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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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pretty_name: ModelNet_Splats |
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size_categories: |
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- 10B<n<100B |
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--- |
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This repository contains ShapeSplats, a large dataset of Gaussian splats spanning 65K objects in 87 unique categories (gathered from ShapeNetCore, ShapeNet-Part, and ModelNet). |
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ModelNet_Splats consists of the 12 objects across 40 categories of ModelNet40. |
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The data is distributed as ply files where information about each Gaussian is encoded in custom vertex attributes. |
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Please see [DATA.md](DATA.md) for details about the data. |
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If you use the ModelNet_Splats data, you agree to abide by the [ModelNet terms of use](https://modelnet.cs.princeton.edu/#). 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. |
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If you use this data, please cite the ShapeSplat paper along with main ShapeNet technical report. |
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``` |
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@article{ma2024shapesplat, |
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title={ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining}, |
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author={Ma, Qi and Li, Yue and Ren, Bin and Sebe, Nicu and Konukoglu, Ender and Gevers, Theo and Van Gool, Luc and Paudel, Danda Pani}, |
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journal={arXiv preprint arXiv:2408.10906}, |
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year={2024} |
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} |
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@misc{wu20153dshapenetsdeeprepresentation, |
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title={3D ShapeNets: A Deep Representation for Volumetric Shapes}, |
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author={Zhirong Wu and Shuran Song and Aditya Khosla and Fisher Yu and Linguang Zhang and Xiaoou Tang and Jianxiong Xiao}, |
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year={2015}, |
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eprint={1406.5670}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/1406.5670}, |
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} |
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``` |
