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Although learning\u2010based stereo matching algorithms have made remarkable progress, two major challenges still persist. Firstly, existing cost aggregation methods that use stacked three\u2010dimensional convolutions are complex, leading to heavy computation and memory costs. Secondly these methods continue to struggle with establishing reliable matches in weakly matchable such as that edges and thin structures. To overcome these limitations, we propose an accurate and efficient network called Attention\u2010guided Aggregation and Error\u2010aware Enhancement Network (AAEE\u2010Net). Our approach involves designing an Attention\u2010guided Aggregation Mechanism (AAM) based on simple image features. This mechanism uses attention weights generated from image features to guide cost aggregation with a more efficient and effective strategy. Additionally, we propose an Error\u2010aware Enhancement Module (EEM) that refines the raw disparity by combining high\u2010frequency information from the original image and warp error between the left and right views. EEM enables the network to learn error correction capabilities that produce excellent subtle details and sharp edges. The experimental results on the SceneFlow and KITTI benchmark datasets demonstrate that AAEE\u2010Net achieves state\u2010of\u2010the\u2010art performance with low inference time. The qualitative results show that AAEE\u2010Net significantly improves predictions, especially for thin structures.<\/jats:p>","DOI":"10.1002\/cpe.7744","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T22:02:51Z","timestamp":1683756171000},"update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AAEE\u2010Net: Attention\u2010guided aggregation and error\u2010aware enhancement network for accurate and efficient stereo matching"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0009-0007-0628-8630","authenticated-orcid":false,"given":"Yujun","family":"Liu","sequence":"first","affiliation":[{"name":"Computer Engineering College Jimei University Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangchen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Engineering College Jimei University Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhe","family":"Su","sequence":"additional","affiliation":[{"name":"Computer Engineering College Jimei University Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-8091-271X","authenticated-orcid":false,"given":"Guorong","family":"Cai","sequence":"additional","affiliation":[{"name":"Computer Engineering College Jimei University Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3145845"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3236900"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.07.002"},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","unstructured":"HeM ZhangJ ShanS ChenX.Enhancing face recognition with self\u2010supervised 3D reconstruction. 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