적응형 및 연속적인 부호거리장 구성을 위한 효율적인 GPU 프레임워크와 이것을 활용한 응용 사례
Efficient GPU Framework for Adaptive and Continuous Signed Distance Field Construction, and Its Applications
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본 논문에서는 적응형 및 연속적인 부호 거리장을 빠르게 계산하기 위한 새로운 GPU 기반 프레임워크를 제안하고, 이를 활용한 렌더링/충돌처리 관련 사례를 살펴본다. 삼각형 메쉬로부터 구성된쿼드트리를 GPU 메모리로 전달하고, 이를 활용하여 삼각형에 대한 유클리디안 거리를 각 스레드 별로 병렬 처리함으로써 적응형 격자 공간에서 불연속 없이 연속적인 최단 거리를 찾는다. 이 과정에서 적응형 부호 거리장의 절단면 보기, 특정 위치에서의 거리 값 조회, 실시간 레이트레이싱 및 충돌처리 작업을 빠르고 효율적으로 수행될 수 있는지를 실험을 통해 보여준다. 제안하는 방법을 사용하면 하이폴리곤 메쉬에서도 1초 내외로 빠르게 적응형 부호 거리장을 계산할 수 있기 때문에 강체뿐만 아니라 변형체에서도 충분히 활용될 수 있는 방법이며, 다양한 모델에서도 정확하게 샘플링하고거리 값을 나타낼 수 있는지 다양한 실험 결과를 통해 알고리즘의 안정성을 보여준다.
Abstract
In this paper, we propose a new GPU-based framework for quickly calculating adaptive and continuous SDF(Signed distance fields), and examine cases related to rendering/collision processing using them. The quadtree constructed from the triangle mesh is transferred to the GPU memory, and the Euclidean distance to the triangle is processed in parallel for each thread by using it to find the shortest continuous distance without discontinuity in the adaptive grid space. In this process, it is shown through experiments that the cut-off view of the adaptive distance field, the distance value inquiry at a specific location, real-time raytracing, and collision handling can be performed quickly and efficiently. Using the proposed method, the adaptive sign distance field can be calculated quickly in about 1 second even on a high polygon mesh, so it is a method that can be fully utilized not only for rigid bodies but also for deformable bodies. It shows the stability of the algorithm through various experimental results whether it can accurately sample and represent distance values in various models.
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