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: this https URL

在 NeRF 训练阶段，通过图像信号处理与 3D ISP 技术分离现代相机的增强，并在后期应用用户期望的增强，从而提高新视角合成的视觉质量。