{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SGCMGE723UNLGDGBRMMG544577","short_pith_number":"pith:SGCMGE72","schema_version":"1.0","canonical_sha256":"9184c313fadd1ab30cc18b186ef39dffe61570aaa6d87bf4c4bd87e17b6b7454","source":{"kind":"arxiv","id":"2303.05164","version":2},"attestation_state":"computed","paper":{"title":"Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guosheng Lin, Jianfei Cai, Yicheng Wu, Zhonghua Wu","submitted_at":"2023-03-09T10:41:57Z","abstract_excerpt":"Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores applying the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with multiple data-specific augmentations, which has not been well studied. We observe that the straightforward way of applying consistency constraints to weakly-supervised point cloud segmentation has two major limitations: noisy pseudo labels due to the conventional confidence-based "},"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":"2303.05164","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-09T10:41:57Z","cross_cats_sorted":[],"title_canon_sha256":"3c1f3d5a9fa912c8ab0f695b91a0801c0624da7f99a3065086efcc8b79e1c1bc","abstract_canon_sha256":"7d5639c7ccb179a36e5997c1ad0b79ee2c8ee643be4b221da48ce489fe21b79d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:56.352543Z","signature_b64":"bhH2XdrnA9dogqVq4LMIah4Mbk9rH85kjL9rPMOEtwMzE1ZKlqcXJKqCsKWioH2eFzgxBhnG80Yx5wXuYNbnDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9184c313fadd1ab30cc18b186ef39dffe61570aaa6d87bf4c4bd87e17b6b7454","last_reissued_at":"2026-07-05T07:23:56.351961Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:56.351961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guosheng Lin, Jianfei Cai, Yicheng Wu, Zhonghua Wu","submitted_at":"2023-03-09T10:41:57Z","abstract_excerpt":"Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores applying the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with multiple data-specific augmentations, which has not been well studied. We observe that the straightforward way of applying consistency constraints to weakly-supervised point cloud segmentation has two major limitations: noisy pseudo labels due to the conventional confidence-based "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05164","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/2303.05164/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":"2303.05164","created_at":"2026-07-05T07:23:56.352030+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.05164v2","created_at":"2026-07-05T07:23:56.352030+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05164","created_at":"2026-07-05T07:23:56.352030+00:00"},{"alias_kind":"pith_short_12","alias_value":"SGCMGE723UNL","created_at":"2026-07-05T07:23:56.352030+00:00"},{"alias_kind":"pith_short_16","alias_value":"SGCMGE723UNLGDGB","created_at":"2026-07-05T07:23:56.352030+00:00"},{"alias_kind":"pith_short_8","alias_value":"SGCMGE72","created_at":"2026-07-05T07:23:56.352030+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/SGCMGE723UNLGDGBRMMG544577","json":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577.json","graph_json":"https://pith.science/api/pith-number/SGCMGE723UNLGDGBRMMG544577/graph.json","events_json":"https://pith.science/api/pith-number/SGCMGE723UNLGDGBRMMG544577/events.json","paper":"https://pith.science/paper/SGCMGE72"},"agent_actions":{"view_html":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577","download_json":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577.json","view_paper":"https://pith.science/paper/SGCMGE72","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.05164&json=true","fetch_graph":"https://pith.science/api/pith-number/SGCMGE723UNLGDGBRMMG544577/graph.json","fetch_events":"https://pith.science/api/pith-number/SGCMGE723UNLGDGBRMMG544577/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577/action/storage_attestation","attest_author":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577/action/author_attestation","sign_citation":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577/action/citation_signature","submit_replication":"https://pith.science/pith/SGCMGE723UNLGDGBRMMG544577/action/replication_record"}},"created_at":"2026-07-05T07:23:56.352030+00:00","updated_at":"2026-07-05T07:23:56.352030+00:00"}