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examples/registration/assets/renderoption.json 0 → 100644
View file @ 92aa2fa8
{
"background_color" : [ 1, 1, 1 ],
"class_name" : "RenderOption",
"default_mesh_color" : [ 0.69999999999999996, 0.69999999999999996, 0.69999999999999996 ],
"image_max_depth" : 3000,
"image_stretch_option" : 0,
"interpolation_option" : 0,
"light0_color" : [ 1, 1, 1 ],
"light0_diffuse_power" : 0.66000000000000003,
"light0_position" : [ 0, 0, 2 ],
"light0_specular_power" : 0.20000000000000001,
"light0_specular_shininess" : 100,
"light1_color" : [ 1, 1, 1 ],
"light1_diffuse_power" : 0.66000000000000003,
"light1_position" : [ 0, 0, 2 ],
"light1_specular_power" : 0.20000000000000001,
"light1_specular_shininess" : 100,
"light2_color" : [ 1, 1, 1 ],
"light2_diffuse_power" : 0.66000000000000003,
"light2_position" : [ 0, 0, -2 ],
"light2_specular_power" : 0.20000000000000001,
"light2_specular_shininess" : 100,
"light3_color" : [ 1, 1, 1 ],
"light3_diffuse_power" : 0.66000000000000003,
"light3_position" : [ 0, 0, -2 ],
"light3_specular_power" : 0.20000000000000001,
"light3_specular_shininess" : 100,
"light_ambient_color" : [ 0, 0, 0 ],
"light_on" : true,
"mesh_color_option" : 1,
"mesh_shade_option" : 0,
"mesh_show_back_face" : false,
"mesh_show_wireframe" : false,
"point_color_option" : 9,
"point_show_normal" : false,
"point_size" : 2,
"show_coordinate_frame" : false,
"version_major" : 1,
"version_minor" : 0
}
examples/registration/assets/tartan_test.txt 0 → 100644
View file @ 92aa2fa8
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P018 524 520
datasets/Tartan/office2/office2/Easy/P009 638 642
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P018 679 681
datasets/Tartan/gascola/gascola/Hard/P007 327 324
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P017 366 363
datasets/Tartan/office2/office2/Hard/P005 669 668
datasets/Tartan/office2/office2/Hard/P007 74 70
datasets/Tartan/gascola/gascola/Hard/P008 1042 1048
datasets/Tartan/gascola/gascola/Easy/P004 2941 2946
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P016 84 81
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datasets/Tartan/gascola/gascola/Easy/P006 1842 1832
datasets/Tartan/gascola/gascola/Hard/P002 1390 1395
datasets/Tartan/seasidetown/seasidetown/Easy/P001 213 219
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datasets/Tartan/gascola/gascola/Hard/P003 251 245
datasets/Tartan/gascola/gascola/Easy/P008 33 27
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datasets/Tartan/seasidetown/seasidetown/Easy/P003 171 170
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P010 1032 1039
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datasets/Tartan/office2/office2/Easy/P006 408 402
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P011 917 913
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P013 165 161
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datasets/Tartan/gascola/gascola/Easy/P007 2167 2162
datasets/Tartan/westerndesert/westerndesert/Hard/P007 41 44
datasets/Tartan/westerndesert/westerndesert/Hard/P005 232 229
datasets/Tartan/westerndesert/westerndesert/Easy/P010 363 360
datasets/Tartan/office2/office2/Hard/P009 616 674
datasets/Tartan/gascola/gascola/Easy/P006 1760 1769
datasets/Tartan/office2/office2/Hard/P005 230 225
datasets/Tartan/westerndesert/westerndesert/Easy/P008 239 140
datasets/Tartan/office2/office2/Easy/P004 1552 1559
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datasets/Tartan/seasidetown/seasidetown/Easy/P009 828 824
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P018 554 552
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datasets/Tartan/westerndesert/westerndesert/Easy/P009 288 300
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P013 476 477
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datasets/Tartan/gascola/gascola/Easy/P004 2024 2020
datasets/Tartan/westerndesert/westerndesert/Easy/P008 258 108
datasets/Tartan/office2/office2/Easy/P000 127 125
datasets/Tartan/office2/office2/Hard/P003 468 472
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datasets/Tartan/gascola/gascola/Hard/P008 138 145
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P014 441 442
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datasets/Tartan/westerndesert/westerndesert/Hard/P000 197 195
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datasets/Tartan/westerndesert/westerndesert/Easy/P008 57 58
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datasets/Tartan/westerndesert/westerndesert/Hard/P002 319 321
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datasets/Tartan/westerndesert/westerndesert/Hard/P000 161 165
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datasets/Tartan/office2/office2/Easy/P004 1204 1208
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P018 697 698
datasets/Tartan/gascola/gascola/Easy/P006 2528 2532
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datasets/Tartan/seasidetown/seasidetown/Hard/P001 135 127
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datasets/Tartan/office2/office2/Easy/P007 84 77
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datasets/Tartan/seasidetown/seasidetown/Easy/P006 598 606
datasets/Tartan/seasidetown/seasidetown/Easy/P001 90 67
datasets/Tartan/seasidetown/seasidetown/Hard/P004 464 459
datasets/Tartan/gascola/gascola/Easy/P006 214 207
datasets/Tartan/office2/office2/Easy/P004 287 290
datasets/Tartan/seasidetown/seasidetown/Easy/P007 832 834
datasets/Tartan/gascola/gascola/Easy/P004 1959 1965
datasets/Tartan/gascola/gascola/Easy/P005 912 936
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P014 81 84
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P016 148 145
datasets/Tartan/westerndesert/westerndesert/Hard/P002 810 802
datasets/Tartan/seasidetown/seasidetown/Easy/P007 3 2
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datasets/Tartan/gascola/gascola/Easy/P005 1641 1639
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datasets/Tartan/office2/office2/Easy/P006 850 852
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datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P012 583 599
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datasets/Tartan/gascola/gascola/Hard/P004 1138 1137
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P018 517 514
datasets/Tartan/westerndesert/westerndesert/Hard/P003 59 56
datasets/Tartan/westerndesert/westerndesert/Hard/P003 20 23
datasets/Tartan/westerndesert/westerndesert/Easy/P013 243 237
datasets/Tartan/gascola/gascola/Easy/P005 932 936
datasets/Tartan/seasidetown/seasidetown/Easy/P008 214 216
datasets/Tartan/westerndesert/westerndesert/Easy/P007 453 449
datasets/Tartan/office2/office2/Hard/P000 292 281
datasets/Tartan/office2/office2/Easy/P006 579 701
datasets/Tartan/gascola/gascola/Hard/P002 7 2
datasets/Tartan/gascola/gascola/Easy/P003 846 860
datasets/Tartan/gascola/gascola/Hard/P004 1724 1723
datasets/Tartan/office2/office2/Hard/P009 54 52
datasets/Tartan/office2/office2/Easy/P006 605 610
datasets/Tartan/gascola/gascola/Easy/P004 1187 1182
datasets/Tartan/office2/office2/Hard/P010 410 409
datasets/Tartan/westerndesert/westerndesert/Easy/P013 12 21
datasets/Tartan/gascola/gascola/Easy/P008 141 174
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P016 276 267
datasets/Tartan/gascola/gascola/Easy/P008 835 840
datasets/Tartan/gascola/gascola/Hard/P004 210 182
datasets/Tartan/gascola/gascola/Easy/P005 1483 1487
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P018 61 64
datasets/Tartan/office2/office2/Hard/P008 561 564
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P017 609 614
datasets/Tartan/seasidetown/seasidetown/Easy/P005 436 438
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P013 256 240
datasets/Tartan/westerndesert/westerndesert/Hard/P004 9 10
datasets/Tartan/seasidetown/seasidetown/Easy/P006 321 319
datasets/Tartan/westerndesert/westerndesert/Easy/P013 236 238
datasets/Tartan/westerndesert/westerndesert/Easy/P004 88 89
datasets/Tartan/gascola/gascola/Easy/P007 734 732
datasets/Tartan/seasidetown/seasidetown/Hard/P002 665 659
datasets/Tartan/gascola/gascola/Easy/P004 1111 1121
datasets/Tartan/office2/office2/Hard/P008 540 527
datasets/Tartan/office2/office2/Hard/P001 25 22
datasets/Tartan/gascola/gascola/Hard/P002 5 17
datasets/Tartan/seasidetown/seasidetown/Easy/P008 429 229
datasets/Tartan/gascola/gascola/Easy/P007 1564 1565
datasets/Tartan/gascola/gascola/Hard/P009 23 27
datasets/Tartan/office2/office2/Hard/P005 102 113
datasets/Tartan/westerndesert/westerndesert/Hard/P004 364 366
datasets/Tartan/gascola/gascola/Hard/P006 129 122
datasets/Tartan/gascola/gascola/Hard/P002 762 764
datasets/Tartan/office2/office2/Hard/P000 249 244
datasets/Tartan/gascola/gascola/Hard/P006 957 933
datasets/Tartan/gascola/gascola/Easy/P004 920 926
datasets/Tartan/office2/office2/Easy/P008 981 1007
datasets/Tartan/office2/office2/Hard/P010 469 467
datasets/Tartan/gascola/gascola/Hard/P008 586 581
datasets/Tartan/seasidetown/seasidetown/Hard/P000 254 249
datasets/Tartan/office2/office2/Hard/P002 268 267
datasets/Tartan/gascola/gascola/Hard/P006 567 575
datasets/Tartan/seasidetown/seasidetown/Easy/P009 349 345
datasets/Tartan/gascola/gascola/Hard/P004 531 536
datasets/Tartan/westerndesert/westerndesert/Easy/P007 142 141
datasets/Tartan/office2/office2/Hard/P002 689 687
datasets/Tartan/gascola/gascola/Easy/P003 117 96
datasets/Tartan/gascola/gascola/Hard/P004 282 279
datasets/Tartan/office2/office2/Easy/P003 9 19
datasets/Tartan/gascola/gascola/Easy/P001 103 98
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P010 203 204
datasets/Tartan/gascola/gascola/Hard/P001 83 84
datasets/Tartan/office2/office2/Hard/P003 1155 1156
datasets/Tartan/gascola/gascola/Hard/P008 1234 1236
datasets/Tartan/westerndesert/westerndesert/Easy/P006 426 422
datasets/Tartan/gascola/gascola/Easy/P004 437 446
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P014 204 215
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P011 161 152
datasets/Tartan/gascola/gascola/Hard/P008 162 182
datasets/Tartan/office2/office2/Easy/P011 379 385
datasets/Tartan/gascola/gascola/Easy/P005 1042 1055
datasets/Tartan/gascola/gascola/Hard/P003 528 526
datasets/Tartan/office2/office2/Easy/P008 481 472
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P016 26 36
datasets/Tartan/office2/office2/Easy/P008 630 633
datasets/Tartan/gascola/gascola/Hard/P004 49 57
datasets/Tartan/gascola/gascola/Easy/P004 2615 2620
datasets/Tartan/gascola/gascola/Easy/P004 2087 2076
datasets/Tartan/gascola/gascola/Hard/P006 749 751
datasets/Tartan/seasidetown/seasidetown/Easy/P007 89 83
datasets/Tartan/westerndesert/westerndesert/Easy/P013 121 126
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P011 631 641
datasets/Tartan/gascola/gascola/Easy/P001 226 247
datasets/Tartan/gascola/gascola/Easy/P005 475 483
datasets/Tartan/gascola/gascola/Hard/P004 1190 1191
datasets/Tartan/gascola/gascola/Easy/P007 606 609
datasets/Tartan/gascola/gascola/Easy/P008 278 276
datasets/Tartan/gascola/gascola/Easy/P007 1909 1917
datasets/Tartan/office2/office2/Easy/P005 172 170
datasets/Tartan/gascola/gascola/Easy/P004 829 825
datasets/Tartan/gascola/gascola/Easy/P006 523 528
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P011 195 196
datasets/Tartan/westerndesert/westerndesert/Easy/P004 809 794
datasets/Tartan/westerndesert/westerndesert/Hard/P000 642 638
datasets/Tartan/westerndesert/westerndesert/Easy/P011 69 71
datasets/Tartan/westerndesert/westerndesert/Hard/P004 227 225
datasets/Tartan/office2/office2/Hard/P006 135 132
datasets/Tartan/gascola/gascola/Hard/P008 353 357
datasets/Tartan/gascola/gascola/Hard/P005 674 659
datasets/Tartan/gascola/gascola/Hard/P007 303 309
datasets/Tartan/gascola/gascola/Easy/P003 296 303
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P011 482 480
datasets/Tartan/gascola/gascola/Easy/P007 1092 1077
datasets/Tartan/gascola/gascola/Easy/P007 404 409
datasets/Tartan/office2/office2/Easy/P011 128 122
datasets/Tartan/gascola/gascola/Easy/P004 2135 2139
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P011 135 138
datasets/Tartan/office2/office2/Easy/P004 376 368
datasets/Tartan/office2/office2/Easy/P009 858 827
datasets/Tartan/gascola/gascola/Hard/P003 290 301
datasets/Tartan/gascola/gascola/Hard/P004 1662 1691
datasets/Tartan/office2/office2/Easy/P005 509 512
datasets/Tartan/office2/office2/Easy/P000 67 78
datasets/Tartan/gascola/gascola/Hard/P008 1116 1118
datasets/Tartan/gascola/gascola/Easy/P007 757 753
datasets/Tartan/gascola/gascola/Hard/P008 346 350
datasets/Tartan/office2/office2/Easy/P006 728 725
datasets/Tartan/office2/office2/Hard/P008 360 357
datasets/Tartan/seasidetown/seasidetown/Easy/P001 269 252
datasets/Tartan/gascola/gascola/Easy/P005 219 198
datasets/Tartan/office2/office2/Hard/P008 50 51
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P015 361 364
datasets/Tartan/gascola/gascola/Easy/P008 1248 1242
datasets/Tartan/gascola/gascola/Hard/P006 1131 1126
datasets/Tartan/westerndesert/westerndesert/Easy/P010 283 300
datasets/Tartan/gascola/gascola/Easy/P007 2201 2198
datasets/Tartan/gascola/gascola/Hard/P006 406 427
datasets/Tartan/gascola/gascola/Easy/P003 412 404
datasets/Tartan/gascola/gascola/Easy/P004 1041 1035
datasets/Tartan/gascola/gascola/Hard/P006 1480 1477
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P013 228 211
datasets/Tartan/seasidetown/seasidetown/Easy/P007 529 527
datasets/Tartan/office2/office2/Hard/P002 501 510
datasets/Tartan/westerndesert/westerndesert/Easy/P002 133 142
datasets/Tartan/office2/office2/Easy/P006 644 642
datasets/Tartan/westerndesert/westerndesert/Easy/P010 290 299
datasets/Tartan/gascola/gascola/Easy/P005 1513 1511
datasets/Tartan/office2/office2/Easy/P008 595 592
datasets/Tartan/gascola/gascola/Hard/P004 1218 1226
datasets/Tartan/gascola/gascola/Hard/P008 963 987
datasets/Tartan/gascola/gascola/Easy/P007 909 904
datasets/Tartan/gascola/gascola/Easy/P006 2087 2058
datasets/Tartan/gascola/gascola/Easy/P005 430 398
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P016 315 296
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P013 20 33
datasets/Tartan/gascola/gascola/Hard/P004 1781 1797
datasets/Tartan/gascola/gascola/Easy/P006 2025 2021
datasets/Tartan/gascola/gascola/Hard/P005 1230 1236
datasets/Tartan/office2/office2/Easy/P009 56 39
datasets/Tartan/gascola/gascola/Hard/P003 481 478
datasets/Tartan/westerndesert/westerndesert/Hard/P003 456 458
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P010 247 238
datasets/Tartan/office2/office2/Easy/P008 586 584
datasets/Tartan/seasidetown/seasidetown/Hard/P002 182 175
datasets/Tartan/gascola/gascola/Easy/P004 1027 1014
datasets/Tartan/office2/office2/Hard/P005 311 309
datasets/Tartan/gascola/gascola/Easy/P005 291 264
datasets/Tartan/seasidetown/seasidetown/Easy/P006 402 389
datasets/Tartan/seasidetown/seasidetown/Hard/P002 65 64
datasets/Tartan/gascola/gascola/Hard/P008 1138 1147
datasets/Tartan/office2/office2/Hard/P004 219 214
datasets/Tartan/westerndesert/westerndesert/Hard/P002 807 810
datasets/Tartan/gascola/gascola/Hard/P008 308 305
datasets/Tartan/office2/office2/Easy/P010 61 84
datasets/Tartan/seasidetown/seasidetown/Easy/P006 459 465
datasets/Tartan/westerndesert/westerndesert/Hard/P003 39 44
datasets/Tartan/gascola/gascola/Easy/P003 1046 1034
datasets/Tartan/westerndesert/westerndesert/Hard/P004 108 115
datasets/Tartan/office2/office2/Easy/P004 206 224
datasets/Tartan/gascola/gascola/Hard/P003 332 354
datasets/Tartan/westerndesert/westerndesert/Hard/P000 603 594
datasets/Tartan/gascola/gascola/Hard/P006 1007 1011
datasets/Tartan/gascola/gascola/Hard/P008 1256 1249
datasets/Tartan/office2/office2/Hard/P009 473 467
datasets/Tartan/westerndesert/westerndesert/Hard/P005 225 226
datasets/Tartan/seasidetown/seasidetown/Hard/P003 227 229
datasets/Tartan/gascola/gascola/Easy/P003 1493 1489
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P017 985 986
datasets/Tartan/gascola/gascola/Easy/P008 845 835
datasets/Tartan/office2/office2/Easy/P004 1289 1285
datasets/Tartan/westerndesert/westerndesert/Hard/P002 179 174
datasets/Tartan/seasidetown/seasidetown/Easy/P000 281 273
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P011 1369 1370
datasets/Tartan/office2/office2/Hard/P008 543 546
datasets/Tartan/office2/office2/Hard/P002 265 266
datasets/Tartan/seasidetown/seasidetown/Easy/P003 273 279
datasets/Tartan/office2/office2/Easy/P009 1225 1203
datasets/Tartan/office2/office2/Easy/P004 1391 1397
datasets/Tartan/gascola/gascola/Easy/P008 912 908
datasets/Tartan/office2/office2/Easy/P000 175 180
datasets/Tartan/gascola/gascola/Easy/P004 929 912
datasets/Tartan/gascola/gascola/Hard/P006 1277 1278
datasets/Tartan/office2/office2/Easy/P008 242 239
datasets/Tartan/westerndesert/westerndesert/Easy/P006 156 167
datasets/Tartan/westerndesert/westerndesert/Easy/P013 512 515
datasets/Tartan/seasidetown/seasidetown/Easy/P002 269 110
datasets/Tartan/gascola/gascola/Easy/P004 2486 2482
datasets/Tartan/office2/office2/Easy/P008 908 909
datasets/Tartan/gascola/gascola/Hard/P003 177 178
datasets/Tartan/office2/office2/Hard/P009 923 925
datasets/Tartan/gascola/gascola/Hard/P006 990 986
datasets/Tartan/gascola/gascola/Hard/P004 1792 1801
datasets/Tartan/gascola/gascola/Easy/P008 723 728
datasets/Tartan/gascola/gascola/Easy/P006 2224 2227
datasets/Tartan/gascola/gascola/Easy/P007 373 388
datasets/Tartan/gascola/gascola/Hard/P006 80 45
datasets/Tartan/office2/office2/Hard/P005 197 195
datasets/Tartan/westerndesert/westerndesert/Hard/P000 193 196
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P017 486 485
datasets/Tartan/gascola/gascola/Hard/P004 1154 1153
datasets/Tartan/gascola/gascola/Easy/P004 2359 2352
datasets/Tartan/westerndesert/westerndesert/Hard/P005 46 91
datasets/Tartan/westerndesert/westerndesert/Hard/P006 154 152
datasets/Tartan/gascola/gascola/Hard/P005 104 97
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P017 193 197
datasets/Tartan/seasidetown/seasidetown/Hard/P004 547 552
datasets/Tartan/office2/office2/Easy/P004 1363 1377
datasets/Tartan/westerndesert/westerndesert/Easy/P009 13 30
datasets/Tartan/office2/office2/Hard/P004 328 327
datasets/Tartan/seasidetown/seasidetown/Easy/P008 219 218
datasets/Tartan/westerndesert/westerndesert/Easy/P009 105 109
datasets/Tartan/office2/office2/Hard/P009 906 904
datasets/Tartan/office2/office2/Easy/P005 12 15
datasets/Tartan/gascola/gascola/Easy/P004 646 645
datasets/Tartan/office2/office2/Easy/P004 1318 1321
datasets/Tartan/gascola/gascola/Easy/P005 86 92
datasets/Tartan/office2/office2/Easy/P009 1500 1497
datasets/Tartan/gascola/gascola/Hard/P004 958 955
datasets/Tartan/office2/office2/Easy/P009 63 77
datasets/Tartan/gascola/gascola/Easy/P007 878 893
datasets/Tartan/office2/office2/Easy/P008 888 886
datasets/Tartan/office2/office2/Easy/P008 1048 1044
datasets/Tartan/gascola/gascola/Easy/P005 1155 1152
datasets/Tartan/seasidetown/seasidetown/Easy/P005 495 497
datasets/Tartan/seasidetown/seasidetown/Hard/P002 64 61
datasets/Tartan/gascola/gascola/Easy/P005 1197 1202
datasets/Tartan/gascola/gascola/Hard/P008 830 828
datasets/Tartan/gascola/gascola/Easy/P004 309 313
datasets/Tartan/gascola/gascola/Easy/P006 2082 2098
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P010 1004 1007
datasets/Tartan/gascola/gascola/Easy/P005 1505 1509
datasets/Tartan/gascola/gascola/Hard/P006 1433 1439
datasets/Tartan/gascola/gascola/Hard/P000 155 145
datasets/Tartan/office2/office2/Easy/P011 124 120
datasets/Tartan/office2/office2/Hard/P010 377 376
datasets/Tartan/westerndesert/westerndesert/Easy/P012 88 91
datasets/Tartan/gascola/gascola/Easy/P003 434 426
datasets/Tartan/office2/office2/Hard/P001 77 73
datasets/Tartan/office2/office2/Easy/P006 238 259
datasets/Tartan/office2/office2/Easy/P000 151 146
datasets/Tartan/gascola/gascola/Easy/P007 1438 1432
datasets/Tartan/westerndesert/westerndesert/Hard/P004 23 24
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P010 1529 1525
datasets/Tartan/gascola/gascola/Hard/P004 1285 1286
datasets/Tartan/seasidetown/seasidetown/Easy/P003 146 145
datasets/Tartan/gascola/gascola/Easy/P008 1182 1188
datasets/Tartan/office2/office2/Easy/P004 225 237
datasets/Tartan/seasidetown/seasidetown/Hard/P003 39 331
datasets/Tartan/westerndesert/westerndesert/Hard/P007 0 1
datasets/Tartan/office2/office2/Easy/P011 671 684
datasets/Tartan/gascola/gascola/Hard/P003 490 491
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P017 953 962
datasets/Tartan/westerndesert/westerndesert/Easy/P008 261 118
datasets/Tartan/westerndesert/westerndesert/Easy/P007 123 119
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P018 245 243
datasets/Tartan/westerndesert/westerndesert/Hard/P004 413 421
datasets/Tartan/office2/office2/Easy/P006 495 491
datasets/Tartan/gascola/gascola/Easy/P008 158 169
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P012 192 195
datasets/Tartan/gascola/gascola/Easy/P001 0 8
datasets/Tartan/seasidetown/seasidetown/Easy/P005 578 582
datasets/Tartan/seasonsforest_winter/seasonsforest_winter/Hard/P017 143 136
datasets/Tartan/gascola/gascola/Hard/P002 484 492
datasets/Tartan/gascola/gascola/Easy/P008 360 355
datasets/Tartan/office2/office2/Easy/P006 32 25
datasets/Tartan/gascola/gascola/Hard/P007 361 343
datasets/Tartan/office2/office2/Hard/P010 41 46
datasets/Tartan/seasidetown/seasidetown/Hard/P004 451 452
datasets/Tartan/seasidetown/seasidetown/Hard/P002 795 798
datasets/Tartan/seasidetown/seasidetown/Easy/P003 20 24
datasets/Tartan/gascola/gascola/Hard/P002 24 33
datasets/Tartan/office2/office2/Easy/P011 558 545
datasets/Tartan/gascola/gascola/Easy/P006 2382 2380
datasets/Tartan/office2/office2/Hard/P008 205 221
datasets/Tartan/gascola/gascola/Hard/P004 640 634
datasets/Tartan/seasidetown/seasidetown/Hard/P004 367 377
datasets/Tartan/westerndesert/westerndesert/Easy/P006 410 403
datasets/Tartan/office2/office2/Hard/P003 76 75
datasets/Tartan/gascola/gascola/Hard/P007 516 525
datasets/Tartan/seasidetown/seasidetown/Hard/P004 409 378
datasets/Tartan/westerndesert/westerndesert/Easy/P004 270 266
datasets/Tartan/office2/office2/Easy/P009 552 547
datasets/Tartan/gascola/gascola/Easy/P004 1496 1493
datasets/Tartan/office2/office2/Easy/P008 520 532
datasets/Tartan/gascola/gascola/Easy/P006 1446 1416
datasets/Tartan/seasidetown/seasidetown/Easy/P006 440 445
examples/registration/demo.py 0 → 100644
View file @ 92aa2fa8
import sys
sys.path.append('../core')
import argparse
import torch
import cv2
import numpy as np
from viz import sim3_visualization
from lietorch import SO3, SE3, Sim3
from networks.sim3_net import Sim3Net
def normalize_images(images):
images = images[:, :, [2,1,0]]
mean = torch.as_tensor([0.485, 0.456, 0.406], device=images.device)
std = torch.as_tensor([0.229, 0.224, 0.225], device=images.device)
return (images/255.0).sub_(mean[:, None, None]).div_(std[:, None, None])
def load_example(i=0):
""" get demo example """
DEPTH_SCALE = 5.0
if i==0:
image1 = cv2.imread('assets/image1.png')
image2 = cv2.imread('assets/image2.png')
depth1 = np.load('assets/depth1.npy') / DEPTH_SCALE
depth2 = np.load('assets/depth2.npy') / DEPTH_SCALE
elif i==1:
image1 = cv2.imread('assets/image3.png')
image2 = cv2.imread('assets/image4.png')
depth1 = np.load('assets/depth3.npy') / DEPTH_SCALE
depth2 = np.load('assets/depth4.npy') / DEPTH_SCALE
images = np.stack([image1, image2], 0)
images = torch.from_numpy(images).permute(0,3,1,2)
depths = np.stack([depth1, depth2], 0)
depths = torch.from_numpy(depths).float()
intrinsics = np.array([320.0, 320.0, 320.0, 240.0])
intrinsics = np.tile(intrinsics[None], (2,1))
intrinsics = torch.from_numpy(intrinsics).float()
return images[None].cuda(), depths[None].cuda(), intrinsics[None].cuda()
@torch.no_grad()
def demo(model, index=0):
images, depths, intrinsics = load_example(index)
# initial transformation estimate
if args.transformation == 'SE3':
Gs = SE3.Identity(1, 2, device='cuda')
elif args.transformation == 'Sim3':
Gs = Sim3.Identity(1, 2, device='cuda')
depths[:,0] *= 2**(2*torch.rand(1) - 1.0).cuda()
images1 = normalize_images(images)
ests, _ = model(Gs, images1, depths, intrinsics, num_steps=12)
# only care about last transformation
Gs = ests[-1]
T = Gs[:,0] * Gs[:,1].inv()
T = T[0].matrix().double().cpu().numpy()
sim3_visualization(T, images, depths, intrinsics)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--transformation', default='SE3', help='checkpoint to restore')
parser.add_argument('--ckpt', help='checkpoint to restore')
args = parser.parse_args()
model = Sim3Net(args)
model.load_state_dict(torch.load(args.ckpt))
model.cuda()
model.eval()
# run two demos
demo(model, 0)
demo(model, 1)
examples/registration/main.py 0 → 100644
View file @ 92aa2fa8
import sys
sys.path.append('../core')
import argparse
import torch
import cv2
import numpy as np
from collections import OrderedDict
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from data_readers.tartan import TartanAir, TartanAirTest
from lietorch import SO3, SE3, Sim3
from geom.losses import *
# network
from networks.sim3_net import Sim3Net
from logger import Logger
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey()
def normalize_images(images):
images = images[:, :, [2,1,0]]
mean = torch.as_tensor([0.485, 0.456, 0.406], device=images.device)
std = torch.as_tensor([0.229, 0.224, 0.225], device=images.device)
return (images/255.0).sub_(mean[:, None, None]).div_(std[:, None, None])
@torch.no_grad()
def evaluate(model):
""" evaluate trained model """
model.cuda()
model.eval()
R_THRESHOLD = 0.1
T_THRESHOLD = 0.01
S_THRESHOLD = 0.01
model.eval()
db = TartanAirTest()
test_loader = DataLoader(db, batch_size=1, shuffle=False, num_workers=4)
# random scales, make sure they are the same every time
from numpy.random import default_rng
rng = default_rng(1234)
scales = 2 ** rng.uniform(-1.0, 1.0, 2000)
scales = scales.astype(np.float32)
metrics = {'t': [], 'r': [], 's': []}
for i_batch, item in enumerate(test_loader):
images, poses, depths, intrinsics = [x.to('cuda') for x in item]
# convert poses w2c -> c2w
Ps = SE3(poses).inv()
batch, num = images.shape[:2]
if args.transformation == 'SE3':
Gs = SE3.Identity(Ps.shape, device='cuda')
elif args.transformation == 'Sim3':
Ps = Sim3(Ps)
Gs = Sim3.Identity(Ps.shape, device='cuda')
s = torch.as_tensor(scales[i_batch]).cuda().unsqueeze(0)
phi = torch.zeros(batch, num, 7, device='cuda')
phi[:,0,6] = s.log()
Ps = Sim3.exp(phi) * Ps
depths[:,0] *= s[:,None,None]
images = normalize_images(images)
Gs, _ = model(Gs, images, depths, intrinsics, num_steps=16)
Gs = Gs[-1]
dP = Ps[:,1] * Ps[:,0].inv()
dG = Gs[:,1] * Gs[:,0].inv()
dE = Sim3(dP.inv() * dG)
r_err, t_err, s_err = pose_metrics(dE)
t_err = t_err * TartanAir.DEPTH_SCALE
metrics['t'].append(t_err.item())
metrics['r'].append(r_err.item())
metrics['s'].append(s_err.item())
rlist = np.array(metrics['r'])
tlist = np.array(metrics['t'])
slist = np.array(metrics['s'])
r_all = np.count_nonzero(rlist < R_THRESHOLD) / len(metrics['r'])
t_all = np.count_nonzero(tlist < T_THRESHOLD) / len(metrics['t'])
s_all = np.count_nonzero(slist < S_THRESHOLD) / len(metrics['s'])
print("Rotation Acc: ", r_all)
print("Translation Acc: ", t_all)
print("Scale Acc: ", s_all)
def train(args):
""" Test to make sure project transform correctly maps points """
model = Sim3Net(args)
model.cuda()
model.train()
if args.ckpt is not None:
model.load_state_dict(torch.load(args.ckpt))
db = TartanAir(mode='training', n_frames=2, do_aug=True, fmin=8.0, fmax=100.0)
train_loader = DataLoader(db, batch_size=args.batch, shuffle=True, num_workers=4)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-5)
scheduler = optim.lr_scheduler.OneCycleLR(optimizer,
args.lr, 100000, pct_start=0.01, cycle_momentum=False)
from collections import OrderedDict
graph = OrderedDict()
graph[0] = [1]
graph[1] = [0]
logger = Logger(args.name, scheduler)
should_keep_training = True
total_steps = 0
while should_keep_training:
for i_batch, item in enumerate(train_loader):
optimizer.zero_grad()
images, poses, depths, intrinsics = [x.to('cuda') for x in item]
# convert poses w2c -> c2w
Ps = SE3(poses).inv()
batch, num = images.shape[:2]
if args.transformation == 'SE3':
Gs = SE3.Identity(Ps.shape, device='cuda')
elif args.transformation == 'Sim3':
Ps = Sim3(Ps)
Gs = Sim3.Identity(Ps.shape, device='cuda')
s = 2**(2*torch.rand(batch) - 1.0).cuda()
phi = torch.zeros(batch, num, 7, device='cuda')
phi[:,0,6] = s.log()
Ps = Sim3.exp(phi) * Ps
depths[:,0] *= s[:,None,None]
images = normalize_images(images)
Gs, residuals = model(Gs, images, depths, intrinsics, num_steps=args.iters)
geo_loss, geo_metrics = geodesic_loss(Ps, Gs, graph)
res_loss, res_metrics = residual_loss(residuals)
metrics = {}
metrics.update(geo_metrics)
metrics.update(res_metrics)
loss = args.w1 * geo_loss + args.w2 * res_loss
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
scheduler.step()
logger.push(metrics)
total_steps += 1
if total_steps % 5000 == 0:
PATH = 'checkpoints/%s_%06d.pth' % (args.name, total_steps)
torch.save(model.state_dict(), PATH)
model.train()
return model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--name', default='bla', help='name your experiment')
parser.add_argument('--transformation', default='SE3', help='checkpoint to restore')
parser.add_argument('--ckpt', help='checkpoint to restore')
parser.add_argument('--train', action='store_true')
parser.add_argument('--batch', type=int, default=4)
parser.add_argument('--iters', type=int, default=8)
parser.add_argument('--lr', type=float, default=0.00025)
parser.add_argument('--clip', type=float, default=2.5)
parser.add_argument('--w1', type=float, default=10.0)
parser.add_argument('--w2', type=float, default=0.1)
args = parser.parse_args()
if args.train:
import os
if not os.path.isdir('checkpoints'):
os.mkdir('checkpoints')
model = train(args)
else:
model = Sim3Net(args)
model.load_state_dict(torch.load(args.ckpt))
evaluate(model)
examples/registration/readme.md 0 → 100644
View file @ 92aa2fa8
## SE3 / Sim3 Registration
Estimate the 3D transformation between two RGB-D frames
<img src="assets/registration.gif" width="480">
### Models
| Model | Rot. Acc. | Tr. Acc. | Scale Acc. |
| --------- | --------- | -------- | ---------- |
| [se3.pth](https://drive.google.com/file/d/17pgeY5m-GXnrY3oFLPRaIrTZYvae_l9u/view?usp=sharing) | 91.90 | 77.70 | - |
| [sim3.pth](https://drive.google.com/file/d/1LMnKND_4DAmd9DMTSKdz_zAoCgja6X43/view?usp=sharing) | 93.45 | 76.05 | 98.70 |
for thresholds 0.1 deg. rotation error, 1cm translation error, and 1% scale error.
### Demo
Download one of the models to run the demo (requres Open3D)
```python
python demo.py --transformation=SE3 --ckpt=se3.pth
python demo.py --transformation=Sim3 --ckpt=sim3.pth
```
### Training and Evaluation
Training and evaluation is performed on the [TartanAir](https://theairlab.org/tartanair-dataset/) (only depth_left and image_left need to be downloaded). Note: our dataloader computes the optical flow between every pair of frames which can take several hours on the first run. However, this result is cached so that future loads will only take a few seconds.
The training script expects the dataset to be in the directory datasets/TartanAir.
To train a Sim3 network:
```python
python main.py --train --transformation=Sim3 --name=sim3
```
A trained model can then be evaluated:
```python
python main.py --transformation=Sim3 --ckpt=sim3.pth
```
examples/registration/viz.py 0 → 100644
View file @ 92aa2fa8
import sys
sys.path.append('../core')
import argparse
import torch
import scipy
import numpy as np
import geom.projective_ops as pops
import open3d as o3d
def make_point_cloud(image, depth, intrinsics, max_depth=5.0):
""" create a point cloud """
colors = image.permute(1,2,0).view(-1,3)
colors = colors[...,[2,1,0]] / 255.0
clr = colors.cpu().numpy()
inv_depth = 1.0 / depth[None,None]
points = pops.iproj(inv_depth, intrinsics[None,None])
points = (points[..., :3] / points[..., 3:]).view(-1,3)
pts = points.cpu().numpy()
# open3d point cloud
pc = o3d.geometry.PointCloud()
keep = pts[:,2] < max_depth
pc.points = o3d.utility.Vector3dVector(pts[keep])
pc.colors = o3d.utility.Vector3dVector(clr[keep])
return pc
def set_camera_pose(vis):
""" set initial camera position """
cam = vis.get_view_control().convert_to_pinhole_camera_parameters()
cam.extrinsic = np.array(
[[ 0.91396544, 0.1462376, -0.37852575, 0.94374719],
[-0.13923432, 0.98919177, 0.04597225, 1.01177687],
[ 0.38115743, 0.01068673, 0.92444838, 3.35964868],
[ 0., 0., 0., 1. ]])
vis.get_view_control().convert_from_pinhole_camera_parameters(cam)
def sim3_visualization(T, images, depths, intrinsics):
""" convert depth to open3d point clouds """
images = images.squeeze(0)
depths = depths.squeeze(0)
intrinsics = intrinsics.squeeze(0)
pc1 = make_point_cloud(images[0], depths[0], intrinsics[0])
pc2 = make_point_cloud(images[1], depths[1], intrinsics[1])
sim3_visualization.index = 1
sim3_visualization.pc2 = pc2
NUM_STEPS = 100
dt = scipy.linalg.logm(T) / NUM_STEPS
dT = scipy.linalg.expm(dt)
sim3_visualization.transform = dT
def animation_callback(vis):
sim3_visualization.index += 1
pc2 = sim3_visualization.pc2
if sim3_visualization.index >= NUM_STEPS and \
sim3_visualization.index < 2*NUM_STEPS:
pc2.transform(sim3_visualization.transform)
vis.update_geometry(pc2)
vis.poll_events()
vis.update_renderer()
vis = o3d.visualization.Visualizer()
vis.register_animation_callback(animation_callback)
vis.create_window(height=540, width=960)
vis.add_geometry(pc1)
vis.add_geometry(pc2)
vis.get_render_option().load_from_json("assets/renderoption.json")
set_camera_pose(vis)
print("Press q to move to next example")
vis.run()
vis.destroy_window()
examples/rgbdslam/assets/renderoption.json 0 → 100644
View file @ 92aa2fa8
{
"background_color" : [ 1, 1, 1 ],
"class_name" : "RenderOption",
"default_mesh_color" : [ 0.69999999999999996, 0.69999999999999996, 0.69999999999999996 ],
"image_max_depth" : 3000,
"image_stretch_option" : 0,
"interpolation_option" : 0,
"light0_color" : [ 1, 1, 1 ],
"light0_diffuse_power" : 0.66000000000000003,
"light0_position" : [ 0, 0, 2 ],
"light0_specular_power" : 0.20000000000000001,
"light0_specular_shininess" : 100,
"light1_color" : [ 1, 1, 1 ],
"light1_diffuse_power" : 0.66000000000000003,
"light1_position" : [ 0, 0, 2 ],
"light1_specular_power" : 0.20000000000000001,
"light1_specular_shininess" : 100,
"light2_color" : [ 1, 1, 1 ],
"light2_diffuse_power" : 0.66000000000000003,
"light2_position" : [ 0, 0, -2 ],
"light2_specular_power" : 0.20000000000000001,
"light2_specular_shininess" : 100,
"light3_color" : [ 1, 1, 1 ],
"light3_diffuse_power" : 0.66000000000000003,
"light3_position" : [ 0, 0, -2 ],
"light3_specular_power" : 0.20000000000000001,
"light3_specular_shininess" : 100,
"light_ambient_color" : [ 0, 0, 0 ],
"light_on" : true,
"mesh_color_option" : 1,
"mesh_shade_option" : 0,
"mesh_show_back_face" : false,
"mesh_show_wireframe" : false,
"point_color_option" : 9,
"point_show_normal" : false,
"point_size" : 2,
"show_coordinate_frame" : false,
"version_major" : 1,
"version_minor" : 0
}
examples/rgbdslam/demo.py 0 → 100644
View file @ 92aa2fa8
import sys
sys.path.append('../core')
from tqdm import tqdm
import numpy as np
import torch
import cv2
import os
from viz import SLAMFrontend
from lietorch import SE3
from networks.slam_system import SLAMSystem
from data_readers import factory
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(10)
def evaluate(poses_gt, poses_est):
from rgbd_benchmark.evaluate_ate import evaluate_ate
poses_gt = poses_gt.cpu().numpy()
poses_est = poses_est.cpu().numpy()
N = poses_gt.shape[0]
poses_gt = dict([(i, poses_gt[i]) for i in range(N)])
poses_est = dict([(i, poses_est[i]) for i in range(N)])
results = evaluate_ate(poses_gt, poses_est)
print(results)
@torch.no_grad()
def run_slam(tracker, datapath, frame_rate=8.0):
""" run slam over full sequence """
torch.multiprocessing.set_sharing_strategy('file_system')
stream = factory.create_datastream(args.datapath, frame_rate=frame_rate)
# start the frontend thread
if args.viz:
frontend = SLAMFrontend().start()
tracker.set_frontend(frontend)
# store groundtruth poses for evaluation
poses_gt = []
for (tstamp, image, depth, pose, intrinsics) in tqdm(stream):
tracker.track(tstamp, image[None].cuda(), depth.cuda(), intrinsics.cuda())
poses_gt.append(pose)
if args.viz:
show_image(image[0])
frontend.update_pose(tstamp, pose[0], gt=True)
# global optimization / loop closure
if args.go:
tracker.global_refinement()
poses_gt = torch.cat(poses_gt, 0)
poses_est = tracker.raw_poses()
evaluate(poses_gt, poses_est)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--datapath', help='path to video for slam')
parser.add_argument('--ckpt', help='saved network weights')
parser.add_argument('--viz', action='store_true', help='run visualization frontent')
parser.add_argument('--go', action='store_true', help='use global optimization')
parser.add_argument('--frame_rate', type=float, default=8.0, help='frame rate')
args = parser.parse_args()
# initialize tracker / load weights
tracker = SLAMSystem(args)
tracker.load_state_dict(torch.load(args.ckpt))
tracker.eval()
tracker.cuda()
run_slam(tracker, args.datapath, args.frame_rate)
examples/rgbdslam/evaluate.py 0 → 100644
View file @ 92aa2fa8
import sys
sys.path.append('../core')
from tqdm import tqdm
import numpy as np
import torch
import cv2
import os
from lietorch import SE3
from networks.slam_system import SLAMSystem
from data_readers import factory
def evaluate(poses_gt, poses_est):
from rgbd_benchmark.evaluate_ate import evaluate_ate
poses_gt = poses_gt.cpu().numpy()
poses_est = poses_est.cpu().numpy()
N = poses_gt.shape[0]
poses_gt = dict([(i, poses_gt[i]) for i in range(N)])
poses_est = dict([(i, poses_est[i]) for i in range(N)])
results = evaluate_ate(poses_gt, poses_est)
print(results)
return results['absolute_translational_error.rmse']
@torch.no_grad()
def run_slam(tracker, datapath, global_optimization=False, frame_rate=3):
""" run slam over full sequence """
torch.multiprocessing.set_sharing_strategy('file_system')
stream = factory.create_datastream(datapath, frame_rate=frame_rate)
# store groundtruth poses for evaluatino
poses_gt = []
for (tstamp, image, depth, pose, intrinsics) in tqdm(stream):
tracker.track(tstamp, image[None].cuda(), depth.cuda(), intrinsics.cuda())
poses_gt.append(pose)
if global_optimization:
tracker.global_refinement()
poses_gt = torch.cat(poses_gt, 0)
poses_est = tracker.raw_poses()
ate = evaluate(poses_gt, poses_est)
return ate
def run_evaluation(ckpt, frame_rate=8.0):
validation_scenes = [
'rgbd_dataset_freiburg1_360',
'rgbd_dataset_freiburg1_desk',
'rgbd_dataset_freiburg1_desk2',
'rgbd_dataset_freiburg1_floor',
'rgbd_dataset_freiburg1_plant',
'rgbd_dataset_freiburg1_room',
'rgbd_dataset_freiburg1_rpy',
'rgbd_dataset_freiburg1_teddy',
'rgbd_dataset_freiburg1_xyz',
]
results = {}
for scene in validation_scenes:
# initialize tracker / load weights
tracker = SLAMSystem(None)
tracker.load_state_dict(torch.load(ckpt))
tracker.eval()
tracker.cuda()
datapath = os.path.join('datasets/TUM-RGBD', scene)
results[scene] = run_slam(tracker, datapath,
global_optimization=args.go, frame_rate=frame_rate)
print("Aggregate Results: ")
for scene in results:
print(scene, results[scene])
print("MEAN: ", np.mean([results[key] for key in results]))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--ckpt', help='saved network weights')
parser.add_argument('--frame_rate', type=float, default=8.0, help='frame rate')
parser.add_argument('--go', action='store_true', help='use global optimization')
args = parser.parse_args()
run_evaluation(args.ckpt, frame_rate=args.frame_rate)
examples/rgbdslam/readme.md 0 → 100644
View file @ 92aa2fa8
## RGB-D SLAM / VO
<img src="assets/floor.png" alt="floor" height="280"/> <img src="assets/room.png" alt="room" height="280"/>
### Pretrained Model
Absolute Trajectory Error (ATE) on all freiburg1 sequences. The default model acts as a visual odometry system (no loop closure). The model rgbdslam.pth + go performs global optimization at the end of tracking to correct for drift.
| Model | 360 | desk | desk2 | floor | plant | room | rpy | teddy | xyz | avg |
| ----- | --- | ---- | ----- | ----- | ----- | ---- | --- | ----- | --- | --- |
| DeepV2D | 0.072 | 0.069 | 0.074 | 0.317 | 0.046 | 0.213 | 0.082 | 0.114 | 0.028 | 0.113 |
| [lietorch_rgbdslam.pth](https://drive.google.com/file/d/1SVQTFCchZuhFeSucS5jLeNbOWyff4BA8/view?usp=sharing) | 0.076 | 0.045 | 0.054 | 0.057 | 0.032 | 0.143 | 0.064 | 0.092 | 0.033 | 0.066 |
| [lietorch_rgbdslam.pth](https://drive.google.com/file/d/1SVQTFCchZuhFeSucS5jLeNbOWyff4BA8/view?usp=sharing) + go | 0.047 | 0.018 | 0.023 | 0.017 | 0.015 | 0.029 | 0.019 | 0.030 | 0.009 | 0.023 |
### Demo
Requires a GPU with at least 8gb of memory. First download a sequence from the [TUM-RGBD dataset](https://vision.in.tum.de/data/datasets/rgbd-dataset/download), then run the demo. You can interact with the Open3D window during tracking.
```python
python demo.py --ckpt=lietorch_rgbdslam.pth --datapath=<sequence path> --frame_rate=8.0 --go --viz
```
The `--frame_rate` flag determines the rate images are subsampled from the video (e.g `--frame_rate=8.0` subsamples the video at a rate of 8 fps). With a RTX-3090 GPU and visualization disabled, `--frame_rate <= 8.0` gives real-time performance.
### Evaluation
Assuming all TUM-RGBD sequences have been download, a trained model can be evaluated on the TUM-RGBD dataset
```
python evaluate.py --ckpt=rgbdslam.pth --datapath=<tum-rgbd root> --go --frame_rate=8.0
```
### Training
We provide data_loaders for [NYUv2](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html), [ScanNet](http://www.scan-net.org/), [ETH3D-SLAM](https://www.eth3d.net/slam_datasets), and [TartanAir](https://theairlab.org/tartanair-dataset/). The dataloaders will work directly on ScanNet, ETH3D, and TartanAir. For NYUv2, you will need to first extract the depths and images from the raw format then run ORB-SLAM2 to generate psuedo-groundtruth poses. Send me an email (Zachary Teed) if you need a link to the preprocessed NYU data.
You can train on any subset of the datasets by listing their keys {`nyu`, `scannet`, `eth`, `tartan`}. The provided models are trained on scannet and nyu. Note: our dataloader computes the optical flow between every pair of frames which can take several hours on the first run. However, this result is cached so that future loads will only take a few seconds. The default training setttings require a GPU with 24 Gb of memory.
```
python train.py --batch=3 --iters=12 --lr=0.00025 --name nyu_scannet_eth_v2 --datasets nyu scannet
```
#### Training on your own dataset
Additional datasets can easily be added by subclassing `RGBDDataset`, see `nyu2.py` or `scannet.py` as examples. To verify the dataloading is correct, you can use the `reprojection_test.py` script to verify that the warped images align.
examples/rgbdslam/reprojection_test.py 0 → 100644
View file @ 92aa2fa8
import sys
sys.path.append('../core')
import torch
import cv2
import numpy as np
from torch.utils.data import DataLoader
from data_readers.factory import dataset_factory
from lietorch import SO3, SE3, Sim3
import geom.projective_ops as pops
from geom.sampler_utils import bilinear_sampler
def show_image(image):
if len(image.shape) == 3:
image = image.permute(1, 2, 0)
image = image.cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey()
def reproj_test(args, N=2):
""" Test to make sure project transform correctly maps points """
db = dataset_factory(args.datasets, n_frames=N)
train_loader = DataLoader(db, batch_size=1, shuffle=True, num_workers=0)
for item in train_loader:
images, poses, depths, intrinsics = [x.to('cuda') for x in item]
poses = SE3(poses).inv()
disps = 1.0 / depths
coords, _ = pops.projective_transform(poses, disps, intrinsics, [0], [1])
imagew = bilinear_sampler(images[:,[1]], coords[...,[0,1]])
# these two image should show camera motion
show_image(images[0,0])
show_image(images[0,1])
# these two images should show the camera motion removed by reprojection / warping
show_image(images[0,0])
show_image(imagew[0,0])
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--datasets', nargs='+', help='lists of datasets for training')
args = parser.parse_args()
reproj_test(args)
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