  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 algorithm
for neural filtering of motion-inconsistent and poorly-conditioned minimal
samples. We train NeFSAC to predict the probability of a minimal sample leading
to an accurate relative pose, only based on the pixel coordinates of the image
correspondences. Our neural filtering model learns typical motion patterns of
samples which lead to unstable poses, and regularities in the possible motions
to favour well-conditioned and likely-correct samples. The novel lightweight
architecture implements the main invariants of minimal samples for pose
estimation, and a novel training scheme addresses the problem of extreme class
imbalance. NeFSAC can be plugged into any existing RANSAC-based pipeline. We
integrate it into USAC and show that it consistently provides strong speed-ups
even under extreme train-test domain gaps - for example, the model trained for
the autonomous driving scenario works on PhotoTourism too. We tested NeFSAC on
more than 100k image pairs from three publicly available real-world datasets
and found that it leads to one order of magnitude speed-up, while often finding
more accurate results than USAC alone. The source code is available at
https://github.com/cavalli1234/NeFSAC.
