Convex Decomposition And Efficient Shape Representation Using Deformable Convex Polytopes
This addresses shape analysis and processing tasks in computer graphics and vision, but it appears incremental as it builds on existing convex decomposition concepts with a new parametric model.
The paper tackles the problem of decomposing shapes into convex parts for applications like shape representation and collision detection by proposing a novel convex decomposition method using deformable convex polytopes, resulting in a robust decomposition and efficient part-based representation.
Decomposition of shapes into (approximate) convex parts is essential for applications such as part-based shape representation, shape matching, and collision detection. In this paper, we propose a novel convex decomposition using a parametric implicit shape model called Disjunctive Normal Shape Model (DNSM). The DNSM is formed as a union of polytopes which themselves are formed by intersections of halfspaces. The key idea is by deforming the polytopes, which naturally remain convex during the evolution, the polytopes capture convex parts without the need to compute convexity. The major contributions of this paper include a robust convex decomposition which also results in an efficient part-based shape representation, and a novel shape convexity measure. The experimental results show the potential of the proposed method.