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MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment

This repo contains an official implementation of MegBA.

MegBA is a fast and distributed library for large-scale Bundle Adjustment (BA). MegBA has a novel end-to-end vectorised BA algorithm which can fully exploit the massive parallel cores on GPUs, thus speeding up the entire BA computation. It also has a novel distributed BA algorithm that can automatically partition BA problems, and solve BA sub-problems using distributed GPUs. The GPUs synchronise intermediate solving state using network-efficient collective communication, and the synchronisation is designed to minimise communication cost. MegBA has a memory-efficient GPU runtime and it exposes g2o-compatible APIs. Experiments show that MegBA can out-perform state-of-the-art BA libraries (i.e., Ceres and DeepLM) by ~50x and ~5x respectively, in public large-scale BA benchmarks.

Version

  • 2021/12/06 Beta version released! It corresponds to this paper
  • 2022/02/18 Stable version released! We have refactored MegBA and fixed some existing bugs, e.g., incorrect rollback in the LM reject step.
  • 2022/02/25 Analytical differentiation module available; We also provide BAL_X_analytical.cpp under examples/. Compared with automatic diff, time and space are reduced by ~30% and ~40%, respectively.

Todo

  • memory-efficient version with implicit Hessian (TBD)
  • IMU factor, prior factor (TBD)

Paper: https://arxiv.org/abs/2112.01349 (updated version)

Quickstart

Dependencies:

You can also easily install all dependencies with script: script

Demo with BAL dataset:

  • Download any pre.txt.bz2 file from BAL Dataset: https://grail.cs.washington.edu/projects/bal/ and uncompressed.

  • Compile

    If you want to use the distributed feature, use cmake -DMEGBA_ENABLE_NCCL .. instead of cmake ...

    mkdir build
cd build
cmake ..  # enable nccl by using cmake -DMEGBA_ENABLE_NCCL ..
make -j4 BAL_Double

  • Run the demo (Venice-1778)

    cd examples
./BAL_Double --path /path/to/your/dataset --world_size 2 --max_iter 100 --solver_tol 1e-1 --solver_refuse_ratio 1 --solver_max_iter 100 --tau 1e4 --epsilon1 1 --epsilon2 1e-10
    • world_size: number of GPUs available
    • max_iter: the maximal number of LM iteration
    • epsilon1 & epsilon2: threshold in LM
    • solver_tol: tolerance of solver (distributed PCG solver)
    • solver_refuse_ratio: early stop for the solver
    • solver_max_iter: the maximal iteration of solver
    • tau: the initial region

Notes for the practitioners

  • Currently, MegBA implements automatic differentiation only for generalizability. Please consider implementing your own analytical differentiation module. Analytical differentiation module is provided.
  • If you use devices without modern inter-device communication (i.e., NVLinks..), you might find the data transfer is the bottleneck.
  • Empirically, we found it is necessary to customize the LM trust-region strategies and tune its hyper-parameters to further boost the performance.

Documentation

Under doc/ (Coming soon...)

Collaborate with Us

Please check here for MegBA's future plan.

If you are interested in MegBA and want to collaborate, you can:

  • Sorry, we can no longer host Interns.
  • As an external collaborator (coding), just fork this repo and send PRs. We will review your PR carefully (and merge it into MegBA).
  • As an algorithm/novelty contributor, please send an email to MegBA@googlegroups.com.
  • Any new feature request, you can send an email to MegBA@googlegroups.com as well. Note that it is not guaranteed the requested feature will be added or added soon

Contact Information:

BibTeX Citation

If you find MegBA useful for your project, please consider citing:

@inproceedings{2021megba,
  title={MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment}, 
  author={Jie Ren and Wenteng Liang and Ran Yan and Luo Mai and Shiwen Liu and Xiao Liu},
  booktitle={European Conference on Computer Vision},
  year={2022}
}

License

MegBA is licensed under the Apache License, Version 2.0.