3D Shape Attributes

David F. Fouhey, Abhinav Gupta, Andrew Zisserman

CVPR 2016

doi:10.1109/CVPR.2016.168 /cvpr/2016/fouhey2016cvpr-3d/

Abstract

In this paper we investigate 3D attributes as a means to understand the shape of an object in a single image. To this end, we make a number of contributions: (i) we introduce and define a set of 3D Shape attributes, including planarity, symmetry and occupied space; (ii) we show that such properties can be successfully inferred from a single image using a Convolutional Neural Network (CNN); (iii) we introduce a 143K image dataset of sculptures with 2197 works over 242 artists for training and evaluating the CNN; (iv) we show that the 3D attributes trained on this dataset generalize to images of other (non-sculpture) object classes; and furthermore (v) we show that the CNN also provides a shape embedding that can be used to match previously unseen sculptures largely independent of viewpoint.

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Cite

Text

Fouhey et al. "3D Shape Attributes." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.168

Markdown

[Fouhey et al. "3D Shape Attributes." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/fouhey2016cvpr-3d/) doi:10.1109/CVPR.2016.168

BibTeX

@inproceedings{fouhey2016cvpr-3d,
  title     = {{3D Shape Attributes}},
  author    = {Fouhey, David F. and Gupta, Abhinav and Zisserman, Andrew},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2016},
  doi       = {10.1109/CVPR.2016.168},
  url       = {https://mlanthology.org/cvpr/2016/fouhey2016cvpr-3d/}
}