{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T06:50:53Z","timestamp":1744181453234,"version":"3.37.3"},"reference-count":160,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision applications. This paper reviews the recent literature on key tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion. The review discusses the historical development of these methods, explores different model architectures, learning algorithms, and training datasets, and provides a comprehensive summary of the state-of-the-art in this domain. The paper presents a critical evaluation of the current limitations and challenges in the field, and identifies potential areas for future research. Furthermore, the emergence of transformative methodologies like PoinTr and SnowflakeNet is examined, highlighting their contributions and potential impact on the field. The potential cross-disciplinary applications of these techniques are also discussed, underscoring the broad scope and impact of these developments. This review fills a knowledge gap by offering a focused and comprehensive synthesis of recent research on deep learning techniques for 3D point cloud data processing, thereby serving as a useful resource for both novice and experienced researchers in the field.<\/jats:p>","DOI":"10.3390\/robotics12040100","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T04:50:41Z","timestamp":1689137441000},"page":"100","source":"Crossref","is-referenced-by-count":18,"title":["Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9713-2701","authenticated-orcid":false,"given":"Zifeng","family":"Ding","sequence":"first","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3299-1580","authenticated-orcid":false,"given":"Yuxuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6002-0094","authenticated-orcid":false,"given":"Sijin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Science, The University of Southampton, University Rd., Southampton SO17 1BJ, UK"}]},{"given":"Yan","family":"Pan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2162-3798","authenticated-orcid":false,"given":"Yanhong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical Engineering, Beijing Information Science and Technology University, NO.12 Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2944-7151","authenticated-orcid":false,"given":"Zebing","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama Meguro-Ku, Tokyo 152-8550, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"658280","DOI":"10.3389\/fnbot.2021.658280","article-title":"Robotics dexterous grasping: The methods based on point cloud and deep learning","volume":"15","author":"Duan","year":"2021","journal-title":"Front. 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