Difference between revisions of "CNF Example Meshes"
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==CFF_14052019== | ==CFF_14052019== | ||
===GPDGK16Numerical_140519=== | ===GPDGK16Numerical_140519=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 34: | Line 35: | ||
===GPDMMS13_140519=== | ===GPDMMS13_140519=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 56: | Line 58: | ||
===GPDVGG99_140519=== | ===GPDVGG99_140519=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 78: | Line 81: | ||
===NT_140519=== | ===NT_140519=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 107: | Line 111: | ||
===OBS_ALU_140519=== | ===OBS_ALU_140519=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 129: | Line 134: | ||
===OBS_CS_140519=== | ===OBS_CS_140519=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 152: | Line 158: | ||
==CFF_DATA== | ==CFF_DATA== | ||
===cff_E.data_IM=== | ===cff_E.data_IM=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
| Line 176: | Line 183: | ||
===cff_E.data_REAL=== | ===cff_E.data_REAL=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
| Line 198: | Line 206: | ||
===cff_H.data_IM=== | ===cff_H.data_IM=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
| Line 220: | Line 229: | ||
===cff_H.data_REAL=== | ===cff_H.data_REAL=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
| Line 242: | Line 252: | ||
===cff_Ht.data_IM=== | ===cff_Ht.data_IM=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
| Line 264: | Line 275: | ||
===cff_Ht.data_REAL=== | ===cff_Ht.data_REAL=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 8,000,000 cells | * Input distribution size: 8,000,000 cells | ||
* Uniform: 745,291 tetrahedra | * Uniform: 745,291 tetrahedra | ||
| Line 287: | Line 299: | ||
==DATA_04252019== | ==DATA_04252019== | ||
===CFF_E_im=== | ===CFF_E_im=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 309: | Line 322: | ||
===CFF_E_re=== | ===CFF_E_re=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 331: | Line 345: | ||
===CFF_H_im=== | ===CFF_H_im=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 353: | Line 368: | ||
===CFF_H_re=== | ===CFF_H_re=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 375: | Line 391: | ||
===GPD_H_down=== | ===GPD_H_down=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 397: | Line 414: | ||
===GPD_H_up=== | ===GPD_H_up=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 768,033 tetrahedra | * Uniform: 768,033 tetrahedra | ||
| Line 419: | Line 437: | ||
===OBS_ALU=== | ===OBS_ALU=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform with background-value = 0: 301,772 tetrahedra | * Uniform with background-value = 0: 301,772 tetrahedra | ||
| Line 461: | Line 480: | ||
==phase_space_000== | ==phase_space_000== | ||
===phase_space_000=== | ===phase_space_000=== | ||
| + | * [ Input Image] | ||
* Input distribution size: 15,625 cells | * Input distribution size: 15,625 cells | ||
* Uniform: 17,961 tetrahedra | * Uniform: 17,961 tetrahedra | ||
| Line 485: | Line 505: | ||
The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/exyxfqttfgpryxsi44mb7vv6ybs6gy4w CNF_Data]. | The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/exyxfqttfgpryxsi44mb7vv6ybs6gy4w CNF_Data]. | ||
== Synthetic Gaussian Data == | == Synthetic Gaussian Data == | ||
| + | * [ Input Image] | ||
* Input distribution size: 1,000,000 cells | * Input distribution size: 1,000,000 cells | ||
* Uniform: 30,949 triangles | * Uniform: 30,949 triangles | ||
| Line 508: | Line 529: | ||
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519 | The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519 | ||
| + | * [ Input Image] | ||
* Input distribution size: 10,000 cells | * Input distribution size: 10,000 cells | ||
* Uniform: 7,587 triangles | * Uniform: 7,587 triangles | ||
| Line 540: | Line 562: | ||
== NT_140519 == | == NT_140519 == | ||
| + | The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519 | ||
| + | |||
| + | * [ Input Image] | ||
* Input distribution size: 10,000 cells | * Input distribution size: 10,000 cells | ||
* Uniform: 7,587 triangles | * Uniform: 7,587 triangles | ||
* Adaptive: 1,038 triangles | * Adaptive: 1,038 triangles | ||
| − | |||
| − | |||
| − | |||
<gallery mode="packed" heights=300px> | <gallery mode="packed" heights=300px> | ||
| Line 565: | Line 587: | ||
== OBS_ALU_Y50 == | == OBS_ALU_Y50 == | ||
| + | The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU | ||
| + | |||
| + | * [ Input Image] | ||
* Input distribution size: 10,000 cells | * Input distribution size: 10,000 cells | ||
* Uniform: 7,587 triangles | * Uniform: 7,587 triangles | ||
* Adaptive: 1,018 triangles | * Adaptive: 1,018 triangles | ||
| − | |||
| − | |||
<gallery mode="packed" heights=300px> | <gallery mode="packed" heights=300px> | ||
Revision as of 19:24, 30 March 2020
Contents
3D Example Meshes
The directory containing the 3D input data is located in the 3D folder of CNF_Data.
CFF_14052019
GPDGK16Numerical_140519
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive (min edge = 1): 273,716 tetrahedra
- Adaptive (min edge = 2): 67,935 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform --output ./GPDGK16Numerical_140519,d=1,uniform.vtk
Adaptive (min edge = 1): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=1.vtk
Adaptive (min edge = 2): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 2 --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=2.vtk
GPDMMS13_140519
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 264,762 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform --output ./GPDMMS13_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk
GPDVGG99_140519
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 261,485 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform --output ./GPDVGG99_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk
NT_140519
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive (min edge = 1): 253,965 tetrahedra
- Adaptive (min edge = 2): 120,168 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform --output ./NT_140519,d=1,uniform.vtk
Adaptive (min edge = 1): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --output ./NT_140519,d=5,wl=0.07,me=1.vtk
Adaptive (min edge = 2): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --min-edge 2 --output ./NT_140519,d=5,wl=0.07,me=2.vtk
OBS_ALU_140519
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 259,269 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform --output ./OBS_ALU_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-adaptive --weight-limit 0.13 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk
OBS_CS_140519
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 25,168 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform --output ./OBS_CS_140519,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-adaptive --weight-limit 0.01 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk
CFF_DATA
cff_E.data_IM
- [ Input Image]
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 358,637 tetrahedra
- Adaptive: 358,637 tetrahedra (other side of the same adaptive case)
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform --output ./cff_E.data_IM,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-adaptive --weight-limit 0.01 --output ./cff_E.data_IM,d=10,wl=0.01,me=2.vtk
cff_E.data_REAL
- [ Input Image]
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 314,990 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform --output ./cff_E.data_REAL,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive --output ./cff_E.data_REAL,d=10,wl=0.1,me=2.vtk
cff_H.data_IM
- [ Input Image]
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 289,855 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform --output ./cff_H.data_IM,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-adaptive --weight-limit 0.05 --output ./cff_H.data_IM,d=10,wl=0.05,me=2.vtk
cff_H.data_REAL
- [ Input Image]
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 372,016 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform --output ./cff_H.data_REAL,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive --output ./cff_H.data_REAL,d=10,wl=0.1,me=2.vtk
cff_Ht.data_IM
- [ Input Image]
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 337,772 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform --output ./cff_Ht.data_IM,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive --output ./cff_Ht.data_IM,d=10,wl=0.1,me=2.vtk
cff_Ht.data_REAL
- [ Input Image]
- Input distribution size: 8,000,000 cells
- Uniform: 745,291 tetrahedra
- Adaptive: 394,632 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform --output ./cff_Ht.data_REAL,d=2,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive --output ./cff_Ht.data_REAL,d=10,wl=0.1,me=2.vtk
DATA_04252019
CFF_E_im
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 236,512 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform --output ./CFF_E_im,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-adaptive --weight-limit 0.04 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk
CFF_E_re
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 260,349 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform --output ./CFF_E_re,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-adaptive --weight-limit 0.08 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk
CFF_H_im
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 263,040 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform --output ./CFF_H_im,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-adaptive --weight-limit 0.06 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk
CFF_H_re
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 249,257 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform --output ./CFF_H_re,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-adaptive --weight-limit 0.13 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk
GPD_H_down
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 300,117 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform --output ./GPD_H_down,d=1,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive --output ./GPD_H_down,d=5,wl=0.1,me=1.vtk
GPD_H_up
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 768,033 tetrahedra
- Adaptive: 295,671 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform --output ./GPD_H_up,d=1,uniform.vtk
Adaptive:Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive --output ./GPD_H_up,d=5,wl=0.1,me=1.vtk
OBS_ALU
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform with background-value = 0: 301,772 tetrahedra
- Adaptive with background-value = 0: 279,721 tetrahedra
- Uniform with background-value = default: 768,033 tetrahedra
- Adaptive with background-value = default: 284,256 tetrahedra
Commands to generate meshes:
Uniform with background-value = 0: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0 --output ./OBS_ALU,d=1,bv=0,uniform.vtk
Adaptive with background-value = 0: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --background-value 0 --weight-limit 0.07 --output ./OBS_ALU,d=5,bv=0,wl=0.07,me=1.vtk
Uniform with background-value = default: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --output ./OBS_ALU,d=1,uniform.vtk
Adaptive with background-value = default: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07 --output ./OBS_ALU,d=5,wl=0.07,me=1.vtk
Note: In this case, we want to exclude the entries with value 0 (lower part, see figure) since they are not of interest. Using the flag --background-value 0, the entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive.
phase_space_000
phase_space_000
- [ Input Image]
- Input distribution size: 15,625 cells
- Uniform: 17,961 tetrahedra
- Adaptive: 10,593 tetrahedra
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.25 --cnf-uniform --output ./phase_space_000,d=0.25,uniform.vtk
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.004 --min-edge 2 --output ./phase_space_000,d=2,wl=0.004,me=2.vtk
2D Example Meshes
The directory containing the 2D input data is located in the 2D folder of CNF_Data.
Synthetic Gaussian Data
- [ Input Image]
- Input distribution size: 1,000,000 cells
- Uniform: 30,949 triangles
- Adaptive: 3,788 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/Gaussian2.vtk --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/Gaussian2.vtk --output Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10
GPDGK16Numerical_140519
The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519
- [ Input Image]
- Input distribution size: 10,000 cells
- Uniform: 7,587 triangles
- Adaptive (min edge = 2): 623 triangles
- Adaptive (min edge = 0.5): 1,409 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive (min edge = 2): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
Adaptive (min edge = 0.5): Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --output GPDGK16Numerical_140519_me_0.5_wl_1e-1.vtk --min-edge=0.5 --weight-limit=0.1
Note: By using min-edge less than 1 we are essentially generating triangles with an edge smaller than the input pixels. Using values much smaller than 1 is not expected to help the discretization since we are essentially packing more element into a pixel which has a constant value.
NT_140519
The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
- [ Input Image]
- Input distribution size: 10,000 cells
- Uniform: 7,587 triangles
- Adaptive: 1,038 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/NT_140519_50_X.vtk --output NT_140519_X50_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/NT_140519_50_X.vtk --output NT_140519_X50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
OBS_ALU_Y50
The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU
- [ Input Image]
- Input distribution size: 10,000 cells
- Uniform: 7,587 triangles
- Adaptive: 1,018 triangles
Commands to generate meshes:
Uniform: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_uniform.vtk --min-edge=2 --uniform
Adaptive: Output Mesh
docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --output OBS_ALU_Y50_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
