The implementation of the paper 'HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation' (CVPR2024). ArXiv

• CUDA 11.1 or 11.6
• torch 1.13.1 and torchvision 0.14.1
• Open3d
• normalSpeed, a fast and light-weight normal map estimator
• RandLA-Net operators
• bop_toolkit

Setting up the environment can be tedious, so we've provided a Dockerfile to simplify the process. Please refer to the README in the Docker directory for more information.

1. Download the dataset from the BOP benchmark. Currently, our focus is on the LMO, TLESS, and YCBV datasets. We recommend using the LMO dataset for testing purposes due to its smaller size.

2. Download required ground truth (GT) folders of zebrapose from owncloud. The folders are models_GT_color, XX_GT (e.g. train_pbr_GT and test_GT) and models (models is optional, only if you want to generate GT from scratch, it contains more files needed to generate GT, but also contains all the origin files from BOP).

3. The expected data structure:

   .
   └── BOP ROOT PATH/
       ├── lmo   
       ├── ycbv/
       │   ├── models            #(from step 1 or step 2, both OK)
       │   ├── models_eval
       │   ├── test              #(testing datasets)
       │   ├── train_pbr         #(training datasets)
       │   ├── train_real        #(not needed; we exclusively trained on PBR data.)
       │   ├── ...               #(other files from BOP page)
       │   ├── models_GT_color   #(from step 2)
       │   ├── train_pbr_GT      #(from step 2)
       │   ├── train_real_GT     #(from step 2)
       │   └── test_GT           #(from step 2)
       └── tless
   

4. (Optional) Instead of download the ground truth, you can also generate them from scratch, details in Generate_GT.md.

Download our trained model from this link. python test.py --cfg config/test_lmo_config.txt --obj_name ape --ckpt_file /path/to/lmo/lmo_convnext_ape/0_7824step86000 --eval_output /path/to/eval_output --new_solver_version True --region_bit 10

The script will save the last 3 checkpoints and the best checkpoint, as well as tensorboard log. Adjust the paths in the config files, and train the network with train.py, e.g. python train.py --cfg config/train_lmo_config.txt --obj_name ape

The primary difference between train_config.txt and test_config.txt lies in the detection files they use. The provided checkpoints were trained using train_config.txt, and the results reported in the paper were obtained using test_config.txt. However, it should be perfectly acceptable to train using test_config.txt or to test using train_config.txt.

Merge the .csv files generated in the last step using tools_for_BOP/merge_csv.py, e.g.

python merge_csv.py --input_dir /dir/to/pose_result_bop/lmo --output_fn hipose_lmo-test.csv We also provide our csv files from this link.

And then evaluate it according to bop_toolkit.

Some code are adapted from ZebraPose, FFB6D, Pix2Pose, SingleShotPose, GDR-Net.

@inproceedings{lin2024hipose,
  title={Hipose: Hierarchical binary surface encoding and correspondence pruning for rgb-d 6dof object pose estimation},
  author={Lin, Yongliang and Su, Yongzhi and Nathan, Praveen and Inuganti, Sandeep and Di, Yan and Sundermeyer, Martin and Manhardt, Fabian and Stricker, Didier and Rambach, Jason and Zhang, Yu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10148--10158},
  year={2024}
}