EPNet | | | 95.20 77 | 94.56 83 | 97.14 59 | 92.80 296 | 92.68 72 | 97.85 54 | 94.87 284 | 96.64 1 | 92.46 142 | 97.80 72 | 86.23 113 | 99.65 46 | 93.72 93 | 98.62 83 | 99.10 67 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
NCCC | | | 97.30 11 | 97.03 11 | 98.11 10 | 98.77 44 | 95.06 15 | 97.34 104 | 98.04 73 | 95.96 2 | 97.09 35 | 97.88 62 | 93.18 14 | 99.71 34 | 95.84 42 | 99.17 59 | 99.56 19 |
|
CNVR-MVS | | | 97.68 4 | 97.44 7 | 98.37 4 | 98.90 41 | 95.86 3 | 97.27 111 | 98.08 58 | 95.81 3 | 97.87 17 | 98.31 38 | 94.26 6 | 99.68 42 | 97.02 6 | 99.49 27 | 99.57 17 |
|
HPM-MVS++ | | | 97.34 10 | 96.97 14 | 98.47 2 | 99.08 33 | 96.16 1 | 97.55 85 | 97.97 87 | 95.59 4 | 96.61 44 | 97.89 60 | 92.57 24 | 99.84 16 | 95.95 39 | 99.51 23 | 99.40 42 |
|
MSP-MVS | | | 97.59 6 | 97.54 4 | 97.73 30 | 99.40 8 | 93.77 46 | 98.53 9 | 98.29 24 | 95.55 5 | 98.56 8 | 97.81 70 | 93.90 8 | 99.65 46 | 96.62 17 | 99.21 56 | 99.77 1 |
|
DeepPCF-MVS | | 93.97 1 | 96.61 41 | 97.09 9 | 95.15 147 | 98.09 91 | 86.63 243 | 96.00 216 | 98.15 45 | 95.43 6 | 97.95 13 | 98.56 12 | 93.40 12 | 99.36 100 | 96.77 14 | 99.48 28 | 99.45 36 |
|
CANet | | | 96.39 47 | 96.02 50 | 97.50 42 | 97.62 113 | 93.38 55 | 97.02 132 | 97.96 88 | 95.42 7 | 94.86 97 | 97.81 70 | 87.38 101 | 99.82 22 | 96.88 9 | 99.20 57 | 99.29 51 |
|
save fliter | | | | | | 98.91 40 | 94.28 26 | 97.02 132 | 98.02 76 | 95.35 8 | | | | | | | |
|
SteuartSystems-ACMMP | | | 97.62 5 | 97.53 5 | 97.87 18 | 98.39 67 | 94.25 28 | 98.43 16 | 98.27 28 | 95.34 9 | 98.11 10 | 98.56 12 | 94.53 5 | 99.71 34 | 96.57 21 | 99.62 10 | 99.65 7 |
Skip Steuart: Steuart Systems R&D Blog. |
Regformer-2 | | | 97.16 15 | 96.99 13 | 97.67 35 | 98.32 73 | 93.84 41 | 96.83 149 | 98.10 55 | 95.24 10 | 97.49 20 | 98.25 45 | 92.57 24 | 99.61 53 | 96.80 11 | 99.29 47 | 99.56 19 |
|
DELS-MVS | | | 96.61 41 | 96.38 43 | 97.30 49 | 97.79 105 | 93.19 60 | 95.96 218 | 98.18 41 | 95.23 11 | 95.87 72 | 97.65 81 | 91.45 46 | 99.70 39 | 95.87 40 | 99.44 34 | 99.00 77 |
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 |
Regformer-1 | | | 97.10 17 | 96.96 15 | 97.54 41 | 98.32 73 | 93.48 52 | 96.83 149 | 97.99 85 | 95.20 12 | 97.46 21 | 98.25 45 | 92.48 27 | 99.58 61 | 96.79 13 | 99.29 47 | 99.55 21 |
|
DPE-MVS | | | 97.86 2 | 97.65 3 | 98.47 2 | 99.17 28 | 95.78 4 | 97.21 120 | 98.35 19 | 95.16 13 | 98.71 6 | 98.80 6 | 95.05 4 | 99.89 3 | 96.70 16 | 99.73 1 | 99.73 5 |
|
Regformer-4 | | | 96.97 25 | 96.87 18 | 97.25 53 | 98.34 70 | 92.66 73 | 96.96 138 | 98.01 79 | 95.12 14 | 97.14 31 | 98.42 23 | 91.82 39 | 99.61 53 | 96.90 8 | 99.13 62 | 99.50 29 |
|
zzz-MVS | | | 97.07 19 | 96.77 27 | 97.97 16 | 99.37 13 | 94.42 23 | 97.15 126 | 98.08 58 | 95.07 15 | 96.11 62 | 98.59 10 | 90.88 58 | 99.90 1 | 96.18 34 | 99.50 25 | 99.58 15 |
|
MTAPA | | | 97.08 18 | 96.78 26 | 97.97 16 | 99.37 13 | 94.42 23 | 97.24 113 | 98.08 58 | 95.07 15 | 96.11 62 | 98.59 10 | 90.88 58 | 99.90 1 | 96.18 34 | 99.50 25 | 99.58 15 |
|
Regformer-3 | | | 96.85 31 | 96.80 25 | 97.01 62 | 98.34 70 | 92.02 92 | 96.96 138 | 97.76 103 | 95.01 17 | 97.08 36 | 98.42 23 | 91.71 41 | 99.54 74 | 96.80 11 | 99.13 62 | 99.48 33 |
|
DVP-MVS | | | 97.91 1 | 97.81 1 | 98.22 7 | 99.45 2 | 95.36 8 | 98.21 30 | 97.85 99 | 94.92 18 | 98.73 4 | 98.87 4 | 95.08 2 | 99.84 16 | 97.52 2 | 99.67 6 | 99.48 33 |
|
test0726 | | | | | | 99.45 2 | 95.36 8 | 98.31 21 | 98.29 24 | 94.92 18 | 98.99 2 | 98.92 2 | 95.08 2 | | | | |
|
XVS | | | 97.18 13 | 96.96 15 | 97.81 22 | 99.38 11 | 94.03 38 | 98.59 7 | 98.20 37 | 94.85 20 | 96.59 46 | 98.29 42 | 91.70 42 | 99.80 25 | 95.66 45 | 99.40 37 | 99.62 11 |
|
X-MVStestdata | | | 91.71 183 | 89.67 233 | 97.81 22 | 99.38 11 | 94.03 38 | 98.59 7 | 98.20 37 | 94.85 20 | 96.59 46 | 32.69 332 | 91.70 42 | 99.80 25 | 95.66 45 | 99.40 37 | 99.62 11 |
|
HQP_MVS | | | 93.78 116 | 93.43 112 | 94.82 162 | 96.21 174 | 89.99 152 | 97.74 63 | 97.51 130 | 94.85 20 | 91.34 166 | 96.64 133 | 81.32 188 | 98.60 167 | 93.02 106 | 92.23 196 | 95.86 209 |
|
plane_prior2 | | | | | | | | 97.74 63 | | 94.85 20 | | | | | | | |
|
SD-MVS | | | 97.41 8 | 97.53 5 | 97.06 61 | 98.57 59 | 94.46 21 | 97.92 48 | 98.14 47 | 94.82 24 | 99.01 1 | 98.55 14 | 94.18 7 | 97.41 276 | 96.94 7 | 99.64 8 | 99.32 49 |
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 |
UA-Net | | | 95.95 58 | 95.53 58 | 97.20 58 | 97.67 110 | 92.98 66 | 97.65 75 | 98.13 48 | 94.81 25 | 96.61 44 | 98.35 29 | 88.87 77 | 99.51 82 | 90.36 149 | 97.35 116 | 99.11 66 |
|
DeepC-MVS_fast | | 93.89 2 | 96.93 28 | 96.64 31 | 97.78 25 | 98.64 54 | 94.30 25 | 97.41 96 | 98.04 73 | 94.81 25 | 96.59 46 | 98.37 28 | 91.24 50 | 99.64 52 | 95.16 59 | 99.52 21 | 99.42 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
test_0728_THIRD | | | | | | | | | | 94.78 27 | 98.73 4 | 98.87 4 | 95.87 1 | 99.84 16 | 97.45 4 | 99.72 2 | 99.77 1 |
|
APDe-MVS | | | 97.82 3 | 97.73 2 | 98.08 11 | 99.15 29 | 94.82 17 | 98.81 2 | 98.30 23 | 94.76 28 | 98.30 9 | 98.90 3 | 93.77 10 | 99.68 42 | 97.93 1 | 99.69 3 | 99.75 3 |
|
EI-MVSNet-Vis-set | | | 96.51 43 | 96.47 39 | 96.63 70 | 98.24 79 | 91.20 119 | 96.89 144 | 97.73 106 | 94.74 29 | 96.49 50 | 98.49 16 | 90.88 58 | 99.58 61 | 96.44 23 | 98.32 89 | 99.13 62 |
|
EI-MVSNet-UG-set | | | 96.34 48 | 96.30 44 | 96.47 81 | 98.20 84 | 90.93 130 | 96.86 145 | 97.72 109 | 94.67 30 | 96.16 61 | 98.46 18 | 90.43 63 | 99.58 61 | 96.23 27 | 97.96 99 | 98.90 86 |
|
MSLP-MVS++ | | | 96.94 27 | 97.06 10 | 96.59 73 | 98.72 46 | 91.86 96 | 97.67 72 | 98.49 12 | 94.66 31 | 97.24 26 | 98.41 26 | 92.31 30 | 98.94 137 | 96.61 18 | 99.46 30 | 98.96 79 |
|
3Dnovator+ | | 91.43 4 | 95.40 69 | 94.48 88 | 98.16 9 | 96.90 140 | 95.34 11 | 98.48 14 | 97.87 95 | 94.65 32 | 88.53 234 | 98.02 55 | 83.69 143 | 99.71 34 | 93.18 103 | 98.96 72 | 99.44 38 |
|
EIA-MVS | | | 96.02 55 | 95.89 53 | 96.40 86 | 97.16 127 | 92.44 79 | 97.47 93 | 97.77 102 | 94.55 33 | 96.48 51 | 94.51 232 | 91.23 51 | 98.92 138 | 95.65 48 | 98.19 92 | 97.82 155 |
|
canonicalmvs | | | 96.02 55 | 95.45 61 | 97.75 29 | 97.59 116 | 95.15 14 | 98.28 24 | 97.60 121 | 94.52 34 | 96.27 58 | 96.12 161 | 87.65 94 | 99.18 112 | 96.20 33 | 94.82 164 | 98.91 85 |
|
plane_prior3 | | | | | | | 90.00 150 | | | 94.46 35 | 91.34 166 | | | | | | |
|
UGNet | | | 94.04 108 | 93.28 117 | 96.31 93 | 96.85 141 | 91.19 120 | 97.88 50 | 97.68 114 | 94.40 36 | 93.00 134 | 96.18 158 | 73.39 276 | 99.61 53 | 91.72 127 | 98.46 86 | 98.13 138 |
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 |
alignmvs | | | 95.87 60 | 95.23 68 | 97.78 25 | 97.56 118 | 95.19 12 | 97.86 51 | 97.17 169 | 94.39 37 | 96.47 52 | 96.40 150 | 85.89 118 | 99.20 109 | 96.21 32 | 95.11 160 | 98.95 81 |
|
CANet_DTU | | | 94.37 96 | 93.65 103 | 96.55 74 | 96.46 164 | 92.13 88 | 96.21 204 | 96.67 213 | 94.38 38 | 93.53 122 | 97.03 114 | 79.34 220 | 99.71 34 | 90.76 143 | 98.45 87 | 97.82 155 |
|
Vis-MVSNet | | | 95.23 75 | 94.81 76 | 96.51 78 | 97.18 126 | 91.58 104 | 98.26 26 | 98.12 50 | 94.38 38 | 94.90 96 | 98.15 48 | 82.28 172 | 98.92 138 | 91.45 136 | 98.58 85 | 99.01 75 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS_111021_HR | | | 96.68 40 | 96.58 34 | 96.99 63 | 98.46 61 | 92.31 81 | 96.20 205 | 98.90 2 | 94.30 40 | 95.86 73 | 97.74 75 | 92.33 28 | 99.38 99 | 96.04 37 | 99.42 35 | 99.28 54 |
|
TSAR-MVS + GP. | | | 96.69 38 | 96.49 38 | 97.27 52 | 98.31 75 | 93.39 54 | 96.79 153 | 96.72 205 | 94.17 41 | 97.44 23 | 97.66 80 | 92.76 18 | 99.33 101 | 96.86 10 | 97.76 105 | 99.08 68 |
|
3Dnovator | | 91.36 5 | 95.19 78 | 94.44 90 | 97.44 44 | 96.56 157 | 93.36 57 | 98.65 6 | 98.36 16 | 94.12 42 | 89.25 225 | 98.06 52 | 82.20 174 | 99.77 27 | 93.41 100 | 99.32 45 | 99.18 57 |
|
plane_prior | | | | | | | 89.99 152 | 97.24 113 | | 94.06 43 | | | | | | 92.16 200 | |
|
casdiffmvs | | | 95.64 64 | 95.49 59 | 96.08 104 | 96.76 149 | 90.45 144 | 97.29 110 | 97.44 146 | 94.00 44 | 95.46 90 | 97.98 58 | 87.52 98 | 98.73 155 | 95.64 49 | 97.33 117 | 99.08 68 |
|
MVS_111021_LR | | | 96.24 51 | 96.19 49 | 96.39 88 | 98.23 83 | 91.35 112 | 96.24 203 | 98.79 4 | 93.99 45 | 95.80 75 | 97.65 81 | 89.92 71 | 99.24 108 | 95.87 40 | 99.20 57 | 98.58 106 |
|
DeepC-MVS | | 93.07 3 | 96.06 53 | 95.66 56 | 97.29 50 | 97.96 95 | 93.17 61 | 97.30 109 | 98.06 66 | 93.92 46 | 93.38 126 | 98.66 9 | 86.83 107 | 99.73 30 | 95.60 54 | 99.22 55 | 98.96 79 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
VNet | | | 95.89 59 | 95.45 61 | 97.21 57 | 98.07 93 | 92.94 67 | 97.50 88 | 98.15 45 | 93.87 47 | 97.52 19 | 97.61 87 | 85.29 124 | 99.53 77 | 95.81 43 | 95.27 156 | 99.16 58 |
|
Effi-MVS+-dtu | | | 93.08 135 | 93.21 118 | 92.68 248 | 96.02 185 | 83.25 281 | 97.14 127 | 96.72 205 | 93.85 48 | 91.20 175 | 93.44 274 | 83.08 153 | 98.30 187 | 91.69 130 | 95.73 149 | 96.50 192 |
|
mvs-test1 | | | 93.63 120 | 93.69 101 | 93.46 225 | 96.02 185 | 84.61 267 | 97.24 113 | 96.72 205 | 93.85 48 | 92.30 149 | 95.76 180 | 83.08 153 | 98.89 143 | 91.69 130 | 96.54 136 | 96.87 183 |
|
PS-MVSNAJ | | | 95.37 70 | 95.33 66 | 95.49 136 | 97.35 120 | 90.66 139 | 95.31 246 | 97.48 132 | 93.85 48 | 96.51 49 | 95.70 185 | 88.65 81 | 99.65 46 | 94.80 73 | 98.27 90 | 96.17 199 |
|
SR-MVS | | | 97.01 24 | 96.86 19 | 97.47 43 | 99.09 31 | 93.27 59 | 97.98 43 | 98.07 63 | 93.75 51 | 97.45 22 | 98.48 17 | 91.43 47 | 99.59 58 | 96.22 28 | 99.27 50 | 99.54 23 |
|
TSAR-MVS + MP. | | | 97.42 7 | 97.33 8 | 97.69 34 | 99.25 23 | 94.24 29 | 98.07 39 | 97.85 99 | 93.72 52 | 98.57 7 | 98.35 29 | 93.69 11 | 99.40 96 | 97.06 5 | 99.46 30 | 99.44 38 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
OPM-MVS | | | 93.28 130 | 92.76 126 | 94.82 162 | 94.63 248 | 90.77 136 | 96.65 165 | 97.18 167 | 93.72 52 | 91.68 159 | 97.26 103 | 79.33 221 | 98.63 164 | 92.13 117 | 92.28 195 | 95.07 253 |
|
xiu_mvs_v2_base | | | 95.32 72 | 95.29 67 | 95.40 141 | 97.22 123 | 90.50 142 | 95.44 240 | 97.44 146 | 93.70 54 | 96.46 53 | 96.18 158 | 88.59 84 | 99.53 77 | 94.79 75 | 97.81 102 | 96.17 199 |
|
baseline | | | 95.58 66 | 95.42 63 | 96.08 104 | 96.78 146 | 90.41 146 | 97.16 124 | 97.45 142 | 93.69 55 | 95.65 84 | 97.85 66 | 87.29 102 | 98.68 160 | 95.66 45 | 97.25 120 | 99.13 62 |
|
ETV-MVS | | | 95.53 68 | 95.47 60 | 95.71 123 | 97.06 135 | 89.63 161 | 97.82 56 | 97.87 95 | 93.57 56 | 93.92 114 | 95.04 209 | 90.61 61 | 98.95 136 | 94.62 77 | 98.68 80 | 98.54 108 |
|
HQP-NCC | | | | | | 95.86 188 | | 96.65 165 | | 93.55 57 | 90.14 188 | | | | | | |
|
ACMP_Plane | | | | | | 95.86 188 | | 96.65 165 | | 93.55 57 | 90.14 188 | | | | | | |
|
HQP-MVS | | | 93.19 133 | 92.74 129 | 94.54 175 | 95.86 188 | 89.33 178 | 96.65 165 | 97.39 152 | 93.55 57 | 90.14 188 | 95.87 170 | 80.95 191 | 98.50 175 | 92.13 117 | 92.10 201 | 95.78 216 |
|
MCST-MVS | | | 97.18 13 | 96.84 21 | 98.20 8 | 99.30 20 | 95.35 10 | 97.12 128 | 98.07 63 | 93.54 60 | 96.08 64 | 97.69 77 | 93.86 9 | 99.71 34 | 96.50 22 | 99.39 39 | 99.55 21 |
|
MG-MVS | | | 95.61 65 | 95.38 64 | 96.31 93 | 98.42 64 | 90.53 141 | 96.04 212 | 97.48 132 | 93.47 61 | 95.67 83 | 98.10 49 | 89.17 74 | 99.25 107 | 91.27 139 | 98.77 77 | 99.13 62 |
|
CS-MVS | | | 95.80 61 | 95.65 57 | 96.24 100 | 97.32 121 | 91.43 110 | 98.10 35 | 97.91 90 | 93.38 62 | 95.16 94 | 94.57 230 | 90.21 66 | 98.98 134 | 95.53 55 | 98.67 81 | 98.30 134 |
|
FC-MVSNet-test | | | 93.94 111 | 93.57 104 | 95.04 151 | 95.48 201 | 91.45 109 | 98.12 34 | 98.71 5 | 93.37 63 | 90.23 187 | 96.70 128 | 87.66 93 | 97.85 242 | 91.49 134 | 90.39 228 | 95.83 213 |
|
MP-MVS | | | 96.77 35 | 96.45 41 | 97.72 31 | 99.39 10 | 93.80 42 | 98.41 17 | 98.06 66 | 93.37 63 | 95.54 88 | 98.34 32 | 90.59 62 | 99.88 4 | 94.83 70 | 99.54 19 | 99.49 31 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
FIs | | | 94.09 105 | 93.70 100 | 95.27 143 | 95.70 195 | 92.03 91 | 98.10 35 | 98.68 7 | 93.36 65 | 90.39 184 | 96.70 128 | 87.63 95 | 97.94 232 | 92.25 113 | 90.50 227 | 95.84 212 |
|
abl_6 | | | 96.40 46 | 96.21 47 | 96.98 64 | 98.89 42 | 92.20 86 | 97.89 49 | 98.03 75 | 93.34 66 | 97.22 27 | 98.42 23 | 87.93 90 | 99.72 33 | 95.10 62 | 99.07 67 | 99.02 71 |
|
mPP-MVS | | | 96.86 30 | 96.60 32 | 97.64 38 | 99.40 8 | 93.44 53 | 98.50 13 | 98.09 57 | 93.27 67 | 95.95 71 | 98.33 35 | 91.04 54 | 99.88 4 | 95.20 58 | 99.57 17 | 99.60 14 |
|
HFP-MVS | | | 97.14 16 | 96.92 17 | 97.83 19 | 99.42 6 | 94.12 34 | 98.52 10 | 98.32 20 | 93.21 68 | 97.18 28 | 98.29 42 | 92.08 32 | 99.83 19 | 95.63 50 | 99.59 13 | 99.54 23 |
|
ACMMPR | | | 97.07 19 | 96.84 21 | 97.79 24 | 99.44 5 | 93.88 40 | 98.52 10 | 98.31 22 | 93.21 68 | 97.15 30 | 98.33 35 | 91.35 48 | 99.86 7 | 95.63 50 | 99.59 13 | 99.62 11 |
|
IS-MVSNet | | | 94.90 86 | 94.52 86 | 96.05 107 | 97.67 110 | 90.56 140 | 98.44 15 | 96.22 228 | 93.21 68 | 93.99 111 | 97.74 75 | 85.55 122 | 98.45 178 | 89.98 152 | 97.86 100 | 99.14 61 |
|
region2R | | | 97.07 19 | 96.84 21 | 97.77 27 | 99.46 1 | 93.79 43 | 98.52 10 | 98.24 31 | 93.19 71 | 97.14 31 | 98.34 32 | 91.59 45 | 99.87 6 | 95.46 56 | 99.59 13 | 99.64 8 |
|
EPNet_dtu | | | 91.71 183 | 91.28 177 | 92.99 241 | 93.76 274 | 83.71 275 | 96.69 162 | 95.28 261 | 93.15 72 | 87.02 264 | 95.95 167 | 83.37 148 | 97.38 278 | 79.46 289 | 96.84 127 | 97.88 150 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
UniMVSNet (Re) | | | 93.31 129 | 92.55 136 | 95.61 128 | 95.39 204 | 93.34 58 | 97.39 100 | 98.71 5 | 93.14 73 | 90.10 196 | 94.83 218 | 87.71 92 | 98.03 218 | 91.67 132 | 83.99 287 | 95.46 230 |
|
APD-MVS_3200maxsize | | | 96.81 33 | 96.71 30 | 97.12 60 | 99.01 37 | 92.31 81 | 97.98 43 | 98.06 66 | 93.11 74 | 97.44 23 | 98.55 14 | 90.93 56 | 99.55 72 | 96.06 36 | 99.25 52 | 99.51 28 |
|
testdata1 | | | | | | | | 95.26 251 | | 93.10 75 | | | | | | | |
|
DU-MVS | | | 92.90 145 | 92.04 149 | 95.49 136 | 94.95 231 | 92.83 68 | 97.16 124 | 98.24 31 | 93.02 76 | 90.13 192 | 95.71 183 | 83.47 145 | 97.85 242 | 91.71 128 | 83.93 288 | 95.78 216 |
|
xiu_mvs_v1_base_debu | | | 95.01 80 | 94.76 77 | 95.75 118 | 96.58 154 | 91.71 97 | 96.25 200 | 97.35 158 | 92.99 77 | 96.70 39 | 96.63 137 | 82.67 162 | 99.44 91 | 96.22 28 | 97.46 109 | 96.11 204 |
|
xiu_mvs_v1_base | | | 95.01 80 | 94.76 77 | 95.75 118 | 96.58 154 | 91.71 97 | 96.25 200 | 97.35 158 | 92.99 77 | 96.70 39 | 96.63 137 | 82.67 162 | 99.44 91 | 96.22 28 | 97.46 109 | 96.11 204 |
|
xiu_mvs_v1_base_debi | | | 95.01 80 | 94.76 77 | 95.75 118 | 96.58 154 | 91.71 97 | 96.25 200 | 97.35 158 | 92.99 77 | 96.70 39 | 96.63 137 | 82.67 162 | 99.44 91 | 96.22 28 | 97.46 109 | 96.11 204 |
|
CP-MVS | | | 97.02 22 | 96.81 24 | 97.64 38 | 99.33 18 | 93.54 50 | 98.80 3 | 98.28 26 | 92.99 77 | 96.45 54 | 98.30 40 | 91.90 38 | 99.85 13 | 95.61 52 | 99.68 4 | 99.54 23 |
|
ACMMP | | | 96.27 50 | 95.93 51 | 97.28 51 | 99.24 24 | 92.62 74 | 98.25 27 | 98.81 3 | 92.99 77 | 94.56 101 | 98.39 27 | 88.96 76 | 99.85 13 | 94.57 78 | 97.63 106 | 99.36 47 |
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 |
UniMVSNet_NR-MVSNet | | | 93.37 127 | 92.67 131 | 95.47 139 | 95.34 207 | 92.83 68 | 97.17 123 | 98.58 10 | 92.98 82 | 90.13 192 | 95.80 175 | 88.37 86 | 97.85 242 | 91.71 128 | 83.93 288 | 95.73 222 |
|
VPNet | | | 92.23 170 | 91.31 175 | 94.99 153 | 95.56 198 | 90.96 128 | 97.22 119 | 97.86 98 | 92.96 83 | 90.96 176 | 96.62 140 | 75.06 263 | 98.20 193 | 91.90 122 | 83.65 293 | 95.80 215 |
|
nrg030 | | | 94.05 107 | 93.31 116 | 96.27 97 | 95.22 218 | 94.59 20 | 98.34 19 | 97.46 137 | 92.93 84 | 91.21 174 | 96.64 133 | 87.23 104 | 98.22 191 | 94.99 67 | 85.80 265 | 95.98 208 |
|
TranMVSNet+NR-MVSNet | | | 92.50 155 | 91.63 163 | 95.14 148 | 94.76 241 | 92.07 89 | 97.53 86 | 98.11 53 | 92.90 85 | 89.56 213 | 96.12 161 | 83.16 150 | 97.60 263 | 89.30 167 | 83.20 297 | 95.75 220 |
|
diffmvs | | | 95.25 74 | 95.13 71 | 95.63 126 | 96.43 166 | 89.34 177 | 95.99 217 | 97.35 158 | 92.83 86 | 96.31 56 | 97.37 99 | 86.44 111 | 98.67 161 | 96.26 25 | 97.19 122 | 98.87 90 |
|
ACMMP_NAP | | | 97.20 12 | 96.86 19 | 98.23 6 | 99.09 31 | 95.16 13 | 97.60 81 | 98.19 39 | 92.82 87 | 97.93 14 | 98.74 8 | 91.60 44 | 99.86 7 | 96.26 25 | 99.52 21 | 99.67 6 |
|
test_prior3 | | | 96.46 45 | 96.20 48 | 97.23 54 | 98.67 49 | 92.99 64 | 96.35 190 | 98.00 81 | 92.80 88 | 96.03 65 | 97.59 88 | 92.01 34 | 99.41 94 | 95.01 64 | 99.38 40 | 99.29 51 |
|
test_prior2 | | | | | | | | 96.35 190 | | 92.80 88 | 96.03 65 | 97.59 88 | 92.01 34 | | 95.01 64 | 99.38 40 | |
|
testtj | | | 96.93 28 | 96.56 35 | 98.05 12 | 99.10 30 | 94.66 19 | 97.78 60 | 98.22 35 | 92.74 90 | 97.59 18 | 98.20 47 | 91.96 37 | 99.86 7 | 94.21 81 | 99.25 52 | 99.63 9 |
|
GST-MVS | | | 96.85 31 | 96.52 37 | 97.82 21 | 99.36 15 | 94.14 33 | 98.29 23 | 98.13 48 | 92.72 91 | 96.70 39 | 98.06 52 | 91.35 48 | 99.86 7 | 94.83 70 | 99.28 49 | 99.47 35 |
|
CLD-MVS | | | 92.98 140 | 92.53 138 | 94.32 183 | 96.12 183 | 89.20 185 | 95.28 247 | 97.47 135 | 92.66 92 | 89.90 201 | 95.62 188 | 80.58 198 | 98.40 180 | 92.73 109 | 92.40 194 | 95.38 238 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
NR-MVSNet | | | 92.34 162 | 91.27 178 | 95.53 133 | 94.95 231 | 93.05 63 | 97.39 100 | 98.07 63 | 92.65 93 | 84.46 285 | 95.71 183 | 85.00 128 | 97.77 251 | 89.71 157 | 83.52 294 | 95.78 216 |
|
#test# | | | 97.02 22 | 96.75 28 | 97.83 19 | 99.42 6 | 94.12 34 | 98.15 33 | 98.32 20 | 92.57 94 | 97.18 28 | 98.29 42 | 92.08 32 | 99.83 19 | 95.12 61 | 99.59 13 | 99.54 23 |
|
PS-MVSNAJss | | | 93.74 117 | 93.51 108 | 94.44 177 | 93.91 269 | 89.28 183 | 97.75 62 | 97.56 127 | 92.50 95 | 89.94 200 | 96.54 143 | 88.65 81 | 98.18 196 | 93.83 92 | 90.90 221 | 95.86 209 |
|
VDD-MVS | | | 93.82 114 | 93.08 119 | 96.02 108 | 97.88 102 | 89.96 156 | 97.72 68 | 95.85 239 | 92.43 96 | 95.86 73 | 98.44 20 | 68.42 298 | 99.39 97 | 96.31 24 | 94.85 162 | 98.71 102 |
|
LCM-MVSNet-Re | | | 92.50 155 | 92.52 139 | 92.44 251 | 96.82 145 | 81.89 289 | 96.92 142 | 93.71 303 | 92.41 97 | 84.30 287 | 94.60 229 | 85.08 127 | 97.03 288 | 91.51 133 | 97.36 115 | 98.40 127 |
|
VPA-MVSNet | | | 93.24 131 | 92.48 141 | 95.51 134 | 95.70 195 | 92.39 80 | 97.86 51 | 98.66 9 | 92.30 98 | 92.09 154 | 95.37 198 | 80.49 200 | 98.40 180 | 93.95 86 | 85.86 264 | 95.75 220 |
|
PGM-MVS | | | 96.81 33 | 96.53 36 | 97.65 36 | 99.35 17 | 93.53 51 | 97.65 75 | 98.98 1 | 92.22 99 | 97.14 31 | 98.44 20 | 91.17 52 | 99.85 13 | 94.35 79 | 99.46 30 | 99.57 17 |
|
Vis-MVSNet (Re-imp) | | | 94.15 101 | 93.88 96 | 94.95 158 | 97.61 114 | 87.92 216 | 98.10 35 | 95.80 241 | 92.22 99 | 93.02 133 | 97.45 96 | 84.53 134 | 97.91 239 | 88.24 185 | 97.97 98 | 99.02 71 |
|
thres100view900 | | | 92.43 158 | 91.58 165 | 94.98 155 | 97.92 99 | 89.37 176 | 97.71 70 | 94.66 286 | 92.20 101 | 93.31 128 | 94.90 214 | 78.06 242 | 99.08 125 | 81.40 275 | 94.08 173 | 96.48 193 |
|
baseline1 | | | 92.82 150 | 91.90 155 | 95.55 132 | 97.20 125 | 90.77 136 | 97.19 121 | 94.58 289 | 92.20 101 | 92.36 146 | 96.34 153 | 84.16 139 | 98.21 192 | 89.20 173 | 83.90 291 | 97.68 160 |
|
tfpn200view9 | | | 92.38 161 | 91.52 168 | 94.95 158 | 97.85 103 | 89.29 181 | 97.41 96 | 94.88 281 | 92.19 103 | 93.27 130 | 94.46 237 | 78.17 239 | 99.08 125 | 81.40 275 | 94.08 173 | 96.48 193 |
|
thres400 | | | 92.42 159 | 91.52 168 | 95.12 150 | 97.85 103 | 89.29 181 | 97.41 96 | 94.88 281 | 92.19 103 | 93.27 130 | 94.46 237 | 78.17 239 | 99.08 125 | 81.40 275 | 94.08 173 | 96.98 177 |
|
thres600view7 | | | 92.49 157 | 91.60 164 | 95.18 146 | 97.91 100 | 89.47 170 | 97.65 75 | 94.66 286 | 92.18 105 | 93.33 127 | 94.91 213 | 78.06 242 | 99.10 120 | 81.61 272 | 94.06 176 | 96.98 177 |
|
Fast-Effi-MVS+-dtu | | | 92.29 166 | 91.99 152 | 93.21 236 | 95.27 213 | 85.52 255 | 97.03 130 | 96.63 216 | 92.09 106 | 89.11 226 | 95.14 206 | 80.33 204 | 98.08 208 | 87.54 203 | 94.74 167 | 96.03 207 |
|
thres200 | | | 92.23 170 | 91.39 171 | 94.75 168 | 97.61 114 | 89.03 190 | 96.60 172 | 95.09 271 | 92.08 107 | 93.28 129 | 94.00 255 | 78.39 237 | 99.04 131 | 81.26 279 | 94.18 172 | 96.19 198 |
|
mvs_tets | | | 92.31 164 | 91.76 158 | 93.94 202 | 93.41 284 | 88.29 204 | 97.63 80 | 97.53 128 | 92.04 108 | 88.76 230 | 96.45 147 | 74.62 266 | 98.09 207 | 93.91 88 | 91.48 210 | 95.45 231 |
|
OMC-MVS | | | 95.09 79 | 94.70 80 | 96.25 99 | 98.46 61 | 91.28 113 | 96.43 180 | 97.57 124 | 92.04 108 | 94.77 99 | 97.96 59 | 87.01 106 | 99.09 123 | 91.31 138 | 96.77 129 | 98.36 131 |
|
jajsoiax | | | 92.42 159 | 91.89 156 | 94.03 193 | 93.33 288 | 88.50 201 | 97.73 65 | 97.53 128 | 92.00 110 | 88.85 229 | 96.50 145 | 75.62 261 | 98.11 203 | 93.88 90 | 91.56 209 | 95.48 227 |
|
XVG-OURS | | | 93.72 118 | 93.35 115 | 94.80 165 | 97.07 132 | 88.61 198 | 94.79 257 | 97.46 137 | 91.97 111 | 93.99 111 | 97.86 65 | 81.74 183 | 98.88 144 | 92.64 110 | 92.67 191 | 96.92 181 |
|
WR-MVS | | | 92.34 162 | 91.53 167 | 94.77 167 | 95.13 223 | 90.83 133 | 96.40 185 | 97.98 86 | 91.88 112 | 89.29 222 | 95.54 193 | 82.50 167 | 97.80 247 | 89.79 156 | 85.27 270 | 95.69 223 |
|
PAPM_NR | | | 95.01 80 | 94.59 82 | 96.26 98 | 98.89 42 | 90.68 138 | 97.24 113 | 97.73 106 | 91.80 113 | 92.93 139 | 96.62 140 | 89.13 75 | 99.14 117 | 89.21 172 | 97.78 103 | 98.97 78 |
|
testgi | | | 87.97 265 | 87.21 264 | 90.24 292 | 92.86 294 | 80.76 294 | 96.67 164 | 94.97 277 | 91.74 114 | 85.52 277 | 95.83 173 | 62.66 316 | 94.47 317 | 76.25 301 | 88.36 247 | 95.48 227 |
|
CP-MVSNet | | | 91.89 180 | 91.24 179 | 93.82 207 | 95.05 226 | 88.57 199 | 97.82 56 | 98.19 39 | 91.70 115 | 88.21 242 | 95.76 180 | 81.96 178 | 97.52 267 | 87.86 191 | 84.65 280 | 95.37 239 |
|
XVG-OURS-SEG-HR | | | 93.86 113 | 93.55 105 | 94.81 164 | 97.06 135 | 88.53 200 | 95.28 247 | 97.45 142 | 91.68 116 | 94.08 110 | 97.68 78 | 82.41 170 | 98.90 141 | 93.84 91 | 92.47 193 | 96.98 177 |
|
OurMVSNet-221017-0 | | | 90.51 230 | 90.19 214 | 91.44 275 | 93.41 284 | 81.25 292 | 96.98 137 | 96.28 224 | 91.68 116 | 86.55 270 | 96.30 154 | 74.20 269 | 97.98 222 | 88.96 177 | 87.40 256 | 95.09 252 |
|
ACMP | | 89.59 10 | 92.62 154 | 92.14 147 | 94.05 192 | 96.40 167 | 88.20 209 | 97.36 103 | 97.25 166 | 91.52 118 | 88.30 238 | 96.64 133 | 78.46 235 | 98.72 158 | 91.86 125 | 91.48 210 | 95.23 249 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
APD-MVS | | | 96.95 26 | 96.60 32 | 98.01 13 | 99.03 36 | 94.93 16 | 97.72 68 | 98.10 55 | 91.50 119 | 98.01 12 | 98.32 37 | 92.33 28 | 99.58 61 | 94.85 69 | 99.51 23 | 99.53 27 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ITE_SJBPF | | | | | 92.43 252 | 95.34 207 | 85.37 258 | | 95.92 236 | 91.47 120 | 87.75 250 | 96.39 151 | 71.00 285 | 97.96 229 | 82.36 269 | 89.86 233 | 93.97 292 |
|
PS-CasMVS | | | 91.55 191 | 90.84 190 | 93.69 214 | 94.96 230 | 88.28 205 | 97.84 55 | 98.24 31 | 91.46 121 | 88.04 244 | 95.80 175 | 79.67 216 | 97.48 269 | 87.02 214 | 84.54 283 | 95.31 242 |
|
WR-MVS_H | | | 92.00 177 | 91.35 172 | 93.95 199 | 95.09 225 | 89.47 170 | 98.04 41 | 98.68 7 | 91.46 121 | 88.34 236 | 94.68 225 | 85.86 119 | 97.56 264 | 85.77 234 | 84.24 285 | 94.82 265 |
|
MVSFormer | | | 95.37 70 | 95.16 70 | 95.99 110 | 96.34 170 | 91.21 117 | 98.22 28 | 97.57 124 | 91.42 123 | 96.22 59 | 97.32 100 | 86.20 115 | 97.92 236 | 94.07 83 | 99.05 68 | 98.85 91 |
|
test_djsdf | | | 93.07 136 | 92.76 126 | 94.00 194 | 93.49 282 | 88.70 197 | 98.22 28 | 97.57 124 | 91.42 123 | 90.08 198 | 95.55 192 | 82.85 159 | 97.92 236 | 94.07 83 | 91.58 208 | 95.40 236 |
|
ACMM | | 89.79 8 | 92.96 141 | 92.50 140 | 94.35 181 | 96.30 172 | 88.71 196 | 97.58 82 | 97.36 157 | 91.40 125 | 90.53 180 | 96.65 132 | 79.77 214 | 98.75 154 | 91.24 140 | 91.64 206 | 95.59 225 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PEN-MVS | | | 91.20 207 | 90.44 201 | 93.48 223 | 94.49 252 | 87.91 218 | 97.76 61 | 98.18 41 | 91.29 126 | 87.78 249 | 95.74 182 | 80.35 203 | 97.33 280 | 85.46 238 | 82.96 298 | 95.19 251 |
|
LPG-MVS_test | | | 92.94 143 | 92.56 135 | 94.10 189 | 96.16 179 | 88.26 206 | 97.65 75 | 97.46 137 | 91.29 126 | 90.12 194 | 97.16 107 | 79.05 224 | 98.73 155 | 92.25 113 | 91.89 204 | 95.31 242 |
|
LGP-MVS_train | | | | | 94.10 189 | 96.16 179 | 88.26 206 | | 97.46 137 | 91.29 126 | 90.12 194 | 97.16 107 | 79.05 224 | 98.73 155 | 92.25 113 | 91.89 204 | 95.31 242 |
|
9.14 | | | | 96.75 28 | | 98.93 38 | | 97.73 65 | 98.23 34 | 91.28 129 | 97.88 16 | 98.44 20 | 93.00 15 | 99.65 46 | 95.76 44 | 99.47 29 | |
|
MVSTER | | | 93.20 132 | 92.81 125 | 94.37 180 | 96.56 157 | 89.59 164 | 97.06 129 | 97.12 173 | 91.24 130 | 91.30 169 | 95.96 166 | 82.02 177 | 98.05 214 | 93.48 97 | 90.55 225 | 95.47 229 |
|
test_yl | | | 94.78 91 | 94.23 92 | 96.43 84 | 97.74 107 | 91.22 115 | 96.85 146 | 97.10 176 | 91.23 131 | 95.71 78 | 96.93 116 | 84.30 136 | 99.31 103 | 93.10 104 | 95.12 158 | 98.75 96 |
|
DCV-MVSNet | | | 94.78 91 | 94.23 92 | 96.43 84 | 97.74 107 | 91.22 115 | 96.85 146 | 97.10 176 | 91.23 131 | 95.71 78 | 96.93 116 | 84.30 136 | 99.31 103 | 93.10 104 | 95.12 158 | 98.75 96 |
|
MVS_Test | | | 94.89 87 | 94.62 81 | 95.68 124 | 96.83 144 | 89.55 166 | 96.70 160 | 97.17 169 | 91.17 133 | 95.60 85 | 96.11 163 | 87.87 91 | 98.76 153 | 93.01 108 | 97.17 123 | 98.72 100 |
|
HPM-MVS | | | 96.69 38 | 96.45 41 | 97.40 45 | 99.36 15 | 93.11 62 | 98.87 1 | 98.06 66 | 91.17 133 | 96.40 55 | 97.99 57 | 90.99 55 | 99.58 61 | 95.61 52 | 99.61 11 | 99.49 31 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
test-LLR | | | 91.42 196 | 91.19 182 | 92.12 256 | 94.59 249 | 80.66 295 | 94.29 269 | 92.98 308 | 91.11 135 | 90.76 178 | 92.37 288 | 79.02 226 | 98.07 211 | 88.81 179 | 96.74 130 | 97.63 161 |
|
test0.0.03 1 | | | 89.37 250 | 88.70 247 | 91.41 276 | 92.47 301 | 85.63 253 | 95.22 252 | 92.70 310 | 91.11 135 | 86.91 267 | 93.65 268 | 79.02 226 | 93.19 323 | 78.00 295 | 89.18 238 | 95.41 232 |
|
XVG-ACMP-BASELINE | | | 90.93 216 | 90.21 213 | 93.09 238 | 94.31 260 | 85.89 250 | 95.33 244 | 97.26 164 | 91.06 137 | 89.38 218 | 95.44 197 | 68.61 296 | 98.60 167 | 89.46 164 | 91.05 217 | 94.79 270 |
|
Effi-MVS+ | | | 94.93 85 | 94.45 89 | 96.36 91 | 96.61 151 | 91.47 107 | 96.41 182 | 97.41 151 | 91.02 138 | 94.50 102 | 95.92 168 | 87.53 97 | 98.78 150 | 93.89 89 | 96.81 128 | 98.84 93 |
|
SCA | | | 91.84 181 | 91.18 183 | 93.83 206 | 95.59 197 | 84.95 263 | 94.72 258 | 95.58 249 | 90.82 139 | 92.25 150 | 93.69 264 | 75.80 258 | 98.10 204 | 86.20 224 | 95.98 142 | 98.45 121 |
|
SixPastTwentyTwo | | | 89.15 251 | 88.54 250 | 90.98 280 | 93.49 282 | 80.28 302 | 96.70 160 | 94.70 285 | 90.78 140 | 84.15 290 | 95.57 190 | 71.78 280 | 97.71 255 | 84.63 248 | 85.07 274 | 94.94 257 |
|
DTE-MVSNet | | | 90.56 228 | 89.75 231 | 93.01 240 | 93.95 267 | 87.25 228 | 97.64 79 | 97.65 117 | 90.74 141 | 87.12 260 | 95.68 186 | 79.97 211 | 97.00 292 | 83.33 259 | 81.66 303 | 94.78 271 |
|
GA-MVS | | | 91.38 198 | 90.31 204 | 94.59 170 | 94.65 246 | 87.62 223 | 94.34 266 | 96.19 229 | 90.73 142 | 90.35 185 | 93.83 258 | 71.84 279 | 97.96 229 | 87.22 210 | 93.61 182 | 98.21 136 |
|
EPP-MVSNet | | | 95.22 76 | 95.04 73 | 95.76 116 | 97.49 119 | 89.56 165 | 98.67 5 | 97.00 190 | 90.69 143 | 94.24 107 | 97.62 86 | 89.79 72 | 98.81 148 | 93.39 101 | 96.49 137 | 98.92 84 |
|
MP-MVS-pluss | | | 96.70 37 | 96.27 45 | 97.98 15 | 99.23 26 | 94.71 18 | 96.96 138 | 98.06 66 | 90.67 144 | 95.55 86 | 98.78 7 | 91.07 53 | 99.86 7 | 96.58 20 | 99.55 18 | 99.38 45 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
IterMVS-LS | | | 92.29 166 | 91.94 154 | 93.34 230 | 96.25 173 | 86.97 236 | 96.57 176 | 97.05 183 | 90.67 144 | 89.50 216 | 94.80 220 | 86.59 108 | 97.64 260 | 89.91 153 | 86.11 263 | 95.40 236 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 93.03 138 | 92.88 124 | 93.48 223 | 95.77 193 | 86.98 235 | 96.44 178 | 97.12 173 | 90.66 146 | 91.30 169 | 97.64 84 | 86.56 109 | 98.05 214 | 89.91 153 | 90.55 225 | 95.41 232 |
|
K. test v3 | | | 87.64 269 | 86.75 268 | 90.32 291 | 93.02 293 | 79.48 308 | 96.61 170 | 92.08 315 | 90.66 146 | 80.25 308 | 94.09 253 | 67.21 304 | 96.65 299 | 85.96 232 | 80.83 306 | 94.83 263 |
|
tttt0517 | | | 92.96 141 | 92.33 144 | 94.87 161 | 97.11 130 | 87.16 233 | 97.97 45 | 92.09 314 | 90.63 148 | 93.88 115 | 97.01 115 | 76.50 252 | 99.06 128 | 90.29 151 | 95.45 153 | 98.38 129 |
|
BH-RMVSNet | | | 92.72 153 | 91.97 153 | 94.97 156 | 97.16 127 | 87.99 215 | 96.15 207 | 95.60 247 | 90.62 149 | 91.87 157 | 97.15 109 | 78.41 236 | 98.57 170 | 83.16 260 | 97.60 107 | 98.36 131 |
|
IterMVS-SCA-FT | | | 90.31 233 | 89.81 227 | 91.82 264 | 95.52 199 | 84.20 270 | 94.30 268 | 96.15 231 | 90.61 150 | 87.39 256 | 94.27 247 | 75.80 258 | 96.44 300 | 87.34 206 | 86.88 259 | 94.82 265 |
|
WTY-MVS | | | 94.71 93 | 94.02 94 | 96.79 66 | 97.71 109 | 92.05 90 | 96.59 173 | 97.35 158 | 90.61 150 | 94.64 100 | 96.93 116 | 86.41 112 | 99.39 97 | 91.20 141 | 94.71 168 | 98.94 82 |
|
ET-MVSNet_ETH3D | | | 91.49 193 | 90.11 215 | 95.63 126 | 96.40 167 | 91.57 105 | 95.34 243 | 93.48 305 | 90.60 152 | 75.58 316 | 95.49 195 | 80.08 208 | 96.79 297 | 94.25 80 | 89.76 234 | 98.52 110 |
|
DI_MVS_plusplus_test | | | 92.01 176 | 90.77 192 | 95.73 121 | 93.34 286 | 89.78 160 | 96.14 208 | 96.18 230 | 90.58 153 | 81.80 299 | 93.50 271 | 74.95 264 | 98.90 141 | 93.51 95 | 96.94 126 | 98.51 112 |
|
SMA-MVS | | | 97.35 9 | 97.03 11 | 98.30 5 | 99.06 35 | 95.42 7 | 97.94 46 | 98.18 41 | 90.57 154 | 98.85 3 | 98.94 1 | 93.33 13 | 99.83 19 | 96.72 15 | 99.68 4 | 99.63 9 |
|
LFMVS | | | 93.60 121 | 92.63 132 | 96.52 75 | 98.13 90 | 91.27 114 | 97.94 46 | 93.39 306 | 90.57 154 | 96.29 57 | 98.31 38 | 69.00 294 | 99.16 114 | 94.18 82 | 95.87 145 | 99.12 65 |
|
HPM-MVS_fast | | | 96.51 43 | 96.27 45 | 97.22 56 | 99.32 19 | 92.74 70 | 98.74 4 | 98.06 66 | 90.57 154 | 96.77 38 | 98.35 29 | 90.21 66 | 99.53 77 | 94.80 73 | 99.63 9 | 99.38 45 |
|
DPM-MVS | | | 95.69 62 | 94.92 74 | 98.01 13 | 98.08 92 | 95.71 6 | 95.27 249 | 97.62 120 | 90.43 157 | 95.55 86 | 97.07 112 | 91.72 40 | 99.50 84 | 89.62 162 | 98.94 73 | 98.82 94 |
|
PVSNet_Blended_VisFu | | | 95.27 73 | 94.91 75 | 96.38 89 | 98.20 84 | 90.86 132 | 97.27 111 | 98.25 30 | 90.21 158 | 94.18 108 | 97.27 102 | 87.48 99 | 99.73 30 | 93.53 94 | 97.77 104 | 98.55 107 |
|
PVSNet_BlendedMVS | | | 94.06 106 | 93.92 95 | 94.47 176 | 98.27 76 | 89.46 172 | 96.73 157 | 98.36 16 | 90.17 159 | 94.36 104 | 95.24 203 | 88.02 87 | 99.58 61 | 93.44 98 | 90.72 223 | 94.36 282 |
|
thisisatest0530 | | | 93.03 138 | 92.21 146 | 95.49 136 | 97.07 132 | 89.11 189 | 97.49 92 | 92.19 313 | 90.16 160 | 94.09 109 | 96.41 149 | 76.43 255 | 99.05 129 | 90.38 148 | 95.68 151 | 98.31 133 |
|
CNLPA | | | 94.28 98 | 93.53 107 | 96.52 75 | 98.38 68 | 92.55 76 | 96.59 173 | 96.88 199 | 90.13 161 | 91.91 156 | 97.24 104 | 85.21 125 | 99.09 123 | 87.64 200 | 97.83 101 | 97.92 147 |
|
BH-untuned | | | 92.94 143 | 92.62 133 | 93.92 204 | 97.22 123 | 86.16 248 | 96.40 185 | 96.25 227 | 90.06 162 | 89.79 205 | 96.17 160 | 83.19 149 | 98.35 184 | 87.19 211 | 97.27 119 | 97.24 174 |
|
IterMVS | | | 90.15 239 | 89.67 233 | 91.61 271 | 95.48 201 | 83.72 274 | 94.33 267 | 96.12 232 | 89.99 163 | 87.31 259 | 94.15 252 | 75.78 260 | 96.27 303 | 86.97 215 | 86.89 258 | 94.83 263 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
AdaColmap | | | 94.34 97 | 93.68 102 | 96.31 93 | 98.59 56 | 91.68 100 | 96.59 173 | 97.81 101 | 89.87 164 | 92.15 152 | 97.06 113 | 83.62 144 | 99.54 74 | 89.34 166 | 98.07 96 | 97.70 159 |
|
UnsupCasMVSNet_eth | | | 85.99 281 | 84.45 283 | 90.62 287 | 89.97 313 | 82.40 287 | 93.62 285 | 97.37 155 | 89.86 165 | 78.59 313 | 92.37 288 | 65.25 312 | 95.35 314 | 82.27 270 | 70.75 321 | 94.10 289 |
|
PHI-MVS | | | 96.77 35 | 96.46 40 | 97.71 33 | 98.40 65 | 94.07 36 | 98.21 30 | 98.45 15 | 89.86 165 | 97.11 34 | 98.01 56 | 92.52 26 | 99.69 40 | 96.03 38 | 99.53 20 | 99.36 47 |
|
mvs_anonymous | | | 93.82 114 | 93.74 99 | 94.06 191 | 96.44 165 | 85.41 257 | 95.81 225 | 97.05 183 | 89.85 167 | 90.09 197 | 96.36 152 | 87.44 100 | 97.75 252 | 93.97 85 | 96.69 133 | 99.02 71 |
|
PatchFormer-LS_test | | | 91.68 186 | 91.18 183 | 93.19 237 | 95.24 217 | 83.63 278 | 95.53 236 | 95.44 253 | 89.82 168 | 91.37 164 | 92.58 285 | 80.85 196 | 98.52 173 | 89.65 161 | 90.16 230 | 97.42 172 |
|
ab-mvs | | | 93.57 123 | 92.55 136 | 96.64 68 | 97.28 122 | 91.96 95 | 95.40 241 | 97.45 142 | 89.81 169 | 93.22 132 | 96.28 155 | 79.62 217 | 99.46 88 | 90.74 144 | 93.11 185 | 98.50 114 |
|
FMVSNet3 | | | 91.78 182 | 90.69 196 | 95.03 152 | 96.53 159 | 92.27 83 | 97.02 132 | 96.93 194 | 89.79 170 | 89.35 219 | 94.65 227 | 77.01 250 | 97.47 271 | 86.12 227 | 88.82 240 | 95.35 240 |
|
test_normal | | | 79.26 295 | 75.88 298 | 89.42 297 | 84.67 324 | 76.93 315 | 58.84 332 | 97.02 188 | 89.63 171 | 59.33 326 | 75.16 323 | 46.20 328 | 97.48 269 | 87.30 208 | 84.76 279 | 93.85 294 |
|
v2v482 | | | 91.59 188 | 90.85 189 | 93.80 208 | 93.87 271 | 88.17 211 | 96.94 141 | 96.88 199 | 89.54 172 | 89.53 214 | 94.90 214 | 81.70 184 | 98.02 219 | 89.25 170 | 85.04 276 | 95.20 250 |
|
PatchmatchNet | | | 91.91 179 | 91.35 172 | 93.59 218 | 95.38 205 | 84.11 271 | 93.15 293 | 95.39 254 | 89.54 172 | 92.10 153 | 93.68 266 | 82.82 160 | 98.13 199 | 84.81 245 | 95.32 155 | 98.52 110 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EPMVS | | | 90.70 225 | 89.81 227 | 93.37 229 | 94.73 243 | 84.21 269 | 93.67 283 | 88.02 325 | 89.50 174 | 92.38 145 | 93.49 272 | 77.82 246 | 97.78 249 | 86.03 230 | 92.68 190 | 98.11 142 |
|
v148 | | | 90.99 214 | 90.38 203 | 92.81 244 | 93.83 272 | 85.80 251 | 96.78 155 | 96.68 211 | 89.45 175 | 88.75 231 | 93.93 257 | 82.96 157 | 97.82 246 | 87.83 192 | 83.25 295 | 94.80 268 |
|
anonymousdsp | | | 92.16 172 | 91.55 166 | 93.97 197 | 92.58 300 | 89.55 166 | 97.51 87 | 97.42 150 | 89.42 176 | 88.40 235 | 94.84 217 | 80.66 197 | 97.88 241 | 91.87 124 | 91.28 214 | 94.48 278 |
|
baseline2 | | | 91.63 187 | 90.86 187 | 93.94 202 | 94.33 258 | 86.32 245 | 95.92 220 | 91.64 318 | 89.37 177 | 86.94 265 | 94.69 224 | 81.62 185 | 98.69 159 | 88.64 182 | 94.57 169 | 96.81 185 |
|
IB-MVS | | 87.33 17 | 89.91 242 | 88.28 253 | 94.79 166 | 95.26 216 | 87.70 222 | 95.12 254 | 93.95 302 | 89.35 178 | 87.03 263 | 92.49 286 | 70.74 287 | 99.19 110 | 89.18 174 | 81.37 304 | 97.49 170 |
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 |
jason | | | 94.84 89 | 94.39 91 | 96.18 102 | 95.52 199 | 90.93 130 | 96.09 210 | 96.52 218 | 89.28 179 | 96.01 69 | 97.32 100 | 84.70 131 | 98.77 152 | 95.15 60 | 98.91 75 | 98.85 91 |
jason: jason. |
TAMVS | | | 94.01 109 | 93.46 110 | 95.64 125 | 96.16 179 | 90.45 144 | 96.71 159 | 96.89 198 | 89.27 180 | 93.46 124 | 96.92 119 | 87.29 102 | 97.94 232 | 88.70 181 | 95.74 148 | 98.53 109 |
|
testing_2 | | | 87.33 271 | 85.03 279 | 94.22 185 | 87.77 322 | 89.32 180 | 94.97 255 | 97.11 175 | 89.22 181 | 71.64 319 | 88.73 309 | 55.16 324 | 97.94 232 | 91.95 121 | 88.73 244 | 95.41 232 |
|
API-MVS | | | 94.84 89 | 94.49 87 | 95.90 112 | 97.90 101 | 92.00 93 | 97.80 58 | 97.48 132 | 89.19 182 | 94.81 98 | 96.71 126 | 88.84 78 | 99.17 113 | 88.91 178 | 98.76 78 | 96.53 190 |
|
XXY-MVS | | | 92.16 172 | 91.23 180 | 94.95 158 | 94.75 242 | 90.94 129 | 97.47 93 | 97.43 149 | 89.14 183 | 88.90 227 | 96.43 148 | 79.71 215 | 98.24 189 | 89.56 163 | 87.68 251 | 95.67 224 |
|
pm-mvs1 | | | 90.72 224 | 89.65 235 | 93.96 198 | 94.29 261 | 89.63 161 | 97.79 59 | 96.82 202 | 89.07 184 | 86.12 274 | 95.48 196 | 78.61 233 | 97.78 249 | 86.97 215 | 81.67 302 | 94.46 279 |
|
HY-MVS | | 89.66 9 | 93.87 112 | 92.95 122 | 96.63 70 | 97.10 131 | 92.49 78 | 95.64 232 | 96.64 214 | 89.05 185 | 93.00 134 | 95.79 178 | 85.77 121 | 99.45 90 | 89.16 175 | 94.35 170 | 97.96 144 |
|
CSCG | | | 96.05 54 | 95.91 52 | 96.46 83 | 99.24 24 | 90.47 143 | 98.30 22 | 98.57 11 | 89.01 186 | 93.97 113 | 97.57 90 | 92.62 23 | 99.76 28 | 94.66 76 | 99.27 50 | 99.15 60 |
|
v8 | | | 91.29 205 | 90.53 200 | 93.57 220 | 94.15 262 | 88.12 213 | 97.34 104 | 97.06 182 | 88.99 187 | 88.32 237 | 94.26 249 | 83.08 153 | 98.01 220 | 87.62 201 | 83.92 290 | 94.57 277 |
|
PAPR | | | 94.18 100 | 93.42 114 | 96.48 80 | 97.64 112 | 91.42 111 | 95.55 234 | 97.71 113 | 88.99 187 | 92.34 148 | 95.82 174 | 89.19 73 | 99.11 119 | 86.14 226 | 97.38 114 | 98.90 86 |
|
CDS-MVSNet | | | 94.14 103 | 93.54 106 | 95.93 111 | 96.18 177 | 91.46 108 | 96.33 193 | 97.04 185 | 88.97 189 | 93.56 119 | 96.51 144 | 87.55 96 | 97.89 240 | 89.80 155 | 95.95 143 | 98.44 124 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
sss | | | 94.51 95 | 93.80 98 | 96.64 68 | 97.07 132 | 91.97 94 | 96.32 194 | 98.06 66 | 88.94 190 | 94.50 102 | 96.78 123 | 84.60 132 | 99.27 106 | 91.90 122 | 96.02 141 | 98.68 104 |
|
lupinMVS | | | 94.99 84 | 94.56 83 | 96.29 96 | 96.34 170 | 91.21 117 | 95.83 224 | 96.27 225 | 88.93 191 | 96.22 59 | 96.88 121 | 86.20 115 | 98.85 145 | 95.27 57 | 99.05 68 | 98.82 94 |
|
D2MVS | | | 91.30 204 | 90.95 185 | 92.35 253 | 94.71 244 | 85.52 255 | 96.18 206 | 98.21 36 | 88.89 192 | 86.60 269 | 93.82 260 | 79.92 212 | 97.95 231 | 89.29 168 | 90.95 220 | 93.56 297 |
|
v7n | | | 90.76 220 | 89.86 224 | 93.45 226 | 93.54 279 | 87.60 224 | 97.70 71 | 97.37 155 | 88.85 193 | 87.65 251 | 94.08 254 | 81.08 190 | 98.10 204 | 84.68 247 | 83.79 292 | 94.66 275 |
|
PVSNet_Blended | | | 94.87 88 | 94.56 83 | 95.81 115 | 98.27 76 | 89.46 172 | 95.47 239 | 98.36 16 | 88.84 194 | 94.36 104 | 96.09 164 | 88.02 87 | 99.58 61 | 93.44 98 | 98.18 93 | 98.40 127 |
|
ACMH+ | | 87.92 14 | 90.20 237 | 89.18 242 | 93.25 233 | 96.48 163 | 86.45 244 | 96.99 136 | 96.68 211 | 88.83 195 | 84.79 284 | 96.22 157 | 70.16 291 | 98.53 172 | 84.42 252 | 88.04 248 | 94.77 272 |
|
GBi-Net | | | 91.35 200 | 90.27 207 | 94.59 170 | 96.51 160 | 91.18 121 | 97.50 88 | 96.93 194 | 88.82 196 | 89.35 219 | 94.51 232 | 73.87 270 | 97.29 282 | 86.12 227 | 88.82 240 | 95.31 242 |
|
test1 | | | 91.35 200 | 90.27 207 | 94.59 170 | 96.51 160 | 91.18 121 | 97.50 88 | 96.93 194 | 88.82 196 | 89.35 219 | 94.51 232 | 73.87 270 | 97.29 282 | 86.12 227 | 88.82 240 | 95.31 242 |
|
FMVSNet2 | | | 91.31 203 | 90.08 216 | 94.99 153 | 96.51 160 | 92.21 84 | 97.41 96 | 96.95 192 | 88.82 196 | 88.62 232 | 94.75 222 | 73.87 270 | 97.42 275 | 85.20 242 | 88.55 246 | 95.35 240 |
|
V42 | | | 91.58 189 | 90.87 186 | 93.73 210 | 94.05 266 | 88.50 201 | 97.32 107 | 96.97 191 | 88.80 199 | 89.71 206 | 94.33 243 | 82.54 166 | 98.05 214 | 89.01 176 | 85.07 274 | 94.64 276 |
|
agg_prior1 | | | 96.22 52 | 95.77 55 | 97.56 40 | 98.67 49 | 93.79 43 | 96.28 198 | 98.00 81 | 88.76 200 | 95.68 80 | 97.55 94 | 92.70 22 | 99.57 69 | 95.01 64 | 99.32 45 | 99.32 49 |
|
BH-w/o | | | 92.14 174 | 91.75 159 | 93.31 231 | 96.99 139 | 85.73 252 | 95.67 229 | 95.69 243 | 88.73 201 | 89.26 224 | 94.82 219 | 82.97 156 | 98.07 211 | 85.26 241 | 96.32 140 | 96.13 203 |
|
test20.03 | | | 86.14 280 | 85.40 277 | 88.35 298 | 90.12 311 | 80.06 304 | 95.90 221 | 95.20 266 | 88.59 202 | 81.29 301 | 93.62 269 | 71.43 282 | 92.65 324 | 71.26 317 | 81.17 305 | 92.34 309 |
|
train_agg | | | 96.30 49 | 95.83 54 | 97.72 31 | 98.70 47 | 94.19 30 | 96.41 182 | 98.02 76 | 88.58 203 | 96.03 65 | 97.56 92 | 92.73 20 | 99.59 58 | 95.04 63 | 99.37 44 | 99.39 43 |
|
test_8 | | | | | | 98.67 49 | 94.06 37 | 96.37 189 | 98.01 79 | 88.58 203 | 95.98 70 | 97.55 94 | 92.73 20 | 99.58 61 | | | |
|
tpmrst | | | 91.44 195 | 91.32 174 | 91.79 266 | 95.15 221 | 79.20 310 | 93.42 288 | 95.37 256 | 88.55 205 | 93.49 123 | 93.67 267 | 82.49 168 | 98.27 188 | 90.41 147 | 89.34 237 | 97.90 148 |
|
ACMH | | 87.59 16 | 90.53 229 | 89.42 237 | 93.87 205 | 96.21 174 | 87.92 216 | 97.24 113 | 96.94 193 | 88.45 206 | 83.91 293 | 96.27 156 | 71.92 278 | 98.62 166 | 84.43 251 | 89.43 236 | 95.05 254 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Baseline_NR-MVSNet | | | 91.20 207 | 90.62 197 | 92.95 242 | 93.83 272 | 88.03 214 | 97.01 135 | 95.12 270 | 88.42 207 | 89.70 207 | 95.13 207 | 83.47 145 | 97.44 273 | 89.66 160 | 83.24 296 | 93.37 301 |
|
v1144 | | | 91.37 199 | 90.60 198 | 93.68 215 | 93.89 270 | 88.23 208 | 96.84 148 | 97.03 187 | 88.37 208 | 89.69 208 | 94.39 239 | 82.04 176 | 97.98 222 | 87.80 193 | 85.37 268 | 94.84 262 |
|
DP-MVS Recon | | | 95.68 63 | 95.12 72 | 97.37 46 | 99.19 27 | 94.19 30 | 97.03 130 | 98.08 58 | 88.35 209 | 95.09 95 | 97.65 81 | 89.97 70 | 99.48 86 | 92.08 120 | 98.59 84 | 98.44 124 |
|
tpm | | | 90.25 235 | 89.74 232 | 91.76 269 | 93.92 268 | 79.73 306 | 93.98 275 | 93.54 304 | 88.28 210 | 91.99 155 | 93.25 276 | 77.51 248 | 97.44 273 | 87.30 208 | 87.94 249 | 98.12 139 |
|
v10 | | | 91.04 213 | 90.23 210 | 93.49 222 | 94.12 263 | 88.16 212 | 97.32 107 | 97.08 179 | 88.26 211 | 88.29 239 | 94.22 250 | 82.17 175 | 97.97 225 | 86.45 221 | 84.12 286 | 94.33 283 |
|
Fast-Effi-MVS+ | | | 93.46 125 | 92.75 128 | 95.59 129 | 96.77 147 | 90.03 149 | 96.81 152 | 97.13 172 | 88.19 212 | 91.30 169 | 94.27 247 | 86.21 114 | 98.63 164 | 87.66 199 | 96.46 139 | 98.12 139 |
|
DWT-MVSNet_test | | | 90.76 220 | 89.89 223 | 93.38 228 | 95.04 227 | 83.70 276 | 95.85 223 | 94.30 297 | 88.19 212 | 90.46 182 | 92.80 280 | 73.61 274 | 98.50 175 | 88.16 186 | 90.58 224 | 97.95 146 |
|
TEST9 | | | | | | 98.70 47 | 94.19 30 | 96.41 182 | 98.02 76 | 88.17 214 | 96.03 65 | 97.56 92 | 92.74 19 | 99.59 58 | | | |
|
MDTV_nov1_ep13 | | | | 90.76 193 | | 95.22 218 | 80.33 300 | 93.03 296 | 95.28 261 | 88.14 215 | 92.84 140 | 93.83 258 | 81.34 187 | 98.08 208 | 82.86 263 | 94.34 171 | |
|
MAR-MVS | | | 94.22 99 | 93.46 110 | 96.51 78 | 98.00 94 | 92.19 87 | 97.67 72 | 97.47 135 | 88.13 216 | 93.00 134 | 95.84 172 | 84.86 130 | 99.51 82 | 87.99 189 | 98.17 94 | 97.83 154 |
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 |
UniMVSNet_ETH3D | | | 91.34 202 | 90.22 212 | 94.68 169 | 94.86 237 | 87.86 219 | 97.23 118 | 97.46 137 | 87.99 217 | 89.90 201 | 96.92 119 | 66.35 306 | 98.23 190 | 90.30 150 | 90.99 219 | 97.96 144 |
|
PatchMatch-RL | | | 92.90 145 | 92.02 151 | 95.56 130 | 98.19 86 | 90.80 134 | 95.27 249 | 97.18 167 | 87.96 218 | 91.86 158 | 95.68 186 | 80.44 201 | 98.99 133 | 84.01 254 | 97.54 108 | 96.89 182 |
|
thisisatest0515 | | | 92.29 166 | 91.30 176 | 95.25 144 | 96.60 152 | 88.90 193 | 94.36 265 | 92.32 312 | 87.92 219 | 93.43 125 | 94.57 230 | 77.28 249 | 99.00 132 | 89.42 165 | 95.86 146 | 97.86 151 |
|
PVSNet | | 86.66 18 | 92.24 169 | 91.74 161 | 93.73 210 | 97.77 106 | 83.69 277 | 92.88 297 | 96.72 205 | 87.91 220 | 93.00 134 | 94.86 216 | 78.51 234 | 99.05 129 | 86.53 218 | 97.45 113 | 98.47 119 |
|
LTVRE_ROB | | 88.41 13 | 90.99 214 | 89.92 222 | 94.19 186 | 96.18 177 | 89.55 166 | 96.31 195 | 97.09 178 | 87.88 221 | 85.67 276 | 95.91 169 | 78.79 232 | 98.57 170 | 81.50 273 | 89.98 231 | 94.44 280 |
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 |
CPTT-MVS | | | 95.57 67 | 95.19 69 | 96.70 67 | 99.27 22 | 91.48 106 | 98.33 20 | 98.11 53 | 87.79 222 | 95.17 93 | 98.03 54 | 87.09 105 | 99.61 53 | 93.51 95 | 99.42 35 | 99.02 71 |
|
v1192 | | | 91.07 211 | 90.23 210 | 93.58 219 | 93.70 275 | 87.82 220 | 96.73 157 | 97.07 180 | 87.77 223 | 89.58 211 | 94.32 244 | 80.90 195 | 97.97 225 | 86.52 219 | 85.48 266 | 94.95 255 |
|
F-COLMAP | | | 93.58 122 | 92.98 121 | 95.37 142 | 98.40 65 | 88.98 191 | 97.18 122 | 97.29 163 | 87.75 224 | 90.49 181 | 97.10 111 | 85.21 125 | 99.50 84 | 86.70 217 | 96.72 132 | 97.63 161 |
|
1314 | | | 92.81 151 | 92.03 150 | 95.14 148 | 95.33 210 | 89.52 169 | 96.04 212 | 97.44 146 | 87.72 225 | 86.25 272 | 95.33 199 | 83.84 141 | 98.79 149 | 89.26 169 | 97.05 125 | 97.11 175 |
|
test-mter | | | 90.19 238 | 89.54 236 | 92.12 256 | 94.59 249 | 80.66 295 | 94.29 269 | 92.98 308 | 87.68 226 | 90.76 178 | 92.37 288 | 67.67 300 | 98.07 211 | 88.81 179 | 96.74 130 | 97.63 161 |
|
TR-MVS | | | 91.48 194 | 90.59 199 | 94.16 188 | 96.40 167 | 87.33 225 | 95.67 229 | 95.34 260 | 87.68 226 | 91.46 162 | 95.52 194 | 76.77 251 | 98.35 184 | 82.85 264 | 93.61 182 | 96.79 186 |
|
LF4IMVS | | | 87.94 266 | 87.25 262 | 89.98 294 | 92.38 304 | 80.05 305 | 94.38 264 | 95.25 264 | 87.59 228 | 84.34 286 | 94.74 223 | 64.31 313 | 97.66 259 | 84.83 244 | 87.45 253 | 92.23 310 |
|
miper_lstm_enhance | | | 90.50 231 | 90.06 219 | 91.83 263 | 95.33 210 | 83.74 273 | 93.86 277 | 96.70 210 | 87.56 229 | 87.79 248 | 93.81 261 | 83.45 147 | 96.92 294 | 87.39 205 | 84.62 281 | 94.82 265 |
|
TransMVSNet (Re) | | | 88.94 252 | 87.56 259 | 93.08 239 | 94.35 257 | 88.45 203 | 97.73 65 | 95.23 265 | 87.47 230 | 84.26 288 | 95.29 200 | 79.86 213 | 97.33 280 | 79.44 290 | 74.44 318 | 93.45 300 |
|
v144192 | | | 91.06 212 | 90.28 206 | 93.39 227 | 93.66 277 | 87.23 230 | 96.83 149 | 97.07 180 | 87.43 231 | 89.69 208 | 94.28 246 | 81.48 186 | 98.00 221 | 87.18 212 | 84.92 278 | 94.93 259 |
|
原ACMM1 | | | | | 96.38 89 | 98.59 56 | 91.09 125 | | 97.89 91 | 87.41 232 | 95.22 92 | 97.68 78 | 90.25 64 | 99.54 74 | 87.95 190 | 99.12 65 | 98.49 116 |
|
v1921920 | | | 90.85 218 | 90.03 220 | 93.29 232 | 93.55 278 | 86.96 237 | 96.74 156 | 97.04 185 | 87.36 233 | 89.52 215 | 94.34 242 | 80.23 206 | 97.97 225 | 86.27 222 | 85.21 271 | 94.94 257 |
|
USDC | | | 88.94 252 | 87.83 257 | 92.27 254 | 94.66 245 | 84.96 262 | 93.86 277 | 95.90 237 | 87.34 234 | 83.40 295 | 95.56 191 | 67.43 302 | 98.19 195 | 82.64 268 | 89.67 235 | 93.66 296 |
|
PLC | | 91.00 6 | 94.11 104 | 93.43 112 | 96.13 103 | 98.58 58 | 91.15 124 | 96.69 162 | 97.39 152 | 87.29 235 | 91.37 164 | 96.71 126 | 88.39 85 | 99.52 81 | 87.33 207 | 97.13 124 | 97.73 157 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tfpnnormal | | | 89.70 247 | 88.40 251 | 93.60 217 | 95.15 221 | 90.10 148 | 97.56 84 | 98.16 44 | 87.28 236 | 86.16 273 | 94.63 228 | 77.57 247 | 98.05 214 | 74.48 305 | 84.59 282 | 92.65 305 |
|
TESTMET0.1,1 | | | 90.06 240 | 89.42 237 | 91.97 259 | 94.41 256 | 80.62 297 | 94.29 269 | 91.97 316 | 87.28 236 | 90.44 183 | 92.47 287 | 68.79 295 | 97.67 257 | 88.50 184 | 96.60 135 | 97.61 165 |
|
v1240 | | | 90.70 225 | 89.85 225 | 93.23 234 | 93.51 281 | 86.80 238 | 96.61 170 | 97.02 188 | 87.16 238 | 89.58 211 | 94.31 245 | 79.55 218 | 97.98 222 | 85.52 237 | 85.44 267 | 94.90 260 |
|
Patchmatch-RL test | | | 87.38 270 | 86.24 270 | 90.81 283 | 88.74 318 | 78.40 313 | 88.12 322 | 93.17 307 | 87.11 239 | 82.17 298 | 89.29 308 | 81.95 179 | 95.60 310 | 88.64 182 | 77.02 312 | 98.41 126 |
|
CDPH-MVS | | | 95.97 57 | 95.38 64 | 97.77 27 | 98.93 38 | 94.44 22 | 96.35 190 | 97.88 93 | 86.98 240 | 96.65 43 | 97.89 60 | 91.99 36 | 99.47 87 | 92.26 111 | 99.46 30 | 99.39 43 |
|
PM-MVS | | | 83.48 289 | 81.86 292 | 88.31 299 | 87.83 321 | 77.59 314 | 93.43 287 | 91.75 317 | 86.91 241 | 80.63 304 | 89.91 305 | 44.42 329 | 95.84 306 | 85.17 243 | 76.73 314 | 91.50 316 |
|
CR-MVSNet | | | 90.82 219 | 89.77 229 | 93.95 199 | 94.45 254 | 87.19 231 | 90.23 314 | 95.68 245 | 86.89 242 | 92.40 143 | 92.36 291 | 80.91 193 | 97.05 286 | 81.09 280 | 93.95 177 | 97.60 166 |
|
1112_ss | | | 93.37 127 | 92.42 142 | 96.21 101 | 97.05 137 | 90.99 126 | 96.31 195 | 96.72 205 | 86.87 243 | 89.83 204 | 96.69 130 | 86.51 110 | 99.14 117 | 88.12 187 | 93.67 179 | 98.50 114 |
|
FMVSNet1 | | | 89.88 244 | 88.31 252 | 94.59 170 | 95.41 203 | 91.18 121 | 97.50 88 | 96.93 194 | 86.62 244 | 87.41 255 | 94.51 232 | 65.94 310 | 97.29 282 | 83.04 262 | 87.43 254 | 95.31 242 |
|
CHOSEN 280x420 | | | 93.12 134 | 92.72 130 | 94.34 182 | 96.71 150 | 87.27 227 | 90.29 313 | 97.72 109 | 86.61 245 | 91.34 166 | 95.29 200 | 84.29 138 | 98.41 179 | 93.25 102 | 98.94 73 | 97.35 173 |
|
MVS_0304 | | | 88.79 256 | 87.57 258 | 92.46 250 | 94.65 246 | 86.15 249 | 96.40 185 | 97.17 169 | 86.44 246 | 88.02 245 | 91.71 300 | 56.68 322 | 97.03 288 | 84.47 250 | 92.58 192 | 94.19 288 |
|
MIMVSNet | | | 88.50 261 | 86.76 267 | 93.72 212 | 94.84 238 | 87.77 221 | 91.39 306 | 94.05 299 | 86.41 247 | 87.99 246 | 92.59 284 | 63.27 314 | 95.82 307 | 77.44 296 | 92.84 188 | 97.57 168 |
|
tpmvs | | | 89.83 246 | 89.15 243 | 91.89 261 | 94.92 233 | 80.30 301 | 93.11 294 | 95.46 252 | 86.28 248 | 88.08 243 | 92.65 282 | 80.44 201 | 98.52 173 | 81.47 274 | 89.92 232 | 96.84 184 |
|
PAPM | | | 91.52 192 | 90.30 205 | 95.20 145 | 95.30 212 | 89.83 158 | 93.38 289 | 96.85 201 | 86.26 249 | 88.59 233 | 95.80 175 | 84.88 129 | 98.15 198 | 75.67 304 | 95.93 144 | 97.63 161 |
|
VDDNet | | | 93.05 137 | 92.07 148 | 96.02 108 | 96.84 142 | 90.39 147 | 98.08 38 | 95.85 239 | 86.22 250 | 95.79 76 | 98.46 18 | 67.59 301 | 99.19 110 | 94.92 68 | 94.85 162 | 98.47 119 |
|
MS-PatchMatch | | | 90.27 234 | 89.77 229 | 91.78 267 | 94.33 258 | 84.72 266 | 95.55 234 | 96.73 204 | 86.17 251 | 86.36 271 | 95.28 202 | 71.28 283 | 97.80 247 | 84.09 253 | 98.14 95 | 92.81 304 |
|
MVP-Stereo | | | 90.74 223 | 90.08 216 | 92.71 246 | 93.19 290 | 88.20 209 | 95.86 222 | 96.27 225 | 86.07 252 | 84.86 283 | 94.76 221 | 77.84 245 | 97.75 252 | 83.88 257 | 98.01 97 | 92.17 312 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Anonymous202405211 | | | 92.07 175 | 90.83 191 | 95.76 116 | 98.19 86 | 88.75 195 | 97.58 82 | 95.00 274 | 86.00 253 | 93.64 118 | 97.45 96 | 66.24 308 | 99.53 77 | 90.68 146 | 92.71 189 | 99.01 75 |
|
CVMVSNet | | | 91.23 206 | 91.75 159 | 89.67 296 | 95.77 193 | 74.69 319 | 96.44 178 | 94.88 281 | 85.81 254 | 92.18 151 | 97.64 84 | 79.07 223 | 95.58 311 | 88.06 188 | 95.86 146 | 98.74 98 |
|
our_test_3 | | | 88.78 257 | 87.98 256 | 91.20 278 | 92.45 302 | 82.53 284 | 93.61 286 | 95.69 243 | 85.77 255 | 84.88 282 | 93.71 263 | 79.99 210 | 96.78 298 | 79.47 288 | 86.24 260 | 94.28 286 |
|
MSDG | | | 91.42 196 | 90.24 209 | 94.96 157 | 97.15 129 | 88.91 192 | 93.69 282 | 96.32 223 | 85.72 256 | 86.93 266 | 96.47 146 | 80.24 205 | 98.98 134 | 80.57 281 | 95.05 161 | 96.98 177 |
|
CHOSEN 1792x2688 | | | 94.15 101 | 93.51 108 | 96.06 106 | 98.27 76 | 89.38 175 | 95.18 253 | 98.48 14 | 85.60 257 | 93.76 117 | 97.11 110 | 83.15 151 | 99.61 53 | 91.33 137 | 98.72 79 | 99.19 56 |
|
AllTest | | | 90.23 236 | 88.98 244 | 93.98 195 | 97.94 97 | 86.64 240 | 96.51 177 | 95.54 250 | 85.38 258 | 85.49 278 | 96.77 124 | 70.28 289 | 99.15 115 | 80.02 284 | 92.87 186 | 96.15 201 |
|
TestCases | | | | | 93.98 195 | 97.94 97 | 86.64 240 | | 95.54 250 | 85.38 258 | 85.49 278 | 96.77 124 | 70.28 289 | 99.15 115 | 80.02 284 | 92.87 186 | 96.15 201 |
|
Test_1112_low_res | | | 92.84 149 | 91.84 157 | 95.85 114 | 97.04 138 | 89.97 155 | 95.53 236 | 96.64 214 | 85.38 258 | 89.65 210 | 95.18 204 | 85.86 119 | 99.10 120 | 87.70 195 | 93.58 184 | 98.49 116 |
|
EU-MVSNet | | | 88.72 258 | 88.90 245 | 88.20 300 | 93.15 291 | 74.21 320 | 96.63 169 | 94.22 298 | 85.18 261 | 87.32 258 | 95.97 165 | 76.16 256 | 94.98 315 | 85.27 240 | 86.17 261 | 95.41 232 |
|
LS3D | | | 93.57 123 | 92.61 134 | 96.47 81 | 97.59 116 | 91.61 101 | 97.67 72 | 97.72 109 | 85.17 262 | 90.29 186 | 98.34 32 | 84.60 132 | 99.73 30 | 83.85 258 | 98.27 90 | 98.06 143 |
|
dp | | | 88.90 254 | 88.26 254 | 90.81 283 | 94.58 251 | 76.62 316 | 92.85 298 | 94.93 279 | 85.12 263 | 90.07 199 | 93.07 277 | 75.81 257 | 98.12 202 | 80.53 282 | 87.42 255 | 97.71 158 |
|
HyFIR lowres test | | | 93.66 119 | 92.92 123 | 95.87 113 | 98.24 79 | 89.88 157 | 94.58 260 | 98.49 12 | 85.06 264 | 93.78 116 | 95.78 179 | 82.86 158 | 98.67 161 | 91.77 126 | 95.71 150 | 99.07 70 |
|
new-patchmatchnet | | | 83.18 290 | 81.87 291 | 87.11 304 | 86.88 323 | 75.99 318 | 93.70 281 | 95.18 267 | 85.02 265 | 77.30 314 | 88.40 311 | 65.99 309 | 93.88 320 | 74.19 309 | 70.18 322 | 91.47 317 |
|
TDRefinement | | | 86.53 276 | 84.76 282 | 91.85 262 | 82.23 327 | 84.25 268 | 96.38 188 | 95.35 257 | 84.97 266 | 84.09 291 | 94.94 211 | 65.76 311 | 98.34 186 | 84.60 249 | 74.52 317 | 92.97 302 |
|
OpenMVS | | 89.19 12 | 92.86 147 | 91.68 162 | 96.40 86 | 95.34 207 | 92.73 71 | 98.27 25 | 98.12 50 | 84.86 267 | 85.78 275 | 97.75 74 | 78.89 231 | 99.74 29 | 87.50 204 | 98.65 82 | 96.73 187 |
|
gm-plane-assit | | | | | | 93.22 289 | 78.89 312 | | | 84.82 268 | | 93.52 270 | | 98.64 163 | 87.72 194 | | |
|
PMMVS | | | 92.86 147 | 92.34 143 | 94.42 179 | 94.92 233 | 86.73 239 | 94.53 262 | 96.38 221 | 84.78 269 | 94.27 106 | 95.12 208 | 83.13 152 | 98.40 180 | 91.47 135 | 96.49 137 | 98.12 139 |
|
pmmvs4 | | | 90.93 216 | 89.85 225 | 94.17 187 | 93.34 286 | 90.79 135 | 94.60 259 | 96.02 234 | 84.62 270 | 87.45 253 | 95.15 205 | 81.88 181 | 97.45 272 | 87.70 195 | 87.87 250 | 94.27 287 |
|
MDA-MVSNet-bldmvs | | | 85.00 286 | 82.95 289 | 91.17 279 | 93.13 292 | 83.33 280 | 94.56 261 | 95.00 274 | 84.57 271 | 65.13 324 | 92.65 282 | 70.45 288 | 95.85 305 | 73.57 310 | 77.49 311 | 94.33 283 |
|
QAPM | | | 93.45 126 | 92.27 145 | 96.98 64 | 96.77 147 | 92.62 74 | 98.39 18 | 98.12 50 | 84.50 272 | 88.27 240 | 97.77 73 | 82.39 171 | 99.81 23 | 85.40 239 | 98.81 76 | 98.51 112 |
|
ppachtmachnet_test | | | 88.35 263 | 87.29 261 | 91.53 272 | 92.45 302 | 83.57 279 | 93.75 280 | 95.97 235 | 84.28 273 | 85.32 281 | 94.18 251 | 79.00 230 | 96.93 293 | 75.71 303 | 84.99 277 | 94.10 289 |
|
pmmvs5 | | | 89.86 245 | 88.87 246 | 92.82 243 | 92.86 294 | 86.23 247 | 96.26 199 | 95.39 254 | 84.24 274 | 87.12 260 | 94.51 232 | 74.27 268 | 97.36 279 | 87.61 202 | 87.57 252 | 94.86 261 |
|
CostFormer | | | 91.18 210 | 90.70 195 | 92.62 249 | 94.84 238 | 81.76 290 | 94.09 274 | 94.43 291 | 84.15 275 | 92.72 141 | 93.77 262 | 79.43 219 | 98.20 193 | 90.70 145 | 92.18 199 | 97.90 148 |
|
FMVSNet5 | | | 87.29 272 | 85.79 274 | 91.78 267 | 94.80 240 | 87.28 226 | 95.49 238 | 95.28 261 | 84.09 276 | 83.85 294 | 91.82 297 | 62.95 315 | 94.17 318 | 78.48 293 | 85.34 269 | 93.91 293 |
|
MIMVSNet1 | | | 84.93 287 | 83.05 288 | 90.56 288 | 89.56 316 | 84.84 265 | 95.40 241 | 95.35 257 | 83.91 277 | 80.38 306 | 92.21 295 | 57.23 320 | 93.34 322 | 70.69 319 | 82.75 301 | 93.50 298 |
|
RPSCF | | | 90.75 222 | 90.86 187 | 90.42 290 | 96.84 142 | 76.29 317 | 95.61 233 | 96.34 222 | 83.89 278 | 91.38 163 | 97.87 63 | 76.45 253 | 98.78 150 | 87.16 213 | 92.23 196 | 96.20 197 |
|
MDTV_nov1_ep13_2view | | | | | | | 70.35 324 | 93.10 295 | | 83.88 279 | 93.55 120 | | 82.47 169 | | 86.25 223 | | 98.38 129 |
|
无先验 | | | | | | | | 95.79 226 | 97.87 95 | 83.87 280 | | | | 99.65 46 | 87.68 197 | | 98.89 88 |
|
PVSNet_0 | | 82.17 19 | 85.46 285 | 83.64 287 | 90.92 281 | 95.27 213 | 79.49 307 | 90.55 312 | 95.60 247 | 83.76 281 | 83.00 296 | 89.95 304 | 71.09 284 | 97.97 225 | 82.75 266 | 60.79 326 | 95.31 242 |
|
TinyColmap | | | 86.82 275 | 85.35 278 | 91.21 277 | 94.91 235 | 82.99 282 | 93.94 276 | 94.02 301 | 83.58 282 | 81.56 300 | 94.68 225 | 62.34 317 | 98.13 199 | 75.78 302 | 87.35 257 | 92.52 307 |
|
Anonymous20231206 | | | 87.09 273 | 86.14 272 | 89.93 295 | 91.22 309 | 80.35 299 | 96.11 209 | 95.35 257 | 83.57 283 | 84.16 289 | 93.02 278 | 73.54 275 | 95.61 309 | 72.16 313 | 86.14 262 | 93.84 295 |
|
pmmvs-eth3d | | | 86.22 279 | 84.45 283 | 91.53 272 | 88.34 319 | 87.25 228 | 94.47 263 | 95.01 273 | 83.47 284 | 79.51 311 | 89.61 307 | 69.75 293 | 95.71 308 | 83.13 261 | 76.73 314 | 91.64 313 |
|
EG-PatchMatch MVS | | | 87.02 274 | 85.44 276 | 91.76 269 | 92.67 298 | 85.00 261 | 96.08 211 | 96.45 219 | 83.41 285 | 79.52 310 | 93.49 272 | 57.10 321 | 97.72 254 | 79.34 291 | 90.87 222 | 92.56 306 |
|
ADS-MVSNet2 | | | 89.45 248 | 88.59 249 | 92.03 258 | 95.86 188 | 82.26 288 | 90.93 309 | 94.32 296 | 83.23 286 | 91.28 172 | 91.81 298 | 79.01 228 | 95.99 304 | 79.52 286 | 91.39 212 | 97.84 152 |
|
ADS-MVSNet | | | 89.89 243 | 88.68 248 | 93.53 221 | 95.86 188 | 84.89 264 | 90.93 309 | 95.07 272 | 83.23 286 | 91.28 172 | 91.81 298 | 79.01 228 | 97.85 242 | 79.52 286 | 91.39 212 | 97.84 152 |
|
COLMAP_ROB | | 87.81 15 | 90.40 232 | 89.28 240 | 93.79 209 | 97.95 96 | 87.13 234 | 96.92 142 | 95.89 238 | 82.83 288 | 86.88 268 | 97.18 106 | 73.77 273 | 99.29 105 | 78.44 294 | 93.62 181 | 94.95 255 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
testdata | | | | | 95.46 140 | 98.18 88 | 88.90 193 | | 97.66 115 | 82.73 289 | 97.03 37 | 98.07 51 | 90.06 68 | 98.85 145 | 89.67 159 | 98.98 71 | 98.64 105 |
|
DP-MVS | | | 92.76 152 | 91.51 170 | 96.52 75 | 98.77 44 | 90.99 126 | 97.38 102 | 96.08 233 | 82.38 290 | 89.29 222 | 97.87 63 | 83.77 142 | 99.69 40 | 81.37 278 | 96.69 133 | 98.89 88 |
|
MDA-MVSNet_test_wron | | | 85.87 282 | 84.23 285 | 90.80 285 | 92.38 304 | 82.57 283 | 93.17 291 | 95.15 268 | 82.15 291 | 67.65 321 | 92.33 294 | 78.20 238 | 95.51 312 | 77.33 297 | 79.74 307 | 94.31 285 |
|
YYNet1 | | | 85.87 282 | 84.23 285 | 90.78 286 | 92.38 304 | 82.46 286 | 93.17 291 | 95.14 269 | 82.12 292 | 67.69 320 | 92.36 291 | 78.16 241 | 95.50 313 | 77.31 298 | 79.73 308 | 94.39 281 |
|
PatchT | | | 88.87 255 | 87.42 260 | 93.22 235 | 94.08 265 | 85.10 260 | 89.51 318 | 94.64 288 | 81.92 293 | 92.36 146 | 88.15 314 | 80.05 209 | 97.01 291 | 72.43 312 | 93.65 180 | 97.54 169 |
|
TAPA-MVS | | 90.10 7 | 92.30 165 | 91.22 181 | 95.56 130 | 98.33 72 | 89.60 163 | 96.79 153 | 97.65 117 | 81.83 294 | 91.52 161 | 97.23 105 | 87.94 89 | 98.91 140 | 71.31 316 | 98.37 88 | 98.17 137 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
旧先验2 | | | | | | | | 95.94 219 | | 81.66 295 | 97.34 25 | | | 98.82 147 | 92.26 111 | | |
|
新几何1 | | | | | 97.32 48 | 98.60 55 | 93.59 49 | | 97.75 104 | 81.58 296 | 95.75 77 | 97.85 66 | 90.04 69 | 99.67 44 | 86.50 220 | 99.13 62 | 98.69 103 |
|
1121 | | | 94.71 93 | 93.83 97 | 97.34 47 | 98.57 59 | 93.64 48 | 96.04 212 | 97.73 106 | 81.56 297 | 95.68 80 | 97.85 66 | 90.23 65 | 99.65 46 | 87.68 197 | 99.12 65 | 98.73 99 |
|
Patchmatch-test | | | 89.42 249 | 87.99 255 | 93.70 213 | 95.27 213 | 85.11 259 | 88.98 320 | 94.37 294 | 81.11 298 | 87.10 262 | 93.69 264 | 82.28 172 | 97.50 268 | 74.37 307 | 94.76 165 | 98.48 118 |
|
test_0402 | | | 86.46 277 | 84.79 281 | 91.45 274 | 95.02 228 | 85.55 254 | 96.29 197 | 94.89 280 | 80.90 299 | 82.21 297 | 93.97 256 | 68.21 299 | 97.29 282 | 62.98 324 | 88.68 245 | 91.51 315 |
|
gg-mvs-nofinetune | | | 87.82 267 | 85.61 275 | 94.44 177 | 94.46 253 | 89.27 184 | 91.21 308 | 84.61 330 | 80.88 300 | 89.89 203 | 74.98 324 | 71.50 281 | 97.53 266 | 85.75 235 | 97.21 121 | 96.51 191 |
|
JIA-IIPM | | | 88.26 264 | 87.04 266 | 91.91 260 | 93.52 280 | 81.42 291 | 89.38 319 | 94.38 293 | 80.84 301 | 90.93 177 | 80.74 321 | 79.22 222 | 97.92 236 | 82.76 265 | 91.62 207 | 96.38 195 |
|
Patchmtry | | | 88.64 259 | 87.25 262 | 92.78 245 | 94.09 264 | 86.64 240 | 89.82 317 | 95.68 245 | 80.81 302 | 87.63 252 | 92.36 291 | 80.91 193 | 97.03 288 | 78.86 292 | 85.12 273 | 94.67 274 |
|
tpm2 | | | 89.96 241 | 89.21 241 | 92.23 255 | 94.91 235 | 81.25 292 | 93.78 279 | 94.42 292 | 80.62 303 | 91.56 160 | 93.44 274 | 76.44 254 | 97.94 232 | 85.60 236 | 92.08 203 | 97.49 170 |
|
pmmvs6 | | | 87.81 268 | 86.19 271 | 92.69 247 | 91.32 308 | 86.30 246 | 97.34 104 | 96.41 220 | 80.59 304 | 84.05 292 | 94.37 241 | 67.37 303 | 97.67 257 | 84.75 246 | 79.51 309 | 94.09 291 |
|
Anonymous20231211 | | | 90.63 227 | 89.42 237 | 94.27 184 | 98.24 79 | 89.19 187 | 98.05 40 | 97.89 91 | 79.95 305 | 88.25 241 | 94.96 210 | 72.56 277 | 98.13 199 | 89.70 158 | 85.14 272 | 95.49 226 |
|
cascas | | | 91.20 207 | 90.08 216 | 94.58 174 | 94.97 229 | 89.16 188 | 93.65 284 | 97.59 123 | 79.90 306 | 89.40 217 | 92.92 279 | 75.36 262 | 98.36 183 | 92.14 116 | 94.75 166 | 96.23 196 |
|
Anonymous20240529 | | | 91.98 178 | 90.73 194 | 95.73 121 | 98.14 89 | 89.40 174 | 97.99 42 | 97.72 109 | 79.63 307 | 93.54 121 | 97.41 98 | 69.94 292 | 99.56 71 | 91.04 142 | 91.11 216 | 98.22 135 |
|
PCF-MVS | | 89.48 11 | 91.56 190 | 89.95 221 | 96.36 91 | 96.60 152 | 92.52 77 | 92.51 302 | 97.26 164 | 79.41 308 | 88.90 227 | 96.56 142 | 84.04 140 | 99.55 72 | 77.01 300 | 97.30 118 | 97.01 176 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
test222 | | | | | | 98.24 79 | 92.21 84 | 95.33 244 | 97.60 121 | 79.22 309 | 95.25 91 | 97.84 69 | 88.80 79 | | | 99.15 60 | 98.72 100 |
|
UnsupCasMVSNet_bld | | | 82.13 293 | 79.46 295 | 90.14 293 | 88.00 320 | 82.47 285 | 90.89 311 | 96.62 217 | 78.94 310 | 75.61 315 | 84.40 319 | 56.63 323 | 96.31 302 | 77.30 299 | 66.77 325 | 91.63 314 |
|
N_pmnet | | | 78.73 296 | 78.71 296 | 78.79 310 | 92.80 296 | 46.50 335 | 94.14 273 | 43.71 338 | 78.61 311 | 80.83 302 | 91.66 301 | 74.94 265 | 96.36 301 | 67.24 321 | 84.45 284 | 93.50 298 |
|
ANet_high | | | 63.94 301 | 59.58 303 | 77.02 311 | 61.24 335 | 66.06 327 | 85.66 325 | 87.93 326 | 78.53 312 | 42.94 330 | 71.04 327 | 25.42 336 | 80.71 330 | 52.60 327 | 30.83 330 | 84.28 323 |
|
114514_t | | | 93.95 110 | 93.06 120 | 96.63 70 | 99.07 34 | 91.61 101 | 97.46 95 | 97.96 88 | 77.99 313 | 93.00 134 | 97.57 90 | 86.14 117 | 99.33 101 | 89.22 171 | 99.15 60 | 98.94 82 |
|
DSMNet-mixed | | | 86.34 278 | 86.12 273 | 87.00 305 | 89.88 314 | 70.43 323 | 94.93 256 | 90.08 323 | 77.97 314 | 85.42 280 | 92.78 281 | 74.44 267 | 93.96 319 | 74.43 306 | 95.14 157 | 96.62 189 |
|
RPMNet | | | 88.52 260 | 86.72 269 | 93.95 199 | 94.45 254 | 87.19 231 | 90.23 314 | 94.99 276 | 77.87 315 | 92.40 143 | 87.55 316 | 80.17 207 | 97.05 286 | 68.84 320 | 93.95 177 | 97.60 166 |
|
new_pmnet | | | 82.89 291 | 81.12 294 | 88.18 301 | 89.63 315 | 80.18 303 | 91.77 305 | 92.57 311 | 76.79 316 | 75.56 317 | 88.23 313 | 61.22 318 | 94.48 316 | 71.43 315 | 82.92 299 | 89.87 320 |
|
tpm cat1 | | | 88.36 262 | 87.21 264 | 91.81 265 | 95.13 223 | 80.55 298 | 92.58 301 | 95.70 242 | 74.97 317 | 87.45 253 | 91.96 296 | 78.01 244 | 98.17 197 | 80.39 283 | 88.74 243 | 96.72 188 |
|
CMPMVS | | 62.92 21 | 85.62 284 | 84.92 280 | 87.74 302 | 89.14 317 | 73.12 322 | 94.17 272 | 96.80 203 | 73.98 318 | 73.65 318 | 94.93 212 | 66.36 305 | 97.61 262 | 83.95 256 | 91.28 214 | 92.48 308 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
OpenMVS_ROB | | 81.14 20 | 84.42 288 | 82.28 290 | 90.83 282 | 90.06 312 | 84.05 272 | 95.73 228 | 94.04 300 | 73.89 319 | 80.17 309 | 91.53 302 | 59.15 319 | 97.64 260 | 66.92 322 | 89.05 239 | 90.80 318 |
|
MVS | | | 91.71 183 | 90.44 201 | 95.51 134 | 95.20 220 | 91.59 103 | 96.04 212 | 97.45 142 | 73.44 320 | 87.36 257 | 95.60 189 | 85.42 123 | 99.10 120 | 85.97 231 | 97.46 109 | 95.83 213 |
|
pmmvs3 | | | 79.97 294 | 77.50 297 | 87.39 303 | 82.80 326 | 79.38 309 | 92.70 300 | 90.75 322 | 70.69 321 | 78.66 312 | 87.47 317 | 51.34 326 | 93.40 321 | 73.39 311 | 69.65 323 | 89.38 321 |
|
MVS-HIRNet | | | 82.47 292 | 81.21 293 | 86.26 307 | 95.38 205 | 69.21 326 | 88.96 321 | 89.49 324 | 66.28 322 | 80.79 303 | 74.08 326 | 68.48 297 | 97.39 277 | 71.93 314 | 95.47 152 | 92.18 311 |
|
DeepMVS_CX | | | | | 74.68 314 | 90.84 310 | 64.34 330 | | 81.61 333 | 65.34 323 | 67.47 322 | 88.01 315 | 48.60 327 | 80.13 331 | 62.33 325 | 73.68 320 | 79.58 325 |
|
PMMVS2 | | | 70.19 299 | 66.92 301 | 80.01 309 | 76.35 328 | 65.67 328 | 86.22 323 | 87.58 327 | 64.83 324 | 62.38 325 | 80.29 322 | 26.78 335 | 88.49 327 | 63.79 323 | 54.07 327 | 85.88 322 |
|
FPMVS | | | 71.27 298 | 69.85 299 | 75.50 312 | 74.64 329 | 59.03 331 | 91.30 307 | 91.50 319 | 58.80 325 | 57.92 327 | 88.28 312 | 29.98 333 | 85.53 329 | 53.43 326 | 82.84 300 | 81.95 324 |
|
LCM-MVSNet | | | 72.55 297 | 69.39 300 | 82.03 308 | 70.81 333 | 65.42 329 | 90.12 316 | 94.36 295 | 55.02 326 | 65.88 323 | 81.72 320 | 24.16 337 | 89.96 325 | 74.32 308 | 68.10 324 | 90.71 319 |
|
Gipuma | | | 67.86 300 | 65.41 302 | 75.18 313 | 92.66 299 | 73.45 321 | 66.50 331 | 94.52 290 | 53.33 327 | 57.80 328 | 66.07 328 | 30.81 331 | 89.20 326 | 48.15 328 | 78.88 310 | 62.90 328 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS | | 53.92 22 | 58.58 302 | 55.40 304 | 68.12 315 | 51.00 336 | 48.64 333 | 78.86 328 | 87.10 329 | 46.77 328 | 35.84 334 | 74.28 325 | 8.76 338 | 86.34 328 | 42.07 329 | 73.91 319 | 69.38 326 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
E-PMN | | | 53.28 303 | 52.56 306 | 55.43 317 | 74.43 330 | 47.13 334 | 83.63 327 | 76.30 334 | 42.23 329 | 42.59 331 | 62.22 330 | 28.57 334 | 74.40 332 | 31.53 331 | 31.51 329 | 44.78 329 |
|
EMVS | | | 52.08 305 | 51.31 307 | 54.39 318 | 72.62 332 | 45.39 336 | 83.84 326 | 75.51 335 | 41.13 330 | 40.77 332 | 59.65 331 | 30.08 332 | 73.60 333 | 28.31 332 | 29.90 331 | 44.18 330 |
|
MVE | | 50.73 23 | 53.25 304 | 48.81 308 | 66.58 316 | 65.34 334 | 57.50 332 | 72.49 330 | 70.94 336 | 40.15 331 | 39.28 333 | 63.51 329 | 6.89 340 | 73.48 334 | 38.29 330 | 42.38 328 | 68.76 327 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | 51.94 306 | 53.82 305 | 46.29 319 | 33.73 337 | 45.30 337 | 78.32 329 | 67.24 337 | 18.02 332 | 50.93 329 | 87.05 318 | 52.99 325 | 53.11 335 | 70.76 318 | 25.29 332 | 40.46 331 |
|
wuyk23d | | | 25.11 307 | 24.57 310 | 26.74 320 | 73.98 331 | 39.89 338 | 57.88 333 | 9.80 339 | 12.27 333 | 10.39 335 | 6.97 337 | 7.03 339 | 36.44 336 | 25.43 333 | 17.39 333 | 3.89 334 |
|
testmvs | | | 13.36 309 | 16.33 311 | 4.48 322 | 5.04 338 | 2.26 340 | 93.18 290 | 3.28 340 | 2.70 334 | 8.24 336 | 21.66 333 | 2.29 342 | 2.19 337 | 7.58 334 | 2.96 334 | 9.00 333 |
|
test123 | | | 13.04 310 | 15.66 312 | 5.18 321 | 4.51 339 | 3.45 339 | 92.50 303 | 1.81 341 | 2.50 335 | 7.58 337 | 20.15 334 | 3.67 341 | 2.18 338 | 7.13 335 | 1.07 335 | 9.90 332 |
|
cdsmvs_eth3d_5k | | | 23.24 308 | 30.99 309 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 97.63 119 | 0.00 336 | 0.00 338 | 96.88 121 | 84.38 135 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
pcd_1.5k_mvsjas | | | 7.39 312 | 9.85 314 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 88.65 81 | 0.00 339 | 0.00 336 | 0.00 336 | 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 | 0.00 336 | 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 | 0.00 336 | 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 | 0.00 336 | 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 | 0.00 336 | 0.00 335 |
|
ab-mvs-re | | | 8.06 311 | 10.74 313 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 96.69 130 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 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 | 0.00 336 | 0.00 335 |
|
save filter2 | | | | | | | | | | | 97.90 15 | 98.30 40 | 92.94 16 | 99.81 23 | 96.61 18 | 99.61 11 | 99.44 38 |
|
test_0728_SECOND | | | | | 98.51 1 | 99.45 2 | 95.93 2 | 98.21 30 | 98.28 26 | | | | | 99.86 7 | 97.52 2 | 99.67 6 | 99.75 3 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.45 121 |
|
test_part2 | | | | | | 99.28 21 | 95.74 5 | | | | 98.10 11 | | | | | | |
|
test_part1 | | | | | 0.00 323 | | 0.00 341 | 0.00 334 | 98.26 29 | | | | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
sam_mvs1 | | | | | | | | | | | | | 82.76 161 | | | | 98.45 121 |
|
sam_mvs | | | | | | | | | | | | | 81.94 180 | | | | |
|
ambc | | | | | 86.56 306 | 83.60 325 | 70.00 325 | 85.69 324 | 94.97 277 | | 80.60 305 | 88.45 310 | 37.42 330 | 96.84 296 | 82.69 267 | 75.44 316 | 92.86 303 |
|
MTGPA | | | | | | | | | 98.08 58 | | | | | | | | |
|
test_post1 | | | | | | | | 92.81 299 | | | | 16.58 336 | 80.53 199 | 97.68 256 | 86.20 224 | | |
|
test_post | | | | | | | | | | | | 17.58 335 | 81.76 182 | 98.08 208 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 90.45 303 | 82.65 165 | 98.10 204 | | | |
|
GG-mvs-BLEND | | | | | 93.62 216 | 93.69 276 | 89.20 185 | 92.39 304 | 83.33 331 | | 87.98 247 | 89.84 306 | 71.00 285 | 96.87 295 | 82.08 271 | 95.40 154 | 94.80 268 |
|
MTMP | | | | | | | | 97.86 51 | 82.03 332 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 94.81 72 | 99.38 40 | 99.45 36 |
|
agg_prior2 | | | | | | | | | | | | | | | 93.94 87 | 99.38 40 | 99.50 29 |
|
agg_prior | | | | | | 98.67 49 | 93.79 43 | | 98.00 81 | | 95.68 80 | | | 99.57 69 | | | |
|
test_prior4 | | | | | | | 93.66 47 | 96.42 181 | | | | | | | | | |
|
test_prior | | | | | 97.23 54 | 98.67 49 | 92.99 64 | | 98.00 81 | | | | | 99.41 94 | | | 99.29 51 |
|
新几何2 | | | | | | | | 95.79 226 | | | | | | | | | |
|
旧先验1 | | | | | | 98.38 68 | 93.38 55 | | 97.75 104 | | | 98.09 50 | 92.30 31 | | | 99.01 70 | 99.16 58 |
|
原ACMM2 | | | | | | | | 95.67 229 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.67 44 | 85.96 232 | | |
|
segment_acmp | | | | | | | | | | | | | 92.89 17 | | | | |
|
test12 | | | | | 97.65 36 | 98.46 61 | 94.26 27 | | 97.66 115 | | 95.52 89 | | 90.89 57 | 99.46 88 | | 99.25 52 | 99.22 55 |
|
plane_prior7 | | | | | | 96.21 174 | 89.98 154 | | | | | | | | | | |
|
plane_prior6 | | | | | | 96.10 184 | 90.00 150 | | | | | | 81.32 188 | | | | |
|
plane_prior5 | | | | | | | | | 97.51 130 | | | | | 98.60 167 | 93.02 106 | 92.23 196 | 95.86 209 |
|
plane_prior4 | | | | | | | | | | | | 96.64 133 | | | | | |
|
plane_prior1 | | | | | | 96.14 182 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 342 | | | | | | | | |
|
nn | | | | | | | | | 0.00 342 | | | | | | | | |
|
door-mid | | | | | | | | | 91.06 321 | | | | | | | | |
|
lessismore_v0 | | | | | 90.45 289 | 91.96 307 | 79.09 311 | | 87.19 328 | | 80.32 307 | 94.39 239 | 66.31 307 | 97.55 265 | 84.00 255 | 76.84 313 | 94.70 273 |
|
test11 | | | | | | | | | 97.88 93 | | | | | | | | |
|
door | | | | | | | | | 91.13 320 | | | | | | | | |
|
HQP5-MVS | | | | | | | 89.33 178 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 92.13 117 | | |
|
HQP4-MVS | | | | | | | | | | | 90.14 188 | | | 98.50 175 | | | 95.78 216 |
|
HQP3-MVS | | | | | | | | | 97.39 152 | | | | | | | 92.10 201 | |
|
HQP2-MVS | | | | | | | | | | | | | 80.95 191 | | | | |
|
NP-MVS | | | | | | 95.99 187 | 89.81 159 | | | | | 95.87 170 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 229 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.02 218 | |
|
Test By Simon | | | | | | | | | | | | | 88.73 80 | | | | |
|