Multi-level Space-time Registration of Growing Plants

In this project, we implement a new method for the space-time registration of a growing plant based on matching the plant at different geometric scales.

Set up

The following python packages are needed to run the sample code

• pyntcloud
• tqdm
• numpy
• pandas
• open3d
• networkx
• scipy
• matplotlib
• Dijkstar
• scikit-learn
• SpharaPy
• polyscope
• robust-laplacian
• joblib
• imageio

Install these python packages by pip install -r requirements.txt

Usage

Registration

• Download the data from [https://media.romi-project.eu/data/4d_plant_analysis_data.zip]

• Unzip the data compressed in to data/ fold.

• Run the registration pipeline with: python3 run_registration_pipeline.py --type XXX --method YYY

  Replace XXX by the target type of plant in arabidopsis, tomato, maize. If it's not explicitly specified, it's set by default to tomato.

  Replace YYY by the method. We provide two choices of method: local_icp and fm, with local_icp our method and fm the functional map method as the reference method. If it's not explicitly specified, it's set by default to local_icp

• Attention: every time rerun the registration process, please delete the existed data/{type}/registration_result directory

• The expected visualization results:

registration result of tomato:

registration result of maize:

• the running time and metric scores might not be exactly the same as presented in the paper. Since the scores are computed on random sampled subsets. But they should be of the same order.

Interpolation

• First run the registration framework to get the point-wise correspondence. Check data/XXX/registration_result to see if the registration work is done
• Run the interpolation pipeline with: python3 run_interpolation.py --type XXX with XXX the type of plant
• The interpolated point clouds will be shown frame by frame. You can save the images and generate a video with them.
• The expected result:

compared with the interpolation video produced by funtional map based method:

We can see that the interpolation produced by our method is more smooth and less noisy.