{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DIAFMEMN56KJDHZWX5U6GYNVPK","short_pith_number":"pith:DIAFMEMN","schema_version":"1.0","canonical_sha256":"1a0056118def94919f36bf69e361b57a883a5239c3eaeccf60db8ef02114d88e","source":{"kind":"arxiv","id":"1910.11296","version":1},"attestation_state":"computed","paper":{"title":"Identifying Unknown Instances for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Kelvin Wong, Mengye Ren, Ming Liang, Raquel Urtasun, Shenlong Wang","submitted_at":"2019-10-24T17:24:43Z","abstract_excerpt":"In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categories of interest, which represent only a small fraction of the potential categories that robots need to handle in the real-world. Thus, identifying objects from unknown classes remains a challenging yet crucial task. In this paper, we develop a novel open-set instance segmentation algorithm for point clouds which can segment objects from both known and unknown classes in a holistic way. Our method uses a deep convolutio"},"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":"1910.11296","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-10-24T17:24:43Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"0b5f1c699d64fc292f8f3e628c8e1cd6ae7cac61b1938ac285ecc43f54615991","abstract_canon_sha256":"4ddf21167d215528b028b1f54fabeb596915b719fb904173c28726075ef5da24"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:14:42.084382Z","signature_b64":"7tON59RgqwLMB+QPtOeNJ4l9V4KPlNIwICwYtbC/RaBvjwPNR1FzbHhlS161OVraytK5wheDaiqGf70AuI3lDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a0056118def94919f36bf69e361b57a883a5239c3eaeccf60db8ef02114d88e","last_reissued_at":"2026-07-05T00:14:42.083868Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:14:42.083868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying Unknown Instances for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Kelvin Wong, Mengye Ren, Ming Liang, Raquel Urtasun, Shenlong Wang","submitted_at":"2019-10-24T17:24:43Z","abstract_excerpt":"In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categories of interest, which represent only a small fraction of the potential categories that robots need to handle in the real-world. Thus, identifying objects from unknown classes remains a challenging yet crucial task. In this paper, we develop a novel open-set instance segmentation algorithm for point clouds which can segment objects from both known and unknown classes in a holistic way. Our method uses a deep convolutio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.11296","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/1910.11296/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":"1910.11296","created_at":"2026-07-05T00:14:42.083938+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.11296v1","created_at":"2026-07-05T00:14:42.083938+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.11296","created_at":"2026-07-05T00:14:42.083938+00:00"},{"alias_kind":"pith_short_12","alias_value":"DIAFMEMN56KJ","created_at":"2026-07-05T00:14:42.083938+00:00"},{"alias_kind":"pith_short_16","alias_value":"DIAFMEMN56KJDHZW","created_at":"2026-07-05T00:14:42.083938+00:00"},{"alias_kind":"pith_short_8","alias_value":"DIAFMEMN","created_at":"2026-07-05T00:14:42.083938+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.10765","citing_title":"Neural Network Meta Classifier: Improving the Reliability of Anomaly Segmentation","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK","json":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK.json","graph_json":"https://pith.science/api/pith-number/DIAFMEMN56KJDHZWX5U6GYNVPK/graph.json","events_json":"https://pith.science/api/pith-number/DIAFMEMN56KJDHZWX5U6GYNVPK/events.json","paper":"https://pith.science/paper/DIAFMEMN"},"agent_actions":{"view_html":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK","download_json":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK.json","view_paper":"https://pith.science/paper/DIAFMEMN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.11296&json=true","fetch_graph":"https://pith.science/api/pith-number/DIAFMEMN56KJDHZWX5U6GYNVPK/graph.json","fetch_events":"https://pith.science/api/pith-number/DIAFMEMN56KJDHZWX5U6GYNVPK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK/action/storage_attestation","attest_author":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK/action/author_attestation","sign_citation":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK/action/citation_signature","submit_replication":"https://pith.science/pith/DIAFMEMN56KJDHZWX5U6GYNVPK/action/replication_record"}},"created_at":"2026-07-05T00:14:42.083938+00:00","updated_at":"2026-07-05T00:14:42.083938+00:00"}