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__TOC__ | __TOC__ | ||
+ | |||
+ | =3D Example Meshes= | ||
+ | The directory containing the 3D input data is located in the 3D folder of [https://odu.box.com/s/sr60emxeep20smr3itpecsaiorskp8o5 CNF_Data]. | ||
+ | |||
+ | ==Summer 2020== | ||
+ | ===GPDGK16=== | ||
+ | ====GPDGK16_uH_img==== | ||
+ | * [https://odu.box.com/s/6pl2q075h45wglbd0mbedb5epxvw1g3c Input Image] | ||
+ | * Input distribution size: 1,000 cells | ||
+ | * Adaptive Meshes which deal with the input as an image: | ||
+ | ** (PODM) delta = 2, min edge = 0.85, weight limit = 0.12, max edge = 0.2 * diagonal: 1,208 tetrahedra, [https://odu.box.com/s/01qnlk7sg3eec6jdt5lz833utddf7wcc Output Mesh] | ||
+ | ** (PODM) delta = 1, min edge = 0.2, weight limit = 0.1, max edge = 0.2 * diagonal: tetrahedra 8,690, [https://odu.box.com/s/r3ngfoweb520nb30log0vrlz939ohpb9 Output Mesh] | ||
+ | * Meshes which deal with the input as a CAD geometry: | ||
+ | ** (Constrained Mesher) quality = 2, min edge = 0.5, weight limit = 0.2, max edge = 0.2 * diagonal: 641 tetrahedra, [https://odu.box.com/s/re5p4b72xhdvad5cxjafrymetdqi5nzs Output Mesh] | ||
+ | ** (CDT3D): 535 tetrahedra, [https://odu.box.com/s/dixu4u853vw8w03funvutqu40jq30tjo Output Mesh] | ||
+ | ** (CDT3D): 1032 tetrahedra, [https://odu.box.com/s/1b8fypd0taale03byxdodvnamh087mko Output Mesh] | ||
+ | ** (CDT3D): 1205 tetrahedra, [https://odu.box.com/s/nn6sxl38s866nucpfa3ze5n3741jwa3h Output Mesh] | ||
+ | |||
+ | <gallery mode="packed" heights=300px> | ||
+ | File:GPDGK16_uH_img-d_2-e_0.85-w_0.12-maxEdge_0.2diagonal.png | ||
+ | File:GPDGK16_uH_img-d_1-e_0.2-w_0.1-maxEdge_0.2diagonal.png | ||
+ | </gallery> | ||
+ | |||
+ | <gallery mode="packed" heights=300px> | ||
+ | File:GPDGK16_uH_img-q_2-e_0.5-w_0.2-maxEdge_0.2diagonal.png | ||
+ | File:cdt3d_constrained_surface_535_tets.png | ||
+ | </gallery> | ||
+ | |||
+ | '''Adaptive ((PODM) delta = 2, min edge = 0.85, weight limit = 0.12, max edge = 0.2 * diagonal):''' | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/GPDGK16/GPDGK16_uH_img.nrrd --cnf-adaptive --delta 2 --min-edge = 0.85 --weight-limit 0.12 --output ./GPDGK16_uH_img-d_2-e_0.85-w_0.12-maxEdge_0.2diagonal.vtk | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive ((PODM) delta = 1, min edge = 0.2, weight limit = 0.1, max edge = 0.2 * diagonal):''' | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/GPDGK16/GPDGK16_uH_img.nrrd --cnf-adaptive --delta 1 --min-edge = 0.2 --weight-limit 0.1 --output ./GPDGK16_uH_img-d_1-e_0.2-w_0.1-maxEdge_0.2diagonal.vtk | ||
+ | </pre> | ||
+ | |||
+ | ====GPDGK16_uH_img_nxi=211==== | ||
+ | * [ Input Image] | ||
+ | * Input distribution size: 21,100 cells | ||
+ | * Number of bins: Xi=211 t=20 Q^2=5 | ||
+ | * Adaptive Meshes which deal with the input as an image: | ||
+ | ** (PODM) delta = 2, min edge = 0.85, weight limit = 0.12: 11964 tetrahedra | ||
+ | ** (PODM) delta = 1, min edge = 0.2, weight limit = 0.1: 124608 tetrahedra | ||
+ | <gallery mode="packed" heights=300px> | ||
+ | GPDGK16_uH_img-nxi_210-d_2-e_0.85-w_0.12.png | ||
+ | File:GPDGK16 uH img-nxi 210-d 1-e 0.2-w 0.1.png | ||
+ | </gallery> | ||
+ | |||
+ | ==Fall 2019== | ||
+ | ===CFF_14052019=== | ||
+ | ====GPDGK16Numerical_140519==== | ||
+ | * [https://odu.box.com/s/xix6kb0jrzvn9dect2d2akdsie4vsu2i Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive (min edge = default): 277,701 tetrahedra | ||
+ | * Adaptive (min edge = 1): 92,216 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:GPDGK16Numerical_140519,d=1,uniform.png | ||
+ | File:GPDGK16Numerical_140519,d=5,wl=0.1,me=1.png | ||
+ | File:GPDGK16Numerical_140519,d=5,wl=0.1,me=2.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/s26icsaf9dkvm3b6wmwbflpleqrar3qo Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive (min edge = default):''' [https://odu.box.com/s/cc5uvbip8oti0hd2z8bwa5v2fp9slkt0 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive (min edge = 1):''' [https://odu.box.com/s/fhgvmc0lxu81p8dmy5l3nh8plpto43r3 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDGK16Numerical_140519.nrrd --cnf-adaptive --min-edge 1 | ||
+ | </pre> | ||
+ | |||
+ | ====GPDMMS13_140519==== | ||
+ | * [https://odu.box.com/s/1tznkuz92u7vrl5ldkp6ikas7579ahrp Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 270,453 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:GPDMMS13_140519,d=1,uniform.png | ||
+ | File:GPDMMS13_140519,d=5,wl=0.05,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/h48toji5cii2rk6xkmofptnw4nntt8zk Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/q2tp29hv8h463ld0sxidjarkji4e6il3 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDMMS13_140519.nrrd --cnf-adaptive --weight-limit 0.05 | ||
+ | </pre> | ||
+ | |||
+ | ====GPDVGG99_140519==== | ||
+ | * [https://odu.box.com/s/o0o24vtbp895ow4kje442jbq6sqh9n7z Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 266,731 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:GPDVGG99_140519,d=1,uniform.png | ||
+ | File:GPDVGG99_140519,d=5,wl=0.05,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/gjg4t3u3gxmp5guq3saln26o7vybycrh Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/o1miy5xmsqvcl0uyut089o5qbi3doj8p Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/GPDVGG99_140519.nrrd --cnf-adaptive --weight-limit 0.05 | ||
+ | </pre> | ||
+ | |||
+ | ====NT_140519==== | ||
+ | * [https://odu.box.com/s/m1qu1ocseyiltswmj9smd2n1tr6rvcsh Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive (min edge = default): 257,041 tetrahedra | ||
+ | * Adaptive (min edge = 1): 140,527 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:NT_140519,d=1,uniform.png | ||
+ | File:NT_140519,d=5,wl=0.07,me=1.png | ||
+ | File:NT_140519,d=5,wl=0.07,me=2.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/o3hv59auwjni9wv95af9div82jyap0el Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive (min edge = default):''' [https://odu.box.com/s/hroxm6c8h4ix30nzkd9xgqucznpjo74z Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive (min edge = 1):''' [https://odu.box.com/s/x6wg3io5ca3ix0qua9i68hckio7j9mh2 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/NT_140519.nrrd --cnf-adaptive --weight-limit 0.07 --min-edge 1 | ||
+ | </pre> | ||
+ | |||
+ | ====OBS_ALU_140519==== | ||
+ | * [https://odu.box.com/s/5mnepdpzeu3d17pagg22vwxs9wa6qqbg Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 262,055 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:OBS_ALU_140519,d=1,uniform.png | ||
+ | File:OBS_ALU_140519,d=5,wl=0.13,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/1yjcx52p9jz6hvumpjt5sd9vi6aa3vry Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/ds9cobknetx03wc58821laj8k2dkd7h2 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_ALU_140519.nrrd --cnf-adaptive --weight-limit 0.13 | ||
+ | </pre> | ||
+ | |||
+ | ====OBS_CS_140519==== | ||
+ | * [https://odu.box.com/s/qbctvffvjvc7qh61xqcmua9o4vdydg0a Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 25,476 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:OBS_CS_140519,d=1,uniform.png | ||
+ | File:OBS_CS_140519,d=5,wl=0.01,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/n4taf4o43xajwg9cks6r35e90hg21p17 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/fs4g3d7yfs8o80t6u3oi4me84yksneot Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_14052019/OBS_CS_140519.nrrd --cnf-adaptive --weight-limit 0.01 | ||
+ | </pre> | ||
+ | |||
+ | ===CFF_DATA=== | ||
+ | ====cff_E.data_IM==== | ||
+ | * [https://odu.box.com/s/d34bcmi2w6f5uh57ni0l16ghf3peo9yz Input Image] | ||
+ | * Input distribution size: 8,000,000 cells | ||
+ | * Uniform: 745,291 tetrahedra | ||
+ | * Adaptive: 362,804 tetrahedra | ||
+ | * Adaptive: 362,804 tetrahedra (other side of the same adaptive case) | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:cff_E.data_IM,d=2,uniform.png | ||
+ | File:cff_E.data_IM,d=10,wl=0.01,me=2.png | ||
+ | File:cff_E.data_IM,d=10,wl=0.01,me=2,OtherSide.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/5rfdhrmm0uj2ohxck4i08v9kds8hcxkr Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/nopuegnozgjdh5is49xsqoqz86r55zux Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_IM.nrrd --cnf-adaptive --weight-limit 0.01 | ||
+ | </pre> | ||
+ | |||
+ | ====cff_E.data_REAL==== | ||
+ | * [https://odu.box.com/s/ptjjqi8p1psg69ah00ikkcux5mxgvs41 Input Image] | ||
+ | * Input distribution size: 8,000,000 cells | ||
+ | * Uniform: 745,291 tetrahedra | ||
+ | * Adaptive: 318,128 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:cff_E.data_REAL,d=2,uniform.png | ||
+ | File:cff_E.data_REAL,d=10,wl=0.1,me=2.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/43qq7cl2ygw6dlx1penmeiqucxittyzm Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/3ildscx8w4t966nzv3w20gkvxt4zxcxz Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_E.data_REAL.nrrd --cnf-adaptive | ||
+ | </pre> | ||
+ | |||
+ | ====cff_H.data_IM==== | ||
+ | * [https://odu.box.com/s/78eg0jujg4koeei5re96imanvlejkslq Input Image] | ||
+ | * Input distribution size: 8,000,000 cells | ||
+ | * Uniform: 745,291 tetrahedra | ||
+ | * Adaptive: 293,560 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:cff_H.data_IM,d=2,uniform.png | ||
+ | File:cff_H.data_IM,d=10,wl=0.05,me=2.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/pvz8oes781atu01namd42a2b4vezi8x4 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/j39hie2a1clrvwzw6nn7kuffty4887l2 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_IM.nrrd --cnf-adaptive --weight-limit 0.05 | ||
+ | </pre> | ||
+ | |||
+ | ====cff_H.data_REAL==== | ||
+ | * [https://odu.box.com/s/ethp2uvks6od9hel9bl8tczjbew2ae1f Input Image] | ||
+ | * Input distribution size: 8,000,000 cells | ||
+ | * Uniform: 745,291 tetrahedra | ||
+ | * Adaptive: 375,705 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:cff_H.data_REAL,d=2,uniform.png | ||
+ | File:cff_H.data_REAL,d=10,wl=0.1,me=2.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/ow7q9ec6w8n46zhzs45issz0powet2bp Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/hmtot3rslyf0gy0gh9wijk4fbts2vnzg Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_H.data_REAL.nrrd --cnf-adaptive | ||
+ | </pre> | ||
+ | |||
+ | ====cff_Ht.data_IM==== | ||
+ | * [https://odu.box.com/s/ogbelxa3nyhj061a2u001wfr1fdyn0v6 Input Image] | ||
+ | * Input distribution size: 8,000,000 cells | ||
+ | * Uniform: 745,291 tetrahedra | ||
+ | * Adaptive: 341,159 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:cff_Ht.data_IM,d=2,uniform.png | ||
+ | File:cff_Ht.data_IM,d=10,wl=0.1,me=2.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/s8clpr339nzitwbamrhnja75wu030oqn Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/94vc0typeqrtudqnvjstr9u7ff5y4h7y Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_IM.nrrd --cnf-adaptive | ||
+ | </pre> | ||
+ | |||
+ | ====cff_Ht.data_REAL==== | ||
+ | * [https://odu.box.com/s/sp4p98s6nhb1tgoz6gjbs0cj9amzxjez Input Image] | ||
+ | * Input distribution size: 8,000,000 cells | ||
+ | * Uniform: 745,291 tetrahedra | ||
+ | * Adaptive: 398,937 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:cff_Ht.data_REAL,d=2,uniform.png | ||
+ | File:cff_Ht.data_REAL,d=10,wl=0.1,me=2.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/ln4z1ncmjaekb9bapcq6zevp8m9w7v2t Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/7ah3un9py97rw935vwwrzwm099156vwd Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/CFF_DATA/cff_Ht.data_REAL.nrrd --cnf-adaptive | ||
+ | </pre> | ||
+ | |||
+ | ===DATA_04252019=== | ||
+ | ====CFF_E_im==== | ||
+ | * [https://odu.box.com/s/sg3trope39jtxliowy3hgoun34mtxic4 Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 240,150 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:CFF_E_im,d=1,uniform.png | ||
+ | File:CFF_E_im,d=5,wl=0.04,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/7121pnrof8y2dtstctun35s2pm9nfz6b Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/v7ptl6shwnhudjukuqllbcn7jzyhg1ku Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_im.nrrd --cnf-adaptive --weight-limit 0.04 | ||
+ | </pre> | ||
+ | |||
+ | ====CFF_E_re==== | ||
+ | * [https://odu.box.com/s/liknum84lzdann15vtuppq0sfsgk8qpc Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 261,918 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:CFF_E_re,d=1,uniform.png | ||
+ | File:CFF_E_re,d=5,wl=0.08,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/7hc4oll2k15i5soe1u09j9imkf03uaol Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/h1b91ms5na6e1a8735lx4r00nz9nwxq6 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_E_re.nrrd --cnf-adaptive --weight-limit 0.08 | ||
+ | </pre> | ||
+ | |||
+ | ====CFF_H_im==== | ||
+ | * [https://odu.box.com/s/09q1lgj9zjd3pxgonl3izzb9lvhiynre Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 266,306 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:CFF_H_im,d=1,uniform.png | ||
+ | File:CFF_H_im,d=5,wl=0.06,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/5s1i0rwh6lt4a0rgmgnpmf51yh8oawmv Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/v6madkz4ahdxq02nrwnkx7o72t1qkntu Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_im.nrrd --cnf-adaptive --weight-limit 0.06 | ||
+ | </pre> | ||
+ | |||
+ | ====CFF_H_re==== | ||
+ | * [https://odu.box.com/s/qz4ob9up67hwxdhmc3vk3m0pgauu5i7s Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 251,186 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:CFF_H_re,d=1,uniform.png | ||
+ | File:CFF_H_re,d=5,wl=0.13,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/tbbrfxnata2hhfpzqmzuqfnmnjfajnph Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/euxix0on74rp6pqy6u4slj9ij7ss28m0 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/CFF_H_re.nrrd --cnf-adaptive --weight-limit 0.13 | ||
+ | </pre> | ||
+ | |||
+ | ====GPD_H_down==== | ||
+ | * [https://odu.box.com/s/c4of5f4pz4y71x6mtskfek5mpdbieonj Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 307,082 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:GPD_H_down,d=1,uniform.png | ||
+ | File:GPD_H_down,d=5,wl=0.1,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/02crgjhj81ztfdj1ts45lih02wl1aahw Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/4qsu8gwr13mzicfmnjqxsxvewfnslbro Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_down.nrrd --cnf-adaptive | ||
+ | </pre> | ||
+ | |||
+ | ====GPD_H_up==== | ||
+ | * [https://odu.box.com/s/bvh5hhh8zaoz1gj0rmzxgnl7num58e88 Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform: 768,033 tetrahedra | ||
+ | * Adaptive: 301,979 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:GPD_H_up,d=1,uniform.png | ||
+ | File:GPD_H_up,d=5,wl=0.1,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/ojhugubus797nc3e8kzztul5hnlqe1fx Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive:''' [https://odu.box.com/s/s4pb42n345gq84w06ne5x629b46buxm6 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/GPD_H_up.nrrd --cnf-adaptive | ||
+ | </pre> | ||
+ | |||
+ | ====OBS_ALU==== | ||
+ | * [https://odu.box.com/s/e5kzeqmtpx5loayh6ymtloo5vhrene8t Input Image] | ||
+ | * Input distribution size: 1,000,000 cells | ||
+ | * Uniform with background-value = 0: 301,772 tetrahedra | ||
+ | * Adaptive with background-value = 0: 282,102 tetrahedra | ||
+ | * Uniform with background-value = default: 768,033 tetrahedra | ||
+ | * Adaptive with background-value = default: 286,978 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:OBS_ALU,d=1,bv=0,uniform.png | ||
+ | File:OBS_ALU,d=5,bv=0,wl=0.07,me=1.png | ||
+ | </gallery> | ||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:OBS_ALU,d=1,uniform.png | ||
+ | File:OBS_ALU,d=5,wl=0.07,me=1.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform with background-value = 0:''' [https://odu.box.com/s/ynpwegb1khqmjjwtz1rg0503mqbmjs9y Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform --background-value 0 | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive with background-value = 0:''' [https://odu.box.com/s/her4h7lrtygcurufj6isx57hxru02c3g Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --background-value 0 --weight-limit 0.07 | ||
+ | </pre> | ||
+ | |||
+ | '''Uniform with background-value = default:''' [https://odu.box.com/s/b5licz0d25mb0ed0ttlz80adpsdoth4z Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-uniform | ||
+ | </pre> | ||
+ | |||
+ | '''Adaptive with background-value = default:''' [https://odu.box.com/s/h93jztz1bdtplmzmfjdveq0xkzntil18 Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/DATA_04252019/OBS_ALU.nrrd --cnf-adaptive --weight-limit 0.07 | ||
+ | </pre> | ||
+ | |||
+ | Note: In this case, we want to exclude the entries with value 0 (lower part, see figure) since they are not of interest. Using the flag --background-value 0, the | ||
+ | entries are excluded from mesh generation. This allows reducing the number of cells by 70% for the uniform case and 30% for the adaptive. | ||
+ | |||
+ | ===phase_space_000=== | ||
+ | ====phase_space_000==== | ||
+ | * [https://odu.box.com/s/7e66j3gnr0ffyj8mixe9akh6cftaujq1 Input Image] | ||
+ | * Input distribution size: 15,625 cells | ||
+ | * Uniform: 17,961 tetrahedra | ||
+ | * Adaptive: 11,494 tetrahedra | ||
+ | |||
+ | <gallery mode="packed" heights=350px> | ||
+ | File:phase_space_000,d=0.25,uniform.png | ||
+ | File:phase_space_000-d_2-g-s-f-w_0.2-m_2.5-M_20-l.png | ||
+ | </gallery> | ||
+ | |||
+ | Commands to generate meshes: | ||
+ | |||
+ | '''Uniform:''' [https://odu.box.com/s/ype3j19yixez8uwy1k9mgc1585oepugq Output Mesh] | ||
+ | <pre> | ||
+ | docker run -v $(pwd):/data/ crtc_i2m tessellate3d --input ./CNF_Data/3D/phase_space_000/phase_space_000.nrrd --delta 0.25 --cnf-uniform | ||
+ | </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= | =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> | |
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | + | '''Adaptive:''' [https://odu.box.com/s/h177u63uk3us6pm8k3m4swv9qxnd2a45 Output Mesh] | |
− | + | <pre> | |
− | + | docker run -v $(pwd):/data/ crtc_i2m tessellate2d --input ./CNF_Data/2D/OBS_ALU_Y50.vtk --cnf-adaptive --min-edge 1 | |
+ | </pre> |
Latest revision as of 20:44, 23 July 2020
Contents
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