Kacper Kania, Maciej Zięba, Tomasz Kajdanowicz Wrocław University of Science and Technology

This is the official implementation of the paper "UCSG-Net - Unsupervised Discovering of Constructive Solid Geometry Tree".

$ conda env create -f env.yml
$ conda activate ucsg

You can also run parts of experiments in the Docker, however note, that visualizations are performed in yet another container. Hence, only evaluation itself, without visualizations can be performed in the Docker. Run:

$ docker build -t ucsgnet:1.0 [-f Dockerfile.gpu] .
$ docker run [--gpus all] --rm -it -v $(pwd):/usr/src/ucsgnet --name ucsgnet ucsgnet:1.0 

to enter the container. All changes in the docker will be reflected in your directory.

You can also create whole ecosystem with the rendering in docker by running:

$ docker network create --driver bridge ucsgnet
$ git submodule update --init --recursive
$ cd mitsuba-flask-service/ &&  docker build -t mitsuba . 
$ docker run -it -p 8000:8000 --name mitsuba --network ucsgnet mitsuba:latest 
# or `$ docker start mitsuba --attach` if you built it previously
# get to the other terminal
$ docker build -t ucsgnet:1.0 [-f Dockerfile.gpu] . 
$ docker run [--gpus all] --network ucsgnet --rm -it -v $(pwd):/usr/src/ucsgnet --name ucsgnet ucsgnet:1.0 

Download from here and place them in models folder, so the structure is similar to <project-folder>/models/3d_64 and <project-folder>/models/cad_main. Note that running dvc repro will remove these models (since they're going to be from the beginning). Check dvc pipeline show --ascii output to see which stages of the pipeline are going to remove the models (pipelines with run_experiment.dvc in their names retrain the model from the beginning).

Due to license restrictions and memory limitations, we are not able to attach datasets directly to the repo. We described below, how to organize datasets so the experiments can be run smoothly.

CAD dataset can be downloaded from here (62.5 MB). Then:

$ mkdir data/cad
$ mv cad.h5 data/cad

The ShapeNet including sampling was already preprocessed by Zhiqin et al.. We provide their downloadable link that includes all the data necessary to run experiments. Check this link to find out more about preprocessing steps. After downloading and unzipping the dataset, move files to appropriate directories:

$ mkdir data/hdf5
$ mv IM-NET/IMSVR/data/*.hdf5 IM-NET/IMSVR/data/*.txt data/hdf5/

Additionally, ground truth in the form of points with normals is needed. You can download it from here. Then, you can unzip it directly to necessary folder:

$ mkdir data/pointcloud_surface
$ unzip pointcloud_surface.zip -d data/pointcloud_surface

To visualize the ground truth ShapeNet dataset, needed for visualization, you need download official ShapeNetV2 repository from here (requires registration). After that, you need put all files into data/shapenet so the taxonomy.json file, located in the original dataset, is located at data/shapenet/taxonomy.json.

$ mkdir data/shapenet
$ mv <path-to-shapenet-folders> data/shapenet/
$ cat data/shapenet/taxonomy.json

For the visualization, we used Mitsuba renderer. Here, you can find a Flask service that runs mitsuba in the background at gives an API to render objects.

All experiments are defined in a Dvcfile (from the dvc dependency included in the env.yml file). To visualize the pipeline, use:

$ dvc pipeline show --ascii Dvcfile

From there, you can notice, which experiment/visualization to run using which file with the command:

$ dvc repro dvc/<folder>/<experiment_name>.dvc

You can also run experiments manully, one by one, as described below.

Note that running any experiment as a dvc file will remove any dependencies that were produced by that file. In case of models, it will remove pretrained models. Please, read below how to avoid such a situation.

$ dvc repro [-s] dvc/cad_experiment/evaluate_model.dvc

It will output models in models/cad_main and metrics in paper-stuff/metrics/cad_experiment. To obtain visualizations, run:

$ dvc repro [-s] dvc/paper/visualize_2d_predicted_shapes_cad.dvc

This will produce all visualizations in paper-stuff/2d-shapes-visualization/cad.

As it is the most demanding task, the experiment takes about 2 days to run on the Nvidia TITAN RTX GPU. To evaluate metrics, run:

$ dvc repro dvc/3d_experiment/evaluate_model.dvc

or

$ dvc repro -s dvc/3d_experiment/reconstruct_3d_shapes.dvc
$ dvc repro -s dvc/3d_experiment/generate_edge_data.dvc
$ dvc repro -s dvc/3d_experiment/evaluate_model.dvc

to avoid retraining model and removal of attached weights.

It will output models in models/3d_64 and metrics in paper-stuff/metrics/3d_experiment.

For visualizations, run:

$ dvc repro [-s] dvc/3d_experiment/visualize_reconstructions.dvc

which will output renders to the data/3d_renders directory.

All other visualizations, that were used in the paper, can be reproduced by following the command schema:

$ dvc repro dvc/paper/example_csg_on_binary_sets.dvc
$ dvc repro dvc/paper/prediction_layer_example.dvc

Files visualize_2d_predicted_shapes_cad.dvc require pretrained cad models. Visualizations of CSG trees were performed manually by using 3d_renders/.../csg_path output folder from the experiments. The same applies for the CAD experiment.

*If any script crashes due to lack of appropriate folders, follow the instructions provided in the output of the command.

All models and logs are saved to models/ directory. To visualize learning, you have to run:

tensorboard --logdir models/

Some tensorboard files are already provided.