Datasets:
File size: 3,178 Bytes
6406f6e 514d9e3 6406f6e 514d9e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | ---
license: cc-by-nc-4.0
task_categories:
- image-segmentation
tags:
- glass-surface-detection
- rgb-d
- depth-estimation
- scene-understanding
pretty_name: RGBD-GSD (RGB-D Glass Surface Detection Dataset)
size_categories:
- 1K<n<10K
---
# RGBD-GSD — RGB-D Glass Surface Detection Dataset
RGBD-GSD is the first large-scale **RGB-D glass surface detection** dataset, introduced in:
> **Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection**
> Jiaying Lin\*, Yuen-Hei Yeung\*, Shuquan Ye, Rynson W. H. Lau
> AAAI 2025
> [arXiv](https://arxiv.org/abs/2206.11250) · [Project Page](https://jiaying.link/aaai2025-rgbdglass/)
## Dataset Summary
RGBD-GSD contains **3,009 RGB-D images** across a wide range of real-world glass surface categories, each paired with a precise binary segmentation mask and a depth map. Depth maps are captured with 3D sensors; blank (missing) regions in depth correspond to glass surfaces, providing a complementary detection cue to the RGB image.
| Split | Samples |
|-------|--------:|
| train | 2,400 |
| test | 609 |
| **total** | **3,009** |
## Dataset Structure
Each sample has four columns:
| Column | Type | Description |
|------------|--------|-------------|
| `image_id` | string | Original filename stem, e.g. `00000001`. Enables round-trip fidelity. |
| `image` | Image | JPEG RGB image |
| `mask` | Image | PNG binary segmentation mask (glass = white, background = black) |
| `depth` | Image | PNG depth map (blank/missing regions often correspond to glass surfaces) |
The original on-disk layout is:
```
RGBD-GSD/
train/
images/ # {id}.jpg
masks/ # {id}.png
depths/ # {id}.png
test/
…
```
## Loading the Dataset
```python
from datasets import load_dataset
ds = load_dataset("garrying/RGBD-GSD")
# or load a single split:
train_ds = load_dataset("garrying/RGBD-GSD", split="train")
test_ds = load_dataset("garrying/RGBD-GSD", split="test")
sample = train_ds[0]
print(sample["image_id"]) # e.g. "00000001"
sample["image"].show()
sample["mask"].show()
sample["depth"].show()
```
## Converting Back to Raw Files
A helper script `parquet_to_raw.py` is included in this repo to restore the original directory structure:
```bash
# Download the helper
huggingface-cli download garrying/RGBD-GSD parquet_to_raw.py --repo-type dataset
# Restore all splits from HuggingFace
python parquet_to_raw.py --repo garrying/RGBD-GSD
# Restore only the test split to a custom directory
python parquet_to_raw.py --repo garrying/RGBD-GSD --splits test --out RGBD-GSD_test
```
Output structure matches the original:
```
RGBD-GSD/
train/images/{id}.jpg train/masks/{id}.png train/depths/{id}.png
test/…
```
## Citation
```bibtex
@article{aaai2025_rgbdglass,
author = {Lin, Jiaying and Yeung, Yuen-Hei and Ye, Shuquan and Lau, Rynson W.H.},
title = {Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection},
journal = {AAAI},
year = {2025},
}
```
## License
This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial use only.
|