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Decomposition of shapes into (approximate) convex parts is essential forapplications such as part-based shape representation, shape matching, andcollision detection. In this paper, we propose a novel convex decompositionusing a parametric implicit shape model called Disjunctive Normal Shape Model(DNSM). The DNSM is formed as a union of polytopes which themselves are formedby intersections of halfspaces. The key idea is by deforming the polytopes,which naturally remain convex during the evolution, the polytopes captureconvex parts without the need to compute convexity. The major contributions ofthis paper include a robust convex decomposition which also results in anefficient part-based shape representation, and a novel shape convexity measure.The experimental results show the potential of the proposed method.

Convex Decomposition And Efficient Shape Representation Using Deformable Convex Polytopes