Density-based Denoising of Point Cloud
This addresses noise removal in point clouds for applications like 3D modeling, but it appears incremental as it combines existing techniques like particle-swarm optimization and mean-shift clustering.
The paper tackles the problem of noise and outliers in point cloud data for surface reconstruction by presenting a density-based denoising method that removes outliers and smooths points, with experimental results showing it is robust and efficient.
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.