{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3TSI6TPL7YNBBASWJR7MF4TVOJ","short_pith_number":"pith:3TSI6TPL","schema_version":"1.0","canonical_sha256":"dce48f4debfe1a1082564c7ec2f275724fcf12740eb0ba8933c4b7a93d3a25eb","source":{"kind":"arxiv","id":"2203.10546","version":1},"attestation_state":"computed","paper":{"title":"Towards 3D Scene Understanding by Referring Synthetic Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dawei Wang, Nenglun Chen, Ruigang Yang, Runnan Chen, Wei Li, Wenping Wang, Xinge Zhu, Yuexin Ma","submitted_at":"2022-03-20T13:06:15Z","abstract_excerpt":"Promising performance has been achieved for visual perception on the point cloud. However, the current methods typically rely on labour-extensive annotations on the scene scans. In this paper, we explore how synthetic models alleviate the real scene annotation burden, i.e., taking the labelled 3D synthetic models as reference for supervision, the neural network aims to recognize specific categories of objects on a real scene scan (without scene annotation for supervision). The problem studies how to transfer knowledge from synthetic 3D models to real 3D scenes and is named Referring Transfer L"},"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":"2203.10546","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-20T13:06:15Z","cross_cats_sorted":[],"title_canon_sha256":"8bf52b6c9ce3948cfc4338a5cd5bd9ff33f3a615d836ab39d0340aef0d441ca9","abstract_canon_sha256":"46fcea45a6f4c5ed4dde9119998d5cd4bf15066d79b79db473459c9b989a02a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:54.248980Z","signature_b64":"vbySkNUORitEZ487bwY57av5RcCs96TxpmJnl9pNOmNdxDuM1kJuLIb7rqrG88K0zJer2WnGSbWfGpPuXHulAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dce48f4debfe1a1082564c7ec2f275724fcf12740eb0ba8933c4b7a93d3a25eb","last_reissued_at":"2026-07-05T04:06:54.248613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:54.248613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards 3D Scene Understanding by Referring Synthetic Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dawei Wang, Nenglun Chen, Ruigang Yang, Runnan Chen, Wei Li, Wenping Wang, Xinge Zhu, Yuexin Ma","submitted_at":"2022-03-20T13:06:15Z","abstract_excerpt":"Promising performance has been achieved for visual perception on the point cloud. However, the current methods typically rely on labour-extensive annotations on the scene scans. In this paper, we explore how synthetic models alleviate the real scene annotation burden, i.e., taking the labelled 3D synthetic models as reference for supervision, the neural network aims to recognize specific categories of objects on a real scene scan (without scene annotation for supervision). The problem studies how to transfer knowledge from synthetic 3D models to real 3D scenes and is named Referring Transfer L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.10546","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/2203.10546/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":"2203.10546","created_at":"2026-07-05T04:06:54.248670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.10546v1","created_at":"2026-07-05T04:06:54.248670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10546","created_at":"2026-07-05T04:06:54.248670+00:00"},{"alias_kind":"pith_short_12","alias_value":"3TSI6TPL7YNB","created_at":"2026-07-05T04:06:54.248670+00:00"},{"alias_kind":"pith_short_16","alias_value":"3TSI6TPL7YNBBASW","created_at":"2026-07-05T04:06:54.248670+00:00"},{"alias_kind":"pith_short_8","alias_value":"3TSI6TPL","created_at":"2026-07-05T04:06:54.248670+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/3TSI6TPL7YNBBASWJR7MF4TVOJ","json":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ.json","graph_json":"https://pith.science/api/pith-number/3TSI6TPL7YNBBASWJR7MF4TVOJ/graph.json","events_json":"https://pith.science/api/pith-number/3TSI6TPL7YNBBASWJR7MF4TVOJ/events.json","paper":"https://pith.science/paper/3TSI6TPL"},"agent_actions":{"view_html":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ","download_json":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ.json","view_paper":"https://pith.science/paper/3TSI6TPL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.10546&json=true","fetch_graph":"https://pith.science/api/pith-number/3TSI6TPL7YNBBASWJR7MF4TVOJ/graph.json","fetch_events":"https://pith.science/api/pith-number/3TSI6TPL7YNBBASWJR7MF4TVOJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ/action/storage_attestation","attest_author":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ/action/author_attestation","sign_citation":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ/action/citation_signature","submit_replication":"https://pith.science/pith/3TSI6TPL7YNBBASWJR7MF4TVOJ/action/replication_record"}},"created_at":"2026-07-05T04:06:54.248670+00:00","updated_at":"2026-07-05T04:06:54.248670+00:00"}