  We address the problem of learning accurate 3D shape and camera pose from a
collection of unlabeled category-specific images. We train a convolutional
network to predict both the shape and the pose from a single image by
minimizing the reprojection error: given several views of an object, the
projections of the predicted shapes to the predicted camera poses should match
the provided views. To deal with pose ambiguity, we introduce an ensemble of
pose predictors which we then distill to a single "student" model. To allow for
efficient learning of high-fidelity shapes, we represent the shapes by point
clouds and devise a formulation allowing for differentiable projection of
these. Our experiments show that the distilled ensemble of pose predictors
learns to estimate the pose accurately, while the point cloud representation
allows to predict detailed shape models. The supplementary video can be found
at https://www.youtube.com/watch?v=LuIGovKeo60
