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Modern technologies are commonly used to inventory different architectural or industrial objects (especially cultural heritage objects and sites) to generate architectural documentation or 3D models. The Terrestrial Laser Scanning (TLS) method is one of the standard technologies researchers investigate for accurate data acquisition and processing required for architectural documentation. The processing of TLS data to generate high-resolution architectural documentation is a multi-stage process that begins with point cloud registration. In this step, it is a common practice to identify corresponding points manually, semi-manually or automatically. There are several challenges for the TLS point cloud processing in the data registration process: correct spatial distribution, marking of control points, automation, and robustness analysis. This is particularly important when large, complex heritage sites are investigated, where it is impossible to distribute marked control points. On the other hand, when orientating multi-temporal data, there is also the problem of corresponding reference points. For this reason, it is necessary to use automatic tie-point detection methods. Therefore, this article aims to evaluate the quality and completeness of the TLS registration process using 2D raster data in the form of spherical images and Affine Hand-crafted and Learned-based detectors in the multi-stage TLS point cloud registration as test data; point clouds were used for the historic 17th-century cellars of the Royal Castle in Warsaw without decorative structures, two baroque rooms in the King John III Palace Museum in Wilanów with decorative elements, ornaments and materials on the walls and flat frescoes, and two modern test fields, narrow office, and empty shopping mall. The extended Structure-from-Motion was used to determine the tie points for the complete TLS registration and reliability analysis. The evaluation of detectors demonstrates that for the test sites exhibiting rich textures and numerous ornaments, a combination of AFAST, ASURF, ASIFT, SuperGlue and LoFTR can be effectively employed. For the point cloud registration of less textured buildings, it is advisable to use AFAST/ASIFT. The robust method for point cloud registration exhibits comparable outcomes to the conventional target-based and Iterative Closest Points methods. [ABSTRACT FROM AUTHOR]

Accurate registration of 3D scans is crucial in creating precise and detailed 3D models for various applications in cultural heritage. The dataset used in this study comprised numerous point clouds collected from different rooms in the Museum of King Jan III's Palace in Warsaw using a structured light scanner. Point clouds from three relatively small rooms at Wilanow Palace: The King's Chinese Cabinet, The King's Wardrobe, and The Queen's Antecabinet exhibit intricate geometric and decorative surfaces with diverse colour and reflective properties. As a result, creating a high-resolution full 3D model require a complex and time-consuming registration process. This process often consists of several steps: data preparation, registering point clouds, final relaxation, and evaluation of the resulting model. Registering two-point clouds is the most fundamental part of this process; therefore, an effective registration workflow capable of precisely registering two-point clouds representing various cultural heritage interiors is proposed in this paper. Fast Adaptive Multimodal Feature Registration (FAMFR) workflow is based on two different handcrafted features, utilising the colour and shape of the object to accurately register point clouds with extensive surface geometry details or geometrically deficient but with rich colour decorations. Furthermore, this work emphasises the challenges associated with high-resolution point clouds registration, providing an overview of various registration techniques ranging from feature-based classic approaches to new ones based on deep learning. A comparison shows that the algorithm explicitly created for this data achieved much better results than traditional feature-based or deep learning methods by at least 35%. [ABSTRACT FROM AUTHOR]