README.md

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].

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 extension for farthest points sampling (fps):

  sh core/csrc/compile.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 including: