  A new method for robust estimation, MAGSAC++, is proposed. It introduces a
new model quality (scoring) function that does not require the inlier-outlier
decision, and a novel marginalization procedure formulated as an iteratively
re-weighted least-squares approach. We also propose a new sampler, Progressive
NAPSAC, for RANSAC-like robust estimators. Exploiting the fact that nearby
points often originate from the same model in real-world data, it finds local
structures earlier than global samplers. The progressive transition from local
to global sampling does not suffer from the weaknesses of purely localized
samplers. On six publicly available real-world datasets for homography and
fundamental matrix fitting, MAGSAC++ produces results superior to
state-of-the-art robust methods. It is faster, more geometrically accurate and
fails less often.
