{"as_of":"2026-08-09T19:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:77f94564d63f1f4bc8cd15190ed32927404753a46cd7cc9c0b4b4514af6b661d","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T22:31:42.149330Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.15727/citation-record","integrity":"/paper/2607.15727/integrity","json":"/paper/2607.15727/citation-record.json","paper":"/paper/2607.15727"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T22:31:39.139106Z","title":"NeRF: Representing scenes as neural radiance fields for view synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:39.139106Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:f7f797de859c32599ceb0d4df747dcd628c9ebfda252fc972157713d443adc26","observation_id":"95977881-37e0-47aa-aa35-45a3242fdebc","resolution":{"observed_at":"2026-08-01T22:31:39.139106Z","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":"2026-08-01T22:31:39.220853Z","title":"3D Gaussian Splatting for real-time radiance field rendering,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:39.220853Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:ee7df63a58c54cde8919ad0ee487ffa73190c9870fa904ba18e28b8dfc4c7381","observation_id":"84de44d2-325d-4285-8a0e-77d83d91bfb2","resolution":{"observed_at":"2026-08-01T22:31:39.220853Z","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":"2026-08-01T22:31:39.288778Z","title":"Dust3R: Geometric 3D vision made easy,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:39.288778Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:1665761efd876f823133b5c7a14d4c2db68fa06a4786d83e75111d8a234a2e97","observation_id":"0221beb1-263c-439a-81d1-d5d9eaca042c","resolution":{"observed_at":"2026-08-01T22:31:39.288778Z","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":"2026-08-01T22:31:39.425701Z","title":"Continuous 3D perception model with persistent state,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:39.425701Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:5c85fb2e408d7e21abaa154533c19bb06552f80d255b9acdbb60c608a217c342","observation_id":"bd37aced-75bf-4a88-a6ff-06b01a520bd9","resolution":{"observed_at":"2026-08-01T22:31:39.425701Z","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":"2026-08-01T22:31:39.597776Z","title":"VGGT: Visual geometry grounded transformer,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:39.597776Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:f666681295291d4465b3c57393a2ea2b0ecf89f04bf725159d2e1ece769fb4fe","observation_id":"7dd27620-e885-49f2-a6d9-c1941af4180c","resolution":{"observed_at":"2026-08-01T22:31:39.597776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14959","last_updated":"2024-10-10T07:41:26Z","snapshot_observed_at":"2026-08-03T07:05:13.555103Z","submitted_at":"2024-05-23T18:10:26Z","title":"EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14959","snapshot_observed_at":"2026-08-01T22:31:39.746300Z","title":"EvGGS: A collaborative learning framework for event-based generalizable Gaussian Splatting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:39.746300Z"},"links":{"cited_paper":"/paper/2405.14959","citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:18e5b1279cdc2b11ada9deafcdef4a5208dcb9ed171346a86a77d80c8676349d","observation_id":"3b40dfbb-ca03-4e6c-af05-aa9827878581","resolution":{"observed_at":"2026-08-01T22:31:39.746300Z","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":"2026-08-01T22:31:39.883956Z","title":"Event3dgs: Event-based 3d gaussian splatting for high- speed robot egomotion,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:39.883956Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:94bedbee002a1d6d20d20843cb70cc673d1495559fd56ca55b47dba1adaf107a","observation_id":"d4c1fc69-1946-44c5-b5ed-2d2e2ae425e2","resolution":{"observed_at":"2026-08-01T22:31:39.883956Z","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":"2026-08-01T22:31:40.101002Z","title":"IncEventGS: Pose-free Gaussian Splatting from a single event camera,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.101002Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:facce672231f880474048729f6bc543ce8326a18f1f471f63a5c04545e7c87d6","observation_id":"af31a9f8-e287-4699-889d-0586c9e1eb78","resolution":{"observed_at":"2026-08-01T22:31:40.101002Z","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":"2026-08-01T22:31:40.186792Z","title":"PEOD: A pixel-aligned event-rgb benchmark for object detection under challenging conditions,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.186792Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:6d30bf756df728c10f0fdffa7aab9ba2acb068dd39f27e23178c01bc4e9334d7","observation_id":"62b7b7cd-0fdd-4419-9924-20b79abed33e","resolution":{"observed_at":"2026-08-01T22:31:40.186792Z","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":"2026-08-01T22:31:40.242898Z","title":"Structure-From-Motion revisited,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.242898Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:6bdf2a12a190e1948e46dbbcacc35c721d283df415d356cc809946a9791fa21c","observation_id":"bbab1957-ed22-4cf4-a48b-bef5bfc9b3d3","resolution":{"observed_at":"2026-08-01T22:31:40.242898Z","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":"2026-08-01T22:31:40.307712Z","title":"CREPES: Cooperative RElative pose estimation system,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.307712Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:753107ee60da15458c58c4c36b2c05ba1b0eea7803359fa25a340b2d5f815c10","observation_id":"586cba39-03cf-4155-8585-cd67d0380501","resolution":{"observed_at":"2026-08-01T22:31:40.307712Z","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":"2026-08-01T22:31:40.358964Z","title":"Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.358964Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:d5b63bfa350a61179a063026f8efaf3638369146a5f02470720acb074904a5fb","observation_id":"667851db-645b-47a0-92df-bf92f22a15b9","resolution":{"observed_at":"2026-08-01T22:31:40.358964Z","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":"2026-08-01T22:31:40.409220Z","title":"COLMAP-free 3D Gaussian Splatting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.409220Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:d5e586c990b5ba9cae1e3cb6e999b1dcee65740d08bb5cb45906386197643fa5","observation_id":"878ed67f-25d9-4843-8acb-2801eeeb4d40","resolution":{"observed_at":"2026-08-01T22:31:40.409220Z","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":"2026-08-01T22:31:40.490101Z","title":"Grounding image matching in 3D with MASt3R,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.490101Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:4192517b323bc606b50b507fc7330364346e29b1bd28dd88f428ee85680c8c20","observation_id":"c8dc1282-d42c-4db5-b2d7-8c350b4143a1","resolution":{"observed_at":"2026-08-01T22:31:40.490101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.08448","last_updated":"2025-07-11T09:41:54Z","snapshot_observed_at":"2026-08-08T11:04:41.526877Z","submitted_at":"2025-07-11T09:41:54Z","title":"Review of Feed-forward 3D Reconstruction: From DUSt3R to VGGT","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.08448","snapshot_observed_at":"2026-08-01T22:31:40.552686Z","title":"Review of Feed-Forward 3D recon- struction: From DUSt3R to VGGT,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.552686Z"},"links":{"cited_paper":"/paper/2507.08448","citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:36f8d0ec36b723455b3f304156bc4d2c810e813c0a39964a8213566d1fa0bbf7","observation_id":"1964f772-2f90-4ecf-9a3f-66766924654c","resolution":{"observed_at":"2026-08-01T22:31:40.552686Z","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":"2026-08-01T22:31:40.621824Z","title":"Real-time 3D recon- struction and 6-DoF tracking with an event camera,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.621824Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:c656fd3f191cb22d894fcb1f7b82be4850f01ee64433751334bdc7c9368657ba","observation_id":"1b3477ab-4807-4d64-aee6-f6da633c539a","resolution":{"observed_at":"2026-08-01T22:31:40.621824Z","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":"2026-08-01T22:31:40.685579Z","title":"EMVS: Event-based multi-view stereo—3D reconstruction with an event camera in real- time,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.685579Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:3c55399e33ff7ec0769af14c68b3d158a967541fe3a267b59f56f547318f8982","observation_id":"3c108c9d-8d25-47d5-a410-cf3a9b674194","resolution":{"observed_at":"2026-08-01T22:31:40.685579Z","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":"2026-08-01T22:31:40.764065Z","title":"EVO: A geometric approach to event-based 6-DOF parallel tracking and mapping in real time,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.764065Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:6aa01ad6070a2abba4c87a358f85c43a58a04ca5a265d254f1ae805ad35ec678","observation_id":"5d5cb84b-5105-49b2-8291-81724331d9f0","resolution":{"observed_at":"2026-08-01T22:31:40.764065Z","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":"2026-08-01T22:31:40.872471Z","title":"Semi-dense 3D reconstruction with a stereo event camera,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.872471Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:0c8cbd2525ff493c8d26d5bd400d1777f814778bd264d18ccba65356a47b264e","observation_id":"e5d3fb0a-2598-426d-8319-27e9ecf37793","resolution":{"observed_at":"2026-08-01T22:31:40.872471Z","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":"2026-08-01T22:31:40.935553Z","title":"E-NeRF: Neural radiance fields from a moving event camera,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:40.935553Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:7b784c7a9c7fb3a9822b67c4acee19292c587fe6133166d31efffc2ec9dc3311","observation_id":"a5af5178-604f-46f5-a65a-f163d854cd4c","resolution":{"observed_at":"2026-08-01T22:31:40.935553Z","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":"2026-08-01T22:31:41.053569Z","title":"EV-NeRF: Event based neural radiance field,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.053569Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:872fbc266b382c00f25319b3b0af4c78422bae2706c22ca5a7dfde91cb667911","observation_id":"da849e14-0c1f-47b2-b0c2-9890184b6132","resolution":{"observed_at":"2026-08-01T22:31:41.053569Z","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":"2026-08-01T22:31:41.140048Z","title":"EventNeRF: Neural radiance fields from a single colour event camera,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.140048Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:53654faa5dbaef5a5ba2e7d8e66d496ea1dcd931724da84c495925c13b95c728","observation_id":"c06f79c4-906c-4957-bdbb-9a5cddc1345f","resolution":{"observed_at":"2026-08-01T22:31:41.140048Z","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":"2026-08-01T22:31:41.227979Z","title":"EventSplat: 3D Gaussian Splatting from moving event cameras for real-time rendering,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.227979Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:c47cd9e92845f2602bdfa8546bb5c84c07d96386184812d2087853e4aee374ad","observation_id":"de0f5d6d-b893-411c-9c1c-bce3a07ab758","resolution":{"observed_at":"2026-08-01T22:31:41.227979Z","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":"2026-08-01T22:31:41.283208Z","title":"Event-3DGS: Event-based 3D reconstruc- tion using 3D Gaussian Splatting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.283208Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:3ed546b5413e2bafc24de2cf5395e2f4e583435dc2afa77f3d409dbcbf6ec623","observation_id":"ce977733-addf-49ad-a1a8-fc9f933fa72c","resolution":{"observed_at":"2026-08-01T22:31:41.283208Z","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":"2026-08-01T22:31:41.386045Z","title":"EAG3R: Event-augmented 3D geometry estimation for dynamic and extreme-lighting scenes,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.386045Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:adbf1db9f13516f926e9442338f3304f901a6769147f8130b3725a704ae7b9bf","observation_id":"c85f4cbf-4671-4cf6-b451-54c476f7584c","resolution":{"observed_at":"2026-08-01T22:31:41.386045Z","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":"2026-08-01T22:31:41.485689Z","title":"Masked event modeling: Self-supervised pretraining for event cameras,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.485689Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:71a1caf47b222c56f8a3e1d635785e57981d85325c92c26405cfafdf0d1cb767","observation_id":"7cbfef53-9e6e-4a5d-a4c1-1c88a62b0a6d","resolution":{"observed_at":"2026-08-01T22:31:41.485689Z","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":"2026-08-01T22:31:41.558222Z","title":"MatrixCity: A large-scale city dataset for city-scale neural rendering and beyond,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.558222Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:9f7df49618a413477d77463ec186bab9a5b177a4bd232374bfc3fff43b67065c","observation_id":"42c8506a-d1e7-4da7-bd41-c4abb83de18d","resolution":{"observed_at":"2026-08-01T22:31:41.558222Z","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":"2026-08-01T22:31:41.620218Z","title":"TUM-VIE: The TUM stereo visual-inertial event dataset,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.620218Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:f19ff7d5d930e3d25026562a16f078137767979387b94e351d1a26f6587dd0a0","observation_id":"4a85af49-6fc7-4c8a-be36-3df3aa1d4a5c","resolution":{"observed_at":"2026-08-01T22:31:41.620218Z","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":"2026-08-01T22:31:41.705792Z","title":"Video to events: Recycling video datasets for event cameras,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.705792Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:0596f241c22e1a404f80e3219d2f4e4e4cbe34d17a342124dd24e197296284d3","observation_id":"904361e6-8cf7-4d82-89fa-593e2c1d9ba9","resolution":{"observed_at":"2026-08-01T22:31:41.705792Z","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":"2026-08-01T22:31:41.766130Z","title":"The MultiVehicle stereo event camera dataset: An event camera dataset for 3D perception,","venue":null,"work_id":null,"year":2032},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.766130Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:8e155c722678d855db7d5fd5e7d34104812eb36bf71a5d2aa150a6e3eae4c380","observation_id":"3470eb79-7431-4054-bbc4-f042c3ede377","resolution":{"observed_at":"2026-08-01T22:31:41.766130Z","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":"2026-08-01T22:31:41.843542Z","title":"Learning monocular dense depth from events,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.843542Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:6fecbdbef998d7ac52b7b7e3de1384fe45f5b3eb797f2dd4975bfe3653f48f76","observation_id":"a0f5ed26-951e-4b45-a7af-0c5bf42c44d3","resolution":{"observed_at":"2026-08-01T22:31:41.843542Z","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":"2026-08-01T22:31:41.926915Z","title":"DERD-Net: Learning depth from event-based ray densities,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.926915Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:536c2a4d4918db08c1a6103f10f70572eea480a24810c62ee0d0ac002d467470","observation_id":"e1786974-36e3-4ee9-9a75-765fcaf06bde","resolution":{"observed_at":"2026-08-01T22:31:41.926915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.02791","last_updated":"2022-12-06T07:06:59Z","snapshot_observed_at":"2026-07-06T14:27:14.966862Z","submitted_at":"2022-12-06T07:06:59Z","title":"Event-based Monocular Dense Depth Estimation with Recurrent Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.02791","snapshot_observed_at":"2026-08-01T22:31:41.991374Z","title":"Event-based monocular dense depth esti- mation with recurrent transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:41.991374Z"},"links":{"cited_paper":"/paper/2212.02791","citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:472315564da058f146eb0c404676e082a9b426c005ac8fc7e75a21c304b4cc60","observation_id":"08a4700b-4449-47fd-93c4-fa4e7c149c1e","resolution":{"observed_at":"2026-08-01T22:31:41.991374Z","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":"2026-08-01T22:31:42.067861Z","title":"Deep event visual odometry,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:42.067861Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:03bd17d763d82209749be83f6e1ddb4747f182dcb71985fbebd12b666a4ff6fe","observation_id":"74b8fe98-a100-48ec-b0b0-1889e0789d70","resolution":{"observed_at":"2026-08-01T22:31:42.067861Z","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":"2026-08-01T22:31:42.149330Z","title":"Events-to-Video: Bringing modern computer vision to event cameras,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T22:31:42.149330Z"},"links":{"citing_paper":"/paper/2607.15727"},"observation_digest":"sha256:093688638cc3278766e992e18e55907860a2a36dadd5a4bf04e4f8325fc9ad60","observation_id":"d7745f07-572b-489d-9b46-406347107bf7","resolution":{"observed_at":"2026-08-01T22:31:42.149330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.15727","last_updated":"2026-07-17T08:04:43Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T16:57:21.578382Z","submitted_at":"2026-07-17T08:04:43Z","title":"Event3R: Asynchronous-to-Global 3D Reconstruction from Event Camera via Spatial-Temporal Feature Aggregation"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":35},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.15727."}