{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BNTIW3LHL2U2JNLRUXZFEEO6GX","short_pith_number":"pith:BNTIW3LH","schema_version":"1.0","canonical_sha256":"0b668b6d675ea9a4b571a5f25211de35dcab4acbb0b872b53da1813c9550759a","source":{"kind":"arxiv","id":"2411.07184","version":2},"attestation_state":"computed","paper":{"title":"SAMPart3D: Segment Any Part in 3D Objects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Edmund Y. Lam, Liangjun Lu, Xiaoyang Wu, Xihui Liu, Yan-Pei Cao, Yuan-Chen Guo, Yukun Huang, Yunhan Yang","submitted_at":"2024-11-11T17:59:10Z","abstract_excerpt":"3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harness the powerful Vision Language Models (VLMs) for 2D-to-3D knowledge distillation, achieving zero-shot 3D part segmentation. However, these methods are limited by their reliance on text prompts, which restricts the scalability to large-scale unlabeled datasets and the flexibility in handling part ambiguities. In this work, we introduce SAMPart3D, a scalable zero-shot 3D part segmentation framework that segments any 3D"},"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":"2411.07184","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-11T17:59:10Z","cross_cats_sorted":[],"title_canon_sha256":"9c688a4dc865df4bdbb36f273e1419a16b91ef5986848153256696a98b1c0708","abstract_canon_sha256":"057784578af098fae9f432212cb147a20cc22c9e66237c732e686756858fdfe0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:16.326923Z","signature_b64":"NKuHtphWpNetKQSSDS9PsSHdxwA3jLK/bDuI9DViuN9rLULNAg4a+jX4KX7cLf3ayf8G2RTXIsiCc6TpJV/DCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b668b6d675ea9a4b571a5f25211de35dcab4acbb0b872b53da1813c9550759a","last_reissued_at":"2026-07-05T09:36:16.326422Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:16.326422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SAMPart3D: Segment Any Part in 3D Objects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Edmund Y. Lam, Liangjun Lu, Xiaoyang Wu, Xihui Liu, Yan-Pei Cao, Yuan-Chen Guo, Yukun Huang, Yunhan Yang","submitted_at":"2024-11-11T17:59:10Z","abstract_excerpt":"3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harness the powerful Vision Language Models (VLMs) for 2D-to-3D knowledge distillation, achieving zero-shot 3D part segmentation. However, these methods are limited by their reliance on text prompts, which restricts the scalability to large-scale unlabeled datasets and the flexibility in handling part ambiguities. In this work, we introduce SAMPart3D, a scalable zero-shot 3D part segmentation framework that segments any 3D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.07184","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/2411.07184/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":"2411.07184","created_at":"2026-07-05T09:36:16.326486+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.07184v2","created_at":"2026-07-05T09:36:16.326486+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07184","created_at":"2026-07-05T09:36:16.326486+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNTIW3LHL2U2","created_at":"2026-07-05T09:36:16.326486+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNTIW3LHL2U2JNLR","created_at":"2026-07-05T09:36:16.326486+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNTIW3LH","created_at":"2026-07-05T09:36:16.326486+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":16,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07187","citing_title":"EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning","ref_index":93,"is_internal_anchor":true},{"citing_arxiv_id":"2606.17824","citing_title":"Human-in-the-Loop Atlas-Based 3D Asset Segmentation for Interactive Content Workflows","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12069","citing_title":"Tac-DINO: Learning Vision-Tactile Features with Patch Alignment","ref_index":89,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06485","citing_title":"PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05975","citing_title":"T-FunS3D: Task-Driven Hierarchical Open-Vocabulary 3D Functionality Segmentation","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30608","citing_title":"UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30608","citing_title":"UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29786","citing_title":"OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2603.27309","citing_title":"MeshTailor: Cutting Seams via Generative Mesh Traversal","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16065","citing_title":"Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2512.00995","citing_title":"S2AM3D: Scale-controllable Part Segmentation of 3D Point Clouds","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23629","citing_title":"From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation","ref_index":156,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23629","citing_title":"From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation","ref_index":156,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05411","citing_title":"Creative Robot Tool Use by Counterfactual Reasoning","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05070","citing_title":"Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14927","citing_title":"STEP-Parts: Geometric Partitioning of Boundary Representations for Large-Scale CAD Processing","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX","json":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX.json","graph_json":"https://pith.science/api/pith-number/BNTIW3LHL2U2JNLRUXZFEEO6GX/graph.json","events_json":"https://pith.science/api/pith-number/BNTIW3LHL2U2JNLRUXZFEEO6GX/events.json","paper":"https://pith.science/paper/BNTIW3LH"},"agent_actions":{"view_html":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX","download_json":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX.json","view_paper":"https://pith.science/paper/BNTIW3LH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.07184&json=true","fetch_graph":"https://pith.science/api/pith-number/BNTIW3LHL2U2JNLRUXZFEEO6GX/graph.json","fetch_events":"https://pith.science/api/pith-number/BNTIW3LHL2U2JNLRUXZFEEO6GX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX/action/storage_attestation","attest_author":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX/action/author_attestation","sign_citation":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX/action/citation_signature","submit_replication":"https://pith.science/pith/BNTIW3LHL2U2JNLRUXZFEEO6GX/action/replication_record"}},"created_at":"2026-07-05T09:36:16.326486+00:00","updated_at":"2026-07-05T09:36:16.326486+00:00"}