WR-MVS_H | | | 97.06 9 | 97.78 7 | 96.23 14 | 96.74 37 | 98.04 3 | 98.25 25 | 97.32 1 | 94.40 32 | 93.71 51 | 98.55 10 | 98.89 10 | 92.97 37 | 98.91 9 | 98.45 6 | 98.38 27 | 97.19 13 |
|
PS-CasMVS | | | 97.22 5 | 97.84 6 | 96.50 5 | 97.08 25 | 97.92 6 | 98.17 29 | 97.02 2 | 94.71 25 | 95.32 20 | 98.52 12 | 98.97 8 | 92.91 40 | 99.04 4 | 98.47 5 | 98.49 18 | 97.24 11 |
|
CP-MVSNet | | | 96.97 10 | 97.42 13 | 96.44 7 | 97.06 26 | 97.82 8 | 98.12 31 | 96.98 3 | 93.50 44 | 95.21 22 | 97.98 22 | 98.44 23 | 92.83 43 | 98.93 8 | 98.37 8 | 98.46 21 | 96.91 25 |
|
UniMVSNet_ETH3D | | | 96.15 22 | 97.71 10 | 94.33 52 | 97.31 17 | 96.71 38 | 95.06 104 | 96.91 4 | 97.86 5 | 90.42 117 | 98.55 10 | 99.60 1 | 88.01 112 | 98.51 13 | 97.81 16 | 98.26 28 | 94.95 63 |
|
DTE-MVSNet | | | 97.16 6 | 97.75 8 | 96.47 6 | 97.40 12 | 97.95 5 | 98.20 28 | 96.89 5 | 95.30 18 | 95.15 23 | 98.66 7 | 98.80 14 | 92.77 44 | 98.97 7 | 98.27 9 | 98.44 22 | 96.28 40 |
|
PEN-MVS | | | 97.16 6 | 97.87 5 | 96.33 12 | 97.20 21 | 97.97 4 | 98.25 25 | 96.86 6 | 95.09 23 | 94.93 25 | 98.66 7 | 99.16 5 | 92.27 50 | 98.98 6 | 98.39 7 | 98.49 18 | 96.83 29 |
|
WR-MVS | | | 97.53 3 | 98.20 3 | 96.76 3 | 96.93 29 | 98.17 1 | 98.60 10 | 96.67 7 | 96.39 13 | 94.46 31 | 99.14 1 | 98.92 9 | 94.57 15 | 99.06 3 | 98.80 2 | 99.32 1 | 96.92 24 |
|
gg-mvs-nofinetune | | | 88.32 151 | 88.81 151 | 87.75 161 | 93.07 135 | 89.37 164 | 89.06 188 | 95.94 8 | 95.29 19 | 87.15 147 | 97.38 41 | 76.38 189 | 68.05 199 | 91.04 150 | 89.10 160 | 93.24 157 | 83.10 176 |
|
Fast-Effi-MVS+-dtu | | | 89.57 140 | 88.42 156 | 90.92 119 | 93.35 127 | 91.57 145 | 93.01 146 | 95.71 9 | 78.94 189 | 87.65 145 | 84.68 186 | 93.14 140 | 82.00 151 | 90.84 151 | 91.01 142 | 93.78 151 | 88.77 152 |
|
UA-Net | | | 96.56 12 | 96.73 22 | 96.36 10 | 98.99 1 | 97.90 7 | 97.79 41 | 95.64 10 | 92.78 56 | 92.54 72 | 96.23 65 | 95.02 120 | 94.31 18 | 98.43 15 | 98.12 11 | 98.89 3 | 98.58 2 |
|
DU-MVS | | | 95.51 41 | 95.68 50 | 95.33 32 | 96.45 48 | 96.44 47 | 96.61 67 | 95.32 11 | 89.97 109 | 93.78 47 | 97.46 39 | 98.07 37 | 91.19 68 | 97.03 49 | 96.53 47 | 98.61 13 | 94.22 77 |
|
NR-MVSNet | | | 94.55 60 | 95.66 52 | 93.25 86 | 94.26 102 | 96.44 47 | 96.69 62 | 95.32 11 | 89.97 109 | 91.79 93 | 97.46 39 | 98.39 25 | 82.85 143 | 96.87 58 | 96.48 50 | 98.57 14 | 93.98 83 |
|
UniMVSNet (Re) | | | 95.46 42 | 95.86 47 | 95.00 43 | 96.09 57 | 96.60 39 | 96.68 64 | 94.99 13 | 90.36 103 | 92.13 83 | 97.64 35 | 98.13 35 | 91.38 62 | 96.90 55 | 96.74 44 | 98.73 6 | 94.63 71 |
|
TranMVSNet+NR-MVSNet | | | 95.72 40 | 96.42 30 | 94.91 44 | 96.21 54 | 96.77 37 | 96.90 56 | 94.99 13 | 92.62 59 | 91.92 87 | 98.51 13 | 98.63 19 | 90.82 75 | 97.27 44 | 96.83 43 | 98.63 12 | 94.31 76 |
|
LTVRE_ROB | | 95.06 1 | 97.73 1 | 98.39 1 | 96.95 1 | 96.33 50 | 96.94 33 | 98.30 21 | 94.90 15 | 98.61 1 | 97.73 3 | 97.97 23 | 98.57 21 | 95.74 4 | 99.24 1 | 98.70 4 | 98.72 7 | 98.70 1 |
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 |
UniMVSNet_NR-MVSNet | | | 95.34 46 | 95.51 54 | 95.14 39 | 95.80 71 | 96.55 40 | 96.61 67 | 94.79 16 | 90.04 108 | 93.78 47 | 97.51 38 | 97.25 62 | 91.19 68 | 96.68 63 | 96.31 54 | 98.65 11 | 94.22 77 |
|
SR-MVS | | | | | | 97.13 23 | | | 94.77 17 | | | | 97.77 51 | | | | | |
|
CP-MVS | | | 96.21 19 | 96.16 41 | 96.27 13 | 97.56 8 | 97.13 32 | 98.43 15 | 94.70 18 | 92.62 59 | 94.13 40 | 92.71 119 | 98.03 41 | 94.54 16 | 98.00 25 | 97.60 21 | 98.23 30 | 97.05 20 |
|
HFP-MVS | | | 96.18 20 | 96.53 27 | 95.77 21 | 97.34 16 | 97.26 26 | 98.16 30 | 94.54 19 | 94.45 29 | 92.52 73 | 95.05 86 | 96.95 70 | 93.89 23 | 97.28 43 | 97.46 27 | 98.19 31 | 97.25 9 |
|
ACMMPR | | | 96.54 13 | 96.71 23 | 96.35 11 | 97.55 9 | 97.63 11 | 98.62 9 | 94.54 19 | 94.45 29 | 94.19 37 | 95.04 88 | 97.35 61 | 94.92 10 | 97.85 29 | 97.50 26 | 98.26 28 | 97.17 14 |
|
Baseline_NR-MVSNet | | | 94.85 53 | 95.35 58 | 94.26 54 | 96.45 48 | 93.86 112 | 96.70 60 | 94.54 19 | 90.07 107 | 90.17 122 | 98.77 4 | 97.89 45 | 90.64 80 | 97.03 49 | 96.16 56 | 97.04 72 | 93.67 89 |
|
MP-MVS | | | 96.13 23 | 95.93 46 | 96.37 9 | 98.19 4 | 97.31 25 | 98.49 14 | 94.53 22 | 91.39 87 | 94.38 34 | 94.32 101 | 96.43 84 | 94.59 14 | 97.75 35 | 97.44 29 | 98.04 39 | 96.88 27 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
zzz-MVS | | | 96.18 20 | 96.01 43 | 96.38 8 | 98.30 2 | 96.18 52 | 98.51 13 | 94.48 23 | 94.56 27 | 94.81 29 | 91.73 128 | 96.96 69 | 94.30 19 | 98.09 19 | 97.83 15 | 97.91 44 | 96.73 31 |
|
SixPastTwentyTwo | | | 97.36 4 | 97.73 9 | 96.92 2 | 97.36 13 | 96.15 53 | 98.29 22 | 94.43 24 | 96.50 11 | 96.96 6 | 98.74 5 | 98.74 16 | 96.04 3 | 99.03 5 | 97.74 17 | 98.44 22 | 97.22 12 |
|
X-MVS | | | 95.33 47 | 95.13 62 | 95.57 26 | 97.35 14 | 97.48 17 | 98.43 15 | 94.28 25 | 92.30 67 | 93.28 58 | 86.89 175 | 96.82 75 | 91.87 55 | 97.85 29 | 97.59 22 | 98.19 31 | 96.95 23 |
|
DeepC-MVS | | 92.47 4 | 96.44 15 | 96.75 21 | 96.08 17 | 97.57 7 | 97.19 29 | 97.96 35 | 94.28 25 | 95.29 19 | 94.92 26 | 98.31 17 | 96.92 71 | 93.69 27 | 96.81 60 | 96.50 49 | 98.06 38 | 96.27 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SteuartSystems-ACMMP | | | 95.96 31 | 96.13 42 | 95.76 22 | 97.06 26 | 97.36 22 | 98.40 19 | 94.24 27 | 91.49 81 | 91.91 88 | 94.50 97 | 96.89 72 | 94.99 8 | 98.01 24 | 97.44 29 | 97.97 42 | 97.25 9 |
Skip Steuart: Steuart Systems R&D Blog. |
TSAR-MVS + ACMM | | | 95.17 51 | 95.95 44 | 94.26 54 | 96.07 59 | 96.46 46 | 95.67 92 | 94.21 28 | 93.84 42 | 90.99 108 | 97.18 46 | 95.24 117 | 93.55 29 | 96.60 65 | 95.61 70 | 95.06 130 | 96.69 33 |
|
PGM-MVS | | | 95.90 34 | 95.72 49 | 96.10 16 | 97.53 10 | 97.45 21 | 98.55 12 | 94.12 29 | 90.25 104 | 93.71 51 | 93.20 114 | 97.18 64 | 94.63 13 | 97.68 37 | 97.34 35 | 98.08 36 | 96.97 22 |
|
HPM-MVS++ | | | 95.21 50 | 94.89 65 | 95.59 24 | 97.79 6 | 95.39 75 | 97.68 42 | 94.05 30 | 91.91 74 | 94.35 35 | 93.38 112 | 95.07 119 | 92.94 39 | 96.01 74 | 95.88 64 | 96.73 76 | 96.61 35 |
|
3Dnovator+ | | 92.82 3 | 95.22 49 | 95.16 60 | 95.29 35 | 96.17 56 | 96.55 40 | 97.64 43 | 94.02 31 | 94.16 37 | 94.29 36 | 92.09 125 | 93.71 135 | 91.90 53 | 96.68 63 | 96.51 48 | 97.70 51 | 96.40 37 |
|
APD-MVS | | | 95.38 45 | 95.68 50 | 95.03 42 | 97.30 18 | 96.90 35 | 97.83 40 | 93.92 32 | 89.40 116 | 90.35 118 | 95.41 79 | 97.69 55 | 92.97 37 | 97.24 46 | 97.17 37 | 97.83 46 | 95.96 45 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
TDRefinement | | | 97.59 2 | 98.32 2 | 96.73 4 | 95.90 66 | 98.10 2 | 99.08 2 | 93.92 32 | 98.24 3 | 96.44 12 | 98.12 19 | 97.86 50 | 96.06 2 | 99.24 1 | 98.93 1 | 99.00 2 | 97.77 4 |
|
MSP-MVS | | | 95.32 48 | 96.28 35 | 94.19 56 | 96.87 30 | 97.77 10 | 98.27 23 | 93.88 34 | 94.15 38 | 89.63 129 | 95.36 80 | 98.37 26 | 90.73 76 | 94.37 106 | 97.53 24 | 95.77 111 | 96.40 37 |
|
LGP-MVS_train | | | 96.10 25 | 96.29 34 | 95.87 20 | 96.72 38 | 97.35 24 | 98.43 15 | 93.83 35 | 90.81 101 | 92.67 71 | 95.05 86 | 98.86 12 | 95.01 7 | 98.11 18 | 97.37 34 | 98.52 16 | 96.50 36 |
|
ACMM | | 90.06 9 | 96.31 16 | 96.42 30 | 96.19 15 | 97.21 20 | 97.16 31 | 98.71 5 | 93.79 36 | 94.35 33 | 93.81 45 | 92.80 118 | 98.23 31 | 95.11 6 | 98.07 21 | 97.45 28 | 98.51 17 | 96.86 28 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMMP | | | 96.12 24 | 96.27 37 | 95.93 19 | 97.20 21 | 97.60 12 | 98.64 7 | 93.74 37 | 92.47 61 | 93.13 65 | 93.23 113 | 98.06 38 | 94.51 17 | 97.99 26 | 97.57 23 | 98.39 26 | 96.99 21 |
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 |
DPE-MVS | | | 96.00 28 | 96.80 20 | 95.06 41 | 95.87 69 | 97.47 20 | 98.25 25 | 93.73 38 | 92.38 63 | 91.57 97 | 97.55 37 | 97.97 43 | 92.98 36 | 97.49 41 | 97.61 20 | 97.96 43 | 97.16 15 |
|
SMA-MVS | | | 95.99 29 | 96.48 28 | 95.41 30 | 97.43 11 | 97.36 22 | 97.55 46 | 93.70 39 | 94.05 39 | 93.79 46 | 97.02 51 | 94.53 125 | 92.28 49 | 97.53 40 | 97.19 36 | 97.73 48 | 97.67 6 |
|
APDe-MVS | | | 96.23 18 | 97.22 16 | 95.08 40 | 96.66 41 | 97.56 14 | 98.63 8 | 93.69 40 | 94.62 26 | 89.80 125 | 97.73 31 | 98.13 35 | 93.84 25 | 97.79 33 | 97.63 19 | 97.87 45 | 97.08 19 |
|
CPTT-MVS | | | 95.00 52 | 94.52 73 | 95.57 26 | 96.84 34 | 96.78 36 | 97.88 38 | 93.67 41 | 92.20 68 | 92.35 79 | 85.87 183 | 97.56 58 | 94.98 9 | 96.96 53 | 96.07 60 | 97.70 51 | 96.18 42 |
|
DVP-MVS | | | 96.10 25 | 97.23 15 | 94.79 47 | 96.28 53 | 97.49 15 | 97.90 37 | 93.60 42 | 95.47 16 | 89.57 130 | 97.32 42 | 97.72 53 | 93.89 23 | 97.74 36 | 97.53 24 | 97.51 54 | 97.34 8 |
|
EPNet | | | 90.17 133 | 89.07 148 | 91.45 112 | 97.25 19 | 90.62 157 | 94.84 108 | 93.54 43 | 80.96 173 | 91.85 89 | 86.98 174 | 85.88 172 | 77.79 174 | 92.30 133 | 92.58 119 | 93.41 154 | 94.20 79 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMMP_NAP | | | 95.86 36 | 96.18 38 | 95.47 29 | 97.11 24 | 97.26 26 | 98.37 20 | 93.48 44 | 93.49 45 | 93.99 43 | 95.61 72 | 94.11 130 | 92.49 45 | 97.87 28 | 97.44 29 | 97.40 57 | 97.52 7 |
|
OPM-MVS | | | 95.96 31 | 96.59 25 | 95.23 36 | 96.67 40 | 96.52 44 | 97.86 39 | 93.28 45 | 95.27 21 | 93.46 55 | 96.26 62 | 98.85 13 | 92.89 41 | 97.09 48 | 96.37 52 | 97.22 66 | 95.78 49 |
|
ACMP | | 89.62 11 | 95.96 31 | 96.28 35 | 95.59 24 | 96.58 43 | 97.23 28 | 98.26 24 | 93.22 46 | 92.33 66 | 92.31 80 | 94.29 102 | 98.73 17 | 94.68 12 | 98.04 22 | 97.14 39 | 98.47 20 | 96.17 43 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
SD-MVS | | | 95.77 39 | 96.17 39 | 95.30 34 | 96.72 38 | 96.19 51 | 97.01 51 | 93.04 47 | 94.03 40 | 92.71 68 | 96.45 60 | 96.78 79 | 93.91 22 | 96.79 61 | 95.89 63 | 98.42 24 | 97.09 18 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
Effi-MVS+ | | | 92.93 95 | 92.16 120 | 93.83 68 | 94.29 100 | 93.53 121 | 95.04 105 | 92.98 48 | 85.27 150 | 94.46 31 | 90.24 142 | 95.34 111 | 89.99 92 | 93.72 116 | 94.23 99 | 96.22 97 | 92.79 104 |
|
MVS_0304 | | | 93.92 67 | 93.81 92 | 94.05 60 | 96.06 60 | 96.00 57 | 96.43 74 | 92.76 49 | 85.99 145 | 94.43 33 | 94.04 106 | 97.08 66 | 88.12 111 | 94.65 102 | 94.20 100 | 96.47 84 | 94.71 68 |
|
NCCC | | | 93.87 73 | 93.42 102 | 94.40 50 | 96.84 34 | 95.42 72 | 96.47 72 | 92.62 50 | 92.36 65 | 92.05 84 | 83.83 189 | 95.55 103 | 91.84 56 | 95.89 76 | 95.23 76 | 96.56 81 | 95.63 51 |
|
ACMH+ | | 89.90 10 | 96.27 17 | 97.52 12 | 94.81 45 | 95.19 83 | 97.18 30 | 97.97 34 | 92.52 51 | 96.72 9 | 90.50 116 | 97.31 43 | 99.11 6 | 94.10 20 | 98.67 12 | 97.90 14 | 98.56 15 | 95.79 48 |
|
Gipuma | | | 95.86 36 | 96.17 39 | 95.50 28 | 95.92 65 | 94.59 97 | 94.77 110 | 92.50 52 | 97.82 6 | 97.90 2 | 95.56 75 | 97.88 48 | 94.71 11 | 98.02 23 | 94.81 87 | 97.23 65 | 94.48 75 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
v7n | | | 96.49 14 | 97.20 17 | 95.65 23 | 95.57 76 | 96.04 55 | 97.93 36 | 92.49 53 | 96.40 12 | 97.13 5 | 98.99 2 | 99.41 3 | 93.79 26 | 97.84 31 | 96.15 57 | 97.00 73 | 95.60 52 |
|
TSAR-MVS + GP. | | | 94.25 62 | 94.81 68 | 93.60 76 | 96.52 46 | 95.80 64 | 94.37 118 | 92.47 54 | 90.89 97 | 88.92 134 | 95.34 81 | 94.38 127 | 92.85 42 | 96.36 70 | 95.62 69 | 96.47 84 | 95.28 58 |
|
pmmvs6 | | | 94.58 58 | 97.30 14 | 91.40 113 | 94.84 90 | 94.61 96 | 93.40 138 | 92.43 55 | 98.51 2 | 85.61 156 | 98.73 6 | 99.53 2 | 84.40 134 | 97.88 27 | 97.03 40 | 97.72 49 | 94.79 66 |
|
LS3D | | | 95.83 38 | 96.35 32 | 95.22 37 | 96.47 47 | 97.49 15 | 97.99 32 | 92.35 56 | 94.92 24 | 94.58 30 | 94.88 92 | 95.11 118 | 91.52 61 | 98.48 14 | 98.05 12 | 98.42 24 | 95.49 53 |
|
IterMVS-LS | | | 92.10 117 | 92.33 115 | 91.82 106 | 93.18 130 | 93.66 115 | 92.80 152 | 92.27 57 | 90.82 99 | 90.59 114 | 97.19 45 | 90.97 151 | 87.76 113 | 89.60 162 | 90.94 143 | 94.34 144 | 93.16 98 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CR-MVSNet | | | 85.32 171 | 81.58 184 | 89.69 139 | 90.36 178 | 84.79 183 | 86.72 198 | 92.22 58 | 75.38 199 | 90.73 109 | 90.41 139 | 67.88 204 | 84.86 130 | 83.76 189 | 85.74 176 | 93.24 157 | 83.14 174 |
|
Patchmtry | | | | | | | 83.74 187 | 86.72 198 | 92.22 58 | | 90.73 109 | | | | | | | |
|
FMVSNet1 | | | 92.86 98 | 95.26 59 | 90.06 132 | 92.40 151 | 95.16 78 | 94.37 118 | 92.22 58 | 93.18 52 | 82.16 178 | 96.76 55 | 97.48 59 | 81.85 153 | 95.32 87 | 94.98 82 | 97.34 62 | 93.93 84 |
|
train_agg | | | 93.89 70 | 93.46 101 | 94.40 50 | 97.35 14 | 93.78 114 | 97.63 44 | 92.19 61 | 88.12 126 | 90.52 115 | 93.57 111 | 95.78 100 | 92.31 48 | 94.78 99 | 93.46 110 | 96.36 88 | 94.70 70 |
|
DeepC-MVS_fast | | 91.38 6 | 94.73 56 | 94.98 63 | 94.44 48 | 96.83 36 | 96.12 54 | 96.69 62 | 92.17 62 | 92.98 54 | 93.72 49 | 94.14 103 | 95.45 107 | 90.49 86 | 95.73 81 | 95.30 74 | 96.71 77 | 95.13 61 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Effi-MVS+-dtu | | | 92.32 114 | 91.66 125 | 93.09 90 | 95.13 85 | 94.73 91 | 94.57 115 | 92.14 63 | 81.74 170 | 90.33 119 | 88.13 163 | 95.91 98 | 89.24 97 | 94.23 112 | 93.65 109 | 97.12 67 | 93.23 96 |
|
IS_MVSNet | | | 92.76 100 | 93.25 106 | 92.19 101 | 94.91 89 | 95.56 69 | 95.86 85 | 92.12 64 | 88.10 127 | 82.71 173 | 93.15 115 | 88.30 163 | 88.86 100 | 97.29 42 | 96.95 41 | 98.66 10 | 93.38 94 |
|
RPMNet | | | 83.42 178 | 78.40 194 | 89.28 144 | 89.79 181 | 84.79 183 | 90.64 176 | 92.11 65 | 75.38 199 | 87.10 148 | 79.80 200 | 61.99 213 | 82.79 145 | 81.88 195 | 82.07 184 | 93.23 159 | 82.87 177 |
|
UGNet | | | 92.31 115 | 94.70 70 | 89.53 142 | 90.99 173 | 95.53 70 | 96.19 80 | 92.10 66 | 91.35 88 | 85.76 153 | 95.31 82 | 95.48 106 | 76.84 182 | 95.22 92 | 94.79 89 | 95.32 117 | 95.19 59 |
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 |
SCA | | | 84.69 175 | 81.10 185 | 88.87 148 | 89.02 186 | 90.31 160 | 92.21 161 | 92.09 67 | 82.72 167 | 89.68 127 | 86.83 176 | 73.08 193 | 85.80 126 | 80.50 197 | 77.51 193 | 84.45 194 | 76.80 196 |
|
ACMH | | 90.17 8 | 96.61 11 | 97.69 11 | 95.35 31 | 95.29 80 | 96.94 33 | 98.43 15 | 92.05 68 | 98.04 4 | 95.38 18 | 98.07 20 | 99.25 4 | 93.23 33 | 98.35 16 | 97.16 38 | 97.72 49 | 96.00 44 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EPP-MVSNet | | | 93.63 78 | 93.95 85 | 93.26 84 | 95.15 84 | 96.54 43 | 96.18 81 | 91.97 69 | 91.74 75 | 85.76 153 | 94.95 90 | 84.27 176 | 91.60 60 | 97.61 39 | 97.38 33 | 98.87 4 | 95.18 60 |
|
CNVR-MVS | | | 94.24 63 | 94.47 74 | 93.96 65 | 96.56 44 | 95.67 67 | 96.43 74 | 91.95 70 | 92.08 71 | 91.28 101 | 90.51 136 | 95.35 110 | 91.20 67 | 96.34 71 | 95.50 72 | 96.34 90 | 95.88 47 |
|
CANet | | | 93.07 93 | 93.05 108 | 93.10 89 | 95.90 66 | 95.41 73 | 95.88 84 | 91.94 71 | 84.77 153 | 93.36 56 | 94.05 105 | 95.25 116 | 86.25 123 | 94.33 107 | 93.94 102 | 95.30 118 | 93.58 91 |
|
MSLP-MVS++ | | | 93.91 68 | 94.30 80 | 93.45 78 | 95.51 77 | 95.83 63 | 93.12 144 | 91.93 72 | 91.45 84 | 91.40 98 | 87.42 170 | 96.12 94 | 93.27 31 | 96.57 66 | 96.40 51 | 95.49 115 | 96.29 39 |
|
CDPH-MVS | | | 93.96 65 | 93.86 88 | 94.08 59 | 96.31 51 | 95.84 62 | 96.92 54 | 91.85 73 | 87.21 135 | 91.25 103 | 92.83 116 | 96.06 95 | 91.05 72 | 95.57 83 | 94.81 87 | 97.12 67 | 94.72 67 |
|
MAR-MVS | | | 91.86 121 | 91.14 130 | 92.71 93 | 94.29 100 | 94.24 103 | 94.91 107 | 91.82 74 | 81.66 171 | 93.32 57 | 84.51 187 | 93.42 138 | 86.86 118 | 95.16 94 | 94.44 97 | 95.05 131 | 94.53 74 |
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 |
AdaColmap | | | 92.41 110 | 91.49 127 | 93.48 77 | 95.96 63 | 95.02 84 | 95.37 100 | 91.73 75 | 87.97 130 | 91.28 101 | 82.82 193 | 91.04 150 | 90.62 82 | 95.82 79 | 95.07 79 | 95.95 105 | 92.67 107 |
|
COLMAP_ROB | | 93.74 2 | 97.09 8 | 97.98 4 | 96.05 18 | 95.97 62 | 97.78 9 | 98.56 11 | 91.72 76 | 97.53 7 | 96.01 14 | 98.14 18 | 98.76 15 | 95.28 5 | 98.76 11 | 98.23 10 | 98.77 5 | 96.67 34 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
TSAR-MVS + MP. | | | 95.99 29 | 96.57 26 | 95.31 33 | 96.87 30 | 96.50 45 | 98.71 5 | 91.58 77 | 93.25 49 | 92.71 68 | 96.86 53 | 96.57 82 | 93.92 21 | 98.09 19 | 97.91 13 | 98.08 36 | 96.81 30 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
PMVS | | 87.16 16 | 95.88 35 | 96.47 29 | 95.19 38 | 97.00 28 | 96.02 56 | 96.70 60 | 91.57 78 | 94.43 31 | 95.33 19 | 97.16 47 | 95.37 109 | 92.39 46 | 98.89 10 | 98.72 3 | 98.17 33 | 94.71 68 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Vis-MVSNet | | | 94.39 61 | 95.85 48 | 92.68 94 | 90.91 174 | 95.88 61 | 97.62 45 | 91.41 79 | 91.95 73 | 89.20 132 | 97.29 44 | 96.26 87 | 90.60 85 | 96.95 54 | 95.91 61 | 96.32 92 | 96.71 32 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CS-MVS | | | 92.04 119 | 90.08 139 | 94.32 53 | 95.94 64 | 94.95 88 | 96.72 59 | 91.36 80 | 80.81 176 | 94.18 38 | 81.09 197 | 90.28 155 | 90.30 89 | 95.84 78 | 95.57 71 | 97.41 56 | 92.05 119 |
|
Anonymous202405211 | | | | 94.63 71 | | 94.51 98 | 94.96 87 | 93.94 128 | 91.35 81 | 90.82 99 | | 95.60 74 | 95.85 99 | 81.74 156 | 96.47 67 | 95.84 65 | 97.39 59 | 92.85 102 |
|
DCV-MVSNet | | | 93.49 83 | 95.15 61 | 91.55 109 | 94.05 107 | 95.92 60 | 95.15 102 | 91.21 82 | 92.76 58 | 87.01 149 | 89.71 147 | 97.16 65 | 83.90 138 | 97.65 38 | 96.87 42 | 97.99 41 | 95.95 46 |
|
Vis-MVSNet (Re-imp) | | | 90.68 126 | 92.18 118 | 88.92 147 | 94.63 93 | 92.75 129 | 92.91 148 | 91.20 83 | 89.21 118 | 75.01 196 | 93.96 109 | 89.07 161 | 82.72 146 | 95.88 77 | 95.30 74 | 97.08 69 | 89.08 148 |
|
FC-MVSNet-test | | | 91.49 123 | 94.43 75 | 88.07 158 | 94.97 87 | 90.53 158 | 95.42 98 | 91.18 84 | 93.24 50 | 72.94 199 | 98.37 15 | 93.86 133 | 78.78 165 | 97.82 32 | 96.13 59 | 95.13 126 | 91.05 131 |
|
v10 | | | 93.96 65 | 94.12 83 | 93.77 74 | 93.37 126 | 95.45 71 | 96.83 58 | 91.13 85 | 89.70 113 | 95.02 24 | 97.88 27 | 98.23 31 | 91.27 65 | 92.39 131 | 92.18 124 | 94.99 132 | 93.00 100 |
|
ETV-MVS | | | 92.65 103 | 91.68 123 | 93.79 72 | 96.20 55 | 93.41 124 | 96.66 65 | 91.10 86 | 85.28 148 | 91.19 105 | 89.89 145 | 87.36 166 | 90.20 90 | 96.98 51 | 96.20 55 | 97.40 57 | 92.61 111 |
|
PHI-MVS | | | 94.65 57 | 94.84 67 | 94.44 48 | 94.95 88 | 96.55 40 | 96.46 73 | 91.10 86 | 88.96 119 | 96.00 15 | 94.55 96 | 95.32 112 | 90.67 78 | 96.97 52 | 96.69 46 | 97.44 55 | 94.84 64 |
|
HQP-MVS | | | 92.87 97 | 92.49 114 | 93.31 81 | 95.75 72 | 95.01 85 | 95.64 93 | 91.06 88 | 88.54 123 | 91.62 96 | 88.16 162 | 96.25 88 | 89.47 96 | 92.26 134 | 91.81 129 | 96.34 90 | 95.40 54 |
|
CSCG | | | 96.07 27 | 97.15 18 | 94.81 45 | 96.06 60 | 97.58 13 | 96.52 70 | 90.98 89 | 96.51 10 | 93.60 53 | 97.13 48 | 98.55 22 | 93.01 35 | 97.17 47 | 95.36 73 | 98.68 9 | 97.78 3 |
|
MCST-MVS | | | 93.60 79 | 93.40 104 | 93.83 68 | 95.30 79 | 95.40 74 | 96.49 71 | 90.87 90 | 90.08 106 | 91.72 94 | 90.28 141 | 95.99 97 | 91.69 58 | 93.94 115 | 92.99 115 | 96.93 74 | 95.13 61 |
|
v8 | | | 93.60 79 | 93.82 90 | 93.34 79 | 93.13 133 | 95.06 81 | 96.39 76 | 90.75 91 | 89.90 111 | 94.03 42 | 97.70 33 | 98.21 33 | 91.08 71 | 92.36 132 | 91.47 136 | 94.63 139 | 92.07 118 |
|
FMVSNet2 | | | 90.28 130 | 92.04 121 | 88.23 156 | 91.22 169 | 94.05 105 | 92.88 149 | 90.69 92 | 86.53 140 | 79.89 185 | 94.38 100 | 92.73 142 | 78.54 168 | 91.64 145 | 92.26 123 | 96.17 99 | 92.67 107 |
|
DeepPCF-MVS | | 90.68 7 | 94.56 59 | 94.92 64 | 94.15 57 | 94.11 106 | 95.71 66 | 97.03 50 | 90.65 93 | 93.39 48 | 94.08 41 | 95.29 83 | 94.15 129 | 93.21 34 | 95.22 92 | 94.92 85 | 95.82 110 | 95.75 50 |
|
Fast-Effi-MVS+ | | | 92.93 95 | 92.64 113 | 93.27 83 | 93.81 117 | 93.88 111 | 95.90 83 | 90.61 94 | 83.98 159 | 92.71 68 | 92.81 117 | 96.22 90 | 90.67 78 | 94.90 98 | 93.92 103 | 95.92 106 | 92.77 105 |
|
PVSNet_Blended_VisFu | | | 93.60 79 | 93.41 103 | 93.83 68 | 96.31 51 | 95.65 68 | 95.71 90 | 90.58 95 | 88.08 128 | 93.17 63 | 95.29 83 | 92.20 143 | 90.72 77 | 94.69 101 | 93.41 112 | 96.51 83 | 94.54 73 |
|
canonicalmvs | | | 93.38 85 | 94.36 77 | 92.24 100 | 93.94 113 | 96.41 49 | 94.18 125 | 90.47 96 | 93.07 53 | 88.47 140 | 88.66 157 | 93.78 134 | 88.80 101 | 95.74 80 | 95.75 67 | 97.57 53 | 97.13 16 |
|
EIA-MVS | | | 91.95 120 | 90.36 135 | 93.81 71 | 96.54 45 | 94.65 93 | 95.38 99 | 90.40 97 | 78.01 194 | 93.72 49 | 86.70 178 | 91.95 145 | 89.93 93 | 95.67 82 | 94.72 94 | 96.89 75 | 90.79 134 |
|
PCF-MVS | | 87.46 14 | 92.44 108 | 91.80 122 | 93.19 87 | 94.66 92 | 95.80 64 | 96.37 77 | 90.19 98 | 87.57 132 | 92.23 81 | 89.26 152 | 93.97 131 | 89.24 97 | 91.32 148 | 90.82 144 | 96.46 86 | 93.86 85 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PLC | | 87.27 15 | 93.08 92 | 92.92 109 | 93.26 84 | 94.67 91 | 95.03 82 | 94.38 117 | 90.10 99 | 91.69 76 | 92.14 82 | 87.24 171 | 93.91 132 | 91.61 59 | 95.05 96 | 94.73 93 | 96.67 79 | 92.80 103 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
EPNet_dtu | | | 87.40 163 | 86.27 171 | 88.72 150 | 95.68 74 | 83.37 188 | 92.09 162 | 90.08 100 | 78.11 193 | 91.29 100 | 86.33 179 | 89.74 157 | 75.39 188 | 89.07 166 | 87.89 166 | 87.81 180 | 89.38 144 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
GBi-Net | | | 89.35 142 | 90.58 131 | 87.91 159 | 91.22 169 | 94.05 105 | 92.88 149 | 90.05 101 | 79.40 181 | 78.60 187 | 90.58 133 | 87.05 168 | 78.54 168 | 95.32 87 | 94.98 82 | 96.17 99 | 92.67 107 |
|
test1 | | | 89.35 142 | 90.58 131 | 87.91 159 | 91.22 169 | 94.05 105 | 92.88 149 | 90.05 101 | 79.40 181 | 78.60 187 | 90.58 133 | 87.05 168 | 78.54 168 | 95.32 87 | 94.98 82 | 96.17 99 | 92.67 107 |
|
FMVSNet3 | | | 87.90 160 | 88.63 154 | 87.04 165 | 89.78 182 | 93.46 122 | 91.62 169 | 90.05 101 | 79.40 181 | 78.60 187 | 90.58 133 | 87.05 168 | 77.07 181 | 88.03 175 | 89.86 151 | 95.12 127 | 92.04 120 |
|
Anonymous20231211 | | | 93.19 88 | 95.50 55 | 90.49 126 | 93.77 118 | 95.29 77 | 94.36 122 | 90.04 104 | 91.44 85 | 84.59 163 | 96.72 56 | 97.65 56 | 82.45 148 | 97.25 45 | 96.32 53 | 97.74 47 | 93.79 86 |
|
v1144 | | | 93.83 74 | 93.87 87 | 93.78 73 | 93.72 121 | 94.57 98 | 96.85 57 | 89.98 105 | 91.31 89 | 95.90 16 | 97.89 26 | 98.40 24 | 91.13 70 | 92.01 139 | 92.01 127 | 95.10 128 | 90.94 133 |
|
test20.03 | | | 88.20 156 | 91.26 129 | 84.63 181 | 96.64 42 | 89.39 163 | 90.73 175 | 89.97 106 | 91.07 94 | 72.02 201 | 94.98 89 | 95.45 107 | 69.35 195 | 92.70 126 | 91.19 140 | 89.06 175 | 84.02 170 |
|
pm-mvs1 | | | 93.27 87 | 95.94 45 | 90.16 130 | 94.13 105 | 93.66 115 | 92.61 154 | 89.91 107 | 95.73 15 | 84.28 168 | 98.51 13 | 98.29 28 | 82.80 144 | 96.44 68 | 95.76 66 | 97.25 64 | 93.21 97 |
|
CDS-MVSNet | | | 88.41 150 | 89.79 141 | 86.79 168 | 94.55 97 | 90.82 153 | 92.50 156 | 89.85 108 | 83.26 163 | 80.52 183 | 91.05 129 | 89.93 156 | 69.11 196 | 93.17 125 | 92.71 118 | 94.21 146 | 87.63 158 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v2v482 | | | 93.42 84 | 93.49 100 | 93.32 80 | 93.44 125 | 94.05 105 | 96.36 79 | 89.76 109 | 91.41 86 | 95.24 21 | 97.63 36 | 98.34 27 | 90.44 87 | 91.65 144 | 91.76 131 | 94.69 136 | 89.62 143 |
|
v1192 | | | 93.98 64 | 93.94 86 | 94.01 61 | 93.91 114 | 94.63 94 | 97.00 52 | 89.75 110 | 91.01 95 | 96.50 9 | 97.93 24 | 98.26 30 | 91.74 57 | 92.06 136 | 92.05 126 | 95.18 125 | 91.66 129 |
|
MVS_111021_HR | | | 93.82 75 | 94.26 82 | 93.31 81 | 95.01 86 | 93.97 110 | 95.73 89 | 89.75 110 | 92.06 72 | 92.49 74 | 94.01 108 | 96.05 96 | 90.61 84 | 95.95 75 | 94.78 90 | 96.28 93 | 93.04 99 |
|
MIMVSNet1 | | | 92.52 106 | 94.88 66 | 89.77 136 | 96.09 57 | 91.99 141 | 96.92 54 | 89.68 112 | 95.92 14 | 84.55 164 | 96.64 58 | 98.21 33 | 78.44 171 | 96.08 73 | 95.10 78 | 92.91 163 | 90.22 140 |
|
DPM-MVS | | | 90.67 128 | 89.86 140 | 91.63 108 | 95.29 80 | 94.16 104 | 94.52 116 | 89.63 113 | 89.59 115 | 89.67 128 | 81.95 195 | 88.64 162 | 85.75 127 | 90.46 153 | 90.43 146 | 94.91 134 | 93.77 87 |
|
TransMVSNet (Re) | | | 93.55 82 | 96.32 33 | 90.32 129 | 94.38 99 | 94.05 105 | 93.30 141 | 89.53 114 | 97.15 8 | 85.12 159 | 98.83 3 | 97.89 45 | 82.21 149 | 96.75 62 | 96.14 58 | 97.35 61 | 93.46 93 |
|
v144192 | | | 93.89 70 | 93.85 89 | 93.94 66 | 93.50 124 | 94.33 99 | 97.12 47 | 89.49 115 | 90.89 97 | 96.49 10 | 97.78 29 | 98.27 29 | 91.89 54 | 92.17 135 | 91.70 132 | 95.19 124 | 91.78 126 |
|
MIMVSNet | | | 84.76 174 | 86.75 168 | 82.44 185 | 91.71 163 | 85.95 178 | 89.74 183 | 89.49 115 | 85.28 148 | 69.69 204 | 87.93 165 | 90.88 152 | 64.85 201 | 88.26 173 | 87.74 167 | 89.18 174 | 81.24 180 |
|
TSAR-MVS + COLMAP | | | 93.06 94 | 93.65 95 | 92.36 97 | 94.62 94 | 94.28 102 | 95.36 101 | 89.46 117 | 92.18 69 | 91.64 95 | 95.55 76 | 95.27 115 | 88.60 104 | 93.24 121 | 92.50 120 | 94.46 141 | 92.55 113 |
|
FC-MVSNet-train | | | 92.75 101 | 95.40 57 | 89.66 140 | 95.21 82 | 94.82 89 | 97.00 52 | 89.40 118 | 91.13 92 | 81.71 179 | 97.72 32 | 96.43 84 | 77.57 177 | 96.89 56 | 96.72 45 | 97.05 70 | 94.09 80 |
|
v1240 | | | 93.89 70 | 93.72 93 | 94.09 58 | 93.98 111 | 94.31 100 | 97.12 47 | 89.37 119 | 90.74 102 | 96.92 7 | 98.05 21 | 97.89 45 | 92.15 51 | 91.53 146 | 91.60 133 | 94.99 132 | 91.93 122 |
|
OMC-MVS | | | 94.74 55 | 95.46 56 | 93.91 67 | 94.62 94 | 96.26 50 | 96.64 66 | 89.36 120 | 94.20 35 | 94.15 39 | 94.02 107 | 97.73 52 | 91.34 64 | 96.15 72 | 95.04 81 | 97.37 60 | 94.80 65 |
|
v1921920 | | | 93.90 69 | 93.82 90 | 94.00 62 | 93.74 120 | 94.31 100 | 97.12 47 | 89.33 121 | 91.13 92 | 96.77 8 | 97.90 25 | 98.06 38 | 91.95 52 | 91.93 140 | 91.54 135 | 95.10 128 | 91.85 123 |
|
pmmvs-eth3d | | | 92.34 112 | 92.33 115 | 92.34 98 | 92.67 144 | 90.67 155 | 96.37 77 | 89.06 122 | 90.98 96 | 93.60 53 | 97.13 48 | 97.02 68 | 88.29 107 | 90.20 155 | 91.42 137 | 94.07 147 | 88.89 151 |
|
DI_MVS_plusplus_trai | | | 90.68 126 | 90.40 134 | 91.00 118 | 92.43 150 | 92.61 132 | 94.17 126 | 88.98 123 | 88.32 125 | 88.76 138 | 93.67 110 | 87.58 165 | 86.44 122 | 89.74 160 | 90.33 147 | 95.24 121 | 90.56 138 |
|
abl_6 | | | | | 91.88 105 | 93.76 119 | 94.98 86 | 95.64 93 | 88.97 124 | 86.20 143 | 90.00 123 | 86.31 180 | 94.50 126 | 87.31 114 | | | 95.60 113 | 92.48 114 |
|
TinyColmap | | | 93.17 89 | 93.33 105 | 93.00 92 | 93.84 116 | 92.76 128 | 94.75 112 | 88.90 125 | 93.97 41 | 97.48 4 | 95.28 85 | 95.29 113 | 88.37 106 | 95.31 90 | 91.58 134 | 94.65 138 | 89.10 147 |
|
HyFIR lowres test | | | 88.19 157 | 86.56 170 | 90.09 131 | 91.24 168 | 92.17 137 | 94.30 123 | 88.79 126 | 84.06 156 | 85.45 157 | 89.52 150 | 85.64 174 | 88.64 103 | 85.40 187 | 87.28 168 | 92.14 167 | 81.87 179 |
|
casdiffmvs | | | 92.42 109 | 93.99 84 | 90.60 124 | 93.25 129 | 93.82 113 | 94.28 124 | 88.73 127 | 91.53 80 | 84.53 166 | 97.74 30 | 98.64 18 | 86.60 120 | 93.21 123 | 91.20 139 | 96.21 98 | 91.76 128 |
|
ET-MVSNet_ETH3D | | | 88.06 158 | 85.75 174 | 90.74 121 | 92.82 142 | 90.68 154 | 93.77 130 | 88.59 128 | 81.22 172 | 89.78 126 | 89.15 154 | 66.79 207 | 84.29 135 | 91.72 143 | 91.34 138 | 95.22 122 | 89.36 145 |
|
thisisatest0515 | | | 93.79 76 | 94.41 76 | 93.06 91 | 94.14 103 | 92.50 133 | 95.56 96 | 88.55 129 | 91.61 78 | 92.45 75 | 96.84 54 | 95.71 101 | 90.62 82 | 94.58 103 | 95.07 79 | 97.05 70 | 94.58 72 |
|
MVS_Test | | | 90.19 132 | 90.58 131 | 89.74 137 | 92.12 158 | 91.74 143 | 92.51 155 | 88.54 130 | 82.80 165 | 87.50 146 | 94.62 94 | 95.02 120 | 83.97 136 | 88.69 170 | 89.32 156 | 93.79 150 | 91.85 123 |
|
tfpnnormal | | | 92.45 107 | 94.77 69 | 89.74 137 | 93.95 112 | 93.44 123 | 93.25 142 | 88.49 131 | 95.27 21 | 83.20 171 | 96.51 59 | 96.23 89 | 83.17 142 | 95.47 84 | 94.52 96 | 96.38 87 | 91.97 121 |
|
EG-PatchMatch MVS | | | 94.81 54 | 95.53 53 | 93.97 64 | 95.89 68 | 94.62 95 | 95.55 97 | 88.18 132 | 92.77 57 | 94.88 27 | 97.04 50 | 98.61 20 | 93.31 30 | 96.89 56 | 95.19 77 | 95.99 104 | 93.56 92 |
|
tfpn200view9 | | | 87.94 159 | 87.51 166 | 88.44 152 | 92.28 155 | 93.63 117 | 93.35 139 | 88.11 133 | 80.90 174 | 80.89 181 | 78.25 202 | 82.25 178 | 79.65 164 | 94.27 109 | 94.76 91 | 96.36 88 | 88.48 153 |
|
RPSCF | | | 95.46 42 | 96.95 19 | 93.73 75 | 95.72 73 | 95.94 59 | 95.58 95 | 88.08 134 | 95.31 17 | 91.34 99 | 96.26 62 | 98.04 40 | 93.63 28 | 98.28 17 | 97.67 18 | 98.01 40 | 97.13 16 |
|
thres600view7 | | | 89.14 144 | 88.83 150 | 89.51 143 | 93.71 122 | 93.55 119 | 93.93 129 | 88.02 135 | 87.30 134 | 82.40 174 | 81.18 196 | 80.63 186 | 82.69 147 | 94.27 109 | 95.90 62 | 96.27 94 | 88.94 149 |
|
thres200 | | | 88.29 153 | 87.88 162 | 88.76 149 | 92.50 148 | 93.55 119 | 92.47 157 | 88.02 135 | 84.80 152 | 81.44 180 | 79.28 201 | 82.20 180 | 81.83 154 | 94.27 109 | 93.67 108 | 96.27 94 | 87.40 160 |
|
3Dnovator | | 91.81 5 | 93.36 86 | 94.27 81 | 92.29 99 | 92.99 137 | 95.03 82 | 95.76 87 | 87.79 137 | 93.82 43 | 92.38 78 | 92.19 124 | 93.37 139 | 88.14 110 | 95.26 91 | 94.85 86 | 96.69 78 | 95.40 54 |
|
pmmvs4 | | | 89.95 136 | 89.32 147 | 90.69 123 | 91.60 164 | 89.17 165 | 94.37 118 | 87.63 138 | 88.07 129 | 91.02 107 | 94.50 97 | 90.50 154 | 86.13 124 | 86.33 184 | 89.40 155 | 93.39 155 | 87.29 162 |
|
thisisatest0530 | | | 89.54 141 | 87.99 161 | 91.35 114 | 93.17 131 | 91.31 148 | 93.45 136 | 87.53 139 | 82.96 164 | 89.17 133 | 90.45 137 | 70.32 199 | 88.21 108 | 93.37 120 | 93.79 105 | 96.54 82 | 93.71 88 |
|
tttt0517 | | | 89.64 138 | 88.05 159 | 91.49 110 | 93.52 123 | 91.65 144 | 93.67 131 | 87.53 139 | 82.77 166 | 89.39 131 | 90.37 140 | 70.05 200 | 88.21 108 | 93.71 117 | 93.79 105 | 96.63 80 | 94.04 81 |
|
QAPM | | | 92.57 105 | 93.51 98 | 91.47 111 | 92.91 139 | 94.82 89 | 93.01 146 | 87.51 141 | 91.49 81 | 91.21 104 | 92.24 122 | 91.70 146 | 88.74 102 | 94.54 104 | 94.39 98 | 95.41 116 | 95.37 57 |
|
IterMVS-SCA-FT | | | 90.24 131 | 89.37 146 | 91.26 115 | 92.50 148 | 92.11 139 | 91.69 168 | 87.48 142 | 87.05 137 | 91.82 91 | 95.76 71 | 87.25 167 | 91.36 63 | 89.02 167 | 85.53 178 | 92.68 164 | 88.90 150 |
|
dps | | | 81.42 187 | 77.88 199 | 85.56 174 | 87.67 196 | 85.17 182 | 88.37 192 | 87.46 143 | 74.37 202 | 84.55 164 | 86.80 177 | 62.18 212 | 80.20 160 | 81.13 196 | 77.52 192 | 85.10 189 | 77.98 194 |
|
USDC | | | 92.17 116 | 92.17 119 | 92.18 102 | 92.93 138 | 92.22 136 | 93.66 132 | 87.41 144 | 93.49 45 | 97.99 1 | 94.10 104 | 96.68 80 | 86.46 121 | 92.04 138 | 89.18 158 | 94.61 140 | 87.47 159 |
|
PatchT | | | 83.44 177 | 81.10 185 | 86.18 172 | 77.92 208 | 82.58 192 | 89.87 180 | 87.39 145 | 75.88 197 | 90.73 109 | 89.86 146 | 66.71 208 | 84.86 130 | 83.76 189 | 85.74 176 | 86.33 188 | 83.14 174 |
|
test-LLR | | | 80.62 188 | 77.20 202 | 84.62 182 | 93.99 109 | 75.11 202 | 87.04 195 | 87.32 146 | 70.11 207 | 78.59 190 | 83.17 191 | 71.60 195 | 73.88 191 | 82.32 193 | 79.20 189 | 86.91 185 | 78.87 192 |
|
test0.0.03 1 | | | 81.51 186 | 83.30 180 | 79.42 193 | 93.99 109 | 86.50 177 | 85.93 202 | 87.32 146 | 78.16 192 | 61.62 207 | 80.78 198 | 81.78 182 | 59.87 204 | 88.40 172 | 87.27 169 | 87.78 182 | 80.19 185 |
|
PatchmatchNet | | | 82.44 180 | 78.69 193 | 86.83 167 | 89.81 180 | 81.55 195 | 90.78 174 | 87.27 148 | 82.39 169 | 88.85 135 | 88.31 161 | 70.96 198 | 81.90 152 | 78.58 201 | 74.33 202 | 82.35 199 | 74.69 200 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
DELS-MVS | | | 92.33 113 | 93.61 97 | 90.83 120 | 92.84 141 | 95.13 80 | 94.76 111 | 87.22 149 | 87.78 131 | 88.42 142 | 95.78 70 | 95.28 114 | 85.71 128 | 94.44 105 | 93.91 104 | 96.01 103 | 92.97 101 |
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 |
v148 | | | 92.38 111 | 92.78 111 | 91.91 104 | 92.86 140 | 92.13 138 | 94.84 108 | 87.03 150 | 91.47 83 | 93.07 66 | 96.92 52 | 98.89 10 | 90.10 91 | 92.05 137 | 89.69 152 | 93.56 152 | 88.27 156 |
|
TAPA-MVS | | 88.94 13 | 93.78 77 | 94.31 79 | 93.18 88 | 94.14 103 | 95.99 58 | 95.74 88 | 86.98 151 | 93.43 47 | 93.88 44 | 90.16 143 | 96.88 73 | 91.05 72 | 94.33 107 | 93.95 101 | 97.28 63 | 95.40 54 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
thres400 | | | 88.54 149 | 88.15 158 | 88.98 145 | 93.17 131 | 92.84 127 | 93.56 134 | 86.93 152 | 86.45 141 | 82.37 175 | 79.96 199 | 81.46 184 | 81.83 154 | 93.21 123 | 94.76 91 | 96.04 102 | 88.39 154 |
|
MVS_111021_LR | | | 93.15 90 | 93.65 95 | 92.56 95 | 93.89 115 | 92.28 135 | 95.09 103 | 86.92 153 | 91.26 91 | 92.99 67 | 94.46 99 | 96.22 90 | 90.64 80 | 95.11 95 | 93.45 111 | 95.85 108 | 92.74 106 |
|
baseline1 | | | 86.96 164 | 87.58 165 | 86.24 171 | 93.07 135 | 90.44 159 | 89.24 187 | 86.85 154 | 85.14 151 | 77.26 193 | 90.45 137 | 76.09 191 | 75.79 187 | 91.80 142 | 91.81 129 | 95.20 123 | 87.35 161 |
|
PatchMatch-RL | | | 89.59 139 | 88.80 152 | 90.51 125 | 92.20 157 | 88.00 171 | 91.72 166 | 86.64 155 | 84.75 154 | 88.25 143 | 87.10 173 | 90.66 153 | 89.85 95 | 93.23 122 | 92.28 122 | 94.41 143 | 85.60 168 |
|
GA-MVS | | | 88.76 147 | 88.04 160 | 89.59 141 | 92.32 154 | 91.46 146 | 92.28 160 | 86.62 156 | 83.82 161 | 89.84 124 | 92.51 121 | 81.94 181 | 83.53 140 | 89.41 164 | 89.27 157 | 92.95 162 | 87.90 157 |
|
MVS-HIRNet | | | 78.28 198 | 75.28 206 | 81.79 188 | 80.33 205 | 69.38 209 | 76.83 208 | 86.59 157 | 70.76 206 | 86.66 151 | 89.57 149 | 81.04 185 | 77.74 175 | 77.81 203 | 71.65 203 | 82.62 197 | 66.73 208 |
|
CNLPA | | | 93.14 91 | 93.67 94 | 92.53 96 | 94.62 94 | 94.73 91 | 95.00 106 | 86.57 158 | 92.85 55 | 92.43 76 | 90.94 131 | 94.67 122 | 90.35 88 | 95.41 85 | 93.70 107 | 96.23 96 | 93.37 95 |
|
tpm cat1 | | | 80.03 189 | 75.93 205 | 84.81 179 | 89.31 183 | 83.26 190 | 88.86 189 | 86.55 159 | 79.24 186 | 86.10 152 | 84.22 188 | 63.62 211 | 77.37 179 | 73.43 206 | 70.88 205 | 80.67 200 | 76.87 195 |
|
Anonymous20231206 | | | 87.45 162 | 89.66 144 | 84.87 178 | 94.00 108 | 87.73 174 | 91.36 170 | 86.41 160 | 88.89 121 | 75.03 195 | 92.59 120 | 96.82 75 | 72.48 193 | 89.72 161 | 88.06 165 | 89.93 172 | 83.81 172 |
|
CostFormer | | | 82.15 182 | 79.54 189 | 85.20 177 | 88.92 187 | 85.70 179 | 90.87 173 | 86.26 161 | 79.19 187 | 83.87 169 | 87.89 167 | 69.20 202 | 76.62 184 | 77.50 204 | 75.28 199 | 84.69 191 | 82.02 178 |
|
MDA-MVSNet-bldmvs | | | 89.75 137 | 91.67 124 | 87.50 163 | 74.25 210 | 90.88 152 | 94.68 113 | 85.89 162 | 91.64 77 | 91.03 106 | 95.86 68 | 94.35 128 | 89.10 99 | 96.87 58 | 86.37 174 | 90.04 171 | 85.72 167 |
|
PVSNet_BlendedMVS | | | 90.09 134 | 90.12 137 | 90.05 133 | 92.40 151 | 92.74 130 | 91.74 164 | 85.89 162 | 80.54 177 | 90.30 120 | 88.54 158 | 95.51 104 | 84.69 132 | 92.64 128 | 90.25 148 | 95.28 119 | 90.61 136 |
|
PVSNet_Blended | | | 90.09 134 | 90.12 137 | 90.05 133 | 92.40 151 | 92.74 130 | 91.74 164 | 85.89 162 | 80.54 177 | 90.30 120 | 88.54 158 | 95.51 104 | 84.69 132 | 92.64 128 | 90.25 148 | 95.28 119 | 90.61 136 |
|
IB-MVS | | 86.01 17 | 88.24 154 | 87.63 164 | 88.94 146 | 92.03 160 | 91.77 142 | 92.40 159 | 85.58 165 | 78.24 191 | 84.85 161 | 71.99 206 | 93.45 137 | 83.96 137 | 93.48 119 | 92.33 121 | 94.84 135 | 92.15 117 |
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 |
OpenMVS | | 89.22 12 | 91.09 124 | 91.42 128 | 90.71 122 | 92.79 143 | 93.61 118 | 92.74 153 | 85.47 166 | 86.10 144 | 90.73 109 | 85.71 184 | 93.07 141 | 86.69 119 | 94.07 114 | 93.34 113 | 95.86 107 | 94.02 82 |
|
MSDG | | | 92.09 118 | 92.84 110 | 91.22 116 | 92.55 146 | 92.97 125 | 93.42 137 | 85.43 167 | 90.24 105 | 91.83 90 | 94.70 93 | 94.59 123 | 88.48 105 | 94.91 97 | 93.31 114 | 95.59 114 | 89.15 146 |
|
anonymousdsp | | | 95.45 44 | 96.70 24 | 93.99 63 | 88.43 191 | 92.05 140 | 99.18 1 | 85.42 168 | 94.29 34 | 96.10 13 | 98.63 9 | 99.08 7 | 96.11 1 | 97.77 34 | 97.41 32 | 98.70 8 | 97.69 5 |
|
V42 | | | 92.67 102 | 93.50 99 | 91.71 107 | 91.41 165 | 92.96 126 | 95.71 90 | 85.00 169 | 89.67 114 | 93.22 61 | 97.67 34 | 98.01 42 | 91.02 74 | 92.65 127 | 92.12 125 | 93.86 149 | 91.42 130 |
|
MDTV_nov1_ep13_2view | | | 88.22 155 | 87.85 163 | 88.65 151 | 91.40 166 | 86.75 176 | 94.07 127 | 84.97 170 | 88.86 122 | 93.20 62 | 96.11 66 | 96.21 92 | 83.70 139 | 87.29 180 | 80.29 187 | 84.56 192 | 79.46 189 |
|
testgi | | | 86.49 167 | 90.31 136 | 82.03 186 | 95.63 75 | 88.18 168 | 93.47 135 | 84.89 171 | 93.23 51 | 69.54 205 | 87.16 172 | 97.96 44 | 60.66 203 | 91.90 141 | 89.90 150 | 87.99 178 | 83.84 171 |
|
diffmvs | | | 90.44 129 | 92.23 117 | 88.35 154 | 91.36 167 | 91.38 147 | 92.45 158 | 84.84 172 | 89.88 112 | 85.09 160 | 96.69 57 | 97.71 54 | 83.33 141 | 90.01 159 | 88.96 162 | 93.03 161 | 91.00 132 |
|
PM-MVS | | | 92.65 103 | 93.20 107 | 92.00 103 | 92.11 159 | 90.16 161 | 95.99 82 | 84.81 173 | 91.31 89 | 92.41 77 | 95.87 67 | 96.64 81 | 92.35 47 | 93.65 118 | 92.91 116 | 94.34 144 | 91.85 123 |
|
gm-plane-assit | | | 86.15 169 | 82.51 182 | 90.40 127 | 95.81 70 | 92.29 134 | 97.99 32 | 84.66 174 | 92.15 70 | 93.15 64 | 97.84 28 | 44.65 215 | 78.60 167 | 88.02 176 | 85.95 175 | 92.20 166 | 76.69 197 |
|
thres100view900 | | | 86.46 168 | 86.00 173 | 86.99 166 | 92.28 155 | 91.03 151 | 91.09 171 | 84.49 175 | 80.90 174 | 80.89 181 | 78.25 202 | 82.25 178 | 77.57 177 | 90.17 156 | 92.84 117 | 95.63 112 | 86.57 165 |
|
baseline2 | | | 84.95 172 | 82.68 181 | 87.59 162 | 92.64 145 | 88.41 167 | 90.09 178 | 84.25 176 | 75.88 197 | 85.23 158 | 82.49 194 | 71.15 197 | 80.14 161 | 88.21 174 | 87.21 171 | 93.21 160 | 85.39 169 |
|
tpm | | | 81.58 185 | 78.84 191 | 84.79 180 | 91.11 172 | 79.50 196 | 89.79 182 | 83.75 177 | 79.30 185 | 92.05 84 | 90.98 130 | 64.78 210 | 74.54 189 | 80.50 197 | 76.67 196 | 77.49 204 | 80.15 186 |
|
ADS-MVSNet | | | 79.11 194 | 79.38 190 | 78.80 198 | 81.90 204 | 75.59 201 | 84.36 203 | 83.69 178 | 87.31 133 | 76.76 194 | 87.58 168 | 76.90 188 | 68.55 198 | 78.70 200 | 75.56 198 | 77.53 203 | 74.07 202 |
|
FPMVS | | | 90.81 125 | 91.60 126 | 89.88 135 | 92.52 147 | 88.18 168 | 93.31 140 | 83.62 179 | 91.59 79 | 88.45 141 | 88.96 155 | 89.73 158 | 86.96 116 | 96.42 69 | 95.69 68 | 94.43 142 | 90.65 135 |
|
EPMVS | | | 79.26 191 | 78.20 197 | 80.49 189 | 87.04 199 | 78.86 197 | 86.08 201 | 83.51 180 | 82.63 168 | 73.94 198 | 89.59 148 | 68.67 203 | 72.03 194 | 78.17 202 | 75.08 200 | 80.37 201 | 74.37 201 |
|
new-patchmatchnet | | | 84.45 176 | 88.75 153 | 79.43 192 | 93.28 128 | 81.87 194 | 81.68 204 | 83.48 181 | 94.47 28 | 71.53 202 | 98.33 16 | 97.88 48 | 58.61 206 | 90.35 154 | 77.33 194 | 87.99 178 | 81.05 182 |
|
CANet_DTU | | | 88.95 146 | 89.51 145 | 88.29 155 | 93.12 134 | 91.22 150 | 93.61 133 | 83.47 182 | 80.07 180 | 90.71 113 | 89.19 153 | 93.68 136 | 76.27 186 | 91.44 147 | 91.17 141 | 92.59 165 | 89.83 142 |
|
CHOSEN 1792x2688 | | | 86.64 166 | 86.62 169 | 86.65 170 | 90.33 179 | 87.86 173 | 93.19 143 | 83.30 183 | 83.95 160 | 82.32 176 | 87.93 165 | 89.34 159 | 86.92 117 | 85.64 186 | 84.95 179 | 83.85 195 | 86.68 164 |
|
MVSTER | | | 84.79 173 | 83.79 177 | 85.96 173 | 89.14 185 | 89.80 162 | 89.39 185 | 82.99 184 | 74.16 203 | 82.78 172 | 85.97 182 | 66.81 206 | 76.84 182 | 90.77 152 | 88.83 164 | 94.66 137 | 90.19 141 |
|
MS-PatchMatch | | | 87.72 161 | 88.62 155 | 86.66 169 | 90.81 176 | 88.18 168 | 90.92 172 | 82.25 185 | 85.86 146 | 80.40 184 | 90.14 144 | 89.29 160 | 84.93 129 | 89.39 165 | 89.12 159 | 90.67 169 | 88.34 155 |
|
pmmvs5 | | | 88.63 148 | 89.70 143 | 87.39 164 | 89.24 184 | 90.64 156 | 91.87 163 | 82.13 186 | 83.34 162 | 87.86 144 | 94.58 95 | 96.15 93 | 79.87 162 | 87.33 179 | 89.07 161 | 93.39 155 | 86.76 163 |
|
MDTV_nov1_ep13 | | | 82.33 181 | 79.66 188 | 85.45 175 | 88.83 188 | 83.88 186 | 90.09 178 | 81.98 187 | 79.07 188 | 88.82 136 | 88.70 156 | 73.77 192 | 78.41 172 | 80.29 199 | 76.08 197 | 84.56 192 | 75.83 198 |
|
IterMVS | | | 88.32 151 | 88.25 157 | 88.41 153 | 90.83 175 | 91.24 149 | 93.07 145 | 81.69 188 | 86.77 138 | 88.55 139 | 95.61 72 | 86.91 171 | 87.01 115 | 87.38 178 | 83.77 180 | 89.29 173 | 86.06 166 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpmrst | | | 78.81 196 | 76.18 204 | 81.87 187 | 88.56 190 | 77.45 199 | 86.74 197 | 81.52 189 | 80.08 179 | 83.48 170 | 90.84 132 | 66.88 205 | 74.54 189 | 73.04 207 | 71.02 204 | 76.38 205 | 73.95 203 |
|
EU-MVSNet | | | 91.63 122 | 92.73 112 | 90.35 128 | 88.36 192 | 87.89 172 | 96.53 69 | 81.51 190 | 92.45 62 | 91.82 91 | 96.44 61 | 97.05 67 | 93.26 32 | 94.10 113 | 88.94 163 | 90.61 170 | 92.24 116 |
|
CLD-MVS | | | 92.81 99 | 94.32 78 | 91.05 117 | 95.39 78 | 95.31 76 | 95.82 86 | 81.44 191 | 89.40 116 | 91.94 86 | 95.86 68 | 97.36 60 | 85.83 125 | 95.35 86 | 94.59 95 | 95.85 108 | 92.34 115 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
DWT-MVSNet_training | | | 79.22 193 | 73.99 207 | 85.33 176 | 88.57 189 | 84.41 185 | 90.56 177 | 80.96 192 | 73.90 204 | 85.72 155 | 75.62 204 | 50.09 214 | 81.30 157 | 76.91 205 | 77.02 195 | 84.88 190 | 79.97 188 |
|
CVMVSNet | | | 88.97 145 | 89.73 142 | 88.10 157 | 87.33 198 | 85.22 181 | 94.68 113 | 78.68 193 | 88.94 120 | 86.98 150 | 95.55 76 | 85.71 173 | 89.87 94 | 91.19 149 | 89.69 152 | 91.05 168 | 91.78 126 |
|
FMVSNet5 | | | 79.08 195 | 78.83 192 | 79.38 194 | 87.52 197 | 86.78 175 | 87.64 193 | 78.15 194 | 69.54 209 | 70.64 203 | 65.97 209 | 65.44 209 | 63.87 202 | 90.17 156 | 90.46 145 | 88.48 177 | 83.45 173 |
|
baseline | | | 86.71 165 | 88.89 149 | 84.16 183 | 87.85 194 | 85.23 180 | 89.82 181 | 77.69 195 | 84.03 158 | 84.75 162 | 94.91 91 | 94.59 123 | 77.19 180 | 86.57 183 | 86.51 173 | 87.66 183 | 90.36 139 |
|
N_pmnet | | | 79.33 190 | 84.22 175 | 73.62 202 | 91.72 162 | 73.72 205 | 86.11 200 | 76.36 196 | 92.38 63 | 53.38 208 | 95.54 78 | 95.62 102 | 59.14 205 | 84.23 188 | 74.84 201 | 75.03 207 | 73.25 204 |
|
CMPMVS | | 66.55 18 | 85.55 170 | 87.46 167 | 83.32 184 | 84.99 200 | 81.97 193 | 79.19 207 | 75.93 197 | 79.32 184 | 88.82 136 | 85.09 185 | 91.07 149 | 82.12 150 | 92.56 130 | 89.63 154 | 88.84 176 | 92.56 112 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
CHOSEN 280x420 | | | 79.24 192 | 78.26 196 | 80.38 190 | 79.60 206 | 68.80 210 | 89.32 186 | 75.38 198 | 77.25 195 | 78.02 192 | 75.57 205 | 76.17 190 | 81.19 158 | 88.61 171 | 81.39 185 | 78.79 202 | 80.03 187 |
|
TAMVS | | | 82.96 179 | 86.15 172 | 79.24 195 | 90.57 177 | 83.12 191 | 87.29 194 | 75.12 199 | 84.06 156 | 65.81 206 | 92.22 123 | 88.27 164 | 69.11 196 | 88.72 168 | 87.26 170 | 87.56 184 | 79.38 190 |
|
MVE | | 60.41 19 | 73.21 203 | 80.84 187 | 64.30 204 | 56.34 212 | 57.24 212 | 75.28 211 | 72.76 200 | 87.14 136 | 41.39 211 | 86.31 180 | 85.30 175 | 80.66 159 | 86.17 185 | 83.36 181 | 59.35 210 | 80.38 184 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 77.81 199 | 77.88 199 | 77.73 201 | 88.26 193 | 70.48 208 | 80.19 206 | 71.20 201 | 86.66 139 | 72.89 200 | 88.09 164 | 81.74 183 | 78.75 166 | 90.02 158 | 68.30 206 | 75.10 206 | 59.85 209 |
|
EMVS | | | 77.65 200 | 77.49 201 | 77.83 199 | 87.75 195 | 71.02 207 | 81.13 205 | 70.54 202 | 86.38 142 | 74.52 197 | 89.38 151 | 80.19 187 | 78.22 173 | 89.48 163 | 67.13 207 | 74.83 208 | 58.84 210 |
|
new_pmnet | | | 76.65 202 | 83.52 178 | 68.63 203 | 82.60 202 | 72.08 206 | 76.76 209 | 64.17 203 | 84.41 155 | 49.73 210 | 91.77 126 | 91.53 147 | 56.16 207 | 86.59 181 | 83.26 182 | 82.37 198 | 75.02 199 |
|
PMMVS | | | 81.93 183 | 83.48 179 | 80.12 191 | 72.35 211 | 75.05 204 | 88.54 190 | 64.01 204 | 77.02 196 | 82.22 177 | 87.51 169 | 91.12 148 | 79.70 163 | 86.59 181 | 86.64 172 | 93.88 148 | 80.41 183 |
|
PMMVS2 | | | 69.86 204 | 82.14 183 | 55.52 205 | 75.19 209 | 63.08 211 | 75.52 210 | 60.97 205 | 88.50 124 | 25.11 213 | 91.77 126 | 96.44 83 | 25.43 208 | 88.70 169 | 79.34 188 | 70.93 209 | 67.17 207 |
|
test-mter | | | 78.71 197 | 78.35 195 | 79.12 197 | 84.03 201 | 76.58 200 | 88.51 191 | 59.06 206 | 71.06 205 | 78.87 186 | 83.73 190 | 71.83 194 | 76.44 185 | 83.41 192 | 80.61 186 | 87.79 181 | 81.24 180 |
|
pmmvs3 | | | 81.69 184 | 83.83 176 | 79.19 196 | 78.33 207 | 78.57 198 | 89.53 184 | 58.71 207 | 78.88 190 | 84.34 167 | 88.36 160 | 91.96 144 | 77.69 176 | 87.48 177 | 82.42 183 | 86.54 187 | 79.18 191 |
|
TESTMET0.1,1 | | | 77.47 201 | 77.20 202 | 77.78 200 | 81.94 203 | 75.11 202 | 87.04 195 | 58.33 208 | 70.11 207 | 78.59 190 | 83.17 191 | 71.60 195 | 73.88 191 | 82.32 193 | 79.20 189 | 86.91 185 | 78.87 192 |
|
DeepMVS_CX | | | | | | | 47.68 213 | 53.20 213 | 19.21 209 | 63.24 210 | 26.96 212 | 66.50 208 | 69.82 201 | 66.91 200 | 64.27 208 | | 54.91 211 | 72.72 205 |
|
tmp_tt | | | | | 28.44 206 | 36.05 213 | 15.86 214 | 21.29 214 | 6.40 210 | 54.52 211 | 51.96 209 | 50.37 210 | 38.68 216 | 9.55 209 | 61.75 209 | 59.66 208 | 45.36 212 | |
|
testmvs | | | 2.38 206 | 3.35 208 | 1.26 209 | 0.83 214 | 0.96 216 | 1.53 216 | 0.83 211 | 3.59 212 | 1.63 216 | 6.03 211 | 2.93 217 | 1.55 211 | 3.49 210 | 2.51 210 | 1.21 214 | 3.92 212 |
|
test123 | | | 2.16 207 | 2.82 209 | 1.41 208 | 0.62 215 | 1.18 215 | 1.53 216 | 0.82 212 | 2.78 213 | 2.27 215 | 4.18 212 | 1.98 218 | 1.64 210 | 2.58 211 | 3.01 209 | 1.56 213 | 4.00 211 |
|
GG-mvs-BLEND | | | 54.28 205 | 77.89 198 | 26.72 207 | 0.37 216 | 83.31 189 | 70.04 212 | 0.39 213 | 74.71 201 | 5.36 214 | 68.78 207 | 83.06 177 | 0.62 212 | 83.73 191 | 78.99 191 | 83.55 196 | 72.68 206 |
|
sosnet-low-res | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
|
sosnet | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
|
our_test_3 | | | | | | 91.78 161 | 88.87 166 | 94.37 118 | | | | | | | | | | |
|
test_part1 | | | | | | | | | | | | | | | | | | 96.89 26 |
|
ambc | | | | 94.61 72 | | 98.09 5 | 95.14 79 | 91.71 167 | | 94.18 36 | 96.46 11 | 96.26 62 | 96.30 86 | 91.26 66 | 94.70 100 | 92.00 128 | 93.45 153 | 93.67 89 |
|
MTAPA | | | | | | | | | | | 94.88 27 | | 96.88 73 | | | | | |
|
MTMP | | | | | | | | | | | 95.43 17 | | 97.25 62 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.96 215 | | | | | | | | | | |
|
XVS | | | | | | 96.86 32 | 97.48 17 | 98.73 3 | | | 93.28 58 | | 96.82 75 | | | | 98.17 33 | |
|
X-MVStestdata | | | | | | 96.86 32 | 97.48 17 | 98.73 3 | | | 93.28 58 | | 96.82 75 | | | | 98.17 33 | |
|
mPP-MVS | | | | | | 98.24 3 | | | | | | | 97.65 56 | | | | | |
|
NP-MVS | | | | | | | | | | 85.48 147 | | | | | | | | |
|