{"as_of":"2026-08-04T15:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:911aa29892ac3d056872900ab19f7a8d62a717d02095c7836bf33ea1510ffb69","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T03:32:56.218448Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2604.19257/citation-record","integrity":"/paper/2604.19257/integrity","json":"/paper/2604.19257/citation-record.json","paper":"/paper/2604.19257"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"nuscenes: A multi- modal dataset for autonomous driving","venue":null,"work_id":"b28fd986-8b3d-47ab-a849-366a993539b3","year":2020},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:1e5f8554b4152b2c55fc8cf53d3b1a9bce60934e0cc4880b0b887860cf2b4f17","observation_id":"787943e3-62bb-40c7-a7f7-9c5705ae60d2","resolution":{"observed_at":"2026-05-22T16:21:48.130001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Car full view dataset: Fine-grained predictions of car orientation from images.Electronics, 12(24):4947","venue":null,"work_id":"26da24e6-d504-4e00-88c4-6ac858123247","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:327fe22c24382d06b6a7f73dcf2f60f985cf553377ba81575403f8135dfb302f","observation_id":"b97ba153-7b75-4c56-9853-ad1cb6123c3c","resolution":{"observed_at":"2026-05-22T16:21:48.109375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-02T21:55:14.093687Z","submitted_at":"2015-12-09T19:42:48Z","title":"ShapeNet: An Information-Rich 3D Model Repository","version":1},"cited_work":{"arxiv_id":"1512.03012","doi":"10.1111/2041-210x.12301","metadata_source":"pith","pith_arxiv_id":"1512.03012","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"ShapeNet: An Information-Rich 3D Model Repository","venue":"cs.GR","work_id":"b2ac5b60-daa9-435b-9369-12271e126edd","year":2015},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:fc7f39a549bdf8677dbfd7198cb9f66db3358bbd5de91fb8f2c10b739455a644","observation_id":"ccf315a9-9251-4a05-abcb-73c59239f6f7","resolution":{"observed_at":"2026-05-11T16:09:06.508173Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Geosim: Realistic video sim- ulation via geometry-aware composition for self-driving","venue":null,"work_id":"b4bde659-fea2-4c92-b30a-e80a2b0b7551","year":2021},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:2ba72f62bd7dbb40f847bb1a2359c29944b1000c6fe6b4174b1c2c80010e6bb4","observation_id":"15dc1151-2d4a-4c03-8ea5-e6201a8fb078","resolution":{"observed_at":"2026-05-22T16:21:48.134040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Objaverse-xl: A universe of 10m+ 3d objects.Advances in Neural Informa- tion Processing Systems, 36:35799–35813","venue":null,"work_id":"571a4c9d-3c64-4081-a1bf-54002b1adb5b","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:3d76900fbb4fc3fa1261678450e15bf8774eb97e18bf31d698ced9e6fd428072","observation_id":"c9d6651d-3720-49f0-98e5-7448ee542d65","resolution":{"observed_at":"2026-05-22T16:21:48.100186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"V oxel r-cnn: Towards high performance voxel-based 3d object detection","venue":null,"work_id":"904009fc-a851-4549-b10c-17e21b38c053","year":2021},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:f62b492ece3f1fa9f3e9f584d9893cd8435bdd9426d65163b4f892d13cbdbbd2","observation_id":"abe38a66-275c-4f14-936d-3b34eb15267b","resolution":{"observed_at":"2026-05-22T16:21:48.122828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Dreamcar: Leveraging car-specific prior for in-the-wild 3d car reconstruction.IEEE Robotics and Automation Let- ters","venue":null,"work_id":"4b79c5f7-f963-4beb-a988-b617caf95f05","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:0bcbe2bfa6ab52b9b45271cfa7acd40db645674a448c740cedd717f11ece758a","observation_id":"5a1e8c87-60f1-4c3f-85dd-9d4ffdfefcc5","resolution":{"observed_at":"2026-05-22T16:21:48.090465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"3drealcar: An in-the-wild rgb-d car dataset with 360-degree views","venue":null,"work_id":"d599610f-b43b-4b23-bce3-d5f663e0460d","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:632a7d1dc6af3dac82fac3b53f6c4fe350063092714551963e8f43aeb9e6e663","observation_id":"796aad36-89be-4498-af32-741fb60f0503","resolution":{"observed_at":"2026-05-22T16:21:48.106019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Get3d: A generative model of high quality 3d tex- tured shapes learned from images.Advances in neural infor- mation processing systems, 35:31841–31854","venue":null,"work_id":"b2b16068-f0e0-492a-85ea-fe1937b9f3d4","year":2022},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:f8f189ff6f4a04879e7a91d3db3cbde17ff2370c04f59463d2cc805f0bd9d8c2","observation_id":"4409f13e-1c81-4c89-adc6-3deb06e8c250","resolution":{"observed_at":"2026-05-22T16:21:48.057981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Tan et al","venue":null,"work_id":"d5352e4a-2055-48bd-9940-99b596af7229","year":2021},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:5abca09b70979baa8e12a862a7a5a780a31fca0e51cf5cedbfb21a3e63c830b4","observation_id":"1184d9b0-4792-49af-9f61-37cee8cb8135","resolution":{"observed_at":"2026-05-22T16:21:48.083216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.04400","last_updated":"2024-03-09T10:47:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-11-08T00:03:52Z","title":"LRM: Large Reconstruction Model for Single Image to 3D","version":2},"cited_work":{"arxiv_id":"2311.04400","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.04400","snapshot_observed_at":"2026-07-09T18:46:26.549595Z","title":"LRM: Large Reconstruction Model for Single Image to 3D","venue":"cs.CV","work_id":"0662dc2c-cc1c-4358-99bd-2a5f34795738","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2311.04400","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:ae6013501175dece528f926c778b56fbd4153503ca1cde137d17175cf8bc9db7","observation_id":"ee127335-9b53-4df4-a4f3-a07b76605d26","resolution":{"observed_at":"2026-05-15T10:11:00.998012Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"No pose at all: Self-supervised pose-free 3d gaussian splatting from sparse views","venue":null,"work_id":"c2e92aa5-d24b-4c83-94c5-f196b635fe28","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:3c67774bf83dd8e1a9a0945e6e1eec4b48761843714dc8b7e1a12fb93f85f131","observation_id":"5c9b3213-0143-4a7b-b77d-a9c283ad04d8","resolution":{"observed_at":"2026-05-22T16:21:48.093805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Mvsmamba: Multi-view stereo with state space model","venue":null,"work_id":"3fbc24c0-1480-47b1-abc7-937dc21c77bd","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:0bab251b08e31683051198cd86d358daf600f9ee91abc86248a672414c5655ba","observation_id":"94508e21-56d5-4a82-96d6-e00efe6f42d8","resolution":{"observed_at":"2026-05-22T16:21:48.074281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Ultralytics YOLO","venue":null,"work_id":"dd7b3a6a-3881-4664-ac63-4150d05c5ae6","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:379940d40959c5c065910c77afa492b997d21f51abe3e2117dd3832511bf6f02","observation_id":"8c63610d-081a-43f1-a1c8-8e66ccdc86b7","resolution":{"observed_at":"2026-05-22T16:21:48.103055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02463","last_updated":"2023-05-03T23:59:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-03T23:59:13Z","title":"Shap-E: Generating Conditional 3D Implicit Functions","version":1},"cited_work":{"arxiv_id":"2305.02463","doi":"10.48550/arxiv.2305.02463","metadata_source":"pith","pith_arxiv_id":"2305.02463","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Shap-E: Generating Conditional 3D Implicit Functions","venue":"cs.CV","work_id":"d292f6b0-b538-459b-b332-ee3b7a0e4fbe","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2305.02463","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:ed4d849a8b0b3ae5f0cc8317de38a56266e8fd9c08b84f4cb3b99101774fcae9","observation_id":"880c4c6b-8795-4f87-852c-82ec097769bf","resolution":{"observed_at":"2026-05-16T15:32:06.918889Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-12T21:49:47.59901+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T21:49:47.59901+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2506.21520","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T16:09:56.641353Z","title":"Madrive: Memory-augmented driving scene modeling","venue":null,"work_id":"4f492b26-450f-4c3c-873e-84bd35da5d32","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:2e8ed3c524a7297755ecabd9f5f7de6d89e63a72bad72aebcd5f28602922f01a","observation_id":"14cb8454-9cb5-400f-8653-2edc5c444da4","resolution":{"observed_at":"2026-05-11T12:31:03.749983Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Gsnet: Joint vehicle pose and shape recon- struction with geometrical and scene-aware supervision","venue":null,"work_id":"8a141e8f-789a-47ea-850f-2a4a273b78fd","year":2020},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:a5750b10636f96fe04a935961d2efec317db868c90fa36f4e9d3233663559c3a","observation_id":"1138c879-1922-4783-adfc-ca2a5325cea6","resolution":{"observed_at":"2026-05-22T16:21:48.096934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-09T07:46:04.037239Z","title":"3d gaussian splatting for real-time radiance field rendering.ACM Trans","venue":null,"work_id":"f02b3a8e-cb41-4f1f-ae1c-75b487ba20f9","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:068a2793bc74a89ad8c1ac73adfc47d08c01f69d289eac7061aae7e4c983d5f7","observation_id":"4bc758e5-7984-46f5-8abc-fd9d542a81c0","resolution":{"observed_at":"2026-05-22T16:21:48.138330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Habitat synthetic scenes dataset (hssd-200): An analysis of 3d scene scale and realism tradeoffs for objectgoal naviga- tion","venue":null,"work_id":"29ea42a0-3187-4941-a49d-9563f289e404","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:eecc34f28e98fcee56d40865231e4b5ffecdc989cd335027eb7976c9bf3ef941","observation_id":"1f916490-7722-4b64-b51b-ac925406fe63","resolution":{"observed_at":"2026-05-22T16:21:48.126298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06214","last_updated":"2023-11-23T08:55:49Z","snapshot_observed_at":"2026-07-06T16:45:47.395736Z","submitted_at":"2023-11-10T18:03:44Z","title":"Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model","version":2},"cited_work":{"arxiv_id":"2311.06214","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.06214","snapshot_observed_at":"2026-07-04T16:19:56.523174Z","title":"Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model","venue":null,"work_id":"0c23e789-12f2-4cd9-900d-b96ddd3b811f","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2311.06214","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:2a59adb8f269d7eb6e85b82355dfc39a39f45b22b015ddbd7a2a3392904a76df","observation_id":"bf580274-f346-4dd8-bcf1-1e6b4dc1ccfb","resolution":{"observed_at":"2026-05-11T12:31:03.641375Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Photorealistic object insertion with diffusion-guided inverse rendering","venue":null,"work_id":"84a71a8d-bed3-4017-a454-4f22f14da90e","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:bd0030cb4f70d20eea97e8567410b3490e51b5bc9176e78e5b1c3be1ea9e7902","observation_id":"08ced50f-5a89-4faa-b870-d6fe540152e3","resolution":{"observed_at":"2026-05-22T16:21:48.060799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching","venue":null,"work_id":"edfac989-7746-41f2-98d9-f41aa8aad2e6","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:28e6035f05d1c4cc1f2e535c46f9768497f461a0c17f4b05cf6e39c2c1093f4b","observation_id":"d5205b8f-0b7e-4207-b147-7dbc01cbad8f","resolution":{"observed_at":"2026-05-22T16:21:48.077110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Magic3d: High-resolution text-to-3d content creation","venue":null,"work_id":"fe2302b3-c67f-4295-b7c8-5441befdb0f9","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:284da54a431f0eec669b3496dda432e5e4c7ef5adfc3213db47d2ea45b8a6b14","observation_id":"31e18084-485c-4d1a-af9a-496ad309bae0","resolution":{"observed_at":"2026-05-22T16:21:48.067765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.12015","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Instdrive: Instance-aware 3d gaussian splatting for driving scenes","venue":null,"work_id":"7528fdc8-350d-4827-a02f-74f0e2d8fe96","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:c5946722b686e9313d9b8030381b33da496faf0e8968af3d112d381dfd2dfec5","observation_id":"f154b884-69b6-476f-9f69-a47e4463a9d7","resolution":{"observed_at":"2026-05-11T12:31:03.632303Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Protocar: Learning 3d vehicle prototypes from single-view and unconstrained driving scene images","venue":null,"work_id":"699932c2-433a-408b-9941-e5f0df44c347","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:7b3f832a83a090c4592726401151a0700eadc5a8b3d0f2fdb3d4cc7b760d4369","observation_id":"986e814c-c5d2-4393-88ac-603a0be4f04f","resolution":{"observed_at":"2026-05-22T16:21:48.144912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d dif- fusion","venue":null,"work_id":"533c95b8-2070-4064-a198-4de055ab8aba","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:5c8de8047c666e775f1ee81041a9fede168262df304ef8c70ad9d9f6c9cb0a1f","observation_id":"96e35bd5-12b9-4f25-a2fb-7c713656c237","resolution":{"observed_at":"2026-05-22T16:21:48.087311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Zero-1-to- 3: Zero-shot one image to 3d object","venue":null,"work_id":"78ef3ecd-23ad-4fac-bb70-dd88fa5e2040","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:1189036906ee63a9da9a66896bb3351df7b215cd8285312dd8fe540bae029da2","observation_id":"9764334f-96d3-4ca1-aa00-c26079c45a5c","resolution":{"observed_at":"2026-05-22T16:21:48.064236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Car-studio: learning car radiance fields from single- view and unlimited in-the-wild images.IEEE Robotics and Automation Letters, 9(3)","venue":null,"work_id":"ee8c079f-30a4-4ef8-9508-f9cd9be8f784","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:3e33884d14826780c711e6d2acad789d20687742f7a4af844c27e8b119c78fe6","observation_id":"4d6e403b-e53f-444c-bf98-b3795b4666c7","resolution":{"observed_at":"2026-05-22T16:21:48.071114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07826","last_updated":"2026-04-19T08:51:28Z","snapshot_observed_at":"2026-07-06T21:39:08.559358Z","submitted_at":"2025-06-09T14:50:19Z","title":"R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation","version":2},"cited_work":{"arxiv_id":"2506.07826","doi":"10.48550/arxiv.2506.07826","metadata_source":"pith","pith_arxiv_id":"2506.07826","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation","venue":"cs.CV","work_id":"492c10ff-e46a-4e28-9948-77303d2579a6","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2506.07826","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:684b4c8b1a52e0cf5b52ca175a48b3b011c339d86e796cde078ea307cf88c341","observation_id":"20effcb4-bd84-4a65-81b4-d32f4e172460","resolution":{"observed_at":"2026-05-11T12:31:03.817355Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Ur- bancad: Towards highly controllable and photorealistic 3d vehicles for urban scene simulation","venue":null,"work_id":"99d898d9-c86a-4aaf-bf65-4049d87fba91","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:64731e84bb91483f9eb9a62a369b1202cbf6b14ea96fd79acf00009b66a20ca0","observation_id":"fcec082f-5285-4c59-a6aa-8f5d08fd88f2","resolution":{"observed_at":"2026-05-22T16:21:48.116250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-08T05:54:34.262354Z","title":"Nerf: Representing scenes as neural radiance fields for view syn- thesis.Communications of the ACM, 65(1):99–106","venue":null,"work_id":"cb45bd9c-4741-435d-ac44-8e1c90e27505","year":2021},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:c2bbd6391c867bce28c49fba44562cc06c7f7aa8d452e6689879ed719e8aa04b","observation_id":"80d8fe9e-a80e-491f-ad32-cf3d41e822a4","resolution":{"observed_at":"2026-05-22T16:21:48.113024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"3d bounding box estimation using deep learn- ing and geometry","venue":null,"work_id":"bd1fa88b-6599-454d-894b-05f1b0bede7d","year":2017},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:2329ae32989fb2d590b40a05fe82ed42be11cab511d573cc0ff6259ae66e718c","observation_id":"927badf3-2b3c-42eb-b544-a0cc86b174a1","resolution":{"observed_at":"2026-05-22T16:21:48.141819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Autorf: Learning 3d object radiance fields from single view observations","venue":null,"work_id":"7075bfd0-4d3a-4b4c-afa4-fd98f9b29880","year":2022},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:9d131fe1b751f6b7f63278f9367f803bca90a7ef452e8d379be05b1aa060a67a","observation_id":"d8ee4556-07b0-4e9b-9a7c-ee97e1340430","resolution":{"observed_at":"2026-05-22T16:21:48.080223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08751","last_updated":"2022-12-16T23:22:59Z","snapshot_observed_at":"2026-07-06T14:31:54.932806Z","submitted_at":"2022-12-16T23:22:59Z","title":"Point-E: A System for Generating 3D Point Clouds from Complex Prompts","version":1},"cited_work":{"arxiv_id":"2212.08751","doi":"10.48550/arxiv.2212.08751","metadata_source":"pith","pith_arxiv_id":"2212.08751","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Point-E: A System for Generating 3D Point Clouds from Complex Prompts","venue":"cs.CV","work_id":"9d7f0b29-b9ca-457f-9518-4a1506b43369","year":2022},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2212.08751","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:e88514efe137373334de5092bdcea6fb494ca34339bafba8123b2412784874aa","observation_id":"2cc04241-781f-4692-9045-92159b8c967b","resolution":{"observed_at":"2026-05-14T20:51:35.400576Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":"2304.07193","doi":"10.48550/arxiv.2304.07193","metadata_source":"pith","pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-07-11T00:07:42.299741Z","title":"DINOv2: Learning Robust Visual Features without Supervision","venue":"cs.CV","work_id":"26b304e5-b54a-4f26-be7e-83299eca52e4","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:958d3f489106e15e28f014ae1f796cc60e489bcf68e442d7de52e22c968abb1e","observation_id":"24bf27f1-4d88-4f65-9eda-58028cb733ba","resolution":{"observed_at":"2026-05-11T12:31:03.793117Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Neural scene graphs for dynamic scenes","venue":null,"work_id":"d117481d-36d0-4e4e-80fa-36c931da3e38","year":2021},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:edb813320c8c2cdd449cc8ec3fddea6831d487a671a9415b2b6a64d143518b39","observation_id":"509cf385-2a4c-4d45-95df-17a0969c35b1","resolution":{"observed_at":"2026-05-22T16:21:48.119403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12036","last_updated":"2024-03-18T17:59:40Z","snapshot_observed_at":"2026-07-06T17:46:29.153377Z","submitted_at":"2024-03-18T17:59:40Z","title":"One-Step Image Translation with Text-to-Image Models","version":1},"cited_work":{"arxiv_id":"2403.12036","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12036","snapshot_observed_at":"2026-07-04T02:59:25.755505Z","title":"One-step image translation with text-to-image models","venue":null,"work_id":"2195f713-f393-41e9-b1d8-0e3b7cbf5536","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2403.12036","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:3c084313d5aeaaba0bc9264685641a4d902c648bd1f562225f896c23a998e2c7","observation_id":"a20d5587-a51f-4173-8c26-4d8262cc347c","resolution":{"observed_at":"2026-05-11T12:31:03.691538Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-08T05:14:35.715460Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":"292e4202-5927-473f-b73e-1d6883498f14","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:c86ee26df87ca54937489b7582b890e5854d73a3e0a12cec8fba9459e777bc53","observation_id":"56a36336-85b0-415b-90dc-998957e6da6c","resolution":{"observed_at":"2026-05-22T16:21:48.154929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14988","last_updated":"2022-09-29T17:50:40Z","snapshot_observed_at":"2026-07-06T13:57:54.539656Z","submitted_at":"2022-09-29T17:50:40Z","title":"DreamFusion: Text-to-3D using 2D Diffusion","version":1},"cited_work":{"arxiv_id":"2209.14988","doi":"10.48550/arxiv.2209.14988","metadata_source":"pith","pith_arxiv_id":"2209.14988","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"DreamFusion: Text-to-3D using 2D Diffusion","venue":"cs.CV","work_id":"7529df29-9980-4a8a-b55e-307e3c2f357b","year":2022},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2209.14988","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:d6d8c49b1fb506fafd54ba684b1f38fd9e8cf8dc6a982c01e6c4f84b7b92ec87","observation_id":"c419988e-fd0d-40c7-b424-a5f318d06da9","resolution":{"observed_at":"2026-05-11T12:31:03.682941Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-14T13:50:01.924447+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T13:50:01.924447+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-05T06:40:46.053221Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"8e9db76b-fe9d-4487-bb62-b413b9491305","year":2022},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:e9a172219279f0ac9e5239bb0153a22f749bb85f1e6e811636e80d909d6fc47c","observation_id":"20f9b398-641e-46d9-af55-41cf303c9981","resolution":{"observed_at":"2026-05-22T16:21:48.158655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-05T06:40:46.062579Z","title":"Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494","venue":null,"work_id":"271cb104-9bce-4478-8e63-2b1884940c89","year":2022},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:7c31a72f21943fa13dbe17d0dcc811fdbdab35a5ef56db74bc854ba773c53bc8","observation_id":"4c5486fc-d0ae-4b25-a89d-5f7c8f379b1f","resolution":{"observed_at":"2026-05-22T16:21:48.168840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Structure- from-motion revisited","venue":null,"work_id":"9d7da8fa-0571-43b3-af66-081051017c7b","year":2016},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:8d9ac83992ed1a043558e7cf04fe8ac2039478a587a4920f1b0c9eb94b7df58f","observation_id":"8a73018c-7345-4180-bbf0-dd7c59df96cb","resolution":{"observed_at":"2026-05-22T16:21:48.222804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Gina-3d: Learning to generate implicit neural assets in the wild","venue":null,"work_id":"b69369e8-eca7-42b0-9713-af00bd4b2b46","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:a9e1d2182826fc746da77bead67457b36b797376ca2cc305041d4f9eac58f420","observation_id":"3c4eadaf-31d3-4761-a980-793254e7f60c","resolution":{"observed_at":"2026-05-22T16:21:48.151463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Scalability in perception for autonomous driving: Waymo open dataset","venue":null,"work_id":"057f4663-18f1-4a66-9208-a5231b87e930","year":2020},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:8626e740aa57d7d50f90e674585d1203cf14bf777377bc67a1ce84a4cdea5dbb","observation_id":"3a19d972-89c4-402b-885c-23c32c342880","resolution":{"observed_at":"2026-05-22T16:21:48.208444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16653","last_updated":"2024-03-29T08:39:23Z","snapshot_observed_at":"2026-07-06T16:25:05.493379Z","submitted_at":"2023-09-28T17:55:05Z","title":"DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation","version":2},"cited_work":{"arxiv_id":"2309.16653","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.16653","snapshot_observed_at":"2026-07-08T01:44:26.267895Z","title":"DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation","venue":"cs.CV","work_id":"790b039f-2f31-4657-93ac-f802a85cd72a","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2309.16653","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:e9371b84f10a0869a02a2317a6cc508929379d1286d50f57d45b30eb70d0eb72","observation_id":"ecc21263-9959-426d-aef8-dd5c2bc32b99","resolution":{"observed_at":"2026-05-16T10:18:06.729150Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Lgm: Large multi-view gaussian model for high-resolution 3d content creation","venue":null,"work_id":"df73f17b-fb47-4f59-a18d-4b2e45fa725b","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:b8a6451a0441495e79c6f59b6994b1ce7f08080011ae5adda4314e40a3c2a180","observation_id":"01443283-2717-4446-999a-a97ce9c83f65","resolution":{"observed_at":"2026-05-22T16:21:48.165018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Openpcdet: An open- source toolbox for 3d object detection from point clouds","venue":null,"work_id":"781a705b-838c-4368-9cf9-c1cdbc9404f1","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:9f4c8dcfe6e94bba8d4529cea22b0c2b33427257799cde8839383d06a8814041","observation_id":"bf0f7c19-fbfc-4873-8b4b-b2aa539a3d44","resolution":{"observed_at":"2026-05-22T16:21:48.219214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01447","last_updated":"2023-11-02T17:56:59Z","snapshot_observed_at":"2026-07-06T16:42:19.938941Z","submitted_at":"2023-11-02T17:56:59Z","title":"CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation","version":1},"cited_work":{"arxiv_id":"2311.01447","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.01447","snapshot_observed_at":"2026-07-04T16:39:57.888998Z","title":"Cadsim: Robust and scalable in-the- wild 3d reconstruction for controllable sensor simulation","venue":null,"work_id":"dc7137b7-f76b-4476-9c4b-a6e7d91a04ca","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2311.01447","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:469af6d78a456440956e7bcd9bcfb450eaaee851cf3d0e6060acae3ed42616b9","observation_id":"db4c84cc-210a-45e0-9242-d022d3af1d99","resolution":{"observed_at":"2026-05-11T12:31:03.664923Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Vggsfm: Visual geometry grounded deep structure from motion","venue":null,"work_id":"4e66275c-8075-4c5b-945c-db34cbd6b03f","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:2d6850e4f7a45d67ff0144ce38b9ea6cf8e3d2c018c0705f0efec9c6f1fc0c42","observation_id":"8adc5381-2fcc-43b8-835c-316f86987e60","resolution":{"observed_at":"2026-05-22T16:21:48.225905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Vggt: Vi- sual geometry grounded transformer","venue":null,"work_id":"201b5fc5-8d5c-4399-a229-6db2130ee463","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:48512afd7f849e5c5b830526214ed5df65234fccf9065ffbab5ef5f53123a36f","observation_id":"4091c57a-ff7f-420b-b735-7c02fc73db76","resolution":{"observed_at":"2026-05-22T16:21:48.162033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Dust3r: Geometric 3d vi- sion made easy","venue":null,"work_id":"19527a6e-a98f-48a1-a408-7a9c0bcfa7b5","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:d0701fed30a39923a8dd44221cec668b68809878650c11e6f4bb79cef5480351","observation_id":"7a17b5bd-ff0c-46a4-a63d-4484c7219c71","resolution":{"observed_at":"2026-05-22T16:21:48.205062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in neural information processing systems, 36: 8406–8441","venue":null,"work_id":"67d5ac7a-afd1-4721-986f-82fd58ce660d","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:76c903c089e27998eb5bd04e9aba0accb98409139fe67754cbb98e1a06cfd973","observation_id":"051a9e6b-23f5-427f-92eb-5ab399b2df26","resolution":{"observed_at":"2026-05-22T16:21:48.229993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.18605","last_updated":"2024-12-24T18:58:43Z","snapshot_observed_at":"2026-07-06T20:12:54.710203Z","submitted_at":"2024-12-24T18:58:43Z","title":"Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models","version":1},"cited_work":{"arxiv_id":"2412.18605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.18605","snapshot_observed_at":"2026-07-04T08:59:43.205863Z","title":"Orient anything: Learn- ing robust object orientation estimation from rendering 3d models","venue":null,"work_id":"c5b7666c-e46a-4729-b933-1457ddcf103a","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2412.18605","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:17df542224920a53c363c339b4569f96bf69f7828eee376368e947fee700e016","observation_id":"99458766-5d70-459f-a79a-c5c61555bf04","resolution":{"observed_at":"2026-05-11T12:31:03.722254Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Dycrowd: Towards dynamic crowd reconstruction from a large-scene video.IEEE Transactions on Pattern Analysis and Machine Intelligence","venue":null,"work_id":"a197e63c-b21e-4a04-80f3-7ff62d8dbad7","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:d35afdef61672698ac5e29514901ef7669db757abb191129dea3b5a21256e1ae","observation_id":"b50f2af5-e8de-4319-a7a2-83847673e83f","resolution":{"observed_at":"2026-05-22T16:21:48.233692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Mars: An instance-aware, mod- ular and realistic simulator for autonomous driving","venue":null,"work_id":"4f6957a1-e083-4ff8-9143-4d24ab33a5b8","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:eeb4ec10977d7e316792c0a8fcc11571383516ee02c0883d162b609f0fdd1735","observation_id":"b16b1f0d-c1cd-4e8b-a9fa-b8505233647e","resolution":{"observed_at":"2026-05-22T16:21:48.191160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Structured 3d latents for scalable and versatile 3d gen- eration","venue":null,"work_id":"0bccd22d-442d-4824-9f94-e6b4f14fd9ce","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:f976a638480f5fbec713d835b26200cc74b115af9ccfbfccb8f4d6c3554071ea","observation_id":"f8b0514a-7472-4ca8-b539-45cfee628629","resolution":{"observed_at":"2026-05-22T16:21:48.215365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Data-driven 3d voxel patterns for object category recogni- tion","venue":null,"work_id":"32688e5a-d709-48ce-b2c4-65b7a0b255f2","year":1903},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:25bbe17884c5eedabbeda74e443843506710db86640c661d27ca2acb20cc1581","observation_id":"e5d59582-c26b-4a74-a60e-e68c02c72581","resolution":{"observed_at":"2026-05-22T16:21:48.194588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Pandaset: Advanced sensor suite dataset for autonomous driving","venue":null,"work_id":"2c4be313-2c2d-4809-a133-7f4b43a54e12","year":2021},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:4e18f68ade742313b1a22d7f365c7d876f2017edd4d73d1067f6a6a10cb49c71","observation_id":"9b89da43-b25e-4844-a49e-fca8ce87e0d2","resolution":{"observed_at":"2026-05-22T16:21:48.187470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07191","last_updated":"2024-04-14T16:54:24Z","snapshot_observed_at":"2026-07-06T17:58:25.894552Z","submitted_at":"2024-04-10T17:48:37Z","title":"InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models","version":2},"cited_work":{"arxiv_id":"2404.07191","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.07191","snapshot_observed_at":"2026-07-08T01:44:26.212652Z","title":"InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models","venue":"cs.CV","work_id":"fc55eabb-0871-4dc4-8bab-b572e0d2aac4","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2404.07191","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:9797622c4c8d1a5077625b5f1874d4ed51c0310dcdbfce949666977d6067199e","observation_id":"9ac34841-17f9-4dd3-b16c-13d25c633423","resolution":{"observed_at":"2026-05-13T21:15:33.601715Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Street gaussians: Modeling dynamic urban scenes with gaussian splatting","venue":null,"work_id":"8e6c2f45-41d2-4d74-9f2c-962b30621867","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:0f29fbefaccb0924cc6330df6ba7a96d1fdf07d2f9bb23da24214ec1c55bbbb3","observation_id":"b0ecaa5c-ece8-481d-aa8b-5aaf4e63098c","resolution":{"observed_at":"2026-05-22T16:21:48.183826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Unisim: A neural closed-loop sensor simulator","venue":null,"work_id":"463a935d-dfba-4dad-91c8-8201bf555bd1","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:5840d16246f68f66c9fd048a65ecb6709f89cb72327b633b58974eb785c9ba2f","observation_id":"c80a4071-86b1-4878-8907-33894938f517","resolution":{"observed_at":"2026-05-22T16:21:48.148252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Get3dgs: Generate 3d gaussians based on points deformation fields.IEEE Transactions on Circuits and Systems for Video Technology","venue":null,"work_id":"bae9b954-eb51-4fc0-ad43-eff7c2242cbd","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:30bd280d8f540313b6babadc60fc8f401ea77c66ae21843a2a964544fed79f03","observation_id":"1bb31796-ef93-4f88-ab79-1f6c47faf8eb","resolution":{"observed_at":"2026-05-22T16:21:48.176274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.07856","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Xyzcylinder: Towards compatible feed-forward 3d gaussian splatting for driving scenes via unified cylinder lifting method","venue":null,"work_id":"dfc6af48-e877-4953-907d-91a012656ecc","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:d454374656dd398f0694fc88349f2c8bdb64042fcf8b9e5d8ccefebde51b420a","observation_id":"780c735c-2c37-4460-ad57-cf82cd871aad","resolution":{"observed_at":"2026-05-11T12:31:03.709812Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.14289","last_updated":"2023-07-01T07:26:22Z","snapshot_observed_at":"2026-07-06T15:46:29.060519Z","submitted_at":"2023-06-25T16:37:25Z","title":"Faster Segment Anything: Towards Lightweight SAM for Mobile Applications","version":2},"cited_work":{"arxiv_id":"2306.14289","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.14289","snapshot_observed_at":"2026-07-04T21:00:09.639501Z","title":"Faster Segment Anything: Towards Lightweight SAM for Mobile Applications","venue":"cs.CV","work_id":"d159afc6-0f47-4693-a3c0-908e661ff652","year":2023},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"cited_paper":"/paper/2306.14289","citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:914393d84e0da86fabd118ce81ee211da54608cbae3b645aab14a965c5f15d72","observation_id":"3459ec34-4a5e-42bf-bb78-c053973ca3e0","resolution":{"observed_at":"2026-05-17T22:41:43.533652Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Hugsim: A real-time, photo-realistic and closed-loop simulator for autonomous driving.IEEE Trans- actions on Pattern Analysis and Machine Intelligence","venue":null,"work_id":"79f90ece-5e00-409b-88fb-1f8ec7f2ccb1","year":2025},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:b9f3ba588bb0c510ac4ab5d0cb82d00bc89693c429c60742c011ad215ad33b70","observation_id":"fdc7bd95-3a7b-4582-b2c1-20b6dd8e25b4","resolution":{"observed_at":"2026-05-22T16:21:48.212247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Triplane meets gaussian splatting: Fast and generalizable single-view 3d reconstruction with transformers","venue":null,"work_id":"13770eff-e127-4a30-aa4c-01562cdb67f0","year":2024},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:1bed7c39c43d0485cb79f6bb0bda15e9761fdf5b30fb9b7fd0f7dac2567ca58d","observation_id":"839c0156-5ecf-4973-a05d-a95a7d7278c2","resolution":{"observed_at":"2026-05-22T16:21:48.172292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Network Architectures Texture Block cross self mlp zpatch𝑃𝑃 𝑃𝑃 𝑃𝑃 Geometry Block cross self mlp zcls × 5 × 10 Figure 5","venue":null,"work_id":"f91ec8f9-5b1a-4e64-801f-3e7ced45a0bf","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:01549ef4ad7570fed7bad8f42dad7feca241469b86183848bd8c92012a0da580","observation_id":"b0ce5632-f4c3-4d14-ada3-f724f3624376","resolution":{"observed_at":"2026-05-22T16:21:48.197981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"8630caaa-b163-4ccf-a8f1-18fe228036d4","year":2033},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:ec0f535f5e19dce617ceac418342bdb4404d3e16eb5e9b7365cde218f547e32f","observation_id":"63709105-6a11-4476-8029-e63e60e493fb","resolution":{"observed_at":"2026-05-22T16:21:48.180007Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In addition we do not explic- itly model shadows cast by environmental illumination","venue":null,"work_id":"39abe4a9-aadc-4696-926d-ed0265a41144","year":null},"citing_paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-10T03:32:56.218448Z"},"links":{"citing_paper":"/paper/2604.19257"},"observation_digest":"sha256:5d790c124caa90ae70d8f1c65052c64300cc25c02116b04753e3d83658ad3db2","observation_id":"5cad0c42-cdd6-476f-b52c-2ebd09f09bcd","resolution":{"observed_at":"2026-05-22T16:21:48.201493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.19257","last_updated":"2026-04-21T09:20:39Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T09:20:39Z","title":"Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":17,"verified_fuzzy":51},"total_outbound_references":69},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2604.19257."}