| --- |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - image-segmentation |
| modalities: |
| - image |
| tags: |
| - synthetic |
| - robotics |
| --- |
| |
| # TransFrag27K: Transparent Fragments Dataset |
|
|
| [](https://arxiv.org/abs/2603.20290) |
|
|
| ## Paper |
| [**Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly**](https://arxiv.org/abs/2603.20290) |
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|
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| [**Code**](https://github.com/Keithllin/Transparent-Fragments-Contour-Estimation) |
|
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|
|
| ## Dataset Summary |
| TransFrag27K is the first large-scale transparent fragments 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="https://huggingface.co/datasets/chenbr7/TransFrag27K/resolve/main/demonstration.png" alt="Demonstration" width="1000"/> |
| </p> |
|
|
| ## Automated Synthetic Dataset Generation |
| 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 fragments is challenging because they are difficult to perceive and exhibit highly diverse and random shapes, which makes accurate annotation prone to errors. |
|
|
| 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 pipeline used to generate TransFrag27K is generalizable to arbitrary objects placed on a horizontal plane (e.g., tabletop or ground scenarios), with randomized camera viewpoints and adjustable environmental configurations. For implementation details, please refer to our [GitHub repository](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 category’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: |
|
|
| ```bibtex |
| @misc{lin2026transparentfragmentscontourestimation, |
| title={Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly}, |
| author={Qihao Lin and Borui Chen and Yuping Zhou and Jianing Wu and Yulan Guo and Weishi Zheng and Chongkun Xia}, |
| year={2026}, |
| eprint={2603.20290}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2603.20290}, |
| } |
| ``` |
