{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:S5OSQZKPVL3MUF3AZN63CPCRLI","short_pith_number":"pith:S5OSQZKP","schema_version":"1.0","canonical_sha256":"975d28654faaf6ca1760cb7db13c515a1e3c7fe3c72eae2bb045d64591e05ef5","source":{"kind":"arxiv","id":"1909.12573","version":2},"attestation_state":"computed","paper":{"title":"RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Atsuhiro Noguchi, Tatsuya Harada","submitted_at":"2019-09-27T09:10:12Z","abstract_excerpt":"Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images. The proposed method enables camera parameter-conditional image generation and depth image generation without any 3D annotations, such as camera poses or depth. We use an explicit 3D consistency loss for two RGBD images generated from different camera parameters, in addition to the"},"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":"1909.12573","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-27T09:10:12Z","cross_cats_sorted":[],"title_canon_sha256":"40910ce5cc0b3f1bf740f5a20e2288ea337fa79e71de5baa504b5b5fa558cdf8","abstract_canon_sha256":"b844bfe98232fa2505002b10d60ff97b963802067042f71d569a3c80bf986c9d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:05:24.096384Z","signature_b64":"Ku3zqBhmYUtN1I6HKL3aw6qBtjFkq6p9qULyOFiorw6BCUhp4YySRQJ4B6br1Y8eMW/NBYDEjnN4HWtfFCmWCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"975d28654faaf6ca1760cb7db13c515a1e3c7fe3c72eae2bb045d64591e05ef5","last_reissued_at":"2026-07-05T01:05:24.095966Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:05:24.095966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Atsuhiro Noguchi, Tatsuya Harada","submitted_at":"2019-09-27T09:10:12Z","abstract_excerpt":"Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images. The proposed method enables camera parameter-conditional image generation and depth image generation without any 3D annotations, such as camera poses or depth. We use an explicit 3D consistency loss for two RGBD images generated from different camera parameters, in addition to the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.12573","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/1909.12573/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":"1909.12573","created_at":"2026-07-05T01:05:24.096035+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.12573v2","created_at":"2026-07-05T01:05:24.096035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.12573","created_at":"2026-07-05T01:05:24.096035+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5OSQZKPVL3M","created_at":"2026-07-05T01:05:24.096035+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5OSQZKPVL3MUF3A","created_at":"2026-07-05T01:05:24.096035+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5OSQZKP","created_at":"2026-07-05T01:05:24.096035+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/S5OSQZKPVL3MUF3AZN63CPCRLI","json":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI.json","graph_json":"https://pith.science/api/pith-number/S5OSQZKPVL3MUF3AZN63CPCRLI/graph.json","events_json":"https://pith.science/api/pith-number/S5OSQZKPVL3MUF3AZN63CPCRLI/events.json","paper":"https://pith.science/paper/S5OSQZKP"},"agent_actions":{"view_html":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI","download_json":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI.json","view_paper":"https://pith.science/paper/S5OSQZKP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.12573&json=true","fetch_graph":"https://pith.science/api/pith-number/S5OSQZKPVL3MUF3AZN63CPCRLI/graph.json","fetch_events":"https://pith.science/api/pith-number/S5OSQZKPVL3MUF3AZN63CPCRLI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI/action/storage_attestation","attest_author":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI/action/author_attestation","sign_citation":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI/action/citation_signature","submit_replication":"https://pith.science/pith/S5OSQZKPVL3MUF3AZN63CPCRLI/action/replication_record"}},"created_at":"2026-07-05T01:05:24.096035+00:00","updated_at":"2026-07-05T01:05:24.096035+00:00"}