5 accepted papers
"We propose StereoGlue, a method designed for joint feature matching and robust estimation that effectively reduces the combinatorial complexity of these tasks using single-point minimal solvers. StereoGlue is applicable to a range of problems, including but not limited to relative pose and homograp…
Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effe…
We propose the Model Quality Network, MQ-Net in short, for predicting the quality, e.g. the pose error of essential matrices, of models generated inside RANSAC. It replaces the traditionally used scoring techniques, e.g., inlier counting of RANSAC, truncated loss of MSAC, and the marginalization-bas…
"Since RANSAC, a great deal of research has been devoted to improving both its accuracy and run-time. Still, only a few methods aim at recognizing invalid minimal samples early, before the often expensive model estimation and quality calculation are done. To this end, we propose NeFSAC, an efficient…
Local feature matching is a critical part of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a…