MG-MVS | | | 87.11 26 | 86.27 34 | 89.62 6 | 97.79 1 | 76.27 4 | 94.96 38 | 94.49 36 | 78.74 60 | 83.87 53 | 92.94 97 | 64.34 67 | 96.94 87 | 75.19 117 | 94.09 28 | 95.66 37 |
|
MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 3 | 97.66 2 | 73.37 8 | 97.13 2 | 95.58 8 | 89.33 1 | 85.77 32 | 96.26 16 | 72.84 17 | 99.38 1 | 92.64 3 | 95.93 6 | 97.08 5 |
|
DP-MVS Recon | | | 82.73 91 | 81.65 97 | 85.98 73 | 97.31 3 | 67.06 94 | 95.15 31 | 91.99 127 | 69.08 208 | 76.50 118 | 93.89 80 | 54.48 164 | 98.20 27 | 70.76 148 | 85.66 113 | 92.69 138 |
|
testtj | | | 86.62 34 | 86.66 33 | 86.50 58 | 96.95 4 | 65.70 131 | 94.41 44 | 93.45 69 | 67.74 217 | 86.19 27 | 96.39 13 | 64.38 66 | 97.91 34 | 87.33 33 | 93.14 44 | 95.90 33 |
|
CNVR-MVS | | | 90.32 4 | 90.89 4 | 88.61 13 | 96.76 5 | 70.65 20 | 96.47 10 | 94.83 22 | 84.83 8 | 89.07 14 | 96.80 7 | 70.86 24 | 99.06 7 | 92.64 3 | 95.71 7 | 96.12 25 |
|
NCCC | | | 89.07 10 | 89.46 10 | 87.91 18 | 96.60 6 | 69.05 46 | 96.38 11 | 94.64 31 | 84.42 9 | 86.74 25 | 96.20 17 | 66.56 46 | 98.76 14 | 89.03 22 | 94.56 24 | 95.92 32 |
|
test_0728_SECOND | | | | | 88.70 11 | 96.45 7 | 70.43 23 | 96.64 6 | 94.37 42 | | | | | 99.15 2 | 91.91 5 | 94.90 14 | 96.51 15 |
|
DVP-MVS | | | 89.41 8 | 89.73 9 | 88.45 15 | 96.40 8 | 69.99 28 | 96.64 6 | 94.52 34 | 71.92 161 | 90.55 8 | 96.93 5 | 73.77 12 | 99.08 5 | 91.91 5 | 94.90 14 | 96.29 21 |
|
test0726 | | | | | | 96.40 8 | 69.99 28 | 96.76 4 | 94.33 43 | 71.92 161 | 91.89 2 | 97.11 4 | 73.77 12 | | | | |
|
AdaColmap | | | 78.94 150 | 77.00 163 | 84.76 113 | 96.34 10 | 65.86 127 | 92.66 109 | 87.97 253 | 62.18 257 | 70.56 172 | 92.37 113 | 43.53 244 | 97.35 59 | 64.50 202 | 82.86 132 | 91.05 169 |
|
test_part2 | | | | | | 96.29 11 | 68.16 68 | | | | 90.78 6 | | | | | | |
|
DPE-MVS | | | 88.77 11 | 89.21 11 | 87.45 30 | 96.26 12 | 67.56 81 | 94.17 47 | 94.15 48 | 68.77 211 | 90.74 7 | 97.27 2 | 76.09 7 | 98.49 19 | 90.58 12 | 94.91 13 | 96.30 20 |
|
MAR-MVS | | | 84.18 68 | 83.43 68 | 86.44 60 | 96.25 13 | 65.93 126 | 94.28 46 | 94.27 45 | 74.41 108 | 79.16 91 | 95.61 29 | 53.99 168 | 98.88 12 | 69.62 156 | 93.26 42 | 94.50 84 |
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 |
API-MVS | | | 82.28 98 | 80.53 111 | 87.54 28 | 96.13 14 | 70.59 21 | 93.63 74 | 91.04 167 | 65.72 234 | 75.45 127 | 92.83 103 | 56.11 148 | 98.89 11 | 64.10 204 | 89.75 85 | 93.15 127 |
|
APDe-MVS | | | 87.54 20 | 87.84 17 | 86.65 50 | 96.07 15 | 66.30 116 | 94.84 40 | 93.78 54 | 69.35 202 | 88.39 16 | 96.34 15 | 67.74 39 | 97.66 44 | 90.62 11 | 93.44 40 | 96.01 29 |
|
PAPR | | | 85.15 51 | 84.47 55 | 87.18 36 | 96.02 16 | 68.29 63 | 91.85 138 | 93.00 91 | 76.59 84 | 79.03 92 | 95.00 47 | 61.59 94 | 97.61 48 | 78.16 101 | 89.00 87 | 95.63 38 |
|
APD-MVS | | | 85.93 43 | 85.99 39 | 85.76 83 | 95.98 17 | 65.21 141 | 93.59 76 | 92.58 107 | 66.54 226 | 86.17 28 | 95.88 22 | 63.83 72 | 97.00 79 | 86.39 41 | 92.94 45 | 95.06 63 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS_fast | | 79.48 2 | 87.95 16 | 88.00 16 | 87.79 21 | 95.86 18 | 68.32 62 | 95.74 17 | 94.11 49 | 83.82 11 | 83.49 54 | 96.19 18 | 64.53 65 | 98.44 21 | 83.42 62 | 94.88 17 | 96.61 10 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DP-MVS | | | 69.90 241 | 66.48 247 | 80.14 210 | 95.36 19 | 62.93 194 | 89.56 203 | 76.11 306 | 50.27 304 | 57.69 274 | 85.23 196 | 39.68 255 | 95.73 129 | 33.35 315 | 71.05 215 | 81.78 285 |
|
114514_t | | | 79.17 146 | 77.67 150 | 83.68 140 | 95.32 20 | 65.53 136 | 92.85 101 | 91.60 145 | 63.49 246 | 67.92 206 | 90.63 136 | 46.65 228 | 95.72 133 | 67.01 177 | 83.54 130 | 89.79 181 |
|
Regformer-1 | | | 87.24 24 | 87.60 22 | 86.15 71 | 95.14 21 | 65.83 129 | 93.95 60 | 95.12 14 | 82.11 21 | 84.25 45 | 95.73 25 | 67.88 38 | 98.35 24 | 85.60 45 | 88.64 90 | 94.26 89 |
|
Regformer-2 | | | 87.00 28 | 87.43 24 | 85.71 86 | 95.14 21 | 64.73 153 | 93.95 60 | 94.95 19 | 81.69 27 | 84.03 51 | 95.73 25 | 67.35 41 | 98.19 28 | 85.40 47 | 88.64 90 | 94.20 91 |
|
HPM-MVS++ | | | 89.37 9 | 89.95 8 | 87.64 23 | 95.10 23 | 68.23 67 | 95.24 28 | 94.49 36 | 82.43 17 | 88.90 15 | 96.35 14 | 71.89 23 | 98.63 16 | 88.76 24 | 96.40 3 | 96.06 26 |
|
CSCG | | | 86.87 29 | 86.26 35 | 88.72 10 | 95.05 24 | 70.79 19 | 93.83 70 | 95.33 11 | 68.48 215 | 77.63 105 | 94.35 68 | 73.04 15 | 98.45 20 | 84.92 51 | 93.71 36 | 96.92 7 |
|
LFMVS | | | 84.34 63 | 82.73 84 | 89.18 9 | 94.76 25 | 73.25 9 | 94.99 37 | 91.89 132 | 71.90 163 | 82.16 60 | 93.49 87 | 47.98 219 | 97.05 74 | 82.55 69 | 84.82 118 | 97.25 3 |
|
CDPH-MVS | | | 85.71 46 | 85.46 47 | 86.46 59 | 94.75 26 | 67.19 90 | 93.89 65 | 92.83 96 | 70.90 186 | 83.09 56 | 95.28 37 | 63.62 76 | 97.36 58 | 80.63 84 | 94.18 27 | 94.84 71 |
|
test_prior3 | | | 87.38 22 | 87.70 19 | 86.42 61 | 94.71 27 | 67.35 86 | 95.10 33 | 93.10 87 | 75.40 97 | 85.25 39 | 95.61 29 | 67.94 35 | 96.84 91 | 87.47 30 | 94.77 19 | 95.05 64 |
|
test_prior | | | | | 86.42 61 | 94.71 27 | 67.35 86 | | 93.10 87 | | | | | 96.84 91 | | | 95.05 64 |
|
test12 | | | | | 87.09 40 | 94.60 29 | 68.86 50 | | 92.91 93 | | 82.67 58 | | 65.44 57 | 97.55 49 | | 93.69 37 | 94.84 71 |
|
test_yl | | | 84.28 64 | 83.16 75 | 87.64 23 | 94.52 30 | 69.24 42 | 95.78 14 | 95.09 17 | 69.19 205 | 81.09 69 | 92.88 101 | 57.00 134 | 97.44 53 | 81.11 82 | 81.76 140 | 96.23 23 |
|
DCV-MVSNet | | | 84.28 64 | 83.16 75 | 87.64 23 | 94.52 30 | 69.24 42 | 95.78 14 | 95.09 17 | 69.19 205 | 81.09 69 | 92.88 101 | 57.00 134 | 97.44 53 | 81.11 82 | 81.76 140 | 96.23 23 |
|
CANet | | | 89.61 7 | 89.99 7 | 88.46 14 | 94.39 32 | 69.71 36 | 96.53 9 | 93.78 54 | 86.89 4 | 89.68 11 | 95.78 23 | 65.94 51 | 99.10 3 | 92.99 1 | 93.91 32 | 96.58 12 |
|
test_8 | | | | | | 94.19 33 | 67.19 90 | 94.15 50 | 93.42 72 | 71.87 165 | 85.38 37 | 95.35 33 | 68.19 32 | 96.95 86 | | | |
|
TEST9 | | | | | | 94.18 34 | 67.28 88 | 94.16 48 | 93.51 66 | 71.75 172 | 85.52 35 | 95.33 34 | 68.01 34 | 97.27 67 | | | |
|
train_agg | | | 87.21 25 | 87.42 25 | 86.60 52 | 94.18 34 | 67.28 88 | 94.16 48 | 93.51 66 | 71.87 165 | 85.52 35 | 95.33 34 | 68.19 32 | 97.27 67 | 89.09 20 | 94.90 14 | 95.25 57 |
|
Regformer-3 | | | 85.80 45 | 85.92 40 | 85.46 93 | 94.17 36 | 65.09 147 | 92.95 97 | 95.11 15 | 81.13 31 | 81.68 63 | 95.04 45 | 65.82 53 | 98.32 25 | 83.02 64 | 84.36 122 | 92.97 133 |
|
Regformer-4 | | | 85.45 48 | 85.69 45 | 84.73 114 | 94.17 36 | 63.23 188 | 92.95 97 | 94.83 22 | 80.66 34 | 81.29 66 | 95.04 45 | 65.12 59 | 98.08 31 | 82.74 66 | 84.36 122 | 92.88 137 |
|
agg_prior1 | | | 87.02 27 | 87.26 27 | 86.28 68 | 94.16 38 | 66.97 98 | 94.08 53 | 93.31 75 | 71.85 167 | 84.49 43 | 95.39 32 | 68.91 28 | 96.75 95 | 88.84 23 | 94.32 26 | 95.13 60 |
|
agg_prior | | | | | | 94.16 38 | 66.97 98 | | 93.31 75 | | 84.49 43 | | | 96.75 95 | | | |
|
PAPM_NR | | | 82.97 88 | 81.84 94 | 86.37 64 | 94.10 40 | 66.76 104 | 87.66 235 | 92.84 95 | 69.96 196 | 74.07 138 | 93.57 85 | 63.10 84 | 97.50 51 | 70.66 149 | 90.58 78 | 94.85 70 |
|
VNet | | | 86.20 39 | 85.65 46 | 87.84 20 | 93.92 41 | 69.99 28 | 95.73 19 | 95.94 6 | 78.43 62 | 86.00 30 | 93.07 94 | 58.22 120 | 97.00 79 | 85.22 48 | 84.33 125 | 96.52 14 |
|
9.14 | | | | 87.63 20 | | 93.86 42 | | 94.41 44 | 94.18 47 | 72.76 142 | 86.21 26 | 96.51 10 | 66.64 44 | 97.88 37 | 90.08 14 | 94.04 29 | |
|
save fliter | | | | | | 93.84 43 | 67.89 74 | 95.05 35 | 92.66 102 | 78.19 64 | | | | | | | |
|
PVSNet_BlendedMVS | | | 83.38 82 | 83.43 68 | 83.22 148 | 93.76 44 | 67.53 83 | 94.06 54 | 93.61 62 | 79.13 52 | 81.00 72 | 85.14 197 | 63.19 82 | 97.29 64 | 87.08 36 | 73.91 194 | 84.83 251 |
|
PVSNet_Blended | | | 86.73 33 | 86.86 30 | 86.31 67 | 93.76 44 | 67.53 83 | 96.33 12 | 93.61 62 | 82.34 18 | 81.00 72 | 93.08 92 | 63.19 82 | 97.29 64 | 87.08 36 | 91.38 68 | 94.13 97 |
|
HFP-MVS | | | 84.73 57 | 84.40 58 | 85.72 84 | 93.75 46 | 65.01 148 | 93.50 80 | 93.19 81 | 72.19 155 | 79.22 89 | 94.93 50 | 59.04 115 | 97.67 41 | 81.55 75 | 92.21 54 | 94.49 85 |
|
#test# | | | 84.98 54 | 84.74 54 | 85.72 84 | 93.75 46 | 65.01 148 | 94.09 52 | 93.19 81 | 73.55 128 | 79.22 89 | 94.93 50 | 59.04 115 | 97.67 41 | 82.66 67 | 92.21 54 | 94.49 85 |
|
Anonymous202405211 | | | 77.96 168 | 75.33 179 | 85.87 78 | 93.73 48 | 64.52 155 | 94.85 39 | 85.36 275 | 62.52 255 | 76.11 119 | 90.18 143 | 29.43 301 | 97.29 64 | 68.51 166 | 77.24 179 | 95.81 35 |
|
SD-MVS | | | 87.49 21 | 87.49 23 | 87.50 29 | 93.60 49 | 68.82 52 | 93.90 64 | 92.63 105 | 76.86 80 | 87.90 18 | 95.76 24 | 66.17 48 | 97.63 46 | 89.06 21 | 91.48 67 | 96.05 27 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
ACMMPR | | | 84.37 61 | 84.06 60 | 85.28 100 | 93.56 50 | 64.37 164 | 93.50 80 | 93.15 84 | 72.19 155 | 78.85 96 | 94.86 54 | 56.69 141 | 97.45 52 | 81.55 75 | 92.20 56 | 94.02 104 |
|
region2R | | | 84.36 62 | 84.03 61 | 85.36 98 | 93.54 51 | 64.31 166 | 93.43 83 | 92.95 92 | 72.16 158 | 78.86 95 | 94.84 55 | 56.97 136 | 97.53 50 | 81.38 79 | 92.11 58 | 94.24 90 |
|
TSAR-MVS + GP. | | | 87.96 15 | 88.37 14 | 86.70 49 | 93.51 52 | 65.32 138 | 95.15 31 | 93.84 53 | 78.17 65 | 85.93 31 | 94.80 57 | 75.80 8 | 98.21 26 | 89.38 16 | 88.78 88 | 96.59 11 |
|
PHI-MVS | | | 86.83 31 | 86.85 31 | 86.78 47 | 93.47 53 | 65.55 135 | 95.39 26 | 95.10 16 | 71.77 171 | 85.69 34 | 96.52 9 | 62.07 91 | 98.77 13 | 86.06 43 | 95.60 8 | 96.03 28 |
|
SR-MVS | | | 82.81 90 | 82.58 86 | 83.50 145 | 93.35 54 | 61.16 219 | 92.23 120 | 91.28 158 | 64.48 240 | 81.27 67 | 95.28 37 | 53.71 172 | 95.86 124 | 82.87 65 | 88.77 89 | 93.49 118 |
|
EPNet | | | 87.84 18 | 88.38 13 | 86.23 69 | 93.30 55 | 66.05 121 | 95.26 27 | 94.84 21 | 87.09 3 | 88.06 17 | 94.53 61 | 66.79 43 | 97.34 60 | 83.89 59 | 91.68 63 | 95.29 52 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
XVS | | | 83.87 75 | 83.47 66 | 85.05 105 | 93.22 56 | 63.78 175 | 92.92 99 | 92.66 102 | 73.99 115 | 78.18 99 | 94.31 71 | 55.25 154 | 97.41 55 | 79.16 93 | 91.58 65 | 93.95 106 |
|
X-MVStestdata | | | 76.86 179 | 74.13 193 | 85.05 105 | 93.22 56 | 63.78 175 | 92.92 99 | 92.66 102 | 73.99 115 | 78.18 99 | 10.19 334 | 55.25 154 | 97.41 55 | 79.16 93 | 91.58 65 | 93.95 106 |
|
SMA-MVS | | | 88.14 12 | 88.29 15 | 87.67 22 | 93.21 58 | 68.72 54 | 93.85 66 | 94.03 50 | 74.18 113 | 91.74 3 | 96.67 8 | 65.61 56 | 98.42 23 | 89.24 19 | 96.08 4 | 95.88 34 |
|
原ACMM1 | | | | | 84.42 124 | 93.21 58 | 64.27 169 | | 93.40 73 | 65.39 235 | 79.51 86 | 92.50 107 | 58.11 122 | 96.69 97 | 65.27 198 | 93.96 30 | 92.32 148 |
|
MVS_111021_HR | | | 86.19 40 | 85.80 43 | 87.37 32 | 93.17 60 | 69.79 34 | 93.99 58 | 93.76 57 | 79.08 54 | 78.88 94 | 93.99 78 | 62.25 90 | 98.15 29 | 85.93 44 | 91.15 72 | 94.15 96 |
|
CP-MVS | | | 83.71 79 | 83.40 71 | 84.65 118 | 93.14 61 | 63.84 173 | 94.59 42 | 92.28 113 | 71.03 184 | 77.41 108 | 94.92 52 | 55.21 157 | 96.19 108 | 81.32 80 | 90.70 76 | 93.91 108 |
|
DELS-MVS | | | 90.05 5 | 90.09 6 | 89.94 3 | 93.14 61 | 73.88 7 | 97.01 3 | 94.40 40 | 88.32 2 | 85.71 33 | 94.91 53 | 74.11 11 | 98.91 9 | 87.26 35 | 95.94 5 | 97.03 6 |
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 6 | 91.38 3 | 84.72 116 | 93.00 63 | 58.16 256 | 96.72 5 | 94.41 39 | 86.50 5 | 90.25 10 | 97.83 1 | 75.46 9 | 98.67 15 | 92.78 2 | 95.49 9 | 97.32 2 |
|
PLC | | 68.80 14 | 75.23 201 | 73.68 199 | 79.86 218 | 92.93 64 | 58.68 253 | 90.64 182 | 88.30 245 | 60.90 265 | 64.43 235 | 90.53 137 | 42.38 248 | 94.57 163 | 56.52 242 | 76.54 183 | 86.33 226 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
DWT-MVSNet_test | | | 83.95 73 | 82.80 82 | 87.41 31 | 92.90 65 | 70.07 27 | 89.12 213 | 94.42 38 | 82.15 20 | 77.64 104 | 91.77 121 | 70.81 25 | 96.22 107 | 65.03 199 | 81.36 144 | 95.94 30 |
|
MSP-MVS | | | 90.38 3 | 91.87 1 | 85.88 76 | 92.83 66 | 64.03 172 | 93.06 91 | 94.33 43 | 82.19 19 | 93.65 1 | 96.15 19 | 85.89 1 | 97.19 69 | 91.02 10 | 97.75 1 | 96.43 17 |
|
mPP-MVS | | | 82.96 89 | 82.44 88 | 84.52 121 | 92.83 66 | 62.92 196 | 92.76 102 | 91.85 134 | 71.52 178 | 75.61 125 | 94.24 73 | 53.48 176 | 96.99 82 | 78.97 96 | 90.73 75 | 93.64 115 |
|
GST-MVS | | | 84.63 60 | 84.29 59 | 85.66 88 | 92.82 68 | 65.27 139 | 93.04 93 | 93.13 85 | 73.20 132 | 78.89 93 | 94.18 74 | 59.41 111 | 97.85 38 | 81.45 77 | 92.48 53 | 93.86 110 |
|
WTY-MVS | | | 86.32 37 | 85.81 42 | 87.85 19 | 92.82 68 | 69.37 41 | 95.20 29 | 95.25 12 | 82.71 15 | 81.91 61 | 94.73 58 | 67.93 37 | 97.63 46 | 79.55 90 | 82.25 136 | 96.54 13 |
|
PGM-MVS | | | 83.25 83 | 82.70 85 | 84.92 108 | 92.81 70 | 64.07 171 | 90.44 185 | 92.20 121 | 71.28 181 | 77.23 111 | 94.43 62 | 55.17 158 | 97.31 63 | 79.33 92 | 91.38 68 | 93.37 119 |
|
EI-MVSNet-Vis-set | | | 83.77 77 | 83.67 63 | 84.06 131 | 92.79 71 | 63.56 184 | 91.76 143 | 94.81 24 | 79.65 44 | 77.87 101 | 94.09 75 | 63.35 80 | 97.90 35 | 79.35 91 | 79.36 154 | 90.74 171 |
|
MVSTER | | | 82.47 95 | 82.05 91 | 83.74 136 | 92.68 72 | 69.01 47 | 91.90 135 | 93.21 78 | 79.83 39 | 72.14 160 | 85.71 194 | 74.72 10 | 94.72 160 | 75.72 113 | 72.49 204 | 87.50 206 |
|
MP-MVS | | | 85.02 52 | 84.97 51 | 85.17 104 | 92.60 73 | 64.27 169 | 93.24 86 | 92.27 114 | 73.13 134 | 79.63 85 | 94.43 62 | 61.90 92 | 97.17 70 | 85.00 49 | 92.56 50 | 94.06 102 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
thres200 | | | 79.66 138 | 78.33 140 | 83.66 142 | 92.54 74 | 65.82 130 | 93.06 91 | 96.31 3 | 74.90 105 | 73.30 143 | 88.66 156 | 59.67 108 | 95.61 137 | 47.84 271 | 78.67 161 | 89.56 186 |
|
APD-MVS_3200maxsize | | | 81.64 108 | 81.32 100 | 82.59 161 | 92.36 75 | 58.74 252 | 91.39 156 | 91.01 168 | 63.35 247 | 79.72 84 | 94.62 60 | 51.82 186 | 96.14 110 | 79.71 88 | 87.93 95 | 92.89 136 |
|
PatchFormer-LS_test | | | 83.14 85 | 81.81 95 | 87.12 38 | 92.34 76 | 69.92 32 | 88.64 221 | 93.32 74 | 82.07 23 | 74.87 130 | 91.62 126 | 68.91 28 | 96.08 114 | 66.07 187 | 78.45 165 | 95.37 45 |
|
新几何1 | | | | | 84.73 114 | 92.32 77 | 64.28 168 | | 91.46 151 | 59.56 275 | 79.77 83 | 92.90 99 | 56.95 137 | 96.57 100 | 63.40 208 | 92.91 46 | 93.34 120 |
|
1121 | | | 81.25 112 | 80.05 114 | 84.87 111 | 92.30 78 | 64.31 166 | 87.91 231 | 91.39 153 | 59.44 276 | 79.94 81 | 92.91 98 | 57.09 130 | 97.01 77 | 66.63 179 | 92.81 48 | 93.29 123 |
|
EI-MVSNet-UG-set | | | 83.14 85 | 82.96 78 | 83.67 141 | 92.28 79 | 63.19 190 | 91.38 158 | 94.68 29 | 79.22 49 | 76.60 116 | 93.75 81 | 62.64 87 | 97.76 39 | 78.07 102 | 78.01 166 | 90.05 179 |
|
HPM-MVS | | | 83.25 83 | 82.95 79 | 84.17 129 | 92.25 80 | 62.88 198 | 90.91 174 | 91.86 133 | 70.30 193 | 77.12 112 | 93.96 79 | 56.75 139 | 96.28 105 | 82.04 72 | 91.34 70 | 93.34 120 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
HY-MVS | | 76.49 5 | 84.28 64 | 83.36 73 | 87.02 42 | 92.22 81 | 67.74 77 | 84.65 254 | 94.50 35 | 79.15 51 | 82.23 59 | 87.93 170 | 66.88 42 | 96.94 87 | 80.53 85 | 82.20 137 | 96.39 19 |
|
tfpn200view9 | | | 78.79 153 | 77.43 156 | 82.88 152 | 92.21 82 | 64.49 156 | 92.05 128 | 96.28 4 | 73.48 129 | 71.75 165 | 88.26 164 | 60.07 104 | 95.32 146 | 45.16 280 | 77.58 171 | 88.83 189 |
|
thres400 | | | 78.68 156 | 77.43 156 | 82.43 163 | 92.21 82 | 64.49 156 | 92.05 128 | 96.28 4 | 73.48 129 | 71.75 165 | 88.26 164 | 60.07 104 | 95.32 146 | 45.16 280 | 77.58 171 | 87.48 207 |
|
PS-MVSNAJ | | | 88.14 12 | 87.61 21 | 89.71 5 | 92.06 84 | 76.72 1 | 95.75 16 | 93.26 77 | 83.86 10 | 89.55 12 | 96.06 20 | 53.55 173 | 97.89 36 | 91.10 8 | 93.31 41 | 94.54 81 |
|
MSLP-MVS++ | | | 86.27 38 | 85.91 41 | 87.35 33 | 92.01 85 | 68.97 49 | 95.04 36 | 92.70 99 | 79.04 55 | 81.50 65 | 96.50 11 | 58.98 117 | 96.78 93 | 83.49 61 | 93.93 31 | 96.29 21 |
|
旧先验1 | | | | | | 91.94 86 | 60.74 226 | | 91.50 149 | | | 94.36 64 | 65.23 58 | | | 91.84 60 | 94.55 79 |
|
thres600view7 | | | 78.00 166 | 76.66 166 | 82.03 181 | 91.93 87 | 63.69 180 | 91.30 163 | 96.33 1 | 72.43 148 | 70.46 174 | 87.89 171 | 60.31 101 | 94.92 155 | 42.64 292 | 76.64 182 | 87.48 207 |
|
LS3D | | | 69.17 246 | 66.40 248 | 77.50 248 | 91.92 88 | 56.12 272 | 85.12 251 | 80.37 300 | 46.96 311 | 56.50 278 | 87.51 176 | 37.25 272 | 93.71 199 | 32.52 320 | 79.40 153 | 82.68 277 |
|
GG-mvs-BLEND | | | | | 86.53 57 | 91.91 89 | 69.67 38 | 75.02 303 | 94.75 26 | | 78.67 98 | 90.85 132 | 77.91 4 | 94.56 164 | 72.25 136 | 93.74 35 | 95.36 46 |
|
thres100view900 | | | 78.37 162 | 77.01 162 | 82.46 162 | 91.89 90 | 63.21 189 | 91.19 169 | 96.33 1 | 72.28 152 | 70.45 175 | 87.89 171 | 60.31 101 | 95.32 146 | 45.16 280 | 77.58 171 | 88.83 189 |
|
zzz-MVS | | | 84.73 57 | 84.47 55 | 85.50 91 | 91.89 90 | 65.16 142 | 91.55 150 | 92.23 116 | 75.32 99 | 80.53 76 | 95.21 43 | 56.06 149 | 97.16 71 | 84.86 52 | 92.55 51 | 94.18 92 |
|
MTAPA | | | 83.91 74 | 83.38 72 | 85.50 91 | 91.89 90 | 65.16 142 | 81.75 274 | 92.23 116 | 75.32 99 | 80.53 76 | 95.21 43 | 56.06 149 | 97.16 71 | 84.86 52 | 92.55 51 | 94.18 92 |
|
canonicalmvs | | | 86.85 30 | 86.25 36 | 88.66 12 | 91.80 93 | 71.92 11 | 93.54 78 | 91.71 140 | 80.26 38 | 87.55 20 | 95.25 41 | 63.59 78 | 96.93 89 | 88.18 26 | 84.34 124 | 97.11 4 |
|
TSAR-MVS + MP. | | | 88.11 14 | 88.64 12 | 86.54 56 | 91.73 94 | 68.04 70 | 90.36 188 | 93.55 65 | 82.89 13 | 91.29 5 | 92.89 100 | 72.27 20 | 96.03 119 | 87.99 27 | 94.77 19 | 95.54 41 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
ACMMP | | | 81.49 109 | 80.67 108 | 83.93 133 | 91.71 95 | 62.90 197 | 92.13 122 | 92.22 120 | 71.79 170 | 71.68 167 | 93.49 87 | 50.32 196 | 96.96 85 | 78.47 99 | 84.22 129 | 91.93 154 |
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 |
BH-RMVSNet | | | 79.46 143 | 77.65 151 | 84.89 109 | 91.68 96 | 65.66 132 | 93.55 77 | 88.09 249 | 72.93 139 | 73.37 142 | 91.12 129 | 46.20 233 | 96.12 111 | 56.28 244 | 85.61 114 | 92.91 135 |
|
baseline1 | | | 81.84 106 | 81.03 105 | 84.28 128 | 91.60 97 | 66.62 107 | 91.08 171 | 91.66 143 | 81.87 25 | 74.86 131 | 91.67 125 | 69.98 27 | 94.92 155 | 71.76 141 | 64.75 252 | 91.29 166 |
|
ACMMP_NAP | | | 86.05 41 | 85.80 43 | 86.80 46 | 91.58 98 | 67.53 83 | 91.79 140 | 93.49 68 | 74.93 104 | 84.61 41 | 95.30 36 | 59.42 110 | 97.92 33 | 86.13 42 | 94.92 12 | 94.94 69 |
|
MVS_Test | | | 84.16 69 | 83.20 74 | 87.05 41 | 91.56 99 | 69.82 33 | 89.99 198 | 92.05 124 | 77.77 69 | 82.84 57 | 86.57 184 | 63.93 71 | 96.09 112 | 74.91 123 | 89.18 86 | 95.25 57 |
|
HPM-MVS_fast | | | 80.25 126 | 79.55 125 | 82.33 168 | 91.55 100 | 59.95 238 | 91.32 162 | 89.16 224 | 65.23 238 | 74.71 132 | 93.07 94 | 47.81 221 | 95.74 128 | 74.87 125 | 88.23 92 | 91.31 165 |
|
CPTT-MVS | | | 79.59 139 | 79.16 133 | 80.89 203 | 91.54 101 | 59.80 240 | 92.10 124 | 88.54 242 | 60.42 268 | 72.96 145 | 93.28 89 | 48.27 215 | 92.80 217 | 78.89 98 | 86.50 109 | 90.06 178 |
|
CS-MVS | | | 86.61 35 | 86.85 31 | 85.88 76 | 91.52 102 | 66.25 118 | 95.42 24 | 92.25 115 | 80.36 37 | 84.10 50 | 94.82 56 | 62.88 86 | 96.08 114 | 88.25 25 | 92.07 59 | 95.30 51 |
|
CNLPA | | | 74.31 209 | 72.30 214 | 80.32 207 | 91.49 103 | 61.66 212 | 90.85 176 | 80.72 299 | 56.67 289 | 63.85 239 | 90.64 134 | 46.75 227 | 90.84 258 | 53.79 252 | 75.99 186 | 88.47 197 |
|
MP-MVS-pluss | | | 85.24 50 | 85.13 50 | 85.56 90 | 91.42 104 | 65.59 134 | 91.54 151 | 92.51 109 | 74.56 107 | 80.62 75 | 95.64 28 | 59.15 114 | 97.00 79 | 86.94 38 | 93.80 33 | 94.07 101 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
gg-mvs-nofinetune | | | 77.18 177 | 74.31 189 | 85.80 81 | 91.42 104 | 68.36 61 | 71.78 305 | 94.72 27 | 49.61 305 | 77.12 112 | 45.92 322 | 77.41 5 | 93.98 190 | 67.62 172 | 93.16 43 | 95.05 64 |
|
xiu_mvs_v2_base | | | 87.92 17 | 87.38 26 | 89.55 8 | 91.41 106 | 76.43 3 | 95.74 17 | 93.12 86 | 83.53 12 | 89.55 12 | 95.95 21 | 53.45 177 | 97.68 40 | 91.07 9 | 92.62 49 | 94.54 81 |
|
ETV-MVS | | | 84.84 56 | 84.88 52 | 84.69 117 | 91.30 107 | 62.36 205 | 93.85 66 | 92.04 125 | 79.45 45 | 79.33 88 | 94.28 72 | 62.42 89 | 96.35 104 | 80.05 87 | 91.25 71 | 95.38 44 |
|
alignmvs | | | 87.28 23 | 86.97 28 | 88.24 17 | 91.30 107 | 71.14 17 | 95.61 21 | 93.56 64 | 79.30 47 | 87.07 24 | 95.25 41 | 68.43 30 | 96.93 89 | 87.87 28 | 84.33 125 | 96.65 9 |
|
EPMVS | | | 78.49 161 | 75.98 173 | 86.02 72 | 91.21 109 | 69.68 37 | 80.23 285 | 91.20 159 | 75.25 101 | 72.48 155 | 78.11 269 | 54.65 162 | 93.69 200 | 57.66 240 | 83.04 131 | 94.69 74 |
|
FMVSNet3 | | | 77.73 171 | 76.04 172 | 82.80 153 | 91.20 110 | 68.99 48 | 91.87 136 | 91.99 127 | 73.35 131 | 67.04 217 | 83.19 216 | 56.62 142 | 92.14 237 | 59.80 232 | 69.34 223 | 87.28 213 |
|
Anonymous20240529 | | | 76.84 181 | 74.15 192 | 84.88 110 | 91.02 111 | 64.95 151 | 93.84 69 | 91.09 164 | 53.57 296 | 73.00 144 | 87.42 177 | 35.91 281 | 97.32 62 | 69.14 161 | 72.41 206 | 92.36 146 |
|
tpmvs | | | 72.88 223 | 69.76 234 | 82.22 173 | 90.98 112 | 67.05 95 | 78.22 297 | 88.30 245 | 63.10 251 | 64.35 236 | 74.98 288 | 55.09 159 | 94.27 175 | 43.25 286 | 69.57 222 | 85.34 246 |
|
MVS | | | 84.66 59 | 82.86 81 | 90.06 2 | 90.93 113 | 74.56 6 | 87.91 231 | 95.54 9 | 68.55 213 | 72.35 159 | 94.71 59 | 59.78 107 | 98.90 10 | 81.29 81 | 94.69 23 | 96.74 8 |
|
PVSNet | | 73.49 8 | 80.05 130 | 78.63 137 | 84.31 126 | 90.92 114 | 64.97 150 | 92.47 115 | 91.05 166 | 79.18 50 | 72.43 157 | 90.51 138 | 37.05 277 | 94.06 184 | 68.06 167 | 86.00 111 | 93.90 109 |
|
3Dnovator+ | | 73.60 7 | 82.10 103 | 80.60 110 | 86.60 52 | 90.89 115 | 66.80 103 | 95.20 29 | 93.44 71 | 74.05 114 | 67.42 213 | 92.49 109 | 49.46 205 | 97.65 45 | 70.80 147 | 91.68 63 | 95.33 47 |
|
VDD-MVS | | | 83.06 87 | 81.81 95 | 86.81 45 | 90.86 116 | 67.70 78 | 95.40 25 | 91.50 149 | 75.46 94 | 81.78 62 | 92.34 114 | 40.09 254 | 97.13 73 | 86.85 39 | 82.04 138 | 95.60 39 |
|
BH-w/o | | | 80.49 123 | 79.30 130 | 84.05 132 | 90.83 117 | 64.36 165 | 93.60 75 | 89.42 214 | 74.35 110 | 69.09 190 | 90.15 144 | 55.23 156 | 95.61 137 | 64.61 201 | 86.43 110 | 92.17 152 |
|
ET-MVSNet_ETH3D | | | 84.01 71 | 83.15 77 | 86.58 54 | 90.78 118 | 70.89 18 | 94.74 41 | 94.62 32 | 81.44 28 | 58.19 270 | 93.64 83 | 73.64 14 | 92.35 234 | 82.66 67 | 78.66 162 | 96.50 16 |
|
Anonymous20231211 | | | 73.08 218 | 70.39 228 | 81.13 195 | 90.62 119 | 63.33 186 | 91.40 154 | 90.06 195 | 51.84 300 | 64.46 234 | 80.67 246 | 36.49 279 | 94.07 183 | 63.83 206 | 64.17 257 | 85.98 232 |
|
TR-MVS | | | 78.77 154 | 77.37 159 | 82.95 151 | 90.49 120 | 60.88 221 | 93.67 73 | 90.07 192 | 70.08 195 | 74.51 133 | 91.37 127 | 45.69 234 | 95.70 134 | 60.12 230 | 80.32 149 | 92.29 149 |
|
SteuartSystems-ACMMP | | | 86.82 32 | 86.90 29 | 86.58 54 | 90.42 121 | 66.38 113 | 96.09 13 | 93.87 52 | 77.73 70 | 84.01 52 | 95.66 27 | 63.39 79 | 97.94 32 | 87.40 32 | 93.55 39 | 95.42 42 |
Skip Steuart: Steuart Systems R&D Blog. |
TAPA-MVS | | 70.22 12 | 74.94 205 | 73.53 200 | 79.17 231 | 90.40 122 | 52.07 290 | 89.19 211 | 89.61 209 | 62.69 253 | 70.07 180 | 92.67 105 | 48.89 213 | 94.32 172 | 38.26 306 | 79.97 150 | 91.12 168 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
mvs_anonymous | | | 81.36 111 | 79.99 116 | 85.46 93 | 90.39 123 | 68.40 60 | 86.88 245 | 90.61 177 | 74.41 108 | 70.31 178 | 84.67 202 | 63.79 73 | 92.32 235 | 73.13 127 | 85.70 112 | 95.67 36 |
|
CANet_DTU | | | 84.09 70 | 83.52 64 | 85.81 80 | 90.30 124 | 66.82 101 | 91.87 136 | 89.01 231 | 85.27 6 | 86.09 29 | 93.74 82 | 47.71 222 | 96.98 83 | 77.90 104 | 89.78 84 | 93.65 114 |
|
Fast-Effi-MVS+ | | | 81.14 114 | 80.01 115 | 84.51 122 | 90.24 125 | 65.86 127 | 94.12 51 | 89.15 225 | 73.81 122 | 75.37 128 | 88.26 164 | 57.26 128 | 94.53 167 | 66.97 178 | 84.92 117 | 93.15 127 |
|
EIA-MVS | | | 86.01 42 | 86.11 38 | 85.70 87 | 90.21 126 | 67.02 97 | 93.43 83 | 91.92 131 | 81.21 30 | 84.13 49 | 94.07 77 | 60.93 98 | 95.63 135 | 89.28 18 | 89.81 82 | 94.46 87 |
|
tpmrst | | | 80.57 120 | 79.14 134 | 84.84 112 | 90.10 127 | 68.28 64 | 81.70 275 | 89.72 207 | 77.63 72 | 75.96 120 | 79.54 261 | 64.94 63 | 92.71 220 | 75.43 115 | 77.28 178 | 93.55 116 |
|
PVSNet_Blended_VisFu | | | 83.97 72 | 83.50 65 | 85.39 97 | 90.02 128 | 66.59 109 | 93.77 71 | 91.73 138 | 77.43 76 | 77.08 114 | 89.81 149 | 63.77 74 | 96.97 84 | 79.67 89 | 88.21 93 | 92.60 141 |
|
UGNet | | | 79.87 134 | 78.68 136 | 83.45 147 | 89.96 129 | 61.51 214 | 92.13 122 | 90.79 170 | 76.83 81 | 78.85 96 | 86.33 187 | 38.16 263 | 96.17 109 | 67.93 169 | 87.17 100 | 92.67 139 |
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 |
CHOSEN 1792x2688 | | | 84.98 54 | 83.45 67 | 89.57 7 | 89.94 130 | 75.14 5 | 92.07 127 | 92.32 112 | 81.87 25 | 75.68 122 | 88.27 163 | 60.18 103 | 98.60 17 | 80.46 86 | 90.27 81 | 94.96 68 |
|
abl_6 | | | 79.82 135 | 79.20 132 | 81.70 187 | 89.85 131 | 58.34 254 | 88.47 224 | 90.07 192 | 62.56 254 | 77.71 103 | 93.08 92 | 47.65 223 | 96.78 93 | 77.94 103 | 85.45 115 | 89.99 180 |
|
BH-untuned | | | 78.68 156 | 77.08 160 | 83.48 146 | 89.84 132 | 63.74 177 | 92.70 105 | 88.59 240 | 71.57 176 | 66.83 220 | 88.65 157 | 51.75 188 | 95.39 144 | 59.03 235 | 84.77 119 | 91.32 164 |
|
test222 | | | | | | 89.77 133 | 61.60 213 | 89.55 204 | 89.42 214 | 56.83 288 | 77.28 110 | 92.43 111 | 52.76 180 | | | 91.14 73 | 93.09 129 |
|
PMMVS | | | 81.98 105 | 82.04 92 | 81.78 183 | 89.76 134 | 56.17 271 | 91.13 170 | 90.69 173 | 77.96 67 | 80.09 80 | 93.57 85 | 46.33 231 | 94.99 151 | 81.41 78 | 87.46 98 | 94.17 94 |
|
DPM-MVS | | | 90.70 2 | 90.52 5 | 91.24 1 | 89.68 135 | 76.68 2 | 97.29 1 | 95.35 10 | 82.87 14 | 91.58 4 | 97.22 3 | 79.93 2 | 99.10 3 | 83.12 63 | 97.64 2 | 97.94 1 |
|
QAPM | | | 79.95 133 | 77.39 158 | 87.64 23 | 89.63 136 | 71.41 13 | 93.30 85 | 93.70 59 | 65.34 237 | 67.39 215 | 91.75 123 | 47.83 220 | 98.96 8 | 57.71 239 | 89.81 82 | 92.54 143 |
|
3Dnovator | | 73.91 6 | 82.69 94 | 80.82 106 | 88.31 16 | 89.57 137 | 71.26 14 | 92.60 110 | 94.39 41 | 78.84 57 | 67.89 208 | 92.48 110 | 48.42 214 | 98.52 18 | 68.80 165 | 94.40 25 | 95.15 59 |
|
Effi-MVS+ | | | 83.82 76 | 82.76 83 | 86.99 43 | 89.56 138 | 69.40 40 | 91.35 160 | 86.12 269 | 72.59 144 | 83.22 55 | 92.81 104 | 59.60 109 | 96.01 121 | 81.76 74 | 87.80 96 | 95.56 40 |
|
PatchmatchNet | | | 77.46 173 | 74.63 183 | 85.96 74 | 89.55 139 | 70.35 24 | 79.97 289 | 89.55 210 | 72.23 153 | 70.94 170 | 76.91 280 | 57.03 132 | 92.79 218 | 54.27 250 | 81.17 145 | 94.74 73 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PatchMatch-RL | | | 72.06 229 | 69.98 229 | 78.28 240 | 89.51 140 | 55.70 274 | 83.49 261 | 83.39 291 | 61.24 264 | 63.72 240 | 82.76 218 | 34.77 284 | 93.03 211 | 53.37 255 | 77.59 170 | 86.12 230 |
|
thisisatest0515 | | | 83.41 81 | 82.49 87 | 86.16 70 | 89.46 141 | 68.26 65 | 93.54 78 | 94.70 28 | 74.31 111 | 75.75 121 | 90.92 130 | 72.62 18 | 96.52 102 | 69.64 154 | 81.50 142 | 93.71 112 |
|
UA-Net | | | 80.02 131 | 79.65 121 | 81.11 196 | 89.33 142 | 57.72 260 | 86.33 248 | 89.00 232 | 77.44 75 | 81.01 71 | 89.15 153 | 59.33 112 | 95.90 122 | 61.01 224 | 84.28 127 | 89.73 183 |
|
dp | | | 75.01 204 | 72.09 216 | 83.76 135 | 89.28 143 | 66.22 120 | 79.96 290 | 89.75 202 | 71.16 183 | 67.80 210 | 77.19 277 | 51.81 187 | 92.54 226 | 50.39 261 | 71.44 213 | 92.51 144 |
|
sss | | | 82.71 93 | 82.38 89 | 83.73 138 | 89.25 144 | 59.58 243 | 92.24 119 | 94.89 20 | 77.96 67 | 79.86 82 | 92.38 112 | 56.70 140 | 97.05 74 | 77.26 107 | 80.86 148 | 94.55 79 |
|
MVSFormer | | | 83.75 78 | 82.88 80 | 86.37 64 | 89.24 145 | 71.18 15 | 89.07 214 | 90.69 173 | 65.80 232 | 87.13 22 | 94.34 69 | 64.99 61 | 92.67 223 | 72.83 130 | 91.80 61 | 95.27 54 |
|
lupinMVS | | | 87.74 19 | 87.77 18 | 87.63 27 | 89.24 145 | 71.18 15 | 96.57 8 | 92.90 94 | 82.70 16 | 87.13 22 | 95.27 39 | 64.99 61 | 95.80 125 | 89.34 17 | 91.80 61 | 95.93 31 |
|
IB-MVS | | 77.80 4 | 82.18 99 | 80.46 112 | 87.35 33 | 89.14 147 | 70.28 25 | 95.59 22 | 95.17 13 | 78.85 56 | 70.19 179 | 85.82 192 | 70.66 26 | 97.67 41 | 72.19 139 | 66.52 243 | 94.09 99 |
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 |
MDTV_nov1_ep13 | | | | 72.61 210 | | 89.06 148 | 68.48 58 | 80.33 283 | 90.11 191 | 71.84 168 | 71.81 164 | 75.92 285 | 53.01 179 | 93.92 193 | 48.04 270 | 73.38 196 | |
|
testdata | | | | | 81.34 191 | 89.02 149 | 57.72 260 | | 89.84 200 | 58.65 280 | 85.32 38 | 94.09 75 | 57.03 132 | 93.28 207 | 69.34 159 | 90.56 79 | 93.03 131 |
|
CostFormer | | | 82.33 97 | 81.15 101 | 85.86 79 | 89.01 150 | 68.46 59 | 82.39 272 | 93.01 89 | 75.59 92 | 80.25 79 | 81.57 231 | 72.03 22 | 94.96 152 | 79.06 95 | 77.48 175 | 94.16 95 |
|
GBi-Net | | | 75.65 195 | 73.83 197 | 81.10 197 | 88.85 151 | 65.11 144 | 90.01 195 | 90.32 181 | 70.84 187 | 67.04 217 | 80.25 253 | 48.03 216 | 91.54 251 | 59.80 232 | 69.34 223 | 86.64 222 |
|
test1 | | | 75.65 195 | 73.83 197 | 81.10 197 | 88.85 151 | 65.11 144 | 90.01 195 | 90.32 181 | 70.84 187 | 67.04 217 | 80.25 253 | 48.03 216 | 91.54 251 | 59.80 232 | 69.34 223 | 86.64 222 |
|
FMVSNet2 | | | 76.07 188 | 74.01 195 | 82.26 172 | 88.85 151 | 67.66 79 | 91.33 161 | 91.61 144 | 70.84 187 | 65.98 223 | 82.25 223 | 48.03 216 | 92.00 242 | 58.46 237 | 68.73 229 | 87.10 215 |
|
DeepC-MVS | | 77.85 3 | 85.52 47 | 85.24 49 | 86.37 64 | 88.80 154 | 66.64 106 | 92.15 121 | 93.68 60 | 81.07 32 | 76.91 115 | 93.64 83 | 62.59 88 | 98.44 21 | 85.50 46 | 92.84 47 | 94.03 103 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
EPP-MVSNet | | | 81.79 107 | 81.52 98 | 82.61 160 | 88.77 155 | 60.21 235 | 93.02 95 | 93.66 61 | 68.52 214 | 72.90 147 | 90.39 139 | 72.19 21 | 94.96 152 | 74.93 122 | 79.29 156 | 92.67 139 |
|
1112_ss | | | 80.56 121 | 79.83 119 | 82.77 154 | 88.65 156 | 60.78 223 | 92.29 117 | 88.36 244 | 72.58 145 | 72.46 156 | 94.95 48 | 65.09 60 | 93.42 206 | 66.38 183 | 77.71 168 | 94.10 98 |
|
tpm cat1 | | | 75.30 200 | 72.21 215 | 84.58 120 | 88.52 157 | 67.77 76 | 78.16 298 | 88.02 251 | 61.88 261 | 68.45 202 | 76.37 281 | 60.65 99 | 94.03 188 | 53.77 253 | 74.11 191 | 91.93 154 |
|
LCM-MVSNet-Re | | | 72.93 221 | 71.84 218 | 76.18 262 | 88.49 158 | 48.02 306 | 80.07 288 | 70.17 319 | 73.96 118 | 52.25 290 | 80.09 256 | 49.98 200 | 88.24 284 | 67.35 173 | 84.23 128 | 92.28 150 |
|
Vis-MVSNet | | | 80.92 118 | 79.98 117 | 83.74 136 | 88.48 159 | 61.80 211 | 93.44 82 | 88.26 248 | 73.96 118 | 77.73 102 | 91.76 122 | 49.94 201 | 94.76 157 | 65.84 190 | 90.37 80 | 94.65 78 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Vis-MVSNet (Re-imp) | | | 79.24 145 | 79.57 122 | 78.24 242 | 88.46 160 | 52.29 289 | 90.41 187 | 89.12 226 | 74.24 112 | 69.13 189 | 91.91 119 | 65.77 54 | 90.09 271 | 59.00 236 | 88.09 94 | 92.33 147 |
|
ab-mvs | | | 80.18 127 | 78.31 141 | 85.80 81 | 88.44 161 | 65.49 137 | 83.00 269 | 92.67 101 | 71.82 169 | 77.36 109 | 85.01 198 | 54.50 163 | 96.59 98 | 76.35 112 | 75.63 187 | 95.32 49 |
|
gm-plane-assit | | | | | | 88.42 162 | 67.04 96 | | | 78.62 61 | | 91.83 120 | | 97.37 57 | 76.57 110 | | |
|
MVS_111021_LR | | | 82.02 104 | 81.52 98 | 83.51 144 | 88.42 162 | 62.88 198 | 89.77 201 | 88.93 233 | 76.78 82 | 75.55 126 | 93.10 90 | 50.31 197 | 95.38 145 | 83.82 60 | 87.02 101 | 92.26 151 |
|
baseline | | | 85.01 53 | 84.44 57 | 86.71 48 | 88.33 164 | 68.73 53 | 90.24 192 | 91.82 136 | 81.05 33 | 81.18 68 | 92.50 107 | 63.69 75 | 96.08 114 | 84.45 54 | 86.71 105 | 95.32 49 |
|
tpm2 | | | 79.80 136 | 77.95 147 | 85.34 99 | 88.28 165 | 68.26 65 | 81.56 277 | 91.42 152 | 70.11 194 | 77.59 107 | 80.50 248 | 67.40 40 | 94.26 177 | 67.34 174 | 77.35 176 | 93.51 117 |
|
thisisatest0530 | | | 81.15 113 | 80.07 113 | 84.39 125 | 88.26 166 | 65.63 133 | 91.40 154 | 94.62 32 | 71.27 182 | 70.93 171 | 89.18 152 | 72.47 19 | 96.04 118 | 65.62 193 | 76.89 181 | 91.49 159 |
|
casdiffmvs | | | 85.37 49 | 84.87 53 | 86.84 44 | 88.25 167 | 69.07 45 | 93.04 93 | 91.76 137 | 81.27 29 | 80.84 74 | 92.07 117 | 64.23 68 | 96.06 117 | 84.98 50 | 87.43 99 | 95.39 43 |
|
Test_1112_low_res | | | 79.56 140 | 78.60 138 | 82.43 163 | 88.24 168 | 60.39 232 | 92.09 125 | 87.99 252 | 72.10 159 | 71.84 163 | 87.42 177 | 64.62 64 | 93.04 210 | 65.80 191 | 77.30 177 | 93.85 111 |
|
PAPM | | | 85.89 44 | 85.46 47 | 87.18 36 | 88.20 169 | 72.42 10 | 92.41 116 | 92.77 97 | 82.11 21 | 80.34 78 | 93.07 94 | 68.27 31 | 95.02 150 | 78.39 100 | 93.59 38 | 94.09 99 |
|
TESTMET0.1,1 | | | 82.41 96 | 81.98 93 | 83.72 139 | 88.08 170 | 63.74 177 | 92.70 105 | 93.77 56 | 79.30 47 | 77.61 106 | 87.57 175 | 58.19 121 | 94.08 182 | 73.91 126 | 86.68 106 | 93.33 122 |
|
ADS-MVSNet2 | | | 66.90 263 | 63.44 267 | 77.26 254 | 88.06 171 | 60.70 227 | 68.01 314 | 75.56 309 | 57.57 282 | 64.48 232 | 69.87 305 | 38.68 257 | 84.10 301 | 40.87 297 | 67.89 235 | 86.97 216 |
|
ADS-MVSNet | | | 68.54 251 | 64.38 263 | 81.03 200 | 88.06 171 | 66.90 100 | 68.01 314 | 84.02 284 | 57.57 282 | 64.48 232 | 69.87 305 | 38.68 257 | 89.21 278 | 40.87 297 | 67.89 235 | 86.97 216 |
|
EPNet_dtu | | | 78.80 152 | 79.26 131 | 77.43 250 | 88.06 171 | 49.71 302 | 91.96 134 | 91.95 130 | 77.67 71 | 76.56 117 | 91.28 128 | 58.51 119 | 90.20 269 | 56.37 243 | 80.95 147 | 92.39 145 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IS-MVSNet | | | 80.14 128 | 79.41 127 | 82.33 168 | 87.91 174 | 60.08 237 | 91.97 133 | 88.27 247 | 72.90 140 | 71.44 169 | 91.73 124 | 61.44 95 | 93.66 201 | 62.47 217 | 86.53 108 | 93.24 124 |
|
CLD-MVS | | | 82.73 91 | 82.35 90 | 83.86 134 | 87.90 175 | 67.65 80 | 95.45 23 | 92.18 123 | 85.06 7 | 72.58 152 | 92.27 115 | 52.46 183 | 95.78 126 | 84.18 55 | 79.06 157 | 88.16 201 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HyFIR lowres test | | | 81.03 116 | 79.56 123 | 85.43 95 | 87.81 176 | 68.11 69 | 90.18 193 | 90.01 197 | 70.65 190 | 72.95 146 | 86.06 190 | 63.61 77 | 94.50 169 | 75.01 121 | 79.75 152 | 93.67 113 |
|
1314 | | | 80.70 119 | 78.95 135 | 85.94 75 | 87.77 177 | 67.56 81 | 87.91 231 | 92.55 108 | 72.17 157 | 67.44 212 | 93.09 91 | 50.27 198 | 97.04 76 | 71.68 143 | 87.64 97 | 93.23 125 |
|
HQP-NCC | | | | | | 87.54 178 | | 94.06 54 | | 79.80 40 | 74.18 134 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 178 | | 94.06 54 | | 79.80 40 | 74.18 134 | | | | | | |
|
HQP-MVS | | | 81.14 114 | 80.64 109 | 82.64 159 | 87.54 178 | 63.66 182 | 94.06 54 | 91.70 141 | 79.80 40 | 74.18 134 | 90.30 141 | 51.63 190 | 95.61 137 | 77.63 105 | 78.90 158 | 88.63 193 |
|
NP-MVS | | | | | | 87.41 181 | 63.04 191 | | | | | 90.30 141 | | | | | |
|
diffmvs | | | 84.28 64 | 83.83 62 | 85.61 89 | 87.40 182 | 68.02 71 | 90.88 175 | 89.24 219 | 80.54 35 | 81.64 64 | 92.52 106 | 59.83 106 | 94.52 168 | 87.32 34 | 85.11 116 | 94.29 88 |
|
baseline2 | | | 83.68 80 | 83.42 70 | 84.48 123 | 87.37 183 | 66.00 123 | 90.06 194 | 95.93 7 | 79.71 43 | 69.08 191 | 90.39 139 | 77.92 3 | 96.28 105 | 78.91 97 | 81.38 143 | 91.16 167 |
|
plane_prior6 | | | | | | 87.23 184 | 62.32 206 | | | | | | 50.66 194 | | | | |
|
tttt0517 | | | 79.50 141 | 78.53 139 | 82.41 166 | 87.22 185 | 61.43 216 | 89.75 202 | 94.76 25 | 69.29 203 | 67.91 207 | 88.06 169 | 72.92 16 | 95.63 135 | 62.91 212 | 73.90 195 | 90.16 177 |
|
plane_prior1 | | | | | | 87.15 186 | | | | | | | | | | | |
|
cascas | | | 78.18 164 | 75.77 176 | 85.41 96 | 87.14 187 | 69.11 44 | 92.96 96 | 91.15 161 | 66.71 225 | 70.47 173 | 86.07 189 | 37.49 271 | 96.48 103 | 70.15 152 | 79.80 151 | 90.65 172 |
|
CHOSEN 280x420 | | | 77.35 175 | 76.95 164 | 78.55 237 | 87.07 188 | 62.68 202 | 69.71 310 | 82.95 293 | 68.80 210 | 71.48 168 | 87.27 180 | 66.03 50 | 84.00 304 | 76.47 111 | 82.81 134 | 88.95 188 |
|
HQP_MVS | | | 80.34 125 | 79.75 120 | 82.12 176 | 86.94 189 | 62.42 203 | 93.13 89 | 91.31 155 | 78.81 58 | 72.53 153 | 89.14 154 | 50.66 194 | 95.55 141 | 76.74 108 | 78.53 163 | 88.39 198 |
|
plane_prior7 | | | | | | 86.94 189 | 61.51 214 | | | | | | | | | | |
|
test-LLR | | | 80.10 129 | 79.56 123 | 81.72 185 | 86.93 191 | 61.17 217 | 92.70 105 | 91.54 146 | 71.51 179 | 75.62 123 | 86.94 181 | 53.83 169 | 92.38 231 | 72.21 137 | 84.76 120 | 91.60 157 |
|
test-mter | | | 79.96 132 | 79.38 129 | 81.72 185 | 86.93 191 | 61.17 217 | 92.70 105 | 91.54 146 | 73.85 120 | 75.62 123 | 86.94 181 | 49.84 203 | 92.38 231 | 72.21 137 | 84.76 120 | 91.60 157 |
|
SCA | | | 75.82 193 | 72.76 207 | 85.01 107 | 86.63 193 | 70.08 26 | 81.06 279 | 89.19 222 | 71.60 175 | 70.01 181 | 77.09 278 | 45.53 235 | 90.25 264 | 60.43 227 | 73.27 197 | 94.68 75 |
|
xiu_mvs_v1_base_debu | | | 82.16 100 | 81.12 102 | 85.26 101 | 86.42 194 | 68.72 54 | 92.59 112 | 90.44 178 | 73.12 135 | 84.20 46 | 94.36 64 | 38.04 265 | 95.73 129 | 84.12 56 | 86.81 102 | 91.33 161 |
|
xiu_mvs_v1_base | | | 82.16 100 | 81.12 102 | 85.26 101 | 86.42 194 | 68.72 54 | 92.59 112 | 90.44 178 | 73.12 135 | 84.20 46 | 94.36 64 | 38.04 265 | 95.73 129 | 84.12 56 | 86.81 102 | 91.33 161 |
|
xiu_mvs_v1_base_debi | | | 82.16 100 | 81.12 102 | 85.26 101 | 86.42 194 | 68.72 54 | 92.59 112 | 90.44 178 | 73.12 135 | 84.20 46 | 94.36 64 | 38.04 265 | 95.73 129 | 84.12 56 | 86.81 102 | 91.33 161 |
|
F-COLMAP | | | 70.66 236 | 68.44 239 | 77.32 252 | 86.37 197 | 55.91 273 | 88.00 229 | 86.32 264 | 56.94 287 | 57.28 276 | 88.07 168 | 33.58 287 | 92.49 228 | 51.02 259 | 68.37 231 | 83.55 260 |
|
CDS-MVSNet | | | 81.43 110 | 80.74 107 | 83.52 143 | 86.26 198 | 64.45 159 | 92.09 125 | 90.65 176 | 75.83 91 | 73.95 140 | 89.81 149 | 63.97 70 | 92.91 216 | 71.27 144 | 82.82 133 | 93.20 126 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
VDDNet | | | 80.50 122 | 78.26 142 | 87.21 35 | 86.19 199 | 69.79 34 | 94.48 43 | 91.31 155 | 60.42 268 | 79.34 87 | 90.91 131 | 38.48 261 | 96.56 101 | 82.16 70 | 81.05 146 | 95.27 54 |
|
jason | | | 86.40 36 | 86.17 37 | 87.11 39 | 86.16 200 | 70.54 22 | 95.71 20 | 92.19 122 | 82.00 24 | 84.58 42 | 94.34 69 | 61.86 93 | 95.53 143 | 87.76 29 | 90.89 74 | 95.27 54 |
jason: jason. |
PCF-MVS | | 73.15 9 | 79.29 144 | 77.63 152 | 84.29 127 | 86.06 201 | 65.96 125 | 87.03 241 | 91.10 163 | 69.86 197 | 69.79 186 | 90.64 134 | 57.54 127 | 96.59 98 | 64.37 203 | 82.29 135 | 90.32 175 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MS-PatchMatch | | | 77.90 170 | 76.50 167 | 82.12 176 | 85.99 202 | 69.95 31 | 91.75 145 | 92.70 99 | 73.97 117 | 62.58 250 | 84.44 205 | 41.11 251 | 95.78 126 | 63.76 207 | 92.17 57 | 80.62 293 |
|
FIs | | | 79.47 142 | 79.41 127 | 79.67 222 | 85.95 203 | 59.40 245 | 91.68 147 | 93.94 51 | 78.06 66 | 68.96 194 | 88.28 162 | 66.61 45 | 91.77 246 | 66.20 186 | 74.99 188 | 87.82 203 |
|
VPA-MVSNet | | | 79.03 147 | 78.00 146 | 82.11 179 | 85.95 203 | 64.48 158 | 93.22 88 | 94.66 30 | 75.05 103 | 74.04 139 | 84.95 199 | 52.17 185 | 93.52 203 | 74.90 124 | 67.04 239 | 88.32 200 |
|
tpm | | | 78.58 159 | 77.03 161 | 83.22 148 | 85.94 205 | 64.56 154 | 83.21 267 | 91.14 162 | 78.31 63 | 73.67 141 | 79.68 259 | 64.01 69 | 92.09 240 | 66.07 187 | 71.26 214 | 93.03 131 |
|
OpenMVS | | 70.45 11 | 78.54 160 | 75.92 174 | 86.41 63 | 85.93 206 | 71.68 12 | 92.74 103 | 92.51 109 | 66.49 228 | 64.56 231 | 91.96 118 | 43.88 243 | 98.10 30 | 54.61 248 | 90.65 77 | 89.44 187 |
|
OMC-MVS | | | 78.67 158 | 77.91 149 | 80.95 202 | 85.76 207 | 57.40 264 | 88.49 223 | 88.67 238 | 73.85 120 | 72.43 157 | 92.10 116 | 49.29 207 | 94.55 165 | 72.73 132 | 77.89 167 | 90.91 170 |
|
EI-MVSNet | | | 78.97 149 | 78.22 143 | 81.25 192 | 85.33 208 | 62.73 201 | 89.53 206 | 93.21 78 | 72.39 150 | 72.14 160 | 90.13 145 | 60.99 96 | 94.72 160 | 67.73 171 | 72.49 204 | 86.29 227 |
|
CVMVSNet | | | 74.04 211 | 74.27 190 | 73.33 278 | 85.33 208 | 43.94 317 | 89.53 206 | 88.39 243 | 54.33 295 | 70.37 176 | 90.13 145 | 49.17 209 | 84.05 302 | 61.83 221 | 79.36 154 | 91.99 153 |
|
ACMH | | 63.93 17 | 68.62 249 | 64.81 256 | 80.03 213 | 85.22 210 | 63.25 187 | 87.72 234 | 84.66 279 | 60.83 266 | 51.57 293 | 79.43 262 | 27.29 307 | 94.96 152 | 41.76 293 | 64.84 250 | 81.88 283 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TAMVS | | | 80.37 124 | 79.45 126 | 83.13 150 | 85.14 211 | 63.37 185 | 91.23 165 | 90.76 172 | 74.81 106 | 72.65 150 | 88.49 158 | 60.63 100 | 92.95 213 | 69.41 158 | 81.95 139 | 93.08 130 |
|
MSDG | | | 69.54 244 | 65.73 250 | 80.96 201 | 85.11 212 | 63.71 179 | 84.19 256 | 83.28 292 | 56.95 286 | 54.50 281 | 84.03 207 | 31.50 295 | 96.03 119 | 42.87 290 | 69.13 226 | 83.14 270 |
|
ACMP | | 71.68 10 | 75.58 198 | 74.23 191 | 79.62 224 | 84.97 213 | 59.64 241 | 90.80 178 | 89.07 229 | 70.39 192 | 62.95 246 | 87.30 179 | 38.28 262 | 93.87 195 | 72.89 129 | 71.45 212 | 85.36 245 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
FC-MVSNet-test | | | 77.99 167 | 78.08 145 | 77.70 245 | 84.89 214 | 55.51 275 | 90.27 190 | 93.75 58 | 76.87 79 | 66.80 221 | 87.59 174 | 65.71 55 | 90.23 268 | 62.89 213 | 73.94 193 | 87.37 210 |
|
PVSNet_0 | | 68.08 15 | 71.81 230 | 68.32 241 | 82.27 170 | 84.68 215 | 62.31 207 | 88.68 219 | 90.31 184 | 75.84 90 | 57.93 273 | 80.65 247 | 37.85 268 | 94.19 178 | 69.94 153 | 29.05 325 | 90.31 176 |
|
WR-MVS | | | 76.76 182 | 75.74 177 | 79.82 219 | 84.60 216 | 62.27 208 | 92.60 110 | 92.51 109 | 76.06 88 | 67.87 209 | 85.34 195 | 56.76 138 | 90.24 267 | 62.20 218 | 63.69 262 | 86.94 219 |
|
ACMH+ | | 65.35 16 | 67.65 258 | 64.55 259 | 76.96 256 | 84.59 217 | 57.10 267 | 88.08 228 | 80.79 298 | 58.59 281 | 53.00 287 | 81.09 242 | 26.63 309 | 92.95 213 | 46.51 275 | 61.69 275 | 80.82 290 |
|
VPNet | | | 78.82 151 | 77.53 154 | 82.70 156 | 84.52 218 | 66.44 112 | 93.93 62 | 92.23 116 | 80.46 36 | 72.60 151 | 88.38 161 | 49.18 208 | 93.13 209 | 72.47 135 | 63.97 260 | 88.55 195 |
|
IterMVS-LS | | | 76.49 184 | 75.18 181 | 80.43 206 | 84.49 219 | 62.74 200 | 90.64 182 | 88.80 236 | 72.40 149 | 65.16 226 | 81.72 230 | 60.98 97 | 92.27 236 | 67.74 170 | 64.65 254 | 86.29 227 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UniMVSNet_NR-MVSNet | | | 78.15 165 | 77.55 153 | 79.98 214 | 84.46 220 | 60.26 233 | 92.25 118 | 93.20 80 | 77.50 74 | 68.88 195 | 86.61 183 | 66.10 49 | 92.13 238 | 66.38 183 | 62.55 264 | 87.54 205 |
|
FMVSNet5 | | | 68.04 255 | 65.66 251 | 75.18 266 | 84.43 221 | 57.89 257 | 83.54 260 | 86.26 266 | 61.83 262 | 53.64 286 | 73.30 293 | 37.15 275 | 85.08 298 | 48.99 266 | 61.77 271 | 82.56 279 |
|
MVS-HIRNet | | | 60.25 286 | 55.55 291 | 74.35 271 | 84.37 222 | 56.57 270 | 71.64 306 | 74.11 312 | 34.44 322 | 45.54 310 | 42.24 325 | 31.11 298 | 89.81 272 | 40.36 300 | 76.10 185 | 76.67 312 |
|
LPG-MVS_test | | | 75.82 193 | 74.58 185 | 79.56 226 | 84.31 223 | 59.37 246 | 90.44 185 | 89.73 205 | 69.49 200 | 64.86 227 | 88.42 159 | 38.65 259 | 94.30 173 | 72.56 133 | 72.76 201 | 85.01 249 |
|
LGP-MVS_train | | | | | 79.56 226 | 84.31 223 | 59.37 246 | | 89.73 205 | 69.49 200 | 64.86 227 | 88.42 159 | 38.65 259 | 94.30 173 | 72.56 133 | 72.76 201 | 85.01 249 |
|
ACMM | | 69.62 13 | 74.34 208 | 72.73 208 | 79.17 231 | 84.25 225 | 57.87 258 | 90.36 188 | 89.93 198 | 63.17 250 | 65.64 224 | 86.04 191 | 37.79 269 | 94.10 180 | 65.89 189 | 71.52 211 | 85.55 242 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UniMVSNet (Re) | | | 77.58 172 | 76.78 165 | 79.98 214 | 84.11 226 | 60.80 222 | 91.76 143 | 93.17 83 | 76.56 85 | 69.93 185 | 84.78 201 | 63.32 81 | 92.36 233 | 64.89 200 | 62.51 266 | 86.78 221 |
|
test_0402 | | | 64.54 273 | 61.09 278 | 74.92 267 | 84.10 227 | 60.75 225 | 87.95 230 | 79.71 302 | 52.03 299 | 52.41 289 | 77.20 276 | 32.21 293 | 91.64 248 | 23.14 324 | 61.03 278 | 72.36 317 |
|
LTVRE_ROB | | 59.60 19 | 66.27 266 | 63.54 266 | 74.45 270 | 84.00 228 | 51.55 292 | 67.08 317 | 83.53 288 | 58.78 279 | 54.94 280 | 80.31 251 | 34.54 285 | 93.23 208 | 40.64 299 | 68.03 233 | 78.58 307 |
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 |
miper_lstm_enhance | | | 73.05 219 | 71.73 220 | 77.03 255 | 83.80 229 | 58.32 255 | 81.76 273 | 88.88 234 | 69.80 198 | 61.01 254 | 78.23 268 | 57.19 129 | 87.51 290 | 65.34 197 | 59.53 284 | 85.27 248 |
|
Patchmatch-test | | | 65.86 268 | 60.94 279 | 80.62 204 | 83.75 230 | 58.83 251 | 58.91 324 | 75.26 311 | 44.50 317 | 50.95 297 | 77.09 278 | 58.81 118 | 87.90 287 | 35.13 312 | 64.03 258 | 95.12 61 |
|
nrg030 | | | 80.93 117 | 79.86 118 | 84.13 130 | 83.69 231 | 68.83 51 | 93.23 87 | 91.20 159 | 75.55 93 | 75.06 129 | 88.22 167 | 63.04 85 | 94.74 159 | 81.88 73 | 66.88 240 | 88.82 191 |
|
GA-MVS | | | 78.33 163 | 76.23 170 | 84.65 118 | 83.65 232 | 66.30 116 | 91.44 152 | 90.14 190 | 76.01 89 | 70.32 177 | 84.02 208 | 42.50 247 | 94.72 160 | 70.98 145 | 77.00 180 | 92.94 134 |
|
FMVSNet1 | | | 72.71 226 | 69.91 232 | 81.10 197 | 83.60 233 | 65.11 144 | 90.01 195 | 90.32 181 | 63.92 243 | 63.56 241 | 80.25 253 | 36.35 280 | 91.54 251 | 54.46 249 | 66.75 241 | 86.64 222 |
|
OPM-MVS | | | 79.00 148 | 78.09 144 | 81.73 184 | 83.52 234 | 63.83 174 | 91.64 149 | 90.30 185 | 76.36 87 | 71.97 162 | 89.93 148 | 46.30 232 | 95.17 149 | 75.10 118 | 77.70 169 | 86.19 229 |
|
tfpnnormal | | | 70.10 239 | 67.36 243 | 78.32 239 | 83.45 235 | 60.97 220 | 88.85 216 | 92.77 97 | 64.85 239 | 60.83 256 | 78.53 265 | 43.52 245 | 93.48 204 | 31.73 321 | 61.70 274 | 80.52 294 |
|
Effi-MVS+-dtu | | | 76.14 187 | 75.28 180 | 78.72 236 | 83.22 236 | 55.17 277 | 89.87 199 | 87.78 254 | 75.42 95 | 67.98 205 | 81.43 232 | 45.08 238 | 92.52 227 | 75.08 119 | 71.63 209 | 88.48 196 |
|
mvs-test1 | | | 78.74 155 | 77.95 147 | 81.14 194 | 83.22 236 | 57.13 266 | 93.96 59 | 87.78 254 | 75.42 95 | 72.68 149 | 90.80 133 | 45.08 238 | 94.54 166 | 75.08 119 | 77.49 174 | 91.74 156 |
|
CR-MVSNet | | | 73.79 215 | 70.82 226 | 82.70 156 | 83.15 238 | 67.96 72 | 70.25 307 | 84.00 285 | 73.67 126 | 69.97 183 | 72.41 296 | 57.82 124 | 89.48 275 | 52.99 256 | 73.13 198 | 90.64 173 |
|
RPMNet | | | 69.58 243 | 65.21 255 | 82.70 156 | 83.15 238 | 67.96 72 | 70.25 307 | 86.15 268 | 46.83 313 | 69.97 183 | 65.10 312 | 56.48 145 | 89.48 275 | 35.79 311 | 73.13 198 | 90.64 173 |
|
DU-MVS | | | 76.86 179 | 75.84 175 | 79.91 216 | 82.96 240 | 60.26 233 | 91.26 164 | 91.54 146 | 76.46 86 | 68.88 195 | 86.35 185 | 56.16 146 | 92.13 238 | 66.38 183 | 62.55 264 | 87.35 211 |
|
NR-MVSNet | | | 76.05 189 | 74.59 184 | 80.44 205 | 82.96 240 | 62.18 209 | 90.83 177 | 91.73 138 | 77.12 77 | 60.96 255 | 86.35 185 | 59.28 113 | 91.80 245 | 60.74 225 | 61.34 277 | 87.35 211 |
|
XXY-MVS | | | 77.94 169 | 76.44 168 | 82.43 163 | 82.60 242 | 64.44 160 | 92.01 130 | 91.83 135 | 73.59 127 | 70.00 182 | 85.82 192 | 54.43 165 | 94.76 157 | 69.63 155 | 68.02 234 | 88.10 202 |
|
TranMVSNet+NR-MVSNet | | | 75.86 192 | 74.52 187 | 79.89 217 | 82.44 243 | 60.64 229 | 91.37 159 | 91.37 154 | 76.63 83 | 67.65 211 | 86.21 188 | 52.37 184 | 91.55 250 | 61.84 220 | 60.81 280 | 87.48 207 |
|
IterMVS | | | 72.65 228 | 70.83 225 | 78.09 243 | 82.17 244 | 62.96 193 | 87.64 236 | 86.28 265 | 71.56 177 | 60.44 257 | 78.85 264 | 45.42 237 | 86.66 294 | 63.30 209 | 61.83 270 | 84.65 253 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Patchmtry | | | 67.53 260 | 63.93 264 | 78.34 238 | 82.12 245 | 64.38 163 | 68.72 311 | 84.00 285 | 48.23 310 | 59.24 262 | 72.41 296 | 57.82 124 | 89.27 277 | 46.10 277 | 56.68 293 | 81.36 286 |
|
PatchT | | | 69.11 247 | 65.37 254 | 80.32 207 | 82.07 246 | 63.68 181 | 67.96 316 | 87.62 256 | 50.86 303 | 69.37 187 | 65.18 311 | 57.09 130 | 88.53 282 | 41.59 295 | 66.60 242 | 88.74 192 |
|
MIMVSNet | | | 71.64 231 | 68.44 239 | 81.23 193 | 81.97 247 | 64.44 160 | 73.05 304 | 88.80 236 | 69.67 199 | 64.59 230 | 74.79 289 | 32.79 289 | 87.82 288 | 53.99 251 | 76.35 184 | 91.42 160 |
|
MVP-Stereo | | | 77.12 178 | 76.23 170 | 79.79 220 | 81.72 248 | 66.34 115 | 89.29 208 | 90.88 169 | 70.56 191 | 62.01 253 | 82.88 217 | 49.34 206 | 94.13 179 | 65.55 195 | 93.80 33 | 78.88 304 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
IterMVS-SCA-FT | | | 71.55 232 | 69.97 230 | 76.32 260 | 81.48 249 | 60.67 228 | 87.64 236 | 85.99 270 | 66.17 230 | 59.50 261 | 78.88 263 | 45.53 235 | 83.65 306 | 62.58 215 | 61.93 269 | 84.63 254 |
|
COLMAP_ROB | | 57.96 20 | 62.98 281 | 59.65 282 | 72.98 282 | 81.44 250 | 53.00 286 | 83.75 258 | 75.53 310 | 48.34 309 | 48.81 302 | 81.40 234 | 24.14 312 | 90.30 263 | 32.95 317 | 60.52 282 | 75.65 314 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
JIA-IIPM | | | 66.06 267 | 62.45 273 | 76.88 257 | 81.42 251 | 54.45 281 | 57.49 325 | 88.67 238 | 49.36 306 | 63.86 238 | 46.86 321 | 56.06 149 | 90.25 264 | 49.53 265 | 68.83 227 | 85.95 233 |
|
WR-MVS_H | | | 70.59 237 | 69.94 231 | 72.53 285 | 81.03 252 | 51.43 293 | 87.35 239 | 92.03 126 | 67.38 222 | 60.23 258 | 80.70 244 | 55.84 153 | 83.45 308 | 46.33 276 | 58.58 288 | 82.72 274 |
|
DI_MVS_plusplus_test | | | 79.78 137 | 77.50 155 | 86.62 51 | 80.90 253 | 69.46 39 | 90.69 181 | 91.97 129 | 77.00 78 | 59.07 266 | 82.34 221 | 46.82 226 | 95.88 123 | 82.14 71 | 86.59 107 | 94.53 83 |
|
Fast-Effi-MVS+-dtu | | | 75.04 203 | 73.37 202 | 80.07 212 | 80.86 254 | 59.52 244 | 91.20 168 | 85.38 274 | 71.90 163 | 65.20 225 | 84.84 200 | 41.46 250 | 92.97 212 | 66.50 182 | 72.96 200 | 87.73 204 |
|
Baseline_NR-MVSNet | | | 73.99 212 | 72.83 206 | 77.48 249 | 80.78 255 | 59.29 248 | 91.79 140 | 84.55 280 | 68.85 209 | 68.99 193 | 80.70 244 | 56.16 146 | 92.04 241 | 62.67 214 | 60.98 279 | 81.11 287 |
|
CP-MVSNet | | | 70.50 238 | 69.91 232 | 72.26 288 | 80.71 256 | 51.00 296 | 87.23 240 | 90.30 185 | 67.84 216 | 59.64 260 | 82.69 219 | 50.23 199 | 82.30 315 | 51.28 258 | 59.28 285 | 83.46 264 |
|
v8 | | | 75.35 199 | 73.26 203 | 81.61 188 | 80.67 257 | 66.82 101 | 89.54 205 | 89.27 218 | 71.65 173 | 63.30 244 | 80.30 252 | 54.99 160 | 94.06 184 | 67.33 175 | 62.33 267 | 83.94 257 |
|
PS-MVSNAJss | | | 77.26 176 | 76.31 169 | 80.13 211 | 80.64 258 | 59.16 249 | 90.63 184 | 91.06 165 | 72.80 141 | 68.58 200 | 84.57 204 | 53.55 173 | 93.96 191 | 72.97 128 | 71.96 208 | 87.27 214 |
|
TransMVSNet (Re) | | | 70.07 240 | 67.66 242 | 77.31 253 | 80.62 259 | 59.13 250 | 91.78 142 | 84.94 278 | 65.97 231 | 60.08 259 | 80.44 249 | 50.78 193 | 91.87 243 | 48.84 267 | 45.46 312 | 80.94 289 |
|
v2v482 | | | 77.42 174 | 75.65 178 | 82.73 155 | 80.38 260 | 67.13 93 | 91.85 138 | 90.23 187 | 75.09 102 | 69.37 187 | 83.39 214 | 53.79 171 | 94.44 170 | 71.77 140 | 65.00 249 | 86.63 225 |
|
MVS_0304 | | | 68.99 248 | 67.23 246 | 74.28 273 | 80.36 261 | 52.54 287 | 87.01 243 | 86.36 263 | 59.89 274 | 66.22 222 | 73.56 292 | 24.25 311 | 88.03 286 | 57.34 241 | 70.11 217 | 82.27 280 |
|
PS-CasMVS | | | 69.86 242 | 69.13 236 | 72.07 291 | 80.35 262 | 50.57 298 | 87.02 242 | 89.75 202 | 67.27 223 | 59.19 264 | 82.28 222 | 46.58 229 | 82.24 316 | 50.69 260 | 59.02 286 | 83.39 266 |
|
v10 | | | 74.77 206 | 72.54 212 | 81.46 189 | 80.33 263 | 66.71 105 | 89.15 212 | 89.08 228 | 70.94 185 | 63.08 245 | 79.86 257 | 52.52 182 | 94.04 187 | 65.70 192 | 62.17 268 | 83.64 259 |
|
test0.0.03 1 | | | 72.76 224 | 72.71 209 | 72.88 283 | 80.25 264 | 47.99 307 | 91.22 166 | 89.45 212 | 71.51 179 | 62.51 251 | 87.66 173 | 53.83 169 | 85.06 299 | 50.16 262 | 67.84 237 | 85.58 240 |
|
v1144 | | | 76.73 183 | 74.88 182 | 82.27 170 | 80.23 265 | 66.60 108 | 91.68 147 | 90.21 189 | 73.69 124 | 69.06 192 | 81.89 227 | 52.73 181 | 94.40 171 | 69.21 160 | 65.23 246 | 85.80 236 |
|
v148 | | | 76.19 186 | 74.47 188 | 81.36 190 | 80.05 266 | 64.44 160 | 91.75 145 | 90.23 187 | 73.68 125 | 67.13 216 | 80.84 243 | 55.92 152 | 93.86 197 | 68.95 163 | 61.73 273 | 85.76 239 |
|
v1192 | | | 75.98 191 | 73.92 196 | 82.15 175 | 79.73 267 | 66.24 119 | 91.22 166 | 89.75 202 | 72.67 143 | 68.49 201 | 81.42 233 | 49.86 202 | 94.27 175 | 67.08 176 | 65.02 248 | 85.95 233 |
|
AllTest | | | 61.66 282 | 58.06 284 | 72.46 286 | 79.57 268 | 51.42 294 | 80.17 286 | 68.61 321 | 51.25 301 | 45.88 306 | 81.23 236 | 19.86 321 | 86.58 295 | 38.98 303 | 57.01 291 | 79.39 301 |
|
TestCases | | | | | 72.46 286 | 79.57 268 | 51.42 294 | | 68.61 321 | 51.25 301 | 45.88 306 | 81.23 236 | 19.86 321 | 86.58 295 | 38.98 303 | 57.01 291 | 79.39 301 |
|
MDA-MVSNet-bldmvs | | | 61.54 284 | 57.70 286 | 73.05 281 | 79.53 270 | 57.00 268 | 83.08 268 | 81.23 296 | 57.57 282 | 34.91 322 | 72.45 295 | 32.79 289 | 86.26 297 | 35.81 310 | 41.95 316 | 75.89 313 |
|
v144192 | | | 76.05 189 | 74.03 194 | 82.12 176 | 79.50 271 | 66.55 110 | 91.39 156 | 89.71 208 | 72.30 151 | 68.17 203 | 81.33 235 | 51.75 188 | 94.03 188 | 67.94 168 | 64.19 256 | 85.77 237 |
|
v1921920 | | | 75.63 197 | 73.49 201 | 82.06 180 | 79.38 272 | 66.35 114 | 91.07 173 | 89.48 211 | 71.98 160 | 67.99 204 | 81.22 238 | 49.16 210 | 93.90 194 | 66.56 181 | 64.56 255 | 85.92 235 |
|
PEN-MVS | | | 69.46 245 | 68.56 238 | 72.17 290 | 79.27 273 | 49.71 302 | 86.90 244 | 89.24 219 | 67.24 224 | 59.08 265 | 82.51 220 | 47.23 225 | 83.54 307 | 48.42 269 | 57.12 289 | 83.25 267 |
|
v1240 | | | 75.21 202 | 72.98 205 | 81.88 182 | 79.20 274 | 66.00 123 | 90.75 180 | 89.11 227 | 71.63 174 | 67.41 214 | 81.22 238 | 47.36 224 | 93.87 195 | 65.46 196 | 64.72 253 | 85.77 237 |
|
pmmvs4 | | | 73.92 213 | 71.81 219 | 80.25 209 | 79.17 275 | 65.24 140 | 87.43 238 | 87.26 260 | 67.64 221 | 63.46 242 | 83.91 209 | 48.96 212 | 91.53 254 | 62.94 211 | 65.49 245 | 83.96 256 |
|
D2MVS | | | 73.80 214 | 72.02 217 | 79.15 233 | 79.15 276 | 62.97 192 | 88.58 222 | 90.07 192 | 72.94 138 | 59.22 263 | 78.30 266 | 42.31 249 | 92.70 222 | 65.59 194 | 72.00 207 | 81.79 284 |
|
V42 | | | 76.46 185 | 74.55 186 | 82.19 174 | 79.14 277 | 67.82 75 | 90.26 191 | 89.42 214 | 73.75 123 | 68.63 199 | 81.89 227 | 51.31 192 | 94.09 181 | 71.69 142 | 64.84 250 | 84.66 252 |
|
pm-mvs1 | | | 72.89 222 | 71.09 224 | 78.26 241 | 79.10 278 | 57.62 262 | 90.80 178 | 89.30 217 | 67.66 219 | 62.91 247 | 81.78 229 | 49.11 211 | 92.95 213 | 60.29 229 | 58.89 287 | 84.22 255 |
|
our_test_3 | | | 68.29 253 | 64.69 258 | 79.11 234 | 78.92 279 | 64.85 152 | 88.40 226 | 85.06 276 | 60.32 270 | 52.68 288 | 76.12 283 | 40.81 252 | 89.80 274 | 44.25 285 | 55.65 294 | 82.67 278 |
|
ppachtmachnet_test | | | 67.72 257 | 63.70 265 | 79.77 221 | 78.92 279 | 66.04 122 | 88.68 219 | 82.90 294 | 60.11 272 | 55.45 279 | 75.96 284 | 39.19 256 | 90.55 260 | 39.53 301 | 52.55 303 | 82.71 275 |
|
TinyColmap | | | 60.32 285 | 56.42 290 | 72.00 292 | 78.78 281 | 53.18 285 | 78.36 296 | 75.64 308 | 52.30 298 | 41.59 318 | 75.82 286 | 14.76 326 | 88.35 283 | 35.84 309 | 54.71 299 | 74.46 315 |
|
SixPastTwentyTwo | | | 64.92 271 | 61.78 277 | 74.34 272 | 78.74 282 | 49.76 301 | 83.42 264 | 79.51 303 | 62.86 252 | 50.27 298 | 77.35 273 | 30.92 299 | 90.49 262 | 45.89 278 | 47.06 310 | 82.78 271 |
|
EG-PatchMatch MVS | | | 68.55 250 | 65.41 253 | 77.96 244 | 78.69 283 | 62.93 194 | 89.86 200 | 89.17 223 | 60.55 267 | 50.27 298 | 77.73 272 | 22.60 316 | 94.06 184 | 47.18 274 | 72.65 203 | 76.88 311 |
|
pmmvs5 | | | 73.35 217 | 71.52 221 | 78.86 235 | 78.64 284 | 60.61 230 | 91.08 171 | 86.90 261 | 67.69 218 | 63.32 243 | 83.64 210 | 44.33 242 | 90.53 261 | 62.04 219 | 66.02 244 | 85.46 243 |
|
UniMVSNet_ETH3D | | | 72.74 225 | 70.53 227 | 79.36 228 | 78.62 285 | 56.64 269 | 85.01 252 | 89.20 221 | 63.77 245 | 64.84 229 | 84.44 205 | 34.05 286 | 91.86 244 | 63.94 205 | 70.89 216 | 89.57 185 |
|
XVG-OURS | | | 74.25 210 | 72.46 213 | 79.63 223 | 78.45 286 | 57.59 263 | 80.33 283 | 87.39 257 | 63.86 244 | 68.76 197 | 89.62 151 | 40.50 253 | 91.72 247 | 69.00 162 | 74.25 190 | 89.58 184 |
|
XVG-OURS-SEG-HR | | | 74.70 207 | 73.08 204 | 79.57 225 | 78.25 287 | 57.33 265 | 80.49 281 | 87.32 258 | 63.22 249 | 68.76 197 | 90.12 147 | 44.89 240 | 91.59 249 | 70.55 150 | 74.09 192 | 89.79 181 |
|
MDA-MVSNet_test_wron | | | 63.78 278 | 60.16 280 | 74.64 268 | 78.15 288 | 60.41 231 | 83.49 261 | 84.03 283 | 56.17 292 | 39.17 320 | 71.59 302 | 37.22 273 | 83.24 311 | 42.87 290 | 48.73 308 | 80.26 297 |
|
YYNet1 | | | 63.76 279 | 60.14 281 | 74.62 269 | 78.06 289 | 60.19 236 | 83.46 263 | 83.99 287 | 56.18 291 | 39.25 319 | 71.56 303 | 37.18 274 | 83.34 309 | 42.90 289 | 48.70 309 | 80.32 296 |
|
DTE-MVSNet | | | 68.46 252 | 67.33 244 | 71.87 293 | 77.94 290 | 49.00 305 | 86.16 249 | 88.58 241 | 66.36 229 | 58.19 270 | 82.21 224 | 46.36 230 | 83.87 305 | 44.97 283 | 55.17 296 | 82.73 273 |
|
USDC | | | 67.43 262 | 64.51 260 | 76.19 261 | 77.94 290 | 55.29 276 | 78.38 295 | 85.00 277 | 73.17 133 | 48.36 303 | 80.37 250 | 21.23 318 | 92.48 229 | 52.15 257 | 64.02 259 | 80.81 291 |
|
jajsoiax | | | 73.05 219 | 71.51 222 | 77.67 246 | 77.46 292 | 54.83 278 | 88.81 217 | 90.04 196 | 69.13 207 | 62.85 248 | 83.51 212 | 31.16 297 | 92.75 219 | 70.83 146 | 69.80 219 | 85.43 244 |
|
mvs_tets | | | 72.71 226 | 71.11 223 | 77.52 247 | 77.41 293 | 54.52 280 | 88.45 225 | 89.76 201 | 68.76 212 | 62.70 249 | 83.26 215 | 29.49 300 | 92.71 220 | 70.51 151 | 69.62 221 | 85.34 246 |
|
N_pmnet | | | 50.55 295 | 49.11 297 | 54.88 309 | 77.17 294 | 4.02 338 | 84.36 255 | 2.00 338 | 48.59 307 | 45.86 308 | 68.82 307 | 32.22 292 | 82.80 312 | 31.58 322 | 51.38 305 | 77.81 309 |
|
test_djsdf | | | 73.76 216 | 72.56 211 | 77.39 251 | 77.00 295 | 53.93 282 | 89.07 214 | 90.69 173 | 65.80 232 | 63.92 237 | 82.03 226 | 43.14 246 | 92.67 223 | 72.83 130 | 68.53 230 | 85.57 241 |
|
OpenMVS_ROB | | 61.12 18 | 66.39 265 | 62.92 270 | 76.80 258 | 76.51 296 | 57.77 259 | 89.22 209 | 83.41 290 | 55.48 293 | 53.86 285 | 77.84 271 | 26.28 310 | 93.95 192 | 34.90 313 | 68.76 228 | 78.68 306 |
|
v7n | | | 71.31 233 | 68.65 237 | 79.28 229 | 76.40 297 | 60.77 224 | 86.71 246 | 89.45 212 | 64.17 242 | 58.77 269 | 78.24 267 | 44.59 241 | 93.54 202 | 57.76 238 | 61.75 272 | 83.52 262 |
|
K. test v3 | | | 63.09 280 | 59.61 283 | 73.53 277 | 76.26 298 | 49.38 304 | 83.27 265 | 77.15 305 | 64.35 241 | 47.77 304 | 72.32 298 | 28.73 302 | 87.79 289 | 49.93 264 | 36.69 322 | 83.41 265 |
|
RPSCF | | | 64.24 275 | 61.98 276 | 71.01 294 | 76.10 299 | 45.00 314 | 75.83 302 | 75.94 307 | 46.94 312 | 58.96 267 | 84.59 203 | 31.40 296 | 82.00 317 | 47.76 272 | 60.33 283 | 86.04 231 |
|
OurMVSNet-221017-0 | | | 64.68 272 | 62.17 275 | 72.21 289 | 76.08 300 | 47.35 310 | 80.67 280 | 81.02 297 | 56.19 290 | 51.60 292 | 79.66 260 | 27.05 308 | 88.56 281 | 53.60 254 | 53.63 301 | 80.71 292 |
|
Anonymous20231206 | | | 67.53 260 | 65.78 249 | 72.79 284 | 74.95 301 | 47.59 309 | 88.23 227 | 87.32 258 | 61.75 263 | 58.07 272 | 77.29 275 | 37.79 269 | 87.29 292 | 42.91 288 | 63.71 261 | 83.48 263 |
|
ITE_SJBPF | | | | | 70.43 295 | 74.44 302 | 47.06 311 | | 77.32 304 | 60.16 271 | 54.04 284 | 83.53 211 | 23.30 315 | 84.01 303 | 43.07 287 | 61.58 276 | 80.21 298 |
|
EU-MVSNet | | | 64.01 276 | 63.01 269 | 67.02 303 | 74.40 303 | 38.86 325 | 83.27 265 | 86.19 267 | 45.11 315 | 54.27 282 | 81.15 241 | 36.91 278 | 80.01 320 | 48.79 268 | 57.02 290 | 82.19 282 |
|
XVG-ACMP-BASELINE | | | 68.04 255 | 65.53 252 | 75.56 264 | 74.06 304 | 52.37 288 | 78.43 294 | 85.88 271 | 62.03 258 | 58.91 268 | 81.21 240 | 20.38 319 | 91.15 257 | 60.69 226 | 68.18 232 | 83.16 269 |
|
anonymousdsp | | | 71.14 234 | 69.37 235 | 76.45 259 | 72.95 305 | 54.71 279 | 84.19 256 | 88.88 234 | 61.92 260 | 62.15 252 | 79.77 258 | 38.14 264 | 91.44 256 | 68.90 164 | 67.45 238 | 83.21 268 |
|
lessismore_v0 | | | | | 73.72 276 | 72.93 306 | 47.83 308 | | 61.72 329 | | 45.86 308 | 73.76 291 | 28.63 304 | 89.81 272 | 47.75 273 | 31.37 324 | 83.53 261 |
|
pmmvs6 | | | 67.57 259 | 64.76 257 | 76.00 263 | 72.82 307 | 53.37 284 | 88.71 218 | 86.78 262 | 53.19 297 | 57.58 275 | 78.03 270 | 35.33 283 | 92.41 230 | 55.56 246 | 54.88 298 | 82.21 281 |
|
testgi | | | 64.48 274 | 62.87 271 | 69.31 297 | 71.24 308 | 40.62 321 | 85.49 250 | 79.92 301 | 65.36 236 | 54.18 283 | 83.49 213 | 23.74 314 | 84.55 300 | 41.60 294 | 60.79 281 | 82.77 272 |
|
Patchmatch-RL test | | | 68.17 254 | 64.49 261 | 79.19 230 | 71.22 309 | 53.93 282 | 70.07 309 | 71.54 318 | 69.22 204 | 56.79 277 | 62.89 314 | 56.58 143 | 88.61 279 | 69.53 157 | 52.61 302 | 95.03 67 |
|
Gipuma | | | 34.91 301 | 31.44 303 | 45.30 313 | 70.99 310 | 39.64 324 | 19.85 333 | 72.56 315 | 20.10 328 | 16.16 330 | 21.47 331 | 5.08 335 | 71.16 324 | 13.07 328 | 43.70 315 | 25.08 328 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
UnsupCasMVSNet_eth | | | 65.79 269 | 63.10 268 | 73.88 274 | 70.71 311 | 50.29 300 | 81.09 278 | 89.88 199 | 72.58 145 | 49.25 301 | 74.77 290 | 32.57 291 | 87.43 291 | 55.96 245 | 41.04 318 | 83.90 258 |
|
CMPMVS | | 48.56 21 | 66.77 264 | 64.41 262 | 73.84 275 | 70.65 312 | 50.31 299 | 77.79 299 | 85.73 273 | 45.54 314 | 44.76 312 | 82.14 225 | 35.40 282 | 90.14 270 | 63.18 210 | 74.54 189 | 81.07 288 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test20.03 | | | 63.83 277 | 62.65 272 | 67.38 302 | 70.58 313 | 39.94 322 | 86.57 247 | 84.17 282 | 63.29 248 | 51.86 291 | 77.30 274 | 37.09 276 | 82.47 313 | 38.87 305 | 54.13 300 | 79.73 299 |
|
testing_2 | | | 71.09 235 | 67.32 245 | 82.40 167 | 69.82 314 | 66.52 111 | 83.64 259 | 90.77 171 | 72.21 154 | 45.12 311 | 71.07 304 | 27.60 306 | 93.74 198 | 75.71 114 | 69.96 218 | 86.95 218 |
|
MIMVSNet1 | | | 60.16 287 | 57.33 287 | 68.67 298 | 69.71 315 | 44.13 316 | 78.92 292 | 84.21 281 | 55.05 294 | 44.63 313 | 71.85 300 | 23.91 313 | 81.54 319 | 32.63 319 | 55.03 297 | 80.35 295 |
|
pmmvs-eth3d | | | 65.53 270 | 62.32 274 | 75.19 265 | 69.39 316 | 59.59 242 | 82.80 270 | 83.43 289 | 62.52 255 | 51.30 295 | 72.49 294 | 32.86 288 | 87.16 293 | 55.32 247 | 50.73 306 | 78.83 305 |
|
UnsupCasMVSNet_bld | | | 61.60 283 | 57.71 285 | 73.29 279 | 68.73 317 | 51.64 291 | 78.61 293 | 89.05 230 | 57.20 285 | 46.11 305 | 61.96 315 | 28.70 303 | 88.60 280 | 50.08 263 | 38.90 320 | 79.63 300 |
|
new-patchmatchnet | | | 59.30 289 | 56.48 289 | 67.79 300 | 65.86 318 | 44.19 315 | 82.47 271 | 81.77 295 | 59.94 273 | 43.65 316 | 66.20 310 | 27.67 305 | 81.68 318 | 39.34 302 | 41.40 317 | 77.50 310 |
|
PM-MVS | | | 59.40 288 | 56.59 288 | 67.84 299 | 63.63 319 | 41.86 318 | 76.76 300 | 63.22 327 | 59.01 278 | 51.07 296 | 72.27 299 | 11.72 328 | 83.25 310 | 61.34 222 | 50.28 307 | 78.39 308 |
|
DSMNet-mixed | | | 56.78 291 | 54.44 292 | 63.79 305 | 63.21 320 | 29.44 329 | 64.43 319 | 64.10 326 | 42.12 319 | 51.32 294 | 71.60 301 | 31.76 294 | 75.04 322 | 36.23 308 | 65.20 247 | 86.87 220 |
|
new_pmnet | | | 49.31 296 | 46.44 298 | 57.93 307 | 62.84 321 | 40.74 320 | 68.47 313 | 62.96 328 | 36.48 321 | 35.09 321 | 57.81 317 | 14.97 325 | 72.18 323 | 32.86 318 | 46.44 311 | 60.88 323 |
|
LF4IMVS | | | 54.01 294 | 52.12 293 | 59.69 306 | 62.41 322 | 39.91 323 | 68.59 312 | 68.28 323 | 42.96 318 | 44.55 314 | 75.18 287 | 14.09 327 | 68.39 325 | 41.36 296 | 51.68 304 | 70.78 318 |
|
ambc | | | | | 69.61 296 | 61.38 323 | 41.35 319 | 49.07 328 | 85.86 272 | | 50.18 300 | 66.40 309 | 10.16 329 | 88.14 285 | 45.73 279 | 44.20 313 | 79.32 303 |
|
test_normal | | | 57.36 290 | 52.06 294 | 73.28 280 | 60.52 324 | 46.40 312 | 26.00 331 | 88.06 250 | 66.54 226 | 32.27 323 | 49.48 319 | 19.92 320 | 90.75 259 | 62.55 216 | 63.18 263 | 82.70 276 |
|
TDRefinement | | | 55.28 293 | 51.58 295 | 66.39 304 | 59.53 325 | 46.15 313 | 76.23 301 | 72.80 314 | 44.60 316 | 42.49 317 | 76.28 282 | 15.29 324 | 82.39 314 | 33.20 316 | 43.75 314 | 70.62 319 |
|
pmmvs3 | | | 55.51 292 | 51.50 296 | 67.53 301 | 57.90 326 | 50.93 297 | 80.37 282 | 73.66 313 | 40.63 320 | 44.15 315 | 64.75 313 | 16.30 323 | 78.97 321 | 44.77 284 | 40.98 319 | 72.69 316 |
|
DeepMVS_CX | | | | | 34.71 316 | 51.45 327 | 24.73 333 | | 28.48 337 | 31.46 323 | 17.49 329 | 52.75 318 | 5.80 334 | 42.60 334 | 18.18 326 | 19.42 326 | 36.81 326 |
|
FPMVS | | | 45.64 297 | 43.10 299 | 53.23 311 | 51.42 328 | 36.46 326 | 64.97 318 | 71.91 316 | 29.13 324 | 27.53 324 | 61.55 316 | 9.83 330 | 65.01 329 | 16.00 327 | 55.58 295 | 58.22 324 |
|
wuyk23d | | | 11.30 308 | 10.95 310 | 12.33 320 | 48.05 329 | 19.89 334 | 25.89 332 | 1.92 339 | 3.58 332 | 3.12 335 | 1.37 335 | 0.64 340 | 15.77 336 | 6.23 333 | 7.77 333 | 1.35 332 |
|
PMMVS2 | | | 37.93 300 | 33.61 302 | 50.92 312 | 46.31 330 | 24.76 332 | 60.55 323 | 50.05 330 | 28.94 325 | 20.93 326 | 47.59 320 | 4.41 337 | 65.13 328 | 25.14 323 | 18.55 327 | 62.87 322 |
|
E-PMN | | | 24.61 303 | 24.00 306 | 26.45 317 | 43.74 331 | 18.44 335 | 60.86 321 | 39.66 332 | 15.11 329 | 9.53 333 | 22.10 330 | 6.52 333 | 46.94 332 | 8.31 331 | 10.14 329 | 13.98 330 |
|
EMVS | | | 23.76 305 | 23.20 308 | 25.46 318 | 41.52 332 | 16.90 336 | 60.56 322 | 38.79 335 | 14.62 330 | 8.99 334 | 20.24 333 | 7.35 332 | 45.82 333 | 7.25 332 | 9.46 330 | 13.64 331 |
|
LCM-MVSNet | | | 40.54 298 | 35.79 300 | 54.76 310 | 36.92 333 | 30.81 328 | 51.41 326 | 69.02 320 | 22.07 326 | 24.63 325 | 45.37 323 | 4.56 336 | 65.81 327 | 33.67 314 | 34.50 323 | 67.67 320 |
|
ANet_high | | | 40.27 299 | 35.20 301 | 55.47 308 | 34.74 334 | 34.47 327 | 63.84 320 | 71.56 317 | 48.42 308 | 18.80 328 | 41.08 326 | 9.52 331 | 64.45 330 | 20.18 325 | 8.66 332 | 67.49 321 |
|
MVE | | 24.84 23 | 24.35 304 | 19.77 309 | 38.09 315 | 34.56 335 | 26.92 331 | 26.57 330 | 38.87 334 | 11.73 331 | 11.37 332 | 27.44 328 | 1.37 339 | 50.42 331 | 11.41 329 | 14.60 328 | 36.93 325 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PMVS | | 26.43 22 | 31.84 302 | 28.16 304 | 42.89 314 | 25.87 336 | 27.58 330 | 50.92 327 | 49.78 331 | 21.37 327 | 14.17 331 | 40.81 327 | 2.01 338 | 66.62 326 | 9.61 330 | 38.88 321 | 34.49 327 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 22.26 306 | 23.75 307 | 17.80 319 | 5.23 337 | 12.06 337 | 35.26 329 | 39.48 333 | 2.82 333 | 18.94 327 | 44.20 324 | 22.23 317 | 24.64 335 | 36.30 307 | 9.31 331 | 16.69 329 |
|
testmvs | | | 7.23 310 | 9.62 312 | 0.06 322 | 0.04 338 | 0.02 340 | 84.98 253 | 0.02 340 | 0.03 334 | 0.18 336 | 1.21 336 | 0.01 342 | 0.02 337 | 0.14 334 | 0.01 334 | 0.13 334 |
|
test123 | | | 6.92 311 | 9.21 313 | 0.08 321 | 0.03 339 | 0.05 339 | 81.65 276 | 0.01 341 | 0.02 335 | 0.14 337 | 0.85 337 | 0.03 341 | 0.02 337 | 0.12 335 | 0.00 335 | 0.16 333 |
|
cdsmvs_eth3d_5k | | | 19.86 307 | 26.47 305 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 93.45 69 | 0.00 336 | 0.00 338 | 95.27 39 | 49.56 204 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
pcd_1.5k_mvsjas | | | 4.46 312 | 5.95 314 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 53.55 173 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
sosnet-low-res | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
sosnet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
uncertanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
Regformer | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
ab-mvs-re | | | 7.91 309 | 10.55 311 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 94.95 48 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
uanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
save filter2 | | | | | | | | | | | 87.82 19 | 96.42 12 | 66.33 47 | 97.33 61 | 90.39 13 | 94.81 18 | 95.11 62 |
|
test_0728_THIRD | | | | | | | | | | 72.48 147 | 90.55 8 | 96.93 5 | 76.24 6 | 99.08 5 | 91.53 7 | 94.99 10 | 96.43 17 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.68 75 |
|
test_part1 | | | | | 0.00 323 | | 0.00 341 | 0.00 334 | 94.26 46 | | | | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 335 | 0.00 335 |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 123 | | | | 94.68 75 |
|
sam_mvs | | | | | | | | | | | | | 54.91 161 | | | | |
|
MTGPA | | | | | | | | | 92.23 116 | | | | | | | | |
|
test_post1 | | | | | | | | 78.95 291 | | | | 20.70 332 | 53.05 178 | 91.50 255 | 60.43 227 | | |
|
test_post | | | | | | | | | | | | 23.01 329 | 56.49 144 | 92.67 223 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 308 | 57.62 126 | 90.25 264 | | | |
|
MTMP | | | | | | | | 93.77 71 | 32.52 336 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 15 | 94.96 11 | 95.29 52 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 40 | 94.75 22 | 95.33 47 |
|
test_prior4 | | | | | | | 67.18 92 | 93.92 63 | | | | | | | | | |
|
test_prior2 | | | | | | | | 95.10 33 | | 75.40 97 | 85.25 39 | 95.61 29 | 67.94 35 | | 87.47 30 | 94.77 19 | |
|
旧先验2 | | | | | | | | 92.00 132 | | 59.37 277 | 87.54 21 | | | 93.47 205 | 75.39 116 | | |
|
新几何2 | | | | | | | | 91.41 153 | | | | | | | | | |
|
无先验 | | | | | | | | 92.71 104 | 92.61 106 | 62.03 258 | | | | 97.01 77 | 66.63 179 | | 93.97 105 |
|
原ACMM2 | | | | | | | | 92.01 130 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 96.09 112 | 61.26 223 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 51 | | | | |
|
testdata1 | | | | | | | | 89.21 210 | | 77.55 73 | | | | | | | |
|
plane_prior5 | | | | | | | | | 91.31 155 | | | | | 95.55 141 | 76.74 108 | 78.53 163 | 88.39 198 |
|
plane_prior4 | | | | | | | | | | | | 89.14 154 | | | | | |
|
plane_prior3 | | | | | | | 61.95 210 | | | 79.09 53 | 72.53 153 | | | | | | |
|
plane_prior2 | | | | | | | | 93.13 89 | | 78.81 58 | | | | | | | |
|
plane_prior | | | | | | | 62.42 203 | 93.85 66 | | 79.38 46 | | | | | | 78.80 160 | |
|
n2 | | | | | | | | | 0.00 342 | | | | | | | | |
|
nn | | | | | | | | | 0.00 342 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 325 | | | | | | | | |
|
test11 | | | | | | | | | 93.01 89 | | | | | | | | |
|
door | | | | | | | | | 66.57 324 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 182 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 105 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 134 | | | 95.61 137 | | | 88.63 193 |
|
HQP3-MVS | | | | | | | | | 91.70 141 | | | | | | | 78.90 158 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 190 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 239 | 80.13 287 | | 67.65 220 | 72.79 148 | | 54.33 166 | | 59.83 231 | | 92.58 142 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 209 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 220 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 167 | | | | |
|