Graph-Based Parallel Large Scale Structure from Motion
This work addresses scalability issues in 3D reconstruction for computer vision applications, representing an incremental improvement through novel graph-based optimizations.
The paper tackles the challenge of large-scale Structure from Motion (SfM) by framing it as a graph problem and using a divide-and-conquer approach with clustering, expansion, and tree-based transformations, achieving superior accuracy and efficiency over state-of-the-art methods on various datasets.
While Structure from Motion (SfM) achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. In this work, large scale SfM is deemed as a graph problem, and we tackle it in a divide-and-conquer manner. Firstly, the images clustering algorithm divides images into clusters with strong connectivity, leading to robust local reconstructions. Then followed with an image expansion step, the connection and completeness of scenes are enhanced by expanding along with a maximum spanning tree. After local reconstructions, we construct a minimum spanning tree (MinST) to find accurate similarity transformations. Then the MinST is transformed into a Minimum Height Tree (MHT) to find a proper anchor node and is further utilized to prevent error accumulation. When evaluated on different kinds of datasets, our approach shows superiority over the state-of-the-art in accuracy and efficiency. Our algorithm is open-sourced at https://github.com/AIBluefisher/GraphSfM.