Datasets:
Tasks:
Image-to-3D
Modalities:
Image
Formats:
imagefolder
Size:
< 1K
ArXiv:
Tags:
scene-inpainting
License:
| 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}, | |
| } | |
| ``` | |