{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:PJBKZ2N5QCZXWWMD3R3B2QN3X6","short_pith_number":"pith:PJBKZ2N5","schema_version":"1.0","canonical_sha256":"7a42ace9bd80b37b5983dc761d41bbbf949b965bb862cc801d43cb0556462275","source":{"kind":"arxiv","id":"2104.13854","version":1},"attestation_state":"computed","paper":{"title":"D-OccNet: Detailed 3D Reconstruction Using Cross-Domain Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Minhaj Uddin Ansari, Naeem Akhter, Talha Bilal","submitted_at":"2021-04-28T16:00:54Z","abstract_excerpt":"Deep learning based 3D reconstruction of single view 2D image is becoming increasingly popular due to their wide range of real-world applications, but this task is inherently challenging because of the partial observability of an object from a single perspective. Recently, state of the art probability based Occupancy Networks reconstructed 3D surfaces from three different types of input domains: single view 2D image, point cloud and voxel. In this study, we extend the work on Occupancy Networks by exploiting cross-domain learning of image and point cloud domains. Specifically, we first convert"},"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":"2104.13854","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-28T16:00:54Z","cross_cats_sorted":[],"title_canon_sha256":"ad4342e4f3b09fa380f5f9f148074561d219475f63922e4839361e9b2f56f6c1","abstract_canon_sha256":"f87c0bcea1bf76a7c62c3613079d46e76d201961d29afeb1264c2679ae93b171"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:36:04.868353Z","signature_b64":"YiC5v+6hLU6ErYEMXYv/Dot/i2ZCjsAGpMeY9oKPfJKdmhTcVlx/NAcUs8rwmV74JYtCaYEaSXDzmBUBGslfCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a42ace9bd80b37b5983dc761d41bbbf949b965bb862cc801d43cb0556462275","last_reissued_at":"2026-07-05T02:36:04.867953Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:36:04.867953Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"D-OccNet: Detailed 3D Reconstruction Using Cross-Domain Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Minhaj Uddin Ansari, Naeem Akhter, Talha Bilal","submitted_at":"2021-04-28T16:00:54Z","abstract_excerpt":"Deep learning based 3D reconstruction of single view 2D image is becoming increasingly popular due to their wide range of real-world applications, but this task is inherently challenging because of the partial observability of an object from a single perspective. Recently, state of the art probability based Occupancy Networks reconstructed 3D surfaces from three different types of input domains: single view 2D image, point cloud and voxel. In this study, we extend the work on Occupancy Networks by exploiting cross-domain learning of image and point cloud domains. Specifically, we first convert"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.13854","kind":"arxiv","version":1},"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/2104.13854/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":"2104.13854","created_at":"2026-07-05T02:36:04.868018+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.13854v1","created_at":"2026-07-05T02:36:04.868018+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.13854","created_at":"2026-07-05T02:36:04.868018+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJBKZ2N5QCZX","created_at":"2026-07-05T02:36:04.868018+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJBKZ2N5QCZXWWMD","created_at":"2026-07-05T02:36:04.868018+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJBKZ2N5","created_at":"2026-07-05T02:36:04.868018+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/PJBKZ2N5QCZXWWMD3R3B2QN3X6","json":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6.json","graph_json":"https://pith.science/api/pith-number/PJBKZ2N5QCZXWWMD3R3B2QN3X6/graph.json","events_json":"https://pith.science/api/pith-number/PJBKZ2N5QCZXWWMD3R3B2QN3X6/events.json","paper":"https://pith.science/paper/PJBKZ2N5"},"agent_actions":{"view_html":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6","download_json":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6.json","view_paper":"https://pith.science/paper/PJBKZ2N5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.13854&json=true","fetch_graph":"https://pith.science/api/pith-number/PJBKZ2N5QCZXWWMD3R3B2QN3X6/graph.json","fetch_events":"https://pith.science/api/pith-number/PJBKZ2N5QCZXWWMD3R3B2QN3X6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6/action/storage_attestation","attest_author":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6/action/author_attestation","sign_citation":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6/action/citation_signature","submit_replication":"https://pith.science/pith/PJBKZ2N5QCZXWWMD3R3B2QN3X6/action/replication_record"}},"created_at":"2026-07-05T02:36:04.868018+00:00","updated_at":"2026-07-05T02:36:04.868018+00:00"}