Abstract
Point cloud registration aims to find a rigid transformation for aligning one point cloud to another. Such registration is a fundamental problem in computer vision and robotics, and has been widely used in various applications, including 3D reconstruction, simultaneous localization and mapping, and autonomous driving. Over the last decades, numerous researchers have devoted themselves to tackling this challenging problem. The success of deep learning in high-level vision tasks has recently been extended to different geometric vision tasks. Various types of deep learning based point cloud registration methods have been proposed to exploit different aspects of the problem. However, a comprehensive overview of these approaches remains missing. To this end, in this paper, we summarize the recent progress in this area and present a comprehensive overview regarding deep learning based point cloud registration. We classify the popular approaches into different categories such as correspondences-based and correspondences-free approaches, with effective modules, i.e., feature extractor, matching, outlier rejection, and motion estimation modules. Furthermore, we discuss the merits and demerits of such approaches in detail. Finally, we provide a systematic and compact framework for currently proposed methods and discuss directions of future research.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.