I have a camera matrix world of a 3D scene (let's call it A):
[[ 6.86118087e-01 -7.27490186e-01 1.11022302e-16 0.00000000e+00]
[ 3.82193007e-01 3.60457832e-01 8.50881106e-01 0.00000000e+00]
[-6.19007654e-01 -5.83804917e-01 5.25358300e-01 0.00000000e+00]
[-2.89658661e+00 5.19178303e-01 -3.50478367e-02 1.00000000e+00]]
And, I have 1000 2D images of the scene. For each image, I have pose (camera to world) as follows:
[[-9.55421e-01 1.19616e-01 -2.69932e-01 2.65583e+00]
[ 2.95248e-01 3.88339e-01 -8.72939e-01 2.98160e+00]
[ 4.07581e-04 -9.13720e-01 -4.06343e-01 1.36865e+00]
[ 0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00]]
I want to find the image that is closest to A by comparing pose matrix. How do I compare A with the camera pose of each image?
What is closest?
Just a little background, we asked annotators to write descriptions of an object in a 3D scene. Along with that, we also captured camera parameters, such as matrix world (see A above), center, lookat, and dof, so that we can estimate where in 3D scene were they looking at when they wrote the description. So, now I am trying to create a training set of images (one image for each scene from 1000 available) and would like to find the image that best matches the recorded camera parameters.