# Surface reconstruction with R(CGAL)

**Saturn Elephant**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Eric Dunipace recently released a new package on CRAN:
**RcppCGAL**. It allows to link to the C++ library
**CGAL** in **Rcpp**. The
**CGAL** library provides an extensive set of algorithms
for computational geometry.

I made a package based on **RcppCGAL**, which I called
**RCGAL**. Unfortunately, R CMD CHECK throws some warnings
on Windows, so the package is not acceptable by CRAN, until this issue
will be resolved. To install it:

remotes::install_github( "stla/RCGAL", dependencies = TRUE, build_opts = "--no-multiarch" )

The compilation fails on Windows for R 32-bits, that is why I set the
option `build_opts = "--no-multiarch"`

. Fortunately, CRAN
will soon abandon the 32-bits version of R.

The **RCGAL** package allows to do convex hulls and
Delaunay tessellations in 2D or 3D, and provides two techniques of
surface reconstruction: the
*advanced front surface reconstruction* and the
*Poisson surface reconstruction*. That is
**CGAL** which does almost all the job, but the package
also resorts to pure R programming.

Here we will have a look at the surface reconstruction methods.

# The solid Möbius strip: construction, sampling, reconstruction

The *solid Möbius strip* is an isosurface I found in
this paper, and I like it.

Here is the code I use to construct a **rgl** mesh of the
solid Möbius strip:

# solid Möbius strip: f(x,y,z)=0 f <- function(x, y, z, a = 0.4, b = 0.1){ ((x*x+y*y+1)*(a*x*x+b*y*y)+z*z*(b*x*x+a*y*y)-2*(a-b)*x*y*z-a*b*(x*x+y*y))^2 - 4*(x*x+y*y)*(a*x*x+b*y*y-x*y*z*(a-b))^2 } # run the marching cubes algorithm #### library(misc3d) nx <- 120; ny <- 120; nz <- 120 x <- seq(-1.4, 1.4, length.out = nx) y <- seq(-1.7, 1.7, length.out = ny) z <- seq(-0.7, 0.7, length.out = nz) G <- expand.grid(x = x, y = y, z = z) voxel <- array(with(G, f(x, y, z)), dim = c(nx, ny, nz)) surface <- computeContour3d( voxel, maxvol = max(voxel), level = 0, x = x, y = y, z = z ) # make rgl mesh library(rgl) mesh0 <- misc3d:::t2ve(makeTriangles(surface)) mesh <- addNormals(tmesh3d( vertices = mesh0[["vb"]], indices = mesh0[["ib"]] ))

This mesh is quite smooth. It has \(73544\) (non-duplicated) vertices:

mesh ## mesh3d object with 73544 vertices, 147088 triangles.

## Sampling the solid Möbius strip

Here we sample a subset of the vertices of the solid Möbius strip mesh,
and later we will reconstruct the surface from this sample. I could
select some vertices at random, but I prefer to use the uniform sampling
performed by the `vcgUniformRemesh`

function of the
**Rvcg** package:

library(Rvcg) ## ## Attaching package: 'Rvcg' ## The following object is masked _by_ '.GlobalEnv': ## ## nverts resample_mesh <- vcgUniformRemesh(mesh, voxelSize = 0.06) ## Resampling mesh using a volume of 58 x 69 x 35 ## VoxelSize is 0.060000, offset is 0.000000 ## Mesh Box is 2.630913 3.263203 1.264488 str(resample_mesh) ## List of 3 ## $ vb : num [1:4, 1:7948] -0.302 -1.59 -0.118 1 -0.322 ... ## $ it : int [1:3, 1:15896] 1 2 3 3 2 4 1 5 6 3 ... ## $ normals: num [1:4, 1:7948] 0.207 0.951 0.23 1 0.271 ... ## - attr(*, "class")= chr "mesh3d" SolidMobiusStrip_cloud <- t(resample_mesh[["vb"]][-4L, ])

Here is our points cloud (I mean the sample):

open3d(windowRect = c(50, 50, 562, 562)) view3d(0, -50, zoom = 0.75) spheres3d(SolidMobiusStrip_cloud, radius = 0.015)

It has \(7948\) points:

nrow(SolidMobiusStrip_cloud) ## [1] 7948

## Advanced front surface reconstruction

We run the first surface reconstruction algorithm, the
*advanced front surface reconstruction*. Is is performed by the
`AFSreconstruction`

function of the
**RCGAL** package, which has no parameters arguments; it
only takes the points cloud as argument:

library(RCGAL) afs_mesh <- AFSreconstruction(SolidMobiusStrip_cloud)

Let’s plot this mesh (this is a triangular **rgl** mesh, of
class `mesh3d`

):

open3d(windowRect = c(50, 50, 562, 562)) view3d(0, -50, zoom = 0.75) shade3d(afs_mesh, color = "darkred")

Well it is not very smooth, but not too bad. Note that the advanced front surface reconstruction algorithm does not alter the vertices of the given points cloud, it doesn’t change them at all. So this mesh has \(7948\) vertices:

afs_mesh ## mesh3d object with 7948 vertices, 15896 triangles.

Remember that the original mesh had \(73544\) vertices.

Let’s compare with the *ball-pivoting* algorithm provided by the
**Rvcg** package:

bp_mesh <- addNormals(vcgBallPivoting( SolidMobiusStrip_cloud, angle = pi/6, clustering = 0.01 ))

The smoothness is similar but there is a couple of holes in the mesh:

open3d(windowRect = c(50, 50, 562, 562)) view3d(0, -50, zoom = 0.75) shade3d(bp_mesh, color = "firebrick")

We can get a smoother mesh and get rid of these holes by applying a mesh
smoothing technique, such as the ones offered by the
`vcgSmooth`

function of the **Rvcg** package:

smooth_bp_mesh <- vcgSmooth(bp_mesh, iteration = 50)

This is indeed better:

open3d(windowRect = c(50, 50, 562, 562)) view3d(0, -50, zoom = 0.75) shade3d(smooth_bp_mesh, color = "firebrick1")

The smooth mesh still has \(7948\) vertices:

smooth_bp_mesh ## mesh3d object with 7948 vertices, 15822 triangles.

Of course we could apply `vcgSmooth`

to our
`afs_mesh`

as well.

## Poisson reconstruction of the solid Möbius strip

Now let’s try the *Poisson surface reconstruction*, available in
**RCGAL**.

psr_mesh <- PoissonReconstruction(SolidMobiusStrip_cloud) ## Poisson reconstruction using average spacing: 0.04682.

open3d(windowRect = c(50, 50, 562, 562)) view3d(0, -50, zoom = 0.75) shade3d(psr_mesh, color = "orangered") wire3d(psr_mesh)

Clearly, that’s not smooth! But wait, there are only \(604\) vertices in this mesh:

psr_mesh ## mesh3d object with 604 vertices, 1208 triangles.

The Poisson reconstruction algorithm takes some parameters as input, and
we can reduce the `spacing`

parameter to get a more precise
mesh, at the cost of a higher computation time:

psr_mesh <- PoissonReconstruction(SolidMobiusStrip_cloud, spacing = 0.005)

open3d(windowRect = c(50, 50, 562, 562)) view3d(0, -50, zoom = 0.75) shade3d(psr_mesh, color = "orangered") wire3d(psr_mesh)

On one hand, the mesh is better, but on the other hand it has some small defaults (not highly visible on this view, try to reproduce the mesh and rotate it, you’ll see). It has \(28726\) vertices:

psr_mesh ## mesh3d object with 28726 vertices, 57556 triangles.

It has some defaults because, I think, some triangles are too small. We
can increase the trianges while keeping the
`spacing`

parameter by increasing the
`sm_distance`

parameter (whose defaut value is
\(0.375\)):

psr_mesh <- PoissonReconstruction( SolidMobiusStrip_cloud, spacing = 0.005, sm_distance = 0.9 )

This reduces the computation time. Here is the result:

open3d(windowRect = c(50, 50, 562, 562)) view3d(0, -50, zoom = 0.75) shade3d(psr_mesh, color = "darkorange") wire3d(psr_mesh)

Quite good! And the mesh has only \(3055\) vertices:

psr_mesh ## mesh3d object with 3055 vertices, 6110 triangles.

# The Stanford bunny

Now let’s try these surface reconstruction techniques to another points
cloud, a famous one: the *Stanford bunny* points cloud. It has
\(35947\) points:

data(bunny, package = "onion") nrow(bunny) ## [1] 35947

This set of points is dense. Plotting it almost gives a totally black shape:

open3d(windowRect = c(50, 50, 562, 562)) view3d(zoom = 0.75) points3d(bunny)

Firstly, let’s try the advanced front surface reconstruction:

afs_mesh <- AFSreconstruction(bunny)

open3d(windowRect = c(50, 50, 562, 562)) view3d(zoom = 0.75) shade3d(afs_mesh, color = "violetred")

Quite nice. Now here is a Poisson reconstruction, with some parameters chosen by myself (the mesh is not precise enough with the default values of the parameters):

psr_mesh <- PoissonReconstruction(bunny, spacing = 0.0001, sm_distance = 0.9)

open3d(windowRect = c(50, 50, 562, 562)) view3d(zoom = 0.75) shade3d(psr_mesh, color = "darkviolet")

The mesh has less details than the previous one but it has only \(20693\) vertices:

psr_mesh ## mesh3d object with 20693 vertices, 41382 triangles.

# The Stanford dragon

Finally, let’s play with the *Stanford dragon*. I found a points
cloud of it containing
\(100250\) points. It is so dense that
its plot is a totally black shape:

open3d(windowRect = c(50, 50, 562, 562)) view3d(zoom = 0.75) points3d(StanfordDragon)

Let’s start with the advanced front surface reconstruction (the
`StanfordDragon`

matrix is provided by
**RCGAL**):

afs_mesh <- AFSreconstruction(StanfordDragon)

open3d(windowRect = c(50, 50, 562, 562)) view3d(-20, zoom = 0.8) shade3d(afs_mesh, color = "darkolivegreen4")

Very nice. And to finish, let’s try a Poisson reconstruction.

psr_mesh <- PoissonReconstruction(StanfordDragon, spacing = 0.0003)

open3d(windowRect = c(50, 50, 562, 562)) view3d(-20, zoom = 0.8) shade3d(psr_mesh, color = "forestgreen")

Less vertices, less details!

psr_mesh ## mesh3d object with 32064 vertices, 64152 triangles.

# Acknowledgments

I am grateful to the **CGAL** members, especially
**@sloriot**
and
**@afabri**, for the help they provided to me and for the attention they pay to my
questions.

**leave a comment**for the author, please follow the link and comment on their blog:

**Saturn Elephant**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.