Abstract:
Understanding 3D physical world requires 3D data with rich geometry of objects and scenes. The emergence of large-scale 3D datasets necessitates a powerful 3D generative model for 3D shape synthesis and analysis. This paper proposes a deep 3D energy-based volumetric shape model. Maximum likelihood model training uses “analysis by synthesis.” The benefits of the proposed model are six-fold: first, unlike GANs and VAEs, the model training does not rely on any auxiliary models; second, the model can synthesize realistic 3D shapes by Markov chain Monte Carlo (MCMC); third, the conditional model can be applied to 3D object recovery and super resolution; fourth, the model can serve as a building block in a multi-grid modeling and sampling framework for high-resolution 3D shape synthesis; and fifth, the Experiments show that the proposed model can generate high-quality 3D shape patterns for a variety of 3D shape analysis.
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