{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VBRK3FHC6TU3OFD5XKLY5QYYZX","short_pith_number":"pith:VBRK3FHC","canonical_record":{"source":{"id":"1807.03149","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T15:50:58Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"fb3e442925feedbb4c0a38e3b233ab048ab6134860f127aed1b579bb1c524297","abstract_canon_sha256":"d5fe8405105160e0f197d4c3ea7364df8ba1094426ece7a1183bc5ff9f5f744f"},"schema_version":"1.0"},"canonical_sha256":"a862ad94e2f4e9b7147dba978ec318cdf25d8d9beabf41596f1c31747ebb0ef7","source":{"kind":"arxiv","id":"1807.03149","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03149","created_at":"2026-05-17T23:58:30Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03149v2","created_at":"2026-05-17T23:58:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03149","created_at":"2026-05-17T23:58:30Z"},{"alias_kind":"pith_short_12","alias_value":"VBRK3FHC6TU3","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VBRK3FHC6TU3OFD5","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VBRK3FHC","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VBRK3FHC6TU3OFD5XKLY5QYYZX","target":"record","payload":{"canonical_record":{"source":{"id":"1807.03149","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T15:50:58Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"fb3e442925feedbb4c0a38e3b233ab048ab6134860f127aed1b579bb1c524297","abstract_canon_sha256":"d5fe8405105160e0f197d4c3ea7364df8ba1094426ece7a1183bc5ff9f5f744f"},"schema_version":"1.0"},"canonical_sha256":"a862ad94e2f4e9b7147dba978ec318cdf25d8d9beabf41596f1c31747ebb0ef7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:30.691478Z","signature_b64":"fW3WUYR3o/8jYcpRNHhW4DaLc4rMgxZeDYaKPT19BPg5BeLO/6+HTz+Ag2kFkiqRcByIF/a+RkwQE75KspjpCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a862ad94e2f4e9b7147dba978ec318cdf25d8d9beabf41596f1c31747ebb0ef7","last_reissued_at":"2026-05-17T23:58:30.690809Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:30.690809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.03149","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":"2026-05-17T23:58:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iD9FnfJotgG/zUfEWBKer968hArikuRNIKezfEPNRFB13ZyoAOk24pZy0BL5x4zG6De0e8QbJKjX5Ed/vvmcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T08:41:43.283483Z"},"content_sha256":"d43e5cc3295619d825fa0b9d2d3884f2f8db823090c8a64df4931fff249a85ab","schema_version":"1.0","event_id":"sha256:d43e5cc3295619d825fa0b9d2d3884f2f8db823090c8a64df4931fff249a85ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VBRK3FHC6TU3OFD5XKLY5QYYZX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning models for visual 3D localization with implicit mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Danilo J. Rezende, Dan Rosenbaum, Fabio Viola, Frederic Besse, S. M. Ali Eslami","submitted_at":"2018-07-04T15:50:58Z","abstract_excerpt":"We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels. The goal is to learn an implicit representation of the environment at a higher, more abstract level. We propose to use a generative approach based on Generative Query Networks (GQNs, Eslami et al. 2018), asking the following questions: 1) Can GQN capture more complex scenes than those it was originally demonstrated on? 2) Can GQN be used for localization in those scenes? To study this approach we consider procedurally generated Minecraft worlds"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03149","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":"2026-05-17T23:58:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BeI6g6enkGxFCYh8CZTzKLUkw2IFqZyrg6dw5ntHk3TAqfAs2bGmZME1L84koH6GpE3Re12bREqTugaisGqtAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T08:41:43.283847Z"},"content_sha256":"493ea7b449952b3efef9c88e982efee6dc886c259328ef92c523abaab1b6d017","schema_version":"1.0","event_id":"sha256:493ea7b449952b3efef9c88e982efee6dc886c259328ef92c523abaab1b6d017"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VBRK3FHC6TU3OFD5XKLY5QYYZX/bundle.json","state_url":"https://pith.science/pith/VBRK3FHC6TU3OFD5XKLY5QYYZX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VBRK3FHC6TU3OFD5XKLY5QYYZX/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":"2026-05-28T08:41:43Z","links":{"resolver":"https://pith.science/pith/VBRK3FHC6TU3OFD5XKLY5QYYZX","bundle":"https://pith.science/pith/VBRK3FHC6TU3OFD5XKLY5QYYZX/bundle.json","state":"https://pith.science/pith/VBRK3FHC6TU3OFD5XKLY5QYYZX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VBRK3FHC6TU3OFD5XKLY5QYYZX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VBRK3FHC6TU3OFD5XKLY5QYYZX","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":"d5fe8405105160e0f197d4c3ea7364df8ba1094426ece7a1183bc5ff9f5f744f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T15:50:58Z","title_canon_sha256":"fb3e442925feedbb4c0a38e3b233ab048ab6134860f127aed1b579bb1c524297"},"schema_version":"1.0","source":{"id":"1807.03149","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03149","created_at":"2026-05-17T23:58:30Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03149v2","created_at":"2026-05-17T23:58:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03149","created_at":"2026-05-17T23:58:30Z"},{"alias_kind":"pith_short_12","alias_value":"VBRK3FHC6TU3","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VBRK3FHC6TU3OFD5","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VBRK3FHC","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:493ea7b449952b3efef9c88e982efee6dc886c259328ef92c523abaab1b6d017","target":"graph","created_at":"2026-05-17T23:58:30Z","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 consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels. The goal is to learn an implicit representation of the environment at a higher, more abstract level. We propose to use a generative approach based on Generative Query Networks (GQNs, Eslami et al. 2018), asking the following questions: 1) Can GQN capture more complex scenes than those it was originally demonstrated on? 2) Can GQN be used for localization in those scenes? To study this approach we consider procedurally generated Minecraft worlds","authors_text":"Danilo J. Rezende, Dan Rosenbaum, Fabio Viola, Frederic Besse, S. M. Ali Eslami","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T15:50:58Z","title":"Learning models for visual 3D localization with implicit mapping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03149","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:d43e5cc3295619d825fa0b9d2d3884f2f8db823090c8a64df4931fff249a85ab","target":"record","created_at":"2026-05-17T23:58:30Z","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":"d5fe8405105160e0f197d4c3ea7364df8ba1094426ece7a1183bc5ff9f5f744f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-04T15:50:58Z","title_canon_sha256":"fb3e442925feedbb4c0a38e3b233ab048ab6134860f127aed1b579bb1c524297"},"schema_version":"1.0","source":{"id":"1807.03149","kind":"arxiv","version":2}},"canonical_sha256":"a862ad94e2f4e9b7147dba978ec318cdf25d8d9beabf41596f1c31747ebb0ef7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a862ad94e2f4e9b7147dba978ec318cdf25d8d9beabf41596f1c31747ebb0ef7","first_computed_at":"2026-05-17T23:58:30.690809Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:30.690809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fW3WUYR3o/8jYcpRNHhW4DaLc4rMgxZeDYaKPT19BPg5BeLO/6+HTz+Ag2kFkiqRcByIF/a+RkwQE75KspjpCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:30.691478Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.03149","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d43e5cc3295619d825fa0b9d2d3884f2f8db823090c8a64df4931fff249a85ab","sha256:493ea7b449952b3efef9c88e982efee6dc886c259328ef92c523abaab1b6d017"],"state_sha256":"101fa8accff8f7a522d02d0fd880152ebb80fb964382e7b49f1c4d9323c2ebab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"krzDxSGsqGHoEk5F6OCQEcKRCwoXRPZj7a/wypO5FosczMTLuqTnktfh0JGrL6/o2VMZttH0sagf2PgPjq6MAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T08:41:43.285847Z","bundle_sha256":"d0fac26e73ede51b054137b63b6e1b64eddc49654f4d95ae6144f4b5a860738c"}}