We review the most recent RANSAC-like hypothesize-and-verify robust estimators. The best performing ones are combined to create a state-of-the-art version of the Universal Sample Consensus (USAC) algorithm. A recent objective is to implement a modular and optimized framework, making future RANSAC modules easy to be included. The proposed method, USACv20, is tested on eight publicly available real-world datasets, estimating homographies, fundamental and essential matrices. On average, USACv20 leads to the most geometrically accurate models and it is the fastest in comparison to the state-of-the-art robust estimators. All reported properties improved performance of original USAC algorithm significantly. The pipeline will be made available after publication.
@article{arxiv.2104.05044,
title = {USACv20: robust essential, fundamental and homography matrix estimation},
author = {Maksym Ivashechkin and Daniel Barath and Jiri Matas},
journal= {arXiv preprint arXiv:2104.05044},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:1912.05909