Fast approximate convex decomposition using relative concavity

Handle URI:
http://hdl.handle.net/10754/598315
Title:
Fast approximate convex decomposition using relative concavity
Authors:
Ghosh, Mukulika; Amato, Nancy M.; Lu, Yanyan; Lien, Jyh-Ming
Abstract:
Approximate 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) [31]. © 2012 Elsevier Ltd. All rights reserved.
Citation:
Ghosh 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.
Publisher:
Elsevier BV
Journal:
Computer-Aided Design
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Feb-2013
DOI:
10.1016/j.cad.2012.10.032
Type:
Article
ISSN:
0010-4485
Sponsors:
The 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.
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Full metadata record

DC FieldValue Language
dc.contributor.authorGhosh, Mukulikaen
dc.contributor.authorAmato, Nancy M.en
dc.contributor.authorLu, Yanyanen
dc.contributor.authorLien, Jyh-Mingen
dc.date.accessioned2016-02-25T13:18:34Zen
dc.date.available2016-02-25T13:18:34Zen
dc.date.issued2013-02en
dc.identifier.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.en
dc.identifier.issn0010-4485en
dc.identifier.doi10.1016/j.cad.2012.10.032en
dc.identifier.urihttp://hdl.handle.net/10754/598315en
dc.description.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) [31]. © 2012 Elsevier Ltd. All rights reserved.en
dc.description.sponsorshipThe 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.en
dc.publisherElsevier BVen
dc.subjectApproximate convex decompositionen
dc.subjectComputational geometryen
dc.subjectMesh segmentationen
dc.titleFast approximate convex decomposition using relative concavityen
dc.typeArticleen
dc.identifier.journalComputer-Aided Designen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionGeorge Mason University, Fairfax, United Statesen
kaust.grant.numberKUS-C1-016-04en
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