Shape segmentation is a very important topic in shape analysis. In this paper, a novel hierarchical shape segmentation method based on splats for 3D shapes is proposed for both patch-aware and part-aware purposes. The authors present a hierarchical splat clustering framework. Using the splats as the initial clusters, the authors calculate an improved variational shape approximation (VSA) with an L~(2, 1) metric. The patch-aware similarity metric and part-aware similarity metric are then denned and combined for merging the neighboring clusters hierarchically to produce a hierarchy of regions. The method generates the hierarchical clustering output as a binary tree denoting the different levels of segmentation. A boundary smoothing would be applied in the post-processing step to improve the segmentation quality in the implementation. Extensive experiments on different models, such as CAD model, scanned object, organic shape, large-scale mesh, and noisy model, show impressive segmentation results. The runtime table in the paper demonstrates its great efficiency.
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