{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:Y42IVNJQXKZFX4HAFIK2YKEGFO","short_pith_number":"pith:Y42IVNJQ","canonical_record":{"source":{"id":"1809.04734","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-13T01:36:55Z","cross_cats_sorted":[],"title_canon_sha256":"1438da3a742f19984fae839f44165a20393329530270ecc8a0604f48d91ea8af","abstract_canon_sha256":"5c596db869d7cd3978382ba09f771f31a735384e4a7a3b0d70e52c2df73cdddc"},"schema_version":"1.0"},"canonical_sha256":"c7348ab530bab25bf0e02a15ac28862b91c00a4abaa969a4154efd46eb6e456d","source":{"kind":"arxiv","id":"1809.04734","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04734","created_at":"2026-05-17T23:46:46Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04734v2","created_at":"2026-05-17T23:46:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04734","created_at":"2026-05-17T23:46:46Z"},{"alias_kind":"pith_short_12","alias_value":"Y42IVNJQXKZF","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Y42IVNJQXKZFX4HA","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Y42IVNJQ","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:Y42IVNJQXKZFX4HAFIK2YKEGFO","target":"record","payload":{"canonical_record":{"source":{"id":"1809.04734","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-13T01:36:55Z","cross_cats_sorted":[],"title_canon_sha256":"1438da3a742f19984fae839f44165a20393329530270ecc8a0604f48d91ea8af","abstract_canon_sha256":"5c596db869d7cd3978382ba09f771f31a735384e4a7a3b0d70e52c2df73cdddc"},"schema_version":"1.0"},"canonical_sha256":"c7348ab530bab25bf0e02a15ac28862b91c00a4abaa969a4154efd46eb6e456d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:46.159284Z","signature_b64":"oFIaKVNihIhx1u7I3MJk9afnLAcHlwf0pWu/G8Fm/Lak+VfXcPLjnlPKBYDjtWGyX4II61XSrlBy/ErW791OAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7348ab530bab25bf0e02a15ac28862b91c00a4abaa969a4154efd46eb6e456d","last_reissued_at":"2026-05-17T23:46:46.158629Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:46.158629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.04734","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":"2026-05-17T23:46:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gz90zkGrn8u9nRytVWycOAVPL8fXtphOwEy3eNkW5NjNSZ49+0ZXxH/YFofwWRVEttwrvK7mtkhYxCG7Eg2TBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T22:31:46.551433Z"},"content_sha256":"39fad4c86a16fa64452559403c75970380794c5759a614d03363aea1e0e4d3f8","schema_version":"1.0","event_id":"sha256:39fad4c86a16fa64452559403c75970380794c5759a614d03363aea1e0e4d3f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:Y42IVNJQXKZFX4HAFIK2YKEGFO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junming Zhang, Katherine A. Skinner, Matthew Johnson-Roberson, Ram Vasudevan","submitted_at":"2018-09-13T01:36:55Z","abstract_excerpt":"Recent work has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low-texture, occlusions and reflections. Concurrently, deep learning for semantic segmentation has shown great progress in recent years. In this paper, we design a CNN architecture that combines these two tasks to improve the quality and accuracy of disparity estimation with the help of semantic segmentation. Specifically, we propose a network structure in which these two tasks are highly coupled. One key novelty of this a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04734","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":"2026-05-17T23:46:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mqRFIN9cBORSXecM8IrETVfnoy536X7gdBYjv87M/k87C+9oku1PcZORmdns8bHT4XiBRW8V/I712Hqhyf8YDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T22:31:46.552105Z"},"content_sha256":"eb5fa37efd239a5fc44b2b31eec284c180d7fe8ba6294096faaf5d9ccb7671bb","schema_version":"1.0","event_id":"sha256:eb5fa37efd239a5fc44b2b31eec284c180d7fe8ba6294096faaf5d9ccb7671bb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y42IVNJQXKZFX4HAFIK2YKEGFO/bundle.json","state_url":"https://pith.science/pith/Y42IVNJQXKZFX4HAFIK2YKEGFO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y42IVNJQXKZFX4HAFIK2YKEGFO/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":"2026-05-23T22:31:46Z","links":{"resolver":"https://pith.science/pith/Y42IVNJQXKZFX4HAFIK2YKEGFO","bundle":"https://pith.science/pith/Y42IVNJQXKZFX4HAFIK2YKEGFO/bundle.json","state":"https://pith.science/pith/Y42IVNJQXKZFX4HAFIK2YKEGFO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y42IVNJQXKZFX4HAFIK2YKEGFO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:Y42IVNJQXKZFX4HAFIK2YKEGFO","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":"5c596db869d7cd3978382ba09f771f31a735384e4a7a3b0d70e52c2df73cdddc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-13T01:36:55Z","title_canon_sha256":"1438da3a742f19984fae839f44165a20393329530270ecc8a0604f48d91ea8af"},"schema_version":"1.0","source":{"id":"1809.04734","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04734","created_at":"2026-05-17T23:46:46Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04734v2","created_at":"2026-05-17T23:46:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04734","created_at":"2026-05-17T23:46:46Z"},{"alias_kind":"pith_short_12","alias_value":"Y42IVNJQXKZF","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Y42IVNJQXKZFX4HA","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Y42IVNJQ","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:eb5fa37efd239a5fc44b2b31eec284c180d7fe8ba6294096faaf5d9ccb7671bb","target":"graph","created_at":"2026-05-17T23:46:46Z","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":"Recent work has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low-texture, occlusions and reflections. Concurrently, deep learning for semantic segmentation has shown great progress in recent years. In this paper, we design a CNN architecture that combines these two tasks to improve the quality and accuracy of disparity estimation with the help of semantic segmentation. Specifically, we propose a network structure in which these two tasks are highly coupled. One key novelty of this a","authors_text":"Junming Zhang, Katherine A. Skinner, Matthew Johnson-Roberson, Ram Vasudevan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-13T01:36:55Z","title":"DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04734","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":"sha256:39fad4c86a16fa64452559403c75970380794c5759a614d03363aea1e0e4d3f8","target":"record","created_at":"2026-05-17T23:46:46Z","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":"5c596db869d7cd3978382ba09f771f31a735384e4a7a3b0d70e52c2df73cdddc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-13T01:36:55Z","title_canon_sha256":"1438da3a742f19984fae839f44165a20393329530270ecc8a0604f48d91ea8af"},"schema_version":"1.0","source":{"id":"1809.04734","kind":"arxiv","version":2}},"canonical_sha256":"c7348ab530bab25bf0e02a15ac28862b91c00a4abaa969a4154efd46eb6e456d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c7348ab530bab25bf0e02a15ac28862b91c00a4abaa969a4154efd46eb6e456d","first_computed_at":"2026-05-17T23:46:46.158629Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:46.158629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oFIaKVNihIhx1u7I3MJk9afnLAcHlwf0pWu/G8Fm/Lak+VfXcPLjnlPKBYDjtWGyX4II61XSrlBy/ErW791OAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:46.159284Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.04734","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:39fad4c86a16fa64452559403c75970380794c5759a614d03363aea1e0e4d3f8","sha256:eb5fa37efd239a5fc44b2b31eec284c180d7fe8ba6294096faaf5d9ccb7671bb"],"state_sha256":"ed2bb3cc84be9494929181af2d6a2401b40fb43b08335bea93d151ee32885cd2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yfOxO+8npy5Gl2DFHvWdbR8zcRJ30GXq4hTnTt8Fs1PeJyycUgD8+01dxa5gJUxwhzR5ddRcRIz72dl5QR7ADQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T22:31:46.555721Z","bundle_sha256":"22d261bb4566e3dd1590472bc9b8c8cd5ee389e3b6aa1ff4e47666153f10106e"}}