About test evaluation content

[FujiwaraZayako]

In your paper, the evaluation comparison demonstrated includes the accuracy of the prediction. But I opened loftr_ pred_ eval. NPY found that the only content in it was' pair name '', identifier '', mconf '', mkpts1f '', mkpts0f '', EPI '', R '', tT'', inliers'。
How to show the prediction accurac？


y

[zehongs]

Please try the test script?

[FujiwaraZayako]

Please try the test script?
The result of my test is this, and there is no prediction.

After viewing the source code in plotting, I try to get the number of "epi_err" in the "loftr_pred_eval" data that is less than the threshold.Is that right？

[FujiwaraZayako]

Now I have new questions about the accuracy of matching point pairs.

“demo_single_pair”, the color of match lines is based on mconf. Mconf is the tensor value of 0-1,So when mconf is greater than or less than what value will it be judged that the matching image block is correct

[zehongs]

Hi, how do you define "the accuracy of the prediction"? Do you mean error AUC of all test pairs? Or matching precision for a single pair?

[FujiwaraZayako]

matching precision

[zehongs]

“demo_single_pair”, the color of match lines is based on mconf. Mconf is the tensor value of 0-1,So when mconf is greater than or less than what value will it be judged that the matching image block is correct

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LoFTR/src/utils/plotting.py

Line 20 in 2122156

def make_matching_figure(

We're not changing colors in this function.

[zehongs]

After viewing the source code in plotting, I try to get the number of "epi_err" in the "loftr_pred_eval" data that is less than the threshold.Is that right？

Yes.

[FujiwaraZayako]

LoFTR/src/utils/plotting.py

Line 20 in 2122156

def make_matching_figure(

We're not changing colors in this function.
This part of the function is to calculate the error by reading the npz information of the corresponding numbered data set in the process of training and testing, and then draw the graph. I saw the corresponding information in the process of reading tensorboard, but when testing other data, I can't calculate the information of accuracy.

I mean, when testing with other images instead of megadepth dataset images, can we calculate the key error information such as "epierr", "ter", "rerr" and use it to predict the image block matching accuracy index.?If not, can the confidence of "cm. Jet" used to distinguish colors be used as an indicator to calculate the matching accuracy.

[zehongs]

Yes, when the K,R and t of image pairs are provided.
cm.jet is a color map converter, I don't think you can use it to calculate matching accuracy. And I also want to point out that the epipolar error can only give you a rough estimate of matching accuracy, as there are still faulty matches with low epipolar error. That's one of the reason that we don't use this metric in our paper.

Are you looking for this?

LoFTR/src/utils/metrics.py

Line 50 in 2122156

def compute_symmetrical_epipolar_errors(data):

LoFTR/src/utils/metrics.py

Line 30 in 2122156

def symmetric_epipolar_distance(pts0, pts1, E, K0, K1):

[FujiwaraZayako]

Are you looking for this?

LoFTR/src/utils/metrics.py

Line 50 in 2122156

def compute_symmetrical_epipolar_errors(data):

LoFTR/src/utils/metrics.py

Line 30 in 2122156

def symmetric_epipolar_distance(pts0, pts1, E, K0, K1):

I have read this part and come to the conclusion that such evaluation indicators cannot be calculated on other data. But I'm not sure if I'm right, so I ask you for help.
Thank you for your generous answer