Each group should produce code for automatic evaluation of the estimated camera positions on the dinosaur dataset. This should be done using the following procedure:

1. Extract the global position and orientation of cameras in the ground truth. This results in a set of K poses: (R_k,t_k)
2. Extract the global position and orientation of your estimated cameras on the same dataset. This results in a set of K poses: (R^est_k,t^est_k)
3. Align the two sets by estimating a similarity transformation that maps your camera positions t^est_k to the ground truth positions t_k, i.e.
   t_k=sMt^est_k+m,
   where s is a scale change, M is the global rotation, and m is the global translation. (See e.g. the IREG compendium, or Horns article.)
4. Map your estimated camera centers and orientations using (s,M,m).
   The camera positions are mapped as:
   t^mapped_k=sMt^est_k+m,
   and the orientations are mapped as:
   R^mapped_k=R^est_kM^T
5. Compute the individual errors for the camera positions as:
   e_k=||t_k-t^mapped_k||
   and the individual angular errors for the camera orientations using the 3D angular error (the geodesic distance on SO(3)):
   a_k=2sin^-1(||R^mapped_k-R_k||/sqrt(8))
   See e.g. the article by Hartley et al. for a derivation of this error measure.
6. Summarize the errors for the entire set of poses using the mean-absolute errors (MAE) for each of the two errors above.

If any of the steps above is unclear to you, ask your project guide.

References

• B.K.P. Horn, Closed-form solution of absolute orientation using unit quaternions, JOSA, Vol 4, 1987.
• Klas Nordberg, Introduction to Representations and Estimation in Geometry (IREG).
• R. Hartley, J. Trumpf, Y. Dai, and H. Li. Rotation averaging. International Journal of Computer Vision, 103(3):267-305, July 2013