{"as_of":"2026-08-18T00:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d70aa69612605fb5081efb7d20ecf317a925dc4b00e313ba7424682cddad5c4d","coverage":[{"denominator":78,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":78,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:17:22.380837Z","state":"measured"},{"denominator":79,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":79,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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":"2026-05-10T18:25:15.494462Z","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":"2026-05-11T00:35:52.829379Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"cited_work":{"arxiv_id":"2507.21960","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.21960","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"PanoSplatt3R: Leveraging Per- spective Pretraining for Generalized Unposed Wide- Baseline Panorama Reconstruction","venue":null,"work_id":"11195ae0-c5a9-42af-a013-7b6954186853","year":2025},"citing_paper":{"arxiv_id":"2604.07105","last_updated":"2026-04-28T01:48:41Z","snapshot_observed_at":"2026-08-15T11:52:38.071683Z","submitted_at":"2026-04-08T13:57:18Z","title":"Genie Sim PanoRecon: Fast Immersive Scene Generation from Single-View Panorama","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T18:25:15.494462Z"},"links":{"cited_paper":"/paper/2507.21960","citing_paper":"/paper/2604.07105"},"observation_digest":"sha256:ad5c252ff832637782765fa96a051b42cea85cacb491a60c1b1542c94454ad7e","observation_id":"448ff9c9-3b32-4099-9ec7-ccfa4ae0e829","resolution":{"observed_at":"2026-05-11T00:35:52.831250Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.21960/citation-record","integrity":"/paper/2507.21960/integrity","json":"/paper/2507.21960/citation-record.json","paper":"/paper/2507.21960"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:17:23.200547Z","title":"Elite360d: Towards efficient 360 depth estimation via semantic-and distance-aware bi- projection fusion","venue":null,"work_id":"7d44fed3-3e51-4aef-9cb6-49b5ccc2f6a9","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.110305Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:d22a6c92e11ab31a09193ad17a578b344947bf6eed18e49fef9d9eae5953d3a7","observation_id":"72c90086-6267-4537-b1eb-5dd90969d49c","resolution":{"observed_at":"2026-08-06T12:17:23.204362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.189421Z","title":"Hrdfuse: Monocular 360deg depth estimation by collaboratively learning holistic-with-regional depth distri- butions","venue":null,"work_id":"8ee68255-ead4-43cd-8a98-aa9d7c7bf60e","year":2023},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.115446Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:f7f6afffacf288a608cb7bc988096b83ec57e4549db623520d17fbc9a73d241d","observation_id":"88852037-94ec-43fc-9e9c-1604bd6c0656","resolution":{"observed_at":"2026-08-06T12:17:23.194033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.176569Z","title":"Virtual reality and 360 panorama technology: a media comparison to study changes in sense of presence, anxiety, and positive emotions","venue":null,"work_id":"70a74cb7-6bb9-488d-aa44-a9fd76e2d9a3","year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.119091Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:2811e8c869e04d0984cf3c9813523f9c793f4b76e9b45a949a87dcaaf7377967","observation_id":"2355d54c-5c91-428c-9bfe-cfaf82ff2a96","resolution":{"observed_at":"2026-08-06T12:17:23.181449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.165836Z","title":"pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction","venue":null,"work_id":"47b3048e-d599-4d5c-9351-08022898ba97","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.122840Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:e92410fba63c6414f022a299a6e0f1bce590e3c0a61748909536579ee0627935","observation_id":"5943ffa7-e75b-430d-8b01-fa2315b38162","resolution":{"observed_at":"2026-08-06T12:17:23.169411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.08207","last_updated":"2024-09-12T16:47:57Z","snapshot_observed_at":"2026-08-16T13:19:04.660331Z","submitted_at":"2024-09-12T16:47:57Z","title":"VI3DRM:Towards meticulous 3D Reconstruction from Sparse Views via Photo-Realistic Novel View Synthesis","version":1},"cited_work":{"arxiv_id":"2409.08207","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.08207","snapshot_observed_at":"2026-08-06T12:17:22.513881Z","title":"VI3DRM:Towards meticulous 3D Reconstruction from Sparse Views via Photo-Realistic Novel View Synthesis","venue":"cs.CV","work_id":"01032b42-9991-4eb1-957e-6e7b20328c91","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.126361Z"},"links":{"cited_paper":"/paper/2409.08207","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:87b2951714ad8709df49fc1f62d049318d1d8caf1cf5288557250ba25ad53e68","observation_id":"db71a7e4-7c32-4803-ad4e-81e06a4beefb","resolution":{"observed_at":"2026-08-06T12:17:22.517788Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15779","last_updated":"2024-11-24T11:20:48Z","snapshot_observed_at":"2026-08-14T12:23:21.272399Z","submitted_at":"2024-11-24T11:20:48Z","title":"ZeroGS: Training 3D Gaussian Splatting from Unposed Images","version":1},"cited_work":{"arxiv_id":"2411.15779","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.15779","snapshot_observed_at":"2026-08-06T12:17:22.496530Z","title":"ZeroGS: Training 3D Gaussian Splatting from Unposed Images","venue":"cs.CV","work_id":"6c036824-73a6-4fd4-9d88-ff5d7af80561","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.130050Z"},"links":{"cited_paper":"/paper/2411.15779","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:2acb90118ce791644678b5701f687270bc00434cdb61d03c0c926b17b230b5f1","observation_id":"e7d9b26c-b488-4bbb-959d-63686fd1a680","resolution":{"observed_at":"2026-08-06T12:17:22.502573Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.154080Z","title":"Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images","venue":null,"work_id":"2d51f529-51a8-41dd-8603-130f50de20d2","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.133741Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:4acd94322dbbb005d3cd2b6bb63a953dc36af9109a11706fe6b5361431d3a287","observation_id":"ce58159e-0ab6-45b2-9db9-7b38e823135d","resolution":{"observed_at":"2026-08-06T12:17:23.158037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.133964Z","title":"Panogrf: generalizable spherical radiance fields for wide-baseline panoramas","venue":null,"work_id":"a5668006-ed88-4836-a631-c9f28f46fad2","year":2023},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.140915Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:545b0bd4409a094a02da8e001fd0c5643d063ce45e62cf55dc40c7b46665dbe9","observation_id":"1a942e16-4f42-424c-90e0-c5e937d3e885","resolution":{"observed_at":"2026-08-06T12:17:23.137428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16877","last_updated":"2024-11-25T19:16:29Z","snapshot_observed_at":"2026-08-14T13:32:39.103757Z","submitted_at":"2024-11-25T19:16:29Z","title":"PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.16877","snapshot_observed_at":"2026-08-06T12:17:22.144019Z","title":"Pref3r: Pose- free feed-forward 3d gaussian splatting from variable-length image sequence","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.144019Z"},"links":{"cited_paper":"/paper/2411.16877","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:75117a9e896cb65c85f8469f96e82407658286e66da7c8d38cf51e1829e9c23d","observation_id":"313d6783-f725-4fd9-8c12-a17d5290068b","resolution":{"observed_at":"2026-08-06T12:17:22.144019Z","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":"2026-08-06T12:17:23.122971Z","title":"Splatter-360: Generalizable 360 gaussian splatting for wide- baseline panoramic images","venue":null,"work_id":"4016c9a4-f683-49dc-b19c-aceee0ff1299","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.147820Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:e4626837b8b92fe5689ef19f5447be711542e1b419341737d6eee99957ae9e4f","observation_id":"7f2d32d3-26fa-41fe-adf3-8e06dd6d314d","resolution":{"observed_at":"2026-08-06T12:17:23.126655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.111566Z","title":"Spherenet: Learning spherical representations for detection and classification in omnidirectional images","venue":null,"work_id":"27550601-e24a-49d5-af4e-1b2622c404fb","year":2018},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.151011Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:2833d41df444da7b3489b073adfb1916bc8a740e43ccba975556a3f2d262a09c","observation_id":"47de64fe-a815-4dc5-ba7b-761d425a8606","resolution":{"observed_at":"2026-08-06T12:17:23.115431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.100804Z","title":"Eliminating the blind spot: Adapting 3d object detection and monocular depth estimation to 360 panoramic imagery","venue":null,"work_id":"c74da42a-881d-429d-8818-a320c608a501","year":2018},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.154753Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:97f763a7622b443a0951b1edb85918263f4bd6685bef8ad3cb65b627382586ec","observation_id":"4b5ba402-184b-48c1-a5a9-16d923b175e5","resolution":{"observed_at":"2026-08-06T12:17:23.104553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.091103Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":"84b40266-f92d-4a9f-967d-78e9bcbe2de2","year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.158101Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:0cb50cb21421b573022f89ba7f6ae3e55792712022853f136139617ce61bc2c5","observation_id":"2763b3bc-8402-4694-a0b6-dba80099b8e9","resolution":{"observed_at":"2026-08-06T12:17:23.094535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.081438Z","title":"Pano popups: In- door 3d reconstruction with a plane-aware network","venue":null,"work_id":"80fe23d5-14d2-401e-8a85-6ae912a83880","year":2019},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.161333Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:e21d093231fa4b8d92450801d1cfab71f9b9f055d08dfc445ecf418d8f984e63","observation_id":"10f9d6b6-9b2a-407d-8dfe-80c11476b835","resolution":{"observed_at":"2026-08-06T12:17:23.084980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.069467Z","title":"Tangent images for mitigating spherical distortion","venue":null,"work_id":"160b6450-607c-4498-8fc1-a4b449c05d89","year":2020},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.164928Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:99763764d749485429b6978fcfee56c96fed15dc4602718ca6942d15f8294931","observation_id":"5a68ab0b-c86f-454c-86aa-a2908d0371b5","resolution":{"observed_at":"2026-08-06T12:17:23.074418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.059204Z","title":"Desktop-based safety training using 360-degree panorama and static virtual reality techniques: A comparative exper- imental study","venue":null,"work_id":"f9c76084-b561-4acf-b970-2ff5ba6f7f54","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.168648Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:f8e36ea44f08b17fcf6ed3ed8ff3c2fd8f26526e078ef08d3065285906a86e64","observation_id":"e5f2b652-2d9b-4280-b75e-70a8b759afb1","resolution":{"observed_at":"2026-08-06T12:17:23.063189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.20309","last_updated":"2025-07-03T22:28:07Z","snapshot_observed_at":"2026-08-16T14:05:08.931099Z","submitted_at":"2024-03-29T17:29:58Z","title":"InstantSplat: Sparse-view Gaussian Splatting in Seconds","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.20309","snapshot_observed_at":"2026-08-06T12:17:22.172980Z","title":"Instantsplat: Un- bounded sparse-view pose-free gaussian splatting in 40 sec- onds","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.172980Z"},"links":{"cited_paper":"/paper/2403.20309","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:b35cea490ad7690ca024e8004f28ba9040c3b320074d9f3833381c775390f948","observation_id":"95e0de2b-b6d8-408c-ae18-eeebd0c788c1","resolution":{"observed_at":"2026-08-06T12:17:22.172980Z","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":"2026-08-06T12:17:23.047635Z","title":"Large spatial model: End-to-end unposed images to semantic 3d","venue":null,"work_id":"2b6ba592-b359-4ec3-be2d-86a2ba86841a","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.177325Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:9cd9ffd79fea4a7fd80d45325047dff659fe873b25b575406f99de997c14dfa6","observation_id":"a797cb6f-0413-4eb0-929d-592533690c5b","resolution":{"observed_at":"2026-08-06T12:17:23.052265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.036002Z","title":"Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981","venue":null,"work_id":"7ec7dde9-17d6-4bfb-b3a2-f44791cc4da1","year":1981},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.180581Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:a83f827a62e3438b9c504733639dc077ee6af7f9bb43e63ee7b4f05d99973905","observation_id":"ca0b95d8-2e95-4a94-b12c-c5a61be0edb6","resolution":{"observed_at":"2026-08-06T12:17:23.040564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.025497Z","title":"Forward flow for novel view synthesis of dynamic scenes","venue":null,"work_id":"2b98a760-3b83-4314-bee8-bd7f00d3eb8e","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.183858Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:d5b483e704f1cf05dc759d48a6c554dd49c13d7373f29456275468bc081c397a","observation_id":"48f41d97-d0fb-46bb-a437-2fa36b8e7b3c","resolution":{"observed_at":"2026-08-06T12:17:23.029141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.013391Z","title":"Somsi: Spherical novel view synthesis with soft occlusion multi-sphere images","venue":null,"work_id":"8ad993c4-bc3a-452b-99c0-a92b4181b546","year":2022},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.187426Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:61ca6b74b93c515bddb2e9df83e97dea36311f37394c9b6dc943c151a3c44496","observation_id":"498110ea-e23a-48fa-97f2-59439b07e5c1","resolution":{"observed_at":"2026-08-06T12:17:23.017882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.999722Z","title":"In defense of the eight-point algorithm","venue":null,"work_id":"73b2d6ce-1001-4210-b0da-7f1fc4127962","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.191682Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:0b88f74758c3ba671e6c5283cbacad55ee4fb7a7d99b96e797894b00ebcb3989","observation_id":"21debe34-d80a-4270-94c1-277c7d027a2d","resolution":{"observed_at":"2026-08-06T12:17:23.004237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.986730Z","title":"Rotary position embedding for vision transformer","venue":null,"work_id":"27fab55f-be8a-474b-bbf2-68ef597f943a","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.195592Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:6b01588fc55addff6031e57841b281a9d7a5594f782061688811c6de6061be53","observation_id":"fe0ecb9a-e880-4a4f-9033-49faac9fc7e8","resolution":{"observed_at":"2026-08-06T12:17:22.991611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.974690Z","title":"2d gaussian splatting for geometrically ac- curate radiance fields","venue":null,"work_id":"ba89bd0f-7635-4d98-a794-4f1f57ecbd42","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.198884Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:6cdba06c90c5f6ff0005ce13c94d29a799ec92bfd41589cfa3d38c35759f3129","observation_id":"0ffa1fa9-c592-4e75-9f88-d8977a279e69","resolution":{"observed_at":"2026-08-06T12:17:22.978792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.964200Z","title":"Unifuse: Unidirectional fusion for 360 panorama depth estimation","venue":null,"work_id":"ce731e99-a47d-47a6-b2e7-274d6da05fbf","year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.202105Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:318cc6926ff294003827e13a66a1144bbb558c6fd7e29d303e961e5536eed1d1","observation_id":"e4f080dc-5a2e-4a46-b880-e2d298e45b3a","resolution":{"observed_at":"2026-08-06T12:17:22.967794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.953929Z","title":"Stereo4d: Learning how 9 things move in 3d from internet stereo videos","venue":null,"work_id":"388b18d8-dde0-4fba-be73-efd966584e92","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.205718Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:f80a586f6541ee2c114f79d660370d571441e96a369ae890608d2424971ead79","observation_id":"9c5a03a6-e14d-4f7f-90ad-7a6f91c77baf","resolution":{"observed_at":"2026-08-06T12:17:22.957677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.943549Z","title":"Selfsplat: Pose-free and 3d prior-free generalizable 3d gaussian splatting","venue":null,"work_id":"60d798fb-4bfc-4e63-9ceb-7eb58954950f","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.209214Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:fedfeaf72cfb33275013a261c761cd4fdfa904f6fcd2e999397dadae6907806b","observation_id":"bb57aeaa-0a90-4b96-ac80-996f00382d89","resolution":{"observed_at":"2026-08-06T12:17:22.947088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.212595Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.212595Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:7d65d12f9f53a29224569c3c94d98cb722683bbe8caf8372f143178f5b99df82","observation_id":"37484b48-842a-4baf-bbc6-43a4a4f856ac","resolution":{"observed_at":"2026-08-06T12:17:22.212595Z","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":"2026-08-06T12:17:22.927896Z","title":"xformers: A modular and hackable trans- former modelling library","venue":null,"work_id":"bccd834a-a242-4ae5-8c56-3bc8610f00c7","year":2022},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.216168Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:4e8f9dabf983ac5e2f6c896ad20532267207169622d14c50241d0a6dd31ef509","observation_id":"0c9118f2-6535-4f4a-829b-a78e5f2e1f72","resolution":{"observed_at":"2026-08-06T12:17:22.931450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.918665Z","title":"Slam with panoramic vision","venue":null,"work_id":"1d999401-b8a7-4968-ac38-445dc865f187","year":2007},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.219519Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:d172f82a52e1681070044a12e4c2799f881f197179cab24a416ff93d46ec1a2c","observation_id":"8bc6c4b4-0819-475f-972f-6f110d50fc91","resolution":{"observed_at":"2026-08-06T12:17:22.921827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.908860Z","title":"Ep n p: An accurate o (n) solution to the p n p problem","venue":null,"work_id":"ad0cb75d-341d-4a8d-9a78-be063b972d28","year":2009},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.222722Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:3c57882f8cb33b6f9bed0f2bbe35bc55a19f67fc199e0351f3bd282b0a39d34d","observation_id":"86a0404c-02b3-41b4-9899-e6c46d164629","resolution":{"observed_at":"2026-08-06T12:17:22.912453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.897005Z","title":"Ground- ing image matching in 3d with mast3r","venue":null,"work_id":"d8e9af96-d5cd-41c7-929d-d865431032b9","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.226082Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:549cb063c9d60249c54b76ca9c24be8065813b5295ff4c58839ce0c7193338f4","observation_id":"23cfa5e2-ecba-46e1-8787-0e8b81dcf67c","resolution":{"observed_at":"2026-08-06T12:17:22.901200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16767","last_updated":"2025-06-25T07:19:44Z","snapshot_observed_at":"2026-08-16T13:22:49.851326Z","submitted_at":"2024-08-29T17:59:40Z","title":"ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16767","snapshot_observed_at":"2026-08-06T12:17:22.229251Z","title":"Re- conx: Reconstruct any scene from sparse views with video diffusion model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.229251Z"},"links":{"cited_paper":"/paper/2408.16767","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:8a1d3b5eadb7da9a445968ae6683985ec7a947aae295af9187fdd371d4514a1f","observation_id":"60df0833-000b-485a-9ba3-c56506844e3e","resolution":{"observed_at":"2026-08-06T12:17:22.229251Z","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":"2026-08-06T12:17:22.886821Z","title":"Augmented reality: a novel approach for navigating in panorama-based virtual environments (pbve)","venue":null,"work_id":"538760b5-3783-460b-b58d-4d8a9231ba3f","year":2003},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.233029Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:7d20e6e298c9b5c3dba1a0380daea304758475f873590e9faa955c685da2cab2","observation_id":"1d2012a8-896d-4019-9a9f-c53f292c5449","resolution":{"observed_at":"2026-08-06T12:17:22.890644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.876576Z","title":"Neural rays for occlusion-aware image-based ren- dering","venue":null,"work_id":"31602365-d049-4462-925f-1e07d8faa3bc","year":2022},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.237024Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:9cf33cae62a4a584907ec349eed4f5417b9f823cde02b8bb42533569eb3ad968","observation_id":"51026a94-4da7-4709-aefb-140393362200","resolution":{"observed_at":"2026-08-06T12:17:22.880714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.866855Z","title":"Slam3r: Real- time dense scene reconstruction from monocular rgb videos","venue":null,"work_id":"52aa584b-ba17-4ca7-8c2a-0038eade863c","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.240474Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:a7d81be351542091677d70175a9e6e0de02408ef8ed701f0daea9534c10520bf","observation_id":"90dd5e9c-0e66-4dad-8f2a-10ce74728608","resolution":{"observed_at":"2026-08-06T12:17:22.870331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.856490Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":"34c81890-6029-4175-8546-ea9ec870e8c2","year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.243777Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:c332a36e03d7cf92c638c58075b9c453bfe61d3cb691ee01723830a0690c650f","observation_id":"b0dcb0ce-383b-4b09-b921-b996ca4e57ac","resolution":{"observed_at":"2026-08-06T12:17:22.860288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-06T12:17:22.247119Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.247119Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:02b8f0f57875746e1f92cb4bded6bae267c04dc4a8936be50aadb23becbb8de8","observation_id":"5e9f02a7-2f95-489e-b374-e1efcb8649eb","resolution":{"observed_at":"2026-08-06T12:17:22.247119Z","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":"2026-08-06T12:17:22.845922Z","title":"Distinctive image features from scale- invariant keypoints","venue":null,"work_id":"e238b9e9-1962-43ed-a4a3-d0e19e72eb95","year":2004},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.250564Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:a1caf71e120f02eceb4dc849c3ab6132a3747f93df343e66b075367f51811df1","observation_id":"b97128b8-da51-4aae-9c84-cb7f162e2ceb","resolution":{"observed_at":"2026-08-06T12:17:22.849732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.836320Z","title":"Align3r: Aligned monocular depth estima- tion for dynamic videos","venue":null,"work_id":"0e90f986-f470-4c4c-ba16-80d5e56be38b","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.253746Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:397a71fb8c2177110824d75158119b5e72de11bebc9b9a668d368e3a44a36482","observation_id":"8bd51354-11e7-4c63-b5c9-f3b4c89c1393","resolution":{"observed_at":"2026-08-06T12:17:22.839798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.825656Z","title":"3d geometry-aware deformable gaussian splatting for dynamic view synthesis","venue":null,"work_id":"542e3c7a-75de-456c-b27a-976e8c6ba29c","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.257653Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:fd24ef04aaa91576fcdcb7f7a0afe239804ad0dd1fb1db2bbb9c8e90ad66ff18","observation_id":"2b5af00c-d28a-4510-8100-7467dd7ede0b","resolution":{"observed_at":"2026-08-06T12:17:22.830029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.814386Z","title":"Fit: Flexible vision trans- former for diffusion model","venue":null,"work_id":"71ac63e2-e54b-48db-9fae-279da1bf73b9","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.261199Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:fd991d1a70ac6f8d5f43263d18662165a7b352185ddbb64c70931f8f9ee1717c","observation_id":"86ac9c12-0e35-40b9-92dc-2e2e61a56fc3","resolution":{"observed_at":"2026-08-06T12:17:22.818001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.803148Z","title":"Nerf: Representing scenes as neural radiance fields for view syn- thesis","venue":null,"work_id":"4fd4465d-3f56-4b50-9b97-c895be0b8a86","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.264516Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:f417013c5ab08c627c4a3676689eb693083af7b90c82aa9d0f677b469ce8d071","observation_id":"2da80b1c-1d79-4af8-82b8-971e04d9ebe9","resolution":{"observed_at":"2026-08-06T12:17:22.807028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.790867Z","title":"Epipolar-free 3d gaussian splatting for generalizable novel view synthesis","venue":null,"work_id":"e6b7230e-464a-44da-baf6-308ed3210c77","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.268747Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:d5e25df9c015d8a6dae885fc3758421733b93cd18a5aa36b00c8605db8b41f86","observation_id":"dc0154d8-8807-4205-ba31-34cb2a99d302","resolution":{"observed_at":"2026-08-06T12:17:22.795592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.779863Z","title":"High-resolution depth estimation for 360deg panoramas through perspective and panoramic depth images registration","venue":null,"work_id":"08b5798f-8dea-425a-af4d-498438e4fe94","year":2023},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.272103Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:4a127bed7d044693d298bd90cd8768faa5b614dd406473f6a6bd005dcb708ecd","observation_id":"530d6d89-2bfc-4418-94e2-98e25a08c123","resolution":{"observed_at":"2026-08-06T12:17:22.783420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.08238","last_updated":"2021-09-16T22:01:24Z","snapshot_observed_at":"2026-08-15T09:50:41.483833Z","submitted_at":"2021-09-16T22:01:24Z","title":"Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.08238","snapshot_observed_at":"2026-08-06T12:17:22.275400Z","title":"Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.275400Z"},"links":{"cited_paper":"/paper/2109.08238","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:1bb3acaaad980e3018a238e1ebd3da080f690f9d508c9c86147229ab85fe6144","observation_id":"7dc95146-5054-46d3-a824-b38b0afe7558","resolution":{"observed_at":"2026-08-06T12:17:22.275400Z","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":"2026-08-06T12:17:22.769593Z","title":"Vi- sion transformers for dense prediction","venue":null,"work_id":"153c484b-28b9-4357-9b5d-c8d5df622158","year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.278895Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:dea39b357b421b05e9c5fbb315422584fbccb9a3b94f3bce191d7ed12a034105","observation_id":"e17ffe53-e363-485b-a365-5bdc1eaec268","resolution":{"observed_at":"2026-08-06T12:17:22.772985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.759786Z","title":"360monodepth: High-resolution 360deg monocular depth estimation","venue":null,"work_id":"052cbd40-d78c-49d9-997b-40c942a08232","year":2022},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.282256Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:a25a55b888494d07def185982e6a70ebd5e9e59d11d14ffbf80d128b2e5f14d6","observation_id":"14782010-ae63-4491-b1c8-74c1e3f1a303","resolution":{"observed_at":"2026-08-06T12:17:22.763196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.749863Z","title":"Habitat: A plat- form for embodied ai research","venue":null,"work_id":"4d07b222-3bfe-4c1c-b52f-8aee77832d26","year":2019},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.285376Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:09942615c39ac622aba295df979abd55f377f69de551b6c1584c0ec15badbb03","observation_id":"8f5f8ab2-2507-49d8-b7b5-928a7172df46","resolution":{"observed_at":"2026-08-06T12:17:22.753378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.740404Z","title":"Panoformer: Panorama transformer for indoor 360 depth estimation","venue":null,"work_id":"40bdced1-26ed-45a6-a9e9-af5519c9d942","year":2022},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.289816Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:d139e73ef963cb0a18ded8262571a2699176c69aecce6b16dfb3bd51bcc8e7d2","observation_id":"d4f90a02-4a1b-4c03-bec8-132c802ef171","resolution":{"observed_at":"2026-08-06T12:17:22.743660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13912","last_updated":"2024-08-27T19:06:57Z","snapshot_observed_at":"2026-08-15T11:25:58.014520Z","submitted_at":"2024-08-25T18:27:20Z","title":"Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13912","snapshot_observed_at":"2026-08-06T12:17:22.293175Z","title":"Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.293175Z"},"links":{"cited_paper":"/paper/2408.13912","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:ff66bc95163e58cc00c05eb0c431f662a98078d616be481e0bd0f8ccf6f9d6c1","observation_id":"23c9bb5d-5a5a-4baf-a3a7-9e830f336b85","resolution":{"observed_at":"2026-08-06T12:17:22.293175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05797","last_updated":"2019-06-13T16:29:58Z","snapshot_observed_at":"2026-08-16T20:27:41.336890Z","submitted_at":"2019-06-13T16:29:58Z","title":"The Replica Dataset: A Digital Replica of Indoor Spaces","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.05797","snapshot_observed_at":"2026-08-06T12:17:22.297496Z","title":"The replica dataset: A digital replica of indoor spaces","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.297496Z"},"links":{"cited_paper":"/paper/1906.05797","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:05152a174e249653b0d3e34d6a554285d177a706ba6fb9761037cf8ebaa37c5d","observation_id":"544fca2d-5886-40b1-8b71-722bcf55ce23","resolution":{"observed_at":"2026-08-06T12:17:22.297496Z","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":"2026-08-06T12:17:22.301243Z","title":"Roformer: Enhanced transformer with rotary position embedding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.301243Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:d25dd6b55f72a9bc28a94189c7d9dd72d24aefc829d90d8d501155826cddb0d2","observation_id":"791c1fa9-3f12-45ec-9d2c-1be9ca87dec9","resolution":{"observed_at":"2026-08-06T12:17:22.301243Z","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":"2026-08-06T12:17:22.725005Z","title":"Hohonet: 360 indoor holistic understanding with latent horizontal fea- tures","venue":null,"work_id":"3cf77608-aa69-4403-9202-855ee235e5cb","year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.304955Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:44ae724b5479de7bff464a799b940c076c26a1095b79bc9f8a7ecb2e8b618fe7","observation_id":"4a4a5751-46fd-445d-ac55-09d25f6b86c9","resolution":{"observed_at":"2026-08-06T12:17:22.728139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.714449Z","title":"Hisplat: Hierarchical 3d gaus- sian splatting for generalizable sparse-view reconstruction","venue":null,"work_id":"0ded15e7-d42f-4548-86be-15e152214f80","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.308175Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:949e2d2ee3de4f928665cb06fcd1a5b22d936f8654b0ce29eb72ec78f8f6cf1f","observation_id":"0e96af74-1191-4b10-a8fe-57b5c240646f","resolution":{"observed_at":"2026-08-06T12:17:22.718705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.704501Z","title":"Mv-dust3r+: Single-stage scene reconstruction from sparse views in 2 seconds","venue":null,"work_id":"45513456-8766-4d39-a8f1-8e72800cb7f4","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.311519Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:49e3974b66c0565186d664e934b964619d8086c3e5ce330daf2b140452e27996","observation_id":"629bba7d-18c1-4bad-b5e9-c4671e4f3256","resolution":{"observed_at":"2026-08-06T12:17:22.707977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.694979Z","title":"Distortion-aware convolutional filters for dense prediction in panoramic images","venue":null,"work_id":"de3dfbed-b6e9-4c5b-85ec-2c1a5b816cf0","year":2018},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.314775Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:04ecb43f8ff200ead565fae92e55d616c395849a239ee9c43ac49a39585a2f75","observation_id":"8f32902b-0e77-4f11-9d40-0692b4aad34a","resolution":{"observed_at":"2026-08-06T12:17:22.698299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.685095Z","title":"Bifuse: Monocular 360 depth estimation via bi-projection fusion","venue":null,"work_id":"5e6ffe8e-bba4-49b5-a268-1a7b9c69248c","year":2020},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.318248Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:e7f1bf7c4dcc3aa5c4f68432ce5db3dfd69789d947d21fc29631158d9f46ffb0","observation_id":"5d6ec5ba-46d7-422f-8957-65dff9997895","resolution":{"observed_at":"2026-08-06T12:17:22.688422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.674476Z","title":"3d reconstruction with spatial memory","venue":null,"work_id":"bbac38cd-cb4b-43f8-a788-19b83b9a5641","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.321494Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:79a791e6062732cebe49d4869b08de27f2585224bdfae78c83bea39116eebd7f","observation_id":"e17c96c0-730c-4220-be5a-c8bbb9ed129a","resolution":{"observed_at":"2026-08-06T12:17:22.678930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.665430Z","title":"Ibr- net: Learning multi-view image-based rendering","venue":null,"work_id":"13a5cc59-b49c-43f2-b96e-ebf50f954380","year":2021},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.324641Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:8417ccb31a50bd209628ed2c8816d81bbf8905d42086d409a3b3e3db0e4f58a3","observation_id":"4d6542ff-d835-47f6-83cc-71b63a89bc22","resolution":{"observed_at":"2026-08-06T12:17:22.668782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.656161Z","title":"Continuous 3d per- ception model with persistent state","venue":null,"work_id":"a6341386-1ad1-454e-a1fd-ee2066739822","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.327868Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:0efd8a338e245ac7896cf3e57f4f35967b8cd519b15bc3055f5406776461ff61","observation_id":"dd0a544b-e0b3-42bd-84d9-62e67209dd07","resolution":{"observed_at":"2026-08-06T12:17:22.659700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.647129Z","title":"Dust3r: Geometric 3d vi- sion made easy","venue":null,"work_id":"ecb6ea10-00ca-4fa7-90c8-4ccb92b3c2e2","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.330847Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:391d82f8a73bc2a80142df373e1e54b6300b25ae3a3a9e3c6035ad04a073e682","observation_id":"c0e42c0d-fd26-43b5-9644-a711c8bbdb52","resolution":{"observed_at":"2026-08-06T12:17:22.650268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.637888Z","title":"Looprefine: Deep camera pose estimation with loop consistency","venue":null,"work_id":"ea25fec2-32af-4c8f-be97-99a08516f211","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.334778Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:f4a090a847dacbaade0c6e4e5b5439c05a894f7912f95a289bcc93d54148151a","observation_id":"2bbf30bf-3d57-4ec7-bf95-8d42726c161d","resolution":{"observed_at":"2026-08-06T12:17:22.641285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.628423Z","title":"Croco v2: Improved cross-view completion pre- training for stereo matching and optical flow","venue":null,"work_id":"bfea9a55-747e-4660-9da9-e517b7ff4786","year":2023},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.337994Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:ac31a4dadd435c7bac96bfbf345f7d76cb6525ef82bd5637ea320520015432df","observation_id":"66c24636-660a-416c-8870-ce7dca25f835","resolution":{"observed_at":"2026-08-06T12:17:22.631881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.619117Z","title":"Depthsplat: Connecting gaussian splatting and depth","venue":null,"work_id":"844d5056-50f4-4cf4-9a0f-7a3adf028f4d","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.341046Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:c64dc71e43945b3a1da1e579eb9f21cf1f2f34dd422564e37392085dad507eb4","observation_id":"f11ca78f-92f1-4cea-bb93-b2c26fc39f95","resolution":{"observed_at":"2026-08-06T12:17:22.622439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.09573","last_updated":"2025-09-01T06:27:11Z","snapshot_observed_at":"2026-08-11T20:41:38.705633Z","submitted_at":"2024-12-12T18:52:53Z","title":"FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.09573","snapshot_observed_at":"2026-08-06T12:17:22.344418Z","title":"Freesplatter: Pose- free gaussian splatting for sparse-view 3d reconstruction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.344418Z"},"links":{"cited_paper":"/paper/2412.09573","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:7b557a2ef90767177a80dbef3d034fa4f864c5031a50419ef9b2b1a84b9bb53d","observation_id":"f8260450-ab37-4d4f-86b0-97aae7889ec1","resolution":{"observed_at":"2026-08-06T12:17:22.344418Z","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":"2026-08-06T12:17:22.610025Z","title":"Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass","venue":null,"work_id":"f9f4b576-f7fe-471a-b2ab-8bf2e15e46ee","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.347725Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:b51b683b36e4be7281159b208072f3f13ea7ebb1e8f4841f59c9621b726c6de1","observation_id":"a7c7d863-126d-4ab9-8786-ea99ba43bfeb","resolution":{"observed_at":"2026-08-06T12:17:22.613221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.600253Z","title":"No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images","venue":null,"work_id":"fdbff795-071c-40ac-bbe7-9e558dbd318e","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.351702Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:aa20f7aec8829db272ae25a7e27234c46e47d693af9310a41d8abee7985e68ab","observation_id":"156c4d7b-ff45-4bca-8092-8d532110f148","resolution":{"observed_at":"2026-08-06T12:17:22.603883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.590110Z","title":"pixelnerf: Neural radiance fields from one or few images","venue":null,"work_id":"963d4385-8a55-4933-ba23-02d3f7e90ccd","year":null},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.354897Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:8bca00380d211e710c4d021f5d3d85280a5fb1548fde27edd3b34beb6c262857","observation_id":"8f7ea072-5aed-4ecf-bed7-36a3aba9e662","resolution":{"observed_at":"2026-08-06T12:17:22.593842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.580294Z","title":"Panelnet: Understanding 360 indoor environment via panel representation","venue":null,"work_id":"305d5798-e3c3-4af1-871a-3d8161027f98","year":2023},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.357977Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:84524e40a4836cf8fe89fc827f2a2252b78c6d217cb058f54a6e360bfba38d88","observation_id":"bd037148-0b13-4157-8d2d-d7de43eb6f77","resolution":{"observed_at":"2026-08-06T12:17:22.583757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.02048","last_updated":"2024-09-03T16:53:19Z","snapshot_observed_at":"2026-08-14T12:22:44.567374Z","submitted_at":"2024-09-03T16:53:19Z","title":"ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.02048","snapshot_observed_at":"2026-08-06T12:17:22.361041Z","title":"Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.361041Z"},"links":{"cited_paper":"/paper/2409.02048","citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:6ee0026b0229b8a4574822632988fa3f5bd8695ad8a255838fa79a2a86008beb","observation_id":"1718a443-94b0-4fca-9eec-6a83062b7fbd","resolution":{"observed_at":"2026-08-06T12:17:22.361041Z","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":"2026-08-06T12:17:22.570610Z","title":"Egformer: Equirectangular geometry- biased transformer for 360 depth estimation","venue":null,"work_id":"9c2f80d0-425d-47d1-a25a-a87c6113c7c5","year":2023},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.364344Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:418aab049e1d60aa31d35cc8976cd74ecec06ee28ec9622145da41c73a4e1be6","observation_id":"308e18b7-1391-418a-8e2c-c7ed2d6f29f2","resolution":{"observed_at":"2026-08-06T12:17:22.573874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.560700Z","title":"Pansplat: 4k panorama synthesis with feed-forward gaussian splatting","venue":null,"work_id":"3b0b6ddb-5c66-4ec0-ac8d-53defdc6a310","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.367557Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:efee9d14db633093f815b6f0ccf830ce17177416e0f41f2b258b0d44fc579e67","observation_id":"4066a0de-ea9b-4e9d-bc8a-611afa17d4d6","resolution":{"observed_at":"2026-08-06T12:17:22.564210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.550757Z","title":"Bending reality: Distortion-aware transformers for adapting to panoramic se- mantic segmentation","venue":null,"work_id":"577ec29f-3896-4658-8711-b4c0fbcca8f0","year":2022},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.370813Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:1721f4b9dddcd8a0c20f386a33828ba0d81a762e1a45c9640b165aa73e4f2852","observation_id":"425d6644-9c25-4e80-947a-8433b3f2df1c","resolution":{"observed_at":"2026-08-06T12:17:22.554155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.540579Z","title":"Monst3r: A simple approach for estimating geometry in the presence of motion","venue":null,"work_id":"eec29aca-6778-42da-a7cc-7f221d4fa0fd","year":2025},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.373924Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:b86e66e171a720d3dfe0e306772f73ea5277be129a27270884ff97cd16e85dd9","observation_id":"10cc0482-cd5d-425e-8eb1-20ee3566ce34","resolution":{"observed_at":"2026-08-06T12:17:22.544043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:22.377166Z","title":"Acdnet: Adaptively combined dilated con- volution for monocular panorama depth estimation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.377166Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:c08e56b4a9be22df0d0f2edccd780451d12d6c3975f68471ebe561a36c5c1b77","observation_id":"50dfda41-0d2b-4a4a-854e-d5577beb156a","resolution":{"observed_at":"2026-08-06T12:17:22.377166Z","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":"2026-08-06T12:17:22.524870Z","title":"Omnidepth: Dense depth estimation for indoors spherical panoramas","venue":null,"work_id":"6611fedd-1c44-4ca8-b9e1-dca9576b5fa6","year":2018},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.380837Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:75697b87765bb851395eebabdb81bc3006e92b611c6c70ad3510ce5eb89388dd","observation_id":"ff8a38df-8f79-43c6-97fb-58e28a23cadd","resolution":{"observed_at":"2026-08-06T12:17:22.528283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:17:23.144292Z","title":"2, 5, 6, 7, 13, 14","venue":null,"work_id":"2be78dcc-431e-4082-aaa4-62f7fda30512","year":2024},"citing_paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction","version":1},"reference_index":386,"source":"pdf_text","source_observed_at":"2026-08-06T12:17:22.137413Z"},"links":{"citing_paper":"/paper/2507.21960"},"observation_digest":"sha256:a79999ce45eeea3096b054232dbdaa8b43fbbce20743df6790121e3cbf0b5fef","observation_id":"e240cd7c-cac7-4a4e-b637-0a0c79e25c76","resolution":{"observed_at":"2026-08-06T12:17:23.147643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21960","last_updated":"2025-07-29T16:10:39Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T20:10:28.077800Z","submitted_at":"2025-07-29T16:10:39Z","title":"PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction"},"reference_resolution":{"displayed":78,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":64},"total_outbound_references":78},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 1 inbound Pith citation observation for arXiv:2507.21960."}