摘要
由于获取点云数据时的误差可能造成法向量不准确,在三维重建之前有必要重新对法向量进行估值.文中在分析已有估值算法的基础上,提出了适合于任意形状物体的模糊法向量估值算法.该算法将点云数据的k邻近距离和曲率输入模糊推理系统,根据模糊推理规则将点云数据分类,将模型中具有薄片特征和尖锐特征的区域区分出来,分别用检测器和附加点的算法对这些特殊区域进行专门的法向量估值.采用同时具有几种特征的牙齿模型对所提出的模糊估值算法进行验证,结果表明,此算法估算准确,简单可行.
Before a three-dimension reconstruction, the normal vector should be estimated because it may be unreliable due to the error of getting point clouds. In this paper, a fuzzy normal vector estimation algorithm for the object with any shape is proposed after analyzing the existing estimation algorithms. In this algorithm, first, the k-nearest neighbor value and the curvature of the cloud point data are input into a fuzzy inference system. Next, the point clouds are classified according to fuzzy inference rules, and the parts with thin or sharp features are distinguished from the point clouds of model and are then estimated with a checker and with the attachment point algorithm. Finally, the proposed algorithm is evaluated by using a denture model with several kinds of point clouds. The results show that the algorithm is of high estimation accuracy, simplicity and feasibility.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第5期68-72,79,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51205093
50675054)
黑龙江省教育厅项目(12511599)
关键词
三维点云
模糊推理
法向量估值
尖锐特征
薄片特征
three-dimension point cloud
fuzzy inference
normal vector estimation
sharp feature
thin feature
