AI-Powered LiDAR Point Cloud Understanding and Processing: An Updated Survey

Current advances have enhanced the efficiency and availability of 3D data processing and scene understanding technologies, which confirms the pivotal status of point cloud data structure in 3D data transmission and storage. However, the intrinsic defects of point cloud data structure have always been a considerable challenge for model complexity and accuracy. As substantial researches have been conducted over the years, multitudinous compelling architectures applied in LiDAR point clouds are proposed in succession. To facilitate further research, we present a systematic integrated survey focusing explicitly on more than 200 key contributions to deep learning-based 3D LiDAR point cloud processing over the recent five years, detailing the revolution of feature extraction techniques of point cloud and specific deep learning-based tasks. Based on an introduction of the hardware sensors and devices of several LiDAR systems, the working mechanism and principle of 3D LiDAR point cloud acquisition and storage are interpreted. Moreover, about 30 publicly accessible datasets, including classical and latest outputs, are summarized and collated in accordance with different tasks. Comprehensive insights into the research challenges and opportunities in this topic are suggested. The contribution of this paper is to offer an up-to-date and all-sided overview of this realm, inspiring innovative explorations and further achievements in the AI-powered LiDAR point cloud understanding and processing.