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This paper proposes a 3D shape descriptor network, which is a deepconvolutional energy-based model, for modeling volumetric shape patterns. Themaximum likelihood training of the model follows an "analysis by synthesis"scheme and can be interpreted as a mode seeking and mode shifting process. Themodel can synthesize 3D shape patterns by sampling from the probabilitydistribution via MCMC such as Langevin dynamics. The model can be used to traina 3D generator network via MCMC teaching. The conditional version of the 3Dshape descriptor net can be used for 3D object recovery and 3D objectsuper-resolution. Experiments demonstrate that the proposed model can generaterealistic 3D shape patterns and can be useful for 3D shape analysis.

Learning Descriptor Networks for 3D Shape Synthesis and Analysis