{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZCUWQRJVESXTQLQ6IRKKPCQ6HG","short_pith_number":"pith:ZCUWQRJV","schema_version":"1.0","canonical_sha256":"c8a968453524af382e1e4454a78a1e39bd76261bb6d9c9b2195ff12d7a9b2b72","source":{"kind":"arxiv","id":"1812.04605","version":4},"attestation_state":"computed","paper":{"title":"DeepV2D: Video to Depth with Differentiable Structure from Motion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jia Deng, Zachary Teed","submitted_at":"2018-12-11T18:47:12Z","abstract_excerpt":"We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are alternated and converge to accurate depth. Code is available https://github.com/princeton-vl/DeepV2D."},"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":"1812.04605","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-11T18:47:12Z","cross_cats_sorted":[],"title_canon_sha256":"f112cc0d61b5a00dc52384fbbe73b7cd939e2bf277c730322feae82ac1173c72","abstract_canon_sha256":"7e245cf754e7b2f44e0ce9099ca38f90dfa64481bc78190224e30c6854836647"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:58:31.457793Z","signature_b64":"nbc23XRuM/W6RSBzEsmaD0H10D7nVFhsXv8ozmd3+UaK9T9rPvGo/fA6+IfkUdLuzqAxL3s2ymweM+NdtuJ1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8a968453524af382e1e4454a78a1e39bd76261bb6d9c9b2195ff12d7a9b2b72","last_reissued_at":"2026-07-05T00:58:31.457300Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:58:31.457300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepV2D: Video to Depth with Differentiable Structure from Motion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jia Deng, Zachary Teed","submitted_at":"2018-12-11T18:47:12Z","abstract_excerpt":"We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are alternated and converge to accurate depth. Code is available https://github.com/princeton-vl/DeepV2D."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04605","kind":"arxiv","version":4},"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/1812.04605/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":"1812.04605","created_at":"2026-07-05T00:58:31.457372+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.04605v4","created_at":"2026-07-05T00:58:31.457372+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04605","created_at":"2026-07-05T00:58:31.457372+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZCUWQRJVESXT","created_at":"2026-07-05T00:58:31.457372+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZCUWQRJVESXTQLQ6","created_at":"2026-07-05T00:58:31.457372+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZCUWQRJV","created_at":"2026-07-05T00:58:31.457372+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.19257","citing_title":"PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19257","citing_title":"PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19727","citing_title":"Tango3D: Towards Alignment for Global and Local 2D-3D Correspondence","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2507.16443","citing_title":"VGGT-Long: Chunk it, Loop it, Align it -- Pushing VGGT's Limits on Kilometer-scale Long RGB Sequences","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2410.03825","citing_title":"MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion","ref_index":148,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08320","citing_title":"Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks","ref_index":70,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22339","citing_title":"Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06830","citing_title":"VGGT-SLAM++","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2511.10647","citing_title":"Depth Anything 3: Recovering the Visual Space from Any Views","ref_index":86,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG","json":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG.json","graph_json":"https://pith.science/api/pith-number/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/graph.json","events_json":"https://pith.science/api/pith-number/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/events.json","paper":"https://pith.science/paper/ZCUWQRJV"},"agent_actions":{"view_html":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG","download_json":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG.json","view_paper":"https://pith.science/paper/ZCUWQRJV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.04605&json=true","fetch_graph":"https://pith.science/api/pith-number/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/graph.json","fetch_events":"https://pith.science/api/pith-number/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/action/storage_attestation","attest_author":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/action/author_attestation","sign_citation":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/action/citation_signature","submit_replication":"https://pith.science/pith/ZCUWQRJVESXTQLQ6IRKKPCQ6HG/action/replication_record"}},"created_at":"2026-07-05T00:58:31.457372+00:00","updated_at":"2026-07-05T00:58:31.457372+00:00"}