SPVNAS introduces sparse point-voxel convolution and applies neural architecture search to find efficient 3D deep learning architectures optimized for point cloud processing. The method achieved state-of-the-art results on SemanticKITTI and won challenges on NuScenes for LiDAR semantic and panoptic segmentation. Built with PyTorch, torchsparse, and torchpack for efficient sparse tensor operations.