@article{
 title = {TILDE: A Temporally Invariant Learned DEtector},
 type = {article},
 year = {2015},
 pages = {5279-5288},
 volume = {07-12-June},
 id = {0bef2a58-d1b5-3b7f-8dfe-d082ae62a36d},
 created = {2022-09-19T09:01:26.102Z},
 file_attached = {true},
 profile_id = {276016a7-2c9d-3507-8888-093db7c54774},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2022-09-19T09:03:09.114Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {02fb5526-03ff-44ad-8d5c-42bd496c3100},
 private_publication = {false},
 abstract = {We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in multiple training images taken from the same viewpoint. We then train a regressor to predict a score map whose maxima are those points so that they can be found by simple non-maximum suppression. As there are no standard datasets to test the influence of these kinds of changes, we created our own, which we will make publicly available. We will show that our method significantly outperforms the state-of-the-art methods in such challenging conditions, while still achieving state-of-the-art performance on untrained standard datasets.},
 bibtype = {article},
 author = {Verdie, Yannick and Yi, Kwang Moo and Fua, Pascal and Lepetit, Vincent},
 doi = {10.1109/CVPR.2015.7299165},
 journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition}
}

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