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Effective re-parameterization and GA based knot structure optimization for high quality T-spline surface fitting

机译:高质量的T条状表面配件有效的重新参数化和GA基结结构优化

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T-spline surface fitting from input triangular mesh is a common task in T-splines related CAD applications. One major objective to this problem is creating T-spline surface with fewer control points and higher accuracy. This paper proposes several effective approaches to improve fitting results. The proposed approaches include an incremental sampling strategy for robust initial fitting, a global effective re-parameterization algorithm called NUFR (non-uniform faithful re-parameterization) for a proper mesh parameterization, and a GA (genetic algorithm) based T-mesh knot structure optimization process for an optimal knot structure. The tradeoff between mesh simplicity and fitting accuracy can be adjusted with a few input parameters. Experiments on different models are provided to demonstrate the effectiveness of these approaches. Compared with the classic adaptive fitting result, the result of the proposed algorithm has smaller RMS error. And typically, the number of control points will be reduced by about 30%. (C) 2019 Elsevier B.V. All rights reserved.
机译:从输入三角网格拟合的T条状表面拟合是T样曲线相关CAD应用中的常用任务。这个问题的一个主要目的是创建具有较少控制点和更高精度的T条状表面。本文提出了几种有效的改善拟合结果方法。所提出的方法包括用于稳健的初始拟合的增量采样策略,是一种名为NuFR(非均匀忠实的重新参数化)的全局有效的重新参数化算法,用于适当的网格参数化,以及基于GA(遗传算法)的T-Mesh结结构最佳结结构的优化过程。可以使用少数输入参数调整网状简单性和拟合精度之间的权衡。提供了对不同模型的实验,以证明这些方法的有效性。与经典的自适应拟合结果相比,所提出的算法的结果较小的误差误差。通常,控制点的数量将减少约30%。 (c)2019 Elsevier B.v.保留所有权利。

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