Gene

Quantum mechanics

Function (biology)

Projective test

Algorithm

Scale (ratio)

Projective space

Biology

Statistics

Robustness (evolution)

Biochemistry

Computer science

Mathematical optimization

Filter (signal processing)

Evolutionary biology

Outlier

Physics

Kernel (algebra)

Mathematics

Estimator

Image (mathematics)

Sampling (signal processing)

Artificial intelligence

Geometry

Combinatorics

Point (geometry)

Chemistry

RANSAC

Homography

Noise (video)

Robust statistics

Computer vision

A new method for robust estimation, MAGSAC++ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , 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 M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. 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 realworld datasets for homography and fundamental matrix fitting, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is faster, more geometrically accurate and fails less often.

MAGSAC++, a Fast, Reliable and Accurate Robust Estimator