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Feature detection of triangular meshes via neighbor supporting

Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第6期13卷 440-451页

作者： Xiao-chao WANG Jun-jie CAO Xiu-ping LIU Bao-jun LI Xi-quan SHI Yi-zhen SUN School of Mathematical Sciences Dalian University of TechnologyDalian 116024China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of TechnologyDalian 116024China State Key Laboratory of Structural Analysis for Industrial Equipment School of Automotive EngineeringFaculty of Vehicle Engineering and MechanicsDalian University of TechnologyDalian 116024China Department of Mathematical Sciences Delaware State UniversityDoverDE 19901USA

We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages： feature detection and feature refinement. Firsts the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.

关键词： Feature detection Neighbor supporting normal tensor voting Salient measure

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