crtc.cs.odu.edu - User contributions [en]

File:CentroidError lnReal.png

2020-08-13T15:54:59Z

Mkerv001:

File:UH lnReal.png

2020-08-13T15:40:38Z

Mkerv001:

File:LnRealNoCrinkle.gif

2020-08-13T15:39:28Z

Mkerv001:

File:CentroidError.png

2020-07-23T17:13:34Z

Mkerv001:

File:MC ln(CFF) uH img.png

2020-07-16T16:19:47Z

Mkerv001:

File:MC CFF uH img.png

2020-07-16T16:19:31Z

Mkerv001:

CNF Example Meshes

2020-07-16T16:01:40Z

Mkerv001: /* Summer 2020 */

__TOC__

=3D Example Meshes=
The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].

==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]

<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 = 1): 273,716 tetrahedra
* Adaptive (min edge = 2): 67,935 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 --output ./GPDGK16Numerical_140519,d=1,uniform.vtk
</pre>

'''Adaptive (min edge = 1):''' [https://odu.box.com/s/zvuuq7e6bx1cqxqbna5rpfnxfaqezgqa Output Mesh]
<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
</pre>

'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddyvbwcdqykta5b1nxi3gwxjzgdzz8yk Output Mesh]
<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
</pre>

====GPDMMS13_140519====
* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 264,762 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 --output ./GPDMMS13_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/bcg7aw4lv9rvp4531va7pqg7j2os7tny 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 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk
</pre>

====GPDVGG99_140519====
* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 261,485 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 --output ./GPDVGG99_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/51rvexl5t83zr4svn2xuw221v16jmtph 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 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk
</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 = 1): 253,965 tetrahedra
* Adaptive (min edge = 2): 120,168 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 --output ./NT_140519,d=1,uniform.vtk
</pre>

'''Adaptive (min edge = 1):''' [https://odu.box.com/s/1gviwtmh053fzqixo4thaueshjhgqdnq 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 --output ./NT_140519,d=5,wl=0.07,me=1.vtk
</pre>

'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddllau93m3itsax55pcb9ifhljp7bd3u 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 2 --output ./NT_140519,d=5,wl=0.07,me=2.vtk
</pre>

====OBS_ALU_140519====
* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 259,269 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 --output ./OBS_ALU_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/gei8k0bjh7k090vr568xo30fulslnpi5 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 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk
</pre>

====OBS_CS_140519====
* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 25,168 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 --output ./OBS_CS_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/j9zpcjp6fchtr82r6xk0dynf0qfkgi4d 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 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk
</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: 358,637 tetrahedra
* Adaptive: 358,637 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 --output ./cff_E.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/byo618ec2140sh6jopidyy1358nmk7ch 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 --output ./cff_E.data_IM,d=10,wl=0.01,me=2.vtk
</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: 314,990 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 --output ./cff_E.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/m7kn4kcmatmx86mofb06f37j8kkc35e4 Output Mesh]
<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
</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: 289,855 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 --output ./cff_H.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/ilbviax5y7vrji1anf3a4g8nd8v1wbdw 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 --output ./cff_H.data_IM,d=10,wl=0.05,me=2.vtk
</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: 372,016 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 --output ./cff_H.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/3ww0semcpqqd36xk9eysczxzajwz56uu Output Mesh]
<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
</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: 337,772 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 --output ./cff_Ht.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/2g7n0thzu4uov8zhskykct9kohosqtzm Output Mesh]
<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
</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: 394,632 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 --output ./cff_Ht.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/laao7ehbda56xyrm17jfn9xw2sz407qi Output Mesh]
<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
</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: 236,512 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 --output ./CFF_E_im,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/62hvu23fevbylbn8htm2os70adamxl3d 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 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk
</pre>

====CFF_E_re====
* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 260,349 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 --output ./CFF_E_re,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/294puxiw7kg9ykeaanuojx7ha071ibn6 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 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk
</pre>

====CFF_H_im====
* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 263,040 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 --output ./CFF_H_im,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/6b8in611r4b0i345ymvpynif45j0pnt2 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 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk
</pre>

====CFF_H_re====
* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 249,257 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 --output ./CFF_H_re,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/40loorarxv19xn40qsvlo56hwpaac2xu 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 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk
</pre>

====GPD_H_down====
* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 300,117 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 --output ./GPD_H_down,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/peljqrn1v8gf9mco4qrly85guxhyef0l Output Mesh]
<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
</pre>

====GPD_H_up====
* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 295,671 tetrahedra

<gallery mode="packed" heights=350px>
File:GPD_H_up,d=1,uniform.png
File:GPD_H_up,d=5,wl=0.1,me=1.png
</gallery>

Commands to generate meshes:

'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
<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
</pre>

'''Adaptive:'''[https://odu.box.com/s/nxrn5xnia27kd4b0kyditafd86atix7b Output Mesh]
<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
</pre>

====OBS_ALU====
* [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: 279,721 tetrahedra
* Uniform with background-value = default: 768,033 tetrahedra
* Adaptive with background-value = default: 284,256 tetrahedra

<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>

Commands to generate meshes:

'''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 --output ./OBS_ALU,d=1,bv=0,uniform.vtk
</pre>

'''Adaptive with background-value = 0:''' [https://odu.box.com/s/sprttnvtuz5imm3cdrdd34cuasifv4n0 Output Mesh]
<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
</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====
* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
* Input distribution size: 15,625 cells
* Uniform: 17,961 tetrahedra
* Adaptive: 10,593 tetrahedra

<gallery mode="packed" heights=350px>
File:phase_space_000,d=0.25,uniform.png
File:phase_space_000,d=2,wl=0.004,me=2.png
</gallery>

Commands to generate meshes:

'''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 --output ./phase_space_000,d=0.25,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/9k8b3wv9ejkvpf5b7xcbb2lc85zrz3b6 Output Mesh]
<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 0.004 --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].
==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: 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>

CNF Example Meshes

2020-07-16T16:00:50Z

Mkerv001: /* GPDGK16_uH_img_nxi=211 */

__TOC__

=3D Example Meshes=
The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].

==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]

<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 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 = 1): 273,716 tetrahedra
* Adaptive (min edge = 2): 67,935 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 --output ./GPDGK16Numerical_140519,d=1,uniform.vtk
</pre>

'''Adaptive (min edge = 1):''' [https://odu.box.com/s/zvuuq7e6bx1cqxqbna5rpfnxfaqezgqa Output Mesh]
<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
</pre>

'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddyvbwcdqykta5b1nxi3gwxjzgdzz8yk Output Mesh]
<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
</pre>

====GPDMMS13_140519====
* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 264,762 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 --output ./GPDMMS13_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/bcg7aw4lv9rvp4531va7pqg7j2os7tny 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 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk
</pre>

====GPDVGG99_140519====
* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 261,485 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 --output ./GPDVGG99_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/51rvexl5t83zr4svn2xuw221v16jmtph 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 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk
</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 = 1): 253,965 tetrahedra
* Adaptive (min edge = 2): 120,168 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 --output ./NT_140519,d=1,uniform.vtk
</pre>

'''Adaptive (min edge = 1):''' [https://odu.box.com/s/1gviwtmh053fzqixo4thaueshjhgqdnq 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 --output ./NT_140519,d=5,wl=0.07,me=1.vtk
</pre>

'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddllau93m3itsax55pcb9ifhljp7bd3u 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 2 --output ./NT_140519,d=5,wl=0.07,me=2.vtk
</pre>

====OBS_ALU_140519====
* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 259,269 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 --output ./OBS_ALU_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/gei8k0bjh7k090vr568xo30fulslnpi5 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 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk
</pre>

====OBS_CS_140519====
* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 25,168 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 --output ./OBS_CS_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/j9zpcjp6fchtr82r6xk0dynf0qfkgi4d 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 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk
</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: 358,637 tetrahedra
* Adaptive: 358,637 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 --output ./cff_E.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/byo618ec2140sh6jopidyy1358nmk7ch 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 --output ./cff_E.data_IM,d=10,wl=0.01,me=2.vtk
</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: 314,990 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 --output ./cff_E.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/m7kn4kcmatmx86mofb06f37j8kkc35e4 Output Mesh]
<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
</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: 289,855 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 --output ./cff_H.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/ilbviax5y7vrji1anf3a4g8nd8v1wbdw 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 --output ./cff_H.data_IM,d=10,wl=0.05,me=2.vtk
</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: 372,016 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 --output ./cff_H.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/3ww0semcpqqd36xk9eysczxzajwz56uu Output Mesh]
<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
</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: 337,772 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 --output ./cff_Ht.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/2g7n0thzu4uov8zhskykct9kohosqtzm Output Mesh]
<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
</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: 394,632 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 --output ./cff_Ht.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/laao7ehbda56xyrm17jfn9xw2sz407qi Output Mesh]
<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
</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: 236,512 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 --output ./CFF_E_im,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/62hvu23fevbylbn8htm2os70adamxl3d 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 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk
</pre>

====CFF_E_re====
* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 260,349 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 --output ./CFF_E_re,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/294puxiw7kg9ykeaanuojx7ha071ibn6 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 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk
</pre>

====CFF_H_im====
* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 263,040 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 --output ./CFF_H_im,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/6b8in611r4b0i345ymvpynif45j0pnt2 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 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk
</pre>

====CFF_H_re====
* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 249,257 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 --output ./CFF_H_re,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/40loorarxv19xn40qsvlo56hwpaac2xu 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 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk
</pre>

====GPD_H_down====
* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 300,117 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 --output ./GPD_H_down,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/peljqrn1v8gf9mco4qrly85guxhyef0l Output Mesh]
<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
</pre>

====GPD_H_up====
* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 295,671 tetrahedra

<gallery mode="packed" heights=350px>
File:GPD_H_up,d=1,uniform.png
File:GPD_H_up,d=5,wl=0.1,me=1.png
</gallery>

Commands to generate meshes:

'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
<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
</pre>

'''Adaptive:'''[https://odu.box.com/s/nxrn5xnia27kd4b0kyditafd86atix7b Output Mesh]
<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
</pre>

====OBS_ALU====
* [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: 279,721 tetrahedra
* Uniform with background-value = default: 768,033 tetrahedra
* Adaptive with background-value = default: 284,256 tetrahedra

<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>

Commands to generate meshes:

'''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 --output ./OBS_ALU,d=1,bv=0,uniform.vtk
</pre>

'''Adaptive with background-value = 0:''' [https://odu.box.com/s/sprttnvtuz5imm3cdrdd34cuasifv4n0 Output Mesh]
<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
</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====
* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
* Input distribution size: 15,625 cells
* Uniform: 17,961 tetrahedra
* Adaptive: 10,593 tetrahedra

<gallery mode="packed" heights=350px>
File:phase_space_000,d=0.25,uniform.png
File:phase_space_000,d=2,wl=0.004,me=2.png
</gallery>

Commands to generate meshes:

'''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 --output ./phase_space_000,d=0.25,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/9k8b3wv9ejkvpf5b7xcbb2lc85zrz3b6 Output Mesh]
<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 0.004 --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].
==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: 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>

File:GPDGK16 uH img-nxi 210-d 2-e 0.85-w 0.12.png

2020-07-16T16:00:37Z

Mkerv001:

CNF Example Meshes

2020-07-16T16:00:15Z

Mkerv001: /* GPDGK16 */

__TOC__

=3D Example Meshes=
The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].

==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]

<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 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>
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 = 1): 273,716 tetrahedra
* Adaptive (min edge = 2): 67,935 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 --output ./GPDGK16Numerical_140519,d=1,uniform.vtk
</pre>

'''Adaptive (min edge = 1):''' [https://odu.box.com/s/zvuuq7e6bx1cqxqbna5rpfnxfaqezgqa Output Mesh]
<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
</pre>

'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddyvbwcdqykta5b1nxi3gwxjzgdzz8yk Output Mesh]
<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
</pre>

====GPDMMS13_140519====
* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 264,762 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 --output ./GPDMMS13_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/bcg7aw4lv9rvp4531va7pqg7j2os7tny 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 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk
</pre>

====GPDVGG99_140519====
* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 261,485 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 --output ./GPDVGG99_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/51rvexl5t83zr4svn2xuw221v16jmtph 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 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk
</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 = 1): 253,965 tetrahedra
* Adaptive (min edge = 2): 120,168 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 --output ./NT_140519,d=1,uniform.vtk
</pre>

'''Adaptive (min edge = 1):''' [https://odu.box.com/s/1gviwtmh053fzqixo4thaueshjhgqdnq 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 --output ./NT_140519,d=5,wl=0.07,me=1.vtk
</pre>

'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddllau93m3itsax55pcb9ifhljp7bd3u 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 2 --output ./NT_140519,d=5,wl=0.07,me=2.vtk
</pre>

====OBS_ALU_140519====
* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 259,269 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 --output ./OBS_ALU_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/gei8k0bjh7k090vr568xo30fulslnpi5 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 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk
</pre>

====OBS_CS_140519====
* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 25,168 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 --output ./OBS_CS_140519,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/j9zpcjp6fchtr82r6xk0dynf0qfkgi4d 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 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk
</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: 358,637 tetrahedra
* Adaptive: 358,637 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 --output ./cff_E.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/byo618ec2140sh6jopidyy1358nmk7ch 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 --output ./cff_E.data_IM,d=10,wl=0.01,me=2.vtk
</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: 314,990 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 --output ./cff_E.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/m7kn4kcmatmx86mofb06f37j8kkc35e4 Output Mesh]
<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
</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: 289,855 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 --output ./cff_H.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/ilbviax5y7vrji1anf3a4g8nd8v1wbdw 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 --output ./cff_H.data_IM,d=10,wl=0.05,me=2.vtk
</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: 372,016 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 --output ./cff_H.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/3ww0semcpqqd36xk9eysczxzajwz56uu Output Mesh]
<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
</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: 337,772 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 --output ./cff_Ht.data_IM,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/2g7n0thzu4uov8zhskykct9kohosqtzm Output Mesh]
<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
</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: 394,632 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 --output ./cff_Ht.data_REAL,d=2,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/laao7ehbda56xyrm17jfn9xw2sz407qi Output Mesh]
<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
</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: 236,512 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 --output ./CFF_E_im,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/62hvu23fevbylbn8htm2os70adamxl3d 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 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk
</pre>

====CFF_E_re====
* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 260,349 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 --output ./CFF_E_re,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/294puxiw7kg9ykeaanuojx7ha071ibn6 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 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk
</pre>

====CFF_H_im====
* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 263,040 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 --output ./CFF_H_im,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/6b8in611r4b0i345ymvpynif45j0pnt2 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 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk
</pre>

====CFF_H_re====
* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 249,257 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 --output ./CFF_H_re,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/40loorarxv19xn40qsvlo56hwpaac2xu 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 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk
</pre>

====GPD_H_down====
* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 300,117 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 --output ./GPD_H_down,d=1,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/peljqrn1v8gf9mco4qrly85guxhyef0l Output Mesh]
<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
</pre>

====GPD_H_up====
* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
* Input distribution size: 1,000,000 cells
* Uniform: 768,033 tetrahedra
* Adaptive: 295,671 tetrahedra

<gallery mode="packed" heights=350px>
File:GPD_H_up,d=1,uniform.png
File:GPD_H_up,d=5,wl=0.1,me=1.png
</gallery>

Commands to generate meshes:

'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
<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
</pre>

'''Adaptive:'''[https://odu.box.com/s/nxrn5xnia27kd4b0kyditafd86atix7b Output Mesh]
<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
</pre>

====OBS_ALU====
* [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: 279,721 tetrahedra
* Uniform with background-value = default: 768,033 tetrahedra
* Adaptive with background-value = default: 284,256 tetrahedra

<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>

Commands to generate meshes:

'''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 --output ./OBS_ALU,d=1,bv=0,uniform.vtk
</pre>

'''Adaptive with background-value = 0:''' [https://odu.box.com/s/sprttnvtuz5imm3cdrdd34cuasifv4n0 Output Mesh]
<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
</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====
* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
* Input distribution size: 15,625 cells
* Uniform: 17,961 tetrahedra
* Adaptive: 10,593 tetrahedra

<gallery mode="packed" heights=350px>
File:phase_space_000,d=0.25,uniform.png
File:phase_space_000,d=2,wl=0.004,me=2.png
</gallery>

Commands to generate meshes:

'''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 --output ./phase_space_000,d=0.25,uniform.vtk
</pre>

'''Adaptive:''' [https://odu.box.com/s/9k8b3wv9ejkvpf5b7xcbb2lc85zrz3b6 Output Mesh]
<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 0.004 --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].
==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: 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>

File:GPDGK16 uH img-nxi 210-d 1-e 0.2-w 0.1.png

2020-07-16T15:59:00Z

Mkerv001: