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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.
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields. https://arxiv.org/abs/2210.13641 + Sigma-Fusion: Probabilistic Volumetric Fusion for Dense Monocular SLAM https://arxiv.org/abs/2210.01276
| Date | Stars |
|---|---|
| 2026-07-24 | 1267 |
| 2026-07-25 | 1267 |
| 2026-07-28 | 1267 |
| 2026-07-30 | 1267 |
| 2026-08-06 | 1267 |
Today
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Momentum
0.0
growth rate 0.00%/day
<div align="center">
<a href="http://mit.edu/sparklab/">
<img align="left" src="./media/sparklab_logo.png" width="80" alt="sparklab">
</a>
<a href="https://marinerobotics.mit.edu/">
<img align="center" src="./media/mrg_logo.png" width="150" alt="kimera">
</a>
<a href="https://www.mit.edu/~arosinol/">
<img align="right" src="./media/mit.png" width="100" alt="mit">
</a>
</div>
<p align="center">
<div align="center">
<h1>NeRF-SLAM</h1>
</div>
<h1 align="center">
Real-Time Dense Monocular SLAM with Neural Radiance Fields</h1>
<p align="center">
<a href="https://www.mit.edu/~arosinol/"><strong>Antoni Rosinol</strong></a>
·
<a href="https://marinerobotics.mit.edu/"><strong>John J. Leonard</strong></a>
·
<a href="https://web.mit.edu/sparklab/"><strong>Luca Carlone</strong></a>
</p>
<!-- <h2 align="center">In Review</h2> -->
<h3 align="center">
<a href="https://arxiv.org/abs/2210.13641">Paper</a> |
<a href="https://www.youtube.com/watch?v=-6ufRJugcEU">Video</a> |
<!-- <a href="">Project Page</a>-->
</h3>
<div align="center"></div>
</p>
<p align="center">
<a href="#">
<img src="./media/intro.gif" alt="" width="90%">
</a>
</p>
<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="#install">Install</a>
</li>
<li>
<a href="#download-sample-data">Download Datasets</a>
</li>
<li>
<a href="#run">Run</a>
</li>
<li>
<a href="#citation">Citation</a>
</li>
<li>
<a href="#license">License</a>
</li>
<li>
<a href="#acknowledgments">Acknowledgments</a>
</li>
<li>
<a href="#contact">Contact</a>
</li>
</ol>
</details>
## Install
Clone repo with submodules:
```
git clone https://github.com/ToniRV/NeRF-SLAM.git --recurse-submodules
git submodule update --init --recursive
```
From this point on, use a virtual environment...
Install torch (see [here](https://pytorch.org/get-started/previous-versions) for other versions):
```
# CUDA 11.3
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
```
Pip install requirements:
```
pip install -r requirements.txt
pip install -r ./thirdparty/gtsam/python/requirements.txt
```
Compile ngp (you need cmake>3.22):
```
cmake ./thirdparty/instant-ngp -B build_ngp
cmake --build build_ngp --config RelWithDebInfo -j
```
Compile gtsam and enable the python wrapper:
```
cmake ./thirdparty/gtsam -DGTSAM_BUILD_PYTHON=1 -B build_gtsam
cmake --build build_gtsam --config RelWithDebInfo -j
cd build_gtsam
make python-install
```
Install:
```
python setup.py install
```
## Download Sample Data
This will just download one of the replica scenes:
```
./scripts/download_replica_sample.bash
```
## Run
```
python ./examples/slam_demo.py --dataset_dir=./datasets/Replica/office0 --dataset_name=nerf --buffer=100 --slam --parallel_run --img_stride=2 --fusion='nerf' --multi_gpu --gui
```
This repo also implements [Sigma-Fusion](https://arxiv.org/abs/2210.01276): just change `--fusion='sigma'` to run that.
## FAQ
### GPU Memory
This is a GPU memory intensive pipeline, to monitor your GPU usage, I'd recommend to use `nvitop`.
Install nvitop in a local env:
```
pip3 install --upgrade nvitop
```
Keep it running on a terminal, and monitor GPU memory usage:
```
nvitop --monitor
```
If you consistently see "out-of-memory" errors, you may either need to change parameters or buy better GPUs :).
The memory consuming parts of this pipeline are:
- Frame to frame correlation volumes (but can be avoided using on-the-fly correlation computation).
- Volumetric rendering (intrinsically memory intensive, tricks exist, but ultimately we need to move to light fields or some better representation (OpenVDB?)).
### Installation issues
1. Gtsam not working: check that the python wrExcerpt of 6,171 characters
Read on GitHub
Toni Rosinol · @stackai · United States
24
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:84becee473a93bde, topic:slam, name:slam, desc:slam
matched fp:84becee473a93bde, topic:nerf, desc:neural radiance, readme:neural radiance