KAUST Grant NumberKUS-C1-016-04
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AbstractApproximate convex decomposition (ACD) is a technique that partitions an input object into approximately convex components. Decomposition into approximately convex pieces is both more efficient to compute than exact convex decomposition and can also generate a more manageable number of components. It can be used as a basis of divide-and-conquer algorithms for applications such as collision detection, skeleton extraction and mesh generation. In this paper, we propose a new method called Fast Approximate Convex Decomposition (FACD) that improves the quality of the decomposition and reduces the cost of computing it for both 2D and 3D models. In particular, we propose a new strategy for evaluating potential cuts that aims to reduce the relative concavity, rather than absolute concavity. As shown in our results, this leads to more natural and smaller decompositions that include components for small but important features such as toes or fingers while not decomposing larger components, such as the torso, that may have concavities due to surface texture. Second, instead of decomposing a component into two pieces at each step, as in the original ACD, we propose a new strategy that uses a dynamic programming approach to select a set of n c non-crossing (independent) cuts that can be simultaneously applied to decompose the component into n c+1 components. This reduces the depth of recursion and, together with a more efficient method for computing the concavity measure, leads to significant gains in efficiency. We provide comparative results for 2D and 3D models illustrating the improvements obtained by FACD over ACD and we compare with the segmentation methods in the Princeton Shape Benchmark by Chen et al. (2009) . © 2012 Elsevier Ltd. All rights reserved.
CitationGhosh M, Amato NM, Lu Y, Lien J-M (2013) Fast approximate convex decomposition using relative concavity. Computer-Aided Design 45: 494–504. Available: http://dx.doi.org/10.1016/j.cad.2012.10.032.
SponsorsThe research of NMA and MG was supported in part by NSF awards CRI-0551685, CCF-0833199, CCF-0830753, IIS-096053, and IIS-0917266, by THECB NHARP award 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The research of J-ML and YL was supported in part by NSF IIS-096053, Autodesk and FHWA.