This paper presents an original approach for 3D mesh approximate convex decomposition. The proposed algorithm computes a hierarchical segmentation of the mesh triangles by applying a set of topological decimation operations to its dual graph. The decimation strategy is guided by a cost function describing the concavity and the shape of the detected clusters. The generated segmentation is finally exploited to construct a faithful approximation of the original mesh by a set of convex surfaces. This new representation is particularly adapted for collision detection. The experimental evaluation we conducted shows that the proposed technique efficiently decomposes a concave 3D mesh into a small set (with respect to the number of its facets) of nearly convex surfaces. Furthermore, it automatically detects the anatomical structure of the analyzed 3D models, which makes it an ideal candidate for skeleton extraction and patterns recognition applications.
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