GDRNPP for BOP2022

This repo provides code and models for GDRNPP_BOP2022.

TODO: add authors

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).

The structure of datasets folder should look like below:

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

MODELS

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

Requirements

• Ubuntu 16.04/18.04/20.04, CUDA 10.1/10.2, python >= 3.6, PyTorch >= 1.6, torchvision
• Install detectron2 from source
• sh scripts/install_deps.sh
• Compile the cpp extension for farthest points sampling (fps):

  sh core/csrc/compile.sh

Detection

TODO: tjw

Pose Estimation

The difference between this repo and gdrn conference version mainly including:

• 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)

Testing

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

Pose Refinement

TODO: rudy