Version: 0.18

Shape completion using Gaussian process regression

In this tutorial we will show how GP regression can be used to predict missing parts of a shape.


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{StatisticalModelIO, MeshIO, LandmarkIO}
import scalismo.statisticalmodel._
import scalismo.numerics.UniformMeshSampler3D
import scalismo.kernels._
import breeze.linalg.{DenseMatrix, DenseVector}
implicit val rng = scalismo.utils.Random(42)
val ui = ScalismoUI()

We also load a dataset that we want to reconstruct. In this case, it is a face without nose:

val noseless = MeshIO.readMesh(new"datasets/noseless.ply")).get
val targetGroup = ui.createGroup("target"), noseless,"noseless")

Finally, we also load the face model.

val smallModel = StatisticalModelIO.readStatisticalMeshModel(new"datasets/model.h5")).get

Enlarging the flexibility of a shape model

The model, which we just loaded, was built from only a small dataset. Therefore, the chances that it manages to reconstruct the missing nose properly are rather slim.

To increase the shape variability of the model, we add smooth some additional smooth shape deformations, modelled by a GP with symmetric Gaussian kernel. The code should be familiar from the previous tutorials.

val scalarValuedKernel = GaussianKernel[_3D](70) * 10.0
case class XmirroredKernel(kernel : PDKernel[_3D]) extends PDKernel[_3D] {
override def domain = RealSpace[_3D]
override def k(x: Point[_3D], y: Point[_3D]) = kernel(Point(x(0) * -1f ,x(1), x(2)), y)
def symmetrizeKernel(kernel : PDKernel[_3D]) : MatrixValuedPDKernel[_3D] = {
val xmirrored = XmirroredKernel(kernel)
val k1 = DiagonalKernel(kernel, 3)
val k2 = DiagonalKernel(xmirrored * -1f, xmirrored, xmirrored)
k1 + k2
val gp = GaussianProcess[_3D, EuclideanVector[_3D]](symmetrizeKernel(scalarValuedKernel))
val lowrankGP = LowRankGaussianProcess.approximateGPCholesky(
relativeTolerance = 0.01,
interpolator = NearestNeighborInterpolator())
val model = StatisticalMeshModel.augmentModel(smallModel, lowrankGP)
val modelGroup = ui.createGroup("face model")
val ssmView =, model, "model")

The new model should now contain much more flexibility, while still preserving the typical face-specific deformations.

Note: This step here is mainly motivated by the fact that we only have 10 face examples available to build the model. However, even if sufficient data is available, it might still be a good idea to slighly enlarge the flexibility of a model before attempting a reconstruction of missing parts. It gives the model some extra slack to account for bias in the data and explain minor shape variations, which have not been prominent in the dataset.

Equipped with our new model, we will perform the reconstruction in three steps:

  1. We fit the face model to the given partial face using Gaussian process regression.
  2. We restrict the model to the nose part by marginalizing and select a suitable nose shape.
  3. We choose a suitable nose from the model

As we saw previously, to perform GP regression we need observations of the deformation vectors at some points. We will discussed in Tutorial 10 how such observations can be obtained fully automatically. Here, we have done this already in a separate step and saved 200 corresponding points as landmarks, which we will now load and visualize:

val referenceLandmarks = LandmarkIO.readLandmarksJson[_3D](new"datasets/modelLandmarks.json")).get
val referencePoints : Seq[Point[_3D]] = => lm.point)
val referenceLandmarkViews = =>, lm, s"lm-${}"))
val noselessLandmarks = LandmarkIO.readLandmarksJson[_3D](new"datasets/noselessLandmarks.json")).get
val noselessPoints : Seq[Point[_3D]] = => lm.point)
val noselessLandmarkViews = =>, lm, s"lm-${}"))

These correspondences define how each selected point of the model should be deformed to its corresponding point on the target mesh. In other words, we observed a few deformation vectors at the selected model points. We use these deformation vectors and build a deformation field:

val domain = UnstructuredPointsDomain(referencePoints.toIndexedSeq)
val deformations = (0 until referencePoints.size).map(i => noselessPoints(i) - referencePoints(i) )
val defField = DiscreteField[_3D, UnstructuredPointsDomain[_3D], EuclideanVector[_3D]](domain, deformations), defField, "partial_Field")

We can now perform GP regression and retrieve the rest of the deformations fitting our observations:

val littleNoise = MultivariateNormalDistribution(DenseVector.zeros[Double](3), DenseMatrix.eye[Double](3) * 0.5)
val regressionData = for ((refPoint, noselessPoint) <- referencePoints zip noselessPoints) yield {
val refPointId = model.referenceMesh.pointSet.findClosestPoint(refPoint).id
(refPointId, noselessPoint, littleNoise)
val posterior = model.posterior(regressionData.toIndexedSeq)
val posteriorGroup = ui.createGroup("posterior-model"), posterior, "posterior")

With this posterior model, we get a normal distribution of faces satisfying our observations by having the selected characteristic points at the indicated positions.

Finally, as we are interested in the nose region only, we marginalize our posterior to obtain a posterior nose model as we did in a previous tutorial:

val nosePtIDs = model.referenceMesh.pointSet.pointIds.filter { id =>
(model.referenceMesh.pointSet.point(id) - model.referenceMesh.pointSet.point(PointId(8152))).norm <= 42
val posteriorNoseModel = posterior.marginal(nosePtIDs.toIndexedSeq)
val posteriorNoseGroup = ui.createGroup("posterior-nose-model"), posteriorNoseModel, "posteriorNoseModel")

We now have a probability distribution over fitting nose shapes. Our last task is to select one nose as a reconstruction. A simple and often reasonable choice is to use the mean of this model, as this is the reconstruction with the highest probability under the model.

val bestReconstruction = posteriorNoseModel.mean

Having a full posterior distribution over fitting nose shapes gives us, however, much more freedom in choosing a reconstruction. By introducing a utility function, which models the utility of each reconstruction for a given task, we can use this distribution as the basis for decision making by minimizing the expected posterior loss.