点云数据光顺是点模型数字几何处理的一个重要研究内容。在海量数据规模应用中，不仅需要较高的光顺质量，而且需要有快速的处理速度。传统的基于CPU的光顺算法串行地处理每个采样点，导致巨大的时间开销。本文提出一种适应于图形处理器的点云快速光顺算法，将多个采样点处的协方差矩阵组织成一个大规模稀疏矩阵，以纹理图像的形式保存该稀疏矩阵，在像素程序中利用图形处理器强大的并行计算能力迭代求解协方差矩阵的最小特征值与特征向量，并据此计算光顺的速度和方向。实验在配有GeForce 8600GTS显卡的平台上进行。实验结果表明，基于GPU的点云光顺算法较之基于CPU的算法能够显著提高计算效率，从而为快速点云处理提供了良好的支持。

The smoothing of cloud points is an important topic in the field of digital geometry processing. Applications based on huge sampled points require fast processing speed and high quality smoothing quality. The traditional CPU based methods deal with every point in a serial manner which leads to great time consumption. a novel approach using graphics processing units(GPUs) is proposed for cloud points smoothing processors in this paper. Many covariance matrices are organized into a large scale spare matrix that is compressed in several textures. The least eigenvalues and corresponding eigenvectors of the matrices are calculated in pixel programs using GPU which has the powerful parallel processing capability. Then the smoothing speed and normal direction can be determined. Experiments are conducted in a PC with the GeForce 8600GTS graphic card. The results show that the efficiency of smoothing processing is improved greatly by using the GPUbased algorithm. Therefore, they well support the applications of the fast processing of cloud points.