Bilateral filtering de-noising method of point cloud data based on KDTree

Abstract

The invention discloses a bilateral filtering de-noising method of point cloud data based on KDTree when a ground laser scanning technology is used to carry out measured object surface reconstruction. In a bilateral filtering algorithm, a scattered point cloud data point field is established through the KDTree so as to obtain a field point set of the data point. An inverse iteration method is used to calculate the field point so as to obtain a normal vector ni. A filtering function parameter and a filtering Gauss parameter are calculated so as to obtain a bilateral filtering power factor lambda. Finally, a new data point after the filtering can be obtained, wherein the new data point p'i=pi+ lambdani. The bilateral filtering method of the invention is used to filter the noise of the point cloud data. The method is simple and effective. An operation speed is fast. A characteristic can be maintained and simultaneously the noise can be removed. The method is suitable for processing the noise in a small scope.

Description

technical field [0001] The invention relates to the fields of surveying and mapping science and technology, reverse engineering, and digital protection of ancient buildings, in particular to a method for bilateral filtering and denoising of point cloud data based on KDTree when ground laser scanning technology is used for object surface reconstruction. Background technique [0002] Ground 3D laser scanning technology is a very important part of 3D laser scanning technology, which is widely used in the field of surveying and mapping. The original point cloud data obtained by TLS is dense, discretely distributed, and a collection of massive data, and there are a lot of noise and redundant data at the same time. For the denoising of TLS mass point cloud data, a set of effective optimization methods is needed to achieve the purpose of refining, compressing and smoothing point cloud data. [0003] The point cloud data collected by TLS can delete noise points that can be recogniz...

AI Technical Summary

Problems solved by technology

[0003] The point cloud data collected by TLS can delete the noise points that can be recognized by the naked eye, but there are still "floating points" and "bad points" on the surface of the point cloud of the object that cannot be removed, and need to be filtered

Among them, the more mature methods such as median, mean, Gaussian and average curvature flow filtering are time consuming and inefficient for the filtering of massive point cloud data; Laplace, Wiener and Kalman filtering will make The model shrinks to different degrees, resulting in model deformation; wavelet denoising and mean shift algorithms are effective, but the algorithm is complex and difficult to implement

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