PGM-MVS | | | 98.86 29 | 99.35 24 | 98.29 32 | 99.77 1 | 99.63 24 | 99.67 5 | 95.63 43 | 98.66 109 | 95.27 47 | 99.11 23 | 99.82 40 | 99.67 4 | 99.33 22 | 99.19 20 | 99.73 49 | 99.74 66 |
|
SMA-MVS | | | 99.38 4 | 99.60 2 | 99.12 8 | 99.76 2 | 99.62 28 | 99.39 28 | 98.23 16 | 99.52 15 | 98.03 14 | 99.45 9 | 99.98 1 | 99.64 5 | 99.58 7 | 99.30 11 | 99.68 85 | 99.76 55 |
|
CSCG | | | 98.90 28 | 98.93 49 | 98.85 22 | 99.75 3 | 99.72 5 | 99.49 20 | 96.58 40 | 99.38 23 | 98.05 13 | 98.97 30 | 97.87 71 | 99.49 19 | 97.78 117 | 98.92 34 | 99.78 28 | 99.90 3 |
|
APDe-MVS | | | 99.49 1 | 99.64 1 | 99.32 2 | 99.74 4 | 99.74 4 | 99.75 1 | 98.34 3 | 99.56 10 | 98.72 4 | 99.57 5 | 99.97 6 | 99.53 16 | 99.65 2 | 99.25 14 | 99.84 5 | 99.77 51 |
|
ACMMP_NAP | | | 99.05 23 | 99.45 12 | 98.58 28 | 99.73 5 | 99.60 38 | 99.64 8 | 98.28 11 | 99.23 43 | 94.57 58 | 99.35 12 | 99.97 6 | 99.55 14 | 99.63 3 | 98.66 48 | 99.70 74 | 99.74 66 |
|
zzz-MVS | | | 99.31 7 | 99.44 15 | 99.16 5 | 99.73 5 | 99.65 16 | 99.63 11 | 98.26 12 | 99.27 37 | 98.01 15 | 99.27 14 | 99.97 6 | 99.60 7 | 99.59 6 | 98.58 53 | 99.71 65 | 99.73 70 |
|
DVP-MVS | | | 99.45 2 | 99.54 5 | 99.35 1 | 99.72 7 | 99.76 1 | 99.63 11 | 98.37 2 | 99.63 6 | 99.03 2 | 98.95 32 | 99.98 1 | 99.60 7 | 99.60 5 | 99.05 24 | 99.74 42 | 99.79 39 |
|
HFP-MVS | | | 99.32 6 | 99.53 7 | 99.07 12 | 99.69 8 | 99.59 40 | 99.63 11 | 98.31 7 | 99.56 10 | 97.37 23 | 99.27 14 | 99.97 6 | 99.70 3 | 99.35 20 | 99.24 16 | 99.71 65 | 99.76 55 |
|
HPM-MVS++ | | | 99.10 19 | 99.30 25 | 98.86 21 | 99.69 8 | 99.48 56 | 99.59 15 | 98.34 3 | 99.26 40 | 96.55 34 | 99.10 24 | 99.96 11 | 99.36 27 | 99.25 25 | 98.37 66 | 99.64 106 | 99.66 100 |
|
APD-MVS | | | 99.25 11 | 99.38 19 | 99.09 10 | 99.69 8 | 99.58 43 | 99.56 16 | 98.32 6 | 98.85 87 | 97.87 17 | 98.91 35 | 99.92 27 | 99.30 33 | 99.45 14 | 99.38 8 | 99.79 25 | 99.58 114 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MSP-MVS | | | 99.34 5 | 99.52 8 | 99.14 7 | 99.68 11 | 99.75 3 | 99.64 8 | 98.31 7 | 99.44 19 | 98.10 11 | 99.28 13 | 99.98 1 | 99.30 33 | 99.34 21 | 99.05 24 | 99.81 16 | 99.79 39 |
|
SR-MVS | | | | | | 99.67 12 | | | 98.25 13 | | | | 99.94 24 | | | | | |
|
X-MVS | | | 98.93 27 | 99.37 20 | 98.42 29 | 99.67 12 | 99.62 28 | 99.60 14 | 98.15 21 | 99.08 63 | 93.81 77 | 98.46 56 | 99.95 16 | 99.59 10 | 99.49 12 | 99.21 19 | 99.68 85 | 99.75 62 |
|
MCST-MVS | | | 99.11 18 | 99.27 27 | 98.93 19 | 99.67 12 | 99.33 79 | 99.51 19 | 98.31 7 | 99.28 35 | 96.57 33 | 99.10 24 | 99.90 30 | 99.71 2 | 99.19 26 | 98.35 67 | 99.82 10 | 99.71 84 |
|
ACMMPR | | | 99.30 8 | 99.54 5 | 99.03 15 | 99.66 15 | 99.64 21 | 99.68 4 | 98.25 13 | 99.56 10 | 97.12 27 | 99.19 17 | 99.95 16 | 99.72 1 | 99.43 15 | 99.25 14 | 99.72 55 | 99.77 51 |
|
SteuartSystems-ACMMP | | | 99.20 14 | 99.51 9 | 98.83 24 | 99.66 15 | 99.66 14 | 99.71 3 | 98.12 25 | 99.14 55 | 96.62 31 | 99.16 19 | 99.98 1 | 99.12 44 | 99.63 3 | 99.19 20 | 99.78 28 | 99.83 22 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 99.23 13 | 99.28 26 | 99.17 4 | 99.65 17 | 99.34 76 | 99.46 23 | 98.21 17 | 99.28 35 | 98.47 6 | 98.89 37 | 99.94 24 | 99.50 17 | 99.42 16 | 98.61 51 | 99.73 49 | 99.52 125 |
|
DPE-MVS | | | 99.39 3 | 99.55 4 | 99.20 3 | 99.63 18 | 99.71 8 | 99.66 6 | 98.33 5 | 99.29 34 | 98.40 9 | 99.64 4 | 99.98 1 | 99.31 31 | 99.56 8 | 98.96 31 | 99.85 3 | 99.70 86 |
|
MP-MVS | | | 99.07 21 | 99.36 21 | 98.74 25 | 99.63 18 | 99.57 45 | 99.66 6 | 98.25 13 | 99.00 74 | 95.62 41 | 98.97 30 | 99.94 24 | 99.54 15 | 99.51 11 | 98.79 45 | 99.71 65 | 99.73 70 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
NCCC | | | 99.05 23 | 99.08 37 | 99.02 16 | 99.62 20 | 99.38 68 | 99.43 27 | 98.21 17 | 99.36 27 | 97.66 20 | 97.79 74 | 99.90 30 | 99.45 22 | 99.17 27 | 98.43 61 | 99.77 33 | 99.51 129 |
|
CP-MVS | | | 99.27 9 | 99.44 15 | 99.08 11 | 99.62 20 | 99.58 43 | 99.53 17 | 98.16 19 | 99.21 46 | 97.79 18 | 99.15 20 | 99.96 11 | 99.59 10 | 99.54 10 | 98.86 39 | 99.78 28 | 99.74 66 |
|
AdaColmap | | | 99.06 22 | 98.98 47 | 99.15 6 | 99.60 22 | 99.30 82 | 99.38 29 | 98.16 19 | 99.02 72 | 98.55 5 | 98.71 47 | 99.57 52 | 99.58 13 | 99.09 32 | 97.84 95 | 99.64 106 | 99.36 143 |
|
CPTT-MVS | | | 99.14 17 | 99.20 31 | 99.06 13 | 99.58 23 | 99.53 49 | 99.45 24 | 97.80 34 | 99.19 49 | 98.32 10 | 98.58 50 | 99.95 16 | 99.60 7 | 99.28 24 | 98.20 78 | 99.64 106 | 99.69 90 |
|
QAPM | | | 98.62 38 | 99.04 43 | 98.13 36 | 99.57 24 | 99.48 56 | 99.17 37 | 94.78 53 | 99.57 9 | 96.16 36 | 96.73 99 | 99.80 41 | 99.33 29 | 98.79 53 | 99.29 13 | 99.75 37 | 99.64 107 |
|
3Dnovator | | 96.92 7 | 98.67 35 | 99.05 40 | 98.23 35 | 99.57 24 | 99.45 60 | 99.11 41 | 94.66 56 | 99.69 3 | 96.80 30 | 96.55 108 | 99.61 49 | 99.40 25 | 98.87 49 | 99.49 3 | 99.85 3 | 99.66 100 |
|
DeepC-MVS_fast | | 98.34 1 | 99.17 15 | 99.45 12 | 98.85 22 | 99.55 26 | 99.37 70 | 99.64 8 | 98.05 29 | 99.53 13 | 96.58 32 | 98.93 33 | 99.92 27 | 99.49 19 | 99.46 13 | 99.32 10 | 99.80 24 | 99.64 107 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | | | | 99.53 27 | | | | | | | 99.89 32 | | | | | |
|
3Dnovator+ | | 96.92 7 | 98.71 34 | 99.05 40 | 98.32 31 | 99.53 27 | 99.34 76 | 99.06 45 | 94.61 57 | 99.65 4 | 97.49 21 | 96.75 98 | 99.86 35 | 99.44 23 | 98.78 54 | 99.30 11 | 99.81 16 | 99.67 96 |
|
MSLP-MVS++ | | | 99.15 16 | 99.24 29 | 99.04 14 | 99.52 29 | 99.49 55 | 99.09 43 | 98.07 27 | 99.37 25 | 98.47 6 | 97.79 74 | 99.89 32 | 99.50 17 | 98.93 42 | 99.45 4 | 99.61 113 | 99.76 55 |
|
OpenMVS | | 96.23 11 | 97.95 54 | 98.45 63 | 97.35 49 | 99.52 29 | 99.42 65 | 98.91 52 | 94.61 57 | 98.87 84 | 92.24 101 | 94.61 134 | 99.05 58 | 99.10 46 | 98.64 64 | 99.05 24 | 99.74 42 | 99.51 129 |
|
PLC | | 97.93 2 | 99.02 26 | 98.94 48 | 99.11 9 | 99.46 31 | 99.24 87 | 99.06 45 | 97.96 31 | 99.31 31 | 99.16 1 | 97.90 72 | 99.79 43 | 99.36 27 | 98.71 60 | 98.12 82 | 99.65 102 | 99.52 125 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MVS_111021_HR | | | 98.59 39 | 99.36 21 | 97.68 45 | 99.42 32 | 99.61 34 | 98.14 81 | 94.81 52 | 99.31 31 | 95.00 52 | 99.51 7 | 99.79 43 | 99.00 53 | 98.94 41 | 98.83 41 | 99.69 76 | 99.57 119 |
|
OMC-MVS | | | 98.84 30 | 99.01 46 | 98.65 27 | 99.39 33 | 99.23 88 | 99.22 34 | 96.70 39 | 99.40 22 | 97.77 19 | 97.89 73 | 99.80 41 | 99.21 35 | 99.02 37 | 98.65 49 | 99.57 135 | 99.07 160 |
|
TSAR-MVS + ACMM | | | 98.77 31 | 99.45 12 | 97.98 41 | 99.37 34 | 99.46 58 | 99.44 26 | 98.13 24 | 99.65 4 | 92.30 99 | 98.91 35 | 99.95 16 | 99.05 49 | 99.42 16 | 98.95 32 | 99.58 131 | 99.82 23 |
|
MVS_111021_LR | | | 98.67 35 | 99.41 18 | 97.81 44 | 99.37 34 | 99.53 49 | 98.51 63 | 95.52 45 | 99.27 37 | 94.85 54 | 99.56 6 | 99.69 47 | 99.04 50 | 99.36 19 | 98.88 37 | 99.60 121 | 99.58 114 |
|
train_agg | | | 98.73 33 | 99.11 35 | 98.28 33 | 99.36 36 | 99.35 74 | 99.48 22 | 97.96 31 | 98.83 92 | 93.86 76 | 98.70 48 | 99.86 35 | 99.44 23 | 99.08 34 | 98.38 64 | 99.61 113 | 99.58 114 |
|
ACMMP | | | 98.74 32 | 99.03 44 | 98.40 30 | 99.36 36 | 99.64 21 | 99.20 35 | 97.75 35 | 98.82 94 | 95.24 48 | 98.85 38 | 99.87 34 | 99.17 41 | 98.74 59 | 97.50 108 | 99.71 65 | 99.76 55 |
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 |
MAR-MVS | | | 97.71 60 | 98.04 80 | 97.32 50 | 99.35 38 | 98.91 104 | 97.65 97 | 91.68 102 | 98.00 139 | 97.01 28 | 97.72 78 | 94.83 106 | 98.85 62 | 98.44 80 | 98.86 39 | 99.41 160 | 99.52 125 |
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 |
abl_6 | | | | | 98.09 37 | 99.33 39 | 99.22 89 | 98.79 56 | 94.96 51 | 98.52 118 | 97.00 29 | 97.30 85 | 99.86 35 | 98.76 63 | | | 99.69 76 | 99.41 138 |
|
CDPH-MVS | | | 98.41 41 | 99.10 36 | 97.61 47 | 99.32 40 | 99.36 71 | 99.49 20 | 96.15 42 | 98.82 94 | 91.82 103 | 98.41 57 | 99.66 48 | 99.10 46 | 98.93 42 | 98.97 30 | 99.75 37 | 99.58 114 |
|
CNLPA | | | 99.03 25 | 99.05 40 | 99.01 17 | 99.27 41 | 99.22 89 | 99.03 47 | 97.98 30 | 99.34 29 | 99.00 3 | 98.25 63 | 99.71 46 | 99.31 31 | 98.80 52 | 98.82 42 | 99.48 150 | 99.17 153 |
|
MSDG | | | 98.27 46 | 98.29 67 | 98.24 34 | 99.20 42 | 99.22 89 | 99.20 35 | 97.82 33 | 99.37 25 | 94.43 63 | 95.90 119 | 97.31 77 | 99.12 44 | 98.76 56 | 98.35 67 | 99.67 93 | 99.14 157 |
|
PHI-MVS | | | 99.08 20 | 99.43 17 | 98.67 26 | 99.15 43 | 99.59 40 | 99.11 41 | 97.35 37 | 99.14 55 | 97.30 24 | 99.44 10 | 99.96 11 | 99.32 30 | 98.89 47 | 99.39 7 | 99.79 25 | 99.58 114 |
|
PatchMatch-RL | | | 97.77 58 | 98.25 68 | 97.21 55 | 99.11 44 | 99.25 85 | 97.06 120 | 94.09 65 | 98.72 107 | 95.14 50 | 98.47 55 | 96.29 88 | 98.43 77 | 98.65 63 | 97.44 114 | 99.45 154 | 98.94 163 |
|
TAPA-MVS | | 97.53 5 | 98.41 41 | 98.84 53 | 97.91 42 | 99.08 45 | 99.33 79 | 99.15 38 | 97.13 38 | 99.34 29 | 93.20 85 | 97.75 76 | 99.19 55 | 99.20 36 | 98.66 62 | 98.13 81 | 99.66 98 | 99.48 133 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
EPNet | | | 98.05 52 | 98.86 51 | 97.10 57 | 99.02 46 | 99.43 64 | 98.47 64 | 94.73 54 | 99.05 69 | 95.62 41 | 98.93 33 | 97.62 75 | 95.48 157 | 98.59 71 | 98.55 54 | 99.29 169 | 99.84 18 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet_dtu | | | 96.30 102 | 98.53 60 | 93.70 124 | 98.97 47 | 98.24 149 | 97.36 104 | 94.23 64 | 98.85 87 | 79.18 175 | 99.19 17 | 98.47 64 | 94.09 178 | 97.89 112 | 98.21 77 | 98.39 184 | 98.85 169 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
COLMAP_ROB | | 96.15 12 | 97.78 57 | 98.17 74 | 97.32 50 | 98.84 48 | 99.45 60 | 99.28 32 | 95.43 46 | 99.48 17 | 91.80 104 | 94.83 133 | 98.36 66 | 98.90 59 | 98.09 95 | 97.85 94 | 99.68 85 | 99.15 154 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DeepPCF-MVS | | 97.74 3 | 98.34 43 | 99.46 11 | 97.04 60 | 98.82 49 | 99.33 79 | 96.28 136 | 97.47 36 | 99.58 8 | 94.70 57 | 98.99 29 | 99.85 38 | 97.24 109 | 99.55 9 | 99.34 9 | 97.73 192 | 99.56 120 |
|
SD-MVS | | | 99.25 11 | 99.50 10 | 98.96 18 | 98.79 50 | 99.55 47 | 99.33 31 | 98.29 10 | 99.75 1 | 97.96 16 | 99.15 20 | 99.95 16 | 99.61 6 | 99.17 27 | 99.06 23 | 99.81 16 | 99.84 18 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
TSAR-MVS + MP. | | | 99.27 9 | 99.57 3 | 98.92 20 | 98.78 51 | 99.53 49 | 99.72 2 | 98.11 26 | 99.73 2 | 97.43 22 | 99.15 20 | 99.96 11 | 99.59 10 | 99.73 1 | 99.07 22 | 99.88 1 | 99.82 23 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
DPM-MVS | | | 98.31 45 | 98.53 60 | 98.05 38 | 98.76 52 | 98.77 112 | 99.13 39 | 98.07 27 | 99.10 60 | 94.27 69 | 96.70 100 | 99.84 39 | 98.70 65 | 97.90 111 | 98.11 83 | 99.40 162 | 99.28 146 |
|
PCF-MVS | | 97.50 6 | 98.18 48 | 98.35 66 | 97.99 40 | 98.65 53 | 99.36 71 | 98.94 50 | 98.14 23 | 98.59 111 | 93.62 80 | 96.61 104 | 99.76 45 | 99.03 51 | 97.77 118 | 97.45 113 | 99.57 135 | 98.89 168 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DeepC-MVS | | 97.63 4 | 98.33 44 | 98.57 58 | 98.04 39 | 98.62 54 | 99.65 16 | 99.45 24 | 98.15 21 | 99.51 16 | 92.80 92 | 95.74 123 | 96.44 86 | 99.46 21 | 99.37 18 | 99.50 2 | 99.78 28 | 99.81 28 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 98.46 40 | 99.16 32 | 97.64 46 | 98.48 55 | 99.64 21 | 99.35 30 | 94.71 55 | 99.53 13 | 95.17 49 | 97.63 80 | 99.59 50 | 98.38 78 | 98.88 48 | 98.99 29 | 99.74 42 | 99.86 15 |
|
LS3D | | | 97.79 56 | 98.25 68 | 97.26 54 | 98.40 56 | 99.63 24 | 99.53 17 | 98.63 1 | 99.25 42 | 88.13 120 | 96.93 95 | 94.14 116 | 99.19 37 | 99.14 29 | 99.23 17 | 99.69 76 | 99.42 137 |
|
CHOSEN 280x420 | | | 97.99 53 | 99.24 29 | 96.53 77 | 98.34 57 | 99.61 34 | 98.36 72 | 89.80 136 | 99.27 37 | 95.08 51 | 99.81 1 | 98.58 62 | 98.64 69 | 99.02 37 | 98.92 34 | 98.93 178 | 99.48 133 |
|
DELS-MVS | | | 98.19 47 | 98.77 55 | 97.52 48 | 98.29 58 | 99.71 8 | 99.12 40 | 94.58 60 | 98.80 97 | 95.38 46 | 96.24 113 | 98.24 68 | 97.92 91 | 99.06 35 | 99.52 1 | 99.82 10 | 99.79 39 |
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 |
RPSCF | | | 97.61 63 | 98.16 75 | 96.96 68 | 98.10 59 | 99.00 97 | 98.84 54 | 93.76 73 | 99.45 18 | 94.78 56 | 99.39 11 | 99.31 54 | 98.53 75 | 96.61 152 | 95.43 162 | 97.74 190 | 97.93 184 |
|
PVSNet_BlendedMVS | | | 97.51 67 | 97.71 90 | 97.28 52 | 98.06 60 | 99.61 34 | 97.31 106 | 95.02 49 | 99.08 63 | 95.51 43 | 98.05 67 | 90.11 136 | 98.07 86 | 98.91 45 | 98.40 62 | 99.72 55 | 99.78 44 |
|
PVSNet_Blended | | | 97.51 67 | 97.71 90 | 97.28 52 | 98.06 60 | 99.61 34 | 97.31 106 | 95.02 49 | 99.08 63 | 95.51 43 | 98.05 67 | 90.11 136 | 98.07 86 | 98.91 45 | 98.40 62 | 99.72 55 | 99.78 44 |
|
MVS_0304 | | | 98.14 49 | 99.03 44 | 97.10 57 | 98.05 62 | 99.63 24 | 99.27 33 | 94.33 62 | 99.63 6 | 93.06 88 | 97.32 84 | 99.05 58 | 98.09 85 | 98.82 51 | 98.87 38 | 99.81 16 | 99.89 6 |
|
CHOSEN 1792x2688 | | | 96.41 99 | 96.99 116 | 95.74 96 | 98.01 63 | 99.72 5 | 97.70 96 | 90.78 121 | 99.13 58 | 90.03 113 | 87.35 186 | 95.36 100 | 98.33 79 | 98.59 71 | 98.91 36 | 99.59 127 | 99.87 12 |
|
HyFIR lowres test | | | 95.99 109 | 96.56 124 | 95.32 101 | 97.99 64 | 99.65 16 | 96.54 129 | 88.86 145 | 98.44 121 | 89.77 116 | 84.14 195 | 97.05 81 | 99.03 51 | 98.55 73 | 98.19 79 | 99.73 49 | 99.86 15 |
|
OPM-MVS | | | 96.22 104 | 95.85 144 | 96.65 74 | 97.75 65 | 98.54 131 | 99.00 49 | 95.53 44 | 96.88 171 | 89.88 114 | 95.95 118 | 86.46 157 | 98.07 86 | 97.65 126 | 96.63 131 | 99.67 93 | 98.83 170 |
|
tmp_tt | | | | | 82.25 200 | 97.73 66 | 88.71 208 | 80.18 207 | 68.65 210 | 99.15 52 | 86.98 128 | 99.47 8 | 85.31 166 | 68.35 208 | 87.51 202 | 83.81 204 | 91.64 207 | |
|
TSAR-MVS + COLMAP | | | 96.79 86 | 96.55 125 | 97.06 59 | 97.70 67 | 98.46 136 | 99.07 44 | 96.23 41 | 99.38 23 | 91.32 107 | 98.80 40 | 85.61 163 | 98.69 67 | 97.64 127 | 96.92 124 | 99.37 164 | 99.06 161 |
|
PVSNet_Blended_VisFu | | | 97.41 70 | 98.49 62 | 96.15 85 | 97.49 68 | 99.76 1 | 96.02 140 | 93.75 75 | 99.26 40 | 93.38 84 | 93.73 142 | 99.35 53 | 96.47 132 | 98.96 39 | 98.46 58 | 99.77 33 | 99.90 3 |
|
MS-PatchMatch | | | 95.99 109 | 97.26 110 | 94.51 110 | 97.46 69 | 98.76 115 | 97.27 108 | 86.97 163 | 99.09 61 | 89.83 115 | 93.51 145 | 97.78 72 | 96.18 138 | 97.53 131 | 95.71 159 | 99.35 165 | 98.41 176 |
|
XVS | | | | | | 97.42 70 | 99.62 28 | 98.59 61 | | | 93.81 77 | | 99.95 16 | | | | 99.69 76 | |
|
X-MVStestdata | | | | | | 97.42 70 | 99.62 28 | 98.59 61 | | | 93.81 77 | | 99.95 16 | | | | 99.69 76 | |
|
LGP-MVS_train | | | 96.23 103 | 96.89 118 | 95.46 100 | 97.32 72 | 98.77 112 | 98.81 55 | 93.60 78 | 98.58 112 | 85.52 137 | 99.08 26 | 86.67 154 | 97.83 98 | 97.87 113 | 97.51 107 | 99.69 76 | 99.73 70 |
|
CMPMVS | | 70.31 18 | 90.74 187 | 91.06 194 | 90.36 180 | 97.32 72 | 97.43 184 | 92.97 184 | 87.82 159 | 93.50 201 | 75.34 191 | 83.27 197 | 84.90 169 | 92.19 193 | 92.64 195 | 91.21 200 | 96.50 203 | 94.46 202 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
HQP-MVS | | | 96.37 100 | 96.58 123 | 96.13 86 | 97.31 74 | 98.44 138 | 98.45 65 | 95.22 47 | 98.86 85 | 88.58 118 | 98.33 61 | 87.00 148 | 97.67 100 | 97.23 140 | 96.56 134 | 99.56 138 | 99.62 110 |
|
ACMM | | 96.26 9 | 96.67 94 | 96.69 122 | 96.66 73 | 97.29 75 | 98.46 136 | 96.48 132 | 95.09 48 | 99.21 46 | 93.19 86 | 98.78 42 | 86.73 153 | 98.17 80 | 97.84 115 | 96.32 140 | 99.74 42 | 99.49 132 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UA-Net | | | 97.13 77 | 99.14 33 | 94.78 106 | 97.21 76 | 99.38 68 | 97.56 98 | 92.04 95 | 98.48 119 | 88.03 121 | 98.39 59 | 99.91 29 | 94.03 179 | 99.33 22 | 99.23 17 | 99.81 16 | 99.25 149 |
|
UGNet | | | 97.66 62 | 99.07 39 | 96.01 90 | 97.19 77 | 99.65 16 | 97.09 118 | 93.39 80 | 99.35 28 | 94.40 65 | 98.79 41 | 99.59 50 | 94.24 176 | 98.04 103 | 98.29 74 | 99.73 49 | 99.80 31 |
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 |
TSAR-MVS + GP. | | | 98.66 37 | 99.36 21 | 97.85 43 | 97.16 78 | 99.46 58 | 99.03 47 | 94.59 59 | 99.09 61 | 97.19 26 | 99.73 3 | 99.95 16 | 99.39 26 | 98.95 40 | 98.69 47 | 99.75 37 | 99.65 103 |
|
CANet_DTU | | | 96.64 95 | 99.08 37 | 93.81 120 | 97.10 79 | 99.42 65 | 98.85 53 | 90.01 130 | 99.31 31 | 79.98 171 | 99.78 2 | 99.10 57 | 97.42 106 | 98.35 82 | 98.05 86 | 99.47 152 | 99.53 122 |
|
IB-MVS | | 93.96 15 | 95.02 127 | 96.44 134 | 93.36 134 | 97.05 80 | 99.28 83 | 90.43 194 | 93.39 80 | 98.02 138 | 96.02 37 | 94.92 132 | 92.07 129 | 83.52 201 | 95.38 178 | 95.82 156 | 99.72 55 | 99.59 113 |
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 |
ACMP | | 96.25 10 | 96.62 97 | 96.72 121 | 96.50 80 | 96.96 81 | 98.75 116 | 97.80 91 | 94.30 63 | 98.85 87 | 93.12 87 | 98.78 42 | 86.61 155 | 97.23 110 | 97.73 121 | 96.61 132 | 99.62 111 | 99.71 84 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 95.42 14 | 95.27 124 | 95.96 140 | 94.45 111 | 96.83 82 | 98.78 111 | 94.72 167 | 91.67 103 | 98.95 77 | 86.82 130 | 96.42 110 | 83.67 174 | 97.00 114 | 97.48 133 | 96.68 129 | 99.69 76 | 99.76 55 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 96.74 89 | 96.51 128 | 97.01 65 | 96.71 83 | 98.62 125 | 98.73 57 | 94.38 61 | 98.94 80 | 94.46 62 | 97.33 83 | 87.03 147 | 98.07 86 | 97.20 142 | 96.87 125 | 99.72 55 | 99.54 121 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TDRefinement | | | 93.04 163 | 93.57 179 | 92.41 143 | 96.58 84 | 98.77 112 | 97.78 93 | 91.96 98 | 98.12 135 | 80.84 164 | 89.13 173 | 79.87 196 | 87.78 197 | 96.44 157 | 94.50 183 | 99.54 144 | 98.15 180 |
|
Anonymous202405211 | | | | 97.40 102 | | 96.45 85 | 99.54 48 | 98.08 86 | 93.79 72 | 98.24 130 | | 93.55 143 | 94.41 112 | 98.88 61 | 98.04 103 | 98.24 76 | 99.75 37 | 99.76 55 |
|
DCV-MVSNet | | | 97.56 65 | 98.36 65 | 96.62 76 | 96.44 86 | 98.36 145 | 98.37 70 | 91.73 101 | 99.11 59 | 94.80 55 | 98.36 60 | 96.28 89 | 98.60 72 | 98.12 92 | 98.44 59 | 99.76 35 | 99.87 12 |
|
ACMH+ | | 95.51 13 | 95.40 119 | 96.00 138 | 94.70 107 | 96.33 87 | 98.79 109 | 96.79 124 | 91.32 111 | 98.77 103 | 87.18 127 | 95.60 127 | 85.46 164 | 96.97 115 | 97.15 143 | 96.59 133 | 99.59 127 | 99.65 103 |
|
Anonymous20231211 | | | 97.10 78 | 97.06 114 | 97.14 56 | 96.32 88 | 99.52 52 | 98.16 80 | 93.76 73 | 98.84 91 | 95.98 38 | 90.92 159 | 94.58 111 | 98.90 59 | 97.72 122 | 98.10 84 | 99.71 65 | 99.75 62 |
|
thres100view900 | | | 96.72 90 | 96.47 131 | 97.00 66 | 96.31 89 | 99.52 52 | 98.28 76 | 94.01 66 | 97.35 158 | 94.52 59 | 95.90 119 | 86.93 149 | 99.09 48 | 98.07 98 | 97.87 93 | 99.81 16 | 99.63 109 |
|
tfpn200view9 | | | 96.75 88 | 96.51 128 | 97.03 61 | 96.31 89 | 99.67 11 | 98.41 66 | 93.99 68 | 97.35 158 | 94.52 59 | 95.90 119 | 86.93 149 | 99.14 43 | 98.26 85 | 97.80 97 | 99.82 10 | 99.70 86 |
|
thres200 | | | 96.76 87 | 96.53 126 | 97.03 61 | 96.31 89 | 99.67 11 | 98.37 70 | 93.99 68 | 97.68 155 | 94.49 61 | 95.83 122 | 86.77 152 | 99.18 39 | 98.26 85 | 97.82 96 | 99.82 10 | 99.66 100 |
|
thres600view7 | | | 96.69 92 | 96.43 135 | 97.00 66 | 96.28 92 | 99.67 11 | 98.41 66 | 93.99 68 | 97.85 149 | 94.29 67 | 95.96 117 | 85.91 161 | 99.19 37 | 98.26 85 | 97.63 102 | 99.82 10 | 99.73 70 |
|
thres400 | | | 96.71 91 | 96.45 133 | 97.02 63 | 96.28 92 | 99.63 24 | 98.41 66 | 94.00 67 | 97.82 150 | 94.42 64 | 95.74 123 | 86.26 158 | 99.18 39 | 98.20 89 | 97.79 98 | 99.81 16 | 99.70 86 |
|
baseline1 | | | 97.58 64 | 98.05 79 | 97.02 63 | 96.21 94 | 99.45 60 | 97.71 95 | 93.71 77 | 98.47 120 | 95.75 40 | 98.78 42 | 93.20 125 | 98.91 58 | 98.52 75 | 98.44 59 | 99.81 16 | 99.53 122 |
|
canonicalmvs | | | 97.31 72 | 97.81 89 | 96.72 71 | 96.20 95 | 99.45 60 | 98.21 78 | 91.60 104 | 99.22 44 | 95.39 45 | 98.48 54 | 90.95 133 | 99.16 42 | 97.66 124 | 99.05 24 | 99.76 35 | 99.90 3 |
|
IS_MVSNet | | | 97.86 55 | 98.86 51 | 96.68 72 | 96.02 96 | 99.72 5 | 98.35 73 | 93.37 82 | 98.75 106 | 94.01 70 | 96.88 97 | 98.40 65 | 98.48 76 | 99.09 32 | 99.42 5 | 99.83 8 | 99.80 31 |
|
USDC | | | 94.26 143 | 94.83 155 | 93.59 126 | 96.02 96 | 98.44 138 | 97.84 89 | 88.65 149 | 98.86 85 | 82.73 156 | 94.02 139 | 80.56 190 | 96.76 121 | 97.28 139 | 96.15 147 | 99.55 140 | 98.50 174 |
|
FC-MVSNet-train | | | 97.04 79 | 97.91 86 | 96.03 89 | 96.00 98 | 98.41 141 | 96.53 131 | 93.42 79 | 99.04 71 | 93.02 89 | 98.03 69 | 94.32 114 | 97.47 105 | 97.93 109 | 97.77 99 | 99.75 37 | 99.88 10 |
|
Vis-MVSNet (Re-imp) | | | 97.40 71 | 98.89 50 | 95.66 98 | 95.99 99 | 99.62 28 | 97.82 90 | 93.22 85 | 98.82 94 | 91.40 106 | 96.94 94 | 98.56 63 | 95.70 149 | 99.14 29 | 99.41 6 | 99.79 25 | 99.75 62 |
|
MVSTER | | | 97.16 76 | 97.71 90 | 96.52 78 | 95.97 100 | 98.48 134 | 98.63 60 | 92.10 94 | 98.68 108 | 95.96 39 | 99.23 16 | 91.79 130 | 96.87 118 | 98.76 56 | 97.37 117 | 99.57 135 | 99.68 95 |
|
baseline | | | 97.45 69 | 98.70 57 | 95.99 91 | 95.89 101 | 99.36 71 | 98.29 75 | 91.37 110 | 99.21 46 | 92.99 90 | 98.40 58 | 96.87 83 | 97.96 90 | 98.60 69 | 98.60 52 | 99.42 159 | 99.86 15 |
|
TinyColmap | | | 94.00 147 | 94.35 163 | 93.60 125 | 95.89 101 | 98.26 147 | 97.49 101 | 88.82 146 | 98.56 114 | 83.21 150 | 91.28 158 | 80.48 192 | 96.68 124 | 97.34 137 | 96.26 143 | 99.53 146 | 98.24 179 |
|
DWT-MVSNet_training | | | 95.38 120 | 95.05 151 | 95.78 93 | 95.86 103 | 98.88 105 | 97.55 99 | 90.09 129 | 98.23 131 | 96.49 35 | 97.62 81 | 86.92 151 | 97.16 111 | 92.03 199 | 94.12 185 | 97.52 195 | 97.50 187 |
|
EPMVS | | | 95.05 126 | 96.86 120 | 92.94 140 | 95.84 104 | 98.96 102 | 96.68 125 | 79.87 191 | 99.05 69 | 90.15 111 | 97.12 91 | 95.99 95 | 97.49 104 | 95.17 182 | 94.75 180 | 97.59 194 | 96.96 195 |
|
CS-MVS | | | 98.06 51 | 99.12 34 | 96.82 69 | 95.83 105 | 99.66 14 | 98.93 51 | 93.12 88 | 98.95 77 | 94.29 67 | 98.55 51 | 99.05 58 | 98.94 55 | 99.05 36 | 98.78 46 | 99.83 8 | 99.80 31 |
|
PMMVS | | | 97.52 66 | 98.39 64 | 96.51 79 | 95.82 106 | 98.73 119 | 97.80 91 | 93.05 90 | 98.76 104 | 94.39 66 | 99.07 27 | 97.03 82 | 98.55 73 | 98.31 84 | 97.61 103 | 99.43 157 | 99.21 152 |
|
diffmvs | | | 96.83 85 | 97.33 105 | 96.25 83 | 95.76 107 | 99.34 76 | 98.06 87 | 93.22 85 | 99.43 20 | 92.30 99 | 96.90 96 | 89.83 140 | 98.55 73 | 98.00 106 | 98.14 80 | 99.64 106 | 99.70 86 |
|
MVS_Test | | | 97.30 73 | 98.54 59 | 95.87 92 | 95.74 108 | 99.28 83 | 98.19 79 | 91.40 109 | 99.18 50 | 91.59 105 | 98.17 65 | 96.18 91 | 98.63 70 | 98.61 67 | 98.55 54 | 99.66 98 | 99.78 44 |
|
EIA-MVS | | | 97.70 61 | 98.78 54 | 96.44 81 | 95.72 109 | 99.65 16 | 98.14 81 | 93.72 76 | 98.30 126 | 92.31 98 | 98.63 49 | 97.90 70 | 98.97 54 | 98.92 44 | 98.30 73 | 99.78 28 | 99.80 31 |
|
casdiffmvs | | | 96.93 83 | 97.43 101 | 96.34 82 | 95.70 110 | 99.50 54 | 97.75 94 | 93.22 85 | 98.98 76 | 92.64 93 | 94.97 130 | 91.71 131 | 98.93 56 | 98.62 66 | 98.52 57 | 99.82 10 | 99.72 81 |
|
tpmrst | | | 93.86 152 | 95.88 142 | 91.50 162 | 95.69 111 | 98.62 125 | 95.64 146 | 79.41 194 | 98.80 97 | 83.76 146 | 95.63 126 | 96.13 92 | 97.25 108 | 92.92 193 | 92.31 194 | 97.27 198 | 96.74 196 |
|
ADS-MVSNet | | | 94.65 135 | 97.04 115 | 91.88 158 | 95.68 112 | 98.99 99 | 95.89 141 | 79.03 198 | 99.15 52 | 85.81 135 | 96.96 93 | 98.21 69 | 97.10 112 | 94.48 189 | 94.24 184 | 97.74 190 | 97.21 191 |
|
EPP-MVSNet | | | 97.75 59 | 98.71 56 | 96.63 75 | 95.68 112 | 99.56 46 | 97.51 100 | 93.10 89 | 99.22 44 | 94.99 53 | 97.18 90 | 97.30 78 | 98.65 68 | 98.83 50 | 98.93 33 | 99.84 5 | 99.92 1 |
|
ETV-MVS | | | 98.11 50 | 99.25 28 | 96.77 70 | 95.66 114 | 99.62 28 | 98.38 69 | 93.91 71 | 99.41 21 | 93.87 75 | 98.85 38 | 99.17 56 | 98.92 57 | 99.12 31 | 98.80 43 | 99.86 2 | 99.81 28 |
|
DI_MVS_plusplus_trai | | | 96.90 84 | 97.49 96 | 96.21 84 | 95.61 115 | 99.40 67 | 98.72 58 | 92.11 93 | 99.14 55 | 92.98 91 | 93.08 153 | 95.14 102 | 98.13 84 | 98.05 102 | 97.91 91 | 99.74 42 | 99.73 70 |
|
thisisatest0530 | | | 97.23 74 | 98.25 68 | 96.05 87 | 95.60 116 | 99.59 40 | 96.96 122 | 93.23 83 | 99.17 51 | 92.60 95 | 98.75 45 | 96.19 90 | 98.17 80 | 98.19 90 | 96.10 148 | 99.72 55 | 99.77 51 |
|
tttt0517 | | | 97.23 74 | 98.24 71 | 96.04 88 | 95.60 116 | 99.60 38 | 96.94 123 | 93.23 83 | 99.15 52 | 92.56 96 | 98.74 46 | 96.12 93 | 98.17 80 | 98.21 88 | 96.10 148 | 99.73 49 | 99.78 44 |
|
SCA | | | 94.95 128 | 97.44 100 | 92.04 150 | 95.55 118 | 99.16 92 | 96.26 137 | 79.30 195 | 99.02 72 | 85.73 136 | 98.18 64 | 97.13 80 | 97.69 99 | 96.03 171 | 94.91 175 | 97.69 193 | 97.65 186 |
|
dps | | | 94.63 136 | 95.31 150 | 93.84 119 | 95.53 119 | 98.71 120 | 96.54 129 | 80.12 190 | 97.81 152 | 97.21 25 | 96.98 92 | 92.37 126 | 96.34 135 | 92.46 196 | 91.77 197 | 97.26 199 | 97.08 193 |
|
PatchmatchNet | | | 94.70 133 | 97.08 113 | 91.92 155 | 95.53 119 | 98.85 107 | 95.77 143 | 79.54 193 | 98.95 77 | 85.98 133 | 98.52 52 | 96.45 84 | 97.39 107 | 95.32 179 | 94.09 186 | 97.32 197 | 97.38 190 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
test-LLR | | | 95.50 117 | 97.32 106 | 93.37 133 | 95.49 121 | 98.74 117 | 96.44 134 | 90.82 119 | 98.18 132 | 82.75 154 | 96.60 105 | 94.67 109 | 95.54 155 | 98.09 95 | 96.00 150 | 99.20 172 | 98.93 164 |
|
test0.0.03 1 | | | 96.69 92 | 98.12 77 | 95.01 104 | 95.49 121 | 98.99 99 | 95.86 142 | 90.82 119 | 98.38 123 | 92.54 97 | 96.66 102 | 97.33 76 | 95.75 147 | 97.75 120 | 98.34 69 | 99.60 121 | 99.40 141 |
|
CostFormer | | | 94.25 144 | 94.88 154 | 93.51 130 | 95.43 123 | 98.34 146 | 96.21 138 | 80.64 188 | 97.94 144 | 94.01 70 | 98.30 62 | 86.20 160 | 97.52 102 | 92.71 194 | 92.69 192 | 97.23 200 | 98.02 183 |
|
MDTV_nov1_ep13 | | | 95.57 115 | 97.48 97 | 93.35 135 | 95.43 123 | 98.97 101 | 97.19 113 | 83.72 184 | 98.92 83 | 87.91 123 | 97.75 76 | 96.12 93 | 97.88 95 | 96.84 151 | 95.64 160 | 97.96 188 | 98.10 181 |
|
tpm cat1 | | | 94.06 145 | 94.90 153 | 93.06 138 | 95.42 125 | 98.52 133 | 96.64 127 | 80.67 187 | 97.82 150 | 92.63 94 | 93.39 147 | 95.00 104 | 96.06 142 | 91.36 200 | 91.58 199 | 96.98 201 | 96.66 198 |
|
Vis-MVSNet | | | 96.16 106 | 98.22 72 | 93.75 121 | 95.33 126 | 99.70 10 | 97.27 108 | 90.85 118 | 98.30 126 | 85.51 138 | 95.72 125 | 96.45 84 | 93.69 185 | 98.70 61 | 99.00 28 | 99.84 5 | 99.69 90 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CVMVSNet | | | 95.33 123 | 97.09 112 | 93.27 136 | 95.23 127 | 98.39 143 | 95.49 149 | 92.58 92 | 97.71 154 | 83.00 153 | 94.44 137 | 93.28 123 | 93.92 182 | 97.79 116 | 98.54 56 | 99.41 160 | 99.45 135 |
|
IterMVS-LS | | | 96.12 107 | 97.48 97 | 94.53 109 | 95.19 128 | 97.56 178 | 97.15 114 | 89.19 143 | 99.08 63 | 88.23 119 | 94.97 130 | 94.73 108 | 97.84 97 | 97.86 114 | 98.26 75 | 99.60 121 | 99.88 10 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 95.81 111 | 97.31 109 | 94.06 116 | 95.09 129 | 99.35 74 | 97.24 110 | 88.22 154 | 98.54 115 | 85.38 139 | 98.52 52 | 88.68 141 | 98.70 65 | 98.32 83 | 97.93 89 | 99.74 42 | 99.84 18 |
|
testgi | | | 95.67 114 | 97.48 97 | 93.56 127 | 95.07 130 | 99.00 97 | 95.33 153 | 88.47 151 | 98.80 97 | 86.90 129 | 97.30 85 | 92.33 127 | 95.97 144 | 97.66 124 | 97.91 91 | 99.60 121 | 99.38 142 |
|
RPMNet | | | 94.66 134 | 97.16 111 | 91.75 159 | 94.98 131 | 98.59 128 | 97.00 121 | 78.37 202 | 97.98 140 | 83.78 144 | 96.27 112 | 94.09 119 | 96.91 117 | 97.36 136 | 96.73 127 | 99.48 150 | 99.09 159 |
|
CR-MVSNet | | | 94.57 140 | 97.34 104 | 91.33 166 | 94.90 132 | 98.59 128 | 97.15 114 | 79.14 196 | 97.98 140 | 80.42 167 | 96.59 107 | 93.50 122 | 96.85 119 | 98.10 93 | 97.49 109 | 99.50 149 | 99.15 154 |
|
gg-mvs-nofinetune | | | 90.85 186 | 94.14 165 | 87.02 192 | 94.89 133 | 99.25 85 | 98.64 59 | 76.29 206 | 88.24 206 | 57.50 209 | 79.93 201 | 95.45 99 | 95.18 166 | 98.77 55 | 98.07 85 | 99.62 111 | 99.24 150 |
|
IterMVS-SCA-FT | | | 94.89 130 | 97.87 87 | 91.42 163 | 94.86 134 | 97.70 165 | 97.24 110 | 84.88 178 | 98.93 81 | 75.74 187 | 94.26 138 | 98.25 67 | 96.69 123 | 98.52 75 | 97.68 101 | 99.10 176 | 99.73 70 |
|
IterMVS | | | 94.81 132 | 97.71 90 | 91.42 163 | 94.83 135 | 97.63 171 | 97.38 103 | 85.08 175 | 98.93 81 | 75.67 188 | 94.02 139 | 97.64 73 | 96.66 126 | 98.45 78 | 97.60 104 | 98.90 179 | 99.72 81 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchT | | | 93.96 149 | 97.36 103 | 90.00 182 | 94.76 136 | 98.65 123 | 90.11 197 | 78.57 201 | 97.96 143 | 80.42 167 | 96.07 115 | 94.10 118 | 96.85 119 | 98.10 93 | 97.49 109 | 99.26 170 | 99.15 154 |
|
baseline2 | | | 96.36 101 | 97.82 88 | 94.65 108 | 94.60 137 | 99.09 95 | 96.45 133 | 89.63 138 | 98.36 124 | 91.29 108 | 97.60 82 | 94.13 117 | 96.37 133 | 98.45 78 | 97.70 100 | 99.54 144 | 99.41 138 |
|
CDS-MVSNet | | | 96.59 98 | 98.02 82 | 94.92 105 | 94.45 138 | 98.96 102 | 97.46 102 | 91.75 100 | 97.86 148 | 90.07 112 | 96.02 116 | 97.25 79 | 96.21 136 | 98.04 103 | 98.38 64 | 99.60 121 | 99.65 103 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm | | | 92.38 178 | 94.79 156 | 89.56 185 | 94.30 139 | 97.50 181 | 94.24 179 | 78.97 199 | 97.72 153 | 74.93 192 | 97.97 71 | 82.91 179 | 96.60 128 | 93.65 192 | 94.81 179 | 98.33 185 | 98.98 162 |
|
Fast-Effi-MVS+ | | | 95.38 120 | 96.52 127 | 94.05 117 | 94.15 140 | 99.14 94 | 97.24 110 | 86.79 164 | 98.53 116 | 87.62 125 | 94.51 135 | 87.06 146 | 98.76 63 | 98.60 69 | 98.04 87 | 99.72 55 | 99.77 51 |
|
Effi-MVS+-dtu | | | 95.74 113 | 98.04 80 | 93.06 138 | 93.92 141 | 99.16 92 | 97.90 88 | 88.16 156 | 99.07 68 | 82.02 159 | 98.02 70 | 94.32 114 | 96.74 122 | 98.53 74 | 97.56 105 | 99.61 113 | 99.62 110 |
|
UniMVSNet_ETH3D | | | 93.15 160 | 92.33 192 | 94.11 115 | 93.91 142 | 98.61 127 | 94.81 164 | 90.98 116 | 97.06 167 | 87.51 126 | 82.27 199 | 76.33 204 | 97.87 96 | 94.79 188 | 97.47 112 | 99.56 138 | 99.81 28 |
|
Fast-Effi-MVS+-dtu | | | 95.38 120 | 98.20 73 | 92.09 149 | 93.91 142 | 98.87 106 | 97.35 105 | 85.01 177 | 99.08 63 | 81.09 163 | 98.10 66 | 96.36 87 | 95.62 152 | 98.43 81 | 97.03 121 | 99.55 140 | 99.50 131 |
|
TAMVS | | | 95.53 116 | 96.50 130 | 94.39 112 | 93.86 144 | 99.03 96 | 96.67 126 | 89.55 140 | 97.33 160 | 90.64 110 | 93.02 154 | 91.58 132 | 96.21 136 | 97.72 122 | 97.43 115 | 99.43 157 | 99.36 143 |
|
GBi-Net | | | 96.98 81 | 98.00 83 | 95.78 93 | 93.81 145 | 97.98 154 | 98.09 83 | 91.32 111 | 98.80 97 | 93.92 72 | 97.21 87 | 95.94 96 | 97.89 92 | 98.07 98 | 98.34 69 | 99.68 85 | 99.67 96 |
|
test1 | | | 96.98 81 | 98.00 83 | 95.78 93 | 93.81 145 | 97.98 154 | 98.09 83 | 91.32 111 | 98.80 97 | 93.92 72 | 97.21 87 | 95.94 96 | 97.89 92 | 98.07 98 | 98.34 69 | 99.68 85 | 99.67 96 |
|
FMVSNet2 | | | 96.64 95 | 97.50 95 | 95.63 99 | 93.81 145 | 97.98 154 | 98.09 83 | 90.87 117 | 98.99 75 | 93.48 82 | 93.17 150 | 95.25 101 | 97.89 92 | 98.63 65 | 98.80 43 | 99.68 85 | 99.67 96 |
|
MVS-HIRNet | | | 92.51 173 | 95.97 139 | 88.48 189 | 93.73 148 | 98.37 144 | 90.33 195 | 75.36 208 | 98.32 125 | 77.78 181 | 89.15 172 | 94.87 105 | 95.14 167 | 97.62 128 | 96.39 138 | 98.51 181 | 97.11 192 |
|
GA-MVS | | | 93.93 150 | 96.31 137 | 91.16 170 | 93.61 149 | 98.79 109 | 95.39 152 | 90.69 124 | 98.25 129 | 73.28 196 | 96.15 114 | 88.42 142 | 94.39 174 | 97.76 119 | 95.35 164 | 99.58 131 | 99.45 135 |
|
FC-MVSNet-test | | | 96.07 108 | 97.94 85 | 93.89 118 | 93.60 150 | 98.67 122 | 96.62 128 | 90.30 128 | 98.76 104 | 88.62 117 | 95.57 128 | 97.63 74 | 94.48 172 | 97.97 107 | 97.48 111 | 99.71 65 | 99.52 125 |
|
FMVSNet3 | | | 97.02 80 | 98.12 77 | 95.73 97 | 93.59 151 | 97.98 154 | 98.34 74 | 91.32 111 | 98.80 97 | 93.92 72 | 97.21 87 | 95.94 96 | 97.63 101 | 98.61 67 | 98.62 50 | 99.61 113 | 99.65 103 |
|
FMVSNet1 | | | 95.77 112 | 96.41 136 | 95.03 103 | 93.42 152 | 97.86 161 | 97.11 117 | 89.89 133 | 98.53 116 | 92.00 102 | 89.17 171 | 93.23 124 | 98.15 83 | 98.07 98 | 98.34 69 | 99.61 113 | 99.69 90 |
|
tfpnnormal | | | 93.85 153 | 94.12 167 | 93.54 129 | 93.22 153 | 98.24 149 | 95.45 150 | 91.96 98 | 94.61 197 | 83.91 142 | 90.74 161 | 81.75 187 | 97.04 113 | 97.49 132 | 96.16 146 | 99.68 85 | 99.84 18 |
|
TransMVSNet (Re) | | | 93.45 156 | 94.08 168 | 92.72 142 | 92.83 154 | 97.62 174 | 94.94 158 | 91.54 107 | 95.65 194 | 83.06 152 | 88.93 174 | 83.53 175 | 94.25 175 | 97.41 134 | 97.03 121 | 99.67 93 | 98.40 178 |
|
LTVRE_ROB | | 93.20 16 | 92.84 165 | 94.92 152 | 90.43 179 | 92.83 154 | 98.63 124 | 97.08 119 | 87.87 158 | 97.91 145 | 68.42 202 | 93.54 144 | 79.46 198 | 96.62 127 | 97.55 130 | 97.40 116 | 99.74 42 | 99.92 1 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
TESTMET0.1,1 | | | 94.95 128 | 97.32 106 | 92.20 147 | 92.62 156 | 98.74 117 | 96.44 134 | 86.67 166 | 98.18 132 | 82.75 154 | 96.60 105 | 94.67 109 | 95.54 155 | 98.09 95 | 96.00 150 | 99.20 172 | 98.93 164 |
|
pm-mvs1 | | | 94.27 142 | 95.57 146 | 92.75 141 | 92.58 157 | 98.13 152 | 94.87 162 | 90.71 123 | 96.70 177 | 83.78 144 | 89.94 167 | 89.85 139 | 94.96 170 | 97.58 129 | 97.07 120 | 99.61 113 | 99.72 81 |
|
NR-MVSNet | | | 94.01 146 | 94.51 160 | 93.44 131 | 92.56 158 | 97.77 162 | 95.67 144 | 91.57 105 | 97.17 164 | 85.84 134 | 93.13 151 | 80.53 191 | 95.29 163 | 97.01 147 | 96.17 145 | 99.69 76 | 99.75 62 |
|
EG-PatchMatch MVS | | | 92.45 174 | 93.92 174 | 90.72 176 | 92.56 158 | 98.43 140 | 94.88 161 | 84.54 180 | 97.18 163 | 79.55 173 | 86.12 193 | 83.23 178 | 93.15 189 | 97.22 141 | 96.00 150 | 99.67 93 | 99.27 148 |
|
test-mter | | | 94.86 131 | 97.32 106 | 92.00 152 | 92.41 160 | 98.82 108 | 96.18 139 | 86.35 170 | 98.05 137 | 82.28 157 | 96.48 109 | 94.39 113 | 95.46 159 | 98.17 91 | 96.20 144 | 99.32 167 | 99.13 158 |
|
our_test_3 | | | | | | 92.30 161 | 97.58 176 | 90.09 198 | | | | | | | | | | |
|
pmmvs4 | | | 95.09 125 | 95.90 141 | 94.14 114 | 92.29 162 | 97.70 165 | 95.45 150 | 90.31 126 | 98.60 110 | 90.70 109 | 93.25 148 | 89.90 138 | 96.67 125 | 97.13 144 | 95.42 163 | 99.44 156 | 99.28 146 |
|
FMVSNet5 | | | 95.42 118 | 96.47 131 | 94.20 113 | 92.26 163 | 95.99 199 | 95.66 145 | 87.15 162 | 97.87 147 | 93.46 83 | 96.68 101 | 93.79 120 | 97.52 102 | 97.10 146 | 97.21 119 | 99.11 175 | 96.62 199 |
|
UniMVSNet (Re) | | | 94.58 139 | 95.34 148 | 93.71 123 | 92.25 164 | 98.08 153 | 94.97 157 | 91.29 115 | 97.03 169 | 87.94 122 | 93.97 141 | 86.25 159 | 96.07 141 | 96.27 165 | 95.97 153 | 99.72 55 | 99.79 39 |
|
SixPastTwentyTwo | | | 93.44 157 | 95.32 149 | 91.24 168 | 92.11 165 | 98.40 142 | 92.77 185 | 88.64 150 | 98.09 136 | 77.83 180 | 93.51 145 | 85.74 162 | 96.52 131 | 96.91 149 | 94.89 178 | 99.59 127 | 99.73 70 |
|
v8 | | | 92.87 164 | 93.87 176 | 91.72 161 | 92.05 166 | 97.50 181 | 94.79 165 | 88.20 155 | 96.85 173 | 80.11 170 | 90.01 166 | 82.86 181 | 95.48 157 | 95.15 183 | 94.90 176 | 99.66 98 | 99.80 31 |
|
thisisatest0515 | | | 94.61 137 | 96.89 118 | 91.95 154 | 92.00 167 | 98.47 135 | 92.01 189 | 90.73 122 | 98.18 132 | 83.96 141 | 94.51 135 | 95.13 103 | 93.38 186 | 97.38 135 | 94.74 181 | 99.61 113 | 99.79 39 |
|
WR-MVS_H | | | 93.54 155 | 94.67 158 | 92.22 145 | 91.95 168 | 97.91 159 | 94.58 173 | 88.75 147 | 96.64 178 | 83.88 143 | 90.66 163 | 85.13 167 | 94.40 173 | 96.54 156 | 95.91 155 | 99.73 49 | 99.89 6 |
|
V42 | | | 93.05 162 | 93.90 175 | 92.04 150 | 91.91 169 | 97.66 169 | 94.91 159 | 89.91 132 | 96.85 173 | 80.58 166 | 89.66 168 | 83.43 177 | 95.37 161 | 95.03 186 | 94.90 176 | 99.59 127 | 99.78 44 |
|
EU-MVSNet | | | 92.80 167 | 94.76 157 | 90.51 177 | 91.88 170 | 96.74 196 | 92.48 187 | 88.69 148 | 96.21 183 | 79.00 176 | 91.51 155 | 87.82 143 | 91.83 194 | 95.87 175 | 96.27 141 | 99.21 171 | 98.92 167 |
|
N_pmnet | | | 92.21 182 | 94.60 159 | 89.42 186 | 91.88 170 | 97.38 187 | 89.15 200 | 89.74 137 | 97.89 146 | 73.75 194 | 87.94 183 | 92.23 128 | 93.85 183 | 96.10 169 | 93.20 191 | 98.15 187 | 97.43 189 |
|
UniMVSNet_NR-MVSNet | | | 94.59 138 | 95.47 147 | 93.55 128 | 91.85 172 | 97.89 160 | 95.03 155 | 92.00 96 | 97.33 160 | 86.12 131 | 93.19 149 | 87.29 145 | 96.60 128 | 96.12 168 | 96.70 128 | 99.72 55 | 99.80 31 |
|
pmmvs6 | | | 91.90 184 | 92.53 191 | 91.17 169 | 91.81 173 | 97.63 171 | 93.23 182 | 88.37 153 | 93.43 202 | 80.61 165 | 77.32 203 | 87.47 144 | 94.12 177 | 96.58 154 | 95.72 158 | 98.88 180 | 99.53 122 |
|
v10 | | | 92.79 168 | 94.06 169 | 91.31 167 | 91.78 174 | 97.29 190 | 94.87 162 | 86.10 171 | 96.97 170 | 79.82 172 | 88.16 180 | 84.56 171 | 95.63 151 | 96.33 163 | 95.31 165 | 99.65 102 | 99.80 31 |
|
MIMVSNet | | | 94.49 141 | 97.59 94 | 90.87 175 | 91.74 175 | 98.70 121 | 94.68 169 | 78.73 200 | 97.98 140 | 83.71 147 | 97.71 79 | 94.81 107 | 96.96 116 | 97.97 107 | 97.92 90 | 99.40 162 | 98.04 182 |
|
v1144 | | | 92.81 166 | 94.03 170 | 91.40 165 | 91.68 176 | 97.60 175 | 94.73 166 | 88.40 152 | 96.71 176 | 78.48 178 | 88.14 181 | 84.46 172 | 95.45 160 | 96.31 164 | 95.22 167 | 99.65 102 | 99.76 55 |
|
DU-MVS | | | 93.98 148 | 94.44 162 | 93.44 131 | 91.66 177 | 97.77 162 | 95.03 155 | 91.57 105 | 97.17 164 | 86.12 131 | 93.13 151 | 81.13 189 | 96.60 128 | 95.10 184 | 97.01 123 | 99.67 93 | 99.80 31 |
|
Baseline_NR-MVSNet | | | 93.87 151 | 93.98 172 | 93.75 121 | 91.66 177 | 97.02 191 | 95.53 148 | 91.52 108 | 97.16 166 | 87.77 124 | 87.93 184 | 83.69 173 | 96.35 134 | 95.10 184 | 97.23 118 | 99.68 85 | 99.73 70 |
|
CP-MVSNet | | | 93.25 159 | 94.00 171 | 92.38 144 | 91.65 179 | 97.56 178 | 94.38 176 | 89.20 142 | 96.05 188 | 83.16 151 | 89.51 169 | 81.97 185 | 96.16 140 | 96.43 158 | 96.56 134 | 99.71 65 | 99.89 6 |
|
v148 | | | 92.36 180 | 92.88 187 | 91.75 159 | 91.63 180 | 97.66 169 | 92.64 186 | 90.55 125 | 96.09 186 | 83.34 149 | 88.19 179 | 80.00 194 | 92.74 190 | 93.98 191 | 94.58 182 | 99.58 131 | 99.69 90 |
|
PS-CasMVS | | | 92.72 170 | 93.36 183 | 91.98 153 | 91.62 181 | 97.52 180 | 94.13 180 | 88.98 144 | 95.94 191 | 81.51 162 | 87.35 186 | 79.95 195 | 95.91 145 | 96.37 160 | 96.49 136 | 99.70 74 | 99.89 6 |
|
v2v482 | | | 92.77 169 | 93.52 182 | 91.90 157 | 91.59 182 | 97.63 171 | 94.57 174 | 90.31 126 | 96.80 175 | 79.22 174 | 88.74 176 | 81.55 188 | 96.04 143 | 95.26 180 | 94.97 174 | 99.66 98 | 99.69 90 |
|
v1192 | | | 92.43 176 | 93.61 178 | 91.05 171 | 91.53 183 | 97.43 184 | 94.61 172 | 87.99 157 | 96.60 179 | 76.72 183 | 87.11 188 | 82.74 182 | 95.85 146 | 96.35 162 | 95.30 166 | 99.60 121 | 99.74 66 |
|
WR-MVS | | | 93.43 158 | 94.48 161 | 92.21 146 | 91.52 184 | 97.69 167 | 94.66 171 | 89.98 131 | 96.86 172 | 83.43 148 | 90.12 165 | 85.03 168 | 93.94 181 | 96.02 172 | 95.82 156 | 99.71 65 | 99.82 23 |
|
v144192 | | | 92.38 178 | 93.55 181 | 91.00 172 | 91.44 185 | 97.47 183 | 94.27 177 | 87.41 161 | 96.52 181 | 78.03 179 | 87.50 185 | 82.65 183 | 95.32 162 | 95.82 176 | 95.15 169 | 99.55 140 | 99.78 44 |
|
pmmvs5 | | | 92.71 172 | 94.27 164 | 90.90 174 | 91.42 186 | 97.74 164 | 93.23 182 | 86.66 167 | 95.99 190 | 78.96 177 | 91.45 156 | 83.44 176 | 95.55 154 | 97.30 138 | 95.05 172 | 99.58 131 | 98.93 164 |
|
v1921920 | | | 92.36 180 | 93.57 179 | 90.94 173 | 91.39 187 | 97.39 186 | 94.70 168 | 87.63 160 | 96.60 179 | 76.63 184 | 86.98 189 | 82.89 180 | 95.75 147 | 96.26 166 | 95.14 170 | 99.55 140 | 99.73 70 |
|
gm-plane-assit | | | 89.44 193 | 92.82 190 | 85.49 196 | 91.37 188 | 95.34 202 | 79.55 209 | 82.12 185 | 91.68 205 | 64.79 206 | 87.98 182 | 80.26 193 | 95.66 150 | 98.51 77 | 97.56 105 | 99.45 154 | 98.41 176 |
|
v1240 | | | 91.99 183 | 93.33 184 | 90.44 178 | 91.29 189 | 97.30 189 | 94.25 178 | 86.79 164 | 96.43 182 | 75.49 190 | 86.34 192 | 81.85 186 | 95.29 163 | 96.42 159 | 95.22 167 | 99.52 147 | 99.73 70 |
|
PEN-MVS | | | 92.72 170 | 93.20 185 | 92.15 148 | 91.29 189 | 97.31 188 | 94.67 170 | 89.81 134 | 96.19 184 | 81.83 160 | 88.58 177 | 79.06 199 | 95.61 153 | 95.21 181 | 96.27 141 | 99.72 55 | 99.82 23 |
|
TranMVSNet+NR-MVSNet | | | 93.67 154 | 94.14 165 | 93.13 137 | 91.28 191 | 97.58 176 | 95.60 147 | 91.97 97 | 97.06 167 | 84.05 140 | 90.64 164 | 82.22 184 | 96.17 139 | 94.94 187 | 96.78 126 | 99.69 76 | 99.78 44 |
|
anonymousdsp | | | 93.12 161 | 95.86 143 | 89.93 184 | 91.09 192 | 98.25 148 | 95.12 154 | 85.08 175 | 97.44 157 | 73.30 195 | 90.89 160 | 90.78 134 | 95.25 165 | 97.91 110 | 95.96 154 | 99.71 65 | 99.82 23 |
|
MDTV_nov1_ep13_2view | | | 92.44 175 | 95.66 145 | 88.68 187 | 91.05 193 | 97.92 158 | 92.17 188 | 79.64 192 | 98.83 92 | 76.20 185 | 91.45 156 | 93.51 121 | 95.04 168 | 95.68 177 | 93.70 189 | 97.96 188 | 98.53 173 |
|
DTE-MVSNet | | | 92.42 177 | 92.85 188 | 91.91 156 | 90.87 194 | 96.97 192 | 94.53 175 | 89.81 134 | 95.86 193 | 81.59 161 | 88.83 175 | 77.88 202 | 95.01 169 | 94.34 190 | 96.35 139 | 99.64 106 | 99.73 70 |
|
v7n | | | 91.61 185 | 92.95 186 | 90.04 181 | 90.56 195 | 97.69 167 | 93.74 181 | 85.59 173 | 95.89 192 | 76.95 182 | 86.60 191 | 78.60 201 | 93.76 184 | 97.01 147 | 94.99 173 | 99.65 102 | 99.87 12 |
|
test20.03 | | | 90.65 189 | 93.71 177 | 87.09 191 | 90.44 196 | 96.24 197 | 89.74 199 | 85.46 174 | 95.59 195 | 72.99 197 | 90.68 162 | 85.33 165 | 84.41 200 | 95.94 174 | 95.10 171 | 99.52 147 | 97.06 194 |
|
FPMVS | | | 83.82 198 | 84.61 200 | 82.90 199 | 90.39 197 | 90.71 207 | 90.85 193 | 84.10 183 | 95.47 196 | 65.15 204 | 83.44 196 | 74.46 205 | 75.48 203 | 81.63 204 | 79.42 206 | 91.42 208 | 87.14 207 |
|
Anonymous20231206 | | | 90.70 188 | 93.93 173 | 86.92 193 | 90.21 198 | 96.79 194 | 90.30 196 | 86.61 168 | 96.05 188 | 69.25 201 | 88.46 178 | 84.86 170 | 85.86 199 | 97.11 145 | 96.47 137 | 99.30 168 | 97.80 185 |
|
new_pmnet | | | 90.45 190 | 92.84 189 | 87.66 190 | 88.96 199 | 96.16 198 | 88.71 201 | 84.66 179 | 97.56 156 | 71.91 200 | 85.60 194 | 86.58 156 | 93.28 187 | 96.07 170 | 93.54 190 | 98.46 182 | 94.39 203 |
|
ET-MVSNet_ETH3D | | | 96.17 105 | 96.99 116 | 95.21 102 | 88.53 200 | 98.54 131 | 98.28 76 | 92.61 91 | 98.85 87 | 93.60 81 | 99.06 28 | 90.39 135 | 98.63 70 | 95.98 173 | 96.68 129 | 99.61 113 | 99.41 138 |
|
PM-MVS | | | 89.55 192 | 90.30 196 | 88.67 188 | 87.06 201 | 95.60 200 | 90.88 192 | 84.51 181 | 96.14 185 | 75.75 186 | 86.89 190 | 63.47 210 | 94.64 171 | 96.85 150 | 93.89 187 | 99.17 174 | 99.29 145 |
|
pmmvs-eth3d | | | 89.81 191 | 89.65 197 | 90.00 182 | 86.94 202 | 95.38 201 | 91.08 190 | 86.39 169 | 94.57 198 | 82.27 158 | 83.03 198 | 64.94 207 | 93.96 180 | 96.57 155 | 93.82 188 | 99.35 165 | 99.24 150 |
|
new-patchmatchnet | | | 86.12 197 | 87.30 199 | 84.74 197 | 86.92 203 | 95.19 204 | 83.57 206 | 84.42 182 | 92.67 203 | 65.66 203 | 80.32 200 | 64.72 208 | 89.41 196 | 92.33 198 | 89.21 201 | 98.43 183 | 96.69 197 |
|
pmmvs3 | | | 88.19 195 | 91.27 193 | 84.60 198 | 85.60 204 | 93.66 205 | 85.68 204 | 81.13 186 | 92.36 204 | 63.66 208 | 89.51 169 | 77.10 203 | 93.22 188 | 96.37 160 | 92.40 193 | 98.30 186 | 97.46 188 |
|
Gipuma | | | 81.40 199 | 81.78 201 | 80.96 201 | 83.21 205 | 85.61 210 | 79.73 208 | 76.25 207 | 97.33 160 | 64.21 207 | 55.32 207 | 55.55 211 | 86.04 198 | 92.43 197 | 92.20 196 | 96.32 204 | 93.99 204 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MDA-MVSNet-bldmvs | | | 87.84 196 | 89.22 198 | 86.23 194 | 81.74 206 | 96.77 195 | 83.74 205 | 89.57 139 | 94.50 199 | 72.83 198 | 96.64 103 | 64.47 209 | 92.71 191 | 81.43 205 | 92.28 195 | 96.81 202 | 98.47 175 |
|
MIMVSNet1 | | | 88.61 194 | 90.68 195 | 86.19 195 | 81.56 207 | 95.30 203 | 87.78 202 | 85.98 172 | 94.19 200 | 72.30 199 | 78.84 202 | 78.90 200 | 90.06 195 | 96.59 153 | 95.47 161 | 99.46 153 | 95.49 201 |
|
PMVS | | 72.60 17 | 76.39 201 | 77.66 204 | 74.92 202 | 81.04 208 | 69.37 214 | 68.47 211 | 80.54 189 | 85.39 207 | 65.07 205 | 73.52 204 | 72.91 206 | 65.67 209 | 80.35 206 | 76.81 207 | 88.71 209 | 85.25 210 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 80.99 202 | | 80.04 209 | 90.84 206 | 90.91 191 | | 96.09 186 | 74.18 193 | 62.81 206 | 30.59 216 | 82.44 202 | 96.25 167 | 91.77 197 | 95.91 205 | 98.56 172 |
|
PMMVS2 | | | 77.26 200 | 79.47 203 | 74.70 203 | 76.00 210 | 88.37 209 | 74.22 210 | 76.34 205 | 78.31 208 | 54.13 210 | 69.96 205 | 52.50 212 | 70.14 207 | 84.83 203 | 88.71 202 | 97.35 196 | 93.58 205 |
|
EMVS | | | 68.12 204 | 68.11 206 | 68.14 205 | 75.51 211 | 71.76 212 | 55.38 214 | 77.20 204 | 77.78 209 | 37.79 213 | 53.59 208 | 43.61 213 | 74.72 204 | 67.05 209 | 76.70 208 | 88.27 211 | 86.24 208 |
|
E-PMN | | | 68.30 203 | 68.43 205 | 68.15 204 | 74.70 212 | 71.56 213 | 55.64 213 | 77.24 203 | 77.48 210 | 39.46 212 | 51.95 210 | 41.68 214 | 73.28 205 | 70.65 208 | 79.51 205 | 88.61 210 | 86.20 209 |
|
MVE | | 67.97 19 | 65.53 205 | 67.43 207 | 63.31 206 | 59.33 213 | 74.20 211 | 53.09 215 | 70.43 209 | 66.27 211 | 43.13 211 | 45.98 211 | 30.62 215 | 70.65 206 | 79.34 207 | 86.30 203 | 83.25 212 | 89.33 206 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 31.24 206 | 40.15 208 | 20.86 208 | 12.61 214 | 17.99 215 | 25.16 216 | 13.30 211 | 48.42 212 | 24.82 214 | 53.07 209 | 30.13 217 | 28.47 210 | 42.73 210 | 37.65 209 | 20.79 213 | 51.04 211 |
|
test123 | | | 26.75 207 | 34.25 209 | 18.01 209 | 7.93 215 | 17.18 216 | 24.85 217 | 12.36 212 | 44.83 213 | 16.52 215 | 41.80 212 | 18.10 218 | 28.29 211 | 33.08 211 | 34.79 210 | 18.10 214 | 49.95 212 |
|
GG-mvs-BLEND | | | 69.11 202 | 98.13 76 | 35.26 207 | 3.49 216 | 98.20 151 | 94.89 160 | 2.38 213 | 98.42 122 | 5.82 216 | 96.37 111 | 98.60 61 | 5.97 212 | 98.75 58 | 97.98 88 | 99.01 177 | 98.61 171 |
|
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 | | | | | | | | | | | | | | | | | | 99.62 110 |
|
MTAPA | | | | | | | | | | | 98.09 12 | | 99.97 6 | | | | | |
|
MTMP | | | | | | | | | | | 98.46 8 | | 99.96 11 | | | | | |
|
Patchmatch-RL test | | | | | | | | 66.86 212 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 98.57 113 | | | | | | | | |
|
Patchmtry | | | | | | | 98.59 128 | 97.15 114 | 79.14 196 | | 80.42 167 | | | | | | | |
|
DeepMVS_CX | | | | | | | 96.85 193 | 87.43 203 | 89.27 141 | 98.30 126 | 75.55 189 | 95.05 129 | 79.47 197 | 92.62 192 | 89.48 201 | | 95.18 206 | 95.96 200 |
|