  Self-driving cars need to understand 3D scenes efficiently and accurately in
order to drive safely. Given the limited hardware resources, existing 3D
perception models are not able to recognize small instances (e.g., pedestrians,
cyclists) very well due to the low-resolution voxelization and aggressive
downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv),
a lightweight 3D module that equips the vanilla Sparse Convolution with the
high-resolution point-based branch. With negligible overhead, this point-based
branch is able to preserve the fine details even from large outdoor scenes. To
explore the spectrum of efficient 3D models, we first define a flexible
architecture design space based on SPVConv, and we then present 3D Neural
Architecture Search (3D-NAS) to search the optimal network architecture over
this diverse design space efficiently and effectively. Experimental results
validate that the resulting SPVNAS model is fast and accurate: it outperforms
the state-of-the-art MinkowskiNet by 3.3%, ranking 1st on the competitive
SemanticKITTI leaderboard. It also achieves 8x computation reduction and 3x
measured speedup over MinkowskiNet with higher accuracy. Finally, we transfer
our method to 3D object detection, and it achieves consistent improvements over
the one-stage detection baseline on KITTI.
