A GPU‐based Streaming Algorithm for High‐Resolution Cloth Simulation

Tang, Min; Tong, Ruofeng; Narain, Rahul; Meng, Chang; Manocha, Dinesh

doi:10.1111/cgf.12208

Cited by 38 publications

(45 citation statements)

References 42 publications

(69 reference statements)

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“…Our method uses the following data structure: BVH, BVTT, front, nonadjacent triangle pair, and orphan set. 13,37,46,47 BVH: A BVH is a tree structure on a set of geometric objects that accelerate queries. BVHs have been widely used for collision detection, in which different BVs are selected.…”

Section: Collision Detection and Responsementioning

confidence: 99%

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A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

Tian

Shen

2018

Computer Animation & Virtual

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This paper offers a fast multi‐graphics processing unit (GPU) parallel simulation framework to the problem of real‐time and nonlinear finite element computation of brain deformation. A load balancing strategy is proposed to ensure the efficient distribution of nonlinear finite element computation on multi‐GPU. A data storage structure is designed to minimize the amount of data transfer and make full use of the overlay technique of GPU to reduce the transferring latency between multi‐GPUs. We further present a fast central processing unit (CPU)–GPU parallel continuous collision detection and response method, which not only can deal with the collision between the brain and skull but also can handle the self‐collision of the brain. Our method can make full use of CPU and GPU to implement a parallel computation about deformation and collision detection. Our experimental results show that our method is able to handle a brain geometric model with high detail gyrus composed of more than 40,000 tetrahedron elements. This can facilitate the fidelity of the current virtual brain surgery simulator. We evaluate our approach qualitatively and quantitatively and compare it with related works.

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Section: Collision Detection and Responsementioning

confidence: 99%

“…Our collision detection method needs to be integrated into our multi‐GPU parallel framework, so we designed a hybrid CPU/GPU algorithm inspired by the above parallel CCD algorithms. Our method uses the following data structure: BVH, BVTT, front, nonadjacent triangle pair, and orphan set …”

Section: Introductionmentioning

confidence: 99%

A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

Tian

Shen

2018

Computer Animation & Virtual

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“…An advantage of our yarn-level cloth model is the strong regularity of the system matrix A, which allows a highly efficient implementation of numerical integration on GPUs, similar to GPU cloth solvers for regular triangle meshes [Tang et al 2013]. We parallelize the computation of internal forces, their Jacobians, and the solution to the linear system on the GPU, but we execute collision detection on the CPU.…”

Section: Gpu-parallel Time Integrationmentioning

confidence: 99%

Yarn-level simulation of woven cloth

Cirio¹,

López‐Moreno²,

Miraut³

et al. 2014

ACM Trans. Graph.

109

110

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Figure 1: Yarn-level simulation of a shirt with 2023 yarns and 350530 crossing nodes. We produce a snag on the shirt by pulling on a seam node. Fine-scale deformations showing yarn sliding and thin wrinkles are combined with large-scale motion of the shirt. AbstractThe large-scale mechanical behavior of woven cloth is determined by the mechanical properties of the yarns, the weave pattern, and frictional contact between yarns. Using standard simulation methods for elastic rod models and yarn-yarn contact handling, the simulation of woven garments at realistic yarn densities is deemed intractable. This paper introduces an efficient solution for simulating woven cloth at the yarn level. Central to our solution is a novel discretization of interlaced yarns based on yarn crossings and yarn sliding, which allows modeling yarn-yarn contact implicitly, avoiding contact handling at yarn crossings altogether. Combined with models for internal yarn forces and inter-yarn frictional contact, as well as a massively parallel solver, we are able to simulate garments with hundreds of thousands of yarn crossings at practical framerates on a desktop machine, showing combinations of large-scale and fine-scale effects induced by yarn-level mechanics.

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“…For example, many Intel processors use 80-bit internal registers for floating-point operations, and this may result in higher accuracy for CPU-based implementations. We have also integrated BSC-float and El-Topo-float into a GPU-based cloth simulation system [Tang et al 2013] and compared the runtime query performance of both CCD algorithms within that system. Figure 7 highlights the performance of BSC-float-GPU and El-Topo-float-GPU.…”

Section: Implementation and Performancementioning

confidence: 99%

Fast and exact continuous collision detection with Bernstein sign classification

et al. 2014

Self Cite

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We present fast algorithms to perform accurate CCD queries between triangulated models. Our formulation uses properties of the Bernstein basis and Bézier curves and reduces the problem to evaluating signs of polynomials. We present a geometrically exact CCD algorithm based on the exact geometric computation paradigm to perform reliable Boolean collision queries. Our algorithm is more than an order of magnitude faster than prior exact algorithms. We evaluate its performance for cloth and FEM simulations on CPUs and GPUs, and highlight the benefits.

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A GPU‐based Streaming Algorithm for High‐Resolution Cloth Simulation

Cited by 38 publications

References 42 publications

A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

Yarn-level simulation of woven cloth

Fast and exact continuous collision detection with Bernstein sign classification

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