虚拟格网分类支持下的密集匹配点云布料模拟滤波

Cloth simulation filtering method of dense matching point cloud based on virtual grid classification

为了提高倾斜影像密集匹配点云滤波算法在地形复杂场景的精度、效率及自适应性,针对传统的布料模拟滤波算法的不足,本文提出了一种基于虚拟格网分类的布料模拟滤波方法。该方法首先构建带有缓冲区的虚拟格网;然后将虚拟格网按其范围内的点云坡度划分为平地、缓坡、陡坡3种类型;最后针对不同类型的虚拟格网,使用相应的布料参数进行布料模拟滤波,从而实现倾斜影像匹配点云在地形复杂场景中的点云滤波,与单一参数的布料模拟滤波进行对比试验。结果表明,基于虚拟格网分类的布料模拟滤波方法结果更接近于目视解译,该方法适合混合地形滤波。

In order to improve the accuracy, efficiency and adaptability of the dense matching point cloud filtering algorithm for oblique images in complex terrain scenes, for the shortage of the traditional cloth simulation filtering algorithm, this paper proposes a cloth simulation filtering method based on virtual grid classification. Firstly, this method constructs a virtual grid with a buffer zone;And then divides the virtual grid into three types: flat ground, gentle slope, and steep slope according to the point cloud slope within its range;Finally, different types of virtual grids use the corresponding cloth parameters for cloth simulate filtering, then realize the point cloud classification of oblique image matching point cloud in complex terrain scenes. The results are compared with the single-parameter cloth simulation filtering. The experiment show that the results of the cloth simulation filtering method based on virtual grid classification are closer to the result of visual interpretation, and this method is suitable for hybrid terrain filtering.