Incremental Topological Surface Reconstruction from Massive Point Cloud Data
Bo Zhicheng1), Sun Dianzhu1)*, Li Yanrui2), and Xu Zhao1)
1) (School of Mechanical Engineering, Shandong University of Technology, Zibo 255049)2) (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049)
The current surface reconstruction algorithms are difficult to achieve a trade-off between efficiency and topological correctness of reconstruction for large point sets. For solving this problem, we present an incremental topological surface reconstruction algorithm based on local Delaunay triangulation. Taking the strategy of advancing fronts method, the local reconstruction process spread to the adjacent area of each point, so the whole sampling point set can be incrementally reconstructed. By updating the front wave in the way of expanding and splitting and eliminating duplicate facets in the meantime, the oriented 2-Manifolds mesh interpolated on sampling point set can be output. In the process of local reconstruction, the feature areas are reconstructed by Localized Cocone algorithm and the flat regions are reconstructed by 2D Delaunay triangulation of projection points. In order to ensure the correctness of local mesh, auxiliary points are added to the local regions. The experiment results show that this algorithm has high efficiency which makes it suitable for reconstructing closed and non-closed massive point cloud. When sampling density condition requirement is met, its final reconstructed triangular mesh is topologically equivalent to the original surface.
surface reconstruction; Delaunay triangulation; massive point cloud; local reconstruction