{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:DCGLZAOF6FARG4WEYXOYAOLGYG","short_pith_number":"pith:DCGLZAOF","canonical_record":{"source":{"id":"2607.28132","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T12:41:50Z","cross_cats_sorted":[],"title_canon_sha256":"d424818150e8ad867eef07f0d0ef17477721588779023d6db17c6fb53a2b915d","abstract_canon_sha256":"1382d661a3de4ff939df64fc7c5177c73e291fee255994edb8e6d71f14fe9e52"},"schema_version":"1.0"},"canonical_sha256":"188cbc81c5f1411372c4c5dd803966c1bb024c2b85db87780a333fe1de8fdd4b","source":{"kind":"arxiv","id":"2607.28132","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.28132","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.28132v1","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28132","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"pith_short_12","alias_value":"DCGLZAOF6FAR","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"pith_short_16","alias_value":"DCGLZAOF6FARG4WE","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"pith_short_8","alias_value":"DCGLZAOF","created_at":"2026-07-31T01:35:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:DCGLZAOF6FARG4WEYXOYAOLGYG","target":"record","payload":{"canonical_record":{"source":{"id":"2607.28132","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T12:41:50Z","cross_cats_sorted":[],"title_canon_sha256":"d424818150e8ad867eef07f0d0ef17477721588779023d6db17c6fb53a2b915d","abstract_canon_sha256":"1382d661a3de4ff939df64fc7c5177c73e291fee255994edb8e6d71f14fe9e52"},"schema_version":"1.0"},"canonical_sha256":"188cbc81c5f1411372c4c5dd803966c1bb024c2b85db87780a333fe1de8fdd4b","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"188cbc81c5f1411372c4c5dd803966c1bb024c2b85db87780a333fe1de8fdd4b","last_reissued_at":"2026-07-31T01:35:50.355283Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:35:50.355283Z"},"source_kind":"arxiv","source_id":"2607.28132","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T01:35:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M3BHgibzzfqvKDx/wsFzEbzqSQwLsAiq7Uhm5J/N102dJ8piUnT5+juniCigH32+MbghJVvYOaGN5SkTJ7e2BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T19:12:46.860907Z"},"content_sha256":"1b637df15a476742c2452361fe154f40112ea7137c8af0ce8a91420a9b05dc8b","schema_version":"1.0","event_id":"sha256:1b637df15a476742c2452361fe154f40112ea7137c8af0ce8a91420a9b05dc8b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:DCGLZAOF6FARG4WEYXOYAOLGYG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Heeseok Oh, Jiwoo Kang, Juheon Hwang, Taewan Kim","submitted_at":"2026-07-30T12:41:50Z","abstract_excerpt":"We propose a convolutional neural shading (CNS), a novel pipeline to reconstruct high-quality 3D shapes from multi-view images. Several recent studies have used neural radiance fields and other neural differentiable rendering methods to understand 3D geometry. However, these approaches rely on single-point geometric information, such as positions and normals of the surface, leading to a lack of detailed local geometry. Our approach addresses the inherent limitations of single-point information by leveraging a neural shader to capture variations even in dark and textureless regions with a convo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28132","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/2607.28132/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T01:35:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xRasfgAGmiq2HskkYwtVGxB8iGTCuz8wnGfXnyVCUyytCEtc51NtPc/wHKQ+krFiyORwtdJDbMz86mK1/W3QBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T19:12:46.861461Z"},"content_sha256":"5ce0eda93ca006f2c0a4f5be7af5953aaa2fac7db021e573eafe8671d0bbc4ee","schema_version":"1.0","event_id":"sha256:5ce0eda93ca006f2c0a4f5be7af5953aaa2fac7db021e573eafe8671d0bbc4ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DCGLZAOF6FARG4WEYXOYAOLGYG/bundle.json","state_url":"https://pith.science/pith/DCGLZAOF6FARG4WEYXOYAOLGYG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DCGLZAOF6FARG4WEYXOYAOLGYG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-01T19:12:46Z","links":{"resolver":"https://pith.science/pith/DCGLZAOF6FARG4WEYXOYAOLGYG","bundle":"https://pith.science/pith/DCGLZAOF6FARG4WEYXOYAOLGYG/bundle.json","state":"https://pith.science/pith/DCGLZAOF6FARG4WEYXOYAOLGYG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DCGLZAOF6FARG4WEYXOYAOLGYG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DCGLZAOF6FARG4WEYXOYAOLGYG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1382d661a3de4ff939df64fc7c5177c73e291fee255994edb8e6d71f14fe9e52","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T12:41:50Z","title_canon_sha256":"d424818150e8ad867eef07f0d0ef17477721588779023d6db17c6fb53a2b915d"},"schema_version":"1.0","source":{"id":"2607.28132","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.28132","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.28132v1","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28132","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"pith_short_12","alias_value":"DCGLZAOF6FAR","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"pith_short_16","alias_value":"DCGLZAOF6FARG4WE","created_at":"2026-07-31T01:35:50Z"},{"alias_kind":"pith_short_8","alias_value":"DCGLZAOF","created_at":"2026-07-31T01:35:50Z"}],"graph_snapshots":[{"event_id":"sha256:5ce0eda93ca006f2c0a4f5be7af5953aaa2fac7db021e573eafe8671d0bbc4ee","target":"graph","created_at":"2026-07-31T01:35:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.28132/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a convolutional neural shading (CNS), a novel pipeline to reconstruct high-quality 3D shapes from multi-view images. Several recent studies have used neural radiance fields and other neural differentiable rendering methods to understand 3D geometry. However, these approaches rely on single-point geometric information, such as positions and normals of the surface, leading to a lack of detailed local geometry. Our approach addresses the inherent limitations of single-point information by leveraging a neural shader to capture variations even in dark and textureless regions with a convo","authors_text":"Heeseok Oh, Jiwoo Kang, Juheon Hwang, Taewan Kim","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T12:41:50Z","title":"Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28132","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1b637df15a476742c2452361fe154f40112ea7137c8af0ce8a91420a9b05dc8b","target":"record","created_at":"2026-07-31T01:35:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1382d661a3de4ff939df64fc7c5177c73e291fee255994edb8e6d71f14fe9e52","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T12:41:50Z","title_canon_sha256":"d424818150e8ad867eef07f0d0ef17477721588779023d6db17c6fb53a2b915d"},"schema_version":"1.0","source":{"id":"2607.28132","kind":"arxiv","version":1}},"canonical_sha256":"188cbc81c5f1411372c4c5dd803966c1bb024c2b85db87780a333fe1de8fdd4b","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"188cbc81c5f1411372c4c5dd803966c1bb024c2b85db87780a333fe1de8fdd4b","first_computed_at":"2026-07-31T01:35:50.355283Z","kind":"pith_receipt","last_reissued_at":"2026-07-31T01:35:50.355283Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.28132","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b637df15a476742c2452361fe154f40112ea7137c8af0ce8a91420a9b05dc8b","sha256:5ce0eda93ca006f2c0a4f5be7af5953aaa2fac7db021e573eafe8671d0bbc4ee"],"state_sha256":"57d4a4bc2472ff6c8d6f1736965b5d04298a8b45025462736e07de95a4bd6291"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fkihzFoJn8SMaXnjs6sby2lJ22TYEzM/ZBKi3XaKhX1V2efvJbYD3ytex8c1y/IXBAZXshd5pWukPmillIzeBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T19:12:46.865211Z","bundle_sha256":"43dab964b59c94c0dc61241a7dba0f9e9307e477bd9e4c45fc64d23c03c2e9f2"}}