Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
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
Anisotropic diffusion of surfaces and functions on surfaces
ACM Transactions on Graphics (TOG)
A simple algorithm for surface denoising
Proceedings of the conference on Visualization '01
Geometric surface smoothing via anisotropic diffusion of normals
Proceedings of the conference on Visualization '02
A Review of Nonlinear Diffusion Filtering
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
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)
Estimating the tensor of curvature of a surface from a polyhedral approximation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Non-iterative, feature-preserving mesh smoothing
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2003 Papers
The trilateral filter for high contrast images and meshes
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
Adaptive Smoothing Tangential Direction Fields on Polygonal Surfaces
PG '01 Proceedings of the 9th Pacific Conference on Computer Graphics and Applications
SMI '02 Proceedings of the Shape Modeling International 2002 (SMI'02)
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Geometric surface processing via normal maps
ACM Transactions on Graphics (TOG)
A novel cubic-order algorithm for approximating principal direction vectors
ACM Transactions on Graphics (TOG)
A unified approach for fairing arbitrary polygonal meshes
Graphical Models
Fast and robust detection of crest lines on meshes
Proceedings of the 2005 ACM symposium on Solid and physical 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 New Bilateral Mesh Smoothing Method by Recognizing Features
CAD-CG '05 Proceedings of the Ninth International Conference on Computer Aided Design and Computer Graphics
Bilateral Recovering of Sharp Edges on Feature-Insensitive Sampled Meshes
IEEE Transactions on Visualization and Computer Graphics
Smooth feature lines on surface meshes
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Geometric fairing of irregular meshes for free-form surface design
Computer Aided Geometric Design
IEEE Transactions on Image Processing
Automatic mesh generation and transformation for topology optimization methods
Computer-Aided Design
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In this paper, we propose a direction-oriented sharpness dependent filter for smoothing a noisy polygon mesh model that contains a large amount of noise. The goal is to remove the noise so that the model conforms to the shape of the original model. The proposed filter consists of a pre-processing step and a filtering process. In the pre-processing step, a smoothed reference model is derived from the input noisy mesh model such that the flow direction field of the smoothed reference model is almost identical to that of the original model. The subsequent filtering process is a linear combination of isotropic smoothing and anisotropic smoothing. This design allows the filter to gradually recover fine structures and remove noise by reconstructing the face direction of the input noisy mesh model so that it is the same as that of the smoothed reference model. Our experiment results show that the proposed direction-oriented sharpness dependent filter can effectively recover noisy models so that they conform to the shape of the original models.