  Object detection in point cloud data is one of the key components in computer
vision systems, especially for autonomous driving applications. In this work,
we present Voxel-FPN, a novel one-stage 3D object detector that utilizes raw
data from LIDAR sensors only. The core framework consists of an encoder network
and a corresponding decoder followed by a region proposal network. Encoder
extracts multi-scale voxel information in a bottom-up manner while decoder
fuses multiple feature maps from various scales in a top-down way. Extensive
experiments show that the proposed method has better performance on extracting
features from point data and demonstrates its superiority over some baselines
on the challenging KITTI-3D benchmark, obtaining good performance on both speed
and accuracy in real-world scenarios.
