摘要
点云中提取的特征线在点云处理中具有重要的应用价值,已被应用于对称性检测、表面重建及点云与图像之间的注册等。然而,已有的点云特征线提取算法无法有效地处理点云中不可避免的噪声、外点和数据缺失,而随机采样一致性RANSAC由于具有较高的鲁棒性,在图像和三维模型处理中具有广泛的应用。为此,针对由建筑物或机械部件等具有平面特征的物体扫描得到的点云,提出了一种基于RANSAC的特征线提取算法。本算法首先基于RANSAC在点云中检测出多个平面,然后将每个平面参数化域的边界点作为候选,在这些候选点上再应用基于全局约束的RANSAC得到最终的特征线。实验结果表明,该算法对点云中的噪声、外点和数据缺失具有很强的鲁棒性。
The line features extracted from point clouds are very useful in the processing of point clouds, including symmetry detection, surface reconstruction, the registration from image to point clouds, etc. However, the ability of existing line feature extraction approaches to deal with noise, outliers, and missing parts in the data is limited. On the other hand, RANSAC （RANdom SAmpling Consensus） based methods are widely used in the fields of image processing and 3D model processing because of the robustness. Thus, a RANSAC based line features detection algorithm is proposed in this paper, in which RANSAC is first used to detect all the possible planes in the point clouds, then to detect the line features from the boundary points of the parameterization field of the planes with global constraints. This method is designed especially for point clouds obtained from architectures or mechanical parts, in which planar features are dominant. Result of experiments validates the robustness of the proposed algorithm in handling with various defections of point clouds, e.g. noise, outliers, and data missing.
出处
《计算机工程与科学》
CSCD
北大核心
2013年第2期147-153,共7页
Computer Engineering & Science
基金
国家自然科学基金资助项目(61202334
61103084
60970094)
