  In this paper, we address the problem of reconstructing an object's surface
from a single image using generative networks. First, we represent a 3D surface
with an aggregation of dense point clouds from multiple views. Each point cloud
is embedded in a regular 2D grid aligned on an image plane of a viewpoint,
making the point cloud convolution-favored and ordered so as to fit into deep
network architectures. The point clouds can be easily triangulated by
exploiting connectivities of the 2D grids to form mesh-based surfaces. Second,
we propose an encoder-decoder network that generates such kind of multiple
view-dependent point clouds from a single image by regressing their 3D
coordinates and visibilities. We also introduce a novel geometric loss that is
able to interpret discrepancy over 3D surfaces as opposed to 2D projective
planes, resorting to the surface discretization on the constructed meshes. We
demonstrate that the multi-view point regression network outperforms
state-of-the-art methods with a significant improvement on challenging
datasets.
