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.
@article{arxiv.2405.11903,
title = {A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation},
author = {Sushmita Sarker and Prithul Sarker and Gunner Stone and Ryan Gorman and Alireza Tavakkoli and George Bebis and Javad Sattarvand},
journal= {arXiv preprint arXiv:2405.11903},
year = {2024}
}
Comments
Published in Springer Nature (Machine Vision and Applications)