{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:JIIKF6ALUCLWDL6VLWGYHGI5QI","short_pith_number":"pith:JIIKF6AL","canonical_record":{"source":{"id":"1803.07721","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T02:36:11Z","cross_cats_sorted":[],"title_canon_sha256":"810cd17f4c07dad78945b79cd851a13666d60cf2d258bace2df9d19c1cbc5285","abstract_canon_sha256":"6d9d58a96224044a955275c4044fd715239316150b63d086a195acce35d780ac"},"schema_version":"1.0"},"canonical_sha256":"4a10a2f80ba09761afd55d8d83991d822951d3988c4d08609c8bc8d41a189a6f","source":{"kind":"arxiv","id":"1803.07721","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07721","created_at":"2026-05-18T00:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07721v6","created_at":"2026-05-18T00:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07721","created_at":"2026-05-18T00:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"JIIKF6ALUCLW","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JIIKF6ALUCLWDL6V","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JIIKF6AL","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:JIIKF6ALUCLWDL6VLWGYHGI5QI","target":"record","payload":{"canonical_record":{"source":{"id":"1803.07721","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T02:36:11Z","cross_cats_sorted":[],"title_canon_sha256":"810cd17f4c07dad78945b79cd851a13666d60cf2d258bace2df9d19c1cbc5285","abstract_canon_sha256":"6d9d58a96224044a955275c4044fd715239316150b63d086a195acce35d780ac"},"schema_version":"1.0"},"canonical_sha256":"4a10a2f80ba09761afd55d8d83991d822951d3988c4d08609c8bc8d41a189a6f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:23.450600Z","signature_b64":"1VetcZTRGSsIAaH1N8H2QafP5X8O8m1/pbnEHq0eToJeJSw7ibby349GC3Ve0yXajkgOC4gDn3F0s1yWk16jDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a10a2f80ba09761afd55d8d83991d822951d3988c4d08609c8bc8d41a189a6f","last_reissued_at":"2026-05-18T00:04:23.449997Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:23.449997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.07721","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EJg4vzNWgW7jn+lTcy8h0iF/U2ehFmXOgrZkq6h/TGUAfCQZNcSIY2aezRIK3PuzIf6J2nd7iYVuZq0uZy04BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:51:04.453990Z"},"content_sha256":"7c0bb9dcb44fe9dcebbb0bcb11342ae7e70df30978a3e1028601d7deacf60248","schema_version":"1.0","event_id":"sha256:7c0bb9dcb44fe9dcebbb0bcb11342ae7e70df30978a3e1028601d7deacf60248"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:JIIKF6ALUCLWDL6VLWGYHGI5QI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Camera Effects to Improve Visual Learning from Synthetic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandra Carlson, Katherine A. Skinner, Matthew Johnson-Roberson, Ram Vasudevan","submitted_at":"2018-03-21T02:36:11Z","abstract_excerpt":"Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chrom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07721","kind":"arxiv","version":6},"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":"2026-05-18T00:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bwMFQ7sD6M2JPM4p6ph3VsMtXNPcrgqgJoNYFLAyUs5wv8Zh3CQ5wm4RqidYiK8DHefU+TvbtS0owRUMj1frAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:51:04.454706Z"},"content_sha256":"773ea9113e4a4dcd82efd68bccbb15501a5f343ccead68adfdd39436b519282e","schema_version":"1.0","event_id":"sha256:773ea9113e4a4dcd82efd68bccbb15501a5f343ccead68adfdd39436b519282e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JIIKF6ALUCLWDL6VLWGYHGI5QI/bundle.json","state_url":"https://pith.science/pith/JIIKF6ALUCLWDL6VLWGYHGI5QI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JIIKF6ALUCLWDL6VLWGYHGI5QI/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":"2026-05-27T03:51:04Z","links":{"resolver":"https://pith.science/pith/JIIKF6ALUCLWDL6VLWGYHGI5QI","bundle":"https://pith.science/pith/JIIKF6ALUCLWDL6VLWGYHGI5QI/bundle.json","state":"https://pith.science/pith/JIIKF6ALUCLWDL6VLWGYHGI5QI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JIIKF6ALUCLWDL6VLWGYHGI5QI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JIIKF6ALUCLWDL6VLWGYHGI5QI","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":"6d9d58a96224044a955275c4044fd715239316150b63d086a195acce35d780ac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T02:36:11Z","title_canon_sha256":"810cd17f4c07dad78945b79cd851a13666d60cf2d258bace2df9d19c1cbc5285"},"schema_version":"1.0","source":{"id":"1803.07721","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07721","created_at":"2026-05-18T00:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07721v6","created_at":"2026-05-18T00:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07721","created_at":"2026-05-18T00:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"JIIKF6ALUCLW","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JIIKF6ALUCLWDL6V","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JIIKF6AL","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:773ea9113e4a4dcd82efd68bccbb15501a5f343ccead68adfdd39436b519282e","target":"graph","created_at":"2026-05-18T00:04:23Z","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":"Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chrom","authors_text":"Alexandra Carlson, Katherine A. Skinner, Matthew Johnson-Roberson, Ram Vasudevan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T02:36:11Z","title":"Modeling Camera Effects to Improve Visual Learning from Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07721","kind":"arxiv","version":6},"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":"sha256:7c0bb9dcb44fe9dcebbb0bcb11342ae7e70df30978a3e1028601d7deacf60248","target":"record","created_at":"2026-05-18T00:04:23Z","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":"6d9d58a96224044a955275c4044fd715239316150b63d086a195acce35d780ac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-21T02:36:11Z","title_canon_sha256":"810cd17f4c07dad78945b79cd851a13666d60cf2d258bace2df9d19c1cbc5285"},"schema_version":"1.0","source":{"id":"1803.07721","kind":"arxiv","version":6}},"canonical_sha256":"4a10a2f80ba09761afd55d8d83991d822951d3988c4d08609c8bc8d41a189a6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4a10a2f80ba09761afd55d8d83991d822951d3988c4d08609c8bc8d41a189a6f","first_computed_at":"2026-05-18T00:04:23.449997Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:23.449997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1VetcZTRGSsIAaH1N8H2QafP5X8O8m1/pbnEHq0eToJeJSw7ibby349GC3Ve0yXajkgOC4gDn3F0s1yWk16jDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:23.450600Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.07721","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c0bb9dcb44fe9dcebbb0bcb11342ae7e70df30978a3e1028601d7deacf60248","sha256:773ea9113e4a4dcd82efd68bccbb15501a5f343ccead68adfdd39436b519282e"],"state_sha256":"2bd0e0004e17ba5e8d410e24587cb9638278acab492c5b6ebb2cac97d2e467e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"15K2M0qtQ7CtQdMzxCqkdYXxu7e3jo5I1v+qB00NMInF2/nRt7yKRA8PyXDLl0aga6lY+ua0jyI3ngSPP14kBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T03:51:04.458116Z","bundle_sha256":"d72947e97a63f81f4da22ff111b4711cea876a8f87c2dc8bffca6aa6ddc75f5e"}}