Graph-Based Parallel Large Scale Structure from Motion

TL;DR

This paper presents a graph-based divide-and-conquer approach for large-scale Structure from Motion, improving accuracy and efficiency through clustering, scene expansion, and error prevention techniques.

Contribution

It introduces a novel graph-based framework that enhances large-scale SfM by combining clustering, scene expansion, and a minimum height tree for robust reconstruction.

Findings

Outperforms state-of-the-art in accuracy

Achieves higher efficiency in large-scale scenes

Provides open-source implementation

Abstract

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.