MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
Abstract
Large-scale Bundle Adjustment (BA) requires massive memory and computation resources which are difficult to be fulfilled by existing BA libraries. In this paper, we propose MegBA, a GPU-based distributed BA library. MegBA can provide massive aggregated memory by automatically partitioning large BA problems, and assigning the solvers of sub-problems to parallel nodes. The parallel solvers adopt distributed Precondition Conjugate Gradient and distributed Schur Elimination, so that an effective solution, which can match the precision of those computed by a single node, can be efficiently computed. To accelerate BA computation, we implement end-to-end BA computation using high-performance primitives available on commodity GPUs. MegBA exposes easy-to-use APIs that are compatible with existing popular BA libraries. Experiments show that MegBA can significantly outperform state-of-the-art BA libraries: Ceres (41.45×), RootBA (64.576×) and DeepLM (6.769×) in several large-scale BA benchmarks.
Cite
Text
Ren et al. "MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-19836-6Markdown
[Ren et al. "MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/ren2022eccv-megba/) doi:10.1007/978-3-031-19836-6BibTeX
@inproceedings{ren2022eccv-megba,
title = {{MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment}},
author = {Ren, Jie and Liang, Wenteng and Yan, Ran and Mai, Luo and Liu, Shiwen and Liu, Xiao},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-19836-6},
url = {https://mlanthology.org/eccv/2022/ren2022eccv-megba/}
}