  We present a robust estimator for fitting multiple parametric models of the
same form to noisy measurements. Applications include finding multiple
vanishing points in man-made scenes, fitting planes to architectural imagery,
or estimating multiple rigid motions within the same sequence. In contrast to
previous works, which resorted to hand-crafted search strategies for multiple
model detection, we learn the search strategy from data. A neural network
conditioned on previously detected models guides a RANSAC estimator to
different subsets of all measurements, thereby finding model instances one
after another. We train our method supervised as well as self-supervised. For
supervised training of the search strategy, we contribute a new dataset for
vanishing point estimation. Leveraging this dataset, the proposed algorithm is
superior with respect to other robust estimators as well as to designated
vanishing point estimation algorithms. For self-supervised learning of the
search, we evaluate the proposed algorithm on multi-homography estimation and
demonstrate an accuracy that is superior to state-of-the-art methods.
