Daniel Barath

magsac. The MAGSAC algorithm for robust model fitting without using an inlier-outlier threshold

graph-cut-ransac. The Graph-Cut RANSAC algorithm proposed in paper: Daniel Barath and Jiri Matas; Graph-Cut RANSAC, Conference on Computer Vision and Pattern Recognition, 2018. It is available at http://openaccess.thecvf.com/content_cvpr_2018/papers/Barath_Graph-Cut_RANSAC_CVPR_2018_paper.pdf

superansac. C++

progressive-x. The Progressive-X algorithm proposed in paper: Daniel Barath and Jiri Matas; Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm, International Conference on Computer Vision, 2019. It is available at https://arxiv.org/pdf/1906.02290

homography-benchmark. Python

pose-graph-initialization. C++

affine-correspondences-for-camera-geometry. C

stereoglue. C++

multi-h. The C++ implementation of Multi-H algorithm, which is a multi-plane fitting technique. If you use this work for Academic purposes, please cite Barath, D. and Matas, J. and Hajder, L., Multi-H: Efficient Recovery of Tangent Planes in Stereo Images. 27th British Machine Vision Conference, 2016

homography-from-sift-features. MATLAB

absolute-pose-from-oriented-and-scaled-features. C++

multi-x. The Multi-X algorithm proposed in paper: Daniel Barath and Jiri Matas, Multi-class model fitting by energy-minimization and mode-seeking, European Conference on Computer Vision, 2018. It is available at http://openaccess.thecvf.com/content_ECCV_2018/papers/Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.pdf

five-point-fundamental. The Matlab implementation of the 5 point fundamental matrix estimator. If you use this work for Academic purposes, please cite Barath, D., Five-point fundamental matrix estimation for uncalibrated cameras, Conference on Computer Vision and Pattern Recognition, 2018

clustering-in-consensus-space. Jupyter Notebook

cvpr2022-affine-tutorial. The official site of the CVPR 2022 Affine Correspondences and Their Applications tutorial

learning-good-models-in-ransac.

recovering-affine-features. D. Barath, "Recovering affine features from orientation-and scale-invariant ones", Asian Conference on Computer Vision (ACCV), 2019

ransac-2025-tutorial. HTML

robust-line-based-estimator. Jupyter Notebook

sutd_hololens_mapping. Python

DeepLSD. Implementation of the paper "DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients"