BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream
This addresses the challenge of 3D scene reconstruction for computer vision and graphics applications, offering a novel approach that reduces reliance on multiple clear images, though it is incremental in combining blur and event data.
The paper tackles the problem of reconstructing 3D neural radiance fields (NeRF) from a single blurry image and an event stream, without pre-computed camera poses, achieving high-quality rendering of sharp images from the learned representation.
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.