[Insight-users] ICP with kd-trees
Nick Tustison
ntustison at gmail.com
Fri Aug 9 14:31:06 EDT 2013
Also, FYI, other point set metrics exist in the toolkit including
https://github.com/Kitware/ITK/blob/master/Modules/Registration/Metricsv4/include/itkJensenHavrdaCharvatTsallisPointSetToPointSetMetricv4.h
https://github.com/Kitware/ITK/blob/master/Modules/Registration/Metricsv4/include/itkExpectationBasedPointSetToPointSetMetricv4.h
NIck
On Aug 9, 2013, at 1:06 PM, Ramón Casero Cañas <rcasero at gmail.com> wrote:
> Nope, I'm a fool.
>
> itk::EuclideanDistancePointSetToPointSetMetricv4
>
> has a line
>
> PointIdentifier pointId = this->m_MovingTransformedPointsLocator->FindClosestPoint( point );
>
> https://github.com/Kitware/ITK/blob/master/Modules/Registration/Metricsv4/include/itkEuclideanDistancePointSetToPointSetMetricv4.hxx
>
> I suppose this is an itk::PointsLocator: "This class accelerates the search for the closest point to a user-provided point, by using constructing a Kd-Tree structure for the PointSetContainer."
>
> http://www.itk.org/Doxygen/html/classitk_1_1PointsLocator.html
>
> So the functionality is already there, and I can just use the ITK class directly.
>
> Best regards,
>
> Ramon.
>
>
>
> On 9 August 2013 17:30, Ramón Casero Cañas <rcasero at gmail.com> wrote:
> Hi Nick,
>
> Thanks again. OK, so if I understand it correctly, your itk::ManifoldParzenWindowsPointSetFunction is an interpolator of a set of points. You used kd-trees to find nearest neighbours faster, but no metric class derived from itk::PointSetToPointSetRegistrationMethod was written.
>
> This is the class of metric that I need to pass to the registration algorithm.
>
> Basically, I think that the way forward for me is to rewrite itk::EuclideanDistancePointMetric, but using a kd-tree instead of the distance map, and PointLocator2 instead of sampling the distance map, if that makes any sense.
>
> Best regards,
>
> Ramon.
>
>
>
>
> On 9 August 2013 16:36, Ramón Casero Cañas <rcasero at gmail.com> wrote:
> Hi Nick,
>
> I hit send before receiving your reply. Many thanks, I'm starting to look into your pointers and will report back.
>
>
> Best regards,
>
> Ramon.
>
>
> On 9 August 2013 16:24, Nick Tustison <ntustison at gmail.com> wrote:
> HI Ramon,
>
> A couple years ago or so, I contributed the following to the Insight Journal
>
> http://www.insight-journal.org/browse/publication/317
>
> based on
>
> http://www.ncbi.nlm.nih.gov/pubmed/20937578
>
> We used kd-trees to speed up the point search. You can see this in
> the function where we use the kd-tree directory
>
> https://github.com/midas-journal/midas-journal-317/blob/master/Source/JHCT/itkManifoldParzenWindowsPointSetFunction.txx
>
> starting at line 63. Part of our ITKv4 registration refactoring included
> this work so these functions are part of the current repository albeit
> slightly modified and we ended up using the PointsLocator class
>
> ITK/Modules/Registration/Metricsv4/include/itkManifoldParzenWindowsPointSetFunction.hxx
>
> Nick
>
>
>
> On Aug 9, 2013, at 8:23 AM, Ramón Casero Cañas <rcasero at gmail.com> wrote:
>
>> Dear all,
>>
>> I'm trying to build an Iterative Closest Point algorithm with ITK.
>>
>> I have googled advice on how it can be put together generally, and checked the three examples
>>
>> Insight/Examples/Registration/
>> IterativeClosestPoint1.cxx
>> IterativeClosestPoint2.cxx
>> IterativeClosestPoint3.cxx
>>
>> recommended here
>>
>> http://www.itk.org/pipermail/insight-users/2004-June/009092.html
>>
>> These examples don't use kd-trees (the last ones uses a distance transform).
>>
>> I have also found an example of a kd-tree, that however doesn't use point sets
>>
>> http://www.itk.org/Wiki/ITK/Examples/Statistics/KdTree
>>
>> I found an "itkPointLocator2.h" class developed for itkQuadEdgeMeshRigidRegistration in the ITK journal. I downloaded it from a more up-to-date publication that makes use of it, "Mesh Similarity Calculator" by Li and Magnotta (2010)
>>
>> http://www.insight-journal.org/browse/publication/762
>>
>> What I don't know is how to extract a metric from said locator, to pass it to the registration object.
>>
>> This is the relevant part of the code I have so far
>>
>> <CODE>
>> PointSetType::Pointer fixedPointSet = PointSetType::New();
>> PointSetType::Pointer movingPointSet = PointSetType::New();
>>
>> // do something to read the points into the point sets
>>
>> // construct a Kd-tree structure for the reference point set, to
>> // accelerate the search for closest point
>> typedef itk::PointLocator2<PointSetType> PointLocatorType;
>> PointLocatorType::Pointer locator = PointLocatorType::New();
>>
>> locator->SetPointSet(fixedPointSet);
>> locator->Initialize(); // pre-compute the kd-tree structure
>> </CODE>
>>
>> I think I could get the metric from the kd-tree with something like
>>
>> tree->GetDistanceMetric()
>>
>> but I cannot get the tree from the locator, can I?
>>
>> https://github.com/midas-journal/midas-journal-762/blob/master/QuadEdgeMeshRigidRegistration/Source/itkPointLocator2.h
>>
>> Is this just a matter of adding a GetKdTree() method to itkPointLocator2.h, or is there some better solution to this?
>>
>>
>>
>> Alternatively, if we not only have point sets at the input, but triangular meshes, it would be possible to compute shortest distances from the vertices of movingPointSet to a triangular mesh fixedTriMesh. This is efficient using an AABB tree (which can internally use a kd-tree), as in the CGAL library
>>
>> http://www.cgal.org/Manual/latest/doc_html/cgal_manual/AABB_tree/Chapter_main.html
>>
>> but this probably requires a vast amount of work? Or would it be relatively easy to write a new metric PointToTriMesh for ITK that imports CGAL AABB trees? I'm asking because I have no idea of how involved writing a new Metric class would be.
>>
>>
>> Best regards,
>>
>> Ramon.
>>
>>
>>
>>
>>
>>
>>
>>
>> --
>> Dr. Ramón Casero Cañas
>>
>> Oxford e-Research Centre (OeRC)
>> University of Oxford
>> 7 Keble Rd
>> Oxford OX1 3QG
>>
>> tlf +44 (0) 1865 610739
>> web http://www.cs.ox.ac.uk/people/Ramon.CaseroCanas
>> photos http://www.flickr.com/photos/rcasero/
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>
>
>
>
> --
> Dr. Ramón Casero Cañas
>
> Oxford e-Research Centre (OeRC)
> University of Oxford
> 7 Keble Rd
> Oxford OX1 3QG
>
> tlf +44 (0) 1865 610739
> web http://www.cs.ox.ac.uk/people/Ramon.CaseroCanas
> photos http://www.flickr.com/photos/rcasero/
>
>
>
> --
> Dr. Ramón Casero Cañas
>
> Oxford e-Research Centre (OeRC)
> University of Oxford
> 7 Keble Rd
> Oxford OX1 3QG
>
> tlf +44 (0) 1865 610739
> web http://www.cs.ox.ac.uk/people/Ramon.CaseroCanas
> photos http://www.flickr.com/photos/rcasero/
>
>
>
> --
> Dr. Ramón Casero Cañas
>
> Oxford e-Research Centre (OeRC)
> University of Oxford
> 7 Keble Rd
> Oxford OX1 3QG
>
> tlf +44 (0) 1865 610739
> web http://www.cs.ox.ac.uk/people/Ramon.CaseroCanas
> photos http://www.flickr.com/photos/rcasero/
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