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.
- General version code release (Expected Dec 31 2021)
- memory-efficient version with implicit Hessian (TBD)
- analytical differential module, IMU factor, prior factor (TBD)
Paper:
- First Draft: https://arxiv.org/abs/2112.01349 (an updated version will be released by Dec 10)
Quickstart
Dependencies:
- C++14
- CMake (>= 3.15)
- CUDA Toolkit (with thrust) https://developer.nvidia.com/cuda-downloads
- Eigen (MegBA Beta depends on a modified version of Eigen under third_party/. Eigen-free version will be provided in the general release.)
- NCCL (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
mkdir build cd build cmake .. make -j4 BAL_Double -
Run the demo (Venice-1778)
cd examples ./BAL_Double --name=Venice --world_size=2 --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
- iter: the maximal number of LM iteration
- epsilon: 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 differentation only for generalizability. Please consider implementing your own analytical differentiation module.
- 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 intereted in MegBA and want to collaborate, you can:
- Apply for an Internship at Megvii Research 3D, please send your resume to [email protected], with your expected starting date. (subject: 3D组CUDA实习生-Name) Unfortunately, now we are only able to host interns with work authorization in China.
- 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:
@misc{2021megba,
title={MegBA: A High-Performance and 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},
year={2021},
eprint={2112.01349},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
License
MegBA is licensed under the Apache License, Version 2.0.
51 Dec 13, 2022
34 Dec 5, 2022
14.5k Jan 8, 2023
21.1k Jan 1, 2023
86 Dec 7, 2022
53 Dec 23, 2022
47 Dec 28, 2022
2 Jan 29, 2022
56 Oct 26, 2022
539 Dec 28, 2022
128 Nov 29, 2022
9 Jul 11, 2022
6.9k Jan 4, 2023
943 Jan 7, 2023
1.2k Jan 4, 2023