# EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images

arXiv: https://arxiv.org/abs/2302.10544 · HTML: https://mlsanity.com/paper/2302.10544/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 74 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 22 of 22)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose](https://mlsanity.com/paper/1803.02276/) (2018)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency](https://mlsanity.com/paper/1809.01649/) (2018)
- [SfM-Net: Learning of Structure and Motion from Video](https://mlsanity.com/paper/1704.07804/) (2017)
- [DeepSFM: Structure From Motion Via Deep Bundle Adjustment](https://mlsanity.com/paper/1912.09697/) (2019)
- [Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation](https://mlsanity.com/paper/2010.14851/) (2020)
- [Extreme Rotation Estimation using Dense Correlation Volumes](https://mlsanity.com/paper/2104.13530/) (2021)
- [Robust Camera Location Estimation by Convex Programming](https://mlsanity.com/paper/1412.0165/) (2014)
- [Deep Two-View Structure-from-Motion Revisited](https://mlsanity.com/paper/2104.00556/) (2021)
- [Visual-Inertial Monocular SLAM with Map Reuse](https://mlsanity.com/paper/1610.05949/) (2016)
- [A Pose-only Solution to Visual Reconstruction and Navigation](https://mlsanity.com/paper/2103.01530/) (2021)
- [Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach](https://mlsanity.com/paper/2101.09116/) (2021)
- [Depth-Guided Sparse Structure-from-Motion for Movies and TV Shows](https://mlsanity.com/paper/2204.02509/) (2022)
- [ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion](https://mlsanity.com/paper/1510.08012/) (2015)
- [Complete Scene Reconstruction by Merging Images and Laser Scans](https://mlsanity.com/paper/1904.09568/) (2019)

## Built on by (most cited first, 9 of 9)

- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation](https://mlsanity.com/paper/2501.05763/) (2025)
- [A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding](https://mlsanity.com/paper/2411.01893/) (2024)
- [Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis](https://mlsanity.com/paper/2304.05218/) (2023)
- [Co-VisiON: Co-Visibility ReasONing on Sparse Image Sets of Indoor Scenes](https://mlsanity.com/paper/2506.16805/) (2025)
- [SceneGlue: Scene-Aware Transformer for Feature Matching without Scene-Level Annotation](https://mlsanity.com/paper/2604.13941/) (2026)
- [Metamon-GS: Enhancing Representability with Variance-Guided Densification and Light Encoding](https://mlsanity.com/paper/2504.14460/) (2025)
- [Image-Plane Geometric Decoding for View-Invariant Indoor Scene Reconstruction](https://mlsanity.com/paper/2509.25744/) (2025)
- [HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction](https://mlsanity.com/paper/2609.04718/) (2026)

## Abstract (arXiv)

Structure-from-Motion is a technology used to obtain scene structure through image collection, which is a fundamental problem in computer vision. For unordered Internet images, SfM is very slow due to the lack of prior knowledge about image overlap. For sequential images, knowing the large overlap between adjacent frames, SfM can adopt a variety of acceleration strategies, which are only applicable to sequential data. To further improve the reconstruction efficiency and break the gap of strategies between these two kinds of data, this paper presents an efficient covisibility-based incremental SfM. Different from previous methods, we exploit covisibility and registration dependency to describe the image connection which is suitable to any kind of data. Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two. Experiments on the unordered images and mixed data verify the effectiveness of the proposed method, which is three times faster than the state of the art on feature matching, and an order of magnitude faster on reconstruction without sacrificing the accuracy. The source code is publicly available at https://github.com/openxrlab/xrsfm