Abstract

Neural Radiance Fields (NeRF) achieves 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 often occurred in the wild. To solve this problem, we propose a novel Event-Enhanced NeRF (E²NeRF) by utilizing the combination data of a bio-inspired event camera and a standard RGB camera. To effectively introduce event stream into the learning process of neural volumetric representation, we propose a blur rendering loss and an event rendering loss, which guide the network via modelling real blur process and event generation process, respectively. Moreover, a camera pose estimation framework for real-world data is built with the guidance of event stream to generalize the method to practical applications. In contrast to previous image-based or event-based NeRF, our framework effectively utilizes the internal relationship between events and images. As a result, E²NeRF not only achieves image deblurring but also achieves high-quality novel view image generation. Extensive experiments on both synthetic data and real-world data demonstrate that E²NeRF can effectively learn a sharp NeRF from blurry images, especially in complex and low-light scenes.

Supplementary Video