  This paper presents a novel 3D object detection framework that processes
LiDAR data directly on its native representation: range images. Benefiting from
the compactness of range images, 2D convolutions can efficiently process dense
LiDAR data of a scene. To overcome scale sensitivity in this perspective view,
a novel range-conditioned dilation (RCD) layer is proposed to dynamically
adjust a continuous dilation rate as a function of the measured range.
Furthermore, localized soft range gating combined with a 3D box-refinement
stage improves robustness in occluded areas, and produces overall more accurate
bounding box predictions. On the public large-scale Waymo Open Dataset, our
method sets a new baseline for range-based 3D detection, outperforming
multiview and voxel-based methods over all ranges with unparalleled performance
at long range detection.
