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. The code of MegBA is available at: https://github.com/MegviiRobot/MegBA.
CITATION STYLE
Ren, J., Liang, W., Yan, R., Mai, L., Liu, S., & Liu, X. (2022). MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13697 LNCS, pp. 715–731). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19836-6_40
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