Random walks for feature-preserving mesh denoising
Computer Aided Geometric Design
Feature-preserving mesh denoising via attenuated bilateral normal filtering and quadrics
Proceedings of the 26th Spring Conference on Computer Graphics
The gradient of the maximal curvature estimation for crest lines extraction
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
A cascaded approach for feature-preserving surface mesh denoising
Computer-Aided Design
Surface mesh denoising with normal tensor framework
Graphical Models
Accurate reconstruction of engineered models with surfaces of revolution
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
A robust algorithm for denoising meshes with high-resolution details
CVM'12 Proceedings of the First international conference on Computational Visual Media
Mesh denoising via L0 minimization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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In this paper, we propose a feature-preserving mesh denoising algorithm which is effective, simple and easy to implement. The proposed method is a two-stage procedure with a bilateral surface normal filtering followed by integration of the normals for least squares error (LSE) vertex position updates. It is well-known that normal variations offer more intuitive geometric meaning than vertex position variations. A smooth surface can be described as one having smoothly varying normals whereas features such as edges and corners appear as discontinuities in the normals. Thus we cast featurepreserving mesh denoising as a robust surface normal estimation using bilateral filtering. Our definition of "intensity difference" used in the influence weighting function of the bilateral filter robustly prevents features such as sharp edges and corners from being washed out. We will demonstrate this capability by comparing the results from smoothing CAD-like models with other smoothing algorithms.