  In this paper, we analyze and improve into the recently proposed DeDoDe
keypoint detector. We focus our analysis on some key issues. First, we find
that DeDoDe keypoints tend to cluster together, which we fix by performing
non-max suppression on the target distribution of the detector during training.
Second, we address issues related to data augmentation. In particular, the
DeDoDe detector is sensitive to large rotations. We fix this by including
90-degree rotations as well as horizontal flips. Finally, the decoupled nature
of the DeDoDe detector makes evaluation of downstream usefulness problematic.
We fix this by matching the keypoints with a pretrained dense matcher (RoMa)
and evaluating two-view pose estimates. We find that the original long training
is detrimental to performance, and therefore propose a much shorter training
schedule. We integrate all these improvements into our proposed detector DeDoDe
v2 and evaluate it with the original DeDoDe descriptor on the MegaDepth-1500
and IMC2022 benchmarks. Our proposed detector significantly increases pose
estimation results, notably from 75.9 to 78.3 mAA on the IMC2022 challenge.
Code and weights are available at https://github.com/Parskatt/DeDoDe
