GitHub - fbernardpi/sparsePdmFitting: Code for our paper "Shape-aware surface reconstruction from sparse 3D point-clouds", Medical Image Analysis, 38, 2017.

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Code for our paper "Shape-aware surface reconstruction from sparse 3D point-clouds", Medical Image Analysis, 38, 2017.

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Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
LICENSE.txt		LICENSE.txt
README.txt		README.txt
computeAnisotropicCovariances.m		computeAnisotropicCovariances.m
computeAnisotropicObjective.m		computeAnisotropicObjective.m
demo_2d.m		demo_2d.m
demo_3d.m		demo_3d.m
downsampleMesh.m		downsampleMesh.m
ellipsoids_pdm.mat		ellipsoids_pdm.mat
fitPdmAnisotropic.m		fitPdmAnisotropic.m
fitPdmIcp.m		fitPdmIcp.m
rectangle2d_pdm.mat		rectangle2d_pdm.mat
sampleMeshSurfacePointsUniformly.m		sampleMeshSurfacePointsUniformly.m
vec.m		vec.m

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*************** GENERAL ***************
This is an implementation of the method described in [1] for fitting a 
statistical shape model to a sparse set of points. When using this 
code, please cite [1]. 

[1] F. Bernard, L. Salamanca, J. Thunberg, A. Tack, D. Jentsch, H. Lamecker,
S. Zachow, F. Hertel, J. Goncalves, P. Gemmar: Shape-aware Surface 
Reconstruction from Sparse 3D Point-Clouds. Medical Image Analysis, 38, 
pp. 77-89, May 2017
http://dx.doi.org/10.1016/j.media.2017.02.005

For feedback, suggestions or other comments please contact Florian Bernard 
per email (f.bernardpi[at]gmail[dot]com).

*************** USAGE *****************
The files demo_2d.m and demo_3d.m contain simple examples that shows how to use the 
method.