3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these static objects. This paper introduces a novel method for automatically generating accurate and temporally consistent 3D bounding box annotations for traffic lights and signs, effective up to a range of 200 meters. These annotations are suitable for training real-time models used in self-driving cars, which need a large amount of training data. The proposed method relies only on RGB images with 2D bounding boxes of traffic management objects, which can be automatically obtained using an off-the-shelf image-space detector neural network, along with GNSS/INS data, eliminating the need for LiDAR point cloud data.

翻译：交通管理对象（如交通信号灯与道路标志）的三维检测对自动驾驶汽车至关重要，特别是在点对点导航场景中，车辆会频繁通过设有此类静态物体的交叉路口。本文提出一种新颖方法，能够为交通信号灯与标志自动生成精确且具有时间一致性的三维边界框标注，有效检测范围可达200米。此类标注适用于训练自动驾驶汽车所需的实时模型，这些模型通常需要海量训练数据。该方法仅需依赖包含交通管理对象二维边界框的RGB图像（可通过现成的图像空间检测器神经网络自动获取）以及GNSS/INS数据，无需使用激光雷达点云数据。