Density based Denoising of Point Cloud
Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density based point cloud denoising method is presented to remove outliers and noisy points. First, particle swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.
ArXiv