Initial code commit

.gitignore

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 *.class
 *.log
+*.iml
+*.ipr
+*.iws
+.idea
+out
+.cache/
+.history/
+.lib/
+dist/*
+target/
+bin/
+libexec/
+lib_managed/
+src_managed/
+project/boot/
+project/plugins/project/
+logs/
+project/*-shim.sbt
+project/project/
+project/target/
+target/
+.scala_dependencies
+.worksheet

README.md

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-# icp-proposal
-Source code to the paper "A Closest Point Proposal for MCMC-based Probabilistic Surface Registration"
+# A Closest Point Proposal for MCMC-based Probabilistic Surface Registration
+
+This repository contains all the code to reproduce our results from our 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"](https://arxiv.org/abs/1907.01414) 
+IN: arXiv preprint (2019)
+
+Markov Chain Monte Carlo for shape registration with examples using [Scalismo](https://github.com/unibas-gravis/scalismo).
+
+
+## Overview
+
+
+## Femur experiments
+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
+
+### Data preparation
+The test dataset we use is the same as used in the [Statistical Shape Modelling course on futurelearn](https://www.futurelearn.com/courses/statistical-shape-modelling) 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](https://www.smir.ch/Account/Register).
+- 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)](https://www.smir.ch/courses/FutureLearnSSM/2016) 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.
+
+
+## BFM experiments
+
+### Data preparation
+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/**.

build.sbt

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+organization  := "ch.unibas.cs.gravis"
+
+name := """ICP-PROPOSAL-REGISTRATION"""
+
+version       := "0.1"
+
+scalaVersion  := "2.12.8"
+
+scalacOptions := Seq("-unchecked", "-deprecation", "-encoding", "utf8")
+
+resolvers += Resolver.bintrayRepo("unibas-gravis", "maven")
+
+
+libraryDependencies ++= Seq(
+  "ch.unibas.cs.gravis" %% "scalismo-ui" % "0.13.0",
+  "ch.unibas.cs.gravis" %% "scalismo-faces" % "0.9.0",
+  "ch.qos.logback" % "logback-classic" % "1.2.3",
+  "com.typesafe.scala-logging" %% "scala-logging" % "3.9.0"
+).map(_.force())
+
+libraryDependencies ~= { _.map(_.exclude("org.slf4j", "slf4j-nop")) }

data/bfm/bfm.json

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+[{"id":"right.lips.corner","coordinates":[-23.70504379272461,-33.45827865600586,98.3342056274414],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"right.eye.corner_outer","coordinates":[-44.02754592895508,32.43998718261719,86.61490631103516],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"left.eye.corner_outer","coordinates":[43.03085708618164,32.5797233581543,86.72027587890625],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"left.lips.corner","coordinates":[23.517236709594727,-33.23805618286133,97.9552230834961],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"center.nose.attachement_to_philtrum","coordinates":[0.025348693132400513,-10.950613975524902,116.14006042480469],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"center.lips.lower.inner","coordinates":[-0.008524144068360329,-32.59530258178711,109.80796813964844],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"right.eye.corner_inner","coordinates":[-17.09498405456543,31.7714786529541,93.12824249267578],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"center.lips.upper.inner","coordinates":[0.0029204045422375202,-32.44647216796875,109.90381622314453],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"left.eye.corner_inner","coordinates":[16.157331466674805,31.860828399658203,92.98885345458984],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}},{"id":"center.nose.tip","coordinates":[0.2037338763475418,0.9716929197311401,131.8238067626953],"uncertainty":{"stddevs":[1.0,1.0,1.0],"pcvectors":[[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]]}}]

data/bfm/initial/landmarks/00001_20061015_00418_neutral_face05.json

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data/bfm/initial/landmarks/00002_20061015_00448_neutral_face05.json

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data/bfm/initial/landmarks/00006_20080430_04384_neutral_face05.json

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data/bfm/initial/landmarks/00014_20080430_04338_neutral_face05.json

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data/bfm/initial/landmarks/00017_20061201_00812_neutral_face05.json

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data/bfm/initial/landmarks/00022_20070307_02486_neutral_face05.json

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data/bfm/initial/landmarks/00052_20061024_00526_neutral_face05.json

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data/bfm/initial/landmarks/00053_20061024_00542_neutral_face05.json

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data/bfm/initial/landmarks/00293_20080104_03413_neutral_face05.json

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data/bfm/initial/landmarks/00323_20080125_03764_neutral_face05.json

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