contrib/vtkSurfaceReconstructionFilter : data/examples ?

[Sebastien Barre]

Dear users, Tim,

There is a nice looking class in contrib/, called
vtkSurfaceReconstructionFilter.

I thought I would have a look at the corresponding example
contrib/examplesTcl/reconstructSurface, but ran out of luck, as the script
complained about a missing file : SampledPoints/cactus.3337.pts

The line is :
   set file [open "$VTK_DATA/SampledPoints/cactus.3337.pts" r]

Could anyone forward me that file ? It's not in the vtk20data* files, and
the MissingData.tar.gz file is empty (bug ?) today (nevertheless, I
downloaded it once some months ago, and no .pts were to be found).

BTW Tim, once again this class might help me, as we are working on the same
kind of stuff which involve 3D reconstruction of facial stuff. How are you
using it ? Did you compare this class with the usual approach described in
the VTK examples/books (optional stuff in brackets) : 

	- Image/Volume16Reader
	- MarchingCubes
	- CleanPolyData
	- [PolyDataConnectivityFilter]
	- [SmoothPolyDataFilter] 
	- Decimate[Pro]
	- [SmoothPolyDataFilter]
	- [PolyDataNormals]
	- [Stripper]

What part of this scheme are you using ?

On my Pentium II, 300 Mhz, 196 RAM, reconstructing the "bones" for a tissue
thresholded at 1190 (not segmented sadly) from 240 CT slices (224x224
pixels) takes quite a long time (more than 6 minutes) :

bone reader:        13 s. => 13 s.
bone mcubes:        25 s. => 38 s. (942668 polys, 471700 points)
bone cleaner:       13 s. => 51 s. (942668 polys, 471682 points)
bone connect:       9 s. => 60 s. (902114 polys, 471682 points)
bone smoother 1:    133 s. => 194 s. (902114 polys, 471682 points)
bone decimator pro: 129 s. => 323 s. (360844 polys, 178954 points)
bone smoother 2:    52 s. => 375 s. (360844 polys, 178954 points)
bone transformer:   0 s. => 375 s.
bone normals:       18 s. => 394 s.
bone stripper:      3 s. => 397 s. (100177 strips, 265443 points)
bone writer:        2 s. => 399 s.

Well, the SmoothPolyDataFilter is obviously computer intensive, but it
gives better results with my data. Anyway, Decimation is greedy too
(although I'm not able to achieve satisfying results with a target higher
than 65%). Therefore I was wondering if you pipeline is more efficient than
mine (I'm sure so :) ? How do you feed SurfaceReconstructionFilter with 3D
points, do you just sample them on each slice ?

Thanks


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