  We propose a novel 3d shape representation for 3d shape reconstruction from a
single image. Rather than predicting a shape directly, we train a network to
generate a training set which will be fed into another learning algorithm to
define the shape. The nested optimization problem can be modeled by bi-level
optimization. Specifically, the algorithms for bi-level optimization are also
being used in meta learning approaches for few-shot learning. Our framework
establishes a link between 3D shape analysis and few-shot learning. We combine
training data generating networks with bi-level optimization algorithms to
obtain a complete framework for which all components can be jointly trained. We
improve upon recent work on standard benchmarks for 3d shape reconstruction.
