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gps-py

Python demos for the publication
Gaussian-Product Subdivision Surfaces
Website: https://cgv.tugraz.at/preiner/gps
DOI: 10.1145/3306346.3323026

Copyright 2020 Reinhold Preiner

Demos

This repository contains two examples from the paper, demoing the functionality of Gaussian-Product subdivision (GPS) and Gaussian inference in covariance meshes.

  • tweety-inference (Fig. 7)
    Demoes the automatic enrichment of a given input triangle mesh with covariances based on Eq. 16, using the tweety model. The resulting covmesh leads to a GPS surface with sharper features than ordinary with loop subdivision.

  • cones (Fig. 10) Shows the different GPS variants of a fixed cone control mesh under varying Gaussian covariances at the apex.

Files

The demos are provided in two versions:

  • as plain python scripts (subdir 'py'). Here the subdivison results are written to ./data directory.
  • as ineractive Jupyter Notebooks (subdir 'jupyter-nb'). Here the subdivision results are directly visualized using meshplot.

Installation/Dependencies

  • The scripts depend on the python bindings of libigl for mesh file i/o as well as linear subdivision (https://libigl.github.io/libigl-python-bindings/). Install via

    conda install -c conda-forge igl

  • To run the Jupyter scripts and perform interactive mesh visualization, you also need to install jupyter and meshplot (https://skoch9.github.io/meshplot/):

    conda install -c conda-forge jupyter
    conda install -c conda-forge meshplot