This is a collection of scripts to voxelize OFF models into regular voxel grids.
To be able to run the voxelizer, first compile the C extension. Simply run in the same folder:
gcc -shared -o libgrid.so -fPIC -fopenmp tribox.c
Note that if you change the name of the library, you will also have to change the name (and location) on the python wrapper script.
Now we can use the file create.py, e.g.:
python create.py --resolution 128 file1.off file2.off
If no files list is given, it will search for the ModelNet10 files in m10, and otherwise download ModelNet10
For further options, such as output directory and number of threads, run python create.py --help.
By default, the files will be stored in the directory preprocessed-res-$RESOLUTION. Each file is a 1D np.array of 1s for voxels and 0s for empty space in WHD order.
To make importing the data easier into TensorFlow™, we should merge the files into a TFRecord. The command python tfrecorder.py preprocessed-res-32 will merge the .vox files in the directory into one training.tfrecord, one test.tfrecord, and a labels.txt label to id correspondence file. The categories in the records are encoded as one-hot vectors.
It assumes the input files have the following format: {test,train}_LABEL_number.vox, where LABEL is the category of the object, and number an arbitrary identifier. It will output one file for the test data, and one for the training data, and a text file with a label-class id correspondence.
Example file name: test_bathtub_0229.vox
Run with the --help option for more options.
Simply run python train.py DIR RESOLUTION, where DIR is the data directory with the TFRecords and RESOLUTION is the voxel grid resolution. It will train the model and evaluate it with the test data and write tensorboard files into DIR/train/ and DIR/test respectively.
To view the TensorBoard results, run
tensorboard --logdir DIR --port 6006
and you can now view it on Internet Explorer 6 at localhost:6006.