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--- |
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license: cc-by-4.0 |
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pretty_name: IPD |
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--- |
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# Industrial Plenoptic Dataset (IPD) |
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To download the data and extract it into BOP format simply execute: |
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``` |
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export SRC=https://huggingface.co/datasets/bop-benchmark |
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wget $SRC/ipd/resolve/main/ipd_base.zip # Base archive with camera parameters, etc |
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wget $SRC/ipd/resolve/main/ipd_models.zip # 3D object models |
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wget $SRC/ipd/resolve/main/ipd_val.zip # Validation images |
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wget $SRC/ipd/resolve/main/ipd_test_all.zip # All test images part 1 |
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wget $SRC/ipd/resolve/main/ipd_test_all.z01 # All test images part 2 |
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wget $SRC/ipd/resolve/main/ipd_train_pbr.zip # PBR training images part 1 |
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wget $SRC/ipd/resolve/main/ipd_train_pbr.z01 # PBR training images part 2 |
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wget $SRC/ipd/resolve/main/ipd_train_pbr.z02 # PBR training images part 3 |
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wget $SRC/ipd/resolve/main/ipd_train_pbr.z03 # PBR training images part 4 |
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7z x ipd_base.zip # Contains folder "ipd" |
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7z x ipd_models.zip -oipd # Unpacks to "ipd" |
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7z x ipd_val.zip -oipd # Unpacks to "ipd" |
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7z x ipd_test_all.zip -oipd # Unpacks to "ipd" |
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7z x ipd_train_pbr.zip -oipd # Unpacks to "ipd" |
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``` |
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If you downloaded the ipd_train_pbr files before March 20, please also download and unzip ipd_train_pbr_patch.zip! |
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## Dataset parameters |
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* Objects: 10 |
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* Object models: Mesh models |
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* Modalities: Three cameras are placed in each scene. Image, depth, angle of linear |
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polarization (AOLP), and degree of linear polarization (DOLP) data |
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are rendered for each camera. |
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## Training PBR images splits |
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Scenes 000000–000024 contain objects 0, 8, 18, 19, 20. |
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Scenes 000025–000049 contain objects 1, 4, 10, 11, 14. |
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## Dataset format |
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General information about the dataset format can be found in: |
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https://github.com/thodan/bop_toolkit/blob/master/docs/bop_datasets_format.md |
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## References |
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[1] Agastya Kalra, Guy Stoppi, Dmitrii Marin, Vage Taamazyan, Aarrushi Shandilya, |
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Rishav Agarwal, Anton Boykov, Tze Hao Chong, Michael Stark; Proceedings of the |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, |
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pp. 22691-22701 |
