---
license: mit
pipeline_tag: other
tags:
- pose-estimation
- point-cloud
- 3d
- cvpr2022
---

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

Pretrained model checkpoints 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), a sim-to-real method for category-level 9D pose estimation trained solely on synthetic ShapeNet models.

- **Code**: https://github.com/qq456cvb/CPPF
- **Project page**: https://qq456cvb.github.io/projects/cppf
- **Companion dataset (training/eval data)**: https://huggingface.co/datasets/qq456cvb/CPPF

## Contents

One folder per ShapeNet category, each containing:

| File | Description |
|---|---|
| `point_encoder_epochbest.pth` | Point encoder weights (best epoch) |
| `ppf_encoder_epochbest.pth` | PPF encoder weights (best epoch) |
| `.hydra/*.yaml` | Hydra config snapshot used for training |

Categories: `bathtub`, `bed`, `bookshelf`, `bottle`, `bowl`, `bowl_reg` (regression variant), `camera`, `can`, `chair`, `laptop`, `laptop_aux` (auxiliary lid/base segmenter), `mug`, `sofa`, `table`.

## Usage

Download and place the category folders under `checkpoints/` in the [CPPF repository](https://github.com/qq456cvb/CPPF):

```bash
pip install -U "huggingface_hub[cli]"
hf download qq456cvb/CPPF --local-dir checkpoints
```

## 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}
}
```