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README.md

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+# Object Pose Estimation Using Implicit Representation for Transparent Objects
+
+This dataset consists of raw rendered Physically Based Rendering (PBR) data and 3D mesh assets designed for training and fine-tuning pose-estimation models, specifically adapting Megapose for transparent objects. Created by Varun Burde in November 2024 and prepared for distribution in February 2026, the data provides a comprehensive resource for implicit representation research.
+
+The dataset is organized into zipped archives for easier accessibility, split into supporting assets (`meshes_and_meta.zip`) and sequence libraries (`train_pbr_xxx.zip`). Internally, it follows the conventions of the **BOP (Benchmark for 6D Object Pose Estimation) Toolkit**, with each sequence containing RGB images, depth maps, visibility masks, and ground truth annotations (scene_gt.json) within structured scene directories.
+
+This work is associated with the research published in *Object Pose Estimation Using Implicit Representation for Transparent Objects*, available at [SpringerView](https://link.springer.com/chapter/10.1007/978-3-031-91569-7_15).
+
+In creating this dataset, we utilized the **BOP Toolkit** for standardized formatting and **BlenderProc** for the underlying synthetic data generation. This work was supported by the European Union under the project *Robotics and advanced industrial production* (reg. no. CZ.02.01.01/00/22_008/0004590).
+
+The dataset is distributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. Users are free to share and adapt the material provided they give appropriate credit to the authors and the associated publication.
+
+---
+
+If you find this dataset useful, please cite it using:
+
+```bibtex
+@InProceedings{10.1007/978-3-031-91569-7_15,
+author="Burde, Varun
+and Moroz, Artem
+and Zeman, V{\'i}t
+and Burget, Pavel",
+editor="Del Bue, Alessio
+and Canton, Cristian
+and Pont-Tuset, Jordi
+and Tommasi, Tatiana",
+title="Object Pose Estimation Using Implicit Representation for Transparent Objects",
+booktitle="Computer Vision -- ECCV 2024 Workshops",
+year="2025",
+publisher="Springer Nature Switzerland",
+address="Cham",
+pages="226--247",
+isbn="978-3-031-91569-7"
+}
+```

meshes_and_meta.zip

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train_pbr_000_049.zip

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train_pbr_050_099.zip

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