{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:HJ3GT2UZU24UK2RXACV43CA6A5","short_pith_number":"pith:HJ3GT2UZ","canonical_record":{"source":{"id":"1509.05909","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-09-19T16:01:05Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"92e85694b537920b33cc817a81f7a2174dee42986cce356a5f26f66999daf869","abstract_canon_sha256":"eaf7cd0d812f1c250b1e5e097044b70136104b2907017cf1f6982f3727b643b0"},"schema_version":"1.0"},"canonical_sha256":"3a7669ea99a6b9456a3700abcd881e074ded2ca5f0aaffe9bf6cac82792d397a","source":{"kind":"arxiv","id":"1509.05909","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.05909","created_at":"2026-05-18T01:20:25Z"},{"alias_kind":"arxiv_version","alias_value":"1509.05909v2","created_at":"2026-05-18T01:20:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.05909","created_at":"2026-05-18T01:20:25Z"},{"alias_kind":"pith_short_12","alias_value":"HJ3GT2UZU24U","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"HJ3GT2UZU24UK2RX","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"HJ3GT2UZ","created_at":"2026-05-18T12:29:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:HJ3GT2UZU24UK2RXACV43CA6A5","target":"record","payload":{"canonical_record":{"source":{"id":"1509.05909","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-09-19T16:01:05Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"92e85694b537920b33cc817a81f7a2174dee42986cce356a5f26f66999daf869","abstract_canon_sha256":"eaf7cd0d812f1c250b1e5e097044b70136104b2907017cf1f6982f3727b643b0"},"schema_version":"1.0"},"canonical_sha256":"3a7669ea99a6b9456a3700abcd881e074ded2ca5f0aaffe9bf6cac82792d397a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:25.948454Z","signature_b64":"2wj98gwWxMVo3lPSR1hNcqzRhYNE6QEotbDysIA8Znke6coyC6DWjkJoBdfP4hpXNCQy/WlFQVjQnRRHyYJyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a7669ea99a6b9456a3700abcd881e074ded2ca5f0aaffe9bf6cac82792d397a","last_reissued_at":"2026-05-18T01:20:25.947896Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:25.947896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1509.05909","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:20:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r8xAPhQXwIcfIiH5+Oyiuh3G3w3AGxeosnfm23T1MquDyh1607iaeHi7BDLKcDIHo7Dcw1h0kWOxfi0N0FN9Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T03:19:40.170619Z"},"content_sha256":"a23a2b4ff53c4ad62f13b25fe65450ab6c53f8b6bcd54f8330cb68e6fac126e2","schema_version":"1.0","event_id":"sha256:a23a2b4ff53c4ad62f13b25fe65450ab6c53f8b6bcd54f8330cb68e6fac126e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:HJ3GT2UZU24UK2RXACV43CA6A5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modelling Uncertainty in Deep Learning for Camera Relocalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Alex Kendall, Roberto Cipolla","submitted_at":"2015-09-19T16:01:05Z","abstract_excerpt":"We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. It is trained in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking under 6ms to compute. It obtains approximately 2m and 6 degrees accuracy for very large scale outdoor scenes and 0.5m and 10 degrees accuracy indoors. Using a Bayesian convolutional neural network implementation we obtain an estima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.05909","kind":"arxiv","version":2},"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":"2026-05-18T01:20:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kRRcPZSVgSLmzZyLsOqQtdwOfSJ7XVPNqHBQqjysdHgaCJDh5LnBZjR299n3ZxnzfCKu/170Ttx+VzUyY7+WDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T03:19:40.171080Z"},"content_sha256":"97195c2fad784288790bb34d27924fa32c1a1f965e214f14bab665cd7298b80d","schema_version":"1.0","event_id":"sha256:97195c2fad784288790bb34d27924fa32c1a1f965e214f14bab665cd7298b80d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HJ3GT2UZU24UK2RXACV43CA6A5/bundle.json","state_url":"https://pith.science/pith/HJ3GT2UZU24UK2RXACV43CA6A5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HJ3GT2UZU24UK2RXACV43CA6A5/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":"2026-08-20T03:19:40Z","links":{"resolver":"https://pith.science/pith/HJ3GT2UZU24UK2RXACV43CA6A5","bundle":"https://pith.science/pith/HJ3GT2UZU24UK2RXACV43CA6A5/bundle.json","state":"https://pith.science/pith/HJ3GT2UZU24UK2RXACV43CA6A5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HJ3GT2UZU24UK2RXACV43CA6A5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:HJ3GT2UZU24UK2RXACV43CA6A5","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":"eaf7cd0d812f1c250b1e5e097044b70136104b2907017cf1f6982f3727b643b0","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-09-19T16:01:05Z","title_canon_sha256":"92e85694b537920b33cc817a81f7a2174dee42986cce356a5f26f66999daf869"},"schema_version":"1.0","source":{"id":"1509.05909","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.05909","created_at":"2026-05-18T01:20:25Z"},{"alias_kind":"arxiv_version","alias_value":"1509.05909v2","created_at":"2026-05-18T01:20:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.05909","created_at":"2026-05-18T01:20:25Z"},{"alias_kind":"pith_short_12","alias_value":"HJ3GT2UZU24U","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"HJ3GT2UZU24UK2RX","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"HJ3GT2UZ","created_at":"2026-05-18T12:29:25Z"}],"graph_snapshots":[{"event_id":"sha256:97195c2fad784288790bb34d27924fa32c1a1f965e214f14bab665cd7298b80d","target":"graph","created_at":"2026-05-18T01:20:25Z","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":"We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. It is trained in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking under 6ms to compute. It obtains approximately 2m and 6 degrees accuracy for very large scale outdoor scenes and 0.5m and 10 degrees accuracy indoors. Using a Bayesian convolutional neural network implementation we obtain an estima","authors_text":"Alex Kendall, Roberto Cipolla","cross_cats":["cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-09-19T16:01:05Z","title":"Modelling Uncertainty in Deep Learning for Camera Relocalization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.05909","kind":"arxiv","version":2},"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:a23a2b4ff53c4ad62f13b25fe65450ab6c53f8b6bcd54f8330cb68e6fac126e2","target":"record","created_at":"2026-05-18T01:20:25Z","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":"eaf7cd0d812f1c250b1e5e097044b70136104b2907017cf1f6982f3727b643b0","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-09-19T16:01:05Z","title_canon_sha256":"92e85694b537920b33cc817a81f7a2174dee42986cce356a5f26f66999daf869"},"schema_version":"1.0","source":{"id":"1509.05909","kind":"arxiv","version":2}},"canonical_sha256":"3a7669ea99a6b9456a3700abcd881e074ded2ca5f0aaffe9bf6cac82792d397a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a7669ea99a6b9456a3700abcd881e074ded2ca5f0aaffe9bf6cac82792d397a","first_computed_at":"2026-05-18T01:20:25.947896Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:25.947896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2wj98gwWxMVo3lPSR1hNcqzRhYNE6QEotbDysIA8Znke6coyC6DWjkJoBdfP4hpXNCQy/WlFQVjQnRRHyYJyBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:25.948454Z","signed_message":"canonical_sha256_bytes"},"source_id":"1509.05909","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a23a2b4ff53c4ad62f13b25fe65450ab6c53f8b6bcd54f8330cb68e6fac126e2","sha256:97195c2fad784288790bb34d27924fa32c1a1f965e214f14bab665cd7298b80d"],"state_sha256":"c9ae27d914d52cafa8b5000c310016490f1c4e0153486e8470a0b64bca1bc726"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rEmwZNh5Y+R30DgLMCh1yI67b8EDgmat8S8jgSaZgew5d7IMkb0olaZ2M7OlVcQH9Zod65qS3SKGqNPDEXfgDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T03:19:40.175936Z","bundle_sha256":"f44c3cbf1f8bc8ee1d203d4ef19b59df3bdf7c3a69aa5b2dcac39497667bfa3a"}}