Awesome

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:

C++14
CMake (>= 3.15)
CUDA (>= 11.2)
gflags
NCCL2 (if you need Distributed features) https://developer.nvidia.com/nccl/nccl-download

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 ...

git submodule update --init
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.