An R package implementing the efficient marching cubes algorithm written by Thomas Lewiner. Minor changes have been made to the code in order to work with the armadillo C++ library.

The key and only function exported in this package is `contour3d()`

, taking a 3-dimensional array of values and returning the calculated 3d mesh object fit to this data. A similar function with more flexibility for different inputs and outputs is provided in the `misc3d`

package. The implementation here has two key advantages, firstly since the implementation is based on compiled C++ code the result should be considerably quicker, perhaps by orders of magnitude, secondly normals are additionally calculated and returned for each vertex making up the 3d contour.

```
# Function to generate values decreasing in a sphere-like way
f <- function(coords) coords[1]^2 + coords[2]^2 + coords[3]^2
# Set grid coordinates at which to calculate values
x <- seq(-2,2,len = 20)
y <- seq(-2,2,len = 20)
z <- seq(-2,2,len = 20)
# Calculate values across grid coordinates
grid_coords <- expand.grid(x, y, z)
grid_values <- apply(grid_coords, 1, f)
# Convert to a 3d array
grid_array <- array(grid_values, dim = c(length(x), length(y), length(z)))
# Calculate 3d contour from the grid data at a contour level of value 4
contour_shape <- contour3d(
griddata = grid_array,
level = 4,
x = x,
y = y,
z = z
)
# Optionally view the output using the r3js package
# devtools::install_github("shwilks/r3js")
# Setup plot object
data3js <- r3js::plot3js(
x = x,
y = y,
z = z,
type = "n"
)
# Add shape according to the calculated contours
data3js <- r3js::shape3js(
data3js,
vertices = contour_shape$vertices,
faces = contour_shape$triangles,
normals = contour_shape$normals,
col = "red"
)
# View the plot
r3js::r3js(data3js)
```