光学学报, 2017, 37 (11): 1115007, 网络出版: 2018-09-07   

空间栅格动态划分的点云精简方法 下载: 897次

Point Cloud Simplification Method Based on Space Grid Dynamic Partitioning
作者单位
南昌大学机电工程学院, 江西 南昌 330031
摘要
常规的特征保持点云精简方法需计算全部点云的微分信息,但直接计算高密度或含噪点云的微分信息存在一定偏差,导致点云精简效果不佳。提出一种基于栅格动态划分的点云精简方法。首先对模型进行空间栅格初划分,利用随机采样一致性算法剔除栅格内的干扰点,然后采用最小二乘法对剩余点进行平面拟合并计算平整度值,根据平整度值判别该栅格是否细分,将平坦区域压入大间距栅格内,特征丰富区域划分至小栅格中。针对小栅格内的点引入高斯函数降低远距离点对特征识别贡献的权重,综合曲面变化度和邻域法向量夹角信息共同识别特征点并保留,大栅格内的点根据栅格间距大小采用不同的采样率采样。与随机采样法、栅格法、曲率精简法对比实验结果表明,该方法能较好地保持模型细微特征且避免孔洞的出现,精简后模型的最大偏差为1.502 mm,远小于其他三种方法;随着噪声强度的增加,本文方法的精简误差相对较小且变化平缓,在35 dB噪声下,平均偏差仅为随机采样法和栅格法的40%,曲率精简法的50%。
Abstract
The conventional feature preserving point cloud simplification method needs to calculate the differential information of all point clouds, but there is a certain deviation in the results by direct calculation with the high density or noise-containing point cloud, resulting in poor effect of point cloud simplification. We present a point cloud simplification method based on grid dynamic partitioning. Firstly, the model is divided into space grids in which the interference points are eliminated with the random sample consensus method. Secondly, the flatness value of grid is calculated by using the least squares method in remaining points, judging whether the grid needs to be subdivided according to the flatness value. Thirdly, the flat areas are achieved and pressed into large spacing grid, and the features-rich areas are divided into small grids as well. For the points in small grids, Gaussian function is introduced to reduce the weight of distant points for recognition features, and the feature points are identified by integration of the surface variation and neighborhood normal vector angle information and then retained. Points in the large grid are sampled at different sampling rates according to the grid spacing. Comparative experiments are carried out with the random sampling method, grid method, curvature method and the proposed method. It is shown that this method can maintain the fine features of model and avoid the appearance of holes, and the maximum deviation of the simplified model is 1.502 mm, much smaller than those of the other three methods. Moreover, as the noise intensity increases, the simplification error of this method is small and gentle. Under the noise condition of 35 dB, the average deviation is only 40% of those of random sampling method and grid method, as well as 50% of that of the curvature method.

傅思勇, 吴禄慎, 陈华伟. 空间栅格动态划分的点云精简方法[J]. 光学学报, 2017, 37(11): 1115007. Siyong Fu, Lushen Wu, Huawei Chen. Point Cloud Simplification Method Based on Space Grid Dynamic Partitioning[J]. Acta Optica Sinica, 2017, 37(11): 1115007.

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