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The idea of style similarity metrics has been recently developed for variousmedia types such as 2D clip art and 3D shapes. We explore this style metricproblem and improve existing style similarity metrics of 3D shapes in fournovel ways. First, we consider the color and texture of 3D shapes which areimportant properties that have not been previously considered. Second, weexplore the effect of clustering a dataset of 3D models by comparing betweenstyle metrics for a single object type and style metrics that combine clustersof object types. Third, we explore the idea of user-guided learning for thisproblem. Fourth, we introduce an iterative approach that can learn a metricfrom a general set of 3D models. We demonstrate these contributions withvarious classes of 3D shapes and with applications such as style-basedsimilarity search and scene composition.

Improving Style Similarity Metrics of 3D Shapes