{"as_of":"2026-08-07T04:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b26420aff5835695b2a841c0f51c3c9df25fa1bcb4764ae454840106eaffe953","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T11:58:53.399042Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2601.04860/citation-record","integrity":"/paper/2601.04860/integrity","json":"/paper/2601.04860/citation-record.json","paper":"/paper/2601.04860"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:50.503592Z","title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation.IEEE transactions on pattern anal- ysis and machine intelligence, 39(12):2481–2495, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:50.503592Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:8314faf8b2abe0092d311120f0a88f7c8d7dcc3b310b208c39b821f7883bced8","observation_id":"69852e4a-4697-4977-bdf5-55849e9a9794","resolution":{"observed_at":"2026-08-03T11:58:50.503592Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:50.556943Z","title":"Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:50.556943Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:5a7631841631c05afa89e6e5d67266766f1e86ce7b14d26aa5297be7e074d0d1","observation_id":"0a91b3ca-e74c-427a-8b14-6f6b4e766090","resolution":{"observed_at":"2026-08-03T11:58:50.556943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:50.633152Z","title":"Mip-nerf 360: Unbounded anti-aliased neural radiance fields","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:50.633152Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:fa1ff6f8ea87b310ffcdee4e4785d35da1d6521b8e73eaee82f794a86a525163","observation_id":"ebd3ae3a-8af6-4f5e-97f4-e8bfc56a2003","resolution":{"observed_at":"2026-08-03T11:58:50.633152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:50.699728Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:50.699728Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:5ebbd28f59f3b35638f69f7969b87b57db7c917686493f9e355ecc45c18ae1d1","observation_id":"760b223c-e6b6-4c06-95e5-d2a5291dbdf3","resolution":{"observed_at":"2026-08-03T11:58:50.699728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:50.789242Z","title":"Seg- ment anything in 3d with nerfs.Advances in Neural Infor- mation Processing Systems, 36:25971–25990, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:50.789242Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:138a73211318f129baf08066404a79937211ca62e216b8439fb04ebb2a833f6d","observation_id":"511f13a9-707f-4a25-ba5e-6381d01cb273","resolution":{"observed_at":"2026-08-03T11:58:50.789242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:50.853740Z","title":"Tensorf: Tensorial radiance fields","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:50.853740Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:e4c6320ec04cc87bce07c103bc1378e178264223b987f289f754457173c37f43","observation_id":"c24da50d-c075-404e-9f3d-fadd01c735df","resolution":{"observed_at":"2026-08-03T11:58:50.853740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:50.922758Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:50.922758Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:ae0f723f0cc9645a727abaaa30e73c5f367f2a99fca48f45d6985eba3dac151b","observation_id":"10bb894f-fa59-4414-8429-55a87dea4294","resolution":{"observed_at":"2026-08-03T11:58:50.922758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-03T11:58:51.040953Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale.arXiv preprint arXiv:2010.11929, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.040953Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:5b17af438ec57bfe10287e0c1376f45861f95bc25eba804ff4496cf7e4d22965","observation_id":"b4efe1d9-b1c0-445c-952a-f7e4196faf7e","resolution":{"observed_at":"2026-08-03T11:58:51.040953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.139777Z","title":"Plenoxels: Radiance fields without neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.139777Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:aa1f05b71a54b8dd3ba5f724ea4764a07fdcd7e2056fe939307a3c632b2d8e4d","observation_id":"c316c1fd-011f-4529-879a-cf35df61e85e","resolution":{"observed_at":"2026-08-03T11:58:51.139777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.272047Z","title":"Fastnerf: High-fidelity neu- ral rendering at 200fps","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.272047Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:6df391ae95ba1eb8c7ddb58cc1f88dfd4f7e9504615af246e0a2a32cc84b2a74","observation_id":"21f713b4-20d8-4712-8c38-c86a89fd91ac","resolution":{"observed_at":"2026-08-03T11:58:51.272047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.391971Z","title":"Interactive segmentation of radiance fields","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.391971Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:ef6dad1aa01429654c569b9baa570f19270d8218ee1495e45ca8333c5b820c91","observation_id":"a7441fe4-0181-465f-ad8e-8d829ff15177","resolution":{"observed_at":"2026-08-03T11:58:51.391971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.481236Z","title":"Measurement of areas on a sphere using fibonacci and latitude–longitude lattices.Mathematical geo- sciences, 42(1):49–64, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.481236Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:81a06a2d9d2bf0f5b5d59743cf9b0cbe7f2eb127c684a76a8e978f43265d8622","observation_id":"7d843b99-408d-4cb6-a101-5e4e59553f3b","resolution":{"observed_at":"2026-08-03T11:58:51.481236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.514857Z","title":"Mask r-cnn","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.514857Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:1417e9ff0680aeecc776d0e90aba6eadc97cd4428263dda2c8aac98f367056ad","observation_id":"a0f6db97-057f-4ff7-bb72-29be52d8523b","resolution":{"observed_at":"2026-08-03T11:58:51.514857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.539087Z","title":"Lerf: Language embedded radiance fields","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.539087Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:30fab7d9f7348fff90720959a5af16c81e6f944163d115a6cc94dc34ccdc3207","observation_id":"a2edbd70-cbd8-4e9a-a55d-b514490e307a","resolution":{"observed_at":"2026-08-03T11:58:51.539087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.607623Z","title":"Segment any- thing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.607623Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:a72da13a7815c10a93407c494e40c195b4a9bfbdd25c20b756bf7c25acd475d7","observation_id":"9e514d07-d38c-4e1f-97ae-dd5742d64c83","resolution":{"observed_at":"2026-08-03T11:58:51.607623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.691771Z","title":"Decomposing nerf for editing via feature field distil- lation.Advances in neural information processing systems, 35:23311–23330, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.691771Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:d386cfb1ceea2529c669f61733836a10ac7c0bd1cdd3669e21948354a28cc925","observation_id":"59882d31-68d3-4211-8161-8096b3066672","resolution":{"observed_at":"2026-08-03T11:58:51.691771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.773363Z","title":"Sanerf-hq: Segment anything for nerf in high quality","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.773363Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:0e8c8a92969d3a32b5591e2cc30dc697120b85028faac22fa5dc1522532c7730","observation_id":"972c0436-5b25-478e-9d9a-086c25a54b86","resolution":{"observed_at":"2026-08-03T11:58:51.773363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.804917Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.804917Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:8b65e1e60461dc46fbcf855f6aff142af5702a79befe29b0441e53dfb4ce55b4","observation_id":"80a14468-12b1-4a15-9fc5-99209a2116a3","resolution":{"observed_at":"2026-08-03T11:58:51.804917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:51.923557Z","title":"Local light field fusion: Practical view syn- thesis with prescriptive sampling guidelines.ACM Transac- tions on Graphics (ToG), 38(4):1–14, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:51.923557Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:64ec97feb0d924f242bd08323aeccb64744ffe0015ae9217bb48e7d08ff3481f","observation_id":"670ea766-06b6-4efd-8386-d42b8faa5662","resolution":{"observed_at":"2026-08-03T11:58:51.923557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:52.106529Z","title":"Nerf: Representing scenes as neural radiance fields for view syn- thesis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:52.106529Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:bf6703b225e048854a4fc181a95dc13a4b04aff1c89c62d4ef3db7cfccf6e5a4","observation_id":"ae8a57fa-8ee6-4070-aab8-fa43eb7c1668","resolution":{"observed_at":"2026-08-03T11:58:52.106529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:52.254963Z","title":"Instant neural graphics primitives with a multires- olution hash encoding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:52.254963Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:5fcfbad45ba58b68c7d5447521c692177cd02fcce536b46a2ad5d54d3314eafe","observation_id":"7d2a394e-f387-4de3-8758-78f2c6aa16a8","resolution":{"observed_at":"2026-08-03T11:58:52.254963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:52.410769Z","title":"Langsplat: 3d language gaussian splatting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:52.410769Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:58b177835d86ed37d7cf3be107af53010fe47317de887d81e2d8a43ad403c5f0","observation_id":"19f490a8-dacb-415e-8b3d-b8ba3df0c6e1","resolution":{"observed_at":"2026-08-03T11:58:52.410769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:52.565867Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:52.565867Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:9fd8cd8fef61db48ea410a11ebc24a62952e77eaf0db53cc815f79502a22999d","observation_id":"0dad425c-b8a8-4177-8cd2-aebae429c886","resolution":{"observed_at":"2026-08-03T11:58:52.565867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:52.715512Z","title":"Neural volumetric object selection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:52.715512Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:e767c4b4baf6c2100bd24fe7ee5d6accc6193a3661cb9db68f92048e73e08c54","observation_id":"238588b1-8d64-4b65-baa8-24a7666e3d53","resolution":{"observed_at":"2026-08-03T11:58:52.715512Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:52.834559Z","title":"Segmenter: Transformer for semantic segmenta- tion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:52.834559Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:9bf5213e827cb73876a8a08622d3c129bde2baf426fef108ea78ca68817f0b35","observation_id":"5a78bd3a-e23a-499a-af9a-0d96bdf94f17","resolution":{"observed_at":"2026-08-03T11:58:52.834559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:52.885391Z","title":"Neural feature fusion fields: 3d distillation of self-supervised 2d image representations","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:52.885391Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:afb2021ad52c3fc29bf94cceffe67ab5fe15337e498da9af88c5c5c94aaa072b","observation_id":"9a3857a2-88de-4817-ab4a-25e3215de581","resolution":{"observed_at":"2026-08-03T11:58:52.885391Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:53.106055Z","title":"Nto3d: Neural target object 3d reconstruction with segment anything","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:53.106055Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:98123514f9389d2f88aa9bed602518f3773d751903505bddecceb9577138eb4e","observation_id":"0951cd3e-c7cf-444f-962e-a92d23662202","resolution":{"observed_at":"2026-08-03T11:58:53.106055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:53.172615Z","title":"Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:53.172615Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:ebf3d9db963061519e1ea0d3bd35e5a8c57994f2e83e62993a41b5a253c47580","observation_id":"20de403c-7894-4813-a11b-9ba34436e61f","resolution":{"observed_at":"2026-08-03T11:58:53.172615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:53.252619Z","title":"Efficientsam: Leveraged masked image pretraining for efficient segment anything","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:53.252619Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:4ff06dff35d9a9ad6f8a6a72d1b730a0625265ae18edb6627df397cab4b222b2","observation_id":"e8c2c7bb-f637-4bda-98b2-15cd8d3c2ba1","resolution":{"observed_at":"2026-08-03T11:58:53.252619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.14289","last_updated":"2023-07-01T07:26:22Z","snapshot_observed_at":"2026-07-06T15:46:29.060519Z","submitted_at":"2023-06-25T16:37:25Z","title":"Faster Segment Anything: Towards Lightweight SAM for Mobile Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.14289","snapshot_observed_at":"2026-08-03T11:58:53.323946Z","title":"Faster segment anything: Towards lightweight sam for mo- bile applications.arXiv preprint arXiv:2306.14289, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:53.323946Z"},"links":{"cited_paper":"/paper/2306.14289","citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:4976e24c7d36b5d1acddc73610845c9ae5e3ca8a3112aa0121fca4bd8d26ee00","observation_id":"d201a854-448a-4c31-a97b-137f1dd42971","resolution":{"observed_at":"2026-08-03T11:58:53.323946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:58:53.355755Z","title":"In-place scene labelling and understanding with implicit scene representation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:53.355755Z"},"links":{"citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:7c1894a7bc7720d6d1cdf158c1d721ee03f0a8a314e2a5ee7c3599cf068c1565","observation_id":"9cf37b76-77d7-4c4b-a72a-7430b970e267","resolution":{"observed_at":"2026-08-03T11:58:53.355755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00874","last_updated":"2024-12-04T23:51:25Z","snapshot_observed_at":"2026-08-04T09:38:10.661882Z","submitted_at":"2024-08-01T18:49:45Z","title":"Medical SAM 2: Segment medical images as video via Segment Anything Model 2","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00874","snapshot_observed_at":"2026-08-03T11:58:53.399042Z","title":"Medical sam 2: Segment medical images as video via segment anything model 2.arXiv preprint arXiv:2408.00874, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T11:58:53.399042Z"},"links":{"cited_paper":"/paper/2408.00874","citing_paper":"/paper/2601.04860"},"observation_digest":"sha256:b6a8c97689d576bed48cedf624435e93c714b04b0272e8c160a250437afa8b7e","observation_id":"4c8a7fea-1bb0-4640-abf4-aec798455829","resolution":{"observed_at":"2026-08-03T11:58:53.399042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.04860","last_updated":"2026-06-29T06:37:09Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T19:15:46.012295Z","submitted_at":"2026-01-08T11:53:04Z","title":"DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":32},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2601.04860."}