  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.
