{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5K425MWV7MG4MBQJZF2CQFRLIL","short_pith_number":"pith:5K425MWV","schema_version":"1.0","canonical_sha256":"eab9aeb2d5fb0dc60609c97428162b42d58e15a3a2346653b315cfb49651d394","source":{"kind":"arxiv","id":"1904.03167","version":2},"attestation_state":"computed","paper":{"title":"HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Ivan Shugurov, Roman Kaskman, Sergey Zakharov, Slobodan Ilic","submitted_at":"2019-04-05T17:16:09Z","abstract_excerpt":"Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such datasets is growing continuously. Despite the fact that some of exist, they are scarce and typically have restricted setups, such as a single object per sequence, or they focus on specific object types, such as textureless industrial parts. Besides, two significant components are often ignored: training using only available 3D models instead of real data and scalability, i.e. training one method to detect all objects r"},"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":"1904.03167","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-05T17:16:09Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"a26e61de8b12366739ed59f3adb6e054b544f44bf9671183b727df28d7060118","abstract_canon_sha256":"e5d8a3cc10cc8ce27e46343ef768519e8e867cdd9df341c66b34d35844d417e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:08:29.662613Z","signature_b64":"PwIKofoq7zOjT96CYSSxfJari/NfjR3AO3T6MQ3OccrKPFssfUNSljKG9xS+MkEr0qsv2EL+9HZm2gbpqaxfDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eab9aeb2d5fb0dc60609c97428162b42d58e15a3a2346653b315cfb49651d394","last_reissued_at":"2026-07-05T00:08:29.662141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:08:29.662141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Ivan Shugurov, Roman Kaskman, Sergey Zakharov, Slobodan Ilic","submitted_at":"2019-04-05T17:16:09Z","abstract_excerpt":"Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such datasets is growing continuously. Despite the fact that some of exist, they are scarce and typically have restricted setups, such as a single object per sequence, or they focus on specific object types, such as textureless industrial parts. Besides, two significant components are often ignored: training using only available 3D models instead of real data and scalability, i.e. training one method to detect all objects r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.03167","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/1904.03167/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":"1904.03167","created_at":"2026-07-05T00:08:29.662197+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.03167v2","created_at":"2026-07-05T00:08:29.662197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.03167","created_at":"2026-07-05T00:08:29.662197+00:00"},{"alias_kind":"pith_short_12","alias_value":"5K425MWV7MG4","created_at":"2026-07-05T00:08:29.662197+00:00"},{"alias_kind":"pith_short_16","alias_value":"5K425MWV7MG4MBQJ","created_at":"2026-07-05T00:08:29.662197+00:00"},{"alias_kind":"pith_short_8","alias_value":"5K425MWV","created_at":"2026-07-05T00:08:29.662197+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20272","citing_title":"Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL","json":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL.json","graph_json":"https://pith.science/api/pith-number/5K425MWV7MG4MBQJZF2CQFRLIL/graph.json","events_json":"https://pith.science/api/pith-number/5K425MWV7MG4MBQJZF2CQFRLIL/events.json","paper":"https://pith.science/paper/5K425MWV"},"agent_actions":{"view_html":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL","download_json":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL.json","view_paper":"https://pith.science/paper/5K425MWV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.03167&json=true","fetch_graph":"https://pith.science/api/pith-number/5K425MWV7MG4MBQJZF2CQFRLIL/graph.json","fetch_events":"https://pith.science/api/pith-number/5K425MWV7MG4MBQJZF2CQFRLIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL/action/storage_attestation","attest_author":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL/action/author_attestation","sign_citation":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL/action/citation_signature","submit_replication":"https://pith.science/pith/5K425MWV7MG4MBQJZF2CQFRLIL/action/replication_record"}},"created_at":"2026-07-05T00:08:29.662197+00:00","updated_at":"2026-07-05T00:08:29.662197+00:00"}