WR-MVS | | | 89.79 22 | 93.66 4 | 85.27 37 | 91.32 22 | 88.27 43 | 93.49 36 | 79.86 8 | 92.75 8 | 75.37 111 | 96.86 1 | 98.38 6 | 75.10 67 | 95.93 8 | 94.07 15 | 96.46 5 | 89.39 56 |
|
DTE-MVSNet | | | 88.99 34 | 92.77 11 | 84.59 41 | 93.31 2 | 88.10 46 | 90.96 50 | 83.09 2 | 91.38 12 | 76.21 105 | 96.03 2 | 98.04 11 | 70.78 113 | 95.65 14 | 92.32 33 | 93.18 50 | 87.84 69 |
|
PEN-MVS | | | 88.86 37 | 92.92 8 | 84.11 49 | 92.92 5 | 88.05 48 | 90.83 52 | 82.67 5 | 91.04 16 | 74.83 114 | 95.97 3 | 98.47 4 | 70.38 114 | 95.70 13 | 92.43 31 | 93.05 54 | 88.78 62 |
|
WR-MVS_H | | | 88.99 34 | 93.28 5 | 83.99 50 | 91.92 11 | 89.13 37 | 91.95 44 | 83.23 1 | 90.14 29 | 71.92 131 | 95.85 4 | 98.01 13 | 71.83 104 | 95.82 9 | 93.19 23 | 93.07 53 | 90.83 46 |
|
PS-CasMVS | | | 89.07 31 | 93.23 6 | 84.21 47 | 92.44 8 | 88.23 45 | 90.54 60 | 82.95 3 | 90.50 22 | 75.31 112 | 95.80 5 | 98.37 7 | 71.16 107 | 96.30 5 | 93.32 22 | 92.88 55 | 90.11 50 |
|
TranMVSNet+NR-MVSNet | | | 85.23 63 | 89.38 54 | 80.39 91 | 88.78 50 | 83.77 75 | 87.40 101 | 76.75 33 | 85.47 70 | 68.99 144 | 95.18 6 | 97.55 19 | 67.13 131 | 91.61 54 | 89.13 61 | 93.26 48 | 82.95 108 |
|
CP-MVSNet | | | 88.71 39 | 92.63 13 | 84.13 48 | 92.39 9 | 88.09 47 | 90.47 65 | 82.86 4 | 88.79 41 | 75.16 113 | 94.87 7 | 97.68 17 | 71.05 109 | 96.16 6 | 93.18 24 | 92.85 56 | 89.64 54 |
|
v7n | | | 87.11 47 | 90.46 47 | 83.19 53 | 85.22 76 | 83.69 76 | 90.03 71 | 68.20 85 | 91.01 17 | 86.71 35 | 94.80 8 | 98.46 5 | 77.69 46 | 91.10 64 | 85.98 87 | 91.30 76 | 88.19 65 |
|
TransMVSNet (Re) | | | 79.05 131 | 86.66 76 | 70.18 157 | 83.32 99 | 75.99 154 | 77.54 169 | 63.98 132 | 90.68 21 | 55.84 179 | 94.80 8 | 96.06 48 | 53.73 187 | 86.27 106 | 83.22 114 | 86.65 144 | 79.61 136 |
|
Baseline_NR-MVSNet | | | 82.79 95 | 86.51 77 | 78.44 112 | 88.30 54 | 75.62 160 | 87.81 93 | 74.97 44 | 81.53 110 | 66.84 155 | 94.71 10 | 96.46 33 | 66.90 132 | 91.79 50 | 83.37 113 | 85.83 162 | 82.09 118 |
|
v52 | | | 86.26 55 | 90.85 41 | 80.91 74 | 72.49 191 | 81.25 105 | 90.55 58 | 60.30 172 | 90.43 25 | 87.24 23 | 94.64 11 | 98.30 10 | 83.16 18 | 92.86 44 | 86.82 80 | 91.69 70 | 91.65 37 |
|
V4 | | | 86.26 55 | 90.85 41 | 80.91 74 | 72.49 191 | 81.25 105 | 90.55 58 | 60.31 171 | 90.44 24 | 87.23 25 | 94.64 11 | 98.31 9 | 83.17 16 | 92.87 43 | 86.82 80 | 91.69 70 | 91.64 38 |
|
Anonymous20231211 | | | 85.16 65 | 91.64 34 | 77.61 117 | 88.54 52 | 79.81 121 | 83.12 136 | 74.68 45 | 98.37 1 | 66.79 156 | 94.56 13 | 99.60 1 | 61.64 151 | 91.49 56 | 89.82 53 | 90.91 80 | 87.80 70 |
|
SixPastTwentyTwo | | | 89.14 28 | 92.19 27 | 85.58 32 | 84.62 80 | 82.56 85 | 90.53 61 | 71.93 57 | 91.95 10 | 85.89 37 | 94.22 14 | 97.25 22 | 85.42 5 | 95.73 12 | 91.71 40 | 95.08 28 | 91.89 34 |
|
v748 | | | 85.21 64 | 89.62 52 | 80.08 93 | 80.71 133 | 80.27 118 | 85.05 127 | 63.79 134 | 90.47 23 | 83.54 62 | 94.21 15 | 98.52 2 | 76.84 53 | 90.97 69 | 84.25 102 | 90.53 83 | 88.62 63 |
|
Anonymous20240521 | | | 83.87 76 | 89.96 50 | 76.76 121 | 87.27 63 | 82.39 87 | 86.26 119 | 69.89 67 | 88.91 38 | 60.94 170 | 94.18 16 | 97.20 24 | 63.54 144 | 93.18 37 | 88.64 64 | 92.63 59 | 85.22 84 |
|
COLMAP_ROB | | 85.66 2 | 91.85 2 | 95.01 2 | 88.16 12 | 88.98 48 | 92.86 2 | 95.51 20 | 72.17 56 | 94.95 5 | 91.27 3 | 94.11 17 | 97.77 14 | 84.22 8 | 96.49 4 | 95.27 5 | 96.79 2 | 93.60 11 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
TDRefinement | | | 93.16 1 | 95.57 1 | 90.36 1 | 88.79 49 | 93.57 1 | 97.27 1 | 78.23 20 | 95.55 2 | 93.00 1 | 93.98 18 | 96.01 50 | 87.53 1 | 97.69 1 | 96.81 1 | 97.33 1 | 95.34 4 |
|
pmmvs6 | | | 80.46 120 | 88.34 64 | 71.26 144 | 81.96 125 | 77.51 134 | 77.54 169 | 68.83 77 | 93.72 6 | 55.92 178 | 93.94 19 | 98.03 12 | 55.94 171 | 89.21 82 | 85.61 90 | 87.36 135 | 80.38 129 |
|
ACMH | | 78.40 12 | 88.94 36 | 92.62 14 | 84.65 40 | 86.45 67 | 87.16 55 | 91.47 46 | 68.79 78 | 95.49 3 | 89.74 6 | 93.55 20 | 98.50 3 | 77.96 45 | 94.14 33 | 89.57 57 | 93.49 44 | 89.94 52 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
APDe-MVS | | | 89.85 20 | 92.91 9 | 86.29 27 | 90.47 37 | 91.34 7 | 96.04 16 | 76.41 38 | 91.11 15 | 78.50 100 | 93.44 21 | 95.82 54 | 81.55 27 | 93.16 38 | 91.90 38 | 94.77 33 | 93.58 14 |
|
DeepC-MVS | | 83.59 4 | 90.37 12 | 92.56 16 | 87.82 16 | 91.26 26 | 92.33 3 | 94.72 29 | 80.04 7 | 90.01 31 | 84.61 45 | 93.33 22 | 94.22 91 | 80.59 29 | 92.90 42 | 92.52 29 | 95.69 21 | 92.57 26 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
UniMVSNet_NR-MVSNet | | | 84.62 71 | 88.00 68 | 80.68 83 | 88.18 56 | 83.83 74 | 87.06 111 | 76.47 37 | 81.46 112 | 70.49 137 | 93.24 23 | 95.56 64 | 68.13 125 | 90.43 74 | 88.47 65 | 93.78 43 | 83.02 105 |
|
FC-MVSNet-test | | | 75.91 148 | 83.59 129 | 66.95 184 | 76.63 179 | 69.07 186 | 85.33 125 | 64.97 117 | 84.87 76 | 41.95 219 | 93.17 24 | 87.04 153 | 47.78 208 | 91.09 65 | 85.56 91 | 85.06 170 | 74.34 159 |
|
pm-mvs1 | | | 78.21 134 | 85.68 91 | 69.50 163 | 80.38 136 | 75.73 158 | 76.25 184 | 65.04 115 | 87.59 49 | 54.47 184 | 93.16 25 | 95.99 52 | 54.20 180 | 86.37 104 | 82.98 116 | 86.64 145 | 77.96 149 |
|
LTVRE_ROB | | 86.82 1 | 91.55 3 | 94.43 3 | 88.19 11 | 83.19 104 | 86.35 62 | 93.60 35 | 78.79 17 | 95.48 4 | 91.79 2 | 93.08 26 | 97.21 23 | 86.34 3 | 97.06 2 | 96.27 3 | 95.46 23 | 95.56 3 |
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 |
TSAR-MVS + ACMM | | | 89.14 28 | 92.11 28 | 85.67 31 | 89.27 45 | 90.61 23 | 90.98 49 | 79.48 11 | 88.86 39 | 79.80 91 | 93.01 27 | 93.53 99 | 83.17 16 | 92.75 46 | 92.45 30 | 91.32 75 | 93.59 12 |
|
PMVS | | 79.51 9 | 90.23 15 | 92.67 12 | 87.39 21 | 90.16 38 | 88.75 39 | 93.64 34 | 75.78 41 | 90.00 32 | 83.70 57 | 92.97 28 | 92.22 114 | 86.13 4 | 97.01 3 | 96.79 2 | 94.94 29 | 90.96 44 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
TSAR-MVS + MP. | | | 89.67 23 | 92.25 24 | 86.65 25 | 91.53 17 | 90.98 16 | 96.15 14 | 73.30 53 | 87.88 47 | 81.83 76 | 92.92 29 | 95.15 74 | 82.23 22 | 93.58 34 | 92.25 34 | 94.87 30 | 93.01 23 |
|
anonymousdsp | | | 85.62 58 | 90.53 45 | 79.88 94 | 64.64 217 | 76.35 149 | 96.28 13 | 53.53 205 | 85.63 69 | 81.59 82 | 92.81 30 | 97.71 16 | 86.88 2 | 94.56 26 | 92.83 25 | 96.35 6 | 93.84 9 |
|
SD-MVS | | | 89.91 18 | 92.23 26 | 87.19 22 | 91.31 23 | 89.79 33 | 94.31 31 | 75.34 43 | 89.26 35 | 81.79 77 | 92.68 31 | 95.08 76 | 83.88 11 | 93.10 39 | 92.69 26 | 96.54 4 | 93.02 22 |
|
v13 | | | 83.75 78 | 86.20 82 | 80.89 76 | 83.38 98 | 81.93 91 | 88.58 83 | 66.09 98 | 83.55 85 | 84.28 47 | 92.67 32 | 96.79 27 | 74.67 72 | 84.42 132 | 79.72 147 | 88.36 107 | 84.31 90 |
|
DU-MVS | | | 84.88 68 | 88.27 65 | 80.92 73 | 88.30 54 | 83.59 77 | 87.06 111 | 78.35 18 | 80.64 120 | 70.49 137 | 92.67 32 | 96.91 25 | 68.13 125 | 91.79 50 | 89.29 60 | 93.20 49 | 83.02 105 |
|
NR-MVSNet | | | 82.89 93 | 87.43 73 | 77.59 118 | 83.91 90 | 83.59 77 | 87.10 110 | 78.35 18 | 80.64 120 | 68.85 145 | 92.67 32 | 96.50 31 | 54.19 181 | 87.19 100 | 88.68 63 | 93.16 52 | 82.75 111 |
|
UniMVSNet (Re) | | | 84.95 67 | 88.53 59 | 80.78 78 | 87.82 59 | 84.21 72 | 88.03 90 | 76.50 36 | 81.18 117 | 69.29 141 | 92.63 35 | 96.83 26 | 69.07 121 | 91.23 61 | 89.60 56 | 93.97 42 | 84.00 94 |
|
FC-MVSNet-train | | | 79.20 130 | 86.29 81 | 70.94 149 | 84.06 86 | 77.67 133 | 85.68 120 | 64.11 130 | 82.90 90 | 52.22 197 | 92.57 36 | 93.69 95 | 49.52 205 | 88.30 90 | 86.93 76 | 90.03 87 | 81.95 120 |
|
v12 | | | 83.59 80 | 86.00 88 | 80.77 81 | 83.30 100 | 81.83 92 | 88.45 84 | 65.95 101 | 83.20 87 | 84.15 48 | 92.54 37 | 96.71 28 | 74.50 74 | 84.19 134 | 79.64 148 | 88.30 108 | 83.93 95 |
|
v11 | | | 83.30 86 | 85.58 93 | 80.64 84 | 83.53 95 | 81.74 94 | 88.30 87 | 65.46 110 | 82.75 93 | 84.63 44 | 92.49 38 | 96.17 45 | 73.90 83 | 82.69 151 | 79.59 149 | 88.04 116 | 83.66 97 |
|
ESAPD | | | 89.27 27 | 91.76 31 | 86.36 26 | 90.60 36 | 90.40 27 | 95.08 25 | 77.43 29 | 87.49 50 | 80.35 89 | 92.38 39 | 94.32 90 | 80.59 29 | 92.69 47 | 91.58 41 | 94.13 39 | 93.44 17 |
|
V9 | | | 83.42 84 | 85.81 90 | 80.63 85 | 83.20 103 | 81.73 95 | 88.29 88 | 65.78 105 | 82.87 91 | 83.99 53 | 92.38 39 | 96.60 30 | 74.30 75 | 83.93 135 | 79.58 150 | 88.24 111 | 83.55 99 |
|
ACMH+ | | 79.05 11 | 89.62 25 | 93.08 7 | 85.58 32 | 88.58 51 | 89.26 36 | 92.18 43 | 74.23 49 | 93.55 7 | 82.66 68 | 92.32 41 | 98.35 8 | 80.29 31 | 95.28 18 | 92.34 32 | 95.52 22 | 90.43 47 |
|
V14 | | | 83.23 87 | 85.59 92 | 80.48 89 | 83.09 106 | 81.63 97 | 88.13 89 | 65.61 107 | 82.53 95 | 83.81 55 | 92.17 42 | 96.50 31 | 74.07 80 | 83.66 137 | 79.51 152 | 88.17 113 | 83.16 103 |
|
v15 | | | 83.06 91 | 85.39 94 | 80.35 92 | 83.01 107 | 81.53 99 | 87.98 92 | 65.47 109 | 82.19 100 | 83.66 58 | 92.00 43 | 96.40 37 | 73.87 84 | 83.39 139 | 79.44 153 | 88.10 115 | 82.76 110 |
|
new-patchmatchnet | | | 62.59 207 | 73.79 183 | 49.53 225 | 76.98 171 | 53.57 221 | 53.46 236 | 54.64 197 | 85.43 71 | 28.81 235 | 91.94 44 | 96.41 36 | 25.28 232 | 76.80 183 | 53.66 229 | 57.99 226 | 58.69 215 |
|
Gipuma | | | 86.47 52 | 89.25 55 | 83.23 52 | 83.88 91 | 78.78 125 | 85.35 124 | 68.42 82 | 92.69 9 | 89.03 12 | 91.94 44 | 96.32 41 | 81.80 25 | 94.45 27 | 86.86 78 | 90.91 80 | 83.69 96 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
v1240 | | | 83.57 81 | 84.94 104 | 81.97 64 | 84.05 87 | 81.27 104 | 89.46 75 | 66.06 99 | 81.31 116 | 87.50 21 | 91.88 46 | 95.46 67 | 76.25 57 | 81.16 167 | 80.51 139 | 88.52 105 | 82.98 107 |
|
v1192 | | | 83.61 79 | 85.23 98 | 81.72 67 | 84.05 87 | 82.15 89 | 89.54 73 | 66.20 96 | 81.38 114 | 86.76 34 | 91.79 47 | 96.03 49 | 74.88 70 | 81.81 162 | 80.92 134 | 88.91 99 | 82.50 113 |
|
v1921920 | | | 83.49 82 | 84.94 104 | 81.80 66 | 83.78 92 | 81.20 108 | 89.50 74 | 65.91 102 | 81.64 108 | 87.18 27 | 91.70 48 | 95.39 68 | 75.85 61 | 81.56 165 | 80.27 142 | 88.60 103 | 82.80 109 |
|
v1144 | | | 83.22 88 | 85.01 101 | 81.14 71 | 83.76 93 | 81.60 98 | 88.95 79 | 65.58 108 | 81.89 102 | 85.80 38 | 91.68 49 | 95.84 53 | 74.04 81 | 82.12 159 | 80.56 138 | 88.70 102 | 81.41 123 |
|
APD-MVS | | | 89.14 28 | 91.25 40 | 86.67 24 | 91.73 15 | 91.02 15 | 95.50 21 | 77.74 23 | 84.04 84 | 79.47 95 | 91.48 50 | 94.85 81 | 81.14 28 | 92.94 41 | 92.20 36 | 94.47 37 | 92.24 29 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
v7 | | | 82.76 96 | 84.65 107 | 80.55 87 | 83.27 101 | 81.77 93 | 88.66 81 | 65.10 114 | 79.23 138 | 83.60 59 | 91.47 51 | 95.47 65 | 74.12 77 | 82.61 152 | 80.66 135 | 88.52 105 | 81.35 124 |
|
v144192 | | | 83.43 83 | 84.97 103 | 81.63 69 | 83.43 96 | 81.23 107 | 89.42 76 | 66.04 100 | 81.45 113 | 86.40 36 | 91.46 52 | 95.70 61 | 75.76 63 | 82.14 158 | 80.23 143 | 88.74 100 | 82.57 112 |
|
v10 | | | 83.17 90 | 85.22 99 | 80.78 78 | 83.26 102 | 82.99 81 | 88.66 81 | 66.49 94 | 79.24 137 | 83.60 59 | 91.46 52 | 95.47 65 | 74.12 77 | 82.60 153 | 80.66 135 | 88.53 104 | 84.11 93 |
|
v1 | | | 82.27 100 | 84.32 112 | 79.87 95 | 82.86 110 | 80.32 114 | 87.57 98 | 63.47 140 | 81.87 104 | 84.13 49 | 91.34 54 | 96.29 42 | 73.23 94 | 82.39 154 | 79.08 164 | 87.94 118 | 78.98 141 |
|
v1141 | | | 82.26 101 | 84.32 112 | 79.85 96 | 82.86 110 | 80.31 115 | 87.58 96 | 63.48 138 | 81.86 105 | 84.03 52 | 91.33 55 | 96.28 43 | 73.23 94 | 82.39 154 | 79.08 164 | 87.93 119 | 78.97 142 |
|
divwei89l23v2f112 | | | 82.26 101 | 84.32 112 | 79.85 96 | 82.86 110 | 80.31 115 | 87.58 96 | 63.48 138 | 81.88 103 | 84.05 51 | 91.33 55 | 96.27 44 | 73.23 94 | 82.39 154 | 79.08 164 | 87.93 119 | 78.97 142 |
|
gm-plane-assit | | | 71.56 177 | 69.99 190 | 73.39 137 | 84.43 84 | 73.21 171 | 90.42 67 | 51.36 212 | 84.08 82 | 76.00 107 | 91.30 57 | 37.09 238 | 59.01 159 | 73.65 198 | 70.24 195 | 79.09 188 | 60.37 211 |
|
v2v482 | | | 82.20 104 | 84.26 116 | 79.81 98 | 82.67 113 | 80.18 119 | 87.67 95 | 63.96 133 | 81.69 107 | 84.73 43 | 91.27 58 | 96.33 40 | 72.05 103 | 81.94 161 | 79.56 151 | 87.79 122 | 78.84 144 |
|
SMA-MVS | | | 90.37 12 | 92.54 17 | 87.83 15 | 91.78 13 | 90.56 25 | 95.35 22 | 77.47 27 | 90.80 20 | 88.51 15 | 91.24 59 | 92.22 114 | 79.16 36 | 94.32 30 | 93.72 19 | 94.75 34 | 94.93 5 |
|
tfpnnormal | | | 77.16 137 | 84.26 116 | 68.88 166 | 81.02 132 | 75.02 161 | 76.52 181 | 63.30 142 | 87.29 53 | 52.40 195 | 91.24 59 | 93.97 92 | 54.85 179 | 85.46 114 | 81.08 132 | 85.18 169 | 75.76 156 |
|
v6 | | | 81.77 110 | 83.96 122 | 79.22 104 | 82.41 115 | 80.45 113 | 87.26 102 | 62.91 150 | 79.29 134 | 81.65 79 | 91.08 61 | 95.74 57 | 73.32 89 | 82.84 144 | 79.21 160 | 87.73 124 | 79.07 138 |
|
OPM-MVS | | | 89.82 21 | 92.24 25 | 86.99 23 | 90.86 32 | 89.35 35 | 95.07 26 | 75.91 40 | 91.16 14 | 86.87 32 | 91.07 62 | 97.29 21 | 79.13 37 | 93.32 35 | 91.99 37 | 94.12 40 | 91.49 40 |
|
v1neww | | | 81.76 111 | 83.95 123 | 79.21 105 | 82.41 115 | 80.46 111 | 87.26 102 | 62.93 146 | 79.28 135 | 81.62 80 | 91.06 63 | 95.72 59 | 73.31 90 | 82.83 145 | 79.22 158 | 87.73 124 | 79.07 138 |
|
v7new | | | 81.76 111 | 83.95 123 | 79.21 105 | 82.41 115 | 80.46 111 | 87.26 102 | 62.93 146 | 79.28 135 | 81.62 80 | 91.06 63 | 95.72 59 | 73.31 90 | 82.83 145 | 79.22 158 | 87.73 124 | 79.07 138 |
|
v8 | | | 82.20 104 | 84.56 109 | 79.45 99 | 82.42 114 | 81.65 96 | 87.26 102 | 64.27 125 | 79.36 133 | 81.70 78 | 91.04 65 | 95.75 56 | 73.30 92 | 82.82 147 | 79.18 161 | 87.74 123 | 82.09 118 |
|
v17 | | | 82.09 106 | 84.45 110 | 79.33 101 | 82.41 115 | 81.31 102 | 87.26 102 | 64.50 124 | 78.72 140 | 80.73 86 | 90.90 66 | 95.57 62 | 73.37 88 | 83.06 140 | 79.25 157 | 87.70 127 | 82.35 116 |
|
v16 | | | 81.92 109 | 84.32 112 | 79.12 107 | 82.31 120 | 81.29 103 | 87.20 107 | 64.51 123 | 78.16 144 | 79.76 92 | 90.86 67 | 95.23 71 | 73.29 93 | 83.05 141 | 79.29 156 | 87.63 128 | 82.34 117 |
|
v18 | | | 81.62 114 | 83.99 121 | 78.86 108 | 82.08 124 | 81.12 109 | 86.93 114 | 64.24 126 | 77.44 146 | 79.47 95 | 90.53 68 | 94.99 79 | 72.99 97 | 82.72 150 | 79.18 161 | 87.48 131 | 81.91 121 |
|
IterMVS-LS | | | 79.79 123 | 82.56 135 | 76.56 124 | 81.83 127 | 77.85 132 | 79.90 155 | 69.42 73 | 78.93 139 | 71.21 134 | 90.47 69 | 85.20 161 | 70.86 112 | 80.54 172 | 80.57 137 | 86.15 153 | 84.36 89 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
LGP-MVS_train | | | 90.56 9 | 92.38 20 | 88.43 10 | 90.88 31 | 91.15 11 | 95.35 22 | 77.65 24 | 86.26 64 | 87.23 25 | 90.45 70 | 97.35 20 | 83.20 15 | 95.44 16 | 93.41 21 | 96.28 8 | 92.63 25 |
|
HSP-MVS | | | 88.32 40 | 90.71 44 | 85.53 34 | 90.63 35 | 92.01 4 | 96.15 14 | 77.52 26 | 86.02 65 | 81.39 84 | 90.21 71 | 96.08 47 | 76.38 56 | 88.30 90 | 86.70 82 | 91.12 79 | 95.64 1 |
|
ACMMP_Plus | | | 89.86 19 | 91.96 29 | 87.42 20 | 91.00 29 | 90.08 29 | 96.00 17 | 76.61 35 | 89.28 34 | 87.73 18 | 90.04 72 | 91.80 122 | 78.71 39 | 94.36 29 | 93.82 18 | 94.48 36 | 94.32 6 |
|
CSCG | | | 88.12 43 | 91.45 36 | 84.23 46 | 88.12 57 | 90.59 24 | 90.57 56 | 68.60 80 | 91.37 13 | 83.45 65 | 89.94 73 | 95.14 75 | 78.71 39 | 91.45 57 | 88.21 69 | 95.96 12 | 93.44 17 |
|
Vis-MVSNet | | | 83.32 85 | 88.12 67 | 77.71 115 | 77.91 164 | 83.44 79 | 90.58 55 | 69.49 71 | 81.11 118 | 67.10 154 | 89.85 74 | 91.48 126 | 71.71 105 | 91.34 58 | 89.37 58 | 89.48 94 | 90.26 48 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
V42 | | | 79.59 127 | 83.59 129 | 74.93 132 | 69.61 203 | 77.05 142 | 86.59 117 | 55.84 194 | 78.42 143 | 77.29 103 | 89.84 75 | 95.08 76 | 74.12 77 | 83.05 141 | 80.11 145 | 86.12 154 | 81.59 122 |
|
LS3D | | | 89.02 32 | 91.69 33 | 85.91 30 | 89.72 42 | 90.81 19 | 92.56 42 | 71.69 58 | 90.83 19 | 87.24 23 | 89.71 76 | 92.07 118 | 78.37 42 | 94.43 28 | 92.59 28 | 95.86 13 | 91.35 41 |
|
TSAR-MVS + COLMAP | | | 85.51 59 | 88.36 63 | 82.19 63 | 86.05 71 | 87.69 50 | 90.50 63 | 70.60 64 | 86.40 61 | 82.33 69 | 89.69 77 | 92.52 109 | 74.01 82 | 87.53 94 | 86.84 79 | 89.63 91 | 87.80 70 |
|
ACMMPR | | | 91.30 4 | 92.88 10 | 89.46 4 | 91.92 11 | 91.61 5 | 96.60 5 | 79.46 12 | 90.08 30 | 88.53 14 | 89.54 78 | 95.57 62 | 84.25 7 | 95.24 20 | 94.27 13 | 95.97 11 | 93.85 8 |
|
v148 | | | 79.33 129 | 82.32 137 | 75.84 126 | 80.14 138 | 75.74 157 | 81.98 143 | 57.06 190 | 81.51 111 | 79.36 97 | 89.42 79 | 96.42 35 | 71.32 106 | 81.54 166 | 75.29 182 | 85.20 168 | 76.32 152 |
|
DeepPCF-MVS | | 81.61 6 | 87.95 45 | 90.29 48 | 85.22 38 | 87.48 60 | 90.01 30 | 93.79 33 | 73.54 51 | 88.93 37 | 83.89 54 | 89.40 80 | 90.84 131 | 80.26 33 | 90.62 73 | 90.19 50 | 92.36 64 | 92.03 32 |
|
EG-PatchMatch MVS | | | 84.35 72 | 87.55 71 | 80.62 86 | 86.38 68 | 82.24 88 | 86.75 115 | 64.02 131 | 84.24 80 | 78.17 102 | 89.38 81 | 95.03 78 | 78.78 38 | 89.95 78 | 86.33 84 | 89.59 92 | 85.65 83 |
|
pmmvs-eth3d | | | 79.64 125 | 82.06 138 | 76.83 120 | 80.05 139 | 72.64 173 | 87.47 100 | 66.59 93 | 80.83 119 | 73.50 122 | 89.32 82 | 93.20 102 | 67.78 127 | 80.78 170 | 81.64 128 | 85.58 165 | 76.01 153 |
|
EPP-MVSNet | | | 82.76 96 | 86.47 79 | 78.45 111 | 86.00 72 | 84.47 71 | 85.39 123 | 68.42 82 | 84.17 81 | 62.97 165 | 89.26 83 | 76.84 187 | 72.13 102 | 92.56 49 | 90.40 48 | 95.76 20 | 87.56 73 |
|
train_agg | | | 86.67 50 | 87.73 70 | 85.43 35 | 91.51 18 | 82.72 82 | 94.47 30 | 74.22 50 | 81.71 106 | 81.54 83 | 89.20 84 | 92.87 105 | 78.33 43 | 90.12 76 | 88.47 65 | 92.51 63 | 89.04 59 |
|
HFP-MVS | | | 90.32 14 | 92.37 21 | 87.94 14 | 91.46 20 | 90.91 17 | 95.69 19 | 79.49 10 | 89.94 33 | 83.50 63 | 89.06 85 | 94.44 88 | 81.68 26 | 94.17 32 | 94.19 14 | 95.81 17 | 93.87 7 |
|
MP-MVS | | | 90.84 6 | 91.95 30 | 89.55 3 | 92.92 5 | 90.90 18 | 96.56 6 | 79.60 9 | 86.83 58 | 88.75 13 | 89.00 86 | 94.38 89 | 84.01 9 | 94.94 25 | 94.34 11 | 95.45 24 | 93.24 21 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
RPSCF | | | 88.05 44 | 92.61 15 | 82.73 62 | 84.24 85 | 88.40 41 | 90.04 70 | 66.29 95 | 91.46 11 | 82.29 70 | 88.93 87 | 96.01 50 | 79.38 34 | 95.15 21 | 94.90 6 | 94.15 38 | 93.40 19 |
|
ACMP | | 80.00 8 | 90.12 16 | 92.30 23 | 87.58 19 | 90.83 33 | 91.10 12 | 94.96 27 | 76.06 39 | 87.47 51 | 85.33 41 | 88.91 88 | 97.65 18 | 82.13 23 | 95.31 17 | 93.44 20 | 96.14 10 | 92.22 30 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
UA-Net | | | 89.02 32 | 91.44 37 | 86.20 28 | 94.88 1 | 89.84 32 | 94.76 28 | 77.45 28 | 85.41 72 | 74.79 115 | 88.83 89 | 88.90 144 | 78.67 41 | 96.06 7 | 95.45 4 | 96.66 3 | 95.58 2 |
|
CLD-MVS | | | 82.75 98 | 87.22 75 | 77.54 119 | 88.01 58 | 85.76 66 | 90.23 68 | 54.52 198 | 82.28 99 | 82.11 75 | 88.48 90 | 95.27 69 | 63.95 141 | 89.41 80 | 88.29 67 | 86.45 150 | 81.01 127 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
SteuartSystems-ACMMP | | | 90.00 17 | 91.73 32 | 87.97 13 | 91.21 28 | 90.29 28 | 96.51 7 | 78.00 22 | 86.33 62 | 85.32 42 | 88.23 91 | 94.67 84 | 82.08 24 | 95.13 22 | 93.88 17 | 94.72 35 | 93.59 12 |
Skip Steuart: Steuart Systems R&D Blog. |
MDA-MVSNet-bldmvs | | | 76.51 141 | 82.87 134 | 69.09 165 | 50.71 235 | 74.72 165 | 84.05 133 | 60.27 173 | 81.62 109 | 71.16 135 | 88.21 92 | 91.58 123 | 69.62 118 | 92.78 45 | 77.48 173 | 78.75 189 | 73.69 169 |
|
gg-mvs-nofinetune | | | 72.68 170 | 75.21 177 | 69.73 160 | 81.48 129 | 69.04 187 | 70.48 208 | 76.67 34 | 86.92 57 | 67.80 153 | 88.06 93 | 64.67 212 | 42.12 216 | 77.60 180 | 73.65 185 | 79.81 185 | 66.57 194 |
|
HPM-MVS++ | | | 88.74 38 | 89.54 53 | 87.80 17 | 92.58 7 | 85.69 67 | 95.10 24 | 78.01 21 | 87.08 55 | 87.66 20 | 87.89 94 | 92.07 118 | 80.28 32 | 90.97 69 | 91.41 42 | 93.17 51 | 91.69 35 |
|
MIMVSNet1 | | | 73.40 159 | 81.85 139 | 63.55 198 | 72.90 188 | 64.37 201 | 84.58 130 | 53.60 204 | 90.84 18 | 53.92 186 | 87.75 95 | 96.10 46 | 45.31 211 | 85.37 116 | 79.32 155 | 70.98 205 | 69.18 188 |
|
TinyColmap | | | 83.79 77 | 86.12 84 | 81.07 72 | 83.42 97 | 81.44 100 | 85.42 122 | 68.55 81 | 88.71 42 | 89.46 8 | 87.60 96 | 92.72 106 | 70.34 115 | 89.29 81 | 81.94 126 | 89.20 96 | 81.12 126 |
|
Vis-MVSNet (Re-imp) | | | 76.15 145 | 80.84 141 | 70.68 151 | 83.66 94 | 74.80 164 | 81.66 145 | 69.59 68 | 80.48 123 | 46.94 213 | 87.44 97 | 80.63 174 | 53.14 189 | 86.87 101 | 84.56 100 | 89.12 97 | 71.12 178 |
|
ambc | | | | 88.38 61 | | 91.62 16 | 87.97 49 | 84.48 131 | | 88.64 43 | 87.93 17 | 87.38 98 | 94.82 83 | 74.53 73 | 89.14 83 | 83.86 107 | 85.94 160 | 86.84 75 |
|
ACMM | | 80.67 7 | 90.67 7 | 92.46 18 | 88.57 8 | 91.35 21 | 89.93 31 | 96.34 12 | 77.36 31 | 90.17 28 | 86.88 31 | 87.32 99 | 96.63 29 | 83.32 14 | 95.79 10 | 94.49 10 | 96.19 9 | 92.91 24 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
IS_MVSNet | | | 81.72 113 | 85.01 101 | 77.90 114 | 86.19 69 | 82.64 84 | 85.56 121 | 70.02 66 | 80.11 126 | 63.52 162 | 87.28 100 | 81.18 172 | 67.26 129 | 91.08 66 | 89.33 59 | 94.82 32 | 83.42 101 |
|
ACMMP | | | 90.63 8 | 92.40 19 | 88.56 9 | 91.24 27 | 91.60 6 | 96.49 9 | 77.53 25 | 87.89 46 | 86.87 32 | 87.24 101 | 96.46 33 | 82.87 19 | 95.59 15 | 94.50 9 | 96.35 6 | 93.51 16 |
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 |
FMVSNet1 | | | 78.20 135 | 84.83 106 | 70.46 154 | 78.62 154 | 79.03 123 | 77.90 168 | 67.53 90 | 83.02 89 | 55.10 181 | 87.19 102 | 93.18 103 | 55.65 173 | 85.57 111 | 83.39 110 | 87.98 117 | 82.40 114 |
|
diffmvs | | | 73.65 157 | 77.10 161 | 69.63 161 | 73.21 186 | 69.52 184 | 79.35 162 | 57.48 187 | 73.80 163 | 68.08 151 | 87.10 103 | 82.39 168 | 61.36 152 | 74.27 192 | 74.51 183 | 78.31 190 | 78.14 148 |
|
PGM-MVS | | | 90.42 10 | 91.58 35 | 89.05 6 | 91.77 14 | 91.06 13 | 96.51 7 | 78.94 15 | 85.41 72 | 87.67 19 | 87.02 104 | 95.26 70 | 83.62 13 | 95.01 24 | 93.94 16 | 95.79 19 | 93.40 19 |
|
CP-MVS | | | 91.09 5 | 92.33 22 | 89.65 2 | 92.16 10 | 90.41 26 | 96.46 10 | 80.38 6 | 88.26 44 | 89.17 11 | 87.00 105 | 96.34 39 | 83.95 10 | 95.77 11 | 94.72 8 | 95.81 17 | 93.78 10 |
|
CDPH-MVS | | | 86.66 51 | 88.52 60 | 84.48 43 | 89.61 43 | 88.27 43 | 92.86 40 | 72.69 55 | 80.55 122 | 82.71 67 | 86.92 106 | 93.32 101 | 75.55 64 | 91.00 67 | 89.85 52 | 93.47 45 | 89.71 53 |
|
OMC-MVS | | | 88.16 41 | 91.34 39 | 84.46 44 | 86.85 64 | 90.63 22 | 93.01 39 | 67.00 91 | 90.35 26 | 87.40 22 | 86.86 107 | 96.35 38 | 77.66 47 | 92.63 48 | 90.84 43 | 94.84 31 | 91.68 36 |
|
MDTV_nov1_ep13_2view | | | 72.96 168 | 75.59 173 | 69.88 159 | 71.15 200 | 64.86 200 | 82.31 142 | 54.45 199 | 76.30 154 | 78.32 101 | 86.52 108 | 91.58 123 | 61.35 153 | 76.80 183 | 66.83 203 | 71.70 200 | 66.26 195 |
|
N_pmnet | | | 54.95 226 | 65.90 204 | 42.18 230 | 66.37 214 | 43.86 236 | 57.92 233 | 39.79 225 | 79.54 132 | 17.24 239 | 86.31 109 | 87.91 150 | 25.44 231 | 64.68 224 | 51.76 231 | 46.33 233 | 47.23 229 |
|
MSDG | | | 81.39 117 | 84.23 118 | 78.09 113 | 82.40 119 | 82.47 86 | 85.31 126 | 60.91 169 | 79.73 129 | 80.26 90 | 86.30 110 | 88.27 149 | 69.67 117 | 87.20 99 | 84.98 96 | 89.97 88 | 80.67 128 |
|
DELS-MVS | | | 79.71 124 | 83.74 127 | 75.01 130 | 79.31 148 | 82.68 83 | 84.79 129 | 60.06 175 | 75.43 159 | 69.09 143 | 86.13 111 | 89.38 138 | 67.16 130 | 85.12 118 | 83.87 106 | 89.65 90 | 83.57 98 |
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 |
PM-MVS | | | 80.42 122 | 83.63 128 | 76.67 122 | 78.04 159 | 72.37 175 | 87.14 109 | 60.18 174 | 80.13 125 | 71.75 132 | 86.12 112 | 93.92 93 | 77.08 51 | 86.56 102 | 85.12 95 | 85.83 162 | 81.18 125 |
|
DeepC-MVS_fast | | 81.78 5 | 87.38 46 | 89.64 51 | 84.75 39 | 89.89 41 | 90.70 21 | 92.74 41 | 74.45 47 | 86.02 65 | 82.16 74 | 86.05 113 | 91.99 121 | 75.84 62 | 91.16 62 | 90.44 46 | 93.41 46 | 91.09 43 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MVS_Test | | | 76.72 140 | 79.40 146 | 73.60 136 | 78.85 153 | 74.99 162 | 79.91 154 | 61.56 165 | 69.67 180 | 72.44 126 | 85.98 114 | 90.78 132 | 63.50 146 | 78.30 178 | 75.74 181 | 85.33 167 | 80.31 133 |
|
zzz-MVS | | | 90.38 11 | 91.35 38 | 89.25 5 | 93.08 3 | 86.59 59 | 96.45 11 | 79.00 14 | 90.23 27 | 89.30 10 | 85.87 115 | 94.97 80 | 82.54 21 | 95.05 23 | 94.83 7 | 95.14 27 | 91.94 33 |
|
UGNet | | | 79.62 126 | 85.91 89 | 72.28 142 | 73.52 184 | 83.91 73 | 86.64 116 | 69.51 70 | 79.85 128 | 62.57 167 | 85.82 116 | 89.63 137 | 53.18 188 | 88.39 89 | 87.35 72 | 88.28 110 | 86.43 78 |
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 |
conf0.05thres1000 | | | 77.12 138 | 82.38 136 | 70.98 147 | 82.30 121 | 77.95 131 | 79.86 156 | 64.74 119 | 86.63 59 | 53.93 185 | 85.74 117 | 75.63 197 | 56.85 165 | 88.98 84 | 84.10 103 | 88.20 112 | 77.61 150 |
|
3Dnovator+ | | 83.71 3 | 88.13 42 | 90.00 49 | 85.94 29 | 86.82 65 | 91.06 13 | 94.26 32 | 75.39 42 | 88.85 40 | 85.76 39 | 85.74 117 | 86.92 154 | 78.02 44 | 93.03 40 | 92.21 35 | 95.39 25 | 92.21 31 |
|
DI_MVS_plusplus_trai | | | 77.64 136 | 79.64 144 | 75.31 129 | 79.87 143 | 76.89 143 | 81.55 146 | 63.64 135 | 76.21 155 | 72.03 130 | 85.59 119 | 82.97 166 | 66.63 133 | 79.27 175 | 77.78 170 | 88.14 114 | 78.76 145 |
|
MVS_111021_LR | | | 83.20 89 | 85.33 95 | 80.73 82 | 82.88 109 | 78.23 129 | 89.61 72 | 65.23 113 | 82.08 101 | 81.19 85 | 85.31 120 | 92.04 120 | 75.22 65 | 89.50 79 | 85.90 88 | 90.24 85 | 84.23 91 |
|
IterMVS | | | 73.62 158 | 76.53 167 | 70.23 156 | 71.83 196 | 77.18 140 | 80.69 150 | 53.22 206 | 72.23 171 | 66.62 157 | 85.21 121 | 78.96 177 | 69.54 119 | 76.28 188 | 71.63 191 | 79.45 186 | 74.25 162 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MVS_0304 | | | 84.73 70 | 86.19 83 | 83.02 54 | 88.32 53 | 86.71 58 | 91.55 45 | 70.87 62 | 73.79 164 | 82.88 66 | 85.13 122 | 93.35 100 | 72.55 99 | 88.62 86 | 87.69 71 | 91.93 68 | 88.05 68 |
|
TSAR-MVS + GP. | | | 85.32 62 | 87.41 74 | 82.89 59 | 90.07 40 | 85.69 67 | 89.07 78 | 72.99 54 | 82.45 96 | 74.52 118 | 85.09 123 | 87.67 151 | 79.24 35 | 91.11 63 | 90.41 47 | 91.45 73 | 89.45 55 |
|
3Dnovator | | 79.41 10 | 82.21 103 | 86.07 86 | 77.71 115 | 79.31 148 | 84.61 70 | 87.18 108 | 61.02 168 | 85.65 68 | 76.11 106 | 85.07 124 | 85.38 160 | 70.96 111 | 87.22 98 | 86.47 83 | 91.66 72 | 88.12 67 |
|
PHI-MVS | | | 86.37 53 | 88.14 66 | 84.30 45 | 86.65 66 | 87.56 51 | 90.76 53 | 70.16 65 | 82.55 94 | 89.65 7 | 84.89 125 | 92.40 111 | 75.97 60 | 90.88 71 | 89.70 54 | 92.58 60 | 89.03 60 |
|
MVS_111021_HR | | | 83.95 75 | 86.10 85 | 81.44 70 | 84.62 80 | 80.29 117 | 90.51 62 | 68.05 86 | 84.07 83 | 80.38 88 | 84.74 126 | 91.37 127 | 74.23 76 | 90.37 75 | 87.25 73 | 90.86 82 | 84.59 87 |
|
CANet | | | 82.84 94 | 84.60 108 | 80.78 78 | 87.30 61 | 85.20 69 | 90.23 68 | 69.00 76 | 72.16 172 | 78.73 99 | 84.49 127 | 90.70 133 | 69.54 119 | 87.65 93 | 86.17 85 | 89.87 89 | 85.84 82 |
|
pmmvs4 | | | 75.92 147 | 77.48 158 | 74.10 135 | 78.21 158 | 70.94 177 | 84.06 132 | 64.78 118 | 75.13 160 | 68.47 149 | 84.12 128 | 83.32 164 | 64.74 140 | 75.93 189 | 79.14 163 | 84.31 173 | 73.77 167 |
|
PVSNet_Blended_VisFu | | | 83.00 92 | 84.16 119 | 81.65 68 | 82.17 123 | 86.01 63 | 88.03 90 | 71.23 60 | 76.05 156 | 79.54 94 | 83.88 129 | 83.44 163 | 77.49 49 | 87.38 95 | 84.93 97 | 91.41 74 | 87.40 74 |
|
CNLPA | | | 85.50 60 | 88.58 58 | 81.91 65 | 84.55 82 | 87.52 52 | 90.89 51 | 63.56 136 | 88.18 45 | 84.06 50 | 83.85 130 | 91.34 128 | 76.46 55 | 91.27 59 | 89.00 62 | 91.96 67 | 88.88 61 |
|
Fast-Effi-MVS+ | | | 81.42 116 | 83.82 126 | 78.62 110 | 82.24 122 | 80.62 110 | 87.72 94 | 63.51 137 | 73.01 165 | 74.75 116 | 83.80 131 | 92.70 107 | 73.44 87 | 88.15 92 | 85.26 93 | 90.05 86 | 83.17 102 |
|
GA-MVS | | | 75.01 152 | 76.39 168 | 73.39 137 | 78.37 155 | 75.66 159 | 80.03 152 | 58.40 184 | 70.51 178 | 75.85 108 | 83.24 132 | 76.14 191 | 63.75 142 | 77.28 182 | 76.62 177 | 83.97 174 | 75.30 158 |
|
CNVR-MVS | | | 86.93 48 | 88.98 57 | 84.54 42 | 90.11 39 | 87.41 53 | 93.23 38 | 73.47 52 | 86.31 63 | 82.25 71 | 82.96 133 | 92.15 116 | 76.04 59 | 91.69 53 | 90.69 44 | 92.17 66 | 91.64 38 |
|
test20.03 | | | 69.91 181 | 76.20 170 | 62.58 200 | 84.01 89 | 67.34 193 | 75.67 193 | 65.88 103 | 79.98 127 | 40.28 224 | 82.65 134 | 89.31 140 | 39.63 218 | 77.41 181 | 73.28 186 | 69.98 206 | 63.40 203 |
|
pmmvs5 | | | 68.91 185 | 74.35 180 | 62.56 201 | 67.45 210 | 66.78 195 | 71.70 204 | 51.47 211 | 67.17 191 | 56.25 177 | 82.41 135 | 88.59 146 | 47.21 209 | 73.21 202 | 74.23 184 | 81.30 184 | 68.03 191 |
|
USDC | | | 81.39 117 | 83.07 132 | 79.43 100 | 81.48 129 | 78.95 124 | 82.62 140 | 66.17 97 | 87.45 52 | 90.73 4 | 82.40 136 | 93.65 96 | 66.57 134 | 83.63 138 | 77.97 168 | 89.00 98 | 77.45 151 |
|
Effi-MVS+ | | | 82.33 99 | 83.87 125 | 80.52 88 | 84.51 83 | 81.32 101 | 87.53 99 | 68.05 86 | 74.94 161 | 79.67 93 | 82.37 137 | 92.31 113 | 72.21 101 | 85.06 119 | 86.91 77 | 91.18 77 | 84.20 92 |
|
FMVSNet2 | | | 74.43 154 | 79.70 143 | 68.27 169 | 76.76 173 | 77.36 136 | 75.77 189 | 65.36 112 | 72.28 170 | 52.97 191 | 81.92 138 | 85.61 158 | 52.73 192 | 80.66 171 | 79.73 146 | 86.04 157 | 80.37 130 |
|
no-one | | | 78.59 132 | 85.28 96 | 70.79 150 | 59.01 224 | 68.77 189 | 76.62 179 | 46.06 216 | 80.25 124 | 75.75 109 | 81.85 139 | 97.75 15 | 83.63 12 | 90.99 68 | 87.20 74 | 83.67 175 | 90.14 49 |
|
QAPM | | | 80.43 121 | 84.34 111 | 75.86 125 | 79.40 147 | 82.06 90 | 79.86 156 | 61.94 163 | 83.28 86 | 74.73 117 | 81.74 140 | 85.44 159 | 70.97 110 | 84.99 126 | 84.71 99 | 88.29 109 | 88.14 66 |
|
MCST-MVS | | | 84.79 69 | 86.48 78 | 82.83 60 | 87.30 61 | 87.03 57 | 90.46 66 | 69.33 74 | 83.14 88 | 82.21 73 | 81.69 141 | 92.14 117 | 75.09 68 | 87.27 97 | 84.78 98 | 92.58 60 | 89.30 57 |
|
tpm | | | 62.79 205 | 63.25 214 | 62.26 202 | 70.09 202 | 53.78 220 | 71.65 205 | 47.31 215 | 65.72 200 | 76.70 104 | 80.62 142 | 56.40 224 | 48.11 207 | 64.20 227 | 58.54 220 | 59.70 224 | 63.47 202 |
|
1111 | | | 55.38 225 | 59.51 227 | 50.57 224 | 72.41 193 | 48.16 230 | 69.76 212 | 57.08 188 | 76.79 151 | 32.10 232 | 80.12 143 | 35.41 239 | 25.87 229 | 67.23 216 | 57.74 223 | 46.17 234 | 51.09 227 |
|
.test1245 | | | 43.71 232 | 44.35 234 | 42.95 229 | 72.41 193 | 48.16 230 | 69.76 212 | 57.08 188 | 76.79 151 | 32.10 232 | 80.12 143 | 35.41 239 | 25.87 229 | 67.23 216 | 1.08 236 | 0.48 239 | 1.68 236 |
|
EU-MVSNet | | | 76.48 142 | 80.53 142 | 71.75 143 | 67.62 208 | 70.30 180 | 81.74 144 | 54.06 201 | 75.47 158 | 71.01 136 | 80.10 145 | 93.17 104 | 73.67 85 | 83.73 136 | 77.85 169 | 82.40 181 | 83.07 104 |
|
CR-MVSNet | | | 69.56 184 | 68.34 199 | 70.99 146 | 72.78 190 | 67.63 191 | 64.47 225 | 67.74 88 | 59.93 221 | 72.30 127 | 80.10 145 | 56.77 221 | 65.04 138 | 71.64 206 | 72.91 187 | 83.61 179 | 69.40 186 |
|
CPTT-MVS | | | 89.63 24 | 90.52 46 | 88.59 7 | 90.95 30 | 90.74 20 | 95.71 18 | 79.13 13 | 87.70 48 | 85.68 40 | 80.05 147 | 95.74 57 | 84.77 6 | 94.28 31 | 92.68 27 | 95.28 26 | 92.45 28 |
|
CVMVSNet | | | 75.65 149 | 77.62 157 | 73.35 139 | 71.95 195 | 69.89 182 | 83.04 139 | 60.84 170 | 69.12 182 | 68.76 146 | 79.92 148 | 78.93 178 | 73.64 86 | 81.02 168 | 81.01 133 | 81.86 183 | 83.43 100 |
|
LP | | | 65.71 197 | 69.91 191 | 60.81 205 | 56.75 228 | 61.37 207 | 69.55 215 | 56.80 191 | 73.01 165 | 60.48 171 | 79.76 149 | 70.57 207 | 55.47 176 | 72.77 203 | 67.19 202 | 65.81 215 | 64.71 198 |
|
PLC | | 76.06 15 | 85.38 61 | 87.46 72 | 82.95 58 | 85.79 73 | 88.84 38 | 88.86 80 | 68.70 79 | 87.06 56 | 83.60 59 | 79.02 150 | 90.05 136 | 77.37 50 | 90.88 71 | 89.66 55 | 93.37 47 | 86.74 76 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
X-MVS | | | 89.36 26 | 90.73 43 | 87.77 18 | 91.50 19 | 91.23 8 | 96.76 4 | 78.88 16 | 87.29 53 | 87.14 28 | 78.98 151 | 94.53 85 | 76.47 54 | 95.25 19 | 94.28 12 | 95.85 14 | 93.55 15 |
|
TAPA-MVS | | 78.00 13 | 85.88 57 | 88.37 62 | 82.96 57 | 84.69 79 | 88.62 40 | 90.62 54 | 64.22 127 | 89.15 36 | 88.05 16 | 78.83 152 | 93.71 94 | 76.20 58 | 90.11 77 | 88.22 68 | 94.00 41 | 89.97 51 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PatchT | | | 66.25 196 | 66.76 202 | 65.67 194 | 55.87 229 | 60.75 208 | 70.17 209 | 59.00 180 | 59.80 223 | 72.30 127 | 78.68 153 | 54.12 226 | 65.04 138 | 71.64 206 | 72.91 187 | 71.63 202 | 69.40 186 |
|
HQP-MVS | | | 85.02 66 | 86.41 80 | 83.40 51 | 89.19 46 | 86.59 59 | 91.28 47 | 71.60 59 | 82.79 92 | 83.48 64 | 78.65 154 | 93.54 98 | 72.55 99 | 86.49 103 | 85.89 89 | 92.28 65 | 90.95 45 |
|
TAMVS | | | 63.02 203 | 69.30 193 | 55.70 214 | 70.12 201 | 56.89 214 | 69.63 214 | 45.13 217 | 70.23 179 | 38.00 229 | 77.79 155 | 75.15 198 | 42.60 214 | 74.48 191 | 72.81 189 | 68.70 210 | 57.75 218 |
|
FPMVS | | | 81.56 115 | 84.04 120 | 78.66 109 | 82.92 108 | 75.96 155 | 86.48 118 | 65.66 106 | 84.67 77 | 71.47 133 | 77.78 156 | 83.22 165 | 77.57 48 | 91.24 60 | 90.21 49 | 87.84 121 | 85.21 85 |
|
canonicalmvs | | | 81.22 119 | 86.04 87 | 75.60 127 | 83.17 105 | 83.18 80 | 80.29 151 | 65.82 104 | 85.97 67 | 67.98 152 | 77.74 157 | 91.51 125 | 65.17 137 | 88.62 86 | 86.15 86 | 91.17 78 | 89.09 58 |
|
MSLP-MVS++ | | | 86.29 54 | 89.10 56 | 83.01 55 | 85.71 74 | 89.79 33 | 87.04 113 | 74.39 48 | 85.17 74 | 78.92 98 | 77.59 158 | 93.57 97 | 82.60 20 | 93.23 36 | 91.88 39 | 89.42 95 | 92.46 27 |
|
PCF-MVS | | 76.59 14 | 84.11 74 | 85.27 97 | 82.76 61 | 86.12 70 | 88.30 42 | 91.24 48 | 69.10 75 | 82.36 98 | 84.45 46 | 77.56 159 | 90.40 135 | 72.91 98 | 85.88 110 | 83.88 105 | 92.72 58 | 88.53 64 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
EPMVS | | | 56.62 221 | 59.77 226 | 52.94 219 | 62.41 218 | 50.55 228 | 60.66 230 | 52.83 207 | 65.15 204 | 41.80 221 | 77.46 160 | 57.28 220 | 42.68 213 | 59.81 231 | 54.82 226 | 57.23 227 | 53.35 222 |
|
tfpn1000 | | | 72.27 172 | 76.88 165 | 66.88 185 | 79.01 152 | 74.04 166 | 76.60 180 | 61.15 167 | 79.65 130 | 45.52 215 | 77.41 161 | 67.98 210 | 52.47 195 | 85.22 117 | 82.99 115 | 86.54 148 | 70.89 179 |
|
CDS-MVSNet | | | 73.07 166 | 77.02 162 | 68.46 168 | 81.62 128 | 72.89 172 | 79.56 160 | 70.78 63 | 69.56 181 | 52.52 194 | 77.37 162 | 81.12 173 | 42.60 214 | 84.20 133 | 83.93 104 | 83.65 176 | 70.07 183 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
OpenMVS | | 75.38 16 | 78.44 133 | 81.39 140 | 74.99 131 | 80.46 135 | 79.85 120 | 79.99 153 | 58.31 185 | 77.34 148 | 73.85 121 | 77.19 163 | 82.33 170 | 68.60 124 | 84.67 129 | 81.95 125 | 88.72 101 | 86.40 79 |
|
CostFormer | | | 66.81 194 | 66.94 201 | 66.67 186 | 72.79 189 | 68.25 190 | 79.55 161 | 55.57 195 | 65.52 201 | 62.77 166 | 76.98 164 | 60.09 216 | 56.73 167 | 65.69 223 | 62.35 211 | 72.59 198 | 69.71 184 |
|
tfpn | | | 72.99 167 | 75.25 176 | 70.36 155 | 81.87 126 | 77.09 141 | 79.28 163 | 64.16 128 | 79.58 131 | 53.14 189 | 76.97 165 | 48.75 231 | 56.35 169 | 87.31 96 | 82.75 118 | 87.35 136 | 74.31 160 |
|
tpmrst | | | 59.42 214 | 60.02 225 | 58.71 208 | 67.56 209 | 53.10 222 | 66.99 222 | 51.88 209 | 63.80 209 | 57.68 173 | 76.73 166 | 56.49 223 | 48.73 206 | 56.47 233 | 55.55 225 | 59.43 225 | 58.02 217 |
|
HyFIR lowres test | | | 73.29 160 | 74.14 181 | 72.30 141 | 73.08 187 | 78.33 128 | 83.12 136 | 62.41 158 | 63.81 208 | 62.13 168 | 76.67 167 | 78.50 179 | 71.09 108 | 74.13 193 | 77.47 174 | 81.98 182 | 70.10 182 |
|
EPNet | | | 79.36 128 | 79.44 145 | 79.27 103 | 89.51 44 | 77.20 139 | 88.35 86 | 77.35 32 | 68.27 186 | 74.29 119 | 76.31 168 | 79.22 176 | 59.63 156 | 85.02 125 | 85.45 92 | 86.49 149 | 84.61 86 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PatchmatchNet | | | 64.81 200 | 63.74 213 | 66.06 192 | 69.21 204 | 58.62 211 | 73.16 200 | 60.01 176 | 65.92 198 | 66.19 159 | 76.27 169 | 59.09 217 | 60.45 154 | 66.58 220 | 61.47 218 | 67.33 212 | 58.24 216 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
Effi-MVS+-dtu | | | 82.04 107 | 83.39 131 | 80.48 89 | 85.48 75 | 86.57 61 | 88.40 85 | 68.28 84 | 69.04 184 | 73.13 125 | 76.26 170 | 91.11 130 | 74.74 71 | 88.40 88 | 87.76 70 | 92.84 57 | 84.57 88 |
|
tfpn_n400 | | | 73.26 161 | 77.94 153 | 67.79 179 | 79.91 141 | 73.32 168 | 76.38 182 | 62.04 160 | 84.26 78 | 48.53 209 | 76.23 171 | 71.50 204 | 53.83 184 | 86.22 108 | 81.59 129 | 86.05 155 | 72.47 173 |
|
tfpnconf | | | 73.26 161 | 77.94 153 | 67.79 179 | 79.91 141 | 73.32 168 | 76.38 182 | 62.04 160 | 84.26 78 | 48.53 209 | 76.23 171 | 71.50 204 | 53.83 184 | 86.22 108 | 81.59 129 | 86.05 155 | 72.47 173 |
|
tfpnview11 | | | 72.88 169 | 77.37 159 | 67.65 181 | 79.81 144 | 73.43 167 | 76.23 185 | 61.97 162 | 81.37 115 | 48.53 209 | 76.23 171 | 71.50 204 | 53.78 186 | 85.45 115 | 82.77 117 | 85.56 166 | 70.87 181 |
|
GBi-Net | | | 73.17 163 | 77.64 155 | 67.95 174 | 76.76 173 | 77.36 136 | 75.77 189 | 64.57 120 | 62.99 212 | 51.83 198 | 76.05 174 | 77.76 182 | 52.73 192 | 85.57 111 | 83.39 110 | 86.04 157 | 80.37 130 |
|
test1 | | | 73.17 163 | 77.64 155 | 67.95 174 | 76.76 173 | 77.36 136 | 75.77 189 | 64.57 120 | 62.99 212 | 51.83 198 | 76.05 174 | 77.76 182 | 52.73 192 | 85.57 111 | 83.39 110 | 86.04 157 | 80.37 130 |
|
FMVSNet3 | | | 71.40 179 | 75.20 178 | 66.97 183 | 75.00 181 | 76.59 144 | 74.29 195 | 64.57 120 | 62.99 212 | 51.83 198 | 76.05 174 | 77.76 182 | 51.49 202 | 76.58 186 | 77.03 176 | 84.62 172 | 79.43 137 |
|
PMMVS2 | | | 48.13 231 | 64.06 210 | 29.55 233 | 44.06 237 | 36.69 238 | 51.95 237 | 29.97 231 | 74.75 162 | 8.90 241 | 76.02 177 | 91.24 129 | 7.53 235 | 73.78 197 | 55.91 224 | 34.87 236 | 40.01 235 |
|
NCCC | | | 86.74 49 | 87.97 69 | 85.31 36 | 90.64 34 | 87.25 54 | 93.27 37 | 74.59 46 | 86.50 60 | 83.72 56 | 75.92 178 | 92.39 112 | 77.08 51 | 91.72 52 | 90.68 45 | 92.57 62 | 91.30 42 |
|
Anonymous20231206 | | | 67.28 192 | 73.41 184 | 60.12 206 | 76.45 180 | 63.61 204 | 74.21 196 | 56.52 192 | 76.35 153 | 42.23 218 | 75.81 179 | 90.47 134 | 41.51 217 | 74.52 190 | 69.97 196 | 69.83 207 | 63.17 204 |
|
CANet_DTU | | | 75.04 151 | 78.45 148 | 71.07 145 | 77.27 169 | 77.96 130 | 83.88 134 | 58.00 186 | 64.11 207 | 68.67 148 | 75.65 180 | 88.37 148 | 53.92 183 | 82.05 160 | 81.11 131 | 84.67 171 | 79.88 135 |
|
view800 | | | 74.68 153 | 78.74 147 | 69.94 158 | 81.12 131 | 76.59 144 | 78.94 165 | 63.24 145 | 78.56 142 | 53.06 190 | 75.61 181 | 76.26 190 | 56.07 170 | 86.32 105 | 83.75 108 | 87.18 141 | 74.10 163 |
|
dps | | | 65.14 198 | 64.50 209 | 65.89 193 | 71.41 199 | 65.81 198 | 71.44 207 | 61.59 164 | 58.56 224 | 61.43 169 | 75.45 182 | 52.70 229 | 58.06 162 | 69.57 212 | 64.65 206 | 71.39 203 | 64.77 197 |
|
ADS-MVSNet | | | 56.89 220 | 61.09 220 | 52.00 220 | 59.48 222 | 48.10 232 | 58.02 232 | 54.37 200 | 72.82 168 | 49.19 208 | 75.32 183 | 65.97 211 | 37.96 219 | 59.34 232 | 54.66 227 | 52.99 232 | 51.42 226 |
|
PVSNet_BlendedMVS | | | 76.45 143 | 78.12 151 | 74.49 133 | 76.76 173 | 78.46 126 | 79.65 158 | 63.26 143 | 65.42 202 | 73.15 123 | 75.05 184 | 88.96 142 | 66.51 135 | 82.73 148 | 77.66 171 | 87.61 129 | 78.60 146 |
|
PVSNet_Blended | | | 76.45 143 | 78.12 151 | 74.49 133 | 76.76 173 | 78.46 126 | 79.65 158 | 63.26 143 | 65.42 202 | 73.15 123 | 75.05 184 | 88.96 142 | 66.51 135 | 82.73 148 | 77.66 171 | 87.61 129 | 78.60 146 |
|
MS-PatchMatch | | | 71.18 180 | 73.99 182 | 67.89 178 | 77.16 170 | 71.76 176 | 77.18 172 | 56.38 193 | 67.35 188 | 55.04 182 | 74.63 186 | 75.70 192 | 62.38 150 | 76.62 185 | 75.97 180 | 79.22 187 | 75.90 154 |
|
MDTV_nov1_ep13 | | | 64.96 199 | 64.77 208 | 65.18 196 | 67.08 211 | 62.46 205 | 75.80 188 | 51.10 213 | 62.27 217 | 69.74 139 | 74.12 187 | 62.65 213 | 55.64 174 | 68.19 215 | 62.16 215 | 71.70 200 | 61.57 210 |
|
view600 | | | 74.08 156 | 78.15 150 | 69.32 164 | 80.27 137 | 75.82 156 | 78.27 167 | 62.20 159 | 77.26 149 | 52.80 192 | 74.07 188 | 76.86 186 | 55.57 175 | 84.90 127 | 84.43 101 | 86.84 143 | 73.71 168 |
|
thres600view7 | | | 74.34 155 | 78.43 149 | 69.56 162 | 80.47 134 | 76.28 150 | 78.65 166 | 62.56 155 | 77.39 147 | 52.53 193 | 74.03 189 | 76.78 188 | 55.90 172 | 85.06 119 | 85.19 94 | 87.25 139 | 74.29 161 |
|
AdaColmap | | | 84.15 73 | 85.14 100 | 83.00 56 | 89.08 47 | 87.14 56 | 90.56 57 | 70.90 61 | 82.40 97 | 80.41 87 | 73.82 190 | 84.69 162 | 75.19 66 | 91.58 55 | 89.90 51 | 91.87 69 | 86.48 77 |
|
testgi | | | 68.20 189 | 76.05 171 | 59.04 207 | 79.99 140 | 67.32 194 | 81.16 147 | 51.78 210 | 84.91 75 | 39.36 227 | 73.42 191 | 95.19 72 | 32.79 224 | 76.54 187 | 70.40 194 | 69.14 209 | 64.55 199 |
|
new_pmnet | | | 52.29 228 | 63.16 215 | 39.61 232 | 58.89 225 | 44.70 235 | 48.78 238 | 34.73 228 | 65.88 199 | 17.85 238 | 73.42 191 | 80.00 175 | 23.06 234 | 67.00 219 | 62.28 214 | 54.36 229 | 48.81 228 |
|
abl_6 | | | | | 79.30 102 | 84.98 78 | 85.78 65 | 90.50 63 | 66.88 92 | 77.08 150 | 74.02 120 | 73.29 193 | 89.34 139 | 68.94 122 | | | 90.49 84 | 85.98 80 |
|
EMVS | | | 58.97 217 | 62.63 218 | 54.70 216 | 66.26 216 | 48.71 229 | 61.74 229 | 42.71 219 | 72.80 169 | 46.00 214 | 73.01 194 | 71.66 201 | 57.91 163 | 80.41 173 | 50.68 233 | 53.55 231 | 41.11 234 |
|
thres400 | | | 73.13 165 | 76.99 164 | 68.62 167 | 79.46 146 | 74.93 163 | 77.23 171 | 61.23 166 | 75.54 157 | 52.31 196 | 72.20 195 | 77.10 185 | 54.89 177 | 82.92 143 | 82.62 123 | 86.57 147 | 73.66 170 |
|
PatchMatch-RL | | | 76.05 146 | 76.64 166 | 75.36 128 | 77.84 165 | 69.87 183 | 81.09 148 | 63.43 141 | 71.66 174 | 68.34 150 | 71.70 196 | 81.76 171 | 74.98 69 | 84.83 128 | 83.44 109 | 86.45 150 | 73.22 171 |
|
CHOSEN 1792x2688 | | | 68.80 186 | 71.09 188 | 66.13 190 | 69.11 205 | 68.89 188 | 78.98 164 | 54.68 196 | 61.63 218 | 56.69 175 | 71.56 197 | 78.39 180 | 67.69 128 | 72.13 204 | 72.01 190 | 69.63 208 | 73.02 172 |
|
EPNet_dtu | | | 71.90 175 | 73.03 185 | 70.59 152 | 78.28 156 | 61.64 206 | 82.44 141 | 64.12 129 | 63.26 210 | 69.74 139 | 71.47 198 | 82.41 167 | 51.89 201 | 78.83 177 | 78.01 167 | 77.07 191 | 75.60 157 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MIMVSNet | | | 63.02 203 | 69.02 195 | 56.01 212 | 68.20 206 | 59.26 210 | 70.01 211 | 53.79 203 | 71.56 175 | 41.26 223 | 71.38 199 | 82.38 169 | 36.38 220 | 71.43 208 | 67.32 201 | 66.45 214 | 59.83 213 |
|
MAR-MVS | | | 81.98 108 | 82.92 133 | 80.88 77 | 85.18 77 | 85.85 64 | 89.13 77 | 69.52 69 | 71.21 176 | 82.25 71 | 71.28 200 | 88.89 145 | 69.69 116 | 88.71 85 | 86.96 75 | 89.52 93 | 87.57 72 |
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 |
tpm cat1 | | | 64.79 201 | 62.74 217 | 67.17 182 | 74.61 183 | 65.91 197 | 76.18 186 | 59.32 178 | 64.88 205 | 66.41 158 | 71.21 201 | 53.56 228 | 59.17 158 | 61.53 229 | 58.16 222 | 67.33 212 | 63.95 200 |
|
pmmvs3 | | | 62.72 206 | 68.71 196 | 55.74 213 | 50.74 234 | 57.10 213 | 70.05 210 | 28.82 232 | 61.57 220 | 57.39 174 | 71.19 202 | 85.73 156 | 53.96 182 | 73.36 201 | 69.43 198 | 73.47 196 | 62.55 206 |
|
test1235678 | | | 60.73 211 | 68.46 197 | 51.71 222 | 61.76 219 | 56.73 216 | 73.40 197 | 42.24 221 | 67.34 189 | 39.55 225 | 70.90 203 | 92.54 108 | 28.75 227 | 73.84 195 | 66.00 204 | 64.57 217 | 51.90 224 |
|
testmv | | | 60.72 212 | 68.44 198 | 51.71 222 | 61.76 219 | 56.70 217 | 73.40 197 | 42.24 221 | 67.31 190 | 39.54 226 | 70.88 204 | 92.49 110 | 28.75 227 | 73.83 196 | 66.00 204 | 64.56 218 | 51.89 225 |
|
MVS-HIRNet | | | 59.74 213 | 58.74 231 | 60.92 204 | 57.74 227 | 45.81 234 | 56.02 234 | 58.69 183 | 55.69 228 | 65.17 160 | 70.86 205 | 71.66 201 | 56.75 166 | 61.11 230 | 53.74 228 | 71.17 204 | 52.28 223 |
|
Fast-Effi-MVS+-dtu | | | 76.92 139 | 77.18 160 | 76.62 123 | 79.55 145 | 79.17 122 | 84.80 128 | 77.40 30 | 64.46 206 | 68.75 147 | 70.81 206 | 86.57 155 | 63.36 148 | 81.74 163 | 81.76 127 | 85.86 161 | 75.78 155 |
|
CMPMVS | | 55.74 18 | 71.56 177 | 76.26 169 | 66.08 191 | 68.11 207 | 63.91 203 | 63.17 228 | 50.52 214 | 68.79 185 | 75.49 110 | 70.78 207 | 85.67 157 | 63.54 144 | 81.58 164 | 77.20 175 | 75.63 192 | 85.86 81 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
tpmp4_e23 | | | 68.32 188 | 66.04 203 | 70.98 147 | 77.52 168 | 69.23 185 | 80.99 149 | 65.46 110 | 68.09 187 | 69.25 142 | 70.77 208 | 54.03 227 | 59.35 157 | 69.01 213 | 63.02 210 | 73.34 197 | 68.15 190 |
|
E-PMN | | | 59.07 216 | 62.79 216 | 54.72 215 | 67.01 213 | 47.81 233 | 60.44 231 | 43.40 218 | 72.95 167 | 44.63 216 | 70.42 209 | 73.17 200 | 58.73 160 | 80.97 169 | 51.98 230 | 54.14 230 | 42.26 233 |
|
MVSTER | | | 68.08 191 | 69.73 192 | 66.16 189 | 66.33 215 | 70.06 181 | 75.71 192 | 52.36 208 | 55.18 230 | 58.64 172 | 70.23 210 | 56.72 222 | 57.34 164 | 79.68 174 | 76.03 179 | 86.61 146 | 80.20 134 |
|
thres200 | | | 72.41 171 | 76.00 172 | 68.21 170 | 78.28 156 | 76.28 150 | 74.94 194 | 62.56 155 | 72.14 173 | 51.35 201 | 69.59 211 | 76.51 189 | 54.89 177 | 85.06 119 | 80.51 139 | 87.25 139 | 71.92 176 |
|
PMMVS | | | 61.98 209 | 65.61 205 | 57.74 209 | 45.03 236 | 51.76 227 | 69.54 216 | 35.05 227 | 55.49 229 | 55.32 180 | 68.23 212 | 78.39 180 | 58.09 161 | 70.21 211 | 71.56 192 | 83.42 180 | 63.66 201 |
|
conf200view11 | | | 72.00 174 | 75.40 174 | 68.04 172 | 77.97 160 | 76.44 146 | 77.04 173 | 62.68 151 | 66.81 192 | 50.69 204 | 67.30 213 | 75.67 193 | 52.46 196 | 85.06 119 | 82.64 119 | 87.42 132 | 73.87 164 |
|
thres100view900 | | | 69.86 182 | 72.97 186 | 66.24 188 | 77.97 160 | 72.49 174 | 73.29 199 | 59.12 179 | 66.81 192 | 50.82 202 | 67.30 213 | 75.67 193 | 50.54 204 | 78.24 179 | 79.40 154 | 85.71 164 | 70.88 180 |
|
tfpn200view9 | | | 72.01 173 | 75.40 174 | 68.06 171 | 77.97 160 | 76.44 146 | 77.04 173 | 62.67 153 | 66.81 192 | 50.82 202 | 67.30 213 | 75.67 193 | 52.46 196 | 85.06 119 | 82.64 119 | 87.41 134 | 73.86 166 |
|
test-mter | | | 59.39 215 | 61.59 219 | 56.82 211 | 53.21 230 | 54.82 218 | 73.12 201 | 26.57 234 | 53.19 231 | 56.31 176 | 64.71 216 | 60.47 215 | 56.36 168 | 68.69 214 | 64.27 207 | 75.38 193 | 65.00 196 |
|
test12356 | | | 54.63 227 | 63.78 212 | 43.96 228 | 51.77 231 | 51.90 226 | 65.92 224 | 30.12 230 | 62.44 215 | 30.38 234 | 64.65 217 | 89.07 141 | 30.62 225 | 73.53 200 | 62.11 216 | 54.92 228 | 42.78 232 |
|
RPMNet | | | 67.02 193 | 63.99 211 | 70.56 153 | 71.55 198 | 67.63 191 | 75.81 187 | 69.44 72 | 59.93 221 | 63.24 163 | 64.32 218 | 47.51 232 | 59.68 155 | 70.37 210 | 69.64 197 | 83.64 177 | 68.49 189 |
|
MVE | | 41.12 19 | 51.80 229 | 60.92 221 | 41.16 231 | 35.21 238 | 34.14 239 | 48.45 239 | 41.39 223 | 69.11 183 | 19.53 237 | 63.33 219 | 73.80 199 | 63.56 143 | 67.19 218 | 61.51 217 | 38.85 235 | 57.38 219 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test-LLR | | | 62.15 208 | 59.46 228 | 65.29 195 | 79.07 150 | 52.66 223 | 69.46 217 | 62.93 146 | 50.76 234 | 53.81 187 | 63.11 220 | 58.91 218 | 52.87 190 | 66.54 221 | 62.34 212 | 73.59 194 | 61.87 208 |
|
TESTMET0.1,1 | | | 57.21 219 | 59.46 228 | 54.60 217 | 50.95 233 | 52.66 223 | 69.46 217 | 26.91 233 | 50.76 234 | 53.81 187 | 63.11 220 | 58.91 218 | 52.87 190 | 66.54 221 | 62.34 212 | 73.59 194 | 61.87 208 |
|
conf0.01 | | | 69.59 183 | 71.01 189 | 67.95 174 | 77.74 166 | 76.09 152 | 77.04 173 | 62.58 154 | 66.81 192 | 50.54 206 | 63.00 222 | 51.78 230 | 52.46 196 | 84.53 130 | 82.64 119 | 87.32 137 | 72.19 175 |
|
DWT-MVSNet_training | | | 63.07 202 | 60.04 224 | 66.61 187 | 71.64 197 | 65.27 199 | 76.80 178 | 53.82 202 | 55.90 227 | 63.07 164 | 62.23 223 | 41.87 237 | 62.54 149 | 64.32 226 | 63.71 208 | 71.78 199 | 66.97 192 |
|
tfpn111 | | | 71.60 176 | 74.66 179 | 68.04 172 | 77.97 160 | 76.44 146 | 77.04 173 | 62.68 151 | 66.81 192 | 50.69 204 | 62.10 224 | 75.67 193 | 52.46 196 | 85.06 119 | 82.64 119 | 87.42 132 | 73.87 164 |
|
thresconf0.02 | | | 66.71 195 | 68.28 200 | 64.89 197 | 76.83 172 | 70.38 179 | 71.62 206 | 58.90 182 | 77.64 145 | 47.04 212 | 62.10 224 | 46.01 233 | 51.32 203 | 78.85 176 | 76.09 178 | 83.62 178 | 66.85 193 |
|
test0.0.03 1 | | | 61.79 210 | 65.33 206 | 57.65 210 | 79.07 150 | 64.09 202 | 68.51 221 | 62.93 146 | 61.59 219 | 33.71 231 | 61.58 226 | 71.58 203 | 33.43 223 | 70.95 209 | 68.68 199 | 68.26 211 | 58.82 214 |
|
conf0.002 | | | 68.60 187 | 69.17 194 | 67.92 177 | 77.66 167 | 76.01 153 | 77.04 173 | 62.56 155 | 66.81 192 | 50.51 207 | 61.21 227 | 44.01 235 | 52.46 196 | 84.44 131 | 80.29 141 | 87.31 138 | 71.44 177 |
|
tfpn_ndepth | | | 68.20 189 | 72.18 187 | 63.55 198 | 74.64 182 | 73.24 170 | 72.41 202 | 59.76 177 | 70.54 177 | 41.93 220 | 60.96 228 | 68.69 209 | 46.23 210 | 82.16 157 | 80.14 144 | 86.34 152 | 69.56 185 |
|
IB-MVS | | 71.28 17 | 75.21 150 | 77.00 163 | 73.12 140 | 76.76 173 | 77.45 135 | 83.05 138 | 58.92 181 | 63.01 211 | 64.31 161 | 59.99 229 | 87.57 152 | 68.64 123 | 86.26 107 | 82.34 124 | 87.05 142 | 82.36 115 |
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 |
testus | | | 57.41 218 | 64.98 207 | 48.58 227 | 59.39 223 | 57.17 212 | 68.81 220 | 32.86 229 | 62.32 216 | 43.25 217 | 57.59 230 | 88.49 147 | 24.19 233 | 71.68 205 | 63.20 209 | 62.99 220 | 54.42 221 |
|
testpf | | | 55.64 224 | 50.84 233 | 61.24 203 | 67.03 212 | 54.45 219 | 72.29 203 | 65.04 115 | 37.23 236 | 54.99 183 | 53.99 231 | 43.12 236 | 44.34 212 | 55.22 234 | 51.59 232 | 63.76 219 | 60.25 212 |
|
test2356 | | | 51.28 230 | 53.40 232 | 48.80 226 | 58.53 226 | 52.10 225 | 63.63 227 | 40.83 224 | 51.94 233 | 39.35 228 | 53.46 232 | 45.22 234 | 28.78 226 | 64.39 225 | 60.77 219 | 61.70 221 | 45.92 230 |
|
GG-mvs-BLEND | | | 41.63 233 | 60.36 222 | 19.78 234 | 0.14 242 | 66.04 196 | 55.66 235 | 0.17 239 | 57.64 225 | 2.42 242 | 51.82 233 | 69.42 208 | 0.28 239 | 64.11 228 | 58.29 221 | 60.02 223 | 55.18 220 |
|
CHOSEN 280x420 | | | 56.32 223 | 58.85 230 | 53.36 218 | 51.63 232 | 39.91 237 | 69.12 219 | 38.61 226 | 56.29 226 | 36.79 230 | 48.84 234 | 62.59 214 | 63.39 147 | 73.61 199 | 67.66 200 | 60.61 222 | 63.07 205 |
|
FMVSNet5 | | | 56.37 222 | 60.14 223 | 51.98 221 | 60.83 221 | 59.58 209 | 66.85 223 | 42.37 220 | 52.68 232 | 41.33 222 | 47.09 235 | 54.68 225 | 35.28 221 | 73.88 194 | 70.77 193 | 65.24 216 | 62.26 207 |
|
DeepMVS_CX | | | | | | | 17.78 240 | 20.40 240 | 6.69 235 | 31.41 237 | 9.80 240 | 38.61 236 | 34.88 241 | 33.78 222 | 28.41 236 | | 23.59 237 | 45.77 231 |
|
tmp_tt | | | | | 13.54 235 | 16.73 239 | 6.42 241 | 8.49 241 | 2.36 236 | 28.69 238 | 27.44 236 | 18.40 237 | 13.51 242 | 3.70 236 | 33.23 235 | 36.26 234 | 22.54 238 | |
|
testmvs | | | 0.93 235 | 1.37 236 | 0.41 237 | 0.36 241 | 0.36 243 | 0.62 243 | 0.39 237 | 1.48 239 | 0.18 244 | 2.41 238 | 1.31 244 | 0.41 238 | 1.25 238 | 1.08 236 | 0.48 239 | 1.68 236 |
|
test123 | | | 1.06 234 | 1.41 235 | 0.64 236 | 0.39 240 | 0.48 242 | 0.52 244 | 0.25 238 | 1.11 240 | 1.37 243 | 2.01 239 | 1.98 243 | 0.87 237 | 1.43 237 | 1.27 235 | 0.46 241 | 1.62 238 |
|
sosnet-low-res | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
sosnet | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
our_test_3 | | | | | | 73.27 185 | 70.91 178 | 83.26 135 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 89.37 9 | | 94.85 81 | | | | | |
|
MTMP | | | | | | | | | | | 90.54 5 | | 95.16 73 | | | | | |
|
Patchmatch-RL test | | | | | | | | 4.13 242 | | | | | | | | | | |
|
XVS | | | | | | 91.28 24 | 91.23 8 | 96.89 2 | | | 87.14 28 | | 94.53 85 | | | | 95.84 15 | |
|
X-MVStestdata | | | | | | 91.28 24 | 91.23 8 | 96.89 2 | | | 87.14 28 | | 94.53 85 | | | | 95.84 15 | |
|
mPP-MVS | | | | | | 93.05 4 | | | | | | | 95.77 55 | | | | | |
|
NP-MVS | | | | | | | | | | 78.65 141 | | | | | | | | |
|
Patchmtry | | | | | | | 56.88 215 | 64.47 225 | 67.74 88 | | 72.30 127 | | | | | | | |
|