calcampose in Eigen Free code

[zjhthu]

It seems the calcampose function in the PnP problem solves a closed-form ICP problem to enforce the orthogonality constraint of the rotation matrix.

But I am a little confused about this line.

As shown in the Eq. 10 of this paper, after finding the ration matrix R2, the translation can be recovered from t2 = uc - np.matmul(R2, uw). But in the implementation of calcampose, there is an additional coefficient c2 in t2 = uc - c2 * np.matmul(R2, uw) . So why there is a c2?

@kmyid @Dangzheng

[zjhthu]

Besides, in the evaluate_R_t_pnp function, the rotation error is computed by the difference between the quaternion vector and ground truth vector rather than the angular difference as done in the original evaluate_R_t function.
But it seems the code still reports the angular error as the metric for both the rotation and translation, which may be inconsistent?

[Dangzheng]

Hi,

About the metric, we use the standard rotation and trainslation error metric as in the paper. 'Robust 3D Object Tracking from Monocular'

About the calcampose, we convert it from a matlab script. We really forget where I find it. Here it is.

Best regards,
Zheng