  3D vehicle detection based on point cloud is a challenging task in real-world
applications such as autonomous driving. Despite significant progress has been
made, we observe two aspects to be further improved. First, the semantic
context information in LiDAR is seldom explored in previous works, which may
help identify ambiguous vehicles. Second, the distribution of point cloud on
vehicles varies continuously with increasing depths, which may not be well
modeled by a single model. In this work, we propose a unified model SegVoxelNet
to address the above two problems. A semantic context encoder is proposed to
leverage the free-of-charge semantic segmentation masks in the bird's eye view.
Suspicious regions could be highlighted while noisy regions are suppressed by
this module. To better deal with vehicles at different depths, a novel
depth-aware head is designed to explicitly model the distribution differences
and each part of the depth-aware head is made to focus on its own target
detection range. Extensive experiments on the KITTI dataset show that the
proposed method outperforms the state-of-the-art alternatives in both accuracy
and efficiency with point cloud as input only.
