## 1 Introduction  
Photogrammetry has found widespread application in the field of cultural heritage documentation, thanks to the non-contact nature of the measurement [1] and the high archival value of the collected data [2]. However, in the last two decades it has been more frequently used mainly thanks to computer vision techniques. Algorithms such as SIFT (scale invariant feature transform) [3] and RANSAC (random sample consensus) [4], in combination with SfM (structure from motion) [5, 6], currently allow efficient and fully automated orientation of images, without the need for artificial coded targets. Many software solutions using this technology exist, such as Metshape by Agisoft LLC, RealityCapture by Capturing Reality, Pix4D Mapper by Pix4D SA, Context Capture by Bentley Systems, 3 DF Zephyr by 3 DFlow, and others. Each of these photogrammetric software programs uses slightly different algorithms, either in the process of orientation of the images or in the surface reconstruction. In the first step, it is necessary to solve the relative, and usually the interior, orientation of the images. The parameters of the interior orientation (focal length, distortions, etc.) are determined using calibration. The main advantage of SfM is its support of self-calibration as a component of the bundle adjustment. This allows determining the interior orientation parameters based on processing of the images of the documented object, without any additional calibration fields. By increasing computing performance, it is possible to process an increasingly large number of images at the same time, which leads to an increase in the effectiveness of field work, since it is not necessary to consider every single image taken. However, SfM also has its weaknesses, which are most evident in larger objects with complicated shapes, flat textures, and difficult accessibility. SfM can approach the relative orientation of images using different strategies: incremental, global, or hierarchical [7; 8; 9], each of which has its advantages and disadvantages and can significantly influence the processing results, in terms of the accuracy and completeness of the oriented image strips. Extensive image blocks can suffer from deformations that are often detectable only with the use of control geodetic measurement [10]. Deformations are most often caused by insufficient overlap of the image strips, inappropriate placement of ground control points (GCPs), or an inappropriate self-calibration model for the camera system used. This is also why photogrammetry is currently often combined with terrestrial laser scanning (TLS), which can ensure the accurate and robust geometry of the entire scanned scene [11]. The purpose of combining both technologies is, not only to ensure the high geometric accuracy from TLS and high resolution of textural information from photogrammetry, but also a complete documentation of an object. Photogrammetry requires suitable lighting conditions. As soon as there are strong shadows with an unreadable texture, these areas will not be modelled reliably. On the contrary, TLS can collect data even in complete darkness, and the laser beam penetrates even into areas that can be a challenging obstacle for photogrammetry. On the other hand, the TLS needs to stand on a stable tripod on the ground, while the camera can be placed on an UAV (unmanned aerial vehicle) and soar above the roof of the object or be attached to a selfie stick and put into spaces inaccessible to the scanner. Mobile laser scanners are currently available, such as the Leica BLK2 GO, however, the accuracy of Grand SLAM technology is still not at the level of conventional TLS, which has already been proven by several studies [12; 13]. This technology may be the future of laser scanning, but in applications where an accuracy of better than 1 cm and high detail is required, it should not be used.