Abstract
We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability. We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.
References
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Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets
Ieee Transactions On Signal And Information Processing Over Networks. 2022-01-01. Vol. 8, p. 63-77. DOI : 10.1109/TSIPN.2022.3140477.LIFT: Learned Invariant Feature Transform
2016. European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, October 8-16, 2016. p. 467-483. DOI : 10.1007/978-3-319-46466-4_28.Teaser Video
This teaser video shows feature matching results with our integrated LIFT pipeline and SIFT, for selected sequences of all three datasets, Strecha, DTU, and Webcam. Our results are significantly better overall compared to SIFT. Note that, in our experiments, SIFT still gives results that are on par with the state-of-the-art when evaluated as a whole pipeline. Please see the paper for details.
Supplementary material
Click the following link for the supplementary appendix for implementation details.
Dataset and Codes
Datasets used in the paper.
Codes for LIFT with learned modules.
Contacts
| Kwang Moo Yi | [e-mail] |
| Eduard Trulls | [e-mail] |
| Vincent Lepetit | [e-mail] |
| Pascal Fua | [e-mail] |