Multiscale hierarchy denoising method for heritage building point cloud model noise removal

Abstract In recent years, there has been a growing interest in the three-dimensional data documentation of heritage buildings. The collection of detailed and accurate 3D point cloud information through the acquisition of heritage building data has facilitated various applications. These applications encompass historical architectural information retrieval, preservation and monitoring, augmented reality and virtual reality, as well as the generation of Heritage Building Information Models (HBIM). Point cloud originates from 3D scanners, human-computer interaction, and other devices exposed to unnecessary environments. In this context, point cloud will inevitably be influenced by noise and outliers. Factors contributing to noise include the limitations of sensors, the defect of device, and the characteristics of illumination or reflection of the studied objects. Thus, dealing with noise and outliers presents a challenge when storing point cloud data. Denoising is a critical step in data processing for point cloud when applied to heritage architecture. The accuracy of the point cloud model in heritage architecture is highly dependent on noise and outliers. This study proposes Multiscale Hierarchy Denoising method, whose process is as follows. First, divide the point cloud model of heritage architecture according to the architectural structure. Then, use the DBSCAN algorithm to carry out large-scale point cloud denoising. For small-scale noise, its denoising is achieved on a macroscopic basis by systematically removing noise and outliers from the heritage architectural point cloud model, using statistical filtering and bilateral filtering techniques. This process can improve the quality and accuracy of point cloud data related to heritage building.