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 · Project Page

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

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:

# 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

@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. Non-commercial use only.