KPConv-PyTorch/doc/scene_segmentation_guide.md

Scene Segmentation on S3DIS

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.

S3DIS dataset can be downloaded here (4.8 GB). Download the file named Stanford3dDataset_v1.2.zip, uncompress the data and move it to ../../Data/S3DIS.

N.B. If you want to place your data anywhere else, you just have to change the variable self.path of S3DISDataset class (here).

Training

Simply run the following script to start the training:

    python3 training_S3DIS.py

Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called S3DISConfig, 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