Graph-based parallel large scale structure from motion

While Structure from Motion achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. Incremental SfM approaches are robust to outliers, but are limited by low efficiency and easy suffer from drift problem. Though Global SfM methods are more efficient than incremental approaches, they are sensitive to outliers, and would also meet memory limitation and time bottleneck. In this work, large scale SfM is deemed as a graph problem, where graph are respectively constructed in image clustering step and local reconstructions merging step. By leveraging the graph structure, we are able to handle large scale dataset in divide-and-conquer manner. Firstly, images are modelled as graph nodes, with edges are retrieved from geometric information after feature matching. Then images are divided into independent clusters by a image clustering algorithm, and followed by a subgraph expansion step, the connection and completeness of scenes are enhanced by walking along a maximum spanning tree, which is utilized to construct overlapping images between clusters. Secondly, Image clusters are distributed into servers to execute SfM in parallel mode. Thirdly, after local reconstructions complete, we construct a minimum spanning tree to find accurate similarity transformations. Then the minimum spanning tree is transformed into a Minimum Height Tree to find a proper anchor node, and is further utilized to prevent error accumulation. We evaluate our approach on various kinds of datasets and our approach shows superiority over the state-of-the-art in accuracy and efficiency. Our algorithm is open-sourced in https://github.com/AIBluefisher/GraphSfM. (C) 2020 Elsevier Ltd. All rights reserved.