E$^3$NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images

• URL: http://arxiv.org/abs/2408.01840v1
• Date: Sat, 3 Aug 2024 18:47:31 GMT
• Title: E$^3$NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images
• Authors: Yunshan Qi, Jia Li, Yifan Zhao, Yu Zhang, Lin Zhu,
• Abstract summary: We propose a novel Efficient Event-Enhanced NeRF (E$3$NeRF) We leverage spatial-temporal information from the event stream to evenly distribute learning attention over temporal blur. Experiments on both synthetic data and real-world data demonstrate that E$3$NeRF can effectively learn a sharp NeRF from blurry images.
• Score: 25.304680391243537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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