{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HLTA2M6EVQGE5N6SB3VHM2N75L","short_pith_number":"pith:HLTA2M6E","schema_version":"1.0","canonical_sha256":"3ae60d33c4ac0c4eb7d20eea7669bfeaf7f22463cd2b5d30e19b529eddc5e595","source":{"kind":"arxiv","id":"2302.01838","version":2},"attestation_state":"computed","paper":{"title":"vMAP: Vectorised Object Mapping for Neural Field SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew J. Davison, Marwan Taher, Shikun Liu, Xin Kong","submitted_at":"2023-02-03T16:27:34Z","abstract_excerpt":"We present vMAP, an object-level dense SLAM system using neural field representations. Each object is represented by a small MLP, enabling efficient, watertight object modelling without the need for 3D priors. As an RGB-D camera browses a scene with no prior information, vMAP detects object instances on-the-fly, and dynamically adds them to its map. Specifically, thanks to the power of vectorised training, vMAP can optimise as many as 50 individual objects in a single scene, with an extremely efficient training speed of 5Hz map update. We experimentally demonstrate significantly improved scene"},"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":"2302.01838","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-03T16:27:34Z","cross_cats_sorted":[],"title_canon_sha256":"c2b4e28595a9689dca88a27071930209f1d06a4fbec0d68a891f520541ebb724","abstract_canon_sha256":"64a2c53d3071a4f714275120a61551c745a2a3cf5ae5e2c192a21315219f137c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:51:00.912898Z","signature_b64":"0PGRhtsd73HbbVfFbd/3cVohBDRaeTlXDrNEkoOVXDLG4jvCRv1y9IzJMtYNDKn2mPK8DiLHvBwRNtZmCgCqAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ae60d33c4ac0c4eb7d20eea7669bfeaf7f22463cd2b5d30e19b529eddc5e595","last_reissued_at":"2026-07-05T05:51:00.912546Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:51:00.912546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"vMAP: Vectorised Object Mapping for Neural Field SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew J. Davison, Marwan Taher, Shikun Liu, Xin Kong","submitted_at":"2023-02-03T16:27:34Z","abstract_excerpt":"We present vMAP, an object-level dense SLAM system using neural field representations. Each object is represented by a small MLP, enabling efficient, watertight object modelling without the need for 3D priors. As an RGB-D camera browses a scene with no prior information, vMAP detects object instances on-the-fly, and dynamically adds them to its map. Specifically, thanks to the power of vectorised training, vMAP can optimise as many as 50 individual objects in a single scene, with an extremely efficient training speed of 5Hz map update. We experimentally demonstrate significantly improved scene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.01838","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/2302.01838/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":"2302.01838","created_at":"2026-07-05T05:51:00.912601+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.01838v2","created_at":"2026-07-05T05:51:00.912601+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.01838","created_at":"2026-07-05T05:51:00.912601+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLTA2M6EVQGE","created_at":"2026-07-05T05:51:00.912601+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLTA2M6EVQGE5N6S","created_at":"2026-07-05T05:51:00.912601+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLTA2M6E","created_at":"2026-07-05T05:51:00.912601+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04120","citing_title":"Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L","json":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L.json","graph_json":"https://pith.science/api/pith-number/HLTA2M6EVQGE5N6SB3VHM2N75L/graph.json","events_json":"https://pith.science/api/pith-number/HLTA2M6EVQGE5N6SB3VHM2N75L/events.json","paper":"https://pith.science/paper/HLTA2M6E"},"agent_actions":{"view_html":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L","download_json":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L.json","view_paper":"https://pith.science/paper/HLTA2M6E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.01838&json=true","fetch_graph":"https://pith.science/api/pith-number/HLTA2M6EVQGE5N6SB3VHM2N75L/graph.json","fetch_events":"https://pith.science/api/pith-number/HLTA2M6EVQGE5N6SB3VHM2N75L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L/action/storage_attestation","attest_author":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L/action/author_attestation","sign_citation":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L/action/citation_signature","submit_replication":"https://pith.science/pith/HLTA2M6EVQGE5N6SB3VHM2N75L/action/replication_record"}},"created_at":"2026-07-05T05:51:00.912601+00:00","updated_at":"2026-07-05T05:51:00.912601+00:00"}