Learning Shape Abstractions by Assembling Volumetric Primitives

Shubham Tulsiani, Hao Su, Leonidas J. Guibas, Alexei A. Efros, Jitendra Malik

CVPR 2017

doi:10.1109/CVPR.2017.160 /cvpr/2017/tulsiani2017cvpr-learning/

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.

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Cite

Text

Tulsiani et al. "Learning Shape Abstractions by Assembling Volumetric Primitives." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.160

Markdown

[Tulsiani et al. "Learning Shape Abstractions by Assembling Volumetric Primitives." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/tulsiani2017cvpr-learning/) doi:10.1109/CVPR.2017.160

BibTeX

@inproceedings{tulsiani2017cvpr-learning,
  title     = {{Learning Shape Abstractions by Assembling Volumetric Primitives}},
  author    = {Tulsiani, Shubham and Su, Hao and Guibas, Leonidas J. and Efros, Alexei A. and Malik, Jitendra},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.160},
  url       = {https://mlanthology.org/cvpr/2017/tulsiani2017cvpr-learning/}
}