ParallelQSlim
Shape aware parallel mesh simplification algorithm
This work elaborates a parallel algorithm based on quadric error metric and adaptive thresholding to simplify a triangle mesh. The approach emphasizes planar surfaces as a target to simplify. The main goal was to create a framework able to produce high quality progressive meshes based on reconstructed ones from environment. Those meshes are characterized by constant resoulution of reconstruction, therefore storing vertices for planar surfaces evenly accros the shape, which is not necessary.
The project implements QSlim algorithm and extends it with parallel approach and global planar surfaces simplification. There are 3 available quadric metrics:
Geometry
Color + Geometry
Normals + Color + Geoemtry
The project needs two libraries:
Installation:
sudo apt-get install libboost-all-dev libeigen3-dev
Check out discord #general for more info or help.
Here you can read the report which summarizes the whole work.
Default reader accepts as an input mesh only those with binary encoding.
Usage:
mkdir build
cd build
cmake ../
make -j 4
./main --in ../resources/armadillo.ply --out ../output/simply.ply -f -a 7.5 -c 2 -t 4 -r 8
Allowed options:
-h [ --help ] Produce help message
--in arg Path fine input mesh
--out arg Output path of simplified mesh
-v [ --verbose ] Show debug output
-f [ --force ] Enable file overwrite
-s [ --smooth ] Smooth the mesh using Taubin
-w [ --weight ] arg (=0) Quadric error weighting strategy
0 = none
1 = area
-r [ --reduction ] arg (=75) The percentage reduction which we want to
achieve; e.g. 10 of the input mesh
-i [ --max-iter ] arg (=10) Max iterations to perform
-t [ --threads ] arg (=1) Number of threads
-q [ --quadric ] arg (=3) Type of quadric metric
3 = [geometry]
6 = [geometry, color]
9 = [geometry, color, normal]
-c [ --clusters ] arg (=2) Number of clusters e.g.
2 will be 2x2x2=8, 3x3x3=27 clusters
-m [ --attributes ] arg (=1) Input mesh attributes
1 = [geometry]
2 = [geometry, color, normal]
-a [ --aggressiveness ] arg (=3) Aggressiveness (directly relates to the
maximum permissive error) [1.0-10.0]