  In this work, we propose a novel two-stage framework for the efficient 3D
point cloud object detection. Instead of transforming point clouds into 2D bird
eye view projections, we parse the raw point cloud data directly in the 3D
space yet achieve impressive efficiency and accuracy. To achieve this goal, we
propose dynamic voxelization, a method that voxellizes points at local scale
on-the-fly. By doing so, we preserve the point cloud geometry with 3D voxels,
and therefore waive the dependence on expensive MLPs to learn from point
coordinates. On the other hand, we inherently still follow the same processing
pattern as point-wise methods (e.g., PointNet) and no longer suffer from the
quantization issue like conventional convolutions. For further speed
optimization, we propose the grid-based downsampling and voxelization method,
and provide different CUDA implementations to accommodate to the discrepant
requirements during training and inference phases. We highlight our efficiency
on KITTI 3D object detection dataset with 75 FPS and on Waymo Open dataset with
25 FPS inference speed with satisfactory accuracy.
