(* Equal contributions)
1 Hikvision Research Institute, Santa Clara, USA
2 University of California, Los Angeles (UCLA), USA
3 Beijing Institute of Technology
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.
The paper can be downloaded here.
The tex file can be downloaded here.
The poster can be downloaded here.
The oral presentation can be downloaded here.
The Python code using tensorflow can be downloaded here
If you wish to use our code, please cite the following paper:
ContentsExp 1 : Experiment on 3D object synthesis
Exp 4.1: Interpolation
Exp 4.2: 3D Object Arithmetic