  We present an endpoint box regression module(epBRM), which is designed for
predicting precise 3D bounding boxes using raw LiDAR 3D point clouds. The
proposed epBRM is built with sequence of small networks and is computationally
lightweight. Our approach can improve a 3D object detection performance by
predicting more precise 3D bounding box coordinates. The proposed approach
requires 40 minutes of training to improve the detection performance. Moreover,
epBRM imposes less than 12ms to network inference time for up-to 20 objects.
  The proposed approach utilizes a spatial transformation mechanism to simplify
the box regression task. Adopting spatial transformation mechanism into epBRM
makes it possible to improve the quality of detection with a small sized
network.
  We conduct in-depth analysis of the effect of various spatial transformation
mechanisms applied on raw LiDAR 3D point clouds. We also evaluate the proposed
epBRM by applying it to several state-of-the-art 3D object detection systems.
  We evaluate our approach on KITTI dataset, a standard 3D object detection
benchmark for autonomous vehicles. The proposed epBRM enhances the overlaps
between ground truth bounding boxes and detected bounding boxes, and improves
3D object detection. Our proposed method evaluated in KITTI test server
outperforms current state-of-the-art approaches.
