| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - person-detection |
| - action-recognition |
| - physical-ai |
| - spatial-reasoning |
| - spatial-intelligence |
| pretty_name: SynDORBench |
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| extra_gated_description: "SynDORBench is released for non-commercial research under CC BY-NC 4.0, for evaluation only." |
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| By requesting access you agree to use SynDORBench under the CC BY-NC 4.0 license — |
| for non-commercial purposes only, with attribution — to use it solely for evaluation |
| and benchmarking and NOT to train, fine-tune, or otherwise fit models on it, and to |
| cite the SynDORBench paper in any resulting work. |
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| Affiliation: text |
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| Intended research use: text |
| I agree to the CC BY-NC terms (non-commercial use, with attribution): checkbox |
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| --- |
| |
| # SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions |
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| Figure 1. Conceptual overview of SynDORBench: The benchmark systematically varies physical visibility factors (lighting, scene type, camera azimuth/elevation, action pose) and DORI-calibrated viewing distance, evaluating models on 3 tasks: 1) binary human-presence classification and 2) action recognition (MCQ and open-ended) |
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| ## Description |
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| SynDORBench is a synthetic benchmark for **robust perception** under |
| controlled distance and lighting conditions. Scenes span the DORI regions |
| (Detection / Observation / Recognition) distance scale crossed with |
| lighting bands (day / night / night-vision), for fine-grained evaluation of how |
| perception degrades at range and in low light. |
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| Figure 2. Sample images from SynDORBench. |
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| SyndDORBench consists of 87k QA pairs: |
| - action recognition (open-ended): 27k |
| - action mcq : 27k |
| - human-presence classification : 27k + 5k empty scenes |
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| ## Contents |
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| ```text |
| SynDORBench/ |
| ├── images/ # rendered frames + annotations |
| └── index/ |
| ├── 20k_index.jsonl # unified record index across all tasks |
| ├── human_detection/ # human-presence classification task |
| │ ├── {day,night,nv}.jsonl # GT person-present index |
| │ ├── empty_{day,night,nv}.jsonl # empty-scene (person-absent) negatives |
| ├── action_recognition/ # open-ended action recognition (similarity-scored) |
| │ └── {day,night,nv}.jsonl # action-label index |
| └── action_mcq/ # 8-way multiple-choice action recognition |
| └── {day,night,nv}.jsonl # MCQ index |
| ``` |
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| Modalities: **day** = RGB daytime · **night** = RGB low-light · **nv** = "night-vision" achieved through image enhancement. |
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| ## Citation |
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| If you use SynDORBench, please cite the SynDORBench paper. |
| ``` |
| @article{yee2026syndorbench, |
| title={SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions}, |
| author={Yee, Jeremy Stephen Gabriel and Wang, Zhengkui and Zhang, Zhiyuan and Anand, Avinash and Liu, Timothy and Chan, Benedict and Ng, Aik Beng and See, Simon}, |
| journal={arXiv preprint arXiv:2609.31823}, |
| year={2026} |
| } |
| ``` |
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| ## License |
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| This dataset is released under |
| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — non-commercial use, |
| with attribution. |
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