Patch-collaborative spectral point-cloud denoising

We present a new framework for point cloud denoising by patch-collaborative spectral analysis. A collaborative generalization of each surface patch is defined, combining similar patches from the denoised 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, yet preserves sharp surface features. The resulting denoising algorithm competes favourably with state-of-the-art approaches, and extends patch-based 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. We present a new framework for point cloud denoising by patch-collaborative spectral analysis. A collaborative generalization of each surface patch is defined, combining similar patches from the denoised 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, yet preserves sharp surface features. © 2013 The Authors Computer Graphics Forum © 2013 The Eurographics Association and John Wiley & Sons Ltd.

Duke Scholars

Author Guy Rosman Surgery, Minimally Invasive Surgery