SAmple Consensus

Sample Consensus (SAC) methods, primarily exemplified by RANSAC, aim to robustly estimate model parameters from noisy data containing outliers by iteratively sampling subsets and evaluating their consistency. Current research focuses on accelerating SAC through techniques like prioritized sampling, parallel processing, and integration with neural networks to guide the sampling process and improve efficiency. These advancements are significantly impacting computer vision tasks (e.g., pose estimation, 3D reconstruction) and other fields like distributed machine learning, enabling more efficient and accurate model fitting in challenging real-world scenarios.