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Today, automatic image orientation and automatic image-based dense reconstruction algorithms are often implemented in professional photogrammetric software. At the same time progress in computer vision brings many advantages to everyone who is dealing with image-based object modeling. A number of free, often on-line software for automatic sparse or dense reconstruction have appeared during the last years [8]. The majority of them allow only for fully automatic reconstruction [1, 7] which is good for amateurs but bad for specialists, as controlling some important aspects of processing, like camera calibration is not available. As a result generated models are out of scale and proper angular orientation, and cannot be used as metric products. Addressed problems have been overcome in professional photogrammetric software where the bundle adjustment with control points is a standard. However, control points collection is a time consuming process even for small objects. Besides it involves using additional survey equipment. As an alternative approach the direct geo-referencing methods seem to be a very att ractive solution. Nowadays a number of cameras are equipped with integrated GPS, or have the interface open for an external receiver. Smartphones and tablets as a standard are equipped with GPS, MEMS inertial sensors and cameras which still improve their resolution and image quality. Low-cost devices providing image direct geo-referencing could be potentially utilized to improve the accuracy of obtained photogrammetric products, bringing new functionality to free, image-based modelling software. The only problem with low-cost GPS receivers is the low accuracy of positioning. The positioning errors can be in the considerable extend compensated by the photogrammetric observations [2, 5] however not completely. GPS measurements of projection centers, when conducted in the network with good controllability, allow

Combined bundle adjustment with distances and GPS observations.