Semi-global matching (SGM) is a widespread algorithm for dense image matching which is used for very different applications, ranging from real-time applications (e.g., for generating 3D data for driver assistance systems) to aerial image matching. Originally developed for stereo-image matching, several extensions have been proposed to use more than two images within the matching pro- cess (multi-baseline matching and multi-view stereo). These extensions perform the image matching in (rectified) stereo images and combine the pairwise results afterwards to cre- ate the final solution. This paper proposes an alternative approach that is suitable for the introduction of an arbitrary number of unrectified images into the matching process. The new method differs from the original SGM method mainly in two aspects: first, the cost calculation is formulated in object space within a dense voxel raster using the grey (or colour) values of all images instead of pairwise cost calculation in image space. Second, the semi-global (pathwise) minimiza-tion process is transferred into object space as well, so that the result of semi-global optimization leads to index maps (instead of disparity maps) which directly indicate the 3D positions of the best matches. Altogether, this yields a sim-plification of the matching process compared to multi-view stereo (MVS) approaches. After a description of the new method, results achieved from different data sets (close-range and aerial, nadir and oblique) are presented and discussed.