用图像和定位数据自动生成200米内交通灯与标志的3D框，无需激光雷达。
Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
- 仅需RGB图和2D框，结合定位信息生成3D标注
- 在200米范围内实现高精度且时间一致的3D框
- 适用于自动驾驶实时模型训练，节省标注成本
交通管理物体（如交通灯和路标）的3D检测对自动驾驶至关重要，尤其在地址到地址导航中，车辆会频繁经过带有这些静态物体的交叉口。本文提出一种新方法，可自动生成准确且时间一致的交通灯与路标3D边界框标注，有效范围达200米。该标注可用于训练自动驾驶汽车所需的实时模型，而这类模型需要大量训练数据。所提方法仅依赖于带有交通管理物体2D边界框的RGB图像（可通过现成的图像空间检测神经网络自动获取），以及GNSS/INS数据，无需激光雷达点云数据。
原文摘要 · Abstract (English)
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.
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