HSP-MVS | | | 99.31 3 | 99.43 13 | 99.17 2 | 99.68 10 | 99.75 2 | 99.72 2 | 98.31 5 | 99.45 16 | 98.16 9 | 99.28 12 | 99.98 1 | 99.30 30 | 99.34 19 | 98.41 58 | 99.81 26 | 99.81 30 |
|
SteuartSystems-ACMMP | | | 99.20 12 | 99.51 5 | 98.83 22 | 99.66 13 | 99.66 19 | 99.71 4 | 98.12 22 | 99.14 52 | 96.62 29 | 99.16 18 | 99.98 1 | 99.12 49 | 99.63 3 | 99.19 19 | 99.78 37 | 99.83 24 |
Skip Steuart: Steuart Systems R&D Blog. |
ESAPD | | | 99.23 10 | 99.41 15 | 99.01 14 | 99.70 6 | 99.69 11 | 99.40 26 | 98.31 5 | 98.94 74 | 97.70 17 | 99.40 9 | 99.97 3 | 99.17 41 | 99.54 7 | 98.67 42 | 99.78 37 | 99.67 104 |
|
ACMMP_Plus | | | 99.05 21 | 99.45 8 | 98.58 26 | 99.73 4 | 99.60 41 | 99.64 8 | 98.28 10 | 99.23 42 | 94.57 57 | 99.35 11 | 99.97 3 | 99.55 12 | 99.63 3 | 98.66 43 | 99.70 77 | 99.74 64 |
|
SMA-MVS | | | 99.31 3 | 99.44 11 | 99.16 4 | 99.73 4 | 99.65 20 | 99.63 10 | 98.26 11 | 99.27 35 | 98.01 12 | 99.27 13 | 99.97 3 | 99.60 6 | 99.59 5 | 98.58 49 | 99.71 69 | 99.73 68 |
|
MTAPA | | | | | | | | | | | 98.09 10 | | 99.97 3 | | | | | |
|
HFP-MVS | | | 99.32 2 | 99.53 4 | 99.07 9 | 99.69 7 | 99.59 43 | 99.63 10 | 98.31 5 | 99.56 9 | 97.37 21 | 99.27 13 | 99.97 3 | 99.70 3 | 99.35 18 | 99.24 15 | 99.71 69 | 99.76 52 |
|
APDe-MVS | | | 99.49 1 | 99.64 1 | 99.32 1 | 99.74 3 | 99.74 3 | 99.75 1 | 98.34 2 | 99.56 9 | 98.72 3 | 99.57 4 | 99.97 3 | 99.53 14 | 99.65 2 | 99.25 13 | 99.84 5 | 99.77 48 |
|
TSAR-MVS + MP. | | | 99.27 6 | 99.57 2 | 98.92 18 | 98.78 48 | 99.53 51 | 99.72 2 | 98.11 23 | 99.73 2 | 97.43 20 | 99.15 19 | 99.96 9 | 99.59 8 | 99.73 1 | 99.07 21 | 99.88 1 | 99.82 25 |
|
MTMP | | | | | | | | | | | 98.46 7 | | 99.96 9 | | | | | |
|
HPM-MVS++ | | | 99.10 17 | 99.30 23 | 98.86 19 | 99.69 7 | 99.48 56 | 99.59 13 | 98.34 2 | 99.26 38 | 96.55 32 | 99.10 23 | 99.96 9 | 99.36 25 | 99.25 23 | 98.37 63 | 99.64 117 | 99.66 113 |
|
CP-MVS | | | 99.27 6 | 99.44 11 | 99.08 8 | 99.62 17 | 99.58 45 | 99.53 15 | 98.16 16 | 99.21 45 | 97.79 15 | 99.15 19 | 99.96 9 | 99.59 8 | 99.54 7 | 98.86 35 | 99.78 37 | 99.74 64 |
|
PHI-MVS | | | 99.08 18 | 99.43 13 | 98.67 24 | 99.15 40 | 99.59 43 | 99.11 38 | 97.35 33 | 99.14 52 | 97.30 22 | 99.44 8 | 99.96 9 | 99.32 28 | 98.89 42 | 99.39 7 | 99.79 34 | 99.58 129 |
|
XVS | | | | | | 97.42 66 | 99.62 33 | 98.59 57 | | | 93.81 79 | | 99.95 14 | | | | 99.69 79 | |
|
X-MVStestdata | | | | | | 97.42 66 | 99.62 33 | 98.59 57 | | | 93.81 79 | | 99.95 14 | | | | 99.69 79 | |
|
X-MVS | | | 98.93 25 | 99.37 18 | 98.42 27 | 99.67 11 | 99.62 33 | 99.60 12 | 98.15 18 | 99.08 59 | 93.81 79 | 98.46 53 | 99.95 14 | 99.59 8 | 99.49 10 | 99.21 18 | 99.68 88 | 99.75 61 |
|
SD-MVS | | | 99.25 8 | 99.50 6 | 98.96 16 | 98.79 47 | 99.55 50 | 99.33 29 | 98.29 9 | 99.75 1 | 97.96 13 | 99.15 19 | 99.95 14 | 99.61 5 | 99.17 25 | 99.06 22 | 99.81 26 | 99.84 20 |
|
TSAR-MVS + ACMM | | | 98.77 29 | 99.45 8 | 97.98 38 | 99.37 31 | 99.46 58 | 99.44 24 | 98.13 21 | 99.65 4 | 92.30 95 | 98.91 35 | 99.95 14 | 99.05 55 | 99.42 14 | 98.95 28 | 99.58 149 | 99.82 25 |
|
ACMMPR | | | 99.30 5 | 99.54 3 | 99.03 12 | 99.66 13 | 99.64 25 | 99.68 5 | 98.25 12 | 99.56 9 | 97.12 25 | 99.19 16 | 99.95 14 | 99.72 1 | 99.43 13 | 99.25 13 | 99.72 60 | 99.77 48 |
|
TSAR-MVS + GP. | | | 98.66 35 | 99.36 19 | 97.85 40 | 97.16 74 | 99.46 58 | 99.03 44 | 94.59 55 | 99.09 57 | 97.19 24 | 99.73 3 | 99.95 14 | 99.39 24 | 98.95 36 | 98.69 41 | 99.75 45 | 99.65 116 |
|
CPTT-MVS | | | 99.14 15 | 99.20 28 | 99.06 10 | 99.58 20 | 99.53 51 | 99.45 22 | 97.80 30 | 99.19 48 | 98.32 8 | 98.58 47 | 99.95 14 | 99.60 6 | 99.28 22 | 98.20 75 | 99.64 117 | 99.69 92 |
|
MP-MVS | | | 99.07 19 | 99.36 19 | 98.74 23 | 99.63 16 | 99.57 47 | 99.66 7 | 98.25 12 | 99.00 69 | 95.62 37 | 98.97 28 | 99.94 22 | 99.54 13 | 99.51 9 | 98.79 40 | 99.71 69 | 99.73 68 |
|
CNVR-MVS | | | 99.23 10 | 99.28 24 | 99.17 2 | 99.65 15 | 99.34 74 | 99.46 21 | 98.21 14 | 99.28 33 | 98.47 5 | 98.89 37 | 99.94 22 | 99.50 15 | 99.42 14 | 98.61 46 | 99.73 55 | 99.52 140 |
|
APD-MVS | | | 99.25 8 | 99.38 17 | 99.09 7 | 99.69 7 | 99.58 45 | 99.56 14 | 98.32 4 | 98.85 81 | 97.87 14 | 98.91 35 | 99.92 24 | 99.30 30 | 99.45 12 | 99.38 8 | 99.79 34 | 99.58 129 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS_fast | | 98.34 1 | 99.17 13 | 99.45 8 | 98.85 20 | 99.55 23 | 99.37 69 | 99.64 8 | 98.05 25 | 99.53 12 | 96.58 30 | 98.93 30 | 99.92 24 | 99.49 17 | 99.46 11 | 99.32 10 | 99.80 33 | 99.64 120 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
UA-Net | | | 97.13 73 | 99.14 30 | 94.78 106 | 97.21 72 | 99.38 67 | 97.56 99 | 92.04 94 | 98.48 111 | 88.03 121 | 98.39 56 | 99.91 26 | 94.03 194 | 99.33 20 | 99.23 16 | 99.81 26 | 99.25 162 |
|
MCST-MVS | | | 99.11 16 | 99.27 25 | 98.93 17 | 99.67 11 | 99.33 76 | 99.51 17 | 98.31 5 | 99.28 33 | 96.57 31 | 99.10 23 | 99.90 27 | 99.71 2 | 99.19 24 | 98.35 65 | 99.82 13 | 99.71 84 |
|
NCCC | | | 99.05 21 | 99.08 33 | 99.02 13 | 99.62 17 | 99.38 67 | 99.43 25 | 98.21 14 | 99.36 23 | 97.66 18 | 97.79 70 | 99.90 27 | 99.45 20 | 99.17 25 | 98.43 55 | 99.77 42 | 99.51 144 |
|
MSLP-MVS++ | | | 99.15 14 | 99.24 26 | 99.04 11 | 99.52 26 | 99.49 55 | 99.09 40 | 98.07 24 | 99.37 21 | 98.47 5 | 97.79 70 | 99.89 29 | 99.50 15 | 98.93 38 | 99.45 4 | 99.61 132 | 99.76 52 |
|
mPP-MVS | | | | | | 99.53 24 | | | | | | | 99.89 29 | | | | | |
|
ACMMP | | | 98.74 30 | 99.03 40 | 98.40 28 | 99.36 33 | 99.64 25 | 99.20 33 | 97.75 31 | 98.82 86 | 95.24 45 | 98.85 38 | 99.87 31 | 99.17 41 | 98.74 54 | 97.50 108 | 99.71 69 | 99.76 52 |
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 |
train_agg | | | 98.73 31 | 99.11 31 | 98.28 31 | 99.36 33 | 99.35 72 | 99.48 20 | 97.96 27 | 98.83 84 | 93.86 78 | 98.70 44 | 99.86 32 | 99.44 21 | 99.08 31 | 98.38 61 | 99.61 132 | 99.58 129 |
|
abl_6 | | | | | 98.09 35 | 99.33 36 | 99.22 90 | 98.79 52 | 94.96 47 | 98.52 110 | 97.00 27 | 97.30 80 | 99.86 32 | 98.76 62 | | | 99.69 79 | 99.41 154 |
|
3Dnovator+ | | 96.92 7 | 98.71 32 | 99.05 36 | 98.32 29 | 99.53 24 | 99.34 74 | 99.06 42 | 94.61 53 | 99.65 4 | 97.49 19 | 96.75 94 | 99.86 32 | 99.44 21 | 98.78 49 | 99.30 11 | 99.81 26 | 99.67 104 |
|
DeepPCF-MVS | | 97.74 3 | 98.34 41 | 99.46 7 | 97.04 58 | 98.82 46 | 99.33 76 | 96.28 134 | 97.47 32 | 99.58 7 | 94.70 56 | 98.99 27 | 99.85 35 | 97.24 106 | 99.55 6 | 99.34 9 | 97.73 210 | 99.56 135 |
|
PGM-MVS | | | 98.86 27 | 99.35 22 | 98.29 30 | 99.77 1 | 99.63 29 | 99.67 6 | 95.63 39 | 98.66 101 | 95.27 44 | 99.11 22 | 99.82 36 | 99.67 4 | 99.33 20 | 99.19 19 | 99.73 55 | 99.74 64 |
|
QAPM | | | 98.62 36 | 99.04 39 | 98.13 34 | 99.57 21 | 99.48 56 | 99.17 35 | 94.78 49 | 99.57 8 | 96.16 34 | 96.73 96 | 99.80 37 | 99.33 27 | 98.79 48 | 99.29 12 | 99.75 45 | 99.64 120 |
|
OMC-MVS | | | 98.84 28 | 99.01 42 | 98.65 25 | 99.39 30 | 99.23 89 | 99.22 32 | 96.70 35 | 99.40 18 | 97.77 16 | 97.89 69 | 99.80 37 | 99.21 34 | 99.02 33 | 98.65 44 | 99.57 153 | 99.07 173 |
|
MVS_111021_HR | | | 98.59 37 | 99.36 19 | 97.68 42 | 99.42 29 | 99.61 37 | 98.14 81 | 94.81 48 | 99.31 30 | 95.00 50 | 99.51 6 | 99.79 39 | 99.00 59 | 98.94 37 | 98.83 37 | 99.69 79 | 99.57 134 |
|
PLC | | 97.93 2 | 99.02 24 | 98.94 44 | 99.11 6 | 99.46 28 | 99.24 88 | 99.06 42 | 97.96 27 | 99.31 30 | 99.16 1 | 97.90 68 | 99.79 39 | 99.36 25 | 98.71 55 | 98.12 78 | 99.65 106 | 99.52 140 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PCF-MVS | | 97.50 6 | 98.18 45 | 98.35 61 | 97.99 37 | 98.65 49 | 99.36 70 | 98.94 47 | 98.14 20 | 98.59 103 | 93.62 82 | 96.61 100 | 99.76 41 | 99.03 57 | 97.77 117 | 97.45 112 | 99.57 153 | 98.89 181 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CNLPA | | | 99.03 23 | 99.05 36 | 99.01 14 | 99.27 38 | 99.22 90 | 99.03 44 | 97.98 26 | 99.34 28 | 99.00 2 | 98.25 59 | 99.71 42 | 99.31 29 | 98.80 47 | 98.82 38 | 99.48 166 | 99.17 166 |
|
MVS_111021_LR | | | 98.67 33 | 99.41 15 | 97.81 41 | 99.37 31 | 99.53 51 | 98.51 59 | 95.52 41 | 99.27 35 | 94.85 53 | 99.56 5 | 99.69 43 | 99.04 56 | 99.36 17 | 98.88 33 | 99.60 139 | 99.58 129 |
|
CDPH-MVS | | | 98.41 39 | 99.10 32 | 97.61 44 | 99.32 37 | 99.36 70 | 99.49 18 | 96.15 38 | 98.82 86 | 91.82 98 | 98.41 54 | 99.66 44 | 99.10 52 | 98.93 38 | 98.97 27 | 99.75 45 | 99.58 129 |
|
3Dnovator | | 96.92 7 | 98.67 33 | 99.05 36 | 98.23 33 | 99.57 21 | 99.45 60 | 99.11 38 | 94.66 52 | 99.69 3 | 96.80 28 | 96.55 104 | 99.61 45 | 99.40 23 | 98.87 44 | 99.49 3 | 99.85 4 | 99.66 113 |
|
CANet | | | 98.46 38 | 99.16 29 | 97.64 43 | 98.48 51 | 99.64 25 | 99.35 28 | 94.71 51 | 99.53 12 | 95.17 46 | 97.63 76 | 99.59 46 | 98.38 75 | 98.88 43 | 98.99 26 | 99.74 49 | 99.86 15 |
|
UGNet | | | 97.66 57 | 99.07 35 | 96.01 92 | 97.19 73 | 99.65 20 | 97.09 118 | 93.39 85 | 99.35 25 | 94.40 66 | 98.79 40 | 99.59 46 | 94.24 191 | 98.04 105 | 98.29 72 | 99.73 55 | 99.80 32 |
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 |
AdaColmap | | | 99.06 20 | 98.98 43 | 99.15 5 | 99.60 19 | 99.30 80 | 99.38 27 | 98.16 16 | 99.02 68 | 98.55 4 | 98.71 43 | 99.57 48 | 99.58 11 | 99.09 29 | 97.84 91 | 99.64 117 | 99.36 157 |
|
PVSNet_Blended_VisFu | | | 97.41 64 | 98.49 56 | 96.15 86 | 97.49 64 | 99.76 1 | 96.02 137 | 93.75 81 | 99.26 38 | 93.38 85 | 93.73 143 | 99.35 49 | 96.47 129 | 98.96 35 | 98.46 54 | 99.77 42 | 99.90 3 |
|
RPSCF | | | 97.61 58 | 98.16 71 | 96.96 70 | 98.10 55 | 99.00 97 | 98.84 50 | 93.76 80 | 99.45 16 | 94.78 55 | 99.39 10 | 99.31 50 | 98.53 71 | 96.61 149 | 95.43 159 | 97.74 208 | 97.93 199 |
|
TAPA-MVS | | 97.53 5 | 98.41 39 | 98.84 49 | 97.91 39 | 99.08 42 | 99.33 76 | 99.15 36 | 97.13 34 | 99.34 28 | 93.20 86 | 97.75 72 | 99.19 51 | 99.20 35 | 98.66 57 | 98.13 77 | 99.66 100 | 99.48 149 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CANet_DTU | | | 96.64 92 | 99.08 33 | 93.81 118 | 97.10 75 | 99.42 64 | 98.85 49 | 90.01 132 | 99.31 30 | 79.98 176 | 99.78 2 | 99.10 52 | 97.42 103 | 98.35 78 | 98.05 81 | 99.47 168 | 99.53 138 |
|
MVS_0304 | | | 98.14 46 | 99.03 40 | 97.10 54 | 98.05 58 | 99.63 29 | 99.27 31 | 94.33 58 | 99.63 6 | 93.06 89 | 97.32 79 | 99.05 53 | 98.09 85 | 98.82 46 | 98.87 34 | 99.81 26 | 99.89 7 |
|
OpenMVS | | 96.23 11 | 97.95 49 | 98.45 57 | 97.35 46 | 99.52 26 | 99.42 64 | 98.91 48 | 94.61 53 | 98.87 78 | 92.24 96 | 94.61 137 | 99.05 53 | 99.10 52 | 98.64 61 | 99.05 23 | 99.74 49 | 99.51 144 |
|
GG-mvs-BLEND | | | 69.11 227 | 98.13 72 | 35.26 233 | 3.49 239 | 98.20 148 | 94.89 158 | 2.38 237 | 98.42 113 | 5.82 242 | 96.37 108 | 98.60 55 | 5.97 237 | 98.75 53 | 97.98 84 | 99.01 191 | 98.61 185 |
|
CHOSEN 280x420 | | | 97.99 48 | 99.24 26 | 96.53 78 | 98.34 53 | 99.61 37 | 98.36 72 | 89.80 138 | 99.27 35 | 95.08 48 | 99.81 1 | 98.58 56 | 98.64 67 | 99.02 33 | 98.92 30 | 98.93 192 | 99.48 149 |
|
Vis-MVSNet (Re-imp) | | | 97.40 65 | 98.89 46 | 95.66 99 | 95.99 105 | 99.62 33 | 97.82 92 | 93.22 88 | 98.82 86 | 91.40 102 | 96.94 91 | 98.56 57 | 95.70 145 | 99.14 27 | 99.41 6 | 99.79 34 | 99.75 61 |
|
EPNet_dtu | | | 96.30 101 | 98.53 55 | 93.70 122 | 98.97 44 | 98.24 146 | 97.36 105 | 94.23 61 | 98.85 81 | 79.18 190 | 99.19 16 | 98.47 58 | 94.09 193 | 97.89 111 | 98.21 74 | 98.39 200 | 98.85 183 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IS_MVSNet | | | 97.86 50 | 98.86 47 | 96.68 74 | 96.02 102 | 99.72 4 | 98.35 73 | 93.37 87 | 98.75 98 | 94.01 72 | 96.88 93 | 98.40 59 | 98.48 72 | 99.09 29 | 99.42 5 | 99.83 9 | 99.80 32 |
|
COLMAP_ROB | | 96.15 12 | 97.78 52 | 98.17 70 | 97.32 47 | 98.84 45 | 99.45 60 | 99.28 30 | 95.43 42 | 99.48 15 | 91.80 99 | 94.83 135 | 98.36 60 | 98.90 60 | 98.09 96 | 97.85 90 | 99.68 88 | 99.15 167 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DELS-MVS | | | 98.19 44 | 98.77 50 | 97.52 45 | 98.29 54 | 99.71 8 | 99.12 37 | 94.58 56 | 98.80 89 | 95.38 43 | 96.24 110 | 98.24 61 | 97.92 90 | 99.06 32 | 99.52 1 | 99.82 13 | 99.79 39 |
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 |
ADS-MVSNet | | | 94.65 132 | 97.04 104 | 91.88 159 | 95.68 114 | 98.99 99 | 95.89 138 | 79.03 217 | 99.15 50 | 85.81 135 | 96.96 90 | 98.21 62 | 97.10 109 | 94.48 204 | 94.24 199 | 97.74 208 | 97.21 207 |
|
CSCG | | | 98.90 26 | 98.93 45 | 98.85 20 | 99.75 2 | 99.72 4 | 99.49 18 | 96.58 36 | 99.38 19 | 98.05 11 | 98.97 28 | 97.87 63 | 99.49 17 | 97.78 116 | 98.92 30 | 99.78 37 | 99.90 3 |
|
MS-PatchMatch | | | 95.99 108 | 97.26 100 | 94.51 109 | 97.46 65 | 98.76 114 | 97.27 109 | 86.97 168 | 99.09 57 | 89.83 115 | 93.51 145 | 97.78 64 | 96.18 134 | 97.53 129 | 95.71 156 | 99.35 180 | 98.41 190 |
|
IterMVS | | | 94.81 129 | 97.71 84 | 91.42 168 | 94.83 134 | 97.63 173 | 97.38 104 | 85.08 183 | 98.93 76 | 75.67 205 | 94.02 140 | 97.64 65 | 96.66 123 | 98.45 73 | 97.60 104 | 98.90 193 | 99.72 80 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
FC-MVSNet-test | | | 96.07 107 | 97.94 80 | 93.89 116 | 93.60 148 | 98.67 121 | 96.62 127 | 90.30 127 | 98.76 96 | 88.62 117 | 95.57 129 | 97.63 66 | 94.48 187 | 97.97 107 | 97.48 111 | 99.71 69 | 99.52 140 |
|
EPNet | | | 98.05 47 | 98.86 47 | 97.10 54 | 99.02 43 | 99.43 63 | 98.47 60 | 94.73 50 | 99.05 65 | 95.62 37 | 98.93 30 | 97.62 67 | 95.48 156 | 98.59 67 | 98.55 50 | 99.29 184 | 99.84 20 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test0.0.03 1 | | | 96.69 89 | 98.12 73 | 95.01 104 | 95.49 119 | 98.99 99 | 95.86 139 | 90.82 116 | 98.38 114 | 92.54 94 | 96.66 98 | 97.33 68 | 95.75 143 | 97.75 119 | 98.34 67 | 99.60 139 | 99.40 155 |
|
MSDG | | | 98.27 43 | 98.29 64 | 98.24 32 | 99.20 39 | 99.22 90 | 99.20 33 | 97.82 29 | 99.37 21 | 94.43 64 | 95.90 118 | 97.31 69 | 99.12 49 | 98.76 51 | 98.35 65 | 99.67 95 | 99.14 170 |
|
EPP-MVSNet | | | 97.75 54 | 98.71 51 | 96.63 77 | 95.68 114 | 99.56 48 | 97.51 101 | 93.10 89 | 99.22 43 | 94.99 51 | 97.18 85 | 97.30 70 | 98.65 66 | 98.83 45 | 98.93 29 | 99.84 5 | 99.92 1 |
|
CDS-MVSNet | | | 96.59 95 | 98.02 77 | 94.92 105 | 94.45 136 | 98.96 102 | 97.46 103 | 91.75 99 | 97.86 140 | 90.07 112 | 96.02 114 | 97.25 71 | 96.21 132 | 98.04 105 | 98.38 61 | 99.60 139 | 99.65 116 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
HyFIR lowres test | | | 95.99 108 | 96.56 112 | 95.32 102 | 97.99 60 | 99.65 20 | 96.54 128 | 88.86 146 | 98.44 112 | 89.77 116 | 84.14 214 | 97.05 72 | 99.03 57 | 98.55 69 | 98.19 76 | 99.73 55 | 99.86 15 |
|
PMMVS | | | 97.52 60 | 98.39 58 | 96.51 80 | 95.82 111 | 98.73 118 | 97.80 94 | 93.05 90 | 98.76 96 | 94.39 67 | 99.07 26 | 97.03 73 | 98.55 70 | 98.31 80 | 97.61 103 | 99.43 173 | 99.21 165 |
|
diffmvs | | | 97.50 63 | 98.63 52 | 96.18 84 | 95.88 108 | 99.26 85 | 98.19 79 | 91.08 113 | 99.36 23 | 94.32 69 | 98.24 60 | 96.83 74 | 98.22 81 | 98.45 73 | 98.42 56 | 99.42 175 | 99.86 15 |
|
Vis-MVSNet | | | 96.16 105 | 98.22 67 | 93.75 119 | 95.33 125 | 99.70 10 | 97.27 109 | 90.85 115 | 98.30 116 | 85.51 137 | 95.72 125 | 96.45 75 | 93.69 200 | 98.70 56 | 99.00 25 | 99.84 5 | 99.69 92 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
PatchmatchNet | | | 94.70 130 | 97.08 103 | 91.92 156 | 95.53 117 | 98.85 107 | 95.77 140 | 79.54 212 | 98.95 71 | 85.98 133 | 98.52 48 | 96.45 75 | 97.39 104 | 95.32 177 | 94.09 201 | 97.32 218 | 97.38 206 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
DeepC-MVS | | 97.63 4 | 98.33 42 | 98.57 53 | 98.04 36 | 98.62 50 | 99.65 20 | 99.45 22 | 98.15 18 | 99.51 14 | 92.80 92 | 95.74 123 | 96.44 77 | 99.46 19 | 99.37 16 | 99.50 2 | 99.78 37 | 99.81 30 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Fast-Effi-MVS+-dtu | | | 95.38 119 | 98.20 69 | 92.09 149 | 93.91 140 | 98.87 106 | 97.35 106 | 85.01 185 | 99.08 59 | 81.09 161 | 98.10 62 | 96.36 78 | 95.62 149 | 98.43 76 | 97.03 120 | 99.55 157 | 99.50 146 |
|
PatchMatch-RL | | | 97.77 53 | 98.25 65 | 97.21 52 | 99.11 41 | 99.25 86 | 97.06 120 | 94.09 64 | 98.72 99 | 95.14 47 | 98.47 52 | 96.29 79 | 98.43 73 | 98.65 58 | 97.44 113 | 99.45 170 | 98.94 176 |
|
MVS_Test | | | 97.30 70 | 98.54 54 | 95.87 93 | 95.74 112 | 99.28 83 | 98.19 79 | 91.40 107 | 99.18 49 | 91.59 101 | 98.17 61 | 96.18 80 | 98.63 68 | 98.61 63 | 98.55 50 | 99.66 100 | 99.78 41 |
|
tpmrst | | | 93.86 148 | 95.88 134 | 91.50 166 | 95.69 113 | 98.62 124 | 95.64 143 | 79.41 213 | 98.80 89 | 83.76 144 | 95.63 127 | 96.13 81 | 97.25 105 | 92.92 210 | 92.31 216 | 97.27 219 | 96.74 214 |
|
MDTV_nov1_ep13 | | | 95.57 114 | 97.48 91 | 93.35 133 | 95.43 121 | 98.97 101 | 97.19 113 | 83.72 199 | 98.92 77 | 87.91 123 | 97.75 72 | 96.12 82 | 97.88 94 | 96.84 148 | 95.64 157 | 97.96 206 | 98.10 195 |
|
EPMVS | | | 95.05 125 | 96.86 108 | 92.94 139 | 95.84 110 | 98.96 102 | 96.68 124 | 79.87 208 | 99.05 65 | 90.15 111 | 97.12 86 | 95.99 83 | 97.49 101 | 95.17 185 | 94.75 194 | 97.59 213 | 96.96 211 |
|
GBi-Net | | | 96.98 76 | 98.00 78 | 95.78 94 | 93.81 143 | 97.98 151 | 98.09 82 | 91.32 108 | 98.80 89 | 93.92 75 | 97.21 82 | 95.94 84 | 97.89 91 | 98.07 99 | 98.34 67 | 99.68 88 | 99.67 104 |
|
test1 | | | 96.98 76 | 98.00 78 | 95.78 94 | 93.81 143 | 97.98 151 | 98.09 82 | 91.32 108 | 98.80 89 | 93.92 75 | 97.21 82 | 95.94 84 | 97.89 91 | 98.07 99 | 98.34 67 | 99.68 88 | 99.67 104 |
|
FMVSNet3 | | | 97.02 75 | 98.12 73 | 95.73 98 | 93.59 149 | 97.98 151 | 98.34 74 | 91.32 108 | 98.80 89 | 93.92 75 | 97.21 82 | 95.94 84 | 97.63 98 | 98.61 63 | 98.62 45 | 99.61 132 | 99.65 116 |
|
gg-mvs-nofinetune | | | 90.85 203 | 94.14 163 | 87.02 209 | 94.89 133 | 99.25 86 | 98.64 55 | 76.29 226 | 88.24 227 | 57.50 231 | 79.93 222 | 95.45 87 | 95.18 178 | 98.77 50 | 98.07 79 | 99.62 129 | 99.24 163 |
|
CHOSEN 1792x2688 | | | 96.41 96 | 96.99 105 | 95.74 97 | 98.01 59 | 99.72 4 | 97.70 97 | 90.78 118 | 99.13 56 | 90.03 113 | 87.35 202 | 95.36 88 | 98.33 77 | 98.59 67 | 98.91 32 | 99.59 145 | 99.87 13 |
|
FMVSNet2 | | | 96.64 92 | 97.50 89 | 95.63 100 | 93.81 143 | 97.98 151 | 98.09 82 | 90.87 114 | 98.99 70 | 93.48 83 | 93.17 150 | 95.25 89 | 97.89 91 | 98.63 62 | 98.80 39 | 99.68 88 | 99.67 104 |
|
DI_MVS_plusplus_trai | | | 96.90 79 | 97.49 90 | 96.21 83 | 95.61 116 | 99.40 66 | 98.72 54 | 92.11 92 | 99.14 52 | 92.98 91 | 93.08 153 | 95.14 90 | 98.13 84 | 98.05 103 | 97.91 87 | 99.74 49 | 99.73 68 |
|
tpm cat1 | | | 94.06 141 | 94.90 148 | 93.06 136 | 95.42 123 | 98.52 130 | 96.64 126 | 80.67 203 | 97.82 143 | 92.63 93 | 93.39 147 | 95.00 91 | 96.06 138 | 91.36 223 | 91.58 222 | 96.98 223 | 96.66 216 |
|
MVS-HIRNet | | | 92.51 179 | 95.97 131 | 88.48 206 | 93.73 146 | 98.37 141 | 90.33 211 | 75.36 229 | 98.32 115 | 77.78 196 | 89.15 178 | 94.87 92 | 95.14 179 | 97.62 126 | 96.39 136 | 98.51 196 | 97.11 208 |
|
MAR-MVS | | | 97.71 55 | 98.04 75 | 97.32 47 | 99.35 35 | 98.91 104 | 97.65 98 | 91.68 100 | 98.00 128 | 97.01 26 | 97.72 74 | 94.83 93 | 98.85 61 | 98.44 75 | 98.86 35 | 99.41 176 | 99.52 140 |
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 |
MIMVSNet | | | 94.49 137 | 97.59 88 | 90.87 185 | 91.74 185 | 98.70 120 | 94.68 179 | 78.73 219 | 97.98 129 | 83.71 145 | 97.71 75 | 94.81 94 | 96.96 113 | 97.97 107 | 97.92 86 | 99.40 178 | 98.04 197 |
|
tfpn_ndepth | | | 97.71 55 | 98.30 63 | 97.02 63 | 96.31 83 | 99.56 48 | 98.05 86 | 93.94 75 | 98.95 71 | 95.59 39 | 98.40 55 | 94.79 95 | 98.39 74 | 98.40 77 | 98.42 56 | 99.86 2 | 99.56 135 |
|
IterMVS-LS | | | 96.12 106 | 97.48 91 | 94.53 108 | 95.19 127 | 97.56 179 | 97.15 114 | 89.19 144 | 99.08 59 | 88.23 119 | 94.97 133 | 94.73 96 | 97.84 95 | 97.86 113 | 98.26 73 | 99.60 139 | 99.88 11 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
test-LLR | | | 95.50 116 | 97.32 96 | 93.37 131 | 95.49 119 | 98.74 116 | 96.44 132 | 90.82 116 | 98.18 120 | 82.75 152 | 96.60 101 | 94.67 97 | 95.54 152 | 98.09 96 | 96.00 146 | 99.20 187 | 98.93 177 |
|
TESTMET0.1,1 | | | 94.95 127 | 97.32 96 | 92.20 146 | 92.62 154 | 98.74 116 | 96.44 132 | 86.67 171 | 98.18 120 | 82.75 152 | 96.60 101 | 94.67 97 | 95.54 152 | 98.09 96 | 96.00 146 | 99.20 187 | 98.93 177 |
|
test-mter | | | 94.86 128 | 97.32 96 | 92.00 153 | 92.41 158 | 98.82 108 | 96.18 136 | 86.35 175 | 98.05 126 | 82.28 155 | 96.48 105 | 94.39 99 | 95.46 162 | 98.17 91 | 96.20 142 | 99.32 182 | 99.13 171 |
|
Effi-MVS+-dtu | | | 95.74 112 | 98.04 75 | 93.06 136 | 93.92 139 | 99.16 94 | 97.90 90 | 88.16 158 | 99.07 64 | 82.02 157 | 98.02 66 | 94.32 100 | 96.74 120 | 98.53 70 | 97.56 105 | 99.61 132 | 99.62 123 |
|
FC-MVSNet-train | | | 97.04 74 | 97.91 81 | 96.03 91 | 96.00 104 | 98.41 138 | 96.53 130 | 93.42 84 | 99.04 67 | 93.02 90 | 98.03 65 | 94.32 100 | 97.47 102 | 97.93 109 | 97.77 98 | 99.75 45 | 99.88 11 |
|
tfpn_n400 | | | 97.32 66 | 98.38 59 | 96.09 89 | 96.07 99 | 99.30 80 | 98.00 87 | 93.84 78 | 99.35 25 | 90.50 108 | 98.93 30 | 94.24 102 | 98.30 79 | 98.65 58 | 98.60 47 | 99.83 9 | 99.60 125 |
|
tfpnconf | | | 97.32 66 | 98.38 59 | 96.09 89 | 96.07 99 | 99.30 80 | 98.00 87 | 93.84 78 | 99.35 25 | 90.50 108 | 98.93 30 | 94.24 102 | 98.30 79 | 98.65 58 | 98.60 47 | 99.83 9 | 99.60 125 |
|
tfpnview11 | | | 97.32 66 | 98.33 62 | 96.14 87 | 96.07 99 | 99.31 79 | 98.08 85 | 93.96 73 | 99.25 40 | 90.50 108 | 98.93 30 | 94.24 102 | 98.38 75 | 98.61 63 | 98.36 64 | 99.84 5 | 99.59 127 |
|
LS3D | | | 97.79 51 | 98.25 65 | 97.26 51 | 98.40 52 | 99.63 29 | 99.53 15 | 98.63 1 | 99.25 40 | 88.13 120 | 96.93 92 | 94.14 105 | 99.19 37 | 99.14 27 | 99.23 16 | 99.69 79 | 99.42 153 |
|
PatchT | | | 93.96 145 | 97.36 94 | 90.00 197 | 94.76 135 | 98.65 122 | 90.11 213 | 78.57 220 | 97.96 132 | 80.42 169 | 96.07 113 | 94.10 106 | 96.85 117 | 98.10 94 | 97.49 109 | 99.26 185 | 99.15 167 |
|
RPMNet | | | 94.66 131 | 97.16 101 | 91.75 162 | 94.98 130 | 98.59 126 | 97.00 121 | 78.37 221 | 97.98 129 | 83.78 142 | 96.27 109 | 94.09 107 | 96.91 114 | 97.36 133 | 96.73 126 | 99.48 166 | 99.09 172 |
|
FMVSNet5 | | | 95.42 117 | 96.47 120 | 94.20 112 | 92.26 160 | 95.99 204 | 95.66 142 | 87.15 165 | 97.87 138 | 93.46 84 | 96.68 97 | 93.79 108 | 97.52 99 | 97.10 143 | 97.21 118 | 99.11 190 | 96.62 217 |
|
tfpn1000 | | | 97.60 59 | 98.21 68 | 96.89 72 | 96.32 82 | 99.60 41 | 97.99 89 | 93.85 77 | 99.21 45 | 95.03 49 | 98.49 50 | 93.69 109 | 98.31 78 | 98.50 72 | 98.31 71 | 99.86 2 | 99.70 86 |
|
MDTV_nov1_ep13_2view | | | 92.44 181 | 95.66 138 | 88.68 204 | 91.05 205 | 97.92 155 | 92.17 203 | 79.64 210 | 98.83 84 | 76.20 203 | 91.45 156 | 93.51 110 | 95.04 180 | 95.68 174 | 93.70 204 | 97.96 206 | 98.53 187 |
|
CR-MVSNet | | | 94.57 136 | 97.34 95 | 91.33 170 | 94.90 132 | 98.59 126 | 97.15 114 | 79.14 215 | 97.98 129 | 80.42 169 | 96.59 103 | 93.50 111 | 96.85 117 | 98.10 94 | 97.49 109 | 99.50 165 | 99.15 167 |
|
CVMVSNet | | | 95.33 122 | 97.09 102 | 93.27 134 | 95.23 126 | 98.39 140 | 95.49 146 | 92.58 91 | 97.71 147 | 83.00 151 | 94.44 139 | 93.28 112 | 93.92 197 | 97.79 115 | 98.54 52 | 99.41 176 | 99.45 151 |
|
FMVSNet1 | | | 95.77 111 | 96.41 128 | 95.03 103 | 93.42 150 | 97.86 158 | 97.11 117 | 89.89 135 | 98.53 108 | 92.00 97 | 89.17 177 | 93.23 113 | 98.15 83 | 98.07 99 | 98.34 67 | 99.61 132 | 99.69 92 |
|
LP | | | 92.12 195 | 94.60 154 | 89.22 202 | 94.96 131 | 98.45 134 | 93.01 198 | 77.58 222 | 97.85 141 | 77.26 199 | 89.80 173 | 93.00 114 | 94.54 184 | 93.69 207 | 92.58 212 | 98.00 205 | 96.83 213 |
|
testpf | | | 91.80 200 | 94.43 160 | 88.74 203 | 93.89 141 | 95.30 217 | 92.05 204 | 71.77 230 | 97.52 150 | 87.24 126 | 94.77 136 | 92.68 115 | 91.48 209 | 91.75 222 | 92.11 219 | 96.02 227 | 96.89 212 |
|
dps | | | 94.63 133 | 95.31 145 | 93.84 117 | 95.53 117 | 98.71 119 | 96.54 128 | 80.12 207 | 97.81 145 | 97.21 23 | 96.98 89 | 92.37 116 | 96.34 131 | 92.46 216 | 91.77 220 | 97.26 220 | 97.08 209 |
|
testgi | | | 95.67 113 | 97.48 91 | 93.56 125 | 95.07 129 | 99.00 97 | 95.33 150 | 88.47 152 | 98.80 89 | 86.90 129 | 97.30 80 | 92.33 117 | 95.97 140 | 97.66 122 | 97.91 87 | 99.60 139 | 99.38 156 |
|
N_pmnet | | | 92.21 192 | 94.60 154 | 89.42 201 | 91.88 172 | 97.38 191 | 89.15 215 | 89.74 139 | 97.89 137 | 73.75 211 | 87.94 199 | 92.23 118 | 93.85 198 | 96.10 168 | 93.20 207 | 98.15 204 | 97.43 205 |
|
IB-MVS | | 93.96 15 | 95.02 126 | 96.44 125 | 93.36 132 | 97.05 76 | 99.28 83 | 90.43 210 | 93.39 85 | 98.02 127 | 96.02 35 | 94.92 134 | 92.07 119 | 83.52 222 | 95.38 176 | 95.82 152 | 99.72 60 | 99.59 127 |
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 |
MVSTER | | | 97.16 72 | 97.71 84 | 96.52 79 | 95.97 106 | 98.48 131 | 98.63 56 | 92.10 93 | 98.68 100 | 95.96 36 | 99.23 15 | 91.79 120 | 96.87 116 | 98.76 51 | 97.37 116 | 99.57 153 | 99.68 99 |
|
TAMVS | | | 95.53 115 | 96.50 119 | 94.39 111 | 93.86 142 | 99.03 96 | 96.67 125 | 89.55 141 | 97.33 159 | 90.64 106 | 93.02 154 | 91.58 121 | 96.21 132 | 97.72 121 | 97.43 114 | 99.43 173 | 99.36 157 |
|
thresconf0.02 | | | 97.18 71 | 97.81 82 | 96.45 82 | 96.11 98 | 99.20 93 | 98.21 77 | 94.26 60 | 99.14 52 | 91.72 100 | 98.65 45 | 91.51 122 | 98.57 69 | 98.22 89 | 98.47 53 | 99.82 13 | 99.50 146 |
|
canonicalmvs | | | 97.31 69 | 97.81 82 | 96.72 73 | 96.20 96 | 99.45 60 | 98.21 77 | 91.60 102 | 99.22 43 | 95.39 42 | 98.48 51 | 90.95 123 | 99.16 43 | 97.66 122 | 99.05 23 | 99.76 44 | 99.90 3 |
|
anonymousdsp | | | 93.12 157 | 95.86 135 | 89.93 199 | 91.09 204 | 98.25 145 | 95.12 151 | 85.08 183 | 97.44 151 | 73.30 212 | 90.89 159 | 90.78 124 | 95.25 177 | 97.91 110 | 95.96 150 | 99.71 69 | 99.82 25 |
|
PVSNet_BlendedMVS | | | 97.51 61 | 97.71 84 | 97.28 49 | 98.06 56 | 99.61 37 | 97.31 107 | 95.02 45 | 99.08 59 | 95.51 40 | 98.05 63 | 90.11 125 | 98.07 86 | 98.91 40 | 98.40 59 | 99.72 60 | 99.78 41 |
|
PVSNet_Blended | | | 97.51 61 | 97.71 84 | 97.28 49 | 98.06 56 | 99.61 37 | 97.31 107 | 95.02 45 | 99.08 59 | 95.51 40 | 98.05 63 | 90.11 125 | 98.07 86 | 98.91 40 | 98.40 59 | 99.72 60 | 99.78 41 |
|
pmmvs4 | | | 95.09 124 | 95.90 133 | 94.14 113 | 92.29 159 | 97.70 165 | 95.45 147 | 90.31 125 | 98.60 102 | 90.70 104 | 93.25 148 | 89.90 127 | 96.67 122 | 97.13 141 | 95.42 160 | 99.44 172 | 99.28 160 |
|
pm-mvs1 | | | 94.27 138 | 95.57 141 | 92.75 140 | 92.58 155 | 98.13 149 | 94.87 162 | 90.71 119 | 96.70 183 | 83.78 142 | 89.94 172 | 89.85 128 | 94.96 182 | 97.58 127 | 97.07 119 | 99.61 132 | 99.72 80 |
|
Effi-MVS+ | | | 95.81 110 | 97.31 99 | 94.06 114 | 95.09 128 | 99.35 72 | 97.24 111 | 88.22 155 | 98.54 107 | 85.38 138 | 98.52 48 | 88.68 129 | 98.70 64 | 98.32 79 | 97.93 85 | 99.74 49 | 99.84 20 |
|
GA-MVS | | | 93.93 146 | 96.31 129 | 91.16 176 | 93.61 147 | 98.79 109 | 95.39 149 | 90.69 120 | 98.25 118 | 73.28 213 | 96.15 112 | 88.42 130 | 94.39 189 | 97.76 118 | 95.35 163 | 99.58 149 | 99.45 151 |
|
EU-MVSNet | | | 92.80 168 | 94.76 152 | 90.51 191 | 91.88 172 | 96.74 201 | 92.48 202 | 88.69 149 | 96.21 198 | 79.00 191 | 91.51 155 | 87.82 131 | 91.83 208 | 95.87 172 | 96.27 139 | 99.21 186 | 98.92 180 |
|
pmmvs6 | | | 91.90 199 | 92.53 209 | 91.17 175 | 91.81 177 | 97.63 173 | 93.23 196 | 88.37 154 | 93.43 220 | 80.61 165 | 77.32 224 | 87.47 132 | 94.12 192 | 96.58 151 | 95.72 155 | 98.88 194 | 99.53 138 |
|
UniMVSNet_NR-MVSNet | | | 94.59 134 | 95.47 142 | 93.55 126 | 91.85 174 | 97.89 157 | 95.03 153 | 92.00 95 | 97.33 159 | 86.12 131 | 93.19 149 | 87.29 133 | 96.60 125 | 96.12 167 | 96.70 127 | 99.72 60 | 99.80 32 |
|
Fast-Effi-MVS+ | | | 95.38 119 | 96.52 115 | 94.05 115 | 94.15 138 | 99.14 95 | 97.24 111 | 86.79 169 | 98.53 108 | 87.62 125 | 94.51 138 | 87.06 134 | 98.76 62 | 98.60 66 | 98.04 82 | 99.72 60 | 99.77 48 |
|
CLD-MVS | | | 96.74 84 | 96.51 116 | 97.01 65 | 96.71 79 | 98.62 124 | 98.73 53 | 94.38 57 | 98.94 74 | 94.46 63 | 97.33 78 | 87.03 135 | 98.07 86 | 97.20 139 | 96.87 124 | 99.72 60 | 99.54 137 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HQP-MVS | | | 96.37 97 | 96.58 111 | 96.13 88 | 97.31 70 | 98.44 135 | 98.45 61 | 95.22 43 | 98.86 79 | 88.58 118 | 98.33 57 | 87.00 136 | 97.67 97 | 97.23 137 | 96.56 132 | 99.56 156 | 99.62 123 |
|
tfpn111 | | | 96.96 78 | 96.91 106 | 97.03 59 | 96.31 83 | 99.67 13 | 98.41 63 | 93.99 67 | 97.35 153 | 94.50 60 | 98.65 45 | 86.93 137 | 99.14 44 | 98.26 83 | 97.80 93 | 99.82 13 | 99.70 86 |
|
conf200view11 | | | 96.75 82 | 96.51 116 | 97.03 59 | 96.31 83 | 99.67 13 | 98.41 63 | 93.99 67 | 97.35 153 | 94.50 60 | 95.90 118 | 86.93 137 | 99.14 44 | 98.26 83 | 97.80 93 | 99.82 13 | 99.70 86 |
|
thres100view900 | | | 96.72 85 | 96.47 120 | 97.00 67 | 96.31 83 | 99.52 54 | 98.28 76 | 94.01 65 | 97.35 153 | 94.52 58 | 95.90 118 | 86.93 137 | 99.09 54 | 98.07 99 | 97.87 89 | 99.81 26 | 99.63 122 |
|
tfpn200view9 | | | 96.75 82 | 96.51 116 | 97.03 59 | 96.31 83 | 99.67 13 | 98.41 63 | 93.99 67 | 97.35 153 | 94.52 58 | 95.90 118 | 86.93 137 | 99.14 44 | 98.26 83 | 97.80 93 | 99.82 13 | 99.70 86 |
|
DWT-MVSNet_training | | | 95.38 119 | 95.05 146 | 95.78 94 | 95.86 109 | 98.88 105 | 97.55 100 | 90.09 131 | 98.23 119 | 96.49 33 | 97.62 77 | 86.92 141 | 97.16 108 | 92.03 219 | 94.12 200 | 97.52 214 | 97.50 202 |
|
thres200 | | | 96.76 81 | 96.53 114 | 97.03 59 | 96.31 83 | 99.67 13 | 98.37 71 | 93.99 67 | 97.68 148 | 94.49 62 | 95.83 122 | 86.77 142 | 99.18 39 | 98.26 83 | 97.82 92 | 99.82 13 | 99.66 113 |
|
ACMM | | 96.26 9 | 96.67 91 | 96.69 110 | 96.66 75 | 97.29 71 | 98.46 132 | 96.48 131 | 95.09 44 | 99.21 45 | 93.19 87 | 98.78 41 | 86.73 143 | 98.17 82 | 97.84 114 | 96.32 138 | 99.74 49 | 99.49 148 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
LGP-MVS_train | | | 96.23 102 | 96.89 107 | 95.46 101 | 97.32 68 | 98.77 112 | 98.81 51 | 93.60 82 | 98.58 104 | 85.52 136 | 99.08 25 | 86.67 144 | 97.83 96 | 97.87 112 | 97.51 107 | 99.69 79 | 99.73 68 |
|
ACMP | | 96.25 10 | 96.62 94 | 96.72 109 | 96.50 81 | 96.96 77 | 98.75 115 | 97.80 94 | 94.30 59 | 98.85 81 | 93.12 88 | 98.78 41 | 86.61 145 | 97.23 107 | 97.73 120 | 96.61 130 | 99.62 129 | 99.71 84 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
new_pmnet | | | 90.45 207 | 92.84 206 | 87.66 207 | 88.96 214 | 96.16 203 | 88.71 216 | 84.66 188 | 97.56 149 | 71.91 217 | 85.60 213 | 86.58 146 | 93.28 201 | 96.07 169 | 93.54 205 | 98.46 198 | 94.39 221 |
|
OPM-MVS | | | 96.22 103 | 95.85 136 | 96.65 76 | 97.75 61 | 98.54 129 | 99.00 46 | 95.53 40 | 96.88 177 | 89.88 114 | 95.95 117 | 86.46 147 | 98.07 86 | 97.65 124 | 96.63 129 | 99.67 95 | 98.83 184 |
|
thres400 | | | 96.71 86 | 96.45 123 | 97.02 63 | 96.28 93 | 99.63 29 | 98.41 63 | 94.00 66 | 97.82 143 | 94.42 65 | 95.74 123 | 86.26 148 | 99.18 39 | 98.20 90 | 97.79 97 | 99.81 26 | 99.70 86 |
|
UniMVSNet (Re) | | | 94.58 135 | 95.34 143 | 93.71 121 | 92.25 161 | 98.08 150 | 94.97 155 | 91.29 112 | 97.03 170 | 87.94 122 | 93.97 142 | 86.25 149 | 96.07 137 | 96.27 164 | 95.97 149 | 99.72 60 | 99.79 39 |
|
CostFormer | | | 94.25 140 | 94.88 149 | 93.51 128 | 95.43 121 | 98.34 142 | 96.21 135 | 80.64 204 | 97.94 134 | 94.01 72 | 98.30 58 | 86.20 150 | 97.52 99 | 92.71 211 | 92.69 211 | 97.23 222 | 98.02 198 |
|
view600 | | | 96.70 87 | 96.44 125 | 97.01 65 | 96.28 93 | 99.67 13 | 98.42 62 | 93.99 67 | 97.87 138 | 94.34 68 | 95.99 115 | 85.94 151 | 99.20 35 | 98.26 83 | 97.64 101 | 99.82 13 | 99.73 68 |
|
thres600view7 | | | 96.69 89 | 96.43 127 | 97.00 67 | 96.28 93 | 99.67 13 | 98.41 63 | 93.99 67 | 97.85 141 | 94.29 70 | 95.96 116 | 85.91 152 | 99.19 37 | 98.26 83 | 97.63 102 | 99.82 13 | 99.73 68 |
|
SixPastTwentyTwo | | | 93.44 154 | 95.32 144 | 91.24 174 | 92.11 164 | 98.40 139 | 92.77 200 | 88.64 151 | 98.09 125 | 77.83 195 | 93.51 145 | 85.74 153 | 96.52 128 | 96.91 146 | 94.89 191 | 99.59 145 | 99.73 68 |
|
TSAR-MVS + COLMAP | | | 96.79 80 | 96.55 113 | 97.06 57 | 97.70 63 | 98.46 132 | 99.07 41 | 96.23 37 | 99.38 19 | 91.32 103 | 98.80 39 | 85.61 154 | 98.69 65 | 97.64 125 | 96.92 123 | 99.37 179 | 99.06 174 |
|
view800 | | | 96.70 87 | 96.45 123 | 96.99 69 | 96.29 90 | 99.69 11 | 98.39 70 | 93.95 74 | 97.92 135 | 94.25 71 | 96.23 111 | 85.57 155 | 99.22 32 | 98.28 81 | 97.71 99 | 99.82 13 | 99.76 52 |
|
ACMH+ | | 95.51 13 | 95.40 118 | 96.00 130 | 94.70 107 | 96.33 81 | 98.79 109 | 96.79 123 | 91.32 108 | 98.77 95 | 87.18 127 | 95.60 128 | 85.46 156 | 96.97 112 | 97.15 140 | 96.59 131 | 99.59 145 | 99.65 116 |
|
test20.03 | | | 90.65 206 | 93.71 181 | 87.09 208 | 90.44 211 | 96.24 202 | 89.74 214 | 85.46 179 | 95.59 212 | 72.99 214 | 90.68 161 | 85.33 157 | 84.41 220 | 95.94 171 | 95.10 171 | 99.52 163 | 97.06 210 |
|
tmp_tt | | | | | 82.25 220 | 97.73 62 | 88.71 230 | 80.18 225 | 68.65 234 | 99.15 50 | 86.98 128 | 99.47 7 | 85.31 158 | 68.35 231 | 87.51 226 | 83.81 227 | 91.64 230 | |
|
conf0.05thres1000 | | | 96.34 99 | 96.47 120 | 96.17 85 | 96.16 97 | 99.71 8 | 97.82 92 | 93.46 83 | 98.10 124 | 90.69 105 | 96.75 94 | 85.26 159 | 99.11 51 | 98.05 103 | 97.65 100 | 99.82 13 | 99.80 32 |
|
WR-MVS_H | | | 93.54 152 | 94.67 153 | 92.22 144 | 91.95 170 | 97.91 156 | 94.58 185 | 88.75 148 | 96.64 187 | 83.88 141 | 90.66 162 | 85.13 160 | 94.40 188 | 96.54 154 | 95.91 151 | 99.73 55 | 99.89 7 |
|
WR-MVS | | | 93.43 155 | 94.48 158 | 92.21 145 | 91.52 196 | 97.69 169 | 94.66 181 | 89.98 133 | 96.86 178 | 83.43 146 | 90.12 164 | 85.03 161 | 93.94 196 | 96.02 170 | 95.82 152 | 99.71 69 | 99.82 25 |
|
CMPMVS | | 70.31 18 | 90.74 204 | 91.06 212 | 90.36 194 | 97.32 68 | 97.43 188 | 92.97 199 | 87.82 162 | 93.50 219 | 75.34 208 | 83.27 217 | 84.90 162 | 92.19 207 | 92.64 214 | 91.21 223 | 96.50 225 | 94.46 220 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Anonymous20231206 | | | 90.70 205 | 93.93 175 | 86.92 210 | 90.21 213 | 96.79 199 | 90.30 212 | 86.61 173 | 96.05 203 | 69.25 218 | 88.46 191 | 84.86 163 | 85.86 217 | 97.11 142 | 96.47 135 | 99.30 183 | 97.80 201 |
|
v7 | | | 92.97 163 | 94.11 166 | 91.65 165 | 91.83 175 | 97.55 181 | 94.86 165 | 88.19 157 | 96.96 173 | 79.72 181 | 88.16 194 | 84.68 164 | 95.63 147 | 96.33 161 | 95.30 165 | 99.65 106 | 99.77 48 |
|
v10 | | | 92.79 170 | 94.06 168 | 91.31 172 | 91.78 180 | 97.29 195 | 94.87 162 | 86.10 176 | 96.97 172 | 79.82 178 | 88.16 194 | 84.56 165 | 95.63 147 | 96.33 161 | 95.31 164 | 99.65 106 | 99.80 32 |
|
v1144 | | | 92.81 166 | 94.03 169 | 91.40 169 | 91.68 188 | 97.60 177 | 94.73 176 | 88.40 153 | 96.71 182 | 78.48 193 | 88.14 196 | 84.46 166 | 95.45 163 | 96.31 163 | 95.22 167 | 99.65 106 | 99.76 52 |
|
tpmp4_e23 | | | 93.84 150 | 94.58 156 | 92.98 138 | 95.41 124 | 98.29 143 | 96.81 122 | 80.57 205 | 98.15 122 | 90.53 107 | 97.00 88 | 84.39 167 | 96.91 114 | 93.69 207 | 92.45 214 | 97.67 211 | 98.06 196 |
|
v11 | | | 92.43 182 | 93.77 180 | 90.85 186 | 91.72 186 | 95.58 212 | 94.87 162 | 84.07 198 | 96.98 171 | 79.28 187 | 88.03 197 | 84.22 168 | 95.53 154 | 96.55 153 | 95.36 162 | 99.65 106 | 99.70 86 |
|
v16 | | | 92.66 176 | 93.80 179 | 91.32 171 | 92.13 162 | 95.62 207 | 94.89 158 | 85.12 182 | 97.20 162 | 80.66 164 | 89.96 171 | 83.93 169 | 95.49 155 | 95.17 185 | 95.04 173 | 99.63 123 | 99.68 99 |
|
Baseline_NR-MVSNet | | | 93.87 147 | 93.98 171 | 93.75 119 | 91.66 189 | 97.02 196 | 95.53 145 | 91.52 106 | 97.16 166 | 87.77 124 | 87.93 200 | 83.69 170 | 96.35 130 | 95.10 196 | 97.23 117 | 99.68 88 | 99.73 68 |
|
ACMH | | 95.42 14 | 95.27 123 | 95.96 132 | 94.45 110 | 96.83 78 | 98.78 111 | 94.72 177 | 91.67 101 | 98.95 71 | 86.82 130 | 96.42 107 | 83.67 171 | 97.00 111 | 97.48 131 | 96.68 128 | 99.69 79 | 99.76 52 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TransMVSNet (Re) | | | 93.45 153 | 94.08 167 | 92.72 141 | 92.83 152 | 97.62 176 | 94.94 156 | 91.54 105 | 95.65 211 | 83.06 150 | 88.93 180 | 83.53 172 | 94.25 190 | 97.41 132 | 97.03 120 | 99.67 95 | 98.40 192 |
|
pmmvs5 | | | 92.71 175 | 94.27 162 | 90.90 183 | 91.42 198 | 97.74 161 | 93.23 196 | 86.66 172 | 95.99 205 | 78.96 192 | 91.45 156 | 83.44 173 | 95.55 151 | 97.30 135 | 95.05 172 | 99.58 149 | 98.93 177 |
|
V42 | | | 93.05 161 | 93.90 177 | 92.04 150 | 91.91 171 | 97.66 171 | 94.91 157 | 89.91 134 | 96.85 179 | 80.58 166 | 89.66 174 | 83.43 174 | 95.37 168 | 95.03 199 | 94.90 189 | 99.59 145 | 99.78 41 |
|
v6 | | | 93.11 158 | 93.98 171 | 92.10 148 | 92.01 167 | 97.71 162 | 94.86 165 | 90.15 128 | 96.96 173 | 80.47 168 | 90.01 167 | 83.26 175 | 95.48 156 | 95.17 185 | 95.01 178 | 99.64 117 | 99.76 52 |
|
EG-PatchMatch MVS | | | 92.45 180 | 93.92 176 | 90.72 188 | 92.56 156 | 98.43 137 | 94.88 161 | 84.54 189 | 97.18 163 | 79.55 184 | 86.12 212 | 83.23 176 | 93.15 203 | 97.22 138 | 96.00 146 | 99.67 95 | 99.27 161 |
|
v18 | | | 92.63 177 | 93.67 182 | 91.43 167 | 92.13 162 | 95.65 205 | 95.09 152 | 85.44 180 | 97.06 168 | 80.78 163 | 90.06 165 | 83.06 177 | 95.47 161 | 95.16 189 | 95.01 178 | 99.64 117 | 99.67 104 |
|
v17 | | | 92.55 178 | 93.65 183 | 91.27 173 | 92.11 164 | 95.63 206 | 94.89 158 | 85.15 181 | 97.12 167 | 80.39 172 | 90.02 166 | 83.02 178 | 95.45 163 | 95.17 185 | 94.92 188 | 99.66 100 | 99.68 99 |
|
v1neww | | | 93.06 159 | 93.94 173 | 92.03 151 | 91.99 168 | 97.70 165 | 94.79 169 | 90.14 129 | 96.93 175 | 80.13 173 | 89.97 169 | 83.01 179 | 95.48 156 | 95.16 189 | 95.01 178 | 99.63 123 | 99.76 52 |
|
v7new | | | 93.06 159 | 93.94 173 | 92.03 151 | 91.99 168 | 97.70 165 | 94.79 169 | 90.14 129 | 96.93 175 | 80.13 173 | 89.97 169 | 83.01 179 | 95.48 156 | 95.16 189 | 95.01 178 | 99.63 123 | 99.76 52 |
|
V14 | | | 92.31 189 | 93.41 192 | 91.03 179 | 91.80 178 | 95.59 210 | 94.79 169 | 84.70 187 | 96.58 190 | 79.83 177 | 88.79 183 | 82.98 181 | 95.41 165 | 95.22 179 | 95.02 177 | 99.65 106 | 99.67 104 |
|
tpm | | | 92.38 185 | 94.79 151 | 89.56 200 | 94.30 137 | 97.50 184 | 94.24 191 | 78.97 218 | 97.72 146 | 74.93 209 | 97.97 67 | 82.91 182 | 96.60 125 | 93.65 209 | 94.81 192 | 98.33 201 | 98.98 175 |
|
v1921920 | | | 92.36 187 | 93.57 187 | 90.94 182 | 91.39 199 | 97.39 190 | 94.70 178 | 87.63 163 | 96.60 188 | 76.63 202 | 86.98 205 | 82.89 183 | 95.75 143 | 96.26 165 | 95.14 170 | 99.55 157 | 99.73 68 |
|
v8 | | | 92.87 164 | 93.87 178 | 91.72 164 | 92.05 166 | 97.50 184 | 94.79 169 | 88.20 156 | 96.85 179 | 80.11 175 | 90.01 167 | 82.86 184 | 95.48 156 | 95.15 193 | 94.90 189 | 99.66 100 | 99.80 32 |
|
v1192 | | | 92.43 182 | 93.61 184 | 91.05 177 | 91.53 195 | 97.43 188 | 94.61 183 | 87.99 159 | 96.60 188 | 76.72 201 | 87.11 204 | 82.74 185 | 95.85 142 | 96.35 160 | 95.30 165 | 99.60 139 | 99.74 64 |
|
v144192 | | | 92.38 185 | 93.55 190 | 91.00 180 | 91.44 197 | 97.47 187 | 94.27 189 | 87.41 164 | 96.52 193 | 78.03 194 | 87.50 201 | 82.65 186 | 95.32 172 | 95.82 173 | 95.15 169 | 99.55 157 | 99.78 41 |
|
v13 | | | 92.16 194 | 93.28 198 | 90.85 186 | 91.75 182 | 95.58 212 | 94.65 182 | 84.23 196 | 96.49 196 | 79.51 185 | 88.40 192 | 82.58 187 | 95.31 174 | 95.21 182 | 95.03 175 | 99.66 100 | 99.68 99 |
|
divwei89l23v2f112 | | | 92.80 168 | 93.60 186 | 91.86 160 | 91.75 182 | 97.71 162 | 94.75 174 | 90.32 123 | 96.54 192 | 79.35 186 | 88.59 187 | 82.55 188 | 95.35 170 | 95.15 193 | 94.96 185 | 99.63 123 | 99.72 80 |
|
v1141 | | | 92.79 170 | 93.61 184 | 91.84 161 | 91.75 182 | 97.71 162 | 94.74 175 | 90.33 122 | 96.58 190 | 79.21 189 | 88.59 187 | 82.53 189 | 95.36 169 | 95.16 189 | 94.96 185 | 99.63 123 | 99.72 80 |
|
v1 | | | 92.81 166 | 93.57 187 | 91.94 155 | 91.79 179 | 97.70 165 | 94.80 168 | 90.32 123 | 96.52 193 | 79.75 179 | 88.47 190 | 82.46 190 | 95.32 172 | 95.14 195 | 94.96 185 | 99.63 123 | 99.73 68 |
|
v15 | | | 92.27 190 | 93.33 194 | 91.04 178 | 91.83 175 | 95.60 208 | 94.79 169 | 84.88 186 | 96.66 185 | 79.66 182 | 88.72 185 | 82.45 191 | 95.40 166 | 95.19 184 | 95.00 182 | 99.65 106 | 99.67 104 |
|
V9 | | | 92.24 191 | 93.32 196 | 90.98 181 | 91.76 181 | 95.58 212 | 94.83 167 | 84.50 191 | 96.68 184 | 79.73 180 | 88.66 186 | 82.39 192 | 95.39 167 | 95.22 179 | 95.03 175 | 99.65 106 | 99.67 104 |
|
TranMVSNet+NR-MVSNet | | | 93.67 151 | 94.14 163 | 93.13 135 | 91.28 203 | 97.58 178 | 95.60 144 | 91.97 96 | 97.06 168 | 84.05 139 | 90.64 163 | 82.22 193 | 96.17 135 | 94.94 200 | 96.78 125 | 99.69 79 | 99.78 41 |
|
V4 | | | 91.92 198 | 93.10 200 | 90.55 190 | 90.64 207 | 97.51 183 | 93.93 194 | 87.02 166 | 95.81 210 | 77.61 198 | 86.93 206 | 82.19 194 | 94.50 186 | 94.72 201 | 94.68 196 | 99.62 129 | 99.85 18 |
|
v52 | | | 91.94 197 | 93.10 200 | 90.57 189 | 90.62 208 | 97.50 184 | 93.98 193 | 87.02 166 | 95.86 208 | 77.67 197 | 86.93 206 | 82.16 195 | 94.53 185 | 94.71 202 | 94.70 195 | 99.61 132 | 99.85 18 |
|
v12 | | | 92.18 193 | 93.29 197 | 90.88 184 | 91.70 187 | 95.59 210 | 94.61 183 | 84.36 193 | 96.65 186 | 79.59 183 | 88.85 181 | 82.03 196 | 95.35 170 | 95.22 179 | 95.04 173 | 99.65 106 | 99.68 99 |
|
CP-MVSNet | | | 93.25 156 | 94.00 170 | 92.38 143 | 91.65 191 | 97.56 179 | 94.38 188 | 89.20 143 | 96.05 203 | 83.16 149 | 89.51 175 | 81.97 197 | 96.16 136 | 96.43 156 | 96.56 132 | 99.71 69 | 99.89 7 |
|
conf0.01 | | | 96.35 98 | 95.71 137 | 97.10 54 | 96.30 89 | 99.65 20 | 98.41 63 | 94.10 63 | 97.35 153 | 94.82 54 | 95.44 131 | 81.88 198 | 99.14 44 | 98.16 92 | 97.80 93 | 99.82 13 | 99.69 92 |
|
v1240 | | | 91.99 196 | 93.33 194 | 90.44 192 | 91.29 201 | 97.30 194 | 94.25 190 | 86.79 169 | 96.43 197 | 75.49 207 | 86.34 210 | 81.85 199 | 95.29 175 | 96.42 157 | 95.22 167 | 99.52 163 | 99.73 68 |
|
tfpnnormal | | | 93.85 149 | 94.12 165 | 93.54 127 | 93.22 151 | 98.24 146 | 95.45 147 | 91.96 97 | 94.61 215 | 83.91 140 | 90.74 160 | 81.75 200 | 97.04 110 | 97.49 130 | 96.16 144 | 99.68 88 | 99.84 20 |
|
v2v482 | | | 92.77 172 | 93.52 191 | 91.90 158 | 91.59 194 | 97.63 173 | 94.57 186 | 90.31 125 | 96.80 181 | 79.22 188 | 88.74 184 | 81.55 201 | 96.04 139 | 95.26 178 | 94.97 184 | 99.66 100 | 99.69 92 |
|
DU-MVS | | | 93.98 144 | 94.44 159 | 93.44 129 | 91.66 189 | 97.77 159 | 95.03 153 | 91.57 103 | 97.17 164 | 86.12 131 | 93.13 151 | 81.13 202 | 96.60 125 | 95.10 196 | 97.01 122 | 99.67 95 | 99.80 32 |
|
conf0.002 | | | 96.31 100 | 95.63 139 | 97.11 53 | 96.29 90 | 99.64 25 | 98.41 63 | 94.11 62 | 97.35 153 | 94.86 52 | 95.49 130 | 81.06 203 | 99.14 44 | 98.14 93 | 98.02 83 | 99.82 13 | 99.69 92 |
|
USDC | | | 94.26 139 | 94.83 150 | 93.59 124 | 96.02 102 | 98.44 135 | 97.84 91 | 88.65 150 | 98.86 79 | 82.73 154 | 94.02 140 | 80.56 204 | 96.76 119 | 97.28 136 | 96.15 145 | 99.55 157 | 98.50 188 |
|
NR-MVSNet | | | 94.01 142 | 94.51 157 | 93.44 129 | 92.56 156 | 97.77 159 | 95.67 141 | 91.57 103 | 97.17 164 | 85.84 134 | 93.13 151 | 80.53 205 | 95.29 175 | 97.01 144 | 96.17 143 | 99.69 79 | 99.75 61 |
|
TinyColmap | | | 94.00 143 | 94.35 161 | 93.60 123 | 95.89 107 | 98.26 144 | 97.49 102 | 88.82 147 | 98.56 106 | 83.21 148 | 91.28 158 | 80.48 206 | 96.68 121 | 97.34 134 | 96.26 141 | 99.53 162 | 98.24 193 |
|
gm-plane-assit | | | 89.44 210 | 92.82 207 | 85.49 213 | 91.37 200 | 95.34 216 | 79.55 227 | 82.12 201 | 91.68 223 | 64.79 226 | 87.98 198 | 80.26 207 | 95.66 146 | 98.51 71 | 97.56 105 | 99.45 170 | 98.41 190 |
|
v148 | | | 92.36 187 | 92.88 203 | 91.75 162 | 91.63 192 | 97.66 171 | 92.64 201 | 90.55 121 | 96.09 201 | 83.34 147 | 88.19 193 | 80.00 208 | 92.74 204 | 93.98 206 | 94.58 197 | 99.58 149 | 99.69 92 |
|
PS-CasMVS | | | 92.72 173 | 93.36 193 | 91.98 154 | 91.62 193 | 97.52 182 | 94.13 192 | 88.98 145 | 95.94 206 | 81.51 160 | 87.35 202 | 79.95 209 | 95.91 141 | 96.37 158 | 96.49 134 | 99.70 77 | 99.89 7 |
|
TDRefinement | | | 93.04 162 | 93.57 187 | 92.41 142 | 96.58 80 | 98.77 112 | 97.78 96 | 91.96 97 | 98.12 123 | 80.84 162 | 89.13 179 | 79.87 210 | 87.78 213 | 96.44 155 | 94.50 198 | 99.54 161 | 98.15 194 |
|
DeepMVS_CX | | | | | | | 96.85 198 | 87.43 218 | 89.27 142 | 98.30 116 | 75.55 206 | 95.05 132 | 79.47 211 | 92.62 206 | 89.48 225 | | 95.18 229 | 95.96 218 |
|
LTVRE_ROB | | 93.20 16 | 92.84 165 | 94.92 147 | 90.43 193 | 92.83 152 | 98.63 123 | 97.08 119 | 87.87 161 | 97.91 136 | 68.42 220 | 93.54 144 | 79.46 212 | 96.62 124 | 97.55 128 | 97.40 115 | 99.74 49 | 99.92 1 |
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 |
tfpn | | | 96.22 103 | 95.62 140 | 96.93 71 | 96.29 90 | 99.72 4 | 98.34 74 | 93.94 75 | 97.96 132 | 93.94 74 | 96.45 106 | 79.09 213 | 99.22 32 | 98.28 81 | 98.06 80 | 99.83 9 | 99.78 41 |
|
PEN-MVS | | | 92.72 173 | 93.20 199 | 92.15 147 | 91.29 201 | 97.31 193 | 94.67 180 | 89.81 136 | 96.19 199 | 81.83 158 | 88.58 189 | 79.06 214 | 95.61 150 | 95.21 182 | 96.27 139 | 99.72 60 | 99.82 25 |
|
MIMVSNet1 | | | 88.61 213 | 90.68 213 | 86.19 212 | 81.56 229 | 95.30 217 | 87.78 217 | 85.98 177 | 94.19 218 | 72.30 216 | 78.84 223 | 78.90 215 | 90.06 211 | 96.59 150 | 95.47 158 | 99.46 169 | 95.49 219 |
|
v7n | | | 91.61 201 | 92.95 202 | 90.04 196 | 90.56 210 | 97.69 169 | 93.74 195 | 85.59 178 | 95.89 207 | 76.95 200 | 86.60 209 | 78.60 216 | 93.76 199 | 97.01 144 | 94.99 183 | 99.65 106 | 99.87 13 |
|
DTE-MVSNet | | | 92.42 184 | 92.85 205 | 91.91 157 | 90.87 206 | 96.97 197 | 94.53 187 | 89.81 136 | 95.86 208 | 81.59 159 | 88.83 182 | 77.88 217 | 95.01 181 | 94.34 205 | 96.35 137 | 99.64 117 | 99.73 68 |
|
pmmvs3 | | | 88.19 214 | 91.27 211 | 84.60 215 | 85.60 220 | 93.66 221 | 85.68 222 | 81.13 202 | 92.36 222 | 63.66 228 | 89.51 175 | 77.10 218 | 93.22 202 | 96.37 158 | 92.40 215 | 98.30 202 | 97.46 203 |
|
v748 | | | 91.12 202 | 91.95 210 | 90.16 195 | 90.60 209 | 97.35 192 | 91.11 205 | 87.92 160 | 94.75 214 | 80.54 167 | 86.26 211 | 75.97 219 | 91.13 210 | 94.63 203 | 94.81 192 | 99.65 106 | 99.90 3 |
|
test2356 | | | 88.81 211 | 92.86 204 | 84.09 218 | 87.85 216 | 93.46 222 | 87.07 220 | 83.60 200 | 96.50 195 | 62.08 229 | 97.06 87 | 75.04 220 | 85.17 218 | 95.08 198 | 95.42 160 | 98.75 195 | 97.46 203 |
|
FPMVS | | | 83.82 218 | 84.61 222 | 82.90 219 | 90.39 212 | 90.71 225 | 90.85 209 | 84.10 197 | 95.47 213 | 65.15 224 | 83.44 215 | 74.46 221 | 75.48 224 | 81.63 228 | 79.42 230 | 91.42 231 | 87.14 229 |
|
testus | | | 88.77 212 | 92.77 208 | 84.10 217 | 88.24 215 | 93.95 220 | 87.16 219 | 84.24 194 | 97.37 152 | 61.54 230 | 95.70 126 | 73.10 222 | 84.90 219 | 95.56 175 | 95.82 152 | 98.51 196 | 97.88 200 |
|
PMVS | | 72.60 17 | 76.39 225 | 77.66 227 | 74.92 226 | 81.04 230 | 69.37 238 | 68.47 234 | 80.54 206 | 85.39 231 | 65.07 225 | 73.52 227 | 72.91 223 | 65.67 232 | 80.35 230 | 76.81 231 | 88.71 233 | 85.25 233 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
1111 | | | 82.87 219 | 85.67 220 | 79.62 222 | 81.86 226 | 89.62 226 | 74.44 229 | 68.81 232 | 87.44 228 | 66.59 221 | 76.83 225 | 70.33 224 | 87.71 214 | 92.65 212 | 93.37 206 | 98.28 203 | 89.42 227 |
|
.test1245 | | | 69.67 226 | 72.22 228 | 66.70 230 | 81.86 226 | 89.62 226 | 74.44 229 | 68.81 232 | 87.44 228 | 66.59 221 | 76.83 225 | 70.33 224 | 87.71 214 | 92.65 212 | 37.65 233 | 20.79 237 | 51.04 234 |
|
pmmvs-eth3d | | | 89.81 208 | 89.65 215 | 90.00 197 | 86.94 218 | 95.38 215 | 91.08 206 | 86.39 174 | 94.57 216 | 82.27 156 | 83.03 218 | 64.94 226 | 93.96 195 | 96.57 152 | 93.82 203 | 99.35 180 | 99.24 163 |
|
new-patchmatchnet | | | 86.12 216 | 87.30 217 | 84.74 214 | 86.92 219 | 95.19 219 | 83.57 224 | 84.42 192 | 92.67 221 | 65.66 223 | 80.32 221 | 64.72 227 | 89.41 212 | 92.33 218 | 89.21 224 | 98.43 199 | 96.69 215 |
|
MDA-MVSNet-bldmvs | | | 87.84 215 | 89.22 216 | 86.23 211 | 81.74 228 | 96.77 200 | 83.74 223 | 89.57 140 | 94.50 217 | 72.83 215 | 96.64 99 | 64.47 228 | 92.71 205 | 81.43 229 | 92.28 217 | 96.81 224 | 98.47 189 |
|
PM-MVS | | | 89.55 209 | 90.30 214 | 88.67 205 | 87.06 217 | 95.60 208 | 90.88 208 | 84.51 190 | 96.14 200 | 75.75 204 | 86.89 208 | 63.47 229 | 94.64 183 | 96.85 147 | 93.89 202 | 99.17 189 | 99.29 159 |
|
test1235678 | | | 81.83 220 | 86.26 218 | 76.66 223 | 84.10 222 | 89.41 229 | 74.29 231 | 79.64 210 | 90.60 225 | 51.84 236 | 82.11 219 | 63.07 230 | 72.61 227 | 91.94 220 | 92.75 209 | 97.49 215 | 93.94 223 |
|
testmv | | | 81.83 220 | 86.26 218 | 76.66 223 | 84.10 222 | 89.42 228 | 74.29 231 | 79.65 209 | 90.61 224 | 51.85 235 | 82.11 219 | 63.06 231 | 72.61 227 | 91.94 220 | 92.75 209 | 97.49 215 | 93.94 223 |
|
Anonymous20231211 | | | 83.86 217 | 83.39 223 | 84.40 216 | 85.29 221 | 93.44 223 | 86.29 221 | 84.24 194 | 85.55 230 | 68.63 219 | 61.25 230 | 59.57 232 | 84.33 221 | 92.50 215 | 92.52 213 | 97.65 212 | 98.89 181 |
|
test12356 | | | 80.53 223 | 84.80 221 | 75.54 225 | 82.31 225 | 88.05 232 | 75.99 228 | 79.31 214 | 88.53 226 | 53.24 234 | 83.30 216 | 56.38 233 | 65.16 233 | 90.87 224 | 93.10 208 | 97.25 221 | 93.34 226 |
|
Gipuma | | | 81.40 222 | 81.78 224 | 80.96 221 | 83.21 224 | 85.61 233 | 79.73 226 | 76.25 227 | 97.33 159 | 64.21 227 | 55.32 231 | 55.55 234 | 86.04 216 | 92.43 217 | 92.20 218 | 96.32 226 | 93.99 222 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMMVS2 | | | 77.26 224 | 79.47 226 | 74.70 227 | 76.00 232 | 88.37 231 | 74.22 233 | 76.34 225 | 78.31 232 | 54.13 232 | 69.96 228 | 52.50 235 | 70.14 230 | 84.83 227 | 88.71 225 | 97.35 217 | 93.58 225 |
|
EMVS | | | 68.12 229 | 68.11 230 | 68.14 229 | 75.51 233 | 71.76 236 | 55.38 237 | 77.20 224 | 77.78 233 | 37.79 239 | 53.59 232 | 43.61 236 | 74.72 225 | 67.05 234 | 76.70 232 | 88.27 235 | 86.24 231 |
|
no-one | | | 66.79 230 | 67.62 231 | 65.81 231 | 73.06 235 | 81.79 234 | 51.90 239 | 76.20 228 | 61.07 236 | 54.05 233 | 51.62 235 | 41.72 237 | 49.18 234 | 67.26 233 | 82.83 228 | 90.47 232 | 87.07 230 |
|
E-PMN | | | 68.30 228 | 68.43 229 | 68.15 228 | 74.70 234 | 71.56 237 | 55.64 236 | 77.24 223 | 77.48 234 | 39.46 238 | 51.95 234 | 41.68 238 | 73.28 226 | 70.65 232 | 79.51 229 | 88.61 234 | 86.20 232 |
|
MVE | | 67.97 19 | 65.53 231 | 67.43 232 | 63.31 232 | 59.33 236 | 74.20 235 | 53.09 238 | 70.43 231 | 66.27 235 | 43.13 237 | 45.98 236 | 30.62 239 | 70.65 229 | 79.34 231 | 86.30 226 | 83.25 236 | 89.33 228 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
ambc | | | | 80.99 225 | | 80.04 231 | 90.84 224 | 90.91 207 | | 96.09 201 | 74.18 210 | 62.81 229 | 30.59 240 | 82.44 223 | 96.25 166 | 91.77 220 | 95.91 228 | 98.56 186 |
|
testmvs | | | 31.24 232 | 40.15 233 | 20.86 234 | 12.61 237 | 17.99 239 | 25.16 240 | 13.30 235 | 48.42 237 | 24.82 240 | 53.07 233 | 30.13 241 | 28.47 235 | 42.73 235 | 37.65 233 | 20.79 237 | 51.04 234 |
|
test123 | | | 26.75 233 | 34.25 234 | 18.01 235 | 7.93 238 | 17.18 240 | 24.85 241 | 12.36 236 | 44.83 238 | 16.52 241 | 41.80 237 | 18.10 242 | 28.29 236 | 33.08 236 | 34.79 235 | 18.10 239 | 49.95 236 |
|
sosnet-low-res | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
sosnet | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
Patchmatch-RL test | | | | | | | | 66.86 235 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 98.57 105 | | | | | | | | |
|
Patchmtry | | | | | | | 98.59 126 | 97.15 114 | 79.14 215 | | 80.42 169 | | | | | | | |
|