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One of the most commonly used pose estimation methods first detects the 2D pose keypoints in the input image and then calculates the 6D pose using a pose solver. Recently, deep learning is widely used in pose keypoint detection and performs excellent accuracy and adaptability. However, its over\u2010reliance on sufficient and high\u2010quality samples and supervision is prominent, particularly in the industrial field, leading to high data cost. Based on domain adaptation and computer\u2010aided\u2010design (CAD) models, herein, a virtual\u2010to\u2010real knowledge transfer method for pose keypoint detection to reduce the data cost of deep learning is proposed. To address the disorder of knowledge flow, a viewpoint\u2010driven feature alignment strategy is proposed to simultaneously eliminate interdomain differences and preserve intradomain differences. The shape invariance of rigid objects is then introduced as constraints to address the large assumption space problem in the regressive domain adaptation. The multidimensional experimental results demonstrate the superiority of the method. Without real annotations, the normalized pixel error of keypoint detection is reported as 0.033, and the proportion of pixel errors lower than 0.05 is up to 92.77%.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200214","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:14:15Z","timestamp":1674173655000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Knowledge Transfer Method for Unsupervised Pose Keypoint Detection Based on Domain Adaptation and CAD Models"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5599-1313","authenticated-orcid":false,"given":"Fuzhou","family":"Du","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Automation Beihang University  37 College Road Haidian Beijing 100191 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2731-1736","authenticated-orcid":false,"given":"Feifei","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation Beihang University  37 College Road Haidian Beijing 100191 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2773-2263","authenticated-orcid":false,"given":"Delong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation Beihang University  37 College Road Haidian Beijing 100191 China"}]}],"member":"311","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/app11010228"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"M.Rad V.Lepetit inIEEE Int. 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