EvHDR-NeRF: Building High Dynamic Range Radiance Fields with Single Exposure Images and Events
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.
Authors
- Huajin Tang (ORCID: https://orcid.org/0000-0002-9857-1543)
- Zehao Chen (ORCID: https://orcid.org/0000-0003-2432-2234)
- Gang Pan (ORCID: https://orcid.org/0000-0002-4049-6181)
- Q. Zheng (ORCID: https://orcid.org/0000-0002-1212-2342)
- Zhanfeng Liao
- De Ma (ORCID: https://orcid.org/0000-0001-8700-938X)
Institutions
- Zhejiang University (CN)
Publication Details
- Journal
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2025-04-11
- DOI
- https://doi.org/10.1609/aaai.v39i3.32238
- Citations
- 4
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.24
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities