LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation

Environment Setup

To install the required dependencies, use the following commands:

conda env create -f Lapose.yaml

Data Preparation

• Download the data from NOCS.
• Download the segmentation predictions on CAMERA25 and REAL275 from DualPose-Net

Run the following scripts to prepare training instances:

python prepare_data/pose_data.py
python prepare_data/shape_data.py

Change the "dataset_dir" in config/config.py to your dataset directory.

Train

• Train on the CAMERA+Real dataset.

python engine/train.py --model_save="./output/model_save"

• Train on the CAMERA dataset.

python engine/train.py  --model_save="./output/model_save_CAMERA" --dataset=CAMERA

• Train scale net.

python engine/train_scale_net.py --model_save="./output_scale_net/model_save"

Evaluate

• Evaluate on the Real dataset.

python evaluation/evaluate.py --resume_model="./output/model_save/model.pth" --dataset=Real --use_scale_net --sn_path='./output_scale_net/model_save/model.pth'

• Evaluate on the CAMERA dataset.

python evaluation/evaluate.py --resume_model="./output/model_save_CAMERA/model.pth" --dataset=CAMERA --use_scale_net --sn_path='./output_scale_net/model_save/model.pth'

Citation

If you find our work useful, please cite:

@inproceedings{zhang2024lapose,
  title={LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation},
  author={Zhang, Ruida and Huang, Ziqin and Wang, Gu and Zhang, Chenyangguang and Di, Yan and Zuo, Xingxing and Tang, Jiwen and Ji, Xiangyang},
  booktitle={European Conference on Computer Vision},
  pages={467--484},
  year={2024},
  organization={Springer}
}