---
license: cc-by-nc-4.0
---
# NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
[](https://opensource.org/licenses/MIT)
This repository is the pytorch implementation of our paper:
**NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes**
[__***Muhammad Zubair Irshad***__](https://zubairirshad.com), [Sergey Zakharov](https://zakharos.github.io/), [Katherine Liu](https://www.thekatherineliu.com/), [Vitor Guizilini](https://www.linkedin.com/in/vitorguizilini), [Thomas Kollar](http://www.tkollar.com/site/), [Adrien Gaidon](https://adriengaidon.com/), [Zsolt Kira](https://faculty.cc.gatech.edu/~zk15/), [Rares Ambrus](https://www.tri.global/about-us/dr-rares-ambrus)
International Conference on Computer Vision (ICCV), 2023
[[Project Page](https://zubair-irshad.github.io/projects/neo360.html)] [[arXiv](https://arxiv.org/abs/2308.12967)] [[PDF](https://arxiv.org/pdf/2308.12967.pdf)] [[Video](https://youtu.be/avmylyL_V8c?si=eeTPhl0xJxM3fSF7)]
### Code Coming Soon!
## 📊 Dataset
### NERDS 360 Multi-View dataset for Outdoor Scenes
NeRDS 360: "NeRF for Reconstruction, Decomposition and Scene Synthesis of 360° outdoor scenes” dataset comprising 75 unbounded scenes with full multi-view annotations and diverse scenes for generalizable NeRF training and evaluation.
#### Download the dataset:
* [NERDS360 Training Set](https://tri-ml-public.s3.amazonaws.com/github/neo360/datasets/PDMultiObjv6.tar.gz) - 75 Scenes (19.5 GB)
* [NERDS360 Test Set](https://tri-ml-public.s3.amazonaws.com/github/neo360/datasets/PD_v6_test.tar.gz) - 5 Scenes (2.1 GB)
#### Visualizing the dataset (Coming Soon):
We will release our visualization scripts to generate visualizations like below i.e. plot accumulated pointclouds, multi-view camera annotations etc.
## Citation
If you find this repository or our NERDS 360 dataset useful, please consider citing:
```
@inproceedings{irshad2023neo360,
title={NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes},
author={Muhammad Zubair Irshad and Sergey Zakharov and Katherine Liu and Vitor Guizilini and Thomas Kollar and Adrien Gaidon and Zsolt Kira and Rares Ambrus},
journal={Interntaional Conference on Computer Vision (ICCV)},
year={2023},
url={https://arxiv.org/abs/2308.12967},
}
```