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Hi, thanks for pointing this out!
The implementation actually follows the prior work CDPN, and I did not ablate using the scales after shift/scale. In my view, using gt box size can encode the true size prior to the delta center learning. Thus if the predicted bbox is closer to gt bbox, the results would be better.
BTW, if you find using the augmented size can lead to better results, please do let me know.
Hi,
when you are transforming the relative scale-invariant translation parameter into absolute coordinates in these lines of code:
https://github.com/THU-DA-6D-Pose-Group/GDR-Net/blob/9814a182e7e4d14431088eab2fea6747616b9bc6/core/gdrn_modeling/models/pose_from_pred_centroid_z.py#L175C5-L182C6
it seems that you use the width and height of the initial detected bounding box in the variable
roi_whs
coming from here:https://github.com/shanice-l/gdrnpp_bop2022/blob/f3ca18632f4b68c15ab3306119c364a0497282a7/core/gdrn_modeling/datasets/data_loader_online.py#L470C9-L482C49
instead of the width and height of the shifted one, which is the input to the network. Is this intentional?
Best,
Philipp
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