Sparse point-voxel aggregation network for efficient point cloud semantic segmentation

Abstract

Effective and efficient semantic segmentation of 3D point cloud data is important for many tasks. Many methods for point cloud semantic segmentation rely on computationally expensive sampling and grouping layers to process irregular points, while others convert irregular points into regular volumetric grids and process them with a 3D U-Net-based semantic segmentation network. However, most of these methods suffer from high computational costs and cannot be applied to the real-time processing of large-scale point clouds. To address these issues, we propose a computationally efficient point-voxel-based network architecture named Sparse Point-Voxel Aggregation Network (SPVAN) for point cloud semantic segmentation. It consists of an encoding layer that consists of sparse convolution and MLP layers and a new decoding layer called Point Feature Aggregation Layer (PFAL) that is only composed of feature interpolation and MLP layers. Compared with recent popular point-voxel-based methods with the U-Net-based network, our method does not need 3D convolution networks in the decoding layer and thus achieves a higher speed. Experimental results on the large-scale SemanticKITTI dataset show that our method gets a good balance between the efficiency and the performance. Moreover, our method achieves on-par or better performance than previous methods for semantic segmentation on the challenging S3DIS dataset.