DeepPCF-MVS | | 93.56 1 | 96.55 33 | 97.84 5 | 92.68 193 | 98.71 72 | 78.11 304 | 99.70 10 | 97.71 78 | 98.18 1 | 97.36 37 | 99.76 1 | 90.37 38 | 99.94 23 | 99.27 3 | 99.54 42 | 99.99 1 |
|
MCST-MVS | | | 98.18 2 | 97.95 4 | 98.86 1 | 99.85 3 | 96.60 5 | 99.70 10 | 97.98 52 | 97.18 2 | 95.96 62 | 99.33 9 | 92.62 12 | 100.00 1 | 98.99 6 | 99.93 1 | 99.98 2 |
|
MG-MVS | | | 97.24 12 | 96.83 21 | 98.47 9 | 99.79 5 | 95.71 12 | 99.07 72 | 99.06 15 | 94.45 18 | 96.42 57 | 98.70 73 | 88.81 52 | 99.74 61 | 95.35 65 | 99.86 8 | 99.97 3 |
|
CNVR-MVS | | | 98.46 1 | 98.38 1 | 98.72 3 | 99.80 4 | 96.19 9 | 99.80 7 | 97.99 51 | 97.05 3 | 99.41 1 | 99.59 2 | 92.89 11 | 100.00 1 | 98.99 6 | 99.90 4 | 99.96 4 |
|
DeepC-MVS_fast | | 93.52 2 | 97.16 15 | 96.84 20 | 98.13 15 | 99.61 17 | 94.45 40 | 98.85 98 | 97.64 88 | 96.51 6 | 95.88 63 | 99.39 8 | 87.35 79 | 99.99 4 | 96.61 42 | 99.69 28 | 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 |
NCCC | | | 98.12 3 | 98.11 3 | 98.13 15 | 99.76 6 | 94.46 39 | 99.81 5 | 97.88 57 | 96.54 4 | 98.84 7 | 99.46 6 | 92.55 13 | 99.98 9 | 98.25 23 | 99.93 1 | 99.94 6 |
|
APDe-MVS | | | 97.53 7 | 97.47 8 | 97.70 26 | 99.58 19 | 93.63 52 | 99.56 21 | 97.52 109 | 93.59 32 | 98.01 25 | 99.12 32 | 90.80 32 | 99.55 79 | 99.26 4 | 99.79 17 | 99.93 7 |
|
agg_prior2 | | | | | | | | | | | | | | | 97.84 28 | 99.87 5 | 99.91 8 |
|
test_part1 | | | | | | | | | 97.69 79 | | | | 93.96 6 | | | 99.83 12 | 99.90 9 |
|
ESAPD | | | 97.97 4 | 97.82 6 | 98.43 10 | 99.54 27 | 95.42 14 | 99.43 33 | 97.69 79 | 92.81 44 | 98.13 17 | 99.48 4 | 93.96 6 | 99.97 14 | 99.52 1 | 99.83 12 | 99.90 9 |
|
test9_res | | | | | | | | | | | | | | | 98.60 11 | 99.87 5 | 99.90 9 |
|
SteuartSystems-ACMMP | | | 97.25 11 | 97.34 12 | 97.01 51 | 97.38 107 | 91.46 91 | 99.75 8 | 97.66 83 | 94.14 21 | 98.13 17 | 99.26 11 | 92.16 14 | 99.66 66 | 97.91 27 | 99.64 31 | 99.90 9 |
Skip Steuart: Steuart Systems R&D Blog. |
PHI-MVS | | | 96.65 30 | 96.46 29 | 97.21 45 | 99.34 40 | 91.77 82 | 99.70 10 | 98.05 47 | 86.48 186 | 98.05 22 | 99.20 18 | 89.33 46 | 99.96 18 | 98.38 18 | 99.62 35 | 99.90 9 |
|
ACMMP_Plus | | | 96.59 31 | 96.18 36 | 97.81 24 | 98.82 69 | 93.55 54 | 98.88 97 | 97.59 96 | 90.66 80 | 97.98 26 | 99.14 29 | 86.59 90 | 100.00 1 | 96.47 45 | 99.46 45 | 99.89 14 |
|
train_agg | | | 97.20 14 | 97.08 14 | 97.57 32 | 99.57 23 | 93.17 60 | 99.38 40 | 97.66 83 | 90.18 93 | 98.39 12 | 99.18 21 | 90.94 27 | 99.66 66 | 98.58 14 | 99.85 9 | 99.88 15 |
|
agg_prior3 | | | 97.09 18 | 96.97 17 | 97.45 35 | 99.56 25 | 92.79 71 | 99.36 44 | 97.67 82 | 89.59 103 | 98.36 14 | 99.16 25 | 90.57 34 | 99.68 63 | 98.58 14 | 99.85 9 | 99.88 15 |
|
MSLP-MVS++ | | | 97.50 9 | 97.45 10 | 97.63 28 | 99.65 13 | 93.21 59 | 99.70 10 | 98.13 45 | 94.61 16 | 97.78 31 | 99.46 6 | 89.85 41 | 99.81 53 | 97.97 25 | 99.91 3 | 99.88 15 |
|
APD-MVS | | | 96.95 22 | 96.72 24 | 97.63 28 | 99.51 34 | 93.58 53 | 99.16 58 | 97.44 122 | 90.08 98 | 98.59 10 | 99.07 36 | 89.06 48 | 99.42 95 | 97.92 26 | 99.66 29 | 99.88 15 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
agg_prior1 | | | 97.12 16 | 97.03 15 | 97.38 41 | 99.54 27 | 92.66 72 | 99.35 46 | 97.64 88 | 90.38 88 | 97.98 26 | 99.17 23 | 90.84 31 | 99.61 75 | 98.57 16 | 99.78 19 | 99.87 19 |
|
MVS | | | 93.92 87 | 92.28 108 | 98.83 2 | 95.69 160 | 96.82 3 | 96.22 253 | 98.17 41 | 84.89 209 | 84.34 196 | 98.61 78 | 79.32 167 | 99.83 48 | 93.88 85 | 99.43 48 | 99.86 20 |
|
æ— å…ˆéªŒ | | | | | | | | 98.52 136 | 97.82 63 | 87.20 174 | | | | 99.90 31 | 87.64 151 | | 99.85 21 |
|
SMA-MVS | | | 97.21 13 | 96.98 16 | 97.91 21 | 99.30 44 | 93.93 48 | 99.16 58 | 97.58 98 | 89.53 107 | 99.35 2 | 99.52 3 | 90.24 39 | 99.99 4 | 98.32 21 | 99.77 20 | 99.82 22 |
|
region2R | | | 96.30 42 | 96.17 38 | 96.70 77 | 99.70 7 | 90.31 125 | 99.46 30 | 97.66 83 | 90.55 84 | 97.07 41 | 99.07 36 | 86.85 87 | 99.97 14 | 95.43 63 | 99.74 21 | 99.81 23 |
|
test222 | | | | | | 98.32 79 | 91.21 101 | 98.08 186 | 97.58 98 | 83.74 230 | 95.87 64 | 99.02 42 | 86.74 88 | | | 99.64 31 | 99.81 23 |
|
TSAR-MVS + GP. | | | 96.95 22 | 96.91 18 | 97.07 48 | 98.88 66 | 91.62 87 | 99.58 18 | 96.54 179 | 95.09 15 | 96.84 50 | 98.63 77 | 91.16 17 | 99.77 58 | 99.04 5 | 96.42 109 | 99.81 23 |
|
test_prior3 | | | 97.07 19 | 97.09 13 | 97.01 51 | 99.58 19 | 91.77 82 | 99.57 19 | 97.57 102 | 91.43 70 | 98.12 20 | 98.97 48 | 90.43 36 | 99.49 87 | 98.33 19 | 99.81 15 | 99.79 26 |
|
test_prior | | | | | 97.01 51 | 99.58 19 | 91.77 82 | | 97.57 102 | | | | | 99.49 87 | | | 99.79 26 |
|
æ–°å‡ ä½•1 | | | | | 97.40 39 | 98.92 64 | 92.51 78 | | 97.77 72 | 85.52 195 | 96.69 55 | 99.06 38 | 88.08 65 | 99.89 34 | 84.88 174 | 99.62 35 | 99.79 26 |
|
1121 | | | 95.19 64 | 94.45 66 | 97.42 37 | 98.88 66 | 92.58 76 | 96.22 253 | 97.75 73 | 85.50 197 | 96.86 47 | 99.01 46 | 88.59 56 | 99.90 31 | 87.64 151 | 99.60 38 | 99.79 26 |
|
HFP-MVS | | | 96.42 38 | 96.26 35 | 96.90 62 | 99.69 8 | 90.96 112 | 99.47 27 | 97.81 66 | 90.54 85 | 96.88 44 | 99.05 39 | 87.57 69 | 99.96 18 | 95.65 58 | 99.72 23 | 99.78 30 |
|
#test# | | | 96.48 35 | 96.34 33 | 96.90 62 | 99.69 8 | 90.96 112 | 99.53 24 | 97.81 66 | 90.94 78 | 96.88 44 | 99.05 39 | 87.57 69 | 99.96 18 | 95.87 57 | 99.72 23 | 99.78 30 |
|
XVS | | | 96.47 36 | 96.37 31 | 96.77 70 | 99.62 15 | 90.66 121 | 99.43 33 | 97.58 98 | 92.41 54 | 96.86 47 | 98.96 51 | 87.37 75 | 99.87 38 | 95.65 58 | 99.43 48 | 99.78 30 |
|
X-MVStestdata | | | 90.69 164 | 88.66 177 | 96.77 70 | 99.62 15 | 90.66 121 | 99.43 33 | 97.58 98 | 92.41 54 | 96.86 47 | 29.59 358 | 87.37 75 | 99.87 38 | 95.65 58 | 99.43 48 | 99.78 30 |
|
testdata | | | | | 95.26 132 | 98.20 81 | 87.28 188 | | 97.60 95 | 85.21 201 | 98.48 11 | 99.15 27 | 88.15 63 | 98.72 128 | 90.29 123 | 99.45 47 | 99.78 30 |
|
SD-MVS | | | 97.51 8 | 97.40 11 | 97.81 24 | 99.01 59 | 93.79 51 | 99.33 49 | 97.38 129 | 93.73 29 | 98.83 8 | 99.02 42 | 90.87 30 | 99.88 35 | 98.69 10 | 99.74 21 | 99.77 35 |
|
Regformer-1 | | | 96.97 21 | 96.80 22 | 97.47 34 | 99.46 37 | 93.11 62 | 98.89 95 | 97.94 53 | 92.89 41 | 96.90 43 | 99.02 42 | 89.78 42 | 99.53 81 | 97.06 33 | 99.26 57 | 99.75 36 |
|
Regformer-2 | | | 96.94 24 | 96.78 23 | 97.42 37 | 99.46 37 | 92.97 67 | 98.89 95 | 97.93 54 | 92.86 43 | 96.88 44 | 99.02 42 | 89.74 43 | 99.53 81 | 97.03 34 | 99.26 57 | 99.75 36 |
|
ACMMPR | | | 96.28 43 | 96.14 41 | 96.73 74 | 99.68 10 | 90.47 123 | 99.47 27 | 97.80 68 | 90.54 85 | 96.83 51 | 99.03 41 | 86.51 93 | 99.95 21 | 95.65 58 | 99.72 23 | 99.75 36 |
|
mPP-MVS | | | 95.90 51 | 95.75 49 | 96.38 95 | 99.58 19 | 89.41 148 | 99.26 51 | 97.41 126 | 90.66 80 | 94.82 81 | 98.95 53 | 86.15 99 | 99.98 9 | 95.24 68 | 99.64 31 | 99.74 39 |
|
PAPR | | | 96.35 39 | 95.82 46 | 97.94 20 | 99.63 14 | 94.19 46 | 99.42 37 | 97.55 105 | 92.43 50 | 93.82 98 | 99.12 32 | 87.30 80 | 99.91 29 | 94.02 82 | 99.06 61 | 99.74 39 |
|
API-MVS | | | 94.78 70 | 94.18 71 | 96.59 86 | 99.21 50 | 90.06 134 | 98.80 103 | 97.78 71 | 83.59 234 | 93.85 96 | 99.21 17 | 83.79 122 | 99.97 14 | 92.37 106 | 99.00 64 | 99.74 39 |
|
CSCG | | | 94.87 68 | 94.71 63 | 95.36 128 | 99.54 27 | 86.49 206 | 99.34 48 | 98.15 43 | 82.71 253 | 90.15 143 | 99.25 12 | 89.48 45 | 99.86 43 | 94.97 72 | 98.82 73 | 99.72 42 |
|
zzz-MVS | | | 96.21 45 | 95.96 42 | 96.96 59 | 99.29 45 | 91.19 102 | 98.69 113 | 97.45 119 | 92.58 46 | 94.39 86 | 99.24 14 | 86.43 95 | 99.99 4 | 96.22 49 | 99.40 51 | 99.71 43 |
|
MTAPA | | | 96.09 47 | 95.80 48 | 96.96 59 | 99.29 45 | 91.19 102 | 97.23 215 | 97.45 119 | 92.58 46 | 94.39 86 | 99.24 14 | 86.43 95 | 99.99 4 | 96.22 49 | 99.40 51 | 99.71 43 |
|
APD-MVS_3200maxsize | | | 95.64 58 | 95.65 51 | 95.62 118 | 99.24 49 | 87.80 172 | 98.42 150 | 97.22 139 | 88.93 125 | 96.64 56 | 98.98 47 | 85.49 106 | 99.36 100 | 96.68 41 | 99.27 56 | 99.70 45 |
|
CP-MVS | | | 96.22 44 | 96.15 40 | 96.42 93 | 99.67 11 | 89.62 143 | 99.70 10 | 97.61 94 | 90.07 99 | 96.00 59 | 99.16 25 | 87.43 73 | 99.92 27 | 96.03 55 | 99.72 23 | 99.70 45 |
|
HSP-MVS | | | 97.73 5 | 98.15 2 | 96.44 92 | 99.54 27 | 90.14 128 | 99.41 38 | 97.47 117 | 95.46 14 | 98.60 9 | 99.19 19 | 95.71 4 | 99.49 87 | 98.15 24 | 99.85 9 | 99.69 47 |
|
HPM-MVS++ | | | 97.72 6 | 97.59 7 | 98.14 14 | 99.53 33 | 94.76 30 | 99.19 53 | 97.75 73 | 95.66 11 | 98.21 16 | 99.29 10 | 91.10 19 | 99.99 4 | 97.68 29 | 99.87 5 | 99.68 48 |
|
CDPH-MVS | | | 96.56 32 | 96.18 36 | 97.70 26 | 99.59 18 | 93.92 49 | 99.13 69 | 97.44 122 | 89.02 120 | 97.90 29 | 99.22 16 | 88.90 51 | 99.49 87 | 94.63 78 | 99.79 17 | 99.68 48 |
|
PAPM_NR | | | 95.43 59 | 95.05 60 | 96.57 87 | 99.42 39 | 90.14 128 | 98.58 131 | 97.51 111 | 90.65 82 | 92.44 109 | 98.90 58 | 87.77 68 | 99.90 31 | 90.88 118 | 99.32 54 | 99.68 48 |
|
canonicalmvs | | | 95.02 66 | 93.96 79 | 98.20 12 | 97.53 101 | 95.92 11 | 98.71 109 | 96.19 200 | 91.78 64 | 95.86 65 | 98.49 86 | 79.53 165 | 99.03 116 | 96.12 52 | 91.42 167 | 99.66 51 |
|
PGM-MVS | | | 95.85 52 | 95.65 51 | 96.45 91 | 99.50 35 | 89.77 140 | 98.22 173 | 98.90 17 | 89.19 114 | 96.74 53 | 98.95 53 | 85.91 101 | 99.92 27 | 93.94 83 | 99.46 45 | 99.66 51 |
|
DELS-MVS | | | 97.12 16 | 96.60 27 | 98.68 5 | 98.03 86 | 96.57 6 | 99.84 3 | 97.84 61 | 96.36 7 | 95.20 76 | 98.24 93 | 88.17 62 | 99.83 48 | 96.11 53 | 99.60 38 | 99.64 53 |
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 | 93.43 101 | 91.84 121 | 98.17 13 | 95.73 159 | 95.08 20 | 98.92 88 | 97.04 156 | 91.42 72 | 81.48 238 | 97.60 109 | 74.60 198 | 99.79 56 | 90.84 119 | 98.97 65 | 99.64 53 |
|
CANet | | | 97.00 20 | 96.49 28 | 98.55 6 | 98.86 68 | 96.10 10 | 99.83 4 | 97.52 109 | 95.90 8 | 97.21 38 | 98.90 58 | 82.66 143 | 99.93 25 | 98.71 9 | 98.80 74 | 99.63 55 |
|
114514_t | | | 94.06 86 | 93.05 94 | 97.06 49 | 99.08 56 | 92.26 80 | 98.97 84 | 97.01 160 | 82.58 255 | 92.57 107 | 98.22 94 | 80.68 161 | 99.30 105 | 89.34 135 | 99.02 63 | 99.63 55 |
|
PAPM | | | 96.35 39 | 95.94 43 | 97.58 30 | 94.10 198 | 95.25 16 | 98.93 86 | 98.17 41 | 94.26 19 | 93.94 94 | 98.72 71 | 89.68 44 | 97.88 158 | 96.36 48 | 99.29 55 | 99.62 57 |
|
MVS_0304 | | | 96.12 46 | 95.26 56 | 98.69 4 | 98.44 78 | 96.54 7 | 99.70 10 | 96.89 165 | 95.76 10 | 97.53 33 | 99.12 32 | 72.42 231 | 99.93 25 | 98.75 8 | 98.69 77 | 99.61 58 |
|
TSAR-MVS + MP. | | | 97.44 10 | 97.46 9 | 97.39 40 | 99.12 53 | 93.49 57 | 98.52 136 | 97.50 114 | 94.46 17 | 98.99 3 | 98.64 76 | 91.58 16 | 99.08 115 | 98.49 17 | 99.83 12 | 99.60 59 |
|
旧先验1 | | | | | | 98.97 60 | 92.90 69 | | 97.74 75 | | | 99.15 27 | 91.05 20 | | | 99.33 53 | 99.60 59 |
|
test12 | | | | | 97.83 23 | 99.33 43 | 94.45 40 | | 97.55 105 | | 97.56 32 | | 88.60 54 | 99.50 86 | | 99.71 27 | 99.55 61 |
|
HY-MVS | | 88.56 7 | 95.29 63 | 94.23 70 | 98.48 8 | 97.72 91 | 96.41 8 | 94.03 291 | 98.74 19 | 92.42 53 | 95.65 69 | 94.76 182 | 86.52 92 | 99.49 87 | 95.29 67 | 92.97 143 | 99.53 62 |
|
MP-MVS | | | 96.00 48 | 95.82 46 | 96.54 88 | 99.47 36 | 90.13 130 | 99.36 44 | 97.41 126 | 90.64 83 | 95.49 71 | 98.95 53 | 85.51 105 | 99.98 9 | 96.00 56 | 99.59 40 | 99.52 63 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
alignmvs | | | 95.77 56 | 95.00 61 | 98.06 18 | 97.35 108 | 95.68 13 | 99.71 9 | 97.50 114 | 91.50 68 | 96.16 58 | 98.61 78 | 86.28 97 | 99.00 117 | 96.19 51 | 91.74 161 | 99.51 64 |
|
WTY-MVS | | | 95.97 49 | 95.11 59 | 98.54 7 | 97.62 94 | 96.65 4 | 99.44 31 | 98.74 19 | 92.25 57 | 95.21 75 | 98.46 90 | 86.56 91 | 99.46 94 | 95.00 71 | 92.69 147 | 99.50 65 |
|
Regformer-3 | | | 96.50 34 | 96.36 32 | 96.91 61 | 99.34 40 | 91.72 85 | 98.71 109 | 97.90 56 | 92.48 49 | 96.00 59 | 98.95 53 | 88.60 54 | 99.52 84 | 96.44 46 | 98.83 71 | 99.49 66 |
|
Regformer-4 | | | 96.45 37 | 96.33 34 | 96.81 69 | 99.34 40 | 91.44 92 | 98.71 109 | 97.88 57 | 92.43 50 | 95.97 61 | 98.95 53 | 88.42 58 | 99.51 85 | 96.40 47 | 98.83 71 | 99.49 66 |
|
DP-MVS Recon | | | 95.85 52 | 95.15 58 | 97.95 19 | 99.87 2 | 94.38 43 | 99.60 17 | 97.48 116 | 86.58 184 | 94.42 85 | 99.13 31 | 87.36 78 | 99.98 9 | 93.64 90 | 98.33 85 | 99.48 68 |
|
HPM-MVS | | | 95.41 61 | 95.22 57 | 95.99 107 | 99.29 45 | 89.14 149 | 99.17 57 | 97.09 152 | 87.28 173 | 95.40 72 | 98.48 87 | 84.93 112 | 99.38 98 | 95.64 62 | 99.65 30 | 99.47 69 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
MVS_111021_HR | | | 96.69 28 | 96.69 25 | 96.72 76 | 98.58 76 | 91.00 111 | 99.14 66 | 99.45 1 | 93.86 26 | 95.15 77 | 98.73 69 | 88.48 57 | 99.76 59 | 97.23 32 | 99.56 41 | 99.40 70 |
|
lupinMVS | | | 96.32 41 | 95.94 43 | 97.44 36 | 95.05 182 | 94.87 22 | 99.86 2 | 96.50 180 | 93.82 27 | 98.04 23 | 98.77 65 | 85.52 103 | 98.09 147 | 96.98 38 | 98.97 65 | 99.37 71 |
|
mvs_anonymous | | | 92.50 128 | 91.65 125 | 95.06 139 | 96.60 136 | 89.64 142 | 97.06 222 | 96.44 184 | 86.64 183 | 84.14 197 | 93.93 193 | 82.49 145 | 96.17 255 | 91.47 112 | 96.08 118 | 99.35 72 |
|
HPM-MVS_fast | | | 94.89 67 | 94.62 64 | 95.70 117 | 99.11 54 | 88.44 163 | 99.14 66 | 97.11 148 | 85.82 192 | 95.69 68 | 98.47 88 | 83.46 126 | 99.32 104 | 93.16 98 | 99.63 34 | 99.35 72 |
|
1314 | | | 93.44 100 | 91.98 119 | 97.84 22 | 95.24 169 | 94.38 43 | 96.22 253 | 97.92 55 | 90.18 93 | 82.28 225 | 97.71 106 | 77.63 180 | 99.80 55 | 91.94 111 | 98.67 79 | 99.34 74 |
|
LFMVS | | | 92.23 132 | 90.84 146 | 96.42 93 | 98.24 80 | 91.08 109 | 98.24 171 | 96.22 198 | 83.39 242 | 94.74 83 | 98.31 92 | 61.12 302 | 98.85 119 | 94.45 81 | 92.82 144 | 99.32 75 |
|
Effi-MVS+ | | | 93.87 89 | 93.15 92 | 96.02 106 | 95.79 156 | 90.76 117 | 96.70 235 | 95.78 225 | 86.98 177 | 95.71 67 | 97.17 131 | 79.58 164 | 98.01 153 | 94.57 79 | 96.09 117 | 99.31 76 |
|
CHOSEN 1792x2688 | | | 94.35 82 | 93.82 84 | 95.95 110 | 97.40 106 | 88.74 157 | 98.41 152 | 98.27 28 | 92.18 59 | 91.43 121 | 96.40 160 | 78.88 169 | 99.81 53 | 93.59 91 | 97.81 89 | 99.30 77 |
|
DWT-MVSNet_test | | | 94.36 81 | 93.95 80 | 95.62 118 | 96.99 120 | 89.47 146 | 96.62 238 | 97.38 129 | 90.96 77 | 93.07 104 | 97.27 123 | 93.73 8 | 98.09 147 | 85.86 168 | 93.65 139 | 99.29 78 |
|
ACMMP | | | 94.67 75 | 94.30 68 | 95.79 114 | 99.25 48 | 88.13 166 | 98.41 152 | 98.67 23 | 90.38 88 | 91.43 121 | 98.72 71 | 82.22 151 | 99.95 21 | 93.83 87 | 95.76 123 | 99.29 78 |
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 54 | 95.30 54 | 97.29 43 | 98.95 63 | 92.66 72 | 98.59 130 | 97.14 145 | 88.95 123 | 93.12 102 | 99.25 12 | 85.62 102 | 99.94 23 | 96.56 44 | 99.48 44 | 99.28 80 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
EPMVS | | | 92.59 127 | 91.59 127 | 95.59 120 | 97.22 112 | 90.03 135 | 91.78 311 | 98.04 48 | 90.42 87 | 91.66 115 | 90.65 257 | 86.49 94 | 97.46 186 | 81.78 209 | 96.31 112 | 99.28 80 |
|
AdaColmap | | | 93.82 90 | 93.06 93 | 96.10 105 | 99.88 1 | 89.07 150 | 98.33 158 | 97.55 105 | 86.81 182 | 90.39 140 | 98.65 75 | 75.09 191 | 99.98 9 | 93.32 96 | 97.53 96 | 99.26 82 |
|
VNet | | | 95.08 65 | 94.26 69 | 97.55 33 | 98.07 85 | 93.88 50 | 98.68 116 | 98.73 21 | 90.33 90 | 97.16 40 | 97.43 115 | 79.19 168 | 99.53 81 | 96.91 40 | 91.85 159 | 99.24 83 |
|
CNLPA | | | 93.64 97 | 92.74 99 | 96.36 96 | 98.96 62 | 90.01 136 | 99.19 53 | 95.89 222 | 86.22 189 | 89.40 156 | 98.85 61 | 80.66 162 | 99.84 46 | 88.57 143 | 96.92 103 | 99.24 83 |
|
3Dnovator | | 87.35 11 | 93.17 113 | 91.77 123 | 97.37 42 | 95.41 167 | 93.07 64 | 98.82 101 | 97.85 60 | 91.53 67 | 82.56 220 | 97.58 110 | 71.97 236 | 99.82 51 | 91.01 116 | 99.23 59 | 99.22 85 |
|
GG-mvs-BLEND | | | | | 96.98 57 | 96.53 137 | 94.81 29 | 87.20 325 | 97.74 75 | | 93.91 95 | 96.40 160 | 96.56 2 | 96.94 209 | 95.08 69 | 98.95 68 | 99.20 86 |
|
Patchmatch-test | | | 86.25 232 | 84.06 244 | 92.82 188 | 94.42 193 | 82.88 267 | 82.88 341 | 94.23 285 | 71.58 317 | 79.39 256 | 90.62 259 | 89.00 50 | 96.42 233 | 63.03 319 | 91.37 168 | 99.16 87 |
|
gg-mvs-nofinetune | | | 90.00 174 | 87.71 190 | 96.89 66 | 96.15 149 | 94.69 33 | 85.15 331 | 97.74 75 | 68.32 330 | 92.97 106 | 60.16 344 | 96.10 3 | 96.84 211 | 93.89 84 | 98.87 69 | 99.14 88 |
|
MVS_Test | | | 93.67 96 | 92.67 101 | 96.69 78 | 96.72 134 | 92.66 72 | 97.22 216 | 96.03 206 | 87.69 163 | 95.12 78 | 94.03 189 | 81.55 155 | 98.28 142 | 89.17 139 | 96.46 107 | 99.14 88 |
|
HyFIR lowres test | | | 93.68 95 | 93.29 89 | 94.87 143 | 97.57 100 | 88.04 168 | 98.18 178 | 98.47 24 | 87.57 165 | 91.24 125 | 95.05 178 | 85.49 106 | 97.46 186 | 93.22 97 | 92.82 144 | 99.10 90 |
|
Vis-MVSNet (Re-imp) | | | 93.26 110 | 93.00 96 | 94.06 165 | 96.14 150 | 86.71 202 | 98.68 116 | 96.70 169 | 88.30 143 | 89.71 151 | 97.64 108 | 85.43 109 | 96.39 236 | 88.06 147 | 96.32 111 | 99.08 91 |
|
Patchmatch-test1 | | | 90.10 171 | 88.61 178 | 94.57 151 | 94.95 185 | 88.83 153 | 96.26 249 | 97.21 140 | 90.06 100 | 90.03 144 | 90.68 253 | 66.61 277 | 95.83 268 | 77.31 244 | 94.36 134 | 99.05 92 |
|
PatchmatchNet | | | 92.05 142 | 91.04 135 | 95.06 139 | 96.17 148 | 89.04 151 | 91.26 315 | 97.26 134 | 89.56 106 | 90.64 133 | 90.56 263 | 88.35 60 | 97.11 202 | 79.53 226 | 96.07 119 | 99.03 93 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PatchFormer-LS_test | | | 94.08 85 | 93.60 87 | 95.53 125 | 96.92 121 | 89.57 144 | 96.51 241 | 97.34 133 | 91.29 74 | 92.22 112 | 97.18 129 | 91.66 15 | 98.02 152 | 87.05 155 | 92.21 154 | 99.00 94 |
|
EPNet | | | 96.82 26 | 96.68 26 | 97.25 44 | 98.65 73 | 93.10 63 | 99.48 26 | 98.76 18 | 96.54 4 | 97.84 30 | 98.22 94 | 87.49 72 | 99.66 66 | 95.35 65 | 97.78 92 | 99.00 94 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
sss | | | 94.85 69 | 93.94 81 | 97.58 30 | 96.43 140 | 94.09 47 | 98.93 86 | 99.16 14 | 89.50 108 | 95.27 74 | 97.85 100 | 81.50 156 | 99.65 70 | 92.79 104 | 94.02 137 | 98.99 96 |
|
Patchmatch-RL test | | | 81.90 274 | 80.13 275 | 87.23 293 | 80.71 331 | 70.12 328 | 84.07 337 | 88.19 344 | 83.16 246 | 70.57 301 | 82.18 314 | 87.18 81 | 92.59 319 | 82.28 201 | 62.78 320 | 98.98 97 |
|
PVSNet | | 87.13 12 | 93.69 93 | 92.83 98 | 96.28 98 | 97.99 87 | 90.22 127 | 99.38 40 | 98.93 16 | 91.42 72 | 93.66 99 | 97.68 107 | 71.29 244 | 99.64 72 | 87.94 148 | 97.20 101 | 98.98 97 |
|
MVSFormer | | | 94.71 74 | 94.08 74 | 96.61 85 | 95.05 182 | 94.87 22 | 97.77 199 | 96.17 201 | 86.84 180 | 98.04 23 | 98.52 82 | 85.52 103 | 95.99 261 | 89.83 126 | 98.97 65 | 98.96 99 |
|
jason | | | 95.40 62 | 94.86 62 | 97.03 50 | 92.91 227 | 94.23 45 | 99.70 10 | 96.30 191 | 93.56 33 | 96.73 54 | 98.52 82 | 81.46 157 | 97.91 155 | 96.08 54 | 98.47 83 | 98.96 99 |
jason: jason. |
CostFormer | | | 92.89 116 | 92.48 105 | 94.12 163 | 94.99 184 | 85.89 228 | 92.89 301 | 97.00 161 | 86.98 177 | 95.00 80 | 90.78 246 | 90.05 40 | 97.51 185 | 92.92 102 | 91.73 162 | 98.96 99 |
|
MAR-MVS | | | 94.43 80 | 94.09 73 | 95.45 127 | 99.10 55 | 87.47 179 | 98.39 156 | 97.79 70 | 88.37 141 | 94.02 93 | 99.17 23 | 78.64 175 | 99.91 29 | 92.48 105 | 98.85 70 | 98.96 99 |
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 | | | | | | | 91.17 104 | 91.38 313 | | 87.45 167 | 93.08 103 | | 86.67 89 | | 87.02 156 | | 98.95 103 |
|
CVMVSNet | | | 90.30 166 | 90.91 144 | 88.46 275 | 94.32 195 | 73.58 317 | 97.61 204 | 97.59 96 | 90.16 96 | 88.43 162 | 97.10 134 | 76.83 184 | 92.86 307 | 82.64 198 | 93.54 140 | 98.93 104 |
|
ab-mvs | | | 91.05 156 | 89.17 167 | 96.69 78 | 95.96 153 | 91.72 85 | 92.62 303 | 97.23 138 | 85.61 194 | 89.74 149 | 93.89 195 | 68.55 261 | 99.42 95 | 91.09 114 | 87.84 199 | 98.92 105 |
|
IS-MVSNet | | | 93.00 115 | 92.51 104 | 94.49 152 | 96.14 150 | 87.36 186 | 98.31 161 | 95.70 231 | 88.58 132 | 90.17 142 | 97.50 112 | 83.02 139 | 97.22 198 | 87.06 154 | 96.07 119 | 98.90 106 |
|
CPTT-MVS | | | 94.60 78 | 94.43 67 | 95.09 136 | 99.66 12 | 86.85 196 | 99.44 31 | 97.47 117 | 83.22 244 | 94.34 88 | 98.96 51 | 82.50 144 | 99.55 79 | 94.81 74 | 99.50 43 | 98.88 107 |
|
Vis-MVSNet | | | 92.64 125 | 91.85 120 | 95.03 141 | 95.12 178 | 88.23 164 | 98.48 143 | 96.81 166 | 91.61 66 | 92.16 113 | 97.22 127 | 71.58 242 | 98.00 154 | 85.85 169 | 97.81 89 | 98.88 107 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
GSMVS | | | | | | | | | | | | | | | | | 98.84 109 |
|
sam_mvs1 | | | | | | | | | | | | | 88.39 59 | | | | 98.84 109 |
|
PMMVS | | | 93.62 98 | 93.90 83 | 92.79 189 | 96.79 132 | 81.40 277 | 98.85 98 | 96.81 166 | 91.25 75 | 96.82 52 | 98.15 98 | 77.02 183 | 98.13 146 | 93.15 99 | 96.30 113 | 98.83 111 |
|
1112_ss | | | 92.71 122 | 91.55 128 | 96.20 99 | 95.56 163 | 91.12 105 | 98.48 143 | 94.69 273 | 88.29 144 | 86.89 181 | 98.50 84 | 87.02 84 | 98.66 131 | 84.75 175 | 89.77 188 | 98.81 112 |
|
Test_1112_low_res | | | 92.27 131 | 90.97 142 | 96.18 100 | 95.53 164 | 91.10 107 | 98.47 145 | 94.66 274 | 88.28 145 | 86.83 182 | 93.50 206 | 87.00 85 | 98.65 132 | 84.69 176 | 89.74 189 | 98.80 113 |
|
tpmp4_e23 | | | 91.05 156 | 90.07 158 | 93.97 169 | 95.77 158 | 85.30 239 | 92.64 302 | 97.09 152 | 84.42 216 | 91.53 119 | 90.31 269 | 87.38 74 | 97.82 162 | 80.86 217 | 90.62 177 | 98.79 114 |
|
PatchT | | | 85.44 244 | 83.19 248 | 92.22 198 | 93.13 226 | 83.00 262 | 83.80 339 | 96.37 186 | 70.62 320 | 90.55 134 | 79.63 333 | 84.81 115 | 94.87 289 | 58.18 331 | 91.59 164 | 98.79 114 |
|
PVSNet_Blended | | | 95.94 50 | 95.66 50 | 96.75 72 | 98.77 70 | 91.61 88 | 99.88 1 | 98.04 48 | 93.64 31 | 94.21 90 | 97.76 104 | 83.50 124 | 99.87 38 | 97.41 30 | 97.75 93 | 98.79 114 |
|
DeepC-MVS | | 91.02 4 | 94.56 79 | 93.92 82 | 96.46 90 | 97.16 114 | 90.76 117 | 98.39 156 | 97.11 148 | 93.92 22 | 88.66 161 | 98.33 91 | 78.14 177 | 99.85 45 | 95.02 70 | 98.57 81 | 98.78 117 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
tpmrst | | | 92.78 117 | 92.16 112 | 94.65 149 | 96.27 144 | 87.45 180 | 91.83 310 | 97.10 151 | 89.10 119 | 94.68 84 | 90.69 251 | 88.22 61 | 97.73 173 | 89.78 128 | 91.80 160 | 98.77 118 |
|
原ACMM1 | | | | | 96.18 100 | 99.03 58 | 90.08 131 | | 97.63 92 | 88.98 121 | 97.00 42 | 98.97 48 | 88.14 64 | 99.71 62 | 88.23 145 | 99.62 35 | 98.76 119 |
|
tpm2 | | | 91.77 144 | 91.09 134 | 93.82 173 | 94.83 188 | 85.56 237 | 92.51 304 | 97.16 144 | 84.00 221 | 93.83 97 | 90.66 256 | 87.54 71 | 97.17 200 | 87.73 150 | 91.55 165 | 98.72 120 |
|
TAPA-MVS | | 87.50 9 | 90.35 165 | 89.05 169 | 94.25 160 | 98.48 77 | 85.17 242 | 98.42 150 | 96.58 176 | 82.44 259 | 87.24 177 | 98.53 81 | 82.77 142 | 98.84 120 | 59.09 329 | 97.88 88 | 98.72 120 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
EI-MVSNet-Vis-set | | | 95.76 57 | 95.63 53 | 96.17 102 | 99.14 52 | 90.33 124 | 98.49 142 | 97.82 63 | 91.92 61 | 94.75 82 | 98.88 60 | 87.06 83 | 99.48 92 | 95.40 64 | 97.17 102 | 98.70 122 |
|
DP-MVS | | | 88.75 195 | 86.56 203 | 95.34 129 | 98.92 64 | 87.45 180 | 97.64 203 | 93.52 295 | 70.55 321 | 81.49 237 | 97.25 124 | 74.43 205 | 99.88 35 | 71.14 301 | 94.09 136 | 98.67 123 |
|
abl_6 | | | 94.63 77 | 94.48 65 | 95.09 136 | 98.61 75 | 86.96 193 | 98.06 188 | 96.97 162 | 89.31 110 | 95.86 65 | 98.56 80 | 79.82 163 | 99.64 72 | 94.53 80 | 98.65 80 | 98.66 124 |
|
TESTMET0.1,1 | | | 93.82 90 | 93.26 90 | 95.49 126 | 95.21 171 | 90.25 126 | 99.15 63 | 97.54 108 | 89.18 116 | 91.79 114 | 94.87 180 | 89.13 47 | 97.63 177 | 86.21 162 | 96.29 114 | 98.60 125 |
|
DI_MVS_plusplus_test | | | 89.41 182 | 87.24 197 | 95.92 112 | 89.06 290 | 90.75 119 | 98.18 178 | 96.63 170 | 89.29 112 | 70.54 302 | 90.31 269 | 63.50 292 | 98.40 138 | 92.25 108 | 95.44 127 | 98.60 125 |
|
Test4 | | | 85.71 243 | 82.59 260 | 95.07 138 | 84.45 322 | 89.84 139 | 97.20 217 | 95.73 228 | 89.19 114 | 64.59 327 | 87.58 298 | 40.59 341 | 96.77 214 | 88.95 142 | 95.01 130 | 98.60 125 |
|
test_normal | | | 89.37 183 | 87.18 199 | 95.93 111 | 88.94 294 | 90.83 115 | 98.24 171 | 96.62 171 | 89.31 110 | 70.38 304 | 90.20 276 | 63.50 292 | 98.37 139 | 92.06 110 | 95.41 128 | 98.59 128 |
|
dp | | | 90.16 170 | 88.83 174 | 94.14 162 | 96.38 142 | 86.42 208 | 91.57 312 | 97.06 155 | 84.76 211 | 88.81 159 | 90.19 277 | 84.29 119 | 97.43 188 | 75.05 270 | 91.35 169 | 98.56 129 |
|
EPP-MVSNet | | | 93.75 92 | 93.67 85 | 94.01 167 | 95.86 155 | 85.70 234 | 98.67 118 | 97.66 83 | 84.46 214 | 91.36 123 | 97.18 129 | 91.16 17 | 97.79 164 | 92.93 101 | 93.75 138 | 98.53 130 |
|
Fast-Effi-MVS+ | | | 91.72 145 | 90.79 149 | 94.49 152 | 95.89 154 | 87.40 183 | 99.54 23 | 95.70 231 | 85.01 207 | 89.28 157 | 95.68 170 | 77.75 179 | 97.57 184 | 83.22 191 | 95.06 129 | 98.51 131 |
|
CDS-MVSNet | | | 93.47 99 | 93.04 95 | 94.76 145 | 94.75 190 | 89.45 147 | 98.82 101 | 97.03 158 | 87.91 155 | 90.97 128 | 96.48 158 | 89.06 48 | 96.36 238 | 89.50 130 | 92.81 146 | 98.49 132 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
LCM-MVSNet-Re | | | 88.59 197 | 88.61 178 | 88.51 274 | 95.53 164 | 72.68 320 | 96.85 228 | 88.43 343 | 88.45 136 | 73.14 293 | 90.63 258 | 75.82 187 | 94.38 297 | 92.95 100 | 95.71 124 | 98.48 133 |
|
TAMVS | | | 92.62 126 | 92.09 116 | 94.20 161 | 94.10 198 | 87.68 174 | 98.41 152 | 96.97 162 | 87.53 166 | 89.74 149 | 96.04 167 | 84.77 116 | 96.49 227 | 88.97 141 | 92.31 151 | 98.42 134 |
|
diffmvs | | | 92.07 138 | 90.77 150 | 95.97 109 | 96.41 141 | 91.32 100 | 96.46 242 | 95.98 207 | 81.73 266 | 94.33 89 | 93.36 207 | 78.72 173 | 98.20 143 | 84.28 179 | 95.66 125 | 98.41 135 |
|
CR-MVSNet | | | 88.83 191 | 87.38 194 | 93.16 182 | 93.47 217 | 86.24 215 | 84.97 333 | 94.20 286 | 88.92 126 | 90.76 131 | 86.88 306 | 84.43 117 | 94.82 291 | 70.64 302 | 92.17 156 | 98.41 135 |
|
RPMNet | | | 84.62 250 | 81.78 264 | 93.16 182 | 93.47 217 | 86.24 215 | 84.97 333 | 96.28 195 | 64.85 336 | 90.76 131 | 78.80 335 | 80.95 160 | 94.82 291 | 53.76 334 | 92.17 156 | 98.41 135 |
|
BH-RMVSNet | | | 91.25 153 | 89.99 159 | 95.03 141 | 96.75 133 | 88.55 160 | 98.65 120 | 94.95 267 | 87.74 160 | 87.74 171 | 97.80 102 | 68.27 263 | 98.14 145 | 80.53 223 | 97.49 97 | 98.41 135 |
|
UA-Net | | | 93.30 107 | 92.62 102 | 95.34 129 | 96.27 144 | 88.53 162 | 95.88 267 | 96.97 162 | 90.90 79 | 95.37 73 | 97.07 136 | 82.38 149 | 99.10 114 | 83.91 187 | 94.86 132 | 98.38 139 |
|
tpm | | | 89.67 178 | 88.95 171 | 91.82 205 | 92.54 230 | 81.43 276 | 92.95 300 | 95.92 216 | 87.81 157 | 90.50 135 | 89.44 284 | 84.99 111 | 95.65 272 | 83.67 190 | 82.71 233 | 98.38 139 |
|
MVS_111021_LR | | | 95.78 55 | 95.94 43 | 95.28 131 | 98.19 83 | 87.69 173 | 98.80 103 | 99.26 13 | 93.39 34 | 95.04 79 | 98.69 74 | 84.09 120 | 99.76 59 | 96.96 39 | 99.06 61 | 98.38 139 |
|
test-LLR | | | 93.11 114 | 92.68 100 | 94.40 155 | 94.94 186 | 87.27 189 | 99.15 63 | 97.25 135 | 90.21 91 | 91.57 116 | 94.04 187 | 84.89 113 | 97.58 180 | 85.94 165 | 96.13 115 | 98.36 142 |
|
test-mter | | | 93.27 109 | 92.89 97 | 94.40 155 | 94.94 186 | 87.27 189 | 99.15 63 | 97.25 135 | 88.95 123 | 91.57 116 | 94.04 187 | 88.03 66 | 97.58 180 | 85.94 165 | 96.13 115 | 98.36 142 |
|
IB-MVS | | 89.43 6 | 92.12 137 | 90.83 148 | 95.98 108 | 95.40 168 | 90.78 116 | 99.81 5 | 98.06 46 | 91.23 76 | 85.63 187 | 93.66 201 | 90.63 33 | 98.78 121 | 91.22 113 | 71.85 300 | 98.36 142 |
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 | | | 91.24 154 | 90.18 157 | 94.45 154 | 97.08 117 | 85.84 232 | 98.40 155 | 96.10 204 | 86.99 175 | 93.36 100 | 98.16 97 | 54.27 320 | 99.20 106 | 96.59 43 | 90.63 176 | 98.31 145 |
|
PVSNet_Blended_VisFu | | | 94.67 75 | 94.11 72 | 96.34 97 | 97.14 115 | 91.10 107 | 99.32 50 | 97.43 124 | 92.10 60 | 91.53 119 | 96.38 163 | 83.29 130 | 99.68 63 | 93.42 95 | 96.37 110 | 98.25 146 |
|
EI-MVSNet-UG-set | | | 95.43 59 | 95.29 55 | 95.86 113 | 99.07 57 | 89.87 137 | 98.43 149 | 97.80 68 | 91.78 64 | 94.11 92 | 98.77 65 | 86.25 98 | 99.48 92 | 94.95 73 | 96.45 108 | 98.22 147 |
|
QAPM | | | 91.41 150 | 89.49 162 | 97.17 47 | 95.66 162 | 93.42 58 | 98.60 128 | 97.51 111 | 80.92 275 | 81.39 239 | 97.41 116 | 72.89 228 | 99.87 38 | 82.33 200 | 98.68 78 | 98.21 148 |
|
CHOSEN 280x420 | | | 96.80 27 | 96.85 19 | 96.66 80 | 97.85 88 | 94.42 42 | 94.76 283 | 98.36 26 | 92.50 48 | 95.62 70 | 97.52 111 | 97.92 1 | 97.38 195 | 98.31 22 | 98.80 74 | 98.20 149 |
|
TR-MVS | | | 90.77 161 | 89.44 163 | 94.76 145 | 96.31 143 | 88.02 169 | 97.92 192 | 95.96 211 | 85.52 195 | 88.22 163 | 97.23 126 | 66.80 275 | 98.09 147 | 84.58 177 | 92.38 149 | 98.17 150 |
|
GA-MVS | | | 90.10 171 | 88.69 176 | 94.33 157 | 92.44 231 | 87.97 170 | 99.08 71 | 96.26 196 | 89.65 102 | 86.92 180 | 93.11 214 | 68.09 264 | 96.96 207 | 82.54 199 | 90.15 182 | 98.05 151 |
|
tfpn_ndepth | | | 93.28 108 | 92.32 106 | 96.16 103 | 97.74 90 | 92.86 70 | 99.01 80 | 98.19 39 | 85.50 197 | 89.84 148 | 97.12 133 | 93.57 9 | 97.58 180 | 79.39 229 | 90.50 178 | 98.04 152 |
|
OMC-MVS | | | 93.90 88 | 93.62 86 | 94.73 147 | 98.63 74 | 87.00 192 | 98.04 189 | 96.56 177 | 92.19 58 | 92.46 108 | 98.73 69 | 79.49 166 | 99.14 112 | 92.16 109 | 94.34 135 | 98.03 153 |
|
xiu_mvs_v2_base | | | 96.66 29 | 96.17 38 | 98.11 17 | 97.11 116 | 96.96 2 | 99.01 80 | 97.04 156 | 95.51 13 | 98.86 6 | 99.11 35 | 82.19 152 | 99.36 100 | 98.59 13 | 98.14 86 | 98.00 154 |
|
PS-MVSNAJ | | | 96.87 25 | 96.40 30 | 98.29 11 | 97.35 108 | 97.29 1 | 99.03 77 | 97.11 148 | 95.83 9 | 98.97 4 | 99.14 29 | 82.48 146 | 99.60 77 | 98.60 11 | 99.08 60 | 98.00 154 |
|
thresconf0.02 | | | 92.14 133 | 90.99 136 | 95.58 121 | 96.84 124 | 91.39 93 | 98.31 161 | 98.20 32 | 83.57 235 | 88.08 164 | 97.34 117 | 91.05 20 | 97.40 189 | 75.80 260 | 89.74 189 | 97.94 156 |
|
tfpn_n400 | | | 92.14 133 | 90.99 136 | 95.58 121 | 96.84 124 | 91.39 93 | 98.31 161 | 98.20 32 | 83.57 235 | 88.08 164 | 97.34 117 | 91.05 20 | 97.40 189 | 75.80 260 | 89.74 189 | 97.94 156 |
|
tfpnconf | | | 92.14 133 | 90.99 136 | 95.58 121 | 96.84 124 | 91.39 93 | 98.31 161 | 98.20 32 | 83.57 235 | 88.08 164 | 97.34 117 | 91.05 20 | 97.40 189 | 75.80 260 | 89.74 189 | 97.94 156 |
|
tfpnview11 | | | 92.14 133 | 90.99 136 | 95.58 121 | 96.84 124 | 91.39 93 | 98.31 161 | 98.20 32 | 83.57 235 | 88.08 164 | 97.34 117 | 91.05 20 | 97.40 189 | 75.80 260 | 89.74 189 | 97.94 156 |
|
tpm cat1 | | | 88.89 188 | 87.27 196 | 93.76 174 | 95.79 156 | 85.32 238 | 90.76 319 | 97.09 152 | 76.14 307 | 85.72 186 | 88.59 292 | 82.92 140 | 98.04 151 | 76.96 248 | 91.43 166 | 97.90 160 |
|
tfpn1000 | | | 92.67 124 | 91.64 126 | 95.78 115 | 97.61 99 | 92.34 79 | 98.69 113 | 98.18 40 | 84.15 219 | 88.80 160 | 96.99 140 | 93.56 10 | 97.21 199 | 76.56 254 | 90.19 181 | 97.77 161 |
|
ADS-MVSNet2 | | | 87.62 206 | 86.88 201 | 89.86 246 | 96.21 146 | 79.14 293 | 87.15 326 | 92.99 301 | 83.01 248 | 89.91 146 | 87.27 302 | 78.87 170 | 92.80 311 | 74.20 278 | 92.27 152 | 97.64 162 |
|
ADS-MVSNet | | | 88.99 186 | 87.30 195 | 94.07 164 | 96.21 146 | 87.56 177 | 87.15 326 | 96.78 168 | 83.01 248 | 89.91 146 | 87.27 302 | 78.87 170 | 97.01 206 | 74.20 278 | 92.27 152 | 97.64 162 |
|
BH-w/o | | | 92.32 129 | 91.79 122 | 93.91 170 | 96.85 123 | 86.18 218 | 99.11 70 | 95.74 227 | 88.13 148 | 84.81 191 | 97.00 139 | 77.26 182 | 97.91 155 | 89.16 140 | 98.03 87 | 97.64 162 |
|
LS3D | | | 90.19 169 | 88.72 175 | 94.59 150 | 98.97 60 | 86.33 213 | 96.90 227 | 96.60 172 | 74.96 310 | 84.06 199 | 98.74 68 | 75.78 188 | 99.83 48 | 74.93 271 | 97.57 94 | 97.62 165 |
|
VDDNet | | | 90.08 173 | 88.54 183 | 94.69 148 | 94.41 194 | 87.68 174 | 98.21 176 | 96.40 185 | 76.21 306 | 93.33 101 | 97.75 105 | 54.93 318 | 98.77 122 | 94.71 77 | 90.96 170 | 97.61 166 |
|
EPNet_dtu | | | 92.28 130 | 92.15 113 | 92.70 192 | 97.29 110 | 84.84 245 | 98.64 122 | 97.82 63 | 92.91 40 | 93.02 105 | 97.02 138 | 85.48 108 | 95.70 271 | 72.25 297 | 94.89 131 | 97.55 167 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
BH-untuned | | | 91.46 149 | 90.84 146 | 93.33 179 | 96.51 139 | 84.83 246 | 98.84 100 | 95.50 246 | 86.44 188 | 83.50 201 | 96.70 149 | 75.49 190 | 97.77 166 | 86.78 161 | 97.81 89 | 97.40 168 |
|
thres200 | | | 93.69 93 | 92.59 103 | 96.97 58 | 97.76 89 | 94.74 31 | 99.35 46 | 99.36 2 | 89.23 113 | 91.21 126 | 96.97 141 | 83.42 127 | 98.77 122 | 85.08 172 | 90.96 170 | 97.39 169 |
|
JIA-IIPM | | | 85.97 235 | 84.85 233 | 89.33 260 | 93.23 224 | 73.68 316 | 85.05 332 | 97.13 147 | 69.62 326 | 91.56 118 | 68.03 342 | 88.03 66 | 96.96 207 | 77.89 242 | 93.12 141 | 97.34 170 |
|
PVSNet_0 | | 83.28 16 | 87.31 209 | 85.16 227 | 93.74 175 | 94.78 189 | 84.59 248 | 98.91 89 | 98.69 22 | 89.81 101 | 78.59 264 | 93.23 211 | 61.95 298 | 99.34 103 | 94.75 75 | 55.72 339 | 97.30 171 |
|
PLC | | 91.07 3 | 94.23 84 | 94.01 75 | 94.87 143 | 99.17 51 | 87.49 178 | 99.25 52 | 96.55 178 | 88.43 139 | 91.26 124 | 98.21 96 | 85.92 100 | 99.86 43 | 89.77 129 | 97.57 94 | 97.24 172 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
thres100view900 | | | 93.34 105 | 92.15 113 | 96.90 62 | 97.62 94 | 94.84 24 | 99.06 74 | 99.36 2 | 87.96 151 | 90.47 136 | 96.78 145 | 83.29 130 | 98.75 124 | 84.11 183 | 90.69 172 | 97.12 173 |
|
tfpn200view9 | | | 93.43 101 | 92.27 109 | 96.90 62 | 97.68 92 | 94.84 24 | 99.18 55 | 99.36 2 | 88.45 136 | 90.79 129 | 96.90 143 | 83.31 128 | 98.75 124 | 84.11 183 | 90.69 172 | 97.12 173 |
|
tpmvs | | | 89.16 184 | 87.76 188 | 93.35 178 | 97.19 113 | 84.75 247 | 90.58 321 | 97.36 131 | 81.99 262 | 84.56 193 | 89.31 287 | 83.98 121 | 98.17 144 | 74.85 273 | 90.00 187 | 97.12 173 |
|
PCF-MVS | | 89.78 5 | 91.26 151 | 89.63 161 | 96.16 103 | 95.44 166 | 91.58 90 | 95.29 279 | 96.10 204 | 85.07 205 | 82.75 216 | 97.45 114 | 78.28 176 | 99.78 57 | 80.60 222 | 95.65 126 | 97.12 173 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MIMVSNet | | | 84.48 254 | 81.83 263 | 92.42 196 | 91.73 243 | 87.36 186 | 85.52 329 | 94.42 281 | 81.40 269 | 81.91 233 | 87.58 298 | 51.92 325 | 92.81 310 | 73.84 283 | 88.15 198 | 97.08 177 |
|
CANet_DTU | | | 94.31 83 | 93.35 88 | 97.20 46 | 97.03 119 | 94.71 32 | 98.62 124 | 95.54 243 | 95.61 12 | 97.21 38 | 98.47 88 | 71.88 237 | 99.84 46 | 88.38 144 | 97.46 98 | 97.04 178 |
|
PatchMatch-RL | | | 91.47 148 | 90.54 154 | 94.26 159 | 98.20 81 | 86.36 212 | 96.94 225 | 97.14 145 | 87.75 159 | 88.98 158 | 95.75 169 | 71.80 239 | 99.40 97 | 80.92 215 | 97.39 99 | 97.02 179 |
|
tfpn111 | | | 93.20 111 | 92.00 117 | 96.83 68 | 97.62 94 | 94.84 24 | 99.06 74 | 99.36 2 | 87.96 151 | 90.47 136 | 96.78 145 | 83.29 130 | 98.71 129 | 82.93 195 | 90.47 179 | 96.94 180 |
|
conf0.01 | | | 92.06 140 | 90.99 136 | 95.24 133 | 96.84 124 | 91.39 93 | 98.31 161 | 98.20 32 | 83.57 235 | 88.08 164 | 97.34 117 | 91.05 20 | 97.40 189 | 75.80 260 | 89.74 189 | 96.94 180 |
|
conf0.002 | | | 92.06 140 | 90.99 136 | 95.24 133 | 96.84 124 | 91.39 93 | 98.31 161 | 98.20 32 | 83.57 235 | 88.08 164 | 97.34 117 | 91.05 20 | 97.40 189 | 75.80 260 | 89.74 189 | 96.94 180 |
|
conf200view11 | | | 93.32 106 | 92.15 113 | 96.84 67 | 97.62 94 | 94.84 24 | 99.06 74 | 99.36 2 | 87.96 151 | 90.47 136 | 96.78 145 | 83.29 130 | 98.75 124 | 84.11 183 | 90.69 172 | 96.94 180 |
|
UGNet | | | 91.91 143 | 90.85 145 | 95.10 135 | 97.06 118 | 88.69 158 | 98.01 190 | 98.24 30 | 92.41 54 | 92.39 110 | 93.61 202 | 60.52 303 | 99.68 63 | 88.14 146 | 97.25 100 | 96.92 184 |
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 | | | 91.57 146 | 92.20 111 | 89.70 251 | 95.15 176 | 74.34 313 | 99.51 25 | 95.40 254 | 91.92 61 | 91.02 127 | 97.25 124 | 74.27 208 | 98.08 150 | 89.45 131 | 95.83 122 | 96.67 185 |
|
view600 | | | 92.78 117 | 91.50 129 | 96.63 81 | 97.51 102 | 94.66 34 | 98.91 89 | 99.36 2 | 87.31 169 | 89.64 152 | 96.59 152 | 83.26 135 | 98.63 133 | 80.76 218 | 90.15 182 | 96.61 186 |
|
view800 | | | 92.78 117 | 91.50 129 | 96.63 81 | 97.51 102 | 94.66 34 | 98.91 89 | 99.36 2 | 87.31 169 | 89.64 152 | 96.59 152 | 83.26 135 | 98.63 133 | 80.76 218 | 90.15 182 | 96.61 186 |
|
conf0.05thres1000 | | | 92.78 117 | 91.50 129 | 96.63 81 | 97.51 102 | 94.66 34 | 98.91 89 | 99.36 2 | 87.31 169 | 89.64 152 | 96.59 152 | 83.26 135 | 98.63 133 | 80.76 218 | 90.15 182 | 96.61 186 |
|
tfpn | | | 92.78 117 | 91.50 129 | 96.63 81 | 97.51 102 | 94.66 34 | 98.91 89 | 99.36 2 | 87.31 169 | 89.64 152 | 96.59 152 | 83.26 135 | 98.63 133 | 80.76 218 | 90.15 182 | 96.61 186 |
|
thres600view7 | | | 93.18 112 | 92.00 117 | 96.75 72 | 97.62 94 | 94.92 21 | 99.07 72 | 99.36 2 | 87.96 151 | 90.47 136 | 96.78 145 | 83.29 130 | 98.71 129 | 82.93 195 | 90.47 179 | 96.61 186 |
|
thres400 | | | 93.39 103 | 92.27 109 | 96.73 74 | 97.68 92 | 94.84 24 | 99.18 55 | 99.36 2 | 88.45 136 | 90.79 129 | 96.90 143 | 83.31 128 | 98.75 124 | 84.11 183 | 90.69 172 | 96.61 186 |
|
xiu_mvs_v1_base_debu | | | 94.73 71 | 93.98 76 | 96.99 54 | 95.19 172 | 95.24 17 | 98.62 124 | 96.50 180 | 92.99 37 | 97.52 34 | 98.83 62 | 72.37 232 | 99.15 109 | 97.03 34 | 96.74 104 | 96.58 192 |
|
xiu_mvs_v1_base | | | 94.73 71 | 93.98 76 | 96.99 54 | 95.19 172 | 95.24 17 | 98.62 124 | 96.50 180 | 92.99 37 | 97.52 34 | 98.83 62 | 72.37 232 | 99.15 109 | 97.03 34 | 96.74 104 | 96.58 192 |
|
xiu_mvs_v1_base_debi | | | 94.73 71 | 93.98 76 | 96.99 54 | 95.19 172 | 95.24 17 | 98.62 124 | 96.50 180 | 92.99 37 | 97.52 34 | 98.83 62 | 72.37 232 | 99.15 109 | 97.03 34 | 96.74 104 | 96.58 192 |
|
F-COLMAP | | | 92.07 138 | 91.75 124 | 93.02 185 | 98.16 84 | 82.89 266 | 98.79 106 | 95.97 209 | 86.54 185 | 87.92 170 | 97.80 102 | 78.69 174 | 99.65 70 | 85.97 164 | 95.93 121 | 96.53 195 |
|
MSDG | | | 88.29 201 | 86.37 205 | 94.04 166 | 96.90 122 | 86.15 220 | 96.52 240 | 94.36 283 | 77.89 303 | 79.22 258 | 96.95 142 | 69.72 252 | 99.59 78 | 73.20 290 | 92.58 148 | 96.37 196 |
|
OpenMVS | | 85.28 14 | 90.75 162 | 88.84 173 | 96.48 89 | 93.58 215 | 93.51 56 | 98.80 103 | 97.41 126 | 82.59 254 | 78.62 262 | 97.49 113 | 68.00 266 | 99.82 51 | 84.52 178 | 98.55 82 | 96.11 197 |
|
DSMNet-mixed | | | 81.60 278 | 81.43 269 | 82.10 314 | 84.36 323 | 60.79 335 | 93.63 296 | 86.74 345 | 79.00 285 | 79.32 257 | 87.15 304 | 63.87 290 | 89.78 332 | 66.89 311 | 91.92 158 | 95.73 198 |
|
cascas | | | 90.93 159 | 89.33 166 | 95.76 116 | 95.69 160 | 93.03 66 | 98.99 83 | 96.59 173 | 80.49 277 | 86.79 183 | 94.45 185 | 65.23 285 | 98.60 137 | 93.52 92 | 92.18 155 | 95.66 199 |
|
XVG-OURS-SEG-HR | | | 90.95 158 | 90.66 153 | 91.83 204 | 95.18 175 | 81.14 283 | 95.92 264 | 95.92 216 | 88.40 140 | 90.33 141 | 97.85 100 | 70.66 247 | 99.38 98 | 92.83 103 | 88.83 196 | 94.98 200 |
|
XVG-OURS | | | 90.83 160 | 90.49 155 | 91.86 203 | 95.23 170 | 81.25 281 | 95.79 272 | 95.92 216 | 88.96 122 | 90.02 145 | 98.03 99 | 71.60 241 | 99.35 102 | 91.06 115 | 87.78 200 | 94.98 200 |
|
Effi-MVS+-dtu | | | 89.97 175 | 90.68 152 | 87.81 288 | 95.15 176 | 71.98 322 | 97.87 196 | 95.40 254 | 91.92 61 | 87.57 172 | 91.44 234 | 74.27 208 | 96.84 211 | 89.45 131 | 93.10 142 | 94.60 202 |
|
Fast-Effi-MVS+-dtu | | | 88.84 190 | 88.59 181 | 89.58 254 | 93.44 220 | 78.18 302 | 98.65 120 | 94.62 275 | 88.46 135 | 84.12 198 | 95.37 176 | 68.91 258 | 96.52 225 | 82.06 203 | 91.70 163 | 94.06 203 |
|
test0.0.03 1 | | | 88.96 187 | 88.61 178 | 90.03 244 | 91.09 250 | 84.43 249 | 98.97 84 | 97.02 159 | 90.21 91 | 80.29 245 | 96.31 164 | 84.89 113 | 91.93 327 | 72.98 293 | 85.70 212 | 93.73 204 |
|
MVS-HIRNet | | | 79.01 295 | 75.13 302 | 90.66 230 | 93.82 211 | 81.69 275 | 85.16 330 | 93.75 291 | 54.54 342 | 74.17 289 | 59.15 346 | 57.46 309 | 96.58 217 | 63.74 317 | 94.38 133 | 93.72 205 |
|
AllTest | | | 84.97 247 | 83.12 249 | 90.52 233 | 96.82 130 | 78.84 297 | 95.89 265 | 92.17 317 | 77.96 300 | 75.94 279 | 95.50 172 | 55.48 315 | 99.18 107 | 71.15 299 | 87.14 201 | 93.55 206 |
|
TestCases | | | | | 90.52 233 | 96.82 130 | 78.84 297 | | 92.17 317 | 77.96 300 | 75.94 279 | 95.50 172 | 55.48 315 | 99.18 107 | 71.15 299 | 87.14 201 | 93.55 206 |
|
RPSCF | | | 85.33 245 | 85.55 222 | 84.67 308 | 94.63 192 | 62.28 334 | 93.73 294 | 93.76 290 | 74.38 313 | 85.23 190 | 97.06 137 | 64.09 288 | 98.31 140 | 80.98 213 | 86.08 209 | 93.41 208 |
|
HQP4-MVS | | | | | | | | | | | 87.57 172 | | | 97.77 166 | | | 92.72 209 |
|
HQP-MVS | | | 91.50 147 | 91.23 133 | 92.29 197 | 93.95 202 | 86.39 210 | 99.16 58 | 96.37 186 | 93.92 22 | 87.57 172 | 96.67 150 | 73.34 221 | 97.77 166 | 93.82 88 | 86.29 204 | 92.72 209 |
|
HQP_MVS | | | 91.26 151 | 90.95 143 | 92.16 199 | 93.84 209 | 86.07 223 | 99.02 78 | 96.30 191 | 93.38 35 | 86.99 178 | 96.52 156 | 72.92 226 | 97.75 171 | 93.46 93 | 86.17 207 | 92.67 211 |
|
plane_prior5 | | | | | | | | | 96.30 191 | | | | | 97.75 171 | 93.46 93 | 86.17 207 | 92.67 211 |
|
nrg030 | | | 90.23 167 | 88.87 172 | 94.32 158 | 91.53 245 | 93.54 55 | 98.79 106 | 95.89 222 | 88.12 149 | 84.55 194 | 94.61 184 | 78.80 172 | 96.88 210 | 92.35 107 | 75.21 264 | 92.53 213 |
|
CLD-MVS | | | 91.06 155 | 90.71 151 | 92.10 200 | 94.05 201 | 86.10 221 | 99.55 22 | 96.29 194 | 94.16 20 | 84.70 192 | 97.17 131 | 69.62 253 | 97.82 162 | 94.74 76 | 86.08 209 | 92.39 214 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
VPNet | | | 88.30 200 | 86.57 202 | 93.49 176 | 91.95 238 | 91.35 99 | 98.18 178 | 97.20 141 | 88.61 131 | 84.52 195 | 94.89 179 | 62.21 297 | 96.76 215 | 89.34 135 | 72.26 296 | 92.36 215 |
|
DU-MVS | | | 88.83 191 | 87.51 192 | 92.79 189 | 91.46 246 | 90.07 132 | 98.71 109 | 97.62 93 | 88.87 127 | 83.21 204 | 93.68 199 | 74.63 196 | 95.93 265 | 86.95 157 | 72.47 292 | 92.36 215 |
|
NR-MVSNet | | | 87.74 205 | 86.00 210 | 92.96 186 | 91.46 246 | 90.68 120 | 96.65 237 | 97.42 125 | 88.02 150 | 73.42 291 | 93.68 199 | 77.31 181 | 95.83 268 | 84.26 180 | 71.82 301 | 92.36 215 |
|
LP | | | 77.80 303 | 74.39 305 | 88.01 284 | 91.93 240 | 79.02 295 | 80.88 343 | 92.90 307 | 65.43 334 | 72.00 300 | 81.29 325 | 65.78 281 | 92.73 316 | 43.76 344 | 75.58 262 | 92.27 218 |
|
FIs | | | 90.70 163 | 89.87 160 | 93.18 181 | 92.29 232 | 91.12 105 | 98.17 181 | 98.25 29 | 89.11 118 | 83.44 202 | 94.82 181 | 82.26 150 | 96.17 255 | 87.76 149 | 82.76 232 | 92.25 219 |
|
UniMVSNet_NR-MVSNet | | | 89.60 179 | 88.55 182 | 92.75 191 | 92.17 235 | 90.07 132 | 98.74 108 | 98.15 43 | 88.37 141 | 83.21 204 | 93.98 192 | 82.86 141 | 95.93 265 | 86.95 157 | 72.47 292 | 92.25 219 |
|
VPA-MVSNet | | | 89.10 185 | 87.66 191 | 93.45 177 | 92.56 229 | 91.02 110 | 97.97 191 | 98.32 27 | 86.92 179 | 86.03 185 | 92.01 226 | 68.84 260 | 97.10 204 | 90.92 117 | 75.34 263 | 92.23 221 |
|
TranMVSNet+NR-MVSNet | | | 87.75 203 | 86.31 206 | 92.07 201 | 90.81 253 | 88.56 159 | 98.33 158 | 97.18 142 | 87.76 158 | 81.87 235 | 93.90 194 | 72.45 230 | 95.43 277 | 83.13 193 | 71.30 304 | 92.23 221 |
|
pcd1.5k->3k | | | 35.91 331 | 37.64 331 | 30.74 344 | 89.49 285 | 0.00 363 | 0.00 354 | 96.36 189 | 0.00 358 | 0.00 359 | 0.00 360 | 69.17 257 | 0.00 361 | 0.00 358 | 83.71 225 | 92.21 223 |
|
FC-MVSNet-test | | | 90.22 168 | 89.40 164 | 92.67 194 | 91.78 242 | 89.86 138 | 97.89 193 | 98.22 31 | 88.81 128 | 82.96 211 | 94.66 183 | 81.90 153 | 95.96 263 | 85.89 167 | 82.52 235 | 92.20 224 |
|
PS-MVSNAJss | | | 89.54 180 | 89.05 169 | 91.00 224 | 88.77 295 | 84.36 250 | 97.39 207 | 95.97 209 | 88.47 133 | 81.88 234 | 93.80 197 | 82.48 146 | 96.50 226 | 89.34 135 | 83.34 228 | 92.15 225 |
|
testgi | | | 82.29 269 | 81.00 273 | 86.17 299 | 87.24 314 | 74.84 312 | 97.39 207 | 91.62 325 | 88.63 130 | 75.85 281 | 95.42 175 | 46.07 334 | 91.55 329 | 66.87 312 | 79.94 244 | 92.12 226 |
|
WR-MVS | | | 88.54 198 | 87.22 198 | 92.52 195 | 91.93 240 | 89.50 145 | 98.56 132 | 97.84 61 | 86.99 175 | 81.87 235 | 93.81 196 | 74.25 210 | 95.92 267 | 85.29 170 | 74.43 271 | 92.12 226 |
|
MVSTER | | | 92.71 122 | 92.32 106 | 93.86 171 | 97.29 110 | 92.95 68 | 99.01 80 | 96.59 173 | 90.09 97 | 85.51 188 | 94.00 191 | 94.61 5 | 96.56 219 | 90.77 121 | 83.03 230 | 92.08 228 |
|
ACMM | | 86.95 13 | 88.77 194 | 88.22 187 | 90.43 235 | 93.61 214 | 81.34 279 | 98.50 140 | 95.92 216 | 87.88 156 | 83.85 200 | 95.20 177 | 67.20 272 | 97.89 157 | 86.90 159 | 84.90 216 | 92.06 229 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XXY-MVS | | | 87.75 203 | 86.02 209 | 92.95 187 | 90.46 257 | 89.70 141 | 97.71 201 | 95.90 220 | 84.02 220 | 80.95 240 | 94.05 186 | 67.51 270 | 97.10 204 | 85.16 171 | 78.41 250 | 92.04 230 |
|
FMVSNet3 | | | 88.81 193 | 87.08 200 | 93.99 168 | 96.52 138 | 94.59 38 | 98.08 186 | 96.20 199 | 85.85 191 | 82.12 228 | 91.60 233 | 74.05 213 | 95.40 279 | 79.04 231 | 80.24 241 | 91.99 231 |
|
FMVSNet2 | | | 86.90 220 | 84.79 235 | 93.24 180 | 95.11 179 | 92.54 77 | 97.67 202 | 95.86 224 | 82.94 250 | 80.55 242 | 91.17 237 | 62.89 294 | 95.29 281 | 77.23 245 | 79.71 247 | 91.90 232 |
|
UniMVSNet (Re) | | | 89.50 181 | 88.32 185 | 93.03 184 | 92.21 234 | 90.96 112 | 98.90 94 | 98.39 25 | 89.13 117 | 83.22 203 | 92.03 224 | 81.69 154 | 96.34 244 | 86.79 160 | 72.53 291 | 91.81 233 |
|
testing_2 | | | 80.92 286 | 77.24 294 | 91.98 202 | 78.88 336 | 87.83 171 | 93.96 292 | 95.72 229 | 84.27 218 | 56.20 337 | 80.42 328 | 38.64 343 | 96.40 235 | 87.20 153 | 79.85 245 | 91.72 234 |
|
EU-MVSNet | | | 84.19 259 | 84.42 241 | 83.52 311 | 88.64 298 | 67.37 331 | 96.04 261 | 95.76 226 | 85.29 200 | 78.44 267 | 93.18 212 | 70.67 246 | 91.48 330 | 75.79 266 | 75.98 259 | 91.70 235 |
|
EI-MVSNet | | | 89.87 176 | 89.38 165 | 91.36 219 | 94.32 195 | 85.87 229 | 97.61 204 | 96.59 173 | 85.10 203 | 85.51 188 | 97.10 134 | 81.30 159 | 96.56 219 | 83.85 189 | 83.03 230 | 91.64 236 |
|
IterMVS-LS | | | 88.34 199 | 87.44 193 | 91.04 223 | 94.10 198 | 85.85 231 | 98.10 184 | 95.48 248 | 85.12 202 | 82.03 232 | 91.21 236 | 81.35 158 | 95.63 273 | 83.86 188 | 75.73 261 | 91.63 237 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
GBi-Net | | | 86.67 224 | 84.96 229 | 91.80 206 | 95.11 179 | 88.81 154 | 96.77 230 | 95.25 261 | 82.94 250 | 82.12 228 | 90.25 271 | 62.89 294 | 94.97 286 | 79.04 231 | 80.24 241 | 91.62 238 |
|
test1 | | | 86.67 224 | 84.96 229 | 91.80 206 | 95.11 179 | 88.81 154 | 96.77 230 | 95.25 261 | 82.94 250 | 82.12 228 | 90.25 271 | 62.89 294 | 94.97 286 | 79.04 231 | 80.24 241 | 91.62 238 |
|
FMVSNet1 | | | 83.94 263 | 81.32 271 | 91.80 206 | 91.94 239 | 88.81 154 | 96.77 230 | 95.25 261 | 77.98 298 | 78.25 269 | 90.25 271 | 50.37 329 | 94.97 286 | 73.27 289 | 77.81 254 | 91.62 238 |
|
jajsoiax | | | 87.35 208 | 86.51 204 | 89.87 245 | 87.75 309 | 81.74 274 | 97.03 223 | 95.98 207 | 88.47 133 | 80.15 247 | 93.80 197 | 61.47 299 | 96.36 238 | 89.44 133 | 84.47 220 | 91.50 241 |
|
ACMP | | 87.39 10 | 88.71 196 | 88.24 186 | 90.12 241 | 93.91 207 | 81.06 284 | 98.50 140 | 95.67 233 | 89.43 109 | 80.37 244 | 95.55 171 | 65.67 282 | 97.83 161 | 90.55 122 | 84.51 218 | 91.47 242 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LPG-MVS_test | | | 88.86 189 | 88.47 184 | 90.06 242 | 93.35 222 | 80.95 285 | 98.22 173 | 95.94 213 | 87.73 161 | 83.17 206 | 96.11 165 | 66.28 279 | 97.77 166 | 90.19 124 | 85.19 213 | 91.46 243 |
|
LGP-MVS_train | | | | | 90.06 242 | 93.35 222 | 80.95 285 | | 95.94 213 | 87.73 161 | 83.17 206 | 96.11 165 | 66.28 279 | 97.77 166 | 90.19 124 | 85.19 213 | 91.46 243 |
|
mvs_tets | | | 87.09 217 | 86.22 207 | 89.71 250 | 87.87 305 | 81.39 278 | 96.73 234 | 95.90 220 | 88.19 147 | 79.99 248 | 93.61 202 | 59.96 305 | 96.31 248 | 89.40 134 | 84.34 221 | 91.43 245 |
|
CP-MVSNet | | | 86.54 227 | 85.45 224 | 89.79 249 | 91.02 252 | 82.78 269 | 97.38 209 | 97.56 104 | 85.37 199 | 79.53 255 | 93.03 215 | 71.86 238 | 95.25 282 | 79.92 224 | 73.43 286 | 91.34 246 |
|
test_djsdf | | | 88.26 202 | 87.73 189 | 89.84 247 | 88.05 304 | 82.21 271 | 97.77 199 | 96.17 201 | 86.84 180 | 82.41 224 | 91.95 229 | 72.07 235 | 95.99 261 | 89.83 126 | 84.50 219 | 91.32 247 |
|
v2v482 | | | 87.27 212 | 85.76 216 | 91.78 210 | 89.59 281 | 87.58 176 | 98.56 132 | 95.54 243 | 84.53 213 | 82.51 221 | 91.78 230 | 73.11 225 | 96.47 230 | 82.07 202 | 74.14 279 | 91.30 248 |
|
OPM-MVS | | | 89.76 177 | 89.15 168 | 91.57 214 | 90.53 256 | 85.58 236 | 98.11 183 | 95.93 215 | 92.88 42 | 86.05 184 | 96.47 159 | 67.06 274 | 97.87 159 | 89.29 138 | 86.08 209 | 91.26 249 |
|
PS-CasMVS | | | 85.81 239 | 84.58 238 | 89.49 258 | 90.77 254 | 82.11 272 | 97.20 217 | 97.36 131 | 84.83 210 | 79.12 259 | 92.84 218 | 67.42 271 | 95.16 284 | 78.39 238 | 73.25 287 | 91.21 250 |
|
pmmvs5 | | | 85.87 236 | 84.40 242 | 90.30 238 | 88.53 299 | 84.23 251 | 98.60 128 | 93.71 292 | 81.53 268 | 80.29 245 | 92.02 225 | 64.51 287 | 95.52 275 | 82.04 204 | 78.34 251 | 91.15 251 |
|
COLMAP_ROB | | 82.69 18 | 84.54 253 | 82.82 253 | 89.70 251 | 96.72 134 | 78.85 296 | 95.89 265 | 92.83 310 | 71.55 318 | 77.54 273 | 95.89 168 | 59.40 306 | 99.14 112 | 67.26 309 | 88.26 197 | 91.11 252 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
v6 | | | 87.27 212 | 85.86 214 | 91.50 215 | 89.97 265 | 86.84 198 | 98.45 146 | 95.67 233 | 83.85 226 | 83.11 208 | 90.97 242 | 74.46 203 | 96.58 217 | 81.97 205 | 74.34 273 | 91.09 253 |
|
divwei89l23v2f112 | | | 87.23 214 | 85.75 218 | 91.66 212 | 89.88 270 | 87.40 183 | 98.53 135 | 95.62 239 | 83.91 223 | 82.84 214 | 90.67 254 | 74.75 192 | 96.49 227 | 81.55 210 | 74.05 282 | 91.08 254 |
|
v1 | | | 87.23 214 | 85.76 216 | 91.66 212 | 89.88 270 | 87.37 185 | 98.54 134 | 95.64 238 | 83.91 223 | 82.88 213 | 90.70 249 | 74.64 194 | 96.53 223 | 81.54 211 | 74.08 280 | 91.08 254 |
|
v1141 | | | 87.23 214 | 85.75 218 | 91.67 211 | 89.88 270 | 87.43 182 | 98.52 136 | 95.62 239 | 83.91 223 | 82.83 215 | 90.69 251 | 74.70 193 | 96.49 227 | 81.53 212 | 74.08 280 | 91.07 256 |
|
v1neww | | | 87.29 210 | 85.88 212 | 91.50 215 | 90.07 258 | 86.87 194 | 98.45 146 | 95.66 236 | 83.84 227 | 83.07 209 | 90.99 240 | 74.58 200 | 96.56 219 | 81.96 206 | 74.33 274 | 91.07 256 |
|
v7new | | | 87.29 210 | 85.88 212 | 91.50 215 | 90.07 258 | 86.87 194 | 98.45 146 | 95.66 236 | 83.84 227 | 83.07 209 | 90.99 240 | 74.58 200 | 96.56 219 | 81.96 206 | 74.33 274 | 91.07 256 |
|
PEN-MVS | | | 85.21 246 | 83.93 246 | 89.07 265 | 89.89 269 | 81.31 280 | 97.09 221 | 97.24 137 | 84.45 215 | 78.66 261 | 92.68 220 | 68.44 262 | 94.87 289 | 75.98 258 | 70.92 305 | 91.04 259 |
|
ACMH | | 83.09 17 | 84.60 251 | 82.61 259 | 90.57 231 | 93.18 225 | 82.94 263 | 96.27 248 | 94.92 268 | 81.01 273 | 72.61 299 | 93.61 202 | 56.54 311 | 97.79 164 | 74.31 276 | 81.07 240 | 90.99 260 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OurMVSNet-221017-0 | | | 84.13 262 | 83.59 247 | 85.77 302 | 87.81 306 | 70.24 326 | 94.89 282 | 93.65 294 | 86.08 190 | 76.53 275 | 93.28 210 | 61.41 300 | 96.14 257 | 80.95 214 | 77.69 255 | 90.93 261 |
|
XVG-ACMP-BASELINE | | | 85.86 237 | 84.95 231 | 88.57 272 | 89.90 268 | 77.12 307 | 94.30 287 | 95.60 242 | 87.40 168 | 82.12 228 | 92.99 217 | 53.42 323 | 97.66 175 | 85.02 173 | 83.83 223 | 90.92 262 |
|
Patchmtry | | | 83.61 267 | 81.64 266 | 89.50 256 | 93.36 221 | 82.84 268 | 84.10 336 | 94.20 286 | 69.47 327 | 79.57 254 | 86.88 306 | 84.43 117 | 94.78 293 | 68.48 307 | 74.30 276 | 90.88 263 |
|
IterMVS | | | 85.81 239 | 84.67 237 | 89.22 261 | 93.51 216 | 83.67 257 | 96.32 247 | 94.80 269 | 85.09 204 | 78.69 260 | 90.17 278 | 66.57 278 | 93.17 303 | 79.48 228 | 77.42 256 | 90.81 264 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1921920 | | | 86.02 234 | 84.44 240 | 90.77 228 | 89.32 288 | 85.20 240 | 98.10 184 | 95.35 258 | 82.19 260 | 82.25 226 | 90.71 248 | 70.73 245 | 96.30 251 | 76.85 251 | 74.49 270 | 90.80 265 |
|
v144192 | | | 86.40 229 | 84.89 232 | 90.91 226 | 89.48 286 | 85.59 235 | 98.21 176 | 95.43 253 | 82.45 258 | 82.62 219 | 90.58 262 | 72.79 229 | 96.36 238 | 78.45 237 | 74.04 283 | 90.79 266 |
|
v1192 | | | 86.32 231 | 84.71 236 | 91.17 221 | 89.53 284 | 86.40 209 | 98.13 182 | 95.44 252 | 82.52 257 | 82.42 223 | 90.62 259 | 71.58 242 | 96.33 245 | 77.23 245 | 74.88 266 | 90.79 266 |
|
semantic-postprocess | | | | | 89.00 266 | 93.46 219 | 82.90 265 | | 94.70 272 | 85.02 206 | 78.62 262 | 90.35 267 | 66.63 276 | 93.33 302 | 79.38 230 | 77.36 257 | 90.76 268 |
|
SixPastTwentyTwo | | | 82.63 268 | 81.58 267 | 85.79 301 | 88.12 303 | 71.01 325 | 95.17 280 | 92.54 313 | 84.33 217 | 72.93 296 | 92.08 223 | 60.41 304 | 95.61 274 | 74.47 275 | 74.15 278 | 90.75 269 |
|
v1240 | | | 85.77 241 | 84.11 243 | 90.73 229 | 89.26 289 | 85.15 243 | 97.88 195 | 95.23 265 | 81.89 265 | 82.16 227 | 90.55 264 | 69.60 254 | 96.31 248 | 75.59 268 | 74.87 267 | 90.72 270 |
|
v7 | | | 86.91 219 | 85.45 224 | 91.29 220 | 90.06 260 | 86.73 200 | 98.26 169 | 95.49 247 | 83.08 247 | 82.95 212 | 90.96 243 | 73.37 219 | 96.42 233 | 79.90 225 | 74.97 265 | 90.71 271 |
|
v148 | | | 86.38 230 | 85.06 228 | 90.37 237 | 89.47 287 | 84.10 252 | 98.52 136 | 95.48 248 | 83.80 229 | 80.93 241 | 90.22 274 | 74.60 198 | 96.31 248 | 80.92 215 | 71.55 302 | 90.69 272 |
|
K. test v3 | | | 81.04 284 | 79.77 278 | 84.83 306 | 87.41 313 | 70.23 327 | 95.60 276 | 93.93 289 | 83.70 232 | 67.51 321 | 89.35 286 | 55.76 313 | 93.58 301 | 76.67 253 | 68.03 312 | 90.67 273 |
|
v1144 | | | 86.83 221 | 85.31 226 | 91.40 218 | 89.75 275 | 87.21 191 | 98.31 161 | 95.45 251 | 83.22 244 | 82.70 218 | 90.78 246 | 73.36 220 | 96.36 238 | 79.49 227 | 74.69 269 | 90.63 274 |
|
ACMH+ | | 83.78 15 | 84.21 257 | 82.56 261 | 89.15 263 | 93.73 213 | 79.16 292 | 96.43 243 | 94.28 284 | 81.09 272 | 74.00 290 | 94.03 189 | 54.58 319 | 97.67 174 | 76.10 257 | 78.81 249 | 90.63 274 |
|
lessismore_v0 | | | | | 85.08 304 | 85.59 319 | 69.28 329 | | 90.56 332 | | 67.68 318 | 90.21 275 | 54.21 321 | 95.46 276 | 73.88 282 | 62.64 321 | 90.50 276 |
|
pmmvs4 | | | 87.58 207 | 86.17 208 | 91.80 206 | 89.58 282 | 88.92 152 | 97.25 213 | 95.28 260 | 82.54 256 | 80.49 243 | 93.17 213 | 75.62 189 | 96.05 260 | 82.75 197 | 78.90 248 | 90.42 277 |
|
WR-MVS_H | | | 86.53 228 | 85.49 223 | 89.66 253 | 91.04 251 | 83.31 260 | 97.53 206 | 98.20 32 | 84.95 208 | 79.64 252 | 90.90 245 | 78.01 178 | 95.33 280 | 76.29 256 | 72.81 288 | 90.35 278 |
|
V42 | | | 87.00 218 | 85.68 221 | 90.98 225 | 89.91 266 | 86.08 222 | 98.32 160 | 95.61 241 | 83.67 233 | 82.72 217 | 90.67 254 | 74.00 214 | 96.53 223 | 81.94 208 | 74.28 277 | 90.32 279 |
|
DTE-MVSNet | | | 84.14 261 | 82.80 254 | 88.14 282 | 88.95 293 | 79.87 291 | 96.81 229 | 96.24 197 | 83.50 241 | 77.60 272 | 92.52 222 | 67.89 268 | 94.24 298 | 72.64 296 | 69.05 309 | 90.32 279 |
|
YYNet1 | | | 79.64 294 | 77.04 296 | 87.43 292 | 87.80 307 | 79.98 288 | 96.23 251 | 94.44 279 | 73.83 315 | 51.83 339 | 87.53 300 | 67.96 267 | 92.07 326 | 66.00 314 | 67.75 314 | 90.23 281 |
|
MDA-MVSNet_test_wron | | | 79.65 293 | 77.05 295 | 87.45 291 | 87.79 308 | 80.13 287 | 96.25 250 | 94.44 279 | 73.87 314 | 51.80 340 | 87.47 301 | 68.04 265 | 92.12 325 | 66.02 313 | 67.79 313 | 90.09 282 |
|
MDA-MVSNet-bldmvs | | | 77.82 302 | 74.75 304 | 87.03 294 | 88.33 300 | 78.52 300 | 96.34 246 | 92.85 309 | 75.57 308 | 48.87 342 | 87.89 295 | 57.32 310 | 92.49 321 | 60.79 324 | 64.80 318 | 90.08 283 |
|
our_test_3 | | | 84.47 255 | 82.80 254 | 89.50 256 | 89.01 291 | 83.90 255 | 97.03 223 | 94.56 276 | 81.33 270 | 75.36 284 | 90.52 265 | 71.69 240 | 94.54 296 | 68.81 305 | 76.84 258 | 90.07 284 |
|
v7n | | | 84.42 256 | 82.75 257 | 89.43 259 | 88.15 302 | 81.86 273 | 96.75 233 | 95.67 233 | 80.53 276 | 78.38 268 | 89.43 285 | 69.89 249 | 96.35 243 | 73.83 284 | 72.13 298 | 90.07 284 |
|
v8 | | | 86.11 233 | 84.45 239 | 91.10 222 | 89.99 264 | 86.85 196 | 97.24 214 | 95.36 256 | 81.99 262 | 79.89 250 | 89.86 280 | 74.53 202 | 96.39 236 | 78.83 235 | 72.32 294 | 90.05 286 |
|
PVSNet_BlendedMVS | | | 93.36 104 | 93.20 91 | 93.84 172 | 98.77 70 | 91.61 88 | 99.47 27 | 98.04 48 | 91.44 69 | 94.21 90 | 92.63 221 | 83.50 124 | 99.87 38 | 97.41 30 | 83.37 227 | 90.05 286 |
|
ITE_SJBPF | | | | | 87.93 286 | 92.26 233 | 76.44 308 | | 93.47 296 | 87.67 164 | 79.95 249 | 95.49 174 | 56.50 312 | 97.38 195 | 75.24 269 | 82.33 236 | 89.98 288 |
|
v748 | | | 83.84 264 | 82.31 262 | 88.41 277 | 87.65 310 | 79.10 294 | 96.66 236 | 95.51 245 | 80.09 279 | 77.65 271 | 88.53 293 | 69.81 250 | 96.23 253 | 75.67 267 | 69.25 307 | 89.91 289 |
|
pm-mvs1 | | | 84.68 249 | 82.78 256 | 90.40 236 | 89.58 282 | 85.18 241 | 97.31 210 | 94.73 271 | 81.93 264 | 76.05 278 | 92.01 226 | 65.48 284 | 96.11 258 | 78.75 236 | 69.14 308 | 89.91 289 |
|
LTVRE_ROB | | 81.71 19 | 84.59 252 | 82.72 258 | 90.18 239 | 92.89 228 | 83.18 261 | 93.15 299 | 94.74 270 | 78.99 286 | 75.14 285 | 92.69 219 | 65.64 283 | 97.63 177 | 69.46 303 | 81.82 238 | 89.74 291 |
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 | | | 86.69 223 | 85.75 218 | 89.53 255 | 86.46 318 | 82.94 263 | 96.39 244 | 95.71 230 | 83.97 222 | 79.63 253 | 90.70 249 | 68.85 259 | 95.94 264 | 86.01 163 | 84.02 222 | 89.72 292 |
|
ppachtmachnet_test | | | 83.63 266 | 81.57 268 | 89.80 248 | 89.01 291 | 85.09 244 | 97.13 220 | 94.50 277 | 78.84 287 | 76.14 277 | 91.00 239 | 69.78 251 | 94.61 295 | 63.40 318 | 74.36 272 | 89.71 293 |
|
v10 | | | 85.73 242 | 84.01 245 | 90.87 227 | 90.03 261 | 86.73 200 | 97.20 217 | 95.22 266 | 81.25 271 | 79.85 251 | 89.75 281 | 73.30 224 | 96.28 252 | 76.87 249 | 72.64 290 | 89.61 294 |
|
UnsupCasMVSNet_eth | | | 78.90 296 | 76.67 298 | 85.58 303 | 82.81 328 | 74.94 311 | 91.98 309 | 96.31 190 | 84.64 212 | 65.84 326 | 87.71 297 | 51.33 326 | 92.23 323 | 72.89 295 | 56.50 338 | 89.56 295 |
|
USDC | | | 84.74 248 | 82.93 250 | 90.16 240 | 91.73 243 | 83.54 258 | 95.00 281 | 93.30 297 | 88.77 129 | 73.19 292 | 93.30 209 | 53.62 322 | 97.65 176 | 75.88 259 | 81.54 239 | 89.30 296 |
|
FMVSNet5 | | | 82.29 269 | 80.54 274 | 87.52 290 | 93.79 212 | 84.01 253 | 93.73 294 | 92.47 314 | 76.92 305 | 74.27 288 | 86.15 310 | 63.69 291 | 89.24 333 | 69.07 304 | 74.79 268 | 89.29 297 |
|
Anonymous20231206 | | | 80.76 287 | 79.42 283 | 84.79 307 | 84.78 321 | 72.98 318 | 96.53 239 | 92.97 302 | 79.56 283 | 74.33 287 | 88.83 290 | 61.27 301 | 92.15 324 | 60.59 325 | 75.92 260 | 89.24 298 |
|
pmmvs6 | | | 79.90 292 | 77.31 293 | 87.67 289 | 84.17 324 | 78.13 303 | 95.86 269 | 93.68 293 | 67.94 331 | 72.67 298 | 89.62 283 | 50.98 328 | 95.75 270 | 74.80 274 | 66.04 315 | 89.14 299 |
|
N_pmnet | | | 70.19 313 | 69.87 312 | 71.12 328 | 88.24 301 | 30.63 359 | 95.85 270 | 28.70 360 | 70.18 324 | 68.73 307 | 86.55 308 | 64.04 289 | 93.81 299 | 53.12 335 | 73.46 285 | 88.94 300 |
|
MIMVSNet1 | | | 75.92 306 | 73.30 307 | 83.81 310 | 81.29 329 | 75.57 310 | 92.26 307 | 92.05 320 | 73.09 316 | 67.48 322 | 86.18 309 | 40.87 340 | 87.64 336 | 55.78 332 | 70.68 306 | 88.21 301 |
|
test2356 | | | 80.96 285 | 81.77 265 | 78.52 322 | 81.02 330 | 62.33 333 | 98.22 173 | 94.49 278 | 79.38 284 | 74.56 286 | 90.34 268 | 70.65 248 | 85.10 341 | 60.83 323 | 86.42 203 | 88.14 302 |
|
test1235678 | | | 71.07 312 | 69.53 314 | 75.71 325 | 71.87 343 | 55.27 345 | 94.32 285 | 90.76 331 | 70.23 323 | 57.61 336 | 79.06 334 | 43.13 336 | 83.72 343 | 50.48 336 | 68.30 311 | 88.14 302 |
|
TransMVSNet (Re) | | | 81.97 272 | 79.61 281 | 89.08 264 | 89.70 277 | 84.01 253 | 97.26 212 | 91.85 323 | 78.84 287 | 73.07 295 | 91.62 232 | 67.17 273 | 95.21 283 | 67.50 308 | 59.46 334 | 88.02 304 |
|
MS-PatchMatch | | | 86.75 222 | 85.92 211 | 89.22 261 | 91.97 237 | 82.47 270 | 96.91 226 | 96.14 203 | 83.74 230 | 77.73 270 | 93.53 205 | 58.19 307 | 97.37 197 | 76.75 252 | 98.35 84 | 87.84 305 |
|
Baseline_NR-MVSNet | | | 85.83 238 | 84.82 234 | 88.87 268 | 88.73 296 | 83.34 259 | 98.63 123 | 91.66 324 | 80.41 278 | 82.44 222 | 91.35 235 | 74.63 196 | 95.42 278 | 84.13 182 | 71.39 303 | 87.84 305 |
|
V4 | | | 84.20 258 | 82.92 251 | 88.02 283 | 87.59 312 | 79.91 290 | 96.21 256 | 95.36 256 | 79.88 280 | 78.51 265 | 89.00 289 | 69.52 255 | 96.32 246 | 77.96 240 | 72.29 295 | 87.83 307 |
|
v52 | | | 84.19 259 | 82.92 251 | 88.01 284 | 87.64 311 | 79.92 289 | 96.23 251 | 95.32 259 | 79.87 281 | 78.51 265 | 89.05 288 | 69.50 256 | 96.32 246 | 77.95 241 | 72.24 297 | 87.79 308 |
|
ambc | | | | | 79.60 320 | 72.76 342 | 56.61 343 | 76.20 345 | 92.01 321 | | 68.25 312 | 80.23 331 | 23.34 349 | 94.73 294 | 73.78 285 | 60.81 325 | 87.48 309 |
|
TinyColmap | | | 80.42 290 | 77.94 290 | 87.85 287 | 92.09 236 | 78.58 299 | 93.74 293 | 89.94 337 | 74.99 309 | 69.77 305 | 91.78 230 | 46.09 333 | 97.58 180 | 65.17 316 | 77.89 253 | 87.38 310 |
|
TDRefinement | | | 78.01 300 | 75.31 301 | 86.10 300 | 70.06 344 | 73.84 315 | 93.59 297 | 91.58 326 | 74.51 312 | 73.08 294 | 91.04 238 | 49.63 330 | 97.12 201 | 74.88 272 | 59.47 333 | 87.33 311 |
|
CMPMVS | | 58.40 21 | 80.48 289 | 80.11 277 | 81.59 318 | 85.10 320 | 59.56 337 | 94.14 290 | 95.95 212 | 68.54 329 | 60.71 331 | 93.31 208 | 55.35 317 | 97.87 159 | 83.06 194 | 84.85 217 | 87.33 311 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
testus | | | 77.11 304 | 76.95 297 | 77.58 323 | 80.02 333 | 58.93 339 | 97.78 197 | 90.48 333 | 79.68 282 | 72.84 297 | 90.61 261 | 37.72 344 | 86.57 340 | 60.28 327 | 83.18 229 | 87.23 313 |
|
LF4IMVS | | | 81.94 273 | 81.17 272 | 84.25 309 | 87.23 315 | 68.87 330 | 93.35 298 | 91.93 322 | 83.35 243 | 75.40 283 | 93.00 216 | 49.25 331 | 96.65 216 | 78.88 234 | 78.11 252 | 87.22 314 |
|
tfpnnormal | | | 83.65 265 | 81.35 270 | 90.56 232 | 91.37 248 | 88.06 167 | 97.29 211 | 97.87 59 | 78.51 292 | 76.20 276 | 90.91 244 | 64.78 286 | 96.47 230 | 61.71 322 | 73.50 284 | 87.13 315 |
|
EG-PatchMatch MVS | | | 79.92 291 | 77.59 291 | 86.90 295 | 87.06 316 | 77.90 306 | 96.20 257 | 94.06 288 | 74.61 311 | 66.53 325 | 88.76 291 | 40.40 342 | 96.20 254 | 67.02 310 | 83.66 226 | 86.61 316 |
|
test20.03 | | | 78.51 299 | 77.48 292 | 81.62 317 | 83.07 327 | 71.03 324 | 96.11 259 | 92.83 310 | 81.66 267 | 69.31 306 | 89.68 282 | 57.53 308 | 87.29 337 | 58.65 330 | 68.47 310 | 86.53 317 |
|
MVP-Stereo | | | 86.61 226 | 85.83 215 | 88.93 267 | 88.70 297 | 83.85 256 | 96.07 260 | 94.41 282 | 82.15 261 | 75.64 282 | 91.96 228 | 67.65 269 | 96.45 232 | 77.20 247 | 98.72 76 | 86.51 318 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
v18 | | | 82.00 271 | 79.76 279 | 88.72 269 | 90.03 261 | 86.81 199 | 96.17 258 | 93.12 298 | 78.70 289 | 68.39 308 | 82.10 315 | 74.64 194 | 93.00 304 | 74.21 277 | 60.45 327 | 86.35 319 |
|
v17 | | | 81.87 276 | 79.61 281 | 88.64 271 | 89.91 266 | 86.64 204 | 96.01 262 | 93.08 299 | 78.54 290 | 68.27 310 | 81.96 317 | 74.44 204 | 92.95 306 | 74.03 280 | 60.22 329 | 86.34 320 |
|
v16 | | | 81.90 274 | 79.65 280 | 88.65 270 | 90.02 263 | 86.66 203 | 96.01 262 | 93.07 300 | 78.53 291 | 68.27 310 | 82.05 316 | 74.39 206 | 92.96 305 | 74.02 281 | 60.48 326 | 86.33 321 |
|
OpenMVS_ROB | | 73.86 20 | 77.99 301 | 75.06 303 | 86.77 296 | 83.81 326 | 77.94 305 | 96.38 245 | 91.53 327 | 67.54 332 | 68.38 309 | 87.13 305 | 43.94 335 | 96.08 259 | 55.03 333 | 81.83 237 | 86.29 322 |
|
V9 | | | 81.46 280 | 79.15 286 | 88.39 279 | 89.75 275 | 86.17 219 | 95.62 275 | 92.92 305 | 78.22 295 | 67.65 319 | 81.64 320 | 73.95 215 | 92.80 311 | 73.15 291 | 59.43 335 | 86.21 323 |
|
v15 | | | 81.62 277 | 79.32 284 | 88.52 273 | 89.80 273 | 86.56 205 | 95.83 271 | 92.96 303 | 78.50 293 | 67.88 314 | 81.68 319 | 74.22 211 | 92.82 309 | 73.46 287 | 59.55 330 | 86.18 324 |
|
v12 | | | 81.37 282 | 79.05 287 | 88.33 280 | 89.68 278 | 86.05 225 | 95.48 277 | 92.92 305 | 78.08 296 | 67.55 320 | 81.58 321 | 73.75 216 | 92.75 314 | 73.05 292 | 59.37 336 | 86.18 324 |
|
V14 | | | 81.55 279 | 79.26 285 | 88.42 276 | 89.80 273 | 86.33 213 | 95.72 274 | 92.96 303 | 78.35 294 | 67.82 315 | 81.70 318 | 74.13 212 | 92.78 313 | 73.32 288 | 59.50 332 | 86.16 326 |
|
v13 | | | 81.30 283 | 78.99 289 | 88.25 281 | 89.61 280 | 85.87 229 | 95.39 278 | 92.90 307 | 77.93 302 | 67.45 323 | 81.52 322 | 73.66 217 | 92.75 314 | 72.91 294 | 59.53 331 | 86.14 327 |
|
v11 | | | 81.38 281 | 79.03 288 | 88.41 277 | 89.68 278 | 86.43 207 | 95.74 273 | 92.82 312 | 78.03 297 | 67.74 316 | 81.45 323 | 73.33 223 | 92.69 317 | 72.23 298 | 60.27 328 | 86.11 328 |
|
UnsupCasMVSNet_bld | | | 73.85 309 | 70.14 311 | 84.99 305 | 79.44 334 | 75.73 309 | 88.53 324 | 95.24 264 | 70.12 325 | 61.94 330 | 74.81 338 | 41.41 339 | 93.62 300 | 68.65 306 | 51.13 345 | 85.62 329 |
|
pmmvs-eth3d | | | 78.71 298 | 76.16 300 | 86.38 297 | 80.25 332 | 81.19 282 | 94.17 289 | 92.13 319 | 77.97 299 | 66.90 324 | 82.31 313 | 55.76 313 | 92.56 320 | 73.63 286 | 62.31 323 | 85.38 330 |
|
PM-MVS | | | 74.88 307 | 72.85 308 | 80.98 319 | 78.98 335 | 64.75 332 | 90.81 318 | 85.77 347 | 80.95 274 | 68.23 313 | 82.81 312 | 29.08 347 | 92.84 308 | 76.54 255 | 62.46 322 | 85.36 331 |
|
test_0402 | | | 78.81 297 | 76.33 299 | 86.26 298 | 91.18 249 | 78.44 301 | 95.88 267 | 91.34 328 | 68.55 328 | 70.51 303 | 89.91 279 | 52.65 324 | 94.99 285 | 47.14 339 | 79.78 246 | 85.34 332 |
|
new-patchmatchnet | | | 74.80 308 | 72.40 309 | 81.99 315 | 78.36 337 | 72.20 321 | 94.44 284 | 92.36 315 | 77.06 304 | 63.47 328 | 79.98 332 | 51.04 327 | 88.85 334 | 60.53 326 | 54.35 340 | 84.92 333 |
|
Anonymous20231211 | | | 67.10 314 | 63.29 317 | 78.54 321 | 75.68 338 | 60.00 336 | 92.05 308 | 88.86 341 | 49.84 343 | 59.35 334 | 78.48 336 | 26.15 348 | 90.76 331 | 45.96 341 | 53.24 342 | 84.88 334 |
|
DeepMVS_CX | | | | | 76.08 324 | 90.74 255 | 51.65 347 | | 90.84 330 | 86.47 187 | 57.89 335 | 87.98 294 | 35.88 345 | 92.60 318 | 65.77 315 | 65.06 317 | 83.97 335 |
|
pmmvs3 | | | 72.86 310 | 69.76 313 | 82.17 313 | 73.86 339 | 74.19 314 | 94.20 288 | 89.01 340 | 64.23 337 | 67.72 317 | 80.91 327 | 41.48 338 | 88.65 335 | 62.40 320 | 54.02 341 | 83.68 336 |
|
new_pmnet | | | 76.02 305 | 73.71 306 | 82.95 312 | 83.88 325 | 72.85 319 | 91.26 315 | 92.26 316 | 70.44 322 | 62.60 329 | 81.37 324 | 47.64 332 | 92.32 322 | 61.85 321 | 72.10 299 | 83.68 336 |
|
LCM-MVSNet | | | 60.07 319 | 56.37 320 | 71.18 327 | 54.81 354 | 48.67 349 | 82.17 342 | 89.48 339 | 37.95 346 | 49.13 341 | 69.12 339 | 13.75 358 | 81.76 345 | 59.28 328 | 51.63 344 | 83.10 338 |
|
testpf | | | 80.59 288 | 80.13 275 | 81.97 316 | 94.25 197 | 71.65 323 | 60.37 351 | 95.46 250 | 70.99 319 | 76.97 274 | 87.74 296 | 73.58 218 | 91.67 328 | 76.86 250 | 84.97 215 | 82.60 339 |
|
1111 | | | 72.28 311 | 71.36 310 | 75.02 326 | 73.04 340 | 57.38 341 | 92.30 305 | 90.22 335 | 62.27 338 | 59.46 332 | 80.36 329 | 76.23 185 | 87.07 338 | 44.29 342 | 64.08 319 | 80.59 340 |
|
test12356 | | | 66.36 315 | 65.12 315 | 70.08 331 | 66.92 345 | 50.46 348 | 89.96 322 | 88.58 342 | 66.00 333 | 53.38 338 | 78.13 337 | 32.89 346 | 82.87 344 | 48.36 338 | 61.87 324 | 76.92 341 |
|
testmv | | | 60.41 318 | 57.98 319 | 67.69 332 | 58.16 353 | 47.14 350 | 89.09 323 | 86.74 345 | 61.52 341 | 44.30 344 | 68.44 340 | 20.98 350 | 79.92 349 | 40.94 346 | 51.67 343 | 76.01 342 |
|
PMMVS2 | | | 58.97 320 | 55.07 321 | 70.69 330 | 62.72 346 | 55.37 344 | 85.97 328 | 80.52 351 | 49.48 344 | 45.94 343 | 68.31 341 | 15.73 356 | 80.78 347 | 49.79 337 | 37.12 346 | 75.91 343 |
|
FPMVS | | | 61.57 316 | 60.32 318 | 65.34 333 | 60.14 350 | 42.44 353 | 91.02 317 | 89.72 338 | 44.15 345 | 42.63 345 | 80.93 326 | 19.02 351 | 80.59 348 | 42.50 345 | 72.76 289 | 73.00 344 |
|
ANet_high | | | 50.71 324 | 46.17 325 | 64.33 334 | 44.27 357 | 52.30 346 | 76.13 346 | 78.73 352 | 64.95 335 | 27.37 351 | 55.23 349 | 14.61 357 | 67.74 353 | 36.01 349 | 18.23 352 | 72.95 345 |
|
no-one | | | 56.69 321 | 51.89 324 | 71.08 329 | 59.35 352 | 58.65 340 | 83.78 340 | 84.81 350 | 61.73 340 | 36.46 348 | 56.52 348 | 18.15 354 | 84.78 342 | 47.03 340 | 19.19 350 | 69.81 346 |
|
wuykxyi23d | | | 43.53 327 | 37.95 330 | 60.27 336 | 45.36 356 | 44.79 351 | 68.27 348 | 74.26 355 | 33.48 349 | 18.21 356 | 40.16 357 | 3.64 361 | 71.01 351 | 38.85 347 | 19.31 349 | 65.02 347 |
|
PNet_i23d | | | 48.05 325 | 44.98 326 | 57.28 337 | 60.15 348 | 42.39 354 | 80.85 344 | 73.14 356 | 36.78 347 | 27.46 350 | 56.66 347 | 6.38 359 | 68.34 352 | 36.65 348 | 26.72 348 | 61.10 348 |
|
tmp_tt | | | 53.66 323 | 52.86 322 | 56.05 338 | 32.75 359 | 41.97 355 | 73.42 347 | 76.12 354 | 21.91 354 | 39.68 347 | 96.39 162 | 42.59 337 | 65.10 354 | 78.00 239 | 14.92 354 | 61.08 349 |
|
PMVS | | 41.42 23 | 45.67 326 | 42.50 327 | 55.17 339 | 34.28 358 | 32.37 357 | 66.24 349 | 78.71 353 | 30.72 350 | 22.04 354 | 59.59 345 | 4.59 360 | 77.85 350 | 27.49 351 | 58.84 337 | 55.29 350 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 44.00 22 | 41.70 328 | 37.64 331 | 53.90 340 | 49.46 355 | 43.37 352 | 65.09 350 | 66.66 357 | 26.19 353 | 25.77 353 | 48.53 351 | 3.58 363 | 63.35 355 | 26.15 352 | 27.28 347 | 54.97 351 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
Gipuma | | | 54.77 322 | 52.22 323 | 62.40 335 | 86.50 317 | 59.37 338 | 50.20 352 | 90.35 334 | 36.52 348 | 41.20 346 | 49.49 350 | 18.33 353 | 81.29 346 | 32.10 350 | 65.34 316 | 46.54 352 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
E-PMN | | | 41.02 329 | 40.93 328 | 41.29 342 | 61.97 347 | 33.83 356 | 84.00 338 | 65.17 358 | 27.17 351 | 27.56 349 | 46.72 352 | 17.63 355 | 60.41 356 | 19.32 353 | 18.82 351 | 29.61 353 |
|
EMVS | | | 39.96 330 | 39.88 329 | 40.18 343 | 59.57 351 | 32.12 358 | 84.79 335 | 64.57 359 | 26.27 352 | 26.14 352 | 44.18 355 | 18.73 352 | 59.29 357 | 17.03 354 | 17.67 353 | 29.12 354 |
|
test123 | | | 16.58 335 | 19.47 335 | 7.91 346 | 3.59 361 | 5.37 361 | 94.32 285 | 1.39 363 | 2.49 357 | 13.98 357 | 44.60 354 | 2.91 364 | 2.65 359 | 11.35 357 | 0.57 358 | 15.70 355 |
|
.test1245 | | | 61.50 317 | 64.44 316 | 52.65 341 | 73.04 340 | 57.38 341 | 92.30 305 | 90.22 335 | 62.27 338 | 59.46 332 | 80.36 329 | 76.23 185 | 87.07 338 | 44.29 342 | 1.80 356 | 13.50 356 |
|
testmvs | | | 18.81 333 | 23.05 334 | 6.10 347 | 4.48 360 | 2.29 362 | 97.78 197 | 3.00 362 | 3.27 356 | 18.60 355 | 62.71 343 | 1.53 365 | 2.49 360 | 14.26 356 | 1.80 356 | 13.50 356 |
|
wuyk23d | | | 16.71 334 | 16.73 336 | 16.65 345 | 60.15 348 | 25.22 360 | 41.24 353 | 5.17 361 | 6.56 355 | 5.48 358 | 3.61 359 | 3.64 361 | 22.72 358 | 15.20 355 | 9.52 355 | 1.99 358 |
|
cdsmvs_eth3d_5k | | | 22.52 332 | 30.03 333 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 97.17 143 | 0.00 358 | 0.00 359 | 98.77 65 | 74.35 207 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
pcd_1.5k_mvsjas | | | 6.87 337 | 9.16 338 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 82.48 146 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
sosnet-low-res | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
sosnet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
uncertanet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
Regformer | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
ab-mvs-re | | | 8.21 336 | 10.94 337 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 98.50 84 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
uanet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
test_part3 | | | | | | | | 99.43 33 | | 92.81 44 | | 99.48 4 | | 99.97 14 | 99.52 1 | | |
|
test_part2 | | | | | | 99.54 27 | 95.42 14 | | | | 98.13 17 | | | | | | |
|
sam_mvs | | | | | | | | | | | | | 87.08 82 | | | | |
|
MTGPA | | | | | | | | | 97.45 119 | | | | | | | | |
|
test_post1 | | | | | | | | 90.74 320 | | | | 41.37 356 | 85.38 110 | 96.36 238 | 83.16 192 | | |
|
test_post | | | | | | | | | | | | 46.00 353 | 87.37 75 | 97.11 202 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 84.86 311 | 88.73 53 | 96.81 213 | | | |
|
MTMP | | | | | | | | | 91.09 329 | | | | | | | | |
|
gm-plane-assit | | | | | | 94.69 191 | 88.14 165 | | | 88.22 146 | | 97.20 128 | | 98.29 141 | 90.79 120 | | |
|
TEST9 | | | | | | 99.57 23 | 93.17 60 | 99.38 40 | 97.66 83 | 89.57 105 | 98.39 12 | 99.18 21 | 90.88 29 | 99.66 66 | | | |
|
test_8 | | | | | | 99.55 26 | 93.07 64 | 99.37 43 | 97.64 88 | 90.18 93 | 98.36 14 | 99.19 19 | 90.94 27 | 99.64 72 | | | |
|
agg_prior | | | | | | 99.54 27 | 92.66 72 | | 97.64 88 | | 97.98 26 | | | 99.61 75 | | | |
|
test_prior4 | | | | | | | 92.00 81 | 99.41 38 | | | | | | | | | |
|
test_prior2 | | | | | | | | 99.57 19 | | 91.43 70 | 98.12 20 | 98.97 48 | 90.43 36 | | 98.33 19 | 99.81 15 | |
|
旧先验2 | | | | | | | | 98.67 118 | | 85.75 193 | 98.96 5 | | | 98.97 118 | 93.84 86 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 98.26 169 | | | | | | | | | |
|
原ACMM2 | | | | | | | | 98.69 113 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.88 35 | 84.16 181 | | |
|
segment_acmp | | | | | | | | | | | | | 90.56 35 | | | | |
|
testdata1 | | | | | | | | 97.89 193 | | 92.43 50 | | | | | | | |
|
plane_prior7 | | | | | | 93.84 209 | 85.73 233 | | | | | | | | | | |
|
plane_prior6 | | | | | | 93.92 206 | 86.02 226 | | | | | | 72.92 226 | | | | |
|
plane_prior4 | | | | | | | | | | | | 96.52 156 | | | | | |
|
plane_prior3 | | | | | | | 85.91 227 | | | 93.65 30 | 86.99 178 | | | | | | |
|
plane_prior2 | | | | | | | | 99.02 78 | | 93.38 35 | | | | | | | |
|
plane_prior1 | | | | | | 93.90 208 | | | | | | | | | | | |
|
plane_prior | | | | | | | 86.07 223 | 99.14 66 | | 93.81 28 | | | | | | 86.26 206 | |
|
n2 | | | | | | | | | 0.00 364 | | | | | | | | |
|
nn | | | | | | | | | 0.00 364 | | | | | | | | |
|
door-mid | | | | | | | | | 84.90 349 | | | | | | | | |
|
test11 | | | | | | | | | 97.68 81 | | | | | | | | |
|
door | | | | | | | | | 85.30 348 | | | | | | | | |
|
HQP5-MVS | | | | | | | 86.39 210 | | | | | | | | | | |
|
HQP-NCC | | | | | | 93.95 202 | | 99.16 58 | | 93.92 22 | 87.57 172 | | | | | | |
|
ACMP_Plane | | | | | | 93.95 202 | | 99.16 58 | | 93.92 22 | 87.57 172 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 93.82 88 | | |
|
HQP3-MVS | | | | | | | | | 96.37 186 | | | | | | | 86.29 204 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.34 221 | | | | |
|
NP-MVS | | | | | | 93.94 205 | 86.22 217 | | | | | 96.67 150 | | | | | |
|
MDTV_nov1_ep13 | | | | 90.47 156 | | 96.14 150 | 88.55 160 | 91.34 314 | 97.51 111 | 89.58 104 | 92.24 111 | 90.50 266 | 86.99 86 | 97.61 179 | 77.64 243 | 92.34 150 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 82.64 234 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 83.83 223 | |
|
Test By Simon | | | | | | | | | | | | | 83.62 123 | | | | |
|