# Parametric, non-rigid registration:

We have seen how non-rigid ICP can be used to establish correspondences. In this tutorial we discuss a different approach to model-fitting and non-rigid registration. We are formulating the registration problem as an optimization problem, which we optimize using gradient-based optimization.

This registration is more general than ICP, in the sense that it can not only be used for surface-to-surface registration, but also for image-to-image-registration. In this tutorial we show the complete work-flow involved in a typical registration task, from building the Gaussian process model to performing the actual optimization.

The following resources from our online course may provide some helpful context for this tutorial:

• Model-fitting and correspondence (Video)
##### Preparation

As in the previous tutorials, we start by importing some commonly used objects and initializing the system.

import scalismo.geometry._
import scalismo.common._
import scalismo.ui.api._
import scalismo.mesh._
import scalismo.registration._
import scalismo.io.{MeshIO}
import scalismo.numerics._
import scalismo.kernels._
import scalismo.statisticalmodel._
import breeze.linalg.{DenseVector}

scalismo.initialize()
implicit val rng = scalismo.utils.Random(42)

val ui = ScalismoUI()


We start by loading and visualizing the reference mesh, which we will later use as the domain for our Gaussian Process model.

val referenceMesh = MeshIO.readMesh(new java.io.File("datasets/quickstart/facemesh.ply")).get

val modelGroup = ui.createGroup("model")
val refMeshView = ui.show(modelGroup, referenceMesh, "referenceMesh")
refMeshView.color = java.awt.Color.RED


## Building a Gaussian process shape model

We assume that our reference surface represents an approximately average face. This justifies the use of a zero-mean Gaussian process. As a covariance function we use a Gaussian kernel and choose to treat the x,y,z component of the vector field to be uncorrelated (indicated by the use of the DiagonalKernel).

val mean = VectorField(RealSpace[_3D], (_ : Point[_3D]) => EuclideanVector.zeros[_3D])
val kernel = DiagonalKernel[_3D](GaussianKernel(sigma = 70) * 50.0, outputDim = 3)
val gp = GaussianProcess(mean, kernel)


We then perform a low-rank approximation, to get a parametric representation of the Gaussian process:

val lowRankGP = LowRankGaussianProcess.approximateGPCholesky(
referenceMesh.pointSet,
gp,
relativeTolerance = 0.05,
interpolator = NearestNeighborInterpolator()
)


To visualize the effect of this Gaussian process, we add it to the model group as a transformation.

val gpView = ui.addTransformation(modelGroup, lowRankGP, "gp")


This has the effect, that the transformations represented by this GP, are applied to all the geometric objects, which are present in the group. In this case, it is the mean of the Gaussian process, which is applied to the reference mesh we loaded previously. By changing the parameters in the ui, we can visualize different transformations, as we did previously for statistical shape models.

Note: Adding the reference mesh to the scene, followed by a Gaussian process transformation is indeed what happend internally, we visualized Statistical Shape Models in the previous tutorials

Having visualized the Gaussian process, we can now draw random samples, to assess whether out choice of parameters of the Gaussian process leads to reasonable deformations. If not, we adjust the parameters until we are happy with the deformations that are modelled.

## Registration

In the next step we perform the registration to a target mesh. We start by loading the target mesh and displaying it.

val targetGroup = ui.createGroup("target")
val targetMesh = MeshIO.readMesh(new java.io.File("datasets/quickstart/face-2.ply")).get
val targetMeshView = ui.show(targetGroup, targetMesh, "targetMesh")


To visualize a registration, it is best to change the perspective in the graphical user interface to “orthogonal slices”. You can find this functionality in the “View -> Perspective” menu.

To define a registration, we need to define four things:

1. a transformation space that models the possible transformations of the reference surface (or the ambient space)
2. a metric to measure the distance between the model (the deformed reference mesh) an the target surface.
3. a regularizer, which penalizes unlikely transformations.
4. an optimizer.

For non-rigid registration we usually model the possible transformations using a Gaussian process. We use the Gaussian process that we have defined above to define the transformation space.

val transformationSpace = GaussianProcessTransformationSpace(lowRankGP)


As a metric, we use a simple mean squares metric. Currently, all metrics that are available in scalismo are implemented as image to image metrics. These can, however, easily be used for surface registration by representing the surface as a distance image. In addition to the images, the metric also needs to know the possible transformations (as modelled by the transformation space) and a sampler. The sampler determines the points where the metric is evaluated. In our case we choose uniformely sampled points on the reference mesh.

val fixedImage = referenceMesh.operations.toDistanceImage
val movingImage = targetMesh.operations.toDistanceImage
val sampler = FixedPointsUniformMeshSampler3D(referenceMesh, numberOfPoints = 1000)
val metric = MeanSquaresMetric(fixedImage, movingImage, transformationSpace, sampler)


As an optimizer, we choose an LBFGS Optimizer

val optimizer = LBFGSOptimizer(maxNumberOfIterations = 100)


and for regularization we choose to penalize the L2 norm using the L2Regularizer:

val regularizer = L2Regularizer(transformationSpace)


We are now ready to define Scalismo’s registration object.

val registration = Registration(metric, regularizer, regularizationWeight = 1e-5, optimizer)


Registration is an iterative process. Consequently, we work with the registration using an iterator. We obtain an iterator by calling the iterator method, where we also provide a starting position for the iteration (which is in this case the zero vector):

val initialCoefficients = DenseVector.zeros[Double](lowRankGP.rank)
val registrationIterator = registration.iterator(initialCoefficients)


Before running the registration, we change the iterator such that it prints in each iteration to current objective value, and updates the visualization. This lets us visually inspect the progress of the registration procedure.

val visualizingRegistrationIterator = for ((it, itnum) <- registrationIterator.zipWithIndex) yield {
println(s"object value in iteration $itnum is${it.value}")
gpView.coefficients = it.parameters
it
}


Note that the above code does not yet run the registration. It simply returns a new iterator, which augments the original iteration with visualization. The actual registration is executed once we “consume” the iterator. This can, for example be achieved by converting it to a sequence. The resulting sequence holds all the intermediate states of the registration. We are usually only interested in the last one:

val registrationResult = visualizingRegistrationIterator.toSeq.last


You should see in the graphical user interface, how the face mesh slowly adapts to the shape of the target mesh.

The final mesh representation can be obtained by obtaining the transform corresponding to the parameters and to warp the reference mesh with this tranform:

val registrationTransformation = transformationSpace.transformForParameters(registrationResult.parameters)
val fittedMesh = referenceMesh.transform(registrationTransformation)


### Working with the registration result

The fittedMesh that we obtained above is a surface that approximates the target surface. It corresponds to the best representation of the target in the model. For most tasks, this approximation is sufficient. However, sometimes, we need an exact representation of the target mesh. This can be achieved by defining a projection function, which projects each point onto its closest point on the target.

val targetMeshOperations = targetMesh.operations
val projection = (pt : Point[_3D]) => {
targetMeshOperations.closestPointOnSurface(pt).point
}


Composing the result of the registration with this projection, will give us a mapping that identifies for each point of the reference mesh the corresponding point of the target mesh.

val finalTransformation = registrationTransformation.andThen(projection)


To check this last point, we warp the reference mesh with the finalTransform and visualize it. Note that the projected target now coincides with the target mesh..

val projectedMesh = referenceMesh.transform(finalTransformation)
val resultGroup = ui.createGroup("result")
val projectionView = ui.show(resultGroup, projectedMesh, "projection")


### Improving registrations for more complex shapes.

This registration procedure outlined above works reasonably well for simple cases. In complex cases, in particular if you have large shape variations, you may find it difficult to find a suitable regularization weight. When you choose the regularization weight large, the procedure will result in a nice and smooth mesh, but fails to closely fit the surface. If you choose it small, it may result in folds and bad correspondences. In such cases it has proven extremely useful to simply iterate the registration procedure, with decreasing regularization weights. In the following we illustrate this procedure. We start by defining a case class, which collects all relevant parameters:

case class RegistrationParameters(regularizationWeight : Double, numberOfIterations : Int, numberOfSampledPoints : Int)


We put all the registration code into a function, which takes (among others) the registration parameters as an argument.

def doRegistration(
lowRankGP : LowRankGaussianProcess[_3D, EuclideanVector[_3D]],
referenceMesh : TriangleMesh[_3D],
targetmesh : TriangleMesh[_3D],
registrationParameters : RegistrationParameters,
initialCoefficients : DenseVector[Double]
) : DenseVector[Double] =
{
val transformationSpace = GaussianProcessTransformationSpace(lowRankGP)
val fixedImage = referenceMesh.operations.toDistanceImage
val movingImage = targetMesh.operations.toDistanceImage
val sampler = FixedPointsUniformMeshSampler3D(
referenceMesh,
registrationParameters.numberOfSampledPoints
)
val metric = MeanSquaresMetric(
fixedImage,
movingImage,
transformationSpace,
sampler
)
val optimizer = LBFGSOptimizer(registrationParameters.numberOfIterations)
val regularizer = L2Regularizer(transformationSpace)
val registration = Registration(
metric,
regularizer,
registrationParameters.regularizationWeight,
optimizer
)
val registrationIterator = registration.iterator(initialCoefficients)
val visualizingRegistrationIterator = for ((it, itnum) <- registrationIterator.zipWithIndex) yield {
println(s"object value in iteration $itnum is${it.value}")
gpView.coefficients = it.parameters
it
}
val registrationResult = visualizingRegistrationIterator.toSeq.last
registrationResult.parameters
}


Finally, we define the parameters and run the registration. Note that for large regularization weights, we sample fewer points on the surface to save some computation time. This is justified as, a strongly regularized model will not be able to adapt to fine details and hence it is not necessary to have a very accurate sampling of the surface.

val registrationParameters = Seq(
RegistrationParameters(regularizationWeight = 1e-1, numberOfIterations = 20, numberOfSampledPoints = 1000),
RegistrationParameters(regularizationWeight = 1e-2, numberOfIterations = 30, numberOfSampledPoints = 1000),
RegistrationParameters(regularizationWeight = 1e-4, numberOfIterations = 40, numberOfSampledPoints = 2000),
RegistrationParameters(regularizationWeight = 1e-6, numberOfIterations = 50, numberOfSampledPoints = 4000)
)

val finalCoefficients = registrationParameters.foldLeft(initialCoefficients)((modelCoefficients, regParameters) =>
doRegistration(lowRankGP, referenceMesh, targetMesh, regParameters, modelCoefficients))


From this point we use the procedure described above to work with the registration result.