LoFTR uses only 15,300=153*100 image training pairs from MegaDepth. While DKM iteratively sample 150,000 pairs each iteration from 10,661,614 total traning pairs in 53 iterations.

Congratulations on the great work! Do you have plans for releasing the training code ? I'm thinking on fine-tuning your weights for more specific tasks.

Hi!

While trying to rerpduce the results using Mega + Synthetic Dataset using "train_mega_synthetic.py", I notice that in the training code, model is set to be DKM (version 1), in Line 31-33. Does this indicate the training scripts "train_mega_synthetic.py" is designed for DKM (v1). Or the scripts are suitable for both versions?

Thanks!

Hello. Not completely sure if this is an issue, but I am a bit confused about what the conf_thresh parameter should be used for. I'm referring to this line in dkm.py. Shouldn't this line come after the filtering with relative_confidence_threshold? Is it correct to say that conf_thresh filters out outliers and the inliers are given the same probability?

Thanks for your amazing work!

While trying to utilize this amazing work, I can not understand on implementation in the code.

In funciton "upsample_preds" (used only in function match), the final flow is further refined using "self.conv_refiner", however, the estimated residual flow is not added over the original final flow. Instead, a re-sampling is executed.

Related Codes:

query_coords = torch.meshgrid((
        torch.linspace(-1 + 1 / h, 1 - 1 / h, h, device="cuda:0"),
        torch.linspace(-1 + 1 / w, 1 - 1 / w, w, device="cuda:0"),
    ))
query_coords = torch.stack((query_coords[1], query_coords[0]))
query_coords = query_coords[None].expand(b, 2, h, w)
delta_certainty, delta_flow = self.conv_refiner["1"](query, support, flow)
displaced_query_coords = torch.stack((
        query_coords[:, 0] + delta_flow[:, 0] / (4 * w),
        query_coords[:, 1] + delta_flow[:, 1] / (4 * h),
    ),dim=1,)
flow = F.grid_sample(flow, displaced_query_coords.permute(0, 2, 3, 1))

Hi Johan, thanks for your excellent work!

I wonder why np.random.choice is used for selecting good_samples instead of ordering the confidences. Is it for selecting more sparse keypoints?

When using

dense_matches, dense_certainty = model.match(img1PIL, img2PIL, check_cycle_consistency=False, do_pred_in_og_res=True)

I got:

    738             if do_pred_in_og_res:  # Will assume that there is no batching going on.
    739                 og_query, og_support = self.og_transforms((im1, im2))
--> 740                 query_to_support, dense_certainty = self.matcher.upsample_preds(
    741                     query_to_support,
    742                     dense_certainty,

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __getattr__(self, name)
   1129                 return modules[name]
   1130         raise AttributeError("'{}' object has no attribute '{}'".format(
-> 1131             type(self).__name__, name))
   1132 
   1133     def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:

AttributeError: 'RegressionMatcher' object has no attribute 'matcher'

I belive you need to rename the variable self.matcher to self.decoder in line 740 of dkm.py (click here to see)