  The objective of this work is to infer the 3D shape of an object from a
single image. We use sculptures as our training and test bed, as these have
great variety in shape and appearance.
  To achieve this we build on the success of multiple view geometry (MVG) which
is able to accurately provide correspondences between images of 3D objects
under varying viewpoint and illumination conditions, and make the following
contributions: first, we introduce a new loss function that can harness
image-to-image correspondences to provide a supervisory signal to train a deep
network to infer a depth map. The network is trained end-to-end by
differentiating through the camera. Second, we develop a processing pipeline to
automatically generate a large scale multi-view set of correspondences for
training the network. Finally, we demonstrate that we can indeed obtain a depth
map of a novel object from a single image for a variety of sculptures with
varying shape/texture, and that the network generalises at test time to new
domains (e.g. synthetic images).

Computer Vision and Pattern Recognition