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There's a good paper on this: https://demuc.de/papers/schoenberger2017comparative.pdf https://demuc.de/papers/schoenberger2017comparative.pdf "Our evaluation
by alleycat5000 7y ago
There's a good paper on this:
https://demuc.de/papers/schoenberger2017comparative.pdf https://demuc.de/papers/schoenberger2017comparative.pdf
"Our evaluation confirms that, as expected, learned descriptors often surpass SIFT on all evaluation metrics. However, we also observe that advanced versions of hand-crafted descriptors perform on par or better than the state-of-the-art learned feature descriptors, especially in the more complex SFM scenarios. As such, our paper demonstrates that there is still significant room for improvement for learning more powerful feature
descriptors."
- Rochetshipz 7y agoThis paper is a bit "old" by the way. An excellent paper was released a week ago : https://arxiv.org/abs/2003.01587 https://arxiv.org/abs/2003.01587 Findings are mostly the same. For day/day images, with a properly tuned pipeline, SIFT is really good.