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	__TOC__		__TOC__
−	=2D Example Meshes=
−	The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/wd0i18giti2zo330y19xhkqu8wjzgf7j CNF_Data].
−	== Synthetic Gaussian Data ==
−	* Input distribution size: 1,000,000 cells
−	* Uniform: 30,949 triangles
−	* Adaptive:  3,788 triangles
−	<gallery mode="packed" heights=300px>	+	=3D Example Meshes=
−	File:Gaussian_me_10_uniform.png	+	The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].
−	File:Gaussian me 10 wl 1e-1 adapted.png
−	</gallery>
−	Commands to generate meshes :	+	==Summer 2020==
−		+	===GPDGK16===
−	'''Uniform:''' [https://odu.box.com/s/2ktd6ecfueq4zmbjzmpgujf3sxfucp5j Output Mesh]	+	====GPDGK16_uH_img====
−	<pre>	+	* [https://odu.box.com/s/6pl2q075h45wglbd0mbedb5epxvw1g3c Input Image]
−	docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk  --output Gaussian_me_10_uniform.vtk --uniform --min-edge=10	+	* Input distribution size: 1,000 cells
−	</pre>	+	* Adaptive Meshes which deal with the input as an image:
−	'''Adaptive:''' [https://odu.box.com/s/1omr8szilea3r6fde39w49gu7u5xyui8 Output Mesh]	+	** (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]
−	<pre>	+	** (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]
−	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	+	* Meshes which deal with the input as a CAD geometry:
−	</pre>	+	** (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]
−	== GPDGK16Numerical_140519 ==	+	** (CDT3D): 1032 tetrahedra, [https://odu.box.com/s/1b8fypd0taale03byxdodvnamh087mko Output Mesh]
−	The 2D case created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) GPDGK16Numerical_140519	+	** (CDT3D): 1205 tetrahedra, [https://odu.box.com/s/nn6sxl38s866nucpfa3ze5n3741jwa3h Output Mesh]
−
−	* 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>		<gallery mode="packed" heights=300px>
−	File:GPDGK16Numerical 140519 X50 me2 uniform.png	+	File:GPDGK16_uH_img-d_2-e_0.85-w_0.12-maxEdge_0.2diagonal.png
−	File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png	+	File:GPDGK16_uH_img-d_1-e_0.2-w_0.1-maxEdge_0.2diagonal.png
−	File:GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png
	</gallery>		</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 ==
−	* Input distribution size: 10,000 cells
−	* Uniform: 7,587 triangles
−	* Adaptive:  1,038  triangles
−
−	The 2D image was created by extracting a 2D slice at X=50 out of the 3D distribution (see 3D case below) NT_140519
−
	<gallery mode="packed" heights=300px>		<gallery mode="packed" heights=300px>
−	File:NT 140519 X50 me2 uniform.png	+	File:GPDGK16_uH_img-q_2-e_0.5-w_0.2-maxEdge_0.2diagonal.png
−	File:NT 140519 X50 me2 me2 wl 1e-1.png	+	File:cdt3d_constrained_surface_535_tets.png
	</gallery>		</gallery>
−	Commands to generate meshes :	+	'''Adaptive ((PODM) delta = 2, min edge = 0.85, weight limit = 0.12, max edge = 0.2 * diagonal):'''
−
−	'''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 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>		</pre>
−	'''Adaptive:''' [https://odu.box.com/s/oytjqxeque11wvbxu62fhwc3830fbpcy Output Mesh]	+	'''Adaptive ((PODM) delta = 1, min edge = 0.2, weight limit = 0.1, max edge = 0.2 * diagonal):'''
	<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 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>		</pre>
−	== OBS_ALU_Y50 ==	+	====GPDGK16_uH_img_nxi=211====
−	* Input distribution size: 10,000 cells	+	* [ Input Image]
−	* Uniform: 7,587 triangles	+	* Input distribution size: 21,100 cells
−	* Adaptive: 1,018  triangles	+	* Number of bins: Xi=211 t=20 Q^2=5
−		+	* Adaptive Meshes which deal with the input as an image:
−	The 2D image was created by extracting a 2D slice at Y=50 out of the 3D distribution (see 3D case below) OBS_ALU	+	** (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>		<gallery mode="packed" heights=300px>
−	File:OBS ALU Y50 me 2 uniform.vtk.png	+	GPDGK16_uH_img-nxi_210-d_2-e_0.85-w_0.12.png
−	File:OBS ALU Y50 me 2 wl 1e-1.png	+	File:GPDGK16 uH img-nxi 210-d 1-e 0.2-w 0.1.png
	</gallery>		</gallery>
−	Commands to generate meshes :	+	==Fall 2019==
−		+	===CFF_14052019===
−	'''Uniform:''' [https://odu.box.com/s/irrcuttg0ceogzgnn4x86mgf9eskk1wg Output Mesh]	+	====GPDGK16Numerical_140519====
−	<pre>	+	* [https://odu.box.com/s/xix6kb0jrzvn9dect2d2akdsie4vsu2i Input Image]
−	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>
−
−	=3D Example Meshes=
−	The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/wd0i18giti2zo330y19xhkqu8wjzgf7j CNF_Data].
−	==CFF_14052019==
−	===GPDGK16Numerical_140519===
	* Input distribution size: 1,000,000 cells		* Input distribution size: 1,000,000 cells
	* Uniform: 768,033 tetrahedra		* Uniform: 768,033 tetrahedra
−	* Adaptive (min edge = 1): 273,716 tetrahedra	+	* Adaptive (min edge = default): 277,701 tetrahedra
−	* Adaptive (min edge = 2):  67,935 tetrahedra	+	* Adaptive (min edge = 1):  92,216 tetrahedra
	<gallery mode="packed" heights=350px>		<gallery mode="packed" heights=350px>
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	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh]		'''Uniform:''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform --output ./GPDGK16Numerical_140519,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform
	</pre>		</pre>
−	'''Adaptive (min edge = 1):''' [https://odu.box.com/s/zvuuq7e6bx1cqxqbna5rpfnxfaqezgqa Output Mesh]	+	'''Adaptive (min edge = default):''' [https://odu.box.com/s/cc5uvbip8oti0hd2z8bwa5v2fp9slkt0 Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=1.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive
	</pre>		</pre>
−	'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddyvbwcdqykta5b1nxi3gwxjzgdzz8yk Output Mesh]	+	'''Adaptive (min edge = 1):''' [https://odu.box.com/s/fhgvmc0lxu81p8dmy5l3nh8plpto43r3 Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 2 --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=2.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 1
	</pre>		</pre>
−	===GPDMMS13_140519===	+	====GPDMMS13_140519====
		+	* [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image]
	* Input distribution size: 1,000,000 cells		* Input distribution size: 1,000,000 cells
	* Uniform: 768,033 tetrahedra		* Uniform: 768,033 tetrahedra
−	* Adaptive: 264,762 tetrahedra	+	* Adaptive: 270,453 tetrahedra
	<gallery mode="packed" heights=350px>		<gallery mode="packed" heights=350px>
Line 148:		Line 93:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh]		'''Uniform:''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform --output ./GPDMMS13_140519,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform
	</pre>		</pre>
−	'''Adaptive:''' [https://odu.box.com/s/bcg7aw4lv9rvp4531va7pqg7j2os7tny Output Mesh]	+	'''Adaptive:''' [https://odu.box.com/s/q2tp29hv8h463ld0sxidjarkji4e6il3 Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05
	</pre>		</pre>
−	===GPDVGG99_140519===	+	====GPDVGG99_140519====
		+	* [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image]
	* Input distribution size: 1,000,000 cells		* Input distribution size: 1,000,000 cells
	* Uniform: 768,033 tetrahedra		* Uniform: 768,033 tetrahedra
−	* Adaptive: 261,485 tetrahedra	+	* Adaptive: 266,731 tetrahedra
	<gallery mode="packed" heights=350px>		<gallery mode="packed" heights=350px>
Line 170:		Line 116:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh]		'''Uniform:''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform --output ./GPDVGG99_140519,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform
	</pre>		</pre>
−	'''Adaptive:''' [https://odu.box.com/s/51rvexl5t83zr4svn2xuw221v16jmtph Output Mesh]	+	'''Adaptive:''' [https://odu.box.com/s/o1miy5xmsqvcl0uyut089o5qbi3doj8p Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05
	</pre>		</pre>
−	===NT_140519===	+	====NT_140519====
		+	* [https://odu.box.com/s/m1qu1ocseyiltswmj9smd2n1tr6rvcsh Input Image]
	* Input distribution size: 1,000,000 cells		* Input distribution size: 1,000,000 cells
	* Uniform: 768,033 tetrahedra		* Uniform: 768,033 tetrahedra
−	* Adaptive (min edge = 1): 253,965 tetrahedra	+	* Adaptive (min edge = default): 257,041 tetrahedra
−	* Adaptive (min edge = 2): 120,168 tetrahedra	+	* Adaptive (min edge = 1): 140,527 tetrahedra
	<gallery mode="packed" heights=350px>		<gallery mode="packed" heights=350px>
Line 194:		Line 141:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh]		'''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform --output ./NT_140519,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform
	</pre>		</pre>
−	'''Adaptive (min edge = 1):''' [https://odu.box.com/s/1gviwtmh053fzqixo4thaueshjhgqdnq Output Mesh]	+	'''Adaptive (min edge = default):''' [https://odu.box.com/s/hroxm6c8h4ix30nzkd9xgqucznpjo74z Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --output ./NT_140519,d=5,wl=0.07,me=1.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07
	</pre>		</pre>
−	'''Adaptive (min edge = 2):''' [https://odu.box.com/s/ddllau93m3itsax55pcb9ifhljp7bd3u Output Mesh]	+	'''Adaptive (min edge = 1):''' [https://odu.box.com/s/x6wg3io5ca3ix0qua9i68hckio7j9mh2 Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --min-edge 2 --output ./NT_140519,d=5,wl=0.07,me=2.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --min-edge 1
	</pre>		</pre>
−	===OBS_ALU_140519===	+	====OBS_ALU_140519====
		+	* [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image]
	* 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 221:		Line 169:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh]		'''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform --output ./OBS_ALU_140519,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform
	</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>
−	===OBS_CS_140519===	+	====OBS_CS_140519====
		+	* [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image]
	* 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 243:		Line 192:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh]		'''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform --output ./OBS_CS_140519,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform
	</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>
−	==CFF_DATA==	+	===CFF_DATA===
−	===cff_E.data_IM===	+	====cff_E.data_IM====
		+	* [https://odu.box.com/s/d34bcmi2w6f5uh57ni0l16ghf3peo9yz Input Image]
	* 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 268:		Line 218:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh]		'''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform --output ./cff_E.data_IM,d=2,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform
	</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>
−	===cff_E.data_REAL===	+	====cff_E.data_REAL====
		+	* [https://odu.box.com/s/ptjjqi8p1psg69ah00ikkcux5mxgvs41 Input Image]
	* 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 290:		Line 241:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh]		'''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform --output ./cff_E.data_REAL,d=2,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform
	</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>
−	===cff_H.data_IM===	+	====cff_H.data_IM====
		+	* [https://odu.box.com/s/78eg0jujg4koeei5re96imanvlejkslq Input Image]
	* 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 312:		Line 264:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh]		'''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform --output ./cff_H.data_IM,d=2,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform
	</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>
−	===cff_H.data_REAL===	+	====cff_H.data_REAL====
		+	* [https://odu.box.com/s/ethp2uvks6od9hel9bl8tczjbew2ae1f Input Image]
	* 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 334:		Line 287:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh]		'''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform --output ./cff_H.data_REAL,d=2,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform
	</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>
−	===cff_Ht.data_IM===	+	====cff_Ht.data_IM====
		+	* [https://odu.box.com/s/ogbelxa3nyhj061a2u001wfr1fdyn0v6 Input Image]
	* 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 356:		Line 310:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh]		'''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform --output ./cff_Ht.data_IM,d=2,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform
	</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>
−	===cff_Ht.data_REAL===	+	====cff_Ht.data_REAL====
		+	* [https://odu.box.com/s/sp4p98s6nhb1tgoz6gjbs0cj9amzxjez Input Image]
	* 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 378:		Line 333:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh]		'''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform --output ./cff_Ht.data_REAL,d=2,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform
	</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>
−	==DATA_04252019==	+	===DATA_04252019===
−	===CFF_E_im===	+	====CFF_E_im====
		+	* [https://odu.box.com/s/sg3trope39jtxliowy3hgoun34mtxic4 Input Image]
	* 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 401:		Line 357:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh]		'''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform --output ./CFF_E_im,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform
	</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>
−	===CFF_E_re===	+	====CFF_E_re====
		+	* [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image]
	* 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 423:		Line 380:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh]		'''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform --output ./CFF_E_re,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform
	</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>
−	===CFF_H_im===	+	====CFF_H_im====
		+	* [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image]
	* 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 445:		Line 403:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh]		'''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform --output ./CFF_H_im,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform
	</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>
−	===CFF_H_re===	+	====CFF_H_re====
		+	* [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image]
	* 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 467:		Line 426:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh]		'''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform --output ./CFF_H_re,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform
	</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>
−	===GPD_H_down===	+	====GPD_H_down====
		+	* [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image]
	* 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 489:		Line 449:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh]		'''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform --output ./GPD_H_down,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform
	</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>
−	===GPD_H_up===	+	====GPD_H_up====
		+	* [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image]
	* 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 511:		Line 472:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]		'''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh]
	<pre>		<pre>
−	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform --output ./GPD_H_up,d=1,uniform.vtk	+	docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform
	</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>
−	===OBS_ALU===	+	====OBS_ALU====
		+	* [https://odu.box.com/s/e5kzeqmtpx5loayh6ymtloo5vhrene8t 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
−	* Adaptive with background-value = 0: 279,721 tetrahedra	+	* Adaptive with background-value = 0: 282,102 tetrahedra
−	* Uniform with background-value = +oo: tetrahedra	+	* Uniform with background-value = default: 768,033 tetrahedra
−	* Adaptive with background-value = +oo: tetrahedra	+	* Adaptive with background-value = default: 286,978 tetrahedra
	<gallery mode="packed" heights=350px>		<gallery mode="packed" heights=350px>
Line 539:		Line 501:
	</gallery>		</gallery>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''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 = +oo:''' [ 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 = +oo:''' [ 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>
−	Note: In this case, we are want to exclude the entries with value 0 (upped part, see figure) since they are not of interest. Using the flag --background-value 0, the	+	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 adapted.	+	entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive.
−	==phase_space_000==
	===phase_space_000===		===phase_space_000===
		+	====phase_space_000====
		+	* [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image]
	* 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>
−	Commands to generate meshes :	+	Commands to generate meshes:
	'''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>
		+
		+	'''Adaptive:''' [https://odu.box.com/s/5uqvlvsu7683whps0efgon6ebs8v776y 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 0.02 --min-edge 2 --max-edge 20
		+	</pre>
		+
		+	=2D Example Meshes=
		+	The directory containing the 2D input data is located in the 2D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data].
		+	==Fall 2019==
		+	===Synthetic Gaussian Data===
		+	* [https://odu.box.com/s/quykwjks6ib6501y95cmyv6ctgf8elhq Input Image]
		+	* Input distribution size: 1,000,000 cells
		+	* Uniform: 30,949 triangles
		+	* Adaptive:  7,509 triangles
		+
		+	<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 --cnf-uniform --area 50
		+	</pre>
		+	'''Adaptive:''' [https://odu.box.com/s/946ll0p0qd65ahhm4s2zgnc7xjszrf0w Output Mesh]
		+	<pre>
		+	docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/Gaussian2.vtk --cnf-adaptive --weight-limit 0.05
		+	</pre>
		+
		+	===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:  1,208  triangles
		+
		+	<gallery mode="packed" heights=300px>
		+	File:GPDGK16Numerical 140519 X50 me2 uniform.png
		+	File:GPDGK16Numerical 140519 X50 me2 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 --cnf-uniform --area 2
		+	</pre>
		+
		+	'''Adaptive:''' [https://odu.box.com/s/e6ghjq0in1w3m9usvbhdye21wlraq271 Output Mesh]
		+	<pre>
		+	docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/GPDGK16Numerical_140519_X50.vtk --cnf-adaptive
		+	</pre>
		+
		+	===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:  1,181  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 --cnf-uniform --area 2
		+	</pre>
		+
		+	'''Adaptive:''' [https://odu.box.com/s/dovy2udxoor8l51hndq4nh7yalg6g52y Output Mesh]
		+	<pre>
		+	docker run -v $(pwd):/data/ crtc_i2m tessellate2d  --input ./CNF_Data/2D/NT_140519_50_X.vtk --cnf-adaptive --min-edge 1
		+	</pre>
		+
		+	===OBS_ALU_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 --cnf-uniform --area 2
	</pre>		</pre>
−	'''Adaptive:''' [https://odu.box.com/s/9k8b3wv9ejkvpf5b7xcbb2lc85zrz3b6 Output Mesh]	+	'''Adaptive:''' [https://odu.box.com/s/h177u63uk3us6pm8k3m4swv9qxnd2a45 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 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

• 
• 

• 
• 

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

• 
• 

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

• 
• 
• 

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

• 
• 

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

• 
• 

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

• 
• 
• 

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

• 
• 

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

• 
• 

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)

• 
• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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

• 
• 

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