  We introduce a novel learning-based method to reconstruct the high-quality
geometry and complex, spatially-varying BRDF of an arbitrary object from a
sparse set of only six images captured by wide-baseline cameras under
collocated point lighting. We first estimate per-view depth maps using a deep
multi-view stereo network; these depth maps are used to coarsely align the
different views. We propose a novel multi-view reflectance estimation network
architecture that is trained to pool features from these coarsely aligned
images and predict per-view spatially-varying diffuse albedo, surface normals,
specular roughness and specular albedo. We do this by jointly optimizing the
latent space of our multi-view reflectance network to minimize the photometric
error between images rendered with our predictions and the input images. While
previous state-of-the-art methods fail on such sparse acquisition setups, we
demonstrate, via extensive experiments on synthetic and real data, that our
method produces high-quality reconstructions that can be used to render
photorealistic images.
