Uncertainty-Aware Semi-Supervised Semantic Key Point Detection via Bundle Adjustment

This repository contains the code and data of our paper: "Uncertainty-Aware Semi-Supervised Semantic Key Point Detection via Bundle Adjustment" submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.

Dataset Preparation

YCB-Video

https://rse-lab.cs.washington.edu/projects/posecnn/

Real-world data

For the real-world drone data used in the paper, you can download from:
https://pan.baidu.com/s/1KyvN9--4radHq7ZZAiqnig?pwd=128y
password: 128y

Code for data preprocessing

We currently provide code for data preprocessing.

Environment setup

conda env create -f environment.yaml
conda activate semi-super-skp

Generate heatmap labels

1. Change 'img_dir' in config_kpts.py to your custom path.
2. Run

python utils/generate_ycb_labels.py
python utils/generate_drone_labels.py

Code for model and pose optimization

Code for this part will be released later.

Others

This project is licensed under the MIT License.
If you have any questions, please contact likai [at] westlake [dot] edu [dot] cn