An Efficient and Robust Hybrid SfM Method for Large-Scale Scenes

The structure from motion (SfM) method has achieved great success in 3D sparse reconstruction, but it still faces serious challenges in large-scale scenes. Existing hybrid SfM methods usually do not fully consider the compactness between images and the connectivity between subclusters, resulting in a loose spatial distribution of images within subclusters, unbalanced connectivity between subclusters, and poor robustness in the merging stage. In this paper, an efficient and robust hybrid SfM method is proposed. First, the multifactor joint scene partition measure and the preassignment balanced image expansion algorithm among subclusters are constructed, which effectively solves the loose spatial distribution of images in subclusters problem and improves the degree of connection among subclusters. Second, the global GlobalACSfM method is used to complete the local sparse reconstruction of the subclusters under the cluster parallel framework. Then, a decentralized dynamic merging rule considering the connectivity of subclusters is proposed to realize robust merging among subclusters. Finally, public datasets and oblique photography datasets are used for experimental verification. The results show that the method proposed in this paper is superior to the state-of-the-art methods in terms of accuracy and robustness and has good feasibility and advancement prospects.

Automated summary

In this paper, an efficient and robust hybrid SfM method is proposed to address the challenges of loose spatial distribution of images within subclusters and unbalanced connectivity between subclusters. The method employs a multifactor joint scene partition measure and a preassignment balanced image expansion algorithm to improve the degree of connection among subclusters. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness.