  We present a learning-based method to estimate the object bounding box from
its 2D bird's-eye view (BEV) LiDAR points. Our method, entitled BoxNet,
exploits a simple deep neural network that can efficiently handle unordered
points. The method takes as input the 2D coordinates of all the points and the
output is a vector consisting of both the box pose (position and orientation in
LiDAR coordinate system) and its size (width and length). In order to deal with
the angle discontinuity problem, we propose to estimate the double-angle
sinusoidal values rather than the angle itself. We also predict the center
relative to the point cloud mean to boost the performance of estimating the
location of the box. The proposed method does not rely on the ordering of
points as in many existing approaches, and can accurately predict the actual
size of the bounding box based on the prior information that is obtained from
the training data. BoxNet is validated using the KITTI 3D object dataset, with
significant improvement compared with the state-of-the-art non-learning based
methods
