  In this paper, we propose an anchor-free single-stage LiDAR-based 3D object
detector -- RangeDet. The most notable difference with previous works is that
our method is purely based on the range view representation. Compared with the
commonly used voxelized or Bird's Eye View (BEV) representations, the range
view representation is more compact and without quantization error. Although
there are works adopting it for semantic segmentation, its performance in
object detection is largely behind voxelized or BEV counterparts. We first
analyze the existing range-view-based methods and find two issues overlooked by
previous works: 1) the scale variation between nearby and far away objects; 2)
the inconsistency between the 2D range image coordinates used in feature
extraction and the 3D Cartesian coordinates used in output. Then we
deliberately design three components to address these issues in our RangeDet.
We test our RangeDet in the large-scale Waymo Open Dataset (WOD). Our best
model achieves 72.9/75.9/65.8 3D AP on vehicle/pedestrian/cyclist. These
results outperform other range-view-based methods by a large margin (~20 3D AP
in vehicle detection), and are overall comparable with the state-of-the-art
multi-view-based methods. Codes will be public.
