Difference between revisions of "CNF Example Meshes"

From crtc.cs.odu.edu
Jump to: navigation, search
(→‎phase_space_000)
(→‎OBS_ALU_Y50)
 
(257 intermediate revisions by 4 users not shown)
Line 1: Line 1:
 
__TOC__
 
__TOC__
−
=2D Example Meshes=
+
 
 
=3D Example Meshes=
 
=3D Example Meshes=
−
==CFF_14052019==
+
The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].
−
GPDGK16Numerical_140519: [https://odu.app.box.com/file/457595295061 Input Image] [https://odu.app.box.com/file/560417595948 Output Mesh]
+
 
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDGK16Numerical_140519.nrrd --delta 5 --weight-limit 0.1 --min-edge 1 --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=1.vtk</pre>
+
==Summer 2020==
 +
===GPDGK16===
 +
====GPDGK16_uH_img====
 +
* [https://odu.box.com/s/6pl2q075h45wglbd0mbedb5epxvw1g3c Input Image]
 +
* Input distribution size: 1,000 cells
 +
* Adaptive Meshes which deal with the input as an image:
 +
** (PODM) delta = 2, min edge = 0.85, weight limit = 0.12, max edge = 0.2 * diagonal: 1,208 tetrahedra, [https://odu.box.com/s/01qnlk7sg3eec6jdt5lz833utddf7wcc Output Mesh]
 +
** (PODM) delta = 1, min edge = 0.2, weight limit = 0.1, max edge = 0.2 * diagonal:  tetrahedra 8,690, [https://odu.box.com/s/r3ngfoweb520nb30log0vrlz939ohpb9 Output Mesh]
 +
* Meshes which deal with the input as a CAD geometry:
 +
** (Constrained Mesher) quality = 2, min edge = 0.5, weight limit = 0.2, max edge = 0.2 * diagonal: 641 tetrahedra, [https://odu.box.com/s/re5p4b72xhdvad5cxjafrymetdqi5nzs Output Mesh]
 +
** (CDT3D): 535 tetrahedra, [https://odu.box.com/s/dixu4u853vw8w03funvutqu40jq30tjo Output Mesh]
 +
** (CDT3D): 1032 tetrahedra, [https://odu.box.com/s/1b8fypd0taale03byxdodvnamh087mko Output Mesh]
 +
** (CDT3D): 1205 tetrahedra, [https://odu.box.com/s/nn6sxl38s866nucpfa3ze5n3741jwa3h Output Mesh]
 +
 
 +
<gallery mode="packed" heights=300px>
 +
File:GPDGK16_uH_img-d_2-e_0.85-w_0.12-maxEdge_0.2diagonal.png
 +
File:GPDGK16_uH_img-d_1-e_0.2-w_0.1-maxEdge_0.2diagonal.png
 +
</gallery>
 +
 
 +
<gallery mode="packed" heights=300px>
 +
File:GPDGK16_uH_img-q_2-e_0.5-w_0.2-maxEdge_0.2diagonal.png
 +
File:cdt3d_constrained_surface_535_tets.png
 +
</gallery>
 +
 
 +
'''Adaptive ((PODM) delta = 2, min edge = 0.85, weight limit = 0.12, max edge = 0.2 * diagonal):'''
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/GPDGK16/GPDGK16_uH_img.nrrd --cnf-adaptive --delta 2 --min-edge = 0.85 --weight-limit 0.12 --output ./GPDGK16_uH_img-d_2-e_0.85-w_0.12-maxEdge_0.2diagonal.vtk
 +
</pre>
 +
 
 +
'''Adaptive ((PODM) delta = 1, min edge = 0.2, weight limit = 0.1, max edge = 0.2 * diagonal):'''
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/GPDGK16/GPDGK16_uH_img.nrrd --cnf-adaptive --delta 1 --min-edge = 0.2 --weight-limit 0.1 --output ./GPDGK16_uH_img-d_1-e_0.2-w_0.1-maxEdge_0.2diagonal.vtk
 +
</pre>
 +
 
 +
====GPDGK16_uH_img_nxi=211====
 +
* [ Input Image]
 +
* Input distribution size: 21,100 cells
 +
* Number of bins: Xi=211 t=20 Q^2=5
 +
* Adaptive Meshes which deal with the input as an image:
 +
** (PODM) delta = 2, min edge = 0.85, weight limit = 0.12: 11964 tetrahedra
 +
** (PODM) delta = 1, min edge = 0.2, weight limit = 0.1: 124608 tetrahedra
 +
<gallery mode="packed" heights=300px>
 +
GPDGK16_uH_img-nxi_210-d_2-e_0.85-w_0.12.png
 +
File:GPDGK16 uH img-nxi 210-d 1-e 0.2-w 0.1.png
 +
</gallery>
 +
 
 +
==Fall 2019==
 +
===CFF_14052019===
 +
====GPDGK16Numerical_140519====
 +
* [https://odu.box.com/s/xix6kb0jrzvn9dect2d2akdsie4vsu2i Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive (min edge = default): 277,701 tetrahedra
 +
* Adaptive (min edge = 1):  92,216 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:GPDGK16Numerical_140519,d=1,uniform.png
 +
File:GPDGK16Numerical_140519,d=5,wl=0.1,me=1.png
 +
File:GPDGK16Numerical_140519,d=5,wl=0.1,me=2.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive (min edge = default):''' [https://odu.box.com/s/cc5uvbip8oti0hd2z8bwa5v2fp9slkt0 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive
 +
</pre>
 +
 
 +
'''Adaptive (min edge = 1):''' [https://odu.box.com/s/fhgvmc0lxu81p8dmy5l3nh8plpto43r3 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 1
 +
</pre>
 +
 
 +
====GPDMMS13_140519====
 +
* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 270,453 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:GPDMMS13_140519,d=1,uniform.png
 +
File:GPDMMS13_140519,d=5,wl=0.05,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/q2tp29hv8h463ld0sxidjarkji4e6il3 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05
 +
</pre>
 +
 
 +
====GPDVGG99_140519====
 +
* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 266,731 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:GPDVGG99_140519,d=1,uniform.png
 +
File:GPDVGG99_140519,d=5,wl=0.05,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/o1miy5xmsqvcl0uyut089o5qbi3doj8p Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05
 +
</pre>
 +
 
 +
====NT_140519====
 +
* [https://odu.box.com/s/m1qu1ocseyiltswmj9smd2n1tr6rvcsh Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive (min edge = default): 257,041 tetrahedra
 +
* Adaptive (min edge = 1): 140,527 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:NT_140519,d=1,uniform.png
 +
File:NT_140519,d=5,wl=0.07,me=1.png
 +
File:NT_140519,d=5,wl=0.07,me=2.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive (min edge = default):''' [https://odu.box.com/s/hroxm6c8h4ix30nzkd9xgqucznpjo74z Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07
 +
</pre>
 +
 
 +
'''Adaptive (min edge = 1):''' [https://odu.box.com/s/x6wg3io5ca3ix0qua9i68hckio7j9mh2 Output Mesh]
 +
<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 1
 +
</pre>
 +
 
 +
====OBS_ALU_140519====
 +
* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 262,055 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:OBS_ALU_140519,d=1,uniform.png
 +
File:OBS_ALU_140519,d=5,wl=0.13,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/ds9cobknetx03wc58821laj8k2dkd7h2 Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
====OBS_CS_140519====
 +
* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive:  25,476 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:OBS_CS_140519,d=1,uniform.png
 +
File:OBS_CS_140519,d=5,wl=0.01,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/fs4g3d7yfs8o80t6u3oi4me84yksneot Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
===CFF_DATA===
 +
====cff_E.data_IM====
 +
* [https://odu.box.com/s/d34bcmi2w6f5uh57ni0l16ghf3peo9yz Input Image]
 +
* Input distribution size: 8,000,000 cells
 +
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 362,804 tetrahedra
 +
* Adaptive: 362,804 tetrahedra (other side of the same adaptive case)
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:cff_E.data_IM,d=2,uniform.png
 +
File:cff_E.data_IM,d=10,wl=0.01,me=2.png
 +
File:cff_E.data_IM,d=10,wl=0.01,me=2,OtherSide.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/nopuegnozgjdh5is49xsqoqz86r55zux Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
====cff_E.data_REAL====
 +
* [https://odu.box.com/s/ptjjqi8p1psg69ah00ikkcux5mxgvs41 Input Image]
 +
* Input distribution size: 8,000,000 cells
 +
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 318,128 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:cff_E.data_REAL,d=2,uniform.png
 +
File:cff_E.data_REAL,d=10,wl=0.1,me=2.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/3ildscx8w4t966nzv3w20gkvxt4zxcxz Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive
 +
</pre>
 +
 
 +
====cff_H.data_IM====
 +
* [https://odu.box.com/s/78eg0jujg4koeei5re96imanvlejkslq Input Image]
 +
* Input distribution size: 8,000,000 cells
 +
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 293,560 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:cff_H.data_IM,d=2,uniform.png
 +
File:cff_H.data_IM,d=10,wl=0.05,me=2.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/j39hie2a1clrvwzw6nn7kuffty4887l2 Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
====cff_H.data_REAL====
 +
* [https://odu.box.com/s/ethp2uvks6od9hel9bl8tczjbew2ae1f Input Image]
 +
* Input distribution size: 8,000,000 cells
 +
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 375,705 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:cff_H.data_REAL,d=2,uniform.png
 +
File:cff_H.data_REAL,d=10,wl=0.1,me=2.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/hmtot3rslyf0gy0gh9wijk4fbts2vnzg Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive
 +
</pre>
 +
 
 +
====cff_Ht.data_IM====
 +
* [https://odu.box.com/s/ogbelxa3nyhj061a2u001wfr1fdyn0v6 Input Image]
 +
* Input distribution size: 8,000,000 cells
 +
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 341,159 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:cff_Ht.data_IM,d=2,uniform.png
 +
File:cff_Ht.data_IM,d=10,wl=0.1,me=2.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/94vc0typeqrtudqnvjstr9u7ff5y4h7y Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive
 +
</pre>
 +
 
 +
====cff_Ht.data_REAL====
 +
* [https://odu.box.com/s/sp4p98s6nhb1tgoz6gjbs0cj9amzxjez Input Image]
 +
* Input distribution size: 8,000,000 cells
 +
* Uniform: 745,291 tetrahedra
 +
* Adaptive: 398,937 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:cff_Ht.data_REAL,d=2,uniform.png
 +
File:cff_Ht.data_REAL,d=10,wl=0.1,me=2.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/7ah3un9py97rw935vwwrzwm099156vwd Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive
 +
</pre>
 +
 
 +
===DATA_04252019===
 +
====CFF_E_im====
 +
* [https://odu.box.com/s/sg3trope39jtxliowy3hgoun34mtxic4 Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 240,150 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:CFF_E_im,d=1,uniform.png
 +
File:CFF_E_im,d=5,wl=0.04,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/v7ptl6shwnhudjukuqllbcn7jzyhg1ku Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
====CFF_E_re====
 +
* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 261,918 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:CFF_E_re,d=1,uniform.png
 +
File:CFF_E_re,d=5,wl=0.08,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/h1b91ms5na6e1a8735lx4r00nz9nwxq6 Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
====CFF_H_im====
 +
* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 266,306 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:CFF_H_im,d=1,uniform.png
 +
File:CFF_H_im,d=5,wl=0.06,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/v6madkz4ahdxq02nrwnkx7o72t1qkntu Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
====CFF_H_re====
 +
* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 251,186 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:CFF_H_re,d=1,uniform.png
 +
File:CFF_H_re,d=5,wl=0.13,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform
 +
</pre>
 +
 
 +
'''Adaptive:''' [https://odu.box.com/s/euxix0on74rp6pqy6u4slj9ij7ss28m0 Output Mesh]
 +
<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
 +
</pre>
 +
 
 +
====GPD_H_down====
 +
* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 307,082 tetrahedra
 +
 
 +
<gallery mode="packed" heights=350px>
 +
File:GPD_H_down,d=1,uniform.png
 +
File:GPD_H_down,d=5,wl=0.1,me=1.png
 +
</gallery>
 +
 
 +
Commands to generate meshes:
 +
 
 +
'''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform
 +
</pre>
  
−
[[File:GPDGK16Numerical_140519,d=5,wl=0.1,me=1.png|500px|GPDGK16Numerical_140519,d=5,wl=0.1,me=1]]
+
'''Adaptive:''' [https://odu.box.com/s/4qsu8gwr13mzicfmnjqxsxvewfnslbro Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive
 +
</pre>
  
−
GPDMMS13_140519: [https://odu.app.box.com/file/457597889650 Input Image] [https://odu.app.box.com/file/560418993018 Output Mesh]
+
====GPD_H_up====
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDMMS13_140519.nrrd --delta 5 --weight-limit 0.05 --min-edge 1 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk</pre>
+
* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 768,033 tetrahedra
 +
* Adaptive: 301,979 tetrahedra
  
−
[[File:GPDMMS13_140519,d=5,wl=0.05,me=1.png|500px|GPDMMS13_140519,d=5,wl=0.05,me=1]]
+
<gallery mode="packed" heights=350px>
−
+
File:GPD_H_up,d=1,uniform.png
−
GPDVGG99_140519: [https://odu.app.box.com/file/457597862395 Input Image] [https://odu.app.box.com/file/560412156590 Output Mesh]
+
File:GPD_H_up,d=5,wl=0.1,me=1.png
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDVGG99_140519.nrrd --delta 5 --weight-limit 0.05 --min-edge 1 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk</pre>
+
</gallery>
  
−
[[File:GPDVGG99_140519,d=5,wl=0.05,me=1.png|500px|GPDVGG99_140519,d=5,wl=0.05,me=1]]
+
Commands to generate meshes:
  
−
NT_140519: [https://odu.app.box.com/file/457595187755 Input Image] [https://odu.app.box.com/file/560417456108 Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/NT_140519.nrrd --delta 5 --weight-limit 0.07 --min-edge 1 --output ./NT_140519,d=5,wl=0.07,me=1.vtk</pre>
+
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform
 +
</pre>
  
−
[[File:NT_140519,d=5,wl=0.07,me=1.png|500px|NT_140519,d=5,wl=0.07,me=1]]
+
'''Adaptive:''' [https://odu.box.com/s/s4pb42n345gq84w06ne5x629b46buxm6 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive
 +
</pre>
  
−
OBS_ALU_140519: [https://odu.app.box.com/file/457603095071 Input Image] [https://odu.app.box.com/file/560419803420 Output Mesh]
+
====OBS_ALU====
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/OBS_ALU_140519.nrrd --delta 5 --weight-limit 0.13 --min-edge 1 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk</pre>
+
* [https://odu.box.com/s/e5kzeqmtpx5loayh6ymtloo5vhrene8t Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform with background-value = 0: 301,772 tetrahedra
 +
* Adaptive with background-value = 0: 282,102 tetrahedra
 +
* Uniform with background-value = default: 768,033 tetrahedra
 +
* Adaptive with background-value = default: 286,978 tetrahedra
  
−
[[File:OBS_ALU_140519,d=5,wl=0.13,me=1.png|500px|OBS_ALU_140519,d=5,wl=0.13,me=1]]
+
<gallery mode="packed" heights=350px>
 +
File:OBS_ALU,d=1,bv=0,uniform.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>
  
−
OBS_CS_140519: [https://odu.app.box.com/file/457597843866 Input Image] [https://odu.app.box.com/file/560412481982 Output Mesh]
+
Commands to generate meshes:
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/OBS_CS_140519.nrrd --delta 5 --weight-limit 0.01 --min-edge 1 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk</pre>
 
  
−
[[File:OBS_CS_140519,d=5,wl=0.01,me=1.png|500px|OBS_CS_140519,d=5,wl=0.01,me=1]]
+
'''Uniform with background-value = 0:''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0
 +
</pre>
  
−
==CFF_DATA==
+
'''Adaptive with background-value = 0:''' [https://odu.box.com/s/her4h7lrtygcurufj6isx57hxru02c3g Output Mesh]
−
cff_E.data_IM: [https://odu.app.box.com/file/440188013797 Input Image] [https://odu.app.box.com/file/560455612588 Output Mesh]
+
<pre>
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_E.data_IM.nrrd --delta 10 --weight-limit 0.01 --min-edge 1 --output ./cff_E.data_IM,d=10,wl=0.01,me=1.vtk</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
 +
</pre>
  
−
[[File:cff_E.data_IM,d=10,wl=0.01,me=1.png|500px|cff_E.data_IM,d=10,wl=0.01,me=1]]
+
'''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
 +
</pre>
  
−
cff_E.data_REAL: [https://odu.app.box.com/file/440199404775 Input Image] [https://odu.app.box.com/file/560476079164 Output Mesh]
+
'''Adaptive with background-value = default:''' [https://odu.box.com/s/h93jztz1bdtplmzmfjdveq0xkzntil18 Output Mesh]
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_E.data_REAL.nrrd --delta 10 --weight-limit 0.1 --min-edge 1 --output ./cff_E.data_REAL,d=10,wl=0.1,me=1.vtk</pre>
+
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07
 +
</pre>
  
−
[[File:cff_E.data_REAL,d=10,wl=0.1,me=1.png|500px|cff_E.data_REAL,d=10,wl=0.1,me=1]]
+
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.
  
−
cff_H.data_IM: [https://odu.app.box.com/file/440195394761 Input Image] [https://odu.app.box.com/file/560474153565 Output Mesh]
+
===phase_space_000===
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_H.data_IM.nrrd --delta 10 --weight-limit 0.05 --min-edge 1 --output ./cff_H.data_IM,d=10,wl=0.05,me=1.vtk</pre>
+
====phase_space_000====
 +
* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
 +
* Input distribution size: 15,625 cells
 +
* Uniform: 17,961 tetrahedra
 +
* Adaptive: 11,494 tetrahedra
  
−
[[File:cff_H.data_IM,d=10,wl=0.05,me=1.png|500px|cff_H.data_IM,d=10,wl=0.05,me=1]]
+
<gallery mode="packed" heights=350px>
 +
File:phase_space_000,d=0.25,uniform.png
 +
File:phase_space_000-d_2-g-s-f-w_0.2-m_2.5-M_20-l.png
 +
</gallery>
  
−
cff_H.data_REAL: [https://odu.app.box.com/file/440197214881 Input Image] [https://odu.app.box.com/file/560472812787 Output Mesh]
+
Commands to generate meshes:
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_H.data_REAL.nrrd --delta 10 --weight-limit 0.1 --min-edge 1 --output ./cff_H.data_REAL,d=10,wl=0.1,me=1.vtk</pre>
 
  
−
[[File:cff_H.data_REAL,d=10,wl=0.1,me=1.png|500px|cff_H.data_REAL,d=10,wl=0.1,me=1]]
+
'''Uniform:''' [https://odu.box.com/s/ype3j19yixez8uwy1k9mgc1585oepugq Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.25 --cnf-uniform
 +
</pre>
  
−
cff_Ht.data_IM: [https://odu.app.box.com/file/440188769890 Input Image] [https://odu.app.box.com/file/560472245094 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/5uqvlvsu7683whps0efgon6ebs8v776y Output Mesh]
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_Ht.data_IM.nrrd --delta 10 --weight-limit 0.1 --min-edge 1 --output ./cff_Ht.data_IM,d=10,wl=0.1,me=1.vtk</pre>
+
<pre>
 +
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 0.02 --min-edge 2 --max-edge 20
 +
</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].  
 +
==Fall 2019==
 +
===Synthetic Gaussian Data===
 +
* [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image]
 +
* Input distribution size: 1,000,000 cells
 +
* Uniform: 30,949 triangles
 +
* Adaptive:  7,509 triangles
  
−
[[File:cff_Ht.data_IM,d=10,wl=0.1,me=1.png|500px|cff_Ht.data_IM,d=10,wl=0.1,me=1]]
+
<gallery mode="packed" heights=300px>
 +
File:Gaussian_me_10_uniform.png
 +
File:Gaussian me 10 wl 1e-1 adapted.png
 +
</gallery>
  
−
cff_Ht.data_REAL: [https://odu.app.box.com/file/440193326284 Input Image] [https://odu.app.box.com/file/560456410980 Output Mesh]
+
Commands to generate meshes:
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_Ht.data_REAL.nrrd --delta 10 --weight-limit 0.1 --min-edge 1 --output ./cff_Ht.data_REAL,d=10,wl=0.1,me=1.vtk</pre>
 
  
−
[[File:cff_Ht.data_REAL,d=10,wl=0.1,me=1.png|500px|cff_Ht.data_REAL,d=10,wl=0.1,me=1]]
+
'''Uniform:''' [https://odu.box.com/s/2ktd6ecfueq4zmbjzmpgujf3sxfucp5j Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk --cnf-uniform --area 50
 +
</pre>
 +
'''Adaptive:''' [https://odu.box.com/s/946ll0p0qd65ahhm4s2zgnc7xjszrf0w Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk --cnf-adaptive --weight-limit 0.05
 +
</pre>
  
−
==DATA_04252019==
+
===GPDGK16Numerical_140519===
−
CFF_E_im: [https://odu.app.box.com/file/447005118740 Input Image] [https://odu.app.box.com/file/560480465505 Output Mesh]
+
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_E_im.nrrd --delta 5 --weight-limit 0.04 --min-edge 1 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk</pre>
 
  
−
[[File:CFF_E_im,d=5,wl=0.04,me=1.png|500px|CFF_E_im,d=5,wl=0.04,me=1]]
+
* [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image]
 +
* Input distribution size: 10,000 cells
 +
* Uniform: 7,587 triangles
 +
* Adaptive:  1,208  triangles
  
−
CFF_E_re: [https://odu.app.box.com/file/447003110701 Input Image] [https://odu.app.box.com/file/560479601862 Output Mesh]
+
<gallery mode="packed" heights=300px>
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_E_re.nrrd --delta 5 --weight-limit 0.08 --min-edge 1 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk</pre>
+
File:GPDGK16Numerical 140519 X50 me2 uniform.png
 +
File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
 +
</gallery>
  
−
[[File:CFF_E_re,d=5,wl=0.08,me=1.png|500px|CFF_E_re,d=5,wl=0.08,me=1]]
+
Commands to generate meshes:
  
−
CFF_H_im: [https://odu.app.box.com/file/446986172238 Input Image] [https://odu.app.box.com/file/560479304543 Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/jgqtydxkdf33iji5c125j70xx5mvi7n6 Output Mesh]
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_H_im.nrrd --delta 5 --weight-limit 0.06 --min-edge 1 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk</pre>
+
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --cnf-uniform --area 2
 +
</pre>
  
−
[[File:CFF_H_im,d=5,wl=0.06,me=1.png|500px|CFF_H_im,d=5,wl=0.06,me=1]]
+
'''Adaptive:''' [https://odu.box.com/s/e6ghjq0in1w3m9usvbhdye21wlraq271 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --cnf-adaptive
 +
</pre>
  
−
CFF_H_re: [https://odu.app.box.com/file/447002648008 Input Image] [https://odu.app.box.com/file/560474710634 Output Mesh]
+
===NT_140519===
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_H_re.nrrd --delta 5 --weight-limit 0.13 --min-edge 1 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk</pre>
+
The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
  
−
[[File:CFF_H_re,d=5,wl=0.13,me=1.png|500px|CFF_H_re,d=5,wl=0.13,me=1]]
+
* [https://odu.box.com/s/nzhrcmfhrmi64ria7vldlb591797n7ph Input Image]
 +
* Input distribution size: 10,000 cells
 +
* Uniform: 7,587 triangles
 +
* Adaptive:  1,181  triangles
  
−
GPD_H_down: [https://odu.app.box.com/file/447005150002 Input Image] [https://odu.app.box.com/file/560460154195 Output Mesh]
+
<gallery mode="packed" heights=300px>
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/GPD_H_down.nrrd --delta 5 --weight-limit 0.1 --min-edge 1 --output ./GPD_H_down,d=5,wl=0.1,me=1.vtk</pre>
+
File:NT 140519 X50 me2 uniform.png
 +
File:NT 140519 X50 me2 me2 wl 1e-1.png
 +
</gallery>
  
−
[[File:GPD_H_down,d=5,wl=0.1,me=1.png|500px|GPD_H_down,d=5,wl=0.1,me=1]]
+
Commands to generate meshes:
  
−
GPD_H_up: [https://odu.app.box.com/file/446986337826 Input Image] [https://odu.app.box.com/file/560467337847 Output Mesh]
+
'''Uniform:''' [https://odu.box.com/s/87wruxwbks5k9q5wst9d7rx6z4eycwyz Output Mesh]
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/GPD_H_up.nrrd --delta 5 --weight-limit 0.1 --min-edge 1 --output ./GPD_H_up,d=5,wl=0.1,me=1.vtk</pre>
+
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk --cnf-uniform --area 2
 +
</pre>
  
−
[[File:GPD_H_up,d=5,wl=0.1,me=1.png|500px|GPD_H_up,d=5,wl=0.1,me=1]]
+
'''Adaptive:''' [https://odu.box.com/s/dovy2udxoor8l51hndq4nh7yalg6g52y Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk --cnf-adaptive --min-edge 1
 +
</pre>
  
−
OBS_ALU: [https://odu.app.box.com/file/447605517863 Input Image] [https://odu.app.box.com/file/560478677472 Output Mesh]
+
===OBS_ALU_Y50===
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/OBS_ALU.nrrd --delta 5 --background-value 0 --weight-limit 0.07 --min-edge 1 --output ./OBS_ALU,d=5,bg=0,wl=0.07,me=1.vtk</pre>
+
The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU
  
−
[[File:OBS_ALU,d=5,bg=0,wl=0.07,me=1.png|500px|OBS_ALU,d=5,bg=0,wl=0.07,me=1]]
+
* [https://odu.box.com/s/o4qxxjebb3rxu71ncmm8kdgh9mvvsvqr Input Image]
 +
* Input distribution size: 10,000 cells
 +
* Uniform: 7,587 triangles
 +
* Adaptive:  1,018  triangles
  
−
OBS_ALU: [https://odu.app.box.com/file/447605517863 Input Image] [https://odu.app.box.com/file/560470121974 Output Mesh]
+
<gallery mode="packed" heights=300px>
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/OBS_ALU.nrrd --delta 2 --background-value 0 --uniform --output ./OBS_ALU,d=5,bg=0,uniform.vtk</pre>
+
File:OBS ALU Y50 me 2 uniform.vtk.png
 +
File:OBS ALU Y50 me 2 wl 1e-1.png
 +
</gallery>
  
−
[[File:OBS_ALU,d=2,bg=0,uniform.png|500px|OBS_ALU,d=2,bg=0,uniform]]
+
Commands to generate meshes:
  
−
==phase_space_000==
+
'''Uniform:''' [https://odu.box.com/s/irrcuttg0ceogzgnn4x86mgf9eskk1wg Output Mesh]
−
phase_space_000: [https://odu.app.box.com/file/431638871277 Input Image] [https://odu.app.box.com/file/560471425013 Output Mesh]
+
<pre>
−
<pre>docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/phase_space_000/phase_space_000.nrrd --delta 1.5 --weight-limit 0.005 --min-edge 1 --output ./phase_space_000,d=1.5,wl=0.005,me=1.vtk</pre>
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-uniform --area 2
 +
</pre>
  
−
[[File:phase_space_000,d=1.5,wl=0.005,me=1.png|500px|phase_space_000,d=1.5,wl=0.005,me=1]]
+
'''Adaptive:''' [https://odu.box.com/s/h177u63uk3us6pm8k3m4swv9qxnd2a45 Output Mesh]
 +
<pre>
 +
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-adaptive --min-edge 1
 +
</pre>

Latest revision as of 20:44, 23 July 2020

3D Example Meshes

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

Summer 2020

GPDGK16

GPDGK16_uH_img

  • Input Image
  • Input distribution size: 1,000 cells
  • Adaptive Meshes which deal with the input as an image:
    • (PODM) delta = 2, min edge = 0.85, weight limit = 0.12, max edge = 0.2 * diagonal: 1,208 tetrahedra, Output Mesh
    • (PODM) delta = 1, min edge = 0.2, weight limit = 0.1, max edge = 0.2 * diagonal: tetrahedra 8,690, Output Mesh
  • Meshes which deal with the input as a CAD geometry:
    • (Constrained Mesher) quality = 2, min edge = 0.5, weight limit = 0.2, max edge = 0.2 * diagonal: 641 tetrahedra, Output Mesh
    • (CDT3D): 535 tetrahedra, Output Mesh
    • (CDT3D): 1032 tetrahedra, Output Mesh
    • (CDT3D): 1205 tetrahedra, Output Mesh
  • GPDGK16 uH img-d 2-e 0.85-w 0.12-maxEdge 0.2diagonal.png
  • GPDGK16 uH img-d 1-e 0.2-w 0.1-maxEdge 0.2diagonal.png
  • GPDGK16 uH img-q 2-e 0.5-w 0.2-maxEdge 0.2diagonal.png
  • Cdt3d constrained surface 535 tets.png

Adaptive ((PODM) delta = 2, min edge = 0.85, weight limit = 0.12, max edge = 0.2 * diagonal): 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/GPDGK16/GPDGK16_uH_img.nrrd --cnf-adaptive --delta 2 --min-edge = 0.85 --weight-limit 0.12 --output ./GPDGK16_uH_img-d_2-e_0.85-w_0.12-maxEdge_0.2diagonal.vtk

Adaptive ((PODM) delta = 1, min edge = 0.2, weight limit = 0.1, max edge = 0.2 * diagonal): 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/GPDGK16/GPDGK16_uH_img.nrrd --cnf-adaptive --delta 1 --min-edge = 0.2 --weight-limit 0.1 --output ./GPDGK16_uH_img-d_1-e_0.2-w_0.1-maxEdge_0.2diagonal.vtk

GPDGK16_uH_img_nxi=211

  • [ Input Image]
  • Input distribution size: 21,100 cells
  • Number of bins: Xi=211 t=20 Q^2=5
  • Adaptive Meshes which deal with the input as an image:
    • (PODM) delta = 2, min edge = 0.85, weight limit = 0.12: 11964 tetrahedra
    • (PODM) delta = 1, min edge = 0.2, weight limit = 0.1: 124608 tetrahedra
  • GPDGK16 uH img-nxi 210-d 2-e 0.85-w 0.12.png
  • GPDGK16 uH img-nxi 210-d 1-e 0.2-w 0.1.png

Fall 2019

CFF_14052019

GPDGK16Numerical_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive (min edge = default): 277,701 tetrahedra
  • Adaptive (min edge = 1): 92,216 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

Adaptive (min edge = default): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive

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 --min-edge 1

GPDMMS13_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 270,453 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

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

GPDVGG99_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 266,731 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

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

NT_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive (min edge = default): 257,041 tetrahedra
  • Adaptive (min edge = 1): 140,527 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

Adaptive (min edge = default): 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

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 --min-edge 1

OBS_ALU_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 262,055 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

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

OBS_CS_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 25,476 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

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

CFF_DATA

cff_E.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 362,804 tetrahedra
  • Adaptive: 362,804 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

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

cff_E.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 318,128 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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive

cff_H.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 293,560 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

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

cff_H.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 375,705 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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive

cff_Ht.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 341,159 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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive

cff_Ht.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 398,937 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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive

DATA_04252019

CFF_E_im

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 240,150 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

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

CFF_E_re

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 261,918 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

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

CFF_H_im

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 266,306 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

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

CFF_H_re

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 251,186 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

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

GPD_H_down

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 307,082 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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive

GPD_H_up

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 301,979 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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive

OBS_ALU

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform with background-value = 0: 301,772 tetrahedra
  • Adaptive with background-value = 0: 282,102 tetrahedra
  • Uniform with background-value = default: 768,033 tetrahedra
  • Adaptive with background-value = default: 286,978 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

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

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

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

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: 11,494 tetrahedra
  • Phase space 000,d=0.25,uniform.png
  • Phase space 000-d 2-g-s-f-w 0.2-m 2.5-M 20-l.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

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 0.02 --min-edge 2 --max-edge 20

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: 7,509 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 --cnf-uniform --area 50

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk --cnf-adaptive --weight-limit 0.05

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: 1,208 triangles
  • GPDGK16Numerical 140519 X50 me2 uniform.png
  • GPDGK16Numerical 140519 X50 me2 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 --cnf-uniform --area 2

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --cnf-adaptive

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: 1,181 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 --cnf-uniform --area 2

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk --cnf-adaptive --min-edge 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 --cnf-uniform --area 2

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-adaptive --min-edge 1
Retrieved from "https://crtc.cs.odu.edu/index.php?title=CNF_Example_Meshes&oldid=7108"