Some benchmark of the modern local features with LightGlue on #IMC2021 dataset.
0) Use ALIKED features.
1) ALIKED 2k is on par with other features 8k
2) DISK overfits to buildings
3) DoG detector benefits from 8k the most

📢Join us tomorrow Friday morning at our #ICCV2023 poster S-6 to learn more about LightGlue!
💥We have released the training code at github.com/cvg/glue-facto…
➡️You can now train LightGlue with SIFT, ALIKED, or your own local features, on your own dataset
⏩More models coming soon!

You liked SuperGlue? You'll love ⚡️LightGlue⚡️, our new deep network for light-speed image matching!
➡️Faster, stronger, easier to train than SuperGlue
➡️Code: github.com/cvg/LightGlue
➡️Paper: arxiv.org/pdf/2306.13643…
Fantastic work by Philipp Lindenberger for #ICCV2023 , with Marc Pollefeys
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Best Student Paper ICCV2021

Pixel-Perfect Structure-from-Motion with Featuremetric Refinement

Philipp Lindenberger (ETH Zurich), Paul-Edouard Sarlin (ETH Zurich), Viktor Larsson, Marc Pollefeys

[Session 5 A/B]

🚨 This week at #ICCV2021 , check out 'Pixel-Perfect Structure-from-Motion with Featuremetric Refinement' w/ Philipp Lindenberger V. Larsson Marc Pollefeys
➡️ oral & *best student paper award*
Website: psarlin.com/pixsfm
Paper: arxiv.org/abs/2108.08291
Video: youtu.be/2HuCMuraFk0
thread ⬇️