An improved volumetric grid deep network model for point cloud segmentation

Abstract

Voxel grid is widely used in point cloud segmentation due to its regularity. However, the memory consumption caused by high resolution restricts the performance of voxel grid. This paper proposes an improved voxel grid deep network (IVDN) model to represent more comprehensive point cloud features at the same resolution, thus improving the segmentation performance of point cloud. Firstly, the point cloud data are structured within a voxel bounding box to correspond with the three-dimensional(3D) convolution kernel, and a fixed number of point coordinates are selected to generate the point feature vector. Then, in order to consider the distribution characteristics, the reliability coefficient is used as an equivalent descriptor of the point cloud distribution density. Finally, a corresponding deep network is constructed to deal with the above features. Experimental results show that the proposed IVDN model can improve the mean classification accuracy and segmentation index mIoU(Mean Intersection over Union) effectively, with a 0.45% and 0.3% improvement on Shape net16 dataset respectively.