Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
<|reference_start|>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.<|reference_end|>
@article{kunsági-máté2024accurate,
    title={Accurate Automatic 3D Annotation of Traffic Lights and Signs for
  Autonomous Driving},
    author={S'andor Kuns'agi-M'at'e, Levente PetH{o}, Lehel Seres, Tam'as
  Matuszka},
    journal={arXiv preprint arXiv:2409.12620},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12620},
    primaryClass={cs.CV}
}