  We consider the non-Lambertian object intrinsic problem of recovering diffuse
albedo, shading, and specular highlights from a single image of an object.
  We build a large-scale object intrinsics database based on existing 3D models
in the ShapeNet database. Rendered with realistic environment maps, millions of
synthetic images of objects and their corresponding albedo, shading, and
specular ground-truth images are used to train an encoder-decoder CNN. Once
trained, the network can decompose an image into the product of albedo and
shading components, along with an additive specular component.
  Our CNN delivers accurate and sharp results in this classical inverse problem
of computer vision, sharp details attributed to skip layer connections at
corresponding resolutions from the encoder to the decoder. Benchmarked on our
ShapeNet and MIT intrinsics datasets, our model consistently outperforms the
state-of-the-art by a large margin.
  We train and test our CNN on different object categories. Perhaps surprising
especially from the CNN classification perspective, our intrinsics CNN
generalizes very well across categories. Our analysis shows that feature
learning at the encoder stage is more crucial for developing a universal
representation across categories.
  We apply our synthetic data trained model to images and videos downloaded
from the internet, and observe robust and realistic intrinsics results. Quality
non-Lambertian intrinsics could open up many interesting applications such as
image-based albedo and specular editing.
