Abstract
Due to the memory limitation and lack of computing power of consumer level computers, there is a need for suitable methods to achieve 3D surface reconstruction of large-scale point cloud data. A method based on the idea of divide and conquer approaches is proposed. Firstly, the kd-tree index was created for the point cloud data. Then, the Delaunay triangulation algorithm of multicore parallel computing was used to construct the point cloud data in the leaf nodes. Finally, the complete 3D mesh model was realized by constrained Delaunay tetrahedralization based on piecewise linear system and graph cut. The proposed method performed surface reconstruction on the point cloud in the multicore parallel computing architecture, in which memory release and reallocation were implemented to reduce the memory occupation and improve the running efficiency while ensuring the quality of the triangular mesh. The proposed algorithm was compared with two classical surface reconstruction algorithms using multigroup point cloud data, and the applicability experiment of the algorithm was carried out; the results verify the effectiveness and practicability of the proposed approach.
Highlights
Introduction e3D surface reconstruction of point cloud data is a critical problem in the field of computer vision, and many scholars have done extensive research on it
In order to verify the effectiveness and applicability of the algorithm, firstly, seven point cloud datasets with different amounts are used to compare the time and memory of the algorithm in this paper with the classical BallPivoting algorithm [5] and the state-of-the-art Restricted Voronoi Cells in Parallel (RVCP) algorithm proposed by Boltcheva and Levy [20], which are both popular Delaunay triangulation algorithms in mesh reconstruction
In terms of mesh reconstruction quality, the mesh reconstruction accuracy of the proposed method is significantly better than the RVCP algorithm
Summary
The segments and facets in PLS are scattered into small segments or triangles, and the Delaunay tetrahedralization of the discrete vertex set and constraint point set is performed; Second, the existence of each discretized small segment or small triangle in the tetrahedral mesh is checked If it does not exist, it is restored by using the constrained Delaunay tetrahedral criterion. Circumscribed sphere of tetrahedron and the length of its shortest edge and dihedral angle between two faces), and the mesh cells outside the boundary are deleted
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