  This paper focuses on the challenging task of learning 3D object surface
reconstructions from RGB images. Existingmethods achieve varying degrees of
success by using different surface representations. However, they all have
their own drawbacks,and cannot properly reconstruct the surface shapes of
complex topologies, arguably due to a lack of constraints on the
topologicalstructures in their learning frameworks. To this end, we propose to
learn and use the topology-preserved, skeletal shape representationto assist
the downstream task of object surface reconstruction from RGB images.
Technically, we propose the novelSkeletonNetdesign that learns a volumetric
representation of a skeleton via a bridged learning of a skeletal point set,
where we use paralleldecoders each responsible for the learning of points on 1D
skeletal curves and 2D skeletal sheets, as well as an efficient module
ofglobally guided subvolume synthesis for a refined, high-resolution skeletal
volume; we present a differentiablePoint2Voxellayer tomake SkeletonNet
end-to-end and trainable. With the learned skeletal volumes, we propose two
models, the Skeleton-Based GraphConvolutional Neural Network (SkeGCNN) and the
Skeleton-Regularized Deep Implicit Surface Network (SkeDISN), which
respectivelybuild upon and improve over the existing frameworks of explicit
mesh deformation and implicit field learning for the downstream
surfacereconstruction task. We conduct thorough experiments that verify the
efficacy of our proposed SkeletonNet. SkeGCNN and SkeDISNoutperform existing
methods as well, and they have their own merits when measured by different
metrics. Additional results ingeneralized task settings further demonstrate the
usefulness of our proposed methods. We have made both our implementation
codeand the ShapeNet-Skeleton dataset publicly available at ble at
https://github.com/tangjiapeng/SkeletonNet.
