|
|
--- |
|
|
license: cc-by-nc-4.0 |
|
|
modalities: |
|
|
- image |
|
|
task_categories: |
|
|
- image-segmentation |
|
|
tags: |
|
|
- synthetic |
|
|
size_categories: |
|
|
- 10K<n<100K |
|
|
|
|
|
arxiv: |
|
|
|
|
|
--- |
|
|
|
|
|
# TransFrag27K: Transparent Fragment Dataset |
|
|
|
|
|
## Dataset Summary |
|
|
TransFrag27K is the first large-scale transparent fragment dataset, which contains **27,000 images and masks** at a resolution of 640×480. The dataset covers fragments of common everyday glassware and incorporates **more than 150 background textures** and **100 HDRI environment lightings**. |
|
|
|
|
|
<p align="center"> |
|
|
<img src="./demonstration.png" alt="Demonstration" width="1000"/> |
|
|
</p> |
|
|
|
|
|
|
|
|
Transparent objects, being a special category, have refractive and transmissive material properties that make their visual features highly sensitive to environmental lighting and background. In real-world scenarios, collecting data of transparent objects with diverse backgrounds and lighting conditions is challenging, and annotations are prone to errors due to difficulties in recognition. |
|
|
|
|
|
To address this, we designed an **automated dataset generation pipeline in Blender**: |
|
|
- Objects are randomly fractured using the Cell Fracture add-on. |
|
|
- Parametric scripts batch-adjust lighting, backgrounds, and camera poses. |
|
|
- Rendering is performed automatically to output paired RGB images and binary masks. |
|
|
|
|
|
The Blender script we used to generate TransFrag27k also supports batch dataset generation with any scene in which objects are placed at a horizontal plane. For implementation details, please refer to: |
|
|
[Transparent-Fragments-Contour-Estimation](https://github.com/Keithllin/Transparent-Fragments-Contour-Estimation) |
|
|
|
|
|
|
|
|
--- |
|
|
|
|
|
## Supported Tasks |
|
|
- Semantic Segmentation for various transparent fragments. |
|
|
|
|
|
--- |
|
|
|
|
|
|
|
|
## Dataset Structure |
|
|
In our released dataset, to facilitate subsequent customized processing, we organize each object’s data in the following structure: |
|
|
|
|
|
```├─TransFrag27K |
|
|
│ ├─Planar1 |
|
|
│ │ ├─anno_mask |
|
|
│ │ └─rgb |
|
|
│ ├─Planar2 |
|
|
|
|
|
│ │ ├─anno_mask |
|
|
│ │ └─rgb |
|
|
│ ├─Curved1 |
|
|
│ │ ├─anno_mask |
|
|
|
|
|
│ │ └─rgb |
|
|
│ ├─Curved2 |
|
|
│ │ ├─anno_mask |
|
|
│ │ └─rgb |
|
|
│ ├─Irregular1 |
|
|
│ │ ├─anno_mask |
|
|
│ │ └─rgb |
|
|
│ ├─Irregular2 |
|
|
│ │ ├─anno_mask |
|
|
│ │ └─rgb |
|
|
│ ├─Irregular3 |
|
|
│ │ ├─anno_mask |
|
|
│ │ └─rgb |
|
|
``` |
|
|
|
|
|
|
|
|
We mainly organize the dataset according to the **shape classes** of transparent fragments: |
|
|
- **Planar** |
|
|
Mainly includes fragments from flat regions such as dish bottoms and glass bases. |
|
|
- **Curved** |
|
|
Mainly includes fragments from objects with cylindrical or spherical curvature, such as cups, bottles, and bowls. |
|
|
- **Irregular** |
|
|
Mainly includes fragments with multiple curvature patterns or discontinuous surfaces, such as the intersection of a cup wall and bottom, special bottle necks, wine glass stems, and handles. |
|
|
|
|
|
--- |
|
|
|
|
|
## Citation |
|
|
If you find this dataset or the associated work useful for your research, please cite the paper: |
|
|
``` |
|
|
@article{lin2025transparent, |
|
|
title={Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly}, |
|
|
author= |
|
|
year= |
|
|
|
|
|
} |
|
|
``` |
