mlsanityData to 14 Oct 2026

Exploiting Sparsity in Automotive Radar Object Detection Networks

arXiv 2308.07748 ↗First posted 15 Aug 2023cs.CV cs.AI cs.LGcited by 2

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Abstract (arXiv)

Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fundamental part of such systems. CNN-based object detectors showed good performance in this context, but they require large compute resources. This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources. We investigate radar specific challenges and propose sparse kernel point pillars (SKPP) and dual voxel point convolutions (DVPC) as remedies for the grid rendering and sparse backbone architectures. We evaluate our SKPP-DPVCN architecture on nuScenes, which outperforms the baseline by 5.89% and the previous state of the art by 4.19% in Car AP4.0. Moreover, SKPP-DPVCN reduces the average scale error (ASE) by 21.41% over the baseline.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

26 of 47 references.

References · 26

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
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  11. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 410
  12. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
14 more
  1. 2017Submanifold Sparse Convolutional Networkscited by 223
  2. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  3. 2015Sparse 3D convolutional neural networkscited by 72
  4. 2020CNN based Road User Detection using the 3D Radar Cubecited by 47
  5. 20192D Car Detection in Radar Data with PointNetscited by 38
  6. 2014Spatially-sparse convolutional neural networkscited by 33
  7. 2022Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radarcited by 33
  8. 2022Pointillism: Accurate 3D bounding box estimation with multi-radarscited by 23
  9. 2019Deep Radar Detectorcited by 22
  10. 2023HVDetFusion: A Simple and Robust Camera-Radar Fusion Frameworkcited by 18
  11. 2022DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radarscited by 16
  12. 2021Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systemscited by 11
  13. 2020DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflectionscited by 9
  14. 2023Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networkscited by 6

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Drivingcited by 7
  2. 2025BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentationcited by 0