LTM-NeRF: Embedding 3D Local Tone Mapping in HDR Neural Radiance Field

Recent advances in Neural Radiance Fields (NeRF) have provided a new geometric primitive for novel view synthesis. High Dynamic Range NeRF (HDR NeRF) can render novel views with a higher dynamic range. However, effectively displaying the scene contents of HDR NeRF on diverse devices with limited dynamic range poses a significant challenge. To address this, we present LTM-NeRF, a method designed to recover HDR NeRF and support 3D local tone mapping. LTM-NeRF allows for the synthesis of HDR views, tone-mapped views, and LDR views under different exposure settings, using only the multi-view multi-exposure LDR inputs for supervision. Specifically, we propose a differentiable Camera Response Function (CRF) module for HDR NeRF reconstruction, globally mapping the scene's HDR radiance to LDR pixels. Moreover, we introduce a Neural Exposure Field (NeEF) to represent the spatially varying exposure time of an HDR NeRF to achieve 3D local tone mapping, for compatibility with various displays. Comprehensive experiments demonstrate that our method can not only synthesize HDR views and exposure-varying LDR views accurately but also render locally tone-mapped views naturally.

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Publication Details

Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2024-08-23
DOI
https://doi.org/10.1109/tpami.2024.3448620
Citations
5
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
1.25

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article

LTM-NeRF: Embedding 3D Local Tone Mapping in HDR Neural Radiance Field

Qi Zhang, Ying Feng, Qing Wang, Hongdong Li et al.
5 citations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Enhancement Techniques
1.25
article

LTM-NeRF: Embedding 3D Local Tone Mapping in HDR Neural Radiance Field

Qi Zhang, Ying Feng, Qing Wang, Hongdong Li, Xin Huang
article en
5 citations

Abstract

Recent advances in Neural Radiance Fields (NeRF) have provided a new geometric primitive for novel view synthesis. High Dynamic Range NeRF (HDR NeRF) can render novel views with a higher dynamic range. However, effectively displaying the scene contents of HDR NeRF on diverse devices with limited dynamic range poses a significant challenge. To address this, we present LTM-NeRF, a method designed to recover HDR NeRF and support 3D local tone mapping. LTM-NeRF allows for the synthesis of HDR views, tone-mapped views, and LDR views under different exposure settings, using only the multi-view multi-exposure LDR inputs for supervision. Specifically, we propose a differentiable Camera Response Function (CRF) module for HDR NeRF reconstruction, globally mapping the scene's HDR radiance to LDR pixels. Moreover, we introduce a Neural Exposure Field (NeEF) to represent the spatially varying exposure time of an HDR NeRF to achieve 3D local tone mapping, for compatibility with various displays. Comprehensive experiments demonstrate that our method can not only synthesize HDR views and exposure-varying LDR views accurately but also render locally tone-mapped views naturally.

IEEE Transactions on Pattern Analysis and Machine IntelligenceVol. 46(12)
Australian National University (AU), Northwestern Polytechnical University (CN), Tencent (China) (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 19%
Image Enhancement Techniques
1.25
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LTM-NeRF: Embedding 3D Local Tone Mapping in HDR Neural Radiance Field — Qi Zhang, Ying Feng, et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence (2024) | TGRS Research Map | TGRS