  Lidar-based perception pipelines rely on 3D object detection models to
interpret complex scenes. While multiple representations for lidar exist, the
range-view is enticing since it losslessly encodes the entire lidar sensor
output. In this work, we achieve state-of-the-art amongst range-view 3D object
detection models without using multiple techniques proposed in past range-view
literature. We explore range-view 3D object detection across two modern
datasets with substantially different properties: Argoverse 2 and Waymo Open.
Our investigation reveals key insights: (1) input feature dimensionality
significantly influences the overall performance, (2) surprisingly, employing a
classification loss grounded in 3D spatial proximity works as well or better
compared to more elaborate IoU-based losses, and (3) addressing non-uniform
lidar density via a straightforward range subsampling technique outperforms
existing multi-resolution, range-conditioned networks. Our experiments reveal
that techniques proposed in recent range-view literature are not needed to
achieve state-of-the-art performance. Combining the above findings, we
establish a new state-of-the-art model for range-view 3D object detection --
improving AP by 2.2% on the Waymo Open dataset while maintaining a runtime of
10 Hz. We establish the first range-view model on the Argoverse 2 dataset and
outperform strong voxel-based baselines. All models are multi-class and
open-source. Code is available at
https://github.com/benjaminrwilson/range-view-3d-detection.
