{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:V72NK5TUTVK5DEQIN7UE6QIB36","short_pith_number":"pith:V72NK5TU","canonical_record":{"source":{"id":"1708.08844","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-29T15:58:18Z","cross_cats_sorted":[],"title_canon_sha256":"3e53f7210d8cddeb4564de860c1e757630018fc2aa7ef371d5c0c737d31bad90","abstract_canon_sha256":"8baf82c3d13e1afcf87d5d615d7e373e78579e584a058e8d53ca6bae4f510e48"},"schema_version":"1.0"},"canonical_sha256":"aff4d576749d55d192086fe84f4101df9749f930f95c425e77c518db9e93fc1b","source":{"kind":"arxiv","id":"1708.08844","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.08844","created_at":"2026-05-18T00:36:25Z"},{"alias_kind":"arxiv_version","alias_value":"1708.08844v1","created_at":"2026-05-18T00:36:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08844","created_at":"2026-05-18T00:36:25Z"},{"alias_kind":"pith_short_12","alias_value":"V72NK5TUTVK5","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"V72NK5TUTVK5DEQI","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"V72NK5TU","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:V72NK5TUTVK5DEQIN7UE6QIB36","target":"record","payload":{"canonical_record":{"source":{"id":"1708.08844","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-29T15:58:18Z","cross_cats_sorted":[],"title_canon_sha256":"3e53f7210d8cddeb4564de860c1e757630018fc2aa7ef371d5c0c737d31bad90","abstract_canon_sha256":"8baf82c3d13e1afcf87d5d615d7e373e78579e584a058e8d53ca6bae4f510e48"},"schema_version":"1.0"},"canonical_sha256":"aff4d576749d55d192086fe84f4101df9749f930f95c425e77c518db9e93fc1b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:25.473169Z","signature_b64":"O9sh7CIw36NPA4ROcP7tTVr3uuEEM0dncP8B8ODUMDcscUS6v2alQK7QyKk15e3yLEZhuQ2o6hMu1jjpE9aQDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aff4d576749d55d192086fe84f4101df9749f930f95c425e77c518db9e93fc1b","last_reissued_at":"2026-05-18T00:36:25.472479Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:25.472479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.08844","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:36:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GNSz9cZujCB5UHyH8MHg9SXJxVX796Av6krO5X3XnIkbPkn8fMTZbaMLA0TTAXAqiKAa/7OqHcerY/Azn7uOAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T16:40:57.788655Z"},"content_sha256":"2766aab1eb16aab4c18292e744c8dc4fa4a3d8edc46a54da3708ac67cbcd84a5","schema_version":"1.0","event_id":"sha256:2766aab1eb16aab4c18292e744c8dc4fa4a3d8edc46a54da3708ac67cbcd84a5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:V72NK5TUTVK5DEQIN7UE6QIB36","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semantic Texture for Robust Dense Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Davison, Jan Czarnowski, Stefan Leutenegger","submitted_at":"2017-08-29T15:58:18Z","abstract_excerpt":"We argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values. We use a straightforward Lucas-Kanade formulation of image alignment, with a schedule of iterations over the coarse-to-fine levels of a pyramid, and simply replace the usual image pyramid by the hierarchy of convolutional feature maps from a pre-trained CNN. The resulting dense alignment performance is much more robust to l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08844","kind":"arxiv","version":1},"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":"2026-05-18T00:36:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V2qKN/RiAfwF/ZQrfT/bIqT1U7DKR8hXgr/JfgiStWmKwkC6XJDIObPDO8h3E7s7URh4jmxescSyZL9yAMWIAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T16:40:57.789000Z"},"content_sha256":"b71d7b85fee33beb660c360c1d66df7e2b22a679dca4b23919f8a8fbf5ee7e91","schema_version":"1.0","event_id":"sha256:b71d7b85fee33beb660c360c1d66df7e2b22a679dca4b23919f8a8fbf5ee7e91"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V72NK5TUTVK5DEQIN7UE6QIB36/bundle.json","state_url":"https://pith.science/pith/V72NK5TUTVK5DEQIN7UE6QIB36/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V72NK5TUTVK5DEQIN7UE6QIB36/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":"2026-05-29T16:40:57Z","links":{"resolver":"https://pith.science/pith/V72NK5TUTVK5DEQIN7UE6QIB36","bundle":"https://pith.science/pith/V72NK5TUTVK5DEQIN7UE6QIB36/bundle.json","state":"https://pith.science/pith/V72NK5TUTVK5DEQIN7UE6QIB36/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V72NK5TUTVK5DEQIN7UE6QIB36/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:V72NK5TUTVK5DEQIN7UE6QIB36","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":"8baf82c3d13e1afcf87d5d615d7e373e78579e584a058e8d53ca6bae4f510e48","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-29T15:58:18Z","title_canon_sha256":"3e53f7210d8cddeb4564de860c1e757630018fc2aa7ef371d5c0c737d31bad90"},"schema_version":"1.0","source":{"id":"1708.08844","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.08844","created_at":"2026-05-18T00:36:25Z"},{"alias_kind":"arxiv_version","alias_value":"1708.08844v1","created_at":"2026-05-18T00:36:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08844","created_at":"2026-05-18T00:36:25Z"},{"alias_kind":"pith_short_12","alias_value":"V72NK5TUTVK5","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"V72NK5TUTVK5DEQI","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"V72NK5TU","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:b71d7b85fee33beb660c360c1d66df7e2b22a679dca4b23919f8a8fbf5ee7e91","target":"graph","created_at":"2026-05-18T00:36:25Z","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 argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values. We use a straightforward Lucas-Kanade formulation of image alignment, with a schedule of iterations over the coarse-to-fine levels of a pyramid, and simply replace the usual image pyramid by the hierarchy of convolutional feature maps from a pre-trained CNN. The resulting dense alignment performance is much more robust to l","authors_text":"Andrew Davison, Jan Czarnowski, Stefan Leutenegger","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-29T15:58:18Z","title":"Semantic Texture for Robust Dense Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08844","kind":"arxiv","version":1},"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":"sha256:2766aab1eb16aab4c18292e744c8dc4fa4a3d8edc46a54da3708ac67cbcd84a5","target":"record","created_at":"2026-05-18T00:36:25Z","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":"8baf82c3d13e1afcf87d5d615d7e373e78579e584a058e8d53ca6bae4f510e48","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-29T15:58:18Z","title_canon_sha256":"3e53f7210d8cddeb4564de860c1e757630018fc2aa7ef371d5c0c737d31bad90"},"schema_version":"1.0","source":{"id":"1708.08844","kind":"arxiv","version":1}},"canonical_sha256":"aff4d576749d55d192086fe84f4101df9749f930f95c425e77c518db9e93fc1b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aff4d576749d55d192086fe84f4101df9749f930f95c425e77c518db9e93fc1b","first_computed_at":"2026-05-18T00:36:25.472479Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:25.472479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O9sh7CIw36NPA4ROcP7tTVr3uuEEM0dncP8B8ODUMDcscUS6v2alQK7QyKk15e3yLEZhuQ2o6hMu1jjpE9aQDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:25.473169Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.08844","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2766aab1eb16aab4c18292e744c8dc4fa4a3d8edc46a54da3708ac67cbcd84a5","sha256:b71d7b85fee33beb660c360c1d66df7e2b22a679dca4b23919f8a8fbf5ee7e91"],"state_sha256":"badbe410978960e3eb8fcdca999e406542a0f13d9d55ffbbeb66951f391644a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M9rYcJIqr/PTxRgCbwN/l5LGlIwjJCaO2fFjabQw9Ir743s2TPs3japnG6KWFAbGD2YK1HrXfxdn7N8OFnDaCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T16:40:57.791249Z","bundle_sha256":"6cee92e96a57e119ba5882040bb7abf8eceacfde0c6245ddf1a65f4c0e9c73c2"}}