3D shape attributes

Conference item

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.

Authors: Fouhey, D; Gupta, A; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Host title: IEEE Conference on Computer Vision and Pattern Recognition, 2016
• Journal: IEEE Conference on Computer Vision and Pattern Recognition, 2016
• Publication date: 2010-04-29
• Acceptance date: 2016-03-02
• Event location: Las Vegas, USA
• Event start date: 2016-06-26
• DOI: 10.1109/CVPR.2016.168
• EISSN: 1063-6919
• Keywords: SBTMR