Multi-camera systems are increasingly vital in the environmental perception of autonomous vehicles and robotics. Their physical configuration offers inherent fixed relative pose constraints that benefit Structure-from-Motion (SfM). However, traditional global SfM systems struggle with robustness due to their optimization framework. We propose a novel global motion averaging framework for multi-camera systems, featuring two core components: a decoupled rotation averaging module and a hybrid translation averaging module. Our rotation averaging employs a hierarchical strategy by first estimating relative rotations within rigid camera units and then computing global rigid unit rotations. To enhance the robustness of translation averaging, we incorporate both camera-to-camera and camera-to-point constraints to initialize camera positions and 3D points with a convex distance-based objective function and refine them with an unbiased non-bilinear angle-based objective function. Experiments on large-scale datasets show that our system matches or exceeds incremental SfM accuracy while significantly improving efficiency. Our framework outperforms existing global SfM methods, establishing itself as a robust solution for real-world multi-camera SfM applications. The code is available at https://github.com/3dv-casia/MGSfM/.

Global translation estimation is a highly challenging step in the global structure from motion (SfM) algorithm. Many existing methods rely solely on relative translations, leading to inaccuracies in low parallax scenes and degradation under collinear camera motion. While recent approaches aim to address these issues by incorporating feature tracks into objective functions, they are often sensitive to outliers. In this paper, we first revisit global translation estimation methods with feature tracks and categorize them into explicit and implicit methods. Then, we highlight the superiority of the objective function based on the cross-product distance metric and propose a novel explicit global translation estimation framework that integrates both relative translations and feature tracks as input. To enhance the accuracy of input observations, we re-estimate relative translations with the coplanarity constraint of the epipolar plane and propose a simple yet effective strategy to select reliable feature tracks. Finally, we demonstrate the effectiveness of our approach through experiments on urban image sequences and unordered Internet images, showcasing its superior accuracy and robustness compared to many state-of-the-art techniques.