BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream
<|reference_start|>BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream: Neural implicit representation of visual scenes has attracted a lot of attention in recent research of computer vision and graphics. Most prior methods focus on how to reconstruct 3D scene representation from a set of images. In this work, we demonstrate the possibility to recover the neural radiance fields (NeRF) from a single blurry image and its corresponding event stream. We model the camera motion with a cubic B-Spline in SE(3) space. Both the blurry image and the brightness change within a time interval, can then be synthesized from the 3D scene representation given the 6-DoF poses interpolated from the cubic B-Spline. Our method can jointly learn both the implicit neural scene representation and recover the camera motion by minimizing the differences between the synthesized data and the real measurements without pre-computed camera poses from COLMAP. We evaluate the proposed method with both synthetic and real datasets. The experimental results demonstrate that we are able to render view-consistent latent sharp images from the learned NeRF and bring a blurry image alive in high quality. Code and data are available at https://github.com/wu-cvgl/BeNeRF.<|reference_end|>
@article{li2024benerf:,
    title={BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event
  Stream},
    author={Wenpu Li, Pian Wan, Peng Wang, Jinghang Li, Yi Zhou, Peidong Liu},
    journal={arXiv preprint arXiv:2407.02174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02174},
    primaryClass={cs.CV}
}