  We introduce a new approach to intrinsic image decomposition, the task of
decomposing a single image into albedo and shading components. Our strategy,
which we term direct intrinsics, is to learn a convolutional neural network
(CNN) that directly predicts output albedo and shading channels from an input
RGB image patch. Direct intrinsics is a departure from classical techniques for
intrinsic image decomposition, which typically rely on physically-motivated
priors and graph-based inference algorithms.
  The large-scale synthetic ground-truth of the MPI Sintel dataset plays a key
role in training direct intrinsics. We demonstrate results on both the
synthetic images of Sintel and the real images of the classic MIT intrinsic
image dataset. On Sintel, direct intrinsics, using only RGB input, outperforms
all prior work, including methods that rely on RGB+Depth input. Direct
intrinsics also generalizes across modalities; it produces quite reasonable
decompositions on the real images of the MIT dataset. Our results indicate that
the marriage of CNNs with synthetic training data may be a powerful new
technique for tackling classic problems in computer vision.
