Clusac: Clustering Sample Consensus For Fundamental Matrix Estimation

In the process of model fitting for fundamental matrix estimation, RANSAC and its variants disregard and fail to reduce the interference of outliers. These methods select correspondences and calculate the model scores from the original dataset. In this work, we propose an inlier filtering method that can filter inliers from the original dataset. Using the filtered inliers can substantially reduce the interference of outliers. Based on the filtered inliers, we propose a new algorithm called CLUSAC, which calculates model quality scores on all filtered inliers. Our approach is evaluated through estimating the fundamental matrix in the dataset kusvod2, and it shows superior performance to other compared RANSAC variants in terms of precision.