{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2XUYT52FWMB3WJTRIJJIZ7FOWR","short_pith_number":"pith:2XUYT52F","canonical_record":{"source":{"id":"1803.02286","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-06T16:26:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a299a3c9c4e9145582d3f45872d3ddeb788e270568d5fc995df5cee6d0921b76","abstract_canon_sha256":"f71fd19d6f02fe8ca8b6656a34740ece916e61ba7ef1cd152bea644c17b63f1c"},"schema_version":"1.0"},"canonical_sha256":"d5e989f745b303bb267142528cfcaeb459dadbe5751887457d9c4ea90a7a0773","source":{"kind":"arxiv","id":"1803.02286","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.02286","created_at":"2026-05-18T00:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"1803.02286v2","created_at":"2026-05-18T00:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.02286","created_at":"2026-05-18T00:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"2XUYT52FWMB3","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2XUYT52FWMB3WJTR","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2XUYT52F","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2XUYT52FWMB3WJTRIJJIZ7FOWR","target":"record","payload":{"canonical_record":{"source":{"id":"1803.02286","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-06T16:26:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a299a3c9c4e9145582d3f45872d3ddeb788e270568d5fc995df5cee6d0921b76","abstract_canon_sha256":"f71fd19d6f02fe8ca8b6656a34740ece916e61ba7ef1cd152bea644c17b63f1c"},"schema_version":"1.0"},"canonical_sha256":"d5e989f745b303bb267142528cfcaeb459dadbe5751887457d9c4ea90a7a0773","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:52.566295Z","signature_b64":"canDwiVLEfc8Q0lIhzL4roXF6Jkd7OnU6URKa/CvuVEzgHx51VFBBSYZ75vBpJprINYmycFNwZMA3zmdbc+TDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5e989f745b303bb267142528cfcaeb459dadbe5751887457d9c4ea90a7a0773","last_reissued_at":"2026-05-18T00:09:52.565682Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:52.565682Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.02286","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":"2026-05-18T00:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"05fcbQkQa4yEOId1os1Zl7Q4lRaXhIMFZ54ONghE2eNwIwonYuwnni4uEYThtnErx8IDcjmEtfL3DVKSnPGNAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T14:52:37.242650Z"},"content_sha256":"683ebfbf96a0df55c88b1d1eb03483d298ca9e42679cf039d94473327f231108","schema_version":"1.0","event_id":"sha256:683ebfbf96a0df55c88b1d1eb03483d298ca9e42679cf039d94473327f231108"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2XUYT52FWMB3WJTRIJJIZ7FOWR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning monocular visual odometry with dense 3D mapping from dense 3D flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Cheng Zhao, Li Sun, Pulak Purkait, Rustam Stolkin, Tom Duckett","submitted_at":"2018-03-06T16:26:06Z","abstract_excerpt":"This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow. Given this 3D flow, the dual-stream L-VO network can then predict the 6DOF relative pose and furthermore reconstruct the vehicle trajectory. In order to learn the correlation between motion directions, the Bivariate Gauss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.02286","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":"2026-05-18T00:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xJPG01+SUssRwFslx56txERlJWKfD1HcCsowGhkHxFrQ1/wmsn/qnandQWKuJASa+1LgsVSAg2UrlVueX50VDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T14:52:37.243260Z"},"content_sha256":"8f76017c5cd675b6c6d30bbb10ba609761309c7513630aa0607c1be69653232a","schema_version":"1.0","event_id":"sha256:8f76017c5cd675b6c6d30bbb10ba609761309c7513630aa0607c1be69653232a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2XUYT52FWMB3WJTRIJJIZ7FOWR/bundle.json","state_url":"https://pith.science/pith/2XUYT52FWMB3WJTRIJJIZ7FOWR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2XUYT52FWMB3WJTRIJJIZ7FOWR/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":"2026-05-31T14:52:37Z","links":{"resolver":"https://pith.science/pith/2XUYT52FWMB3WJTRIJJIZ7FOWR","bundle":"https://pith.science/pith/2XUYT52FWMB3WJTRIJJIZ7FOWR/bundle.json","state":"https://pith.science/pith/2XUYT52FWMB3WJTRIJJIZ7FOWR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2XUYT52FWMB3WJTRIJJIZ7FOWR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2XUYT52FWMB3WJTRIJJIZ7FOWR","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":"f71fd19d6f02fe8ca8b6656a34740ece916e61ba7ef1cd152bea644c17b63f1c","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-06T16:26:06Z","title_canon_sha256":"a299a3c9c4e9145582d3f45872d3ddeb788e270568d5fc995df5cee6d0921b76"},"schema_version":"1.0","source":{"id":"1803.02286","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.02286","created_at":"2026-05-18T00:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"1803.02286v2","created_at":"2026-05-18T00:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.02286","created_at":"2026-05-18T00:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"2XUYT52FWMB3","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2XUYT52FWMB3WJTR","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2XUYT52F","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:8f76017c5cd675b6c6d30bbb10ba609761309c7513630aa0607c1be69653232a","target":"graph","created_at":"2026-05-18T00:09:52Z","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":"This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow. Given this 3D flow, the dual-stream L-VO network can then predict the 6DOF relative pose and furthermore reconstruct the vehicle trajectory. In order to learn the correlation between motion directions, the Bivariate Gauss","authors_text":"Cheng Zhao, Li Sun, Pulak Purkait, Rustam Stolkin, Tom Duckett","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-06T16:26:06Z","title":"Learning monocular visual odometry with dense 3D mapping from dense 3D flow"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.02286","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":"sha256:683ebfbf96a0df55c88b1d1eb03483d298ca9e42679cf039d94473327f231108","target":"record","created_at":"2026-05-18T00:09:52Z","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":"f71fd19d6f02fe8ca8b6656a34740ece916e61ba7ef1cd152bea644c17b63f1c","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-06T16:26:06Z","title_canon_sha256":"a299a3c9c4e9145582d3f45872d3ddeb788e270568d5fc995df5cee6d0921b76"},"schema_version":"1.0","source":{"id":"1803.02286","kind":"arxiv","version":2}},"canonical_sha256":"d5e989f745b303bb267142528cfcaeb459dadbe5751887457d9c4ea90a7a0773","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d5e989f745b303bb267142528cfcaeb459dadbe5751887457d9c4ea90a7a0773","first_computed_at":"2026-05-18T00:09:52.565682Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:52.565682Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"canDwiVLEfc8Q0lIhzL4roXF6Jkd7OnU6URKa/CvuVEzgHx51VFBBSYZ75vBpJprINYmycFNwZMA3zmdbc+TDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:52.566295Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.02286","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:683ebfbf96a0df55c88b1d1eb03483d298ca9e42679cf039d94473327f231108","sha256:8f76017c5cd675b6c6d30bbb10ba609761309c7513630aa0607c1be69653232a"],"state_sha256":"a5b4f560c0ccb7d160c8a1005170b60fc658f8da32799e97972954729df547ae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VQo/cjpF8bj5W1YURFzN5FizhGwkaZGITZTJHsC6KtG/vbcHb97WgvI8VzF1MQRs7LiUSu9oMjkD+5veA8VOBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T14:52:37.246784Z","bundle_sha256":"740ed1b1ba73afc941b002b66f2b4d01742f0cb23e6428ef19c835dfacd3ed55"}}