<p>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. </p>
<p>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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14504817 |
| Date | 01 June 2021 |
| Creators | Jichun Zheng (10719285) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/An_Experimental_Fast_Approach_of_Self-collision_Handling_in_Cloth_Simulation_Using_GPU/14504817 |
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