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_ycb_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_ycb_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

zhanhz/KRF

KRF

Installation

Create Dataset

Training

Evaluation