MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 2 | 97.66 2 | 73.37 7 | 97.13 1 | 95.58 12 | 89.33 1 | 85.77 23 | 96.26 7 | 72.84 9 | 99.38 1 | 92.64 4 | 95.93 4 | 97.08 4 |
|
DELS-MVS | | | 90.05 4 | 90.09 5 | 89.94 2 | 93.14 49 | 73.88 6 | 97.01 2 | 94.40 38 | 88.32 2 | 85.71 24 | 94.91 45 | 74.11 7 | 98.91 6 | 87.26 26 | 95.94 3 | 97.03 5 |
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 |
EPNet | | | 87.84 15 | 88.38 10 | 86.23 61 | 93.30 44 | 66.05 125 | 95.26 21 | 94.84 22 | 87.09 3 | 88.06 11 | 94.53 52 | 66.79 33 | 97.34 50 | 83.89 48 | 91.68 54 | 95.29 38 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CANet | | | 89.61 6 | 89.99 6 | 88.46 13 | 94.39 24 | 69.71 32 | 96.53 5 | 93.78 45 | 86.89 4 | 89.68 4 | 95.78 15 | 65.94 40 | 99.10 2 | 92.99 1 | 93.91 25 | 96.58 11 |
|
DeepPCF-MVS | | 81.17 1 | 89.72 5 | 91.38 3 | 84.72 103 | 93.00 52 | 58.16 255 | 96.72 3 | 94.41 37 | 86.50 5 | 90.25 3 | 97.83 1 | 75.46 5 | 98.67 12 | 92.78 2 | 95.49 7 | 97.32 1 |
|
MVS_0304 | | | 88.39 9 | 88.35 12 | 88.50 12 | 93.01 51 | 70.11 23 | 95.90 10 | 92.20 109 | 86.27 6 | 88.70 9 | 95.92 13 | 56.76 121 | 99.02 4 | 92.68 3 | 93.76 28 | 96.37 15 |
|
CANet_DTU | | | 84.09 58 | 83.52 53 | 85.81 69 | 90.30 111 | 66.82 93 | 91.87 122 | 89.01 221 | 85.27 7 | 86.09 20 | 93.74 70 | 47.71 221 | 96.98 71 | 77.90 87 | 89.78 73 | 93.65 97 |
|
CLD-MVS | | | 82.73 75 | 82.35 73 | 83.86 118 | 87.90 158 | 67.65 69 | 95.45 18 | 92.18 112 | 85.06 8 | 72.58 132 | 92.27 98 | 52.46 182 | 95.78 107 | 84.18 44 | 79.06 141 | 88.16 179 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CNVR-MVS | | | 90.32 3 | 90.89 4 | 88.61 11 | 96.76 4 | 70.65 18 | 96.47 6 | 94.83 23 | 84.83 9 | 89.07 7 | 96.80 2 | 70.86 14 | 99.06 3 | 92.64 4 | 95.71 5 | 96.12 18 |
|
NCCC | | | 89.07 8 | 89.46 8 | 87.91 16 | 96.60 5 | 69.05 40 | 96.38 7 | 94.64 31 | 84.42 10 | 86.74 18 | 96.20 8 | 66.56 35 | 98.76 11 | 89.03 16 | 94.56 18 | 95.92 25 |
|
PS-MVSNAJ | | | 88.14 10 | 87.61 17 | 89.71 4 | 92.06 72 | 76.72 1 | 95.75 11 | 93.26 68 | 83.86 11 | 89.55 5 | 96.06 11 | 53.55 169 | 97.89 30 | 91.10 6 | 93.31 35 | 94.54 66 |
|
DeepC-MVS_fast | | 79.48 2 | 87.95 13 | 88.00 13 | 87.79 19 | 95.86 12 | 68.32 55 | 95.74 12 | 94.11 41 | 83.82 12 | 83.49 44 | 96.19 9 | 64.53 53 | 98.44 17 | 83.42 51 | 94.88 12 | 96.61 9 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
xiu_mvs_v2_base | | | 87.92 14 | 87.38 22 | 89.55 7 | 91.41 97 | 76.43 2 | 95.74 12 | 93.12 76 | 83.53 13 | 89.55 5 | 95.95 12 | 53.45 174 | 97.68 32 | 91.07 7 | 92.62 42 | 94.54 66 |
|
TSAR-MVS + MP. | | | 88.11 11 | 88.64 9 | 86.54 47 | 91.73 83 | 68.04 61 | 90.36 173 | 93.55 56 | 82.89 14 | 91.29 2 | 92.89 87 | 72.27 10 | 96.03 100 | 87.99 20 | 94.77 13 | 95.54 31 |
|
WTY-MVS | | | 86.32 33 | 85.81 36 | 87.85 17 | 92.82 57 | 69.37 37 | 95.20 23 | 95.25 15 | 82.71 15 | 81.91 51 | 94.73 49 | 67.93 26 | 97.63 38 | 79.55 73 | 82.25 124 | 96.54 12 |
|
lupinMVS | | | 87.74 16 | 87.77 15 | 87.63 22 | 89.24 130 | 71.18 14 | 96.57 4 | 92.90 84 | 82.70 16 | 87.13 15 | 95.27 30 | 64.99 49 | 95.80 106 | 89.34 12 | 91.80 52 | 95.93 24 |
|
HPM-MVS++ | | | 89.37 7 | 89.95 7 | 87.64 20 | 95.10 17 | 68.23 59 | 95.24 22 | 94.49 34 | 82.43 17 | 88.90 8 | 96.35 5 | 71.89 13 | 98.63 13 | 88.76 18 | 96.40 2 | 96.06 19 |
|
PVSNet_Blended | | | 86.73 31 | 86.86 27 | 86.31 59 | 93.76 35 | 67.53 72 | 96.33 8 | 93.61 53 | 82.34 18 | 81.00 57 | 93.08 79 | 63.19 67 | 97.29 52 | 87.08 27 | 91.38 59 | 94.13 81 |
|
HSP-MVS | | | 90.38 2 | 91.89 1 | 85.84 68 | 92.83 55 | 64.03 168 | 93.06 74 | 94.52 32 | 82.19 19 | 93.65 1 | 96.15 10 | 85.89 1 | 97.19 57 | 91.02 8 | 97.75 1 | 96.29 16 |
|
DWT-MVSNet_test | | | 83.95 60 | 82.80 67 | 87.41 25 | 92.90 54 | 70.07 25 | 89.12 199 | 94.42 36 | 82.15 20 | 77.64 87 | 91.77 103 | 70.81 15 | 96.22 92 | 65.03 183 | 81.36 128 | 95.94 23 |
|
Regformer-1 | | | 87.24 21 | 87.60 18 | 86.15 62 | 95.14 15 | 65.83 131 | 93.95 49 | 95.12 17 | 82.11 21 | 84.25 37 | 95.73 17 | 67.88 27 | 98.35 19 | 85.60 36 | 88.64 78 | 94.26 73 |
|
PAPM | | | 85.89 39 | 85.46 41 | 87.18 30 | 88.20 152 | 72.42 9 | 92.41 97 | 92.77 87 | 82.11 21 | 80.34 62 | 93.07 81 | 68.27 20 | 95.02 130 | 78.39 83 | 93.59 32 | 94.09 84 |
|
PatchFormer-LS_test | | | 83.14 70 | 81.81 78 | 87.12 32 | 92.34 64 | 69.92 28 | 88.64 206 | 93.32 65 | 82.07 23 | 74.87 112 | 91.62 107 | 68.91 17 | 96.08 99 | 66.07 174 | 78.45 148 | 95.37 33 |
|
jason | | | 86.40 32 | 86.17 32 | 87.11 33 | 86.16 180 | 70.54 20 | 95.71 15 | 92.19 111 | 82.00 24 | 84.58 34 | 94.34 60 | 61.86 76 | 95.53 122 | 87.76 22 | 90.89 64 | 95.27 40 |
jason: jason. |
CHOSEN 1792x2688 | | | 84.98 47 | 83.45 56 | 89.57 6 | 89.94 116 | 75.14 4 | 92.07 107 | 92.32 101 | 81.87 25 | 75.68 103 | 88.27 140 | 60.18 90 | 98.60 14 | 80.46 70 | 90.27 71 | 94.96 55 |
|
Regformer-2 | | | 87.00 25 | 87.43 20 | 85.71 76 | 95.14 15 | 64.73 150 | 93.95 49 | 94.95 20 | 81.69 26 | 84.03 41 | 95.73 17 | 67.35 31 | 98.19 23 | 85.40 38 | 88.64 78 | 94.20 75 |
|
Regformer-3 | | | 85.80 40 | 85.92 34 | 85.46 80 | 94.17 29 | 65.09 146 | 92.95 78 | 95.11 18 | 81.13 27 | 81.68 53 | 95.04 37 | 65.82 42 | 98.32 20 | 83.02 52 | 84.36 109 | 92.97 116 |
|
DeepC-MVS | | 77.85 3 | 85.52 42 | 85.24 43 | 86.37 56 | 88.80 139 | 66.64 108 | 92.15 101 | 93.68 51 | 81.07 28 | 76.91 98 | 93.64 71 | 62.59 72 | 98.44 17 | 85.50 37 | 92.84 40 | 94.03 88 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Regformer-4 | | | 85.45 43 | 85.69 39 | 84.73 101 | 94.17 29 | 63.23 183 | 92.95 78 | 94.83 23 | 80.66 29 | 81.29 55 | 95.04 37 | 65.12 47 | 98.08 26 | 82.74 53 | 84.36 109 | 92.88 120 |
|
VPNet | | | 78.82 135 | 77.53 135 | 82.70 139 | 84.52 198 | 66.44 117 | 93.93 51 | 92.23 104 | 80.46 30 | 72.60 131 | 88.38 138 | 49.18 207 | 93.13 200 | 72.47 118 | 63.97 249 | 88.55 172 |
|
canonicalmvs | | | 86.85 28 | 86.25 31 | 88.66 10 | 91.80 82 | 71.92 10 | 93.54 63 | 91.71 127 | 80.26 31 | 87.55 13 | 95.25 32 | 63.59 63 | 96.93 77 | 88.18 19 | 84.34 111 | 97.11 3 |
|
MVSTER | | | 82.47 79 | 82.05 74 | 83.74 120 | 92.68 60 | 69.01 41 | 91.90 121 | 93.21 69 | 79.83 32 | 72.14 140 | 85.71 174 | 74.72 6 | 94.72 141 | 75.72 96 | 72.49 190 | 87.50 184 |
|
HQP-NCC | | | | | | 87.54 161 | | 94.06 43 | | 79.80 33 | 74.18 115 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 161 | | 94.06 43 | | 79.80 33 | 74.18 115 | | | | | | |
|
HQP-MVS | | | 81.14 96 | 80.64 91 | 82.64 142 | 87.54 161 | 63.66 178 | 94.06 43 | 91.70 128 | 79.80 33 | 74.18 115 | 90.30 120 | 51.63 189 | 95.61 116 | 77.63 88 | 78.90 142 | 88.63 170 |
|
EI-MVSNet-Vis-set | | | 83.77 64 | 83.67 52 | 84.06 115 | 92.79 59 | 63.56 180 | 91.76 129 | 94.81 25 | 79.65 36 | 77.87 84 | 94.09 64 | 63.35 65 | 97.90 29 | 79.35 74 | 79.36 138 | 90.74 150 |
|
plane_prior | | | | | | | 62.42 198 | 93.85 55 | | 79.38 37 | | | | | | 78.80 144 | |
|
alignmvs | | | 87.28 20 | 86.97 25 | 88.24 15 | 91.30 98 | 71.14 16 | 95.61 16 | 93.56 55 | 79.30 38 | 87.07 17 | 95.25 32 | 68.43 19 | 96.93 77 | 87.87 21 | 84.33 112 | 96.65 8 |
|
TESTMET0.1,1 | | | 82.41 80 | 81.98 76 | 83.72 123 | 88.08 153 | 63.74 173 | 92.70 86 | 93.77 47 | 79.30 38 | 77.61 89 | 87.57 152 | 58.19 106 | 94.08 174 | 73.91 109 | 86.68 92 | 93.33 105 |
|
EI-MVSNet-UG-set | | | 83.14 70 | 82.96 63 | 83.67 125 | 92.28 67 | 63.19 186 | 91.38 143 | 94.68 28 | 79.22 40 | 76.60 99 | 93.75 69 | 62.64 71 | 97.76 31 | 78.07 85 | 78.01 149 | 90.05 157 |
|
PVSNet | | 73.49 8 | 80.05 113 | 78.63 118 | 84.31 111 | 90.92 103 | 64.97 149 | 92.47 96 | 91.05 151 | 79.18 41 | 72.43 137 | 90.51 118 | 37.05 280 | 94.06 175 | 68.06 154 | 86.00 99 | 93.90 94 |
|
HY-MVS | | 76.49 5 | 84.28 55 | 83.36 61 | 87.02 36 | 92.22 69 | 67.74 66 | 84.65 248 | 94.50 33 | 79.15 42 | 82.23 49 | 87.93 146 | 66.88 32 | 96.94 75 | 80.53 69 | 82.20 125 | 96.39 14 |
|
PVSNet_BlendedMVS | | | 83.38 67 | 83.43 57 | 83.22 131 | 93.76 35 | 67.53 72 | 94.06 43 | 93.61 53 | 79.13 43 | 81.00 57 | 85.14 178 | 63.19 67 | 97.29 52 | 87.08 27 | 73.91 181 | 84.83 239 |
|
plane_prior3 | | | | | | | 61.95 208 | | | 79.09 44 | 72.53 133 | | | | | | |
|
MVS_111021_HR | | | 86.19 36 | 85.80 37 | 87.37 26 | 93.17 48 | 69.79 30 | 93.99 47 | 93.76 48 | 79.08 45 | 78.88 76 | 93.99 66 | 62.25 73 | 98.15 24 | 85.93 35 | 91.15 62 | 94.15 80 |
|
MSLP-MVS++ | | | 86.27 34 | 85.91 35 | 87.35 27 | 92.01 73 | 68.97 43 | 95.04 29 | 92.70 89 | 79.04 46 | 81.50 54 | 96.50 4 | 58.98 102 | 96.78 81 | 83.49 50 | 93.93 24 | 96.29 16 |
|
IB-MVS | | 77.80 4 | 82.18 83 | 80.46 94 | 87.35 27 | 89.14 132 | 70.28 22 | 95.59 17 | 95.17 16 | 78.85 47 | 70.19 159 | 85.82 172 | 70.66 16 | 97.67 33 | 72.19 122 | 66.52 226 | 94.09 84 |
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 |
3Dnovator | | 73.91 6 | 82.69 78 | 80.82 88 | 88.31 14 | 89.57 122 | 71.26 13 | 92.60 91 | 94.39 39 | 78.84 48 | 67.89 197 | 92.48 93 | 48.42 213 | 98.52 15 | 68.80 152 | 94.40 19 | 95.15 46 |
|
HQP_MVS | | | 80.34 107 | 79.75 101 | 82.12 166 | 86.94 169 | 62.42 198 | 93.13 72 | 91.31 142 | 78.81 49 | 72.53 133 | 89.14 131 | 50.66 193 | 95.55 120 | 76.74 91 | 78.53 146 | 88.39 175 |
|
plane_prior2 | | | | | | | | 93.13 72 | | 78.81 49 | | | | | | | |
|
MG-MVS | | | 87.11 23 | 86.27 29 | 89.62 5 | 97.79 1 | 76.27 3 | 94.96 31 | 94.49 34 | 78.74 51 | 83.87 43 | 92.94 84 | 64.34 54 | 96.94 75 | 75.19 100 | 94.09 22 | 95.66 27 |
|
gm-plane-assit | | | | | | 88.42 148 | 67.04 85 | | | 78.62 52 | | 91.83 102 | | 97.37 47 | 76.57 93 | | |
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VNet | | | 86.20 35 | 85.65 40 | 87.84 18 | 93.92 34 | 69.99 26 | 95.73 14 | 95.94 11 | 78.43 53 | 86.00 21 | 93.07 81 | 58.22 105 | 97.00 67 | 85.22 39 | 84.33 112 | 96.52 13 |
|
tpm | | | 78.58 143 | 77.03 143 | 83.22 131 | 85.94 185 | 64.56 151 | 83.21 261 | 91.14 148 | 78.31 54 | 73.67 122 | 79.68 246 | 64.01 55 | 92.09 232 | 66.07 174 | 71.26 199 | 93.03 114 |
|
TSAR-MVS + GP. | | | 87.96 12 | 88.37 11 | 86.70 41 | 93.51 42 | 65.32 138 | 95.15 25 | 93.84 44 | 78.17 55 | 85.93 22 | 94.80 48 | 75.80 4 | 98.21 21 | 89.38 11 | 88.78 77 | 96.59 10 |
|
FIs | | | 79.47 125 | 79.41 108 | 79.67 214 | 85.95 183 | 59.40 244 | 91.68 133 | 93.94 42 | 78.06 56 | 68.96 180 | 88.28 139 | 66.61 34 | 91.77 237 | 66.20 173 | 74.99 175 | 87.82 181 |
|
sss | | | 82.71 77 | 82.38 72 | 83.73 122 | 89.25 128 | 59.58 242 | 92.24 100 | 94.89 21 | 77.96 57 | 79.86 66 | 92.38 95 | 56.70 124 | 97.05 62 | 77.26 90 | 80.86 132 | 94.55 64 |
|
PMMVS | | | 81.98 89 | 82.04 75 | 81.78 173 | 89.76 120 | 56.17 269 | 91.13 156 | 90.69 159 | 77.96 57 | 80.09 64 | 93.57 72 | 46.33 231 | 94.99 131 | 81.41 64 | 87.46 86 | 94.17 78 |
|
MVS_Test | | | 84.16 57 | 83.20 62 | 87.05 35 | 91.56 87 | 69.82 29 | 89.99 181 | 92.05 114 | 77.77 59 | 82.84 47 | 86.57 164 | 63.93 57 | 96.09 97 | 74.91 106 | 89.18 75 | 95.25 43 |
|
SteuartSystems-ACMMP | | | 86.82 30 | 86.90 26 | 86.58 46 | 90.42 108 | 66.38 118 | 96.09 9 | 93.87 43 | 77.73 60 | 84.01 42 | 95.66 19 | 63.39 64 | 97.94 27 | 87.40 25 | 93.55 33 | 95.42 32 |
Skip Steuart: Steuart Systems R&D Blog. |
EPNet_dtu | | | 78.80 136 | 79.26 112 | 77.43 251 | 88.06 154 | 49.71 299 | 91.96 114 | 91.95 119 | 77.67 61 | 76.56 100 | 91.28 109 | 58.51 104 | 90.20 257 | 56.37 234 | 80.95 131 | 92.39 128 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tpmrst | | | 80.57 102 | 79.14 115 | 84.84 99 | 90.10 113 | 68.28 57 | 81.70 270 | 89.72 198 | 77.63 62 | 75.96 102 | 79.54 249 | 64.94 51 | 92.71 214 | 75.43 98 | 77.28 162 | 93.55 99 |
|
testdata1 | | | | | | | | 89.21 196 | | 77.55 63 | | | | | | | |
|
UniMVSNet_NR-MVSNet | | | 78.15 150 | 77.55 134 | 79.98 207 | 84.46 200 | 60.26 230 | 92.25 99 | 93.20 71 | 77.50 64 | 68.88 181 | 86.61 163 | 66.10 38 | 92.13 230 | 66.38 170 | 62.55 252 | 87.54 183 |
|
UA-Net | | | 80.02 114 | 79.65 102 | 81.11 189 | 89.33 126 | 57.72 259 | 86.33 241 | 89.00 222 | 77.44 65 | 81.01 56 | 89.15 130 | 59.33 97 | 95.90 103 | 61.01 215 | 84.28 114 | 89.73 161 |
|
PVSNet_Blended_VisFu | | | 83.97 59 | 83.50 54 | 85.39 85 | 90.02 114 | 66.59 111 | 93.77 57 | 91.73 125 | 77.43 66 | 77.08 97 | 89.81 127 | 63.77 60 | 96.97 72 | 79.67 72 | 88.21 81 | 92.60 124 |
|
NR-MVSNet | | | 76.05 184 | 74.59 178 | 80.44 198 | 82.96 219 | 62.18 203 | 90.83 161 | 91.73 125 | 77.12 67 | 60.96 240 | 86.35 165 | 59.28 98 | 91.80 236 | 60.74 216 | 61.34 265 | 87.35 194 |
|
DI_MVS_plusplus_test | | | 79.78 120 | 77.50 136 | 86.62 43 | 80.90 233 | 69.46 35 | 90.69 165 | 91.97 118 | 77.00 68 | 59.07 250 | 82.34 204 | 46.82 225 | 95.88 104 | 82.14 57 | 86.59 94 | 94.53 68 |
|
test_normal | | | 79.66 121 | 77.36 141 | 86.54 47 | 80.72 237 | 69.21 38 | 90.68 166 | 92.16 113 | 76.99 69 | 58.63 254 | 82.03 213 | 46.70 227 | 95.86 105 | 81.74 61 | 86.63 93 | 94.56 63 |
|
FC-MVSNet-test | | | 77.99 152 | 78.08 126 | 77.70 246 | 84.89 194 | 55.51 273 | 90.27 175 | 93.75 49 | 76.87 70 | 66.80 210 | 87.59 151 | 65.71 44 | 90.23 256 | 62.89 203 | 73.94 180 | 87.37 192 |
|
SD-MVS | | | 87.49 18 | 87.49 19 | 87.50 24 | 93.60 39 | 68.82 46 | 93.90 53 | 92.63 94 | 76.86 71 | 87.90 12 | 95.76 16 | 66.17 36 | 97.63 38 | 89.06 15 | 91.48 58 | 96.05 20 |
|
UGNet | | | 79.87 117 | 78.68 117 | 83.45 130 | 89.96 115 | 61.51 212 | 92.13 102 | 90.79 156 | 76.83 72 | 78.85 78 | 86.33 167 | 38.16 266 | 96.17 94 | 67.93 156 | 87.17 87 | 92.67 122 |
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 |
MVS_111021_LR | | | 82.02 88 | 81.52 81 | 83.51 128 | 88.42 148 | 62.88 193 | 89.77 188 | 88.93 223 | 76.78 73 | 75.55 107 | 93.10 77 | 50.31 196 | 95.38 124 | 83.82 49 | 87.02 88 | 92.26 133 |
|
Test4 | | | 76.45 180 | 73.45 195 | 85.45 82 | 76.07 295 | 67.61 70 | 88.38 210 | 90.83 155 | 76.71 74 | 53.06 283 | 79.65 248 | 31.61 297 | 94.35 159 | 78.47 81 | 86.22 98 | 94.40 72 |
|
TranMVSNet+NR-MVSNet | | | 75.86 187 | 74.52 181 | 79.89 210 | 82.44 222 | 60.64 225 | 91.37 144 | 91.37 141 | 76.63 75 | 67.65 200 | 86.21 168 | 52.37 183 | 91.55 241 | 61.84 211 | 60.81 268 | 87.48 185 |
|
PAPR | | | 85.15 45 | 84.47 47 | 87.18 30 | 96.02 10 | 68.29 56 | 91.85 124 | 93.00 81 | 76.59 76 | 79.03 75 | 95.00 39 | 61.59 77 | 97.61 40 | 78.16 84 | 89.00 76 | 95.63 28 |
|
UniMVSNet (Re) | | | 77.58 156 | 76.78 148 | 79.98 207 | 84.11 206 | 60.80 218 | 91.76 129 | 93.17 74 | 76.56 77 | 69.93 165 | 84.78 182 | 63.32 66 | 92.36 226 | 64.89 184 | 62.51 254 | 86.78 204 |
|
DU-MVS | | | 76.86 173 | 75.84 159 | 79.91 209 | 82.96 219 | 60.26 230 | 91.26 149 | 91.54 132 | 76.46 78 | 68.88 181 | 86.35 165 | 56.16 130 | 92.13 230 | 66.38 170 | 62.55 252 | 87.35 194 |
|
OPM-MVS | | | 79.00 131 | 78.09 125 | 81.73 174 | 83.52 213 | 63.83 170 | 91.64 136 | 90.30 174 | 76.36 79 | 71.97 142 | 89.93 126 | 46.30 232 | 95.17 129 | 75.10 101 | 77.70 152 | 86.19 212 |
|
WR-MVS | | | 76.76 176 | 75.74 161 | 79.82 212 | 84.60 196 | 62.27 202 | 92.60 91 | 92.51 98 | 76.06 80 | 67.87 198 | 85.34 175 | 56.76 121 | 90.24 255 | 62.20 209 | 63.69 251 | 86.94 202 |
|
GA-MVS | | | 78.33 147 | 76.23 154 | 84.65 104 | 83.65 211 | 66.30 121 | 91.44 139 | 90.14 182 | 76.01 81 | 70.32 157 | 84.02 188 | 42.50 248 | 94.72 141 | 70.98 133 | 77.00 163 | 92.94 117 |
|
PVSNet_0 | | 68.08 15 | 71.81 222 | 68.32 236 | 82.27 159 | 84.68 195 | 62.31 201 | 88.68 205 | 90.31 173 | 75.84 82 | 57.93 257 | 80.65 234 | 37.85 271 | 94.19 166 | 69.94 141 | 29.05 330 | 90.31 155 |
|
CDS-MVSNet | | | 81.43 93 | 80.74 89 | 83.52 127 | 86.26 178 | 64.45 155 | 92.09 105 | 90.65 162 | 75.83 83 | 73.95 121 | 89.81 127 | 63.97 56 | 92.91 208 | 71.27 129 | 82.82 121 | 93.20 109 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
CostFormer | | | 82.33 81 | 81.15 84 | 85.86 67 | 89.01 135 | 68.46 51 | 82.39 266 | 93.01 79 | 75.59 84 | 80.25 63 | 81.57 219 | 72.03 12 | 94.96 132 | 79.06 78 | 77.48 159 | 94.16 79 |
|
nrg030 | | | 80.93 99 | 79.86 99 | 84.13 114 | 83.69 210 | 68.83 45 | 93.23 70 | 91.20 145 | 75.55 85 | 75.06 111 | 88.22 144 | 63.04 70 | 94.74 140 | 81.88 59 | 66.88 223 | 88.82 168 |
|
VDD-MVS | | | 83.06 72 | 81.81 78 | 86.81 38 | 90.86 105 | 67.70 67 | 95.40 19 | 91.50 135 | 75.46 86 | 81.78 52 | 92.34 97 | 40.09 258 | 97.13 61 | 86.85 30 | 82.04 126 | 95.60 29 |
|
Effi-MVS+-dtu | | | 76.14 182 | 75.28 173 | 78.72 234 | 83.22 215 | 55.17 275 | 89.87 186 | 87.78 243 | 75.42 87 | 67.98 195 | 81.43 220 | 45.08 238 | 92.52 220 | 75.08 102 | 71.63 194 | 88.48 173 |
|
mvs-test1 | | | 78.74 139 | 77.95 128 | 81.14 188 | 83.22 215 | 57.13 265 | 93.96 48 | 87.78 243 | 75.42 87 | 72.68 129 | 90.80 113 | 45.08 238 | 94.54 147 | 75.08 102 | 77.49 158 | 91.74 138 |
|
test_prior3 | | | 87.38 19 | 87.70 16 | 86.42 53 | 94.71 21 | 67.35 75 | 95.10 27 | 93.10 77 | 75.40 89 | 85.25 30 | 95.61 21 | 67.94 24 | 96.84 79 | 87.47 23 | 94.77 13 | 95.05 50 |
|
test_prior2 | | | | | | | | 95.10 27 | | 75.40 89 | 85.25 30 | 95.61 21 | 67.94 24 | | 87.47 23 | 94.77 13 | |
|
MPTG | | | 84.73 49 | 84.47 47 | 85.50 78 | 91.89 78 | 65.16 141 | 91.55 137 | 92.23 104 | 75.32 91 | 80.53 60 | 95.21 34 | 56.06 133 | 97.16 59 | 84.86 42 | 92.55 44 | 94.18 76 |
|
MTAPA | | | 83.91 61 | 83.38 60 | 85.50 78 | 91.89 78 | 65.16 141 | 81.75 269 | 92.23 104 | 75.32 91 | 80.53 60 | 95.21 34 | 56.06 133 | 97.16 59 | 84.86 42 | 92.55 44 | 94.18 76 |
|
EPMVS | | | 78.49 145 | 75.98 157 | 86.02 63 | 91.21 99 | 69.68 33 | 80.23 283 | 91.20 145 | 75.25 93 | 72.48 135 | 78.11 256 | 54.65 155 | 93.69 191 | 57.66 232 | 83.04 119 | 94.69 61 |
|
v2v482 | | | 77.42 158 | 75.65 165 | 82.73 138 | 80.38 251 | 67.13 82 | 91.85 124 | 90.23 176 | 75.09 94 | 69.37 173 | 83.39 194 | 53.79 167 | 94.44 150 | 71.77 123 | 65.00 240 | 86.63 208 |
|
VPA-MVSNet | | | 79.03 130 | 78.00 127 | 82.11 169 | 85.95 183 | 64.48 154 | 93.22 71 | 94.66 29 | 75.05 95 | 74.04 120 | 84.95 180 | 52.17 184 | 93.52 194 | 74.90 107 | 67.04 222 | 88.32 177 |
|
ACMMP_Plus | | | 86.05 37 | 85.80 37 | 86.80 39 | 91.58 86 | 67.53 72 | 91.79 126 | 93.49 59 | 74.93 96 | 84.61 33 | 95.30 28 | 59.42 96 | 97.92 28 | 86.13 33 | 94.92 9 | 94.94 56 |
|
thres200 | | | 79.66 121 | 78.33 121 | 83.66 126 | 92.54 62 | 65.82 132 | 93.06 74 | 96.31 8 | 74.90 97 | 73.30 124 | 88.66 133 | 59.67 94 | 95.61 116 | 47.84 264 | 78.67 145 | 89.56 163 |
|
TAMVS | | | 80.37 106 | 79.45 107 | 83.13 133 | 85.14 191 | 63.37 181 | 91.23 150 | 90.76 158 | 74.81 98 | 72.65 130 | 88.49 135 | 60.63 86 | 92.95 204 | 69.41 146 | 81.95 127 | 93.08 113 |
|
MP-MVS-pluss | | | 85.24 44 | 85.13 44 | 85.56 77 | 91.42 95 | 65.59 134 | 91.54 138 | 92.51 98 | 74.56 99 | 80.62 59 | 95.64 20 | 59.15 99 | 97.00 67 | 86.94 29 | 93.80 26 | 94.07 86 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
v1141 | | | 77.28 164 | 75.57 166 | 82.42 152 | 80.63 245 | 66.73 101 | 91.96 114 | 90.42 167 | 74.41 100 | 69.46 170 | 82.12 210 | 55.09 145 | 94.40 155 | 70.99 132 | 65.05 236 | 86.12 214 |
|
divwei89l23v2f112 | | | 77.28 164 | 75.57 166 | 82.42 152 | 80.62 246 | 66.72 103 | 91.96 114 | 90.42 167 | 74.41 100 | 69.46 170 | 82.12 210 | 55.11 144 | 94.40 155 | 71.00 130 | 65.04 237 | 86.12 214 |
|
v1 | | | 77.29 163 | 75.57 166 | 82.42 152 | 80.61 249 | 66.73 101 | 91.96 114 | 90.42 167 | 74.41 100 | 69.46 170 | 82.12 210 | 55.14 143 | 94.40 155 | 71.00 130 | 65.04 237 | 86.13 213 |
|
mvs_anonymous | | | 81.36 94 | 79.99 97 | 85.46 80 | 90.39 110 | 68.40 52 | 86.88 236 | 90.61 163 | 74.41 100 | 70.31 158 | 84.67 183 | 63.79 59 | 92.32 227 | 73.13 110 | 85.70 100 | 95.67 26 |
|
MAR-MVS | | | 84.18 56 | 83.43 57 | 86.44 52 | 96.25 7 | 65.93 128 | 94.28 35 | 94.27 40 | 74.41 100 | 79.16 74 | 95.61 21 | 53.99 164 | 98.88 9 | 69.62 144 | 93.26 36 | 94.50 69 |
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 |
BH-w/o | | | 80.49 105 | 79.30 111 | 84.05 116 | 90.83 106 | 64.36 161 | 93.60 60 | 89.42 205 | 74.35 105 | 69.09 177 | 90.15 122 | 55.23 140 | 95.61 116 | 64.61 186 | 86.43 97 | 92.17 134 |
|
Vis-MVSNet (Re-imp) | | | 79.24 128 | 79.57 103 | 78.24 241 | 88.46 146 | 52.29 286 | 90.41 172 | 89.12 216 | 74.24 106 | 69.13 176 | 91.91 101 | 65.77 43 | 90.09 262 | 59.00 226 | 88.09 82 | 92.33 129 |
|
v1neww | | | 77.39 159 | 75.71 162 | 82.44 146 | 80.69 239 | 66.83 91 | 91.94 118 | 90.18 179 | 74.19 107 | 69.60 167 | 82.51 200 | 54.99 149 | 94.44 150 | 71.68 125 | 65.60 229 | 86.05 217 |
|
v7new | | | 77.39 159 | 75.71 162 | 82.44 146 | 80.69 239 | 66.83 91 | 91.94 118 | 90.18 179 | 74.19 107 | 69.60 167 | 82.51 200 | 54.99 149 | 94.44 150 | 71.68 125 | 65.60 229 | 86.05 217 |
|
v6 | | | 77.39 159 | 75.71 162 | 82.44 146 | 80.67 241 | 66.82 93 | 91.94 118 | 90.18 179 | 74.19 107 | 69.60 167 | 82.50 203 | 55.00 148 | 94.44 150 | 71.68 125 | 65.60 229 | 86.05 217 |
|
3Dnovator+ | | 73.60 7 | 82.10 87 | 80.60 92 | 86.60 44 | 90.89 104 | 66.80 99 | 95.20 23 | 93.44 61 | 74.05 110 | 67.42 202 | 92.49 92 | 49.46 204 | 97.65 37 | 70.80 135 | 91.68 54 | 95.33 35 |
|
XVS | | | 83.87 62 | 83.47 55 | 85.05 94 | 93.22 45 | 63.78 171 | 92.92 80 | 92.66 92 | 73.99 111 | 78.18 82 | 94.31 62 | 55.25 138 | 97.41 45 | 79.16 76 | 91.58 56 | 93.95 91 |
|
X-MVStestdata | | | 76.86 173 | 74.13 186 | 85.05 94 | 93.22 45 | 63.78 171 | 92.92 80 | 92.66 92 | 73.99 111 | 78.18 82 | 10.19 342 | 55.25 138 | 97.41 45 | 79.16 76 | 91.58 56 | 93.95 91 |
|
MS-PatchMatch | | | 77.90 154 | 76.50 151 | 82.12 166 | 85.99 182 | 69.95 27 | 91.75 131 | 92.70 89 | 73.97 113 | 62.58 236 | 84.44 186 | 41.11 255 | 95.78 107 | 63.76 192 | 92.17 49 | 80.62 288 |
|
LCM-MVSNet-Re | | | 72.93 213 | 71.84 210 | 76.18 262 | 88.49 144 | 48.02 303 | 80.07 286 | 70.17 326 | 73.96 114 | 52.25 286 | 80.09 243 | 49.98 199 | 88.24 281 | 67.35 160 | 84.23 115 | 92.28 132 |
|
Vis-MVSNet | | | 80.92 100 | 79.98 98 | 83.74 120 | 88.48 145 | 61.80 209 | 93.44 66 | 88.26 238 | 73.96 114 | 77.73 85 | 91.76 104 | 49.94 200 | 94.76 138 | 65.84 177 | 90.37 70 | 94.65 62 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
test-mter | | | 79.96 115 | 79.38 110 | 81.72 175 | 86.93 171 | 61.17 214 | 92.70 86 | 91.54 132 | 73.85 116 | 75.62 104 | 86.94 161 | 49.84 202 | 92.38 224 | 72.21 120 | 84.76 107 | 91.60 139 |
|
OMC-MVS | | | 78.67 142 | 77.91 130 | 80.95 195 | 85.76 187 | 57.40 263 | 88.49 207 | 88.67 227 | 73.85 116 | 72.43 137 | 92.10 99 | 49.29 206 | 94.55 146 | 72.73 115 | 77.89 150 | 90.91 149 |
|
Fast-Effi-MVS+ | | | 81.14 96 | 80.01 96 | 84.51 109 | 90.24 112 | 65.86 129 | 94.12 40 | 89.15 215 | 73.81 118 | 75.37 109 | 88.26 141 | 57.26 113 | 94.53 148 | 66.97 165 | 84.92 104 | 93.15 110 |
|
V42 | | | 76.46 179 | 74.55 180 | 82.19 164 | 79.14 273 | 67.82 64 | 90.26 176 | 89.42 205 | 73.75 119 | 68.63 185 | 81.89 215 | 51.31 191 | 94.09 173 | 71.69 124 | 64.84 241 | 84.66 240 |
|
v1144 | | | 76.73 177 | 74.88 176 | 82.27 159 | 80.23 259 | 66.60 109 | 91.68 133 | 90.21 178 | 73.69 120 | 69.06 178 | 81.89 215 | 52.73 179 | 94.40 155 | 69.21 148 | 65.23 233 | 85.80 224 |
|
v148 | | | 76.19 181 | 74.47 182 | 81.36 180 | 80.05 263 | 64.44 156 | 91.75 131 | 90.23 176 | 73.68 121 | 67.13 205 | 80.84 231 | 55.92 136 | 93.86 188 | 68.95 150 | 61.73 261 | 85.76 227 |
|
CR-MVSNet | | | 73.79 209 | 70.82 218 | 82.70 139 | 83.15 217 | 67.96 62 | 70.25 309 | 84.00 284 | 73.67 122 | 69.97 163 | 72.41 288 | 57.82 109 | 89.48 271 | 52.99 248 | 73.13 184 | 90.64 152 |
|
XXY-MVS | | | 77.94 153 | 76.44 152 | 82.43 149 | 82.60 221 | 64.44 156 | 92.01 110 | 91.83 123 | 73.59 123 | 70.00 162 | 85.82 172 | 54.43 159 | 94.76 138 | 69.63 143 | 68.02 217 | 88.10 180 |
|
#test# | | | 84.98 47 | 84.74 46 | 85.72 74 | 93.75 37 | 65.01 147 | 94.09 41 | 93.19 72 | 73.55 124 | 79.22 72 | 94.93 42 | 59.04 100 | 97.67 33 | 82.66 54 | 92.21 46 | 94.49 70 |
|
tfpn200view9 | | | 78.79 137 | 77.43 137 | 82.88 135 | 92.21 70 | 64.49 152 | 92.05 108 | 96.28 9 | 73.48 125 | 71.75 145 | 88.26 141 | 60.07 91 | 95.32 125 | 45.16 273 | 77.58 154 | 88.83 166 |
|
thres400 | | | 78.68 140 | 77.43 137 | 82.43 149 | 92.21 70 | 64.49 152 | 92.05 108 | 96.28 9 | 73.48 125 | 71.75 145 | 88.26 141 | 60.07 91 | 95.32 125 | 45.16 273 | 77.58 154 | 87.48 185 |
|
v7 | | | 76.83 175 | 75.01 175 | 82.29 158 | 80.35 252 | 66.70 105 | 91.68 133 | 89.97 188 | 73.47 127 | 69.22 175 | 82.22 207 | 52.52 180 | 94.43 154 | 69.73 142 | 65.96 228 | 85.74 228 |
|
FMVSNet3 | | | 77.73 155 | 76.04 156 | 82.80 136 | 91.20 100 | 68.99 42 | 91.87 122 | 91.99 116 | 73.35 128 | 67.04 206 | 83.19 196 | 56.62 126 | 92.14 229 | 59.80 222 | 69.34 206 | 87.28 196 |
|
USDC | | | 67.43 261 | 64.51 259 | 76.19 261 | 77.94 284 | 55.29 274 | 78.38 295 | 85.00 276 | 73.17 129 | 48.36 299 | 80.37 237 | 21.23 320 | 92.48 222 | 52.15 249 | 64.02 248 | 80.81 286 |
|
MP-MVS | | | 85.02 46 | 84.97 45 | 85.17 92 | 92.60 61 | 64.27 165 | 93.24 69 | 92.27 103 | 73.13 130 | 79.63 69 | 94.43 53 | 61.90 75 | 97.17 58 | 85.00 40 | 92.56 43 | 94.06 87 |
|
xiu_mvs_v1_base_debu | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 174 | 68.72 47 | 92.59 93 | 90.44 164 | 73.12 131 | 84.20 38 | 94.36 55 | 38.04 268 | 95.73 110 | 84.12 45 | 86.81 89 | 91.33 142 |
|
xiu_mvs_v1_base | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 174 | 68.72 47 | 92.59 93 | 90.44 164 | 73.12 131 | 84.20 38 | 94.36 55 | 38.04 268 | 95.73 110 | 84.12 45 | 86.81 89 | 91.33 142 |
|
xiu_mvs_v1_base_debi | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 174 | 68.72 47 | 92.59 93 | 90.44 164 | 73.12 131 | 84.20 38 | 94.36 55 | 38.04 268 | 95.73 110 | 84.12 45 | 86.81 89 | 91.33 142 |
|
BH-RMVSNet | | | 79.46 126 | 77.65 132 | 84.89 97 | 91.68 85 | 65.66 133 | 93.55 62 | 88.09 239 | 72.93 134 | 73.37 123 | 91.12 110 | 46.20 233 | 96.12 96 | 56.28 235 | 85.61 102 | 92.91 118 |
|
IS-MVSNet | | | 80.14 111 | 79.41 108 | 82.33 156 | 87.91 157 | 60.08 236 | 91.97 113 | 88.27 237 | 72.90 135 | 71.44 149 | 91.73 106 | 61.44 78 | 93.66 192 | 62.47 208 | 86.53 95 | 93.24 107 |
|
PS-MVSNAJss | | | 77.26 166 | 76.31 153 | 80.13 204 | 80.64 244 | 59.16 248 | 90.63 169 | 91.06 150 | 72.80 136 | 68.58 186 | 84.57 185 | 53.55 169 | 93.96 182 | 72.97 111 | 71.96 193 | 87.27 197 |
|
v1192 | | | 75.98 186 | 73.92 189 | 82.15 165 | 79.73 264 | 66.24 123 | 91.22 151 | 89.75 193 | 72.67 137 | 68.49 191 | 81.42 221 | 49.86 201 | 94.27 163 | 67.08 163 | 65.02 239 | 85.95 221 |
|
Effi-MVS+ | | | 83.82 63 | 82.76 68 | 86.99 37 | 89.56 123 | 69.40 36 | 91.35 145 | 86.12 268 | 72.59 138 | 83.22 45 | 92.81 89 | 59.60 95 | 96.01 102 | 81.76 60 | 87.80 84 | 95.56 30 |
|
UnsupCasMVSNet_eth | | | 65.79 268 | 63.10 267 | 73.88 273 | 70.71 309 | 50.29 297 | 81.09 275 | 89.88 190 | 72.58 139 | 49.25 297 | 74.77 278 | 32.57 293 | 87.43 286 | 55.96 236 | 41.04 320 | 83.90 246 |
|
1112_ss | | | 80.56 103 | 79.83 100 | 82.77 137 | 88.65 141 | 60.78 219 | 92.29 98 | 88.36 234 | 72.58 139 | 72.46 136 | 94.95 40 | 65.09 48 | 93.42 197 | 66.38 170 | 77.71 151 | 94.10 83 |
|
thres600view7 | | | 78.00 151 | 76.66 149 | 82.03 171 | 91.93 75 | 63.69 176 | 91.30 148 | 96.33 5 | 72.43 141 | 70.46 153 | 87.89 147 | 60.31 87 | 94.92 135 | 42.64 285 | 76.64 164 | 87.48 185 |
|
IterMVS-LS | | | 76.49 178 | 75.18 174 | 80.43 199 | 84.49 199 | 62.74 195 | 90.64 167 | 88.80 225 | 72.40 142 | 65.16 214 | 81.72 218 | 60.98 80 | 92.27 228 | 67.74 157 | 64.65 244 | 86.29 210 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 78.97 132 | 78.22 124 | 81.25 182 | 85.33 188 | 62.73 196 | 89.53 192 | 93.21 69 | 72.39 143 | 72.14 140 | 90.13 123 | 60.99 79 | 94.72 141 | 67.73 158 | 72.49 190 | 86.29 210 |
|
v144192 | | | 76.05 184 | 74.03 187 | 82.12 166 | 79.50 268 | 66.55 113 | 91.39 141 | 89.71 199 | 72.30 144 | 68.17 193 | 81.33 223 | 51.75 187 | 94.03 179 | 67.94 155 | 64.19 246 | 85.77 225 |
|
conf200view11 | | | 78.32 148 | 77.01 144 | 82.27 159 | 91.89 78 | 63.21 184 | 91.19 154 | 96.33 5 | 72.28 145 | 70.45 154 | 87.89 147 | 60.31 87 | 95.32 125 | 45.16 273 | 77.58 154 | 88.27 178 |
|
thres100view900 | | | 78.37 146 | 77.01 144 | 82.46 145 | 91.89 78 | 63.21 184 | 91.19 154 | 96.33 5 | 72.28 145 | 70.45 154 | 87.89 147 | 60.31 87 | 95.32 125 | 45.16 273 | 77.58 154 | 88.83 166 |
|
PatchmatchNet | | | 77.46 157 | 74.63 177 | 85.96 65 | 89.55 124 | 70.35 21 | 79.97 287 | 89.55 201 | 72.23 147 | 70.94 150 | 76.91 268 | 57.03 116 | 92.79 212 | 54.27 241 | 81.17 129 | 94.74 60 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
testing_2 | | | 71.09 231 | 67.32 244 | 82.40 155 | 69.82 312 | 66.52 115 | 83.64 253 | 90.77 157 | 72.21 148 | 45.12 309 | 71.07 302 | 27.60 309 | 93.74 189 | 75.71 97 | 69.96 201 | 86.95 201 |
|
HFP-MVS | | | 84.73 49 | 84.40 49 | 85.72 74 | 93.75 37 | 65.01 147 | 93.50 64 | 93.19 72 | 72.19 149 | 79.22 72 | 94.93 42 | 59.04 100 | 97.67 33 | 81.55 62 | 92.21 46 | 94.49 70 |
|
ACMMPR | | | 84.37 52 | 84.06 50 | 85.28 88 | 93.56 40 | 64.37 160 | 93.50 64 | 93.15 75 | 72.19 149 | 78.85 78 | 94.86 46 | 56.69 125 | 97.45 44 | 81.55 62 | 92.20 48 | 94.02 89 |
|
1314 | | | 80.70 101 | 78.95 116 | 85.94 66 | 87.77 160 | 67.56 71 | 87.91 218 | 92.55 97 | 72.17 151 | 67.44 201 | 93.09 78 | 50.27 197 | 97.04 64 | 71.68 125 | 87.64 85 | 93.23 108 |
|
region2R | | | 84.36 53 | 84.03 51 | 85.36 86 | 93.54 41 | 64.31 162 | 93.43 67 | 92.95 82 | 72.16 152 | 78.86 77 | 94.84 47 | 56.97 118 | 97.53 42 | 81.38 65 | 92.11 50 | 94.24 74 |
|
Test_1112_low_res | | | 79.56 124 | 78.60 119 | 82.43 149 | 88.24 151 | 60.39 228 | 92.09 105 | 87.99 241 | 72.10 153 | 71.84 143 | 87.42 154 | 64.62 52 | 93.04 201 | 65.80 178 | 77.30 161 | 93.85 95 |
|
v1921920 | | | 75.63 191 | 73.49 194 | 82.06 170 | 79.38 269 | 66.35 119 | 91.07 158 | 89.48 202 | 71.98 154 | 67.99 194 | 81.22 226 | 49.16 209 | 93.90 185 | 66.56 168 | 64.56 245 | 85.92 223 |
|
Fast-Effi-MVS+-dtu | | | 75.04 197 | 73.37 196 | 80.07 205 | 80.86 234 | 59.52 243 | 91.20 153 | 85.38 275 | 71.90 155 | 65.20 213 | 84.84 181 | 41.46 254 | 92.97 203 | 66.50 169 | 72.96 186 | 87.73 182 |
|
LFMVS | | | 84.34 54 | 82.73 69 | 89.18 8 | 94.76 19 | 73.25 8 | 94.99 30 | 91.89 120 | 71.90 155 | 82.16 50 | 93.49 74 | 47.98 218 | 97.05 62 | 82.55 55 | 84.82 105 | 97.25 2 |
|
train_agg | | | 87.21 22 | 87.42 21 | 86.60 44 | 94.18 27 | 67.28 77 | 94.16 36 | 93.51 57 | 71.87 157 | 85.52 26 | 95.33 26 | 68.19 21 | 97.27 54 | 89.09 13 | 94.90 10 | 95.25 43 |
|
test_8 | | | | | | 94.19 26 | 67.19 79 | 94.15 38 | 93.42 62 | 71.87 157 | 85.38 28 | 95.35 25 | 68.19 21 | 96.95 74 | | | |
|
agg_prior1 | | | 87.02 24 | 87.26 23 | 86.28 60 | 94.16 31 | 66.97 86 | 94.08 42 | 93.31 66 | 71.85 159 | 84.49 35 | 95.39 24 | 68.91 17 | 96.75 83 | 88.84 17 | 94.32 20 | 95.13 47 |
|
MDTV_nov1_ep13 | | | | 72.61 203 | | 89.06 133 | 68.48 50 | 80.33 281 | 90.11 183 | 71.84 160 | 71.81 144 | 75.92 273 | 53.01 176 | 93.92 184 | 48.04 263 | 73.38 182 | |
|
ab-mvs | | | 80.18 109 | 78.31 122 | 85.80 70 | 88.44 147 | 65.49 137 | 83.00 263 | 92.67 91 | 71.82 161 | 77.36 92 | 85.01 179 | 54.50 156 | 96.59 86 | 76.35 95 | 75.63 169 | 95.32 37 |
|
agg_prior3 | | | 86.93 26 | 87.08 24 | 86.48 50 | 94.21 25 | 66.95 88 | 94.14 39 | 93.40 63 | 71.80 162 | 84.86 32 | 95.13 36 | 66.16 37 | 97.25 56 | 89.09 13 | 94.90 10 | 95.25 43 |
|
ACMMP | | | 81.49 92 | 80.67 90 | 83.93 117 | 91.71 84 | 62.90 192 | 92.13 102 | 92.22 108 | 71.79 163 | 71.68 147 | 93.49 74 | 50.32 195 | 96.96 73 | 78.47 81 | 84.22 116 | 91.93 136 |
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 |
PHI-MVS | | | 86.83 29 | 86.85 28 | 86.78 40 | 93.47 43 | 65.55 135 | 95.39 20 | 95.10 19 | 71.77 164 | 85.69 25 | 96.52 3 | 62.07 74 | 98.77 10 | 86.06 34 | 95.60 6 | 96.03 21 |
|
TEST9 | | | | | | 94.18 27 | 67.28 77 | 94.16 36 | 93.51 57 | 71.75 165 | 85.52 26 | 95.33 26 | 68.01 23 | 97.27 54 | | | |
|
v8 | | | 75.35 193 | 73.26 197 | 81.61 178 | 80.67 241 | 66.82 93 | 89.54 191 | 89.27 209 | 71.65 166 | 63.30 230 | 80.30 239 | 54.99 149 | 94.06 175 | 67.33 162 | 62.33 255 | 83.94 245 |
|
v1240 | | | 75.21 196 | 72.98 199 | 81.88 172 | 79.20 271 | 66.00 126 | 90.75 164 | 89.11 217 | 71.63 167 | 67.41 203 | 81.22 226 | 47.36 223 | 93.87 186 | 65.46 181 | 64.72 243 | 85.77 225 |
|
Patchmatch-test1 | | | 75.00 199 | 71.80 212 | 84.58 106 | 86.63 173 | 70.08 24 | 81.06 276 | 89.19 212 | 71.60 168 | 70.01 161 | 77.16 266 | 45.53 235 | 88.63 275 | 51.79 250 | 73.27 183 | 95.02 54 |
|
BH-untuned | | | 78.68 140 | 77.08 142 | 83.48 129 | 89.84 118 | 63.74 173 | 92.70 86 | 88.59 230 | 71.57 169 | 66.83 209 | 88.65 134 | 51.75 187 | 95.39 123 | 59.03 225 | 84.77 106 | 91.32 145 |
|
IterMVS | | | 72.65 219 | 70.83 217 | 78.09 244 | 82.17 223 | 62.96 188 | 87.64 224 | 86.28 264 | 71.56 170 | 60.44 242 | 78.85 253 | 45.42 237 | 86.66 289 | 63.30 196 | 61.83 258 | 84.65 241 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
mPP-MVS | | | 82.96 74 | 82.44 71 | 84.52 108 | 92.83 55 | 62.92 191 | 92.76 83 | 91.85 122 | 71.52 171 | 75.61 106 | 94.24 63 | 53.48 173 | 96.99 70 | 78.97 79 | 90.73 65 | 93.64 98 |
|
test-LLR | | | 80.10 112 | 79.56 104 | 81.72 175 | 86.93 171 | 61.17 214 | 92.70 86 | 91.54 132 | 71.51 172 | 75.62 104 | 86.94 161 | 53.83 165 | 92.38 224 | 72.21 120 | 84.76 107 | 91.60 139 |
|
test0.0.03 1 | | | 72.76 216 | 72.71 202 | 72.88 281 | 80.25 258 | 47.99 304 | 91.22 151 | 89.45 203 | 71.51 172 | 62.51 237 | 87.66 150 | 53.83 165 | 85.06 294 | 50.16 255 | 67.84 220 | 85.58 229 |
|
PGM-MVS | | | 83.25 68 | 82.70 70 | 84.92 96 | 92.81 58 | 64.07 167 | 90.44 170 | 92.20 109 | 71.28 174 | 77.23 94 | 94.43 53 | 55.17 142 | 97.31 51 | 79.33 75 | 91.38 59 | 93.37 102 |
|
dp | | | 75.01 198 | 72.09 209 | 83.76 119 | 89.28 127 | 66.22 124 | 79.96 288 | 89.75 193 | 71.16 175 | 67.80 199 | 77.19 264 | 51.81 186 | 92.54 219 | 50.39 254 | 71.44 198 | 92.51 127 |
|
CP-MVS | | | 83.71 66 | 83.40 59 | 84.65 104 | 93.14 49 | 63.84 169 | 94.59 33 | 92.28 102 | 71.03 176 | 77.41 91 | 94.92 44 | 55.21 141 | 96.19 93 | 81.32 66 | 90.70 66 | 93.91 93 |
|
v10 | | | 74.77 201 | 72.54 205 | 81.46 179 | 80.33 256 | 66.71 104 | 89.15 198 | 89.08 218 | 70.94 177 | 63.08 231 | 79.86 244 | 52.52 180 | 94.04 178 | 65.70 179 | 62.17 256 | 83.64 247 |
|
CDPH-MVS | | | 85.71 41 | 85.46 41 | 86.46 51 | 94.75 20 | 67.19 79 | 93.89 54 | 92.83 86 | 70.90 178 | 83.09 46 | 95.28 29 | 63.62 61 | 97.36 48 | 80.63 68 | 94.18 21 | 94.84 58 |
|
GBi-Net | | | 75.65 189 | 73.83 190 | 81.10 190 | 88.85 136 | 65.11 143 | 90.01 178 | 90.32 170 | 70.84 179 | 67.04 206 | 80.25 240 | 48.03 215 | 91.54 242 | 59.80 222 | 69.34 206 | 86.64 205 |
|
test1 | | | 75.65 189 | 73.83 190 | 81.10 190 | 88.85 136 | 65.11 143 | 90.01 178 | 90.32 170 | 70.84 179 | 67.04 206 | 80.25 240 | 48.03 215 | 91.54 242 | 59.80 222 | 69.34 206 | 86.64 205 |
|
FMVSNet2 | | | 76.07 183 | 74.01 188 | 82.26 162 | 88.85 136 | 67.66 68 | 91.33 146 | 91.61 130 | 70.84 179 | 65.98 211 | 82.25 206 | 48.03 215 | 92.00 234 | 58.46 228 | 68.73 212 | 87.10 198 |
|
view600 | | | 76.93 169 | 75.50 169 | 81.23 183 | 91.44 91 | 62.00 204 | 89.94 182 | 96.56 1 | 70.68 182 | 68.54 187 | 87.31 155 | 60.79 81 | 94.19 166 | 38.90 298 | 75.31 171 | 87.48 185 |
|
view800 | | | 76.93 169 | 75.50 169 | 81.23 183 | 91.44 91 | 62.00 204 | 89.94 182 | 96.56 1 | 70.68 182 | 68.54 187 | 87.31 155 | 60.79 81 | 94.19 166 | 38.90 298 | 75.31 171 | 87.48 185 |
|
conf0.05thres1000 | | | 76.93 169 | 75.50 169 | 81.23 183 | 91.44 91 | 62.00 204 | 89.94 182 | 96.56 1 | 70.68 182 | 68.54 187 | 87.31 155 | 60.79 81 | 94.19 166 | 38.90 298 | 75.31 171 | 87.48 185 |
|
tfpn | | | 76.93 169 | 75.50 169 | 81.23 183 | 91.44 91 | 62.00 204 | 89.94 182 | 96.56 1 | 70.68 182 | 68.54 187 | 87.31 155 | 60.79 81 | 94.19 166 | 38.90 298 | 75.31 171 | 87.48 185 |
|
HyFIR lowres test | | | 81.03 98 | 79.56 104 | 85.43 83 | 87.81 159 | 68.11 60 | 90.18 177 | 90.01 187 | 70.65 186 | 72.95 126 | 86.06 170 | 63.61 62 | 94.50 149 | 75.01 104 | 79.75 136 | 93.67 96 |
|
MVP-Stereo | | | 77.12 168 | 76.23 154 | 79.79 213 | 81.72 227 | 66.34 120 | 89.29 194 | 90.88 154 | 70.56 187 | 62.01 239 | 82.88 197 | 49.34 205 | 94.13 171 | 65.55 180 | 93.80 26 | 78.88 301 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
diffmvs | | | 80.18 109 | 78.55 120 | 85.07 93 | 88.56 142 | 66.93 89 | 86.70 239 | 88.62 229 | 70.42 188 | 78.69 80 | 85.26 176 | 56.93 120 | 94.77 137 | 68.68 153 | 83.09 118 | 93.51 100 |
|
tpmp4_e23 | | | 78.85 134 | 76.55 150 | 85.77 72 | 89.25 128 | 68.39 53 | 81.63 273 | 91.38 140 | 70.40 189 | 75.21 110 | 79.22 251 | 67.37 30 | 94.79 136 | 58.98 227 | 75.51 170 | 94.13 81 |
|
ACMP | | 71.68 10 | 75.58 192 | 74.23 185 | 79.62 216 | 84.97 193 | 59.64 240 | 90.80 162 | 89.07 219 | 70.39 190 | 62.95 232 | 87.30 159 | 38.28 265 | 93.87 186 | 72.89 112 | 71.45 197 | 85.36 234 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
HPM-MVS | | | 83.25 68 | 82.95 64 | 84.17 113 | 92.25 68 | 62.88 193 | 90.91 159 | 91.86 121 | 70.30 191 | 77.12 95 | 93.96 67 | 56.75 123 | 96.28 91 | 82.04 58 | 91.34 61 | 93.34 103 |
|
tpm2 | | | 79.80 119 | 77.95 128 | 85.34 87 | 88.28 150 | 68.26 58 | 81.56 274 | 91.42 138 | 70.11 192 | 77.59 90 | 80.50 235 | 67.40 29 | 94.26 165 | 67.34 161 | 77.35 160 | 93.51 100 |
|
TR-MVS | | | 78.77 138 | 77.37 140 | 82.95 134 | 90.49 107 | 60.88 217 | 93.67 58 | 90.07 184 | 70.08 193 | 74.51 114 | 91.37 108 | 45.69 234 | 95.70 115 | 60.12 220 | 80.32 133 | 92.29 131 |
|
PAPM_NR | | | 82.97 73 | 81.84 77 | 86.37 56 | 94.10 33 | 66.76 100 | 87.66 223 | 92.84 85 | 69.96 194 | 74.07 119 | 93.57 72 | 63.10 69 | 97.50 43 | 70.66 137 | 90.58 68 | 94.85 57 |
|
PCF-MVS | | 73.15 9 | 79.29 127 | 77.63 133 | 84.29 112 | 86.06 181 | 65.96 127 | 87.03 231 | 91.10 149 | 69.86 195 | 69.79 166 | 90.64 114 | 57.54 112 | 96.59 86 | 64.37 190 | 82.29 123 | 90.32 154 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MIMVSNet | | | 71.64 225 | 68.44 234 | 81.23 183 | 81.97 226 | 64.44 156 | 73.05 305 | 88.80 225 | 69.67 196 | 64.59 217 | 74.79 277 | 32.79 291 | 87.82 284 | 53.99 242 | 76.35 166 | 91.42 141 |
|
LPG-MVS_test | | | 75.82 188 | 74.58 179 | 79.56 218 | 84.31 203 | 59.37 245 | 90.44 170 | 89.73 196 | 69.49 197 | 64.86 215 | 88.42 136 | 38.65 262 | 94.30 161 | 72.56 116 | 72.76 187 | 85.01 237 |
|
LGP-MVS_train | | | | | 79.56 218 | 84.31 203 | 59.37 245 | | 89.73 196 | 69.49 197 | 64.86 215 | 88.42 136 | 38.65 262 | 94.30 161 | 72.56 116 | 72.76 187 | 85.01 237 |
|
APDe-MVS | | | 87.54 17 | 87.84 14 | 86.65 42 | 96.07 9 | 66.30 121 | 94.84 32 | 93.78 45 | 69.35 199 | 88.39 10 | 96.34 6 | 67.74 28 | 97.66 36 | 90.62 9 | 93.44 34 | 96.01 22 |
|
Patchmatch-RL test | | | 68.17 254 | 64.49 260 | 79.19 229 | 71.22 307 | 53.93 280 | 70.07 311 | 71.54 325 | 69.22 200 | 56.79 263 | 62.89 316 | 56.58 127 | 88.61 276 | 69.53 145 | 52.61 293 | 95.03 53 |
|
jajsoiax | | | 73.05 212 | 71.51 214 | 77.67 247 | 77.46 286 | 54.83 276 | 88.81 203 | 90.04 186 | 69.13 201 | 62.85 234 | 83.51 192 | 31.16 300 | 92.75 213 | 70.83 134 | 69.80 202 | 85.43 233 |
|
DP-MVS Recon | | | 82.73 75 | 81.65 80 | 85.98 64 | 97.31 3 | 67.06 83 | 95.15 25 | 91.99 116 | 69.08 202 | 76.50 101 | 93.89 68 | 54.48 158 | 98.20 22 | 70.76 136 | 85.66 101 | 92.69 121 |
|
v18 | | | 71.94 221 | 69.43 224 | 79.50 220 | 80.74 236 | 66.82 93 | 88.16 212 | 86.66 254 | 68.95 203 | 55.55 266 | 72.66 283 | 55.03 147 | 90.15 258 | 64.78 185 | 52.30 294 | 81.54 270 |
|
Baseline_NR-MVSNet | | | 73.99 207 | 72.83 200 | 77.48 250 | 80.78 235 | 59.29 247 | 91.79 126 | 84.55 279 | 68.85 204 | 68.99 179 | 80.70 232 | 56.16 130 | 92.04 233 | 62.67 206 | 60.98 267 | 81.11 282 |
|
CHOSEN 280x420 | | | 77.35 162 | 76.95 147 | 78.55 235 | 87.07 168 | 62.68 197 | 69.71 312 | 82.95 294 | 68.80 205 | 71.48 148 | 87.27 160 | 66.03 39 | 84.00 301 | 76.47 94 | 82.81 122 | 88.95 165 |
|
mvs_tets | | | 72.71 217 | 71.11 215 | 77.52 248 | 77.41 287 | 54.52 278 | 88.45 209 | 89.76 192 | 68.76 206 | 62.70 235 | 83.26 195 | 29.49 304 | 92.71 214 | 70.51 139 | 69.62 204 | 85.34 235 |
|
v17 | | | 71.77 224 | 69.20 227 | 79.46 222 | 80.62 246 | 66.81 97 | 87.93 216 | 86.63 256 | 68.71 207 | 55.25 268 | 72.49 285 | 54.72 154 | 90.11 261 | 64.50 188 | 51.97 296 | 81.47 271 |
|
v16 | | | 71.81 222 | 69.26 226 | 79.47 221 | 80.66 243 | 66.81 97 | 87.93 216 | 86.63 256 | 68.70 208 | 55.35 267 | 72.51 284 | 54.75 153 | 90.12 260 | 64.51 187 | 52.28 295 | 81.47 271 |
|
v15 | | | 71.40 226 | 68.75 229 | 79.35 223 | 80.39 250 | 66.70 105 | 87.57 225 | 86.64 255 | 68.66 209 | 54.68 270 | 72.00 292 | 54.50 156 | 89.98 263 | 63.69 193 | 50.66 301 | 81.38 275 |
|
MVS | | | 84.66 51 | 82.86 66 | 90.06 1 | 90.93 102 | 74.56 5 | 87.91 218 | 95.54 13 | 68.55 210 | 72.35 139 | 94.71 50 | 59.78 93 | 98.90 7 | 81.29 67 | 94.69 17 | 96.74 7 |
|
EPP-MVSNet | | | 81.79 90 | 81.52 81 | 82.61 143 | 88.77 140 | 60.21 232 | 93.02 76 | 93.66 52 | 68.52 211 | 72.90 127 | 90.39 119 | 72.19 11 | 94.96 132 | 74.93 105 | 79.29 140 | 92.67 122 |
|
CSCG | | | 86.87 27 | 86.26 30 | 88.72 9 | 95.05 18 | 70.79 17 | 93.83 56 | 95.33 14 | 68.48 212 | 77.63 88 | 94.35 59 | 73.04 8 | 98.45 16 | 84.92 41 | 93.71 30 | 96.92 6 |
|
V14 | | | 71.29 228 | 68.61 231 | 79.31 224 | 80.34 254 | 66.65 107 | 87.39 227 | 86.61 258 | 68.41 213 | 54.49 272 | 71.91 293 | 54.25 161 | 89.96 264 | 63.50 194 | 50.62 302 | 81.33 277 |
|
V9 | | | 71.16 229 | 68.46 233 | 79.27 226 | 80.26 257 | 66.60 109 | 87.21 230 | 86.56 259 | 68.17 214 | 54.26 275 | 71.81 295 | 54.00 163 | 89.93 265 | 63.28 197 | 50.57 303 | 81.27 278 |
|
v11 | | | 71.05 232 | 68.32 236 | 79.23 227 | 80.34 254 | 66.57 112 | 87.01 233 | 86.55 260 | 68.11 215 | 54.40 273 | 71.66 297 | 52.94 177 | 89.91 266 | 62.71 205 | 51.12 299 | 81.21 279 |
|
v12 | | | 71.02 233 | 68.29 238 | 79.22 228 | 80.18 260 | 66.53 114 | 87.01 233 | 86.54 261 | 67.90 216 | 54.00 278 | 71.70 296 | 53.66 168 | 89.91 266 | 63.09 199 | 50.51 304 | 81.21 279 |
|
CP-MVSNet | | | 70.50 238 | 69.91 221 | 72.26 286 | 80.71 238 | 51.00 293 | 87.23 229 | 90.30 174 | 67.84 217 | 59.64 245 | 82.69 199 | 50.23 198 | 82.30 312 | 51.28 251 | 59.28 272 | 83.46 252 |
|
pmmvs5 | | | 73.35 211 | 71.52 213 | 78.86 233 | 78.64 279 | 60.61 226 | 91.08 157 | 86.90 252 | 67.69 218 | 63.32 229 | 83.64 190 | 44.33 243 | 90.53 250 | 62.04 210 | 66.02 227 | 85.46 232 |
|
pm-mvs1 | | | 72.89 214 | 71.09 216 | 78.26 240 | 79.10 275 | 57.62 261 | 90.80 162 | 89.30 208 | 67.66 219 | 62.91 233 | 81.78 217 | 49.11 210 | 92.95 204 | 60.29 219 | 58.89 277 | 84.22 243 |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 238 | 80.13 285 | | 67.65 220 | 72.79 128 | | 54.33 160 | | 59.83 221 | | 92.58 125 |
|
pmmvs4 | | | 73.92 208 | 71.81 211 | 80.25 202 | 79.17 272 | 65.24 139 | 87.43 226 | 87.26 251 | 67.64 221 | 63.46 228 | 83.91 189 | 48.96 211 | 91.53 245 | 62.94 202 | 65.49 232 | 83.96 244 |
|
v13 | | | 70.90 234 | 68.15 239 | 79.15 232 | 80.08 261 | 66.45 116 | 86.83 237 | 86.50 262 | 67.62 222 | 53.78 280 | 71.61 298 | 53.51 172 | 89.87 268 | 62.89 203 | 50.50 305 | 81.14 281 |
|
WR-MVS_H | | | 70.59 236 | 69.94 220 | 72.53 283 | 81.03 232 | 51.43 290 | 87.35 228 | 92.03 115 | 67.38 223 | 60.23 243 | 80.70 232 | 55.84 137 | 83.45 305 | 46.33 269 | 58.58 278 | 82.72 263 |
|
PS-CasMVS | | | 69.86 242 | 69.13 228 | 72.07 289 | 80.35 252 | 50.57 295 | 87.02 232 | 89.75 193 | 67.27 224 | 59.19 248 | 82.28 205 | 46.58 229 | 82.24 313 | 50.69 253 | 59.02 275 | 83.39 254 |
|
PEN-MVS | | | 69.46 247 | 68.56 232 | 72.17 288 | 79.27 270 | 49.71 299 | 86.90 235 | 89.24 211 | 67.24 225 | 59.08 249 | 82.51 200 | 47.23 224 | 83.54 304 | 48.42 262 | 57.12 280 | 83.25 255 |
|
cascas | | | 78.18 149 | 75.77 160 | 85.41 84 | 87.14 167 | 69.11 39 | 92.96 77 | 91.15 147 | 66.71 226 | 70.47 152 | 86.07 169 | 37.49 274 | 96.48 90 | 70.15 140 | 79.80 135 | 90.65 151 |
|
APD-MVS | | | 85.93 38 | 85.99 33 | 85.76 73 | 95.98 11 | 65.21 140 | 93.59 61 | 92.58 96 | 66.54 227 | 86.17 19 | 95.88 14 | 63.83 58 | 97.00 67 | 86.39 32 | 92.94 38 | 95.06 49 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
OpenMVS | | 70.45 11 | 78.54 144 | 75.92 158 | 86.41 55 | 85.93 186 | 71.68 11 | 92.74 84 | 92.51 98 | 66.49 228 | 64.56 218 | 91.96 100 | 43.88 244 | 98.10 25 | 54.61 239 | 90.65 67 | 89.44 164 |
|
DTE-MVSNet | | | 68.46 253 | 67.33 243 | 71.87 292 | 77.94 284 | 49.00 302 | 86.16 243 | 88.58 231 | 66.36 229 | 58.19 255 | 82.21 208 | 46.36 230 | 83.87 302 | 44.97 277 | 55.17 287 | 82.73 262 |
|
semantic-postprocess | | | | | 76.32 260 | 81.48 228 | 60.67 224 | | 85.99 270 | 66.17 230 | 59.50 246 | 78.88 252 | 45.51 236 | 83.65 303 | 62.58 207 | 61.93 257 | 84.63 242 |
|
TransMVSNet (Re) | | | 70.07 240 | 67.66 241 | 77.31 254 | 80.62 246 | 59.13 249 | 91.78 128 | 84.94 277 | 65.97 231 | 60.08 244 | 80.44 236 | 50.78 192 | 91.87 235 | 48.84 260 | 45.46 314 | 80.94 284 |
|
MVSFormer | | | 83.75 65 | 82.88 65 | 86.37 56 | 89.24 130 | 71.18 14 | 89.07 200 | 90.69 159 | 65.80 232 | 87.13 15 | 94.34 60 | 64.99 49 | 92.67 216 | 72.83 113 | 91.80 52 | 95.27 40 |
|
test_djsdf | | | 73.76 210 | 72.56 204 | 77.39 252 | 77.00 289 | 53.93 280 | 89.07 200 | 90.69 159 | 65.80 232 | 63.92 223 | 82.03 213 | 43.14 247 | 92.67 216 | 72.83 113 | 68.53 213 | 85.57 230 |
|
API-MVS | | | 82.28 82 | 80.53 93 | 87.54 23 | 96.13 8 | 70.59 19 | 93.63 59 | 91.04 152 | 65.72 234 | 75.45 108 | 92.83 88 | 56.11 132 | 98.89 8 | 64.10 191 | 89.75 74 | 93.15 110 |
|
原ACMM1 | | | | | 84.42 110 | 93.21 47 | 64.27 165 | | 93.40 63 | 65.39 235 | 79.51 70 | 92.50 91 | 58.11 107 | 96.69 85 | 65.27 182 | 93.96 23 | 92.32 130 |
|
testgi | | | 64.48 273 | 62.87 270 | 69.31 296 | 71.24 306 | 40.62 320 | 85.49 244 | 79.92 303 | 65.36 236 | 54.18 276 | 83.49 193 | 23.74 316 | 84.55 295 | 41.60 287 | 60.79 269 | 82.77 261 |
|
QAPM | | | 79.95 116 | 77.39 139 | 87.64 20 | 89.63 121 | 71.41 12 | 93.30 68 | 93.70 50 | 65.34 237 | 67.39 204 | 91.75 105 | 47.83 219 | 98.96 5 | 57.71 231 | 89.81 72 | 92.54 126 |
|
HPM-MVS_fast | | | 80.25 108 | 79.55 106 | 82.33 156 | 91.55 88 | 59.95 237 | 91.32 147 | 89.16 214 | 65.23 238 | 74.71 113 | 93.07 81 | 47.81 220 | 95.74 109 | 74.87 108 | 88.23 80 | 91.31 146 |
|
v748 | | | 70.55 237 | 67.97 240 | 78.27 239 | 75.75 296 | 58.78 251 | 86.29 242 | 89.25 210 | 65.12 239 | 56.66 264 | 77.17 265 | 45.05 240 | 92.95 204 | 58.13 229 | 58.33 279 | 83.10 259 |
|
tfpnnormal | | | 70.10 239 | 67.36 242 | 78.32 237 | 83.45 214 | 60.97 216 | 88.85 202 | 92.77 87 | 64.85 240 | 60.83 241 | 78.53 254 | 43.52 246 | 93.48 195 | 31.73 319 | 61.70 262 | 80.52 289 |
|
v52 | | | 69.80 243 | 67.01 246 | 78.15 242 | 71.84 305 | 60.10 234 | 82.02 267 | 87.39 246 | 64.48 241 | 57.80 258 | 75.97 272 | 41.47 253 | 92.90 209 | 63.00 200 | 59.13 274 | 81.45 273 |
|
V4 | | | 69.80 243 | 67.02 245 | 78.15 242 | 71.86 304 | 60.10 234 | 82.02 267 | 87.39 246 | 64.48 241 | 57.78 259 | 75.98 271 | 41.49 252 | 92.90 209 | 63.00 200 | 59.16 273 | 81.44 274 |
|
K. test v3 | | | 63.09 280 | 59.61 282 | 73.53 276 | 76.26 292 | 49.38 301 | 83.27 259 | 77.15 308 | 64.35 243 | 47.77 300 | 72.32 290 | 28.73 305 | 87.79 285 | 49.93 257 | 36.69 325 | 83.41 253 |
|
v7n | | | 71.31 227 | 68.65 230 | 79.28 225 | 76.40 291 | 60.77 220 | 86.71 238 | 89.45 203 | 64.17 244 | 58.77 253 | 78.24 255 | 44.59 242 | 93.54 193 | 57.76 230 | 61.75 260 | 83.52 250 |
|
FMVSNet1 | | | 72.71 217 | 69.91 221 | 81.10 190 | 83.60 212 | 65.11 143 | 90.01 178 | 90.32 170 | 63.92 245 | 63.56 227 | 80.25 240 | 36.35 282 | 91.54 242 | 54.46 240 | 66.75 224 | 86.64 205 |
|
XVG-OURS | | | 74.25 205 | 72.46 206 | 79.63 215 | 78.45 280 | 57.59 262 | 80.33 281 | 87.39 246 | 63.86 246 | 68.76 183 | 89.62 129 | 40.50 257 | 91.72 238 | 69.00 149 | 74.25 177 | 89.58 162 |
|
114514_t | | | 79.17 129 | 77.67 131 | 83.68 124 | 95.32 14 | 65.53 136 | 92.85 82 | 91.60 131 | 63.49 247 | 67.92 196 | 90.63 116 | 46.65 228 | 95.72 114 | 67.01 164 | 83.54 117 | 89.79 159 |
|
APD-MVS_3200maxsize | | | 81.64 91 | 81.32 83 | 82.59 144 | 92.36 63 | 58.74 252 | 91.39 141 | 91.01 153 | 63.35 248 | 79.72 68 | 94.62 51 | 51.82 185 | 96.14 95 | 79.71 71 | 87.93 83 | 92.89 119 |
|
test20.03 | | | 63.83 277 | 62.65 271 | 67.38 301 | 70.58 311 | 39.94 321 | 86.57 240 | 84.17 281 | 63.29 249 | 51.86 287 | 77.30 261 | 37.09 279 | 82.47 310 | 38.87 302 | 54.13 291 | 79.73 296 |
|
XVG-OURS-SEG-HR | | | 74.70 202 | 73.08 198 | 79.57 217 | 78.25 281 | 57.33 264 | 80.49 279 | 87.32 249 | 63.22 250 | 68.76 183 | 90.12 125 | 44.89 241 | 91.59 240 | 70.55 138 | 74.09 179 | 89.79 159 |
|
ACMM | | 69.62 13 | 74.34 203 | 72.73 201 | 79.17 230 | 84.25 205 | 57.87 257 | 90.36 173 | 89.93 189 | 63.17 251 | 65.64 212 | 86.04 171 | 37.79 272 | 94.10 172 | 65.89 176 | 71.52 196 | 85.55 231 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tpmvs | | | 72.88 215 | 69.76 223 | 82.22 163 | 90.98 101 | 67.05 84 | 78.22 297 | 88.30 235 | 63.10 252 | 64.35 222 | 74.98 276 | 55.09 145 | 94.27 163 | 43.25 279 | 69.57 205 | 85.34 235 |
|
SixPastTwentyTwo | | | 64.92 270 | 61.78 276 | 74.34 272 | 78.74 277 | 49.76 298 | 83.42 258 | 79.51 305 | 62.86 253 | 50.27 294 | 77.35 260 | 30.92 302 | 90.49 251 | 45.89 271 | 47.06 311 | 82.78 260 |
|
TAPA-MVS | | 70.22 12 | 74.94 200 | 73.53 193 | 79.17 230 | 90.40 109 | 52.07 287 | 89.19 197 | 89.61 200 | 62.69 254 | 70.07 160 | 92.67 90 | 48.89 212 | 94.32 160 | 38.26 303 | 79.97 134 | 91.12 147 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
abl_6 | | | 79.82 118 | 79.20 113 | 81.70 177 | 89.85 117 | 58.34 254 | 88.47 208 | 90.07 184 | 62.56 255 | 77.71 86 | 93.08 79 | 47.65 222 | 96.78 81 | 77.94 86 | 85.45 103 | 89.99 158 |
|
pmmvs-eth3d | | | 65.53 269 | 62.32 273 | 75.19 265 | 69.39 314 | 59.59 241 | 82.80 264 | 83.43 288 | 62.52 256 | 51.30 291 | 72.49 285 | 32.86 290 | 87.16 288 | 55.32 238 | 50.73 300 | 78.83 302 |
|
AdaColmap | | | 78.94 133 | 77.00 146 | 84.76 100 | 96.34 6 | 65.86 129 | 92.66 90 | 87.97 242 | 62.18 257 | 70.56 151 | 92.37 96 | 43.53 245 | 97.35 49 | 64.50 188 | 82.86 120 | 91.05 148 |
|
无先验 | | | | | | | | 92.71 85 | 92.61 95 | 62.03 258 | | | | 97.01 65 | 66.63 166 | | 93.97 90 |
|
XVG-ACMP-BASELINE | | | 68.04 255 | 65.53 252 | 75.56 264 | 74.06 300 | 52.37 285 | 78.43 294 | 85.88 272 | 62.03 258 | 58.91 252 | 81.21 228 | 20.38 321 | 91.15 248 | 60.69 217 | 68.18 215 | 83.16 257 |
|
anonymousdsp | | | 71.14 230 | 69.37 225 | 76.45 259 | 72.95 301 | 54.71 277 | 84.19 250 | 88.88 224 | 61.92 260 | 62.15 238 | 79.77 245 | 38.14 267 | 91.44 247 | 68.90 151 | 67.45 221 | 83.21 256 |
|
tpm cat1 | | | 75.30 194 | 72.21 208 | 84.58 106 | 88.52 143 | 67.77 65 | 78.16 298 | 88.02 240 | 61.88 261 | 68.45 192 | 76.37 269 | 60.65 85 | 94.03 179 | 53.77 244 | 74.11 178 | 91.93 136 |
|
FMVSNet5 | | | 68.04 255 | 65.66 251 | 75.18 266 | 84.43 201 | 57.89 256 | 83.54 254 | 86.26 265 | 61.83 262 | 53.64 281 | 73.30 280 | 37.15 278 | 85.08 293 | 48.99 259 | 61.77 259 | 82.56 265 |
|
Anonymous20231206 | | | 67.53 259 | 65.78 249 | 72.79 282 | 74.95 297 | 47.59 306 | 88.23 211 | 87.32 249 | 61.75 263 | 58.07 256 | 77.29 262 | 37.79 272 | 87.29 287 | 42.91 281 | 63.71 250 | 83.48 251 |
|
PatchMatch-RL | | | 72.06 220 | 69.98 219 | 78.28 238 | 89.51 125 | 55.70 272 | 83.49 255 | 83.39 290 | 61.24 264 | 63.72 226 | 82.76 198 | 34.77 287 | 93.03 202 | 53.37 247 | 77.59 153 | 86.12 214 |
|
PLC | | 68.80 14 | 75.23 195 | 73.68 192 | 79.86 211 | 92.93 53 | 58.68 253 | 90.64 167 | 88.30 235 | 60.90 265 | 64.43 221 | 90.53 117 | 42.38 249 | 94.57 144 | 56.52 233 | 76.54 165 | 86.33 209 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMH | | 63.93 17 | 68.62 250 | 64.81 256 | 80.03 206 | 85.22 190 | 63.25 182 | 87.72 222 | 84.66 278 | 60.83 266 | 51.57 289 | 79.43 250 | 27.29 310 | 94.96 132 | 41.76 286 | 64.84 241 | 81.88 268 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EG-PatchMatch MVS | | | 68.55 251 | 65.41 253 | 77.96 245 | 78.69 278 | 62.93 189 | 89.86 187 | 89.17 213 | 60.55 267 | 50.27 294 | 77.73 259 | 22.60 318 | 94.06 175 | 47.18 267 | 72.65 189 | 76.88 309 |
|
VDDNet | | | 80.50 104 | 78.26 123 | 87.21 29 | 86.19 179 | 69.79 30 | 94.48 34 | 91.31 142 | 60.42 268 | 79.34 71 | 90.91 111 | 38.48 264 | 96.56 89 | 82.16 56 | 81.05 130 | 95.27 40 |
|
CPTT-MVS | | | 79.59 123 | 79.16 114 | 80.89 196 | 91.54 89 | 59.80 239 | 92.10 104 | 88.54 232 | 60.42 268 | 72.96 125 | 93.28 76 | 48.27 214 | 92.80 211 | 78.89 80 | 86.50 96 | 90.06 156 |
|
ITE_SJBPF | | | | | 70.43 294 | 74.44 298 | 47.06 308 | | 77.32 307 | 60.16 270 | 54.04 277 | 83.53 191 | 23.30 317 | 84.01 300 | 43.07 280 | 61.58 264 | 80.21 293 |
|
new-patchmatchnet | | | 59.30 290 | 56.48 290 | 67.79 299 | 65.86 318 | 44.19 311 | 82.47 265 | 81.77 295 | 59.94 271 | 43.65 315 | 66.20 309 | 27.67 308 | 81.68 315 | 39.34 295 | 41.40 319 | 77.50 308 |
|
test2356 | | | 64.16 275 | 63.28 266 | 66.81 303 | 69.37 315 | 39.86 323 | 87.76 221 | 86.02 269 | 59.83 272 | 53.54 282 | 73.23 281 | 34.94 286 | 80.67 317 | 39.66 294 | 65.20 234 | 79.89 294 |
|
新几何1 | | | | | 84.73 101 | 92.32 65 | 64.28 164 | | 91.46 137 | 59.56 273 | 79.77 67 | 92.90 86 | 56.95 119 | 96.57 88 | 63.40 195 | 92.91 39 | 93.34 103 |
|
1121 | | | 81.25 95 | 80.05 95 | 84.87 98 | 92.30 66 | 64.31 162 | 87.91 218 | 91.39 139 | 59.44 274 | 79.94 65 | 92.91 85 | 57.09 114 | 97.01 65 | 66.63 166 | 92.81 41 | 93.29 106 |
|
旧先验2 | | | | | | | | 92.00 112 | | 59.37 275 | 87.54 14 | | | 93.47 196 | 75.39 99 | | |
|
PM-MVS | | | 59.40 288 | 56.59 289 | 67.84 298 | 63.63 320 | 41.86 317 | 76.76 300 | 63.22 334 | 59.01 276 | 51.07 292 | 72.27 291 | 11.72 331 | 83.25 307 | 61.34 213 | 50.28 306 | 78.39 305 |
|
LTVRE_ROB | | 59.60 19 | 66.27 265 | 63.54 264 | 74.45 270 | 84.00 208 | 51.55 289 | 67.08 319 | 83.53 287 | 58.78 277 | 54.94 269 | 80.31 238 | 34.54 288 | 93.23 199 | 40.64 292 | 68.03 216 | 78.58 304 |
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 |
testdata | | | | | 81.34 181 | 89.02 134 | 57.72 259 | | 89.84 191 | 58.65 278 | 85.32 29 | 94.09 64 | 57.03 116 | 93.28 198 | 69.34 147 | 90.56 69 | 93.03 114 |
|
ACMH+ | | 65.35 16 | 67.65 257 | 64.55 258 | 76.96 256 | 84.59 197 | 57.10 266 | 88.08 213 | 80.79 300 | 58.59 279 | 53.00 284 | 81.09 230 | 26.63 312 | 92.95 204 | 46.51 268 | 61.69 263 | 80.82 285 |
|
ADS-MVSNet2 | | | 66.90 262 | 63.44 265 | 77.26 255 | 88.06 154 | 60.70 223 | 68.01 316 | 75.56 314 | 57.57 280 | 64.48 219 | 69.87 303 | 38.68 260 | 84.10 297 | 40.87 290 | 67.89 218 | 86.97 199 |
|
ADS-MVSNet | | | 68.54 252 | 64.38 262 | 81.03 193 | 88.06 154 | 66.90 90 | 68.01 316 | 84.02 283 | 57.57 280 | 64.48 219 | 69.87 303 | 38.68 260 | 89.21 274 | 40.87 290 | 67.89 218 | 86.97 199 |
|
MDA-MVSNet-bldmvs | | | 61.54 284 | 57.70 285 | 73.05 279 | 79.53 267 | 57.00 267 | 83.08 262 | 81.23 297 | 57.57 280 | 34.91 325 | 72.45 287 | 32.79 291 | 86.26 292 | 35.81 308 | 41.95 318 | 75.89 311 |
|
testus | | | 59.36 289 | 57.51 286 | 64.90 306 | 66.72 317 | 37.56 326 | 84.98 246 | 81.09 298 | 57.46 283 | 47.72 301 | 72.76 282 | 11.43 333 | 78.78 324 | 36.56 304 | 58.91 276 | 78.36 306 |
|
UnsupCasMVSNet_bld | | | 61.60 283 | 57.71 284 | 73.29 278 | 68.73 316 | 51.64 288 | 78.61 293 | 89.05 220 | 57.20 284 | 46.11 303 | 61.96 318 | 28.70 306 | 88.60 277 | 50.08 256 | 38.90 323 | 79.63 297 |
|
MSDG | | | 69.54 246 | 65.73 250 | 80.96 194 | 85.11 192 | 63.71 175 | 84.19 250 | 83.28 291 | 56.95 285 | 54.50 271 | 84.03 187 | 31.50 298 | 96.03 100 | 42.87 283 | 69.13 209 | 83.14 258 |
|
F-COLMAP | | | 70.66 235 | 68.44 234 | 77.32 253 | 86.37 177 | 55.91 271 | 88.00 214 | 86.32 263 | 56.94 286 | 57.28 262 | 88.07 145 | 33.58 289 | 92.49 221 | 51.02 252 | 68.37 214 | 83.55 248 |
|
test222 | | | | | | 89.77 119 | 61.60 211 | 89.55 190 | 89.42 205 | 56.83 287 | 77.28 93 | 92.43 94 | 52.76 178 | | | 91.14 63 | 93.09 112 |
|
CNLPA | | | 74.31 204 | 72.30 207 | 80.32 200 | 91.49 90 | 61.66 210 | 90.85 160 | 80.72 301 | 56.67 288 | 63.85 225 | 90.64 114 | 46.75 226 | 90.84 249 | 53.79 243 | 75.99 168 | 88.47 174 |
|
OurMVSNet-221017-0 | | | 64.68 271 | 62.17 274 | 72.21 287 | 76.08 294 | 47.35 307 | 80.67 278 | 81.02 299 | 56.19 289 | 51.60 288 | 79.66 247 | 27.05 311 | 88.56 278 | 53.60 245 | 53.63 292 | 80.71 287 |
|
YYNet1 | | | 63.76 279 | 60.14 280 | 74.62 269 | 78.06 283 | 60.19 233 | 83.46 257 | 83.99 286 | 56.18 290 | 39.25 321 | 71.56 301 | 37.18 277 | 83.34 306 | 42.90 282 | 48.70 309 | 80.32 291 |
|
MDA-MVSNet_test_wron | | | 63.78 278 | 60.16 279 | 74.64 268 | 78.15 282 | 60.41 227 | 83.49 255 | 84.03 282 | 56.17 291 | 39.17 322 | 71.59 300 | 37.22 276 | 83.24 308 | 42.87 283 | 48.73 308 | 80.26 292 |
|
OpenMVS_ROB | | 61.12 18 | 66.39 264 | 62.92 269 | 76.80 258 | 76.51 290 | 57.77 258 | 89.22 195 | 83.41 289 | 55.48 292 | 53.86 279 | 77.84 258 | 26.28 313 | 93.95 183 | 34.90 311 | 68.76 211 | 78.68 303 |
|
MIMVSNet1 | | | 60.16 287 | 57.33 287 | 68.67 297 | 69.71 313 | 44.13 312 | 78.92 292 | 84.21 280 | 55.05 293 | 44.63 311 | 71.85 294 | 23.91 315 | 81.54 316 | 32.63 317 | 55.03 288 | 80.35 290 |
|
CVMVSNet | | | 74.04 206 | 74.27 184 | 73.33 277 | 85.33 188 | 43.94 313 | 89.53 192 | 88.39 233 | 54.33 294 | 70.37 156 | 90.13 123 | 49.17 208 | 84.05 298 | 61.83 212 | 79.36 138 | 91.99 135 |
|
pmmvs6 | | | 67.57 258 | 64.76 257 | 76.00 263 | 72.82 303 | 53.37 282 | 88.71 204 | 86.78 253 | 53.19 295 | 57.58 261 | 78.03 257 | 35.33 285 | 92.41 223 | 55.56 237 | 54.88 289 | 82.21 266 |
|
testpf | | | 57.17 291 | 56.93 288 | 57.88 313 | 79.13 274 | 42.40 314 | 34.23 336 | 85.97 271 | 52.64 296 | 47.66 302 | 66.50 307 | 36.33 283 | 79.65 320 | 53.60 245 | 56.31 285 | 51.60 329 |
|
TinyColmap | | | 60.32 285 | 56.42 291 | 72.00 290 | 78.78 276 | 53.18 283 | 78.36 296 | 75.64 312 | 52.30 297 | 41.59 320 | 75.82 274 | 14.76 329 | 88.35 280 | 35.84 307 | 54.71 290 | 74.46 313 |
|
test_0402 | | | 64.54 272 | 61.09 277 | 74.92 267 | 84.10 207 | 60.75 221 | 87.95 215 | 79.71 304 | 52.03 298 | 52.41 285 | 77.20 263 | 32.21 295 | 91.64 239 | 23.14 329 | 61.03 266 | 72.36 316 |
|
test1235678 | | | 55.73 295 | 52.74 295 | 64.68 307 | 60.16 327 | 35.56 328 | 81.65 271 | 81.46 296 | 51.27 299 | 38.93 323 | 62.82 317 | 17.44 324 | 78.58 325 | 30.87 321 | 50.09 307 | 79.89 294 |
|
AllTest | | | 61.66 282 | 58.06 283 | 72.46 284 | 79.57 265 | 51.42 291 | 80.17 284 | 68.61 328 | 51.25 300 | 45.88 304 | 81.23 224 | 19.86 322 | 86.58 290 | 38.98 296 | 57.01 282 | 79.39 298 |
|
TestCases | | | | | 72.46 284 | 79.57 265 | 51.42 291 | | 68.61 328 | 51.25 300 | 45.88 304 | 81.23 224 | 19.86 322 | 86.58 290 | 38.98 296 | 57.01 282 | 79.39 298 |
|
PatchT | | | 69.11 249 | 65.37 254 | 80.32 200 | 82.07 225 | 63.68 177 | 67.96 318 | 87.62 245 | 50.86 302 | 69.37 173 | 65.18 312 | 57.09 114 | 88.53 279 | 41.59 288 | 66.60 225 | 88.74 169 |
|
DP-MVS | | | 69.90 241 | 66.48 247 | 80.14 203 | 95.36 13 | 62.93 189 | 89.56 189 | 76.11 309 | 50.27 303 | 57.69 260 | 85.23 177 | 39.68 259 | 95.73 110 | 33.35 313 | 71.05 200 | 81.78 269 |
|
gg-mvs-nofinetune | | | 77.18 167 | 74.31 183 | 85.80 70 | 91.42 95 | 68.36 54 | 71.78 306 | 94.72 27 | 49.61 304 | 77.12 95 | 45.92 327 | 77.41 3 | 93.98 181 | 67.62 159 | 93.16 37 | 95.05 50 |
|
JIA-IIPM | | | 66.06 266 | 62.45 272 | 76.88 257 | 81.42 231 | 54.45 279 | 57.49 330 | 88.67 227 | 49.36 305 | 63.86 224 | 46.86 326 | 56.06 133 | 90.25 253 | 49.53 258 | 68.83 210 | 85.95 221 |
|
N_pmnet | | | 50.55 300 | 49.11 302 | 54.88 317 | 77.17 288 | 4.02 346 | 84.36 249 | 2.00 347 | 48.59 306 | 45.86 306 | 68.82 305 | 32.22 294 | 82.80 309 | 31.58 320 | 51.38 298 | 77.81 307 |
|
ANet_high | | | 40.27 308 | 35.20 309 | 55.47 316 | 34.74 341 | 34.47 329 | 63.84 323 | 71.56 324 | 48.42 307 | 18.80 333 | 41.08 331 | 9.52 336 | 64.45 336 | 20.18 332 | 8.66 340 | 67.49 324 |
|
COLMAP_ROB | | 57.96 20 | 62.98 281 | 59.65 281 | 72.98 280 | 81.44 230 | 53.00 284 | 83.75 252 | 75.53 315 | 48.34 308 | 48.81 298 | 81.40 222 | 24.14 314 | 90.30 252 | 32.95 315 | 60.52 270 | 75.65 312 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Patchmtry | | | 67.53 259 | 63.93 263 | 78.34 236 | 82.12 224 | 64.38 159 | 68.72 313 | 84.00 284 | 48.23 309 | 59.24 247 | 72.41 288 | 57.82 109 | 89.27 273 | 46.10 270 | 56.68 284 | 81.36 276 |
|
LS3D | | | 69.17 248 | 66.40 248 | 77.50 249 | 91.92 76 | 56.12 270 | 85.12 245 | 80.37 302 | 46.96 310 | 56.50 265 | 87.51 153 | 37.25 275 | 93.71 190 | 32.52 318 | 79.40 137 | 82.68 264 |
|
RPSCF | | | 64.24 274 | 61.98 275 | 71.01 293 | 76.10 293 | 45.00 310 | 75.83 302 | 75.94 311 | 46.94 311 | 58.96 251 | 84.59 184 | 31.40 299 | 82.00 314 | 47.76 265 | 60.33 271 | 86.04 220 |
|
RPMNet | | | 69.58 245 | 65.21 255 | 82.70 139 | 83.15 217 | 67.96 62 | 70.25 309 | 86.15 267 | 46.83 312 | 69.97 163 | 65.10 313 | 56.48 129 | 89.48 271 | 35.79 309 | 73.13 184 | 90.64 152 |
|
1111 | | | 56.66 294 | 54.98 293 | 61.69 309 | 61.99 324 | 31.38 330 | 79.81 289 | 83.17 292 | 45.66 313 | 41.94 317 | 65.44 310 | 41.50 250 | 79.56 321 | 27.64 323 | 47.68 310 | 74.14 314 |
|
.test1245 | | | 46.52 304 | 49.68 300 | 37.02 326 | 61.99 324 | 31.38 330 | 79.81 289 | 83.17 292 | 45.66 313 | 41.94 317 | 65.44 310 | 41.50 250 | 79.56 321 | 27.64 323 | 0.01 342 | 0.13 341 |
|
CMPMVS | | 48.56 21 | 66.77 263 | 64.41 261 | 73.84 274 | 70.65 310 | 50.31 296 | 77.79 299 | 85.73 274 | 45.54 315 | 44.76 310 | 82.14 209 | 35.40 284 | 90.14 259 | 63.18 198 | 74.54 176 | 81.07 283 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
EU-MVSNet | | | 64.01 276 | 63.01 268 | 67.02 302 | 74.40 299 | 38.86 325 | 83.27 259 | 86.19 266 | 45.11 316 | 54.27 274 | 81.15 229 | 36.91 281 | 80.01 318 | 48.79 261 | 57.02 281 | 82.19 267 |
|
no-one | | | 44.13 306 | 38.39 307 | 61.34 310 | 45.91 337 | 41.94 316 | 61.67 325 | 75.07 317 | 45.05 317 | 20.07 331 | 40.68 333 | 11.58 332 | 79.82 319 | 30.18 322 | 15.30 333 | 62.26 326 |
|
TDRefinement | | | 55.28 297 | 51.58 298 | 66.39 304 | 59.53 328 | 46.15 309 | 76.23 301 | 72.80 320 | 44.60 318 | 42.49 316 | 76.28 270 | 15.29 327 | 82.39 311 | 33.20 314 | 43.75 316 | 70.62 320 |
|
Patchmatch-test | | | 65.86 267 | 60.94 278 | 80.62 197 | 83.75 209 | 58.83 250 | 58.91 329 | 75.26 316 | 44.50 319 | 50.95 293 | 77.09 267 | 58.81 103 | 87.90 283 | 35.13 310 | 64.03 247 | 95.12 48 |
|
test12356 | | | 47.51 302 | 44.82 304 | 55.56 315 | 52.53 330 | 21.09 341 | 71.45 308 | 76.03 310 | 44.14 320 | 30.69 326 | 58.18 321 | 9.01 337 | 76.14 326 | 26.95 325 | 34.43 328 | 69.46 322 |
|
LP | | | 56.71 293 | 51.64 297 | 71.91 291 | 80.08 261 | 60.33 229 | 61.72 324 | 75.61 313 | 43.87 321 | 43.76 314 | 60.30 320 | 30.46 303 | 84.05 298 | 22.94 330 | 46.06 313 | 71.34 318 |
|
LF4IMVS | | | 54.01 298 | 52.12 296 | 59.69 311 | 62.41 323 | 39.91 322 | 68.59 314 | 68.28 330 | 42.96 322 | 44.55 312 | 75.18 275 | 14.09 330 | 68.39 331 | 41.36 289 | 51.68 297 | 70.78 319 |
|
testmv | | | 46.98 303 | 43.53 305 | 57.35 314 | 47.75 335 | 30.41 333 | 74.99 304 | 77.69 306 | 42.84 323 | 28.03 327 | 53.36 323 | 8.18 338 | 71.18 329 | 24.36 328 | 34.55 326 | 70.46 321 |
|
DSMNet-mixed | | | 56.78 292 | 54.44 294 | 63.79 308 | 63.21 321 | 29.44 334 | 64.43 322 | 64.10 333 | 42.12 324 | 51.32 290 | 71.60 299 | 31.76 296 | 75.04 327 | 36.23 306 | 65.20 234 | 86.87 203 |
|
pmmvs3 | | | 55.51 296 | 51.50 299 | 67.53 300 | 57.90 329 | 50.93 294 | 80.37 280 | 73.66 319 | 40.63 325 | 44.15 313 | 64.75 314 | 16.30 325 | 78.97 323 | 44.77 278 | 40.98 321 | 72.69 315 |
|
Anonymous20231211 | | | 53.57 299 | 49.43 301 | 66.00 305 | 65.01 319 | 42.08 315 | 80.95 277 | 72.60 321 | 38.46 326 | 41.65 319 | 64.48 315 | 15.72 326 | 84.23 296 | 25.78 326 | 40.24 322 | 71.68 317 |
|
new_pmnet | | | 49.31 301 | 46.44 303 | 57.93 312 | 62.84 322 | 40.74 319 | 68.47 315 | 62.96 335 | 36.48 327 | 35.09 324 | 57.81 322 | 14.97 328 | 72.18 328 | 32.86 316 | 46.44 312 | 60.88 327 |
|
MVS-HIRNet | | | 60.25 286 | 55.55 292 | 74.35 271 | 84.37 202 | 56.57 268 | 71.64 307 | 74.11 318 | 34.44 328 | 45.54 308 | 42.24 330 | 31.11 301 | 89.81 269 | 40.36 293 | 76.10 167 | 76.67 310 |
|
DeepMVS_CX | | | | | 34.71 327 | 51.45 331 | 24.73 340 | | 28.48 346 | 31.46 329 | 17.49 334 | 52.75 324 | 5.80 341 | 42.60 342 | 18.18 334 | 19.42 331 | 36.81 333 |
|
FPMVS | | | 45.64 305 | 43.10 306 | 53.23 319 | 51.42 332 | 36.46 327 | 64.97 321 | 71.91 323 | 29.13 330 | 27.53 328 | 61.55 319 | 9.83 335 | 65.01 335 | 16.00 335 | 55.58 286 | 58.22 328 |
|
PMMVS2 | | | 37.93 309 | 33.61 310 | 50.92 320 | 46.31 336 | 24.76 339 | 60.55 328 | 50.05 338 | 28.94 331 | 20.93 330 | 47.59 325 | 4.41 344 | 65.13 334 | 25.14 327 | 18.55 332 | 62.87 325 |
|
LCM-MVSNet | | | 40.54 307 | 35.79 308 | 54.76 318 | 36.92 340 | 30.81 332 | 51.41 331 | 69.02 327 | 22.07 332 | 24.63 329 | 45.37 328 | 4.56 343 | 65.81 333 | 33.67 312 | 34.50 327 | 67.67 323 |
|
PNet_i23d | | | 32.77 311 | 29.98 313 | 41.11 324 | 48.05 333 | 29.17 335 | 65.82 320 | 50.02 339 | 21.42 333 | 14.74 336 | 37.19 334 | 1.11 347 | 55.11 338 | 19.75 333 | 11.77 335 | 39.06 331 |
|
PMVS | | 26.43 22 | 31.84 312 | 28.16 314 | 42.89 323 | 25.87 344 | 27.58 337 | 50.92 332 | 49.78 340 | 21.37 334 | 14.17 337 | 40.81 332 | 2.01 345 | 66.62 332 | 9.61 338 | 38.88 324 | 34.49 334 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Gipuma | | | 34.91 310 | 31.44 312 | 45.30 322 | 70.99 308 | 39.64 324 | 19.85 339 | 72.56 322 | 20.10 335 | 16.16 335 | 21.47 338 | 5.08 342 | 71.16 330 | 13.07 336 | 43.70 317 | 25.08 335 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
E-PMN | | | 24.61 315 | 24.00 316 | 26.45 329 | 43.74 338 | 18.44 343 | 60.86 326 | 39.66 341 | 15.11 336 | 9.53 339 | 22.10 337 | 6.52 340 | 46.94 340 | 8.31 339 | 10.14 336 | 13.98 337 |
|
EMVS | | | 23.76 317 | 23.20 318 | 25.46 330 | 41.52 339 | 16.90 344 | 60.56 327 | 38.79 344 | 14.62 337 | 8.99 340 | 20.24 341 | 7.35 339 | 45.82 341 | 7.25 340 | 9.46 338 | 13.64 338 |
|
wuykxyi23d | | | 29.03 314 | 23.09 319 | 46.84 321 | 31.67 343 | 28.82 336 | 43.46 334 | 57.72 337 | 14.39 338 | 7.52 341 | 20.84 339 | 0.64 348 | 60.29 337 | 21.57 331 | 10.04 337 | 51.40 330 |
|
MVE | | 24.84 23 | 24.35 316 | 19.77 320 | 38.09 325 | 34.56 342 | 26.92 338 | 26.57 337 | 38.87 343 | 11.73 339 | 11.37 338 | 27.44 335 | 1.37 346 | 50.42 339 | 11.41 337 | 14.60 334 | 36.93 332 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 11.30 320 | 10.95 321 | 12.33 332 | 48.05 333 | 19.89 342 | 25.89 338 | 1.92 348 | 3.58 340 | 3.12 342 | 1.37 343 | 0.64 348 | 15.77 344 | 6.23 341 | 7.77 341 | 1.35 339 |
|
tmp_tt | | | 22.26 318 | 23.75 317 | 17.80 331 | 5.23 345 | 12.06 345 | 35.26 335 | 39.48 342 | 2.82 341 | 18.94 332 | 44.20 329 | 22.23 319 | 24.64 343 | 36.30 305 | 9.31 339 | 16.69 336 |
|
testmvs | | | 7.23 322 | 9.62 323 | 0.06 334 | 0.04 346 | 0.02 348 | 84.98 246 | 0.02 349 | 0.03 342 | 0.18 343 | 1.21 344 | 0.01 351 | 0.02 345 | 0.14 342 | 0.01 342 | 0.13 341 |
|
test123 | | | 6.92 323 | 9.21 324 | 0.08 333 | 0.03 347 | 0.05 347 | 81.65 271 | 0.01 350 | 0.02 343 | 0.14 344 | 0.85 345 | 0.03 350 | 0.02 345 | 0.12 343 | 0.00 344 | 0.16 340 |
|
cdsmvs_eth3d_5k | | | 19.86 319 | 26.47 315 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 93.45 60 | 0.00 344 | 0.00 345 | 95.27 30 | 49.56 203 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
pcd_1.5k_mvsjas | | | 4.46 324 | 5.95 325 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 0.00 351 | 0.00 344 | 0.00 345 | 0.00 346 | 53.55 169 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
pcd1.5k->3k | | | 31.17 313 | 31.85 311 | 29.12 328 | 81.48 228 | 0.00 349 | 0.00 340 | 91.79 124 | 0.00 344 | 0.00 345 | 0.00 346 | 41.05 256 | 0.00 347 | 0.00 344 | 72.34 192 | 87.36 193 |
|
sosnet-low-res | | | 0.00 325 | 0.00 326 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 0.00 351 | 0.00 344 | 0.00 345 | 0.00 346 | 0.00 352 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
sosnet | | | 0.00 325 | 0.00 326 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 0.00 351 | 0.00 344 | 0.00 345 | 0.00 346 | 0.00 352 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
uncertanet | | | 0.00 325 | 0.00 326 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 0.00 351 | 0.00 344 | 0.00 345 | 0.00 346 | 0.00 352 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
Regformer | | | 0.00 325 | 0.00 326 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 0.00 351 | 0.00 344 | 0.00 345 | 0.00 346 | 0.00 352 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
ab-mvs-re | | | 7.91 321 | 10.55 322 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 0.00 351 | 0.00 344 | 0.00 345 | 94.95 40 | 0.00 352 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
uanet | | | 0.00 325 | 0.00 326 | 0.00 335 | 0.00 348 | 0.00 349 | 0.00 340 | 0.00 351 | 0.00 344 | 0.00 345 | 0.00 346 | 0.00 352 | 0.00 347 | 0.00 344 | 0.00 344 | 0.00 343 |
|
ESAPD | | | | | | | | | 94.66 29 | | | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 108 | | | | |
|
sam_mvs | | | | | | | | | | | | | 54.91 152 | | | | |
|
ambc | | | | | 69.61 295 | 61.38 326 | 41.35 318 | 49.07 333 | 85.86 273 | | 50.18 296 | 66.40 308 | 10.16 334 | 88.14 282 | 45.73 272 | 44.20 315 | 79.32 300 |
|
MTGPA | | | | | | | | | 92.23 104 | | | | | | | | |
|
test_post1 | | | | | | | | 78.95 291 | | | | 20.70 340 | 53.05 175 | 91.50 246 | 60.43 218 | | |
|
test_post | | | | | | | | | | | | 23.01 336 | 56.49 128 | 92.67 216 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 306 | 57.62 111 | 90.25 253 | | | |
|
GG-mvs-BLEND | | | | | 86.53 49 | 91.91 77 | 69.67 34 | 75.02 303 | 94.75 26 | | 78.67 81 | 90.85 112 | 77.91 2 | 94.56 145 | 72.25 119 | 93.74 29 | 95.36 34 |
|
MTMP | | | | | | | | | 32.52 345 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 10 | 94.96 8 | 95.29 38 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 31 | 94.75 16 | 95.33 35 |
|
agg_prior | | | | | | 94.16 31 | 66.97 86 | | 93.31 66 | | 84.49 35 | | | 96.75 83 | | | |
|
test_prior4 | | | | | | | 67.18 81 | 93.92 52 | | | | | | | | | |
|
test_prior | | | | | 86.42 53 | 94.71 21 | 67.35 75 | | 93.10 77 | | | | | 96.84 79 | | | 95.05 50 |
|
新几何2 | | | | | | | | 91.41 140 | | | | | | | | | |
|
旧先验1 | | | | | | 91.94 74 | 60.74 222 | | 91.50 135 | | | 94.36 55 | 65.23 46 | | | 91.84 51 | 94.55 64 |
|
原ACMM2 | | | | | | | | 92.01 110 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 96.09 97 | 61.26 214 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 40 | | | | |
|
test12 | | | | | 87.09 34 | 94.60 23 | 68.86 44 | | 92.91 83 | | 82.67 48 | | 65.44 45 | 97.55 41 | | 93.69 31 | 94.84 58 |
|
plane_prior7 | | | | | | 86.94 169 | 61.51 212 | | | | | | | | | | |
|
plane_prior6 | | | | | | 87.23 165 | 62.32 200 | | | | | | 50.66 193 | | | | |
|
plane_prior5 | | | | | | | | | 91.31 142 | | | | | 95.55 120 | 76.74 91 | 78.53 146 | 88.39 175 |
|
plane_prior4 | | | | | | | | | | | | 89.14 131 | | | | | |
|
plane_prior1 | | | | | | 87.15 166 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 351 | | | | | | | | |
|
nn | | | | | | | | | 0.00 351 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 332 | | | | | | | | |
|
lessismore_v0 | | | | | 73.72 275 | 72.93 302 | 47.83 305 | | 61.72 336 | | 45.86 306 | 73.76 279 | 28.63 307 | 89.81 269 | 47.75 266 | 31.37 329 | 83.53 249 |
|
test11 | | | | | | | | | 93.01 79 | | | | | | | | |
|
door | | | | | | | | | 66.57 331 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 178 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 88 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 115 | | | 95.61 116 | | | 88.63 170 |
|
HQP3-MVS | | | | | | | | | 91.70 128 | | | | | | | 78.90 142 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 189 | | | | |
|
NP-MVS | | | | | | 87.41 164 | 63.04 187 | | | | | 90.30 120 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 194 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 203 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 162 | | | | |
|