基于姿态标准化的线特征点云提取方法

从零件三维点云中提取棱边等线特征所对应的点云是零件模型重构的关键,也是点云数据处理的基本操作.基于曲率的线特征点云提取方法易受点云初始姿态以及曲率估计方法的影响,曲面拟合及曲率估计误差较大.提出了一种基于点云姿态标准化的线特征点云提取方法:首先计算点云主方向并将其同z轴对准实现点云姿态的标准化,然后进行曲面拟合并以最大主曲率绝对值作为曲率估计值,最后对曲率值取阈值提取出线特征点云.用不同类型的点云数据进行了实验,结果表明所提方法有较高的提取效率和良好的适用性.

Attitude normalization based line feature point cloud extraction approach

Extracting the point cloud corresponding to the edges from point cloud of a part is a basic work in the part 3D reconstruction, also a key point of point cloud processing. The commonly used curvature based LFPC(line feature point cloud) extraction approach may have large errors in curve surface fitting and curvature estimation due to the arbitrary attitude of original point cloud and the way of curvature estimation. An attitude normalization based LFPC extraction approach is proposed. First the attitude normalization is accomplished by adjust the principle direction of the point cloud to the z axis, then, after the curve surface fitting, the main curvature with the maximal absolute value is treated as curvature, finally, the LFPC is obtained by applying a curvature threshold. Experiments on different models show that the proposed approach has better efficiency and is adaptive to different kinds of models.