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Laser scan technology has proved competitive over photogrammetric methods. The result of that method – an automatically generated point cloud enables prompt and effi cient object modeling. The photogrammetry responses with dense point clouds based on photos. The method has developed thanks to proper digital photo processing algorithms and using them for automatic photo adjustment. Now mostly SLR cameras, calibrated for stabilized image distance (usually for fi xed lens) are used for taking photos in close range photogrammetry. Relatively small sensor matrix size in comparison with the size of a photo taken with an analog measuring camera causes the increase in number of photos covering the captured object. To limit the number of photos, panoramic images taken from partial photos made from the same center of projection are used. Such spherical or cylindrical panorama covers much space, replacing the large format central projection. In the case of photogrammetric photos of large architectural objects (such as churches, palaces, and castles) the use of panoramic images [3–5] enables the signifi cant reduction of time, improves the measurement economics, simplifi es the calculation process and manual processing, as few programs enable the function of automatic processing from panoramas. That is why panoramas taken by special panoramic cameras or properly taken and connected photos from digital cameras are an alternative for classic convergent photos. The accuracy of photogrammetric measurement results is also aff ected by the degree of noise removal that is why one of the goals of this work was to test the effect of removal of distortion from component photos on the accuracy of point clouds generated at the later stage. However, our basic research goal was to analyze the quality and economics of creating point clouds based on convergent photos and spherical panoramas

A comparison of accuracy between point clouds from convergent images and spherical panoramas