{"as_of":"2026-08-08T14:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f1a3be288ec801431150a54df90bcce0a91028cbd8dca4b5a80a1eb6a0818cf3","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:56:22.483270Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T18:47:30.907614Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T22:37:25.869938Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"cited_work":{"arxiv_id":"2505.20610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.20610","snapshot_observed_at":"2026-07-02T22:37:25.869938Z","title":null,"venue":null,"work_id":"327ca3b2-1781-4fc3-9761-a0f784260ee8","year":null},"citing_paper":{"arxiv_id":"2606.08440","last_updated":"2026-06-07T03:37:55Z","snapshot_observed_at":"2026-08-02T10:50:14.191601Z","submitted_at":"2026-06-07T03:37:55Z","title":"GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T18:47:30.907614Z"},"links":{"cited_paper":"/paper/2505.20610","citing_paper":"/paper/2606.08440"},"observation_digest":"sha256:35b0bbd941d3ee0309a8957b3af7891d106fe14b54e28f450e00bdcaa1b938e2","observation_id":"9ab42a6a-2df6-46cf-a41e-76d266d70b73","resolution":{"observed_at":"2026-07-02T22:37:25.871324Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.20610/citation-record","integrity":"/paper/2505.20610/integrity","json":"/paper/2505.20610/citation-record.json","paper":"/paper/2505.20610"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:56:26.345617Z","title":"A survey on robot semantic navigation systems for indoor environments","venue":null,"work_id":"daea73e3-6ea6-4aff-b3f4-15dfa3c54a10","year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:18.965420Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:0b4fbb65e5fad1ced5bbe14f43781479c4524cac61686b9ef4cbee9d063d2948","observation_id":"189ed6d0-a25b-4165-b606-77091aa3760c","resolution":{"observed_at":"2026-08-07T13:56:26.417803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:26.205513Z","title":"Contrastive lift: 3d object instance segmentation by slow-fast contrastive fusion","venue":null,"work_id":"0acf731c-277e-4bb9-a8a1-7c4cb9f11d7d","year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.033138Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:46b610c6ec6fa48af619065828f40a61d73edbdebbec001657690754d4e5fb6f","observation_id":"41da6b29-f936-4967-b6c1-a7414ea9c158","resolution":{"observed_at":"2026-08-07T13:56:26.264097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:19.094197Z","title":"Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction.IEEE Transactions on Visualization and Computer Graphics, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.094197Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:68f9ab7df1d7831f77c970bf48b72dad8e8689cdf9f1df32b735ff89352eef41","observation_id":"a662e902-7140-4c3c-8aba-64ed9c727c68","resolution":{"observed_at":"2026-08-07T13:56:19.094197Z","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":"2026-08-07T13:56:26.081488Z","title":"Panoptic vision- language feature fields.IEEE Robotics and Automation Letters, 9(3):2144–2151, 2024","venue":null,"work_id":"ad7ac964-a144-481f-a5d9-fe64ef7a2cb5","year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.142156Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:f1cc78bd98a15ef9872454ede9872d959e42d6b583639bc706ca81bd313de79b","observation_id":"753664a7-8694-4544-92ea-5928975fedca","resolution":{"observed_at":"2026-08-07T13:56:26.127734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:19.185555Z","title":"Mixedgaus- sianavatar: Realistically and geometrically accurate head avatar via mixed 2d-3d gaussian splatting.arXiv preprint arXiv:2412.04955, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.185555Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:6ad887a1e739cdd3811b1e828c9d2cd29436b198fa6a2cdb96c0eb6340383e01","observation_id":"b999fbfc-1fc7-4ace-9a1d-f9b20ef62e34","resolution":{"observed_at":"2026-08-07T13:56:19.185555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16760","last_updated":"2025-04-19T06:59:35Z","snapshot_observed_at":"2026-07-06T19:07:50.782209Z","submitted_at":"2024-08-29T17:56:33Z","title":"OmniRe: Omni Urban Scene Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16760","snapshot_observed_at":"2026-08-07T13:56:19.237397Z","title":"Omnire: Omni urban scene reconstruction.arXiv preprint arXiv:2408.16760, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.237397Z"},"links":{"cited_paper":"/paper/2408.16760","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:b673e2c2acff722273a5033767645cba4fc626b4e43c260b55baf33a0fb1f99c","observation_id":"12948dad-3e7e-4826-b604-a70ef39f3814","resolution":{"observed_at":"2026-08-07T13:56:19.237397Z","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":"2026-08-07T13:56:19.287381Z","title":"Gaussianpro: 3d gaussian splatting with progressive propagation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.287381Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:8e1f3789ffec051e4bde6f5edb345a9d824bf7c2f6d71547ae33da3ba58c0b0a","observation_id":"cfa8675e-2b7f-4db4-969f-63cf8bad4894","resolution":{"observed_at":"2026-08-07T13:56:19.287381Z","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":"2026-08-07T13:56:25.923038Z","title":"Semantic information for robot navigation: A survey.Applied Sciences, 10(2):497, 2020","venue":null,"work_id":"c12e1d42-b6a1-4845-b03a-9c9d2c790146","year":2020},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.317292Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:282b75bf2802139743e1297c31dbbd1c3a2a099743e8726c491e33097e577b91","observation_id":"840dc9da-f143-4ded-8a5f-d0e1f4a9c46c","resolution":{"observed_at":"2026-08-07T13:56:25.990562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:19.368656Z","title":"Scannet: Richly-annotated 3d reconstructions of indoor scenes","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.368656Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:b9855ba37d6b4f7a7e238062f9e8214611ded5a797803026734926eebbed9b51","observation_id":"c50bbc23-5115-4343-a619-d34c43b457eb","resolution":{"observed_at":"2026-08-07T13:56:19.368656Z","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":"2026-08-07T13:56:25.770595Z","title":"Depth-supervised nerf: Fewer views and faster training for free","venue":null,"work_id":"52a883e5-04ba-434c-b554-71cf33ef9e5e","year":2022},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.419070Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:9a39ad47ec0b08c702b4d66fa6f18f7f8a3f72827190757f8322c827e91f3457","observation_id":"aa2496b6-ff06-466b-842f-7d7e249505b0","resolution":{"observed_at":"2026-08-07T13:56:25.831771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:25.607060Z","title":"Geo-neus: Geometry-consistent neural implicit surfaces learning for multi-view reconstruction.Advances in Neural Information Processing Systems, 35:3403–3416, 2022","venue":null,"work_id":"fe276126-2795-40d8-bab1-1fb43c4b3e89","year":2022},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.461848Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:8bd0db3a76115b071d1a12dee20b0fddaff62ffcdd39657dbc265744ff7bb140","observation_id":"490b1c09-6c67-4fb5-a65d-c6a04eb6b909","resolution":{"observed_at":"2026-08-07T13:56:25.661325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:19.503805Z","title":"Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.503805Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:bc6c1b9a997ccc80ec21d2b36c7f42020b21006b62b75fae3831ac1560c1af54","observation_id":"01333673-17b0-4049-ad7f-844563f8e10d","resolution":{"observed_at":"2026-08-07T13:56:19.503805Z","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":"2026-08-07T13:56:19.576771Z","title":"2d gaussian splatting for geometrically accurate radiance fields","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.576771Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:edd173d51f1c18f5523f19214cece155289966779945b92c99fc47f789a4bf21","observation_id":"65bbc8a8-ad01-4908-9d92-a48a992bc314","resolution":{"observed_at":"2026-08-07T13:56:19.576771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20323","last_updated":"2024-05-30T17:57:08Z","snapshot_observed_at":"2026-08-07T23:36:49.030008Z","submitted_at":"2024-05-30T17:57:08Z","title":"$\\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20323","snapshot_observed_at":"2026-08-07T13:56:19.683893Z","title":"S3gaussian: Self-supervised street gaussians for autonomous driving.arXiv preprint arXiv:2405.20323, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.683893Z"},"links":{"cited_paper":"/paper/2405.20323","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:ac4515920f1b398ee2301b20a744c6766a5ecd9f7c41fa8f09f410cc402c720b","observation_id":"299efff8-46db-4c85-8c5e-c1d9c8931f4b","resolution":{"observed_at":"2026-08-07T13:56:19.683893Z","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":"2026-08-07T13:56:19.770084Z","title":"3d gaussian splatting for real-time radiance field rendering.ACM Trans","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.770084Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:90fd9e70d886045f63fda61655c78719b9171eb69773bcda8512a3281f97ec1e","observation_id":"35e04bd8-9585-4964-9156-f36a4dafa68e","resolution":{"observed_at":"2026-08-07T13:56:19.770084Z","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":"2026-08-07T13:56:25.412426Z","title":"Panoptic segmentation","venue":null,"work_id":"3092206b-f00c-4d38-9a81-50de395c15f5","year":2019},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.869473Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:731b6d37b06aec7ad09cff77a43d6b0e87388f96a47c3506ef4694782065031e","observation_id":"10057e18-705c-4a03-bf2c-53f7b1de3f59","resolution":{"observed_at":"2026-08-07T13:56:25.470671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:19.929477Z","title":"Segment anything","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:19.929477Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:5bc2eee44026ce2f8899a1db6a6dcc8ad7dc12a3e0e9ff95587d4d0b0e903b83","observation_id":"ba652684-9f65-40dc-859f-35c55226a09c","resolution":{"observed_at":"2026-08-07T13:56:19.929477Z","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":"2026-08-07T13:56:20.001072Z","title":"Compact 3d gaussian representation for radiance field","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.001072Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:15d749cad0af2a86d0a129f4431389db13bc8f8712e0b74fdb0ffeb70fe27214","observation_id":"08253db9-ee55-4417-a8e9-721f7e2e2517","resolution":{"observed_at":"2026-08-07T13:56:20.001072Z","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":"2026-08-07T13:56:25.203303Z","title":"Neuralangelo: High-fidelity neural surface reconstruction","venue":null,"work_id":"489c4407-3d99-4ae3-bdca-e93b44505146","year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.074792Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:282b399f09ee4739d0696b2930163ef01d8385cb3354b07ed37cc86f81b2ebbf","observation_id":"883c9f0c-58df-4c84-91f7-c1ab34ab2b27","resolution":{"observed_at":"2026-08-07T13:56:25.298652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:25.067031Z","title":"Helixsurf: A robust and efficient neural implicit surface learning of indoor scenes with iterative intertwined regularization","venue":null,"work_id":"cee8b8db-f939-4b63-b6e1-d56c6f91d673","year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.138456Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:444b6ea7d53d72b8c58f7d7857ecc2b0871f0f69d12e89bbb6f321033ce2656a","observation_id":"77478f2b-320f-4ddc-a637-1f1728908c03","resolution":{"observed_at":"2026-08-07T13:56:25.126181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:20.225671Z","title":"Neural sparse voxel fields.Advances in Neural Information Processing Systems, 33:15651–15663, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.225671Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:14470b883e27f014492dc31295105a66811e43d4bab7382e7beccd10dabb67e1","observation_id":"44e3db41-6999-494c-bb9c-d8475de80809","resolution":{"observed_at":"2026-08-07T13:56:20.225671Z","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":"2026-08-07T13:56:20.280253Z","title":"Marching cubes: A high resolution 3d surface construction algorithm","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.280253Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:175408616c8773807aa889ba82fb712d3a30a20b25dbda039990e8e789b5ff46","observation_id":"cce80df2-3707-4630-b6b9-0e3f35a4909e","resolution":{"observed_at":"2026-08-07T13:56:20.280253Z","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":"2026-08-07T13:56:20.348256Z","title":"Scaffold- gs: Structured 3d gaussians for view-adaptive rendering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.348256Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:09c4b7bf7918219f3f605d67a77fcf8f79c9d0a532a3c044bf86791d6dc16226","observation_id":"6300f37e-706b-4bee-bb82-1f8ff81a60a7","resolution":{"observed_at":"2026-08-07T13:56:20.348256Z","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":"2026-08-07T13:56:20.451282Z","title":"Nerf: Representing scenes as neural radiance fields for view synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.451282Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:108876984618bf27dd498fdafb63631323d73f4db3f02f6215ba66376fc8d210","observation_id":"96c808d4-77e6-405a-ba69-18b0e687dcb1","resolution":{"observed_at":"2026-08-07T13:56:20.451282Z","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":"2026-08-07T13:56:20.532786Z","title":"Instant neural graphics primitives with a multiresolution hash encoding.ACM transactions on graphics (TOG), 41(4):1– 15, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.532786Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:8518e5c9fbd32441bd2e7a840dae0826c62a24995d6b9dcef4626ebce5da5313","observation_id":"a92b0598-e4fd-445a-850d-24507c37623f","resolution":{"observed_at":"2026-08-07T13:56:20.532786Z","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":"2026-08-07T13:56:24.846944Z","title":"Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs","venue":null,"work_id":"837eec13-fbdf-4e69-85fc-fb780df1e615","year":2022},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.618198Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:27f6019bd727aee91687cd25e24c565447f022607b8af7706c398c619bb2fb70","observation_id":"8f68b9f2-6910-4a8d-9a89-aca69e6d41bc","resolution":{"observed_at":"2026-08-07T13:56:24.882181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:24.770289Z","title":"Differentiable volu- metric rendering: Learning implicit 3d representations without 3d supervision","venue":null,"work_id":"fc32a2cf-b16b-4c1c-a133-504b5de12a06","year":2020},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.660458Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:9b35b8644e4356bc016ca8372d3da26bf39ed8f427dfe6f32885e5968079ce95","observation_id":"20271821-3742-4d9c-a89e-317363fa4b51","resolution":{"observed_at":"2026-08-07T13:56:24.807966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:20.759941Z","title":"Langsplat: 3d language gaussian splatting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.759941Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:8c9f659e328a98bd224a2cdf6dc375920bb15c013ec4ec55b2cccd16112a8d79","observation_id":"b6572f8e-18fc-41e9-a477-9fc90c2cb9ee","resolution":{"observed_at":"2026-08-07T13:56:20.759941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-07T13:56:20.828590Z","title":"Sam 2: Segment anything in images and videos.arXiv preprint arXiv:2408.00714, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.828590Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:4c064fcdb33d3789538fc2c108b17f7b1236266a05146c3f4599d17336b8f3a6","observation_id":"c686bdf2-9570-4459-88f5-3c2bb222f3fd","resolution":{"observed_at":"2026-08-07T13:56:20.828590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14159","last_updated":"2024-01-25T13:12:09Z","snapshot_observed_at":"2026-07-06T17:20:25.138890Z","submitted_at":"2024-01-25T13:12:09Z","title":"Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14159","snapshot_observed_at":"2026-08-07T13:56:20.936889Z","title":"Grounded sam: Assembling open-world models for diverse visual tasks.arXiv preprint arXiv:2401.14159, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.936889Z"},"links":{"cited_paper":"/paper/2401.14159","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:21fdd2553316bb9ba692568e5d4dda7e63f024416c2a5a4788bb7f7dfc4a917b","observation_id":"bf1dbe76-960b-4abd-896c-ef5cb1b14c0c","resolution":{"observed_at":"2026-08-07T13:56:20.936889Z","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":"2026-08-07T13:56:24.555432Z","title":"Structure-from-motion revisited","venue":null,"work_id":"4b4172b7-777a-4380-ac7f-eb562ca0ad30","year":2016},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:20.999480Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:3aa0a448bd09a6524abc8f1da355f9f01678c110601a2d3b165db54965169b93","observation_id":"a5e1b91a-b504-4425-af9f-9ecfc65a2aa1","resolution":{"observed_at":"2026-08-07T13:56:24.662520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00881","last_updated":"2025-03-02T12:51:38Z","snapshot_observed_at":"2026-08-07T17:35:56.748472Z","submitted_at":"2025-03-02T12:51:38Z","title":"Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.00881","snapshot_observed_at":"2026-08-07T13:56:21.072644Z","title":"Evolving high-quality rendering and reconstruction in a unified framework with contribution-adaptive regularization.arXiv preprint arXiv:2503.00881, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.072644Z"},"links":{"cited_paper":"/paper/2503.00881","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:442aa80aca8a6a8a5a716d1ae8db323b70950d60f798657637f819ece4babf46","observation_id":"29396dc9-007d-4eaf-b2e8-fa95ff350c92","resolution":{"observed_at":"2026-08-07T13:56:21.072644Z","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":"2026-08-07T13:56:21.144345Z","title":"Language embedded 3d gaus- sians for open-vocabulary scene understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.144345Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:bc093131e85b8b274ef8fae41b9aad24692f314fbe7e759c3489935466262c51","observation_id":"99d9dd27-fca5-4d54-ae3a-41665d52395d","resolution":{"observed_at":"2026-08-07T13:56:21.144345Z","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":"2026-08-07T13:56:24.218544Z","title":"Panoptic lifting for 3d scene understanding with neural fields","venue":null,"work_id":"c9703b11-fe2a-4266-b9d8-4fe6af38f995","year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.207154Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:8f856205150119c3fa44ad0766e791cb7330a5f6b40af17bc4eb00696890e7f4","observation_id":"f697dadf-acc4-4465-b18f-99b504da48d3","resolution":{"observed_at":"2026-08-07T13:56:24.380039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:21.291483Z","title":"Dn-splatter: Depth and normal priors for gaussian splatting and meshing","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.291483Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:91aab399b3b90ae7148b3c10241638e233503341ec8cf62535e9c9552e012bbb","observation_id":"bf96ff5e-c7c8-40b2-8085-410d0276d6dc","resolution":{"observed_at":"2026-08-07T13:56:21.291483Z","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":"2026-08-07T13:56:23.940265Z","title":"Sparsenerf: Distilling depth ranking for few-shot novel view synthesis","venue":null,"work_id":"2dd405bc-1073-4231-9224-c789fb9d3930","year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.377165Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:ea7f31df9a5ef833cc398be6b0e40d49424695bf1d16c0baaff33792dd7049d2","observation_id":"1464ce81-ab26-4a3f-90c4-5d4ac5d9be6c","resolution":{"observed_at":"2026-08-07T13:56:24.022783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.09540","last_updated":"2025-07-25T14:34:24Z","snapshot_observed_at":"2026-08-07T16:06:10.234967Z","submitted_at":"2025-04-13T12:10:49Z","title":"EmbodiedOcc++: Boosting Embodied 3D Occupancy Prediction with Plane Regularization and Uncertainty Sampler","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.09540","snapshot_observed_at":"2026-08-07T13:56:21.487197Z","title":"Embodiedocc++: Boosting embodied 3d occupancy prediction with plane regularization and uncertainty sampler.arXiv preprint arXiv:2504.09540, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.487197Z"},"links":{"cited_paper":"/paper/2504.09540","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:629135aa0ee6fbc136efc3e1d40e6e95940351735397803326ee91b1a555c41e","observation_id":"3d1c6b7c-8b8a-4c68-b42c-4565e5be37cc","resolution":{"observed_at":"2026-08-07T13:56:21.487197Z","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":"2026-08-07T13:56:23.777588Z","title":"Neus: learning neural implicit surfaces by volume rendering for multi-view reconstruction","venue":null,"work_id":"acfe54c5-fe06-4aa3-855a-7b9c533f3e46","year":2021},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.546437Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:892555be180232cfb4dcea2ae6e55d9152b5a996eb3d7bd08f065006c38dbd7a","observation_id":"8487544e-c2e7-4a14-a077-2abc6a151233","resolution":{"observed_at":"2026-08-07T13:56:23.824573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.17505","last_updated":"2025-08-03T13:55:14Z","snapshot_observed_at":"2026-08-03T17:22:12.203934Z","submitted_at":"2024-10-23T02:05:05Z","title":"PLGS: Robust Panoptic Lifting with 3D Gaussian Splatting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.17505","snapshot_observed_at":"2026-08-07T13:56:21.593492Z","title":"Plgs: Robust panoptic lifting with 3d gaussian splatting.arXiv preprint arXiv:2410.17505, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.593492Z"},"links":{"cited_paper":"/paper/2410.17505","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:a9e1eef0aaa6d1f0e6b35a886ac4529c62bb48af444bf74cc69f17ad2fda885c","observation_id":"28b3bba7-10a3-415a-80be-08199f5bf050","resolution":{"observed_at":"2026-08-07T13:56:21.593492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12981","last_updated":"2025-07-09T02:41:35Z","snapshot_observed_at":"2026-07-06T19:52:55.369337Z","submitted_at":"2024-11-20T02:15:23Z","title":"GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting","version":2},"cited_work":{"arxiv_id":"2411.12981","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.12981","snapshot_observed_at":"2026-08-07T13:56:22.938750Z","title":"GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting","venue":"cs.CV","work_id":"907b2cab-ae31-49bb-87ea-e3a7ae4c8777","year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.658534Z"},"links":{"cited_paper":"/paper/2411.12981","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:708e48bbdc43855981e0c7190c3bc11d789cb2303d8434b5ed122d17ec1dd2d3","observation_id":"9d5f6b8f-91f7-4869-a3b3-2af5e88cb8c4","resolution":{"observed_at":"2026-08-07T13:56:22.975255Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:23.654277Z","title":"Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environ- ments: A review.Applied Sciences, 13(17):9877, 2023","venue":null,"work_id":"55550891-d2b3-4528-8e0b-c553c440574e","year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.698538Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:115595c56fc3f28a9cde4a913ec59d395f87690ede9093378850072db1a225e8","observation_id":"b4758941-f7d7-4cdf-b085-5c661a490074","resolution":{"observed_at":"2026-08-07T13:56:23.696483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02058","last_updated":"2024-12-06T14:49:23Z","snapshot_observed_at":"2026-08-07T21:29:07.865625Z","submitted_at":"2024-06-04T07:42:33Z","title":"OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02058","snapshot_observed_at":"2026-08-07T13:56:21.745363Z","title":"Opengaussian: Towards point-level 3d gaussian-based open vocabulary understanding.arXiv preprint arXiv:2406.02058, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.745363Z"},"links":{"cited_paper":"/paper/2406.02058","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:78255f2673d8ba21a86a317cb4ee24436de84d49c68cdcdcc8d02872fccb52dd","observation_id":"71fa254f-8308-44cb-a380-370d1c6a53d3","resolution":{"observed_at":"2026-08-07T13:56:21.745363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04380","last_updated":"2025-08-25T05:59:30Z","snapshot_observed_at":"2026-07-06T20:02:21.509050Z","submitted_at":"2024-12-05T17:57:09Z","title":"EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04380","snapshot_observed_at":"2026-08-07T13:56:21.776114Z","title":"Embodiedocc: Embodied 3d occupancy prediction for vision-based online scene understanding.arXiv preprint arXiv:2412.04380, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.776114Z"},"links":{"cited_paper":"/paper/2412.04380","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:1da91dd12e837e9938169bf1e9704ea09d4000d1620bf98e831e2913ce072385","observation_id":"4bcd17e8-c2ea-4774-a1a9-d87991094cda","resolution":{"observed_at":"2026-08-07T13:56:21.776114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19671","last_updated":"2025-04-01T07:49:10Z","snapshot_observed_at":"2026-07-06T18:22:27.156635Z","submitted_at":"2024-05-30T03:46:59Z","title":"GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19671","snapshot_observed_at":"2026-08-07T13:56:21.818806Z","title":"Gaussianroom: Improving 3d gaussian splatting with sdf guidance and monocular cues for indoor scene reconstruction.arXiv preprint arXiv:2405.19671, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.818806Z"},"links":{"cited_paper":"/paper/2405.19671","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:c60363e3e02b6cc79ea785f94d18811fd0f15afb9cfd54c0556ec9af3bbf01c6","observation_id":"c9ea7a1e-e3a3-4db8-84f8-bb26bbc5a4c7","resolution":{"observed_at":"2026-08-07T13:56:21.818806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.18073","last_updated":"2025-03-23T13:45:39Z","snapshot_observed_at":"2026-08-07T16:43:15.254530Z","submitted_at":"2025-03-23T13:45:39Z","title":"PanopticSplatting: End-to-End Panoptic Gaussian Splatting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.18073","snapshot_observed_at":"2026-08-07T13:56:21.877634Z","title":"Panopticsplatting: End-to-end panoptic gaussian splatting.arXiv preprint arXiv:2503.18073, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.877634Z"},"links":{"cited_paper":"/paper/2503.18073","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:6d8d723daf82e2645842d37116836b52b6f31f9f04c8f1cf1f7f953f4c7255f8","observation_id":"31f39828-2c00-4736-b9db-0594d5938a2a","resolution":{"observed_at":"2026-08-07T13:56:21.877634Z","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":"2026-08-07T13:56:23.570350Z","title":"Upsnet: A unified panoptic segmentation network","venue":null,"work_id":"306db54d-b5f0-4f6c-8856-4454ed61b138","year":2019},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:21.969192Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:95ad92be3cc1a46f2bc16322e6a6db86f4835b5dc5dca8da3fb6697733cdff8a","observation_id":"a5f95bfc-cf3c-4e08-8d66-61a72d41a4f8","resolution":{"observed_at":"2026-08-07T13:56:23.615437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:23.469557Z","title":"Gaussian grouping: Segment and edit anything in 3d scenes","venue":null,"work_id":"3df441cf-880c-48b4-bdd4-de2d13406f18","year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.035372Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:7f75d4fa8011b77199ab522c310621c3f5c1273d51ee16aa097d5e78a3cb0bae","observation_id":"d4033cae-85a0-4aef-8c2a-5a0c0fc05b89","resolution":{"observed_at":"2026-08-07T13:56:23.510700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:22.100277Z","title":"Scannet++: A high- fidelity dataset of 3d indoor scenes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.100277Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:d1fa1ae59cd556ac5ea5412f39cba622333e97de1362e213ba3f634fe9f3c61c","observation_id":"6db5e701-d3e7-44cb-9d28-b7c4482d1623","resolution":{"observed_at":"2026-08-07T13:56:22.100277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.16964","last_updated":"2024-10-13T17:52:00Z","snapshot_observed_at":"2026-07-06T17:50:18.017647Z","submitted_at":"2024-03-25T17:22:11Z","title":"GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.16964","snapshot_observed_at":"2026-08-07T13:56:22.160768Z","title":"Gsdf: 3dgs meets sdf for improved rendering and reconstruction.arXiv preprint arXiv:2403.16964, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.160768Z"},"links":{"cited_paper":"/paper/2403.16964","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:d3ee32f0326071bfa59ce72b468c9a6bc6fa5e9adec6095ef0a8b55e878a5bd2","observation_id":"ff9f29ef-09cc-4e76-a4cc-374824858aa8","resolution":{"observed_at":"2026-08-07T13:56:22.160768Z","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":"2026-08-07T13:56:23.366689Z","title":"Panopticrecon: Leverage open-vocabulary instance segmentation for zero-shot panoptic reconstruction","venue":null,"work_id":"f5829b9a-8ded-4990-bc8c-cbc454517c86","year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.231096Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:c2d1c1ca733a2961dcbb8edfd03dcd52294128ecc15566efaf453896eec5522a","observation_id":"dedf76c7-15c6-4387-bfdc-cc4c0f790e02","resolution":{"observed_at":"2026-08-07T13:56:23.397178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:56:22.301645Z","title":"Lever- age cross-attention for end-to-end open-vocabulary panoptic reconstruction.arXiv preprint arXiv:2501.01119, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.301645Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:c99ec1242a92536a63d92c04b54c795366d9a69f2f0a515a4605fcd2ae1d0466","observation_id":"72066fe2-75ff-41d5-b0fe-1bf93cbba623","resolution":{"observed_at":"2026-08-07T13:56:22.301645Z","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":"2026-08-07T13:56:22.359576Z","title":"Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction.Advances in neural information processing systems, 35:25018–25032, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.359576Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:17a9833b665fb96667cf26bb8348f7ac6be4383410ce5dff53fee4555d280206","observation_id":"d0063fc9-8593-48dd-ac88-753d0045dab4","resolution":{"observed_at":"2026-08-07T13:56:22.359576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03428","last_updated":"2024-12-04T16:17:47Z","snapshot_observed_at":"2026-08-07T10:07:48.414809Z","submitted_at":"2024-12-04T16:17:47Z","title":"2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03428","snapshot_observed_at":"2026-08-07T13:56:22.415810Z","title":"2dgs-room: Seed-guided 2d gaussian splatting with geometric constrains for high-fidelity indoor scene reconstruction.arXiv preprint arXiv:2412.03428, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.415810Z"},"links":{"cited_paper":"/paper/2412.03428","citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:1f25a7f5ac82605248d4503ec600d483459b8f0015053760cb2bed124a045b99","observation_id":"1707178e-1027-45a2-a4cc-2229aa88bcce","resolution":{"observed_at":"2026-08-07T13:56:22.415810Z","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":"2026-08-07T13:56:23.248700Z","title":"Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields","venue":null,"work_id":"975ed6cc-1aec-43d5-872b-aca8699e5c4a","year":2024},"citing_paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T13:56:22.483270Z"},"links":{"citing_paper":"/paper/2505.20610"},"observation_digest":"sha256:e8fc8a561e09e4455d47cb59926c8a93cfd43a1b5dcb753d800494e4f064bc00","observation_id":"9fe80e27-79a5-4c8e-8984-241c4dd6620e","resolution":{"observed_at":"2026-08-07T13:56:23.295941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.20610","last_updated":"2025-05-27T01:17:10Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T21:30:52.129139Z","submitted_at":"2025-05-27T01:17:10Z","title":"OmniIndoor3D: Comprehensive Indoor 3D Reconstruction"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":33,"verified_exact":1,"verified_fuzzy":20},"total_outbound_references":54},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2505.20610."}