KRF

Installation

• Install CUDA 10.1 / 10.2
• Set up python3 environment from requirement.txt:

  pip3 install -r requirement.txt 
  

• Install apex:

  git clone https://github.com/NVIDIA/apex
  cd apex
  export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5"  # set the target architecture manually, suggested in issue https://github.com/NVIDIA/apex/issues/605#issuecomment-554453001
  pip3 install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  cd ..
  

• Install normalSpeed, a fast and light-weight normal map estimator:

  git clone https://github.com/hfutcgncas/normalSpeed.git
  cd normalSpeed/normalSpeed
  python3 setup.py install --user
  cd ..
  

• Install tkinter through sudo apt install python3-tk
• Compile for chamfer distance

  cd krf/utils/distance
  python setup.py install
  

• Install KNN-CUDA

  cd KNN-CUDA
  make
  make install
  

• Compile RandLA-Net operators:

  cd krf/models/RandLA/
  sh compile_op.sh
  

Create Dataset

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

  ln -s path_to_unzipped_Linemod_preprocessed krf/dataset/linemod/
  

  Generate rendered and fused data following raster_triangle.

• YCB-Video: Download the YCB-Video Dataset from PoseCNN. Unzip it and link the unzipped YCB_Video_Dataset to krf/datasets/ycb/YCB_Video_Dataset:

  ln -s path_to_unzipped_YCB_Video_Dataset krf/datasets/ycb/
  

• You can download pretrained complete networks and generated data here.

  Then generate colored mesh point cloud for each objects by:

  python generate_color_pts.py
  

• Generate FFB6D estimate results

  Download pretrained model FFB6D-LineMOD, FFB6D-YCB, move it to train_log/linemod/checkpoints/ or train_log/ycb/checkpoints/. Then modify generate_ds.sh and generate estimate results by:

  bash generate_ds.sh
  

Training

• To train the network on YCB Dataset, run the following command:

bash train_ycb_refine_pcn.sh

• To train the network on LineMOD Dataset, run the following command:

# commands in train_lm_refine_pcn.sh
n_gpu=6
cls='ape'
#ckpt_mdl="/home/zhanhz/FFB6D/ffb6d/train_log/linemod/checkpoints/${cls}/FFB6D_${cls}_REFINE_best.pth.tar"
python3 -m torch.distributed.launch --nproc_per_node=$n_gpu train_lm_refine_pcn.py --gpus=$n_gpu --cls=$cls #-checkpoint $ckpt_mdl
# end

bash train_lm_refine_pcn.sh

Evaluation

• To evaluate our method on YCB Dataset, run the following command:

python ycb_refine_test.py -gpu=0 -ckpt=CHECKPOINT_PATH -use_pcld -use_rgb

• To evaluate our method on Occlusion LineMOD Dataset, run the following command for one class:

python lm_refine_test.py -gpu=0 -ckpt=CHECKPOINT_PATH -cls='ape' -use_pcld -use_rgb

or evaluate all class by:

bash test_occ_icp.sh