├── face_area.png ├── annotations.png ├── annotations ├── F_3D_N │ ├── F1005_N.lnd │ ├── F1064_N.lnd │ ├── f1016_N.lnd │ ├── f1022_N.lnd │ ├── f1027_N.lnd │ ├── f1029_N.lnd │ ├── f1051_N.lnd │ ├── f1053_N.lnd │ ├── F1002_N.lnd │ ├── F1007_N.lnd │ ├── F1031_2_N.lnd │ ├── F1046b_N.lnd │ ├── F1060_N.lnd │ ├── F1061_N.lnd │ ├── F1062_N.lnd │ ├── F1063_N.lnd │ ├── F1065_N.lnd │ ├── F1066_N.lnd │ ├── F1068_N.lnd │ ├── F1069_N.lnd │ ├── F1071_N.lnd │ ├── F1072_N.lnd │ ├── F1073_N.lnd │ ├── f1004b_N.lnd │ ├── f1011_N.lnd │ ├── f1013_N.lnd │ ├── f1014_N.lnd │ ├── f1015_N.lnd │ ├── f1017_N.lnd │ ├── f1019_N.lnd │ ├── f1024_N.lnd │ ├── f1025_N.lnd │ ├── f1026_N.lnd │ ├── f1028_N.lnd │ ├── f1031_N.lnd │ ├── f1032_N.lnd │ ├── f1038_N.lnd │ ├── f1039_N.lnd │ ├── f1041_N.lnd │ ├── f1043_N.lnd │ ├── f1047_N.lnd │ ├── f1048_N.lnd │ ├── f1055_N.lnd │ ├── f1056_N.lnd │ ├── f1057_N.lnd │ ├── f1058_N.lnd │ ├── F1001_N.lnd │ ├── F1003_N.lnd │ ├── F1004_N.lnd │ ├── F1006_N.lnd │ ├── F1008_N.lnd │ ├── F1009_N.lnd │ ├── F1010_N.lnd │ ├── F1055b_N.lnd │ ├── F1067_N.lnd │ ├── f1012_N.lnd │ ├── f1021_N.lnd │ ├── f1023_N.lnd │ ├── f1034_N.lnd │ ├── f1035_N.lnd │ ├── f1036_N.lnd │ ├── f1037_N.lnd │ ├── f1042_N.lnd │ ├── f1045_N.lnd │ ├── f1046_N.lnd │ ├── f1049_N.lnd │ ├── f1050_N.lnd │ ├── f1054_N.lnd │ ├── f1059_N.lnd │ ├── f1074_N.lnd │ ├── F1013_NH_2.lnd │ ├── f1020_N.lnd │ ├── f1030_N.lnd │ ├── f1040_N.lnd │ └── f1052_N.lnd └── M_3D_N │ ├── M1005_N.lnd │ ├── M1006_2_N.lnd │ ├── m1011_N.lnd │ ├── m1017_N.lnd │ ├── m1020_N.lnd │ ├── m1027_N.lnd │ ├── m1034_N.lnd │ ├── m1047_N.lnd │ ├── m1048_N.lnd │ ├── m1049_N.lnd │ ├── m1050_N.lnd │ ├── M1002_N.lnd │ ├── M1008_N.lnd │ ├── M1009_N.lnd │ ├── M1011B_N.lnd │ ├── M1056_N.lnd │ ├── m1000b_N.lnd │ ├── m1000b_NH.lnd │ ├── m1006_N.lnd │ ├── m1007_N.lnd │ ├── m1014_N.lnd │ ├── m1015_N.lnd │ ├── m1016_N.lnd │ ├── m1018_N.lnd │ ├── m1019_N.lnd │ ├── m1024_N.lnd │ ├── m1026_N.lnd │ ├── m1028_N.lnd │ ├── m1028_NH.lnd │ ├── m1031_N.lnd │ ├── m1033_N.lnd │ ├── m1040_N.lnd │ ├── m1045_N.lnd │ ├── m1046_N.lnd │ ├── m1046_NH.lnd │ ├── m1047_NH.lnd │ ├── m1052_N.lnd │ ├── m1054_N.lnd │ ├── m1055_N.lnd │ ├── m1057_N.lnd │ ├── m1058_N.lnd │ ├── m1059_N.lnd │ ├── m1060_N.lnd │ ├── m1062_N.lnd │ ├── m1063_N.lnd │ ├── M1000_N.lnd │ ├── M1000_NH.lnd │ ├── M1051_N.lnd │ ├── m1001_N.lnd │ ├── m1003_N.lnd │ ├── m1004_N.lnd │ ├── m1010_N.lnd │ ├── m1012_N.lnd │ ├── m1013_N.lnd │ ├── m1021_N.lnd │ ├── m1022_N.lnd │ ├── m1023_N.lnd │ ├── m1025_N.lnd │ ├── m1029_N.lnd │ ├── m1030_N.lnd │ ├── m1032_N.lnd │ ├── m1035_N.lnd │ ├── m1036_N.lnd │ ├── m1037_N.lnd │ ├── m1038_N.lnd │ ├── m1039_N.lnd │ ├── m1041_N.lnd │ ├── m1042_N.lnd │ ├── m1043_N.lnd │ ├── m1044_N.lnd │ ├── m1044_NH.lnd │ └── m1053_N.lnd ├── LICENSE ├── run_example.py ├── README.md └── compute_vertices_to_mesh_distances.py /face_area.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/patrikhuber/fg2018-competition/HEAD/face_area.png -------------------------------------------------------------------------------- /annotations.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/patrikhuber/fg2018-competition/HEAD/annotations.png -------------------------------------------------------------------------------- /annotations/F_3D_N/F1005_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -26.7041 26.3637 11.427 2 | 2 1.15276 25.3768 15.2682 3 | 3 36.3764 24.8836 13.3017 4 | 4 64.0825 26.4565 12.495 5 | 5 17.9003 -15.4816 38.056 6 | 6 -7.38412 -34.8907 24.1575 7 | 7 43.7038 -36.0372 25.8044 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1064_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -15.9518 39.3025 -2.30615 2 | 2 12.203 41.7922 8.20887 3 | 3 45.6424 42.7485 11.7789 4 | 4 74.8993 45.2415 6.11563 5 | 5 29.2043 3.81027 31.4001 6 | 6 4.57028 -21.1454 15.853 7 | 7 58.6966 -18.7181 19.9654 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1016_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -21.9104 35.0573 16.8258 2 | 2 6.19053 35.1924 26.2684 3 | 3 39.1953 34.9458 26.205 4 | 4 68.0169 34.682 22.0167 5 | 5 20.2497 -10.0067 50.0425 6 | 6 -4.66685 -33.8942 33.0503 7 | 7 50.366 -32.949 39.2461 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1022_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -31.0037 28.6073 31.4633 2 | 2 -0.37512 28.5188 43.2972 3 | 3 28.893 31.3924 43.4668 4 | 4 58.8599 33.5493 35.94 5 | 5 13.3427 -9.63492 67.8589 6 | 6 -10.203 -33.871 54.9822 7 | 7 36.801 -31.5565 56.4525 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1027_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -6.09146 8.84115 12.3382 2 | 2 23.2297 11.8962 21.4795 3 | 3 59.1218 14.1287 21.853 4 | 4 89.5649 14.9657 15.0656 5 | 5 39.9883 -27.738 49.3544 6 | 6 15.8885 -56.9653 29.3479 7 | 7 68.0939 -55.3779 31.1208 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1029_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -22.388 5.37273 34.281 2 | 2 6.4286 5.26824 48.773 3 | 3 37.2228 5.9812 54.6495 4 | 4 69.2738 7.84588 51.836 5 | 5 19.4322 -39.6078 70.4077 6 | 6 -3.91058 -57.1346 48.5933 7 | 7 48.0833 -56.1962 55.1613 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1051_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -36.6966 37.112 -9.52439 2 | 2 -9.50536 35.8461 4.17469 3 | 3 19.5312 35.839 7.85035 4 | 4 48.3852 36.6585 2.5991 5 | 5 2.77165 -0.8979 28.7987 6 | 6 -18.8803 -22.2028 11.8287 7 | 7 28.3531 -21.6376 15.7007 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1053_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -32.2217 51.077 27.0839 2 | 2 -2.25405 49.9364 37.343 3 | 3 27.8189 50.9699 39.0493 4 | 4 57.2559 52.2662 35.0422 5 | 5 10.8353 4.44592 64.9166 6 | 6 -10.5566 -14.4226 42.5962 7 | 7 36.2934 -13.1664 46.7806 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/M1005_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -28.6291 3.2274 1.559 2 | 2 -1.94461 4.79707 6.21148 3 | 3 29.7105 3.48901 5.68244 4 | 4 56.1333 2.44256 0.300353 5 | 5 15.711 -36.7433 36.6596 6 | 6 -10.3737 -67.2602 18.81 7 | 7 39.3419 -66.8166 17.6841 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/M1006_2_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -45.224 49.3425 2.93763 2 | 2 -18.0816 49.6618 6.2371 3 | 3 22.153 49.9811 4.93176 4 | 4 51.2114 49.0232 -0.897118 5 | 5 1.7164 5.8282 36.3695 6 | 6 -23.5101 -21.134 17.0839 7 | 7 29.1493 -20.7601 16.7306 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1011_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -54.5667 29.8793 14.007 2 | 2 -27.1872 27.5977 16.251 3 | 3 9.03352 26.7421 13.6917 4 | 4 35.5574 30.4498 9.20621 5 | 5 -7.65579 -18.9264 38.1607 6 | 6 -32.488 -41.9455 19.2637 7 | 7 16.2979 -40.756 13.9201 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1017_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -40.5516 22.9723 5.60014 2 | 2 -8.7383 24.2199 13.0355 3 | 3 20.8918 23.908 12.4407 4 | 4 51.7694 26.715 6.12446 5 | 5 7.53433 -21.7383 31.1513 6 | 6 -13.4478 -43.3963 8.58695 7 | 7 32.1403 -44.2645 11.1296 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1020_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -41.5804 50.0513 16.7591 2 | 2 -13.9491 49.7262 20.3446 3 | 3 25.3849 49.7262 23.423 4 | 4 53.6663 50.7014 21.7711 5 | 5 5.0494 3.90512 46.9852 6 | 6 -21.6311 -25.9766 28.59 7 | 7 28.8063 -27.5292 29.0338 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1027_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -45.0126 15.7289 6.27043 2 | 2 -19.6513 18.93 11.1243 3 | 3 13.4147 19.5738 14.3453 4 | 4 41.3471 16.7656 9.33238 5 | 5 -1.48364 -26.2249 31.7644 6 | 6 -23.4545 -48.7242 15.182 7 | 7 20.0935 -48.4364 20.43 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1034_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -31.3738 21.708 22.1065 2 | 2 -4.71314 24.6758 30.9871 3 | 3 29.5715 28.9821 36.6886 4 | 4 57.3398 29.8783 33.912 5 | 5 13.7398 -21.5421 61.0268 6 | 6 -5.94363 -46.641 30.7403 7 | 7 44.8983 -40.5145 38.1615 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1047_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -41.0096 18.5535 -0.573663 2 | 2 -9.31013 18.7434 6.0511 3 | 3 27.4292 21.4026 6.6385 4 | 4 57.687 23.691 3.42292 5 | 5 10.0261 -24.6675 36.0032 6 | 6 -18.6217 -57.702 17.5025 7 | 7 41.3746 -53.2006 17.5417 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1048_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -38.4555 20.9919 40.0605 2 | 2 -12.6621 21.9575 45.341 3 | 3 21.195 24.681 46.7841 4 | 4 45.969 25.0237 43.8785 5 | 5 3.02136 -13.7914 85.1529 6 | 6 -22.9779 -43.0711 64.2074 7 | 7 32.1705 -41.2774 67.2735 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1049_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -56.1793 47.986 13.3217 2 | 2 -26.6514 49.3539 22.2742 3 | 3 10.826 53.2723 22.5377 4 | 4 40.2333 54.0745 18.685 5 | 5 -6.90619 11.2307 46.9842 6 | 6 -29.3244 -19.0923 28.2835 7 | 7 17.6258 -16.6261 30.3553 8 | -------------------------------------------------------------------------------- /annotations/M_3D_N/m1050_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -27.0725 49.7031 0.68504 2 | 2 4.94024 46.9121 9.28901 3 | 3 41.4129 44.2971 11.0414 4 | 4 71.7364 43.6967 6.25742 5 | 5 17.927 0.48909 39.2566 6 | 6 -9.4953 -27.1094 26.2628 7 | 7 45.8635 -29.1809 28.2879 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1002_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -29.5138 32.3317 19.6598 2 | 2 -2.6951 30.5102 24.5825 3 | 3 33.5387 30.7316 23.5034 4 | 4 58.1042 32.8655 20.1092 5 | 5 12.8464 -10.1154 46.5941 6 | 6 -10.7168 -34.0361 26.954 7 | 7 38.7479 -32.7692 26.9016 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1007_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -25.5992 10.0588 21.3477 2 | 2 2.42398 12.0042 31.3204 3 | 3 36.6146 10.2951 31.4929 4 | 4 64.1689 7.21986 25.6255 5 | 5 18.0588 -29.1011 51.6557 6 | 6 -7.03242 -48.1465 31.7418 7 | 7 44.3706 -49.6798 34.8796 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1031_2_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -45.5218 43.4509 31.7817 2 | 2 -17.127 44.9258 38.2083 3 | 3 15.8121 43.5102 40.1568 4 | 4 45.2776 42.7231 36.0568 5 | 5 -1.288 1.50705 59.4061 6 | 6 -26.9041 -20.1066 39.6959 7 | 7 25.4451 -20.5579 39.9328 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1046b_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -58.9081 23.051 -2.48948 2 | 2 -30.0981 25.254 5.34921 3 | 3 4.55361 26.3585 7.77069 4 | 4 33.6274 29.5648 0.501396 5 | 5 -12.69 -13.6189 31.0459 6 | 6 -35.1702 -37.8214 9.10381 7 | 7 15.2148 -34.3112 13.8592 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1060_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -49.5436 37.3209 38.6152 2 | 2 -23.2492 36.0899 47.1513 3 | 3 10.4373 38.1616 49.2652 4 | 4 38.9008 36.8475 41.6362 5 | 5 -7.35211 -3.61106 68.4232 6 | 6 -29.7704 -23.2739 52.8259 7 | 7 13.6668 -23.003 55.2642 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1061_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -35.3047 29.7896 74.5887 2 | 2 -5.33438 26.8356 84.6627 3 | 3 28.3643 22.4961 89.0759 4 | 4 58.5055 22.4632 82.2546 5 | 5 7.25813 -22.1135 102.821 6 | 6 -21.4279 -41.2006 89.6661 7 | 7 32.3575 -46.7047 92.1576 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1062_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -44.6564 57.5337 32.5311 2 | 2 -14.3574 57.5744 42.5008 3 | 3 24.043 57.9407 46.4498 4 | 4 54.1588 57.5068 44.3315 5 | 5 -0.433225 12.0397 66.9227 6 | 6 -24.404 -11.1111 46.3023 7 | 7 28.0426 -12.2559 52.0642 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1063_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -52.8007 25.0521 32.6008 2 | 2 -25.2881 25.409 40.911 3 | 3 6.78992 26.3667 42.4073 4 | 4 34.7974 24.3637 35.1246 5 | 5 -7.62138 -10.4838 75.2779 6 | 6 -32.5476 -39.4059 61.9927 7 | 7 17.0145 -35.5538 61.9389 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1065_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -37.919 35.0129 14.6678 2 | 2 -6.02806 32.7441 25.8421 3 | 3 26.4319 32.0154 26.1157 4 | 4 58.2354 31.4122 17.3627 5 | 5 8.27409 -10.2837 44.7737 6 | 6 -14.4961 -31.7069 24.0691 7 | 7 34.3421 -33.5402 26.5938 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1066_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -45.4094 20.6599 23.486 2 | 2 -15.9313 18.7051 34.452 3 | 3 12.8238 19.879 38.1984 4 | 4 43.9608 21.0584 35.3025 5 | 5 -4.58694 -26.1174 54.4316 6 | 6 -22.5048 -48.1487 32.7356 7 | 7 21.3707 -44.2852 36.6751 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1068_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -67.6794 31.2292 -3.05417 2 | 2 -39.556 32.2681 5.42122 3 | 3 1.97847 32.6588 6.87581 4 | 4 30.8322 35.3521 -0.179664 5 | 5 -18.6043 -13.3112 24.4741 6 | 6 -41.0846 -39.11 8.54305 7 | 7 4.26037 -37.1072 9.45205 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1069_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -65.3365 24.8191 14.5268 2 | 2 -39.6662 27.008 18.924 3 | 3 -5.46212 29.7009 21.4981 4 | 4 22.0211 31.0437 16.9861 5 | 5 -18.9861 -15.7912 45.4839 6 | 6 -44.2098 -43.8811 28.1588 7 | 7 12.7018 -38.132 33.5717 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1071_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -57.6407 7.29809 36.0609 2 | 2 -25.7476 5.56521 42.5377 3 | 3 3.08222 3.76014 43.6079 4 | 4 34.5438 6.19974 38.9144 5 | 5 -13.3754 -43.797 63.9606 6 | 6 -38.2752 -62.3016 45.0324 7 | 7 10.251 -63.7951 46.2791 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1072_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -67.9633 32.3278 28.8983 2 | 2 -39.2689 31.6913 37.6533 3 | 3 -6.35181 32.8919 37.5253 4 | 4 22.9284 31.188 30.8578 5 | 5 -22.0743 -6.50414 62.0193 6 | 6 -47.7839 -35.5163 51.4603 7 | 7 3.27002 -34.9575 51.9922 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/F1073_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -32.5783 11.9351 41.357 2 | 2 -6.5134 11.4178 45.5239 3 | 3 23.8939 12.1684 45.2209 4 | 4 50.6298 11.9736 39.6449 5 | 5 8.03441 -26.1648 68.1852 6 | 6 -15.3449 -48.3493 53.2576 7 | 7 31.8225 -49.0895 52.8784 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1004b_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -37.9427 31.9277 -30.4569 2 | 2 -10.5704 30.9391 -25.6724 3 | 3 20.0294 32.574 -26.4913 4 | 4 45.7601 33.2204 -30.2779 5 | 5 3.52555 -12.5268 -6.4875 6 | 6 -16.7789 -38.2434 -24.148 7 | 7 28.99 -35.6399 -24.123 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1011_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -47.8335 11.9149 -12.4742 2 | 2 -19.7423 11.5733 -2.76677 3 | 3 13.1229 11.6731 -2.86856 4 | 4 41.3595 10.3061 -8.85595 5 | 5 -1.68248 -26.9035 20.066 6 | 6 -25.37 -51.4861 8.93498 7 | 7 22.3779 -49.8938 6.38705 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1013_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -14.9391 22.9825 49.3598 2 | 2 14.0902 24.8588 56.1045 3 | 3 45.4393 25.1067 54.5374 4 | 4 74.8842 26.0608 48.7116 5 | 5 30.7976 -16.0496 65.3175 6 | 6 9.64456 -36.5391 52.6773 7 | 7 58.6942 -34.0758 48.7695 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1014_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -36.3573 51.3178 -15.0199 2 | 2 -8.67789 52.9144 -10.0732 3 | 3 22.6925 51.7151 -8.4064 4 | 4 51.508 53.3176 -13.5026 5 | 5 10.4872 5.06971 9.76151 6 | 6 -13.439 -15.3973 -9.59476 7 | 7 34.0589 -13.5492 -10.4553 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1015_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -38.1113 28.8219 47.4391 2 | 2 -12.6089 28.5176 51.9708 3 | 3 24.9921 26.9226 52.0134 4 | 4 48.9073 30.0252 48.416 5 | 5 5.13149 -12.9795 78.5386 6 | 6 -23.2576 -39.2162 61.2883 7 | 7 34.0317 -42.1997 61.5662 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1017_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -46.3338 40.8653 11.9623 2 | 2 -16.6116 38.5465 17.0123 3 | 3 20.8149 41.537 15.0845 4 | 4 50.2952 44.9382 9.49445 5 | 5 4.46434 -4.87907 31.1505 6 | 6 -20.6196 -25.5418 11.2943 7 | 7 30.4846 -22.0591 10.7201 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1019_N.lnd: -------------------------------------------------------------------------------- 1 | 1 -33.1645 51.6169 -8.12606 2 | 2 -7.1626 51.1927 -0.804159 3 | 3 29.5435 51.6932 -2.02846 4 | 4 54.5582 50.22 -8.37393 5 | 5 8.87662 9.06017 28.4865 6 | 6 -17.0107 -11.4483 13.3675 7 | 7 37.4263 -13.7151 13.3968 8 | -------------------------------------------------------------------------------- /annotations/F_3D_N/f1024_N.lnd: 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-------------------------------------------------------------------------------- 1 | 1 -42.0166 -4.12337 -90.5876 2 | 2 -12.2827 -4.57656 -83.4815 3 | 3 20.9643 -4.69667 -84.2468 4 | 4 51.3261 -6.41609 -91.4797 5 | 5 6.00214 -45.6309 -57.3587 6 | 6 -17.241 -71.0346 -71.1031 7 | 7 32.9918 -67.7279 -72.6408 8 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Patrik Huber 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /run_example.py: -------------------------------------------------------------------------------- 1 | import pymesh 2 | import compute_vertices_to_mesh_distances as fg 3 | 4 | # Load a ground truth scan and its landmark annotations: 5 | groundtruth_scan = pymesh.load_mesh('F1008_A.obj') # from the challenge dataset 6 | grundtruth_landmark_points = fg.read_groundtruth('annotations/F1008_A.lnd') 7 | 8 | # Load your predicted mesh - the script needs a list of vertices and faces (triangles): 9 | predicted_mesh = pymesh.load_mesh('your_mesh.obj') 10 | # Load your annotations: The 3D coordinates of the 7 defined landmarks (see the README.md) 11 | predicted_mesh_landmark_points = [ # These are example values! 12 | [-46.166801, 34.721901, -35.938000], # right eye outer corner 13 | [-18.926001, 31.543200, -29.964100], # right eye inner corner 14 | [19.257401, 31.576700, -30.229000], # left eye inner corner 15 | [46.191399, 34.452000, -36.131699], # left eye outer corner 16 | [-0.134549, -12.905100, -10.940000], # bottom of the nose 17 | [-22.698500, -35.266102, -27.407700], # right mouth corner 18 | [22.639299, -35.315800, -27.732201] # left mouth corner 19 | ] 20 | 21 | # Compute the errors and save to a file: 22 | out_file = "F1008_A_distances.txt" 23 | fg.compute_vertices_to_mesh_distances(groundtruth_scan.vertices, grundtruth_landmark_points, predicted_mesh.vertices, 24 | predicted_mesh.faces, predicted_mesh_landmark_points, out_file) 25 | print("Computed and saved distances.") 26 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # IEEE FG 2018 Workshop on Dense 3D Reconstruction of 2D Face Images in the Wild - Special Session: Competition 2 | 3 | This repository contains the evaluation scripts for the FG2018 3D face reconstruction challenge. 4 | 5 | Website: https://facer2vm.org/fg2018.html 6 | 7 | # Installation 8 | 9 | The script requires numpy and pymesh. We recommend to do the installation in a virtualenv. The installation is tested with python3.6, but may work with other versions. 10 | 11 | 1) Create virtualenv: `mkvirtualenv fg2018`. Make sure you're inside the virtualenv - your command prompt should show something like `(fg2018) [user@computer ~]$`. 12 | 2) Install numpy: `pip install numpy` 13 | 3) Install PyMesh. Unfortunately PyMesh is not on pip (there is a "pymesh" package on pip, but it is a different pymesh!). 14 | We recommend to follow the PyMesh installation manual: http://pymesh.readthedocs.io/en/latest/installation.html and after cloning and setting `PYMESH_PATH`, do: 15 | `./setup.py build` and `./setup.py install`. Do this from **inside** the virtualenv. It should compile, create a python package and install it into the virtualenv. 16 | The script should now be able to import PyMesh via `import pymesh`. 17 | 18 | # Evaluation 19 | 20 | The task of the challenge is to reconstruct a face in 3D from a single 2D image input. The script provided here compares a reconstructed 3D mesh with a high-resolution 3D ground truth scan. 21 | For more information regarding the data and submission deadlines, please see the workshop website https://facer2vm.org/fg2018.html. 22 | 23 | **Challenge rules**: One set of parameters has to be used for all images of all subjects. No fine-tuning is allowed on a per-image or per-subject basis. 24 | 25 | **Evaluation area**: 26 | The reconstructed 3D face shape will be compared with the ground truth 3D face shape, over an area consisting of the inner face. The area is depicted below for an example scan. 27 | 28 | Evaluation area 29 | 30 | **Evaluation metric**: 31 | The script performs the following steps to compute the 3DRMSE between a predicted 3D face shape and the 3D ground truth scan: 32 | 1) The predicted and ground truth meshes will be rigidly aligned (by translation, rotation, and scaling). Scaling is compensated for because participants resulting meshes might be in a different coordinate system, whereas the ground truth scans are in the unit of millimetres. The rigid alignment is based on 7 points: Both inner and outer eye corners, the nose-bottom, and the mouth corners. The ground truth is annotated with these 7 points, whereas participants are expected to specify the 3D coordinates for these 7 points on their resulting meshes. 33 | 34 | Annotations 35 | 36 | 2) For each vertex in the ground truth scan, the distance is computed to the closest point on the surface of the predicted mesh. 37 | 3) These distances, along with the corresponding vertex index of the ground truth scan, are saved to a text file and used to compute the 3DRMSE as well as more specific analysis by the organisers, e.g. the distribution of errors across different face regions. 38 | 39 | ## Submission format 40 | The participants submit the resulting text files containing the computed mesh distances, along with a declaration that they have abided to the rules and protocol set out in this document (e.g. not having tuned the parameters for individual images). The files should be named having the same name as the ground truth scan, adding the text `_distances`, and with extension `.txt`, e.g. `F1008_A_distances.txt`. 41 | 42 | # First run example 43 | 44 | The file `run_example.py` provides an example of how to use the provided script. It loads a ground truth scan with its annotation, and shows how a predicted mesh can be loaded in the same way (e.g. from an `.obj` file). 45 | In principle, participants results can be in any form, as long as they can be converted to a mesh, i.e. a list of vertices and a list of faces (triangles). 46 | 47 | The script `compute_vertices_to_mesh_distances(groundtruth_vertices, grundtruth_landmark_points, predicted_mesh_vertices, predicted_mesh_faces, predicted_mesh_landmark_points, out_filename)` then aligns the meshes based on the given annotations and then computes the distance values. 48 | 49 | After running the script, please verify the output `.txt` file contains a list of vertex indices alongside floating point distance values, like e.g. so: 50 | ``` 51 | 1843 1.543212 52 | 1846 1.284938 53 | 2385 2.168093 54 | [...] 55 | ``` 56 | 57 | # Issues & help 58 | 59 | For any issues or help, please open a GitHub issue here. 60 | -------------------------------------------------------------------------------- /compute_vertices_to_mesh_distances.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pymesh 3 | from math import sqrt 4 | import csv 5 | 6 | 7 | def compute_vertices_to_mesh_distances(groundtruth_vertices, grundtruth_landmark_points, predicted_mesh_vertices, 8 | predicted_mesh_faces, 9 | predicted_mesh_landmark_points, out_filename): 10 | """ 11 | This script computes the reconstruction error between an input mesh and a ground truth mesh. 12 | :param groundtruth_vertices: An n x 3 numpy array of vertices from a ground truth scan. 13 | :param grundtruth_landmark_points: A 7 x 3 list with annotations of the ground truth scan. 14 | :param predicted_mesh_vertices: An m x 3 numpy array of vertices from a predicted mesh. 15 | :param predicted_mesh_faces: A k x 3 numpy array of vertex indices composing the predicted mesh. 16 | :param predicted_mesh_landmark_points: A 7 x 3 list containing the annotated 3D point locations in the predicted mesh. 17 | :param out_filename: Filename to write the resulting distances to (e.g. F1008_A_distances.txt). 18 | :return: A list of distances (errors), one for each vertex in the groundtruth mesh, and the associated vertex index in the ground truth scan. 19 | 20 | The grundtruth_landmark_points and predicted_mesh_landmark_points have to contain points in the following order: 21 | (1) right eye outer corner, (2) right eye inner corner, (3) left eye inner corner, (4) left eye outer corner, 22 | (5) nose bottom, (6) right mouth corner, (7) left mouth corner. 23 | """ 24 | 25 | # Do procrustes based on the 7 points: 26 | # The ground truth scan is in mm, so by aligning the prediction to the ground truth, we get meaningful units. 27 | d, Z, tform = procrustes(np.array(grundtruth_landmark_points), np.array(predicted_mesh_landmark_points), 28 | scaling=True, reflection='best') 29 | # Use tform to transform all vertices in predicted_mesh_vertices to the ground truth reference space: 30 | predicted_mesh_vertices_aligned = [] 31 | for v in predicted_mesh_vertices: 32 | s = tform['scale'] 33 | R = tform['rotation'] 34 | t = tform['translation'] 35 | transformed_vertex = s * np.dot(v, R) + t 36 | predicted_mesh_vertices_aligned.append(transformed_vertex) 37 | 38 | # Compute the mask: A circular area around the center of the face. Take the nose-bottom and go upwards a bit: 39 | nose_bottom = np.array(grundtruth_landmark_points[4]) 40 | nose_bridge = (np.array(grundtruth_landmark_points[1]) + np.array( 41 | grundtruth_landmark_points[2])) / 2 # between the inner eye corners 42 | face_centre = nose_bottom + 0.3 * (nose_bridge - nose_bottom) 43 | # Compute the radius for the face mask: 44 | outer_eye_dist = np.linalg.norm(np.array(grundtruth_landmark_points[0]) - np.array(grundtruth_landmark_points[3])) 45 | nose_dist = np.linalg.norm(nose_bridge - nose_bottom) 46 | mask_radius = 1.2 * (outer_eye_dist + nose_dist) / 2 47 | 48 | # Find all the vertex indices in the ground truth scan that lie within the mask area: 49 | vertex_indices_mask = [] # vertex indices in the source mesh (the ground truth scan) 50 | points_on_groundtruth_scan_to_measure_from = [] 51 | for vertex_idx, vertex in enumerate(groundtruth_vertices): 52 | dist = np.linalg.norm(vertex - face_centre) # We use Euclidean distance for the mask area for now. 53 | if dist <= mask_radius: 54 | vertex_indices_mask.append(vertex_idx) 55 | points_on_groundtruth_scan_to_measure_from.append(vertex) 56 | assert len(vertex_indices_mask) == len(points_on_groundtruth_scan_to_measure_from) 57 | 58 | # For each vertex on the ground truth mesh, find the closest point on the surface of the predicted mesh: 59 | predicted_mesh_pymesh = pymesh.meshio.form_mesh(np.array(predicted_mesh_vertices_aligned), predicted_mesh_faces) 60 | squared_distances, face_indices, closest_points = pymesh.distance_to_mesh(predicted_mesh_pymesh, 61 | points_on_groundtruth_scan_to_measure_from) 62 | distances = [sqrt(d2) for d2 in squared_distances] 63 | 64 | # Save the distances to a file, alongside with each vertex id of the ground truth scan that the distance has been computed for: 65 | with open(out_filename, 'w') as csv_file: 66 | wr = csv.writer(csv_file, delimiter=' ') 67 | vertex_indices_with_distances = [[v_idx, v] for v_idx, v in zip(vertex_indices_mask, distances)] 68 | wr.writerows(vertex_indices_with_distances) 69 | 70 | 71 | def read_groundtruth(filename): 72 | """ 73 | Reads a file with 7 annotations for a ground truth scan. 74 | :param filename: Filename of the text file to load. 75 | :return: A list of 7 annotations in the order (1) right eye outer corner, (2) right eye inner corner, (3) left eye inner corner, (4) left eye outer corner, 76 | (5) nose bottom, (6) right mouth corner, (7) left mouth corner. 77 | """ 78 | groundtruth_points = [] 79 | with open(filename, newline='') as csvfile: 80 | reader = csv.reader(csvfile, delimiter=' ') 81 | for row in reader: 82 | if row: # there might be an empty line at the end of the file 83 | groundtruth_points.append([float(row[1]), float(row[2]), float(row[3])]) 84 | if len(groundtruth_points) != 7: 85 | raise Exception("Error: Expected 7 landmarks in the given ground truth file " + filename + ".") 86 | return groundtruth_points 87 | 88 | 89 | def procrustes(X, Y, scaling=True, reflection='best'): 90 | """ 91 | A port of MATLAB's `procrustes` function to Numpy. 92 | Code from: https://stackoverflow.com/a/18927641. 93 | 94 | Procrustes analysis determines a linear transformation (translation, 95 | reflection, orthogonal rotation and scaling) of the points in Y to best 96 | conform them to the points in matrix X, using the sum of squared errors 97 | as the goodness of fit criterion. 98 | 99 | d, Z, [tform] = procrustes(X, Y) 100 | 101 | Inputs: 102 | ------------ 103 | X, Y 104 | matrices of target and input coordinates. they must have equal 105 | numbers of points (rows), but Y may have fewer dimensions 106 | (columns) than X. 107 | 108 | scaling 109 | if False, the scaling component of the transformation is forced 110 | to 1 111 | 112 | reflection 113 | if 'best' (default), the transformation solution may or may not 114 | include a reflection component, depending on which fits the data 115 | best. setting reflection to True or False forces a solution with 116 | reflection or no reflection respectively. 117 | 118 | Outputs 119 | ------------ 120 | d 121 | the residual sum of squared errors, normalized according to a 122 | measure of the scale of X, ((X - X.mean(0))**2).sum() 123 | 124 | Z 125 | the matrix of transformed Y-values 126 | 127 | tform 128 | a dict specifying the rotation, translation and scaling that 129 | maps X --> Y 130 | 131 | """ 132 | 133 | n, m = X.shape 134 | ny, my = Y.shape 135 | 136 | muX = X.mean(0) 137 | muY = Y.mean(0) 138 | 139 | X0 = X - muX 140 | Y0 = Y - muY 141 | 142 | ssX = (X0 ** 2.).sum() 143 | ssY = (Y0 ** 2.).sum() 144 | 145 | # centred Frobenius norm 146 | normX = np.sqrt(ssX) 147 | normY = np.sqrt(ssY) 148 | 149 | # scale to equal (unit) norm 150 | X0 /= normX 151 | Y0 /= normY 152 | 153 | if my < m: 154 | Y0 = np.concatenate((Y0, np.zeros(n, m - my)), 0) 155 | 156 | # optimum rotation matrix of Y 157 | A = np.dot(X0.T, Y0) 158 | U, s, Vt = np.linalg.svd(A, full_matrices=False) 159 | V = Vt.T 160 | T = np.dot(V, U.T) 161 | 162 | if reflection is not 'best': 163 | 164 | # does the current solution use a reflection? 165 | have_reflection = np.linalg.det(T) < 0 166 | 167 | # if that's not what was specified, force another reflection 168 | if reflection != have_reflection: 169 | V[:, -1] *= -1 170 | s[-1] *= -1 171 | T = np.dot(V, U.T) 172 | 173 | traceTA = s.sum() 174 | 175 | if scaling: 176 | 177 | # optimum scaling of Y 178 | b = traceTA * normX / normY 179 | 180 | # standarised distance between X and b*Y*T + c 181 | d = 1 - traceTA ** 2 182 | 183 | # transformed coords 184 | Z = normX * traceTA * np.dot(Y0, T) + muX 185 | 186 | else: 187 | b = 1 188 | d = 1 + ssY / ssX - 2 * traceTA * normY / normX 189 | Z = normY * np.dot(Y0, T) + muX 190 | 191 | # transformation matrix 192 | if my < m: 193 | T = T[:my, :] 194 | c = muX - b * np.dot(muY, T) 195 | 196 | # transformation values 197 | tform = {'rotation': T, 'scale': b, 'translation': c} 198 | 199 | return d, Z, tform 200 | --------------------------------------------------------------------------------