Google DeepMind
4f0293c6cb
Now we can load flex with format 2.2 binary and ASCII. There are two different parsing strategies. One for gmsh app and the other is for ftetwild. Used the shark model from Scanned Objects by Google Research: https://app.gazebosim.org/GoogleResearch/fuel/models/Weisshai_Great_White_Shark PiperOrigin-RevId: 619229499 Change-Id: I6eb876afcbc8a00631914b093c45bde10c0caaf6
3979 lines
81 KiB
Plaintext
3979 lines
81 KiB
Plaintext
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529 6.26363
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530 6.83661
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532 3.43579
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925 3.90953
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930 5.27177
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953 4.97386
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987 3.86094
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999 6.64228
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1000 3.66927
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1001 7.59005
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1002 7.84943
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1003 6.12621
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1004 4.28124
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1005 5.25678
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1006 4.49358
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1007 3.83982
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1008 4.1934
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1009 4.15133
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1011 6.99563
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1013 8.90109
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1014 7.652
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1015 7.06981
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1017 4.43713
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1018 3.94864
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1020 5.67571
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1022 5.55705
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1024 3.53053
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1025 4.95254
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1028 3.64351
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1029 4.37971
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1030 6.08977
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1031 8.17508
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1032 9.08688
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1033 6.27308
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1034 5.95361
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1035 5.76509
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1036 4.96722
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1038 4.46812
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1039 5.33303
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1040 6.1708
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1042 5.29678
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1043 4.55251
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1045 3.51383
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1051 3.98189
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1052 5.41173
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1054 3.74779
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1055 3.72462
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1056 4.19243
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1057 8.98073
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1060 3.37577
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1061 5.38447
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1062 7.73223
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1063 8.13835
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1064 4.25916
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1065 4.74505
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1067 7.34217
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1068 4.67887
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1069 7.30568
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1070 4.68001
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1071 4.1343
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1072 4.30236
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1074 3.81816
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1075 4.33604
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1076 7.30661
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1077 3.71466
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1078 5.48295
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1079 5.02695
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1080 5.64749
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1081 4.97985
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1082 5.0635
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1084 6.63653
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1085 6.50817
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1086 4.06997
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1088 3.9456
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1089 5.86381
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1090 4.34958
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1091 4.72955
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1092 7.68562
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1093 4.76352
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1094 4.38867
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1095 7.24025
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1096 8.56583
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1097 7.12327
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1098 7.63126
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1099 4.98995
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1100 5.51299
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1101 6.22274
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1102 8.55485
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1103 4.67541
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1104 5.56093
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1105 4.11592
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1106 5.39012
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1107 4.91738
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1108 4.55802
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1109 9.81191
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1110 9.03824
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1111 4.91096
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1112 5.68686
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1113 5.43295
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1114 4.5376
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$EndElementData
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