Detection and Recovery of Sharp Features for 3D Mesh Models

The sharp features1, including edges, corners and boundaries, usually express the most important geometric information for triangular meshes. The purpose of this investigation is to reconstruct sharp edges from blended or chamfered features for mesh processing with high accuracy. The proposed approach involves two processes: sharpness-based region-recognizing, which identifies the feature vertices based on the vertex sharpness, and sharp-features reconstructing. In the process of region-recognizing, the angle variations of normal vectors are introduced to indicate the vertex sharpness. Then, the feature regions comprised of "sharp" vertices and facets could be identified. Furthermore, during the process of sharp feature reconstructing, the coordinates of concerned vertices are adjusted gradually using an iterative filtering algorithm depending on sharpness, which updates the feature regions from the inside-outside. Finally, the experimental results validate the effectiveness and robustness of the proposed method in sharpening meshes.

[1]  Charlie C. L. Wang,et al.  Bilateral recovering of sharp edges on feature-insensitive sampled meshes , 2006, IEEE Transactions on Visualization and Computer Graphics.

[2]  S. Zhang,et al.  Mesh sharpening via normal filtering , 2009 .

[3]  Ralph R. Martin,et al.  Fast and Effective Feature-Preserving Mesh Denoising , 2007, IEEE Transactions on Visualization and Computer Graphics.

[4]  Liu Sheng-lan A Mesh Smoothing Algorithm for Feature Enhancing , 2004 .

[5]  Marco Attene,et al.  Sharpen&Bend: recovering curved sharp edges in triangle meshes produced by feature-insensitive sampling , 2005, IEEE Transactions on Visualization and Computer Graphics.

[6]  Francesco Bianconi,et al.  Bridging the gap between CAD and CAE using STL files , 2002 .

[7]  Yutaka Ohtake,et al.  Mesh smoothing via mean and median filtering applied to face normals , 2002, Geometric Modeling and Processing. Theory and Applications. GMP 2002. Proceedings.

[8]  Jiansong Deng,et al.  Recovery of Sharp Features in Mesh Models , 2015 .

[9]  Bert Lauwers,et al.  STL Model Segmentation for Multi-Axis Machining Operations Planning , 2004 .

[10]  Kuo-Young Cheng,et al.  A Sharpness-Dependent Filter for Recovering Sharp Features in Repaired 3D Mesh Models , 2008, IEEE Transactions on Visualization and Computer Graphics.

[11]  Wenzhi Chen,et al.  An efficient approach for feature-preserving mesh denoising , 2017 .

[12]  Yutaka Ohtake,et al.  Mesh regularization and adaptive smoothing , 2001, Comput. Aided Des..

[13]  Weishi Li,et al.  Tensor Voting Guided Mesh Denoising , 2017, IEEE Transactions on Automation Science and Engineering.

[14]  Ming Zeng,et al.  Feature-preserving filtering with L0 gradient minimization , 2014, Comput. Graph..

[15]  Charlie C. L. Wang Incremental reconstruction of sharp edges on mesh surfaces , 2006, Comput. Aided Des..

[16]  Kwan H. Lee,et al.  Feature detection of triangular meshes based on tensor voting theory , 2009, Comput. Aided Des..

[17]  Qian Xie,et al.  Surface reconstruction with data-driven exemplar priors , 2017, Comput. Aided Des..

[18]  Alberto Signoroni,et al.  Advancing mesh completion for digital modeling and manufacturing , 2018, Comput. Aided Geom. Des..