---
license: mit
task_categories:
- other
tags:
- pose-estimation
- point-cloud
- 3d
- cvpr2022
pretty_name: CPPF Training and Evaluation Data
---

# CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild — Data

Auxiliary training and evaluation data for [CPPF (CVPR 2022)](https://openaccess.thecvf.com/content/CVPR2022/html/You_CPPF_Towards_Robust_Category-Level_9D_Pose_Estimation_in_the_Wild_CVPR_2022_paper.html).

- **Code**: https://github.com/qq456cvb/CPPF
- **Project page**: https://qq456cvb.github.io/projects/cppf
- **Pretrained models**: https://huggingface.co/qq456cvb/CPPF

## Contents

| Path | Size | Description |
|---|---|---|
| `laptop.zip` | ~1.1 GB | Blender physically rendered laptop images, used to train the auxiliary lid/keyboard-base segmentation network (`train_laptop_aux.py`). Extract under `data/laptop`. |
| `nocs_seg.zip` | ~20 MB | Detection priors for NOCS REAL275 evaluation with instance segmentation or bounding box masks. Extract under `data/nocs_seg`. |
| `sunrgbd_extra/` | ~4 GB | Prepared extra data for SUN RGB-D evaluation (`sunrgbd_pc_bbox_votes_50k_v1_val.zip` point clouds with bbox votes, plus per-category scan name lists). Place under `data/sunrgbd_extra` and unzip the zip files. |

## Usage

```bash
pip install -U "huggingface_hub[cli]"
hf download qq456cvb/CPPF --repo-type dataset --local-dir cppf_data
```

## Citation

```bibtex
@inproceedings{you2022cppf,
  title={CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild},
  author={You, Yang and Shi, Ruoxi and Wang, Weiming and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}
```