{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZLFQOGIUBNWQUQO2K7WPRXGNSD","short_pith_number":"pith:ZLFQOGIU","schema_version":"1.0","canonical_sha256":"cacb0719140b6d0a41da57ecf8dccd90febbc29fecf7e8ef5ddb86451c4c8d68","source":{"kind":"arxiv","id":"2403.09799","version":2},"attestation_state":"computed","paper":{"title":"BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bertram Drost, Carsten Rother, Eric Brachmann, Gu Wang, Jiri Matas, Martin Sundermeyer, Tomas Hodan, Van Nguyen Nguyen, Vincent Lepetit, Yann Labbe","submitted_at":"2024-03-14T18:37:46Z","abstract_excerpt":"We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in model-based 6D object pose estimation from an RGB/RGB-D image and related tasks. Besides the three tasks from 2022 (model-based 2D detection, 2D segmentation, and 6D localization of objects seen during training), the 2023 challenge introduced new variants of these tasks focused on objects unseen during training. In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, "},"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":"2403.09799","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-14T18:37:46Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"34f62ce0dd47225440f5f769c22aa010103087de3e745bf8b1cf27eaa51e4502","abstract_canon_sha256":"6ae4608a780e02dfcba1b04ef400689c23d19c1227e1072f62588932dc32dd63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:08:55.944900Z","signature_b64":"nmqN6yrQNN6Ty1HR8E4RMODQj7x1B906YGjWCgYr7XxvYoAIEhLR5ROQAu40sTo/17E/KEisl5sv1WBSf6q2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cacb0719140b6d0a41da57ecf8dccd90febbc29fecf7e8ef5ddb86451c4c8d68","last_reissued_at":"2026-07-05T08:08:55.944430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:08:55.944430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bertram Drost, Carsten Rother, Eric Brachmann, Gu Wang, Jiri Matas, Martin Sundermeyer, Tomas Hodan, Van Nguyen Nguyen, Vincent Lepetit, Yann Labbe","submitted_at":"2024-03-14T18:37:46Z","abstract_excerpt":"We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in model-based 6D object pose estimation from an RGB/RGB-D image and related tasks. Besides the three tasks from 2022 (model-based 2D detection, 2D segmentation, and 6D localization of objects seen during training), the 2023 challenge introduced new variants of these tasks focused on objects unseen during training. In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09799","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.09799/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":"2403.09799","created_at":"2026-07-05T08:08:55.944481+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.09799v2","created_at":"2026-07-05T08:08:55.944481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09799","created_at":"2026-07-05T08:08:55.944481+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZLFQOGIUBNWQ","created_at":"2026-07-05T08:08:55.944481+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZLFQOGIUBNWQUQO2","created_at":"2026-07-05T08:08:55.944481+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZLFQOGIU","created_at":"2026-07-05T08:08:55.944481+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02708","citing_title":"Temporally Consistent Object 6D Pose Estimation for Robot Control","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD","json":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD.json","graph_json":"https://pith.science/api/pith-number/ZLFQOGIUBNWQUQO2K7WPRXGNSD/graph.json","events_json":"https://pith.science/api/pith-number/ZLFQOGIUBNWQUQO2K7WPRXGNSD/events.json","paper":"https://pith.science/paper/ZLFQOGIU"},"agent_actions":{"view_html":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD","download_json":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD.json","view_paper":"https://pith.science/paper/ZLFQOGIU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.09799&json=true","fetch_graph":"https://pith.science/api/pith-number/ZLFQOGIUBNWQUQO2K7WPRXGNSD/graph.json","fetch_events":"https://pith.science/api/pith-number/ZLFQOGIUBNWQUQO2K7WPRXGNSD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD/action/storage_attestation","attest_author":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD/action/author_attestation","sign_citation":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD/action/citation_signature","submit_replication":"https://pith.science/pith/ZLFQOGIUBNWQUQO2K7WPRXGNSD/action/replication_record"}},"created_at":"2026-07-05T08:08:55.944481+00:00","updated_at":"2026-07-05T08:08:55.944481+00:00"}