Transformer Enhanced Hierarchical 3D Point Cloud Semantic Segmentation

Point cloud can represent 3D geometry conveniently, but its challenging for computers to process it. In this work, we design a transformer enhanced hierarchical neural network for accurate large scale point cloud semantic segmentation. We use semantic space’s transformer block to learn global feature correlation. In this way, we can expand the receptive field of network to the whole input point cloud. Experimental results on S3DIS 3d semantic segmentation dataset show that, compared with the traditional hierarchical 3d semantic segmentation model, our transformer-enhanced hierarchical model achieved higher performance on overall accuracy and mloU.