Patch-Collaborative Spectral Point-Cloud Denoising
Article first published online: 3 JUN 2013
© 2013 The Authors Computer Graphics Forum © 2013 The Eurographics Association and John Wiley & Sons Ltd.
Computer Graphics Forum
Volume 32, Issue 8, pages 1–12, December 2013
How to Cite
Rosman, G., Dubrovina, A. and Kimmel, R. (2013), Patch-Collaborative Spectral Point-Cloud Denoising. Computer Graphics Forum, 32: 1–12. doi: 10.1111/cgf.12139
- Issue published online: 27 NOV 2013
- Article first published online: 3 JUN 2013
- European Community's FP7- ERC. Grant Number: 267414
- point cloud;
- G.1.2 [Mathematics of Computing]: Approximation—Approximation of surfaces and contours;
- I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling—Geometric algorithms languages and systems;
- I.4.8 [Image Processing and Computer Vision]: Scene Analysis—Surface fitting
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.