RANSAC for (Quasi-)Degenerate data (QDEGSAC)

The computation of relations from a number of potential matches is a major task in computer vision. Often RANSAC is employed for the robust computation of relations such as the fundamental matrix. For (quasi-)degenerate data however, it often fails to compute the correct relation. The computed relation is always consistent with the data but RANSAC does not verify that it is unique. The paper proposes a framework that estimates the correct relation with the same robustness as RANSAC even for (quasi-)degenerate data. The approach is based on a hierarchical RANSAC over the number of constraints provided by the data. In contrast to all previously presented algorithms for (quasi-)degenerate data our technique does not require problem specific tests or models to deal with degenerate configurations. Accordingly it can be applied for the estimation of any relation on any data and is not limited to a special type of relation as previous approaches. The results are equivalent to the results achieved by state of the art approaches that employ knowledge about degeneracies.