Bootstrap Based Bilateral Smoothing of Point Sets.

Boostrap is a statistical method widely used in applications such as model averaging and noise estimation. In this paper, we propose a bilateral smoothing algorithm based on bootstrap noise estimates for denoising 3D point sets. Following a classic denoising technique, for a given neighbourhood of the point set, we first fit a polynomial surface and then update the position of each point of the neighbourhood by moving it towards its projection on the fitted surface. However, in the proposed algorithm, the amount by which we move each vertex towards its projection depends on the bootstrap error estimate of the polynomial fitting. As a result, low quality polynomial fittings with high bootstrap error estimates tend have smaller effect on the denoising process and do not degrade the final result. We also propose a multi-pass, density adaptive variant of the proposed bilateral smoothing and experimentally show that it can further improve the results. [ABSTRACT FROM AUTHOR]