多源数据的点云融合算法在智慧城市三维建模中的应用

Application of Point Cloud Fusion Algorithm of Multi-source Data in 3D Modeling of Smart City

基于多源数据构建三维模型技术,建立的三维模型精度不但相较于传统的基于单一点云构建的模型精度大幅度提高,同时解决了无人机影像三维建模中的部分缺陷,完善了三维模型的部分纹理细节。但是该技术往往是不同种点云数据的叠加,不仅会造成数据量的增加,而且会影响整个数据的质量。基于此,提出了一种点云融合算法,首先是将倾斜摄影测量技术、三维激光扫描技术、贴近摄影测量技术三者相结合,将各自形成的点云数据,利用点云融合算法进行处理,将融合后的点云进行重建,最终完成三维模型构建。经过实验测试,在保证一定数据量的情况下,模型精度大幅度提高。

Based on three-dimensional modeling technology with multi-source data,the accuracy of the established three-dimensional model is not only greatly improved compared with the traditional model based on a single point cloud,but also some defects in the UAV image three-dimensional modeling are reduced,withthetexture details refined.However,the technology is often the superposition of different kinds of point cloud data,which will not only increases the amount of data,but also affects the quality of the whole data.Considering this problem,a point cloud fusion algorithm was proposed in this paper.Firstly,the tilt photogrammetry technology,3D laser scanning technology and proximity photogrammetry technology were combined to process the point cloud data formed respectively by the point cloud fusion algorithm,and then the fusion point cloud was reconstructed to complete the 3D model construction finally.The experimental results showed that the accuracy of the model was greatly improved under the condition of ensuring a certain amount of data.