We present a deep learning approach to reconstruct scene appearance from
unstructured images captured under collocated point lighting. At the heart of
Deep Reflectance Volumes is a novel volumetric scene representation consisting
of opacity, surface normal and reflectance voxel grids. We present a novel
physically-based differentiable volume ray marching framework to render these
scene volumes under arbitrary viewpoint and lighting. This allows us to
optimize the scene volumes to minimize the error between their rendered images
and the captured images. Our method is able to reconstruct real scenes with
challenging non-Lambertian reflectance and complex geometry with occlusions and
shadowing. Moreover, it accurately generalizes to novel viewpoints and
lighting, including non-collocated lighting, rendering photorealistic images
that are significantly better than state-of-the-art mesh-based methods. We also
show that our learned reflectance volumes are editable, allowing for modifying
the materials of the captured scenes.

本文提出了一种基于深度学习的场景重建方法，应用了一种新颖的体积场景表示方法 —— 反射体积网格，通过物理可行的可微分体积光线行走框架，渲染出高质量的图像，实现了对具备挑战性的真实场景的重建，而重建得到的反射体积网格可进行编辑，可用于修改捕捉的场景材料。