MGSfM: Multi-Camera Geometry Driven Global Structure-from-Motion
- URL: http://arxiv.org/abs/2507.03306v1
- Date: Fri, 04 Jul 2025 05:25:00 GMT
- Title: MGSfM: Multi-Camera Geometry Driven Global Structure-from-Motion
- Authors: Peilin Tao, Hainan Cui, Diantao Tu, Shuhan Shen,
- Abstract summary: We propose a novel global motion averaging framework for multi-camera systems.<n>Our system matches or exceeds incremental SfM accuracy while significantly improving efficiency.
- Score: 13.24058110580706
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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/.
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