基于学习的网格特征边界边识别

Learning-Based Identification for Feature Boundary Edges of Meshes

特征边界边识别是复杂三角网格模型后续应用的基础,采用单一阈值和判定规则很难识别符合实际要求的特征边。对特征边界线的几何特征的深入分析,基于机器学习的方法,提出和实现一个基于学习的三角网格模型特征边界边识别方法。该方法将特征边界线识别形式化为三角边的分类问题;分析和构建了一个由三角边两面角、边顶点邻域曲率及形状直径等特征组成的17维特征向量;通过人工标注获取特征向量训练数据集,训练通用BP-AdaBoost分类器,获得能够识别特征边界线的分类器;对待识别的三角网格模型进行特征边识别。经过实例验证,识别结果符合预期。

Feature boundary edge recognition is the basis for subsequent applications of complex triangular mesh models. It is difficult to identify feature edges that can meet actual requirements by using single threshold and decision rules. In this paper, the geometric characteristics of the feature boundary edge are analyzed. Based on the machine learning method, a learning-based method for identifying feature boundary edges of triangular mesh model is proposed and implemented. In this method, the feature boundary edge recognition is formalized as the classification problem of triangular edges. A 17-dimensional eigenvector to describe the geometric characteristics of feature boundary edges is analyzed and constructed, which consists of a dihedral angle, curvatures and a shape diameter. The eigenvector training data set is obtained by manual annotation, and is in-putted into the general BP-AdaBoost classifier to train it in order to make it have the capability to identify feature boundary edges. The trained BP-AdaBoost classifier can identify the feature boundary edges correctly. It is proved by examples that the identification result is in line with the expectation.