{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GUSDUHCCI6GFVFY5ZLMBRWJDZ3","short_pith_number":"pith:GUSDUHCC","schema_version":"1.0","canonical_sha256":"35243a1c42478c5a971dcad818d923cef1279d30093559ae6b12af93c7239f5e","source":{"kind":"arxiv","id":"1804.00103","version":1},"attestation_state":"computed","paper":{"title":"A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alberto L. Sangiovanni-Vincentelli, Bichen Wu, Kurt Keutzer, Sanjit A. Seshia, Xiangyu Yue","submitted_at":"2018-03-31T01:32:11Z","abstract_excerpt":"3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point cloud"},"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":"1804.00103","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-31T01:32:11Z","cross_cats_sorted":[],"title_canon_sha256":"2e0486579df543be6fd39dfe4f2840c98faa6970cf62926db2873f4da990bbca","abstract_canon_sha256":"eec6b9f3a7bdc0a19af4f4e99001f486698308b9563ba81649fc27d89d35d721"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:38.380333Z","signature_b64":"O83KB5LvqiWx0OeQrvg3pD/lU+8s1tz+kvbbMfHG0VIDKCJUKkPQZYJKQZYHBkH75ovU12mCl9ZztgipSenpBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35243a1c42478c5a971dcad818d923cef1279d30093559ae6b12af93c7239f5e","last_reissued_at":"2026-05-18T00:19:38.379873Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:38.379873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alberto L. Sangiovanni-Vincentelli, Bichen Wu, Kurt Keutzer, Sanjit A. Seshia, Xiangyu Yue","submitted_at":"2018-03-31T01:32:11Z","abstract_excerpt":"3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point cloud"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00103","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":"1804.00103","created_at":"2026-05-18T00:19:38.379943+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.00103v1","created_at":"2026-05-18T00:19:38.379943+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00103","created_at":"2026-05-18T00:19:38.379943+00:00"},{"alias_kind":"pith_short_12","alias_value":"GUSDUHCCI6GF","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GUSDUHCCI6GFVFY5","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GUSDUHCC","created_at":"2026-05-18T12:32:25.280505+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/GUSDUHCCI6GFVFY5ZLMBRWJDZ3","json":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3.json","graph_json":"https://pith.science/api/pith-number/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/graph.json","events_json":"https://pith.science/api/pith-number/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/events.json","paper":"https://pith.science/paper/GUSDUHCC"},"agent_actions":{"view_html":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3","download_json":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3.json","view_paper":"https://pith.science/paper/GUSDUHCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.00103&json=true","fetch_graph":"https://pith.science/api/pith-number/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/graph.json","fetch_events":"https://pith.science/api/pith-number/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/action/storage_attestation","attest_author":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/action/author_attestation","sign_citation":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/action/citation_signature","submit_replication":"https://pith.science/pith/GUSDUHCCI6GFVFY5ZLMBRWJDZ3/action/replication_record"}},"created_at":"2026-05-18T00:19:38.379943+00:00","updated_at":"2026-05-18T00:19:38.379943+00:00"}