This repository contains all the code to reproduce results from recent publication:
- Dennis Madsen, Andreas Morel-Forster, Patrick Kahr, Dana Rahbani, Thomas Vetter and Marcel Lüthi "A Closest Point Proposal for MCMC-based Probabilistic Surface Registration" IN: arXiv preprint (2019)
Markov Chain Monte Carlo for shape registration with examples using Scalismo.
Data avaialble for this experiment was supplied by Prof Albert Mehl(University of Zurich) and is confidential.
Creation of model using CBCT data
-
Load full samples data
-
Align meshes
-
Registeration
apps/teeth/registeration/
- Non Rigid ICP proposal
- Gradient based optimization
- ICP proposal for MCMC
- Build SSM model from CBCT data
Augmenting SSM model with partial data from laser scans
apps/teeth/definition/changepointKernelForPartialMesh.scala
apps/teeth/definition/changepointKernel.scala
-
Load partial data
-
Partial data registeration
api/sampling/evaluators/IndependentPointDistanceEvaluator.scala api/sampling/MixedProposalDistributions.scala apps/teeth/registeration/
-
Non rigid ICP proposal
-
ICP proposal for MCMC
- Creation of SSM Model from CBCT and laser scan data
The experiments are found under apps/femur. The repository already contains Gaussian Process Morphable Models (GPMMs) of the femur - approximated with 50 and 100 basis functions. A synthetic target is also provided. Without downloading the full test dataset from SMIR, the registration can be performed on the target mesh.
- IcpRegistration: Perform a normal ICP registration.
- IcpProposalRegistration: Performs a probabilistic registration using our method described in the paper.
- GreateGPmodel: Create a new GPMM with a user defined number of basis functions.
The log file for the target registration with 100.000 samples is avilable, the following scripts can be used for replay and visualisation:
- ReplayFittingFromLog: Replay a fitting from a log file
- PosteriorVariabilityToMeshColor: Create a color map on the MAP solution to visualise the registration uncertainty
The test dataset we use is the same as used in the Statistical Shape Modelling course on futurelearn and can be downloaded from SMIR: [comment]: <> (Registration procedure from the SSM course https://www.futurelearn.com/courses/statistical-shape-modelling/0/steps/16884)
- Go to the SMIR registraiton page.
- Fill in your details, and select SSM.FUTURELEARN.COM as a research unit.
- This will send a request to an administrator to authorize your account creation. Please bare in mind that this might take up to 24h. You will be informed by Email once your account creation is authorized.
- Follow the instructions on Sicas Medical Image Repository (SMIR) to download the required femur surfaces and corresponding landmarks.
- We only use the data from Step 2 of the project
- Extract the folder under data/femur/SMIR, such that the mesh 0.stl can be found under data/femur/SMIR/step2/meshes
- Align all the test meshes to the model by running the apps/femur/AlignShapes script
To compare the Markov-Chain random-walk to our ICP-proposal, run the script: apps/femur/RunMHrandomInitComparson. This script will start 5 fittings in parallel with different initial starting points for the femur GPMM.
To compare the standard ICP fitting to our ICP-proposal either with Euclidean average evaluator or Hausdorff evaluator, run the script: apps/femur/StdIcpVsChainICPrandomInitComparsonAll. This will perform registration on all the femurs from SMIR and do so 100 times for each mesh, each registration having a different initial starting point for the femur GPMM.
Download the Basel Face Model (BFM) 2017 https://faces.dmi.unibas.ch/bfm/bfm2017.html. For the experiments we use the cropped model: model2017-1_face12_nomouth.h5, which is cropped to the face region. Place the model such that it is located: data/bfm/model2017-1_face12_nomouth.h5 (same place where our provided bfm.json landmark file is located).
The test face scans are available here: https://faces.dmi.unibas.ch/bfm/main.php?nav=1-2&id=downloads
- Download the 3D face scans & renderings of ten individuals (94 MB).
- Unpack the scans in the bfm/initial folder such that the .ply meshes are to be found under data/bfm/initial/PublicMM1/03_scans_ply.
- Then run the apps/bfm/AlignShapes script to scale and align all faces to the face model as well as creating partial target meshes.
To run the face registration, run the script: apps/bfm/BfmFitting. As with the femur, the face registration can be replayed and the posterior can be visualised with the similar scripts found under apps/bfm/.
This segment of the project is not included in the open source repository since the model created contained folds,hence was not smooth when sampled at different possible variance, using metroplis hastings algorithm. A new method is being worked on to create a model instead since this did not work out.