基于深度学习的大规模三维点云处理综述

随着三维视觉的快速发展, 基于深度学习的大规模三维点云实时处理成为研究热点. 以三维空间分布无序的大规模三维点云为背景, 综合分析介绍并对比深度学习实时处理三维视觉问题的最新进展, 对点云分割、形状分类、目标检测等方面算法优势与不足进行详细分析, 给出详细的性能分析与优劣对比, 并对点云常用数据集进行简要介绍, 并给出不同数据集的算法性能对比. 最后, 指出未来在基于深度学习方法处理三维点云问题上的研究方向.

Survey on Large Scale 3D Point Cloud Processing Using Deep Learning

With the rapid development of 3D vision, large-scale 3D point cloud processing in real time based on deep learning has become a research hotspot. Taking a large-scale 3D point cloud with disordered spatial distribution as the background, this study comprehensively analyzes, introduces and compares the latest progress of deep learning in real-time processing of 3D vision problems. Then, it analyzes in detail and compares the advantages and disadvantages of algorithms in terms of point cloud segmentation, shape classification and target detection. Further, it briefly introduces the common data sets of point clouds and compares the algorithm performance of different data sets. Finally, the study points out the future research direction of 3D point cloud processing based on deep learning.