OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909089647296512 |
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| author | Liu, Isabella Chen, Linghao Fu, Ziyang Wu, Liwen Jin, Haian Li, Zhong Wong, Chin Ming Ryan Xu, Yi Ramamoorthi, Ravi Xu, Zexiang Su, Hao |
| author_facet | Liu, Isabella Chen, Linghao Fu, Ziyang Wu, Liwen Jin, Haian Li, Zhong Wong, Chin Ming Ryan Xu, Yi Ramamoorthi, Ravi Xu, Zexiang Su, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_07921 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects Liu, Isabella Chen, Linghao Fu, Ziyang Wu, Liwen Jin, Haian Li, Zhong Wong, Chin Ming Ryan Xu, Yi Ramamoorthi, Ravi Xu, Zexiang Su, Hao Computer Vision and Pattern Recognition 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. |
| title | OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.07921 |