Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving

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.

@article{kunsági-máté2025_2409.12620,
  title={ Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving },
  author={ Sándor Kunsági-Máté and Levente Pető and Lehel Seres and Tamás Matuszka },
  journal={arXiv preprint arXiv:2409.12620},
  year={ 2025 }
}