A Perceptual Aesthetics Measure for 3D Shapes
This work addresses the lack of automated aesthetics assessment for 3D shapes, which could benefit fields like computer graphics and design, though it appears incremental as it extends image aesthetics methods to 3D.
The paper tackles the problem of measuring aesthetics for 3D shapes by learning an autonomous measure from raw voxel data using deep learning, without relying on manually-defined features, and demonstrates its application in visualization, search, and scene composition.
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.