  The idea of style similarity metrics has been recently developed for various
media types such as 2D clip art and 3D shapes. We explore this style metric
problem and improve existing style similarity metrics of 3D shapes in four
novel ways. First, we consider the color and texture of 3D shapes which are
important properties that have not been previously considered. Second, we
explore the effect of clustering a dataset of 3D models by comparing between
style metrics for a single object type and style metrics that combine clusters
of object types. Third, we explore the idea of user-guided learning for this
problem. Fourth, we introduce an iterative approach that can learn a metric
from a general set of 3D models. We demonstrate these contributions with
various classes of 3D shapes and with applications such as style-based
similarity search and scene composition.
