Fast hypothesis filtering for multi-structure geometric model fitting

We propose a fast and efficient two-stage hypothesis filtering technique that can improve performance of clustering based robust multi-model fitting algorithms. Sampling based hypothesis generation is nondeterministic and permits little control over generating poor model hypotheses, often leading to a significant proportion of bad hypotheses. Our novel filtering approach leverages the asymmetry in the distributions of points around the inlier/outlier boundary via the sample skewness computed in the residual space. The output is a set of promising hypotheses which aid multi-model fitting algorithms in improving accuracy as well as running time. We validate our approach on the AdelaideRMF dataset and show favorable results along with comparisons to state-of-the-art.