A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

TL;DR

This paper reviews recent deep learning methods for 3D point cloud classification and segmentation, highlighting challenges and future directions in processing complex, unordered 3D data for applications like autonomous driving and robotics.

Contribution

It provides a comprehensive overview of deep learning techniques for 3D point cloud analysis, identifying key challenges and proposing future research directions.

Findings

Summarizes recent deep learning approaches for point cloud classification and segmentation.

Identifies challenges such as data irregularity and noise in 3D point cloud processing.

Suggests potential future research directions to improve 3D point cloud analysis.

Abstract

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.