# Progressive Structure from Motion

arXiv: https://arxiv.org/abs/1803.07349 · HTML: https://mlsanity.com/paper/1803.07349/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging](https://mlsanity.com/paper/2103.08292/) (2021)
- [AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion](https://mlsanity.com/paper/2301.12135/) (2023)
- [ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration](https://mlsanity.com/paper/2503.17093/) (2025)
- [On the Robustness of Multi-View Rotation Averaging](https://mlsanity.com/paper/2102.05454/) (2021)
- [Robust Global Structure-from-Motion via View Graph Pruning](https://mlsanity.com/paper/2608.22054/) (2026)

## Abstract (arXiv)

Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art.