PGM-MVS | | | 98.86 28 | 99.35 23 | 98.29 32 | 99.77 1 | 99.63 30 | 99.67 6 | 95.63 41 | 98.66 107 | 95.27 47 | 99.11 24 | 99.82 38 | 99.67 4 | 99.33 22 | 99.19 20 | 99.73 60 | 99.74 70 |
|
SMA-MVS | | | 99.38 3 | 99.60 2 | 99.12 7 | 99.76 2 | 99.62 34 | 99.39 28 | 98.23 15 | 99.52 14 | 98.03 13 | 99.45 9 | 99.98 1 | 99.64 5 | 99.58 6 | 99.30 11 | 99.68 94 | 99.76 55 |
|
CSCG | | | 98.90 27 | 98.93 46 | 98.85 22 | 99.75 3 | 99.72 4 | 99.49 19 | 96.58 38 | 99.38 21 | 98.05 12 | 98.97 30 | 97.87 65 | 99.49 18 | 97.78 122 | 98.92 32 | 99.78 39 | 99.90 4 |
|
APDe-MVS | | | 99.49 1 | 99.64 1 | 99.32 1 | 99.74 4 | 99.74 3 | 99.75 1 | 98.34 2 | 99.56 9 | 98.72 3 | 99.57 5 | 99.97 5 | 99.53 15 | 99.65 2 | 99.25 14 | 99.84 6 | 99.77 51 |
|
ACMMP_Plus | | | 99.05 22 | 99.45 10 | 98.58 28 | 99.73 5 | 99.60 43 | 99.64 9 | 98.28 11 | 99.23 44 | 94.57 61 | 99.35 13 | 99.97 5 | 99.55 13 | 99.63 3 | 98.66 45 | 99.70 83 | 99.74 70 |
|
zzz-MVS | | | 99.31 5 | 99.44 13 | 99.16 5 | 99.73 5 | 99.65 21 | 99.63 11 | 98.26 12 | 99.27 37 | 98.01 14 | 99.27 15 | 99.97 5 | 99.60 7 | 99.59 5 | 98.58 51 | 99.71 74 | 99.73 74 |
|
v1.0 | | | 91.56 207 | 85.17 227 | 99.01 16 | 99.70 7 | 99.69 12 | 99.40 27 | 98.31 6 | 98.94 79 | 97.70 19 | 99.40 11 | 99.97 5 | 99.17 43 | 99.54 9 | 98.67 44 | 99.78 39 | 0.00 242 |
|
HFP-MVS | | | 99.32 4 | 99.53 6 | 99.07 11 | 99.69 8 | 99.59 45 | 99.63 11 | 98.31 6 | 99.56 9 | 97.37 23 | 99.27 15 | 99.97 5 | 99.70 3 | 99.35 20 | 99.24 16 | 99.71 74 | 99.76 55 |
|
HPM-MVS++ | | | 99.10 18 | 99.30 24 | 98.86 21 | 99.69 8 | 99.48 60 | 99.59 14 | 98.34 2 | 99.26 40 | 96.55 34 | 99.10 25 | 99.96 11 | 99.36 26 | 99.25 25 | 98.37 66 | 99.64 124 | 99.66 119 |
|
APD-MVS | | | 99.25 10 | 99.38 18 | 99.09 9 | 99.69 8 | 99.58 47 | 99.56 15 | 98.32 5 | 98.85 86 | 97.87 16 | 98.91 37 | 99.92 26 | 99.30 32 | 99.45 14 | 99.38 8 | 99.79 36 | 99.58 135 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HSP-MVS | | | 99.31 5 | 99.43 15 | 99.17 3 | 99.68 11 | 99.75 2 | 99.72 2 | 98.31 6 | 99.45 18 | 98.16 10 | 99.28 14 | 99.98 1 | 99.30 32 | 99.34 21 | 98.41 61 | 99.81 27 | 99.81 32 |
|
X-MVS | | | 98.93 26 | 99.37 19 | 98.42 29 | 99.67 12 | 99.62 34 | 99.60 13 | 98.15 20 | 99.08 62 | 93.81 84 | 98.46 57 | 99.95 16 | 99.59 9 | 99.49 12 | 99.21 19 | 99.68 94 | 99.75 66 |
|
MCST-MVS | | | 99.11 17 | 99.27 26 | 98.93 19 | 99.67 12 | 99.33 82 | 99.51 18 | 98.31 6 | 99.28 35 | 96.57 33 | 99.10 25 | 99.90 29 | 99.71 2 | 99.19 26 | 98.35 68 | 99.82 14 | 99.71 90 |
|
ACMMPR | | | 99.30 7 | 99.54 5 | 99.03 14 | 99.66 14 | 99.64 26 | 99.68 5 | 98.25 13 | 99.56 9 | 97.12 27 | 99.19 18 | 99.95 16 | 99.72 1 | 99.43 15 | 99.25 14 | 99.72 65 | 99.77 51 |
|
SteuartSystems-ACMMP | | | 99.20 13 | 99.51 7 | 98.83 24 | 99.66 14 | 99.66 20 | 99.71 4 | 98.12 24 | 99.14 54 | 96.62 31 | 99.16 20 | 99.98 1 | 99.12 51 | 99.63 3 | 99.19 20 | 99.78 39 | 99.83 26 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 99.23 12 | 99.28 25 | 99.17 3 | 99.65 16 | 99.34 80 | 99.46 22 | 98.21 16 | 99.28 35 | 98.47 5 | 98.89 39 | 99.94 24 | 99.50 16 | 99.42 16 | 98.61 48 | 99.73 60 | 99.52 146 |
|
ESAPD | | | 99.39 2 | 99.55 4 | 99.20 2 | 99.63 17 | 99.71 8 | 99.66 7 | 98.33 4 | 99.29 34 | 98.40 8 | 99.64 4 | 99.98 1 | 99.31 30 | 99.56 7 | 98.96 29 | 99.85 4 | 99.70 92 |
|
MP-MVS | | | 99.07 20 | 99.36 20 | 98.74 25 | 99.63 17 | 99.57 49 | 99.66 7 | 98.25 13 | 99.00 74 | 95.62 40 | 98.97 30 | 99.94 24 | 99.54 14 | 99.51 11 | 98.79 42 | 99.71 74 | 99.73 74 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
NCCC | | | 99.05 22 | 99.08 34 | 99.02 15 | 99.62 19 | 99.38 72 | 99.43 26 | 98.21 16 | 99.36 25 | 97.66 20 | 97.79 75 | 99.90 29 | 99.45 21 | 99.17 27 | 98.43 59 | 99.77 44 | 99.51 150 |
|
CP-MVS | | | 99.27 8 | 99.44 13 | 99.08 10 | 99.62 19 | 99.58 47 | 99.53 16 | 98.16 18 | 99.21 47 | 97.79 17 | 99.15 21 | 99.96 11 | 99.59 9 | 99.54 9 | 98.86 37 | 99.78 39 | 99.74 70 |
|
AdaColmap | | | 99.06 21 | 98.98 44 | 99.15 6 | 99.60 21 | 99.30 86 | 99.38 29 | 98.16 18 | 99.02 73 | 98.55 4 | 98.71 45 | 99.57 50 | 99.58 12 | 99.09 31 | 97.84 97 | 99.64 124 | 99.36 163 |
|
CPTT-MVS | | | 99.14 16 | 99.20 29 | 99.06 12 | 99.58 22 | 99.53 54 | 99.45 23 | 97.80 32 | 99.19 50 | 98.32 9 | 98.58 51 | 99.95 16 | 99.60 7 | 99.28 24 | 98.20 79 | 99.64 124 | 99.69 99 |
|
QAPM | | | 98.62 37 | 99.04 40 | 98.13 36 | 99.57 23 | 99.48 60 | 99.17 37 | 94.78 51 | 99.57 8 | 96.16 36 | 96.73 101 | 99.80 39 | 99.33 28 | 98.79 50 | 99.29 13 | 99.75 48 | 99.64 126 |
|
3Dnovator | | 96.92 7 | 98.67 34 | 99.05 37 | 98.23 35 | 99.57 23 | 99.45 64 | 99.11 40 | 94.66 54 | 99.69 3 | 96.80 30 | 96.55 109 | 99.61 47 | 99.40 24 | 98.87 46 | 99.49 3 | 99.85 4 | 99.66 119 |
|
DeepC-MVS_fast | | 98.34 1 | 99.17 14 | 99.45 10 | 98.85 22 | 99.55 25 | 99.37 74 | 99.64 9 | 98.05 27 | 99.53 12 | 96.58 32 | 98.93 32 | 99.92 26 | 99.49 18 | 99.46 13 | 99.32 10 | 99.80 34 | 99.64 126 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | | | | 99.53 26 | | | | | | | 99.89 31 | | | | | |
|
3Dnovator+ | | 96.92 7 | 98.71 33 | 99.05 37 | 98.32 31 | 99.53 26 | 99.34 80 | 99.06 44 | 94.61 55 | 99.65 4 | 97.49 21 | 96.75 99 | 99.86 34 | 99.44 22 | 98.78 51 | 99.30 11 | 99.81 27 | 99.67 111 |
|
MSLP-MVS++ | | | 99.15 15 | 99.24 27 | 99.04 13 | 99.52 28 | 99.49 59 | 99.09 42 | 98.07 26 | 99.37 23 | 98.47 5 | 97.79 75 | 99.89 31 | 99.50 16 | 98.93 40 | 99.45 4 | 99.61 139 | 99.76 55 |
|
OpenMVS | | 96.23 11 | 97.95 50 | 98.45 58 | 97.35 48 | 99.52 28 | 99.42 68 | 98.91 50 | 94.61 55 | 98.87 83 | 92.24 102 | 94.61 142 | 99.05 55 | 99.10 54 | 98.64 63 | 99.05 24 | 99.74 54 | 99.51 150 |
|
PLC | | 97.93 2 | 99.02 25 | 98.94 45 | 99.11 8 | 99.46 30 | 99.24 94 | 99.06 44 | 97.96 29 | 99.31 31 | 99.16 1 | 97.90 73 | 99.79 41 | 99.36 26 | 98.71 57 | 98.12 82 | 99.65 113 | 99.52 146 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MVS_111021_HR | | | 98.59 38 | 99.36 20 | 97.68 44 | 99.42 31 | 99.61 39 | 98.14 85 | 94.81 50 | 99.31 31 | 95.00 53 | 99.51 7 | 99.79 41 | 99.00 61 | 98.94 39 | 98.83 39 | 99.69 85 | 99.57 140 |
|
OMC-MVS | | | 98.84 29 | 99.01 43 | 98.65 27 | 99.39 32 | 99.23 95 | 99.22 34 | 96.70 37 | 99.40 20 | 97.77 18 | 97.89 74 | 99.80 39 | 99.21 36 | 99.02 35 | 98.65 46 | 99.57 160 | 99.07 179 |
|
TSAR-MVS + ACMM | | | 98.77 30 | 99.45 10 | 97.98 40 | 99.37 33 | 99.46 62 | 99.44 25 | 98.13 23 | 99.65 4 | 92.30 101 | 98.91 37 | 99.95 16 | 99.05 57 | 99.42 16 | 98.95 30 | 99.58 156 | 99.82 27 |
|
MVS_111021_LR | | | 98.67 34 | 99.41 17 | 97.81 43 | 99.37 33 | 99.53 54 | 98.51 61 | 95.52 43 | 99.27 37 | 94.85 56 | 99.56 6 | 99.69 45 | 99.04 58 | 99.36 19 | 98.88 35 | 99.60 146 | 99.58 135 |
|
train_agg | | | 98.73 32 | 99.11 32 | 98.28 33 | 99.36 35 | 99.35 78 | 99.48 21 | 97.96 29 | 98.83 90 | 93.86 83 | 98.70 46 | 99.86 34 | 99.44 22 | 99.08 33 | 98.38 64 | 99.61 139 | 99.58 135 |
|
ACMMP | | | 98.74 31 | 99.03 41 | 98.40 30 | 99.36 35 | 99.64 26 | 99.20 35 | 97.75 33 | 98.82 92 | 95.24 48 | 98.85 40 | 99.87 33 | 99.17 43 | 98.74 56 | 97.50 115 | 99.71 74 | 99.76 55 |
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 |
MAR-MVS | | | 97.71 56 | 98.04 78 | 97.32 49 | 99.35 37 | 98.91 110 | 97.65 104 | 91.68 105 | 98.00 135 | 97.01 28 | 97.72 79 | 94.83 97 | 98.85 65 | 98.44 77 | 98.86 37 | 99.41 183 | 99.52 146 |
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 |
abl_6 | | | | | 98.09 37 | 99.33 38 | 99.22 96 | 98.79 54 | 94.96 49 | 98.52 116 | 97.00 29 | 97.30 85 | 99.86 34 | 98.76 67 | | | 99.69 85 | 99.41 160 |
|
CDPH-MVS | | | 98.41 40 | 99.10 33 | 97.61 46 | 99.32 39 | 99.36 76 | 99.49 19 | 96.15 40 | 98.82 92 | 91.82 104 | 98.41 58 | 99.66 46 | 99.10 54 | 98.93 40 | 98.97 28 | 99.75 48 | 99.58 135 |
|
CNLPA | | | 99.03 24 | 99.05 37 | 99.01 16 | 99.27 40 | 99.22 96 | 99.03 46 | 97.98 28 | 99.34 29 | 99.00 2 | 98.25 64 | 99.71 44 | 99.31 30 | 98.80 49 | 98.82 40 | 99.48 173 | 99.17 172 |
|
MSDG | | | 98.27 44 | 98.29 67 | 98.24 34 | 99.20 41 | 99.22 96 | 99.20 35 | 97.82 31 | 99.37 23 | 94.43 70 | 95.90 123 | 97.31 71 | 99.12 51 | 98.76 53 | 98.35 68 | 99.67 102 | 99.14 176 |
|
PHI-MVS | | | 99.08 19 | 99.43 15 | 98.67 26 | 99.15 42 | 99.59 45 | 99.11 40 | 97.35 35 | 99.14 54 | 97.30 24 | 99.44 10 | 99.96 11 | 99.32 29 | 98.89 44 | 99.39 7 | 99.79 36 | 99.58 135 |
|
PatchMatch-RL | | | 97.77 54 | 98.25 68 | 97.21 54 | 99.11 43 | 99.25 92 | 97.06 127 | 94.09 66 | 98.72 105 | 95.14 50 | 98.47 56 | 96.29 81 | 98.43 79 | 98.65 60 | 97.44 120 | 99.45 177 | 98.94 182 |
|
TAPA-MVS | | 97.53 5 | 98.41 40 | 98.84 50 | 97.91 41 | 99.08 44 | 99.33 82 | 99.15 38 | 97.13 36 | 99.34 29 | 93.20 91 | 97.75 77 | 99.19 53 | 99.20 37 | 98.66 59 | 98.13 81 | 99.66 107 | 99.48 155 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
EPNet | | | 98.05 48 | 98.86 48 | 97.10 57 | 99.02 45 | 99.43 67 | 98.47 62 | 94.73 52 | 99.05 70 | 95.62 40 | 98.93 32 | 97.62 69 | 95.48 163 | 98.59 70 | 98.55 53 | 99.29 191 | 99.84 22 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet_dtu | | | 96.30 106 | 98.53 56 | 93.70 128 | 98.97 46 | 98.24 153 | 97.36 112 | 94.23 63 | 98.85 86 | 79.18 196 | 99.19 18 | 98.47 60 | 94.09 200 | 97.89 117 | 98.21 78 | 98.39 207 | 98.85 188 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
COLMAP_ROB | | 96.15 12 | 97.78 53 | 98.17 73 | 97.32 49 | 98.84 47 | 99.45 64 | 99.28 32 | 95.43 44 | 99.48 17 | 91.80 105 | 94.83 140 | 98.36 62 | 98.90 62 | 98.09 101 | 97.85 96 | 99.68 94 | 99.15 173 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DeepPCF-MVS | | 97.74 3 | 98.34 42 | 99.46 9 | 97.04 61 | 98.82 48 | 99.33 82 | 96.28 141 | 97.47 34 | 99.58 7 | 94.70 60 | 98.99 29 | 99.85 37 | 97.24 113 | 99.55 8 | 99.34 9 | 97.73 217 | 99.56 141 |
|
SD-MVS | | | 99.25 10 | 99.50 8 | 98.96 18 | 98.79 49 | 99.55 52 | 99.33 31 | 98.29 10 | 99.75 1 | 97.96 15 | 99.15 21 | 99.95 16 | 99.61 6 | 99.17 27 | 99.06 23 | 99.81 27 | 99.84 22 |
|
TSAR-MVS + MP. | | | 99.27 8 | 99.57 3 | 98.92 20 | 98.78 50 | 99.53 54 | 99.72 2 | 98.11 25 | 99.73 2 | 97.43 22 | 99.15 21 | 99.96 11 | 99.59 9 | 99.73 1 | 99.07 22 | 99.88 1 | 99.82 27 |
|
PCF-MVS | | 97.50 6 | 98.18 46 | 98.35 63 | 97.99 39 | 98.65 51 | 99.36 76 | 98.94 49 | 98.14 22 | 98.59 109 | 93.62 87 | 96.61 105 | 99.76 43 | 99.03 59 | 97.77 123 | 97.45 119 | 99.57 160 | 98.89 187 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DeepC-MVS | | 97.63 4 | 98.33 43 | 98.57 54 | 98.04 38 | 98.62 52 | 99.65 21 | 99.45 23 | 98.15 20 | 99.51 16 | 92.80 97 | 95.74 128 | 96.44 78 | 99.46 20 | 99.37 18 | 99.50 2 | 99.78 39 | 99.81 32 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 98.46 39 | 99.16 30 | 97.64 45 | 98.48 53 | 99.64 26 | 99.35 30 | 94.71 53 | 99.53 12 | 95.17 49 | 97.63 81 | 99.59 48 | 98.38 81 | 98.88 45 | 98.99 27 | 99.74 54 | 99.86 18 |
|
LS3D | | | 97.79 52 | 98.25 68 | 97.26 53 | 98.40 54 | 99.63 30 | 99.53 16 | 98.63 1 | 99.25 42 | 88.13 126 | 96.93 97 | 94.14 111 | 99.19 39 | 99.14 29 | 99.23 17 | 99.69 85 | 99.42 159 |
|
CHOSEN 280x420 | | | 97.99 49 | 99.24 27 | 96.53 82 | 98.34 55 | 99.61 39 | 98.36 75 | 89.80 145 | 99.27 37 | 95.08 51 | 99.81 1 | 98.58 58 | 98.64 72 | 99.02 35 | 98.92 32 | 98.93 199 | 99.48 155 |
|
DELS-MVS | | | 98.19 45 | 98.77 51 | 97.52 47 | 98.29 56 | 99.71 8 | 99.12 39 | 94.58 58 | 98.80 95 | 95.38 46 | 96.24 115 | 98.24 63 | 97.92 97 | 99.06 34 | 99.52 1 | 99.82 14 | 99.79 42 |
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 |
RPSCF | | | 97.61 60 | 98.16 74 | 96.96 73 | 98.10 57 | 99.00 103 | 98.84 52 | 93.76 83 | 99.45 18 | 94.78 59 | 99.39 12 | 99.31 52 | 98.53 77 | 96.61 156 | 95.43 166 | 97.74 215 | 97.93 204 |
|
PVSNet_BlendedMVS | | | 97.51 64 | 97.71 88 | 97.28 51 | 98.06 58 | 99.61 39 | 97.31 114 | 95.02 47 | 99.08 62 | 95.51 43 | 98.05 68 | 90.11 132 | 98.07 93 | 98.91 42 | 98.40 62 | 99.72 65 | 99.78 44 |
|
PVSNet_Blended | | | 97.51 64 | 97.71 88 | 97.28 51 | 98.06 58 | 99.61 39 | 97.31 114 | 95.02 47 | 99.08 62 | 95.51 43 | 98.05 68 | 90.11 132 | 98.07 93 | 98.91 42 | 98.40 62 | 99.72 65 | 99.78 44 |
|
MVS_0304 | | | 98.14 47 | 99.03 41 | 97.10 57 | 98.05 60 | 99.63 30 | 99.27 33 | 94.33 60 | 99.63 6 | 93.06 94 | 97.32 84 | 99.05 55 | 98.09 92 | 98.82 48 | 98.87 36 | 99.81 27 | 99.89 8 |
|
CHOSEN 1792x2688 | | | 96.41 101 | 96.99 111 | 95.74 103 | 98.01 61 | 99.72 4 | 97.70 103 | 90.78 125 | 99.13 58 | 90.03 119 | 87.35 209 | 95.36 91 | 98.33 84 | 98.59 70 | 98.91 34 | 99.59 152 | 99.87 14 |
|
HyFIR lowres test | | | 95.99 113 | 96.56 118 | 95.32 108 | 97.99 62 | 99.65 21 | 96.54 135 | 88.86 153 | 98.44 118 | 89.77 122 | 84.14 221 | 97.05 74 | 99.03 59 | 98.55 72 | 98.19 80 | 99.73 60 | 99.86 18 |
|
OPM-MVS | | | 96.22 108 | 95.85 142 | 96.65 79 | 97.75 63 | 98.54 135 | 99.00 48 | 95.53 42 | 96.88 184 | 89.88 120 | 95.95 122 | 86.46 154 | 98.07 93 | 97.65 131 | 96.63 136 | 99.67 102 | 98.83 189 |
|
tmp_tt | | | | | 82.25 225 | 97.73 64 | 88.71 237 | 80.18 232 | 68.65 240 | 99.15 52 | 86.98 134 | 99.47 8 | 85.31 165 | 68.35 237 | 87.51 232 | 83.81 233 | 91.64 236 | |
|
TSAR-MVS + COLMAP | | | 96.79 85 | 96.55 119 | 97.06 60 | 97.70 65 | 98.46 138 | 99.07 43 | 96.23 39 | 99.38 21 | 91.32 109 | 98.80 41 | 85.61 161 | 98.69 70 | 97.64 132 | 96.92 130 | 99.37 186 | 99.06 180 |
|
PVSNet_Blended_VisFu | | | 97.41 66 | 98.49 57 | 96.15 91 | 97.49 66 | 99.76 1 | 96.02 144 | 93.75 85 | 99.26 40 | 93.38 90 | 93.73 148 | 99.35 51 | 96.47 136 | 98.96 37 | 98.46 57 | 99.77 44 | 99.90 4 |
|
MS-PatchMatch | | | 95.99 113 | 97.26 105 | 94.51 115 | 97.46 67 | 98.76 120 | 97.27 116 | 86.97 175 | 99.09 60 | 89.83 121 | 93.51 151 | 97.78 66 | 96.18 141 | 97.53 136 | 95.71 163 | 99.35 187 | 98.41 195 |
|
XVS | | | | | | 97.42 68 | 99.62 34 | 98.59 59 | | | 93.81 84 | | 99.95 16 | | | | 99.69 85 | |
|
X-MVStestdata | | | | | | 97.42 68 | 99.62 34 | 98.59 59 | | | 93.81 84 | | 99.95 16 | | | | 99.69 85 | |
|
LGP-MVS_train | | | 96.23 107 | 96.89 113 | 95.46 107 | 97.32 70 | 98.77 118 | 98.81 53 | 93.60 86 | 98.58 110 | 85.52 142 | 99.08 27 | 86.67 151 | 97.83 103 | 97.87 118 | 97.51 114 | 99.69 85 | 99.73 74 |
|
CMPMVS | | 70.31 18 | 90.74 210 | 91.06 218 | 90.36 200 | 97.32 70 | 97.43 196 | 92.97 206 | 87.82 169 | 93.50 226 | 75.34 214 | 83.27 224 | 84.90 169 | 92.19 214 | 92.64 221 | 91.21 229 | 96.50 231 | 94.46 225 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
HQP-MVS | | | 96.37 102 | 96.58 117 | 96.13 93 | 97.31 72 | 98.44 141 | 98.45 63 | 95.22 45 | 98.86 84 | 88.58 124 | 98.33 62 | 87.00 143 | 97.67 104 | 97.23 144 | 96.56 139 | 99.56 163 | 99.62 129 |
|
ACMM | | 96.26 9 | 96.67 96 | 96.69 116 | 96.66 78 | 97.29 73 | 98.46 138 | 96.48 138 | 95.09 46 | 99.21 47 | 93.19 92 | 98.78 43 | 86.73 150 | 98.17 89 | 97.84 120 | 96.32 145 | 99.74 54 | 99.49 154 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UA-Net | | | 97.13 76 | 99.14 31 | 94.78 112 | 97.21 74 | 99.38 72 | 97.56 106 | 92.04 98 | 98.48 117 | 88.03 127 | 98.39 60 | 99.91 28 | 94.03 201 | 99.33 22 | 99.23 17 | 99.81 27 | 99.25 168 |
|
UGNet | | | 97.66 59 | 99.07 36 | 96.01 97 | 97.19 75 | 99.65 21 | 97.09 125 | 93.39 89 | 99.35 26 | 94.40 72 | 98.79 42 | 99.59 48 | 94.24 198 | 98.04 110 | 98.29 75 | 99.73 60 | 99.80 35 |
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 |
TSAR-MVS + GP. | | | 98.66 36 | 99.36 20 | 97.85 42 | 97.16 76 | 99.46 62 | 99.03 46 | 94.59 57 | 99.09 60 | 97.19 26 | 99.73 3 | 99.95 16 | 99.39 25 | 98.95 38 | 98.69 43 | 99.75 48 | 99.65 122 |
|
CANet_DTU | | | 96.64 97 | 99.08 34 | 93.81 124 | 97.10 77 | 99.42 68 | 98.85 51 | 90.01 139 | 99.31 31 | 79.98 182 | 99.78 2 | 99.10 54 | 97.42 110 | 98.35 80 | 98.05 87 | 99.47 175 | 99.53 144 |
|
IB-MVS | | 93.96 15 | 95.02 131 | 96.44 131 | 93.36 138 | 97.05 78 | 99.28 89 | 90.43 217 | 93.39 89 | 98.02 134 | 96.02 37 | 94.92 139 | 92.07 126 | 83.52 228 | 95.38 183 | 95.82 159 | 99.72 65 | 99.59 133 |
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 |
ACMP | | 96.25 10 | 96.62 99 | 96.72 115 | 96.50 85 | 96.96 79 | 98.75 121 | 97.80 100 | 94.30 61 | 98.85 86 | 93.12 93 | 98.78 43 | 86.61 152 | 97.23 114 | 97.73 126 | 96.61 137 | 99.62 136 | 99.71 90 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 95.42 14 | 95.27 128 | 95.96 138 | 94.45 116 | 96.83 80 | 98.78 117 | 94.72 184 | 91.67 106 | 98.95 76 | 86.82 136 | 96.42 112 | 83.67 178 | 97.00 118 | 97.48 138 | 96.68 135 | 99.69 85 | 99.76 55 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 96.74 89 | 96.51 122 | 97.01 68 | 96.71 81 | 98.62 130 | 98.73 55 | 94.38 59 | 98.94 79 | 94.46 68 | 97.33 83 | 87.03 142 | 98.07 93 | 97.20 146 | 96.87 131 | 99.72 65 | 99.54 143 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TDRefinement | | | 93.04 167 | 93.57 193 | 92.41 148 | 96.58 82 | 98.77 118 | 97.78 102 | 91.96 101 | 98.12 130 | 80.84 168 | 89.13 186 | 79.87 217 | 87.78 220 | 96.44 162 | 94.50 205 | 99.54 168 | 98.15 199 |
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Anonymous202405211 | | | | 97.40 98 | | 96.45 83 | 99.54 53 | 98.08 89 | 93.79 82 | 98.24 125 | | 93.55 149 | 94.41 104 | 98.88 64 | 98.04 110 | 98.24 77 | 99.75 48 | 99.76 55 |
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Anonymous20240521 | | | 97.56 62 | 98.36 62 | 96.62 81 | 96.44 84 | 98.36 148 | 98.37 73 | 91.73 104 | 99.11 59 | 94.80 58 | 98.36 61 | 96.28 82 | 98.60 74 | 98.12 97 | 98.44 58 | 99.76 46 | 99.87 14 |
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ACMH+ | | 95.51 13 | 95.40 123 | 96.00 136 | 94.70 113 | 96.33 85 | 98.79 115 | 96.79 130 | 91.32 115 | 98.77 101 | 87.18 133 | 95.60 133 | 85.46 163 | 96.97 119 | 97.15 147 | 96.59 138 | 99.59 152 | 99.65 122 |
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tfpn1000 | | | 97.60 61 | 98.21 71 | 96.89 75 | 96.32 86 | 99.60 43 | 97.99 94 | 93.85 79 | 99.21 47 | 95.03 52 | 98.49 54 | 93.69 115 | 98.31 85 | 98.50 75 | 98.31 74 | 99.86 2 | 99.70 92 |
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Anonymous20231211 | | | 97.10 77 | 97.06 109 | 97.14 55 | 96.32 86 | 99.52 57 | 98.16 84 | 93.76 83 | 98.84 89 | 95.98 38 | 90.92 165 | 94.58 103 | 98.90 62 | 97.72 127 | 98.10 84 | 99.71 74 | 99.75 66 |
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tfpn111 | | | 96.96 82 | 96.91 112 | 97.03 62 | 96.31 88 | 99.67 14 | 98.41 65 | 93.99 69 | 97.35 160 | 94.50 65 | 98.65 48 | 86.93 144 | 99.14 46 | 98.26 86 | 97.80 100 | 99.82 14 | 99.70 92 |
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tfpn_ndepth | | | 97.71 56 | 98.30 66 | 97.02 66 | 96.31 88 | 99.56 50 | 98.05 91 | 93.94 77 | 98.95 76 | 95.59 42 | 98.40 59 | 94.79 99 | 98.39 80 | 98.40 79 | 98.42 60 | 99.86 2 | 99.56 141 |
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conf200view11 | | | 96.75 87 | 96.51 122 | 97.03 62 | 96.31 88 | 99.67 14 | 98.41 65 | 93.99 69 | 97.35 160 | 94.50 65 | 95.90 123 | 86.93 144 | 99.14 46 | 98.26 86 | 97.80 100 | 99.82 14 | 99.70 92 |
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thres100view900 | | | 96.72 90 | 96.47 126 | 97.00 70 | 96.31 88 | 99.52 57 | 98.28 79 | 94.01 67 | 97.35 160 | 94.52 63 | 95.90 123 | 86.93 144 | 99.09 56 | 98.07 104 | 97.87 95 | 99.81 27 | 99.63 128 |
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tfpn200view9 | | | 96.75 87 | 96.51 122 | 97.03 62 | 96.31 88 | 99.67 14 | 98.41 65 | 93.99 69 | 97.35 160 | 94.52 63 | 95.90 123 | 86.93 144 | 99.14 46 | 98.26 86 | 97.80 100 | 99.82 14 | 99.70 92 |
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thres200 | | | 96.76 86 | 96.53 120 | 97.03 62 | 96.31 88 | 99.67 14 | 98.37 73 | 93.99 69 | 97.68 155 | 94.49 67 | 95.83 127 | 86.77 149 | 99.18 41 | 98.26 86 | 97.82 99 | 99.82 14 | 99.66 119 |
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conf0.01 | | | 96.35 103 | 95.71 143 | 97.10 57 | 96.30 94 | 99.65 21 | 98.41 65 | 94.10 65 | 97.35 160 | 94.82 57 | 95.44 136 | 81.88 205 | 99.14 46 | 98.16 95 | 97.80 100 | 99.82 14 | 99.69 99 |
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conf0.002 | | | 96.31 105 | 95.63 145 | 97.11 56 | 96.29 95 | 99.64 26 | 98.41 65 | 94.11 64 | 97.35 160 | 94.86 55 | 95.49 135 | 81.06 210 | 99.14 46 | 98.14 96 | 98.02 89 | 99.82 14 | 99.69 99 |
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view800 | | | 96.70 92 | 96.45 129 | 96.99 72 | 96.29 95 | 99.69 12 | 98.39 72 | 93.95 76 | 97.92 142 | 94.25 76 | 96.23 116 | 85.57 162 | 99.22 34 | 98.28 83 | 97.71 106 | 99.82 14 | 99.76 55 |
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tfpn | | | 96.22 108 | 95.62 146 | 96.93 74 | 96.29 95 | 99.72 4 | 98.34 77 | 93.94 77 | 97.96 139 | 93.94 79 | 96.45 111 | 79.09 220 | 99.22 34 | 98.28 83 | 98.06 86 | 99.83 10 | 99.78 44 |
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view600 | | | 96.70 92 | 96.44 131 | 97.01 68 | 96.28 98 | 99.67 14 | 98.42 64 | 93.99 69 | 97.87 145 | 94.34 74 | 95.99 120 | 85.94 158 | 99.20 37 | 98.26 86 | 97.64 108 | 99.82 14 | 99.73 74 |
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thres600view7 | | | 96.69 94 | 96.43 133 | 97.00 70 | 96.28 98 | 99.67 14 | 98.41 65 | 93.99 69 | 97.85 148 | 94.29 75 | 95.96 121 | 85.91 159 | 99.19 39 | 98.26 86 | 97.63 109 | 99.82 14 | 99.73 74 |
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thres400 | | | 96.71 91 | 96.45 129 | 97.02 66 | 96.28 98 | 99.63 30 | 98.41 65 | 94.00 68 | 97.82 150 | 94.42 71 | 95.74 128 | 86.26 155 | 99.18 41 | 98.20 93 | 97.79 104 | 99.81 27 | 99.70 92 |
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canonicalmvs | | | 97.31 72 | 97.81 86 | 96.72 76 | 96.20 101 | 99.45 64 | 98.21 81 | 91.60 107 | 99.22 45 | 95.39 45 | 98.48 55 | 90.95 130 | 99.16 45 | 97.66 129 | 99.05 24 | 99.76 46 | 99.90 4 |
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conf0.05thres1000 | | | 96.34 104 | 96.47 126 | 96.17 90 | 96.16 102 | 99.71 8 | 97.82 98 | 93.46 87 | 98.10 131 | 90.69 111 | 96.75 99 | 85.26 166 | 99.11 53 | 98.05 108 | 97.65 107 | 99.82 14 | 99.80 35 |
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thresconf0.02 | | | 97.18 74 | 97.81 86 | 96.45 87 | 96.11 103 | 99.20 99 | 98.21 81 | 94.26 62 | 99.14 54 | 91.72 106 | 98.65 48 | 91.51 129 | 98.57 75 | 98.22 92 | 98.47 56 | 99.82 14 | 99.50 152 |
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tfpn_n400 | | | 97.32 69 | 98.38 60 | 96.09 94 | 96.07 104 | 99.30 86 | 98.00 92 | 93.84 80 | 99.35 26 | 90.50 114 | 98.93 32 | 94.24 108 | 98.30 86 | 98.65 60 | 98.60 49 | 99.83 10 | 99.60 131 |
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tfpnconf | | | 97.32 69 | 98.38 60 | 96.09 94 | 96.07 104 | 99.30 86 | 98.00 92 | 93.84 80 | 99.35 26 | 90.50 114 | 98.93 32 | 94.24 108 | 98.30 86 | 98.65 60 | 98.60 49 | 99.83 10 | 99.60 131 |
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tfpnview11 | | | 97.32 69 | 98.33 64 | 96.14 92 | 96.07 104 | 99.31 85 | 98.08 89 | 93.96 75 | 99.25 42 | 90.50 114 | 98.93 32 | 94.24 108 | 98.38 81 | 98.61 65 | 98.36 67 | 99.84 6 | 99.59 133 |
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IS_MVSNet | | | 97.86 51 | 98.86 48 | 96.68 77 | 96.02 107 | 99.72 4 | 98.35 76 | 93.37 91 | 98.75 104 | 94.01 77 | 96.88 98 | 98.40 61 | 98.48 78 | 99.09 31 | 99.42 5 | 99.83 10 | 99.80 35 |
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USDC | | | 94.26 144 | 94.83 156 | 93.59 130 | 96.02 107 | 98.44 141 | 97.84 97 | 88.65 157 | 98.86 84 | 82.73 160 | 94.02 145 | 80.56 211 | 96.76 126 | 97.28 143 | 96.15 152 | 99.55 164 | 98.50 193 |
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FC-MVSNet-train | | | 97.04 78 | 97.91 84 | 96.03 96 | 96.00 109 | 98.41 144 | 96.53 137 | 93.42 88 | 99.04 72 | 93.02 95 | 98.03 70 | 94.32 106 | 97.47 109 | 97.93 115 | 97.77 105 | 99.75 48 | 99.88 12 |
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casdiffmvs1 | | | 97.69 58 | 98.72 52 | 96.49 86 | 96.00 109 | 99.40 70 | 98.26 80 | 91.54 110 | 99.52 14 | 94.56 62 | 98.61 50 | 96.41 79 | 98.79 66 | 98.60 68 | 98.58 51 | 99.80 34 | 99.91 3 |
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Vis-MVSNet (Re-imp) | | | 97.40 67 | 98.89 47 | 95.66 105 | 95.99 111 | 99.62 34 | 97.82 98 | 93.22 92 | 98.82 92 | 91.40 108 | 96.94 96 | 98.56 59 | 95.70 152 | 99.14 29 | 99.41 6 | 99.79 36 | 99.75 66 |
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MVSTER | | | 97.16 75 | 97.71 88 | 96.52 83 | 95.97 112 | 98.48 137 | 98.63 58 | 92.10 97 | 98.68 106 | 95.96 39 | 99.23 17 | 91.79 127 | 96.87 123 | 98.76 53 | 97.37 123 | 99.57 160 | 99.68 106 |
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TinyColmap | | | 94.00 148 | 94.35 167 | 93.60 129 | 95.89 113 | 98.26 151 | 97.49 109 | 88.82 154 | 98.56 112 | 83.21 154 | 91.28 164 | 80.48 213 | 96.68 128 | 97.34 141 | 96.26 148 | 99.53 169 | 98.24 198 |
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DWT-MVSNet_training | | | 95.38 124 | 95.05 152 | 95.78 100 | 95.86 114 | 98.88 111 | 97.55 107 | 90.09 138 | 98.23 126 | 96.49 35 | 97.62 82 | 86.92 148 | 97.16 115 | 92.03 225 | 94.12 207 | 97.52 220 | 97.50 207 |
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EPMVS | | | 95.05 130 | 96.86 114 | 92.94 145 | 95.84 115 | 98.96 108 | 96.68 131 | 79.87 214 | 99.05 70 | 90.15 117 | 97.12 91 | 95.99 86 | 97.49 108 | 95.17 192 | 94.75 201 | 97.59 219 | 96.96 216 |
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PMMVS | | | 97.52 63 | 98.39 59 | 96.51 84 | 95.82 116 | 98.73 124 | 97.80 100 | 93.05 94 | 98.76 102 | 94.39 73 | 99.07 28 | 97.03 75 | 98.55 76 | 98.31 82 | 97.61 110 | 99.43 181 | 99.21 171 |
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casdiffmvs | | | 97.36 68 | 98.33 64 | 96.23 88 | 95.78 117 | 99.37 74 | 97.62 105 | 91.41 113 | 99.07 68 | 94.45 69 | 98.68 47 | 94.90 95 | 98.37 83 | 98.27 85 | 98.12 82 | 99.75 48 | 99.87 14 |
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MVS_Test | | | 97.30 73 | 98.54 55 | 95.87 98 | 95.74 118 | 99.28 89 | 98.19 83 | 91.40 114 | 99.18 51 | 91.59 107 | 98.17 65 | 96.18 83 | 98.63 73 | 98.61 65 | 98.55 53 | 99.66 107 | 99.78 44 |
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diffmvs | | | 96.92 83 | 97.86 85 | 95.82 99 | 95.70 119 | 99.28 89 | 97.98 95 | 91.13 120 | 99.08 62 | 92.48 100 | 98.09 67 | 92.81 121 | 98.18 88 | 98.11 98 | 97.83 98 | 99.44 179 | 99.81 32 |
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tpmrst | | | 93.86 153 | 95.88 140 | 91.50 172 | 95.69 120 | 98.62 130 | 95.64 150 | 79.41 219 | 98.80 95 | 83.76 150 | 95.63 132 | 96.13 84 | 97.25 112 | 92.92 217 | 92.31 222 | 97.27 225 | 96.74 219 |
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ADS-MVSNet | | | 94.65 137 | 97.04 110 | 91.88 165 | 95.68 121 | 98.99 105 | 95.89 145 | 79.03 223 | 99.15 52 | 85.81 141 | 96.96 95 | 98.21 64 | 97.10 116 | 94.48 211 | 94.24 206 | 97.74 215 | 97.21 212 |
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EPP-MVSNet | | | 97.75 55 | 98.71 53 | 96.63 80 | 95.68 121 | 99.56 50 | 97.51 108 | 93.10 93 | 99.22 45 | 94.99 54 | 97.18 90 | 97.30 72 | 98.65 71 | 98.83 47 | 98.93 31 | 99.84 6 | 99.92 1 |
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DI_MVS_plusplus_trai | | | 96.90 84 | 97.49 94 | 96.21 89 | 95.61 123 | 99.40 70 | 98.72 56 | 92.11 96 | 99.14 54 | 92.98 96 | 93.08 159 | 95.14 93 | 98.13 91 | 98.05 108 | 97.91 93 | 99.74 54 | 99.73 74 |
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dps | | | 94.63 138 | 95.31 151 | 93.84 123 | 95.53 124 | 98.71 125 | 96.54 135 | 80.12 213 | 97.81 152 | 97.21 25 | 96.98 94 | 92.37 123 | 96.34 138 | 92.46 222 | 91.77 226 | 97.26 226 | 97.08 214 |
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PatchmatchNet | | | 94.70 135 | 97.08 108 | 91.92 162 | 95.53 124 | 98.85 113 | 95.77 147 | 79.54 218 | 98.95 76 | 85.98 139 | 98.52 52 | 96.45 76 | 97.39 111 | 95.32 184 | 94.09 208 | 97.32 224 | 97.38 211 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
test-LLR | | | 95.50 121 | 97.32 101 | 93.37 137 | 95.49 126 | 98.74 122 | 96.44 139 | 90.82 123 | 98.18 127 | 82.75 158 | 96.60 106 | 94.67 101 | 95.54 159 | 98.09 101 | 96.00 153 | 99.20 194 | 98.93 183 |
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test0.0.03 1 | | | 96.69 94 | 98.12 76 | 95.01 110 | 95.49 126 | 98.99 105 | 95.86 146 | 90.82 123 | 98.38 120 | 92.54 99 | 96.66 103 | 97.33 70 | 95.75 150 | 97.75 125 | 98.34 70 | 99.60 146 | 99.40 161 |
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CostFormer | | | 94.25 145 | 94.88 155 | 93.51 134 | 95.43 128 | 98.34 149 | 96.21 142 | 80.64 210 | 97.94 141 | 94.01 77 | 98.30 63 | 86.20 157 | 97.52 106 | 92.71 218 | 92.69 218 | 97.23 228 | 98.02 203 |
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MDTV_nov1_ep13 | | | 95.57 119 | 97.48 95 | 93.35 139 | 95.43 128 | 98.97 107 | 97.19 120 | 83.72 205 | 98.92 82 | 87.91 129 | 97.75 77 | 96.12 85 | 97.88 101 | 96.84 155 | 95.64 164 | 97.96 213 | 98.10 200 |
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tpm cat1 | | | 94.06 146 | 94.90 154 | 93.06 142 | 95.42 130 | 98.52 136 | 96.64 133 | 80.67 209 | 97.82 150 | 92.63 98 | 93.39 153 | 95.00 94 | 96.06 145 | 91.36 229 | 91.58 228 | 96.98 229 | 96.66 221 |
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tpmp4_e23 | | | 93.84 155 | 94.58 162 | 92.98 144 | 95.41 131 | 98.29 150 | 96.81 129 | 80.57 211 | 98.15 129 | 90.53 113 | 97.00 93 | 84.39 174 | 96.91 121 | 93.69 214 | 92.45 220 | 97.67 218 | 98.06 201 |
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Vis-MVSNet | | | 96.16 110 | 98.22 70 | 93.75 125 | 95.33 132 | 99.70 11 | 97.27 116 | 90.85 122 | 98.30 122 | 85.51 143 | 95.72 130 | 96.45 76 | 93.69 207 | 98.70 58 | 99.00 26 | 99.84 6 | 99.69 99 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CVMVSNet | | | 95.33 127 | 97.09 107 | 93.27 140 | 95.23 133 | 98.39 146 | 95.49 153 | 92.58 95 | 97.71 154 | 83.00 157 | 94.44 144 | 93.28 118 | 93.92 204 | 97.79 121 | 98.54 55 | 99.41 183 | 99.45 157 |
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IterMVS-LS | | | 96.12 111 | 97.48 95 | 94.53 114 | 95.19 134 | 97.56 187 | 97.15 121 | 89.19 151 | 99.08 62 | 88.23 125 | 94.97 138 | 94.73 100 | 97.84 102 | 97.86 119 | 98.26 76 | 99.60 146 | 99.88 12 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 95.81 115 | 97.31 104 | 94.06 120 | 95.09 135 | 99.35 78 | 97.24 118 | 88.22 162 | 98.54 113 | 85.38 144 | 98.52 52 | 88.68 136 | 98.70 69 | 98.32 81 | 97.93 91 | 99.74 54 | 99.84 22 |
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testgi | | | 95.67 118 | 97.48 95 | 93.56 131 | 95.07 136 | 99.00 103 | 95.33 157 | 88.47 159 | 98.80 95 | 86.90 135 | 97.30 85 | 92.33 124 | 95.97 147 | 97.66 129 | 97.91 93 | 99.60 146 | 99.38 162 |
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RPMNet | | | 94.66 136 | 97.16 106 | 91.75 168 | 94.98 137 | 98.59 132 | 97.00 128 | 78.37 227 | 97.98 136 | 83.78 148 | 96.27 114 | 94.09 113 | 96.91 121 | 97.36 140 | 96.73 133 | 99.48 173 | 99.09 178 |
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LP | | | 92.12 200 | 94.60 160 | 89.22 208 | 94.96 138 | 98.45 140 | 93.01 205 | 77.58 228 | 97.85 148 | 77.26 205 | 89.80 180 | 93.00 120 | 94.54 191 | 93.69 214 | 92.58 219 | 98.00 212 | 96.83 218 |
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CR-MVSNet | | | 94.57 141 | 97.34 100 | 91.33 176 | 94.90 139 | 98.59 132 | 97.15 121 | 79.14 221 | 97.98 136 | 80.42 175 | 96.59 108 | 93.50 117 | 96.85 124 | 98.10 99 | 97.49 116 | 99.50 172 | 99.15 173 |
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gg-mvs-nofinetune | | | 90.85 209 | 94.14 169 | 87.02 215 | 94.89 140 | 99.25 92 | 98.64 57 | 76.29 232 | 88.24 234 | 57.50 236 | 79.93 229 | 95.45 90 | 95.18 185 | 98.77 52 | 98.07 85 | 99.62 136 | 99.24 169 |
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IterMVS | | | 94.81 134 | 97.71 88 | 91.42 174 | 94.83 141 | 97.63 180 | 97.38 111 | 85.08 190 | 98.93 81 | 75.67 211 | 94.02 145 | 97.64 67 | 96.66 130 | 98.45 76 | 97.60 111 | 98.90 200 | 99.72 86 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchT | | | 93.96 150 | 97.36 99 | 90.00 203 | 94.76 142 | 98.65 128 | 90.11 220 | 78.57 226 | 97.96 139 | 80.42 175 | 96.07 118 | 94.10 112 | 96.85 124 | 98.10 99 | 97.49 116 | 99.26 192 | 99.15 173 |
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CDS-MVSNet | | | 96.59 100 | 98.02 80 | 94.92 111 | 94.45 143 | 98.96 108 | 97.46 110 | 91.75 103 | 97.86 147 | 90.07 118 | 96.02 119 | 97.25 73 | 96.21 139 | 98.04 110 | 98.38 64 | 99.60 146 | 99.65 122 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm | | | 92.38 190 | 94.79 157 | 89.56 206 | 94.30 144 | 97.50 192 | 94.24 198 | 78.97 224 | 97.72 153 | 74.93 215 | 97.97 72 | 82.91 189 | 96.60 132 | 93.65 216 | 94.81 199 | 98.33 208 | 98.98 181 |
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Fast-Effi-MVS+ | | | 95.38 124 | 96.52 121 | 94.05 121 | 94.15 145 | 99.14 101 | 97.24 118 | 86.79 176 | 98.53 114 | 87.62 131 | 94.51 143 | 87.06 141 | 98.76 67 | 98.60 68 | 98.04 88 | 99.72 65 | 99.77 51 |
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Effi-MVS+-dtu | | | 95.74 117 | 98.04 78 | 93.06 142 | 93.92 146 | 99.16 100 | 97.90 96 | 88.16 165 | 99.07 68 | 82.02 163 | 98.02 71 | 94.32 106 | 96.74 127 | 98.53 73 | 97.56 112 | 99.61 139 | 99.62 129 |
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Fast-Effi-MVS+-dtu | | | 95.38 124 | 98.20 72 | 92.09 155 | 93.91 147 | 98.87 112 | 97.35 113 | 85.01 192 | 99.08 62 | 81.09 167 | 98.10 66 | 96.36 80 | 95.62 156 | 98.43 78 | 97.03 127 | 99.55 164 | 99.50 152 |
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testpf | | | 91.80 205 | 94.43 166 | 88.74 209 | 93.89 148 | 95.30 225 | 92.05 211 | 71.77 236 | 97.52 157 | 87.24 132 | 94.77 141 | 92.68 122 | 91.48 216 | 91.75 228 | 92.11 225 | 96.02 233 | 96.89 217 |
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TAMVS | | | 95.53 120 | 96.50 125 | 94.39 117 | 93.86 149 | 99.03 102 | 96.67 132 | 89.55 148 | 97.33 166 | 90.64 112 | 93.02 160 | 91.58 128 | 96.21 139 | 97.72 127 | 97.43 121 | 99.43 181 | 99.36 163 |
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GBi-Net | | | 96.98 80 | 98.00 81 | 95.78 100 | 93.81 150 | 97.98 158 | 98.09 86 | 91.32 115 | 98.80 95 | 93.92 80 | 97.21 87 | 95.94 87 | 97.89 98 | 98.07 104 | 98.34 70 | 99.68 94 | 99.67 111 |
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test1 | | | 96.98 80 | 98.00 81 | 95.78 100 | 93.81 150 | 97.98 158 | 98.09 86 | 91.32 115 | 98.80 95 | 93.92 80 | 97.21 87 | 95.94 87 | 97.89 98 | 98.07 104 | 98.34 70 | 99.68 94 | 99.67 111 |
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FMVSNet2 | | | 96.64 97 | 97.50 93 | 95.63 106 | 93.81 150 | 97.98 158 | 98.09 86 | 90.87 121 | 98.99 75 | 93.48 88 | 93.17 156 | 95.25 92 | 97.89 98 | 98.63 64 | 98.80 41 | 99.68 94 | 99.67 111 |
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MVS-HIRNet | | | 92.51 184 | 95.97 137 | 88.48 212 | 93.73 153 | 98.37 147 | 90.33 218 | 75.36 235 | 98.32 121 | 77.78 202 | 89.15 185 | 94.87 96 | 95.14 186 | 97.62 133 | 96.39 143 | 98.51 203 | 97.11 213 |
|
GA-MVS | | | 93.93 151 | 96.31 135 | 91.16 182 | 93.61 154 | 98.79 115 | 95.39 156 | 90.69 127 | 98.25 124 | 73.28 219 | 96.15 117 | 88.42 137 | 94.39 196 | 97.76 124 | 95.35 170 | 99.58 156 | 99.45 157 |
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FC-MVSNet-test | | | 96.07 112 | 97.94 83 | 93.89 122 | 93.60 155 | 98.67 127 | 96.62 134 | 90.30 134 | 98.76 102 | 88.62 123 | 95.57 134 | 97.63 68 | 94.48 194 | 97.97 113 | 97.48 118 | 99.71 74 | 99.52 146 |
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FMVSNet3 | | | 97.02 79 | 98.12 76 | 95.73 104 | 93.59 156 | 97.98 158 | 98.34 77 | 91.32 115 | 98.80 95 | 93.92 80 | 97.21 87 | 95.94 87 | 97.63 105 | 98.61 65 | 98.62 47 | 99.61 139 | 99.65 122 |
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FMVSNet1 | | | 95.77 116 | 96.41 134 | 95.03 109 | 93.42 157 | 97.86 165 | 97.11 124 | 89.89 142 | 98.53 114 | 92.00 103 | 89.17 184 | 93.23 119 | 98.15 90 | 98.07 104 | 98.34 70 | 99.61 139 | 99.69 99 |
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tfpnnormal | | | 93.85 154 | 94.12 171 | 93.54 133 | 93.22 158 | 98.24 153 | 95.45 154 | 91.96 101 | 94.61 222 | 83.91 146 | 90.74 167 | 81.75 207 | 97.04 117 | 97.49 137 | 96.16 151 | 99.68 94 | 99.84 22 |
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TransMVSNet (Re) | | | 93.45 158 | 94.08 173 | 92.72 147 | 92.83 159 | 97.62 183 | 94.94 163 | 91.54 110 | 95.65 218 | 83.06 156 | 88.93 187 | 83.53 179 | 94.25 197 | 97.41 139 | 97.03 127 | 99.67 102 | 98.40 197 |
|
LTVRE_ROB | | 93.20 16 | 92.84 170 | 94.92 153 | 90.43 199 | 92.83 159 | 98.63 129 | 97.08 126 | 87.87 168 | 97.91 143 | 68.42 225 | 93.54 150 | 79.46 219 | 96.62 131 | 97.55 135 | 97.40 122 | 99.74 54 | 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 |
TESTMET0.1,1 | | | 94.95 132 | 97.32 101 | 92.20 152 | 92.62 161 | 98.74 122 | 96.44 139 | 86.67 178 | 98.18 127 | 82.75 158 | 96.60 106 | 94.67 101 | 95.54 159 | 98.09 101 | 96.00 153 | 99.20 194 | 98.93 183 |
|
pm-mvs1 | | | 94.27 143 | 95.57 147 | 92.75 146 | 92.58 162 | 98.13 156 | 94.87 169 | 90.71 126 | 96.70 190 | 83.78 148 | 89.94 179 | 89.85 135 | 94.96 189 | 97.58 134 | 97.07 126 | 99.61 139 | 99.72 86 |
|
NR-MVSNet | | | 94.01 147 | 94.51 163 | 93.44 135 | 92.56 163 | 97.77 166 | 95.67 148 | 91.57 108 | 97.17 171 | 85.84 140 | 93.13 157 | 80.53 212 | 95.29 182 | 97.01 151 | 96.17 150 | 99.69 85 | 99.75 66 |
|
EG-PatchMatch MVS | | | 92.45 185 | 93.92 182 | 90.72 194 | 92.56 163 | 98.43 143 | 94.88 168 | 84.54 196 | 97.18 170 | 79.55 190 | 86.12 219 | 83.23 183 | 93.15 210 | 97.22 145 | 96.00 153 | 99.67 102 | 99.27 167 |
|
test-mter | | | 94.86 133 | 97.32 101 | 92.00 159 | 92.41 165 | 98.82 114 | 96.18 143 | 86.35 182 | 98.05 133 | 82.28 161 | 96.48 110 | 94.39 105 | 95.46 169 | 98.17 94 | 96.20 149 | 99.32 189 | 99.13 177 |
|
our_test_3 | | | | | | 92.30 166 | 97.58 185 | 90.09 221 | | | | | | | | | | |
|
pmmvs4 | | | 95.09 129 | 95.90 139 | 94.14 119 | 92.29 167 | 97.70 172 | 95.45 154 | 90.31 132 | 98.60 108 | 90.70 110 | 93.25 154 | 89.90 134 | 96.67 129 | 97.13 148 | 95.42 167 | 99.44 179 | 99.28 166 |
|
FMVSNet5 | | | 95.42 122 | 96.47 126 | 94.20 118 | 92.26 168 | 95.99 212 | 95.66 149 | 87.15 172 | 97.87 145 | 93.46 89 | 96.68 102 | 93.79 114 | 97.52 106 | 97.10 150 | 97.21 125 | 99.11 197 | 96.62 222 |
|
UniMVSNet (Re) | | | 94.58 140 | 95.34 149 | 93.71 127 | 92.25 169 | 98.08 157 | 94.97 162 | 91.29 119 | 97.03 177 | 87.94 128 | 93.97 147 | 86.25 156 | 96.07 144 | 96.27 171 | 95.97 156 | 99.72 65 | 99.79 42 |
|
v18 | | | 92.63 182 | 93.67 188 | 91.43 173 | 92.13 170 | 95.65 213 | 95.09 159 | 85.44 187 | 97.06 175 | 80.78 169 | 90.06 172 | 83.06 184 | 95.47 168 | 95.16 196 | 95.01 185 | 99.64 124 | 99.67 111 |
|
v16 | | | 92.66 181 | 93.80 185 | 91.32 177 | 92.13 170 | 95.62 215 | 94.89 165 | 85.12 189 | 97.20 169 | 80.66 170 | 89.96 178 | 83.93 176 | 95.49 162 | 95.17 192 | 95.04 180 | 99.63 130 | 99.68 106 |
|
v17 | | | 92.55 183 | 93.65 189 | 91.27 179 | 92.11 172 | 95.63 214 | 94.89 165 | 85.15 188 | 97.12 174 | 80.39 178 | 90.02 173 | 83.02 185 | 95.45 170 | 95.17 192 | 94.92 195 | 99.66 107 | 99.68 106 |
|
SixPastTwentyTwo | | | 93.44 159 | 95.32 150 | 91.24 180 | 92.11 172 | 98.40 145 | 92.77 207 | 88.64 158 | 98.09 132 | 77.83 201 | 93.51 151 | 85.74 160 | 96.52 135 | 96.91 153 | 94.89 198 | 99.59 152 | 99.73 74 |
|
v8 | | | 92.87 169 | 93.87 184 | 91.72 170 | 92.05 174 | 97.50 192 | 94.79 176 | 88.20 163 | 96.85 186 | 80.11 181 | 90.01 174 | 82.86 191 | 95.48 163 | 95.15 200 | 94.90 196 | 99.66 107 | 99.80 35 |
|
v6 | | | 93.11 163 | 93.98 177 | 92.10 154 | 92.01 175 | 97.71 169 | 94.86 172 | 90.15 135 | 96.96 180 | 80.47 174 | 90.01 174 | 83.26 182 | 95.48 163 | 95.17 192 | 95.01 185 | 99.64 124 | 99.76 55 |
|
v1neww | | | 93.06 164 | 93.94 179 | 92.03 157 | 91.99 176 | 97.70 172 | 94.79 176 | 90.14 136 | 96.93 182 | 80.13 179 | 89.97 176 | 83.01 186 | 95.48 163 | 95.16 196 | 95.01 185 | 99.63 130 | 99.76 55 |
|
v7new | | | 93.06 164 | 93.94 179 | 92.03 157 | 91.99 176 | 97.70 172 | 94.79 176 | 90.14 136 | 96.93 182 | 80.13 179 | 89.97 176 | 83.01 186 | 95.48 163 | 95.16 196 | 95.01 185 | 99.63 130 | 99.76 55 |
|
WR-MVS_H | | | 93.54 157 | 94.67 159 | 92.22 150 | 91.95 178 | 97.91 163 | 94.58 192 | 88.75 155 | 96.64 194 | 83.88 147 | 90.66 169 | 85.13 167 | 94.40 195 | 96.54 161 | 95.91 158 | 99.73 60 | 99.89 8 |
|
V42 | | | 93.05 166 | 93.90 183 | 92.04 156 | 91.91 179 | 97.66 178 | 94.91 164 | 89.91 141 | 96.85 186 | 80.58 172 | 89.66 181 | 83.43 181 | 95.37 175 | 95.03 206 | 94.90 196 | 99.59 152 | 99.78 44 |
|
EU-MVSNet | | | 92.80 173 | 94.76 158 | 90.51 197 | 91.88 180 | 96.74 209 | 92.48 209 | 88.69 156 | 96.21 205 | 79.00 197 | 91.51 161 | 87.82 138 | 91.83 215 | 95.87 179 | 96.27 146 | 99.21 193 | 98.92 186 |
|
N_pmnet | | | 92.21 197 | 94.60 160 | 89.42 207 | 91.88 180 | 97.38 199 | 89.15 223 | 89.74 146 | 97.89 144 | 73.75 217 | 87.94 206 | 92.23 125 | 93.85 205 | 96.10 175 | 93.20 214 | 98.15 211 | 97.43 210 |
|
UniMVSNet_NR-MVSNet | | | 94.59 139 | 95.47 148 | 93.55 132 | 91.85 182 | 97.89 164 | 95.03 160 | 92.00 99 | 97.33 166 | 86.12 137 | 93.19 155 | 87.29 140 | 96.60 132 | 96.12 174 | 96.70 134 | 99.72 65 | 99.80 35 |
|
v15 | | | 92.27 195 | 93.33 200 | 91.04 184 | 91.83 183 | 95.60 216 | 94.79 176 | 84.88 193 | 96.66 192 | 79.66 188 | 88.72 192 | 82.45 198 | 95.40 173 | 95.19 191 | 95.00 189 | 99.65 113 | 99.67 111 |
|
v7 | | | 92.97 168 | 94.11 172 | 91.65 171 | 91.83 183 | 97.55 189 | 94.86 172 | 88.19 164 | 96.96 180 | 79.72 187 | 88.16 201 | 84.68 171 | 95.63 154 | 96.33 168 | 95.30 172 | 99.65 113 | 99.77 51 |
|
pmmvs6 | | | 91.90 204 | 92.53 215 | 91.17 181 | 91.81 185 | 97.63 180 | 93.23 203 | 88.37 161 | 93.43 227 | 80.61 171 | 77.32 231 | 87.47 139 | 94.12 199 | 96.58 158 | 95.72 162 | 98.88 201 | 99.53 144 |
|
V14 | | | 92.31 194 | 93.41 198 | 91.03 185 | 91.80 186 | 95.59 218 | 94.79 176 | 84.70 194 | 96.58 197 | 79.83 183 | 88.79 190 | 82.98 188 | 95.41 172 | 95.22 186 | 95.02 184 | 99.65 113 | 99.67 111 |
|
v1 | | | 92.81 171 | 93.57 193 | 91.94 161 | 91.79 187 | 97.70 172 | 94.80 175 | 90.32 130 | 96.52 200 | 79.75 185 | 88.47 197 | 82.46 197 | 95.32 179 | 95.14 202 | 94.96 192 | 99.63 130 | 99.73 74 |
|
v10 | | | 92.79 175 | 94.06 174 | 91.31 178 | 91.78 188 | 97.29 203 | 94.87 169 | 86.10 183 | 96.97 179 | 79.82 184 | 88.16 201 | 84.56 172 | 95.63 154 | 96.33 168 | 95.31 171 | 99.65 113 | 99.80 35 |
|
V9 | | | 92.24 196 | 93.32 202 | 90.98 187 | 91.76 189 | 95.58 220 | 94.83 174 | 84.50 198 | 96.68 191 | 79.73 186 | 88.66 193 | 82.39 199 | 95.39 174 | 95.22 186 | 95.03 182 | 99.65 113 | 99.67 111 |
|
v1141 | | | 92.79 175 | 93.61 190 | 91.84 167 | 91.75 190 | 97.71 169 | 94.74 182 | 90.33 129 | 96.58 197 | 79.21 195 | 88.59 194 | 82.53 196 | 95.36 176 | 95.16 196 | 94.96 192 | 99.63 130 | 99.72 86 |
|
divwei89l23v2f112 | | | 92.80 173 | 93.60 192 | 91.86 166 | 91.75 190 | 97.71 169 | 94.75 181 | 90.32 130 | 96.54 199 | 79.35 192 | 88.59 194 | 82.55 195 | 95.35 177 | 95.15 200 | 94.96 192 | 99.63 130 | 99.72 86 |
|
v13 | | | 92.16 199 | 93.28 204 | 90.85 192 | 91.75 190 | 95.58 220 | 94.65 189 | 84.23 202 | 96.49 203 | 79.51 191 | 88.40 199 | 82.58 194 | 95.31 181 | 95.21 189 | 95.03 182 | 99.66 107 | 99.68 106 |
|
MIMVSNet | | | 94.49 142 | 97.59 92 | 90.87 191 | 91.74 193 | 98.70 126 | 94.68 186 | 78.73 225 | 97.98 136 | 83.71 151 | 97.71 80 | 94.81 98 | 96.96 120 | 97.97 113 | 97.92 92 | 99.40 185 | 98.04 202 |
|
v11 | | | 92.43 187 | 93.77 186 | 90.85 192 | 91.72 194 | 95.58 220 | 94.87 169 | 84.07 204 | 96.98 178 | 79.28 193 | 88.03 204 | 84.22 175 | 95.53 161 | 96.55 160 | 95.36 169 | 99.65 113 | 99.70 92 |
|
v12 | | | 92.18 198 | 93.29 203 | 90.88 190 | 91.70 195 | 95.59 218 | 94.61 190 | 84.36 200 | 96.65 193 | 79.59 189 | 88.85 188 | 82.03 203 | 95.35 177 | 95.22 186 | 95.04 180 | 99.65 113 | 99.68 106 |
|
v1144 | | | 92.81 171 | 94.03 175 | 91.40 175 | 91.68 196 | 97.60 184 | 94.73 183 | 88.40 160 | 96.71 189 | 78.48 199 | 88.14 203 | 84.46 173 | 95.45 170 | 96.31 170 | 95.22 174 | 99.65 113 | 99.76 55 |
|
DU-MVS | | | 93.98 149 | 94.44 165 | 93.44 135 | 91.66 197 | 97.77 166 | 95.03 160 | 91.57 108 | 97.17 171 | 86.12 137 | 93.13 157 | 81.13 209 | 96.60 132 | 95.10 203 | 97.01 129 | 99.67 102 | 99.80 35 |
|
Baseline_NR-MVSNet | | | 93.87 152 | 93.98 177 | 93.75 125 | 91.66 197 | 97.02 204 | 95.53 152 | 91.52 112 | 97.16 173 | 87.77 130 | 87.93 207 | 83.69 177 | 96.35 137 | 95.10 203 | 97.23 124 | 99.68 94 | 99.73 74 |
|
CP-MVSNet | | | 93.25 161 | 94.00 176 | 92.38 149 | 91.65 199 | 97.56 187 | 94.38 195 | 89.20 150 | 96.05 210 | 83.16 155 | 89.51 182 | 81.97 204 | 96.16 143 | 96.43 163 | 96.56 139 | 99.71 74 | 99.89 8 |
|
v148 | | | 92.36 192 | 92.88 209 | 91.75 168 | 91.63 200 | 97.66 178 | 92.64 208 | 90.55 128 | 96.09 208 | 83.34 153 | 88.19 200 | 80.00 215 | 92.74 211 | 93.98 213 | 94.58 204 | 99.58 156 | 99.69 99 |
|
PS-CasMVS | | | 92.72 178 | 93.36 199 | 91.98 160 | 91.62 201 | 97.52 190 | 94.13 199 | 88.98 152 | 95.94 213 | 81.51 166 | 87.35 209 | 79.95 216 | 95.91 148 | 96.37 165 | 96.49 141 | 99.70 83 | 99.89 8 |
|
v2v482 | | | 92.77 177 | 93.52 197 | 91.90 164 | 91.59 202 | 97.63 180 | 94.57 193 | 90.31 132 | 96.80 188 | 79.22 194 | 88.74 191 | 81.55 208 | 96.04 146 | 95.26 185 | 94.97 191 | 99.66 107 | 99.69 99 |
|
v1192 | | | 92.43 187 | 93.61 190 | 91.05 183 | 91.53 203 | 97.43 196 | 94.61 190 | 87.99 166 | 96.60 195 | 76.72 207 | 87.11 211 | 82.74 192 | 95.85 149 | 96.35 167 | 95.30 172 | 99.60 146 | 99.74 70 |
|
WR-MVS | | | 93.43 160 | 94.48 164 | 92.21 151 | 91.52 204 | 97.69 176 | 94.66 188 | 89.98 140 | 96.86 185 | 83.43 152 | 90.12 171 | 85.03 168 | 93.94 203 | 96.02 177 | 95.82 159 | 99.71 74 | 99.82 27 |
|
v144192 | | | 92.38 190 | 93.55 196 | 91.00 186 | 91.44 205 | 97.47 195 | 94.27 196 | 87.41 171 | 96.52 200 | 78.03 200 | 87.50 208 | 82.65 193 | 95.32 179 | 95.82 180 | 95.15 176 | 99.55 164 | 99.78 44 |
|
pmmvs5 | | | 92.71 180 | 94.27 168 | 90.90 189 | 91.42 206 | 97.74 168 | 93.23 203 | 86.66 179 | 95.99 212 | 78.96 198 | 91.45 162 | 83.44 180 | 95.55 158 | 97.30 142 | 95.05 179 | 99.58 156 | 98.93 183 |
|
v1921920 | | | 92.36 192 | 93.57 193 | 90.94 188 | 91.39 207 | 97.39 198 | 94.70 185 | 87.63 170 | 96.60 195 | 76.63 208 | 86.98 212 | 82.89 190 | 95.75 150 | 96.26 172 | 95.14 177 | 99.55 164 | 99.73 74 |
|
gm-plane-assit | | | 89.44 216 | 92.82 213 | 85.49 219 | 91.37 208 | 95.34 224 | 79.55 234 | 82.12 207 | 91.68 230 | 64.79 231 | 87.98 205 | 80.26 214 | 95.66 153 | 98.51 74 | 97.56 112 | 99.45 177 | 98.41 195 |
|
v1240 | | | 91.99 201 | 93.33 200 | 90.44 198 | 91.29 209 | 97.30 202 | 94.25 197 | 86.79 176 | 96.43 204 | 75.49 213 | 86.34 217 | 81.85 206 | 95.29 182 | 96.42 164 | 95.22 174 | 99.52 170 | 99.73 74 |
|
PEN-MVS | | | 92.72 178 | 93.20 205 | 92.15 153 | 91.29 209 | 97.31 201 | 94.67 187 | 89.81 143 | 96.19 206 | 81.83 164 | 88.58 196 | 79.06 221 | 95.61 157 | 95.21 189 | 96.27 146 | 99.72 65 | 99.82 27 |
|
TranMVSNet+NR-MVSNet | | | 93.67 156 | 94.14 169 | 93.13 141 | 91.28 211 | 97.58 185 | 95.60 151 | 91.97 100 | 97.06 175 | 84.05 145 | 90.64 170 | 82.22 200 | 96.17 142 | 94.94 207 | 96.78 132 | 99.69 85 | 99.78 44 |
|
anonymousdsp | | | 93.12 162 | 95.86 141 | 89.93 205 | 91.09 212 | 98.25 152 | 95.12 158 | 85.08 190 | 97.44 158 | 73.30 218 | 90.89 166 | 90.78 131 | 95.25 184 | 97.91 116 | 95.96 157 | 99.71 74 | 99.82 27 |
|
MDTV_nov1_ep13_2view | | | 92.44 186 | 95.66 144 | 88.68 210 | 91.05 213 | 97.92 162 | 92.17 210 | 79.64 216 | 98.83 90 | 76.20 209 | 91.45 162 | 93.51 116 | 95.04 187 | 95.68 181 | 93.70 211 | 97.96 213 | 98.53 192 |
|
DTE-MVSNet | | | 92.42 189 | 92.85 211 | 91.91 163 | 90.87 214 | 96.97 205 | 94.53 194 | 89.81 143 | 95.86 215 | 81.59 165 | 88.83 189 | 77.88 224 | 95.01 188 | 94.34 212 | 96.35 144 | 99.64 124 | 99.73 74 |
|
V4 | | | 91.92 203 | 93.10 206 | 90.55 196 | 90.64 215 | 97.51 191 | 93.93 201 | 87.02 173 | 95.81 217 | 77.61 204 | 86.93 213 | 82.19 201 | 94.50 193 | 94.72 208 | 94.68 203 | 99.62 136 | 99.85 20 |
|
v52 | | | 91.94 202 | 93.10 206 | 90.57 195 | 90.62 216 | 97.50 192 | 93.98 200 | 87.02 173 | 95.86 215 | 77.67 203 | 86.93 213 | 82.16 202 | 94.53 192 | 94.71 209 | 94.70 202 | 99.61 139 | 99.85 20 |
|
v748 | | | 91.12 208 | 91.95 216 | 90.16 201 | 90.60 217 | 97.35 200 | 91.11 212 | 87.92 167 | 94.75 221 | 80.54 173 | 86.26 218 | 75.97 226 | 91.13 217 | 94.63 210 | 94.81 199 | 99.65 113 | 99.90 4 |
|
v7n | | | 91.61 206 | 92.95 208 | 90.04 202 | 90.56 218 | 97.69 176 | 93.74 202 | 85.59 185 | 95.89 214 | 76.95 206 | 86.60 216 | 78.60 223 | 93.76 206 | 97.01 151 | 94.99 190 | 99.65 113 | 99.87 14 |
|
test20.03 | | | 90.65 212 | 93.71 187 | 87.09 214 | 90.44 219 | 96.24 210 | 89.74 222 | 85.46 186 | 95.59 219 | 72.99 220 | 90.68 168 | 85.33 164 | 84.41 227 | 95.94 178 | 95.10 178 | 99.52 170 | 97.06 215 |
|
FPMVS | | | 83.82 223 | 84.61 229 | 82.90 224 | 90.39 220 | 90.71 232 | 90.85 216 | 84.10 203 | 95.47 220 | 65.15 229 | 83.44 222 | 74.46 228 | 75.48 230 | 81.63 234 | 79.42 236 | 91.42 237 | 87.14 234 |
|
Anonymous20231206 | | | 90.70 211 | 93.93 181 | 86.92 216 | 90.21 221 | 96.79 207 | 90.30 219 | 86.61 180 | 96.05 210 | 69.25 224 | 88.46 198 | 84.86 170 | 85.86 224 | 97.11 149 | 96.47 142 | 99.30 190 | 97.80 206 |
|
new_pmnet | | | 90.45 213 | 92.84 212 | 87.66 213 | 88.96 222 | 96.16 211 | 88.71 224 | 84.66 195 | 97.56 156 | 71.91 223 | 85.60 220 | 86.58 153 | 93.28 208 | 96.07 176 | 93.54 212 | 98.46 205 | 94.39 226 |
|
testus | | | 88.77 218 | 92.77 214 | 84.10 222 | 88.24 223 | 93.95 228 | 87.16 227 | 84.24 201 | 97.37 159 | 61.54 235 | 95.70 131 | 73.10 229 | 84.90 226 | 95.56 182 | 95.82 159 | 98.51 203 | 97.88 205 |
|
test2356 | | | 88.81 217 | 92.86 210 | 84.09 223 | 87.85 224 | 93.46 230 | 87.07 228 | 83.60 206 | 96.50 202 | 62.08 234 | 97.06 92 | 75.04 227 | 85.17 225 | 95.08 205 | 95.42 167 | 98.75 202 | 97.46 208 |
|
PM-MVS | | | 89.55 215 | 90.30 220 | 88.67 211 | 87.06 225 | 95.60 216 | 90.88 215 | 84.51 197 | 96.14 207 | 75.75 210 | 86.89 215 | 63.47 236 | 94.64 190 | 96.85 154 | 93.89 209 | 99.17 196 | 99.29 165 |
|
pmmvs-eth3d | | | 89.81 214 | 89.65 221 | 90.00 203 | 86.94 226 | 95.38 223 | 91.08 213 | 86.39 181 | 94.57 223 | 82.27 162 | 83.03 225 | 64.94 233 | 93.96 202 | 96.57 159 | 93.82 210 | 99.35 187 | 99.24 169 |
|
new-patchmatchnet | | | 86.12 222 | 87.30 223 | 84.74 220 | 86.92 227 | 95.19 227 | 83.57 231 | 84.42 199 | 92.67 228 | 65.66 228 | 80.32 228 | 64.72 234 | 89.41 219 | 92.33 224 | 89.21 230 | 98.43 206 | 96.69 220 |
|
pmmvs3 | | | 88.19 220 | 91.27 217 | 84.60 221 | 85.60 228 | 93.66 229 | 85.68 229 | 81.13 208 | 92.36 229 | 63.66 233 | 89.51 182 | 77.10 225 | 93.22 209 | 96.37 165 | 92.40 221 | 98.30 209 | 97.46 208 |
|
testmv | | | 81.83 225 | 86.26 224 | 76.66 228 | 84.10 229 | 89.42 235 | 74.29 238 | 79.65 215 | 90.61 231 | 51.85 240 | 82.11 226 | 63.06 238 | 72.61 233 | 91.94 226 | 92.75 216 | 97.49 221 | 93.94 228 |
|
test1235678 | | | 81.83 225 | 86.26 224 | 76.66 228 | 84.10 229 | 89.41 236 | 74.29 238 | 79.64 216 | 90.60 232 | 51.84 241 | 82.11 226 | 63.07 237 | 72.61 233 | 91.94 226 | 92.75 216 | 97.49 221 | 93.94 228 |
|
Gipuma | | | 81.40 227 | 81.78 230 | 80.96 226 | 83.21 231 | 85.61 240 | 79.73 233 | 76.25 233 | 97.33 166 | 64.21 232 | 55.32 237 | 55.55 240 | 86.04 223 | 92.43 223 | 92.20 224 | 96.32 232 | 93.99 227 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test12356 | | | 80.53 228 | 84.80 228 | 75.54 230 | 82.31 232 | 88.05 239 | 75.99 235 | 79.31 220 | 88.53 233 | 53.24 239 | 83.30 223 | 56.38 239 | 65.16 239 | 90.87 230 | 93.10 215 | 97.25 227 | 93.34 231 |
|
1111 | | | 82.87 224 | 85.67 226 | 79.62 227 | 81.86 233 | 89.62 233 | 74.44 236 | 68.81 238 | 87.44 235 | 66.59 226 | 76.83 232 | 70.33 231 | 87.71 221 | 92.65 219 | 93.37 213 | 98.28 210 | 89.42 232 |
|
.test1245 | | | 69.67 231 | 72.22 234 | 66.70 235 | 81.86 233 | 89.62 233 | 74.44 236 | 68.81 238 | 87.44 235 | 66.59 226 | 76.83 232 | 70.33 231 | 87.71 221 | 92.65 219 | 37.65 239 | 20.79 243 | 51.04 239 |
|
MDA-MVSNet-bldmvs | | | 87.84 221 | 89.22 222 | 86.23 217 | 81.74 235 | 96.77 208 | 83.74 230 | 89.57 147 | 94.50 224 | 72.83 221 | 96.64 104 | 64.47 235 | 92.71 212 | 81.43 235 | 92.28 223 | 96.81 230 | 98.47 194 |
|
MIMVSNet1 | | | 88.61 219 | 90.68 219 | 86.19 218 | 81.56 236 | 95.30 225 | 87.78 225 | 85.98 184 | 94.19 225 | 72.30 222 | 78.84 230 | 78.90 222 | 90.06 218 | 96.59 157 | 95.47 165 | 99.46 176 | 95.49 224 |
|
PMVS | | 72.60 17 | 76.39 230 | 77.66 233 | 74.92 231 | 81.04 237 | 69.37 245 | 68.47 241 | 80.54 212 | 85.39 237 | 65.07 230 | 73.52 234 | 72.91 230 | 65.67 238 | 80.35 236 | 76.81 237 | 88.71 239 | 85.25 238 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 80.99 231 | | 80.04 238 | 90.84 231 | 90.91 214 | | 96.09 208 | 74.18 216 | 62.81 236 | 30.59 246 | 82.44 229 | 96.25 173 | 91.77 226 | 95.91 234 | 98.56 191 |
|
PMMVS2 | | | 77.26 229 | 79.47 232 | 74.70 232 | 76.00 239 | 88.37 238 | 74.22 240 | 76.34 231 | 78.31 238 | 54.13 237 | 69.96 235 | 52.50 241 | 70.14 236 | 84.83 233 | 88.71 231 | 97.35 223 | 93.58 230 |
|
EMVS | | | 68.12 234 | 68.11 236 | 68.14 234 | 75.51 240 | 71.76 243 | 55.38 244 | 77.20 230 | 77.78 239 | 37.79 244 | 53.59 238 | 43.61 242 | 74.72 231 | 67.05 240 | 76.70 238 | 88.27 241 | 86.24 236 |
|
E-PMN | | | 68.30 233 | 68.43 235 | 68.15 233 | 74.70 241 | 71.56 244 | 55.64 243 | 77.24 229 | 77.48 240 | 39.46 243 | 51.95 240 | 41.68 244 | 73.28 232 | 70.65 238 | 79.51 235 | 88.61 240 | 86.20 237 |
|
no-one | | | 66.79 235 | 67.62 237 | 65.81 236 | 73.06 242 | 81.79 241 | 51.90 246 | 76.20 234 | 61.07 242 | 54.05 238 | 51.62 241 | 41.72 243 | 49.18 240 | 67.26 239 | 82.83 234 | 90.47 238 | 87.07 235 |
|
MVE | | 67.97 19 | 65.53 236 | 67.43 238 | 63.31 237 | 59.33 243 | 74.20 242 | 53.09 245 | 70.43 237 | 66.27 241 | 43.13 242 | 45.98 242 | 30.62 245 | 70.65 235 | 79.34 237 | 86.30 232 | 83.25 242 | 89.33 233 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 31.24 237 | 40.15 239 | 20.86 239 | 12.61 244 | 17.99 246 | 25.16 247 | 13.30 241 | 48.42 243 | 24.82 245 | 53.07 239 | 30.13 247 | 28.47 241 | 42.73 241 | 37.65 239 | 20.79 243 | 51.04 239 |
|
test123 | | | 26.75 238 | 34.25 240 | 18.01 240 | 7.93 245 | 17.18 247 | 24.85 248 | 12.36 242 | 44.83 244 | 16.52 246 | 41.80 243 | 18.10 248 | 28.29 242 | 33.08 242 | 34.79 241 | 18.10 245 | 49.95 241 |
|
GG-mvs-BLEND | | | 69.11 232 | 98.13 75 | 35.26 238 | 3.49 246 | 98.20 155 | 94.89 165 | 2.38 243 | 98.42 119 | 5.82 247 | 96.37 113 | 98.60 57 | 5.97 243 | 98.75 55 | 97.98 90 | 99.01 198 | 98.61 190 |
|
sosnet-low-res | | | 0.00 239 | 0.00 241 | 0.00 241 | 0.00 247 | 0.00 248 | 0.00 249 | 0.00 244 | 0.00 245 | 0.00 248 | 0.00 244 | 0.00 249 | 0.00 244 | 0.00 243 | 0.00 242 | 0.00 246 | 0.00 242 |
|
sosnet | | | 0.00 239 | 0.00 241 | 0.00 241 | 0.00 247 | 0.00 248 | 0.00 249 | 0.00 244 | 0.00 245 | 0.00 248 | 0.00 244 | 0.00 249 | 0.00 244 | 0.00 243 | 0.00 242 | 0.00 246 | 0.00 242 |
|
MTAPA | | | | | | | | | | | 98.09 11 | | 99.97 5 | | | | | |
|
MTMP | | | | | | | | | | | 98.46 7 | | 99.96 11 | | | | | |
|
Patchmatch-RL test | | | | | | | | 66.86 242 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 98.57 111 | | | | | | | | |
|
Patchmtry | | | | | | | 98.59 132 | 97.15 121 | 79.14 221 | | 80.42 175 | | | | | | | |
|
DeepMVS_CX | | | | | | | 96.85 206 | 87.43 226 | 89.27 149 | 98.30 122 | 75.55 212 | 95.05 137 | 79.47 218 | 92.62 213 | 89.48 231 | | 95.18 235 | 95.96 223 |
|