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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]

This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to the raster form, (2) register of TLS data in pairs in all possible combinations using the SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms, (3) the quality analysis of relative orientation of processed pairs, and (4) the final bundle adjustment. The following two problems, related to the influence of the spherical image, the orthoimage and the Mercator representation of the point cloud, are discussed: The correctness of the automatic tie points detection and distribution and the influence of the TLS position on the completeness of the registration process and the quality assessment. The majority of popular software applications use manually or semi-automatically determined corresponding points. However, the authors propose an original software tool to address the first issue, which automatically detects and matches corresponding points on each TLS raster representation, utilizing different algorithms (SURF and FAST). To address the second task, the authors present a series of analyses: The time of detection of characteristic points, the percentage of incorrectly detected points and adjusted characteristic points, the number of detected control and check points, the orientation accuracy of control and check points, and the distribution of control and check points. Selection of an appropriate method for the TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process, and the accuracy of data orientation. The results of the performed experiments show that fully automatic registration of the TLS point clouds in the raster forms is possible; however, it is not possible to propose one, universal form of the point cloud, because a priori knowledge concerning the scanner positions is required. If scanner stations are located close to one another in raster images or in spherical images, Mercator projections are recommended. In the case where fragments of the surface are measured under different angles from different distances and heights of the TLS, orthoimages are suggested. [ABSTRACT FROM AUTHOR]

The aim of this article is to present the influence of detector selection for the image-based Terrestrial Laser Scanning (TLS) registration method. The presented results are the extended continuation of investigations presented in the article, 'The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration'. In order to obtain the correct results of the TLS registration process, it is necessary to detect and match the correct tie points, which are evenly distributed across the entire area. Commonly, for TLS data registration manually or semi-manually corresponding points are detected. However, when large, complicated cultural heritage objects are investigated, it is sometimes impossible to place marked control points. The only possibility of resolving this problem is the use of image-based TLS data registration. One of the most important factors that influences the quality and ability to use it correctly, is accurate selection. For this purpose, the authors decided to test three blob detectors ASIFT, SURF, CenSurE, and two point detectors FAST and BRISK. The results indicated that selection depends on two factors: if the time required for data processing is not important, the ASIFT algorithm should be used, which allows for full registration, but if not, a combination of other algorithms with results supervision should be considered. [ABSTRACT FROM AUTHOR]