MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 2 | 97.66 2 | 73.37 7 | 97.13 1 | 95.58 10 | 89.33 1 | 85.77 23 | 96.26 7 | 72.84 9 | 99.38 1 | 92.64 4 | 95.93 4 | 97.08 4 |
|
HSP-MVS | | | 90.38 2 | 91.89 1 | 85.84 68 | 92.83 55 | 64.03 168 | 93.06 74 | 94.52 30 | 82.19 19 | 93.65 1 | 96.15 10 | 85.89 1 | 97.19 57 | 91.02 8 | 97.75 1 | 96.29 16 |
|
CNVR-MVS | | | 90.32 3 | 90.89 4 | 88.61 11 | 96.76 4 | 70.65 18 | 96.47 6 | 94.83 21 | 84.83 9 | 89.07 7 | 96.80 2 | 70.86 14 | 99.06 3 | 92.64 4 | 95.71 5 | 96.12 18 |
|
DELS-MVS | | | 90.05 4 | 90.09 5 | 89.94 2 | 93.14 49 | 73.88 6 | 97.01 2 | 94.40 36 | 88.32 2 | 85.71 24 | 94.91 45 | 74.11 7 | 98.91 6 | 87.26 26 | 95.94 3 | 97.03 5 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
DeepPCF-MVS | | 81.17 1 | 89.72 5 | 91.38 3 | 84.72 103 | 93.00 52 | 58.16 253 | 96.72 3 | 94.41 35 | 86.50 5 | 90.25 3 | 97.83 1 | 75.46 5 | 98.67 12 | 92.78 2 | 95.49 7 | 97.32 1 |
|
CANet | | | 89.61 6 | 89.99 6 | 88.46 13 | 94.39 24 | 69.71 32 | 96.53 5 | 93.78 43 | 86.89 4 | 89.68 4 | 95.78 15 | 65.94 40 | 99.10 2 | 92.99 1 | 93.91 25 | 96.58 11 |
|
HPM-MVS++ | | | 89.37 7 | 89.95 7 | 87.64 20 | 95.10 17 | 68.23 59 | 95.24 22 | 94.49 32 | 82.43 17 | 88.90 8 | 96.35 5 | 71.89 13 | 98.63 13 | 88.76 18 | 96.40 2 | 96.06 19 |
|
NCCC | | | 89.07 8 | 89.46 8 | 87.91 16 | 96.60 5 | 69.05 40 | 96.38 7 | 94.64 29 | 84.42 10 | 86.74 18 | 96.20 8 | 66.56 35 | 98.76 11 | 89.03 16 | 94.56 18 | 95.92 25 |
|
MVS_0304 | | | 88.39 9 | 88.35 12 | 88.50 12 | 93.01 51 | 70.11 23 | 95.90 10 | 92.20 107 | 86.27 6 | 88.70 9 | 95.92 13 | 56.76 119 | 99.02 4 | 92.68 3 | 93.76 28 | 96.37 15 |
|
PS-MVSNAJ | | | 88.14 10 | 87.61 17 | 89.71 4 | 92.06 72 | 76.72 1 | 95.75 11 | 93.26 66 | 83.86 11 | 89.55 5 | 96.06 11 | 53.55 167 | 97.89 30 | 91.10 6 | 93.31 35 | 94.54 66 |
|
TSAR-MVS + MP. | | | 88.11 11 | 88.64 9 | 86.54 47 | 91.73 81 | 68.04 61 | 90.36 171 | 93.55 54 | 82.89 14 | 91.29 2 | 92.89 87 | 72.27 10 | 96.03 100 | 87.99 20 | 94.77 13 | 95.54 31 |
|
TSAR-MVS + GP. | | | 87.96 12 | 88.37 11 | 86.70 41 | 93.51 42 | 65.32 138 | 95.15 25 | 93.84 42 | 78.17 55 | 85.93 22 | 94.80 48 | 75.80 4 | 98.21 21 | 89.38 11 | 88.78 77 | 96.59 10 |
|
DeepC-MVS_fast | | 79.48 2 | 87.95 13 | 88.00 13 | 87.79 19 | 95.86 12 | 68.32 55 | 95.74 12 | 94.11 39 | 83.82 12 | 83.49 44 | 96.19 9 | 64.53 53 | 98.44 17 | 83.42 51 | 94.88 12 | 96.61 9 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
xiu_mvs_v2_base | | | 87.92 14 | 87.38 22 | 89.55 7 | 91.41 95 | 76.43 2 | 95.74 12 | 93.12 74 | 83.53 13 | 89.55 5 | 95.95 12 | 53.45 172 | 97.68 32 | 91.07 7 | 92.62 42 | 94.54 66 |
|
EPNet | | | 87.84 15 | 88.38 10 | 86.23 61 | 93.30 44 | 66.05 125 | 95.26 21 | 94.84 20 | 87.09 3 | 88.06 11 | 94.53 52 | 66.79 33 | 97.34 50 | 83.89 48 | 91.68 54 | 95.29 38 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
lupinMVS | | | 87.74 16 | 87.77 15 | 87.63 22 | 89.24 128 | 71.18 14 | 96.57 4 | 92.90 82 | 82.70 16 | 87.13 15 | 95.27 30 | 64.99 49 | 95.80 106 | 89.34 12 | 91.80 52 | 95.93 24 |
|
APDe-MVS | | | 87.54 17 | 87.84 14 | 86.65 42 | 96.07 9 | 66.30 121 | 94.84 32 | 93.78 43 | 69.35 197 | 88.39 10 | 96.34 6 | 67.74 28 | 97.66 36 | 90.62 9 | 93.44 34 | 96.01 22 |
|
SD-MVS | | | 87.49 18 | 87.49 19 | 87.50 24 | 93.60 39 | 68.82 46 | 93.90 53 | 92.63 92 | 76.86 71 | 87.90 12 | 95.76 16 | 66.17 36 | 97.63 38 | 89.06 15 | 91.48 58 | 96.05 20 |
|
test_prior3 | | | 87.38 19 | 87.70 16 | 86.42 53 | 94.71 21 | 67.35 75 | 95.10 27 | 93.10 75 | 75.40 89 | 85.25 30 | 95.61 21 | 67.94 24 | 96.84 79 | 87.47 23 | 94.77 13 | 95.05 50 |
|
alignmvs | | | 87.28 20 | 86.97 25 | 88.24 15 | 91.30 96 | 71.14 16 | 95.61 16 | 93.56 53 | 79.30 38 | 87.07 17 | 95.25 32 | 68.43 19 | 96.93 77 | 87.87 21 | 84.33 112 | 96.65 8 |
|
Regformer-1 | | | 87.24 21 | 87.60 18 | 86.15 62 | 95.14 15 | 65.83 131 | 93.95 49 | 95.12 15 | 82.11 21 | 84.25 37 | 95.73 17 | 67.88 27 | 98.35 19 | 85.60 36 | 88.64 78 | 94.26 73 |
|
train_agg | | | 87.21 22 | 87.42 21 | 86.60 44 | 94.18 27 | 67.28 77 | 94.16 36 | 93.51 55 | 71.87 155 | 85.52 26 | 95.33 26 | 68.19 21 | 97.27 54 | 89.09 13 | 94.90 10 | 95.25 43 |
|
MG-MVS | | | 87.11 23 | 86.27 29 | 89.62 5 | 97.79 1 | 76.27 3 | 94.96 31 | 94.49 32 | 78.74 51 | 83.87 43 | 92.94 84 | 64.34 54 | 96.94 75 | 75.19 100 | 94.09 22 | 95.66 27 |
|
agg_prior1 | | | 87.02 24 | 87.26 23 | 86.28 60 | 94.16 31 | 66.97 86 | 94.08 42 | 93.31 64 | 71.85 157 | 84.49 35 | 95.39 24 | 68.91 17 | 96.75 83 | 88.84 17 | 94.32 20 | 95.13 47 |
|
Regformer-2 | | | 87.00 25 | 87.43 20 | 85.71 76 | 95.14 15 | 64.73 150 | 93.95 49 | 94.95 18 | 81.69 26 | 84.03 41 | 95.73 17 | 67.35 31 | 98.19 23 | 85.40 38 | 88.64 78 | 94.20 75 |
|
agg_prior3 | | | 86.93 26 | 87.08 24 | 86.48 50 | 94.21 25 | 66.95 88 | 94.14 39 | 93.40 61 | 71.80 160 | 84.86 32 | 95.13 36 | 66.16 37 | 97.25 56 | 89.09 13 | 94.90 10 | 95.25 43 |
|
CSCG | | | 86.87 27 | 86.26 30 | 88.72 9 | 95.05 18 | 70.79 17 | 93.83 56 | 95.33 12 | 68.48 210 | 77.63 88 | 94.35 59 | 73.04 8 | 98.45 16 | 84.92 41 | 93.71 30 | 96.92 6 |
|
canonicalmvs | | | 86.85 28 | 86.25 31 | 88.66 10 | 91.80 80 | 71.92 10 | 93.54 63 | 91.71 125 | 80.26 31 | 87.55 13 | 95.25 32 | 63.59 63 | 96.93 77 | 88.18 19 | 84.34 111 | 97.11 3 |
|
PHI-MVS | | | 86.83 29 | 86.85 28 | 86.78 40 | 93.47 43 | 65.55 135 | 95.39 20 | 95.10 17 | 71.77 162 | 85.69 25 | 96.52 3 | 62.07 74 | 98.77 10 | 86.06 34 | 95.60 6 | 96.03 21 |
|
SteuartSystems-ACMMP | | | 86.82 30 | 86.90 26 | 86.58 46 | 90.42 106 | 66.38 118 | 96.09 9 | 93.87 41 | 77.73 60 | 84.01 42 | 95.66 19 | 63.39 64 | 97.94 27 | 87.40 25 | 93.55 33 | 95.42 32 |
Skip Steuart: Steuart Systems R&D Blog. |
PVSNet_Blended | | | 86.73 31 | 86.86 27 | 86.31 59 | 93.76 35 | 67.53 72 | 96.33 8 | 93.61 51 | 82.34 18 | 81.00 57 | 93.08 79 | 63.19 67 | 97.29 52 | 87.08 27 | 91.38 59 | 94.13 81 |
|
jason | | | 86.40 32 | 86.17 32 | 87.11 33 | 86.16 178 | 70.54 20 | 95.71 15 | 92.19 109 | 82.00 24 | 84.58 34 | 94.34 60 | 61.86 76 | 95.53 122 | 87.76 22 | 90.89 64 | 95.27 40 |
jason: jason. |
WTY-MVS | | | 86.32 33 | 85.81 36 | 87.85 17 | 92.82 57 | 69.37 37 | 95.20 23 | 95.25 13 | 82.71 15 | 81.91 51 | 94.73 49 | 67.93 26 | 97.63 38 | 79.55 73 | 82.25 124 | 96.54 12 |
|
MSLP-MVS++ | | | 86.27 34 | 85.91 35 | 87.35 27 | 92.01 73 | 68.97 43 | 95.04 29 | 92.70 87 | 79.04 46 | 81.50 54 | 96.50 4 | 58.98 100 | 96.78 81 | 83.49 50 | 93.93 24 | 96.29 16 |
|
VNet | | | 86.20 35 | 85.65 40 | 87.84 18 | 93.92 34 | 69.99 26 | 95.73 14 | 95.94 9 | 78.43 53 | 86.00 21 | 93.07 81 | 58.22 103 | 97.00 67 | 85.22 39 | 84.33 112 | 96.52 13 |
|
MVS_111021_HR | | | 86.19 36 | 85.80 37 | 87.37 26 | 93.17 48 | 69.79 30 | 93.99 47 | 93.76 46 | 79.08 45 | 78.88 76 | 93.99 66 | 62.25 73 | 98.15 24 | 85.93 35 | 91.15 62 | 94.15 80 |
|
ACMMP_Plus | | | 86.05 37 | 85.80 37 | 86.80 39 | 91.58 84 | 67.53 72 | 91.79 126 | 93.49 57 | 74.93 96 | 84.61 33 | 95.30 28 | 59.42 94 | 97.92 28 | 86.13 33 | 94.92 9 | 94.94 56 |
|
APD-MVS | | | 85.93 38 | 85.99 33 | 85.76 73 | 95.98 11 | 65.21 140 | 93.59 61 | 92.58 94 | 66.54 225 | 86.17 19 | 95.88 14 | 63.83 58 | 97.00 67 | 86.39 32 | 92.94 38 | 95.06 49 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
PAPM | | | 85.89 39 | 85.46 41 | 87.18 30 | 88.20 150 | 72.42 9 | 92.41 97 | 92.77 85 | 82.11 21 | 80.34 62 | 93.07 81 | 68.27 20 | 95.02 128 | 78.39 83 | 93.59 32 | 94.09 84 |
|
Regformer-3 | | | 85.80 40 | 85.92 34 | 85.46 80 | 94.17 29 | 65.09 146 | 92.95 78 | 95.11 16 | 81.13 27 | 81.68 53 | 95.04 37 | 65.82 42 | 98.32 20 | 83.02 52 | 84.36 109 | 92.97 116 |
|
CDPH-MVS | | | 85.71 41 | 85.46 41 | 86.46 51 | 94.75 20 | 67.19 79 | 93.89 54 | 92.83 84 | 70.90 176 | 83.09 46 | 95.28 29 | 63.62 61 | 97.36 48 | 80.63 68 | 94.18 21 | 94.84 58 |
|
DeepC-MVS | | 77.85 3 | 85.52 42 | 85.24 43 | 86.37 56 | 88.80 137 | 66.64 108 | 92.15 101 | 93.68 49 | 81.07 28 | 76.91 98 | 93.64 71 | 62.59 72 | 98.44 17 | 85.50 37 | 92.84 40 | 94.03 88 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Regformer-4 | | | 85.45 43 | 85.69 39 | 84.73 101 | 94.17 29 | 63.23 183 | 92.95 78 | 94.83 21 | 80.66 29 | 81.29 55 | 95.04 37 | 65.12 47 | 98.08 26 | 82.74 53 | 84.36 109 | 92.88 120 |
|
MP-MVS-pluss | | | 85.24 44 | 85.13 44 | 85.56 77 | 91.42 93 | 65.59 134 | 91.54 138 | 92.51 96 | 74.56 99 | 80.62 59 | 95.64 20 | 59.15 97 | 97.00 67 | 86.94 29 | 93.80 26 | 94.07 86 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
PAPR | | | 85.15 45 | 84.47 47 | 87.18 30 | 96.02 10 | 68.29 56 | 91.85 124 | 93.00 79 | 76.59 76 | 79.03 75 | 95.00 39 | 61.59 77 | 97.61 40 | 78.16 84 | 89.00 76 | 95.63 28 |
|
MP-MVS | | | 85.02 46 | 84.97 45 | 85.17 92 | 92.60 61 | 64.27 165 | 93.24 69 | 92.27 101 | 73.13 130 | 79.63 69 | 94.43 53 | 61.90 75 | 97.17 58 | 85.00 40 | 92.56 43 | 94.06 87 |
|
#test# | | | 84.98 47 | 84.74 46 | 85.72 74 | 93.75 37 | 65.01 147 | 94.09 41 | 93.19 70 | 73.55 124 | 79.22 72 | 94.93 42 | 59.04 98 | 97.67 33 | 82.66 54 | 92.21 46 | 94.49 70 |
|
CHOSEN 1792x2688 | | | 84.98 47 | 83.45 56 | 89.57 6 | 89.94 114 | 75.14 4 | 92.07 107 | 92.32 99 | 81.87 25 | 75.68 103 | 88.27 140 | 60.18 88 | 98.60 14 | 80.46 70 | 90.27 71 | 94.96 55 |
|
MPTG | | | 84.73 49 | 84.47 47 | 85.50 78 | 91.89 78 | 65.16 141 | 91.55 137 | 92.23 102 | 75.32 91 | 80.53 60 | 95.21 34 | 56.06 131 | 97.16 59 | 84.86 42 | 92.55 44 | 94.18 76 |
|
HFP-MVS | | | 84.73 49 | 84.40 49 | 85.72 74 | 93.75 37 | 65.01 147 | 93.50 64 | 93.19 70 | 72.19 147 | 79.22 72 | 94.93 42 | 59.04 98 | 97.67 33 | 81.55 62 | 92.21 46 | 94.49 70 |
|
MVS | | | 84.66 51 | 82.86 66 | 90.06 1 | 90.93 100 | 74.56 5 | 87.91 216 | 95.54 11 | 68.55 208 | 72.35 139 | 94.71 50 | 59.78 91 | 98.90 7 | 81.29 67 | 94.69 17 | 96.74 7 |
|
ACMMPR | | | 84.37 52 | 84.06 50 | 85.28 88 | 93.56 40 | 64.37 160 | 93.50 64 | 93.15 73 | 72.19 147 | 78.85 78 | 94.86 46 | 56.69 123 | 97.45 44 | 81.55 62 | 92.20 48 | 94.02 89 |
|
region2R | | | 84.36 53 | 84.03 51 | 85.36 86 | 93.54 41 | 64.31 162 | 93.43 67 | 92.95 80 | 72.16 150 | 78.86 77 | 94.84 47 | 56.97 116 | 97.53 42 | 81.38 65 | 92.11 50 | 94.24 74 |
|
LFMVS | | | 84.34 54 | 82.73 69 | 89.18 8 | 94.76 19 | 73.25 8 | 94.99 30 | 91.89 118 | 71.90 153 | 82.16 50 | 93.49 74 | 47.98 216 | 97.05 62 | 82.55 55 | 84.82 105 | 97.25 2 |
|
HY-MVS | | 76.49 5 | 84.28 55 | 83.36 61 | 87.02 36 | 92.22 69 | 67.74 66 | 84.65 246 | 94.50 31 | 79.15 42 | 82.23 49 | 87.93 146 | 66.88 32 | 96.94 75 | 80.53 69 | 82.20 125 | 96.39 14 |
|
MAR-MVS | | | 84.18 56 | 83.43 57 | 86.44 52 | 96.25 7 | 65.93 128 | 94.28 35 | 94.27 38 | 74.41 100 | 79.16 74 | 95.61 21 | 53.99 162 | 98.88 9 | 69.62 144 | 93.26 36 | 94.50 69 |
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 |
MVS_Test | | | 84.16 57 | 83.20 62 | 87.05 35 | 91.56 85 | 69.82 29 | 89.99 179 | 92.05 112 | 77.77 59 | 82.84 47 | 86.57 162 | 63.93 57 | 96.09 97 | 74.91 106 | 89.18 75 | 95.25 43 |
|
CANet_DTU | | | 84.09 58 | 83.52 53 | 85.81 69 | 90.30 109 | 66.82 93 | 91.87 122 | 89.01 219 | 85.27 7 | 86.09 20 | 93.74 70 | 47.71 219 | 96.98 71 | 77.90 87 | 89.78 73 | 93.65 97 |
|
PVSNet_Blended_VisFu | | | 83.97 59 | 83.50 54 | 85.39 85 | 90.02 112 | 66.59 111 | 93.77 57 | 91.73 123 | 77.43 66 | 77.08 97 | 89.81 127 | 63.77 60 | 96.97 72 | 79.67 72 | 88.21 81 | 92.60 124 |
|
DWT-MVSNet_test | | | 83.95 60 | 82.80 67 | 87.41 25 | 92.90 54 | 70.07 25 | 89.12 197 | 94.42 34 | 82.15 20 | 77.64 87 | 91.77 103 | 70.81 15 | 96.22 92 | 65.03 183 | 81.36 128 | 95.94 23 |
|
MTAPA | | | 83.91 61 | 83.38 60 | 85.50 78 | 91.89 78 | 65.16 141 | 81.75 267 | 92.23 102 | 75.32 91 | 80.53 60 | 95.21 34 | 56.06 131 | 97.16 59 | 84.86 42 | 92.55 44 | 94.18 76 |
|
XVS | | | 83.87 62 | 83.47 55 | 85.05 94 | 93.22 45 | 63.78 171 | 92.92 80 | 92.66 90 | 73.99 111 | 78.18 82 | 94.31 62 | 55.25 136 | 97.41 45 | 79.16 76 | 91.58 56 | 93.95 91 |
|
Effi-MVS+ | | | 83.82 63 | 82.76 68 | 86.99 37 | 89.56 121 | 69.40 36 | 91.35 145 | 86.12 266 | 72.59 138 | 83.22 45 | 92.81 89 | 59.60 93 | 96.01 102 | 81.76 60 | 87.80 84 | 95.56 30 |
|
EI-MVSNet-Vis-set | | | 83.77 64 | 83.67 52 | 84.06 115 | 92.79 59 | 63.56 180 | 91.76 129 | 94.81 23 | 79.65 36 | 77.87 84 | 94.09 64 | 63.35 65 | 97.90 29 | 79.35 74 | 79.36 138 | 90.74 150 |
|
MVSFormer | | | 83.75 65 | 82.88 65 | 86.37 56 | 89.24 128 | 71.18 14 | 89.07 198 | 90.69 157 | 65.80 230 | 87.13 15 | 94.34 60 | 64.99 49 | 92.67 214 | 72.83 113 | 91.80 52 | 95.27 40 |
|
CP-MVS | | | 83.71 66 | 83.40 59 | 84.65 104 | 93.14 49 | 63.84 169 | 94.59 33 | 92.28 100 | 71.03 174 | 77.41 91 | 94.92 44 | 55.21 139 | 96.19 93 | 81.32 66 | 90.70 66 | 93.91 93 |
|
PVSNet_BlendedMVS | | | 83.38 67 | 83.43 57 | 83.22 131 | 93.76 35 | 67.53 72 | 94.06 43 | 93.61 51 | 79.13 43 | 81.00 57 | 85.14 176 | 63.19 67 | 97.29 52 | 87.08 27 | 73.91 179 | 84.83 237 |
|
PGM-MVS | | | 83.25 68 | 82.70 70 | 84.92 96 | 92.81 58 | 64.07 167 | 90.44 168 | 92.20 107 | 71.28 172 | 77.23 94 | 94.43 53 | 55.17 140 | 97.31 51 | 79.33 75 | 91.38 59 | 93.37 102 |
|
HPM-MVS | | | 83.25 68 | 82.95 64 | 84.17 113 | 92.25 68 | 62.88 191 | 90.91 157 | 91.86 119 | 70.30 189 | 77.12 95 | 93.96 67 | 56.75 121 | 96.28 91 | 82.04 58 | 91.34 61 | 93.34 103 |
|
EI-MVSNet-UG-set | | | 83.14 70 | 82.96 63 | 83.67 125 | 92.28 67 | 63.19 184 | 91.38 143 | 94.68 26 | 79.22 40 | 76.60 99 | 93.75 69 | 62.64 71 | 97.76 31 | 78.07 85 | 78.01 149 | 90.05 157 |
|
PatchFormer-LS_test | | | 83.14 70 | 81.81 78 | 87.12 32 | 92.34 64 | 69.92 28 | 88.64 204 | 93.32 63 | 82.07 23 | 74.87 112 | 91.62 107 | 68.91 17 | 96.08 99 | 66.07 174 | 78.45 148 | 95.37 33 |
|
VDD-MVS | | | 83.06 72 | 81.81 78 | 86.81 38 | 90.86 103 | 67.70 67 | 95.40 19 | 91.50 133 | 75.46 86 | 81.78 52 | 92.34 97 | 40.09 256 | 97.13 61 | 86.85 30 | 82.04 126 | 95.60 29 |
|
PAPM_NR | | | 82.97 73 | 81.84 77 | 86.37 56 | 94.10 33 | 66.76 100 | 87.66 221 | 92.84 83 | 69.96 192 | 74.07 119 | 93.57 72 | 63.10 69 | 97.50 43 | 70.66 137 | 90.58 68 | 94.85 57 |
|
mPP-MVS | | | 82.96 74 | 82.44 71 | 84.52 108 | 92.83 55 | 62.92 189 | 92.76 83 | 91.85 120 | 71.52 169 | 75.61 106 | 94.24 63 | 53.48 171 | 96.99 70 | 78.97 79 | 90.73 65 | 93.64 98 |
|
DP-MVS Recon | | | 82.73 75 | 81.65 80 | 85.98 64 | 97.31 3 | 67.06 83 | 95.15 25 | 91.99 114 | 69.08 200 | 76.50 101 | 93.89 68 | 54.48 156 | 98.20 22 | 70.76 136 | 85.66 101 | 92.69 121 |
|
CLD-MVS | | | 82.73 75 | 82.35 73 | 83.86 118 | 87.90 156 | 67.65 69 | 95.45 18 | 92.18 110 | 85.06 8 | 72.58 132 | 92.27 98 | 52.46 180 | 95.78 107 | 84.18 44 | 79.06 141 | 88.16 177 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
sss | | | 82.71 77 | 82.38 72 | 83.73 122 | 89.25 126 | 59.58 240 | 92.24 100 | 94.89 19 | 77.96 57 | 79.86 66 | 92.38 95 | 56.70 122 | 97.05 62 | 77.26 90 | 80.86 132 | 94.55 64 |
|
3Dnovator | | 73.91 6 | 82.69 78 | 80.82 88 | 88.31 14 | 89.57 120 | 71.26 13 | 92.60 91 | 94.39 37 | 78.84 48 | 67.89 195 | 92.48 93 | 48.42 211 | 98.52 15 | 68.80 152 | 94.40 19 | 95.15 46 |
|
MVSTER | | | 82.47 79 | 82.05 74 | 83.74 120 | 92.68 60 | 69.01 41 | 91.90 121 | 93.21 67 | 79.83 32 | 72.14 140 | 85.71 172 | 74.72 6 | 94.72 139 | 75.72 96 | 72.49 188 | 87.50 182 |
|
TESTMET0.1,1 | | | 82.41 80 | 81.98 76 | 83.72 123 | 88.08 151 | 63.74 173 | 92.70 86 | 93.77 45 | 79.30 38 | 77.61 89 | 87.57 150 | 58.19 104 | 94.08 172 | 73.91 109 | 86.68 92 | 93.33 105 |
|
CostFormer | | | 82.33 81 | 81.15 84 | 85.86 67 | 89.01 133 | 68.46 51 | 82.39 264 | 93.01 77 | 75.59 84 | 80.25 63 | 81.57 217 | 72.03 12 | 94.96 130 | 79.06 78 | 77.48 157 | 94.16 79 |
|
API-MVS | | | 82.28 82 | 80.53 93 | 87.54 23 | 96.13 8 | 70.59 19 | 93.63 59 | 91.04 150 | 65.72 232 | 75.45 108 | 92.83 88 | 56.11 130 | 98.89 8 | 64.10 191 | 89.75 74 | 93.15 110 |
|
IB-MVS | | 77.80 4 | 82.18 83 | 80.46 94 | 87.35 27 | 89.14 130 | 70.28 22 | 95.59 17 | 95.17 14 | 78.85 47 | 70.19 157 | 85.82 170 | 70.66 16 | 97.67 33 | 72.19 122 | 66.52 224 | 94.09 84 |
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 |
xiu_mvs_v1_base_debu | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 172 | 68.72 47 | 92.59 93 | 90.44 162 | 73.12 131 | 84.20 38 | 94.36 55 | 38.04 266 | 95.73 110 | 84.12 45 | 86.81 89 | 91.33 142 |
|
xiu_mvs_v1_base | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 172 | 68.72 47 | 92.59 93 | 90.44 162 | 73.12 131 | 84.20 38 | 94.36 55 | 38.04 266 | 95.73 110 | 84.12 45 | 86.81 89 | 91.33 142 |
|
xiu_mvs_v1_base_debi | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 172 | 68.72 47 | 92.59 93 | 90.44 162 | 73.12 131 | 84.20 38 | 94.36 55 | 38.04 266 | 95.73 110 | 84.12 45 | 86.81 89 | 91.33 142 |
|
3Dnovator+ | | 73.60 7 | 82.10 87 | 80.60 92 | 86.60 44 | 90.89 102 | 66.80 99 | 95.20 23 | 93.44 59 | 74.05 110 | 67.42 200 | 92.49 92 | 49.46 202 | 97.65 37 | 70.80 135 | 91.68 54 | 95.33 35 |
|
MVS_111021_LR | | | 82.02 88 | 81.52 81 | 83.51 128 | 88.42 146 | 62.88 191 | 89.77 186 | 88.93 221 | 76.78 73 | 75.55 107 | 93.10 77 | 50.31 194 | 95.38 124 | 83.82 49 | 87.02 88 | 92.26 133 |
|
PMMVS | | | 81.98 89 | 82.04 75 | 81.78 171 | 89.76 118 | 56.17 267 | 91.13 154 | 90.69 157 | 77.96 57 | 80.09 64 | 93.57 72 | 46.33 229 | 94.99 129 | 81.41 64 | 87.46 86 | 94.17 78 |
|
EPP-MVSNet | | | 81.79 90 | 81.52 81 | 82.61 143 | 88.77 138 | 60.21 230 | 93.02 76 | 93.66 50 | 68.52 209 | 72.90 127 | 90.39 119 | 72.19 11 | 94.96 130 | 74.93 105 | 79.29 140 | 92.67 122 |
|
APD-MVS_3200maxsize | | | 81.64 91 | 81.32 83 | 82.59 144 | 92.36 63 | 58.74 250 | 91.39 141 | 91.01 151 | 63.35 246 | 79.72 68 | 94.62 51 | 51.82 183 | 96.14 95 | 79.71 71 | 87.93 83 | 92.89 119 |
|
ACMMP | | | 81.49 92 | 80.67 90 | 83.93 117 | 91.71 82 | 62.90 190 | 92.13 102 | 92.22 106 | 71.79 161 | 71.68 147 | 93.49 74 | 50.32 193 | 96.96 73 | 78.47 81 | 84.22 116 | 91.93 136 |
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 |
CDS-MVSNet | | | 81.43 93 | 80.74 89 | 83.52 127 | 86.26 176 | 64.45 155 | 92.09 105 | 90.65 160 | 75.83 83 | 73.95 121 | 89.81 127 | 63.97 56 | 92.91 206 | 71.27 129 | 82.82 121 | 93.20 109 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
mvs_anonymous | | | 81.36 94 | 79.99 97 | 85.46 80 | 90.39 108 | 68.40 52 | 86.88 234 | 90.61 161 | 74.41 100 | 70.31 156 | 84.67 181 | 63.79 59 | 92.32 225 | 73.13 110 | 85.70 100 | 95.67 26 |
|
1121 | | | 81.25 95 | 80.05 95 | 84.87 98 | 92.30 66 | 64.31 162 | 87.91 216 | 91.39 137 | 59.44 272 | 79.94 65 | 92.91 85 | 57.09 112 | 97.01 65 | 66.63 166 | 92.81 41 | 93.29 106 |
|
Fast-Effi-MVS+ | | | 81.14 96 | 80.01 96 | 84.51 109 | 90.24 110 | 65.86 129 | 94.12 40 | 89.15 213 | 73.81 118 | 75.37 109 | 88.26 141 | 57.26 111 | 94.53 146 | 66.97 165 | 84.92 104 | 93.15 110 |
|
HQP-MVS | | | 81.14 96 | 80.64 91 | 82.64 142 | 87.54 159 | 63.66 178 | 94.06 43 | 91.70 126 | 79.80 33 | 74.18 115 | 90.30 120 | 51.63 187 | 95.61 116 | 77.63 88 | 78.90 142 | 88.63 169 |
|
HyFIR lowres test | | | 81.03 98 | 79.56 104 | 85.43 83 | 87.81 157 | 68.11 60 | 90.18 175 | 90.01 185 | 70.65 184 | 72.95 126 | 86.06 168 | 63.61 62 | 94.50 147 | 75.01 104 | 79.75 136 | 93.67 96 |
|
nrg030 | | | 80.93 99 | 79.86 99 | 84.13 114 | 83.69 208 | 68.83 45 | 93.23 70 | 91.20 143 | 75.55 85 | 75.06 111 | 88.22 144 | 63.04 70 | 94.74 138 | 81.88 59 | 66.88 221 | 88.82 167 |
|
Vis-MVSNet | | | 80.92 100 | 79.98 98 | 83.74 120 | 88.48 143 | 61.80 207 | 93.44 66 | 88.26 236 | 73.96 114 | 77.73 85 | 91.76 104 | 49.94 198 | 94.76 136 | 65.84 177 | 90.37 70 | 94.65 62 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
1314 | | | 80.70 101 | 78.95 116 | 85.94 66 | 87.77 158 | 67.56 71 | 87.91 216 | 92.55 95 | 72.17 149 | 67.44 199 | 93.09 78 | 50.27 195 | 97.04 64 | 71.68 125 | 87.64 85 | 93.23 108 |
|
tpmrst | | | 80.57 102 | 79.14 115 | 84.84 99 | 90.10 111 | 68.28 57 | 81.70 268 | 89.72 196 | 77.63 62 | 75.96 102 | 79.54 247 | 64.94 51 | 92.71 212 | 75.43 98 | 77.28 160 | 93.55 99 |
|
1112_ss | | | 80.56 103 | 79.83 100 | 82.77 137 | 88.65 139 | 60.78 217 | 92.29 98 | 88.36 232 | 72.58 139 | 72.46 136 | 94.95 40 | 65.09 48 | 93.42 195 | 66.38 170 | 77.71 151 | 94.10 83 |
|
VDDNet | | | 80.50 104 | 78.26 123 | 87.21 29 | 86.19 177 | 69.79 30 | 94.48 34 | 91.31 140 | 60.42 266 | 79.34 71 | 90.91 111 | 38.48 262 | 96.56 89 | 82.16 56 | 81.05 130 | 95.27 40 |
|
BH-w/o | | | 80.49 105 | 79.30 111 | 84.05 116 | 90.83 104 | 64.36 161 | 93.60 60 | 89.42 203 | 74.35 105 | 69.09 175 | 90.15 122 | 55.23 138 | 95.61 116 | 64.61 186 | 86.43 97 | 92.17 134 |
|
TAMVS | | | 80.37 106 | 79.45 107 | 83.13 133 | 85.14 189 | 63.37 181 | 91.23 150 | 90.76 156 | 74.81 98 | 72.65 130 | 88.49 135 | 60.63 86 | 92.95 202 | 69.41 146 | 81.95 127 | 93.08 113 |
|
HQP_MVS | | | 80.34 107 | 79.75 101 | 82.12 164 | 86.94 167 | 62.42 196 | 93.13 72 | 91.31 140 | 78.81 49 | 72.53 133 | 89.14 131 | 50.66 191 | 95.55 120 | 76.74 91 | 78.53 146 | 88.39 174 |
|
HPM-MVS_fast | | | 80.25 108 | 79.55 106 | 82.33 155 | 91.55 86 | 59.95 235 | 91.32 147 | 89.16 212 | 65.23 236 | 74.71 113 | 93.07 81 | 47.81 218 | 95.74 109 | 74.87 108 | 88.23 80 | 91.31 146 |
|
diffmvs | | | 80.18 109 | 78.55 120 | 85.07 93 | 88.56 140 | 66.93 89 | 86.70 237 | 88.62 227 | 70.42 186 | 78.69 80 | 85.26 174 | 56.93 118 | 94.77 135 | 68.68 153 | 83.09 118 | 93.51 100 |
|
ab-mvs | | | 80.18 109 | 78.31 122 | 85.80 70 | 88.44 145 | 65.49 137 | 83.00 261 | 92.67 89 | 71.82 159 | 77.36 92 | 85.01 177 | 54.50 154 | 96.59 86 | 76.35 95 | 75.63 167 | 95.32 37 |
|
IS-MVSNet | | | 80.14 111 | 79.41 108 | 82.33 155 | 87.91 155 | 60.08 234 | 91.97 113 | 88.27 235 | 72.90 135 | 71.44 149 | 91.73 106 | 61.44 78 | 93.66 190 | 62.47 208 | 86.53 95 | 93.24 107 |
|
test-LLR | | | 80.10 112 | 79.56 104 | 81.72 173 | 86.93 169 | 61.17 212 | 92.70 86 | 91.54 130 | 71.51 170 | 75.62 104 | 86.94 159 | 53.83 163 | 92.38 222 | 72.21 120 | 84.76 107 | 91.60 139 |
|
PVSNet | | 73.49 8 | 80.05 113 | 78.63 118 | 84.31 111 | 90.92 101 | 64.97 149 | 92.47 96 | 91.05 149 | 79.18 41 | 72.43 137 | 90.51 118 | 37.05 278 | 94.06 173 | 68.06 154 | 86.00 99 | 93.90 94 |
|
UA-Net | | | 80.02 114 | 79.65 102 | 81.11 187 | 89.33 124 | 57.72 257 | 86.33 239 | 89.00 220 | 77.44 65 | 81.01 56 | 89.15 130 | 59.33 95 | 95.90 103 | 61.01 215 | 84.28 114 | 89.73 161 |
|
test-mter | | | 79.96 115 | 79.38 110 | 81.72 173 | 86.93 169 | 61.17 212 | 92.70 86 | 91.54 130 | 73.85 116 | 75.62 104 | 86.94 159 | 49.84 200 | 92.38 222 | 72.21 120 | 84.76 107 | 91.60 139 |
|
QAPM | | | 79.95 116 | 77.39 139 | 87.64 20 | 89.63 119 | 71.41 12 | 93.30 68 | 93.70 48 | 65.34 235 | 67.39 202 | 91.75 105 | 47.83 217 | 98.96 5 | 57.71 231 | 89.81 72 | 92.54 126 |
|
UGNet | | | 79.87 117 | 78.68 117 | 83.45 130 | 89.96 113 | 61.51 210 | 92.13 102 | 90.79 154 | 76.83 72 | 78.85 78 | 86.33 165 | 38.16 264 | 96.17 94 | 67.93 156 | 87.17 87 | 92.67 122 |
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 |
abl_6 | | | 79.82 118 | 79.20 113 | 81.70 175 | 89.85 115 | 58.34 252 | 88.47 206 | 90.07 182 | 62.56 253 | 77.71 86 | 93.08 79 | 47.65 220 | 96.78 81 | 77.94 86 | 85.45 103 | 89.99 158 |
|
tpm2 | | | 79.80 119 | 77.95 128 | 85.34 87 | 88.28 148 | 68.26 58 | 81.56 272 | 91.42 136 | 70.11 190 | 77.59 90 | 80.50 233 | 67.40 29 | 94.26 163 | 67.34 161 | 77.35 158 | 93.51 100 |
|
DI_MVS_plusplus_test | | | 79.78 120 | 77.50 136 | 86.62 43 | 80.90 231 | 69.46 35 | 90.69 163 | 91.97 116 | 77.00 68 | 59.07 248 | 82.34 202 | 46.82 223 | 95.88 104 | 82.14 57 | 86.59 94 | 94.53 68 |
|
test_normal | | | 79.66 121 | 77.36 141 | 86.54 47 | 80.72 235 | 69.21 38 | 90.68 164 | 92.16 111 | 76.99 69 | 58.63 252 | 82.03 211 | 46.70 225 | 95.86 105 | 81.74 61 | 86.63 93 | 94.56 63 |
|
thres200 | | | 79.66 121 | 78.33 121 | 83.66 126 | 92.54 62 | 65.82 132 | 93.06 74 | 96.31 6 | 74.90 97 | 73.30 124 | 88.66 133 | 59.67 92 | 95.61 116 | 47.84 264 | 78.67 145 | 89.56 163 |
|
CPTT-MVS | | | 79.59 123 | 79.16 114 | 80.89 194 | 91.54 87 | 59.80 237 | 92.10 104 | 88.54 230 | 60.42 266 | 72.96 125 | 93.28 76 | 48.27 212 | 92.80 209 | 78.89 80 | 86.50 96 | 90.06 156 |
|
Test_1112_low_res | | | 79.56 124 | 78.60 119 | 82.43 148 | 88.24 149 | 60.39 226 | 92.09 105 | 87.99 239 | 72.10 151 | 71.84 143 | 87.42 152 | 64.62 52 | 93.04 199 | 65.80 178 | 77.30 159 | 93.85 95 |
|
FIs | | | 79.47 125 | 79.41 108 | 79.67 212 | 85.95 181 | 59.40 242 | 91.68 133 | 93.94 40 | 78.06 56 | 68.96 178 | 88.28 139 | 66.61 34 | 91.77 235 | 66.20 173 | 74.99 173 | 87.82 179 |
|
BH-RMVSNet | | | 79.46 126 | 77.65 132 | 84.89 97 | 91.68 83 | 65.66 133 | 93.55 62 | 88.09 237 | 72.93 134 | 73.37 123 | 91.12 110 | 46.20 231 | 96.12 96 | 56.28 235 | 85.61 102 | 92.91 118 |
|
PCF-MVS | | 73.15 9 | 79.29 127 | 77.63 133 | 84.29 112 | 86.06 179 | 65.96 127 | 87.03 229 | 91.10 147 | 69.86 193 | 69.79 164 | 90.64 114 | 57.54 110 | 96.59 86 | 64.37 190 | 82.29 123 | 90.32 154 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Vis-MVSNet (Re-imp) | | | 79.24 128 | 79.57 103 | 78.24 239 | 88.46 144 | 52.29 284 | 90.41 170 | 89.12 214 | 74.24 106 | 69.13 174 | 91.91 101 | 65.77 43 | 90.09 260 | 59.00 226 | 88.09 82 | 92.33 129 |
|
114514_t | | | 79.17 129 | 77.67 131 | 83.68 124 | 95.32 14 | 65.53 136 | 92.85 82 | 91.60 129 | 63.49 245 | 67.92 194 | 90.63 116 | 46.65 226 | 95.72 114 | 67.01 164 | 83.54 117 | 89.79 159 |
|
VPA-MVSNet | | | 79.03 130 | 78.00 127 | 82.11 167 | 85.95 181 | 64.48 154 | 93.22 71 | 94.66 27 | 75.05 95 | 74.04 120 | 84.95 178 | 52.17 182 | 93.52 192 | 74.90 107 | 67.04 220 | 88.32 176 |
|
OPM-MVS | | | 79.00 131 | 78.09 125 | 81.73 172 | 83.52 211 | 63.83 170 | 91.64 136 | 90.30 172 | 76.36 79 | 71.97 142 | 89.93 126 | 46.30 230 | 95.17 127 | 75.10 101 | 77.70 152 | 86.19 210 |
|
EI-MVSNet | | | 78.97 132 | 78.22 124 | 81.25 180 | 85.33 186 | 62.73 194 | 89.53 190 | 93.21 67 | 72.39 143 | 72.14 140 | 90.13 123 | 60.99 79 | 94.72 139 | 67.73 158 | 72.49 188 | 86.29 208 |
|
AdaColmap | | | 78.94 133 | 77.00 144 | 84.76 100 | 96.34 6 | 65.86 129 | 92.66 90 | 87.97 240 | 62.18 255 | 70.56 151 | 92.37 96 | 43.53 243 | 97.35 49 | 64.50 188 | 82.86 120 | 91.05 148 |
|
tpmp4_e23 | | | 78.85 134 | 76.55 148 | 85.77 72 | 89.25 126 | 68.39 53 | 81.63 271 | 91.38 138 | 70.40 187 | 75.21 110 | 79.22 249 | 67.37 30 | 94.79 134 | 58.98 227 | 75.51 168 | 94.13 81 |
|
VPNet | | | 78.82 135 | 77.53 135 | 82.70 139 | 84.52 196 | 66.44 117 | 93.93 51 | 92.23 102 | 80.46 30 | 72.60 131 | 88.38 138 | 49.18 205 | 93.13 198 | 72.47 118 | 63.97 247 | 88.55 171 |
|
EPNet_dtu | | | 78.80 136 | 79.26 112 | 77.43 249 | 88.06 152 | 49.71 297 | 91.96 114 | 91.95 117 | 77.67 61 | 76.56 100 | 91.28 109 | 58.51 102 | 90.20 255 | 56.37 234 | 80.95 131 | 92.39 128 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tfpn200view9 | | | 78.79 137 | 77.43 137 | 82.88 135 | 92.21 70 | 64.49 152 | 92.05 108 | 96.28 7 | 73.48 125 | 71.75 145 | 88.26 141 | 60.07 89 | 95.32 125 | 45.16 273 | 77.58 154 | 88.83 166 |
|
TR-MVS | | | 78.77 138 | 77.37 140 | 82.95 134 | 90.49 105 | 60.88 215 | 93.67 58 | 90.07 182 | 70.08 191 | 74.51 114 | 91.37 108 | 45.69 232 | 95.70 115 | 60.12 220 | 80.32 133 | 92.29 131 |
|
mvs-test1 | | | 78.74 139 | 77.95 128 | 81.14 186 | 83.22 213 | 57.13 263 | 93.96 48 | 87.78 241 | 75.42 87 | 72.68 129 | 90.80 113 | 45.08 236 | 94.54 145 | 75.08 102 | 77.49 156 | 91.74 138 |
|
thres400 | | | 78.68 140 | 77.43 137 | 82.43 148 | 92.21 70 | 64.49 152 | 92.05 108 | 96.28 7 | 73.48 125 | 71.75 145 | 88.26 141 | 60.07 89 | 95.32 125 | 45.16 273 | 77.58 154 | 87.48 183 |
|
BH-untuned | | | 78.68 140 | 77.08 142 | 83.48 129 | 89.84 116 | 63.74 173 | 92.70 86 | 88.59 228 | 71.57 167 | 66.83 207 | 88.65 134 | 51.75 185 | 95.39 123 | 59.03 225 | 84.77 106 | 91.32 145 |
|
OMC-MVS | | | 78.67 142 | 77.91 130 | 80.95 193 | 85.76 185 | 57.40 261 | 88.49 205 | 88.67 225 | 73.85 116 | 72.43 137 | 92.10 99 | 49.29 204 | 94.55 144 | 72.73 115 | 77.89 150 | 90.91 149 |
|
tpm | | | 78.58 143 | 77.03 143 | 83.22 131 | 85.94 183 | 64.56 151 | 83.21 259 | 91.14 146 | 78.31 54 | 73.67 122 | 79.68 244 | 64.01 55 | 92.09 230 | 66.07 174 | 71.26 197 | 93.03 114 |
|
OpenMVS | | 70.45 11 | 78.54 144 | 75.92 156 | 86.41 55 | 85.93 184 | 71.68 11 | 92.74 84 | 92.51 96 | 66.49 226 | 64.56 216 | 91.96 100 | 43.88 242 | 98.10 25 | 54.61 239 | 90.65 67 | 89.44 164 |
|
EPMVS | | | 78.49 145 | 75.98 155 | 86.02 63 | 91.21 97 | 69.68 33 | 80.23 281 | 91.20 143 | 75.25 93 | 72.48 135 | 78.11 254 | 54.65 153 | 93.69 189 | 57.66 232 | 83.04 119 | 94.69 61 |
|
GA-MVS | | | 78.33 146 | 76.23 152 | 84.65 104 | 83.65 209 | 66.30 121 | 91.44 139 | 90.14 180 | 76.01 81 | 70.32 155 | 84.02 186 | 42.50 246 | 94.72 139 | 70.98 133 | 77.00 161 | 92.94 117 |
|
cascas | | | 78.18 147 | 75.77 158 | 85.41 84 | 87.14 165 | 69.11 39 | 92.96 77 | 91.15 145 | 66.71 224 | 70.47 152 | 86.07 167 | 37.49 272 | 96.48 90 | 70.15 140 | 79.80 135 | 90.65 151 |
|
UniMVSNet_NR-MVSNet | | | 78.15 148 | 77.55 134 | 79.98 205 | 84.46 198 | 60.26 228 | 92.25 99 | 93.20 69 | 77.50 64 | 68.88 179 | 86.61 161 | 66.10 38 | 92.13 228 | 66.38 170 | 62.55 250 | 87.54 181 |
|
thres600view7 | | | 78.00 149 | 76.66 147 | 82.03 169 | 91.93 75 | 63.69 176 | 91.30 148 | 96.33 5 | 72.43 141 | 70.46 153 | 87.89 147 | 60.31 87 | 94.92 133 | 42.64 283 | 76.64 162 | 87.48 183 |
|
FC-MVSNet-test | | | 77.99 150 | 78.08 126 | 77.70 244 | 84.89 192 | 55.51 271 | 90.27 173 | 93.75 47 | 76.87 70 | 66.80 208 | 87.59 149 | 65.71 44 | 90.23 254 | 62.89 203 | 73.94 178 | 87.37 190 |
|
XXY-MVS | | | 77.94 151 | 76.44 150 | 82.43 148 | 82.60 219 | 64.44 156 | 92.01 110 | 91.83 121 | 73.59 123 | 70.00 160 | 85.82 170 | 54.43 157 | 94.76 136 | 69.63 143 | 68.02 215 | 88.10 178 |
|
MS-PatchMatch | | | 77.90 152 | 76.50 149 | 82.12 164 | 85.99 180 | 69.95 27 | 91.75 131 | 92.70 87 | 73.97 113 | 62.58 234 | 84.44 184 | 41.11 253 | 95.78 107 | 63.76 192 | 92.17 49 | 80.62 286 |
|
FMVSNet3 | | | 77.73 153 | 76.04 154 | 82.80 136 | 91.20 98 | 68.99 42 | 91.87 122 | 91.99 114 | 73.35 128 | 67.04 204 | 83.19 194 | 56.62 124 | 92.14 227 | 59.80 222 | 69.34 204 | 87.28 194 |
|
UniMVSNet (Re) | | | 77.58 154 | 76.78 146 | 79.98 205 | 84.11 204 | 60.80 216 | 91.76 129 | 93.17 72 | 76.56 77 | 69.93 163 | 84.78 180 | 63.32 66 | 92.36 224 | 64.89 184 | 62.51 252 | 86.78 202 |
|
PatchmatchNet | | | 77.46 155 | 74.63 175 | 85.96 65 | 89.55 122 | 70.35 21 | 79.97 285 | 89.55 199 | 72.23 145 | 70.94 150 | 76.91 266 | 57.03 114 | 92.79 210 | 54.27 241 | 81.17 129 | 94.74 60 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
v2v482 | | | 77.42 156 | 75.65 163 | 82.73 138 | 80.38 249 | 67.13 82 | 91.85 124 | 90.23 174 | 75.09 94 | 69.37 171 | 83.39 192 | 53.79 165 | 94.44 148 | 71.77 123 | 65.00 238 | 86.63 206 |
|
v1neww | | | 77.39 157 | 75.71 160 | 82.44 145 | 80.69 237 | 66.83 91 | 91.94 118 | 90.18 177 | 74.19 107 | 69.60 165 | 82.51 198 | 54.99 147 | 94.44 148 | 71.68 125 | 65.60 227 | 86.05 215 |
|
v7new | | | 77.39 157 | 75.71 160 | 82.44 145 | 80.69 237 | 66.83 91 | 91.94 118 | 90.18 177 | 74.19 107 | 69.60 165 | 82.51 198 | 54.99 147 | 94.44 148 | 71.68 125 | 65.60 227 | 86.05 215 |
|
v6 | | | 77.39 157 | 75.71 160 | 82.44 145 | 80.67 239 | 66.82 93 | 91.94 118 | 90.18 177 | 74.19 107 | 69.60 165 | 82.50 201 | 55.00 146 | 94.44 148 | 71.68 125 | 65.60 227 | 86.05 215 |
|
CHOSEN 280x420 | | | 77.35 160 | 76.95 145 | 78.55 233 | 87.07 166 | 62.68 195 | 69.71 310 | 82.95 292 | 68.80 203 | 71.48 148 | 87.27 158 | 66.03 39 | 84.00 299 | 76.47 94 | 82.81 122 | 88.95 165 |
|
v1 | | | 77.29 161 | 75.57 164 | 82.42 151 | 80.61 247 | 66.73 101 | 91.96 114 | 90.42 165 | 74.41 100 | 69.46 168 | 82.12 208 | 55.14 141 | 94.40 153 | 71.00 130 | 65.04 235 | 86.13 211 |
|
v1141 | | | 77.28 162 | 75.57 164 | 82.42 151 | 80.63 243 | 66.73 101 | 91.96 114 | 90.42 165 | 74.41 100 | 69.46 168 | 82.12 208 | 55.09 143 | 94.40 153 | 70.99 132 | 65.05 234 | 86.12 212 |
|
divwei89l23v2f112 | | | 77.28 162 | 75.57 164 | 82.42 151 | 80.62 244 | 66.72 103 | 91.96 114 | 90.42 165 | 74.41 100 | 69.46 168 | 82.12 208 | 55.11 142 | 94.40 153 | 71.00 130 | 65.04 235 | 86.12 212 |
|
PS-MVSNAJss | | | 77.26 164 | 76.31 151 | 80.13 202 | 80.64 242 | 59.16 246 | 90.63 167 | 91.06 148 | 72.80 136 | 68.58 184 | 84.57 183 | 53.55 167 | 93.96 180 | 72.97 111 | 71.96 191 | 87.27 195 |
|
gg-mvs-nofinetune | | | 77.18 165 | 74.31 181 | 85.80 70 | 91.42 93 | 68.36 54 | 71.78 304 | 94.72 25 | 49.61 302 | 77.12 95 | 45.92 325 | 77.41 3 | 93.98 179 | 67.62 159 | 93.16 37 | 95.05 50 |
|
MVP-Stereo | | | 77.12 166 | 76.23 152 | 79.79 211 | 81.72 225 | 66.34 120 | 89.29 192 | 90.88 152 | 70.56 185 | 62.01 237 | 82.88 195 | 49.34 203 | 94.13 169 | 65.55 180 | 93.80 26 | 78.88 299 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
view600 | | | 76.93 167 | 75.50 167 | 81.23 181 | 91.44 89 | 62.00 202 | 89.94 180 | 96.56 1 | 70.68 180 | 68.54 185 | 87.31 153 | 60.79 81 | 94.19 164 | 38.90 296 | 75.31 169 | 87.48 183 |
|
view800 | | | 76.93 167 | 75.50 167 | 81.23 181 | 91.44 89 | 62.00 202 | 89.94 180 | 96.56 1 | 70.68 180 | 68.54 185 | 87.31 153 | 60.79 81 | 94.19 164 | 38.90 296 | 75.31 169 | 87.48 183 |
|
conf0.05thres1000 | | | 76.93 167 | 75.50 167 | 81.23 181 | 91.44 89 | 62.00 202 | 89.94 180 | 96.56 1 | 70.68 180 | 68.54 185 | 87.31 153 | 60.79 81 | 94.19 164 | 38.90 296 | 75.31 169 | 87.48 183 |
|
tfpn | | | 76.93 167 | 75.50 167 | 81.23 181 | 91.44 89 | 62.00 202 | 89.94 180 | 96.56 1 | 70.68 180 | 68.54 185 | 87.31 153 | 60.79 81 | 94.19 164 | 38.90 296 | 75.31 169 | 87.48 183 |
|
X-MVStestdata | | | 76.86 171 | 74.13 184 | 85.05 94 | 93.22 45 | 63.78 171 | 92.92 80 | 92.66 90 | 73.99 111 | 78.18 82 | 10.19 340 | 55.25 136 | 97.41 45 | 79.16 76 | 91.58 56 | 93.95 91 |
|
DU-MVS | | | 76.86 171 | 75.84 157 | 79.91 207 | 82.96 217 | 60.26 228 | 91.26 149 | 91.54 130 | 76.46 78 | 68.88 179 | 86.35 163 | 56.16 128 | 92.13 228 | 66.38 170 | 62.55 250 | 87.35 192 |
|
v7 | | | 76.83 173 | 75.01 173 | 82.29 157 | 80.35 250 | 66.70 105 | 91.68 133 | 89.97 186 | 73.47 127 | 69.22 173 | 82.22 205 | 52.52 178 | 94.43 152 | 69.73 142 | 65.96 226 | 85.74 226 |
|
WR-MVS | | | 76.76 174 | 75.74 159 | 79.82 210 | 84.60 194 | 62.27 200 | 92.60 91 | 92.51 96 | 76.06 80 | 67.87 196 | 85.34 173 | 56.76 119 | 90.24 253 | 62.20 209 | 63.69 249 | 86.94 200 |
|
v1144 | | | 76.73 175 | 74.88 174 | 82.27 158 | 80.23 257 | 66.60 109 | 91.68 133 | 90.21 176 | 73.69 120 | 69.06 176 | 81.89 213 | 52.73 177 | 94.40 153 | 69.21 148 | 65.23 231 | 85.80 222 |
|
IterMVS-LS | | | 76.49 176 | 75.18 172 | 80.43 197 | 84.49 197 | 62.74 193 | 90.64 165 | 88.80 223 | 72.40 142 | 65.16 212 | 81.72 216 | 60.98 80 | 92.27 226 | 67.74 157 | 64.65 242 | 86.29 208 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
V42 | | | 76.46 177 | 74.55 178 | 82.19 162 | 79.14 271 | 67.82 64 | 90.26 174 | 89.42 203 | 73.75 119 | 68.63 183 | 81.89 213 | 51.31 189 | 94.09 171 | 71.69 124 | 64.84 239 | 84.66 238 |
|
Test4 | | | 76.45 178 | 73.45 193 | 85.45 82 | 76.07 293 | 67.61 70 | 88.38 208 | 90.83 153 | 76.71 74 | 53.06 281 | 79.65 246 | 31.61 295 | 94.35 157 | 78.47 81 | 86.22 98 | 94.40 72 |
|
v148 | | | 76.19 179 | 74.47 180 | 81.36 178 | 80.05 261 | 64.44 156 | 91.75 131 | 90.23 174 | 73.68 121 | 67.13 203 | 80.84 229 | 55.92 134 | 93.86 186 | 68.95 150 | 61.73 259 | 85.76 225 |
|
Effi-MVS+-dtu | | | 76.14 180 | 75.28 171 | 78.72 232 | 83.22 213 | 55.17 273 | 89.87 184 | 87.78 241 | 75.42 87 | 67.98 193 | 81.43 218 | 45.08 236 | 92.52 218 | 75.08 102 | 71.63 192 | 88.48 172 |
|
FMVSNet2 | | | 76.07 181 | 74.01 186 | 82.26 160 | 88.85 134 | 67.66 68 | 91.33 146 | 91.61 128 | 70.84 177 | 65.98 209 | 82.25 204 | 48.03 213 | 92.00 232 | 58.46 228 | 68.73 210 | 87.10 196 |
|
v144192 | | | 76.05 182 | 74.03 185 | 82.12 164 | 79.50 266 | 66.55 113 | 91.39 141 | 89.71 197 | 72.30 144 | 68.17 191 | 81.33 221 | 51.75 185 | 94.03 177 | 67.94 155 | 64.19 244 | 85.77 223 |
|
NR-MVSNet | | | 76.05 182 | 74.59 176 | 80.44 196 | 82.96 217 | 62.18 201 | 90.83 159 | 91.73 123 | 77.12 67 | 60.96 238 | 86.35 163 | 59.28 96 | 91.80 234 | 60.74 216 | 61.34 263 | 87.35 192 |
|
v1192 | | | 75.98 184 | 73.92 187 | 82.15 163 | 79.73 262 | 66.24 123 | 91.22 151 | 89.75 191 | 72.67 137 | 68.49 189 | 81.42 219 | 49.86 199 | 94.27 161 | 67.08 163 | 65.02 237 | 85.95 219 |
|
TranMVSNet+NR-MVSNet | | | 75.86 185 | 74.52 179 | 79.89 208 | 82.44 220 | 60.64 223 | 91.37 144 | 91.37 139 | 76.63 75 | 67.65 198 | 86.21 166 | 52.37 181 | 91.55 239 | 61.84 211 | 60.81 266 | 87.48 183 |
|
LPG-MVS_test | | | 75.82 186 | 74.58 177 | 79.56 216 | 84.31 201 | 59.37 243 | 90.44 168 | 89.73 194 | 69.49 195 | 64.86 213 | 88.42 136 | 38.65 260 | 94.30 159 | 72.56 116 | 72.76 185 | 85.01 235 |
|
GBi-Net | | | 75.65 187 | 73.83 188 | 81.10 188 | 88.85 134 | 65.11 143 | 90.01 176 | 90.32 168 | 70.84 177 | 67.04 204 | 80.25 238 | 48.03 213 | 91.54 240 | 59.80 222 | 69.34 204 | 86.64 203 |
|
test1 | | | 75.65 187 | 73.83 188 | 81.10 188 | 88.85 134 | 65.11 143 | 90.01 176 | 90.32 168 | 70.84 177 | 67.04 204 | 80.25 238 | 48.03 213 | 91.54 240 | 59.80 222 | 69.34 204 | 86.64 203 |
|
v1921920 | | | 75.63 189 | 73.49 192 | 82.06 168 | 79.38 267 | 66.35 119 | 91.07 156 | 89.48 200 | 71.98 152 | 67.99 192 | 81.22 224 | 49.16 207 | 93.90 183 | 66.56 168 | 64.56 243 | 85.92 221 |
|
ACMP | | 71.68 10 | 75.58 190 | 74.23 183 | 79.62 214 | 84.97 191 | 59.64 238 | 90.80 160 | 89.07 217 | 70.39 188 | 62.95 230 | 87.30 157 | 38.28 263 | 93.87 184 | 72.89 112 | 71.45 195 | 85.36 232 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v8 | | | 75.35 191 | 73.26 195 | 81.61 176 | 80.67 239 | 66.82 93 | 89.54 189 | 89.27 207 | 71.65 164 | 63.30 228 | 80.30 237 | 54.99 147 | 94.06 173 | 67.33 162 | 62.33 253 | 83.94 243 |
|
tpm cat1 | | | 75.30 192 | 72.21 206 | 84.58 106 | 88.52 141 | 67.77 65 | 78.16 296 | 88.02 238 | 61.88 259 | 68.45 190 | 76.37 267 | 60.65 85 | 94.03 177 | 53.77 244 | 74.11 176 | 91.93 136 |
|
PLC | | 68.80 14 | 75.23 193 | 73.68 190 | 79.86 209 | 92.93 53 | 58.68 251 | 90.64 165 | 88.30 233 | 60.90 263 | 64.43 219 | 90.53 117 | 42.38 247 | 94.57 142 | 56.52 233 | 76.54 163 | 86.33 207 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
v1240 | | | 75.21 194 | 72.98 197 | 81.88 170 | 79.20 269 | 66.00 126 | 90.75 162 | 89.11 215 | 71.63 165 | 67.41 201 | 81.22 224 | 47.36 221 | 93.87 184 | 65.46 181 | 64.72 241 | 85.77 223 |
|
Fast-Effi-MVS+-dtu | | | 75.04 195 | 73.37 194 | 80.07 203 | 80.86 232 | 59.52 241 | 91.20 153 | 85.38 273 | 71.90 153 | 65.20 211 | 84.84 179 | 41.46 252 | 92.97 201 | 66.50 169 | 72.96 184 | 87.73 180 |
|
dp | | | 75.01 196 | 72.09 207 | 83.76 119 | 89.28 125 | 66.22 124 | 79.96 286 | 89.75 191 | 71.16 173 | 67.80 197 | 77.19 262 | 51.81 184 | 92.54 217 | 50.39 254 | 71.44 196 | 92.51 127 |
|
Patchmatch-test1 | | | 75.00 197 | 71.80 210 | 84.58 106 | 86.63 171 | 70.08 24 | 81.06 274 | 89.19 210 | 71.60 166 | 70.01 159 | 77.16 264 | 45.53 233 | 88.63 273 | 51.79 250 | 73.27 181 | 95.02 54 |
|
TAPA-MVS | | 70.22 12 | 74.94 198 | 73.53 191 | 79.17 228 | 90.40 107 | 52.07 285 | 89.19 195 | 89.61 198 | 62.69 252 | 70.07 158 | 92.67 90 | 48.89 210 | 94.32 158 | 38.26 301 | 79.97 134 | 91.12 147 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
v10 | | | 74.77 199 | 72.54 203 | 81.46 177 | 80.33 254 | 66.71 104 | 89.15 196 | 89.08 216 | 70.94 175 | 63.08 229 | 79.86 242 | 52.52 178 | 94.04 176 | 65.70 179 | 62.17 254 | 83.64 245 |
|
XVG-OURS-SEG-HR | | | 74.70 200 | 73.08 196 | 79.57 215 | 78.25 279 | 57.33 262 | 80.49 277 | 87.32 247 | 63.22 248 | 68.76 181 | 90.12 125 | 44.89 239 | 91.59 238 | 70.55 138 | 74.09 177 | 89.79 159 |
|
ACMM | | 69.62 13 | 74.34 201 | 72.73 199 | 79.17 228 | 84.25 203 | 57.87 255 | 90.36 171 | 89.93 187 | 63.17 249 | 65.64 210 | 86.04 169 | 37.79 270 | 94.10 170 | 65.89 176 | 71.52 194 | 85.55 229 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CNLPA | | | 74.31 202 | 72.30 205 | 80.32 198 | 91.49 88 | 61.66 208 | 90.85 158 | 80.72 299 | 56.67 286 | 63.85 223 | 90.64 114 | 46.75 224 | 90.84 247 | 53.79 243 | 75.99 166 | 88.47 173 |
|
XVG-OURS | | | 74.25 203 | 72.46 204 | 79.63 213 | 78.45 278 | 57.59 260 | 80.33 279 | 87.39 244 | 63.86 244 | 68.76 181 | 89.62 129 | 40.50 255 | 91.72 236 | 69.00 149 | 74.25 175 | 89.58 162 |
|
CVMVSNet | | | 74.04 204 | 74.27 182 | 73.33 275 | 85.33 186 | 43.94 311 | 89.53 190 | 88.39 231 | 54.33 292 | 70.37 154 | 90.13 123 | 49.17 206 | 84.05 296 | 61.83 212 | 79.36 138 | 91.99 135 |
|
Baseline_NR-MVSNet | | | 73.99 205 | 72.83 198 | 77.48 248 | 80.78 233 | 59.29 245 | 91.79 126 | 84.55 277 | 68.85 202 | 68.99 177 | 80.70 230 | 56.16 128 | 92.04 231 | 62.67 206 | 60.98 265 | 81.11 280 |
|
pmmvs4 | | | 73.92 206 | 71.81 209 | 80.25 200 | 79.17 270 | 65.24 139 | 87.43 224 | 87.26 249 | 67.64 219 | 63.46 226 | 83.91 187 | 48.96 209 | 91.53 243 | 62.94 202 | 65.49 230 | 83.96 242 |
|
CR-MVSNet | | | 73.79 207 | 70.82 216 | 82.70 139 | 83.15 215 | 67.96 62 | 70.25 307 | 84.00 282 | 73.67 122 | 69.97 161 | 72.41 286 | 57.82 107 | 89.48 269 | 52.99 248 | 73.13 182 | 90.64 152 |
|
test_djsdf | | | 73.76 208 | 72.56 202 | 77.39 250 | 77.00 287 | 53.93 278 | 89.07 198 | 90.69 157 | 65.80 230 | 63.92 221 | 82.03 211 | 43.14 245 | 92.67 214 | 72.83 113 | 68.53 211 | 85.57 228 |
|
pmmvs5 | | | 73.35 209 | 71.52 211 | 78.86 231 | 78.64 277 | 60.61 224 | 91.08 155 | 86.90 250 | 67.69 216 | 63.32 227 | 83.64 188 | 44.33 241 | 90.53 248 | 62.04 210 | 66.02 225 | 85.46 230 |
|
jajsoiax | | | 73.05 210 | 71.51 212 | 77.67 245 | 77.46 284 | 54.83 274 | 88.81 201 | 90.04 184 | 69.13 199 | 62.85 232 | 83.51 190 | 31.16 298 | 92.75 211 | 70.83 134 | 69.80 200 | 85.43 231 |
|
LCM-MVSNet-Re | | | 72.93 211 | 71.84 208 | 76.18 260 | 88.49 142 | 48.02 301 | 80.07 284 | 70.17 324 | 73.96 114 | 52.25 284 | 80.09 241 | 49.98 197 | 88.24 279 | 67.35 160 | 84.23 115 | 92.28 132 |
|
pm-mvs1 | | | 72.89 212 | 71.09 214 | 78.26 238 | 79.10 273 | 57.62 259 | 90.80 160 | 89.30 206 | 67.66 217 | 62.91 231 | 81.78 215 | 49.11 208 | 92.95 202 | 60.29 219 | 58.89 275 | 84.22 241 |
|
tpmvs | | | 72.88 213 | 69.76 221 | 82.22 161 | 90.98 99 | 67.05 84 | 78.22 295 | 88.30 233 | 63.10 250 | 64.35 220 | 74.98 274 | 55.09 143 | 94.27 161 | 43.25 277 | 69.57 203 | 85.34 233 |
|
test0.0.03 1 | | | 72.76 214 | 72.71 200 | 72.88 279 | 80.25 256 | 47.99 302 | 91.22 151 | 89.45 201 | 71.51 170 | 62.51 235 | 87.66 148 | 53.83 163 | 85.06 292 | 50.16 255 | 67.84 218 | 85.58 227 |
|
mvs_tets | | | 72.71 215 | 71.11 213 | 77.52 246 | 77.41 285 | 54.52 276 | 88.45 207 | 89.76 190 | 68.76 204 | 62.70 233 | 83.26 193 | 29.49 302 | 92.71 212 | 70.51 139 | 69.62 202 | 85.34 233 |
|
FMVSNet1 | | | 72.71 215 | 69.91 219 | 81.10 188 | 83.60 210 | 65.11 143 | 90.01 176 | 90.32 168 | 63.92 243 | 63.56 225 | 80.25 238 | 36.35 280 | 91.54 240 | 54.46 240 | 66.75 222 | 86.64 203 |
|
IterMVS | | | 72.65 217 | 70.83 215 | 78.09 242 | 82.17 221 | 62.96 186 | 87.64 222 | 86.28 262 | 71.56 168 | 60.44 240 | 78.85 251 | 45.42 235 | 86.66 287 | 63.30 196 | 61.83 256 | 84.65 239 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchMatch-RL | | | 72.06 218 | 69.98 217 | 78.28 236 | 89.51 123 | 55.70 270 | 83.49 253 | 83.39 288 | 61.24 262 | 63.72 224 | 82.76 196 | 34.77 285 | 93.03 200 | 53.37 247 | 77.59 153 | 86.12 212 |
|
v18 | | | 71.94 219 | 69.43 222 | 79.50 218 | 80.74 234 | 66.82 93 | 88.16 210 | 86.66 252 | 68.95 201 | 55.55 264 | 72.66 281 | 55.03 145 | 90.15 256 | 64.78 185 | 52.30 292 | 81.54 268 |
|
v16 | | | 71.81 220 | 69.26 224 | 79.47 219 | 80.66 241 | 66.81 97 | 87.93 214 | 86.63 254 | 68.70 206 | 55.35 265 | 72.51 282 | 54.75 151 | 90.12 258 | 64.51 187 | 52.28 293 | 81.47 269 |
|
PVSNet_0 | | 68.08 15 | 71.81 220 | 68.32 234 | 82.27 158 | 84.68 193 | 62.31 199 | 88.68 203 | 90.31 171 | 75.84 82 | 57.93 255 | 80.65 232 | 37.85 269 | 94.19 164 | 69.94 141 | 29.05 328 | 90.31 155 |
|
v17 | | | 71.77 222 | 69.20 225 | 79.46 220 | 80.62 244 | 66.81 97 | 87.93 214 | 86.63 254 | 68.71 205 | 55.25 266 | 72.49 283 | 54.72 152 | 90.11 259 | 64.50 188 | 51.97 294 | 81.47 269 |
|
MIMVSNet | | | 71.64 223 | 68.44 232 | 81.23 181 | 81.97 224 | 64.44 156 | 73.05 303 | 88.80 223 | 69.67 194 | 64.59 215 | 74.79 275 | 32.79 289 | 87.82 282 | 53.99 242 | 76.35 164 | 91.42 141 |
|
v15 | | | 71.40 224 | 68.75 227 | 79.35 221 | 80.39 248 | 66.70 105 | 87.57 223 | 86.64 253 | 68.66 207 | 54.68 268 | 72.00 290 | 54.50 154 | 89.98 261 | 63.69 193 | 50.66 299 | 81.38 273 |
|
v7n | | | 71.31 225 | 68.65 228 | 79.28 223 | 76.40 289 | 60.77 218 | 86.71 236 | 89.45 201 | 64.17 242 | 58.77 251 | 78.24 253 | 44.59 240 | 93.54 191 | 57.76 230 | 61.75 258 | 83.52 248 |
|
V14 | | | 71.29 226 | 68.61 229 | 79.31 222 | 80.34 252 | 66.65 107 | 87.39 225 | 86.61 256 | 68.41 211 | 54.49 270 | 71.91 291 | 54.25 159 | 89.96 262 | 63.50 194 | 50.62 300 | 81.33 275 |
|
V9 | | | 71.16 227 | 68.46 231 | 79.27 224 | 80.26 255 | 66.60 109 | 87.21 228 | 86.56 257 | 68.17 212 | 54.26 273 | 71.81 293 | 54.00 161 | 89.93 263 | 63.28 197 | 50.57 301 | 81.27 276 |
|
anonymousdsp | | | 71.14 228 | 69.37 223 | 76.45 257 | 72.95 299 | 54.71 275 | 84.19 248 | 88.88 222 | 61.92 258 | 62.15 236 | 79.77 243 | 38.14 265 | 91.44 245 | 68.90 151 | 67.45 219 | 83.21 254 |
|
testing_2 | | | 71.09 229 | 67.32 242 | 82.40 154 | 69.82 310 | 66.52 115 | 83.64 251 | 90.77 155 | 72.21 146 | 45.12 307 | 71.07 300 | 27.60 307 | 93.74 187 | 75.71 97 | 69.96 199 | 86.95 199 |
|
v11 | | | 71.05 230 | 68.32 234 | 79.23 225 | 80.34 252 | 66.57 112 | 87.01 231 | 86.55 258 | 68.11 213 | 54.40 271 | 71.66 295 | 52.94 175 | 89.91 264 | 62.71 205 | 51.12 297 | 81.21 277 |
|
v12 | | | 71.02 231 | 68.29 236 | 79.22 226 | 80.18 258 | 66.53 114 | 87.01 231 | 86.54 259 | 67.90 214 | 54.00 276 | 71.70 294 | 53.66 166 | 89.91 264 | 63.09 199 | 50.51 302 | 81.21 277 |
|
v13 | | | 70.90 232 | 68.15 237 | 79.15 230 | 80.08 259 | 66.45 116 | 86.83 235 | 86.50 260 | 67.62 220 | 53.78 278 | 71.61 296 | 53.51 170 | 89.87 266 | 62.89 203 | 50.50 303 | 81.14 279 |
|
F-COLMAP | | | 70.66 233 | 68.44 232 | 77.32 251 | 86.37 175 | 55.91 269 | 88.00 212 | 86.32 261 | 56.94 284 | 57.28 260 | 88.07 145 | 33.58 287 | 92.49 219 | 51.02 252 | 68.37 212 | 83.55 246 |
|
WR-MVS_H | | | 70.59 234 | 69.94 218 | 72.53 281 | 81.03 230 | 51.43 288 | 87.35 226 | 92.03 113 | 67.38 221 | 60.23 241 | 80.70 230 | 55.84 135 | 83.45 303 | 46.33 269 | 58.58 276 | 82.72 261 |
|
v748 | | | 70.55 235 | 67.97 238 | 78.27 237 | 75.75 294 | 58.78 249 | 86.29 240 | 89.25 208 | 65.12 237 | 56.66 262 | 77.17 263 | 45.05 238 | 92.95 202 | 58.13 229 | 58.33 277 | 83.10 257 |
|
CP-MVSNet | | | 70.50 236 | 69.91 219 | 72.26 284 | 80.71 236 | 51.00 291 | 87.23 227 | 90.30 172 | 67.84 215 | 59.64 243 | 82.69 197 | 50.23 196 | 82.30 310 | 51.28 251 | 59.28 270 | 83.46 250 |
|
tfpnnormal | | | 70.10 237 | 67.36 240 | 78.32 235 | 83.45 212 | 60.97 214 | 88.85 200 | 92.77 85 | 64.85 238 | 60.83 239 | 78.53 252 | 43.52 244 | 93.48 193 | 31.73 317 | 61.70 260 | 80.52 287 |
|
TransMVSNet (Re) | | | 70.07 238 | 67.66 239 | 77.31 252 | 80.62 244 | 59.13 247 | 91.78 128 | 84.94 275 | 65.97 229 | 60.08 242 | 80.44 234 | 50.78 190 | 91.87 233 | 48.84 260 | 45.46 312 | 80.94 282 |
|
DP-MVS | | | 69.90 239 | 66.48 245 | 80.14 201 | 95.36 13 | 62.93 187 | 89.56 187 | 76.11 307 | 50.27 301 | 57.69 258 | 85.23 175 | 39.68 257 | 95.73 110 | 33.35 311 | 71.05 198 | 81.78 267 |
|
PS-CasMVS | | | 69.86 240 | 69.13 226 | 72.07 287 | 80.35 250 | 50.57 293 | 87.02 230 | 89.75 191 | 67.27 222 | 59.19 246 | 82.28 203 | 46.58 227 | 82.24 311 | 50.69 253 | 59.02 273 | 83.39 252 |
|
v52 | | | 69.80 241 | 67.01 244 | 78.15 240 | 71.84 303 | 60.10 232 | 82.02 265 | 87.39 244 | 64.48 239 | 57.80 256 | 75.97 270 | 41.47 251 | 92.90 207 | 63.00 200 | 59.13 272 | 81.45 271 |
|
V4 | | | 69.80 241 | 67.02 243 | 78.15 240 | 71.86 302 | 60.10 232 | 82.02 265 | 87.39 244 | 64.48 239 | 57.78 257 | 75.98 269 | 41.49 250 | 92.90 207 | 63.00 200 | 59.16 271 | 81.44 272 |
|
RPMNet | | | 69.58 243 | 65.21 253 | 82.70 139 | 83.15 215 | 67.96 62 | 70.25 307 | 86.15 265 | 46.83 310 | 69.97 161 | 65.10 311 | 56.48 127 | 89.48 269 | 35.79 307 | 73.13 182 | 90.64 152 |
|
MSDG | | | 69.54 244 | 65.73 248 | 80.96 192 | 85.11 190 | 63.71 175 | 84.19 248 | 83.28 289 | 56.95 283 | 54.50 269 | 84.03 185 | 31.50 296 | 96.03 100 | 42.87 281 | 69.13 207 | 83.14 256 |
|
PEN-MVS | | | 69.46 245 | 68.56 230 | 72.17 286 | 79.27 268 | 49.71 297 | 86.90 233 | 89.24 209 | 67.24 223 | 59.08 247 | 82.51 198 | 47.23 222 | 83.54 302 | 48.42 262 | 57.12 278 | 83.25 253 |
|
LS3D | | | 69.17 246 | 66.40 246 | 77.50 247 | 91.92 76 | 56.12 268 | 85.12 243 | 80.37 300 | 46.96 308 | 56.50 263 | 87.51 151 | 37.25 273 | 93.71 188 | 32.52 316 | 79.40 137 | 82.68 262 |
|
PatchT | | | 69.11 247 | 65.37 252 | 80.32 198 | 82.07 223 | 63.68 177 | 67.96 316 | 87.62 243 | 50.86 300 | 69.37 171 | 65.18 310 | 57.09 112 | 88.53 277 | 41.59 286 | 66.60 223 | 88.74 168 |
|
ACMH | | 63.93 17 | 68.62 248 | 64.81 254 | 80.03 204 | 85.22 188 | 63.25 182 | 87.72 220 | 84.66 276 | 60.83 264 | 51.57 287 | 79.43 248 | 27.29 308 | 94.96 130 | 41.76 284 | 64.84 239 | 81.88 266 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EG-PatchMatch MVS | | | 68.55 249 | 65.41 251 | 77.96 243 | 78.69 276 | 62.93 187 | 89.86 185 | 89.17 211 | 60.55 265 | 50.27 292 | 77.73 257 | 22.60 316 | 94.06 173 | 47.18 267 | 72.65 187 | 76.88 307 |
|
ADS-MVSNet | | | 68.54 250 | 64.38 260 | 81.03 191 | 88.06 152 | 66.90 90 | 68.01 314 | 84.02 281 | 57.57 278 | 64.48 217 | 69.87 301 | 38.68 258 | 89.21 272 | 40.87 288 | 67.89 216 | 86.97 197 |
|
DTE-MVSNet | | | 68.46 251 | 67.33 241 | 71.87 290 | 77.94 282 | 49.00 300 | 86.16 241 | 88.58 229 | 66.36 227 | 58.19 253 | 82.21 206 | 46.36 228 | 83.87 300 | 44.97 275 | 55.17 285 | 82.73 260 |
|
Patchmatch-RL test | | | 68.17 252 | 64.49 258 | 79.19 227 | 71.22 305 | 53.93 278 | 70.07 309 | 71.54 323 | 69.22 198 | 56.79 261 | 62.89 314 | 56.58 125 | 88.61 274 | 69.53 145 | 52.61 291 | 95.03 53 |
|
XVG-ACMP-BASELINE | | | 68.04 253 | 65.53 250 | 75.56 262 | 74.06 298 | 52.37 283 | 78.43 292 | 85.88 270 | 62.03 256 | 58.91 250 | 81.21 226 | 20.38 319 | 91.15 246 | 60.69 217 | 68.18 213 | 83.16 255 |
|
FMVSNet5 | | | 68.04 253 | 65.66 249 | 75.18 264 | 84.43 199 | 57.89 254 | 83.54 252 | 86.26 263 | 61.83 260 | 53.64 279 | 73.30 278 | 37.15 276 | 85.08 291 | 48.99 259 | 61.77 257 | 82.56 263 |
|
ACMH+ | | 65.35 16 | 67.65 255 | 64.55 256 | 76.96 254 | 84.59 195 | 57.10 264 | 88.08 211 | 80.79 298 | 58.59 277 | 53.00 282 | 81.09 228 | 26.63 310 | 92.95 202 | 46.51 268 | 61.69 261 | 80.82 283 |
|
pmmvs6 | | | 67.57 256 | 64.76 255 | 76.00 261 | 72.82 301 | 53.37 280 | 88.71 202 | 86.78 251 | 53.19 293 | 57.58 259 | 78.03 255 | 35.33 283 | 92.41 221 | 55.56 237 | 54.88 287 | 82.21 264 |
|
Anonymous20231206 | | | 67.53 257 | 65.78 247 | 72.79 280 | 74.95 295 | 47.59 304 | 88.23 209 | 87.32 247 | 61.75 261 | 58.07 254 | 77.29 260 | 37.79 270 | 87.29 285 | 42.91 279 | 63.71 248 | 83.48 249 |
|
Patchmtry | | | 67.53 257 | 63.93 261 | 78.34 234 | 82.12 222 | 64.38 159 | 68.72 311 | 84.00 282 | 48.23 307 | 59.24 245 | 72.41 286 | 57.82 107 | 89.27 271 | 46.10 270 | 56.68 282 | 81.36 274 |
|
USDC | | | 67.43 259 | 64.51 257 | 76.19 259 | 77.94 282 | 55.29 272 | 78.38 293 | 85.00 274 | 73.17 129 | 48.36 297 | 80.37 235 | 21.23 318 | 92.48 220 | 52.15 249 | 64.02 246 | 80.81 284 |
|
ADS-MVSNet2 | | | 66.90 260 | 63.44 263 | 77.26 253 | 88.06 152 | 60.70 221 | 68.01 314 | 75.56 312 | 57.57 278 | 64.48 217 | 69.87 301 | 38.68 258 | 84.10 295 | 40.87 288 | 67.89 216 | 86.97 197 |
|
CMPMVS | | 48.56 21 | 66.77 261 | 64.41 259 | 73.84 272 | 70.65 308 | 50.31 294 | 77.79 297 | 85.73 272 | 45.54 313 | 44.76 308 | 82.14 207 | 35.40 282 | 90.14 257 | 63.18 198 | 74.54 174 | 81.07 281 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
OpenMVS_ROB | | 61.12 18 | 66.39 262 | 62.92 267 | 76.80 256 | 76.51 288 | 57.77 256 | 89.22 193 | 83.41 287 | 55.48 290 | 53.86 277 | 77.84 256 | 26.28 311 | 93.95 181 | 34.90 309 | 68.76 209 | 78.68 301 |
|
LTVRE_ROB | | 59.60 19 | 66.27 263 | 63.54 262 | 74.45 268 | 84.00 206 | 51.55 287 | 67.08 317 | 83.53 285 | 58.78 275 | 54.94 267 | 80.31 236 | 34.54 286 | 93.23 197 | 40.64 290 | 68.03 214 | 78.58 302 |
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 |
JIA-IIPM | | | 66.06 264 | 62.45 270 | 76.88 255 | 81.42 229 | 54.45 277 | 57.49 328 | 88.67 225 | 49.36 303 | 63.86 222 | 46.86 324 | 56.06 131 | 90.25 251 | 49.53 258 | 68.83 208 | 85.95 219 |
|
Patchmatch-test | | | 65.86 265 | 60.94 276 | 80.62 195 | 83.75 207 | 58.83 248 | 58.91 327 | 75.26 314 | 44.50 317 | 50.95 291 | 77.09 265 | 58.81 101 | 87.90 281 | 35.13 308 | 64.03 245 | 95.12 48 |
|
UnsupCasMVSNet_eth | | | 65.79 266 | 63.10 265 | 73.88 271 | 70.71 307 | 50.29 295 | 81.09 273 | 89.88 188 | 72.58 139 | 49.25 295 | 74.77 276 | 32.57 291 | 87.43 284 | 55.96 236 | 41.04 318 | 83.90 244 |
|
pmmvs-eth3d | | | 65.53 267 | 62.32 271 | 75.19 263 | 69.39 312 | 59.59 239 | 82.80 262 | 83.43 286 | 62.52 254 | 51.30 289 | 72.49 283 | 32.86 288 | 87.16 286 | 55.32 238 | 50.73 298 | 78.83 300 |
|
SixPastTwentyTwo | | | 64.92 268 | 61.78 274 | 74.34 270 | 78.74 275 | 49.76 296 | 83.42 256 | 79.51 303 | 62.86 251 | 50.27 292 | 77.35 258 | 30.92 300 | 90.49 249 | 45.89 271 | 47.06 309 | 82.78 258 |
|
OurMVSNet-221017-0 | | | 64.68 269 | 62.17 272 | 72.21 285 | 76.08 292 | 47.35 305 | 80.67 276 | 81.02 297 | 56.19 287 | 51.60 286 | 79.66 245 | 27.05 309 | 88.56 276 | 53.60 245 | 53.63 290 | 80.71 285 |
|
test_0402 | | | 64.54 270 | 61.09 275 | 74.92 265 | 84.10 205 | 60.75 219 | 87.95 213 | 79.71 302 | 52.03 296 | 52.41 283 | 77.20 261 | 32.21 293 | 91.64 237 | 23.14 327 | 61.03 264 | 72.36 314 |
|
testgi | | | 64.48 271 | 62.87 268 | 69.31 294 | 71.24 304 | 40.62 318 | 85.49 242 | 79.92 301 | 65.36 234 | 54.18 274 | 83.49 191 | 23.74 314 | 84.55 293 | 41.60 285 | 60.79 267 | 82.77 259 |
|
RPSCF | | | 64.24 272 | 61.98 273 | 71.01 291 | 76.10 291 | 45.00 308 | 75.83 300 | 75.94 309 | 46.94 309 | 58.96 249 | 84.59 182 | 31.40 297 | 82.00 312 | 47.76 265 | 60.33 269 | 86.04 218 |
|
test2356 | | | 64.16 273 | 63.28 264 | 66.81 301 | 69.37 313 | 39.86 321 | 87.76 219 | 86.02 267 | 59.83 270 | 53.54 280 | 73.23 279 | 34.94 284 | 80.67 315 | 39.66 292 | 65.20 232 | 79.89 292 |
|
EU-MVSNet | | | 64.01 274 | 63.01 266 | 67.02 300 | 74.40 297 | 38.86 323 | 83.27 257 | 86.19 264 | 45.11 314 | 54.27 272 | 81.15 227 | 36.91 279 | 80.01 316 | 48.79 261 | 57.02 279 | 82.19 265 |
|
test20.03 | | | 63.83 275 | 62.65 269 | 67.38 299 | 70.58 309 | 39.94 319 | 86.57 238 | 84.17 279 | 63.29 247 | 51.86 285 | 77.30 259 | 37.09 277 | 82.47 308 | 38.87 300 | 54.13 289 | 79.73 294 |
|
MDA-MVSNet_test_wron | | | 63.78 276 | 60.16 277 | 74.64 266 | 78.15 280 | 60.41 225 | 83.49 253 | 84.03 280 | 56.17 289 | 39.17 320 | 71.59 298 | 37.22 274 | 83.24 306 | 42.87 281 | 48.73 306 | 80.26 290 |
|
YYNet1 | | | 63.76 277 | 60.14 278 | 74.62 267 | 78.06 281 | 60.19 231 | 83.46 255 | 83.99 284 | 56.18 288 | 39.25 319 | 71.56 299 | 37.18 275 | 83.34 304 | 42.90 280 | 48.70 307 | 80.32 289 |
|
K. test v3 | | | 63.09 278 | 59.61 280 | 73.53 274 | 76.26 290 | 49.38 299 | 83.27 257 | 77.15 306 | 64.35 241 | 47.77 298 | 72.32 288 | 28.73 303 | 87.79 283 | 49.93 257 | 36.69 323 | 83.41 251 |
|
COLMAP_ROB | | 57.96 20 | 62.98 279 | 59.65 279 | 72.98 278 | 81.44 228 | 53.00 282 | 83.75 250 | 75.53 313 | 48.34 306 | 48.81 296 | 81.40 220 | 24.14 312 | 90.30 250 | 32.95 313 | 60.52 268 | 75.65 310 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
AllTest | | | 61.66 280 | 58.06 281 | 72.46 282 | 79.57 263 | 51.42 289 | 80.17 282 | 68.61 326 | 51.25 298 | 45.88 302 | 81.23 222 | 19.86 320 | 86.58 288 | 38.98 294 | 57.01 280 | 79.39 296 |
|
UnsupCasMVSNet_bld | | | 61.60 281 | 57.71 282 | 73.29 276 | 68.73 314 | 51.64 286 | 78.61 291 | 89.05 218 | 57.20 282 | 46.11 301 | 61.96 316 | 28.70 304 | 88.60 275 | 50.08 256 | 38.90 321 | 79.63 295 |
|
MDA-MVSNet-bldmvs | | | 61.54 282 | 57.70 283 | 73.05 277 | 79.53 265 | 57.00 265 | 83.08 260 | 81.23 295 | 57.57 278 | 34.91 323 | 72.45 285 | 32.79 289 | 86.26 290 | 35.81 306 | 41.95 316 | 75.89 309 |
|
TinyColmap | | | 60.32 283 | 56.42 289 | 72.00 288 | 78.78 274 | 53.18 281 | 78.36 294 | 75.64 310 | 52.30 295 | 41.59 318 | 75.82 272 | 14.76 327 | 88.35 278 | 35.84 305 | 54.71 288 | 74.46 311 |
|
MVS-HIRNet | | | 60.25 284 | 55.55 290 | 74.35 269 | 84.37 200 | 56.57 266 | 71.64 305 | 74.11 316 | 34.44 326 | 45.54 306 | 42.24 328 | 31.11 299 | 89.81 267 | 40.36 291 | 76.10 165 | 76.67 308 |
|
MIMVSNet1 | | | 60.16 285 | 57.33 285 | 68.67 295 | 69.71 311 | 44.13 310 | 78.92 290 | 84.21 278 | 55.05 291 | 44.63 309 | 71.85 292 | 23.91 313 | 81.54 314 | 32.63 315 | 55.03 286 | 80.35 288 |
|
PM-MVS | | | 59.40 286 | 56.59 287 | 67.84 296 | 63.63 318 | 41.86 315 | 76.76 298 | 63.22 332 | 59.01 274 | 51.07 290 | 72.27 289 | 11.72 329 | 83.25 305 | 61.34 213 | 50.28 304 | 78.39 303 |
|
testus | | | 59.36 287 | 57.51 284 | 64.90 304 | 66.72 315 | 37.56 324 | 84.98 244 | 81.09 296 | 57.46 281 | 47.72 299 | 72.76 280 | 11.43 331 | 78.78 322 | 36.56 302 | 58.91 274 | 78.36 304 |
|
new-patchmatchnet | | | 59.30 288 | 56.48 288 | 67.79 297 | 65.86 316 | 44.19 309 | 82.47 263 | 81.77 293 | 59.94 269 | 43.65 313 | 66.20 307 | 27.67 306 | 81.68 313 | 39.34 293 | 41.40 317 | 77.50 306 |
|
testpf | | | 57.17 289 | 56.93 286 | 57.88 311 | 79.13 272 | 42.40 312 | 34.23 334 | 85.97 269 | 52.64 294 | 47.66 300 | 66.50 305 | 36.33 281 | 79.65 318 | 53.60 245 | 56.31 283 | 51.60 327 |
|
DSMNet-mixed | | | 56.78 290 | 54.44 292 | 63.79 306 | 63.21 319 | 29.44 332 | 64.43 320 | 64.10 331 | 42.12 322 | 51.32 288 | 71.60 297 | 31.76 294 | 75.04 325 | 36.23 304 | 65.20 232 | 86.87 201 |
|
LP | | | 56.71 291 | 51.64 295 | 71.91 289 | 80.08 259 | 60.33 227 | 61.72 322 | 75.61 311 | 43.87 319 | 43.76 312 | 60.30 318 | 30.46 301 | 84.05 296 | 22.94 328 | 46.06 311 | 71.34 316 |
|
1111 | | | 56.66 292 | 54.98 291 | 61.69 307 | 61.99 322 | 31.38 328 | 79.81 287 | 83.17 290 | 45.66 311 | 41.94 315 | 65.44 308 | 41.50 248 | 79.56 319 | 27.64 321 | 47.68 308 | 74.14 312 |
|
test1235678 | | | 55.73 293 | 52.74 293 | 64.68 305 | 60.16 325 | 35.56 326 | 81.65 269 | 81.46 294 | 51.27 297 | 38.93 321 | 62.82 315 | 17.44 322 | 78.58 323 | 30.87 319 | 50.09 305 | 79.89 292 |
|
pmmvs3 | | | 55.51 294 | 51.50 297 | 67.53 298 | 57.90 327 | 50.93 292 | 80.37 278 | 73.66 317 | 40.63 323 | 44.15 311 | 64.75 312 | 16.30 323 | 78.97 321 | 44.77 276 | 40.98 319 | 72.69 313 |
|
TDRefinement | | | 55.28 295 | 51.58 296 | 66.39 302 | 59.53 326 | 46.15 307 | 76.23 299 | 72.80 318 | 44.60 316 | 42.49 314 | 76.28 268 | 15.29 325 | 82.39 309 | 33.20 312 | 43.75 314 | 70.62 318 |
|
LF4IMVS | | | 54.01 296 | 52.12 294 | 59.69 309 | 62.41 321 | 39.91 320 | 68.59 312 | 68.28 328 | 42.96 320 | 44.55 310 | 75.18 273 | 14.09 328 | 68.39 329 | 41.36 287 | 51.68 295 | 70.78 317 |
|
Anonymous20231211 | | | 53.57 297 | 49.43 299 | 66.00 303 | 65.01 317 | 42.08 313 | 80.95 275 | 72.60 319 | 38.46 324 | 41.65 317 | 64.48 313 | 15.72 324 | 84.23 294 | 25.78 324 | 40.24 320 | 71.68 315 |
|
N_pmnet | | | 50.55 298 | 49.11 300 | 54.88 315 | 77.17 286 | 4.02 344 | 84.36 247 | 2.00 345 | 48.59 304 | 45.86 304 | 68.82 303 | 32.22 292 | 82.80 307 | 31.58 318 | 51.38 296 | 77.81 305 |
|
new_pmnet | | | 49.31 299 | 46.44 301 | 57.93 310 | 62.84 320 | 40.74 317 | 68.47 313 | 62.96 333 | 36.48 325 | 35.09 322 | 57.81 320 | 14.97 326 | 72.18 326 | 32.86 314 | 46.44 310 | 60.88 325 |
|
test12356 | | | 47.51 300 | 44.82 302 | 55.56 313 | 52.53 328 | 21.09 339 | 71.45 306 | 76.03 308 | 44.14 318 | 30.69 324 | 58.18 319 | 9.01 335 | 76.14 324 | 26.95 323 | 34.43 326 | 69.46 320 |
|
testmv | | | 46.98 301 | 43.53 303 | 57.35 312 | 47.75 333 | 30.41 331 | 74.99 302 | 77.69 304 | 42.84 321 | 28.03 325 | 53.36 321 | 8.18 336 | 71.18 327 | 24.36 326 | 34.55 324 | 70.46 319 |
|
.test1245 | | | 46.52 302 | 49.68 298 | 37.02 324 | 61.99 322 | 31.38 328 | 79.81 287 | 83.17 290 | 45.66 311 | 41.94 315 | 65.44 308 | 41.50 248 | 79.56 319 | 27.64 321 | 0.01 340 | 0.13 339 |
|
FPMVS | | | 45.64 303 | 43.10 304 | 53.23 317 | 51.42 330 | 36.46 325 | 64.97 319 | 71.91 321 | 29.13 328 | 27.53 326 | 61.55 317 | 9.83 333 | 65.01 333 | 16.00 333 | 55.58 284 | 58.22 326 |
|
no-one | | | 44.13 304 | 38.39 305 | 61.34 308 | 45.91 335 | 41.94 314 | 61.67 323 | 75.07 315 | 45.05 315 | 20.07 329 | 40.68 331 | 11.58 330 | 79.82 317 | 30.18 320 | 15.30 331 | 62.26 324 |
|
LCM-MVSNet | | | 40.54 305 | 35.79 306 | 54.76 316 | 36.92 338 | 30.81 330 | 51.41 329 | 69.02 325 | 22.07 330 | 24.63 327 | 45.37 326 | 4.56 341 | 65.81 331 | 33.67 310 | 34.50 325 | 67.67 321 |
|
ANet_high | | | 40.27 306 | 35.20 307 | 55.47 314 | 34.74 339 | 34.47 327 | 63.84 321 | 71.56 322 | 48.42 305 | 18.80 331 | 41.08 329 | 9.52 334 | 64.45 334 | 20.18 330 | 8.66 338 | 67.49 322 |
|
PMMVS2 | | | 37.93 307 | 33.61 308 | 50.92 318 | 46.31 334 | 24.76 337 | 60.55 326 | 50.05 336 | 28.94 329 | 20.93 328 | 47.59 323 | 4.41 342 | 65.13 332 | 25.14 325 | 18.55 330 | 62.87 323 |
|
Gipuma | | | 34.91 308 | 31.44 310 | 45.30 320 | 70.99 306 | 39.64 322 | 19.85 337 | 72.56 320 | 20.10 333 | 16.16 333 | 21.47 336 | 5.08 340 | 71.16 328 | 13.07 334 | 43.70 315 | 25.08 333 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PNet_i23d | | | 32.77 309 | 29.98 311 | 41.11 322 | 48.05 331 | 29.17 333 | 65.82 318 | 50.02 337 | 21.42 331 | 14.74 334 | 37.19 332 | 1.11 345 | 55.11 336 | 19.75 331 | 11.77 333 | 39.06 329 |
|
PMVS | | 26.43 22 | 31.84 310 | 28.16 312 | 42.89 321 | 25.87 342 | 27.58 335 | 50.92 330 | 49.78 338 | 21.37 332 | 14.17 335 | 40.81 330 | 2.01 343 | 66.62 330 | 9.61 336 | 38.88 322 | 34.49 332 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
pcd1.5k->3k | | | 31.17 311 | 31.85 309 | 29.12 326 | 81.48 226 | 0.00 347 | 0.00 338 | 91.79 122 | 0.00 342 | 0.00 343 | 0.00 344 | 41.05 254 | 0.00 345 | 0.00 342 | 72.34 190 | 87.36 191 |
|
wuykxyi23d | | | 29.03 312 | 23.09 317 | 46.84 319 | 31.67 341 | 28.82 334 | 43.46 332 | 57.72 335 | 14.39 336 | 7.52 339 | 20.84 337 | 0.64 346 | 60.29 335 | 21.57 329 | 10.04 335 | 51.40 328 |
|
E-PMN | | | 24.61 313 | 24.00 314 | 26.45 327 | 43.74 336 | 18.44 341 | 60.86 324 | 39.66 339 | 15.11 334 | 9.53 337 | 22.10 335 | 6.52 338 | 46.94 338 | 8.31 337 | 10.14 334 | 13.98 335 |
|
MVE | | 24.84 23 | 24.35 314 | 19.77 318 | 38.09 323 | 34.56 340 | 26.92 336 | 26.57 335 | 38.87 341 | 11.73 337 | 11.37 336 | 27.44 333 | 1.37 344 | 50.42 337 | 11.41 335 | 14.60 332 | 36.93 330 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EMVS | | | 23.76 315 | 23.20 316 | 25.46 328 | 41.52 337 | 16.90 342 | 60.56 325 | 38.79 342 | 14.62 335 | 8.99 338 | 20.24 339 | 7.35 337 | 45.82 339 | 7.25 338 | 9.46 336 | 13.64 336 |
|
tmp_tt | | | 22.26 316 | 23.75 315 | 17.80 329 | 5.23 343 | 12.06 343 | 35.26 333 | 39.48 340 | 2.82 339 | 18.94 330 | 44.20 327 | 22.23 317 | 24.64 341 | 36.30 303 | 9.31 337 | 16.69 334 |
|
cdsmvs_eth3d_5k | | | 19.86 317 | 26.47 313 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 93.45 58 | 0.00 342 | 0.00 343 | 95.27 30 | 49.56 201 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
wuyk23d | | | 11.30 318 | 10.95 319 | 12.33 330 | 48.05 331 | 19.89 340 | 25.89 336 | 1.92 346 | 3.58 338 | 3.12 340 | 1.37 341 | 0.64 346 | 15.77 342 | 6.23 339 | 7.77 339 | 1.35 337 |
|
ab-mvs-re | | | 7.91 319 | 10.55 320 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 0.00 349 | 0.00 342 | 0.00 343 | 94.95 40 | 0.00 350 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
testmvs | | | 7.23 320 | 9.62 321 | 0.06 332 | 0.04 344 | 0.02 346 | 84.98 244 | 0.02 347 | 0.03 340 | 0.18 341 | 1.21 342 | 0.01 349 | 0.02 343 | 0.14 340 | 0.01 340 | 0.13 339 |
|
test123 | | | 6.92 321 | 9.21 322 | 0.08 331 | 0.03 345 | 0.05 345 | 81.65 269 | 0.01 348 | 0.02 341 | 0.14 342 | 0.85 343 | 0.03 348 | 0.02 343 | 0.12 341 | 0.00 342 | 0.16 338 |
|
pcd_1.5k_mvsjas | | | 4.46 322 | 5.95 323 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 0.00 349 | 0.00 342 | 0.00 343 | 0.00 344 | 53.55 167 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
sosnet-low-res | | | 0.00 323 | 0.00 324 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 0.00 349 | 0.00 342 | 0.00 343 | 0.00 344 | 0.00 350 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
sosnet | | | 0.00 323 | 0.00 324 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 0.00 349 | 0.00 342 | 0.00 343 | 0.00 344 | 0.00 350 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
uncertanet | | | 0.00 323 | 0.00 324 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 0.00 349 | 0.00 342 | 0.00 343 | 0.00 344 | 0.00 350 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
Regformer | | | 0.00 323 | 0.00 324 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 0.00 349 | 0.00 342 | 0.00 343 | 0.00 344 | 0.00 350 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
uanet | | | 0.00 323 | 0.00 324 | 0.00 333 | 0.00 346 | 0.00 347 | 0.00 338 | 0.00 349 | 0.00 342 | 0.00 343 | 0.00 344 | 0.00 350 | 0.00 345 | 0.00 342 | 0.00 342 | 0.00 341 |
|
ESAPD | | | | | | | | | 94.66 27 | | | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 106 | | | | |
|
sam_mvs | | | | | | | | | | | | | 54.91 150 | | | | |
|
semantic-postprocess | | | | | 76.32 258 | 81.48 226 | 60.67 222 | | 85.99 268 | 66.17 228 | 59.50 244 | 78.88 250 | 45.51 234 | 83.65 301 | 62.58 207 | 61.93 255 | 84.63 240 |
|
ambc | | | | | 69.61 293 | 61.38 324 | 41.35 316 | 49.07 331 | 85.86 271 | | 50.18 294 | 66.40 306 | 10.16 332 | 88.14 280 | 45.73 272 | 44.20 313 | 79.32 298 |
|
MTGPA | | | | | | | | | 92.23 102 | | | | | | | | |
|
test_post1 | | | | | | | | 78.95 289 | | | | 20.70 338 | 53.05 173 | 91.50 244 | 60.43 218 | | |
|
test_post | | | | | | | | | | | | 23.01 334 | 56.49 126 | 92.67 214 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 304 | 57.62 109 | 90.25 251 | | | |
|
GG-mvs-BLEND | | | | | 86.53 49 | 91.91 77 | 69.67 34 | 75.02 301 | 94.75 24 | | 78.67 81 | 90.85 112 | 77.91 2 | 94.56 143 | 72.25 119 | 93.74 29 | 95.36 34 |
|
MTMP | | | | | | | | | 32.52 343 | | | | | | | | |
|
gm-plane-assit | | | | | | 88.42 146 | 67.04 85 | | | 78.62 52 | | 91.83 102 | | 97.37 47 | 76.57 93 | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 10 | 94.96 8 | 95.29 38 |
|
TEST9 | | | | | | 94.18 27 | 67.28 77 | 94.16 36 | 93.51 55 | 71.75 163 | 85.52 26 | 95.33 26 | 68.01 23 | 97.27 54 | | | |
|
test_8 | | | | | | 94.19 26 | 67.19 79 | 94.15 38 | 93.42 60 | 71.87 155 | 85.38 28 | 95.35 25 | 68.19 21 | 96.95 74 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 31 | 94.75 16 | 95.33 35 |
|
agg_prior | | | | | | 94.16 31 | 66.97 86 | | 93.31 64 | | 84.49 35 | | | 96.75 83 | | | |
|
TestCases | | | | | 72.46 282 | 79.57 263 | 51.42 289 | | 68.61 326 | 51.25 298 | 45.88 302 | 81.23 222 | 19.86 320 | 86.58 288 | 38.98 294 | 57.01 280 | 79.39 296 |
|
test_prior4 | | | | | | | 67.18 81 | 93.92 52 | | | | | | | | | |
|
test_prior2 | | | | | | | | 95.10 27 | | 75.40 89 | 85.25 30 | 95.61 21 | 67.94 24 | | 87.47 23 | 94.77 13 | |
|
test_prior | | | | | 86.42 53 | 94.71 21 | 67.35 75 | | 93.10 75 | | | | | 96.84 79 | | | 95.05 50 |
|
旧先验2 | | | | | | | | 92.00 112 | | 59.37 273 | 87.54 14 | | | 93.47 194 | 75.39 99 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 91.41 140 | | | | | | | | | |
|
æ–°å‡ ä½•1 | | | | | 84.73 101 | 92.32 65 | 64.28 164 | | 91.46 135 | 59.56 271 | 79.77 67 | 92.90 86 | 56.95 117 | 96.57 88 | 63.40 195 | 92.91 39 | 93.34 103 |
|
旧先验1 | | | | | | 91.94 74 | 60.74 220 | | 91.50 133 | | | 94.36 55 | 65.23 46 | | | 91.84 51 | 94.55 64 |
|
æ— å…ˆéªŒ | | | | | | | | 92.71 85 | 92.61 93 | 62.03 256 | | | | 97.01 65 | 66.63 166 | | 93.97 90 |
|
原ACMM2 | | | | | | | | 92.01 110 | | | | | | | | | |
|
原ACMM1 | | | | | 84.42 110 | 93.21 47 | 64.27 165 | | 93.40 61 | 65.39 233 | 79.51 70 | 92.50 91 | 58.11 105 | 96.69 85 | 65.27 182 | 93.96 23 | 92.32 130 |
|
test222 | | | | | | 89.77 117 | 61.60 209 | 89.55 188 | 89.42 203 | 56.83 285 | 77.28 93 | 92.43 94 | 52.76 176 | | | 91.14 63 | 93.09 112 |
|
testdata2 | | | | | | | | | | | | | | 96.09 97 | 61.26 214 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 40 | | | | |
|
testdata | | | | | 81.34 179 | 89.02 132 | 57.72 257 | | 89.84 189 | 58.65 276 | 85.32 29 | 94.09 64 | 57.03 114 | 93.28 196 | 69.34 147 | 90.56 69 | 93.03 114 |
|
testdata1 | | | | | | | | 89.21 194 | | 77.55 63 | | | | | | | |
|
test12 | | | | | 87.09 34 | 94.60 23 | 68.86 44 | | 92.91 81 | | 82.67 48 | | 65.44 45 | 97.55 41 | | 93.69 31 | 94.84 58 |
|
plane_prior7 | | | | | | 86.94 167 | 61.51 210 | | | | | | | | | | |
|
plane_prior6 | | | | | | 87.23 163 | 62.32 198 | | | | | | 50.66 191 | | | | |
|
plane_prior5 | | | | | | | | | 91.31 140 | | | | | 95.55 120 | 76.74 91 | 78.53 146 | 88.39 174 |
|
plane_prior4 | | | | | | | | | | | | 89.14 131 | | | | | |
|
plane_prior3 | | | | | | | 61.95 206 | | | 79.09 44 | 72.53 133 | | | | | | |
|
plane_prior2 | | | | | | | | 93.13 72 | | 78.81 49 | | | | | | | |
|
plane_prior1 | | | | | | 87.15 164 | | | | | | | | | | | |
|
plane_prior | | | | | | | 62.42 196 | 93.85 55 | | 79.38 37 | | | | | | 78.80 144 | |
|
n2 | | | | | | | | | 0.00 349 | | | | | | | | |
|
nn | | | | | | | | | 0.00 349 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 330 | | | | | | | | |
|
lessismore_v0 | | | | | 73.72 273 | 72.93 300 | 47.83 303 | | 61.72 334 | | 45.86 304 | 73.76 277 | 28.63 305 | 89.81 267 | 47.75 266 | 31.37 327 | 83.53 247 |
|
LGP-MVS_train | | | | | 79.56 216 | 84.31 201 | 59.37 243 | | 89.73 194 | 69.49 195 | 64.86 213 | 88.42 136 | 38.65 260 | 94.30 159 | 72.56 116 | 72.76 185 | 85.01 235 |
|
test11 | | | | | | | | | 93.01 77 | | | | | | | | |
|
door | | | | | | | | | 66.57 329 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 178 | | | | | | | | | | |
|
HQP-NCC | | | | | | 87.54 159 | | 94.06 43 | | 79.80 33 | 74.18 115 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 159 | | 94.06 43 | | 79.80 33 | 74.18 115 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 88 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 115 | | | 95.61 116 | | | 88.63 169 |
|
HQP3-MVS | | | | | | | | | 91.70 126 | | | | | | | 78.90 142 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 187 | | | | |
|
NP-MVS | | | | | | 87.41 162 | 63.04 185 | | | | | 90.30 120 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 236 | 80.13 283 | | 67.65 218 | 72.79 128 | | 54.33 158 | | 59.83 221 | | 92.58 125 |
|
MDTV_nov1_ep13 | | | | 72.61 201 | | 89.06 131 | 68.48 50 | 80.33 279 | 90.11 181 | 71.84 158 | 71.81 144 | 75.92 271 | 53.01 174 | 93.92 182 | 48.04 263 | 73.38 180 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 192 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 201 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 160 | | | | |
|
ITE_SJBPF | | | | | 70.43 292 | 74.44 296 | 47.06 306 | | 77.32 305 | 60.16 268 | 54.04 275 | 83.53 189 | 23.30 315 | 84.01 298 | 43.07 278 | 61.58 262 | 80.21 291 |
|
DeepMVS_CX | | | | | 34.71 325 | 51.45 329 | 24.73 338 | | 28.48 344 | 31.46 327 | 17.49 332 | 52.75 322 | 5.80 339 | 42.60 340 | 18.18 332 | 19.42 329 | 36.81 331 |
|