  Existing online 3D shape repositories contain thousands of 3D models but lack
photorealistic appearance. We present an approach to automatically assign
high-quality, realistic appearance models to large scale 3D shape collections.
The key idea is to jointly leverage three types of online data -- shape
collections, material collections, and photo collections, using the photos as
reference to guide assignment of materials to shapes. By generating a large
number of synthetic renderings, we train a convolutional neural network to
classify materials in real photos, and employ 3D-2D alignment techniques to
transfer materials to different parts of each shape model. Our system produces
photorealistic, relightable, 3D shapes (PhotoShapes).
