Patch-Collaborative Spectral Surface Denoising

We present a new framework for denoising of point clouds by patch-collaborative spectral analysis. A collaborative generalization of each surface patch is dened, combining similar patches from the surface. The Laplace-Beltrami operator of the collaborative patch is then used to selectively smooth the surface in a robust manner that can gracefully handle high levels of noise. The resulting denoising algorithm competes favorably with state-of-the-art approaches, and extends patch-matching algorithms from the image processing domain to point clouds of arbitrary sampling. We demonstrate the accuracy and noise-robustness of the proposed algorithm on standard benchmark models as well as range scans, and compare it to existing methods for point cloud denoising.