Neural Radiance Fields (NeRF) achieves unprecedented performance in synthesizing novel view synthesis, utilizing multi-view consistency. When capturing multiple inputs, image signal processing (ISP) in modern cameras will independently enhance them, including exposure adjustment, color correction, local tone mapping, etc. While these processings greatly improve image quality, they often break the multi-view consistency assumption, leading to "floaters" in the reconstructed radiance fields. To address this concern without compromising visual aesthetics, we aim to first disentangle the enhancement by ISP at the NeRF training stage and re-apply user-desired enhancements to the reconstructed radiance fields at the finishing stage. Furthermore, to make the re-applied enhancements consistent between novel views, we need to perform imaging signal processing in 3D space (i.e. "3D ISP"). For this goal, we adopt the bilateral grid, a locally-affine model, as a generalized representation of ISP processing. Specifically, we optimize per-view 3D bilateral grids with radiance fields to approximate the effects of camera pipelines for each input view. To achieve user-adjustable 3D finishing, we propose to learn a low-rank 4D bilateral grid from a given single view edit, lifting photo enhancements to the whole 3D scene. We demonstrate our approach can boost the visual quality of novel view synthesis by effectively removing floaters and performing enhancements from user retouching. The source code and our data are available at: https://bilarfpro.github.io.

翻译：神经辐射场（NeRF）利用多视角一致性，在新视角合成方面取得了前所未有的性能。在捕获多张输入图像时，现代相机中的图像信号处理（ISP）会独立地对每张图像进行增强，包括曝光调整、色彩校正、局部色调映射等。虽然这些处理显著提升了图像质量，但它们往往会破坏多视角一致性假设，导致重建的辐射场中出现“漂浮物”。为了在不损害视觉美感的前提下解决这一问题，我们的目标是在NeRF训练阶段首先解耦ISP所做的增强，并在最终处理阶段将用户期望的增强重新应用于重建的辐射场。此外，为了使重新应用的增强在不同新视角之间保持一致，我们需要在三维空间中进行图像信号处理（即“3D ISP”）。为此，我们采用局部仿射模型——双边网格，作为ISP处理的广义表示。具体而言，我们优化与辐射场结合的各视角三维双边网格，以近似模拟每个输入视图对应的相机处理流程的效果。为实现用户可调节的三维最终处理，我们提出从给定的单视图编辑中学习一个低秩四维双边网格，从而将照片增强提升至整个三维场景。我们通过实验证明，我们的方法能够有效去除漂浮物并应用用户修饰带来的增强，从而显著提升新视角合成的视觉质量。源代码和数据可在以下网址获取：https://bilarfpro.github.io。