EvHDR-NeRF: Building High Dynamic Range Radiance Fields with Single Exposure Images and Events

Authors

  • Zehao Chen Zhejiang University
  • Zhanfeng Liao Zhejiang University
  • De Ma Zhejiang University
  • Huajin Tang Zhejiang University
  • Qian Zheng Zhejiang University
  • Gang Pan Zhejiang University

DOI:

https://doi.org/10.1609/aaai.v39i3.32238

Abstract

We present EvHDR-NeRF to recover a High Dynamic Range (HDR) radiance field from event streams and a set of Low Dynamic Range (LDR) views with single exposures. Using the EvHDR-NeRF, we can generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the new relationship between events streams and LDR images, which considers both the Camera Response Function (CRF) and exposure time. Based on this relationship, we categorize events into inter-frame events and intra-exposure. The former is utilized for building HDR radiance field and the latter is used to deblur potentially blurred images. Compared to existing methods, this method can effectively reconstruct the HDR radiance field even when the input images are degraded. Experimental results demonstrate that our method achieves state-of-the-art HDR reconstruction, providing a more adaptable and accurate solution for complex imaging applications.

Published

2025-04-11

How to Cite

Chen, Z., Liao, Z., Ma, D., Tang, H., Zheng, Q., & Pan, G. (2025). EvHDR-NeRF: Building High Dynamic Range Radiance Fields with Single Exposure Images and Events. Proceedings of the AAAI Conference on Artificial Intelligence, 39(3), 2376–2384. https://doi.org/10.1609/aaai.v39i3.32238

Issue

Section

AAAI Technical Track on Computer Vision II