Difference between revisions of "CNF Example Meshes"

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__TOC__
 
__TOC__
=2D Example Meshes=
 
The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/wd0i18giti2zo330y19xhkqu8wjzgf7j CNF_Data].
 
== Synthetic Gaussian Data ==
 
* Input distribution size : 1,000,000 cells
 
* Uniform case: 30,949 triangles
 
* Adapted case:  3,788 triangles
 
 
<gallery mode="packed" heights=300px>
 
File:Gaussian_me_10_uniform.png
 
File:Gaussian me 10 wl 1e-1 adapted.png
 
</gallery>
 
 
Commands to generate meshes :
 
 
'''Uniform :''' [https://odu.box.com/s/2ktd6ecfueq4zmbjzmpgujf3sxfucp5j Output Mesh]
 
<pre>
 
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk  --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10
 
</pre>
 
'''Adapted :''' [https://odu.box.com/s/1omr8szilea3r6fde39w49gu7u5xyui8 Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
== 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 distribution size : 10,000 cells
 
* Uniform case: 7,587 triangles
 
* Adapted (min edge = 2)  case:  623  triangles
 
* Adapted (min edge = 0.5) case:  1,409 triangles
 
 
 
<gallery mode="packed" heights=300px>
 
File:GPDGK16Numerical 140519 X50 me2 uniform.png
 
File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
 
File:GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png
 
</gallery>
 
 
Commands to generate meshes :
 
 
'''Uniform :''' [https://odu.box.com/s/jgqtydxkdf33iji5c125j70xx5mvi7n6 Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
'''Adapted (min edge = 2):''' [https://odu.box.com/s/3yp9jod0hcjxipfu81ywk0jrxphonasa Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
 
'''Adapted (min edge = 0.5):''' [https://odu.box.com/s/7zuszll7jn8tt8bpge6vau2tkbatkihz Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
'''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 ==
 
* Input distribution size : 10,000 cells
 
* Uniform case: 7,587 triangles
 
* Adapted  case:  1,038  triangles
 
 
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
 
 
 
<gallery mode="packed" heights=300px>
 
File:NT 140519 X50 me2 uniform.png
 
File:NT 140519 X50 me2 me2 wl 1e-1.png
 
</gallery>
 
 
Commands to generate meshes :
 
 
'''Uniform :''' [https://odu.box.com/s/87wruxwbks5k9q5wst9d7rx6z4eycwyz Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
'''Adapted :''' [https://odu.box.com/s/oytjqxeque11wvbxu62fhwc3830fbpcy Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
== OBS_ALU_Y50 ==
 
* Input distribution size : 10,000 cells
 
* Uniform case: 7,587 triangles
 
* Adapted  case:  1,018  triangles
 
 
The 2D case created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU
 
 
<gallery mode="packed" heights=300px>
 
File:OBS ALU Y50 me 2 uniform.vtk.png
 
File:OBS ALU Y50 me 2 wl 1e-1.png
 
</gallery>
 
 
Commands to generate meshes :
 
 
'''Uniform :''' [https://odu.box.com/s/irrcuttg0ceogzgnn4x86mgf9eskk1wg Output Mesh]
 
<pre>
 
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
 
</pre>
 
 
'''Adapted :''' [https://odu.box.com/s/8369kd2q52weqp76811h6p54sax3nj37 Output Mesh]
 
<pre>
 
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
 
</pre>
 
  
 
=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/wd0i18giti2zo330y19xhkqu8wjzgf7j 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==
 
==CFF_14052019==
 
===GPDGK16Numerical_140519===  
 
===GPDGK16Numerical_140519===  
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/xix6kb0jrzvn9dect2d2akdsie4vsu2i Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted (min edge = 1) case: 273,716 tetrahedra
+
* Uniform: 768,033 tetrahedra
* Adapted (min edge = 2) case:  67,935 tetrahedra
+
* Adaptive (min edge = 1): 273,716 tetrahedra
 +
* Adaptive (min edge = 2):  67,935 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 122: Line 17:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh]
 
<pre>
 
<pre>
docker run -v $(pwd):/data/ crtc_i2m tessellate3d -i ../../Data/3D/CNF_SHARE/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform --output ./GPDGK16Numerical_140519,d=1,uniform.vtk
+
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
 
</pre>
 
</pre>
  
'''Adapted (min edge = 1):''' [https://odu.box.com/s/zvuuq7e6bx1cqxqbna5rpfnxfaqezgqa Output Mesh]
+
'''Adaptive (min edge = 1):''' [https://odu.box.com/s/zvuuq7e6bx1cqxqbna5rpfnxfaqezgqa Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted (min edge = 2):''' [https://odu.box.com/s/ddyvbwcdqykta5b1nxi3gwxjzgdzz8yk Output Mesh]
+
'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddyvbwcdqykta5b1nxi3gwxjzgdzz8yk Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 140: Line 35:
  
 
===GPDMMS13_140519===
 
===GPDMMS13_140519===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 264,762 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 264,762 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 149: Line 45:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/bcg7aw4lv9rvp4531va7pqg7j2os7tny Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/bcg7aw4lv9rvp4531va7pqg7j2os7tny Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 162: Line 58:
  
 
===GPDVGG99_140519===
 
===GPDVGG99_140519===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 261,485 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 261,485 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 171: Line 68:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/51rvexl5t83zr4svn2xuw221v16jmtph Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/51rvexl5t83zr4svn2xuw221v16jmtph Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 184: Line 81:
  
 
===NT_140519===  
 
===NT_140519===  
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/m1qu1ocseyiltswmj9smd2n1tr6rvcsh Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted (min edge = 1) case: 253,965 tetrahedra
+
* Uniform: 768,033 tetrahedra
* Adapted (min edge = 2) case: 120,168 tetrahedra
+
* Adaptive (min edge = 1): 253,965 tetrahedra
 +
* Adaptive (min edge = 2): 120,168 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 195: Line 93:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted (min edge = 1):''' [https://odu.box.com/s/1gviwtmh053fzqixo4thaueshjhgqdnq Output Mesh]
+
'''Adaptive (min edge = 1):''' [https://odu.box.com/s/1gviwtmh053fzqixo4thaueshjhgqdnq Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted (min edge = 2):''' [https://odu.box.com/s/ddllau93m3itsax55pcb9ifhljp7bd3u Output Mesh]
+
'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddllau93m3itsax55pcb9ifhljp7bd3u Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 213: Line 111:
  
 
===OBS_ALU_140519===
 
===OBS_ALU_140519===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 259,269 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 259,269 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 222: Line 121:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/gei8k0bjh7k090vr568xo30fulslnpi5 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/gei8k0bjh7k090vr568xo30fulslnpi5 Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 235: Line 134:
  
 
===OBS_CS_140519===
 
===OBS_CS_140519===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case:  25,168 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive:  25,168 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 244: Line 144:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/j9zpcjp6fchtr82r6xk0dynf0qfkgi4d Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/j9zpcjp6fchtr82r6xk0dynf0qfkgi4d Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 258: Line 158:
 
==CFF_DATA==
 
==CFF_DATA==
 
===cff_E.data_IM===
 
===cff_E.data_IM===
* Input distribution size : 8,000,000 cells
+
* [https://odu.box.com/s/d34bcmi2w6f5uh57ni0l16ghf3peo9yz Input Image]
* Uniform case: 745,291 tetrahedra
+
* Input distribution size: 8,000,000 cells
* Adapted case: 358,637 tetrahedra
+
* Uniform: 745,291 tetrahedra
* Adapted case: 358,637 tetrahedra (other side of the same adapted case)
+
* Adaptive: 358,637 tetrahedra
 +
* Adaptive: 358,637 tetrahedra (other side of the same adaptive case)
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 269: Line 170:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/byo618ec2140sh6jopidyy1358nmk7ch Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/byo618ec2140sh6jopidyy1358nmk7ch Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 282: Line 183:
  
 
===cff_E.data_REAL===
 
===cff_E.data_REAL===
* Input distribution size : 8,000,000 cells
+
* [https://odu.box.com/s/ptjjqi8p1psg69ah00ikkcux5mxgvs41 Input Image]
* Uniform case: 745,291 tetrahedra
+
* Input distribution size: 8,000,000 cells
* Adapted case: 314,990 tetrahedra
+
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 314,990 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 291: Line 193:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/m7kn4kcmatmx86mofb06f37j8kkc35e4 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/m7kn4kcmatmx86mofb06f37j8kkc35e4 Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 304: Line 206:
  
 
===cff_H.data_IM===
 
===cff_H.data_IM===
* Input distribution size : 8,000,000 cells
+
* [https://odu.box.com/s/78eg0jujg4koeei5re96imanvlejkslq Input Image]
* Uniform case: 745,291 tetrahedra
+
* Input distribution size: 8,000,000 cells
* Adapted case: 289,855 tetrahedra
+
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 289,855 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 313: Line 216:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/ilbviax5y7vrji1anf3a4g8nd8v1wbdw Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/ilbviax5y7vrji1anf3a4g8nd8v1wbdw Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 326: Line 229:
  
 
===cff_H.data_REAL===
 
===cff_H.data_REAL===
* Input distribution size : 8,000,000 cells
+
* [https://odu.box.com/s/ethp2uvks6od9hel9bl8tczjbew2ae1f Input Image]
* Uniform case: 745,291 tetrahedra
+
* Input distribution size: 8,000,000 cells
* Adapted case: 372,016 tetrahedra
+
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 372,016 tetrahedra
  
[[File:cff_H.data_REAL,d=10,wl=0.1,me=2.png|500px|File:cff_H.data_REAL,d=10,wl=0.1,me=2.png]]
 
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:cff_H.data_REAL,d=2,uniform.png
 
File:cff_H.data_REAL,d=2,uniform.png
File:cff_H.data_REAL,d=10,wl=0.1,me=1.png
+
File:cff_H.data_REAL,d=10,wl=0.1,me=2.png
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/y90lrcbd2qtdjrc73tpqkn7rpissws9a Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/3ww0semcpqqd36xk9eysczxzajwz56uu Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 349: Line 252:
  
 
===cff_Ht.data_IM===
 
===cff_Ht.data_IM===
* Input distribution size : 8,000,000 cells
+
* [https://odu.box.com/s/ogbelxa3nyhj061a2u001wfr1fdyn0v6 Input Image]
* Uniform case:   745,291 tetrahedra
+
* Input distribution size: 8,000,000 cells
* Adapted case: 1,594,872 tetrahedra
+
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 337,772 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:cff_Ht.data_IM,d=2,uniform.png
 
File:cff_Ht.data_IM,d=2,uniform.png
File:cff_Ht.data_IM,d=10,wl=0.1,me=1.png
+
File:cff_Ht.data_IM,d=10,wl=0.1,me=2.png
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/lvg21o3d1bviuuknhhfuzl30csq90a5e Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/2g7n0thzu4uov8zhskykct9kohosqtzm Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 371: Line 275:
  
 
===cff_Ht.data_REAL===
 
===cff_Ht.data_REAL===
* Input distribution size : 8,000,000 cells
+
* [https://odu.box.com/s/sp4p98s6nhb1tgoz6gjbs0cj9amzxjez Input Image]
* Uniform case:   745,291 tetrahedra
+
* Input distribution size: 8,000,000 cells
* Adapted case: 1,821,299 tetrahedra
+
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 394,632 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:cff_Ht.data_REAL,d=2,uniform.png
 
File:cff_Ht.data_REAL,d=2,uniform.png
File:cff_Ht.data_REAL,d=10,wl=0.1,me=1.png
+
File:cff_Ht.data_REAL,d=10,wl=0.1,me=2.png
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/obz3uae6y392qlxqcrf5afgobrvibuew Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/laao7ehbda56xyrm17jfn9xw2sz407qi Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 394: Line 299:
 
==DATA_04252019==
 
==DATA_04252019==
 
===CFF_E_im===
 
===CFF_E_im===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/sg3trope39jtxliowy3hgoun34mtxic4 Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 236,512 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 236,512 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 403: Line 309:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/62hvu23fevbylbn8htm2os70adamxl3d Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/62hvu23fevbylbn8htm2os70adamxl3d Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 416: Line 322:
  
 
===CFF_E_re===
 
===CFF_E_re===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 260,349 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 260,349 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 425: Line 332:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/294puxiw7kg9ykeaanuojx7ha071ibn6 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/294puxiw7kg9ykeaanuojx7ha071ibn6 Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 438: Line 345:
  
 
===CFF_H_im===
 
===CFF_H_im===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 263,040 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 263,040 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 447: Line 355:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/6b8in611r4b0i345ymvpynif45j0pnt2 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/6b8in611r4b0i345ymvpynif45j0pnt2 Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 460: Line 368:
  
 
===CFF_H_re===
 
===CFF_H_re===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 249,257 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 249,257 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 469: Line 378:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/40loorarxv19xn40qsvlo56hwpaac2xu Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/40loorarxv19xn40qsvlo56hwpaac2xu Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 482: Line 391:
  
 
===GPD_H_down===
 
===GPD_H_down===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 300,117 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 300,117 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 491: Line 401:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/peljqrn1v8gf9mco4qrly85guxhyef0l Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/peljqrn1v8gf9mco4qrly85guxhyef0l Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 504: Line 414:
  
 
===GPD_H_up===
 
===GPD_H_up===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
* Uniform case: 768,033 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 295,671 tetrahedra
+
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 295,671 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 513: Line 424:
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :'''[https://odu.box.com/s/nxrn5xnia27kd4b0kyditafd86atix7b Output Mesh]
+
'''Adaptive:'''[https://odu.box.com/s/nxrn5xnia27kd4b0kyditafd86atix7b Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
Line 526: Line 437:
  
 
===OBS_ALU===
 
===OBS_ALU===
* Input distribution size : 1,000,000 cells
+
* [https://odu.box.com/s/e5kzeqmtpx5loayh6ymtloo5vhrene8t Input Image]
* Uniform case: 301,772 tetrahedra
+
* Input distribution size: 1,000,000 cells
* Adapted case: 279,721 tetrahedra
+
* 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
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:OBS_ALU,d=1,bv=0,uniform.png
 
File:OBS_ALU,d=1,bv=0,uniform.png
 
File:OBS_ALU,d=5,bv=0,wl=0.07,me=1.png
 
File:OBS_ALU,d=5,bv=0,wl=0.07,me=1.png
 +
</gallery>
 +
<gallery mode="packed" heights=350px>
 +
File:OBS_ALU,d=1,uniform.png
 +
File:OBS_ALU,d=5,wl=0.07,me=1.png
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh]
+
'''Uniform with background-value = 0:''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/sprttnvtuz5imm3cdrdd34cuasifv4n0 Output Mesh]
+
'''Adaptive with background-value = 0:''' [https://odu.box.com/s/sprttnvtuz5imm3cdrdd34cuasifv4n0 Output Mesh]
 
<pre>
 
<pre>
 
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
 
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
 
</pre>
 
</pre>
 +
 +
'''Uniform with background-value = default:''' [https://odu.box.com/s/b5licz0d25mb0ed0ttlz80adpsdoth4z Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 +
'''Adaptive with background-value = default:''' [https://odu.box.com/s/lz86mqwrukhupuhq9okfwim5vx6a2avr Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 +
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==
 
===phase_space_000===
 
===phase_space_000===
* Input distribution size : 15,625 cells
+
* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
* Uniform case: 26,410 tetrahedra
+
* Input distribution size: 15,625 cells
* Adapted case: 21,392 tetrahedra
+
* Uniform: 17,961 tetrahedra
 +
* Adaptive: 10,593 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
File:phase_space_000,d=0.5,bv=0,uniform.png
+
File:phase_space_000,d=0.25,uniform.png
File:phase_space_000,d=1.5,bv=0,wl=0.004,me=1.png
+
File:phase_space_000,d=2,wl=0.004,me=2.png
 
</gallery>
 
</gallery>
  
Commands to generate meshes :
+
Commands to generate meshes:
  
'''Uniform :''' [https://odu.box.com/s/vd9kr6392nfuibvwva77khcw7b59jvxc Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/ype3j19yixez8uwy1k9mgc1585oepugq Output Mesh]
 
<pre>
 
<pre>
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.5 --cnf-uniform --output ./phase_space_000,d=0.5,bv=0,uniform.vtk
+
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
 
</pre>
 
</pre>
  
'''Adapted :''' [https://odu.box.com/s/bm23n0nb7cmvphayj2mivby628sqwzm6 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/9k8b3wv9ejkvpf5b7xcbb2lc85zrz3b6 Output Mesh]
 
<pre>
 
<pre>
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 1.5 --cnf-adaptive --weight-limit 0.004 --output ./phase_space_000,d=1.5,bv=0,wl=0.004,me=1.vtk
+
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
 +
</pre>
 +
 
 +
=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].
 +
== Synthetic Gaussian Data ==
 +
* [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 30,949 triangles
 +
* Adaptive:  3,788 triangles
 +
 
 +
<gallery mode="packed" heights=300px>
 +
File:Gaussian_me_10_uniform.png
 +
File:Gaussian me 10 wl 1e-1 adapted.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/2ktd6ecfueq4zmbjzmpgujf3sxfucp5j Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk  --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10
 +
</pre>
 +
'''Adaptive:''' [https://odu.box.com/s/1omr8szilea3r6fde39w49gu7u5xyui8 Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 
 +
== 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
 +
 
 +
* [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq 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
 +
 
 +
<gallery mode="packed" heights=300px>
 +
File:GPDGK16Numerical 140519 X50 me2 uniform.png
 +
File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
 +
File:GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/jgqtydxkdf33iji5c125j70xx5mvi7n6 Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 
 +
'''Adaptive (min edge = 2):''' [https://odu.box.com/s/3yp9jod0hcjxipfu81ywk0jrxphonasa Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 
 +
 
 +
'''Adaptive (min edge = 0.5):''' [https://odu.box.com/s/7zuszll7jn8tt8bpge6vau2tkbatkihz Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 
 +
'''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
 +
 
 +
* [https://odu.box.com/s/nzhrcmfhrmi64ria7vldlb591797n7ph Input Image]
 +
* Input distribution size: 10,000 cells
 +
* Uniform: 7,587 triangles
 +
* Adaptive:  1,038  triangles
 +
 
 +
<gallery mode="packed" heights=300px>
 +
File:NT 140519 X50 me2 uniform.png
 +
File:NT 140519 X50 me2 me2 wl 1e-1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/87wruxwbks5k9q5wst9d7rx6z4eycwyz Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/oytjqxeque11wvbxu62fhwc3830fbpcy Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 
 +
== 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
 +
 
 +
* [https://odu.box.com/s/o4qxxjebb3rxu71ncmm8kdgh9mvvsvqr Input Image]
 +
* Input distribution size: 10,000 cells
 +
* Uniform: 7,587 triangles
 +
* Adaptive:  1,018  triangles
 +
 
 +
<gallery mode="packed" heights=300px>
 +
File:OBS ALU Y50 me 2 uniform.vtk.png
 +
File:OBS ALU Y50 me 2 wl 1e-1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/irrcuttg0ceogzgnn4x86mgf9eskk1wg Output Mesh]
 +
<pre>
 +
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
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/8369kd2q52weqp76811h6p54sax3nj37 Output Mesh]
 +
<pre>
 +
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
 
</pre>
 
</pre>

Revision as of 16:02, 2 June 2020

3D Example Meshes

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Synthetic Gaussian Data

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 30,949 triangles
  • Adaptive: 3,788 triangles

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

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

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

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