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}.
翻译:根据环形摄像系统进行的3D探测是自动驾驶的关键技术。在这项工作中,我们提出了3D探测的极地偏移法,它重新配置了极地坐标系统中的位置平衡、速度分解、感知范围、标签分配和损失功能。极地对称法在图像模式和预测目标之间建立了明确的联系,利用环形摄像机的对称法作为感应偏差,以方便优化和提升性能。根据极地对称法,我们提议采用3D D D D D D 脱轨环绕法,称为PolorDETR。极地DETR在不同骨干配置上实现了有希望的快速性能交换。此外,在提交时,在核星基准的领先板上,北极DETR在3D检测和3D跟踪方面排名第一(Mar. 4, 2022)。代码将在<url{https://github.com/hstvl/PolarDETR}发布。