A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation
It provides a comprehensive overview for researchers and practitioners working on point cloud analysis in areas like computer vision and autonomous driving, but it is incremental as it synthesizes existing methods rather than introducing new ones.
This paper reviews deep learning techniques for 3D point cloud classification and semantic segmentation, analyzing recent progress and identifying challenges to guide future research in the field.
Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation.