{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CMTD3YDDPZCSG5ALQW43OIHHDC","short_pith_number":"pith:CMTD3YDD","schema_version":"1.0","canonical_sha256":"13263de0637e4523740b85b9b720e71897a5b7b7ba3d9b87a92856af6eef5d23","source":{"kind":"arxiv","id":"2212.09735","version":2},"attestation_state":"computed","paper":{"title":"Correspondence Distillation from NeRF-based GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Dai, Chen Change Loy, Yushi Lan","submitted_at":"2022-12-19T18:54:59Z","abstract_excerpt":"The neural radiance field (NeRF) has shown promising results in preserving the fine details of objects and scenes. However, unlike mesh-based representations, it remains an open problem to build dense correspondences across different NeRFs of the same category, which is essential in many downstream tasks. The main difficulties of this problem lie in the implicit nature of NeRF and the lack of ground-truth correspondence annotations. In this paper, we show it is possible to bypass these challenges by leveraging the rich semantics and structural priors encapsulated in a pre-trained NeRF-based GA"},"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":"2212.09735","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-19T18:54:59Z","cross_cats_sorted":[],"title_canon_sha256":"a9777a83984474672feb6ae891ee0a734715b0185a9e7374fecc221d721e56f9","abstract_canon_sha256":"0304cafa3ee32f86d55ccbe8124b2ba7fe400652244dd4e9835b818508bc8574"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:26:59.733859Z","signature_b64":"BhAZwzpSGaM5G4pqnFq9dZqxILIqHNQqaQ74QtmIOzoyZ6uXvQp1OH992RGx9l6WJgggX6N7ghIFsFXaR3ADDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13263de0637e4523740b85b9b720e71897a5b7b7ba3d9b87a92856af6eef5d23","last_reissued_at":"2026-07-05T05:26:59.733375Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:26:59.733375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Correspondence Distillation from NeRF-based GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Dai, Chen Change Loy, Yushi Lan","submitted_at":"2022-12-19T18:54:59Z","abstract_excerpt":"The neural radiance field (NeRF) has shown promising results in preserving the fine details of objects and scenes. However, unlike mesh-based representations, it remains an open problem to build dense correspondences across different NeRFs of the same category, which is essential in many downstream tasks. The main difficulties of this problem lie in the implicit nature of NeRF and the lack of ground-truth correspondence annotations. In this paper, we show it is possible to bypass these challenges by leveraging the rich semantics and structural priors encapsulated in a pre-trained NeRF-based GA"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09735","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/2212.09735/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":"2212.09735","created_at":"2026-07-05T05:26:59.733440+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09735v2","created_at":"2026-07-05T05:26:59.733440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09735","created_at":"2026-07-05T05:26:59.733440+00:00"},{"alias_kind":"pith_short_12","alias_value":"CMTD3YDDPZCS","created_at":"2026-07-05T05:26:59.733440+00:00"},{"alias_kind":"pith_short_16","alias_value":"CMTD3YDDPZCSG5AL","created_at":"2026-07-05T05:26:59.733440+00:00"},{"alias_kind":"pith_short_8","alias_value":"CMTD3YDD","created_at":"2026-07-05T05:26:59.733440+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/CMTD3YDDPZCSG5ALQW43OIHHDC","json":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC.json","graph_json":"https://pith.science/api/pith-number/CMTD3YDDPZCSG5ALQW43OIHHDC/graph.json","events_json":"https://pith.science/api/pith-number/CMTD3YDDPZCSG5ALQW43OIHHDC/events.json","paper":"https://pith.science/paper/CMTD3YDD"},"agent_actions":{"view_html":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC","download_json":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC.json","view_paper":"https://pith.science/paper/CMTD3YDD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09735&json=true","fetch_graph":"https://pith.science/api/pith-number/CMTD3YDDPZCSG5ALQW43OIHHDC/graph.json","fetch_events":"https://pith.science/api/pith-number/CMTD3YDDPZCSG5ALQW43OIHHDC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC/action/storage_attestation","attest_author":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC/action/author_attestation","sign_citation":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC/action/citation_signature","submit_replication":"https://pith.science/pith/CMTD3YDDPZCSG5ALQW43OIHHDC/action/replication_record"}},"created_at":"2026-07-05T05:26:59.733440+00:00","updated_at":"2026-07-05T05:26:59.733440+00:00"}