{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QTBCE3BBBK5SON5HJIMDLTTHEQ","short_pith_number":"pith:QTBCE3BB","schema_version":"1.0","canonical_sha256":"84c2226c210abb2737a74a1835ce67241610d355af9de2f80d2370455193a56f","source":{"kind":"arxiv","id":"2309.10649","version":2},"attestation_state":"computed","paper":{"title":"Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dai-Jie Wu, Huitong Yang, Jacky Keung, Jingyu Zhang, Xinge Zhu, Xuesong Li, Yuexin Ma","submitted_at":"2023-09-19T14:29:57Z","abstract_excerpt":"Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations. A natural option is to explore the unsupervised methodology for 3D perception tasks. However, such methods often face substantial performance-drop difficulties. Fortunately, we found that there exist amounts of image-based datasets and an alternative can be proposed, i.e., transferring the knowledge in the 2D images to 3D point clouds. Specifically, we propose a novel approach for the challenging cross-modal and cross-domain adaptation task by ful"},"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":"2309.10649","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-19T14:29:57Z","cross_cats_sorted":[],"title_canon_sha256":"0cd78cfe600a927c1eea9c3ad64f6b777af083b55e558be6debc9241f3b4cd81","abstract_canon_sha256":"e1f898b9e5df61955ff58659ced419a5f4fe23b0efc4f0a0a39524b1d8d4e86f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:05.726942Z","signature_b64":"F2cXX9WZN7xQI6LYG1N0+7qgVm6Bw2LXUyXrNhH7GuisAOBSVLQFtp7KZczCEuSagi7s47yfjQ69A/nKior+Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84c2226c210abb2737a74a1835ce67241610d355af9de2f80d2370455193a56f","last_reissued_at":"2026-07-05T07:01:05.726487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:05.726487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dai-Jie Wu, Huitong Yang, Jacky Keung, Jingyu Zhang, Xinge Zhu, Xuesong Li, Yuexin Ma","submitted_at":"2023-09-19T14:29:57Z","abstract_excerpt":"Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations. A natural option is to explore the unsupervised methodology for 3D perception tasks. However, such methods often face substantial performance-drop difficulties. Fortunately, we found that there exist amounts of image-based datasets and an alternative can be proposed, i.e., transferring the knowledge in the 2D images to 3D point clouds. Specifically, we propose a novel approach for the challenging cross-modal and cross-domain adaptation task by ful"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10649","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/2309.10649/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":"2309.10649","created_at":"2026-07-05T07:01:05.726544+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.10649v2","created_at":"2026-07-05T07:01:05.726544+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10649","created_at":"2026-07-05T07:01:05.726544+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTBCE3BBBK5S","created_at":"2026-07-05T07:01:05.726544+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTBCE3BBBK5SON5H","created_at":"2026-07-05T07:01:05.726544+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTBCE3BB","created_at":"2026-07-05T07:01:05.726544+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ","json":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ.json","graph_json":"https://pith.science/api/pith-number/QTBCE3BBBK5SON5HJIMDLTTHEQ/graph.json","events_json":"https://pith.science/api/pith-number/QTBCE3BBBK5SON5HJIMDLTTHEQ/events.json","paper":"https://pith.science/paper/QTBCE3BB"},"agent_actions":{"view_html":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ","download_json":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ.json","view_paper":"https://pith.science/paper/QTBCE3BB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.10649&json=true","fetch_graph":"https://pith.science/api/pith-number/QTBCE3BBBK5SON5HJIMDLTTHEQ/graph.json","fetch_events":"https://pith.science/api/pith-number/QTBCE3BBBK5SON5HJIMDLTTHEQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ/action/storage_attestation","attest_author":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ/action/author_attestation","sign_citation":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ/action/citation_signature","submit_replication":"https://pith.science/pith/QTBCE3BBBK5SON5HJIMDLTTHEQ/action/replication_record"}},"created_at":"2026-07-05T07:01:05.726544+00:00","updated_at":"2026-07-05T07:01:05.726544+00:00"}