Deformable object simulation based on an improved numerical integration

被引:0
|
作者
Hu, Xinrong [1 ]
Li, Dehua [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Educ Commiss, Lab Image Proc & Intelligence Control, Inst Pattern Recognit & Artificial Intelligence, Wuhan 430074, Peoples R China
来源
MIPPR 2007: MULTISPECTRAL IMAGE PROCESSING | 2007年 / 6787卷
关键词
deformable object; simulation; numerical integration; embedded Runge-Kutta; ordinary differential equation; spring-mass model; time step;
D O I
10.1117/12.750207
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A satisfied deformable object simulation should be general, accurate, efficient and stable. Explicit, implicit and semi-implicit integration methods have contributed to large performance enhancements in the field of deformable simulation. Cloth is the most representative deformable object. In this paper, we propose an improved embedded Runge-Kutta method to solve the deformable simulation that takes cloth for example based on classical spring-mass model. Traditional embedded Runge-Kutta methods generally apply some optimized coefficients to solve ordinary differential equations of deformable object simulation. Most of them tend to concentrate on the efficiency of the simulation process. and not the fidelity of the simulation result. We investigate and determine the extent to which the overall quality must be compromised in order for the stable conditions to be satisfied. The improved Runge-Kutta method proposed in our paper incorporates the fixed time step and adaptive time step in solving cloth motion equations to achieve a controllable error evaluation. Compared with the other Runge-Kutta methods, the proposed method has some advantages for cloth simulation: controllable error evaluation without extra computations, excellent efficiency, good stability and satisfied precision. Experiment demonstrates that the method improves the simulation efficiency and is considerable practicable.
引用
收藏
页数:7
相关论文
共 50 条
  • [41] Occlusion-robust Deformable Object Tracking without Physics Simulation
    Chi, Cheng
    Berenson, Dmitry
    2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2019, : 6443 - 6450
  • [42] An Adaptive Spherical Collision Detection and Resolution Method for Deformable Object Simulation
    Qian, Kun
    Yang, Xiaosong
    Zhang, Jianjun
    Wang, Meili
    2015 14TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN AND COMPUTER GRAPHICS (CAD/GRAPHICS), 2015, : 8 - 17
  • [43] Versatile cutting fracture evolution modeling for deformable object cutting simulation
    He, Sirui
    Qian, Yinling
    Zhu, Xin
    Liao, Xiangyun
    Heng, Pheng-Ann
    Feng, Ziliang
    Wang, Qiong
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 219
  • [44] A PROJECT-BASED APPROACH FOR TEACHING NUMERICAL INTEGRATION IN CONTINUOUS SIMULATION
    Shen, Yuzhong
    Sosonkina, Masha
    2023 ANNUAL MODELING AND SIMULATION CONFERENCE, ANNSIM, 2023, : 462 - 471
  • [46] Object detection and recognition based on multiscale deformable template
    Yu, Li
    Wang, Run-Sheng
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2002, 39 (10):
  • [47] Deformable-model based textured object segmentation
    Huang, XL
    Qian, Z
    Huang, R
    Metaxas, D
    ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, 2005, 3757 : 119 - 135
  • [48] OBJECT DETECTION ALGORITHM BASED ON DEFORMABLE PART MODELS
    Guo Jie
    Zhang Honggang
    Chen Daiwu
    Zhang Nannan
    2014 4TH IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT (IEEE IC-NIDC), 2014, : 90 - 94
  • [49] Numerical simulation of the drying of a deformable material: Evaluation of the diffusion coefficient
    Mabrouk, SB
    Belghith, A
    DRYING TECHNOLOGY, 1995, 13 (8-9) : 1789 - 1805
  • [50] Numerical simulation of a bilayer PMN ceramic bimorph deformable mirror
    Sun Q.
    Jiang Z.-F.
    Optoelectronics Letters, 2010, 6 (05) : 342 - 345