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.

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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

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article

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

Huajin Tang, Zehao Chen, Gang Pan, Q. Zheng et al.
4 citations
Proceedings of the AAAI Conference on Artificial Intelligence
Medical Imaging Techniques and Applications
4.24
article

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

Huajin Tang, Zehao Chen, Gang Pan, Q. Zheng, Zhanfeng Liao, De Ma
article en
4 citations

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.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(3)
Zhejiang University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 4%
Medical Imaging Techniques and Applications
4.24
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