{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3BEN3GLHN5WU76QZHQMKWRBNMW","short_pith_number":"pith:3BEN3GLH","schema_version":"1.0","canonical_sha256":"d848dd99676f6d4ffa193c18ab442d65901fc4573c33138b27004f7b562503dd","source":{"kind":"arxiv","id":"1801.03910","version":2},"attestation_state":"computed","paper":{"title":"Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Jitendra Malik, Shubham Tulsiani","submitted_at":"2018-01-11T18:15:47Z","abstract_excerpt":"We present a framework for learning single-view shape and pose prediction without using direct supervision for either. Our approach allows leveraging multi-view observations from unknown poses as supervisory signal during training. Our proposed training setup enforces geometric consistency between the independently predicted shape and pose from two views of the same instance. We consequently learn to predict shape in an emergent canonical (view-agnostic) frame along with a corresponding pose predictor. We show empirical and qualitative results using the ShapeNet dataset and observe encouraging"},"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":"1801.03910","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-11T18:15:47Z","cross_cats_sorted":[],"title_canon_sha256":"54ee4937c7c338427320a3d9896001a005b63f851a691069737473ea29a52e63","abstract_canon_sha256":"4b01bd52c539efc36facc13a3f5db411a65b4bcdc945b97b5ad10fb8df6976c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:41.205215Z","signature_b64":"fT0r3cbJ7gydH/uqHsnM+E9eewQUiDHeiDwQjCfPayv1vsyFDQz4n4MVbTGjnmHfG/IiTQNCqbrY+YJYNA56Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d848dd99676f6d4ffa193c18ab442d65901fc4573c33138b27004f7b562503dd","last_reissued_at":"2026-05-18T00:17:41.204659Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:41.204659Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Jitendra Malik, Shubham Tulsiani","submitted_at":"2018-01-11T18:15:47Z","abstract_excerpt":"We present a framework for learning single-view shape and pose prediction without using direct supervision for either. Our approach allows leveraging multi-view observations from unknown poses as supervisory signal during training. Our proposed training setup enforces geometric consistency between the independently predicted shape and pose from two views of the same instance. We consequently learn to predict shape in an emergent canonical (view-agnostic) frame along with a corresponding pose predictor. We show empirical and qualitative results using the ShapeNet dataset and observe encouraging"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.03910","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":"1801.03910","created_at":"2026-05-18T00:17:41.204732+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.03910v2","created_at":"2026-05-18T00:17:41.204732+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.03910","created_at":"2026-05-18T00:17:41.204732+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BEN3GLHN5WU","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BEN3GLHN5WU76QZ","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BEN3GLH","created_at":"2026-05-18T12:32:02.567920+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/3BEN3GLHN5WU76QZHQMKWRBNMW","json":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW.json","graph_json":"https://pith.science/api/pith-number/3BEN3GLHN5WU76QZHQMKWRBNMW/graph.json","events_json":"https://pith.science/api/pith-number/3BEN3GLHN5WU76QZHQMKWRBNMW/events.json","paper":"https://pith.science/paper/3BEN3GLH"},"agent_actions":{"view_html":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW","download_json":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW.json","view_paper":"https://pith.science/paper/3BEN3GLH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.03910&json=true","fetch_graph":"https://pith.science/api/pith-number/3BEN3GLHN5WU76QZHQMKWRBNMW/graph.json","fetch_events":"https://pith.science/api/pith-number/3BEN3GLHN5WU76QZHQMKWRBNMW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW/action/storage_attestation","attest_author":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW/action/author_attestation","sign_citation":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW/action/citation_signature","submit_replication":"https://pith.science/pith/3BEN3GLHN5WU76QZHQMKWRBNMW/action/replication_record"}},"created_at":"2026-05-18T00:17:41.204732+00:00","updated_at":"2026-05-18T00:17:41.204732+00:00"}