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/}.

翻译：神经辐射场（NeRF）及其扩展方法在表示三维场景和合成新视角图像方面取得了巨大成功。然而，大多数NeRF方法采用低动态范围（LDR）图像，在非均匀光照条件下容易丢失细节。部分先行NeRF方法尝试引入高动态范围（HDR）技术，但主要针对静态场景。为将HDR NeRF方法推广至更广泛的应用场景，我们提出一种动态HDR NeRF框架——HDR-HexPlane，该框架能够从不同曝光条件下拍摄的动态二维图像中学习三维场景。通过构建可学习的曝光映射函数，为每幅图像获取自适应曝光值。基于单调递增先验设计相机响应函数，确保稳定学习。借助所提模型，可在任意时间点以任意期望曝光度渲染高质量的新视角图像。我们进一步构建了一个包含多个不同曝光条件下动态场景的数据集用于评估。所有数据集与代码均已开源，访问地址为：\url{https://guanjunwu.github.io/HDR-HexPlane/}。