In the process of 3D laser scanning, the acquisition of point cloud data is often affected by factors such as object occlusion and uneven illumination, which is easy to cause the blind spot of complex shape objects and form holes. At the same time, due to the limited range of scanning measurement, it can not be completely measured for a large range of scenes, and it must be scanned and measured several times. The scanning results are often multiple pieces of point cloud data with different coordinate systems and noise, which cannot fully meet people's requirements for the authenticity and real-time performance of digital models, so it is necessary to conduct pre-processing such as de-noising, simplification, registration and hole filling for three-dimensional point cloud data.
Through data preprocessing, the noise and external points in the point cloud can be effectively eliminated, the point cloud data can be simplified on the basis of maintaining geometric characteristics, and the point clouds scanned from different angles can be unified into the same coordinate system, which provides a robust data basis for the subsequent surface construction and 3D solid model generation.Point cloud modeling requires the use of reality modeling software to better evaluate point clouds and generate more accurate 3D models.
The reality modeling software can enhance, segment and classify the point cloud data, and combine it with the engineering model. Model completion conditions quickly and efficiently and support the design process with advanced 3D modeling, cross section cutting, fracture lines and terrain extraction.
It is also possible to generate very large scalable terrain models based on point cloud data. By synchronizing with the original data source, scalable terrain models can be updated to the latest in real time. The value of this is to have a global, up-to-date and comprehensive representation of all the data and to use various display modes and perform analysis, as well as to generate animations and visualizations.