icp: Iterative closest point matching between two triangular...

In zarquon42b/mesheR: Meshing Operations on Triangular Meshes

Iterative closest point matching between two triangular meshes.

Description

performs rigid body transformations (and scaling if requested) to map a reference mesh onto a target mesh

Usage

icp(
  mesh1,
  mesh2,
  iterations = 3,
  lm1 = NULL,
  lm2 = NULL,
  uprange = 1,
  maxdist = NULL,
  minclost = 50,
  distinc = 0.5,
  rhotol = pi,
  k = 50,
  reflection = FALSE,
  silent = FALSE,
  subsample = NULL,
  subsampletype = c("km", "pd"),
  type = c("rigid", "similarity", "affine"),
  getTransform = FALSE,
  pcAlign = FALSE,
  pcOptim = TRUE,
  threads = 0,
  weights = NULL
)

Arguments

mesh1	object of class "mesh3d".
mesh2	object of class "mesh3d".
iterations	integer
lm1	optional: kx3 matrix containing reference points on mesh1.
lm2	optional: kx3 matrix containing reference points on mesh2.
uprange	quantile of distances between vertices of mesh1 and closest points on mesh2. All hit points on mesh2 farther away than the specified quantile are not used for matching.
maxdist	maximum distance for closest points to be included for matching. Overrides uprange, if specified.
minclost	integer: only valid if maxdist is specified. If less than maxdist closest points are found, maxdist is increased by distinc (see below) until the specified number is reached.
distinc	numeric: amount to increment maxdist until minclost points are within this disatnce.
rhotol	maximum allowed angle of which normals are allowed to differ between reference points and hit target points. Hit points above this threshold will be neglected.
k	integer: number of closest triangles on target that will be searched for hits.
reflection	logical: allow reflection. closemeshKD from the package Morpho, while vcg calls vcgClostKD from Rvcg. If the tow meshes have only little or no overlap, "vcg" can be really slow. Otherwise very fast. "morpho" is the stable but somewhat slower algorithm.
silent	logical: no verbosity
subsample	integer use a subsample (using kmeans clustering) to find closest points for - subsample specifies the size of this subsample.
subsampletype	select type of subsampling (see vcgSample for details)
type	set type of affine transformation: options are "affine", "rigid" and "similarity" (rigid + scale)
getTransform	logical: if TRUE, a list containing the transformed mesh and the 4x4 transformation matrix.
pcAlign	if TRUE, surfaces are prealigned by principal axis. Overrides intial landmark based alignment.
pcOptim	if TRUE, all posible alignments to the PC-Axes are evaluated and the one with the lowest LSE will be used. Can be time consuming for large meshes.
threads	integer: threads to use in closest point search.
weights	vector containing weights for inital landmark transform

Details

the registration is done by minimising squared distances between reference vertices and closest points on the target surface (a.k.a. Procrustes registration)

Value

if getTransform=FALSE, the tranformed mesh1 is returned and otherwise a list containing

mesh	tranformed mesh1
transform	4x4 transformation matrix

Author(s)

Stefan Schlager

References

Zhang Z. 1994. Iterative point matching for registration of free-form curves and surfaces International Journal of Computer Vision 13:119-152.

See Also

rotmesh.onto, rotonto

Examples

require(Morpho)
data(nose)
longnose.mesh <- tps3d(shortnose.mesh,shortnose.lm,longnose.lm)
rotnose <- icp(longnose.mesh,shortnose.mesh,lm1=longnose.lm,lm2=shortnose.lm,rhotol=0.7,uprange=0.9)
## Not run: 
require(rgl)
shade3d(rotnose,col=2,alpha=0.7)
shade3d(shortnose.mesh,col=3,alpha=0.7)

## End(Not run)

zarquon42b/mesheR documentation built on Jan. 28, 2024, 2:17 p.m.