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Proceedings Paper

Multi-image semi-global matching in object space
Author(s): F. Bethmann; T. Luhmann
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Paper 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.

Paper Details

Date Published: 21 June 2015
PDF: 10 pages
Proc. SPIE 9528, Videometrics, Range Imaging, and Applications XIII, 95280N (21 June 2015); doi: 10.1117/12.2184696
Show Author Affiliations
F. Bethmann, Institute for Applied Photogrammetry and Geoinformatics (Germany)
T. Luhmann, Institute for Applied Photogrammetry and Geoinformatics (Germany)


Published in SPIE Proceedings Vol. 9528:
Videometrics, Range Imaging, and Applications XIII
Fabio Remondino; Mark R. Shortis, Editor(s)

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