  We propose a new sampler for robust estimators that always selects the sample
with the highest probability of consisting only of inliers. After every
unsuccessful iteration, the inlier probabilities are updated in a principled
way via a Bayesian approach. The probabilities obtained by the deep network are
used as prior (so-called neural guidance) inside the sampler. Moreover, we
introduce a new loss that exploits, in a geometrically justifiable manner, the
orientation and scale that can be estimated for any type of feature, e.g., SIFT
or SuperPoint, to estimate two-view geometry. The new loss helps to learn
higher-order information about the underlying scene geometry. Benefiting from
the new sampler and the proposed loss, we combine the neural guidance with the
state-of-the-art MAGSAC++. Adaptive Reordering Sampler with Neurally Guided
MAGSAC (ARS-MAGSAC) is superior to the state-of-the-art in terms of accuracy
and run-time on the PhotoTourism and KITTI datasets for essential and
fundamental matrix estimation. The code and trained models are available at
https://github.com/weitong8591/ars_magsac.
