Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/659527
Title: Efficient Penetration Depth Computation Between Rigid Models Using Contact Space Propagation Sampling
Authors: Liang He;Jia Pan;Danwei Li;Dinesh Manocha
subject: Contact Modelling|Simulation and Animation
Year: 2016
Publisher: IEEE
Abstract: We present a novel method to compute the approximate global penetration depth (PD) between two nonconvex geometric models. Our approach consists of two phases: offline precomputation and run-time queries. In the first phase, our formulation uses a novel sampling algorithm to precompute an approximation of the high-dimensional contact space between the pair of models. As compared with prior random sampling algorithms for contact space approximation, our propagation sampling considerably speeds up the precomputation and yields a high quality approximation. At run-time, we perform a nearest-neighbor query and local projection to efficiently compute the translational or generalized PD. We demonstrate the performance of our approach on complex 3-D benchmarks with tens or hundreds or thousands of triangles, and we observe significant improvement over previous methods in terms of accuracy, with a modest improvement in the run-time performance.
Description: 
URI: http://localhost/handle/Hannan/170547
http://localhost/handle/Hannan/659527
ISSN: 2377-3766
volume: 1
issue: 1
Appears in Collections:2016

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Title: Efficient Penetration Depth Computation Between Rigid Models Using Contact Space Propagation Sampling
Authors: Liang He;Jia Pan;Danwei Li;Dinesh Manocha
subject: Contact Modelling|Simulation and Animation
Year: 2016
Publisher: IEEE
Abstract: We present a novel method to compute the approximate global penetration depth (PD) between two nonconvex geometric models. Our approach consists of two phases: offline precomputation and run-time queries. In the first phase, our formulation uses a novel sampling algorithm to precompute an approximation of the high-dimensional contact space between the pair of models. As compared with prior random sampling algorithms for contact space approximation, our propagation sampling considerably speeds up the precomputation and yields a high quality approximation. At run-time, we perform a nearest-neighbor query and local projection to efficiently compute the translational or generalized PD. We demonstrate the performance of our approach on complex 3-D benchmarks with tens or hundreds or thousands of triangles, and we observe significant improvement over previous methods in terms of accuracy, with a modest improvement in the run-time performance.
Description: 
URI: http://localhost/handle/Hannan/170547
http://localhost/handle/Hannan/659527
ISSN: 2377-3766
volume: 1
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7342933.pdf721.03 kBAdobe PDFThumbnail
Preview File
Title: Efficient Penetration Depth Computation Between Rigid Models Using Contact Space Propagation Sampling
Authors: Liang He;Jia Pan;Danwei Li;Dinesh Manocha
subject: Contact Modelling|Simulation and Animation
Year: 2016
Publisher: IEEE
Abstract: We present a novel method to compute the approximate global penetration depth (PD) between two nonconvex geometric models. Our approach consists of two phases: offline precomputation and run-time queries. In the first phase, our formulation uses a novel sampling algorithm to precompute an approximation of the high-dimensional contact space between the pair of models. As compared with prior random sampling algorithms for contact space approximation, our propagation sampling considerably speeds up the precomputation and yields a high quality approximation. At run-time, we perform a nearest-neighbor query and local projection to efficiently compute the translational or generalized PD. We demonstrate the performance of our approach on complex 3-D benchmarks with tens or hundreds or thousands of triangles, and we observe significant improvement over previous methods in terms of accuracy, with a modest improvement in the run-time performance.
Description: 
URI: http://localhost/handle/Hannan/170547
http://localhost/handle/Hannan/659527
ISSN: 2377-3766
volume: 1
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7342933.pdf721.03 kBAdobe PDFThumbnail
Preview File