摘要： 基于网格曲面特征线的稀疏分布，提出一种优化的特征线提取算法。对于给定的网格，在每个面上计算一个值或向量作为输入。对输入的度量建立L₀优化模型，使其在网格边上的跃变尽可能少且优化前后的变化较小。给出基于变量分裂技术与罚函数方法的交替方向优化算法，并引入一种迭代的策略提升解的稀疏性，以取得更高质量的特征线。实验结果表明，该算法能有效提取网格曲面的特征线，与Crest lines算法、变分算法等相比，提高了特征线提取的质量和带噪数据的鲁棒性。

关键词: 网格曲面, 特征线提取, L₀优化, 变量分裂, 交替方向优化算法

Abstract: Based on the sparse distribution of feature lines on meshes,the paper proposes an optimized algorithm for feature lines extraction.For a given mesh,compute a scalar or a vector for each triangle as the input.Then,an optimized model is established for the measurement of the input so that the jumps on the edge of the meshes are as few as possible and the changes before and after optimization are minimized.An alternating direction optimization algorithm based on variable splitting technique and penalty function approach.In addition,a heuristic strategy is introduced to improve the sparsity of the solution,thus achieving higher quality of feature lines.Experimental results demonstrate that this method can effectively extract feature lines.Compared with some state-of-the-art methods such as Crest lines algorithm,variation algorithm and others,the proposed algorithm can improve the quality of feature lines extraction and the robustness for noisy data.

Key words: meshes, feature lines extraction, L₀ optimization, variable splitting, alternating direction optimization algorithm

中图分类号: 

• TP391