# xrsfm

**Repository Path**: OpenXRLab/xrsfm

## Basic Information

- **Project Name**: xrsfm
- **Description**: OpenXRLab Structure-from-Motion Toolbox and Benchmark
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2022-12-18
- **Last Updated**: 2025-09-16

## Categories & Tags

**Categories**: vrar

**Tags**: None

## README

# XRSfM

<div align="left">

[![actions](https://github.com/openxrlab/xrsfm/workflows/build/badge.svg)](https://github.com/openxrlab/xrsfm/actions)
[![LICENSE](https://img.shields.io/github/license/openxrlab/xrsfm)](https://github.com/openxrlab/xrsfm/blob/main/LICENSE)

</div>

## Introduction

[English](README.md) | [简体中文]

XRSfM 是一个开源的运动恢复结构代码仓库，它是[OpenXRLab](https://openxrlab.org.cn/)项目的一部分.
关于XRSfM更详细的介绍放在[introduction.md](docs/en/introduction.md).

## Citation

如果你的研究过程中使用了该仓库，请考虑引用:

```bibtex
@misc{xrsfm,
    title={OpenXRLab Structure-from-Motion Toolbox and Benchmark},
    author={XRSfM Contributors},
    howpublished = {\url{https://github.com/openxrlab/xrsfm}},
    year={2022}
}
```

如果你的研究过程中使用了该仓库中的基于共视的匹配方法，请考虑引用:

```bibtex
@inproceedings{ye2020efficient,
  title={Efficient covisibility-based image matching for large-scale sfm},
  author={Ye, Zhichao and Zhang, Guofeng and Bao, Hujun},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2020}
}
```
```bibtex
@misc{ye2023ecsfm,
  title = {EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images},
  author = {Ye, Zhichao and Bao, Chong and Zhou, Xin and Liu, Haomin and Bao, Hujun and Zhang, Guofeng},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2023},
  publisher={IEEE}
}
```

## Getting Started

1.参考[installation.md](docs/zh/installation.md)进行安装编译.

2.下载提供的[测试数据](https://openxrlab-share-mainland.oss-cn-hangzhou.aliyuncs.com/xrsfm/test_data.zip?versionId=CAEQQBiBgMCi_6mllxgiIGI2ZjM1YjE1NjBmNTRmYjc5NzZlMzZkNWY1ZTk1YWFj) 或者按照相同格式准备你自己的数据.

3.运行以下脚本进行重建:
```
python3 ./scripts/run_test_data.py --workspace_path ${workspace_path}$
```
更多细节请查看[tutorial.md](docs/zh/tutorial.md)

## Build ARDemo

除了重建功能, OpenXRLab 项目还提供了定位功能。
你可以构建自己的端云定位ARDemo，更多的信息请查看[ARDemo](http://doc.openxrlab.org.cn/openxrlab_document/ARDemo/ARdemo.html#).

## License
本代码库的许可证是[Apache-2.0](LICENSE)。请注意，本许可证仅适用于我们库中的代码，这些代码的依赖项是独立的，并单独许可。我们十分感谢这些依赖项的贡献者。
本项目所使用依赖项的内容可能会影响我们代码库的许可证，一些依赖方法带有[附加许可证](docs/en/additional_licenses.md)。


## Acknowledgement

XRSfM是一个开源项目，由学术界和行业的研究人员和工程师共同参与。
我们感谢所有实现其方法或添加新功能的贡献者，以及提供宝贵反馈的用户。

## Projects in OpenXRLab

- [XRPrimer](https://github.com/openxrlab/xrprimer): OpenXRLab foundational library for XR-related algorithms.
- [XRSLAM](https://github.com/openxrlab/xrslam): OpenXRLab Visual-inertial SLAM Toolbox and Benchmark.
- [XRSfM](https://github.com/openxrlab/xrsfm): OpenXRLab Structure-from-Motion Toolbox and Benchmark.
- [XRLocalization](https://github.com/openxrlab/xrlocalization): OpenXRLab Visual Localization Toolbox and Server.
- [XRMoCap](https://github.com/openxrlab/xrmocap): OpenXRLab Multi-view Motion Capture Toolbox and Benchmark.
- [XRMoGen](https://github.com/openxrlab/xrmogen): OpenXRLab Human Motion Generation Toolbox and Benchmark.
- [XRNeRF](https://github.com/openxrlab/xrnerf): OpenXRLab Neural Radiance Field (NeRF) Toolbox and Benchmark.