{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ABOQO66KZIPHQAGUFM4NXD6AW3","short_pith_number":"pith:ABOQO66K","canonical_record":{"source":{"id":"1806.03994","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-11T14:01:31Z","cross_cats_sorted":[],"title_canon_sha256":"dbfc82ecf0a838bb0ed034bc82afdc5dce79657f0387f1fba93d94148cebab4d","abstract_canon_sha256":"6bd46bfc6d91af6e9dcc95d054363a6a9fad639559a6079911b3ad8a5b75a6ac"},"schema_version":"1.0"},"canonical_sha256":"005d077bcaca1e7800d42b38db8fc0b6e67b4f4700cd43f3c139e825e2819268","source":{"kind":"arxiv","id":"1806.03994","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.03994","created_at":"2026-05-18T00:08:20Z"},{"alias_kind":"arxiv_version","alias_value":"1806.03994v3","created_at":"2026-05-18T00:08:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03994","created_at":"2026-05-18T00:08:20Z"},{"alias_kind":"pith_short_12","alias_value":"ABOQO66KZIPH","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"ABOQO66KZIPHQAGU","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"ABOQO66K","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ABOQO66KZIPHQAGUFM4NXD6AW3","target":"record","payload":{"canonical_record":{"source":{"id":"1806.03994","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-11T14:01:31Z","cross_cats_sorted":[],"title_canon_sha256":"dbfc82ecf0a838bb0ed034bc82afdc5dce79657f0387f1fba93d94148cebab4d","abstract_canon_sha256":"6bd46bfc6d91af6e9dcc95d054363a6a9fad639559a6079911b3ad8a5b75a6ac"},"schema_version":"1.0"},"canonical_sha256":"005d077bcaca1e7800d42b38db8fc0b6e67b4f4700cd43f3c139e825e2819268","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:20.568012Z","signature_b64":"pOz9CXc6pqeau2EvYsAigdHeKCpNJ/y2ckPZwx0Dw+XfnmQi/eWCdGZfKcHeQrsDWDjL6vlK821YfPwvw5HRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"005d077bcaca1e7800d42b38db8fc0b6e67b4f4700cd43f3c139e825e2819268","last_reissued_at":"2026-05-18T00:08:20.567289Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:20.567289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.03994","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:08:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AAtb+MSrz+/1pa1hJ1tQBNIxVFQAK1KAmqGeXvhe1EkNNtB+3L74KarwpACdrIqGOuEXkKcnDXMgfsJkX0P3AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T17:53:19.737733Z"},"content_sha256":"3aae550055871ef982b005adb7e79d4c048a70d76354896329e46b7745ab6e2f","schema_version":"1.0","event_id":"sha256:3aae550055871ef982b005adb7e79d4c048a70d76354896329e46b7745ab6e2f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ABOQO66KZIPHQAGUFM4NXD6AW3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Estimate Indoor Lighting from 3D Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Donald Pr\\'evost, Henrique Weber, Jean-Fran\\c{c}ois Lalonde","submitted_at":"2018-06-11T14:01:31Z","abstract_excerpt":"In this work, we propose a step towards a more accurate prediction of the environment light given a single picture of a known object. To achieve this, we developed a deep learning method that is able to encode the latent space of indoor lighting using few parameters and that is trained on a database of environment maps. This latent space is then used to generate predictions of the light that are both more realistic and accurate than previous methods. To achieve this, our first contribution is a deep autoencoder which is capable of learning the feature space that compactly models lighting. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03994","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":""},"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":"2026-05-18T00:08:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y5crgn3YBi4Fz9FPQeUj+0x9CsDwLTpgbgiRLsMVm3ru4KOXKhc5s2gSh8WWD2HCVvqqFm5LtpUzF09CzekdBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T17:53:19.738112Z"},"content_sha256":"8a08bfc4ffecb5aa6fa3e402f2f4fdb8e5d2cea36b8b39263c6edf56bd6cc629","schema_version":"1.0","event_id":"sha256:8a08bfc4ffecb5aa6fa3e402f2f4fdb8e5d2cea36b8b39263c6edf56bd6cc629"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ABOQO66KZIPHQAGUFM4NXD6AW3/bundle.json","state_url":"https://pith.science/pith/ABOQO66KZIPHQAGUFM4NXD6AW3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ABOQO66KZIPHQAGUFM4NXD6AW3/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":"2026-06-05T17:53:19Z","links":{"resolver":"https://pith.science/pith/ABOQO66KZIPHQAGUFM4NXD6AW3","bundle":"https://pith.science/pith/ABOQO66KZIPHQAGUFM4NXD6AW3/bundle.json","state":"https://pith.science/pith/ABOQO66KZIPHQAGUFM4NXD6AW3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ABOQO66KZIPHQAGUFM4NXD6AW3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ABOQO66KZIPHQAGUFM4NXD6AW3","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":"6bd46bfc6d91af6e9dcc95d054363a6a9fad639559a6079911b3ad8a5b75a6ac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-11T14:01:31Z","title_canon_sha256":"dbfc82ecf0a838bb0ed034bc82afdc5dce79657f0387f1fba93d94148cebab4d"},"schema_version":"1.0","source":{"id":"1806.03994","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.03994","created_at":"2026-05-18T00:08:20Z"},{"alias_kind":"arxiv_version","alias_value":"1806.03994v3","created_at":"2026-05-18T00:08:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03994","created_at":"2026-05-18T00:08:20Z"},{"alias_kind":"pith_short_12","alias_value":"ABOQO66KZIPH","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"ABOQO66KZIPHQAGU","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"ABOQO66K","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:8a08bfc4ffecb5aa6fa3e402f2f4fdb8e5d2cea36b8b39263c6edf56bd6cc629","target":"graph","created_at":"2026-05-18T00:08:20Z","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"},"paper":{"abstract_excerpt":"In this work, we propose a step towards a more accurate prediction of the environment light given a single picture of a known object. To achieve this, we developed a deep learning method that is able to encode the latent space of indoor lighting using few parameters and that is trained on a database of environment maps. This latent space is then used to generate predictions of the light that are both more realistic and accurate than previous methods. To achieve this, our first contribution is a deep autoencoder which is capable of learning the feature space that compactly models lighting. Our ","authors_text":"Donald Pr\\'evost, Henrique Weber, Jean-Fran\\c{c}ois Lalonde","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-11T14:01:31Z","title":"Learning to Estimate Indoor Lighting from 3D Objects"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03994","kind":"arxiv","version":3},"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":"sha256:3aae550055871ef982b005adb7e79d4c048a70d76354896329e46b7745ab6e2f","target":"record","created_at":"2026-05-18T00:08:20Z","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":"6bd46bfc6d91af6e9dcc95d054363a6a9fad639559a6079911b3ad8a5b75a6ac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-11T14:01:31Z","title_canon_sha256":"dbfc82ecf0a838bb0ed034bc82afdc5dce79657f0387f1fba93d94148cebab4d"},"schema_version":"1.0","source":{"id":"1806.03994","kind":"arxiv","version":3}},"canonical_sha256":"005d077bcaca1e7800d42b38db8fc0b6e67b4f4700cd43f3c139e825e2819268","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"005d077bcaca1e7800d42b38db8fc0b6e67b4f4700cd43f3c139e825e2819268","first_computed_at":"2026-05-18T00:08:20.567289Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:20.567289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pOz9CXc6pqeau2EvYsAigdHeKCpNJ/y2ckPZwx0Dw+XfnmQi/eWCdGZfKcHeQrsDWDjL6vlK821YfPwvw5HRDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:20.568012Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.03994","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3aae550055871ef982b005adb7e79d4c048a70d76354896329e46b7745ab6e2f","sha256:8a08bfc4ffecb5aa6fa3e402f2f4fdb8e5d2cea36b8b39263c6edf56bd6cc629"],"state_sha256":"d35ef5302cdd692f175d427306664b10888ed218870b66b429396b208d2a5471"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KQIqHhkBkWTpiFrjzmd02CYZi+ciMBehJ3CKbyUf/W3bClB1bOFQLLzoQX3CCruFYVB4gYMM5V72uGc+oiuuCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T17:53:19.740153Z","bundle_sha256":"e51e3dff9b8bc208d446c6825bf35b5c04ab8bf6cf0733bb0d11f325a3f8c6a6"}}