{"as_of":"2026-08-13T00:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aa0a4662fefa549d4ecd335d9b5c9181b51386aa5b10346221a3f3ed596cacd4","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T00:21:15.561750Z","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":"2026-07-02T13:46:58.812876Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2003.03653","last_updated":"2024-11-20T20:47:25Z","snapshot_observed_at":"2026-08-11T00:10:39.821879Z","submitted_at":"2020-03-07T20:17:06Z","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03653","snapshot_observed_at":"2026-08-09T00:21:15.561750Z","title":"SalsaNext: Fast, 7 Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.03901","last_updated":"2025-02-06T09:24:47Z","snapshot_observed_at":"2026-08-12T22:28:08.290637Z","submitted_at":"2025-02-06T09:24:47Z","title":"LeAP: Consistent multi-domain 3D labeling using Foundation Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T00:21:15.561750Z"},"links":{"cited_paper":"/paper/2003.03653","citing_paper":"/paper/2502.03901"},"observation_digest":"sha256:2046b1a8f9c69683f037e57a0a3fb200a75a26e21f31c5f12e31d4e38a60ef15","observation_id":"48296489-b7da-4b17-8f26-8adc150991c3","resolution":{"observed_at":"2026-08-09T00:21:15.561750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03653","last_updated":"2024-11-20T20:47:25Z","snapshot_observed_at":"2026-08-11T00:10:39.821879Z","submitted_at":"2020-03-07T20:17:06Z","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03653","snapshot_observed_at":"2026-08-07T04:50:30.502221Z","title":"CoRRabs/2003.03653(2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.09552","last_updated":"2025-06-11T09:36:07Z","snapshot_observed_at":"2026-08-09T10:48:14.418804Z","submitted_at":"2025-06-11T09:36:07Z","title":"Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T04:50:30.502221Z"},"links":{"cited_paper":"/paper/2003.03653","citing_paper":"/paper/2506.09552"},"observation_digest":"sha256:2c6449e5043bb7e4eba779d33e15adf01aa3731ed3ebe2ae953d92da75b91c27","observation_id":"45e0b179-4c62-472f-a7f9-78aa45fd6e38","resolution":{"observed_at":"2026-08-07T04:50:30.502221Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03653","last_updated":"2024-11-20T20:47:25Z","snapshot_observed_at":"2026-08-11T00:10:39.821879Z","submitted_at":"2020-03-07T20:17:06Z","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03653","snapshot_observed_at":"2026-08-03T07:48:20.599613Z","title":"Cortinhal, G","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.19128","last_updated":"2026-07-05T03:19:22Z","snapshot_observed_at":"2026-08-10T05:27:44.306706Z","submitted_at":"2026-01-27T02:52:28Z","title":"Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T07:48:20.599613Z"},"links":{"cited_paper":"/paper/2003.03653","citing_paper":"/paper/2601.19128"},"observation_digest":"sha256:3c0e825f0af1f8b591687d30a723604cf28250bd1cc588597a9a08161b1aeddc","observation_id":"9471a12b-3696-4b45-a1d8-66056697b4f4","resolution":{"observed_at":"2026-08-03T07:48:20.599613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03653","last_updated":"2024-11-20T20:47:25Z","snapshot_observed_at":"2026-08-11T00:10:39.821879Z","submitted_at":"2020-03-07T20:17:06Z","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","version":4},"cited_work":{"arxiv_id":"2003.03653","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.03653","snapshot_observed_at":"2026-07-02T13:46:58.812876Z","title":"Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds for autonomous driving.arXiv preprint arXiv:2003.03653, 2020","venue":null,"work_id":"8ea57dc9-5c4d-4af9-85e3-3ab3ab536578","year":2003},"citing_paper":{"arxiv_id":"2607.00978","last_updated":"2026-07-01T14:12:21Z","snapshot_observed_at":"2026-08-03T23:36:26.501126Z","submitted_at":"2026-07-01T14:12:21Z","title":"Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-02T13:38:39.744509Z"},"links":{"cited_paper":"/paper/2003.03653","citing_paper":"/paper/2607.00978"},"observation_digest":"sha256:c93bc9511521aac379cda3dd8e3353cb40888d4d924b82b5a11f957227b545b1","observation_id":"881b1bb5-fd06-45e8-ab5a-f8b95dd6149d","resolution":{"observed_at":"2026-07-02T13:46:58.814442Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03653","last_updated":"2024-11-20T20:47:25Z","snapshot_observed_at":"2026-08-11T00:10:39.821879Z","submitted_at":"2020-03-07T20:17:06Z","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03653","snapshot_observed_at":"2026-08-05T00:38:19.697910Z","title":"2020 , booktitle =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.00687","last_updated":"2026-08-01T14:24:08Z","snapshot_observed_at":"2026-08-11T21:40:40.490862Z","submitted_at":"2026-08-01T14:24:08Z","title":"Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-05T00:38:19.697910Z"},"links":{"cited_paper":"/paper/2003.03653","citing_paper":"/paper/2608.00687"},"observation_digest":"sha256:a5976d7d521921ac55504bdbe715caf5b6bc46875cbd1102d45ade2bb4ad6872","observation_id":"19640423-0ea8-4808-a958-978b3883a40d","resolution":{"observed_at":"2026-08-05T00:38:19.697910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2003.03653/citation-record","integrity":"/paper/2003.03653/integrity","json":"/paper/2003.03653/citation-record.json","paper":"/paper/2003.03653"},"outbound":[],"paper":{"arxiv_id":"2003.03653","last_updated":"2024-11-20T20:47:25Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T00:10:39.821879Z","submitted_at":"2020-03-07T20:17:06Z","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2003.03653."}