Fully Polar Coordinate Object Detection: A Constraint-Based Polar Bounding Box Approach for LiDAR and Scanning Radar

Abstract

Polar coordinates are widely used in segmentation tasks for range sensors such as LiDAR and radar, owing to their ability to naturally align with point cloud sparsity and distribution. However, their use in detection is limited by feature distortion. Existing polar-based detection works focused on undistorting features from the polar coordinates back to canonical Cartesian representations, but their results remain unsuccessful. In this work, we propose fully polar coordinate object detection, performing training and evaluation entirely in polar coordinates without relying on Cartesian metrics. To achieve this, we design a constraint-based polar bounding box representation, that enables the direct conversion of Cartesian bounding boxes via a constrained minimum bounding rectangle (MBR). Using the state-of-the-art polar-based detector as our baseline, we conduct experiments on the Boreas dataset. The results demonstrate that our approach improves the LiDAR detection AP30 metric by 2.88%, and yields a 2.17% gain over Cartesian-based detection methods. On more challenging scanning radar detection experiments, our method achieves an 13.11% improvement in AP30 compared to Cartesian-based detection methods. These findings validate the feasibility of fully polar coordinate object detection and demonstrate its robustness and generalizability across multiple range sensor modalities.