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A Global Laplacian Smoothing Approach with Feature Preservation

机译:具有特征保留的全局拉普拉斯平滑方法

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This paper presents a novel approach for surface smoothing with feature preservation on arbitrary meshes. Laplacian operator is performed in a global way over the mesh. The surface smoothing is formulated as a quadratic optimization problem, which is easily solved a sparse linear system. The cost function to be optimized penalizes deviations from the global Laplacian operator while maintaining the overall shape of the original mesh. The features of the original mesh can be preserved by adding feature constraints and barycenter constraints in the system. Our approach is simple, non-iterative, fast, and does not cause surface shrinkage and distortion. Many experimental results are presented to show the applicability and flexibility of the approach.
机译:本文提出了一种在任意网格上保留特征的表面平滑方法。拉普拉斯算子在网格上以全局方式执行。将表面平滑化表示为二次优化问题,可以轻松解决稀疏线性系统。要优化的成本函数可以补偿与全局拉普拉斯算子的偏差,同时保持原始网格的整体形状。可以通过在系统中添加特征约束和重心约束来保留原始网格的特征。我们的方法简单,非迭代,快速,并且不会引起表面收缩和变形。提出了许多实验结果以表明该方法的适用性和灵活性。

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