Difference between revisions of "CNF Example Meshes"

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(→‎NT_140519)
(→‎OBS_ALU_Y50)
 
(30 intermediate revisions by the same user not shown)
Line 145: Line 145:
 
'''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform --output ./NT_140519,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
Line 162: Line 162:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive: 259,269 tetrahedra
+
* Adaptive: 262,055 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 173: Line 173:
 
'''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform --output ./OBS_ALU_140519,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/gei8k0bjh7k090vr568xo30fulslnpi5 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/ds9cobknetx03wc58821laj8k2dkd7h2 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-adaptive --weight-limit 0.13 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-adaptive --weight-limit 0.13
 
</pre>
 
</pre>
  
Line 185: Line 185:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive:  25,168 tetrahedra
+
* Adaptive:  25,476 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 196: Line 196:
 
'''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform --output ./OBS_CS_140519,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/j9zpcjp6fchtr82r6xk0dynf0qfkgi4d Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/fs4g3d7yfs8o80t6u3oi4me84yksneot Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-adaptive --weight-limit 0.01 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-adaptive --weight-limit 0.01
 
</pre>
 
</pre>
  
Line 209: Line 209:
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
 
* Uniform: 745,291 tetrahedra
 
* Uniform: 745,291 tetrahedra
−
* Adaptive: 358,637 tetrahedra
+
* Adaptive: 362,804 tetrahedra
−
* Adaptive: 358,637 tetrahedra (other side of the same adaptive case)
+
* Adaptive: 362,804 tetrahedra (other side of the same adaptive case)
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 222: Line 222:
 
'''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform --output ./cff_E.data_IM,d=2,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/byo618ec2140sh6jopidyy1358nmk7ch Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/nopuegnozgjdh5is49xsqoqz86r55zux Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-adaptive --weight-limit 0.01 --output ./cff_E.data_IM,d=10,wl=0.01,me=2.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-adaptive --weight-limit 0.01
 
</pre>
 
</pre>
  
Line 234: Line 234:
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
 
* Uniform: 745,291 tetrahedra
 
* Uniform: 745,291 tetrahedra
−
* Adaptive: 314,990 tetrahedra
+
* Adaptive: 318,128 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 245: Line 245:
 
'''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform --output ./cff_E.data_REAL,d=2,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/m7kn4kcmatmx86mofb06f37j8kkc35e4 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/3ildscx8w4t966nzv3w20gkvxt4zxcxz Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive --output ./cff_E.data_REAL,d=10,wl=0.1,me=2.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive
 
</pre>
 
</pre>
  
Line 257: Line 257:
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
 
* Uniform: 745,291 tetrahedra
 
* Uniform: 745,291 tetrahedra
−
* Adaptive: 289,855 tetrahedra
+
* Adaptive: 293,560 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 268: Line 268:
 
'''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform --output ./cff_H.data_IM,d=2,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/ilbviax5y7vrji1anf3a4g8nd8v1wbdw Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/j39hie2a1clrvwzw6nn7kuffty4887l2 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-adaptive --weight-limit 0.05 --output ./cff_H.data_IM,d=10,wl=0.05,me=2.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-adaptive --weight-limit 0.05
 
</pre>
 
</pre>
  
Line 280: Line 280:
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
 
* Uniform: 745,291 tetrahedra
 
* Uniform: 745,291 tetrahedra
−
* Adaptive: 372,016 tetrahedra
+
* Adaptive: 375,705 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 291: Line 291:
 
'''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform --output ./cff_H.data_REAL,d=2,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/3ww0semcpqqd36xk9eysczxzajwz56uu Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/hmtot3rslyf0gy0gh9wijk4fbts2vnzg Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive --output ./cff_H.data_REAL,d=10,wl=0.1,me=2.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive
 
</pre>
 
</pre>
  
Line 303: Line 303:
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
 
* Uniform: 745,291 tetrahedra
 
* Uniform: 745,291 tetrahedra
−
* Adaptive: 337,772 tetrahedra
+
* Adaptive: 341,159 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 314: Line 314:
 
'''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform --output ./cff_Ht.data_IM,d=2,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/2g7n0thzu4uov8zhskykct9kohosqtzm Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/94vc0typeqrtudqnvjstr9u7ff5y4h7y Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive --output ./cff_Ht.data_IM,d=10,wl=0.1,me=2.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive
 
</pre>
 
</pre>
  
Line 326: Line 326:
 
* Input distribution size: 8,000,000 cells
 
* Input distribution size: 8,000,000 cells
 
* Uniform: 745,291 tetrahedra
 
* Uniform: 745,291 tetrahedra
−
* Adaptive: 394,632 tetrahedra
+
* Adaptive: 398,937 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 337: Line 337:
 
'''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform --output ./cff_Ht.data_REAL,d=2,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/laao7ehbda56xyrm17jfn9xw2sz407qi Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/7ah3un9py97rw935vwwrzwm099156vwd Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive --output ./cff_Ht.data_REAL,d=10,wl=0.1,me=2.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive
 
</pre>
 
</pre>
  
Line 350: Line 350:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive: 236,512 tetrahedra
+
* Adaptive: 240,150 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 361: Line 361:
 
'''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform --output ./CFF_E_im,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/62hvu23fevbylbn8htm2os70adamxl3d Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/v7ptl6shwnhudjukuqllbcn7jzyhg1ku Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-adaptive --weight-limit 0.04 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-adaptive --weight-limit 0.04
 
</pre>
 
</pre>
  
Line 373: Line 373:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive: 260,349 tetrahedra
+
* Adaptive: 261,918 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 384: Line 384:
 
'''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform --output ./CFF_E_re,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/294puxiw7kg9ykeaanuojx7ha071ibn6 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/h1b91ms5na6e1a8735lx4r00nz9nwxq6 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-adaptive --weight-limit 0.08 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-adaptive --weight-limit 0.08
 
</pre>
 
</pre>
  
Line 396: Line 396:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive: 263,040 tetrahedra
+
* Adaptive: 266,306 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 407: Line 407:
 
'''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform --output ./CFF_H_im,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/6b8in611r4b0i345ymvpynif45j0pnt2 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/v6madkz4ahdxq02nrwnkx7o72t1qkntu Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-adaptive --weight-limit 0.06 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-adaptive --weight-limit 0.06
 
</pre>
 
</pre>
  
Line 419: Line 419:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive: 249,257 tetrahedra
+
* Adaptive: 251,186 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 430: Line 430:
 
'''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform --output ./CFF_H_re,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/40loorarxv19xn40qsvlo56hwpaac2xu Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/euxix0on74rp6pqy6u4slj9ij7ss28m0 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-adaptive --weight-limit 0.13 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-adaptive --weight-limit 0.13
 
</pre>
 
</pre>
  
Line 442: Line 442:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive: 300,117 tetrahedra
+
* Adaptive: 307,082 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 453: Line 453:
 
'''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform --output ./GPD_H_down,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/peljqrn1v8gf9mco4qrly85guxhyef0l Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/4qsu8gwr13mzicfmnjqxsxvewfnslbro Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive --output ./GPD_H_down,d=5,wl=0.1,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive
 
</pre>
 
</pre>
  
Line 465: Line 465:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 768,033 tetrahedra
 
* Uniform: 768,033 tetrahedra
−
* Adaptive: 295,671 tetrahedra
+
* Adaptive: 301,979 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 476: Line 476:
 
'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform --output ./GPD_H_up,d=1,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:'''[https://odu.box.com/s/nxrn5xnia27kd4b0kyditafd86atix7b Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/s4pb42n345gq84w06ne5x629b46buxm6 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive --output ./GPD_H_up,d=5,wl=0.1,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive
 
</pre>
 
</pre>
  
Line 488: Line 488:
 
* 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
−
* Adaptive with background-value = 0: 279,721 tetrahedra
+
* Adaptive with background-value = 0: 282,102 tetrahedra
 
* Uniform with background-value = default: 768,033 tetrahedra
 
* Uniform with background-value = default: 768,033 tetrahedra
−
* Adaptive with background-value = default: 284,256 tetrahedra
+
* Adaptive with background-value = default: 286,978 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
Line 505: Line 505:
 
'''Uniform with background-value = 0:''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh]
 
'''Uniform with background-value = 0:''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0 --output ./OBS_ALU,d=1,bv=0,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0
 
</pre>
 
</pre>
  
−
'''Adaptive with background-value = 0:''' [https://odu.box.com/s/sprttnvtuz5imm3cdrdd34cuasifv4n0 Output Mesh]
+
'''Adaptive with background-value = 0:''' [https://odu.box.com/s/her4h7lrtygcurufj6isx57hxru02c3g Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --background-value 0 --weight-limit 0.07 --output ./OBS_ALU,d=5,bv=0,wl=0.07,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --background-value 0 --weight-limit 0.07
 
</pre>
 
</pre>
  
 
'''Uniform with background-value = default:''' [https://odu.box.com/s/b5licz0d25mb0ed0ttlz80adpsdoth4z Output Mesh]
 
'''Uniform with background-value = default:''' [https://odu.box.com/s/b5licz0d25mb0ed0ttlz80adpsdoth4z Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive with background-value = default:''' [https://odu.box.com/s/lz86mqwrukhupuhq9okfwim5vx6a2avr Output Mesh]
+
'''Adaptive with background-value = default:''' [https://odu.box.com/s/h93jztz1bdtplmzmfjdveq0xkzntil18 Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07 --output ./OBS_ALU,d=5,wl=0.07,me=1.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07
 
</pre>
 
</pre>
  
Line 531: Line 531:
 
* Input distribution size: 15,625 cells
 
* Input distribution size: 15,625 cells
 
* Uniform: 17,961 tetrahedra
 
* Uniform: 17,961 tetrahedra
−
* Adaptive: 10,593 tetrahedra
+
* Adaptive: 11,494 tetrahedra
  
 
<gallery mode="packed" heights=350px>
 
<gallery mode="packed" heights=350px>
 
File:phase_space_000,d=0.25,uniform.png
 
File:phase_space_000,d=0.25,uniform.png
−
File:phase_space_000,d=2,wl=0.004,me=2.png
+
File:phase_space_000-d_2-g-s-f-w_0.2-m_2.5-M_20-l.png
 
</gallery>
 
</gallery>
  
Line 542: Line 542:
 
'''Uniform:''' [https://odu.box.com/s/ype3j19yixez8uwy1k9mgc1585oepugq Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/ype3j19yixez8uwy1k9mgc1585oepugq Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.25 --cnf-uniform --output ./phase_space_000,d=0.25,uniform.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.25 --cnf-uniform
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/9k8b3wv9ejkvpf5b7xcbb2lc85zrz3b6 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/5uqvlvsu7683whps0efgon6ebs8v776y Output Mesh]
 
<pre>
 
<pre>
−
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.004 --min-edge 2 --output ./phase_space_000,d=2,wl=0.004,me=2.vtk
+
docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.02 --min-edge 2 --max-edge 20
 
</pre>
 
</pre>
  
Line 557: Line 557:
 
* Input distribution size: 1,000,000 cells
 
* Input distribution size: 1,000,000 cells
 
* Uniform: 30,949 triangles
 
* Uniform: 30,949 triangles
−
* Adaptive:  3,788 triangles
+
* Adaptive:  7,509 triangles
  
 
<gallery mode="packed" heights=300px>
 
<gallery mode="packed" heights=300px>
Line 568: Line 568:
 
'''Uniform:''' [https://odu.box.com/s/2ktd6ecfueq4zmbjzmpgujf3sxfucp5j Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/2ktd6ecfueq4zmbjzmpgujf3sxfucp5j Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk --cnf-uniform --area 50
 
</pre>
 
</pre>
−
'''Adaptive:''' [https://odu.box.com/s/1omr8szilea3r6fde39w49gu7u5xyui8 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/946ll0p0qd65ahhm4s2zgnc7xjszrf0w Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk --cnf-adaptive --weight-limit 0.05
 
</pre>
 
</pre>
  
Line 581: Line 581:
 
* Input distribution size: 10,000 cells
 
* Input distribution size: 10,000 cells
 
* Uniform: 7,587 triangles
 
* Uniform: 7,587 triangles
−
* Adaptive (min edge = 2):  623  triangles
+
* Adaptive:  1,208  triangles
−
* Adaptive (min edge = 0.5):  1,409 triangles
 
  
 
<gallery mode="packed" heights=300px>
 
<gallery mode="packed" heights=300px>
 
File:GPDGK16Numerical 140519 X50 me2 uniform.png
 
File:GPDGK16Numerical 140519 X50 me2 uniform.png
 
File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
 
File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
−
File:GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png
 
 
</gallery>
 
</gallery>
  
Line 594: Line 592:
 
'''Uniform:''' [https://odu.box.com/s/jgqtydxkdf33iji5c125j70xx5mvi7n6 Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/jgqtydxkdf33iji5c125j70xx5mvi7n6 Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --cnf-uniform --area 2
 
</pre>
 
</pre>
  
−
'''Adaptive (min edge = 2):''' [https://odu.box.com/s/3yp9jod0hcjxipfu81ywk0jrxphonasa Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/e6ghjq0in1w3m9usvbhdye21wlraq271 Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --cnf-adaptive
 
</pre>
 
</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===
 
===NT_140519===
Line 616: Line 606:
 
* 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:  1,181   triangles
  
 
<gallery mode="packed" heights=300px>
 
<gallery mode="packed" heights=300px>
Line 627: Line 617:
 
'''Uniform:''' [https://odu.box.com/s/87wruxwbks5k9q5wst9d7rx6z4eycwyz Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/87wruxwbks5k9q5wst9d7rx6z4eycwyz Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk --cnf-uniform --area 2
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/oytjqxeque11wvbxu62fhwc3830fbpcy Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/dovy2udxoor8l51hndq4nh7yalg6g52y Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk --cnf-adaptive --min-edge 1
 
</pre>
 
</pre>
  
Line 652: Line 642:
 
'''Uniform:''' [https://odu.box.com/s/irrcuttg0ceogzgnn4x86mgf9eskk1wg Output Mesh]
 
'''Uniform:''' [https://odu.box.com/s/irrcuttg0ceogzgnn4x86mgf9eskk1wg Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-uniform --area 2
 
</pre>
 
</pre>
  
−
'''Adaptive:''' [https://odu.box.com/s/8369kd2q52weqp76811h6p54sax3nj37 Output Mesh]
+
'''Adaptive:''' [https://odu.box.com/s/h177u63uk3us6pm8k3m4swv9qxnd2a45 Output Mesh]
 
<pre>
 
<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
+
docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-adaptive --min-edge 1
 
</pre>
 
</pre>

Latest revision as of 20:44, 23 July 2020

3D Example Meshes

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

Summer 2020

GPDGK16

GPDGK16_uH_img

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

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

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

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

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

GPDGK16_uH_img_nxi=211

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

Fall 2019

CFF_14052019

GPDGK16Numerical_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive (min edge = default): 277,701 tetrahedra
  • Adaptive (min edge = 1): 92,216 tetrahedra
  • GPDGK16Numerical 140519,d=1,uniform.png
  • GPDGK16Numerical 140519,d=5,wl=0.1,me=1.png
  • GPDGK16Numerical 140519,d=5,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive (min edge = default): Output Mesh 

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

Adaptive (min edge = 1): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 1

GPDMMS13_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 270,453 tetrahedra
  • GPDMMS13 140519,d=1,uniform.png
  • GPDMMS13 140519,d=5,wl=0.05,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05

GPDVGG99_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 266,731 tetrahedra
  • GPDVGG99 140519,d=1,uniform.png
  • GPDVGG99 140519,d=5,wl=0.05,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05

NT_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive (min edge = default): 257,041 tetrahedra
  • Adaptive (min edge = 1): 140,527 tetrahedra
  • NT 140519,d=1,uniform.png
  • NT 140519,d=5,wl=0.07,me=1.png
  • NT 140519,d=5,wl=0.07,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform

Adaptive (min edge = default): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07

Adaptive (min edge = 1): Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --min-edge 1

OBS_ALU_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 262,055 tetrahedra
  • OBS ALU 140519,d=1,uniform.png
  • OBS ALU 140519,d=5,wl=0.13,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-adaptive --weight-limit 0.13

OBS_CS_140519

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 25,476 tetrahedra
  • OBS CS 140519,d=1,uniform.png
  • OBS CS 140519,d=5,wl=0.01,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-adaptive --weight-limit 0.01

CFF_DATA

cff_E.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 362,804 tetrahedra
  • Adaptive: 362,804 tetrahedra (other side of the same adaptive case)
  • Cff E.data IM,d=2,uniform.png
  • Cff E.data IM,d=10,wl=0.01,me=2.png
  • Cff E.data IM,d=10,wl=0.01,me=2,OtherSide.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-adaptive --weight-limit 0.01

cff_E.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 318,128 tetrahedra
  • Cff E.data REAL,d=2,uniform.png
  • Cff E.data REAL,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

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

cff_H.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 293,560 tetrahedra
  • Cff H.data IM,d=2,uniform.png
  • Cff H.data IM,d=10,wl=0.05,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-adaptive --weight-limit 0.05

cff_H.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 375,705 tetrahedra
  • Cff H.data REAL,d=2,uniform.png
  • Cff H.data REAL,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

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

cff_Ht.data_IM

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 341,159 tetrahedra
  • Cff Ht.data IM,d=2,uniform.png
  • Cff Ht.data IM,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

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

cff_Ht.data_REAL

  • Input Image
  • Input distribution size: 8,000,000 cells
  • Uniform: 745,291 tetrahedra
  • Adaptive: 398,937 tetrahedra
  • Cff Ht.data REAL,d=2,uniform.png
  • Cff Ht.data REAL,d=10,wl=0.1,me=2.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

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

DATA_04252019

CFF_E_im

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 240,150 tetrahedra
  • CFF E im,d=1,uniform.png
  • CFF E im,d=5,wl=0.04,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-adaptive --weight-limit 0.04

CFF_E_re

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 261,918 tetrahedra
  • CFF E re,d=1,uniform.png
  • CFF E re,d=5,wl=0.08,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-adaptive --weight-limit 0.08

CFF_H_im

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 266,306 tetrahedra
  • CFF H im,d=1,uniform.png
  • CFF H im,d=5,wl=0.06,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-adaptive --weight-limit 0.06

CFF_H_re

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 251,186 tetrahedra
  • CFF H re,d=1,uniform.png
  • CFF H re,d=5,wl=0.13,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-adaptive --weight-limit 0.13

GPD_H_down

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 307,082 tetrahedra
  • GPD H down,d=1,uniform.png
  • GPD H down,d=5,wl=0.1,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

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

GPD_H_up

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 768,033 tetrahedra
  • Adaptive: 301,979 tetrahedra
  • GPD H up,d=1,uniform.png
  • GPD H up,d=5,wl=0.1,me=1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

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

OBS_ALU

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform with background-value = 0: 301,772 tetrahedra
  • Adaptive with background-value = 0: 282,102 tetrahedra
  • Uniform with background-value = default: 768,033 tetrahedra
  • Adaptive with background-value = default: 286,978 tetrahedra
  • OBS ALU,d=1,bv=0,uniform.png
  • OBS ALU,d=5,bv=0,wl=0.07,me=1.png
  • OBS ALU,d=1,uniform.png
  • OBS ALU,d=5,wl=0.07,me=1.png

Commands to generate meshes:

Uniform with background-value = 0: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0

Adaptive with background-value = 0: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --background-value 0 --weight-limit 0.07

Uniform with background-value = default: Output Mesh 

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

Adaptive with background-value = default: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07

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

phase_space_000

phase_space_000

  • Input Image
  • Input distribution size: 15,625 cells
  • Uniform: 17,961 tetrahedra
  • Adaptive: 11,494 tetrahedra
  • Phase space 000,d=0.25,uniform.png
  • Phase space 000-d 2-g-s-f-w 0.2-m 2.5-M 20-l.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 2 --cnf-adaptive --weight-limit 0.02 --min-edge 2 --max-edge 20

2D Example Meshes

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

Fall 2019

Synthetic Gaussian Data

  • Input Image
  • Input distribution size: 1,000,000 cells
  • Uniform: 30,949 triangles
  • Adaptive: 7,509 triangles
  • Gaussian me 10 uniform.png
  • Gaussian me 10 wl 1e-1 adapted.png

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk --cnf-uniform --area 50

Adaptive: Output Mesh 

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

GPDGK16Numerical_140519

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

  • Input Image
  • Input distribution size: 10,000 cells
  • Uniform: 7,587 triangles
  • Adaptive: 1,208 triangles
  • GPDGK16Numerical 140519 X50 me2 uniform.png
  • GPDGK16Numerical 140519 X50 me2 wl 1e-1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

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

NT_140519

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

  • Input Image
  • Input distribution size: 10,000 cells
  • Uniform: 7,587 triangles
  • Adaptive: 1,181 triangles
  • NT 140519 X50 me2 uniform.png
  • NT 140519 X50 me2 me2 wl 1e-1.png

Commands to generate meshes:

Uniform: Output Mesh 

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

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk --cnf-adaptive --min-edge 1

OBS_ALU_Y50

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

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

Commands to generate meshes:

Uniform: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-uniform --area 2

Adaptive: Output Mesh 

docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-adaptive --min-edge 1
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