| --- |
| license: cc-by-nc-3.0 |
| task_categories: |
| - object-detection |
| tags: |
| - synthetic-data |
| - sim-to-real |
| - domain-randomization |
| - industrial |
| - robotics |
| --- |
| |
| # SynthRender_Robotics: Synthetic Training Sets for the Robotics Sim-to-Real Benchmark |
| |
| This repository hosts the synthetic training sets generated with **SynthRender** and used to benchmark sim-to-real transfer on the public **Robotics** dataset (Horváth et al., 2022), as reported in *"SynthRender and IRIS: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception"* ([arXiv:2602.21141](https://arxiv.org/abs/2602.21141)). |
| |
| ## Sample Images (720x720) |
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| |---|---|---|---| |
| |  |  |  |  | |
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| ## Dataset Summary |
| |
| SynthRender is an open-source, scriptable Domain Randomization (DR) engine built on BlenderProc, used in the paper to systematically ablate rendering design choices (physics-based placement, exponential light sampling, RGB lighting, material randomization) and quantify their effect on sim-to-real transfer. The synthetic sets in this repository correspond to the configurations used to train detectors evaluated against the **Robotics** benchmark (10 classes, 190 real test images, 920 annotated instances), on which the proposed framework reached **99.1% mAP@50**. |
| |
| The repository contains two synthetic datasets, generated at different rendering resolutions: |
| |
| | Resolution | Total Images | Train (80%) | Validation (20%) | |
| |------------|-------------|-------------|-------------------| |
| | 1024x1024 | 4,000 | 3,200 | 800 | |
| | 720x720 | 4,000 | 3,200 | 800 | |
| |
| - **Annotation format:** YOLO-style bounding box labels (`class x_center y_center width height`, normalized) |
| - **Modality:** RGB images with text-based annotation files |
| - **Classes:** 10, matching the target Robotics benchmark |
| |
| ## Relation to the Paper |
| |
| The paper's contribution statement notes that multiple synthetic training sets of 4,000 domain-randomized images each were generated to support the ablation studies. This repository provides two such sets, at 1024x1024 and 720x720 resolution, used for the Robotics benchmark specifically. It is a companion resource to the [IRIS dataset](https://huggingface.co/datasets/moiaraya/IRIS) and to the [SynthRender framework](https://github.com/Moiso/SynthRender) itself. |
| |
| ## Intended Use |
| |
| For research on synthetic-to-real domain transfer, domain randomization ablations, and object detection benchmarking against the public Robotics dataset. Not intended for commercial use. |
| |
| ## License |
| |
| Released under **CC BY-NC 3.0**. Commercial use is not permitted; attribution is required. |
| |
| ## Citation |
| |
| ```bibtex |
| @article{araya2026synthrender, |
| title={SynthRender and IRIS: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception}, |
| author={Araya-Martinez, Jose Moises and Tom, Thushar and Sanchis Reig, Adri{\'a}n and Rey Valiente, Pablo and Lambrecht, Jens and Kr{\"u}ger, J{\"o}rg}, |
| journal={arXiv preprint arXiv:2602.21141}, |
| year={2026} |
| } |
| ``` |
| |
| J. M. Araya-Martinez, T. Tom, A. S. Reig, P. R. Valiente, J. Lambrecht, and J. Krüger, |
| “Synthrender and iris: Open-source framework and dataset for bidirectional sim-real transfer in industrial object perception,” |
| 2026. [Online]. Available: https://arxiv.org/abs/2602.21141 |
| |
| ## Contact |
| |
| For questions about the dataset, please open a discussion on this repository. |
