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In recent years, interest in the three-dimensional (3D) data documentation of heritage buildings has been growing. The collection of detailed and accurate 3D point-cloud information by acquiring heritage-building data has facilitated various applications. These applications encompass historical architectural information retrieval, preservation and monitoring, augmented reality, virtual reality, and the generation of heritage building information models. Point clouds originate from 3D scanners, human–computer interactions, and other devices exposed to unnecessary environments. In this context, point clouds are inevitably affected by noise and outliers. Factors contributing to noise include the limitations of sensors, device defects, and the illumination or reflection characteristics of the studied objects. Thus, addressing noise and outliers presents a challenge when storing point cloud data. Denoising is a critical step in data processing for point clouds 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 a multiscale hierarchy denoising method, the process of which is as follows. First, we divided the point cloud model of heritage architecture according to the architectural structure. The density-based spatial clustering of applications with noise algorithm was then used to perform large-scale point cloud denoising. For small-scale noise, denoising is achieved on a macroscopic basis by systematically removing noise and outliers from the heritage architectural point cloud model using statistical and bilateral filtering techniques. This process improves the quality and accuracy of point cloud data related to heritage buildings.