  In this paper we show how to perform scene-level inverse rendering to recover
shape, reflectance and lighting from a single, uncontrolled image using a fully
convolutional neural network. The network takes an RGB image as input,
regresses albedo, shadow and normal maps from which we infer least squares
optimal spherical harmonic lighting coefficients. Our network is trained using
large uncontrolled multiview and timelapse image collections without ground
truth. By incorporating a differentiable renderer, our network can learn from
self-supervision. Since the problem is ill-posed we introduce additional
supervision. Our key insight is to perform offline multiview stereo (MVS) on
images containing rich illumination variation. From the MVS pose and depth
maps, we can cross project between overlapping views such that Siamese training
can be used to ensure consistent estimation of photometric invariants. MVS
depth also provides direct coarse supervision for normal map estimation. We
believe this is the first attempt to use MVS supervision for learning inverse
rendering. In addition, we learn a statistical natural illumination prior. We
evaluate performance on inverse rendering, normal map estimation and intrinsic
image decomposition benchmarks.
