GDRNPP for BOP2022

This repo provides code and models for GDRNPP_BOP2022, winner (most of the awards) of the BOP Challenge 2022 at ECCV'22 [slides].

News

[18/03/2025] Our paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)! Paper is available at [arXiv].

Path Setting

Dataset Preparation

Download the 6D pose datasets from the BOP website and VOC 2012 for background images. Please also download the test_bboxes from here OneDrive (password: groupji) or BaiDuYunPan(password: vp58).

The structure of datasets folder should look like below:

datasets/
├── BOP_DATASETS   # https://bop.felk.cvut.cz/datasets/
    ├──tudl
    ├──lmo
    ├──ycbv
    ├──icbin
    ├──hb
    ├──itodd
    └──tless
└──VOCdevkit

Models

Download the trained models at Onedrive (password: groupji) or BaiDuYunPan(password: 10t3) and put them in the folder ./output.

Requirements

• Ubuntu 18.04/20.04, CUDA 10.1/10.2/11.6, python >= 3.7, PyTorch >= 1.9, torchvision
• Install detectron2 from source
• sh scripts/install_deps.sh
• Compile the cpp extensions for

1. farthest points sampling (fps)

2. flow

3. uncertainty pnp

4. ransac_voting

5. chamfer distance

6. egl renderer

   sh ./scripts/compile_all.sh
   

Detection

We adopt yolox as the detection method. We used stronger data augmentation and ranger optimizer.

Training

Download the pretrained model at Onedrive (password: groupji) or BaiDuYunPan(password: aw68) and put it in the folder pretrained_models/yolox. Then use the following command:

./det/yolox/tools/train_yolox.sh <config_path> <gpu_ids> (other args)

Testing

./det/yolox/tools/test_yolox.sh <config_path> <gpu_ids> <ckpt_path> (other args)

Pose Estimation

The difference between this repo and GDR-Net (CVPR2021) mainly includes:

• Domain Randomization: We used stronger domain randomization operations than the conference version during training.
• Network Architecture: We used a more powerful backbone Convnext rather than resnet-34, and two mask heads for predicting amodal mask and visible mask separately.
• Other training details, such as learning rate, weight decay, visible threshold, and bounding box type.

Training

./core/gdrn_modeling/train_gdrn.sh <config_path> <gpu_ids> (other args)

For example:

./core/gdrn_modeling/train_gdrn.sh configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py 0

Testing

./core/gdrn_modeling/test_gdrn.sh <config_path> <gpu_ids> <ckpt_path> (other args)

For example:

./core/gdrn_modeling/test_gdrn.sh configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py 0 output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth

Pose Refinement

We utilize depth information to further refine the estimated pose. We provide two types of refinement: fast refinement and iterative refinement.

For fast refinement, we compare the rendered object depth and the observed depth to refine translation. Run

./core/gdrn_modeling/test_gdrn_depth_refine.sh <config_path> <gpu_ids> <ckpt_path> (other args)

For iterative refinement, please checkout to the pose_refine branch for details.

Citing GDRNPP

If you use GDRNPP in your research, please use the following BibTeX entries.

@article{liu2025gdrnpp,
  title     = {GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator},
  author    = {Liu, Xingyu and Zhang, Ruida and Zhang, Chenyangguang and Wang, Gu and Tang, Jiwen and Li, Zhigang and Ji, Xiangyang},
  journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year      = {2025},
}

@InProceedings{Wang_2021_GDRN,
    title     = {{GDR-Net}: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation},
    author    = {Wang, Gu and Manhardt, Fabian and Tombari, Federico and Ji, Xiangyang},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {16611-16621}
}