{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BR6XK6YL5MSNPK25MFRTD4WUWM","short_pith_number":"pith:BR6XK6YL","schema_version":"1.0","canonical_sha256":"0c7d757b0beb24d7ab5d616331f2d4b3296c6faa8dd82830231a73d40e0fe779","source":{"kind":"arxiv","id":"2405.12057","version":3},"attestation_state":"computed","paper":{"title":"NPLMV-PS: Neural Point-Light Multi-View Photometric Stereo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fotios Logothetis, Ignas Budvytis, Roberto Cipolla","submitted_at":"2024-05-20T14:26:07Z","abstract_excerpt":"In this work we present a novel multi-view photometric stereo (MVPS) method. Like many works in 3D reconstruction we are leveraging neural shape representations and learnt renderers. However, our work differs from the state-of-the-art multi-view PS methods such as PS-NeRF or Supernormal in that we explicitly leverage per-pixel intensity renderings rather than relying mainly on estimated normals.\n  We model point light attenuation and explicitly raytrace cast shadows in order to best approximate the incoming radiance for each point. The estimated incoming radiance is used as input to a fully ne"},"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":"2405.12057","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-20T14:26:07Z","cross_cats_sorted":[],"title_canon_sha256":"867d965426a561bd3d608d705668228d421b3a964f2a0785021e9147e512f129","abstract_canon_sha256":"04beb7f38a7ff709846db69f6f78a7d045c4e5c88550d486eb01ae348f4413a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:36.923869Z","signature_b64":"vEG+rWvNt8Pdbz+EBI1LUOndDVBofV8CEfSIZT9jtYxwn0BnioJ40ORTbMVPfpKyJR1i9wOzMYw6BWpP9d1HAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c7d757b0beb24d7ab5d616331f2d4b3296c6faa8dd82830231a73d40e0fe779","last_reissued_at":"2026-07-05T09:41:36.923375Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:36.923375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NPLMV-PS: Neural Point-Light Multi-View Photometric Stereo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fotios Logothetis, Ignas Budvytis, Roberto Cipolla","submitted_at":"2024-05-20T14:26:07Z","abstract_excerpt":"In this work we present a novel multi-view photometric stereo (MVPS) method. Like many works in 3D reconstruction we are leveraging neural shape representations and learnt renderers. However, our work differs from the state-of-the-art multi-view PS methods such as PS-NeRF or Supernormal in that we explicitly leverage per-pixel intensity renderings rather than relying mainly on estimated normals.\n  We model point light attenuation and explicitly raytrace cast shadows in order to best approximate the incoming radiance for each point. The estimated incoming radiance is used as input to a fully ne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.12057","kind":"arxiv","version":3},"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/2405.12057/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":"2405.12057","created_at":"2026-07-05T09:41:36.923440+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.12057v3","created_at":"2026-07-05T09:41:36.923440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.12057","created_at":"2026-07-05T09:41:36.923440+00:00"},{"alias_kind":"pith_short_12","alias_value":"BR6XK6YL5MSN","created_at":"2026-07-05T09:41:36.923440+00:00"},{"alias_kind":"pith_short_16","alias_value":"BR6XK6YL5MSNPK25","created_at":"2026-07-05T09:41:36.923440+00:00"},{"alias_kind":"pith_short_8","alias_value":"BR6XK6YL","created_at":"2026-07-05T09:41:36.923440+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.23162","citing_title":"Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo Cues","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM","json":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM.json","graph_json":"https://pith.science/api/pith-number/BR6XK6YL5MSNPK25MFRTD4WUWM/graph.json","events_json":"https://pith.science/api/pith-number/BR6XK6YL5MSNPK25MFRTD4WUWM/events.json","paper":"https://pith.science/paper/BR6XK6YL"},"agent_actions":{"view_html":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM","download_json":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM.json","view_paper":"https://pith.science/paper/BR6XK6YL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.12057&json=true","fetch_graph":"https://pith.science/api/pith-number/BR6XK6YL5MSNPK25MFRTD4WUWM/graph.json","fetch_events":"https://pith.science/api/pith-number/BR6XK6YL5MSNPK25MFRTD4WUWM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM/action/storage_attestation","attest_author":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM/action/author_attestation","sign_citation":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM/action/citation_signature","submit_replication":"https://pith.science/pith/BR6XK6YL5MSNPK25MFRTD4WUWM/action/replication_record"}},"created_at":"2026-07-05T09:41:36.923440+00:00","updated_at":"2026-07-05T09:41:36.923440+00:00"}