  Deep learning based 3D reconstruction of single view 2D image is becoming
increasingly popular due to their wide range of real-world applications, but
this task is inherently challenging because of the partial observability of an
object from a single perspective. Recently, state of the art probability based
Occupancy Networks reconstructed 3D surfaces from three different types of
input domains: single view 2D image, point cloud and voxel. In this study, we
extend the work on Occupancy Networks by exploiting cross-domain learning of
image and point cloud domains. Specifically, we first convert the single view
2D image into a simpler point cloud representation, and then reconstruct a 3D
surface from it. Our network, the Double Occupancy Network (D-OccNet)
outperforms Occupancy Networks in terms of visual quality and details captured
in the 3D reconstruction.
