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

The problem of self-collision detection has always been evasive in real time cloth animation as it is very expensive to be implemented. While as rapid as today’s evolution of graphics hardware, self-collision handling of cloth is hardly to be seen in almost all kinds of electronic graphics product. 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