## 1 Introduction  
Nowadays, two groups of methods for shape recognition are commonly used in photogrammetry, i.e., passive methods based on digital image processing and active methods, which include, among others, Terrestrial Laser Scanning (TLS) [1]. Each of these methods requires appropriate data pipeline processing to be performed, with the use of different algorithms that may be mutually complementary. Recent research focuses on TLS data registration of raster images generated from scans [1; 2; 3; 4; 5; 6]. Corresponding points are recognized on these images using point detectors. Corresponding geometric shapes are recognized using blob detectors and these are used to determine elements of scan orientation [7; 8; 9; 10; 11; 12]. These points are used in the Structure for Motion (SfM) approach, which allows for automatic determination of exterior orientation elements based on raster data [13; 14; 15; 16; 17]. The use of spatial TLS data processed into a specified raster form and the use of computer vision (CV) algorithms (such as feature detectors and descriptors) allow for automatically connecting point. Using this approach, it is also possible to detect a greater number of tie points, to shorten the time for tie point detection and matching and to eliminate errors resulting from the 3D matching of point clouds, (e.g., the ICP (Iterative Closest Point) method). Such an approach requires that the detected tie points are processed and analysed in appropriate stages; at the first stage, they are burdened with gross errors. The key stage that influences the completeness and correctness of TLS data orientation is not only the selection of appropriate detectors and descriptors allowing for correct selection [9] of tie points, but also the method of cartographic transformation of the point cloud to the 2D form [11; 12; 18]. When the appropriate cartographic transformation of the point cloud is applied, it is possible to decrease the number of incorrectly detected and adjusted tie points. Selection of the appropriate TLS data transformation and 2D detectors allows increased registration accuracy, correctly distributed tie points, and a reduction in the time of detection and matching of points, as well as automation of the data orientation process. The aim of this project was to test the influence of the selection of the cartographic transformation of the TLS point cloud on the accuracy and completeness of the automatic data orientation process. The novel of this work comprises the use of an unordered and raw TLS dataset of interiors, both objects of smooth walls and ceilings (with the law number of details) and historical surfaces of the decorative structure and design; for such objects, it is not possible to distribute points utilised in the data orientation process using the target-based method. The presented TLS data processing is an original approach; as a result of this process, a decision may be made whether the obtained results meet the assumed accuracy criteria or they are considered as an approximation of the ICP method. For that purpose, the original application, as well as external software tools (LupoScan3D and ArcMap) were applied. In this paper three cartographic transformations-to a spherical image, to a raster image in the Mercator projection and to an orthoimage-were analysed. At the first stage, the time for searching and matching of points and the number of correctly adjusted points were analysed. During the second stage, the accuracy of the data orientation process for control and check points was analysed and the correctness of the distribution of tie points was checked. At the final stage, the completeness and the accuracy of the orientation process were analysed with consideration of the influence of selection of a method of TLS data conversion using SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms. This allowed defining TLS data conversion to the raster, depending on the analysed object.