  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.
