Purdue University Graduate School
Thesis_JichunZheng.pdf (1.94 MB)

An Experimental Fast Approach of Self-collision Handling in Cloth Simulation Using GPU

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posted on 2021-06-01, 00:33 authored by Jichun ZhengJichun Zheng

This study describes a fast approach using GPU to process self-collision in cloth animation without significant compromise in physical accuracy. The proposed fast approach is built and works effectively on a modification of Mass Spring Model which is seen in a variety of cloth simulation study. Instead of using hierarchical data structure which needs to be updated each frame, this fast approach adopts a spatial hashing technique which virtually partitions the space where the cloth object locates into small cubes and stores the information of the particles being held in the cells with an integer array. With the data of the particles and the cells holding information of the particles, self-collision detection can be processed in a very limited cost in each thread launched in GPU regardless of the increase in the amount of particles. This method is capable of visualizing self-collision detection and response in real time with limited cost in accessing memory on the GPU.

The idea of the proposed fast approach is extremely straightforward, however, the amount of memory which is needed to be consumed by this method is its weakness. Also, this method sacrifices physical accuracy in exchange for the performance.


Degree Type

  • Master of Science


  • Computer Graphics Technology

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. David Whitthinghill

Additional Committee Member 2

Dr. Tim McGraw

Additional Committee Member 3

Dr. Christos Mousas

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