GPU-Based Parallel Collision Detection for Deformable Objects

KIPS Transactions on Software and Data Engineering, Vol. 7, No.1, pp.25-32, January 2018
10.3745/KTSDE.2018.7.1.025, Full Text

Abstract

Due to heavy computational cost, deformable object simulation requires more effective collision detection method than rigid body simulation. However, when the CPU-based collision detection algorithm is purely applied to the GPU environment, the collision detection algorithm and the data structure optimized for the GPU environment are essential because the performance of the GPU can not be used properly. Therefore, we propose a GPU-based parallel collision detection algorithm for mass-spring system which is widely used for deformable object representation in this paper. The proposed method uses a parallel algorithm and data structure to reduce collision detection cost through GPU-based curling algorithm using AABB-Octree structure. In this paper, we prove the effectiveness of the proposed method by comparing the intersection test of all triangle pairs in parallel. The results of experimental tests show that the proposed method improves the performance by about 24% on average. Therefore, it is expected that the proposed method can improve the performance of real-time simulation for deformable objects.


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Cite this paper

[KIPS Transactions Style]
N. Sung, K. M. Sang, M. Hong, and Y. Choi, "GPU-Based Parallel Collision Detection for Deformable Objects," KIPS Transactions on Software and Data Engineering, Vol.7, No.1, pp.25-32, 2018, DOI: 10.3745/KTSDE.2018.7.1.025.

[IEEE Style]
Nak-Jun Sung, Kim Min Sang, Min Hong, and Yoo-Joo Choi, "GPU-Based Parallel Collision Detection for Deformable Objects," KIPS Transactions on Software and Data Engineering, vol. 7, no. 1, pp. 25-32, 2018. DOI: 10.3745/KTSDE.2018.7.1.025.

[ACM Style]
Sung, N., Sang, K. M., Hong, M., and Choi, Y. 2018. GPU-Based Parallel Collision Detection for Deformable Objects. KIPS Transactions on Software and Data Engineering, 7, 1, (2018), 25-32. DOI: 10.3745/KTSDE.2018.7.1.025.