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[CVPR'22] NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
| Date | Stars |
|---|---|
| 2026-07-24 | 1592 |
| 2026-07-25 | 1592 |
| 2026-07-28 | 1592 |
| 2026-07-30 | 1592 |
| 2026-08-06 | 1592 |
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<!-- PROJECT LOGO -->
<p align="center">
<h1 align="center"><img src="media/logo.png" width="60">NICE-SLAM: Neural Implicit Scalable Encoding for SLAM</h1>
<p align="center">
<a href="https://zzh2000.github.io"><strong>Zihan Zhu*</strong></a>
·
<a href="https://pengsongyou.github.io"><strong>Songyou Peng*</strong></a>
·
<a href="http://people.inf.ethz.ch/vlarsson/"><strong>Viktor Larsson</strong></a>
·
<a href="http://www.cad.zju.edu.cn/home/weiweixu/weiweixu_en.htm"><strong>Weiwei Xu</strong></a>
·
<a href="http://www.cad.zju.edu.cn/home/bao/"><strong>Hujun Bao</strong></a>
<br>
<a href="https://zhpcui.github.io/"><strong>Zhaopeng Cui</strong></a>
·
<a href="http://people.inf.ethz.ch/moswald/"><strong>Martin R. Oswald</strong></a>
·
<a href="https://people.inf.ethz.ch/pomarc/"><strong>Marc Pollefeys</strong></a>
</p>
<p align="center"><strong>(* Equal Contribution)</strong></p>
<h2 align="center">CVPR 2022</h2>
<h3 align="center"><a href="https://arxiv.org/abs/2112.12130">Paper</a> | <a href="https://youtu.be/V5hYTz5os0M">Video</a> | <a href="https://pengsongyou.github.io/nice-slam">Project Page</a></h3>
<div align="center"></div>
</p>
<p align="center">
<a href="">
<img src="./media/apartment.gif" alt="Logo" width="80%">
</a>
</p>
<p align="center">
NICE-SLAM produces accurate dense geometry and camera tracking on large-scale indoor scenes.
</p>
<p align="center">
(The black / red lines are the ground truth / predicted camera trajectory)
</p>
<br>
<br>
<!-- 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="#visualizing-nice-slam-results">Visualization</a>
</li>
<li>
<a href="#demo">Demo</a>
</li>
<li>
<a href="#run">Run</a>
</li>
<li>
<a href="#imap">iMAP*</a>
</li>
<li>
<a href="#evaluation">Evaluation</a>
</li>
<li>
<a href="#acknowledgement">Acknowledgement</a>
</li>
<li>
<a href="#citation">Citation</a>
</li>
<li>
<a href="#contact">Contact</a>
</li>
</ol>
</details>
## Installation
First you have to make sure that you have all dependencies in place.
The simplest way to do so, is to use [anaconda](https://www.anaconda.com/).
You can create an anaconda environment called `nice-slam`. For linux, you need to install **libopenexr-dev** before creating the environment.
```bash
sudo apt-get install libopenexr-dev
conda env create -f environment.yaml
conda activate nice-slam
```
## Visualizing NICE-SLAM Results
We provide the results of NICE-SLAM ready for download. You can run our **interactive visualizer** as following.
### Self-captured Apartment
To visualize our results on the self-captured apartment, as shown in the teaser:
```bash
bash scripts/download_vis_apartment.sh
python visualizer.py configs/Apartment/apartment.yaml --output output/vis/Apartment
```
**Note for users from China:** If you encounter slow speed in downloading, check in all the `scripts/download_*.sh` scripts, where we also provide the 和彩云 links for you to download manually.
### ScanNet
```bash
bash scripts/download_vis_scene0000.sh
python visualizer.py configs/ScanNet/scene0000.yaml --output output/vis/scannet/scans/scene0000_00
```
<p align="center">
<img src="./media/scannet.gif" width="60%" />
</p>
You can find the results of NICE-SLAM on other scenes in ScanNet [here](https://cvg-data.inf.ethz.ch/nice-slam/vis/scannet/).
### Replica
```bash
bash scripts/download_vis_room1.sh
python visualizer.py configs/Replica/room1.yaml --output output/vis/Replica/room1
```
<p align="center">
<img src="./media/replica.gif" width="70%" />
</p
You can find the results of NICE-SLAM on other scenes in Replica [here](https://cvg-data.inExcerpt of 16,615 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:087ac03709a64e33, topic:slam, name:slam, desc:slam
matched fp:087ac03709a64e33, topic:deep-learning
matched fp:087ac03709a64e33, topic:3d-reconstruction