S. Tulsiani, H. Su, L.J. Guibas, A.A. Efros, J. Malik, Learning Shape Abstractions by Assembling Volumetric Primitives, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

Abstract:

We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives. In addition to generating simple and geometrically interpretable explanations of 3D objects, our framework also allows us to automatically discover and exploit consistent structure in the data. We demonstrate that using our method allows predicting shape representations which can be leveraged for obtaining a consistent parsing across the instances of a shape collection and constructing an interpretable shape similarity measure. We also examine applications for image-based prediction as well as shape manipulation.

Bibtex:

@inProceedings{tsgem-lsabavp-17,
  title={Learning Shape Abstractions by Assembling Volumetric Primitives},
  author = {Shubham Tulsiani and Hao Su and Leonidas J. Guibas and Alexei A. Efros and Jitendra Malik},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017}
}