Created by Hugues THOMAS

A step-by-step installation guide for Ubuntu 16.04 is provided in INSTALL.md. Windows is currently not supported as the code uses tensorflow custom operations.

N.B. For reviews, an anonymous version of this code can be downloaded here (110 KB).

Regularly sampled clouds from ModelNet40 dataset can be downloaded here (1.6 GB). Uncompress the folder and move it to a folder called Data/ModelNet40/modelnet40_normal_resampled.

N.B. If you want to place your data anywhere else, you just have to change the variable self.path of ModelNet40Dataset class (line 142 of the file datasets/ModelNet40.py). The same can be done for the other datasets.

Simply run the following script to start the training:

    python3 training_ModelNet40.py

This file contains a configuration subclass ModelNet40Config, inherited from the general configuration class Config defined in utils/config.py. The value of every parameter can be modified in the subclass. The first run of this script will precompute structures for the dataset which might take some time.

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 model you want to test. Follow them and then run the script :

    python3 test_any_model.py

For any model, run:

    python3 visualize_features.py

More details in the script.

For any model, run:

    python3 plot_convergence.py

With this script, you can show the evolution of the training loss, validation accuracy, learning rate. You can also compare different logs. More details in the script.

ShapeNetPart dataset can be downloaded here (635 MB). Uncompress the folder and move it to Data/ShapeNetPart/shapenetcore_partanno_segmentation_benchmark_v0.

Simply run the following script to start the training:

    python3 training_ShapeNetPart.py

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

See [ModelNet40 section].

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

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.

See [ModelNet40 section].

Our code is released under Apache 2.0 License (see LICENSE file for details).

• 17/03/2019: New version of KPConv.
• 11/12/2018: Added general visualization code.
• 10/12/2018: Added training code for S3DIS and general test code.
• 26/11/2018: Added training code for ModelNet40/ShapeNetPart.