  We present Hybrid Voxel Network (HVNet), a novel one-stage unified network
for point cloud based 3D object detection for autonomous driving. Recent
studies show that 2D voxelization with per voxel PointNet style feature
extractor leads to accurate and efficient detector for large 3D scenes. Since
the size of the feature map determines the computation and memory cost, the
size of the voxel becomes a parameter that is hard to balance. A smaller voxel
size gives a better performance, especially for small objects, but a longer
inference time. A larger voxel can cover the same area with a smaller feature
map, but fails to capture intricate features and accurate location for smaller
objects. We present a Hybrid Voxel network that solves this problem by fusing
voxel feature encoder (VFE) of different scales at point-wise level and project
into multiple pseudo-image feature maps. We further propose an attentive voxel
feature encoding that outperforms plain VFE and a feature fusion pyramid
network to aggregate multi-scale information at feature map level. Experiments
on the KITTI benchmark show that a single HVNet achieves the best mAP among all
existing methods with a real time inference speed of 31Hz.
