{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XFP7GDEGKZNA2FNUMFSEIPKUO7","short_pith_number":"pith:XFP7GDEG","schema_version":"1.0","canonical_sha256":"b95ff30c86565a0d15b46164443d5477f010cb6902167995279e80fae6b71551","source":{"kind":"arxiv","id":"2302.12883","version":1},"attestation_state":"computed","paper":{"title":"3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun-Jee Chao, Nicolai H\\\"ani, Volkan Isler","submitted_at":"2023-02-24T20:37:27Z","abstract_excerpt":"Reconstructing the underlying 3D surface of an object from a single image is a challenging problem that has received extensive attention from the computer vision community. Many learning-based approaches tackle this problem by learning a 3D shape prior from either ground truth 3D data or multi-view observations. To achieve state-of-the-art results, these methods assume that the objects are specified with respect to a fixed canonical coordinate frame, where instances of the same category are perfectly aligned. In this work, we present a new method for joint category-specific 3D reconstruction a"},"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":"2302.12883","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-24T20:37:27Z","cross_cats_sorted":[],"title_canon_sha256":"41601cf85a37cc3acdac7c7a6e02ad533c2dafcdcca42b2e12bd1a65246c49ac","abstract_canon_sha256":"449ce1a22ceda1c155263b6254e7dbaf94319904d3cc2e4ebc2fa3d49f14223f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:45:40.455942Z","signature_b64":"vsOOn/kBjtlDZzawDgWza2mWBL1OZWn6jH4gqDRBdpuLsscUiYo7eb9lcb/4ju2W5p1Byu4Pfxhu/jXVN1ckAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b95ff30c86565a0d15b46164443d5477f010cb6902167995279e80fae6b71551","last_reissued_at":"2026-07-05T05:45:40.455463Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:45:40.455463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun-Jee Chao, Nicolai H\\\"ani, Volkan Isler","submitted_at":"2023-02-24T20:37:27Z","abstract_excerpt":"Reconstructing the underlying 3D surface of an object from a single image is a challenging problem that has received extensive attention from the computer vision community. Many learning-based approaches tackle this problem by learning a 3D shape prior from either ground truth 3D data or multi-view observations. To achieve state-of-the-art results, these methods assume that the objects are specified with respect to a fixed canonical coordinate frame, where instances of the same category are perfectly aligned. In this work, we present a new method for joint category-specific 3D reconstruction a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.12883","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/2302.12883/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":"2302.12883","created_at":"2026-07-05T05:45:40.455518+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.12883v1","created_at":"2026-07-05T05:45:40.455518+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.12883","created_at":"2026-07-05T05:45:40.455518+00:00"},{"alias_kind":"pith_short_12","alias_value":"XFP7GDEGKZNA","created_at":"2026-07-05T05:45:40.455518+00:00"},{"alias_kind":"pith_short_16","alias_value":"XFP7GDEGKZNA2FNU","created_at":"2026-07-05T05:45:40.455518+00:00"},{"alias_kind":"pith_short_8","alias_value":"XFP7GDEG","created_at":"2026-07-05T05:45:40.455518+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.22718","citing_title":"Part Segmentation and Motion Estimation for Articulated Objects with Dynamic 3D Gaussians","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7","json":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7.json","graph_json":"https://pith.science/api/pith-number/XFP7GDEGKZNA2FNUMFSEIPKUO7/graph.json","events_json":"https://pith.science/api/pith-number/XFP7GDEGKZNA2FNUMFSEIPKUO7/events.json","paper":"https://pith.science/paper/XFP7GDEG"},"agent_actions":{"view_html":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7","download_json":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7.json","view_paper":"https://pith.science/paper/XFP7GDEG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.12883&json=true","fetch_graph":"https://pith.science/api/pith-number/XFP7GDEGKZNA2FNUMFSEIPKUO7/graph.json","fetch_events":"https://pith.science/api/pith-number/XFP7GDEGKZNA2FNUMFSEIPKUO7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7/action/storage_attestation","attest_author":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7/action/author_attestation","sign_citation":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7/action/citation_signature","submit_replication":"https://pith.science/pith/XFP7GDEGKZNA2FNUMFSEIPKUO7/action/replication_record"}},"created_at":"2026-07-05T05:45:40.455518+00:00","updated_at":"2026-07-05T05:45:40.455518+00:00"}