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GPU-Based Parallel Collision Detection for Deformable Objects

변형 물체를 위한 GPU 기반 병렬 충돌 감지

  • 성낙준 (순천향대학교 컴퓨터학과) ;
  • 김민상 (순천향대학교 컴퓨터학과) ;
  • 홍민 (순천향대학교 컴퓨터소프트웨어공학과) ;
  • 최유주 (서울미디어대학원대학교 뉴미디어학부)
  • Received : 2017.11.14
  • Accepted : 2017.11.28
  • Published : 2018.01.31

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.

변형물체 시뮬레이션은 강체 시뮬레이션에 비해 많은 연산량을 요구하기 때문에 효과적인 충돌 검사 방법을 필요하다. 그러나 CPU 기반의 충돌 검사 알고리즘을 그대로 GPU 환경에 적용할 경우 GPU의 성능을 제대로 사용할 수 없기 때문에 GPU 환경에 최적화된 충돌 감지 알고리즘과 자료구조가 필요하다. 따라서 본 연구에서는 변형 물체 표현을 위해 널리 사용되고 있는 질량-스프링 시스템을 위한 GPU 기반의 병렬 충돌 감지 알고리즘을 제안한다. 제안하는 방법은 AABB-옥트리 구조를 이용한 GPU 기반의 컬링 알고리즘을 통해 충돌 감지 비용을 줄이는 병렬 알고리즘과 자료 구조를 사용하였다. 본 연구에서는 모든 삼각형 쌍의 충돌을 병렬로 검사하는 기존 방법과의 비교실험을 통하여 제안 알고리즘의 효율성을 입증하였다. 실험결과, 제안된 방법은 기존의 방법에 비해서 평균 약 24%의 성능 개선을 보였다. 따라서 제안하는 방법을 통해서 변형 물체에 대한 실시간 시뮬레이션의 성능 개선이 가능할 것으로 기대한다.

Keywords

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