Abstract
This paper proposes a 3D shape descriptor network,
which is a deep convolutional energy-based model, for
modeling volumetric shape patterns. The maximum likelihood training of the model follows an “analysis by synthesis” scheme and can be interpreted as a mode seeking and
mode shifting process. The model can synthesize 3D shape
patterns by sampling from the probability distribution via
MCMC such as Langevin dynamics. The model can be used
to train a 3D generator network via MCMC teaching. The
conditional version of the 3D shape descriptor net can be
used for 3D object recovery and 3D object super-resolution.
Experiments demonstrate that the proposed model can generate realistic 3D shape patterns and can be useful for 3D
shape analysis