Point Cloud Reduction and Denoising Based on Optimized Downsampling and Bilateral Filtering

Abstract

For the issue of using the center of gravity during down-sampling, some points of their feature will be lost. We propose a new method, FWD(Farthest point Weighted mean Down-sampling), this method uses down-sampling to find the center of gravity, it is added to the furthest point sampling and performed ten iterations. The obtained 11-point distance is weighted average to find the feature point. Influences of environmental noise and self-noises on the subsequent processing of point cloud are considered. A PWB (Principal component analysis Wavelet function Bilateral Filtering) method is proposed. The normal vector of points is calculated by PCA. The distance between two points in the optimal neighborhood is obtained by the particle swarm optimization(PSO) method. This method performs wavelet smoothing and utilizes the Gaussian function to retain the edge eigenvalues. FWD simplified 90840 points in 48 seconds in the case of retaining the complete feature points. Compared with other latest methods, better results have been obtained. PWB reached de-noising precision of 0.9696 within 72.31s. Accuracy of de-noising is superior to the latest method. The loss of feature points is completed by FWD, the removal of noise is by PWB. Images of de-noising precision prove the priority of the method. The verification shows that the feature points are retained and the noise is eliminated.