{"as_of":"2026-08-10T00:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4119f530de776bebea93c77279f771c7167258ae1df4e1a7d4423b5f2e6d48a3","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T05:49:19.366635Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.08285/citation-record","integrity":"/paper/2502.08285/integrity","json":"/paper/2502.08285/citation-record.json","paper":"/paper/2502.08285"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T05:49:20.077079Z","title":"Spinnet: Learning a general surface descrip- tor for 3d point cloud registration","venue":null,"work_id":"d7101a76-b8d2-486d-b3d2-be4d3e376d2b","year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.191379Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:e15b59b394adba07310d40fd3dce9931abbd2e04529b2da070567cffbece0129","observation_id":"fa032eae-73d4-46bf-b15e-35f1eb80d7e5","resolution":{"observed_at":"2026-08-08T05:49:20.081773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:20.067300Z","title":"Pointnetlk: Robust & efficient point cloud registration using pointnet","venue":null,"work_id":"0ad6e308-86c3-4de1-8d56-bba1a454545d","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.196175Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:c03be83008e068321d7fc46cb3f3238c24b7299b783f8686834b06064fef4054","observation_id":"585ede96-0a23-4073-bf98-49dc4a243ad4","resolution":{"observed_at":"2026-08-08T05:49:20.070792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:20.055953Z","title":"D3feat: Joint learning of dense de- tection and description of 3d local features","venue":null,"work_id":"33e486dd-a976-4e65-b5f9-f3ece1817a69","year":2020},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.199733Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:6d946c668ee6b2ac1a9575791a4de48a53f50ae85715fd88ec52c4104b077aae","observation_id":"5ab9c88f-5e98-44f1-bb56-b6d4b0693f24","resolution":{"observed_at":"2026-08-08T05:49:20.060560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:20.040060Z","title":"Pointdsc: Robust point cloud registra- tion using deep spatial consistency","venue":null,"work_id":"4e032c37-4feb-4bb3-9308-f574477cd4b1","year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.203310Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:f79e206516d727743ec004d57e2314e2383642b42666cd03e3199273a1a6a0a0","observation_id":"0999c815-d39c-4af1-adb8-8c97ba659e6d","resolution":{"observed_at":"2026-08-08T05:49:20.044328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:20.025798Z","title":"Point cloud registration: a mini-review of current state, challenging is- sues and future directions.AIMS Geosciences, 9(1):68–85,","venue":null,"work_id":"33b40b34-2025-4e2f-8f0a-eee22608dbfc","year":null},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.208209Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:71d9cccafdd3c2fac4ccda4489158d378b35deedc9adb8162af80160bdd4fd64","observation_id":"e118050c-9b57-4fd0-898c-31560fd6f06b","resolution":{"observed_at":"2026-08-08T05:49:20.030203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:20.011642Z","title":"3d shape knowledge graph for cross-domain 3d shape retrieval.CAAI Transactions on Intelligence Tech- nology, 9(5):1199–1216, 2024","venue":null,"work_id":"2a51c204-99b1-479f-a6f3-de4b39eb4a11","year":2024},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.211886Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:136f7001bc1452fc2e6381a71f37d36ba1b73e6559e6e3b0bea82f031c659ff4","observation_id":"1e1fc48f-9ab4-4cb9-bd09-f8e88508a063","resolution":{"observed_at":"2026-08-08T05:49:20.015916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.995067Z","title":"Deep global registration","venue":null,"work_id":"1339968a-db79-48df-8432-82e1255b3df7","year":2020},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.215368Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:7df0487d2fd7969e557b0668486cd3e18913f75e729e27e5e3328fe9b9f5de54","observation_id":"08bea7ae-e939-440d-b5db-dc5cdb5f1a3b","resolution":{"observed_at":"2026-08-08T05:49:20.000097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.983088Z","title":"Fully convolutional geometric features","venue":null,"work_id":"da40c1f8-e433-4325-a4e6-81f0c7e931c3","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.220029Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:6d7058f222928a3d9d7017b546ed145ce7681c1ada55889b1e7099d46b7cc2de","observation_id":"9e95929c-e50e-4247-b6b6-234c9ca17b59","resolution":{"observed_at":"2026-08-08T05:49:19.987109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.970067Z","title":"Corsetti, D","venue":null,"work_id":"5a8abeed-3be7-4ee7-9297-2fc0db9d7eac","year":2023},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.223626Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:37ea19321eee15d8110ee768079c26a63c565f6bd2d2ace232982b3403ecbb88","observation_id":"dc0ecddc-1f99-4e02-a4f7-07fd1cd310c2","resolution":{"observed_at":"2026-08-08T05:49:19.974834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.956875Z","title":"Sinkhorn distances: Lightspeed computation of optimal transport.NeurIPS, 26:2292–2300, 2013","venue":null,"work_id":"a6dba23a-f26d-4eae-9589-2ef929defabc","year":2013},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.227269Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:78d224c76506f2387f94bb6758973113e1a98546e2079bf097c05b6d515dd783","observation_id":"56ba828d-6b16-4a52-9681-695078e53b79","resolution":{"observed_at":"2026-08-08T05:49:19.961880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.942058Z","title":"Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.COMMUN ACM, 24(6):381–395, 1981","venue":null,"work_id":"6afe56e2-72bd-4b18-baf6-723ad1c4e010","year":1981},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.230829Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:7f51bf2ab9f59a58d5886502f89f0eec18b5c6f46ff341651c92df8d52a0781c","observation_id":"ef21eeca-a1f4-420e-b57c-824961b1828c","resolution":{"observed_at":"2026-08-08T05:49:19.946338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.928942Z","title":"Robust point cloud registration frame- work based on deep graph matching","venue":null,"work_id":"5b78c07e-58fd-44dd-bc2c-4c341c20cf1a","year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.234373Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:f34b0bf60ed970ae1883b064ce81a833c113009ff268822bfb083cd7bf4b21dd","observation_id":"7c44ad0f-4850-4473-84ea-1190c7e8e3fd","resolution":{"observed_at":"2026-08-08T05:49:19.933158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.916730Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","venue":null,"work_id":"734d312d-4be8-463a-8a8e-5519f387be37","year":2012},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.237866Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:5e923edc21195c845897be2236ebbb6d302f58742dc56513543a66b181f3eb77","observation_id":"bf0a4860-7bfb-482a-bfc5-a02de700782d","resolution":{"observed_at":"2026-08-08T05:49:19.921219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.903780Z","title":"Predator: Registration of 3d point clouds with low overlap","venue":null,"work_id":"57f93092-0837-4718-a371-e6e6b4424bec","year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.241145Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:f00d272cf15633459fd639d873d7ac3ec238b93ebb2049d422ec6896ac0f8b71","observation_id":"f0d6ff1a-f69c-450d-a70d-aed545435fab","resolution":{"observed_at":"2026-08-08T05:49:19.908137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.244496Z","title":"Feature- metric registration: A fast semi-supervised approach for ro- bust point cloud registration without correspondences","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.244496Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:1656e3ed9b11290f425c977de0f6e9c9f356a607ad29a93015cba96d14e34b13","observation_id":"96f573fa-6366-4102-9cfc-1cec940a6ac1","resolution":{"observed_at":"2026-08-08T05:49:19.244496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.02690","last_updated":"2021-03-05T05:59:07Z","snapshot_observed_at":"2026-08-04T03:43:34.316114Z","submitted_at":"2021-03-03T21:17:06Z","title":"A comprehensive survey on point cloud registration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.02690","snapshot_observed_at":"2026-08-08T05:49:19.247766Z","title":"A comprehensive survey on point cloud registration","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.247766Z"},"links":{"cited_paper":"/paper/2103.02690","citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:ed96cc85f9ffc087dc3fd47aecb9aaeb0a2b10d186f1d609dca598ad6eaa39db","observation_id":"49854936-4dc9-430b-9c90-731f53084241","resolution":{"observed_at":"2026-08-08T05:49:19.247766Z","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":"2026-08-08T05:49:19.883378Z","title":"Unsupervised point cloud regis- tration by learning unified gaussian mixture models.RA-L, 7 (3):7028–7035, 2022","venue":null,"work_id":"74b53d7c-868c-44b0-858e-da43bb94effc","year":2022},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.251488Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:c717879b31da69a819e0bf74f2aa01882fcb2006f9c42bfefa7a2650b1f442ff","observation_id":"f6e8808a-5542-4588-93e9-87066109283a","resolution":{"observed_at":"2026-08-08T05:49:19.887692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.871349Z","title":"Slam-driven robotic mapping and registration of 3d point clouds.Au- tomation in Construction, 89:38–48, 2018","venue":null,"work_id":"a7675488-17ad-43de-8487-1b8b2a4e6cc7","year":2018},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.255974Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:75b5ac2286dda1bb81bde7c2596c77ec6c076725fdedc854e96cb9d5331fdc87","observation_id":"3a960718-abb3-46e3-84bf-54f15d4a9d0b","resolution":{"observed_at":"2026-08-08T05:49:19.875850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.01322","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T05:49:19.512186Z","title":"Freeinsert: Disentangled text-guided object insertion in 3d gaussian scene without spatial priors.arXiv preprint arXiv:2505.01322, 2025","venue":null,"work_id":"e5d57ec2-4e3a-4973-abba-62ab4f537472","year":2025},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.259758Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:e5d4d3ac90f484298aad86407581fa7e4c7163130540983119fa7cb507b01cca","observation_id":"7b770190-dccc-41fc-9dea-3e1482074a66","resolution":{"observed_at":"2026-08-08T05:49:19.518750Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.858129Z","title":"Iterative distance- aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration","venue":null,"work_id":"0fe30b70-d80c-460b-9af2-21117d6e8533","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.263272Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:22963a62d4d0c855765536bb14e8faa3f5db8e677a10e3aead61e1633880b4fe","observation_id":"bb64ca27-5ac6-44ac-a0b0-e9bcc3feade3","resolution":{"observed_at":"2026-08-08T05:49:19.862499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.845457Z","title":"Point cloud registration with self-supervised feature learning and beam search","venue":null,"work_id":"c6c8dd9b-3693-40b3-aec1-01921724d298","year":null},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.266702Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:23623e1958fca987f22f7447eca7034b585fb7bc7aa9dcdc8ed70ffd603558b2","observation_id":"62188d16-8ca6-488e-a1e5-7ead13b8fb93","resolution":{"observed_at":"2026-08-08T05:49:19.850664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.825684Z","title":"Overlap-guided coarse-to-fine correspondence prediction for point cloud registration","venue":null,"work_id":"389ae4f7-047d-4257-9ebe-982ad1a6e256","year":2022},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.270580Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:bc4508679d57f2cc4bb1871da8eae1b28e607f1e5ff09bc9b5ec112cce89fd04","observation_id":"ac0af339-6209-4273-84e8-4bd361ce5b97","resolution":{"observed_at":"2026-08-08T05:49:19.829032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.816837Z","title":null,"venue":null,"work_id":"90d81276-1cef-4bf9-bc69-154b59c07cae","year":2022},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.273883Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:ae9a9097768b39b96761200e62c70a90bafb957f71ef71bf414054902844ad73","observation_id":"5e4cfb93-d770-4ada-9b22-98656003068b","resolution":{"observed_at":"2026-08-08T05:49:19.819908Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.807048Z","title":"Overlap-guided gaussian mix- ture models for point cloud registration","venue":null,"work_id":"405b3b55-ac63-4869-a8d5-3b772bc02c8f","year":2023},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.277397Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:e863a97dcdac5652787b5e2d8003f602e1d45d387d5c2e73c7ab5c1236ce09c9","observation_id":"66185ead-5e25-4d0e-8704-8cc96883ddf8","resolution":{"observed_at":"2026-08-08T05:49:19.811189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.797605Z","title":"Unsu- pervised deep probabilistic approach for partial point cloud registration","venue":null,"work_id":"4702c842-ef87-43a7-a36f-53f0d34d7fc1","year":2023},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.280857Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:5cf70595b4eb2170d673edf3ba00a257a397e6541d873773bcf6c61f39f18f44","observation_id":"c4e211e5-475f-4ad1-b331-e64fdf26364f","resolution":{"observed_at":"2026-08-08T05:49:19.800877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.788581Z","title":"Hgan: Holistic generative adversarial networks for two- dimensional image-based three-dimensional object retrieval","venue":null,"work_id":"3bd16bce-5d92-4224-a277-33a1a7077870","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.284169Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:19d2228cd938386f00195a933c56453efa64f3bc1596d4de5690c752fc8997c5","observation_id":"3a8b7fbe-d378-48cc-93e6-5da1eea01719","resolution":{"observed_at":"2026-08-08T05:49:19.791970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.779005Z","title":"T2td: Text-3d generation model based on prior knowledge guidance.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024","venue":null,"work_id":"c7f5f5c8-73a2-4255-81fc-ebb3b0a18a41","year":2024},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.287517Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:9c19716b1f95211078789ed41d05c31dc02287aa3adb5edc6e90b4b2e50573e8","observation_id":"cbd3d18f-407d-4a1d-8e90-dac9f8cc515b","resolution":{"observed_at":"2026-08-08T05:49:19.782849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.769998Z","title":"3dregnet: A deep neural network for 3d point registration","venue":null,"work_id":"b4407f85-733e-4fb4-bfec-1eb8f246577f","year":2020},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.291170Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:ccf3da8f202bec05172673a52986d3ff8c908649331d5cd14a9223add335baa3","observation_id":"03131662-1fd7-4606-bdf2-4ebe5cada769","resolution":{"observed_at":"2026-08-08T05:49:19.773274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.760767Z","title":"Computational optimal transport: With applications to data science.F oundations and Trends® in Machine Learning, 11(5-6):355–607, 2019","venue":null,"work_id":"ad596134-e57e-4b17-94d5-382053de0838","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.294440Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:ed8a58ea71813a43810cddbac20dd31e613f6471e47a1304c1a20995f6c3c4ba","observation_id":"c36005f7-1974-44ec-8ed8-e8c133a05c48","resolution":{"observed_at":"2026-08-08T05:49:19.764236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.10382","last_updated":"2022-05-12T19:47:29Z","snapshot_observed_at":"2026-08-06T06:30:06.952978Z","submitted_at":"2021-05-21T14:47:55Z","title":"Learning general and distinctive 3D local deep descriptors for point cloud registration","version":3},"cited_work":{"arxiv_id":"2105.10382","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.10382","snapshot_observed_at":"2026-08-08T05:49:19.409795Z","title":"Learning general and distinctive 3D local deep descriptors for point cloud registration","venue":"cs.CV","work_id":"6549a619-cf3c-45e0-bd76-767d7749d1ea","year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.297735Z"},"links":{"cited_paper":"/paper/2105.10382","citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:41631ad90c58302e15245a462c4b1a2577d5d32ab984c2dfb5c6b6fb6fdb50d4","observation_id":"88a0205f-2af5-4d03-bb9d-bef11d746f52","resolution":{"observed_at":"2026-08-08T05:49:19.415469Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.750212Z","title":"Geometric transformer for fast and ro- bust point cloud registration","venue":null,"work_id":"6a0c3f4e-cc27-488c-a51f-56fd911c03c2","year":null},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.301437Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:718683a272d23f23561b979ae49d13ff4b0f8be8bd41d2426ee1b5370965c23f","observation_id":"c784b6f6-9f58-43ae-9053-6f62677992ce","resolution":{"observed_at":"2026-08-08T05:49:19.753920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.739150Z","title":"Kpconv: Flexible and deformable convolution for point clouds","venue":null,"work_id":"5b0b2296-cc79-4ef1-b520-0bef3791c302","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.304949Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:e07994f6498dcca6e2825b3e25d8c5feb0adec43c58384da5cccd59994161228","observation_id":"65e43c2a-58d7-439a-8913-446ae43b57cf","resolution":{"observed_at":"2026-08-08T05:49:19.742767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.728128Z","title":"Attention is all you need.NeurIPS, 30, 2017","venue":null,"work_id":"ce8fc6ae-34fc-4535-bee1-930884735e97","year":2017},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.308255Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:2cbe02956feb2a0f20a359ff63a5e750583a55caf4d9c7dd66b181357bf8d7fc","observation_id":"30b15b53-f601-44d8-bc92-5e5a6400ce77","resolution":{"observed_at":"2026-08-08T05:49:19.731892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.716453Z","title":"You only hypothesize once: Point cloud registration with rotation-equivariant descriptors","venue":null,"work_id":"37fd09d0-3202-464c-b70e-d057d40f9cb8","year":2022},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.311557Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:eee1aa14b94fd7e980d9c32d08131131cc9a6fbed98bdb0669b78d0914372a1e","observation_id":"4c15d4aa-a828-4d4e-8fff-bfef3085bfa6","resolution":{"observed_at":"2026-08-08T05:49:19.720234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.705033Z","title":"Roreg: Pairwise point cloud registration with oriented descriptors and local rotations.TPAMI, 2023","venue":null,"work_id":"ccc54c91-323d-4e48-b069-b65371d9584c","year":2023},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.314908Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:bf5f274183f80834e51ee8cb190287a240978ab5d51a13601873a846bd08642a","observation_id":"e46abc1f-b760-4842-a92d-82316dc34321","resolution":{"observed_at":"2026-08-08T05:49:19.709117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.692565Z","title":"Zeroreg: Zero-shot point cloud registration with foundation models,","venue":null,"work_id":"4d914e53-87c9-49ba-ad96-a41ed26a25b7","year":null},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.318410Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:5083dc24627a1544805904822c8aa05964ba650567e66a782f44f3a02b8cb586","observation_id":"14330b2c-ce86-43a7-8231-77fb2ecbe162","resolution":{"observed_at":"2026-08-08T05:49:19.696700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.681441Z","title":"Uvmap-id: A controllable and personalized uv map generative model","venue":null,"work_id":"b8b3c74f-8594-4b3b-9f43-08a54e82d7e7","year":2024},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.322687Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:047c3feabb5cada4c37086af150ad175b85865a140639506c9bd74a930189e02","observation_id":"33d213c7-8df1-44e7-ab42-410a5ec8df18","resolution":{"observed_at":"2026-08-08T05:49:19.685220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.669448Z","title":"Deep closest point: Learn- ing representations for point cloud registration","venue":null,"work_id":"6e810803-772f-41bb-91a9-7c754e2d943d","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.326158Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:9616f6b5e763b66dc0639f6e8516e2fe44106b1b3b665c1189ef484a3fd12366","observation_id":"478a3d94-eb53-41da-8e56-29793e7ac1cc","resolution":{"observed_at":"2026-08-08T05:49:19.673729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.657845Z","title":"Prnet: Self-supervised learning for partial-to-partial registration","venue":null,"work_id":"6ce27aa5-740d-4fdc-bb41-db3939f9c330","year":2019},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.329519Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:3c7ea79be445e5c5ad7e791122a6580c6964c9967e97ec27ca7e7b6b28e88da5","observation_id":"a0f36f13-43b2-4106-ade1-9908ed09c66d","resolution":{"observed_at":"2026-08-08T05:49:19.661762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.646018Z","title":"A bayesian regularization network approach to thermal distortion control in 3d printing.Computational Me- chanics, pages 1–18, 2023","venue":null,"work_id":"1cab8097-0b63-444e-8407-06a60cad4249","year":2023},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.332795Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:f2b2bddf4c27b63a9566890d74d66a987c5c95541df05439e51e4a5717ba5a29","observation_id":"72bb65fe-0bd8-4de5-8e4e-b2d5fc380755","resolution":{"observed_at":"2026-08-08T05:49:19.650130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.634395Z","title":"Omnet: Learning overlapping mask for partial- to-partial point cloud registration","venue":null,"work_id":"179b4e8f-4f13-41f1-974d-602b9e2f8a7d","year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.336208Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:7ad94c5bf321ab00c2d2a92073185fb40a2eb1d7c05bbe6fa462d6dd76879a04","observation_id":"811e16e1-d0f2-48cf-843c-41e7172cd41c","resolution":{"observed_at":"2026-08-08T05:49:19.638347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.622423Z","title":"Glorn: Strong generalization fully convolutional network for low- overlap point cloud registration.T-GE, 60:1–14, 2022","venue":null,"work_id":"297102c0-d9e3-4cde-ac87-e27aa4aed3b7","year":2022},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.339442Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:869e77784ddccfc650b823bb4dcc03f4cc29cf069e6e6ba9baa2489a4d2ecfbe","observation_id":"b303b0bc-07a4-4754-9e67-332a4a91c998","resolution":{"observed_at":"2026-08-08T05:49:19.626408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.611188Z","title":"Rpm-net: Robust point matching using learned features","venue":null,"work_id":"49528391-96e6-45ff-8fdc-7e47ec0f485b","year":2020},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.342882Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:bb9bcc4535023b84d12d219d441367b03173cda0ad279331c769e003ac51c4ad","observation_id":"93f368c3-6216-4416-8bd0-625c6109673d","resolution":{"observed_at":"2026-08-08T05:49:19.614917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.597628Z","title":"Regtr: End-to-end point cloud correspondences with transformers","venue":null,"work_id":"487891e5-6540-4fcd-9495-0c713746e5d3","year":2022},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.346651Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:3947f93f9b8b2bfbbcc06fc16bf98ee05e05f15898ed78cb575ef95475212b30","observation_id":"7ed753c0-c7b7-4da4-86b7-db4379cc7961","resolution":{"observed_at":"2026-08-08T05:49:19.603129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.585395Z","title":"Cofinet: Reliable coarse-to-fine correspon- dences for robust pointcloud registration.NeurIPS, 34, 2021","venue":null,"work_id":"4bd0d0d5-de99-4375-9af7-5fa454298e1d","year":2021},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.349893Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:3104e815ffee45b0e625cfd1800c684473a59731786a48fbed20ee512ecabac4","observation_id":"ffb96856-c764-4f3e-b79a-fe6132be06f2","resolution":{"observed_at":"2026-08-08T05:49:19.589258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.573515Z","title":"Rotation-invariant transformer for point cloud matching","venue":null,"work_id":"f08afd36-9221-4dc1-be7e-bcd333a26d90","year":2023},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.353152Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:c2a800e4f035f213b4e065743af47815b19ff0c5c663a55b6c2eae8ff0fa6617","observation_id":"acadbade-0cc3-4bbb-82a3-35a496114db9","resolution":{"observed_at":"2026-08-08T05:49:19.577609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.561660Z","title":"3dmatch: Learning local geometric descriptors from rgb-d reconstruc- tions","venue":null,"work_id":"6d884581-1193-4498-8241-cc5de8a3602e","year":2017},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.356710Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:062e93d1254356aecba279e5b78a8085b23a59c095596f4acbd66d6750996d83","observation_id":"0a1b30a1-35a6-4114-a95f-de37fdf44f9b","resolution":{"observed_at":"2026-08-08T05:49:19.565455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.549920Z","title":"Patchformer: An efficient point transformer with patch at- tention","venue":null,"work_id":"312d5d5c-5e39-4973-9c1c-0a728af54274","year":2022},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.360092Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:79f3fca0dbc8be0ecb176b1144a7534cbee3d1c2e8162a626947489afe771c0a","observation_id":"74325c72-cad6-4b41-b663-fbffe6e0e6a3","resolution":{"observed_at":"2026-08-08T05:49:19.554267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:49:19.537471Z","title":"Deep learning based point cloud registration: an overview.VRIH, 2(3):222– 246, 2020","venue":null,"work_id":"7c46d05b-b2da-4186-9a30-ed0252d4ef90","year":2020},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.363385Z"},"links":{"citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:84fb56451a2728948545a19f5d1eb7333c0e299b536e826a5ac4fb3214975956","observation_id":"672aaff0-919d-4936-97a5-7c7fa3f2a293","resolution":{"observed_at":"2026-08-08T05:49:19.541303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.09847","last_updated":"2018-01-30T04:33:20Z","snapshot_observed_at":"2026-07-06T06:20:51.245757Z","submitted_at":"2018-01-30T04:33:20Z","title":"Open3D: A Modern Library for 3D Data Processing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.09847","snapshot_observed_at":"2026-08-08T05:49:19.366635Z","title":"Open3d: A modern library for 3d data processing.arXiv preprint arXiv:1801.09847, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T05:49:19.366635Z"},"links":{"cited_paper":"/paper/1801.09847","citing_paper":"/paper/2502.08285"},"observation_digest":"sha256:aaf6c2621ed731803fc90f081e07d9829ac823b386dc9b7c2c5060b5e5327d92","observation_id":"bba84585-a313-4b2b-9505-78e7aa23a028","resolution":{"observed_at":"2026-08-08T05:49:19.366635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.08285","last_updated":"2025-06-08T16:11:07Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T05:40:38.119016Z","submitted_at":"2025-02-12T10:44:36Z","title":"Fully-Geometric Cross-Attention for Point Cloud Registration"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":44},"total_outbound_references":50},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2502.08285."}