---
tags:
- scene-inpainting
task_categories:
- image-to-3d
license: apache-2.0
---

## Introduction
We present the ***360-USID dataset*** in [AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360° Unbounded Scene Inpainting](https://arxiv.org/abs/2502.05176).

We introduce first 360° Unbounded Scenes Inpaint-
ing Dataset (360-USID), consisting of seven scenes with
training views (RGB images and object masks), novel test-
ing views (inpainting ground truth), camera poses, and a reference view
(without objects) for evaluating with other reference-based
methods.

We further collect and process some 360 unbounded scenes in ***Other-360*** for qualitative comparison. These scenes are from [Mip-NeRF 360](https://jonbarron.info/mipnerf360/), [NeRF-Real-360](https://www.matthewtancik.com/nerf), [Instruct-NeRF2NeRF](https://instruct-nerf2nerf.github.io/).

## Download
```
huggingface-cli login
huggingface-cli download kkennethwu/360-USID --repo-type dataset --local-dir ./data --resume-download --quiet --max-workers 32
```


## Data tree
#### ***360-USID***:
```
360-USID/{scene}
    ├── images
    ├── object_masks
    ├── reference
    ├── sparse
    ├── test_images # gt images with objects removed.
    ├── test_object_masks # object masks in testing views rendered by Masked-GS, used for evaluation.
    └── unseen_masks
```
#### ***Other-360***:
```
Other-360/{scene}
    ├── images
    ├── object_masks
    ├── reference
    ├── sparse
    └── unseen_masks
```

## Citation
If you find this dataset helpful, please cite this paper and give us a ⭐️.
```
@misc{wu2025aurafusion360augmentedunseenregion,
      title={AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360{\deg} Unbounded Scene Inpainting}, 
      author={Chung-Ho Wu and Yang-Jung Chen and Ying-Huan Chen and Jie-Ying Lee and Bo-Hsu Ke and Chun-Wei Tuan Mu and Yi-Chuan Huang and Chin-Yang Lin and Min-Hung Chen and Yen-Yu Lin and Yu-Lun Liu},
      year={2025},
      eprint={2502.05176},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.05176}, 
}
```