  We present a convolutional network capable of inferring a 3D representation
of a previously unseen object given a single image of this object. Concretely,
the network can predict an RGB image and a depth map of the object as seen from
an arbitrary view. Several of these depth maps fused together give a full point
cloud of the object. The point cloud can in turn be transformed into a surface
mesh. The network is trained on renderings of synthetic 3D models of cars and
chairs. It successfully deals with objects on cluttered background and
generates reasonable predictions for real images of cars.
