Improved Single Camera BEV Perception Using Multi-Camera Training
<|reference_start|>Improved Single Camera BEV Perception Using Multi-Camera Training: Bird's Eye View (BEV) map prediction is essential for downstream autonomous driving tasks like trajectory prediction. In the past, this was accomplished through the use of a sophisticated sensor configuration that captured a surround view from multiple cameras. However, in large-scale production, cost efficiency is an optimization goal, so that using fewer cameras becomes more relevant. But the consequence of fewer input images correlates with a performance drop. This raises the problem of developing a BEV perception model that provides a sufficient performance on a low-cost sensor setup. Although, primarily relevant for inference time on production cars, this cost restriction is less problematic on a test vehicle during training. Therefore, the objective of our approach is to reduce the aforementioned performance drop as much as possible using a modern multi-camera surround view model reduced for single-camera inference. The approach includes three features, a modern masking technique, a cyclic Learning Rate (LR) schedule, and a feature reconstruction loss for supervising the transition from six-camera inputs to one-camera input during training. Our method outperforms versions trained strictly with one camera or strictly with six-camera surround view for single-camera inference resulting in reduced hallucination and better quality of the BEV map.<|reference_end|>
@article{busch2024improved,
    title={Improved Single Camera BEV Perception Using Multi-Camera Training},
    author={Daniel Busch, Ido Freeman, Richard Meyes and Tobias Meisen},
    journal={arXiv preprint arXiv:2409.02676},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02676},
    primaryClass={cs.CV}
}