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Computational analysis of mesh simplification using global error

机译:使用全局误差的网格简化计算分析

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Meshes with (recursive) subdivision connectivity, such as subdivision surfaces, are increasingly popular in computer graphics. They present several advantages over their Delaunay-type based counterparts, e.g., Triangulated Irregular Networks (TINs), such as efficient processing, compact storage and numerical robustness. A mesh having subdivision connectivity can be described using a tree structure and recent work exploits this inherent hierarchy in applications such as progressive terrain visualization, surface compression and transmission. We propose a hierarchical, fine to coarse (i.e., using vertex decimation) algorithm to reduce the number of vertices in meshes whose connectivity is based on quadrilateral quadrisection (e.g., subdivision surfaces obtained from Catmull-Clark or 4-8 subdivision rules). Our method is derived from optimal tree pruning algorithms used in modeling of adaptive quantizers for compression. The main advantage of our method is that it allows control of the global error of the approximation, whereas previous methods are based on local error heuristics only. We present a set of operations allowing the use of global error and use them to build an O(n log n) simplification algorithm transforming an input mesh of n vertices into a multiresolution hierarchy. Note that a single approximation having κ < n vertices is obtained in linear running time. We show that, without using these operations, mesh simplification using global error has o(n~2) computational complexity in the RAM model. Our approach uses a generalized vertex decimation method which allows for choosing the optimal vertex in the rate-distortion sense. Additionally, our algorithm can also be applied to other types of subdivision connectivity such as triangular quadrisection, e.g., obtained from Loop subdivision.
机译:具有(递归)细分连通性的网格(例如细分曲面)在计算机图形学中越来越流行。与基于Delaunay类型的同类对象相比,它们具有一些优点,例如,不规则三角网(TIN),例如高效的处理,紧凑的存储和数值鲁棒性。可以使用树结构来描述具有细分连通性的网格,并且最近的工作在诸如渐进式地形可视化,表面压缩和传输之类的应用中利用了这种固有的层次结构。我们提出了一种从细到粗的分层算法(即使用顶点抽取),以减少其网格基于四边形四边形(例如,从Catmull-Clark或4-8细分规则获得的细分曲面)的网格中的顶点数量。我们的方法源自用于自适应量化器建模的最佳树修剪算法。我们方法的主要优点是,它允许控制近似值的全局误差,而先前的方法仅基于局部误差启发法。我们提出了一组允许使用全局误差的操作,并使用它们来构建O(n log n)简化算法,将n个顶点的输入网格转换为多分辨率层次结构。注意,在线性运行时间中获得具有κ

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