{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:572BVSZNPRH57Z6C5OI6GEQQAD","short_pith_number":"pith:572BVSZN","schema_version":"1.0","canonical_sha256":"eff41acb2d7c4fdfe7c2eb91e3121000da819e629490f029c14e43e2db692fb7","source":{"kind":"arxiv","id":"2410.21739","version":2},"attestation_state":"computed","paper":{"title":"SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Heng Zhou, Kairui Wen, Xiaoyang Guo, Yong-jin Liu, Yubin Hu","submitted_at":"2024-10-29T04:54:45Z","abstract_excerpt":"Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-truths often lack the necessary precision to evaluate surface positions and do not provide data for assessing surface normals. To overcome these challenges, we introduce the SS3DM dataset, comprising precise \\textbf{S}ynthetic \\textbf{S}tree"},"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":"2410.21739","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-29T04:54:45Z","cross_cats_sorted":[],"title_canon_sha256":"3c65c361afd82fb513a4749125288a7d303eab724e689749d66d14f7d94d6a84","abstract_canon_sha256":"4970abbf0496a09ef08a26a590acb53fbf46a6d1060c7fd495ccb204fe6daea9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:12.912398Z","signature_b64":"Sg4euLz3jbBwMcTb1M8u9KxyX59d0VZAlh3c36i9Gsg3rlekx6OGf2Ce6KbWzz6gyt7gjT8F4lC5NPJqDDrLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eff41acb2d7c4fdfe7c2eb91e3121000da819e629490f029c14e43e2db692fb7","last_reissued_at":"2026-07-05T09:32:12.911965Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:12.911965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Heng Zhou, Kairui Wen, Xiaoyang Guo, Yong-jin Liu, Yubin Hu","submitted_at":"2024-10-29T04:54:45Z","abstract_excerpt":"Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-truths often lack the necessary precision to evaluate surface positions and do not provide data for assessing surface normals. To overcome these challenges, we introduce the SS3DM dataset, comprising precise \\textbf{S}ynthetic \\textbf{S}tree"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.21739","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/2410.21739/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":"2410.21739","created_at":"2026-07-05T09:32:12.912024+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.21739v2","created_at":"2026-07-05T09:32:12.912024+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.21739","created_at":"2026-07-05T09:32:12.912024+00:00"},{"alias_kind":"pith_short_12","alias_value":"572BVSZNPRH5","created_at":"2026-07-05T09:32:12.912024+00:00"},{"alias_kind":"pith_short_16","alias_value":"572BVSZNPRH57Z6C","created_at":"2026-07-05T09:32:12.912024+00:00"},{"alias_kind":"pith_short_8","alias_value":"572BVSZN","created_at":"2026-07-05T09:32:12.912024+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/572BVSZNPRH57Z6C5OI6GEQQAD","json":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD.json","graph_json":"https://pith.science/api/pith-number/572BVSZNPRH57Z6C5OI6GEQQAD/graph.json","events_json":"https://pith.science/api/pith-number/572BVSZNPRH57Z6C5OI6GEQQAD/events.json","paper":"https://pith.science/paper/572BVSZN"},"agent_actions":{"view_html":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD","download_json":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD.json","view_paper":"https://pith.science/paper/572BVSZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.21739&json=true","fetch_graph":"https://pith.science/api/pith-number/572BVSZNPRH57Z6C5OI6GEQQAD/graph.json","fetch_events":"https://pith.science/api/pith-number/572BVSZNPRH57Z6C5OI6GEQQAD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD/action/storage_attestation","attest_author":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD/action/author_attestation","sign_citation":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD/action/citation_signature","submit_replication":"https://pith.science/pith/572BVSZNPRH57Z6C5OI6GEQQAD/action/replication_record"}},"created_at":"2026-07-05T09:32:12.912024+00:00","updated_at":"2026-07-05T09:32:12.912024+00:00"}