This paper presents a novel randomized algorithm for robust point cloud registration without
correspondences. Most existing registration approaches require a set of putative …

Robustly fitting a linear model from outlier-contaminated data is an important and basic task
in many scientific fields, and it is often tackled by consensus set maximization. There have …

Robust model fitting is a core algorithm in several computer vision applications. Despite
being studied for decades, solving this problem efficiently for datasets that are heavily …

Robust model fitting is a core algorithm in a large number of computer vision applications.
Solving this problem efficiently for highly contaminated datasets is, however, still challenging …

Efficiently registering point clouds of spatial targets or defunct debris is a formidable
challenge. SDRSAC is a robust point cloud registration method based on correspondence …

This paper revisits the application of M-estimators for a wide range of robust estimation
problems in computer vision, especially with the maximum consensus criterion. Current …