SRX Net: A point cloud segmentation framework based on surface representation and X-Net

Abstract

We propose a novel unified point cloud segmentation framework called SRX Net. This framework is composed of our proposed SurRep Group module and X-Net architecture, reconstructing point cloud segmentation models from two aspects: point cloud surface representation and network architecture. The SurRep Group can model the local surface structure of the point cloud through structures such as the Ellipsoid Surface Representation and Umbrella Surface Representation, providing rich geometric information to the network. The XNet establishes an inverse encoding-decoding path to deepen the modeling of shallow semantics, solving the interference of shallow semantics on deep semantics in prediction in the U-Net architecture. It also introduces a GLFEnhancement module to enhance each point’s perception of global information. As a unified framework, SRX Net can improve the semantic segmentation effects of various existing models. Extensive experimental results show that our model produces more accurate segmentation edges and significantly reduces discrete predicted points. Based on Point Transformer, our SRX-PT Net achieves state-of-the-art performance of 72.1 mIoU on S3DIS, Scannet V2, and WCS3D datasets, respectively.