Abstract: The segmentation of point cloud is one of the key technologies of reverse engineering reconstruction, however, the computation time of point cloud feature is heavy. So it is of significance to accelerate the algorithm by heterogeneous computing with OpenCL. This paper aims to segment unordered point cloud efficiently with OpenCL. The algorithm is mainly divided into three steps: compute parallel eigenvalues of point cloud data, compute parallel normal, and curvature computation of point cloud. In the process of calculation, the data storage structure, the efficiency of data access, and the complexity of the algorithm have been optimized and improved according to the GPU parallel architecture and hardware features. Experimental results show that the algorithm takes full advantage of the parallel processing capabilities of OpenCL, the running time is 16 times faster than implementation of the CPU.

Key words: OpenCL, Graphics Processing Unit（GPU）, point cloud segmentation

摘要： 点云分割是逆向工程中模型重建的关键技术之一，然而在求取点云特征时非常耗时，通过OpenCL异构计算对其进行性能加速有着重要的现实意义。以散乱无序的点云为研究对象，通过OpenCL对点云分割算法加以改进。算法主要分为并行计算点云数据的特征值，并行计算点云数据的法向量和曲率3个步骤。在计算中，根据GPU的并行结构和硬件特点，优化了数据存储结构，提高了数据访问效率，降低了算法复杂度。实验结果表明，算法充分利用了OpenCL的并行处理能力，运行效率是基于CPU实现的16倍。

关键词: OpenCL, 图形处理器（GPU）, 点云分割