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XYZ-IBD: A High-precision Bin-picking Dataset for Object 6D Pose Estimation Capturing Real-world Industrial Complexity

Welcome to the official repository of XYZ-IBD, a bin-picking dataset that captures real-world industrial complexity for 6D pose estimation.

🔥 Note in this repo, we provide the Docker usage for our bin-picking simulation rendering. The code is based on the BlenderProc.

📦 Dataset Access

🏆 Ongoing Challenges and Workshops

🚀 Baseline Implementations

We provide reference implementations to kick-start research and benchmarking. Please refer to the baseline repository for the running details.

🛠️ Data Processing & Evaluation

Docker usage for our bin-picking simulation rendering

Build Docker

cd docker 
docker build -t "render_xyz_synthetic" .

Run Docker

docker run --gpus all render_xyz_synthetic
cd "examples\datasets\deep-tote"
### Modify the OUTPUT_DIR and OBJECT FILE in config.yaml to your custom path before run scripts!

### Generate the whole synthetic dataset for all objects
blenderproc run render.py --config=config.yaml
### Generate the synthetic dataset for specific object
blenderproc run custom.py --config=config_photoneo_qiuxiao1.yaml
### Post Process the synthetic dataset to BOP Format
blenderproc run gen_masks.py --config=config_photoneo_qiuxiao1.yaml

Citation

If you find this dataset helpful, please cite this paper

@misc{huang2025xyzibdhighprecisionbinpickingdataset,
      title={XYZ-IBD: High-precision Bin-picking Dataset for Object 6D Pose Estimation Capturing Real-world Industrial Complexity}, 
      author={Junwen Huang and Jizhong Liang and Jiaqi Hu and Martin Sundermeyer and Peter KT Yu and Nassir Navab and Benjamin Busam},
      year={2025},
      eprint={2506.00599},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.00599}, 
}

📫 Contact

For questions, issues, or collaboration inquiries, feel free to open an issue or reach out via the project page.

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A High-precision Bin-picking Dataset for Object 6D pose estimation and Monocular Depth Estimation

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