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.

翻译：视觉场景的神经隐式表示在计算机视觉与图形学领域近年受到广泛关注。现有方法多聚焦于如何从一组图像重建三维场景表示。本工作证明了从单张模糊图像及其对应事件流中恢复神经辐射场（NeRF）的可能性。我们采用SE(3)空间中的三次B样条对相机运动进行建模。基于三次B样条插值得到的6自由度位姿，模糊图像及时间区间内的亮度变化均可从三维场景表示中合成。该方法通过最小化合成数据与真实测量值之间的差异，能够联合学习隐式神经场景表示并恢复相机运动，无需依赖COLMAP预计算的相机位姿。我们在合成与真实数据集上评估了所提方法。实验结果表明，我们能够从学习到的NeRF中渲染视角一致的潜在清晰图像，并以高质量还原模糊图像的动态细节。代码与数据公开于https://github.com/WU-CVGL/BeNeRF。