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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}
}