In this paper, we introduce a feature preserving surface reconstruction algorithm to produce a high fidelity triangulated mesh from an input point set. The concept of local Delaunay triangulation is applied to speed up the reconstruction procedure and to preserve features. The proposed algorithm has running time complexity of $O(nlogn)$, where n is the number of points. Additionally, the local Delaunay triangulation improves the memory efficiency of the proposed method compared to global Delaunay-based methods. We introduced the concept of minimum circum-radius triangle to select the prime-triangle in the local Delaunay mesh. Based on the local projection and minimum circum-radius, our triangle selection criteria ensures the output mesh will be free from non-manifold edges and fold-over triangles. On top of that, we have provided the theoretical correctness of the proposed algorithm with the assumption of ϵ-sampling model. The experimental results show that the proposed method is capable of producing high fidelity meshes from large real-world non-uniform data. We also show the effectiveness of the proposed method compared to the state-of-the-art methods in terms of visual and quantitative analysis.

在本文中，我们介绍了一种保留特征的曲面重建算法，以根据输入点集生成高保真三角网格。应用局部Delaunay三角剖分的概念可加快重建过程并保留特征。所提算法的运行时间复杂度为$Ø（ñ升ØGñ）$，其中n是点数。此外，与基于全局Delaunay的方法相比，局部Delaunay三角剖分提高了所提出方法的存储效率。我们引入了最小外接圆半径三角形的概念来选择局部Delaunay网格中的质数三角形。基于局部投影和最小外接半径，我们的三角形选择标准可确保输出网格不包含非流形边缘和折叠三角形。最重要的是，我们已经提供了该算法的理论的正确性与假设ε抽样模型。实验结果表明，该方法能够从大量真实的非均匀数据中生成高保真网格。在视觉和定量分析方面，我们还显示了与最新方法相比所提出的方法的有效性。