{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6AHHY4DVDLOT5SCAUVL2AUEJL6","short_pith_number":"pith:6AHHY4DV","canonical_record":{"source":{"id":"1811.01292","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-03T22:24:00Z","cross_cats_sorted":[],"title_canon_sha256":"8dbda491927cab0523c170604fb06e164399dac2a3685508d5d5dea3470a89f1","abstract_canon_sha256":"2b1f411859f85b48a8e6d6235272cb9231e176bd56f2e4e44bf1d931746a74bc"},"schema_version":"1.0"},"canonical_sha256":"f00e7c70751add3ec840a557a050895fbbc6a2a6cdce2b2da88493e8f44b91fd","source":{"kind":"arxiv","id":"1811.01292","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.01292","created_at":"2026-05-18T00:00:42Z"},{"alias_kind":"arxiv_version","alias_value":"1811.01292v2","created_at":"2026-05-18T00:00:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01292","created_at":"2026-05-18T00:00:42Z"},{"alias_kind":"pith_short_12","alias_value":"6AHHY4DVDLOT","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"6AHHY4DVDLOT5SCA","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"6AHHY4DV","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6AHHY4DVDLOT5SCAUVL2AUEJL6","target":"record","payload":{"canonical_record":{"source":{"id":"1811.01292","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-03T22:24:00Z","cross_cats_sorted":[],"title_canon_sha256":"8dbda491927cab0523c170604fb06e164399dac2a3685508d5d5dea3470a89f1","abstract_canon_sha256":"2b1f411859f85b48a8e6d6235272cb9231e176bd56f2e4e44bf1d931746a74bc"},"schema_version":"1.0"},"canonical_sha256":"f00e7c70751add3ec840a557a050895fbbc6a2a6cdce2b2da88493e8f44b91fd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:42.876828Z","signature_b64":"Mg8tRbWTrAJRDTDhwMupGXtds0fNEkK76Ht86MBdPnKze6e2i3oD3G/rnqs32V7vDNuG85ZSqB57xTo9AxT7Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f00e7c70751add3ec840a557a050895fbbc6a2a6cdce2b2da88493e8f44b91fd","last_reissued_at":"2026-05-18T00:00:42.876475Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:42.876475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.01292","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":"2026-05-18T00:00:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n1wzUatGOVzggO6rHr1eApnybZQztyMT5JOEsUsLDDBkjJrbbq+u2D7ZXRcDN9SlpbCiP13oun2jfrjIdgGbBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T22:53:39.617824Z"},"content_sha256":"b015b2a233eeec19a1e9a7bfdba9f5a2126b7d841436b0e9f103c9a9e421f7f0","schema_version":"1.0","event_id":"sha256:b015b2a233eeec19a1e9a7bfdba9f5a2126b7d841436b0e9f103c9a9e421f7f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6AHHY4DVDLOT5SCAUVL2AUEJL6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geometry-Aware Recurrent Neural Networks for Active Visual Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Katerina Fragkiadaki, Ricson Cheng, Ziyan Wang","submitted_at":"2018-11-03T22:24:00Z","abstract_excerpt":"We present recurrent geometry-aware neural networks that integrate visual information across multiple views of a scene into 3D latent feature tensors, while maintaining an one-to-one mapping between 3D physical locations in the world scene and latent feature locations. Object detection, object segmentation, and 3D reconstruction is then carried out directly using the constructed 3D feature memory, as opposed to any of the input 2D images. The proposed models are equipped with differentiable egomotion-aware feature warping and (learned) depth-aware unprojection operations to achieve geometrical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01292","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":"2026-05-18T00:00:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sr2jM+iUB1y2YqTjrYgGqjYy9Ho4Xcnwi9SaFF/0aFUiLT79O9jlJS7Pwqoo30fcE1MNRHq01sVUbhpY3e0bAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T22:53:39.618467Z"},"content_sha256":"1d855ce9385f3d9a39f2d0310047a9f2658120186c06b79c4103b71675af02ef","schema_version":"1.0","event_id":"sha256:1d855ce9385f3d9a39f2d0310047a9f2658120186c06b79c4103b71675af02ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6AHHY4DVDLOT5SCAUVL2AUEJL6/bundle.json","state_url":"https://pith.science/pith/6AHHY4DVDLOT5SCAUVL2AUEJL6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6AHHY4DVDLOT5SCAUVL2AUEJL6/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":"2026-05-29T22:53:39Z","links":{"resolver":"https://pith.science/pith/6AHHY4DVDLOT5SCAUVL2AUEJL6","bundle":"https://pith.science/pith/6AHHY4DVDLOT5SCAUVL2AUEJL6/bundle.json","state":"https://pith.science/pith/6AHHY4DVDLOT5SCAUVL2AUEJL6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6AHHY4DVDLOT5SCAUVL2AUEJL6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6AHHY4DVDLOT5SCAUVL2AUEJL6","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":"2b1f411859f85b48a8e6d6235272cb9231e176bd56f2e4e44bf1d931746a74bc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-03T22:24:00Z","title_canon_sha256":"8dbda491927cab0523c170604fb06e164399dac2a3685508d5d5dea3470a89f1"},"schema_version":"1.0","source":{"id":"1811.01292","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.01292","created_at":"2026-05-18T00:00:42Z"},{"alias_kind":"arxiv_version","alias_value":"1811.01292v2","created_at":"2026-05-18T00:00:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01292","created_at":"2026-05-18T00:00:42Z"},{"alias_kind":"pith_short_12","alias_value":"6AHHY4DVDLOT","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"6AHHY4DVDLOT5SCA","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"6AHHY4DV","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:1d855ce9385f3d9a39f2d0310047a9f2658120186c06b79c4103b71675af02ef","target":"graph","created_at":"2026-05-18T00:00:42Z","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 recurrent geometry-aware neural networks that integrate visual information across multiple views of a scene into 3D latent feature tensors, while maintaining an one-to-one mapping between 3D physical locations in the world scene and latent feature locations. Object detection, object segmentation, and 3D reconstruction is then carried out directly using the constructed 3D feature memory, as opposed to any of the input 2D images. The proposed models are equipped with differentiable egomotion-aware feature warping and (learned) depth-aware unprojection operations to achieve geometrical","authors_text":"Katerina Fragkiadaki, Ricson Cheng, Ziyan Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-03T22:24:00Z","title":"Geometry-Aware Recurrent Neural Networks for Active Visual Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01292","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:b015b2a233eeec19a1e9a7bfdba9f5a2126b7d841436b0e9f103c9a9e421f7f0","target":"record","created_at":"2026-05-18T00:00:42Z","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":"2b1f411859f85b48a8e6d6235272cb9231e176bd56f2e4e44bf1d931746a74bc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-03T22:24:00Z","title_canon_sha256":"8dbda491927cab0523c170604fb06e164399dac2a3685508d5d5dea3470a89f1"},"schema_version":"1.0","source":{"id":"1811.01292","kind":"arxiv","version":2}},"canonical_sha256":"f00e7c70751add3ec840a557a050895fbbc6a2a6cdce2b2da88493e8f44b91fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f00e7c70751add3ec840a557a050895fbbc6a2a6cdce2b2da88493e8f44b91fd","first_computed_at":"2026-05-18T00:00:42.876475Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:42.876475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mg8tRbWTrAJRDTDhwMupGXtds0fNEkK76Ht86MBdPnKze6e2i3oD3G/rnqs32V7vDNuG85ZSqB57xTo9AxT7Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:42.876828Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.01292","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b015b2a233eeec19a1e9a7bfdba9f5a2126b7d841436b0e9f103c9a9e421f7f0","sha256:1d855ce9385f3d9a39f2d0310047a9f2658120186c06b79c4103b71675af02ef"],"state_sha256":"43cc8b5de16624d0864c185a7e4246e9f87404ee0ab9c245c182fdeda187ce47"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/sT85xOBPZwjcKC1k/0euSJdgpfAtBRvVHeMwn5VoiFNgg/qsoyHeGGyCzD0NhB6hRekWCAQ2L+MIWVHpC9GDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T22:53:39.621257Z","bundle_sha256":"eea930e4fae2b04abf0f85b399a35e9a6caf3880621c22cb2161afb06449712e"}}