自适应的基于点云的CAD模型重建方法  

Adaptive approach for point cloud based CAD model reconstruction

作　　者：刘进[1,2] 

机构地区：[1]山东财经大学计算机科学与技术学院,济南250014 [2]北京师范大学信息科学与技术学院,北京100875

出　　处：《计算机应用》2013年第9期2617-2622,共6页journal of Computer Applications

摘　　要：基本的随机抽样一致性(RANSAC)算法无法根据点云模型的噪声自适应地设定分割参数,并有效判断点云数据是否被合理分割。针对该问题,提出了一种自适应的基于点云模型的计算机辅助设计(CAD)模型重建方法。该方法采用RANSAC算法从点云数据中提取基本形状体素,使用直方图法分析点到相应形状体素表面的投影距离。对分割不合理的区域,按照该点云面片的高斯噪声设置新的分割参数,再次进行形状提取。经过一定轮数的迭代,该方法可以合理提取点云模型中的细小形状体素。然后通过校准形状体素的位置和方向、根据相邻形状体素之间的交线裁剪形状体素,实现CAD模型的重建。最后,以误差分布图和直方图分析了原始点云数据中点到CAD模型表面投影距离,有70.71%的点的投影距离不超过点云模型包围盒高度的1%。实验结果表明,以点云包围盒高度的1%为尺度向实验数据中加入噪声时,该方法仍能够通过自适应设置分割参数提取出合理的细小体素。Basic RANdom SAmple Consensus （RANSAC） approach cannot set segmentation parameters adaptively by the noise of point clouds and has no efficient way to determine whether the segmentation results are reasonable. In order to solve these problems, an adaptive approach for point cloud based CAD model reconstruction was presented. First, the approach extracted primitive shapes from point clouds by RANSAC algorithm, then it analyzed deviations of points from the fitted primitive shapes by histograms. For unreasonably segmented point cloud patches, the approach updated parameters of segmentation and repeated the primitive shape detection process. After certain rounds of iteration, the approach detected primitive shapes from point clouds reasonably. By calibrating primitive shapes＇ position and orientation and trimming primitive shapes according to intersection curves, the approach reconstructed the CAD model. Deviations from points to the surface of the CAD model were analyzed by error distribution graph and histogram, which demonstrated that 70.71% of the points whose projection distance were no more than 1% of the bounding box height. The experimental results show that, by setting segmentation parameters adaptively, the approach can extract small primitive shapes from the experimental point cloud data distorted by noise with scale equal to 1% of the bounding box height.

关 键 词：点云 随机抽样一致性算法 高斯噪声 基本形状体素 CAD模型重建 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]