wgmmreg | R Documentation |
Rigid registration of two point sets by minimizing the Wasserstein distance between GMMs
wgmmreg( X, Y, CX, CY, wx = NULL, wy = NULL, maxIter = 200, subsample = NULL, tol = 1e-08 )
X |
reference point set, a N x D matrix |
Y |
point set to transform, a M x D matrix, |
CX |
array of covariance matrices for each point in X |
CY |
array of covariance matrices for each point in Y |
wx |
(optional) vector of mixture weights for X. |
wy |
(optional) vector of mixture weights for Y. |
maxIter |
maximum number of iterations to perform (default: 200) |
subsample |
if set, use this randomly selected fraction of the points |
tol |
tolerance for determining convergence (default: 1e-8) |
a list of
Y: transformed point set,
R: rotation matrix,
t: translation vector,
c: final value of the cost function,
converged: logical, whether the algorithm converged.
data.file1 <- system.file("test_data", "parasaurolophusA.txt", package = "LOMAR", mustWork = TRUE) PS1 <- read.csv(data.file1, sep = '\t', header = FALSE) data.file2 <- system.file("test_data", "parasaurolophusB.txt", package = "LOMAR", mustWork = TRUE) C1 <- diag(0.1, ncol(PS1)) + jitter(0.01, amount = 0.01) C1 <- replicate(nrow(PS1),C1) PS2 <- read.csv(data.file2, sep = '\t', header = FALSE) C2 <- diag(0.1, ncol(PS2)) + jitter(0.01, amount = 0.01) C2 <- replicate(nrow(PS2),C2) transformation <- wgmmreg(PS1, PS2, C1, C2, subsample = 0.1, maxIter = 30, tol = 1e-4) ## Not run: # Visualize registration outcome library(rgl) plot3d(PS1, col = "blue") points3d(PS2, col = "green") points3d(transformation[['Y']], col = "magenta") ## End(Not run)
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