{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:CICZ5AZXMAWHC3ZPXA25BE7T4E","short_pith_number":"pith:CICZ5AZX","schema_version":"1.0","canonical_sha256":"12059e8337602c716f2fb835d093f3e13d495247af13affb8cc81c925115915b","source":{"kind":"arxiv","id":"1803.06199","version":2},"attestation_state":"computed","paper":{"title":"Complex-YOLO: Real-time 3D Object Detection on Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Horst-Michael Gross, Karl Amende, Martin Simon, Stefan Milz","submitted_at":"2018-03-16T12:54:40Z","abstract_excerpt":"Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object"},"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":"1803.06199","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-16T12:54:40Z","cross_cats_sorted":[],"title_canon_sha256":"03f3b95fdc2cbe829b50ce1032d708e70e9a78206493b5d054c046a53b754bc6","abstract_canon_sha256":"3f80d5047493e125507e2ac13c7d7da2e9e15abbc48c4363104d025c90d4aac4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:05.105481Z","signature_b64":"ujzeNipkTRuN+zRvVs86ZUwhR+axbA9hWLSThg5Avdmxzenmkeb9oGYjVePN0X/Cqs9eIaC0Fao/XttkJOJAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12059e8337602c716f2fb835d093f3e13d495247af13affb8cc81c925115915b","last_reissued_at":"2026-05-18T00:05:05.104883Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:05.104883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Complex-YOLO: Real-time 3D Object Detection on Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Horst-Michael Gross, Karl Amende, Martin Simon, Stefan Milz","submitted_at":"2018-03-16T12:54:40Z","abstract_excerpt":"Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.06199","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":""},"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":"1803.06199","created_at":"2026-05-18T00:05:05.104968+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.06199v2","created_at":"2026-05-18T00:05:05.104968+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.06199","created_at":"2026-05-18T00:05:05.104968+00:00"},{"alias_kind":"pith_short_12","alias_value":"CICZ5AZXMAWH","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"CICZ5AZXMAWHC3ZP","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"CICZ5AZX","created_at":"2026-05-18T12:32:16.446611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.07061","citing_title":"How much real data do we actually need: Analyzing object detection performance using synthetic and real data","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E","json":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E.json","graph_json":"https://pith.science/api/pith-number/CICZ5AZXMAWHC3ZPXA25BE7T4E/graph.json","events_json":"https://pith.science/api/pith-number/CICZ5AZXMAWHC3ZPXA25BE7T4E/events.json","paper":"https://pith.science/paper/CICZ5AZX"},"agent_actions":{"view_html":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E","download_json":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E.json","view_paper":"https://pith.science/paper/CICZ5AZX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.06199&json=true","fetch_graph":"https://pith.science/api/pith-number/CICZ5AZXMAWHC3ZPXA25BE7T4E/graph.json","fetch_events":"https://pith.science/api/pith-number/CICZ5AZXMAWHC3ZPXA25BE7T4E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E/action/storage_attestation","attest_author":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E/action/author_attestation","sign_citation":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E/action/citation_signature","submit_replication":"https://pith.science/pith/CICZ5AZXMAWHC3ZPXA25BE7T4E/action/replication_record"}},"created_at":"2026-05-18T00:05:05.104968+00:00","updated_at":"2026-05-18T00:05:05.104968+00:00"}