Abstract
Abstract. The stereo matching method semi-global matching (SGM) relies on consistency constraints during the cost aggregation which are enforced by so-called penalty terms. This paper proposes new and evaluates four penalty functions for SGM. Due to mutual dependencies, two types of matching cost calculation, census and rank transform, are considered. Performance is measured using original and degenerated images exhibiting radiometric changes and noise from the Middlebury benchmark. The two best performing penalty functions are inversely proportional and negatively linear to the intensity gradient and perform equally with 6.05% and 5.91% average error, respectively. The experiments also show that adaptive penalty terms are mandatory when dealing with difficult imaging conditions. Consequently, for highest algorithmic performance in real-world systems, selection of a suitable penalty function and thorough parametrization with respect to the expected image quality is essential.
Highlights
Calculating depth information by stereo matching is a common image processing task in many remote sensing applications
The benchmark originated from the studies in (Scharstein and Szeliski, 2002) comparing state-of-the-art stereo methods using a controlled set of test images with complex scene structure and varying texture
While for highly structured images taken under near ideal conditions constant penalty functions (P2,c) perform well, they tend to become overfitted to the particular imaging conditions and performance is not stable over different conditions
Summary
Calculating depth information by stereo matching (disparity estimation) is a common image processing task in many remote sensing applications. Crucial aspects for real-world suitability is accuracy and density of the depth map, which are especially difficult to achieve at in untextured areas. These requirements are further impacted by noise and difficult lighting conditions. All of these effects occur in real-world scenarios. The semi-global matching algorithm (SGM) (Hirschmüller, 2008) is among the top-performing algorithms in the ongoing Middlebury benchmark (Scharstein and Szeliski, 2012). The benchmark originated from the studies in (Scharstein and Szeliski, 2002) comparing state-of-the-art stereo methods using a controlled set of test images with complex scene structure and varying texture. Several combinations of matching cost functions and stereo methods were evaluated using original and degraded test images (e. g. noise, exposure differences)
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