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Aiming at the problems in the matching method of constructing matching cost volume, we propose a disparity prediction algorithm based on Transformer, which specifically comprises the Swin-SPP module for feature extraction based on Swin Transformer, Transformer disparity matching network based on self-attention and cross-attention mechanism, and occlusion prediction sub-network. In addition, we propose a double skip connection fully connected layer to solve the problems of gradient vanishing and explosion during the training process for the Transformer model, thus further enhancing inference accuracy. The proposed model in this paper achieved an EPE (Absolute error) of 0.57 and 0.61, and a 3PE (Percentage error greater than 3 px) of 1.74% and 1.56% on KITTI 2012 and KITTI 2015 datasets, respectively, with an inference time of 0.46 s and parameters as low as only 2.6 M, showing great advantages compared with other algorithms in various evaluation metrics.<\/jats:p>","DOI":"10.3390\/s22197577","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"7577","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3056-7713","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3389-7518","authenticated-orcid":false,"given":"Xintao","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"School of Microelectronics, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bajian","family":"Xiang","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3157-0172","authenticated-orcid":false,"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Gong","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5928-9705","authenticated-orcid":false,"given":"Huaxiang","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100089, China"},{"name":"Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences, Beijing 200031, China"},{"name":"College of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ohta, Y., and Kanade, T. 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