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Feature Sensitive Out-of-Core Chartification of Large Polygonal Meshes

机译:具有大型多边形网格的特征敏感的核心图表

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Mesh chartification is an important tool for processing meshes in various applications. In this paper, we present a novel feature sensitive mesh chartification technique that can handle huge meshes with limited main memory. Our technique adapts the mesh chartification approach using Lloyd-Max quantization to out-of-core processing. While the previous approach updates chartification globally at each iteration of Lloyd-Max quantization, we propose a local update algorithm where only a part of the chartification is processed at a time. By repeating the local updates, we can obtain a chartification of a huge mesh that cannot fit into the main memory. We verify the accuracy of the serialized local updates by comparing the results with the global update approach. We demonstrate that our technique can successfully process huge meshes for applications, such as mesh compression, shape approximation, and remeshing.
机译:网格图是处理各种应用中网格的重要工具。在本文中,我们提出了一种新颖的特征敏感网格化学技术,可以处理具有有限主存储器的巨大网格。我们的技术使用Lloyd-Max量化与核心外处理来适应网眼图形方法。虽然先前的方法在LLOYD-MAX量化的每次迭代时全局更新,但我们提出了一种本地更新算法,其中一次仅处理图表的一部分。通过重复本地更新,我们可以获得一个无法拟合到主内存的大型网格的图表。通过将结果与全局更新方法进行比较,我们验证了序列化本地更新的准确性。我们展示了我们的技术可以成功地处理巨大网格的应用程序,例如网格压缩,形状近似和回忆。

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