Quality Assessment of Non-Dense Image Correspondences

Abstract

Non-dense image correspondence estimation algorithms are known for their speed, robustness and accuracy. However, current evaluation methods evaluate correspondences point-wise and consider only correspondences that are actually estimated. They cannot evaluate the fact that some algorithms might leave important scene correspondences undetected - correspondences which might be vital for succeeding applications. Additionally, often the reference correspondences for real world scenes are also sparse. Outliers that do not hit a reference measurement can remain undetected with the current, point-wise evaluation methods. To assess the quality of correspondence fields we propose a histogram based evaluation metric that does not rely on point-wise comparison and is therefore robust to sparsity in estimate as well as reference.