A Perceptual Aesthetics Measure for 3D Shapes

Understand

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.