基于方法库的点云去噪与精简算法

Point Cloud Denoising and Simplification Algorithm Based on Method Library

为了减少不同尺度噪声对三维点云模型重建效果的影响,提出一种基于直通滤波、统计滤波、半径滤波、改进的双边滤波、体素栅格滤波的方法库的点云模型去噪与精简算法。首先利用直通滤波将目标物体提取出来,再依据噪声点离模型主体的距离,将其分为小尺度噪声和大尺度噪声,然后利用统计滤波结合半径滤波去除大尺度噪声,利用改进的双边滤波去除小尺度噪声,最后通过体素栅格滤波进行点云精简来降低空间复杂度,并以三角网格面重建展示该算法的精度效果。实验结果表明,该算法可有效去除点云模型的不同尺度噪声,在不破坏点云本身几何结构的前提下,保证点云精简的均匀化,而且算法执行速度快,重建效率高。

In order to reduce the influence of different scales of noise on the reconstruction of three-dimensional point cloud models, a denoising and simplification algorithm based on the method library of the passthrough filter, statistical filter, radius filter, improved bilateral filter and voxel grid filter is proposed. Firstly, the object is extracted by the passthrough filter. Then according to the distance between the noise points and the model body, the noise points are divided into the small scale noise and the large scale noise. The large scale noise is removed by the statistical filter and the radius filter, and the small scale noise is removed by the improved bilateral filter. Finally, the three-dimensional point cloud is simplified by the voxel grid filter to reduce the space complexity and the accuracy of the proposed algorithm is shown via the triangular mesh reconstruction. The experimental results show that the proposed algorithm can effectively remove different scales of noise existing in the point cloud model and ensure the uniformity of point cloud simplification under the precondition of not destroying the geometrical structure of the point cloud. In addition, this algorithm runs quickly and has high reconstruction efficiency,