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For those curious, keep in mind that the use case for open cv has been almost exclusively indoor, close range, or otherwise loose constraints. Graph models for

For those curious, keep in mind that the use case for open cv has been almost exclusively indoor, close range, or otherwise loose constraints. Graph models for localization famously make use of quite primitive features but tons of observations over time to produce excellent estimates and loop closures. There are applications that require pulling almost magical levels of signal from very small numbers of pixels, and for those extremely accurate results you really need to drill down on the camera model in particular. Even modelling the environment or atmosphere to get accurate results. For those types of applications opencv is absolutely not the right choice. It just so happens that dima has an accessible software kit that is more for the latter case. But all the better because it's perfectly fine to use this tool to calibrate any camera and then go use it with any other CV pipeline. His pain your gain.

midjji 2y ago
Nonsense. Opencv has been used widely for outdoor localisation too, in particular since it is much easier to do outdoor than indoor. Such algorithms always create a graph over the images, but if you mean the graphslam graph filter methods, those are substantially subpar compared to classic feature based methods as well as the more modern, dense and semidense methods.
- jvanderbot 2y ago
  OK I was imprecise in my comments. let me try again. For localization work (indoor or outdoor), in which you mostly want to close loops and track 3D pose of features using measurements from, say 100s of m or less, (or perhaps scene recognition using effectively flat "very far" images), calibration using opencv is probably highly performant. It's standard, and I've certainly seen much success using it before feeding into regular gtsam etc. There are some unspoken assumptions about localization that don't translate to, say, tracking necessarily. Generally they are: Many observations, close-ish range (relative to stereo offset), mostly-correctly-pointed camera rigs (e.g., forward on a car), or perhaps assumptions about density of features, correlation across images, existence of dense prior maps, etc. I believe the use case for something like mrcal is to improve calibration for cameras and applications that don't fit this model well. In particular, you may need to track a target at extreme range, using a pixel or two in the corner of your image, with a particularly wide FOV. These specific use cases, mentioned in top level comment, do require additional care, especially in calibration. Thus mrcal. It just so happens that out of a domain where the very particular details of calibration matter, you may find a tool that helps with all calibration and I think that's the point in bringing up mrcal on a thread discussing calibration in general. I think that's as precise and non-controversial as I can be.
  - midjji 2y ago
    Fair enough, it is very hard to be worse than opencv in terms of user friendlyness, so even if it did nothing more than improve that, its better.

midjji 2y ago

Nonsense. Opencv has been used widely for outdoor localisation too, in particular since it is much easier to do outdoor than indoor. Such algorithms always create a graph over the images, but if you mean the graphslam graph filter methods, those are substantially subpar compared to classic feature based methods as well as the more modern, dense and semidense methods.

jvanderbot 2y ago
OK I was imprecise in my comments. let me try again. For localization work (indoor or outdoor), in which you mostly want to close loops and track 3D pose of features using measurements from, say 100s of m or less, (or perhaps scene recognition using effectively flat "very far" images), calibration using opencv is probably highly performant. It's standard, and I've certainly seen much success using it before feeding into regular gtsam etc. There are some unspoken assumptions about localization that don't translate to, say, tracking necessarily. Generally they are: Many observations, close-ish range (relative to stereo offset), mostly-correctly-pointed camera rigs (e.g., forward on a car), or perhaps assumptions about density of features, correlation across images, existence of dense prior maps, etc. I believe the use case for something like mrcal is to improve calibration for cameras and applications that don't fit this model well. In particular, you may need to track a target at extreme range, using a pixel or two in the corner of your image, with a particularly wide FOV. These specific use cases, mentioned in top level comment, do require additional care, especially in calibration. Thus mrcal. It just so happens that out of a domain where the very particular details of calibration matter, you may find a tool that helps with all calibration and I think that's the point in bringing up mrcal on a thread discussing calibration in general. I think that's as precise and non-controversial as I can be.
- midjji 2y ago
  Fair enough, it is very hard to be worse than opencv in terms of user friendlyness, so even if it did nothing more than improve that, its better.