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Richer models that fit better are often a trap, in particular for beginners. Use the simplest model you can get away with. Unless you know what you are doing, m
by midjji 2y ago
Richer models that fit better are often a trap, in particular for beginners. Use the simplest model you can get away with. Unless you know what you are doing, most people are better of with a simpler model as it will be more robust to observability issues. If you dont know what those are, use a simpler model.
Uncertainty propagation is very difficult to use for vision, and largely just modelling errors in vision as the error distributions, e.g. for anything observed or reconstructed from images are either subpixel accurate, or too non linear.
- dima55 2y agoAvoiding rich models is a great thing to do if you don't model uncertainty: a beginner that didn't get enough useful calibration data will see poor uncertainties in the results. So I now use the splined models in pretty much all applications, and there are very few downsides. In my experience, every lens fits noticeably better with the richer model (the mrcal validation shows you this explicitly). I think you should look at the tour of mrcal; it's friendly.