  Random hypothesis sampling lies at the core of many popular robust fitting
techniques such as RANSAC. In this paper, we propose a novel hypothesis
sampling scheme based on incremental computation of distances between partial
rankings (top-$k$ lists) derived from residual sorting information. Our method
simultaneously (1) guides the sampling such that hypotheses corresponding to
all true structures can be quickly retrieved and (2) filters the hypotheses
such that only a small but very promising subset remain. This permits the usage
of simple agglomerative clustering on the surviving hypotheses for accurate
model selection. The outcome is a highly efficient multi-structure robust
estimation technique. Experiments on synthetic and real data show the superior
performance of our approach over previous methods.
