  While the problem of image aesthetics has been well explored, the study of 3D
shape aesthetics has focused on specific manually defined features. In this
paper, we learn an aesthetics measure for 3D shapes autonomously from raw voxel
data and without manually-crafted features by leveraging the strength of deep
learning. We collect data from humans on their aesthetics preferences for
various 3D shape classes. We take a deep convolutional 3D shape ranking
approach to compute a measure that gives an aesthetics score for a 3D shape. We
demonstrate our approach with various types of shapes and for applications such
as aesthetics-based visualization, search, and scene composition.
