Concerning the problems of low efficiency and less processing points of the traditional algorithm for point cloud simplification, according to the slicing principle in the rapid prototyping with feature-preserving and low computational complexity, a parallel slicing algorithm was designed and implemented for more than ten millions point cloud of Light Detection And Ranging (LiDAR) data. The point cloud model was layed with the slicing principle and every layer was sorted according to the angle. Incorporating the parallel computation framework of Compute Unified Device Architecture (CUDA) proposed by NVIDA and taking the highly parallel performance advantages of the programmable Graphics Processing Unit (GPU), and parallel execution of the single slice point cloud simplification with the multi-thread of GPU was done, which improved the algorithm efficiency. Finally, a comparing experiment was done with three groups of point cloud data in different order of magnitudes. The experimental results show that the efficiency of the proposed algorithm has 1-2 order of magnitude higher than that of traditional algorithm under the condition of keeping the model characteristics and not changing the compression ratio.

Spherical Voronoi diagram generating algorithm based on distance computation and comparison of Quaternary Triangular Mesh (QTM) has a higher precision relative to dilation algorithm. However, massive distance computation and comparison lead to low efficiency. To improve efficiency, Graphic Processing Unit (GPU) parallel computation was used to implement the algorithm. Then, the algorithm was optimized with respect to the access to GPU shared memory, constant memory and register. At last, an experimental system was developed by using C++ and Compute Unified Device Architecture (CUDA) to compare the efficiency before and after the optimization. The experimental results show that efficiency can be improved to a great extent by using different GPU memories reasonably. In addition, a higher speed-up ratio can be acquired when the data scale is larger.