A deep generative model of 3D volumetric shapes
Implementation of a 3D shape generative model based on deep convolutional generative adversarial nets (DCGAN) with techniques of improved-gan.
Experimental results on ShapeNetCore dataset are shown below. For training the networks, I used all 3D models in ShapeNetCore.
This is an application for visualizing linear interpolation and saving generated data as binvox. You can run this application with the following command:
$ python application.py
I strongly recommend running the app on GPU because it is very slow on CPU.
To train the networks, you need to install three python packages.
The following python packages are required for running the application. If you are using anaconda, you can easily install VTK5 and PyQt4 (or they may already be installed). I show installation commands with conda for VTK5 and PyQt4.
$ conda install -c anaconda vtk=5.10.1
$ conda install -c anaconda pyqt=4.11.4
$ pip install qdarkstyle
$ git clone https://github.com/maxorange/voxel-dcgan.git
$ cd voxel-dcgan
config.py
:...
dataset_path = "path/to/dataset/*.binvox"
params_path = "path/to/model"
...
$ python train.py
$ python visualize.py
or
$ python application.py
More details are here.