3D Shape Attributes

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.