3D Shape Attributes

David F. Fouhey, Abhinav Gupta, Andrew Zisserman; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1516-1524

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.