SMA-MVS | | | 99.34 6 | 99.79 4 | 98.81 9 | 99.69 1 | 99.94 17 | 99.75 12 | 96.91 7 | 99.98 3 | 96.76 11 | 99.37 38 | 100.00 1 | 99.90 4 | 99.88 9 | 99.46 16 | 99.84 31 | 99.92 121 |
|
CNVR-MVS | | | 99.39 2 | 99.75 11 | 98.98 1 | 99.69 1 | 99.95 12 | 99.76 6 | 96.91 7 | 99.98 3 | 97.59 5 | 99.64 19 | 100.00 1 | 99.93 1 | 99.94 2 | 98.75 47 | 99.97 11 | 99.97 81 |
|
PGM-MVS | | | 98.47 29 | 99.73 15 | 97.00 34 | 99.68 3 | 99.94 17 | 99.76 6 | 91.74 42 | 99.84 46 | 91.17 54 | 100.00 1 | 99.69 47 | 99.81 11 | 99.38 26 | 99.30 24 | 99.82 41 | 99.95 109 |
|
AdaColmap | | | 99.21 10 | 99.45 35 | 98.92 4 | 99.67 4 | 99.95 12 | 99.65 24 | 96.77 18 | 99.97 7 | 97.67 3 | 100.00 1 | 99.69 47 | 99.93 1 | 99.26 28 | 97.25 89 | 99.85 29 | 100.00 1 |
|
NCCC | | | 99.24 8 | 99.75 11 | 98.65 12 | 99.63 5 | 99.96 7 | 99.76 6 | 96.91 7 | 99.97 7 | 95.86 20 | 99.67 11 | 100.00 1 | 99.75 14 | 99.85 12 | 98.80 42 | 99.98 9 | 99.97 81 |
|
v1.0 | | | 91.56 143 | 85.41 218 | 98.74 10 | 99.62 6 | 99.94 17 | 99.79 3 | 96.87 11 | 99.93 25 | 96.33 16 | 99.59 24 | 100.00 1 | 99.84 8 | 99.88 9 | 98.50 54 | 100.00 1 | 0.00 242 |
|
ACMMP_Plus | | | 98.68 25 | 99.58 29 | 97.62 28 | 99.62 6 | 99.92 36 | 99.72 19 | 96.78 17 | 99.71 63 | 90.13 71 | 99.66 15 | 99.99 27 | 99.64 24 | 99.78 14 | 98.14 61 | 99.82 41 | 99.89 139 |
|
HPM-MVS++ | | | 98.98 21 | 99.62 25 | 98.22 21 | 99.62 6 | 99.94 17 | 99.74 15 | 96.95 3 | 99.87 38 | 93.76 31 | 99.49 32 | 100.00 1 | 99.39 36 | 99.73 17 | 98.35 56 | 99.89 25 | 99.96 100 |
|
APDe-MVS | | | 99.40 1 | 99.81 2 | 98.92 4 | 99.62 6 | 99.96 7 | 99.76 6 | 96.87 11 | 99.95 20 | 97.66 4 | 99.57 27 | 100.00 1 | 99.63 25 | 99.88 9 | 99.28 25 | 100.00 1 | 100.00 1 |
|
HSP-MVS | | | 99.36 5 | 99.79 4 | 98.85 7 | 99.61 10 | 99.96 7 | 99.71 20 | 96.94 5 | 99.97 7 | 97.11 9 | 99.60 23 | 100.00 1 | 99.70 17 | 99.96 1 | 99.12 30 | 100.00 1 | 99.96 100 |
|
APD-MVS | | | 99.33 7 | 99.85 1 | 98.73 11 | 99.61 10 | 99.92 36 | 99.77 5 | 96.91 7 | 99.93 25 | 96.31 17 | 99.59 24 | 99.95 34 | 99.84 8 | 99.73 17 | 99.84 8 | 99.95 14 | 100.00 1 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ESAPD | | | 99.37 4 | 99.74 14 | 98.94 3 | 99.60 12 | 99.94 17 | 99.87 1 | 96.95 3 | 99.94 22 | 97.42 7 | 99.62 21 | 100.00 1 | 99.80 13 | 99.91 5 | 98.78 44 | 99.98 9 | 100.00 1 |
|
MSLP-MVS++ | | | 99.39 2 | 99.76 8 | 98.95 2 | 99.60 12 | 99.99 1 | 99.83 2 | 96.82 14 | 99.92 30 | 97.58 6 | 99.58 26 | 100.00 1 | 99.93 1 | 98.98 32 | 99.86 7 | 99.96 12 | 100.00 1 |
|
CSCG | | | 98.22 31 | 98.37 59 | 98.04 23 | 99.60 12 | 99.82 57 | 99.45 30 | 93.59 38 | 99.16 101 | 96.46 15 | 98.22 75 | 95.86 91 | 99.41 35 | 96.33 128 | 99.22 27 | 99.75 88 | 99.94 113 |
|
MCST-MVS | | | 99.08 17 | 99.72 17 | 98.33 19 | 99.59 15 | 99.97 3 | 99.78 4 | 96.96 2 | 99.95 20 | 93.72 32 | 99.67 11 | 100.00 1 | 99.90 4 | 99.91 5 | 98.55 51 | 100.00 1 | 100.00 1 |
|
zzz-MVS | | | 99.12 15 | 99.52 34 | 98.65 12 | 99.58 16 | 99.93 30 | 99.74 15 | 96.72 21 | 99.44 85 | 96.47 14 | 99.62 21 | 100.00 1 | 99.63 25 | 99.74 16 | 97.97 65 | 99.77 68 | 99.94 113 |
|
HFP-MVS | | | 99.19 11 | 99.77 7 | 98.51 16 | 99.55 17 | 99.94 17 | 99.76 6 | 96.84 13 | 99.88 35 | 95.27 24 | 99.67 11 | 100.00 1 | 99.85 7 | 99.56 22 | 99.36 20 | 99.79 56 | 99.97 81 |
|
X-MVS | | | 98.62 26 | 99.75 11 | 97.29 29 | 99.50 18 | 99.94 17 | 99.71 20 | 96.55 26 | 99.85 43 | 88.58 87 | 99.65 16 | 99.98 29 | 99.67 21 | 99.60 21 | 99.26 26 | 99.77 68 | 99.97 81 |
|
DeepC-MVS_fast | | 98.03 2 | 99.05 19 | 99.78 6 | 98.21 22 | 99.47 19 | 99.97 3 | 99.75 12 | 96.80 15 | 99.97 7 | 93.58 35 | 98.68 64 | 99.94 35 | 99.69 18 | 99.93 4 | 99.95 2 | 99.96 12 | 99.98 68 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PLC | | 98.06 1 | 99.17 12 | 99.38 37 | 98.92 4 | 99.47 19 | 99.90 44 | 99.48 29 | 96.47 28 | 99.96 16 | 98.73 1 | 99.52 30 | 100.00 1 | 99.55 30 | 98.54 52 | 97.73 79 | 99.84 31 | 99.99 49 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMMPR | | | 99.12 15 | 99.76 8 | 98.36 18 | 99.45 21 | 99.94 17 | 99.75 12 | 96.70 22 | 99.93 25 | 94.65 28 | 99.65 16 | 99.96 32 | 99.84 8 | 99.51 24 | 99.35 21 | 99.79 56 | 99.96 100 |
|
CP-MVS | | | 99.14 14 | 99.67 20 | 98.53 15 | 99.45 21 | 99.94 17 | 99.63 26 | 96.62 25 | 99.82 48 | 95.92 19 | 99.65 16 | 100.00 1 | 99.71 16 | 99.76 15 | 98.56 50 | 99.83 37 | 100.00 1 |
|
CPTT-MVS | | | 99.08 17 | 99.53 33 | 98.57 14 | 99.44 23 | 99.93 30 | 99.60 27 | 95.92 33 | 99.77 55 | 97.01 10 | 99.67 11 | 100.00 1 | 99.72 15 | 99.56 22 | 97.76 76 | 99.70 112 | 99.98 68 |
|
SteuartSystems-ACMMP | | | 98.95 22 | 99.80 3 | 97.95 25 | 99.43 24 | 99.96 7 | 99.76 6 | 96.45 29 | 99.82 48 | 93.63 33 | 99.64 19 | 100.00 1 | 98.56 76 | 99.90 8 | 99.31 23 | 99.84 31 | 100.00 1 |
Skip Steuart: Steuart Systems R&D Blog. |
MP-MVS | | | 98.82 24 | 99.63 23 | 97.88 27 | 99.41 25 | 99.91 43 | 99.74 15 | 96.76 19 | 99.88 35 | 91.89 43 | 99.50 31 | 99.94 35 | 99.65 23 | 99.71 20 | 98.49 55 | 99.82 41 | 99.97 81 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
OMC-MVS | | | 98.59 28 | 99.07 39 | 98.03 24 | 99.41 25 | 99.90 44 | 99.26 35 | 94.33 37 | 99.94 22 | 96.03 18 | 96.68 91 | 99.72 46 | 99.42 33 | 98.86 35 | 98.84 39 | 99.72 108 | 99.58 179 |
|
3Dnovator | | 95.01 8 | 97.98 37 | 98.89 43 | 96.92 36 | 99.36 27 | 99.76 73 | 98.72 49 | 89.98 55 | 99.98 3 | 93.99 30 | 94.60 116 | 99.43 57 | 99.50 31 | 98.55 49 | 99.91 4 | 99.99 6 | 99.98 68 |
|
QAPM | | | 97.90 39 | 98.89 43 | 96.74 37 | 99.35 28 | 99.80 69 | 98.84 45 | 90.20 54 | 99.94 22 | 92.85 36 | 94.17 119 | 99.78 43 | 99.42 33 | 98.71 38 | 99.87 6 | 99.79 56 | 99.98 68 |
|
CNLPA | | | 99.24 8 | 99.58 29 | 98.85 7 | 99.34 29 | 99.95 12 | 99.32 32 | 96.65 23 | 99.96 16 | 98.44 2 | 98.97 52 | 100.00 1 | 99.57 28 | 98.66 40 | 99.56 14 | 99.76 75 | 99.97 81 |
|
MAR-MVS | | | 97.03 50 | 98.00 72 | 95.89 47 | 99.32 30 | 99.74 74 | 96.76 94 | 84.89 105 | 99.97 7 | 94.86 26 | 98.29 70 | 90.58 118 | 99.67 21 | 98.02 81 | 99.50 15 | 99.82 41 | 99.92 121 |
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 |
OpenMVS | | 94.03 11 | 96.87 52 | 98.10 69 | 95.44 53 | 99.29 31 | 99.78 71 | 98.46 57 | 89.92 58 | 99.47 83 | 85.78 102 | 91.05 140 | 98.50 66 | 99.30 40 | 98.49 57 | 99.41 17 | 99.89 25 | 99.98 68 |
|
3Dnovator+ | | 95.21 7 | 98.17 32 | 99.08 38 | 97.12 32 | 99.28 32 | 99.78 71 | 98.61 51 | 89.93 57 | 99.93 25 | 95.36 23 | 95.50 102 | 100.00 1 | 99.56 29 | 98.58 47 | 99.80 9 | 99.95 14 | 99.97 81 |
|
MVS_111021_HR | | | 97.94 38 | 99.59 27 | 96.02 46 | 99.27 33 | 99.97 3 | 97.03 87 | 90.44 50 | 99.89 32 | 90.75 58 | 100.00 1 | 99.73 44 | 98.68 75 | 98.67 39 | 98.89 37 | 99.95 14 | 99.97 81 |
|
mPP-MVS | | | | | | 99.23 34 | | | | | | | 99.87 38 | | | | | |
|
MVS_111021_LR | | | 98.15 34 | 99.69 19 | 96.36 42 | 99.23 34 | 99.93 30 | 97.79 62 | 91.84 41 | 99.87 38 | 90.53 65 | 100.00 1 | 99.57 52 | 98.93 62 | 99.44 25 | 99.08 32 | 99.85 29 | 99.95 109 |
|
abl_6 | | | | | 97.06 33 | 99.17 36 | 99.82 57 | 98.68 50 | 90.86 48 | 100.00 1 | 94.53 29 | 97.40 86 | 100.00 1 | 99.17 51 | | | 99.93 18 | 99.99 49 |
|
TSAR-MVS + ACMM | | | 98.30 30 | 99.64 22 | 96.74 37 | 99.08 37 | 99.94 17 | 99.67 23 | 96.73 20 | 99.97 7 | 86.30 101 | 98.30 69 | 99.99 27 | 98.78 70 | 99.73 17 | 99.57 13 | 99.88 28 | 99.98 68 |
|
train_agg | | | 98.62 26 | 99.76 8 | 97.28 30 | 99.03 38 | 99.93 30 | 99.65 24 | 96.37 30 | 99.98 3 | 89.24 82 | 99.53 28 | 99.83 40 | 99.59 27 | 99.85 12 | 99.19 28 | 99.80 52 | 100.00 1 |
|
CDPH-MVS | | | 97.88 40 | 99.59 27 | 95.89 47 | 98.90 39 | 99.95 12 | 99.40 31 | 92.86 40 | 99.86 42 | 85.33 104 | 98.62 65 | 99.45 56 | 99.06 58 | 99.29 27 | 99.94 3 | 99.81 49 | 100.00 1 |
|
ACMMP | | | 98.16 33 | 99.01 40 | 97.18 31 | 98.86 40 | 99.92 36 | 98.77 48 | 95.73 34 | 99.31 97 | 91.15 55 | 100.00 1 | 99.81 42 | 98.82 68 | 98.11 74 | 95.91 128 | 99.77 68 | 99.97 81 |
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 |
PatchMatch-RL | | | 96.84 53 | 98.03 71 | 95.47 50 | 98.84 41 | 99.81 65 | 95.61 115 | 89.20 65 | 99.65 66 | 91.28 52 | 99.39 34 | 93.46 107 | 98.18 85 | 98.05 77 | 96.28 115 | 99.69 119 | 99.55 184 |
|
tmp_tt | | | | | 78.81 215 | 98.80 42 | 85.73 231 | 70.08 232 | 77.87 171 | 98.68 119 | 83.71 110 | 99.53 28 | 74.55 172 | 54.97 237 | 78.28 232 | 72.43 235 | 87.45 236 | |
|
TAPA-MVS | | 96.62 5 | 97.60 42 | 98.46 57 | 96.60 40 | 98.73 43 | 99.90 44 | 99.30 33 | 94.96 36 | 99.46 84 | 87.57 92 | 96.05 100 | 98.53 65 | 99.26 47 | 98.04 79 | 97.33 88 | 99.77 68 | 99.88 144 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MSDG | | | 97.29 45 | 97.55 82 | 97.00 34 | 98.66 44 | 99.71 75 | 99.03 41 | 96.15 31 | 99.59 71 | 89.67 80 | 92.77 133 | 94.86 95 | 98.75 71 | 98.22 69 | 97.94 66 | 99.72 108 | 99.76 164 |
|
PHI-MVS | | | 98.85 23 | 99.67 20 | 97.89 26 | 98.63 45 | 99.93 30 | 98.95 43 | 95.20 35 | 99.84 46 | 94.94 25 | 99.74 10 | 100.00 1 | 99.69 18 | 98.40 59 | 99.75 10 | 99.93 18 | 99.99 49 |
|
DeepPCF-MVS | | 97.16 4 | 97.58 43 | 99.72 17 | 95.07 58 | 98.45 46 | 99.96 7 | 93.83 140 | 95.93 32 | 100.00 1 | 90.79 57 | 98.38 68 | 99.85 39 | 95.28 130 | 99.94 2 | 99.97 1 | 96.15 226 | 99.97 81 |
|
EPNet | | | 98.11 35 | 99.63 23 | 96.34 43 | 98.44 47 | 99.88 50 | 98.55 52 | 90.25 53 | 99.93 25 | 92.60 40 | 100.00 1 | 99.73 44 | 98.41 78 | 98.87 34 | 99.02 33 | 99.82 41 | 99.97 81 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet_dtu | | | 95.10 96 | 98.81 48 | 90.78 112 | 98.38 48 | 98.47 130 | 96.54 97 | 89.36 62 | 99.78 54 | 65.65 194 | 99.31 39 | 98.24 73 | 94.79 135 | 98.28 66 | 99.35 21 | 99.93 18 | 98.27 206 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
SD-MVS | | | 99.16 13 | 99.73 15 | 98.49 17 | 97.93 49 | 99.95 12 | 99.74 15 | 96.94 5 | 99.96 16 | 96.60 13 | 99.47 33 | 100.00 1 | 99.88 6 | 99.15 30 | 99.59 12 | 99.84 31 | 100.00 1 |
|
TSAR-MVS + MP. | | | 98.99 20 | 99.61 26 | 98.27 20 | 97.88 50 | 99.92 36 | 99.71 20 | 96.80 15 | 99.96 16 | 95.58 22 | 98.71 63 | 100.00 1 | 99.68 20 | 99.91 5 | 98.78 44 | 99.99 6 | 100.00 1 |
|
COLMAP_ROB | | 93.56 12 | 96.03 66 | 96.83 103 | 95.11 56 | 97.87 51 | 99.52 85 | 98.81 47 | 91.40 45 | 99.42 88 | 84.97 106 | 90.46 142 | 96.82 83 | 98.05 87 | 96.46 123 | 96.19 118 | 99.54 141 | 98.92 201 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PCF-MVS | | 97.20 3 | 97.49 44 | 98.20 66 | 96.66 39 | 97.62 52 | 99.92 36 | 98.93 44 | 96.64 24 | 98.53 130 | 88.31 90 | 94.04 121 | 99.58 51 | 98.94 60 | 97.53 94 | 97.79 74 | 99.54 141 | 99.97 81 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DeepC-MVS | | 96.33 6 | 97.05 47 | 97.59 81 | 96.42 41 | 97.37 53 | 99.92 36 | 99.10 39 | 96.54 27 | 99.34 96 | 86.64 99 | 91.93 137 | 93.15 109 | 99.11 56 | 99.11 31 | 99.68 11 | 99.73 104 | 99.97 81 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 97.62 41 | 98.94 42 | 96.08 45 | 97.19 54 | 99.93 30 | 99.29 34 | 90.38 51 | 99.87 38 | 91.00 56 | 95.79 101 | 99.51 53 | 98.72 74 | 98.53 53 | 99.00 34 | 99.90 24 | 99.99 49 |
|
DELS-MVS | | | 97.05 47 | 98.05 70 | 95.88 49 | 97.09 55 | 99.99 1 | 98.82 46 | 90.30 52 | 98.44 135 | 91.40 49 | 92.91 130 | 96.57 84 | 97.68 100 | 98.56 48 | 99.88 5 | 100.00 1 | 100.00 1 |
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 |
LS3D | | | 96.44 60 | 97.31 88 | 95.41 54 | 97.06 56 | 99.87 51 | 99.51 28 | 97.48 1 | 99.57 72 | 79.00 127 | 95.39 104 | 89.19 125 | 99.81 11 | 98.55 49 | 98.84 39 | 99.62 129 | 99.78 162 |
|
CHOSEN 280x420 | | | 97.16 46 | 99.58 29 | 94.35 80 | 96.95 57 | 99.97 3 | 97.19 82 | 81.55 146 | 99.92 30 | 91.75 44 | 100.00 1 | 100.00 1 | 98.84 67 | 98.55 49 | 98.65 48 | 99.79 56 | 99.97 81 |
|
RPSCF | | | 95.86 70 | 96.94 102 | 94.61 74 | 96.52 58 | 98.67 128 | 98.54 53 | 88.43 86 | 99.56 73 | 90.51 68 | 99.39 34 | 98.70 63 | 97.72 97 | 93.77 173 | 92.00 181 | 95.93 227 | 96.50 220 |
|
PVSNet_BlendedMVS | | | 96.01 67 | 96.48 113 | 95.46 51 | 96.47 59 | 99.89 48 | 95.64 112 | 91.23 46 | 99.75 59 | 91.59 45 | 96.80 87 | 82.44 151 | 98.05 87 | 98.53 53 | 97.92 71 | 99.80 52 | 100.00 1 |
|
PVSNet_Blended | | | 96.01 67 | 96.48 113 | 95.46 51 | 96.47 59 | 99.89 48 | 95.64 112 | 91.23 46 | 99.75 59 | 91.59 45 | 96.80 87 | 82.44 151 | 98.05 87 | 98.53 53 | 97.92 71 | 99.80 52 | 100.00 1 |
|
MVS_0304 | | | 97.04 49 | 98.72 50 | 95.08 57 | 96.32 61 | 99.90 44 | 99.15 37 | 89.61 61 | 99.89 32 | 87.22 97 | 95.47 103 | 98.22 74 | 98.22 84 | 98.63 44 | 98.90 36 | 99.93 18 | 100.00 1 |
|
TSAR-MVS + COLMAP | | | 95.20 91 | 95.03 135 | 95.41 54 | 96.17 62 | 98.69 127 | 99.11 38 | 93.40 39 | 99.97 7 | 84.89 108 | 98.23 74 | 75.01 170 | 99.34 38 | 97.27 110 | 96.37 114 | 99.58 134 | 99.64 174 |
|
CHOSEN 1792x2688 | | | 93.69 112 | 94.89 138 | 92.28 105 | 96.17 62 | 99.84 52 | 95.69 111 | 83.17 127 | 98.54 129 | 82.04 120 | 77.58 210 | 91.15 115 | 96.90 110 | 98.36 62 | 98.82 41 | 99.73 104 | 99.98 68 |
|
HyFIR lowres test | | | 93.13 121 | 94.48 142 | 91.56 109 | 96.12 64 | 99.68 77 | 93.52 142 | 79.98 154 | 97.24 155 | 81.73 123 | 72.66 221 | 95.74 93 | 98.29 82 | 98.27 67 | 97.79 74 | 99.70 112 | 100.00 1 |
|
OPM-MVS | | | 93.50 115 | 93.00 153 | 94.07 81 | 95.82 65 | 98.26 137 | 98.49 56 | 91.62 43 | 94.69 180 | 81.93 121 | 92.82 132 | 76.18 168 | 96.82 112 | 96.12 133 | 94.57 145 | 99.74 92 | 98.39 204 |
|
MS-PatchMatch | | | 93.46 119 | 95.91 127 | 90.61 115 | 95.48 66 | 99.31 102 | 95.62 114 | 77.23 175 | 99.42 88 | 81.88 122 | 88.92 148 | 96.06 90 | 93.80 147 | 96.45 125 | 93.11 169 | 99.65 123 | 98.10 210 |
|
CMPMVS | | 65.66 17 | 84.62 210 | 85.02 219 | 84.15 192 | 95.40 67 | 97.79 145 | 88.35 202 | 79.22 161 | 89.66 226 | 60.71 213 | 72.20 222 | 73.94 176 | 87.32 213 | 86.73 226 | 84.55 230 | 93.90 232 | 90.31 229 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PVSNet_Blended_VisFu | | | 95.37 89 | 97.44 86 | 92.95 99 | 95.20 68 | 99.80 69 | 92.68 147 | 88.41 87 | 99.12 104 | 87.64 91 | 88.31 151 | 99.10 61 | 94.07 145 | 98.27 67 | 97.51 84 | 99.73 104 | 100.00 1 |
|
HQP-MVS | | | 94.48 103 | 95.39 133 | 93.42 90 | 95.10 69 | 98.35 133 | 98.19 58 | 91.41 44 | 99.77 55 | 79.79 124 | 99.30 40 | 77.08 160 | 96.25 120 | 96.93 112 | 96.28 115 | 99.76 75 | 99.99 49 |
|
XVS | | | | | | 95.09 70 | 99.94 17 | 97.49 72 | | | 88.58 87 | | 99.98 29 | | | | 99.78 63 | |
|
X-MVStestdata | | | | | | 95.09 70 | 99.94 17 | 97.49 72 | | | 88.58 87 | | 99.98 29 | | | | 99.78 63 | |
|
LGP-MVS_train | | | 93.60 113 | 95.05 134 | 91.90 108 | 94.90 72 | 98.29 136 | 97.93 60 | 88.06 88 | 99.14 103 | 74.83 142 | 99.26 41 | 76.50 164 | 96.07 122 | 96.31 129 | 95.90 130 | 99.59 132 | 99.97 81 |
|
ACMM | | 94.44 10 | 94.26 108 | 94.62 140 | 93.84 85 | 94.86 73 | 97.73 146 | 93.48 143 | 90.76 49 | 99.27 98 | 87.46 93 | 99.04 47 | 76.60 163 | 96.76 115 | 96.37 127 | 93.76 160 | 99.74 92 | 99.55 184 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CANet_DTU | | | 94.90 99 | 98.98 41 | 90.13 120 | 94.74 74 | 99.81 65 | 98.53 54 | 82.23 137 | 99.97 7 | 66.76 182 | 100.00 1 | 98.50 66 | 98.74 72 | 97.52 95 | 97.19 97 | 99.76 75 | 99.88 144 |
|
TSAR-MVS + GP. | | | 98.06 36 | 99.55 32 | 96.32 44 | 94.72 75 | 99.92 36 | 99.22 36 | 89.98 55 | 99.97 7 | 94.77 27 | 99.94 9 | 100.00 1 | 99.43 32 | 98.52 56 | 98.53 52 | 99.79 56 | 100.00 1 |
|
UGNet | | | 96.05 65 | 98.55 54 | 93.13 93 | 94.64 76 | 99.65 79 | 94.70 129 | 87.78 90 | 99.40 91 | 89.69 79 | 98.25 72 | 99.25 60 | 92.12 162 | 96.50 119 | 97.08 99 | 99.84 31 | 99.72 168 |
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 |
IB-MVS | | 90.59 15 | 92.70 130 | 95.70 129 | 89.21 128 | 94.62 77 | 99.45 93 | 83.77 215 | 88.92 70 | 99.53 74 | 92.82 37 | 98.86 57 | 86.08 135 | 75.24 227 | 92.81 191 | 93.17 167 | 99.89 25 | 100.00 1 |
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 |
UA-Net | | | 94.95 98 | 98.66 51 | 90.63 114 | 94.60 78 | 98.94 120 | 96.03 106 | 85.28 100 | 98.01 148 | 78.92 128 | 97.42 85 | 99.96 32 | 89.09 203 | 98.95 33 | 98.80 42 | 99.82 41 | 98.57 203 |
|
ACMP | | 94.49 9 | 94.19 109 | 94.74 139 | 93.56 88 | 94.25 79 | 98.32 135 | 96.02 107 | 89.35 64 | 98.90 116 | 87.28 96 | 99.14 44 | 76.41 166 | 94.94 133 | 96.07 136 | 94.35 154 | 99.49 152 | 99.99 49 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 92.34 14 | 91.59 142 | 93.02 152 | 89.92 122 | 93.97 80 | 97.98 142 | 90.10 185 | 84.70 107 | 98.46 133 | 76.80 136 | 93.38 128 | 71.94 185 | 94.39 140 | 95.34 147 | 94.04 156 | 99.54 141 | 100.00 1 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 94.53 101 | 94.45 143 | 94.61 74 | 93.85 81 | 98.36 132 | 98.12 59 | 89.68 59 | 99.35 95 | 89.62 81 | 95.19 106 | 77.08 160 | 96.66 117 | 95.51 143 | 95.67 133 | 99.74 92 | 100.00 1 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TDRefinement | | | 87.79 173 | 88.76 199 | 86.66 154 | 93.54 82 | 98.02 140 | 95.76 109 | 85.18 103 | 96.57 161 | 67.90 168 | 80.51 186 | 66.51 222 | 78.37 224 | 93.20 185 | 89.73 217 | 99.22 209 | 96.75 217 |
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Anonymous202405211 | | | | 95.78 128 | | 93.26 83 | 99.52 85 | 96.70 95 | 88.55 81 | 97.93 149 | | 88.99 147 | 90.68 117 | 98.99 59 | 96.46 123 | 97.02 103 | 99.64 125 | 99.89 139 |
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Anonymous20240521 | | | 95.85 72 | 97.53 83 | 93.89 83 | 93.20 84 | 97.01 156 | 97.14 84 | 84.77 106 | 99.16 101 | 90.38 70 | 98.96 53 | 93.73 104 | 98.23 83 | 96.57 118 | 97.37 87 | 99.64 125 | 99.93 116 |
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tfpn_ndepth | | | 96.84 53 | 98.58 53 | 94.81 69 | 93.18 85 | 99.62 82 | 96.83 92 | 88.75 77 | 99.73 61 | 92.38 41 | 98.45 67 | 96.34 88 | 97.90 93 | 98.34 64 | 97.59 82 | 99.84 31 | 99.99 49 |
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conf0.01 | | | 96.20 64 | 97.19 92 | 95.05 60 | 93.11 86 | 99.83 53 | 97.67 64 | 89.06 68 | 98.62 122 | 91.38 50 | 99.19 42 | 89.09 126 | 99.28 42 | 97.48 97 | 96.10 119 | 99.76 75 | 100.00 1 |
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conf0.002 | | | 96.51 57 | 97.75 78 | 95.07 58 | 93.11 86 | 99.83 53 | 97.67 64 | 89.10 67 | 98.62 122 | 91.47 48 | 99.39 34 | 91.68 112 | 99.28 42 | 97.49 96 | 97.24 90 | 99.76 75 | 100.00 1 |
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tfpn111 | | | 95.79 75 | 96.55 107 | 94.89 63 | 93.10 88 | 99.82 57 | 97.67 64 | 88.85 71 | 98.62 122 | 90.69 59 | 99.07 45 | 84.86 138 | 99.28 42 | 97.41 101 | 96.10 119 | 99.76 75 | 99.99 49 |
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conf200view11 | | | 95.78 76 | 96.54 109 | 94.89 63 | 93.10 88 | 99.82 57 | 97.67 64 | 88.85 71 | 98.62 122 | 90.69 59 | 99.00 48 | 84.86 138 | 99.28 42 | 97.41 101 | 96.10 119 | 99.76 75 | 99.99 49 |
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thres100view900 | | | 95.86 70 | 96.62 105 | 94.97 61 | 93.10 88 | 99.83 53 | 97.76 63 | 89.15 66 | 98.62 122 | 90.69 59 | 99.00 48 | 84.86 138 | 99.30 40 | 97.57 93 | 96.48 109 | 99.81 49 | 100.00 1 |
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tfpn200view9 | | | 95.78 76 | 96.54 109 | 94.89 63 | 93.10 88 | 99.82 57 | 97.67 64 | 88.85 71 | 98.62 122 | 90.69 59 | 99.00 48 | 84.86 138 | 99.28 42 | 97.41 101 | 96.10 119 | 99.76 75 | 99.99 49 |
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thres200 | | | 95.77 79 | 96.55 107 | 94.86 66 | 93.09 92 | 99.82 57 | 97.63 70 | 88.85 71 | 98.49 131 | 90.66 63 | 98.99 51 | 84.86 138 | 99.20 48 | 97.41 101 | 96.28 115 | 99.76 75 | 100.00 1 |
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tfpn1000 | | | 96.58 56 | 98.37 59 | 94.50 76 | 93.04 93 | 99.59 83 | 96.53 98 | 88.54 82 | 99.73 61 | 91.59 45 | 98.28 71 | 95.76 92 | 97.46 102 | 98.19 70 | 97.10 98 | 99.82 41 | 99.96 100 |
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ACMH+ | | 92.61 13 | 91.80 140 | 93.03 151 | 90.37 117 | 93.03 94 | 98.17 138 | 94.00 138 | 84.13 119 | 98.12 145 | 77.39 134 | 91.95 136 | 74.62 171 | 94.36 142 | 94.62 158 | 93.82 159 | 99.32 199 | 99.87 148 |
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Anonymous20231211 | | | 94.96 97 | 94.99 136 | 94.91 62 | 93.01 95 | 99.44 96 | 96.85 91 | 88.49 85 | 98.78 117 | 92.61 39 | 83.94 165 | 90.25 121 | 98.94 60 | 95.87 139 | 96.77 106 | 99.58 134 | 99.89 139 |
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thres400 | | | 95.72 83 | 96.48 113 | 94.84 67 | 93.00 96 | 99.83 53 | 97.55 71 | 88.93 69 | 98.49 131 | 90.61 64 | 98.86 57 | 84.63 143 | 99.20 48 | 97.45 98 | 96.10 119 | 99.77 68 | 99.99 49 |
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view600 | | | 95.64 84 | 96.38 116 | 94.79 70 | 92.96 97 | 99.82 57 | 97.48 75 | 88.85 71 | 98.38 136 | 90.52 66 | 98.84 59 | 84.61 144 | 99.15 52 | 97.41 101 | 95.60 136 | 99.76 75 | 99.99 49 |
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view800 | | | 95.62 86 | 96.38 116 | 94.73 72 | 92.96 97 | 99.81 65 | 97.38 77 | 88.75 77 | 98.35 141 | 90.43 69 | 98.81 61 | 84.54 146 | 99.13 55 | 97.35 107 | 95.82 131 | 99.76 75 | 99.98 68 |
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canonicalmvs | | | 95.80 74 | 97.02 96 | 94.37 78 | 92.96 97 | 99.47 91 | 97.49 72 | 84.58 108 | 99.44 85 | 92.05 42 | 98.54 66 | 86.65 133 | 99.37 37 | 96.18 131 | 98.93 35 | 99.77 68 | 99.92 121 |
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thres600view7 | | | 95.64 84 | 96.38 116 | 94.79 70 | 92.96 97 | 99.82 57 | 97.48 75 | 88.85 71 | 98.38 136 | 90.52 66 | 98.84 59 | 84.61 144 | 99.15 52 | 97.41 101 | 95.60 136 | 99.76 75 | 99.99 49 |
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tfpn | | | 95.93 69 | 97.06 95 | 94.62 73 | 92.94 101 | 99.81 65 | 97.25 81 | 88.71 80 | 98.32 142 | 89.98 73 | 98.79 62 | 88.55 128 | 99.11 56 | 97.26 111 | 96.71 107 | 99.75 88 | 99.98 68 |
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thresconf0.02 | | | 96.46 58 | 98.87 45 | 93.64 87 | 92.77 102 | 99.11 107 | 97.05 86 | 89.36 62 | 99.64 68 | 85.14 105 | 99.07 45 | 96.84 82 | 97.72 97 | 98.72 37 | 98.76 46 | 99.78 63 | 99.95 109 |
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MVSTER | | | 97.00 51 | 98.85 46 | 94.83 68 | 92.71 103 | 97.43 151 | 99.03 41 | 85.52 98 | 99.82 48 | 92.74 38 | 99.15 43 | 99.94 35 | 99.19 50 | 98.66 40 | 96.99 104 | 99.79 56 | 99.98 68 |
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conf0.05thres1000 | | | 94.50 102 | 95.70 129 | 93.11 94 | 92.68 104 | 99.67 78 | 96.04 105 | 87.81 89 | 97.52 154 | 83.71 110 | 96.20 98 | 84.52 147 | 98.73 73 | 96.39 126 | 95.66 134 | 99.71 110 | 99.92 121 |
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tfpnview11 | | | 95.78 76 | 98.17 68 | 93.01 97 | 92.58 105 | 99.04 113 | 96.64 96 | 88.72 79 | 99.63 70 | 83.08 114 | 98.90 54 | 94.24 100 | 97.25 105 | 98.35 63 | 97.21 91 | 99.77 68 | 99.80 161 |
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DWT-MVSNet_training | | | 96.26 62 | 98.44 58 | 93.72 86 | 92.58 105 | 99.34 101 | 96.15 104 | 83.00 129 | 99.76 57 | 93.63 33 | 97.89 79 | 99.46 54 | 97.23 106 | 94.43 160 | 98.19 59 | 99.70 112 | 100.00 1 |
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USDC | | | 90.36 151 | 91.68 159 | 88.82 133 | 92.58 105 | 98.02 140 | 96.27 103 | 79.83 155 | 98.37 139 | 70.61 161 | 89.05 146 | 67.50 217 | 94.17 143 | 95.77 140 | 94.43 149 | 99.46 162 | 98.62 202 |
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PMMVS | | | 96.45 59 | 98.24 63 | 94.36 79 | 92.58 105 | 99.01 114 | 97.08 85 | 87.42 93 | 99.88 35 | 90.06 72 | 99.39 34 | 94.63 96 | 99.33 39 | 97.85 87 | 96.99 104 | 99.70 112 | 99.96 100 |
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tfpn_n400 | | | 95.76 80 | 98.21 64 | 92.90 100 | 92.57 109 | 99.05 111 | 96.42 99 | 88.50 83 | 99.49 78 | 83.08 114 | 98.90 54 | 94.24 100 | 97.07 107 | 98.10 75 | 97.93 68 | 99.74 92 | 99.76 164 |
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tfpnconf | | | 95.76 80 | 98.21 64 | 92.90 100 | 92.57 109 | 99.05 111 | 96.42 99 | 88.50 83 | 99.49 78 | 83.08 114 | 98.90 54 | 94.24 100 | 97.07 107 | 98.10 75 | 97.93 68 | 99.74 92 | 99.76 164 |
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casdiffmvs1 | | | 96.22 63 | 98.26 62 | 93.85 84 | 92.52 111 | 99.45 93 | 97.35 79 | 84.50 111 | 99.87 38 | 89.96 74 | 97.60 83 | 93.89 103 | 98.79 69 | 98.49 57 | 98.51 53 | 99.95 14 | 100.00 1 |
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EPMVS | | | 94.08 110 | 98.54 55 | 88.87 131 | 92.51 112 | 99.47 91 | 94.18 136 | 66.53 215 | 99.68 65 | 82.40 118 | 95.24 105 | 99.40 58 | 97.86 94 | 98.12 73 | 97.99 64 | 99.75 88 | 99.88 144 |
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TinyColmap | | | 89.94 152 | 90.88 165 | 88.84 132 | 92.43 113 | 97.91 144 | 95.59 116 | 80.10 153 | 98.12 145 | 71.33 158 | 84.56 161 | 67.46 218 | 94.15 144 | 95.57 142 | 94.27 155 | 99.43 171 | 98.26 207 |
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IS_MVSNet | | | 96.66 55 | 98.62 52 | 94.38 77 | 92.41 114 | 99.70 76 | 97.19 82 | 87.67 92 | 99.05 109 | 91.27 53 | 95.09 108 | 98.46 70 | 97.95 92 | 98.64 42 | 99.37 18 | 99.79 56 | 100.00 1 |
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Vis-MVSNet (Re-imp) | | | 95.60 87 | 98.52 56 | 92.19 106 | 92.37 115 | 99.56 84 | 96.37 101 | 87.41 94 | 98.95 112 | 84.77 109 | 94.88 114 | 98.48 69 | 92.44 159 | 98.63 44 | 99.37 18 | 99.76 75 | 99.77 163 |
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FC-MVSNet-train | | | 94.61 100 | 96.27 120 | 92.68 104 | 92.35 116 | 97.14 154 | 93.45 144 | 87.73 91 | 98.93 113 | 87.31 94 | 96.42 94 | 89.35 123 | 95.67 125 | 96.06 137 | 96.01 126 | 99.56 138 | 99.98 68 |
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MVS_Test | | | 95.74 82 | 98.18 67 | 92.90 100 | 92.16 117 | 99.49 90 | 97.36 78 | 84.30 114 | 99.79 52 | 84.94 107 | 96.65 92 | 93.63 106 | 98.85 66 | 98.61 46 | 99.10 31 | 99.81 49 | 100.00 1 |
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casdiffmvs | | | 95.82 73 | 97.83 77 | 93.47 89 | 92.15 118 | 99.52 85 | 96.32 102 | 84.29 115 | 99.50 76 | 89.73 78 | 97.82 80 | 91.67 113 | 98.38 79 | 98.30 65 | 98.00 63 | 99.92 22 | 100.00 1 |
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tpmrst | | | 92.52 134 | 97.45 85 | 86.77 153 | 92.15 118 | 99.36 100 | 92.53 150 | 65.95 220 | 99.53 74 | 72.50 147 | 92.22 135 | 99.83 40 | 97.81 96 | 95.18 150 | 96.05 125 | 99.69 119 | 100.00 1 |
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diffmvs | | | 95.11 95 | 96.95 101 | 92.96 98 | 92.09 120 | 99.44 96 | 97.26 80 | 83.80 121 | 99.44 85 | 86.43 100 | 96.77 90 | 87.25 132 | 98.49 77 | 97.92 84 | 97.93 68 | 99.70 112 | 99.90 134 |
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ADS-MVSNet | | | 92.91 126 | 97.97 73 | 87.01 150 | 92.07 121 | 99.27 103 | 92.70 146 | 65.39 224 | 99.85 43 | 75.40 140 | 94.93 113 | 98.26 71 | 96.86 111 | 96.09 134 | 97.52 83 | 99.65 123 | 99.84 153 |
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dps | | | 94.29 107 | 97.33 87 | 90.75 113 | 92.02 122 | 99.21 104 | 94.31 134 | 66.97 214 | 99.50 76 | 95.61 21 | 96.22 97 | 98.64 64 | 96.08 121 | 93.71 175 | 94.03 157 | 99.52 145 | 99.98 68 |
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PatchmatchNet | | | 93.48 118 | 98.84 47 | 87.22 148 | 91.93 123 | 99.39 98 | 92.55 149 | 66.06 219 | 99.71 63 | 75.61 139 | 98.24 73 | 99.59 50 | 97.35 103 | 97.87 86 | 97.64 81 | 99.83 37 | 99.43 189 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpm cat1 | | | 93.29 120 | 96.53 111 | 89.50 126 | 91.84 124 | 99.18 106 | 94.70 129 | 67.70 210 | 98.38 136 | 86.67 98 | 89.16 145 | 99.38 59 | 96.66 117 | 94.33 161 | 95.30 139 | 99.43 171 | 100.00 1 |
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tpmp4_e23 | | | 92.95 125 | 96.28 119 | 89.06 130 | 91.80 125 | 98.81 125 | 94.95 125 | 67.56 213 | 99.21 99 | 82.97 117 | 96.54 93 | 88.52 129 | 97.47 101 | 94.47 159 | 96.42 112 | 99.61 130 | 100.00 1 |
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EPP-MVSNet | | | 96.29 61 | 98.34 61 | 93.90 82 | 91.77 126 | 99.38 99 | 95.45 120 | 87.25 95 | 99.38 92 | 91.36 51 | 94.86 115 | 98.49 68 | 97.83 95 | 98.01 82 | 98.23 58 | 99.75 88 | 99.99 49 |
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DI_MVS_plusplus_trai | | | 95.29 90 | 97.02 96 | 93.28 92 | 91.76 127 | 99.52 85 | 97.84 61 | 85.67 97 | 99.08 108 | 87.29 95 | 87.76 154 | 97.46 80 | 97.31 104 | 97.83 88 | 97.48 85 | 99.83 37 | 100.00 1 |
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MDTV_nov1_ep13 | | | 94.32 105 | 98.77 49 | 89.14 129 | 91.70 128 | 99.52 85 | 95.21 122 | 72.09 206 | 99.80 51 | 78.91 129 | 96.32 95 | 99.62 49 | 97.71 99 | 98.39 60 | 97.71 80 | 99.22 209 | 100.00 1 |
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test-LLR | | | 93.71 111 | 97.23 90 | 89.60 124 | 91.69 129 | 99.10 108 | 94.68 131 | 83.60 122 | 99.36 93 | 71.94 152 | 93.82 123 | 96.51 85 | 95.96 123 | 97.42 99 | 94.37 151 | 99.74 92 | 99.99 49 |
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test0.0.03 1 | | | 95.15 94 | 97.87 76 | 91.99 107 | 91.69 129 | 98.82 124 | 93.04 145 | 83.60 122 | 99.65 66 | 88.80 85 | 94.15 120 | 97.67 78 | 94.97 132 | 96.62 117 | 98.16 60 | 99.83 37 | 100.00 1 |
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CostFormer | | | 93.50 115 | 96.50 112 | 90.00 121 | 91.69 129 | 98.65 129 | 93.88 139 | 67.64 211 | 98.97 110 | 89.16 83 | 97.79 81 | 88.92 127 | 97.97 91 | 95.14 151 | 96.06 124 | 99.63 127 | 100.00 1 |
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CVMVSNet | | | 92.13 138 | 95.40 132 | 88.32 142 | 91.29 132 | 97.29 153 | 91.85 154 | 86.42 96 | 96.71 160 | 71.84 154 | 89.56 144 | 91.18 114 | 88.98 205 | 96.17 132 | 97.76 76 | 99.51 149 | 99.14 197 |
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Vis-MVSNet | | | 93.08 123 | 96.76 104 | 88.78 135 | 91.14 133 | 99.63 81 | 94.85 126 | 83.34 125 | 97.19 156 | 74.78 143 | 91.92 138 | 93.15 109 | 88.81 206 | 97.59 92 | 98.35 56 | 99.78 63 | 99.49 188 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS-LS | | | 93.50 115 | 96.22 121 | 90.33 119 | 90.93 134 | 95.50 195 | 94.83 127 | 80.54 150 | 98.92 114 | 79.11 126 | 90.64 141 | 93.70 105 | 96.79 113 | 96.93 112 | 97.85 73 | 99.78 63 | 99.99 49 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 93.06 124 | 95.94 126 | 89.70 123 | 90.82 135 | 99.45 93 | 95.71 110 | 78.94 165 | 98.72 118 | 74.71 144 | 97.92 78 | 80.73 155 | 98.35 80 | 97.72 89 | 97.05 102 | 99.70 112 | 100.00 1 |
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RPMNet | | | 92.64 132 | 97.88 75 | 86.53 155 | 90.79 136 | 98.95 118 | 95.13 123 | 64.44 228 | 99.09 106 | 72.36 148 | 93.58 126 | 99.01 62 | 96.74 116 | 98.05 77 | 96.45 111 | 99.71 110 | 100.00 1 |
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testgi | | | 92.47 135 | 95.68 131 | 88.73 136 | 90.68 137 | 98.35 133 | 91.67 157 | 79.50 159 | 98.96 111 | 77.12 135 | 95.17 107 | 85.84 136 | 93.95 146 | 95.75 141 | 96.47 110 | 99.45 165 | 99.21 195 |
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LP | | | 88.31 163 | 93.18 149 | 82.63 206 | 90.66 138 | 97.98 142 | 87.32 207 | 63.49 231 | 97.17 157 | 63.02 205 | 82.08 172 | 90.47 119 | 91.92 164 | 92.75 192 | 93.42 165 | 99.38 193 | 98.37 205 |
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CR-MVSNet | | | 92.32 137 | 97.97 73 | 85.74 167 | 90.63 139 | 98.95 118 | 95.46 118 | 65.50 222 | 99.09 106 | 67.51 173 | 94.20 118 | 98.18 75 | 95.59 128 | 98.16 71 | 97.20 95 | 99.74 92 | 100.00 1 |
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gg-mvs-nofinetune | | | 86.69 197 | 91.30 163 | 81.30 211 | 90.42 140 | 99.64 80 | 98.50 55 | 61.68 233 | 79.23 235 | 40.35 236 | 66.58 229 | 97.14 81 | 96.92 109 | 98.64 42 | 97.94 66 | 99.91 23 | 99.97 81 |
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PatchT | | | 91.06 146 | 97.66 79 | 83.36 203 | 90.32 141 | 98.96 117 | 82.30 220 | 64.72 227 | 98.45 134 | 67.51 173 | 93.28 129 | 97.60 79 | 95.59 128 | 98.16 71 | 97.20 95 | 99.70 112 | 100.00 1 |
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IterMVS | | | 91.65 141 | 96.62 105 | 85.85 164 | 90.27 142 | 95.80 185 | 95.32 121 | 74.15 190 | 98.91 115 | 60.95 211 | 88.79 150 | 97.76 77 | 94.69 138 | 98.04 79 | 97.07 100 | 99.73 104 | 100.00 1 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpm | | | 89.60 154 | 94.93 137 | 83.39 201 | 89.94 143 | 97.11 155 | 90.09 186 | 65.28 225 | 98.67 120 | 60.03 215 | 96.79 89 | 84.38 148 | 95.66 127 | 91.90 196 | 95.65 135 | 99.32 199 | 99.98 68 |
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CDS-MVSNet | | | 94.32 105 | 97.00 98 | 91.19 111 | 89.82 144 | 98.71 126 | 95.51 117 | 85.14 104 | 96.85 158 | 82.33 119 | 92.48 134 | 96.40 87 | 94.71 136 | 96.86 114 | 97.76 76 | 99.63 127 | 99.92 121 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
testpf | | | 91.26 145 | 97.28 89 | 84.23 190 | 89.52 145 | 97.45 150 | 88.08 204 | 56.08 237 | 99.76 57 | 78.71 130 | 95.06 112 | 98.26 71 | 93.44 151 | 94.72 156 | 95.69 132 | 99.57 136 | 99.99 49 |
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FMVSNet3 | | | 95.59 88 | 97.51 84 | 93.34 91 | 89.48 146 | 96.57 163 | 97.67 64 | 84.17 116 | 99.48 80 | 89.76 75 | 95.09 108 | 94.35 97 | 99.14 54 | 98.37 61 | 98.86 38 | 99.82 41 | 99.89 139 |
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GBi-Net | | | 95.19 92 | 96.99 99 | 93.09 95 | 89.11 147 | 96.47 165 | 96.90 88 | 84.17 116 | 99.48 80 | 89.76 75 | 95.09 108 | 94.35 97 | 98.87 63 | 96.50 119 | 97.21 91 | 99.74 92 | 99.81 157 |
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test1 | | | 95.19 92 | 96.99 99 | 93.09 95 | 89.11 147 | 96.47 165 | 96.90 88 | 84.17 116 | 99.48 80 | 89.76 75 | 95.09 108 | 94.35 97 | 98.87 63 | 96.50 119 | 97.21 91 | 99.74 92 | 99.81 157 |
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FMVSNet2 | | | 94.48 103 | 95.95 125 | 92.77 103 | 89.11 147 | 96.47 165 | 96.90 88 | 83.38 124 | 99.11 105 | 88.64 86 | 87.50 159 | 92.26 111 | 98.87 63 | 97.91 85 | 98.60 49 | 99.74 92 | 99.81 157 |
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Fast-Effi-MVS+ | | | 92.11 139 | 94.33 144 | 89.52 125 | 89.06 150 | 99.00 115 | 95.13 123 | 76.72 180 | 98.59 128 | 78.21 133 | 89.99 143 | 77.35 159 | 98.34 81 | 97.97 83 | 97.44 86 | 99.67 121 | 99.96 100 |
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Fast-Effi-MVS+-dtu | | | 92.73 129 | 97.62 80 | 87.02 149 | 88.91 151 | 98.83 123 | 95.79 108 | 73.98 194 | 99.89 32 | 68.62 167 | 97.73 82 | 93.30 108 | 95.21 131 | 97.67 90 | 95.96 127 | 99.59 132 | 100.00 1 |
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MVS-HIRNet | | | 88.27 164 | 94.05 146 | 81.51 210 | 88.90 152 | 98.93 121 | 83.38 218 | 60.52 236 | 98.06 147 | 63.78 201 | 80.67 184 | 90.36 120 | 92.94 154 | 97.29 109 | 96.41 113 | 99.56 138 | 96.66 218 |
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Effi-MVS+-dtu | | | 93.13 121 | 97.13 93 | 88.47 139 | 88.86 153 | 99.19 105 | 96.79 93 | 79.08 163 | 99.64 68 | 70.01 162 | 97.51 84 | 89.38 122 | 96.53 119 | 97.60 91 | 96.55 108 | 99.57 136 | 100.00 1 |
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TAMVS | | | 92.43 136 | 94.21 145 | 90.35 118 | 88.68 154 | 98.85 122 | 94.15 137 | 81.53 147 | 95.58 167 | 83.61 112 | 87.05 160 | 86.45 134 | 94.71 136 | 96.27 130 | 95.91 128 | 99.42 177 | 99.38 191 |
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GA-MVS | | | 90.38 150 | 94.59 141 | 85.46 172 | 88.30 155 | 98.44 131 | 92.18 151 | 83.30 126 | 97.89 150 | 58.05 218 | 92.86 131 | 84.25 149 | 91.27 187 | 96.65 116 | 92.61 176 | 99.66 122 | 99.43 189 |
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FC-MVSNet-test | | | 92.78 128 | 96.19 123 | 88.80 134 | 88.00 156 | 97.54 148 | 93.60 141 | 82.36 136 | 98.16 143 | 79.71 125 | 91.55 139 | 95.41 94 | 89.65 198 | 96.09 134 | 95.23 140 | 99.49 152 | 99.31 192 |
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FMVSNet1 | | | 92.55 133 | 93.66 147 | 91.26 110 | 87.91 157 | 96.12 172 | 94.75 128 | 81.69 145 | 97.67 151 | 85.63 103 | 80.56 185 | 87.88 131 | 98.15 86 | 96.50 119 | 97.21 91 | 99.41 189 | 99.71 169 |
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tfpnnormal | | | 89.09 158 | 89.71 174 | 88.38 140 | 87.37 158 | 96.78 159 | 91.46 158 | 85.20 102 | 90.33 222 | 72.35 149 | 83.45 166 | 69.30 213 | 94.45 139 | 95.29 148 | 92.86 172 | 99.44 170 | 99.93 116 |
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TESTMET0.1,1 | | | 92.87 127 | 97.23 90 | 87.79 145 | 86.96 159 | 99.10 108 | 94.68 131 | 77.46 174 | 99.36 93 | 71.94 152 | 93.82 123 | 96.51 85 | 95.96 123 | 97.42 99 | 94.37 151 | 99.74 92 | 99.99 49 |
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FMVSNet5 | | | 93.53 114 | 96.09 124 | 90.56 116 | 86.74 160 | 92.84 213 | 92.64 148 | 77.50 173 | 99.41 90 | 88.97 84 | 98.02 77 | 97.81 76 | 98.00 90 | 94.85 154 | 95.43 138 | 99.50 151 | 94.25 225 |
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test-mter | | | 92.67 131 | 97.13 93 | 87.47 147 | 86.72 161 | 99.07 110 | 94.28 135 | 76.90 178 | 99.21 99 | 71.53 156 | 93.63 125 | 96.32 89 | 95.67 125 | 97.32 108 | 94.36 153 | 99.74 92 | 99.99 49 |
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TransMVSNet (Re) | | | 88.33 162 | 89.55 180 | 86.91 152 | 86.65 162 | 95.56 192 | 90.48 168 | 84.44 113 | 92.02 221 | 71.07 160 | 80.13 187 | 72.48 183 | 89.41 200 | 95.05 153 | 94.44 148 | 99.39 191 | 97.14 215 |
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LTVRE_ROB | | 88.65 16 | 87.87 171 | 91.11 164 | 84.10 193 | 86.64 163 | 97.47 149 | 94.40 133 | 78.41 169 | 96.13 164 | 52.02 225 | 87.95 152 | 65.92 223 | 93.59 150 | 95.29 148 | 95.09 142 | 99.52 145 | 99.95 109 |
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 |
EG-PatchMatch MVS | | | 86.96 191 | 89.56 179 | 83.93 197 | 86.29 164 | 97.61 147 | 90.75 164 | 73.31 199 | 95.43 171 | 66.08 190 | 75.88 218 | 71.31 191 | 87.55 212 | 94.79 155 | 92.74 173 | 99.61 130 | 99.13 198 |
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pm-mvs1 | | | 89.68 153 | 92.00 156 | 86.96 151 | 86.23 165 | 96.62 162 | 90.36 174 | 83.05 128 | 93.97 192 | 72.15 151 | 81.77 180 | 82.10 153 | 90.69 193 | 95.38 146 | 94.50 147 | 99.29 203 | 99.65 171 |
|
NR-MVSNet | | | 89.52 155 | 90.71 166 | 88.14 144 | 86.19 166 | 96.20 169 | 92.07 152 | 84.58 108 | 95.54 168 | 75.27 141 | 87.52 157 | 67.96 216 | 91.24 189 | 94.33 161 | 93.45 164 | 99.49 152 | 99.97 81 |
|
our_test_3 | | | | | | 85.89 167 | 96.09 175 | 82.15 221 | | | | | | | | | | |
|
pmmvs4 | | | 91.41 144 | 93.05 150 | 89.49 127 | 85.85 168 | 96.52 164 | 91.70 156 | 82.49 131 | 98.14 144 | 83.17 113 | 87.57 156 | 81.76 154 | 94.39 140 | 95.47 144 | 92.62 175 | 99.33 198 | 99.29 193 |
|
UniMVSNet (Re) | | | 90.41 149 | 91.96 157 | 88.59 138 | 85.71 169 | 96.73 160 | 90.82 162 | 84.11 120 | 95.23 173 | 78.54 131 | 88.91 149 | 76.41 166 | 92.84 156 | 93.40 182 | 93.05 170 | 99.55 140 | 100.00 1 |
|
v18 | | | 87.14 189 | 88.96 192 | 85.01 176 | 85.57 170 | 92.03 215 | 90.89 161 | 74.62 188 | 94.80 179 | 67.90 168 | 82.02 173 | 71.28 192 | 91.63 173 | 91.53 202 | 91.44 193 | 99.47 159 | 99.60 176 |
|
v16 | | | 87.15 188 | 89.13 187 | 84.83 179 | 85.55 171 | 91.94 217 | 90.50 166 | 74.13 192 | 95.06 175 | 67.72 170 | 81.84 177 | 72.55 182 | 91.65 170 | 91.50 205 | 91.42 197 | 99.42 177 | 99.60 176 |
|
v17 | | | 86.99 190 | 88.90 195 | 84.76 181 | 85.52 172 | 91.96 216 | 90.50 166 | 74.17 189 | 94.88 177 | 67.33 178 | 81.94 174 | 71.21 193 | 91.57 176 | 91.49 206 | 91.20 206 | 99.48 156 | 99.60 176 |
|
v8 | | | 87.54 176 | 89.33 186 | 85.45 173 | 85.41 173 | 95.50 195 | 90.32 179 | 78.94 165 | 94.35 190 | 66.93 181 | 81.90 175 | 70.99 198 | 91.62 174 | 91.49 206 | 91.22 205 | 99.48 156 | 99.87 148 |
|
SixPastTwentyTwo | | | 88.35 161 | 91.51 161 | 84.66 182 | 85.39 174 | 96.96 157 | 86.57 208 | 79.62 158 | 96.57 161 | 63.73 202 | 87.86 153 | 75.18 169 | 93.43 152 | 94.03 165 | 90.37 213 | 99.24 208 | 99.58 179 |
|
v6 | | | 87.96 168 | 89.58 177 | 86.08 157 | 85.34 175 | 96.14 171 | 90.44 169 | 82.19 138 | 94.56 181 | 67.43 177 | 81.90 175 | 71.57 190 | 91.62 174 | 91.54 201 | 91.43 194 | 99.43 171 | 99.92 121 |
|
v1neww | | | 87.88 169 | 89.51 183 | 85.97 162 | 85.32 176 | 96.12 172 | 90.33 176 | 82.17 139 | 94.51 182 | 66.96 179 | 81.84 177 | 71.21 193 | 91.64 171 | 91.52 203 | 91.43 194 | 99.42 177 | 99.92 121 |
|
v7new | | | 87.88 169 | 89.51 183 | 85.97 162 | 85.32 176 | 96.12 172 | 90.33 176 | 82.17 139 | 94.51 182 | 66.96 179 | 81.84 177 | 71.21 193 | 91.64 171 | 91.52 203 | 91.43 194 | 99.42 177 | 99.92 121 |
|
MIMVSNet | | | 91.01 147 | 96.22 121 | 84.93 178 | 85.24 178 | 98.09 139 | 90.40 172 | 64.96 226 | 97.55 153 | 72.65 145 | 96.23 96 | 90.81 116 | 96.79 113 | 96.69 115 | 97.06 101 | 99.52 145 | 97.09 216 |
|
V42 | | | 87.84 172 | 89.42 185 | 85.99 161 | 85.16 179 | 96.01 181 | 90.52 165 | 81.78 144 | 94.43 188 | 67.59 171 | 81.32 181 | 71.87 186 | 91.48 178 | 91.25 213 | 91.16 208 | 99.43 171 | 99.92 121 |
|
WR-MVS_H | | | 88.47 160 | 90.55 168 | 86.04 158 | 85.13 180 | 96.07 178 | 89.86 194 | 79.80 156 | 94.37 189 | 72.32 150 | 83.12 168 | 74.44 174 | 89.60 199 | 93.52 179 | 92.40 177 | 99.51 149 | 99.96 100 |
|
N_pmnet | | | 87.31 184 | 91.51 161 | 82.41 209 | 85.13 180 | 95.57 191 | 80.59 223 | 81.79 143 | 96.20 163 | 58.52 217 | 78.62 205 | 85.66 137 | 89.36 201 | 94.64 157 | 92.14 180 | 99.08 214 | 97.72 214 |
|
EU-MVSNet | | | 87.20 186 | 90.47 169 | 83.38 202 | 85.11 182 | 93.85 211 | 86.10 210 | 79.76 157 | 93.30 211 | 65.39 197 | 84.41 162 | 78.43 157 | 85.04 217 | 92.20 195 | 93.03 171 | 98.86 216 | 98.05 211 |
|
UniMVSNet_NR-MVSNet | | | 90.50 148 | 92.31 155 | 88.38 140 | 85.04 183 | 96.34 168 | 90.94 159 | 85.32 99 | 95.87 166 | 75.69 137 | 87.68 155 | 78.49 156 | 93.78 148 | 93.21 184 | 94.60 144 | 99.53 144 | 99.97 81 |
|
v7 | | | 87.72 175 | 89.75 173 | 85.35 174 | 85.01 184 | 95.79 186 | 90.43 171 | 78.98 164 | 94.50 185 | 66.39 185 | 78.87 201 | 73.65 178 | 91.85 167 | 93.69 176 | 91.86 185 | 99.45 165 | 99.92 121 |
|
v15 | | | 86.50 200 | 88.32 204 | 84.37 184 | 85.00 185 | 91.86 218 | 90.30 180 | 73.76 195 | 93.90 195 | 66.28 188 | 79.78 192 | 70.37 205 | 91.45 180 | 91.48 208 | 91.27 202 | 99.43 171 | 99.58 179 |
|
V14 | | | 86.54 199 | 88.41 203 | 84.35 185 | 84.94 186 | 91.83 219 | 90.28 181 | 73.48 197 | 93.73 200 | 66.50 184 | 79.89 190 | 71.12 197 | 91.46 179 | 91.48 208 | 91.25 203 | 99.42 177 | 99.58 179 |
|
v10 | | | 87.40 182 | 89.62 176 | 84.80 180 | 84.93 187 | 95.07 203 | 90.44 169 | 75.63 184 | 94.51 182 | 66.52 183 | 78.87 201 | 73.47 179 | 91.86 166 | 93.69 176 | 91.87 184 | 99.45 165 | 99.86 151 |
|
v1 | | | 87.48 178 | 88.91 194 | 85.81 165 | 84.93 187 | 96.07 178 | 90.33 176 | 82.45 134 | 93.65 202 | 66.39 185 | 79.38 198 | 70.40 204 | 91.33 184 | 91.58 200 | 91.38 200 | 99.42 177 | 99.93 116 |
|
pmmvs6 | | | 85.75 207 | 86.97 215 | 84.34 187 | 84.88 189 | 95.59 190 | 87.41 206 | 79.19 162 | 87.81 228 | 67.56 172 | 63.05 232 | 77.76 158 | 89.15 202 | 93.45 181 | 91.90 183 | 97.83 223 | 99.21 195 |
|
V9 | | | 86.42 201 | 88.26 205 | 84.27 189 | 84.88 189 | 91.80 220 | 90.34 175 | 73.18 201 | 93.92 194 | 66.37 187 | 79.68 194 | 70.25 206 | 91.42 181 | 91.43 210 | 91.23 204 | 99.42 177 | 99.55 184 |
|
v1141 | | | 87.45 181 | 88.98 190 | 85.67 170 | 84.86 191 | 96.08 176 | 90.23 182 | 82.46 132 | 93.75 197 | 65.64 196 | 79.57 195 | 70.52 202 | 91.41 182 | 91.63 198 | 91.39 198 | 99.42 177 | 99.92 121 |
|
divwei89l23v2f112 | | | 87.46 179 | 88.97 191 | 85.70 169 | 84.85 192 | 96.08 176 | 90.23 182 | 82.46 132 | 93.69 201 | 65.83 192 | 79.57 195 | 70.54 201 | 91.39 183 | 91.60 199 | 91.39 198 | 99.43 171 | 99.92 121 |
|
v13 | | | 86.27 203 | 88.16 208 | 84.06 196 | 84.85 192 | 91.77 223 | 90.00 188 | 72.77 204 | 93.56 204 | 66.06 191 | 79.25 199 | 70.50 203 | 91.25 188 | 91.35 212 | 91.15 209 | 99.42 177 | 99.55 184 |
|
v11 | | | 86.74 195 | 89.01 188 | 84.09 195 | 84.79 194 | 91.79 222 | 90.39 173 | 72.53 205 | 94.47 186 | 65.75 193 | 78.64 204 | 72.96 181 | 91.66 169 | 93.92 168 | 91.69 189 | 99.42 177 | 99.61 175 |
|
v12 | | | 86.32 202 | 88.22 206 | 84.10 193 | 84.76 195 | 91.80 220 | 89.94 191 | 72.97 203 | 93.85 196 | 66.18 189 | 79.98 189 | 69.72 212 | 91.33 184 | 91.40 211 | 91.20 206 | 99.42 177 | 99.56 183 |
|
v1144 | | | 87.49 177 | 89.64 175 | 84.97 177 | 84.73 196 | 95.84 184 | 90.17 184 | 79.30 160 | 93.96 193 | 64.65 199 | 78.83 203 | 73.38 180 | 91.51 177 | 93.77 173 | 91.77 186 | 99.45 165 | 99.93 116 |
|
DU-MVS | | | 89.49 156 | 90.60 167 | 88.19 143 | 84.71 197 | 96.20 169 | 90.94 159 | 84.58 108 | 95.54 168 | 75.69 137 | 87.52 157 | 68.74 215 | 93.78 148 | 91.10 214 | 95.13 141 | 99.47 159 | 99.97 81 |
|
Baseline_NR-MVSNet | | | 89.13 157 | 89.53 181 | 88.66 137 | 84.71 197 | 94.43 206 | 91.79 155 | 84.49 112 | 95.54 168 | 78.28 132 | 78.52 207 | 72.46 184 | 93.29 153 | 91.10 214 | 94.82 143 | 99.42 177 | 99.86 151 |
|
v148 | | | 86.63 198 | 87.79 210 | 85.28 175 | 84.65 199 | 95.97 182 | 86.46 209 | 82.84 130 | 92.91 214 | 71.52 157 | 78.99 200 | 66.74 221 | 86.83 214 | 89.28 222 | 90.69 211 | 99.41 189 | 99.94 113 |
|
CP-MVSNet | | | 88.09 166 | 89.57 178 | 86.36 156 | 84.63 200 | 95.46 197 | 89.48 196 | 80.53 151 | 93.42 207 | 71.26 159 | 81.25 182 | 69.90 208 | 92.78 157 | 93.30 183 | 93.69 161 | 99.47 159 | 99.96 100 |
|
v2v482 | | | 87.46 179 | 88.90 195 | 85.78 166 | 84.58 201 | 95.95 183 | 89.90 193 | 82.43 135 | 94.19 191 | 65.65 194 | 79.80 191 | 69.12 214 | 92.67 158 | 91.88 197 | 91.46 192 | 99.45 165 | 99.93 116 |
|
PS-CasMVS | | | 87.24 185 | 88.52 202 | 85.73 168 | 84.58 201 | 95.35 199 | 89.03 199 | 80.17 152 | 93.11 213 | 68.86 166 | 77.71 209 | 66.89 219 | 92.30 160 | 93.13 187 | 93.50 163 | 99.46 162 | 99.96 100 |
|
v1192 | | | 86.93 192 | 89.01 188 | 84.50 183 | 84.46 203 | 95.51 194 | 89.93 192 | 78.65 167 | 93.75 197 | 62.29 207 | 77.19 211 | 70.88 199 | 92.28 161 | 93.84 170 | 91.96 182 | 99.38 193 | 99.90 134 |
|
WR-MVS | | | 88.23 165 | 90.15 170 | 86.00 160 | 84.39 204 | 95.64 188 | 89.96 190 | 81.80 142 | 94.46 187 | 71.60 155 | 82.10 171 | 74.36 175 | 88.76 207 | 92.48 193 | 92.20 179 | 99.46 162 | 99.83 155 |
|
v144192 | | | 86.80 194 | 88.90 195 | 84.35 185 | 84.33 205 | 95.56 192 | 89.34 197 | 77.74 172 | 93.60 203 | 64.03 200 | 77.82 208 | 70.76 200 | 91.28 186 | 92.91 190 | 91.74 188 | 99.37 195 | 99.90 134 |
|
pmmvs5 | | | 87.33 183 | 90.01 171 | 84.20 191 | 84.31 206 | 96.04 180 | 87.63 205 | 76.59 181 | 93.17 212 | 65.35 198 | 84.30 164 | 71.68 187 | 91.91 165 | 95.41 145 | 91.37 201 | 99.39 191 | 98.13 208 |
|
v1921920 | | | 86.81 193 | 88.93 193 | 84.33 188 | 84.23 207 | 95.41 198 | 90.09 186 | 78.10 170 | 93.74 199 | 62.17 208 | 76.98 213 | 71.14 196 | 92.05 163 | 93.69 176 | 91.69 189 | 99.32 199 | 99.88 144 |
|
gm-plane-assit | | | 84.93 209 | 91.61 160 | 77.14 219 | 84.14 208 | 91.29 225 | 66.18 235 | 69.70 208 | 85.22 231 | 47.95 231 | 78.58 206 | 89.24 124 | 94.90 134 | 98.82 36 | 98.12 62 | 99.99 6 | 100.00 1 |
|
TranMVSNet+NR-MVSNet | | | 88.88 159 | 89.90 172 | 87.69 146 | 84.06 209 | 95.68 187 | 91.88 153 | 85.23 101 | 95.16 174 | 72.54 146 | 83.06 169 | 70.14 207 | 92.93 155 | 90.81 217 | 94.53 146 | 99.48 156 | 99.89 139 |
|
v1240 | | | 86.24 204 | 88.56 201 | 83.54 198 | 84.05 210 | 95.21 202 | 89.27 198 | 76.76 179 | 93.42 207 | 60.68 214 | 75.99 217 | 69.80 210 | 91.21 190 | 93.83 172 | 91.76 187 | 99.29 203 | 99.91 133 |
|
PEN-MVS | | | 87.20 186 | 88.22 206 | 86.01 159 | 84.01 211 | 94.93 205 | 90.00 188 | 81.52 149 | 93.46 206 | 69.29 164 | 79.69 193 | 65.51 224 | 91.72 168 | 91.01 216 | 93.12 168 | 99.49 152 | 99.84 153 |
|
MDTV_nov1_ep13_2view | | | 87.75 174 | 93.32 148 | 81.26 212 | 83.74 212 | 96.64 161 | 85.66 211 | 66.20 218 | 98.36 140 | 61.61 209 | 84.34 163 | 87.95 130 | 91.12 192 | 94.01 166 | 92.66 174 | 99.22 209 | 99.27 194 |
|
anonymousdsp | | | 87.98 167 | 92.38 154 | 82.85 204 | 83.68 213 | 96.79 158 | 90.78 163 | 74.06 193 | 95.29 172 | 57.91 219 | 83.33 167 | 83.12 150 | 91.15 191 | 95.96 138 | 92.37 178 | 99.52 145 | 99.76 164 |
|
DTE-MVSNet | | | 86.70 196 | 87.66 214 | 85.58 171 | 83.30 214 | 94.29 207 | 89.74 195 | 81.53 147 | 92.77 215 | 68.93 165 | 80.13 187 | 64.00 227 | 90.62 194 | 89.45 221 | 93.34 166 | 99.32 199 | 99.67 170 |
|
FPMVS | | | 73.80 223 | 74.62 230 | 72.84 225 | 83.09 215 | 84.44 232 | 83.89 213 | 73.64 196 | 92.20 219 | 48.50 229 | 72.19 223 | 59.51 230 | 63.16 232 | 69.13 235 | 66.26 240 | 84.74 237 | 78.59 239 |
|
V4 | | | 85.78 206 | 87.74 211 | 83.50 200 | 82.90 216 | 95.33 200 | 88.62 201 | 77.05 176 | 92.14 220 | 63.45 204 | 76.91 214 | 69.85 209 | 89.72 197 | 90.07 218 | 90.05 216 | 99.27 206 | 99.81 157 |
|
v52 | | | 85.80 205 | 87.74 211 | 83.53 199 | 82.87 217 | 95.31 201 | 88.71 200 | 77.04 177 | 92.23 218 | 63.53 203 | 76.91 214 | 69.80 210 | 89.78 196 | 90.05 219 | 90.07 215 | 99.26 207 | 99.82 156 |
|
v748 | | | 84.47 211 | 86.06 216 | 82.62 207 | 82.85 218 | 95.02 204 | 83.73 216 | 78.48 168 | 90.20 224 | 67.45 176 | 75.86 219 | 61.27 229 | 83.84 218 | 89.87 220 | 90.28 214 | 99.34 196 | 99.90 134 |
|
v7n | | | 85.39 208 | 87.70 213 | 82.70 205 | 82.77 219 | 95.64 188 | 88.27 203 | 74.83 186 | 92.30 217 | 62.58 206 | 76.37 216 | 64.80 226 | 88.38 209 | 94.29 163 | 90.61 212 | 99.34 196 | 99.87 148 |
|
test20.03 | | | 83.86 214 | 88.73 200 | 78.16 217 | 82.60 220 | 93.00 212 | 81.61 222 | 74.68 187 | 92.36 216 | 57.50 220 | 83.01 170 | 74.48 173 | 73.30 230 | 92.40 194 | 91.14 210 | 99.29 203 | 94.75 224 |
|
Anonymous20231206 | | | 84.28 212 | 89.53 181 | 78.17 216 | 82.31 221 | 94.16 209 | 82.57 219 | 76.51 182 | 93.38 210 | 52.98 224 | 79.47 197 | 73.74 177 | 75.45 226 | 95.07 152 | 94.41 150 | 99.18 212 | 96.46 221 |
|
new_pmnet | | | 84.12 213 | 87.89 209 | 79.72 214 | 80.43 222 | 94.14 210 | 80.26 224 | 74.14 191 | 96.01 165 | 56.30 223 | 74.94 220 | 76.45 165 | 88.59 208 | 93.11 188 | 89.31 218 | 98.59 219 | 91.27 228 |
|
testus | | | 82.22 218 | 88.82 198 | 74.52 224 | 79.14 223 | 89.37 228 | 78.38 226 | 72.99 202 | 97.57 152 | 44.54 235 | 93.44 127 | 58.13 231 | 74.20 229 | 92.96 189 | 93.67 162 | 97.89 222 | 96.58 219 |
|
test2356 | | | 83.84 215 | 91.77 158 | 74.59 223 | 78.71 224 | 89.10 229 | 78.24 228 | 72.07 207 | 96.78 159 | 45.18 234 | 96.19 99 | 76.77 162 | 74.87 228 | 93.17 186 | 94.01 158 | 98.44 220 | 96.38 222 |
|
PM-MVS | | | 82.79 217 | 84.51 220 | 80.77 213 | 77.22 225 | 92.13 214 | 83.61 217 | 73.31 199 | 93.50 205 | 61.06 210 | 77.15 212 | 46.52 236 | 90.55 195 | 94.14 164 | 89.05 221 | 98.85 217 | 99.12 199 |
|
pmmvs-eth3d | | | 82.92 216 | 83.31 222 | 82.47 208 | 76.97 226 | 91.76 224 | 83.79 214 | 76.10 183 | 90.33 222 | 69.95 163 | 71.04 225 | 48.09 233 | 89.02 204 | 93.85 169 | 89.14 219 | 99.02 215 | 98.96 200 |
|
new-patchmatchnet | | | 78.17 222 | 80.82 224 | 75.07 222 | 76.93 227 | 91.20 226 | 71.90 231 | 73.32 198 | 86.59 230 | 48.91 228 | 67.11 228 | 47.85 235 | 81.19 220 | 88.18 223 | 87.02 226 | 98.19 221 | 97.79 213 |
|
pmmvs3 | | | 80.91 219 | 85.62 217 | 75.42 221 | 75.01 228 | 89.09 230 | 75.31 229 | 68.70 209 | 86.99 229 | 46.74 233 | 81.18 183 | 62.91 228 | 87.95 210 | 93.84 170 | 89.06 220 | 98.80 218 | 96.23 223 |
|
testmv | | | 71.50 225 | 77.62 227 | 64.36 228 | 72.64 229 | 81.28 236 | 59.32 241 | 66.24 216 | 83.91 232 | 35.02 240 | 69.74 226 | 46.18 237 | 57.12 235 | 85.60 228 | 87.48 224 | 95.84 228 | 89.16 231 |
|
test1235678 | | | 71.50 225 | 77.61 228 | 64.36 228 | 72.64 229 | 81.26 237 | 59.31 242 | 66.22 217 | 83.90 233 | 35.02 240 | 69.74 226 | 46.18 237 | 57.12 235 | 85.60 228 | 87.47 225 | 95.84 228 | 89.15 232 |
|
Gipuma | | | 71.02 227 | 72.60 233 | 69.19 226 | 71.31 231 | 75.11 240 | 66.36 234 | 61.65 234 | 94.93 176 | 47.29 232 | 38.74 239 | 38.52 240 | 75.52 225 | 86.09 227 | 85.92 229 | 93.01 233 | 88.87 233 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test12356 | | | 69.94 229 | 75.85 229 | 63.04 230 | 70.04 232 | 79.32 239 | 61.62 237 | 65.84 221 | 80.56 234 | 36.30 239 | 71.45 224 | 39.38 239 | 48.79 241 | 83.64 230 | 88.02 223 | 95.64 230 | 88.56 234 |
|
1111 | | | 73.79 224 | 78.62 226 | 68.16 227 | 69.34 233 | 81.48 234 | 59.42 239 | 52.46 239 | 78.55 236 | 50.42 226 | 62.43 233 | 71.67 188 | 80.43 222 | 86.79 224 | 88.22 222 | 96.87 224 | 81.17 238 |
|
.test1245 | | | 70.78 228 | 79.90 225 | 60.13 233 | 69.34 233 | 81.48 234 | 59.42 239 | 52.46 239 | 78.55 236 | 50.42 226 | 62.43 233 | 71.67 188 | 80.43 222 | 86.79 224 | 78.71 232 | 48.74 243 | 99.65 171 |
|
MDA-MVSNet-bldmvs | | | 80.30 221 | 82.83 223 | 77.34 218 | 69.16 235 | 94.29 207 | 72.16 230 | 81.97 141 | 90.14 225 | 57.32 221 | 94.01 122 | 47.97 234 | 86.81 215 | 68.74 236 | 86.82 227 | 96.63 225 | 97.86 212 |
|
MIMVSNet1 | | | 80.64 220 | 83.97 221 | 76.76 220 | 68.91 236 | 91.15 227 | 78.32 227 | 75.47 185 | 89.58 227 | 56.64 222 | 65.10 230 | 65.17 225 | 82.14 219 | 93.51 180 | 91.64 191 | 99.10 213 | 91.66 227 |
|
PMVS | | 60.14 18 | 62.67 232 | 64.05 235 | 61.06 232 | 68.32 237 | 53.27 247 | 52.23 244 | 67.63 212 | 75.07 239 | 48.30 230 | 58.27 235 | 57.43 232 | 49.99 240 | 67.20 237 | 62.42 241 | 79.87 241 | 74.68 241 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 74.33 231 | | 66.84 238 | 84.26 233 | 84.17 212 | | 93.39 209 | 58.99 216 | 45.93 238 | 18.06 247 | 70.61 231 | 93.94 167 | 86.62 228 | 92.61 235 | 98.13 208 |
|
PMMVS2 | | | 65.18 231 | 68.25 234 | 61.59 231 | 61.37 239 | 79.72 238 | 59.18 243 | 61.80 232 | 64.72 240 | 37.33 237 | 53.82 236 | 35.59 241 | 54.46 239 | 73.94 234 | 80.52 231 | 95.40 231 | 89.43 230 |
|
EMVS | | | 55.14 235 | 55.29 238 | 54.97 234 | 60.87 240 | 57.52 244 | 38.58 246 | 63.57 230 | 64.54 241 | 23.36 245 | 36.96 240 | 27.99 243 | 60.69 233 | 51.17 240 | 66.61 239 | 82.73 240 | 82.25 236 |
|
E-PMN | | | 55.33 234 | 55.79 237 | 54.81 235 | 59.81 241 | 57.23 245 | 38.83 245 | 63.59 229 | 64.06 242 | 24.66 244 | 35.33 241 | 26.40 244 | 58.69 234 | 55.41 239 | 70.54 237 | 83.26 238 | 81.56 237 |
|
no-one | | | 52.34 236 | 53.36 240 | 51.14 236 | 57.63 242 | 69.39 241 | 35.07 248 | 61.58 235 | 44.14 244 | 37.06 238 | 34.80 242 | 26.36 245 | 32.65 242 | 50.68 241 | 70.83 236 | 82.88 239 | 77.30 240 |
|
MVE | | 58.81 19 | 52.07 237 | 55.15 239 | 48.48 238 | 42.45 243 | 62.35 243 | 36.41 247 | 54.70 238 | 49.88 243 | 27.65 243 | 29.98 243 | 18.08 246 | 54.87 238 | 65.93 238 | 77.26 234 | 74.79 242 | 82.59 235 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 61.76 233 | 72.90 232 | 48.76 237 | 21.21 244 | 68.61 242 | 66.11 236 | 37.38 241 | 94.83 178 | 33.06 242 | 64.31 231 | 29.72 242 | 86.08 216 | 74.44 233 | 78.71 232 | 48.74 243 | 99.65 171 |
|
test123 | | | 48.14 238 | 58.11 236 | 36.51 239 | 8.71 245 | 56.81 246 | 59.55 238 | 24.08 242 | 77.50 238 | 14.41 246 | 49.20 237 | 11.94 248 | 80.98 221 | 41.62 242 | 69.81 238 | 31.32 245 | 99.90 134 |
|
GG-mvs-BLEND | | | 69.85 230 | 99.39 36 | 35.39 240 | 3.67 246 | 99.94 17 | 99.10 39 | 1.69 243 | 99.85 43 | 3.19 247 | 98.13 76 | 99.46 54 | 4.92 243 | 99.23 29 | 99.14 29 | 99.80 52 | 100.00 1 |
|
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 | | | | | | | | | | | 96.61 12 | | 100.00 1 | | | | | |
|
MTMP | | | | | | | | | | | 97.42 7 | | 100.00 1 | | | | | |
|
Patchmatch-RL test | | | | | | | | 68.01 233 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 99.79 52 | | | | | | | | |
|
Patchmtry | | | | | | | 99.00 115 | 95.46 118 | 65.50 222 | | 67.51 173 | | | | | | | |
|
DeepMVS_CX | | | | | | | 97.31 152 | 79.48 225 | 89.65 60 | 98.66 121 | 60.89 212 | 94.40 117 | 66.89 219 | 87.65 211 | 81.69 231 | | 92.76 234 | 94.24 226 |
|