  Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has
drawn huge attention from researchers in computer vision and deep learning
communities. However, it is not practical to assume that 2D input images and
their associated ground truth 3D shapes are always available during training.
In this paper, we propose a framework for semi-supervised 3D reconstruction.
This is realized by our introduced 2D-3D self-consistency, which aligns the
predicted 3D models and the projected 2D foreground segmentation masks.
Moreover, our model not only enables recovering 3D shapes with the
corresponding 2D masks, camera pose information can be jointly disentangled and
predicted, even such supervision is never available during training. In the
experiments, we qualitatively and quantitatively demonstrate the effectiveness
of our model, which performs favorably against state-of-the-art approaches in
either supervised or semi-supervised settings.
