LCM-MVSNet | | | 95.70 1 | 96.40 1 | 93.61 2 | 98.67 1 | 85.39 29 | 95.54 3 | 97.36 1 | 96.97 1 | 99.04 1 | 99.05 1 | 96.61 1 | 95.92 8 | 85.07 36 | 99.27 3 | 99.54 1 |
|
TDRefinement | | | 93.52 2 | 93.39 3 | 93.88 1 | 95.94 13 | 90.26 4 | 95.70 2 | 96.46 2 | 90.58 8 | 92.86 41 | 96.29 18 | 88.16 26 | 94.17 65 | 86.07 32 | 98.48 19 | 97.22 26 |
|
LTVRE_ROB | | 86.10 1 | 93.04 3 | 93.44 2 | 91.82 19 | 93.73 47 | 85.72 28 | 96.79 1 | 95.51 4 | 88.86 13 | 95.63 10 | 96.99 8 | 84.81 52 | 93.16 119 | 91.10 1 | 97.53 56 | 96.58 39 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
abl_6 | | | 93.02 4 | 93.16 4 | 92.60 4 | 94.73 38 | 88.99 7 | 93.26 10 | 94.19 19 | 89.11 11 | 94.43 19 | 95.27 41 | 91.86 4 | 95.09 40 | 87.54 18 | 98.02 39 | 93.71 110 |
|
HPM-MVS_fast | | | 92.50 5 | 92.54 5 | 92.37 5 | 95.93 14 | 85.81 27 | 92.99 11 | 94.23 16 | 85.21 24 | 92.51 48 | 95.13 45 | 90.65 11 | 95.34 30 | 88.06 10 | 98.15 34 | 95.95 51 |
|
HPM-MVS | | | 92.13 6 | 92.20 7 | 91.91 15 | 95.58 23 | 84.67 38 | 93.51 6 | 94.85 9 | 82.88 41 | 91.77 61 | 93.94 89 | 90.55 13 | 95.73 16 | 88.50 8 | 98.23 31 | 95.33 69 |
|
APD-MVS_3200maxsize | | | 92.05 7 | 92.24 6 | 91.48 20 | 93.02 62 | 85.17 31 | 92.47 21 | 95.05 8 | 87.65 19 | 93.21 36 | 94.39 71 | 90.09 14 | 95.08 41 | 86.67 24 | 97.60 54 | 94.18 94 |
|
COLMAP_ROB | | 83.01 3 | 91.97 8 | 91.95 8 | 92.04 10 | 93.68 48 | 86.15 18 | 93.37 8 | 95.10 7 | 90.28 9 | 92.11 53 | 95.03 47 | 89.75 15 | 94.93 45 | 79.95 101 | 98.27 29 | 95.04 76 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
ACMMP | | | 91.91 9 | 91.87 14 | 92.03 11 | 95.53 24 | 85.91 22 | 93.35 9 | 94.16 20 | 82.52 45 | 92.39 52 | 94.14 79 | 89.15 17 | 95.62 18 | 87.35 19 | 98.24 30 | 94.56 81 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
mPP-MVS | | | 91.69 10 | 91.47 20 | 92.37 5 | 96.04 11 | 88.48 8 | 92.72 14 | 92.60 68 | 83.09 38 | 91.54 64 | 94.25 75 | 87.67 33 | 95.51 26 | 87.21 23 | 98.11 35 | 93.12 125 |
|
CP-MVS | | | 91.67 11 | 91.58 17 | 91.96 12 | 95.29 27 | 87.62 9 | 93.38 7 | 93.36 35 | 83.16 37 | 91.06 70 | 94.00 82 | 88.26 23 | 95.71 17 | 87.28 22 | 98.39 23 | 92.55 136 |
|
XVS | | | 91.54 12 | 91.36 22 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 37 | 85.07 25 | 89.99 84 | 94.03 81 | 86.57 41 | 95.80 13 | 87.35 19 | 97.62 52 | 94.20 92 |
|
MTAPA | | | 91.52 13 | 91.60 16 | 91.29 25 | 96.59 3 | 86.29 14 | 92.02 24 | 91.81 87 | 84.07 30 | 92.00 56 | 94.40 69 | 86.63 39 | 95.28 34 | 88.59 4 | 98.31 26 | 92.30 143 |
|
UA-Net | | | 91.49 14 | 91.53 18 | 91.39 22 | 94.98 31 | 82.95 50 | 93.52 5 | 92.79 61 | 88.22 16 | 88.53 121 | 97.64 3 | 83.45 63 | 94.55 56 | 86.02 33 | 98.60 14 | 96.67 36 |
|
ACMMPR | | | 91.49 14 | 91.35 24 | 91.92 14 | 95.74 18 | 85.88 24 | 92.58 18 | 93.25 43 | 81.99 51 | 91.40 67 | 94.17 78 | 87.51 34 | 95.87 10 | 87.74 11 | 97.76 47 | 93.99 99 |
|
LPG-MVS_test | | | 91.47 16 | 91.68 15 | 90.82 34 | 94.75 35 | 81.69 51 | 90.00 39 | 94.27 13 | 82.35 46 | 93.67 30 | 94.82 54 | 91.18 6 | 95.52 24 | 85.36 34 | 98.73 8 | 95.23 72 |
|
region2R | | | 91.44 17 | 91.30 26 | 91.87 16 | 95.75 17 | 85.90 23 | 92.63 17 | 93.30 40 | 81.91 53 | 90.88 75 | 94.21 76 | 87.75 30 | 95.87 10 | 87.60 16 | 97.71 50 | 93.83 103 |
|
HFP-MVS | | | 91.30 18 | 91.39 21 | 91.02 29 | 95.43 25 | 84.66 39 | 92.58 18 | 93.29 41 | 81.99 51 | 91.47 65 | 93.96 85 | 88.35 21 | 95.56 21 | 87.74 11 | 97.74 48 | 92.85 128 |
|
MPTG | | | 91.27 19 | 91.26 27 | 91.29 25 | 96.59 3 | 86.29 14 | 88.94 62 | 91.81 87 | 84.07 30 | 92.00 56 | 94.40 69 | 86.63 39 | 95.28 34 | 88.59 4 | 98.31 26 | 92.30 143 |
|
APDe-MVS | | | 91.22 20 | 91.92 9 | 89.14 56 | 92.97 64 | 78.04 77 | 92.84 12 | 94.14 21 | 83.33 35 | 93.90 26 | 95.73 29 | 88.77 18 | 96.41 1 | 87.60 16 | 97.98 42 | 92.98 127 |
|
PGM-MVS | | | 91.20 21 | 90.95 33 | 91.93 13 | 95.67 20 | 85.85 25 | 90.00 39 | 93.90 26 | 80.32 67 | 91.74 62 | 94.41 68 | 88.17 25 | 95.98 5 | 86.37 25 | 97.99 41 | 93.96 101 |
|
SteuartSystems-ACMMP | | | 91.16 22 | 91.36 22 | 90.55 37 | 93.91 46 | 80.97 58 | 91.49 29 | 93.48 34 | 82.82 42 | 92.60 47 | 93.97 83 | 88.19 24 | 96.29 3 | 87.61 15 | 98.20 33 | 94.39 90 |
Skip Steuart: Steuart Systems R&D Blog. |
MP-MVS | | | 91.14 23 | 90.91 34 | 91.83 18 | 96.18 10 | 86.88 11 | 92.20 22 | 93.03 52 | 82.59 44 | 88.52 122 | 94.37 72 | 86.74 38 | 95.41 28 | 86.32 26 | 98.21 32 | 93.19 124 |
|
MP-MVS-pluss | | | 90.81 24 | 91.08 29 | 89.99 46 | 95.97 12 | 79.88 63 | 88.13 73 | 94.51 11 | 75.79 123 | 92.94 38 | 94.96 49 | 88.36 20 | 95.01 43 | 90.70 2 | 98.40 21 | 95.09 75 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
ACMH+ | | 77.89 11 | 90.73 25 | 91.50 19 | 88.44 62 | 93.00 63 | 76.26 97 | 89.65 49 | 95.55 3 | 87.72 18 | 93.89 27 | 94.94 50 | 91.62 5 | 93.44 106 | 78.35 115 | 98.76 5 | 95.61 64 |
|
ACMMP_Plus | | | 90.65 26 | 91.07 30 | 89.42 52 | 95.93 14 | 79.54 68 | 89.95 42 | 93.68 29 | 77.65 101 | 91.97 58 | 94.89 51 | 88.38 19 | 95.45 27 | 89.27 3 | 97.87 45 | 93.27 120 |
|
ACMM | | 79.39 9 | 90.65 26 | 90.99 31 | 89.63 49 | 95.03 30 | 83.53 44 | 89.62 50 | 93.35 36 | 79.20 80 | 93.83 28 | 93.60 94 | 90.81 9 | 92.96 127 | 85.02 38 | 98.45 20 | 92.41 139 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
LS3D | | | 90.60 28 | 90.34 40 | 91.38 23 | 89.03 147 | 84.23 42 | 93.58 4 | 94.68 10 | 90.65 7 | 90.33 79 | 93.95 88 | 84.50 55 | 95.37 29 | 80.87 86 | 95.50 117 | 94.53 85 |
|
ACMP | | 79.16 10 | 90.54 29 | 90.60 37 | 90.35 41 | 94.36 40 | 80.98 57 | 89.16 59 | 94.05 23 | 79.03 84 | 92.87 40 | 93.74 92 | 90.60 12 | 95.21 38 | 82.87 65 | 98.76 5 | 94.87 77 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
#test# | | | 90.49 30 | 90.31 41 | 91.02 29 | 95.43 25 | 84.66 39 | 90.65 34 | 93.29 41 | 77.00 110 | 91.47 65 | 93.96 85 | 88.35 21 | 95.56 21 | 84.88 39 | 97.74 48 | 92.85 128 |
|
Anonymous20231211 | | | 90.14 31 | 91.88 12 | 84.92 116 | 94.75 35 | 64.47 176 | 90.13 38 | 92.97 54 | 91.68 3 | 95.35 12 | 98.79 2 | 93.19 3 | 91.76 158 | 71.67 169 | 98.40 21 | 98.52 7 |
|
v7n | | | 90.13 32 | 90.96 32 | 87.65 75 | 91.95 91 | 71.06 136 | 89.99 41 | 93.05 49 | 86.53 21 | 94.29 22 | 96.27 19 | 82.69 70 | 94.08 68 | 86.25 29 | 97.63 51 | 97.82 11 |
|
PMVS | | 80.48 6 | 90.08 33 | 90.66 36 | 88.34 64 | 96.71 2 | 92.97 2 | 90.31 36 | 89.57 158 | 88.51 15 | 90.11 80 | 95.12 46 | 90.98 8 | 88.92 213 | 77.55 122 | 97.07 65 | 83.13 264 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PS-CasMVS | | | 90.06 34 | 91.92 9 | 84.47 130 | 96.56 6 | 58.83 248 | 89.04 60 | 92.74 63 | 91.40 5 | 96.12 4 | 96.06 24 | 87.23 35 | 95.57 20 | 79.42 109 | 98.74 7 | 99.00 2 |
|
PEN-MVS | | | 90.03 35 | 91.88 12 | 84.48 129 | 96.57 5 | 58.88 247 | 88.95 61 | 93.19 44 | 91.62 4 | 96.01 6 | 96.16 22 | 87.02 36 | 95.60 19 | 78.69 113 | 98.72 10 | 98.97 3 |
|
OurMVSNet-221017-0 | | | 90.01 36 | 89.74 46 | 90.83 33 | 93.16 59 | 80.37 59 | 91.91 27 | 93.11 46 | 81.10 60 | 95.32 13 | 97.24 6 | 72.94 185 | 94.85 47 | 85.07 36 | 97.78 46 | 97.26 23 |
|
DTE-MVSNet | | | 89.98 37 | 91.91 11 | 84.21 138 | 96.51 7 | 57.84 251 | 88.93 63 | 92.84 60 | 91.92 2 | 96.16 3 | 96.23 20 | 86.95 37 | 95.99 4 | 79.05 110 | 98.57 16 | 98.80 6 |
|
XVG-ACMP-BASELINE | | | 89.98 37 | 89.84 44 | 90.41 39 | 94.91 33 | 84.50 41 | 89.49 55 | 93.98 24 | 79.68 73 | 92.09 54 | 93.89 90 | 83.80 59 | 93.10 122 | 82.67 69 | 98.04 36 | 93.64 112 |
|
v52 | | | 89.97 39 | 90.60 37 | 88.07 68 | 88.69 152 | 72.01 124 | 91.35 30 | 92.64 66 | 82.22 48 | 95.97 8 | 96.31 16 | 84.82 51 | 93.98 72 | 88.59 4 | 94.83 139 | 98.23 8 |
|
V4 | | | 89.97 39 | 90.60 37 | 88.07 68 | 88.69 152 | 72.01 124 | 91.35 30 | 92.64 66 | 82.22 48 | 95.98 7 | 96.31 16 | 84.80 53 | 93.98 72 | 88.59 4 | 94.83 139 | 98.23 8 |
|
3Dnovator+ | | 83.92 2 | 89.97 39 | 89.66 47 | 90.92 32 | 91.27 110 | 81.66 54 | 91.25 32 | 94.13 22 | 88.89 12 | 88.83 116 | 94.26 74 | 77.55 122 | 95.86 12 | 84.88 39 | 95.87 108 | 95.24 71 |
|
WR-MVS_H | | | 89.91 42 | 91.31 25 | 85.71 107 | 96.32 9 | 62.39 210 | 89.54 53 | 93.31 39 | 90.21 10 | 95.57 11 | 95.66 31 | 81.42 91 | 95.90 9 | 80.94 85 | 98.80 4 | 98.84 5 |
|
OPM-MVS | | | 89.80 43 | 89.97 42 | 89.27 54 | 94.76 34 | 79.86 64 | 86.76 97 | 92.78 62 | 78.78 87 | 92.51 48 | 93.64 93 | 88.13 27 | 93.84 79 | 84.83 41 | 97.55 55 | 94.10 97 |
|
mvs_tets | | | 89.78 44 | 89.27 51 | 91.30 24 | 93.51 50 | 84.79 36 | 89.89 44 | 90.63 124 | 70.00 184 | 94.55 18 | 96.67 11 | 87.94 29 | 93.59 90 | 84.27 48 | 95.97 103 | 95.52 65 |
|
anonymousdsp | | | 89.73 45 | 88.88 56 | 92.27 7 | 89.82 138 | 86.67 12 | 90.51 35 | 90.20 145 | 69.87 185 | 95.06 14 | 96.14 23 | 84.28 56 | 93.07 126 | 87.68 13 | 96.34 87 | 97.09 30 |
|
test_djsdf | | | 89.62 46 | 89.01 53 | 91.45 21 | 92.36 79 | 82.98 49 | 91.98 25 | 90.08 146 | 71.54 172 | 94.28 23 | 96.54 13 | 81.57 89 | 94.27 58 | 86.26 27 | 96.49 83 | 97.09 30 |
|
XVG-OURS-SEG-HR | | | 89.59 47 | 89.37 50 | 90.28 42 | 94.47 39 | 85.95 21 | 86.84 93 | 93.91 25 | 80.07 70 | 86.75 147 | 93.26 96 | 93.64 2 | 90.93 177 | 84.60 44 | 90.75 211 | 93.97 100 |
|
APD-MVS | | | 89.54 48 | 89.63 48 | 89.26 55 | 92.57 72 | 81.34 56 | 90.19 37 | 93.08 48 | 80.87 62 | 91.13 69 | 93.19 97 | 86.22 46 | 95.97 6 | 82.23 73 | 97.18 63 | 90.45 184 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
jajsoiax | | | 89.41 49 | 88.81 58 | 91.19 28 | 93.38 54 | 84.72 37 | 89.70 46 | 90.29 140 | 69.27 188 | 94.39 20 | 96.38 15 | 86.02 48 | 93.52 100 | 83.96 51 | 95.92 106 | 95.34 68 |
|
CPTT-MVS | | | 89.39 50 | 88.98 55 | 90.63 36 | 95.09 29 | 86.95 10 | 92.09 23 | 92.30 74 | 79.74 72 | 87.50 136 | 92.38 117 | 81.42 91 | 93.28 114 | 83.07 62 | 97.24 61 | 91.67 158 |
|
ACMH | | 76.49 14 | 89.34 51 | 91.14 28 | 83.96 144 | 92.50 75 | 70.36 140 | 89.55 51 | 93.84 27 | 81.89 54 | 94.70 16 | 95.44 39 | 90.69 10 | 88.31 220 | 83.33 58 | 98.30 28 | 93.20 123 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CP-MVSNet | | | 89.27 52 | 90.91 34 | 84.37 133 | 96.34 8 | 58.61 250 | 88.66 68 | 92.06 79 | 90.78 6 | 95.67 9 | 95.17 44 | 81.80 87 | 95.54 23 | 79.00 111 | 98.69 11 | 98.95 4 |
|
XVG-OURS | | | 89.18 53 | 88.83 57 | 90.23 43 | 94.28 41 | 86.11 20 | 85.91 109 | 93.60 32 | 80.16 69 | 89.13 112 | 93.44 95 | 83.82 58 | 90.98 175 | 83.86 54 | 95.30 124 | 93.60 114 |
|
DeepC-MVS | | 82.31 4 | 89.15 54 | 89.08 52 | 89.37 53 | 93.64 49 | 79.07 70 | 88.54 69 | 94.20 17 | 73.53 142 | 89.71 95 | 94.82 54 | 85.09 50 | 95.77 15 | 84.17 50 | 98.03 38 | 93.26 121 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SD-MVS | | | 88.96 55 | 89.88 43 | 86.22 94 | 91.63 97 | 77.07 87 | 89.82 45 | 93.77 28 | 78.90 85 | 92.88 39 | 92.29 121 | 86.11 47 | 90.22 197 | 86.24 30 | 97.24 61 | 91.36 165 |
|
HPM-MVS++ | | | 88.93 56 | 88.45 62 | 90.38 40 | 94.92 32 | 85.85 25 | 89.70 46 | 91.27 112 | 78.20 95 | 86.69 148 | 92.28 122 | 80.36 100 | 95.06 42 | 86.17 31 | 96.49 83 | 90.22 188 |
|
v748 | | | 88.91 57 | 89.82 45 | 86.19 98 | 90.06 134 | 68.53 152 | 88.81 65 | 91.48 97 | 84.36 28 | 94.19 24 | 95.98 25 | 82.52 73 | 92.67 136 | 84.30 47 | 96.67 76 | 97.37 20 |
|
test_0402 | | | 88.65 58 | 89.58 49 | 85.88 104 | 92.55 73 | 72.22 123 | 84.01 136 | 89.44 160 | 88.63 14 | 94.38 21 | 95.77 28 | 86.38 45 | 93.59 90 | 79.84 102 | 95.21 125 | 91.82 155 |
|
HSP-MVS | | | 88.63 59 | 87.84 67 | 91.02 29 | 95.76 16 | 86.14 19 | 92.75 13 | 91.01 119 | 78.43 92 | 89.16 111 | 92.25 123 | 72.03 201 | 96.36 2 | 88.21 9 | 90.93 206 | 90.55 182 |
|
DP-MVS | | | 88.60 60 | 89.01 53 | 87.36 79 | 91.30 108 | 77.50 82 | 87.55 81 | 92.97 54 | 87.95 17 | 89.62 101 | 92.87 106 | 84.56 54 | 93.89 76 | 77.65 121 | 96.62 77 | 90.70 175 |
|
wuykxyi23d | | | 88.46 61 | 88.80 59 | 87.44 78 | 90.96 118 | 93.03 1 | 85.85 111 | 81.96 234 | 74.58 132 | 98.58 2 | 97.29 5 | 87.73 31 | 87.31 227 | 82.84 67 | 99.41 1 | 81.99 277 |
|
PS-MVSNAJss | | | 88.31 62 | 87.90 66 | 89.56 51 | 93.31 55 | 77.96 78 | 87.94 75 | 91.97 82 | 70.73 178 | 94.19 24 | 96.67 11 | 76.94 130 | 94.57 54 | 83.07 62 | 96.28 89 | 96.15 42 |
|
OMC-MVS | | | 88.19 63 | 87.52 72 | 90.19 44 | 91.94 93 | 81.68 53 | 87.49 83 | 93.17 45 | 76.02 119 | 88.64 119 | 91.22 144 | 84.24 57 | 93.37 109 | 77.97 120 | 97.03 66 | 95.52 65 |
|
TSAR-MVS + MP. | | | 88.14 64 | 87.82 68 | 89.09 57 | 95.72 19 | 76.74 91 | 92.49 20 | 91.19 115 | 67.85 201 | 86.63 149 | 94.84 53 | 79.58 106 | 95.96 7 | 87.62 14 | 94.50 148 | 94.56 81 |
|
RPSCF | | | 88.00 65 | 86.93 84 | 91.22 27 | 90.08 132 | 89.30 6 | 89.68 48 | 91.11 116 | 79.26 79 | 89.68 96 | 94.81 57 | 82.44 74 | 87.74 224 | 76.54 132 | 88.74 232 | 96.61 38 |
|
AllTest | | | 87.97 66 | 87.40 76 | 89.68 47 | 91.59 98 | 83.40 45 | 89.50 54 | 95.44 5 | 79.47 75 | 88.00 129 | 93.03 100 | 82.66 71 | 91.47 163 | 70.81 171 | 96.14 96 | 94.16 95 |
|
TranMVSNet+NR-MVSNet | | | 87.86 67 | 88.76 60 | 85.18 113 | 94.02 43 | 64.13 178 | 84.38 131 | 91.29 111 | 84.88 27 | 92.06 55 | 93.84 91 | 86.45 43 | 93.73 80 | 73.22 155 | 98.66 12 | 97.69 12 |
|
nrg030 | | | 87.85 68 | 88.49 61 | 85.91 102 | 90.07 133 | 69.73 142 | 87.86 76 | 94.20 17 | 74.04 137 | 92.70 45 | 94.66 58 | 85.88 49 | 91.50 162 | 79.72 103 | 97.32 59 | 96.50 40 |
|
CNVR-MVS | | | 87.81 69 | 87.68 71 | 88.21 65 | 92.87 66 | 77.30 86 | 85.25 118 | 91.23 113 | 77.31 106 | 87.07 143 | 91.47 140 | 82.94 68 | 94.71 50 | 84.67 43 | 96.27 91 | 92.62 135 |
|
HQP_MVS | | | 87.75 70 | 87.43 75 | 88.70 60 | 93.45 51 | 76.42 95 | 89.45 56 | 93.61 30 | 79.44 77 | 86.55 150 | 92.95 104 | 74.84 150 | 95.22 36 | 80.78 88 | 95.83 110 | 94.46 86 |
|
NCCC | | | 87.36 71 | 86.87 85 | 88.83 58 | 92.32 82 | 78.84 73 | 86.58 104 | 91.09 117 | 78.77 88 | 84.85 174 | 90.89 162 | 80.85 95 | 95.29 32 | 81.14 82 | 95.32 121 | 92.34 142 |
|
v13 | | | 87.31 72 | 88.10 63 | 84.94 115 | 88.84 149 | 63.75 182 | 87.85 77 | 91.47 100 | 79.12 81 | 93.72 29 | 95.82 27 | 75.20 144 | 93.58 93 | 84.76 42 | 96.16 94 | 97.48 17 |
|
DeepPCF-MVS | | 81.24 5 | 87.28 73 | 86.21 96 | 90.49 38 | 91.48 105 | 84.90 34 | 83.41 159 | 92.38 73 | 70.25 183 | 89.35 109 | 90.68 169 | 82.85 69 | 94.57 54 | 79.55 105 | 95.95 104 | 92.00 150 |
|
SixPastTwentyTwo | | | 87.20 74 | 87.45 74 | 86.45 88 | 92.52 74 | 69.19 150 | 87.84 78 | 88.05 176 | 81.66 56 | 94.64 17 | 96.53 14 | 65.94 224 | 94.75 49 | 83.02 64 | 96.83 72 | 95.41 67 |
|
v12 | | | 87.15 75 | 87.91 65 | 84.84 118 | 88.69 152 | 63.52 185 | 87.58 80 | 91.46 101 | 78.74 89 | 93.57 32 | 95.66 31 | 74.94 148 | 93.57 94 | 84.50 45 | 96.08 99 | 97.43 18 |
|
v11 | | | 86.96 76 | 87.78 69 | 84.51 127 | 88.50 158 | 62.60 205 | 87.21 86 | 91.63 91 | 78.08 98 | 93.40 34 | 95.56 36 | 75.07 145 | 93.57 94 | 84.46 46 | 96.08 99 | 97.36 21 |
|
V9 | | | 86.96 76 | 87.70 70 | 84.74 122 | 88.52 157 | 63.27 191 | 87.31 85 | 91.45 103 | 78.28 94 | 93.43 33 | 95.45 38 | 74.59 156 | 93.57 94 | 84.23 49 | 96.01 102 | 97.38 19 |
|
UniMVSNet (Re) | | | 86.87 78 | 86.98 82 | 86.55 86 | 93.11 61 | 68.48 153 | 83.80 145 | 92.87 57 | 80.37 65 | 89.61 103 | 91.81 132 | 77.72 119 | 94.18 63 | 75.00 141 | 98.53 17 | 96.99 34 |
|
Vis-MVSNet | | | 86.86 79 | 86.58 89 | 87.72 73 | 92.09 87 | 77.43 83 | 87.35 84 | 92.09 78 | 78.87 86 | 84.27 186 | 94.05 80 | 78.35 114 | 93.65 83 | 80.54 92 | 91.58 198 | 92.08 149 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
UniMVSNet_NR-MVSNet | | | 86.84 80 | 87.06 79 | 86.17 99 | 92.86 68 | 67.02 161 | 82.55 182 | 91.56 93 | 83.08 39 | 90.92 72 | 91.82 131 | 78.25 115 | 93.99 70 | 74.16 144 | 98.35 24 | 97.49 16 |
|
DU-MVS | | | 86.80 81 | 86.99 81 | 86.21 96 | 93.24 57 | 67.02 161 | 83.16 166 | 92.21 75 | 81.73 55 | 90.92 72 | 91.97 125 | 77.20 124 | 93.99 70 | 74.16 144 | 98.35 24 | 97.61 13 |
|
V14 | | | 86.75 82 | 87.46 73 | 84.62 125 | 88.35 161 | 63.00 196 | 87.02 91 | 91.42 105 | 77.78 100 | 93.27 35 | 95.23 43 | 74.22 157 | 93.56 97 | 83.95 52 | 95.93 105 | 97.31 22 |
|
Regformer-2 | | | 86.74 83 | 86.08 98 | 88.73 59 | 84.18 238 | 79.20 69 | 83.52 153 | 89.33 161 | 83.33 35 | 89.92 90 | 85.07 247 | 83.23 66 | 93.16 119 | 83.39 57 | 92.72 184 | 93.83 103 |
|
IS-MVSNet | | | 86.66 84 | 86.82 88 | 86.17 99 | 92.05 89 | 66.87 163 | 91.21 33 | 88.64 168 | 86.30 23 | 89.60 104 | 92.59 112 | 69.22 211 | 94.91 46 | 73.89 148 | 97.89 44 | 96.72 35 |
|
v15 | | | 86.56 85 | 87.25 77 | 84.51 127 | 88.15 168 | 62.72 201 | 86.72 101 | 91.40 107 | 77.38 104 | 93.11 37 | 95.00 48 | 73.93 162 | 93.55 98 | 83.67 56 | 95.86 109 | 97.26 23 |
|
v10 | | | 86.54 86 | 87.10 78 | 84.84 118 | 88.16 167 | 63.28 190 | 86.64 103 | 92.20 76 | 75.42 128 | 92.81 42 | 94.50 64 | 74.05 160 | 94.06 69 | 83.88 53 | 96.28 89 | 97.17 28 |
|
pmmvs6 | | | 86.52 87 | 88.06 64 | 81.90 184 | 92.22 85 | 62.28 216 | 84.66 126 | 89.15 163 | 83.54 34 | 89.85 91 | 97.32 4 | 88.08 28 | 86.80 234 | 70.43 178 | 97.30 60 | 96.62 37 |
|
Regformer-4 | | | 86.41 88 | 85.71 103 | 88.52 61 | 84.27 234 | 77.57 81 | 84.07 134 | 88.00 178 | 82.82 42 | 89.84 92 | 85.48 237 | 82.06 79 | 92.77 133 | 83.83 55 | 91.04 203 | 95.22 74 |
|
PHI-MVS | | | 86.38 89 | 85.81 101 | 88.08 67 | 88.44 160 | 77.34 84 | 89.35 58 | 93.05 49 | 73.15 151 | 84.76 175 | 87.70 215 | 78.87 110 | 94.18 63 | 80.67 90 | 96.29 88 | 92.73 132 |
|
v17 | | | 86.32 90 | 86.95 83 | 84.44 131 | 88.00 170 | 62.62 204 | 86.74 99 | 91.48 97 | 77.17 107 | 92.74 43 | 94.56 60 | 73.74 166 | 93.53 99 | 83.27 59 | 94.87 138 | 97.18 27 |
|
test_prior3 | | | 86.31 91 | 86.31 93 | 86.32 90 | 90.59 125 | 71.99 126 | 83.37 160 | 92.85 58 | 75.43 126 | 84.58 177 | 91.57 136 | 81.92 85 | 94.17 65 | 79.54 106 | 96.97 67 | 92.80 130 |
|
CSCG | | | 86.26 92 | 86.47 91 | 85.60 109 | 90.87 120 | 74.26 106 | 87.98 74 | 91.85 85 | 80.35 66 | 89.54 107 | 88.01 209 | 79.09 108 | 92.13 146 | 75.51 136 | 95.06 130 | 90.41 185 |
|
v16 | | | 86.24 93 | 86.85 86 | 84.43 132 | 87.96 172 | 62.59 206 | 86.73 100 | 91.48 97 | 77.17 107 | 92.67 46 | 94.55 61 | 73.63 167 | 93.52 100 | 83.26 60 | 94.16 152 | 97.17 28 |
|
DeepC-MVS_fast | | 80.27 8 | 86.23 94 | 85.65 105 | 87.96 72 | 91.30 108 | 76.92 88 | 87.19 87 | 91.99 81 | 70.56 179 | 84.96 170 | 90.69 168 | 80.01 103 | 95.14 39 | 78.37 114 | 95.78 112 | 91.82 155 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v8 | | | 86.22 95 | 86.83 87 | 84.36 134 | 87.82 174 | 62.35 211 | 86.42 106 | 91.33 110 | 76.78 112 | 92.73 44 | 94.48 65 | 73.41 174 | 93.72 81 | 83.10 61 | 95.41 118 | 97.01 33 |
|
CDPH-MVS | | | 86.17 96 | 85.54 106 | 88.05 71 | 92.25 83 | 75.45 100 | 83.85 142 | 92.01 80 | 65.91 213 | 86.19 157 | 91.75 134 | 83.77 60 | 94.98 44 | 77.43 125 | 96.71 75 | 93.73 109 |
|
Regformer-1 | | | 86.00 97 | 85.50 107 | 87.49 76 | 84.18 238 | 76.90 89 | 83.52 153 | 87.94 180 | 82.18 50 | 89.19 110 | 85.07 247 | 82.28 77 | 91.89 152 | 82.40 71 | 92.72 184 | 93.69 111 |
|
NR-MVSNet | | | 86.00 97 | 86.22 95 | 85.34 111 | 93.24 57 | 64.56 175 | 82.21 193 | 90.46 128 | 80.99 61 | 88.42 124 | 91.97 125 | 77.56 121 | 93.85 77 | 72.46 163 | 98.65 13 | 97.61 13 |
|
v18 | | | 85.99 99 | 86.55 90 | 84.30 136 | 87.73 180 | 62.29 215 | 86.40 107 | 91.49 96 | 76.64 113 | 92.40 51 | 94.20 77 | 73.28 178 | 93.52 100 | 82.87 65 | 93.99 156 | 97.09 30 |
|
train_agg | | | 85.98 100 | 85.28 110 | 88.07 68 | 92.34 80 | 79.70 66 | 83.94 138 | 90.32 133 | 65.79 214 | 84.49 179 | 90.97 158 | 81.93 83 | 93.63 85 | 81.21 80 | 96.54 80 | 90.88 171 |
|
FC-MVSNet-test | | | 85.93 101 | 87.05 80 | 82.58 175 | 92.25 83 | 56.44 261 | 85.75 112 | 93.09 47 | 77.33 105 | 91.94 59 | 94.65 59 | 74.78 152 | 93.41 108 | 75.11 139 | 98.58 15 | 97.88 10 |
|
Effi-MVS+-dtu | | | 85.82 102 | 83.38 150 | 93.14 3 | 87.13 195 | 91.15 3 | 87.70 79 | 88.42 170 | 74.57 133 | 83.56 193 | 85.65 234 | 78.49 112 | 94.21 62 | 72.04 166 | 92.88 182 | 94.05 98 |
|
agg_prior3 | | | 85.76 103 | 84.95 116 | 88.16 66 | 92.43 77 | 79.92 62 | 83.98 137 | 90.03 148 | 65.11 224 | 83.66 191 | 90.64 173 | 81.00 94 | 93.67 82 | 81.21 80 | 96.54 80 | 90.88 171 |
|
agg_prior1 | | | 85.72 104 | 85.20 111 | 87.28 80 | 91.58 101 | 77.69 79 | 83.69 148 | 90.30 137 | 66.29 209 | 84.32 183 | 91.07 155 | 82.13 78 | 93.18 117 | 81.02 83 | 96.36 86 | 90.98 166 |
|
TAPA-MVS | | 77.73 12 | 85.71 105 | 84.83 118 | 88.37 63 | 88.78 151 | 79.72 65 | 87.15 89 | 93.50 33 | 69.17 189 | 85.80 162 | 89.56 191 | 80.76 96 | 92.13 146 | 73.21 159 | 95.51 116 | 93.25 122 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
canonicalmvs | | | 85.50 106 | 86.14 97 | 83.58 156 | 87.97 171 | 67.13 160 | 87.55 81 | 94.32 12 | 73.44 144 | 88.47 123 | 87.54 218 | 86.45 43 | 91.06 174 | 75.76 135 | 93.76 163 | 92.54 137 |
|
EPP-MVSNet | | | 85.47 107 | 85.04 113 | 86.77 83 | 91.52 104 | 69.37 145 | 91.63 28 | 87.98 179 | 81.51 58 | 87.05 144 | 91.83 130 | 66.18 223 | 95.29 32 | 70.75 173 | 96.89 69 | 95.64 59 |
|
FIs | | | 85.35 108 | 86.27 94 | 82.60 174 | 91.86 94 | 57.31 255 | 85.10 120 | 93.05 49 | 75.83 122 | 91.02 71 | 93.97 83 | 73.57 171 | 92.91 131 | 73.97 147 | 98.02 39 | 97.58 15 |
|
K. test v3 | | | 85.14 109 | 84.73 119 | 86.37 89 | 91.13 115 | 69.63 144 | 85.45 116 | 76.68 261 | 84.06 32 | 92.44 50 | 96.99 8 | 62.03 236 | 94.65 51 | 80.58 91 | 93.24 175 | 94.83 80 |
|
EI-MVSNet-Vis-set | | | 85.12 110 | 84.53 131 | 86.88 81 | 84.01 240 | 72.76 114 | 83.91 141 | 85.18 216 | 80.44 64 | 88.75 117 | 85.49 236 | 80.08 102 | 91.92 150 | 82.02 74 | 90.85 209 | 95.97 49 |
|
Regformer-3 | | | 85.06 111 | 84.67 124 | 86.22 94 | 84.27 234 | 73.43 110 | 84.07 134 | 85.26 214 | 80.77 63 | 88.62 120 | 85.48 237 | 80.56 99 | 90.39 193 | 81.99 75 | 91.04 203 | 94.85 79 |
|
EI-MVSNet-UG-set | | | 85.04 112 | 84.44 133 | 86.85 82 | 83.87 243 | 72.52 117 | 83.82 143 | 85.15 217 | 80.27 68 | 88.75 117 | 85.45 240 | 79.95 104 | 91.90 151 | 81.92 76 | 90.80 210 | 96.13 43 |
|
X-MVStestdata | | | 85.04 112 | 82.70 156 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 37 | 85.07 25 | 89.99 84 | 16.05 330 | 86.57 41 | 95.80 13 | 87.35 19 | 97.62 52 | 94.20 92 |
|
MSLP-MVS++ | | | 85.00 114 | 86.03 99 | 81.90 184 | 91.84 95 | 71.56 134 | 86.75 98 | 93.02 53 | 75.95 120 | 87.12 140 | 89.39 193 | 77.98 116 | 89.40 210 | 77.46 123 | 94.78 141 | 84.75 244 |
|
F-COLMAP | | | 84.97 115 | 83.42 149 | 89.63 49 | 92.39 78 | 83.40 45 | 88.83 64 | 91.92 84 | 73.19 150 | 80.18 229 | 89.15 196 | 77.04 128 | 93.28 114 | 65.82 214 | 92.28 191 | 92.21 147 |
|
v7 | | | 84.81 116 | 85.00 114 | 84.23 137 | 88.15 168 | 63.27 191 | 83.79 146 | 91.39 108 | 71.10 176 | 90.07 81 | 91.28 142 | 74.04 161 | 93.63 85 | 81.48 79 | 93.67 166 | 95.79 52 |
|
3Dnovator | | 80.37 7 | 84.80 117 | 84.71 122 | 85.06 114 | 86.36 209 | 74.71 103 | 88.77 66 | 90.00 149 | 75.65 124 | 84.96 170 | 93.17 98 | 74.06 159 | 91.19 170 | 78.28 117 | 91.09 201 | 89.29 198 |
|
IterMVS-LS | | | 84.73 118 | 84.98 115 | 83.96 144 | 87.35 188 | 63.66 183 | 83.25 163 | 89.88 151 | 76.06 117 | 89.62 101 | 92.37 120 | 73.40 176 | 92.52 139 | 78.16 118 | 94.77 143 | 95.69 57 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MVS_111021_HR | | | 84.63 119 | 84.34 140 | 85.49 110 | 90.18 131 | 75.86 99 | 79.23 233 | 87.13 192 | 73.35 145 | 85.56 166 | 89.34 194 | 83.60 62 | 90.50 191 | 76.64 131 | 94.05 155 | 90.09 193 |
|
HQP-MVS | | | 84.61 120 | 84.06 143 | 86.27 92 | 91.19 111 | 70.66 138 | 84.77 122 | 92.68 64 | 73.30 146 | 80.55 224 | 90.17 182 | 72.10 197 | 94.61 52 | 77.30 126 | 94.47 149 | 93.56 116 |
|
v1192 | | | 84.57 121 | 84.69 123 | 84.21 138 | 87.75 179 | 62.88 198 | 83.02 168 | 91.43 104 | 69.08 190 | 89.98 86 | 90.89 162 | 72.70 190 | 93.62 89 | 82.41 70 | 94.97 133 | 96.13 43 |
|
mvs-test1 | | | 84.55 122 | 82.12 166 | 91.84 17 | 87.13 195 | 89.54 5 | 85.05 121 | 88.42 170 | 74.57 133 | 80.60 221 | 82.98 267 | 78.49 112 | 93.98 72 | 72.04 166 | 89.77 223 | 92.00 150 |
|
FMVSNet1 | | | 84.55 122 | 85.45 108 | 81.85 187 | 90.27 130 | 61.05 232 | 86.83 94 | 88.27 173 | 78.57 91 | 89.66 97 | 95.64 33 | 75.43 142 | 90.68 186 | 69.09 187 | 95.33 120 | 93.82 105 |
|
v1144 | | | 84.54 124 | 84.72 121 | 84.00 142 | 87.67 182 | 62.55 207 | 82.97 169 | 90.93 120 | 70.32 182 | 89.80 93 | 90.99 157 | 73.50 172 | 93.48 104 | 81.69 78 | 94.65 146 | 95.97 49 |
|
Gipuma | | | 84.44 125 | 86.33 92 | 78.78 222 | 84.20 237 | 73.57 109 | 89.55 51 | 90.44 129 | 84.24 29 | 84.38 181 | 94.89 51 | 76.35 139 | 80.40 276 | 76.14 133 | 96.80 74 | 82.36 272 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
v1neww | | | 84.43 126 | 84.66 125 | 83.75 149 | 87.81 175 | 62.34 212 | 83.59 149 | 90.27 141 | 72.33 162 | 89.93 88 | 91.22 144 | 73.28 178 | 93.29 111 | 80.25 97 | 93.25 173 | 95.62 60 |
|
v7new | | | 84.43 126 | 84.66 125 | 83.75 149 | 87.81 175 | 62.34 212 | 83.59 149 | 90.27 141 | 72.33 162 | 89.93 88 | 91.22 144 | 73.28 178 | 93.29 111 | 80.25 97 | 93.25 173 | 95.62 60 |
|
v6 | | | 84.43 126 | 84.66 125 | 83.75 149 | 87.81 175 | 62.34 212 | 83.59 149 | 90.26 143 | 72.33 162 | 89.94 87 | 91.19 148 | 73.30 177 | 93.29 111 | 80.26 96 | 93.26 172 | 95.62 60 |
|
testing_2 | | | 84.36 129 | 84.64 128 | 83.50 162 | 86.74 203 | 63.97 181 | 84.56 128 | 90.31 135 | 66.22 210 | 91.62 63 | 94.55 61 | 75.88 140 | 91.95 149 | 77.02 129 | 94.89 135 | 94.56 81 |
|
MCST-MVS | | | 84.36 129 | 83.93 146 | 85.63 108 | 91.59 98 | 71.58 133 | 83.52 153 | 92.13 77 | 61.82 246 | 83.96 187 | 89.75 188 | 79.93 105 | 93.46 105 | 78.33 116 | 94.34 151 | 91.87 154 |
|
VDDNet | | | 84.35 131 | 85.39 109 | 81.25 197 | 95.13 28 | 59.32 244 | 85.42 117 | 81.11 240 | 86.41 22 | 87.41 137 | 96.21 21 | 73.61 170 | 90.61 189 | 66.33 208 | 96.85 70 | 93.81 108 |
|
v1240 | | | 84.30 132 | 84.51 132 | 83.65 154 | 87.65 183 | 61.26 228 | 82.85 172 | 91.54 94 | 67.94 199 | 90.68 77 | 90.65 171 | 71.71 203 | 93.64 84 | 82.84 67 | 94.78 141 | 96.07 45 |
|
MVS_111021_LR | | | 84.28 133 | 83.76 147 | 85.83 106 | 89.23 144 | 83.07 48 | 80.99 214 | 83.56 226 | 72.71 156 | 86.07 158 | 89.07 197 | 81.75 88 | 86.19 240 | 77.11 128 | 93.36 168 | 88.24 207 |
|
v144192 | | | 84.24 134 | 84.41 134 | 83.71 153 | 87.59 185 | 61.57 224 | 82.95 170 | 91.03 118 | 67.82 202 | 89.80 93 | 90.49 174 | 73.28 178 | 93.51 103 | 81.88 77 | 94.89 135 | 96.04 47 |
|
v1921920 | | | 84.23 135 | 84.37 139 | 83.79 147 | 87.64 184 | 61.71 219 | 82.91 171 | 91.20 114 | 67.94 199 | 90.06 82 | 90.34 176 | 72.04 200 | 93.59 90 | 82.32 72 | 94.91 134 | 96.07 45 |
|
VDD-MVS | | | 84.23 135 | 84.58 130 | 83.20 165 | 91.17 114 | 65.16 172 | 83.25 163 | 84.97 222 | 79.79 71 | 87.18 139 | 94.27 73 | 74.77 153 | 90.89 180 | 69.24 185 | 96.54 80 | 93.55 118 |
|
v1141 | | | 84.16 137 | 84.38 136 | 83.52 159 | 87.32 190 | 61.70 221 | 82.79 174 | 89.74 152 | 71.90 169 | 89.64 98 | 91.12 151 | 72.68 191 | 93.10 122 | 80.39 95 | 93.80 161 | 95.75 54 |
|
divwei89l23v2f112 | | | 84.16 137 | 84.38 136 | 83.52 159 | 87.32 190 | 61.70 221 | 82.79 174 | 89.74 152 | 71.90 169 | 89.64 98 | 91.12 151 | 72.68 191 | 93.10 122 | 80.40 93 | 93.81 160 | 95.75 54 |
|
v1 | | | 84.16 137 | 84.38 136 | 83.52 159 | 87.33 189 | 61.71 219 | 82.79 174 | 89.73 154 | 71.89 171 | 89.64 98 | 91.11 153 | 72.72 188 | 93.10 122 | 80.40 93 | 93.79 162 | 95.75 54 |
|
v2v482 | | | 84.09 140 | 84.24 141 | 83.62 155 | 87.13 195 | 61.40 225 | 82.71 179 | 89.71 155 | 72.19 165 | 89.55 105 | 91.41 141 | 70.70 208 | 93.20 116 | 81.02 83 | 93.76 163 | 96.25 41 |
|
EG-PatchMatch MVS | | | 84.08 141 | 84.11 142 | 83.98 143 | 92.22 85 | 72.61 116 | 82.20 195 | 87.02 196 | 72.63 157 | 88.86 114 | 91.02 156 | 78.52 111 | 91.11 172 | 73.41 154 | 91.09 201 | 88.21 208 |
|
DP-MVS Recon | | | 84.05 142 | 83.22 152 | 86.52 87 | 91.73 96 | 75.27 101 | 83.23 165 | 92.40 71 | 72.04 166 | 82.04 205 | 88.33 205 | 77.91 118 | 93.95 75 | 66.17 209 | 95.12 128 | 90.34 187 |
|
TransMVSNet (Re) | | | 84.02 143 | 85.74 102 | 78.85 221 | 91.00 117 | 55.20 270 | 82.29 189 | 87.26 187 | 79.65 74 | 88.38 126 | 95.52 37 | 83.00 67 | 86.88 232 | 67.97 197 | 96.60 78 | 94.45 88 |
|
Baseline_NR-MVSNet | | | 84.00 144 | 85.90 100 | 78.29 229 | 91.47 106 | 53.44 279 | 82.29 189 | 87.00 197 | 79.06 83 | 89.55 105 | 95.72 30 | 77.20 124 | 86.14 241 | 72.30 164 | 98.51 18 | 95.28 70 |
|
TSAR-MVS + GP. | | | 83.95 145 | 82.69 157 | 87.72 73 | 89.27 143 | 81.45 55 | 83.72 147 | 81.58 239 | 74.73 131 | 85.66 163 | 86.06 232 | 72.56 194 | 92.69 135 | 75.44 137 | 95.21 125 | 89.01 203 |
|
alignmvs | | | 83.94 146 | 83.98 145 | 83.80 146 | 87.80 178 | 67.88 157 | 84.54 129 | 91.42 105 | 73.27 149 | 88.41 125 | 87.96 210 | 72.33 196 | 90.83 181 | 76.02 134 | 94.11 153 | 92.69 134 |
|
Effi-MVS+ | | | 83.90 147 | 84.01 144 | 83.57 157 | 87.22 193 | 65.61 170 | 86.55 105 | 92.40 71 | 78.64 90 | 81.34 215 | 84.18 256 | 83.65 61 | 92.93 129 | 74.22 143 | 87.87 242 | 92.17 148 |
|
pm-mvs1 | | | 83.69 148 | 84.95 116 | 79.91 209 | 90.04 136 | 59.66 241 | 82.43 184 | 87.44 184 | 75.52 125 | 87.85 131 | 95.26 42 | 81.25 93 | 85.65 247 | 68.74 191 | 96.04 101 | 94.42 89 |
|
AdaColmap | | | 83.66 149 | 83.69 148 | 83.57 157 | 90.05 135 | 72.26 122 | 86.29 108 | 90.00 149 | 78.19 96 | 81.65 210 | 87.16 220 | 83.40 64 | 94.24 61 | 61.69 234 | 94.76 144 | 84.21 249 |
|
MIMVSNet1 | | | 83.63 150 | 84.59 129 | 80.74 202 | 94.06 42 | 62.77 200 | 82.72 178 | 84.53 224 | 77.57 103 | 90.34 78 | 95.92 26 | 76.88 136 | 85.83 245 | 61.88 232 | 97.42 57 | 93.62 113 |
|
WR-MVS | | | 83.56 151 | 84.40 135 | 81.06 200 | 93.43 53 | 54.88 271 | 78.67 239 | 85.02 220 | 81.24 59 | 90.74 76 | 91.56 138 | 72.85 186 | 91.08 173 | 68.00 196 | 98.04 36 | 97.23 25 |
|
CNLPA | | | 83.55 152 | 83.10 155 | 84.90 117 | 89.34 142 | 83.87 43 | 84.54 129 | 88.77 165 | 79.09 82 | 83.54 194 | 88.66 203 | 74.87 149 | 81.73 272 | 66.84 206 | 92.29 190 | 89.11 199 |
|
LCM-MVSNet-Re | | | 83.48 153 | 85.06 112 | 78.75 223 | 85.94 216 | 55.75 266 | 80.05 220 | 94.27 13 | 76.47 114 | 96.09 5 | 94.54 63 | 83.31 65 | 89.75 206 | 59.95 245 | 94.89 135 | 90.75 174 |
|
V42 | | | 83.47 154 | 83.37 151 | 83.75 149 | 83.16 248 | 63.33 189 | 81.31 209 | 90.23 144 | 69.51 187 | 90.91 74 | 90.81 165 | 74.16 158 | 92.29 143 | 80.06 99 | 90.22 218 | 95.62 60 |
|
VPA-MVSNet | | | 83.47 154 | 84.73 119 | 79.69 213 | 90.29 129 | 57.52 254 | 81.30 211 | 88.69 167 | 76.29 115 | 87.58 134 | 94.44 66 | 80.60 98 | 87.20 228 | 66.60 207 | 96.82 73 | 94.34 91 |
|
MVS_0305 | | | 83.28 156 | 82.18 165 | 86.58 84 | 86.07 215 | 72.49 118 | 83.49 158 | 91.61 92 | 76.81 111 | 73.06 273 | 86.94 223 | 72.39 195 | 95.33 31 | 76.65 130 | 92.63 186 | 91.93 153 |
|
PAPM_NR | | | 83.23 157 | 83.19 154 | 83.33 163 | 90.90 119 | 65.98 167 | 88.19 72 | 90.78 121 | 78.13 97 | 80.87 219 | 87.92 213 | 73.49 173 | 92.42 140 | 70.07 179 | 88.40 233 | 91.60 160 |
|
CLD-MVS | | | 83.18 158 | 82.64 158 | 84.79 120 | 89.05 146 | 67.82 158 | 77.93 244 | 92.52 69 | 68.33 194 | 85.07 169 | 81.54 285 | 82.06 79 | 92.96 127 | 69.35 184 | 97.91 43 | 93.57 115 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
ANet_high | | | 83.17 159 | 85.68 104 | 75.65 251 | 81.24 260 | 45.26 310 | 79.94 222 | 92.91 56 | 83.83 33 | 91.33 68 | 96.88 10 | 80.25 101 | 85.92 243 | 68.89 189 | 95.89 107 | 95.76 53 |
|
114514_t | | | 83.10 160 | 82.54 161 | 84.77 121 | 92.90 65 | 69.10 151 | 86.65 102 | 90.62 125 | 54.66 277 | 81.46 212 | 90.81 165 | 76.98 129 | 94.38 57 | 72.62 162 | 96.18 93 | 90.82 173 |
|
UGNet | | | 82.78 161 | 81.64 172 | 86.21 96 | 86.20 213 | 76.24 98 | 86.86 92 | 85.68 209 | 77.07 109 | 73.76 268 | 92.82 107 | 69.64 209 | 91.82 155 | 69.04 188 | 93.69 165 | 90.56 181 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
LF4IMVS | | | 82.75 162 | 81.93 170 | 85.19 112 | 82.08 254 | 80.15 61 | 85.53 115 | 88.76 166 | 68.01 196 | 85.58 165 | 87.75 214 | 71.80 202 | 86.85 233 | 74.02 146 | 93.87 159 | 88.58 206 |
|
EI-MVSNet | | | 82.61 163 | 82.42 163 | 83.20 165 | 83.25 246 | 63.66 183 | 83.50 156 | 85.07 218 | 76.06 117 | 86.55 150 | 85.10 245 | 73.41 174 | 90.25 194 | 78.15 119 | 90.67 213 | 95.68 58 |
|
QAPM | | | 82.59 164 | 82.59 160 | 82.58 175 | 86.44 204 | 66.69 164 | 89.94 43 | 90.36 132 | 67.97 198 | 84.94 172 | 92.58 114 | 72.71 189 | 92.18 145 | 70.63 176 | 87.73 244 | 88.85 205 |
|
Fast-Effi-MVS+-dtu | | | 82.54 165 | 81.41 175 | 85.90 103 | 85.60 217 | 76.53 94 | 83.07 167 | 89.62 157 | 73.02 153 | 79.11 236 | 83.51 261 | 80.74 97 | 90.24 196 | 68.76 190 | 89.29 225 | 90.94 168 |
|
MVS_Test | | | 82.47 166 | 83.22 152 | 80.22 208 | 82.62 253 | 57.75 253 | 82.54 183 | 91.96 83 | 71.16 175 | 82.89 198 | 92.52 116 | 77.41 123 | 90.50 191 | 80.04 100 | 87.84 243 | 92.40 140 |
|
v148 | | | 82.31 167 | 82.48 162 | 81.81 190 | 85.59 218 | 59.66 241 | 81.47 207 | 86.02 205 | 72.85 154 | 88.05 128 | 90.65 171 | 70.73 207 | 90.91 179 | 75.15 138 | 91.79 195 | 94.87 77 |
|
API-MVS | | | 82.28 168 | 82.61 159 | 81.30 195 | 86.29 211 | 69.79 141 | 88.71 67 | 87.67 182 | 78.42 93 | 82.15 204 | 84.15 258 | 77.98 116 | 91.59 161 | 65.39 215 | 92.75 183 | 82.51 271 |
|
MVSFormer | | | 82.23 169 | 81.57 174 | 84.19 140 | 85.54 219 | 69.26 147 | 91.98 25 | 90.08 146 | 71.54 172 | 76.23 250 | 85.07 247 | 58.69 253 | 94.27 58 | 86.26 27 | 88.77 230 | 89.03 201 |
|
PCF-MVS | | 74.62 15 | 82.15 170 | 80.92 182 | 85.84 105 | 89.43 140 | 72.30 121 | 80.53 217 | 91.82 86 | 57.36 265 | 87.81 132 | 89.92 185 | 77.67 120 | 93.63 85 | 58.69 251 | 95.08 129 | 91.58 161 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PLC | | 73.85 16 | 82.09 171 | 80.31 187 | 87.45 77 | 90.86 121 | 80.29 60 | 85.88 110 | 90.65 123 | 68.17 195 | 76.32 249 | 86.33 229 | 73.12 183 | 92.61 138 | 61.40 238 | 90.02 221 | 89.44 196 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
GBi-Net | | | 82.02 172 | 82.07 167 | 81.85 187 | 86.38 206 | 61.05 232 | 86.83 94 | 88.27 173 | 72.43 158 | 86.00 159 | 95.64 33 | 63.78 231 | 90.68 186 | 65.95 210 | 93.34 169 | 93.82 105 |
|
test1 | | | 82.02 172 | 82.07 167 | 81.85 187 | 86.38 206 | 61.05 232 | 86.83 94 | 88.27 173 | 72.43 158 | 86.00 159 | 95.64 33 | 63.78 231 | 90.68 186 | 65.95 210 | 93.34 169 | 93.82 105 |
|
OpenMVS | | 76.72 13 | 81.98 174 | 82.00 169 | 81.93 183 | 84.42 232 | 68.22 155 | 88.50 70 | 89.48 159 | 66.92 205 | 81.80 209 | 91.86 127 | 72.59 193 | 90.16 199 | 71.19 170 | 91.25 199 | 87.40 219 |
|
PVSNet_Blended_VisFu | | | 81.55 175 | 80.49 186 | 84.70 124 | 91.58 101 | 73.24 112 | 84.21 132 | 91.67 90 | 62.86 238 | 80.94 217 | 87.16 220 | 67.27 218 | 92.87 132 | 69.82 181 | 88.94 229 | 87.99 212 |
|
DELS-MVS | | | 81.44 176 | 81.25 177 | 82.03 182 | 84.27 234 | 62.87 199 | 76.47 258 | 92.49 70 | 70.97 177 | 81.64 211 | 83.83 259 | 75.03 146 | 92.70 134 | 74.29 142 | 92.22 193 | 90.51 183 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
Test4 | | | 81.31 177 | 81.13 179 | 81.88 186 | 84.89 225 | 63.05 195 | 82.37 186 | 90.50 127 | 62.75 239 | 89.00 113 | 88.29 206 | 67.55 217 | 91.68 159 | 73.55 152 | 91.24 200 | 90.89 170 |
|
FMVSNet2 | | | 81.31 177 | 81.61 173 | 80.41 206 | 86.38 206 | 58.75 249 | 83.93 140 | 86.58 200 | 72.43 158 | 87.65 133 | 92.98 102 | 63.78 231 | 90.22 197 | 66.86 204 | 93.92 158 | 92.27 145 |
|
DI_MVS_plusplus_test | | | 81.27 179 | 81.26 176 | 81.29 196 | 84.98 223 | 61.65 223 | 81.98 198 | 87.25 188 | 63.56 231 | 87.56 135 | 89.60 190 | 73.62 168 | 91.83 154 | 72.20 165 | 90.59 217 | 90.38 186 |
|
TinyColmap | | | 81.25 180 | 82.34 164 | 77.99 234 | 85.33 221 | 60.68 236 | 82.32 188 | 88.33 172 | 71.26 174 | 86.97 145 | 92.22 124 | 77.10 127 | 86.98 231 | 62.37 229 | 95.17 127 | 86.31 229 |
|
test_normal | | | 81.23 181 | 81.16 178 | 81.43 194 | 84.77 227 | 61.99 218 | 81.46 208 | 86.95 198 | 63.16 236 | 87.22 138 | 89.63 189 | 73.62 168 | 91.65 160 | 72.92 160 | 90.70 212 | 90.65 178 |
|
Fast-Effi-MVS+ | | | 81.04 182 | 80.57 183 | 82.46 179 | 87.50 186 | 63.22 193 | 78.37 240 | 89.63 156 | 68.01 196 | 81.87 207 | 82.08 280 | 82.31 76 | 92.65 137 | 67.10 202 | 88.30 238 | 91.51 163 |
|
BH-untuned | | | 80.96 183 | 80.99 180 | 80.84 201 | 88.55 156 | 68.23 154 | 80.33 218 | 88.46 169 | 72.79 155 | 86.55 150 | 86.76 225 | 74.72 154 | 91.77 157 | 61.79 233 | 88.99 228 | 82.52 270 |
|
1121 | | | 80.86 184 | 79.81 194 | 84.02 141 | 93.93 45 | 78.70 74 | 81.64 204 | 80.18 245 | 55.43 274 | 83.67 190 | 91.15 149 | 71.29 205 | 91.41 167 | 67.95 198 | 93.06 178 | 81.96 278 |
|
xiu_mvs_v1_base_debu | | | 80.84 185 | 80.14 190 | 82.93 170 | 88.31 162 | 71.73 129 | 79.53 226 | 87.17 189 | 65.43 218 | 79.59 231 | 82.73 273 | 76.94 130 | 90.14 200 | 73.22 155 | 88.33 234 | 86.90 224 |
|
xiu_mvs_v1_base | | | 80.84 185 | 80.14 190 | 82.93 170 | 88.31 162 | 71.73 129 | 79.53 226 | 87.17 189 | 65.43 218 | 79.59 231 | 82.73 273 | 76.94 130 | 90.14 200 | 73.22 155 | 88.33 234 | 86.90 224 |
|
xiu_mvs_v1_base_debi | | | 80.84 185 | 80.14 190 | 82.93 170 | 88.31 162 | 71.73 129 | 79.53 226 | 87.17 189 | 65.43 218 | 79.59 231 | 82.73 273 | 76.94 130 | 90.14 200 | 73.22 155 | 88.33 234 | 86.90 224 |
|
BH-RMVSNet | | | 80.53 188 | 80.22 189 | 81.49 193 | 87.19 194 | 66.21 166 | 77.79 246 | 86.23 202 | 74.21 136 | 83.69 189 | 88.50 204 | 73.25 182 | 90.75 183 | 63.18 228 | 87.90 241 | 87.52 217 |
|
EPNet | | | 80.37 189 | 78.41 201 | 86.23 93 | 76.75 294 | 73.28 111 | 87.18 88 | 77.45 255 | 76.24 116 | 68.14 294 | 88.93 199 | 65.41 226 | 93.85 77 | 69.47 183 | 96.12 98 | 91.55 162 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MG-MVS | | | 80.32 190 | 80.94 181 | 78.47 228 | 88.18 165 | 52.62 286 | 82.29 189 | 85.01 221 | 72.01 167 | 79.24 235 | 92.54 115 | 69.36 210 | 93.36 110 | 70.65 175 | 89.19 227 | 89.45 195 |
|
VPNet | | | 80.25 191 | 81.68 171 | 75.94 249 | 92.46 76 | 47.98 305 | 76.70 254 | 81.67 238 | 73.45 143 | 84.87 173 | 92.82 107 | 74.66 155 | 86.51 237 | 61.66 235 | 96.85 70 | 93.33 119 |
|
MAR-MVS | | | 80.24 192 | 78.74 199 | 84.73 123 | 86.87 202 | 78.18 76 | 85.75 112 | 87.81 181 | 65.67 217 | 77.84 239 | 78.50 298 | 73.79 165 | 90.53 190 | 61.59 237 | 90.87 208 | 85.49 238 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
PM-MVS | | | 80.20 193 | 79.00 197 | 83.78 148 | 88.17 166 | 86.66 13 | 81.31 209 | 66.81 309 | 69.64 186 | 88.33 127 | 90.19 180 | 64.58 227 | 83.63 264 | 71.99 168 | 90.03 220 | 81.06 295 |
|
LFMVS | | | 80.15 194 | 80.56 184 | 78.89 220 | 89.19 145 | 55.93 263 | 85.22 119 | 73.78 275 | 82.96 40 | 84.28 185 | 92.72 111 | 57.38 260 | 90.07 204 | 63.80 223 | 95.75 113 | 90.68 176 |
|
MSDG | | | 80.06 195 | 79.99 193 | 80.25 207 | 83.91 242 | 68.04 156 | 77.51 249 | 89.19 162 | 77.65 101 | 81.94 206 | 83.45 263 | 76.37 138 | 86.31 239 | 63.31 227 | 86.59 254 | 86.41 227 |
|
ab-mvs | | | 79.67 196 | 80.56 184 | 76.99 239 | 88.48 159 | 56.93 257 | 84.70 125 | 86.06 204 | 68.95 192 | 80.78 220 | 93.08 99 | 75.30 143 | 84.62 256 | 56.78 260 | 90.90 207 | 89.43 197 |
|
VNet | | | 79.31 197 | 80.27 188 | 76.44 244 | 87.92 173 | 53.95 275 | 75.58 265 | 84.35 225 | 74.39 135 | 82.23 202 | 90.72 167 | 72.84 187 | 84.39 258 | 60.38 244 | 93.98 157 | 90.97 167 |
|
diffmvs | | | 79.20 198 | 79.04 196 | 79.69 213 | 78.64 282 | 58.90 246 | 81.79 199 | 87.61 183 | 65.07 225 | 73.65 270 | 89.80 186 | 73.10 184 | 87.79 223 | 75.02 140 | 86.63 253 | 92.38 141 |
|
PAPR | | | 78.84 199 | 78.10 202 | 81.07 199 | 85.17 222 | 60.22 239 | 82.21 193 | 90.57 126 | 62.51 241 | 75.32 259 | 84.61 254 | 74.99 147 | 92.30 142 | 59.48 249 | 88.04 240 | 90.68 176 |
|
PVSNet_BlendedMVS | | | 78.80 200 | 77.84 203 | 81.65 192 | 84.43 230 | 63.41 186 | 79.49 229 | 90.44 129 | 61.70 249 | 75.43 257 | 87.07 222 | 69.11 212 | 91.44 165 | 60.68 242 | 92.24 192 | 90.11 192 |
|
FMVSNet3 | | | 78.80 200 | 78.55 200 | 79.57 216 | 82.89 250 | 56.89 259 | 81.76 201 | 85.77 208 | 69.04 191 | 86.00 159 | 90.44 175 | 51.75 280 | 90.09 203 | 65.95 210 | 93.34 169 | 91.72 157 |
|
pmmvs-eth3d | | | 78.42 202 | 77.04 207 | 82.57 177 | 87.44 187 | 74.41 105 | 80.86 216 | 79.67 248 | 55.68 272 | 84.69 176 | 90.31 179 | 60.91 239 | 85.42 248 | 62.20 230 | 91.59 197 | 87.88 215 |
|
mvs_anonymous | | | 78.13 203 | 78.76 198 | 76.23 248 | 79.24 276 | 50.31 299 | 78.69 238 | 84.82 223 | 61.60 250 | 83.09 197 | 92.82 107 | 73.89 164 | 87.01 229 | 68.33 195 | 86.41 256 | 91.37 164 |
|
TAMVS | | | 78.08 204 | 76.36 212 | 83.23 164 | 90.62 124 | 72.87 113 | 79.08 234 | 80.01 247 | 61.72 248 | 81.35 214 | 86.92 224 | 63.96 230 | 88.78 214 | 50.61 284 | 93.01 180 | 88.04 211 |
|
Vis-MVSNet (Re-imp) | | | 77.82 205 | 77.79 204 | 77.92 235 | 88.82 150 | 51.29 291 | 83.28 162 | 71.97 286 | 74.04 137 | 82.23 202 | 89.78 187 | 57.38 260 | 89.41 209 | 57.22 258 | 95.41 118 | 93.05 126 |
|
OpenMVS_ROB | | 70.19 17 | 77.77 206 | 77.46 205 | 78.71 224 | 84.39 233 | 61.15 230 | 81.18 213 | 82.52 230 | 62.45 243 | 83.34 195 | 87.37 219 | 66.20 222 | 88.66 217 | 64.69 218 | 85.02 269 | 86.32 228 |
|
MDA-MVSNet-bldmvs | | | 77.47 207 | 76.90 208 | 79.16 219 | 79.03 278 | 64.59 174 | 66.58 301 | 75.67 265 | 73.15 151 | 88.86 114 | 88.99 198 | 66.94 219 | 81.23 273 | 64.71 217 | 88.22 239 | 91.64 159 |
|
jason | | | 77.42 208 | 75.75 218 | 82.43 180 | 87.10 198 | 69.27 146 | 77.99 243 | 81.94 236 | 51.47 297 | 77.84 239 | 85.07 247 | 60.32 242 | 89.00 211 | 70.74 174 | 89.27 226 | 89.03 201 |
jason: jason. |
CDS-MVSNet | | | 77.32 209 | 75.40 220 | 83.06 167 | 89.00 148 | 72.48 119 | 77.90 245 | 82.17 233 | 60.81 253 | 78.94 237 | 83.49 262 | 59.30 250 | 88.76 215 | 54.64 274 | 92.37 189 | 87.93 214 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
xiu_mvs_v2_base | | | 77.19 210 | 76.75 209 | 78.52 227 | 87.01 200 | 61.30 227 | 75.55 266 | 87.12 194 | 61.24 252 | 74.45 265 | 78.79 297 | 77.20 124 | 90.93 177 | 64.62 220 | 84.80 273 | 83.32 260 |
|
MVSTER | | | 77.09 211 | 75.70 219 | 81.25 197 | 75.27 308 | 61.08 231 | 77.49 250 | 85.07 218 | 60.78 254 | 86.55 150 | 88.68 202 | 43.14 311 | 90.25 194 | 73.69 150 | 90.67 213 | 92.42 138 |
|
PS-MVSNAJ | | | 77.04 212 | 76.53 211 | 78.56 226 | 87.09 199 | 61.40 225 | 75.26 267 | 87.13 192 | 61.25 251 | 74.38 266 | 77.22 303 | 76.94 130 | 90.94 176 | 64.63 219 | 84.83 272 | 83.35 259 |
|
IterMVS | | | 76.91 213 | 76.34 213 | 78.64 225 | 80.91 264 | 64.03 179 | 76.30 259 | 79.03 249 | 64.88 228 | 83.11 196 | 89.16 195 | 59.90 246 | 84.46 257 | 68.61 193 | 85.15 268 | 87.42 218 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MVS_dtu | | | 76.84 214 | 75.19 222 | 81.80 191 | 82.87 251 | 62.54 208 | 82.78 177 | 85.63 210 | 65.12 223 | 64.44 310 | 82.73 273 | 55.61 269 | 92.24 144 | 67.42 201 | 90.13 219 | 90.61 179 |
|
TR-MVS | | | 76.77 215 | 75.79 216 | 79.72 212 | 86.10 214 | 65.79 169 | 77.14 251 | 83.02 228 | 65.20 222 | 81.40 213 | 82.10 279 | 66.30 221 | 90.73 185 | 55.57 267 | 85.27 265 | 82.65 266 |
|
USDC | | | 76.63 216 | 76.73 210 | 76.34 246 | 83.46 244 | 57.20 256 | 80.02 221 | 88.04 177 | 52.14 292 | 83.65 192 | 91.25 143 | 63.24 234 | 86.65 236 | 54.66 273 | 94.11 153 | 85.17 239 |
|
BH-w/o | | | 76.57 217 | 76.07 215 | 78.10 232 | 86.88 201 | 65.92 168 | 77.63 247 | 86.33 201 | 65.69 216 | 80.89 218 | 79.95 293 | 68.97 214 | 90.74 184 | 53.01 279 | 85.25 266 | 77.62 300 |
|
Patchmtry | | | 76.56 218 | 77.46 205 | 73.83 258 | 79.37 275 | 46.60 307 | 82.41 185 | 76.90 258 | 73.81 140 | 85.56 166 | 92.38 117 | 48.07 289 | 83.98 261 | 63.36 226 | 95.31 123 | 90.92 169 |
|
PVSNet_Blended | | | 76.49 219 | 75.40 220 | 79.76 211 | 84.43 230 | 63.41 186 | 75.14 268 | 90.44 129 | 57.36 265 | 75.43 257 | 78.30 299 | 69.11 212 | 91.44 165 | 60.68 242 | 87.70 245 | 84.42 247 |
|
lupinMVS | | | 76.37 220 | 74.46 228 | 82.09 181 | 85.54 219 | 69.26 147 | 76.79 252 | 80.77 243 | 50.68 304 | 76.23 250 | 82.82 271 | 58.69 253 | 88.94 212 | 69.85 180 | 88.77 230 | 88.07 209 |
|
cascas | | | 76.29 221 | 74.81 223 | 80.72 204 | 84.47 229 | 62.94 197 | 73.89 276 | 87.34 185 | 55.94 271 | 75.16 261 | 76.53 306 | 63.97 229 | 91.16 171 | 65.00 216 | 90.97 205 | 88.06 210 |
|
MVS_test0326 | | | 76.19 222 | 74.70 224 | 80.65 205 | 80.68 268 | 61.16 229 | 81.77 200 | 85.93 206 | 65.03 226 | 63.13 312 | 81.14 288 | 57.18 263 | 91.80 156 | 67.65 200 | 89.79 222 | 88.99 204 |
|
RPMNet | | | 76.06 223 | 75.79 216 | 76.85 242 | 79.58 271 | 62.64 202 | 82.58 180 | 71.75 290 | 74.80 130 | 75.72 255 | 92.59 112 | 48.69 287 | 84.07 259 | 73.48 153 | 82.91 283 | 83.85 251 |
|
GA-MVS | | | 75.83 224 | 74.61 225 | 79.48 218 | 81.87 256 | 59.25 245 | 73.42 279 | 82.88 229 | 68.68 193 | 79.75 230 | 81.80 282 | 50.62 282 | 89.46 207 | 66.85 205 | 85.64 262 | 89.72 194 |
|
MVP-Stereo | | | 75.81 225 | 73.51 233 | 82.71 173 | 89.35 141 | 73.62 108 | 80.06 219 | 85.20 215 | 60.30 256 | 73.96 267 | 87.94 211 | 57.89 257 | 89.45 208 | 52.02 282 | 74.87 310 | 85.06 241 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
wuyk23d | | | 75.13 226 | 79.30 195 | 62.63 298 | 75.56 302 | 75.18 102 | 80.89 215 | 73.10 279 | 75.06 129 | 94.76 15 | 95.32 40 | 87.73 31 | 52.85 328 | 34.16 322 | 97.11 64 | 59.85 321 |
|
EU-MVSNet | | | 75.12 227 | 74.43 229 | 77.18 238 | 83.11 249 | 59.48 243 | 85.71 114 | 82.43 231 | 39.76 325 | 85.64 164 | 88.76 200 | 44.71 307 | 87.88 222 | 73.86 149 | 85.88 260 | 84.16 250 |
|
HyFIR lowres test | | | 75.12 227 | 72.66 240 | 82.50 178 | 91.44 107 | 65.19 171 | 72.47 281 | 87.31 186 | 46.79 313 | 80.29 227 | 84.30 255 | 52.70 278 | 92.10 148 | 51.88 283 | 86.73 252 | 90.22 188 |
|
CMPMVS | | 59.41 20 | 75.12 227 | 73.57 232 | 79.77 210 | 75.84 300 | 67.22 159 | 81.21 212 | 82.18 232 | 50.78 302 | 76.50 246 | 87.66 216 | 55.20 272 | 82.99 266 | 62.17 231 | 90.64 216 | 89.09 200 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
pmmvs4 | | | 74.92 230 | 72.98 238 | 80.73 203 | 84.95 224 | 71.71 132 | 76.23 260 | 77.59 254 | 52.83 286 | 77.73 242 | 86.38 228 | 56.35 264 | 84.97 252 | 57.72 257 | 87.05 250 | 85.51 237 |
|
1112_ss | | | 74.82 231 | 73.74 230 | 78.04 233 | 89.57 139 | 60.04 240 | 76.49 257 | 87.09 195 | 54.31 278 | 73.66 269 | 79.80 294 | 60.25 243 | 86.76 235 | 58.37 252 | 84.15 276 | 87.32 220 |
|
Patchmatch-RL test | | | 74.48 232 | 73.68 231 | 76.89 241 | 84.83 226 | 66.54 165 | 72.29 282 | 69.16 300 | 57.70 263 | 86.76 146 | 86.33 229 | 45.79 298 | 82.59 268 | 69.63 182 | 90.65 215 | 81.54 286 |
|
PatchMatch-RL | | | 74.48 232 | 73.22 235 | 78.27 230 | 87.70 181 | 85.26 30 | 75.92 261 | 70.09 295 | 64.34 230 | 76.09 252 | 81.25 287 | 65.87 225 | 78.07 281 | 53.86 276 | 83.82 277 | 71.48 311 |
|
XXY-MVS | | | 74.44 234 | 76.19 214 | 69.21 278 | 84.61 228 | 52.43 287 | 71.70 284 | 77.18 256 | 60.73 255 | 80.60 221 | 90.96 160 | 75.44 141 | 69.35 302 | 56.13 263 | 88.33 234 | 85.86 234 |
|
CR-MVSNet | | | 74.00 235 | 73.04 237 | 76.85 242 | 79.58 271 | 62.64 202 | 82.58 180 | 76.90 258 | 50.50 305 | 75.72 255 | 92.38 117 | 48.07 289 | 84.07 259 | 68.72 192 | 82.91 283 | 83.85 251 |
|
Test_1112_low_res | | | 73.90 236 | 73.08 236 | 76.35 245 | 90.35 128 | 55.95 262 | 73.40 280 | 86.17 203 | 50.70 303 | 73.14 271 | 85.94 233 | 58.31 255 | 85.90 244 | 56.51 261 | 83.22 280 | 87.20 221 |
|
test20.03 | | | 73.75 237 | 74.59 227 | 71.22 272 | 81.11 262 | 51.12 293 | 70.15 289 | 72.10 285 | 70.42 180 | 80.28 228 | 91.50 139 | 64.21 228 | 74.72 292 | 46.96 301 | 94.58 147 | 87.82 216 |
|
1314 | | | 73.22 238 | 72.56 243 | 75.20 252 | 80.41 270 | 57.84 251 | 81.64 204 | 85.36 213 | 51.68 295 | 73.10 272 | 76.65 305 | 61.45 238 | 85.19 250 | 63.54 224 | 79.21 301 | 82.59 267 |
|
MVS | | | 73.21 239 | 72.59 242 | 75.06 253 | 80.97 263 | 60.81 235 | 81.64 204 | 85.92 207 | 46.03 316 | 71.68 280 | 77.54 300 | 68.47 215 | 89.77 205 | 55.70 266 | 85.39 263 | 74.60 306 |
|
HY-MVS | | 64.64 18 | 73.03 240 | 72.47 244 | 74.71 255 | 83.36 245 | 54.19 273 | 82.14 196 | 81.96 234 | 56.76 270 | 69.57 290 | 86.21 231 | 60.03 244 | 84.83 255 | 49.58 290 | 82.65 285 | 85.11 240 |
|
EPNet_dtu | | | 72.87 241 | 71.33 251 | 77.49 237 | 77.72 288 | 60.55 237 | 82.35 187 | 75.79 263 | 66.49 208 | 58.39 325 | 81.06 289 | 53.68 276 | 85.98 242 | 53.55 277 | 92.97 181 | 85.95 232 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Patchmatch-test1 | | | 72.75 242 | 72.61 241 | 73.19 261 | 81.62 258 | 55.86 264 | 78.89 236 | 71.37 292 | 61.73 247 | 74.93 262 | 82.15 278 | 60.46 241 | 81.80 270 | 59.68 247 | 82.63 287 | 81.92 280 |
|
CVMVSNet | | | 72.62 243 | 71.41 250 | 76.28 247 | 83.25 246 | 60.34 238 | 83.50 156 | 79.02 250 | 37.77 326 | 76.33 248 | 85.10 245 | 49.60 285 | 87.41 226 | 70.54 177 | 77.54 306 | 81.08 293 |
|
CHOSEN 1792x2688 | | | 72.45 244 | 70.56 253 | 78.13 231 | 90.02 137 | 63.08 194 | 68.72 292 | 83.16 227 | 42.99 322 | 75.92 253 | 85.46 239 | 57.22 262 | 85.18 251 | 49.87 288 | 81.67 289 | 86.14 230 |
|
testgi | | | 72.36 245 | 74.61 225 | 65.59 291 | 80.56 269 | 42.82 318 | 68.29 293 | 73.35 278 | 66.87 206 | 81.84 208 | 89.93 184 | 72.08 199 | 66.92 310 | 46.05 303 | 92.54 187 | 87.01 223 |
|
FPMVS | | | 72.29 246 | 72.00 246 | 73.14 262 | 88.63 155 | 85.00 32 | 74.65 271 | 67.39 303 | 71.94 168 | 77.80 241 | 87.66 216 | 50.48 283 | 75.83 288 | 49.95 286 | 79.51 297 | 58.58 323 |
|
FMVSNet5 | | | 72.10 247 | 71.69 248 | 73.32 259 | 81.57 259 | 53.02 282 | 76.77 253 | 78.37 251 | 63.31 234 | 76.37 247 | 91.85 128 | 36.68 321 | 78.98 280 | 47.87 297 | 92.45 188 | 87.95 213 |
|
PAPM | | | 71.77 248 | 70.06 259 | 76.92 240 | 86.39 205 | 53.97 274 | 76.62 255 | 86.62 199 | 53.44 283 | 63.97 311 | 84.73 253 | 57.79 258 | 92.34 141 | 39.65 313 | 81.33 292 | 84.45 246 |
|
IB-MVS | | 62.13 19 | 71.64 249 | 68.97 263 | 79.66 215 | 80.80 267 | 62.26 217 | 73.94 275 | 76.90 258 | 63.27 235 | 68.63 293 | 76.79 304 | 33.83 324 | 91.84 153 | 59.28 250 | 87.26 248 | 84.88 242 |
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021 |
UnsupCasMVSNet_eth | | | 71.63 250 | 72.30 245 | 69.62 276 | 76.47 296 | 52.70 285 | 70.03 290 | 80.97 242 | 59.18 259 | 79.36 234 | 88.21 207 | 60.50 240 | 69.12 303 | 58.33 254 | 77.62 305 | 87.04 222 |
|
no-one | | | 71.52 251 | 70.43 256 | 74.81 254 | 78.45 284 | 63.41 186 | 57.73 318 | 77.03 257 | 51.46 298 | 77.17 244 | 90.33 177 | 54.96 274 | 80.35 277 | 47.41 298 | 99.29 2 | 80.68 297 |
|
Anonymous20231206 | | | 71.38 252 | 71.88 247 | 69.88 273 | 86.31 210 | 54.37 272 | 70.39 288 | 74.62 271 | 52.57 288 | 76.73 245 | 88.76 200 | 59.94 245 | 72.06 295 | 44.35 306 | 93.23 176 | 83.23 262 |
|
MIMVSNet | | | 71.09 253 | 71.59 249 | 69.57 277 | 87.23 192 | 50.07 300 | 78.91 235 | 71.83 288 | 60.20 257 | 71.26 282 | 91.76 133 | 55.08 273 | 76.09 286 | 41.06 311 | 87.02 251 | 82.54 269 |
|
MS-PatchMatch | | | 70.93 254 | 70.22 257 | 73.06 263 | 81.85 257 | 62.50 209 | 73.82 277 | 77.90 252 | 52.44 289 | 75.92 253 | 81.27 286 | 55.67 268 | 81.75 271 | 55.37 269 | 77.70 304 | 74.94 305 |
|
pmmvs5 | | | 70.73 255 | 70.07 258 | 72.72 265 | 77.03 293 | 52.73 284 | 74.14 273 | 75.65 266 | 50.36 306 | 72.17 278 | 85.37 243 | 55.42 271 | 80.67 275 | 52.86 280 | 87.59 246 | 84.77 243 |
|
PatchT | | | 70.52 256 | 72.76 239 | 63.79 297 | 79.38 274 | 33.53 326 | 77.63 247 | 65.37 311 | 73.61 141 | 71.77 279 | 92.79 110 | 44.38 308 | 75.65 289 | 64.53 221 | 85.37 264 | 82.18 275 |
|
testmv | | | 70.47 257 | 70.70 252 | 69.77 275 | 86.22 212 | 53.89 276 | 67.32 298 | 71.91 287 | 63.32 233 | 78.16 238 | 89.47 192 | 56.12 266 | 73.10 293 | 36.43 319 | 87.33 247 | 82.33 273 |
|
N_pmnet | | | 70.20 258 | 68.80 265 | 74.38 256 | 80.91 264 | 84.81 35 | 59.12 315 | 76.45 262 | 55.06 275 | 75.31 260 | 82.36 277 | 55.74 267 | 54.82 327 | 47.02 300 | 87.24 249 | 83.52 255 |
|
tpmvs | | | 70.16 259 | 69.56 261 | 71.96 270 | 74.71 312 | 48.13 303 | 79.63 224 | 75.45 267 | 65.02 227 | 70.26 287 | 81.88 281 | 45.34 302 | 85.68 246 | 58.34 253 | 75.39 309 | 82.08 276 |
|
new-patchmatchnet | | | 70.10 260 | 73.37 234 | 60.29 306 | 81.23 261 | 16.95 333 | 59.54 312 | 74.62 271 | 62.93 237 | 80.97 216 | 87.93 212 | 62.83 235 | 71.90 296 | 55.24 270 | 95.01 132 | 92.00 150 |
|
YYNet1 | | | 70.06 261 | 70.44 254 | 68.90 279 | 73.76 314 | 53.42 280 | 58.99 316 | 67.20 305 | 58.42 261 | 87.10 141 | 85.39 242 | 59.82 247 | 67.32 307 | 59.79 246 | 83.50 279 | 85.96 231 |
|
MDA-MVSNet_test_wron | | | 70.05 262 | 70.44 254 | 68.88 280 | 73.84 313 | 53.47 278 | 58.93 317 | 67.28 304 | 58.43 260 | 87.09 142 | 85.40 241 | 59.80 248 | 67.25 308 | 59.66 248 | 83.54 278 | 85.92 233 |
|
CostFormer | | | 69.98 263 | 68.68 266 | 73.87 257 | 77.14 291 | 50.72 297 | 79.26 232 | 74.51 273 | 51.94 294 | 70.97 285 | 84.75 252 | 45.16 305 | 87.49 225 | 55.16 271 | 79.23 300 | 83.40 258 |
|
PatchmatchNet | | | 69.71 264 | 68.83 264 | 72.33 268 | 77.66 289 | 53.60 277 | 79.29 231 | 69.99 296 | 57.66 264 | 72.53 275 | 82.93 270 | 46.45 293 | 80.08 279 | 60.91 241 | 72.09 315 | 83.31 261 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpmp4_e23 | | | 69.43 265 | 67.33 272 | 75.72 250 | 78.53 283 | 52.75 283 | 82.13 197 | 74.91 268 | 49.23 310 | 66.37 299 | 84.17 257 | 41.28 315 | 88.67 216 | 49.73 289 | 79.63 296 | 85.75 235 |
|
LP | | | 69.42 266 | 68.30 268 | 72.77 264 | 71.48 325 | 56.84 260 | 73.66 278 | 74.84 269 | 63.52 232 | 70.95 286 | 83.35 265 | 49.55 286 | 77.15 284 | 57.13 259 | 70.21 318 | 84.33 248 |
|
JIA-IIPM | | | 69.41 267 | 66.64 277 | 77.70 236 | 73.19 317 | 71.24 135 | 75.67 263 | 65.56 310 | 70.42 180 | 65.18 305 | 92.97 103 | 33.64 325 | 83.06 265 | 53.52 278 | 69.61 322 | 78.79 299 |
|
UnsupCasMVSNet_bld | | | 69.21 268 | 69.68 260 | 67.82 285 | 79.42 273 | 51.15 292 | 67.82 297 | 75.79 263 | 54.15 279 | 77.47 243 | 85.36 244 | 59.26 251 | 70.64 298 | 48.46 294 | 79.35 299 | 81.66 284 |
|
gg-mvs-nofinetune | | | 68.96 269 | 69.11 262 | 68.52 284 | 76.12 299 | 45.32 309 | 83.59 149 | 55.88 326 | 86.68 20 | 64.62 309 | 97.01 7 | 30.36 328 | 83.97 262 | 44.78 305 | 82.94 282 | 76.26 303 |
|
tpm2 | | | 68.45 270 | 66.83 274 | 73.30 260 | 78.93 279 | 48.50 302 | 79.76 223 | 71.76 289 | 47.50 312 | 69.92 289 | 83.60 260 | 42.07 314 | 88.40 218 | 48.44 295 | 79.51 297 | 83.01 265 |
|
tpm | | | 67.95 271 | 68.08 270 | 67.55 286 | 78.74 281 | 43.53 316 | 75.60 264 | 67.10 308 | 54.92 276 | 72.23 277 | 88.10 208 | 42.87 312 | 75.97 287 | 52.21 281 | 80.95 295 | 83.15 263 |
|
PatchFormer-LS_test | | | 67.91 272 | 66.49 278 | 72.17 269 | 75.29 307 | 51.85 289 | 75.68 262 | 73.62 277 | 57.23 267 | 68.64 291 | 68.13 322 | 42.19 313 | 82.76 267 | 64.06 222 | 73.51 312 | 81.89 281 |
|
WTY-MVS | | | 67.91 272 | 68.35 267 | 66.58 289 | 80.82 266 | 48.12 304 | 65.96 302 | 72.60 281 | 53.67 282 | 71.20 283 | 81.68 284 | 58.97 252 | 69.06 304 | 48.57 293 | 81.67 289 | 82.55 268 |
|
test-LLR | | | 67.21 274 | 66.74 275 | 68.63 282 | 76.45 297 | 55.21 268 | 67.89 294 | 67.14 306 | 62.43 244 | 65.08 306 | 72.39 314 | 43.41 309 | 69.37 300 | 61.00 239 | 84.89 270 | 81.31 288 |
|
sss | | | 66.92 275 | 67.26 273 | 65.90 290 | 77.23 290 | 51.10 294 | 64.79 303 | 71.72 291 | 52.12 293 | 70.13 288 | 80.18 291 | 57.96 256 | 65.36 317 | 50.21 285 | 81.01 294 | 81.25 290 |
|
tpm cat1 | | | 66.76 276 | 65.21 281 | 71.42 271 | 77.09 292 | 50.62 298 | 78.01 242 | 73.68 276 | 44.89 318 | 68.64 291 | 79.00 296 | 45.51 299 | 82.42 269 | 49.91 287 | 70.15 319 | 81.23 292 |
|
DWT-MVSNet_test | | | 66.43 277 | 64.37 282 | 72.63 266 | 74.86 311 | 50.86 296 | 76.52 256 | 72.74 280 | 54.06 280 | 65.50 303 | 68.30 321 | 32.13 326 | 84.84 254 | 61.63 236 | 73.59 311 | 82.19 274 |
|
PVSNet | | 58.17 21 | 66.41 278 | 65.63 280 | 68.75 281 | 81.96 255 | 49.88 301 | 62.19 308 | 72.51 283 | 51.03 300 | 68.04 295 | 75.34 310 | 50.84 281 | 74.77 290 | 45.82 304 | 82.96 281 | 81.60 285 |
|
tpmrst | | | 66.28 279 | 66.69 276 | 65.05 295 | 72.82 321 | 39.33 321 | 78.20 241 | 70.69 294 | 53.16 285 | 67.88 296 | 80.36 290 | 48.18 288 | 74.75 291 | 58.13 255 | 70.79 317 | 81.08 293 |
|
Patchmatch-test | | | 65.91 280 | 67.38 271 | 61.48 303 | 75.51 304 | 43.21 317 | 68.84 291 | 63.79 313 | 62.48 242 | 72.80 274 | 83.42 264 | 44.89 306 | 59.52 324 | 48.27 296 | 86.45 255 | 81.70 282 |
|
ADS-MVSNet2 | | | 65.87 281 | 63.64 286 | 72.55 267 | 73.16 318 | 56.92 258 | 67.10 299 | 74.81 270 | 49.74 307 | 66.04 301 | 82.97 268 | 46.71 291 | 77.26 282 | 42.29 308 | 69.96 320 | 83.46 256 |
|
test1235678 | | | 65.57 282 | 65.73 279 | 65.06 294 | 82.84 252 | 50.90 295 | 62.90 306 | 69.26 298 | 57.17 268 | 72.36 276 | 83.04 266 | 46.02 295 | 70.10 299 | 32.79 324 | 85.24 267 | 74.19 307 |
|
test-mter | | | 65.00 283 | 63.79 284 | 68.63 282 | 76.45 297 | 55.21 268 | 67.89 294 | 67.14 306 | 50.98 301 | 65.08 306 | 72.39 314 | 28.27 331 | 69.37 300 | 61.00 239 | 84.89 270 | 81.31 288 |
|
test0.0.03 1 | | | 64.66 284 | 64.36 283 | 65.57 292 | 75.03 310 | 46.89 306 | 64.69 304 | 61.58 319 | 62.43 244 | 71.18 284 | 77.54 300 | 43.41 309 | 68.47 305 | 40.75 312 | 82.65 285 | 81.35 287 |
|
pmmvs3 | | | 62.47 285 | 60.02 298 | 69.80 274 | 71.58 324 | 64.00 180 | 70.52 287 | 58.44 323 | 39.77 324 | 66.05 300 | 75.84 307 | 27.10 333 | 72.28 294 | 46.15 302 | 84.77 274 | 73.11 309 |
|
EPMVS | | | 62.47 285 | 62.63 289 | 62.01 299 | 70.63 326 | 38.74 322 | 74.76 269 | 52.86 328 | 53.91 281 | 67.71 297 | 80.01 292 | 39.40 319 | 66.60 312 | 55.54 268 | 68.81 324 | 80.68 297 |
|
testus | | | 62.33 287 | 63.03 287 | 60.20 307 | 78.78 280 | 40.74 319 | 59.14 313 | 69.80 297 | 49.26 309 | 71.41 281 | 74.72 312 | 52.33 279 | 63.52 319 | 29.84 326 | 82.01 288 | 76.36 302 |
|
ADS-MVSNet | | | 61.90 288 | 62.19 290 | 61.03 305 | 73.16 318 | 36.42 324 | 67.10 299 | 61.75 317 | 49.74 307 | 66.04 301 | 82.97 268 | 46.71 291 | 63.21 321 | 42.29 308 | 69.96 320 | 83.46 256 |
|
1111 | | | 61.71 289 | 63.77 285 | 55.55 312 | 78.05 285 | 25.74 330 | 60.62 309 | 67.52 301 | 66.09 211 | 74.68 263 | 86.50 226 | 16.00 336 | 59.22 325 | 38.79 314 | 85.65 261 | 81.70 282 |
|
PMMVS | | | 61.65 290 | 60.38 295 | 65.47 293 | 65.40 330 | 69.26 147 | 63.97 305 | 61.73 318 | 36.80 327 | 60.11 318 | 68.43 318 | 59.42 249 | 66.35 313 | 48.97 292 | 78.57 302 | 60.81 320 |
|
E-PMN | | | 61.59 291 | 61.62 291 | 61.49 302 | 66.81 327 | 55.40 267 | 53.77 322 | 60.34 320 | 66.80 207 | 58.90 323 | 65.50 324 | 40.48 318 | 66.12 314 | 55.72 265 | 86.25 258 | 62.95 319 |
|
TESTMET0.1,1 | | | 61.29 292 | 60.32 296 | 64.19 296 | 72.06 322 | 51.30 290 | 67.89 294 | 62.09 315 | 45.27 317 | 60.65 317 | 69.01 317 | 27.93 332 | 64.74 318 | 56.31 262 | 81.65 291 | 76.53 301 |
|
MVS-HIRNet | | | 61.16 293 | 62.92 288 | 55.87 310 | 79.09 277 | 35.34 325 | 71.83 283 | 57.98 325 | 46.56 314 | 59.05 322 | 91.14 150 | 49.95 284 | 76.43 285 | 38.74 316 | 71.92 316 | 55.84 324 |
|
EMVS | | | 61.10 294 | 60.81 294 | 61.99 300 | 65.96 329 | 55.86 264 | 53.10 323 | 58.97 322 | 67.06 203 | 56.89 327 | 63.33 325 | 40.98 316 | 67.03 309 | 54.79 272 | 86.18 259 | 63.08 318 |
|
DSMNet-mixed | | | 60.98 295 | 61.61 292 | 59.09 309 | 72.88 320 | 45.05 312 | 74.70 270 | 46.61 332 | 26.20 328 | 65.34 304 | 90.32 178 | 55.46 270 | 63.12 322 | 41.72 310 | 81.30 293 | 69.09 315 |
|
dp | | | 60.70 296 | 60.29 297 | 61.92 301 | 72.04 323 | 38.67 323 | 70.83 285 | 64.08 312 | 51.28 299 | 60.75 316 | 77.28 302 | 36.59 322 | 71.58 297 | 47.41 298 | 62.34 326 | 75.52 304 |
|
CHOSEN 280x420 | | | 59.08 297 | 56.52 302 | 66.76 288 | 76.51 295 | 64.39 177 | 49.62 325 | 59.00 321 | 43.86 320 | 55.66 328 | 68.41 320 | 35.55 323 | 68.21 306 | 43.25 307 | 76.78 308 | 67.69 316 |
|
testpf | | | 58.55 298 | 61.58 293 | 49.48 315 | 66.03 328 | 40.05 320 | 74.40 272 | 58.07 324 | 64.72 229 | 59.36 320 | 72.67 313 | 22.76 334 | 66.92 310 | 67.07 203 | 69.15 323 | 41.46 326 |
|
test2356 | | | 56.69 299 | 55.15 303 | 61.32 304 | 73.20 316 | 44.11 314 | 54.95 320 | 62.52 314 | 48.75 311 | 62.45 314 | 68.42 319 | 21.10 335 | 65.67 316 | 26.86 328 | 78.08 303 | 74.19 307 |
|
PVSNet_0 | | 51.08 22 | 56.10 300 | 54.97 304 | 59.48 308 | 75.12 309 | 53.28 281 | 55.16 319 | 61.89 316 | 44.30 319 | 59.16 321 | 62.48 326 | 54.22 275 | 65.91 315 | 35.40 321 | 47.01 327 | 59.25 322 |
|
new_pmnet | | | 55.69 301 | 57.66 300 | 49.76 314 | 75.47 305 | 30.59 327 | 59.56 311 | 51.45 330 | 43.62 321 | 62.49 313 | 75.48 308 | 40.96 317 | 49.15 330 | 37.39 318 | 72.52 313 | 69.55 314 |
|
PMMVS2 | | | 55.64 302 | 59.27 299 | 44.74 317 | 64.30 331 | 12.32 334 | 40.60 326 | 49.79 331 | 53.19 284 | 65.06 308 | 84.81 251 | 53.60 277 | 49.76 329 | 32.68 325 | 89.41 224 | 72.15 310 |
|
test12356 | | | 54.91 303 | 57.14 301 | 48.22 316 | 75.83 301 | 17.47 332 | 52.31 324 | 69.20 299 | 51.66 296 | 60.11 318 | 75.40 309 | 29.77 330 | 62.62 323 | 27.64 327 | 72.37 314 | 64.59 317 |
|
PNet_i23d | | | 52.13 304 | 51.24 306 | 54.79 313 | 75.56 302 | 45.26 310 | 54.54 321 | 52.55 329 | 66.95 204 | 57.19 326 | 65.82 323 | 13.15 338 | 63.40 320 | 36.39 320 | 39.04 329 | 55.71 325 |
|
MVE | | 40.22 23 | 51.82 305 | 50.47 307 | 55.87 310 | 62.66 332 | 51.91 288 | 31.61 328 | 39.28 333 | 40.65 323 | 50.76 329 | 74.98 311 | 56.24 265 | 44.67 331 | 33.94 323 | 64.11 325 | 71.04 313 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
.test1245 | | | 48.02 306 | 54.41 305 | 28.84 319 | 78.05 285 | 25.74 330 | 60.62 309 | 67.52 301 | 66.09 211 | 74.68 263 | 86.50 226 | 16.00 336 | 59.22 325 | 38.79 314 | 1.47 331 | 1.55 330 |
|
pcd1.5k->3k | | | 38.83 307 | 41.11 308 | 32.01 318 | 93.13 60 | 0.00 338 | 0.00 329 | 91.38 109 | 0.00 333 | 0.00 334 | 0.00 335 | 89.24 16 | 0.00 336 | 0.00 333 | 96.24 92 | 96.02 48 |
|
cdsmvs_eth3d_5k | | | 20.81 308 | 27.75 309 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 85.44 212 | 0.00 333 | 0.00 334 | 82.82 271 | 81.46 90 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
tmp_tt | | | 20.25 309 | 24.50 310 | 7.49 321 | 4.47 334 | 8.70 335 | 34.17 327 | 25.16 335 | 1.00 330 | 32.43 331 | 18.49 329 | 39.37 320 | 9.21 333 | 21.64 329 | 43.75 328 | 4.57 328 |
|
ab-mvs-re | | | 6.65 310 | 8.87 311 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 79.80 294 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
pcd_1.5k_mvsjas | | | 6.41 311 | 8.55 312 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 76.94 130 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
test123 | | | 6.27 312 | 8.08 313 | 0.84 322 | 1.11 336 | 0.57 336 | 62.90 306 | 0.82 337 | 0.54 331 | 1.07 333 | 2.75 334 | 1.26 339 | 0.30 334 | 1.04 331 | 1.26 333 | 1.66 329 |
|
testmvs | | | 5.91 313 | 7.65 314 | 0.72 323 | 1.20 335 | 0.37 337 | 59.14 313 | 0.67 338 | 0.49 332 | 1.11 332 | 2.76 333 | 0.94 340 | 0.24 335 | 1.02 332 | 1.47 331 | 1.55 330 |
|
sosnet-low-res | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sosnet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uncertanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
Regformer | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sam_mvs1 | | | | | | | | | | | | | 46.11 294 | | | | |
|
sam_mvs | | | | | | | | | | | | | 45.92 297 | | | | |
|
semantic-postprocess | | | | | 84.34 135 | 83.93 241 | 69.66 143 | | 81.09 241 | 72.43 158 | 86.47 156 | 90.19 180 | 57.56 259 | 93.15 121 | 77.45 124 | 86.39 257 | 90.22 188 |
|
ambc | | | | | 82.98 168 | 90.55 127 | 64.86 173 | 88.20 71 | 89.15 163 | | 89.40 108 | 93.96 85 | 71.67 204 | 91.38 169 | 78.83 112 | 96.55 79 | 92.71 133 |
|
MTGPA | | | | | | | | | 91.81 87 | | | | | | | | |
|
test_post1 | | | | | | | | 78.85 237 | | | | 3.13 331 | 45.19 304 | 80.13 278 | 58.11 256 | | |
|
test_post | | | | | | | | | | | | 3.10 332 | 45.43 300 | 77.22 283 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 81.71 283 | 45.93 296 | 87.01 229 | | | |
|
GG-mvs-BLEND | | | | | 67.16 287 | 73.36 315 | 46.54 308 | 84.15 133 | 55.04 327 | | 58.64 324 | 61.95 327 | 29.93 329 | 83.87 263 | 38.71 317 | 76.92 307 | 71.07 312 |
|
MTMP | | | | | | | | | 33.14 334 | | | | | | | | |
|
gm-plane-assit | | | | | | 75.42 306 | 44.97 313 | | | 52.17 290 | | 72.36 316 | | 87.90 221 | 54.10 275 | | |
|
test9_res | | | | | | | | | | | | | | | 80.83 87 | 96.45 85 | 90.57 180 |
|
TEST9 | | | | | | 92.34 80 | 79.70 66 | 83.94 138 | 90.32 133 | 65.41 221 | 84.49 179 | 90.97 158 | 82.03 81 | 93.63 85 | | | |
|
test_8 | | | | | | 92.09 87 | 78.87 72 | 83.82 143 | 90.31 135 | 65.79 214 | 84.36 182 | 90.96 160 | 81.93 83 | 93.44 106 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 79.68 104 | 96.16 94 | 90.22 188 |
|
agg_prior | | | | | | 91.58 101 | 77.69 79 | | 90.30 137 | | 84.32 183 | | | 93.18 117 | | | |
|
TestCases | | | | | 89.68 47 | 91.59 98 | 83.40 45 | | 95.44 5 | 79.47 75 | 88.00 129 | 93.03 100 | 82.66 71 | 91.47 163 | 70.81 171 | 96.14 96 | 94.16 95 |
|
test_prior4 | | | | | | | 78.97 71 | 84.59 127 | | | | | | | | | |
|
test_prior2 | | | | | | | | 83.37 160 | | 75.43 126 | 84.58 177 | 91.57 136 | 81.92 85 | | 79.54 106 | 96.97 67 | |
|
test_prior | | | | | 86.32 90 | 90.59 125 | 71.99 126 | | 92.85 58 | | | | | 94.17 65 | | | 92.80 130 |
|
旧先验2 | | | | | | | | 81.73 202 | | 56.88 269 | 86.54 155 | | | 84.90 253 | 72.81 161 | | |
|
新几何2 | | | | | | | | 81.72 203 | | | | | | | | | |
|
新几何1 | | | | | 82.95 169 | 93.96 44 | 78.56 75 | | 80.24 244 | 55.45 273 | 83.93 188 | 91.08 154 | 71.19 206 | 88.33 219 | 65.84 213 | 93.07 177 | 81.95 279 |
|
旧先验1 | | | | | | 91.97 90 | 71.77 128 | | 81.78 237 | | | 91.84 129 | 73.92 163 | | | 93.65 167 | 83.61 254 |
|
无先验 | | | | | | | | 82.81 173 | 85.62 211 | 58.09 262 | | | | 91.41 167 | 67.95 198 | | 84.48 245 |
|
原ACMM2 | | | | | | | | 82.26 192 | | | | | | | | | |
|
原ACMM1 | | | | | 84.60 126 | 92.81 70 | 74.01 107 | | 91.50 95 | 62.59 240 | 82.73 200 | 90.67 170 | 76.53 137 | 94.25 60 | 69.24 185 | 95.69 115 | 85.55 236 |
|
test222 | | | | | | 93.31 55 | 76.54 92 | 79.38 230 | 77.79 253 | 52.59 287 | 82.36 201 | 90.84 164 | 66.83 220 | | | 91.69 196 | 81.25 290 |
|
testdata2 | | | | | | | | | | | | | | 86.43 238 | 63.52 225 | | |
|
segment_acmp | | | | | | | | | | | | | 81.94 82 | | | | |
|
testdata | | | | | 79.54 217 | 92.87 66 | 72.34 120 | | 80.14 246 | 59.91 258 | 85.47 168 | 91.75 134 | 67.96 216 | 85.24 249 | 68.57 194 | 92.18 194 | 81.06 295 |
|
testdata1 | | | | | | | | 79.62 225 | | 73.95 139 | | | | | | | |
|
test12 | | | | | 86.57 85 | 90.74 122 | 72.63 115 | | 90.69 122 | | 82.76 199 | | 79.20 107 | 94.80 48 | | 95.32 121 | 92.27 145 |
|
plane_prior7 | | | | | | 93.45 51 | 77.31 85 | | | | | | | | | | |
|
plane_prior6 | | | | | | 92.61 71 | 76.54 92 | | | | | | 74.84 150 | | | | |
|
plane_prior5 | | | | | | | | | 93.61 30 | | | | | 95.22 36 | 80.78 88 | 95.83 110 | 94.46 86 |
|
plane_prior4 | | | | | | | | | | | | 92.95 104 | | | | | |
|
plane_prior3 | | | | | | | 76.85 90 | | | 77.79 99 | 86.55 150 | | | | | | |
|
plane_prior2 | | | | | | | | 89.45 56 | | 79.44 77 | | | | | | | |
|
plane_prior1 | | | | | | 92.83 69 | | | | | | | | | | | |
|
plane_prior | | | | | | | 76.42 95 | 87.15 89 | | 75.94 121 | | | | | | 95.03 131 | |
|
n2 | | | | | | | | | 0.00 339 | | | | | | | | |
|
nn | | | | | | | | | 0.00 339 | | | | | | | | |
|
door-mid | | | | | | | | | 74.45 274 | | | | | | | | |
|
lessismore_v0 | | | | | 85.95 101 | 91.10 116 | 70.99 137 | | 70.91 293 | | 91.79 60 | 94.42 67 | 61.76 237 | 92.93 129 | 79.52 108 | 93.03 179 | 93.93 102 |
|
LGP-MVS_train | | | | | 90.82 34 | 94.75 35 | 81.69 51 | | 94.27 13 | 82.35 46 | 93.67 30 | 94.82 54 | 91.18 6 | 95.52 24 | 85.36 34 | 98.73 8 | 95.23 72 |
|
test11 | | | | | | | | | 91.46 101 | | | | | | | | |
|
door | | | | | | | | | 72.57 282 | | | | | | | | |
|
HQP5-MVS | | | | | | | 70.66 138 | | | | | | | | | | |
|
HQP-NCC | | | | | | 91.19 111 | | 84.77 122 | | 73.30 146 | 80.55 224 | | | | | | |
|
ACMP_Plane | | | | | | 91.19 111 | | 84.77 122 | | 73.30 146 | 80.55 224 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.30 126 | | |
|
HQP4-MVS | | | | | | | | | | | 80.56 223 | | | 94.61 52 | | | 93.56 116 |
|
HQP3-MVS | | | | | | | | | 92.68 64 | | | | | | | 94.47 149 | |
|
HQP2-MVS | | | | | | | | | | | | | 72.10 197 | | | | |
|
NP-MVS | | | | | | 91.95 91 | 74.55 104 | | | | | 90.17 182 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 27.60 329 | 70.76 286 | | 46.47 315 | 61.27 315 | | 45.20 303 | | 49.18 291 | | 83.75 253 |
|
MDTV_nov1_ep13 | | | | 68.29 269 | | 78.03 287 | 43.87 315 | 74.12 274 | 72.22 284 | 52.17 290 | 67.02 298 | 85.54 235 | 45.36 301 | 80.85 274 | 55.73 264 | 84.42 275 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 95.74 114 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 97.35 58 | |
|
Test By Simon | | | | | | | | | | | | | 79.09 108 | | | | |
|
ITE_SJBPF | | | | | 90.11 45 | 90.72 123 | 84.97 33 | | 90.30 137 | 81.56 57 | 90.02 83 | 91.20 147 | 82.40 75 | 90.81 182 | 73.58 151 | 94.66 145 | 94.56 81 |
|
DeepMVS_CX | | | | | 24.13 320 | 32.95 333 | 29.49 328 | | 21.63 336 | 12.07 329 | 37.95 330 | 45.07 328 | 30.84 327 | 19.21 332 | 17.94 330 | 33.06 330 | 23.69 327 |
|