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

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 …

Feature matching is a fundamental problem in many computer vision tasks. This paper
proposes a novel effective framework for mismatch removal, named LOcality-guided Global …

Finding relative pose between two calibrated images is a fundamental task in computer
vision. Given five point correspondences, the classical five-point methods can be used to …

Robust model fitting plays a vital role in computer vision, and research into algorithms for
robust fitting continues to be active. Arguably the most popular paradigm for robust fitting in …

Geometric model fitting is a fundamental task in computer vision, which serves as the pre-
requisite of many downstream applications. While the problem has a simple intrinsic …

Abstract Point Cloud Registration (PCR) is a fundamental and important issue in
photogrammetry and computer vision. Its goal is to find rigid transformations that register …

False match removal is a crucial and fundamental task in photogrammetry and computer
vision. This paper proposes a robust and efficient mismatch-removal algorithm based on the …

Branch-and-bound-based consensus maximization stands out due to its important ability of
retrieving the globally optimal solution to outlier-affected geometric problems. However …

Robust estimation, which aims to recover the geometric transformation from outlier
contaminated observations, is essential for many remote sensing and photogrammetry …