{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PZTK4IBGPWWG7AUO4WPFILZITT","short_pith_number":"pith:PZTK4IBG","schema_version":"1.0","canonical_sha256":"7e66ae20267dac6f828ee59e542f289ce35c0bebb7e0eac3c4467300f7d8e474","source":{"kind":"arxiv","id":"1902.10272","version":1},"attestation_state":"computed","paper":{"title":"Zero-shot Learning of 3D Point Cloud Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Cheraghian, Lars Petersson, Shafin Rahman","submitted_at":"2019-02-27T00:15:31Z","abstract_excerpt":"Recent deep learning architectures can recognize instances of 3D point cloud objects of previously seen classes quite well. At the same time, current 3D depth camera technology allows generating/segmenting a large amount of 3D point cloud objects from an arbitrary scene, for which there is no previously seen training data. A challenge for a 3D point cloud recognition system is, then, to classify objects from new, unseen, classes. This issue can be resolved by adopting a zero-shot learning (ZSL) approach for 3D data, similar to the 2D image version of the same problem. ZSL attempts to classify "},"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":"1902.10272","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-27T00:15:31Z","cross_cats_sorted":[],"title_canon_sha256":"2a67fdbfba8123bdee3e1a667b5b56559269086393b0b46c34e6a600d6e5e26c","abstract_canon_sha256":"c9c47404a3bd785ebe0f4b76221324a65ce808b2d39c53f87d1097e27a1a11da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:31.323660Z","signature_b64":"XGAKGByINJLJCofI/miuJI3sqCvYxycI8GqYErz31mmWAvpuX+UiiEFtwGzNts+M3ZdERiQQhV2UVxAVZi1mBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e66ae20267dac6f828ee59e542f289ce35c0bebb7e0eac3c4467300f7d8e474","last_reissued_at":"2026-05-17T23:52:31.323204Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:31.323204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Zero-shot Learning of 3D Point Cloud Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Cheraghian, Lars Petersson, Shafin Rahman","submitted_at":"2019-02-27T00:15:31Z","abstract_excerpt":"Recent deep learning architectures can recognize instances of 3D point cloud objects of previously seen classes quite well. At the same time, current 3D depth camera technology allows generating/segmenting a large amount of 3D point cloud objects from an arbitrary scene, for which there is no previously seen training data. A challenge for a 3D point cloud recognition system is, then, to classify objects from new, unseen, classes. This issue can be resolved by adopting a zero-shot learning (ZSL) approach for 3D data, similar to the 2D image version of the same problem. ZSL attempts to classify "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10272","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":""},"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":"1902.10272","created_at":"2026-05-17T23:52:31.323272+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.10272v1","created_at":"2026-05-17T23:52:31.323272+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10272","created_at":"2026-05-17T23:52:31.323272+00:00"},{"alias_kind":"pith_short_12","alias_value":"PZTK4IBGPWWG","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PZTK4IBGPWWG7AUO","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PZTK4IBG","created_at":"2026-05-18T12:33:24.271573+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/PZTK4IBGPWWG7AUO4WPFILZITT","json":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT.json","graph_json":"https://pith.science/api/pith-number/PZTK4IBGPWWG7AUO4WPFILZITT/graph.json","events_json":"https://pith.science/api/pith-number/PZTK4IBGPWWG7AUO4WPFILZITT/events.json","paper":"https://pith.science/paper/PZTK4IBG"},"agent_actions":{"view_html":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT","download_json":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT.json","view_paper":"https://pith.science/paper/PZTK4IBG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.10272&json=true","fetch_graph":"https://pith.science/api/pith-number/PZTK4IBGPWWG7AUO4WPFILZITT/graph.json","fetch_events":"https://pith.science/api/pith-number/PZTK4IBGPWWG7AUO4WPFILZITT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT/action/storage_attestation","attest_author":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT/action/author_attestation","sign_citation":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT/action/citation_signature","submit_replication":"https://pith.science/pith/PZTK4IBGPWWG7AUO4WPFILZITT/action/replication_record"}},"created_at":"2026-05-17T23:52:31.323272+00:00","updated_at":"2026-05-17T23:52:31.323272+00:00"}