尖锐特征诱导的点云自动分片算法

Automatic Sharp Feature Based Segmentation of Point Clouds

点云模型的分片技术是数字几何处理领域的基础技术之一.提出一种尖锐特征诱导的点云模型自动分片算法.算法首先计算点云模型的局部微分属性,并以此来识别模型上的尖锐特征点;然后采用改进的折线生长算法生成并完善特征折线,并基于特征折线采用三次B样条曲线来逼近的尖锐特征点;最后采用区域生长方法将点云模型分割成多个几何特征单一、边界整齐的点云数据面片.实验表明,本文算法运行稳定,可以准确地分割点云模型.该算法可用于点云模型的形状匹配、纹理映射、CAD建模、以及逆向工程等应用中.

Segmentation of point clouds is one of basic and key technologies in digital geometry processing. In this paper, based on extracted sharp features, we present a method for automatic ally segmenting point clouds. Our algorithm first calculates local surface differentials features and uses them to identify sharp feature points. And an improved feature-ployline propagation technique is em- ployed to approximate the feature points by a set of polylines and optimize the feature curves. Then, based on feature ploylines, we approximate the sharp feature points by cubic B-spline curve. Subsequently, based on the extracted feature curves, region growing al- gorithm was applied to segment the point clouds into multiple regions, the geometric feature of the region is consistent and the bound- ary of the patch is neat. Experiments show that the algorithm can segment the point clouds precisely and efficiently. Our algorithm can be used in shape matching, texture mapping, CAD modeling and reverse engineering.