  This paper presents a new approach to 3D object detection that leverages the
properties of the data obtained by a LiDAR sensor. State-of-the-art detectors
use neural network architectures based on assumptions valid for camera images.
However, point clouds obtained from LiDAR are fundamentally different. Most
detectors use shared filter kernels to extract features which do not take into
account the range dependent nature of the point cloud features. To show this,
different detectors are trained on two splits of the KITTI dataset: close range
(objects up to 25 meters from LiDAR) and long-range. Top view images are
generated from point clouds as input for the networks. Combined results
outperform the baseline network trained on the full dataset with a single
backbone. Additional research compares the effect of using different input
features when converting the point cloud to image. The results indicate that
the network focuses on the shape and structure of the objects, rather than
exact values of the input. This work proposes an improvement for 3D object
detectors by taking into account the properties of LiDAR point clouds over
distance. Results show that training separate networks for close-range and
long-range objects boosts performance for all KITTI benchmark difficulties.
