A review of rigid point cloud registration based on deep learning

• Published in: Frontiers in neurorobotics Vol. 17; p. 1281332
• Main Authors: Chen, Lei, Feng, Changzhou, Ma, Yunpeng, Zhao, Yikai, Wang, Chaorong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 04.01.2024; Frontiers Media S.A
• Subjects: Data processing; Deep learning; Methods; network acceleration; neural networks; Neuroscience; Optimization techniques; partial overlap; point cloud registration; Registration; Reviews; Software; deep learning; partial overlap; point cloud registration; neural networks; network acceleration
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2023.1281332

With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.