AbstractSince RANSAC, a great deal of research has been devoted to improving both its accuracy and run-time. Still, only few methods aim at recognizing invalid minimal samples early, before the often expensive model estimation quality calculation are done. To this end, we propose NeFSAC, an efficient algorithm for neural filtering motion-inconsistent poorly-conditioned samples. We train NeFSAC ...