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 ...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.展开更多
基金supported by the National Natural Science Foundation of China (Nos. U0935400, 60873181, and 61173102)the Fundamental Research Funds for the Central Universities, China(No. DUT11SX08)
文摘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.
