{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZCA7GMN6KSTS26EILEGCMSGIW3","short_pith_number":"pith:ZCA7GMN6","schema_version":"1.0","canonical_sha256":"c881f331be54a72d7888590c2648c8b6e832a79c38614de783dd4c307337361b","source":{"kind":"arxiv","id":"1901.02970","version":2},"attestation_state":"computed","paper":{"title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"He Wang, Jingwei Huang, Julien Valentin, Leonidas J. Guibas, Shuran Song, Srinath Sridhar","submitted_at":"2019-01-09T23:31:40Z","abstract_excerpt":"The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to \"instance-level\" 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce a Normalized Object Coordinate Space (NOCS)---a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this"},"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":"1901.02970","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-09T23:31:40Z","cross_cats_sorted":[],"title_canon_sha256":"6bbaf669bbb25ae8e7959ab89bbd2f512fb55f545d9646f45272163699a27b31","abstract_canon_sha256":"36b53d2713146c6b5a053f6bcefd311f933d740f57b235cfa24fd771b4aac2b4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:42.550940Z","signature_b64":"6Yh36eDbLULhKknZofqYPZ6P8RWlWR3GkdI7gx+FAWeoKeY75/rjXej18b5RHokTo+EuDuM8kMwiLmv4LLR/Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c881f331be54a72d7888590c2648c8b6e832a79c38614de783dd4c307337361b","last_reissued_at":"2026-05-17T23:42:42.550205Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:42.550205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"He Wang, Jingwei Huang, Julien Valentin, Leonidas J. Guibas, Shuran Song, Srinath Sridhar","submitted_at":"2019-01-09T23:31:40Z","abstract_excerpt":"The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to \"instance-level\" 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce a Normalized Object Coordinate Space (NOCS)---a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02970","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":"1901.02970","created_at":"2026-05-17T23:42:42.550342+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.02970v2","created_at":"2026-05-17T23:42:42.550342+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02970","created_at":"2026-05-17T23:42:42.550342+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZCA7GMN6KSTS","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZCA7GMN6KSTS26EI","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZCA7GMN6","created_at":"2026-05-18T12:33:33.725879+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/ZCA7GMN6KSTS26EILEGCMSGIW3","json":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3.json","graph_json":"https://pith.science/api/pith-number/ZCA7GMN6KSTS26EILEGCMSGIW3/graph.json","events_json":"https://pith.science/api/pith-number/ZCA7GMN6KSTS26EILEGCMSGIW3/events.json","paper":"https://pith.science/paper/ZCA7GMN6"},"agent_actions":{"view_html":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3","download_json":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3.json","view_paper":"https://pith.science/paper/ZCA7GMN6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.02970&json=true","fetch_graph":"https://pith.science/api/pith-number/ZCA7GMN6KSTS26EILEGCMSGIW3/graph.json","fetch_events":"https://pith.science/api/pith-number/ZCA7GMN6KSTS26EILEGCMSGIW3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3/action/storage_attestation","attest_author":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3/action/author_attestation","sign_citation":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3/action/citation_signature","submit_replication":"https://pith.science/pith/ZCA7GMN6KSTS26EILEGCMSGIW3/action/replication_record"}},"created_at":"2026-05-17T23:42:42.550342+00:00","updated_at":"2026-05-17T23:42:42.550342+00:00"}