We propose the first fast and certifiable algorithm for the registration of two sets of three-
dimensional (3-D) points in the presence of large amounts of outlier correspondences. A …

We review methods designed to compute correspondences between geometric shapes
represented by triangle meshes, contours or point sets. This survey is motivated in part by …

An established approach for 3D point cloud registration is to estimate the registration
function from 3D keypoint correspondences. Typically, a robust technique is required to …

Geometric model fitting is a typical chicken-&-egg problem: data points should be clustered
based on geometric proximity to models whose unknown parameters must be estimated at …

A novel idea on how to make RANSAC repeatable is presented, which will find the optimal
set in nearly every run for certain types of applications. The proposed algorithm can be used …

Consistent correspondences between point clouds are vital to 3D vision tasks such as
registration and recognition. In this paper, we present a mutual voting method for ranking 3D …

Outlier-robust estimation is a fundamental problem and has been extensively investigated
by statisticians and practitioners. The last few years have seen a convergence across …

Nonlinear estimation in robotics and vision is typically plagued with outliers due to wrong
data association or incorrect detections from signal processing and machine learning …

Fitting geometric models onto outlier contaminated data is provably intractable. Many
computer vision systems rely on random sampling heuristics to solve robust fitting, which do …