{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:A2QRADM7D7DOWAWLJSUC5FCZVZ","short_pith_number":"pith:A2QRADM7","schema_version":"1.0","canonical_sha256":"06a1100d9f1fc6eb02cb4ca82e9459ae7ae3f8c82c4bac0f022600ce0bfbe785","source":{"kind":"arxiv","id":"1812.01387","version":1},"attestation_state":"computed","paper":{"title":"Estimating 6D Pose From Localizing Designated Surface Keypoints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Chengkun Li, Gao Peng, Hao-Shu Fang, Haoyu Wang, Zelin Zhao","submitted_at":"2018-12-04T12:55:06Z","abstract_excerpt":"In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image. The core of our approach is that we first designate a set of surface points on target object model as keypoints and then train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can recover the 6D pose according to the 2D-3D relationship of keypoints. Different from recent state-of-the-art CNN-based approaches that rely on a time-consuming post-processing procedure, our method can achieve competitive accuracy without any refinement after pose prediction. Meanwhile, we obtain "},"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":"1812.01387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-04T12:55:06Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"dd8432884bee1dc39e41e9d24757f567a5c2bf1e3dd49c20828c001d641f73ee","abstract_canon_sha256":"59bd1e972dd0950786807981a2c4549f7e0f7581837c1848f18e53ce657c4d53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:12.608380Z","signature_b64":"ccP1o6HGIkg5+4xArSVZ7Xikwmex4G3eomPlxARNnq0zSnhKw9fHsUUDynbshQaNVV3H6dbUdduigvbQdooqAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06a1100d9f1fc6eb02cb4ca82e9459ae7ae3f8c82c4bac0f022600ce0bfbe785","last_reissued_at":"2026-05-17T23:59:12.607821Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:12.607821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating 6D Pose From Localizing Designated Surface Keypoints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Chengkun Li, Gao Peng, Hao-Shu Fang, Haoyu Wang, Zelin Zhao","submitted_at":"2018-12-04T12:55:06Z","abstract_excerpt":"In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image. The core of our approach is that we first designate a set of surface points on target object model as keypoints and then train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can recover the 6D pose according to the 2D-3D relationship of keypoints. Different from recent state-of-the-art CNN-based approaches that rely on a time-consuming post-processing procedure, our method can achieve competitive accuracy without any refinement after pose prediction. Meanwhile, we obtain "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01387","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":"1812.01387","created_at":"2026-05-17T23:59:12.607922+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.01387v1","created_at":"2026-05-17T23:59:12.607922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01387","created_at":"2026-05-17T23:59:12.607922+00:00"},{"alias_kind":"pith_short_12","alias_value":"A2QRADM7D7DO","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"A2QRADM7D7DOWAWL","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"A2QRADM7","created_at":"2026-05-18T12:32:13.499390+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/A2QRADM7D7DOWAWLJSUC5FCZVZ","json":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ.json","graph_json":"https://pith.science/api/pith-number/A2QRADM7D7DOWAWLJSUC5FCZVZ/graph.json","events_json":"https://pith.science/api/pith-number/A2QRADM7D7DOWAWLJSUC5FCZVZ/events.json","paper":"https://pith.science/paper/A2QRADM7"},"agent_actions":{"view_html":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ","download_json":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ.json","view_paper":"https://pith.science/paper/A2QRADM7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.01387&json=true","fetch_graph":"https://pith.science/api/pith-number/A2QRADM7D7DOWAWLJSUC5FCZVZ/graph.json","fetch_events":"https://pith.science/api/pith-number/A2QRADM7D7DOWAWLJSUC5FCZVZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ/action/storage_attestation","attest_author":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ/action/author_attestation","sign_citation":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ/action/citation_signature","submit_replication":"https://pith.science/pith/A2QRADM7D7DOWAWLJSUC5FCZVZ/action/replication_record"}},"created_at":"2026-05-17T23:59:12.607922+00:00","updated_at":"2026-05-17T23:59:12.607922+00:00"}