these days I am working on understanding the solvePnP algorithm used for computer vision. I found the official EPnP algorithm in MATLAB here https://github.com/cvlab-epfl/EPnP.
I am comparing the results of the official implementation with the OpenCV version via the Python API.
Running a simple example with both methods, gives me different R matrices and t vectors. The t vectors are different and the sign of the two upper rows of R are flipped (see output below under the code examples). What am I missing here?
Thanks!
Example Python code:
import numpy as np
import cv2
cam_in = [[2445.72, 0.0, 819.29],
[0.0, 2442.39, 660.13],
[0.0, 0.0, 1.0]]
# 3p model from opencv
objPts = [[ 0. , 0. , 0. ],
[ 82.5, 0. , 0. ],
[165. , 0. , 0. ],
[247.5, 0. , 0. ],
[ 55. , 27.5, 0. ],
[137.5, 27.5, 0. ],
[220. , 27.5, 0. ],
[ 27.5, 55. , 0. ],
[110. , 55. , 0. ],
[192.5, 55. , 0. ],
[ 0. , 82.5, 0. ],
[ 82.5, 82.5, 0. ],
[165. , 82.5, 0. ],
[247.5, 82.5, 0. ],
[ 55. , 110. , 0. ],
[137.5, 110. , 0. ],
[220. , 110. , 0. ],
[ 27.5, 137.5, 0. ],
[110. , 137.5, 0. ],
[192.5, 137.5, 0. ],
[ 0. , 165. , 0. ],
[ 82.5, 165. , 0. ],
[165. , 165. , 0. ],
[247.5, 165. , 0. ]]
imagePts = [[648.84735, 335.1484 ],
[522.6854 , 317.74222],
[400.24448, 301.46362],
[281.39792, 285.43964],
[560.8046 , 366.6523 ],
[437.57022, 349.67358],
[318.269 , 333.33557],
[598.38196, 415.80203],
[475.02866, 397.87906],
[354.60062, 380.84167],
[636.9289 , 465.04666],
[512.3496 , 446.39185],
[391.26932, 428.63168],
[273.65057, 411.7955 ],
[549.6402 , 495.04532],
[428.12842, 476.45554],
[309.60794, 458.5343 ],
[587.5397 , 543.42163],
[465.2291 , 524.39795],
[346.24826, 505.79684],
[624.71814, 591.7365 ],
[502.51782, 572.03394],
[382.8287 , 552.7545 ],
[266.8465 , 534.29364]]
cam_in, objPts, imagePts = [np.array(x) for x in [cam_in, objPts, imagePts]]
distortion = None
ret, rvec, T = cv2.solvePnP(objPts, imagePts, cam_in, distortion, flags=cv2.SOLVEPNP_EPNP)
R, _ = cv2.Rodrigues(rvec)
print('R')
print(R)
print()
print('T:')
print(T)
Example MATLAB code with Octave:
clear all
close all
cam_in = [[2445.72, 0.0, 819.29]; ...
[0.0, 2442.39, 660.13]; ...
[0.0, 0.0, 1.0]]
% 3p model from opencv
objPts = [[ 0. , 0. , 0. ]; ...
[ 82.5, 0. , 0. ]; ...
[165. , 0. , 0. ]; ...
[247.5, 0. , 0. ]; ...
[ 55. , 27.5, 0. ]; ...
[137.5, 27.5, 0. ]; ...
[220. , 27.5, 0. ]; ...
[ 27.5, 55. , 0. ]; ...
[110. , 55. , 0. ]; ...
[192.5, 55. , 0. ]; ...
[ 0. , 82.5, 0. ]; ...
[ 82.5, 82.5, 0. ]; ...
[165. , 82.5, 0. ]; ...
[247.5, 82.5, 0. ]; ...
[ 55. , 110. , 0. ]; ...
[137.5, 110. , 0. ]; ...
[220. , 110. , 0. ]; ...
[ 27.5, 137.5, 0. ]; ...
[110. , 137.5, 0. ]; ...
[192.5, 137.5, 0. ]; ...
[ 0. , 165. , 0. ]; ...
[ 82.5, 165. , 0. ]; ...
[165. , 165. , 0. ]; ...
[247.5, 165. , 0. ]];
imagePts = [[648.84735, 335.1484 ]; ...
[522.6854 , 317.74222]; ...
[400.24448, 301.46362]; ...
[281.39792, 285.43964]; ...
[560.8046 , 366.6523 ]; ...
[437.57022, 349.67358]; ...
[318.269 , 333.33557]; ...
[598.38196, 415.80203]; ...
[475.02866, 397.87906]; ...
[354.60062, 380.84167]; ...
[636.9289 , 465.04666]; ...
[512.3496 , 446.39185]; ...
[391.26932, 428.63168]; ...
[273.65057, 411.7955 ]; ...
[549.6402 , 495.04532]; ...
[428.12842, 476.45554]; ...
[309.60794, 458.5343 ]; ...
[587.5397 , 543.42163]; ...
[465.2291 , 524.39795]; ...
[346.24826, 505.79684]; ...
[624.71814, 591.7365 ]; ...
[502.51782, 572.03394]; ...
[382.8287 , 552.7545 ]; ...
[266.8465 , 534.29364]];
[R T reprojectionPts] = efficient_pnp(objPts, imagePts, cam_in);
display(R)
display(T)
Python output:
R
[[-0.9553479 -0.10390386 0.27661231]
[-0.15995044 0.96896901 -0.18845399]
[-0.24844766 -0.22428338 -0.94232199]]
T:
[[-105.91385446]
[-201.18325419]
[1596.85002509]]
Octave output:
R =
0.95539 0.10395 -0.27644
0.15990 -0.96902 0.18824
-0.24830 -0.22405 -0.94242
T =
-359.306
-80.755
1595.419
