HSP-MVS | | | 97.61 1 | 98.30 1 | 96.81 4 | 98.66 10 | 99.35 4 | 98.00 8 | 94.75 8 | 98.45 2 | 92.78 5 | 97.99 1 | 98.58 5 | 97.41 2 | 98.24 1 | 96.48 10 | 99.27 4 | 98.99 44 |
|
MCST-MVS | | | 96.93 6 | 98.07 4 | 95.61 15 | 98.98 4 | 99.44 3 | 98.04 7 | 95.04 4 | 98.10 3 | 86.55 28 | 97.65 2 | 97.56 7 | 95.60 19 | 97.67 5 | 96.45 11 | 99.43 1 | 99.61 14 |
|
ESAPD | | | 97.61 1 | 98.19 2 | 96.94 3 | 99.03 2 | 99.49 2 | 99.00 1 | 95.35 1 | 97.97 5 | 92.21 10 | 97.50 3 | 99.73 1 | 96.95 3 | 97.13 10 | 95.61 22 | 99.11 6 | 99.87 4 |
|
PGM-MVS | | | 94.64 24 | 95.49 26 | 93.66 26 | 98.55 15 | 98.51 38 | 97.63 20 | 87.77 42 | 94.45 45 | 84.92 36 | 97.23 4 | 91.90 39 | 95.22 21 | 94.56 46 | 93.80 49 | 97.87 82 | 97.97 82 |
|
CNVR-MVS | | | 97.60 3 | 98.08 3 | 97.03 2 | 99.14 1 | 99.55 1 | 98.67 2 | 95.32 2 | 97.91 6 | 92.55 7 | 97.11 5 | 97.23 9 | 97.49 1 | 98.16 2 | 97.05 4 | 99.04 11 | 99.55 15 |
|
train_agg | | | 95.72 16 | 97.37 9 | 93.80 24 | 97.82 26 | 98.92 19 | 97.84 14 | 93.50 19 | 96.86 19 | 81.35 47 | 97.10 6 | 97.71 6 | 94.19 26 | 96.02 23 | 95.37 26 | 98.07 59 | 99.64 12 |
|
SteuartSystems-ACMMP | | | 96.20 12 | 97.22 11 | 95.01 19 | 98.40 17 | 99.11 13 | 97.93 11 | 93.62 18 | 96.28 23 | 87.45 24 | 97.05 7 | 96.00 16 | 94.23 25 | 96.83 15 | 95.97 17 | 98.40 39 | 99.27 26 |
Skip Steuart: Steuart Systems R&D Blog. |
APDe-MVS | | | 97.31 4 | 97.51 8 | 97.08 1 | 98.95 6 | 99.29 9 | 98.58 4 | 95.11 3 | 97.69 12 | 94.16 1 | 96.91 8 | 96.81 13 | 96.57 5 | 96.71 16 | 95.39 25 | 99.08 10 | 99.79 6 |
|
PHI-MVS | | | 94.49 26 | 96.72 16 | 91.88 38 | 97.06 34 | 98.88 20 | 94.99 42 | 89.13 36 | 96.15 26 | 79.70 56 | 96.91 8 | 95.78 19 | 91.87 45 | 94.65 45 | 95.68 20 | 98.53 34 | 98.98 47 |
|
NCCC | | | 97.01 5 | 97.74 5 | 96.16 7 | 99.02 3 | 99.35 4 | 98.63 3 | 95.04 4 | 97.84 9 | 88.95 21 | 96.83 10 | 97.02 12 | 96.39 9 | 97.44 6 | 96.51 9 | 98.90 20 | 99.16 36 |
|
HFP-MVS | | | 96.09 13 | 96.41 19 | 95.72 14 | 98.58 13 | 98.84 22 | 97.95 10 | 93.08 22 | 96.96 17 | 90.24 15 | 96.60 11 | 94.40 25 | 96.52 7 | 95.13 34 | 94.33 40 | 97.93 75 | 98.59 63 |
|
HPM-MVS++ | | | 96.91 7 | 97.70 6 | 96.00 9 | 98.97 5 | 99.16 12 | 97.82 15 | 94.81 7 | 98.04 4 | 89.61 17 | 96.56 12 | 98.60 4 | 96.39 9 | 97.09 11 | 95.22 27 | 98.39 40 | 99.22 30 |
|
SD-MVS | | | 96.87 8 | 97.69 7 | 95.92 10 | 96.38 42 | 99.25 10 | 97.76 16 | 94.75 8 | 97.72 10 | 92.46 9 | 95.94 13 | 99.09 2 | 96.48 8 | 96.01 24 | 96.08 16 | 97.68 87 | 99.73 9 |
|
MP-MVS | | | 95.24 21 | 95.96 20 | 94.40 22 | 98.32 19 | 98.38 44 | 97.12 24 | 92.87 23 | 95.17 39 | 85.50 33 | 95.68 14 | 94.91 23 | 94.58 23 | 95.11 35 | 93.76 50 | 98.05 62 | 98.68 57 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
EPNet | | | 93.69 30 | 95.34 27 | 91.76 39 | 96.98 36 | 98.47 41 | 95.40 39 | 86.79 45 | 95.47 33 | 82.84 41 | 95.66 15 | 89.17 44 | 90.47 64 | 95.25 33 | 94.69 36 | 98.10 55 | 98.68 57 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
APD-MVS | | | 96.79 9 | 96.99 14 | 96.56 5 | 98.76 9 | 98.87 21 | 98.42 5 | 94.93 6 | 97.70 11 | 91.83 11 | 95.52 16 | 95.94 17 | 96.63 4 | 95.94 25 | 95.47 23 | 98.80 24 | 99.47 17 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMMP | | | 93.32 31 | 93.59 36 | 93.00 33 | 97.03 35 | 98.24 47 | 95.27 40 | 91.66 32 | 95.20 37 | 83.25 40 | 95.39 17 | 85.52 62 | 92.80 37 | 92.60 78 | 90.21 86 | 98.01 67 | 97.99 81 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
ACMMPR | | | 95.59 17 | 95.89 21 | 95.25 17 | 98.41 16 | 98.74 25 | 97.69 19 | 92.73 26 | 96.88 18 | 88.95 21 | 95.33 18 | 92.91 32 | 95.79 16 | 94.73 44 | 94.33 40 | 97.92 78 | 98.32 74 |
|
CP-MVS | | | 95.43 20 | 95.67 23 | 95.14 18 | 98.24 22 | 98.60 30 | 97.45 22 | 92.80 24 | 95.98 28 | 89.21 20 | 95.22 19 | 93.60 27 | 95.43 20 | 94.37 48 | 93.22 58 | 97.68 87 | 98.72 55 |
|
TSAR-MVS + GP. | | | 94.59 25 | 96.60 17 | 92.25 36 | 90.25 90 | 98.17 50 | 96.22 31 | 86.53 48 | 97.49 13 | 87.26 25 | 95.21 20 | 97.06 11 | 94.07 30 | 94.34 50 | 94.20 44 | 99.18 5 | 99.71 10 |
|
TSAR-MVS + MP. | | | 96.50 11 | 97.08 12 | 95.82 13 | 96.12 46 | 98.97 18 | 98.00 8 | 94.13 15 | 97.89 7 | 91.49 12 | 95.11 21 | 97.52 8 | 96.26 14 | 96.27 22 | 94.07 46 | 98.91 19 | 99.74 8 |
|
MVS_111021_LR | | | 93.05 35 | 94.53 31 | 91.32 43 | 96.43 41 | 98.38 44 | 92.81 56 | 87.20 44 | 95.94 30 | 81.45 45 | 94.75 22 | 86.08 58 | 92.12 44 | 94.83 39 | 93.34 56 | 97.89 81 | 98.42 70 |
|
X-MVS | | | 94.70 23 | 95.71 22 | 93.52 28 | 98.38 18 | 98.56 32 | 96.99 25 | 92.62 27 | 95.58 32 | 81.00 53 | 94.57 23 | 93.49 28 | 94.16 28 | 94.82 40 | 94.29 42 | 97.99 71 | 98.68 57 |
|
TSAR-MVS + ACMM | | | 94.99 22 | 97.02 13 | 92.61 35 | 97.19 32 | 98.71 27 | 97.74 18 | 93.21 21 | 96.97 16 | 79.27 59 | 94.09 24 | 97.14 10 | 90.84 57 | 96.64 17 | 95.94 18 | 97.42 101 | 99.67 11 |
|
MVS_111021_HR | | | 92.73 37 | 94.83 30 | 90.28 51 | 96.27 43 | 99.10 14 | 92.77 57 | 86.15 51 | 93.41 50 | 77.11 72 | 93.82 25 | 87.39 51 | 90.61 61 | 95.60 27 | 95.15 29 | 98.79 25 | 99.32 21 |
|
ACMMP_Plus | | | 95.81 15 | 96.50 18 | 95.01 19 | 98.79 8 | 99.17 11 | 97.52 21 | 94.20 14 | 96.19 25 | 85.71 32 | 93.80 26 | 96.20 15 | 95.89 15 | 96.62 18 | 94.98 33 | 97.93 75 | 98.52 66 |
|
CPTT-MVS | | | 94.11 29 | 93.99 33 | 94.25 23 | 96.58 39 | 97.66 54 | 97.31 23 | 91.94 29 | 94.84 42 | 88.72 23 | 92.51 27 | 93.04 31 | 95.78 17 | 91.51 91 | 89.97 90 | 95.15 186 | 98.37 71 |
|
SMA-MVS | | | 96.67 10 | 97.36 10 | 95.86 11 | 98.90 7 | 99.34 7 | 97.86 13 | 94.75 8 | 97.31 14 | 89.22 19 | 92.39 28 | 99.04 3 | 96.29 13 | 97.23 9 | 96.87 5 | 98.15 53 | 99.34 20 |
|
TSAR-MVS + COLMAP | | | 89.59 55 | 89.64 61 | 89.53 56 | 93.32 60 | 96.51 69 | 95.03 41 | 88.53 39 | 95.98 28 | 69.10 101 | 91.81 29 | 64.53 131 | 93.40 35 | 93.53 55 | 91.35 71 | 97.77 83 | 93.75 161 |
|
HQP-MVS | | | 89.57 56 | 90.57 54 | 88.41 61 | 92.77 62 | 94.71 99 | 94.24 48 | 87.97 40 | 93.44 49 | 68.18 104 | 91.75 30 | 71.54 106 | 89.90 68 | 92.31 85 | 91.43 69 | 97.39 102 | 98.80 53 |
|
zzz-MVS | | | 95.87 14 | 95.63 25 | 96.15 8 | 98.60 12 | 98.83 23 | 97.89 12 | 93.65 17 | 96.24 24 | 93.08 4 | 91.13 31 | 95.46 22 | 95.72 18 | 95.64 26 | 93.67 53 | 97.97 72 | 98.46 69 |
|
abl_6 | | | | | 93.25 30 | 97.12 33 | 98.71 27 | 94.40 46 | 87.81 41 | 97.86 8 | 87.19 26 | 91.07 32 | 95.80 18 | 94.18 27 | | | 98.78 26 | 99.36 19 |
|
CDPH-MVS | | | 93.22 33 | 95.08 28 | 91.04 45 | 97.57 29 | 98.49 40 | 96.74 27 | 89.35 35 | 95.19 38 | 73.57 82 | 90.26 33 | 91.59 40 | 90.68 60 | 95.09 37 | 96.15 14 | 98.31 45 | 98.81 52 |
|
PMMVS | | | 88.56 60 | 91.22 47 | 85.47 88 | 90.04 94 | 95.60 90 | 86.62 115 | 78.49 105 | 93.86 47 | 70.62 95 | 90.00 34 | 80.08 80 | 91.64 46 | 92.36 83 | 89.80 96 | 95.40 181 | 96.84 106 |
|
AdaColmap | | | 94.28 27 | 92.94 38 | 95.84 12 | 98.32 19 | 98.33 46 | 96.06 33 | 94.62 11 | 96.29 22 | 91.22 13 | 89.89 35 | 85.50 64 | 96.38 11 | 91.85 88 | 90.89 74 | 98.44 36 | 97.81 86 |
|
DeepPCF-MVS | | 91.00 2 | 94.15 28 | 96.87 15 | 90.97 46 | 96.82 37 | 99.33 8 | 89.40 89 | 92.76 25 | 98.76 1 | 82.36 43 | 88.74 36 | 95.49 21 | 90.58 63 | 98.13 3 | 97.80 3 | 93.88 195 | 99.88 3 |
|
LGP-MVS_train | | | 86.95 74 | 87.65 77 | 86.12 84 | 91.77 69 | 93.84 109 | 93.04 54 | 82.77 74 | 88.04 81 | 65.33 112 | 87.69 37 | 67.09 120 | 86.79 89 | 90.20 110 | 88.99 111 | 97.05 113 | 97.71 87 |
|
CHOSEN 280x420 | | | 90.61 50 | 94.27 32 | 86.35 80 | 93.12 61 | 98.16 51 | 89.99 84 | 69.62 173 | 92.48 61 | 76.89 75 | 87.28 38 | 96.72 14 | 90.31 66 | 94.81 41 | 92.33 62 | 98.17 50 | 98.08 79 |
|
MSLP-MVS++ | | | 95.49 19 | 94.84 29 | 96.25 6 | 98.64 11 | 98.63 29 | 98.35 6 | 92.37 28 | 95.04 41 | 92.62 6 | 87.12 39 | 93.79 26 | 96.55 6 | 93.53 55 | 96.78 6 | 98.98 15 | 98.99 44 |
|
tfpn111 | | | 87.30 68 | 87.03 83 | 87.61 68 | 90.54 76 | 96.39 71 | 91.35 68 | 83.15 66 | 84.16 109 | 71.65 87 | 86.75 40 | 60.49 137 | 90.91 52 | 92.89 69 | 89.34 98 | 98.05 62 | 99.17 32 |
|
thresconf0.02 | | | 86.84 75 | 89.56 64 | 83.67 99 | 90.08 92 | 95.66 87 | 89.03 91 | 83.62 63 | 87.45 84 | 62.19 121 | 86.75 40 | 80.81 74 | 78.48 135 | 92.24 86 | 91.27 72 | 98.60 31 | 92.72 176 |
|
DeepC-MVS_fast | | 91.53 1 | 95.57 18 | 95.67 23 | 95.45 16 | 98.57 14 | 99.00 17 | 97.76 16 | 94.41 12 | 97.06 15 | 86.84 27 | 86.39 42 | 92.27 37 | 96.38 11 | 97.89 4 | 98.06 2 | 98.73 30 | 99.01 43 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMP | | 85.16 9 | 87.15 72 | 87.04 82 | 87.27 74 | 90.80 73 | 94.45 103 | 89.41 88 | 83.09 72 | 89.15 73 | 76.98 74 | 86.35 43 | 65.80 125 | 86.94 87 | 88.45 123 | 87.52 129 | 96.42 158 | 97.56 93 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CANet_DTU | | | 87.91 61 | 91.57 46 | 83.64 100 | 90.96 71 | 97.12 60 | 91.90 62 | 75.97 123 | 92.83 59 | 53.16 179 | 86.02 44 | 79.02 82 | 90.80 58 | 95.40 31 | 94.15 45 | 99.03 13 | 96.47 124 |
|
EPNet_dtu | | | 84.87 97 | 89.01 68 | 80.05 122 | 95.25 50 | 92.88 115 | 88.84 93 | 84.11 60 | 91.69 63 | 49.28 196 | 85.69 45 | 78.95 83 | 65.39 197 | 92.22 87 | 91.66 66 | 97.43 100 | 89.95 190 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
RPSCF | | | 82.91 114 | 81.86 130 | 84.13 94 | 88.25 113 | 88.32 169 | 87.67 103 | 80.86 89 | 84.78 103 | 76.57 77 | 85.56 46 | 76.00 91 | 84.61 101 | 78.20 208 | 76.52 215 | 86.81 224 | 83.63 211 |
|
MVSTER | | | 91.91 42 | 93.43 37 | 90.14 52 | 89.81 98 | 92.32 123 | 94.53 45 | 81.32 86 | 96.00 27 | 84.77 37 | 85.41 47 | 92.39 35 | 91.32 47 | 96.41 19 | 94.01 47 | 99.11 6 | 97.45 96 |
|
GG-mvs-BLEND | | | 65.67 212 | 93.78 34 | 32.89 232 | 0.47 240 | 99.35 4 | 96.92 26 | 0.22 239 | 93.28 51 | 0.51 242 | 84.07 48 | 92.50 33 | 0.62 239 | 93.59 54 | 93.86 48 | 98.59 32 | 99.79 6 |
|
CSCG | | | 93.16 34 | 92.65 40 | 93.76 25 | 98.32 19 | 99.09 15 | 96.12 32 | 89.91 34 | 93.15 53 | 89.64 16 | 83.62 49 | 88.91 47 | 92.40 41 | 91.09 97 | 93.70 51 | 96.14 168 | 98.99 44 |
|
canonicalmvs | | | 89.62 54 | 89.87 58 | 89.33 57 | 90.47 81 | 97.02 63 | 93.46 52 | 79.67 96 | 92.45 62 | 81.05 52 | 82.84 50 | 73.00 94 | 93.71 33 | 90.38 107 | 94.85 34 | 97.65 90 | 98.54 65 |
|
tfpn_ndepth | | | 86.61 77 | 87.92 75 | 85.08 90 | 90.39 84 | 95.45 93 | 88.21 97 | 82.30 78 | 90.79 68 | 71.22 92 | 82.59 51 | 72.09 100 | 80.42 125 | 91.37 93 | 88.61 117 | 97.93 75 | 94.56 143 |
|
PLC | | 89.12 3 | 92.67 38 | 90.84 49 | 94.81 21 | 97.69 27 | 96.10 80 | 95.42 38 | 91.70 30 | 95.82 31 | 92.52 8 | 81.24 52 | 86.01 59 | 94.36 24 | 92.44 82 | 90.27 83 | 97.19 110 | 93.99 151 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MAR-MVS | | | 90.44 51 | 91.17 48 | 89.59 55 | 97.48 30 | 97.92 53 | 90.96 79 | 79.80 93 | 95.07 40 | 77.03 73 | 80.83 53 | 79.10 81 | 94.68 22 | 93.16 62 | 94.46 39 | 97.59 93 | 97.63 88 |
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 |
tfpn_n400 | | | 83.32 110 | 84.61 102 | 81.81 113 | 89.50 103 | 94.81 97 | 87.41 107 | 81.65 81 | 80.24 126 | 58.99 128 | 80.80 54 | 66.64 121 | 75.84 168 | 90.09 112 | 89.33 105 | 97.46 95 | 90.37 186 |
|
tfpnconf | | | 83.32 110 | 84.61 102 | 81.81 113 | 89.50 103 | 94.81 97 | 87.41 107 | 81.65 81 | 80.24 126 | 58.99 128 | 80.80 54 | 66.64 121 | 75.84 168 | 90.09 112 | 89.33 105 | 97.46 95 | 90.37 186 |
|
tfpnview11 | | | 83.86 105 | 85.36 95 | 82.10 112 | 89.66 100 | 94.55 100 | 87.73 102 | 81.81 80 | 85.72 94 | 58.99 128 | 80.80 54 | 66.64 121 | 76.13 166 | 90.79 98 | 88.15 122 | 98.26 46 | 90.90 184 |
|
OMC-MVS | | | 92.05 41 | 91.88 44 | 92.25 36 | 96.51 40 | 97.94 52 | 93.18 53 | 88.97 38 | 96.53 20 | 84.47 38 | 80.79 57 | 87.85 49 | 93.25 36 | 92.48 80 | 91.81 65 | 97.12 111 | 95.73 128 |
|
TAPA-MVS | | 87.40 6 | 90.98 47 | 90.71 50 | 91.30 44 | 96.14 45 | 97.66 54 | 94.80 43 | 89.00 37 | 94.74 44 | 77.42 70 | 80.22 58 | 86.70 54 | 92.27 42 | 91.65 90 | 90.17 88 | 98.15 53 | 93.83 156 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CLD-MVS | | | 88.99 58 | 88.07 73 | 90.07 53 | 89.61 101 | 94.94 96 | 93.82 51 | 85.70 55 | 92.73 60 | 82.73 42 | 79.97 59 | 69.59 111 | 90.44 65 | 90.32 109 | 89.93 92 | 98.10 55 | 99.04 41 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
PatchMatch-RL | | | 86.75 76 | 85.43 94 | 88.29 62 | 94.06 55 | 96.37 78 | 86.82 114 | 82.94 73 | 88.94 74 | 79.59 57 | 79.83 60 | 59.17 147 | 89.46 73 | 91.12 96 | 88.81 114 | 96.88 117 | 93.78 158 |
|
tfpn1000 | | | 84.98 94 | 86.47 87 | 83.24 101 | 89.93 95 | 94.98 94 | 86.58 116 | 81.22 87 | 88.54 78 | 67.35 105 | 79.39 61 | 70.93 107 | 76.07 167 | 90.70 100 | 87.37 131 | 98.32 44 | 93.37 166 |
|
PVSNet_BlendedMVS | | | 90.74 48 | 90.66 51 | 90.82 48 | 94.75 52 | 98.54 36 | 91.30 73 | 86.53 48 | 95.43 34 | 85.75 30 | 78.66 62 | 70.67 108 | 87.60 84 | 96.37 20 | 95.08 31 | 98.98 15 | 99.90 1 |
|
PVSNet_Blended | | | 90.74 48 | 90.66 51 | 90.82 48 | 94.75 52 | 98.54 36 | 91.30 73 | 86.53 48 | 95.43 34 | 85.75 30 | 78.66 62 | 70.67 108 | 87.60 84 | 96.37 20 | 95.08 31 | 98.98 15 | 99.90 1 |
|
conf0.002 | | | 87.85 62 | 87.85 76 | 87.84 65 | 90.63 74 | 96.81 65 | 91.35 68 | 83.36 64 | 84.16 109 | 72.61 84 | 78.06 64 | 71.90 103 | 90.91 52 | 93.29 59 | 91.47 68 | 98.20 48 | 99.28 25 |
|
UGNet | | | 87.04 73 | 89.59 63 | 84.07 95 | 90.94 72 | 95.95 83 | 86.02 119 | 81.65 81 | 85.94 91 | 78.54 65 | 78.00 65 | 85.40 66 | 69.62 185 | 91.83 89 | 91.53 67 | 97.63 91 | 98.51 67 |
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_Test | | | 89.02 57 | 90.20 57 | 87.64 67 | 89.83 97 | 97.05 62 | 92.30 59 | 77.59 112 | 92.89 58 | 75.01 79 | 77.36 66 | 76.10 90 | 92.27 42 | 95.30 32 | 95.42 24 | 98.83 23 | 97.30 99 |
|
CANet | | | 93.23 32 | 93.72 35 | 92.65 34 | 95.48 49 | 99.09 15 | 96.55 29 | 86.74 46 | 95.28 36 | 85.22 34 | 77.30 67 | 91.25 41 | 92.60 39 | 97.06 12 | 96.63 7 | 99.31 2 | 99.45 18 |
|
EPP-MVSNet | | | 87.72 64 | 89.74 59 | 85.37 89 | 89.11 108 | 95.57 91 | 86.31 117 | 79.44 97 | 85.83 93 | 75.73 78 | 77.23 68 | 90.05 43 | 84.78 100 | 91.22 95 | 90.25 84 | 96.83 118 | 98.04 80 |
|
MDTV_nov1_ep13 | | | 84.17 103 | 88.03 74 | 79.66 125 | 86.00 126 | 94.41 104 | 85.05 125 | 66.01 195 | 90.36 69 | 64.34 117 | 77.13 69 | 84.56 69 | 82.71 115 | 87.12 133 | 88.92 112 | 93.84 198 | 93.69 162 |
|
MVS_0304 | | | 91.90 43 | 92.93 39 | 90.69 50 | 93.66 57 | 98.78 24 | 96.73 28 | 85.43 58 | 93.13 54 | 78.11 68 | 77.02 70 | 89.09 45 | 91.10 50 | 96.98 13 | 96.54 8 | 99.11 6 | 98.96 48 |
|
test0.0.03 1 | | | 80.99 126 | 84.37 107 | 77.05 156 | 85.32 131 | 89.79 141 | 78.43 186 | 74.18 136 | 84.78 103 | 57.98 137 | 76.06 71 | 72.88 95 | 69.14 189 | 88.02 127 | 87.70 126 | 97.27 106 | 91.37 181 |
|
QAPM | | | 91.68 44 | 91.97 42 | 91.34 42 | 97.86 25 | 98.72 26 | 95.60 37 | 85.72 53 | 90.86 67 | 77.14 71 | 76.06 71 | 90.35 42 | 92.69 38 | 94.10 51 | 94.60 37 | 99.04 11 | 99.09 37 |
|
PCF-MVS | | 88.14 5 | 90.42 52 | 89.56 64 | 91.41 41 | 94.44 54 | 98.18 49 | 94.35 47 | 94.33 13 | 84.55 105 | 76.61 76 | 75.84 73 | 88.47 48 | 91.29 48 | 90.37 108 | 90.66 80 | 97.46 95 | 98.88 51 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
conf0.01 | | | 87.22 69 | 86.71 86 | 87.81 66 | 90.61 75 | 96.75 67 | 91.35 68 | 83.33 65 | 84.16 109 | 72.45 85 | 75.61 74 | 68.65 114 | 90.91 52 | 93.23 60 | 89.34 98 | 98.17 50 | 99.27 26 |
|
test-LLR | | | 85.11 93 | 89.49 66 | 80.00 123 | 85.32 131 | 94.49 101 | 82.27 165 | 74.18 136 | 87.83 82 | 56.70 142 | 75.55 75 | 86.26 55 | 82.75 113 | 93.06 65 | 90.60 81 | 98.77 27 | 98.65 61 |
|
TESTMET0.1,1 | | | 84.62 101 | 89.49 66 | 78.94 132 | 82.18 144 | 94.49 101 | 82.27 165 | 70.94 162 | 87.83 82 | 56.70 142 | 75.55 75 | 86.26 55 | 82.75 113 | 93.06 65 | 90.60 81 | 98.77 27 | 98.65 61 |
|
IS_MVSNet | | | 87.83 63 | 90.66 51 | 84.53 93 | 90.08 92 | 96.79 66 | 88.16 98 | 79.89 92 | 85.44 95 | 72.20 86 | 75.50 77 | 87.14 52 | 80.21 126 | 95.53 28 | 95.22 27 | 96.65 133 | 99.02 42 |
|
ACMM | | 84.23 10 | 86.40 79 | 84.64 101 | 88.46 60 | 91.90 67 | 91.93 128 | 88.11 99 | 85.59 56 | 88.61 77 | 79.13 61 | 75.31 78 | 66.25 124 | 89.86 71 | 89.88 116 | 87.64 127 | 96.16 167 | 92.86 174 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UA-Net | | | 84.69 100 | 87.64 78 | 81.25 117 | 90.38 85 | 95.67 86 | 87.33 109 | 79.41 98 | 72.07 150 | 66.48 109 | 75.09 79 | 92.48 34 | 66.88 194 | 94.03 52 | 94.25 43 | 97.01 116 | 89.88 191 |
|
DWT-MVSNet_training | | | 87.65 65 | 88.45 72 | 86.71 78 | 90.32 88 | 95.64 88 | 87.91 101 | 75.69 127 | 93.27 52 | 81.43 46 | 74.99 80 | 76.48 89 | 86.92 88 | 87.74 129 | 92.29 63 | 98.00 68 | 98.74 54 |
|
test-mter | | | 84.06 104 | 89.00 69 | 78.29 137 | 81.92 146 | 94.23 105 | 81.07 176 | 70.38 166 | 87.12 86 | 56.10 152 | 74.75 81 | 85.80 60 | 81.81 118 | 92.52 79 | 90.10 89 | 98.43 37 | 98.49 68 |
|
DeepC-MVS | | 88.77 4 | 92.39 40 | 91.74 45 | 93.14 32 | 96.21 44 | 98.55 35 | 96.30 30 | 93.84 16 | 93.06 57 | 81.09 51 | 74.69 82 | 85.20 67 | 93.48 34 | 95.41 30 | 96.13 15 | 97.92 78 | 99.18 31 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
testgi | | | 73.22 184 | 75.84 152 | 70.16 204 | 81.67 151 | 85.50 193 | 71.45 205 | 70.81 163 | 69.56 166 | 44.74 211 | 74.52 83 | 49.25 184 | 58.45 211 | 84.10 160 | 83.37 189 | 93.86 197 | 84.56 207 |
|
PatchmatchNet | | | 83.28 112 | 87.57 79 | 78.29 137 | 87.46 121 | 94.95 95 | 83.36 135 | 59.43 214 | 90.20 70 | 58.10 134 | 74.29 84 | 86.20 57 | 84.13 103 | 85.27 149 | 87.39 130 | 97.25 107 | 94.67 142 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
diffmvs | | | 88.92 59 | 90.30 56 | 87.32 73 | 89.46 105 | 96.38 77 | 91.21 75 | 77.89 109 | 93.11 55 | 79.09 62 | 74.17 85 | 87.41 50 | 88.55 81 | 90.20 110 | 92.70 61 | 97.71 86 | 98.13 75 |
|
Fast-Effi-MVS+-dtu | | | 80.57 127 | 83.44 117 | 77.22 152 | 83.98 138 | 91.52 131 | 85.78 123 | 64.54 200 | 80.38 125 | 50.28 192 | 74.06 86 | 62.89 135 | 82.00 117 | 89.10 119 | 88.91 113 | 96.75 122 | 97.21 101 |
|
Vis-MVSNet (Re-imp) | | | 85.89 84 | 89.62 62 | 81.55 115 | 89.85 96 | 96.08 81 | 87.55 105 | 79.80 93 | 84.80 102 | 66.55 108 | 73.70 87 | 86.71 53 | 68.25 192 | 94.40 47 | 94.53 38 | 97.32 105 | 97.09 102 |
|
3Dnovator | | 85.78 8 | 92.53 39 | 91.96 43 | 93.20 31 | 97.99 23 | 98.47 41 | 95.78 35 | 85.94 52 | 93.07 56 | 86.40 29 | 73.43 88 | 89.00 46 | 94.08 29 | 94.74 43 | 96.44 12 | 99.01 14 | 98.57 64 |
|
FC-MVSNet-train | | | 84.88 96 | 84.08 109 | 85.82 87 | 89.21 107 | 91.74 129 | 85.87 120 | 81.20 88 | 81.71 118 | 74.66 81 | 73.38 89 | 64.99 129 | 86.60 91 | 90.75 99 | 88.08 123 | 97.36 103 | 97.90 84 |
|
tmp_tt | | | | | 57.89 222 | 79.94 159 | 59.29 233 | 52.84 228 | 36.65 236 | 94.77 43 | 68.22 103 | 72.96 90 | 65.62 126 | 33.65 229 | 66.20 225 | 58.02 227 | 76.06 230 | |
|
DELS-MVS | | | 91.09 46 | 90.56 55 | 91.71 40 | 95.82 47 | 98.59 31 | 95.74 36 | 86.68 47 | 85.86 92 | 85.12 35 | 72.71 91 | 81.36 73 | 88.06 83 | 97.31 7 | 98.27 1 | 98.86 22 | 99.82 5 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
CostFormer | | | 85.47 89 | 86.98 84 | 83.71 98 | 88.70 110 | 94.02 108 | 88.07 100 | 62.72 204 | 89.78 71 | 78.68 63 | 72.69 92 | 78.37 84 | 87.35 86 | 85.96 142 | 89.32 107 | 96.73 124 | 98.72 55 |
|
EPMVS | | | 83.71 108 | 86.76 85 | 80.16 121 | 89.72 99 | 95.64 88 | 84.68 126 | 59.73 211 | 89.61 72 | 62.67 119 | 72.65 93 | 81.80 72 | 86.22 93 | 86.23 138 | 88.03 125 | 97.96 73 | 93.35 167 |
|
Effi-MVS+ | | | 84.80 99 | 85.71 92 | 83.73 97 | 87.94 116 | 95.76 85 | 90.08 83 | 73.45 141 | 85.12 99 | 62.66 120 | 72.39 94 | 64.97 130 | 90.59 62 | 92.95 68 | 90.69 79 | 97.67 89 | 98.12 76 |
|
3Dnovator+ | | 86.26 7 | 92.90 36 | 92.45 41 | 93.42 29 | 97.25 31 | 98.45 43 | 95.82 34 | 85.71 54 | 93.83 48 | 89.55 18 | 72.31 95 | 92.28 36 | 94.01 31 | 95.10 36 | 95.92 19 | 98.17 50 | 99.23 29 |
|
Effi-MVS+-dtu | | | 81.18 125 | 82.77 122 | 79.33 128 | 84.70 135 | 92.54 119 | 85.81 121 | 71.55 157 | 78.84 132 | 57.06 140 | 71.98 96 | 63.77 133 | 85.09 99 | 88.94 120 | 87.62 128 | 91.79 213 | 95.68 129 |
|
OPM-MVS | | | 85.69 87 | 82.79 121 | 89.06 59 | 93.42 58 | 94.21 106 | 94.21 49 | 87.61 43 | 72.68 147 | 70.79 94 | 71.09 97 | 67.27 119 | 90.74 59 | 91.29 94 | 89.05 110 | 97.61 92 | 93.94 153 |
|
IB-MVS | | 79.58 12 | 83.83 106 | 84.81 98 | 82.68 104 | 91.85 68 | 97.35 57 | 75.75 195 | 82.57 77 | 86.55 89 | 84.01 39 | 70.90 98 | 65.43 127 | 63.18 204 | 84.19 157 | 89.92 94 | 98.74 29 | 99.31 23 |
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 |
conf200view11 | | | 86.07 82 | 84.76 99 | 87.61 68 | 90.54 76 | 96.39 71 | 91.35 68 | 83.15 66 | 84.16 109 | 71.65 87 | 70.86 99 | 60.49 137 | 90.91 52 | 92.89 69 | 89.34 98 | 98.05 62 | 99.17 32 |
|
thres100view900 | | | 86.48 78 | 85.08 97 | 88.12 64 | 90.54 76 | 96.90 64 | 92.39 58 | 84.82 59 | 84.16 109 | 71.65 87 | 70.86 99 | 60.49 137 | 91.23 49 | 93.65 53 | 90.19 87 | 98.10 55 | 99.32 21 |
|
tfpn200view9 | | | 86.07 82 | 84.76 99 | 87.61 68 | 90.54 76 | 96.39 71 | 91.35 68 | 83.15 66 | 84.16 109 | 71.65 87 | 70.86 99 | 60.49 137 | 90.91 52 | 92.89 69 | 89.34 98 | 98.05 62 | 99.17 32 |
|
Vis-MVSNet | | | 82.88 115 | 86.04 89 | 79.20 130 | 87.77 119 | 96.42 70 | 86.10 118 | 76.70 114 | 74.82 142 | 61.38 123 | 70.70 102 | 77.91 85 | 64.83 198 | 93.22 61 | 93.19 59 | 98.43 37 | 96.01 126 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
LS3D | | | 87.19 70 | 85.48 93 | 89.18 58 | 94.96 51 | 95.47 92 | 92.02 61 | 93.36 20 | 88.69 76 | 67.01 106 | 70.56 103 | 72.10 98 | 92.47 40 | 89.96 115 | 89.93 92 | 95.25 183 | 91.68 180 |
|
thres200 | | | 85.80 86 | 84.38 106 | 87.46 71 | 90.51 80 | 96.39 71 | 91.64 63 | 83.15 66 | 81.59 119 | 71.54 91 | 70.24 104 | 60.41 141 | 89.88 69 | 92.89 69 | 89.85 95 | 98.06 60 | 99.26 28 |
|
FC-MVSNet-test | | | 77.95 144 | 81.85 131 | 73.39 188 | 82.31 142 | 88.99 162 | 79.33 182 | 74.24 135 | 78.75 133 | 47.40 204 | 70.22 105 | 72.09 100 | 60.78 210 | 86.66 135 | 85.62 147 | 96.30 162 | 90.61 185 |
|
dps | | | 82.63 116 | 82.64 124 | 82.62 106 | 87.81 118 | 92.81 116 | 84.39 127 | 61.96 205 | 86.43 90 | 81.63 44 | 69.72 106 | 67.60 118 | 84.42 102 | 82.51 185 | 83.90 183 | 95.52 176 | 95.50 134 |
|
CNLPA | | | 91.53 45 | 89.74 59 | 93.63 27 | 96.75 38 | 97.63 56 | 91.16 76 | 91.70 30 | 96.38 21 | 90.82 14 | 69.66 107 | 85.52 62 | 93.76 32 | 90.44 106 | 91.14 73 | 97.55 94 | 97.40 97 |
|
FMVSNet5 | | | 80.56 128 | 82.53 125 | 78.26 139 | 73.80 215 | 81.52 212 | 82.26 168 | 68.36 181 | 88.85 75 | 64.21 118 | 69.09 108 | 84.38 70 | 83.49 109 | 87.13 132 | 86.76 136 | 97.44 98 | 79.95 217 |
|
thres400 | | | 85.59 88 | 84.08 109 | 87.36 72 | 90.45 82 | 96.60 68 | 90.95 80 | 83.67 62 | 80.99 122 | 71.17 93 | 69.08 109 | 60.25 142 | 89.88 69 | 93.14 63 | 89.34 98 | 98.02 66 | 99.17 32 |
|
ADS-MVSNet | | | 80.25 129 | 82.96 119 | 77.08 155 | 87.86 117 | 92.60 118 | 81.82 173 | 56.19 221 | 86.95 88 | 56.16 150 | 68.19 110 | 72.42 96 | 83.70 108 | 82.05 189 | 85.45 152 | 96.75 122 | 93.08 172 |
|
tpmp4_e23 | | | 83.72 107 | 84.45 105 | 82.86 102 | 88.25 113 | 92.54 119 | 88.95 92 | 63.01 202 | 88.20 79 | 74.83 80 | 68.07 111 | 71.99 102 | 86.65 90 | 84.11 159 | 88.74 115 | 95.47 178 | 97.51 95 |
|
view600 | | | 85.15 91 | 83.59 115 | 86.96 76 | 90.38 85 | 96.39 71 | 90.33 81 | 83.15 66 | 80.46 123 | 70.61 96 | 67.96 112 | 60.04 143 | 89.22 74 | 92.89 69 | 88.30 119 | 98.10 55 | 99.08 38 |
|
thres600view7 | | | 85.14 92 | 83.58 116 | 86.96 76 | 90.37 87 | 96.39 71 | 90.33 81 | 83.15 66 | 80.46 123 | 70.60 97 | 67.96 112 | 60.04 143 | 89.22 74 | 92.89 69 | 88.28 120 | 98.06 60 | 99.08 38 |
|
USDC | | | 80.10 131 | 79.33 137 | 81.00 119 | 86.36 125 | 91.71 130 | 88.74 94 | 75.77 125 | 81.90 117 | 54.90 160 | 67.67 114 | 52.05 161 | 83.94 106 | 88.44 125 | 86.25 139 | 96.31 161 | 87.28 201 |
|
view800 | | | 84.86 98 | 83.35 118 | 86.63 79 | 90.31 89 | 96.17 79 | 89.86 86 | 82.67 75 | 79.95 129 | 70.04 98 | 67.25 115 | 59.75 145 | 88.72 77 | 92.64 77 | 88.72 116 | 98.19 49 | 98.95 49 |
|
PVSNet_Blended_VisFu | | | 87.44 67 | 88.72 71 | 85.95 85 | 92.02 66 | 97.26 58 | 86.88 113 | 82.66 76 | 83.86 115 | 79.16 60 | 66.96 116 | 84.91 68 | 77.26 147 | 94.97 38 | 93.48 54 | 97.73 84 | 99.64 12 |
|
CDS-MVSNet | | | 83.13 113 | 83.73 114 | 82.43 110 | 84.52 136 | 92.92 114 | 88.26 96 | 77.67 111 | 72.08 149 | 69.08 102 | 66.96 116 | 74.66 92 | 78.61 132 | 90.70 100 | 91.96 64 | 96.46 157 | 96.86 105 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tfpn | | | 85.32 90 | 84.47 104 | 86.31 83 | 90.24 91 | 95.99 82 | 89.39 90 | 82.28 79 | 79.44 130 | 69.50 99 | 66.59 118 | 67.71 116 | 88.20 82 | 92.47 81 | 90.22 85 | 98.26 46 | 98.89 50 |
|
tpm | | | 78.87 139 | 81.33 133 | 76.00 172 | 85.57 128 | 90.19 138 | 82.81 157 | 59.66 212 | 78.35 134 | 51.40 186 | 66.30 119 | 67.92 115 | 80.94 121 | 83.28 175 | 85.73 144 | 95.65 175 | 97.56 93 |
|
IterMVS-LS | | | 82.62 117 | 82.75 123 | 82.48 107 | 87.09 122 | 87.48 182 | 87.19 111 | 72.85 144 | 79.09 131 | 66.63 107 | 65.22 120 | 72.14 97 | 84.06 105 | 88.33 126 | 91.39 70 | 97.03 115 | 95.60 133 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
SixPastTwentyTwo | | | 72.65 189 | 73.22 181 | 71.98 199 | 78.40 190 | 87.64 179 | 70.09 208 | 70.37 167 | 66.49 189 | 47.60 202 | 65.09 121 | 45.94 202 | 73.09 174 | 78.94 201 | 78.66 210 | 92.33 209 | 89.82 192 |
|
OpenMVS | | 83.41 11 | 89.84 53 | 88.89 70 | 90.95 47 | 97.63 28 | 98.51 38 | 94.64 44 | 85.47 57 | 88.14 80 | 78.39 66 | 65.06 122 | 85.42 65 | 91.04 51 | 93.06 65 | 93.70 51 | 98.53 34 | 98.37 71 |
|
tpmrst | | | 81.71 122 | 83.87 113 | 79.20 130 | 89.01 109 | 93.67 110 | 84.22 128 | 60.14 209 | 87.45 84 | 59.49 127 | 64.97 123 | 71.86 104 | 85.30 98 | 84.72 153 | 86.30 138 | 97.04 114 | 98.09 78 |
|
ACMH | | 78.51 14 | 79.27 135 | 78.08 140 | 80.65 120 | 89.52 102 | 90.40 134 | 80.45 178 | 79.77 95 | 69.54 167 | 54.85 161 | 64.83 124 | 56.16 154 | 83.94 106 | 84.58 155 | 86.01 143 | 95.41 180 | 95.03 139 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
GBi-Net | | | 86.16 80 | 86.00 90 | 86.35 80 | 81.81 148 | 89.52 144 | 91.40 65 | 76.53 115 | 91.69 63 | 81.31 48 | 64.81 125 | 80.64 75 | 88.72 77 | 90.54 103 | 90.72 76 | 98.34 41 | 94.08 148 |
|
test1 | | | 86.16 80 | 86.00 90 | 86.35 80 | 81.81 148 | 89.52 144 | 91.40 65 | 76.53 115 | 91.69 63 | 81.31 48 | 64.81 125 | 80.64 75 | 88.72 77 | 90.54 103 | 90.72 76 | 98.34 41 | 94.08 148 |
|
FMVSNet3 | | | 87.19 70 | 87.32 80 | 87.04 75 | 82.82 140 | 90.21 137 | 92.88 55 | 76.53 115 | 91.69 63 | 81.31 48 | 64.81 125 | 80.64 75 | 89.79 72 | 94.80 42 | 94.76 35 | 98.88 21 | 94.32 146 |
|
IterMVS | | | 78.85 141 | 81.36 132 | 75.93 173 | 84.27 137 | 85.74 191 | 83.83 131 | 66.35 194 | 76.82 136 | 50.48 189 | 63.48 128 | 68.82 113 | 73.99 172 | 89.68 118 | 89.34 98 | 96.63 137 | 95.67 130 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
RPMNet | | | 81.47 123 | 86.24 88 | 75.90 175 | 86.72 123 | 92.12 125 | 82.82 156 | 55.76 222 | 85.21 97 | 53.73 175 | 63.45 129 | 83.16 71 | 80.13 127 | 92.34 84 | 89.52 97 | 96.23 165 | 97.90 84 |
|
MSDG | | | 85.81 85 | 82.29 128 | 89.93 54 | 95.52 48 | 92.61 117 | 91.51 64 | 91.46 33 | 85.12 99 | 78.56 64 | 63.25 130 | 69.01 112 | 85.31 97 | 88.45 123 | 88.23 121 | 97.21 109 | 89.33 193 |
|
Fast-Effi-MVS+ | | | 82.61 118 | 82.51 126 | 82.72 103 | 85.49 130 | 93.06 113 | 87.17 112 | 71.39 159 | 84.18 108 | 64.59 115 | 63.03 131 | 58.89 148 | 90.22 67 | 91.39 92 | 90.83 75 | 97.44 98 | 96.21 125 |
|
GA-MVS | | | 78.86 140 | 80.42 134 | 77.05 156 | 83.27 139 | 92.17 124 | 83.24 138 | 75.73 126 | 73.75 143 | 46.27 207 | 62.43 132 | 57.12 150 | 76.94 155 | 93.14 63 | 89.34 98 | 96.83 118 | 95.00 140 |
|
CVMVSNet | | | 76.86 148 | 79.09 138 | 74.26 180 | 85.29 133 | 89.44 150 | 79.91 181 | 78.47 106 | 68.94 171 | 44.45 212 | 62.35 133 | 69.70 110 | 64.50 200 | 85.82 143 | 87.03 134 | 92.94 206 | 90.33 188 |
|
ACMH+ | | 79.09 13 | 79.12 137 | 77.22 147 | 81.35 116 | 88.50 112 | 90.36 135 | 82.14 170 | 79.38 100 | 72.78 146 | 58.59 131 | 62.31 134 | 56.44 153 | 84.10 104 | 82.03 190 | 84.05 181 | 95.40 181 | 92.55 177 |
|
testpf | | | 71.11 202 | 76.92 148 | 64.33 211 | 81.95 145 | 78.78 217 | 61.99 217 | 43.97 234 | 84.31 107 | 46.81 205 | 61.76 135 | 63.32 134 | 62.03 207 | 77.13 213 | 80.68 203 | 89.25 217 | 92.50 178 |
|
TAMVS | | | 79.23 136 | 78.95 139 | 79.56 126 | 81.89 147 | 92.52 121 | 82.97 150 | 73.70 140 | 67.27 185 | 64.97 114 | 61.66 136 | 65.06 128 | 78.61 132 | 87.12 133 | 88.07 124 | 95.23 184 | 90.95 183 |
|
tpm cat1 | | | 82.39 119 | 82.32 127 | 82.47 108 | 88.13 115 | 92.42 122 | 87.43 106 | 62.79 203 | 85.30 96 | 78.05 69 | 60.14 137 | 72.10 98 | 83.20 110 | 82.26 188 | 85.67 146 | 95.23 184 | 98.35 73 |
|
LTVRE_ROB | | 71.82 16 | 72.62 190 | 71.77 194 | 73.62 185 | 80.74 153 | 87.59 180 | 80.42 179 | 70.37 167 | 49.73 222 | 37.12 222 | 59.76 138 | 42.52 219 | 80.92 122 | 83.20 176 | 85.61 149 | 92.13 210 | 93.95 152 |
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 |
DI_MVS_plusplus_trai | | | 87.63 66 | 87.13 81 | 88.22 63 | 88.61 111 | 95.92 84 | 94.09 50 | 81.41 84 | 87.00 87 | 78.38 67 | 59.70 139 | 80.52 78 | 89.08 76 | 94.37 48 | 93.34 56 | 97.73 84 | 99.05 40 |
|
CR-MVSNet | | | 81.44 124 | 85.29 96 | 76.94 159 | 86.53 124 | 92.12 125 | 83.86 129 | 58.37 216 | 85.21 97 | 56.28 147 | 59.60 140 | 80.39 79 | 80.50 123 | 92.77 75 | 89.32 107 | 96.12 169 | 97.59 91 |
|
MS-PatchMatch | | | 82.16 120 | 82.18 129 | 82.12 111 | 91.65 70 | 93.50 111 | 89.51 87 | 71.95 153 | 81.48 120 | 64.45 116 | 59.58 141 | 77.54 86 | 77.23 148 | 89.88 116 | 85.62 147 | 97.94 74 | 87.68 197 |
|
FMVSNet2 | | | 84.89 95 | 84.02 111 | 85.91 86 | 81.81 148 | 89.52 144 | 91.40 65 | 75.79 124 | 84.45 106 | 79.39 58 | 58.75 142 | 74.35 93 | 88.72 77 | 93.51 57 | 93.46 55 | 98.34 41 | 94.08 148 |
|
pmmvs4 | | | 79.32 133 | 77.78 143 | 81.11 118 | 80.18 156 | 88.96 163 | 83.39 133 | 76.07 121 | 81.27 121 | 69.35 100 | 58.66 143 | 51.19 164 | 82.01 116 | 87.16 131 | 84.39 180 | 95.66 174 | 92.82 175 |
|
COLMAP_ROB | | 75.69 15 | 79.47 132 | 76.90 149 | 82.46 109 | 92.20 63 | 90.53 133 | 85.30 124 | 83.69 61 | 78.27 135 | 61.47 122 | 58.26 144 | 62.75 136 | 78.28 137 | 82.41 186 | 82.13 198 | 93.83 200 | 83.98 209 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
anonymousdsp | | | 75.14 171 | 77.25 146 | 72.69 191 | 76.68 202 | 89.26 156 | 75.26 199 | 68.44 180 | 65.53 195 | 46.65 206 | 58.16 145 | 56.67 152 | 73.96 173 | 87.84 128 | 86.05 142 | 95.13 187 | 97.22 100 |
|
testus | | | 64.41 214 | 66.39 214 | 62.10 215 | 70.01 220 | 72.88 223 | 59.74 223 | 64.99 198 | 65.18 196 | 33.49 231 | 57.35 146 | 30.48 230 | 51.71 218 | 78.09 209 | 80.75 202 | 92.69 207 | 79.97 216 |
|
UniMVSNet_NR-MVSNet | | | 78.89 138 | 78.04 142 | 79.88 124 | 79.40 161 | 89.70 142 | 82.92 152 | 80.17 90 | 76.37 140 | 58.56 132 | 57.10 147 | 54.92 156 | 81.44 119 | 83.51 163 | 87.12 133 | 96.76 121 | 97.60 89 |
|
test2356 | | | 66.34 211 | 69.50 205 | 62.65 213 | 70.77 218 | 74.02 222 | 61.29 218 | 64.23 201 | 67.61 183 | 33.88 230 | 56.51 148 | 44.92 208 | 53.09 214 | 80.01 200 | 82.24 196 | 92.66 208 | 81.22 214 |
|
PatchT | | | 79.28 134 | 83.88 112 | 73.93 182 | 85.54 129 | 90.95 132 | 66.14 215 | 56.53 220 | 83.21 116 | 56.28 147 | 56.50 149 | 76.80 87 | 80.50 123 | 92.77 75 | 89.32 107 | 98.57 33 | 97.59 91 |
|
conf0.05thres1000 | | | 81.86 121 | 79.55 136 | 84.56 92 | 89.39 106 | 94.15 107 | 87.57 104 | 81.36 85 | 69.95 163 | 65.78 111 | 56.38 150 | 59.38 146 | 86.04 94 | 90.58 102 | 88.49 118 | 97.22 108 | 97.97 82 |
|
pmmvs5 | | | 75.46 167 | 75.12 156 | 75.87 176 | 79.39 162 | 89.44 150 | 78.12 188 | 72.27 151 | 65.98 192 | 51.54 184 | 55.83 151 | 46.23 200 | 76.80 156 | 88.77 121 | 85.73 144 | 97.07 112 | 93.84 155 |
|
TinyColmap | | | 75.75 160 | 73.19 183 | 78.74 134 | 84.82 134 | 87.69 177 | 81.59 174 | 74.62 134 | 71.81 151 | 54.01 173 | 55.79 152 | 44.42 212 | 82.89 112 | 84.61 154 | 83.76 185 | 94.50 192 | 84.22 208 |
|
DU-MVS | | | 77.98 143 | 76.71 150 | 79.46 127 | 78.68 178 | 89.26 156 | 82.92 152 | 79.06 102 | 76.52 137 | 58.56 132 | 54.89 153 | 48.35 194 | 81.44 119 | 83.16 178 | 87.21 132 | 96.08 171 | 97.60 89 |
|
NR-MVSNet | | | 77.21 145 | 76.41 151 | 78.14 141 | 80.18 156 | 89.26 156 | 83.38 134 | 79.06 102 | 76.52 137 | 56.59 145 | 54.89 153 | 45.32 207 | 72.89 175 | 85.39 148 | 86.12 140 | 96.71 125 | 97.36 98 |
|
UniMVSNet (Re) | | | 78.00 142 | 77.52 144 | 78.57 135 | 79.66 160 | 90.36 135 | 82.09 171 | 77.86 110 | 76.38 139 | 60.26 124 | 54.63 155 | 52.07 160 | 75.31 170 | 84.97 152 | 86.10 141 | 96.22 166 | 98.11 77 |
|
EU-MVSNet | | | 68.07 210 | 70.25 203 | 65.52 210 | 74.68 212 | 81.30 213 | 68.53 211 | 70.31 169 | 62.40 208 | 37.43 221 | 54.62 156 | 48.36 193 | 51.34 219 | 78.32 207 | 79.27 207 | 90.84 214 | 87.47 199 |
|
MDTV_nov1_ep13_2view | | | 71.65 199 | 73.08 184 | 69.97 205 | 75.22 209 | 86.81 186 | 73.98 202 | 59.61 213 | 69.75 165 | 48.01 201 | 54.21 157 | 53.06 157 | 69.19 188 | 78.50 206 | 80.43 205 | 93.84 198 | 88.79 194 |
|
TranMVSNet+NR-MVSNet | | | 77.02 147 | 75.76 153 | 78.49 136 | 78.46 189 | 88.24 170 | 83.03 149 | 79.97 91 | 73.49 145 | 54.73 164 | 54.00 158 | 48.74 189 | 78.15 139 | 82.36 187 | 86.90 135 | 96.59 142 | 96.55 112 |
|
MIMVSNet | | | 75.71 161 | 77.26 145 | 73.90 184 | 70.93 216 | 88.71 167 | 79.98 180 | 57.67 219 | 73.58 144 | 58.08 136 | 53.93 159 | 58.56 149 | 79.41 130 | 90.04 114 | 89.97 90 | 97.34 104 | 86.04 202 |
|
DeepMVS_CX | | | | | | | 70.68 226 | 59.61 224 | 67.36 190 | 72.12 148 | 38.41 220 | 53.88 160 | 32.44 228 | 55.15 213 | 50.88 231 | | 74.35 232 | 68.42 226 |
|
PM-MVS | | | 70.17 205 | 69.42 206 | 71.04 202 | 70.82 217 | 81.26 214 | 71.25 206 | 67.80 186 | 69.16 170 | 51.04 188 | 53.15 161 | 34.93 224 | 72.19 177 | 80.30 199 | 76.95 214 | 93.16 205 | 90.21 189 |
|
TDRefinement | | | 75.54 164 | 73.22 181 | 78.25 140 | 87.65 120 | 89.65 143 | 85.81 121 | 79.28 101 | 71.14 154 | 56.06 153 | 52.17 162 | 51.96 162 | 68.74 191 | 81.60 191 | 80.58 204 | 91.94 211 | 85.45 203 |
|
test20.03 | | | 65.17 213 | 67.41 213 | 62.55 214 | 75.35 207 | 79.31 216 | 62.22 216 | 68.83 175 | 56.50 217 | 35.35 226 | 51.97 163 | 44.70 211 | 40.01 225 | 80.69 196 | 79.25 208 | 93.55 201 | 79.47 219 |
|
v2v482 | | | 76.25 153 | 74.78 162 | 77.96 142 | 78.50 187 | 89.14 160 | 83.05 148 | 76.02 122 | 68.78 172 | 54.11 171 | 51.36 164 | 48.59 191 | 79.49 129 | 83.53 162 | 85.60 150 | 96.59 142 | 96.49 121 |
|
Anonymous20240521 | | | 74.52 178 | 75.39 154 | 73.50 186 | 76.79 201 | 89.15 159 | 74.95 201 | 72.58 146 | 67.39 184 | 57.14 139 | 51.33 165 | 52.48 158 | 64.12 201 | 85.45 147 | 84.92 169 | 95.42 179 | 96.99 103 |
|
PEN-MVS | | | 72.24 193 | 71.30 198 | 73.33 189 | 77.08 200 | 85.57 192 | 76.75 189 | 72.52 148 | 63.89 200 | 48.12 200 | 50.79 166 | 43.09 217 | 69.03 190 | 78.54 203 | 83.46 187 | 96.50 152 | 93.76 160 |
|
DTE-MVSNet | | | 71.19 201 | 70.45 202 | 72.06 198 | 76.61 203 | 84.59 197 | 75.61 198 | 72.32 150 | 63.12 203 | 45.70 209 | 50.72 167 | 43.02 218 | 65.89 196 | 77.53 212 | 82.23 197 | 96.26 163 | 91.93 179 |
|
v18 | | | 75.49 166 | 74.04 171 | 77.18 153 | 79.31 163 | 82.47 201 | 83.66 132 | 68.68 177 | 71.77 152 | 57.43 138 | 50.71 168 | 51.01 165 | 77.31 146 | 83.35 167 | 85.03 162 | 96.70 127 | 93.91 154 |
|
v17 | | | 75.24 170 | 73.83 174 | 76.89 160 | 79.15 165 | 82.38 203 | 83.16 143 | 68.48 178 | 70.93 156 | 56.69 144 | 50.53 169 | 50.98 166 | 77.13 150 | 83.29 174 | 84.93 168 | 96.71 125 | 93.77 159 |
|
v8 | | | 75.89 159 | 74.74 163 | 77.23 151 | 79.09 166 | 88.00 173 | 83.19 141 | 71.08 161 | 70.03 162 | 56.29 146 | 50.50 170 | 50.88 169 | 77.06 151 | 83.32 170 | 84.99 163 | 96.68 129 | 95.49 135 |
|
CP-MVSNet | | | 73.19 185 | 72.37 192 | 74.15 181 | 77.54 196 | 86.77 188 | 76.34 191 | 72.05 152 | 65.66 194 | 51.47 185 | 50.49 171 | 43.66 214 | 70.90 178 | 80.93 194 | 83.40 188 | 96.59 142 | 95.66 131 |
|
v6 | | | 76.41 150 | 75.11 157 | 77.93 143 | 79.08 167 | 89.48 149 | 83.25 137 | 75.62 128 | 70.21 158 | 55.94 155 | 50.48 172 | 50.81 170 | 77.01 154 | 83.32 170 | 84.97 166 | 96.66 130 | 96.50 119 |
|
v1neww | | | 76.39 151 | 75.09 158 | 77.91 144 | 79.08 167 | 89.49 147 | 83.21 139 | 75.62 128 | 70.20 159 | 55.81 156 | 50.43 173 | 50.74 171 | 77.05 152 | 83.33 168 | 84.99 163 | 96.66 130 | 96.48 122 |
|
v7new | | | 76.39 151 | 75.09 158 | 77.91 144 | 79.08 167 | 89.49 147 | 83.21 139 | 75.62 128 | 70.20 159 | 55.81 156 | 50.43 173 | 50.74 171 | 77.05 152 | 83.33 168 | 84.99 163 | 96.66 130 | 96.48 122 |
|
v16 | | | 75.32 168 | 73.90 173 | 76.98 158 | 79.23 164 | 82.37 204 | 83.27 136 | 68.48 178 | 71.54 153 | 57.06 140 | 50.43 173 | 50.93 167 | 77.18 149 | 83.30 173 | 84.92 169 | 96.70 127 | 93.79 157 |
|
V42 | | | 76.21 154 | 75.04 160 | 77.58 149 | 78.68 178 | 89.33 152 | 82.93 151 | 74.64 133 | 69.84 164 | 56.13 151 | 50.42 176 | 50.93 167 | 76.30 164 | 83.32 170 | 84.89 172 | 96.83 118 | 96.54 113 |
|
WR-MVS | | | 72.93 186 | 73.57 176 | 72.19 196 | 78.14 192 | 87.71 176 | 76.21 193 | 73.02 143 | 67.78 181 | 50.09 193 | 50.35 177 | 50.53 173 | 61.27 209 | 80.42 198 | 83.10 192 | 94.43 193 | 95.11 138 |
|
v1 | | | 76.04 155 | 74.65 165 | 77.66 146 | 78.77 174 | 89.33 152 | 83.18 142 | 76.22 118 | 68.17 175 | 54.58 167 | 50.10 178 | 49.99 176 | 76.70 159 | 83.38 166 | 85.05 161 | 96.50 152 | 96.51 116 |
|
divwei89l23v2f112 | | | 76.03 156 | 74.64 166 | 77.65 148 | 78.78 172 | 89.33 152 | 83.15 144 | 76.21 120 | 68.26 174 | 54.55 169 | 50.08 179 | 49.86 179 | 76.73 158 | 83.39 165 | 85.06 160 | 96.51 151 | 96.51 116 |
|
v1141 | | | 76.03 156 | 74.64 166 | 77.66 146 | 78.78 172 | 89.32 155 | 83.14 146 | 76.22 118 | 68.27 173 | 54.56 168 | 50.06 180 | 49.84 181 | 76.78 157 | 83.40 164 | 85.07 158 | 96.50 152 | 96.51 116 |
|
WR-MVS_H | | | 72.69 188 | 72.80 190 | 72.56 193 | 77.94 193 | 87.83 175 | 75.26 199 | 71.53 158 | 64.75 198 | 52.19 182 | 49.83 181 | 48.62 190 | 61.96 208 | 81.12 193 | 82.44 195 | 96.50 152 | 95.00 140 |
|
FMVSNet1 | | | 80.18 130 | 78.07 141 | 82.65 105 | 78.55 183 | 87.57 181 | 88.41 95 | 73.93 139 | 70.16 161 | 73.57 82 | 49.80 182 | 64.45 132 | 85.35 96 | 90.54 103 | 90.72 76 | 96.10 170 | 93.21 170 |
|
v15 | | | 74.54 177 | 73.06 185 | 76.26 164 | 78.70 177 | 82.14 205 | 82.89 154 | 68.05 182 | 68.07 177 | 54.77 162 | 49.76 183 | 49.88 178 | 76.56 160 | 83.19 177 | 84.76 173 | 96.59 142 | 93.60 163 |
|
v7 | | | 76.00 158 | 75.01 161 | 77.15 154 | 78.73 175 | 88.87 164 | 83.15 144 | 72.40 149 | 69.20 169 | 53.57 176 | 49.73 184 | 49.23 185 | 78.49 134 | 86.15 141 | 85.17 157 | 96.53 149 | 96.73 109 |
|
v11 | | | 74.62 175 | 73.41 180 | 76.03 170 | 78.54 185 | 81.97 208 | 82.34 163 | 67.33 191 | 68.08 176 | 53.39 178 | 49.73 184 | 48.87 188 | 78.01 142 | 86.66 135 | 84.97 166 | 96.56 147 | 93.58 164 |
|
v10 | | | 75.57 163 | 74.67 164 | 76.62 163 | 78.73 175 | 87.46 183 | 83.14 146 | 69.41 174 | 69.27 168 | 53.44 177 | 49.73 184 | 49.21 186 | 78.44 136 | 86.17 140 | 85.18 156 | 96.53 149 | 95.65 132 |
|
V14 | | | 74.48 179 | 73.00 187 | 76.20 165 | 78.65 181 | 82.09 206 | 82.79 158 | 67.88 185 | 68.04 178 | 54.75 163 | 49.68 187 | 49.92 177 | 76.51 161 | 83.12 180 | 84.67 175 | 96.63 137 | 93.44 165 |
|
MDA-MVSNet-bldmvs | | | 62.23 215 | 61.13 217 | 63.52 212 | 58.94 231 | 82.44 202 | 60.71 221 | 73.28 142 | 57.22 215 | 38.42 219 | 49.63 188 | 27.64 232 | 62.83 206 | 54.98 229 | 74.16 218 | 86.96 223 | 81.83 213 |
|
V9 | | | 74.37 180 | 72.87 188 | 76.11 168 | 78.58 182 | 82.02 207 | 82.68 159 | 67.75 187 | 67.80 180 | 54.63 165 | 49.50 189 | 49.86 179 | 76.40 162 | 83.05 181 | 84.59 176 | 96.63 137 | 93.30 168 |
|
v12 | | | 74.29 181 | 72.82 189 | 76.02 171 | 78.52 186 | 81.96 209 | 82.27 165 | 67.65 188 | 67.88 179 | 54.63 165 | 49.40 190 | 49.74 183 | 76.40 162 | 82.99 182 | 84.52 177 | 96.64 135 | 93.23 169 |
|
v1144 | | | 75.54 164 | 74.55 169 | 76.69 161 | 78.33 191 | 88.77 166 | 82.89 154 | 72.76 145 | 67.18 187 | 51.73 183 | 49.34 191 | 48.37 192 | 78.10 140 | 86.22 139 | 85.24 154 | 96.35 160 | 96.74 108 |
|
v13 | | | 74.20 182 | 72.72 191 | 75.92 174 | 78.49 188 | 81.90 210 | 82.28 164 | 67.55 189 | 67.64 182 | 54.29 170 | 49.25 192 | 49.75 182 | 76.30 164 | 82.92 184 | 84.47 178 | 96.63 137 | 93.08 172 |
|
v148 | | | 74.98 172 | 73.52 178 | 76.69 161 | 78.84 171 | 89.02 161 | 78.78 184 | 76.82 113 | 67.22 186 | 59.61 126 | 49.18 193 | 47.94 196 | 70.57 184 | 80.76 195 | 83.99 182 | 95.52 176 | 96.52 115 |
|
PS-CasMVS | | | 72.37 191 | 71.47 197 | 73.43 187 | 77.32 198 | 86.43 189 | 75.99 194 | 71.94 154 | 63.37 201 | 49.24 197 | 49.07 194 | 42.42 220 | 69.60 186 | 80.59 197 | 83.18 191 | 96.48 156 | 95.23 137 |
|
v144192 | | | 74.76 174 | 73.64 175 | 76.06 169 | 77.58 195 | 88.23 171 | 81.87 172 | 71.63 156 | 66.03 191 | 51.08 187 | 48.63 195 | 46.77 199 | 77.59 143 | 84.53 156 | 84.76 173 | 96.64 135 | 96.54 113 |
|
pm-mvs1 | | | 75.61 162 | 74.19 170 | 77.26 150 | 80.16 158 | 88.79 165 | 81.49 175 | 75.49 132 | 59.49 211 | 58.09 135 | 48.32 196 | 55.53 155 | 72.35 176 | 88.61 122 | 85.48 151 | 95.99 172 | 93.12 171 |
|
v1192 | | | 74.96 173 | 73.92 172 | 76.17 166 | 77.76 194 | 88.19 172 | 82.54 160 | 71.94 154 | 66.84 188 | 50.07 194 | 48.10 197 | 46.14 201 | 78.28 137 | 86.30 137 | 85.23 155 | 96.41 159 | 96.67 110 |
|
Baseline_NR-MVSNet | | | 76.71 149 | 74.56 168 | 79.23 129 | 78.68 178 | 84.15 198 | 82.45 161 | 78.87 104 | 75.83 141 | 60.05 125 | 47.92 198 | 50.18 175 | 79.06 131 | 83.16 178 | 83.86 184 | 96.26 163 | 96.80 107 |
|
v1921920 | | | 74.60 176 | 73.56 177 | 75.81 177 | 77.43 197 | 87.94 174 | 82.18 169 | 71.33 160 | 66.48 190 | 49.23 198 | 47.84 199 | 45.56 205 | 78.03 141 | 85.70 145 | 84.92 169 | 96.65 133 | 96.50 119 |
|
v1240 | | | 74.04 183 | 73.04 186 | 75.20 179 | 77.19 199 | 87.69 177 | 80.93 177 | 70.72 165 | 65.08 197 | 48.47 199 | 47.31 200 | 44.71 209 | 77.33 145 | 85.50 146 | 85.07 158 | 96.59 142 | 95.94 127 |
|
TransMVSNet (Re) | | | 72.90 187 | 70.51 201 | 75.69 178 | 80.88 152 | 85.26 195 | 79.25 183 | 78.43 107 | 56.13 218 | 52.81 180 | 46.81 201 | 48.20 195 | 66.77 195 | 85.18 151 | 83.70 186 | 95.98 173 | 88.28 196 |
|
V4 | | | 71.67 197 | 71.15 200 | 72.27 194 | 73.91 213 | 86.82 185 | 75.73 196 | 68.04 183 | 62.49 207 | 50.47 190 | 46.20 202 | 47.74 198 | 70.70 180 | 78.54 203 | 81.76 199 | 94.76 190 | 94.52 145 |
|
v748 | | | 70.94 203 | 70.25 203 | 71.75 201 | 75.58 206 | 86.28 190 | 72.12 203 | 70.25 170 | 60.25 209 | 54.08 172 | 46.18 203 | 44.41 213 | 64.61 199 | 77.92 210 | 82.49 194 | 93.87 196 | 94.19 147 |
|
v52 | | | 71.67 197 | 71.16 199 | 72.26 195 | 73.90 214 | 86.80 187 | 75.72 197 | 68.04 183 | 62.53 206 | 50.43 191 | 46.15 204 | 47.83 197 | 70.73 179 | 78.53 205 | 81.76 199 | 94.75 191 | 94.53 144 |
|
v7n | | | 72.11 194 | 71.66 195 | 72.63 192 | 75.26 208 | 86.85 184 | 76.74 190 | 68.77 176 | 62.70 204 | 49.40 195 | 45.92 205 | 43.51 215 | 70.63 183 | 84.16 158 | 83.21 190 | 94.99 188 | 95.25 136 |
|
CHOSEN 1792x2688 | | | 84.59 102 | 84.30 108 | 84.93 91 | 93.71 56 | 98.23 48 | 89.91 85 | 77.96 108 | 84.81 101 | 65.93 110 | 45.19 206 | 71.76 105 | 83.13 111 | 95.46 29 | 95.13 30 | 98.94 18 | 99.53 16 |
|
pmmvs-eth3d | | | 69.59 206 | 67.57 212 | 71.95 200 | 70.04 219 | 80.05 215 | 71.48 204 | 70.00 172 | 62.57 205 | 55.99 154 | 44.92 207 | 35.73 223 | 70.64 182 | 81.56 192 | 79.69 206 | 93.55 201 | 88.43 195 |
|
N_pmnet | | | 68.54 207 | 67.83 211 | 69.38 206 | 75.77 205 | 81.90 210 | 66.21 214 | 72.53 147 | 65.91 193 | 46.09 208 | 44.67 208 | 45.48 206 | 63.82 203 | 74.66 215 | 77.39 213 | 91.87 212 | 84.77 206 |
|
CMPMVS | | 54.54 17 | 71.74 196 | 67.94 209 | 76.16 167 | 90.41 83 | 93.25 112 | 78.32 187 | 75.60 131 | 59.81 210 | 53.95 174 | 44.64 209 | 51.22 163 | 70.70 180 | 74.59 216 | 75.88 216 | 88.01 218 | 76.23 220 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
gm-plane-assit | | | 71.33 200 | 75.18 155 | 66.83 209 | 79.06 170 | 75.57 220 | 48.05 229 | 60.33 206 | 48.28 223 | 34.67 227 | 44.34 210 | 67.70 117 | 79.78 128 | 97.25 8 | 96.21 13 | 99.10 9 | 96.92 104 |
|
EG-PatchMatch MVS | | | 71.81 195 | 71.54 196 | 72.12 197 | 80.53 155 | 89.94 140 | 78.51 185 | 66.56 193 | 57.38 214 | 47.46 203 | 44.28 211 | 52.22 159 | 63.10 205 | 85.22 150 | 84.42 179 | 96.56 147 | 87.35 200 |
|
tfpnnormal | | | 75.27 169 | 72.12 193 | 78.94 132 | 82.30 143 | 88.52 168 | 82.41 162 | 79.41 98 | 58.03 212 | 55.59 158 | 43.83 212 | 44.71 209 | 77.35 144 | 87.70 130 | 85.45 152 | 96.60 141 | 96.61 111 |
|
LP | | | 68.35 208 | 68.20 208 | 68.53 207 | 82.61 141 | 82.93 199 | 69.42 209 | 53.36 226 | 71.06 155 | 45.32 210 | 41.19 213 | 49.10 187 | 67.20 193 | 73.89 217 | 78.16 211 | 93.25 203 | 81.04 215 |
|
HyFIR lowres test | | | 83.43 109 | 82.94 120 | 84.01 96 | 93.41 59 | 97.10 61 | 87.21 110 | 74.04 138 | 80.15 128 | 64.98 113 | 41.09 214 | 76.61 88 | 86.51 92 | 93.31 58 | 93.01 60 | 97.91 80 | 99.30 24 |
|
new_pmnet | | | 61.60 216 | 62.68 215 | 60.35 218 | 63.02 225 | 74.93 221 | 60.97 220 | 58.86 215 | 64.21 199 | 35.38 225 | 39.51 215 | 39.89 221 | 57.37 212 | 72.78 218 | 72.56 219 | 86.49 225 | 74.85 222 |
|
MVS-HIRNet | | | 72.32 192 | 73.45 179 | 71.00 203 | 80.58 154 | 89.97 139 | 68.51 212 | 55.28 223 | 70.89 157 | 52.27 181 | 39.09 216 | 57.11 151 | 75.02 171 | 85.76 144 | 86.33 137 | 94.36 194 | 85.00 205 |
|
pmmvs3 | | | 60.52 218 | 60.87 218 | 60.12 219 | 61.38 226 | 71.62 225 | 57.42 227 | 53.94 225 | 48.09 224 | 35.95 223 | 38.62 217 | 32.19 229 | 64.12 201 | 75.33 214 | 77.99 212 | 87.89 220 | 82.28 212 |
|
gg-mvs-nofinetune | | | 77.08 146 | 79.79 135 | 73.92 183 | 85.95 127 | 97.23 59 | 92.18 60 | 52.65 227 | 46.19 226 | 27.79 233 | 38.27 218 | 85.63 61 | 85.67 95 | 96.95 14 | 95.62 21 | 99.30 3 | 98.67 60 |
|
new-patchmatchnet | | | 60.74 217 | 59.78 219 | 61.87 216 | 69.52 221 | 76.67 219 | 57.99 226 | 65.78 196 | 52.63 220 | 38.47 218 | 38.08 219 | 32.92 227 | 48.88 221 | 68.50 221 | 69.87 224 | 90.56 215 | 79.75 218 |
|
Anonymous20231206 | | | 68.09 209 | 68.68 207 | 67.39 208 | 75.16 210 | 82.55 200 | 69.33 210 | 70.06 171 | 63.34 202 | 42.28 214 | 37.91 220 | 43.12 216 | 52.67 215 | 83.56 161 | 82.71 193 | 94.84 189 | 87.59 198 |
|
FPMVS | | | 56.54 220 | 52.82 225 | 60.87 217 | 74.90 211 | 67.58 228 | 67.69 213 | 65.38 197 | 57.86 213 | 41.51 215 | 37.83 221 | 34.19 225 | 41.21 224 | 55.88 228 | 53.09 230 | 74.55 231 | 63.31 228 |
|
pmmvs6 | | | 70.29 204 | 67.90 210 | 73.07 190 | 76.17 204 | 85.31 194 | 76.29 192 | 70.75 164 | 47.39 225 | 55.33 159 | 37.15 222 | 50.49 174 | 69.55 187 | 82.96 183 | 80.85 201 | 90.34 216 | 91.18 182 |
|
testmv | | | 53.23 223 | 53.37 223 | 53.06 224 | 64.78 222 | 63.76 231 | 42.27 232 | 60.18 207 | 38.40 229 | 24.60 234 | 33.04 223 | 23.85 233 | 39.28 226 | 68.05 222 | 72.53 220 | 87.23 221 | 73.98 223 |
|
test1235678 | | | 53.22 224 | 53.36 224 | 53.05 225 | 64.78 222 | 63.75 232 | 42.27 232 | 60.17 208 | 38.36 230 | 24.60 234 | 33.03 224 | 23.84 234 | 39.28 226 | 68.04 223 | 72.52 221 | 87.23 221 | 73.96 224 |
|
test12356 | | | 48.96 225 | 49.54 226 | 48.28 227 | 59.74 230 | 57.59 234 | 42.10 234 | 58.32 218 | 36.65 232 | 23.11 236 | 31.44 225 | 19.22 235 | 23.46 233 | 61.17 227 | 71.98 222 | 82.97 227 | 68.75 225 |
|
ambc | | | | 57.08 220 | | 58.68 232 | 67.71 227 | 60.07 222 | | 57.13 216 | 42.79 213 | 30.00 226 | 11.64 238 | 50.18 220 | 78.89 202 | 69.14 225 | 82.64 228 | 85.02 204 |
|
1111 | | | 54.82 222 | 55.44 221 | 54.10 223 | 61.33 228 | 64.37 229 | 42.52 230 | 46.65 232 | 42.29 227 | 34.21 228 | 29.57 227 | 45.65 203 | 51.95 216 | 71.47 219 | 74.60 217 | 87.95 219 | 60.10 229 |
|
.test1245 | | | 40.04 229 | 40.41 230 | 39.60 230 | 61.33 228 | 64.37 229 | 42.52 230 | 46.65 232 | 42.29 227 | 34.21 228 | 29.57 227 | 45.65 203 | 51.95 216 | 71.47 219 | 5.65 235 | 0.92 239 | 23.86 237 |
|
MIMVSNet1 | | | 60.51 219 | 61.43 216 | 59.44 220 | 48.75 235 | 77.21 218 | 60.98 219 | 66.84 192 | 52.09 221 | 38.74 217 | 29.29 229 | 39.40 222 | 48.08 222 | 77.60 211 | 78.87 209 | 93.22 204 | 75.56 221 |
|
PMVS | | 42.57 18 | 45.71 226 | 42.61 228 | 49.32 226 | 61.35 227 | 37.82 238 | 36.96 236 | 60.10 210 | 37.20 231 | 41.50 216 | 28.53 230 | 33.11 226 | 28.82 232 | 53.45 230 | 48.70 232 | 67.22 234 | 59.42 230 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Anonymous20231211 | | | 56.40 221 | 54.23 222 | 58.92 221 | 64.68 224 | 71.87 224 | 59.09 225 | 64.63 199 | 34.66 233 | 35.73 224 | 21.99 231 | 29.42 231 | 45.81 223 | 67.46 224 | 70.30 223 | 83.57 226 | 83.94 210 |
|
PMMVS2 | | | 41.25 228 | 42.55 229 | 39.74 229 | 43.25 236 | 55.05 235 | 38.15 235 | 47.11 231 | 31.78 234 | 11.83 239 | 21.16 232 | 19.12 236 | 20.98 235 | 49.95 232 | 56.09 228 | 77.09 229 | 64.68 227 |
|
no-one | | | 36.24 230 | 35.28 231 | 37.36 231 | 49.42 234 | 52.08 236 | 23.67 238 | 54.16 224 | 20.93 237 | 12.98 238 | 13.94 233 | 12.99 237 | 16.68 236 | 34.98 234 | 55.52 229 | 67.24 233 | 56.51 231 |
|
Gipuma | | | 43.95 227 | 42.62 227 | 45.50 228 | 50.79 233 | 41.20 237 | 35.55 237 | 52.51 228 | 52.95 219 | 29.09 232 | 12.92 234 | 11.48 239 | 38.15 228 | 62.01 226 | 66.62 226 | 66.89 235 | 51.17 232 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
testmvs | | | 5.16 234 | 8.14 235 | 1.69 236 | 0.36 241 | 1.65 242 | 3.02 243 | 0.66 237 | 7.17 239 | 0.50 243 | 12.58 235 | 0.69 243 | 4.67 237 | 5.42 237 | 5.65 235 | 0.92 239 | 23.86 237 |
|
E-PMN | | | 27.87 231 | 24.36 233 | 31.97 233 | 41.27 238 | 25.56 241 | 16.62 240 | 49.16 229 | 22.00 236 | 9.90 240 | 11.75 236 | 7.86 241 | 29.57 231 | 22.22 235 | 34.70 233 | 45.27 236 | 46.41 234 |
|
test123 | | | 4.39 235 | 7.11 236 | 1.21 237 | 0.11 242 | 1.16 243 | 1.67 244 | 0.35 238 | 5.91 240 | 0.16 244 | 11.65 237 | 0.16 244 | 4.45 238 | 1.72 238 | 4.92 237 | 0.51 241 | 24.28 236 |
|
MVE | | 32.98 19 | 27.61 232 | 29.89 232 | 24.94 235 | 21.97 239 | 37.22 239 | 15.56 242 | 38.83 235 | 17.49 238 | 14.72 237 | 11.64 238 | 5.62 242 | 21.26 234 | 35.20 233 | 50.95 231 | 37.29 238 | 51.13 233 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EMVS | | | 26.96 233 | 22.96 234 | 31.63 234 | 41.91 237 | 25.73 240 | 16.30 241 | 49.10 230 | 22.38 235 | 9.03 241 | 11.22 239 | 8.12 240 | 29.93 230 | 20.16 236 | 31.04 234 | 43.49 237 | 42.04 235 |
|
sosnet-low-res | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
sosnet | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
our_test_3 | | | | | | 78.55 183 | 84.98 196 | 70.12 207 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 93.37 3 | | 95.71 20 | | | | | |
|
MTMP | | | | | | | | | | | 93.84 2 | | 94.86 24 | | | | | |
|
Patchmatch-RL test | | | | | | | | 19.65 239 | | | | | | | | | | |
|
XVS | | | | | | 92.16 64 | 98.56 32 | 91.04 77 | | | 81.00 53 | | 93.49 28 | | | | 98.00 68 | |
|
X-MVStestdata | | | | | | 92.16 64 | 98.56 32 | 91.04 77 | | | 81.00 53 | | 93.49 28 | | | | 98.00 68 | |
|
mPP-MVS | | | | | | 97.95 24 | | | | | | | 92.24 38 | | | | | |
|
NP-MVS | | | | | | | | | | 94.12 46 | | | | | | | | |
|
Patchmtry | | | | | | | 92.08 127 | 83.86 129 | 58.37 216 | | 56.28 147 | | | | | | | |
|