Synthetic Ground Truth for Feature Trackers

Good data sets for evaluation of computer vision algorithms are important for the continued progress of the field. There exist good evaluation sets for many applications, but there are others for which good evaluation sets are harder to come by. One such example is feature tracking, where there is an obvious difficulty in the collection of data. Good evaluation data is important both for comparisons of different algorithms, and to detect weaknesses in a specific method.

All image data is a result of light interacting with its environment. These interactions are so well modelled in rendering software that sometimes not even the sharpest human eye can tell the difference between reality and simulation. In this paper we thus propose to use a high quality rendering system to create evaluation data for sparse point correspondence trackers.