Papers › PolarFormer: Multi-camera 3D Object Detection with Polar Transformer

PolarFormer: Multi-camera 3D Object Detection with Polar Transformer

30 Jun 2022arXiv:2206.15398archive 2025-07-28

Yanqin Jiang, Li Zhang, Zhenwei Miao, Xiatian Zhu, Jin Gao, Weiming Hu, Yu-Gang Jiang

3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3D world. Following the conventional wisdom of previous 2D object detection, existing methods often adopt the canonical Cartesian coordinate system with perpendicular axis. However, we conjugate that this does not fit the nature of the ego car's perspective, as each onboard camera perceives the world in shape of wedge intrinsic to the imaging geometry with radical (non-perpendicular) axis. Hence, in this paper we advocate the exploitation of the Polar coordinate system and propose a new Polar Transformer (PolarFormer) for more accurate 3D object detection in the bird's-eye-view (BEV) taking as input only multi-camera 2D images. Specifically, we design a cross attention based Polar detection head without restriction to the shape of input structure to deal with irregular Polar grids. For tackling the unconstrained object scale variations along Polar's distance dimension, we further introduce a multi-scalePolar representation learning strategy. As a result, our model can make best use of the Polar representation rasterized via attending to the corresponding image observation in a sequence-to-sequence fashion subject to the geometric constraints. Thorough experiments on the nuScenes dataset demonstrate that our PolarFormer outperforms significantly state-of-the-art 3D object detection alternatives.

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conv1x1 fudan-zvg/polarformer/projects/mmdet3d_plugin/models/backbones/vovnet.py official repository ran MIT (permissive) · c637c42a302bb63c · report
conv3x3 fudan-zvg/polarformer/projects/mmdet3d_plugin/models/backbones/vovnet.py official repository ran MIT (permissive) · 303f4d3695ad18f1 · report
inverse_sigmoid fudan-zvg/polarformer/projects/mmdet3d_plugin/models/utils/polar_transformer.py official repository ran fingerprinted MIT (permissive) · c12fa9dce0d5fd34 · report
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nchw_to_nlc fudan-zvg/polarformer/projects/mmdet3d_plugin/models/utils/polar_transformer.py official repository unverified MIT (permissive) · f7a6c2f2d2956250 · report
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perspective fudan-zvg/polarformer/projects/mmdet3d_plugin/models/necks/fpn_trans.py official repository unverified MIT (permissive) · 24cccf792e409dc2 · report

Tasks

2D Object Detection3D Object DetectionAutonomous DrivingObjectObject DetectionRepresentation LearningRobust Camera Only 3D Object DetectionSemantic Segmentationobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Object Detection nuScenes Camera Only PolarFormer Future Frame false #17 of 19 Archive leaderboard report
3D Object Detection nuScenes Camera Only PolarFormer NDS 57.2 #17 of 19 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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