{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:XOFVWWGFPYC3LO2O2P3A6JM747","short_pith_number":"pith:XOFVWWGF","schema_version":"1.0","canonical_sha256":"bb8b5b58c57e05b5bb4ed3f60f259fe7e149d3209c1061b56738f7c2c2f20638","source":{"kind":"arxiv","id":"2012.09014","version":1},"attestation_state":"computed","paper":{"title":"I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingtao Ma, Gan Sun, Jiahua Dong, Lichen Wang, Yang Cong","submitted_at":"2020-12-16T15:17:51Z","abstract_excerpt":"3D object classification has attracted appealing attentions in academic researches and industrial applications. However, most existing methods need to access the training data of past 3D object classes when facing the common real-world scenario: new classes of 3D objects arrive in a sequence. Moreover, the performance of advanced approaches degrades dramatically for past learned classes (i.e., catastrophic forgetting), due to the irregular and redundant geometric structures of 3D point cloud data. To address these challenges, we propose a new Incremental 3D Object Learning (i.e., I3DOL) model,"},"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":"2012.09014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-16T15:17:51Z","cross_cats_sorted":[],"title_canon_sha256":"741cc772ca61ddd607b7b2d06b53233761ecfd34a4cd6eb8cc3544b476c1c1a5","abstract_canon_sha256":"8554203c166dcd48880c943232d6a12ebe2efce25cd7dc1af7621210db97c57c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:00:06.015115Z","signature_b64":"CHcMnYZGSh7886oN7uGG0zX9y2iJvjxRf6xNiT0j6rVcc7yFo/C8QWnz9uDROCG62qZT/i6/RF/lrvaiBgXECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb8b5b58c57e05b5bb4ed3f60f259fe7e149d3209c1061b56738f7c2c2f20638","last_reissued_at":"2026-07-05T02:00:06.014735Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:00:06.014735Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingtao Ma, Gan Sun, Jiahua Dong, Lichen Wang, Yang Cong","submitted_at":"2020-12-16T15:17:51Z","abstract_excerpt":"3D object classification has attracted appealing attentions in academic researches and industrial applications. However, most existing methods need to access the training data of past 3D object classes when facing the common real-world scenario: new classes of 3D objects arrive in a sequence. Moreover, the performance of advanced approaches degrades dramatically for past learned classes (i.e., catastrophic forgetting), due to the irregular and redundant geometric structures of 3D point cloud data. To address these challenges, we propose a new Incremental 3D Object Learning (i.e., I3DOL) model,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.09014","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/2012.09014/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":"2012.09014","created_at":"2026-07-05T02:00:06.014790+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.09014v1","created_at":"2026-07-05T02:00:06.014790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.09014","created_at":"2026-07-05T02:00:06.014790+00:00"},{"alias_kind":"pith_short_12","alias_value":"XOFVWWGFPYC3","created_at":"2026-07-05T02:00:06.014790+00:00"},{"alias_kind":"pith_short_16","alias_value":"XOFVWWGFPYC3LO2O","created_at":"2026-07-05T02:00:06.014790+00:00"},{"alias_kind":"pith_short_8","alias_value":"XOFVWWGF","created_at":"2026-07-05T02:00:06.014790+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/XOFVWWGFPYC3LO2O2P3A6JM747","json":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747.json","graph_json":"https://pith.science/api/pith-number/XOFVWWGFPYC3LO2O2P3A6JM747/graph.json","events_json":"https://pith.science/api/pith-number/XOFVWWGFPYC3LO2O2P3A6JM747/events.json","paper":"https://pith.science/paper/XOFVWWGF"},"agent_actions":{"view_html":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747","download_json":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747.json","view_paper":"https://pith.science/paper/XOFVWWGF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.09014&json=true","fetch_graph":"https://pith.science/api/pith-number/XOFVWWGFPYC3LO2O2P3A6JM747/graph.json","fetch_events":"https://pith.science/api/pith-number/XOFVWWGFPYC3LO2O2P3A6JM747/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747/action/storage_attestation","attest_author":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747/action/author_attestation","sign_citation":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747/action/citation_signature","submit_replication":"https://pith.science/pith/XOFVWWGFPYC3LO2O2P3A6JM747/action/replication_record"}},"created_at":"2026-07-05T02:00:06.014790+00:00","updated_at":"2026-07-05T02:00:06.014790+00:00"}