MCST-MVS | | | 91.08 1 | 91.46 1 | 89.94 2 | 97.66 2 | 73.37 6 | 97.13 1 | 95.58 2 | 89.33 1 | 85.77 17 | 96.26 7 | 72.84 7 | 99.38 1 | 92.64 2 | 95.93 3 | 97.08 4 |
|
CNVR-MVS | | | 90.32 2 | 90.89 3 | 88.61 10 | 96.76 4 | 70.65 17 | 96.47 5 | 94.83 13 | 84.83 6 | 89.07 3 | 96.80 2 | 70.86 11 | 99.06 2 | 92.64 2 | 95.71 4 | 96.12 15 |
|
DELS-MVS | | | 90.05 3 | 90.09 4 | 89.94 2 | 93.14 48 | 73.88 5 | 97.01 2 | 94.40 25 | 88.32 2 | 85.71 18 | 94.91 37 | 74.11 5 | 98.91 4 | 87.26 21 | 95.94 2 | 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 4 | 91.38 2 | 84.72 87 | 93.00 50 | 58.16 221 | 96.72 3 | 94.41 24 | 86.50 4 | 90.25 1 | 97.83 1 | 75.46 3 | 98.67 10 | 92.78 1 | 95.49 6 | 97.32 1 |
|
HPM-MVS++ | | | 89.37 5 | 89.95 5 | 87.64 17 | 95.10 17 | 68.23 50 | 95.24 19 | 94.49 21 | 82.43 12 | 88.90 4 | 96.35 5 | 71.89 10 | 98.63 11 | 88.76 14 | 96.40 1 | 96.06 16 |
|
NCCC | | | 89.07 6 | 89.46 6 | 87.91 13 | 96.60 5 | 69.05 34 | 96.38 6 | 94.64 19 | 84.42 7 | 86.74 13 | 96.20 8 | 66.56 32 | 98.76 9 | 89.03 12 | 94.56 15 | 95.92 22 |
|
PS-MVSNAJ | | | 88.14 7 | 87.61 13 | 89.71 4 | 92.06 66 | 76.72 1 | 95.75 9 | 93.26 52 | 83.86 8 | 89.55 2 | 96.06 10 | 53.55 140 | 97.89 27 | 91.10 4 | 93.31 28 | 94.54 56 |
|
TSAR-MVS | | | 87.96 8 | 88.37 8 | 86.70 36 | 93.51 41 | 65.32 122 | 95.15 22 | 93.84 31 | 78.17 49 | 85.93 16 | 94.80 40 | 75.80 2 | 98.21 19 | 89.38 7 | 88.78 66 | 96.59 10 |
|
DeepC-MVS_fast | | 79.48 2 | 87.95 9 | 88.00 9 | 87.79 16 | 95.86 12 | 68.32 46 | 95.74 10 | 94.11 28 | 83.82 9 | 83.49 34 | 96.19 9 | 64.53 48 | 98.44 15 | 83.42 39 | 94.88 10 | 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 |
EPNet | | | 87.84 10 | 88.38 7 | 86.23 54 | 93.30 43 | 66.05 112 | 95.26 18 | 94.84 12 | 87.09 3 | 88.06 6 | 94.53 44 | 66.79 30 | 97.34 46 | 83.89 36 | 91.68 44 | 95.29 32 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
lupinMVS | | | 87.74 11 | 87.77 11 | 87.63 19 | 89.24 107 | 71.18 13 | 96.57 4 | 92.90 67 | 82.70 11 | 87.13 10 | 95.27 24 | 64.99 44 | 95.80 94 | 89.34 8 | 91.80 42 | 95.93 21 |
|
APDe-MVS | | | 87.54 12 | 87.84 10 | 86.65 37 | 96.07 9 | 66.30 108 | 94.84 29 | 93.78 32 | 69.35 168 | 88.39 5 | 96.34 6 | 67.74 25 | 97.66 32 | 90.62 5 | 93.44 27 | 96.01 19 |
|
SD-MVS | | | 87.49 13 | 87.49 15 | 87.50 21 | 93.60 38 | 68.82 40 | 93.90 48 | 92.63 76 | 76.86 65 | 87.90 7 | 95.76 12 | 66.17 33 | 97.63 34 | 89.06 11 | 91.48 48 | 96.05 17 |
|
test_prior3 | | | 87.38 14 | 87.70 12 | 86.42 46 | 94.71 21 | 67.35 63 | 95.10 24 | 93.10 60 | 75.40 80 | 85.25 24 | 95.61 16 | 67.94 21 | 96.84 70 | 87.47 18 | 94.77 11 | 95.05 43 |
|
alignmvs | | | 87.28 15 | 86.97 20 | 88.24 12 | 91.30 78 | 71.14 15 | 95.61 13 | 93.56 41 | 79.30 32 | 87.07 12 | 95.25 26 | 68.43 16 | 96.93 68 | 87.87 16 | 84.33 95 | 96.65 8 |
|
Regformer-1 | | | 87.24 16 | 87.60 14 | 86.15 55 | 95.14 15 | 65.83 117 | 93.95 44 | 95.12 7 | 82.11 15 | 84.25 30 | 95.73 13 | 67.88 24 | 98.35 17 | 85.60 29 | 88.64 67 | 94.26 62 |
|
train_agg | | | 87.21 17 | 87.42 17 | 86.60 39 | 94.18 26 | 67.28 65 | 94.16 33 | 93.51 42 | 71.87 132 | 85.52 20 | 95.33 21 | 68.19 18 | 97.27 50 | 89.09 9 | 94.90 8 | 95.25 37 |
|
MG-MVS | | | 87.11 18 | 86.27 24 | 89.62 5 | 97.79 1 | 76.27 2 | 94.96 28 | 94.49 21 | 78.74 45 | 83.87 33 | 92.94 72 | 64.34 49 | 96.94 66 | 75.19 84 | 94.09 19 | 95.66 24 |
|
agg_prior1 | | | 87.02 19 | 87.26 18 | 86.28 53 | 94.16 30 | 66.97 74 | 94.08 38 | 93.31 50 | 71.85 134 | 84.49 28 | 95.39 19 | 68.91 14 | 96.75 73 | 88.84 13 | 94.32 17 | 95.13 41 |
|
Regformer-2 | | | 87.00 20 | 87.43 16 | 85.71 67 | 95.14 15 | 64.73 131 | 93.95 44 | 94.95 10 | 81.69 20 | 84.03 31 | 95.73 13 | 67.35 28 | 98.19 21 | 85.40 31 | 88.64 67 | 94.20 64 |
|
agg_prior3 | | | 86.93 21 | 87.08 19 | 86.48 43 | 94.21 24 | 66.95 76 | 94.14 36 | 93.40 47 | 71.80 137 | 84.86 26 | 95.13 28 | 66.16 34 | 97.25 52 | 89.09 9 | 94.90 8 | 95.25 37 |
|
CSCG | | | 86.87 22 | 86.26 25 | 88.72 8 | 95.05 18 | 70.79 16 | 93.83 51 | 95.33 4 | 68.48 180 | 77.63 73 | 94.35 48 | 73.04 6 | 98.45 14 | 84.92 34 | 93.71 23 | 96.92 6 |
|
canonicalmvs | | | 86.85 23 | 86.25 26 | 88.66 9 | 91.80 70 | 71.92 9 | 93.54 58 | 91.71 104 | 80.26 25 | 87.55 8 | 95.25 26 | 63.59 57 | 96.93 68 | 88.18 15 | 84.34 94 | 97.11 3 |
|
PHI-MVS | | | 86.83 24 | 86.85 23 | 86.78 35 | 93.47 42 | 65.55 119 | 95.39 17 | 95.10 9 | 71.77 139 | 85.69 19 | 96.52 3 | 62.07 68 | 98.77 8 | 86.06 27 | 95.60 5 | 96.03 18 |
|
SteuartSystems-ACMMP | | | 86.82 25 | 86.90 21 | 86.58 41 | 90.42 88 | 66.38 105 | 96.09 8 | 93.87 30 | 77.73 54 | 84.01 32 | 95.66 15 | 63.39 58 | 97.94 25 | 87.40 20 | 93.55 26 | 95.42 27 |
Skip Steuart: Steuart Systems R&D Blog. |
PVSNet_Blended | | | 86.73 26 | 86.86 22 | 86.31 52 | 93.76 34 | 67.53 61 | 96.33 7 | 93.61 39 | 82.34 13 | 81.00 46 | 93.08 67 | 63.19 61 | 97.29 48 | 87.08 22 | 91.38 49 | 94.13 68 |
|
jason | | | 86.40 27 | 86.17 27 | 87.11 30 | 86.16 151 | 70.54 19 | 95.71 12 | 92.19 88 | 82.00 18 | 84.58 27 | 94.34 49 | 61.86 70 | 95.53 106 | 87.76 17 | 90.89 54 | 95.27 34 |
jason: jason. |
WTY-MVS | | | 86.32 28 | 85.81 31 | 87.85 14 | 92.82 54 | 69.37 31 | 95.20 20 | 95.25 5 | 82.71 10 | 81.91 40 | 94.73 41 | 67.93 23 | 97.63 34 | 79.55 60 | 82.25 106 | 96.54 11 |
|
MSLP-MVS | | | 86.27 29 | 85.91 30 | 87.35 24 | 92.01 67 | 68.97 37 | 95.04 26 | 92.70 71 | 79.04 40 | 81.50 43 | 96.50 4 | 58.98 83 | 96.78 72 | 83.49 38 | 93.93 21 | 96.29 14 |
|
VNet | | | 86.20 30 | 85.65 34 | 87.84 15 | 93.92 33 | 69.99 23 | 95.73 11 | 95.94 1 | 78.43 47 | 86.00 15 | 93.07 69 | 58.22 85 | 97.00 60 | 85.22 32 | 84.33 95 | 96.52 12 |
|
MVS_111021_HR | | | 86.19 31 | 85.80 32 | 87.37 23 | 93.17 47 | 69.79 27 | 93.99 43 | 93.76 34 | 79.08 39 | 78.88 62 | 93.99 55 | 62.25 67 | 98.15 22 | 85.93 28 | 91.15 52 | 94.15 67 |
|
APD-MVS |  | | 85.93 32 | 85.99 28 | 85.76 64 | 95.98 11 | 65.21 123 | 93.59 56 | 92.58 78 | 66.54 192 | 86.17 14 | 95.88 11 | 63.83 53 | 97.00 60 | 86.39 26 | 92.94 31 | 95.06 42 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
PAPM | | | 85.89 33 | 85.46 35 | 87.18 27 | 88.20 129 | 72.42 8 | 92.41 87 | 92.77 70 | 82.11 15 | 80.34 48 | 93.07 69 | 68.27 17 | 95.02 109 | 78.39 70 | 93.59 25 | 94.09 71 |
|
Regformer-3 | | | 85.80 34 | 85.92 29 | 85.46 68 | 94.17 28 | 65.09 127 | 92.95 71 | 95.11 8 | 81.13 21 | 81.68 42 | 95.04 29 | 65.82 37 | 98.32 18 | 83.02 40 | 84.36 92 | 92.97 99 |
|
CDPH-MVS | | | 85.71 35 | 85.46 35 | 86.46 44 | 94.75 20 | 67.19 67 | 93.89 49 | 92.83 69 | 70.90 152 | 83.09 35 | 95.28 23 | 63.62 56 | 97.36 44 | 80.63 55 | 94.18 18 | 94.84 48 |
|
DeepC-MVS | | 77.85 3 | 85.52 36 | 85.24 37 | 86.37 49 | 88.80 116 | 66.64 95 | 92.15 91 | 93.68 37 | 81.07 22 | 76.91 83 | 93.64 59 | 62.59 66 | 98.44 15 | 85.50 30 | 92.84 33 | 94.03 74 |
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 37 | 85.69 33 | 84.73 85 | 94.17 28 | 63.23 159 | 92.95 71 | 94.83 13 | 80.66 23 | 81.29 44 | 95.04 29 | 65.12 42 | 98.08 24 | 82.74 41 | 84.36 92 | 92.88 103 |
|
PAPR | | | 85.15 38 | 84.47 40 | 87.18 27 | 96.02 10 | 68.29 47 | 91.85 111 | 93.00 64 | 76.59 70 | 79.03 61 | 95.00 31 | 61.59 71 | 97.61 36 | 78.16 71 | 89.00 65 | 95.63 25 |
|
MP-MVS |  | | 85.02 39 | 84.97 38 | 85.17 76 | 92.60 58 | 64.27 144 | 93.24 64 | 92.27 84 | 73.13 113 | 79.63 55 | 94.43 45 | 61.90 69 | 97.17 53 | 85.00 33 | 92.56 35 | 94.06 73 |
|
#test# | | | 84.98 40 | 84.74 39 | 85.72 65 | 93.75 36 | 65.01 128 | 94.09 37 | 93.19 56 | 73.55 109 | 79.22 58 | 94.93 34 | 59.04 81 | 97.67 29 | 82.66 42 | 92.21 36 | 94.49 59 |
|
HyFIR | | | 84.98 40 | 83.45 47 | 89.57 6 | 89.94 94 | 75.14 3 | 92.07 97 | 92.32 82 | 81.87 19 | 75.68 88 | 88.27 122 | 60.18 77 | 98.60 12 | 80.46 57 | 90.27 61 | 94.96 46 |
|
HFP-MVS | | | 84.73 42 | 84.40 41 | 85.72 65 | 93.75 36 | 65.01 128 | 93.50 59 | 93.19 56 | 72.19 125 | 79.22 58 | 94.93 34 | 59.04 81 | 97.67 29 | 81.55 49 | 92.21 36 | 94.49 59 |
|
HC-MVS | | | 84.66 43 | 82.86 56 | 90.06 1 | 90.93 82 | 74.56 4 | 87.91 185 | 95.54 3 | 68.55 178 | 72.35 120 | 94.71 42 | 59.78 78 | 98.90 5 | 81.29 54 | 94.69 14 | 96.74 7 |
|
ACMMPR | | | 84.37 44 | 84.06 42 | 85.28 75 | 93.56 39 | 64.37 139 | 93.50 59 | 93.15 59 | 72.19 125 | 78.85 64 | 94.86 38 | 56.69 101 | 97.45 40 | 81.55 49 | 92.20 38 | 94.02 75 |
|
region2R | | | 84.36 45 | 84.03 43 | 85.36 73 | 93.54 40 | 64.31 141 | 93.43 62 | 92.95 65 | 72.16 128 | 78.86 63 | 94.84 39 | 56.97 95 | 97.53 38 | 81.38 52 | 92.11 40 | 94.24 63 |
|
LFMVS | | | 84.34 46 | 82.73 58 | 89.18 7 | 94.76 19 | 73.25 7 | 94.99 27 | 91.89 97 | 71.90 131 | 82.16 39 | 93.49 62 | 47.98 185 | 97.05 55 | 82.55 43 | 84.82 88 | 97.25 2 |
|
HY-MVS | | 76.49 5 | 84.28 47 | 83.36 51 | 87.02 33 | 92.22 65 | 67.74 55 | 84.65 214 | 94.50 20 | 79.15 36 | 82.23 38 | 87.93 125 | 66.88 29 | 96.94 66 | 80.53 56 | 82.20 107 | 96.39 13 |
|
MAR-MVS | | | 84.18 48 | 83.43 48 | 86.44 45 | 96.25 7 | 65.93 115 | 94.28 32 | 94.27 27 | 74.41 86 | 79.16 60 | 95.61 16 | 53.99 135 | 98.88 7 | 69.62 123 | 93.26 29 | 94.50 58 |
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 49 | 83.20 52 | 87.05 32 | 91.56 73 | 69.82 26 | 89.99 155 | 92.05 91 | 77.77 53 | 82.84 36 | 86.57 135 | 63.93 52 | 96.09 87 | 74.91 86 | 89.18 64 | 95.25 37 |
|
PVSNet_Blended_VisFu | | | 83.97 50 | 83.50 45 | 85.39 72 | 90.02 92 | 66.59 98 | 93.77 52 | 91.73 102 | 77.43 60 | 77.08 82 | 89.81 110 | 63.77 55 | 96.97 63 | 79.67 59 | 88.21 70 | 92.60 107 |
|
DWT-MVSNet_test | | | 83.95 51 | 82.80 57 | 87.41 22 | 92.90 52 | 70.07 22 | 89.12 168 | 94.42 23 | 82.15 14 | 77.64 72 | 91.77 89 | 70.81 12 | 96.22 82 | 65.03 159 | 81.36 110 | 95.94 20 |
|
XVS | | | 83.87 52 | 83.47 46 | 85.05 78 | 93.22 44 | 63.78 148 | 92.92 73 | 92.66 74 | 73.99 97 | 78.18 67 | 94.31 51 | 55.25 110 | 97.41 41 | 79.16 63 | 91.58 46 | 93.95 77 |
|
EI-MVSNet-Vis-set | | | 83.77 53 | 83.67 44 | 84.06 97 | 92.79 56 | 63.56 156 | 91.76 115 | 94.81 15 | 79.65 30 | 77.87 69 | 94.09 53 | 63.35 59 | 97.90 26 | 79.35 61 | 79.36 119 | 90.74 129 |
|
MVSFormer | | | 83.75 54 | 82.88 55 | 86.37 49 | 89.24 107 | 71.18 13 | 89.07 169 | 90.69 136 | 65.80 196 | 87.13 10 | 94.34 49 | 64.99 44 | 92.67 183 | 72.83 93 | 91.80 42 | 95.27 34 |
|
CP-MVS | | | 83.71 55 | 83.40 50 | 84.65 88 | 93.14 48 | 63.84 147 | 94.59 30 | 92.28 83 | 71.03 150 | 77.41 76 | 94.92 36 | 55.21 113 | 96.19 83 | 81.32 53 | 90.70 56 | 93.91 79 |
|
PVSNet_BlendedMVS | | | 83.38 56 | 83.43 48 | 83.22 112 | 93.76 34 | 67.53 61 | 94.06 39 | 93.61 39 | 79.13 37 | 81.00 46 | 85.14 148 | 63.19 61 | 97.29 48 | 87.08 22 | 73.91 150 | 84.83 204 |
|
PGM-MVS | | | 83.25 57 | 82.70 59 | 84.92 80 | 92.81 55 | 64.07 146 | 90.44 146 | 92.20 87 | 71.28 148 | 77.23 79 | 94.43 45 | 55.17 114 | 97.31 47 | 79.33 62 | 91.38 49 | 93.37 86 |
|
HPM-MVS | | | 83.25 57 | 82.95 54 | 84.17 95 | 92.25 64 | 62.88 167 | 90.91 136 | 91.86 98 | 70.30 160 | 77.12 80 | 93.96 56 | 56.75 99 | 96.28 81 | 82.04 46 | 91.34 51 | 93.34 87 |
|
EI-MVSNet-UG-set | | | 83.14 59 | 82.96 53 | 83.67 107 | 92.28 63 | 63.19 160 | 91.38 126 | 94.68 17 | 79.22 34 | 76.60 84 | 93.75 58 | 62.64 65 | 97.76 28 | 78.07 72 | 78.01 129 | 90.05 136 |
|
PatchFormer-LS_test | | | 83.14 59 | 81.81 67 | 87.12 29 | 92.34 60 | 69.92 25 | 88.64 174 | 93.32 49 | 82.07 17 | 74.87 96 | 91.62 93 | 68.91 14 | 96.08 89 | 66.07 150 | 78.45 128 | 95.37 28 |
|
VDD-MVS | | | 83.06 61 | 81.81 67 | 86.81 34 | 90.86 85 | 67.70 56 | 95.40 16 | 91.50 112 | 75.46 79 | 81.78 41 | 92.34 83 | 40.09 217 | 97.13 54 | 86.85 24 | 82.04 108 | 95.60 26 |
|
PAPM_NR | | | 82.97 62 | 81.84 66 | 86.37 49 | 94.10 32 | 66.76 87 | 87.66 190 | 92.84 68 | 69.96 163 | 74.07 103 | 93.57 60 | 63.10 63 | 97.50 39 | 70.66 116 | 90.58 58 | 94.85 47 |
|
mPP-MVS | | | 82.96 63 | 82.44 60 | 84.52 91 | 92.83 53 | 62.92 165 | 92.76 76 | 91.85 99 | 71.52 145 | 75.61 91 | 94.24 52 | 53.48 144 | 96.99 62 | 78.97 66 | 90.73 55 | 93.64 82 |
|
DP-MVS Recon | | | 82.73 64 | 81.65 69 | 85.98 57 | 97.31 3 | 67.06 71 | 95.15 22 | 91.99 93 | 69.08 171 | 76.50 86 | 93.89 57 | 54.48 129 | 98.20 20 | 70.76 115 | 85.66 86 | 92.69 104 |
|
CLD-MVS | | | 82.73 64 | 82.35 62 | 83.86 100 | 87.90 135 | 67.65 58 | 95.45 15 | 92.18 89 | 85.06 5 | 72.58 113 | 92.27 84 | 52.46 151 | 95.78 95 | 84.18 35 | 79.06 122 | 88.16 152 |
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 66 | 82.38 61 | 83.73 104 | 89.25 105 | 59.58 210 | 92.24 90 | 94.89 11 | 77.96 51 | 79.86 52 | 92.38 81 | 56.70 100 | 97.05 55 | 77.26 75 | 80.86 114 | 94.55 54 |
|
3Dnovator | | 73.91 6 | 82.69 67 | 80.82 74 | 88.31 11 | 89.57 100 | 71.26 12 | 92.60 84 | 94.39 26 | 78.84 42 | 67.89 165 | 92.48 79 | 48.42 180 | 98.52 13 | 68.80 130 | 94.40 16 | 95.15 40 |
|
MVSTER | | | 82.47 68 | 82.05 63 | 83.74 102 | 92.68 57 | 69.01 35 | 91.90 109 | 93.21 53 | 79.83 26 | 72.14 121 | 85.71 144 | 74.72 4 | 94.72 119 | 75.72 80 | 72.49 157 | 87.50 156 |
|
TESTMET0.1,1 | | | 82.41 69 | 81.98 65 | 83.72 105 | 88.08 130 | 63.74 150 | 92.70 79 | 93.77 33 | 79.30 32 | 77.61 74 | 87.57 128 | 58.19 86 | 94.08 144 | 73.91 89 | 86.68 77 | 93.33 89 |
|
CostFormer | | | 82.33 70 | 81.15 73 | 85.86 60 | 89.01 112 | 68.46 42 | 82.39 230 | 93.01 62 | 75.59 77 | 80.25 49 | 81.57 185 | 72.03 9 | 94.96 111 | 79.06 65 | 77.48 133 | 94.16 66 |
|
API-MVS | | | 82.28 71 | 80.53 79 | 87.54 20 | 96.13 8 | 70.59 18 | 93.63 54 | 91.04 129 | 65.72 198 | 75.45 93 | 92.83 75 | 56.11 106 | 98.89 6 | 64.10 167 | 89.75 63 | 93.15 94 |
|
IB-MVS | | 77.80 4 | 82.18 72 | 80.46 80 | 87.35 24 | 89.14 109 | 70.28 21 | 95.59 14 | 95.17 6 | 78.85 41 | 70.19 133 | 85.82 142 | 70.66 13 | 97.67 29 | 72.19 101 | 66.52 191 | 94.09 71 |
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 |
3Dnovator+ | | 73.60 7 | 82.10 73 | 80.60 78 | 86.60 39 | 90.89 84 | 66.80 86 | 95.20 20 | 93.44 45 | 74.05 96 | 67.42 170 | 92.49 78 | 49.46 173 | 97.65 33 | 70.80 114 | 91.68 44 | 95.33 29 |
|
MVS_111021_LR | | | 82.02 74 | 81.52 70 | 83.51 109 | 88.42 125 | 62.88 167 | 89.77 157 | 88.93 191 | 76.78 67 | 75.55 92 | 93.10 65 | 50.31 165 | 95.38 108 | 83.82 37 | 87.02 76 | 92.26 116 |
|
PMMVS | | | 81.98 75 | 82.04 64 | 81.78 149 | 89.76 98 | 56.17 234 | 91.13 134 | 90.69 136 | 77.96 51 | 80.09 50 | 93.57 60 | 46.33 197 | 94.99 110 | 81.41 51 | 87.46 74 | 94.17 65 |
|
EPP-MVSNet | | | 81.79 76 | 81.52 70 | 82.61 123 | 88.77 117 | 60.21 200 | 93.02 69 | 93.66 38 | 68.52 179 | 72.90 109 | 90.39 103 | 72.19 8 | 94.96 111 | 74.93 85 | 79.29 121 | 92.67 105 |
|
APD-MVS_3200maxsize | | | 81.64 77 | 81.32 72 | 82.59 124 | 92.36 59 | 58.74 218 | 91.39 124 | 91.01 130 | 63.35 212 | 79.72 54 | 94.62 43 | 51.82 154 | 96.14 85 | 79.71 58 | 87.93 72 | 92.89 102 |
|
ACMMP |  | | 81.49 78 | 80.67 76 | 83.93 99 | 91.71 71 | 62.90 166 | 92.13 92 | 92.22 86 | 71.79 138 | 71.68 125 | 93.49 62 | 50.32 164 | 96.96 64 | 78.47 68 | 84.22 99 | 91.93 119 |
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 79 | 80.74 75 | 83.52 108 | 86.26 149 | 64.45 134 | 92.09 95 | 90.65 139 | 75.83 76 | 73.95 105 | 89.81 110 | 63.97 51 | 92.91 175 | 71.27 108 | 82.82 104 | 93.20 93 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
mvs_anonymous | | | 81.36 80 | 79.99 82 | 85.46 68 | 90.39 90 | 68.40 43 | 86.88 202 | 90.61 140 | 74.41 86 | 70.31 132 | 84.67 152 | 63.79 54 | 92.32 191 | 73.13 90 | 85.70 85 | 95.67 23 |
|
liao | | | 81.25 81 | 80.05 81 | 84.87 82 | 92.30 62 | 64.31 141 | 87.91 185 | 91.39 116 | 59.44 235 | 79.94 51 | 92.91 73 | 57.09 91 | 97.01 58 | 66.63 143 | 92.81 34 | 93.29 90 |
|
HQP-MVS | | | 81.14 82 | 80.64 77 | 82.64 122 | 87.54 137 | 63.66 154 | 94.06 39 | 91.70 105 | 79.80 27 | 74.18 99 | 90.30 104 | 51.63 158 | 95.61 101 | 77.63 73 | 78.90 123 | 88.63 145 |
|
nrg030 | | | 80.93 83 | 79.86 84 | 84.13 96 | 83.69 181 | 68.83 39 | 93.23 65 | 91.20 122 | 75.55 78 | 75.06 95 | 88.22 123 | 63.04 64 | 94.74 118 | 81.88 47 | 66.88 188 | 88.82 143 |
|
Vis-MVSNet |  | | 80.92 84 | 79.98 83 | 83.74 102 | 88.48 122 | 61.80 179 | 93.44 61 | 88.26 205 | 73.96 100 | 77.73 70 | 91.76 90 | 49.94 169 | 94.76 116 | 65.84 153 | 90.37 60 | 94.65 52 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
liao1 | | | 80.70 85 | 78.95 99 | 85.94 59 | 87.77 136 | 67.56 60 | 87.91 185 | 92.55 79 | 72.17 127 | 67.44 169 | 93.09 66 | 50.27 166 | 97.04 57 | 71.68 104 | 87.64 73 | 93.23 92 |
|
tpmrst | | | 80.57 86 | 79.14 98 | 84.84 83 | 90.10 91 | 68.28 48 | 81.70 233 | 89.72 170 | 77.63 56 | 75.96 87 | 79.54 213 | 64.94 46 | 92.71 181 | 75.43 82 | 77.28 136 | 93.55 83 |
|
1112_ss | | | 80.56 87 | 79.83 85 | 82.77 117 | 88.65 118 | 60.78 189 | 92.29 88 | 88.36 201 | 72.58 118 | 72.46 117 | 94.95 32 | 65.09 43 | 93.42 166 | 66.38 146 | 77.71 131 | 94.10 70 |
|
VDDNet | | | 80.50 88 | 78.26 105 | 87.21 26 | 86.19 150 | 69.79 27 | 94.48 31 | 91.31 119 | 60.42 230 | 79.34 57 | 90.91 97 | 38.48 223 | 96.56 79 | 82.16 44 | 81.05 112 | 95.27 34 |
|
BH-w/o | | | 80.49 89 | 79.30 95 | 84.05 98 | 90.83 86 | 64.36 140 | 93.60 55 | 89.42 177 | 74.35 91 | 69.09 150 | 90.15 106 | 55.23 112 | 95.61 101 | 64.61 162 | 86.43 82 | 92.17 117 |
|
TAMVS | | | 80.37 90 | 79.45 91 | 83.13 114 | 85.14 163 | 63.37 157 | 91.23 131 | 90.76 135 | 74.81 85 | 72.65 111 | 88.49 117 | 60.63 76 | 92.95 172 | 69.41 125 | 81.95 109 | 93.08 96 |
|
HQP_MVS | | | 80.34 91 | 79.75 86 | 82.12 143 | 86.94 144 | 62.42 171 | 93.13 67 | 91.31 119 | 78.81 43 | 72.53 114 | 89.14 114 | 50.66 162 | 95.55 104 | 76.74 76 | 78.53 126 | 88.39 149 |
|
HPM-MVS_fast | | | 80.25 92 | 79.55 90 | 82.33 134 | 91.55 74 | 59.95 205 | 91.32 129 | 89.16 184 | 65.23 203 | 74.71 97 | 93.07 69 | 47.81 187 | 95.74 97 | 74.87 88 | 88.23 69 | 91.31 125 |
|
diffmvs | | | 80.18 93 | 78.55 103 | 85.07 77 | 88.56 119 | 66.93 77 | 86.70 205 | 88.62 196 | 70.42 157 | 78.69 66 | 85.26 146 | 56.93 97 | 94.77 115 | 68.68 131 | 83.09 101 | 93.51 84 |
|
ab-mvs | | | 80.18 93 | 78.31 104 | 85.80 61 | 88.44 124 | 65.49 121 | 83.00 228 | 92.67 73 | 71.82 136 | 77.36 77 | 85.01 149 | 54.50 127 | 96.59 76 | 76.35 79 | 75.63 142 | 95.32 31 |
|
IS-MVSNet | | | 80.14 95 | 79.41 92 | 82.33 134 | 87.91 134 | 60.08 204 | 91.97 101 | 88.27 204 | 72.90 115 | 71.44 126 | 91.73 92 | 61.44 72 | 93.66 162 | 62.47 182 | 86.53 80 | 93.24 91 |
|
test-LLR | | | 80.10 96 | 79.56 89 | 81.72 150 | 86.93 146 | 61.17 185 | 92.70 79 | 91.54 109 | 71.51 146 | 75.62 89 | 86.94 132 | 53.83 136 | 92.38 188 | 72.21 99 | 84.76 90 | 91.60 121 |
|
PVSNet | | 73.49 8 | 80.05 97 | 78.63 101 | 84.31 93 | 90.92 83 | 64.97 130 | 92.47 86 | 91.05 128 | 79.18 35 | 72.43 118 | 90.51 102 | 37.05 235 | 94.06 145 | 68.06 132 | 86.00 84 | 93.90 80 |
|
UA-Net | | | 80.02 98 | 79.65 87 | 81.11 158 | 89.33 103 | 57.72 225 | 86.33 207 | 89.00 190 | 77.44 59 | 81.01 45 | 89.15 113 | 59.33 79 | 95.90 91 | 61.01 188 | 84.28 97 | 89.73 140 |
|
test-mter | | | 79.96 99 | 79.38 94 | 81.72 150 | 86.93 146 | 61.17 185 | 92.70 79 | 91.54 109 | 73.85 102 | 75.62 89 | 86.94 132 | 49.84 171 | 92.38 188 | 72.21 99 | 84.76 90 | 91.60 121 |
|
QAPM | | | 79.95 100 | 77.39 117 | 87.64 17 | 89.63 99 | 71.41 11 | 93.30 63 | 93.70 36 | 65.34 202 | 67.39 172 | 91.75 91 | 47.83 186 | 98.96 3 | 57.71 203 | 89.81 62 | 92.54 109 |
|
UGNet | | | 79.87 101 | 78.68 100 | 83.45 111 | 89.96 93 | 61.51 182 | 92.13 92 | 90.79 133 | 76.83 66 | 78.85 64 | 86.33 138 | 38.16 225 | 96.17 84 | 67.93 134 | 87.17 75 | 92.67 105 |
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 |
tpm2 | | | 79.80 102 | 77.95 109 | 85.34 74 | 88.28 127 | 68.26 49 | 81.56 237 | 91.42 115 | 70.11 161 | 77.59 75 | 80.50 200 | 67.40 26 | 94.26 139 | 67.34 139 | 77.35 134 | 93.51 84 |
|
DI_MVS_test_dynamic | | | 79.78 103 | 77.50 116 | 86.62 38 | 80.90 199 | 69.46 30 | 90.69 141 | 91.97 95 | 77.00 62 | 59.07 212 | 82.34 171 | 46.82 191 | 95.88 92 | 82.14 45 | 86.59 79 | 94.53 57 |
|
DI_MVS_test_normal | | | 79.66 104 | 77.36 119 | 86.54 42 | 80.72 202 | 69.21 32 | 90.68 142 | 92.16 90 | 76.99 63 | 58.63 216 | 82.03 180 | 46.70 193 | 95.86 93 | 81.74 48 | 86.63 78 | 94.56 53 |
|
CPTT-MVS | | | 79.59 105 | 79.16 97 | 80.89 165 | 91.54 75 | 59.80 207 | 92.10 94 | 88.54 199 | 60.42 230 | 72.96 108 | 93.28 64 | 48.27 181 | 92.80 178 | 78.89 67 | 86.50 81 | 90.06 135 |
|
Test_1112_low_res | | | 79.56 106 | 78.60 102 | 82.43 128 | 88.24 128 | 60.39 196 | 92.09 95 | 87.99 209 | 72.10 129 | 71.84 123 | 87.42 130 | 64.62 47 | 93.04 170 | 65.80 154 | 77.30 135 | 93.85 81 |
|
FIs | | | 79.47 107 | 79.41 92 | 79.67 180 | 85.95 154 | 59.40 211 | 91.68 119 | 93.94 29 | 78.06 50 | 68.96 153 | 88.28 121 | 66.61 31 | 91.77 201 | 66.20 149 | 74.99 144 | 87.82 154 |
|
BH-RMVSNet | | | 79.46 108 | 77.65 112 | 84.89 81 | 91.68 72 | 65.66 118 | 93.55 57 | 88.09 206 | 72.93 114 | 73.37 107 | 91.12 96 | 46.20 198 | 96.12 86 | 56.28 207 | 85.61 87 | 92.91 101 |
|
PCF-MVS | | 73.15 9 | 79.29 109 | 77.63 113 | 84.29 94 | 86.06 152 | 65.96 114 | 87.03 197 | 91.10 126 | 69.86 164 | 69.79 139 | 90.64 98 | 57.54 90 | 96.59 76 | 64.37 166 | 82.29 105 | 90.32 133 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Vis-MVSNet (Re-imp) | | | 79.24 110 | 79.57 88 | 78.24 202 | 88.46 123 | 52.29 248 | 90.41 148 | 89.12 185 | 74.24 92 | 69.13 149 | 91.91 87 | 65.77 38 | 90.09 222 | 59.00 197 | 88.09 71 | 92.33 112 |
|
114514_t | | | 79.17 111 | 77.67 111 | 83.68 106 | 95.32 14 | 65.53 120 | 92.85 75 | 91.60 108 | 63.49 211 | 67.92 164 | 90.63 100 | 46.65 194 | 95.72 99 | 67.01 142 | 83.54 100 | 89.79 138 |
|
VPA-MVSNet | | | 79.03 112 | 78.00 108 | 82.11 146 | 85.95 154 | 64.48 133 | 93.22 66 | 94.66 18 | 75.05 84 | 74.04 104 | 84.95 150 | 52.17 153 | 93.52 164 | 74.90 87 | 67.04 187 | 88.32 151 |
|
EI-MVSNet | | | 78.97 113 | 78.22 106 | 81.25 156 | 85.33 160 | 62.73 170 | 89.53 161 | 93.21 53 | 72.39 121 | 72.14 121 | 90.13 107 | 60.99 73 | 94.72 119 | 67.73 136 | 72.49 157 | 86.29 176 |
|
AdaColmap |  | | 78.94 114 | 77.00 122 | 84.76 84 | 96.34 6 | 65.86 116 | 92.66 83 | 87.97 210 | 62.18 220 | 70.56 128 | 92.37 82 | 43.53 206 | 97.35 45 | 64.50 164 | 82.86 103 | 91.05 127 |
|
tpmp4_e23 | | | 78.85 115 | 76.55 124 | 85.77 63 | 89.25 105 | 68.39 44 | 81.63 236 | 91.38 117 | 70.40 158 | 75.21 94 | 79.22 215 | 67.37 27 | 94.79 114 | 58.98 198 | 75.51 143 | 94.13 68 |
|
VPNet | | | 78.82 116 | 77.53 115 | 82.70 119 | 84.52 170 | 66.44 104 | 93.93 46 | 92.23 85 | 80.46 24 | 72.60 112 | 88.38 120 | 49.18 176 | 93.13 169 | 72.47 98 | 63.97 213 | 88.55 147 |
|
EPNet_dtu | | | 78.80 117 | 79.26 96 | 77.43 212 | 88.06 131 | 49.71 261 | 91.96 102 | 91.95 96 | 77.67 55 | 76.56 85 | 91.28 95 | 58.51 84 | 90.20 217 | 56.37 206 | 80.95 113 | 92.39 111 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
TR-MVS | | | 78.77 118 | 77.37 118 | 82.95 115 | 90.49 87 | 60.88 187 | 93.67 53 | 90.07 157 | 70.08 162 | 74.51 98 | 91.37 94 | 45.69 199 | 95.70 100 | 60.12 191 | 80.32 115 | 92.29 114 |
|
BH-untuned | | | 78.68 119 | 77.08 120 | 83.48 110 | 89.84 96 | 63.74 150 | 92.70 79 | 88.59 197 | 71.57 143 | 66.83 177 | 88.65 116 | 51.75 156 | 95.39 107 | 59.03 196 | 84.77 89 | 91.32 124 |
|
OMC-MVS | | | 78.67 120 | 77.91 110 | 80.95 164 | 85.76 159 | 57.40 228 | 88.49 175 | 88.67 194 | 73.85 102 | 72.43 118 | 92.10 85 | 49.29 175 | 94.55 123 | 72.73 95 | 77.89 130 | 90.91 128 |
|
tpm | | | 78.58 121 | 77.03 121 | 83.22 112 | 85.94 156 | 64.56 132 | 83.21 226 | 91.14 125 | 78.31 48 | 73.67 106 | 79.68 210 | 64.01 50 | 92.09 196 | 66.07 150 | 71.26 165 | 93.03 97 |
|
OpenMVS |  | 70.45 11 | 78.54 122 | 75.92 132 | 86.41 48 | 85.93 157 | 71.68 10 | 92.74 77 | 92.51 80 | 66.49 193 | 64.56 185 | 91.96 86 | 43.88 205 | 98.10 23 | 54.61 210 | 90.65 57 | 89.44 142 |
|
EPMVS | | | 78.49 123 | 75.98 131 | 86.02 56 | 91.21 79 | 69.68 29 | 80.23 244 | 91.20 122 | 75.25 82 | 72.48 116 | 78.11 219 | 54.65 126 | 93.69 161 | 57.66 204 | 83.04 102 | 94.69 51 |
|
GA-MVS | | | 78.33 124 | 76.23 128 | 84.65 88 | 83.65 182 | 66.30 108 | 91.44 122 | 90.14 155 | 76.01 74 | 70.32 131 | 84.02 157 | 42.50 208 | 94.72 119 | 70.98 112 | 77.00 137 | 92.94 100 |
|
cascas | | | 78.18 125 | 75.77 134 | 85.41 71 | 87.14 143 | 69.11 33 | 92.96 70 | 91.15 124 | 66.71 191 | 70.47 129 | 86.07 140 | 37.49 229 | 96.48 80 | 70.15 119 | 79.80 117 | 90.65 130 |
|
UniMVSNet_NR-MVSNet | | | 78.15 126 | 77.55 114 | 79.98 173 | 84.46 172 | 60.26 198 | 92.25 89 | 93.20 55 | 77.50 58 | 68.88 154 | 86.61 134 | 66.10 35 | 92.13 194 | 66.38 146 | 62.55 216 | 87.54 155 |
|
FC-MVSNet-test | | | 77.99 127 | 78.08 107 | 77.70 207 | 84.89 166 | 55.51 238 | 90.27 150 | 93.75 35 | 76.87 64 | 66.80 178 | 87.59 127 | 65.71 39 | 90.23 216 | 62.89 178 | 73.94 149 | 87.37 158 |
|
XXY-MVS | | | 77.94 128 | 76.44 126 | 82.43 128 | 82.60 188 | 64.44 135 | 92.01 98 | 91.83 100 | 73.59 108 | 70.00 135 | 85.82 142 | 54.43 130 | 94.76 116 | 69.63 122 | 68.02 183 | 88.10 153 |
|
MS-PatchMatch | | | 77.90 129 | 76.50 125 | 82.12 143 | 85.99 153 | 69.95 24 | 91.75 117 | 92.70 71 | 73.97 99 | 62.58 200 | 84.44 155 | 41.11 214 | 95.78 95 | 63.76 168 | 92.17 39 | 80.62 249 |
|
FMVSNet3 | | | 77.73 130 | 76.04 130 | 82.80 116 | 91.20 80 | 68.99 36 | 91.87 110 | 91.99 93 | 73.35 111 | 67.04 174 | 83.19 163 | 56.62 102 | 92.14 193 | 59.80 193 | 69.34 172 | 87.28 162 |
|
UniMVSNet (Re) | | | 77.58 131 | 76.78 123 | 79.98 173 | 84.11 178 | 60.80 188 | 91.76 115 | 93.17 58 | 76.56 71 | 69.93 138 | 84.78 151 | 63.32 60 | 92.36 190 | 64.89 160 | 62.51 218 | 86.78 170 |
|
PatchmatchNet |  | | 77.46 132 | 74.63 146 | 85.96 58 | 89.55 101 | 70.35 20 | 79.97 248 | 89.55 173 | 72.23 123 | 70.94 127 | 76.91 228 | 57.03 93 | 92.79 179 | 54.27 212 | 81.17 111 | 94.74 50 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
v2v482 | | | 77.42 133 | 75.65 139 | 82.73 118 | 80.38 216 | 67.13 70 | 91.85 111 | 90.23 149 | 75.09 83 | 69.37 146 | 83.39 161 | 53.79 138 | 94.44 124 | 71.77 102 | 65.00 203 | 86.63 174 |
|
v1neww | | | 77.39 134 | 75.71 136 | 82.44 125 | 80.69 204 | 66.83 79 | 91.94 106 | 90.18 152 | 74.19 93 | 69.60 140 | 82.51 167 | 54.99 121 | 94.44 124 | 71.68 104 | 65.60 193 | 86.05 182 |
|
v7new | | | 77.39 134 | 75.71 136 | 82.44 125 | 80.69 204 | 66.83 79 | 91.94 106 | 90.18 152 | 74.19 93 | 69.60 140 | 82.51 167 | 54.99 121 | 94.44 124 | 71.68 104 | 65.60 193 | 86.05 182 |
|
v6 | | | 77.39 134 | 75.71 136 | 82.44 125 | 80.67 206 | 66.82 81 | 91.94 106 | 90.18 152 | 74.19 93 | 69.60 140 | 82.50 170 | 55.00 120 | 94.44 124 | 71.68 104 | 65.60 193 | 86.05 182 |
|
v1 | | | 77.29 137 | 75.57 140 | 82.42 130 | 80.61 214 | 66.73 88 | 91.96 102 | 90.42 141 | 74.41 86 | 69.46 143 | 82.12 177 | 55.14 115 | 94.40 129 | 71.00 109 | 65.04 200 | 86.13 178 |
|
v1141 | | | 77.28 138 | 75.57 140 | 82.42 130 | 80.63 210 | 66.73 88 | 91.96 102 | 90.42 141 | 74.41 86 | 69.46 143 | 82.12 177 | 55.09 117 | 94.40 129 | 70.99 111 | 65.05 199 | 86.12 179 |
|
divwei89l23v2f112 | | | 77.28 138 | 75.57 140 | 82.42 130 | 80.62 211 | 66.72 90 | 91.96 102 | 90.42 141 | 74.41 86 | 69.46 143 | 82.12 177 | 55.11 116 | 94.40 129 | 71.00 109 | 65.04 200 | 86.12 179 |
|
PS-MVSNAJss | | | 77.26 140 | 76.31 127 | 80.13 171 | 80.64 209 | 59.16 215 | 90.63 145 | 91.06 127 | 72.80 116 | 68.58 159 | 84.57 154 | 53.55 140 | 93.96 152 | 72.97 91 | 71.96 160 | 87.27 163 |
|
gg-mvs-nofinetune | | | 77.18 141 | 74.31 152 | 85.80 61 | 91.42 77 | 68.36 45 | 71.78 265 | 94.72 16 | 49.61 264 | 77.12 80 | 45.92 286 | 77.41 1 | 93.98 151 | 67.62 137 | 93.16 30 | 95.05 43 |
|
MVP-PatchMatch | | | 77.12 142 | 76.23 128 | 79.79 179 | 81.72 194 | 66.34 107 | 89.29 163 | 90.88 131 | 70.56 156 | 62.01 202 | 82.88 164 | 49.34 174 | 94.13 141 | 65.55 156 | 93.80 22 | 78.88 260 |
|
X-MVStestdata | | | 76.86 143 | 74.13 155 | 85.05 78 | 93.22 44 | 63.78 148 | 92.92 73 | 92.66 74 | 73.99 97 | 78.18 67 | 10.19 299 | 55.25 110 | 97.41 41 | 79.16 63 | 91.58 46 | 93.95 77 |
|
DU-MVS | | | 76.86 143 | 75.84 133 | 79.91 175 | 82.96 186 | 60.26 198 | 91.26 130 | 91.54 109 | 76.46 72 | 68.88 154 | 86.35 136 | 56.16 104 | 92.13 194 | 66.38 146 | 62.55 216 | 87.35 160 |
|
v7 | | | 76.83 145 | 75.01 144 | 82.29 136 | 80.35 217 | 66.70 92 | 91.68 119 | 89.97 160 | 73.47 110 | 69.22 148 | 82.22 174 | 52.52 149 | 94.43 128 | 69.73 121 | 65.96 192 | 85.74 193 |
|
WR-MVS | | | 76.76 146 | 75.74 135 | 79.82 178 | 84.60 168 | 62.27 175 | 92.60 84 | 92.51 80 | 76.06 73 | 67.87 166 | 85.34 145 | 56.76 98 | 90.24 215 | 62.20 183 | 63.69 215 | 86.94 168 |
|
v1144 | | | 76.73 147 | 74.88 145 | 82.27 137 | 80.23 224 | 66.60 96 | 91.68 119 | 90.21 151 | 73.69 105 | 69.06 151 | 81.89 182 | 52.73 148 | 94.40 129 | 69.21 127 | 65.23 196 | 85.80 189 |
|
IterMVS-LS | | | 76.49 148 | 75.18 143 | 80.43 167 | 84.49 171 | 62.74 169 | 90.64 143 | 88.80 192 | 72.40 120 | 65.16 181 | 81.72 184 | 60.98 74 | 92.27 192 | 67.74 135 | 64.65 207 | 86.29 176 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
V42 | | | 76.46 149 | 74.55 149 | 82.19 141 | 79.14 237 | 67.82 53 | 90.26 151 | 89.42 177 | 73.75 104 | 68.63 158 | 81.89 182 | 51.31 160 | 94.09 143 | 71.69 103 | 64.84 204 | 84.66 205 |
|
Test4 | | | 76.45 150 | 73.45 164 | 85.45 70 | 76.07 258 | 67.61 59 | 88.38 177 | 90.83 132 | 76.71 68 | 53.06 245 | 79.65 212 | 31.61 251 | 94.35 133 | 78.47 68 | 86.22 83 | 94.40 61 |
|
v148 | | | 76.19 151 | 74.47 151 | 81.36 154 | 80.05 228 | 64.44 135 | 91.75 117 | 90.23 149 | 73.68 106 | 67.13 173 | 80.84 196 | 55.92 108 | 93.86 158 | 68.95 129 | 61.73 224 | 85.76 192 |
|
FMVSNet2 | | | 76.07 152 | 74.01 157 | 82.26 139 | 88.85 113 | 67.66 57 | 91.33 128 | 91.61 107 | 70.84 153 | 65.98 179 | 82.25 173 | 48.03 182 | 92.00 198 | 58.46 199 | 68.73 178 | 87.10 164 |
|
v144192 | | | 76.05 153 | 74.03 156 | 82.12 143 | 79.50 233 | 66.55 100 | 91.39 124 | 89.71 171 | 72.30 122 | 68.17 162 | 81.33 188 | 51.75 156 | 94.03 149 | 67.94 133 | 64.19 210 | 85.77 190 |
|
NR-MVSNet | | | 76.05 153 | 74.59 147 | 80.44 166 | 82.96 186 | 62.18 176 | 90.83 138 | 91.73 102 | 77.12 61 | 60.96 204 | 86.35 136 | 59.28 80 | 91.80 200 | 60.74 189 | 61.34 227 | 87.35 160 |
|
v1192 | | | 75.98 155 | 73.92 158 | 82.15 142 | 79.73 229 | 66.24 110 | 91.22 132 | 89.75 165 | 72.67 117 | 68.49 160 | 81.42 186 | 49.86 170 | 94.27 137 | 67.08 141 | 65.02 202 | 85.95 186 |
|
TranMVSNet+NR-MVSNet | | | 75.86 156 | 74.52 150 | 79.89 176 | 82.44 189 | 60.64 194 | 91.37 127 | 91.37 118 | 76.63 69 | 67.65 168 | 86.21 139 | 52.37 152 | 91.55 205 | 61.84 184 | 60.81 230 | 87.48 157 |
|
LPG-MVS_test | | | 75.82 157 | 74.58 148 | 79.56 184 | 84.31 175 | 59.37 212 | 90.44 146 | 89.73 168 | 69.49 166 | 64.86 182 | 88.42 118 | 38.65 221 | 94.30 135 | 72.56 96 | 72.76 154 | 85.01 202 |
|
GBi-Net | | | 75.65 158 | 73.83 159 | 81.10 159 | 88.85 113 | 65.11 124 | 90.01 152 | 90.32 144 | 70.84 153 | 67.04 174 | 80.25 205 | 48.03 182 | 91.54 206 | 59.80 193 | 69.34 172 | 86.64 171 |
|
test1 | | | 75.65 158 | 73.83 159 | 81.10 159 | 88.85 113 | 65.11 124 | 90.01 152 | 90.32 144 | 70.84 153 | 67.04 174 | 80.25 205 | 48.03 182 | 91.54 206 | 59.80 193 | 69.34 172 | 86.64 171 |
|
v1921920 | | | 75.63 160 | 73.49 163 | 82.06 147 | 79.38 234 | 66.35 106 | 91.07 135 | 89.48 174 | 71.98 130 | 67.99 163 | 81.22 191 | 49.16 178 | 93.90 155 | 66.56 145 | 64.56 208 | 85.92 188 |
|
ACMP | | 71.68 10 | 75.58 161 | 74.23 154 | 79.62 182 | 84.97 165 | 59.64 208 | 90.80 139 | 89.07 188 | 70.39 159 | 62.95 197 | 87.30 131 | 38.28 224 | 93.87 156 | 72.89 92 | 71.45 163 | 85.36 199 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v8 | | | 75.35 162 | 73.26 165 | 81.61 152 | 80.67 206 | 66.82 81 | 89.54 160 | 89.27 180 | 71.65 141 | 63.30 195 | 80.30 204 | 54.99 121 | 94.06 145 | 67.33 140 | 62.33 219 | 83.94 207 |
|
tpm cat1 | | | 75.30 163 | 72.21 176 | 84.58 90 | 88.52 120 | 67.77 54 | 78.16 258 | 88.02 207 | 61.88 223 | 68.45 161 | 76.37 229 | 60.65 75 | 94.03 149 | 53.77 215 | 74.11 147 | 91.93 119 |
|
PLC |  | 68.80 14 | 75.23 164 | 73.68 161 | 79.86 177 | 92.93 51 | 58.68 219 | 90.64 143 | 88.30 202 | 60.90 227 | 64.43 188 | 90.53 101 | 42.38 209 | 94.57 122 | 56.52 205 | 76.54 138 | 86.33 175 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
v1240 | | | 75.21 165 | 72.98 167 | 81.88 148 | 79.20 236 | 66.00 113 | 90.75 140 | 89.11 186 | 71.63 142 | 67.41 171 | 81.22 191 | 47.36 189 | 93.87 156 | 65.46 157 | 64.72 206 | 85.77 190 |
|
dp | | | 75.01 166 | 72.09 177 | 83.76 101 | 89.28 104 | 66.22 111 | 79.96 249 | 89.75 165 | 71.16 149 | 67.80 167 | 77.19 226 | 51.81 155 | 92.54 185 | 50.39 224 | 71.44 164 | 92.51 110 |
|
TAPA-MVS | | 70.22 12 | 74.94 167 | 73.53 162 | 79.17 196 | 90.40 89 | 52.07 249 | 89.19 166 | 89.61 172 | 62.69 218 | 70.07 134 | 92.67 76 | 48.89 179 | 94.32 134 | 38.26 262 | 79.97 116 | 91.12 126 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
v10 | | | 74.77 168 | 72.54 173 | 81.46 153 | 80.33 221 | 66.71 91 | 89.15 167 | 89.08 187 | 70.94 151 | 63.08 196 | 79.86 209 | 52.52 149 | 94.04 148 | 65.70 155 | 62.17 220 | 83.64 209 |
|
XVG-OURS-SEG-HR | | | 74.70 169 | 73.08 166 | 79.57 183 | 78.25 244 | 57.33 229 | 80.49 240 | 87.32 215 | 63.22 214 | 68.76 156 | 90.12 109 | 44.89 203 | 91.59 204 | 70.55 117 | 74.09 148 | 89.79 138 |
|
ACMM | | 69.62 13 | 74.34 170 | 72.73 169 | 79.17 196 | 84.25 177 | 57.87 223 | 90.36 149 | 89.93 161 | 63.17 215 | 65.64 180 | 86.04 141 | 37.79 227 | 94.10 142 | 65.89 152 | 71.52 162 | 85.55 196 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CNLPA | | | 74.31 171 | 72.30 175 | 80.32 168 | 91.49 76 | 61.66 180 | 90.85 137 | 80.72 258 | 56.67 249 | 63.85 192 | 90.64 98 | 46.75 192 | 90.84 210 | 53.79 214 | 75.99 141 | 88.47 148 |
|
XVG-OURS | | | 74.25 172 | 72.46 174 | 79.63 181 | 78.45 242 | 57.59 227 | 80.33 242 | 87.39 212 | 63.86 210 | 68.76 156 | 89.62 112 | 40.50 216 | 91.72 202 | 69.00 128 | 74.25 146 | 89.58 141 |
|
CVMVSNet | | | 74.04 173 | 74.27 153 | 73.33 236 | 85.33 160 | 43.94 275 | 89.53 161 | 88.39 200 | 54.33 255 | 70.37 130 | 90.13 107 | 49.17 177 | 84.05 254 | 61.83 185 | 79.36 119 | 91.99 118 |
|
Baseline_NR-MVSNet | | | 73.99 174 | 72.83 168 | 77.48 211 | 80.78 200 | 59.29 214 | 91.79 113 | 84.55 238 | 68.85 173 | 68.99 152 | 80.70 197 | 56.16 104 | 92.04 197 | 62.67 181 | 60.98 229 | 81.11 242 |
|
CR-MVSNet | | | 73.79 175 | 70.82 182 | 82.70 119 | 83.15 184 | 67.96 51 | 70.25 268 | 84.00 243 | 73.67 107 | 69.97 136 | 72.41 249 | 57.82 88 | 89.48 231 | 52.99 219 | 73.13 152 | 90.64 131 |
|
test_djsdf | | | 73.76 176 | 72.56 172 | 77.39 213 | 77.00 252 | 53.93 243 | 89.07 169 | 90.69 136 | 65.80 196 | 63.92 190 | 82.03 180 | 43.14 207 | 92.67 183 | 72.83 93 | 68.53 179 | 85.57 195 |
|
jajsoiax | | | 73.05 177 | 71.51 179 | 77.67 208 | 77.46 249 | 54.83 240 | 88.81 172 | 90.04 159 | 69.13 170 | 62.85 198 | 83.51 159 | 31.16 254 | 92.75 180 | 70.83 113 | 69.80 168 | 85.43 197 |
|
LCM-MVSNet-Re | | | 72.93 178 | 71.84 178 | 76.18 221 | 88.49 121 | 48.02 266 | 80.07 247 | 70.17 282 | 73.96 100 | 52.25 248 | 80.09 208 | 49.98 168 | 88.24 240 | 67.35 138 | 84.23 98 | 92.28 115 |
|
tpmvs | | | 72.88 179 | 69.76 188 | 82.22 140 | 90.98 81 | 67.05 72 | 78.22 257 | 88.30 202 | 63.10 216 | 64.35 189 | 74.98 236 | 55.09 117 | 94.27 137 | 43.25 243 | 69.57 171 | 85.34 200 |
|
test0.0.03 1 | | | 72.76 180 | 72.71 170 | 72.88 240 | 80.25 223 | 47.99 267 | 91.22 132 | 89.45 175 | 71.51 146 | 62.51 201 | 87.66 126 | 53.83 136 | 85.06 251 | 50.16 225 | 67.84 186 | 85.58 194 |
|
mvs_tets | | | 72.71 181 | 71.11 180 | 77.52 209 | 77.41 250 | 54.52 241 | 88.45 176 | 89.76 164 | 68.76 174 | 62.70 199 | 83.26 162 | 29.49 258 | 92.71 181 | 70.51 118 | 69.62 170 | 85.34 200 |
|
FMVSNet1 | | | 72.71 181 | 69.91 186 | 81.10 159 | 83.60 183 | 65.11 124 | 90.01 152 | 90.32 144 | 63.92 209 | 63.56 194 | 80.25 205 | 36.35 237 | 91.54 206 | 54.46 211 | 66.75 189 | 86.64 171 |
|
IterMVS | | | 72.65 183 | 70.83 181 | 78.09 205 | 82.17 190 | 62.96 162 | 87.64 191 | 86.28 227 | 71.56 144 | 60.44 205 | 78.85 217 | 45.42 200 | 86.66 246 | 63.30 172 | 61.83 221 | 84.65 206 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
pm-mvs1 | | | 72.28 184 | 69.97 184 | 79.20 195 | 78.43 243 | 61.48 184 | 88.92 171 | 88.01 208 | 65.63 199 | 61.92 203 | 79.10 216 | 45.18 201 | 90.70 211 | 58.24 200 | 64.29 209 | 85.43 197 |
|
PatchMatch-RL | | | 72.06 185 | 69.98 183 | 78.28 200 | 89.51 102 | 55.70 237 | 83.49 220 | 83.39 249 | 61.24 226 | 63.72 193 | 82.76 165 | 34.77 241 | 93.03 171 | 53.37 218 | 77.59 132 | 86.12 179 |
|
v18 | | | 71.94 186 | 69.43 189 | 79.50 186 | 80.74 201 | 66.82 81 | 88.16 179 | 86.66 217 | 68.95 172 | 55.55 228 | 72.66 244 | 55.03 119 | 90.15 218 | 64.78 161 | 52.30 255 | 81.54 230 |
|
v16 | | | 71.81 187 | 69.26 190 | 79.47 187 | 80.66 208 | 66.81 84 | 87.93 183 | 86.63 219 | 68.70 176 | 55.35 229 | 72.51 245 | 54.75 124 | 90.12 220 | 64.51 163 | 52.28 256 | 81.47 231 |
|
PVSNet_0 | | 68.08 15 | 71.81 187 | 68.32 200 | 82.27 137 | 84.68 167 | 62.31 174 | 88.68 173 | 90.31 147 | 75.84 75 | 57.93 220 | 80.65 199 | 37.85 226 | 94.19 140 | 69.94 120 | 29.05 288 | 90.31 134 |
|
v17 | | | 71.77 189 | 69.20 191 | 79.46 188 | 80.62 211 | 66.81 84 | 87.93 183 | 86.63 219 | 68.71 175 | 55.25 230 | 72.49 246 | 54.72 125 | 90.11 221 | 64.50 164 | 51.97 257 | 81.47 231 |
|
MIMVSNet | | | 71.64 190 | 68.44 198 | 81.23 157 | 81.97 193 | 64.44 135 | 73.05 264 | 88.80 192 | 69.67 165 | 64.59 184 | 74.79 237 | 32.79 245 | 87.82 241 | 53.99 213 | 76.35 139 | 91.42 123 |
|
v15 | | | 71.40 191 | 68.75 193 | 79.35 189 | 80.39 215 | 66.70 92 | 87.57 192 | 86.64 218 | 68.66 177 | 54.68 232 | 72.00 253 | 54.50 127 | 89.98 223 | 63.69 169 | 50.66 262 | 81.38 235 |
|
v7n | | | 71.31 192 | 68.65 194 | 79.28 191 | 76.40 254 | 60.77 190 | 86.71 204 | 89.45 175 | 64.17 208 | 58.77 215 | 78.24 218 | 44.59 204 | 93.54 163 | 57.76 202 | 61.75 223 | 83.52 212 |
|
V14 | | | 71.29 193 | 68.61 195 | 79.31 190 | 80.34 219 | 66.65 94 | 87.39 193 | 86.61 221 | 68.41 181 | 54.49 234 | 71.91 254 | 54.25 132 | 89.96 224 | 63.50 170 | 50.62 263 | 81.33 237 |
|
V9 | | | 71.16 194 | 68.46 197 | 79.27 192 | 80.26 222 | 66.60 96 | 87.21 196 | 86.56 222 | 68.17 182 | 54.26 237 | 71.81 256 | 54.00 134 | 89.93 225 | 63.28 173 | 50.57 264 | 81.27 238 |
|
testing_2 | | | 71.09 195 | 67.32 207 | 82.40 133 | 69.82 275 | 66.52 102 | 83.64 218 | 90.77 134 | 72.21 124 | 45.12 269 | 71.07 264 | 27.60 262 | 93.74 159 | 75.71 81 | 69.96 167 | 86.95 167 |
|
v11 | | | 71.05 196 | 68.32 200 | 79.23 193 | 80.34 219 | 66.57 99 | 87.01 199 | 86.55 223 | 68.11 183 | 54.40 235 | 71.66 258 | 52.94 146 | 89.91 226 | 62.71 180 | 51.12 260 | 81.21 239 |
|
v12 | | | 71.02 197 | 68.29 202 | 79.22 194 | 80.18 225 | 66.53 101 | 87.01 199 | 86.54 224 | 67.90 184 | 54.00 240 | 71.70 257 | 53.66 139 | 89.91 226 | 63.09 175 | 50.51 265 | 81.21 239 |
|
v13 | | | 70.90 198 | 68.15 203 | 79.15 198 | 80.08 226 | 66.45 103 | 86.83 203 | 86.50 225 | 67.62 187 | 53.78 242 | 71.61 259 | 53.51 143 | 89.87 228 | 62.89 178 | 50.50 266 | 81.14 241 |
|
F-COLMAP | | | 70.66 199 | 68.44 198 | 77.32 214 | 86.37 148 | 55.91 236 | 88.00 181 | 86.32 226 | 56.94 247 | 57.28 224 | 88.07 124 | 33.58 243 | 92.49 186 | 51.02 222 | 68.37 180 | 83.55 210 |
|
WR-MVS_H | | | 70.59 200 | 69.94 185 | 72.53 242 | 81.03 198 | 51.43 252 | 87.35 194 | 92.03 92 | 67.38 188 | 60.23 206 | 80.70 197 | 55.84 109 | 83.45 259 | 46.33 238 | 58.58 239 | 82.72 224 |
|
v748 | | | 70.55 201 | 67.97 204 | 78.27 201 | 75.75 259 | 58.78 217 | 86.29 208 | 89.25 181 | 65.12 204 | 56.66 226 | 77.17 227 | 45.05 202 | 92.95 172 | 58.13 201 | 58.33 240 | 83.10 220 |
|
CP-MVSNet | | | 70.50 202 | 69.91 186 | 72.26 245 | 80.71 203 | 51.00 255 | 87.23 195 | 90.30 148 | 67.84 185 | 59.64 208 | 82.69 166 | 50.23 167 | 82.30 266 | 51.28 221 | 59.28 234 | 83.46 214 |
|
TransMVSNet (Re) | | | 70.07 203 | 67.66 205 | 77.31 215 | 80.62 211 | 59.13 216 | 91.78 114 | 84.94 236 | 65.97 195 | 60.08 207 | 80.44 201 | 50.78 161 | 91.87 199 | 48.84 230 | 45.46 275 | 80.94 244 |
|
DP-MVS | | | 69.90 204 | 66.48 210 | 80.14 170 | 95.36 13 | 62.93 163 | 89.56 158 | 76.11 266 | 50.27 263 | 57.69 223 | 85.23 147 | 39.68 218 | 95.73 98 | 33.35 271 | 71.05 166 | 81.78 229 |
|
PS-CasMVS | | | 69.86 205 | 69.13 192 | 72.07 248 | 80.35 217 | 50.57 257 | 87.02 198 | 89.75 165 | 67.27 189 | 59.19 210 | 82.28 172 | 46.58 195 | 82.24 267 | 50.69 223 | 59.02 237 | 83.39 216 |
|
v52 | | | 69.80 206 | 67.01 209 | 78.15 203 | 71.84 268 | 60.10 202 | 82.02 231 | 87.39 212 | 64.48 205 | 57.80 221 | 75.97 232 | 41.47 213 | 92.90 176 | 63.00 176 | 59.13 236 | 81.45 233 |
|
V4 | | | 69.80 206 | 67.02 208 | 78.15 203 | 71.86 267 | 60.10 202 | 82.02 231 | 87.39 212 | 64.48 205 | 57.78 222 | 75.98 231 | 41.49 212 | 92.90 176 | 63.00 176 | 59.16 235 | 81.44 234 |
|
RPMNet | | | 69.58 208 | 65.21 218 | 82.70 119 | 83.15 184 | 67.96 51 | 70.25 268 | 86.15 230 | 46.83 272 | 69.97 136 | 65.10 272 | 56.48 103 | 89.48 231 | 35.79 268 | 73.13 152 | 90.64 131 |
|
MSDG | | | 69.54 209 | 65.73 213 | 80.96 163 | 85.11 164 | 63.71 152 | 84.19 216 | 83.28 250 | 56.95 246 | 54.50 233 | 84.03 156 | 31.50 252 | 96.03 90 | 42.87 248 | 69.13 175 | 83.14 219 |
|
PEN-MVS | | | 69.46 210 | 68.56 196 | 72.17 247 | 79.27 235 | 49.71 261 | 86.90 201 | 89.24 182 | 67.24 190 | 59.08 211 | 82.51 167 | 47.23 190 | 83.54 258 | 48.42 232 | 57.12 241 | 83.25 217 |
|
LS3D | | | 69.17 211 | 66.40 211 | 77.50 210 | 91.92 69 | 56.12 235 | 85.12 211 | 80.37 259 | 46.96 270 | 56.50 227 | 87.51 129 | 37.25 230 | 93.71 160 | 32.52 276 | 79.40 118 | 82.68 225 |
|
PatchT | | | 69.11 212 | 65.37 217 | 80.32 168 | 82.07 192 | 63.68 153 | 67.96 276 | 87.62 211 | 50.86 262 | 69.37 146 | 65.18 271 | 57.09 91 | 88.53 238 | 41.59 252 | 66.60 190 | 88.74 144 |
|
ACMH | | 63.93 17 | 68.62 213 | 64.81 219 | 80.03 172 | 85.22 162 | 63.25 158 | 87.72 189 | 84.66 237 | 60.83 228 | 51.57 251 | 79.43 214 | 27.29 263 | 94.96 111 | 41.76 250 | 64.84 204 | 81.88 228 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EG-PatchMatch MVS | | | 68.55 214 | 65.41 216 | 77.96 206 | 78.69 241 | 62.93 163 | 89.86 156 | 89.17 183 | 60.55 229 | 50.27 255 | 77.73 221 | 22.60 271 | 94.06 145 | 47.18 236 | 72.65 156 | 76.88 267 |
|
ADS-MVSNet | | | 68.54 215 | 64.38 223 | 81.03 162 | 88.06 131 | 66.90 78 | 68.01 274 | 84.02 242 | 57.57 241 | 64.48 186 | 69.87 265 | 38.68 219 | 89.21 234 | 40.87 254 | 67.89 184 | 86.97 165 |
|
DTE-MVSNet | | | 68.46 216 | 67.33 206 | 71.87 251 | 77.94 247 | 49.00 265 | 86.16 209 | 88.58 198 | 66.36 194 | 58.19 218 | 82.21 175 | 46.36 196 | 83.87 257 | 44.97 241 | 55.17 248 | 82.73 223 |
|
XVG-ACMP-BASELINE | | | 68.04 217 | 65.53 215 | 75.56 222 | 74.06 264 | 52.37 247 | 78.43 254 | 85.88 233 | 62.03 221 | 58.91 214 | 81.21 193 | 20.38 275 | 91.15 209 | 60.69 190 | 68.18 181 | 83.16 218 |
|
FMVSNet5 | | | 68.04 217 | 65.66 214 | 75.18 224 | 84.43 173 | 57.89 222 | 83.54 219 | 86.26 228 | 61.83 224 | 53.64 243 | 73.30 241 | 37.15 233 | 85.08 250 | 48.99 229 | 61.77 222 | 82.56 226 |
|
ACMH+ | | 65.35 16 | 67.65 219 | 64.55 220 | 76.96 217 | 84.59 169 | 57.10 230 | 88.08 180 | 80.79 257 | 58.59 240 | 53.00 246 | 81.09 195 | 26.63 265 | 92.95 172 | 46.51 237 | 61.69 225 | 80.82 245 |
|
Anonymous20231206 | | | 67.53 220 | 65.78 212 | 72.79 241 | 74.95 260 | 47.59 269 | 88.23 178 | 87.32 215 | 61.75 225 | 58.07 219 | 77.29 224 | 37.79 227 | 87.29 244 | 42.91 246 | 63.71 214 | 83.48 213 |
|
Patchmtry | | | 67.53 220 | 63.93 224 | 78.34 199 | 82.12 191 | 64.38 138 | 68.72 271 | 84.00 243 | 48.23 269 | 59.24 209 | 72.41 249 | 57.82 88 | 89.27 233 | 46.10 239 | 56.68 245 | 81.36 236 |
|
USDC | | | 67.43 222 | 64.51 221 | 76.19 220 | 77.94 247 | 55.29 239 | 78.38 255 | 85.00 235 | 73.17 112 | 48.36 259 | 80.37 202 | 21.23 274 | 92.48 187 | 52.15 220 | 64.02 211 | 80.81 246 |
|
ADS-MVSNet2 | | | 66.90 223 | 63.44 226 | 77.26 216 | 88.06 131 | 60.70 193 | 68.01 274 | 75.56 271 | 57.57 241 | 64.48 186 | 69.87 265 | 38.68 219 | 84.10 253 | 40.87 254 | 67.89 184 | 86.97 165 |
|
CMPMVS |  | 48.56 21 | 66.77 224 | 64.41 222 | 73.84 233 | 70.65 273 | 50.31 258 | 77.79 259 | 85.73 234 | 45.54 275 | 44.76 270 | 82.14 176 | 35.40 239 | 90.14 219 | 63.18 174 | 74.54 145 | 81.07 243 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
OpenMVS_ROB |  | 61.12 18 | 66.39 225 | 62.92 230 | 76.80 219 | 76.51 253 | 57.77 224 | 89.22 164 | 83.41 248 | 55.48 253 | 53.86 241 | 77.84 220 | 26.28 266 | 93.95 153 | 34.90 269 | 68.76 177 | 78.68 262 |
|
LTVRE_ROB | | 59.60 19 | 66.27 226 | 63.54 225 | 74.45 228 | 84.00 180 | 51.55 251 | 67.08 277 | 83.53 246 | 58.78 238 | 54.94 231 | 80.31 203 | 34.54 242 | 93.23 168 | 40.64 256 | 68.03 182 | 78.58 263 |
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 | | | 66.06 227 | 62.45 233 | 76.88 218 | 81.42 197 | 54.45 242 | 57.49 287 | 88.67 194 | 49.36 265 | 63.86 191 | 46.86 285 | 56.06 107 | 90.25 214 | 49.53 228 | 68.83 176 | 85.95 186 |
|
UnsupCasMVSNet_eth | | | 65.79 228 | 63.10 228 | 73.88 232 | 70.71 272 | 50.29 259 | 81.09 238 | 89.88 162 | 72.58 118 | 49.25 257 | 74.77 238 | 32.57 247 | 87.43 243 | 55.96 208 | 41.04 279 | 83.90 208 |
|
pmmvs-eth3d | | | 65.53 229 | 62.32 234 | 75.19 223 | 69.39 277 | 59.59 209 | 82.80 229 | 83.43 247 | 62.52 219 | 51.30 253 | 72.49 246 | 32.86 244 | 87.16 245 | 55.32 209 | 50.73 261 | 78.83 261 |
|
SixPastTwentyTwo | | | 64.92 230 | 61.78 237 | 74.34 231 | 78.74 240 | 49.76 260 | 83.42 223 | 79.51 262 | 62.86 217 | 50.27 255 | 77.35 222 | 30.92 256 | 90.49 212 | 45.89 240 | 47.06 272 | 82.78 221 |
|
Patchmatch-RL test | | | 64.87 231 | 61.69 238 | 74.44 229 | 71.22 270 | 53.93 243 | 70.07 270 | 71.54 281 | 69.22 169 | 56.79 225 | 74.43 239 | 19.77 278 | 88.61 235 | 69.53 124 | 52.61 254 | 80.79 247 |
|
OurMVSNet-221017-0 | | | 64.68 232 | 62.17 235 | 72.21 246 | 76.08 257 | 47.35 270 | 80.67 239 | 81.02 256 | 56.19 250 | 51.60 250 | 79.66 211 | 27.05 264 | 88.56 237 | 53.60 216 | 53.63 252 | 80.71 248 |
|
test_0402 | | | 64.54 233 | 61.09 239 | 74.92 225 | 84.10 179 | 60.75 191 | 87.95 182 | 79.71 261 | 52.03 258 | 52.41 247 | 77.20 225 | 32.21 249 | 91.64 203 | 23.14 285 | 61.03 228 | 72.36 274 |
|
testgi | | | 64.48 234 | 62.87 231 | 69.31 254 | 71.24 269 | 40.62 280 | 85.49 210 | 79.92 260 | 65.36 201 | 54.18 238 | 83.49 160 | 23.74 269 | 84.55 252 | 41.60 251 | 60.79 231 | 82.77 222 |
|
RPSCF | | | 64.24 235 | 61.98 236 | 71.01 252 | 76.10 256 | 45.00 273 | 75.83 262 | 75.94 268 | 46.94 271 | 58.96 213 | 84.59 153 | 31.40 253 | 82.00 268 | 47.76 234 | 60.33 233 | 86.04 185 |
|
test2356 | | | 64.16 236 | 63.28 227 | 66.81 260 | 69.37 278 | 39.86 283 | 87.76 188 | 86.02 231 | 59.83 233 | 53.54 244 | 73.23 242 | 34.94 240 | 80.67 270 | 39.66 258 | 65.20 197 | 79.89 254 |
|
EU-MVSNet | | | 64.01 237 | 63.01 229 | 67.02 259 | 74.40 262 | 38.86 285 | 83.27 224 | 86.19 229 | 45.11 276 | 54.27 236 | 81.15 194 | 36.91 236 | 80.01 272 | 48.79 231 | 57.02 242 | 82.19 227 |
|
test20.03 | | | 63.83 238 | 62.65 232 | 67.38 258 | 70.58 274 | 39.94 281 | 86.57 206 | 84.17 240 | 63.29 213 | 51.86 249 | 77.30 223 | 37.09 234 | 82.47 264 | 38.87 261 | 54.13 251 | 79.73 256 |
|
MDA-MVSNet_test_wron | | | 63.78 239 | 60.16 240 | 74.64 226 | 78.15 245 | 60.41 195 | 83.49 220 | 84.03 241 | 56.17 252 | 39.17 280 | 71.59 261 | 37.22 231 | 83.24 262 | 42.87 248 | 48.73 269 | 80.26 252 |
|
YYNet1 | | | 63.76 240 | 60.14 241 | 74.62 227 | 78.06 246 | 60.19 201 | 83.46 222 | 83.99 245 | 56.18 251 | 39.25 279 | 71.56 262 | 37.18 232 | 83.34 260 | 42.90 247 | 48.70 270 | 80.32 251 |
|
K. test v3 | | | 63.09 241 | 59.61 243 | 73.53 235 | 76.26 255 | 49.38 263 | 83.27 224 | 77.15 265 | 64.35 207 | 47.77 260 | 72.32 251 | 28.73 259 | 87.79 242 | 49.93 227 | 36.69 283 | 83.41 215 |
|
COLMAP_ROB |  | 57.96 20 | 62.98 242 | 59.65 242 | 72.98 239 | 81.44 196 | 53.00 246 | 83.75 217 | 75.53 272 | 48.34 268 | 48.81 258 | 81.40 187 | 24.14 267 | 90.30 213 | 32.95 273 | 60.52 232 | 75.65 270 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
AllTest | | | 61.66 243 | 58.06 244 | 72.46 243 | 79.57 230 | 51.42 253 | 80.17 245 | 68.61 284 | 51.25 260 | 45.88 264 | 81.23 189 | 19.86 276 | 86.58 247 | 38.98 259 | 57.01 243 | 79.39 258 |
|
UnsupCasMVSNet_bld | | | 61.60 244 | 57.71 245 | 73.29 237 | 68.73 279 | 51.64 250 | 78.61 253 | 89.05 189 | 57.20 245 | 46.11 263 | 61.96 277 | 28.70 260 | 88.60 236 | 50.08 226 | 38.90 281 | 79.63 257 |
|
MDA-MVSNet-bldmvs | | | 61.54 245 | 57.70 246 | 73.05 238 | 79.53 232 | 57.00 231 | 83.08 227 | 81.23 254 | 57.57 241 | 34.91 284 | 72.45 248 | 32.79 245 | 86.26 249 | 35.81 267 | 41.95 278 | 75.89 269 |
|
TinyColmap | | | 60.32 246 | 56.42 251 | 72.00 249 | 78.78 239 | 53.18 245 | 78.36 256 | 75.64 269 | 52.30 257 | 41.59 278 | 75.82 234 | 14.76 283 | 88.35 239 | 35.84 266 | 54.71 250 | 74.46 271 |
|
MVS-HIRNet | | | 60.25 247 | 55.55 252 | 74.35 230 | 84.37 174 | 56.57 232 | 71.64 266 | 74.11 274 | 34.44 287 | 45.54 268 | 42.24 289 | 31.11 255 | 89.81 229 | 40.36 257 | 76.10 140 | 76.67 268 |
|
MIMVSNet1 | | | 60.16 248 | 57.33 248 | 68.67 255 | 69.71 276 | 44.13 274 | 78.92 252 | 84.21 239 | 55.05 254 | 44.63 271 | 71.85 255 | 23.91 268 | 81.54 269 | 32.63 275 | 55.03 249 | 80.35 250 |
|
PM-MVS | | | 59.40 249 | 56.59 250 | 67.84 256 | 63.63 281 | 41.86 278 | 76.76 260 | 63.22 290 | 59.01 237 | 51.07 254 | 72.27 252 | 11.72 286 | 83.25 261 | 61.34 186 | 50.28 267 | 78.39 264 |
|
testus | | | 59.36 250 | 57.51 247 | 64.90 263 | 66.72 280 | 37.56 286 | 84.98 212 | 81.09 255 | 57.46 244 | 47.72 261 | 72.76 243 | 11.43 288 | 78.78 278 | 36.56 263 | 58.91 238 | 78.36 265 |
|
testpf | | | 57.17 251 | 56.93 249 | 57.88 270 | 79.13 238 | 42.40 276 | 34.23 294 | 85.97 232 | 52.64 256 | 47.66 262 | 66.50 268 | 36.33 238 | 79.65 274 | 53.60 216 | 56.31 246 | 51.60 287 |
|
DSMNet-mixed | | | 56.78 252 | 54.44 254 | 63.79 265 | 63.21 282 | 29.44 294 | 64.43 280 | 64.10 289 | 42.12 284 | 51.32 252 | 71.60 260 | 31.76 250 | 75.04 281 | 36.23 265 | 65.20 197 | 86.87 169 |
|
LP | | | 56.71 253 | 51.64 257 | 71.91 250 | 80.08 226 | 60.33 197 | 61.72 282 | 75.61 270 | 43.87 281 | 43.76 274 | 60.30 279 | 30.46 257 | 84.05 254 | 22.94 286 | 46.06 274 | 71.34 275 |
|
1111 | | | 56.66 254 | 54.98 253 | 61.69 266 | 61.99 285 | 31.38 290 | 79.81 250 | 83.17 251 | 45.66 273 | 41.94 276 | 65.44 269 | 41.50 210 | 79.56 275 | 27.64 280 | 47.68 271 | 74.14 272 |
|
test1235678 | | | 55.73 255 | 52.74 255 | 64.68 264 | 60.16 287 | 35.56 288 | 81.65 234 | 81.46 253 | 51.27 259 | 38.93 281 | 62.82 275 | 17.44 279 | 78.58 279 | 30.87 278 | 50.09 268 | 79.89 254 |
|
pmmvs3 | | | 55.51 256 | 51.50 259 | 67.53 257 | 57.90 289 | 50.93 256 | 80.37 241 | 73.66 276 | 40.63 285 | 44.15 273 | 64.75 273 | 16.30 280 | 78.97 277 | 44.77 242 | 40.98 280 | 72.69 273 |
|
TDRefinement | | | 55.28 257 | 51.58 258 | 66.39 261 | 59.53 288 | 46.15 272 | 76.23 261 | 72.80 277 | 44.60 279 | 42.49 275 | 76.28 230 | 15.29 281 | 82.39 265 | 33.20 272 | 43.75 276 | 70.62 277 |
|
HyFIR lowres test | | | 54.68 258 | 50.80 260 | 66.34 262 | 74.15 263 | 49.02 264 | 53.99 289 | 62.10 292 | 44.68 278 | 37.46 282 | 63.99 274 | 13.84 285 | 80.23 271 | 43.01 245 | 63.99 212 | 69.76 279 |
|
LF4IMVS | | | 54.01 259 | 52.12 256 | 59.69 268 | 62.41 284 | 39.91 282 | 68.59 272 | 68.28 286 | 42.96 282 | 44.55 272 | 75.18 235 | 14.09 284 | 68.39 285 | 41.36 253 | 51.68 258 | 70.78 276 |
|
N_pmnet | | | 50.55 260 | 49.11 262 | 54.88 274 | 77.17 251 | 4.02 306 | 84.36 215 | 2.00 303 | 48.59 266 | 45.86 266 | 68.82 267 | 32.22 248 | 82.80 263 | 31.58 277 | 51.38 259 | 77.81 266 |
|
new_pmnet | | | 49.31 261 | 46.44 263 | 57.93 269 | 62.84 283 | 40.74 279 | 68.47 273 | 62.96 291 | 36.48 286 | 35.09 283 | 57.81 281 | 14.97 282 | 72.18 282 | 32.86 274 | 46.44 273 | 60.88 285 |
|
test12356 | | | 47.51 262 | 44.82 264 | 55.56 272 | 52.53 290 | 21.09 301 | 71.45 267 | 76.03 267 | 44.14 280 | 30.69 285 | 58.18 280 | 9.01 291 | 76.14 280 | 26.95 282 | 34.43 286 | 69.46 280 |
|
testmv | | | 46.98 263 | 43.53 265 | 57.35 271 | 47.75 295 | 30.41 293 | 74.99 263 | 77.69 263 | 42.84 283 | 28.03 286 | 53.36 282 | 8.18 292 | 71.18 283 | 24.36 284 | 34.55 284 | 70.46 278 |
|
.test1245 | | | 46.52 264 | 49.68 261 | 37.02 283 | 61.99 285 | 31.38 290 | 79.81 250 | 83.17 251 | 45.66 273 | 41.94 276 | 65.44 269 | 41.50 210 | 79.56 275 | 27.64 280 | 0.01 300 | 0.13 299 |
|
FPMVS | | | 45.64 265 | 43.10 266 | 53.23 276 | 51.42 292 | 36.46 287 | 64.97 279 | 71.91 279 | 29.13 289 | 27.53 287 | 61.55 278 | 9.83 289 | 65.01 289 | 16.00 291 | 55.58 247 | 58.22 286 |
|
no-one | | | 44.13 266 | 38.39 267 | 61.34 267 | 45.91 297 | 41.94 277 | 61.67 283 | 75.07 273 | 45.05 277 | 20.07 290 | 40.68 292 | 11.58 287 | 79.82 273 | 30.18 279 | 15.30 291 | 62.26 284 |
|
LCM-MVSNet | | | 40.54 267 | 35.79 268 | 54.76 275 | 36.92 301 | 30.81 292 | 51.41 290 | 69.02 283 | 22.07 291 | 24.63 288 | 45.37 287 | 4.56 297 | 65.81 287 | 33.67 270 | 34.50 285 | 67.67 281 |
|
ANet_high | | | 40.27 268 | 35.20 269 | 55.47 273 | 34.74 302 | 34.47 289 | 63.84 281 | 71.56 280 | 48.42 267 | 18.80 292 | 41.08 290 | 9.52 290 | 64.45 290 | 20.18 288 | 8.66 298 | 67.49 282 |
|
PMMVS2 | | | 37.93 269 | 33.61 270 | 50.92 277 | 46.31 296 | 24.76 299 | 60.55 286 | 50.05 295 | 28.94 290 | 20.93 289 | 47.59 284 | 4.41 298 | 65.13 288 | 25.14 283 | 18.55 290 | 62.87 283 |
|
Gipuma |  | | 34.91 270 | 31.44 272 | 45.30 279 | 70.99 271 | 39.64 284 | 19.85 297 | 72.56 278 | 20.10 294 | 16.16 294 | 21.47 296 | 5.08 296 | 71.16 284 | 13.07 292 | 43.70 277 | 25.08 293 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PNet_i23d | | | 32.77 271 | 29.98 273 | 41.11 281 | 48.05 293 | 29.17 295 | 65.82 278 | 50.02 296 | 21.42 292 | 14.74 295 | 37.19 293 | 1.11 301 | 55.11 292 | 19.75 289 | 11.77 293 | 39.06 289 |
|
PMVS |  | 26.43 22 | 31.84 272 | 28.16 274 | 42.89 280 | 25.87 305 | 27.58 297 | 50.92 291 | 49.78 297 | 21.37 293 | 14.17 296 | 40.81 291 | 2.01 299 | 66.62 286 | 9.61 294 | 38.88 282 | 34.49 292 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
pcd1.5k->3k | | | 31.17 273 | 31.85 271 | 29.12 285 | 81.48 195 | 0.00 309 | 0.00 298 | 91.79 101 | 0.00 303 | 0.00 304 | 0.00 303 | 41.05 215 | 0.00 301 | 0.00 300 | 72.34 159 | 87.36 159 |
|
wuykxyi23d | | | 29.03 274 | 23.09 279 | 46.84 278 | 31.67 304 | 28.82 296 | 43.46 292 | 57.72 294 | 14.39 297 | 7.52 300 | 20.84 297 | 0.64 302 | 60.29 291 | 21.57 287 | 10.04 295 | 51.40 288 |
|
E-PMN | | | 24.61 275 | 24.00 276 | 26.45 286 | 43.74 298 | 18.44 303 | 60.86 284 | 39.66 298 | 15.11 295 | 9.53 298 | 22.10 295 | 6.52 294 | 46.94 294 | 8.31 295 | 10.14 294 | 13.98 295 |
|
MVE |  | 24.84 23 | 24.35 276 | 19.77 280 | 38.09 282 | 34.56 303 | 26.92 298 | 26.57 295 | 38.87 300 | 11.73 298 | 11.37 297 | 27.44 294 | 1.37 300 | 50.42 293 | 11.41 293 | 14.60 292 | 36.93 290 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EMVS | | | 23.76 277 | 23.20 278 | 25.46 287 | 41.52 299 | 16.90 304 | 60.56 285 | 38.79 301 | 14.62 296 | 8.99 299 | 20.24 298 | 7.35 293 | 45.82 295 | 7.25 296 | 9.46 296 | 13.64 296 |
|
DUST3R | | | 22.26 278 | 23.75 277 | 17.80 288 | 5.23 306 | 12.06 305 | 35.26 293 | 39.48 299 | 2.82 300 | 18.94 291 | 44.20 288 | 22.23 272 | 24.64 297 | 36.30 264 | 9.31 297 | 16.69 294 |
|
cdsmvs_eth3d_5k | | | 19.86 279 | 26.47 275 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 93.45 44 | 0.00 303 | 0.00 304 | 95.27 24 | 49.56 172 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
wuyk23d | | | 11.30 280 | 10.95 281 | 12.33 289 | 48.05 293 | 19.89 302 | 25.89 296 | 1.92 304 | 3.58 299 | 3.12 301 | 1.37 300 | 0.64 302 | 15.77 298 | 6.23 297 | 7.77 299 | 1.35 297 |
|
ab-mvs-re | | | 7.91 281 | 10.55 282 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 0.00 307 | 0.00 303 | 0.00 304 | 94.95 32 | 0.00 306 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
testmvs | | | 7.23 282 | 9.62 283 | 0.06 291 | 0.04 307 | 0.02 308 | 84.98 212 | 0.02 305 | 0.03 301 | 0.18 302 | 1.21 301 | 0.01 305 | 0.02 299 | 0.14 298 | 0.01 300 | 0.13 299 |
|
test123 | | | 6.92 283 | 9.21 284 | 0.08 290 | 0.03 308 | 0.05 307 | 81.65 234 | 0.01 306 | 0.02 302 | 0.14 303 | 0.85 302 | 0.03 304 | 0.02 299 | 0.12 299 | 0.00 302 | 0.16 298 |
|
pcd_1.5k_mvsjas | | | 4.46 284 | 5.95 285 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 0.00 307 | 0.00 303 | 0.00 304 | 0.00 303 | 53.55 140 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
sosnet-low-res | | | 0.00 285 | 0.00 286 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 0.00 307 | 0.00 303 | 0.00 304 | 0.00 303 | 0.00 306 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
sosnet | | | 0.00 285 | 0.00 286 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 0.00 307 | 0.00 303 | 0.00 304 | 0.00 303 | 0.00 306 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
uncertanet | | | 0.00 285 | 0.00 286 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 0.00 307 | 0.00 303 | 0.00 304 | 0.00 303 | 0.00 306 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
Regformer | | | 0.00 285 | 0.00 286 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 0.00 307 | 0.00 303 | 0.00 304 | 0.00 303 | 0.00 306 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
uanet | | | 0.00 285 | 0.00 286 | 0.00 292 | 0.00 309 | 0.00 309 | 0.00 298 | 0.00 307 | 0.00 303 | 0.00 304 | 0.00 303 | 0.00 306 | 0.00 301 | 0.00 300 | 0.00 302 | 0.00 301 |
|
MTAPA | | | | | | 38.87 300 | | | | | | | | | | | |
|
gm-plane-assit | | | | | | 88.42 125 | 67.04 73 | | | 78.62 46 | | 91.83 88 | | 97.37 43 | 76.57 78 | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 6 | 94.96 7 | 95.29 32 |
|
TEST9 | | | | | | 94.18 26 | 67.28 65 | 94.16 33 | 93.51 42 | 71.75 140 | 85.52 20 | 95.33 21 | 68.01 20 | 97.27 50 | | | |
|
Patchmatch-test1 | | | | | | 72.70 266 | 56.44 233 | 57.17 288 | 74.00 275 | | 58.57 217 | 71.23 263 | 21.52 273 | | | 52.64 253 | |
|
Patchmatch-test | | | | | | 85.80 158 | 62.10 177 | | | | | 62.79 276 | | | | | |
|
test_8 | | | | | | 94.19 25 | 67.19 67 | 94.15 35 | 93.42 46 | 71.87 132 | 85.38 22 | 95.35 20 | 68.19 18 | 96.95 65 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 25 | 94.75 13 | 95.33 29 |
|
agg_prior | | | | | | 94.16 30 | 66.97 74 | | 93.31 50 | | 84.49 28 | | | 96.75 73 | | | |
|
TestCases | | | | | 72.46 243 | 79.57 230 | 51.42 253 | | 68.61 284 | 51.25 260 | 45.88 264 | 81.23 189 | 19.86 276 | 86.58 247 | 38.98 259 | 57.01 243 | 79.39 258 |
|
test_prior4 | | | | | | | 67.18 69 | 93.92 47 | | | | | | | | | |
|
test_prior2 | | | | | | | | 95.10 24 | | 75.40 80 | 85.25 24 | 95.61 16 | 67.94 21 | | 87.47 18 | 94.77 11 | |
|
test_prior | | | | | 86.42 46 | 94.71 21 | 67.35 63 | | 93.10 60 | | | | | 96.84 70 | | | 95.05 43 |
|
旧先验2 | | | | | | | | 92.00 100 | | 59.37 236 | 87.54 9 | | | 93.47 165 | 75.39 83 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 91.41 123 | | | | | | | | | |
|
æ–°å‡ ä½•1 | | | | | 84.73 85 | 92.32 61 | 64.28 143 | | 91.46 114 | 59.56 234 | 79.77 53 | 92.90 74 | 56.95 96 | 96.57 78 | 63.40 171 | 92.91 32 | 93.34 87 |
|
旧先验1 | | | | | | 91.94 68 | 60.74 192 | | 91.50 112 | | | 94.36 47 | 65.23 41 | | | 91.84 41 | 94.55 54 |
|
æ— å…ˆéªŒ | | | | | | | | 92.71 78 | 92.61 77 | 62.03 221 | | | | 97.01 58 | 66.63 143 | | 93.97 76 |
|
原ACMM2 | | | | | | | | 92.01 98 | | | | | | | | | |
|
原ACMM1 | | | | | 84.42 92 | 93.21 46 | 64.27 144 | | 93.40 47 | 65.39 200 | 79.51 56 | 92.50 77 | 58.11 87 | 96.69 75 | 65.27 158 | 93.96 20 | 92.32 113 |
|
test222 | | | | | | 89.77 97 | 61.60 181 | 89.55 159 | 89.42 177 | 56.83 248 | 77.28 78 | 92.43 80 | 52.76 147 | | | 91.14 53 | 93.09 95 |
|
testdata2 | | | | | | | | | | | | | | 96.09 87 | 61.26 187 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 36 | | | | |
|
testdata | | | | | 81.34 155 | 89.02 111 | 57.72 225 | | 89.84 163 | 58.65 239 | 85.32 23 | 94.09 53 | 57.03 93 | 93.28 167 | 69.34 126 | 90.56 59 | 93.03 97 |
|
testdata1 | | | | | | | | 89.21 165 | | 77.55 57 | | | | | | | |
|
test12 | | | | | 87.09 31 | 94.60 23 | 68.86 38 | | 92.91 66 | | 82.67 37 | | 65.44 40 | 97.55 37 | | 93.69 24 | 94.84 48 |
|
plane_prior7 | | | | | | 86.94 144 | 61.51 182 | | | | | | | | | | |
|
plane_prior6 | | | | | | 87.23 141 | 62.32 173 | | | | | | 50.66 162 | | | | |
|
plane_prior5 | | | | | | | | | 91.31 119 | | | | | 95.55 104 | 76.74 76 | 78.53 126 | 88.39 149 |
|
plane_prior4 | | | | | | | | | | | | 89.14 114 | | | | | |
|
plane_prior3 | | | | | | | 61.95 178 | | | 79.09 38 | 72.53 114 | | | | | | |
|
plane_prior2 | | | | | | | | 93.13 67 | | 78.81 43 | | | | | | | |
|
plane_prior1 | | | | | | 87.15 142 | | | | | | | | | | | |
|
plane_prior | | | | | | | 62.42 171 | 93.85 50 | | 79.38 31 | | | | | | 78.80 125 | |
|
abl_6 | | | | | | 89.85 95 | 58.34 220 | | 90.07 157 | | 77.71 71 | 93.08 67 | 47.65 188 | | | | 89.99 137 |
|
n2 | | | | | | | | | 0.00 307 | | | | | | | | |
|
nn | | | | | | | | | 0.00 307 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 288 | | | | | | | | |
|
lessismore_v0 | | | | | 73.72 234 | 72.93 265 | 47.83 268 | | 61.72 293 | | 45.86 266 | 73.76 240 | 28.63 261 | 89.81 229 | 47.75 235 | 31.37 287 | 83.53 211 |
|
LGP-MVS_train | | | | | 79.56 184 | 84.31 175 | 59.37 212 | | 89.73 168 | 69.49 166 | 64.86 182 | 88.42 118 | 38.65 221 | 94.30 135 | 72.56 96 | 72.76 154 | 85.01 202 |
|
test11 | | | | | | | | | 93.01 62 | | | | | | | | |
|
door | | | | | | | | | 66.57 287 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 154 | | | | | | | | | | |
|
HQP-NCC | | | | | | 87.54 137 | | 94.06 39 | | 79.80 27 | 74.18 99 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 137 | | 94.06 39 | | 79.80 27 | 74.18 99 | | | | | | |
|
BP-MVS | | | | | | | | |