HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models
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https://zenodo.org/record/11668765
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资源简介：
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HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024
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Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.
Affiliation: EPFL
Date: June, 2024
Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf
Link to the Arxiv article: https://arxiv.org/abs/2402.17062
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Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.
The overall structure of the data is:
├── ckpts.zip - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)
├── annotations.zip - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.
├── simple_ycb_models.zip - Contains the preprocessed YCB objects for batched evaluation.
├── test.zip - Contains the processed SDF files for DexYCB test set.
├── ho3d_release.zip - Contains the HO3Dv2 submission trained with HO3D training set.
├── ho3d_render_release.zip - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.
The code to reproduce the results is available at: https://github.com/amathislab/HOISDF
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If you find our code, weights, predictions or ideas useful, please cite:
@inproceedings{qi2024hoisdf, title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields}, author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={10392--10402}, year={2024}}
创建时间：
2024-12-31
