Smooth per-vertex data using nearest-neighbor smoothing based on mesh adjacency information.

Smooth per-vertex data using nearest-neighbor smoothing based on mesh adjacency information.

Usage

pervertexdata.smoothnn.adj(
  mesh_adj,
  pvdata,
  num_iter,
  method = "C++",
  silent = getOption("haze.silent", default = TRUE)
)

Arguments

mesh_adj

list of vectors of integers, the adjacency list representation of the mesh. One can use the pre-computed adjacency for some special meshes, see mesh.neigh.pre. Data for vertices should include the vertex itself.

pvdata

numerical vector of per-vertex-data for the mesh, one value per vertex. Data values of NA will be ignored, allowing you to mask parts of the data. If you pass an n x m matrix or data.frame, the n rows will be treated as (independent) overlays that should be smoothed in parallel. To set the number of cores to use for parallel processing, set the 'mc_cores' option like this: options("mc.cores"=22L); before calling this function. Data.frames and matrices with a single row will be converted to vectors, and the return value will be a vector in that case.

num_iter

positive integer, number of smoothing iterations.

method

character string, one of 'C++' or 'R'. The C++ version is much faster (about 30 times faster on our test machine), and there is little reason to ever use the R version in production code, so just ignore this.

silent

logical, whether to suppress output messages.

Value

numerical vector, the smoothed data (for vector input). If pvdata is a matrix or a data.frame (with more than a single column), the result is also a matrix or data.frame.

See also

pervertexdata.smoothnn if you have a mesh and still need the connectivity to be computed.

Examples

if (FALSE) { # \dontrun{
mesh = rgl::tetrahedron3d();
mesh_adj = mesh.adj(mesh, k = 1L);
pvd = rnorm(nrow(mesh$vb), mean = 5.0, sd = 1.0);
pvd_smoothed = pervertexdata.smoothnn.adj(mesh_adj, pvd, num_iter = 30L);
} # }