论文标题
基于视觉的环绕视图3D检测的极性参数化
Polar Parametrization for Vision-based Surround-View 3D Detection
论文作者
论文摘要
基于环绕视图摄像机系统的3D检测是自动驾驶中的一项关键技术。在这项工作中，我们提出了3D检测的极性参数化，该参数重新定义了北极坐标系中的位置参数化，速度分解，感知范围，标签分配和损失函数。极性参数化建立了图像模式与预测目标之间的明确关联，从而利用环绕视觉摄像机的视图对称性作为感应偏置，以减轻优化和增强性能。基于极性参数化，我们提出了一个名为polardetr的环绕图3D检测变压器。 Polardetr在不同的主链配置上实现了有希望的性能速度权衡。此外，在提交时间（2022年3月4日）的3D检测和3D跟踪方面，Polardetr在Nuscenes基准的排行榜上排名第一。代码将以\ url {https://github.com/hustvl/polardetr}发布。
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}.
