Comments (1)
I see, the relevant numbers are .288 vs .313. That's not part of the standard hpatches benchmark (which is patch-based), so we ran it independently with different settings, even though we both used OpenCV. We probably used different image sizes and keypoint numbers, and maybe even sampled the image pairs differently (I think we did first vs rest in every sequence; I'm not sure I'm reading the SP paper correctly but they might've done all possible pairs?).
We should've been more explicit here, but we ran this last-minute for the appendix. The point of this experiment was just to show how learned methods perform against hand-crafted methods w.r.t. the inlier threshold.
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Related Issues (20)
- Find Matches HOT 11
- Training Pipeline HOT 7
- Invalid argument error HOT 2
- Evaluate on HPatches benchmark HOT 1
- Top K pixels gradient backpropagation HOT 3
- Using lf-net in OpenCV dnn HOT 4
- No file is found under the directory "/home" HOT 3
- What are the thetas and inv_thetas of next batch? HOT 3
- raise ValueError('Miss finding argument: unparsed={}\n'.format(unparsed)) HOT 1
- Inference time for images HOT 3
- Will you release a pytorch version? HOT 1
- A little problem with a function"logarithm_so3"? HOT 1
- description match HOT 9
- test datasets keypoints number HOT 1
- inverse_warp_view_2_to_1 HOT 1
- Return also the keypoint scores
- Find matches HOT 13
- Test on Roxford5k benchmark HOT 1
- where is the function 'euclidean_augmentation' in train_lfnet from? HOT 2
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