{"as_of":"2026-08-19T20:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8813b4b83e90693a4067342712b1fd64d3533bc56929e0ad3ab1a71a80ad0c00","coverage":[{"denominator":100,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:42:49.373944Z","state":"measured"},{"denominator":102,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":102,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T01:07:40.373543Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-16T01:05:06.592750Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"cited_work":{"arxiv_id":"2412.16361","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.16361","snapshot_observed_at":"2026-08-16T01:05:06.592750Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","venue":"cs.CV","work_id":"ad2e0545-8571-4bac-8cbe-eb60420dd8d7","year":2024},"citing_paper":{"arxiv_id":"2505.02175","last_updated":"2025-05-04T16:33:47Z","snapshot_observed_at":"2026-08-18T16:44:48.497030Z","submitted_at":"2025-05-04T16:33:47Z","title":"SparSplat: Fast Multi-View Reconstruction with Generalizable 2D Gaussian Splatting","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-16T01:05:06.009602Z"},"links":{"cited_paper":"/paper/2412.16361","citing_paper":"/paper/2505.02175"},"observation_digest":"sha256:d08058f206ff72aba376d3321238f2b87021aebcd5ec853b67a320f107455aaa","observation_id":"7c9e3513-fe43-4a67-bccc-3c4e298f6899","resolution":{"observed_at":"2026-08-16T01:05:06.600783Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16361","snapshot_observed_at":"2026-08-16T01:07:40.373543Z","title":"Toward robust neural reconstruction from sparse point sets.arXiv preprint arXiv:2412.16361,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.02178","last_updated":"2025-07-31T01:22:13Z","snapshot_observed_at":"2026-08-18T09:32:56.797220Z","submitted_at":"2025-05-04T16:40:24Z","title":"Sparfels: Fast Reconstruction from Sparse Unposed Imagery","version":4},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-16T01:07:40.373543Z"},"links":{"cited_paper":"/paper/2412.16361","citing_paper":"/paper/2505.02178"},"observation_digest":"sha256:2c6442e5077f54c7e64079fbb3ddf40f23ba77f5eb48a6bf7a7681830b0e8f9b","observation_id":"efb66ede-8fee-4c67-b6e7-f12a7b25900a","resolution":{"observed_at":"2026-08-16T01:07:40.373543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.16361/citation-record","integrity":"/paper/2412.16361/integrity","json":"/paper/2412.16361/citation-record.json","paper":"/paper/2412.16361"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.004849Z","title":"Neural point-based graphics","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.004849Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:0f45f463b943cbe30103795213938b3a5959f2c87610a4e88961f33c827d7d87","observation_id":"0eddb9f1-99e0-4ffd-a18a-cb960185ef21","resolution":{"observed_at":"2026-08-11T10:42:49.004849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.009967Z","title":"The power crust, unions of balls, and the medial axis transform","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.009967Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:f1d1f7e4770f4b913f02cfb05d9cc9e6ab4c38209eb35097139a40af84dafbf9","observation_id":"66d58d1c-4af3-4cd0-baab-a4a96bd13b06","resolution":{"observed_at":"2026-08-11T10:42:49.009967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.013962Z","title":"Sal: Sign agnostic learn- ing of shapes from raw data","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.013962Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:43bcfe55e170fc106c6a580b27a0aed557e97d7eb331c37f471894f7ddbff5ca","observation_id":"01056861-1552-4bef-b4f3-04314d4b7971","resolution":{"observed_at":"2026-08-11T10:42:49.013962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.018437Z","title":"Sald: Sign agnostic learn- ing with derivatives","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.018437Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:9bf031918a709a49a616b1272f27e499bcc0d00ba33fd917626faa3e3c6afb27","observation_id":"f5f3391d-2b28-4277-85ed-d08e769517ef","resolution":{"observed_at":"2026-08-11T10:42:49.018437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.022346Z","title":"Regu- larization for wasserstein distributionally robust optimization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.022346Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:eaf78d5b1c244c64a0331e21e9741d5325d5740e06b50eb85a1d79d92a1f16c5","observation_id":"e94752eb-dd9b-4283-aa9d-156fddb7f42b","resolution":{"observed_at":"2026-08-11T10:42:49.022346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.026759Z","title":"Digs: Divergence guided shape implicit neu- ral representation for unoriented point clouds","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.026759Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:1e0b62c1ed0d66876aa27ff8163eaeb4fa6179caef4d59091a8380a5c2dedb92","observation_id":"9dfe8d93-ecac-483e-8594-1234a977aa70","resolution":{"observed_at":"2026-08-11T10:42:49.026759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.030750Z","title":"Robust solutions of optimization problems affected by uncertain probabilities","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.030750Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:39cf0fed4d35bed7d076a73a37788c42d86fab1ad0fcf06e1da6ba32a08c15ae","observation_id":"c209c24d-02f5-4826-bdfd-a49894141dae","resolution":{"observed_at":"2026-08-11T10:42:49.030750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.034546Z","title":"The ball-pivoting algorithm for surface reconstruction","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.034546Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:b99a76e30e5d42b59ffcdc3c7356e71054c7a5eeb7da5a2ae393feb3c30179ed","observation_id":"74b7b8dd-a1e8-439d-ba16-183bd3808391","resolution":{"observed_at":"2026-08-11T10:42:49.034546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.038302Z","title":"Data- driven robust optimization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.038302Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:bda79415609b0c12ae715e1e5adb67f342470946f1e9a65a21ce3742c0204167","observation_id":"6c388e35-5d10-4cc8-8432-fc7f1fa2dd75","resolution":{"observed_at":"2026-08-11T10:42:49.038302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.041140Z","title":"Semi-supervised learning based on distributionally robust optimization, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.041140Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:739cc4654d93c200df72cdb0be6975972dac28c611767a9aea14c1610cea1a50","observation_id":"8323d71f-a6a2-4dbc-96fa-73cf75369d4f","resolution":{"observed_at":"2026-08-11T10:42:49.041140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.048246Z","title":"Quantifying distribu- tional model risk via optimal transport","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.048246Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:46ef297ca27b7351340d8837b5be71e3972c29c60e2b24fd273205ef846fbbef","observation_id":"f7ef6edb-8f92-47c7-aba7-f94e1965d186","resolution":{"observed_at":"2026-08-11T10:42:49.048246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.051518Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.051518Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:b8448ab4e4857196bce55fc88cab9528234c92a95226a0eb7978cecc918a3de3","observation_id":"08c50e91-bfc0-44ec-91f1-c106267948bd","resolution":{"observed_at":"2026-08-11T10:42:49.051518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.054856Z","title":"Poco: Point con- volution for surface reconstruction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.054856Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:111e26f6ea0f984e68f0b430c4912f224520db28e3e9569c6bb6dee2388c031c","observation_id":"9b2f98d6-f9fb-45e2-8dfc-91ca2ccd3289","resolution":{"observed_at":"2026-08-11T10:42:49.054856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.058372Z","title":"Needrop: Self-supervised shape represen- tation from sparse point clouds using needle dropping","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.058372Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:4909b91f8a7fc79563f59b1aada99c077e765fd64f27aeffb1401f9006c07357","observation_id":"b8d6fdae-1ad2-4a73-a28e-8aaba650ced2","resolution":{"observed_at":"2026-08-11T10:42:49.058372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.13437","last_updated":"2022-02-27T19:40:29Z","snapshot_observed_at":"2026-08-17T03:52:04.655543Z","submitted_at":"2022-02-27T19:40:29Z","title":"A Unified Wasserstein Distributional Robustness Framework for Adversarial Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.13437","snapshot_observed_at":"2026-08-11T10:42:49.063277Z","title":"A unified wasserstein distributional robust- ness framework for adversarial training","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.063277Z"},"links":{"cited_paper":"/paper/2202.13437","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:041735f26760b39b358b0981213a6925a8982ec7ab0d674774f51b7a241dda01","observation_id":"2f2d15e9-b652-4c9b-9094-1d9f53b66ecd","resolution":{"observed_at":"2026-08-11T10:42:49.063277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.067294Z","title":"Reconstruction and representation of 3d objects with radial basis functions","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.067294Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a3089f9f767950a6c2626a275ef367e46ddbbb50150ac3f30a7fb5e1347629e2","observation_id":"9b15a726-939c-446b-ad61-3dbd4a538e4f","resolution":{"observed_at":"2026-08-11T10:42:49.067294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.071204Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.071204Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a16ceae4a39e5b4ac1d02b9161cb81c1d61539d198efbd86b1a2a419855b84bc","observation_id":"f8a48172-0ee8-425c-9fcc-f44140c2f1e3","resolution":{"observed_at":"2026-08-11T10:42:49.071204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.075207Z","title":"Efficient geometry-aware 3d generative adversarial networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.075207Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:5048569a6ccf28fd69efb9ea0c346509f2b4dc5269985b43d7a2b182ff1e4e2f","observation_id":"4ecac2fb-ac2d-4cfb-aa8b-e4cf7d56d346","resolution":{"observed_at":"2026-08-11T10:42:49.075207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-15T22:26:43.274625Z","submitted_at":"2015-12-09T19:42:48Z","title":"ShapeNet: An Information-Rich 3D Model Repository","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03012","snapshot_observed_at":"2026-08-11T10:42:49.078541Z","title":"Shapenet: An information- rich 3d model repository","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.078541Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:60f2627a31a728c8e5587d656ac7bc850d52726995297be829bad65c4d13c927","observation_id":"b7569c2a-2874-4e79-81c9-17c332fffbda","resolution":{"observed_at":"2026-08-11T10:42:49.078541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.083218Z","title":"Latent partition implicit with surface codes for 3d representation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.083218Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:549d74505917f728989de43653fe57af6b5e7c43df526709d3646645f1370a27","observation_id":"4a09c027-b9a8-423e-b478-20bff224de88","resolution":{"observed_at":"2026-08-11T10:42:49.083218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.087181Z","title":"Unsupervised inference of signed distance functions from single sparse point clouds without learning priors","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.087181Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:236626797d524211b3c2b9424ca00af78e379eb899d3158a1d4924cd934d73de","observation_id":"259d7b55-c398-444e-8dff-69b95038f03c","resolution":{"observed_at":"2026-08-11T10:42:49.087181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.090679Z","title":"Gridpull: To- wards scalability in learning implicit representations from 3d point clouds","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.090679Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:c025e86001feda9c3edc9abf94309826ba0f61410251b58ea94f64ae52da10f7","observation_id":"65e334df-28e8-4cfb-840d-5fc4325d4ad4","resolution":{"observed_at":"2026-08-11T10:42:49.090679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.260081Z","title":"Implicit feature net- works for texture completion from partial 3d data","venue":null,"work_id":"edbf9a63-83a4-4d50-a5f4-f7d3c139f371","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.094459Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:4f82de219ab4b8e045309ed654caa76ed7193c370032860cdbdcb7a9265135aa","observation_id":"8e5b8dec-c3e2-4519-a9c1-d88da5806535","resolution":{"observed_at":"2026-08-11T10:42:50.263242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.249797Z","title":"Sinkhorn distances: Lightspeed computation of optimal transport","venue":null,"work_id":"068c79d8-f11c-4314-846b-2deffe928fac","year":2013},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.097714Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:1210911646258243acfa9e33be1c22233cf7eacdfda90ad3331a86314c279085","observation_id":"ece17715-c64e-4dac-88a7-8b5453af912a","resolution":{"observed_at":"2026-08-11T10:42:50.253316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.238911Z","title":"A point set generation network for 3d object reconstruction from a single image","venue":null,"work_id":"2ab626ed-a3ea-419f-b550-6c5df03af69a","year":2017},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.101523Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:b081f3b92859237aa09fc85310546e60abda9857b5f6251ae39d5ff7ecf953ba","observation_id":"de1e4f4d-3c59-4f77-9e8a-9039d7ea9423","resolution":{"observed_at":"2026-08-11T10:42:50.243058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.228592Z","title":"Distributionally robust stochas- tic optimization with wasserstein distance","venue":null,"work_id":"69d6fd9d-aa66-4fe6-ba8a-a0913634e3c2","year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.105197Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a9fc84cdf8e3b6ad1ea56c37f490981c6a079dc746ca5a0c9a9e0437f4624bf5","observation_id":"b875cb00-0ed8-45bb-9e40-ac416a32f940","resolution":{"observed_at":"2026-08-11T10:42:50.232257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.218898Z","title":"Distributionally robust optimiza- tion and its tractable approximations","venue":null,"work_id":"28fe5ba9-3a0b-4839-9722-0426aecf4680","year":2010},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.108550Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:3d99a51f9b8572de7407c5a4a4bde8c2e4e5feabda285e4ac0cd4d33129117fc","observation_id":"23d51881-96c5-4198-920e-d3287cb1e406","resolution":{"observed_at":"2026-08-11T10:42:50.222422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.209002Z","title":"Implicit geometric regularization for learning shapes","venue":null,"work_id":"4363641e-7b4f-4169-92b8-9692e1f2f361","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.112383Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:379e60783388406dce757dd22d1d9ee62eafdea90457399335a3d5ace324df21","observation_id":"d9b6dcd9-714d-4ecf-9d81-d1f18dc3aa9f","resolution":{"observed_at":"2026-08-11T10:42:50.212982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.195155Z","title":"Algebraic point set surfaces","venue":null,"work_id":"fdb83d02-0829-4719-bb3c-d16397ab9658","year":2007},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.115884Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:6e415f3c0a96f44942ee56c7377e1d307fb10be62093975e154a7ce054ca0c66","observation_id":"db12955a-5eba-4d74-b66f-900cfdf2ee3e","resolution":{"observed_at":"2026-08-11T10:42:50.200386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.09510","last_updated":"2022-09-20T06:56:59Z","snapshot_observed_at":"2026-08-16T16:31:17.614086Z","submitted_at":"2022-09-20T06:56:59Z","title":"Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.09510","snapshot_observed_at":"2026-08-11T10:42:49.119178Z","title":"Iterative poisson surface reconstruction (ipsr) for unoriented points","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.119178Z"},"links":{"cited_paper":"/paper/2209.09510","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:ded1af2f8639041ca13f676b607928c0816241463c438ea4f327cb83ad9e441e","observation_id":"154a5ba6-e6c9-4a04-bfeb-a4122e693b18","resolution":{"observed_at":"2026-08-11T10:42:49.119178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.182466Z","title":"Neusurf: On-surface priors for neural surface reconstruction from sparse input views","venue":null,"work_id":"c2a08f1d-f81b-4971-80bb-96b574e06da6","year":2024},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.123232Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:52bb16d048279266e6057969c6aeb9b1d1a06a2c0a47aad0b3e2dfccfbacbda8","observation_id":"b66af778-f5f6-4827-aeaf-1147809e6f4a","resolution":{"observed_at":"2026-08-11T10:42:50.187288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.170901Z","title":"Neural kernel surface recon- struction","venue":null,"work_id":"c9e3bb11-1b85-454d-8090-08d8703ecbd9","year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.126833Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:4364512092a0a7ec61dacc46928c597b9e6617b150f2305fed39341457a4ea7f","observation_id":"0543d2b5-b831-4f86-9c97-f8c9a2a42411","resolution":{"observed_at":"2026-08-11T10:42:50.174946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.159647Z","title":"Neural kernel surface recon- struction","venue":null,"work_id":"405f3540-6aa3-4e70-b087-db109d5bd59e","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.131015Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:bd3200b8be77e1edc99b08d5ff337dc82adf70970f25ce9e5d53c6c97e3174c8","observation_id":"2d4c4a2e-ac02-4096-b0cd-152a225666a8","resolution":{"observed_at":"2026-08-11T10:42:50.163447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.149568Z","title":"Barron, Pieter Abbeel, and Ben Poole","venue":null,"work_id":"07e4cbef-e1dd-4b0d-b8ed-3ab3dc315e24","year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.134851Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:083e518b7c8b0f201a5cefe07afb516486829d104c09344dac6eb6792a121550","observation_id":"365c5c5c-5428-4f6b-85e6-48bf9dda395c","resolution":{"observed_at":"2026-08-11T10:42:50.152655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.140107Z","title":"Neural mesh-based graphics","venue":null,"work_id":"2e60cb22-cf70-4971-b8f7-756b1df1692f","year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.138631Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:f21d03ed443f693e92c82a8e16297467240dea73ce66e46b3c2dc6fe6fc51565","observation_id":"4db40510-6eed-47b0-bc19-4c9c6ae1cef5","resolution":{"observed_at":"2026-08-11T10:42:50.143405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.129010Z","title":"Geotransfer: Generalizable few-shot multi-view reconstruc- tion via transfer learning","venue":null,"work_id":"7a22a9b3-9be0-46c1-b9dd-322f86a42286","year":2024},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.142460Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:6fb70dbbf957f4e6ba044d1a806637c7e2aeec57473bf298440fca08f67a9ef2","observation_id":"e2cfd8dd-6dd1-4e90-a5d1-5f66dcdb7beb","resolution":{"observed_at":"2026-08-11T10:42:50.132958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.118887Z","title":"Local implicit grid representations for 3d scenes","venue":null,"work_id":"66e91ace-8426-49c2-b1a9-59ed2a92a650","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.146442Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:1bfb588399ca2fcf5ff38f22e1e93b9a2d8b4ae398bc10c4847d9797edd58f22","observation_id":"f781dcdb-c389-4813-968e-83df8e2242e5","resolution":{"observed_at":"2026-08-11T10:42:50.122589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.108076Z","title":"Neu- ral 3d mesh renderer","venue":null,"work_id":"40815ad6-a0f5-4567-ac01-129e8f05126b","year":2018},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.149902Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:05ed70ae30211a47acbcd355a96b6ea9253f7671f9c21517f60d94809a8a14ee","observation_id":"a4c762b5-c608-4ce6-b6d4-3185430fdfb8","resolution":{"observed_at":"2026-08-11T10:42:50.111902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.097009Z","title":"Screened poisson surface reconstruction","venue":null,"work_id":"14ef9bd9-8688-4896-b007-89553983ba02","year":2013},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.155377Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:fa890227186759867910fef1589121588181dcb185207b936930128053ef611a","observation_id":"b4ab562c-10e6-4bed-ade6-0a530003c747","resolution":{"observed_at":"2026-08-11T10:42:50.101054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.085773Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":"fe8f05f5-278e-40de-991f-94fde381d77a","year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.159128Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:1509d2eb79ff96b4cae2786badcbdedd792a5812adca8343c4b3ee52fd1d1fed","observation_id":"6d531589-121a-4695-bf5a-8b9e9cf97339","resolution":{"observed_at":"2026-08-11T10:42:50.089609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.074350Z","title":"Tanks and temples: Benchmarking large-scale scene reconstruction","venue":null,"work_id":"b58b04af-6014-4169-a448-0618100d8d0c","year":2017},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.162713Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:cbaf76be9bc1b4f38c535d272ed7f72acff08430e122db29a69af07581737466","observation_id":"a12a9d0f-4a6e-4acf-881c-a96c4aea5195","resolution":{"observed_at":"2026-08-11T10:42:50.078325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.063734Z","title":"Provably good moving least squares","venue":null,"work_id":"5663f29d-0680-43e7-9b36-d87baf4cc128","year":2008},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.166443Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:4e6a4d6ce5116b1793233d93b8eb5664b75dc883ec7235cfa3ce5dfed6c092ae","observation_id":"bfa4c670-1a95-49c7-a33b-b1129a778907","resolution":{"observed_at":"2026-08-11T10:42:50.067589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.052751Z","title":"Octree guided unoriented surface reconstruc- tion","venue":null,"work_id":"58f009da-567b-4bc7-9468-d8ffa5ec1c19","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.169879Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:987a8c1a67f429c3cf7265a60c41374dc8f46a5ec4b549e3962db9a0e67af112","observation_id":"9f0f9005-5a38-43e8-8310-ee7220ec7bae","resolution":{"observed_at":"2026-08-11T10:42:50.056754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.041932Z","title":"Learn- ing generalizable light field networks from few images","venue":null,"work_id":"0fd5c180-803e-4cd6-86be-400fc0262e57","year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.173132Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a9d027aed7f78a20dfe3884f2608088dfae8df47d73b6a4da0360e424bf7e64b","observation_id":"ac84ed09-f2cd-4497-a997-df77daeea4e7","resolution":{"observed_at":"2026-08-11T10:42:50.045881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.030268Z","title":"Regulariz- ing neural radiance fields from sparse rgb-d inputs","venue":null,"work_id":"b77f38a9-334b-4041-b0e2-5afd692b3b9c","year":2023},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.175945Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:4450d035e658d7fee4de47ec2859fcebe31487ef91e2644f3ae711e6baf944c0","observation_id":"5b8f5d58-6857-43f1-ba82-d6fd40f7623f","resolution":{"observed_at":"2026-08-11T10:42:50.034644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.06334","last_updated":"2018-05-17T16:12:16Z","snapshot_observed_at":"2026-08-14T19:14:39.502319Z","submitted_at":"2018-05-16T13:59:20Z","title":"Auxiliary Tasks in Multi-task Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.06334","snapshot_observed_at":"2026-08-11T10:42:49.178809Z","title":"Auxiliary tasks in multi-task learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.178809Z"},"links":{"cited_paper":"/paper/1805.06334","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:318a511b7eddf10fcdaf8ef34257f1c511573b05cf4f94db292f4a855ee44b24","observation_id":"e38e9510-0348-4151-bd0b-d971d05f5397","resolution":{"observed_at":"2026-08-11T10:42:49.178809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.181953Z","title":"Sur- face reconstruction from point clouds without normals by parametrizing the gauss formula","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.181953Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:3b05c5e86efd5eedf176a55a684a1b3b27ee6cf561409301527d0776149cf6af","observation_id":"a332dee3-94cf-4433-9d08-fcd0bb9c19dd","resolution":{"observed_at":"2026-08-11T10:42:49.181953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.012649Z","title":"Dynamic plane convolutional occupancy networks","venue":null,"work_id":"246a54a3-ac57-43a8-bbff-d5225f621c06","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.185245Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a046d590eb13c3405794c2f2f0db466ca913338fdf2fe343409a89bb584b9691","observation_id":"63419c10-de22-42f6-bb64-a6425284c24b","resolution":{"observed_at":"2026-08-11T10:42:50.015978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:50.002503Z","title":"Phase transitions, distance functions, and implicit neural representations","venue":null,"work_id":"9f5001cd-4574-4f4a-b6af-fa4b524999df","year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.188318Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:6833c7f4424708ecc0a8c5898f9a5681cfca8c55c580f2db1a39f6947b847dd7","observation_id":"d5fe2fb1-a2fe-419e-9e5c-04c7410948c1","resolution":{"observed_at":"2026-08-11T10:42:50.006018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.08345","last_updated":"2022-05-10T17:24:38Z","snapshot_observed_at":"2026-08-16T17:20:44.010272Z","submitted_at":"2022-02-16T21:24:54Z","title":"Learning Smooth Neural Functions via Lipschitz Regularization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.08345","snapshot_observed_at":"2026-08-11T10:42:49.191168Z","title":"Learning smooth neural functions via lipschitz regularization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.191168Z"},"links":{"cited_paper":"/paper/2202.08345","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:18f286cb442c45195171add9c13bf1dc65ac6c45d002d830e03d99e2c35ccc2c","observation_id":"67291194-c887-4fd1-b478-9d77ebb35760","resolution":{"observed_at":"2026-08-11T10:42:49.191168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.991708Z","title":"Meshing point clouds with predicted intrinsic-extrinsic ratio guidance","venue":null,"work_id":"b4a6dea9-d48c-40ee-a99a-e874058670e4","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.195042Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:ba118b0d4e8bd8b679e515b359cc08b950a5bf28d52496d3e91a0a9957b260ae","observation_id":"5c16526b-dfc4-417f-b9e2-599b55c04315","resolution":{"observed_at":"2026-08-11T10:42:49.995298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.980906Z","title":"Deep implicit moving least-squares functions for 3d reconstruction","venue":null,"work_id":"e343a6ae-065c-4867-a410-16861c8e320f","year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.198548Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:f84dcf5ad710c02d966c17690932a7b3e52545d93eb71157eb2831946bda7b91","observation_id":"46af7db5-d306-4d6b-9287-59aed64cfa0e","resolution":{"observed_at":"2026-08-11T10:42:49.984845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.968940Z","title":"Marching cubes: A high resolution 3d surface construction algorithm","venue":null,"work_id":"c5f4575c-93d8-4aad-b156-7f7a896403cd","year":1987},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.202687Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:23b2031d58148561f4c44b2395cb2bdde323c7459397798438cadb8d8cf8201a","observation_id":"0a4e5d5c-7ccf-46ae-8669-95357821fcfa","resolution":{"observed_at":"2026-08-11T10:42:49.972930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.957476Z","title":"Neural-pull: Learning signed distance functions from point clouds by learning to pull space onto surfaces","venue":null,"work_id":"38f46461-21ee-4c5f-8b12-fe6a92c871aa","year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.206909Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:bf1b8640b18dc441d635d24e67a450a4fbd8c2464ab7b85ebcfc5ae560a41c68","observation_id":"2336d481-6ea4-4cc9-9d29-0c8d365ea79b","resolution":{"observed_at":"2026-08-11T10:42:49.961377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.946758Z","title":"Reconstruct- ing surfaces for sparse point clouds with on-surface priors","venue":null,"work_id":"8e6d6a78-c7b3-45dd-8f16-9ea39e4392d6","year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.210374Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:85a9f027d12e255dbbf2d55c43b6f98cc5a749da086e3591f09b20842399c038","observation_id":"961c329c-18d4-4bb6-8f14-86c02433ad84","resolution":{"observed_at":"2026-08-11T10:42:49.950683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.935215Z","title":"Surface reconstruction from point clouds by learning predictive context priors","venue":null,"work_id":"5787e50e-b793-4596-886e-4bbf31d1f583","year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.213939Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:f02221433c35d0a48cce22daec557415d6dbc663459b6738ec5e739c415a2b2a","observation_id":"bdecf81b-fcfd-4db8-9319-625044bc9a25","resolution":{"observed_at":"2026-08-11T10:42:49.939131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.922870Z","title":"Moving level-of-detail surfaces","venue":null,"work_id":"7a4f72b2-7e0d-45a1-ab27-02394877884a","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.217316Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:acf5bc841bda1d77eac4d743fa1a4b9b10d2db6a3f89c997b6e45d9158c42df6","observation_id":"7c9310af-f834-4d6a-9d6d-5b8ef028cdf4","resolution":{"observed_at":"2026-08-11T10:42:49.927437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.909888Z","title":"Occupancy networks: Learning 3d reconstruction in function space","venue":null,"work_id":"5cd80b78-5193-4706-ae5f-88b7fc554f2f","year":2019},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.220794Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:97a6ab9c559da1e6312e442496f1e2b70a0c5227bcc8f0ea8c50f44681a9fe7f","observation_id":"90a012b2-6522-4f3d-a87a-27919c2b4a1c","resolution":{"observed_at":"2026-08-11T10:42:49.913632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.898562Z","title":"Nerf: Representing scenes as neural radiance fields for view synthe- sis","venue":null,"work_id":"599e7d37-4534-4c30-ad16-f7851a82d2e2","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.224896Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:1fb964c197c1be681d68b55f84eed267734c060d9983961e024dd6c07c846427","observation_id":"6aad9081-b72e-4451-a9b9-1f3816736fdb","resolution":{"observed_at":"2026-08-11T10:42:49.902742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1507.00677","last_updated":"2016-06-11T18:22:33Z","snapshot_observed_at":"2026-08-14T22:41:58.116024Z","submitted_at":"2015-07-02T18:01:23Z","title":"Distributional Smoothing with Virtual Adversarial Training","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1507.00677","snapshot_observed_at":"2026-08-11T10:42:49.228846Z","title":"Distributional smoothing with virtual adversarial training","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.228846Z"},"links":{"cited_paper":"/paper/1507.00677","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a7b0d942db35bf47a397bce30ff3db69a6b38425ed08fc0adb7f0c3f63c6fb4c","observation_id":"c28d35f9-9459-4bbb-b553-dd74cd53ac1c","resolution":{"observed_at":"2026-08-11T10:42:49.228846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.887315Z","title":"Data-driven distributionally robust optimization using the wasserstein met- ric: performance guarantees and tractable reformulations","venue":null,"work_id":"7fe7fb24-b7f4-4f8b-8739-05944e78a943","year":2018},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.232540Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:2cfde471402162483e64b0861c10cc45b2677d1977776beabf28222dba2b6f02","observation_id":"2037c511-3347-44e1-9453-44756781523c","resolution":{"observed_at":"2026-08-11T10:42:49.891434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.873192Z","title":"Stochastic gra- dient methods for distributionally robust optimization with f-divergences","venue":null,"work_id":"c2b6242e-ba5c-4083-bce9-b3de1be4842c","year":2016},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.238768Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:145f70c4dc90aca559a2e610fcea368c7c21a9c1d37f5396c314c17391692276","observation_id":"34d6d5f9-f115-4bb8-99c2-35b03c3f0602","resolution":{"observed_at":"2026-08-11T10:42:49.879012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.863202Z","title":"Few’zero level set’- shot learning of shape signed distance functions in feature space","venue":null,"work_id":"cd93624f-716a-4b53-b97f-6a6f8425232c","year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.242969Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:dfb3216975f1c96655a5facdcb337d4c0bcc3a23d5ede474b1d71000089619e8","observation_id":"eda829d7-97f8-4f54-a095-23aa64bc69c5","resolution":{"observed_at":"2026-08-11T10:42:49.866511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15114","last_updated":"2024-08-27T14:54:33Z","snapshot_observed_at":"2026-08-18T18:59:40.106534Z","submitted_at":"2024-08-27T14:54:33Z","title":"Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15114","snapshot_observed_at":"2026-08-11T10:42:49.246944Z","title":"Few-shot unsuper- vised implicit neural shape representation learning with spa- tial adversaries","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.246944Z"},"links":{"cited_paper":"/paper/2408.15114","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:9f5018ffcb273928bbfcd5970b0e939cc57bcb11ca6e6323379ea29355dab31a","observation_id":"d7742e1f-99cf-4b55-85f5-fcb14e8a201f","resolution":{"observed_at":"2026-08-11T10:42:49.246944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.250772Z","title":"Mixing-denoising generalizable occupancy networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.250772Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:d68a533d7fdfe9a7aca75ffecead5321054eabbd732a07462544a23f55cd6bcf","observation_id":"4956ae37-c1c6-4bee-a66d-2843ce1fcf5b","resolution":{"observed_at":"2026-08-11T10:42:49.250772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.844746Z","title":"Robustifying general- izable implicit shape networks with a tunable non-parametric model","venue":null,"work_id":"46d49b63-19c2-4b78-ad00-bcd673045b5d","year":2024},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.254437Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:313e7b6ea7cac1e027dbdec37663abe75f4569fae2c812e8dc91dd734e4352e8","observation_id":"70397a2b-b3c6-4a79-bb71-245c4e70a068","resolution":{"observed_at":"2026-08-11T10:42:49.848477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.834808Z","title":"Unsupervised occu- pancy learning from sparse point cloud","venue":null,"work_id":"19336959-5de7-46c8-b594-1da008fdf7cc","year":2024},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.258191Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:aaf652fb21791f2030084570b92ace03a4de87527e7cff9d91a7e1de55e878f6","observation_id":"2089f0f8-bcd6-471f-988a-23fd44157c88","resolution":{"observed_at":"2026-08-11T10:42:49.838477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.824423Z","title":"Semanticposs: A point cloud dataset with large quantity of dynamic instances","venue":null,"work_id":"63289593-d8a4-4eac-ac9b-28ebc889c911","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.262378Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:21e801920988652d44e4c36b36ba10e157c565b906954279f6f94072b532bb1b","observation_id":"370cf3b5-20e3-4e4d-9281-7ee783404744","resolution":{"observed_at":"2026-08-11T10:42:49.828435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.812518Z","title":"Deepsdf: Learning continuous signed distance functions for shape representation","venue":null,"work_id":"23c7aa22-aeb7-4b26-9afa-e0cc3d0183d5","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.265954Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:d8cf0f409d391564290a9fa6e2cb96ac9411f0dbd3c4b7245a44790591b8cc0e","observation_id":"a7855f3e-76ee-4c4d-a4cc-2fc51920af79","resolution":{"observed_at":"2026-08-11T10:42:49.816974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.801885Z","title":"A linear time histogram metric for improved sift matching","venue":null,"work_id":"2157f867-a7ea-43bc-ad37-f2309de2c726","year":2008},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.269622Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:ff77def14cb25597e5ed10cbcb9a6507a89540f95e3094980227b4d5c7423681","observation_id":"864b5f8f-900b-47ad-ab4d-0e3267d6f641","resolution":{"observed_at":"2026-08-11T10:42:49.805615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.792240Z","title":"Convolutional occupancy net- works","venue":null,"work_id":"e141a1ab-b00f-433e-938d-e1379d28eb1c","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.273178Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:387f084b505467f3de93aba264ddef84a318841dd99d452590ad0753d3b7e9c2","observation_id":"5777ddaa-b31e-4bb7-b467-b6475f5598db","resolution":{"observed_at":"2026-08-11T10:42:49.795880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.781981Z","title":"Shape as points: A differentiable poisson solver","venue":null,"work_id":"7d6239d6-a14c-4742-961c-9f3b78ec332e","year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.276592Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:4cc0db882399880be7542b9d18cff4c5b4ea0fbc6b90d0824735f8300524ed87","observation_id":"1d034553-b186-4c8e-ba32-a288dae2b80a","resolution":{"observed_at":"2026-08-11T10:42:49.785875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.05659","last_updated":"2019-08-13T00:43:41Z","snapshot_observed_at":"2026-08-15T17:50:04.032669Z","submitted_at":"2019-08-13T00:43:41Z","title":"Distributionally Robust Optimization: A Review","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.05659","snapshot_observed_at":"2026-08-11T10:42:49.279885Z","title":"Distribution- ally robust optimization: A review","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.279885Z"},"links":{"cited_paper":"/paper/1908.05659","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:9b9d7ec64c66e5c36409ea42314cce4028ec7eee038dd5699eb626a3868941e2","observation_id":"bf52306f-1704-4fc3-90fc-6a359015d18c","resolution":{"observed_at":"2026-08-11T10:42:49.279885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.771610Z","title":"Differentiable surface triangulation","venue":null,"work_id":"f2d9920d-a73f-4a2b-9398-acd5e2b0ac53","year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.283602Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:bded68b6fec286764f67fe4ff1a1f8583f24b0b389fb4ef31ed0fc5ee6807491","observation_id":"457cf16a-b34a-4f93-852c-8582fc37c0c0","resolution":{"observed_at":"2026-08-11T10:42:49.775341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.761844Z","title":"The earth mover’s distance as a metric for image retrieval","venue":null,"work_id":"df92d5bc-908e-49a1-bc7a-a236c564fa5d","year":2000},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.286970Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:188811cdcde7802544637f00d0703882024b5bd476c5926850362623cee659b5","observation_id":"863dec0c-1f13-4583-b6d0-5419177be270","resolution":{"observed_at":"2026-08-11T10:42:49.765384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.751452Z","title":"A min-max solution of an inventory problem","venue":null,"work_id":"fee34fcd-2c52-474f-b0ef-f6a868455100","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.290698Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:178b196f692ace01c26cd6b8ba3e53b576088cc3f20aadef63aea609ad1d2654","observation_id":"4aa1bba6-b5b9-4bc8-81b6-a3beb7699037","resolution":{"observed_at":"2026-08-11T10:42:49.755376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.739799Z","title":"Kernel methods for implicit surface modeling","venue":null,"work_id":"3c145ac7-c5c3-45be-a43d-5755fdf1d142","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.293997Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:5a497fc88485471f3373745ae0aa20dab3ed857e8bfcc7db3d77f0750ebbc36f","observation_id":"0bb1e7cc-fd0a-4d19-a7f4-de18093ccab0","resolution":{"observed_at":"2026-08-11T10:42:49.743970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.728259Z","title":"Distributionally robust logistic regres- sion","venue":null,"work_id":"cf315459-35a2-443f-acbc-3b9906412b41","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.296894Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:f37297cd701da5286c91aeb49a68813fc18a2a46427966ee20a6ed1050fb02a5","observation_id":"462dd857-4ed8-4da5-bdc6-a225a2c53739","resolution":{"observed_at":"2026-08-11T10:42:49.732378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-11T10:42:49.299692Z","title":"Certifying some distributional robustness with princi- pled adversarial training","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.299692Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a2643d322c00e9ab0aaa0f39e7725719047b6bb9680b626021134c4bc7f13a44","observation_id":"1f5af60f-0547-42fa-9fe7-0ca404aa3417","resolution":{"observed_at":"2026-08-11T10:42:49.299692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.716769Z","title":"Implicit neural representa- tions with periodic activation functions","venue":null,"work_id":"6bd89b3c-52e9-4aa0-a001-61a41ebf06bb","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.302860Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:c7dedd0a44762cc980cad7c3a16254242a36836e18d6cc66790335d1ae7a2943","observation_id":"1c79b1e1-636b-432c-a469-b8d304548fa0","resolution":{"observed_at":"2026-08-11T10:42:49.721174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.706423Z","title":"Earth mover’s distances on discrete surfaces","venue":null,"work_id":"99c631c9-c4b0-4203-a1c1-5dfd0fc57eb7","year":2014},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.305757Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:c43e030d2a55d4d23482764c08ebfd584463fa740632681018e6e2ff332cc050","observation_id":"110e3319-ad74-45c1-83db-d3c0a8c0c45c","resolution":{"observed_at":"2026-08-11T10:42:49.710041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.695974Z","title":"Convolutional wasserstein distances: Ef- ficient optimal transportation on geometric domains","venue":null,"work_id":"89cbf2f4-8faa-4136-947f-424ad1da0180","year":2015},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.308561Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:ce1066c564e956feb87bb83b3b5b60afe872b96d5d9c8834ca42233358a15a84","observation_id":"a30da00c-4b1c-44af-b0fa-acaf42f783c0","resolution":{"observed_at":"2026-08-11T10:42:49.699474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.684080Z","title":"Distributionally robust deep learning as a generalization of adversarial training","venue":null,"work_id":"8807593f-85f6-47e3-bd98-cdcd9d9debda","year":2017},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.311481Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:cb97b47b274c930c8f02046c75052b23a99eb178c93b4d4e31b0ae27276d1606","observation_id":"64f9efe4-115e-4361-aa15-121d3a5cc39e","resolution":{"observed_at":"2026-08-11T10:42:49.688910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.671289Z","title":"Generalizing to unseen domains via adversarial data augmentation","venue":null,"work_id":"7efc9665-be06-4d5e-a732-111e79340620","year":2018},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.315373Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:27b4983ed19341f510c2646a9400a17caa24f3f631f3a284c073fc0a0e29d6fb","observation_id":"0a406806-d0cf-47f9-97c7-ed66192229e6","resolution":{"observed_at":"2026-08-11T10:42:49.675583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11926","last_updated":"2025-03-26T16:30:42Z","snapshot_observed_at":"2026-08-16T17:54:18.685973Z","submitted_at":"2021-09-24T12:40:48Z","title":"Sinkhorn Distributionally Robust Optimization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.11926","snapshot_observed_at":"2026-08-11T10:42:49.319285Z","title":"Sinkhorn distributionally robust optimization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.319285Z"},"links":{"cited_paper":"/paper/2109.11926","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:97972a44cf45911207bdd68a2ab0e99acee3ecf6656db468090b0d48fd0c12d6","observation_id":"e401e11d-aee1-49d9-acf7-edc4490b2358","resolution":{"observed_at":"2026-08-11T10:42:49.319285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.659249Z","title":"Vggsfm: Visual geometry grounded deep structure from motion","venue":null,"work_id":"df990926-a732-40c2-83e7-bbbdb3378dd8","year":2024},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.323084Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:3b265dfe520207205f57f314eb07d5e4f77e1fbb85b2b2bd2de48fe5877a080a","observation_id":"221297ed-f40a-4e48-846f-3167ba43cc0c","resolution":{"observed_at":"2026-08-11T10:42:49.664127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.646520Z","title":"Pixel2mesh: Generating 3d mesh models from single rgb images","venue":null,"work_id":"5d247991-6353-4f41-9f5d-327f9babc4b2","year":2018},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.326406Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:6f656b361c8eec2c0f026f835ada4ca30ff05cd3cd6e196f9f45a913b75dcf75","observation_id":"68e371aa-455a-4c60-83d5-3be82312d32a","resolution":{"observed_at":"2026-08-11T10:42:49.651571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10689","last_updated":"2023-02-01T06:00:21Z","snapshot_observed_at":"2026-08-02T09:26:48.635481Z","submitted_at":"2021-06-20T12:59:42Z","title":"NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10689","snapshot_observed_at":"2026-08-11T10:42:49.330664Z","title":"Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.330664Z"},"links":{"cited_paper":"/paper/2106.10689","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:43f50f51823e50e8835095b90ec6c4e5dbdcc7ad72a7790b55af38958f72287f","observation_id":"3f26b319-46f5-4594-8a82-395c76db94ad","resolution":{"observed_at":"2026-08-11T10:42:49.330664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.634365Z","title":"Deep geometric prior for surface reconstruction","venue":null,"work_id":"da2d89bf-88d6-451c-8aa1-1af7fecccbb0","year":2019},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.334622Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:3551c83e383402dbb18d4e4b2cd40473316f90316a83f22e19c23996eecea1d5","observation_id":"dce7ff3f-20f5-4381-bd3c-621f76259e59","resolution":{"observed_at":"2026-08-11T10:42:49.638883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.621678Z","title":"Neural splines: Fitting 3d surfaces with infinitely-wide neural networks","venue":null,"work_id":"9f88551c-4a88-4c0d-8bdc-3da30a623b5a","year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.338084Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:82e3affbc545fcb1969c839ffc51b2838ac8d7ebadd285bd689b414eb7ed091d","observation_id":"973085f0-1ddd-4467-83f4-e2ff7de0d174","resolution":{"observed_at":"2026-08-11T10:42:49.625812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.341491Z","title":"Neural fields as learnable kernels for 3d reconstruction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.341491Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:a5342f583693d02ff58b7a33998d5cfc97f6182c684fea6c89ce1df12b37a399","observation_id":"8be7220c-c043-48bb-8dd7-b1c2e15a68d2","resolution":{"observed_at":"2026-08-11T10:42:49.341491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.602496Z","title":"Blendedmvs: A large- scale dataset for generalized multi-view stereo networks","venue":null,"work_id":"2c643871-b742-497b-b168-2640da57791c","year":2020},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.345285Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:c4ed1875ce30e9707853306c0987f102580a784f7659d8c6a9af6048e5be2328","observation_id":"e04457f5-203e-46e4-b46d-8f2f7cee2f8f","resolution":{"observed_at":"2026-08-11T10:42:49.607720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.589486Z","title":"V ol- ume rendering of neural implicit surfaces.Advances in Neural Information Processing Systems, 34:4805–4815, 2021","venue":null,"work_id":"b7122371-7ecc-4ac9-b6c7-aed98664b54f","year":2021},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.348754Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:0e72d6c57902cec8ed997c7f0d115d45dfacc6b40ca489b1e2b0ac608c3d544a","observation_id":"388de008-b448-4bbf-a4cc-70aa3a6c91e6","resolution":{"observed_at":"2026-08-11T10:42:49.594518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.577547Z","title":"Spar- secraft: Few-shot neural reconstruction through stereopsis guided geometric linearization","venue":null,"work_id":"99d9c80b-07ac-45e9-8b24-8bbeec84ab4a","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.352314Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:3fc0aa1466b18939051f0c302d1d41951e4a53bddc56e2e4ca60c589ac207fed","observation_id":"1162cd12-b2a6-4c39-baac-a22e0c904c5d","resolution":{"observed_at":"2026-08-11T10:42:49.581691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.566494Z","title":"Dense scene reconstruc- tion with points of interest","venue":null,"work_id":"ee5d71bb-5939-4c14-b9ab-0943e61051a8","year":2013},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.355664Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:9739efcf47b1b01d68cd6ad8b9b1b0c3e9c1bfaa1f9b7e88bbb3a84419ceaa09","observation_id":"62946d1d-3c58-452e-b765-350b48dd818b","resolution":{"observed_at":"2026-08-11T10:42:49.569880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.555087Z","title":"The DRO framework operates by defining an uncertainty set U, typically modeled as a ball of radiusϵ around an empirical distribution ˆQn , such that U = {Q : d(Q, ˆQn) ≤ ϵ}","venue":null,"work_id":"e45b9ba8-5d51-4914-80cf-4e4bc477b396","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.359418Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:0a1e64828c970405a4880cd5418cbc67220cf4d82410b5f62694372070de4b51","observation_id":"7b0ad5fc-025a-4354-bf41-6a482528301b","resolution":{"observed_at":"2026-08-11T10:42:49.559160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.543837Z","title":"Varying the point cloud density In order to assess the performance of our method under vari- ous point cloud densities we perform an ablative analysis on the SRB benchmark [89]","venue":null,"work_id":"f6ba1dca-b887-4a8b-9579-e15f40902595","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.363081Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:93747a0735722408d02b9e8193e1fdc96a1c8cfb83a5b1657326dee5403805e7","observation_id":"a128b8e6-6117-4adc-a9ac-97da330da836","resolution":{"observed_at":"2026-08-11T10:42:49.547827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.532670Z","title":null,"venue":null,"work_id":"b3039806-b7b7-4157-975e-21cad7b6e905","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.366824Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:d3ea82532a7eb97959b9fee0a29f467d2757abbf4d3cf0de6645949c673545c5","observation_id":"7eb9c329-db6e-475e-b348-5b51ac421da2","resolution":{"observed_at":"2026-08-11T10:42:49.536498Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.521365Z","title":null,"venue":null,"work_id":"30b3bfb6-2471-4bf1-85db-fb97c881a364","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.370310Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:5a6e3edf0120e1660271b0aecb2dcfaef0833506a6ec9e6a670cae64a10058cf","observation_id":"8b1985c9-bd11-409d-89c3-5d975f9653b2","resolution":{"observed_at":"2026-08-11T10:42:49.525246Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:42:49.506543Z","title":"Let S and ˆS denote the ground truth and predicted meshes, respectively","venue":null,"work_id":"f81c64db-688a-4869-943b-7ecb3da6e49c","year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.373944Z"},"links":{"citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:ab9ed0c152cf8ddb20d312ca543a3d391a112098b38a735a319851f38aad6fbd","observation_id":"5f240fe2-87bc-490c-8c32-5bbaf0c8d9d2","resolution":{"observed_at":"2026-08-11T10:42:49.512790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T17:05:11.681377Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":0,"verified_fuzzy":64},"total_outbound_references":100},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 2 inbound Pith citation observations for arXiv:2412.16361."}