{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ASTAXT7G74PBQAR5OUW2Q6US5W","short_pith_number":"pith:ASTAXT7G","schema_version":"1.0","canonical_sha256":"04a60bcfe6ff1e18023d752da87a92ed8bc13945085746bed532e748f8335730","source":{"kind":"arxiv","id":"1909.05452","version":1},"attestation_state":"computed","paper":{"title":"Flow-Motion and Depth Network for Monocular Stereo and Beyond","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kaixuan Wang, Shaojie Shen","submitted_at":"2019-09-12T04:49:38Z","abstract_excerpt":"We propose a learning-based method that solves monocular stereo and can be extended to fuse depth information from multiple target frames. Given two unconstrained images from a monocular camera with known intrinsic calibration, our network estimates relative camera poses and the depth map of the source image. The core contribution of the proposed method is threefold. First, a network is tailored for static scenes that jointly estimates the optical flow and camera motion. By the joint estimation, the optical flow search space is gradually reduced resulting in an efficient and accurate flow esti"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1909.05452","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-12T04:49:38Z","cross_cats_sorted":[],"title_canon_sha256":"913be1be6370ebe027aefb04f5fd92b5383cb70a3d4dfba58545512e9d7c76e8","abstract_canon_sha256":"8035e970dbed0cfa210ba0559e15b3c9dda429bb0a2c49b2a6822c0f6294c35b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:14.549227Z","signature_b64":"lYYZ5aORR/so2uC8WIHbsVAb67K8GFhIPAyG63AQ31eDXu1HRDssdxN2VhXRethtRPVAr35scaiKjnwY95FJBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04a60bcfe6ff1e18023d752da87a92ed8bc13945085746bed532e748f8335730","last_reissued_at":"2026-07-05T00:04:14.548761Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:14.548761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Flow-Motion and Depth Network for Monocular Stereo and Beyond","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kaixuan Wang, Shaojie Shen","submitted_at":"2019-09-12T04:49:38Z","abstract_excerpt":"We propose a learning-based method that solves monocular stereo and can be extended to fuse depth information from multiple target frames. Given two unconstrained images from a monocular camera with known intrinsic calibration, our network estimates relative camera poses and the depth map of the source image. The core contribution of the proposed method is threefold. First, a network is tailored for static scenes that jointly estimates the optical flow and camera motion. By the joint estimation, the optical flow search space is gradually reduced resulting in an efficient and accurate flow esti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.05452","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/1909.05452/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1909.05452","created_at":"2026-07-05T00:04:14.548823+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.05452v1","created_at":"2026-07-05T00:04:14.548823+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.05452","created_at":"2026-07-05T00:04:14.548823+00:00"},{"alias_kind":"pith_short_12","alias_value":"ASTAXT7G74PB","created_at":"2026-07-05T00:04:14.548823+00:00"},{"alias_kind":"pith_short_16","alias_value":"ASTAXT7G74PBQAR5","created_at":"2026-07-05T00:04:14.548823+00:00"},{"alias_kind":"pith_short_8","alias_value":"ASTAXT7G","created_at":"2026-07-05T00:04:14.548823+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21300","citing_title":"SCOPE: Scale-Consistent One-Pass Estimation of 3D Geometry","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12368","citing_title":"DepthMaster: Unified Monocular Depth Estimation for Perspective and Panoramic Images","ref_index":81,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30060","citing_title":"Towards Consistent Video Geometry Estimation","ref_index":66,"is_internal_anchor":false},{"citing_arxiv_id":"2507.02546","citing_title":"MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details","ref_index":58,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W","json":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W.json","graph_json":"https://pith.science/api/pith-number/ASTAXT7G74PBQAR5OUW2Q6US5W/graph.json","events_json":"https://pith.science/api/pith-number/ASTAXT7G74PBQAR5OUW2Q6US5W/events.json","paper":"https://pith.science/paper/ASTAXT7G"},"agent_actions":{"view_html":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W","download_json":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W.json","view_paper":"https://pith.science/paper/ASTAXT7G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.05452&json=true","fetch_graph":"https://pith.science/api/pith-number/ASTAXT7G74PBQAR5OUW2Q6US5W/graph.json","fetch_events":"https://pith.science/api/pith-number/ASTAXT7G74PBQAR5OUW2Q6US5W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W/action/storage_attestation","attest_author":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W/action/author_attestation","sign_citation":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W/action/citation_signature","submit_replication":"https://pith.science/pith/ASTAXT7G74PBQAR5OUW2Q6US5W/action/replication_record"}},"created_at":"2026-07-05T00:04:14.548823+00:00","updated_at":"2026-07-05T00:04:14.548823+00:00"}