SUSAN structure preserving filtering for mesh denoising

Authors:
Zhihong Mao;Lizhuang Ma;Mingxi Zhao;Xuezhong Xiao
Affiliations:
Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, 200030, Shanghai, P.R. China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, 200030, Shanghai, P.R. China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, 200030, Shanghai, P.R. China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, 200030, Shanghai, P.R. China
Venue:
The Visual Computer: International Journal of Computer Graphics
Year:
2006

Citing 0
Cited 5

3D distance transform adaptive filtering for smoothing and denoising triangle meshes

Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method

Pattern Recognition
Short Communication to SMI 2011: Mesh filtering algorithm using an adaptive 3D convolution kernel applied to a volume-based vector distance field

Computers and Graphics
Fast communication: Analysis and improvement of SUSAN algorithm

Signal Processing
Surface mesh denoising with normal tensor framework

Graphical Models

Quantified Score

Hi-index	0.00

Visualization

Abstract

Motivated by the impressive effect of the SUSAN operator for low level image processing and its usage simplicity, we extend it to denoise the 3D mesh. We use the angle between the normals on the surface to determine the SUSAN area; each point has associated itself with the SUSAN area that is has a similar continuity feature to the point. The SUSAN area avoids the feature to be taken as noise effectively, so the SUSAN operator gives the maximal number of suitable neighbors with which to take an average, whilst no neighbors from unrelated regions are involved. Thus, the entire structure can be preserved. We also extend the SUSAN operator to two-ring neighbors by a squared umbrella-operator to improve the surface smoothness with little loss of detailed features. Details of the SUSAN structure preserving noise reduction algorithm are discussed along with the test results in this paper.