{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EXKGRUGQF4KU5NVZ54PQRNUUP6","short_pith_number":"pith:EXKGRUGQ","schema_version":"1.0","canonical_sha256":"25d468d0d02f154eb6b9ef1f08b6947fa9bbbcda3bacd7655f0e70701a22f21e","source":{"kind":"arxiv","id":"2302.05094","version":1},"attestation_state":"computed","paper":{"title":"General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Atsuhiko Banno, Kenji Koide, Masashi Yokozuka, Shuji Oishi","submitted_at":"2023-02-10T07:24:28Z","abstract_excerpt":"This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and camera projection models, requires only one pairing of LiDAR and camera data without a calibration target, and is fully automatic. For automatic initial guess estimation, we employ the SuperGlue image matching pipeline to find 2D-3D correspondences between LiDAR and camera data and estimate the LiDAR-camera transformation via RANSAC. Given the initial guess, we refine the transformation estimate with direct LiDAR-camera registration based on the normalized information distance, a mutual information"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2302.05094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-02-10T07:24:28Z","cross_cats_sorted":[],"title_canon_sha256":"be2f51dc99d8c41fead54cd1d6748d079d872b50b021c1cbe4848d8f5d1078cb","abstract_canon_sha256":"603f96f2b01ed66379c474a791f0055d4bfbc54b174659783885e27eafc4effd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:35.380928Z","signature_b64":"eIQ7bnm6E1lGWXQgKqXSI21ni6CFwTglVqKXZXkvJV6H3ZAVz279MC5tm70TnpSndQ5K4+i9fncRqeyOsfIVDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25d468d0d02f154eb6b9ef1f08b6947fa9bbbcda3bacd7655f0e70701a22f21e","last_reissued_at":"2026-07-05T05:40:35.380490Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:35.380490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Atsuhiko Banno, Kenji Koide, Masashi Yokozuka, Shuji Oishi","submitted_at":"2023-02-10T07:24:28Z","abstract_excerpt":"This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and camera projection models, requires only one pairing of LiDAR and camera data without a calibration target, and is fully automatic. For automatic initial guess estimation, we employ the SuperGlue image matching pipeline to find 2D-3D correspondences between LiDAR and camera data and estimate the LiDAR-camera transformation via RANSAC. Given the initial guess, we refine the transformation estimate with direct LiDAR-camera registration based on the normalized information distance, a mutual information"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05094","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.05094/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2302.05094","created_at":"2026-07-05T05:40:35.380544+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.05094v1","created_at":"2026-07-05T05:40:35.380544+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05094","created_at":"2026-07-05T05:40:35.380544+00:00"},{"alias_kind":"pith_short_12","alias_value":"EXKGRUGQF4KU","created_at":"2026-07-05T05:40:35.380544+00:00"},{"alias_kind":"pith_short_16","alias_value":"EXKGRUGQF4KU5NVZ","created_at":"2026-07-05T05:40:35.380544+00:00"},{"alias_kind":"pith_short_8","alias_value":"EXKGRUGQ","created_at":"2026-07-05T05:40:35.380544+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20103","citing_title":"Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11739","citing_title":"Multi-View In-Cabin Monitoring System for Public Transport Vehicles","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6","json":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6.json","graph_json":"https://pith.science/api/pith-number/EXKGRUGQF4KU5NVZ54PQRNUUP6/graph.json","events_json":"https://pith.science/api/pith-number/EXKGRUGQF4KU5NVZ54PQRNUUP6/events.json","paper":"https://pith.science/paper/EXKGRUGQ"},"agent_actions":{"view_html":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6","download_json":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6.json","view_paper":"https://pith.science/paper/EXKGRUGQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.05094&json=true","fetch_graph":"https://pith.science/api/pith-number/EXKGRUGQF4KU5NVZ54PQRNUUP6/graph.json","fetch_events":"https://pith.science/api/pith-number/EXKGRUGQF4KU5NVZ54PQRNUUP6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6/action/storage_attestation","attest_author":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6/action/author_attestation","sign_citation":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6/action/citation_signature","submit_replication":"https://pith.science/pith/EXKGRUGQF4KU5NVZ54PQRNUUP6/action/replication_record"}},"created_at":"2026-07-05T05:40:35.380544+00:00","updated_at":"2026-07-05T05:40:35.380544+00:00"}