  Actions as simple as grasping an object or navigating around it require a
rich understanding of that object's 3D shape from a given viewpoint. In this
paper we repurpose powerful learning machinery, originally developed for object
classification, to discover image cues relevant for recovering the 3D shape of
potentially unfamiliar objects. We cast the problem as one of local prediction
of surface normals and global detection of 3D reflection symmetry planes, which
open the door for extrapolating occluded surfaces from visible ones. We
demonstrate that our method is able to recover accurate 3D shape information
for classes of objects it was not trained on, in both synthetic and real
images.
