OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects

Isabella Liu, Ling Chen, Ziyang Fu, Liwen Wu, Haian Jin +6 more
2/12/2026

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Abstract

We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations. For each image in the dataset, we provide accurate camera parameters, illumination ground truth, and foreground segmentation masks. Our dataset enables the quantitative evaluation of most inverse rendering and material decomposition methods for real objects. We examine several state-of-the-art inverse rendering methods on our dataset and compare their performances. The dataset and code can be found on the project page: https://oppo-us-research.github.io/OpenIllumination.

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@article{liu2026openillumination,
  title  = {OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects},
  author = {Isabella Liu and Ling Chen and Ziyang Fu and Liwen Wu and Haian Jin and Zhong Li and Chin Ming Ryan Wong and Yi Xu and R. Ramamoorthi and Zexiang Xu and Hao Su},
  year   = {2026},
  eprint = {2309.07921},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/pdf/2309.07921},
  journal = {NEURIPS 2023 2023}
}

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