点云数据压缩中的边界特征检测

The Detection of Boundary Point of Point Cloud Compression

点云数据压缩是逆向工程产品建模中必要的数据预处理手段之一。常见的数据压缩算法未考虑点云边界数据点的保留问题,因此在大比例压缩过程中会出现边界数据丢失的情况从而破坏了数据的完整性。为此,提出了一种利用点云数据小邻域内点的相邻关系来检测边界特征点的算法。该算法能检测出点云数据的内、外边界特征点,同时对边界上的点进行排序,检测出边界特征点中的过渡点,最后构建点云轮廓的边界多边线。该算法不仅能满足在点云数据压缩过程中检测并保留边界特征点的要求,而且生成的边界多边线也为后面的3维模型重建奠定了基础。

The compression of point cloud is one step of the necessary preprocessing in the reverse engineering modeling. The issue of detection and preservation of boundary points, however, has not been usually considered in many point cloud compression algorithms. Then, boundary points could be withdrawn during large scale compression, and integrality of data could not be guaranteed. In this paper, we provide an algorithm to detect the boundary points. Obviously, if a point is a boundary point, then its circumambient points will distribute only on one side or around a corner. If a point is not a boundary point, its neighboring points will distribute symmetrically. By this way, boundary points will be detected by analyzing the relation of points in a trivial neighboring region. The algorithm will be effective for inner boundary points as well as outers. It sorts the boundary points further, and detects the transitional points in boundary points, at last constructs boundary polygon lines by transitional points. The distance between a boundary point and its neighboring boundary points is used to detect the transitional points. Therefore, the algorithm not only can satisfy preserving the boundary points in a large scale compression, but also prepare for reversion modeling by boundary polygon lines.