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arXiv · 2401.06052

Fast High Dynamic Range Radiance Fields for Dynamic Scenes

Guanjun Wu·Taoran Yi·Jiemin Fang·Wenyu Liu·Xinggang Wang
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Abstract

Neural Radiances Fields (NeRF) and their extensions have shown great success in representing 3D scenes and synthesizing novel-view images. However, most NeRF methods take in low-dynamic-range (LDR) images, which may lose details, especially with nonuniform illumination. Some previous NeRF methods attempt to introduce high-dynamic-range (HDR) techniques but mainly target static scenes. To extend HDR NeRF methods to wider applications, we propose a dynamic HDR NeRF framework, named HDR-HexPlane, which can learn 3D scenes from dynamic 2D images captured with various exposures. A learnable exposure mapping function is constructed to obtain adaptive exposure values for each image. Based on the monotonically increasing prior, a camera response function is designed for stable learning. With the proposed model, high-quality novel-view images at any time point can be rendered with any desired exposure. We further construct a dataset containing multiple dynamic scenes captured with diverse exposures for evaluation. All the datasets and code are available at \url{https://guanjunwu.github.io/HDR-HexPlane/}.

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Guanjun Wu, Taoran Yi, Jiemin Fang, Wenyu Liu, Xinggang Wang. 2024-01-11. Fast High Dynamic Range Radiance Fields for Dynamic Scenes. https://arxiv.org/abs/2401.06052

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