We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper. The radiance field encodes the scene radiance (values vary from 0 to +infty), which outputs the density and radiance of a ray by giving corresponding ray origin and ray direction. The tone mapper models the mapping process that a ray hitting on the camera sensor becomes a pixel value. The color of the ray is predicted by feeding the radiance and the corresponding exposure time into the tone mapper. We use the classic volume rendering technique to project the output radiance, colors, and densities into HDR and LDR images, while only the input LDR images are used as the supervision. We collect a new forward-facing HDR dataset to evaluate the proposed method. Experimental results on synthetic and real-world scenes validate that our method can not only accurately control the exposures of synthesized views but also render views with a high dynamic range.

翻译：我们提出高动态范围神经辐射场（HDR-NeRF），旨在从一组具有不同曝光的低动态范围（LDR）视角中恢复出HDR辐射场。利用HDR-NeRF，我们能够生成不同曝光下的新视角HDR图像和LDR图像。该方法的核心是对物理成像过程进行建模，该过程通过两个隐式函数（辐射场和色调映射器）将场景点的辐射亮度转换为LDR图像中的像素值。辐射场编码场景辐射亮度（值域为0到正无穷），通过给定射线起点和方向输出射线的密度与辐射亮度。色调映射器模拟射线撞击相机传感器后转换为像素值的映射过程。通过将辐射亮度与对应曝光时间输入色调映射器，即可预测射线的颜色。我们采用经典体渲染技术将输出的辐射亮度、颜色和密度投影为HDR图像与LDR图像，仅使用输入的LDR图像作为监督信号。为评估该方法，我们构建了一个新的前向HDR数据集。在合成场景与真实场景上的实验结果表明，该方法不仅能精确控制合成视图的曝光参数，还能渲染出具有高动态范围的视角图像。