{"as_of":"2026-08-13T06:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9928824b8992784f7855f2b4fcac16b7b36b05f5a3fdf84351607bdd176e9b07","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:11:28.900273Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2506.22433/citation-record","integrity":"/paper/2506.22433/integrity","json":"/paper/2506.22433/citation-record.json","paper":"/paper/2506.22433"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:34.697046Z","title":"Mip-nerf 360: Unbounded anti-aliased neural radiance fields","venue":null,"work_id":"48d5a897-eeb2-4143-b505-9af084298fda","year":2022},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:25.597542Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:5bff6495b99698687b73d876e41f0cf24e8ecf8c6f2a2d983fde95811b9358ab","observation_id":"def8c097-7e7a-42ee-a206-724f1fb3f100","resolution":{"observed_at":"2026-08-06T22:11:34.862653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06521","last_updated":"2025-01-10T12:05:16Z","snapshot_observed_at":"2026-08-12T23:46:13.267458Z","submitted_at":"2024-06-10T17:59:01Z","title":"PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06521","snapshot_observed_at":"2026-08-06T22:11:25.666682Z","title":"Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:25.666682Z"},"links":{"cited_paper":"/paper/2406.06521","citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:8da1fcad8c0dce91657b34211275af95150674f28d12e2a6c0d10bfcb8ace26b","observation_id":"4f9935d2-ad3a-4ef0-bf64-d61017433dc5","resolution":{"observed_at":"2026-08-06T22:11:25.666682Z","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":"2026-08-06T22:11:34.393047Z","title":"Depth-supervised NeRF: Fewer views and faster training for free","venue":null,"work_id":"af6634a3-1fa5-4f93-89d9-617b4477aec7","year":null},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:25.750570Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:2d1da50ff2f7cc784f7ea5b0818c7ddbbcfe1a828fefad5ffc3172fcef29b524","observation_id":"05f0832c-b92f-4241-8f02-5ccf1d34ceb3","resolution":{"observed_at":"2026-08-06T22:11:34.519382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:34.223307Z","title":"Accurate, dense, and ro- bust multiview stereopsis","venue":null,"work_id":"c4e18e6e-d7e3-4b64-9a2c-14c02cfc8247","year":null},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:25.822201Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:adcee7abfcb4efe543dfccb2e23c9cb763bd2db9faa9416d196ea30b982ca7c9","observation_id":"4037ca4c-7dec-4685-85ad-5321c6bcc465","resolution":{"observed_at":"2026-08-06T22:11:34.283882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:34.062950Z","title":"A survey of uncertainty in deep neural networks","venue":null,"work_id":"97747df3-bb12-4aa1-acee-a18e79a5c226","year":2023},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:25.920139Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:7e9083c28644fc55d90078a042ab729c7f587621e4076d7cbfac8b7433f08752","observation_id":"a2aaffee-95c9-4c2b-9254-c1ba3ac28d71","resolution":{"observed_at":"2026-08-06T22:11:34.129690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.931399Z","title":null,"venue":null,"work_id":"60d2efa5-ee8f-48b1-8b79-43c1185ebc56","year":2019},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:25.999833Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:abfce0bbfe18a18a893054bd3dbc2363cf7e448819edcb3f313c73b0ddd0c10e","observation_id":"d8a2a37c-a4c5-4c2d-855e-993a87324a25","resolution":{"observed_at":"2026-08-06T22:11:33.997866Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.808661Z","title":"Bayes’ Rays: Uncertainty quantifica- tion in neural radiance fields","venue":null,"work_id":"7c3ab4cf-8dae-442a-9bc5-771f6ae18c5b","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.070295Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:f508b0973bdafc0b3f342b36099c4eed74391e0e17223992f49025d86d559a92","observation_id":"a7224f1c-1b9d-473d-9b28-bfcb75e99072","resolution":{"observed_at":"2026-08-06T22:11:33.867607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.693621Z","title":"Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering","venue":null,"work_id":"793013d3-a695-40b7-a4d0-1c5c1a16129e","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.159686Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:31a777f38eb3864633d54103b6dc38976e8d9010db9d0e2723ff80149fa379c8","observation_id":"bd0dc827-2a17-44ee-b975-9d725335d85f","resolution":{"observed_at":"2026-08-06T22:11:33.738589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.558806Z","title":"Scone: Surface coverage optimization in unknown environ- ments by volumetric integration","venue":null,"work_id":"8bc55134-615b-4d87-ba41-e47578201c9d","year":2022},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.253146Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:2d924136862d95041acacbab7d9029ede012c3f19244980ad4afb4b5218a5235","observation_id":"44b6fbc4-671a-4fb9-a505-06f77c2f9434","resolution":{"observed_at":"2026-08-06T22:11:33.625313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.428862Z","title":"Macarons: Mapping and coverage anticipation with rgb online self-supervision","venue":null,"work_id":"6d1ab8df-856d-49eb-a0bb-30c1d9b3b339","year":null},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.334327Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:039054f9b63354466143d9849068db4f2358d5f5ee06296e40bc18b599a59300","observation_id":"b2592955-f853-4afd-90f2-0c66139c735b","resolution":{"observed_at":"2026-08-06T22:11:33.474835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.288311Z","title":"Cg-slam: Efficient dense rgb-d slam in a consistent uncertainty-aware 3d gaussian field","venue":null,"work_id":"360f2093-bf12-4c1a-8c7a-9f94c230ccc9","year":null},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.377055Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:aa97cc7820630b6d3c27f73390b587d1cca928a7a36d718ae4ae49886c66efbf","observation_id":"6fff0351-67f1-4fbf-86c8-2a48cb3bf82c","resolution":{"observed_at":"2026-08-06T22:11:33.341689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.141901Z","title":"Fisherrf: Ac- tive view selection and mapping with radiance fields using fisher information","venue":null,"work_id":"584b54ce-af00-44f0-a8cb-a7eb0ccb212c","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.464227Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:af3979095ee6f67d23e28a38ee4c8d2a8dcb60fbcb81e70a8ab9a49da7a0ef45","observation_id":"8a1d0812-d2ab-4f57-8b83-9a80cfa8f62f","resolution":{"observed_at":"2026-08-06T22:11:33.210547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:33.032144Z","title":"What uncertainties do we need in bayesian deep learning for computer vision? In NeurIPS,","venue":null,"work_id":"033d97d2-9844-45e6-b627-817fb968d5f4","year":null},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.556247Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:db32b54aea1b7cb33905803c2da69decf0c197e886e17ce3a12cf3e6214b6971","observation_id":"a2f563e7-2d63-4828-b370-27bbba9aa34b","resolution":{"observed_at":"2026-08-06T22:11:33.083783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:32.831334Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":"be77b027-e48b-43f4-a628-b06e95dfef62","year":2023},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.640258Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:c61e4e85c703e2bc9810cf37595921d3b2b3bf9ac87ce60ecd7836cb1d1d2ecb","observation_id":"40e0ca0c-6449-4fe9-bb30-8ac13b035360","resolution":{"observed_at":"2026-08-06T22:11:32.942611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:32.674451Z","title":"4d gaus- sian splatting in the wild with uncertainty-aware regulariza- tion","venue":null,"work_id":"e733823c-6bfa-4d1f-8b04-ab8053d35fb1","year":2025},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.728867Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:f9439aabded16f9c5fd7d0454fa719aa380957885881aa23163d66ab59744995","observation_id":"9f56e54a-6eee-4f4e-a47f-21cc8b1a79d5","resolution":{"observed_at":"2026-08-06T22:11:32.762259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:32.538059Z","title":"Sources of uncertainty in 3d scene reconstruction","venue":null,"work_id":"beb8048e-40ad-4df9-a08f-a8245008079b","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.812523Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:6902e8b9e496a27beae430bca596f51b450062e040dbb81b38d9e5f25f3b60f7","observation_id":"b638d98e-5103-44f6-b646-a210bae2af91","resolution":{"observed_at":"2026-08-06T22:11:32.601451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:26.898851Z","title":"Tanks and temples: Benchmarking large-scale scene reconstruction","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.898851Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:86ab946fe5700aa1e81a0f715075ced551d61866997a132a06848f196a1193b6","observation_id":"1f2c5728-c91e-4ca2-accc-1fb816088b9b","resolution":{"observed_at":"2026-08-06T22:11:26.898851Z","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":"2026-08-06T22:11:32.350809Z","title":"Uncertainty guided pol- icy for active robotic 3d reconstruction using neural radiance fields","venue":null,"work_id":"310aa1e1-db36-4f4d-90a1-b90f6a935f77","year":2022},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:26.976175Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:f04a90601fc85a8781700b3eca240b062c7d3374dd4880c2d8558035ca802c60","observation_id":"33234e57-e7f0-4496-a239-fd01cb507dda","resolution":{"observed_at":"2026-08-06T22:11:32.452128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:32.215977Z","title":"Manifold sampling for differentiable uncer- tainty in radiance fields","venue":null,"work_id":"46d053f9-703f-4f47-af51-a2430d41be22","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.018479Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:61e022aa9bacf846ee0c885355e2d1ac114be00cf3451dcdda31f3a93796b8dc","observation_id":"4824f142-9e82-439a-a0ac-cfb0cea97aba","resolution":{"observed_at":"2026-08-06T22:11:32.279983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:32.022508Z","title":"Srinivasan, Matthew Tancik, Jonathan T","venue":null,"work_id":"3b3052a2-ed87-48ac-8be8-f3022c6110f8","year":2020},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.067070Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:64837deb84f009938e74fade5972f61f05ec7076c95c4d9bc20abf2e083c81a3","observation_id":"91880e5f-42e0-4018-97db-be6551a1de52","resolution":{"observed_at":"2026-08-06T22:11:32.116824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:31.811412Z","title":"Ac- tivenerf: Learning where to see with uncertainty estimation","venue":null,"work_id":"ae70cb10-21bc-4e24-8288-12372069550e","year":2022},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.148965Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:4a6dfb04e5576e2711ea5e61c68deb632b65bf9ba23d0af464fe85dcd5320727","observation_id":"0e4037f1-c28d-4df3-9850-b1397c2935c8","resolution":{"observed_at":"2026-08-06T22:11:31.902750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:31.645468Z","title":"On the confidence of stereo matching in a deep- learning era: a quantitative evaluation","venue":null,"work_id":"b3a17da0-d07d-4c3f-bef0-93445e52a415","year":2022},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.240896Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:62cf2e1b335b5a73764f6c647ff697e06d4eb74f018619c73a151ecb3366d798","observation_id":"bb1886dd-d561-47c7-be2b-62fae212c058","resolution":{"observed_at":"2026-08-06T22:11:31.747721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:31.524868Z","title":"Neurar: Neural uncertainty for autonomous 3d reconstruction with implicit neural representations","venue":null,"work_id":"4ed514eb-32ff-4564-8ca8-9fb6e4ad34a2","year":2023},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.363539Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:39996e640460455d3566b76044f7dc76c437ef782da223b8f92f7ded75d8be51","observation_id":"ebeee658-3c49-4f96-a865-b010ee7b3c1b","resolution":{"observed_at":"2026-08-06T22:11:31.571725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:27.420286Z","title":"Nerf on-the-go: Exploiting uncertainty for distractor-free nerfs in the wild","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.420286Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:20a969430ec76ffdd634144affa1d5c9bdac9a6b1a87747ac1c2e7ab3cab4103","observation_id":"3527ab3d-e530-40f3-bf87-b7bf2a65ad09","resolution":{"observed_at":"2026-08-06T22:11:27.420286Z","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":"2026-08-06T22:11:31.364227Z","title":"Barron, Ben Mildenhall, Pratul P","venue":null,"work_id":"9cc2c8dc-4711-4665-a2e4-40daff172fc9","year":2022},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.474149Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:d8d1b3c63e1db60e6f974bd5118f3e2eb1d36baa3f883b4f825d3fd9afa197c2","observation_id":"68e91fc9-5843-49db-a913-4de4f674a585","resolution":{"observed_at":"2026-08-06T22:11:31.432625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:31.257016Z","title":"Self-evolving depth-supervised 3d gaussian splatting from rendered stereo pairs","venue":null,"work_id":"c963421f-7cab-450a-8afc-059531dd7644","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.587187Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:02e8930d17a0ade3f50a37a26a31cadd334b02f1fdf6e35ddeb0be3001b73084","observation_id":"f7e3c652-e8b6-45df-8516-f747b8515d21","resolution":{"observed_at":"2026-08-06T22:11:31.301995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:31.036789Z","title":"A multi-view stereo benchmark with high- resolution images and multi-camera videos","venue":null,"work_id":"ff5714a6-3e1e-49f8-9002-07d850af1c67","year":2017},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.685188Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:35c9ec8c8c2db072cb96a58f6bed1e2d7b2251da215000ae735fdc3cd605dbec","observation_id":"fccc7280-7578-4a71-a25b-be95611daaa6","resolution":{"observed_at":"2026-08-06T22:11:31.140749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02123","last_updated":"2021-09-28T08:39:47Z","snapshot_observed_at":"2026-08-12T04:04:45.328201Z","submitted_at":"2021-09-05T16:56:43Z","title":"Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02123","snapshot_observed_at":"2026-08-06T22:11:27.784004Z","title":"Stochastic neural radiance fields: Quan- tifying uncertainty in implicit 3d representations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.784004Z"},"links":{"cited_paper":"/paper/2109.02123","citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:090c6d2a7eb148894fab5dab72cf366e2b121f3f317bd985d465f5b21a5880ef","observation_id":"f19dc2d4-d836-4261-931c-53c547e4c1f1","resolution":{"observed_at":"2026-08-06T22:11:27.784004Z","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":"2026-08-06T22:11:30.875788Z","title":"Conditional-flow nerf: Accurate 3d mod- elling with reliable uncertainty quantification","venue":null,"work_id":"166c9def-d8de-428f-a709-a387e32ecdad","year":null},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.854832Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:f8f9b58317415528630692087323a298c3a17702ee56ab5fd4071457883e63fc","observation_id":"d74dc978-5238-46bf-a19c-6cd717449e41","resolution":{"observed_at":"2026-08-06T22:11:30.972856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:30.654405Z","title":"Estimating 3d uncertainty field: Quantify- ing uncertainty for neural radiance fields","venue":null,"work_id":"94f00a01-ec6e-48fc-ab75-91e3277d9c58","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:27.962547Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:896ad378d538ee75366b23f5ba7fb3a13de1f95b6a53f76214f1d30b0432a53f","observation_id":"3443b3b5-4cbe-4231-826e-ffda635dad12","resolution":{"observed_at":"2026-08-06T22:11:30.755384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:30.508187Z","title":"imap: Implicit mapping and positioning in real-time","venue":null,"work_id":"dbe6ad71-001b-4d46-abb1-61609502aec1","year":2021},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.057966Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:35cf6dcb1166acce7232894794b924bccfd4d9c53ceae275a104b8a0604e345d","observation_id":"074a96e2-179d-429a-93ed-2df4367f977a","resolution":{"observed_at":"2026-08-06T22:11:30.589434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04459","last_updated":"2025-03-17T07:23:56Z","snapshot_observed_at":"2026-08-11T21:21:36.422412Z","submitted_at":"2024-12-05T18:59:11Z","title":"Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04459","snapshot_observed_at":"2026-08-06T22:11:28.145240Z","title":"Sparse voxels rasterization: Real-time high-fidelity radiance field rendering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.145240Z"},"links":{"cited_paper":"/paper/2412.04459","citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:7b236eaaf47b7fbaa51d3bea5ee28a8d5a91c3c18c90bcbb052d1764a0ad3704","observation_id":"2c420039-5504-4103-b14a-6cb7c04db0b2","resolution":{"observed_at":"2026-08-06T22:11:28.145240Z","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":"2026-08-06T22:11:30.335142Z","title":"Density-aware nerf ensembles: Quantifying predictive un- certainty in neural radiance fields","venue":null,"work_id":"4e777466-0b5f-46ce-a08b-188bee09d23c","year":2023},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.189729Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:8249b9a93c04b1e59945a3acfc34178f1c73dc5b657282a2b9ceafdd9eb6af95","observation_id":"8acd6a10-c8af-46c6-b690-d2ee81e67fd2","resolution":{"observed_at":"2026-08-06T22:11:30.400567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:30.173281Z","title":"Dn-splatter: Depth and normal priors for gaussian splatting and meshing","venue":null,"work_id":"fe723abb-d397-42e0-8efa-4d4a1ce7d3a4","year":2025},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.267185Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:af5818a23cd868e4dec4e37acc8e493e1f4de8050c8568c2af088770469ecbe1","observation_id":"9760d681-07d5-4109-9c04-3fadce90f9b3","resolution":{"observed_at":"2026-08-06T22:11:30.257868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:29.985276Z","title":"Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction","venue":null,"work_id":"e3b7ced8-93e0-417c-a831-9ef993cf64a0","year":2021},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.358290Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:8cf583dedcf85961a3e70bfc36a28fa1b141804ce7d325cde701ddbdd04273e8","observation_id":"d8e902e8-6390-46c2-9151-38a7273ad372","resolution":{"observed_at":"2026-08-06T22:11:30.066068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:29.827564Z","title":"Neural visibility field for uncertainty-driven active mapping","venue":null,"work_id":"cb7fe125-6064-408a-9543-422780f0bf15","year":2024},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.457158Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:6be9de6f87725e6cac480b1d09294a80679c4f9561d3ebd83ee1f5bf6b7702ba","observation_id":"60b4d8b8-dcce-4d7c-87f7-8a47afd5d614","resolution":{"observed_at":"2026-08-06T22:11:29.897134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:29.687123Z","title":"Active implicit object reconstruction us- ing uncertainty-guided next-best-view optimization","venue":null,"work_id":"46843e17-5a4b-49a6-bdd0-f88b7d0042cb","year":2023},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.514759Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:7a93be89d194087d3208ba26e8820f89396abf959e369c2a720bcc995eef23f9","observation_id":"fa995bb1-97c2-4b69-8511-60f46c241f5e","resolution":{"observed_at":"2026-08-06T22:11:29.737647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:29.530370Z","title":"Active neural mapping","venue":null,"work_id":"4fa7cd51-9225-4fcb-b089-23e6e8d00639","year":2023},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.606009Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:0b4371e2dac46363a56c50e1b1d91f6ef2fdd9eaafb2a9bfa18bc4608f1b553b","observation_id":"d6aded81-3643-4dc9-8def-ff730c36275d","resolution":{"observed_at":"2026-08-06T22:11:29.590024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:29.355427Z","title":"Scannet++: A high-fidelity dataset of 3d indoor scenes","venue":null,"work_id":"a20b8c19-fa78-4883-85f1-90c75a6c3cc9","year":2023},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.721269Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:bd2e6d97a8cf7abed2fb469b5a188ef15df1a01260cfccb49362a5c23d2ebdbf","observation_id":"94048e4f-78ba-4a86-a276-442f9fed07fb","resolution":{"observed_at":"2026-08-06T22:11:29.430941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:29.205342Z","title":"Efficient 3d object segmentation from densely sampled light fields with applications to 3d re- construction","venue":null,"work_id":"cfb0f9f4-e6fe-48aa-9490-422f88de9a6b","year":2016},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.835126Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:ad9561184dea51de39e32959f7bf551c0bfaa1a044e10f233c66391dcff7959e","observation_id":"d03f2cfe-b690-4108-af28-3677cbb7e944","resolution":{"observed_at":"2026-08-06T22:11:29.265224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T22:11:29.053920Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","venue":null,"work_id":"9f5727b6-172d-48d0-aa7c-8308837738c4","year":2022},"citing_paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:28.900273Z"},"links":{"citing_paper":"/paper/2506.22433"},"observation_digest":"sha256:89dade79c24558e6ee3d14a4b0f0374bb780633b44f785a2b31946dda0042700","observation_id":"3e8b4016-ce22-42d4-96f2-ac4862385750","resolution":{"observed_at":"2026-08-06T22:11:29.107172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.22433","last_updated":"2025-06-27T17:59:13Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T02:44:32.951436Z","submitted_at":"2025-06-27T17:59:13Z","title":"WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":35},"total_outbound_references":41},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.22433."}