{"as_of":"2026-08-15T06:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b7d574b03871fc59b54cc85c89863d4cc8e28f918ee77ba2f14058ccda50cbc5","coverage":[{"denominator":123,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:40:55.868789Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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":"2026-08-11T21:38:19.199720Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-11T21:38:19.258349Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"cited_work":{"arxiv_id":"2412.05548","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.05548","snapshot_observed_at":"2026-08-11T21:38:19.258349Z","title":"Street Gaussians without 3D Object Tracker","venue":"cs.CV","work_id":"15e99fa5-e1d0-4093-b916-484bf388fedd","year":2024},"citing_paper":{"arxiv_id":"2412.04282","last_updated":"2025-03-24T12:53:56Z","snapshot_observed_at":"2026-08-13T15:03:04.792754Z","submitted_at":"2024-12-05T16:03:37Z","title":"Learnable Infinite Taylor Gaussian for Dynamic View Rendering","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:19.199720Z"},"links":{"cited_paper":"/paper/2412.05548","citing_paper":"/paper/2412.04282"},"observation_digest":"sha256:47a2574d9edf8063ffe5f0d977be3114bc62756fb4190066aea26bab0b1267bf","observation_id":"8dd93747-5c72-41a5-97ed-781ddf4125f1","resolution":{"observed_at":"2026-08-11T21:38:19.262139Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.05548/citation-record","integrity":"/paper/2412.05548/integrity","json":"/paper/2412.05548/citation-record.json","paper":"/paper/2412.05548"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:40:55.473692Z","title":"Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.473692Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:175589c11deda04789131175dd7d8d9869e1bcac729562e7f7424a26fe92912e","observation_id":"c4f37fc7-939d-47d7-a959-1642fa11a3f5","resolution":{"observed_at":"2026-08-11T20:40:55.473692Z","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":"2026-08-11T20:40:55.478127Z","title":"Mip-nerf 360: Unbounded anti-aliased neural radiance fields","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.478127Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:d89056aee4eee281c5c3ef69f651f67c024c6db14e5e862469c454ac468a02c3","observation_id":"4b64107a-c350-43d0-afaa-4351bdb89c1e","resolution":{"observed_at":"2026-08-11T20:40:55.478127Z","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":"2026-08-11T20:40:55.482200Z","title":"Zip-nerf: Anti-aliased grid-based neural radiance fields","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.482200Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:0e50e55c55cea49656d3f8d901efc6ad4d8d480d20478bb9fcad10f5d3b96389","observation_id":"dd545dc3-5ea2-438a-b61c-50358f45c25a","resolution":{"observed_at":"2026-08-11T20:40:55.482200Z","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":"2026-08-11T20:40:55.486362Z","title":"Multiple object tracking in recent times: A literature re- view","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.486362Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:ef3348e5b90c077838069632bb4bc00b69a3ecac0fe750a1a6383e9d7025eb27","observation_id":"c6e5aff8-8c99-48e6-8250-c54f0d90cb5a","resolution":{"observed_at":"2026-08-11T20:40:55.486362Z","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":"2026-08-11T20:40:55.490235Z","title":"Simple online and realtime tracking","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.490235Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:8021b527857594a279f0270abc40ac4ec5a17d6d4c93c552e2e2257fdc180569","observation_id":"6f91ff60-5b06-46b6-93db-395f426f054c","resolution":{"observed_at":"2026-08-11T20:40:55.490235Z","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":"2026-08-11T20:40:55.494187Z","title":"nuscenes: A mul- timodal dataset for autonomous driving","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.494187Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:69323397ab9e918ec088a35adc8b9101532fc8ed9a5ef234c93b7490233a15e7","observation_id":"b06206e4-8519-46e0-a3ee-69fd4e6866a4","resolution":{"observed_at":"2026-08-11T20:40:55.494187Z","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":"2026-08-11T20:40:55.497917Z","title":"Hexplane: A fast representa- tion for dynamic scenes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.497917Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:6e3eae163a21390662936db72bdf839270d479e2044da3000666fec4cb0ff628","observation_id":"6e7b5c37-c2e4-49de-8a5a-0c43d3edacc8","resolution":{"observed_at":"2026-08-11T20:40:55.497917Z","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":"2026-08-11T20:40:55.502067Z","title":"End-to-end object detection with transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.502067Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:2ed91bfd12d34f1d9cdac4b6a889c033cd4d1350242cd09e25d45753c5443dd7","observation_id":"edaabc88-1bb8-4efd-a2b8-5b6b01d36a2a","resolution":{"observed_at":"2026-08-11T20:40:55.502067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.01975","last_updated":"2019-06-03T06:42:25Z","snapshot_observed_at":"2026-08-14T22:22:23.886108Z","submitted_at":"2019-04-03T12:40:08Z","title":"D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.01975","snapshot_observed_at":"2026-08-11T20:40:55.505972Z","title":"D2-city: a large-scale dashcam video dataset of diverse traffic scenarios","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.505972Z"},"links":{"cited_paper":"/paper/1904.01975","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:6f0ae2f37408d2d37d4b372d626cae2c722b76ce4896fb6a90c766086b8574aa","observation_id":"298e688c-965b-4461-aac8-a3a34b397e88","resolution":{"observed_at":"2026-08-11T20:40:55.505972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18561","last_updated":"2025-08-23T01:56:43Z","snapshot_observed_at":"2026-08-13T05:13:26.949495Z","submitted_at":"2023-11-30T13:53:50Z","title":"Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.18561","snapshot_observed_at":"2026-08-11T20:40:55.510200Z","title":"Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.510200Z"},"links":{"cited_paper":"/paper/2311.18561","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:c3ff75568cd6e0cf4b994f378856f983bea233827589ec3ed5e6f9ac25096519","observation_id":"d5ca1b8e-a299-499f-af08-8d9a710d9c87","resolution":{"observed_at":"2026-08-11T20:40:55.510200Z","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":"2026-08-11T20:40:55.514205Z","title":"V oxelnext: Fully sparse voxelnet for 3d object detection and tracking","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.514205Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:56a30e120bba9ce35732366b8954788f97ab63836be3045ea72b7dcd37a33649","observation_id":"185de755-c0d6-4093-94d4-e902879cc1f2","resolution":{"observed_at":"2026-08-11T20:40:55.514205Z","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":"2026-08-11T20:40:55.517958Z","title":"Schwing, Alexander Kirillov, and Rohit Girdhar","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.517958Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:789a63f25ec6d6c397a93e3ea0d0ccfb6c3c0c9587aeac47e350392f21e328f8","observation_id":"208a0216-1a6f-4e67-9976-4021a9d631e0","resolution":{"observed_at":"2026-08-11T20:40:55.517958Z","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":"2026-08-11T20:40:55.521663Z","title":"Real-time trajectory planning for autonomous driving with gaussian process and incre- mental refinement","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.521663Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f61a6dac84d6b83be820f5eafe0048ec2b54a3ef1caf472ecaa0b05750569d79","observation_id":"5f25f1b0-c26d-4e10-bf28-eb9feec84c53","resolution":{"observed_at":"2026-08-11T20:40:55.521663Z","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":"2026-08-11T20:40:55.525166Z","title":"Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.525166Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:466cde4bcc4a15baff997cb6088f69839ef989752538d6cea2c1d425804b38d8","observation_id":"2b073a36-5def-4984-8fd9-4a369d26f6c1","resolution":{"observed_at":"2026-08-11T20:40:55.525166Z","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":"2026-08-11T20:40:55.528663Z","title":"High-quality streamable free- viewpoint video","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.528663Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:a8b5176f8012c7c78d8db6d687cb034642dbbaa7dfe3b2879411b68b05fd8c54","observation_id":"7dc65acb-22d3-45a4-ac0c-b79659c80cfb","resolution":{"observed_at":"2026-08-11T20:40:55.528663Z","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":"2026-08-11T20:40:55.532289Z","title":"Parting with misconceptions about learning-based vehicle motion planning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.532289Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:e07bf6c0ce455518130c63c1b48aaa736e8487b1731117464dc323241e2757a9","observation_id":"605c04e8-4e9c-4549-a356-e9ff24465032","resolution":{"observed_at":"2026-08-11T20:40:55.532289Z","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":"2026-08-11T20:40:55.535660Z","title":"Imagenet: A large-scale hierarchical im- age database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.535660Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:da074846f1135145f434c91485d9cbde761feb907d79d92e370204fe08f7d36c","observation_id":"77505f8d-d8b7-485e-8cd6-f1016c7f1a16","resolution":{"observed_at":"2026-08-11T20:40:55.535660Z","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":"2026-08-11T20:40:55.539420Z","title":"Gpv- pose: Category-level object pose estimation via geometry- guided point-wise voting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.539420Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:fd0d0fa2a44404d3c61362f01a8d49c6ce3bdfd6a8c00d5e2352e3d48525cf5a","observation_id":"0d2e98db-0207-4cd8-9a4b-cca12272d54c","resolution":{"observed_at":"2026-08-11T20:40:55.539420Z","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":"2026-08-11T20:40:55.543039Z","title":"3dmotformer: Graph transformer for online 3d multi-object tracking","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.543039Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:5aa1cec5e326ee6357ca8d36fe0e87453c3f58707e6caf4597098463747ddfe7","observation_id":"898b7cfd-0476-473c-8197-1b084e5edf0f","resolution":{"observed_at":"2026-08-11T20:40:55.543039Z","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":"2026-08-11T20:40:55.546244Z","title":"Carla: An open urban driv- ing simulator","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.546244Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:5647ece86f283ca40f62433c880520267497ca26a70baa30de6fee3a69e7e423","observation_id":"ab8af4bc-ceeb-47c8-b656-d0060fb19a44","resolution":{"observed_at":"2026-08-11T20:40:55.546244Z","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":"2026-08-11T20:40:55.549691Z","title":"An empirical study of the generalization ability of lidar 3d object detectors to unseen domains","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.549691Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:3e72ccc1318d30e185fb5a7fda2ef6c70f83d29296bcd5a08c41e7961df89171","observation_id":"b71c3760-d35a-4362-ab77-760e9f015116","resolution":{"observed_at":"2026-08-11T20:40:55.549691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03175","last_updated":"2024-11-23T13:09:50Z","snapshot_observed_at":"2026-08-12T23:49:42.400597Z","submitted_at":"2024-06-05T12:07:39Z","title":"Dynamic 3D Gaussian Fields for Urban Areas","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03175","snapshot_observed_at":"2026-08-11T20:40:55.553164Z","title":"9 Dynamic 3d gaussian fields for urban areas","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.553164Z"},"links":{"cited_paper":"/paper/2406.03175","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:d5b5da934bce88df85dbbd4cfe479eb5a66a693cacc6c015e34fb6db0fc895a9","observation_id":"84e7aa08-9257-4025-85d9-8819dbe4ee20","resolution":{"observed_at":"2026-08-11T20:40:55.553164Z","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":"2026-08-11T20:40:55.556969Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.556969Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f9fa61e53520434e02f55df07e17c64c30b320cc4bddeb0a732d4b895f996a76","observation_id":"b596e46d-f00a-44a5-83a3-bef884e8a7ad","resolution":{"observed_at":"2026-08-11T20:40:55.556969Z","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":"2026-08-11T20:40:55.560655Z","title":"Vision meets robotics: The kitti dataset.The Inter- national Journal of Robotics Research, 32(11):1231–1237,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.560655Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:bee0ab3509e1207f915a631130b6316f941688c5b82d2f7e88fc152afb8aaa74","observation_id":"16dac89d-a747-43ec-90f9-5990fdd35a03","resolution":{"observed_at":"2026-08-11T20:40:55.560655Z","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":"2026-08-11T20:40:55.564546Z","title":"Vip3d: End-to- end visual trajectory prediction via 3d agent queries","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.564546Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:5f4eab5a93be7a35fbd0deaccba83105545036374084ff131d5351ac9860b590","observation_id":"e290f22d-863f-44d5-b9ba-da75c6af32d8","resolution":{"observed_at":"2026-08-11T20:40:55.564546Z","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":"2026-08-11T20:40:55.568797Z","title":"Robust non-rigid motion tracking and sur- face reconstruction using l0 regularization","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.568797Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:2a98670d3faf92ef3e0fb7a32a0cb08accf238a4343f9294abfc7f82f1836cc6","observation_id":"803e3eb5-56a8-42c7-a411-c6bdb6e16783","resolution":{"observed_at":"2026-08-11T20:40:55.568797Z","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":"2026-08-11T20:40:55.572631Z","title":"The re- lightables: V olumetric performance capture of humans with realistic relighting","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.572631Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:370101283bf7ba4dee3129c205c85872e3c2fc4ed95a93048d64f918415d0ea1","observation_id":"6dff8d1c-c554-4dc9-ac9e-4da8bf6e083b","resolution":{"observed_at":"2026-08-11T20:40:55.572631Z","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":"2026-08-11T20:40:55.576568Z","title":"Lvis: A dataset for large vocabulary instance segmentation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.576568Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:485dd37684516bb27643f52b4fbae98d82640e3279767e0f78e11de800fff744","observation_id":"ce8bb40c-813a-42d4-856a-bf1e06ae0b79","resolution":{"observed_at":"2026-08-11T20:40:55.576568Z","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":"2026-08-11T20:40:55.581304Z","title":"St-p3: End-to-end vision- based autonomous driving via spatial-temporal feature learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.581304Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:8471389db9cea1ea13ead721476a858d777ef1cd1978da6e4aafd8c784be934f","observation_id":"b8424304-8310-4556-a833-ae0c05a76f3c","resolution":{"observed_at":"2026-08-11T20:40:55.581304Z","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":"2026-08-11T20:40:55.585139Z","title":"Hvtr: Hybrid volumetric-textural render- ing for human avatars","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.585139Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:a7f91193c0f30471499cf9c6351fc4e1e794a14721b47698b64f945a28a9fa16","observation_id":"6da6902c-33cc-4dcc-a91d-9f592bd97540","resolution":{"observed_at":"2026-08-11T20:40:55.585139Z","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":"2026-08-11T20:40:55.588722Z","title":"Planning-oriented autonomous driving","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.588722Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:4023f54bf7c9e48295b20a78cb34ecf74ca21ee92e94931c14e39a4fe45b6e7d","observation_id":"1f5c6584-adb3-4148-9aa4-e7a92eea0837","resolution":{"observed_at":"2026-08-11T20:40:55.588722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20323","last_updated":"2024-05-30T17:57:08Z","snapshot_observed_at":"2026-08-13T05:43:30.307555Z","submitted_at":"2024-05-30T17:57:08Z","title":"$\\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20323","snapshot_observed_at":"2026-08-11T20:40:55.592095Z","title":"S3gaussian: Self-supervised street gaussians for autonomous driving","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.592095Z"},"links":{"cited_paper":"/paper/2405.20323","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:d934466eebb6061b30fa14de6a826a872fceb27d4c6f23243ab19a1474bb72e8","observation_id":"857ab896-824a-4e42-9acc-a68ba4e6ba26","resolution":{"observed_at":"2026-08-11T20:40:55.592095Z","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":"2026-08-11T20:40:55.595831Z","title":"A survey on trajectory-prediction methods for autonomous driving","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.595831Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:26ffb70f885e83322bbfb0bca11c85c7c208e76da8c09e00b25aaa8c018586af","observation_id":"8343e38e-e832-40ef-95c8-25a6504b3158","resolution":{"observed_at":"2026-08-11T20:40:55.595831Z","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":"2026-08-11T20:40:55.599578Z","title":"Givepose: Gradual intra- class variation elimination for rgb-based category-level ob- ject pose estimation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.599578Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:11606c36c11eec151df6fa9d441d9fc2711a07f26efb5af3e80d193d5c134ba6","observation_id":"69b2a7ad-69a5-48fe-bed1-0568cc537b8a","resolution":{"observed_at":"2026-08-11T20:40:55.599578Z","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":"2026-08-11T20:40:55.603031Z","title":"Vad: Vectorized scene rep- resentation for efficient autonomous driving","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.603031Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:c6b259ef9abadb74635c46aa9d557b4e9ac50b09c3977a621eed65f061d0759f","observation_id":"dbd7c2ec-d075-4f08-bb42-062d00a82f4d","resolution":{"observed_at":"2026-08-11T20:40:55.603031Z","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":"2026-08-11T20:40:55.606579Z","title":"Analysis based on recent deep learning approaches applied in real-time multi-object tracking: a review","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.606579Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:1a2d6f210ae43b7ca8fc1066036bb5442db439a66640198a33bb11baf9b62c19","observation_id":"7a75ee62-d1f5-4433-a561-32e35eb17766","resolution":{"observed_at":"2026-08-11T20:40:55.606579Z","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":"2026-08-11T20:40:55.609688Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.609688Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:5b067e611ae197b9d56a722e20a2d1a625a9784bea353fd52a4cbe3d9642da33","observation_id":"852fcb0e-4280-4c15-8a03-084e0f484a23","resolution":{"observed_at":"2026-08-11T20:40:55.609688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02598","last_updated":"2024-07-04T02:18:54Z","snapshot_observed_at":"2026-08-13T21:45:49.232037Z","submitted_at":"2024-07-02T18:36:50Z","title":"AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02598","snapshot_observed_at":"2026-08-11T20:40:55.613447Z","title":"Autosplat: Constrained gaussian splatting for au- tonomous driving scene reconstruction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.613447Z"},"links":{"cited_paper":"/paper/2407.02598","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:b7d27dc0da8ad1ed615f3344d0174606a6f2905fbf5e91c565d53c7ff9fb4f07","observation_id":"0ce97348-e1c5-4d35-b380-45c700281621","resolution":{"observed_at":"2026-08-11T20:40:55.613447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T20:40:55.617382Z","title":"Adam: A method for stochastic opti- mization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.617382Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:d61a9eb889cfec9d9b2f626a316a583b3736dded1b7bf07475836e5c619a1a76","observation_id":"706b1300-a51e-4060-a0fc-5147754ac7bc","resolution":{"observed_at":"2026-08-11T20:40:55.617382Z","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":"2026-08-11T20:40:55.621018Z","title":"Segment anything","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.621018Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:6e421e9bc482445b673c67b736666d1ce04601af552a3f98d50a06d97e13b8fa","observation_id":"190f15d6-dab2-460f-978f-45e1f455c489","resolution":{"observed_at":"2026-08-11T20:40:55.621018Z","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":"2026-08-11T20:40:55.624503Z","title":"The open images dataset v4: Unified image classifica- tion, object detection, and visual relationship detection at scale","venue":null,"work_id":null,"year":1956},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.624503Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:fecf5b3cea69d97318f55018ca9b37194924593664219b0260b2ccc579568cc3","observation_id":"0af2efc5-f835-48ca-a1f2-ae4ce0a38f40","resolution":{"observed_at":"2026-08-11T20:40:55.624503Z","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":"2026-08-11T20:40:55.628346Z","title":"Pillarnext: Re- thinking network designs for 3d object detection in lidar point clouds","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.628346Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:42b401a50749bdb8af41106c0abed7fedd0b5976ba60a7ab5f4689d373e3a858","observation_id":"21778809-78c0-4a81-81b6-a543d681df79","resolution":{"observed_at":"2026-08-11T20:40:55.628346Z","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":"2026-08-11T20:40:55.632024Z","title":"Aads: Aug- mented autonomous driving simulation using data-driven algorithms","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.632024Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:117149c082d23654a93071ccec98988f1319f3d98a1dd780870d9fea253e1449","observation_id":"c0475831-3c34-4baa-ba75-2fd6365c70e3","resolution":{"observed_at":"2026-08-11T20:40:55.632024Z","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":"2026-08-11T20:40:55.636291Z","title":"Geogaussian: Geometry-aware gaussian splatting for scene rendering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.636291Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:929453e1a227ecf0c0d044317b7db4fd0f56f40a876537b008b16d1969bbf03d","observation_id":"42a08093-984d-4b29-b2d9-3cd0f64db5c3","resolution":{"observed_at":"2026-08-11T20:40:55.636291Z","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":"2026-08-11T20:40:55.639662Z","title":"Ro- bust 3d human motion reconstruction via dynamic template construction","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.639662Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:a865a1dcd15ea17293656ffb33f1783ea73c493c615777a8fc09ea4661df1664","observation_id":"25eda885-a0ef-42fe-a599-f4f406e5ddec","resolution":{"observed_at":"2026-08-11T20:40:55.639662Z","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":"2026-08-11T20:40:55.642977Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.642977Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:b0ffb6088e28d0ed72a89f38465d66e79fa5cda3c0481e8bf873e2a3f6880be5","observation_id":"7e6852d3-3d2f-4a53-93fa-b649cfbae099","resolution":{"observed_at":"2026-08-11T20:40:55.642977Z","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":"2026-08-11T20:40:55.646409Z","title":"Pnpnet: End-to-end per- ception and prediction with tracking in the loop","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.646409Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:500584c6e951d12e2bc77a61fc368993dff1247879edc36139bc734b322c7b75","observation_id":"9fd69eda-58aa-4aaa-b832-a256e02ed852","resolution":{"observed_at":"2026-08-11T20:40:55.646409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11458","last_updated":"2025-01-15T22:17:24Z","snapshot_observed_at":"2026-08-14T13:32:02.694451Z","submitted_at":"2023-12-18T18:59:03Z","title":"GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11458","snapshot_observed_at":"2026-08-11T20:40:55.649933Z","title":"Gaufre: Gaussian deformation fields for real-time dynamic novel view synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.649933Z"},"links":{"cited_paper":"/paper/2312.11458","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:9a935bc7232b12445a8f90b1cf394d3ced10969d3bab3444a29eff1b8349988a","observation_id":"e80ebead-98ee-4dde-914a-52db168a6ec4","resolution":{"observed_at":"2026-08-11T20:40:55.649933Z","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":"2026-08-11T20:40:55.654084Z","title":"Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.654084Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:4303fcd6f86318059dee01da0ce6420356e7e9e83459c8c8be1464c9f9114f77","observation_id":"2e720049-753e-46fe-aab4-8c19a42e9012","resolution":{"observed_at":"2026-08-11T20:40:55.654084Z","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":"2026-08-11T20:40:55.658074Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.658074Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:6300eede6fcd554cdc9f6482d97a538dcb14f2e834a2eed874e1b30d4095d6b0","observation_id":"187b1d97-d008-433e-a6f6-49d58181c330","resolution":{"observed_at":"2026-08-11T20:40:55.658074Z","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":"2026-08-11T20:40:55.661782Z","title":"CATRE: iterative point clouds alignment for category-level object pose refinement","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.661782Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:166e4e8ae774bd876b4abe5ad51848c8dec582f8995541d265ce39ac607557cd","observation_id":"ba18f163-d267-41a7-b4e2-22375bfbec1f","resolution":{"observed_at":"2026-08-11T20:40:55.661782Z","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":"2026-08-11T20:40:56.976384Z","title":"Rasim: A range-aware high- fidelity rgb-d data simulation pipeline for real-world appli- cations","venue":null,"work_id":"36429632-6bf1-4c66-a05d-4dbace6d5306","year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.665059Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:95c8ee89b3d31bcb214853240510c60993d9e157c6113850ecb1e5eb90939de9","observation_id":"6e8fdd2f-3265-44b6-8f2f-36f390404773","resolution":{"observed_at":"2026-08-11T20:40:56.980386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.964619Z","title":"Gfreedet: Exploiting gaussian splatting and foundation models for model-free unseen object detection in the bop challenge","venue":null,"work_id":"e6544f95-1db9-4c75-b006-58c9aa169f85","year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.668875Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f07fba9cb45988abe0dd067c74f567ad153256f372985d384e9ad751797e8bf7","observation_id":"6e27ae97-b8a1-46df-ae6d-97c96b9915d8","resolution":{"observed_at":"2026-08-11T20:40:56.968582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.942189Z","title":"UNOPose: Unseen object pose estimation with an unposed rgb-d refer- ence image","venue":null,"work_id":"e19a4d58-605e-4cd1-97d4-a13993074d6a","year":2025},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.677457Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:dafb9fad16c2de3482d1bcee77f2e2b8bb87832da59e8fbe8d239ba2523e7c41","observation_id":"0557389b-ac3b-4668-b48e-3a124993ab7c","resolution":{"observed_at":"2026-08-11T20:40:56.946117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.930504Z","title":"Gdrnpp: A geometry-guided and fully learning-based object pose es- timator","venue":null,"work_id":"da078506-d89f-47ab-be0c-939e2bb91552","year":2025},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.681123Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:cc724e4874fd6dd5fb3a89d9ca7e3b1948832cb96abd0f74cd0f2f3e57e25676","observation_id":"77e276dd-ee3f-4ca7-859e-91d58bd0301c","resolution":{"observed_at":"2026-08-11T20:40:56.934522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:55.685347Z","title":"Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.685347Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f70a6665954debf022f08e575f2044e21aa9ecdf5d10acd822b00bc42b3255a4","observation_id":"c8e57e10-c2e3-4557-a66c-58c785f3e63f","resolution":{"observed_at":"2026-08-11T20:40:55.685347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.09713","last_updated":"2023-08-18T17:59:21Z","snapshot_observed_at":"2026-08-13T10:32:10.871509Z","submitted_at":"2023-08-18T17:59:21Z","title":"Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.09713","snapshot_observed_at":"2026-08-11T20:40:55.689365Z","title":"Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.689365Z"},"links":{"cited_paper":"/paper/2308.09713","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:866b1289ad83024af4b1a3ca30fe62a078d6d949222c35d0af3f0b2a26d5f780","observation_id":"658414ed-1882-448b-886a-3a4853145975","resolution":{"observed_at":"2026-08-11T20:40:55.689365Z","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":"2026-08-11T20:40:56.912837Z","title":"Multiple object tracking: A literature review","venue":null,"work_id":"e9630163-6a17-4ab9-ae79-fdaefae07aff","year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.693114Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:dae879be3dc8c315596a8ce61ac18db5f911d3118d0732699463c01ca6dc87fc","observation_id":"aba25005-4ea6-44b6-8092-0f030c2fd6a8","resolution":{"observed_at":"2026-08-11T20:40:56.916677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11037","last_updated":"2021-10-25T08:08:25Z","snapshot_observed_at":"2026-08-12T22:32:14.288125Z","submitted_at":"2021-06-21T12:28:08Z","title":"One Million Scenes for Autonomous Driving: ONCE Dataset","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.11037","snapshot_observed_at":"2026-08-11T20:40:55.696694Z","title":"One million scenes for autonomous driving: Once dataset","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.696694Z"},"links":{"cited_paper":"/paper/2106.11037","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:d93422aa983dd31088b426969dc38a05b450d43c0b04dd3d4475d0f67c23e62e","observation_id":"f24292b6-da94-4226-a045-7ee7e4efead5","resolution":{"observed_at":"2026-08-11T20:40:55.696694Z","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":"2026-08-11T20:40:56.901574Z","title":"3d object detection for autonomous driving: A comprehensive survey","venue":null,"work_id":"99287057-2f6f-436a-bd65-b71f6836a775","year":1909},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.700635Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:9cdf7f9b635815d7629b8df51869a72b5d42b34b367e9a32af9a6448d0f2d5b5","observation_id":"58ad5fa6-ddbe-4be1-bb12-e665a0981d4a","resolution":{"observed_at":"2026-08-11T20:40:56.905325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:55.704162Z","title":"Nerf: Representing scenes as neural radiance fields for view syn- thesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.704162Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:724836c637cacd4de6fec4be77f3faaaa980e241a0b5af03258fcd5b53d4218f","observation_id":"bcf12623-03df-4697-904d-ba83168fc8f1","resolution":{"observed_at":"2026-08-11T20:40:55.704162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.06230","last_updated":"2022-07-20T12:24:25Z","snapshot_observed_at":"2026-08-14T07:52:37.232039Z","submitted_at":"2022-05-12T17:20:36Z","title":"Simple Open-Vocabulary Object Detection with Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.06230","snapshot_observed_at":"2026-08-11T20:40:55.707961Z","title":"Simple open-vocabulary object de- tection with vision transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.707961Z"},"links":{"cited_paper":"/paper/2205.06230","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:bc796ea2bbd41e746a7793a31260393ca62c83ea8b11e610781ac60682d00d65","observation_id":"7dedb51a-da3c-464b-8ebf-9777c7a53c74","resolution":{"observed_at":"2026-08-11T20:40:55.707961Z","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":"2026-08-11T20:40:56.882775Z","title":"Instant neural graphics primitives with a multiresolution hash encoding","venue":null,"work_id":"515d9967-adb6-4acb-b550-665def072e91","year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.711977Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:fcabe4488f7561cbab36d8a68ae6a71fb0172fc1aad89bcc01922517438c3dd0","observation_id":"8346188d-d28b-43b2-be08-f86d0115317d","resolution":{"observed_at":"2026-08-11T20:40:56.886713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-11T10:12:11.384939Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-11T20:40:55.721201Z","title":"Dinov2: Learning robust visual features without supervi- sion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.721201Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:5ec6d1f2578d4ac65f2e81ad92b39dc507d98eba039605aad93dafca13e618eb","observation_id":"f7c6780f-9186-4665-8df4-3c2d12b8e807","resolution":{"observed_at":"2026-08-11T20:40:55.721201Z","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":"2026-08-11T20:40:56.869705Z","title":"Neural scene graphs for dynamic scenes","venue":null,"work_id":"f7ff4a72-a885-4bd3-8301-a19f847da770","year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.725850Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:9df8abbb732b73162a69a30abd1ac8fade61b9969c298574c522e64b1b683a76","observation_id":"2ce2c422-e196-47bc-a8d4-96b1c9df0385","resolution":{"observed_at":"2026-08-11T20:40:56.875489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.858911Z","title":"Simpletrack: Understanding and rethinking 3d multi-object tracking","venue":null,"work_id":"051d5ef8-c64c-4981-bcd4-a72bba5fb413","year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.729354Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:0275c017282457097167ffb8a526a9f26d43c50cec76734c616a81aab60eb8d2","observation_id":"b4a83a86-3daf-42c0-9306-363ea9851b19","resolution":{"observed_at":"2026-08-11T20:40:56.862733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.847481Z","title":"Desire-gs: 4d street gaussians for static-dynamic decomposition and surface re- construction for urban driving scenes","venue":null,"work_id":"e3ca5c9e-0c31-4edf-b277-8bd8e25beab8","year":2025},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.733037Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:4a38bb253ad55f02a7d8e72f5b504e701eb56613d0f25e17cdfd6a2127897090","observation_id":"60db325a-99e3-4657-a295-90c7677756af","resolution":{"observed_at":"2026-08-11T20:40:56.851534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.837162Z","title":"Learn- ing transferable visual models from natural language super- vision","venue":null,"work_id":"69853289-3953-4d66-af72-8d281abe08d1","year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.736695Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:c43cbd8a3ec45db5c4b6512b177396ebf517cca172f7dae754facc2bff216b8c","observation_id":"234f3c01-f546-429a-b598-00f28d2bd1ed","resolution":{"observed_at":"2026-08-11T20:40:56.840734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.826540Z","title":"Domain generalization of 3d semantic seg- mentation in autonomous driving","venue":null,"work_id":"b97aca6e-759c-4617-98ff-ce405e4d3138","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.740332Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f31a96c86519c21c8b86033b8b9d73e68c585d50680035e5bd16f9bd8166f271","observation_id":"f7d58412-8c7e-450f-a7b2-3e4e975ec12b","resolution":{"observed_at":"2026-08-11T20:40:56.830626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.816285Z","title":"K- planes: Explicit radiance fields in space, time, and appear- ance","venue":null,"work_id":"b0de68f9-1f4a-4d89-83fc-b7d13ada05d1","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.744424Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:8e9bb383663d50f6bb3b46c1437172c551d38cd991ff2fa56f4b9665367b1222","observation_id":"d2ef5fab-1914-484e-8b22-0a137ab12ccc","resolution":{"observed_at":"2026-08-11T20:40:56.819931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.805462Z","title":"Airsim: High-fidelity visual and physical sim- ulation for autonomous vehicles","venue":null,"work_id":"1051449d-f3a3-4361-b1a5-2fe96b6da69e","year":2018},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.748396Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:97a44bf35481834baf1dbdb69d6c7c98ccdebd9e6466454069c91dbcab7b3399","observation_id":"241de5e9-dd40-422f-899b-78c1ad3079e2","resolution":{"observed_at":"2026-08-11T20:40:56.809101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.04127","last_updated":"2022-10-09T00:44:44Z","snapshot_observed_at":"2026-08-13T14:09:53.213913Z","submitted_at":"2022-10-09T00:44:44Z","title":"Towards Efficient Neural Scene Graphs by Learning Consistency Fields","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.04127","snapshot_observed_at":"2026-08-11T20:40:55.752680Z","title":"Towards efficient neural scene graphs by learning consistency fields","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.752680Z"},"links":{"cited_paper":"/paper/2210.04127","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:441729ab6b17e4672023c3b4d325f13067602bc8d1fbb416212d9d7e53bb8d37","observation_id":"ce23018c-1057-4855-b402-afab4a90fdea","resolution":{"observed_at":"2026-08-11T20:40:55.752680Z","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":"2026-08-11T20:40:56.794770Z","title":"Mdt3d: Multi-dataset training for lidar 3d object detection generalization","venue":null,"work_id":"1c2698d8-647c-4030-932b-33005f8091b8","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.757496Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:28e0ca79456ee8b5771f7d78fd43077d6bd0d777188c602ba2a077fe5580924e","observation_id":"aaf9ddd7-a796-4a44-9974-330caebcde50","resolution":{"observed_at":"2026-08-11T20:40:56.798503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18717","last_updated":"2024-09-10T21:30:31Z","snapshot_observed_at":"2026-08-12T23:34:10.573648Z","submitted_at":"2024-06-26T19:37:07Z","title":"Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18717","snapshot_observed_at":"2026-08-11T20:40:55.762017Z","title":"Dynamic gaussian marbles for novel view synthesis of casual monocular videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.762017Z"},"links":{"cited_paper":"/paper/2406.18717","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:c2f34017255035a68444fd45ffe4d1a2c5b78fa6113f38a65dc35ef50a47db8c","observation_id":"0a832e55-e530-49d2-bac0-91672c040f1d","resolution":{"observed_at":"2026-08-11T20:40:55.762017Z","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":"2026-08-11T20:40:56.783244Z","title":"Robustfusion: Human volumetric capture with data-driven visual cues using a rgbd camera","venue":null,"work_id":"d2561ce2-703c-4177-bcf5-abf89d7e8c86","year":2020},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.766092Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:8099a8a0766be075d82b4f3ad01ec7475134a12f30fb62d2fa4855f79f812483","observation_id":"cbe18e2c-43a0-469d-8551-e1a6f6acd90b","resolution":{"observed_at":"2026-08-11T20:40:56.787685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.770334Z","title":"Scalability in perception for autonomous driving: Waymo open dataset","venue":null,"work_id":"b0f197a8-c25a-4851-af90-287d58f30201","year":2020},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.770441Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:397bac693fddffb0c50c5f4c880a3956ea080ebbe6debccc2c2d36e144acdbea","observation_id":"3319fde1-6e0d-483e-82f3-e50c63594429","resolution":{"observed_at":"2026-08-11T20:40:56.775051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.759476Z","title":"Lidarf: Delving into lidar for neural radiance field on street scenes","venue":null,"work_id":"5fc0d05d-812c-4925-b21b-744475dc0f3a","year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.774600Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:d454040bd3fef09779770980f8ffd53b6bba3817b0c8dcfc1d2ff2b3ae99468d","observation_id":"8e33a55e-a517-4430-860a-a3c31b94051b","resolution":{"observed_at":"2026-08-11T20:40:56.763135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.748598Z","title":"Suds: Scalable urban dynamic scenes","venue":null,"work_id":"80525184-9349-4060-a021-d9b4315d9c74","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.778768Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:9b3e54e8b5f85feb08216e29940bbd9e6bd4aed8ba9e9c7761017de3a95cd140","observation_id":"0a3f1fb7-5587-4e5c-93c0-e790fb40caff","resolution":{"observed_at":"2026-08-11T20:40:56.752491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.737905Z","title":"Train in germany, test in the usa: Making 3d object detectors generalize","venue":null,"work_id":"59534b0b-0f20-4555-98bf-b29671e5e25b","year":2020},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.782810Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:ea02f2ee616ca48e6209ac56f4371e59728e8df07a6a69ad43b90122b63e7cb9","observation_id":"24bb360e-4e42-4503-a65d-03da450c9bca","resolution":{"observed_at":"2026-08-11T20:40:56.741580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16530","last_updated":"2024-08-28T01:08:33Z","snapshot_observed_at":"2026-08-12T22:56:14.572805Z","submitted_at":"2024-08-28T01:08:33Z","title":"A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16530","snapshot_observed_at":"2026-08-11T20:40:55.786603Z","title":"A comprehensive review of 3d object detection in au- tonomous driving: Technological advances and future di- rections","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.786603Z"},"links":{"cited_paper":"/paper/2408.16530","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f887263e86408f00a8e9d11dbdaff0fc33ba56b8fd0ae58b8f8e7d0f4ad3d786","observation_id":"3beac73d-c4b8-4a14-a9b7-37b4d492427b","resolution":{"observed_at":"2026-08-11T20:40:55.786603Z","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":"2026-08-11T20:40:56.727170Z","title":"4d gaussian splatting for real-time dynamic scene rendering","venue":null,"work_id":"3bdf823a-16a3-42d9-a562-c743bded0db1","year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.791388Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:772efc6c346a1a03dfb36c9ab284ab12b77c13d003643c2e4c5f868f3cfe1dd0","observation_id":"26a6b360-ab2c-4622-94f0-83e17e2dc56f","resolution":{"observed_at":"2026-08-11T20:40:56.730927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.716104Z","title":"3d multi-object tracking in point clouds based on prediction confidence-guided data association.IEEE Trans- actions on Intelligent Transportation Systems, 23(6):5668– 5677, 2021","venue":null,"work_id":"c3f8fe62-7978-4b7c-8197-bce9f3d6fe91","year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.795496Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:4430af1b7d359fe0c42544dcc8c838816868d6cbc9200687dd79edc01b92a366","observation_id":"60b846fc-109d-4ebb-9fce-d19965106318","resolution":{"observed_at":"2026-08-11T20:40:56.720258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.705209Z","title":"Casa: A cascade attention network for 3-d object detection from lidar point clouds.IEEE Transactions on Geoscience and Remote Sensing, 60:1–11, 2022","venue":null,"work_id":"8bd994ab-f404-41ce-9e44-cd94173e85da","year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.799506Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:76ca5c6423ac35b814333c500143885f6c0c5d5b316e266c4b218baa2892739d","observation_id":"0b382bc9-526f-4087-82c0-200fc667a461","resolution":{"observed_at":"2026-08-11T20:40:56.709062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.693083Z","title":"General object foundation model for images and videos at scale","venue":null,"work_id":"ba3307f9-682d-4598-be2c-e1c6234526f1","year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.804607Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:28dfc31220cf1d100ae17eaf6cb6d607f44307ee2389758468cf98342b484922","observation_id":"fe10ebc1-0fef-4d0b-ac29-e9162613634b","resolution":{"observed_at":"2026-08-11T20:40:56.697653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.680805Z","title":"Mars: An instance-aware, modu- lar and realistic simulator for autonomous driving","venue":null,"work_id":"b55a4ff2-d1dd-4194-ab21-e69be29661d7","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.808674Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:ada6a4924a19c96a9b9a4ce75b88e8b2a854b53ec96df42feea390e5a484f4c1","observation_id":"f8566800-a82a-4a45-9e7c-3958b6436692","resolution":{"observed_at":"2026-08-11T20:40:56.684958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.669428Z","title":"Pandaset: Advanced sensor suite dataset for au- tonomous driving","venue":null,"work_id":"f5800765-3420-4957-b875-536a9005ad59","year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.813983Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:eba58088e4754a87a6658ba03109e06482a45b78c76b86b4b39c3faae7bdc1fd","observation_id":"b31533f4-8f6e-45f3-aa86-1265711ec0b2","resolution":{"observed_at":"2026-08-11T20:40:56.673334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.00749","last_updated":"2023-03-01T18:59:30Z","snapshot_observed_at":"2026-08-13T12:34:05.584548Z","submitted_at":"2023-03-01T18:59:30Z","title":"S-NeRF: Neural Radiance Fields for Street Views","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.00749","snapshot_observed_at":"2026-08-11T20:40:55.818248Z","title":"S-nerf: Neural radiance fields for street views","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.818248Z"},"links":{"cited_paper":"/paper/2303.00749","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:4fd428ddf06a82ea3469cbab7ad78df509d098909d7708db60e619efad703760","observation_id":"80e2817e-2786-48e0-84d4-2dcced3ca9ae","resolution":{"observed_at":"2026-08-11T20:40:55.818248Z","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":"2026-08-11T20:40:56.657745Z","title":"ViTPose: Simple vision transformer baselines for human pose estimation","venue":null,"work_id":"26960e2a-31be-4bfa-96ca-c7f326d2662c","year":2022},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.822827Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:d6c274d2c0c2d43b261474c7ac35974167f0b73a14fb469d4aa0ab77854c72bf","observation_id":"8458c77c-9174-451b-b8f4-8793e589d1fd","resolution":{"observed_at":"2026-08-11T20:40:56.661795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04246","last_updated":"2023-12-14T08:57:43Z","snapshot_observed_at":"2026-08-13T13:26:05.273624Z","submitted_at":"2022-12-07T12:33:28Z","title":"ViTPose++: Vision Transformer for Generic Body Pose Estimation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04246","snapshot_observed_at":"2026-08-11T20:40:55.826945Z","title":"Vitpose+: Vision transformer foundation model for generic body pose estimation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.826945Z"},"links":{"cited_paper":"/paper/2212.04246","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:272d64c225fad8594db439b5513c3893ef2e06f7d2537f445dc6c343d0a018ba","observation_id":"7a7e3679-da7b-455e-a961-743d7aea62f9","resolution":{"observed_at":"2026-08-11T20:40:55.826945Z","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":"2026-08-11T20:40:56.645388Z","title":"Universal instance percep- tion as object discovery and retrieval","venue":null,"work_id":"f45002f6-b3e8-4401-af8e-19910df3f11c","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.831171Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:983d2fa636f7083a8c3d71ea24479079dff4ca02d9ec18798c2c79e8fa8f2476","observation_id":"2c16416a-1af5-4166-b709-19fdaf6e1c6d","resolution":{"observed_at":"2026-08-11T20:40:56.649625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.634382Z","title":"Street gaussians: Modeling dynamic urban scenes with gaussian splatting","venue":null,"work_id":"53386760-d762-4e45-8e5f-790955ae9af3","year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.835579Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f37177a1ffc9de133c8bb97f79f9d173eb883a20d5cf0884e096924141aca125","observation_id":"577a16cb-4aee-4d5f-9275-2a73f6e84e01","resolution":{"observed_at":"2026-08-11T20:40:56.638286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02077","last_updated":"2023-11-03T17:59:55Z","snapshot_observed_at":"2026-08-13T05:33:13.609614Z","submitted_at":"2023-11-03T17:59:55Z","title":"EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02077","snapshot_observed_at":"2026-08-11T20:40:55.839226Z","title":"Emernerf: Emergent spatial- temporal scene decomposition via self-supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.839226Z"},"links":{"cited_paper":"/paper/2311.02077","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:0af78686e73f55e09489ff3b19d2460dcfc6130fa18037c50d71124143d70cda","observation_id":"e67629a8-197f-4622-b026-f5ad97d265ed","resolution":{"observed_at":"2026-08-11T20:40:55.839226Z","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":"2026-08-11T20:40:55.842813Z","title":"Unisim: A neural closed-loop sensor simulator","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.842813Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:db899b647f9d05d5d7cf922cfbb535014e3703bf762e154b6e40d32e3a231049","observation_id":"d92e8d1f-300c-40d6-86ee-d0e97e99cf70","resolution":{"observed_at":"2026-08-11T20:40:55.842813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10642","last_updated":"2024-02-22T15:08:49Z","snapshot_observed_at":"2026-08-13T05:48:11.830894Z","submitted_at":"2023-10-16T17:57:43Z","title":"Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10642","snapshot_observed_at":"2026-08-11T20:40:55.846219Z","title":"Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.846219Z"},"links":{"cited_paper":"/paper/2310.10642","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:7179185b0323365ab9ae593371f0f784e1ec4451af13b4be3bd5d075cdce13ae","observation_id":"35030919-06f7-4f7b-91c6-1fdd4765f69d","resolution":{"observed_at":"2026-08-11T20:40:55.846219Z","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":"2026-08-11T20:40:56.615069Z","title":"Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction","venue":null,"work_id":"d8d007d8-58c6-4e39-a016-00e86651088d","year":2024},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.850068Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:9bcfe7960dcfd6c003b2ead3aaf84fa9fc44144dfe026eea61984dacf5880b4c","observation_id":"640a7143-5179-499c-8165-3170755a9d1b","resolution":{"observed_at":"2026-08-11T20:40:56.618890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T20:40:56.603752Z","title":"Center-based 3d object detection and tracking","venue":null,"work_id":"ef26f18c-8342-4bf6-af86-00f185fb5643","year":2021},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.853689Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:f63be3883ec26f2d282feddc31c7ed2a0d4dead42c08729f5f61a77d657b04c2","observation_id":"07c45529-862d-402a-b974-ec691b135023","resolution":{"observed_at":"2026-08-11T20:40:56.608228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.04687","last_updated":"2020-04-08T09:25:06Z","snapshot_observed_at":"2026-08-14T19:16:08.121001Z","submitted_at":"2018-05-12T09:24:21Z","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.04687","snapshot_observed_at":"2026-08-11T20:40:55.857454Z","title":"Bdd100k: A diverse driving video database with scalable annotation tooling","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.857454Z"},"links":{"cited_paper":"/paper/1805.04687","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:26a197790609a485da921cbbf29b781ea08fb2a2e090f78d84cd5adc00021e3a","observation_id":"eea381e8-20b7-4871-bbe9-e8b65bfdd3ce","resolution":{"observed_at":"2026-08-11T20:40:55.857454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10430","last_updated":"2023-10-22T02:45:33Z","snapshot_observed_at":"2026-07-06T15:28:49.253125Z","submitted_at":"2023-05-17T17:59:11Z","title":"Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10430","snapshot_observed_at":"2026-08-11T20:40:55.861152Z","title":"Rethinking the open-loop evalua- tion of end-to-end autonomous driving in nuscenes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.861152Z"},"links":{"cited_paper":"/paper/2305.10430","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:b20df86b9ac4f2bdb14ffae702bef2d323622e99df21b807a5c4c8f719774984","observation_id":"af1e9d45-8615-444b-b6c1-d0b00491e632","resolution":{"observed_at":"2026-08-11T20:40:55.861152Z","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":"2026-08-11T20:40:56.591964Z","title":"Uni3d: A unified baseline for multi-dataset 3d object detection","venue":null,"work_id":"cf7bc27c-2e6e-4b18-aef1-93ef5a3695eb","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.865275Z"},"links":{"citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:de10df64fc64ae326df168340412baa42cbc3dd8e59174d98ff19a7020df0f05","observation_id":"f023742f-1c38-4136-aa40-991a210893b8","resolution":{"observed_at":"2026-08-11T20:40:56.595921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15411","last_updated":"2023-11-03T13:15:29Z","snapshot_observed_at":"2026-08-13T10:04:11.821961Z","submitted_at":"2023-09-27T05:32:26Z","title":"3D Multiple Object Tracking on Autonomous Driving: A Literature Review","version":3},"cited_work":{"arxiv_id":"2309.15411","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.15411","snapshot_observed_at":"2026-08-11T20:40:56.004338Z","title":"3D Multiple Object Tracking on Autonomous Driving: A Literature Review","venue":"cs.CV","work_id":"c7cd4d14-47b8-44a7-9b79-fc12ffddf34d","year":2023},"citing_paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker","version":4},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T20:40:55.868789Z"},"links":{"cited_paper":"/paper/2309.15411","citing_paper":"/paper/2412.05548"},"observation_digest":"sha256:0682e956f325ecab8b325a32f337d78eef97fa1ffc129c99525db7c7e9dcaf77","observation_id":"e733c3c6-1084-439a-b37c-0476c5c0d256","resolution":{"observed_at":"2026-08-11T20:40:56.010506Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.05548","last_updated":"2025-08-31T00:33:31Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T20:54:33.374013Z","submitted_at":"2024-12-07T05:49:42Z","title":"Street Gaussians without 3D Object Tracker"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":67,"verified_exact":1,"verified_fuzzy":32},"total_outbound_references":123},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 100 of 123 outbound references and 1 inbound Pith citation observation for arXiv:2412.05548."}