{"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/acvnet-attention-concatenation-volume-for","title":"Attention Concatenation Volume for Accurate and Efficient Stereo Matching","arxiv_id":"2203.02146","date":"2022-03-04","proceeding":"CVPR 2022 1","authors":["Gangwei Xu","Junda Cheng","Peng Guo","Xin Yang"],"abstract":"Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities even for textureless regions. The proposed cost volume is named attention concatenation volume (ACV) which can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the aggregation network can achieve higher accuracy for GwcNet. Furthermore, we design a highly accurate network (ACVNet) based on our ACV, which achieves state-of-the-art performance on several benchmarks.","url_abs":"https://arxiv.org/abs/2203.02146v3","url_pdf":"https://arxiv.org/pdf/2203.02146v3.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":[{"paper_slug":"acvnet-attention-concatenation-volume-for","repo_url":"https://github.com/gangweix/acvnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"acvnet-attention-concatenation-volume-for","repo_url":"https://github.com/ibaiGorordo/ONNX-ACVNet-Stereo-Depth-Estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"patch-matching","task_name":"Patch Matching"},{"task_slug":"stereo-depth-estimation","task_name":"Stereo Depth Estimation"},{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/stereo-depth-estimation-on-spring","task":"Stereo Depth Estimation","dataset":"Spring","model":"ACVNet","rank_in_archive_order":1,"of":4,"metrics":{"1px total":"14.772"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.02146","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02146"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ibaiGorordo/ONNX-ACVNet-Stereo-Depth-Estimation","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/gangweix/acvnet","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/gangweiX/ACVNet","reach":null}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"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":"10888f85cdc8be09","entry":"ACVNet","repo":"gangweiX/ACVNet","repo_kind":"official","path":"models/acv.py","file_url":"https://github.com/gangweiX/ACVNet/blob/HEAD/models/acv.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":"10888f85cdc8be09"}},{"code_sha256_prefix":"b6c41d129f1b1e36","entry":"feature_extraction","repo":"gangweiX/ACVNet","repo_kind":"official","path":"models/acv.py","file_url":"https://github.com/gangweiX/ACVNet/blob/HEAD/models/acv.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":"b6c41d129f1b1e36"}},{"code_sha256_prefix":"59871f207b1cdeff","entry":"hourglass","repo":"gangweiX/ACVNet","repo_kind":"official","path":"models/acv.py","file_url":"https://github.com/gangweiX/ACVNet/blob/HEAD/models/acv.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":"59871f207b1cdeff"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}