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Efficient GPU Framework for Adaptive and Continuous Signed Distance Field Construction, and Its Applications

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(3), pp.63-69
  • DOI : 10.9708/jksci.2022.27.03.063
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 15, 2022
  • Accepted : March 21, 2022
  • Published : March 31, 2022

Jong-Hyun Kim 1

1강남대학교

Accredited

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.

Citation status

* References for papers published after 2022 are currently being built.