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Feature-Based Visibility-Driven CLOD for Terrain

机译:基于功能的可视性驱动的地形

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摘要

View-dependent level-of-detail (LOD) and visibility culling are two powerful tools for accelerating the rendering of very large models in real-time visualization, especially in walkthrough of a large-scale terrain environment. In this paper, we propose a visibility-driven Continuous LOD (CLOD) framework for terrain, which takes advantage of both techniques. The visibility determination is based on the well-known occlusion horizon algorithm. By making use of the features of the terrain extracted in pre-processing stage, a new cascading occlusion culling (COC) algorithm is proposed to cull those regions classified as invisible to current viewpoint in real time. The time consumption and storage overheads that we spend on visibility preprocessing are quite small. Visibility-driven CLOD enhances culling efficiency and improves the frame rates significantly for walkthrough of a terrain environment.
机译:查看依赖性细节水平(LOD)和可见性剔除是两个强大的工具,用于加速实时可视化中非常大型模型的强大工具,特别是在大型地形环境的演练中。在本文中,我们提出了一种用于地形的可见性连续LOD(CLOC)框架,其利用两种技术。可视性确定基于众所周知的遮挡地平线算法。通过利用预处理阶段中提取的地形的特征,提出了一种新的级联遮挡剔除(COC)算法,以实时地将这些区域剔除,这些区域是实时对当前观点的不可见的。我们在可见性预处理上花费的时间消耗和存储开销非常小。可视性驱动的CLOD增强了剔除效率,并提高了地形环境的演练的显着框架速率。

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