{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:T6Q5SVJT72G6YUWCSUWJNSYWVT","short_pith_number":"pith:T6Q5SVJT","canonical_record":{"source":{"id":"2006.03427","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-05T13:26:05Z","cross_cats_sorted":[],"title_canon_sha256":"bd29ccced5a0382091e2e53f33aaa44884b082feea825c7b5c816716036409db","abstract_canon_sha256":"e4b89fe4943391ca8c6616a58d8950f2e3f7562dbe9e1bd5ae582b862bddd03b"},"schema_version":"1.0"},"canonical_sha256":"9fa1d95533fe8dec52c2952c96cb16acc79b74b54ae6a4b16e5eb3b4de44995a","source":{"kind":"arxiv","id":"2006.03427","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.03427","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"arxiv_version","alias_value":"2006.03427v1","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.03427","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"pith_short_12","alias_value":"T6Q5SVJT72G6","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"pith_short_16","alias_value":"T6Q5SVJT72G6YUWC","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"pith_short_8","alias_value":"T6Q5SVJT","created_at":"2026-07-05T01:08:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:T6Q5SVJT72G6YUWCSUWJNSYWVT","target":"record","payload":{"canonical_record":{"source":{"id":"2006.03427","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-05T13:26:05Z","cross_cats_sorted":[],"title_canon_sha256":"bd29ccced5a0382091e2e53f33aaa44884b082feea825c7b5c816716036409db","abstract_canon_sha256":"e4b89fe4943391ca8c6616a58d8950f2e3f7562dbe9e1bd5ae582b862bddd03b"},"schema_version":"1.0"},"canonical_sha256":"9fa1d95533fe8dec52c2952c96cb16acc79b74b54ae6a4b16e5eb3b4de44995a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:18.275956Z","signature_b64":"QkM6P7tyYjJxgb+3WiTvi9/4qj+kursKs2FNw5M4FQ47h7cwZTc0zEG/BYTvYn7Pb37f6Su6dQ6UYUBOqB3aAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fa1d95533fe8dec52c2952c96cb16acc79b74b54ae6a4b16e5eb3b4de44995a","last_reissued_at":"2026-07-05T01:08:18.275502Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:18.275502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.03427","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":"2026-07-05T01:08:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mwFgjI7Qn/ul5n3IZG/4iQdeL9vBjIx2mRUPDucnOK5RRqMuo/Xt3nkSkPKDIJcMrR/x41jKf+En7amUCjgRCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T15:00:22.396787Z"},"content_sha256":"d90f35a3215a845a1af37763ca2036d8dd1087279112ec06c0c8b74d75c72f3d","schema_version":"1.0","event_id":"sha256:d90f35a3215a845a1af37763ca2036d8dd1087279112ec06c0c8b74d75c72f3d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:T6Q5SVJT72G6YUWCSUWJNSYWVT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Neural Light Transport","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Geiger, Lars Mescheder, Paul Sanzenbacher","submitted_at":"2020-06-05T13:26:05Z","abstract_excerpt":"In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data augmentation for training computer vision models. While existing models are able to faithfully learn the image distribution of the training set, they often lack controllability as they operate in 2D pixel space and do not model the physical image formation process. In this work, we investigate the importance of 3D reasoning for photorealistic rendering. We present an approach for learning light transport in static and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.03427","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/2006.03427/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":"2026-07-05T01:08:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lM2NuyuguPuLOCSj5heWVY9UiogzQqEizv7koU2CYrVOOApUDwuKXOfrT4oOksq7Sa2kOR8W/GmD+7Q1V6jbAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T15:00:22.397099Z"},"content_sha256":"294e0d2d8ee5bf869a6e71ab7b7cdfe8a44ffe12bd74c099eea8f065ede4bdf7","schema_version":"1.0","event_id":"sha256:294e0d2d8ee5bf869a6e71ab7b7cdfe8a44ffe12bd74c099eea8f065ede4bdf7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T6Q5SVJT72G6YUWCSUWJNSYWVT/bundle.json","state_url":"https://pith.science/pith/T6Q5SVJT72G6YUWCSUWJNSYWVT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T6Q5SVJT72G6YUWCSUWJNSYWVT/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":"2026-08-11T15:00:22Z","links":{"resolver":"https://pith.science/pith/T6Q5SVJT72G6YUWCSUWJNSYWVT","bundle":"https://pith.science/pith/T6Q5SVJT72G6YUWCSUWJNSYWVT/bundle.json","state":"https://pith.science/pith/T6Q5SVJT72G6YUWCSUWJNSYWVT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T6Q5SVJT72G6YUWCSUWJNSYWVT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:T6Q5SVJT72G6YUWCSUWJNSYWVT","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":"e4b89fe4943391ca8c6616a58d8950f2e3f7562dbe9e1bd5ae582b862bddd03b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-05T13:26:05Z","title_canon_sha256":"bd29ccced5a0382091e2e53f33aaa44884b082feea825c7b5c816716036409db"},"schema_version":"1.0","source":{"id":"2006.03427","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.03427","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"arxiv_version","alias_value":"2006.03427v1","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.03427","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"pith_short_12","alias_value":"T6Q5SVJT72G6","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"pith_short_16","alias_value":"T6Q5SVJT72G6YUWC","created_at":"2026-07-05T01:08:18Z"},{"alias_kind":"pith_short_8","alias_value":"T6Q5SVJT","created_at":"2026-07-05T01:08:18Z"}],"graph_snapshots":[{"event_id":"sha256:294e0d2d8ee5bf869a6e71ab7b7cdfe8a44ffe12bd74c099eea8f065ede4bdf7","target":"graph","created_at":"2026-07-05T01:08:18Z","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/2006.03427/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data augmentation for training computer vision models. While existing models are able to faithfully learn the image distribution of the training set, they often lack controllability as they operate in 2D pixel space and do not model the physical image formation process. In this work, we investigate the importance of 3D reasoning for photorealistic rendering. We present an approach for learning light transport in static and ","authors_text":"Andreas Geiger, Lars Mescheder, Paul Sanzenbacher","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-05T13:26:05Z","title":"Learning Neural Light Transport"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.03427","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:d90f35a3215a845a1af37763ca2036d8dd1087279112ec06c0c8b74d75c72f3d","target":"record","created_at":"2026-07-05T01:08:18Z","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":"e4b89fe4943391ca8c6616a58d8950f2e3f7562dbe9e1bd5ae582b862bddd03b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-05T13:26:05Z","title_canon_sha256":"bd29ccced5a0382091e2e53f33aaa44884b082feea825c7b5c816716036409db"},"schema_version":"1.0","source":{"id":"2006.03427","kind":"arxiv","version":1}},"canonical_sha256":"9fa1d95533fe8dec52c2952c96cb16acc79b74b54ae6a4b16e5eb3b4de44995a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9fa1d95533fe8dec52c2952c96cb16acc79b74b54ae6a4b16e5eb3b4de44995a","first_computed_at":"2026-07-05T01:08:18.275502Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:08:18.275502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QkM6P7tyYjJxgb+3WiTvi9/4qj+kursKs2FNw5M4FQ47h7cwZTc0zEG/BYTvYn7Pb37f6Su6dQ6UYUBOqB3aAA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:08:18.275956Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.03427","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d90f35a3215a845a1af37763ca2036d8dd1087279112ec06c0c8b74d75c72f3d","sha256:294e0d2d8ee5bf869a6e71ab7b7cdfe8a44ffe12bd74c099eea8f065ede4bdf7"],"state_sha256":"8baf60e31d3f40b1bd029fe4f23201aee3a8fada37d35aa014e6b396add6bc0b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8cELOqZXnIfbLrKg5RWYcDE6ietmednUz2lWHo4r04ungclqhMqJ7XxmObzENcZRvJwmwx9tVZCh7ar/k4utBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T15:00:22.399522Z","bundle_sha256":"b17d687fd6a45acc793874e2bf36e21710633b9d5b239a7ce6f88243f863422b"}}