Improving Radial Imbalances with Hybrid Voxelization and RadialMix for LiDAR 3D Semantic Segmentation

Huge progress has been made in LiDAR 3D semantic segmentation, but there are two under-explored imbalances on the radial axis: points are unevenly concentrated on the near side, and the distribution of foreground object instances is skewed to the near side. This leads the training of the model to favor semantics at the near side with the majority of points and object instances. Both the cylindrical and the spherical voxelizations aim to address the problem of imbalanced point distribution by increasing the volume of voxels along the radial distance to include fewer near-side points in a smaller voxel and more far-side points in a bigger voxel. However, this causes a problem of the receptive field enlarging along the radial distance, which is not desirable in LiDAR 3D segmentation. This can be addressed in cubic voxelization which has a fixed volume of voxels. Thus, we propose a new LiDAR 3D semantic segmentation network (Hi-VoxelNet) with Hybrid Voxelization that leverages the advantages of cubic, cylindrical, and spherical voxelizations for hybrid voxel feature learning. To address the radial imbalance of object instances, we propose a novel data augmentation technique termed as RadialMix that uses radial sample duplication to increase the number of distant foreground object instances and mixes the radial duplication with another point cloud for enriching the training samples. With the joint improvements of the radial imbalances, our method archives state-of-the-art performance on nuScenes and SemanticKITTI datasets, and it shows significant improvements along the radial axis. Our code is publicly available at https://github.com/jialeli1/lidarseg3d.