cvg/Hierarchical-Localization

hloc - the hierarchical localization toolbox

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 combines and makes easily accessible years of research on image matching and Structure-from-Motion.

With hloc, you can:

• Reproduce state-of-the-art results on multiple indoor and outdoor visual localization benchmarks
• 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 🔥

Hierachical Localization uses both image retrieval and feature matching

Quick start ➡️

Build 3D maps with Structure-from-Motion and localize any Internet image right from your browser! You can now run hloc and COLMAP in Google Colab with GPU for free. The notebook demo.ipynb shows how to run SfM and localization in just a few steps. Try it with your own data and let us know!

Installation

hloc requires Python >=3.7 and PyTorch >=1.1. Installing the package locally pulls the other dependencies:

git clone --recursive https://github.com/cvg/Hierarchical-Localization/
cd Hierarchical-Localization/
python -m pip install -e .

All dependencies are listed in requirements.txt. Starting with hloc-v1.3, installing COLMAP is not required anymore. This repository includes external local features as git submodules – don't forget to pull submodules with git submodule update --init --recursive.

We also provide a Docker image:

docker build -t hloc:latest .
docker run -it --rm -p 8888:8888 hloc:latest  # for GPU support, add `--runtime=nvidia`
jupyter notebook --ip 0.0.0.0 --port 8888 --no-browser --allow-root

General pipeline

The toolbox is composed of scripts, which roughly perform the following steps:

1. Extract local features, like SuperPoint or DISK, for all database and query images
2. Build a reference 3D SfM model
   1. Find covisible database images, with retrieval or a prior SfM model
   2. Match these database pairs with SuperGlue or the faster LightGlue
   3. Triangulate a new SfM model with COLMAP
3. Find database images relevant to each query, using retrieval
4. Match the query images
5. Run the localization
6. Visualize and debug

The localization can then be evaluated on visuallocalization.net for the supported datasets. When 3D Lidar scans are available, such as for the indoor dataset InLoc, step 2. can be skipped.

Strcture of the toolbox:

• hloc/*.py : top-level scripts
• hloc/extractors/ : interfaces for feature extractors
• hloc/matchers/ : interfaces for feature matchers
• hloc/pipelines/ : entire pipelines for multiple datasets

hloc can be imported as an external package with import hloc or called from the command line with:

python -m hloc.name_of_script --arg1 --arg2

Tasks

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!

Aachen – outdoor localization

Have a look at pipeline_Aachen.ipynb for a step-by-step guide on localizing with Aachen. Play with the visualization, try new local features or matcher, and have fun! Don't like notebooks? You can also run all scripts from the command line.

InLoc – indoor localization

The notebook pipeline_InLoc.ipynb shows the steps for localizing with InLoc. It's much simpler since a 3D SfM model is not needed.

SfM reconstruction from scratch

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.

Results

• Supported local feature extractors: SuperPoint, DISK, D2-Net, SIFT, and R2D2.
• Supported feature matchers: SuperGlue, its faster follow-up LightGlue, and nearest neighbor search with ratio test, distance test, and/or mutual check. hloc also supports dense matching with LoFTR.
• Supported image retrieval: NetVLAD, AP-GeM/DIR, OpenIBL, and MegaLoc.

Using NetVLAD for retrieval, we obtain the following best results:

Check out visuallocalization.net/benchmark for more details and additional baselines.

Supported datasets

We provide in hloc/pipelines/ scripts to run the reconstruction and the localization on the following datasets: Aachen Day-Night (v1.0 and v1.1), InLoc, Extended CMU Seasons, RobotCar Seasons, 4Seasons, Cambridge Landmarks, and 7-Scenes. For example, after downloading the dataset with the instructions given here, we can run the Aachen Day-Night pipeline with SuperPoint+SuperGlue using the command:

python -m hloc.pipelines.Aachen.pipeline [--outputs ./outputs/aachen]

BibTex Citation

If you report any of the above results in a publication, or use any of the tools provided here, please consider citing both Hierarchical Localization and SuperGlue papers:

@inproceedings{sarlin2019coarse,
  title     = {From Coarse to Fine: Robust Hierarchical Localization at Large Scale},
  author    = {Paul-Edouard Sarlin and
               Cesar Cadena and
               Roland Siegwart and
               Marcin Dymczyk},
  booktitle = {CVPR},
  year      = {2019}
}

@inproceedings{sarlin2020superglue,
  title     = {{SuperGlue}: Learning Feature Matching with Graph Neural Networks},
  author    = {Paul-Edouard Sarlin and
               Daniel DeTone and
               Tomasz Malisiewicz and
               Andrew Rabinovich},
  booktitle = {CVPR},
  year      = {2020},
}

Going further

Debugging and Visualization

Using your own local features or matcher

Using your own image retrieval

Reconstruction with known camera parameters

opts = dict(camera_model='SIMPLE_RADIAL', camera_params=','.join(map(str, (f, cx, cy, k))))
model = reconstruction.main(..., image_options=opts)

python -m hloc.reconstruction [...] --image_options camera_model='"SIMPLE_RADIAL"' camera_params='"256,256,256,0"'

reconstruction.main(..., mapper_options=dict(ba_refine_focal_length=False, ba_refine_extra_params=False))

python -m hloc.reconstruction [...] --mapper_options ba_refine_focal_length=False ba_refine_extra_params=False

Versions

Contributions welcome!

External contributions are very much welcome. Please follow the PEP8 style guidelines using a linter like flake8. This is a non-exhaustive list of features that might be valuable additions:

• support for GPS (extraction from EXIF + guided retrieval)
• covisibility clustering for InLoc
• visualization of the raw predictions (features and matches)
• other local features or image retrieval

Created and maintained by Paul-Edouard Sarlin with the help of many contributors.