RANSAC Robust Plane Fitting

Random Sample Consensus (RANSAC) is the workhorse robust-estimation algorithm behind homography and fundamental-matrix fitting, LiDAR ground-plane extraction, and SLAM loop closure — anywhere a computer-vision pipeline must fit a model to data containing gross outliers that an ordinary least-squares fit cannot tolerate. This simulator scatters a 3D point cloud around a hidden true plane, contaminates it with a controllable fraction of pure-noise outliers, and runs the real RANSAC loop live: sample three points, fit the exact plane through them, count how many of all N points lie within a distance threshold, and keep whichever candidate plane wins the largest consensus set. Watch the best-fit plane and its inlier set (green) converge onto the true surface as iterations accumulate, tune outlier fraction, measurement noise and the inlier threshold to see how each degrades or helps convergence, and compare the live iteration count against the closed-form k = ⌈ln(1−P)/ln(1−w³)⌉ that predicts how many random trials are needed for 99% confidence at a given inlier ratio.