Skip to main content

computational geometry - How to perform Loop subdivision on a triangle mesh with Mathematica?


(Cross posted on Wolfram Community)


Every now and then, the question pops up how a given geometric mesh (e.g. a MeshRegion) can be refined to produce a (i) finer and (ii) smoother mesh. For example, the following triangle mesh from the example database is pretty coarse.


R = ExampleData[{"Geometry3D", "Triceratops"}, "MeshRegion"]
MeshCellCount[R, 2]

enter image description here



5660




Well, we could execute this


S = DiscretizeRegion[R, MaxCellMeasure -> {1 -> 0.01}]
MeshCellCount[S, 2]

enter image description here



1332378



only to learn that the visual appearance hasn't improved at all.



So, how can we refine it a smoothing way with Mathematica? There are several subdivision schemes known in geometry processing, e.g. Loop subdivision for triangle meshes and Catmull-Clark subdivision for general polyhedral meshes, but there seem to be no built-in methods for these.



Answer



We need quite a bit of preparation. In the first place we need methods to compute cell adjacency matrices from here. I copied the code for completeness.


CellAdjacencyMatrix[R_MeshRegion, d_, 0] := If[MeshCellCount[R, d] > 0,
Unitize[R["ConnectivityMatrix"[d, 0]]],
{}
];

CellAdjacencyMatrix[R_MeshRegion, 0, d_] := If[MeshCellCount[R, d] > 0,
Unitize[R["ConnectivityMatrix"[0, d]]],

{}
];

CellAdjacencyMatrix[R_MeshRegion, 0, 0] :=
If[MeshCellCount[R, 1] > 0,
With[{A = CellAdjacencyMatrix[R, 0, 1]},
With[{B = A.Transpose[A]},
SparseArray[B - DiagonalMatrix[Diagonal[B]]]
]
],

{}
];

CellAdjacencyMatrix[R_MeshRegion, d1_, d2_] :=
If[(MeshCellCount[R, d1] > 0) && (MeshCellCount[R, d2] > 0),
With[{B = CellAdjacencyMatrix[R, d1, 0].CellAdjacencyMatrix[R, 0, d2]},
SparseArray[
If[d1 == d2,
UnitStep[B - DiagonalMatrix[Diagonal[B]] - d1],
UnitStep[B - (Min[d1, d2] + 1)]

]
]
],
{}
];

Alternatively to copying the code above, simply make sure that you have IGraph/M version 0.3.93 or later installed and run


Needs["IGraphM`"];
CellAdjacencyMatrix = IGMeshCellAdjacencyMatrix;


Next is a CompiledFunction to compute the triangle faces for the new mesh:


getSubdividedTriangles = 
Compile[{{ff, _Integer, 1}, {ee, _Integer, 1}},
{
{Compile`GetElement[ff, 1],Compile`GetElement[ee, 3],Compile`GetElement[ee, 2]},
{Compile`GetElement[ff, 2],Compile`GetElement[ee, 1],Compile`GetElement[ee, 3]},
{Compile`GetElement[ff, 3],Compile`GetElement[ee, 2],Compile`GetElement[ee, 1]},
ee
},
CompilationTarget -> "C",

RuntimeAttributes -> {Listable},
Parallelization -> True,
RuntimeOptions -> "Speed"
];

Finally, the method that webs everything together. It assembles the subdivision matrix (which maps the old vertex coordinates to the new ones), uses it to compute the new positions and calls getSubdividedTriangles in order to generate the new triangle faces.


ClearAll[LoopSubdivide];
Options[LoopSubdivide] = {
"VertexWeightFunction" ->
Function[n, 5./8. - (3./8. + 1./4. Cos[(2. Pi)/n])^2],

"EdgeWeight" -> 3./8.,
"AverageBoundary" -> True
};

LoopSubdivide[R_MeshRegion, opts : OptionsPattern[]] :=
LoopSubdivide[{R, {{0}}}, opts][[1]];

LoopSubdivide[{R_MeshRegion, A_?MatrixQ}, OptionsPattern[]] :=
Module[{A00, A10, A12, A20, B00, B10, n, n0, n1, n2, βn, pts,
newpts, edges, faces, edgelookuptable, triangleneighedges,

newfaces, subdivisionmatrix, bndedgelist, bndedges, bndvertices,
bndedgeQ, intedgeQ, bndvertexQ,
intvertexQ, β, βbnd, η},
pts = MeshCoordinates[R];
A10 = CellAdjacencyMatrix[R, 1, 0];
A20 = CellAdjacencyMatrix[R, 2, 0];
A12 = CellAdjacencyMatrix[R, 1, 2];
edges = MeshCells[R, 1, "Multicells" -> True][[1, 1]];
faces = MeshCells[R, 2, "Multicells" -> True][[1, 1]];
n0 = Length[pts];

n1 = Length[edges];
n2 = Length[faces];
edgelookuptable = SparseArray[
Rule[
Join[edges, Transpose[Transpose[edges][[{2, 1}]]]],
Join[Range[1, Length[edges]], Range[1, Length[edges]]]
],
{n0, n0}];
(*A00=CellAdjacencyMatrix[R,0,0];*)
A00 = Unitize[edgelookuptable];

bndedgelist = Flatten[Position[Total[A12, {2}], 1]];
If[Length[bndedgelist] > 0, bndedges = edges[[bndedgelist]];
bndvertices = Sort[DeleteDuplicates[Flatten[bndedges]]];
bndedgeQ = SparseArray[Partition[bndedgelist, 1] -> 1, {n1}];
bndvertexQ = SparseArray[Partition[bndvertices, 1] -> 1, {n0}];
B00 = SparseArray[Join[bndedges, Reverse /@ bndedges] -> 1, {n0, n0}];
B10 = SparseArray[Transpose[{Join[bndedgelist, bndedgelist], Join @@ Transpose[bndedges]}] -> 1, {n1, n0}];,
bndedgeQ = SparseArray[{}, {Length[edges]}];
bndvertexQ = SparseArray[{}, {n0}];
B00 = SparseArray[{}, {n0, n0}];

B10 = SparseArray[{}, {n1, n0}];
];
intedgeQ = SparseArray[Subtract[1, Normal[bndedgeQ]]];
intvertexQ = SparseArray[Subtract[1, Normal[bndvertexQ]]];
n = Total[A10];
β = OptionValue["VertexWeightFunction"];
η = OptionValue["EdgeWeight"];
βn = β /@ n;
βbnd = If[TrueQ[OptionValue["AverageBoundary"]], 1./8., 0.];
subdivisionmatrix = Join[

Plus[
DiagonalMatrix[SparseArray[1. - βn] intvertexQ + (1. - 2. βbnd) bndvertexQ],
SparseArray[(βn/n intvertexQ)] A00,
βbnd B00
],
Plus @@ {
((3. η - 1.) intedgeQ) (A10),
If[Abs[η - 0.5] < Sqrt[$MachineEpsilon], Nothing, ((0.5 - η) intedgeQ) (A12.A20)], 0.5 B10}
];
newpts = subdivisionmatrix.pts;

triangleneighedges = Module[{f1, f2, f3},
{f1, f2, f3} = Transpose[faces];
Partition[
Extract[
edgelookuptable,
Transpose[{Flatten[Transpose[{f2, f3, f1}]], Flatten[Transpose[{f3, f1, f2}]]}]],
3]
];
newfaces =
Flatten[getSubdividedTriangles[faces, triangleneighedges + n0],

1];

{
MeshRegion[newpts, Polygon[newfaces]],
subdivisionmatrix
}
]

Test examples


So, let's test it. A classical example is subdividing an "Isosahedron":



R = RegionBoundary@PolyhedronData["Icosahedron", "MeshRegion"];
regions = NestList[LoopSubdivide, R, 5]; // AbsoluteTiming // First
g = GraphicsGrid[Partition[regions, 3], ImageSize -> Full]


0.069731



enter image description here


Now, let's tackle the "Triceratops" from above:


R = ExampleData[{"Geometry3D", "Triceratops"}, "MeshRegion"];

regions = NestList[LoopSubdivide, R, 2]; // AbsoluteTiming // First
g = GraphicsGrid[Partition[regions, 3], ImageSize -> Full]


0.270776



enter image description here


The meshes so far had trivial boundary. As for an example with nontrivial boundary, I dug out the "Vase" from the example dataset:


R = ExampleData[{"Geometry3D", "Vase"}, "MeshRegion"];
regions = NestList[LoopSubdivide, R, 2]; // AbsoluteTiming // First

g = GraphicsRow[
Table[Show[S, ViewPoint -> {1.4, -2.1, -2.2},
ViewVertical -> {1.7, -0.6, 0.0}], {S, regions}],
ImageSize -> Full]


1.35325



enter image description here


Edit



Added some performance improvements and incorporated some ideas by Chip Hurst form this post.


Added options for customization of the subdivision process, in particular for planar subdivision (see this post for an application example).


Added a way to return also the subdivision matrix since it can be useful, e.g. for geometric multigrid solvers. Just call with a matrix as second argument, e.g., LoopSubdivide[R,{{1}}].


Fixed a bug that produced dense subdivision matrices in some two-dimensional examples due to not using 0 as "Background" value.


Comments

Popular posts from this blog

plotting - Plot 4D data with color as 4th dimension

I have a list of 4D data (x position, y position, amplitude, wavelength). I want to plot x, y, and amplitude on a 3D plot and have the color of the points correspond to the wavelength. I have seen many examples using functions to define color but my wavelength cannot be expressed by an analytic function. Is there a simple way to do this? Answer Here a another possible way to visualize 4D data: data = Flatten[Table[{x, y, x^2 + y^2, Sin[x - y]}, {x, -Pi, Pi,Pi/10}, {y,-Pi,Pi, Pi/10}], 1]; You can use the function Point along with VertexColors . Now the points are places using the first three elements and the color is determined by the fourth. In this case I used Hue, but you can use whatever you prefer. Graphics3D[ Point[data[[All, 1 ;; 3]], VertexColors -> Hue /@ data[[All, 4]]], Axes -> True, BoxRatios -> {1, 1, 1/GoldenRatio}]

plotting - Filling between two spheres in SphericalPlot3D

Manipulate[ SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, Mesh -> None, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], {n, 0, 1}] I cant' seem to be able to make a filling between two spheres. I've already tried the obvious Filling -> {1 -> {2}} but Mathematica doesn't seem to like that option. Is there any easy way around this or ... Answer There is no built-in filling in SphericalPlot3D . One option is to use ParametricPlot3D to draw the surfaces between the two shells: Manipulate[ Show[SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], ParametricPlot3D[{ r {Sin[t] Cos[1.5 Pi], Sin[t] Sin[1.5 Pi], Cos[t]}, r {Sin[t] Cos[0 Pi], Sin[t] Sin[0 Pi], Cos[t]}}, {r, 1, 2 - n}, {t, 0, Pi}, PlotStyle -> Yellow, Mesh -> {2, 15}]], {n, 0, 1}]

plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1....