{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VNSQKKPTL7I2AVKTCFKUC2FA7X","short_pith_number":"pith:VNSQKKPT","schema_version":"1.0","canonical_sha256":"ab650529f35fd1a0555311554168a0fdd013e8a25288994ec50cdc4b34ec70bf","source":{"kind":"arxiv","id":"2412.05268","version":1},"attestation_state":"computed","paper":{"title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Huazhe Xu, Junyi Zhang, Junzhe Zhu, Kaizhe Hu, Muhan Wang, Yuanchen Ju, Zhecheng Yuan","submitted_at":"2024-12-06T18:55:09Z","abstract_excerpt":"Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effective in generalizing across different object categories. To this end, we present DenseMatcher, a method capable of computing 3D correspondences between in-the-wild objects that share similar structures. DenseMatcher first computes vertex features by projecting multiview 2D features onto meshes and refining them with a 3D network, and subsequently fi"},"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":"2412.05268","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-12-06T18:55:09Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"efa277fd49c2b258c0ee1766081a2fc1dd9e813e5efa763e4814143c5bd61f3b","abstract_canon_sha256":"4b3b27bda36f06beb940eb2170277a5b13270f9d5769a5c326a78557c6c94ff6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:35.635281Z","signature_b64":"3m6YbADMemK4shRDw4QtkThryGcjlR8tGZTyRVsoGb0HoD2UV0cSm2lgfSuaTtFY9MI5/rJ9V+v5a5qQivmTCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab650529f35fd1a0555311554168a0fdd013e8a25288994ec50cdc4b34ec70bf","last_reissued_at":"2026-07-05T09:45:35.634797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:35.634797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Huazhe Xu, Junyi Zhang, Junzhe Zhu, Kaizhe Hu, Muhan Wang, Yuanchen Ju, Zhecheng Yuan","submitted_at":"2024-12-06T18:55:09Z","abstract_excerpt":"Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effective in generalizing across different object categories. To this end, we present DenseMatcher, a method capable of computing 3D correspondences between in-the-wild objects that share similar structures. DenseMatcher first computes vertex features by projecting multiview 2D features onto meshes and refining them with a 3D network, and subsequently fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05268","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/2412.05268/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":"2412.05268","created_at":"2026-07-05T09:45:35.634859+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.05268v1","created_at":"2026-07-05T09:45:35.634859+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05268","created_at":"2026-07-05T09:45:35.634859+00:00"},{"alias_kind":"pith_short_12","alias_value":"VNSQKKPTL7I2","created_at":"2026-07-05T09:45:35.634859+00:00"},{"alias_kind":"pith_short_16","alias_value":"VNSQKKPTL7I2AVKT","created_at":"2026-07-05T09:45:35.634859+00:00"},{"alias_kind":"pith_short_8","alias_value":"VNSQKKPT","created_at":"2026-07-05T09:45:35.634859+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25241","citing_title":"GRAFT: Graph-Based Affordance Transfer via Part Correspondence","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18039","citing_title":"SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals","ref_index":79,"is_internal_anchor":false},{"citing_arxiv_id":"2507.00990","citing_title":"Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations","ref_index":138,"is_internal_anchor":false},{"citing_arxiv_id":"2511.02830","citing_title":"Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2512.01773","citing_title":"IGen: Scalable Data Generation for Robot Learning from Open-World Images","ref_index":79,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10579","citing_title":"AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X","json":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X.json","graph_json":"https://pith.science/api/pith-number/VNSQKKPTL7I2AVKTCFKUC2FA7X/graph.json","events_json":"https://pith.science/api/pith-number/VNSQKKPTL7I2AVKTCFKUC2FA7X/events.json","paper":"https://pith.science/paper/VNSQKKPT"},"agent_actions":{"view_html":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X","download_json":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X.json","view_paper":"https://pith.science/paper/VNSQKKPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.05268&json=true","fetch_graph":"https://pith.science/api/pith-number/VNSQKKPTL7I2AVKTCFKUC2FA7X/graph.json","fetch_events":"https://pith.science/api/pith-number/VNSQKKPTL7I2AVKTCFKUC2FA7X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X/action/storage_attestation","attest_author":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X/action/author_attestation","sign_citation":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X/action/citation_signature","submit_replication":"https://pith.science/pith/VNSQKKPTL7I2AVKTCFKUC2FA7X/action/replication_record"}},"created_at":"2026-07-05T09:45:35.634859+00:00","updated_at":"2026-07-05T09:45:35.634859+00:00"}