Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

SCRAMSAC: Improving RANSAC's Efficiency with a Spatial Consistency Filter
Torsten Sattler, Bastian Leibe, Leif Kobbelt
The current state-of-the-art systems for registering two images first extract local features in
each of the images, match those features using their descriptors and then try to estimate the
transformation between the images. Since some of those correspondences might be wrong, it
is crucial to use a robust estimator when computing the transformation.The most popular
robust estimator is the RANdom SAmple Consensus (RANSAC) algorithm. RANSAC
operates in a hypothesize-and-verify framework by randomly selecting subsets of the
correspondences to compute transformations and then verifying those hypothesizes against
the whole set. It terminates when the probability of finding a better transformation explaining
more correspondences falls below a certain treshold.
In this project, we developed a RANSAC extension that is several orders of magnitude faster
than standard RANSAC and as fast as and more robust to degenerate configurations than
PROSAC, the currently fastest RANSAC extension from the literature. Our proposed method
is simple to implement and does not require parameter tuning. Its main component is a spatial
consistency check that results in a reduced correspondence set with a significantly increased
inlier ratio (see the figure below) , leading to faster convergence of the remaining estimation
steps. In addition, we experimentally demonstrate that RANSAC can operate entirely on the
reduced set not only for sampling, but also for its consensus step, leading to additional speedups.
The resulting approach is widely applicable and can be readily combined with other
extensions from the literature.
Figure: (a) The initial set of correspondences contains mostly false correspondences. (b)
Our spatial consistency check removes nearly all false correspondences, keeping only the
correct ones.
285