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Results

In the following, we assume always to work on the activated virtualenv pd_mesh_net previously created, and we denote the main root of the repo as $PD_MESH_NET_ROOT (cf. install.md).

Data preparation

  • Please download the pretrained models and the training parameters to reproduce the paper results from https://www.dropbox.com/s/rip0mhsytvt14mh/pretrained_models_pd_mesh_net.zip.
  • Extract the archive in a folder of your choice, which we refer to as $UNZIP_FOLDER.
  • Choose a local folder $DATASET_FOLDER that will contain the datasets, which will be automatically downloaded and processed, e.g., 
      export DATASET_FOLDER=/home/user/datasets
  • The training-parameter files contain a placeholder for the dataset folder. Replace it with the folder defined above: 
      cd $UNZIP_FOLDER
  find . -type f -name '*.yml' -exec sed -i "s@DATASET_FOLDER@${DATASET_FOLDER}@g" {} +

Reproducing the accuracies from the pretrained models

  • Choose the experiment of which to reproduce the accuracy and store the path to the associated folder in an enviromental variable $JOB_FOLDER. For instance, to reproduce the experiment on mesh segmentation on the COSEG aliens dataset, set: 
    export JOB_FOLDER=$UNZIP_FOLDER/coseg/aliens
  • Generate the dataset to run the evaluation. Also the training dataset will automatically be generated, because it will be used to compute mean and standard deviation of the features, used to standardize the test dataset: 
    cd $PD_MESH_NET_ROOT/evaluation_utils
python generate_dataset.py --f $JOB_FOLDER
  • Run the evaluation: 
    cd $PD_MESH_NET_ROOT/evaluation_utils
python test.py --f $JOB_FOLDER

For reference, we report below the results from the paper and the variable $JOB_FOLDER that needs to be set to reproduce them:

Experiment name Classification accuracy Face-label accuracy $JOB_FOLDER
shrec16, config A, split 1 100.00%* - $UNZIP_FOLDER/shrec16/split1
shrec16, config A, split 2 100.00%* - $UNZIP_FOLDER/shrec16/split2
shrec16, config A, split 3 99.17%* - $UNZIP_FOLDER/shrec16/split3
shrec16, config B, split 1 100.00%* - $UNZIP_FOLDER/ablation_configs/shrec16/config_B/split1
shrec16, config B, split 2 100.00%* - $UNZIP_FOLDER/ablation_configs/shrec16/config_B/split2
shrec16, config B, split 3 100.00%* - $UNZIP_FOLDER/ablation_configs/shrec16/config_B/split3
shrec16, config C, split 1 100.00%* - $UNZIP_FOLDER/ablation_configs/shrec16/config_C/split1
shrec16, config C, split 2 100.00%* - $UNZIP_FOLDER/ablation_configs/shrec16/config_C/split2
shrec16, config C, split 3 100.00%* - $UNZIP_FOLDER/ablation_configs/shrec16/config_C/split3
shrec10, config A, split 1 99.67%* - $UNZIP_FOLDER/shrec10/split1
shrec10, config A, split 2 98.67%* - $UNZIP_FOLDER/shrec10/split2
shrec10, config A, split 3 99.00%* - $UNZIP_FOLDER/shrec10/split3
shrec10, config B, split 1 100.00%* - $UNZIP_FOLDER/ablation_configs/shrec10/config_B/split1
shrec10, config B, split 2 99.67%* - $UNZIP_FOLDER/ablation_configs/shrec10/config_B/split2
shrec10, config B, split 3 99.67%* - $UNZIP_FOLDER/ablation_configs/shrec10/config_B/split3
shrec10, config C, split 1 99.67%* - $UNZIP_FOLDER/ablation_configs/shrec10/config_C/split1
shrec10, config C, split 2 99.67%* - $UNZIP_FOLDER/ablation_configs/shrec10/config_C/split2
shrec10, config C, split 3 99.67%* - $UNZIP_FOLDER/ablation_configs/shrec10/config_C/split3
cube engraving, config A 94.39% - $UNZIP_FOLDER/cube_engraving/
coseg aliens, config A, U-Net, mean pooling aggregation - 97.63% $UNZIP_FOLDER/coseg/aliens_mean_aggr/
coseg aliens, config A, U-Net, add pooling aggregation - 98.18% $UNZIP_FOLDER/coseg/aliens_add_aggr/
coseg chairs, config A, U-Net, mean pooling aggregation - 97.08% $UNZIP_FOLDER/coseg/chairs_mean_aggr/
coseg chairs, config A, U-Net, add pooling aggregation - 97.23% $UNZIP_FOLDER/coseg/chairs_add_aggr/
coseg vases, config A, U-Net, mean pooling aggregation - 95.47% $UNZIP_FOLDER/coseg/vases_mean_aggr/
coseg vases, config A, U-Net, add pooling aggregation - 95.36% $UNZIP_FOLDER/coseg/vases_add_aggr/
coseg aliens, config A, superpixel-like - 94.75% $UNZIP_FOLDER/ablation_superpixels/coseg_aliens/
coseg chairs, config A, superpixel-like - 93.74% $UNZIP_FOLDER/ablation_superpixels/coseg_chairs/
coseg vases, config A, superpixel-like - 92.79% $UNZIP_FOLDER/ablation_superpixels/coseg_vases/
human body, config A, U-Net, mean pooling aggregation - 84.78% $UNZIP_FOLDER/human_seg_mean_aggr/
human body, config A, U-Net, add pooling aggregation - 85.61% $UNZIP_FOLDER/human_seg_add_aggr/

*Both for shrec16 and shrec10, the results reported in the paper are averaged over the 3 splits (e.g., for shrec10, config B, (100. + 99.67 + 99.67) / 3 = 99.78%).

Retraining

To rerun a training job with the same parameters that allowed to obtain the pretrained models above:

  • Navigate to the folder $JOB_FOLDER corresponding to the job;
  • If you would like to start the training process from scratch using the same parameters that allowed to obtain the results above, move or delete the checkpoints in the checkpoints/ folder. Optionally, the checkpoint can be left in the folder, but when using the training script, the training will resume from the epoch of the saved pretrained model;
  • Start the training job, specifying at which epoch the training job should terminate, as shown below: 
    cd $PD_MESH_NET_ROOT/evaluation_utils
python train.py --f $JOB_FOLDER --last_epoch 200   #e.g., final epoch 200

Reproducing the segmentation accuracies that require predicted edge labels (cf. Sec. H of the Supplementary Material .pdf)

Setting up MeshCNN

Since we compute the accuracy defined by MeshCNN (cf. Supplementary Material .pdf), we use their implementation to compute the accuracy, which relies on internal data representations of their code. It is therefore required to set up MeshCNN as follows:

  • Please download MeshCNN in a folder $DOWNLOAD_FOLDER of you choice, using the official code provided (last tested with commit 15b83cc): 
    cd $DOWNLOAD_FOLDER
git clone https://github.com/ranahanocka/MeshCNN
export MESHCNN_ROOT=$DOWNLOAD_FOLDER/MeshCNN
cd $MESHCNN_ROOT
git checkout 15b83cc
  • Download the (segmentation) datasets in the version of MeshCNN: 
    cd $MESHCNN_ROOT
bash ./scripts/coseg_seg/get_data.sh
bash ./scripts/human_seg/get_data.sh
  • Move the label-conversion scripts in the edge_label_accuracies folder to the MeshCNN folder: 
    cp $PD_MESH_NET_ROOT/evaluation_utils/edge_label_accuracies/* $MESHCNN_ROOT

Converting our face labels to soft edge labels

  • For any of the segmentation experiments above (valid $JOB_FOLDERs are $UNZIP_FOLDER/coseg/aliens/, $UNZIP_FOLDER/coseg/chairs/, $UNZIP_FOLDER/coseg/vases/, $UNZIP_FOLDER/human_seg/) generate the segmented meshes by running the same evaluation script shown in the section above, but with the --save_clusters flag. For instance, for the COSEG aliens experiment (assuming the dataset was already generated, cf. above):

      export JOB_FOLDER=$UNZIP_FOLDER/coseg/aliens/
  cd $PD_MESH_NET_ROOT/evaluation_utils
  python test.py --f $JOB_FOLDER --save_clusters

  • Choose a folder $OUTPUT_SOFT_LABELS_FOLDER where the converted edge soft-labels should be saved, and convert the predicted face labels to edge soft-labels, e.g.,:

    export OUTPUT_SOFT_LABELS_FOLDER=~/output_soft_labels
mkdir $OUTPUT_SOFT_LABELS_FOLDER
export DATASET_NAME=coseg_aliens
# Remember to update JOB_FOLDER accordingly, cf. above.
workon pd_mesh_net
cd $MESHCNN_ROOT
python obtain_soft_edge_labels.py \
  --dataset_name $DATASET_NAME \
  --meshcnn_root $MESHCNN_ROOT \
  --root_segmented_meshes $JOB_FOLDER/segmented_test_meshes \
  --o $OUTPUT_SOFT_LABELS_FOLDER

  • Evaluate the accuracies using the script test_pd_mesh_net.py, as follows:

    python test_pd_mesh_net.py   \
  --dataset_name $DATASET_NAME \
  --meshcnn_root $MESHCNN_ROOT \
  --soft_labels_folder $OUTPUT_SOFT_LABELS_FOLDER

    For reference, we report below the results from the paper and the variables that need to be set to reproduce them:

    Experiment name Acc. ground-truth hard edge labels Acc. ground-truth soft edge labels $JOB_FOLDER $DATASET_NAME
    coseg aliens, config A, U-Net, mean pooling aggregation 96.51% 98.53% $UNZIP_FOLDER/coseg/aliens_mean_aggr/ coseg_aliens
    coseg aliens, config A, U-Net, add pooling aggregation 97.06% 99.03% $UNZIP_FOLDER/coseg/aliens_add_aggr/ coseg_aliens
    coseg chairs, config A, U-Net, mean pooling aggregation 96.26% 97.93% $UNZIP_FOLDER/coseg/chairs_mean_aggr/ coseg_chairs
    coseg chairs, config A, U-Net, add pooling aggregation 96.44% 98.21% $UNZIP_FOLDER/coseg/chairs_add_aggr/ coseg_chairs
    coseg vases, config A, U-Net, mean pooling aggregation 94.70% 97.96% $UNZIP_FOLDER/coseg/vases_mean_aggr/ coseg_vases
    coseg vases, config A, U-Net, add pooling aggregation 94.57% 97.83% $UNZIP_FOLDER/coseg/vases_add_aggr/ coseg_vases
    human body, config A, U-Net, mean pooling aggregation 84.13% 90.44% $UNZIP_FOLDER/human_seg_mean_aggr/ human_seg
    human body, config A, U-Net, add pooling aggregation 85.09% 91.11% $UNZIP_FOLDER/human_seg_add_aggr/ human_seg