Abstract:

With the advancement of spatial intelligence applications such as mixed reality and autonomous driving, demand for high-precision 3D reconstruction technologies in large-scale scenes has continued to increase. In such scenarios, traditional Structure from Motion (SfM) methods are computationally inefficient and prone to severe error accumulation, while existing divide-and-conquer reconstruction strategies fail to mitigate the accuracy loss caused by cumulative errors. To address these issues, a covisibility-guided hierarchical partitioning SfM method for large-scale scenes was proposed. At the global level, keyframes were selected using a greedy strategy, and GPS and visual information were fused via an adaptive covisibility weighting mechanism to rapidly generate a globally consistent sparse scene skeleton. At the local level, the scene was adaptively partitioned into several visually high-cohesion image communities based on covisibility relationships for local reconstruction. In the model fusion stage, a fusion strategy based on bidirectional consistency checking was designed to eliminate erroneous relative transformations, and all local models were aligned under global-skeleton constraints through joint optimization of multi-source transformations. Experimental results indicated that the proposed method significantly improved the efficiency and robustness of large-scale scene reconstruction and effectively suppressed cumulative errors.

Key words: 3D reconstruction, structure from motion, hierarchical reconstruction, global positioning system fusion, model fusion