基于最小曲面距离的快速点云精简算法

Fast Scattered Point Cloud Data Reduction Algorithm Based on Minimum Curvature Distance

提出了一种新的基于最小曲面距离的点云精简算法,算法在简化点云数据的同时不损失特征。点云被划分成一系列的三维子网格,根据子网格,找到最近k邻域。散乱点云的k邻域通过二次参数曲面拟合,进一步得到相关曲率。依据提出的曲面距离,对点云进行精简。选择了一些典型的点云,如冲浪、石头、陶俑、牙齿等数据对算法进行了验证。结果表明,可以直接和有效地减少点云数据,同时保持原始模型的几何形状,对点云精简研究有一定的理论和实践意义。通过实验也证明了该算法的可靠性和准确性。

To simplify the point cloud data while preserving features, a novel algorithm based on the curvature distance is put forward. The whole point cloud is divided into a series of initial sub-clusters with the 3D grid subdivision method, and then k neighborhood is constructed from the partition results. All the points in k neighborhood are approximated by quadratic parametric surface based on scattered point cloud parameterization. The curvatures of fitting surface are further calculated. The judgment of requiring reduction is decided by the novel minimal surface distance of curvature features. Some typical cases with various surface features, such as surf, stone, pottery figurine and tooth, are chosen to verify the new method. The results indicate that the new algorithm is of significance in theory and practice for reduction of point cloud, and enables to reduce data directly and efficiently while maintaining the geometry of the original model. The reliability and accuracy of the algorithm are also proved by experiment.