metadata
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).
- 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
pip install -U "huggingface_hub[cli]"
hf download qq456cvb/CPPF --repo-type dataset --local-dir cppf_data
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}
}