MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment

TL;DR

MegBA is a GPU-based distributed library that efficiently handles large-scale bundle adjustment problems by partitioning tasks and leveraging parallel GPU computation, significantly outperforming existing solutions.

Contribution

Introduces MegBA, a novel GPU-based distributed BA library that automatically partitions large problems and uses parallel solvers for high efficiency and scalability.

Findings

MegBA outperforms Ceres by 41.45 times in large-scale benchmarks.

MegBA outperforms RootBA by 64.576 times.

MegBA outperforms DeepLM by 6.769 times.

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$\times$), RootBA (64.576$\times$) and DeepLM (6.769$\times$) in several large-scale BA benchmarks. The code of MegBA is available at https://github.com/MegviiRobot/MegBA.