KPConv-PyTorch/doc/slam_segmentation_guide.md

Scene Segmentation on SemanticKitti

Data

We consider our experiment folder is located at XXXX/Experiments/KPConv-PyTorch. And we use a common Data folder loacated at XXXX/Data. Therefore the relative path to the Data folder is ../../Data.

SemanticKitti dataset can be downloaded here (80 GB). Download the three file named:

• data_odometry_velodyne.zip (80 GB)
• data_odometry_calib.zip (1 MB)
• data_odometry_labels.zip (179 MB)

uncompress the data and move it to ../../Data/SemanticKitti.

You also need to download the files semantic-kitti-all.yaml and semantic-kitti.yaml. Place them in your ../../Data/SemanticKitti folder.

N.B. If you want to place your data anywhere else, you just have to change the variable self.path of SemanticKittiDataset class (here).

Training

Simply run the following script to start the training:

    python3 training_SemanticKitti.py

Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called SemanticKittiConfig, and the first run of this script might take some time to precompute dataset structures.

Plot a logged training

When you start a new training, it is saved in a results folder. A dated log folder will be created, containing many information including loss values, validation metrics, model checkpoints, etc.

In plot_convergence.py, you will find detailed comments explaining how to choose which training log you want to plot. Follow them and then run the script :

    python3 plot_convergence.py

Test the trained model

The test script is the same for all models (segmentation or classification). In test_any_model.py, you will find detailed comments explaining how to choose which logged trained model you want to test. Follow them and then run the script :

    python3 test_any_model.py