A Papier-Mâché Approach to Learning 3D Surface Generation
Abstract
We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potentialfor other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.
Cite
Text
Groueix et al. "A Papier-Mâché Approach to Learning 3D Surface Generation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00030Markdown
[Groueix et al. "A Papier-Mâché Approach to Learning 3D Surface Generation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/groueix2018cvpr-papiermache/) doi:10.1109/CVPR.2018.00030BibTeX
@inproceedings{groueix2018cvpr-papiermache,
title = {{A Papier-Mâché Approach to Learning 3D Surface Generation}},
author = {Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan C. and Aubry, Mathieu},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00030},
url = {https://mlanthology.org/cvpr/2018/groueix2018cvpr-papiermache/}
}