基于空间自适应剖分的 Lightcuts多光源聚类算法
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(1.海军航空工程学院电子信息工程系,山东烟台 264001;2.空军航空大学航空航天情报系,长春 130022)

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TP391.9

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Lightcuts Multi-Light Source Clustering Algorithm Based on Adaptive Space Subdivision
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(1. Department of Electronic and Information Engineering, NAAU, Yantai Shandong 264001, China; 2. Department of Aeronautic and Astronautic Intelligence, Aviation University of Air Force, Changchun 130022, China)

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    摘要:

    针对 Lightcuts算法在面对大规模复杂光源时计算效率较低的问题,提出了一种基于空间自适应剖分的 Lightcuts多光源聚类算法。该算法采用二叉树森林代替传统 Lightcuts算法中的二叉树,并提出自适应的视景体划分方法对三维场景进行剖分,通过构建包含“簇-光源”对的列表,快速剔除与当前渲染点无关的光源,同时利用空间聚类的相似性减少“光源割”搜索过程中的重复计算。实验结果表明,与传统方法相比,文章提出的算法在“光源割”计算阶段能够将搜索步数平均减少 30.71%~42.09%,绘制时间平均缩短 28.35%~34.84%,有效地加快了 Light. cuts算法的计算速度,提高了多光源三维场景的绘制效率。

    Abstract:

    In order to overcome the shortcomings of low efficiency of lightcuts algorithm when dealing with plenty of com.plex light sources, a lightcuts multi-source clustering algorithm based on adaptive space subdivision was proposed. Binarytree forest that was used in this algorithm, replaced the binary tree in traditional lightcuts algorithm, and a scheme of adap.tive spatial subdivision based on view frustum was proposed to subdivide the 3D scene. For purpose of quickly culling thelight sources which was irrelevant to current rendering point, a list of“cluster-light”pairs was built. At the same time, therepeated computation in the process of finding cuts was reduced based on the similarity of space clustering. The experimen.tal results showed that, compared with the traditional method, the algorithm that was proposed in this paper could reducethe number of search steps by 30.71%-42.09% averagely in the stage of finding cut, and reduce the rendering time by28.35%-34.84% averagely. It could accelerate the calculation speed of lightcuts algorithm significantly, and improve therendering efficiency of multiple light source in 3D scene.

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引用本文

袁昱纬,刘传辉,全吉成,王宏伟,吴晨.基于空间自适应剖分的 Lightcuts多光源聚类算法[J].海军航空大学学报,2017,32(2):181-186, 198
YUAN Yuwei, LIU Chuanhui, QUAN Jicheng, WANG Hongwei, WU Chen. Lightcuts Multi-Light Source Clustering Algorithm Based on Adaptive Space Subdivision[J]. JOURNAL OF NAVAL AVIATION UNIVERSITY,2017,32(2):181-186, 198

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  • 在线发布日期: 2017-06-06
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