mlsanityData to 14 Oct 2026

PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection

arXiv 2308.03982 ↗First posted 8 Aug 2023cs.CVcited by 9

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Abstract (arXiv)

Recently, polar-based representation has shown promising properties in perceptual tasks. In addition to Cartesian-based approaches, which separate point clouds unevenly, representing point clouds as polar grids has been recognized as an alternative due to (1) its advantage in robust performance under different resolutions and (2) its superiority in streaming-based approaches. However, state-of-the-art polar-based detection methods inevitably suffer from the feature distortion problem because of the non-uniform division of polar representation, resulting in a non-negligible performance gap compared to Cartesian-based approaches. To tackle this issue, we present PARTNER, a novel 3D object detector in the polar coordinate. PARTNER alleviates the dilemma of feature distortion with global representation re-alignment and facilitates the regression by introducing instance-level geometric information into the detection head. Extensive experiments show overwhelming advantages in streaming-based detection and different resolutions. Furthermore, our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on Waymo and ONCE validation set, thus achieving competitive results over the state-of-the-art methods.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 8 Aug 2023.

Builds on

37 of 47 references.

References · 37

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
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  11. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  12. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
25 more
  1. 2017Focal Loss for Dense Object Detectioncited by 752
  2. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  3. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  4. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 389
  5. 2019Axial Attention in Multidimensional Transformerscited by 358
  6. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 327
  7. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  8. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 275
  9. 2021Voxel Transformer for 3D Object Detectioncited by 247
  10. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  11. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  12. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  13. 2022Understanding The Robustness in Vision Transformerscited by 144
  14. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  15. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  16. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102
  17. 2022CenterFormer: Center-based Transformer for 3D Object Detectioncited by 99
  18. 2022SWFormer: Sparse Window Transformer for 3D Object Detection in Point Cloudscited by 92
  19. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  20. 2021To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernelscited by 39
  21. 2020RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representationcited by 37
  22. 2022ConQueR: Query Contrast Voxel-DETR for 3D Object Detectioncited by 21
  23. 2020Streaming Object Detection for 3-D Point Cloudscited by 17
  24. 2020StrObe: Streaming Object Detection from LiDAR Packetscited by 17
  25. 2022Point2Seq: Detecting 3D Objects as Sequencescited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 3.

9 papers.

  1. 2024RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radarcited by 33
  2. 2024PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-Viewcited by 5
  3. 2024PVP: Polar Representation Boost for 3D Semantic Occupancy Predictioncited by 2
  4. 2025Towards Streaming LiDAR Object Detection with Point Clouds as Egocentric Sequencescited by 1
  5. 2026AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervisioncited by 1
  6. 2023PolarRec: Radio Interferometric Data Reconstruction with Polar Coordinate Representationcited by 0
  7. 2025Targetless 6DoF Calibration of LiDAR and 2D Scanning Radar Based on Cylindrical Occupancycited by 0
  8. 2026Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Datacited by 0
  9. 2026Geometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setupscited by 0