SMA-MVS | | | 98.66 5 | 98.89 5 | 98.39 8 | 99.60 1 | 99.41 7 | 99.00 19 | 97.63 9 | 97.78 16 | 95.83 16 | 98.33 9 | 99.83 2 | 98.85 10 | 98.93 6 | 98.56 6 | 99.41 41 | 99.40 13 |
|
APDe-MVS | | | 98.87 1 | 98.96 2 | 98.77 1 | 99.58 2 | 99.53 4 | 99.44 1 | 97.81 1 | 98.22 8 | 97.33 3 | 98.70 3 | 99.33 8 | 98.86 8 | 98.96 4 | 98.40 13 | 99.63 3 | 99.57 7 |
|
PGM-MVS | | | 97.81 23 | 98.11 26 | 97.46 27 | 99.55 3 | 99.34 14 | 99.32 7 | 94.51 43 | 96.21 58 | 93.07 35 | 98.05 11 | 97.95 39 | 98.82 12 | 98.22 29 | 97.89 31 | 99.48 22 | 99.09 51 |
|
ACMMP_NAP | | | 98.20 16 | 98.49 11 | 97.85 23 | 99.50 4 | 99.40 8 | 99.26 10 | 97.64 8 | 97.47 30 | 92.62 44 | 97.59 17 | 99.09 18 | 98.71 16 | 98.82 10 | 97.86 33 | 99.40 44 | 99.19 40 |
|
zzz-MVS | | | 98.43 10 | 98.31 21 | 98.57 3 | 99.48 5 | 99.40 8 | 99.32 7 | 97.62 10 | 97.70 19 | 96.67 8 | 96.59 29 | 99.09 18 | 98.86 8 | 98.65 11 | 97.56 40 | 99.45 30 | 99.17 43 |
|
APD-MVS | | | 98.36 13 | 98.32 20 | 98.41 7 | 99.47 6 | 99.26 20 | 99.12 14 | 97.77 5 | 96.73 47 | 96.12 14 | 97.27 25 | 98.88 21 | 98.46 24 | 98.47 16 | 98.39 14 | 99.52 14 | 99.22 36 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
CSCG | | | 97.44 30 | 97.18 38 | 97.75 25 | 99.47 6 | 99.52 5 | 98.55 30 | 95.41 38 | 97.69 21 | 95.72 17 | 94.29 50 | 95.53 59 | 98.10 28 | 96.20 97 | 97.38 48 | 99.24 69 | 99.62 3 |
|
HPM-MVS++ | | | 98.34 14 | 98.47 13 | 98.18 14 | 99.46 8 | 99.15 28 | 99.10 15 | 97.69 6 | 97.67 22 | 94.93 24 | 97.62 16 | 99.70 5 | 98.60 19 | 98.45 17 | 97.46 43 | 99.31 58 | 99.26 29 |
|
SR-MVS | | | | | | 99.45 9 | | | 97.61 12 | | | | 99.20 12 | | | | | |
|
MSP-MVS | | | 98.73 4 | 98.93 3 | 98.50 5 | 99.44 10 | 99.57 2 | 99.36 3 | 97.65 7 | 98.14 10 | 96.51 12 | 98.49 5 | 99.65 6 | 98.67 18 | 98.60 12 | 98.42 11 | 99.40 44 | 99.63 1 |
|
DVP-MVS | | | 98.86 2 | 98.97 1 | 98.75 2 | 99.43 11 | 99.63 1 | 99.25 11 | 97.81 1 | 98.62 1 | 97.69 1 | 97.59 17 | 99.90 1 | 98.93 5 | 98.99 2 | 98.42 11 | 99.37 50 | 99.62 3 |
|
ACMMPR | | | 98.40 11 | 98.49 11 | 98.28 11 | 99.41 12 | 99.40 8 | 99.36 3 | 97.35 19 | 98.30 5 | 95.02 23 | 97.79 14 | 98.39 34 | 99.04 2 | 98.26 26 | 98.10 21 | 99.50 21 | 99.22 36 |
|
X-MVS | | | 97.84 22 | 98.19 25 | 97.42 28 | 99.40 13 | 99.35 11 | 99.06 16 | 97.25 23 | 97.38 31 | 90.85 55 | 96.06 33 | 98.72 26 | 98.53 23 | 98.41 20 | 98.15 20 | 99.46 26 | 99.28 24 |
|
MCST-MVS | | | 98.20 16 | 98.36 16 | 98.01 20 | 99.40 13 | 99.05 31 | 99.00 19 | 97.62 10 | 97.59 26 | 93.70 31 | 97.42 24 | 99.30 9 | 98.77 14 | 98.39 22 | 97.48 42 | 99.59 4 | 99.31 23 |
|
CNVR-MVS | | | 98.47 9 | 98.46 14 | 98.48 6 | 99.40 13 | 99.05 31 | 99.02 18 | 97.54 14 | 97.73 17 | 96.65 9 | 97.20 26 | 99.13 16 | 98.85 10 | 98.91 7 | 98.10 21 | 99.41 41 | 99.08 52 |
|
HFP-MVS | | | 98.48 8 | 98.62 9 | 98.32 9 | 99.39 16 | 99.33 15 | 99.27 9 | 97.42 16 | 98.27 6 | 95.25 21 | 98.34 8 | 98.83 23 | 99.08 1 | 98.26 26 | 98.08 23 | 99.48 22 | 99.26 29 |
|
NCCC | | | 98.10 19 | 98.05 28 | 98.17 16 | 99.38 17 | 99.05 31 | 99.00 19 | 97.53 15 | 98.04 12 | 95.12 22 | 94.80 47 | 99.18 14 | 98.58 21 | 98.49 15 | 97.78 35 | 99.39 46 | 98.98 68 |
|
MP-MVS | | | 98.09 20 | 98.30 22 | 97.84 24 | 99.34 18 | 99.19 26 | 99.23 12 | 97.40 17 | 97.09 40 | 93.03 38 | 97.58 19 | 98.85 22 | 98.57 22 | 98.44 19 | 97.69 36 | 99.48 22 | 99.23 34 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
CP-MVS | | | 98.32 15 | 98.34 19 | 98.29 10 | 99.34 18 | 99.30 16 | 99.15 13 | 97.35 19 | 97.49 28 | 95.58 19 | 97.72 15 | 98.62 30 | 98.82 12 | 98.29 24 | 97.67 37 | 99.51 19 | 99.28 24 |
|
SteuartSystems-ACMMP | | | 98.38 12 | 98.71 8 | 97.99 21 | 99.34 18 | 99.46 6 | 99.34 5 | 97.33 22 | 97.31 32 | 94.25 27 | 98.06 10 | 99.17 15 | 98.13 27 | 98.98 3 | 98.46 9 | 99.55 11 | 99.54 8 |
Skip Steuart: Steuart Systems R&D Blog. |
DPE-MVS | | | 98.75 3 | 98.91 4 | 98.57 3 | 99.21 21 | 99.54 3 | 99.42 2 | 97.78 4 | 97.49 28 | 96.84 6 | 98.94 1 | 99.82 3 | 98.59 20 | 98.90 8 | 98.22 17 | 99.56 10 | 99.48 10 |
|
mPP-MVS | | | | | | 99.21 21 | | | | | | | 98.29 35 | | | | | |
|
AdaColmap | | | 97.53 28 | 96.93 43 | 98.24 12 | 99.21 21 | 98.77 58 | 98.47 33 | 97.34 21 | 96.68 49 | 96.52 11 | 95.11 43 | 96.12 55 | 98.72 15 | 97.19 60 | 96.24 75 | 99.17 83 | 98.39 108 |
|
DeepC-MVS_fast | | 96.13 1 | 98.13 18 | 98.27 23 | 97.97 22 | 99.16 24 | 99.03 36 | 99.05 17 | 97.24 24 | 98.22 8 | 94.17 29 | 95.82 35 | 98.07 36 | 98.69 17 | 98.83 9 | 98.80 2 | 99.52 14 | 99.10 49 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MSLP-MVS++ | | | 98.04 21 | 97.93 30 | 98.18 14 | 99.10 25 | 99.09 30 | 98.34 35 | 96.99 30 | 97.54 27 | 96.60 10 | 94.82 46 | 98.45 32 | 98.89 6 | 97.46 52 | 98.77 4 | 99.17 83 | 99.37 16 |
|
3Dnovator | | 93.79 8 | 97.08 35 | 97.20 36 | 96.95 35 | 99.09 26 | 99.03 36 | 98.20 37 | 93.33 51 | 97.99 13 | 93.82 30 | 90.61 87 | 96.80 46 | 97.82 32 | 97.90 41 | 98.78 3 | 99.47 25 | 99.26 29 |
|
QAPM | | | 96.78 43 | 97.14 40 | 96.36 41 | 99.05 27 | 99.14 29 | 98.02 40 | 93.26 53 | 97.27 34 | 90.84 58 | 91.16 79 | 97.31 41 | 97.64 37 | 97.70 46 | 98.20 18 | 99.33 53 | 99.18 42 |
|
OpenMVS | | 92.33 11 | 95.50 52 | 95.22 69 | 95.82 49 | 98.98 28 | 98.97 43 | 97.67 48 | 93.04 61 | 94.64 96 | 89.18 85 | 84.44 133 | 94.79 61 | 96.79 57 | 97.23 57 | 97.61 38 | 99.24 69 | 98.88 79 |
|
PLC | | 94.95 3 | 97.37 31 | 96.77 47 | 98.07 18 | 98.97 29 | 98.21 81 | 97.94 43 | 96.85 33 | 97.66 23 | 97.58 2 | 93.33 55 | 96.84 45 | 98.01 31 | 97.13 62 | 96.20 77 | 99.09 95 | 98.01 120 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
train_agg | | | 97.65 27 | 98.06 27 | 97.18 31 | 98.94 30 | 98.91 50 | 98.98 23 | 97.07 29 | 96.71 48 | 90.66 61 | 97.43 23 | 99.08 20 | 98.20 25 | 97.96 39 | 97.14 54 | 99.22 75 | 99.19 40 |
|
CDPH-MVS | | | 96.84 41 | 97.49 33 | 96.09 45 | 98.92 31 | 98.85 55 | 98.61 27 | 95.09 39 | 96.00 65 | 87.29 98 | 95.45 40 | 97.42 40 | 97.16 47 | 97.83 43 | 97.94 28 | 99.44 36 | 98.92 74 |
|
CPTT-MVS | | | 97.78 24 | 97.54 31 | 98.05 19 | 98.91 32 | 99.05 31 | 99.00 19 | 96.96 31 | 97.14 37 | 95.92 15 | 95.50 38 | 98.78 25 | 98.99 4 | 97.20 58 | 96.07 79 | 98.54 148 | 99.04 60 |
|
3Dnovator+ | | 93.91 7 | 97.23 33 | 97.22 35 | 97.24 30 | 98.89 33 | 98.85 55 | 98.26 36 | 93.25 55 | 97.99 13 | 95.56 20 | 90.01 93 | 98.03 38 | 98.05 29 | 97.91 40 | 98.43 10 | 99.44 36 | 99.35 18 |
|
ACMMP | | | 97.37 31 | 97.48 34 | 97.25 29 | 98.88 34 | 99.28 18 | 98.47 33 | 96.86 32 | 97.04 42 | 92.15 45 | 97.57 20 | 96.05 57 | 97.67 35 | 97.27 56 | 95.99 84 | 99.46 26 | 99.14 48 |
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 |
PHI-MVS | | | 97.78 24 | 98.44 15 | 97.02 34 | 98.73 35 | 99.25 22 | 98.11 38 | 95.54 37 | 96.66 50 | 92.79 41 | 98.52 4 | 99.38 7 | 97.50 40 | 97.84 42 | 98.39 14 | 99.45 30 | 99.03 61 |
|
OMC-MVS | | | 97.00 37 | 96.92 44 | 97.09 32 | 98.69 36 | 98.66 66 | 97.85 44 | 95.02 40 | 98.09 11 | 94.47 25 | 93.15 56 | 96.90 43 | 97.38 41 | 97.16 61 | 96.82 64 | 99.13 90 | 97.65 133 |
|
MAR-MVS | | | 95.50 52 | 95.60 61 | 95.39 56 | 98.67 37 | 98.18 84 | 95.89 91 | 89.81 101 | 94.55 98 | 91.97 47 | 92.99 58 | 90.21 85 | 97.30 43 | 96.79 71 | 97.49 41 | 98.72 134 | 98.99 66 |
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 |
TSAR-MVS + ACMM | | | 97.71 26 | 98.60 10 | 96.66 38 | 98.64 38 | 99.05 31 | 98.85 24 | 97.23 25 | 98.45 3 | 89.40 81 | 97.51 21 | 99.27 11 | 96.88 56 | 98.53 13 | 97.81 34 | 98.96 111 | 99.59 6 |
|
abl_6 | | | | | 96.82 37 | 98.60 39 | 98.74 60 | 97.74 46 | 93.73 47 | 96.25 56 | 94.37 26 | 94.55 49 | 98.60 31 | 97.25 44 | | | 99.27 64 | 98.61 93 |
|
CNLPA | | | 96.90 39 | 96.28 53 | 97.64 26 | 98.56 40 | 98.63 71 | 96.85 61 | 96.60 34 | 97.73 17 | 97.08 5 | 89.78 95 | 96.28 54 | 97.80 34 | 96.73 74 | 96.63 66 | 98.94 113 | 98.14 119 |
|
EPNet | | | 96.27 48 | 96.97 42 | 95.46 54 | 98.47 41 | 98.28 78 | 97.41 51 | 93.67 48 | 95.86 71 | 92.86 40 | 97.51 21 | 93.79 65 | 91.76 131 | 97.03 65 | 97.03 56 | 98.61 144 | 99.28 24 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_111021_LR | | | 97.16 34 | 98.01 29 | 96.16 44 | 98.47 41 | 98.98 41 | 96.94 58 | 93.89 46 | 97.64 24 | 91.44 49 | 98.89 2 | 96.41 50 | 97.20 46 | 98.02 37 | 97.29 53 | 99.04 106 | 98.85 83 |
|
MVS_111021_HR | | | 97.04 36 | 98.20 24 | 95.69 50 | 98.44 43 | 99.29 17 | 96.59 71 | 93.20 56 | 97.70 19 | 89.94 73 | 98.46 6 | 96.89 44 | 96.71 60 | 98.11 34 | 97.95 27 | 99.27 64 | 99.01 64 |
|
MSDG | | | 94.82 65 | 93.73 97 | 96.09 45 | 98.34 44 | 97.43 99 | 97.06 55 | 96.05 35 | 95.84 72 | 90.56 62 | 86.30 122 | 89.10 95 | 95.55 79 | 96.13 100 | 95.61 94 | 99.00 107 | 95.73 169 |
|
TAPA-MVS | | 94.18 5 | 96.38 46 | 96.49 51 | 96.25 42 | 98.26 45 | 98.66 66 | 98.00 41 | 94.96 41 | 97.17 36 | 89.48 78 | 92.91 60 | 96.35 51 | 97.53 39 | 96.59 79 | 95.90 87 | 99.28 62 | 97.82 124 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DeepC-MVS | | 94.87 4 | 96.76 44 | 96.50 50 | 97.05 33 | 98.21 46 | 99.28 18 | 98.67 26 | 97.38 18 | 97.31 32 | 90.36 67 | 89.19 97 | 93.58 66 | 98.19 26 | 98.31 23 | 98.50 7 | 99.51 19 | 99.36 17 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SD-MVS | | | 98.52 6 | 98.77 7 | 98.23 13 | 98.15 47 | 99.26 20 | 98.79 25 | 97.59 13 | 98.52 2 | 96.25 13 | 97.99 12 | 99.75 4 | 99.01 3 | 98.27 25 | 97.97 26 | 99.59 4 | 99.63 1 |
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 |
TSAR-MVS + MP. | | | 98.49 7 | 98.78 6 | 98.15 17 | 98.14 48 | 99.17 27 | 99.34 5 | 97.18 27 | 98.44 4 | 95.72 17 | 97.84 13 | 99.28 10 | 98.87 7 | 99.05 1 | 98.05 24 | 99.66 1 | 99.60 5 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
DPM-MVS | | | 96.86 40 | 96.82 46 | 96.91 36 | 98.08 49 | 98.20 82 | 98.52 32 | 97.20 26 | 97.24 35 | 91.42 50 | 91.84 72 | 98.45 32 | 97.25 44 | 97.07 63 | 97.40 47 | 98.95 112 | 97.55 136 |
|
EPNet_dtu | | | 92.45 107 | 95.02 74 | 89.46 136 | 98.02 50 | 95.47 158 | 94.79 110 | 92.62 62 | 94.97 91 | 70.11 182 | 94.76 48 | 92.61 72 | 84.07 191 | 95.94 103 | 95.56 95 | 97.15 177 | 95.82 168 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CANet | | | 96.84 41 | 97.20 36 | 96.42 39 | 97.92 51 | 99.24 24 | 98.60 28 | 93.51 50 | 97.11 38 | 93.07 35 | 91.16 79 | 97.24 42 | 96.21 68 | 98.24 28 | 98.05 24 | 99.22 75 | 99.35 18 |
|
LS3D | | | 95.46 55 | 95.14 70 | 95.84 48 | 97.91 52 | 98.90 52 | 98.58 29 | 97.79 3 | 97.07 41 | 83.65 112 | 88.71 101 | 88.64 98 | 97.82 32 | 97.49 51 | 97.42 45 | 99.26 68 | 97.72 132 |
|
DELS-MVS | | | 96.06 50 | 96.04 57 | 96.07 47 | 97.77 53 | 99.25 22 | 98.10 39 | 93.26 53 | 94.42 100 | 92.79 41 | 88.52 105 | 93.48 67 | 95.06 85 | 98.51 14 | 98.83 1 | 99.45 30 | 99.28 24 |
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 |
COLMAP_ROB | | 90.49 14 | 93.27 100 | 92.71 109 | 93.93 83 | 97.75 54 | 97.44 98 | 96.07 85 | 93.17 57 | 95.40 81 | 83.86 110 | 83.76 137 | 88.72 97 | 93.87 104 | 94.25 141 | 94.11 135 | 98.87 119 | 95.28 175 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PCF-MVS | | 93.95 6 | 95.65 51 | 95.14 70 | 96.25 42 | 97.73 55 | 98.73 62 | 97.59 49 | 97.13 28 | 92.50 129 | 89.09 87 | 89.85 94 | 96.65 47 | 96.90 55 | 94.97 129 | 94.89 113 | 99.08 96 | 98.38 109 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PatchMatch-RL | | | 94.69 71 | 94.41 81 | 95.02 62 | 97.63 56 | 98.15 85 | 94.50 116 | 91.99 70 | 95.32 83 | 91.31 51 | 95.47 39 | 83.44 131 | 96.02 71 | 96.56 80 | 95.23 105 | 98.69 137 | 96.67 160 |
|
PVSNet_BlendedMVS | | | 95.41 57 | 95.28 67 | 95.57 52 | 97.42 57 | 99.02 38 | 95.89 91 | 93.10 58 | 96.16 59 | 93.12 33 | 91.99 68 | 85.27 117 | 94.66 90 | 98.09 35 | 97.34 49 | 99.24 69 | 99.08 52 |
|
PVSNet_Blended | | | 95.41 57 | 95.28 67 | 95.57 52 | 97.42 57 | 99.02 38 | 95.89 91 | 93.10 58 | 96.16 59 | 93.12 33 | 91.99 68 | 85.27 117 | 94.66 90 | 98.09 35 | 97.34 49 | 99.24 69 | 99.08 52 |
|
DeepPCF-MVS | | 95.28 2 | 97.00 37 | 98.35 18 | 95.42 55 | 97.30 59 | 98.94 45 | 94.82 109 | 96.03 36 | 98.24 7 | 92.11 46 | 95.80 36 | 98.64 29 | 95.51 80 | 98.95 5 | 98.66 5 | 96.78 180 | 99.20 39 |
|
CHOSEN 280x420 | | | 95.46 55 | 97.01 41 | 93.66 88 | 97.28 60 | 97.98 89 | 96.40 77 | 85.39 150 | 96.10 62 | 91.07 53 | 96.53 30 | 96.34 53 | 95.61 77 | 97.65 47 | 96.95 59 | 96.21 182 | 97.49 137 |
|
MVS_0304 | | | 96.31 47 | 96.91 45 | 95.62 51 | 97.21 61 | 99.20 25 | 98.55 30 | 93.10 58 | 97.04 42 | 89.73 75 | 90.30 89 | 96.35 51 | 95.71 74 | 98.14 31 | 97.93 30 | 99.38 47 | 99.40 13 |
|
CHOSEN 1792x2688 | | | 92.66 105 | 92.49 114 | 92.85 99 | 97.13 62 | 98.89 53 | 95.90 89 | 88.50 117 | 95.32 83 | 83.31 113 | 71.99 186 | 88.96 96 | 94.10 101 | 96.69 75 | 96.49 68 | 98.15 161 | 99.10 49 |
|
HyFIR lowres test | | | 92.03 108 | 91.55 132 | 92.58 100 | 97.13 62 | 98.72 63 | 94.65 113 | 86.54 135 | 93.58 115 | 82.56 116 | 67.75 197 | 90.47 83 | 95.67 75 | 95.87 105 | 95.54 96 | 98.91 116 | 98.93 73 |
|
OPM-MVS | | | 93.61 93 | 92.43 118 | 95.00 63 | 96.94 64 | 97.34 100 | 97.78 45 | 94.23 44 | 89.64 160 | 85.53 105 | 88.70 102 | 82.81 134 | 96.28 67 | 96.28 94 | 95.00 112 | 99.24 69 | 97.22 145 |
|
XVS | | | | | | 96.60 65 | 99.35 11 | 96.82 62 | | | 90.85 55 | | 98.72 26 | | | | 99.46 26 | |
|
X-MVStestdata | | | | | | 96.60 65 | 99.35 11 | 96.82 62 | | | 90.85 55 | | 98.72 26 | | | | 99.46 26 | |
|
TSAR-MVS + COLMAP | | | 94.79 67 | 94.51 79 | 95.11 60 | 96.50 67 | 97.54 94 | 97.99 42 | 94.54 42 | 97.81 15 | 85.88 104 | 96.73 28 | 81.28 141 | 96.99 53 | 96.29 93 | 95.21 106 | 98.76 133 | 96.73 159 |
|
PVSNet_Blended_VisFu | | | 94.77 69 | 95.54 63 | 93.87 84 | 96.48 68 | 98.97 43 | 94.33 118 | 91.84 73 | 94.93 92 | 90.37 66 | 85.04 128 | 94.99 60 | 90.87 146 | 98.12 33 | 97.30 51 | 99.30 60 | 99.45 12 |
|
LGP-MVS_train | | | 94.12 82 | 94.62 77 | 93.53 89 | 96.44 69 | 97.54 94 | 97.40 52 | 91.84 73 | 94.66 95 | 81.09 125 | 95.70 37 | 83.36 132 | 95.10 84 | 96.36 91 | 95.71 92 | 99.32 55 | 99.03 61 |
|
HQP-MVS | | | 94.43 77 | 94.57 78 | 94.27 79 | 96.41 70 | 97.23 103 | 96.89 59 | 93.98 45 | 95.94 68 | 83.68 111 | 95.01 44 | 84.46 123 | 95.58 78 | 95.47 117 | 94.85 117 | 99.07 98 | 99.00 65 |
|
ACMM | | 92.75 10 | 94.41 79 | 93.84 95 | 95.09 61 | 96.41 70 | 96.80 113 | 94.88 108 | 93.54 49 | 96.41 53 | 90.16 68 | 92.31 66 | 83.11 133 | 96.32 66 | 96.22 96 | 94.65 119 | 99.22 75 | 97.35 142 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
RPSCF | | | 94.05 83 | 94.00 91 | 94.12 81 | 96.20 72 | 96.41 127 | 96.61 70 | 91.54 79 | 95.83 73 | 89.73 75 | 96.94 27 | 92.80 70 | 95.35 83 | 91.63 178 | 90.44 182 | 95.27 193 | 93.94 185 |
|
UA-Net | | | 93.96 85 | 95.95 58 | 91.64 108 | 96.06 73 | 98.59 73 | 95.29 99 | 90.00 96 | 91.06 148 | 82.87 114 | 90.64 86 | 98.06 37 | 86.06 178 | 98.14 31 | 98.20 18 | 99.58 7 | 96.96 153 |
|
UGNet | | | 94.92 62 | 96.63 48 | 92.93 98 | 96.03 74 | 98.63 71 | 94.53 115 | 91.52 80 | 96.23 57 | 90.03 70 | 92.87 61 | 96.10 56 | 86.28 177 | 96.68 76 | 96.60 67 | 99.16 86 | 99.32 22 |
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 |
ACMP | | 92.88 9 | 94.43 77 | 94.38 82 | 94.50 75 | 96.01 75 | 97.69 92 | 95.85 94 | 92.09 69 | 95.74 74 | 89.12 86 | 95.14 42 | 82.62 136 | 94.77 86 | 95.73 111 | 94.67 118 | 99.14 89 | 99.06 56 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
IB-MVS | | 89.56 15 | 91.71 113 | 92.50 113 | 90.79 120 | 95.94 76 | 98.44 75 | 87.05 190 | 91.38 82 | 93.15 118 | 92.98 39 | 84.78 129 | 85.14 120 | 78.27 196 | 92.47 167 | 94.44 130 | 99.10 94 | 99.08 52 |
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 |
MS-PatchMatch | | | 91.82 111 | 92.51 112 | 91.02 114 | 95.83 77 | 96.88 108 | 95.05 102 | 84.55 163 | 93.85 110 | 82.01 118 | 82.51 143 | 91.71 74 | 90.52 153 | 95.07 127 | 93.03 157 | 98.13 162 | 94.52 177 |
|
CANet_DTU | | | 93.92 86 | 96.57 49 | 90.83 118 | 95.63 78 | 98.39 76 | 96.99 57 | 87.38 127 | 96.26 55 | 71.97 171 | 96.31 31 | 93.02 68 | 94.53 93 | 97.38 54 | 96.83 63 | 98.49 151 | 97.79 125 |
|
ACMH | | 90.77 13 | 91.51 118 | 91.63 131 | 91.38 111 | 95.62 79 | 96.87 110 | 91.76 163 | 89.66 103 | 91.58 143 | 78.67 134 | 86.73 113 | 78.12 149 | 93.77 107 | 94.59 132 | 94.54 126 | 98.78 131 | 98.98 68 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TSAR-MVS + GP. | | | 97.45 29 | 98.36 16 | 96.39 40 | 95.56 80 | 98.93 47 | 97.74 46 | 93.31 52 | 97.61 25 | 94.24 28 | 98.44 7 | 99.19 13 | 98.03 30 | 97.60 48 | 97.41 46 | 99.44 36 | 99.33 20 |
|
thres600view7 | | | 93.49 96 | 92.37 121 | 94.79 70 | 95.42 81 | 98.93 47 | 96.58 72 | 92.31 64 | 93.04 119 | 87.88 94 | 86.62 115 | 76.94 156 | 97.09 51 | 96.82 68 | 95.63 93 | 99.45 30 | 98.63 92 |
|
thres400 | | | 93.56 94 | 92.43 118 | 94.87 67 | 95.40 82 | 98.91 50 | 96.70 68 | 92.38 63 | 92.93 121 | 88.19 93 | 86.69 114 | 77.35 154 | 97.13 48 | 96.75 73 | 95.85 89 | 99.42 40 | 98.56 95 |
|
thres200 | | | 93.62 92 | 92.54 111 | 94.88 66 | 95.36 83 | 98.93 47 | 96.75 66 | 92.31 64 | 92.84 122 | 88.28 91 | 86.99 111 | 77.81 153 | 97.13 48 | 96.82 68 | 95.92 85 | 99.45 30 | 98.49 101 |
|
thres100view900 | | | 93.55 95 | 92.47 117 | 94.81 69 | 95.33 84 | 98.74 60 | 96.78 65 | 92.30 67 | 92.63 125 | 88.29 89 | 87.21 109 | 78.01 151 | 96.78 58 | 96.38 88 | 95.92 85 | 99.38 47 | 98.40 107 |
|
tfpn200view9 | | | 93.64 91 | 92.57 110 | 94.89 65 | 95.33 84 | 98.94 45 | 96.82 62 | 92.31 64 | 92.63 125 | 88.29 89 | 87.21 109 | 78.01 151 | 97.12 50 | 96.82 68 | 95.85 89 | 99.45 30 | 98.56 95 |
|
IS_MVSNet | | | 95.28 59 | 96.43 52 | 93.94 82 | 95.30 86 | 99.01 40 | 95.90 89 | 91.12 84 | 94.13 106 | 87.50 97 | 91.23 78 | 94.45 63 | 94.17 99 | 98.45 17 | 98.50 7 | 99.65 2 | 99.23 34 |
|
CMPMVS | | 65.18 17 | 84.76 187 | 83.10 193 | 86.69 177 | 95.29 87 | 95.05 170 | 88.37 185 | 85.51 149 | 80.27 201 | 71.31 175 | 68.37 195 | 73.85 168 | 85.25 182 | 87.72 194 | 87.75 192 | 94.38 201 | 88.70 201 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
canonicalmvs | | | 95.25 61 | 95.45 65 | 95.00 63 | 95.27 88 | 98.72 63 | 96.89 59 | 89.82 100 | 96.51 51 | 90.84 58 | 93.72 54 | 86.01 112 | 97.66 36 | 95.78 109 | 97.94 28 | 99.54 13 | 99.50 9 |
|
Vis-MVSNet (Re-imp) | | | 94.46 76 | 96.24 54 | 92.40 101 | 95.23 89 | 98.64 69 | 95.56 97 | 90.99 85 | 94.42 100 | 85.02 107 | 90.88 85 | 94.65 62 | 88.01 168 | 98.17 30 | 98.37 16 | 99.57 9 | 98.53 98 |
|
CLD-MVS | | | 94.79 67 | 94.36 83 | 95.30 57 | 95.21 90 | 97.46 97 | 97.23 53 | 92.24 68 | 96.43 52 | 91.77 48 | 92.69 62 | 84.31 124 | 96.06 69 | 95.52 115 | 95.03 109 | 99.31 58 | 99.06 56 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
baseline1 | | | 94.59 73 | 94.47 80 | 94.72 71 | 95.16 91 | 97.97 90 | 96.07 85 | 91.94 71 | 94.86 93 | 89.98 71 | 91.60 76 | 85.87 114 | 95.64 76 | 97.07 63 | 96.90 60 | 99.52 14 | 97.06 152 |
|
TDRefinement | | | 89.07 151 | 88.15 160 | 90.14 129 | 95.16 91 | 96.88 108 | 95.55 98 | 90.20 94 | 89.68 159 | 76.42 147 | 76.67 160 | 74.30 166 | 84.85 185 | 93.11 157 | 91.91 175 | 98.64 143 | 94.47 178 |
|
ACMH+ | | 90.88 12 | 91.41 119 | 91.13 135 | 91.74 107 | 95.11 93 | 96.95 107 | 93.13 135 | 89.48 107 | 92.42 131 | 79.93 129 | 85.13 127 | 78.02 150 | 93.82 106 | 93.49 152 | 93.88 141 | 98.94 113 | 97.99 121 |
|
DCV-MVSNet | | | 94.76 70 | 95.12 72 | 94.35 78 | 95.10 94 | 95.81 147 | 96.46 76 | 89.49 106 | 96.33 54 | 90.16 68 | 92.55 64 | 90.26 84 | 95.83 73 | 95.52 115 | 96.03 82 | 99.06 101 | 99.33 20 |
|
Anonymous202405211 | | | | 92.18 123 | | 95.04 95 | 98.20 82 | 96.14 83 | 91.79 75 | 93.93 107 | | 74.60 169 | 88.38 101 | 96.48 64 | 95.17 125 | 95.82 91 | 99.00 107 | 99.15 45 |
|
CS-MVS | | | 96.23 49 | 97.15 39 | 95.16 58 | 95.01 96 | 98.98 41 | 97.13 54 | 90.68 89 | 96.00 65 | 91.21 52 | 94.03 51 | 96.48 48 | 97.35 42 | 98.00 38 | 97.43 44 | 99.55 11 | 99.15 45 |
|
FC-MVSNet-train | | | 93.85 87 | 93.91 92 | 93.78 86 | 94.94 97 | 96.79 116 | 94.29 119 | 91.13 83 | 93.84 111 | 88.26 92 | 90.40 88 | 85.23 119 | 94.65 92 | 96.54 82 | 95.31 102 | 99.38 47 | 99.28 24 |
|
EPP-MVSNet | | | 95.27 60 | 96.18 56 | 94.20 80 | 94.88 98 | 98.64 69 | 94.97 104 | 90.70 88 | 95.34 82 | 89.67 77 | 91.66 75 | 93.84 64 | 95.42 82 | 97.32 55 | 97.00 57 | 99.58 7 | 99.47 11 |
|
EIA-MVS | | | 95.50 52 | 96.19 55 | 94.69 72 | 94.83 99 | 98.88 54 | 95.93 88 | 91.50 81 | 94.47 99 | 89.43 79 | 93.14 57 | 92.72 71 | 97.05 52 | 97.82 45 | 97.13 55 | 99.43 39 | 99.15 45 |
|
ETV-MVS | | | 96.42 45 | 97.53 32 | 95.13 59 | 94.80 100 | 98.77 58 | 96.40 77 | 91.63 77 | 97.10 39 | 90.75 60 | 94.90 45 | 96.42 49 | 97.61 38 | 98.40 21 | 97.87 32 | 99.59 4 | 99.25 32 |
|
MVS_Test | | | 94.82 65 | 95.66 60 | 93.84 85 | 94.79 101 | 98.35 77 | 96.49 75 | 89.10 111 | 96.12 61 | 87.09 100 | 92.58 63 | 90.61 82 | 96.48 64 | 96.51 86 | 96.89 61 | 99.11 93 | 98.54 97 |
|
Anonymous20231211 | | | 93.49 96 | 92.33 122 | 94.84 68 | 94.78 102 | 98.00 88 | 96.11 84 | 91.85 72 | 94.86 93 | 90.91 54 | 74.69 168 | 89.18 93 | 96.73 59 | 94.82 130 | 95.51 97 | 98.67 138 | 99.24 33 |
|
baseline | | | 94.83 64 | 95.82 59 | 93.68 87 | 94.75 103 | 97.80 91 | 96.51 74 | 88.53 116 | 97.02 44 | 89.34 83 | 92.93 59 | 92.18 73 | 94.69 89 | 95.78 109 | 96.08 78 | 98.27 159 | 98.97 72 |
|
MVSTER | | | 94.89 63 | 95.07 73 | 94.68 73 | 94.71 104 | 96.68 119 | 97.00 56 | 90.57 91 | 95.18 89 | 93.05 37 | 95.21 41 | 86.41 109 | 93.72 108 | 97.59 49 | 95.88 88 | 99.00 107 | 98.50 100 |
|
EPMVS | | | 90.88 125 | 92.12 124 | 89.44 137 | 94.71 104 | 97.24 102 | 93.55 126 | 76.81 189 | 95.89 69 | 81.77 120 | 91.49 77 | 86.47 108 | 93.87 104 | 90.21 185 | 90.07 184 | 95.92 184 | 93.49 191 |
|
casdiffmvs | | | 94.38 80 | 94.15 90 | 94.64 74 | 94.70 106 | 98.51 74 | 96.03 87 | 91.66 76 | 95.70 75 | 89.36 82 | 86.48 117 | 85.03 122 | 96.60 63 | 97.40 53 | 97.30 51 | 99.52 14 | 98.67 90 |
|
diffmvs | | | 94.31 81 | 94.21 85 | 94.42 77 | 94.64 107 | 98.28 78 | 96.36 79 | 91.56 78 | 96.77 46 | 88.89 88 | 88.97 99 | 84.23 125 | 96.01 72 | 96.05 101 | 96.41 70 | 99.05 105 | 98.79 87 |
|
DWT-MVSNet_training | | | 91.30 120 | 89.73 146 | 93.13 97 | 94.64 107 | 96.87 110 | 94.93 105 | 86.17 140 | 94.22 104 | 93.18 32 | 89.11 98 | 73.28 172 | 93.59 111 | 88.00 193 | 90.73 180 | 96.26 181 | 95.87 166 |
|
DI_MVS_plusplus_trai | | | 94.01 84 | 93.63 99 | 94.44 76 | 94.54 109 | 98.26 80 | 97.51 50 | 90.63 90 | 95.88 70 | 89.34 83 | 80.54 150 | 89.36 90 | 95.48 81 | 96.33 92 | 96.27 74 | 99.17 83 | 98.78 88 |
|
thisisatest0530 | | | 94.54 74 | 95.47 64 | 93.46 91 | 94.51 110 | 98.65 68 | 94.66 112 | 90.72 86 | 95.69 77 | 86.90 101 | 93.80 52 | 89.44 89 | 94.74 87 | 96.98 67 | 94.86 114 | 99.19 82 | 98.85 83 |
|
tttt0517 | | | 94.52 75 | 95.44 66 | 93.44 92 | 94.51 110 | 98.68 65 | 94.61 114 | 90.72 86 | 95.61 79 | 86.84 102 | 93.78 53 | 89.26 92 | 94.74 87 | 97.02 66 | 94.86 114 | 99.20 81 | 98.87 81 |
|
ADS-MVSNet | | | 89.80 140 | 91.33 134 | 88.00 158 | 94.43 112 | 96.71 118 | 92.29 151 | 74.95 199 | 96.07 63 | 77.39 139 | 88.67 103 | 86.09 111 | 93.26 116 | 88.44 191 | 89.57 187 | 95.68 187 | 93.81 188 |
|
tpmrst | | | 88.86 155 | 89.62 147 | 87.97 159 | 94.33 113 | 95.98 137 | 92.62 143 | 76.36 192 | 94.62 97 | 76.94 143 | 85.98 123 | 82.80 135 | 92.80 121 | 86.90 197 | 87.15 195 | 94.77 198 | 93.93 186 |
|
PMMVS | | | 94.61 72 | 95.56 62 | 93.50 90 | 94.30 114 | 96.74 117 | 94.91 107 | 89.56 105 | 95.58 80 | 87.72 95 | 96.15 32 | 92.86 69 | 96.06 69 | 95.47 117 | 95.02 110 | 98.43 156 | 97.09 148 |
|
CostFormer | | | 90.69 126 | 90.48 143 | 90.93 116 | 94.18 115 | 96.08 135 | 94.03 121 | 78.20 185 | 93.47 116 | 89.96 72 | 90.97 84 | 80.30 142 | 93.72 108 | 87.66 196 | 88.75 189 | 95.51 190 | 96.12 164 |
|
USDC | | | 90.69 126 | 90.52 142 | 90.88 117 | 94.17 116 | 96.43 126 | 95.82 95 | 86.76 133 | 93.92 108 | 76.27 149 | 86.49 116 | 74.30 166 | 93.67 110 | 95.04 128 | 93.36 150 | 98.61 144 | 94.13 182 |
|
Vis-MVSNet | | | 92.77 103 | 95.00 75 | 90.16 127 | 94.10 117 | 98.79 57 | 94.76 111 | 88.26 118 | 92.37 134 | 79.95 128 | 88.19 107 | 91.58 75 | 84.38 188 | 97.59 49 | 97.58 39 | 99.52 14 | 98.91 77 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Effi-MVS+ | | | 92.93 102 | 93.86 94 | 91.86 104 | 94.07 118 | 98.09 87 | 95.59 96 | 85.98 143 | 94.27 103 | 79.54 132 | 91.12 82 | 81.81 138 | 96.71 60 | 96.67 77 | 96.06 80 | 99.27 64 | 98.98 68 |
|
IterMVS-LS | | | 92.56 106 | 93.18 106 | 91.84 105 | 93.90 119 | 94.97 172 | 94.99 103 | 86.20 139 | 94.18 105 | 82.68 115 | 85.81 124 | 87.36 105 | 94.43 94 | 95.31 121 | 96.02 83 | 98.87 119 | 98.60 94 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
dps | | | 90.11 138 | 89.37 151 | 90.98 115 | 93.89 120 | 96.21 132 | 93.49 128 | 77.61 187 | 91.95 140 | 92.74 43 | 88.85 100 | 78.77 148 | 92.37 124 | 87.71 195 | 87.71 193 | 95.80 186 | 94.38 180 |
|
tpm cat1 | | | 88.90 153 | 87.78 169 | 90.22 126 | 93.88 121 | 95.39 161 | 93.79 124 | 78.11 186 | 92.55 128 | 89.43 79 | 81.31 146 | 79.84 144 | 91.40 134 | 84.95 198 | 86.34 198 | 94.68 200 | 94.09 183 |
|
PatchmatchNet | | | 90.56 128 | 92.49 114 | 88.31 149 | 93.83 122 | 96.86 112 | 92.42 147 | 76.50 191 | 95.96 67 | 78.31 135 | 91.96 70 | 89.66 88 | 93.48 113 | 90.04 187 | 89.20 188 | 95.32 191 | 93.73 189 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
TinyColmap | | | 89.42 143 | 88.58 155 | 90.40 124 | 93.80 123 | 95.45 159 | 93.96 123 | 86.54 135 | 92.24 137 | 76.49 146 | 80.83 148 | 70.44 184 | 93.37 114 | 94.45 136 | 93.30 153 | 98.26 160 | 93.37 192 |
|
SCA | | | 90.92 124 | 93.04 108 | 88.45 146 | 93.72 124 | 97.33 101 | 92.77 139 | 76.08 194 | 96.02 64 | 78.26 136 | 91.96 70 | 90.86 79 | 93.99 103 | 90.98 182 | 90.04 185 | 95.88 185 | 94.06 184 |
|
RPMNet | | | 90.19 135 | 92.03 127 | 88.05 155 | 93.46 125 | 95.95 140 | 93.41 129 | 74.59 200 | 92.40 132 | 75.91 151 | 84.22 134 | 86.41 109 | 92.49 122 | 94.42 137 | 93.85 143 | 98.44 154 | 96.96 153 |
|
gg-mvs-nofinetune | | | 86.17 182 | 88.57 156 | 83.36 189 | 93.44 126 | 98.15 85 | 96.58 72 | 72.05 203 | 74.12 206 | 49.23 209 | 64.81 201 | 90.85 80 | 89.90 161 | 97.83 43 | 96.84 62 | 98.97 110 | 97.41 140 |
|
MDTV_nov1_ep13 | | | 91.57 116 | 93.18 106 | 89.70 133 | 93.39 127 | 96.97 106 | 93.53 127 | 80.91 180 | 95.70 75 | 81.86 119 | 92.40 65 | 89.93 86 | 93.25 117 | 91.97 176 | 90.80 179 | 95.25 194 | 94.46 179 |
|
CR-MVSNet | | | 90.16 136 | 91.96 128 | 88.06 154 | 93.32 128 | 95.95 140 | 93.36 131 | 75.99 195 | 92.40 132 | 75.19 157 | 83.18 139 | 85.37 116 | 92.05 126 | 95.21 123 | 94.56 124 | 98.47 153 | 97.08 150 |
|
test-LLR | | | 91.62 115 | 93.56 102 | 89.35 139 | 93.31 129 | 96.57 122 | 92.02 159 | 87.06 131 | 92.34 135 | 75.05 160 | 90.20 90 | 88.64 98 | 90.93 142 | 96.19 98 | 94.07 136 | 97.75 171 | 96.90 156 |
|
test0.0.03 1 | | | 91.97 109 | 93.91 92 | 89.72 132 | 93.31 129 | 96.40 128 | 91.34 168 | 87.06 131 | 93.86 109 | 81.67 121 | 91.15 81 | 89.16 94 | 86.02 179 | 95.08 126 | 95.09 107 | 98.91 116 | 96.64 162 |
|
CVMVSNet | | | 89.77 141 | 91.66 130 | 87.56 168 | 93.21 131 | 95.45 159 | 91.94 162 | 89.22 109 | 89.62 161 | 69.34 188 | 83.99 136 | 85.90 113 | 84.81 186 | 94.30 140 | 95.28 103 | 96.85 179 | 97.09 148 |
|
PatchT | | | 89.13 150 | 91.71 129 | 86.11 182 | 92.92 132 | 95.59 154 | 83.64 197 | 75.09 198 | 91.87 141 | 75.19 157 | 82.63 142 | 85.06 121 | 92.05 126 | 95.21 123 | 94.56 124 | 97.76 170 | 97.08 150 |
|
Fast-Effi-MVS+ | | | 91.87 110 | 92.08 125 | 91.62 110 | 92.91 133 | 97.21 104 | 94.93 105 | 84.60 161 | 93.61 114 | 81.49 123 | 83.50 138 | 78.95 146 | 96.62 62 | 96.55 81 | 96.22 76 | 99.16 86 | 98.51 99 |
|
IterMVS-SCA-FT | | | 90.24 133 | 92.48 116 | 87.63 165 | 92.85 134 | 94.30 188 | 93.79 124 | 81.47 179 | 92.66 124 | 69.95 183 | 84.66 131 | 88.38 101 | 89.99 159 | 95.39 120 | 94.34 131 | 97.74 173 | 97.63 134 |
|
baseline2 | | | 93.01 101 | 94.17 88 | 91.64 108 | 92.83 135 | 97.49 96 | 93.40 130 | 87.53 125 | 93.67 113 | 86.07 103 | 91.83 73 | 86.58 106 | 91.36 135 | 96.38 88 | 95.06 108 | 98.67 138 | 98.20 117 |
|
IterMVS | | | 90.20 134 | 92.43 118 | 87.61 166 | 92.82 136 | 94.31 187 | 94.11 120 | 81.54 177 | 92.97 120 | 69.90 184 | 84.71 130 | 88.16 104 | 89.96 160 | 95.25 122 | 94.17 134 | 97.31 175 | 97.46 138 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 92.77 103 | 93.60 100 | 91.80 106 | 92.63 137 | 96.80 113 | 95.24 100 | 89.14 110 | 90.30 157 | 84.58 108 | 86.76 112 | 90.65 81 | 90.42 154 | 95.89 104 | 96.49 68 | 98.79 130 | 98.32 113 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm | | | 87.95 163 | 89.44 150 | 86.21 181 | 92.53 138 | 94.62 182 | 91.40 166 | 76.36 192 | 91.46 144 | 69.80 186 | 87.43 108 | 75.14 161 | 91.55 133 | 89.85 189 | 90.60 181 | 95.61 188 | 96.96 153 |
|
Effi-MVS+-dtu | | | 91.78 112 | 93.59 101 | 89.68 135 | 92.44 139 | 97.11 105 | 94.40 117 | 84.94 157 | 92.43 130 | 75.48 153 | 91.09 83 | 83.75 129 | 93.55 112 | 96.61 78 | 95.47 98 | 97.24 176 | 98.67 90 |
|
testgi | | | 89.42 143 | 91.50 133 | 87.00 175 | 92.40 140 | 95.59 154 | 89.15 184 | 85.27 154 | 92.78 123 | 72.42 169 | 91.75 74 | 76.00 159 | 84.09 190 | 94.38 138 | 93.82 145 | 98.65 142 | 96.15 163 |
|
Fast-Effi-MVS+-dtu | | | 91.19 121 | 93.64 98 | 88.33 148 | 92.19 141 | 96.46 125 | 93.99 122 | 81.52 178 | 92.59 127 | 71.82 172 | 92.17 67 | 85.54 115 | 91.68 132 | 95.73 111 | 94.64 120 | 98.80 128 | 98.34 110 |
|
FC-MVSNet-test | | | 91.63 114 | 93.82 96 | 89.08 140 | 92.02 142 | 96.40 128 | 93.26 133 | 87.26 128 | 93.72 112 | 77.26 140 | 88.61 104 | 89.86 87 | 85.50 181 | 95.72 113 | 95.02 110 | 99.16 86 | 97.44 139 |
|
GA-MVS | | | 89.28 146 | 90.75 141 | 87.57 167 | 91.77 143 | 96.48 124 | 92.29 151 | 87.58 124 | 90.61 154 | 65.77 193 | 84.48 132 | 76.84 157 | 89.46 162 | 95.84 106 | 93.68 146 | 98.52 149 | 97.34 143 |
|
UniMVSNet_ETH3D | | | 88.47 157 | 86.00 186 | 91.35 112 | 91.55 144 | 96.29 130 | 92.53 144 | 88.81 112 | 85.58 189 | 82.33 117 | 67.63 198 | 66.87 197 | 94.04 102 | 91.49 179 | 95.24 104 | 98.84 122 | 98.92 74 |
|
TAMVS | | | 90.54 130 | 90.87 140 | 90.16 127 | 91.48 145 | 96.61 121 | 93.26 133 | 86.08 141 | 87.71 175 | 81.66 122 | 83.11 141 | 84.04 126 | 90.42 154 | 94.54 133 | 94.60 121 | 98.04 166 | 95.48 173 |
|
tfpnnormal | | | 88.50 156 | 87.01 177 | 90.23 125 | 91.36 146 | 95.78 149 | 92.74 140 | 90.09 95 | 83.65 194 | 76.33 148 | 71.46 189 | 69.58 189 | 91.84 129 | 95.54 114 | 94.02 138 | 99.06 101 | 99.03 61 |
|
GBi-Net | | | 93.81 88 | 94.18 86 | 93.38 93 | 91.34 147 | 95.86 143 | 96.22 80 | 88.68 113 | 95.23 86 | 90.40 63 | 86.39 118 | 91.16 76 | 94.40 96 | 96.52 83 | 96.30 71 | 99.21 78 | 97.79 125 |
|
test1 | | | 93.81 88 | 94.18 86 | 93.38 93 | 91.34 147 | 95.86 143 | 96.22 80 | 88.68 113 | 95.23 86 | 90.40 63 | 86.39 118 | 91.16 76 | 94.40 96 | 96.52 83 | 96.30 71 | 99.21 78 | 97.79 125 |
|
FMVSNet2 | | | 93.30 99 | 93.36 105 | 93.22 96 | 91.34 147 | 95.86 143 | 96.22 80 | 88.24 119 | 95.15 90 | 89.92 74 | 81.64 145 | 89.36 90 | 94.40 96 | 96.77 72 | 96.98 58 | 99.21 78 | 97.79 125 |
|
FMVSNet3 | | | 93.79 90 | 94.17 88 | 93.35 95 | 91.21 150 | 95.99 136 | 96.62 69 | 88.68 113 | 95.23 86 | 90.40 63 | 86.39 118 | 91.16 76 | 94.11 100 | 95.96 102 | 96.67 65 | 99.07 98 | 97.79 125 |
|
TransMVSNet (Re) | | | 87.73 169 | 86.79 179 | 88.83 142 | 90.76 151 | 94.40 185 | 91.33 169 | 89.62 104 | 84.73 191 | 75.41 155 | 72.73 182 | 71.41 180 | 86.80 174 | 94.53 134 | 93.93 140 | 99.06 101 | 95.83 167 |
|
LTVRE_ROB | | 87.32 16 | 87.55 170 | 88.25 159 | 86.73 176 | 90.66 152 | 95.80 148 | 93.05 136 | 84.77 158 | 83.35 195 | 60.32 202 | 83.12 140 | 67.39 195 | 93.32 115 | 94.36 139 | 94.86 114 | 98.28 158 | 98.87 81 |
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 |
EG-PatchMatch MVS | | | 86.68 178 | 87.24 174 | 86.02 183 | 90.58 153 | 96.26 131 | 91.08 172 | 81.59 176 | 84.96 190 | 69.80 186 | 71.35 190 | 75.08 163 | 84.23 189 | 94.24 142 | 93.35 151 | 98.82 123 | 95.46 174 |
|
TESTMET0.1,1 | | | 91.07 122 | 93.56 102 | 88.17 150 | 90.43 154 | 96.57 122 | 92.02 159 | 82.83 172 | 92.34 135 | 75.05 160 | 90.20 90 | 88.64 98 | 90.93 142 | 96.19 98 | 94.07 136 | 97.75 171 | 96.90 156 |
|
pm-mvs1 | | | 89.19 149 | 89.02 152 | 89.38 138 | 90.40 155 | 95.74 150 | 92.05 157 | 88.10 121 | 86.13 185 | 77.70 137 | 73.72 177 | 79.44 145 | 88.97 165 | 95.81 108 | 94.51 128 | 99.08 96 | 97.78 130 |
|
NR-MVSNet | | | 89.34 145 | 88.66 154 | 90.13 130 | 90.40 155 | 95.61 152 | 93.04 137 | 89.91 97 | 91.22 146 | 78.96 133 | 77.72 158 | 68.90 192 | 89.16 164 | 94.24 142 | 93.95 139 | 99.32 55 | 98.99 66 |
|
FMVSNet1 | | | 91.54 117 | 90.93 138 | 92.26 102 | 90.35 157 | 95.27 165 | 95.22 101 | 87.16 130 | 91.37 145 | 87.62 96 | 75.45 163 | 83.84 128 | 94.43 94 | 96.52 83 | 96.30 71 | 98.82 123 | 97.74 131 |
|
test-mter | | | 90.95 123 | 93.54 104 | 87.93 160 | 90.28 158 | 96.80 113 | 91.44 165 | 82.68 173 | 92.15 139 | 74.37 164 | 89.57 96 | 88.23 103 | 90.88 145 | 96.37 90 | 94.31 132 | 97.93 168 | 97.37 141 |
|
pmmvs4 | | | 90.55 129 | 89.91 145 | 91.30 113 | 90.26 159 | 94.95 173 | 92.73 141 | 87.94 122 | 93.44 117 | 85.35 106 | 82.28 144 | 76.09 158 | 93.02 120 | 93.56 150 | 92.26 173 | 98.51 150 | 96.77 158 |
|
MVS-HIRNet | | | 85.36 185 | 86.89 178 | 83.57 188 | 90.13 160 | 94.51 183 | 83.57 198 | 72.61 202 | 88.27 172 | 71.22 176 | 68.97 193 | 81.81 138 | 88.91 166 | 93.08 158 | 91.94 174 | 94.97 197 | 89.64 200 |
|
thisisatest0515 | | | 90.12 137 | 92.06 126 | 87.85 161 | 90.03 161 | 96.17 133 | 87.83 187 | 87.45 126 | 91.71 142 | 77.15 141 | 85.40 126 | 84.01 127 | 85.74 180 | 95.41 119 | 93.30 153 | 98.88 118 | 98.43 103 |
|
SixPastTwentyTwo | | | 88.37 158 | 89.47 149 | 87.08 173 | 90.01 162 | 95.93 142 | 87.41 188 | 85.32 151 | 90.26 158 | 70.26 180 | 86.34 121 | 71.95 177 | 90.93 142 | 92.89 162 | 91.72 176 | 98.55 147 | 97.22 145 |
|
UniMVSNet (Re) | | | 90.03 139 | 89.61 148 | 90.51 123 | 89.97 163 | 96.12 134 | 92.32 149 | 89.26 108 | 90.99 149 | 80.95 126 | 78.25 157 | 75.08 163 | 91.14 138 | 93.78 145 | 93.87 142 | 99.41 41 | 99.21 38 |
|
our_test_3 | | | | | | 89.78 164 | 93.84 190 | 85.59 193 | | | | | | | | | | |
|
UniMVSNet_NR-MVSNet | | | 90.35 132 | 89.96 144 | 90.80 119 | 89.66 165 | 95.83 146 | 92.48 145 | 90.53 92 | 90.96 150 | 79.57 130 | 79.33 154 | 77.14 155 | 93.21 118 | 92.91 161 | 94.50 129 | 99.37 50 | 99.05 58 |
|
v8 | | | 88.21 161 | 87.94 166 | 88.51 145 | 89.62 166 | 95.01 171 | 92.31 150 | 84.99 156 | 88.94 163 | 74.70 162 | 75.03 165 | 73.51 170 | 90.67 150 | 92.11 172 | 92.74 165 | 98.80 128 | 98.24 115 |
|
WR-MVS_H | | | 87.93 164 | 87.85 167 | 88.03 157 | 89.62 166 | 95.58 156 | 90.47 177 | 85.55 148 | 87.20 180 | 76.83 144 | 74.42 172 | 72.67 175 | 86.37 176 | 93.22 156 | 93.04 156 | 99.33 53 | 98.83 85 |
|
pmmvs5 | | | 87.83 168 | 88.09 161 | 87.51 170 | 89.59 168 | 95.48 157 | 89.75 182 | 84.73 159 | 86.07 187 | 71.44 174 | 80.57 149 | 70.09 187 | 90.74 149 | 94.47 135 | 92.87 161 | 98.82 123 | 97.10 147 |
|
gm-plane-assit | | | 83.26 191 | 85.29 188 | 80.89 192 | 89.52 169 | 89.89 202 | 70.26 207 | 78.24 184 | 77.11 204 | 58.01 206 | 74.16 174 | 66.90 196 | 90.63 152 | 97.20 58 | 96.05 81 | 98.66 141 | 95.68 170 |
|
v10 | | | 88.00 162 | 87.96 164 | 88.05 155 | 89.44 170 | 94.68 179 | 92.36 148 | 83.35 168 | 89.37 162 | 72.96 168 | 73.98 175 | 72.79 174 | 91.35 136 | 93.59 147 | 92.88 160 | 98.81 126 | 98.42 105 |
|
V42 | | | 88.31 159 | 87.95 165 | 88.73 143 | 89.44 170 | 95.34 162 | 92.23 153 | 87.21 129 | 88.83 165 | 74.49 163 | 74.89 167 | 73.43 171 | 90.41 156 | 92.08 174 | 92.77 164 | 98.60 146 | 98.33 111 |
|
v148 | | | 87.51 171 | 86.79 179 | 88.36 147 | 89.39 172 | 95.21 167 | 89.84 181 | 88.20 120 | 87.61 177 | 77.56 138 | 73.38 180 | 70.32 186 | 86.80 174 | 90.70 183 | 92.31 171 | 98.37 157 | 97.98 123 |
|
CP-MVSNet | | | 87.89 167 | 87.27 173 | 88.62 144 | 89.30 173 | 95.06 169 | 90.60 176 | 85.78 145 | 87.43 179 | 75.98 150 | 74.60 169 | 68.14 194 | 90.76 147 | 93.07 159 | 93.60 147 | 99.30 60 | 98.98 68 |
|
v1144 | | | 87.92 166 | 87.79 168 | 88.07 152 | 89.27 174 | 95.15 168 | 92.17 154 | 85.62 147 | 88.52 169 | 71.52 173 | 73.80 176 | 72.40 176 | 91.06 140 | 93.54 151 | 92.80 162 | 98.81 126 | 98.33 111 |
|
DU-MVS | | | 89.67 142 | 88.84 153 | 90.63 122 | 89.26 175 | 95.61 152 | 92.48 145 | 89.91 97 | 91.22 146 | 79.57 130 | 77.72 158 | 71.18 181 | 93.21 118 | 92.53 165 | 94.57 123 | 99.35 52 | 99.05 58 |
|
WR-MVS | | | 87.93 164 | 88.09 161 | 87.75 162 | 89.26 175 | 95.28 163 | 90.81 174 | 86.69 134 | 88.90 164 | 75.29 156 | 74.31 173 | 73.72 169 | 85.19 184 | 92.26 168 | 93.32 152 | 99.27 64 | 98.81 86 |
|
Baseline_NR-MVSNet | | | 89.27 147 | 88.01 163 | 90.73 121 | 89.26 175 | 93.71 191 | 92.71 142 | 89.78 102 | 90.73 151 | 81.28 124 | 73.53 178 | 72.85 173 | 92.30 125 | 92.53 165 | 93.84 144 | 99.07 98 | 98.88 79 |
|
N_pmnet | | | 84.80 186 | 85.10 190 | 84.45 186 | 89.25 178 | 92.86 194 | 84.04 196 | 86.21 137 | 88.78 166 | 66.73 192 | 72.41 185 | 74.87 165 | 85.21 183 | 88.32 192 | 86.45 196 | 95.30 192 | 92.04 194 |
|
v2v482 | | | 88.25 160 | 87.71 170 | 88.88 141 | 89.23 179 | 95.28 163 | 92.10 155 | 87.89 123 | 88.69 168 | 73.31 167 | 75.32 164 | 71.64 178 | 91.89 128 | 92.10 173 | 92.92 159 | 98.86 121 | 97.99 121 |
|
PS-CasMVS | | | 87.33 174 | 86.68 182 | 88.10 151 | 89.22 180 | 94.93 174 | 90.35 179 | 85.70 146 | 86.44 184 | 74.01 165 | 73.43 179 | 66.59 200 | 90.04 158 | 92.92 160 | 93.52 148 | 99.28 62 | 98.91 77 |
|
TranMVSNet+NR-MVSNet | | | 89.23 148 | 88.48 157 | 90.11 131 | 89.07 181 | 95.25 166 | 92.91 138 | 90.43 93 | 90.31 156 | 77.10 142 | 76.62 161 | 71.57 179 | 91.83 130 | 92.12 171 | 94.59 122 | 99.32 55 | 98.92 74 |
|
v1192 | | | 87.51 171 | 87.31 172 | 87.74 163 | 89.04 182 | 94.87 177 | 92.07 156 | 85.03 155 | 88.49 170 | 70.32 179 | 72.65 183 | 70.35 185 | 91.21 137 | 93.59 147 | 92.80 162 | 98.78 131 | 98.42 105 |
|
v144192 | | | 87.40 173 | 87.20 175 | 87.64 164 | 88.89 183 | 94.88 176 | 91.65 164 | 84.70 160 | 87.80 174 | 71.17 177 | 73.20 181 | 70.91 182 | 90.75 148 | 92.69 163 | 92.49 168 | 98.71 135 | 98.43 103 |
|
PEN-MVS | | | 87.22 176 | 86.50 184 | 88.07 152 | 88.88 184 | 94.44 184 | 90.99 173 | 86.21 137 | 86.53 183 | 73.66 166 | 74.97 166 | 66.56 201 | 89.42 163 | 91.20 181 | 93.48 149 | 99.24 69 | 98.31 114 |
|
v1921920 | | | 87.31 175 | 87.13 176 | 87.52 169 | 88.87 185 | 94.72 178 | 91.96 161 | 84.59 162 | 88.28 171 | 69.86 185 | 72.50 184 | 70.03 188 | 91.10 139 | 93.33 154 | 92.61 167 | 98.71 135 | 98.44 102 |
|
pmmvs6 | | | 85.98 183 | 84.89 191 | 87.25 172 | 88.83 186 | 94.35 186 | 89.36 183 | 85.30 153 | 78.51 203 | 75.44 154 | 62.71 202 | 75.41 160 | 87.65 170 | 93.58 149 | 92.40 170 | 96.89 178 | 97.29 144 |
|
v1240 | | | 86.89 177 | 86.75 181 | 87.06 174 | 88.75 187 | 94.65 181 | 91.30 170 | 84.05 164 | 87.49 178 | 68.94 189 | 71.96 187 | 68.86 193 | 90.65 151 | 93.33 154 | 92.72 166 | 98.67 138 | 98.24 115 |
|
anonymousdsp | | | 88.90 153 | 91.00 137 | 86.44 179 | 88.74 188 | 95.97 138 | 90.40 178 | 82.86 171 | 88.77 167 | 67.33 191 | 81.18 147 | 81.44 140 | 90.22 157 | 96.23 95 | 94.27 133 | 99.12 92 | 99.16 44 |
|
EU-MVSNet | | | 85.62 184 | 87.65 171 | 83.24 190 | 88.54 189 | 92.77 195 | 87.12 189 | 85.32 151 | 86.71 181 | 64.54 195 | 78.52 156 | 75.11 162 | 78.35 195 | 92.25 169 | 92.28 172 | 95.58 189 | 95.93 165 |
|
DTE-MVSNet | | | 86.67 179 | 86.09 185 | 87.35 171 | 88.45 190 | 94.08 189 | 90.65 175 | 86.05 142 | 86.13 185 | 72.19 170 | 74.58 171 | 66.77 199 | 87.61 171 | 90.31 184 | 93.12 155 | 99.13 90 | 97.62 135 |
|
FMVSNet5 | | | 90.36 131 | 90.93 138 | 89.70 133 | 87.99 191 | 92.25 196 | 92.03 158 | 83.51 167 | 92.20 138 | 84.13 109 | 85.59 125 | 86.48 107 | 92.43 123 | 94.61 131 | 94.52 127 | 98.13 162 | 90.85 197 |
|
v7n | | | 86.43 180 | 86.52 183 | 86.33 180 | 87.91 192 | 94.93 174 | 90.15 180 | 83.05 169 | 86.57 182 | 70.21 181 | 71.48 188 | 66.78 198 | 87.72 169 | 94.19 144 | 92.96 158 | 98.92 115 | 98.76 89 |
|
test20.03 | | | 82.92 192 | 85.52 187 | 79.90 195 | 87.75 193 | 91.84 197 | 82.80 199 | 82.99 170 | 82.65 199 | 60.32 202 | 78.90 155 | 70.50 183 | 67.10 202 | 92.05 175 | 90.89 178 | 98.44 154 | 91.80 195 |
|
MDTV_nov1_ep13_2view | | | 86.30 181 | 88.27 158 | 84.01 187 | 87.71 194 | 94.67 180 | 88.08 186 | 76.78 190 | 90.59 155 | 68.66 190 | 80.46 151 | 80.12 143 | 87.58 172 | 89.95 188 | 88.20 191 | 95.25 194 | 93.90 187 |
|
Anonymous20231206 | | | 83.84 190 | 85.19 189 | 82.26 191 | 87.38 195 | 92.87 193 | 85.49 194 | 83.65 166 | 86.07 187 | 63.44 198 | 68.42 194 | 69.01 191 | 75.45 199 | 93.34 153 | 92.44 169 | 98.12 164 | 94.20 181 |
|
FPMVS | | | 75.84 198 | 74.59 200 | 77.29 199 | 86.92 196 | 83.89 207 | 85.01 195 | 80.05 182 | 82.91 197 | 60.61 201 | 65.25 200 | 60.41 204 | 63.86 203 | 75.60 203 | 73.60 205 | 87.29 207 | 80.47 205 |
|
MIMVSNet | | | 88.99 152 | 91.07 136 | 86.57 178 | 86.78 197 | 95.62 151 | 91.20 171 | 75.40 197 | 90.65 153 | 76.57 145 | 84.05 135 | 82.44 137 | 91.01 141 | 95.84 106 | 95.38 100 | 98.48 152 | 93.50 190 |
|
tmp_tt | | | | | 66.88 202 | 86.07 198 | 73.86 209 | 68.22 208 | 33.38 210 | 96.88 45 | 80.67 127 | 88.23 106 | 78.82 147 | 49.78 207 | 82.68 201 | 77.47 203 | 83.19 209 | |
|
PM-MVS | | | 84.72 188 | 84.47 192 | 85.03 185 | 84.67 199 | 91.57 198 | 86.27 192 | 82.31 175 | 87.65 176 | 70.62 178 | 76.54 162 | 56.41 208 | 88.75 167 | 92.59 164 | 89.85 186 | 97.54 174 | 96.66 161 |
|
pmmvs-eth3d | | | 84.33 189 | 82.94 194 | 85.96 184 | 84.16 200 | 90.94 199 | 86.55 191 | 83.79 165 | 84.25 192 | 75.85 152 | 70.64 191 | 56.43 207 | 87.44 173 | 92.20 170 | 90.41 183 | 97.97 167 | 95.68 170 |
|
new-patchmatchnet | | | 78.49 197 | 78.19 199 | 78.84 197 | 84.13 201 | 90.06 201 | 77.11 206 | 80.39 181 | 79.57 202 | 59.64 205 | 66.01 199 | 55.65 209 | 75.62 198 | 84.55 199 | 80.70 201 | 96.14 183 | 90.77 198 |
|
new_pmnet | | | 81.53 193 | 82.68 195 | 80.20 193 | 83.47 202 | 89.47 203 | 82.21 201 | 78.36 183 | 87.86 173 | 60.14 204 | 67.90 196 | 69.43 190 | 82.03 193 | 89.22 190 | 87.47 194 | 94.99 196 | 87.39 202 |
|
ET-MVSNet_ETH3D | | | 93.34 98 | 94.33 84 | 92.18 103 | 83.26 203 | 97.66 93 | 96.72 67 | 89.89 99 | 95.62 78 | 87.17 99 | 96.00 34 | 83.69 130 | 96.99 53 | 93.78 145 | 95.34 101 | 99.06 101 | 98.18 118 |
|
pmmvs3 | | | 79.16 196 | 80.12 198 | 78.05 198 | 79.36 204 | 86.59 205 | 78.13 205 | 73.87 201 | 76.42 205 | 57.51 207 | 70.59 192 | 57.02 206 | 84.66 187 | 90.10 186 | 88.32 190 | 94.75 199 | 91.77 196 |
|
PMVS | | 63.12 18 | 67.27 200 | 66.39 203 | 68.30 201 | 77.98 205 | 60.24 211 | 59.53 211 | 76.82 188 | 66.65 207 | 60.74 200 | 54.39 204 | 59.82 205 | 51.24 206 | 73.92 206 | 70.52 206 | 83.48 208 | 79.17 207 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MDA-MVSNet-bldmvs | | | 80.11 194 | 80.24 197 | 79.94 194 | 77.01 206 | 93.21 192 | 78.86 204 | 85.94 144 | 82.71 198 | 60.86 199 | 79.71 153 | 51.77 210 | 83.71 192 | 75.60 203 | 86.37 197 | 93.28 202 | 92.35 193 |
|
ambc | | | | 73.83 201 | | 76.23 207 | 85.13 206 | 82.27 200 | | 84.16 193 | 65.58 194 | 52.82 205 | 23.31 215 | 73.55 200 | 91.41 180 | 85.26 200 | 92.97 203 | 94.70 176 |
|
Gipuma | | | 68.35 199 | 66.71 202 | 70.27 200 | 74.16 208 | 68.78 210 | 63.93 210 | 71.77 204 | 83.34 196 | 54.57 208 | 34.37 207 | 31.88 212 | 68.69 201 | 83.30 200 | 85.53 199 | 88.48 206 | 79.78 206 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MIMVSNet1 | | | 80.03 195 | 80.93 196 | 78.97 196 | 72.46 209 | 90.73 200 | 80.81 202 | 82.44 174 | 80.39 200 | 63.64 197 | 57.57 203 | 64.93 202 | 76.37 197 | 91.66 177 | 91.55 177 | 98.07 165 | 89.70 199 |
|
PMMVS2 | | | 64.36 202 | 65.94 204 | 62.52 203 | 67.37 210 | 77.44 208 | 64.39 209 | 69.32 208 | 61.47 208 | 34.59 210 | 46.09 206 | 41.03 211 | 48.02 209 | 74.56 205 | 78.23 202 | 91.43 204 | 82.76 204 |
|
EMVS | | | 49.98 204 | 46.76 207 | 53.74 205 | 64.96 211 | 51.29 213 | 37.81 213 | 69.35 207 | 51.83 209 | 22.69 213 | 29.57 209 | 25.06 213 | 57.28 204 | 44.81 209 | 56.11 208 | 70.32 211 | 68.64 210 |
|
E-PMN | | | 50.67 203 | 47.85 206 | 53.96 204 | 64.13 212 | 50.98 214 | 38.06 212 | 69.51 206 | 51.40 210 | 24.60 212 | 29.46 210 | 24.39 214 | 56.07 205 | 48.17 208 | 59.70 207 | 71.40 210 | 70.84 209 |
|
MVE | | 50.86 19 | 49.54 205 | 51.43 205 | 47.33 206 | 44.14 213 | 59.20 212 | 36.45 214 | 60.59 209 | 41.47 211 | 31.14 211 | 29.58 208 | 17.06 216 | 48.52 208 | 62.22 207 | 74.63 204 | 63.12 212 | 75.87 208 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 12.09 206 | 16.94 208 | 6.42 208 | 3.15 214 | 6.08 215 | 9.51 216 | 3.84 211 | 21.46 212 | 5.31 214 | 27.49 211 | 6.76 217 | 10.89 210 | 17.06 210 | 15.01 209 | 5.84 213 | 24.75 211 |
|
GG-mvs-BLEND | | | 66.17 201 | 94.91 76 | 32.63 207 | 1.32 215 | 96.64 120 | 91.40 166 | 0.85 213 | 94.39 102 | 2.20 215 | 90.15 92 | 95.70 58 | 2.27 212 | 96.39 87 | 95.44 99 | 97.78 169 | 95.68 170 |
|
test123 | | | 9.58 207 | 13.53 209 | 4.97 209 | 1.31 216 | 5.47 216 | 8.32 217 | 2.95 212 | 18.14 213 | 2.03 216 | 20.82 212 | 2.34 218 | 10.60 211 | 10.00 211 | 14.16 210 | 4.60 214 | 23.77 212 |
|
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 |
|
test_part1 | | | | | | | | | | | | | | | | | | 99.38 15 |
|
MTAPA | | | | | | | | | | | 96.83 7 | | 99.12 17 | | | | | |
|
MTMP | | | | | | | | | | | 97.18 4 | | 98.83 23 | | | | | |
|
Patchmatch-RL test | | | | | | | | 34.61 215 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 95.32 83 | | | | | | | | |
|
Patchmtry | | | | | | | 95.96 139 | 93.36 131 | 75.99 195 | | 75.19 157 | | | | | | | |
|
DeepMVS_CX | | | | | | | 86.86 204 | 79.50 203 | 70.43 205 | 90.73 151 | 63.66 196 | 80.36 152 | 60.83 203 | 79.68 194 | 76.23 202 | | 89.46 205 | 86.53 203 |
|