Point cloud semantic segmentation based on pillarpointnet

Abstract Aiming at the problem that the deep neural network pointnet does not introduce local features, the segmentation accuracy is not high and the segmentation efficiency of pointnet++ is low. On the basis of pointnet, a two-way feature extraction method, pillarpointnet, is proposed. Our network is divided into upper and lower parallel paths. The upper path is a simplified version of pointnet, which is used to extract global features. In the lower path, we use pillars instead of voxels to reduce the problem of wasting computing power due to inconsistency in density. The features of each voxel are then assigned to the points within the grid to represent the domain features of the points. Finally, the local features, global features and the points are concatenated and put into the segmentation network. The final experimental results show that compared to some current state-of-the-art segmentation networks, pillarpointnet maintains a good balance between speed and accuracy.