HSP-MVS | | | 86.82 2 | 89.95 2 | 83.16 5 | 89.38 22 | 91.60 12 | 85.63 12 | 74.15 7 | 94.20 1 | 75.52 14 | 94.99 1 | 83.21 6 | 85.96 1 | 87.67 3 | 85.88 6 | 88.32 24 | 92.13 46 |
|
DeepPCF-MVS | | 76.94 1 | 83.08 14 | 87.77 6 | 77.60 30 | 90.11 14 | 90.96 14 | 78.48 49 | 72.63 17 | 93.10 2 | 65.84 37 | 80.67 19 | 81.55 13 | 74.80 23 | 85.94 9 | 85.39 8 | 83.75 150 | 96.77 8 |
|
ESAPD | | | 87.78 1 | 90.56 1 | 84.53 1 | 92.88 2 | 93.82 1 | 88.95 1 | 76.05 4 | 92.95 3 | 80.32 2 | 93.12 2 | 86.87 1 | 80.88 4 | 85.54 10 | 84.01 18 | 88.09 32 | 97.62 2 |
|
APDe-MVS | | | 86.37 3 | 88.41 4 | 84.00 4 | 91.43 9 | 91.83 10 | 88.34 2 | 74.67 5 | 91.19 4 | 81.76 1 | 91.13 3 | 81.94 12 | 80.07 5 | 83.38 22 | 82.58 29 | 87.69 36 | 96.78 7 |
|
APD-MVS | | | 84.83 8 | 87.00 8 | 82.30 8 | 89.61 20 | 89.21 29 | 86.51 7 | 73.64 11 | 90.98 5 | 77.99 7 | 89.89 5 | 80.04 17 | 79.18 7 | 82.00 38 | 81.37 41 | 86.88 53 | 95.49 16 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HPM-MVS++ | | | 85.64 6 | 88.43 3 | 82.39 7 | 92.65 3 | 90.24 21 | 85.83 10 | 74.21 6 | 90.68 6 | 75.63 13 | 86.77 9 | 84.15 4 | 78.68 9 | 86.33 6 | 85.26 9 | 87.32 43 | 95.60 14 |
|
SMA-MVS | | | 84.91 7 | 87.95 5 | 81.36 10 | 91.75 7 | 90.84 15 | 86.35 8 | 73.36 13 | 90.22 7 | 72.81 18 | 80.70 18 | 85.67 2 | 76.69 14 | 86.06 8 | 86.14 5 | 87.20 49 | 96.05 10 |
|
TSAR-MVS + MP. | | | 84.39 9 | 86.58 12 | 81.83 9 | 88.09 34 | 86.47 53 | 85.63 12 | 73.62 12 | 90.13 8 | 79.24 4 | 89.67 6 | 82.99 7 | 77.72 11 | 81.22 44 | 80.92 49 | 86.68 56 | 94.66 23 |
|
SD-MVS | | | 84.31 10 | 86.96 10 | 81.22 11 | 88.98 26 | 88.68 32 | 85.65 11 | 73.85 10 | 89.09 9 | 79.63 3 | 87.34 8 | 84.84 3 | 73.71 28 | 82.66 28 | 81.60 38 | 85.48 105 | 94.51 24 |
|
CNVR-MVS | | | 85.96 4 | 87.58 7 | 84.06 3 | 92.58 4 | 92.40 6 | 87.62 4 | 77.77 2 | 88.44 10 | 75.93 12 | 79.49 21 | 81.97 11 | 81.65 3 | 87.04 5 | 86.58 3 | 88.79 14 | 97.18 4 |
|
ACMMP_Plus | | | 83.54 12 | 86.37 13 | 80.25 16 | 89.57 21 | 90.10 23 | 85.27 15 | 71.66 18 | 87.38 11 | 73.08 16 | 84.23 13 | 80.16 15 | 75.31 19 | 84.85 14 | 83.64 21 | 86.57 57 | 94.21 29 |
|
TSAR-MVS + GP. | | | 82.27 18 | 85.98 14 | 77.94 28 | 80.72 66 | 88.25 38 | 81.12 38 | 67.71 40 | 87.10 12 | 73.31 15 | 85.23 11 | 83.68 5 | 76.64 15 | 80.43 51 | 81.47 40 | 88.15 30 | 95.66 13 |
|
zzz-MVS | | | 81.65 20 | 83.10 22 | 79.97 18 | 88.14 33 | 87.62 44 | 83.96 21 | 69.90 26 | 86.92 13 | 77.67 9 | 72.47 31 | 78.74 19 | 74.13 27 | 81.59 42 | 81.15 45 | 86.01 73 | 93.19 37 |
|
abl_6 | | | | | 79.06 24 | 89.68 19 | 92.14 8 | 77.70 54 | 69.68 28 | 86.87 14 | 71.88 20 | 74.29 30 | 80.06 16 | 76.56 16 | | | 88.84 13 | 95.82 11 |
|
NCCC | | | 84.16 11 | 85.46 16 | 82.64 6 | 92.34 5 | 90.57 18 | 86.57 6 | 76.51 3 | 86.85 15 | 72.91 17 | 77.20 27 | 78.69 20 | 79.09 8 | 84.64 16 | 84.88 14 | 88.44 22 | 95.41 17 |
|
train_agg | | | 83.35 13 | 86.93 11 | 79.17 22 | 89.70 18 | 88.41 35 | 85.60 14 | 72.89 16 | 86.31 16 | 66.58 36 | 90.48 4 | 82.24 10 | 73.06 32 | 83.10 24 | 82.64 28 | 87.21 48 | 95.30 19 |
|
TSAR-MVS + ACMM | | | 81.59 21 | 85.84 15 | 76.63 34 | 89.82 17 | 86.53 52 | 86.32 9 | 66.72 45 | 85.96 17 | 65.43 38 | 88.98 7 | 82.29 9 | 67.57 67 | 82.06 37 | 81.33 42 | 83.93 148 | 93.75 33 |
|
MCST-MVS | | | 85.75 5 | 86.99 9 | 84.31 2 | 94.07 1 | 92.80 3 | 88.15 3 | 79.10 1 | 85.66 18 | 70.72 25 | 76.50 28 | 80.45 14 | 82.17 2 | 88.35 1 | 87.49 2 | 91.63 2 | 97.65 1 |
|
HFP-MVS | | | 82.48 17 | 84.12 19 | 80.56 14 | 90.15 13 | 87.55 45 | 84.28 18 | 69.67 29 | 85.22 19 | 77.95 8 | 84.69 12 | 75.94 24 | 75.04 21 | 81.85 39 | 81.17 44 | 86.30 62 | 92.40 44 |
|
OMC-MVS | | | 74.03 53 | 75.82 54 | 71.95 60 | 79.56 68 | 80.98 97 | 75.35 69 | 63.21 68 | 84.48 20 | 61.83 47 | 61.54 52 | 66.89 52 | 69.41 54 | 76.60 77 | 74.07 124 | 82.34 170 | 86.15 114 |
|
ACMMPR | | | 80.62 24 | 82.98 23 | 77.87 29 | 88.41 28 | 87.05 47 | 83.02 24 | 69.18 32 | 83.91 21 | 68.35 32 | 82.89 14 | 73.64 29 | 72.16 37 | 80.78 49 | 81.13 46 | 86.10 67 | 91.43 53 |
|
DeepC-MVS_fast | | 75.41 2 | 81.69 19 | 82.10 28 | 81.20 12 | 91.04 11 | 87.81 43 | 83.42 22 | 74.04 8 | 83.77 22 | 71.09 23 | 66.88 40 | 72.44 32 | 79.48 6 | 85.08 12 | 84.97 13 | 88.12 31 | 93.78 32 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + COLMAP | | | 73.09 56 | 76.86 48 | 68.71 74 | 74.97 102 | 82.49 78 | 74.51 74 | 61.83 85 | 83.16 23 | 49.31 88 | 82.22 16 | 51.62 107 | 68.94 57 | 78.76 62 | 75.52 89 | 82.67 165 | 84.23 128 |
|
SteuartSystems-ACMMP | | | 82.51 16 | 85.35 17 | 79.20 21 | 90.25 12 | 89.39 28 | 84.79 16 | 70.95 20 | 82.86 24 | 68.32 33 | 86.44 10 | 77.19 21 | 73.07 31 | 83.63 21 | 83.64 21 | 87.82 33 | 94.34 26 |
Skip Steuart: Steuart Systems R&D Blog. |
DeepC-MVS | | 74.46 3 | 80.30 25 | 81.05 31 | 79.42 19 | 87.42 36 | 88.50 34 | 83.23 23 | 73.27 14 | 82.78 25 | 71.01 24 | 62.86 48 | 69.93 46 | 74.80 23 | 84.30 17 | 84.20 17 | 86.79 55 | 94.77 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CNLPA | | | 71.37 66 | 70.27 77 | 72.66 57 | 80.79 65 | 81.33 93 | 71.07 111 | 65.75 51 | 82.36 26 | 64.80 40 | 42.46 125 | 56.49 87 | 72.70 34 | 73.00 121 | 70.52 170 | 80.84 181 | 85.76 119 |
|
PHI-MVS | | | 79.43 28 | 84.06 20 | 74.04 50 | 86.15 42 | 91.57 13 | 80.85 41 | 68.90 35 | 82.22 27 | 51.81 78 | 78.10 23 | 74.28 27 | 70.39 47 | 84.01 20 | 84.00 19 | 86.14 66 | 94.24 28 |
|
CSCG | | | 82.90 15 | 84.52 18 | 81.02 13 | 91.85 6 | 93.43 2 | 87.14 5 | 74.01 9 | 81.96 28 | 76.14 10 | 70.84 32 | 82.49 8 | 69.71 50 | 82.32 34 | 85.18 11 | 87.26 45 | 95.40 18 |
|
CP-MVS | | | 79.44 27 | 81.51 30 | 77.02 33 | 86.95 38 | 85.96 59 | 82.00 29 | 68.44 37 | 81.82 29 | 67.39 34 | 77.43 25 | 73.68 28 | 71.62 40 | 79.56 56 | 79.58 54 | 85.73 95 | 92.51 43 |
|
EPNet | | | 79.28 32 | 82.25 25 | 75.83 40 | 88.31 31 | 90.14 22 | 79.43 47 | 68.07 38 | 81.76 30 | 61.26 50 | 77.26 26 | 70.08 45 | 70.06 48 | 82.43 32 | 82.00 33 | 87.82 33 | 92.09 47 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CANet | | | 80.90 23 | 82.93 24 | 78.53 26 | 86.83 40 | 92.26 7 | 81.19 37 | 66.95 43 | 81.60 31 | 69.90 28 | 66.93 39 | 74.80 26 | 76.79 13 | 84.68 15 | 84.77 15 | 89.50 8 | 95.50 15 |
|
NP-MVS | | | | | | | | | | 81.60 31 | | | | | | | | |
|
TAPA-MVS | | 67.10 9 | 71.45 65 | 73.47 63 | 69.10 72 | 77.04 86 | 80.78 100 | 73.81 77 | 62.10 81 | 80.80 33 | 51.28 79 | 60.91 54 | 63.80 62 | 67.98 62 | 74.59 98 | 72.42 149 | 82.37 169 | 80.97 156 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MP-MVS | | | 80.94 22 | 83.49 21 | 77.96 27 | 88.48 27 | 88.16 39 | 82.82 27 | 69.34 31 | 80.79 34 | 69.67 29 | 82.35 15 | 77.13 22 | 71.60 41 | 80.97 48 | 80.96 48 | 85.87 86 | 94.06 30 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
PGM-MVS | | | 79.42 30 | 81.84 29 | 76.60 35 | 88.38 30 | 86.69 50 | 82.97 26 | 65.75 51 | 80.39 35 | 64.94 39 | 81.95 17 | 72.11 37 | 71.41 42 | 80.45 50 | 80.55 52 | 86.18 64 | 90.76 61 |
|
canonicalmvs | | | 77.65 38 | 79.59 36 | 75.39 42 | 81.52 58 | 89.83 27 | 81.32 36 | 60.74 99 | 80.05 36 | 66.72 35 | 68.43 36 | 65.09 56 | 74.72 25 | 78.87 60 | 82.73 27 | 87.32 43 | 92.16 45 |
|
HQP-MVS | | | 78.26 35 | 80.91 32 | 75.17 45 | 85.67 44 | 84.33 68 | 83.01 25 | 69.38 30 | 79.88 37 | 55.83 68 | 79.85 20 | 64.90 58 | 70.81 44 | 82.46 30 | 81.78 35 | 86.30 62 | 93.18 38 |
|
CDPH-MVS | | | 79.39 31 | 82.13 27 | 76.19 38 | 89.22 25 | 88.34 36 | 84.20 19 | 71.00 19 | 79.67 38 | 56.97 67 | 77.77 24 | 72.24 36 | 68.50 60 | 81.33 43 | 82.74 26 | 87.23 46 | 92.84 40 |
|
ACMMP | | | 77.61 39 | 79.59 36 | 75.30 44 | 85.87 43 | 85.58 60 | 81.42 34 | 67.38 42 | 79.38 39 | 62.61 44 | 78.53 22 | 65.79 55 | 68.80 58 | 78.56 63 | 78.50 63 | 85.75 91 | 90.80 59 |
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 |
X-MVS | | | 78.16 36 | 80.55 33 | 75.38 43 | 87.99 35 | 86.27 55 | 81.05 39 | 68.98 33 | 78.33 40 | 61.07 52 | 75.25 29 | 72.27 33 | 67.52 68 | 80.03 53 | 80.52 53 | 85.66 102 | 91.20 55 |
|
MVSTER | | | 76.92 44 | 79.92 34 | 73.42 53 | 74.98 101 | 82.97 73 | 78.15 50 | 63.41 67 | 78.02 41 | 64.41 41 | 67.54 37 | 72.80 31 | 71.05 43 | 83.29 23 | 83.73 20 | 88.53 21 | 91.12 56 |
|
CLD-MVS | | | 77.36 42 | 77.29 45 | 77.45 32 | 82.21 54 | 88.11 40 | 81.92 30 | 68.96 34 | 77.97 42 | 69.62 30 | 62.08 49 | 59.44 76 | 73.57 29 | 81.75 40 | 81.27 43 | 88.41 23 | 90.39 64 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CPTT-MVS | | | 75.43 49 | 77.13 47 | 73.44 52 | 81.43 59 | 82.55 77 | 80.96 40 | 64.35 59 | 77.95 43 | 61.39 49 | 69.20 35 | 70.94 41 | 69.38 55 | 73.89 109 | 73.32 135 | 83.14 161 | 92.06 48 |
|
MAR-MVS | | | 77.19 43 | 78.37 41 | 75.81 41 | 89.87 16 | 90.58 17 | 79.33 48 | 65.56 53 | 77.62 44 | 58.33 62 | 59.24 59 | 67.98 49 | 74.83 22 | 82.37 33 | 83.12 25 | 86.95 52 | 87.67 102 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
MVS_0304 | | | 79.43 28 | 82.20 26 | 76.20 37 | 84.22 47 | 91.79 11 | 81.82 32 | 63.81 64 | 76.83 45 | 61.71 48 | 66.37 41 | 75.52 25 | 76.38 17 | 85.54 10 | 85.03 12 | 89.28 10 | 94.32 27 |
|
AdaColmap | | | 76.23 47 | 73.55 61 | 79.35 20 | 89.38 22 | 85.00 64 | 79.99 45 | 73.04 15 | 76.60 46 | 71.17 22 | 55.18 64 | 57.99 84 | 77.87 10 | 76.82 75 | 76.82 73 | 84.67 133 | 86.45 110 |
|
MSLP-MVS++ | | | 78.57 33 | 77.33 44 | 80.02 17 | 88.39 29 | 84.79 65 | 84.62 17 | 66.17 49 | 75.96 47 | 78.40 5 | 61.59 51 | 71.47 39 | 73.54 30 | 78.43 64 | 78.88 59 | 88.97 12 | 90.18 67 |
|
PVSNet_BlendedMVS | | | 76.84 45 | 78.47 38 | 74.95 46 | 82.37 52 | 89.90 25 | 75.45 67 | 65.45 54 | 74.99 48 | 70.66 26 | 63.07 46 | 58.27 82 | 67.60 65 | 84.24 18 | 81.70 36 | 88.18 28 | 97.10 5 |
|
PVSNet_Blended | | | 76.84 45 | 78.47 38 | 74.95 46 | 82.37 52 | 89.90 25 | 75.45 67 | 65.45 54 | 74.99 48 | 70.66 26 | 63.07 46 | 58.27 82 | 67.60 65 | 84.24 18 | 81.70 36 | 88.18 28 | 97.10 5 |
|
3Dnovator+ | | 70.16 6 | 77.87 37 | 77.29 45 | 78.55 25 | 89.25 24 | 88.32 37 | 80.09 43 | 67.95 39 | 74.89 50 | 71.83 21 | 52.05 76 | 70.68 42 | 76.27 18 | 82.27 35 | 82.04 31 | 85.92 79 | 90.77 60 |
|
3Dnovator | | 70.49 5 | 78.42 34 | 76.77 49 | 80.35 15 | 91.43 9 | 90.27 20 | 81.84 31 | 70.79 21 | 72.10 51 | 71.95 19 | 50.02 83 | 67.86 51 | 77.47 12 | 82.89 25 | 84.24 16 | 88.61 18 | 89.99 68 |
|
CANet_DTU | | | 72.84 57 | 76.63 51 | 68.43 77 | 76.81 89 | 86.62 51 | 75.54 66 | 54.71 157 | 72.06 52 | 43.54 112 | 67.11 38 | 58.46 79 | 72.40 35 | 81.13 47 | 80.82 51 | 87.57 38 | 90.21 66 |
|
PMMVS | | | 70.37 70 | 75.06 56 | 64.90 96 | 71.46 122 | 81.88 82 | 64.10 153 | 55.64 145 | 71.31 53 | 46.69 95 | 70.69 33 | 58.56 77 | 69.53 52 | 79.03 59 | 75.63 86 | 81.96 173 | 88.32 98 |
|
diffmvs | | | 73.50 55 | 75.66 55 | 70.97 63 | 74.96 103 | 86.71 49 | 77.16 58 | 57.42 129 | 71.12 54 | 60.43 57 | 57.20 61 | 70.40 44 | 68.79 59 | 76.11 85 | 76.05 81 | 87.10 51 | 92.06 48 |
|
MVS_111021_LR | | | 74.26 52 | 75.95 53 | 72.27 58 | 79.43 70 | 85.04 63 | 72.71 79 | 65.27 56 | 70.92 55 | 63.58 42 | 69.32 34 | 60.31 74 | 69.43 53 | 77.01 73 | 77.15 71 | 83.22 157 | 91.93 51 |
|
LGP-MVS_train | | | 72.02 63 | 73.18 64 | 70.67 65 | 82.13 55 | 80.26 114 | 79.58 46 | 63.04 71 | 70.09 56 | 51.98 76 | 65.06 43 | 55.62 94 | 62.49 87 | 75.97 87 | 76.32 78 | 84.80 130 | 88.93 79 |
|
EPNet_dtu | | | 66.17 93 | 70.13 78 | 61.54 146 | 81.04 61 | 77.39 143 | 68.87 129 | 62.50 80 | 69.78 57 | 33.51 180 | 63.77 45 | 56.22 88 | 37.65 197 | 72.20 143 | 72.18 151 | 85.69 98 | 79.38 163 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_Test | | | 75.22 50 | 76.69 50 | 73.51 51 | 79.30 71 | 88.82 31 | 80.06 44 | 58.74 109 | 69.77 58 | 57.50 66 | 59.78 58 | 61.35 71 | 75.31 19 | 82.07 36 | 83.60 23 | 90.13 5 | 91.41 54 |
|
ACMP | | 68.86 7 | 72.15 62 | 72.25 65 | 72.03 59 | 80.96 62 | 80.87 99 | 77.93 52 | 64.13 61 | 69.29 59 | 60.79 55 | 64.04 44 | 53.54 101 | 63.91 79 | 73.74 113 | 75.27 90 | 84.45 138 | 88.98 78 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PLC | | 64.00 12 | 68.54 77 | 66.66 97 | 70.74 64 | 80.28 67 | 74.88 162 | 72.64 80 | 63.70 66 | 69.26 60 | 55.71 69 | 47.24 100 | 55.31 95 | 70.42 46 | 72.05 147 | 70.67 168 | 81.66 175 | 77.19 170 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PCF-MVS | | 70.85 4 | 75.73 48 | 76.55 52 | 74.78 49 | 83.67 48 | 88.04 42 | 81.47 33 | 70.62 24 | 69.24 61 | 57.52 65 | 60.59 56 | 69.18 47 | 70.65 45 | 77.11 72 | 77.65 70 | 84.75 131 | 94.01 31 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DWT-MVSNet_training | | | 72.81 58 | 73.98 58 | 71.45 62 | 81.26 60 | 86.37 54 | 72.08 82 | 59.82 106 | 69.13 62 | 58.15 63 | 54.71 65 | 61.33 73 | 67.81 64 | 76.86 74 | 78.63 60 | 89.59 6 | 90.86 58 |
|
GG-mvs-BLEND | | | 54.54 189 | 77.58 42 | 27.67 226 | 0.03 239 | 90.09 24 | 77.20 57 | 0.02 236 | 66.83 63 | 0.05 241 | 59.90 57 | 73.33 30 | 0.04 236 | 78.40 65 | 79.30 56 | 88.65 16 | 95.20 20 |
|
QAPM | | | 77.50 40 | 77.43 43 | 77.59 31 | 91.52 8 | 92.00 9 | 81.41 35 | 70.63 22 | 66.22 64 | 58.05 64 | 54.70 66 | 71.79 38 | 74.49 26 | 82.46 30 | 82.04 31 | 89.46 9 | 92.79 42 |
|
MVS_111021_HR | | | 77.42 41 | 78.40 40 | 76.28 36 | 86.95 38 | 90.68 16 | 77.41 56 | 70.56 25 | 66.21 65 | 62.48 46 | 66.17 42 | 63.98 60 | 72.08 38 | 82.87 26 | 83.15 24 | 88.24 27 | 95.71 12 |
|
OpenMVS | | 67.62 8 | 74.92 51 | 73.91 59 | 76.09 39 | 90.10 15 | 90.38 19 | 78.01 51 | 66.35 47 | 66.09 66 | 62.80 43 | 46.33 108 | 64.55 59 | 71.77 39 | 79.92 54 | 80.88 50 | 87.52 39 | 89.20 75 |
|
CostFormer | | | 72.18 61 | 73.90 60 | 70.18 67 | 79.47 69 | 86.19 58 | 76.94 59 | 48.62 190 | 66.07 67 | 60.40 58 | 54.14 70 | 65.82 54 | 67.98 62 | 75.84 88 | 76.41 77 | 87.67 37 | 92.83 41 |
|
GBi-Net | | | 69.21 73 | 70.40 75 | 67.81 80 | 69.49 133 | 78.65 128 | 74.54 70 | 60.97 95 | 65.32 68 | 51.06 80 | 47.37 97 | 62.05 65 | 63.43 81 | 77.49 68 | 78.22 65 | 87.37 40 | 83.73 131 |
|
test1 | | | 69.21 73 | 70.40 75 | 67.81 80 | 69.49 133 | 78.65 128 | 74.54 70 | 60.97 95 | 65.32 68 | 51.06 80 | 47.37 97 | 62.05 65 | 63.43 81 | 77.49 68 | 78.22 65 | 87.37 40 | 83.73 131 |
|
FMVSNet3 | | | 70.41 69 | 71.89 68 | 68.68 75 | 70.89 128 | 79.42 123 | 75.63 63 | 60.97 95 | 65.32 68 | 51.06 80 | 47.37 97 | 62.05 65 | 64.90 75 | 82.49 29 | 82.27 30 | 88.64 17 | 84.34 127 |
|
DELS-MVS | | | 79.49 26 | 79.84 35 | 79.08 23 | 88.26 32 | 92.49 4 | 84.12 20 | 70.63 22 | 65.27 71 | 69.60 31 | 61.29 53 | 66.50 53 | 72.75 33 | 88.07 2 | 88.03 1 | 89.13 11 | 97.22 3 |
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 |
ACMM | | 66.70 10 | 70.42 67 | 68.49 86 | 72.67 56 | 82.85 49 | 77.76 139 | 77.70 54 | 64.76 58 | 64.61 72 | 60.74 56 | 49.29 85 | 53.97 100 | 65.86 71 | 74.97 95 | 75.57 88 | 84.13 146 | 83.29 136 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tpmp4_e23 | | | 69.38 72 | 69.47 80 | 69.28 71 | 78.20 75 | 82.35 79 | 75.92 60 | 49.20 188 | 64.15 73 | 59.96 59 | 47.93 90 | 55.77 92 | 68.06 61 | 73.05 120 | 74.53 103 | 84.34 140 | 88.50 97 |
|
RPSCF | | | 55.07 183 | 58.06 177 | 51.57 192 | 48.87 224 | 58.95 214 | 53.68 194 | 41.26 218 | 62.42 74 | 45.88 98 | 54.38 69 | 54.26 98 | 53.75 149 | 57.15 206 | 53.53 222 | 66.01 221 | 65.75 207 |
|
DI_MVS_plusplus_trai | | | 73.94 54 | 74.85 57 | 72.88 55 | 76.57 91 | 86.80 48 | 80.41 42 | 61.47 89 | 62.35 75 | 59.44 61 | 47.91 91 | 68.12 48 | 72.24 36 | 82.84 27 | 81.50 39 | 87.15 50 | 94.42 25 |
|
CHOSEN 1792x2688 | | | 72.55 60 | 71.98 66 | 73.22 54 | 86.57 41 | 92.41 5 | 75.63 63 | 66.77 44 | 62.08 76 | 52.32 75 | 30.27 200 | 50.74 110 | 66.14 70 | 86.22 7 | 85.41 7 | 91.90 1 | 96.75 9 |
|
EPMVS | | | 66.21 92 | 67.49 93 | 64.73 101 | 75.81 96 | 84.20 70 | 68.94 128 | 44.37 206 | 61.55 77 | 48.07 91 | 49.21 87 | 54.87 97 | 62.88 84 | 71.82 149 | 71.40 160 | 88.28 26 | 79.37 164 |
|
tpm cat1 | | | 67.47 84 | 67.05 96 | 67.98 79 | 76.63 90 | 81.51 91 | 74.49 75 | 47.65 195 | 61.18 78 | 61.12 51 | 42.51 124 | 53.02 104 | 64.74 77 | 70.11 165 | 71.50 156 | 83.22 157 | 89.49 71 |
|
LS3D | | | 64.54 107 | 62.14 140 | 67.34 84 | 80.85 63 | 75.79 157 | 69.99 120 | 65.87 50 | 60.77 79 | 44.35 109 | 42.43 126 | 45.95 120 | 65.01 73 | 69.88 168 | 68.69 178 | 77.97 199 | 71.43 196 |
|
tpmrst | | | 67.15 87 | 68.12 90 | 66.03 92 | 76.21 93 | 80.98 97 | 71.27 104 | 45.05 202 | 60.69 80 | 50.63 83 | 46.95 105 | 54.15 99 | 65.30 72 | 71.80 150 | 71.77 153 | 87.72 35 | 90.48 63 |
|
PatchmatchNet | | | 65.43 100 | 67.71 91 | 62.78 133 | 73.49 110 | 82.83 74 | 66.42 145 | 45.40 201 | 60.40 81 | 45.27 101 | 49.22 86 | 57.60 85 | 60.01 113 | 70.61 157 | 71.38 163 | 86.08 69 | 81.91 151 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
Effi-MVS+ | | | 70.42 67 | 71.23 72 | 69.47 69 | 78.04 76 | 85.24 62 | 75.57 65 | 58.88 108 | 59.56 82 | 48.47 89 | 52.73 75 | 54.94 96 | 69.69 51 | 78.34 66 | 77.06 72 | 86.18 64 | 90.73 62 |
|
Fast-Effi-MVS+ | | | 67.59 81 | 67.56 92 | 67.62 82 | 73.67 108 | 81.14 96 | 71.12 108 | 54.79 156 | 58.88 83 | 50.61 84 | 46.70 106 | 47.05 117 | 69.12 56 | 76.06 86 | 76.44 76 | 86.43 60 | 86.65 108 |
|
test-LLR | | | 68.23 79 | 71.61 70 | 64.28 114 | 71.37 123 | 81.32 94 | 63.98 157 | 61.03 92 | 58.62 84 | 42.96 124 | 52.74 73 | 61.65 69 | 57.74 135 | 75.64 90 | 78.09 68 | 88.61 18 | 93.21 35 |
|
TESTMET0.1,1 | | | 67.38 85 | 71.61 70 | 62.45 138 | 66.05 171 | 81.32 94 | 63.98 157 | 55.36 149 | 58.62 84 | 42.96 124 | 52.74 73 | 61.65 69 | 57.74 135 | 75.64 90 | 78.09 68 | 88.61 18 | 93.21 35 |
|
MDTV_nov1_ep13 | | | 65.21 101 | 67.28 94 | 62.79 132 | 70.91 127 | 81.72 84 | 69.28 127 | 49.50 185 | 58.08 86 | 43.94 111 | 50.50 82 | 56.02 89 | 58.86 130 | 70.72 156 | 73.37 133 | 84.24 142 | 80.52 157 |
|
MS-PatchMatch | | | 70.34 71 | 69.00 82 | 71.91 61 | 85.20 46 | 85.35 61 | 77.84 53 | 61.77 87 | 58.01 87 | 55.40 71 | 41.26 132 | 58.34 81 | 61.69 90 | 81.70 41 | 78.29 64 | 89.56 7 | 80.02 161 |
|
FMVSNet5 | | | 58.86 167 | 60.24 161 | 57.25 173 | 52.66 217 | 66.25 200 | 63.77 160 | 52.86 175 | 57.85 88 | 37.92 157 | 36.12 179 | 52.22 105 | 51.37 154 | 70.88 155 | 71.43 159 | 84.92 118 | 66.91 204 |
|
dps | | | 64.08 110 | 63.22 123 | 65.08 95 | 75.27 100 | 79.65 119 | 66.68 142 | 46.63 200 | 56.94 89 | 55.67 70 | 43.96 111 | 43.63 128 | 64.00 78 | 69.50 172 | 69.82 173 | 82.25 171 | 79.02 165 |
|
pmmvs4 | | | 63.14 125 | 62.46 137 | 63.94 117 | 66.03 172 | 76.40 152 | 66.82 141 | 57.60 122 | 56.74 90 | 50.26 86 | 40.81 137 | 37.51 174 | 59.26 127 | 71.75 151 | 71.48 157 | 83.68 151 | 82.53 144 |
|
IB-MVS | | 64.48 11 | 69.02 75 | 68.97 83 | 69.09 73 | 81.75 57 | 89.01 30 | 64.50 151 | 64.91 57 | 56.65 91 | 62.59 45 | 47.89 92 | 45.23 122 | 51.99 152 | 69.18 173 | 81.88 34 | 88.77 15 | 92.93 39 |
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 |
MSDG | | | 65.57 98 | 61.57 147 | 70.24 66 | 82.02 56 | 76.47 151 | 74.46 76 | 68.73 36 | 56.52 92 | 50.33 85 | 38.47 159 | 41.10 139 | 62.42 88 | 72.12 145 | 72.94 143 | 83.47 153 | 73.37 186 |
|
PVSNet_Blended_VisFu | | | 71.76 64 | 73.54 62 | 69.69 68 | 79.01 72 | 87.16 46 | 72.05 83 | 61.80 86 | 56.46 93 | 59.66 60 | 53.88 72 | 62.48 63 | 59.08 129 | 81.17 45 | 78.90 58 | 86.53 59 | 94.74 22 |
|
FMVSNet2 | | | 68.06 80 | 68.57 85 | 67.45 83 | 69.49 133 | 78.65 128 | 74.54 70 | 60.23 105 | 56.29 94 | 49.64 87 | 42.13 128 | 57.08 86 | 63.43 81 | 81.15 46 | 80.99 47 | 87.37 40 | 83.73 131 |
|
PatchMatch-RL | | | 62.22 141 | 60.69 154 | 64.01 115 | 68.74 138 | 75.75 158 | 59.27 182 | 60.35 101 | 56.09 95 | 53.80 74 | 47.06 103 | 36.45 180 | 64.80 76 | 68.22 176 | 67.22 185 | 77.10 201 | 74.02 181 |
|
CR-MVSNet | | | 62.31 136 | 64.75 110 | 59.47 157 | 68.63 139 | 71.29 188 | 67.53 135 | 43.18 208 | 55.83 96 | 41.40 136 | 41.04 135 | 55.85 90 | 57.29 138 | 72.76 133 | 73.27 138 | 78.77 196 | 83.23 139 |
|
RPMNet | | | 58.63 172 | 62.80 130 | 53.76 190 | 67.59 163 | 71.29 188 | 54.60 193 | 38.13 223 | 55.83 96 | 35.70 170 | 41.58 131 | 53.04 103 | 47.89 166 | 66.10 181 | 67.38 183 | 78.65 198 | 84.40 126 |
|
IS_MVSNet | | | 67.29 86 | 71.98 66 | 61.82 144 | 76.92 87 | 84.32 69 | 65.90 148 | 58.22 112 | 55.75 98 | 39.22 147 | 54.51 68 | 62.47 64 | 45.99 177 | 78.83 61 | 78.52 62 | 84.70 132 | 89.47 72 |
|
test-mter | | | 64.06 113 | 69.24 81 | 58.01 165 | 59.07 200 | 77.40 142 | 59.13 183 | 48.11 193 | 55.64 99 | 39.18 148 | 51.56 77 | 58.54 78 | 55.38 143 | 73.52 114 | 76.00 82 | 87.22 47 | 92.05 50 |
|
Vis-MVSNet (Re-imp) | | | 62.25 138 | 68.74 84 | 54.68 184 | 73.70 107 | 78.74 127 | 56.51 190 | 57.49 124 | 55.22 100 | 26.86 198 | 54.56 67 | 61.35 71 | 31.06 200 | 73.10 117 | 74.90 93 | 82.49 167 | 83.31 135 |
|
tmp_tt | | | | | 16.09 233 | 13.07 237 | 8.12 240 | 13.61 237 | 2.08 235 | 55.09 101 | 30.10 189 | 40.26 140 | 22.83 221 | 5.35 234 | 29.91 229 | 25.25 231 | 32.33 233 | |
|
FC-MVSNet-train | | | 68.83 76 | 68.29 87 | 69.47 69 | 78.35 74 | 79.94 115 | 64.72 150 | 66.38 46 | 54.96 102 | 54.51 73 | 56.75 62 | 47.91 116 | 66.91 69 | 75.57 92 | 75.75 84 | 85.92 79 | 87.12 105 |
|
USDC | | | 59.69 162 | 60.03 164 | 59.28 159 | 64.04 181 | 71.84 186 | 63.15 166 | 55.36 149 | 54.90 103 | 35.02 175 | 48.34 88 | 29.79 208 | 58.16 132 | 70.60 158 | 71.33 164 | 79.99 188 | 73.42 185 |
|
HyFIR lowres test | | | 68.39 78 | 68.28 88 | 68.52 76 | 80.85 63 | 88.11 40 | 71.08 110 | 58.09 114 | 54.87 104 | 47.80 92 | 27.55 205 | 55.80 91 | 64.97 74 | 79.11 58 | 79.14 57 | 88.31 25 | 93.35 34 |
|
UGNet | | | 67.57 83 | 71.69 69 | 62.76 134 | 69.88 131 | 82.58 76 | 66.43 144 | 58.64 110 | 54.71 105 | 51.87 77 | 61.74 50 | 62.01 68 | 45.46 180 | 74.78 97 | 74.99 92 | 84.24 142 | 91.02 57 |
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 |
CHOSEN 280x420 | | | 62.23 140 | 66.57 98 | 57.17 174 | 59.88 197 | 68.92 194 | 61.20 173 | 42.28 212 | 54.17 106 | 39.57 144 | 47.78 93 | 64.97 57 | 62.68 85 | 73.85 110 | 69.52 175 | 77.43 200 | 86.75 107 |
|
Effi-MVS+-dtu | | | 64.58 105 | 64.08 114 | 65.16 94 | 73.04 113 | 75.17 161 | 70.68 116 | 56.23 139 | 54.12 107 | 44.71 108 | 47.42 96 | 51.10 108 | 63.82 80 | 68.08 177 | 66.32 190 | 82.47 168 | 86.38 112 |
|
PatchT | | | 60.46 158 | 63.85 115 | 56.51 176 | 65.95 173 | 75.68 159 | 47.34 204 | 41.39 215 | 53.89 108 | 41.40 136 | 37.84 168 | 50.30 111 | 57.29 138 | 72.76 133 | 73.27 138 | 85.67 99 | 83.23 139 |
|
EPP-MVSNet | | | 67.58 82 | 71.10 73 | 63.48 123 | 75.71 97 | 83.35 72 | 66.85 140 | 57.83 117 | 53.02 109 | 41.15 139 | 55.82 63 | 67.89 50 | 56.01 141 | 74.40 100 | 72.92 144 | 83.33 155 | 90.30 65 |
|
Fast-Effi-MVS+-dtu | | | 63.05 127 | 64.72 112 | 61.11 148 | 71.21 126 | 76.81 150 | 70.72 115 | 43.13 210 | 52.51 110 | 35.34 172 | 46.55 107 | 46.36 118 | 61.40 97 | 71.57 152 | 71.44 158 | 84.84 124 | 87.79 101 |
|
tpm | | | 64.85 103 | 66.02 104 | 63.48 123 | 74.52 105 | 78.38 131 | 70.98 112 | 44.99 204 | 51.61 111 | 43.28 119 | 47.66 95 | 53.18 102 | 60.57 104 | 70.58 159 | 71.30 165 | 86.54 58 | 89.45 73 |
|
ADS-MVSNet | | | 58.40 173 | 59.16 174 | 57.52 171 | 65.80 175 | 74.57 166 | 60.26 177 | 40.17 220 | 50.51 112 | 38.01 156 | 40.11 141 | 44.72 124 | 59.36 125 | 64.91 185 | 66.55 188 | 81.53 176 | 72.72 190 |
|
IterMVS-LS | | | 66.08 94 | 66.56 99 | 65.51 93 | 73.67 108 | 74.88 162 | 70.89 114 | 53.55 164 | 50.42 113 | 48.32 90 | 50.59 81 | 55.66 93 | 61.83 89 | 73.93 108 | 74.42 110 | 84.82 129 | 86.01 116 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UniMVSNet_NR-MVSNet | | | 62.30 137 | 63.51 118 | 60.89 149 | 69.48 136 | 77.83 137 | 64.07 155 | 63.94 62 | 50.03 114 | 31.17 186 | 44.82 110 | 41.12 138 | 51.37 154 | 71.02 154 | 74.81 95 | 85.30 107 | 84.95 123 |
|
OPM-MVS | | | 72.74 59 | 70.93 74 | 74.85 48 | 85.30 45 | 84.34 67 | 82.82 27 | 69.79 27 | 49.96 115 | 55.39 72 | 54.09 71 | 60.14 75 | 70.04 49 | 80.38 52 | 79.43 55 | 85.74 94 | 88.20 99 |
|
UniMVSNet (Re) | | | 60.62 157 | 62.93 128 | 57.92 166 | 67.64 162 | 77.90 136 | 61.75 170 | 61.24 91 | 49.83 116 | 29.80 190 | 42.57 122 | 40.62 152 | 43.36 184 | 70.49 162 | 73.27 138 | 83.76 149 | 85.81 118 |
|
tfpn_ndepth | | | 62.95 132 | 63.75 116 | 62.02 142 | 76.89 88 | 79.48 122 | 64.09 154 | 60.98 94 | 49.48 117 | 38.73 151 | 49.92 84 | 44.79 123 | 47.37 169 | 71.91 148 | 71.66 154 | 84.07 147 | 79.00 166 |
|
DU-MVS | | | 60.87 156 | 61.82 143 | 59.76 155 | 66.69 167 | 75.87 155 | 64.07 155 | 61.96 82 | 49.31 118 | 31.17 186 | 42.76 119 | 36.95 177 | 51.37 154 | 69.67 170 | 73.20 141 | 83.30 156 | 84.95 123 |
|
NR-MVSNet | | | 61.08 155 | 62.09 141 | 59.90 153 | 71.96 121 | 75.87 155 | 63.60 161 | 61.96 82 | 49.31 118 | 27.95 195 | 42.76 119 | 33.85 197 | 48.82 163 | 74.35 103 | 74.05 125 | 85.13 110 | 84.45 125 |
|
tfpn111 | | | 66.52 91 | 66.12 102 | 66.98 88 | 77.70 79 | 81.58 87 | 71.71 92 | 62.94 75 | 49.16 120 | 43.28 119 | 51.38 78 | 41.34 132 | 61.42 92 | 76.24 80 | 74.63 97 | 84.84 124 | 88.52 93 |
|
conf0.01 | | | 66.60 90 | 66.18 101 | 67.09 86 | 77.90 78 | 82.02 81 | 71.71 92 | 63.05 70 | 49.16 120 | 43.41 116 | 46.23 109 | 45.78 121 | 61.42 92 | 76.55 78 | 74.63 97 | 85.04 116 | 88.87 83 |
|
conf0.002 | | | 67.12 89 | 67.13 95 | 67.11 85 | 77.95 77 | 82.11 80 | 71.71 92 | 63.06 69 | 49.16 120 | 43.43 114 | 47.76 94 | 48.79 113 | 61.42 92 | 76.61 76 | 76.55 75 | 85.07 115 | 88.92 81 |
|
conf200view11 | | | 65.89 96 | 64.96 108 | 66.98 88 | 77.70 79 | 81.58 87 | 71.71 92 | 62.94 75 | 49.16 120 | 43.28 119 | 43.24 115 | 41.34 132 | 61.42 92 | 76.24 80 | 74.63 97 | 84.84 124 | 88.52 93 |
|
thres100view900 | | | 67.14 88 | 66.09 103 | 68.38 78 | 77.70 79 | 83.84 71 | 74.52 73 | 66.33 48 | 49.16 120 | 43.40 117 | 43.24 115 | 41.34 132 | 62.59 86 | 79.31 57 | 75.92 83 | 85.73 95 | 89.81 69 |
|
tfpn200view9 | | | 65.90 95 | 64.96 108 | 67.00 87 | 77.70 79 | 81.58 87 | 71.71 92 | 62.94 75 | 49.16 120 | 43.40 117 | 43.24 115 | 41.34 132 | 61.42 92 | 76.24 80 | 74.63 97 | 84.84 124 | 88.52 93 |
|
Baseline_NR-MVSNet | | | 59.47 163 | 60.28 159 | 58.54 163 | 66.69 167 | 73.90 168 | 61.63 171 | 62.90 78 | 49.15 126 | 26.87 197 | 35.18 185 | 37.62 173 | 48.20 165 | 69.67 170 | 73.61 129 | 84.92 118 | 82.82 142 |
|
v18 | | | 63.31 124 | 62.02 142 | 64.81 100 | 68.48 140 | 73.38 171 | 72.14 81 | 54.28 159 | 48.99 127 | 47.21 93 | 39.56 144 | 41.20 136 | 60.80 101 | 72.89 125 | 74.46 109 | 85.96 78 | 83.64 134 |
|
Vis-MVSNet | | | 65.53 99 | 69.83 79 | 60.52 150 | 70.80 129 | 84.59 66 | 66.37 146 | 55.47 148 | 48.40 128 | 40.62 143 | 57.67 60 | 58.43 80 | 45.37 181 | 77.49 68 | 76.24 79 | 84.47 137 | 85.99 117 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v16 | | | 63.12 126 | 61.78 144 | 64.68 102 | 68.45 141 | 73.29 172 | 71.86 85 | 54.12 160 | 48.36 129 | 47.00 94 | 39.30 149 | 41.01 140 | 60.67 102 | 72.83 131 | 74.40 111 | 86.01 73 | 83.24 138 |
|
v17 | | | 62.99 131 | 61.70 145 | 64.51 105 | 68.40 142 | 73.28 173 | 71.80 90 | 54.11 161 | 47.87 130 | 46.14 97 | 39.29 150 | 41.01 140 | 60.60 103 | 72.81 132 | 74.39 116 | 85.99 76 | 83.25 137 |
|
COLMAP_ROB | | 51.17 15 | 55.13 182 | 52.90 195 | 57.73 170 | 73.47 111 | 67.21 198 | 62.13 168 | 55.82 142 | 47.83 131 | 34.39 176 | 31.60 197 | 34.24 194 | 44.90 182 | 63.88 194 | 62.52 204 | 75.67 205 | 63.02 213 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
IterMVS | | | 61.87 146 | 63.55 117 | 59.90 153 | 67.29 165 | 72.20 183 | 67.34 138 | 48.56 191 | 47.48 132 | 37.86 158 | 47.07 102 | 48.27 114 | 54.08 148 | 72.12 145 | 73.71 128 | 84.30 141 | 83.99 129 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UA-Net | | | 64.62 104 | 68.23 89 | 60.42 151 | 77.53 83 | 81.38 92 | 60.08 179 | 57.47 125 | 47.01 133 | 44.75 107 | 60.68 55 | 71.32 40 | 41.84 188 | 73.27 115 | 72.25 150 | 80.83 182 | 71.68 194 |
|
V42 | | | 62.86 133 | 62.97 127 | 62.74 135 | 60.84 193 | 78.99 126 | 71.46 103 | 57.13 133 | 46.85 134 | 44.28 110 | 38.87 154 | 40.73 148 | 57.63 137 | 72.60 138 | 74.14 121 | 85.09 113 | 88.63 91 |
|
TranMVSNet+NR-MVSNet | | | 60.38 159 | 61.30 149 | 59.30 158 | 68.34 143 | 75.57 160 | 63.38 164 | 63.78 65 | 46.74 135 | 27.73 196 | 42.56 123 | 36.84 178 | 47.66 167 | 70.36 163 | 74.59 101 | 84.91 120 | 82.46 145 |
|
v6 | | | 64.09 109 | 63.40 119 | 64.90 96 | 68.28 144 | 80.78 100 | 71.85 86 | 57.64 121 | 46.73 136 | 45.18 103 | 39.40 145 | 40.89 143 | 60.54 107 | 72.86 126 | 74.40 111 | 85.92 79 | 88.72 88 |
|
v8 | | | 63.44 123 | 62.58 136 | 64.43 107 | 68.28 144 | 78.07 134 | 71.82 89 | 54.85 154 | 46.70 137 | 45.20 102 | 39.40 145 | 40.91 142 | 60.54 107 | 72.85 130 | 74.39 116 | 85.92 79 | 85.76 119 |
|
v1neww | | | 64.08 110 | 63.38 120 | 64.89 98 | 68.27 146 | 80.77 102 | 71.84 87 | 57.65 119 | 46.66 138 | 45.10 104 | 39.40 145 | 40.86 144 | 60.57 104 | 72.86 126 | 74.40 111 | 85.92 79 | 88.71 89 |
|
v7new | | | 64.08 110 | 63.38 120 | 64.89 98 | 68.27 146 | 80.77 102 | 71.84 87 | 57.65 119 | 46.66 138 | 45.10 104 | 39.40 145 | 40.86 144 | 60.57 104 | 72.86 126 | 74.40 111 | 85.92 79 | 88.71 89 |
|
MIMVSNet | | | 57.78 176 | 59.71 167 | 55.53 181 | 54.79 208 | 77.10 148 | 63.89 159 | 45.02 203 | 46.59 140 | 36.79 162 | 28.36 203 | 40.77 147 | 45.84 178 | 74.97 95 | 76.58 74 | 86.87 54 | 73.60 184 |
|
thres200 | | | 65.58 97 | 64.74 111 | 66.56 90 | 77.52 84 | 81.61 85 | 73.44 78 | 62.95 73 | 46.23 141 | 42.45 134 | 42.76 119 | 41.18 137 | 58.12 133 | 76.24 80 | 75.59 87 | 84.89 121 | 89.58 70 |
|
test0.0.03 1 | | | 57.35 178 | 59.89 166 | 54.38 186 | 71.37 123 | 73.45 170 | 52.71 195 | 61.03 92 | 46.11 142 | 26.33 199 | 41.73 130 | 44.08 125 | 29.72 203 | 71.43 153 | 70.90 166 | 85.10 111 | 71.56 195 |
|
ACMH+ | | 60.36 13 | 61.16 153 | 58.38 175 | 64.42 108 | 77.37 85 | 74.35 167 | 68.45 130 | 62.81 79 | 45.86 143 | 38.48 153 | 35.71 181 | 37.35 175 | 59.81 118 | 67.24 179 | 69.80 174 | 79.58 191 | 78.32 168 |
|
FC-MVSNet-test | | | 47.24 208 | 54.37 190 | 38.93 218 | 59.49 199 | 58.25 216 | 34.48 223 | 53.36 166 | 45.66 144 | 6.66 233 | 50.62 80 | 42.02 130 | 16.62 227 | 58.39 201 | 61.21 207 | 62.99 223 | 64.40 209 |
|
v7 | | | 63.61 118 | 63.02 126 | 64.29 113 | 67.88 158 | 80.32 112 | 71.60 100 | 56.63 135 | 45.37 145 | 42.84 129 | 38.54 157 | 38.91 168 | 61.05 99 | 74.39 101 | 74.52 104 | 85.75 91 | 89.10 77 |
|
v10 | | | 63.00 129 | 62.22 139 | 63.90 118 | 67.88 158 | 77.78 138 | 71.59 101 | 54.34 158 | 45.37 145 | 42.76 133 | 38.53 158 | 38.93 167 | 61.05 99 | 74.39 101 | 74.52 104 | 85.75 91 | 86.04 115 |
|
GA-MVS | | | 64.55 106 | 65.76 106 | 63.12 129 | 69.68 132 | 81.56 90 | 69.59 124 | 58.16 113 | 45.23 147 | 35.58 171 | 47.01 104 | 41.82 131 | 59.41 124 | 79.62 55 | 78.54 61 | 86.32 61 | 86.56 109 |
|
thresconf0.02 | | | 63.92 114 | 65.18 107 | 62.46 137 | 75.91 95 | 80.65 108 | 67.51 137 | 63.86 63 | 45.00 148 | 33.32 181 | 51.38 78 | 51.68 106 | 48.34 164 | 75.49 93 | 75.13 91 | 85.84 90 | 76.91 172 |
|
thres400 | | | 65.18 102 | 64.44 113 | 66.04 91 | 76.40 92 | 82.63 75 | 71.52 102 | 64.27 60 | 44.93 149 | 40.69 142 | 41.86 129 | 40.79 146 | 58.12 133 | 77.67 67 | 74.64 96 | 85.26 108 | 88.56 92 |
|
v2v482 | | | 63.68 117 | 62.85 129 | 64.65 104 | 68.01 154 | 80.46 111 | 71.90 84 | 57.60 122 | 44.26 150 | 42.82 132 | 39.80 143 | 38.62 170 | 61.56 91 | 73.06 118 | 74.86 94 | 86.03 72 | 88.90 82 |
|
TDRefinement | | | 52.70 193 | 51.02 201 | 54.66 185 | 57.41 205 | 65.06 204 | 61.47 172 | 54.94 151 | 44.03 151 | 33.93 178 | 30.13 201 | 27.57 211 | 46.17 176 | 61.86 196 | 62.48 205 | 74.01 211 | 66.06 206 |
|
view600 | | | 63.91 115 | 63.27 122 | 64.66 103 | 75.57 98 | 81.73 83 | 69.71 123 | 63.04 71 | 43.97 152 | 39.18 148 | 41.09 133 | 40.24 154 | 55.38 143 | 76.28 79 | 72.04 152 | 85.08 114 | 87.52 103 |
|
thres600view7 | | | 63.77 116 | 63.14 124 | 64.51 105 | 75.49 99 | 81.61 85 | 69.59 124 | 62.95 73 | 43.96 153 | 38.90 150 | 41.09 133 | 40.24 154 | 55.25 145 | 76.24 80 | 71.54 155 | 84.89 121 | 87.30 104 |
|
tfpn1000 | | | 58.35 174 | 59.96 165 | 56.47 177 | 72.78 117 | 77.51 141 | 56.66 189 | 59.16 107 | 43.74 154 | 29.76 191 | 42.79 118 | 42.49 129 | 37.04 198 | 68.92 174 | 68.98 176 | 83.45 154 | 75.25 176 |
|
v15 | | | 62.07 142 | 60.70 153 | 63.67 120 | 68.09 151 | 73.00 174 | 71.27 104 | 53.41 165 | 43.70 155 | 43.43 114 | 38.77 155 | 39.83 156 | 59.87 117 | 72.74 135 | 74.25 118 | 85.98 77 | 82.61 143 |
|
divwei89l23v2f112 | | | 63.48 120 | 62.76 133 | 64.32 110 | 68.13 148 | 80.68 107 | 71.71 92 | 57.43 126 | 43.69 156 | 42.84 129 | 39.01 153 | 39.75 159 | 59.94 115 | 72.93 123 | 74.49 106 | 85.86 87 | 88.75 86 |
|
v1 | | | 63.49 119 | 62.77 132 | 64.32 110 | 68.13 148 | 80.70 105 | 71.70 98 | 57.43 126 | 43.69 156 | 42.89 128 | 39.03 151 | 39.77 158 | 59.93 116 | 72.93 123 | 74.48 108 | 85.86 87 | 88.77 84 |
|
v1141 | | | 63.48 120 | 62.75 134 | 64.32 110 | 68.13 148 | 80.69 106 | 71.69 99 | 57.43 126 | 43.66 158 | 42.83 131 | 39.02 152 | 39.74 160 | 59.95 114 | 72.94 122 | 74.49 106 | 85.86 87 | 88.75 86 |
|
CDS-MVSNet | | | 64.22 108 | 65.89 105 | 62.28 140 | 70.05 130 | 80.59 109 | 69.91 122 | 57.98 115 | 43.53 159 | 46.58 96 | 48.22 89 | 50.76 109 | 46.45 174 | 75.68 89 | 76.08 80 | 82.70 164 | 86.34 113 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
V14 | | | 61.96 145 | 60.56 155 | 63.59 121 | 68.06 152 | 72.93 177 | 71.10 109 | 53.33 167 | 43.47 160 | 43.28 119 | 38.59 156 | 39.78 157 | 59.76 119 | 72.65 137 | 74.19 119 | 86.01 73 | 82.32 148 |
|
FMVSNet1 | | | 63.48 120 | 63.07 125 | 63.97 116 | 65.31 177 | 76.37 153 | 71.77 91 | 57.90 116 | 43.32 161 | 45.66 99 | 35.06 186 | 49.43 112 | 58.57 131 | 77.49 68 | 78.22 65 | 84.59 135 | 81.60 154 |
|
V9 | | | 61.85 147 | 60.42 158 | 63.51 122 | 68.02 153 | 72.85 178 | 70.91 113 | 53.24 168 | 43.25 162 | 43.27 123 | 38.41 160 | 39.73 161 | 59.60 121 | 72.55 139 | 74.13 122 | 86.04 71 | 82.04 150 |
|
view800 | | | 63.02 128 | 62.69 135 | 63.39 125 | 74.79 104 | 80.76 104 | 67.83 133 | 61.93 84 | 43.16 163 | 37.78 159 | 40.43 138 | 39.73 161 | 53.16 150 | 75.01 94 | 73.32 135 | 84.87 123 | 86.43 111 |
|
v12 | | | 61.70 149 | 60.27 160 | 63.38 126 | 68.00 155 | 72.76 179 | 70.63 117 | 53.14 170 | 43.01 164 | 42.95 127 | 38.25 162 | 39.64 163 | 59.48 123 | 72.47 141 | 74.05 125 | 86.06 70 | 81.71 153 |
|
v13 | | | 61.60 151 | 60.13 163 | 63.31 127 | 67.95 157 | 72.67 181 | 70.51 118 | 53.05 171 | 42.80 165 | 42.96 124 | 38.10 167 | 39.57 164 | 59.31 126 | 72.36 142 | 73.98 127 | 86.10 67 | 81.40 155 |
|
v11 | | | 61.74 148 | 60.47 157 | 63.22 128 | 67.83 160 | 72.72 180 | 70.31 119 | 52.95 174 | 42.75 166 | 41.89 135 | 38.16 165 | 38.49 171 | 60.40 111 | 74.35 103 | 74.40 111 | 85.92 79 | 82.39 147 |
|
v1144 | | | 63.00 129 | 62.39 138 | 63.70 119 | 67.72 161 | 80.27 113 | 71.23 106 | 56.40 136 | 42.51 167 | 40.81 141 | 38.12 166 | 37.73 172 | 60.42 110 | 74.46 99 | 74.55 102 | 85.64 103 | 89.12 76 |
|
tfpn | | | 62.54 134 | 62.79 131 | 62.25 141 | 74.16 106 | 79.86 117 | 66.07 147 | 60.97 95 | 42.43 168 | 36.41 163 | 39.88 142 | 43.76 127 | 51.25 157 | 73.85 110 | 74.17 120 | 84.67 133 | 85.57 122 |
|
v148 | | | 62.00 144 | 61.19 150 | 62.96 130 | 67.46 164 | 79.49 121 | 67.87 132 | 57.66 118 | 42.30 169 | 45.02 106 | 38.20 164 | 38.89 169 | 54.77 146 | 69.83 169 | 72.60 148 | 84.96 117 | 87.01 106 |
|
CVMVSNet | | | 54.92 186 | 58.16 176 | 51.13 195 | 62.61 188 | 68.44 195 | 55.45 192 | 52.38 176 | 42.28 170 | 21.45 206 | 47.10 101 | 46.10 119 | 37.96 196 | 64.42 190 | 63.81 198 | 76.92 203 | 75.01 178 |
|
tfpnview11 | | | 58.92 166 | 59.60 168 | 58.13 164 | 72.99 114 | 77.11 147 | 60.48 174 | 60.37 100 | 42.10 171 | 29.10 192 | 43.45 112 | 40.72 149 | 41.67 189 | 70.53 161 | 70.43 171 | 84.17 145 | 72.85 188 |
|
PM-MVS | | | 50.11 200 | 50.38 203 | 49.80 197 | 47.23 226 | 62.08 212 | 50.91 198 | 44.84 205 | 41.90 172 | 36.10 167 | 35.22 184 | 26.05 217 | 46.83 173 | 57.64 204 | 55.42 221 | 72.90 212 | 74.32 180 |
|
CMPMVS | | 43.63 17 | 57.67 177 | 55.43 184 | 60.28 152 | 72.01 120 | 79.00 125 | 62.77 167 | 53.23 169 | 41.77 173 | 45.42 100 | 30.74 199 | 39.03 166 | 53.01 151 | 64.81 187 | 64.65 196 | 75.26 207 | 68.03 202 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
v1192 | | | 62.25 138 | 61.64 146 | 62.96 130 | 66.88 166 | 79.72 118 | 69.96 121 | 55.77 143 | 41.58 174 | 39.42 145 | 37.05 172 | 35.96 185 | 60.50 109 | 74.30 106 | 74.09 123 | 85.24 109 | 88.76 85 |
|
v144192 | | | 62.05 143 | 61.46 148 | 62.73 136 | 66.59 169 | 79.87 116 | 69.30 126 | 55.88 141 | 41.50 175 | 39.41 146 | 37.23 170 | 36.45 180 | 59.62 120 | 72.69 136 | 73.51 130 | 85.61 104 | 88.93 79 |
|
v1921920 | | | 61.66 150 | 61.10 151 | 62.31 139 | 66.32 170 | 79.57 120 | 68.41 131 | 55.49 147 | 41.03 176 | 38.69 152 | 36.64 178 | 35.27 191 | 59.60 121 | 73.23 116 | 73.41 132 | 85.37 106 | 88.51 96 |
|
pmmvs-eth3d | | | 55.20 180 | 53.95 191 | 56.65 175 | 57.34 206 | 67.77 196 | 57.54 187 | 53.74 163 | 40.93 177 | 41.09 140 | 31.19 198 | 29.10 210 | 49.07 162 | 65.54 182 | 67.28 184 | 81.14 179 | 75.81 173 |
|
TinyColmap | | | 52.66 194 | 50.09 204 | 55.65 180 | 59.72 198 | 64.02 208 | 57.15 188 | 52.96 173 | 40.28 178 | 32.51 184 | 32.42 191 | 20.97 223 | 56.65 140 | 63.95 191 | 65.15 195 | 74.91 208 | 63.87 210 |
|
pmmvs5 | | | 59.72 161 | 60.24 161 | 59.11 160 | 62.77 187 | 77.33 144 | 63.17 165 | 54.00 162 | 40.21 179 | 37.23 160 | 40.41 139 | 35.99 184 | 51.75 153 | 72.55 139 | 72.74 146 | 85.72 97 | 82.45 146 |
|
ACMH | | 59.42 14 | 61.59 152 | 59.22 173 | 64.36 109 | 78.92 73 | 78.26 132 | 67.65 134 | 67.48 41 | 39.81 180 | 30.98 188 | 38.25 162 | 34.59 193 | 61.37 98 | 70.55 160 | 73.47 131 | 79.74 190 | 79.59 162 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v1240 | | | 61.09 154 | 60.55 156 | 61.72 145 | 65.92 174 | 79.28 124 | 67.16 139 | 54.91 153 | 39.79 181 | 38.10 155 | 36.08 180 | 34.64 192 | 59.15 128 | 72.86 126 | 73.36 134 | 85.10 111 | 87.84 100 |
|
tfpn_n400 | | | 58.64 170 | 59.27 171 | 57.89 167 | 72.83 115 | 77.26 145 | 60.35 175 | 60.29 103 | 39.77 182 | 29.10 192 | 43.45 112 | 40.72 149 | 41.61 190 | 70.06 166 | 71.39 161 | 83.17 159 | 72.26 191 |
|
tfpnconf | | | 58.64 170 | 59.27 171 | 57.89 167 | 72.83 115 | 77.26 145 | 60.35 175 | 60.29 103 | 39.77 182 | 29.10 192 | 43.45 112 | 40.72 149 | 41.61 190 | 70.06 166 | 71.39 161 | 83.17 159 | 72.26 191 |
|
MVS-HIRNet | | | 53.86 192 | 53.02 193 | 54.85 183 | 60.30 196 | 72.36 182 | 44.63 212 | 42.20 213 | 39.45 184 | 43.47 113 | 21.66 218 | 34.00 196 | 55.47 142 | 65.42 183 | 67.16 186 | 83.02 163 | 71.08 197 |
|
MDTV_nov1_ep13_2view | | | 54.47 190 | 54.61 188 | 54.30 189 | 60.50 194 | 73.82 169 | 57.92 186 | 43.38 207 | 39.43 185 | 32.51 184 | 33.23 189 | 34.05 195 | 47.26 170 | 62.36 195 | 66.21 191 | 84.24 142 | 73.19 187 |
|
TAMVS | | | 58.86 167 | 60.91 152 | 56.47 177 | 62.38 189 | 77.57 140 | 58.97 184 | 52.98 172 | 38.76 186 | 36.17 166 | 42.26 127 | 47.94 115 | 46.45 174 | 70.23 164 | 70.79 167 | 81.86 174 | 78.82 167 |
|
WR-MVS | | | 51.02 197 | 54.56 189 | 46.90 205 | 63.84 182 | 69.23 193 | 44.78 211 | 56.38 137 | 38.19 187 | 14.19 219 | 37.38 169 | 36.82 179 | 22.39 217 | 60.14 200 | 66.20 192 | 79.81 189 | 73.95 183 |
|
CP-MVSNet | | | 50.57 198 | 52.60 198 | 48.21 202 | 58.77 202 | 65.82 202 | 48.17 202 | 56.29 138 | 37.41 188 | 16.59 214 | 37.14 171 | 31.95 202 | 29.21 204 | 56.60 208 | 63.71 199 | 80.22 186 | 75.56 175 |
|
testpf | | | 43.39 213 | 47.17 210 | 38.98 217 | 65.58 176 | 47.38 228 | 36.09 221 | 31.67 230 | 36.97 189 | 19.47 209 | 33.01 190 | 35.62 190 | 23.61 216 | 50.86 222 | 56.08 218 | 57.48 227 | 70.27 199 |
|
PEN-MVS | | | 51.04 196 | 52.94 194 | 48.82 199 | 61.45 191 | 66.00 201 | 48.68 201 | 57.20 131 | 36.87 190 | 15.36 217 | 36.98 173 | 32.72 200 | 28.77 207 | 57.63 205 | 66.37 189 | 81.44 178 | 74.00 182 |
|
Anonymous20231206 | | | 52.23 195 | 52.80 196 | 51.56 193 | 64.70 180 | 69.41 192 | 51.01 197 | 58.60 111 | 36.63 191 | 22.44 205 | 21.80 217 | 31.42 204 | 30.52 201 | 66.79 180 | 67.83 182 | 82.10 172 | 75.73 174 |
|
conf0.05thres1000 | | | 60.33 160 | 59.42 170 | 61.40 147 | 73.15 112 | 78.25 133 | 65.29 149 | 60.30 102 | 36.61 192 | 35.75 169 | 33.25 188 | 39.23 165 | 50.35 160 | 72.18 144 | 72.67 147 | 83.57 152 | 83.74 130 |
|
WR-MVS_H | | | 49.62 202 | 52.63 197 | 46.11 208 | 58.80 201 | 67.58 197 | 46.14 209 | 54.94 151 | 36.51 193 | 13.63 222 | 36.75 176 | 35.67 189 | 22.10 218 | 56.43 209 | 62.76 202 | 81.06 180 | 72.73 189 |
|
PS-CasMVS | | | 50.17 199 | 52.02 199 | 48.02 203 | 58.60 203 | 65.54 203 | 48.04 203 | 56.19 140 | 36.42 194 | 16.42 216 | 35.68 182 | 31.33 205 | 28.85 206 | 56.42 210 | 63.54 200 | 80.01 187 | 75.18 177 |
|
DTE-MVSNet | | | 49.82 201 | 51.92 200 | 47.37 204 | 61.75 190 | 64.38 206 | 45.89 210 | 57.33 130 | 36.11 195 | 12.79 224 | 36.87 174 | 31.93 203 | 25.73 212 | 58.01 202 | 65.22 194 | 80.75 183 | 70.93 198 |
|
N_pmnet | | | 47.67 207 | 47.00 211 | 48.45 201 | 54.72 209 | 62.78 210 | 46.95 206 | 51.25 180 | 36.01 196 | 26.09 200 | 26.59 208 | 25.93 218 | 35.50 199 | 55.67 212 | 59.01 209 | 76.22 204 | 63.04 212 |
|
FPMVS | | | 39.11 219 | 36.39 223 | 42.28 211 | 55.97 207 | 45.94 229 | 46.23 208 | 41.57 214 | 35.73 197 | 22.61 203 | 23.46 212 | 19.82 225 | 28.32 210 | 43.57 224 | 40.67 227 | 58.96 225 | 45.54 224 |
|
LP | | | 48.21 206 | 46.65 212 | 50.03 196 | 60.39 195 | 63.86 209 | 48.73 200 | 38.71 222 | 35.60 198 | 32.99 182 | 23.31 213 | 24.95 219 | 40.07 194 | 57.73 203 | 61.56 206 | 79.29 194 | 59.51 219 |
|
v7n | | | 57.04 179 | 56.64 182 | 57.52 171 | 62.85 186 | 74.75 164 | 61.76 169 | 51.80 178 | 35.58 199 | 36.02 168 | 32.33 192 | 33.61 198 | 50.16 161 | 67.73 178 | 70.34 172 | 82.51 166 | 82.12 149 |
|
pm-mvs1 | | | 59.21 164 | 59.58 169 | 58.77 162 | 67.97 156 | 77.07 149 | 64.12 152 | 57.20 131 | 34.73 200 | 36.86 161 | 35.34 183 | 40.54 153 | 43.34 185 | 74.32 105 | 73.30 137 | 83.13 162 | 81.77 152 |
|
v52 | | | 54.79 187 | 55.15 185 | 54.36 188 | 54.07 212 | 72.13 184 | 59.84 180 | 49.39 186 | 34.50 201 | 35.08 174 | 31.63 196 | 35.74 187 | 47.21 172 | 63.90 192 | 67.92 179 | 80.59 184 | 80.23 158 |
|
v748 | | | 55.19 181 | 54.63 187 | 55.85 179 | 61.44 192 | 72.97 176 | 58.72 185 | 51.62 179 | 34.48 202 | 36.39 165 | 32.09 193 | 33.05 199 | 45.48 179 | 61.85 197 | 67.87 181 | 81.45 177 | 80.08 160 |
|
V4 | | | 54.78 188 | 55.14 186 | 54.37 187 | 54.07 212 | 72.13 184 | 59.83 181 | 49.39 186 | 34.46 203 | 35.11 173 | 31.64 195 | 35.72 188 | 47.22 171 | 63.90 192 | 67.92 179 | 80.59 184 | 80.23 158 |
|
anonymousdsp | | | 54.99 184 | 57.24 180 | 52.36 191 | 53.82 214 | 71.75 187 | 51.49 196 | 48.14 192 | 33.74 204 | 33.66 179 | 38.34 161 | 36.13 183 | 47.54 168 | 64.53 189 | 70.60 169 | 79.53 192 | 85.59 121 |
|
EU-MVSNet | | | 44.84 211 | 47.85 208 | 41.32 215 | 49.26 221 | 56.59 219 | 43.07 213 | 47.64 196 | 33.03 205 | 13.82 220 | 36.78 175 | 30.99 206 | 24.37 215 | 53.80 217 | 55.57 220 | 69.78 217 | 68.21 200 |
|
tfpnnormal | | | 58.97 165 | 56.48 183 | 61.89 143 | 71.27 125 | 76.21 154 | 66.65 143 | 61.76 88 | 32.90 206 | 36.41 163 | 27.83 204 | 29.14 209 | 50.64 159 | 73.06 118 | 73.05 142 | 84.58 136 | 83.15 141 |
|
EG-PatchMatch MVS | | | 58.73 169 | 58.03 178 | 59.55 156 | 72.32 118 | 80.49 110 | 63.44 163 | 55.55 146 | 32.49 207 | 38.31 154 | 28.87 202 | 37.22 176 | 42.84 186 | 74.30 106 | 75.70 85 | 84.84 124 | 77.14 171 |
|
TransMVSNet (Re) | | | 57.83 175 | 56.90 181 | 58.91 161 | 72.26 119 | 74.69 165 | 63.57 162 | 61.42 90 | 32.30 208 | 32.65 183 | 33.97 187 | 35.96 185 | 39.17 195 | 73.84 112 | 72.84 145 | 84.37 139 | 74.69 179 |
|
ambc | | | | 42.30 216 | | 50.36 218 | 49.51 225 | 35.47 222 | | 32.04 209 | 23.53 202 | 17.36 223 | 8.95 235 | 29.06 205 | 64.88 186 | 56.26 217 | 61.29 224 | 67.12 203 |
|
SixPastTwentyTwo | | | 49.11 204 | 49.22 206 | 48.99 198 | 58.54 204 | 64.14 207 | 47.18 205 | 47.75 194 | 31.15 210 | 24.42 201 | 41.01 136 | 26.55 213 | 44.04 183 | 54.76 216 | 58.70 211 | 71.99 215 | 68.21 200 |
|
test2356 | | | 46.29 210 | 47.37 209 | 45.03 210 | 54.38 210 | 57.99 217 | 42.03 214 | 50.32 182 | 30.78 211 | 16.65 213 | 27.40 206 | 23.70 220 | 29.86 202 | 61.20 198 | 64.31 197 | 76.93 202 | 66.22 205 |
|
MDA-MVSNet-bldmvs | | | 44.15 212 | 42.27 218 | 46.34 206 | 38.34 229 | 62.31 211 | 46.28 207 | 55.74 144 | 29.83 212 | 20.98 207 | 27.11 207 | 16.45 230 | 41.98 187 | 41.11 228 | 57.47 213 | 74.72 209 | 61.65 217 |
|
test20.03 | | | 47.23 209 | 48.69 207 | 45.53 209 | 63.28 184 | 64.39 205 | 41.01 217 | 56.93 134 | 29.16 213 | 15.21 218 | 23.90 210 | 30.76 207 | 17.51 226 | 64.63 188 | 65.26 193 | 79.21 195 | 62.71 214 |
|
testgi | | | 48.51 205 | 50.53 202 | 46.16 207 | 64.78 178 | 67.15 199 | 41.54 215 | 54.81 155 | 29.12 214 | 17.03 212 | 32.07 194 | 31.98 201 | 20.15 221 | 65.26 184 | 67.00 187 | 78.67 197 | 61.10 218 |
|
new_pmnet | | | 33.19 223 | 35.52 224 | 30.47 224 | 27.55 235 | 45.31 230 | 29.29 228 | 30.92 231 | 29.00 215 | 9.88 230 | 18.77 220 | 17.64 228 | 26.77 211 | 44.07 223 | 45.98 225 | 58.41 226 | 47.87 223 |
|
new-patchmatchnet | | | 42.21 215 | 42.97 215 | 41.33 214 | 53.05 216 | 59.89 213 | 39.38 218 | 49.61 184 | 28.26 216 | 12.10 225 | 22.17 216 | 21.54 222 | 19.22 222 | 50.96 221 | 56.04 219 | 74.61 210 | 61.92 216 |
|
testus | | | 42.30 214 | 43.69 213 | 40.67 216 | 53.21 215 | 53.50 221 | 31.81 225 | 49.96 183 | 27.06 217 | 11.55 226 | 25.67 209 | 19.00 226 | 25.20 213 | 55.34 213 | 62.59 203 | 72.31 214 | 62.69 215 |
|
MIMVSNet1 | | | 40.84 217 | 43.46 214 | 37.79 220 | 32.14 231 | 58.92 215 | 39.24 219 | 50.83 181 | 27.00 218 | 11.29 227 | 16.76 228 | 26.53 214 | 17.75 225 | 57.14 207 | 61.12 208 | 75.46 206 | 56.78 222 |
|
pmmvs6 | | | 54.20 191 | 53.54 192 | 54.97 182 | 63.22 185 | 72.98 175 | 60.17 178 | 52.32 177 | 26.77 219 | 34.30 177 | 23.29 214 | 36.23 182 | 40.33 193 | 68.77 175 | 68.76 177 | 79.47 193 | 78.00 169 |
|
gg-mvs-nofinetune | | | 62.34 135 | 66.19 100 | 57.86 169 | 76.15 94 | 88.61 33 | 71.18 107 | 41.24 219 | 25.74 220 | 13.16 223 | 22.91 215 | 63.97 61 | 54.52 147 | 85.06 13 | 85.25 10 | 90.92 3 | 91.78 52 |
|
Gipuma | | | 24.91 228 | 24.61 228 | 25.26 229 | 31.47 232 | 21.59 235 | 18.06 233 | 37.53 226 | 25.43 221 | 10.03 229 | 4.18 236 | 4.25 238 | 14.85 228 | 43.20 225 | 47.03 224 | 39.62 232 | 26.55 232 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
DeepMVS_CX | | | | | | | 19.81 237 | 17.01 235 | 10.02 234 | 23.61 222 | 5.85 234 | 17.21 224 | 8.03 237 | 21.13 219 | 22.60 232 | | 21.42 236 | 30.01 230 |
|
pmmvs3 | | | 41.86 216 | 42.29 217 | 41.36 213 | 39.80 227 | 52.66 222 | 38.93 220 | 35.85 229 | 23.40 223 | 20.22 208 | 19.30 219 | 20.84 224 | 40.56 192 | 55.98 211 | 58.79 210 | 72.80 213 | 65.03 208 |
|
1111 | | | 38.93 220 | 38.98 220 | 38.86 219 | 50.10 219 | 50.42 223 | 29.52 226 | 38.00 224 | 22.67 224 | 17.99 210 | 17.40 221 | 26.26 215 | 28.72 208 | 54.86 214 | 58.20 212 | 68.82 220 | 43.08 227 |
|
.test1245 | | | 25.86 227 | 24.56 229 | 27.39 228 | 50.10 219 | 50.42 223 | 29.52 226 | 38.00 224 | 22.67 224 | 17.99 210 | 17.40 221 | 26.26 215 | 28.72 208 | 54.86 214 | 0.05 234 | 0.01 238 | 0.24 236 |
|
gm-plane-assit | | | 54.99 184 | 57.99 179 | 51.49 194 | 69.27 137 | 54.42 220 | 32.32 224 | 42.59 211 | 21.18 226 | 13.71 221 | 23.61 211 | 43.84 126 | 60.21 112 | 87.09 4 | 86.55 4 | 90.81 4 | 89.28 74 |
|
PMVS | | 27.44 18 | 32.08 224 | 29.07 226 | 35.60 223 | 48.33 225 | 24.79 234 | 26.97 231 | 41.34 216 | 20.45 227 | 22.50 204 | 17.11 225 | 18.64 227 | 20.44 220 | 41.99 227 | 38.06 228 | 54.02 230 | 42.44 228 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
LTVRE_ROB | | 47.26 16 | 49.41 203 | 49.91 205 | 48.82 199 | 64.76 179 | 69.79 191 | 49.05 199 | 47.12 197 | 20.36 228 | 16.52 215 | 36.65 177 | 26.96 212 | 50.76 158 | 60.47 199 | 63.16 201 | 64.73 222 | 72.00 193 |
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 |
testmv | | | 37.40 221 | 37.95 221 | 36.76 221 | 48.97 222 | 49.33 226 | 28.65 229 | 46.74 198 | 18.34 229 | 7.68 231 | 16.80 226 | 14.47 231 | 19.18 223 | 51.72 219 | 56.93 214 | 69.36 218 | 58.09 220 |
|
test1235678 | | | 37.40 221 | 37.94 222 | 36.76 221 | 48.97 222 | 49.30 227 | 28.65 229 | 46.73 199 | 18.33 230 | 7.68 231 | 16.79 227 | 14.46 232 | 19.18 223 | 51.72 219 | 56.92 215 | 69.36 218 | 58.07 221 |
|
Anonymous20231211 | | | 40.44 218 | 39.25 219 | 41.84 212 | 54.29 211 | 57.29 218 | 41.10 216 | 49.06 189 | 17.67 231 | 10.15 228 | 10.63 230 | 16.79 229 | 25.15 214 | 52.14 218 | 56.70 216 | 71.30 216 | 63.51 211 |
|
test12356 | | | 29.92 225 | 31.49 225 | 28.08 225 | 38.46 228 | 37.74 232 | 21.36 232 | 40.17 220 | 16.83 232 | 5.61 235 | 15.66 229 | 11.48 233 | 6.60 233 | 42.01 226 | 51.23 223 | 56.29 228 | 45.52 225 |
|
PMMVS2 | | | 20.45 229 | 22.31 230 | 18.27 232 | 20.52 236 | 26.73 233 | 14.85 236 | 28.43 233 | 13.69 233 | 0.79 240 | 10.35 231 | 9.10 234 | 3.83 235 | 27.64 231 | 32.87 229 | 41.17 231 | 35.81 229 |
|
no-one | | | 26.96 226 | 26.51 227 | 27.49 227 | 37.87 230 | 39.14 231 | 17.12 234 | 41.31 217 | 12.02 234 | 3.68 237 | 8.04 232 | 8.42 236 | 10.67 231 | 28.11 230 | 45.96 226 | 54.27 229 | 43.89 226 |
|
EMVS | | | 14.40 231 | 10.71 233 | 18.70 231 | 28.15 234 | 12.09 239 | 7.06 238 | 36.89 227 | 11.00 235 | 3.56 239 | 4.95 234 | 2.27 240 | 13.91 229 | 10.13 235 | 16.06 233 | 22.63 235 | 18.51 234 |
|
E-PMN | | | 15.08 230 | 11.65 232 | 19.08 230 | 28.73 233 | 12.31 238 | 6.95 239 | 36.87 228 | 10.71 236 | 3.63 238 | 5.13 233 | 2.22 241 | 13.81 230 | 11.34 234 | 18.50 232 | 24.49 234 | 21.32 233 |
|
MVE | | 15.98 19 | 14.37 232 | 16.36 231 | 12.04 234 | 7.72 238 | 20.24 236 | 5.90 240 | 29.05 232 | 8.28 237 | 3.92 236 | 4.72 235 | 2.42 239 | 9.57 232 | 18.89 233 | 31.46 230 | 16.07 237 | 28.53 231 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 0.05 233 | 0.08 234 | 0.01 235 | 0.00 240 | 0.01 241 | 0.03 242 | 0.01 237 | 0.05 238 | 0.00 242 | 0.14 238 | 0.01 242 | 0.03 238 | 0.05 236 | 0.05 234 | 0.01 238 | 0.24 236 |
|
test123 | | | 0.05 233 | 0.08 234 | 0.01 235 | 0.00 240 | 0.01 241 | 0.01 243 | 0.00 238 | 0.05 238 | 0.00 242 | 0.16 237 | 0.00 243 | 0.04 236 | 0.02 237 | 0.05 234 | 0.00 240 | 0.26 235 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 240 | 0.00 243 | 0.00 244 | 0.00 238 | 0.00 240 | 0.00 242 | 0.00 239 | 0.00 243 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 240 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 240 | 0.00 243 | 0.00 244 | 0.00 238 | 0.00 240 | 0.00 242 | 0.00 239 | 0.00 243 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 240 | 0.00 238 |
|
our_test_3 | | | | | | 63.32 183 | 71.07 190 | 55.90 191 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 78.32 6 | | 79.42 18 | | | | | |
|
MTMP | | | | | | | | | | | 76.04 11 | | 76.65 23 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.17 241 | | | | | | | | | | |
|
XVS | | | | | | 82.43 50 | 86.27 55 | 75.70 61 | | | 61.07 52 | | 72.27 33 | | | | 85.67 99 | |
|
X-MVStestdata | | | | | | 82.43 50 | 86.27 55 | 75.70 61 | | | 61.07 52 | | 72.27 33 | | | | 85.67 99 | |
|
mPP-MVS | | | | | | 86.96 37 | | | | | | | 70.61 43 | | | | | |
|
Patchmtry | | | | | | | 78.06 135 | 67.53 135 | 43.18 208 | | 41.40 136 | | | | | | | |
|