MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 2 | 97.66 2 | 73.37 7 | 97.13 1 | 95.58 13 | 89.33 1 | 85.77 26 | 96.26 10 | 72.84 11 | 99.38 1 | 92.64 4 | 95.93 5 | 97.08 4 |
|
DELS-MVS | | | 90.05 4 | 90.09 5 | 89.94 2 | 93.14 54 | 73.88 6 | 97.01 2 | 94.40 39 | 88.32 2 | 85.71 27 | 94.91 48 | 74.11 9 | 98.91 6 | 87.26 29 | 95.94 4 | 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 17 | 88.38 11 | 86.23 65 | 93.30 48 | 66.05 129 | 95.26 22 | 94.84 24 | 87.09 3 | 88.06 14 | 94.53 55 | 66.79 36 | 97.34 54 | 83.89 52 | 91.68 57 | 95.29 44 |
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 27 | 69.71 33 | 96.53 5 | 93.78 49 | 86.89 4 | 89.68 7 | 95.78 18 | 65.94 44 | 99.10 2 | 92.99 1 | 93.91 28 | 96.58 11 |
|
DeepPCF-MVS | | 81.17 1 | 89.72 5 | 91.38 3 | 84.72 109 | 93.00 57 | 58.16 267 | 96.72 3 | 94.41 38 | 86.50 5 | 90.25 6 | 97.83 1 | 75.46 7 | 98.67 14 | 92.78 2 | 95.49 8 | 97.32 1 |
|
MVS_0304 | | | 88.39 10 | 88.35 13 | 88.50 12 | 93.01 56 | 70.11 24 | 95.90 10 | 92.20 122 | 86.27 6 | 88.70 12 | 95.92 16 | 56.76 135 | 99.02 4 | 92.68 3 | 93.76 31 | 96.37 15 |
|
CANet_DTU | | | 84.09 62 | 83.52 56 | 85.81 74 | 90.30 121 | 66.82 97 | 91.87 129 | 89.01 237 | 85.27 7 | 86.09 23 | 93.74 73 | 47.71 235 | 96.98 77 | 77.90 92 | 89.78 76 | 93.65 106 |
|
CLD-MVS | | | 82.73 79 | 82.35 77 | 83.86 124 | 87.90 175 | 67.65 74 | 95.45 19 | 92.18 125 | 85.06 8 | 72.58 139 | 92.27 102 | 52.46 196 | 95.78 114 | 84.18 48 | 79.06 146 | 88.16 192 |
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 19 | 96.47 6 | 94.83 25 | 84.83 9 | 89.07 10 | 96.80 4 | 70.86 16 | 99.06 3 | 92.64 4 | 95.71 6 | 96.12 19 |
|
NCCC | | | 89.07 9 | 89.46 9 | 87.91 16 | 96.60 5 | 69.05 42 | 96.38 7 | 94.64 32 | 84.42 10 | 86.74 21 | 96.20 11 | 66.56 39 | 98.76 13 | 89.03 19 | 94.56 21 | 95.92 28 |
|
PS-MVSNAJ | | | 88.14 11 | 87.61 19 | 89.71 4 | 92.06 77 | 76.72 1 | 95.75 12 | 93.26 79 | 83.86 11 | 89.55 8 | 96.06 14 | 53.55 183 | 97.89 33 | 91.10 8 | 93.31 38 | 94.54 74 |
|
DeepC-MVS_fast | | 79.48 2 | 87.95 15 | 88.00 15 | 87.79 19 | 95.86 14 | 68.32 58 | 95.74 13 | 94.11 44 | 83.82 12 | 83.49 48 | 96.19 12 | 64.53 64 | 98.44 20 | 83.42 55 | 94.88 15 | 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 16 | 87.38 24 | 89.55 7 | 91.41 103 | 76.43 2 | 95.74 13 | 93.12 88 | 83.53 13 | 89.55 8 | 95.95 15 | 53.45 188 | 97.68 35 | 91.07 9 | 92.62 45 | 94.54 74 |
|
TSAR-MVS + MP. | | | 88.11 13 | 88.64 10 | 86.54 51 | 91.73 89 | 68.04 66 | 90.36 183 | 93.55 60 | 82.89 14 | 91.29 3 | 92.89 90 | 72.27 12 | 96.03 107 | 87.99 23 | 94.77 16 | 95.54 37 |
|
WTY-MVS | | | 86.32 35 | 85.81 38 | 87.85 17 | 92.82 62 | 69.37 38 | 95.20 24 | 95.25 16 | 82.71 15 | 81.91 55 | 94.73 52 | 67.93 28 | 97.63 41 | 79.55 78 | 82.25 128 | 96.54 12 |
|
lupinMVS | | | 87.74 18 | 87.77 17 | 87.63 26 | 89.24 147 | 71.18 15 | 96.57 4 | 92.90 96 | 82.70 16 | 87.13 18 | 95.27 33 | 64.99 60 | 95.80 113 | 89.34 14 | 91.80 55 | 95.93 27 |
|
HPM-MVS++ | | | 89.37 7 | 89.95 7 | 87.64 23 | 95.10 19 | 68.23 62 | 95.24 23 | 94.49 35 | 82.43 17 | 88.90 11 | 96.35 8 | 71.89 15 | 98.63 15 | 88.76 21 | 96.40 2 | 96.06 22 |
|
PVSNet_Blended | | | 86.73 33 | 86.86 29 | 86.31 63 | 93.76 38 | 67.53 77 | 96.33 8 | 93.61 57 | 82.34 18 | 81.00 62 | 93.08 82 | 63.19 79 | 97.29 57 | 87.08 30 | 91.38 62 | 94.13 89 |
|
HSP-MVS | | | 90.38 2 | 91.89 1 | 85.84 73 | 92.83 60 | 64.03 177 | 93.06 81 | 94.52 33 | 82.19 19 | 93.65 1 | 96.15 13 | 85.89 1 | 97.19 63 | 91.02 10 | 97.75 1 | 96.29 16 |
|
DWT-MVSNet_test | | | 83.95 64 | 82.80 71 | 87.41 29 | 92.90 59 | 70.07 26 | 89.12 210 | 94.42 37 | 82.15 20 | 77.64 92 | 91.77 107 | 70.81 17 | 96.22 99 | 65.03 190 | 81.36 133 | 95.94 26 |
|
Regformer-1 | | | 87.24 23 | 87.60 20 | 86.15 66 | 95.14 17 | 65.83 136 | 93.95 53 | 95.12 18 | 82.11 21 | 84.25 41 | 95.73 20 | 67.88 29 | 98.35 22 | 85.60 39 | 88.64 81 | 94.26 81 |
|
PAPM | | | 85.89 41 | 85.46 43 | 87.18 34 | 88.20 168 | 72.42 9 | 92.41 104 | 92.77 99 | 82.11 21 | 80.34 67 | 93.07 84 | 68.27 22 | 95.02 137 | 78.39 88 | 93.59 35 | 94.09 92 |
|
PatchFormer-LS_test | | | 83.14 74 | 81.81 82 | 87.12 36 | 92.34 69 | 69.92 29 | 88.64 218 | 93.32 76 | 82.07 23 | 74.87 118 | 91.62 111 | 68.91 19 | 96.08 106 | 66.07 181 | 78.45 153 | 95.37 39 |
|
jason | | | 86.40 34 | 86.17 34 | 87.11 37 | 86.16 197 | 70.54 21 | 95.71 16 | 92.19 124 | 82.00 24 | 84.58 37 | 94.34 63 | 61.86 88 | 95.53 129 | 87.76 25 | 90.89 67 | 95.27 46 |
jason: jason. |
CHOSEN 1792x2688 | | | 84.98 50 | 83.45 59 | 89.57 6 | 89.94 127 | 75.14 4 | 92.07 114 | 92.32 114 | 81.87 25 | 75.68 109 | 88.27 153 | 60.18 103 | 98.60 16 | 80.46 75 | 90.27 74 | 94.96 61 |
|
Regformer-2 | | | 87.00 27 | 87.43 22 | 85.71 81 | 95.14 17 | 64.73 158 | 93.95 53 | 94.95 22 | 81.69 26 | 84.03 45 | 95.73 20 | 67.35 33 | 98.19 26 | 85.40 41 | 88.64 81 | 94.20 83 |
|
Regformer-3 | | | 85.80 42 | 85.92 36 | 85.46 85 | 94.17 32 | 65.09 152 | 92.95 85 | 95.11 19 | 81.13 27 | 81.68 57 | 95.04 40 | 65.82 46 | 98.32 23 | 83.02 56 | 84.36 113 | 92.97 124 |
|
DeepC-MVS | | 77.85 3 | 85.52 44 | 85.24 45 | 86.37 60 | 88.80 156 | 66.64 112 | 92.15 108 | 93.68 55 | 81.07 28 | 76.91 103 | 93.64 74 | 62.59 84 | 98.44 20 | 85.50 40 | 92.84 43 | 94.03 96 |
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 45 | 85.69 41 | 84.73 107 | 94.17 32 | 63.23 193 | 92.95 85 | 94.83 25 | 80.66 29 | 81.29 59 | 95.04 40 | 65.12 52 | 98.08 29 | 82.74 57 | 84.36 113 | 92.88 128 |
|
VPNet | | | 78.82 139 | 77.53 139 | 82.70 145 | 84.52 215 | 66.44 121 | 93.93 55 | 92.23 117 | 80.46 30 | 72.60 138 | 88.38 151 | 49.18 221 | 93.13 209 | 72.47 124 | 63.97 266 | 88.55 182 |
|
canonicalmvs | | | 86.85 30 | 86.25 33 | 88.66 10 | 91.80 88 | 71.92 10 | 93.54 70 | 91.71 140 | 80.26 31 | 87.55 16 | 95.25 35 | 63.59 75 | 96.93 83 | 88.18 22 | 84.34 115 | 97.11 3 |
|
MVSTER | | | 82.47 83 | 82.05 78 | 83.74 126 | 92.68 65 | 69.01 43 | 91.90 128 | 93.21 81 | 79.83 32 | 72.14 147 | 85.71 191 | 74.72 8 | 94.72 148 | 75.72 102 | 72.49 199 | 87.50 202 |
|
HQP-NCC | | | | | | 87.54 178 | | 94.06 47 | | 79.80 33 | 74.18 121 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 178 | | 94.06 47 | | 79.80 33 | 74.18 121 | | | | | | |
|
HQP-MVS | | | 81.14 101 | 80.64 95 | 82.64 148 | 87.54 178 | 63.66 187 | 94.06 47 | 91.70 141 | 79.80 33 | 74.18 121 | 90.30 125 | 51.63 203 | 95.61 123 | 77.63 93 | 78.90 147 | 88.63 180 |
|
EI-MVSNet-Vis-set | | | 83.77 68 | 83.67 55 | 84.06 121 | 92.79 64 | 63.56 189 | 91.76 136 | 94.81 27 | 79.65 36 | 77.87 89 | 94.09 67 | 63.35 77 | 97.90 32 | 79.35 79 | 79.36 143 | 90.74 159 |
|
plane_prior | | | | | | | 62.42 209 | 93.85 59 | | 79.38 37 | | | | | | 78.80 149 | |
|
alignmvs | | | 87.28 22 | 86.97 27 | 88.24 15 | 91.30 104 | 71.14 17 | 95.61 17 | 93.56 59 | 79.30 38 | 87.07 20 | 95.25 35 | 68.43 21 | 96.93 83 | 87.87 24 | 84.33 116 | 96.65 8 |
|
TESTMET0.1,1 | | | 82.41 84 | 81.98 80 | 83.72 129 | 88.08 169 | 63.74 182 | 92.70 93 | 93.77 51 | 79.30 38 | 77.61 94 | 87.57 166 | 58.19 119 | 94.08 182 | 73.91 115 | 86.68 96 | 93.33 113 |
|
EI-MVSNet-UG-set | | | 83.14 74 | 82.96 67 | 83.67 131 | 92.28 72 | 63.19 197 | 91.38 151 | 94.68 30 | 79.22 40 | 76.60 104 | 93.75 72 | 62.64 83 | 97.76 34 | 78.07 90 | 78.01 154 | 90.05 166 |
|
PVSNet | | 73.49 8 | 80.05 117 | 78.63 123 | 84.31 117 | 90.92 111 | 64.97 155 | 92.47 103 | 91.05 165 | 79.18 41 | 72.43 144 | 90.51 123 | 37.05 297 | 94.06 184 | 68.06 161 | 86.00 103 | 93.90 103 |
|
HY-MVS | | 76.49 5 | 84.28 58 | 83.36 64 | 87.02 40 | 92.22 74 | 67.74 71 | 84.65 269 | 94.50 34 | 79.15 42 | 82.23 53 | 87.93 159 | 66.88 35 | 96.94 81 | 80.53 74 | 82.20 129 | 96.39 14 |
|
PVSNet_BlendedMVS | | | 83.38 71 | 83.43 60 | 83.22 137 | 93.76 38 | 67.53 77 | 94.06 47 | 93.61 57 | 79.13 43 | 81.00 62 | 85.14 194 | 63.19 79 | 97.29 57 | 87.08 30 | 73.91 189 | 84.83 258 |
|
plane_prior3 | | | | | | | 61.95 219 | | | 79.09 44 | 72.53 140 | | | | | | |
|
MVS_111021_HR | | | 86.19 38 | 85.80 39 | 87.37 30 | 93.17 53 | 69.79 31 | 93.99 51 | 93.76 52 | 79.08 45 | 78.88 81 | 93.99 69 | 62.25 85 | 98.15 27 | 85.93 38 | 91.15 65 | 94.15 88 |
|
MSLP-MVS++ | | | 86.27 36 | 85.91 37 | 87.35 31 | 92.01 78 | 68.97 45 | 95.04 30 | 92.70 101 | 79.04 46 | 81.50 58 | 96.50 7 | 58.98 115 | 96.78 87 | 83.49 54 | 93.93 27 | 96.29 16 |
|
IB-MVS | | 77.80 4 | 82.18 87 | 80.46 98 | 87.35 31 | 89.14 149 | 70.28 23 | 95.59 18 | 95.17 17 | 78.85 47 | 70.19 167 | 85.82 188 | 70.66 18 | 97.67 36 | 72.19 128 | 66.52 242 | 94.09 92 |
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 82 | 80.82 92 | 88.31 14 | 89.57 139 | 71.26 14 | 92.60 98 | 94.39 40 | 78.84 48 | 67.89 205 | 92.48 97 | 48.42 227 | 98.52 17 | 68.80 159 | 94.40 22 | 95.15 52 |
|
HQP_MVS | | | 80.34 112 | 79.75 106 | 82.12 173 | 86.94 186 | 62.42 209 | 93.13 79 | 91.31 155 | 78.81 49 | 72.53 140 | 89.14 144 | 50.66 207 | 95.55 127 | 76.74 97 | 78.53 151 | 88.39 185 |
|
plane_prior2 | | | | | | | | 93.13 79 | | 78.81 49 | | | | | | | |
|
MG-MVS | | | 87.11 25 | 86.27 31 | 89.62 5 | 97.79 1 | 76.27 3 | 94.96 32 | 94.49 35 | 78.74 51 | 83.87 47 | 92.94 87 | 64.34 66 | 96.94 81 | 75.19 106 | 94.09 25 | 95.66 32 |
|
gm-plane-assit | | | | | | 88.42 164 | 67.04 90 | | | 78.62 52 | | 91.83 106 | | 97.37 51 | 76.57 99 | | |
|
VNet | | | 86.20 37 | 85.65 42 | 87.84 18 | 93.92 37 | 69.99 27 | 95.73 15 | 95.94 12 | 78.43 53 | 86.00 24 | 93.07 84 | 58.22 118 | 97.00 73 | 85.22 42 | 84.33 116 | 96.52 13 |
|
tpm | | | 78.58 147 | 77.03 147 | 83.22 137 | 85.94 202 | 64.56 159 | 83.21 282 | 91.14 161 | 78.31 54 | 73.67 128 | 79.68 264 | 64.01 67 | 92.09 242 | 66.07 181 | 71.26 209 | 93.03 122 |
|
TSAR-MVS + GP. | | | 87.96 14 | 88.37 12 | 86.70 45 | 93.51 46 | 65.32 144 | 95.15 26 | 93.84 48 | 78.17 55 | 85.93 25 | 94.80 51 | 75.80 6 | 98.21 24 | 89.38 13 | 88.78 80 | 96.59 10 |
|
FIs | | | 79.47 129 | 79.41 113 | 79.67 224 | 85.95 200 | 59.40 255 | 91.68 140 | 93.94 46 | 78.06 56 | 68.96 188 | 88.28 152 | 66.61 38 | 91.77 247 | 66.20 180 | 74.99 182 | 87.82 199 |
|
sss | | | 82.71 81 | 82.38 76 | 83.73 128 | 89.25 145 | 59.58 253 | 92.24 107 | 94.89 23 | 77.96 57 | 79.86 71 | 92.38 99 | 56.70 138 | 97.05 68 | 77.26 96 | 80.86 137 | 94.55 72 |
|
PMMVS | | | 81.98 93 | 82.04 79 | 81.78 180 | 89.76 131 | 56.17 288 | 91.13 165 | 90.69 173 | 77.96 57 | 80.09 69 | 93.57 75 | 46.33 245 | 94.99 138 | 81.41 68 | 87.46 90 | 94.17 86 |
|
MVS_Test | | | 84.16 61 | 83.20 65 | 87.05 39 | 91.56 93 | 69.82 30 | 89.99 192 | 92.05 127 | 77.77 59 | 82.84 51 | 86.57 180 | 63.93 69 | 96.09 104 | 74.91 112 | 89.18 78 | 95.25 49 |
|
SteuartSystems-ACMMP | | | 86.82 32 | 86.90 28 | 86.58 50 | 90.42 118 | 66.38 122 | 96.09 9 | 93.87 47 | 77.73 60 | 84.01 46 | 95.66 22 | 63.39 76 | 97.94 30 | 87.40 28 | 93.55 36 | 95.42 38 |
Skip Steuart: Steuart Systems R&D Blog. |
EPNet_dtu | | | 78.80 140 | 79.26 117 | 77.43 269 | 88.06 170 | 49.71 319 | 91.96 121 | 91.95 132 | 77.67 61 | 76.56 105 | 91.28 113 | 58.51 117 | 90.20 275 | 56.37 242 | 80.95 136 | 92.39 136 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tpmrst | | | 80.57 107 | 79.14 120 | 84.84 104 | 90.10 124 | 68.28 60 | 81.70 291 | 89.72 214 | 77.63 62 | 75.96 108 | 79.54 267 | 64.94 62 | 92.71 223 | 75.43 104 | 77.28 167 | 93.55 108 |
|
testdata1 | | | | | | | | 89.21 207 | | 77.55 63 | | | | | | | |
|
UniMVSNet_NR-MVSNet | | | 78.15 154 | 77.55 138 | 79.98 216 | 84.46 217 | 60.26 241 | 92.25 106 | 93.20 83 | 77.50 64 | 68.88 189 | 86.61 179 | 66.10 42 | 92.13 240 | 66.38 177 | 62.55 270 | 87.54 201 |
|
UA-Net | | | 80.02 118 | 79.65 107 | 81.11 197 | 89.33 143 | 57.72 271 | 86.33 261 | 89.00 238 | 77.44 65 | 81.01 61 | 89.15 143 | 59.33 110 | 95.90 110 | 61.01 223 | 84.28 118 | 89.73 170 |
|
PVSNet_Blended_VisFu | | | 83.97 63 | 83.50 57 | 85.39 90 | 90.02 125 | 66.59 115 | 93.77 63 | 91.73 138 | 77.43 66 | 77.08 102 | 89.81 139 | 63.77 72 | 96.97 78 | 79.67 77 | 88.21 84 | 92.60 132 |
|
NR-MVSNet | | | 76.05 192 | 74.59 185 | 80.44 206 | 82.96 236 | 62.18 214 | 90.83 170 | 91.73 138 | 77.12 67 | 60.96 257 | 86.35 181 | 59.28 111 | 91.80 246 | 60.74 224 | 61.34 283 | 87.35 212 |
|
casdiffmvs | | | 85.23 47 | 84.38 52 | 87.79 19 | 90.73 115 | 71.38 13 | 90.71 174 | 92.52 110 | 77.08 68 | 84.58 37 | 87.18 176 | 64.43 65 | 96.34 97 | 84.32 47 | 87.86 87 | 95.65 33 |
|
DI_MVS_plusplus_test | | | 79.78 124 | 77.50 140 | 86.62 47 | 80.90 251 | 69.46 36 | 90.69 175 | 91.97 131 | 77.00 69 | 59.07 267 | 82.34 220 | 46.82 239 | 95.88 111 | 82.14 61 | 86.59 98 | 94.53 76 |
|
test_normal | | | 79.66 125 | 77.36 145 | 86.54 51 | 80.72 255 | 69.21 40 | 90.68 176 | 92.16 126 | 76.99 70 | 58.63 271 | 82.03 229 | 46.70 241 | 95.86 112 | 81.74 65 | 86.63 97 | 94.56 71 |
|
FC-MVSNet-test | | | 77.99 157 | 78.08 130 | 77.70 264 | 84.89 211 | 55.51 292 | 90.27 185 | 93.75 53 | 76.87 71 | 66.80 219 | 87.59 165 | 65.71 48 | 90.23 274 | 62.89 211 | 73.94 188 | 87.37 210 |
|
SD-MVS | | | 87.49 20 | 87.49 21 | 87.50 28 | 93.60 43 | 68.82 48 | 93.90 57 | 92.63 106 | 76.86 72 | 87.90 15 | 95.76 19 | 66.17 40 | 97.63 41 | 89.06 18 | 91.48 61 | 96.05 23 |
|
UGNet | | | 79.87 121 | 78.68 122 | 83.45 136 | 89.96 126 | 61.51 223 | 92.13 109 | 90.79 170 | 76.83 73 | 78.85 83 | 86.33 183 | 38.16 283 | 96.17 101 | 67.93 163 | 87.17 91 | 92.67 130 |
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 92 | 81.52 85 | 83.51 134 | 88.42 164 | 62.88 204 | 89.77 199 | 88.93 239 | 76.78 74 | 75.55 113 | 93.10 80 | 50.31 210 | 95.38 131 | 83.82 53 | 87.02 92 | 92.26 142 |
|
Test4 | | | 76.45 187 | 73.45 210 | 85.45 87 | 76.07 315 | 67.61 75 | 88.38 224 | 90.83 169 | 76.71 75 | 53.06 302 | 79.65 266 | 31.61 316 | 94.35 167 | 78.47 86 | 86.22 102 | 94.40 80 |
|
TranMVSNet+NR-MVSNet | | | 75.86 195 | 74.52 188 | 79.89 219 | 82.44 239 | 60.64 236 | 91.37 152 | 91.37 154 | 76.63 76 | 67.65 208 | 86.21 184 | 52.37 197 | 91.55 257 | 61.84 219 | 60.81 286 | 87.48 203 |
|
PAPR | | | 85.15 48 | 84.47 49 | 87.18 34 | 96.02 12 | 68.29 59 | 91.85 131 | 93.00 93 | 76.59 77 | 79.03 80 | 95.00 42 | 61.59 89 | 97.61 43 | 78.16 89 | 89.00 79 | 95.63 34 |
|
diffmvs | | | 81.52 96 | 80.44 99 | 84.76 105 | 87.98 173 | 65.79 138 | 86.97 255 | 88.84 241 | 76.57 78 | 78.24 86 | 85.79 190 | 58.10 121 | 94.55 153 | 77.40 95 | 84.11 121 | 93.95 99 |
|
UniMVSNet (Re) | | | 77.58 162 | 76.78 152 | 79.98 216 | 84.11 223 | 60.80 229 | 91.76 136 | 93.17 86 | 76.56 79 | 69.93 173 | 84.78 198 | 63.32 78 | 92.36 235 | 64.89 191 | 62.51 272 | 86.78 222 |
|
DU-MVS | | | 76.86 179 | 75.84 164 | 79.91 218 | 82.96 236 | 60.26 241 | 91.26 157 | 91.54 145 | 76.46 80 | 68.88 189 | 86.35 181 | 56.16 144 | 92.13 240 | 66.38 177 | 62.55 270 | 87.35 212 |
|
OPM-MVS | | | 79.00 135 | 78.09 129 | 81.73 181 | 83.52 230 | 63.83 179 | 91.64 143 | 90.30 189 | 76.36 81 | 71.97 149 | 89.93 138 | 46.30 246 | 95.17 136 | 75.10 107 | 77.70 157 | 86.19 230 |
|
WR-MVS | | | 76.76 183 | 75.74 166 | 79.82 221 | 84.60 213 | 62.27 213 | 92.60 98 | 92.51 111 | 76.06 82 | 67.87 206 | 85.34 192 | 56.76 135 | 90.24 273 | 62.20 217 | 63.69 268 | 86.94 220 |
|
GA-MVS | | | 78.33 151 | 76.23 159 | 84.65 110 | 83.65 228 | 66.30 125 | 91.44 146 | 90.14 197 | 76.01 83 | 70.32 165 | 84.02 204 | 42.50 262 | 94.72 148 | 70.98 139 | 77.00 169 | 92.94 125 |
|
PVSNet_0 | | 68.08 15 | 71.81 238 | 68.32 252 | 82.27 165 | 84.68 212 | 62.31 212 | 88.68 216 | 90.31 188 | 75.84 84 | 57.93 274 | 80.65 252 | 37.85 288 | 94.19 174 | 69.94 147 | 29.05 349 | 90.31 164 |
|
CDS-MVSNet | | | 81.43 98 | 80.74 93 | 83.52 133 | 86.26 195 | 64.45 164 | 92.09 112 | 90.65 176 | 75.83 85 | 73.95 127 | 89.81 139 | 63.97 68 | 92.91 217 | 71.27 135 | 82.82 125 | 93.20 117 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
CostFormer | | | 82.33 85 | 81.15 88 | 85.86 72 | 89.01 152 | 68.46 54 | 82.39 287 | 93.01 91 | 75.59 86 | 80.25 68 | 81.57 235 | 72.03 14 | 94.96 139 | 79.06 83 | 77.48 164 | 94.16 87 |
|
nrg030 | | | 80.93 104 | 79.86 104 | 84.13 120 | 83.69 227 | 68.83 47 | 93.23 77 | 91.20 158 | 75.55 87 | 75.06 117 | 88.22 157 | 63.04 82 | 94.74 147 | 81.88 63 | 66.88 239 | 88.82 178 |
|
VDD-MVS | | | 83.06 76 | 81.81 82 | 86.81 42 | 90.86 113 | 67.70 72 | 95.40 20 | 91.50 148 | 75.46 88 | 81.78 56 | 92.34 101 | 40.09 274 | 97.13 67 | 86.85 33 | 82.04 130 | 95.60 35 |
|
Effi-MVS+-dtu | | | 76.14 190 | 75.28 179 | 78.72 252 | 83.22 232 | 55.17 294 | 89.87 197 | 87.78 259 | 75.42 89 | 67.98 203 | 81.43 236 | 45.08 252 | 92.52 229 | 75.08 108 | 71.63 204 | 88.48 183 |
|
mvs-test1 | | | 78.74 143 | 77.95 132 | 81.14 195 | 83.22 232 | 57.13 278 | 93.96 52 | 87.78 259 | 75.42 89 | 72.68 136 | 90.80 117 | 45.08 252 | 94.54 155 | 75.08 108 | 77.49 163 | 91.74 147 |
|
test_prior3 | | | 87.38 21 | 87.70 18 | 86.42 57 | 94.71 23 | 67.35 80 | 95.10 28 | 93.10 89 | 75.40 91 | 85.25 33 | 95.61 24 | 67.94 26 | 96.84 85 | 87.47 26 | 94.77 16 | 95.05 56 |
|
test_prior2 | | | | | | | | 95.10 28 | | 75.40 91 | 85.25 33 | 95.61 24 | 67.94 26 | | 87.47 26 | 94.77 16 | |
|
zzz-MVS | | | 84.73 52 | 84.47 49 | 85.50 83 | 91.89 83 | 65.16 147 | 91.55 144 | 92.23 117 | 75.32 93 | 80.53 65 | 95.21 37 | 56.06 147 | 97.16 65 | 84.86 45 | 92.55 47 | 94.18 84 |
|
MTAPA | | | 83.91 65 | 83.38 63 | 85.50 83 | 91.89 83 | 65.16 147 | 81.75 290 | 92.23 117 | 75.32 93 | 80.53 65 | 95.21 37 | 56.06 147 | 97.16 65 | 84.86 45 | 92.55 47 | 94.18 84 |
|
EPMVS | | | 78.49 149 | 75.98 162 | 86.02 67 | 91.21 105 | 69.68 34 | 80.23 303 | 91.20 158 | 75.25 95 | 72.48 142 | 78.11 274 | 54.65 169 | 93.69 200 | 57.66 240 | 83.04 123 | 94.69 67 |
|
v2v482 | | | 77.42 164 | 75.65 170 | 82.73 144 | 80.38 269 | 67.13 87 | 91.85 131 | 90.23 191 | 75.09 96 | 69.37 181 | 83.39 210 | 53.79 181 | 94.44 158 | 71.77 129 | 65.00 256 | 86.63 226 |
|
VPA-MVSNet | | | 79.03 134 | 78.00 131 | 82.11 176 | 85.95 200 | 64.48 163 | 93.22 78 | 94.66 31 | 75.05 97 | 74.04 126 | 84.95 196 | 52.17 198 | 93.52 203 | 74.90 113 | 67.04 238 | 88.32 187 |
|
ACMMP_Plus | | | 86.05 39 | 85.80 39 | 86.80 43 | 91.58 92 | 67.53 77 | 91.79 133 | 93.49 63 | 74.93 98 | 84.61 36 | 95.30 31 | 59.42 109 | 97.92 31 | 86.13 36 | 94.92 12 | 94.94 62 |
|
thres200 | | | 79.66 125 | 78.33 125 | 83.66 132 | 92.54 67 | 65.82 137 | 93.06 81 | 96.31 9 | 74.90 99 | 73.30 130 | 88.66 146 | 59.67 107 | 95.61 123 | 47.84 272 | 78.67 150 | 89.56 172 |
|
TAMVS | | | 80.37 111 | 79.45 112 | 83.13 139 | 85.14 208 | 63.37 190 | 91.23 158 | 90.76 172 | 74.81 100 | 72.65 137 | 88.49 148 | 60.63 98 | 92.95 213 | 69.41 152 | 81.95 131 | 93.08 121 |
|
MP-MVS-pluss | | | 85.24 46 | 85.13 46 | 85.56 82 | 91.42 101 | 65.59 140 | 91.54 145 | 92.51 111 | 74.56 101 | 80.62 64 | 95.64 23 | 59.15 112 | 97.00 73 | 86.94 32 | 93.80 29 | 94.07 94 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
v1141 | | | 77.28 170 | 75.57 171 | 82.42 158 | 80.63 263 | 66.73 105 | 91.96 121 | 90.42 182 | 74.41 102 | 69.46 178 | 82.12 226 | 55.09 159 | 94.40 163 | 70.99 138 | 65.05 252 | 86.12 232 |
|
divwei89l23v2f112 | | | 77.28 170 | 75.57 171 | 82.42 158 | 80.62 264 | 66.72 107 | 91.96 121 | 90.42 182 | 74.41 102 | 69.46 178 | 82.12 226 | 55.11 158 | 94.40 163 | 71.00 136 | 65.04 253 | 86.12 232 |
|
v1 | | | 77.29 169 | 75.57 171 | 82.42 158 | 80.61 267 | 66.73 105 | 91.96 121 | 90.42 182 | 74.41 102 | 69.46 178 | 82.12 226 | 55.14 157 | 94.40 163 | 71.00 136 | 65.04 253 | 86.13 231 |
|
mvs_anonymous | | | 81.36 99 | 79.99 102 | 85.46 85 | 90.39 120 | 68.40 55 | 86.88 257 | 90.61 177 | 74.41 102 | 70.31 166 | 84.67 199 | 63.79 71 | 92.32 236 | 73.13 116 | 85.70 104 | 95.67 31 |
|
MAR-MVS | | | 84.18 60 | 83.43 60 | 86.44 56 | 96.25 9 | 65.93 133 | 94.28 39 | 94.27 41 | 74.41 102 | 79.16 79 | 95.61 24 | 53.99 178 | 98.88 11 | 69.62 150 | 93.26 39 | 94.50 77 |
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 | | | 80.49 110 | 79.30 116 | 84.05 122 | 90.83 114 | 64.36 170 | 93.60 67 | 89.42 221 | 74.35 107 | 69.09 185 | 90.15 128 | 55.23 154 | 95.61 123 | 64.61 193 | 86.43 101 | 92.17 143 |
|
Vis-MVSNet (Re-imp) | | | 79.24 132 | 79.57 108 | 78.24 259 | 88.46 162 | 52.29 305 | 90.41 182 | 89.12 232 | 74.24 108 | 69.13 184 | 91.91 105 | 65.77 47 | 90.09 280 | 59.00 234 | 88.09 85 | 92.33 138 |
|
v1neww | | | 77.39 165 | 75.71 167 | 82.44 152 | 80.69 257 | 66.83 95 | 91.94 125 | 90.18 194 | 74.19 109 | 69.60 175 | 82.51 216 | 54.99 163 | 94.44 158 | 71.68 131 | 65.60 245 | 86.05 235 |
|
v7new | | | 77.39 165 | 75.71 167 | 82.44 152 | 80.69 257 | 66.83 95 | 91.94 125 | 90.18 194 | 74.19 109 | 69.60 175 | 82.51 216 | 54.99 163 | 94.44 158 | 71.68 131 | 65.60 245 | 86.05 235 |
|
v6 | | | 77.39 165 | 75.71 167 | 82.44 152 | 80.67 259 | 66.82 97 | 91.94 125 | 90.18 194 | 74.19 109 | 69.60 175 | 82.50 219 | 55.00 162 | 94.44 158 | 71.68 131 | 65.60 245 | 86.05 235 |
|
SMA-MVS | | | 88.14 11 | 88.31 14 | 87.66 22 | 93.25 49 | 68.72 49 | 93.85 59 | 94.03 45 | 74.18 112 | 91.74 2 | 96.72 5 | 65.61 49 | 98.48 18 | 89.29 15 | 96.08 3 | 95.79 30 |
|
3Dnovator+ | | 73.60 7 | 82.10 91 | 80.60 96 | 86.60 48 | 90.89 112 | 66.80 103 | 95.20 24 | 93.44 71 | 74.05 113 | 67.42 210 | 92.49 96 | 49.46 218 | 97.65 40 | 70.80 141 | 91.68 57 | 95.33 41 |
|
XVS | | | 83.87 66 | 83.47 58 | 85.05 98 | 93.22 50 | 63.78 180 | 92.92 87 | 92.66 104 | 73.99 114 | 78.18 87 | 94.31 65 | 55.25 152 | 97.41 49 | 79.16 81 | 91.58 59 | 93.95 99 |
|
X-MVStestdata | | | 76.86 179 | 74.13 194 | 85.05 98 | 93.22 50 | 63.78 180 | 92.92 87 | 92.66 104 | 73.99 114 | 78.18 87 | 10.19 361 | 55.25 152 | 97.41 49 | 79.16 81 | 91.58 59 | 93.95 99 |
|
MS-PatchMatch | | | 77.90 160 | 76.50 156 | 82.12 173 | 85.99 199 | 69.95 28 | 91.75 138 | 92.70 101 | 73.97 116 | 62.58 253 | 84.44 202 | 41.11 270 | 95.78 114 | 63.76 200 | 92.17 52 | 80.62 310 |
|
LCM-MVSNet-Re | | | 72.93 229 | 71.84 225 | 76.18 280 | 88.49 160 | 48.02 323 | 80.07 306 | 70.17 344 | 73.96 117 | 52.25 306 | 80.09 261 | 49.98 213 | 88.24 301 | 67.35 167 | 84.23 119 | 92.28 141 |
|
Vis-MVSNet | | | 80.92 105 | 79.98 103 | 83.74 126 | 88.48 161 | 61.80 220 | 93.44 73 | 88.26 254 | 73.96 117 | 77.73 90 | 91.76 108 | 49.94 214 | 94.76 145 | 65.84 184 | 90.37 73 | 94.65 70 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
test-mter | | | 79.96 119 | 79.38 115 | 81.72 182 | 86.93 188 | 61.17 225 | 92.70 93 | 91.54 145 | 73.85 119 | 75.62 110 | 86.94 177 | 49.84 216 | 92.38 233 | 72.21 126 | 84.76 111 | 91.60 148 |
|
OMC-MVS | | | 78.67 146 | 77.91 134 | 80.95 203 | 85.76 204 | 57.40 276 | 88.49 220 | 88.67 244 | 73.85 119 | 72.43 144 | 92.10 103 | 49.29 220 | 94.55 153 | 72.73 121 | 77.89 155 | 90.91 158 |
|
Fast-Effi-MVS+ | | | 81.14 101 | 80.01 101 | 84.51 115 | 90.24 123 | 65.86 134 | 94.12 44 | 89.15 231 | 73.81 121 | 75.37 115 | 88.26 154 | 57.26 127 | 94.53 156 | 66.97 172 | 84.92 108 | 93.15 118 |
|
V42 | | | 76.46 186 | 74.55 187 | 82.19 170 | 79.14 291 | 67.82 69 | 90.26 186 | 89.42 221 | 73.75 122 | 68.63 193 | 81.89 231 | 51.31 205 | 94.09 181 | 71.69 130 | 64.84 257 | 84.66 259 |
|
v1144 | | | 76.73 184 | 74.88 183 | 82.27 165 | 80.23 277 | 66.60 113 | 91.68 140 | 90.21 193 | 73.69 123 | 69.06 186 | 81.89 231 | 52.73 193 | 94.40 163 | 69.21 154 | 65.23 249 | 85.80 243 |
|
v148 | | | 76.19 189 | 74.47 189 | 81.36 187 | 80.05 281 | 64.44 165 | 91.75 138 | 90.23 191 | 73.68 124 | 67.13 214 | 80.84 248 | 55.92 150 | 93.86 197 | 68.95 157 | 61.73 279 | 85.76 246 |
|
CR-MVSNet | | | 73.79 224 | 70.82 233 | 82.70 145 | 83.15 234 | 67.96 67 | 70.25 329 | 84.00 302 | 73.67 125 | 69.97 171 | 72.41 308 | 57.82 123 | 89.48 291 | 52.99 256 | 73.13 192 | 90.64 161 |
|
XXY-MVS | | | 77.94 159 | 76.44 157 | 82.43 155 | 82.60 238 | 64.44 165 | 92.01 117 | 91.83 136 | 73.59 126 | 70.00 170 | 85.82 188 | 54.43 173 | 94.76 145 | 69.63 149 | 68.02 233 | 88.10 193 |
|
#test# | | | 84.98 50 | 84.74 48 | 85.72 79 | 93.75 40 | 65.01 153 | 94.09 45 | 93.19 84 | 73.55 127 | 79.22 77 | 94.93 45 | 59.04 113 | 97.67 36 | 82.66 58 | 92.21 49 | 94.49 78 |
|
tfpn200view9 | | | 78.79 141 | 77.43 141 | 82.88 141 | 92.21 75 | 64.49 161 | 92.05 115 | 96.28 10 | 73.48 128 | 71.75 152 | 88.26 154 | 60.07 104 | 95.32 132 | 45.16 282 | 77.58 159 | 88.83 176 |
|
thres400 | | | 78.68 144 | 77.43 141 | 82.43 155 | 92.21 75 | 64.49 161 | 92.05 115 | 96.28 10 | 73.48 128 | 71.75 152 | 88.26 154 | 60.07 104 | 95.32 132 | 45.16 282 | 77.58 159 | 87.48 203 |
|
v7 | | | 76.83 182 | 75.01 182 | 82.29 164 | 80.35 270 | 66.70 109 | 91.68 140 | 89.97 204 | 73.47 130 | 69.22 183 | 82.22 223 | 52.52 194 | 94.43 162 | 69.73 148 | 65.96 244 | 85.74 247 |
|
FMVSNet3 | | | 77.73 161 | 76.04 161 | 82.80 142 | 91.20 106 | 68.99 44 | 91.87 129 | 91.99 129 | 73.35 131 | 67.04 215 | 83.19 212 | 56.62 140 | 92.14 239 | 59.80 230 | 69.34 222 | 87.28 214 |
|
USDC | | | 67.43 280 | 64.51 277 | 76.19 279 | 77.94 304 | 55.29 293 | 78.38 315 | 85.00 294 | 73.17 132 | 48.36 319 | 80.37 255 | 21.23 340 | 92.48 231 | 52.15 257 | 64.02 265 | 80.81 308 |
|
MP-MVS | | | 85.02 49 | 84.97 47 | 85.17 97 | 92.60 66 | 64.27 174 | 93.24 76 | 92.27 116 | 73.13 133 | 79.63 74 | 94.43 56 | 61.90 87 | 97.17 64 | 85.00 43 | 92.56 46 | 94.06 95 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
xiu_mvs_v1_base_debu | | | 82.16 88 | 81.12 89 | 85.26 94 | 86.42 191 | 68.72 49 | 92.59 100 | 90.44 179 | 73.12 134 | 84.20 42 | 94.36 58 | 38.04 285 | 95.73 117 | 84.12 49 | 86.81 93 | 91.33 151 |
|
xiu_mvs_v1_base | | | 82.16 88 | 81.12 89 | 85.26 94 | 86.42 191 | 68.72 49 | 92.59 100 | 90.44 179 | 73.12 134 | 84.20 42 | 94.36 58 | 38.04 285 | 95.73 117 | 84.12 49 | 86.81 93 | 91.33 151 |
|
xiu_mvs_v1_base_debi | | | 82.16 88 | 81.12 89 | 85.26 94 | 86.42 191 | 68.72 49 | 92.59 100 | 90.44 179 | 73.12 134 | 84.20 42 | 94.36 58 | 38.04 285 | 95.73 117 | 84.12 49 | 86.81 93 | 91.33 151 |
|
BH-RMVSNet | | | 79.46 130 | 77.65 136 | 84.89 101 | 91.68 91 | 65.66 139 | 93.55 69 | 88.09 255 | 72.93 137 | 73.37 129 | 91.12 114 | 46.20 247 | 96.12 103 | 56.28 243 | 85.61 106 | 92.91 126 |
|
IS-MVSNet | | | 80.14 115 | 79.41 113 | 82.33 162 | 87.91 174 | 60.08 247 | 91.97 120 | 88.27 253 | 72.90 138 | 71.44 156 | 91.73 110 | 61.44 90 | 93.66 201 | 62.47 216 | 86.53 99 | 93.24 115 |
|
PS-MVSNAJss | | | 77.26 172 | 76.31 158 | 80.13 213 | 80.64 262 | 59.16 259 | 90.63 179 | 91.06 164 | 72.80 139 | 68.58 194 | 84.57 201 | 53.55 183 | 93.96 191 | 72.97 117 | 71.96 203 | 87.27 215 |
|
v1192 | | | 75.98 194 | 73.92 198 | 82.15 171 | 79.73 282 | 66.24 127 | 91.22 159 | 89.75 209 | 72.67 140 | 68.49 199 | 81.42 237 | 49.86 215 | 94.27 171 | 67.08 170 | 65.02 255 | 85.95 240 |
|
Effi-MVS+ | | | 83.82 67 | 82.76 72 | 86.99 41 | 89.56 140 | 69.40 37 | 91.35 153 | 86.12 284 | 72.59 141 | 83.22 49 | 92.81 93 | 59.60 108 | 96.01 109 | 81.76 64 | 87.80 88 | 95.56 36 |
|
UnsupCasMVSNet_eth | | | 65.79 287 | 63.10 286 | 73.88 292 | 70.71 329 | 50.29 316 | 81.09 296 | 89.88 206 | 72.58 142 | 49.25 317 | 74.77 298 | 32.57 312 | 87.43 306 | 55.96 244 | 41.04 340 | 83.90 265 |
|
1112_ss | | | 80.56 108 | 79.83 105 | 82.77 143 | 88.65 158 | 60.78 230 | 92.29 105 | 88.36 250 | 72.58 142 | 72.46 143 | 94.95 43 | 65.09 53 | 93.42 206 | 66.38 177 | 77.71 156 | 94.10 91 |
|
thres600view7 | | | 78.00 155 | 76.66 153 | 82.03 178 | 91.93 80 | 63.69 185 | 91.30 156 | 96.33 5 | 72.43 144 | 70.46 160 | 87.89 160 | 60.31 99 | 94.92 142 | 42.64 295 | 76.64 170 | 87.48 203 |
|
IterMVS-LS | | | 76.49 185 | 75.18 181 | 80.43 207 | 84.49 216 | 62.74 206 | 90.64 177 | 88.80 242 | 72.40 145 | 65.16 230 | 81.72 234 | 60.98 92 | 92.27 238 | 67.74 164 | 64.65 260 | 86.29 228 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 78.97 136 | 78.22 128 | 81.25 189 | 85.33 205 | 62.73 207 | 89.53 203 | 93.21 81 | 72.39 146 | 72.14 147 | 90.13 129 | 60.99 91 | 94.72 148 | 67.73 165 | 72.49 199 | 86.29 228 |
|
v144192 | | | 76.05 192 | 74.03 195 | 82.12 173 | 79.50 286 | 66.55 117 | 91.39 149 | 89.71 215 | 72.30 147 | 68.17 201 | 81.33 239 | 51.75 201 | 94.03 188 | 67.94 162 | 64.19 262 | 85.77 244 |
|
tfpn111 | | | 78.00 155 | 76.62 154 | 82.13 172 | 91.89 83 | 63.21 194 | 91.19 162 | 96.33 5 | 72.28 148 | 70.45 161 | 87.89 160 | 60.31 99 | 94.91 143 | 42.61 296 | 76.64 170 | 88.27 188 |
|
conf200view11 | | | 78.32 152 | 77.01 148 | 82.27 165 | 91.89 83 | 63.21 194 | 91.19 162 | 96.33 5 | 72.28 148 | 70.45 161 | 87.89 160 | 60.31 99 | 95.32 132 | 45.16 282 | 77.58 159 | 88.27 188 |
|
thres100view900 | | | 78.37 150 | 77.01 148 | 82.46 151 | 91.89 83 | 63.21 194 | 91.19 162 | 96.33 5 | 72.28 148 | 70.45 161 | 87.89 160 | 60.31 99 | 95.32 132 | 45.16 282 | 77.58 159 | 88.83 176 |
|
PatchmatchNet | | | 77.46 163 | 74.63 184 | 85.96 69 | 89.55 141 | 70.35 22 | 79.97 307 | 89.55 217 | 72.23 151 | 70.94 157 | 76.91 286 | 57.03 130 | 92.79 221 | 54.27 249 | 81.17 134 | 94.74 66 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
testing_2 | | | 71.09 247 | 67.32 261 | 82.40 161 | 69.82 332 | 66.52 119 | 83.64 274 | 90.77 171 | 72.21 152 | 45.12 329 | 71.07 322 | 27.60 329 | 93.74 198 | 75.71 103 | 69.96 217 | 86.95 219 |
|
HFP-MVS | | | 84.73 52 | 84.40 51 | 85.72 79 | 93.75 40 | 65.01 153 | 93.50 71 | 93.19 84 | 72.19 153 | 79.22 77 | 94.93 45 | 59.04 113 | 97.67 36 | 81.55 66 | 92.21 49 | 94.49 78 |
|
ACMMPR | | | 84.37 55 | 84.06 53 | 85.28 93 | 93.56 44 | 64.37 169 | 93.50 71 | 93.15 87 | 72.19 153 | 78.85 83 | 94.86 49 | 56.69 139 | 97.45 47 | 81.55 66 | 92.20 51 | 94.02 97 |
|
1314 | | | 80.70 106 | 78.95 121 | 85.94 70 | 87.77 177 | 67.56 76 | 87.91 232 | 92.55 109 | 72.17 155 | 67.44 209 | 93.09 81 | 50.27 211 | 97.04 70 | 71.68 131 | 87.64 89 | 93.23 116 |
|
region2R | | | 84.36 56 | 84.03 54 | 85.36 91 | 93.54 45 | 64.31 171 | 93.43 74 | 92.95 94 | 72.16 156 | 78.86 82 | 94.84 50 | 56.97 133 | 97.53 45 | 81.38 69 | 92.11 53 | 94.24 82 |
|
Test_1112_low_res | | | 79.56 128 | 78.60 124 | 82.43 155 | 88.24 167 | 60.39 239 | 92.09 112 | 87.99 257 | 72.10 157 | 71.84 150 | 87.42 168 | 64.62 63 | 93.04 210 | 65.80 185 | 77.30 166 | 93.85 104 |
|
v1921920 | | | 75.63 199 | 73.49 209 | 82.06 177 | 79.38 287 | 66.35 123 | 91.07 167 | 89.48 218 | 71.98 158 | 67.99 202 | 81.22 243 | 49.16 223 | 93.90 194 | 66.56 175 | 64.56 261 | 85.92 242 |
|
Fast-Effi-MVS+-dtu | | | 75.04 206 | 73.37 211 | 80.07 214 | 80.86 252 | 59.52 254 | 91.20 161 | 85.38 291 | 71.90 159 | 65.20 228 | 84.84 197 | 41.46 269 | 92.97 212 | 66.50 176 | 72.96 195 | 87.73 200 |
|
LFMVS | | | 84.34 57 | 82.73 73 | 89.18 8 | 94.76 21 | 73.25 8 | 94.99 31 | 91.89 133 | 71.90 159 | 82.16 54 | 93.49 77 | 47.98 232 | 97.05 68 | 82.55 59 | 84.82 109 | 97.25 2 |
|
train_agg | | | 87.21 24 | 87.42 23 | 86.60 48 | 94.18 30 | 67.28 82 | 94.16 40 | 93.51 61 | 71.87 161 | 85.52 29 | 95.33 29 | 68.19 23 | 97.27 60 | 89.09 16 | 94.90 13 | 95.25 49 |
|
test_8 | | | | | | 94.19 29 | 67.19 84 | 94.15 42 | 93.42 72 | 71.87 161 | 85.38 31 | 95.35 28 | 68.19 23 | 96.95 80 | | | |
|
agg_prior1 | | | 87.02 26 | 87.26 25 | 86.28 64 | 94.16 34 | 66.97 91 | 94.08 46 | 93.31 77 | 71.85 163 | 84.49 39 | 95.39 27 | 68.91 19 | 96.75 89 | 88.84 20 | 94.32 23 | 95.13 53 |
|
MDTV_nov1_ep13 | | | | 72.61 218 | | 89.06 150 | 68.48 53 | 80.33 301 | 90.11 198 | 71.84 164 | 71.81 151 | 75.92 293 | 53.01 190 | 93.92 193 | 48.04 271 | 73.38 190 | |
|
ab-mvs | | | 80.18 114 | 78.31 126 | 85.80 75 | 88.44 163 | 65.49 143 | 83.00 284 | 92.67 103 | 71.82 165 | 77.36 97 | 85.01 195 | 54.50 170 | 96.59 92 | 76.35 101 | 75.63 176 | 95.32 43 |
|
agg_prior3 | | | 86.93 28 | 87.08 26 | 86.48 54 | 94.21 28 | 66.95 93 | 94.14 43 | 93.40 73 | 71.80 166 | 84.86 35 | 95.13 39 | 66.16 41 | 97.25 62 | 89.09 16 | 94.90 13 | 95.25 49 |
|
ACMMP | | | 81.49 97 | 80.67 94 | 83.93 123 | 91.71 90 | 62.90 203 | 92.13 109 | 92.22 121 | 71.79 167 | 71.68 154 | 93.49 77 | 50.32 209 | 96.96 79 | 78.47 86 | 84.22 120 | 91.93 145 |
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 31 | 86.85 30 | 86.78 44 | 93.47 47 | 65.55 141 | 95.39 21 | 95.10 20 | 71.77 168 | 85.69 28 | 96.52 6 | 62.07 86 | 98.77 12 | 86.06 37 | 95.60 7 | 96.03 24 |
|
TEST9 | | | | | | 94.18 30 | 67.28 82 | 94.16 40 | 93.51 61 | 71.75 169 | 85.52 29 | 95.33 29 | 68.01 25 | 97.27 60 | | | |
|
v8 | | | 75.35 201 | 73.26 212 | 81.61 185 | 80.67 259 | 66.82 97 | 89.54 202 | 89.27 225 | 71.65 170 | 63.30 247 | 80.30 257 | 54.99 163 | 94.06 184 | 67.33 169 | 62.33 273 | 83.94 264 |
|
v1240 | | | 75.21 205 | 72.98 214 | 81.88 179 | 79.20 289 | 66.00 131 | 90.75 173 | 89.11 233 | 71.63 171 | 67.41 211 | 81.22 243 | 47.36 237 | 93.87 195 | 65.46 188 | 64.72 259 | 85.77 244 |
|
Patchmatch-test1 | | | 75.00 208 | 71.80 227 | 84.58 112 | 86.63 190 | 70.08 25 | 81.06 297 | 89.19 228 | 71.60 172 | 70.01 169 | 77.16 284 | 45.53 249 | 88.63 295 | 51.79 258 | 73.27 191 | 95.02 60 |
|
BH-untuned | | | 78.68 144 | 77.08 146 | 83.48 135 | 89.84 129 | 63.74 182 | 92.70 93 | 88.59 246 | 71.57 173 | 66.83 218 | 88.65 147 | 51.75 201 | 95.39 130 | 59.03 233 | 84.77 110 | 91.32 154 |
|
IterMVS | | | 72.65 235 | 70.83 232 | 78.09 262 | 82.17 240 | 62.96 199 | 87.64 244 | 86.28 280 | 71.56 174 | 60.44 259 | 78.85 271 | 45.42 251 | 86.66 309 | 63.30 204 | 61.83 276 | 84.65 260 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
mPP-MVS | | | 82.96 78 | 82.44 75 | 84.52 114 | 92.83 60 | 62.92 202 | 92.76 90 | 91.85 135 | 71.52 175 | 75.61 112 | 94.24 66 | 53.48 187 | 96.99 76 | 78.97 84 | 90.73 68 | 93.64 107 |
|
test-LLR | | | 80.10 116 | 79.56 109 | 81.72 182 | 86.93 188 | 61.17 225 | 92.70 93 | 91.54 145 | 71.51 176 | 75.62 110 | 86.94 177 | 53.83 179 | 92.38 233 | 72.21 126 | 84.76 111 | 91.60 148 |
|
test0.0.03 1 | | | 72.76 232 | 72.71 217 | 72.88 300 | 80.25 276 | 47.99 324 | 91.22 159 | 89.45 219 | 71.51 176 | 62.51 254 | 87.66 164 | 53.83 179 | 85.06 314 | 50.16 263 | 67.84 236 | 85.58 248 |
|
PGM-MVS | | | 83.25 72 | 82.70 74 | 84.92 100 | 92.81 63 | 64.07 176 | 90.44 180 | 92.20 122 | 71.28 178 | 77.23 99 | 94.43 56 | 55.17 156 | 97.31 56 | 79.33 80 | 91.38 62 | 93.37 110 |
|
dp | | | 75.01 207 | 72.09 224 | 83.76 125 | 89.28 144 | 66.22 128 | 79.96 308 | 89.75 209 | 71.16 179 | 67.80 207 | 77.19 282 | 51.81 200 | 92.54 228 | 50.39 262 | 71.44 208 | 92.51 135 |
|
CP-MVS | | | 83.71 70 | 83.40 62 | 84.65 110 | 93.14 54 | 63.84 178 | 94.59 37 | 92.28 115 | 71.03 180 | 77.41 96 | 94.92 47 | 55.21 155 | 96.19 100 | 81.32 70 | 90.70 69 | 93.91 102 |
|
v10 | | | 74.77 216 | 72.54 220 | 81.46 186 | 80.33 274 | 66.71 108 | 89.15 209 | 89.08 234 | 70.94 181 | 63.08 248 | 79.86 262 | 52.52 194 | 94.04 187 | 65.70 186 | 62.17 274 | 83.64 266 |
|
CDPH-MVS | | | 85.71 43 | 85.46 43 | 86.46 55 | 94.75 22 | 67.19 84 | 93.89 58 | 92.83 98 | 70.90 182 | 83.09 50 | 95.28 32 | 63.62 73 | 97.36 52 | 80.63 73 | 94.18 24 | 94.84 64 |
|
GBi-Net | | | 75.65 197 | 73.83 199 | 81.10 198 | 88.85 153 | 65.11 149 | 90.01 189 | 90.32 185 | 70.84 183 | 67.04 215 | 80.25 258 | 48.03 229 | 91.54 258 | 59.80 230 | 69.34 222 | 86.64 223 |
|
test1 | | | 75.65 197 | 73.83 199 | 81.10 198 | 88.85 153 | 65.11 149 | 90.01 189 | 90.32 185 | 70.84 183 | 67.04 215 | 80.25 258 | 48.03 229 | 91.54 258 | 59.80 230 | 69.34 222 | 86.64 223 |
|
FMVSNet2 | | | 76.07 191 | 74.01 196 | 82.26 168 | 88.85 153 | 67.66 73 | 91.33 154 | 91.61 143 | 70.84 183 | 65.98 220 | 82.25 222 | 48.03 229 | 92.00 244 | 58.46 236 | 68.73 228 | 87.10 216 |
|
view600 | | | 76.93 175 | 75.50 174 | 81.23 190 | 91.44 97 | 62.00 215 | 89.94 193 | 96.56 1 | 70.68 186 | 68.54 195 | 87.31 170 | 60.79 93 | 94.19 174 | 38.90 310 | 75.31 178 | 87.48 203 |
|
view800 | | | 76.93 175 | 75.50 174 | 81.23 190 | 91.44 97 | 62.00 215 | 89.94 193 | 96.56 1 | 70.68 186 | 68.54 195 | 87.31 170 | 60.79 93 | 94.19 174 | 38.90 310 | 75.31 178 | 87.48 203 |
|
conf0.05thres1000 | | | 76.93 175 | 75.50 174 | 81.23 190 | 91.44 97 | 62.00 215 | 89.94 193 | 96.56 1 | 70.68 186 | 68.54 195 | 87.31 170 | 60.79 93 | 94.19 174 | 38.90 310 | 75.31 178 | 87.48 203 |
|
tfpn | | | 76.93 175 | 75.50 174 | 81.23 190 | 91.44 97 | 62.00 215 | 89.94 193 | 96.56 1 | 70.68 186 | 68.54 195 | 87.31 170 | 60.79 93 | 94.19 174 | 38.90 310 | 75.31 178 | 87.48 203 |
|
HyFIR lowres test | | | 81.03 103 | 79.56 109 | 85.43 88 | 87.81 176 | 68.11 65 | 90.18 187 | 90.01 203 | 70.65 190 | 72.95 133 | 86.06 186 | 63.61 74 | 94.50 157 | 75.01 110 | 79.75 141 | 93.67 105 |
|
MVP-Stereo | | | 77.12 174 | 76.23 159 | 79.79 222 | 81.72 245 | 66.34 124 | 89.29 205 | 90.88 168 | 70.56 191 | 62.01 256 | 82.88 213 | 49.34 219 | 94.13 179 | 65.55 187 | 93.80 29 | 78.88 323 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
tpmp4_e23 | | | 78.85 138 | 76.55 155 | 85.77 77 | 89.25 145 | 68.39 56 | 81.63 294 | 91.38 153 | 70.40 192 | 75.21 116 | 79.22 269 | 67.37 32 | 94.79 144 | 58.98 235 | 75.51 177 | 94.13 89 |
|
ACMP | | 71.68 10 | 75.58 200 | 74.23 192 | 79.62 226 | 84.97 210 | 59.64 251 | 90.80 171 | 89.07 235 | 70.39 193 | 62.95 249 | 87.30 174 | 38.28 282 | 93.87 195 | 72.89 118 | 71.45 207 | 85.36 253 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
HPM-MVS | | | 83.25 72 | 82.95 68 | 84.17 119 | 92.25 73 | 62.88 204 | 90.91 168 | 91.86 134 | 70.30 194 | 77.12 100 | 93.96 70 | 56.75 137 | 96.28 98 | 82.04 62 | 91.34 64 | 93.34 111 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
tpm2 | | | 79.80 123 | 77.95 132 | 85.34 92 | 88.28 166 | 68.26 61 | 81.56 295 | 91.42 151 | 70.11 195 | 77.59 95 | 80.50 253 | 67.40 31 | 94.26 173 | 67.34 168 | 77.35 165 | 93.51 109 |
|
TR-MVS | | | 78.77 142 | 77.37 144 | 82.95 140 | 90.49 117 | 60.88 228 | 93.67 65 | 90.07 199 | 70.08 196 | 74.51 120 | 91.37 112 | 45.69 248 | 95.70 122 | 60.12 228 | 80.32 138 | 92.29 140 |
|
PAPM_NR | | | 82.97 77 | 81.84 81 | 86.37 60 | 94.10 36 | 66.76 104 | 87.66 243 | 92.84 97 | 69.96 197 | 74.07 125 | 93.57 75 | 63.10 81 | 97.50 46 | 70.66 143 | 90.58 71 | 94.85 63 |
|
PCF-MVS | | 73.15 9 | 79.29 131 | 77.63 137 | 84.29 118 | 86.06 198 | 65.96 132 | 87.03 251 | 91.10 162 | 69.86 198 | 69.79 174 | 90.64 118 | 57.54 126 | 96.59 92 | 64.37 197 | 82.29 127 | 90.32 163 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MIMVSNet | | | 71.64 241 | 68.44 250 | 81.23 190 | 81.97 244 | 64.44 165 | 73.05 325 | 88.80 242 | 69.67 199 | 64.59 233 | 74.79 297 | 32.79 310 | 87.82 304 | 53.99 250 | 76.35 173 | 91.42 150 |
|
LPG-MVS_test | | | 75.82 196 | 74.58 186 | 79.56 228 | 84.31 220 | 59.37 256 | 90.44 180 | 89.73 212 | 69.49 200 | 64.86 231 | 88.42 149 | 38.65 279 | 94.30 169 | 72.56 122 | 72.76 196 | 85.01 256 |
|
LGP-MVS_train | | | | | 79.56 228 | 84.31 220 | 59.37 256 | | 89.73 212 | 69.49 200 | 64.86 231 | 88.42 149 | 38.65 279 | 94.30 169 | 72.56 122 | 72.76 196 | 85.01 256 |
|
APDe-MVS | | | 87.54 19 | 87.84 16 | 86.65 46 | 96.07 11 | 66.30 125 | 94.84 36 | 93.78 49 | 69.35 202 | 88.39 13 | 96.34 9 | 67.74 30 | 97.66 39 | 90.62 11 | 93.44 37 | 96.01 25 |
|
Patchmatch-RL test | | | 68.17 272 | 64.49 278 | 79.19 239 | 71.22 327 | 53.93 299 | 70.07 331 | 71.54 343 | 69.22 203 | 56.79 281 | 62.89 335 | 56.58 141 | 88.61 296 | 69.53 151 | 52.61 312 | 95.03 59 |
|
0601test | | | 84.28 58 | 83.16 66 | 87.64 23 | 94.52 26 | 69.24 39 | 95.78 11 | 95.09 21 | 69.19 204 | 81.09 60 | 92.88 91 | 57.00 132 | 97.44 48 | 81.11 72 | 81.76 132 | 96.23 18 |
|
jajsoiax | | | 73.05 228 | 71.51 229 | 77.67 265 | 77.46 306 | 54.83 295 | 88.81 214 | 90.04 202 | 69.13 205 | 62.85 251 | 83.51 208 | 31.16 319 | 92.75 222 | 70.83 140 | 69.80 218 | 85.43 252 |
|
DP-MVS Recon | | | 82.73 79 | 81.65 84 | 85.98 68 | 97.31 3 | 67.06 88 | 95.15 26 | 91.99 129 | 69.08 206 | 76.50 106 | 93.89 71 | 54.48 172 | 98.20 25 | 70.76 142 | 85.66 105 | 92.69 129 |
|
v18 | | | 71.94 237 | 69.43 240 | 79.50 230 | 80.74 254 | 66.82 97 | 88.16 226 | 86.66 270 | 68.95 207 | 55.55 284 | 72.66 303 | 55.03 161 | 90.15 276 | 64.78 192 | 52.30 314 | 81.54 292 |
|
Baseline_NR-MVSNet | | | 73.99 222 | 72.83 215 | 77.48 268 | 80.78 253 | 59.29 258 | 91.79 133 | 84.55 297 | 68.85 208 | 68.99 187 | 80.70 249 | 56.16 144 | 92.04 243 | 62.67 214 | 60.98 285 | 81.11 304 |
|
CHOSEN 280x420 | | | 77.35 168 | 76.95 151 | 78.55 253 | 87.07 185 | 62.68 208 | 69.71 332 | 82.95 312 | 68.80 209 | 71.48 155 | 87.27 175 | 66.03 43 | 84.00 320 | 76.47 100 | 82.81 126 | 88.95 175 |
|
mvs_tets | | | 72.71 233 | 71.11 230 | 77.52 266 | 77.41 307 | 54.52 297 | 88.45 222 | 89.76 208 | 68.76 210 | 62.70 252 | 83.26 211 | 29.49 323 | 92.71 223 | 70.51 145 | 69.62 220 | 85.34 254 |
|
v17 | | | 71.77 240 | 69.20 243 | 79.46 232 | 80.62 264 | 66.81 101 | 87.93 230 | 86.63 272 | 68.71 211 | 55.25 287 | 72.49 305 | 54.72 168 | 90.11 279 | 64.50 195 | 51.97 316 | 81.47 293 |
|
v16 | | | 71.81 238 | 69.26 242 | 79.47 231 | 80.66 261 | 66.81 101 | 87.93 230 | 86.63 272 | 68.70 212 | 55.35 286 | 72.51 304 | 54.75 167 | 90.12 278 | 64.51 194 | 52.28 315 | 81.47 293 |
|
v15 | | | 71.40 242 | 68.75 245 | 79.35 233 | 80.39 268 | 66.70 109 | 87.57 245 | 86.64 271 | 68.66 213 | 54.68 289 | 72.00 312 | 54.50 170 | 89.98 281 | 63.69 201 | 50.66 321 | 81.38 297 |
|
MVS | | | 84.66 54 | 82.86 70 | 90.06 1 | 90.93 110 | 74.56 5 | 87.91 232 | 95.54 14 | 68.55 214 | 72.35 146 | 94.71 53 | 59.78 106 | 98.90 7 | 81.29 71 | 94.69 20 | 96.74 7 |
|
test_part3 | | | | | | | | 94.96 32 | | 68.52 215 | | 97.23 2 | | 98.90 7 | 91.52 6 | | |
|
ESAPD | | | 89.08 8 | 89.53 8 | 87.72 21 | 96.29 7 | 68.16 63 | 94.96 32 | 94.26 42 | 68.52 215 | 90.78 4 | 97.23 2 | 77.03 4 | 98.90 7 | 91.52 6 | 95.18 9 | 96.11 20 |
|
EPP-MVSNet | | | 81.79 94 | 81.52 85 | 82.61 149 | 88.77 157 | 60.21 243 | 93.02 83 | 93.66 56 | 68.52 215 | 72.90 134 | 90.39 124 | 72.19 13 | 94.96 139 | 74.93 111 | 79.29 145 | 92.67 130 |
|
CSCG | | | 86.87 29 | 86.26 32 | 88.72 9 | 95.05 20 | 70.79 18 | 93.83 62 | 95.33 15 | 68.48 218 | 77.63 93 | 94.35 62 | 73.04 10 | 98.45 19 | 84.92 44 | 93.71 33 | 96.92 6 |
|
V14 | | | 71.29 244 | 68.61 247 | 79.31 234 | 80.34 272 | 66.65 111 | 87.39 247 | 86.61 274 | 68.41 219 | 54.49 291 | 71.91 313 | 54.25 175 | 89.96 282 | 63.50 202 | 50.62 322 | 81.33 299 |
|
V9 | | | 71.16 245 | 68.46 249 | 79.27 236 | 80.26 275 | 66.60 113 | 87.21 250 | 86.56 275 | 68.17 220 | 54.26 294 | 71.81 315 | 54.00 177 | 89.93 283 | 63.28 205 | 50.57 323 | 81.27 300 |
|
v11 | | | 71.05 248 | 68.32 252 | 79.23 237 | 80.34 272 | 66.57 116 | 87.01 253 | 86.55 276 | 68.11 221 | 54.40 292 | 71.66 317 | 52.94 191 | 89.91 284 | 62.71 213 | 51.12 319 | 81.21 301 |
|
v12 | | | 71.02 249 | 68.29 254 | 79.22 238 | 80.18 278 | 66.53 118 | 87.01 253 | 86.54 277 | 67.90 222 | 54.00 297 | 71.70 316 | 53.66 182 | 89.91 284 | 63.09 207 | 50.51 324 | 81.21 301 |
|
CP-MVSNet | | | 70.50 254 | 69.91 237 | 72.26 305 | 80.71 256 | 51.00 312 | 87.23 249 | 90.30 189 | 67.84 223 | 59.64 262 | 82.69 215 | 50.23 212 | 82.30 331 | 51.28 259 | 59.28 290 | 83.46 271 |
|
pmmvs5 | | | 73.35 226 | 71.52 228 | 78.86 247 | 78.64 299 | 60.61 237 | 91.08 166 | 86.90 268 | 67.69 224 | 63.32 246 | 83.64 206 | 44.33 257 | 90.53 268 | 62.04 218 | 66.02 243 | 85.46 251 |
|
pm-mvs1 | | | 72.89 230 | 71.09 231 | 78.26 258 | 79.10 293 | 57.62 273 | 90.80 171 | 89.30 224 | 67.66 225 | 62.91 250 | 81.78 233 | 49.11 224 | 92.95 213 | 60.29 227 | 58.89 295 | 84.22 262 |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 249 | 80.13 305 | | 67.65 226 | 72.79 135 | | 54.33 174 | | 59.83 229 | | 92.58 133 |
|
pmmvs4 | | | 73.92 223 | 71.81 226 | 80.25 210 | 79.17 290 | 65.24 145 | 87.43 246 | 87.26 267 | 67.64 227 | 63.46 245 | 83.91 205 | 48.96 225 | 91.53 261 | 62.94 210 | 65.49 248 | 83.96 263 |
|
v13 | | | 70.90 250 | 68.15 255 | 79.15 242 | 80.08 279 | 66.45 120 | 86.83 258 | 86.50 278 | 67.62 228 | 53.78 299 | 71.61 318 | 53.51 186 | 89.87 286 | 62.89 211 | 50.50 325 | 81.14 303 |
|
WR-MVS_H | | | 70.59 252 | 69.94 236 | 72.53 302 | 81.03 250 | 51.43 309 | 87.35 248 | 92.03 128 | 67.38 229 | 60.23 260 | 80.70 249 | 55.84 151 | 83.45 324 | 46.33 278 | 58.58 296 | 82.72 283 |
|
PS-CasMVS | | | 69.86 258 | 69.13 244 | 72.07 308 | 80.35 270 | 50.57 314 | 87.02 252 | 89.75 209 | 67.27 230 | 59.19 265 | 82.28 221 | 46.58 243 | 82.24 332 | 50.69 261 | 59.02 293 | 83.39 273 |
|
PEN-MVS | | | 69.46 264 | 68.56 248 | 72.17 307 | 79.27 288 | 49.71 319 | 86.90 256 | 89.24 227 | 67.24 231 | 59.08 266 | 82.51 216 | 47.23 238 | 83.54 323 | 48.42 270 | 57.12 298 | 83.25 275 |
|
cascas | | | 78.18 153 | 75.77 165 | 85.41 89 | 87.14 184 | 69.11 41 | 92.96 84 | 91.15 160 | 66.71 232 | 70.47 159 | 86.07 185 | 37.49 291 | 96.48 96 | 70.15 146 | 79.80 140 | 90.65 160 |
|
APD-MVS | | | 85.93 40 | 85.99 35 | 85.76 78 | 95.98 13 | 65.21 146 | 93.59 68 | 92.58 108 | 66.54 233 | 86.17 22 | 95.88 17 | 63.83 70 | 97.00 73 | 86.39 35 | 92.94 41 | 95.06 55 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
OpenMVS | | 70.45 11 | 78.54 148 | 75.92 163 | 86.41 59 | 85.93 203 | 71.68 11 | 92.74 91 | 92.51 111 | 66.49 234 | 64.56 234 | 91.96 104 | 43.88 258 | 98.10 28 | 54.61 247 | 90.65 70 | 89.44 173 |
|
DTE-MVSNet | | | 68.46 270 | 67.33 260 | 71.87 311 | 77.94 304 | 49.00 322 | 86.16 263 | 88.58 247 | 66.36 235 | 58.19 272 | 82.21 224 | 46.36 244 | 83.87 321 | 44.97 286 | 55.17 306 | 82.73 282 |
|
semantic-postprocess | | | | | 76.32 278 | 81.48 246 | 60.67 235 | | 85.99 286 | 66.17 236 | 59.50 263 | 78.88 270 | 45.51 250 | 83.65 322 | 62.58 215 | 61.93 275 | 84.63 261 |
|
TransMVSNet (Re) | | | 70.07 256 | 67.66 258 | 77.31 272 | 80.62 264 | 59.13 260 | 91.78 135 | 84.94 295 | 65.97 237 | 60.08 261 | 80.44 254 | 50.78 206 | 91.87 245 | 48.84 268 | 45.46 334 | 80.94 306 |
|
tfpn_ndepth | | | 76.45 187 | 75.22 180 | 80.14 211 | 90.97 109 | 58.92 261 | 90.11 188 | 93.24 80 | 65.96 238 | 67.37 213 | 90.52 122 | 66.67 37 | 92.29 237 | 37.71 316 | 74.44 184 | 89.21 174 |
|
MVSFormer | | | 83.75 69 | 82.88 69 | 86.37 60 | 89.24 147 | 71.18 15 | 89.07 211 | 90.69 173 | 65.80 239 | 87.13 18 | 94.34 63 | 64.99 60 | 92.67 225 | 72.83 119 | 91.80 55 | 95.27 46 |
|
test_djsdf | | | 73.76 225 | 72.56 219 | 77.39 270 | 77.00 309 | 53.93 299 | 89.07 211 | 90.69 173 | 65.80 239 | 63.92 240 | 82.03 229 | 43.14 261 | 92.67 225 | 72.83 119 | 68.53 229 | 85.57 249 |
|
API-MVS | | | 82.28 86 | 80.53 97 | 87.54 27 | 96.13 10 | 70.59 20 | 93.63 66 | 91.04 166 | 65.72 241 | 75.45 114 | 92.83 92 | 56.11 146 | 98.89 10 | 64.10 198 | 89.75 77 | 93.15 118 |
|
原ACMM1 | | | | | 84.42 116 | 93.21 52 | 64.27 174 | | 93.40 73 | 65.39 242 | 79.51 75 | 92.50 95 | 58.11 120 | 96.69 91 | 65.27 189 | 93.96 26 | 92.32 139 |
|
testgi | | | 64.48 292 | 62.87 289 | 69.31 315 | 71.24 326 | 40.62 339 | 85.49 265 | 79.92 322 | 65.36 243 | 54.18 295 | 83.49 209 | 23.74 336 | 84.55 315 | 41.60 298 | 60.79 287 | 82.77 281 |
|
QAPM | | | 79.95 120 | 77.39 143 | 87.64 23 | 89.63 138 | 71.41 12 | 93.30 75 | 93.70 54 | 65.34 244 | 67.39 212 | 91.75 109 | 47.83 233 | 98.96 5 | 57.71 239 | 89.81 75 | 92.54 134 |
|
HPM-MVS_fast | | | 80.25 113 | 79.55 111 | 82.33 162 | 91.55 94 | 59.95 248 | 91.32 155 | 89.16 230 | 65.23 245 | 74.71 119 | 93.07 84 | 47.81 234 | 95.74 116 | 74.87 114 | 88.23 83 | 91.31 155 |
|
conf0.01 | | | 74.95 209 | 73.61 202 | 78.96 245 | 89.65 132 | 56.94 281 | 87.72 236 | 93.45 64 | 65.14 246 | 65.68 221 | 89.99 132 | 65.09 53 | 91.67 249 | 35.16 323 | 70.61 211 | 88.27 188 |
|
conf0.002 | | | 74.95 209 | 73.61 202 | 78.96 245 | 89.65 132 | 56.94 281 | 87.72 236 | 93.45 64 | 65.14 246 | 65.68 221 | 89.99 132 | 65.09 53 | 91.67 249 | 35.16 323 | 70.61 211 | 88.27 188 |
|
thresconf0.02 | | | 74.92 212 | 73.61 202 | 78.85 248 | 89.65 132 | 56.94 281 | 87.72 236 | 93.45 64 | 65.14 246 | 65.68 221 | 89.99 132 | 65.09 53 | 91.67 249 | 35.16 323 | 70.61 211 | 87.94 195 |
|
tfpn_n400 | | | 74.92 212 | 73.61 202 | 78.85 248 | 89.65 132 | 56.94 281 | 87.72 236 | 93.45 64 | 65.14 246 | 65.68 221 | 89.99 132 | 65.09 53 | 91.67 249 | 35.16 323 | 70.61 211 | 87.94 195 |
|
tfpnconf | | | 74.92 212 | 73.61 202 | 78.85 248 | 89.65 132 | 56.94 281 | 87.72 236 | 93.45 64 | 65.14 246 | 65.68 221 | 89.99 132 | 65.09 53 | 91.67 249 | 35.16 323 | 70.61 211 | 87.94 195 |
|
tfpnview11 | | | 74.92 212 | 73.61 202 | 78.85 248 | 89.65 132 | 56.94 281 | 87.72 236 | 93.45 64 | 65.14 246 | 65.68 221 | 89.99 132 | 65.09 53 | 91.67 249 | 35.16 323 | 70.61 211 | 87.94 195 |
|
v748 | | | 70.55 253 | 67.97 257 | 78.27 257 | 75.75 316 | 58.78 263 | 86.29 262 | 89.25 226 | 65.12 252 | 56.66 282 | 77.17 283 | 45.05 254 | 92.95 213 | 58.13 237 | 58.33 297 | 83.10 279 |
|
tfpnnormal | | | 70.10 255 | 67.36 259 | 78.32 255 | 83.45 231 | 60.97 227 | 88.85 213 | 92.77 99 | 64.85 253 | 60.83 258 | 78.53 272 | 43.52 260 | 93.48 204 | 31.73 339 | 61.70 280 | 80.52 311 |
|
v52 | | | 69.80 259 | 67.01 263 | 78.15 260 | 71.84 325 | 60.10 245 | 82.02 288 | 87.39 262 | 64.48 254 | 57.80 275 | 75.97 291 | 41.47 268 | 92.90 218 | 63.00 208 | 59.13 292 | 81.45 295 |
|
V4 | | | 69.80 259 | 67.02 262 | 78.15 260 | 71.86 324 | 60.10 245 | 82.02 288 | 87.39 262 | 64.48 254 | 57.78 276 | 75.98 290 | 41.49 267 | 92.90 218 | 63.00 208 | 59.16 291 | 81.44 296 |
|
K. test v3 | | | 63.09 299 | 59.61 301 | 73.53 295 | 76.26 312 | 49.38 321 | 83.27 280 | 77.15 327 | 64.35 256 | 47.77 320 | 72.32 310 | 28.73 325 | 87.79 305 | 49.93 265 | 36.69 344 | 83.41 272 |
|
v7n | | | 71.31 243 | 68.65 246 | 79.28 235 | 76.40 311 | 60.77 231 | 86.71 259 | 89.45 219 | 64.17 257 | 58.77 270 | 78.24 273 | 44.59 256 | 93.54 202 | 57.76 238 | 61.75 278 | 83.52 269 |
|
tfpn1000 | | | 75.25 203 | 74.00 197 | 79.03 244 | 90.30 121 | 57.56 275 | 88.55 219 | 93.36 75 | 64.14 258 | 65.17 229 | 89.76 141 | 67.06 34 | 91.46 263 | 34.54 331 | 73.09 194 | 88.06 194 |
|
FMVSNet1 | | | 72.71 233 | 69.91 237 | 81.10 198 | 83.60 229 | 65.11 149 | 90.01 189 | 90.32 185 | 63.92 259 | 63.56 244 | 80.25 258 | 36.35 300 | 91.54 258 | 54.46 248 | 66.75 240 | 86.64 223 |
|
Anonymous20240521 | | | 69.68 261 | 68.15 255 | 74.28 291 | 82.04 243 | 49.91 317 | 85.92 264 | 90.52 178 | 63.87 260 | 57.61 278 | 81.33 239 | 41.82 264 | 89.57 290 | 46.86 276 | 63.36 269 | 83.37 274 |
|
XVG-OURS | | | 74.25 220 | 72.46 221 | 79.63 225 | 78.45 300 | 57.59 274 | 80.33 301 | 87.39 262 | 63.86 261 | 68.76 191 | 89.62 142 | 40.50 273 | 91.72 248 | 69.00 156 | 74.25 185 | 89.58 171 |
|
114514_t | | | 79.17 133 | 77.67 135 | 83.68 130 | 95.32 16 | 65.53 142 | 92.85 89 | 91.60 144 | 63.49 262 | 67.92 204 | 90.63 120 | 46.65 242 | 95.72 121 | 67.01 171 | 83.54 122 | 89.79 168 |
|
APD-MVS_3200maxsize | | | 81.64 95 | 81.32 87 | 82.59 150 | 92.36 68 | 58.74 264 | 91.39 149 | 91.01 167 | 63.35 263 | 79.72 73 | 94.62 54 | 51.82 199 | 96.14 102 | 79.71 76 | 87.93 86 | 92.89 127 |
|
test20.03 | | | 63.83 296 | 62.65 290 | 67.38 320 | 70.58 331 | 39.94 340 | 86.57 260 | 84.17 299 | 63.29 264 | 51.86 307 | 77.30 279 | 37.09 296 | 82.47 329 | 38.87 314 | 54.13 310 | 79.73 318 |
|
XVG-OURS-SEG-HR | | | 74.70 217 | 73.08 213 | 79.57 227 | 78.25 301 | 57.33 277 | 80.49 299 | 87.32 265 | 63.22 265 | 68.76 191 | 90.12 131 | 44.89 255 | 91.59 256 | 70.55 144 | 74.09 187 | 89.79 168 |
|
ACMM | | 69.62 13 | 74.34 218 | 72.73 216 | 79.17 240 | 84.25 222 | 57.87 269 | 90.36 183 | 89.93 205 | 63.17 266 | 65.64 227 | 86.04 187 | 37.79 289 | 94.10 180 | 65.89 183 | 71.52 206 | 85.55 250 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tpmvs | | | 72.88 231 | 69.76 239 | 82.22 169 | 90.98 108 | 67.05 89 | 78.22 317 | 88.30 251 | 63.10 267 | 64.35 239 | 74.98 296 | 55.09 159 | 94.27 171 | 43.25 289 | 69.57 221 | 85.34 254 |
|
SixPastTwentyTwo | | | 64.92 289 | 61.78 295 | 74.34 290 | 78.74 297 | 49.76 318 | 83.42 279 | 79.51 324 | 62.86 268 | 50.27 314 | 77.35 278 | 30.92 321 | 90.49 269 | 45.89 280 | 47.06 331 | 82.78 280 |
|
TAPA-MVS | | 70.22 12 | 74.94 211 | 73.53 208 | 79.17 240 | 90.40 119 | 52.07 306 | 89.19 208 | 89.61 216 | 62.69 269 | 70.07 168 | 92.67 94 | 48.89 226 | 94.32 168 | 38.26 315 | 79.97 139 | 91.12 156 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
abl_6 | | | 79.82 122 | 79.20 118 | 81.70 184 | 89.85 128 | 58.34 266 | 88.47 221 | 90.07 199 | 62.56 270 | 77.71 91 | 93.08 82 | 47.65 236 | 96.78 87 | 77.94 91 | 85.45 107 | 89.99 167 |
|
Anonymous202405211 | | | 77.96 158 | 75.33 178 | 85.87 71 | 93.73 42 | 64.52 160 | 94.85 35 | 85.36 292 | 62.52 271 | 76.11 107 | 90.18 127 | 29.43 324 | 97.29 57 | 68.51 160 | 77.24 168 | 95.81 29 |
|
pmmvs-eth3d | | | 65.53 288 | 62.32 292 | 75.19 283 | 69.39 334 | 59.59 252 | 82.80 285 | 83.43 306 | 62.52 271 | 51.30 311 | 72.49 305 | 32.86 309 | 87.16 308 | 55.32 246 | 50.73 320 | 78.83 324 |
|
AdaColmap | | | 78.94 137 | 77.00 150 | 84.76 105 | 96.34 6 | 65.86 134 | 92.66 97 | 87.97 258 | 62.18 273 | 70.56 158 | 92.37 100 | 43.53 259 | 97.35 53 | 64.50 195 | 82.86 124 | 91.05 157 |
|
无先验 | | | | | | | | 92.71 92 | 92.61 107 | 62.03 274 | | | | 97.01 71 | 66.63 173 | | 93.97 98 |
|
XVG-ACMP-BASELINE | | | 68.04 273 | 65.53 269 | 75.56 282 | 74.06 320 | 52.37 304 | 78.43 314 | 85.88 288 | 62.03 274 | 58.91 269 | 81.21 245 | 20.38 341 | 91.15 265 | 60.69 225 | 68.18 231 | 83.16 277 |
|
anonymousdsp | | | 71.14 246 | 69.37 241 | 76.45 277 | 72.95 321 | 54.71 296 | 84.19 271 | 88.88 240 | 61.92 276 | 62.15 255 | 79.77 263 | 38.14 284 | 91.44 264 | 68.90 158 | 67.45 237 | 83.21 276 |
|
tpm cat1 | | | 75.30 202 | 72.21 223 | 84.58 112 | 88.52 159 | 67.77 70 | 78.16 318 | 88.02 256 | 61.88 277 | 68.45 200 | 76.37 287 | 60.65 97 | 94.03 188 | 53.77 252 | 74.11 186 | 91.93 145 |
|
FMVSNet5 | | | 68.04 273 | 65.66 268 | 75.18 284 | 84.43 218 | 57.89 268 | 83.54 275 | 86.26 281 | 61.83 278 | 53.64 300 | 73.30 300 | 37.15 295 | 85.08 313 | 48.99 267 | 61.77 277 | 82.56 287 |
|
Anonymous20231206 | | | 67.53 278 | 65.78 266 | 72.79 301 | 74.95 317 | 47.59 326 | 88.23 225 | 87.32 265 | 61.75 279 | 58.07 273 | 77.29 280 | 37.79 289 | 87.29 307 | 42.91 291 | 63.71 267 | 83.48 270 |
|
PatchMatch-RL | | | 72.06 236 | 69.98 235 | 78.28 256 | 89.51 142 | 55.70 291 | 83.49 276 | 83.39 308 | 61.24 280 | 63.72 243 | 82.76 214 | 34.77 306 | 93.03 211 | 53.37 255 | 77.59 158 | 86.12 232 |
|
PLC | | 68.80 14 | 75.23 204 | 73.68 201 | 79.86 220 | 92.93 58 | 58.68 265 | 90.64 177 | 88.30 251 | 60.90 281 | 64.43 238 | 90.53 121 | 42.38 263 | 94.57 151 | 56.52 241 | 76.54 172 | 86.33 227 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMH | | 63.93 17 | 68.62 267 | 64.81 273 | 80.03 215 | 85.22 207 | 63.25 192 | 87.72 236 | 84.66 296 | 60.83 282 | 51.57 309 | 79.43 268 | 27.29 330 | 94.96 139 | 41.76 297 | 64.84 257 | 81.88 290 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EG-PatchMatch MVS | | | 68.55 268 | 65.41 270 | 77.96 263 | 78.69 298 | 62.93 200 | 89.86 198 | 89.17 229 | 60.55 283 | 50.27 314 | 77.73 277 | 22.60 338 | 94.06 184 | 47.18 275 | 72.65 198 | 76.88 331 |
|
VDDNet | | | 80.50 109 | 78.26 127 | 87.21 33 | 86.19 196 | 69.79 31 | 94.48 38 | 91.31 155 | 60.42 284 | 79.34 76 | 90.91 115 | 38.48 281 | 96.56 95 | 82.16 60 | 81.05 135 | 95.27 46 |
|
CPTT-MVS | | | 79.59 127 | 79.16 119 | 80.89 204 | 91.54 95 | 59.80 250 | 92.10 111 | 88.54 248 | 60.42 284 | 72.96 132 | 93.28 79 | 48.27 228 | 92.80 220 | 78.89 85 | 86.50 100 | 90.06 165 |
|
our_test_3 | | | 68.29 271 | 64.69 275 | 79.11 243 | 78.92 294 | 64.85 157 | 88.40 223 | 85.06 293 | 60.32 286 | 52.68 304 | 76.12 289 | 40.81 272 | 89.80 289 | 44.25 288 | 55.65 304 | 82.67 286 |
|
ITE_SJBPF | | | | | 70.43 313 | 74.44 318 | 47.06 328 | | 77.32 326 | 60.16 287 | 54.04 296 | 83.53 207 | 23.30 337 | 84.01 319 | 43.07 290 | 61.58 282 | 80.21 315 |
|
ppachtmachnet_test | | | 67.72 275 | 63.70 282 | 79.77 223 | 78.92 294 | 66.04 130 | 88.68 216 | 82.90 313 | 60.11 288 | 55.45 285 | 75.96 292 | 39.19 276 | 90.55 267 | 39.53 306 | 52.55 313 | 82.71 284 |
|
new-patchmatchnet | | | 59.30 309 | 56.48 309 | 67.79 318 | 65.86 338 | 44.19 331 | 82.47 286 | 81.77 314 | 59.94 289 | 43.65 335 | 66.20 329 | 27.67 328 | 81.68 334 | 39.34 307 | 41.40 339 | 77.50 330 |
|
test2356 | | | 64.16 294 | 63.28 285 | 66.81 322 | 69.37 335 | 39.86 342 | 87.76 235 | 86.02 285 | 59.83 290 | 53.54 301 | 73.23 301 | 34.94 305 | 80.67 336 | 39.66 305 | 65.20 250 | 79.89 316 |
|
新几何1 | | | | | 84.73 107 | 92.32 70 | 64.28 173 | | 91.46 150 | 59.56 291 | 79.77 72 | 92.90 89 | 56.95 134 | 96.57 94 | 63.40 203 | 92.91 42 | 93.34 111 |
|
1121 | | | 81.25 100 | 80.05 100 | 84.87 103 | 92.30 71 | 64.31 171 | 87.91 232 | 91.39 152 | 59.44 292 | 79.94 70 | 92.91 88 | 57.09 128 | 97.01 71 | 66.63 173 | 92.81 44 | 93.29 114 |
|
旧先验2 | | | | | | | | 92.00 119 | | 59.37 293 | 87.54 17 | | | 93.47 205 | 75.39 105 | | |
|
PM-MVS | | | 59.40 307 | 56.59 308 | 67.84 317 | 63.63 339 | 41.86 336 | 76.76 320 | 63.22 352 | 59.01 294 | 51.07 312 | 72.27 311 | 11.72 350 | 83.25 326 | 61.34 221 | 50.28 326 | 78.39 327 |
|
LTVRE_ROB | | 59.60 19 | 66.27 284 | 63.54 283 | 74.45 288 | 84.00 225 | 51.55 308 | 67.08 339 | 83.53 305 | 58.78 295 | 54.94 288 | 80.31 256 | 34.54 307 | 93.23 208 | 40.64 303 | 68.03 232 | 78.58 326 |
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 | | | | | 81.34 188 | 89.02 151 | 57.72 271 | | 89.84 207 | 58.65 296 | 85.32 32 | 94.09 67 | 57.03 130 | 93.28 207 | 69.34 153 | 90.56 72 | 93.03 122 |
|
ACMH+ | | 65.35 16 | 67.65 276 | 64.55 276 | 76.96 274 | 84.59 214 | 57.10 279 | 88.08 227 | 80.79 319 | 58.59 297 | 53.00 303 | 81.09 247 | 26.63 332 | 92.95 213 | 46.51 277 | 61.69 281 | 80.82 307 |
|
ADS-MVSNet2 | | | 66.90 281 | 63.44 284 | 77.26 273 | 88.06 170 | 60.70 234 | 68.01 336 | 75.56 333 | 57.57 298 | 64.48 235 | 69.87 323 | 38.68 277 | 84.10 316 | 40.87 301 | 67.89 234 | 86.97 217 |
|
ADS-MVSNet | | | 68.54 269 | 64.38 280 | 81.03 201 | 88.06 170 | 66.90 94 | 68.01 336 | 84.02 301 | 57.57 298 | 64.48 235 | 69.87 323 | 38.68 277 | 89.21 294 | 40.87 301 | 67.89 234 | 86.97 217 |
|
MDA-MVSNet-bldmvs | | | 61.54 303 | 57.70 304 | 73.05 298 | 79.53 285 | 57.00 280 | 83.08 283 | 81.23 316 | 57.57 298 | 34.91 344 | 72.45 307 | 32.79 310 | 86.26 312 | 35.81 321 | 41.95 338 | 75.89 333 |
|
testus | | | 59.36 308 | 57.51 305 | 64.90 324 | 66.72 337 | 37.56 345 | 84.98 267 | 81.09 317 | 57.46 301 | 47.72 321 | 72.76 302 | 11.43 352 | 78.78 343 | 36.56 317 | 58.91 294 | 78.36 328 |
|
UnsupCasMVSNet_bld | | | 61.60 302 | 57.71 303 | 73.29 297 | 68.73 336 | 51.64 307 | 78.61 313 | 89.05 236 | 57.20 302 | 46.11 323 | 61.96 337 | 28.70 326 | 88.60 297 | 50.08 264 | 38.90 342 | 79.63 319 |
|
MSDG | | | 69.54 263 | 65.73 267 | 80.96 202 | 85.11 209 | 63.71 184 | 84.19 271 | 83.28 309 | 56.95 303 | 54.50 290 | 84.03 203 | 31.50 317 | 96.03 107 | 42.87 293 | 69.13 225 | 83.14 278 |
|
F-COLMAP | | | 70.66 251 | 68.44 250 | 77.32 271 | 86.37 194 | 55.91 290 | 88.00 228 | 86.32 279 | 56.94 304 | 57.28 280 | 88.07 158 | 33.58 308 | 92.49 230 | 51.02 260 | 68.37 230 | 83.55 267 |
|
test222 | | | | | | 89.77 130 | 61.60 222 | 89.55 201 | 89.42 221 | 56.83 305 | 77.28 98 | 92.43 98 | 52.76 192 | | | 91.14 66 | 93.09 120 |
|
CNLPA | | | 74.31 219 | 72.30 222 | 80.32 208 | 91.49 96 | 61.66 221 | 90.85 169 | 80.72 320 | 56.67 306 | 63.85 242 | 90.64 118 | 46.75 240 | 90.84 266 | 53.79 251 | 75.99 175 | 88.47 184 |
|
OurMVSNet-221017-0 | | | 64.68 290 | 62.17 293 | 72.21 306 | 76.08 314 | 47.35 327 | 80.67 298 | 81.02 318 | 56.19 307 | 51.60 308 | 79.66 265 | 27.05 331 | 88.56 298 | 53.60 253 | 53.63 311 | 80.71 309 |
|
YYNet1 | | | 63.76 298 | 60.14 299 | 74.62 287 | 78.06 303 | 60.19 244 | 83.46 278 | 83.99 304 | 56.18 308 | 39.25 340 | 71.56 321 | 37.18 294 | 83.34 325 | 42.90 292 | 48.70 329 | 80.32 313 |
|
MDA-MVSNet_test_wron | | | 63.78 297 | 60.16 298 | 74.64 286 | 78.15 302 | 60.41 238 | 83.49 276 | 84.03 300 | 56.17 309 | 39.17 341 | 71.59 320 | 37.22 293 | 83.24 327 | 42.87 293 | 48.73 328 | 80.26 314 |
|
OpenMVS_ROB | | 61.12 18 | 66.39 283 | 62.92 288 | 76.80 276 | 76.51 310 | 57.77 270 | 89.22 206 | 83.41 307 | 55.48 310 | 53.86 298 | 77.84 276 | 26.28 333 | 93.95 192 | 34.90 330 | 68.76 227 | 78.68 325 |
|
MIMVSNet1 | | | 60.16 306 | 57.33 306 | 68.67 316 | 69.71 333 | 44.13 332 | 78.92 312 | 84.21 298 | 55.05 311 | 44.63 331 | 71.85 314 | 23.91 335 | 81.54 335 | 32.63 337 | 55.03 307 | 80.35 312 |
|
CVMVSNet | | | 74.04 221 | 74.27 191 | 73.33 296 | 85.33 205 | 43.94 333 | 89.53 203 | 88.39 249 | 54.33 312 | 70.37 164 | 90.13 129 | 49.17 222 | 84.05 317 | 61.83 220 | 79.36 143 | 91.99 144 |
|
Anonymous20240529 | | | 76.84 181 | 74.15 193 | 84.88 102 | 91.02 107 | 64.95 156 | 93.84 61 | 91.09 163 | 53.57 313 | 73.00 131 | 87.42 168 | 35.91 302 | 97.32 55 | 69.14 155 | 72.41 201 | 92.36 137 |
|
pmmvs6 | | | 67.57 277 | 64.76 274 | 76.00 281 | 72.82 323 | 53.37 301 | 88.71 215 | 86.78 269 | 53.19 314 | 57.58 279 | 78.03 275 | 35.33 304 | 92.41 232 | 55.56 245 | 54.88 308 | 82.21 288 |
|
testpf | | | 57.17 310 | 56.93 307 | 57.88 331 | 79.13 292 | 42.40 334 | 34.23 356 | 85.97 287 | 52.64 315 | 47.66 322 | 66.50 327 | 36.33 301 | 79.65 339 | 53.60 253 | 56.31 303 | 51.60 350 |
|
TinyColmap | | | 60.32 304 | 56.42 310 | 72.00 309 | 78.78 296 | 53.18 302 | 78.36 316 | 75.64 331 | 52.30 316 | 41.59 339 | 75.82 294 | 14.76 348 | 88.35 300 | 35.84 320 | 54.71 309 | 74.46 335 |
|
test_0402 | | | 64.54 291 | 61.09 296 | 74.92 285 | 84.10 224 | 60.75 232 | 87.95 229 | 79.71 323 | 52.03 317 | 52.41 305 | 77.20 281 | 32.21 314 | 91.64 255 | 23.14 348 | 61.03 284 | 72.36 338 |
|
Anonymous20231211 | | | 73.08 227 | 70.39 234 | 81.13 196 | 90.62 116 | 63.33 191 | 91.40 148 | 90.06 201 | 51.84 318 | 64.46 237 | 80.67 251 | 36.49 299 | 94.07 183 | 63.83 199 | 64.17 263 | 85.98 239 |
|
test1235678 | | | 55.73 314 | 52.74 314 | 64.68 325 | 60.16 346 | 35.56 347 | 81.65 292 | 81.46 315 | 51.27 319 | 38.93 342 | 62.82 336 | 17.44 344 | 78.58 344 | 30.87 341 | 50.09 327 | 79.89 316 |
|
AllTest | | | 61.66 301 | 58.06 302 | 72.46 303 | 79.57 283 | 51.42 310 | 80.17 304 | 68.61 346 | 51.25 320 | 45.88 324 | 81.23 241 | 19.86 342 | 86.58 310 | 38.98 308 | 57.01 300 | 79.39 320 |
|
TestCases | | | | | 72.46 303 | 79.57 283 | 51.42 310 | | 68.61 346 | 51.25 320 | 45.88 324 | 81.23 241 | 19.86 342 | 86.58 310 | 38.98 308 | 57.01 300 | 79.39 320 |
|
PatchT | | | 69.11 266 | 65.37 271 | 80.32 208 | 82.07 242 | 63.68 186 | 67.96 338 | 87.62 261 | 50.86 322 | 69.37 181 | 65.18 332 | 57.09 128 | 88.53 299 | 41.59 299 | 66.60 241 | 88.74 179 |
|
DP-MVS | | | 69.90 257 | 66.48 264 | 80.14 211 | 95.36 15 | 62.93 200 | 89.56 200 | 76.11 328 | 50.27 323 | 57.69 277 | 85.23 193 | 39.68 275 | 95.73 117 | 33.35 333 | 71.05 210 | 81.78 291 |
|
gg-mvs-nofinetune | | | 77.18 173 | 74.31 190 | 85.80 75 | 91.42 101 | 68.36 57 | 71.78 326 | 94.72 29 | 49.61 324 | 77.12 100 | 45.92 346 | 77.41 3 | 93.98 190 | 67.62 166 | 93.16 40 | 95.05 56 |
|
JIA-IIPM | | | 66.06 285 | 62.45 291 | 76.88 275 | 81.42 249 | 54.45 298 | 57.49 350 | 88.67 244 | 49.36 325 | 63.86 241 | 46.86 345 | 56.06 147 | 90.25 271 | 49.53 266 | 68.83 226 | 85.95 240 |
|
N_pmnet | | | 50.55 318 | 49.11 320 | 54.88 335 | 77.17 308 | 4.02 365 | 84.36 270 | 2.00 365 | 48.59 326 | 45.86 326 | 68.82 325 | 32.22 313 | 82.80 328 | 31.58 340 | 51.38 318 | 77.81 329 |
|
ANet_high | | | 40.27 326 | 35.20 327 | 55.47 334 | 34.74 360 | 34.47 348 | 63.84 343 | 71.56 342 | 48.42 327 | 18.80 352 | 41.08 350 | 9.52 355 | 64.45 355 | 20.18 351 | 8.66 359 | 67.49 345 |
|
COLMAP_ROB | | 57.96 20 | 62.98 300 | 59.65 300 | 72.98 299 | 81.44 248 | 53.00 303 | 83.75 273 | 75.53 334 | 48.34 328 | 48.81 318 | 81.40 238 | 24.14 334 | 90.30 270 | 32.95 335 | 60.52 288 | 75.65 334 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Patchmtry | | | 67.53 278 | 63.93 281 | 78.34 254 | 82.12 241 | 64.38 168 | 68.72 333 | 84.00 302 | 48.23 329 | 59.24 264 | 72.41 308 | 57.82 123 | 89.27 293 | 46.10 279 | 56.68 302 | 81.36 298 |
|
LS3D | | | 69.17 265 | 66.40 265 | 77.50 267 | 91.92 81 | 56.12 289 | 85.12 266 | 80.37 321 | 46.96 330 | 56.50 283 | 87.51 167 | 37.25 292 | 93.71 199 | 32.52 338 | 79.40 142 | 82.68 285 |
|
RPSCF | | | 64.24 293 | 61.98 294 | 71.01 312 | 76.10 313 | 45.00 330 | 75.83 322 | 75.94 330 | 46.94 331 | 58.96 268 | 84.59 200 | 31.40 318 | 82.00 333 | 47.76 273 | 60.33 289 | 86.04 238 |
|
RPMNet | | | 69.58 262 | 65.21 272 | 82.70 145 | 83.15 234 | 67.96 67 | 70.25 329 | 86.15 283 | 46.83 332 | 69.97 171 | 65.10 333 | 56.48 143 | 89.48 291 | 35.79 322 | 73.13 192 | 90.64 161 |
|
1111 | | | 56.66 313 | 54.98 312 | 61.69 327 | 61.99 343 | 31.38 349 | 79.81 309 | 83.17 310 | 45.66 333 | 41.94 337 | 65.44 330 | 41.50 265 | 79.56 340 | 27.64 343 | 47.68 330 | 74.14 336 |
|
.test1245 | | | 46.52 322 | 49.68 319 | 37.02 344 | 61.99 343 | 31.38 349 | 79.81 309 | 83.17 310 | 45.66 333 | 41.94 337 | 65.44 330 | 41.50 265 | 79.56 340 | 27.64 343 | 0.01 361 | 0.13 362 |
|
CMPMVS | | 48.56 21 | 66.77 282 | 64.41 279 | 73.84 293 | 70.65 330 | 50.31 315 | 77.79 319 | 85.73 290 | 45.54 335 | 44.76 330 | 82.14 225 | 35.40 303 | 90.14 277 | 63.18 206 | 74.54 183 | 81.07 305 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
EU-MVSNet | | | 64.01 295 | 63.01 287 | 67.02 321 | 74.40 319 | 38.86 344 | 83.27 280 | 86.19 282 | 45.11 336 | 54.27 293 | 81.15 246 | 36.91 298 | 80.01 337 | 48.79 269 | 57.02 299 | 82.19 289 |
|
no-one | | | 44.13 324 | 38.39 325 | 61.34 328 | 45.91 356 | 41.94 335 | 61.67 345 | 75.07 336 | 45.05 337 | 20.07 350 | 40.68 352 | 11.58 351 | 79.82 338 | 30.18 342 | 15.30 352 | 62.26 347 |
|
TDRefinement | | | 55.28 316 | 51.58 317 | 66.39 323 | 59.53 347 | 46.15 329 | 76.23 321 | 72.80 339 | 44.60 338 | 42.49 336 | 76.28 288 | 15.29 346 | 82.39 330 | 33.20 334 | 43.75 336 | 70.62 341 |
|
Patchmatch-test | | | 65.86 286 | 60.94 297 | 80.62 205 | 83.75 226 | 58.83 262 | 58.91 349 | 75.26 335 | 44.50 339 | 50.95 313 | 77.09 285 | 58.81 116 | 87.90 303 | 35.13 329 | 64.03 264 | 95.12 54 |
|
test12356 | | | 47.51 320 | 44.82 322 | 55.56 333 | 52.53 349 | 21.09 360 | 71.45 328 | 76.03 329 | 44.14 340 | 30.69 345 | 58.18 340 | 9.01 356 | 76.14 345 | 26.95 345 | 34.43 347 | 69.46 343 |
|
LP | | | 56.71 312 | 51.64 316 | 71.91 310 | 80.08 279 | 60.33 240 | 61.72 344 | 75.61 332 | 43.87 341 | 43.76 334 | 60.30 339 | 30.46 322 | 84.05 317 | 22.94 349 | 46.06 333 | 71.34 339 |
|
LF4IMVS | | | 54.01 317 | 52.12 315 | 59.69 329 | 62.41 342 | 39.91 341 | 68.59 334 | 68.28 348 | 42.96 342 | 44.55 332 | 75.18 295 | 14.09 349 | 68.39 350 | 41.36 300 | 51.68 317 | 70.78 340 |
|
testmv | | | 46.98 321 | 43.53 323 | 57.35 332 | 47.75 354 | 30.41 352 | 74.99 324 | 77.69 325 | 42.84 343 | 28.03 346 | 53.36 342 | 8.18 357 | 71.18 348 | 24.36 347 | 34.55 345 | 70.46 342 |
|
DSMNet-mixed | | | 56.78 311 | 54.44 313 | 63.79 326 | 63.21 340 | 29.44 353 | 64.43 342 | 64.10 351 | 42.12 344 | 51.32 310 | 71.60 319 | 31.76 315 | 75.04 346 | 36.23 319 | 65.20 250 | 86.87 221 |
|
pmmvs3 | | | 55.51 315 | 51.50 318 | 67.53 319 | 57.90 348 | 50.93 313 | 80.37 300 | 73.66 338 | 40.63 345 | 44.15 333 | 64.75 334 | 16.30 345 | 78.97 342 | 44.77 287 | 40.98 341 | 72.69 337 |
|
new_pmnet | | | 49.31 319 | 46.44 321 | 57.93 330 | 62.84 341 | 40.74 338 | 68.47 335 | 62.96 353 | 36.48 346 | 35.09 343 | 57.81 341 | 14.97 347 | 72.18 347 | 32.86 336 | 46.44 332 | 60.88 348 |
|
MVS-HIRNet | | | 60.25 305 | 55.55 311 | 74.35 289 | 84.37 219 | 56.57 287 | 71.64 327 | 74.11 337 | 34.44 347 | 45.54 328 | 42.24 349 | 31.11 320 | 89.81 287 | 40.36 304 | 76.10 174 | 76.67 332 |
|
DeepMVS_CX | | | | | 34.71 345 | 51.45 350 | 24.73 359 | | 28.48 364 | 31.46 348 | 17.49 353 | 52.75 343 | 5.80 360 | 42.60 361 | 18.18 353 | 19.42 350 | 36.81 354 |
|
FPMVS | | | 45.64 323 | 43.10 324 | 53.23 337 | 51.42 351 | 36.46 346 | 64.97 341 | 71.91 341 | 29.13 349 | 27.53 347 | 61.55 338 | 9.83 354 | 65.01 354 | 16.00 354 | 55.58 305 | 58.22 349 |
|
PMMVS2 | | | 37.93 327 | 33.61 328 | 50.92 338 | 46.31 355 | 24.76 358 | 60.55 348 | 50.05 356 | 28.94 350 | 20.93 349 | 47.59 344 | 4.41 363 | 65.13 353 | 25.14 346 | 18.55 351 | 62.87 346 |
|
LCM-MVSNet | | | 40.54 325 | 35.79 326 | 54.76 336 | 36.92 359 | 30.81 351 | 51.41 351 | 69.02 345 | 22.07 351 | 24.63 348 | 45.37 347 | 4.56 362 | 65.81 352 | 33.67 332 | 34.50 346 | 67.67 344 |
|
PNet_i23d | | | 32.77 329 | 29.98 331 | 41.11 342 | 48.05 352 | 29.17 354 | 65.82 340 | 50.02 357 | 21.42 352 | 14.74 355 | 37.19 353 | 1.11 366 | 55.11 357 | 19.75 352 | 11.77 354 | 39.06 352 |
|
PMVS | | 26.43 22 | 31.84 330 | 28.16 332 | 42.89 341 | 25.87 363 | 27.58 356 | 50.92 352 | 49.78 358 | 21.37 353 | 14.17 356 | 40.81 351 | 2.01 364 | 66.62 351 | 9.61 357 | 38.88 343 | 34.49 355 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Gipuma | | | 34.91 328 | 31.44 330 | 45.30 340 | 70.99 328 | 39.64 343 | 19.85 359 | 72.56 340 | 20.10 354 | 16.16 354 | 21.47 357 | 5.08 361 | 71.16 349 | 13.07 355 | 43.70 337 | 25.08 356 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
E-PMN | | | 24.61 333 | 24.00 334 | 26.45 347 | 43.74 357 | 18.44 362 | 60.86 346 | 39.66 359 | 15.11 355 | 9.53 358 | 22.10 356 | 6.52 359 | 46.94 359 | 8.31 358 | 10.14 355 | 13.98 358 |
|
EMVS | | | 23.76 335 | 23.20 336 | 25.46 348 | 41.52 358 | 16.90 363 | 60.56 347 | 38.79 362 | 14.62 356 | 8.99 359 | 20.24 360 | 7.35 358 | 45.82 360 | 7.25 359 | 9.46 357 | 13.64 359 |
|
wuykxyi23d | | | 29.03 332 | 23.09 337 | 46.84 339 | 31.67 362 | 28.82 355 | 43.46 354 | 57.72 355 | 14.39 357 | 7.52 360 | 20.84 358 | 0.64 367 | 60.29 356 | 21.57 350 | 10.04 356 | 51.40 351 |
|
MVE | | 24.84 23 | 24.35 334 | 19.77 338 | 38.09 343 | 34.56 361 | 26.92 357 | 26.57 357 | 38.87 361 | 11.73 358 | 11.37 357 | 27.44 354 | 1.37 365 | 50.42 358 | 11.41 356 | 14.60 353 | 36.93 353 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 11.30 338 | 10.95 339 | 12.33 350 | 48.05 352 | 19.89 361 | 25.89 358 | 1.92 366 | 3.58 359 | 3.12 361 | 1.37 362 | 0.64 367 | 15.77 363 | 6.23 360 | 7.77 360 | 1.35 360 |
|
tmp_tt | | | 22.26 336 | 23.75 335 | 17.80 349 | 5.23 364 | 12.06 364 | 35.26 355 | 39.48 360 | 2.82 360 | 18.94 351 | 44.20 348 | 22.23 339 | 24.64 362 | 36.30 318 | 9.31 358 | 16.69 357 |
|
testmvs | | | 7.23 340 | 9.62 341 | 0.06 352 | 0.04 365 | 0.02 367 | 84.98 267 | 0.02 367 | 0.03 361 | 0.18 362 | 1.21 363 | 0.01 370 | 0.02 364 | 0.14 361 | 0.01 361 | 0.13 362 |
|
test123 | | | 6.92 341 | 9.21 342 | 0.08 351 | 0.03 366 | 0.05 366 | 81.65 292 | 0.01 368 | 0.02 362 | 0.14 363 | 0.85 364 | 0.03 369 | 0.02 364 | 0.12 362 | 0.00 363 | 0.16 361 |
|
cdsmvs_eth3d_5k | | | 19.86 337 | 26.47 333 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 93.45 64 | 0.00 363 | 0.00 364 | 95.27 33 | 49.56 217 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
pcd_1.5k_mvsjas | | | 4.46 342 | 5.95 343 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 53.55 183 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
pcd1.5k->3k | | | 31.17 331 | 31.85 329 | 29.12 346 | 81.48 246 | 0.00 368 | 0.00 360 | 91.79 137 | 0.00 363 | 0.00 364 | 0.00 365 | 41.05 271 | 0.00 366 | 0.00 363 | 72.34 202 | 87.36 211 |
|
sosnet-low-res | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
sosnet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
uncertanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
Regformer | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
ab-mvs-re | | | 7.91 339 | 10.55 340 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 94.95 43 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
uanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 363 | 0.00 364 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.68 68 |
|
test_part2 | | | | | | 96.29 7 | 68.16 63 | | | | 90.78 4 | | | | | | |
|
test_part1 | | | | | | | | | 94.26 42 | | | | 77.03 4 | | | 95.18 9 | 96.11 20 |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 122 | | | | 94.68 68 |
|
sam_mvs | | | | | | | | | | | | | 54.91 166 | | | | |
|
ambc | | | | | 69.61 314 | 61.38 345 | 41.35 337 | 49.07 353 | 85.86 289 | | 50.18 316 | 66.40 328 | 10.16 353 | 88.14 302 | 45.73 281 | 44.20 335 | 79.32 322 |
|
MTGPA | | | | | | | | | 92.23 117 | | | | | | | | |
|
test_post1 | | | | | | | | 78.95 311 | | | | 20.70 359 | 53.05 189 | 91.50 262 | 60.43 226 | | |
|
test_post | | | | | | | | | | | | 23.01 355 | 56.49 142 | 92.67 225 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 326 | 57.62 125 | 90.25 271 | | | |
|
GG-mvs-BLEND | | | | | 86.53 53 | 91.91 82 | 69.67 35 | 75.02 323 | 94.75 28 | | 78.67 85 | 90.85 116 | 77.91 2 | 94.56 152 | 72.25 125 | 93.74 32 | 95.36 40 |
|
MTMP | | | | | | | | 93.77 63 | 32.52 363 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 12 | 94.96 11 | 95.29 44 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 34 | 94.75 19 | 95.33 41 |
|
agg_prior | | | | | | 94.16 34 | 66.97 91 | | 93.31 77 | | 84.49 39 | | | 96.75 89 | | | |
|
test_prior4 | | | | | | | 67.18 86 | 93.92 56 | | | | | | | | | |
|
test_prior | | | | | 86.42 57 | 94.71 23 | 67.35 80 | | 93.10 89 | | | | | 96.84 85 | | | 95.05 56 |
|
新几何2 | | | | | | | | 91.41 147 | | | | | | | | | |
|
旧先验1 | | | | | | 91.94 79 | 60.74 233 | | 91.50 148 | | | 94.36 58 | 65.23 51 | | | 91.84 54 | 94.55 72 |
|
原ACMM2 | | | | | | | | 92.01 117 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 96.09 104 | 61.26 222 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 44 | | | | |
|
test12 | | | | | 87.09 38 | 94.60 25 | 68.86 46 | | 92.91 95 | | 82.67 52 | | 65.44 50 | 97.55 44 | | 93.69 34 | 94.84 64 |
|
plane_prior7 | | | | | | 86.94 186 | 61.51 223 | | | | | | | | | | |
|
plane_prior6 | | | | | | 87.23 182 | 62.32 211 | | | | | | 50.66 207 | | | | |
|
plane_prior5 | | | | | | | | | 91.31 155 | | | | | 95.55 127 | 76.74 97 | 78.53 151 | 88.39 185 |
|
plane_prior4 | | | | | | | | | | | | 89.14 144 | | | | | |
|
plane_prior1 | | | | | | 87.15 183 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 369 | | | | | | | | |
|
nn | | | | | | | | | 0.00 369 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 350 | | | | | | | | |
|
lessismore_v0 | | | | | 73.72 294 | 72.93 322 | 47.83 325 | | 61.72 354 | | 45.86 326 | 73.76 299 | 28.63 327 | 89.81 287 | 47.75 274 | 31.37 348 | 83.53 268 |
|
test11 | | | | | | | | | 93.01 91 | | | | | | | | |
|
door | | | | | | | | | 66.57 349 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 187 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 93 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 121 | | | 95.61 123 | | | 88.63 180 |
|
HQP3-MVS | | | | | | | | | 91.70 141 | | | | | | | 78.90 147 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 203 | | | | |
|
NP-MVS | | | | | | 87.41 181 | 63.04 198 | | | | | 90.30 125 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 204 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 219 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 176 | | | | |
|