This work is trained on YCB-Video and LineMOD dataset, and tested on YCB-Video and LineMOD&Occlusion LineMOD respectively.

• YCB-Video Dataset:
  • Download YCB-Video dataset from PoseCNN.
  • Download the predicted masks from PVN3D/FFB6D(The results reported in paper use the masks predicted by FFB6D).
• LineMOD Dataset: Download preprocessed LineMOD dataset and also the masks from DenseFusion
• Occlusion-LineMOD
  • Download Occlusion LineMOD dataset from HybridPose, and also the masks predicted by HybridPose for evaluation.
• Put the downloaded datasets and masks into the "datasets" folder, please refer to Code Structure section for more detail.

• Install torch1.3.0 and CUDA10.1 .
• Install dependencies:

    matplotlib==3.3.4
    seaborn==0.11.2
    opencv-python==4.6.0
    sklearn
    open3d==0.8.0.0
    gorilla-core==0.2.7.8
    torchvision==0.4.1
    h5py
    autolab_core
    transforms3d
    ipdb
    pillow==6.1
  

• Compile and install the libraries:

    conda install --yes -c bioconda   google-sparsehash
    cd libs/spconv/
    bash install.sh
    cd ../pointgroup_ops 
    bash install.sh 
    cd ../pointnet_sp
    bash install.sh
    cd ../pointnet_lib    
    bash install.sh
  

Download and unzip these files, and put them into "log" directory for evaluation, please refer to Code Structure section for more detail.

After you have downloaded and setup the environment, simply execute the corresponding shell script to train or evaluate DCL-Net.

bash scripts/script_eval_YCBV_stage1.sh
bash scripts/script_train_YCBV_stage1.sh

DCL_Net
|--- configs
|--- |--- config_LM.yaml: Configuration for the experiments of LineMOD/Occlusion LineMOD dataset.
|--- |--- config_YCBV_32/40.yaml: Configurations for the experiments of YCB-Video dataset.

|--- datasets
|--- |--- Linemod_preprocessed: The LineMOD dataset, .
|--- |--- occlusion_linemod: The Occlusion LineMOD dataset, from HybridPose.
|--- |--- LMO_Masks: The masks for the evaluation of Occlusion LineMOD(the prediction of HybridPose.
|--- |--- YCB_Video_Dataset: The YCB-Video dataset.
|--- |--- YCBV_Masks: The masks for the evaluation of YCB-Video dataset.
|--- |--- |--- Masks_PVN3D: The masks predicted by PVN3D.
|--- |--- |--- Masks_FFB6D: The masks predicted by FFB6D.

|--- LM:
|--- |--- dataloader_train_LM.py: The dataloader for the training set of LineMOD dataset.
|--- |--- dataloader_test_LM.py: The dataloader for the test set of LineMOD dataset.
|--- |--- dataloader_test_LMO.py: The dataloader for the test set of Occlusion LineMOD dataset.

|--- YCBV
|--- |--- dataloader_train_YCBV.py: The dataloader for the training set of YCB-Video dataset.
|--- |--- dataloader_test_YCBV.py: The dataloader for the test set of YCB-Video dataset.
|--- |--- utils_YCBV
|--- |--- |--- train_data_list.txt: The list of the training set for YCB-Video dataset.
|--- |--- |--- test_data_list.txt: The list of the test set for YCB-Video dataset.
|--- |--- CADs: The CAD models of YCB-Video dataset.

|--- libs
|--- |--- pointgroup_ops: Some operations of sparse tensor, refers to PointGroup.
|--- |--- pointnet_ops: Some operations for the pointcloud, such as knn, refers to PointNet++.
|--- |--- pointnet_sp: Some operations for the pointcloud, where the dimension of batch_id is also considered.
|--- |--- spconv: The lib of Sparse Convolution, refers to spconv.

|--- models
|--- |--- DCL_Net.py: The proposed DCL-Net.
|--- |--- refiner.py: The proposed refiner.
|--- |--- Modules.py: Some basic modules for the construction of network.

|--- scripts
|--- |--- script_train_LM.sh: A shell script to execute the training process of DCL-Net on LineMOD dataset conveniently.
|--- |--- script_eval_LM.sh: A shell script to execute the evaluation of DCL-Net on LineMOD dataset conveniently.
|--- |--- script_eval_LMO.sh: A shell script to execute the evaluation of DCL-Net on Occlusion LineMOD dataset conveniently.
|--- |--- script_train_YCBV_stage1.sh: A shell script to execute the training of DCL-Net on YCB-Video dataset conveniently.
|--- |--- script_train_YCBV_stage2.sh: A shell script to execute the training of refiner on YCB-Video dataset conveniently.
|--- |--- script_eval_YCBV_stage1.sh: A shell script to execute the evaluation of DCL-Net on YCB-Video dataset conveniently.
|--- |--- script_eval_YCBV_stage2.sh: A shell script to execute the evaluation of DCL-Net+refiner on YCB-Video dataset conveniently.

|--- tools
|--- |--- train_LM.py: A python script describe the detailed training process of DCL-Net on LineMOD dataset.
|--- |--- test_LM.py: A python script describe the detailed evaluation process of DCL-Net on LineMOD dataset.
|--- |--- test_LMO.py: A python script describe the detailed evaluation process of DCL-Net on Occlusion LineMOD dataset.
|--- |--- train_YCBV_stage1.py: A python script, describing the detailed training process of DCL-Net on YCB-Video dataset.
|--- |--- train_YCBV_stage2.py: A python script, describing the detailed training process of refiner on YCB-Video dataset.
|--- |--- test_YCBV_stage1.py: A python script, describing the detailed evaluation process of DCL-Net on YCB-Video dataset.
|--- |--- test_YCBV_stage2.py: A python script, describing the detailed evaluation process of DCL-Net+refiner on YCB-Video dataset.

|--- log
|--- |--- DCL_Net_config_YCBV_bs32_id0: Contains the checkpoint and also the training/test log of an experiment that is based on the "DCL-Net" model and "config_YCBV_bs32" configuration on YCB-Video dataset.
|--- |--- refiner_refiner_config_YCBV_bs40_id0_model_DCL_Net_config_YCBV_bs32_id0_epoch_84: Contains the checkpoint and also the training/test log of an experiment for refiner based on "DCL_Net_config_YCBV_bs32_id0" on YCB-Video dataset.
|--- |--- DCL_Net_config_LM_id0: Contains the checkpoint and also the training/test log of an experiment that is based on the "DCL-Net" model and "config_LM" configuration on LineMOD.

|--- utils:

|--- figs

|--- README.md