Learning Scale-Adaptive Representations for Point-Level LiDAR Semantic Segmentation

A large number of objects with various scales and categories in autonomous driving scenes pose a great challenge to LiDAR semantic segmentation. Voxel-based 3D convolutional networks have been widely employed by existing state-of-the-art methods to extract features with different spatial scales. However, the voxel network architecture limits its effectiveness in combining multi-scale features for point-level discrimination. In this paper, we propose point-wise prediction by taking the geometric structure of the original point cloud into account. We propose a Scale-Adaptive Fusion (SAF) module that progressively and selectively fuses multi-scale features to deal with scale variations across objects adaptively. Moreover, we propose a novel Local Point Refinement (LPR) module to address the quantization loss problem of voxel-based methods. Our approach achieves state-of-the-art performance on three public datasets, i.e., Semantic-KITTI, Semantic-POSS, and nuScenes dataset, while greatly improving the computational and memory efficiency.