E$^3$NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images
<|reference_start|>E$^3$NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images: Neural Radiance Fields (NeRF) achieve impressive rendering performance by learning volumetric 3D representation from several images of different views. However, it is difficult to reconstruct a sharp NeRF from blurry input as it often occurs in the wild. To solve this problem, we propose a novel Efficient Event-Enhanced NeRF (E$^3$NeRF) by utilizing the combination of RGB images and event streams. To effectively introduce event streams into the neural volumetric representation learning process, we propose an event-enhanced blur rendering loss and an event rendering loss, which guide the network via modeling the real blur process and event generation process, respectively. Specifically, we leverage spatial-temporal information from the event stream to evenly distribute learning attention over temporal blur while simultaneously focusing on blurry texture through the spatial attention. Moreover, a camera pose estimation framework for real-world data is built with the guidance of the events to generalize the method to practical applications. Compared to previous image-based or event-based NeRF, our framework makes more profound use of the internal relationship between events and images. Extensive experiments on both synthetic data and real-world data demonstrate that E$^3$NeRF can effectively learn a sharp NeRF from blurry images, especially in non-uniform motion and low-light scenes.<|reference_end|>
@article{qi2024e$^3$nerf:,
    title={E$^3$NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry
  Images},
    author={Yunshan Qi, Jia Li, Yifan Zhao, Yu Zhang, and Lin Zhu},
    journal={arXiv preprint arXiv:2408.01840},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01840},
    primaryClass={cs.CV}
}