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Add paper and GitHub links, update citation and metadata

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Hi, I'm Niels from the Hugging Face community science team. This pull request improves the dataset card for TransFrag27K by:
- Adding direct links to the research paper and the official GitHub repository.
- Including the full list of authors.
- Updating the BibTeX citation with complete information found in the repository.
- Adding the `robotics` tag to the metadata to reflect the dataset's primary application.

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  1. README.md +26 -25
README.md CHANGED
@@ -1,61 +1,59 @@
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  ---
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  license: cc-by-nc-4.0
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- modalities:
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- - image
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  task_categories:
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- - image-segmentation
 
 
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  tags:
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- - synthetic
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- size_categories:
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- - 10K<n<100K
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-
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- arxiv:
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-
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  ---
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  # TransFrag27K: Transparent Fragment Dataset
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  ## Dataset Summary
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  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**.
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  <p align="center">
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- <img src="./demonstration.png" alt="Demonstration" width="1000"/>
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  </p>
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-
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  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.
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- To address this, we designed an **automated dataset generation pipeline in Blender**:
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  - Objects are randomly fractured using the Cell Fracture add-on.
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  - Parametric scripts batch-adjust lighting, backgrounds, and camera poses.
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  - Rendering is performed automatically to output paired RGB images and binary masks.
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- 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:
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- [Transparent-Fragments-Contour-Estimation](https://github.com/Keithllin/Transparent-Fragments-Contour-Estimation)
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-
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  ---
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  ## Supported Tasks
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  - Semantic Segmentation for various transparent fragments.
 
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  ---
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-
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  ## Dataset Structure
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  In our released dataset, to facilitate subsequent customized processing, we organize each object’s data in the following structure:
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- ```├─TransFrag27K
 
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  │ ├─Planar1
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  │ │ ├─anno_mask
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  │ │ └─rgb
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  │ ├─Planar2
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-
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  │ │ ├─anno_mask
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  │ │ └─rgb
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  │ ├─Curved1
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  │ │ ├─anno_mask
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-
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  │ │ └─rgb
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  │ ├─Curved2
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  │ │ ├─anno_mask
@@ -71,7 +69,6 @@ In our released dataset, to facilitate subsequent customized processing, we orga
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  │ │ └─rgb
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  ```
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-
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  We mainly organize the dataset according to the **shape classes** of transparent fragments:
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  - **Planar**
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  Mainly includes fragments from flat regions such as dish bottoms and glass bases.
@@ -84,11 +81,15 @@ We mainly organize the dataset according to the **shape classes** of transparent
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  ## Citation
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  If you find this dataset or the associated work useful for your research, please cite the paper:
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- ```
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- @article{lin2025transparent,
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- title={Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly},
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- author=
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- year=
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  }
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  ```
 
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  ---
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  license: cc-by-nc-4.0
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+ size_categories:
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+ - 10K<n<100K
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  task_categories:
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+ - image-segmentation
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+ modalities:
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+ - image
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  tags:
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+ - synthetic
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+ - robotics
 
 
 
 
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  ---
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  # TransFrag27K: Transparent Fragment Dataset
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+ [**Paper**](https://huggingface.co/papers/2603.20290) | [**Code**](https://github.com/Keithllin/Transparent-Fragments-Contour-Estimation)
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+
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+ **Authors:** Qihao Lin, Borui Chen, Yuping Zhou, Jianing Wu, Yulan Guo, Weishi Zheng, Chongkun Xia.
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+
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  ## Dataset Summary
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  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**.
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  <p align="center">
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+ <img src="https://huggingface.co/datasets/chenbr7/TransFrag27K/resolve/main/demonstration.png" alt="Demonstration" width="1000"/>
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  </p>
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  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.
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+ To address this, the authors designed an **automated dataset generation pipeline in Blender**:
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  - Objects are randomly fractured using the Cell Fracture add-on.
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  - Parametric scripts batch-adjust lighting, backgrounds, and camera poses.
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  - Rendering is performed automatically to output paired RGB images and binary masks.
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+ The Blender script 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 the [official GitHub repository](https://github.com/Keithllin/Transparent-Fragments-Contour-Estimation).
 
 
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  ---
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  ## Supported Tasks
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  - Semantic Segmentation for various transparent fragments.
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+ - Contour estimation for autonomous reassembly.
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  ---
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  ## Dataset Structure
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  In our released dataset, to facilitate subsequent customized processing, we organize each object’s data in the following structure:
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+ ```
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+ ├─TransFrag27K
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  │ ├─Planar1
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  │ │ ├─anno_mask
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  │ │ └─rgb
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  │ ├─Planar2
 
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  │ │ ├─anno_mask
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  │ │ └─rgb
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  │ ├─Curved1
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  │ │ ├─anno_mask
 
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  │ │ └─rgb
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  │ ├─Curved2
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  │ │ ├─anno_mask
 
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  │ │ └─rgb
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  ```
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  We mainly organize the dataset according to the **shape classes** of transparent fragments:
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  - **Planar**
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  Mainly includes fragments from flat regions such as dish bottoms and glass bases.
 
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  ## Citation
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  If you find this dataset or the associated work useful for your research, please cite the paper:
 
 
 
 
 
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+ ```bibtex
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+ @misc{lin2026transparentfragmentscontourestimation,
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+ title={Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly},
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+ author={Qihao Lin and Borui Chen and Yuping Zhou and Jianing Wu and Yulan Guo and Weishi Zheng and Chongkun Xia},
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+ year={2026},
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+ eprint={2603.20290},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/2603.20290},
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  }
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  ```