The submissions to our @CVPR workshop OpenSUN3D on Open-World 3D Scene Understanding and Representations are open! 🏔️🤖☀️
➡️ The deadline for the proceedings track is on the 5th of March!🏃♀️
🌍: https://t.co/4nSXaJGNpR
📝: https://t.co/TYZKnuKeSo
RaCo: Ranking and Covariance for Practical Learned Keypoints
Abhiram Shenoi @PhilippCSE@pesarlin@mapo1
tl;dr: ALIKED arch + DaD RL training with full 360deg rotaug with detector+covariance heads, separate ranker (sorter) model . No IMC eval
#3DV2026
https://t.co/avrkyYFQ2L
We propose to tackle this problem by fusing two popular research branches: direct image geo-localization via classification, and cross-view retrieval against aerial imagery. We show that these tasks not only synergize, but eliminate fundamental limitations of the other approach.
At #NeurIPS2025, we're presenting our work on Scaling Image Geo-Localization to Continent Level.
Website: https://t.co/vWCyOIuGiu
Paper: https://t.co/czDjRDJDUg
If you are at the conference, say 👋:
📍 Poster #4812, Dec 3 (Wed), 4:30–7:30 PM PST
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
@ducha_aiki Thank you for this great blog post! We also observed this phenomenon, and my best guess is that the spatial distribution of matched points and noisy pose estimation perturb the results. However, we found that non-linear pose refinement (e.g. with PoseLib) reduced this error.
📢Join us tomorrow Friday morning at our #ICCV2023 poster S-6 to learn more about LightGlue!
💥We have released the training code at https://t.co/ZnKGFut80j
➡️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: https://t.co/SLLvKOwbEo
➡️Paper: https://t.co/8b38JCoFdg
Fantastic work by @PhilippCSE for #ICCV2023, with @mapo1
1/
Best Student Paper @ICCV_2021
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/ @PhilippCSE V. Larsson @mapo1
➡️ oral & *best student paper award*
Website: https://t.co/1QAdVO2A5y
Paper: https://t.co/ZL7qKT3UsE
Video: https://t.co/HwkfsSGGqx
thread ⬇️