{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SIPDGAOR5L3RJ5B5JN5EDFE5HA","short_pith_number":"pith:SIPDGAOR","schema_version":"1.0","canonical_sha256":"921e3301d1eaf714f43d4b7a41949d383f9a949d8dc1afce28890400996e86e6","source":{"kind":"arxiv","id":"2503.15300","version":2},"attestation_state":"computed","paper":{"title":"SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hugo Ledoux, Liangliang Nan, Weixiao Gao","submitted_at":"2025-03-19T15:22:23Z","abstract_excerpt":"Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. This paper introduces SUM Parts, the first large-scale dataset for urban textured meshes with part-level semantic labels, covering about 2.5 km2 with 21 classes. The dataset was created using our own annotation tool, which supports both face- and texture-based annotations with efficient interactive selection. We also provide a comprehensive evaluation of 3D semantic segmentation and interactive annotation methods on t"},"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":"2503.15300","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-19T15:22:23Z","cross_cats_sorted":[],"title_canon_sha256":"0351bcf299dfeccd59004eab303e4d7ebf7e57cc42dae11dbce355f877a3eeaa","abstract_canon_sha256":"8607be1ea8a918582f9ef1a29b307872c84a18db57e30da9e220e113871ca7b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:36:39.591948Z","signature_b64":"rK70OGfRZx1IE5wGVbBqGkgwCxqHMj0TRIDJdYfdmQhPcmHIoL2tEAJCEIWmrAHc3kxyD5Jzev4jXj4Q9MZSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"921e3301d1eaf714f43d4b7a41949d383f9a949d8dc1afce28890400996e86e6","last_reissued_at":"2026-07-05T10:36:39.591264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:36:39.591264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hugo Ledoux, Liangliang Nan, Weixiao Gao","submitted_at":"2025-03-19T15:22:23Z","abstract_excerpt":"Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. This paper introduces SUM Parts, the first large-scale dataset for urban textured meshes with part-level semantic labels, covering about 2.5 km2 with 21 classes. The dataset was created using our own annotation tool, which supports both face- and texture-based annotations with efficient interactive selection. We also provide a comprehensive evaluation of 3D semantic segmentation and interactive annotation methods on t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.15300","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/2503.15300/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":"2503.15300","created_at":"2026-07-05T10:36:39.591360+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.15300v2","created_at":"2026-07-05T10:36:39.591360+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.15300","created_at":"2026-07-05T10:36:39.591360+00:00"},{"alias_kind":"pith_short_12","alias_value":"SIPDGAOR5L3R","created_at":"2026-07-05T10:36:39.591360+00:00"},{"alias_kind":"pith_short_16","alias_value":"SIPDGAOR5L3RJ5B5","created_at":"2026-07-05T10:36:39.591360+00:00"},{"alias_kind":"pith_short_8","alias_value":"SIPDGAOR","created_at":"2026-07-05T10:36:39.591360+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/SIPDGAOR5L3RJ5B5JN5EDFE5HA","json":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA.json","graph_json":"https://pith.science/api/pith-number/SIPDGAOR5L3RJ5B5JN5EDFE5HA/graph.json","events_json":"https://pith.science/api/pith-number/SIPDGAOR5L3RJ5B5JN5EDFE5HA/events.json","paper":"https://pith.science/paper/SIPDGAOR"},"agent_actions":{"view_html":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA","download_json":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA.json","view_paper":"https://pith.science/paper/SIPDGAOR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.15300&json=true","fetch_graph":"https://pith.science/api/pith-number/SIPDGAOR5L3RJ5B5JN5EDFE5HA/graph.json","fetch_events":"https://pith.science/api/pith-number/SIPDGAOR5L3RJ5B5JN5EDFE5HA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA/action/storage_attestation","attest_author":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA/action/author_attestation","sign_citation":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA/action/citation_signature","submit_replication":"https://pith.science/pith/SIPDGAOR5L3RJ5B5JN5EDFE5HA/action/replication_record"}},"created_at":"2026-07-05T10:36:39.591360+00:00","updated_at":"2026-07-05T10:36:39.591360+00:00"}