DeepPCF-MVS | | 93.56 1 | 96.55 35 | 97.84 7 | 92.68 195 | 98.71 79 | 78.11 293 | 99.70 15 | 97.71 72 | 98.18 1 | 97.36 43 | 99.76 1 | 90.37 38 | 99.94 29 | 99.27 4 | 99.54 47 | 99.99 1 |
|
MCST-MVS | | | 98.18 2 | 97.95 6 | 98.86 2 | 99.85 3 | 96.60 6 | 99.70 15 | 97.98 43 | 97.18 2 | 95.96 70 | 99.33 14 | 92.62 18 | 100.00 1 | 98.99 8 | 99.93 1 | 99.98 2 |
|
MG-MVS | | | 97.24 14 | 96.83 24 | 98.47 10 | 99.79 5 | 95.71 13 | 99.07 83 | 99.06 9 | 94.45 20 | 96.42 65 | 98.70 79 | 88.81 54 | 99.74 66 | 95.35 74 | 99.86 9 | 99.97 3 |
|
test_0728_SECOND | | | | | 98.77 4 | 99.66 13 | 96.37 9 | 99.72 12 | 97.68 76 | | | | | 99.98 11 | 99.64 1 | 99.82 15 | 99.96 4 |
|
CNVR-MVS | | | 98.46 1 | 98.38 1 | 98.72 5 | 99.80 4 | 96.19 10 | 99.80 8 | 97.99 42 | 97.05 3 | 99.41 1 | 99.59 2 | 92.89 17 | 100.00 1 | 98.99 8 | 99.90 4 | 99.96 4 |
|
DeepC-MVS_fast | | 93.52 2 | 97.16 18 | 96.84 23 | 98.13 17 | 99.61 21 | 94.45 38 | 98.85 105 | 97.64 84 | 96.51 7 | 95.88 73 | 99.39 13 | 87.35 82 | 99.99 5 | 96.61 48 | 99.69 32 | 99.96 4 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
test_0728_THIRD | | | | | | | | | | 93.01 42 | 99.07 5 | 99.46 8 | 94.66 9 | 99.97 18 | 99.25 6 | 99.82 15 | 99.95 7 |
|
DPE-MVS | | | 98.11 4 | 98.00 4 | 98.44 11 | 99.50 37 | 95.39 16 | 99.29 59 | 97.72 71 | 94.50 18 | 98.64 13 | 99.54 3 | 93.32 14 | 99.97 18 | 99.58 3 | 99.90 4 | 99.95 7 |
|
MSP-MVS | | | 97.77 7 | 98.18 2 | 96.53 89 | 99.54 30 | 90.14 125 | 99.41 47 | 97.70 73 | 95.46 15 | 98.60 14 | 99.19 25 | 95.71 4 | 99.49 94 | 98.15 27 | 99.85 10 | 99.95 7 |
|
testtj | | | 97.23 16 | 97.05 17 | 97.75 28 | 99.75 7 | 93.34 59 | 99.16 68 | 97.74 67 | 91.28 83 | 98.40 18 | 99.29 15 | 89.95 41 | 99.98 11 | 98.20 26 | 99.70 31 | 99.94 10 |
|
DPM-MVS | | | 97.86 6 | 97.25 14 | 99.68 1 | 98.25 88 | 99.10 1 | 99.76 10 | 97.78 62 | 96.61 4 | 98.15 23 | 99.53 5 | 93.62 13 | 100.00 1 | 91.79 125 | 99.80 20 | 99.94 10 |
|
NCCC | | | 98.12 3 | 98.11 3 | 98.13 17 | 99.76 6 | 94.46 37 | 99.81 6 | 97.88 48 | 96.54 5 | 98.84 11 | 99.46 8 | 92.55 19 | 99.98 11 | 98.25 25 | 99.93 1 | 99.94 10 |
|
APDe-MVS | | | 97.53 9 | 97.47 9 | 97.70 29 | 99.58 23 | 93.63 52 | 99.56 28 | 97.52 108 | 93.59 36 | 98.01 31 | 99.12 37 | 90.80 33 | 99.55 84 | 99.26 5 | 99.79 21 | 99.93 13 |
|
agg_prior2 | | | | | | | | | | | | | | | 97.84 33 | 99.87 6 | 99.91 14 |
|
test9_res | | | | | | | | | | | | | | | 98.60 14 | 99.87 6 | 99.90 15 |
|
SteuartSystems-ACMMP | | | 97.25 13 | 97.34 13 | 97.01 55 | 97.38 111 | 91.46 92 | 99.75 11 | 97.66 78 | 94.14 23 | 98.13 24 | 99.26 17 | 92.16 20 | 99.66 71 | 97.91 31 | 99.64 35 | 99.90 15 |
Skip Steuart: Steuart Systems R&D Blog. |
PHI-MVS | | | 96.65 32 | 96.46 32 | 97.21 49 | 99.34 45 | 91.77 83 | 99.70 15 | 98.05 38 | 86.48 198 | 98.05 28 | 99.20 24 | 89.33 48 | 99.96 23 | 98.38 20 | 99.62 39 | 99.90 15 |
|
ACMMP_NAP | | | 96.59 33 | 96.18 39 | 97.81 26 | 98.82 76 | 93.55 54 | 98.88 104 | 97.59 96 | 90.66 91 | 97.98 32 | 99.14 34 | 86.59 96 | 100.00 1 | 96.47 52 | 99.46 50 | 99.89 18 |
|
train_agg | | | 97.20 17 | 97.08 16 | 97.57 35 | 99.57 27 | 93.17 61 | 99.38 49 | 97.66 78 | 90.18 105 | 98.39 19 | 99.18 27 | 90.94 28 | 99.66 71 | 98.58 17 | 99.85 10 | 99.88 19 |
|
MSLP-MVS++ | | | 97.50 11 | 97.45 11 | 97.63 31 | 99.65 17 | 93.21 60 | 99.70 15 | 98.13 36 | 94.61 17 | 97.78 37 | 99.46 8 | 89.85 42 | 99.81 58 | 97.97 29 | 99.91 3 | 99.88 19 |
|
APD-MVS | | | 96.95 24 | 96.72 27 | 97.63 31 | 99.51 36 | 93.58 53 | 99.16 68 | 97.44 123 | 90.08 110 | 98.59 15 | 99.07 41 | 89.06 50 | 99.42 104 | 97.92 30 | 99.66 33 | 99.88 19 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
agg_prior1 | | | 97.12 19 | 97.03 18 | 97.38 43 | 99.54 30 | 92.66 73 | 99.35 54 | 97.64 84 | 90.38 100 | 97.98 32 | 99.17 29 | 90.84 32 | 99.61 80 | 98.57 18 | 99.78 23 | 99.87 22 |
|
MVS | | | 93.92 100 | 92.28 123 | 98.83 3 | 95.69 163 | 96.82 4 | 96.22 252 | 98.17 31 | 84.89 221 | 84.34 203 | 98.61 86 | 79.32 174 | 99.83 53 | 93.88 99 | 99.43 54 | 99.86 23 |
|
æ— å…ˆéªŒ | | | | | | | | 98.52 143 | 97.82 54 | 87.20 183 | | | | 99.90 36 | 87.64 167 | | 99.85 24 |
|
SMA-MVS | | | 97.24 14 | 96.99 19 | 98.00 21 | 99.30 51 | 94.20 44 | 99.16 68 | 97.65 83 | 89.55 121 | 99.22 4 | 99.52 6 | 90.34 39 | 99.99 5 | 98.32 23 | 99.83 12 | 99.82 25 |
|
region2R | | | 96.30 44 | 96.17 41 | 96.70 80 | 99.70 8 | 90.31 121 | 99.46 39 | 97.66 78 | 90.55 95 | 97.07 47 | 99.07 41 | 86.85 90 | 99.97 18 | 95.43 72 | 99.74 24 | 99.81 26 |
|
test222 | | | | | | 98.32 87 | 91.21 96 | 98.08 185 | 97.58 98 | 83.74 234 | 95.87 74 | 99.02 47 | 86.74 93 | | | 99.64 35 | 99.81 26 |
|
TSAR-MVS + GP. | | | 96.95 24 | 96.91 20 | 97.07 52 | 98.88 73 | 91.62 88 | 99.58 25 | 96.54 184 | 95.09 16 | 96.84 56 | 98.63 85 | 91.16 24 | 99.77 63 | 99.04 7 | 96.42 118 | 99.81 26 |
|
test_prior3 | | | 97.07 21 | 97.09 15 | 97.01 55 | 99.58 23 | 91.77 83 | 99.57 26 | 97.57 101 | 91.43 78 | 98.12 26 | 98.97 53 | 90.43 36 | 99.49 94 | 98.33 21 | 99.81 18 | 99.79 29 |
|
test_prior | | | | | 97.01 55 | 99.58 23 | 91.77 83 | | 97.57 101 | | | | | 99.49 94 | | | 99.79 29 |
|
æ–°å‡ ä½•1 | | | | | 97.40 41 | 98.92 71 | 92.51 80 | | 97.77 64 | 85.52 208 | 96.69 63 | 99.06 43 | 88.08 67 | 99.89 39 | 84.88 194 | 99.62 39 | 99.79 29 |
|
1121 | | | 95.19 71 | 94.45 75 | 97.42 39 | 98.88 73 | 92.58 78 | 96.22 252 | 97.75 65 | 85.50 210 | 96.86 53 | 99.01 51 | 88.59 58 | 99.90 36 | 87.64 167 | 99.60 43 | 99.79 29 |
|
HFP-MVS | | | 96.42 40 | 96.26 38 | 96.90 66 | 99.69 9 | 90.96 108 | 99.47 35 | 97.81 57 | 90.54 96 | 96.88 50 | 99.05 44 | 87.57 73 | 99.96 23 | 95.65 65 | 99.72 26 | 99.78 33 |
|
#test# | | | 96.48 37 | 96.34 36 | 96.90 66 | 99.69 9 | 90.96 108 | 99.53 31 | 97.81 57 | 90.94 89 | 96.88 50 | 99.05 44 | 87.57 73 | 99.96 23 | 95.87 64 | 99.72 26 | 99.78 33 |
|
XVS | | | 96.47 38 | 96.37 34 | 96.77 73 | 99.62 19 | 90.66 117 | 99.43 44 | 97.58 98 | 92.41 60 | 96.86 53 | 98.96 56 | 87.37 78 | 99.87 43 | 95.65 65 | 99.43 54 | 99.78 33 |
|
X-MVStestdata | | | 90.69 165 | 88.66 180 | 96.77 73 | 99.62 19 | 90.66 117 | 99.43 44 | 97.58 98 | 92.41 60 | 96.86 53 | 29.59 336 | 87.37 78 | 99.87 43 | 95.65 65 | 99.43 54 | 99.78 33 |
|
testdata | | | | | 95.26 133 | 98.20 90 | 87.28 186 | | 97.60 92 | 85.21 213 | 98.48 17 | 99.15 32 | 88.15 65 | 98.72 137 | 90.29 138 | 99.45 52 | 99.78 33 |
|
SD-MVS | | | 97.51 10 | 97.40 12 | 97.81 26 | 99.01 66 | 93.79 51 | 99.33 57 | 97.38 130 | 93.73 33 | 98.83 12 | 99.02 47 | 90.87 31 | 99.88 40 | 98.69 12 | 99.74 24 | 99.77 38 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
SR-MVS | | | 96.13 48 | 96.16 43 | 96.07 106 | 99.42 43 | 89.04 151 | 98.59 138 | 97.33 135 | 90.44 98 | 96.84 56 | 99.12 37 | 86.75 92 | 99.41 106 | 97.47 35 | 99.44 53 | 99.76 39 |
|
Regformer-1 | | | 96.97 23 | 96.80 25 | 97.47 37 | 99.46 41 | 93.11 63 | 98.89 102 | 97.94 44 | 92.89 47 | 96.90 49 | 99.02 47 | 89.78 43 | 99.53 87 | 97.06 39 | 99.26 63 | 99.75 40 |
|
Regformer-2 | | | 96.94 26 | 96.78 26 | 97.42 39 | 99.46 41 | 92.97 70 | 98.89 102 | 97.93 45 | 92.86 49 | 96.88 50 | 99.02 47 | 89.74 45 | 99.53 87 | 97.03 40 | 99.26 63 | 99.75 40 |
|
ACMMPR | | | 96.28 45 | 96.14 45 | 96.73 77 | 99.68 11 | 90.47 119 | 99.47 35 | 97.80 59 | 90.54 96 | 96.83 58 | 99.03 46 | 86.51 99 | 99.95 26 | 95.65 65 | 99.72 26 | 99.75 40 |
|
save filter2 | | | | | | | | | | | 99.29 3 | 99.48 7 | 91.45 23 | 99.99 5 | 98.94 10 | 99.83 12 | 99.74 43 |
|
mPP-MVS | | | 95.90 55 | 95.75 55 | 96.38 96 | 99.58 23 | 89.41 148 | 99.26 60 | 97.41 127 | 90.66 91 | 94.82 91 | 98.95 58 | 86.15 106 | 99.98 11 | 95.24 77 | 99.64 35 | 99.74 43 |
|
PAPR | | | 96.35 41 | 95.82 52 | 97.94 23 | 99.63 18 | 94.19 45 | 99.42 46 | 97.55 104 | 92.43 56 | 93.82 110 | 99.12 37 | 87.30 83 | 99.91 34 | 94.02 96 | 99.06 68 | 99.74 43 |
|
API-MVS | | | 94.78 78 | 94.18 82 | 96.59 85 | 99.21 57 | 90.06 132 | 98.80 110 | 97.78 62 | 83.59 238 | 93.85 108 | 99.21 23 | 83.79 131 | 99.97 18 | 92.37 120 | 99.00 71 | 99.74 43 |
|
CSCG | | | 94.87 76 | 94.71 70 | 95.36 129 | 99.54 30 | 86.49 197 | 99.34 56 | 98.15 34 | 82.71 250 | 90.15 157 | 99.25 18 | 89.48 47 | 99.86 48 | 94.97 82 | 98.82 80 | 99.72 47 |
|
zzz-MVS | | | 96.21 47 | 95.96 47 | 96.96 63 | 99.29 52 | 91.19 97 | 98.69 122 | 97.45 120 | 92.58 51 | 94.39 98 | 99.24 20 | 86.43 101 | 99.99 5 | 96.22 56 | 99.40 57 | 99.71 48 |
|
MTAPA | | | 96.09 49 | 95.80 54 | 96.96 63 | 99.29 52 | 91.19 97 | 97.23 216 | 97.45 120 | 92.58 51 | 94.39 98 | 99.24 20 | 86.43 101 | 99.99 5 | 96.22 56 | 99.40 57 | 99.71 48 |
|
APD-MVS_3200maxsize | | | 95.64 63 | 95.65 58 | 95.62 121 | 99.24 56 | 87.80 173 | 98.42 155 | 97.22 141 | 88.93 138 | 96.64 64 | 98.98 52 | 85.49 114 | 99.36 110 | 96.68 47 | 99.27 62 | 99.70 50 |
|
CP-MVS | | | 96.22 46 | 96.15 44 | 96.42 94 | 99.67 12 | 89.62 143 | 99.70 15 | 97.61 91 | 90.07 111 | 96.00 67 | 99.16 31 | 87.43 77 | 99.92 32 | 96.03 62 | 99.72 26 | 99.70 50 |
|
DVP-MVS | | | 98.07 5 | 98.00 4 | 98.29 12 | 99.66 13 | 95.20 21 | 99.72 12 | 97.47 118 | 93.95 24 | 99.07 5 | 99.46 8 | 93.18 15 | 99.97 18 | 99.64 1 | 99.82 15 | 99.69 52 |
|
HPM-MVS++ | | | 97.72 8 | 97.59 8 | 98.14 16 | 99.53 35 | 94.76 31 | 99.19 63 | 97.75 65 | 95.66 12 | 98.21 22 | 99.29 15 | 91.10 26 | 99.99 5 | 97.68 34 | 99.87 6 | 99.68 53 |
|
CDPH-MVS | | | 96.56 34 | 96.18 39 | 97.70 29 | 99.59 22 | 93.92 48 | 99.13 80 | 97.44 123 | 89.02 133 | 97.90 35 | 99.22 22 | 88.90 53 | 99.49 94 | 94.63 89 | 99.79 21 | 99.68 53 |
|
PAPM_NR | | | 95.43 64 | 95.05 67 | 96.57 87 | 99.42 43 | 90.14 125 | 98.58 140 | 97.51 110 | 90.65 93 | 92.44 124 | 98.90 63 | 87.77 72 | 99.90 36 | 90.88 133 | 99.32 60 | 99.68 53 |
|
canonicalmvs | | | 95.02 74 | 93.96 90 | 98.20 14 | 97.53 109 | 95.92 12 | 98.71 117 | 96.19 205 | 91.78 71 | 95.86 75 | 98.49 94 | 79.53 172 | 99.03 126 | 96.12 59 | 91.42 182 | 99.66 56 |
|
PGM-MVS | | | 95.85 56 | 95.65 58 | 96.45 92 | 99.50 37 | 89.77 140 | 98.22 171 | 98.90 11 | 89.19 128 | 96.74 61 | 98.95 58 | 85.91 109 | 99.92 32 | 93.94 97 | 99.46 50 | 99.66 56 |
|
DELS-MVS | | | 97.12 19 | 96.60 30 | 98.68 6 | 98.03 96 | 96.57 7 | 99.84 3 | 97.84 52 | 96.36 8 | 95.20 86 | 98.24 103 | 88.17 64 | 99.83 53 | 96.11 60 | 99.60 43 | 99.64 58 |
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 |
3Dnovator+ | | 87.72 8 | 93.43 115 | 91.84 134 | 98.17 15 | 95.73 162 | 95.08 23 | 98.92 99 | 97.04 158 | 91.42 80 | 81.48 239 | 97.60 124 | 74.60 198 | 99.79 61 | 90.84 134 | 98.97 72 | 99.64 58 |
|
CANet | | | 97.00 22 | 96.49 31 | 98.55 7 | 98.86 75 | 96.10 11 | 99.83 5 | 97.52 108 | 95.90 9 | 97.21 44 | 98.90 63 | 82.66 146 | 99.93 31 | 98.71 11 | 98.80 81 | 99.63 60 |
|
114514_t | | | 94.06 96 | 93.05 109 | 97.06 53 | 99.08 63 | 92.26 81 | 98.97 95 | 97.01 162 | 82.58 252 | 92.57 122 | 98.22 104 | 80.68 165 | 99.30 115 | 89.34 150 | 99.02 70 | 99.63 60 |
|
PAPM | | | 96.35 41 | 95.94 48 | 97.58 33 | 94.10 205 | 95.25 17 | 98.93 97 | 98.17 31 | 94.26 21 | 93.94 106 | 98.72 76 | 89.68 46 | 97.88 170 | 96.36 55 | 99.29 61 | 99.62 62 |
|
TSAR-MVS + MP. | | | 97.44 12 | 97.46 10 | 97.39 42 | 99.12 60 | 93.49 57 | 98.52 143 | 97.50 113 | 94.46 19 | 98.99 7 | 98.64 83 | 91.58 22 | 99.08 125 | 98.49 19 | 99.83 12 | 99.60 63 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
旧先验1 | | | | | | 98.97 67 | 92.90 72 | | 97.74 67 | | | 99.15 32 | 91.05 27 | | | 99.33 59 | 99.60 63 |
|
test12 | | | | | 97.83 25 | 99.33 50 | 94.45 38 | | 97.55 104 | | 97.56 38 | | 88.60 56 | 99.50 93 | | 99.71 30 | 99.55 65 |
|
HY-MVS | | 88.56 7 | 95.29 68 | 94.23 79 | 98.48 9 | 97.72 100 | 96.41 8 | 94.03 281 | 98.74 13 | 92.42 59 | 95.65 79 | 94.76 190 | 86.52 98 | 99.49 94 | 95.29 76 | 92.97 154 | 99.53 66 |
|
GST-MVS | | | 95.97 52 | 95.66 56 | 96.90 66 | 99.49 39 | 91.22 95 | 99.45 41 | 97.48 116 | 89.69 116 | 95.89 72 | 98.72 76 | 86.37 103 | 99.95 26 | 94.62 90 | 99.22 66 | 99.52 67 |
|
MP-MVS | | | 96.00 50 | 95.82 52 | 96.54 88 | 99.47 40 | 90.13 127 | 99.36 53 | 97.41 127 | 90.64 94 | 95.49 81 | 98.95 58 | 85.51 113 | 99.98 11 | 96.00 63 | 99.59 45 | 99.52 67 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
alignmvs | | | 95.77 61 | 95.00 68 | 98.06 20 | 97.35 112 | 95.68 14 | 99.71 14 | 97.50 113 | 91.50 76 | 96.16 66 | 98.61 86 | 86.28 104 | 99.00 127 | 96.19 58 | 91.74 176 | 99.51 69 |
|
WTY-MVS | | | 95.97 52 | 95.11 66 | 98.54 8 | 97.62 104 | 96.65 5 | 99.44 42 | 98.74 13 | 92.25 63 | 95.21 85 | 98.46 98 | 86.56 97 | 99.46 101 | 95.00 81 | 92.69 158 | 99.50 70 |
|
Regformer-3 | | | 96.50 36 | 96.36 35 | 96.91 65 | 99.34 45 | 91.72 86 | 98.71 117 | 97.90 47 | 92.48 55 | 96.00 67 | 98.95 58 | 88.60 56 | 99.52 90 | 96.44 53 | 98.83 78 | 99.49 71 |
|
Regformer-4 | | | 96.45 39 | 96.33 37 | 96.81 72 | 99.34 45 | 91.44 93 | 98.71 117 | 97.88 48 | 92.43 56 | 95.97 69 | 98.95 58 | 88.42 60 | 99.51 91 | 96.40 54 | 98.83 78 | 99.49 71 |
|
DP-MVS Recon | | | 95.85 56 | 95.15 65 | 97.95 22 | 99.87 2 | 94.38 41 | 99.60 23 | 97.48 116 | 86.58 195 | 94.42 97 | 99.13 36 | 87.36 81 | 99.98 11 | 93.64 104 | 98.33 93 | 99.48 73 |
|
HPM-MVS | | | 95.41 66 | 95.22 64 | 95.99 109 | 99.29 52 | 89.14 149 | 99.17 67 | 97.09 155 | 87.28 182 | 95.40 82 | 98.48 95 | 84.93 121 | 99.38 108 | 95.64 69 | 99.65 34 | 99.47 74 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
test_yl | | | 95.27 69 | 94.60 72 | 97.28 46 | 98.53 84 | 92.98 68 | 99.05 86 | 98.70 16 | 86.76 192 | 94.65 95 | 97.74 117 | 87.78 70 | 99.44 102 | 95.57 70 | 92.61 159 | 99.44 75 |
|
DCV-MVSNet | | | 95.27 69 | 94.60 72 | 97.28 46 | 98.53 84 | 92.98 68 | 99.05 86 | 98.70 16 | 86.76 192 | 94.65 95 | 97.74 117 | 87.78 70 | 99.44 102 | 95.57 70 | 92.61 159 | 99.44 75 |
|
CS-MVS | | | 95.85 56 | 95.86 51 | 95.82 115 | 96.80 131 | 89.78 139 | 99.84 3 | 96.60 176 | 92.60 50 | 96.81 60 | 98.70 79 | 85.04 119 | 98.25 149 | 97.90 32 | 98.43 91 | 99.42 77 |
|
MVS_111021_HR | | | 96.69 30 | 96.69 28 | 96.72 79 | 98.58 83 | 91.00 107 | 99.14 77 | 99.45 1 | 93.86 30 | 95.15 87 | 98.73 74 | 88.48 59 | 99.76 64 | 97.23 38 | 99.56 46 | 99.40 78 |
|
lupinMVS | | | 96.32 43 | 95.94 48 | 97.44 38 | 95.05 189 | 94.87 25 | 99.86 2 | 96.50 185 | 93.82 31 | 98.04 29 | 98.77 70 | 85.52 111 | 98.09 156 | 96.98 44 | 98.97 72 | 99.37 79 |
|
mvs_anonymous | | | 92.50 137 | 91.65 138 | 95.06 137 | 96.60 136 | 89.64 142 | 97.06 222 | 96.44 189 | 86.64 194 | 84.14 204 | 93.93 202 | 82.49 148 | 96.17 252 | 91.47 126 | 96.08 127 | 99.35 80 |
|
HPM-MVS_fast | | | 94.89 75 | 94.62 71 | 95.70 120 | 99.11 61 | 88.44 164 | 99.14 77 | 97.11 151 | 85.82 205 | 95.69 78 | 98.47 96 | 83.46 135 | 99.32 114 | 93.16 112 | 99.63 38 | 99.35 80 |
|
1314 | | | 93.44 114 | 91.98 132 | 97.84 24 | 95.24 174 | 94.38 41 | 96.22 252 | 97.92 46 | 90.18 105 | 82.28 225 | 97.71 119 | 77.63 186 | 99.80 60 | 91.94 124 | 98.67 85 | 99.34 82 |
|
LFMVS | | | 92.23 141 | 90.84 150 | 96.42 94 | 98.24 89 | 91.08 104 | 98.24 170 | 96.22 202 | 83.39 240 | 94.74 93 | 98.31 100 | 61.12 282 | 98.85 129 | 94.45 93 | 92.82 155 | 99.32 83 |
|
Effi-MVS+ | | | 93.87 103 | 93.15 107 | 96.02 107 | 95.79 159 | 90.76 112 | 96.70 236 | 95.78 231 | 86.98 186 | 95.71 77 | 97.17 143 | 79.58 170 | 98.01 165 | 94.57 91 | 96.09 126 | 99.31 84 |
|
CHOSEN 1792x2688 | | | 94.35 92 | 93.82 95 | 95.95 111 | 97.40 110 | 88.74 158 | 98.41 157 | 98.27 25 | 92.18 65 | 91.43 136 | 96.40 167 | 78.88 176 | 99.81 58 | 93.59 105 | 97.81 97 | 99.30 85 |
|
DWT-MVSNet_test | | | 94.36 91 | 93.95 91 | 95.62 121 | 96.99 125 | 89.47 146 | 96.62 238 | 97.38 130 | 90.96 88 | 93.07 118 | 97.27 134 | 93.73 12 | 98.09 156 | 85.86 187 | 93.65 149 | 99.29 86 |
|
ACMMP | | | 94.67 84 | 94.30 77 | 95.79 117 | 99.25 55 | 88.13 167 | 98.41 157 | 98.67 19 | 90.38 100 | 91.43 136 | 98.72 76 | 82.22 154 | 99.95 26 | 93.83 101 | 95.76 132 | 99.29 86 |
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 |
MP-MVS-pluss | | | 95.80 59 | 95.30 61 | 97.29 45 | 98.95 70 | 92.66 73 | 98.59 138 | 97.14 147 | 88.95 136 | 93.12 116 | 99.25 18 | 85.62 110 | 99.94 29 | 96.56 50 | 99.48 49 | 99.28 88 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
EPMVS | | | 92.59 135 | 91.59 139 | 95.59 124 | 97.22 116 | 90.03 133 | 91.78 297 | 98.04 39 | 90.42 99 | 91.66 131 | 90.65 258 | 86.49 100 | 97.46 197 | 81.78 227 | 96.31 121 | 99.28 88 |
|
AdaColmap | | | 93.82 104 | 93.06 108 | 96.10 105 | 99.88 1 | 89.07 150 | 98.33 163 | 97.55 104 | 86.81 191 | 90.39 154 | 98.65 82 | 75.09 195 | 99.98 11 | 93.32 110 | 97.53 105 | 99.26 90 |
|
ET-MVSNet_ETH3D | | | 92.56 136 | 91.45 141 | 95.88 113 | 96.39 142 | 94.13 46 | 99.46 39 | 96.97 164 | 92.18 65 | 66.94 311 | 98.29 102 | 94.65 10 | 94.28 297 | 94.34 94 | 83.82 226 | 99.24 91 |
|
VNet | | | 95.08 73 | 94.26 78 | 97.55 36 | 98.07 95 | 93.88 49 | 98.68 124 | 98.73 15 | 90.33 102 | 97.16 46 | 97.43 131 | 79.19 175 | 99.53 87 | 96.91 46 | 91.85 174 | 99.24 91 |
|
CNLPA | | | 93.64 111 | 92.74 115 | 96.36 97 | 98.96 69 | 90.01 135 | 99.19 63 | 95.89 227 | 86.22 201 | 89.40 165 | 98.85 66 | 80.66 166 | 99.84 51 | 88.57 157 | 96.92 112 | 99.24 91 |
|
3Dnovator | | 87.35 11 | 93.17 125 | 91.77 136 | 97.37 44 | 95.41 172 | 93.07 65 | 98.82 108 | 97.85 51 | 91.53 75 | 82.56 220 | 97.58 126 | 71.97 223 | 99.82 56 | 91.01 131 | 99.23 65 | 99.22 94 |
|
GG-mvs-BLEND | | | | | 96.98 61 | 96.53 138 | 94.81 30 | 87.20 309 | 97.74 67 | | 93.91 107 | 96.40 167 | 96.56 2 | 96.94 213 | 95.08 78 | 98.95 75 | 99.20 95 |
|
ETV-MVS | | | 95.11 72 | 95.27 63 | 94.64 150 | 96.34 144 | 86.51 196 | 99.59 24 | 96.62 174 | 92.51 53 | 94.08 104 | 98.64 83 | 86.05 107 | 98.24 150 | 95.07 79 | 98.50 89 | 99.18 96 |
|
Patchmatch-test | | | 86.25 229 | 84.06 243 | 92.82 190 | 94.42 201 | 82.88 257 | 82.88 324 | 94.23 283 | 71.58 304 | 79.39 258 | 90.62 260 | 89.00 52 | 96.42 234 | 63.03 309 | 91.37 183 | 99.16 97 |
|
gg-mvs-nofinetune | | | 90.00 175 | 87.71 192 | 96.89 71 | 96.15 151 | 94.69 34 | 85.15 315 | 97.74 67 | 68.32 315 | 92.97 120 | 60.16 325 | 96.10 3 | 96.84 215 | 93.89 98 | 98.87 76 | 99.14 98 |
|
MVS_Test | | | 93.67 110 | 92.67 117 | 96.69 81 | 96.72 134 | 92.66 73 | 97.22 217 | 96.03 211 | 87.69 175 | 95.12 88 | 94.03 198 | 81.55 159 | 98.28 148 | 89.17 154 | 96.46 116 | 99.14 98 |
|
HyFIR lowres test | | | 93.68 109 | 93.29 104 | 94.87 141 | 97.57 108 | 88.04 169 | 98.18 175 | 98.47 21 | 87.57 177 | 91.24 140 | 95.05 186 | 85.49 114 | 97.46 197 | 93.22 111 | 92.82 155 | 99.10 100 |
|
Anonymous202405211 | | | 88.84 190 | 87.03 203 | 94.27 159 | 98.14 94 | 84.18 240 | 98.44 153 | 95.58 244 | 76.79 292 | 89.34 166 | 96.88 156 | 53.42 305 | 99.54 86 | 87.53 169 | 87.12 203 | 99.09 101 |
|
baseline | | | 93.91 101 | 93.30 103 | 95.72 119 | 95.10 187 | 90.07 129 | 97.48 206 | 95.91 224 | 91.03 86 | 93.54 112 | 97.68 120 | 79.58 170 | 98.02 163 | 94.27 95 | 95.14 138 | 99.08 102 |
|
Vis-MVSNet (Re-imp) | | | 93.26 123 | 93.00 112 | 94.06 167 | 96.14 152 | 86.71 195 | 98.68 124 | 96.70 171 | 88.30 156 | 89.71 164 | 97.64 123 | 85.43 117 | 96.39 236 | 88.06 163 | 96.32 120 | 99.08 102 |
|
PatchmatchNet | | | 92.05 144 | 91.04 145 | 95.06 137 | 96.17 150 | 89.04 151 | 91.26 301 | 97.26 136 | 89.56 120 | 90.64 148 | 90.56 264 | 88.35 62 | 97.11 206 | 79.53 236 | 96.07 128 | 99.03 104 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PatchFormer-LS_test | | | 94.08 95 | 93.60 99 | 95.53 125 | 96.92 126 | 89.57 144 | 96.51 241 | 97.34 134 | 91.29 82 | 92.22 127 | 97.18 141 | 91.66 21 | 98.02 163 | 87.05 172 | 92.21 168 | 99.00 105 |
|
EPNet | | | 96.82 28 | 96.68 29 | 97.25 48 | 98.65 80 | 93.10 64 | 99.48 34 | 98.76 12 | 96.54 5 | 97.84 36 | 98.22 104 | 87.49 76 | 99.66 71 | 95.35 74 | 97.78 100 | 99.00 105 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
sss | | | 94.85 77 | 93.94 92 | 97.58 33 | 96.43 141 | 94.09 47 | 98.93 97 | 99.16 8 | 89.50 122 | 95.27 84 | 97.85 110 | 81.50 160 | 99.65 75 | 92.79 118 | 94.02 147 | 98.99 107 |
|
Patchmatch-RL test | | | 81.90 270 | 80.13 272 | 87.23 280 | 80.71 318 | 70.12 316 | 84.07 321 | 88.19 326 | 83.16 244 | 70.57 298 | 82.18 310 | 87.18 84 | 92.59 309 | 82.28 222 | 62.78 313 | 98.98 108 |
|
PVSNet | | 87.13 12 | 93.69 107 | 92.83 114 | 96.28 99 | 97.99 97 | 90.22 124 | 99.38 49 | 98.93 10 | 91.42 80 | 93.66 111 | 97.68 120 | 71.29 231 | 99.64 77 | 87.94 164 | 97.20 110 | 98.98 108 |
|
MVSFormer | | | 94.71 83 | 94.08 85 | 96.61 84 | 95.05 189 | 94.87 25 | 97.77 197 | 96.17 206 | 86.84 189 | 98.04 29 | 98.52 90 | 85.52 111 | 95.99 258 | 89.83 141 | 98.97 72 | 98.96 110 |
|
jason | | | 95.40 67 | 94.86 69 | 97.03 54 | 92.91 236 | 94.23 43 | 99.70 15 | 96.30 195 | 93.56 37 | 96.73 62 | 98.52 90 | 81.46 161 | 97.91 167 | 96.08 61 | 98.47 90 | 98.96 110 |
jason: jason. |
CostFormer | | | 92.89 128 | 92.48 121 | 94.12 165 | 94.99 191 | 85.89 216 | 92.89 291 | 97.00 163 | 86.98 186 | 95.00 90 | 90.78 251 | 90.05 40 | 97.51 196 | 92.92 116 | 91.73 177 | 98.96 110 |
|
MAR-MVS | | | 94.43 90 | 94.09 84 | 95.45 127 | 99.10 62 | 87.47 180 | 98.39 161 | 97.79 61 | 88.37 154 | 94.02 105 | 99.17 29 | 78.64 181 | 99.91 34 | 92.48 119 | 98.85 77 | 98.96 110 |
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 |
MDTV_nov1_ep13_2view | | | | | | | 91.17 99 | 91.38 299 | | 87.45 180 | 93.08 117 | | 86.67 94 | | 87.02 173 | | 98.95 114 |
|
CVMVSNet | | | 90.30 168 | 90.91 148 | 88.46 271 | 94.32 203 | 73.58 306 | 97.61 203 | 97.59 96 | 90.16 108 | 88.43 173 | 97.10 145 | 76.83 190 | 92.86 304 | 82.64 218 | 93.54 150 | 98.93 115 |
|
ab-mvs | | | 91.05 158 | 89.17 170 | 96.69 81 | 95.96 156 | 91.72 86 | 92.62 292 | 97.23 140 | 85.61 207 | 89.74 162 | 93.89 204 | 68.55 243 | 99.42 104 | 91.09 129 | 87.84 199 | 98.92 116 |
|
IS-MVSNet | | | 93.00 127 | 92.51 120 | 94.49 152 | 96.14 152 | 87.36 184 | 98.31 166 | 95.70 237 | 88.58 145 | 90.17 156 | 97.50 128 | 83.02 142 | 97.22 203 | 87.06 171 | 96.07 128 | 98.90 117 |
|
CPTT-MVS | | | 94.60 87 | 94.43 76 | 95.09 135 | 99.66 13 | 86.85 192 | 99.44 42 | 97.47 118 | 83.22 242 | 94.34 100 | 98.96 56 | 82.50 147 | 99.55 84 | 94.81 84 | 99.50 48 | 98.88 118 |
|
Vis-MVSNet | | | 92.64 132 | 91.85 133 | 95.03 139 | 95.12 183 | 88.23 165 | 98.48 150 | 96.81 168 | 91.61 73 | 92.16 128 | 97.22 138 | 71.58 229 | 98.00 166 | 85.85 188 | 97.81 97 | 98.88 118 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
casdiffmvs | | | 93.98 99 | 93.43 101 | 95.61 123 | 95.07 188 | 89.86 137 | 98.80 110 | 95.84 230 | 90.98 87 | 92.74 121 | 97.66 122 | 79.71 169 | 98.10 155 | 94.72 87 | 95.37 137 | 98.87 120 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.84 121 |
|
sam_mvs1 | | | | | | | | | | | | | 88.39 61 | | | | 98.84 121 |
|
SCA | | | 90.64 166 | 89.25 169 | 94.83 143 | 94.95 192 | 88.83 154 | 96.26 249 | 97.21 142 | 90.06 112 | 90.03 158 | 90.62 260 | 66.61 258 | 96.81 217 | 83.16 212 | 94.36 144 | 98.84 121 |
|
PMMVS | | | 93.62 112 | 93.90 94 | 92.79 191 | 96.79 132 | 81.40 267 | 98.85 105 | 96.81 168 | 91.25 84 | 96.82 59 | 98.15 108 | 77.02 189 | 98.13 153 | 93.15 113 | 96.30 122 | 98.83 124 |
|
EIA-MVS | | | 96.00 50 | 96.00 46 | 96.00 108 | 96.56 137 | 91.05 105 | 99.63 22 | 96.61 175 | 93.26 41 | 97.39 42 | 98.30 101 | 86.62 95 | 98.13 153 | 98.07 28 | 97.57 102 | 98.82 125 |
|
1112_ss | | | 92.71 130 | 91.55 140 | 96.20 100 | 95.56 167 | 91.12 100 | 98.48 150 | 94.69 272 | 88.29 157 | 86.89 186 | 98.50 92 | 87.02 87 | 98.66 139 | 84.75 195 | 89.77 194 | 98.81 126 |
|
Test_1112_low_res | | | 92.27 140 | 90.97 146 | 96.18 101 | 95.53 169 | 91.10 102 | 98.47 152 | 94.66 273 | 88.28 158 | 86.83 188 | 93.50 215 | 87.00 88 | 98.65 140 | 84.69 196 | 89.74 195 | 98.80 127 |
|
PatchT | | | 85.44 242 | 83.19 248 | 92.22 200 | 93.13 233 | 83.00 252 | 83.80 323 | 96.37 191 | 70.62 306 | 90.55 149 | 79.63 317 | 84.81 124 | 94.87 286 | 58.18 319 | 91.59 179 | 98.79 128 |
|
PVSNet_Blended | | | 95.94 54 | 95.66 56 | 96.75 75 | 98.77 77 | 91.61 89 | 99.88 1 | 98.04 39 | 93.64 35 | 94.21 101 | 97.76 115 | 83.50 133 | 99.87 43 | 97.41 36 | 97.75 101 | 98.79 128 |
|
DeepC-MVS | | 91.02 4 | 94.56 89 | 93.92 93 | 96.46 91 | 97.16 118 | 90.76 112 | 98.39 161 | 97.11 151 | 93.92 26 | 88.66 170 | 98.33 99 | 78.14 183 | 99.85 50 | 95.02 80 | 98.57 87 | 98.78 130 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
tpmrst | | | 92.78 129 | 92.16 127 | 94.65 149 | 96.27 146 | 87.45 181 | 91.83 296 | 97.10 154 | 89.10 132 | 94.68 94 | 90.69 255 | 88.22 63 | 97.73 185 | 89.78 143 | 91.80 175 | 98.77 131 |
|
原ACMM1 | | | | | 96.18 101 | 99.03 65 | 90.08 128 | | 97.63 88 | 88.98 134 | 97.00 48 | 98.97 53 | 88.14 66 | 99.71 67 | 88.23 161 | 99.62 39 | 98.76 132 |
|
tpm2 | | | 91.77 146 | 91.09 144 | 93.82 175 | 94.83 195 | 85.56 224 | 92.51 293 | 97.16 146 | 84.00 231 | 93.83 109 | 90.66 257 | 87.54 75 | 97.17 204 | 87.73 166 | 91.55 180 | 98.72 133 |
|
TAPA-MVS | | 87.50 9 | 90.35 167 | 89.05 172 | 94.25 161 | 98.48 86 | 85.17 228 | 98.42 155 | 96.58 181 | 82.44 256 | 87.24 182 | 98.53 89 | 82.77 145 | 98.84 130 | 59.09 317 | 97.88 96 | 98.72 133 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
EI-MVSNet-Vis-set | | | 95.76 62 | 95.63 60 | 96.17 103 | 99.14 59 | 90.33 120 | 98.49 149 | 97.82 54 | 91.92 68 | 94.75 92 | 98.88 65 | 87.06 86 | 99.48 99 | 95.40 73 | 97.17 111 | 98.70 135 |
|
diffmvs | | | 94.59 88 | 94.19 80 | 95.81 116 | 95.54 168 | 90.69 115 | 98.70 121 | 95.68 239 | 91.61 73 | 95.96 70 | 97.81 112 | 80.11 167 | 98.06 160 | 96.52 51 | 95.76 132 | 98.67 136 |
|
DP-MVS | | | 88.75 196 | 86.56 206 | 95.34 130 | 98.92 71 | 87.45 181 | 97.64 202 | 93.52 293 | 70.55 307 | 81.49 238 | 97.25 135 | 74.43 201 | 99.88 40 | 71.14 291 | 94.09 146 | 98.67 136 |
|
abl_6 | | | 94.63 86 | 94.48 74 | 95.09 135 | 98.61 82 | 86.96 191 | 98.06 187 | 96.97 164 | 89.31 125 | 95.86 75 | 98.56 88 | 79.82 168 | 99.64 77 | 94.53 92 | 98.65 86 | 98.66 138 |
|
TESTMET0.1,1 | | | 93.82 104 | 93.26 105 | 95.49 126 | 95.21 176 | 90.25 122 | 99.15 74 | 97.54 107 | 89.18 129 | 91.79 129 | 94.87 188 | 89.13 49 | 97.63 189 | 86.21 181 | 96.29 123 | 98.60 139 |
|
DI_MVS_plusplus_test | | | 89.41 183 | 87.24 200 | 95.92 112 | 89.06 283 | 90.75 114 | 98.18 175 | 96.63 173 | 89.29 126 | 70.54 299 | 90.31 270 | 63.50 273 | 98.40 143 | 92.25 122 | 95.44 136 | 98.60 139 |
|
dp | | | 90.16 172 | 88.83 177 | 94.14 164 | 96.38 143 | 86.42 199 | 91.57 298 | 97.06 157 | 84.76 223 | 88.81 169 | 90.19 277 | 84.29 128 | 97.43 199 | 75.05 269 | 91.35 184 | 98.56 141 |
|
EPP-MVSNet | | | 93.75 106 | 93.67 97 | 94.01 169 | 95.86 158 | 85.70 221 | 98.67 126 | 97.66 78 | 84.46 226 | 91.36 138 | 97.18 141 | 91.16 24 | 97.79 176 | 92.93 115 | 93.75 148 | 98.53 142 |
|
Fast-Effi-MVS+ | | | 91.72 147 | 90.79 153 | 94.49 152 | 95.89 157 | 87.40 183 | 99.54 30 | 95.70 237 | 85.01 219 | 89.28 167 | 95.68 178 | 77.75 185 | 97.57 195 | 83.22 211 | 95.06 139 | 98.51 143 |
|
CDS-MVSNet | | | 93.47 113 | 93.04 110 | 94.76 144 | 94.75 197 | 89.45 147 | 98.82 108 | 97.03 160 | 87.91 167 | 90.97 143 | 96.48 165 | 89.06 50 | 96.36 238 | 89.50 145 | 92.81 157 | 98.49 144 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
LCM-MVSNet-Re | | | 88.59 198 | 88.61 181 | 88.51 270 | 95.53 169 | 72.68 309 | 96.85 228 | 88.43 325 | 88.45 149 | 73.14 292 | 90.63 259 | 75.82 191 | 94.38 296 | 92.95 114 | 95.71 134 | 98.48 145 |
|
TAMVS | | | 92.62 133 | 92.09 130 | 94.20 162 | 94.10 205 | 87.68 175 | 98.41 157 | 96.97 164 | 87.53 179 | 89.74 162 | 96.04 174 | 84.77 125 | 96.49 230 | 88.97 156 | 92.31 165 | 98.42 146 |
|
CR-MVSNet | | | 88.83 192 | 87.38 197 | 93.16 184 | 93.47 224 | 86.24 205 | 84.97 317 | 94.20 284 | 88.92 139 | 90.76 146 | 86.88 301 | 84.43 126 | 94.82 288 | 70.64 292 | 92.17 170 | 98.41 147 |
|
RPMNet | | | 84.62 249 | 81.78 262 | 93.16 184 | 93.47 224 | 86.24 205 | 84.97 317 | 96.28 199 | 64.85 321 | 90.76 146 | 78.80 318 | 80.95 164 | 94.82 288 | 53.76 322 | 92.17 170 | 98.41 147 |
|
BH-RMVSNet | | | 91.25 155 | 89.99 161 | 95.03 139 | 96.75 133 | 88.55 161 | 98.65 128 | 94.95 265 | 87.74 172 | 87.74 176 | 97.80 113 | 68.27 245 | 98.14 152 | 80.53 234 | 97.49 106 | 98.41 147 |
|
UA-Net | | | 93.30 120 | 92.62 118 | 95.34 130 | 96.27 146 | 88.53 163 | 95.88 262 | 96.97 164 | 90.90 90 | 95.37 83 | 97.07 147 | 82.38 152 | 99.10 124 | 83.91 207 | 94.86 141 | 98.38 150 |
|
tpm | | | 89.67 179 | 88.95 174 | 91.82 207 | 92.54 239 | 81.43 266 | 92.95 290 | 95.92 220 | 87.81 169 | 90.50 151 | 89.44 284 | 84.99 120 | 95.65 268 | 83.67 210 | 82.71 235 | 98.38 150 |
|
MVS_111021_LR | | | 95.78 60 | 95.94 48 | 95.28 132 | 98.19 92 | 87.69 174 | 98.80 110 | 99.26 7 | 93.39 38 | 95.04 89 | 98.69 81 | 84.09 129 | 99.76 64 | 96.96 45 | 99.06 68 | 98.38 150 |
|
test-LLR | | | 93.11 126 | 92.68 116 | 94.40 155 | 94.94 193 | 87.27 187 | 99.15 74 | 97.25 137 | 90.21 103 | 91.57 132 | 94.04 196 | 84.89 122 | 97.58 192 | 85.94 184 | 96.13 124 | 98.36 153 |
|
test-mter | | | 93.27 122 | 92.89 113 | 94.40 155 | 94.94 193 | 87.27 187 | 99.15 74 | 97.25 137 | 88.95 136 | 91.57 132 | 94.04 196 | 88.03 68 | 97.58 192 | 85.94 184 | 96.13 124 | 98.36 153 |
|
IB-MVS | | 89.43 6 | 92.12 142 | 90.83 152 | 95.98 110 | 95.40 173 | 90.78 111 | 99.81 6 | 98.06 37 | 91.23 85 | 85.63 194 | 93.66 210 | 90.63 34 | 98.78 131 | 91.22 128 | 71.85 296 | 98.36 153 |
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 |
VDD-MVS | | | 91.24 156 | 90.18 160 | 94.45 154 | 97.08 121 | 85.84 219 | 98.40 160 | 96.10 209 | 86.99 184 | 93.36 113 | 98.16 107 | 54.27 302 | 99.20 116 | 96.59 49 | 90.63 190 | 98.31 156 |
|
PVSNet_Blended_VisFu | | | 94.67 84 | 94.11 83 | 96.34 98 | 97.14 119 | 91.10 102 | 99.32 58 | 97.43 125 | 92.10 67 | 91.53 135 | 96.38 170 | 83.29 139 | 99.68 69 | 93.42 109 | 96.37 119 | 98.25 157 |
|
thisisatest0515 | | | 94.75 79 | 94.19 80 | 96.43 93 | 96.13 155 | 92.64 77 | 99.47 35 | 97.60 92 | 87.55 178 | 93.17 115 | 97.59 125 | 94.71 8 | 98.42 142 | 88.28 160 | 93.20 151 | 98.24 158 |
|
EI-MVSNet-UG-set | | | 95.43 64 | 95.29 62 | 95.86 114 | 99.07 64 | 89.87 136 | 98.43 154 | 97.80 59 | 91.78 71 | 94.11 103 | 98.77 70 | 86.25 105 | 99.48 99 | 94.95 83 | 96.45 117 | 98.22 159 |
|
QAPM | | | 91.41 152 | 89.49 164 | 97.17 51 | 95.66 165 | 93.42 58 | 98.60 136 | 97.51 110 | 80.92 273 | 81.39 240 | 97.41 132 | 72.89 216 | 99.87 43 | 82.33 221 | 98.68 84 | 98.21 160 |
|
CHOSEN 280x420 | | | 96.80 29 | 96.85 22 | 96.66 83 | 97.85 98 | 94.42 40 | 94.76 274 | 98.36 23 | 92.50 54 | 95.62 80 | 97.52 127 | 97.92 1 | 97.38 200 | 98.31 24 | 98.80 81 | 98.20 161 |
|
TR-MVS | | | 90.77 162 | 89.44 165 | 94.76 144 | 96.31 145 | 88.02 170 | 97.92 191 | 95.96 215 | 85.52 208 | 88.22 174 | 97.23 137 | 66.80 257 | 98.09 156 | 84.58 197 | 92.38 163 | 98.17 162 |
|
GA-MVS | | | 90.10 173 | 88.69 179 | 94.33 157 | 92.44 240 | 87.97 171 | 99.08 82 | 96.26 200 | 89.65 117 | 86.92 185 | 93.11 222 | 68.09 246 | 96.96 211 | 82.54 220 | 90.15 192 | 98.05 163 |
|
OMC-MVS | | | 93.90 102 | 93.62 98 | 94.73 147 | 98.63 81 | 87.00 190 | 98.04 188 | 96.56 182 | 92.19 64 | 92.46 123 | 98.73 74 | 79.49 173 | 99.14 122 | 92.16 123 | 94.34 145 | 98.03 164 |
|
xiu_mvs_v2_base | | | 96.66 31 | 96.17 41 | 98.11 19 | 97.11 120 | 96.96 3 | 99.01 91 | 97.04 158 | 95.51 14 | 98.86 10 | 99.11 40 | 82.19 155 | 99.36 110 | 98.59 16 | 98.14 94 | 98.00 165 |
|
PS-MVSNAJ | | | 96.87 27 | 96.40 33 | 98.29 12 | 97.35 112 | 97.29 2 | 99.03 88 | 97.11 151 | 95.83 10 | 98.97 8 | 99.14 34 | 82.48 149 | 99.60 82 | 98.60 14 | 99.08 67 | 98.00 165 |
|
thisisatest0530 | | | 94.00 98 | 93.52 100 | 95.43 128 | 95.76 161 | 90.02 134 | 98.99 93 | 97.60 92 | 86.58 195 | 91.74 130 | 97.36 133 | 94.78 7 | 98.34 144 | 86.37 180 | 92.48 162 | 97.94 167 |
|
tpm cat1 | | | 88.89 188 | 87.27 199 | 93.76 176 | 95.79 159 | 85.32 225 | 90.76 305 | 97.09 155 | 76.14 294 | 85.72 193 | 88.59 290 | 82.92 143 | 98.04 162 | 76.96 255 | 91.43 181 | 97.90 168 |
|
tttt0517 | | | 93.30 120 | 93.01 111 | 94.17 163 | 95.57 166 | 86.47 198 | 98.51 146 | 97.60 92 | 85.99 203 | 90.55 149 | 97.19 140 | 94.80 6 | 98.31 145 | 85.06 192 | 91.86 173 | 97.74 169 |
|
ADS-MVSNet2 | | | 87.62 209 | 86.88 204 | 89.86 243 | 96.21 148 | 79.14 284 | 87.15 310 | 92.99 296 | 83.01 245 | 89.91 160 | 87.27 297 | 78.87 177 | 92.80 307 | 74.20 276 | 92.27 166 | 97.64 170 |
|
ADS-MVSNet | | | 88.99 186 | 87.30 198 | 94.07 166 | 96.21 148 | 87.56 178 | 87.15 310 | 96.78 170 | 83.01 245 | 89.91 160 | 87.27 297 | 78.87 177 | 97.01 210 | 74.20 276 | 92.27 166 | 97.64 170 |
|
BH-w/o | | | 92.32 138 | 91.79 135 | 93.91 172 | 96.85 128 | 86.18 208 | 99.11 81 | 95.74 234 | 88.13 161 | 84.81 198 | 97.00 150 | 77.26 188 | 97.91 167 | 89.16 155 | 98.03 95 | 97.64 170 |
|
LS3D | | | 90.19 171 | 88.72 178 | 94.59 151 | 98.97 67 | 86.33 204 | 96.90 227 | 96.60 176 | 74.96 297 | 84.06 206 | 98.74 73 | 75.78 192 | 99.83 53 | 74.93 270 | 97.57 102 | 97.62 173 |
|
VDDNet | | | 90.08 174 | 88.54 185 | 94.69 148 | 94.41 202 | 87.68 175 | 98.21 173 | 96.40 190 | 76.21 293 | 93.33 114 | 97.75 116 | 54.93 300 | 98.77 132 | 94.71 88 | 90.96 185 | 97.61 174 |
|
EPNet_dtu | | | 92.28 139 | 92.15 128 | 92.70 194 | 97.29 114 | 84.84 232 | 98.64 130 | 97.82 54 | 92.91 46 | 93.02 119 | 97.02 149 | 85.48 116 | 95.70 267 | 72.25 288 | 94.89 140 | 97.55 175 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
BH-untuned | | | 91.46 151 | 90.84 150 | 93.33 181 | 96.51 140 | 84.83 233 | 98.84 107 | 95.50 247 | 86.44 200 | 83.50 208 | 96.70 160 | 75.49 194 | 97.77 178 | 86.78 179 | 97.81 97 | 97.40 176 |
|
thres200 | | | 93.69 107 | 92.59 119 | 96.97 62 | 97.76 99 | 94.74 32 | 99.35 54 | 99.36 2 | 89.23 127 | 91.21 141 | 96.97 151 | 83.42 136 | 98.77 132 | 85.08 191 | 90.96 185 | 97.39 177 |
|
JIA-IIPM | | | 85.97 232 | 84.85 231 | 89.33 258 | 93.23 231 | 73.68 305 | 85.05 316 | 97.13 149 | 69.62 311 | 91.56 134 | 68.03 322 | 88.03 68 | 96.96 211 | 77.89 250 | 93.12 152 | 97.34 178 |
|
baseline1 | | | 92.61 134 | 91.28 142 | 96.58 86 | 97.05 123 | 94.63 35 | 97.72 199 | 96.20 203 | 89.82 114 | 88.56 171 | 96.85 157 | 86.85 90 | 97.82 174 | 88.42 158 | 80.10 246 | 97.30 179 |
|
PVSNet_0 | | 83.28 16 | 87.31 212 | 85.16 225 | 93.74 177 | 94.78 196 | 84.59 235 | 98.91 100 | 98.69 18 | 89.81 115 | 78.59 267 | 93.23 219 | 61.95 278 | 99.34 113 | 94.75 85 | 55.72 322 | 97.30 179 |
|
PLC | | 91.07 3 | 94.23 94 | 94.01 86 | 94.87 141 | 99.17 58 | 87.49 179 | 99.25 61 | 96.55 183 | 88.43 152 | 91.26 139 | 98.21 106 | 85.92 108 | 99.86 48 | 89.77 144 | 97.57 102 | 97.24 181 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
Anonymous20240529 | | | 87.66 208 | 85.58 220 | 93.92 171 | 97.59 107 | 85.01 231 | 98.13 179 | 97.13 149 | 66.69 319 | 88.47 172 | 96.01 175 | 55.09 299 | 99.51 91 | 87.00 174 | 84.12 222 | 97.23 182 |
|
thres100view900 | | | 93.34 119 | 92.15 128 | 96.90 66 | 97.62 104 | 94.84 27 | 99.06 85 | 99.36 2 | 87.96 165 | 90.47 152 | 96.78 158 | 83.29 139 | 98.75 134 | 84.11 204 | 90.69 187 | 97.12 183 |
|
tfpn200view9 | | | 93.43 115 | 92.27 124 | 96.90 66 | 97.68 102 | 94.84 27 | 99.18 65 | 99.36 2 | 88.45 149 | 90.79 144 | 96.90 154 | 83.31 137 | 98.75 134 | 84.11 204 | 90.69 187 | 97.12 183 |
|
tpmvs | | | 89.16 184 | 87.76 190 | 93.35 180 | 97.19 117 | 84.75 234 | 90.58 307 | 97.36 132 | 81.99 260 | 84.56 200 | 89.31 287 | 83.98 130 | 98.17 151 | 74.85 272 | 90.00 193 | 97.12 183 |
|
PCF-MVS | | 89.78 5 | 91.26 153 | 89.63 163 | 96.16 104 | 95.44 171 | 91.58 91 | 95.29 270 | 96.10 209 | 85.07 217 | 82.75 216 | 97.45 130 | 78.28 182 | 99.78 62 | 80.60 233 | 95.65 135 | 97.12 183 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MIMVSNet | | | 84.48 253 | 81.83 261 | 92.42 198 | 91.73 252 | 87.36 184 | 85.52 313 | 94.42 279 | 81.40 267 | 81.91 233 | 87.58 294 | 51.92 308 | 92.81 306 | 73.84 279 | 88.15 198 | 97.08 187 |
|
CANet_DTU | | | 94.31 93 | 93.35 102 | 97.20 50 | 97.03 124 | 94.71 33 | 98.62 132 | 95.54 245 | 95.61 13 | 97.21 44 | 98.47 96 | 71.88 224 | 99.84 51 | 88.38 159 | 97.46 107 | 97.04 188 |
|
PatchMatch-RL | | | 91.47 150 | 90.54 157 | 94.26 160 | 98.20 90 | 86.36 203 | 96.94 225 | 97.14 147 | 87.75 171 | 88.98 168 | 95.75 177 | 71.80 226 | 99.40 107 | 80.92 230 | 97.39 108 | 97.02 189 |
|
UGNet | | | 91.91 145 | 90.85 149 | 95.10 134 | 97.06 122 | 88.69 159 | 98.01 189 | 98.24 27 | 92.41 60 | 92.39 125 | 93.61 211 | 60.52 283 | 99.68 69 | 88.14 162 | 97.25 109 | 96.92 190 |
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 |
mvs-test1 | | | 91.57 148 | 92.20 126 | 89.70 248 | 95.15 181 | 74.34 302 | 99.51 32 | 95.40 253 | 91.92 68 | 91.02 142 | 97.25 135 | 74.27 203 | 98.08 159 | 89.45 146 | 95.83 131 | 96.67 191 |
|
thres600view7 | | | 93.18 124 | 92.00 131 | 96.75 75 | 97.62 104 | 94.92 24 | 99.07 83 | 99.36 2 | 87.96 165 | 90.47 152 | 96.78 158 | 83.29 139 | 98.71 138 | 82.93 216 | 90.47 191 | 96.61 192 |
|
thres400 | | | 93.39 117 | 92.27 124 | 96.73 77 | 97.68 102 | 94.84 27 | 99.18 65 | 99.36 2 | 88.45 149 | 90.79 144 | 96.90 154 | 83.31 137 | 98.75 134 | 84.11 204 | 90.69 187 | 96.61 192 |
|
xiu_mvs_v1_base_debu | | | 94.73 80 | 93.98 87 | 96.99 58 | 95.19 177 | 95.24 18 | 98.62 132 | 96.50 185 | 92.99 43 | 97.52 39 | 98.83 67 | 72.37 219 | 99.15 119 | 97.03 40 | 96.74 113 | 96.58 194 |
|
xiu_mvs_v1_base | | | 94.73 80 | 93.98 87 | 96.99 58 | 95.19 177 | 95.24 18 | 98.62 132 | 96.50 185 | 92.99 43 | 97.52 39 | 98.83 67 | 72.37 219 | 99.15 119 | 97.03 40 | 96.74 113 | 96.58 194 |
|
xiu_mvs_v1_base_debi | | | 94.73 80 | 93.98 87 | 96.99 58 | 95.19 177 | 95.24 18 | 98.62 132 | 96.50 185 | 92.99 43 | 97.52 39 | 98.83 67 | 72.37 219 | 99.15 119 | 97.03 40 | 96.74 113 | 96.58 194 |
|
F-COLMAP | | | 92.07 143 | 91.75 137 | 93.02 187 | 98.16 93 | 82.89 256 | 98.79 114 | 95.97 213 | 86.54 197 | 87.92 175 | 97.80 113 | 78.69 180 | 99.65 75 | 85.97 183 | 95.93 130 | 96.53 197 |
|
MSDG | | | 88.29 202 | 86.37 208 | 94.04 168 | 96.90 127 | 86.15 209 | 96.52 240 | 94.36 281 | 77.89 289 | 79.22 260 | 96.95 152 | 69.72 237 | 99.59 83 | 73.20 284 | 92.58 161 | 96.37 198 |
|
UniMVSNet_ETH3D | | | 85.65 241 | 83.79 246 | 91.21 216 | 90.41 267 | 80.75 278 | 95.36 269 | 95.78 231 | 78.76 283 | 81.83 237 | 94.33 194 | 49.86 313 | 96.66 221 | 84.30 199 | 83.52 229 | 96.22 199 |
|
OpenMVS | | 85.28 14 | 90.75 163 | 88.84 176 | 96.48 90 | 93.58 222 | 93.51 56 | 98.80 110 | 97.41 127 | 82.59 251 | 78.62 265 | 97.49 129 | 68.00 248 | 99.82 56 | 84.52 198 | 98.55 88 | 96.11 200 |
|
baseline2 | | | 94.04 97 | 93.80 96 | 94.74 146 | 93.07 234 | 90.25 122 | 98.12 181 | 98.16 33 | 89.86 113 | 86.53 190 | 96.95 152 | 95.56 5 | 98.05 161 | 91.44 127 | 94.53 142 | 95.93 201 |
|
DSMNet-mixed | | | 81.60 271 | 81.43 266 | 82.10 302 | 84.36 311 | 60.79 323 | 93.63 286 | 86.74 327 | 79.00 279 | 79.32 259 | 87.15 299 | 63.87 271 | 89.78 320 | 66.89 301 | 91.92 172 | 95.73 202 |
|
cascas | | | 90.93 160 | 89.33 168 | 95.76 118 | 95.69 163 | 93.03 67 | 98.99 93 | 96.59 178 | 80.49 275 | 86.79 189 | 94.45 193 | 65.23 266 | 98.60 141 | 93.52 106 | 92.18 169 | 95.66 203 |
|
XVG-OURS-SEG-HR | | | 90.95 159 | 90.66 156 | 91.83 206 | 95.18 180 | 81.14 274 | 95.92 259 | 95.92 220 | 88.40 153 | 90.33 155 | 97.85 110 | 70.66 234 | 99.38 108 | 92.83 117 | 88.83 196 | 94.98 204 |
|
XVG-OURS | | | 90.83 161 | 90.49 158 | 91.86 205 | 95.23 175 | 81.25 271 | 95.79 267 | 95.92 220 | 88.96 135 | 90.02 159 | 98.03 109 | 71.60 228 | 99.35 112 | 91.06 130 | 87.78 200 | 94.98 204 |
|
Effi-MVS+-dtu | | | 89.97 176 | 90.68 155 | 87.81 275 | 95.15 181 | 71.98 311 | 97.87 195 | 95.40 253 | 91.92 68 | 87.57 177 | 91.44 242 | 74.27 203 | 96.84 215 | 89.45 146 | 93.10 153 | 94.60 206 |
|
Fast-Effi-MVS+-dtu | | | 88.84 190 | 88.59 183 | 89.58 252 | 93.44 227 | 78.18 291 | 98.65 128 | 94.62 274 | 88.46 148 | 84.12 205 | 95.37 184 | 68.91 240 | 96.52 228 | 82.06 224 | 91.70 178 | 94.06 207 |
|
test0.0.03 1 | | | 88.96 187 | 88.61 181 | 90.03 241 | 91.09 259 | 84.43 237 | 98.97 95 | 97.02 161 | 90.21 103 | 80.29 246 | 96.31 171 | 84.89 122 | 91.93 317 | 72.98 285 | 85.70 213 | 93.73 208 |
|
MVS-HIRNet | | | 79.01 281 | 75.13 288 | 90.66 227 | 93.82 218 | 81.69 265 | 85.16 314 | 93.75 289 | 54.54 323 | 74.17 288 | 59.15 327 | 57.46 290 | 96.58 224 | 63.74 307 | 94.38 143 | 93.72 209 |
|
AllTest | | | 84.97 245 | 83.12 249 | 90.52 230 | 96.82 129 | 78.84 286 | 95.89 260 | 92.17 305 | 77.96 287 | 75.94 279 | 95.50 180 | 55.48 296 | 99.18 117 | 71.15 289 | 87.14 201 | 93.55 210 |
|
TestCases | | | | | 90.52 230 | 96.82 129 | 78.84 286 | | 92.17 305 | 77.96 287 | 75.94 279 | 95.50 180 | 55.48 296 | 99.18 117 | 71.15 289 | 87.14 201 | 93.55 210 |
|
RPSCF | | | 85.33 243 | 85.55 221 | 84.67 295 | 94.63 199 | 62.28 322 | 93.73 284 | 93.76 288 | 74.38 300 | 85.23 197 | 97.06 148 | 64.09 269 | 98.31 145 | 80.98 228 | 86.08 210 | 93.41 212 |
|
HQP4-MVS | | | | | | | | | | | 87.57 177 | | | 97.77 178 | | | 92.72 213 |
|
HQP-MVS | | | 91.50 149 | 91.23 143 | 92.29 199 | 93.95 209 | 86.39 201 | 99.16 68 | 96.37 191 | 93.92 26 | 87.57 177 | 96.67 161 | 73.34 210 | 97.77 178 | 93.82 102 | 86.29 205 | 92.72 213 |
|
HQP_MVS | | | 91.26 153 | 90.95 147 | 92.16 201 | 93.84 216 | 86.07 212 | 99.02 89 | 96.30 195 | 93.38 39 | 86.99 183 | 96.52 163 | 72.92 214 | 97.75 183 | 93.46 107 | 86.17 208 | 92.67 215 |
|
plane_prior5 | | | | | | | | | 96.30 195 | | | | | 97.75 183 | 93.46 107 | 86.17 208 | 92.67 215 |
|
nrg030 | | | 90.23 169 | 88.87 175 | 94.32 158 | 91.53 254 | 93.54 55 | 98.79 114 | 95.89 227 | 88.12 162 | 84.55 201 | 94.61 192 | 78.80 179 | 96.88 214 | 92.35 121 | 75.21 266 | 92.53 217 |
|
CLD-MVS | | | 91.06 157 | 90.71 154 | 92.10 202 | 94.05 208 | 86.10 210 | 99.55 29 | 96.29 198 | 94.16 22 | 84.70 199 | 97.17 143 | 69.62 238 | 97.82 174 | 94.74 86 | 86.08 210 | 92.39 218 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
VPNet | | | 88.30 201 | 86.57 205 | 93.49 178 | 91.95 247 | 91.35 94 | 98.18 175 | 97.20 143 | 88.61 144 | 84.52 202 | 94.89 187 | 62.21 277 | 96.76 220 | 89.34 150 | 72.26 293 | 92.36 219 |
|
DU-MVS | | | 88.83 192 | 87.51 194 | 92.79 191 | 91.46 255 | 90.07 129 | 98.71 117 | 97.62 90 | 88.87 140 | 83.21 211 | 93.68 208 | 74.63 196 | 95.93 262 | 86.95 175 | 72.47 290 | 92.36 219 |
|
NR-MVSNet | | | 87.74 207 | 86.00 214 | 92.96 188 | 91.46 255 | 90.68 116 | 96.65 237 | 97.42 126 | 88.02 164 | 73.42 290 | 93.68 208 | 77.31 187 | 95.83 265 | 84.26 200 | 71.82 297 | 92.36 219 |
|
FIs | | | 90.70 164 | 89.87 162 | 93.18 183 | 92.29 241 | 91.12 100 | 98.17 178 | 98.25 26 | 89.11 131 | 83.44 209 | 94.82 189 | 82.26 153 | 96.17 252 | 87.76 165 | 82.76 234 | 92.25 222 |
|
UniMVSNet_NR-MVSNet | | | 89.60 180 | 88.55 184 | 92.75 193 | 92.17 244 | 90.07 129 | 98.74 116 | 98.15 34 | 88.37 154 | 83.21 211 | 93.98 201 | 82.86 144 | 95.93 262 | 86.95 175 | 72.47 290 | 92.25 222 |
|
VPA-MVSNet | | | 89.10 185 | 87.66 193 | 93.45 179 | 92.56 238 | 91.02 106 | 97.97 190 | 98.32 24 | 86.92 188 | 86.03 192 | 92.01 234 | 68.84 242 | 97.10 208 | 90.92 132 | 75.34 265 | 92.23 224 |
|
TranMVSNet+NR-MVSNet | | | 87.75 205 | 86.31 209 | 92.07 203 | 90.81 262 | 88.56 160 | 98.33 163 | 97.18 144 | 87.76 170 | 81.87 235 | 93.90 203 | 72.45 218 | 95.43 273 | 83.13 214 | 71.30 300 | 92.23 224 |
|
FC-MVSNet-test | | | 90.22 170 | 89.40 166 | 92.67 196 | 91.78 251 | 89.86 137 | 97.89 192 | 98.22 28 | 88.81 141 | 82.96 215 | 94.66 191 | 81.90 157 | 95.96 260 | 85.89 186 | 82.52 237 | 92.20 226 |
|
PS-MVSNAJss | | | 89.54 181 | 89.05 172 | 91.00 220 | 88.77 287 | 84.36 238 | 97.39 207 | 95.97 213 | 88.47 146 | 81.88 234 | 93.80 206 | 82.48 149 | 96.50 229 | 89.34 150 | 83.34 231 | 92.15 227 |
|
testgi | | | 82.29 266 | 81.00 270 | 86.17 286 | 87.24 303 | 74.84 301 | 97.39 207 | 91.62 313 | 88.63 143 | 75.85 281 | 95.42 183 | 46.07 318 | 91.55 318 | 66.87 302 | 79.94 247 | 92.12 228 |
|
WR-MVS | | | 88.54 199 | 87.22 201 | 92.52 197 | 91.93 249 | 89.50 145 | 98.56 141 | 97.84 52 | 86.99 184 | 81.87 235 | 93.81 205 | 74.25 205 | 95.92 264 | 85.29 189 | 74.43 272 | 92.12 228 |
|
MVSTER | | | 92.71 130 | 92.32 122 | 93.86 173 | 97.29 114 | 92.95 71 | 99.01 91 | 96.59 178 | 90.09 109 | 85.51 195 | 94.00 200 | 94.61 11 | 96.56 225 | 90.77 136 | 83.03 232 | 92.08 230 |
|
ACMM | | 86.95 13 | 88.77 195 | 88.22 189 | 90.43 232 | 93.61 221 | 81.34 269 | 98.50 147 | 95.92 220 | 87.88 168 | 83.85 207 | 95.20 185 | 67.20 254 | 97.89 169 | 86.90 177 | 84.90 216 | 92.06 231 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XXY-MVS | | | 87.75 205 | 86.02 213 | 92.95 189 | 90.46 266 | 89.70 141 | 97.71 200 | 95.90 225 | 84.02 230 | 80.95 241 | 94.05 195 | 67.51 252 | 97.10 208 | 85.16 190 | 78.41 253 | 92.04 232 |
|
FMVSNet3 | | | 88.81 194 | 87.08 202 | 93.99 170 | 96.52 139 | 94.59 36 | 98.08 185 | 96.20 203 | 85.85 204 | 82.12 228 | 91.60 241 | 74.05 206 | 95.40 275 | 79.04 240 | 80.24 243 | 91.99 233 |
|
FMVSNet2 | | | 86.90 216 | 84.79 233 | 93.24 182 | 95.11 184 | 92.54 79 | 97.67 201 | 95.86 229 | 82.94 247 | 80.55 243 | 91.17 245 | 62.89 274 | 95.29 278 | 77.23 252 | 79.71 250 | 91.90 234 |
|
UniMVSNet (Re) | | | 89.50 182 | 88.32 187 | 93.03 186 | 92.21 243 | 90.96 108 | 98.90 101 | 98.39 22 | 89.13 130 | 83.22 210 | 92.03 232 | 81.69 158 | 96.34 244 | 86.79 178 | 72.53 289 | 91.81 235 |
|
testing_2 | | | 80.92 273 | 77.24 281 | 91.98 204 | 78.88 322 | 87.83 172 | 93.96 282 | 95.72 235 | 84.27 229 | 56.20 320 | 80.42 314 | 38.64 326 | 96.40 235 | 87.20 170 | 79.85 248 | 91.72 236 |
|
EU-MVSNet | | | 84.19 257 | 84.42 240 | 83.52 298 | 88.64 290 | 67.37 319 | 96.04 258 | 95.76 233 | 85.29 212 | 78.44 268 | 93.18 220 | 70.67 233 | 91.48 319 | 75.79 266 | 75.98 262 | 91.70 237 |
|
EI-MVSNet | | | 89.87 177 | 89.38 167 | 91.36 215 | 94.32 203 | 85.87 217 | 97.61 203 | 96.59 178 | 85.10 215 | 85.51 195 | 97.10 145 | 81.30 163 | 96.56 225 | 83.85 209 | 83.03 232 | 91.64 238 |
|
IterMVS-LS | | | 88.34 200 | 87.44 195 | 91.04 219 | 94.10 205 | 85.85 218 | 98.10 183 | 95.48 248 | 85.12 214 | 82.03 232 | 91.21 244 | 81.35 162 | 95.63 269 | 83.86 208 | 75.73 264 | 91.63 239 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
GBi-Net | | | 86.67 221 | 84.96 227 | 91.80 208 | 95.11 184 | 88.81 155 | 96.77 230 | 95.25 258 | 82.94 247 | 82.12 228 | 90.25 272 | 62.89 274 | 94.97 283 | 79.04 240 | 80.24 243 | 91.62 240 |
|
test1 | | | 86.67 221 | 84.96 227 | 91.80 208 | 95.11 184 | 88.81 155 | 96.77 230 | 95.25 258 | 82.94 247 | 82.12 228 | 90.25 272 | 62.89 274 | 94.97 283 | 79.04 240 | 80.24 243 | 91.62 240 |
|
FMVSNet1 | | | 83.94 261 | 81.32 268 | 91.80 208 | 91.94 248 | 88.81 155 | 96.77 230 | 95.25 258 | 77.98 285 | 78.25 270 | 90.25 272 | 50.37 312 | 94.97 283 | 73.27 283 | 77.81 257 | 91.62 240 |
|
Anonymous20231211 | | | 84.72 247 | 82.65 258 | 90.91 222 | 97.71 101 | 84.55 236 | 97.28 212 | 96.67 172 | 66.88 318 | 79.18 261 | 90.87 250 | 58.47 287 | 96.60 223 | 82.61 219 | 74.20 276 | 91.59 243 |
|
jajsoiax | | | 87.35 211 | 86.51 207 | 89.87 242 | 87.75 301 | 81.74 264 | 97.03 223 | 95.98 212 | 88.47 146 | 80.15 248 | 93.80 206 | 61.47 279 | 96.36 238 | 89.44 148 | 84.47 220 | 91.50 244 |
|
ACMP | | 87.39 10 | 88.71 197 | 88.24 188 | 90.12 238 | 93.91 214 | 81.06 275 | 98.50 147 | 95.67 240 | 89.43 123 | 80.37 245 | 95.55 179 | 65.67 263 | 97.83 173 | 90.55 137 | 84.51 218 | 91.47 245 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LPG-MVS_test | | | 88.86 189 | 88.47 186 | 90.06 239 | 93.35 229 | 80.95 276 | 98.22 171 | 95.94 217 | 87.73 173 | 83.17 213 | 96.11 172 | 66.28 261 | 97.77 178 | 90.19 139 | 85.19 214 | 91.46 246 |
|
LGP-MVS_train | | | | | 90.06 239 | 93.35 229 | 80.95 276 | | 95.94 217 | 87.73 173 | 83.17 213 | 96.11 172 | 66.28 261 | 97.77 178 | 90.19 139 | 85.19 214 | 91.46 246 |
|
mvs_tets | | | 87.09 214 | 86.22 210 | 89.71 247 | 87.87 297 | 81.39 268 | 96.73 235 | 95.90 225 | 88.19 160 | 79.99 250 | 93.61 211 | 59.96 285 | 96.31 246 | 89.40 149 | 84.34 221 | 91.43 248 |
|
CP-MVSNet | | | 86.54 224 | 85.45 223 | 89.79 246 | 91.02 261 | 82.78 259 | 97.38 209 | 97.56 103 | 85.37 211 | 79.53 257 | 93.03 223 | 71.86 225 | 95.25 279 | 79.92 235 | 73.43 284 | 91.34 249 |
|
test_djsdf | | | 88.26 203 | 87.73 191 | 89.84 244 | 88.05 296 | 82.21 261 | 97.77 197 | 96.17 206 | 86.84 189 | 82.41 224 | 91.95 237 | 72.07 222 | 95.99 258 | 89.83 141 | 84.50 219 | 91.32 250 |
|
v2v482 | | | 87.27 213 | 85.76 217 | 91.78 212 | 89.59 275 | 87.58 177 | 98.56 141 | 95.54 245 | 84.53 225 | 82.51 221 | 91.78 238 | 73.11 213 | 96.47 231 | 82.07 223 | 74.14 278 | 91.30 251 |
|
OPM-MVS | | | 89.76 178 | 89.15 171 | 91.57 213 | 90.53 265 | 85.58 223 | 98.11 182 | 95.93 219 | 92.88 48 | 86.05 191 | 96.47 166 | 67.06 256 | 97.87 171 | 89.29 153 | 86.08 210 | 91.26 252 |
|
PS-CasMVS | | | 85.81 236 | 84.58 237 | 89.49 256 | 90.77 263 | 82.11 262 | 97.20 218 | 97.36 132 | 84.83 222 | 79.12 262 | 92.84 226 | 67.42 253 | 95.16 281 | 78.39 247 | 73.25 285 | 91.21 253 |
|
pmmvs5 | | | 85.87 233 | 84.40 241 | 90.30 235 | 88.53 291 | 84.23 239 | 98.60 136 | 93.71 290 | 81.53 266 | 80.29 246 | 92.02 233 | 64.51 268 | 95.52 271 | 82.04 225 | 78.34 254 | 91.15 254 |
|
miper_lstm_enhance | | | 86.90 216 | 86.20 211 | 89.00 264 | 94.53 200 | 81.19 272 | 96.74 234 | 95.24 261 | 82.33 257 | 80.15 248 | 90.51 267 | 81.99 156 | 94.68 293 | 80.71 232 | 73.58 280 | 91.12 255 |
|
COLMAP_ROB | | 82.69 18 | 84.54 252 | 82.82 251 | 89.70 248 | 96.72 134 | 78.85 285 | 95.89 260 | 92.83 299 | 71.55 305 | 77.54 274 | 95.89 176 | 59.40 286 | 99.14 122 | 67.26 299 | 88.26 197 | 91.11 256 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PEN-MVS | | | 85.21 244 | 83.93 245 | 89.07 263 | 89.89 272 | 81.31 270 | 97.09 221 | 97.24 139 | 84.45 227 | 78.66 264 | 92.68 228 | 68.44 244 | 94.87 286 | 75.98 264 | 70.92 301 | 91.04 257 |
|
ACMH | | 83.09 17 | 84.60 250 | 82.61 259 | 90.57 228 | 93.18 232 | 82.94 253 | 96.27 247 | 94.92 266 | 81.01 271 | 72.61 297 | 93.61 211 | 56.54 292 | 97.79 176 | 74.31 275 | 81.07 242 | 90.99 258 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OurMVSNet-221017-0 | | | 84.13 259 | 83.59 247 | 85.77 289 | 87.81 298 | 70.24 314 | 94.89 273 | 93.65 292 | 86.08 202 | 76.53 275 | 93.28 218 | 61.41 280 | 96.14 254 | 80.95 229 | 77.69 258 | 90.93 259 |
|
XVG-ACMP-BASELINE | | | 85.86 234 | 84.95 229 | 88.57 268 | 89.90 271 | 77.12 296 | 94.30 277 | 95.60 243 | 87.40 181 | 82.12 228 | 92.99 225 | 53.42 305 | 97.66 187 | 85.02 193 | 83.83 224 | 90.92 260 |
|
Patchmtry | | | 83.61 264 | 81.64 263 | 89.50 254 | 93.36 228 | 82.84 258 | 84.10 320 | 94.20 284 | 69.47 312 | 79.57 256 | 86.88 301 | 84.43 126 | 94.78 290 | 68.48 297 | 74.30 274 | 90.88 261 |
|
IterMVS | | | 85.81 236 | 84.67 235 | 89.22 259 | 93.51 223 | 83.67 246 | 96.32 246 | 94.80 268 | 85.09 216 | 78.69 263 | 90.17 278 | 66.57 260 | 93.17 303 | 79.48 238 | 77.42 259 | 90.81 262 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1921920 | | | 86.02 231 | 84.44 239 | 90.77 225 | 89.32 281 | 85.20 226 | 98.10 183 | 95.35 256 | 82.19 258 | 82.25 226 | 90.71 253 | 70.73 232 | 96.30 249 | 76.85 257 | 74.49 271 | 90.80 263 |
|
v144192 | | | 86.40 226 | 84.89 230 | 90.91 222 | 89.48 279 | 85.59 222 | 98.21 173 | 95.43 252 | 82.45 255 | 82.62 219 | 90.58 263 | 72.79 217 | 96.36 238 | 78.45 246 | 74.04 279 | 90.79 264 |
|
v1192 | | | 86.32 228 | 84.71 234 | 91.17 217 | 89.53 278 | 86.40 200 | 98.13 179 | 95.44 251 | 82.52 254 | 82.42 223 | 90.62 260 | 71.58 229 | 96.33 245 | 77.23 252 | 74.88 267 | 90.79 264 |
|
IterMVS-SCA-FT | | | 85.73 239 | 84.64 236 | 89.00 264 | 93.46 226 | 82.90 255 | 96.27 247 | 94.70 271 | 85.02 218 | 78.62 265 | 90.35 269 | 66.61 258 | 93.33 302 | 79.38 239 | 77.36 260 | 90.76 266 |
|
SixPastTwentyTwo | | | 82.63 265 | 81.58 264 | 85.79 288 | 88.12 295 | 71.01 313 | 95.17 271 | 92.54 301 | 84.33 228 | 72.93 295 | 92.08 231 | 60.41 284 | 95.61 270 | 74.47 274 | 74.15 277 | 90.75 267 |
|
MVS_0304 | | | 84.13 259 | 82.66 257 | 88.52 269 | 93.07 234 | 80.15 279 | 95.81 266 | 98.21 29 | 79.27 278 | 86.85 187 | 86.40 304 | 41.33 323 | 94.69 292 | 76.36 261 | 86.69 204 | 90.73 268 |
|
v1240 | | | 85.77 238 | 84.11 242 | 90.73 226 | 89.26 282 | 85.15 229 | 97.88 194 | 95.23 263 | 81.89 264 | 82.16 227 | 90.55 265 | 69.60 239 | 96.31 246 | 75.59 267 | 74.87 268 | 90.72 269 |
|
v148 | | | 86.38 227 | 85.06 226 | 90.37 234 | 89.47 280 | 84.10 241 | 98.52 143 | 95.48 248 | 83.80 233 | 80.93 242 | 90.22 275 | 74.60 198 | 96.31 246 | 80.92 230 | 71.55 298 | 90.69 270 |
|
K. test v3 | | | 81.04 272 | 79.77 274 | 84.83 293 | 87.41 302 | 70.23 315 | 95.60 268 | 93.93 287 | 83.70 236 | 67.51 309 | 89.35 286 | 55.76 294 | 93.58 301 | 76.67 259 | 68.03 306 | 90.67 271 |
|
v1144 | | | 86.83 218 | 85.31 224 | 91.40 214 | 89.75 273 | 87.21 189 | 98.31 166 | 95.45 250 | 83.22 242 | 82.70 218 | 90.78 251 | 73.36 209 | 96.36 238 | 79.49 237 | 74.69 270 | 90.63 272 |
|
ACMH+ | | 83.78 15 | 84.21 256 | 82.56 260 | 89.15 261 | 93.73 220 | 79.16 283 | 96.43 242 | 94.28 282 | 81.09 270 | 74.00 289 | 94.03 198 | 54.58 301 | 97.67 186 | 76.10 263 | 78.81 252 | 90.63 272 |
|
lessismore_v0 | | | | | 85.08 291 | 85.59 308 | 69.28 317 | | 90.56 319 | | 67.68 308 | 90.21 276 | 54.21 303 | 95.46 272 | 73.88 278 | 62.64 314 | 90.50 274 |
|
pmmvs4 | | | 87.58 210 | 86.17 212 | 91.80 208 | 89.58 276 | 88.92 153 | 97.25 214 | 95.28 257 | 82.54 253 | 80.49 244 | 93.17 221 | 75.62 193 | 96.05 257 | 82.75 217 | 78.90 251 | 90.42 275 |
|
WR-MVS_H | | | 86.53 225 | 85.49 222 | 89.66 251 | 91.04 260 | 83.31 250 | 97.53 205 | 98.20 30 | 84.95 220 | 79.64 254 | 90.90 249 | 78.01 184 | 95.33 276 | 76.29 262 | 72.81 286 | 90.35 276 |
|
V42 | | | 87.00 215 | 85.68 219 | 90.98 221 | 89.91 270 | 86.08 211 | 98.32 165 | 95.61 242 | 83.67 237 | 82.72 217 | 90.67 256 | 74.00 207 | 96.53 227 | 81.94 226 | 74.28 275 | 90.32 277 |
|
DTE-MVSNet | | | 84.14 258 | 82.80 252 | 88.14 272 | 88.95 286 | 79.87 282 | 96.81 229 | 96.24 201 | 83.50 239 | 77.60 273 | 92.52 230 | 67.89 250 | 94.24 298 | 72.64 287 | 69.05 304 | 90.32 277 |
|
YYNet1 | | | 79.64 280 | 77.04 283 | 87.43 279 | 87.80 299 | 79.98 281 | 96.23 251 | 94.44 277 | 73.83 302 | 51.83 321 | 87.53 295 | 67.96 249 | 92.07 316 | 66.00 304 | 67.75 308 | 90.23 279 |
|
MDA-MVSNet_test_wron | | | 79.65 279 | 77.05 282 | 87.45 278 | 87.79 300 | 80.13 280 | 96.25 250 | 94.44 277 | 73.87 301 | 51.80 322 | 87.47 296 | 68.04 247 | 92.12 315 | 66.02 303 | 67.79 307 | 90.09 280 |
|
MDA-MVSNet-bldmvs | | | 77.82 288 | 74.75 290 | 87.03 281 | 88.33 292 | 78.52 289 | 96.34 245 | 92.85 298 | 75.57 295 | 48.87 324 | 87.89 292 | 57.32 291 | 92.49 311 | 60.79 313 | 64.80 312 | 90.08 281 |
|
our_test_3 | | | 84.47 254 | 82.80 252 | 89.50 254 | 89.01 284 | 83.90 244 | 97.03 223 | 94.56 275 | 81.33 268 | 75.36 284 | 90.52 266 | 71.69 227 | 94.54 295 | 68.81 295 | 76.84 261 | 90.07 282 |
|
v7n | | | 84.42 255 | 82.75 255 | 89.43 257 | 88.15 294 | 81.86 263 | 96.75 233 | 95.67 240 | 80.53 274 | 78.38 269 | 89.43 285 | 69.89 235 | 96.35 243 | 73.83 280 | 72.13 294 | 90.07 282 |
|
v8 | | | 86.11 230 | 84.45 238 | 91.10 218 | 89.99 269 | 86.85 192 | 97.24 215 | 95.36 255 | 81.99 260 | 79.89 252 | 89.86 280 | 74.53 200 | 96.39 236 | 78.83 244 | 72.32 292 | 90.05 284 |
|
PVSNet_BlendedMVS | | | 93.36 118 | 93.20 106 | 93.84 174 | 98.77 77 | 91.61 89 | 99.47 35 | 98.04 39 | 91.44 77 | 94.21 101 | 92.63 229 | 83.50 133 | 99.87 43 | 97.41 36 | 83.37 230 | 90.05 284 |
|
ITE_SJBPF | | | | | 87.93 273 | 92.26 242 | 76.44 297 | | 93.47 294 | 87.67 176 | 79.95 251 | 95.49 182 | 56.50 293 | 97.38 200 | 75.24 268 | 82.33 238 | 89.98 286 |
|
pm-mvs1 | | | 84.68 248 | 82.78 254 | 90.40 233 | 89.58 276 | 85.18 227 | 97.31 210 | 94.73 270 | 81.93 263 | 76.05 278 | 92.01 234 | 65.48 265 | 96.11 255 | 78.75 245 | 69.14 303 | 89.91 287 |
|
LTVRE_ROB | | 81.71 19 | 84.59 251 | 82.72 256 | 90.18 236 | 92.89 237 | 83.18 251 | 93.15 289 | 94.74 269 | 78.99 280 | 75.14 285 | 92.69 227 | 65.64 264 | 97.63 189 | 69.46 293 | 81.82 240 | 89.74 288 |
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 |
anonymousdsp | | | 86.69 220 | 85.75 218 | 89.53 253 | 86.46 307 | 82.94 253 | 96.39 243 | 95.71 236 | 83.97 232 | 79.63 255 | 90.70 254 | 68.85 241 | 95.94 261 | 86.01 182 | 84.02 223 | 89.72 289 |
|
ppachtmachnet_test | | | 83.63 263 | 81.57 265 | 89.80 245 | 89.01 284 | 85.09 230 | 97.13 220 | 94.50 276 | 78.84 281 | 76.14 277 | 91.00 247 | 69.78 236 | 94.61 294 | 63.40 308 | 74.36 273 | 89.71 290 |
|
v10 | | | 85.73 239 | 84.01 244 | 90.87 224 | 90.03 268 | 86.73 194 | 97.20 218 | 95.22 264 | 81.25 269 | 79.85 253 | 89.75 281 | 73.30 212 | 96.28 250 | 76.87 256 | 72.64 288 | 89.61 291 |
|
UnsupCasMVSNet_eth | | | 78.90 282 | 76.67 284 | 85.58 290 | 82.81 316 | 74.94 300 | 91.98 295 | 96.31 194 | 84.64 224 | 65.84 314 | 87.71 293 | 51.33 309 | 92.23 313 | 72.89 286 | 56.50 321 | 89.56 292 |
|
USDC | | | 84.74 246 | 82.93 250 | 90.16 237 | 91.73 252 | 83.54 247 | 95.00 272 | 93.30 295 | 88.77 142 | 73.19 291 | 93.30 217 | 53.62 304 | 97.65 188 | 75.88 265 | 81.54 241 | 89.30 293 |
|
FMVSNet5 | | | 82.29 266 | 80.54 271 | 87.52 277 | 93.79 219 | 84.01 242 | 93.73 284 | 92.47 302 | 76.92 291 | 74.27 287 | 86.15 306 | 63.69 272 | 89.24 321 | 69.07 294 | 74.79 269 | 89.29 294 |
|
Anonymous20231206 | | | 80.76 274 | 79.42 276 | 84.79 294 | 84.78 310 | 72.98 307 | 96.53 239 | 92.97 297 | 79.56 277 | 74.33 286 | 88.83 288 | 61.27 281 | 92.15 314 | 60.59 314 | 75.92 263 | 89.24 295 |
|
pmmvs6 | | | 79.90 278 | 77.31 280 | 87.67 276 | 84.17 312 | 78.13 292 | 95.86 264 | 93.68 291 | 67.94 316 | 72.67 296 | 89.62 283 | 50.98 311 | 95.75 266 | 74.80 273 | 66.04 309 | 89.14 296 |
|
N_pmnet | | | 70.19 295 | 69.87 296 | 71.12 310 | 88.24 293 | 30.63 337 | 95.85 265 | 28.70 338 | 70.18 309 | 68.73 303 | 86.55 303 | 64.04 270 | 93.81 299 | 53.12 323 | 73.46 283 | 88.94 297 |
|
D2MVS | | | 87.96 204 | 87.39 196 | 89.70 248 | 91.84 250 | 83.40 248 | 98.31 166 | 98.49 20 | 88.04 163 | 78.23 271 | 90.26 271 | 73.57 208 | 96.79 219 | 84.21 201 | 83.53 228 | 88.90 298 |
|
test_normal | | | 68.74 296 | 64.03 298 | 82.86 300 | 73.54 325 | 64.63 321 | 41.77 332 | 94.81 267 | 81.96 262 | 42.22 327 | 60.30 324 | 30.12 328 | 95.33 276 | 77.97 249 | 73.56 281 | 88.61 299 |
|
MIMVSNet1 | | | 75.92 290 | 73.30 292 | 83.81 297 | 81.29 317 | 75.57 299 | 92.26 294 | 92.05 308 | 73.09 303 | 67.48 310 | 86.18 305 | 40.87 324 | 87.64 324 | 55.78 320 | 70.68 302 | 88.21 300 |
|
TransMVSNet (Re) | | | 81.97 268 | 79.61 275 | 89.08 262 | 89.70 274 | 84.01 242 | 97.26 213 | 91.85 311 | 78.84 281 | 73.07 294 | 91.62 240 | 67.17 255 | 95.21 280 | 67.50 298 | 59.46 319 | 88.02 301 |
|
MS-PatchMatch | | | 86.75 219 | 85.92 215 | 89.22 259 | 91.97 246 | 82.47 260 | 96.91 226 | 96.14 208 | 83.74 234 | 77.73 272 | 93.53 214 | 58.19 288 | 97.37 202 | 76.75 258 | 98.35 92 | 87.84 302 |
|
Baseline_NR-MVSNet | | | 85.83 235 | 84.82 232 | 88.87 267 | 88.73 288 | 83.34 249 | 98.63 131 | 91.66 312 | 80.41 276 | 82.44 222 | 91.35 243 | 74.63 196 | 95.42 274 | 84.13 203 | 71.39 299 | 87.84 302 |
|
ambc | | | | | 79.60 307 | 72.76 326 | 56.61 326 | 76.20 326 | 92.01 309 | | 68.25 305 | 80.23 315 | 23.34 330 | 94.73 291 | 73.78 281 | 60.81 317 | 87.48 304 |
|
TinyColmap | | | 80.42 276 | 77.94 277 | 87.85 274 | 92.09 245 | 78.58 288 | 93.74 283 | 89.94 321 | 74.99 296 | 69.77 301 | 91.78 238 | 46.09 317 | 97.58 192 | 65.17 306 | 77.89 256 | 87.38 305 |
|
TDRefinement | | | 78.01 286 | 75.31 287 | 86.10 287 | 70.06 327 | 73.84 304 | 93.59 287 | 91.58 314 | 74.51 299 | 73.08 293 | 91.04 246 | 49.63 314 | 97.12 205 | 74.88 271 | 59.47 318 | 87.33 306 |
|
CMPMVS | | 58.40 21 | 80.48 275 | 80.11 273 | 81.59 305 | 85.10 309 | 59.56 324 | 94.14 280 | 95.95 216 | 68.54 314 | 60.71 318 | 93.31 216 | 55.35 298 | 97.87 171 | 83.06 215 | 84.85 217 | 87.33 306 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
LF4IMVS | | | 81.94 269 | 81.17 269 | 84.25 296 | 87.23 304 | 68.87 318 | 93.35 288 | 91.93 310 | 83.35 241 | 75.40 283 | 93.00 224 | 49.25 315 | 96.65 222 | 78.88 243 | 78.11 255 | 87.22 308 |
|
tfpnnormal | | | 83.65 262 | 81.35 267 | 90.56 229 | 91.37 257 | 88.06 168 | 97.29 211 | 97.87 50 | 78.51 284 | 76.20 276 | 90.91 248 | 64.78 267 | 96.47 231 | 61.71 312 | 73.50 282 | 87.13 309 |
|
EG-PatchMatch MVS | | | 79.92 277 | 77.59 278 | 86.90 282 | 87.06 305 | 77.90 295 | 96.20 255 | 94.06 286 | 74.61 298 | 66.53 313 | 88.76 289 | 40.40 325 | 96.20 251 | 67.02 300 | 83.66 227 | 86.61 310 |
|
test20.03 | | | 78.51 285 | 77.48 279 | 81.62 304 | 83.07 315 | 71.03 312 | 96.11 256 | 92.83 299 | 81.66 265 | 69.31 302 | 89.68 282 | 57.53 289 | 87.29 325 | 58.65 318 | 68.47 305 | 86.53 311 |
|
MVP-Stereo | | | 86.61 223 | 85.83 216 | 88.93 266 | 88.70 289 | 83.85 245 | 96.07 257 | 94.41 280 | 82.15 259 | 75.64 282 | 91.96 236 | 67.65 251 | 96.45 233 | 77.20 254 | 98.72 83 | 86.51 312 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
OpenMVS_ROB | | 73.86 20 | 77.99 287 | 75.06 289 | 86.77 283 | 83.81 314 | 77.94 294 | 96.38 244 | 91.53 315 | 67.54 317 | 68.38 304 | 87.13 300 | 43.94 319 | 96.08 256 | 55.03 321 | 81.83 239 | 86.29 313 |
|
UnsupCasMVSNet_bld | | | 73.85 293 | 70.14 295 | 84.99 292 | 79.44 320 | 75.73 298 | 88.53 308 | 95.24 261 | 70.12 310 | 61.94 317 | 74.81 319 | 41.41 322 | 93.62 300 | 68.65 296 | 51.13 326 | 85.62 314 |
|
pmmvs-eth3d | | | 78.71 284 | 76.16 286 | 86.38 284 | 80.25 319 | 81.19 272 | 94.17 279 | 92.13 307 | 77.97 286 | 66.90 312 | 82.31 309 | 55.76 294 | 92.56 310 | 73.63 282 | 62.31 316 | 85.38 315 |
|
PM-MVS | | | 74.88 291 | 72.85 293 | 80.98 306 | 78.98 321 | 64.75 320 | 90.81 304 | 85.77 328 | 80.95 272 | 68.23 306 | 82.81 308 | 29.08 329 | 92.84 305 | 76.54 260 | 62.46 315 | 85.36 316 |
|
test_0402 | | | 78.81 283 | 76.33 285 | 86.26 285 | 91.18 258 | 78.44 290 | 95.88 262 | 91.34 316 | 68.55 313 | 70.51 300 | 89.91 279 | 52.65 307 | 94.99 282 | 47.14 325 | 79.78 249 | 85.34 317 |
|
new-patchmatchnet | | | 74.80 292 | 72.40 294 | 81.99 303 | 78.36 323 | 72.20 310 | 94.44 275 | 92.36 303 | 77.06 290 | 63.47 315 | 79.98 316 | 51.04 310 | 88.85 322 | 60.53 315 | 54.35 323 | 84.92 318 |
|
DeepMVS_CX | | | | | 76.08 308 | 90.74 264 | 51.65 329 | | 90.84 318 | 86.47 199 | 57.89 319 | 87.98 291 | 35.88 327 | 92.60 308 | 65.77 305 | 65.06 311 | 83.97 319 |
|
pmmvs3 | | | 72.86 294 | 69.76 297 | 82.17 301 | 73.86 324 | 74.19 303 | 94.20 278 | 89.01 324 | 64.23 322 | 67.72 307 | 80.91 313 | 41.48 321 | 88.65 323 | 62.40 310 | 54.02 324 | 83.68 320 |
|
new_pmnet | | | 76.02 289 | 73.71 291 | 82.95 299 | 83.88 313 | 72.85 308 | 91.26 301 | 92.26 304 | 70.44 308 | 62.60 316 | 81.37 311 | 47.64 316 | 92.32 312 | 61.85 311 | 72.10 295 | 83.68 320 |
|
LCM-MVSNet | | | 60.07 298 | 56.37 300 | 71.18 309 | 54.81 333 | 48.67 330 | 82.17 325 | 89.48 323 | 37.95 326 | 49.13 323 | 69.12 320 | 13.75 337 | 81.76 326 | 59.28 316 | 51.63 325 | 83.10 322 |
|
PMMVS2 | | | 58.97 299 | 55.07 301 | 70.69 311 | 62.72 328 | 55.37 327 | 85.97 312 | 80.52 331 | 49.48 324 | 45.94 325 | 68.31 321 | 15.73 335 | 80.78 328 | 49.79 324 | 37.12 327 | 75.91 323 |
|
FPMVS | | | 61.57 297 | 60.32 299 | 65.34 312 | 60.14 331 | 42.44 332 | 91.02 303 | 89.72 322 | 44.15 325 | 42.63 326 | 80.93 312 | 19.02 331 | 80.59 329 | 42.50 326 | 72.76 287 | 73.00 324 |
|
ANet_high | | | 50.71 302 | 46.17 304 | 64.33 313 | 44.27 335 | 52.30 328 | 76.13 327 | 78.73 332 | 64.95 320 | 27.37 331 | 55.23 328 | 14.61 336 | 67.74 331 | 36.01 327 | 18.23 330 | 72.95 325 |
|
tmp_tt | | | 53.66 301 | 52.86 302 | 56.05 315 | 32.75 337 | 41.97 333 | 73.42 328 | 76.12 334 | 21.91 332 | 39.68 329 | 96.39 169 | 42.59 320 | 65.10 332 | 78.00 248 | 14.92 332 | 61.08 326 |
|
PMVS | | 41.42 23 | 45.67 303 | 42.50 305 | 55.17 316 | 34.28 336 | 32.37 335 | 66.24 329 | 78.71 333 | 30.72 328 | 22.04 334 | 59.59 326 | 4.59 338 | 77.85 330 | 27.49 329 | 58.84 320 | 55.29 327 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 44.00 22 | 41.70 304 | 37.64 308 | 53.90 317 | 49.46 334 | 43.37 331 | 65.09 330 | 66.66 335 | 26.19 331 | 25.77 333 | 48.53 330 | 3.58 340 | 63.35 333 | 26.15 330 | 27.28 328 | 54.97 328 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
Gipuma | | | 54.77 300 | 52.22 303 | 62.40 314 | 86.50 306 | 59.37 325 | 50.20 331 | 90.35 320 | 36.52 327 | 41.20 328 | 49.49 329 | 18.33 333 | 81.29 327 | 32.10 328 | 65.34 310 | 46.54 329 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
E-PMN | | | 41.02 305 | 40.93 306 | 41.29 318 | 61.97 329 | 33.83 334 | 84.00 322 | 65.17 336 | 27.17 329 | 27.56 330 | 46.72 331 | 17.63 334 | 60.41 334 | 19.32 331 | 18.82 329 | 29.61 330 |
|
EMVS | | | 39.96 306 | 39.88 307 | 40.18 319 | 59.57 332 | 32.12 336 | 84.79 319 | 64.57 337 | 26.27 330 | 26.14 332 | 44.18 334 | 18.73 332 | 59.29 335 | 17.03 332 | 17.67 331 | 29.12 331 |
|
test123 | | | 16.58 310 | 19.47 311 | 7.91 321 | 3.59 339 | 5.37 339 | 94.32 276 | 1.39 341 | 2.49 335 | 13.98 336 | 44.60 333 | 2.91 341 | 2.65 337 | 11.35 335 | 0.57 335 | 15.70 332 |
|
testmvs | | | 18.81 308 | 23.05 310 | 6.10 322 | 4.48 338 | 2.29 340 | 97.78 196 | 3.00 340 | 3.27 334 | 18.60 335 | 62.71 323 | 1.53 342 | 2.49 338 | 14.26 334 | 1.80 334 | 13.50 333 |
|
wuyk23d | | | 16.71 309 | 16.73 312 | 16.65 320 | 60.15 330 | 25.22 338 | 41.24 333 | 5.17 339 | 6.56 333 | 5.48 337 | 3.61 337 | 3.64 339 | 22.72 336 | 15.20 333 | 9.52 333 | 1.99 334 |
|
test_part1 | | | | | 0.00 323 | | 0.00 341 | 0.00 334 | 97.69 75 | | | | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
cdsmvs_eth3d_5k | | | 22.52 307 | 30.03 309 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 97.17 145 | 0.00 336 | 0.00 338 | 98.77 70 | 74.35 202 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
pcd_1.5k_mvsjas | | | 6.87 312 | 9.16 314 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 82.48 149 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
sosnet-low-res | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
sosnet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
uncertanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
Regformer | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
ab-mvs-re | | | 8.21 311 | 10.94 313 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 98.50 92 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
uanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
9.14 | | | | 96.87 21 | | 99.34 45 | | 99.50 33 | 97.49 115 | 89.41 124 | 98.59 15 | 99.43 12 | 89.78 43 | 99.69 68 | 98.69 12 | 99.62 39 | |
|
save fliter | | | | | | 99.34 45 | 93.85 50 | 99.65 21 | 97.63 88 | 95.69 11 | | | | | | | |
|
test0726 | | | | | | 99.66 13 | 95.20 21 | 99.77 9 | 97.70 73 | 93.95 24 | 99.35 2 | 99.54 3 | 93.18 15 | | | | |
|
test_part2 | | | | | | 99.54 30 | 95.42 15 | | | | 98.13 24 | | | | | | |
|
sam_mvs | | | | | | | | | | | | | 87.08 85 | | | | |
|
MTGPA | | | | | | | | | 97.45 120 | | | | | | | | |
|
test_post1 | | | | | | | | 90.74 306 | | | | 41.37 335 | 85.38 118 | 96.36 238 | 83.16 212 | | |
|
test_post | | | | | | | | | | | | 46.00 332 | 87.37 78 | 97.11 206 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 84.86 307 | 88.73 55 | 96.81 217 | | | |
|
MTMP | | | | | | | | 99.21 62 | 91.09 317 | | | | | | | | |
|
gm-plane-assit | | | | | | 94.69 198 | 88.14 166 | | | 88.22 159 | | 97.20 139 | | 98.29 147 | 90.79 135 | | |
|
TEST9 | | | | | | 99.57 27 | 93.17 61 | 99.38 49 | 97.66 78 | 89.57 119 | 98.39 19 | 99.18 27 | 90.88 30 | 99.66 71 | | | |
|
test_8 | | | | | | 99.55 29 | 93.07 65 | 99.37 52 | 97.64 84 | 90.18 105 | 98.36 21 | 99.19 25 | 90.94 28 | 99.64 77 | | | |
|
agg_prior | | | | | | 99.54 30 | 92.66 73 | | 97.64 84 | | 97.98 32 | | | 99.61 80 | | | |
|
test_prior4 | | | | | | | 92.00 82 | 99.41 47 | | | | | | | | | |
|
test_prior2 | | | | | | | | 99.57 26 | | 91.43 78 | 98.12 26 | 98.97 53 | 90.43 36 | | 98.33 21 | 99.81 18 | |
|
旧先验2 | | | | | | | | 98.67 126 | | 85.75 206 | 98.96 9 | | | 98.97 128 | 93.84 100 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 98.26 169 | | | | | | | | | |
|
原ACMM2 | | | | | | | | 98.69 122 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.88 40 | 84.16 202 | | |
|
segment_acmp | | | | | | | | | | | | | 90.56 35 | | | | |
|
testdata1 | | | | | | | | 97.89 192 | | 92.43 56 | | | | | | | |
|
plane_prior7 | | | | | | 93.84 216 | 85.73 220 | | | | | | | | | | |
|
plane_prior6 | | | | | | 93.92 213 | 86.02 214 | | | | | | 72.92 214 | | | | |
|
plane_prior4 | | | | | | | | | | | | 96.52 163 | | | | | |
|
plane_prior3 | | | | | | | 85.91 215 | | | 93.65 34 | 86.99 183 | | | | | | |
|
plane_prior2 | | | | | | | | 99.02 89 | | 93.38 39 | | | | | | | |
|
plane_prior1 | | | | | | 93.90 215 | | | | | | | | | | | |
|
plane_prior | | | | | | | 86.07 212 | 99.14 77 | | 93.81 32 | | | | | | 86.26 207 | |
|
n2 | | | | | | | | | 0.00 342 | | | | | | | | |
|
nn | | | | | | | | | 0.00 342 | | | | | | | | |
|
door-mid | | | | | | | | | 84.90 330 | | | | | | | | |
|
test11 | | | | | | | | | 97.68 76 | | | | | | | | |
|
door | | | | | | | | | 85.30 329 | | | | | | | | |
|
HQP5-MVS | | | | | | | 86.39 201 | | | | | | | | | | |
|
HQP-NCC | | | | | | 93.95 209 | | 99.16 68 | | 93.92 26 | 87.57 177 | | | | | | |
|
ACMP_Plane | | | | | | 93.95 209 | | 99.16 68 | | 93.92 26 | 87.57 177 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 93.82 102 | | |
|
HQP3-MVS | | | | | | | | | 96.37 191 | | | | | | | 86.29 205 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.34 210 | | | | |
|
NP-MVS | | | | | | 93.94 212 | 86.22 207 | | | | | 96.67 161 | | | | | |
|
MDTV_nov1_ep13 | | | | 90.47 159 | | 96.14 152 | 88.55 161 | 91.34 300 | 97.51 110 | 89.58 118 | 92.24 126 | 90.50 268 | 86.99 89 | 97.61 191 | 77.64 251 | 92.34 164 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 82.64 236 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 83.83 224 | |
|
Test By Simon | | | | | | | | | | | | | 83.62 132 | | | | |
|