{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-intra-view-and-cross-view-geometric","title":"Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching","arxiv_id":"2402.19270","date":"2024-02-29","proceeding":"CVPR 2024 1","authors":["Rui Gong","Weide Liu","Zaiwang Gu","Xulei Yang","Jun Cheng"],"abstract":"Geometric knowledge has been shown to be beneficial for the stereo matching task. However, prior attempts to integrate geometric insights into stereo matching algorithms have largely focused on geometric knowledge from single images while crucial cross-view factors such as occlusion and matching uniqueness have been overlooked. To address this gap, we propose a novel Intra-view and Cross-view Geometric knowledge learning Network (ICGNet), specifically crafted to assimilate both intra-view and cross-view geometric knowledge. ICGNet harnesses the power of interest points to serve as a channel for intra-view geometric understanding. Simultaneously, it employs the correspondences among these points to capture cross-view geometric relationships. This dual incorporation empowers the proposed ICGNet to leverage both intra-view and cross-view geometric knowledge in its learning process, substantially improving its ability to estimate disparities. Our extensive experiments demonstrate the superiority of the ICGNet over contemporary leading models.","url_abs":"https://arxiv.org/abs/2402.19270v2","url_pdf":"https://arxiv.org/pdf/2402.19270v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.19270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19270"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/DFSDDDDD1199/ICGNet","reach":null}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"found_in_text":{"samples":2,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"a73b41942e43e0b4","entry":"Combined_Geo_Encoding_Volume","repo":"DFSDDDDD1199/ICGNet","repo_kind":"found_in_text","path":"core/geometry.py","file_url":"https://github.com/DFSDDDDD1199/ICGNet/blob/HEAD/core/geometry.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a73b41942e43e0b4"}},{"code_sha256_prefix":"e33c148d74a24460","entry":"bilinear_sampler","repo":"DFSDDDDD1199/ICGNet","repo_kind":"found_in_text","path":"core/geometry.py","file_url":"https://github.com/DFSDDDDD1199/ICGNet/blob/HEAD/core/geometry.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e33c148d74a24460"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}