A New Semantic Segmentation Method of Point Cloud Based on PointNet and VoxelNet

Abstract

Three-dimensional laser point cloud technology has been widely used in machine vision and three-dimensional reconstruction engineering, but scene recognition and semantic segmentation of three-dimensional point cloud is still developing and improving. With the gradual maturity of artificial neural networks of deep learning in the field of computer vision, it is possible to identify and segment three-dimensional point cloud scenes. However, most methods perform slowly and inaccurately in real-world outdoor scenes that are large and noisy. This paper proposes a new scenario segmentation method based on PointNet and VoxelNet to improve the efficiency and accuracy of semantic segmentation. Firstly, the voxel meshing method is used to downsample the point cloud scene while keeping the original information of the point cloud. Then the point cloud data is filtered by multiple methods to remove noises and smooth the surface of point clouds. Finally, the processed point cloud scene is trained and the ideal semantic segmentation result is obtained. The results of our method are evaluated on the datasets of BDCI and our own datasets.