Similarity based filtering of point clouds

Denoising surfaces is a a crucial step in the surface processing pipeline. This is even more challenging when no underlying structure of the surface is known, id est when the surface is represented as a set of unorganized points. In this paper, a denoising method based on local similarities is introduced. The contributions are threefold: first, we do not denoise directly the point positions but use a low/high frequency decomposition and denoise only the high frequency. Second, we introduce a local surface parameterization which is proved stable. Finally, this method works directly on point clouds, thus avoiding building a mesh of a noisy surface which is a difficult problem. Our approach is based on denoising a height vector field by comparing the neighborhood of the point with neighborhoods of other points on the surface. It falls into the non-local denoising framework that has been extensively used in image processing, but extends it to unorganized point clouds.