{"as_of":"2026-08-14T22:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b817db717f1ccba9b9cc4c38cdeb1e4f59d21aed6c061c621930182ea19d424e","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:49:50.166246Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T09:35:40.927753Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-12T19:49:50.166246Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10316","last_updated":"2025-05-21T14:09:13Z","snapshot_observed_at":"2026-08-14T17:56:01.794249Z","submitted_at":"2024-11-15T16:14:48Z","title":"M3TR: A Generalist Model for Real-World HD Map Completion","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:49:50.166246Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2411.10316"},"observation_digest":"sha256:67657eb44ea7d9d3fc0a25e5ed5e09c48dd31f32096fed59b06e0031ef0c494b","observation_id":"a5250313-74e8-419e-a5a5-24a59c73ff9d","resolution":{"observed_at":"2026-08-12T19:49:50.166246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-12T19:06:54.132445Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided kernel transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11002","last_updated":"2024-11-17T08:38:18Z","snapshot_observed_at":"2026-08-14T17:56:00.442656Z","submitted_at":"2024-11-17T08:38:18Z","title":"Unveiling the Hidden: Online Vectorized HD Map Construction with Clip-Level Token Interaction and Propagation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:06:54.132445Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2411.11002"},"observation_digest":"sha256:d96c3869bda6581a9551749a2a85f832bd6e5a831dd72ccb57a2c34679a57afc","observation_id":"194d5044-f7b0-4ce9-a97d-bb0ddf3b264f","resolution":{"observed_at":"2026-08-12T19:06:54.132445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-12T17:53:33.403335Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.12177","last_updated":"2024-11-19T02:40:42Z","snapshot_observed_at":"2026-08-14T17:56:00.103455Z","submitted_at":"2024-11-19T02:40:42Z","title":"Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T17:53:33.403335Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2411.12177"},"observation_digest":"sha256:bca42df0db860fa78082356ba45f20e3b1f15ead68a6b977b9b75f5ee8100847","observation_id":"2508f327-451f-4f78-8bc6-7a6b242614db","resolution":{"observed_at":"2026-08-12T17:53:33.403335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-12T04:22:33.653948Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01595","last_updated":"2024-12-02T15:15:10Z","snapshot_observed_at":"2026-08-13T20:32:24.484512Z","submitted_at":"2024-12-02T15:15:10Z","title":"Epipolar Attention Field Transformers for Bird's Eye View Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T04:22:33.653948Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2412.01595"},"observation_digest":"sha256:7cf07ec3d120a3dacaf2a72313b1074962689ec1d318f576490a2240230bc9e9","observation_id":"d1a11595-0afa-4dd7-ae2f-5b72a736bb25","resolution":{"observed_at":"2026-08-12T04:22:33.653948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-11T06:08:56.061421Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided kernel transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16889","last_updated":"2024-12-22T06:52:10Z","snapshot_observed_at":"2026-08-14T03:10:15.169889Z","submitted_at":"2024-12-22T06:52:10Z","title":"Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T06:08:56.061421Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2412.16889"},"observation_digest":"sha256:fc1ed2d7e2600996882bb88e8765da9c186b58e4f0eacc7e75e6c81466bfd291","observation_id":"494e946a-0069-41b6-b04c-7b468d606761","resolution":{"observed_at":"2026-08-11T06:08:56.061421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-09T05:10:34.431838Z","title":"Efficient and robust 2d-to-bev representation learning via geometry- guided kernel transformer","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04377","last_updated":"2025-02-05T16:25:45Z","snapshot_observed_at":"2026-08-13T10:31:10.608402Z","submitted_at":"2025-02-05T16:25:45Z","title":"MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map Construction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T05:10:34.431838Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2502.04377"},"observation_digest":"sha256:4869f1b5f5ab5aeedc2112bb9b0c12ae6588b8c01c837d15126cb93e525786f0","observation_id":"485267ea-929e-4410-98cf-a0e487f6a070","resolution":{"observed_at":"2026-08-09T05:10:34.431838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-06T21:13:14.372263Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided kernel transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00861","last_updated":"2025-07-01T15:28:09Z","snapshot_observed_at":"2026-08-14T02:41:10.624890Z","submitted_at":"2025-07-01T15:28:09Z","title":"SafeMap: Robust HD Map Construction from Incomplete Observations","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T21:13:14.372263Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2507.00861"},"observation_digest":"sha256:db2f0384f1de94d68424ef07214437a8b577616b88cf932eeb3445a8933379db","observation_id":"6659439d-8450-4767-b2ca-e651d3ee6891","resolution":{"observed_at":"2026-08-06T21:13:14.372263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-06T21:06:04.808718Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00980","last_updated":"2025-07-30T02:43:16Z","snapshot_observed_at":"2026-08-11T09:45:30.840404Z","submitted_at":"2025-07-01T17:32:30Z","title":"RTMap: Real-Time Recursive Mapping with Change Detection and Localization","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:06:04.808718Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2507.00980"},"observation_digest":"sha256:9f0967edcdd70c53ffc965f78c02b61c2c96dd6f264669826031bd978af4ce63","observation_id":"637d046f-5a6d-40b0-8b72-87aa98f0c199","resolution":{"observed_at":"2026-08-06T21:06:04.808718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-06T20:57:36.874639Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01397","last_updated":"2025-07-28T12:57:34Z","snapshot_observed_at":"2026-08-12T09:06:41.418208Z","submitted_at":"2025-07-02T06:26:17Z","title":"Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:57:36.874639Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2507.01397"},"observation_digest":"sha256:d364766ea48a3b79813ec72e6a3cb2d8ddc7f48b644ee20e906f76a31b22afe9","observation_id":"adb29325-079b-4295-90d7-6ea9a48a932f","resolution":{"observed_at":"2026-08-06T20:57:36.874639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-06T20:57:10.241524Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided kernel transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01484","last_updated":"2025-07-02T08:46:27Z","snapshot_observed_at":"2026-08-11T22:33:13.805894Z","submitted_at":"2025-07-02T08:46:27Z","title":"What Really Matters for Robust Multi-Sensor HD Map Construction?","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T20:57:10.241524Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2507.01484"},"observation_digest":"sha256:495d532cd8927de1d25bada33c4d5c369f4bd16e6647e015a23ab686489754ac","observation_id":"fc5538a8-621c-4e01-b062-a73fc912a104","resolution":{"observed_at":"2026-08-06T20:57:10.241524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-06T23:27:22.893753Z","title":"Efficient and robust 2d-to-bev representation learning via geometry- guided kernel transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02899","last_updated":"2025-07-11T05:01:20Z","snapshot_observed_at":"2026-08-14T15:58:52.760150Z","submitted_at":"2025-06-23T04:29:08Z","title":"Learning to Generate Vectorized Maps at Intersections with Multiple Roadside Cameras","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:22.893753Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2507.02899"},"observation_digest":"sha256:fd7c46abb8367572ccc38ac1fb643e33c679492e63c5720debe7d6ab11b2a0f7","observation_id":"6f77bb9e-58fc-4971-8df9-3ac23ad9d09b","resolution":{"observed_at":"2026-08-06T23:27:22.893753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-08-05T14:07:04.178856Z","title":"Efficient and robust 2D-to-BEV representation learning via geometry- guided kernel transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.21689","last_updated":"2025-08-29T14:55:33Z","snapshot_observed_at":"2026-08-14T18:50:52.441575Z","submitted_at":"2025-08-29T14:55:33Z","title":"Mapping like a Skeptic: Probabilistic BEV Projection for Online HD Mapping","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T14:07:04.178856Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2508.21689"},"observation_digest":"sha256:ca074fa679393c936cdbe67acd54f122697ec7bfcc4eb4f52b004bc67240b119","observation_id":"ab695ebc-58c5-4823-851f-596978f4a500","resolution":{"observed_at":"2026-08-05T14:07:04.178856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":"2206.04584","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-07-01T09:35:40.927753Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer","venue":null,"work_id":"cd74b41d-ba97-45a0-b330-975beb7573a1","year":2022},"citing_paper":{"arxiv_id":"2603.01558","last_updated":"2026-04-08T14:31:28Z","snapshot_observed_at":"2026-08-13T02:53:20.349558Z","submitted_at":"2026-03-02T07:33:46Z","title":"TopoMaskV3: 3D Mask Head with Dense Offset and Height Predictions for Road Topology Understanding","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T18:04:49.834498Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2603.01558"},"observation_digest":"sha256:6867996a857be962f9ee6da94e8225873f8ab76e80bfe62894080cb46142f877","observation_id":"f16c7c07-8f1c-467d-97bf-16c329344879","resolution":{"observed_at":"2026-05-15T18:06:25.429076Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":"2206.04584","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-07-01T09:35:40.927753Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer","venue":null,"work_id":"cd74b41d-ba97-45a0-b330-975beb7573a1","year":2022},"citing_paper":{"arxiv_id":"2605.08911","last_updated":"2026-05-09T12:12:14Z","snapshot_observed_at":"2026-08-13T14:01:25.218216Z","submitted_at":"2026-05-09T12:12:14Z","title":"Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-12T02:02:43.011302Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2605.08911"},"observation_digest":"sha256:e977b4b08b7c919be9a4d9219eafdc3bd90f79c9a5d486193498c723aa4a882e","observation_id":"dd34156d-124a-4152-b17f-0f3b98c76700","resolution":{"observed_at":"2026-05-12T02:06:15.476059Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","version":1},"cited_work":{"arxiv_id":"2206.04584","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.04584","snapshot_observed_at":"2026-07-01T09:35:40.927753Z","title":"Efficient and robust 2d-to-bev representation learning via geometry-guided ker- nel transformer","venue":null,"work_id":"cd74b41d-ba97-45a0-b330-975beb7573a1","year":2022},"citing_paper":{"arxiv_id":"2606.31177","last_updated":"2026-06-30T06:08:42Z","snapshot_observed_at":"2026-08-14T05:45:42.580009Z","submitted_at":"2026-06-30T06:08:42Z","title":"GaussianMap: Learning Gaussian Representation for Multi-Sensor Online HD Map Construction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-01T06:23:40.909967Z"},"links":{"cited_paper":"/paper/2206.04584","citing_paper":"/paper/2606.31177"},"observation_digest":"sha256:7d4a0166c1f91c3ad70edb98130750e7f0efb63a481c162f173caee3d18e7f69","observation_id":"ffd83958-4e98-4780-bbdf-a3436e12155e","resolution":{"observed_at":"2026-07-01T09:35:40.931379Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2206.04584/citation-record","integrity":"/paper/2206.04584/integrity","json":"/paper/2206.04584/citation-record.json","paper":"/paper/2206.04584"},"outbound":[],"paper":{"arxiv_id":"2206.04584","last_updated":"2022-06-09T16:05:08Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T17:55:14.665846Z","submitted_at":"2022-06-09T16:05:08Z","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2206.04584."}