Development of time-series point cloud data changes and automatic structure recognition system using Unreal Engine

We have developed a point cloud processing system within the Unreal Engine to analyze changes in large time-series point cloud data collected by laser scanners and extract structured information. Currently, human interaction is required to create CAD data associated with the time-series point cloud data. The Unreal Engine, known for its 3D visualization capabilities, was chosen due to its suitability for data visualization and automation. Our system features a user interface that automates update procedures with a single button press, allowing for efficient evaluation of the interface’s effectiveness. The system effectively visualizes structural changes by extracting differences between pre- and post-change data, recognizing shape variations, and meshing the data. The difference extraction involves isolating only the added or deleted point clouds between the two datasets using the K-D tree method. Subsequent shape recognition utilizes pre-prepared training data associated with pipes and tanks, improving accuracy through classification into nine types and leveraging PointNet +  + for deep learning recognition. Meshing of the shape-recognized point clouds, particularly those to be added, employs the ball pivoting algorithm (BPA), which was proven effective. Finally, the updated structural data are visualized by color-coding added and deleted data in red and blue, respectively, within the Unreal Engine. Despite increased processing time with a higher number of point cloud data, down sampling prior to difference extraction significantly reduces the automatic update time, enhancing overall efficiency.