brep2sdf/code/pre_processing

Data pre-processing

Please first download the prepared ABC dataset from BaiduYun or OneDrive, and unzip it under current folder. We split the models with multiple components. There will be 3 items for each model:

*.obj: surface mesh of the model.
*.yml: parametric curve and patch information.
*.fea: curve segments on the mesh. Starts with the number of curve segments n, followed by n lines, where each line contains the two vertex indices of a curve segment. Can be extracted from 'vert_indices' of curves in the *.yml files.

[Optional] If you want to split the models and generate the correponding *.fea files from the raw ABC dataset, please first put the *.yml and *.obj files in folder abc_data (make sure that file in different formats share the same prefix). Install the PyYAML package via:

$ pip install PyYAML

and run:

$ python split_and_gen_fea.py

You will find the split models and *.fea files in raw_input.

Installation

Please first install the Boost and eigen3 library:

$ sudo apt install libboost-all-dev
$ sudo apt install libeigen3-dev

Then run:

$ cd PATH_TO_NH-REP/code/pre_processing
$ mkdir build && cd build
$ cmake ..
$ make

You can generate the training data:

$ cd ..
$ python gen_training_data_yaml.py

The generated training data can be found in training_data folder.

If you do not have a yaml file and want to generate sample points from meshes, you can prepare the *.fea file as sharp feature curves of the meshes, then run:

$ python gen_training_data_mesh.py

Please make sure that you set 'in_path' in gen_training_data_yaml.py and gen_training_data_mesh.py as the path containing the *.fea files.

When patch decomposition is conducted (like model 00007974_5), there will be *fixtree.obj and *fixtree.fea in training_data, which can be used for generating point samples in later round:

$ python gen_training_data_yaml.py -r

or

$ python gen_training_data_mesh.py -r

You can find the generated training data of the decomposed patch in training_data_repair. By default we only decompose one patch and it is enough for most models. But if you find *fixtree.obj and *fixtree.fea in training_data_repair, that means that more patches need to decomposed. There are two ways to achieve this. First, you can copy training_data_repair./*fixtree.obj and training_data_repair./*fixtree.fea to training_data, and re-run 'python gen_training_data_yaml.py -r', until enough patches are decomposed (i.e. *.conf files can be found in training_data_repair). Another way is to decompose all patches at once, to achieve this, simple uncomment the following line in FeatureSample/helper.cpp:

42ae22bf8f/code/pre_processing/FeatureSample/helper.cpp (L722)

After that, rebuild the executable files, and re-run 'python gen_training_data_yaml.py' and 'python gen_training_data_yaml.py -r'. There will be generated training data in training_data_repair.

docker run -it --name brep_processor -v ~/NH-Rep/code/pre_processing:/app brep_pre_processing:v1