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We review the most recent RANSAC-like hypothesize-and-verify robustestimators. The best performing ones are combined to create a state-of-the-artversion of the Universal Sample Consensus (USAC) algorithm. A recent objectiveis to implement a modular and optimized framework, making future RANSAC moduleseasy to be included. The proposed method, USACv20, is tested on eight publiclyavailable real-world datasets, estimating homographies, fundamental andessential matrices. On average, USACv20 leads to the most geometricallyaccurate models and it is the fastest in comparison to the state-of-the-artrobust estimators. All reported properties improved performance of originalUSAC algorithm significantly. The pipeline will be made available afterpublication.

USACv20: robust essential, fundamental and homography matrix estimation