한국컴퓨터그래픽스학회논문지 (Journal of the Korea Computer Graphics Society)

  • 제25권3호
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  • Pages.105-113
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  • 2019
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  • 1975-7883(pISSN)
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  • 2383-529X(eISSN)
한국컴퓨터그래픽스학회 (Korea Computer Graphics Society)
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CNN을 이용한 Quad Tree 기반 2D Smoke Super-resolution

Quad Tree Based 2D Smoke Super-resolution with CNN

  • 투고 : 2019.06.11
  • 심사 : 2019.06.23
  • 발행 : 2019.07.14
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초록

물리 기반 유체 시뮬레이션은 고해상도 연산을 위해 많은 시간이 필요하다. 이 문제를 해결하기 위해 저해상도 유체 시뮬레이션의 한계를 딥 러닝으로 보완하는 연구들이 있으며, 그중에서는 저해상도의 시뮬레이션 데이터를 고해상도로 변환해주는 Super-resolution 분야가 있다. 하지만 기존 기법들은 전체 데이터 공간에서 밀도 데이터가 없는 부분까지 연산하므로 전체 시뮬레이션 속도 면에서 효율성이 떨어지며, 입력 해상도가 큰 경우에는 GPU 메모리가 부족해 연산할 수 없는 경우가 발생할 수 있다. 본 연구에서는 공간 분할 법 중 하나인 쿼드 트리를 활용하여 시뮬레이션 공간을 분할 및 분류하여 Super-resolution 하는 기법을 제안한다. 본 기법은 필요 공간만 Super-resolution 하므로 전체 시뮬레이션 가속화가 가능하고, 입력 데이터를 분할 연산하므로 GPU 메모리 문제를 해결할 수 있게 된다.

Physically-based fluid simulation takes a lot of time for high resolution. To solve this problem, there are studies that make up the limitation of low resolution fluid simulation by using deep running. Among them, Super-resolution, which converts low-resolution simulation data to high resolution is under way. However, traditional techniques require to the entire space where there are no density data, so there are problems that are inefficient in terms of the full simulation speed and that cannot be computed with the lack of GPU memory as input resolution increases. In this paper, we propose a new method that divides and classifies 2D smoke simulation data into the space using the quad tree, one of the spatial partitioning methods, and performs Super-resolution only required space. This technique accelerates the simulation speed by computing only necessary space. It also processes the divided input data, which can solve GPU memory problems.

키워드

참고문헌

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피인용 문헌

  1. Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method vol.26, pp.4, 2021, https://doi.org/10.9708/jksci.2021.26.04.047