Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[CVPR 2025]MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM
| Date | Stars |
|---|---|
| 2026-07-24 | 337 |
| 2026-07-25 | 337 |
| 2026-07-28 | 337 |
| 2026-07-30 | 337 |
| 2026-07-31 | 337 |
| 2026-08-06 | 337 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center">
<h1 align="center">MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM</h1>
<h3 align="center">CVPR 2025</h3>
<p align="center">
<a href="https://vladimiryugay.github.io/"><strong>Vladimir Yugay</strong></a>
·
<a href="https://staff.fnwi.uva.nl/th.gevers/"><strong>Theo Gevers</strong></a>
·
<a href="https://oswaldm.github.io/"><strong>Martin Oswald</strong></a>
</p>
<h3 align="center"><a href="https://vladimiryugay.github.io/magic_slam">Project Page</a></h3>
<div align="center"></div>
</p>
<p align="center">
<a href="">
<img src="./assets/magic_slam_demo.gif" width="90%">
</a>
</p>
## ⚙️ Setting Things Up
Clone the repo:
```
git clone https://github.com/VladimirYugay/MAGiC-SLAM.git
```
We tested the installation with ```gcc``` and ```g++``` of versions 10, 11 and 12. Also, make sure that ```nvcc --version``` matches ```nvidia-smi``` version.
Run the following commands to set up the environment
```
conda create -n magic-slam python=3.11
conda activate magic-slam
```
Install pytorch:
```
# CUDA 11.8
conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=11.8 -c pytorch -c nvidia
# CUDA 12.1
conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.1 -c pytorch -c nvidia
# CUDA 12.4
conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
```
Install other dependencies:
```
conda install -c conda-forge faiss-gpu=1.8.0
pip install -r requirements.txt
```
In case you are on a local machine and want to run visualization script run:
```
pip install rerun-sdk
```
We tested our code on RTX3090 and RTX A6000 GPUs with Ubuntu22 and CentOS7.5 respectively.
## 🔨 Running MAGiC-SLAM
Here we elaborate on how to load the necessary data, configure Gaussian-SLAM for your use-case, debug it, and how to reproduce the results mentioned in the paper.
<details>
<summary><b>Getting the Data</b></summary>
We tested our code on ReplicaMultiagent and AriaMultiagent datasets. Make sure to install git lfs and hugging face cli before proceeding.
<br>
<br>
**ReplicaMultiagent** was created by CP-SLAM authors. However, it is a bit tricky to find it on the web. Therefore, we uploaded it to HF datasets for easier access. Install git lfs and download it by running: <br>
<code>git lfs install</code> <br>
<code>git clone https://huggingface.co/datasets/voviktyl/ReplicaMultiagent</code> <br>
**AriaMultiagent** consists of clips from <a href="https://www.projectaria.com/datasets/adt/">AriaDigitalTwin</a>.
You can download already processed data with the command: <br>
<code>git clone https://huggingface.co/datasets/voviktyl/AriaMultiagent</code> <br>
Alternatively, you can create the data yourself.
First, download raw AriaDigitalTwin sequences following instructions <a href="https://www.projectaria.com/datasets/adt/">here</a>. We recommend using the data tools described <a href="https://facebookresearch.github.io/projectaria_tools/docs/open_datasets/aria_digital_twin_dataset/dataset_download">here</a>. You can find the names of the raw videos we used in <code>prepare_aria_room_data.py</code> in <code>DATASET_DICT</code>. After you have downloaded the raw videos, process them using <code>prepare_aria_room_data.py</code>. This will create a folder with the format matching our repository.
</details>
<details>
<summary><b>Running the code</b></summary>
Ideally, our system needs <code>n + 1</code> GPUs where <code>n</code> is the nubmer of agents. If you want to run the system for debugging purposes set <code>multi_gpu: False</code> and <code>agent_ids: [0]</code>. In this way, you will run a single agent and use the same GPU for the server and the agent. Start the system by running:
```
python run_slam.py configs/<dataset_name>/<config_name> --input_path <path_to_the_scene> --output_path <output_path>
```
For example:
```
python run_slam.py configs/AriaMultiagent/room0.yaml --input_path <path_toExcerpt of 6,002 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cec4a072b7523d7f, topic:computer-vision, topic:gaussian-splatting
matched fp:cec4a072b7523d7f, topic:slam, name:slam, desc:slam