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pretrained_models_guide.md

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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.