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面向反光工件点云缺陷的点云增强算法

Point Cloud Consolidation Algorithm for Reflective Workpieces with Point Cloud Defects

  • 摘要: 针对反光工件的三维扫描点云存在的缺陷以及点云处理时间长的问题,提出一种点云增强的算法以改善缺陷点云的质量、缩短点云处理的时间,从而提高点云三维重建的精度和效率.首先输入大量的、分布不均匀且包含离群点和噪声的无组织点云,使用基于kD-tree 的点云均匀重采样将点云数据分成多个子集;然后使用改进的局部最优投影算法对点云进行降噪处理;最后使用边缘感知重采样对点云进行增采样,达到保留和增强点云特征的效果.实验结果证明,该算法的处理是有效的.

     

    Abstract: Considering the problems of point cloud defects of reflective workpiece through three-dimensional scanning process and long processing time of the point cloud, this paper proposes a point cloud consolida- tion algorithm to improve the quality of the scanned three-dimensional data and to reduce its processing time. The algorithm can thus improve the accuracy and efficiency of the three-dimensional reconstruction. The first step of the algorithm was to acquire the unorganized point cloud with nonuniform distribution, outliers and noise, then a kD-tree based resampling method was used to subdivide the points into multiple subsets. For each of the point subset, noise reduction was performed through an improved local optimal projection algorithm. Finally, the edge-aware resampling method was adopted to upsample the points to preserve and consolidate the feature of the point cloud. Experimental results show that the point cloud consolidation algo- rithm is effective.

     

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