KPConv-PyTorch/doc/pretrained_models_guide.md

S3DIS Pretrained Models

Models

We provide pretrained weights for S3DIS dataset. The raw weights come with a parameter file describing the architecture and network hyperparameters. THe code can thus load the network automatically.

The instructions to run these models are in the S3DIS documentation, section Test the trained model.

Name (link)	KPConv Type	Description	Score
Light_KPFCNN	rigid	A network with small in_radius for light GPU consumption (~8GB)	65.4%
Heavy_KPFCNN	rigid	A network with better performances but needing bigger GPU (>18GB).	66.4%

Instructions

1. Unzip and place the folder in your 'results' folder.

2. In the test script test_any_model.py, set the variable chosen_log to the path were you placed the folder.

3. Run the test script

    python3 test_any_model.py
   

4. You will see the performance (on the subsampled input clouds) increase as the test goes on.

    Confusion on sub clouds
    65.08 | 92.11 98.40 81.83  0.00 18.71 55.41 68.65 90.93 79.79 74.83 65.31 63.41 56.62
   

5. After a few minutes, the script will reproject the results form the subsampled input clouds to the real data and get you the real score

    Reproject Vote #9
    Done in 2.6 s
   
    Confusion on full clouds
    Done in 2.1 s
   
    --------------------------------------------------------------------------------------
    65.38 | 92.62 98.39 81.77  0.00 18.87 57.80 67.93 91.52 80.27 74.24 66.14 64.01 56.42
    --------------------------------------------------------------------------------------
   

6. The test script creates a folder test/name-of-your-log, where it saves the predictions, potentials, and probabilities per class. You can load them with CloudCompare for visualization.