Point Cloud Data Processing of Three-dimensional Reconstruction Model of Object by 3D Laser Scanning
Sheng Zhan and Hongbo Zhang
Point cloud data processing is an important part of three-dimensional laser scanning. In this paper, the point cloud data processing in three-dimensional reconstruction of objects was studied. The iterative closest points algorithm was used for point cloud registration, curvature-based algorithm and random filtering method were used for denoising respectively. Finally, bilateral filtering algorithm was used for point cloud smoothing to achieve further smoothing of point cloud data. The experimental results showed that the point cloud denoising algorithm proposed in this paper could effectively remove obvious noise points and random noise points, and the overall error of the smoothing algorithm was 2.03%. Compared with the model established by the unprocessed point cloud data, the error of the three-dimensional model established by the processed point cloud data was smaller than that of the entity model, which was only 0.01%. The experimental results proves the reliability of the algorithm proposed in this paper and the importance of point cloud processing and makes some contributions to the better application of three-dimensional laser scanning technology in three-dimensional reconstruction.
Keywords: three-dimensional laser scanning, three-dimensional reconstruction, point cloud data, denoising, smoothing