Object-based multi-image Semi-Global Matching - Concept and first results

Abstract

Semi-Global Matching (SGM) is a widespread algorithm for image matching which is used for very different applications, reaching 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). Most of 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 within a closed solution. The proposed approach differs from the original SGM method in two major 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. The paper provides a detailed description of the approach and it discusses its advantages and disadvantages. Further on, first results and accuracy analysis are presented.