What Matters in Range View 3D Object Detection

Benjamin Wilson, Nicholas Autio Mitchell, Jhony Kaesemodel Pontes, James Hays
Proceedings of The 8th Conference on Robot Learning, PMLR 270:4141-4157, 2025.

Abstract

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 are the first to benchmark a 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v270-wilson25a, title = {What Matters in Range View 3D Object Detection}, author = {Wilson, Benjamin and Mitchell, Nicholas Autio and Pontes, Jhony Kaesemodel and Hays, James}, booktitle = {Proceedings of The 8th Conference on Robot Learning}, pages = {4141--4157}, year = {2025}, editor = {Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram}, volume = {270}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v270/main/assets/wilson25a/wilson25a.pdf}, url = {https://proceedings.mlr.press/v270/wilson25a.html}, abstract = {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 are the first to benchmark a 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.} }
Endnote
%0 Conference Paper %T What Matters in Range View 3D Object Detection %A Benjamin Wilson %A Nicholas Autio Mitchell %A Jhony Kaesemodel Pontes %A James Hays %B Proceedings of The 8th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Pulkit Agrawal %E Oliver Kroemer %E Wolfram Burgard %F pmlr-v270-wilson25a %I PMLR %P 4141--4157 %U https://proceedings.mlr.press/v270/wilson25a.html %V 270 %X 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 are the first to benchmark a 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.
APA
Wilson, B., Mitchell, N.A., Pontes, J.K. & Hays, J.. (2025). What Matters in Range View 3D Object Detection. Proceedings of The 8th Conference on Robot Learning, in Proceedings of Machine Learning Research 270:4141-4157 Available from https://proceedings.mlr.press/v270/wilson25a.html.

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