  3-D object detection is pivotal for autonomous driving. Point cloud based
methods have become increasingly popular for 3-D object detection, owing to
their accurate depth information. NuTonomy's nuScenes dataset greatly extends
commonly used datasets such as KITTI in size, sensor modalities, categories,
and annotation numbers. However, it suffers from severe class imbalance. The
Class-balanced Grouping and Sampling paper addresses this issue and suggests
augmentation and sampling strategy. However, the localization precision of this
model is affected by the loss of spatial information in the downscaled feature
maps. We propose to enhance the performance of the CBGS model by designing an
auxiliary network, that makes full use of the structure information of the 3D
point cloud, in order to improve the localization accuracy. The detachable
auxiliary network is jointly optimized by two point-level supervisions, namely
foreground segmentation and center estimation. The auxiliary network does not
introduce any extra computation during inference, since it can be detached at
test time.
