Abstract: In order to eliminate the noise points in the process of collecting laser point cloud and avoid the impact of noise the data quality of the point cloud, especially some isolated outliers. The scattered and noisy point cloud is transformed into regular and high-precision point cloud, and the method of point cloud denoising based on principal component analysis and surface fitting is adopted. In this paper, a point cloud denoising method based on principal component analysis and surface fitting was proposed. Firstly, the principal component analysis method of point cloud region was proposed. Then, the principal component analysis normal vector was used for rough denoising, and the rough denoising point cloud was used for surface fitting. Finally, the point cloud was filtered according to the synthetic distance between the point and the surface. The experimental result shows that the denoising effect is accurate. From the above experimental results and analysis, it can be seen that this method has high filtering accuracy through fitting operation. The algorithm is simple in structure and can retain the details effectively, the error of the best filter performance is only 0.018mm. This study provides a reference for the denoising and filtering of scattered laser point clouds.