空间超限邻域点云去噪算法的研究与实现

Research and Implementation for Over-Domain of Space Point Clouds Denoising

对获取的点云数据进行降噪处理,是曲面重构过程的关键技术之一。充分利用三维空间散乱点的深度信息,重新诠释图像去噪中的"超限邻域平均法",使其对二维图像的运用转换为对三维图形数据点的操作,并结合运用空间解析几何理论,提出一种直接对三维无组织散乱点去噪算法。从试验结果来看,本算法在去除噪声点的同时很好地保留了散乱点模型的细节特征,噪声去除效果理想。

Point clouds denoising is one of surface reconstruction＇s key technologies.This paper makes full use of space unorganized points＇ inner information,to re-interpret over-domain average method of image denoising theory,so that the theory of two-dimensional space can be applied to three-dimensional space＇s point data.And based on the space analytic geometry theory,it creates a method of three-dimensional space＇s unorganized point clouds denoising.The experimental results show that this method better retains detail features of the unorganized points of 3D shape and noise is obviously suppressed after point clouds denoising.