Meaningful feature curves provide high-level shape representation of the geometrical shapes and are useful in various applications. In this paper, we propose an automatic method on the basis of the quadric surface fitting technique to extract complete feature curve networks (FCNs) from 3D surface meshes, as well as finding cycles and generating a high-quality segmentation. In the initial collection of noisy and fragmented feature curves, we first fit the quadric surfaces of each curve and the corresponding neighbor vertices to filter out non-salient or noisy feature curves. Then we conduct a feature extension step to address the curve intersections and form a closed FCN. Finally, we regard circle curves as cycles in the complete FCN and segment the mesh into patches to reveal a highly structured representation of the input geometry. Experimental results demonstrate that our algorithm is more robust for FCN extraction from complex input meshes and achieves higher quality patch layouts compared with the state-of-the-art approaches. We also verify the validity of extracted feature curve cycles by applying them to surface reconstruction.

有意义的特征曲线提供了几何形状的高级形状表示，可用于各种应用中。在本文中，我们提出了一种基于二次曲面拟合技术的自动方法，该方法可从3D曲面网格中提取完整特征曲线网络（FCN），以及查找循环并生成高质量的分割。在嘈杂和碎片化特征曲线的初始集合中，我们首先拟合每条曲线的二次曲面和相应的相邻顶点，以滤除非突出或嘈杂的特征曲线。然后，我们执行特征扩展步骤以解决曲线的交点并形成封闭的FCN。最后，我们将圆曲线视为完整FCN中的循环，并将网格划分为小块以揭示输入几何的高度结构化表示。实验结果表明，与最新方法相比，我们的算法对于从复杂的输入网格中提取FCN更具鲁棒性，并且可以获得更高质量的面片布局。我们还通过将提取的特征曲线循环应用于曲面重建来验证其有效性。