Polar Parametrization for Vision-based Surround-View 3D Detection

Abstract

3D detection based on surround-view camera system is a critical technique in autopilot. In this work, we present Polar Parametrization for 3D detection, which reformulates position parametrization, velocity decomposition, perception range, label assignment and loss function in polar coordinate system. Polar Parametrization establishes explicit associations between image patterns and prediction targets, exploiting the view symmetry of surround-view cameras as inductive bias to ease optimization and boost performance. Based on Polar Parametrization, we propose a surround-view 3D DEtection TRansformer, named PolarDETR. PolarDETR achieves promising performance-speed trade-off on different backbone configurations. Besides, PolarDETR ranks 1st on the leaderboard of nuScenes benchmark in terms of both 3D detection and 3D tracking at the submission time (Mar. 4th, 2022). Code will be released at \url{https://github.com/hustvl/PolarDETR}.

References in corpus (6)

• BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
• BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection
• Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
• Progressive Coordinate Transforms for Monocular 3D Object Detection
• Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion
• PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars