metadata
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), 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:
pip install -U "huggingface_hub[cli]"
hf download qq456cvb/CPPF --local-dir checkpoints
Citation
@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}
}