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The question about results on Aachen dataset in original paper. #52
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Thanks for your questions.
The entry which you link (here) corresponds to the full localization task, which is different than the local feature challenge since it also involves retrieval. The number are higher because we use an improved pipeline (hloc) for reconstruction and absolute pose estimation. |
Thanks for your reply! it really helps me to solve confusion. :) |
Great, please close this issue if your problem is solved. Thanks! |
Well, now I'm trying to reproduce the model on MegaDepth, and I have other questions about the image pairs generation and ground truth correspondences:
Could you give me more details? Thanks! |
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Anyway, thanks for your hints. So, the relative depth error is also asymmetrical, right? Besides, I saw the discussion that said not using any data augmentation during training. Does that mean I can generate keypoints and descriptors offline for efficiency? But generate keypoints and descriptors with a little randomness (like lightly random brightness and scale) is better? |
Ground truth matchesFor each keypoint in each image: we interpolate the depth, lift the keypoint to 3D, and project it to the other image using the relative pose. We then interpolate the depth at the projected location and check if it is consistent with the 3D point using the relative error. If larger than 10%, the projection is marked as occluded. Two keypoints are deemed matchable if both are not occluded and if the distances to their projections are both lower than some threshold. Data augmentationThe only random data augmentations used for MegaDepth are i) the random cropping (since not all images have the same size), ii) the random additional keypoints (for images that have fewer than 1024 detected keypoints). Both allow to detect and describe offline, and dynamically drop or add keypoints. |
Thanks for your help, now I'm clear :). |
hi, Can you specify the formula for calculating the relative error? 3D point depth := d_3D, projection depth := d_2,relative_errors := abs(d_3D - d_2) / d_3D ? |
Hi, thanks for your great work! The paper is very solid and I think it will has a profound influence.
But I have some questions (uncertain details) about visual localization results in the original paper.
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