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Current methods address the problem primarily from a 2D perspective, which limits the efficient utilization of 3D information. Herein, a novel approach, called pose orientation\u2010aware network (POANet), which leverages normal maps for orientation information embedding, providing abundant and robust head pose information, is introduced. POANet incorporates the axial signal perception module and the rotation matrix perception module, these lightweight modules make the approach achieve state\u2010of\u2010the\u2010art (SOTA) performance with few computational costs. This method can directly analyze various topological 3D data without extensive preprocessing. For depth images, POANet outperforms existing methods on the Biwi Kinect head pose dataset, reducing the mean absolute error (MAE) by \u224830% compared to the SOTA methods. POANet is the first method to perform rigid head registration in a landmark\u2010free manner. It also incorporates few\u2010shot learning capabilities and achieves an MAE of about  on the Headspace dataset. These features make POANet a superior alternative to traditional generalized Procrustes analysis for mesh data processing, offering enhanced convenience for human phenotype studies.<\/jats:p>","DOI":"10.1002\/aisy.202400159","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T02:54:47Z","timestamp":1728874487000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["3D Head Pose Estimation via Normal Maps: A Generalized Solution for Depth Image, Point Cloud, and Mesh"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4159-1911","authenticated-orcid":false,"given":"Jiang","family":"Wu","sequence":"first","affiliation":[{"name":"Beijing Institute of Genomics Chinese Academy of Sciences  Beijing 100101 China"},{"name":"China National Center for Bioinformation  Beijing 100101 China"},{"name":"School of Future Technology University of Chinese Academy of Sciences  Beijing 100049 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9829-6561","authenticated-orcid":false,"given":"Hua","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Institute of Genomics Chinese Academy of Sciences  Beijing 100101 China"},{"name":"China National Center for Bioinformation  Beijing 100101 China"},{"name":"School of Future Technology University of Chinese Academy of Sciences  Beijing 100049 China"},{"name":"CAS Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences  Kunming 650223 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3081873"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"E.Murphy\u2010Chutorian A.Doshi M. 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