This repo contains the data for S2O: Static to Openable Enhancement for Articulated 3D Objects. See the code on GitHub and the paper for details. Please cite S2O [1] if you use ACD.

We provide the mesh, point cloud, and metadata for the two datasets used in S2O.

PM-Openable - This is a subset of 648 openable objects from full PartNet-Mobility [2]. We use a train/val/test split of 460/95/93 objects.
Articulated Container Dataset (ACD) [1] - We take openable container objects from HSSD [3], ABO [4], and 3D-FUTURE [5] and annotate them to form another set of 354 articulated openable objects.

For both datasets, we provide sampled point cloud and static meshes to which the point clouds can be corresponded to.
For ACD, we also provide the articulation annotations and final articulated object after articulation. In addition, we provide metadata with information for each object including the object category, split, etc.

For details on the mesh, point cloud, and annotation files see ANNOTATIONS.md, and see METADATA.md for information on the metadata files.

Let us know if you find any issues with ACD annotations by submitting a GitHub issue in S2O repo.

File structure

pm/ - PartNet-Mobility
- mesh_input/ - meshes processed for S2O pipeline and corresponding annotations
- pcd/ - point clouds
acd/ - ACD
- mesh_input/ - meshes processed for S2O pipeline and corresponding annotations
- mesh_parts/ - meshes split into parts (as separate GLBs or nodes), annotations embedded in glTF extras field
- articulated/ - articulated meshes with interiors added (NOTE - some shapes have know issues, under development)
  - glb/ - GLB meshes with annotations embedded in glTF extras field
- art_anno/ - articulation annotations stored separately
- pcd/ - point clouds
metadata/ - various metadata
- acd_data.json - ACD objects ids (with category) by train/val/test (used for training/evaluation code)
- acd_parts_info.csv - CSV with metadata for all parts in ACD (used to compute statistics)
- acd_objects.csv - summary of ACD objects with different categorization granularity
- pm_data.json - PM object ids (with category) by train/val/test (used for training/evaluation code)
- pm_missing_top.txt - IDs of PM shapes that have countertops missing
- pm_parts_info.csv - analogous to acd_parts_info.csv
- pm_objects.csv - summary of PM objects with different categorization granularity
ckpts/ - S2O method checkpoints
- pg_* - PointGroup checkpoints
  - unet - PG + UNet, trained on PM-Openable
  - swin3d - PG + Swin3D, trained on PM-Openable
  - px - PG + PointNeXt, trained on PM-Openable
    - fpn - PG + PointNeXt + FPN (introduced in S2O paper, trained on PM-Openable)
    - fpn_w_pm_ext - PG + PointNeXt + FPN + trained on PM-Openable and PM-Openable-ext (version without interiors)
    - fpn_w_3df - PG + PointNeXt + FPN + trained on PM-Openable and 3D-FUTURE subset of ACD (see supplement of S2O for details)
    - fpn_w_3df_pm_ext - PG + PointNeXt + FPN + trained on PM-Openable and 3D-FUTURE subset of ACD and PM-Openable-ext
  - shape2motion/ - Shape2Motion checkpoints, trained on PM-Openable
  - mask3d - Mask3D checkpoint, trained on PM-Openable
  - opdformer_p - OPDFormer checkpoint (RGBD)
  - meshwalker - MeshWalker, trained on PM-Openable

References

[1] - S2O: Static to Openable Enhancement for Articulated 3D Objects. Denys Iliash, Hanxiao Jiang, Yiming Zhang, Manolis Savva, Angel X. Chang.

[2] - SAPIEN: A SimulAted Part-based Interactive ENvironment. Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su.

[3] - Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation. Mukul Khanna, Yongsen Mao, Hanxiao Jiang, Sanjay Haresh, Brennan Shacklett, Dhruv Batra, Alexander Clegg, Eric Undersander, Angel X. Chang, Manolis Savva.

[4] - ABO: Dataset and Benchmarks for Real-World 3D Object Understanding. Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F. Yago Vicente, Thomas Dideriksen, Himanshu Arora, Matthieu Guillaumin, Jitendra Malik.

[5] - 3D-FUTURE: 3D Furniture shape with TextURE - Huan Fu, Rongfei Jia, Lin Gao, Mingming Gong, Binqiang Zhao, Steve Maybank, Dacheng Tao.

BibTeX:

@article{iliash2024s2o,
  title={{S2O}: Static to openable enhancement for articulated {3D} objects},
  author={Iliash, Denys and Jiang, Hanxiao and Zhang, Yiming and Savva, Manolis and Chang, Angel X},
  journal={arXiv preprint arXiv:2409.18896},
  year={2024}
}

@inproceedings{xiang2020sapien,
  author = {Xiang, Fanbo and Qin, Yuzhe and Mo, Kaichun and Xia, Yikuan and Zhu, Hao and Liu, Fangchen and Liu, Minghua and Jiang, Hanxiao and Yuan, Yifu and Wang, He and Yi, Li and Chang, Angel X. and Guibas, Leonidas J. and Su, Hao},
  title = {{SAPIEN}: A SimulAted Part-Based Interactive ENvironment},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}

@inproceedings{khanna2023hssd,
  author={{Khanna*}, Mukul and {Mao*}, Yongsen and Jiang, Hanxiao and Haresh, Sanjay and Shacklett, Brennan and Batra, Dhruv and Clegg, Alexander and Undersander, Eric and Chang, Angel X. and Savva, Manolis},
  title={{Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation}},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2024}
}

@inproceedings{collins2022abo,
  title={{ABO}: Dataset and benchmarks for real-world {3D} object understanding},
  author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Luthra, Achleshwar and Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Vicente, Tomas F Yago and Dideriksen, Thomas and Arora, Himanshu and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={21126--21136},
  year={2022}
}

@article{fu20213d,
  title={{3D-FUTURE}: {3D} furniture shape with texture},
  author={Fu, Huan and Jia, Rongfei and Gao, Lin and Gong, Mingming and Zhao, Binqiang and Maybank, Steve and Tao, Dacheng},
  journal={International Journal of Computer Vision},
  volume={129},
  pages={3313--3337},
  year={2021},
  publisher={Springer}
}

Datasets:

3dlg-hcvc
/

s2o

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