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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Fast and efficient narrow volume reconstruction from scattered data
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Fast and efficient narrow volume reconstruction from scattered data

机译:从分散的数据快速有效地缩小体积

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摘要

We describe a fast and efficient numerical algorithm for the process of three-dimensional narrow volume reconstruction from scattered data in three dimensions. The present study is an extension of previous research [Li et al., Surface embedding narrow volume reconstruction from unorganized points, Comput. Vis. Image Underst. 121 (2014) 100-107]. In the previous work, we modified the original Allen-Cahn equation by multiplying a control function to restrict the evolution within a narrow band around the given surface data set. The key idea of the present work is to perform the computations only on a narrow band around the given surface data set. In this way, we can significantly reduce the storage memory and CPU time. The proposed numerical method, based on operator splitting techniques, can employ a large time step size and exhibits unconditional stability. We perform a number of numerical experiments in order to demonstrate the efficiency of this method. (C) 2015 Elsevier Ltd. All rights reserved.
机译:我们描述了一种快速有效的数值算法,用于从三维散乱数据进行三维窄体积重建。本研究是先前研究的延伸[Li等人,《从无组织点嵌入窄体积的曲面嵌入》,计算机。可见图片说明121(2014)100-107]。在先前的工作中,我们通过乘以一个控制函数来限制给定表面数据集周围窄带内的演化,从而修改了原始的Allen-Cahn方程。本工作的关键思想是仅在给定表面数据集周围的窄带上执行计算。这样,我们可以大大减少存储内存和CPU时间。所提出的数值方法基于算子拆分技术,可以采用较大的时间步长,并且显示出无条件的稳定性。为了证明这种方法的有效性,我们进行了许多数值实验。 (C)2015 Elsevier Ltd.保留所有权利。

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