Difference between revisions of "CNF Example Meshes"

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(→‎2D Example Meshes)
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=3D Example Meshes=
 
=3D Example Meshes=
−
The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].  
+
The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].
−
==CFF_14052019==
+
==Fall 2019==
−
===GPDGK16Numerical_140519===  
+
===CFF_14052019===
 +
====GPDGK16Numerical_140519====
 
* [https://odu.box.com/s/xix6kb0jrzvn9dect2d2akdsie4vsu2i Input Image]
 
* [https://odu.box.com/s/xix6kb0jrzvn9dect2d2akdsie4vsu2i Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===GPDMMS13_140519===
+
====GPDMMS13_140519====
 
* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
 
* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===GPDVGG99_140519===
+
====GPDVGG99_140519====
 
* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
 
* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===NT_140519===  
+
====NT_140519====
 
* [https://odu.box.com/s/m1qu1ocseyiltswmj9smd2n1tr6rvcsh Input Image]
 
* [https://odu.box.com/s/m1qu1ocseyiltswmj9smd2n1tr6rvcsh Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===OBS_ALU_140519===
+
====OBS_ALU_140519====
 
* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
 
* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===OBS_CS_140519===
+
====OBS_CS_140519====
 
* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
 
* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
==CFF_DATA==
+
===CFF_DATA===
−
===cff_E.data_IM===
+
====cff_E.data_IM====
 
* [https://odu.box.com/s/d34bcmi2w6f5uh57ni0l16ghf3peo9yz Input Image]
 
* [https://odu.box.com/s/d34bcmi2w6f5uh57ni0l16ghf3peo9yz Input Image]
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
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</pre>
 
</pre>
  
−
===cff_E.data_REAL===
+
====cff_E.data_REAL====
 
* [https://odu.box.com/s/ptjjqi8p1psg69ah00ikkcux5mxgvs41 Input Image]
 
* [https://odu.box.com/s/ptjjqi8p1psg69ah00ikkcux5mxgvs41 Input Image]
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
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</pre>
 
</pre>
  
−
===cff_H.data_IM===
+
====cff_H.data_IM====
 
* [https://odu.box.com/s/78eg0jujg4koeei5re96imanvlejkslq Input Image]
 
* [https://odu.box.com/s/78eg0jujg4koeei5re96imanvlejkslq Input Image]
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
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</pre>
 
</pre>
  
−
===cff_H.data_REAL===
+
====cff_H.data_REAL====
 
* [https://odu.box.com/s/ethp2uvks6od9hel9bl8tczjbew2ae1f Input Image]
 
* [https://odu.box.com/s/ethp2uvks6od9hel9bl8tczjbew2ae1f Input Image]
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
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</pre>
 
</pre>
  
−
===cff_Ht.data_IM===
+
====cff_Ht.data_IM====
 
* [https://odu.box.com/s/ogbelxa3nyhj061a2u001wfr1fdyn0v6 Input Image]
 
* [https://odu.box.com/s/ogbelxa3nyhj061a2u001wfr1fdyn0v6 Input Image]
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
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</pre>
 
</pre>
  
−
===cff_Ht.data_REAL===
+
====cff_Ht.data_REAL====
 
* [https://odu.box.com/s/sp4p98s6nhb1tgoz6gjbs0cj9amzxjez Input Image]
 
* [https://odu.box.com/s/sp4p98s6nhb1tgoz6gjbs0cj9amzxjez Input Image]
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
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</pre>
 
</pre>
  
−
==DATA_04252019==
+
===DATA_04252019===
−
===CFF_E_im===
+
====CFF_E_im====
 
* [https://odu.box.com/s/sg3trope39jtxliowy3hgoun34mtxic4 Input Image]
 
* [https://odu.box.com/s/sg3trope39jtxliowy3hgoun34mtxic4 Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===CFF_E_re===
+
====CFF_E_re====
 
* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
 
* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===CFF_H_im===
+
====CFF_H_im====
 
* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
 
* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===CFF_H_re===
+
====CFF_H_re====
 
* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
 
* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===GPD_H_down===
+
====GPD_H_down====
 
* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
 
* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===GPD_H_up===
+
====GPD_H_up====
 
* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
 
* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
===OBS_ALU===
+
====OBS_ALU====
 
* [https://odu.box.com/s/e5kzeqmtpx5loayh6ymtloo5vhrene8t Input Image]
 
* [https://odu.box.com/s/e5kzeqmtpx5loayh6ymtloo5vhrene8t Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive.
 
entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive.
  
−
==phase_space_000==
 
 
===phase_space_000===
 
===phase_space_000===
 +
====phase_space_000====
 
* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
 
* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
 
* Input distribution size: 15,625 cells
 
* Input distribution size: 15,625 cells
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=2D Example Meshes=
 
=2D Example Meshes=
 
The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].  
 
The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].  
−
== Synthetic Gaussian Data ==
+
==Fall 2019
 +
===Synthetic Gaussian Data===
 
* [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image]
 
* [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image]
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
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</pre>
 
</pre>
  
−
== GPDGK16Numerical_140519 ==
+
===GPDGK16Numerical_140519===
 
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519
 
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519
  
Line 561: Line 563:
 
'''Note: ''' By using ''min-edge'' less than 1 we are essentially generating triangles with an edge smaller than the input pixels. Using values much smaller than 1 is not expected to help the discretization since we are essentially packing more element into a pixel which has a constant value.
 
'''Note: ''' By using ''min-edge'' less than 1 we are essentially generating triangles with an edge smaller than the input pixels. Using values much smaller than 1 is not expected to help the discretization since we are essentially packing more element into a pixel which has a constant value.
  
−
== NT_140519 ==
+
===NT_140519===
 
The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
 
The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
  
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</pre>
 
</pre>
  
−
== OBS_ALU_Y50 ==
+
===OBS_ALU_Y50===
 
The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU
 
The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU
  

Revision as of 16:20, 14 July 2020

3D Example Meshes

The directory containing the 3D input data is located in the 3D folder of CNF_Data.

Fall 2019

CFF_14052019

GPDGK16Numerical_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive (min edge = 1): 273,716 tetrahedra
  • Adaptive (min edge = 2): 67,935 tetrahedra
  • GPDGK16Numerical 140519,d=1,uniform.png
  • GPDGK16Numerical 140519,d=5,wl=0.1,me=1.png
  • GPDGK16Numerical 140519,d=5,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform --output ./GPDGK16Numerical_140519,d=1,uniform.vtk

Adaptive (min edge = 1): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=1.vtk

Adaptive (min edge = 2): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 2 --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=2.vtk

GPDMMS13_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 264,762 tetrahedra
  • GPDMMS13 140519,d=1,uniform.png
  • GPDMMS13 140519,d=5,wl=0.05,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform --output ./GPDMMS13_140519,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk

GPDVGG99_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 261,485 tetrahedra
  • GPDVGG99 140519,d=1,uniform.png
  • GPDVGG99 140519,d=5,wl=0.05,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform --output ./GPDVGG99_140519,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk

NT_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive (min edge = 1): 253,965 tetrahedra
  • Adaptive (min edge = 2): 120,168 tetrahedra
  • NT 140519,d=1,uniform.png
  • NT 140519,d=5,wl=0.07,me=1.png
  • NT 140519,d=5,wl=0.07,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform --output ./NT_140519,d=1,uniform.vtk

Adaptive (min edge = 1): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --output ./NT_140519,d=5,wl=0.07,me=1.vtk

Adaptive (min edge = 2): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --min-edge 2 --output ./NT_140519,d=5,wl=0.07,me=2.vtk

OBS_ALU_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 259,269 tetrahedra
  • OBS ALU 140519,d=1,uniform.png
  • OBS ALU 140519,d=5,wl=0.13,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform --output ./OBS_ALU_140519,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-adaptive --weight-limit 0.13 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk

OBS_CS_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 25,168 tetrahedra
  • OBS CS 140519,d=1,uniform.png
  • OBS CS 140519,d=5,wl=0.01,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform --output ./OBS_CS_140519,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-adaptive --weight-limit 0.01 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk

CFF_DATA

cff_E.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 358,637 tetrahedra
  • Adaptive: 358,637 tetrahedra (other side of the same adaptive case)
  • Cff E.data IM,d=2,uniform.png
  • Cff E.data IM,d=10,wl=0.01,me=2.png
  • Cff E.data IM,d=10,wl=0.01,me=2,OtherSide.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform --output ./cff_E.data_IM,d=2,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-adaptive --weight-limit 0.01 --output ./cff_E.data_IM,d=10,wl=0.01,me=2.vtk

cff_E.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 314,990 tetrahedra
  • Cff E.data REAL,d=2,uniform.png
  • Cff E.data REAL,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform --output ./cff_E.data_REAL,d=2,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive --output ./cff_E.data_REAL,d=10,wl=0.1,me=2.vtk

cff_H.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 289,855 tetrahedra
  • Cff H.data IM,d=2,uniform.png
  • Cff H.data IM,d=10,wl=0.05,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform --output ./cff_H.data_IM,d=2,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-adaptive --weight-limit 0.05 --output ./cff_H.data_IM,d=10,wl=0.05,me=2.vtk

cff_H.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 372,016 tetrahedra
  • Cff H.data REAL,d=2,uniform.png
  • Cff H.data REAL,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform --output ./cff_H.data_REAL,d=2,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive --output ./cff_H.data_REAL,d=10,wl=0.1,me=2.vtk

cff_Ht.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 337,772 tetrahedra
  • Cff Ht.data IM,d=2,uniform.png
  • Cff Ht.data IM,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform --output ./cff_Ht.data_IM,d=2,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive --output ./cff_Ht.data_IM,d=10,wl=0.1,me=2.vtk

cff_Ht.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 394,632 tetrahedra
  • Cff Ht.data REAL,d=2,uniform.png
  • Cff Ht.data REAL,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform --output ./cff_Ht.data_REAL,d=2,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive --output ./cff_Ht.data_REAL,d=10,wl=0.1,me=2.vtk

DATA_04252019

CFF_E_im

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 236,512 tetrahedra
  • CFF E im,d=1,uniform.png
  • CFF E im,d=5,wl=0.04,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform --output ./CFF_E_im,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-adaptive --weight-limit 0.04 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk

CFF_E_re

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 260,349 tetrahedra
  • CFF E re,d=1,uniform.png
  • CFF E re,d=5,wl=0.08,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform --output ./CFF_E_re,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-adaptive --weight-limit 0.08 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk

CFF_H_im

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 263,040 tetrahedra
  • CFF H im,d=1,uniform.png
  • CFF H im,d=5,wl=0.06,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform --output ./CFF_H_im,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-adaptive --weight-limit 0.06 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk

CFF_H_re

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 249,257 tetrahedra
  • CFF H re,d=1,uniform.png
  • CFF H re,d=5,wl=0.13,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform --output ./CFF_H_re,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-adaptive --weight-limit 0.13 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk

GPD_H_down

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 300,117 tetrahedra
  • GPD H down,d=1,uniform.png
  • GPD H down,d=5,wl=0.1,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform --output ./GPD_H_down,d=1,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive --output ./GPD_H_down,d=5,wl=0.1,me=1.vtk

GPD_H_up

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 295,671 tetrahedra
  • GPD H up,d=1,uniform.png
  • GPD H up,d=5,wl=0.1,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform --output ./GPD_H_up,d=1,uniform.vtk

Adaptive:Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive --output ./GPD_H_up,d=5,wl=0.1,me=1.vtk

OBS_ALU

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform with background-value = 0: 301,772 tetrahedra
  • Adaptive with background-value = 0: 279,721 tetrahedra
  • Uniform with background-value = default: 768,033 tetrahedra
  • Adaptive with background-value = default: 284,256 tetrahedra
  • OBS ALU,d=1,bv=0,uniform.png
  • OBS ALU,d=5,bv=0,wl=0.07,me=1.png
  • OBS ALU,d=1,uniform.png
  • OBS ALU,d=5,wl=0.07,me=1.png

Commands to generate meshes:

Uniform with background-value = 0: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0 --output ./OBS_ALU,d=1,bv=0,uniform.vtk

Adaptive with background-value = 0: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --background-value 0 --weight-limit 0.07 --output ./OBS_ALU,d=5,bv=0,wl=0.07,me=1.vtk

Uniform with background-value = default: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --output ./OBS_ALU,d=1,uniform.vtk

Adaptive with background-value = default: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07 --output ./OBS_ALU,d=5,wl=0.07,me=1.vtk

Note: In this case, we want to exclude the entries with value 0 (lower part, see figure) since they are not of interest. Using the flag --background-value 0, the entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive.

phase_space_000

phase_space_000

  • Input Image
  • Input distribution size: 15,625 cells
  • Uniform: 17,961 tetrahedra
  • Adaptive: 10,593 tetrahedra
  • Phase space 000,d=0.25,uniform.png
  • Phase space 000,d=2,wl=0.004,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.25 --cnf-uniform --output ./phase_space_000,d=0.25,uniform.vtk

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.004 --min-edge 2 --output ./phase_space_000,d=2,wl=0.004,me=2.vtk

2D Example Meshes

The directory containing the 2D input data is located in the 2D folder of CNF_Data. ==Fall 2019

Synthetic Gaussian Data

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 30,949 triangles
  • Adaptive: 3,788 triangles
  • Gaussian me 10 uniform.png
  • Gaussian me 10 wl 1e-1 adapted.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk  --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk  --output Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10

GPDGK16Numerical_140519

The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519

  • Input Image
  • Input distribution size: 10,000 cells
  • Uniform: 7,587 triangles
  • Adaptive (min edge = 2): 623 triangles
  • Adaptive (min edge = 0.5): 1,409 triangles
  • GPDGK16Numerical 140519 X50 me2 uniform.png
  • GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
  • GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform

Adaptive (min edge = 2): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1


Adaptive (min edge = 0.5): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_0.5_wl_1e-1.vtk --min-edge=0.5 --weight-limit=0.1

Note: By using min-edge less than 1 we are essentially generating triangles with an edge smaller than the input pixels. Using values much smaller than 1 is not expected to help the discretization since we are essentially packing more element into a pixel which has a constant value.

NT_140519

The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519

  • Input Image
  • Input distribution size: 10,000 cells
  • Uniform: 7,587 triangles
  • Adaptive: 1,038 triangles
  • NT 140519 X50 me2 uniform.png
  • NT 140519 X50 me2 me2 wl 1e-1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk  --output NT_140519_X50_me_2_uniform.vtk --min-edge=2 --uniform

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk  --output NT_140519_X50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1

OBS_ALU_Y50

The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU

  • Input Image
  • Input distribution size: 10,000 cells
  • Uniform: 7,587 triangles
  • Adaptive: 1,018 triangles
  • OBS ALU Y50 me 2 uniform.vtk.png
  • OBS ALU Y50 me 2 wl 1e-1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_uniform.vtk --min-edge=2 --uniform

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
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