TG-Pose (TMM 2024)

Pytorch implementation of TG-Pose: Delving into Topology and Geometry for Category-level Object Pose Estimation. (Paper, Project)

Required environment

• Ubuntu 18.04
• Python 3.8
• Pytorch 1.11.0
• CUDA ≥11.7

Data Preparation

To generate your own dataset, use the data preprocess code provided in this git. Download the detection results in this git. Change the dataset_dir and detection_dir to your own path.

Since the handle visibility labels are not provided in the original NOCS REAL275 train set, please put the handle visibility file ./mug_handle.pkl under YOUR_NOCS_DIR/Real/train/ folder. The mug_handle.pkl is mannually labeled and originally provided by the GPV-Pose.

Training

python -m engine.train --loss_list='TDA_loss' --run_stage='RL_TDA' --dataset_dir YOUR_DATA_DIR --model_save SAVE_DIR

Detailed configurations are in config/config.py.

Evaluation

python -m evaluation.evaluate --dataset_dir YOUR_DATA_DIR --detection_dir DETECTION_DIR --resume 1 --resume_model MODEL_PATH --model_save SAVE_DIR

Citation

Cite us if you found this work useful.

@article{zhan2024tg,
  title={TG-Pose: Delving into Topology and Geometry for Category-level Object Pose Estimation},
  author={Zhan, Yue and Wang, Xin and Nie, Lang and Zhao, Yang and Yang, Tangwen and Ruan, Qiuqi},
  journal={IEEE Transactions on Multimedia},
  year={2024},
  publisher={IEEE}
}