Abstract
The main task of this paper is to describe methods and algorithms used in computer vision for fully automatic reconstruction of exterior orientation in ordered and unordered sets of images captured by digital calibrated cameras without prior informations about camera positions or scene structure. Attention will be paid to the SIFT interest operator for finding key points clearly describing the image areas with respect to scale and rotation, so that these areas could be compared to the regions in other images. There will also be discussed methods of matching key points, calculation of the relative orientation and strategy of linking sub-models to estimate the parameters entering complex bundle adjustment. The paper also compares the results achieved with above system with the results obtained by standard photogrammetric methods in processing of project documentation for reconstruction of the Žinkovy castle.
Highlights
Images captured by digital cameras are one of the most important form of information in documentation of cultural heritage
The automatic process of finding exterior orientation can be divided to three main tasks: Key point finding and matching, relative orientation and bundle adjustment
SIFT detector is based on searching for extremities in the images by finding differences among images incurred by the convolution of image function I(x, y) and Gauss filter G(x, y, δ) with variable values of sigma
Summary
Images captured by digital cameras are one of the most important form of information in documentation of cultural heritage. Effective assignment of camera pose in space is necessary for consequential usage for measuring purposes. The automatic process of finding exterior orientation can be divided to three main tasks: Key point finding and matching, relative orientation and bundle adjustment. Our paper presents practical experiment of such procedure
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