Performance Evaluation of RANSAC Family

TLDR

RANSAC is a widely used robust regression method for data contaminated with outliers and has become a milestone in robust estimation, yet few surveys analyze its performance. This study categorizes RANSAC variants by their goals of accuracy, speed, and robustness. The authors evaluate performance on line fitting across diverse data distributions and demonstrate results on planar homography estimation with real data.

Abstract

RANSAC (Random Sample Consensus) has been popular in regression problem with samples contaminated with outliers. It has been a milestone of many researches on robust estimators, but there are a few survey and performance analysis on them. This paper categorizes them on their objectives: being accurate, being fast, and being robust. Performance evaluation performed on line fitting with various data distribution. Planar homography estimation was utilized to present performance in real data.