Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A signal processing approach to fair surface design
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Implicit fairing of irregular meshes using diffusion and curvature flow
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Anisotropic geometric diffusion in surface processing
Proceedings of the conference on Visualization '00
Mesh Smoothing via Mean and Median Filtering Applied to Face Normals
GMP '02 Proceedings of the Geometric Modeling and Processing — Theory and Applications (GMP'02)
Non-iterative, feature-preserving mesh smoothing
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2003 Papers
SMI '02 Proceedings of the Shape Modeling International 2002 (SMI'02)
Mesh editing with poisson-based gradient field manipulation
ACM SIGGRAPH 2004 Papers
Accuracy of 3D Scanning Technologies in a Face Scanning Scenario
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
A sharpness dependent filter for mesh smoothing
Computer Aided Geometric Design - Special issue: Geometry processing
A Bayesian method for probable surface reconstruction and decimation
ACM Transactions on Graphics (TOG)
Smoothing by Example: Mesh Denoising by Averaging with Similarity-Based Weights
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Feature-preserving non-local denoising of static and time-varying range data
Proceedings of the 2007 ACM symposium on Solid and physical modeling
Random walks for feature-preserving mesh denoising
Computer Aided Geometric Design
Fast and Effective Feature-Preserving Mesh Denoising
IEEE Transactions on Visualization and Computer Graphics
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
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This paper analyses the noise present in range data measured by a Konica Minolta Vivid 910 scanner, in order to better characterise real scanner noise. Methods for denoising 3D mesh data have often assumed the noise to be Gaussian, and independently distributed at each mesh point. We show via measurements of an accurately machined almost planar test surface that real scanner data does not have such properties: the errors are not quite Gaussian, and more importantly, exhibit significant short range correlation. We use this to give a simple model for generating noise with similar characteristics. We also consider how noise varies with such factors as laser intensity, orientation of the surface, and distance from the scanner. Finally, we evaluate the performance of three typical mesh denoising algorithms using real and synthetic test data, and suggest that new denoising algorithms are required for effective removal of real noise.