Platform	Build Status
Ubuntu 20.04.3

Georgia Tech Structure-from-Motion (GTSfM) is an end-to-end SfM pipeline based on GTSAM. GTSfM was designed from the ground-up to natively support parallel computation using Dask.

For more details, please refer to our arXiv preprint.

License

The majority of our code is governed by an MIT license and is suitable for commercial use. However, certain implementations featured in our repo (e.g., SuperPoint, SuperGlue) are governed by a non-commercial license and may not be used commercially.

Installation

GTSfM requires no compilation, as Python wheels are provided for GTSAM. This repository includes external repositories as Git submodules –- don't forget to pull submodules with git submodule update --init --recursive or clone with git clone --recursive https://github.com/borglab/gtsfm.git.

To run GTSfM, first, we need to create a conda environment with the required dependencies.

On Linux, with CUDA support, run:

conda env create -f environment_linux.yml
conda activate gtsfm-v1 # you may need "source activate gtsfm-v1" depending upon your bash and conda set-up

On macOS, there is no CUDA support, so run:

conda env create -f environment_mac.yml
conda activate gtsfm-v1

Now, install gtsfm as a module:

pip install -e .

Make sure that you can run python -c "import gtsfm; import gtsam; print('hello world')" in python, and you are good to go!

Usage Guide (Running 3D Reconstruction)

Before running reconstruction, if you intend to use modules with pre-trained weights, such as SuperPoint, SuperGlue, or PatchmatchNet, please first run:

./download_model_weights.sh

To run SfM with a dataset with only an image directory and EXIF, with image file names ending with "jpg", please create the following file structure like

└── {DATASET_NAME}
       ├── images
               ├── image1.jpg
               ├── image2.jpg
               ├── image3.jpg

and run

python gtsfm/runner/run_scene_optimizer_olssonloader.py --config_name {CONFIG_NAME} --dataset_root {DATASET_ROOT} --num_workers {NUM_WORKERS}

For example, if you had 4 cores available and wanted to use the Deep Front-End (recommended) on the "door" dataset, you should run:

python gtsfm/runner/run_scene_optimizer_olssonloader.py --dataset_root tests/data/set1_lund_door --config_name deep_front_end.yaml --num_workers 4

(or however many workers you desire).

You can view/monitor the distributed computation using the Dask dashboard.

Currently we require EXIF data embedded into your images (or you can provide ground truth intrinsics in the expected format for an Olsson dataset, or COLMAP-exported text data, etc.)

If you would like to compare GTSfM output with COLMAP output, please run:

python gtsfm/runner/run_scene_optimizer_colmaploader.py --config_name {CONFIG_NAME} --images_dir {IMAGES_DIR} --colmap_files_dirpath {COLMAP_FILES_DIRPATH} --num_workers {NUM_WORKERS} --max_frame_lookahead {MAX_FRAME_LOOKAHEAD}

where COLMAP_FILES_DIRPATH is a directory where .txt files such as cameras.txt, images.txt, etc have been saved.

To visualize the result using Open3D, run:

python gtsfm/visualization/view_scene.py

For users that are working with the same dataset repeatedly, we provide functionality to cache front-end results for GTSfM for very fast inference afterwards. For more information, please refer to gtsfm/frontend/cacher/README.md.

For users that want to run GTSfM on a cluster of multiple machines, we provide setup instructions here: CLUSTER.md

The results will be stored at --output_root, which is the results folder in the repo root by default. The poses and 3D tracks are stored in COLMAP format inside the ba_output subdirectory of --output_root. These can be visualized using the COLMAP GUI as well.

Nerfstudio

We provide a preprocessing script to convert the camera poses estimated by GTSfM to nerfstudio format:

python scripts/prepare_nerfstudio.py --results_path {RESULTS_DIR} --images_dir {IMAGES_DIR}

The results are stored in the nerfstudio_input subdirectory inside {RESULTS_DIR}, which can be used directly with nerfstudio if installed:

ns-train nerfacto --data {RESULTS_DIR}/nerfstudio_input

Repository Structure

GTSfM is designed in an extremely modular way. Each module can be swapped out with a new one, as long as it implements the API of the module's abstract base class. The code is organized as follows:

• gtsfm: source code, organized as:
  • averaging
    • rotation: rotation averaging implementations (Shonan, Chordal, etc)
    • translation: translation averaging implementations (1d-SFM, etc)
  • bundle: bundle adjustment implementations
  • common: basic classes used through GTSFM, such as Keypoints, Image, SfmTrack2d, etc
  • data_association: 3d point triangulation (DLT) w/ or w/o RANSAC, from 2d point-tracks
  • densify
  • frontend: SfM front-end code, including:
    • detector: keypoint detector implementations (DoG, etc)
    • descriptor: feature descriptor implementations (SIFT, SuperPoint etc)
    • matcher: descriptor matching implementations (Superglue, etc)
    • verifier: 2d-correspondence verifier implementations (Degensac, OA-Net, etc)
    • cacher: Cache implementations for different stages of the front-end.
  • loader: image data loaders
  • utils: utility functions such as serialization routines and pose comparisons, etc
• tests: unit tests on every function and module

Contributing

Contributions are always welcome! Please be aware of our contribution guidelines for this project.

Citing this work

If you use GTSfM, please cite our paper:

@misc{baid2023distributed,
      title={Distributed Global Structure-from-Motion with a Deep Front-End}, 
      author={Ayush Baid and John Lambert and Travis Driver and Akshay Krishnan and Hayk Stepanyan and Frank Dellaert},
      year={2023},
      eprint={2311.18801},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Citing the open-source Python implementation:

@misc{GTSFM,
    author = {Ayush Baid and Travis Driver and Fan Jiang and Akshay Krishnan and John Lambert
       and Ren Liu and Aditya Singh and Neha Upadhyay and Aishwarya Venkataramanan
       and Sushmita Warrier and Jon Womack and Jing Wu and Xiaolong Wu and Frank Dellaert},
    title = { {GTSFM}: Georgia Tech Structure from Motion},
    howpublished={\url{https://github.com/borglab/gtsfm}},
    year = {2021}
}

Note: authors are listed in alphabetical order (by last name).