  While recent deep neural networks have achieved promising results for 3D
reconstruction from a single-view image, these rely on the availability of RGB
textures in images and extra information as supervision. In this work, we
propose novel stacked hierarchical networks and an end to end training strategy
to tackle a more challenging task for the first time, 3D reconstruction from a
single-view 2D silhouette image. We demonstrate that our model is able to
conduct 3D reconstruction from a single-view silhouette image both
qualitatively and quantitatively. Evaluation is performed using Shapenet for
the single-view reconstruction and results are presented in comparison with a
single network, to highlight the improvements obtained with the proposed
stacked networks and the end to end training strategy. Furthermore, 3D re-
construction in forms of IoU is compared with the state of art 3D
reconstruction from a single-view RGB image, and the proposed model achieves
higher IoU than the state of art of reconstruction from a single view RGB
image.
