Please consider consider citing our ACCV 2020 paper:

@inproceedings{boulch2020fka,
  title={{FKAConv: Feature-Kernel Alignment for Point Cloud Convolution}},
  author={Boulch, Alexandre and Puy, Gilles and Marlet, Renaud},
  booktitle={15th Asian Conference on Computer Vision (ACCV 2020)},
  year={2020}
}

FKAConv code uses LightConvPoint as backend. Please note that we used the v0.2 for experiments. As the library is under develeopment, we do not garanty compatibility with current head. Please download the v0.2 version.

pip install -ve /path/to/fkaconv/repository/

Dataset are prepared according to the LightConvPoint data preparation. We use the provided dataloaders.

We provide examples classification and segmentation datasets:

The training scripts use Sacred to manage options. The options can be managed by modifying the 'config.yaml' files or with the with statement in command line (e.g., python train.py with training.batchsize=16).

In order to train the model, you must modify the config.yaml with the right path for the data (dataset-->dir) and the path where to save the results (training-->savedir). Then, run the folowing commands:

cd examples/modelnet40/
python train.py

To evalutate the model on the test set of ModelNet40:

python test.py -c path_to_save_dir/config.yaml --iter 16 --batchsize 64

The --iter (-i) (the number of iterations per shape) and --batchsize (-b) (the batchsize to be used at test time) are optional. If not provided, their value are the one defined in the config.yaml file.

In order to train the model, you must modify the config.yaml with the right path for the data (dataset-->dir) and the path where to save the results (training-->savedir) and validation area. Then, run the folowing commands:

cd examples/s3dis/
python train.py

To evaluate the model on the validation set (this is the val_area parameter set at the training phase):

python test.py -c path_to_save_dir/config.yaml --step 0.2 --savepts

The --step (-s) (the step of the test sliding window) and --savepts (save the points and labels) are optional. If not provided, their value are the one defined in the config.yaml file.

In order to train the model, you must modify the config.yaml with the right path for the data (dataset-->dir) and the path where to save the results (training-->savedir). Then, run the folowing commands:

cd examples/semantic8/
python train.py

To evaluate the model on the test set of Semantic8, modify the test path parameter in the config.yaml file saved in the save directory and run:

python test.py -c path_to_save_dir/config.yaml --step 0.8 --savepts

The --step (-s) (the step of the test sliding window) and --savepts (save the points and labels) are optional. If not provided, their value are the one defined in the config.yaml file.

In order to train the model, you must modify the config.yaml with the right path for the data (dataset-->dir) and the path where to save the results (training-->savedir). Then, run the folowing commands:

cd examples/npm3d/
python train.py

To evaluate the model on the test set of Semantic8, modify the test path parameter in the config.yaml file saved in the save directory and run:

python test.py -c path_to_save_dir/config.yaml --step 0.8 --savepts

The --step (-s) (the step of the test sliding window) and --savepts (save the points and labels) are optional. If not provided, their value are the one defined in the config.yaml file.

Once two segmentation models have been trained (one with color information and one without), you can train a fusion model. To do so, we use the ``config_fusion.yaml` file. The path to fusion model subdirectory must be filled in this file.

Then train the model:

python train.py with config_fusion.yaml

The test procedure is the same as for single networks.