RANSAC (Random Sample Consensus) is a key algorithm in computer vision for handling noisy data and outliers when estimating mathematical models. It addresses scenarios where traditional methods, like least squares, fail because they assume all data points are valid. In computer vision, tasks like feature matching, camera calibration, and 3D reconstruction often involve datasets contaminated by incorrect measurements. RANSAC solves this by iteratively selecting random subsets of data to propose a model, then validating it by counting how many points agree (inliers). The model with the most inliers is chosen, making the process robust to outliers. For example, when stitching images into a panorama, RANSAC helps compute the correct homography (transformation) between overlapping images by ignoring mismatched keypoints.
A common application is feature-based image alignment. Suppose two images of a scene have hundreds of detected keypoints, with many incorrect matches due to repetitive textures or occlusion. RANSAC randomly selects a minimal set of matched points (e.g., four pairs for homography estimation), computes the transformation, and checks how many other matches fit this model within an error threshold. After many iterations, the best-fitting homography is used to warp the images into alignment. Without RANSAC, even a small percentage of mismatches could severely distort the result. Similarly, in stereo vision, RANSAC estimates the fundamental matrix (which relates two camera views) by filtering out incorrect correspondences, enabling accurate depth estimation.
Beyond image alignment, RANSAC is used in structure-from-motion (SfM) pipelines to iteratively build 3D models from 2D images. For instance, when triangulating 3D points from multiple views, RANSAC ensures that only consistent 2D observations are used, rejecting outliers caused by tracking errors. It’s also critical in object pose estimation, where the goal is to determine an object’s 3D position and orientation from a cluttered scene. While alternatives like MLESAC or PROSAC exist, RANSAC remains popular due to its simplicity and effectiveness. Its computational cost scales with outlier ratio, but optimizations like early termination or pre-filtering (e.g., using Lowe’s ratio for feature matching) make it practical for real-time applications like SLAM (Simultaneous Localization and Mapping).