A simple playground in order to test various pnp (3D pose estimation) methods with a given configuration.
This code is related to the following conversations:
- http://answers.opencv.org/question/87546/solvepnp-fails-with-perfect-coordinates-and-cvposit-passes/
- opencv/opencv#6117
Several methods are proposed
enum SolvePnpStrategy
{
Strategy_MySolvePnp_Epnp, //an home baked adapter of epnp using the epnp library source code
Strategy_MySolvePnpPosit, //an home baked adapter of cvPOSIT (deprecated "OpenCV 1" pose estimation method)
Strategy_solvePnp_P3p, // opencv SOLVEPNP_P3P method
Strategy_solvePnp_Iterative_InitialGuess, // opencv SOLVEPNP_ITERATIVE method with an initial guess
Strategy_solvePnp_Epnp //opencv SOLVEPNP_EPNP method
};
Based on my experimentations,
The order of the 3d points and image points does matter
It has to be adapted depending upon the strategy !
-
With opencv's SOLVEPNP_EPNP the error can go down to 23.03 pixel. The order of the points does matter
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With MySolvePnpEpnp (an home baked adapter of epnp using the epnp library source code), the error is about 6.742 pixels, and the order is important It is strange that this "rewrite" gives different results
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With MySolvePnpPosit, the error is about 4.911 pixels and the order is important (in other cases the reprojection error is about 1278 pixels !)
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With solvePnp_P3p (cv::SOLVEPNP_P3P) the error is about 0.02961 pixels and the order does not matter much
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With solvePnp_Iterative_InitialGuess (cv::SOLVEPNP_ITERATIVE) the error can be 0 pixels if a good initial extrinsic guess is given (otherwise don't hope for any convergence). The order does not matter much with SOLVEPNP_ITERATIVE.