  3D shape reconstruction from a single image is a highly ill-posed problem.
Modern deep learning based systems try to solve this problem by learning an
end-to-end mapping from image to shape via a deep network. In this paper, we
aim to solve this problem via an online optimization framework inspired by
traditional methods. Our framework employs a deep autoencoder to learn a set of
latent codes of 3D object shapes, which are fitted by a probabilistic shape
prior using Gaussian Mixture Model (GMM). At inference, the shape and pose are
jointly optimized guided by both image cues and deep shape prior without
relying on an initialization from any trained deep nets. Surprisingly, our
method achieves comparable performance to state-of-the-art methods even without
training an end-to-end network, which shows a promising step in this direction.
