7 citations · 18 across the 11 of their papers we have counts for
4 papers · 1 filter
Adaptive Reordering Sampler with Neurally Guided MAGSAC
Tong Wei, Jiri Matas, Daniel Barath
We propose a new sampler for robust estimators that always selects the sample with the highest probability of consisting only of inliers. After every unsuccessful iteration, the in…
VSAC: Efficient and Accurate Estimator for H and F
Maksym Ivashechkin, Daniel Barath, Jiri Matas
We present VSAC, a RANSAC-type robust estimator with a number of novelties. It benefits from the introduction of the concept of independent inliers that improves significantly the…
USACv20: robust essential, fundamental and homography matrix estimation
Maksym Ivashechkin, Daniel Barath, Jiri Matas
We review the most recent RANSAC-like hypothesize-and-verify robust estimators. The best performing ones are combined to create a state-of-the-art version of the Universal Sample C…
Finding Geometric Models by Clustering in the Consensus Space
Daniel Barath, Denys Rozumny, Ivan Eichhardt +2
We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively withou…