  Recent advances on 3D object detection heavily rely on how the 3D data are
represented, \emph{i.e.}, voxel-based or point-based representation. Many
existing high performance 3D detectors are point-based because this structure
can better retain precise point positions. Nevertheless, point-level features
lead to high computation overheads due to unordered storage. In contrast, the
voxel-based structure is better suited for feature extraction but often yields
lower accuracy because the input data are divided into grids. In this paper, we
take a slightly different viewpoint -- we find that precise positioning of raw
points is not essential for high performance 3D object detection and that the
coarse voxel granularity can also offer sufficient detection accuracy. Bearing
this view in mind, we devise a simple but effective voxel-based framework,
named Voxel R-CNN. By taking full advantage of voxel features in a two stage
approach, our method achieves comparable detection accuracy with
state-of-the-art point-based models, but at a fraction of the computation cost.
Voxel R-CNN consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region
Proposal Network and a detect head. A voxel RoI pooling is devised to extract
RoI features directly from voxel features for further refinement. Extensive
experiments are conducted on the widely used KITTI Dataset and the more recent
Waymo Open Dataset. Our results show that compared to existing voxel-based
methods, Voxel R-CNN delivers a higher detection accuracy while maintaining a
real-time frame processing rate, \emph{i.e}., at a speed of 25 FPS on an NVIDIA
RTX 2080 Ti GPU. The code is available at
\url{https://github.com/djiajunustc/Voxel-R-CNN}.
