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This repository is publicly accessible, but you have to accept the conditions to access its files and content.
To request access to the PartNet repo, you will need to provide your full name (please provide both your first and last name), the name of your advisor or the principal investigator (PI) of your lab (in the PI/Advisor) fields, and the school or company that you are affiliated with (the Affiliation field). After requesting access to the PartNet repo, you will be considered for access approval.
After access approval, you (the "Researcher") receive permission to use the PartNet database (the "Database") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions: Researcher shall use the Database only for non-commercial research and educational purposes. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted 3D models that he or she may create from the Database. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. The law of the State of New Jersey shall apply to all disputes under this agreement.
For access to the data, please fill in your full name (both first and last name), the name of your advisor or principal investigator (PI), and the name of the school or company you are affliated with. Please actually fill out the fields (DO NOT put the word "Advisor" for PI/Advisor and the word "School" for "Affiliation", please specify the name of your advisor and the name of your school).
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This repository contains archives (zip files) for PartNet, a subset of ShapeNet with part annotations.
The PartNet prerelease v0 (March 29, 2019) consists of the following:
- PartNet v0 annotations (meshes, point clouds, and visualizations) in chunks: data_v0_chunk.zip (302MB), data_v0_chunk.z01-z10 (10GB each)
- HDF5 files for the semantic segmentation task (Sec 5.1 of PartNet paper): sem_seg_h5.zip (8GB)
- HDF5 files for the instance segmentation task (Sec 5.3 of PartNet paper): ins_seg_h5.zip (20GB)
If you use PartNet and ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report and the PartNet paper.
@techreport{shapenet2015,
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
number = {arXiv:1512.03012 [cs.GR]},
institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago},
year = {2015}
}
@inproceedings{mo2019partnet,
title={{PartNet}: A large-scale benchmark for fine-grained and hierarchical part-level {3D} object understanding},
author={Mo, Kaichun and Zhu, Shilin and Chang, Angel X and Yi, Li and Tripathi, Subarna and Guibas, Leonidas J and Su, Hao},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={909--918},
year={2019}
}
If you have any questions, please post issues on the PartNet github issue page. If you have general feedbacks, please fill in this form to let us know. If you observe any data annotation error, please fill in this errata to help improve PartNet.
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