RANSAC Scoring Functions: Analysis and Reality Check
Published:Dec 24, 2025 05:00
•1 min read
•ArXiv Vision
Analysis
This paper presents a thorough analysis of scoring functions used in RANSAC for robust geometric fitting. It revisits the geometric error function, extending it to spherical noises and analyzing its behavior in the presence of outliers. A key finding is the debunking of MAGSAC++, a popular method, showing its score function is numerically equivalent to a simpler Gaussian-uniform likelihood. The paper also proposes a novel experimental methodology for evaluating scoring functions, revealing that many, including learned inlier distributions, perform similarly. This challenges the perceived superiority of complex scoring functions and highlights the importance of rigorous evaluation in robust estimation.
Key Takeaways
- •MAGSAC++ score function is numerically equivalent to a simple Gaussian-uniform likelihood.
- •Complex scoring functions may not offer significant performance advantages over simpler alternatives.
- •Rigorous experimental evaluation is crucial for assessing the effectiveness of scoring functions.
Reference
“We find that all scoring functions, including using a learned inlier distribution, perform identically.”