{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6LZVMLBXN7UDP6LORLUUNTHYTO","short_pith_number":"pith:6LZVMLBX","canonical_record":{"source":{"id":"1709.00930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-04T12:56:18Z","cross_cats_sorted":[],"title_canon_sha256":"7aafb02be1f3a1fed4874cdf7d4de77b848eff709d9e7421e2c75da4ffa07b16","abstract_canon_sha256":"732c02a345b4130ca409a220a76553a3a8d79ba13ba4243c9077ea74d22b3cd9"},"schema_version":"1.0"},"canonical_sha256":"f2f3562c376fe837f96e8ae946ccf89b8a46cee412fa81c60060061221f7ec10","source":{"kind":"arxiv","id":"1709.00930","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.00930","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"arxiv_version","alias_value":"1709.00930v1","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00930","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"pith_short_12","alias_value":"6LZVMLBXN7UD","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6LZVMLBXN7UDP6LO","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6LZVMLBX","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6LZVMLBXN7UDP6LORLUUNTHYTO","target":"record","payload":{"canonical_record":{"source":{"id":"1709.00930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-04T12:56:18Z","cross_cats_sorted":[],"title_canon_sha256":"7aafb02be1f3a1fed4874cdf7d4de77b848eff709d9e7421e2c75da4ffa07b16","abstract_canon_sha256":"732c02a345b4130ca409a220a76553a3a8d79ba13ba4243c9077ea74d22b3cd9"},"schema_version":"1.0"},"canonical_sha256":"f2f3562c376fe837f96e8ae946ccf89b8a46cee412fa81c60060061221f7ec10","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:04.699543Z","signature_b64":"swyLISXmgR/wSUezcKpdg4LFUjvsHfztzRQYg+Y+gv2GDORmi7yg7kYRs186+qOHsC6yjJPpW7BpkGbFA/jwBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2f3562c376fe837f96e8ae946ccf89b8a46cee412fa81c60060061221f7ec10","last_reissued_at":"2026-05-18T00:36:04.699096Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:04.699096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.00930","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":"2026-05-18T00:36:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RZjI/YBm5RT1YGfIiYa/+Ux+3nypbdDCGmyRer+P1jhyYxIFTStcersUAmw1plABwJkl0uNRtfOzCDlM8ewACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T05:57:10.683544Z"},"content_sha256":"87321077c8b32bd7939bbc0e5e781a411dd0c7b9a9968629572c0a2d0c81a88d","schema_version":"1.0","event_id":"sha256:87321077c8b32bd7939bbc0e5e781a411dd0c7b9a9968629572c0a2d0c81a88d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6LZVMLBXN7UDP6LORLUUNTHYTO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Supervised Learning for Stereo Matching with Self-Improving Ability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongdong Li, Yiran Zhong, Yuchao Dai","submitted_at":"2017-09-04T12:56:18Z","abstract_excerpt":"Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations. In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs. Training is performed in an end-to-end fashion without the need of ground-truth disparity maps. The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00930","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":"2026-05-18T00:36:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PcgL/gIjcAGFyBHT4IZlWixOmVc97PG2ojMKMuH4Z6EhGC9skrW3HcCNzEial2/2Esz4QwPWNSqzpawaav5bDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T05:57:10.683947Z"},"content_sha256":"4fe686767851116a3c0c67941e0666b7f2bd90c95470fd7d9a21e247846a9e57","schema_version":"1.0","event_id":"sha256:4fe686767851116a3c0c67941e0666b7f2bd90c95470fd7d9a21e247846a9e57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6LZVMLBXN7UDP6LORLUUNTHYTO/bundle.json","state_url":"https://pith.science/pith/6LZVMLBXN7UDP6LORLUUNTHYTO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6LZVMLBXN7UDP6LORLUUNTHYTO/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":"2026-05-30T05:57:10Z","links":{"resolver":"https://pith.science/pith/6LZVMLBXN7UDP6LORLUUNTHYTO","bundle":"https://pith.science/pith/6LZVMLBXN7UDP6LORLUUNTHYTO/bundle.json","state":"https://pith.science/pith/6LZVMLBXN7UDP6LORLUUNTHYTO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6LZVMLBXN7UDP6LORLUUNTHYTO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6LZVMLBXN7UDP6LORLUUNTHYTO","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":"732c02a345b4130ca409a220a76553a3a8d79ba13ba4243c9077ea74d22b3cd9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-04T12:56:18Z","title_canon_sha256":"7aafb02be1f3a1fed4874cdf7d4de77b848eff709d9e7421e2c75da4ffa07b16"},"schema_version":"1.0","source":{"id":"1709.00930","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.00930","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"arxiv_version","alias_value":"1709.00930v1","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00930","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"pith_short_12","alias_value":"6LZVMLBXN7UD","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6LZVMLBXN7UDP6LO","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6LZVMLBX","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:4fe686767851116a3c0c67941e0666b7f2bd90c95470fd7d9a21e247846a9e57","target":"graph","created_at":"2026-05-18T00:36:04Z","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":"Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations. In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs. Training is performed in an end-to-end fashion without the need of ground-truth disparity maps. The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map th","authors_text":"Hongdong Li, Yiran Zhong, Yuchao Dai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-04T12:56:18Z","title":"Self-Supervised Learning for Stereo Matching with Self-Improving Ability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00930","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":"sha256:87321077c8b32bd7939bbc0e5e781a411dd0c7b9a9968629572c0a2d0c81a88d","target":"record","created_at":"2026-05-18T00:36:04Z","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":"732c02a345b4130ca409a220a76553a3a8d79ba13ba4243c9077ea74d22b3cd9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-04T12:56:18Z","title_canon_sha256":"7aafb02be1f3a1fed4874cdf7d4de77b848eff709d9e7421e2c75da4ffa07b16"},"schema_version":"1.0","source":{"id":"1709.00930","kind":"arxiv","version":1}},"canonical_sha256":"f2f3562c376fe837f96e8ae946ccf89b8a46cee412fa81c60060061221f7ec10","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f2f3562c376fe837f96e8ae946ccf89b8a46cee412fa81c60060061221f7ec10","first_computed_at":"2026-05-18T00:36:04.699096Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:04.699096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"swyLISXmgR/wSUezcKpdg4LFUjvsHfztzRQYg+Y+gv2GDORmi7yg7kYRs186+qOHsC6yjJPpW7BpkGbFA/jwBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:04.699543Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.00930","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:87321077c8b32bd7939bbc0e5e781a411dd0c7b9a9968629572c0a2d0c81a88d","sha256:4fe686767851116a3c0c67941e0666b7f2bd90c95470fd7d9a21e247846a9e57"],"state_sha256":"cc425374f7b49427b63ac3d251470c07267e2ffea2613a552aaa9bbaadcf8671"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rkR0u1dO9J8XPtkuHEce7E9094A9VFQ7plNoqPpZhQw4Mi7LhuxyCM2weLMy89XVtVSw/xeJjIm28CsP0bmuDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T05:57:10.686784Z","bundle_sha256":"82a52e03d629937a9964ed76278f78279f528f3efc86322abe57040a3a78b099"}}