{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6QMEXZX57GCWNBRG24EFPLI4B5","short_pith_number":"pith:6QMEXZX5","schema_version":"1.0","canonical_sha256":"f4184be6fdf985668626d70857ad1c0f779796e5f7c75fc25f15208648c4dfa5","source":{"kind":"arxiv","id":"2409.18261","version":3},"attestation_state":"computed","paper":{"title":"Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dahua Lin, Mengchen Zhang, Tai Wang, Tengfei Wang, Tong Wu, Ziwei Liu","submitted_at":"2024-09-26T20:13:33Z","abstract_excerpt":"6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize across unseen instances within the same category. However, this generalization is limited by the narrow range of categories covered by existing datasets, such as NOCS, which also tend to overlook common real-world challenges like occlusion. To tackle these challenges, we introduce Omni6D, a comprehensive RGBD dataset featuring a wide range of categori"},"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":"2409.18261","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-26T20:13:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"380ed51b51222a5711f014a567ad887863891c3d1d4f2172ea003d0f3083d20e","abstract_canon_sha256":"77546866b97f296b27dbc23328237cf5162527b8f55ea2b9e50a7ca4dfd38953"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:36:29.061645Z","signature_b64":"8xmdmpjmvhc6n+xux06AvaC37oN5RchBkj055lbMakGV3kK0YKl30wwUrg/m03hQU6aXU1QvAPCKDOaYPahLDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4184be6fdf985668626d70857ad1c0f779796e5f7c75fc25f15208648c4dfa5","last_reissued_at":"2026-07-05T10:36:29.060862Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:36:29.060862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dahua Lin, Mengchen Zhang, Tai Wang, Tengfei Wang, Tong Wu, Ziwei Liu","submitted_at":"2024-09-26T20:13:33Z","abstract_excerpt":"6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize across unseen instances within the same category. However, this generalization is limited by the narrow range of categories covered by existing datasets, such as NOCS, which also tend to overlook common real-world challenges like occlusion. To tackle these challenges, we introduce Omni6D, a comprehensive RGBD dataset featuring a wide range of categori"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18261","kind":"arxiv","version":3},"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/2409.18261/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":"2409.18261","created_at":"2026-07-05T10:36:29.060965+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.18261v3","created_at":"2026-07-05T10:36:29.060965+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18261","created_at":"2026-07-05T10:36:29.060965+00:00"},{"alias_kind":"pith_short_12","alias_value":"6QMEXZX57GCW","created_at":"2026-07-05T10:36:29.060965+00:00"},{"alias_kind":"pith_short_16","alias_value":"6QMEXZX57GCWNBRG","created_at":"2026-07-05T10:36:29.060965+00:00"},{"alias_kind":"pith_short_8","alias_value":"6QMEXZX5","created_at":"2026-07-05T10:36:29.060965+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.01312","citing_title":"CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation","ref_index":54,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5","json":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5.json","graph_json":"https://pith.science/api/pith-number/6QMEXZX57GCWNBRG24EFPLI4B5/graph.json","events_json":"https://pith.science/api/pith-number/6QMEXZX57GCWNBRG24EFPLI4B5/events.json","paper":"https://pith.science/paper/6QMEXZX5"},"agent_actions":{"view_html":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5","download_json":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5.json","view_paper":"https://pith.science/paper/6QMEXZX5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.18261&json=true","fetch_graph":"https://pith.science/api/pith-number/6QMEXZX57GCWNBRG24EFPLI4B5/graph.json","fetch_events":"https://pith.science/api/pith-number/6QMEXZX57GCWNBRG24EFPLI4B5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5/action/storage_attestation","attest_author":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5/action/author_attestation","sign_citation":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5/action/citation_signature","submit_replication":"https://pith.science/pith/6QMEXZX57GCWNBRG24EFPLI4B5/action/replication_record"}},"created_at":"2026-07-05T10:36:29.060965+00:00","updated_at":"2026-07-05T10:36:29.060965+00:00"}