Hello, I'm Gabriele Berton, a PhD student from the Vandal Lab at Polytechnic of Turin ! 
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gmberton Goto Github PK
Name: Gabriele Berton
Type: User
Twitter: gabriberton
Hello, I'm Gabriele Berton, a PhD student from the Vandal Lab at Polytechnic of Turin !
Gabriele Berton's Projects
anyloc
Official Code Repository for AnyLoc
awesome-visual-place-recognition
A curated list of awesome Visual Place Recognition papers
biomassters
An algorithm that predicts yearly Aboveground Biomass for Finnish forests using satellite imagery.
cifar10
cosplace
Official code for CVPR 2022 paper "Rethinking Visual Geo-localization for Large-Scale Applications"
deep-visual-geo-localization-benchmark
Official code for CVPR 2022 (Oral) paper "Deep Visual Geo-localization Benchmark"
deepseed_v2
earthloc
Code for CVPR 2024 "EarthLoc: Astronaut Photography Localization by Indexing Earth from Space"
earthmatch
Code for EarthMatch (CVPR 2024 IMW), an iterative coregistration pipeline to localize astronaut photos of Earth
eigenplaces
Official code for ICCV 2023 paper "EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition"
fau_hackaton_pistazien
geo_warp
Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"
gmberton
gmberton.github.io
[Check the website](http://gmberton.github.io)
hierarchical-localization
Visual localization made easy with hloc
image-matching-models
Code to easily try 17 different image matching methods
project_vg
Repository with code and links to datasets for the visual geo-localization project
py3_wget
A tool like wget for python, supporting a progress bar
salad
Optimal Transport Aggregation for Visual Place Recognition
simple_vpr_codebase
This is a simple codebase to train a Visual Geolocalization model through image retrieval methods, using PyTorch Lightning and the PyTorch Metric Learning library
vpr-datasets-downloader
Automatic download VPR datasets in a standard format
vpr-methods-evaluation
Easily download and evaluate pre-trained Visual Place Recognition methods. Code built for the ICCV 2023 paper "EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition"
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