Recovery of Sharp Features in Mesh Models

Due to the shortages of current methods for the recovery of sharp features of mesh models with holes,this paper presents two novel algorithms for the recovery of features(especially sharp features)in mesh models.One algorithm defines an energy that is regarded as the difference between the initial features and the ideal features.The optimal solution of the energy optimization problem modifies the initial features.The algorithm has good performance on sharp features.The other method establishes a plane cluster for each initial feature point to obtain a corresponding modified feature point.If necessary,we can obtain the modified feature line by fitting these modified points.Both methods depend little on the result of fillingmodel holes and result in better features,which maintain the sharp geometric characteristic and the smoothness of the model.The experimental results of the two algorithms demonstrate their superiority and rationality compared with the existing methods.

A combinatorial approach to surface reconstruction

This paper presents a reconstruction algorithm to build a surface mesh approximating an object from an unorganized point sampling of the boundary object. It combines 3D Delaunay tetrahedralization and mesh-growing method and uses only once Delau- nay triangulation. It begins with 3D Delaunay triangulation of the sampling. Then initialize the surface mesh with seed facets se- lected from Delaunay triangulation. Selection is based on the angle formed by the circumscribing ball of incident tetrahedral. Finally, grow until complete the surface mesh based on some heuristic rules. This paper shows several experimental results that demonstrate this method can handle open and close surfaces and work efficiently on various object topologies except non-manifold surface with self-intersections. It can reproduce even the smallest details of well-sampled surfaces but not work properly in every under-sampled situation that point density is too low.