Lower bound on number of samples to learn intrinsics from a video

[r3krut]

Hi! I've read your paper "Self-Supervised Camera Self-Calibration from Videos" and got some inspiration from it.
According to the paper in order to get proper intrinsics one has to train the full pipeline on a big dataset like KITTI, for example.
But it seems that this process is quite redundant, because one should wait for some time(30-50 epochs). Did you try to learn the full pipeline on a few samples, for example 10? It seems that the full pipepline should reach overfiting and after that we can get a plausable intrisics.