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 28 | 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 56 | 73.88 6 | 97.01 2 | 94.40 43 | 88.32 2 | 85.71 29 | 94.91 48 | 74.11 8 | 98.91 6 | 87.26 28 | 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 |
DeepPCF-MVS | | 81.17 1 | 89.72 5 | 91.38 3 | 84.72 114 | 93.00 59 | 58.16 274 | 96.72 3 | 94.41 42 | 86.50 5 | 90.25 7 | 97.83 1 | 75.46 6 | 98.67 12 | 92.78 2 | 95.49 8 | 97.32 1 |
|
lupinMVS | | | 87.74 18 | 87.77 17 | 87.63 27 | 89.24 152 | 71.18 16 | 96.57 4 | 92.90 103 | 82.70 16 | 87.13 20 | 95.27 33 | 64.99 63 | 95.80 117 | 89.34 13 | 91.80 55 | 95.93 28 |
|
CANet | | | 89.61 6 | 89.99 6 | 88.46 13 | 94.39 29 | 69.71 34 | 96.53 5 | 93.78 54 | 86.89 4 | 89.68 8 | 95.78 18 | 65.94 47 | 99.10 2 | 92.99 1 | 93.91 27 | 96.58 11 |
|
CNVR-MVS | | | 90.32 3 | 90.89 4 | 88.61 11 | 96.76 4 | 70.65 20 | 96.47 6 | 94.83 26 | 84.83 9 | 89.07 11 | 96.80 4 | 70.86 18 | 99.06 3 | 92.64 4 | 95.71 6 | 96.12 22 |
|
NCCC | | | 89.07 8 | 89.46 8 | 87.91 17 | 96.60 5 | 69.05 44 | 96.38 7 | 94.64 35 | 84.42 10 | 86.74 23 | 96.20 11 | 66.56 42 | 98.76 11 | 89.03 18 | 94.56 20 | 95.92 29 |
|
PVSNet_Blended | | | 86.73 33 | 86.86 29 | 86.31 65 | 93.76 40 | 67.53 80 | 96.33 8 | 93.61 62 | 82.34 18 | 81.00 65 | 93.08 83 | 63.19 82 | 97.29 58 | 87.08 30 | 91.38 62 | 94.13 91 |
|
SteuartSystems-ACMMP | | | 86.82 32 | 86.90 28 | 86.58 52 | 90.42 122 | 66.38 125 | 96.09 9 | 93.87 52 | 77.73 60 | 84.01 48 | 95.66 22 | 63.39 79 | 97.94 29 | 87.40 27 | 93.55 35 | 95.42 39 |
Skip Steuart: Steuart Systems R&D Blog. |
MVS_0304 | | | 88.39 10 | 88.35 13 | 88.50 12 | 93.01 58 | 70.11 25 | 95.90 10 | 92.20 129 | 86.27 6 | 88.70 13 | 95.92 16 | 56.76 141 | 99.02 4 | 92.68 3 | 93.76 30 | 96.37 15 |
|
0601test | | | 84.28 60 | 83.16 68 | 87.64 23 | 94.52 27 | 69.24 40 | 95.78 11 | 95.09 21 | 69.19 210 | 81.09 62 | 92.88 92 | 57.00 137 | 97.44 48 | 81.11 73 | 81.76 134 | 96.23 19 |
|
Anonymous20240521 | | | 84.28 60 | 83.16 68 | 87.64 23 | 94.52 27 | 69.24 40 | 95.78 11 | 95.09 21 | 69.19 210 | 81.09 62 | 92.88 92 | 57.00 137 | 97.44 48 | 81.11 73 | 81.76 134 | 96.23 19 |
|
PS-MVSNAJ | | | 88.14 11 | 87.61 19 | 89.71 4 | 92.06 80 | 76.72 1 | 95.75 13 | 93.26 84 | 83.86 11 | 89.55 9 | 96.06 14 | 53.55 189 | 97.89 32 | 91.10 6 | 93.31 37 | 94.54 76 |
|
xiu_mvs_v2_base | | | 87.92 16 | 87.38 24 | 89.55 7 | 91.41 106 | 76.43 2 | 95.74 14 | 93.12 94 | 83.53 13 | 89.55 9 | 95.95 15 | 53.45 194 | 97.68 35 | 91.07 7 | 92.62 44 | 94.54 76 |
|
DeepC-MVS_fast | | 79.48 2 | 87.95 15 | 88.00 15 | 87.79 20 | 95.86 15 | 68.32 60 | 95.74 14 | 94.11 49 | 83.82 12 | 83.49 50 | 96.19 12 | 64.53 67 | 98.44 18 | 83.42 55 | 94.88 14 | 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 |
VNet | | | 86.20 38 | 85.65 43 | 87.84 19 | 93.92 39 | 69.99 28 | 95.73 16 | 95.94 12 | 78.43 53 | 86.00 26 | 93.07 85 | 58.22 123 | 97.00 74 | 85.22 42 | 84.33 118 | 96.52 13 |
|
jason | | | 86.40 34 | 86.17 34 | 87.11 39 | 86.16 205 | 70.54 22 | 95.71 17 | 92.19 131 | 82.00 24 | 84.58 39 | 94.34 63 | 61.86 91 | 95.53 134 | 87.76 24 | 90.89 67 | 95.27 47 |
jason: jason. |
alignmvs | | | 87.28 22 | 86.97 27 | 88.24 15 | 91.30 107 | 71.14 18 | 95.61 18 | 93.56 64 | 79.30 38 | 87.07 22 | 95.25 35 | 68.43 23 | 96.93 84 | 87.87 23 | 84.33 118 | 96.65 8 |
|
IB-MVS | | 77.80 4 | 82.18 92 | 80.46 103 | 87.35 33 | 89.14 154 | 70.28 24 | 95.59 19 | 95.17 17 | 78.85 47 | 70.19 174 | 85.82 195 | 70.66 20 | 97.67 36 | 72.19 131 | 66.52 248 | 94.09 94 |
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 |
CLD-MVS | | | 82.73 83 | 82.35 81 | 83.86 130 | 87.90 182 | 67.65 76 | 95.45 20 | 92.18 132 | 85.06 8 | 72.58 145 | 92.27 104 | 52.46 202 | 95.78 118 | 84.18 48 | 79.06 150 | 88.16 198 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
VDD-MVS | | | 83.06 80 | 81.81 86 | 86.81 44 | 90.86 116 | 67.70 74 | 95.40 21 | 91.50 155 | 75.46 90 | 81.78 58 | 92.34 103 | 40.09 279 | 97.13 68 | 86.85 33 | 82.04 132 | 95.60 36 |
|
PHI-MVS | | | 86.83 31 | 86.85 30 | 86.78 46 | 93.47 49 | 65.55 146 | 95.39 22 | 95.10 20 | 71.77 172 | 85.69 30 | 96.52 6 | 62.07 89 | 98.77 10 | 86.06 37 | 95.60 7 | 96.03 25 |
|
EPNet | | | 87.84 17 | 88.38 11 | 86.23 67 | 93.30 50 | 66.05 133 | 95.26 23 | 94.84 25 | 87.09 3 | 88.06 15 | 94.53 55 | 66.79 39 | 97.34 55 | 83.89 52 | 91.68 57 | 95.29 45 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
HPM-MVS++ | | | 89.37 7 | 89.95 7 | 87.64 23 | 95.10 20 | 68.23 65 | 95.24 24 | 94.49 39 | 82.43 17 | 88.90 12 | 96.35 8 | 71.89 17 | 98.63 13 | 88.76 20 | 96.40 2 | 96.06 23 |
|
WTY-MVS | | | 86.32 35 | 85.81 38 | 87.85 18 | 92.82 64 | 69.37 39 | 95.20 25 | 95.25 16 | 82.71 15 | 81.91 57 | 94.73 52 | 67.93 30 | 97.63 41 | 79.55 81 | 82.25 130 | 96.54 12 |
|
3Dnovator+ | | 73.60 7 | 82.10 96 | 80.60 101 | 86.60 50 | 90.89 115 | 66.80 106 | 95.20 25 | 93.44 76 | 74.05 116 | 67.42 218 | 92.49 98 | 49.46 224 | 97.65 40 | 70.80 144 | 91.68 57 | 95.33 42 |
|
TSAR-MVS + GP. | | | 87.96 14 | 88.37 12 | 86.70 47 | 93.51 48 | 65.32 149 | 95.15 27 | 93.84 53 | 78.17 55 | 85.93 27 | 94.80 51 | 75.80 5 | 98.21 23 | 89.38 12 | 88.78 81 | 96.59 10 |
|
DP-MVS Recon | | | 82.73 83 | 81.65 89 | 85.98 71 | 97.31 3 | 67.06 91 | 95.15 27 | 91.99 136 | 69.08 213 | 76.50 111 | 93.89 72 | 54.48 178 | 98.20 24 | 70.76 145 | 85.66 107 | 92.69 133 |
|
test_prior3 | | | 87.38 21 | 87.70 18 | 86.42 59 | 94.71 24 | 67.35 83 | 95.10 29 | 93.10 95 | 75.40 93 | 85.25 35 | 95.61 24 | 67.94 28 | 96.84 86 | 87.47 25 | 94.77 15 | 95.05 57 |
|
test_prior2 | | | | | | | | 95.10 29 | | 75.40 93 | 85.25 35 | 95.61 24 | 67.94 28 | | 87.47 25 | 94.77 15 | |
|
MSLP-MVS++ | | | 86.27 36 | 85.91 37 | 87.35 33 | 92.01 81 | 68.97 47 | 95.04 31 | 92.70 108 | 79.04 46 | 81.50 60 | 96.50 7 | 58.98 120 | 96.78 88 | 83.49 54 | 93.93 26 | 96.29 17 |
|
LFMVS | | | 84.34 59 | 82.73 76 | 89.18 8 | 94.76 22 | 73.25 8 | 94.99 32 | 91.89 140 | 71.90 163 | 82.16 56 | 93.49 78 | 47.98 238 | 97.05 69 | 82.55 59 | 84.82 111 | 97.25 2 |
|
MG-MVS | | | 87.11 25 | 86.27 31 | 89.62 5 | 97.79 1 | 76.27 3 | 94.96 33 | 94.49 39 | 78.74 51 | 83.87 49 | 92.94 88 | 64.34 69 | 96.94 82 | 75.19 109 | 94.09 24 | 95.66 33 |
|
Anonymous202405211 | | | 77.96 165 | 75.33 185 | 85.87 74 | 93.73 44 | 64.52 166 | 94.85 34 | 85.36 299 | 62.52 277 | 76.11 112 | 90.18 130 | 29.43 329 | 97.29 58 | 68.51 164 | 77.24 172 | 95.81 31 |
|
APDe-MVS | | | 87.54 19 | 87.84 16 | 86.65 48 | 96.07 12 | 66.30 128 | 94.84 35 | 93.78 54 | 69.35 207 | 88.39 14 | 96.34 9 | 67.74 33 | 97.66 39 | 90.62 9 | 93.44 36 | 96.01 26 |
|
CP-MVS | | | 83.71 73 | 83.40 64 | 84.65 115 | 93.14 56 | 63.84 184 | 94.59 36 | 92.28 122 | 71.03 185 | 77.41 101 | 94.92 47 | 55.21 161 | 96.19 103 | 81.32 71 | 90.70 69 | 93.91 104 |
|
VDDNet | | | 80.50 115 | 78.26 134 | 87.21 35 | 86.19 204 | 69.79 32 | 94.48 37 | 91.31 162 | 60.42 290 | 79.34 80 | 90.91 118 | 38.48 286 | 96.56 96 | 82.16 60 | 81.05 139 | 95.27 47 |
|
MAR-MVS | | | 84.18 63 | 83.43 62 | 86.44 58 | 96.25 10 | 65.93 137 | 94.28 38 | 94.27 45 | 74.41 104 | 79.16 83 | 95.61 24 | 53.99 184 | 98.88 9 | 69.62 154 | 93.26 38 | 94.50 79 |
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 |
ESAPD | | | 88.77 9 | 89.21 9 | 87.45 30 | 96.26 9 | 67.56 78 | 94.17 39 | 94.15 48 | 68.77 217 | 90.74 6 | 97.27 2 | 76.09 4 | 98.49 16 | 90.58 10 | 94.91 11 | 96.30 16 |
|
TEST9 | | | | | | 94.18 32 | 67.28 85 | 94.16 40 | 93.51 66 | 71.75 173 | 85.52 31 | 95.33 29 | 68.01 27 | 97.27 61 | | | |
|
train_agg | | | 87.21 24 | 87.42 23 | 86.60 50 | 94.18 32 | 67.28 85 | 94.16 40 | 93.51 66 | 71.87 165 | 85.52 31 | 95.33 29 | 68.19 25 | 97.27 61 | 89.09 15 | 94.90 12 | 95.25 50 |
|
test_8 | | | | | | 94.19 31 | 67.19 87 | 94.15 42 | 93.42 77 | 71.87 165 | 85.38 33 | 95.35 28 | 68.19 25 | 96.95 81 | | | |
|
agg_prior3 | | | 86.93 28 | 87.08 26 | 86.48 56 | 94.21 30 | 66.95 96 | 94.14 43 | 93.40 78 | 71.80 170 | 84.86 37 | 95.13 39 | 66.16 44 | 97.25 63 | 89.09 15 | 94.90 12 | 95.25 50 |
|
Fast-Effi-MVS+ | | | 81.14 107 | 80.01 107 | 84.51 120 | 90.24 127 | 65.86 138 | 94.12 44 | 89.15 237 | 73.81 124 | 75.37 121 | 88.26 158 | 57.26 132 | 94.53 162 | 66.97 176 | 84.92 110 | 93.15 122 |
|
#test# | | | 84.98 51 | 84.74 49 | 85.72 82 | 93.75 42 | 65.01 159 | 94.09 45 | 93.19 89 | 73.55 130 | 79.22 81 | 94.93 45 | 59.04 118 | 97.67 36 | 82.66 58 | 92.21 49 | 94.49 80 |
|
agg_prior1 | | | 87.02 26 | 87.26 25 | 86.28 66 | 94.16 36 | 66.97 94 | 94.08 46 | 93.31 82 | 71.85 167 | 84.49 41 | 95.39 27 | 68.91 21 | 96.75 90 | 88.84 19 | 94.32 22 | 95.13 54 |
|
HQP-NCC | | | | | | 87.54 185 | | 94.06 47 | | 79.80 33 | 74.18 127 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 185 | | 94.06 47 | | 79.80 33 | 74.18 127 | | | | | | |
|
PVSNet_BlendedMVS | | | 83.38 75 | 83.43 62 | 83.22 143 | 93.76 40 | 67.53 80 | 94.06 47 | 93.61 62 | 79.13 43 | 81.00 65 | 85.14 201 | 63.19 82 | 97.29 58 | 87.08 30 | 73.91 194 | 84.83 264 |
|
HQP-MVS | | | 81.14 107 | 80.64 100 | 82.64 154 | 87.54 185 | 63.66 193 | 94.06 47 | 91.70 148 | 79.80 33 | 74.18 127 | 90.30 128 | 51.63 209 | 95.61 128 | 77.63 96 | 78.90 151 | 88.63 186 |
|
MVS_111021_HR | | | 86.19 39 | 85.80 39 | 87.37 32 | 93.17 55 | 69.79 32 | 93.99 51 | 93.76 57 | 79.08 45 | 78.88 86 | 93.99 70 | 62.25 88 | 98.15 26 | 85.93 38 | 91.15 65 | 94.15 90 |
|
mvs-test1 | | | 78.74 150 | 77.95 139 | 81.14 202 | 83.22 240 | 57.13 285 | 93.96 52 | 87.78 266 | 75.42 91 | 72.68 142 | 90.80 120 | 45.08 258 | 94.54 161 | 75.08 111 | 77.49 167 | 91.74 151 |
|
Regformer-1 | | | 87.24 23 | 87.60 20 | 86.15 69 | 95.14 18 | 65.83 140 | 93.95 53 | 95.12 18 | 82.11 21 | 84.25 43 | 95.73 20 | 67.88 31 | 98.35 21 | 85.60 39 | 88.64 82 | 94.26 83 |
|
Regformer-2 | | | 87.00 27 | 87.43 22 | 85.71 84 | 95.14 18 | 64.73 164 | 93.95 53 | 94.95 23 | 81.69 26 | 84.03 47 | 95.73 20 | 67.35 36 | 98.19 25 | 85.40 41 | 88.64 82 | 94.20 85 |
|
VPNet | | | 78.82 146 | 77.53 146 | 82.70 151 | 84.52 223 | 66.44 124 | 93.93 55 | 92.23 124 | 80.46 30 | 72.60 144 | 88.38 155 | 49.18 227 | 93.13 215 | 72.47 127 | 63.97 272 | 88.55 188 |
|
test_prior4 | | | | | | | 67.18 89 | 93.92 56 | | | | | | | | | |
|
SD-MVS | | | 87.49 20 | 87.49 21 | 87.50 29 | 93.60 45 | 68.82 50 | 93.90 57 | 92.63 113 | 76.86 72 | 87.90 17 | 95.76 19 | 66.17 43 | 97.63 41 | 89.06 17 | 91.48 61 | 96.05 24 |
|
CDPH-MVS | | | 85.71 44 | 85.46 44 | 86.46 57 | 94.75 23 | 67.19 87 | 93.89 58 | 92.83 105 | 70.90 187 | 83.09 52 | 95.28 32 | 63.62 76 | 97.36 53 | 80.63 75 | 94.18 23 | 94.84 65 |
|
SMA-MVS | | | 88.14 11 | 88.29 14 | 87.67 22 | 93.21 53 | 68.72 51 | 93.85 59 | 94.03 50 | 74.18 115 | 91.74 2 | 96.67 5 | 65.61 52 | 98.42 20 | 89.24 14 | 96.08 3 | 95.88 30 |
|
plane_prior | | | | | | | 62.42 215 | 93.85 59 | | 79.38 37 | | | | | | 78.80 153 | |
|
Anonymous20240529 | | | 76.84 188 | 74.15 200 | 84.88 107 | 91.02 110 | 64.95 162 | 93.84 61 | 91.09 170 | 53.57 319 | 73.00 137 | 87.42 174 | 35.91 307 | 97.32 56 | 69.14 159 | 72.41 207 | 92.36 141 |
|
CSCG | | | 86.87 29 | 86.26 32 | 88.72 9 | 95.05 21 | 70.79 19 | 93.83 62 | 95.33 15 | 68.48 225 | 77.63 98 | 94.35 62 | 73.04 9 | 98.45 17 | 84.92 44 | 93.71 32 | 96.92 6 |
|
MTMP | | | | | | | | 93.77 63 | 32.52 370 | | | | | | | | |
|
PVSNet_Blended_VisFu | | | 83.97 66 | 83.50 59 | 85.39 94 | 90.02 129 | 66.59 118 | 93.77 63 | 91.73 145 | 77.43 66 | 77.08 107 | 89.81 142 | 63.77 75 | 96.97 79 | 79.67 80 | 88.21 85 | 92.60 136 |
|
TR-MVS | | | 78.77 149 | 77.37 151 | 82.95 146 | 90.49 121 | 60.88 235 | 93.67 65 | 90.07 205 | 70.08 201 | 74.51 126 | 91.37 114 | 45.69 254 | 95.70 126 | 60.12 234 | 80.32 142 | 92.29 144 |
|
API-MVS | | | 82.28 91 | 80.53 102 | 87.54 28 | 96.13 11 | 70.59 21 | 93.63 66 | 91.04 173 | 65.72 248 | 75.45 120 | 92.83 94 | 56.11 152 | 98.89 8 | 64.10 203 | 89.75 77 | 93.15 122 |
|
BH-w/o | | | 80.49 116 | 79.30 122 | 84.05 128 | 90.83 117 | 64.36 176 | 93.60 67 | 89.42 227 | 74.35 109 | 69.09 192 | 90.15 131 | 55.23 160 | 95.61 128 | 64.61 198 | 86.43 102 | 92.17 147 |
|
APD-MVS | | | 85.93 41 | 85.99 35 | 85.76 81 | 95.98 14 | 65.21 152 | 93.59 68 | 92.58 115 | 66.54 240 | 86.17 24 | 95.88 17 | 63.83 73 | 97.00 74 | 86.39 35 | 92.94 40 | 95.06 56 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
BH-RMVSNet | | | 79.46 137 | 77.65 143 | 84.89 106 | 91.68 94 | 65.66 143 | 93.55 69 | 88.09 262 | 72.93 141 | 73.37 135 | 91.12 116 | 46.20 253 | 96.12 106 | 56.28 249 | 85.61 108 | 92.91 130 |
|
thisisatest0515 | | | 83.41 74 | 82.49 78 | 86.16 68 | 89.46 147 | 68.26 63 | 93.54 70 | 94.70 32 | 74.31 110 | 75.75 114 | 90.92 117 | 72.62 12 | 96.52 97 | 69.64 152 | 81.50 136 | 93.71 108 |
|
canonicalmvs | | | 86.85 30 | 86.25 33 | 88.66 10 | 91.80 91 | 71.92 10 | 93.54 70 | 91.71 147 | 80.26 31 | 87.55 18 | 95.25 35 | 63.59 78 | 96.93 84 | 88.18 21 | 84.34 117 | 97.11 3 |
|
HFP-MVS | | | 84.73 53 | 84.40 52 | 85.72 82 | 93.75 42 | 65.01 159 | 93.50 72 | 93.19 89 | 72.19 157 | 79.22 81 | 94.93 45 | 59.04 118 | 97.67 36 | 81.55 66 | 92.21 49 | 94.49 80 |
|
ACMMPR | | | 84.37 57 | 84.06 55 | 85.28 97 | 93.56 46 | 64.37 175 | 93.50 72 | 93.15 92 | 72.19 157 | 78.85 88 | 94.86 49 | 56.69 145 | 97.45 47 | 81.55 66 | 92.20 51 | 94.02 99 |
|
Vis-MVSNet | | | 80.92 111 | 79.98 109 | 83.74 132 | 88.48 166 | 61.80 226 | 93.44 74 | 88.26 261 | 73.96 120 | 77.73 95 | 91.76 110 | 49.94 220 | 94.76 151 | 65.84 188 | 90.37 73 | 94.65 71 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
region2R | | | 84.36 58 | 84.03 56 | 85.36 95 | 93.54 47 | 64.31 177 | 93.43 75 | 92.95 101 | 72.16 160 | 78.86 87 | 94.84 50 | 56.97 139 | 97.53 45 | 81.38 70 | 92.11 53 | 94.24 84 |
|
QAPM | | | 79.95 126 | 77.39 150 | 87.64 23 | 89.63 142 | 71.41 12 | 93.30 76 | 93.70 59 | 65.34 251 | 67.39 220 | 91.75 111 | 47.83 239 | 98.96 5 | 57.71 245 | 89.81 75 | 92.54 138 |
|
MP-MVS | | | 85.02 50 | 84.97 48 | 85.17 101 | 92.60 69 | 64.27 180 | 93.24 77 | 92.27 123 | 73.13 137 | 79.63 78 | 94.43 56 | 61.90 90 | 97.17 65 | 85.00 43 | 92.56 45 | 94.06 97 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
nrg030 | | | 80.93 110 | 79.86 110 | 84.13 126 | 83.69 235 | 68.83 49 | 93.23 78 | 91.20 165 | 75.55 89 | 75.06 123 | 88.22 161 | 63.04 85 | 94.74 153 | 81.88 63 | 66.88 245 | 88.82 184 |
|
VPA-MVSNet | | | 79.03 141 | 78.00 138 | 82.11 183 | 85.95 208 | 64.48 169 | 93.22 79 | 94.66 34 | 75.05 99 | 74.04 132 | 84.95 203 | 52.17 204 | 93.52 209 | 74.90 116 | 67.04 244 | 88.32 193 |
|
HQP_MVS | | | 80.34 118 | 79.75 112 | 82.12 180 | 86.94 194 | 62.42 215 | 93.13 80 | 91.31 162 | 78.81 49 | 72.53 146 | 89.14 148 | 50.66 213 | 95.55 132 | 76.74 100 | 78.53 155 | 88.39 191 |
|
plane_prior2 | | | | | | | | 93.13 80 | | 78.81 49 | | | | | | | |
|
HSP-MVS | | | 90.38 2 | 91.89 1 | 85.84 76 | 92.83 62 | 64.03 183 | 93.06 82 | 94.52 37 | 82.19 19 | 93.65 1 | 96.15 13 | 85.89 1 | 97.19 64 | 91.02 8 | 97.75 1 | 96.29 17 |
|
thres200 | | | 79.66 131 | 78.33 132 | 83.66 138 | 92.54 70 | 65.82 141 | 93.06 82 | 96.31 9 | 74.90 101 | 73.30 136 | 88.66 150 | 59.67 111 | 95.61 128 | 47.84 278 | 78.67 154 | 89.56 178 |
|
GST-MVS | | | 84.63 56 | 84.29 54 | 85.66 85 | 92.82 64 | 65.27 150 | 93.04 84 | 93.13 93 | 73.20 135 | 78.89 85 | 94.18 67 | 59.41 114 | 97.85 33 | 81.45 68 | 92.48 48 | 93.86 106 |
|
EPP-MVSNet | | | 81.79 99 | 81.52 90 | 82.61 155 | 88.77 162 | 60.21 250 | 93.02 85 | 93.66 61 | 68.52 223 | 72.90 140 | 90.39 127 | 72.19 15 | 94.96 144 | 74.93 114 | 79.29 149 | 92.67 134 |
|
cascas | | | 78.18 160 | 75.77 172 | 85.41 93 | 87.14 192 | 69.11 43 | 92.96 86 | 91.15 167 | 66.71 239 | 70.47 166 | 86.07 192 | 37.49 296 | 96.48 98 | 70.15 149 | 79.80 144 | 90.65 165 |
|
Regformer-3 | | | 85.80 43 | 85.92 36 | 85.46 89 | 94.17 34 | 65.09 158 | 92.95 87 | 95.11 19 | 81.13 27 | 81.68 59 | 95.04 40 | 65.82 49 | 98.32 22 | 83.02 56 | 84.36 115 | 92.97 128 |
|
Regformer-4 | | | 85.45 46 | 85.69 42 | 84.73 112 | 94.17 34 | 63.23 199 | 92.95 87 | 94.83 26 | 80.66 29 | 81.29 61 | 95.04 40 | 65.12 55 | 98.08 28 | 82.74 57 | 84.36 115 | 92.88 132 |
|
XVS | | | 83.87 69 | 83.47 60 | 85.05 103 | 93.22 51 | 63.78 186 | 92.92 89 | 92.66 111 | 73.99 117 | 78.18 92 | 94.31 65 | 55.25 158 | 97.41 50 | 79.16 84 | 91.58 59 | 93.95 101 |
|
X-MVStestdata | | | 76.86 186 | 74.13 201 | 85.05 103 | 93.22 51 | 63.78 186 | 92.92 89 | 92.66 111 | 73.99 117 | 78.18 92 | 10.19 367 | 55.25 158 | 97.41 50 | 79.16 84 | 91.58 59 | 93.95 101 |
|
114514_t | | | 79.17 140 | 77.67 142 | 83.68 136 | 95.32 17 | 65.53 147 | 92.85 91 | 91.60 151 | 63.49 268 | 67.92 211 | 90.63 123 | 46.65 248 | 95.72 125 | 67.01 175 | 83.54 124 | 89.79 174 |
|
mPP-MVS | | | 82.96 82 | 82.44 79 | 84.52 119 | 92.83 62 | 62.92 208 | 92.76 92 | 91.85 142 | 71.52 179 | 75.61 118 | 94.24 66 | 53.48 193 | 96.99 77 | 78.97 87 | 90.73 68 | 93.64 111 |
|
OpenMVS | | 70.45 11 | 78.54 155 | 75.92 170 | 86.41 61 | 85.93 211 | 71.68 11 | 92.74 93 | 92.51 118 | 66.49 241 | 64.56 242 | 91.96 106 | 43.88 264 | 98.10 27 | 54.61 253 | 90.65 70 | 89.44 179 |
|
æ— å…ˆéªŒ | | | | | | | | 92.71 94 | 92.61 114 | 62.03 280 | | | | 97.01 72 | 66.63 177 | | 93.97 100 |
|
test-LLR | | | 80.10 122 | 79.56 115 | 81.72 189 | 86.93 196 | 61.17 232 | 92.70 95 | 91.54 152 | 71.51 180 | 75.62 116 | 86.94 184 | 53.83 185 | 92.38 239 | 72.21 129 | 84.76 113 | 91.60 152 |
|
TESTMET0.1,1 | | | 82.41 89 | 81.98 84 | 83.72 135 | 88.08 175 | 63.74 188 | 92.70 95 | 93.77 56 | 79.30 38 | 77.61 99 | 87.57 172 | 58.19 124 | 94.08 188 | 73.91 118 | 86.68 97 | 93.33 117 |
|
test-mter | | | 79.96 125 | 79.38 121 | 81.72 189 | 86.93 196 | 61.17 232 | 92.70 95 | 91.54 152 | 73.85 122 | 75.62 116 | 86.94 184 | 49.84 222 | 92.38 239 | 72.21 129 | 84.76 113 | 91.60 152 |
|
BH-untuned | | | 78.68 151 | 77.08 153 | 83.48 141 | 89.84 133 | 63.74 188 | 92.70 95 | 88.59 253 | 71.57 177 | 66.83 226 | 88.65 151 | 51.75 207 | 95.39 135 | 59.03 239 | 84.77 112 | 91.32 159 |
|
AdaColmap | | | 78.94 144 | 77.00 157 | 84.76 110 | 96.34 6 | 65.86 138 | 92.66 99 | 87.97 265 | 62.18 279 | 70.56 165 | 92.37 102 | 43.53 265 | 97.35 54 | 64.50 200 | 82.86 126 | 91.05 162 |
|
WR-MVS | | | 76.76 190 | 75.74 173 | 79.82 228 | 84.60 221 | 62.27 219 | 92.60 100 | 92.51 118 | 76.06 84 | 67.87 214 | 85.34 199 | 56.76 141 | 90.24 279 | 62.20 223 | 63.69 274 | 86.94 226 |
|
3Dnovator | | 73.91 6 | 82.69 86 | 80.82 97 | 88.31 14 | 89.57 143 | 71.26 14 | 92.60 100 | 94.39 44 | 78.84 48 | 67.89 213 | 92.48 99 | 48.42 233 | 98.52 15 | 68.80 163 | 94.40 21 | 95.15 53 |
|
xiu_mvs_v1_base_debu | | | 82.16 93 | 81.12 94 | 85.26 98 | 86.42 199 | 68.72 51 | 92.59 102 | 90.44 185 | 73.12 138 | 84.20 44 | 94.36 58 | 38.04 290 | 95.73 121 | 84.12 49 | 86.81 94 | 91.33 156 |
|
xiu_mvs_v1_base | | | 82.16 93 | 81.12 94 | 85.26 98 | 86.42 199 | 68.72 51 | 92.59 102 | 90.44 185 | 73.12 138 | 84.20 44 | 94.36 58 | 38.04 290 | 95.73 121 | 84.12 49 | 86.81 94 | 91.33 156 |
|
xiu_mvs_v1_base_debi | | | 82.16 93 | 81.12 94 | 85.26 98 | 86.42 199 | 68.72 51 | 92.59 102 | 90.44 185 | 73.12 138 | 84.20 44 | 94.36 58 | 38.04 290 | 95.73 121 | 84.12 49 | 86.81 94 | 91.33 156 |
|
PVSNet | | 73.49 8 | 80.05 123 | 78.63 129 | 84.31 123 | 90.92 114 | 64.97 161 | 92.47 105 | 91.05 172 | 79.18 41 | 72.43 150 | 90.51 126 | 37.05 302 | 94.06 190 | 68.06 165 | 86.00 105 | 93.90 105 |
|
PAPM | | | 85.89 42 | 85.46 44 | 87.18 36 | 88.20 174 | 72.42 9 | 92.41 106 | 92.77 106 | 82.11 21 | 80.34 71 | 93.07 85 | 68.27 24 | 95.02 142 | 78.39 91 | 93.59 34 | 94.09 94 |
|
1112_ss | | | 80.56 114 | 79.83 111 | 82.77 149 | 88.65 163 | 60.78 237 | 92.29 107 | 88.36 257 | 72.58 146 | 72.46 149 | 94.95 43 | 65.09 56 | 93.42 212 | 66.38 181 | 77.71 160 | 94.10 93 |
|
UniMVSNet_NR-MVSNet | | | 78.15 161 | 77.55 145 | 79.98 223 | 84.46 225 | 60.26 248 | 92.25 108 | 93.20 88 | 77.50 64 | 68.88 196 | 86.61 186 | 66.10 45 | 92.13 246 | 66.38 181 | 62.55 275 | 87.54 207 |
|
sss | | | 82.71 85 | 82.38 80 | 83.73 134 | 89.25 150 | 59.58 260 | 92.24 109 | 94.89 24 | 77.96 57 | 79.86 75 | 92.38 101 | 56.70 144 | 97.05 69 | 77.26 99 | 80.86 141 | 94.55 74 |
|
DeepC-MVS | | 77.85 3 | 85.52 45 | 85.24 46 | 86.37 62 | 88.80 161 | 66.64 115 | 92.15 110 | 93.68 60 | 81.07 28 | 76.91 108 | 93.64 75 | 62.59 87 | 98.44 18 | 85.50 40 | 92.84 42 | 94.03 98 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
UGNet | | | 79.87 127 | 78.68 128 | 83.45 142 | 89.96 130 | 61.51 229 | 92.13 111 | 90.79 177 | 76.83 73 | 78.85 88 | 86.33 190 | 38.16 288 | 96.17 104 | 67.93 167 | 87.17 92 | 92.67 134 |
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 |
ACMMP | | | 81.49 102 | 80.67 99 | 83.93 129 | 91.71 93 | 62.90 209 | 92.13 111 | 92.22 128 | 71.79 171 | 71.68 160 | 93.49 78 | 50.32 215 | 96.96 80 | 78.47 89 | 84.22 122 | 91.93 149 |
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 |
CPTT-MVS | | | 79.59 133 | 79.16 125 | 80.89 211 | 91.54 98 | 59.80 257 | 92.10 113 | 88.54 255 | 60.42 290 | 72.96 138 | 93.28 80 | 48.27 234 | 92.80 226 | 78.89 88 | 86.50 101 | 90.06 171 |
|
Test_1112_low_res | | | 79.56 134 | 78.60 130 | 82.43 161 | 88.24 173 | 60.39 246 | 92.09 114 | 87.99 264 | 72.10 161 | 71.84 156 | 87.42 174 | 64.62 66 | 93.04 216 | 65.80 189 | 77.30 170 | 93.85 107 |
|
CDS-MVSNet | | | 81.43 103 | 80.74 98 | 83.52 139 | 86.26 203 | 64.45 170 | 92.09 114 | 90.65 183 | 75.83 87 | 73.95 133 | 89.81 142 | 63.97 71 | 92.91 223 | 71.27 138 | 82.82 127 | 93.20 121 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
CHOSEN 1792x2688 | | | 84.98 51 | 83.45 61 | 89.57 6 | 89.94 131 | 75.14 4 | 92.07 116 | 92.32 121 | 81.87 25 | 75.68 115 | 88.27 157 | 60.18 107 | 98.60 14 | 80.46 77 | 90.27 74 | 94.96 62 |
|
tfpn200view9 | | | 78.79 148 | 77.43 148 | 82.88 147 | 92.21 78 | 64.49 167 | 92.05 117 | 96.28 10 | 73.48 131 | 71.75 158 | 88.26 158 | 60.07 108 | 95.32 137 | 45.16 287 | 77.58 163 | 88.83 182 |
|
thres400 | | | 78.68 151 | 77.43 148 | 82.43 161 | 92.21 78 | 64.49 167 | 92.05 117 | 96.28 10 | 73.48 131 | 71.75 158 | 88.26 158 | 60.07 108 | 95.32 137 | 45.16 287 | 77.58 163 | 87.48 209 |
|
原ACMM2 | | | | | | | | 92.01 119 | | | | | | | | | |
|
XXY-MVS | | | 77.94 166 | 76.44 164 | 82.43 161 | 82.60 246 | 64.44 171 | 92.01 119 | 91.83 143 | 73.59 129 | 70.00 177 | 85.82 195 | 54.43 179 | 94.76 151 | 69.63 153 | 68.02 239 | 88.10 199 |
|
旧先验2 | | | | | | | | 92.00 121 | | 59.37 299 | 87.54 19 | | | 93.47 211 | 75.39 108 | | |
|
IS-MVSNet | | | 80.14 121 | 79.41 119 | 82.33 169 | 87.91 181 | 60.08 254 | 91.97 122 | 88.27 260 | 72.90 142 | 71.44 162 | 91.73 112 | 61.44 93 | 93.66 207 | 62.47 222 | 86.53 100 | 93.24 119 |
|
v1141 | | | 77.28 177 | 75.57 178 | 82.42 164 | 80.63 270 | 66.73 108 | 91.96 123 | 90.42 188 | 74.41 104 | 69.46 185 | 82.12 233 | 55.09 165 | 94.40 169 | 70.99 141 | 65.05 258 | 86.12 238 |
|
divwei89l23v2f112 | | | 77.28 177 | 75.57 178 | 82.42 164 | 80.62 271 | 66.72 110 | 91.96 123 | 90.42 188 | 74.41 104 | 69.46 185 | 82.12 233 | 55.11 164 | 94.40 169 | 71.00 139 | 65.04 259 | 86.12 238 |
|
v1 | | | 77.29 176 | 75.57 178 | 82.42 164 | 80.61 274 | 66.73 108 | 91.96 123 | 90.42 188 | 74.41 104 | 69.46 185 | 82.12 233 | 55.14 163 | 94.40 169 | 71.00 139 | 65.04 259 | 86.13 237 |
|
EPNet_dtu | | | 78.80 147 | 79.26 123 | 77.43 276 | 88.06 177 | 49.71 325 | 91.96 123 | 91.95 139 | 77.67 61 | 76.56 110 | 91.28 115 | 58.51 122 | 90.20 281 | 56.37 248 | 80.95 140 | 92.39 140 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v1neww | | | 77.39 172 | 75.71 174 | 82.44 158 | 80.69 264 | 66.83 98 | 91.94 127 | 90.18 200 | 74.19 112 | 69.60 182 | 82.51 223 | 54.99 169 | 94.44 164 | 71.68 134 | 65.60 251 | 86.05 241 |
|
v7new | | | 77.39 172 | 75.71 174 | 82.44 158 | 80.69 264 | 66.83 98 | 91.94 127 | 90.18 200 | 74.19 112 | 69.60 182 | 82.51 223 | 54.99 169 | 94.44 164 | 71.68 134 | 65.60 251 | 86.05 241 |
|
v6 | | | 77.39 172 | 75.71 174 | 82.44 158 | 80.67 266 | 66.82 100 | 91.94 127 | 90.18 200 | 74.19 112 | 69.60 182 | 82.50 226 | 55.00 168 | 94.44 164 | 71.68 134 | 65.60 251 | 86.05 241 |
|
MVSTER | | | 82.47 88 | 82.05 82 | 83.74 132 | 92.68 68 | 69.01 45 | 91.90 130 | 93.21 86 | 79.83 32 | 72.14 153 | 85.71 198 | 74.72 7 | 94.72 154 | 75.72 105 | 72.49 205 | 87.50 208 |
|
CANet_DTU | | | 84.09 65 | 83.52 58 | 85.81 77 | 90.30 125 | 66.82 100 | 91.87 131 | 89.01 243 | 85.27 7 | 86.09 25 | 93.74 74 | 47.71 241 | 96.98 78 | 77.90 95 | 89.78 76 | 93.65 110 |
|
FMVSNet3 | | | 77.73 168 | 76.04 168 | 82.80 148 | 91.20 109 | 68.99 46 | 91.87 131 | 91.99 136 | 73.35 134 | 67.04 223 | 83.19 219 | 56.62 146 | 92.14 245 | 59.80 236 | 69.34 228 | 87.28 220 |
|
v2v482 | | | 77.42 171 | 75.65 177 | 82.73 150 | 80.38 276 | 67.13 90 | 91.85 133 | 90.23 197 | 75.09 98 | 69.37 188 | 83.39 217 | 53.79 187 | 94.44 164 | 71.77 132 | 65.00 262 | 86.63 232 |
|
PAPR | | | 85.15 49 | 84.47 50 | 87.18 36 | 96.02 13 | 68.29 61 | 91.85 133 | 93.00 100 | 76.59 77 | 79.03 84 | 95.00 42 | 61.59 92 | 97.61 43 | 78.16 92 | 89.00 80 | 95.63 35 |
|
ACMMP_Plus | | | 86.05 40 | 85.80 39 | 86.80 45 | 91.58 95 | 67.53 80 | 91.79 135 | 93.49 68 | 74.93 100 | 84.61 38 | 95.30 31 | 59.42 113 | 97.92 30 | 86.13 36 | 94.92 10 | 94.94 63 |
|
Baseline_NR-MVSNet | | | 73.99 229 | 72.83 222 | 77.48 275 | 80.78 260 | 59.29 265 | 91.79 135 | 84.55 304 | 68.85 215 | 68.99 194 | 80.70 255 | 56.16 150 | 92.04 249 | 62.67 220 | 60.98 290 | 81.11 309 |
|
TransMVSNet (Re) | | | 70.07 263 | 67.66 264 | 77.31 279 | 80.62 271 | 59.13 267 | 91.78 137 | 84.94 302 | 65.97 244 | 60.08 269 | 80.44 260 | 50.78 212 | 91.87 251 | 48.84 274 | 45.46 339 | 80.94 311 |
|
EI-MVSNet-Vis-set | | | 83.77 71 | 83.67 57 | 84.06 127 | 92.79 67 | 63.56 195 | 91.76 138 | 94.81 28 | 79.65 36 | 77.87 94 | 94.09 68 | 63.35 80 | 97.90 31 | 79.35 82 | 79.36 147 | 90.74 164 |
|
UniMVSNet (Re) | | | 77.58 169 | 76.78 159 | 79.98 223 | 84.11 231 | 60.80 236 | 91.76 138 | 93.17 91 | 76.56 80 | 69.93 180 | 84.78 205 | 63.32 81 | 92.36 241 | 64.89 196 | 62.51 277 | 86.78 228 |
|
MS-PatchMatch | | | 77.90 167 | 76.50 163 | 82.12 180 | 85.99 207 | 69.95 29 | 91.75 140 | 92.70 108 | 73.97 119 | 62.58 261 | 84.44 209 | 41.11 275 | 95.78 118 | 63.76 205 | 92.17 52 | 80.62 315 |
|
v148 | | | 76.19 196 | 74.47 196 | 81.36 194 | 80.05 288 | 64.44 171 | 91.75 140 | 90.23 197 | 73.68 127 | 67.13 222 | 80.84 254 | 55.92 156 | 93.86 203 | 68.95 161 | 61.73 284 | 85.76 252 |
|
FIs | | | 79.47 136 | 79.41 119 | 79.67 231 | 85.95 208 | 59.40 262 | 91.68 142 | 93.94 51 | 78.06 56 | 68.96 195 | 88.28 156 | 66.61 41 | 91.77 253 | 66.20 184 | 74.99 187 | 87.82 205 |
|
v1144 | | | 76.73 191 | 74.88 190 | 82.27 172 | 80.23 284 | 66.60 116 | 91.68 142 | 90.21 199 | 73.69 126 | 69.06 193 | 81.89 238 | 52.73 199 | 94.40 169 | 69.21 158 | 65.23 255 | 85.80 249 |
|
v7 | | | 76.83 189 | 75.01 189 | 82.29 171 | 80.35 277 | 66.70 112 | 91.68 142 | 89.97 210 | 73.47 133 | 69.22 190 | 82.22 230 | 52.52 200 | 94.43 168 | 69.73 151 | 65.96 250 | 85.74 253 |
|
OPM-MVS | | | 79.00 142 | 78.09 136 | 81.73 188 | 83.52 238 | 63.83 185 | 91.64 145 | 90.30 195 | 76.36 83 | 71.97 155 | 89.93 141 | 46.30 252 | 95.17 141 | 75.10 110 | 77.70 161 | 86.19 236 |
|
zzz-MVS | | | 84.73 53 | 84.47 50 | 85.50 87 | 91.89 86 | 65.16 153 | 91.55 146 | 92.23 124 | 75.32 95 | 80.53 68 | 95.21 37 | 56.06 153 | 97.16 66 | 84.86 45 | 92.55 46 | 94.18 86 |
|
MP-MVS-pluss | | | 85.24 47 | 85.13 47 | 85.56 86 | 91.42 104 | 65.59 145 | 91.54 147 | 92.51 118 | 74.56 103 | 80.62 67 | 95.64 23 | 59.15 117 | 97.00 74 | 86.94 32 | 93.80 28 | 94.07 96 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
GA-MVS | | | 78.33 158 | 76.23 166 | 84.65 115 | 83.65 236 | 66.30 128 | 91.44 148 | 90.14 203 | 76.01 85 | 70.32 172 | 84.02 211 | 42.50 268 | 94.72 154 | 70.98 142 | 77.00 173 | 92.94 129 |
|
æ–°å‡ ä½•2 | | | | | | | | 91.41 149 | | | | | | | | | |
|
thisisatest0530 | | | 81.15 106 | 80.07 105 | 84.39 122 | 88.26 172 | 65.63 144 | 91.40 150 | 94.62 36 | 71.27 183 | 70.93 164 | 89.18 146 | 72.47 13 | 96.04 110 | 65.62 191 | 76.89 174 | 91.49 154 |
|
Anonymous20231211 | | | 73.08 234 | 70.39 241 | 81.13 203 | 90.62 120 | 63.33 197 | 91.40 150 | 90.06 207 | 51.84 324 | 64.46 245 | 80.67 257 | 36.49 304 | 94.07 189 | 63.83 204 | 64.17 269 | 85.98 245 |
|
v144192 | | | 76.05 199 | 74.03 202 | 82.12 180 | 79.50 293 | 66.55 120 | 91.39 152 | 89.71 221 | 72.30 151 | 68.17 208 | 81.33 246 | 51.75 207 | 94.03 194 | 67.94 166 | 64.19 268 | 85.77 250 |
|
APD-MVS_3200maxsize | | | 81.64 100 | 81.32 92 | 82.59 156 | 92.36 71 | 58.74 271 | 91.39 152 | 91.01 174 | 63.35 269 | 79.72 77 | 94.62 54 | 51.82 205 | 96.14 105 | 79.71 79 | 87.93 87 | 92.89 131 |
|
EI-MVSNet-UG-set | | | 83.14 78 | 82.96 70 | 83.67 137 | 92.28 75 | 63.19 203 | 91.38 154 | 94.68 33 | 79.22 40 | 76.60 109 | 93.75 73 | 62.64 86 | 97.76 34 | 78.07 93 | 78.01 158 | 90.05 172 |
|
TranMVSNet+NR-MVSNet | | | 75.86 202 | 74.52 195 | 79.89 226 | 82.44 247 | 60.64 243 | 91.37 155 | 91.37 161 | 76.63 76 | 67.65 216 | 86.21 191 | 52.37 203 | 91.55 263 | 61.84 225 | 60.81 291 | 87.48 209 |
|
Effi-MVS+ | | | 83.82 70 | 82.76 75 | 86.99 43 | 89.56 144 | 69.40 38 | 91.35 156 | 86.12 291 | 72.59 145 | 83.22 51 | 92.81 95 | 59.60 112 | 96.01 113 | 81.76 64 | 87.80 89 | 95.56 37 |
|
FMVSNet2 | | | 76.07 198 | 74.01 203 | 82.26 175 | 88.85 158 | 67.66 75 | 91.33 157 | 91.61 150 | 70.84 188 | 65.98 228 | 82.25 229 | 48.03 235 | 92.00 250 | 58.46 242 | 68.73 234 | 87.10 222 |
|
HPM-MVS_fast | | | 80.25 119 | 79.55 117 | 82.33 169 | 91.55 97 | 59.95 255 | 91.32 158 | 89.16 236 | 65.23 252 | 74.71 125 | 93.07 85 | 47.81 240 | 95.74 120 | 74.87 117 | 88.23 84 | 91.31 160 |
|
thres600view7 | | | 78.00 162 | 76.66 160 | 82.03 185 | 91.93 83 | 63.69 191 | 91.30 159 | 96.33 5 | 72.43 148 | 70.46 167 | 87.89 165 | 60.31 102 | 94.92 147 | 42.64 300 | 76.64 175 | 87.48 209 |
|
DU-MVS | | | 76.86 186 | 75.84 171 | 79.91 225 | 82.96 244 | 60.26 248 | 91.26 160 | 91.54 152 | 76.46 81 | 68.88 196 | 86.35 188 | 56.16 150 | 92.13 246 | 66.38 181 | 62.55 275 | 87.35 218 |
|
TAMVS | | | 80.37 117 | 79.45 118 | 83.13 145 | 85.14 216 | 63.37 196 | 91.23 161 | 90.76 179 | 74.81 102 | 72.65 143 | 88.49 152 | 60.63 101 | 92.95 219 | 69.41 156 | 81.95 133 | 93.08 125 |
|
v1192 | | | 75.98 201 | 73.92 205 | 82.15 178 | 79.73 289 | 66.24 130 | 91.22 162 | 89.75 215 | 72.67 144 | 68.49 206 | 81.42 244 | 49.86 221 | 94.27 177 | 67.08 174 | 65.02 261 | 85.95 246 |
|
test0.0.03 1 | | | 72.76 239 | 72.71 224 | 72.88 306 | 80.25 283 | 47.99 330 | 91.22 162 | 89.45 225 | 71.51 180 | 62.51 262 | 87.66 170 | 53.83 185 | 85.06 319 | 50.16 269 | 67.84 242 | 85.58 254 |
|
Fast-Effi-MVS+-dtu | | | 75.04 213 | 73.37 218 | 80.07 221 | 80.86 259 | 59.52 261 | 91.20 164 | 85.38 298 | 71.90 163 | 65.20 236 | 84.84 204 | 41.46 274 | 92.97 218 | 66.50 180 | 72.96 201 | 87.73 206 |
|
tfpn111 | | | 78.00 162 | 76.62 161 | 82.13 179 | 91.89 86 | 63.21 200 | 91.19 165 | 96.33 5 | 72.28 152 | 70.45 168 | 87.89 165 | 60.31 102 | 94.91 148 | 42.61 301 | 76.64 175 | 88.27 194 |
|
conf200view11 | | | 78.32 159 | 77.01 155 | 82.27 172 | 91.89 86 | 63.21 200 | 91.19 165 | 96.33 5 | 72.28 152 | 70.45 168 | 87.89 165 | 60.31 102 | 95.32 137 | 45.16 287 | 77.58 163 | 88.27 194 |
|
thres100view900 | | | 78.37 157 | 77.01 155 | 82.46 157 | 91.89 86 | 63.21 200 | 91.19 165 | 96.33 5 | 72.28 152 | 70.45 168 | 87.89 165 | 60.31 102 | 95.32 137 | 45.16 287 | 77.58 163 | 88.83 182 |
|
PMMVS | | | 81.98 98 | 82.04 83 | 81.78 187 | 89.76 135 | 56.17 295 | 91.13 168 | 90.69 180 | 77.96 57 | 80.09 73 | 93.57 76 | 46.33 251 | 94.99 143 | 81.41 69 | 87.46 91 | 94.17 88 |
|
pmmvs5 | | | 73.35 233 | 71.52 235 | 78.86 254 | 78.64 306 | 60.61 244 | 91.08 169 | 86.90 275 | 67.69 231 | 63.32 254 | 83.64 213 | 44.33 263 | 90.53 274 | 62.04 224 | 66.02 249 | 85.46 257 |
|
v1921920 | | | 75.63 206 | 73.49 216 | 82.06 184 | 79.38 294 | 66.35 126 | 91.07 170 | 89.48 224 | 71.98 162 | 67.99 209 | 81.22 249 | 49.16 229 | 93.90 200 | 66.56 179 | 64.56 267 | 85.92 248 |
|
HPM-MVS | | | 83.25 76 | 82.95 71 | 84.17 125 | 92.25 76 | 62.88 210 | 90.91 171 | 91.86 141 | 70.30 199 | 77.12 105 | 93.96 71 | 56.75 143 | 96.28 101 | 82.04 62 | 91.34 64 | 93.34 115 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
CNLPA | | | 74.31 226 | 72.30 229 | 80.32 215 | 91.49 99 | 61.66 227 | 90.85 172 | 80.72 327 | 56.67 312 | 63.85 250 | 90.64 121 | 46.75 246 | 90.84 272 | 53.79 257 | 75.99 180 | 88.47 190 |
|
NR-MVSNet | | | 76.05 199 | 74.59 192 | 80.44 213 | 82.96 244 | 62.18 220 | 90.83 173 | 91.73 145 | 77.12 67 | 60.96 265 | 86.35 188 | 59.28 116 | 91.80 252 | 60.74 230 | 61.34 288 | 87.35 218 |
|
pm-mvs1 | | | 72.89 237 | 71.09 238 | 78.26 265 | 79.10 300 | 57.62 280 | 90.80 174 | 89.30 230 | 67.66 232 | 62.91 258 | 81.78 240 | 49.11 230 | 92.95 219 | 60.29 233 | 58.89 300 | 84.22 268 |
|
ACMP | | 71.68 10 | 75.58 207 | 74.23 199 | 79.62 233 | 84.97 218 | 59.64 258 | 90.80 174 | 89.07 241 | 70.39 198 | 62.95 257 | 87.30 180 | 38.28 287 | 93.87 201 | 72.89 121 | 71.45 213 | 85.36 259 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v1240 | | | 75.21 212 | 72.98 221 | 81.88 186 | 79.20 296 | 66.00 135 | 90.75 176 | 89.11 239 | 71.63 175 | 67.41 219 | 81.22 249 | 47.36 243 | 93.87 201 | 65.46 193 | 64.72 265 | 85.77 250 |
|
casdiffmvs1 | | | 86.26 37 | 85.70 41 | 87.97 16 | 90.76 118 | 71.19 15 | 90.74 177 | 93.07 97 | 76.57 78 | 88.02 16 | 87.85 169 | 67.81 32 | 96.48 98 | 87.22 29 | 89.22 78 | 96.23 19 |
|
casdiffmvs | | | 85.23 48 | 84.38 53 | 87.79 20 | 90.73 119 | 71.38 13 | 90.71 178 | 92.52 117 | 77.08 68 | 84.58 39 | 87.18 182 | 64.43 68 | 96.34 100 | 84.32 47 | 87.86 88 | 95.65 34 |
|
DI_MVS_plusplus_test | | | 79.78 130 | 77.50 147 | 86.62 49 | 80.90 258 | 69.46 37 | 90.69 179 | 91.97 138 | 77.00 69 | 59.07 275 | 82.34 227 | 46.82 245 | 95.88 115 | 82.14 61 | 86.59 99 | 94.53 78 |
|
test_normal | | | 79.66 131 | 77.36 152 | 86.54 53 | 80.72 262 | 69.21 42 | 90.68 180 | 92.16 133 | 76.99 70 | 58.63 279 | 82.03 236 | 46.70 247 | 95.86 116 | 81.74 65 | 86.63 98 | 94.56 73 |
|
IterMVS-LS | | | 76.49 192 | 75.18 188 | 80.43 214 | 84.49 224 | 62.74 212 | 90.64 181 | 88.80 249 | 72.40 149 | 65.16 238 | 81.72 241 | 60.98 95 | 92.27 244 | 67.74 168 | 64.65 266 | 86.29 234 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PLC | | 68.80 14 | 75.23 211 | 73.68 208 | 79.86 227 | 92.93 60 | 58.68 272 | 90.64 181 | 88.30 258 | 60.90 287 | 64.43 246 | 90.53 124 | 42.38 269 | 94.57 157 | 56.52 247 | 76.54 177 | 86.33 233 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PS-MVSNAJss | | | 77.26 179 | 76.31 165 | 80.13 220 | 80.64 269 | 59.16 266 | 90.63 183 | 91.06 171 | 72.80 143 | 68.58 201 | 84.57 208 | 53.55 189 | 93.96 197 | 72.97 120 | 71.96 209 | 87.27 221 |
|
PGM-MVS | | | 83.25 76 | 82.70 77 | 84.92 105 | 92.81 66 | 64.07 182 | 90.44 184 | 92.20 129 | 71.28 182 | 77.23 104 | 94.43 56 | 55.17 162 | 97.31 57 | 79.33 83 | 91.38 62 | 93.37 114 |
|
LPG-MVS_test | | | 75.82 203 | 74.58 193 | 79.56 235 | 84.31 228 | 59.37 263 | 90.44 184 | 89.73 218 | 69.49 205 | 64.86 239 | 88.42 153 | 38.65 284 | 94.30 175 | 72.56 125 | 72.76 202 | 85.01 262 |
|
Vis-MVSNet (Re-imp) | | | 79.24 139 | 79.57 114 | 78.24 266 | 88.46 167 | 52.29 312 | 90.41 186 | 89.12 238 | 74.24 111 | 69.13 191 | 91.91 107 | 65.77 50 | 90.09 286 | 59.00 240 | 88.09 86 | 92.33 142 |
|
TSAR-MVS + MP. | | | 88.11 13 | 88.64 10 | 86.54 53 | 91.73 92 | 68.04 68 | 90.36 187 | 93.55 65 | 82.89 14 | 91.29 3 | 92.89 91 | 72.27 14 | 96.03 111 | 87.99 22 | 94.77 15 | 95.54 38 |
|
ACMM | | 69.62 13 | 74.34 225 | 72.73 223 | 79.17 247 | 84.25 230 | 57.87 276 | 90.36 187 | 89.93 211 | 63.17 272 | 65.64 235 | 86.04 194 | 37.79 294 | 94.10 186 | 65.89 187 | 71.52 212 | 85.55 256 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FC-MVSNet-test | | | 77.99 164 | 78.08 137 | 77.70 271 | 84.89 219 | 55.51 299 | 90.27 189 | 93.75 58 | 76.87 71 | 66.80 227 | 87.59 171 | 65.71 51 | 90.23 280 | 62.89 217 | 73.94 193 | 87.37 216 |
|
V42 | | | 76.46 193 | 74.55 194 | 82.19 177 | 79.14 298 | 67.82 71 | 90.26 190 | 89.42 227 | 73.75 125 | 68.63 200 | 81.89 238 | 51.31 211 | 94.09 187 | 71.69 133 | 64.84 263 | 84.66 265 |
|
HyFIR lowres test | | | 81.03 109 | 79.56 115 | 85.43 92 | 87.81 183 | 68.11 67 | 90.18 191 | 90.01 209 | 70.65 195 | 72.95 139 | 86.06 193 | 63.61 77 | 94.50 163 | 75.01 113 | 79.75 145 | 93.67 109 |
|
tfpn_ndepth | | | 76.45 194 | 75.22 187 | 80.14 218 | 90.97 112 | 58.92 268 | 90.11 192 | 93.24 85 | 65.96 245 | 67.37 221 | 90.52 125 | 66.67 40 | 92.29 243 | 37.71 321 | 74.44 189 | 89.21 180 |
|
GBi-Net | | | 75.65 204 | 73.83 206 | 81.10 205 | 88.85 158 | 65.11 155 | 90.01 193 | 90.32 191 | 70.84 188 | 67.04 223 | 80.25 264 | 48.03 235 | 91.54 264 | 59.80 236 | 69.34 228 | 86.64 229 |
|
test1 | | | 75.65 204 | 73.83 206 | 81.10 205 | 88.85 158 | 65.11 155 | 90.01 193 | 90.32 191 | 70.84 188 | 67.04 223 | 80.25 264 | 48.03 235 | 91.54 264 | 59.80 236 | 69.34 228 | 86.64 229 |
|
FMVSNet1 | | | 72.71 240 | 69.91 244 | 81.10 205 | 83.60 237 | 65.11 155 | 90.01 193 | 90.32 191 | 63.92 266 | 63.56 252 | 80.25 264 | 36.35 305 | 91.54 264 | 54.46 254 | 66.75 246 | 86.64 229 |
|
MVS_Test | | | 84.16 64 | 83.20 67 | 87.05 41 | 91.56 96 | 69.82 31 | 89.99 196 | 92.05 134 | 77.77 59 | 82.84 53 | 86.57 187 | 63.93 72 | 96.09 107 | 74.91 115 | 89.18 79 | 95.25 50 |
|
view600 | | | 76.93 182 | 75.50 181 | 81.23 197 | 91.44 100 | 62.00 221 | 89.94 197 | 96.56 1 | 70.68 191 | 68.54 202 | 87.31 176 | 60.79 96 | 94.19 180 | 38.90 315 | 75.31 183 | 87.48 209 |
|
view800 | | | 76.93 182 | 75.50 181 | 81.23 197 | 91.44 100 | 62.00 221 | 89.94 197 | 96.56 1 | 70.68 191 | 68.54 202 | 87.31 176 | 60.79 96 | 94.19 180 | 38.90 315 | 75.31 183 | 87.48 209 |
|
conf0.05thres1000 | | | 76.93 182 | 75.50 181 | 81.23 197 | 91.44 100 | 62.00 221 | 89.94 197 | 96.56 1 | 70.68 191 | 68.54 202 | 87.31 176 | 60.79 96 | 94.19 180 | 38.90 315 | 75.31 183 | 87.48 209 |
|
tfpn | | | 76.93 182 | 75.50 181 | 81.23 197 | 91.44 100 | 62.00 221 | 89.94 197 | 96.56 1 | 70.68 191 | 68.54 202 | 87.31 176 | 60.79 96 | 94.19 180 | 38.90 315 | 75.31 183 | 87.48 209 |
|
Effi-MVS+-dtu | | | 76.14 197 | 75.28 186 | 78.72 259 | 83.22 240 | 55.17 301 | 89.87 201 | 87.78 266 | 75.42 91 | 67.98 210 | 81.43 243 | 45.08 258 | 92.52 235 | 75.08 111 | 71.63 210 | 88.48 189 |
|
EG-PatchMatch MVS | | | 68.55 274 | 65.41 276 | 77.96 270 | 78.69 305 | 62.93 206 | 89.86 202 | 89.17 235 | 60.55 289 | 50.27 321 | 77.73 283 | 22.60 343 | 94.06 190 | 47.18 281 | 72.65 204 | 76.88 336 |
|
MVS_111021_LR | | | 82.02 97 | 81.52 90 | 83.51 140 | 88.42 169 | 62.88 210 | 89.77 203 | 88.93 245 | 76.78 74 | 75.55 119 | 93.10 81 | 50.31 216 | 95.38 136 | 83.82 53 | 87.02 93 | 92.26 146 |
|
tttt0517 | | | 79.50 135 | 78.53 131 | 82.41 167 | 87.22 190 | 61.43 231 | 89.75 204 | 94.76 29 | 69.29 208 | 67.91 212 | 88.06 163 | 72.92 10 | 95.63 127 | 62.91 216 | 73.90 195 | 90.16 170 |
|
DP-MVS | | | 69.90 264 | 66.48 270 | 80.14 218 | 95.36 16 | 62.93 206 | 89.56 205 | 76.11 335 | 50.27 329 | 57.69 285 | 85.23 200 | 39.68 280 | 95.73 121 | 33.35 338 | 71.05 216 | 81.78 296 |
|
test222 | | | | | | 89.77 134 | 61.60 228 | 89.55 206 | 89.42 227 | 56.83 311 | 77.28 103 | 92.43 100 | 52.76 198 | | | 91.14 66 | 93.09 124 |
|
v8 | | | 75.35 208 | 73.26 219 | 81.61 192 | 80.67 266 | 66.82 100 | 89.54 207 | 89.27 231 | 71.65 174 | 63.30 255 | 80.30 263 | 54.99 169 | 94.06 190 | 67.33 173 | 62.33 278 | 83.94 270 |
|
EI-MVSNet | | | 78.97 143 | 78.22 135 | 81.25 196 | 85.33 213 | 62.73 213 | 89.53 208 | 93.21 86 | 72.39 150 | 72.14 153 | 90.13 132 | 60.99 94 | 94.72 154 | 67.73 169 | 72.49 205 | 86.29 234 |
|
CVMVSNet | | | 74.04 228 | 74.27 198 | 73.33 302 | 85.33 213 | 43.94 339 | 89.53 208 | 88.39 256 | 54.33 318 | 70.37 171 | 90.13 132 | 49.17 228 | 84.05 322 | 61.83 226 | 79.36 147 | 91.99 148 |
|
MVP-Stereo | | | 77.12 181 | 76.23 166 | 79.79 229 | 81.72 252 | 66.34 127 | 89.29 210 | 90.88 175 | 70.56 196 | 62.01 264 | 82.88 220 | 49.34 225 | 94.13 185 | 65.55 192 | 93.80 28 | 78.88 328 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
OpenMVS_ROB | | 61.12 18 | 66.39 289 | 62.92 294 | 76.80 283 | 76.51 317 | 57.77 277 | 89.22 211 | 83.41 314 | 55.48 316 | 53.86 305 | 77.84 282 | 26.28 338 | 93.95 198 | 34.90 335 | 68.76 233 | 78.68 330 |
|
testdata1 | | | | | | | | 89.21 212 | | 77.55 63 | | | | | | | |
|
TAPA-MVS | | 70.22 12 | 74.94 218 | 73.53 215 | 79.17 247 | 90.40 123 | 52.07 313 | 89.19 213 | 89.61 222 | 62.69 275 | 70.07 175 | 92.67 96 | 48.89 232 | 94.32 174 | 38.26 320 | 79.97 143 | 91.12 161 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
v10 | | | 74.77 223 | 72.54 227 | 81.46 193 | 80.33 281 | 66.71 111 | 89.15 214 | 89.08 240 | 70.94 186 | 63.08 256 | 79.86 268 | 52.52 200 | 94.04 193 | 65.70 190 | 62.17 279 | 83.64 272 |
|
DWT-MVSNet_test | | | 83.95 67 | 82.80 74 | 87.41 31 | 92.90 61 | 70.07 27 | 89.12 215 | 94.42 41 | 82.15 20 | 77.64 97 | 91.77 109 | 70.81 19 | 96.22 102 | 65.03 195 | 81.36 137 | 95.94 27 |
|
MVSFormer | | | 83.75 72 | 82.88 72 | 86.37 62 | 89.24 152 | 71.18 16 | 89.07 216 | 90.69 180 | 65.80 246 | 87.13 20 | 94.34 63 | 64.99 63 | 92.67 231 | 72.83 122 | 91.80 55 | 95.27 47 |
|
test_djsdf | | | 73.76 232 | 72.56 226 | 77.39 277 | 77.00 316 | 53.93 306 | 89.07 216 | 90.69 180 | 65.80 246 | 63.92 248 | 82.03 236 | 43.14 267 | 92.67 231 | 72.83 122 | 68.53 235 | 85.57 255 |
|
tfpnnormal | | | 70.10 262 | 67.36 265 | 78.32 262 | 83.45 239 | 60.97 234 | 88.85 218 | 92.77 106 | 64.85 260 | 60.83 266 | 78.53 278 | 43.52 266 | 93.48 210 | 31.73 344 | 61.70 285 | 80.52 316 |
|
jajsoiax | | | 73.05 235 | 71.51 236 | 77.67 272 | 77.46 313 | 54.83 302 | 88.81 219 | 90.04 208 | 69.13 212 | 62.85 259 | 83.51 215 | 31.16 324 | 92.75 228 | 70.83 143 | 69.80 224 | 85.43 258 |
|
pmmvs6 | | | 67.57 283 | 64.76 280 | 76.00 288 | 72.82 330 | 53.37 308 | 88.71 220 | 86.78 276 | 53.19 320 | 57.58 286 | 78.03 281 | 35.33 309 | 92.41 238 | 55.56 251 | 54.88 313 | 82.21 293 |
|
ppachtmachnet_test | | | 67.72 281 | 63.70 288 | 79.77 230 | 78.92 301 | 66.04 134 | 88.68 221 | 82.90 320 | 60.11 294 | 55.45 292 | 75.96 298 | 39.19 281 | 90.55 273 | 39.53 311 | 52.55 318 | 82.71 289 |
|
PVSNet_0 | | 68.08 15 | 71.81 245 | 68.32 259 | 82.27 172 | 84.68 220 | 62.31 218 | 88.68 221 | 90.31 194 | 75.84 86 | 57.93 282 | 80.65 258 | 37.85 293 | 94.19 180 | 69.94 150 | 29.05 354 | 90.31 169 |
|
PatchFormer-LS_test | | | 83.14 78 | 81.81 86 | 87.12 38 | 92.34 72 | 69.92 30 | 88.64 223 | 93.32 81 | 82.07 23 | 74.87 124 | 91.62 113 | 68.91 21 | 96.08 109 | 66.07 185 | 78.45 157 | 95.37 40 |
|
tfpn1000 | | | 75.25 210 | 74.00 204 | 79.03 251 | 90.30 125 | 57.56 282 | 88.55 224 | 93.36 80 | 64.14 265 | 65.17 237 | 89.76 144 | 67.06 37 | 91.46 269 | 34.54 336 | 73.09 200 | 88.06 200 |
|
OMC-MVS | | | 78.67 153 | 77.91 141 | 80.95 210 | 85.76 212 | 57.40 283 | 88.49 225 | 88.67 251 | 73.85 122 | 72.43 150 | 92.10 105 | 49.29 226 | 94.55 159 | 72.73 124 | 77.89 159 | 90.91 163 |
|
abl_6 | | | 79.82 128 | 79.20 124 | 81.70 191 | 89.85 132 | 58.34 273 | 88.47 226 | 90.07 205 | 62.56 276 | 77.71 96 | 93.08 83 | 47.65 242 | 96.78 88 | 77.94 94 | 85.45 109 | 89.99 173 |
|
mvs_tets | | | 72.71 240 | 71.11 237 | 77.52 273 | 77.41 314 | 54.52 304 | 88.45 227 | 89.76 214 | 68.76 218 | 62.70 260 | 83.26 218 | 29.49 328 | 92.71 229 | 70.51 148 | 69.62 226 | 85.34 260 |
|
our_test_3 | | | 68.29 277 | 64.69 281 | 79.11 250 | 78.92 301 | 64.85 163 | 88.40 228 | 85.06 300 | 60.32 292 | 52.68 311 | 76.12 295 | 40.81 277 | 89.80 295 | 44.25 293 | 55.65 309 | 82.67 291 |
|
Test4 | | | 76.45 194 | 73.45 217 | 85.45 91 | 76.07 322 | 67.61 77 | 88.38 229 | 90.83 176 | 76.71 75 | 53.06 309 | 79.65 272 | 31.61 321 | 94.35 173 | 78.47 89 | 86.22 104 | 94.40 82 |
|
Anonymous20231206 | | | 67.53 284 | 65.78 272 | 72.79 307 | 74.95 324 | 47.59 332 | 88.23 230 | 87.32 272 | 61.75 285 | 58.07 281 | 77.29 286 | 37.79 294 | 87.29 312 | 42.91 296 | 63.71 273 | 83.48 276 |
|
v18 | | | 71.94 244 | 69.43 247 | 79.50 237 | 80.74 261 | 66.82 100 | 88.16 231 | 86.66 277 | 68.95 214 | 55.55 291 | 72.66 309 | 55.03 167 | 90.15 282 | 64.78 197 | 52.30 319 | 81.54 297 |
|
ACMH+ | | 65.35 16 | 67.65 282 | 64.55 282 | 76.96 281 | 84.59 222 | 57.10 286 | 88.08 232 | 80.79 326 | 58.59 303 | 53.00 310 | 81.09 253 | 26.63 337 | 92.95 219 | 46.51 282 | 61.69 286 | 80.82 312 |
|
F-COLMAP | | | 70.66 258 | 68.44 257 | 77.32 278 | 86.37 202 | 55.91 297 | 88.00 233 | 86.32 286 | 56.94 310 | 57.28 287 | 88.07 162 | 33.58 313 | 92.49 236 | 51.02 266 | 68.37 236 | 83.55 273 |
|
test_0402 | | | 64.54 297 | 61.09 302 | 74.92 292 | 84.10 232 | 60.75 239 | 87.95 234 | 79.71 330 | 52.03 323 | 52.41 312 | 77.20 287 | 32.21 319 | 91.64 261 | 23.14 353 | 61.03 289 | 72.36 343 |
|
v17 | | | 71.77 247 | 69.20 250 | 79.46 239 | 80.62 271 | 66.81 104 | 87.93 235 | 86.63 279 | 68.71 219 | 55.25 294 | 72.49 311 | 54.72 174 | 90.11 285 | 64.50 200 | 51.97 321 | 81.47 298 |
|
v16 | | | 71.81 245 | 69.26 249 | 79.47 238 | 80.66 268 | 66.81 104 | 87.93 235 | 86.63 279 | 68.70 220 | 55.35 293 | 72.51 310 | 54.75 173 | 90.12 284 | 64.51 199 | 52.28 320 | 81.47 298 |
|
1314 | | | 80.70 112 | 78.95 127 | 85.94 73 | 87.77 184 | 67.56 78 | 87.91 237 | 92.55 116 | 72.17 159 | 67.44 217 | 93.09 82 | 50.27 217 | 97.04 71 | 71.68 134 | 87.64 90 | 93.23 120 |
|
1121 | | | 81.25 105 | 80.05 106 | 84.87 108 | 92.30 74 | 64.31 177 | 87.91 237 | 91.39 159 | 59.44 298 | 79.94 74 | 92.91 89 | 57.09 133 | 97.01 72 | 66.63 177 | 92.81 43 | 93.29 118 |
|
MVS | | | 84.66 55 | 82.86 73 | 90.06 1 | 90.93 113 | 74.56 5 | 87.91 237 | 95.54 14 | 68.55 222 | 72.35 152 | 94.71 53 | 59.78 110 | 98.90 7 | 81.29 72 | 94.69 19 | 96.74 7 |
|
test2356 | | | 64.16 300 | 63.28 291 | 66.81 328 | 69.37 342 | 39.86 348 | 87.76 240 | 86.02 292 | 59.83 296 | 53.54 308 | 73.23 307 | 34.94 310 | 80.67 341 | 39.66 310 | 65.20 256 | 79.89 321 |
|
conf0.01 | | | 74.95 216 | 73.61 209 | 78.96 252 | 89.65 136 | 56.94 288 | 87.72 241 | 93.45 69 | 65.14 253 | 65.68 229 | 89.99 135 | 65.09 56 | 91.67 255 | 35.16 328 | 70.61 217 | 88.27 194 |
|
conf0.002 | | | 74.95 216 | 73.61 209 | 78.96 252 | 89.65 136 | 56.94 288 | 87.72 241 | 93.45 69 | 65.14 253 | 65.68 229 | 89.99 135 | 65.09 56 | 91.67 255 | 35.16 328 | 70.61 217 | 88.27 194 |
|
thresconf0.02 | | | 74.92 219 | 73.61 209 | 78.85 255 | 89.65 136 | 56.94 288 | 87.72 241 | 93.45 69 | 65.14 253 | 65.68 229 | 89.99 135 | 65.09 56 | 91.67 255 | 35.16 328 | 70.61 217 | 87.94 201 |
|
tfpn_n400 | | | 74.92 219 | 73.61 209 | 78.85 255 | 89.65 136 | 56.94 288 | 87.72 241 | 93.45 69 | 65.14 253 | 65.68 229 | 89.99 135 | 65.09 56 | 91.67 255 | 35.16 328 | 70.61 217 | 87.94 201 |
|
tfpnconf | | | 74.92 219 | 73.61 209 | 78.85 255 | 89.65 136 | 56.94 288 | 87.72 241 | 93.45 69 | 65.14 253 | 65.68 229 | 89.99 135 | 65.09 56 | 91.67 255 | 35.16 328 | 70.61 217 | 87.94 201 |
|
tfpnview11 | | | 74.92 219 | 73.61 209 | 78.85 255 | 89.65 136 | 56.94 288 | 87.72 241 | 93.45 69 | 65.14 253 | 65.68 229 | 89.99 135 | 65.09 56 | 91.67 255 | 35.16 328 | 70.61 217 | 87.94 201 |
|
ACMH | | 63.93 17 | 68.62 273 | 64.81 279 | 80.03 222 | 85.22 215 | 63.25 198 | 87.72 241 | 84.66 303 | 60.83 288 | 51.57 316 | 79.43 274 | 27.29 335 | 94.96 144 | 41.76 302 | 64.84 263 | 81.88 295 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PAPM_NR | | | 82.97 81 | 81.84 85 | 86.37 62 | 94.10 38 | 66.76 107 | 87.66 248 | 92.84 104 | 69.96 202 | 74.07 131 | 93.57 76 | 63.10 84 | 97.50 46 | 70.66 146 | 90.58 71 | 94.85 64 |
|
IterMVS | | | 72.65 242 | 70.83 239 | 78.09 269 | 82.17 248 | 62.96 205 | 87.64 249 | 86.28 287 | 71.56 178 | 60.44 267 | 78.85 277 | 45.42 257 | 86.66 314 | 63.30 209 | 61.83 281 | 84.65 266 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v15 | | | 71.40 249 | 68.75 252 | 79.35 240 | 80.39 275 | 66.70 112 | 87.57 250 | 86.64 278 | 68.66 221 | 54.68 296 | 72.00 318 | 54.50 176 | 89.98 287 | 63.69 206 | 50.66 326 | 81.38 302 |
|
diffmvs1 | | | 82.57 87 | 81.71 88 | 85.15 102 | 88.07 176 | 66.09 132 | 87.52 251 | 88.92 246 | 76.45 82 | 80.41 70 | 87.04 183 | 60.29 106 | 94.77 150 | 80.30 78 | 86.36 103 | 94.59 72 |
|
pmmvs4 | | | 73.92 230 | 71.81 233 | 80.25 217 | 79.17 297 | 65.24 151 | 87.43 252 | 87.26 274 | 67.64 234 | 63.46 253 | 83.91 212 | 48.96 231 | 91.53 267 | 62.94 215 | 65.49 254 | 83.96 269 |
|
V14 | | | 71.29 251 | 68.61 254 | 79.31 241 | 80.34 279 | 66.65 114 | 87.39 253 | 86.61 281 | 68.41 226 | 54.49 298 | 71.91 319 | 54.25 181 | 89.96 288 | 63.50 207 | 50.62 327 | 81.33 304 |
|
WR-MVS_H | | | 70.59 259 | 69.94 243 | 72.53 308 | 81.03 257 | 51.43 316 | 87.35 254 | 92.03 135 | 67.38 236 | 60.23 268 | 80.70 255 | 55.84 157 | 83.45 329 | 46.33 283 | 58.58 301 | 82.72 288 |
|
CP-MVSNet | | | 70.50 261 | 69.91 244 | 72.26 311 | 80.71 263 | 51.00 319 | 87.23 255 | 90.30 195 | 67.84 230 | 59.64 270 | 82.69 222 | 50.23 218 | 82.30 336 | 51.28 265 | 59.28 295 | 83.46 277 |
|
V9 | | | 71.16 252 | 68.46 256 | 79.27 243 | 80.26 282 | 66.60 116 | 87.21 256 | 86.56 282 | 68.17 227 | 54.26 301 | 71.81 321 | 54.00 183 | 89.93 289 | 63.28 210 | 50.57 328 | 81.27 305 |
|
PCF-MVS | | 73.15 9 | 79.29 138 | 77.63 144 | 84.29 124 | 86.06 206 | 65.96 136 | 87.03 257 | 91.10 169 | 69.86 203 | 69.79 181 | 90.64 121 | 57.54 131 | 96.59 93 | 64.37 202 | 82.29 129 | 90.32 168 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PS-CasMVS | | | 69.86 265 | 69.13 251 | 72.07 314 | 80.35 277 | 50.57 321 | 87.02 258 | 89.75 215 | 67.27 237 | 59.19 273 | 82.28 228 | 46.58 249 | 82.24 337 | 50.69 267 | 59.02 298 | 83.39 279 |
|
v12 | | | 71.02 256 | 68.29 261 | 79.22 245 | 80.18 285 | 66.53 121 | 87.01 259 | 86.54 284 | 67.90 229 | 54.00 304 | 71.70 322 | 53.66 188 | 89.91 290 | 63.09 212 | 50.51 329 | 81.21 306 |
|
v11 | | | 71.05 255 | 68.32 259 | 79.23 244 | 80.34 279 | 66.57 119 | 87.01 259 | 86.55 283 | 68.11 228 | 54.40 299 | 71.66 323 | 52.94 197 | 89.91 290 | 62.71 219 | 51.12 324 | 81.21 306 |
|
diffmvs | | | 81.52 101 | 80.44 104 | 84.76 110 | 87.98 180 | 65.79 142 | 86.97 261 | 88.84 248 | 76.57 78 | 78.24 91 | 85.79 197 | 58.10 126 | 94.55 159 | 77.40 98 | 84.11 123 | 93.95 101 |
|
PEN-MVS | | | 69.46 270 | 68.56 255 | 72.17 313 | 79.27 295 | 49.71 325 | 86.90 262 | 89.24 233 | 67.24 238 | 59.08 274 | 82.51 223 | 47.23 244 | 83.54 328 | 48.42 276 | 57.12 303 | 83.25 280 |
|
mvs_anonymous | | | 81.36 104 | 79.99 108 | 85.46 89 | 90.39 124 | 68.40 57 | 86.88 263 | 90.61 184 | 74.41 104 | 70.31 173 | 84.67 206 | 63.79 74 | 92.32 242 | 73.13 119 | 85.70 106 | 95.67 32 |
|
v13 | | | 70.90 257 | 68.15 262 | 79.15 249 | 80.08 286 | 66.45 123 | 86.83 264 | 86.50 285 | 67.62 235 | 53.78 306 | 71.61 324 | 53.51 192 | 89.87 292 | 62.89 217 | 50.50 330 | 81.14 308 |
|
v7n | | | 71.31 250 | 68.65 253 | 79.28 242 | 76.40 318 | 60.77 238 | 86.71 265 | 89.45 225 | 64.17 264 | 58.77 278 | 78.24 279 | 44.59 262 | 93.54 208 | 57.76 244 | 61.75 283 | 83.52 275 |
|
test20.03 | | | 63.83 302 | 62.65 296 | 67.38 326 | 70.58 338 | 39.94 346 | 86.57 266 | 84.17 306 | 63.29 270 | 51.86 314 | 77.30 285 | 37.09 301 | 82.47 334 | 38.87 319 | 54.13 315 | 79.73 323 |
|
UA-Net | | | 80.02 124 | 79.65 113 | 81.11 204 | 89.33 148 | 57.72 278 | 86.33 267 | 89.00 244 | 77.44 65 | 81.01 64 | 89.15 147 | 59.33 115 | 95.90 114 | 61.01 229 | 84.28 120 | 89.73 176 |
|
v748 | | | 70.55 260 | 67.97 263 | 78.27 264 | 75.75 323 | 58.78 270 | 86.29 268 | 89.25 232 | 65.12 259 | 56.66 289 | 77.17 289 | 45.05 260 | 92.95 219 | 58.13 243 | 58.33 302 | 83.10 284 |
|
DTE-MVSNet | | | 68.46 276 | 67.33 266 | 71.87 317 | 77.94 311 | 49.00 328 | 86.16 269 | 88.58 254 | 66.36 242 | 58.19 280 | 82.21 231 | 46.36 250 | 83.87 326 | 44.97 291 | 55.17 311 | 82.73 287 |
|
testgi | | | 64.48 298 | 62.87 295 | 69.31 321 | 71.24 333 | 40.62 345 | 85.49 270 | 79.92 329 | 65.36 250 | 54.18 302 | 83.49 216 | 23.74 341 | 84.55 320 | 41.60 303 | 60.79 292 | 82.77 286 |
|
LS3D | | | 69.17 271 | 66.40 271 | 77.50 274 | 91.92 84 | 56.12 296 | 85.12 271 | 80.37 328 | 46.96 336 | 56.50 290 | 87.51 173 | 37.25 297 | 93.71 205 | 32.52 343 | 79.40 146 | 82.68 290 |
|
testmvs | | | 7.23 347 | 9.62 348 | 0.06 358 | 0.04 372 | 0.02 373 | 84.98 272 | 0.02 374 | 0.03 367 | 0.18 369 | 1.21 369 | 0.01 375 | 0.02 369 | 0.14 366 | 0.01 366 | 0.13 367 |
|
testus | | | 59.36 314 | 57.51 311 | 64.90 330 | 66.72 344 | 37.56 351 | 84.98 272 | 81.09 324 | 57.46 307 | 47.72 328 | 72.76 308 | 11.43 357 | 78.78 348 | 36.56 322 | 58.91 299 | 78.36 333 |
|
HY-MVS | | 76.49 5 | 84.28 60 | 83.36 66 | 87.02 42 | 92.22 77 | 67.74 73 | 84.65 274 | 94.50 38 | 79.15 42 | 82.23 55 | 87.93 164 | 66.88 38 | 96.94 82 | 80.53 76 | 82.20 131 | 96.39 14 |
|
N_pmnet | | | 50.55 324 | 49.11 327 | 54.88 341 | 77.17 315 | 4.02 371 | 84.36 275 | 2.00 372 | 48.59 332 | 45.86 333 | 68.82 331 | 32.22 318 | 82.80 333 | 31.58 345 | 51.38 323 | 77.81 334 |
|
anonymousdsp | | | 71.14 253 | 69.37 248 | 76.45 284 | 72.95 328 | 54.71 303 | 84.19 276 | 88.88 247 | 61.92 282 | 62.15 263 | 79.77 269 | 38.14 289 | 91.44 270 | 68.90 162 | 67.45 243 | 83.21 281 |
|
MSDG | | | 69.54 269 | 65.73 273 | 80.96 209 | 85.11 217 | 63.71 190 | 84.19 276 | 83.28 316 | 56.95 309 | 54.50 297 | 84.03 210 | 31.50 322 | 96.03 111 | 42.87 298 | 69.13 231 | 83.14 283 |
|
COLMAP_ROB | | 57.96 20 | 62.98 306 | 59.65 306 | 72.98 305 | 81.44 255 | 53.00 310 | 83.75 278 | 75.53 341 | 48.34 334 | 48.81 325 | 81.40 245 | 24.14 339 | 90.30 276 | 32.95 340 | 60.52 293 | 75.65 339 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
testing_2 | | | 71.09 254 | 67.32 267 | 82.40 168 | 69.82 339 | 66.52 122 | 83.64 279 | 90.77 178 | 72.21 156 | 45.12 336 | 71.07 328 | 27.60 334 | 93.74 204 | 75.71 106 | 69.96 223 | 86.95 225 |
|
FMVSNet5 | | | 68.04 279 | 65.66 274 | 75.18 291 | 84.43 226 | 57.89 275 | 83.54 280 | 86.26 288 | 61.83 284 | 53.64 307 | 73.30 306 | 37.15 300 | 85.08 318 | 48.99 273 | 61.77 282 | 82.56 292 |
|
MDA-MVSNet_test_wron | | | 63.78 303 | 60.16 304 | 74.64 293 | 78.15 309 | 60.41 245 | 83.49 281 | 84.03 307 | 56.17 315 | 39.17 348 | 71.59 326 | 37.22 298 | 83.24 332 | 42.87 298 | 48.73 333 | 80.26 319 |
|
PatchMatch-RL | | | 72.06 243 | 69.98 242 | 78.28 263 | 89.51 146 | 55.70 298 | 83.49 281 | 83.39 315 | 61.24 286 | 63.72 251 | 82.76 221 | 34.77 311 | 93.03 217 | 53.37 261 | 77.59 162 | 86.12 238 |
|
YYNet1 | | | 63.76 304 | 60.14 305 | 74.62 294 | 78.06 310 | 60.19 251 | 83.46 283 | 83.99 311 | 56.18 314 | 39.25 347 | 71.56 327 | 37.18 299 | 83.34 330 | 42.90 297 | 48.70 334 | 80.32 318 |
|
SixPastTwentyTwo | | | 64.92 295 | 61.78 301 | 74.34 297 | 78.74 304 | 49.76 324 | 83.42 284 | 79.51 331 | 62.86 274 | 50.27 321 | 77.35 284 | 30.92 326 | 90.49 275 | 45.89 285 | 47.06 336 | 82.78 285 |
|
EU-MVSNet | | | 64.01 301 | 63.01 293 | 67.02 327 | 74.40 326 | 38.86 350 | 83.27 285 | 86.19 289 | 45.11 342 | 54.27 300 | 81.15 252 | 36.91 303 | 80.01 342 | 48.79 275 | 57.02 304 | 82.19 294 |
|
K. test v3 | | | 63.09 305 | 59.61 307 | 73.53 301 | 76.26 319 | 49.38 327 | 83.27 285 | 77.15 334 | 64.35 263 | 47.77 327 | 72.32 316 | 28.73 330 | 87.79 310 | 49.93 271 | 36.69 349 | 83.41 278 |
|
tpm | | | 78.58 154 | 77.03 154 | 83.22 143 | 85.94 210 | 64.56 165 | 83.21 287 | 91.14 168 | 78.31 54 | 73.67 134 | 79.68 270 | 64.01 70 | 92.09 248 | 66.07 185 | 71.26 215 | 93.03 126 |
|
MDA-MVSNet-bldmvs | | | 61.54 309 | 57.70 310 | 73.05 304 | 79.53 292 | 57.00 287 | 83.08 288 | 81.23 323 | 57.57 304 | 34.91 351 | 72.45 313 | 32.79 315 | 86.26 317 | 35.81 326 | 41.95 343 | 75.89 338 |
|
ab-mvs | | | 80.18 120 | 78.31 133 | 85.80 78 | 88.44 168 | 65.49 148 | 83.00 289 | 92.67 110 | 71.82 169 | 77.36 102 | 85.01 202 | 54.50 176 | 96.59 93 | 76.35 104 | 75.63 181 | 95.32 44 |
|
pmmvs-eth3d | | | 65.53 294 | 62.32 298 | 75.19 290 | 69.39 341 | 59.59 259 | 82.80 290 | 83.43 313 | 62.52 277 | 51.30 318 | 72.49 311 | 32.86 314 | 87.16 313 | 55.32 252 | 50.73 325 | 78.83 329 |
|
new-patchmatchnet | | | 59.30 315 | 56.48 315 | 67.79 324 | 65.86 345 | 44.19 337 | 82.47 291 | 81.77 321 | 59.94 295 | 43.65 342 | 66.20 335 | 27.67 333 | 81.68 339 | 39.34 312 | 41.40 344 | 77.50 335 |
|
CostFormer | | | 82.33 90 | 81.15 93 | 85.86 75 | 89.01 157 | 68.46 56 | 82.39 292 | 93.01 98 | 75.59 88 | 80.25 72 | 81.57 242 | 72.03 16 | 94.96 144 | 79.06 86 | 77.48 168 | 94.16 89 |
|
v52 | | | 69.80 266 | 67.01 269 | 78.15 267 | 71.84 332 | 60.10 252 | 82.02 293 | 87.39 269 | 64.48 261 | 57.80 283 | 75.97 297 | 41.47 273 | 92.90 224 | 63.00 213 | 59.13 297 | 81.45 300 |
|
V4 | | | 69.80 266 | 67.02 268 | 78.15 267 | 71.86 331 | 60.10 252 | 82.02 293 | 87.39 269 | 64.48 261 | 57.78 284 | 75.98 296 | 41.49 272 | 92.90 224 | 63.00 213 | 59.16 296 | 81.44 301 |
|
MTAPA | | | 83.91 68 | 83.38 65 | 85.50 87 | 91.89 86 | 65.16 153 | 81.75 295 | 92.23 124 | 75.32 95 | 80.53 68 | 95.21 37 | 56.06 153 | 97.16 66 | 84.86 45 | 92.55 46 | 94.18 86 |
|
tpmrst | | | 80.57 113 | 79.14 126 | 84.84 109 | 90.10 128 | 68.28 62 | 81.70 296 | 89.72 220 | 77.63 62 | 75.96 113 | 79.54 273 | 64.94 65 | 92.71 229 | 75.43 107 | 77.28 171 | 93.55 112 |
|
test123 | | | 6.92 348 | 9.21 349 | 0.08 357 | 0.03 373 | 0.05 372 | 81.65 297 | 0.01 375 | 0.02 368 | 0.14 370 | 0.85 370 | 0.03 374 | 0.02 369 | 0.12 367 | 0.00 368 | 0.16 366 |
|
test1235678 | | | 55.73 320 | 52.74 320 | 64.68 331 | 60.16 353 | 35.56 353 | 81.65 297 | 81.46 322 | 51.27 325 | 38.93 349 | 62.82 342 | 17.44 349 | 78.58 349 | 30.87 346 | 50.09 332 | 79.89 321 |
|
tpmp4_e23 | | | 78.85 145 | 76.55 162 | 85.77 80 | 89.25 150 | 68.39 58 | 81.63 299 | 91.38 160 | 70.40 197 | 75.21 122 | 79.22 275 | 67.37 35 | 94.79 149 | 58.98 241 | 75.51 182 | 94.13 91 |
|
tpm2 | | | 79.80 129 | 77.95 139 | 85.34 96 | 88.28 171 | 68.26 63 | 81.56 300 | 91.42 158 | 70.11 200 | 77.59 100 | 80.50 259 | 67.40 34 | 94.26 179 | 67.34 172 | 77.35 169 | 93.51 113 |
|
UnsupCasMVSNet_eth | | | 65.79 293 | 63.10 292 | 73.88 298 | 70.71 336 | 50.29 323 | 81.09 301 | 89.88 212 | 72.58 146 | 49.25 324 | 74.77 304 | 32.57 317 | 87.43 311 | 55.96 250 | 41.04 345 | 83.90 271 |
|
Patchmatch-test1 | | | 75.00 215 | 71.80 234 | 84.58 117 | 86.63 198 | 70.08 26 | 81.06 302 | 89.19 234 | 71.60 176 | 70.01 176 | 77.16 290 | 45.53 255 | 88.63 300 | 51.79 264 | 73.27 197 | 95.02 61 |
|
OurMVSNet-221017-0 | | | 64.68 296 | 62.17 299 | 72.21 312 | 76.08 321 | 47.35 333 | 80.67 303 | 81.02 325 | 56.19 313 | 51.60 315 | 79.66 271 | 27.05 336 | 88.56 303 | 53.60 259 | 53.63 316 | 80.71 314 |
|
XVG-OURS-SEG-HR | | | 74.70 224 | 73.08 220 | 79.57 234 | 78.25 308 | 57.33 284 | 80.49 304 | 87.32 272 | 63.22 271 | 68.76 198 | 90.12 134 | 44.89 261 | 91.59 262 | 70.55 147 | 74.09 192 | 89.79 174 |
|
pmmvs3 | | | 55.51 321 | 51.50 324 | 67.53 325 | 57.90 355 | 50.93 320 | 80.37 305 | 73.66 345 | 40.63 351 | 44.15 340 | 64.75 340 | 16.30 350 | 78.97 347 | 44.77 292 | 40.98 346 | 72.69 342 |
|
XVG-OURS | | | 74.25 227 | 72.46 228 | 79.63 232 | 78.45 307 | 57.59 281 | 80.33 306 | 87.39 269 | 63.86 267 | 68.76 198 | 89.62 145 | 40.50 278 | 91.72 254 | 69.00 160 | 74.25 190 | 89.58 177 |
|
MDTV_nov1_ep13 | | | | 72.61 225 | | 89.06 155 | 68.48 55 | 80.33 306 | 90.11 204 | 71.84 168 | 71.81 157 | 75.92 299 | 53.01 196 | 93.92 199 | 48.04 277 | 73.38 196 | |
|
EPMVS | | | 78.49 156 | 75.98 169 | 86.02 70 | 91.21 108 | 69.68 35 | 80.23 308 | 91.20 165 | 75.25 97 | 72.48 148 | 78.11 280 | 54.65 175 | 93.69 206 | 57.66 246 | 83.04 125 | 94.69 68 |
|
AllTest | | | 61.66 307 | 58.06 308 | 72.46 309 | 79.57 290 | 51.42 317 | 80.17 309 | 68.61 353 | 51.25 326 | 45.88 331 | 81.23 247 | 19.86 347 | 86.58 315 | 38.98 313 | 57.01 305 | 79.39 325 |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 256 | 80.13 310 | | 67.65 233 | 72.79 141 | | 54.33 180 | | 59.83 235 | | 92.58 137 |
|
LCM-MVSNet-Re | | | 72.93 236 | 71.84 232 | 76.18 287 | 88.49 165 | 48.02 329 | 80.07 311 | 70.17 351 | 73.96 120 | 52.25 313 | 80.09 267 | 49.98 219 | 88.24 306 | 67.35 171 | 84.23 121 | 92.28 145 |
|
PatchmatchNet | | | 77.46 170 | 74.63 191 | 85.96 72 | 89.55 145 | 70.35 23 | 79.97 312 | 89.55 223 | 72.23 155 | 70.94 163 | 76.91 292 | 57.03 135 | 92.79 227 | 54.27 255 | 81.17 138 | 94.74 67 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
dp | | | 75.01 214 | 72.09 231 | 83.76 131 | 89.28 149 | 66.22 131 | 79.96 313 | 89.75 215 | 71.16 184 | 67.80 215 | 77.19 288 | 51.81 206 | 92.54 234 | 50.39 268 | 71.44 214 | 92.51 139 |
|
1111 | | | 56.66 319 | 54.98 318 | 61.69 333 | 61.99 350 | 31.38 355 | 79.81 314 | 83.17 317 | 45.66 339 | 41.94 344 | 65.44 336 | 41.50 270 | 79.56 345 | 27.64 348 | 47.68 335 | 74.14 341 |
|
.test1245 | | | 46.52 328 | 49.68 325 | 37.02 350 | 61.99 350 | 31.38 355 | 79.81 314 | 83.17 317 | 45.66 339 | 41.94 344 | 65.44 336 | 41.50 270 | 79.56 345 | 27.64 348 | 0.01 366 | 0.13 367 |
|
test_post1 | | | | | | | | 78.95 316 | | | | 20.70 365 | 53.05 195 | 91.50 268 | 60.43 232 | | |
|
MIMVSNet1 | | | 60.16 312 | 57.33 312 | 68.67 322 | 69.71 340 | 44.13 338 | 78.92 317 | 84.21 305 | 55.05 317 | 44.63 338 | 71.85 320 | 23.91 340 | 81.54 340 | 32.63 342 | 55.03 312 | 80.35 317 |
|
UnsupCasMVSNet_bld | | | 61.60 308 | 57.71 309 | 73.29 303 | 68.73 343 | 51.64 314 | 78.61 318 | 89.05 242 | 57.20 308 | 46.11 330 | 61.96 343 | 28.70 331 | 88.60 302 | 50.08 270 | 38.90 347 | 79.63 324 |
|
XVG-ACMP-BASELINE | | | 68.04 279 | 65.53 275 | 75.56 289 | 74.06 327 | 52.37 311 | 78.43 319 | 85.88 295 | 62.03 280 | 58.91 277 | 81.21 251 | 20.38 346 | 91.15 271 | 60.69 231 | 68.18 237 | 83.16 282 |
|
USDC | | | 67.43 286 | 64.51 283 | 76.19 286 | 77.94 311 | 55.29 300 | 78.38 320 | 85.00 301 | 73.17 136 | 48.36 326 | 80.37 261 | 21.23 345 | 92.48 237 | 52.15 263 | 64.02 271 | 80.81 313 |
|
TinyColmap | | | 60.32 310 | 56.42 316 | 72.00 315 | 78.78 303 | 53.18 309 | 78.36 321 | 75.64 338 | 52.30 322 | 41.59 346 | 75.82 300 | 14.76 353 | 88.35 305 | 35.84 325 | 54.71 314 | 74.46 340 |
|
tpmvs | | | 72.88 238 | 69.76 246 | 82.22 176 | 90.98 111 | 67.05 92 | 78.22 322 | 88.30 258 | 63.10 273 | 64.35 247 | 74.98 302 | 55.09 165 | 94.27 177 | 43.25 294 | 69.57 227 | 85.34 260 |
|
tpm cat1 | | | 75.30 209 | 72.21 230 | 84.58 117 | 88.52 164 | 67.77 72 | 78.16 323 | 88.02 263 | 61.88 283 | 68.45 207 | 76.37 293 | 60.65 100 | 94.03 194 | 53.77 258 | 74.11 191 | 91.93 149 |
|
CMPMVS | | 48.56 21 | 66.77 288 | 64.41 285 | 73.84 299 | 70.65 337 | 50.31 322 | 77.79 324 | 85.73 297 | 45.54 341 | 44.76 337 | 82.14 232 | 35.40 308 | 90.14 283 | 63.18 211 | 74.54 188 | 81.07 310 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PM-MVS | | | 59.40 313 | 56.59 314 | 67.84 323 | 63.63 346 | 41.86 342 | 76.76 325 | 63.22 359 | 59.01 300 | 51.07 319 | 72.27 317 | 11.72 355 | 83.25 331 | 61.34 227 | 50.28 331 | 78.39 332 |
|
TDRefinement | | | 55.28 322 | 51.58 323 | 66.39 329 | 59.53 354 | 46.15 335 | 76.23 326 | 72.80 346 | 44.60 344 | 42.49 343 | 76.28 294 | 15.29 351 | 82.39 335 | 33.20 339 | 43.75 341 | 70.62 346 |
|
RPSCF | | | 64.24 299 | 61.98 300 | 71.01 318 | 76.10 320 | 45.00 336 | 75.83 327 | 75.94 337 | 46.94 337 | 58.96 276 | 84.59 207 | 31.40 323 | 82.00 338 | 47.76 279 | 60.33 294 | 86.04 244 |
|
GG-mvs-BLEND | | | | | 86.53 55 | 91.91 85 | 69.67 36 | 75.02 328 | 94.75 30 | | 78.67 90 | 90.85 119 | 77.91 2 | 94.56 158 | 72.25 128 | 93.74 31 | 95.36 41 |
|
testmv | | | 46.98 327 | 43.53 330 | 57.35 338 | 47.75 361 | 30.41 358 | 74.99 329 | 77.69 332 | 42.84 349 | 28.03 353 | 53.36 348 | 8.18 362 | 71.18 353 | 24.36 352 | 34.55 350 | 70.46 347 |
|
MIMVSNet | | | 71.64 248 | 68.44 257 | 81.23 197 | 81.97 251 | 64.44 171 | 73.05 330 | 88.80 249 | 69.67 204 | 64.59 241 | 74.79 303 | 32.79 315 | 87.82 309 | 53.99 256 | 76.35 178 | 91.42 155 |
|
gg-mvs-nofinetune | | | 77.18 180 | 74.31 197 | 85.80 78 | 91.42 104 | 68.36 59 | 71.78 331 | 94.72 31 | 49.61 330 | 77.12 105 | 45.92 352 | 77.41 3 | 93.98 196 | 67.62 170 | 93.16 39 | 95.05 57 |
|
MVS-HIRNet | | | 60.25 311 | 55.55 317 | 74.35 296 | 84.37 227 | 56.57 294 | 71.64 332 | 74.11 344 | 34.44 353 | 45.54 335 | 42.24 355 | 31.11 325 | 89.81 293 | 40.36 309 | 76.10 179 | 76.67 337 |
|
test12356 | | | 47.51 326 | 44.82 329 | 55.56 339 | 52.53 356 | 21.09 366 | 71.45 333 | 76.03 336 | 44.14 346 | 30.69 352 | 58.18 346 | 9.01 361 | 76.14 350 | 26.95 350 | 34.43 352 | 69.46 348 |
|
CR-MVSNet | | | 73.79 231 | 70.82 240 | 82.70 151 | 83.15 242 | 67.96 69 | 70.25 334 | 84.00 309 | 73.67 128 | 69.97 178 | 72.41 314 | 57.82 128 | 89.48 296 | 52.99 262 | 73.13 198 | 90.64 166 |
|
RPMNet | | | 69.58 268 | 65.21 278 | 82.70 151 | 83.15 242 | 67.96 69 | 70.25 334 | 86.15 290 | 46.83 338 | 69.97 178 | 65.10 339 | 56.48 149 | 89.48 296 | 35.79 327 | 73.13 198 | 90.64 166 |
|
Patchmatch-RL test | | | 68.17 278 | 64.49 284 | 79.19 246 | 71.22 334 | 53.93 306 | 70.07 336 | 71.54 350 | 69.22 209 | 56.79 288 | 62.89 341 | 56.58 147 | 88.61 301 | 69.53 155 | 52.61 317 | 95.03 60 |
|
CHOSEN 280x420 | | | 77.35 175 | 76.95 158 | 78.55 260 | 87.07 193 | 62.68 214 | 69.71 337 | 82.95 319 | 68.80 216 | 71.48 161 | 87.27 181 | 66.03 46 | 84.00 325 | 76.47 103 | 82.81 128 | 88.95 181 |
|
Patchmtry | | | 67.53 284 | 63.93 287 | 78.34 261 | 82.12 249 | 64.38 174 | 68.72 338 | 84.00 309 | 48.23 335 | 59.24 272 | 72.41 314 | 57.82 128 | 89.27 298 | 46.10 284 | 56.68 307 | 81.36 303 |
|
LF4IMVS | | | 54.01 323 | 52.12 321 | 59.69 335 | 62.41 349 | 39.91 347 | 68.59 339 | 68.28 355 | 42.96 348 | 44.55 339 | 75.18 301 | 14.09 354 | 68.39 355 | 41.36 305 | 51.68 322 | 70.78 345 |
|
new_pmnet | | | 49.31 325 | 46.44 328 | 57.93 336 | 62.84 348 | 40.74 344 | 68.47 340 | 62.96 360 | 36.48 352 | 35.09 350 | 57.81 347 | 14.97 352 | 72.18 352 | 32.86 341 | 46.44 337 | 60.88 353 |
|
ADS-MVSNet2 | | | 66.90 287 | 63.44 290 | 77.26 280 | 88.06 177 | 60.70 241 | 68.01 341 | 75.56 340 | 57.57 304 | 64.48 243 | 69.87 329 | 38.68 282 | 84.10 321 | 40.87 306 | 67.89 240 | 86.97 223 |
|
ADS-MVSNet | | | 68.54 275 | 64.38 286 | 81.03 208 | 88.06 177 | 66.90 97 | 68.01 341 | 84.02 308 | 57.57 304 | 64.48 243 | 69.87 329 | 38.68 282 | 89.21 299 | 40.87 306 | 67.89 240 | 86.97 223 |
|
PatchT | | | 69.11 272 | 65.37 277 | 80.32 215 | 82.07 250 | 63.68 192 | 67.96 343 | 87.62 268 | 50.86 328 | 69.37 188 | 65.18 338 | 57.09 133 | 88.53 304 | 41.59 304 | 66.60 247 | 88.74 185 |
|
LTVRE_ROB | | 59.60 19 | 66.27 290 | 63.54 289 | 74.45 295 | 84.00 233 | 51.55 315 | 67.08 344 | 83.53 312 | 58.78 301 | 54.94 295 | 80.31 262 | 34.54 312 | 93.23 214 | 40.64 308 | 68.03 238 | 78.58 331 |
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 |
PNet_i23d | | | 32.77 336 | 29.98 338 | 41.11 348 | 48.05 359 | 29.17 360 | 65.82 345 | 50.02 364 | 21.42 358 | 14.74 362 | 37.19 359 | 1.11 371 | 55.11 362 | 19.75 357 | 11.77 359 | 39.06 357 |
|
FPMVS | | | 45.64 329 | 43.10 331 | 53.23 343 | 51.42 358 | 36.46 352 | 64.97 346 | 71.91 348 | 29.13 355 | 27.53 354 | 61.55 344 | 9.83 359 | 65.01 359 | 16.00 359 | 55.58 310 | 58.22 354 |
|
DSMNet-mixed | | | 56.78 317 | 54.44 319 | 63.79 332 | 63.21 347 | 29.44 359 | 64.43 347 | 64.10 358 | 42.12 350 | 51.32 317 | 71.60 325 | 31.76 320 | 75.04 351 | 36.23 324 | 65.20 256 | 86.87 227 |
|
ANet_high | | | 40.27 332 | 35.20 334 | 55.47 340 | 34.74 367 | 34.47 354 | 63.84 348 | 71.56 349 | 48.42 333 | 18.80 359 | 41.08 356 | 9.52 360 | 64.45 360 | 20.18 356 | 8.66 364 | 67.49 350 |
|
LP | | | 56.71 318 | 51.64 322 | 71.91 316 | 80.08 286 | 60.33 247 | 61.72 349 | 75.61 339 | 43.87 347 | 43.76 341 | 60.30 345 | 30.46 327 | 84.05 322 | 22.94 354 | 46.06 338 | 71.34 344 |
|
no-one | | | 44.13 330 | 38.39 332 | 61.34 334 | 45.91 363 | 41.94 341 | 61.67 350 | 75.07 343 | 45.05 343 | 20.07 357 | 40.68 358 | 11.58 356 | 79.82 343 | 30.18 347 | 15.30 357 | 62.26 352 |
|
E-PMN | | | 24.61 340 | 24.00 341 | 26.45 353 | 43.74 364 | 18.44 368 | 60.86 351 | 39.66 366 | 15.11 361 | 9.53 365 | 22.10 362 | 6.52 364 | 46.94 364 | 8.31 363 | 10.14 360 | 13.98 363 |
|
EMVS | | | 23.76 342 | 23.20 343 | 25.46 354 | 41.52 365 | 16.90 369 | 60.56 352 | 38.79 369 | 14.62 362 | 8.99 366 | 20.24 366 | 7.35 363 | 45.82 365 | 7.25 364 | 9.46 362 | 13.64 364 |
|
PMMVS2 | | | 37.93 333 | 33.61 335 | 50.92 344 | 46.31 362 | 24.76 364 | 60.55 353 | 50.05 363 | 28.94 356 | 20.93 356 | 47.59 350 | 4.41 368 | 65.13 358 | 25.14 351 | 18.55 356 | 62.87 351 |
|
Patchmatch-test | | | 65.86 292 | 60.94 303 | 80.62 212 | 83.75 234 | 58.83 269 | 58.91 354 | 75.26 342 | 44.50 345 | 50.95 320 | 77.09 291 | 58.81 121 | 87.90 308 | 35.13 334 | 64.03 270 | 95.12 55 |
|
JIA-IIPM | | | 66.06 291 | 62.45 297 | 76.88 282 | 81.42 256 | 54.45 305 | 57.49 355 | 88.67 251 | 49.36 331 | 63.86 249 | 46.86 351 | 56.06 153 | 90.25 277 | 49.53 272 | 68.83 232 | 85.95 246 |
|
LCM-MVSNet | | | 40.54 331 | 35.79 333 | 54.76 342 | 36.92 366 | 30.81 357 | 51.41 356 | 69.02 352 | 22.07 357 | 24.63 355 | 45.37 353 | 4.56 367 | 65.81 357 | 33.67 337 | 34.50 351 | 67.67 349 |
|
PMVS | | 26.43 22 | 31.84 337 | 28.16 339 | 42.89 347 | 25.87 370 | 27.58 362 | 50.92 357 | 49.78 365 | 21.37 359 | 14.17 363 | 40.81 357 | 2.01 369 | 66.62 356 | 9.61 362 | 38.88 348 | 34.49 360 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | | 69.61 320 | 61.38 352 | 41.35 343 | 49.07 358 | 85.86 296 | | 50.18 323 | 66.40 334 | 10.16 358 | 88.14 307 | 45.73 286 | 44.20 340 | 79.32 327 |
|
wuykxyi23d | | | 29.03 339 | 23.09 344 | 46.84 345 | 31.67 369 | 28.82 361 | 43.46 359 | 57.72 362 | 14.39 363 | 7.52 367 | 20.84 364 | 0.64 372 | 60.29 361 | 21.57 355 | 10.04 361 | 51.40 356 |
|
tmp_tt | | | 22.26 343 | 23.75 342 | 17.80 355 | 5.23 371 | 12.06 370 | 35.26 360 | 39.48 367 | 2.82 366 | 18.94 358 | 44.20 354 | 22.23 344 | 24.64 367 | 36.30 323 | 9.31 363 | 16.69 362 |
|
testpf | | | 57.17 316 | 56.93 313 | 57.88 337 | 79.13 299 | 42.40 340 | 34.23 361 | 85.97 294 | 52.64 321 | 47.66 329 | 66.50 333 | 36.33 306 | 79.65 344 | 53.60 259 | 56.31 308 | 51.60 355 |
|
MVE | | 24.84 23 | 24.35 341 | 19.77 345 | 38.09 349 | 34.56 368 | 26.92 363 | 26.57 362 | 38.87 368 | 11.73 364 | 11.37 364 | 27.44 360 | 1.37 370 | 50.42 363 | 11.41 361 | 14.60 358 | 36.93 358 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 11.30 345 | 10.95 346 | 12.33 356 | 48.05 359 | 19.89 367 | 25.89 363 | 1.92 373 | 3.58 365 | 3.12 368 | 1.37 368 | 0.64 372 | 15.77 368 | 6.23 365 | 7.77 365 | 1.35 365 |
|
Gipuma | | | 34.91 335 | 31.44 337 | 45.30 346 | 70.99 335 | 39.64 349 | 19.85 364 | 72.56 347 | 20.10 360 | 16.16 361 | 21.47 363 | 5.08 366 | 71.16 354 | 13.07 360 | 43.70 342 | 25.08 361 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test_part1 | | | | | 0.00 359 | | 0.00 374 | 0.00 365 | 94.26 46 | | | | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
v1.0 | | | 37.26 334 | 49.67 326 | 0.00 359 | 96.29 7 | 0.00 374 | 0.00 365 | 94.26 46 | 68.52 223 | 90.78 4 | 97.23 3 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
cdsmvs_eth3d_5k | | | 19.86 344 | 26.47 340 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 93.45 69 | 0.00 369 | 0.00 371 | 95.27 33 | 49.56 223 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
pcd_1.5k_mvsjas | | | 4.46 349 | 5.95 350 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 53.55 189 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
pcd1.5k->3k | | | 31.17 338 | 31.85 336 | 29.12 352 | 81.48 253 | 0.00 374 | 0.00 365 | 91.79 144 | 0.00 369 | 0.00 371 | 0.00 371 | 41.05 276 | 0.00 371 | 0.00 368 | 72.34 208 | 87.36 217 |
|
sosnet-low-res | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
sosnet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
uncertanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
Regformer | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
ab-mvs-re | | | 7.91 346 | 10.55 347 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 94.95 43 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
uanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 368 | 0.00 369 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.68 69 |
|
test_part2 | | | | | | 96.29 7 | 68.16 66 | | | | 90.78 4 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 127 | | | | 94.68 69 |
|
sam_mvs | | | | | | | | | | | | | 54.91 172 | | | | |
|
semantic-postprocess | | | | | 76.32 285 | 81.48 253 | 60.67 242 | | 85.99 293 | 66.17 243 | 59.50 271 | 78.88 276 | 45.51 256 | 83.65 327 | 62.58 221 | 61.93 280 | 84.63 267 |
|
MTGPA | | | | | | | | | 92.23 124 | | | | | | | | |
|
test_post | | | | | | | | | | | | 23.01 361 | 56.49 148 | 92.67 231 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 332 | 57.62 130 | 90.25 277 | | | |
|
gm-plane-assit | | | | | | 88.42 169 | 67.04 93 | | | 78.62 52 | | 91.83 108 | | 97.37 52 | 76.57 102 | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 11 | 94.96 9 | 95.29 45 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 34 | 94.75 18 | 95.33 42 |
|
agg_prior | | | | | | 94.16 36 | 66.97 94 | | 93.31 82 | | 84.49 41 | | | 96.75 90 | | | |
|
TestCases | | | | | 72.46 309 | 79.57 290 | 51.42 317 | | 68.61 353 | 51.25 326 | 45.88 331 | 81.23 247 | 19.86 347 | 86.58 315 | 38.98 313 | 57.01 305 | 79.39 325 |
|
test_prior | | | | | 86.42 59 | 94.71 24 | 67.35 83 | | 93.10 95 | | | | | 96.84 86 | | | 95.05 57 |
|
æ–°å‡ ä½•1 | | | | | 84.73 112 | 92.32 73 | 64.28 179 | | 91.46 157 | 59.56 297 | 79.77 76 | 92.90 90 | 56.95 140 | 96.57 95 | 63.40 208 | 92.91 41 | 93.34 115 |
|
旧先验1 | | | | | | 91.94 82 | 60.74 240 | | 91.50 155 | | | 94.36 58 | 65.23 54 | | | 91.84 54 | 94.55 74 |
|
原ACMM1 | | | | | 84.42 121 | 93.21 53 | 64.27 180 | | 93.40 78 | 65.39 249 | 79.51 79 | 92.50 97 | 58.11 125 | 96.69 92 | 65.27 194 | 93.96 25 | 92.32 143 |
|
testdata2 | | | | | | | | | | | | | | 96.09 107 | 61.26 228 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 47 | | | | |
|
testdata | | | | | 81.34 195 | 89.02 156 | 57.72 278 | | 89.84 213 | 58.65 302 | 85.32 34 | 94.09 68 | 57.03 135 | 93.28 213 | 69.34 157 | 90.56 72 | 93.03 126 |
|
test12 | | | | | 87.09 40 | 94.60 26 | 68.86 48 | | 92.91 102 | | 82.67 54 | | 65.44 53 | 97.55 44 | | 93.69 33 | 94.84 65 |
|
plane_prior7 | | | | | | 86.94 194 | 61.51 229 | | | | | | | | | | |
|
plane_prior6 | | | | | | 87.23 189 | 62.32 217 | | | | | | 50.66 213 | | | | |
|
plane_prior5 | | | | | | | | | 91.31 162 | | | | | 95.55 132 | 76.74 100 | 78.53 155 | 88.39 191 |
|
plane_prior4 | | | | | | | | | | | | 89.14 148 | | | | | |
|
plane_prior3 | | | | | | | 61.95 225 | | | 79.09 44 | 72.53 146 | | | | | | |
|
plane_prior1 | | | | | | 87.15 191 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 376 | | | | | | | | |
|
nn | | | | | | | | | 0.00 376 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 357 | | | | | | | | |
|
lessismore_v0 | | | | | 73.72 300 | 72.93 329 | 47.83 331 | | 61.72 361 | | 45.86 333 | 73.76 305 | 28.63 332 | 89.81 293 | 47.75 280 | 31.37 353 | 83.53 274 |
|
LGP-MVS_train | | | | | 79.56 235 | 84.31 228 | 59.37 263 | | 89.73 218 | 69.49 205 | 64.86 239 | 88.42 153 | 38.65 284 | 94.30 175 | 72.56 125 | 72.76 202 | 85.01 262 |
|
test11 | | | | | | | | | 93.01 98 | | | | | | | | |
|
door | | | | | | | | | 66.57 356 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 193 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 96 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 127 | | | 95.61 128 | | | 88.63 186 |
|
HQP3-MVS | | | | | | | | | 91.70 148 | | | | | | | 78.90 151 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 209 | | | | |
|
NP-MVS | | | | | | 87.41 188 | 63.04 204 | | | | | 90.30 128 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 210 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 225 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 182 | | | | |
|
ITE_SJBPF | | | | | 70.43 319 | 74.44 325 | 47.06 334 | | 77.32 333 | 60.16 293 | 54.04 303 | 83.53 214 | 23.30 342 | 84.01 324 | 43.07 295 | 61.58 287 | 80.21 320 |
|
DeepMVS_CX | | | | | 34.71 351 | 51.45 357 | 24.73 365 | | 28.48 371 | 31.46 354 | 17.49 360 | 52.75 349 | 5.80 365 | 42.60 366 | 18.18 358 | 19.42 355 | 36.81 359 |
|