Neural radiance field has achieved fundamental success in novel view
synthesis from input views with the same brightness level captured under fixed
normal lighting. Unfortunately, synthesizing novel views remains to be a
challenge for input views with heterogeneous brightness level captured under
low-light condition. The condition is pretty common in the real world. It
causes low-contrast images where details are concealed in the darkness and
camera sensor noise significantly degrades the image quality. To tackle this
problem, we propose to learn to decompose illumination, reflectance, and noise
from input views according to that reflectance remains invariant across
heterogeneous views. To cope with heterogeneous brightness and noise levels
across multi-views, we learn an illumination embedding and optimize a noise map
individually for each view. To allow intuitive editing of the illumination, we
design an illumination adjustment module to enable either brightening or
darkening of the illumination component. Comprehensive experiments demonstrate
that this approach enables effective intrinsic decomposition for low-light
multi-view noisy images and achieves superior visual quality and numerical
performance for synthesizing novel views compared to state-of-the-art methods.

从具有异质亮度级别的低光条件下捕获的输入视图中合成新视图始终是一项挑战。本文提出了学习将亮度、光反射和噪声从输入视图中分解的方法，以便在异质视图中保持光反射不变。通过学习照明嵌入和优化噪声映射，以及设计照明调整模块来实现对照明的直观编辑，该方法实现了低光多视图噪声图像的有效内在分解，并在合成新视图方面在视觉质量和数值性能上优于现有方法。