{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7QYSJ3F7YFP7OJFANJO5EM54AX","short_pith_number":"pith:7QYSJ3F7","schema_version":"1.0","canonical_sha256":"fc3124ecbfc15ff724a06a5dd233bc05dc55056f48305a4bdfe4dbb927bfdbbf","source":{"kind":"arxiv","id":"2210.15374","version":1},"attestation_state":"computed","paper":{"title":"2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mansi Sharma, Rithvik Anil, Rohit Choudhary","submitted_at":"2022-10-27T12:34:41Z","abstract_excerpt":"Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction with twin convolution towers. These towers have an allowance for different weights between them. Additionally, the input for twin encoders in 2T-UNet are different compared to the existing stereo methods. Generally, a stereo network takes "},"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":"2210.15374","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-27T12:34:41Z","cross_cats_sorted":[],"title_canon_sha256":"89e5d7a396f6996fb6b9bcdf759461e0570742751fbf53c3d635b81bb5994955","abstract_canon_sha256":"8fbfc2a3104114b419b13840a5e9567fbb7a4423251c7b00b0e2d3baf4c833b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:05.563478Z","signature_b64":"J5hhJ9GErqVJtu/tePLBp1AdjdXNoRK1NplySpsp+lf6JXWURdpZlVDf4MuPD9KpfFJLt/svHw34SSzs9weiAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc3124ecbfc15ff724a06a5dd233bc05dc55056f48305a4bdfe4dbb927bfdbbf","last_reissued_at":"2026-07-05T05:11:05.562911Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:05.562911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mansi Sharma, Rithvik Anil, Rohit Choudhary","submitted_at":"2022-10-27T12:34:41Z","abstract_excerpt":"Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction with twin convolution towers. These towers have an allowance for different weights between them. Additionally, the input for twin encoders in 2T-UNet are different compared to the existing stereo methods. Generally, a stereo network takes "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15374","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/2210.15374/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":"2210.15374","created_at":"2026-07-05T05:11:05.562990+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.15374v1","created_at":"2026-07-05T05:11:05.562990+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15374","created_at":"2026-07-05T05:11:05.562990+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QYSJ3F7YFP7","created_at":"2026-07-05T05:11:05.562990+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QYSJ3F7YFP7OJFA","created_at":"2026-07-05T05:11:05.562990+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QYSJ3F7","created_at":"2026-07-05T05:11:05.562990+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX","json":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX.json","graph_json":"https://pith.science/api/pith-number/7QYSJ3F7YFP7OJFANJO5EM54AX/graph.json","events_json":"https://pith.science/api/pith-number/7QYSJ3F7YFP7OJFANJO5EM54AX/events.json","paper":"https://pith.science/paper/7QYSJ3F7"},"agent_actions":{"view_html":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX","download_json":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX.json","view_paper":"https://pith.science/paper/7QYSJ3F7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.15374&json=true","fetch_graph":"https://pith.science/api/pith-number/7QYSJ3F7YFP7OJFANJO5EM54AX/graph.json","fetch_events":"https://pith.science/api/pith-number/7QYSJ3F7YFP7OJFANJO5EM54AX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX/action/storage_attestation","attest_author":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX/action/author_attestation","sign_citation":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX/action/citation_signature","submit_replication":"https://pith.science/pith/7QYSJ3F7YFP7OJFANJO5EM54AX/action/replication_record"}},"created_at":"2026-07-05T05:11:05.562990+00:00","updated_at":"2026-07-05T05:11:05.562990+00:00"}