I-Cloth: Incremental Collision Handling for GPU-Based Interactive Cloth Simulation

by Min Tang1, Tongtong Wang, Zhongyuan Liu1, Ruofeng Tong1, and Dinesh Manocha2,3

1 - Zhejiang University, China

2 - University of North Carolina at Chapel Hill, USA

3 - University of Maryland at College Park, USA

 

Benchmark Andy: Our GPU-based approach can simulate the clothes dressed on a Kung-Fu boy. The meshes of three cloth pieces are represented by 127K triangles. Our simulator performs all of the computations, including implicit time integration and collision handling, in 0.2s per frame (on average) on an NVIDIA GeForce GTX 1080 GPU. We use new algoriths for sparse matrix assembly, incremental collision detection and collision response.

Abstract

We present an incremental collision handling algorithm for GPU-based interactive cloth simulation. Our approach exploits the spatial and temporal coherence between successive iterations of an optimization-based solver for collision response computation. We present an incremental continuous collision detection algorithm that keeps track of deforming vertices and combine it with spatial hashing. We use a non-linear GPU-based impact zone solver to resolve the penetrations. We combine our collision handling algorithm with implicit integration to use large time steps. Our overall algorithm, I-Cloth, can simulate complex cloth deformation with a few hundred thousand vertices at 2 − 8 frames per second on a commodity GPU. We highlight its performance on different benchmarks and observe up to 7 − 10X speedup over prior algorithms.
 

Contents

Paper  (PDF 2.48 MB) Min Tang, Huamin Tang, Le Tang, Roufeng Tong, and Dinesh Manocha, CAMA: Contact-Aware Matrix Assembly with Unified Collision Handling for GPU-based Cloth Simulation, Computer Graphics Forum, 35(2): 511-521, (Proceedings of Eurographics 2016), 2016. Video (63.5 MB)

   @article{cama16,
      author = {Tang, Min and Wang, Huamin and Tang, Le and Tong, Ruofeng and Manocha, Dinesh},
      title = {{CAMA}: Contact-Aware Matrix Assembly with Unified Collision Handling for {GPU}-based Cloth Simulation},
      journal = {Computer Graphics Forum (Proceedings of Eurographics 2016)},
      volume = {35},
      number = {2},
      pages = {511--521},
      year = {2016},
  }

 

Paper  (PDF 2.3 MB) Min Tang, Zhongyuan Liu, Roufeng Tong, and Dinesh Manocha, PSCC: Parallel Self-Collision Culling with Spatial Hashing on GPUs. Proceedings of ACM Symposium on Interactive 3D Graphics and Games, 2018. Video (24 MB)
  

 

Paper  (PDF 3.2 MB) Min Tang, Tongtong Wang, Zhongyuan Liu, Roufeng Tong, and Dinesh Manocha, I-Cloth: Incremental Collision Handling for GPU-Based Interactive Cloth Simulation. Proceedings of ACM SIGGRAPH Asia, 2018. Video (45 MB) WWW
  

 

Related Links

Efficient BVH-based Collision Detection Scheme with Ordering and Restructuring (Paper) and Source Code

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

UNC dynamic model benchmark repository

Collision-Streams: Fast GPU-based Collision Detection for Deformable Models

Fast Continuous Collision Detection using Deforming Non-Penetration Filters

Interactive Continuous Collision Detection between Deformable Models using Connectivity-Based Culling

MCCD: Multi-Core Collision Detection between Deformable Models using Front-Based Decomposition

Fast Collision Detection for Deformable Models using Representative-Triangles

DeformCD: Collision Detection between Deforming Objects

Self-CCD: Continuous Collision Detection for Deforming Objects

Interactive Collision Detection between Deformable Models using Chromatic Decomposition

Fast Proximity Computation Among Deformable Models using Discrete Voronoi Diagrams

CULLIDE: Interactive Collision Detection between Complex Models using Graphics Hardware

RCULLIDE: Fast and Reliable Collision Culling using Graphics Processors

Quick-CULLIDE: Efficient Inter- and Intra-Object Collision Culling using Graphics Hardware

Collision Detection

UNC GAMMA Group

Acknowledgements

This research is supported in part by the National High-Tech Research and Development Program (No.2013AA013903) of China, the National Key Technology R&D Program of China (2012BAD35B01). Min Tang is supported in part by NSFC (61572423, 61170140), Zhejiang Provincial NSFC (LZ16F020003), the Doctoral Fund of Ministry of Education of China (20130101110133), and EU ANNEX project (612627). Dinesh Manocha is supported in part by ARO contract W911NF-14-1-0437 and NSF grant 1547106, and the National Thousand Talents Program of China. Huamin Wang is supported in part by NVIDIA and Adobe. Ruofeng Tong is partly supported by NSFC (61572424, 61170141). We gratefully acknowledge the support of NVIDIA Corporation for the donation of Tesla K20x/K40c GPUs used for this research. We thank FxGear for providing the models for Benchmark Andy, Bishop, and Falling. We also thank Zhendong Wang for useful discussions and making the video.

 

CB #3175, Department of Computer Science
University of North Carolina
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