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--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "dataset-*.tar" |
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- split: test |
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path: "dataset-*.tar" |
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subset: true |
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subset_field: txt |
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subset_mapping: |
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- key: diffuse_train |
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split: |
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- train |
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- key: diffuse_test |
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split: |
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- test |
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license: cc-by-nc-4.0 |
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tags: |
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- croissant |
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- webdataset |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- image-to-3d |
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--- |
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# OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction |
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Zheng Dang<sup>1</sup> · Jialu Huang<sup>2</sup> · Fei Wang<sup>2</sup> · Mathieu Salzmann<sup>1</sup> |
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<sup>1</sup>EPFL CVLAb, Switzerland <sup>2</sup> Xi'an Jiaotong University, China |
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[Paper](https://arxiv.org/abs/2406.08894) |
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[WebPage](https://christy61.github.io/openmaterial.github.io/) |
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 |
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## Dataset |
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[+] 1001 unique shapes |
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[+] 295 material types with laboratory measured IOR |
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[+] 723 lighting conditions |
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[+] Physical based rendering with costomized BSDF for each material type |
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[+] 1001 uniques scenes, for each scene 90 images (50 for training, 40 for testing) with object mask, depth, camera pose, materail type annotations. |
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## Example Images |
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 |
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<details> |
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<summary> Click to see more examples</summary> |
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</details> |
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## Data structure |
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``` |
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. |
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├── name_of_object/[lighing_condition_name]-[material_type]-[material_name] |
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│ ├── train |
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│ │ ├── images |
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│ │ │ ├── 000000.png |
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│ │ │ |-- ... |
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│ │ └── mask |
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│ │ │ ├── 000000.png |
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│ │ │ |-- ... |
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│ │ └── depth |
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│ │ ├── 000000.png |
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│ │ |-- ... |
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│ ├── test |
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│ │ ├── images |
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│ │ │ ├── 000000.png |
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│ │ │ |-- ... |
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│ │ └── mask |
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│ │ │ ├── 000000.png |
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│ │ │ |-- ... |
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│ │ └── depth |
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│ │ ├── 000000.png |
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│ │ |-- ... |
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│ └── transformas_train.json |
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│ └── transformas_test.json |
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``` |
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## Usage |
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Going to have a example code at the link. |
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## Citation |
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If you find our work useful in your research, please cite: |
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``` |
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@article{Dang24, |
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title={OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction}, |
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author={Zheng Dang and Jialu Huang and Fei Wang and Mathieu Salzmann}, |
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journal={arXiv preprint arXiv:2406.08894}, |
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year={2024} |
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} |
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``` |
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