{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QYQDTLHBQTUBY6LJFA3R6YYW3B","short_pith_number":"pith:QYQDTLHB","schema_version":"1.0","canonical_sha256":"862039ace184e81c796928371f6316d85ee849b34fcf68cd8fe66267762b0a81","source":{"kind":"arxiv","id":"2403.01254","version":2},"attestation_state":"computed","paper":{"title":"RKHS-BA: A Robust Correspondence-Free Multi-View Registration Framework with Semantic Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jingwei Song, Jinyuan Zhang, Junzhe Wu, Maani Ghaffari, Ray Zhang, Ryan Eustice, Tianyi Liu, Xiang Gao","submitted_at":"2024-03-02T16:25:09Z","abstract_excerpt":"This work reports a novel multi-frame Bundle Adjustment (BA) framework called RKHS-BA. It uses continuous landmark representations that encode RGB-D/LiDAR and semantic observations in a Reproducing Kernel Hilbert Space (RKHS). With a correspondence-free pose graph formulation, the proposed system constructs a loss function that achieves more generalized convergence than classical point-wise convergence. We demonstrate its applications in multi-view point cloud registration, sliding-window odometry, and global LiDAR mapping on simulated and real data. It shows highly robust pose estimations in "},"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":"2403.01254","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-03-02T16:25:09Z","cross_cats_sorted":[],"title_canon_sha256":"58d2525b801ca44eac08b59f2043dc0d98db8b7c828310b2bcfb494b347b806a","abstract_canon_sha256":"9fc2cd5f96d1d55fb5efab2143ffc064bf0e56e30bb7948adbad1438d2189fec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:23.291467Z","signature_b64":"XZRh+Icg4H0UpkYROfhIFU36VQaJ5sQDPFqVe1FseVmXQJuT2Jo4vVLNjXqLZYUoxkniSp5piCR2oIMZNry2Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"862039ace184e81c796928371f6316d85ee849b34fcf68cd8fe66267762b0a81","last_reissued_at":"2026-07-05T09:42:23.291019Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:23.291019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RKHS-BA: A Robust Correspondence-Free Multi-View Registration Framework with Semantic Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jingwei Song, Jinyuan Zhang, Junzhe Wu, Maani Ghaffari, Ray Zhang, Ryan Eustice, Tianyi Liu, Xiang Gao","submitted_at":"2024-03-02T16:25:09Z","abstract_excerpt":"This work reports a novel multi-frame Bundle Adjustment (BA) framework called RKHS-BA. It uses continuous landmark representations that encode RGB-D/LiDAR and semantic observations in a Reproducing Kernel Hilbert Space (RKHS). With a correspondence-free pose graph formulation, the proposed system constructs a loss function that achieves more generalized convergence than classical point-wise convergence. We demonstrate its applications in multi-view point cloud registration, sliding-window odometry, and global LiDAR mapping on simulated and real data. It shows highly robust pose estimations in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.01254","kind":"arxiv","version":2},"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/2403.01254/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":"2403.01254","created_at":"2026-07-05T09:42:23.291077+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.01254v2","created_at":"2026-07-05T09:42:23.291077+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.01254","created_at":"2026-07-05T09:42:23.291077+00:00"},{"alias_kind":"pith_short_12","alias_value":"QYQDTLHBQTUB","created_at":"2026-07-05T09:42:23.291077+00:00"},{"alias_kind":"pith_short_16","alias_value":"QYQDTLHBQTUBY6LJ","created_at":"2026-07-05T09:42:23.291077+00:00"},{"alias_kind":"pith_short_8","alias_value":"QYQDTLHB","created_at":"2026-07-05T09:42:23.291077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.14095","citing_title":"C-DOG: Multi-View Multi-instance Feature Association Using Connected {\\delta}-Overlap Graphs","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B","json":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B.json","graph_json":"https://pith.science/api/pith-number/QYQDTLHBQTUBY6LJFA3R6YYW3B/graph.json","events_json":"https://pith.science/api/pith-number/QYQDTLHBQTUBY6LJFA3R6YYW3B/events.json","paper":"https://pith.science/paper/QYQDTLHB"},"agent_actions":{"view_html":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B","download_json":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B.json","view_paper":"https://pith.science/paper/QYQDTLHB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.01254&json=true","fetch_graph":"https://pith.science/api/pith-number/QYQDTLHBQTUBY6LJFA3R6YYW3B/graph.json","fetch_events":"https://pith.science/api/pith-number/QYQDTLHBQTUBY6LJFA3R6YYW3B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B/action/storage_attestation","attest_author":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B/action/author_attestation","sign_citation":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B/action/citation_signature","submit_replication":"https://pith.science/pith/QYQDTLHBQTUBY6LJFA3R6YYW3B/action/replication_record"}},"created_at":"2026-07-05T09:42:23.291077+00:00","updated_at":"2026-07-05T09:42:23.291077+00:00"}