Glass Surface Detection (GSD) Dataset

Dataset from the CVPR 2021 paper:

Rich Context Aggregation with Reflection Prior for Glass Surface Detection
Jiaying Lin, Zebang He, Rynson W.H. Lau
Proceedings of CVPR 2021
Project page: https://jiaying.link/cvpr2021-gsd/

Dataset Summary

GSD is a large-scale benchmark for glass surface detection in the wild. The dataset contains three splits:

• train — 2,710 images with full annotations (mask, reflection, edge). This is the split used to train GlassNet.
• extra — 579 additional images with mask and edge annotations but no reflections. Not used to train GlassNet.
• test — 813 images with mask annotations for evaluation.

Dataset Structure

Split	Images	Masks	Reflections	Edges
train	2,710	2,710	2,710	2,710
extra	579	579	—	579
test	813	813	—	—

Fields

• image_id — original filename stem (e.g. glass_0001), unique within each split
• image — RGB photograph containing glass surfaces
• mask — binary segmentation mask (white = glass)
• reflections — RGB reflection image paired with the scene (train only; None otherwise)
• edge — edge annotation map (train and extra only; None for test)

Usage

from datasets import load_dataset

ds = load_dataset("garrying/GSD")
sample = ds["train"][0]
sample["image_id"]     # original filename stem, e.g. "glass_0001"
sample["image"]        # PIL Image
sample["mask"]         # PIL Image (binary mask)
sample["reflections"]  # PIL Image (None for extra/test)
sample["edge"]         # PIL Image (None for test)

Converting Back to Raw Files

A helper script parquet_to_raw.py is included in this repository to convert the dataset back to a folder of raw image files:

# download the script
huggingface-cli download garrying/GSD parquet_to_raw.py --repo-type dataset --local-dir .

# convert all splits to raw PNG files
python parquet_to_raw.py --repo garrying/GSD --out GSD

Output layout:

GSD/
  train/  image/  mask/  reflections/  edge/  metadata.jsonl
  extra/  image/  mask/  edge/          metadata.jsonl
  test/   image/  mask/  metadata.jsonl

Pretrained Model

A pretrained GlassNet checkpoint (GSD.pth) is available in the companion model repository:

👉 garrying/GSD-GlassNet

Inference

# download the checkpoint
huggingface-cli download garrying/GSD-GlassNet GSD.pth --local-dir .

# run inference
python infer.py

Model Architecture

GlassNet uses a ResNeXt-101 backbone with:

• DenseContrastModule — multi-scale dilated convolutions (rates 1/2/4/8) with pairwise feature subtraction to capture cross-context contrast
• SELayer — grouped squeeze-and-excitation for context-aware channel reweighting
• RefNet — a lightweight U-Net-style decoder that jointly predicts the binary glass mask and reconstructs the reflection image as auxiliary output
• CRF post-processing — dense CRF refinement of predicted masks at inference time

Citation

@inproceedings{GSD:2021,
    title     = {Rich Context Aggregation with Reflection Prior for Glass Surface Detection},
    author    = {Lin, Jiaying and He, Zebang and Lau, Rynson W.H.},
    booktitle = {Proc. CVPR},
    year      = {2021}
}

Contact

jiayinlin5-c@my.cityu.edu.hk