HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Synthetic data
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https://zenodo.org/doi/10.5281/zenodo.13348980
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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 rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset.
The overall structure of the data is:
├── render_sdf_ho3d.zip - Contains the rendered images for HO3Dv2.
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}}
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Zenodo创建时间：
2024-08-21
