7 citations · 12 across the 4 of their papers we have counts for
12 papers · 1 filter
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…
Efficient Initial Pose-graph Generation for Global SfM
Daniel Barath, Dmytro Mishkin, Ivan Eichhardt +2
We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. To avoid forming tentative point correspondences by FLANN and geometric v…
Relative Pose from Deep Learned Depth and a Single Affine Correspondence
Ivan Eichhardt, Daniel Barath
We propose a new approach for combining deep-learned non-metric monocular depth with affine correspondences (ACs) to estimate the relative pose of two calibrated cameras from a sin…
EPOS: Estimating 6D Pose of Objects with Symmetries
Tomas Hodan, Daniel Barath, Jiri Matas
We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, in…
MAGSAC++, a fast, reliable and accurate robust estimator
Daniel Barath, Jana Noskova, Maksym Ivashechkin +1
A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not require the inlier-outlier decision, and a novel margi…