SMA-MVS | | | 97.53 5 | 97.93 5 | 97.07 8 | 99.21 1 | 99.02 6 | 98.08 18 | 96.25 8 | 96.36 10 | 93.57 13 | 96.56 11 | 99.27 3 | 96.78 15 | 97.91 2 | 97.43 3 | 98.51 18 | 98.94 11 |
|
APDe-MVS | | | 97.79 3 | 97.96 4 | 97.60 1 | 99.20 2 | 99.10 4 | 98.88 2 | 96.68 2 | 96.81 5 | 94.64 5 | 97.84 2 | 98.02 9 | 97.24 3 | 97.74 6 | 97.02 12 | 98.97 3 | 99.16 4 |
|
zzz-MVS | | | 96.98 13 | 96.68 21 | 97.33 4 | 99.09 3 | 98.71 11 | 98.43 7 | 96.01 13 | 96.11 15 | 95.19 3 | 92.89 30 | 97.32 20 | 96.84 11 | 97.20 15 | 96.09 37 | 98.44 29 | 98.46 29 |
|
HPM-MVS++ | | | 97.22 9 | 97.40 10 | 97.01 9 | 99.08 4 | 98.55 22 | 98.19 13 | 96.48 5 | 96.02 17 | 93.28 18 | 96.26 14 | 98.71 6 | 96.76 16 | 97.30 13 | 96.25 33 | 98.30 46 | 98.68 13 |
|
ACMMP_NAP | | | 96.93 14 | 97.27 12 | 96.53 21 | 99.06 5 | 98.95 7 | 98.24 12 | 96.06 12 | 95.66 19 | 90.96 31 | 95.63 21 | 97.71 14 | 96.53 19 | 97.66 8 | 96.68 18 | 98.30 46 | 98.61 18 |
|
DVP-MVS | | | 97.93 1 | 98.23 1 | 97.58 2 | 99.05 6 | 99.31 1 | 98.64 4 | 96.62 3 | 97.56 1 | 95.08 4 | 96.61 10 | 99.64 1 | 97.32 1 | 97.91 2 | 97.31 6 | 98.77 9 | 99.26 1 |
|
PGM-MVS | | | 96.16 22 | 96.33 26 | 95.95 24 | 99.04 7 | 98.63 17 | 98.32 11 | 92.76 40 | 93.42 45 | 90.49 36 | 96.30 13 | 95.31 38 | 96.71 17 | 96.46 33 | 96.02 38 | 98.38 37 | 98.19 38 |
|
APD-MVS | | | 97.12 10 | 97.05 15 | 97.19 6 | 99.04 7 | 98.63 17 | 98.45 6 | 96.54 4 | 94.81 34 | 93.50 14 | 96.10 16 | 97.40 19 | 96.81 12 | 97.05 19 | 96.82 17 | 98.80 7 | 98.56 19 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
NCCC | | | 96.75 17 | 96.67 22 | 96.85 14 | 99.03 9 | 98.44 30 | 98.15 15 | 96.28 7 | 96.32 11 | 92.39 23 | 92.16 32 | 97.55 17 | 96.68 18 | 97.32 11 | 96.65 20 | 98.55 17 | 98.26 34 |
|
CNVR-MVS | | | 97.30 8 | 97.41 9 | 97.18 7 | 99.02 10 | 98.60 19 | 98.15 15 | 96.24 10 | 96.12 14 | 94.10 9 | 95.54 22 | 97.99 10 | 96.99 7 | 97.97 1 | 97.17 8 | 98.57 16 | 98.50 25 |
|
MSP-MVS | | | 97.70 4 | 98.09 3 | 97.24 5 | 99.00 11 | 99.17 3 | 98.76 3 | 96.41 6 | 96.91 3 | 93.88 12 | 97.72 3 | 99.04 5 | 96.93 10 | 97.29 14 | 97.31 6 | 98.45 28 | 99.23 2 |
|
ACMMPR | | | 96.92 15 | 96.96 16 | 96.87 13 | 98.99 12 | 98.78 9 | 98.38 9 | 95.52 22 | 96.57 8 | 92.81 22 | 96.06 17 | 95.90 33 | 97.07 5 | 96.60 30 | 96.34 30 | 98.46 25 | 98.42 30 |
|
HFP-MVS | | | 97.11 11 | 97.19 13 | 97.00 10 | 98.97 13 | 98.73 10 | 98.37 10 | 95.69 19 | 96.60 7 | 93.28 18 | 96.87 5 | 96.64 25 | 97.27 2 | 96.64 28 | 96.33 31 | 98.44 29 | 98.56 19 |
|
SteuartSystems-ACMMP | | | 97.10 12 | 97.49 8 | 96.65 16 | 98.97 13 | 98.95 7 | 98.43 7 | 95.96 15 | 95.12 26 | 91.46 26 | 96.85 6 | 97.60 16 | 96.37 23 | 97.76 4 | 97.16 9 | 98.68 10 | 98.97 9 |
Skip Steuart: Steuart Systems R&D Blog. |
X-MVS | | | 96.07 24 | 96.33 26 | 95.77 27 | 98.94 15 | 98.66 12 | 97.94 22 | 95.41 28 | 95.12 26 | 88.03 48 | 93.00 29 | 96.06 29 | 95.85 25 | 96.65 27 | 96.35 28 | 98.47 23 | 98.48 26 |
|
SR-MVS | | | | | | 98.93 16 | | | 96.00 14 | | | | 97.75 13 | | | | | |
|
MP-MVS | | | 96.56 19 | 96.72 20 | 96.37 22 | 98.93 16 | 98.48 26 | 98.04 19 | 95.55 21 | 94.32 38 | 90.95 33 | 95.88 19 | 97.02 22 | 96.29 24 | 96.77 25 | 96.01 39 | 98.47 23 | 98.56 19 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MCST-MVS | | | 96.83 16 | 97.06 14 | 96.57 17 | 98.88 18 | 98.47 28 | 98.02 20 | 96.16 11 | 95.58 21 | 90.96 31 | 95.78 20 | 97.84 12 | 96.46 21 | 97.00 21 | 96.17 35 | 98.94 5 | 98.55 24 |
|
CP-MVS | | | 96.68 18 | 96.59 24 | 96.77 15 | 98.85 19 | 98.58 20 | 98.18 14 | 95.51 24 | 95.34 23 | 92.94 21 | 95.21 25 | 96.25 28 | 96.79 14 | 96.44 35 | 95.77 42 | 98.35 38 | 98.56 19 |
|
DPE-MVS | | | 97.83 2 | 98.13 2 | 97.48 3 | 98.83 20 | 99.19 2 | 98.99 1 | 96.70 1 | 96.05 16 | 94.39 7 | 98.30 1 | 99.47 2 | 97.02 6 | 97.75 5 | 97.02 12 | 98.98 2 | 99.10 7 |
|
mPP-MVS | | | | | | 98.76 21 | | | | | | | 95.49 36 | | | | | |
|
CSCG | | | 95.68 28 | 95.46 33 | 95.93 25 | 98.71 22 | 99.07 5 | 97.13 33 | 93.55 35 | 95.48 22 | 93.35 17 | 90.61 42 | 93.82 43 | 95.16 32 | 94.60 73 | 95.57 45 | 97.70 96 | 99.08 8 |
|
DeepC-MVS_fast | | 93.32 1 | 96.48 20 | 96.42 25 | 96.56 18 | 98.70 23 | 98.31 34 | 97.97 21 | 95.76 18 | 96.31 12 | 92.01 25 | 91.43 37 | 95.42 37 | 96.46 21 | 97.65 9 | 97.69 1 | 98.49 22 | 98.12 44 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | | | 95.02 35 | 93.71 46 | 96.54 20 | 98.51 24 | 97.76 51 | 96.69 37 | 95.94 17 | 93.72 43 | 93.50 14 | 89.01 49 | 90.53 62 | 96.49 20 | 94.51 76 | 93.76 75 | 98.07 70 | 96.69 92 |
|
train_agg | | | 96.15 23 | 96.64 23 | 95.58 31 | 98.44 25 | 98.03 42 | 98.14 17 | 95.40 29 | 93.90 42 | 87.72 53 | 96.26 14 | 98.10 8 | 95.75 27 | 96.25 40 | 95.45 47 | 98.01 76 | 98.47 27 |
|
CDPH-MVS | | | 94.80 39 | 95.50 31 | 93.98 44 | 98.34 26 | 98.06 40 | 97.41 28 | 93.23 37 | 92.81 50 | 82.98 90 | 92.51 31 | 94.82 39 | 93.53 55 | 96.08 43 | 96.30 32 | 98.42 32 | 97.94 50 |
|
MSLP-MVS++ | | | 96.05 25 | 95.63 29 | 96.55 19 | 98.33 27 | 98.17 37 | 96.94 34 | 94.61 32 | 94.70 36 | 94.37 8 | 89.20 48 | 95.96 32 | 96.81 12 | 95.57 51 | 97.33 5 | 98.24 54 | 98.47 27 |
|
ACMMP | | | 95.54 29 | 95.49 32 | 95.61 30 | 98.27 28 | 98.53 24 | 97.16 32 | 94.86 30 | 94.88 32 | 89.34 39 | 95.36 24 | 91.74 52 | 95.50 30 | 95.51 52 | 94.16 66 | 98.50 20 | 98.22 36 |
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 |
3Dnovator+ | | 90.56 5 | 95.06 34 | 94.56 40 | 95.65 29 | 98.11 29 | 98.15 38 | 97.19 31 | 91.59 50 | 95.11 28 | 93.23 20 | 81.99 96 | 94.71 40 | 95.43 31 | 96.48 32 | 96.88 16 | 98.35 38 | 98.63 15 |
|
3Dnovator | | 90.28 7 | 94.70 40 | 94.34 43 | 95.11 33 | 98.06 30 | 98.21 35 | 96.89 35 | 91.03 56 | 94.72 35 | 91.45 27 | 82.87 87 | 93.10 46 | 94.61 37 | 96.24 41 | 97.08 11 | 98.63 13 | 98.16 40 |
|
PLC | | 90.69 4 | 94.32 42 | 92.99 53 | 95.87 26 | 97.91 31 | 96.49 83 | 95.95 48 | 94.12 33 | 94.94 30 | 94.09 10 | 85.90 66 | 90.77 59 | 95.58 29 | 94.52 75 | 93.32 88 | 97.55 104 | 95.00 136 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
EPNet | | | 93.92 46 | 94.40 41 | 93.36 51 | 97.89 32 | 96.55 81 | 96.08 44 | 92.14 43 | 91.65 60 | 89.16 41 | 94.07 27 | 90.17 66 | 87.78 115 | 95.24 55 | 94.97 55 | 97.09 123 | 98.15 41 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CPTT-MVS | | | 95.54 29 | 95.07 34 | 96.10 23 | 97.88 33 | 97.98 45 | 97.92 23 | 94.86 30 | 94.56 37 | 92.16 24 | 91.01 39 | 95.71 34 | 96.97 9 | 94.56 74 | 93.50 82 | 96.81 144 | 98.14 42 |
|
QAPM | | | 94.13 44 | 94.33 44 | 93.90 45 | 97.82 34 | 98.37 33 | 96.47 39 | 90.89 57 | 92.73 53 | 85.63 74 | 85.35 70 | 93.87 42 | 94.17 45 | 95.71 50 | 95.90 40 | 98.40 34 | 98.42 30 |
|
DeepC-MVS | | 92.10 3 | 95.22 32 | 94.77 38 | 95.75 28 | 97.77 35 | 98.54 23 | 97.63 27 | 95.96 15 | 95.07 29 | 88.85 44 | 85.35 70 | 91.85 51 | 95.82 26 | 96.88 24 | 97.10 10 | 98.44 29 | 98.63 15 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
OpenMVS | | 88.18 11 | 92.51 57 | 91.61 74 | 93.55 50 | 97.74 36 | 98.02 43 | 95.66 51 | 90.46 60 | 89.14 93 | 86.50 64 | 75.80 129 | 90.38 65 | 92.69 64 | 94.99 58 | 95.30 49 | 98.27 50 | 97.63 62 |
|
TSAR-MVS + ACMM | | | 96.19 21 | 97.39 11 | 94.78 35 | 97.70 37 | 98.41 31 | 97.72 26 | 95.49 25 | 96.47 9 | 86.66 63 | 96.35 12 | 97.85 11 | 93.99 47 | 97.19 16 | 96.37 27 | 97.12 121 | 99.13 5 |
|
MAR-MVS | | | 92.71 56 | 92.63 57 | 92.79 62 | 97.70 37 | 97.15 67 | 93.75 83 | 87.98 90 | 90.71 67 | 85.76 72 | 86.28 63 | 86.38 73 | 94.35 42 | 94.95 59 | 95.49 46 | 97.22 114 | 97.44 69 |
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 |
PHI-MVS | | | 95.86 26 | 96.93 19 | 94.61 39 | 97.60 39 | 98.65 16 | 96.49 38 | 93.13 38 | 94.07 40 | 87.91 52 | 97.12 4 | 97.17 21 | 93.90 50 | 96.46 33 | 96.93 15 | 98.64 12 | 98.10 46 |
|
abl_6 | | | | | 94.78 35 | 97.46 40 | 97.99 44 | 95.76 49 | 91.80 47 | 93.72 43 | 91.25 28 | 91.33 38 | 96.47 26 | 94.28 44 | | | 98.14 63 | 97.39 71 |
|
DPM-MVS | | | 95.07 33 | 94.84 36 | 95.34 32 | 97.44 41 | 97.49 58 | 97.76 25 | 95.52 22 | 94.88 32 | 88.92 43 | 87.25 55 | 96.44 27 | 94.41 39 | 95.78 48 | 96.11 36 | 97.99 78 | 95.95 116 |
|
SD-MVS | | | 97.35 6 | 97.73 6 | 96.90 12 | 97.35 42 | 98.66 12 | 97.85 24 | 96.25 8 | 96.86 4 | 94.54 6 | 96.75 8 | 99.13 4 | 96.99 7 | 96.94 22 | 96.58 21 | 98.39 36 | 99.20 3 |
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 |
MVS_111021_HR | | | 94.84 37 | 95.91 28 | 93.60 49 | 97.35 42 | 98.46 29 | 95.08 56 | 91.19 53 | 94.18 39 | 85.97 67 | 95.38 23 | 92.56 48 | 93.61 54 | 96.61 29 | 96.25 33 | 98.40 34 | 97.92 52 |
|
TSAR-MVS + MP. | | | 97.31 7 | 97.64 7 | 96.92 11 | 97.28 44 | 98.56 21 | 98.61 5 | 95.48 26 | 96.72 6 | 94.03 11 | 96.73 9 | 98.29 7 | 97.15 4 | 97.61 10 | 96.42 25 | 98.96 4 | 99.13 5 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
CANet | | | 94.85 36 | 94.92 35 | 94.78 35 | 97.25 45 | 98.52 25 | 97.20 30 | 91.81 46 | 93.25 47 | 91.06 30 | 86.29 62 | 94.46 41 | 92.99 61 | 97.02 20 | 96.68 18 | 98.34 40 | 98.20 37 |
|
OMC-MVS | | | 94.49 41 | 94.36 42 | 94.64 38 | 97.17 46 | 97.73 53 | 95.49 53 | 92.25 42 | 96.18 13 | 90.34 37 | 88.51 50 | 92.88 47 | 94.90 36 | 94.92 61 | 94.17 65 | 97.69 97 | 96.15 111 |
|
MVS_111021_LR | | | 94.84 37 | 95.57 30 | 94.00 42 | 97.11 47 | 97.72 55 | 94.88 59 | 91.16 54 | 95.24 25 | 88.74 45 | 96.03 18 | 91.52 55 | 94.33 43 | 95.96 45 | 95.01 54 | 97.79 88 | 97.49 68 |
|
CNLPA | | | 93.69 48 | 92.50 59 | 95.06 34 | 97.11 47 | 97.36 61 | 93.88 80 | 93.30 36 | 95.64 20 | 93.44 16 | 80.32 104 | 90.73 60 | 94.99 35 | 93.58 92 | 93.33 86 | 97.67 99 | 96.57 97 |
|
LS3D | | | 91.97 63 | 90.98 81 | 93.12 57 | 97.03 49 | 97.09 70 | 95.33 55 | 95.59 20 | 92.47 54 | 79.26 109 | 81.60 99 | 82.77 94 | 94.39 41 | 94.28 78 | 94.23 64 | 97.14 120 | 94.45 142 |
|
TAPA-MVS | | 90.35 6 | 93.69 48 | 93.52 47 | 93.90 45 | 96.89 50 | 97.62 56 | 96.15 42 | 91.67 49 | 94.94 30 | 85.97 67 | 87.72 54 | 91.96 50 | 94.40 40 | 93.76 90 | 93.06 96 | 98.30 46 | 95.58 124 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DELS-MVS | | | 93.71 47 | 93.47 48 | 94.00 42 | 96.82 51 | 98.39 32 | 96.80 36 | 91.07 55 | 89.51 90 | 89.94 38 | 83.80 80 | 89.29 67 | 90.95 81 | 97.32 11 | 97.65 2 | 98.42 32 | 98.32 33 |
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 |
EPNet_dtu | | | 88.32 107 | 90.61 83 | 85.64 137 | 96.79 52 | 92.27 165 | 92.03 111 | 90.31 61 | 89.05 94 | 65.44 180 | 89.43 46 | 85.90 78 | 74.22 188 | 92.76 105 | 92.09 114 | 95.02 177 | 92.76 163 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MSDG | | | 90.42 85 | 88.25 104 | 92.94 60 | 96.67 53 | 94.41 109 | 93.96 75 | 92.91 39 | 89.59 89 | 86.26 65 | 76.74 122 | 80.92 108 | 90.43 87 | 92.60 110 | 92.08 115 | 97.44 109 | 91.41 169 |
|
DeepPCF-MVS | | 92.65 2 | 95.50 31 | 96.96 16 | 93.79 48 | 96.44 54 | 98.21 35 | 93.51 89 | 94.08 34 | 96.94 2 | 89.29 40 | 93.08 28 | 96.77 24 | 93.82 51 | 97.68 7 | 97.40 4 | 95.59 167 | 98.65 14 |
|
PCF-MVS | | 90.19 8 | 92.98 52 | 92.07 67 | 94.04 41 | 96.39 55 | 97.87 46 | 96.03 45 | 95.47 27 | 87.16 109 | 85.09 84 | 84.81 74 | 93.21 45 | 93.46 57 | 91.98 122 | 91.98 118 | 97.78 89 | 97.51 67 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_0304 | | | 94.30 43 | 94.68 39 | 93.86 47 | 96.33 56 | 98.48 26 | 97.41 28 | 91.20 52 | 92.75 51 | 86.96 60 | 86.03 65 | 93.81 44 | 92.64 65 | 96.89 23 | 96.54 23 | 98.61 14 | 98.24 35 |
|
OPM-MVS | | | 91.08 75 | 89.34 94 | 93.11 58 | 96.18 57 | 96.13 92 | 96.39 40 | 92.39 41 | 82.97 147 | 81.74 93 | 82.55 93 | 80.20 111 | 93.97 49 | 94.62 71 | 93.23 89 | 98.00 77 | 95.73 120 |
|
PVSNet_BlendedMVS | | | 92.80 53 | 92.44 61 | 93.23 52 | 96.02 58 | 97.83 49 | 93.74 84 | 90.58 58 | 91.86 57 | 90.69 34 | 85.87 68 | 82.04 101 | 90.01 88 | 96.39 36 | 95.26 50 | 98.34 40 | 97.81 57 |
|
PVSNet_Blended | | | 92.80 53 | 92.44 61 | 93.23 52 | 96.02 58 | 97.83 49 | 93.74 84 | 90.58 58 | 91.86 57 | 90.69 34 | 85.87 68 | 82.04 101 | 90.01 88 | 96.39 36 | 95.26 50 | 98.34 40 | 97.81 57 |
|
XVS | | | | | | 95.68 60 | 98.66 12 | 94.96 57 | | | 88.03 48 | | 96.06 29 | | | | 98.46 25 | |
|
X-MVStestdata | | | | | | 95.68 60 | 98.66 12 | 94.96 57 | | | 88.03 48 | | 96.06 29 | | | | 98.46 25 | |
|
HQP-MVS | | | 92.39 59 | 92.49 60 | 92.29 67 | 95.65 62 | 95.94 94 | 95.64 52 | 92.12 44 | 92.46 55 | 79.65 107 | 91.97 34 | 82.68 95 | 92.92 63 | 93.47 97 | 92.77 101 | 97.74 92 | 98.12 44 |
|
HyFIR lowres test | | | 87.87 109 | 86.42 126 | 89.57 96 | 95.56 63 | 96.99 73 | 92.37 101 | 84.15 130 | 86.64 113 | 77.17 116 | 57.65 194 | 83.97 85 | 91.08 80 | 92.09 120 | 92.44 106 | 97.09 123 | 95.16 133 |
|
ACMM | | 88.76 10 | 91.70 70 | 90.43 84 | 93.19 54 | 95.56 63 | 95.14 100 | 93.35 92 | 91.48 51 | 92.26 56 | 87.12 58 | 84.02 78 | 79.34 114 | 93.99 47 | 94.07 84 | 92.68 102 | 97.62 103 | 95.50 125 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | | 84.39 15 | 87.61 111 | 86.03 130 | 89.46 97 | 95.54 65 | 94.48 106 | 91.77 115 | 90.14 62 | 87.16 109 | 75.50 121 | 73.41 141 | 76.86 132 | 87.33 121 | 90.05 154 | 89.76 164 | 96.48 148 | 90.46 178 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
LGP-MVS_train | | | 91.83 66 | 92.04 68 | 91.58 73 | 95.46 66 | 96.18 91 | 95.97 47 | 89.85 64 | 90.45 73 | 77.76 112 | 91.92 35 | 80.07 112 | 92.34 69 | 94.27 79 | 93.47 83 | 98.11 67 | 97.90 55 |
|
CHOSEN 1792x2688 | | | 88.57 104 | 87.82 111 | 89.44 98 | 95.46 66 | 96.89 76 | 93.74 84 | 85.87 112 | 89.63 88 | 77.42 115 | 61.38 188 | 83.31 89 | 88.80 110 | 93.44 98 | 93.16 92 | 95.37 172 | 96.95 86 |
|
PVSNet_Blended_VisFu | | | 91.92 64 | 92.39 63 | 91.36 81 | 95.45 68 | 97.85 48 | 92.25 104 | 89.54 71 | 88.53 101 | 87.47 56 | 79.82 106 | 90.53 62 | 85.47 140 | 96.31 39 | 95.16 53 | 97.99 78 | 98.56 19 |
|
PatchMatch-RL | | | 90.30 86 | 88.93 98 | 91.89 69 | 95.41 69 | 95.68 96 | 90.94 118 | 88.67 80 | 89.80 86 | 86.95 61 | 85.90 66 | 72.51 139 | 92.46 66 | 93.56 94 | 92.18 111 | 96.93 136 | 92.89 161 |
|
TSAR-MVS + COLMAP | | | 92.39 59 | 92.31 64 | 92.47 63 | 95.35 70 | 96.46 85 | 96.13 43 | 92.04 45 | 95.33 24 | 80.11 105 | 94.95 26 | 77.35 129 | 94.05 46 | 94.49 77 | 93.08 94 | 97.15 118 | 94.53 140 |
|
ACMP | | 89.13 9 | 92.03 62 | 91.70 73 | 92.41 65 | 94.92 71 | 96.44 87 | 93.95 76 | 89.96 63 | 91.81 59 | 85.48 79 | 90.97 40 | 79.12 115 | 92.42 67 | 93.28 102 | 92.55 105 | 97.76 90 | 97.74 60 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
UA-Net | | | 90.81 79 | 92.58 58 | 88.74 105 | 94.87 72 | 97.44 59 | 92.61 98 | 88.22 85 | 82.35 150 | 78.93 110 | 85.20 72 | 95.61 35 | 79.56 175 | 96.52 31 | 96.57 22 | 98.23 55 | 94.37 143 |
|
IB-MVS | | 85.10 14 | 87.98 108 | 87.97 109 | 87.99 113 | 94.55 73 | 96.86 77 | 84.52 184 | 88.21 86 | 86.48 118 | 88.54 47 | 74.41 135 | 77.74 126 | 74.10 190 | 89.65 160 | 92.85 100 | 98.06 72 | 97.80 59 |
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 |
CANet_DTU | | | 90.74 82 | 92.93 55 | 88.19 110 | 94.36 74 | 96.61 79 | 94.34 66 | 84.66 123 | 90.66 68 | 68.75 159 | 90.41 43 | 86.89 71 | 89.78 90 | 95.46 53 | 94.87 56 | 97.25 113 | 95.62 122 |
|
canonicalmvs | | | 93.08 51 | 93.09 51 | 93.07 59 | 94.24 75 | 97.86 47 | 95.45 54 | 87.86 96 | 94.00 41 | 87.47 56 | 88.32 51 | 82.37 99 | 95.13 33 | 93.96 89 | 96.41 26 | 98.27 50 | 98.73 12 |
|
CS-MVS | | | 93.68 50 | 94.33 44 | 92.93 61 | 94.15 76 | 98.04 41 | 94.43 61 | 87.99 88 | 91.64 61 | 87.54 54 | 88.22 52 | 92.09 49 | 94.56 38 | 96.77 25 | 95.85 41 | 98.88 6 | 97.71 61 |
|
UGNet | | | 91.52 71 | 93.41 49 | 89.32 99 | 94.13 77 | 97.15 67 | 91.83 114 | 89.01 75 | 90.62 70 | 85.86 71 | 86.83 56 | 91.73 53 | 77.40 180 | 94.68 70 | 94.43 61 | 97.71 94 | 98.40 32 |
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 |
thres600view7 | | | 89.28 101 | 87.47 120 | 91.39 78 | 94.12 78 | 97.25 64 | 93.94 78 | 89.74 66 | 85.62 125 | 80.63 103 | 75.24 133 | 69.33 153 | 91.66 76 | 94.92 61 | 93.23 89 | 98.27 50 | 96.72 91 |
|
IS_MVSNet | | | 91.87 65 | 93.35 50 | 90.14 93 | 94.09 79 | 97.73 53 | 93.09 95 | 88.12 87 | 88.71 97 | 79.98 106 | 84.49 75 | 90.63 61 | 87.49 119 | 97.07 18 | 96.96 14 | 98.07 70 | 97.88 56 |
|
TSAR-MVS + GP. | | | 95.86 26 | 96.95 18 | 94.60 40 | 94.07 80 | 98.11 39 | 96.30 41 | 91.76 48 | 95.67 18 | 91.07 29 | 96.82 7 | 97.69 15 | 95.71 28 | 95.96 45 | 95.75 43 | 98.68 10 | 98.63 15 |
|
thres400 | | | 89.40 98 | 87.58 117 | 91.53 75 | 94.06 81 | 97.21 66 | 94.19 72 | 89.83 65 | 85.69 122 | 81.08 99 | 75.50 131 | 69.76 152 | 91.80 72 | 94.79 68 | 93.51 79 | 98.20 58 | 96.60 95 |
|
ACMH | | 85.51 13 | 87.31 114 | 86.59 124 | 88.14 111 | 93.96 82 | 94.51 105 | 89.00 154 | 87.99 88 | 81.58 152 | 70.15 149 | 78.41 113 | 71.78 144 | 90.60 85 | 91.30 131 | 91.99 117 | 97.17 117 | 96.58 96 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MS-PatchMatch | | | 87.63 110 | 87.61 115 | 87.65 118 | 93.95 83 | 94.09 114 | 92.60 99 | 81.52 161 | 86.64 113 | 76.41 119 | 73.46 140 | 85.94 77 | 85.01 144 | 92.23 118 | 90.00 158 | 96.43 151 | 90.93 175 |
|
thres200 | | | 89.49 97 | 87.72 112 | 91.55 74 | 93.95 83 | 97.25 64 | 94.34 66 | 89.74 66 | 85.66 123 | 81.18 96 | 76.12 128 | 70.19 151 | 91.80 72 | 94.92 61 | 93.51 79 | 98.27 50 | 96.40 101 |
|
CLD-MVS | | | 92.50 58 | 91.96 69 | 93.13 56 | 93.93 85 | 96.24 89 | 95.69 50 | 88.77 79 | 92.92 48 | 89.01 42 | 88.19 53 | 81.74 104 | 93.13 60 | 93.63 91 | 93.08 94 | 98.23 55 | 97.91 54 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
thres100view900 | | | 89.36 99 | 87.61 115 | 91.39 78 | 93.90 86 | 96.86 77 | 94.35 65 | 89.66 70 | 85.87 120 | 81.15 97 | 76.46 124 | 70.38 148 | 91.17 78 | 94.09 83 | 93.43 85 | 98.13 64 | 96.16 110 |
|
tfpn200view9 | | | 89.55 96 | 87.86 110 | 91.53 75 | 93.90 86 | 97.26 63 | 94.31 68 | 89.74 66 | 85.87 120 | 81.15 97 | 76.46 124 | 70.38 148 | 91.76 74 | 94.92 61 | 93.51 79 | 98.28 49 | 96.61 94 |
|
EIA-MVS | | | 92.72 55 | 92.96 54 | 92.44 64 | 93.86 88 | 97.76 51 | 93.13 94 | 88.65 81 | 89.78 87 | 86.68 62 | 86.69 59 | 87.57 68 | 93.74 52 | 96.07 44 | 95.32 48 | 98.58 15 | 97.53 66 |
|
CHOSEN 280x420 | | | 90.77 81 | 92.14 66 | 89.17 101 | 93.86 88 | 92.81 153 | 93.16 93 | 80.22 169 | 90.21 78 | 84.67 86 | 89.89 45 | 91.38 56 | 90.57 86 | 94.94 60 | 92.11 113 | 92.52 188 | 93.65 153 |
|
ETV-MVS | | | 94.04 45 | 94.81 37 | 93.14 55 | 93.83 90 | 97.39 60 | 94.15 73 | 88.89 76 | 93.35 46 | 87.93 51 | 90.14 44 | 90.99 57 | 95.04 34 | 97.17 17 | 96.44 24 | 98.99 1 | 98.18 39 |
|
FC-MVSNet-train | | | 90.55 83 | 90.19 87 | 90.97 84 | 93.78 91 | 95.16 99 | 92.11 109 | 88.85 77 | 87.64 106 | 83.38 89 | 84.36 77 | 78.41 120 | 89.53 92 | 94.69 69 | 93.15 93 | 98.15 61 | 97.92 52 |
|
Vis-MVSNet (Re-imp) | | | 90.54 84 | 92.76 56 | 87.94 114 | 93.73 92 | 96.94 75 | 92.17 107 | 87.91 91 | 88.77 96 | 76.12 120 | 83.68 81 | 90.80 58 | 79.49 176 | 96.34 38 | 96.35 28 | 98.21 57 | 96.46 99 |
|
baseline1 | | | 90.81 79 | 90.29 85 | 91.42 77 | 93.67 93 | 95.86 95 | 93.94 78 | 89.69 69 | 89.29 92 | 82.85 91 | 82.91 86 | 80.30 110 | 89.60 91 | 95.05 57 | 94.79 58 | 98.80 7 | 93.82 151 |
|
EPP-MVSNet | | | 92.13 61 | 93.06 52 | 91.05 83 | 93.66 94 | 97.30 62 | 92.18 105 | 87.90 92 | 90.24 77 | 83.63 87 | 86.14 64 | 90.52 64 | 90.76 83 | 94.82 66 | 94.38 62 | 98.18 60 | 97.98 48 |
|
ACMH+ | | 85.75 12 | 87.19 115 | 86.02 131 | 88.56 106 | 93.42 95 | 94.41 109 | 89.91 138 | 87.66 100 | 83.45 144 | 72.25 136 | 76.42 126 | 71.99 143 | 90.78 82 | 89.86 155 | 90.94 132 | 97.32 110 | 95.11 135 |
|
MVS_Test | | | 91.81 67 | 92.19 65 | 91.37 80 | 93.24 96 | 96.95 74 | 94.43 61 | 86.25 109 | 91.45 64 | 83.45 88 | 86.31 61 | 85.15 81 | 92.93 62 | 93.99 85 | 94.71 59 | 97.92 82 | 96.77 90 |
|
MVSTER | | | 91.73 68 | 91.61 74 | 91.86 70 | 93.18 97 | 94.56 103 | 94.37 64 | 87.90 92 | 90.16 81 | 88.69 46 | 89.23 47 | 81.28 106 | 88.92 107 | 95.75 49 | 93.95 72 | 98.12 65 | 96.37 102 |
|
Anonymous202405211 | | | | 88.00 107 | | 93.16 98 | 96.38 88 | 93.58 87 | 89.34 73 | 87.92 105 | | 65.04 178 | 83.03 91 | 92.07 70 | 92.67 107 | 93.33 86 | 96.96 131 | 97.63 62 |
|
casdiffmvs | | | 91.72 69 | 91.16 79 | 92.38 66 | 93.16 98 | 97.15 67 | 93.95 76 | 89.49 72 | 91.58 63 | 86.03 66 | 80.75 103 | 80.95 107 | 93.16 59 | 95.25 54 | 95.22 52 | 98.50 20 | 97.23 77 |
|
tttt0517 | | | 91.01 78 | 91.71 72 | 90.19 91 | 92.98 100 | 97.07 71 | 91.96 113 | 87.63 101 | 90.61 71 | 81.42 95 | 86.76 58 | 82.26 100 | 89.23 99 | 94.86 65 | 93.03 98 | 97.90 83 | 97.36 72 |
|
Effi-MVS+ | | | 89.79 93 | 89.83 92 | 89.74 94 | 92.98 100 | 96.45 86 | 93.48 90 | 84.24 128 | 87.62 107 | 76.45 118 | 81.76 97 | 77.56 128 | 93.48 56 | 94.61 72 | 93.59 78 | 97.82 87 | 97.22 79 |
|
RPSCF | | | 89.68 94 | 89.24 95 | 90.20 90 | 92.97 102 | 92.93 149 | 92.30 102 | 87.69 98 | 90.44 74 | 85.12 83 | 91.68 36 | 85.84 79 | 90.69 84 | 87.34 177 | 86.07 180 | 92.46 189 | 90.37 179 |
|
TDRefinement | | | 84.97 142 | 83.39 157 | 86.81 126 | 92.97 102 | 94.12 113 | 92.18 105 | 87.77 97 | 82.78 148 | 71.31 141 | 68.43 159 | 68.07 159 | 81.10 171 | 89.70 159 | 89.03 171 | 95.55 169 | 91.62 167 |
|
thisisatest0530 | | | 91.04 77 | 91.74 71 | 90.21 89 | 92.93 104 | 97.00 72 | 92.06 110 | 87.63 101 | 90.74 66 | 81.51 94 | 86.81 57 | 82.48 96 | 89.23 99 | 94.81 67 | 93.03 98 | 97.90 83 | 97.33 74 |
|
DCV-MVSNet | | | 91.24 73 | 91.26 77 | 91.22 82 | 92.84 105 | 93.44 131 | 93.82 81 | 86.75 106 | 91.33 65 | 85.61 75 | 84.00 79 | 85.46 80 | 91.27 77 | 92.91 104 | 93.62 77 | 97.02 127 | 98.05 47 |
|
baseline | | | 91.19 74 | 91.89 70 | 90.38 85 | 92.76 106 | 95.04 101 | 93.55 88 | 84.54 126 | 92.92 48 | 85.71 73 | 86.68 60 | 86.96 70 | 89.28 98 | 92.00 121 | 92.62 104 | 96.46 149 | 96.99 84 |
|
EPMVS | | | 85.77 130 | 86.24 128 | 85.23 142 | 92.76 106 | 93.78 121 | 89.91 138 | 73.60 191 | 90.19 79 | 74.22 124 | 82.18 95 | 78.06 122 | 87.55 118 | 85.61 186 | 85.38 185 | 93.32 183 | 88.48 190 |
|
DWT-MVSNet_training | | | 86.83 117 | 84.44 146 | 89.61 95 | 92.75 108 | 93.82 119 | 91.66 116 | 82.85 144 | 88.57 99 | 87.48 55 | 79.00 109 | 64.24 180 | 88.82 109 | 85.18 187 | 87.50 176 | 94.07 180 | 92.79 162 |
|
diffmvs | | | 91.37 72 | 91.09 80 | 91.70 72 | 92.71 109 | 96.47 84 | 94.03 74 | 88.78 78 | 92.74 52 | 85.43 81 | 83.63 82 | 80.37 109 | 91.76 74 | 93.39 99 | 93.78 74 | 97.50 106 | 97.23 77 |
|
DI_MVS_plusplus_trai | | | 91.05 76 | 90.15 88 | 92.11 68 | 92.67 110 | 96.61 79 | 96.03 45 | 88.44 83 | 90.25 76 | 85.92 69 | 73.73 136 | 84.89 83 | 91.92 71 | 94.17 82 | 94.07 70 | 97.68 98 | 97.31 75 |
|
Anonymous20231211 | | | 89.82 92 | 88.18 105 | 91.74 71 | 92.52 111 | 96.09 93 | 93.38 91 | 89.30 74 | 88.95 95 | 85.90 70 | 64.55 182 | 84.39 84 | 92.41 68 | 92.24 117 | 93.06 96 | 96.93 136 | 97.95 49 |
|
tpmrst | | | 83.72 160 | 83.45 154 | 84.03 158 | 92.21 112 | 91.66 177 | 88.74 157 | 73.58 192 | 88.14 103 | 72.67 133 | 77.37 118 | 72.11 142 | 86.34 130 | 82.94 195 | 82.05 194 | 90.63 198 | 89.86 183 |
|
CostFormer | | | 86.78 119 | 86.05 129 | 87.62 120 | 92.15 113 | 93.20 140 | 91.55 117 | 75.83 183 | 88.11 104 | 85.29 82 | 81.76 97 | 76.22 134 | 87.80 114 | 84.45 190 | 85.21 186 | 93.12 184 | 93.42 156 |
|
Vis-MVSNet | | | 89.36 99 | 91.49 76 | 86.88 125 | 92.10 114 | 97.60 57 | 92.16 108 | 85.89 111 | 84.21 136 | 75.20 122 | 82.58 91 | 87.13 69 | 77.40 180 | 95.90 47 | 95.63 44 | 98.51 18 | 97.36 72 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS-LS | | | 88.60 103 | 88.45 100 | 88.78 104 | 92.02 115 | 92.44 163 | 92.00 112 | 83.57 138 | 86.52 116 | 78.90 111 | 78.61 112 | 81.34 105 | 89.12 102 | 90.68 143 | 93.18 91 | 97.10 122 | 96.35 103 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchmatchNet | | | 85.70 131 | 86.65 123 | 84.60 149 | 91.79 116 | 93.40 132 | 89.27 147 | 73.62 190 | 90.19 79 | 72.63 134 | 82.74 90 | 81.93 103 | 87.64 116 | 84.99 188 | 84.29 190 | 92.64 187 | 89.00 186 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpm cat1 | | | 84.13 153 | 81.99 173 | 86.63 129 | 91.74 117 | 91.50 180 | 90.68 120 | 75.69 184 | 86.12 119 | 85.44 80 | 72.39 145 | 70.72 146 | 85.16 142 | 80.89 198 | 81.56 195 | 91.07 196 | 90.71 176 |
|
USDC | | | 86.73 120 | 85.96 133 | 87.63 119 | 91.64 118 | 93.97 116 | 92.76 97 | 84.58 125 | 88.19 102 | 70.67 146 | 80.10 105 | 67.86 160 | 89.43 93 | 91.81 123 | 89.77 163 | 96.69 146 | 90.05 182 |
|
SCA | | | 86.25 122 | 87.52 118 | 84.77 146 | 91.59 119 | 93.90 117 | 89.11 151 | 73.25 195 | 90.38 75 | 72.84 132 | 83.26 83 | 83.79 87 | 88.49 112 | 86.07 184 | 85.56 183 | 93.33 182 | 89.67 184 |
|
gg-mvs-nofinetune | | | 81.83 179 | 83.58 152 | 79.80 186 | 91.57 120 | 96.54 82 | 93.79 82 | 68.80 202 | 62.71 205 | 43.01 209 | 55.28 197 | 85.06 82 | 83.65 154 | 96.13 42 | 94.86 57 | 97.98 81 | 94.46 141 |
|
Fast-Effi-MVS+ | | | 88.56 105 | 87.99 108 | 89.22 100 | 91.56 121 | 95.21 98 | 92.29 103 | 82.69 146 | 86.82 111 | 77.73 113 | 76.24 127 | 73.39 138 | 93.36 58 | 94.22 81 | 93.64 76 | 97.65 100 | 96.43 100 |
|
CMPMVS | | 61.19 17 | 79.86 185 | 77.46 193 | 82.66 176 | 91.54 122 | 91.82 175 | 83.25 187 | 81.57 160 | 70.51 198 | 68.64 160 | 59.89 193 | 66.77 166 | 79.63 174 | 84.00 193 | 84.30 189 | 91.34 194 | 84.89 198 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
ADS-MVSNet | | | 84.08 154 | 84.95 141 | 83.05 171 | 91.53 123 | 91.75 176 | 88.16 161 | 70.70 199 | 89.96 85 | 69.51 154 | 78.83 110 | 76.97 131 | 86.29 131 | 84.08 192 | 84.60 188 | 92.13 192 | 88.48 190 |
|
test-LLR | | | 86.88 116 | 88.28 102 | 85.24 141 | 91.22 124 | 92.07 169 | 87.41 167 | 83.62 136 | 84.58 129 | 69.33 155 | 83.00 84 | 82.79 92 | 84.24 148 | 92.26 115 | 89.81 161 | 95.64 165 | 93.44 154 |
|
test0.0.03 1 | | | 85.58 133 | 87.69 114 | 83.11 168 | 91.22 124 | 92.54 160 | 85.60 183 | 83.62 136 | 85.66 123 | 67.84 166 | 82.79 89 | 79.70 113 | 73.51 192 | 91.15 135 | 90.79 134 | 96.88 140 | 91.23 172 |
|
baseline2 | | | 88.97 102 | 89.50 93 | 88.36 107 | 91.14 126 | 95.30 97 | 90.13 132 | 85.17 120 | 87.24 108 | 80.80 101 | 84.46 76 | 78.44 119 | 85.60 137 | 93.54 95 | 91.87 119 | 97.31 111 | 95.66 121 |
|
Effi-MVS+-dtu | | | 87.51 112 | 88.13 106 | 86.77 127 | 91.10 127 | 94.90 102 | 90.91 119 | 82.67 147 | 83.47 143 | 71.55 138 | 81.11 102 | 77.04 130 | 89.41 94 | 92.65 109 | 91.68 125 | 95.00 178 | 96.09 113 |
|
RPMNet | | | 84.82 144 | 85.90 134 | 83.56 163 | 91.10 127 | 92.10 167 | 88.73 158 | 71.11 198 | 84.75 127 | 68.79 158 | 73.56 137 | 77.62 127 | 85.33 141 | 90.08 153 | 89.43 167 | 96.32 152 | 93.77 152 |
|
CR-MVSNet | | | 85.48 135 | 86.29 127 | 84.53 151 | 91.08 129 | 92.10 167 | 89.18 149 | 73.30 193 | 84.75 127 | 71.08 143 | 73.12 144 | 77.91 124 | 86.27 132 | 91.48 127 | 90.75 137 | 96.27 153 | 93.94 148 |
|
TinyColmap | | | 84.04 155 | 82.01 172 | 86.42 131 | 90.87 130 | 91.84 174 | 88.89 156 | 84.07 132 | 82.11 151 | 69.89 151 | 71.08 148 | 60.81 192 | 89.04 103 | 90.52 145 | 89.19 169 | 95.76 159 | 88.50 189 |
|
tpm | | | 83.16 166 | 83.64 151 | 82.60 177 | 90.75 131 | 91.05 183 | 88.49 159 | 73.99 188 | 82.36 149 | 67.08 172 | 78.10 114 | 68.79 154 | 84.17 150 | 85.95 185 | 85.96 181 | 91.09 195 | 93.23 158 |
|
dps | | | 85.00 141 | 83.21 161 | 87.08 123 | 90.73 132 | 92.55 159 | 89.34 146 | 75.29 185 | 84.94 126 | 87.01 59 | 79.27 108 | 67.69 161 | 87.27 122 | 84.22 191 | 83.56 191 | 92.83 186 | 90.25 180 |
|
MDTV_nov1_ep13 | | | 86.64 121 | 87.50 119 | 85.65 136 | 90.73 132 | 93.69 125 | 89.96 136 | 78.03 178 | 89.48 91 | 76.85 117 | 84.92 73 | 82.42 98 | 86.14 134 | 86.85 181 | 86.15 179 | 92.17 190 | 88.97 187 |
|
CDS-MVSNet | | | 88.34 106 | 88.71 99 | 87.90 115 | 90.70 134 | 94.54 104 | 92.38 100 | 86.02 110 | 80.37 158 | 79.42 108 | 79.30 107 | 83.43 88 | 82.04 163 | 93.39 99 | 94.01 71 | 96.86 142 | 95.93 117 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
IterMVS-SCA-FT | | | 85.44 137 | 86.71 122 | 83.97 159 | 90.59 135 | 90.84 186 | 89.73 142 | 78.34 175 | 84.07 140 | 66.40 175 | 77.27 120 | 78.66 117 | 83.06 156 | 91.20 132 | 90.10 156 | 95.72 162 | 94.78 137 |
|
IterMVS | | | 85.25 139 | 86.49 125 | 83.80 160 | 90.42 136 | 90.77 189 | 90.02 134 | 78.04 177 | 84.10 138 | 66.27 176 | 77.28 119 | 78.41 120 | 83.01 157 | 90.88 137 | 89.72 165 | 95.04 176 | 94.24 144 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Fast-Effi-MVS+-dtu | | | 86.25 122 | 87.70 113 | 84.56 150 | 90.37 137 | 93.70 124 | 90.54 122 | 78.14 176 | 83.50 142 | 65.37 181 | 81.59 100 | 75.83 136 | 86.09 136 | 91.70 125 | 91.70 123 | 96.88 140 | 95.84 118 |
|
FC-MVSNet-test | | | 86.15 125 | 89.10 97 | 82.71 175 | 89.83 138 | 93.18 141 | 87.88 164 | 84.69 122 | 86.54 115 | 62.18 190 | 82.39 94 | 83.31 89 | 74.18 189 | 92.52 112 | 91.86 120 | 97.50 106 | 93.88 150 |
|
GA-MVS | | | 85.08 140 | 85.65 137 | 84.42 152 | 89.77 139 | 94.25 112 | 89.26 148 | 84.62 124 | 81.19 155 | 62.25 189 | 75.72 130 | 68.44 157 | 84.14 151 | 93.57 93 | 91.68 125 | 96.49 147 | 94.71 139 |
|
PMMVS | | | 89.88 91 | 91.19 78 | 88.35 108 | 89.73 140 | 91.97 173 | 90.62 121 | 81.92 156 | 90.57 72 | 80.58 104 | 92.16 32 | 86.85 72 | 91.17 78 | 92.31 114 | 91.35 129 | 96.11 155 | 93.11 160 |
|
tfpnnormal | | | 83.80 159 | 81.26 181 | 86.77 127 | 89.60 141 | 93.26 139 | 89.72 143 | 87.60 103 | 72.78 191 | 70.44 147 | 60.53 191 | 61.15 191 | 85.55 138 | 92.72 106 | 91.44 127 | 97.71 94 | 96.92 87 |
|
CVMVSNet | | | 83.83 158 | 85.53 138 | 81.85 182 | 89.60 141 | 90.92 184 | 87.81 165 | 83.21 142 | 80.11 161 | 60.16 192 | 76.47 123 | 78.57 118 | 76.79 182 | 89.76 156 | 90.13 151 | 93.51 181 | 92.75 164 |
|
testgi | | | 81.94 178 | 84.09 149 | 79.43 187 | 89.53 143 | 90.83 187 | 82.49 190 | 81.75 159 | 80.59 156 | 59.46 194 | 82.82 88 | 65.75 169 | 67.97 194 | 90.10 152 | 89.52 166 | 95.39 171 | 89.03 185 |
|
UniMVSNet_ETH3D | | | 84.57 145 | 81.40 179 | 88.28 109 | 89.34 144 | 94.38 111 | 90.33 124 | 86.50 108 | 74.74 189 | 77.52 114 | 59.90 192 | 62.04 188 | 88.78 111 | 88.82 170 | 92.65 103 | 97.22 114 | 97.24 76 |
|
LTVRE_ROB | | 81.71 16 | 82.44 176 | 81.84 174 | 83.13 167 | 89.01 145 | 92.99 146 | 88.90 155 | 82.32 153 | 66.26 202 | 54.02 202 | 74.68 134 | 59.62 198 | 88.87 108 | 90.71 142 | 92.02 116 | 95.68 164 | 96.62 93 |
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 |
TAMVS | | | 84.94 143 | 84.95 141 | 84.93 145 | 88.82 146 | 93.18 141 | 88.44 160 | 81.28 163 | 77.16 177 | 73.76 128 | 75.43 132 | 76.57 133 | 82.04 163 | 90.59 144 | 90.79 134 | 95.22 174 | 90.94 174 |
|
EG-PatchMatch MVS | | | 81.70 181 | 81.31 180 | 82.15 180 | 88.75 147 | 93.81 120 | 87.14 170 | 78.89 174 | 71.57 194 | 64.12 186 | 61.20 190 | 68.46 156 | 76.73 183 | 91.48 127 | 90.77 136 | 97.28 112 | 91.90 166 |
|
TransMVSNet (Re) | | | 82.67 173 | 80.93 184 | 84.69 148 | 88.71 148 | 91.50 180 | 87.90 163 | 87.15 104 | 71.54 196 | 68.24 163 | 63.69 184 | 64.67 179 | 78.51 179 | 91.65 126 | 90.73 139 | 97.64 101 | 92.73 165 |
|
FMVSNet3 | | | 90.19 89 | 90.06 91 | 90.34 86 | 88.69 149 | 93.85 118 | 94.58 60 | 85.78 113 | 90.03 82 | 85.56 76 | 77.38 115 | 86.13 74 | 89.22 101 | 93.29 101 | 94.36 63 | 98.20 58 | 95.40 130 |
|
GBi-Net | | | 90.21 87 | 90.11 89 | 90.32 87 | 88.66 150 | 93.65 127 | 94.25 69 | 85.78 113 | 90.03 82 | 85.56 76 | 77.38 115 | 86.13 74 | 89.38 95 | 93.97 86 | 94.16 66 | 98.31 43 | 95.47 126 |
|
test1 | | | 90.21 87 | 90.11 89 | 90.32 87 | 88.66 150 | 93.65 127 | 94.25 69 | 85.78 113 | 90.03 82 | 85.56 76 | 77.38 115 | 86.13 74 | 89.38 95 | 93.97 86 | 94.16 66 | 98.31 43 | 95.47 126 |
|
FMVSNet2 | | | 89.61 95 | 89.14 96 | 90.16 92 | 88.66 150 | 93.65 127 | 94.25 69 | 85.44 117 | 88.57 99 | 84.96 85 | 73.53 138 | 83.82 86 | 89.38 95 | 94.23 80 | 94.68 60 | 98.31 43 | 95.47 126 |
|
PatchT | | | 83.86 157 | 85.51 139 | 81.94 181 | 88.41 153 | 91.56 179 | 78.79 197 | 71.57 197 | 84.08 139 | 71.08 143 | 70.62 149 | 76.13 135 | 86.27 132 | 91.48 127 | 90.75 137 | 95.52 170 | 93.94 148 |
|
UniMVSNet (Re) | | | 86.22 124 | 85.46 140 | 87.11 122 | 88.34 154 | 94.42 108 | 89.65 144 | 87.10 105 | 84.39 133 | 74.61 123 | 70.41 153 | 68.10 158 | 85.10 143 | 91.17 134 | 91.79 121 | 97.84 86 | 97.94 50 |
|
NR-MVSNet | | | 85.46 136 | 84.54 145 | 86.52 130 | 88.33 155 | 93.78 121 | 90.45 123 | 87.87 94 | 84.40 131 | 71.61 137 | 70.59 150 | 62.09 187 | 82.79 159 | 91.75 124 | 91.75 122 | 98.10 68 | 97.44 69 |
|
UniMVSNet_NR-MVSNet | | | 86.80 118 | 85.86 135 | 87.89 116 | 88.17 156 | 94.07 115 | 90.15 130 | 88.51 82 | 84.20 137 | 73.45 129 | 72.38 146 | 70.30 150 | 88.95 105 | 90.25 148 | 92.21 110 | 98.12 65 | 97.62 64 |
|
thisisatest0515 | | | 85.70 131 | 87.00 121 | 84.19 155 | 88.16 157 | 93.67 126 | 84.20 186 | 84.14 131 | 83.39 145 | 72.91 131 | 76.79 121 | 74.75 137 | 78.82 178 | 92.57 111 | 91.26 130 | 96.94 133 | 96.56 98 |
|
pm-mvs1 | | | 84.55 146 | 83.46 153 | 85.82 133 | 88.16 157 | 93.39 133 | 89.05 153 | 85.36 119 | 74.03 190 | 72.43 135 | 65.08 177 | 71.11 145 | 82.30 162 | 93.48 96 | 91.70 123 | 97.64 101 | 95.43 129 |
|
gm-plane-assit | | | 77.65 190 | 78.50 188 | 76.66 191 | 87.96 159 | 85.43 201 | 64.70 207 | 74.50 186 | 64.15 204 | 51.26 204 | 61.32 189 | 58.17 200 | 84.11 152 | 95.16 56 | 93.83 73 | 97.45 108 | 91.41 169 |
|
test-mter | | | 86.09 128 | 88.38 101 | 83.43 165 | 87.89 160 | 92.61 157 | 86.89 172 | 77.11 181 | 84.30 134 | 68.62 161 | 82.57 92 | 82.45 97 | 84.34 147 | 92.40 113 | 90.11 155 | 95.74 160 | 94.21 146 |
|
pmmvs4 | | | 86.00 129 | 84.28 148 | 88.00 112 | 87.80 161 | 92.01 172 | 89.94 137 | 84.91 121 | 86.79 112 | 80.98 100 | 73.41 141 | 66.34 168 | 88.12 113 | 89.31 163 | 88.90 172 | 96.24 154 | 93.20 159 |
|
TESTMET0.1,1 | | | 86.11 127 | 88.28 102 | 83.59 162 | 87.80 161 | 92.07 169 | 87.41 167 | 77.12 180 | 84.58 129 | 69.33 155 | 83.00 84 | 82.79 92 | 84.24 148 | 92.26 115 | 89.81 161 | 95.64 165 | 93.44 154 |
|
DU-MVS | | | 86.12 126 | 84.81 143 | 87.66 117 | 87.77 163 | 93.78 121 | 90.15 130 | 87.87 94 | 84.40 131 | 73.45 129 | 70.59 150 | 64.82 177 | 88.95 105 | 90.14 149 | 92.33 107 | 97.76 90 | 97.62 64 |
|
Baseline_NR-MVSNet | | | 85.28 138 | 83.42 156 | 87.46 121 | 87.77 163 | 90.80 188 | 89.90 140 | 87.69 98 | 83.93 141 | 74.16 125 | 64.72 180 | 66.43 167 | 87.48 120 | 90.14 149 | 90.83 133 | 97.73 93 | 97.11 82 |
|
SixPastTwentyTwo | | | 83.12 168 | 83.44 155 | 82.74 174 | 87.71 165 | 93.11 145 | 82.30 191 | 82.33 152 | 79.24 166 | 64.33 184 | 78.77 111 | 62.75 183 | 84.11 152 | 88.11 172 | 87.89 174 | 95.70 163 | 94.21 146 |
|
TranMVSNet+NR-MVSNet | | | 85.57 134 | 84.41 147 | 86.92 124 | 87.67 166 | 93.34 134 | 90.31 126 | 88.43 84 | 83.07 146 | 70.11 150 | 69.99 156 | 65.28 172 | 86.96 124 | 89.73 157 | 92.27 108 | 98.06 72 | 97.17 81 |
|
WR-MVS | | | 83.14 167 | 83.38 158 | 82.87 173 | 87.55 167 | 93.29 136 | 86.36 177 | 84.21 129 | 80.05 162 | 66.41 174 | 66.91 165 | 66.92 165 | 75.66 186 | 88.96 168 | 90.56 142 | 97.05 125 | 96.96 85 |
|
v8 | | | 84.45 151 | 83.30 160 | 85.80 134 | 87.53 168 | 92.95 147 | 90.31 126 | 82.46 151 | 80.46 157 | 71.43 139 | 66.99 164 | 67.16 163 | 86.14 134 | 89.26 164 | 90.22 150 | 96.94 133 | 96.06 114 |
|
WR-MVS_H | | | 82.86 172 | 82.66 166 | 83.10 169 | 87.44 169 | 93.33 135 | 85.71 182 | 83.20 143 | 77.36 176 | 68.20 164 | 66.37 168 | 65.23 173 | 76.05 185 | 89.35 161 | 90.13 151 | 97.99 78 | 96.89 88 |
|
v148 | | | 83.61 161 | 82.10 170 | 85.37 138 | 87.34 170 | 92.94 148 | 87.48 166 | 85.72 116 | 78.92 167 | 73.87 127 | 65.71 174 | 64.69 178 | 81.78 167 | 87.82 173 | 89.35 168 | 96.01 156 | 95.26 132 |
|
v10 | | | 84.18 152 | 83.17 162 | 85.37 138 | 87.34 170 | 92.68 155 | 90.32 125 | 81.33 162 | 79.93 165 | 69.23 157 | 66.33 169 | 65.74 170 | 87.03 123 | 90.84 138 | 90.38 145 | 96.97 129 | 96.29 107 |
|
v2v482 | | | 84.51 147 | 83.05 163 | 86.20 132 | 87.25 172 | 93.28 137 | 90.22 128 | 85.40 118 | 79.94 164 | 69.78 152 | 67.74 161 | 65.15 174 | 87.57 117 | 89.12 166 | 90.55 143 | 96.97 129 | 95.60 123 |
|
CP-MVSNet | | | 83.11 169 | 82.15 169 | 84.23 154 | 87.20 173 | 92.70 154 | 86.42 176 | 83.53 139 | 77.83 174 | 67.67 167 | 66.89 167 | 60.53 194 | 82.47 160 | 89.23 165 | 90.65 141 | 98.08 69 | 97.20 80 |
|
v1144 | | | 84.03 156 | 82.88 164 | 85.37 138 | 87.17 174 | 93.15 144 | 90.18 129 | 83.31 141 | 78.83 168 | 67.85 165 | 65.99 171 | 64.99 175 | 86.79 126 | 90.75 140 | 90.33 147 | 96.90 138 | 96.15 111 |
|
V42 | | | 84.48 149 | 83.36 159 | 85.79 135 | 87.14 175 | 93.28 137 | 90.03 133 | 83.98 133 | 80.30 159 | 71.20 142 | 66.90 166 | 67.17 162 | 85.55 138 | 89.35 161 | 90.27 148 | 96.82 143 | 96.27 108 |
|
pmmvs5 | | | 83.37 164 | 82.68 165 | 84.18 156 | 87.13 176 | 93.18 141 | 86.74 173 | 82.08 155 | 76.48 181 | 67.28 170 | 71.26 147 | 62.70 184 | 84.71 145 | 90.77 139 | 90.12 154 | 97.15 118 | 94.24 144 |
|
FMVSNet1 | | | 87.33 113 | 86.00 132 | 88.89 102 | 87.13 176 | 92.83 152 | 93.08 96 | 84.46 127 | 81.35 154 | 82.20 92 | 66.33 169 | 77.96 123 | 88.96 104 | 93.97 86 | 94.16 66 | 97.54 105 | 95.38 131 |
|
PS-CasMVS | | | 82.53 174 | 81.54 177 | 83.68 161 | 87.08 178 | 92.54 160 | 86.20 178 | 83.46 140 | 76.46 182 | 65.73 179 | 65.71 174 | 59.41 199 | 81.61 168 | 89.06 167 | 90.55 143 | 98.03 74 | 97.07 83 |
|
our_test_3 | | | | | | 86.93 179 | 89.77 190 | 81.61 192 | | | | | | | | | | |
|
PEN-MVS | | | 82.49 175 | 81.58 176 | 83.56 163 | 86.93 179 | 92.05 171 | 86.71 174 | 83.84 134 | 76.94 179 | 64.68 183 | 67.24 162 | 60.11 195 | 81.17 170 | 87.78 174 | 90.70 140 | 98.02 75 | 96.21 109 |
|
v1192 | | | 83.56 162 | 82.35 167 | 84.98 143 | 86.84 181 | 92.84 150 | 90.01 135 | 82.70 145 | 78.54 169 | 66.48 173 | 64.88 179 | 62.91 182 | 86.91 125 | 90.72 141 | 90.25 149 | 96.94 133 | 96.32 105 |
|
v144192 | | | 83.48 163 | 82.23 168 | 84.94 144 | 86.65 182 | 92.84 150 | 89.63 145 | 82.48 150 | 77.87 173 | 67.36 169 | 65.33 176 | 63.50 181 | 86.51 128 | 89.72 158 | 89.99 159 | 97.03 126 | 96.35 103 |
|
DTE-MVSNet | | | 81.76 180 | 81.04 182 | 82.60 177 | 86.63 183 | 91.48 182 | 85.97 180 | 83.70 135 | 76.45 183 | 62.44 188 | 67.16 163 | 59.98 196 | 78.98 177 | 87.15 178 | 89.93 160 | 97.88 85 | 95.12 134 |
|
v1921920 | | | 83.30 165 | 82.09 171 | 84.70 147 | 86.59 184 | 92.67 156 | 89.82 141 | 82.23 154 | 78.32 170 | 65.76 178 | 64.64 181 | 62.35 185 | 86.78 127 | 90.34 147 | 90.02 157 | 97.02 127 | 96.31 106 |
|
v1240 | | | 82.88 171 | 81.66 175 | 84.29 153 | 86.46 185 | 92.52 162 | 89.06 152 | 81.82 158 | 77.16 177 | 65.09 182 | 64.17 183 | 61.50 189 | 86.36 129 | 90.12 151 | 90.13 151 | 96.95 132 | 96.04 115 |
|
anonymousdsp | | | 84.51 147 | 85.85 136 | 82.95 172 | 86.30 186 | 93.51 130 | 85.77 181 | 80.38 168 | 78.25 172 | 63.42 187 | 73.51 139 | 72.20 141 | 84.64 146 | 93.21 103 | 92.16 112 | 97.19 116 | 98.14 42 |
|
pmmvs6 | | | 80.90 182 | 78.77 187 | 83.38 166 | 85.84 187 | 91.61 178 | 86.01 179 | 82.54 149 | 64.17 203 | 70.43 148 | 54.14 201 | 67.06 164 | 80.73 172 | 90.50 146 | 89.17 170 | 94.74 179 | 94.75 138 |
|
MVS-HIRNet | | | 78.16 188 | 77.57 192 | 78.83 188 | 85.83 188 | 87.76 195 | 76.67 198 | 70.22 200 | 75.82 186 | 67.39 168 | 55.61 196 | 70.52 147 | 81.96 165 | 86.67 182 | 85.06 187 | 90.93 197 | 81.58 201 |
|
test20.03 | | | 76.41 192 | 78.49 189 | 73.98 194 | 85.64 189 | 87.50 196 | 75.89 199 | 80.71 167 | 70.84 197 | 51.07 205 | 68.06 160 | 61.40 190 | 54.99 203 | 88.28 171 | 87.20 177 | 95.58 168 | 86.15 194 |
|
v7n | | | 82.25 177 | 81.54 177 | 83.07 170 | 85.55 190 | 92.58 158 | 86.68 175 | 81.10 166 | 76.54 180 | 65.97 177 | 62.91 185 | 60.56 193 | 82.36 161 | 91.07 136 | 90.35 146 | 96.77 145 | 96.80 89 |
|
N_pmnet | | | 77.55 191 | 76.68 194 | 78.56 189 | 85.43 191 | 87.30 198 | 78.84 196 | 81.88 157 | 78.30 171 | 60.61 191 | 61.46 187 | 62.15 186 | 74.03 191 | 82.04 196 | 80.69 198 | 90.59 199 | 84.81 199 |
|
Anonymous20231206 | | | 78.09 189 | 78.11 190 | 78.07 190 | 85.19 192 | 89.17 191 | 80.99 193 | 81.24 165 | 75.46 187 | 58.25 196 | 54.78 200 | 59.90 197 | 66.73 197 | 88.94 169 | 88.26 173 | 96.01 156 | 90.25 180 |
|
MDTV_nov1_ep13_2view | | | 80.43 183 | 80.94 183 | 79.84 185 | 84.82 193 | 90.87 185 | 84.23 185 | 73.80 189 | 80.28 160 | 64.33 184 | 70.05 155 | 68.77 155 | 79.67 173 | 84.83 189 | 83.50 192 | 92.17 190 | 88.25 192 |
|
FPMVS | | | 69.87 198 | 67.10 201 | 73.10 196 | 84.09 194 | 78.35 206 | 79.40 195 | 76.41 182 | 71.92 192 | 57.71 197 | 54.06 202 | 50.04 205 | 56.72 201 | 71.19 202 | 68.70 203 | 84.25 204 | 75.43 204 |
|
EU-MVSNet | | | 78.43 187 | 80.25 185 | 76.30 192 | 83.81 195 | 87.27 199 | 80.99 193 | 79.52 171 | 76.01 184 | 54.12 201 | 70.44 152 | 64.87 176 | 67.40 196 | 86.23 183 | 85.54 184 | 91.95 193 | 91.41 169 |
|
FMVSNet5 | | | 84.47 150 | 84.72 144 | 84.18 156 | 83.30 196 | 88.43 193 | 88.09 162 | 79.42 172 | 84.25 135 | 74.14 126 | 73.15 143 | 78.74 116 | 83.65 154 | 91.19 133 | 91.19 131 | 96.46 149 | 86.07 195 |
|
MIMVSNet | | | 82.97 170 | 84.00 150 | 81.77 183 | 82.23 197 | 92.25 166 | 87.40 169 | 72.73 196 | 81.48 153 | 69.55 153 | 68.79 158 | 72.42 140 | 81.82 166 | 92.23 118 | 92.25 109 | 96.89 139 | 88.61 188 |
|
PM-MVS | | | 80.29 184 | 79.30 186 | 81.45 184 | 81.91 198 | 88.23 194 | 82.61 189 | 79.01 173 | 79.99 163 | 67.15 171 | 69.07 157 | 51.39 204 | 82.92 158 | 87.55 176 | 85.59 182 | 95.08 175 | 93.28 157 |
|
pmmvs-eth3d | | | 79.78 186 | 77.58 191 | 82.34 179 | 81.57 199 | 87.46 197 | 82.92 188 | 81.28 163 | 75.33 188 | 71.34 140 | 61.88 186 | 52.41 203 | 81.59 169 | 87.56 175 | 86.90 178 | 95.36 173 | 91.48 168 |
|
new-patchmatchnet | | | 72.32 195 | 71.09 198 | 73.74 195 | 81.17 200 | 84.86 202 | 72.21 204 | 77.48 179 | 68.32 200 | 54.89 200 | 55.10 198 | 49.31 207 | 63.68 200 | 79.30 199 | 76.46 201 | 93.03 185 | 84.32 200 |
|
ET-MVSNet_ETH3D | | | 89.93 90 | 90.84 82 | 88.87 103 | 79.60 201 | 96.19 90 | 94.43 61 | 86.56 107 | 90.63 69 | 80.75 102 | 90.71 41 | 77.78 125 | 93.73 53 | 91.36 130 | 93.45 84 | 98.15 61 | 95.77 119 |
|
PMVS | | 56.77 18 | 61.27 200 | 58.64 202 | 64.35 200 | 75.66 202 | 54.60 210 | 53.62 209 | 74.23 187 | 53.69 207 | 58.37 195 | 44.27 205 | 49.38 206 | 44.16 206 | 69.51 204 | 65.35 205 | 80.07 206 | 73.66 205 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new_pmnet | | | 72.29 196 | 73.25 196 | 71.16 199 | 75.35 203 | 81.38 203 | 73.72 203 | 69.27 201 | 75.97 185 | 49.84 206 | 56.27 195 | 56.12 202 | 69.08 193 | 81.73 197 | 80.86 197 | 89.72 202 | 80.44 202 |
|
ambc | | | | 67.96 200 | | 73.69 204 | 79.79 205 | 73.82 202 | | 71.61 193 | 59.80 193 | 46.00 203 | 20.79 212 | 66.15 198 | 86.92 180 | 80.11 199 | 89.13 203 | 90.50 177 |
|
pmmvs3 | | | 71.13 197 | 71.06 199 | 71.21 198 | 73.54 205 | 80.19 204 | 71.69 205 | 64.86 204 | 62.04 206 | 52.10 203 | 54.92 199 | 48.00 208 | 75.03 187 | 83.75 194 | 83.24 193 | 90.04 201 | 85.27 196 |
|
MDA-MVSNet-bldmvs | | | 73.81 193 | 72.56 197 | 75.28 193 | 72.52 206 | 88.87 192 | 74.95 201 | 82.67 147 | 71.57 194 | 55.02 199 | 65.96 172 | 42.84 210 | 76.11 184 | 70.61 203 | 81.47 196 | 90.38 200 | 86.59 193 |
|
tmp_tt | | | | | 50.24 203 | 68.55 207 | 46.86 212 | 48.90 211 | 18.28 210 | 86.51 117 | 68.32 162 | 70.19 154 | 65.33 171 | 26.69 209 | 74.37 201 | 66.80 204 | 70.72 209 | |
|
Gipuma | | | 58.52 201 | 56.17 203 | 61.27 201 | 67.14 208 | 58.06 209 | 52.16 210 | 68.40 203 | 69.00 199 | 45.02 208 | 22.79 207 | 20.57 213 | 55.11 202 | 76.27 200 | 79.33 200 | 79.80 207 | 67.16 207 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MIMVSNet1 | | | 73.19 194 | 73.70 195 | 72.60 197 | 65.42 209 | 86.69 200 | 75.56 200 | 79.65 170 | 67.87 201 | 55.30 198 | 45.24 204 | 56.41 201 | 63.79 199 | 86.98 179 | 87.66 175 | 95.85 158 | 85.04 197 |
|
PMMVS2 | | | 53.68 202 | 55.72 204 | 51.30 202 | 58.84 210 | 67.02 208 | 54.23 208 | 60.97 207 | 47.50 208 | 19.42 211 | 34.81 206 | 31.97 211 | 30.88 208 | 65.84 205 | 69.99 202 | 83.47 205 | 72.92 206 |
|
EMVS | | | 39.04 205 | 34.32 207 | 44.54 205 | 58.25 211 | 39.35 213 | 27.61 213 | 62.55 206 | 35.99 209 | 16.40 213 | 20.04 210 | 14.77 214 | 44.80 204 | 33.12 209 | 44.10 208 | 57.61 211 | 52.89 210 |
|
E-PMN | | | 40.00 203 | 35.74 206 | 44.98 204 | 57.69 212 | 39.15 214 | 28.05 212 | 62.70 205 | 35.52 210 | 17.78 212 | 20.90 208 | 14.36 215 | 44.47 205 | 35.89 208 | 47.86 207 | 59.15 210 | 56.47 209 |
|
MVE | | 39.81 19 | 39.52 204 | 41.58 205 | 37.11 206 | 33.93 213 | 49.06 211 | 26.45 214 | 54.22 208 | 29.46 211 | 24.15 210 | 20.77 209 | 10.60 216 | 34.42 207 | 51.12 207 | 65.27 206 | 49.49 212 | 64.81 208 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 4.35 206 | 6.54 208 | 1.79 208 | 0.60 214 | 1.82 215 | 3.06 216 | 0.95 211 | 7.22 212 | 0.88 215 | 12.38 211 | 1.25 217 | 3.87 211 | 6.09 210 | 5.58 209 | 1.40 213 | 11.42 212 |
|
GG-mvs-BLEND | | | 62.84 199 | 90.21 86 | 30.91 207 | 0.57 215 | 94.45 107 | 86.99 171 | 0.34 213 | 88.71 97 | 0.98 214 | 81.55 101 | 91.58 54 | 0.86 212 | 92.66 108 | 91.43 128 | 95.73 161 | 91.11 173 |
|
test123 | | | 3.48 207 | 5.31 209 | 1.34 209 | 0.20 216 | 1.52 216 | 2.17 217 | 0.58 212 | 6.13 213 | 0.31 216 | 9.85 212 | 0.31 218 | 3.90 210 | 2.65 211 | 5.28 210 | 0.87 214 | 11.46 211 |
|
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 | | | | | | | | | | | | | | | | | | 98.96 10 |
|
MTAPA | | | | | | | | | | | 95.36 2 | | 97.46 18 | | | | | |
|
MTMP | | | | | | | | | | | 95.70 1 | | 96.90 23 | | | | | |
|
Patchmatch-RL test | | | | | | | | 18.47 215 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 91.63 62 | | | | | | | | |
|
Patchmtry | | | | | | | 92.39 164 | 89.18 149 | 73.30 193 | | 71.08 143 | | | | | | | |
|
DeepMVS_CX | | | | | | | 71.82 207 | 68.37 206 | 48.05 209 | 77.38 175 | 46.88 207 | 65.77 173 | 47.03 209 | 67.48 195 | 64.27 206 | | 76.89 208 | 76.72 203 |
|