  Surface reconstruction from an unorganized point cloud is an important
problem due to its widespread applications. White noise, possibly clustered
outliers, and noisy perturbation may be generated when a point cloud is sampled
from a surface. Most existing methods handle limited amount of noise. We
develop a method to denoise a point cloud so that the users can run their
surface reconstruction codes or perform other analyses afterwards. Our
experiments demonstrate that our method is computationally efficient and it has
significantly better noise handling ability than several existing surface
reconstruction codes.
