Point cloud simplification method based on frequency domain coding

With the development of intelligent construction, three-dimensional (3D) point cloud data (PCD) simplification has played an important role in back-end scenario analysis, reducing the computational and storage burden. However, the existing simplification methods are not applicable to the modeling analysis tasks in the architecture, engineering and construction industry. To address this issue, this study proposes a new simplification method, named as the frequency domain coding-based maximum difference simplification (FDCMDS). The FDCMDS is able to convert PCD into frequency domain multidimensional features to capture fine-grained structural variations of PCD. To improve the simplification efficiency, a 3D Canny key point detection combined with PCD gradient is proposed as a key point extraction module. Finally, a method for evaluating PCD density is designed by combining existing metrics. The validation experiments on PCD with different density distributions and volumes prove the effectiveness and feasibility of the proposed method.