Skip to content

Latest commit

 

History

History
146 lines (101 loc) · 5.91 KB

README.md

File metadata and controls

146 lines (101 loc) · 5.91 KB
Raw

gproshan: a geometry processing and shape analysis framework

This framework integrates some algorithms and contributions focus on the areas of computer graphics, geometry processing and computational geometry.

Build and Run

Install all dependencies and run:

mkdir build
cd build
cmake ..
make

finally execute:

./gproshan [mesh_paths.(off,obj,ply)]

Dependencies (Linux)

g++ >= 8.3, cuda >= 10.1, libarmadillo, libeigen, libsuitesparse, libopenblas, opengl, glew, gnuplot, libcgal, libgles2-mesa, cimg

In Ubuntu (>= 18.04) you can install them with:

sudo apt install libarmadillo-dev libeigen3-dev libopenblas-dev libsuitesparse-dev libglew-dev freeglut3-dev libgles2-mesa-dev cimg-dev libcgal-dev

Build Status

Ubuntu 18.04 (Bionic)

Build Status

Contributions

CHE implementation

We have implemented a Compact Half-Edge (CHE) data structure to manipulated triangular meshes, also can be extended for other polygonal meshes. See the paper: CHE: A scalable topological data structure for triangular meshes for more details.

Geodesics

We proposed a CPU/GPU parallel algorithm to compute geodesics distances on triangular meshes. Our approach is competitive with the current methods and is simple to implement. Please cite our paper:

A minimalistic approach for fast computation of geodesic distances on triangular meshes

@Article{ROMEROCALLA2019,
  author   = { {Romero Calla}, Luciano A. and {Fuentes Perez}, Lizeth J. and Montenegro, Anselmo A. },
  title    = { A minimalistic approach for fast computation of geodesic distances on triangular meshes },
  journal  = { Computers \& Graphics },
  year     = { 2019 },
  issn     = { 0097-8493 },
  doi      = { https://doi.org/10.1016/j.cag.2019.08.014 },
  keywords = { Geodesic distance, Fast marching, Triangular meshes, Parallel programming, Breadth-first search },
  url      = { http://www.sciencedirect.com/science/article/pii/S0097849319301426 }
}

Also, we have implemented the Fast Marching algorithm, and the Heat method.

Dictionary Learning

We proposed a Dictionary Learning and Sparse Coding framework, to solve the problems of Denoising, Inpainting, and Multiresolution on triangular meshes. This work is still in process. Please cite our work:

A Dictionary Learning-based framework on Triangular Meshes

@ARTICLE{2018arXiv181008266F,
	author	= { {Fuentes Perez}, L.~J. and {Romero Calla}, L.~A. and {Montenegro}, A.~A. },
	title	= { Dictionary Learning-based Inpainting on Triangular Meshes },
	journal	= { ArXiv e-prints },
	eprint	= { 1810.08266 },
	year	= 2018,
	month	= oct,
	url	= { https://arxiv.org/abs/1810.08266 }
}

Hole repairing

We implemented repairing mesh holes in two steps:

  1. Generate a mesh to cover the hole. We modified the algorithm presented in the paper: A robust hole-filling algorithm for triangular mesh, in order to generate a planar triangular mesh using a priority queue.
  2. Fit the surface described by the new points in order to minimize the variation of the surface, solving the Poisson equation (see the Chapter 4 of the book Polygon Mesh Processing) or using Biharmonic splines.

Please see and cite our final undergraduate project: mesh hole repairing report (in Spanish).

Key-Points and Key-Components

We proposed a simple method based on the faces' areas to compute key-points for adaptive meshes.

Please cite our paper (in Spanish):

Efficient approach for interest points detection in non-rigid shapes

@INPROCEEDINGS{7359459,
	author		= { C. J. Lopez Del Alamo and L. A. Romero Calla and L. J. Fuentes Perez },
	booktitle	= { 2015 Latin American Computing Conference (CLEI) },
	title		= { Efficient approach for interest points detection in non-rigid shapes },
	year		= { 2015 },
	month		= { Oct },
	pages		= { 1-8 },
	doi		= { 10.1109/CLEI.2015.7359459 }
}

Computing key-components depends on the accuracy and definition of the key points. We were inspired by the work of Ivan Sipiran, he defined for the first time the notion of a key-component in meshes. We proposed a method based on geodesics to determine the key components.

Please see and cite our final undergraduate project: key-components report (in Spanish).

Decimation

We are implementing the algorithm described by the paper Stellar Mesh Simplification Using Probabilistic Optimization, to compute a mesh simplification.

Fairing

We implemented Spectral and Taubin fairing algorithms to smooth a mesh surface. See the Chapter 4 of the book Polygon Mesh Processing.

Laplacian and signatures

Laplace-Beltrami operator and its eigen decomposition, WKS, HKS, GPS signatures.

Documentation

Execute:

doxygen Doxyfile

to generate the documentation in html and LaTeX.

Viewer

The viewer was initially based in the viewer of https://github.com/dgpdec/course. The current viewer uses VAO and VBO to render, and the shaders have been modified and upgraded.

License

MIT License

Authors