摘要
为了提升基于粒子的流体表面重建效率,提出了一种基于透视网格的自适应窄带表面粒子提取方法。与基于物体空间的方法相比,该方案根据粒子密度、离散系数等信息自适应提取视锥范围内最靠近视点的表面粒子,使表面粒子数、内存消耗仅与可见的表面区域相关,而不是整个流体表面或模拟域。此外,利用透视网格沿视线排布的优势,提出了基于粒子密度的自适应厚度估计方法。实验结果表明,该方案有效减少了40%~76%的表面粒子和30%~50%的内存开销,解决了表面粒子冗余和空洞问题,并以较低的代价获取了厚度信息。该方案为后续的表面重建和渲染带来了明显的性能提升,可以更好地处理大规模粒子集的重建和渲染。
In order to improve the efficiency of particle-based fluid surface reconstruction,this paper proposed a perspective-grid-based adaptive narrow-band surface particle extraction method.Compared with the object-space-based method,this scheme adaptively extracted the surface particles closest to the viewpoint within the frustum according to the particle density,dispersion coefficient,so that the number of surface particles and memory consumption were related to the visible surface area,rather than the entire fluid surface or simulation domain.In addition,this paper presented an adaptive thickness estimation method based on particle density by taking advantage of the arrangement of perspective grids along the view.Experiments show that this scheme effectively reduces surface particles by 40%to 76%and memory overhead by 30%to 50%,solves the problems of surface particle redundancy and holes,and quickly obtains thickness information at a lower cost.This scheme brings significant performance improvement for surface reconstruction and rendering,and can better handle the surface reconstruction and rende-ring of large-scale particle sets.
作者
周志强
吴桐
张严辞
Zhou Zhiqiang;Wu Tong;Zhang Yanci(National Key Laboratory of Fundamental Science on Synthetic Vision,Sichuan University,Chengdu 610065,China;College of Computer Science,Sichuan University,Chengdu 610065,China)
出处
《计算机应用研究》
CSCD
北大核心
2023年第10期3155-3161,共7页
Application Research of Computers
基金
四川省重点研发项目(2023YFG0122)。
关键词
表面重建
表面粒子提取
透视网格
厚度
自适应
窄带
surface reconstruction
surface particle extraction
perspective grid
thickness
adaptive
narrow band
