{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:ZQ2QPB526CTTCKOPEWOK7UUH2P","short_pith_number":"pith:ZQ2QPB52","canonical_record":{"source":{"id":"2006.02535","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-01T13:09:46Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"b4b304dc69f1b201777952c64d08e2f92223a7635126b5408f84be668e5aad3f","abstract_canon_sha256":"4120d9001e25543d9425d2cc251e74a63ba0e11a5d222c71fbaf8ac8492e94a4"},"schema_version":"1.0"},"canonical_sha256":"cc350787baf0a73129cf259cafd287d3fe168d34b102189e754124e333a84d7e","source":{"kind":"arxiv","id":"2006.02535","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.02535","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"arxiv_version","alias_value":"2006.02535v1","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.02535","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZQ2QPB526CTT","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"pith_short_16","alias_value":"ZQ2QPB526CTTCKOP","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"pith_short_8","alias_value":"ZQ2QPB52","created_at":"2026-07-05T02:09:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:ZQ2QPB526CTTCKOPEWOK7UUH2P","target":"record","payload":{"canonical_record":{"source":{"id":"2006.02535","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-01T13:09:46Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"b4b304dc69f1b201777952c64d08e2f92223a7635126b5408f84be668e5aad3f","abstract_canon_sha256":"4120d9001e25543d9425d2cc251e74a63ba0e11a5d222c71fbaf8ac8492e94a4"},"schema_version":"1.0"},"canonical_sha256":"cc350787baf0a73129cf259cafd287d3fe168d34b102189e754124e333a84d7e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:09:10.742515Z","signature_b64":"96F9idPJEOdHmSPxuxbavFZxrZ39UlfKecDf64587bSd/Ewx2XYOT3CKtpHLaNRzIo9IpSCVDkWJMhKsNOKiDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc350787baf0a73129cf259cafd287d3fe168d34b102189e754124e333a84d7e","last_reissued_at":"2026-07-05T02:09:10.742091Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:09:10.742091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.02535","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":"2026-07-05T02:09:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ongWn/YREwR33UwIudDKMb3N1LcH5Yc0RJx21Q7I64OGbByPylWxfB/Od8GtlR9et6MhfcJELMnsuoz3VUr2AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T12:54:49.808982Z"},"content_sha256":"13e1ffcd68d403620bb12c19a729d8a31cd675492bef17f5160fd0f8cecafd11","schema_version":"1.0","event_id":"sha256:13e1ffcd68d403620bb12c19a729d8a31cd675492bef17f5160fd0f8cecafd11"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:ZQ2QPB526CTTCKOPEWOK7UUH2P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on Deep Learning Techniques for Stereo-based Depth Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Farid Boussaid, Hamid Laga, Laurent Valentin Jospin, Mohammed Bennamoun","submitted_at":"2020-06-01T13:09:46Z","abstract_excerpt":"Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large unifo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.02535","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.02535/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:09:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1R7eAEPrWStDbzLuIrI5w25SyDgKrByzndyhUDon0EUiZZHId8UB5dUctkqJj2BLeKV3OL/dNSkkmb9E8mTNCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T12:54:49.809388Z"},"content_sha256":"75e7cc037e6308f4aa96a6f83ac198458834d255f3f02c29884b0492eff29091","schema_version":"1.0","event_id":"sha256:75e7cc037e6308f4aa96a6f83ac198458834d255f3f02c29884b0492eff29091"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZQ2QPB526CTTCKOPEWOK7UUH2P/bundle.json","state_url":"https://pith.science/pith/ZQ2QPB526CTTCKOPEWOK7UUH2P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZQ2QPB526CTTCKOPEWOK7UUH2P/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":"2026-07-05T12:54:49Z","links":{"resolver":"https://pith.science/pith/ZQ2QPB526CTTCKOPEWOK7UUH2P","bundle":"https://pith.science/pith/ZQ2QPB526CTTCKOPEWOK7UUH2P/bundle.json","state":"https://pith.science/pith/ZQ2QPB526CTTCKOPEWOK7UUH2P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZQ2QPB526CTTCKOPEWOK7UUH2P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:ZQ2QPB526CTTCKOPEWOK7UUH2P","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":"4120d9001e25543d9425d2cc251e74a63ba0e11a5d222c71fbaf8ac8492e94a4","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-01T13:09:46Z","title_canon_sha256":"b4b304dc69f1b201777952c64d08e2f92223a7635126b5408f84be668e5aad3f"},"schema_version":"1.0","source":{"id":"2006.02535","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.02535","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"arxiv_version","alias_value":"2006.02535v1","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.02535","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZQ2QPB526CTT","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"pith_short_16","alias_value":"ZQ2QPB526CTTCKOP","created_at":"2026-07-05T02:09:10Z"},{"alias_kind":"pith_short_8","alias_value":"ZQ2QPB52","created_at":"2026-07-05T02:09:10Z"}],"graph_snapshots":[{"event_id":"sha256:75e7cc037e6308f4aa96a6f83ac198458834d255f3f02c29884b0492eff29091","target":"graph","created_at":"2026-07-05T02:09:10Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.02535/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large unifo","authors_text":"Farid Boussaid, Hamid Laga, Laurent Valentin Jospin, Mohammed Bennamoun","cross_cats":["cs.GR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-01T13:09:46Z","title":"A Survey on Deep Learning Techniques for Stereo-based Depth Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.02535","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":"sha256:13e1ffcd68d403620bb12c19a729d8a31cd675492bef17f5160fd0f8cecafd11","target":"record","created_at":"2026-07-05T02:09:10Z","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":"4120d9001e25543d9425d2cc251e74a63ba0e11a5d222c71fbaf8ac8492e94a4","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-01T13:09:46Z","title_canon_sha256":"b4b304dc69f1b201777952c64d08e2f92223a7635126b5408f84be668e5aad3f"},"schema_version":"1.0","source":{"id":"2006.02535","kind":"arxiv","version":1}},"canonical_sha256":"cc350787baf0a73129cf259cafd287d3fe168d34b102189e754124e333a84d7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc350787baf0a73129cf259cafd287d3fe168d34b102189e754124e333a84d7e","first_computed_at":"2026-07-05T02:09:10.742091Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:09:10.742091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"96F9idPJEOdHmSPxuxbavFZxrZ39UlfKecDf64587bSd/Ewx2XYOT3CKtpHLaNRzIo9IpSCVDkWJMhKsNOKiDw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:09:10.742515Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.02535","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:13e1ffcd68d403620bb12c19a729d8a31cd675492bef17f5160fd0f8cecafd11","sha256:75e7cc037e6308f4aa96a6f83ac198458834d255f3f02c29884b0492eff29091"],"state_sha256":"9c9af6593742ae10d4a467c6ab490d148e750efc7b8920da0326dd83713d8c3c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tkk0nfG7CimIEgiSdI3RDhfhFejTEvSLrr/+4olOCYB3l5Tv2c1GXikjxPtfMhWByOQcndJuow5AWNJO6EzoAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T12:54:49.811785Z","bundle_sha256":"31500a037e44b763d904cbdf622bd6ce3b6a0c0695537418e591d301ba6df3ba"}}