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https://github.com/MegviiRobot/MegBA
MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
https://github.com/MegviiRobot/MegBA
bundleadjustment cuda distributed gpu-acceleration graph-optimization high-performance
Last synced: about 2 months ago
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MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
- Host: GitHub
- URL: https://github.com/MegviiRobot/MegBA
- Owner: MegviiRobot
- License: apache-2.0
- Created: 2021-11-18T14:22:53.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-13T12:51:04.000Z (10 months ago)
- Last Synced: 2024-05-08T10:32:25.459Z (5 months ago)
- Topics: bundleadjustment, cuda, distributed, gpu-acceleration, graph-optimization, high-performance
- Language: Cuda
- Homepage:
- Size: 1.3 MB
- Stars: 431
- Watchers: 21
- Forks: 56
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment
This repo contains an official implementation of [MegBA](https://arxiv.org/abs/2112.01349).
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](https://arxiv.org/abs/2112.01349)
* 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](https://developer.nvidia.com/cuda-downloads) (>= 11.2)
- [gflags](https://github.com/gflags/gflags)
- NCCL2 (if you need Distributed features) https://developer.nvidia.com/nccl/nccl-download
You can also easily install all dependencies with script: [script](https://drive.google.com/file/d/154whcVH2VcJCYnTSlnfo_tbIIaQvSax3/view?usp=sharing)
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 ..`.
```bash
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)
```bash
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](https://docs.google.com/document/d/1fHYuw_qRFHrBcGSeQ8Ld4y2wK9oxF0am3xA9r6veUwM/edit?usp=sharing).
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 [email protected].
* Any new feature request, you can send an email to [email protected] as well. *Note that it is not guaranteed the requested feature will be added or added soon*
Contact Information:
* Jie Ren [email protected]
* Wenteng Liang [email protected]
* Ran Yan [email protected]
* Shiwen Liu [email protected]
* Xiao Liu [email protected]
## 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.