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Our goal in this paper is to investigate properties of 3D shape that can bedetermined from a single image. We define 3D shape attributes -- genericproperties of the shape that capture curvature, contact and occupied space. Ourfirst objective is to infer these 3D shape attributes from a single image. Asecond objective is to infer a 3D shape embedding -- a low dimensional vectorrepresenting the 3D shape.  We study how the 3D shape attributes and embedding can be obtained from asingle image by training a Convolutional Neural Network (CNN) for this task. Westart with synthetic images so that the contribution of various cues andnuisance parameters can be controlled. We then turn to real images andintroduce a large scale image dataset of sculptures containing 143K imagescovering 2197 works from 242 artists.  For the CNN trained on the sculpture dataset we show the following: (i) whichregions of the imaged sculpture are used by the CNN to infer the 3D shapeattributes; (ii) that the shape embedding can be used to match previouslyunseen sculptures largely independent of viewpoint; and (iii) that the 3Dattributes generalize to images of other (non-sculpture) object classes.

From Images to 3D Shape Attributes