{"as_of":"2026-08-11T14:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32481bc57698bcd007e2b1ea5839b90eb82db9d795b9a440244381d2a5d5a29b","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T01:21:15.228734Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2607.09225/citation-record","integrity":"/paper/2607.09225/integrity","json":"/paper/2607.09225/citation-record.json","paper":"/paper/2607.09225"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.08897","last_updated":"2022-01-12T08:19:29Z","snapshot_observed_at":"2026-07-06T12:09:19.763667Z","submitted_at":"2021-11-17T04:27:01Z","title":"ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08897","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data.arXiv preprint arXiv:2111.08897, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2111.08897","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:60cdec0dfd7bf2fc752f6e51928b057c1f7615e322c3e9c895cf5733aa9e1976","observation_id":"d2ab1ce4-45cd-4a53-a49e-ab5de712f46a","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Scene coordinate reconstruction: Posing of image collections via incremental learning of a relocalizer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:61c32a90629b83301f1657631e06c1b40ffbff8700380539e745af770b3e4605","observation_id":"112f8238-0a97-43d7-bffc-6cb09238e62a","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.10773","last_updated":"2020-01-29T12:13:20Z","snapshot_observed_at":"2026-07-06T08:53:28.193420Z","submitted_at":"2020-01-29T12:13:20Z","title":"Virtual KITTI 2","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.10773","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Virtual kitti 2.arXiv preprint arXiv:2001.10773, 2020","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2001.10773","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:7944d8d2462edf78f676b12ff969743dcf3cfc7992a80b76a53eea17096c7791","observation_id":"6f43282b-44aa-4dc7-a601-a55e4b3c6739","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.14141","last_updated":"2026-04-16T16:44:56Z","snapshot_observed_at":"2026-08-11T14:11:51.712541Z","submitted_at":"2026-04-15T17:58:13Z","title":"Geometric Context Transformer for Streaming 3D Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.14141","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Geometric context transformer for streaming 3d reconstruction.arXiv preprint arXiv:2604.14141, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2604.14141","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:210d35d7d863356324acfa377e06b0712acec4b801e5661c8685e04a98cd100b","observation_id":"4e816f02-9bad-47c1-bb5d-4601d6502214","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Longstream: Long-sequence streaming autoregressive visual geometry.arXiv preprint arXiv:2602.13172, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:ca5b82a07a89360e45d0d5124a1b3eabf8c9978215f43c274457aaa1537d5aec","observation_id":"57d46f4f-e0e6-4bab-947e-12824fab9379","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Scannet: Richly-annotated 3d reconstructions of indoor scenes","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:41898542062ad50ebd5b5b7b0b65810a5658f13b92ddb7a1c5ac2bd9572a70e5","observation_id":"bf632c6c-ddcc-4582-a835-6951f80b36ca","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Nerf-loam: Neural implicit representation for large-scale incremental lidar odometry and mapping","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:1667b62d17dade86cf1b831c783569128bcd6649d14a37f92efd7245a57f7f38","observation_id":"8d117bc2-4c65-45bc-92b0-5883434f5993","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Boost 3d reconstruction using diffusion-based monocular camera calibration","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:00b15886ccbd199e577bc13bd0ade988aa57180624c93e92cf5740a8e6a37be7","observation_id":"68cde132-ffaf-4133-b5a2-b3208c287d94","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Sail-recon: Large sfm by augmenting scene regression with localization.2026 International Conference on 3D Vision (3DV)., 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:e4f68bb7f8f5a916a6922e779a48e37953c7355fd3a29fc784e97fb8f576fad6","observation_id":"935a6f7f-5c30-4c8e-a1ba-ddd7ac6098d7","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16443","last_updated":"2026-03-16T08:45:25Z","snapshot_observed_at":"2026-08-04T13:08:09.273271Z","submitted_at":"2025-07-22T10:39:04Z","title":"VGGT-Long: Chunk it, Loop it, Align it -- Pushing VGGT's Limits on Kilometer-scale Long RGB Sequences","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.16443","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Vggt-long: Chunk it, loop it, align it – pushing vggt’s limits on kilometer-scale long rgb sequences, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2507.16443","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:5589d6662e0b7765a91415547b15b85e727724bd8e6bcc12301b057367312f1e","observation_id":"7baad372-abe2-4247-aeaf-2620b1c08194","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Reloc-vggt: Visual re-localization with geometry grounded transformer.arXiv preprint arXiv:2512.21883, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:5c439e3b85011862d662855560b01941a1d8d9a754ca28fd5adcc45d7fbe4ccd","observation_id":"6d1c7ec1-082c-4222-a90f-e5dc77c0ffcf","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19152","last_updated":"2024-09-27T21:29:58Z","snapshot_observed_at":"2026-08-04T22:29:38.303623Z","submitted_at":"2024-09-27T21:29:58Z","title":"MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19152","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Mast3r-sfm: a fully-integrated solution for unconstrained structure-from-motion.arXiv preprint arXiv:2409.19152, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2409.19152","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:92057355e5b851283f384e38b163bbb1f1acfb97925cc2c54add84c6c00a1a58","observation_id":"f6da8e37-40f8-4d55-882b-6c638e8cb624","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Dkm: Dense kernelized feature matching for geometry estimation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:1131b4a8f5ab8e2eadd852862eefe819310ad78e1853f8b88dd17721ab080cf2","observation_id":"428a4b66-596f-4a56-98ad-8531b8558805","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Roma: Robust dense feature matching","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:c8e07efeb4ba81c7f3efcf8b7648ec309b29e867cb1ff1772483006e4449cc9a","observation_id":"de3d70c9-6f27-41d6-93f6-43ba2cae7381","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.15706","last_updated":"2026-07-06T10:31:24Z","snapshot_observed_at":"2026-08-07T20:44:09.151132Z","submitted_at":"2025-11-19T18:59:38Z","title":"RoMa v2: Harder Better Faster Denser Feature Matching","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.15706","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Roma v2: Harder better faster denser feature matching.arXiv preprint arXiv:2511.15706, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2511.15706","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:669e188525327d7b497432b60c32ffbb4bd44a651375a4e56dec8fe5d18e1d73","observation_id":"36517afb-70a5-4160-b748-3fdaa3fe4b24","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"More: 3d visual geometry reconstruction meets mixture-of-experts","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:6e0e28b2b9c141763e6fd20871b279cbd05b670bd114f59ec5b954e703093278","observation_id":"c9afb1c5-58d1-4864-b5c4-a287ac38c9ed","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Are we ready for autonomous driving? The KITTI vision benchmark suite","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:854ecedc00d1c19303047e212167da78c3b14cc683c2cb59cfa2e1529590e37f","observation_id":"037ea283-a49d-4b58-aec3-dbe723f2050c","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Combining two-view constraints for motion estimation","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:0b002cf9fb14294453c1bfe6cfbdd6042b6304932c3c47a6b3bdc7e894b45f9e","observation_id":"4aa3fba7-ce5a-41c5-9a8e-ae8549d810ef","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Rotation averaging.International journal of computer vision, 103(3):267–305, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:9d44f2aa81d8290934f90be33467953f9ccffd48001a6cb8869efd8bbbd777d5","observation_id":"b7ab91ec-82e5-4c61-b5a5-da5bf0f49bd2","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Detector- free structure from motion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:de1ac639a838d9b656f8b372574df832617b916df0e26ecdbf47714cb5a8db13","observation_id":"7846b4d9-0b48-4ff4-a76f-94b00bf3fc68","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Deepmvs: Learning multi-view stereopsis","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:f8526b783e9ddc80406884f5457c7782e4fc9c2b5cd2961a9a2a64ff90eb3ecc","observation_id":"aa34b910-6ae1-4058-92ff-2544e36ede35","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Optimal transport aggregation for visual place recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:50905f2e86b68a175fcd9bc0e09f2b70fc2b2328c7044879d186c223fad59c3b","observation_id":"0c69418d-9d83-420a-a05c-b2df8b201f82","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.13414","last_updated":"2026-01-23T18:59:33Z","snapshot_observed_at":"2026-07-06T22:30:07.020487Z","submitted_at":"2025-09-16T18:00:14Z","title":"MapAnything: Universal Feed-Forward Metric 3D Reconstruction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.13414","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Mapanything: Universal feed-forward metric 3d reconstruction.arXiv preprint arXiv:2509.13414, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2509.13414","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:31f737afbc15894aefbfc2e859bd1ae722c3abece682ce798e10669ddde1e462","observation_id":"a7261c78-5aa9-45c9-8f2d-0ca0c1573fb8","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Tanks and temples: Benchmarking large-scale scene reconstruction.ACM Transactions on Graphics (ToG), 36(4):1–13, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:1c08bdb6428d2f45de5a4360d52bce21e4fcac18c122d907e0c23566ab0814ef","observation_id":"26d6dd9b-cda8-4d88-aab1-5d47ab3d4468","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Rago: Recurrent graph optimizer for multiple rota- tion averaging","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:a92f34f865b9403a72e213edf6b9e2119904e59ac8aa2f811aa8f583407e6109","observation_id":"aad3545c-e415-4f3c-ab51-23beb7c42692","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Matrixcity: A large-scale city dataset for city-scale neural rendering and beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:0b1f17b6aee7c1a071cdb9d6fd65ae7771193d5eb070841b894b2ba6a6f7f7a1","observation_id":"38a1ec0f-f4a8-4dfb-a06b-6e340560b531","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Megadepth: Learning single-view depth prediction from internet photos","venue":null,"work_id":null,"year":2041},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:cf8b0ce18e494a0b795dd97696a3a217c99054c79cfe04e860ce9882b9336148","observation_id":"604fde7c-69b0-4370-8ab5-54f90d4d3ffd","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.05296","last_updated":"2025-09-05T17:59:47Z","snapshot_observed_at":"2026-08-05T05:21:39.895646Z","submitted_at":"2025-09-05T17:59:47Z","title":"WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.05296","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Wint3r: Window-based streaming reconstruction with camera token pool","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2509.05296","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:8fa1904f0cb0c5e87bd54656776ef2d7531ce08eb5fe6c9ee9dd9484e5707333","observation_id":"e9d1e0f8-b51a-4046-81c5-75350aa7aa25","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.10647","last_updated":"2025-11-13T18:59:53Z","snapshot_observed_at":"2026-07-06T22:35:46.018050Z","submitted_at":"2025-11-13T18:59:53Z","title":"Depth Anything 3: Recovering the Visual Space from Any Views","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.10647","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2511.10647","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:352b3d6bdfc5685a2d3f2408ae3228979089f29c5578cebb79969268ddd57bba","observation_id":"6962ffce-a36e-447d-ae31-d626c5de76e4","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13643","last_updated":"2023-06-23T17:52:54Z","snapshot_observed_at":"2026-08-11T03:37:26.557319Z","submitted_at":"2023-06-23T17:52:54Z","title":"LightGlue: Local Feature Matching at Light Speed","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13643","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Lightglue: Local feature matching at light speed.arXiv preprint arXiv:2306.13643, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2306.13643","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:9f685c4b5dbe7d24d619bd9e67fcdec49ab36f4ef73bd162554e6e44df8d0fef","observation_id":"d22fdbde-875f-4e9d-b81f-e6153a0971f0","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:132e0d502ba978618382ed9ef1d8bfa6c78fe2b615b76348d812d0ed43b3318d","observation_id":"dc79409d-6ee3-4a74-8708-3320b9d8dc21","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Vggt-slam 2.0: Real-time dense feed-forward scene reconstruction","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:c14bff807b63b3a60fe1d3da7cc0013387f3333189d08b94460f90a584f4b49b","observation_id":"76c23a78-eb6f-4436-9933-cbe3f4868bc2","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12549","last_updated":"2025-05-23T11:59:20Z","snapshot_observed_at":"2026-07-06T21:25:56.796553Z","submitted_at":"2025-05-18T21:33:09Z","title":"VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.12549","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Vggt-slam: Dense rgb slam optimized on the sl (4) manifold.arXiv preprint arXiv:2505.12549, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2505.12549","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:984967c03420a02e767361b0c6b336b0c4fe996c9bc54e31d07a040bd551c0e5","observation_id":"8724e4e4-13f1-49c5-9e41-1157fe2aea64","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Robust rotation and translation estimation in multiview reconstruction","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:d5f5edad4bed1b84ef17875a754ac1b4283190720bd94a83d6303e8bbc3965dc","observation_id":"93c9f5df-33ed-45cf-91cb-f319fa2f6c17","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65 (1):99–106, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:a233e3e141af48f3cefdc3c8fe3dc2dab83ea2a810bbdefca99c73b073b82329","observation_id":"0ab35af8-4887-4124-8923-dd642380ff8f","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Mast3r-slam: Real-time dense slam with 3d reconstruction priors","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:1343798b22ae588b1496d2f37b3e8c4d64dda2b7389914bf7d202c4d8f71ddbd","observation_id":"4575478b-11bf-4c0c-86a4-ee15c5fa6d52","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Global Structure-from-Motion Revisited","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:7e9ad5b263940e70641817927edf5525384e5c24a40dc8c625902245178ce8c2","observation_id":"a88ae4a2-5668-4262-b5d4-70f6eaaffe2d","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Aria digital twin: A new benchmark dataset for egocentric 3d machine perception","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:634ec25683e449dafb7ea18f7107ed3b35853744815a07cfffae6b70e5104fdc","observation_id":"45ad74a2-560d-45ee-b094-ecca6298c110","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:6d48af82db60ae98b7ad135ef8487ba11f2159f00ab03e7a2a2b9d2d6f86f8f4","observation_id":"9379fdba-2e3b-47b8-9058-6d1bd54b2858","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:7ee6317068a26b923561def033a112ed0ed914eee9244c5755a48500555cbc0b","observation_id":"a6b77a99-16ae-4e97-85de-b82de6aeeaca","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Superglue: Learning feature matching with graph neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:5ee4f1425430de41559c2aa9174f21a17b736b99cba7e6112a1eac1472213c9b","observation_id":"fe0b25bb-86f2-429e-90c8-3a0f806d2d36","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Structure-from-motion revisited","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:941dd5afe08ab0cab2f0d05ede1f144d51b02e3e195da8e496efcb29a60a6e72","observation_id":"9149df3c-56e9-4369-8745-b481608d8fa2","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Structure-from-motion revisited","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:59e543aff6f4a075508f29398fbb6e8548a78bdc17bbb84af151257ae1a7ccd3","observation_id":"b2c430f5-7318-4d97-8ac1-521cc484ca53","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:2f68652f6323462f3c7ed39e179bc808e544e850366eadd77df16d3f51bd700d","observation_id":"2989e2b6-75d3-4c7a-b8cc-354e3d14dba5","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02560","last_updated":"2025-11-09T15:12:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-02T17:54:21Z","title":"FastVGGT: Training-Free Acceleration of Visual Geometry Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.02560","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Fastvggt: Training-free acceleration of visual geometry transformer.arXiv preprint arXiv:2509.02560, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2509.02560","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:058aaf0380ac1fe573541c316c80e0968d53e8f96341fa34fdeb520e97425b42","observation_id":"290fe870-ecb9-4f19-8c8c-2caee8585303","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15259","last_updated":"2024-07-23T13:41:03Z","snapshot_observed_at":"2026-07-06T18:04:30.291363Z","submitted_at":"2024-04-23T17:46:50Z","title":"FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15259","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Flowmap: High-quality camera poses, intrinsics, and depth via gradient descent.arXiv preprint arXiv:2404.15259, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2404.15259","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:a6d0dd112c3de835eea3e8f8f28e561a21f4cf020b62fe165fb17a150300823b","observation_id":"0ffc7288-9714-4c10-8008-ce5a7495a06e","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Sturm, N","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:f0fc321894d3a102f78223aec908224d0e24985b96c716712e2b09f4a94b1a2e","observation_id":"8aa5c978-f460-42e9-b5b6-83748656d873","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Loftr: Detector-free local feature matching with transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:b2e94f408fec287bc549c43d66d477d54d2bb02d2328cb2f832e950e1ccc9adc","observation_id":"58fa86e3-35c0-404f-867e-15cb7c806906","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Nerfstudio: A modular framework for neural radiance field development","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:b60235717cc788f861802a7429d869ee6864079078765920aa9348fd7d5b180d","observation_id":"68b779b5-713a-4260-9137-2b2b3addd020","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.04807","last_updated":"2019-08-25T19:20:00Z","snapshot_observed_at":"2026-08-06T09:18:12.942546Z","submitted_at":"2018-06-13T00:51:48Z","title":"BA-Net: Dense Bundle Adjustment Network","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.04807","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Ba-net: Dense bundle adjustment network.arXiv preprint arXiv:1806.04807, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/1806.04807","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:bac2400cb9423b087185526cae18f1254d3faac8fe9b1937b94ca2b6c67b9548","observation_id":"8a20acdc-8a6a-4c5c-b680-ce17828abcac","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.04605","last_updated":"2020-04-27T19:17:43Z","snapshot_observed_at":"2026-07-06T07:20:36.957156Z","submitted_at":"2018-12-11T18:47:12Z","title":"DeepV2D: Video to Depth with Differentiable Structure from Motion","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.04605","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Deepv2d: Video to depth with differentiable structure from motion.arXiv preprint arXiv:1812.04605, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/1812.04605","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:a544f2bd6ad6583b7d87b5e174049a0608c9d8dd6e1f2e7be2ec0b275f09c84f","observation_id":"459df366-3abf-4c5e-be7c-f10592402740","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras.Advances in neural information processing systems, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:d05d0a713dce46cc14bb64b70209cb57eb60ce3e6770da00ef5ba78d3becc010","observation_id":"adae4869-ffd3-4cdd-ba12-21de1ac3a40e","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Smd-nets: Stereo mixture density networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:4fa64b92a78989e6a4f16ab3c58dcd9e6d16d1c4bd6a022359eb17f0306af363","observation_id":"9f767649-309b-40d5-b31e-4074f2b00cde","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Bundle adjustment—a modern synthesis","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:a9479578f5a26597d1f81adc2123dc41b0483f4a8e62d8052684ec06169ab2fe","observation_id":"dd10b285-bdd3-4fc7-875c-00150c159444","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Amb3r: Accurate feed-forward metric-scale 3d reconstruction with backend.arXiv preprint arXiv:2511.20343, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:d2d7849a597bef51fbccbd1165fb5f82c493d9d926f528127bcbd9ef75cc2b77","observation_id":"041cb403-cec1-428d-8774-fc212290882f","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Vggsfm: Visual geometry grounded deep structure from motion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:3103f364fa26d3acaf809a2e39ae3c3cc775130a2420ef4c7ac7200819e8bd83","observation_id":"32b02759-3770-4637-bbe5-8965691fbfa3","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Vggt: Visual geometry grounded transformer","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:d6b81e7d017555bf2ed1960dd4e48ca90e67884f0068f586cbff6247c358fc4c","observation_id":"61dd1545-008e-43f0-bf4a-658570d7960b","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Continuous 3d perception model with persistent state","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:02d187c8eb8679fb82db776a81450f714ac7f3108f315a2e16994760dd33d248","observation_id":"47b0d940-fe40-4389-a38f-63d24066a41a","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Dust3r: Geometric 3d vision made easy","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:2d92f66cdb455ed595e10aece603e935dd16169abd0e70b3fcf8fdeedbbd1934","observation_id":"3c01742a-8742-425b-b231-96e3020e8bce","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Tartanair: A dataset to push the limits of visual slam","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:0cea75aab884e0bc5804822274c08204df27d09fbd7150d5ace0eef0ee4093ed","observation_id":"86b31e6a-2609-4124-bf53-2d8caf93fe52","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.13347","last_updated":"2026-03-07T07:01:59Z","snapshot_observed_at":"2026-08-02T12:12:15.465550Z","submitted_at":"2025-07-17T17:59:53Z","title":"$\\pi^3$: Permutation-Equivariant Visual Geometry Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.13347","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"π3: Scalable permutation-equivariant visual geometry learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2507.13347","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:355f897c8d08aaf749882c958c0a2befdc0b99e2f8da1cb3fb5ec2564401af56","observation_id":"fede3689-9ead-4c6a-84cb-b1b7c26c972f","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Deepsfm: Structure from motion via deep bundle adjustment","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:4c6363ce7c68634cb9ece5e01c4fd3be6193f1c96d99075997279322d501ec80","observation_id":"f2a1a229-f5bf-49d0-ae6a-4b02fe6d43e9","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Robust global translations with 1dsfm","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:b5e79c346ef33caf80281e517fe792b8b44ce57ac40e04f13a7d4bdf16b4f86c","observation_id":"fcd70a1b-79d6-4895-abe2-5564f88c9694","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Point3r: Streaming 3d reconstruction with explicit spatial pointer memory.arXiv preprint arXiv:2507.02863, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:e0ae255158fdc89570c6f2ddc9c31882aa27866302e8f572862edd2e257917f8","observation_id":"0010a889-8135-4107-a40a-c19112ae4779","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Rgbd objects in the wild: Scaling real-world 3d object learning from rgb-d videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:d9a4dd3c1c729c4ac8c927c66ba4838dea9a690aa4bedb439563ecfe66c77d23","observation_id":"8deaf1d2-e74b-4ea8-8bb0-f2675e35b5fb","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.08542","last_updated":"2026-04-09T17:59:50Z","snapshot_observed_at":"2026-07-06T22:57:35.435713Z","submitted_at":"2026-04-09T17:59:50Z","title":"Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.08542","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Scal3r: Scalable test-time training for large-scale 3d reconstruction, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2604.08542","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:fe694482a1720acca7ccb7b7384fe938ac11429618d167b4c3e8f474405b5f37","observation_id":"6ef7c7c0-b611-446d-94c7-b3fd5c2e1aa4","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Vggt-motion: Motion-aware calibration-free monocular slam for long-range consistency.arXiv preprint arXiv:2602.05508, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:d1254fd9e6ddc21115b58187f3d2de3b526abe818c89c51133c93f92dfc203f4","observation_id":"f2c562bb-bb1e-4b48-a5f2-2b7d23167b84","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"360recon: An accurate reconstruction method based on depth fusion from 360 images","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:88e896952224526fc3cc7c83bc13e77dd521e9f30ed34e6d4b67cc9820c77244","observation_id":"1aac989c-25d5-45a3-b716-736238ec9508","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Blendedmvs: A large-scale dataset for generalized multi-view stereo networks.Computer Vision and Pattern Recognition (CVPR), 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:c0e8a635af5bf8c65a03e31088dc163d1a6029385b2c0cf0ad360ab413f9f7a3","observation_id":"94022622-8c34-4fe7-b5c9-173ab5a44c2c","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Scannet++: A high-fidelity dataset of 3d indoor scenes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:fc76c8349f0705e9ffa22d8b30a67dd3f5b0d4e40a53a0bd63cefc67848ba8d9","observation_id":"b80c9360-2d19-4b99-9387-85089cae227c","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12190","last_updated":"2026-05-18T07:05:26Z","snapshot_observed_at":"2026-07-23T23:19:03.109997Z","submitted_at":"2024-09-18T17:59:29Z","title":"Bundle Adjustment in the Eager Mode","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12190","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Bundle adjustment in the eager mode.IEEE Transactions on Robotics, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2409.12190","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:4f85bd1effdc6a6d6ab4e07f2490c22c7ec71b312cce42a74e053c377aae9c4e","observation_id":"0b69a60a-ef3b-42ec-9841-ec8dedcca7b8","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.03269","last_updated":"2026-04-27T16:46:35Z","snapshot_observed_at":"2026-07-06T22:47:41.997192Z","submitted_at":"2026-03-03T18:55:37Z","title":"LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.03269","snapshot_observed_at":"2026-07-13T01:21:15.228734Z","title":"Loger: Long-context geometric reconstruction with hybrid memory.arXiv preprint arXiv:2603.03269, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"cited_paper":"/paper/2603.03269","citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:c04ad411f99a2a45f8577156bf9350882047deedcb74baf68e998e0182f44ded","observation_id":"d41b2f9d-add7-4c47-94e0-4e5f22820951","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Instantsfm: Towards gpu-native sfm for the deep learning era.arXiv preprint arXiv:2510.13310, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:df7511670b943bccd5a9cf7442f0fc198659e6106cfc8bdb449b5f96d8f40f0d","observation_id":"258362a6-6aae-4afc-bb99-6bfbb841b0aa","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":"Omniworld: A multi-domain and multi-modal dataset for 4d world modeling.arXiv preprint arXiv:2509.12201, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:7d8dcbc0f622fc8613f97bcc6bea4cce342729984703c1c274833746119b4171","observation_id":"f475d297-30da-47f2-b7e1-2cdac1262479","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","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":"2026-07-13T01:21:15.228734Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-07-13T01:21:15.228734Z"},"links":{"citing_paper":"/paper/2607.09225"},"observation_digest":"sha256:6d012137cbec9a9d1d78bcb5f79ff98b2d7e7eaf6ccaa02d68ee7d5f004d7791","observation_id":"fb5d51c6-f566-4707-8941-6f490f9c51dc","resolution":{"observed_at":"2026-07-13T01:21:15.228734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.09225","last_updated":"2026-07-10T09:18:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-03T17:26:56.605851Z","submitted_at":"2026-07-10T09:18:35Z","title":"Glob3R: Global Structure-from-Motion with 3D Foundation Models"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":75,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":75},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2607.09225."}