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Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods
March/April 2010 (vol. 16 no. 2)
pp. 312-324
ASCII Text
x 
 
Hanqi Fan, Yizhou Yu, Qunsheng Peng, "Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 2, pp. 312-324, March/April, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2009.70,
author = {Hanqi Fan and Yizhou Yu and Qunsheng Peng},
title = {Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {16},
number = {2},
issn = {1077-2626},
year = {2010},
pages = {312-324},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.70},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods
IS - 2
SN - 1077-2626
SP312
EP324
EPD - 312-324
A1 - Hanqi Fan,
A1 - Yizhou Yu,
A1 - Qunsheng Peng,
PY - 2010
KW - Denoising
KW - features
KW - clustering
KW - shared nearest neighbors
KW - normals
KW - curvature tensors
KW - quadrics
KW - bilateral filtering.
VL - 16
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Hanqi Fan, Zhejiang University, Hangzhou
Yizhou Yu, University of Illinois at Urbana-Champaign, Urbana
Qunsheng Peng, Zhejiang University, Hangzhou
In this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved.

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Index Terms:
Denoising, features, clustering, shared nearest neighbors, normals, curvature tensors, quadrics, bilateral filtering.
Citation:
Hanqi Fan, Yizhou Yu, Qunsheng Peng, "Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 2, pp. 312-324, March-April 2010, doi:10.1109/TVCG.2009.70
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