Abstract
Abstract. Semi-Global Matching (SGM) is a widespread algorithm for 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 process (multi-baseline matching, multi-view stereo). These extensions still perform the image matching in (rectified) stereo images and combine the pairwise results afterwards to create the final solution. This paper proposes an alternative approach which is suitable for the introduction of an arbitrary number of images into the matching process and utilizes image matching by using non-rectified images. The new method differs from the original SGM method mainly in two aspects: Firstly, the cost calculation is formulated in object space within a dense voxel raster by using the grey (or colour) values of all images instead of pairwise cost calculation in image space. Secondly, the semi-global (path-wise) minimization 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 to an essential simplification of the matching process compared to multi-view stereo (MVS) approaches. After a description of the new method, results achieved from two different datasets (close-range and aerial) are presented and discussed.
Highlights
Semi-Global Matching (Hirschmüller, 2005) has proven to be a powerful stereo matching algorithm which is used for a variety of applications and measurement tasks, ranging from closerange and real-time applications to aerial image matching
The presented modification of Semi-Global Matching (SGM) is mainly characterized by transferring the process of cost calculation and path-wise cost aggregation from image into object space
Instead of estimating dense disparity maps, index maps are generated which directly indicate the best matches in 3D space
Summary
Semi-Global Matching (Hirschmüller, 2005) has proven to be a powerful stereo matching algorithm which is used for a variety of applications and measurement tasks, ranging from closerange and real-time applications to aerial image matching. It has become widespread especially due to several advantages compared to other matching algorithms: It is very robust and reduces large outliers in low or non-textured areas while preserving edges and sharp object boundaries It allows for the use of pixel-wise cost functions and is able to resolve fine spatial structures on the object surface. Various tasks focus on the accurate and complete 3D reconstruction of complex scenes (e.g. for aerial image matching, in fields of cultural heritage, archaeology, industrial measurements and so on) For these purposes, dense surface matching has been extended to so-called multi-baseline matching as proposed e.g. in Hirschmüller (2008) or multi-view stereo algorithms as proposed e.g. in Rothermel et al, (2013) and Wenzel et al (2013).
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