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.

中文翻译：

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大规模点云数据的3D表面重建方法

由于存储级别的限制以及消费者级计算机的计算能力不足，因此需要合适的方法来实现大规模点云数据的3D表面重建。提出了一种基于分而治之思想的方法。首先，为点云数据创建了kd-tree索引。然后，采用多核并行计算的Delaunay三角剖分算法在叶节点中构建点云数据。最后，通过基于分段线性系统和图割的约束Delaunay四面体化，实现了完整的3D网格模型。所提出的方法在多核并行计算架构中对点云进行了表面重构，其中实现了内存释放和重新分配，以减少内存占用并提高运行效率，同时确保三角形网格的质量。将该算法与两种基于多组点云数据的经典曲面重构算法进行了比较，并进行了适用性实验。结果验证了所提方法的有效性和实用性。