MRASfM: Multi-Camera Reconstruction and Aggregation through Structure-from-Motion in Driving Scenes

• URL: http://arxiv.org/abs/2510.15467v1
• Date: Fri, 17 Oct 2025 09:20:59 GMT
• Title: MRASfM: Multi-Camera Reconstruction and Aggregation through Structure-from-Motion in Driving Scenes
• Authors: Lingfeng Xuan, Chang Nie, Yiqing Xu, Zhe Liu, Yanzi Miao, Hesheng Wang,
• Abstract summary: We propose a Multi-camera Reconstruction and Aggregation Structure-from-Motion (MRASfM) framework specifically designed for driving scenes.<n>MRASfM enhances the reliability of camera pose estimation by leveraging the fixed spatial relationships within the multi-camera system during the registration process.<n>Treating the multi-camera set as a single unit in Bundle Adjustment (BA) helps reduce optimization variables to boost efficiency.
• Score: 20.625799448587703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structure from Motion (SfM) estimates camera poses and reconstructs point clouds, forming a foundation for various tasks. However, applying SfM to driving scenes captured by multi-camera systems presents significant difficulties, including unreliable pose estimation, excessive outliers in road surface reconstruction, and low reconstruction efficiency. To address these limitations, we propose a Multi-camera Reconstruction and Aggregation Structure-from-Motion (MRASfM) framework specifically designed for driving scenes. MRASfM enhances the reliability of camera pose estimation by leveraging the fixed spatial relationships within the multi-camera system during the registration process. To improve the quality of road surface reconstruction, our framework employs a plane model to effectively remove erroneous points from the triangulated road surface. Moreover, treating the multi-camera set as a single unit in Bundle Adjustment (BA) helps reduce optimization variables to boost efficiency. In addition, MRASfM achieves multi-scene aggregation through scene association and assembly modules in a coarse-to-fine fashion. We deployed multi-camera systems on actual vehicles to validate the generalizability of MRASfM across various scenes and its robustness in challenging conditions through real-world applications. Furthermore, large-scale validation results on public datasets show the state-of-the-art performance of MRASfM, achieving 0.124 absolute pose error on the nuScenes dataset.

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