  This paper focuses on the challenging task of learning 3D object surface
reconstructions from single RGB images. Existing methods achieve varying
degrees of success by using different geometric representations. However, they
all have their own drawbacks, and cannot well reconstruct those surfaces of
complex topologies. To this end, we propose in this paper a skeleton-bridged,
stage-wise learning approach to address the challenge. Our use of skeleton is
due to its nice property of topology preservation, while being of lower
complexity to learn. To learn skeleton from an input image, we design a deep
architecture whose decoder is based on a novel design of parallel streams
respectively for synthesis of curve- and surface-like skeleton points. We use
different shape representations of point cloud, volume, and mesh in our
stage-wise learning, in order to take their respective advantages. We also
propose multi-stage use of the input image to correct prediction errors that
are possibly accumulated in each stage. We conduct intensive experiments to
investigate the efficacy of our proposed approach. Qualitative and quantitative
results on representative object categories of both simple and complex
topologies demonstrate the superiority of our approach over existing ones. We
will make our ShapeNet-Skeleton dataset publicly available.
