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This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using SfM. Just download the datasets and you're reading to go! The notebook pipeline_InLoc.ipynb shows the steps for localizing with InLoc. It's much simpler since a 3D SfM model is not needed. We show in pipeline_SfM.ipynb how to run 3D reconstruction for an unordered set of images. This generates reference poses, and a nice sparse 3D model suitable for localization with the same pipeline as Aachen.
Features
- Reproduce our CVPR 2020 winning results on outdoor (Aachen) and indoor (InLoc) datasets
- Run Structure-from-Motion with SuperPoint+SuperGlue to localize with your own datasets
- Evaluate your own local features or image retrieval for visual localization
- Implement new localization pipelines and debug them easily
- Build 3D maps with Structure-from-Motion
- Localize any Internet image right from your browser
Project Samples
