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.

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%
Deform_KPFCNN	deform	Deformable convolution network needing big GPU (>20GB).	67.3%
Deform_Light_KPFCNN	deform	Lighter version of the deformable architecture (~8GB).	66.7%

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.