# Graph-Based Parallel Large Scale Structure from Motion

arXiv: https://arxiv.org/abs/1912.10659 · HTML: https://mlsanity.com/paper/1912.10659/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 21 of 21)

- [DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields](https://mlsanity.com/paper/2303.14478/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus](https://mlsanity.com/paper/2405.13943/) (2024)
- [LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs](https://mlsanity.com/paper/2306.05410/) (2023)
- [DReg-NeRF: Deep Registration for Neural Radiance Fields](https://mlsanity.com/paper/2308.09386/) (2023)
- [Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Set](https://mlsanity.com/paper/2206.11499/) (2022)
- [OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering](https://mlsanity.com/paper/2503.16177/) (2025)
- [Distributed Global Structure-from-Motion with a Deep Front-End](https://mlsanity.com/paper/2311.18801/) (2023)
- [TC-SfM: Robust Track-Community-Based Structure-from-Motion](https://mlsanity.com/paper/2206.05866/) (2022)
- [AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion](https://mlsanity.com/paper/2301.12135/) (2023)
- [SCALAR-NeRF: SCAlable LARge-scale Neural Radiance Fields for Scene Reconstruction](https://mlsanity.com/paper/2311.16657/) (2023)
- [Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping](https://mlsanity.com/paper/2308.06147/) (2023)
- [MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry](https://mlsanity.com/paper/2603.02351/) (2026)
- [Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor](https://mlsanity.com/paper/2307.04520/) (2023)
- [DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping](https://mlsanity.com/paper/2402.01134/) (2024)
- [Global-Aware Edge Prioritization for Pose Graph Initialization](https://mlsanity.com/paper/2602.21963/) (2026)
- [EAR-Net: Pursuing End-to-End Absolute Rotations from Multi-View Images](https://mlsanity.com/paper/2310.10051/) (2023)
- [MRASfM: Multi-Camera Reconstruction and Aggregation through Structure-from-Motion in Driving Scenes](https://mlsanity.com/paper/2510.15467/) (2025)
- [Robust Global Structure-from-Motion via View Graph Pruning](https://mlsanity.com/paper/2608.22054/) (2026)
- [HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction](https://mlsanity.com/paper/2609.04718/) (2026)
- [G3AR: Graph-Guided Neural Visual Geometry for Scalable Multi-Sequence Aerial Registration](https://mlsanity.com/paper/2609.16603/) (2026)

## Abstract (arXiv)

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.