SMA-MVS | | | 99.34 7 | 99.79 4 | 98.81 10 | 99.69 1 | 99.94 17 | 99.75 13 | 96.91 8 | 99.98 3 | 96.76 12 | 99.37 37 | 100.00 1 | 99.90 4 | 99.88 9 | 99.46 17 | 99.84 31 | 99.92 120 |
|
CNVR-MVS | | | 99.39 2 | 99.75 12 | 98.98 2 | 99.69 1 | 99.95 12 | 99.76 7 | 96.91 8 | 99.98 3 | 97.59 6 | 99.64 19 | 100.00 1 | 99.93 1 | 99.94 2 | 98.75 48 | 99.97 10 | 99.97 82 |
|
PGM-MVS | | | 98.47 31 | 99.73 16 | 97.00 35 | 99.68 3 | 99.94 17 | 99.76 7 | 91.74 44 | 99.84 50 | 91.17 51 | 100.00 1 | 99.69 52 | 99.81 11 | 99.38 27 | 99.30 25 | 99.82 41 | 99.95 108 |
|
AdaColmap | | | 99.21 11 | 99.45 37 | 98.92 5 | 99.67 4 | 99.95 12 | 99.65 25 | 96.77 19 | 99.97 8 | 97.67 4 | 100.00 1 | 99.69 52 | 99.93 1 | 99.26 29 | 97.25 91 | 99.85 27 | 100.00 1 |
|
NCCC | | | 99.24 9 | 99.75 12 | 98.65 12 | 99.63 5 | 99.96 7 | 99.76 7 | 96.91 8 | 99.97 8 | 95.86 20 | 99.67 11 | 100.00 1 | 99.75 14 | 99.85 12 | 98.80 44 | 99.98 8 | 99.97 82 |
|
DVP-MVS | | | 99.38 4 | 99.57 33 | 99.15 1 | 99.62 6 | 99.94 17 | 99.72 20 | 96.99 2 | 99.98 3 | 98.85 1 | 98.21 69 | 100.00 1 | 99.88 6 | 99.88 9 | 98.96 35 | 99.85 27 | 100.00 1 |
|
ACMMP_NAP | | | 98.68 26 | 99.58 30 | 97.62 28 | 99.62 6 | 99.92 36 | 99.72 20 | 96.78 18 | 99.71 66 | 90.13 64 | 99.66 15 | 99.99 29 | 99.64 24 | 99.78 14 | 98.14 62 | 99.82 41 | 99.89 129 |
|
HPM-MVS++ | | | 98.98 22 | 99.62 26 | 98.22 21 | 99.62 6 | 99.94 17 | 99.74 16 | 96.95 4 | 99.87 43 | 93.76 31 | 99.49 32 | 100.00 1 | 99.39 36 | 99.73 17 | 98.35 56 | 99.89 22 | 99.96 101 |
|
APDe-MVS | | | 99.40 1 | 99.81 2 | 98.92 5 | 99.62 6 | 99.96 7 | 99.76 7 | 96.87 12 | 99.95 22 | 97.66 5 | 99.57 26 | 100.00 1 | 99.63 25 | 99.88 9 | 99.28 26 | 100.00 1 | 100.00 1 |
|
SR-MVS | | | | | | 99.61 10 | | | 96.80 15 | | | | 100.00 1 | | | | | |
|
MSP-MVS | | | 99.38 4 | 99.78 5 | 98.91 8 | 99.61 10 | 99.96 7 | 99.85 2 | 96.94 6 | 99.96 16 | 97.38 10 | 99.60 23 | 100.00 1 | 99.70 17 | 99.96 1 | 98.96 35 | 100.00 1 | 100.00 1 |
|
APD-MVS | | | 99.33 8 | 99.85 1 | 98.73 11 | 99.61 10 | 99.92 36 | 99.77 6 | 96.91 8 | 99.93 29 | 96.31 17 | 99.59 24 | 99.95 38 | 99.84 9 | 99.73 17 | 99.84 9 | 99.95 13 | 100.00 1 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DPE-MVS | | | 99.37 6 | 99.74 15 | 98.94 4 | 99.60 13 | 99.94 17 | 99.87 1 | 96.95 4 | 99.94 26 | 97.42 8 | 99.62 21 | 100.00 1 | 99.80 13 | 99.91 5 | 98.78 46 | 99.98 8 | 100.00 1 |
|
MSLP-MVS++ | | | 99.39 2 | 99.76 9 | 98.95 3 | 99.60 13 | 99.99 1 | 99.83 3 | 96.82 14 | 99.92 33 | 97.58 7 | 99.58 25 | 100.00 1 | 99.93 1 | 98.98 33 | 99.86 8 | 99.96 11 | 100.00 1 |
|
CSCG | | | 98.22 33 | 98.37 65 | 98.04 23 | 99.60 13 | 99.82 55 | 99.45 31 | 93.59 40 | 99.16 101 | 96.46 16 | 98.22 68 | 95.86 100 | 99.41 35 | 96.33 124 | 99.22 28 | 99.75 86 | 99.94 113 |
|
MCST-MVS | | | 99.08 18 | 99.72 18 | 98.33 19 | 99.59 16 | 99.97 3 | 99.78 5 | 96.96 3 | 99.95 22 | 93.72 32 | 99.67 11 | 100.00 1 | 99.90 4 | 99.91 5 | 98.55 52 | 100.00 1 | 100.00 1 |
|
zzz-MVS | | | 99.12 16 | 99.52 36 | 98.65 12 | 99.58 17 | 99.93 30 | 99.74 16 | 96.72 22 | 99.44 85 | 96.47 15 | 99.62 21 | 100.00 1 | 99.63 25 | 99.74 16 | 97.97 67 | 99.77 70 | 99.94 113 |
|
HFP-MVS | | | 99.19 12 | 99.77 8 | 98.51 16 | 99.55 18 | 99.94 17 | 99.76 7 | 96.84 13 | 99.88 40 | 95.27 24 | 99.67 11 | 100.00 1 | 99.85 8 | 99.56 22 | 99.36 21 | 99.79 59 | 99.97 82 |
|
X-MVS | | | 98.62 27 | 99.75 12 | 97.29 29 | 99.50 19 | 99.94 17 | 99.71 22 | 96.55 28 | 99.85 47 | 88.58 80 | 99.65 16 | 99.98 31 | 99.67 21 | 99.60 21 | 99.26 27 | 99.77 70 | 99.97 82 |
|
DeepC-MVS_fast | | 98.03 2 | 99.05 20 | 99.78 5 | 98.21 22 | 99.47 20 | 99.97 3 | 99.75 13 | 96.80 15 | 99.97 8 | 93.58 35 | 98.68 56 | 99.94 39 | 99.69 18 | 99.93 4 | 99.95 3 | 99.96 11 | 99.98 71 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PLC | | 98.06 1 | 99.17 13 | 99.38 39 | 98.92 5 | 99.47 20 | 99.90 44 | 99.48 30 | 96.47 30 | 99.96 16 | 98.73 2 | 99.52 30 | 100.00 1 | 99.55 30 | 98.54 52 | 97.73 79 | 99.84 31 | 99.99 53 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMMPR | | | 99.12 16 | 99.76 9 | 98.36 18 | 99.45 22 | 99.94 17 | 99.75 13 | 96.70 24 | 99.93 29 | 94.65 28 | 99.65 16 | 99.96 36 | 99.84 9 | 99.51 25 | 99.35 22 | 99.79 59 | 99.96 101 |
|
CP-MVS | | | 99.14 15 | 99.67 21 | 98.53 15 | 99.45 22 | 99.94 17 | 99.63 27 | 96.62 27 | 99.82 52 | 95.92 19 | 99.65 16 | 100.00 1 | 99.71 16 | 99.76 15 | 98.56 51 | 99.83 37 | 100.00 1 |
|
CPTT-MVS | | | 99.08 18 | 99.53 35 | 98.57 14 | 99.44 24 | 99.93 30 | 99.60 28 | 95.92 35 | 99.77 60 | 97.01 11 | 99.67 11 | 100.00 1 | 99.72 15 | 99.56 22 | 97.76 76 | 99.70 107 | 99.98 71 |
|
SteuartSystems-ACMMP | | | 98.95 23 | 99.80 3 | 97.95 25 | 99.43 25 | 99.96 7 | 99.76 7 | 96.45 31 | 99.82 52 | 93.63 33 | 99.64 19 | 100.00 1 | 98.56 73 | 99.90 8 | 99.31 24 | 99.84 31 | 100.00 1 |
Skip Steuart: Steuart Systems R&D Blog. |
MP-MVS | | | 98.82 25 | 99.63 24 | 97.88 27 | 99.41 26 | 99.91 43 | 99.74 16 | 96.76 20 | 99.88 40 | 91.89 43 | 99.50 31 | 99.94 39 | 99.65 23 | 99.71 20 | 98.49 54 | 99.82 41 | 99.97 82 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
OMC-MVS | | | 98.59 29 | 99.07 41 | 98.03 24 | 99.41 26 | 99.90 44 | 99.26 36 | 94.33 39 | 99.94 26 | 96.03 18 | 96.68 85 | 99.72 51 | 99.42 33 | 98.86 36 | 98.84 41 | 99.72 104 | 99.58 163 |
|
3Dnovator | | 95.01 8 | 97.98 39 | 98.89 48 | 96.92 37 | 99.36 28 | 99.76 66 | 98.72 50 | 89.98 57 | 99.98 3 | 93.99 30 | 94.60 107 | 99.43 62 | 99.50 31 | 98.55 49 | 99.91 5 | 99.99 5 | 99.98 71 |
|
QAPM | | | 97.90 41 | 98.89 48 | 96.74 38 | 99.35 29 | 99.80 62 | 98.84 46 | 90.20 56 | 99.94 26 | 92.85 36 | 94.17 110 | 99.78 47 | 99.42 33 | 98.71 38 | 99.87 7 | 99.79 59 | 99.98 71 |
|
CNLPA | | | 99.24 9 | 99.58 30 | 98.85 9 | 99.34 30 | 99.95 12 | 99.32 33 | 96.65 25 | 99.96 16 | 98.44 3 | 98.97 48 | 100.00 1 | 99.57 28 | 98.66 40 | 99.56 15 | 99.76 77 | 99.97 82 |
|
MAR-MVS | | | 97.03 52 | 98.00 76 | 95.89 48 | 99.32 31 | 99.74 69 | 96.76 90 | 84.89 102 | 99.97 8 | 94.86 26 | 98.29 62 | 90.58 122 | 99.67 21 | 98.02 82 | 99.50 16 | 99.82 41 | 99.92 120 |
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 |
OpenMVS | | 94.03 11 | 96.87 55 | 98.10 73 | 95.44 54 | 99.29 32 | 99.78 64 | 98.46 59 | 89.92 60 | 99.47 83 | 85.78 101 | 91.05 131 | 98.50 74 | 99.30 41 | 98.49 59 | 99.41 18 | 99.89 22 | 99.98 71 |
|
3Dnovator+ | | 95.21 7 | 98.17 34 | 99.08 40 | 97.12 33 | 99.28 33 | 99.78 64 | 98.61 52 | 89.93 59 | 99.93 29 | 95.36 23 | 95.50 94 | 100.00 1 | 99.56 29 | 98.58 47 | 99.80 10 | 99.95 13 | 99.97 82 |
|
MVS_111021_HR | | | 97.94 40 | 99.59 28 | 96.02 47 | 99.27 34 | 99.97 3 | 97.03 83 | 90.44 52 | 99.89 37 | 90.75 55 | 100.00 1 | 99.73 49 | 98.68 70 | 98.67 39 | 98.89 39 | 99.95 13 | 99.97 82 |
|
mPP-MVS | | | | | | 99.23 35 | | | | | | | 99.87 42 | | | | | |
|
MVS_111021_LR | | | 98.15 36 | 99.69 20 | 96.36 43 | 99.23 35 | 99.93 30 | 97.79 64 | 91.84 43 | 99.87 43 | 90.53 60 | 100.00 1 | 99.57 57 | 98.93 58 | 99.44 26 | 99.08 32 | 99.85 27 | 99.95 108 |
|
abl_6 | | | | | 97.06 34 | 99.17 37 | 99.82 55 | 98.68 51 | 90.86 50 | 100.00 1 | 94.53 29 | 97.40 80 | 100.00 1 | 99.17 49 | | | 99.93 16 | 99.99 53 |
|
TSAR-MVS + ACMM | | | 98.30 32 | 99.64 23 | 96.74 38 | 99.08 38 | 99.94 17 | 99.67 24 | 96.73 21 | 99.97 8 | 86.30 98 | 98.30 61 | 99.99 29 | 98.78 66 | 99.73 17 | 99.57 14 | 99.88 25 | 99.98 71 |
|
train_agg | | | 98.62 27 | 99.76 9 | 97.28 30 | 99.03 39 | 99.93 30 | 99.65 25 | 96.37 32 | 99.98 3 | 89.24 74 | 99.53 28 | 99.83 44 | 99.59 27 | 99.85 12 | 99.19 29 | 99.80 52 | 100.00 1 |
|
CDPH-MVS | | | 97.88 42 | 99.59 28 | 95.89 48 | 98.90 40 | 99.95 12 | 99.40 32 | 92.86 42 | 99.86 46 | 85.33 103 | 98.62 58 | 99.45 61 | 99.06 54 | 99.29 28 | 99.94 4 | 99.81 48 | 100.00 1 |
|
ACMMP | | | 98.16 35 | 99.01 43 | 97.18 31 | 98.86 41 | 99.92 36 | 98.77 49 | 95.73 36 | 99.31 97 | 91.15 52 | 100.00 1 | 99.81 46 | 98.82 65 | 98.11 76 | 95.91 125 | 99.77 70 | 99.97 82 |
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 |
PatchMatch-RL | | | 96.84 57 | 98.03 75 | 95.47 51 | 98.84 42 | 99.81 60 | 95.61 107 | 89.20 66 | 99.65 70 | 91.28 49 | 99.39 34 | 93.46 112 | 98.18 81 | 98.05 78 | 96.28 114 | 99.69 113 | 99.55 165 |
|
tmp_tt | | | | | 78.81 192 | 98.80 43 | 85.73 206 | 70.08 207 | 77.87 161 | 98.68 121 | 83.71 109 | 99.53 28 | 74.55 167 | 54.97 208 | 78.28 202 | 72.43 205 | 87.45 207 | |
|
TAPA-MVS | | 96.62 5 | 97.60 44 | 98.46 63 | 96.60 41 | 98.73 44 | 99.90 44 | 99.30 34 | 94.96 38 | 99.46 84 | 87.57 86 | 96.05 92 | 98.53 73 | 99.26 44 | 98.04 80 | 97.33 90 | 99.77 70 | 99.88 134 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MSDG | | | 97.29 47 | 97.55 85 | 97.00 35 | 98.66 45 | 99.71 70 | 99.03 42 | 96.15 33 | 99.59 73 | 89.67 71 | 92.77 124 | 94.86 103 | 98.75 67 | 98.22 70 | 97.94 68 | 99.72 104 | 99.76 155 |
|
PHI-MVS | | | 98.85 24 | 99.67 21 | 97.89 26 | 98.63 46 | 99.93 30 | 98.95 44 | 95.20 37 | 99.84 50 | 94.94 25 | 99.74 10 | 100.00 1 | 99.69 18 | 98.40 60 | 99.75 11 | 99.93 16 | 99.99 53 |
|
DeepPCF-MVS | | 97.16 4 | 97.58 45 | 99.72 18 | 95.07 59 | 98.45 47 | 99.96 7 | 93.83 135 | 95.93 34 | 100.00 1 | 90.79 54 | 98.38 60 | 99.85 43 | 95.28 126 | 99.94 2 | 99.97 1 | 96.15 200 | 99.97 82 |
|
EPNet | | | 98.11 37 | 99.63 24 | 96.34 44 | 98.44 48 | 99.88 50 | 98.55 54 | 90.25 55 | 99.93 29 | 92.60 41 | 100.00 1 | 99.73 49 | 98.41 75 | 98.87 35 | 99.02 33 | 99.82 41 | 99.97 82 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet_dtu | | | 95.10 91 | 98.81 52 | 90.78 107 | 98.38 49 | 98.47 124 | 96.54 92 | 89.36 64 | 99.78 59 | 65.65 174 | 99.31 38 | 98.24 81 | 94.79 131 | 98.28 66 | 99.35 22 | 99.93 16 | 98.27 184 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DPM-MVS | | | 98.58 30 | 99.78 5 | 97.17 32 | 98.02 50 | 99.64 77 | 99.80 4 | 96.72 22 | 99.96 16 | 90.05 66 | 99.57 26 | 100.00 1 | 98.66 71 | 99.56 22 | 99.96 2 | 99.80 52 | 99.80 151 |
|
SD-MVS | | | 99.16 14 | 99.73 16 | 98.49 17 | 97.93 51 | 99.95 12 | 99.74 16 | 96.94 6 | 99.96 16 | 96.60 14 | 99.47 33 | 100.00 1 | 99.88 6 | 99.15 31 | 99.59 13 | 99.84 31 | 100.00 1 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
TSAR-MVS + MP. | | | 98.99 21 | 99.61 27 | 98.27 20 | 97.88 52 | 99.92 36 | 99.71 22 | 96.80 15 | 99.96 16 | 95.58 22 | 98.71 55 | 100.00 1 | 99.68 20 | 99.91 5 | 98.78 46 | 99.99 5 | 100.00 1 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
COLMAP_ROB | | 93.56 12 | 96.03 66 | 96.83 103 | 95.11 57 | 97.87 53 | 99.52 82 | 98.81 48 | 91.40 47 | 99.42 88 | 84.97 104 | 90.46 134 | 96.82 91 | 98.05 84 | 96.46 120 | 96.19 117 | 99.54 136 | 98.92 180 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PCF-MVS | | 97.20 3 | 97.49 46 | 98.20 70 | 96.66 40 | 97.62 54 | 99.92 36 | 98.93 45 | 96.64 26 | 98.53 128 | 88.31 84 | 94.04 113 | 99.58 56 | 98.94 56 | 97.53 95 | 97.79 74 | 99.54 136 | 99.97 82 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DeepC-MVS | | 96.33 6 | 97.05 49 | 97.59 84 | 96.42 42 | 97.37 55 | 99.92 36 | 99.10 40 | 96.54 29 | 99.34 96 | 86.64 95 | 91.93 128 | 93.15 114 | 99.11 52 | 99.11 32 | 99.68 12 | 99.73 100 | 99.97 82 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 97.62 43 | 98.94 46 | 96.08 46 | 97.19 56 | 99.93 30 | 99.29 35 | 90.38 53 | 99.87 43 | 91.00 53 | 95.79 93 | 99.51 58 | 98.72 69 | 98.53 54 | 99.00 34 | 99.90 21 | 99.99 53 |
|
DELS-MVS | | | 97.05 49 | 98.05 74 | 95.88 50 | 97.09 57 | 99.99 1 | 98.82 47 | 90.30 54 | 98.44 134 | 91.40 47 | 92.91 121 | 96.57 93 | 97.68 98 | 98.56 48 | 99.88 6 | 100.00 1 | 100.00 1 |
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 |
LS3D | | | 96.44 61 | 97.31 91 | 95.41 55 | 97.06 58 | 99.87 51 | 99.51 29 | 97.48 1 | 99.57 74 | 79.00 121 | 95.39 96 | 89.19 128 | 99.81 11 | 98.55 49 | 98.84 41 | 99.62 125 | 99.78 152 |
|
CHOSEN 280x420 | | | 97.16 48 | 99.58 30 | 94.35 73 | 96.95 59 | 99.97 3 | 97.19 78 | 81.55 138 | 99.92 33 | 91.75 44 | 100.00 1 | 100.00 1 | 98.84 64 | 98.55 49 | 98.65 49 | 99.79 59 | 99.97 82 |
|
RPSCF | | | 95.86 71 | 96.94 101 | 94.61 68 | 96.52 60 | 98.67 122 | 98.54 55 | 88.43 77 | 99.56 75 | 90.51 62 | 99.39 34 | 98.70 71 | 97.72 94 | 93.77 168 | 92.00 174 | 95.93 201 | 96.50 197 |
|
PVSNet_BlendedMVS | | | 96.01 67 | 96.48 111 | 95.46 52 | 96.47 61 | 99.89 48 | 95.64 104 | 91.23 48 | 99.75 63 | 91.59 45 | 96.80 82 | 82.44 146 | 98.05 84 | 98.53 54 | 97.92 70 | 99.80 52 | 100.00 1 |
|
PVSNet_Blended | | | 96.01 67 | 96.48 111 | 95.46 52 | 96.47 61 | 99.89 48 | 95.64 104 | 91.23 48 | 99.75 63 | 91.59 45 | 96.80 82 | 82.44 146 | 98.05 84 | 98.53 54 | 97.92 70 | 99.80 52 | 100.00 1 |
|
MVS_0304 | | | 97.04 51 | 98.72 54 | 95.08 58 | 96.32 63 | 99.90 44 | 99.15 38 | 89.61 63 | 99.89 37 | 87.22 92 | 95.47 95 | 98.22 82 | 98.22 80 | 98.63 44 | 98.90 38 | 99.93 16 | 100.00 1 |
|
TSAR-MVS + COLMAP | | | 95.20 85 | 95.03 131 | 95.41 55 | 96.17 64 | 98.69 121 | 99.11 39 | 93.40 41 | 99.97 8 | 84.89 106 | 98.23 67 | 75.01 165 | 99.34 38 | 97.27 106 | 96.37 113 | 99.58 129 | 99.64 162 |
|
CHOSEN 1792x2688 | | | 93.69 108 | 94.89 134 | 92.28 98 | 96.17 64 | 99.84 52 | 95.69 103 | 83.17 123 | 98.54 127 | 82.04 114 | 77.58 187 | 91.15 118 | 96.90 104 | 98.36 64 | 98.82 43 | 99.73 100 | 99.98 71 |
|
HyFIR lowres test | | | 93.13 118 | 94.48 138 | 91.56 103 | 96.12 66 | 99.68 72 | 93.52 137 | 79.98 146 | 97.24 149 | 81.73 117 | 72.66 195 | 95.74 101 | 98.29 78 | 98.27 67 | 97.79 74 | 99.70 107 | 100.00 1 |
|
OPM-MVS | | | 93.50 112 | 93.00 149 | 94.07 76 | 95.82 67 | 98.26 131 | 98.49 58 | 91.62 45 | 94.69 170 | 81.93 115 | 92.82 123 | 76.18 163 | 96.82 106 | 96.12 129 | 94.57 139 | 99.74 90 | 98.39 183 |
|
MS-PatchMatch | | | 93.46 116 | 95.91 122 | 90.61 110 | 95.48 68 | 99.31 98 | 95.62 106 | 77.23 165 | 99.42 88 | 81.88 116 | 88.92 142 | 96.06 99 | 93.80 144 | 96.45 122 | 93.11 162 | 99.65 117 | 98.10 188 |
|
CMPMVS | | 65.66 17 | 84.62 188 | 85.02 194 | 84.15 176 | 95.40 69 | 97.79 139 | 88.35 181 | 79.22 153 | 89.66 201 | 60.71 190 | 72.20 196 | 73.94 171 | 87.32 191 | 86.73 199 | 84.55 201 | 93.90 203 | 90.31 205 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PVSNet_Blended_VisFu | | | 95.37 83 | 97.44 89 | 92.95 94 | 95.20 70 | 99.80 62 | 92.68 143 | 88.41 78 | 99.12 104 | 87.64 85 | 88.31 145 | 99.10 67 | 94.07 142 | 98.27 67 | 97.51 85 | 99.73 100 | 100.00 1 |
|
HQP-MVS | | | 94.48 99 | 95.39 129 | 93.42 83 | 95.10 71 | 98.35 127 | 98.19 60 | 91.41 46 | 99.77 60 | 79.79 118 | 99.30 39 | 77.08 156 | 96.25 114 | 96.93 109 | 96.28 114 | 99.76 77 | 99.99 53 |
|
XVS | | | | | | 95.09 72 | 99.94 17 | 97.49 71 | | | 88.58 80 | | 99.98 31 | | | | 99.78 66 | |
|
X-MVStestdata | | | | | | 95.09 72 | 99.94 17 | 97.49 71 | | | 88.58 80 | | 99.98 31 | | | | 99.78 66 | |
|
LGP-MVS_train | | | 93.60 109 | 95.05 130 | 91.90 102 | 94.90 74 | 98.29 130 | 97.93 62 | 88.06 80 | 99.14 103 | 74.83 137 | 99.26 41 | 76.50 159 | 96.07 117 | 96.31 125 | 95.90 127 | 99.59 127 | 99.97 82 |
|
ACMM | | 94.44 10 | 94.26 104 | 94.62 136 | 93.84 79 | 94.86 75 | 97.73 140 | 93.48 138 | 90.76 51 | 99.27 99 | 87.46 87 | 99.04 44 | 76.60 158 | 96.76 109 | 96.37 123 | 93.76 153 | 99.74 90 | 99.55 165 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CANet_DTU | | | 94.90 94 | 98.98 45 | 90.13 115 | 94.74 76 | 99.81 60 | 98.53 56 | 82.23 131 | 99.97 8 | 66.76 171 | 100.00 1 | 98.50 74 | 98.74 68 | 97.52 96 | 97.19 97 | 99.76 77 | 99.88 134 |
|
TSAR-MVS + GP. | | | 98.06 38 | 99.55 34 | 96.32 45 | 94.72 77 | 99.92 36 | 99.22 37 | 89.98 57 | 99.97 8 | 94.77 27 | 99.94 9 | 100.00 1 | 99.43 32 | 98.52 57 | 98.53 53 | 99.79 59 | 100.00 1 |
|
UGNet | | | 96.05 65 | 98.55 57 | 93.13 89 | 94.64 78 | 99.65 76 | 94.70 123 | 87.78 81 | 99.40 91 | 89.69 70 | 98.25 65 | 99.25 66 | 92.12 158 | 96.50 116 | 97.08 100 | 99.84 31 | 99.72 157 |
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 |
IB-MVS | | 90.59 15 | 92.70 126 | 95.70 124 | 89.21 124 | 94.62 79 | 99.45 90 | 83.77 193 | 88.92 69 | 99.53 76 | 92.82 37 | 98.86 51 | 86.08 134 | 75.24 202 | 92.81 183 | 93.17 160 | 99.89 22 | 100.00 1 |
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 |
UA-Net | | | 94.95 93 | 98.66 55 | 90.63 109 | 94.60 80 | 98.94 115 | 96.03 97 | 85.28 97 | 98.01 144 | 78.92 122 | 97.42 79 | 99.96 36 | 89.09 180 | 98.95 34 | 98.80 44 | 99.82 41 | 98.57 182 |
|
ACMP | | 94.49 9 | 94.19 105 | 94.74 135 | 93.56 82 | 94.25 81 | 98.32 129 | 96.02 98 | 89.35 65 | 98.90 117 | 87.28 90 | 99.14 43 | 76.41 161 | 94.94 129 | 96.07 132 | 94.35 148 | 99.49 147 | 99.99 53 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 92.34 14 | 91.59 139 | 93.02 148 | 89.92 117 | 93.97 82 | 97.98 137 | 90.10 168 | 84.70 104 | 98.46 131 | 76.80 130 | 93.38 119 | 71.94 177 | 94.39 137 | 95.34 143 | 94.04 150 | 99.54 136 | 100.00 1 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 94.53 98 | 94.45 139 | 94.61 68 | 93.85 83 | 98.36 126 | 98.12 61 | 89.68 61 | 99.35 95 | 89.62 72 | 95.19 98 | 77.08 156 | 96.66 111 | 95.51 139 | 95.67 128 | 99.74 90 | 100.00 1 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TDRefinement | | | 87.79 166 | 88.76 182 | 86.66 150 | 93.54 84 | 98.02 135 | 95.76 101 | 85.18 100 | 96.57 154 | 67.90 164 | 80.51 173 | 66.51 197 | 78.37 199 | 93.20 179 | 89.73 192 | 99.22 185 | 96.75 195 |
|
Anonymous202405211 | | | | 95.78 123 | | 93.26 85 | 99.52 82 | 96.70 91 | 88.55 74 | 97.93 145 | | 88.99 141 | 90.68 121 | 98.99 55 | 96.46 120 | 97.02 104 | 99.64 121 | 99.89 129 |
|
DCV-MVSNet | | | 95.85 73 | 97.53 86 | 93.89 78 | 93.20 86 | 97.01 151 | 97.14 80 | 84.77 103 | 99.16 101 | 90.38 63 | 98.96 49 | 93.73 109 | 98.23 79 | 96.57 115 | 97.37 89 | 99.64 121 | 99.93 116 |
|
thres100view900 | | | 95.86 71 | 96.62 105 | 94.97 60 | 93.10 87 | 99.83 53 | 97.76 65 | 89.15 67 | 98.62 124 | 90.69 56 | 99.00 45 | 84.86 137 | 99.30 41 | 97.57 94 | 96.48 109 | 99.81 48 | 100.00 1 |
|
tfpn200view9 | | | 95.78 76 | 96.54 108 | 94.89 63 | 93.10 87 | 99.82 55 | 97.67 66 | 88.85 70 | 98.62 124 | 90.69 56 | 99.00 45 | 84.86 137 | 99.28 43 | 97.41 101 | 96.10 118 | 99.76 77 | 99.99 53 |
|
thres200 | | | 95.77 77 | 96.55 107 | 94.86 64 | 93.09 89 | 99.82 55 | 97.63 69 | 88.85 70 | 98.49 129 | 90.66 58 | 98.99 47 | 84.86 137 | 99.20 46 | 97.41 101 | 96.28 114 | 99.76 77 | 100.00 1 |
|
ACMH+ | | 92.61 13 | 91.80 136 | 93.03 147 | 90.37 112 | 93.03 90 | 98.17 132 | 94.00 133 | 84.13 115 | 98.12 141 | 77.39 128 | 91.95 127 | 74.62 166 | 94.36 139 | 94.62 154 | 93.82 152 | 99.32 177 | 99.87 139 |
|
Anonymous20231211 | | | 94.96 92 | 94.99 132 | 94.91 61 | 93.01 91 | 99.44 93 | 96.85 88 | 88.49 76 | 98.78 119 | 92.61 40 | 83.94 159 | 90.25 124 | 98.94 56 | 95.87 135 | 96.77 107 | 99.58 129 | 99.89 129 |
|
thres400 | | | 95.72 79 | 96.48 111 | 94.84 65 | 93.00 92 | 99.83 53 | 97.55 70 | 88.93 68 | 98.49 129 | 90.61 59 | 98.86 51 | 84.63 141 | 99.20 46 | 97.45 97 | 96.10 118 | 99.77 70 | 99.99 53 |
|
canonicalmvs | | | 95.80 75 | 97.02 96 | 94.37 71 | 92.96 93 | 99.47 87 | 97.49 71 | 84.58 105 | 99.44 85 | 92.05 42 | 98.54 59 | 86.65 132 | 99.37 37 | 96.18 127 | 98.93 37 | 99.77 70 | 99.92 120 |
|
thres600view7 | | | 95.64 80 | 96.38 114 | 94.79 67 | 92.96 93 | 99.82 55 | 97.48 75 | 88.85 70 | 98.38 135 | 90.52 61 | 98.84 53 | 84.61 142 | 99.15 50 | 97.41 101 | 95.60 130 | 99.76 77 | 99.99 53 |
|
baseline1 | | | 96.87 55 | 98.55 57 | 94.91 61 | 92.89 95 | 99.45 90 | 96.34 94 | 88.54 75 | 98.88 118 | 92.82 37 | 98.93 50 | 96.58 92 | 99.07 53 | 98.19 72 | 98.04 65 | 99.80 52 | 99.78 152 |
|
MVSTER | | | 97.00 53 | 98.85 50 | 94.83 66 | 92.71 96 | 97.43 145 | 99.03 42 | 85.52 95 | 99.82 52 | 92.74 39 | 99.15 42 | 99.94 39 | 99.19 48 | 98.66 40 | 96.99 105 | 99.79 59 | 99.98 71 |
|
DWT-MVSNet_training | | | 96.26 64 | 98.44 64 | 93.72 81 | 92.58 97 | 99.34 97 | 96.15 96 | 83.00 126 | 99.76 62 | 93.63 33 | 97.89 74 | 99.46 59 | 97.23 102 | 94.43 156 | 98.19 59 | 99.70 107 | 100.00 1 |
|
USDC | | | 90.36 146 | 91.68 154 | 88.82 128 | 92.58 97 | 98.02 135 | 96.27 95 | 79.83 147 | 98.37 137 | 70.61 157 | 89.05 140 | 67.50 192 | 94.17 140 | 95.77 136 | 94.43 143 | 99.46 155 | 98.62 181 |
|
PMMVS | | | 96.45 60 | 98.24 69 | 94.36 72 | 92.58 97 | 99.01 108 | 97.08 82 | 87.42 88 | 99.88 40 | 90.06 65 | 99.39 34 | 94.63 104 | 99.33 39 | 97.85 88 | 96.99 105 | 99.70 107 | 99.96 101 |
|
EPMVS | | | 94.08 106 | 98.54 61 | 88.87 126 | 92.51 100 | 99.47 87 | 94.18 131 | 66.53 192 | 99.68 68 | 82.40 112 | 95.24 97 | 99.40 63 | 97.86 91 | 98.12 75 | 97.99 66 | 99.75 86 | 99.88 134 |
|
TinyColmap | | | 89.94 148 | 90.88 160 | 88.84 127 | 92.43 101 | 97.91 138 | 95.59 108 | 80.10 145 | 98.12 141 | 71.33 154 | 84.56 155 | 67.46 193 | 94.15 141 | 95.57 138 | 94.27 149 | 99.43 163 | 98.26 185 |
|
IS_MVSNet | | | 96.66 59 | 98.62 56 | 94.38 70 | 92.41 102 | 99.70 71 | 97.19 78 | 87.67 83 | 99.05 109 | 91.27 50 | 95.09 100 | 98.46 78 | 97.95 89 | 98.64 42 | 99.37 19 | 99.79 59 | 100.00 1 |
|
Vis-MVSNet (Re-imp) | | | 95.60 81 | 98.52 62 | 92.19 99 | 92.37 103 | 99.56 81 | 96.37 93 | 87.41 89 | 98.95 112 | 84.77 108 | 94.88 105 | 98.48 77 | 92.44 155 | 98.63 44 | 99.37 19 | 99.76 77 | 99.77 154 |
|
FC-MVSNet-train | | | 94.61 95 | 96.27 115 | 92.68 97 | 92.35 104 | 97.14 149 | 93.45 139 | 87.73 82 | 98.93 113 | 87.31 88 | 96.42 88 | 89.35 126 | 95.67 121 | 96.06 133 | 96.01 122 | 99.56 133 | 99.98 71 |
|
baseline | | | 95.85 73 | 98.13 72 | 93.20 88 | 92.29 105 | 99.58 80 | 97.49 71 | 84.33 110 | 99.44 85 | 87.28 90 | 97.00 81 | 94.04 108 | 97.93 90 | 98.36 64 | 98.47 55 | 99.87 26 | 99.99 53 |
|
MVS_Test | | | 95.74 78 | 98.18 71 | 92.90 95 | 92.16 106 | 99.49 86 | 97.36 76 | 84.30 111 | 99.79 57 | 84.94 105 | 96.65 86 | 93.63 111 | 98.85 62 | 98.61 46 | 99.10 31 | 99.81 48 | 100.00 1 |
|
tpmrst | | | 92.52 130 | 97.45 88 | 86.77 149 | 92.15 107 | 99.36 96 | 92.53 146 | 65.95 195 | 99.53 76 | 72.50 142 | 92.22 126 | 99.83 44 | 97.81 93 | 95.18 147 | 96.05 121 | 99.69 113 | 100.00 1 |
|
CS-MVS | | | 96.88 54 | 99.06 42 | 94.34 74 | 92.11 108 | 99.67 73 | 98.58 53 | 87.30 90 | 99.95 22 | 89.40 73 | 98.68 56 | 98.41 79 | 98.85 62 | 98.54 52 | 98.15 61 | 99.80 52 | 99.99 53 |
|
ADS-MVSNet | | | 92.91 122 | 97.97 77 | 87.01 146 | 92.07 109 | 99.27 101 | 92.70 142 | 65.39 199 | 99.85 47 | 75.40 134 | 94.93 104 | 98.26 80 | 96.86 105 | 96.09 130 | 97.52 84 | 99.65 117 | 99.84 144 |
|
dps | | | 94.29 103 | 97.33 90 | 90.75 108 | 92.02 110 | 99.21 102 | 94.31 129 | 66.97 191 | 99.50 79 | 95.61 21 | 96.22 91 | 98.64 72 | 96.08 116 | 93.71 170 | 94.03 151 | 99.52 140 | 99.98 71 |
|
SCA | | | 93.53 110 | 98.90 47 | 87.27 143 | 92.01 111 | 99.30 99 | 93.43 140 | 65.72 196 | 99.80 55 | 75.20 136 | 97.66 77 | 99.74 48 | 97.44 99 | 98.21 71 | 97.62 82 | 99.84 31 | 100.00 1 |
|
diffmvs | | | 94.60 96 | 95.63 126 | 93.41 84 | 91.98 112 | 99.30 99 | 96.86 87 | 87.62 84 | 99.30 98 | 86.07 99 | 94.12 112 | 81.63 150 | 98.16 82 | 97.43 98 | 97.60 83 | 99.76 77 | 100.00 1 |
|
EIA-MVS | | | 96.34 62 | 98.55 57 | 93.76 80 | 91.93 113 | 99.66 74 | 97.14 80 | 88.33 79 | 99.51 78 | 85.98 100 | 98.82 54 | 96.08 98 | 99.33 39 | 98.38 62 | 97.40 88 | 99.81 48 | 100.00 1 |
|
PatchmatchNet | | | 93.48 115 | 98.84 51 | 87.22 144 | 91.93 113 | 99.39 94 | 92.55 145 | 66.06 194 | 99.71 66 | 75.61 133 | 98.24 66 | 99.59 55 | 97.35 100 | 97.87 87 | 97.64 81 | 99.83 37 | 99.43 168 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
casdiffmvs | | | 94.54 97 | 95.56 127 | 93.36 85 | 91.84 115 | 99.46 89 | 95.92 99 | 87.54 87 | 98.45 132 | 86.57 97 | 90.51 133 | 84.72 140 | 98.49 74 | 97.97 84 | 97.80 73 | 99.77 70 | 100.00 1 |
|
tpm cat1 | | | 93.29 117 | 96.53 109 | 89.50 121 | 91.84 115 | 99.18 104 | 94.70 123 | 67.70 188 | 98.38 135 | 86.67 93 | 89.16 139 | 99.38 64 | 96.66 111 | 94.33 157 | 95.30 132 | 99.43 163 | 100.00 1 |
|
ETV-MVS | | | 96.76 58 | 99.01 43 | 94.14 75 | 91.83 117 | 99.66 74 | 97.20 77 | 88.68 73 | 99.95 22 | 88.61 79 | 98.18 70 | 98.73 70 | 98.66 71 | 98.51 58 | 98.07 64 | 99.85 27 | 100.00 1 |
|
EPP-MVSNet | | | 96.29 63 | 98.34 66 | 93.90 77 | 91.77 118 | 99.38 95 | 95.45 112 | 87.25 91 | 99.38 92 | 91.36 48 | 94.86 106 | 98.49 76 | 97.83 92 | 98.01 83 | 98.23 58 | 99.75 86 | 99.99 53 |
|
thisisatest0530 | | | 95.89 69 | 98.32 67 | 93.06 92 | 91.76 119 | 99.75 67 | 94.94 118 | 87.60 85 | 99.91 35 | 86.66 94 | 98.28 63 | 99.98 31 | 97.72 94 | 97.10 107 | 93.24 158 | 99.65 117 | 99.95 108 |
|
DI_MVS_plusplus_trai | | | 95.29 84 | 97.02 96 | 93.28 87 | 91.76 119 | 99.52 82 | 97.84 63 | 85.67 94 | 99.08 108 | 87.29 89 | 87.76 148 | 97.46 88 | 97.31 101 | 97.83 89 | 97.48 86 | 99.83 37 | 100.00 1 |
|
tttt0517 | | | 95.88 70 | 98.31 68 | 93.04 93 | 91.75 121 | 99.75 67 | 94.90 119 | 87.60 85 | 99.91 35 | 86.63 96 | 98.28 63 | 99.98 31 | 97.72 94 | 97.10 107 | 93.24 158 | 99.65 117 | 99.95 108 |
|
MDTV_nov1_ep13 | | | 94.32 101 | 98.77 53 | 89.14 125 | 91.70 122 | 99.52 82 | 95.21 114 | 72.09 185 | 99.80 55 | 78.91 123 | 96.32 89 | 99.62 54 | 97.71 97 | 98.39 61 | 97.71 80 | 99.22 185 | 100.00 1 |
|
test-LLR | | | 93.71 107 | 97.23 92 | 89.60 119 | 91.69 123 | 99.10 105 | 94.68 125 | 83.60 117 | 99.36 93 | 71.94 148 | 93.82 115 | 96.51 94 | 95.96 118 | 97.42 99 | 94.37 145 | 99.74 90 | 99.99 53 |
|
test0.0.03 1 | | | 95.15 89 | 97.87 80 | 91.99 101 | 91.69 123 | 98.82 119 | 93.04 141 | 83.60 117 | 99.65 70 | 88.80 77 | 94.15 111 | 97.67 86 | 94.97 128 | 96.62 114 | 98.16 60 | 99.83 37 | 100.00 1 |
|
CostFormer | | | 93.50 112 | 96.50 110 | 90.00 116 | 91.69 123 | 98.65 123 | 93.88 134 | 67.64 189 | 98.97 110 | 89.16 75 | 97.79 75 | 88.92 129 | 97.97 88 | 95.14 148 | 96.06 120 | 99.63 123 | 100.00 1 |
|
CVMVSNet | | | 92.13 134 | 95.40 128 | 88.32 137 | 91.29 126 | 97.29 147 | 91.85 150 | 86.42 93 | 96.71 153 | 71.84 150 | 89.56 137 | 91.18 117 | 88.98 182 | 96.17 128 | 97.76 76 | 99.51 144 | 99.14 176 |
|
Vis-MVSNet | | | 93.08 120 | 96.76 104 | 88.78 130 | 91.14 127 | 99.63 79 | 94.85 120 | 83.34 121 | 97.19 150 | 74.78 138 | 91.92 129 | 93.15 114 | 88.81 183 | 97.59 93 | 98.35 56 | 99.78 66 | 99.49 167 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS-LS | | | 93.50 112 | 96.22 116 | 90.33 114 | 90.93 128 | 95.50 184 | 94.83 121 | 80.54 142 | 98.92 114 | 79.11 120 | 90.64 132 | 93.70 110 | 96.79 107 | 96.93 109 | 97.85 72 | 99.78 66 | 99.99 53 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 93.06 121 | 95.94 121 | 89.70 118 | 90.82 129 | 99.45 90 | 95.71 102 | 78.94 156 | 98.72 120 | 74.71 139 | 97.92 73 | 80.73 151 | 98.35 76 | 97.72 90 | 97.05 103 | 99.70 107 | 100.00 1 |
|
RPMNet | | | 92.64 128 | 97.88 79 | 86.53 151 | 90.79 130 | 98.95 113 | 95.13 115 | 64.44 203 | 99.09 106 | 72.36 144 | 93.58 118 | 99.01 68 | 96.74 110 | 98.05 78 | 96.45 111 | 99.71 106 | 100.00 1 |
|
testgi | | | 92.47 131 | 95.68 125 | 88.73 131 | 90.68 131 | 98.35 127 | 91.67 153 | 79.50 151 | 98.96 111 | 77.12 129 | 95.17 99 | 85.84 135 | 93.95 143 | 95.75 137 | 96.47 110 | 99.45 158 | 99.21 174 |
|
CR-MVSNet | | | 92.32 133 | 97.97 77 | 85.74 160 | 90.63 132 | 98.95 113 | 95.46 110 | 65.50 197 | 99.09 106 | 67.51 167 | 94.20 109 | 98.18 83 | 95.59 124 | 98.16 73 | 97.20 95 | 99.74 90 | 100.00 1 |
|
gg-mvs-nofinetune | | | 86.69 182 | 91.30 158 | 81.30 188 | 90.42 133 | 99.64 77 | 98.50 57 | 61.68 207 | 79.23 207 | 40.35 209 | 66.58 201 | 97.14 89 | 96.92 103 | 98.64 42 | 97.94 68 | 99.91 20 | 99.97 82 |
|
IterMVS-SCA-FT | | | 91.75 137 | 96.87 102 | 85.78 158 | 90.34 134 | 95.93 173 | 95.06 117 | 73.85 180 | 98.91 115 | 61.01 187 | 89.21 138 | 98.87 69 | 94.66 135 | 98.09 77 | 97.12 99 | 99.76 77 | 99.99 53 |
|
PatchT | | | 91.06 141 | 97.66 82 | 83.36 182 | 90.32 135 | 98.96 112 | 82.30 197 | 64.72 202 | 98.45 132 | 67.51 167 | 93.28 120 | 97.60 87 | 95.59 124 | 98.16 73 | 97.20 95 | 99.70 107 | 100.00 1 |
|
baseline2 | | | 95.13 90 | 98.55 57 | 91.15 106 | 90.29 136 | 99.00 109 | 94.49 127 | 82.00 132 | 99.68 68 | 84.82 107 | 96.47 87 | 99.30 65 | 95.71 120 | 98.24 69 | 97.14 98 | 99.57 131 | 100.00 1 |
|
IterMVS | | | 91.65 138 | 96.62 105 | 85.85 157 | 90.27 137 | 95.80 175 | 95.32 113 | 74.15 176 | 98.91 115 | 60.95 188 | 88.79 144 | 97.76 85 | 94.69 134 | 98.04 80 | 97.07 101 | 99.73 100 | 100.00 1 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpm | | | 89.60 150 | 94.93 133 | 83.39 180 | 89.94 138 | 97.11 150 | 90.09 169 | 65.28 200 | 98.67 122 | 60.03 192 | 96.79 84 | 84.38 143 | 95.66 123 | 91.90 187 | 95.65 129 | 99.32 177 | 99.98 71 |
|
CDS-MVSNet | | | 94.32 101 | 97.00 98 | 91.19 105 | 89.82 139 | 98.71 120 | 95.51 109 | 85.14 101 | 96.85 151 | 82.33 113 | 92.48 125 | 96.40 96 | 94.71 132 | 96.86 111 | 97.76 76 | 99.63 123 | 99.92 120 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
FMVSNet3 | | | 95.59 82 | 97.51 87 | 93.34 86 | 89.48 140 | 96.57 158 | 97.67 66 | 84.17 112 | 99.48 80 | 89.76 67 | 95.09 100 | 94.35 105 | 99.14 51 | 98.37 63 | 98.86 40 | 99.82 41 | 99.89 129 |
|
GBi-Net | | | 95.19 87 | 96.99 99 | 93.09 90 | 89.11 141 | 96.47 160 | 96.90 84 | 84.17 112 | 99.48 80 | 89.76 67 | 95.09 100 | 94.35 105 | 98.87 59 | 96.50 116 | 97.21 92 | 99.74 90 | 99.81 148 |
|
test1 | | | 95.19 87 | 96.99 99 | 93.09 90 | 89.11 141 | 96.47 160 | 96.90 84 | 84.17 112 | 99.48 80 | 89.76 67 | 95.09 100 | 94.35 105 | 98.87 59 | 96.50 116 | 97.21 92 | 99.74 90 | 99.81 148 |
|
FMVSNet2 | | | 94.48 99 | 95.95 120 | 92.77 96 | 89.11 141 | 96.47 160 | 96.90 84 | 83.38 120 | 99.11 105 | 88.64 78 | 87.50 153 | 92.26 116 | 98.87 59 | 97.91 86 | 98.60 50 | 99.74 90 | 99.81 148 |
|
Fast-Effi-MVS+ | | | 92.11 135 | 94.33 140 | 89.52 120 | 89.06 144 | 99.00 109 | 95.13 115 | 76.72 168 | 98.59 126 | 78.21 126 | 89.99 135 | 77.35 155 | 98.34 77 | 97.97 84 | 97.44 87 | 99.67 115 | 99.96 101 |
|
Fast-Effi-MVS+-dtu | | | 92.73 125 | 97.62 83 | 87.02 145 | 88.91 145 | 98.83 118 | 95.79 100 | 73.98 179 | 99.89 37 | 68.62 163 | 97.73 76 | 93.30 113 | 95.21 127 | 97.67 91 | 95.96 124 | 99.59 127 | 100.00 1 |
|
MVS-HIRNet | | | 88.27 159 | 94.05 143 | 81.51 187 | 88.90 146 | 98.93 116 | 83.38 195 | 60.52 209 | 98.06 143 | 63.78 180 | 80.67 171 | 90.36 123 | 92.94 150 | 97.29 105 | 96.41 112 | 99.56 133 | 96.66 196 |
|
Effi-MVS+-dtu | | | 93.13 118 | 97.13 94 | 88.47 134 | 88.86 147 | 99.19 103 | 96.79 89 | 79.08 155 | 99.64 72 | 70.01 158 | 97.51 78 | 89.38 125 | 96.53 113 | 97.60 92 | 96.55 108 | 99.57 131 | 100.00 1 |
|
TAMVS | | | 92.43 132 | 94.21 142 | 90.35 113 | 88.68 148 | 98.85 117 | 94.15 132 | 81.53 139 | 95.58 160 | 83.61 110 | 87.05 154 | 86.45 133 | 94.71 132 | 96.27 126 | 95.91 125 | 99.42 166 | 99.38 170 |
|
UniMVSNet_ETH3D | | | 88.05 162 | 87.01 191 | 89.27 123 | 88.53 149 | 97.49 143 | 90.35 165 | 83.48 119 | 94.57 171 | 77.87 127 | 70.08 199 | 61.75 204 | 96.22 115 | 90.17 195 | 95.21 134 | 99.16 189 | 99.82 147 |
|
GA-MVS | | | 90.38 145 | 94.59 137 | 85.46 164 | 88.30 150 | 98.44 125 | 92.18 147 | 83.30 122 | 97.89 146 | 58.05 195 | 92.86 122 | 84.25 144 | 91.27 167 | 96.65 113 | 92.61 169 | 99.66 116 | 99.43 168 |
|
FC-MVSNet-test | | | 92.78 124 | 96.19 118 | 88.80 129 | 88.00 151 | 97.54 142 | 93.60 136 | 82.36 130 | 98.16 139 | 79.71 119 | 91.55 130 | 95.41 102 | 89.65 175 | 96.09 130 | 95.23 133 | 99.49 147 | 99.31 171 |
|
FMVSNet1 | | | 92.55 129 | 93.66 144 | 91.26 104 | 87.91 152 | 96.12 166 | 94.75 122 | 81.69 137 | 97.67 147 | 85.63 102 | 80.56 172 | 87.88 131 | 98.15 83 | 96.50 116 | 97.21 92 | 99.41 168 | 99.71 158 |
|
tfpnnormal | | | 89.09 154 | 89.71 168 | 88.38 135 | 87.37 153 | 96.78 154 | 91.46 154 | 85.20 99 | 90.33 198 | 72.35 145 | 83.45 160 | 69.30 188 | 94.45 136 | 95.29 144 | 92.86 165 | 99.44 162 | 99.93 116 |
|
TESTMET0.1,1 | | | 92.87 123 | 97.23 92 | 87.79 140 | 86.96 154 | 99.10 105 | 94.68 125 | 77.46 164 | 99.36 93 | 71.94 148 | 93.82 115 | 96.51 94 | 95.96 118 | 97.42 99 | 94.37 145 | 99.74 90 | 99.99 53 |
|
FMVSNet5 | | | 93.53 110 | 96.09 119 | 90.56 111 | 86.74 155 | 92.84 199 | 92.64 144 | 77.50 163 | 99.41 90 | 88.97 76 | 98.02 72 | 97.81 84 | 98.00 87 | 94.85 151 | 95.43 131 | 99.50 146 | 94.25 201 |
|
test-mter | | | 92.67 127 | 97.13 94 | 87.47 142 | 86.72 156 | 99.07 107 | 94.28 130 | 76.90 166 | 99.21 100 | 71.53 152 | 93.63 117 | 96.32 97 | 95.67 121 | 97.32 104 | 94.36 147 | 99.74 90 | 99.99 53 |
|
TransMVSNet (Re) | | | 88.33 158 | 89.55 173 | 86.91 148 | 86.65 157 | 95.56 181 | 90.48 161 | 84.44 109 | 92.02 197 | 71.07 156 | 80.13 174 | 72.48 175 | 89.41 177 | 95.05 150 | 94.44 142 | 99.39 170 | 97.14 193 |
|
LTVRE_ROB | | 88.65 16 | 87.87 164 | 91.11 159 | 84.10 177 | 86.64 158 | 97.47 144 | 94.40 128 | 78.41 159 | 96.13 157 | 52.02 202 | 87.95 146 | 65.92 198 | 93.59 147 | 95.29 144 | 95.09 136 | 99.52 140 | 99.95 108 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
EG-PatchMatch MVS | | | 86.96 177 | 89.56 172 | 83.93 178 | 86.29 159 | 97.61 141 | 90.75 159 | 73.31 183 | 95.43 164 | 66.08 173 | 75.88 193 | 71.31 180 | 87.55 190 | 94.79 152 | 92.74 166 | 99.61 126 | 99.13 177 |
|
pm-mvs1 | | | 89.68 149 | 92.00 152 | 86.96 147 | 86.23 160 | 96.62 157 | 90.36 164 | 83.05 125 | 93.97 178 | 72.15 147 | 81.77 167 | 82.10 148 | 90.69 172 | 95.38 142 | 94.50 141 | 99.29 181 | 99.65 160 |
|
NR-MVSNet | | | 89.52 151 | 90.71 161 | 88.14 139 | 86.19 161 | 96.20 164 | 92.07 148 | 84.58 105 | 95.54 161 | 75.27 135 | 87.52 151 | 67.96 191 | 91.24 168 | 94.33 157 | 93.45 156 | 99.49 147 | 99.97 82 |
|
our_test_3 | | | | | | 85.89 162 | 96.09 167 | 82.15 198 | | | | | | | | | | |
|
pmmvs4 | | | 91.41 140 | 93.05 146 | 89.49 122 | 85.85 163 | 96.52 159 | 91.70 152 | 82.49 128 | 98.14 140 | 83.17 111 | 87.57 150 | 81.76 149 | 94.39 137 | 95.47 140 | 92.62 168 | 99.33 175 | 99.29 172 |
|
UniMVSNet (Re) | | | 90.41 144 | 91.96 153 | 88.59 133 | 85.71 164 | 96.73 155 | 90.82 157 | 84.11 116 | 95.23 166 | 78.54 124 | 88.91 143 | 76.41 161 | 92.84 152 | 93.40 176 | 93.05 163 | 99.55 135 | 100.00 1 |
|
v8 | | | 87.54 168 | 89.33 177 | 85.45 165 | 85.41 165 | 95.50 184 | 90.32 166 | 78.94 156 | 94.35 176 | 66.93 170 | 81.90 166 | 70.99 182 | 91.62 163 | 91.49 189 | 91.22 185 | 99.48 151 | 99.87 139 |
|
SixPastTwentyTwo | | | 88.35 157 | 91.51 156 | 84.66 170 | 85.39 166 | 96.96 152 | 86.57 185 | 79.62 150 | 96.57 154 | 63.73 181 | 87.86 147 | 75.18 164 | 93.43 148 | 94.03 161 | 90.37 191 | 99.24 184 | 99.58 163 |
|
MIMVSNet | | | 91.01 142 | 96.22 116 | 84.93 168 | 85.24 167 | 98.09 134 | 90.40 163 | 64.96 201 | 97.55 148 | 72.65 140 | 96.23 90 | 90.81 120 | 96.79 107 | 96.69 112 | 97.06 102 | 99.52 140 | 97.09 194 |
|
thisisatest0515 | | | 90.28 147 | 94.32 141 | 85.57 163 | 85.23 168 | 97.23 148 | 85.44 189 | 83.09 124 | 96.80 152 | 72.41 143 | 89.82 136 | 90.87 119 | 87.93 188 | 95.27 146 | 90.39 190 | 99.33 175 | 99.88 134 |
|
V42 | | | 87.84 165 | 89.42 176 | 85.99 156 | 85.16 169 | 96.01 170 | 90.52 160 | 81.78 136 | 94.43 174 | 67.59 165 | 81.32 168 | 71.87 178 | 91.48 165 | 91.25 190 | 91.16 186 | 99.43 163 | 99.92 120 |
|
WR-MVS_H | | | 88.47 156 | 90.55 163 | 86.04 153 | 85.13 170 | 96.07 168 | 89.86 175 | 79.80 148 | 94.37 175 | 72.32 146 | 83.12 162 | 74.44 169 | 89.60 176 | 93.52 173 | 92.40 170 | 99.51 144 | 99.96 101 |
|
N_pmnet | | | 87.31 173 | 91.51 156 | 82.41 186 | 85.13 170 | 95.57 180 | 80.59 200 | 81.79 135 | 96.20 156 | 58.52 194 | 78.62 182 | 85.66 136 | 89.36 178 | 94.64 153 | 92.14 173 | 99.08 191 | 97.72 192 |
|
EU-MVSNet | | | 87.20 175 | 90.47 164 | 83.38 181 | 85.11 172 | 93.85 197 | 86.10 187 | 79.76 149 | 93.30 189 | 65.39 176 | 84.41 156 | 78.43 153 | 85.04 195 | 92.20 186 | 93.03 164 | 98.86 193 | 98.05 189 |
|
UniMVSNet_NR-MVSNet | | | 90.50 143 | 92.31 151 | 88.38 135 | 85.04 173 | 96.34 163 | 90.94 155 | 85.32 96 | 95.87 159 | 75.69 131 | 87.68 149 | 78.49 152 | 93.78 145 | 93.21 178 | 94.60 138 | 99.53 139 | 99.97 82 |
|
v10 | | | 87.40 171 | 89.62 170 | 84.80 169 | 84.93 174 | 95.07 190 | 90.44 162 | 75.63 172 | 94.51 172 | 66.52 172 | 78.87 180 | 73.47 173 | 91.86 161 | 93.69 171 | 91.87 177 | 99.45 158 | 99.86 142 |
|
pmmvs6 | | | 85.75 185 | 86.97 192 | 84.34 173 | 84.88 175 | 95.59 179 | 87.41 184 | 79.19 154 | 87.81 203 | 67.56 166 | 63.05 204 | 77.76 154 | 89.15 179 | 93.45 175 | 91.90 176 | 97.83 198 | 99.21 174 |
|
v1144 | | | 87.49 169 | 89.64 169 | 84.97 167 | 84.73 176 | 95.84 174 | 90.17 167 | 79.30 152 | 93.96 179 | 64.65 178 | 78.83 181 | 73.38 174 | 91.51 164 | 93.77 168 | 91.77 178 | 99.45 158 | 99.93 116 |
|
DU-MVS | | | 89.49 152 | 90.60 162 | 88.19 138 | 84.71 177 | 96.20 164 | 90.94 155 | 84.58 105 | 95.54 161 | 75.69 131 | 87.52 151 | 68.74 190 | 93.78 145 | 91.10 191 | 95.13 135 | 99.47 153 | 99.97 82 |
|
Baseline_NR-MVSNet | | | 89.13 153 | 89.53 174 | 88.66 132 | 84.71 177 | 94.43 192 | 91.79 151 | 84.49 108 | 95.54 161 | 78.28 125 | 78.52 184 | 72.46 176 | 93.29 149 | 91.10 191 | 94.82 137 | 99.42 166 | 99.86 142 |
|
v148 | | | 86.63 183 | 87.79 188 | 85.28 166 | 84.65 179 | 95.97 171 | 86.46 186 | 82.84 127 | 92.91 192 | 71.52 153 | 78.99 179 | 66.74 196 | 86.83 192 | 89.28 197 | 90.69 188 | 99.41 168 | 99.94 113 |
|
CP-MVSNet | | | 88.09 161 | 89.57 171 | 86.36 152 | 84.63 180 | 95.46 186 | 89.48 177 | 80.53 143 | 93.42 185 | 71.26 155 | 81.25 169 | 69.90 186 | 92.78 153 | 93.30 177 | 93.69 154 | 99.47 153 | 99.96 101 |
|
v2v482 | | | 87.46 170 | 88.90 180 | 85.78 158 | 84.58 181 | 95.95 172 | 89.90 174 | 82.43 129 | 94.19 177 | 65.65 174 | 79.80 176 | 69.12 189 | 92.67 154 | 91.88 188 | 91.46 183 | 99.45 158 | 99.93 116 |
|
PS-CasMVS | | | 87.24 174 | 88.52 185 | 85.73 161 | 84.58 181 | 95.35 188 | 89.03 180 | 80.17 144 | 93.11 191 | 68.86 162 | 77.71 186 | 66.89 194 | 92.30 156 | 93.13 180 | 93.50 155 | 99.46 155 | 99.96 101 |
|
v1192 | | | 86.93 178 | 89.01 178 | 84.50 171 | 84.46 183 | 95.51 183 | 89.93 173 | 78.65 158 | 93.75 180 | 62.29 183 | 77.19 188 | 70.88 183 | 92.28 157 | 93.84 165 | 91.96 175 | 99.38 172 | 99.90 126 |
|
WR-MVS | | | 88.23 160 | 90.15 165 | 86.00 155 | 84.39 184 | 95.64 177 | 89.96 172 | 81.80 134 | 94.46 173 | 71.60 151 | 82.10 165 | 74.36 170 | 88.76 184 | 92.48 184 | 92.20 172 | 99.46 155 | 99.83 146 |
|
v144192 | | | 86.80 180 | 88.90 180 | 84.35 172 | 84.33 185 | 95.56 181 | 89.34 178 | 77.74 162 | 93.60 182 | 64.03 179 | 77.82 185 | 70.76 184 | 91.28 166 | 92.91 182 | 91.74 180 | 99.37 173 | 99.90 126 |
|
pmmvs5 | | | 87.33 172 | 90.01 166 | 84.20 175 | 84.31 186 | 96.04 169 | 87.63 183 | 76.59 169 | 93.17 190 | 65.35 177 | 84.30 158 | 71.68 179 | 91.91 160 | 95.41 141 | 91.37 184 | 99.39 170 | 98.13 186 |
|
v1921920 | | | 86.81 179 | 88.93 179 | 84.33 174 | 84.23 187 | 95.41 187 | 90.09 169 | 78.10 160 | 93.74 181 | 62.17 184 | 76.98 190 | 71.14 181 | 92.05 159 | 93.69 171 | 91.69 181 | 99.32 177 | 99.88 134 |
|
gm-plane-assit | | | 84.93 187 | 91.61 155 | 77.14 196 | 84.14 188 | 91.29 202 | 66.18 210 | 69.70 186 | 85.22 206 | 47.95 206 | 78.58 183 | 89.24 127 | 94.90 130 | 98.82 37 | 98.12 63 | 99.99 5 | 100.00 1 |
|
TranMVSNet+NR-MVSNet | | | 88.88 155 | 89.90 167 | 87.69 141 | 84.06 189 | 95.68 176 | 91.88 149 | 85.23 98 | 95.16 167 | 72.54 141 | 83.06 163 | 70.14 185 | 92.93 151 | 90.81 194 | 94.53 140 | 99.48 151 | 99.89 129 |
|
v1240 | | | 86.24 184 | 88.56 184 | 83.54 179 | 84.05 190 | 95.21 189 | 89.27 179 | 76.76 167 | 93.42 185 | 60.68 191 | 75.99 192 | 69.80 187 | 91.21 169 | 93.83 167 | 91.76 179 | 99.29 181 | 99.91 125 |
|
PEN-MVS | | | 87.20 175 | 88.22 186 | 86.01 154 | 84.01 191 | 94.93 191 | 90.00 171 | 81.52 141 | 93.46 184 | 69.29 160 | 79.69 177 | 65.51 199 | 91.72 162 | 91.01 193 | 93.12 161 | 99.49 147 | 99.84 144 |
|
MDTV_nov1_ep13_2view | | | 87.75 167 | 93.32 145 | 81.26 189 | 83.74 192 | 96.64 156 | 85.66 188 | 66.20 193 | 98.36 138 | 61.61 185 | 84.34 157 | 87.95 130 | 91.12 171 | 94.01 162 | 92.66 167 | 99.22 185 | 99.27 173 |
|
anonymousdsp | | | 87.98 163 | 92.38 150 | 82.85 183 | 83.68 193 | 96.79 153 | 90.78 158 | 74.06 178 | 95.29 165 | 57.91 196 | 83.33 161 | 83.12 145 | 91.15 170 | 95.96 134 | 92.37 171 | 99.52 140 | 99.76 155 |
|
DTE-MVSNet | | | 86.70 181 | 87.66 190 | 85.58 162 | 83.30 194 | 94.29 193 | 89.74 176 | 81.53 139 | 92.77 193 | 68.93 161 | 80.13 174 | 64.00 202 | 90.62 173 | 89.45 196 | 93.34 157 | 99.32 177 | 99.67 159 |
|
FPMVS | | | 73.80 198 | 74.62 200 | 72.84 200 | 83.09 195 | 84.44 207 | 83.89 191 | 73.64 181 | 92.20 196 | 48.50 204 | 72.19 197 | 59.51 205 | 63.16 205 | 69.13 205 | 66.26 209 | 84.74 208 | 78.59 211 |
|
v7n | | | 85.39 186 | 87.70 189 | 82.70 184 | 82.77 196 | 95.64 177 | 88.27 182 | 74.83 174 | 92.30 195 | 62.58 182 | 76.37 191 | 64.80 201 | 88.38 186 | 94.29 159 | 90.61 189 | 99.34 174 | 99.87 139 |
|
test20.03 | | | 83.86 191 | 88.73 183 | 78.16 194 | 82.60 197 | 93.00 198 | 81.61 199 | 74.68 175 | 92.36 194 | 57.50 197 | 83.01 164 | 74.48 168 | 73.30 203 | 92.40 185 | 91.14 187 | 99.29 181 | 94.75 200 |
|
Anonymous20231206 | | | 84.28 189 | 89.53 174 | 78.17 193 | 82.31 198 | 94.16 195 | 82.57 196 | 76.51 170 | 93.38 188 | 52.98 201 | 79.47 178 | 73.74 172 | 75.45 201 | 95.07 149 | 94.41 144 | 99.18 188 | 96.46 198 |
|
ET-MVSNet_ETH3D | | | 95.20 85 | 97.82 81 | 92.15 100 | 80.77 199 | 98.13 133 | 97.65 68 | 86.93 92 | 99.72 65 | 88.56 83 | 99.29 40 | 97.01 90 | 99.24 45 | 94.58 155 | 95.98 123 | 99.75 86 | 99.99 53 |
|
new_pmnet | | | 84.12 190 | 87.89 187 | 79.72 191 | 80.43 200 | 94.14 196 | 80.26 201 | 74.14 177 | 96.01 158 | 56.30 200 | 74.94 194 | 76.45 160 | 88.59 185 | 93.11 181 | 89.31 193 | 98.59 196 | 91.27 204 |
|
PM-MVS | | | 82.79 193 | 84.51 195 | 80.77 190 | 77.22 201 | 92.13 200 | 83.61 194 | 73.31 183 | 93.50 183 | 61.06 186 | 77.15 189 | 46.52 210 | 90.55 174 | 94.14 160 | 89.05 196 | 98.85 194 | 99.12 178 |
|
pmmvs-eth3d | | | 82.92 192 | 83.31 197 | 82.47 185 | 76.97 202 | 91.76 201 | 83.79 192 | 76.10 171 | 90.33 198 | 69.95 159 | 71.04 198 | 48.09 207 | 89.02 181 | 93.85 164 | 89.14 194 | 99.02 192 | 98.96 179 |
|
new-patchmatchnet | | | 78.17 197 | 80.82 199 | 75.07 199 | 76.93 203 | 91.20 203 | 71.90 206 | 73.32 182 | 86.59 205 | 48.91 203 | 67.11 200 | 47.85 209 | 81.19 197 | 88.18 198 | 87.02 197 | 98.19 197 | 97.79 191 |
|
pmmvs3 | | | 80.91 194 | 85.62 193 | 75.42 198 | 75.01 204 | 89.09 205 | 75.31 204 | 68.70 187 | 86.99 204 | 46.74 208 | 81.18 170 | 62.91 203 | 87.95 187 | 93.84 165 | 89.06 195 | 98.80 195 | 96.23 199 |
|
Gipuma | | | 71.02 199 | 72.60 203 | 69.19 201 | 71.31 205 | 75.11 210 | 66.36 209 | 61.65 208 | 94.93 168 | 47.29 207 | 38.74 209 | 38.52 211 | 75.52 200 | 86.09 200 | 85.92 200 | 93.01 204 | 88.87 207 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MDA-MVSNet-bldmvs | | | 80.30 196 | 82.83 198 | 77.34 195 | 69.16 206 | 94.29 193 | 72.16 205 | 81.97 133 | 90.14 200 | 57.32 198 | 94.01 114 | 47.97 208 | 86.81 193 | 68.74 206 | 86.82 198 | 96.63 199 | 97.86 190 |
|
MIMVSNet1 | | | 80.64 195 | 83.97 196 | 76.76 197 | 68.91 207 | 91.15 204 | 78.32 203 | 75.47 173 | 89.58 202 | 56.64 199 | 65.10 202 | 65.17 200 | 82.14 196 | 93.51 174 | 91.64 182 | 99.10 190 | 91.66 203 |
|
PMVS | | 60.14 18 | 62.67 202 | 64.05 205 | 61.06 203 | 68.32 208 | 53.27 216 | 52.23 214 | 67.63 190 | 75.07 209 | 48.30 205 | 58.27 205 | 57.43 206 | 49.99 211 | 67.20 207 | 62.42 210 | 79.87 211 | 74.68 212 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 74.33 201 | | 66.84 209 | 84.26 208 | 84.17 190 | | 93.39 187 | 58.99 193 | 45.93 208 | 18.06 217 | 70.61 204 | 93.94 163 | 86.62 199 | 92.61 206 | 98.13 186 |
|
PMMVS2 | | | 65.18 201 | 68.25 204 | 61.59 202 | 61.37 210 | 79.72 209 | 59.18 213 | 61.80 206 | 64.72 210 | 37.33 210 | 53.82 206 | 35.59 212 | 54.46 210 | 73.94 204 | 80.52 202 | 95.40 202 | 89.43 206 |
|
EMVS | | | 55.14 205 | 55.29 208 | 54.97 204 | 60.87 211 | 57.52 213 | 38.58 216 | 63.57 205 | 64.54 211 | 23.36 214 | 36.96 210 | 27.99 214 | 60.69 206 | 51.17 210 | 66.61 208 | 82.73 210 | 82.25 209 |
|
E-PMN | | | 55.33 204 | 55.79 207 | 54.81 205 | 59.81 212 | 57.23 214 | 38.83 215 | 63.59 204 | 64.06 212 | 24.66 213 | 35.33 211 | 26.40 215 | 58.69 207 | 55.41 209 | 70.54 206 | 83.26 209 | 81.56 210 |
|
MVE | | 58.81 19 | 52.07 206 | 55.15 209 | 48.48 207 | 42.45 213 | 62.35 212 | 36.41 217 | 54.70 210 | 49.88 213 | 27.65 212 | 29.98 212 | 18.08 216 | 54.87 209 | 65.93 208 | 77.26 204 | 74.79 212 | 82.59 208 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 61.76 203 | 72.90 202 | 48.76 206 | 21.21 214 | 68.61 211 | 66.11 211 | 37.38 211 | 94.83 169 | 33.06 211 | 64.31 203 | 29.72 213 | 86.08 194 | 74.44 203 | 78.71 203 | 48.74 213 | 99.65 160 |
|
test123 | | | 48.14 207 | 58.11 206 | 36.51 208 | 8.71 215 | 56.81 215 | 59.55 212 | 24.08 212 | 77.50 208 | 14.41 215 | 49.20 207 | 11.94 218 | 80.98 198 | 41.62 211 | 69.81 207 | 31.32 214 | 99.90 126 |
|
GG-mvs-BLEND | | | 69.85 200 | 99.39 38 | 35.39 209 | 3.67 216 | 99.94 17 | 99.10 40 | 1.69 213 | 99.85 47 | 3.19 216 | 98.13 71 | 99.46 59 | 4.92 212 | 99.23 30 | 99.14 30 | 99.80 52 | 100.00 1 |
|
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 | | | | | | | | | | | | | | | | | | 100.00 1 |
|
MTAPA | | | | | | | | | | | 96.61 13 | | 100.00 1 | | | | | |
|
MTMP | | | | | | | | | | | 97.42 8 | | 100.00 1 | | | | | |
|
Patchmatch-RL test | | | | | | | | 68.01 208 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 99.79 57 | | | | | | | | |
|
Patchmtry | | | | | | | 99.00 109 | 95.46 110 | 65.50 197 | | 67.51 167 | | | | | | | |
|
DeepMVS_CX | | | | | | | 97.31 146 | 79.48 202 | 89.65 62 | 98.66 123 | 60.89 189 | 94.40 108 | 66.89 194 | 87.65 189 | 81.69 201 | | 92.76 205 | 94.24 202 |
|