  In this paper a semi-supervised deep framework is proposed for the problem of
3D shape inverse rendering from a single 2D input image. The main structure of
proposed framework consists of unsupervised pre-trained components which
significantly reduce the need to labeled data for training the whole framework.
using labeled data has the advantage of achieving to accurate results without
the need to predefined assumptions about image formation process. Three main
components are used in the proposed network: an encoder which maps 2D input
image to a representation space, a 3D decoder which decodes a representation to
a 3D structure and a mapping component in order to map 2D to 3D representation.
The only part that needs label for training is the mapping part with not too
many parameters. The other components in the network can be pre-trained
unsupervised using only 2D images or 3D data in each case. The way of
reconstructing 3D shapes in the decoder component, inspired by the model based
methods for 3D reconstruction, maps a low dimensional representation to 3D
shape space with the advantage of extracting the basis vectors of shape space
from training data itself and is not restricted to a small set of examples as
used in predefined models. Therefore, the proposed framework deals directly
with coordinate values of the point cloud representation which leads to achieve
dense 3D shapes in the output. The experimental results on several benchmark
datasets of objects and human faces and comparing with recent similar methods
shows the power of proposed network in recovering more details from single 2D
images.
