Datasets:
Tasks:
Image Segmentation
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
File size: 4,239 Bytes
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annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
pretty_name: 360BEV
tags:
- panoramic
- bird-eye-view
- semantic-mapping
- indoor-mapping
- 360-camera
- wacv-2024
- stanford2d3d
- matterport3d
---
# 360BEV: Panoramic Semantic Mapping for Indoor Bird’s-Eye View
[IEEE/CVF WACV 2024] Official Dataset Repository for the **360BEV** benchmark.
- **Project Page:** https://jamycheung.github.io/360BEV.html
- **GitHub Repository:** https://github.com/InSAI-Lab/360BEV
- **Paper:** [arXiv:2303.11910](https://arxiv.org/abs/2303.11910)
- **Model Checkpoints:** [InSAI-Lab/360BEV](https://huggingface.co/InSAI-Lab/360BEV)
- **Collection:** [360BEV & 360Mapper (WACV 2024)](https://huggingface.co/collections/InSAI-Lab/360bev-and-360mapper-wacv-2024-6ad1bdcdeb1718eb4753c447)
---
## Dataset Description
In this work, mapping from 360° panoramas to BEV semantics, the **360BEV** task, is established for the first time to achieve holistic representations of indoor scenes in a top-down view. Instead of relying on narrow-FoV image sequences, a panoramic image with depth information is sufficient to generate a holistic BEV semantic map.
To benchmark 360BEV, we present two indoor datasets:
1. **360BEV-Stanford (Stanford-360):** Built on Stanford2D3D, covering 6 indoor areas with egocentric panoramic images, 13 semantic classes, and allocentric top-down BEV semantic maps.
2. **360BEV-Matterport (Matterport-360):** Built on Matterport3D, covering 86 buildings and 2,030 rooms with 20 semantic categories.
3. **360FV-Matterport:** Front-view RGB, depth, and semantic segmentation panorama sequences for Matterport3D.
## Dataset Statistics
| Dataset | Split | Scene | Room | Frame | Category |
| :--- | :---: | :---: | :---: | :---: | :---: |
| **360BEV-Stanford** | Train | 5 | 215 | 1,040 | 13 |
| | Val | 1 | 55 | 373 | 13 |
| | **Total** | **6** | **270** | **1,413** | **13** |
| **360BEV-Matterport** | Train | 61 | -- | 7,829 | 20 |
| | Val | 7 | -- | 772 | 20 |
| | Test | 18 | -- | 2,014 | 20 |
| | **Total** | **86** | **2,030** | **10,615** | **20** |
## Dataset Structure
After unpacking the archives into the `data/` directory:
```text
data/
├── Stanford2D3D
│ └── area_[1|2|3|4|5a|5b|6]
│ ├── rgb/*.png
│ └── semantic/*.png
│
├── 360BEV-Stanford
│ ├── bev/
│ │ └── *.h5
│ ├── train/
│ │ └── *.png
│ └── val/
│ └── *.png
│
├── 360BEV-Matterport
│ ├── bev/
│ │ └── *.h5
│ ├── train/
│ │ └── *.png
│ ├── val/
│ │ └── *.png
│ └── test/
│ └── *.png
│
└── 360FV-Matterport
├── 17DRP5sb8fy/
│ ├── depth/*.png
│ ├── rgb/*.png
│ └── semantic/*.png
└── ...
```
## Quick Start & Download
### Using Hugging Face CLI (`hf`)
```bash
# Download entire dataset repository
hf download InSAI-Lab/360BEV --repo-type dataset --local-dir ./data
# Or download individual archives
hf download InSAI-Lab/360BEV 360BEV-Stanford.tar.gz --repo-type dataset --local-dir ./data
hf download InSAI-Lab/360BEV 360BEV-Matterport.tar.gz --repo-type dataset --local-dir ./data
hf download InSAI-Lab/360BEV 360FV-Matterport.tar.gz --repo-type dataset --local-dir ./data
```
### Unpacking Archives
```bash
cd ./data
tar -xzf 360BEV-Stanford.tar.gz
tar -xzf 360BEV-Matterport.tar.gz
tar -xzf 360FV-Matterport.tar.gz
```
## Citation
```bibtex
@inproceedings{teng2024_360bev,
title={360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View},
author={Teng, Zhifeng and Zhang, Jiaming and Yang, Kailun and Peng, Kunyu and Shi, Hao and Reiß, Simon and Cao, Ke and Stiefelhagen, Rainer},
booktitle={2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year={2024}
}
```
## License
This dataset is released under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
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