mlsanityData to 7 Oct 2026

An Empirical Analysis of Range for 3D Object Detection

arXiv 2308.04054 ↗First posted 8 Aug 2023cs.CV cs.ROcited by 9

Not on the map. Why?

Abstract (arXiv)

LiDAR-based 3D detection plays a vital role in autonomous navigation. Surprisingly, although autonomous vehicles (AVs) must detect both near-field objects (for collision avoidance) and far-field objects (for longer-term planning), contemporary benchmarks focus only on near-field 3D detection. However, AVs must detect far-field objects for safe navigation. In this paper, we present an empirical analysis of far-field 3D detection using the long-range detection dataset Argoverse 2.0 to better understand the problem, and share the following insight: near-field LiDAR measurements are dense and optimally encoded by small voxels, while far-field measurements are sparse and are better encoded with large voxels. We exploit this observation to build a collection of range experts tuned for near-vs-far field detection, and propose simple techniques to efficiently ensemble models for long-range detection that improve efficiency by 33% and boost accuracy by 3.2% CDS.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 33 of its 42 references that are in the map. First posted on arXiv 8 Aug 2023.

Builds on

33 of 42 references.

References · 33

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  9. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  10. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  11. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  12. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
21 more
  1. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  2. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  3. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  4. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  5. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  6. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 388
  7. 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
  8. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  9. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
  10. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  11. 2022PolarFormer: Multi-camera 3D Object Detection with Polar Transformercited by 147
  12. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  13. 2019LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Drivingcited by 133
  14. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  15. 2022Point Density-Aware Voxels for LiDAR 3D Object Detectioncited by 70
  16. 2020An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Cloudscited by 45
  17. 2022Towards Long-Tailed 3D Detectioncited by 40
  18. 2021To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernelscited by 39
  19. 2021FOVEA: Foveated Image Magnification for Autonomous Navigationcited by 31
  20. 2022Forecasting from LiDAR via Future Object Detectioncited by 28
  21. 2020It's All Around You: Range-Guided Cylindrical Network for 3D Object Detectioncited by 13

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 4.

9 papers.

  1. 2024Better Call SAL: Towards Learning to Segment Anything in Lidarcited by 47
  2. 2024I Can't Believe It's Not Scene Flow!cited by 21
  3. 2024Neural Eulerian Scene Flow Fieldscited by 14
  4. 2025RefAV: Towards Planning-Centric Scenario Miningcited by 13
  5. 2023Long-Tailed 3D Detection via Multi-Modal Fusioncited by 10
  6. 2025Towards Learning to Complete Anything in Lidarcited by 7
  7. 2023Towards Long-Range 3D Object Detection for Autonomous Vehiclescited by 5
  8. 2025DGFusion: Dual-guided Fusion for Robust Multi-Modal 3D Object Detectioncited by 2
  9. 2025UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehiclescited by 0