Sampling Strategies for Efficient Segmentation and Object Detection of 3D Point Clouds

This is the authors' abstract. We don't add key points for this paper.

A key aspect of efficient point cloud processing is sampling, which significantly reduces computational overhead, memory requirements, and redundancy. In this paper, we first present a comprehensive review and in-depth analysis of existing representative heuristic-based and learning-based sampling methods, highlighting their merits and limitations. Subsequently, we introduce an efficient semantic segmentation method and an object detection method for large-scale 3D point clouds containing millions of points, with the point cloud sampling strategy serving as a crucial component. Through a detailed examination of the RandLA-Net and IA-SSD algorithms, we aim to provide valuable insights for future research and development in this domain. Our overarching goal is to advance the efficiency, accuracy, and scalability of point cloud processing techniques, ultimately benefiting a wide range of applications across various domains.