Update README.md

README.md

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-license: cc-by-sa-4.0
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+---
+license: cc-by-sa-4.0
+---
+# TransFrag27K: Transparent Fragment Dataset
+
+## Dataset Summary
+This script was used to create the **first large-scale transparent fragment dataset, TransFrag27K**, 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**.  
+
+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 plugin**.  
+- Parametric scripts batch-adjust lighting, backgrounds, and camera poses.  
+- Rendering is performed automatically to output paired RGB images and binary masks.  
+
+The dataset is generated using this Blender script. Please refer to:  
+[GitHub Repository](https://github.com/Keithllin/Transparent-Fragments-Contour-Estimation)  
+
+---
+
+## Related Paper
+**Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly**  
+*(Lin et al., 2025)*  
+
+---
+
+## Supported Tasks
+- **Semantic Segmentation**
+
+---
+
+## 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.  
+
+---
+
+## Usage Example
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("your-username/TransFrag27K")
+print(dataset["train"][0])
+```
+##Citation
+If you use this dataset, please cite the following paper:
+```
+@article{lin2025transparent,
+  title={Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly},
+  author={Lin, XXX and others},
+  year={2025},
+  journal={TBD}
+}
+```