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[3DV 2026] ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
| Date | Stars |
|---|---|
| 2026-07-24 | 269 |
| 2026-07-25 | 269 |
| 2026-07-28 | 269 |
| 2026-07-30 | 269 |
| 2026-08-06 | 269 |
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<!-- PROJECT LOGO -->
<h1 align="center" style="display: flex; align-items: center; justify-content: center; flex-wrap: wrap;">
<img src="./media/vista_logo.png" alt="logo" width="60" style="margin-right: 10px;">
ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
</h1>
<p align="center">
<a href="https://ganlinzhang.xyz" target="_blank"><strong>Ganlin Zhang<sup>1,2</sup></strong></a>
·
<a href="https://shenhanqian.github.io/" target="_blank"><strong>Shenhan Qian<sup>1,2</sup></strong></a>
·
<a href="https://xiwang1212.github.io/homepage/" target="_blank"><strong>Xi Wang<sup>1,2,3</sup></strong></a>
·
<a href="https://cvg.cit.tum.de/members/cremers" target="_blank"><strong>Daniel Cremers<sup>1,2</sup></strong></a>
</p>
<p align="center">
<strong><sup>1 </sup>TU Munich, <sup>2 </sup>MCML, <sup>3 </sup>ETH Zurich</strong>
<strong><h4 align="center"><a href="https://arxiv.org/abs/2509.01584" target="_blank">Paper</a> | <a href="https://ganlinzhang.xyz/vista-slam/" target="_blank">Project Website</a> | <a href="https://app.rerun.io/version/0.24.1/index.html?url=https://cvg.cit.tum.de/webshare/g/papers/vista_slam/0054.rrd&url=https://cvg.cit.tum.de/webshare/g/papers/vista_slam/vista_slam.rbl&hide_welcome_screen=true&allow_fullscreen=true" target="_blank">
Interactive 3D Visualization
</a></h4></strong>
</strong></p>
### Update
- Evaluation results on additional datasets (TUM-RGBD *freiburg2* and *freiburg3* partitions, Replica, and ScanNet) have been released in the supplementary material of the latest version of the paper on <a href="https://arxiv.org/abs/2509.01584" target="_blank">ArXiv</a>. The evaluation scripts for these datasets have also been added to this repository.
- <a href="#run-with-live-camera">Live camera mode</a> is now supported!
https://github.com/user-attachments/assets/ac8732d0-4efd-4814-a168-01eae799df7c
---
<p align="center">
<img src="./media/teaser.gif" alt="teaser_img" width="100%">
<strong>ViSTA-SLAM</strong> is a real-time monocular dense SLAM pipeline that combines a Symmetric Two-view Association (STA) frontend with Sim(3) pose graph optimization and loop closure, enabling accurate camera trajectories and high-quality 3D scene reconstruction from RGB inputs.
</p>
<!-- TABLE OF CONTENTS -->
<details open="open" style='padding: 10px; border-radius:5px 30px 30px 5px; border-style: solid; border-width: 1px;'>
<summary>Table of Contents</summary>
<ol>
<li>
<a href="#installation">Installation</a>
</li>
<li>
<a href="#run">Run</a>
</li>
<li>
<a href="#visualization">Visualization</a>
</li>
<li>
<a href="#evaluation">Evaluation</a>
</li>
<li>
<a href="#acknowledgement">Acknowledgement</a>
</li>
<li>
<a href="#licensing">Licensing</a>
</li>
<li>
<a href="#citation">Citation and Contact</a>
</li>
</ol>
</details>
## Installation
1. Clone the repo
```bash
git clone https://github.com/zhangganlin/vista-slam.git
cd vista-slam
git submodule update --init --recursive
```
2. Creating a new conda environment and install python dependencies.
```bash
conda create -n vista python=3.11 cmake=3.31.2 gcc_linux-64=11.4.0 gxx_linux-64=11.4.0 libopencv=4.12.0 -c conda-forge
conda activate vista
# install torch according to your cuda version
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 xformers --index-url https://download.pytorch.org/whl/cu121
# install python binding of DBoW3, for loop detection
cd DBoW3Py
pip install --no-build-isolation .
cd ..
# install other python dependencies
pip install -r requirements.txt
# optional: accelerate with CUDA-based RoPE
cd vista_slam/sta_model/pos_embed/curope
python setup.py build_ext --inplace
cd ../../../../
```
3. Download pretrained model.
Download the pretained models from HuggingFace (https://huggingface.co/zhangganlin/vista_slam/tree/mExcerpt of 9,041 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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