CMFF6D

Cross-modality Multiscale Feature Fusion Network for 6D Pose Estimation

Installation - From conda

• Install conda environment from conda environment.yml (it might take a while, and don't forget changing the prefix in the end of environment.yml file)

conda env create -f cmff6d/environment.yml

• Activate our cmff6dtest conda environment

conda activate cmff6dtest

• Install mmseg within conda

pip install -r cmff6d/mmseg_install.txt

• Following normalSpeed to install normalSpeed within conda

pip3 install "pybind11[global]"
git clone https://github.com/hfutcgncas/normalSpeed.git
cd normalSpeed
python3 setup.py install --user

• Install some neccessary package

cd models/RandLA
sh compile_op.sh

Code Structure

Datasets

• Download real and prepare synthetic LineMod Dataset

  • Download the preprocessed LineMOD dataset from onedrive link or google drive link (refer from DenseFusion). Unzip it and link the unzipped Linemod_preprocessed/ to ffb6d/datasets/linemod/Linemod_preprocessed:

  ln -s path_to_unzipped_Linemod_preprocessed ffb6d/dataset/linemod/

• Download MP6D Dataset

链接：https://pan.baidu.com/s/1FX9OdbaVrUT1d6W6dkcmUA 提取码：by9r

Training and evaluating

Training on the LineMOD Dataset

• Train the model for the target object. bash sh train_lm.sh

Evaluating on the LineMOD Dataset

• Start evaluation by: bash sh test_lm.sh `

Evaluating on the Occ-LineMOD Dataset

• Start evaluation by: bash sh test_occlm.sh

Training on the MP6D Dataset

• Train the model for the target object.

  bash sh train_meta.sh

Evaluating on the MP6D Dataset

• Start evaluation by: bash sh test_meta.sh