MCST-MVS | | | 85.75 5 | 86.99 9 | 84.31 2 | 94.07 1 | 92.80 4 | 88.15 4 | 79.10 1 | 85.66 19 | 70.72 26 | 76.50 29 | 80.45 15 | 82.17 2 | 88.35 1 | 87.49 2 | 91.63 2 | 97.65 1 |
|
v1.0 | | | 80.27 26 | 76.62 54 | 84.53 1 | 92.88 2 | 93.82 1 | 88.95 1 | 76.05 4 | 92.95 4 | 80.32 3 | 93.12 3 | 86.87 1 | 80.88 5 | 85.54 10 | 84.01 18 | 88.09 34 | 0.00 242 |
|
HPM-MVS++ | | | 85.64 6 | 88.43 3 | 82.39 8 | 92.65 3 | 90.24 23 | 85.83 11 | 74.21 7 | 90.68 7 | 75.63 14 | 86.77 10 | 84.15 5 | 78.68 10 | 86.33 6 | 85.26 9 | 87.32 46 | 95.60 14 |
|
CNVR-MVS | | | 85.96 4 | 87.58 7 | 84.06 4 | 92.58 4 | 92.40 7 | 87.62 5 | 77.77 2 | 88.44 11 | 75.93 13 | 79.49 22 | 81.97 12 | 81.65 3 | 87.04 5 | 86.58 3 | 88.79 16 | 97.18 4 |
|
NCCC | | | 84.16 11 | 85.46 16 | 82.64 7 | 92.34 5 | 90.57 20 | 86.57 8 | 76.51 3 | 86.85 16 | 72.91 19 | 77.20 28 | 78.69 21 | 79.09 9 | 84.64 17 | 84.88 14 | 88.44 24 | 95.41 17 |
|
ESAPD | | | 87.60 1 | 90.44 1 | 84.29 3 | 92.09 6 | 93.44 2 | 88.69 2 | 75.11 5 | 93.06 3 | 80.80 2 | 94.23 2 | 86.70 2 | 81.44 4 | 84.84 15 | 83.52 24 | 87.64 40 | 97.28 2 |
|
CSCG | | | 82.90 15 | 84.52 18 | 81.02 14 | 91.85 7 | 93.43 3 | 87.14 6 | 74.01 10 | 81.96 29 | 76.14 11 | 70.84 33 | 82.49 9 | 69.71 54 | 82.32 35 | 85.18 11 | 87.26 48 | 95.40 18 |
|
SMA-MVS | | | 85.24 7 | 88.27 5 | 81.72 11 | 91.74 8 | 90.71 16 | 86.71 7 | 73.16 15 | 90.56 8 | 74.33 16 | 83.07 15 | 85.88 3 | 77.16 14 | 86.28 7 | 85.58 6 | 87.23 49 | 95.77 11 |
|
QAPM | | | 77.50 42 | 77.43 44 | 77.59 32 | 91.52 9 | 92.00 10 | 81.41 37 | 70.63 23 | 66.22 67 | 58.05 67 | 54.70 70 | 71.79 39 | 74.49 29 | 82.46 31 | 82.04 34 | 89.46 11 | 92.79 43 |
|
APDe-MVS | | | 86.37 3 | 88.41 4 | 84.00 5 | 91.43 10 | 91.83 11 | 88.34 3 | 74.67 6 | 91.19 5 | 81.76 1 | 91.13 4 | 81.94 13 | 80.07 6 | 83.38 23 | 82.58 31 | 87.69 38 | 96.78 7 |
|
3Dnovator | | 70.49 5 | 78.42 35 | 76.77 51 | 80.35 16 | 91.43 10 | 90.27 22 | 81.84 32 | 70.79 22 | 72.10 53 | 71.95 20 | 50.02 87 | 67.86 51 | 77.47 13 | 82.89 26 | 84.24 16 | 88.61 20 | 89.99 72 |
|
DeepC-MVS_fast | | 75.41 2 | 81.69 19 | 82.10 28 | 81.20 13 | 91.04 12 | 87.81 46 | 83.42 23 | 74.04 9 | 83.77 23 | 71.09 24 | 66.88 41 | 72.44 33 | 79.48 7 | 85.08 12 | 84.97 13 | 88.12 33 | 93.78 33 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SteuartSystems-ACMMP | | | 82.51 16 | 85.35 17 | 79.20 22 | 90.25 13 | 89.39 31 | 84.79 17 | 70.95 21 | 82.86 25 | 68.32 34 | 86.44 11 | 77.19 22 | 73.07 34 | 83.63 22 | 83.64 21 | 87.82 35 | 94.34 26 |
Skip Steuart: Steuart Systems R&D Blog. |
HFP-MVS | | | 82.48 17 | 84.12 19 | 80.56 15 | 90.15 14 | 87.55 48 | 84.28 19 | 69.67 30 | 85.22 20 | 77.95 9 | 84.69 13 | 75.94 25 | 75.04 23 | 81.85 40 | 81.17 47 | 86.30 65 | 92.40 45 |
|
DeepPCF-MVS | | 76.94 1 | 83.08 14 | 87.77 6 | 77.60 31 | 90.11 15 | 90.96 15 | 78.48 52 | 72.63 18 | 93.10 2 | 65.84 39 | 80.67 20 | 81.55 14 | 74.80 25 | 85.94 9 | 85.39 8 | 83.75 153 | 96.77 8 |
|
OpenMVS | | 67.62 8 | 74.92 54 | 73.91 61 | 76.09 41 | 90.10 16 | 90.38 21 | 78.01 54 | 66.35 48 | 66.09 69 | 62.80 46 | 46.33 112 | 64.55 60 | 71.77 42 | 79.92 57 | 80.88 53 | 87.52 42 | 89.20 79 |
|
MAR-MVS | | | 77.19 45 | 78.37 42 | 75.81 43 | 89.87 17 | 90.58 19 | 79.33 51 | 65.56 54 | 77.62 45 | 58.33 65 | 59.24 62 | 67.98 49 | 74.83 24 | 82.37 34 | 83.12 26 | 86.95 54 | 87.67 107 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
TSAR-MVS + ACMM | | | 81.59 21 | 85.84 15 | 76.63 35 | 89.82 18 | 86.53 55 | 86.32 10 | 66.72 46 | 85.96 18 | 65.43 40 | 88.98 8 | 82.29 10 | 67.57 72 | 82.06 38 | 81.33 45 | 83.93 151 | 93.75 34 |
|
train_agg | | | 83.35 13 | 86.93 11 | 79.17 23 | 89.70 19 | 88.41 38 | 85.60 15 | 72.89 17 | 86.31 17 | 66.58 37 | 90.48 5 | 82.24 11 | 73.06 35 | 83.10 25 | 82.64 30 | 87.21 52 | 95.30 19 |
|
abl_6 | | | | | 79.06 25 | 89.68 20 | 92.14 9 | 77.70 58 | 69.68 29 | 86.87 15 | 71.88 21 | 74.29 31 | 80.06 17 | 76.56 17 | | | 88.84 15 | 95.82 10 |
|
APD-MVS | | | 84.83 8 | 87.00 8 | 82.30 9 | 89.61 21 | 89.21 32 | 86.51 9 | 73.64 12 | 90.98 6 | 77.99 8 | 89.89 6 | 80.04 18 | 79.18 8 | 82.00 39 | 81.37 44 | 86.88 55 | 95.49 16 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMMP_Plus | | | 83.54 12 | 86.37 13 | 80.25 17 | 89.57 22 | 90.10 25 | 85.27 16 | 71.66 19 | 87.38 12 | 73.08 18 | 84.23 14 | 80.16 16 | 75.31 21 | 84.85 14 | 83.64 21 | 86.57 60 | 94.21 29 |
|
HSP-MVS | | | 86.82 2 | 89.95 2 | 83.16 6 | 89.38 23 | 91.60 13 | 85.63 13 | 74.15 8 | 94.20 1 | 75.52 15 | 94.99 1 | 83.21 7 | 85.96 1 | 87.67 3 | 85.88 5 | 88.32 26 | 92.13 48 |
|
AdaColmap | | | 76.23 49 | 73.55 64 | 79.35 21 | 89.38 23 | 85.00 67 | 79.99 47 | 73.04 16 | 76.60 47 | 71.17 23 | 55.18 68 | 57.99 86 | 77.87 11 | 76.82 79 | 76.82 78 | 84.67 136 | 86.45 115 |
|
3Dnovator+ | | 70.16 6 | 77.87 38 | 77.29 46 | 78.55 26 | 89.25 25 | 88.32 40 | 80.09 45 | 67.95 40 | 74.89 52 | 71.83 22 | 52.05 80 | 70.68 43 | 76.27 20 | 82.27 36 | 82.04 34 | 85.92 82 | 90.77 63 |
|
CDPH-MVS | | | 79.39 32 | 82.13 27 | 76.19 40 | 89.22 26 | 88.34 39 | 84.20 20 | 71.00 20 | 79.67 39 | 56.97 70 | 77.77 25 | 72.24 37 | 68.50 65 | 81.33 45 | 82.74 27 | 87.23 49 | 92.84 41 |
|
SD-MVS | | | 84.31 10 | 86.96 10 | 81.22 12 | 88.98 27 | 88.68 35 | 85.65 12 | 73.85 11 | 89.09 10 | 79.63 4 | 87.34 9 | 84.84 4 | 73.71 31 | 82.66 29 | 81.60 41 | 85.48 108 | 94.51 24 |
|
MP-MVS | | | 80.94 22 | 83.49 21 | 77.96 28 | 88.48 28 | 88.16 42 | 82.82 28 | 69.34 32 | 80.79 35 | 69.67 30 | 82.35 17 | 77.13 23 | 71.60 44 | 80.97 50 | 80.96 51 | 85.87 89 | 94.06 30 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
ACMMPR | | | 80.62 24 | 82.98 23 | 77.87 30 | 88.41 29 | 87.05 51 | 83.02 25 | 69.18 33 | 83.91 22 | 68.35 33 | 82.89 16 | 73.64 30 | 72.16 40 | 80.78 51 | 81.13 49 | 86.10 70 | 91.43 54 |
|
MSLP-MVS++ | | | 78.57 34 | 77.33 45 | 80.02 18 | 88.39 30 | 84.79 68 | 84.62 18 | 66.17 50 | 75.96 48 | 78.40 6 | 61.59 54 | 71.47 40 | 73.54 33 | 78.43 67 | 78.88 63 | 88.97 14 | 90.18 71 |
|
PGM-MVS | | | 79.42 31 | 81.84 29 | 76.60 36 | 88.38 31 | 86.69 53 | 82.97 27 | 65.75 52 | 80.39 36 | 64.94 41 | 81.95 19 | 72.11 38 | 71.41 45 | 80.45 52 | 80.55 55 | 86.18 67 | 90.76 64 |
|
EPNet | | | 79.28 33 | 82.25 25 | 75.83 42 | 88.31 32 | 90.14 24 | 79.43 50 | 68.07 39 | 81.76 31 | 61.26 53 | 77.26 27 | 70.08 45 | 70.06 52 | 82.43 33 | 82.00 36 | 87.82 35 | 92.09 49 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DELS-MVS | | | 79.49 27 | 79.84 35 | 79.08 24 | 88.26 33 | 92.49 5 | 84.12 21 | 70.63 23 | 65.27 74 | 69.60 32 | 61.29 56 | 66.50 53 | 72.75 36 | 88.07 2 | 88.03 1 | 89.13 13 | 97.22 3 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
zzz-MVS | | | 81.65 20 | 83.10 22 | 79.97 19 | 88.14 34 | 87.62 47 | 83.96 22 | 69.90 27 | 86.92 14 | 77.67 10 | 72.47 32 | 78.74 20 | 74.13 30 | 81.59 44 | 81.15 48 | 86.01 76 | 93.19 38 |
|
TSAR-MVS + MP. | | | 84.39 9 | 86.58 12 | 81.83 10 | 88.09 35 | 86.47 56 | 85.63 13 | 73.62 13 | 90.13 9 | 79.24 5 | 89.67 7 | 82.99 8 | 77.72 12 | 81.22 46 | 80.92 52 | 86.68 59 | 94.66 23 |
|
X-MVS | | | 78.16 37 | 80.55 33 | 75.38 45 | 87.99 36 | 86.27 58 | 81.05 41 | 68.98 34 | 78.33 41 | 61.07 55 | 75.25 30 | 72.27 34 | 67.52 73 | 80.03 56 | 80.52 56 | 85.66 105 | 91.20 57 |
|
DeepC-MVS | | 74.46 3 | 80.30 25 | 81.05 31 | 79.42 20 | 87.42 37 | 88.50 37 | 83.23 24 | 73.27 14 | 82.78 26 | 71.01 25 | 62.86 50 | 69.93 46 | 74.80 25 | 84.30 18 | 84.20 17 | 86.79 58 | 94.77 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | | | | 86.96 38 | | | | | | | 70.61 44 | | | | | |
|
CP-MVS | | | 79.44 28 | 81.51 30 | 77.02 34 | 86.95 39 | 85.96 62 | 82.00 30 | 68.44 38 | 81.82 30 | 67.39 35 | 77.43 26 | 73.68 29 | 71.62 43 | 79.56 59 | 79.58 57 | 85.73 98 | 92.51 44 |
|
MVS_111021_HR | | | 77.42 43 | 78.40 41 | 76.28 38 | 86.95 39 | 90.68 17 | 77.41 60 | 70.56 26 | 66.21 68 | 62.48 49 | 66.17 43 | 63.98 61 | 72.08 41 | 82.87 27 | 83.15 25 | 88.24 29 | 95.71 12 |
|
CANet | | | 80.90 23 | 82.93 24 | 78.53 27 | 86.83 41 | 92.26 8 | 81.19 39 | 66.95 44 | 81.60 32 | 69.90 29 | 66.93 40 | 74.80 27 | 76.79 15 | 84.68 16 | 84.77 15 | 89.50 10 | 95.50 15 |
|
CHOSEN 1792x2688 | | | 72.55 62 | 71.98 69 | 73.22 57 | 86.57 42 | 92.41 6 | 75.63 66 | 66.77 45 | 62.08 79 | 52.32 80 | 30.27 205 | 50.74 116 | 66.14 76 | 86.22 8 | 85.41 7 | 91.90 1 | 96.75 9 |
|
PHI-MVS | | | 79.43 29 | 84.06 20 | 74.04 53 | 86.15 43 | 91.57 14 | 80.85 43 | 68.90 36 | 82.22 28 | 51.81 83 | 78.10 24 | 74.28 28 | 70.39 51 | 84.01 21 | 84.00 19 | 86.14 69 | 94.24 28 |
|
ACMMP | | | 77.61 41 | 79.59 36 | 75.30 46 | 85.87 44 | 85.58 63 | 81.42 36 | 67.38 43 | 79.38 40 | 62.61 47 | 78.53 23 | 65.79 55 | 68.80 64 | 78.56 66 | 78.50 67 | 85.75 94 | 90.80 62 |
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 |
HQP-MVS | | | 78.26 36 | 80.91 32 | 75.17 47 | 85.67 45 | 84.33 72 | 83.01 26 | 69.38 31 | 79.88 38 | 55.83 71 | 79.85 21 | 64.90 59 | 70.81 47 | 82.46 31 | 81.78 38 | 86.30 65 | 93.18 39 |
|
OPM-MVS | | | 72.74 61 | 70.93 77 | 74.85 51 | 85.30 46 | 84.34 71 | 82.82 28 | 69.79 28 | 49.96 121 | 55.39 76 | 54.09 75 | 60.14 77 | 70.04 53 | 80.38 54 | 79.43 58 | 85.74 97 | 88.20 103 |
|
MS-PatchMatch | | | 70.34 74 | 69.00 86 | 71.91 64 | 85.20 47 | 85.35 64 | 77.84 57 | 61.77 90 | 58.01 90 | 55.40 75 | 41.26 136 | 58.34 83 | 61.69 96 | 81.70 42 | 78.29 68 | 89.56 9 | 80.02 166 |
|
MVS_0304 | | | 79.43 29 | 82.20 26 | 76.20 39 | 84.22 48 | 91.79 12 | 81.82 33 | 63.81 65 | 76.83 46 | 61.71 51 | 66.37 42 | 75.52 26 | 76.38 19 | 85.54 10 | 85.03 12 | 89.28 12 | 94.32 27 |
|
PCF-MVS | | 70.85 4 | 75.73 51 | 76.55 55 | 74.78 52 | 83.67 49 | 88.04 45 | 81.47 35 | 70.62 25 | 69.24 63 | 57.52 68 | 60.59 59 | 69.18 47 | 70.65 48 | 77.11 76 | 77.65 74 | 84.75 134 | 94.01 31 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
ACMM | | 66.70 10 | 70.42 70 | 68.49 90 | 72.67 59 | 82.85 50 | 77.76 144 | 77.70 58 | 64.76 59 | 64.61 75 | 60.74 59 | 49.29 89 | 53.97 105 | 65.86 77 | 74.97 100 | 75.57 94 | 84.13 149 | 83.29 141 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
casdiffmvs1 | | | 77.74 39 | 78.92 38 | 76.36 37 | 82.58 51 | 90.61 18 | 81.58 34 | 61.31 94 | 75.68 49 | 66.24 38 | 64.21 45 | 65.17 56 | 76.54 18 | 80.07 55 | 82.68 29 | 89.88 7 | 94.00 32 |
|
XVS | | | | | | 82.43 52 | 86.27 58 | 75.70 64 | | | 61.07 55 | | 72.27 34 | | | | 85.67 102 | |
|
X-MVStestdata | | | | | | 82.43 52 | 86.27 58 | 75.70 64 | | | 61.07 55 | | 72.27 34 | | | | 85.67 102 | |
|
PVSNet_BlendedMVS | | | 76.84 47 | 78.47 39 | 74.95 49 | 82.37 54 | 89.90 28 | 75.45 70 | 65.45 55 | 74.99 50 | 70.66 27 | 63.07 48 | 58.27 84 | 67.60 70 | 84.24 19 | 81.70 39 | 88.18 30 | 97.10 5 |
|
PVSNet_Blended | | | 76.84 47 | 78.47 39 | 74.95 49 | 82.37 54 | 89.90 28 | 75.45 70 | 65.45 55 | 74.99 50 | 70.66 27 | 63.07 48 | 58.27 84 | 67.60 70 | 84.24 19 | 81.70 39 | 88.18 30 | 97.10 5 |
|
casdiffmvs | | | 76.13 50 | 76.96 49 | 75.15 48 | 82.26 56 | 90.09 26 | 79.98 48 | 60.64 104 | 70.12 57 | 63.58 44 | 62.04 52 | 60.30 76 | 74.53 28 | 81.62 43 | 82.30 32 | 89.90 6 | 92.27 46 |
|
CLD-MVS | | | 77.36 44 | 77.29 46 | 77.45 33 | 82.21 57 | 88.11 43 | 81.92 31 | 68.96 35 | 77.97 43 | 69.62 31 | 62.08 51 | 59.44 78 | 73.57 32 | 81.75 41 | 81.27 46 | 88.41 25 | 90.39 68 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
LGP-MVS_train | | | 72.02 66 | 73.18 67 | 70.67 69 | 82.13 58 | 80.26 119 | 79.58 49 | 63.04 73 | 70.09 58 | 51.98 81 | 65.06 44 | 55.62 98 | 62.49 93 | 75.97 91 | 76.32 85 | 84.80 133 | 88.93 83 |
|
MSDG | | | 65.57 103 | 61.57 153 | 70.24 70 | 82.02 59 | 76.47 157 | 74.46 82 | 68.73 37 | 56.52 95 | 50.33 90 | 38.47 163 | 41.10 145 | 62.42 94 | 72.12 151 | 72.94 149 | 83.47 156 | 73.37 191 |
|
IB-MVS | | 64.48 11 | 69.02 79 | 68.97 87 | 69.09 78 | 81.75 60 | 89.01 33 | 64.50 157 | 64.91 58 | 56.65 94 | 62.59 48 | 47.89 96 | 45.23 128 | 51.99 158 | 69.18 179 | 81.88 37 | 88.77 17 | 92.93 40 |
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 |
canonicalmvs | | | 77.65 40 | 79.59 36 | 75.39 44 | 81.52 61 | 89.83 30 | 81.32 38 | 60.74 103 | 80.05 37 | 66.72 36 | 68.43 37 | 65.09 57 | 74.72 27 | 78.87 63 | 82.73 28 | 87.32 46 | 92.16 47 |
|
CPTT-MVS | | | 75.43 52 | 77.13 48 | 73.44 55 | 81.43 62 | 82.55 82 | 80.96 42 | 64.35 60 | 77.95 44 | 61.39 52 | 69.20 36 | 70.94 42 | 69.38 60 | 73.89 115 | 73.32 141 | 83.14 166 | 92.06 50 |
|
DWT-MVSNet_training | | | 72.81 60 | 73.98 60 | 71.45 65 | 81.26 63 | 86.37 57 | 72.08 88 | 59.82 111 | 69.13 64 | 58.15 66 | 54.71 69 | 61.33 74 | 67.81 69 | 76.86 78 | 78.63 64 | 89.59 8 | 90.86 60 |
|
EPNet_dtu | | | 66.17 98 | 70.13 81 | 61.54 151 | 81.04 64 | 77.39 149 | 68.87 135 | 62.50 82 | 69.78 59 | 33.51 185 | 63.77 47 | 56.22 92 | 37.65 203 | 72.20 149 | 72.18 157 | 85.69 101 | 79.38 168 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMP | | 68.86 7 | 72.15 65 | 72.25 68 | 72.03 62 | 80.96 65 | 80.87 104 | 77.93 56 | 64.13 62 | 69.29 61 | 60.79 58 | 64.04 46 | 53.54 107 | 63.91 85 | 73.74 119 | 75.27 96 | 84.45 141 | 88.98 82 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
HyFIR lowres test | | | 68.39 83 | 68.28 92 | 68.52 81 | 80.85 66 | 88.11 43 | 71.08 116 | 58.09 120 | 54.87 108 | 47.80 97 | 27.55 211 | 55.80 95 | 64.97 80 | 79.11 61 | 79.14 60 | 88.31 27 | 93.35 35 |
|
LS3D | | | 64.54 112 | 62.14 146 | 67.34 89 | 80.85 66 | 75.79 163 | 69.99 126 | 65.87 51 | 60.77 82 | 44.35 114 | 42.43 130 | 45.95 126 | 65.01 79 | 69.88 174 | 68.69 184 | 77.97 205 | 71.43 201 |
|
CNLPA | | | 71.37 69 | 70.27 80 | 72.66 60 | 80.79 68 | 81.33 98 | 71.07 117 | 65.75 52 | 82.36 27 | 64.80 42 | 42.46 129 | 56.49 91 | 72.70 37 | 73.00 127 | 70.52 176 | 80.84 187 | 85.76 124 |
|
TSAR-MVS + GP. | | | 82.27 18 | 85.98 14 | 77.94 29 | 80.72 69 | 88.25 41 | 81.12 40 | 67.71 41 | 87.10 13 | 73.31 17 | 85.23 12 | 83.68 6 | 76.64 16 | 80.43 53 | 81.47 43 | 88.15 32 | 95.66 13 |
|
PLC | | 64.00 12 | 68.54 81 | 66.66 101 | 70.74 68 | 80.28 70 | 74.88 168 | 72.64 86 | 63.70 67 | 69.26 62 | 55.71 73 | 47.24 104 | 55.31 100 | 70.42 49 | 72.05 153 | 70.67 174 | 81.66 181 | 77.19 175 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
OMC-MVS | | | 74.03 56 | 75.82 57 | 71.95 63 | 79.56 71 | 80.98 102 | 75.35 72 | 63.21 69 | 84.48 21 | 61.83 50 | 61.54 55 | 66.89 52 | 69.41 59 | 76.60 81 | 74.07 130 | 82.34 176 | 86.15 119 |
|
CostFormer | | | 72.18 64 | 73.90 62 | 70.18 71 | 79.47 72 | 86.19 61 | 76.94 62 | 48.62 195 | 66.07 70 | 60.40 60 | 54.14 74 | 65.82 54 | 67.98 67 | 75.84 92 | 76.41 84 | 87.67 39 | 92.83 42 |
|
MVS_111021_LR | | | 74.26 55 | 75.95 56 | 72.27 61 | 79.43 73 | 85.04 66 | 72.71 85 | 65.27 57 | 70.92 56 | 63.58 44 | 69.32 35 | 60.31 75 | 69.43 58 | 77.01 77 | 77.15 75 | 83.22 161 | 91.93 52 |
|
MVS_Test | | | 75.22 53 | 76.69 52 | 73.51 54 | 79.30 74 | 88.82 34 | 80.06 46 | 58.74 114 | 69.77 60 | 57.50 69 | 59.78 61 | 61.35 72 | 75.31 21 | 82.07 37 | 83.60 23 | 90.13 5 | 91.41 55 |
|
PVSNet_Blended_VisFu | | | 71.76 67 | 73.54 65 | 69.69 72 | 79.01 75 | 87.16 50 | 72.05 89 | 61.80 89 | 56.46 96 | 59.66 62 | 53.88 76 | 62.48 64 | 59.08 135 | 81.17 47 | 78.90 61 | 86.53 62 | 94.74 22 |
|
ACMH | | 59.42 14 | 61.59 157 | 59.22 179 | 64.36 114 | 78.92 76 | 78.26 137 | 67.65 140 | 67.48 42 | 39.81 186 | 30.98 193 | 38.25 166 | 34.59 199 | 61.37 104 | 70.55 166 | 73.47 137 | 79.74 196 | 79.59 167 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FC-MVSNet-train | | | 68.83 80 | 68.29 91 | 69.47 73 | 78.35 77 | 79.94 120 | 64.72 156 | 66.38 47 | 54.96 105 | 54.51 78 | 56.75 65 | 47.91 122 | 66.91 74 | 75.57 96 | 75.75 90 | 85.92 82 | 87.12 110 |
|
tpmp4_e23 | | | 69.38 75 | 69.47 83 | 69.28 75 | 78.20 78 | 82.35 84 | 75.92 63 | 49.20 194 | 64.15 76 | 59.96 61 | 47.93 94 | 55.77 96 | 68.06 66 | 73.05 126 | 74.53 109 | 84.34 143 | 88.50 101 |
|
Effi-MVS+ | | | 70.42 70 | 71.23 75 | 69.47 73 | 78.04 79 | 85.24 65 | 75.57 68 | 58.88 113 | 59.56 85 | 48.47 94 | 52.73 79 | 54.94 101 | 69.69 55 | 78.34 69 | 77.06 76 | 86.18 67 | 90.73 65 |
|
Anonymous202405211 | | | | 66.35 105 | | 78.00 80 | 84.41 70 | 74.85 74 | 63.18 70 | 51.00 117 | | 31.37 202 | 53.73 106 | 69.67 56 | 76.28 83 | 76.84 77 | 83.21 163 | 90.85 61 |
|
conf0.002 | | | 67.12 94 | 67.13 99 | 67.11 90 | 77.95 81 | 82.11 85 | 71.71 98 | 63.06 71 | 49.16 126 | 43.43 119 | 47.76 98 | 48.79 119 | 61.42 98 | 76.61 80 | 76.55 82 | 85.07 118 | 88.92 85 |
|
conf0.01 | | | 66.60 95 | 66.18 107 | 67.09 91 | 77.90 82 | 82.02 86 | 71.71 98 | 63.05 72 | 49.16 126 | 43.41 121 | 46.23 113 | 45.78 127 | 61.42 98 | 76.55 82 | 74.63 103 | 85.04 119 | 88.87 87 |
|
tfpn111 | | | 66.52 96 | 66.12 108 | 66.98 93 | 77.70 83 | 81.58 92 | 71.71 98 | 62.94 77 | 49.16 126 | 43.28 124 | 51.38 82 | 41.34 138 | 61.42 98 | 76.24 85 | 74.63 103 | 84.84 127 | 88.52 97 |
|
conf200view11 | | | 65.89 101 | 64.96 114 | 66.98 93 | 77.70 83 | 81.58 92 | 71.71 98 | 62.94 77 | 49.16 126 | 43.28 124 | 43.24 119 | 41.34 138 | 61.42 98 | 76.24 85 | 74.63 103 | 84.84 127 | 88.52 97 |
|
thres100view900 | | | 67.14 93 | 66.09 109 | 68.38 83 | 77.70 83 | 83.84 75 | 74.52 79 | 66.33 49 | 49.16 126 | 43.40 122 | 43.24 119 | 41.34 138 | 62.59 92 | 79.31 60 | 75.92 89 | 85.73 98 | 89.81 73 |
|
tfpn200view9 | | | 65.90 100 | 64.96 114 | 67.00 92 | 77.70 83 | 81.58 92 | 71.71 98 | 62.94 77 | 49.16 126 | 43.40 122 | 43.24 119 | 41.34 138 | 61.42 98 | 76.24 85 | 74.63 103 | 84.84 127 | 88.52 97 |
|
Anonymous20240521 | | | 69.13 78 | 69.07 85 | 69.21 76 | 77.65 87 | 77.52 146 | 74.68 75 | 57.85 123 | 54.92 106 | 55.34 77 | 55.74 67 | 55.56 99 | 66.35 75 | 75.05 98 | 76.56 81 | 83.35 158 | 88.13 104 |
|
Anonymous20231211 | | | 68.44 82 | 66.37 104 | 70.86 67 | 77.58 88 | 83.49 76 | 75.15 73 | 61.89 87 | 52.54 114 | 58.50 64 | 28.89 207 | 56.78 89 | 69.29 61 | 74.96 102 | 76.61 79 | 82.73 169 | 91.36 56 |
|
UA-Net | | | 64.62 109 | 68.23 93 | 60.42 156 | 77.53 89 | 81.38 97 | 60.08 185 | 57.47 132 | 47.01 139 | 44.75 112 | 60.68 58 | 71.32 41 | 41.84 194 | 73.27 121 | 72.25 156 | 80.83 188 | 71.68 199 |
|
thres200 | | | 65.58 102 | 64.74 117 | 66.56 95 | 77.52 90 | 81.61 90 | 73.44 84 | 62.95 75 | 46.23 147 | 42.45 139 | 42.76 123 | 41.18 143 | 58.12 139 | 76.24 85 | 75.59 93 | 84.89 124 | 89.58 74 |
|
ACMH+ | | 60.36 13 | 61.16 158 | 58.38 181 | 64.42 113 | 77.37 91 | 74.35 173 | 68.45 136 | 62.81 81 | 45.86 149 | 38.48 158 | 35.71 185 | 37.35 181 | 59.81 124 | 67.24 185 | 69.80 180 | 79.58 197 | 78.32 173 |
|
TAPA-MVS | | 67.10 9 | 71.45 68 | 73.47 66 | 69.10 77 | 77.04 92 | 80.78 105 | 73.81 83 | 62.10 83 | 80.80 34 | 51.28 84 | 60.91 57 | 63.80 63 | 67.98 67 | 74.59 104 | 72.42 155 | 82.37 175 | 80.97 161 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
IS_MVSNet | | | 67.29 91 | 71.98 69 | 61.82 149 | 76.92 93 | 84.32 73 | 65.90 154 | 58.22 118 | 55.75 101 | 39.22 152 | 54.51 72 | 62.47 65 | 45.99 183 | 78.83 64 | 78.52 66 | 84.70 135 | 89.47 76 |
|
tfpn_ndepth | | | 62.95 137 | 63.75 122 | 62.02 147 | 76.89 94 | 79.48 127 | 64.09 160 | 60.98 98 | 49.48 123 | 38.73 156 | 49.92 88 | 44.79 129 | 47.37 175 | 71.91 154 | 71.66 160 | 84.07 150 | 79.00 171 |
|
CANet_DTU | | | 72.84 59 | 76.63 53 | 68.43 82 | 76.81 95 | 86.62 54 | 75.54 69 | 54.71 163 | 72.06 54 | 43.54 117 | 67.11 39 | 58.46 81 | 72.40 38 | 81.13 49 | 80.82 54 | 87.57 41 | 90.21 70 |
|
tpm cat1 | | | 67.47 89 | 67.05 100 | 67.98 84 | 76.63 96 | 81.51 96 | 74.49 81 | 47.65 200 | 61.18 81 | 61.12 54 | 42.51 128 | 53.02 110 | 64.74 83 | 70.11 171 | 71.50 162 | 83.22 161 | 89.49 75 |
|
DI_MVS_plusplus_trai | | | 73.94 57 | 74.85 59 | 72.88 58 | 76.57 97 | 86.80 52 | 80.41 44 | 61.47 92 | 62.35 78 | 59.44 63 | 47.91 95 | 68.12 48 | 72.24 39 | 82.84 28 | 81.50 42 | 87.15 53 | 94.42 25 |
|
thres400 | | | 65.18 107 | 64.44 119 | 66.04 96 | 76.40 98 | 82.63 80 | 71.52 108 | 64.27 61 | 44.93 155 | 40.69 147 | 41.86 133 | 40.79 152 | 58.12 139 | 77.67 70 | 74.64 102 | 85.26 111 | 88.56 96 |
|
diffmvs | | | 72.46 63 | 73.75 63 | 70.95 66 | 76.33 99 | 87.21 49 | 77.96 55 | 58.43 117 | 66.25 66 | 55.75 72 | 59.11 63 | 56.77 90 | 70.42 49 | 77.35 75 | 78.90 61 | 86.80 57 | 90.64 66 |
|
tpmrst | | | 67.15 92 | 68.12 94 | 66.03 97 | 76.21 100 | 80.98 102 | 71.27 110 | 45.05 207 | 60.69 83 | 50.63 88 | 46.95 109 | 54.15 104 | 65.30 78 | 71.80 156 | 71.77 159 | 87.72 37 | 90.48 67 |
|
gg-mvs-nofinetune | | | 62.34 140 | 66.19 106 | 57.86 174 | 76.15 101 | 88.61 36 | 71.18 113 | 41.24 224 | 25.74 226 | 13.16 228 | 22.91 221 | 63.97 62 | 54.52 153 | 85.06 13 | 85.25 10 | 90.92 3 | 91.78 53 |
|
thresconf0.02 | | | 63.92 119 | 65.18 113 | 62.46 142 | 75.91 102 | 80.65 113 | 67.51 143 | 63.86 64 | 45.00 154 | 33.32 186 | 51.38 82 | 51.68 112 | 48.34 170 | 75.49 97 | 75.13 97 | 85.84 93 | 76.91 177 |
|
EPMVS | | | 66.21 97 | 67.49 97 | 64.73 106 | 75.81 103 | 84.20 74 | 68.94 134 | 44.37 211 | 61.55 80 | 48.07 96 | 49.21 91 | 54.87 102 | 62.88 90 | 71.82 155 | 71.40 166 | 88.28 28 | 79.37 169 |
|
EPP-MVSNet | | | 67.58 87 | 71.10 76 | 63.48 128 | 75.71 104 | 83.35 77 | 66.85 146 | 57.83 124 | 53.02 113 | 41.15 144 | 55.82 66 | 67.89 50 | 56.01 147 | 74.40 106 | 72.92 150 | 83.33 159 | 90.30 69 |
|
view600 | | | 63.91 120 | 63.27 128 | 64.66 108 | 75.57 105 | 81.73 88 | 69.71 129 | 63.04 73 | 43.97 158 | 39.18 153 | 41.09 137 | 40.24 160 | 55.38 149 | 76.28 83 | 72.04 158 | 85.08 117 | 87.52 108 |
|
thres600view7 | | | 63.77 121 | 63.14 130 | 64.51 110 | 75.49 106 | 81.61 90 | 69.59 130 | 62.95 75 | 43.96 159 | 38.90 155 | 41.09 137 | 40.24 160 | 55.25 151 | 76.24 85 | 71.54 161 | 84.89 124 | 87.30 109 |
|
dps | | | 64.08 115 | 63.22 129 | 65.08 100 | 75.27 107 | 79.65 124 | 66.68 148 | 46.63 205 | 56.94 92 | 55.67 74 | 43.96 115 | 43.63 134 | 64.00 84 | 69.50 178 | 69.82 179 | 82.25 177 | 79.02 170 |
|
MVSTER | | | 76.92 46 | 79.92 34 | 73.42 56 | 74.98 108 | 82.97 78 | 78.15 53 | 63.41 68 | 78.02 42 | 64.41 43 | 67.54 38 | 72.80 32 | 71.05 46 | 83.29 24 | 83.73 20 | 88.53 23 | 91.12 58 |
|
TSAR-MVS + COLMAP | | | 73.09 58 | 76.86 50 | 68.71 79 | 74.97 109 | 82.49 83 | 74.51 80 | 61.83 88 | 83.16 24 | 49.31 93 | 82.22 18 | 51.62 113 | 68.94 63 | 78.76 65 | 75.52 95 | 82.67 171 | 84.23 133 |
|
view800 | | | 63.02 133 | 62.69 141 | 63.39 130 | 74.79 110 | 80.76 109 | 67.83 139 | 61.93 86 | 43.16 169 | 37.78 164 | 40.43 142 | 39.73 167 | 53.16 156 | 75.01 99 | 73.32 141 | 84.87 126 | 86.43 116 |
|
tpm | | | 64.85 108 | 66.02 110 | 63.48 128 | 74.52 111 | 78.38 136 | 70.98 118 | 44.99 209 | 51.61 116 | 43.28 124 | 47.66 99 | 53.18 108 | 60.57 110 | 70.58 165 | 71.30 171 | 86.54 61 | 89.45 77 |
|
tfpn | | | 62.54 139 | 62.79 137 | 62.25 146 | 74.16 112 | 79.86 122 | 66.07 153 | 60.97 99 | 42.43 174 | 36.41 168 | 39.88 146 | 43.76 133 | 51.25 163 | 73.85 116 | 74.17 126 | 84.67 136 | 85.57 127 |
|
Vis-MVSNet (Re-imp) | | | 62.25 143 | 68.74 88 | 54.68 189 | 73.70 113 | 78.74 132 | 56.51 196 | 57.49 131 | 55.22 103 | 26.86 203 | 54.56 71 | 61.35 72 | 31.06 206 | 73.10 123 | 74.90 99 | 82.49 173 | 83.31 140 |
|
Fast-Effi-MVS+ | | | 67.59 86 | 67.56 96 | 67.62 87 | 73.67 114 | 81.14 101 | 71.12 114 | 54.79 162 | 58.88 86 | 50.61 89 | 46.70 110 | 47.05 123 | 69.12 62 | 76.06 90 | 76.44 83 | 86.43 63 | 86.65 113 |
|
IterMVS-LS | | | 66.08 99 | 66.56 103 | 65.51 98 | 73.67 114 | 74.88 168 | 70.89 120 | 53.55 170 | 50.42 119 | 48.32 95 | 50.59 85 | 55.66 97 | 61.83 95 | 73.93 114 | 74.42 116 | 84.82 132 | 86.01 121 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchmatchNet | | | 65.43 105 | 67.71 95 | 62.78 138 | 73.49 116 | 82.83 79 | 66.42 151 | 45.40 206 | 60.40 84 | 45.27 106 | 49.22 90 | 57.60 87 | 60.01 119 | 70.61 163 | 71.38 169 | 86.08 72 | 81.91 156 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
COLMAP_ROB | | 51.17 15 | 55.13 187 | 52.90 201 | 57.73 175 | 73.47 117 | 67.21 204 | 62.13 174 | 55.82 148 | 47.83 137 | 34.39 181 | 31.60 201 | 34.24 200 | 44.90 188 | 63.88 200 | 62.52 210 | 75.67 211 | 63.02 217 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
conf0.05thres1000 | | | 60.33 165 | 59.42 176 | 61.40 152 | 73.15 118 | 78.25 138 | 65.29 155 | 60.30 107 | 36.61 198 | 35.75 174 | 33.25 192 | 39.23 171 | 50.35 166 | 72.18 150 | 72.67 153 | 83.57 155 | 83.74 135 |
|
Effi-MVS+-dtu | | | 64.58 110 | 64.08 120 | 65.16 99 | 73.04 119 | 75.17 167 | 70.68 122 | 56.23 145 | 54.12 111 | 44.71 113 | 47.42 100 | 51.10 114 | 63.82 86 | 68.08 183 | 66.32 196 | 82.47 174 | 86.38 117 |
|
tfpnview11 | | | 58.92 171 | 59.60 174 | 58.13 169 | 72.99 120 | 77.11 153 | 60.48 180 | 60.37 105 | 42.10 177 | 29.10 197 | 43.45 116 | 40.72 155 | 41.67 195 | 70.53 167 | 70.43 177 | 84.17 148 | 72.85 193 |
|
tfpn_n400 | | | 58.64 175 | 59.27 177 | 57.89 172 | 72.83 121 | 77.26 151 | 60.35 181 | 60.29 108 | 39.77 188 | 29.10 197 | 43.45 116 | 40.72 155 | 41.61 196 | 70.06 172 | 71.39 167 | 83.17 164 | 72.26 196 |
|
tfpnconf | | | 58.64 175 | 59.27 177 | 57.89 172 | 72.83 121 | 77.26 151 | 60.35 181 | 60.29 108 | 39.77 188 | 29.10 197 | 43.45 116 | 40.72 155 | 41.61 196 | 70.06 172 | 71.39 167 | 83.17 164 | 72.26 196 |
|
tfpn1000 | | | 58.35 179 | 59.96 171 | 56.47 182 | 72.78 123 | 77.51 147 | 56.66 195 | 59.16 112 | 43.74 160 | 29.76 196 | 42.79 122 | 42.49 135 | 37.04 204 | 68.92 180 | 68.98 182 | 83.45 157 | 75.25 181 |
|
EG-PatchMatch MVS | | | 58.73 174 | 58.03 184 | 59.55 161 | 72.32 124 | 80.49 115 | 63.44 169 | 55.55 152 | 32.49 213 | 38.31 159 | 28.87 208 | 37.22 182 | 42.84 192 | 74.30 112 | 75.70 91 | 84.84 127 | 77.14 176 |
|
TransMVSNet (Re) | | | 57.83 180 | 56.90 187 | 58.91 166 | 72.26 125 | 74.69 171 | 63.57 168 | 61.42 93 | 32.30 214 | 32.65 188 | 33.97 191 | 35.96 191 | 39.17 201 | 73.84 118 | 72.84 151 | 84.37 142 | 74.69 184 |
|
CMPMVS | | 43.63 17 | 57.67 182 | 55.43 190 | 60.28 157 | 72.01 126 | 79.00 130 | 62.77 173 | 53.23 175 | 41.77 179 | 45.42 105 | 30.74 204 | 39.03 172 | 53.01 157 | 64.81 193 | 64.65 202 | 75.26 213 | 68.03 207 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
NR-MVSNet | | | 61.08 160 | 62.09 147 | 59.90 158 | 71.96 127 | 75.87 161 | 63.60 167 | 61.96 84 | 49.31 124 | 27.95 200 | 42.76 123 | 33.85 203 | 48.82 169 | 74.35 109 | 74.05 131 | 85.13 113 | 84.45 130 |
|
PMMVS | | | 70.37 73 | 75.06 58 | 64.90 101 | 71.46 128 | 81.88 87 | 64.10 159 | 55.64 151 | 71.31 55 | 46.69 100 | 70.69 34 | 58.56 79 | 69.53 57 | 79.03 62 | 75.63 92 | 81.96 179 | 88.32 102 |
|
test-LLR | | | 68.23 84 | 71.61 73 | 64.28 119 | 71.37 129 | 81.32 99 | 63.98 163 | 61.03 96 | 58.62 87 | 42.96 129 | 52.74 77 | 61.65 70 | 57.74 141 | 75.64 94 | 78.09 72 | 88.61 20 | 93.21 36 |
|
test0.0.03 1 | | | 57.35 183 | 59.89 172 | 54.38 191 | 71.37 129 | 73.45 176 | 52.71 201 | 61.03 96 | 46.11 148 | 26.33 204 | 41.73 134 | 44.08 131 | 29.72 209 | 71.43 159 | 70.90 172 | 85.10 114 | 71.56 200 |
|
tfpnnormal | | | 58.97 170 | 56.48 189 | 61.89 148 | 71.27 131 | 76.21 160 | 66.65 149 | 61.76 91 | 32.90 212 | 36.41 168 | 27.83 210 | 29.14 215 | 50.64 165 | 73.06 124 | 73.05 148 | 84.58 139 | 83.15 146 |
|
Fast-Effi-MVS+-dtu | | | 63.05 132 | 64.72 118 | 61.11 153 | 71.21 132 | 76.81 156 | 70.72 121 | 43.13 215 | 52.51 115 | 35.34 177 | 46.55 111 | 46.36 124 | 61.40 103 | 71.57 158 | 71.44 164 | 84.84 127 | 87.79 106 |
|
MDTV_nov1_ep13 | | | 65.21 106 | 67.28 98 | 62.79 137 | 70.91 133 | 81.72 89 | 69.28 133 | 49.50 191 | 58.08 89 | 43.94 116 | 50.50 86 | 56.02 93 | 58.86 136 | 70.72 162 | 73.37 139 | 84.24 145 | 80.52 162 |
|
FMVSNet3 | | | 70.41 72 | 71.89 71 | 68.68 80 | 70.89 134 | 79.42 128 | 75.63 66 | 60.97 99 | 65.32 71 | 51.06 85 | 47.37 101 | 62.05 66 | 64.90 81 | 82.49 30 | 82.27 33 | 88.64 19 | 84.34 132 |
|
Vis-MVSNet | | | 65.53 104 | 69.83 82 | 60.52 155 | 70.80 135 | 84.59 69 | 66.37 152 | 55.47 154 | 48.40 134 | 40.62 148 | 57.67 64 | 58.43 82 | 45.37 187 | 77.49 71 | 76.24 86 | 84.47 140 | 85.99 122 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CDS-MVSNet | | | 64.22 113 | 65.89 111 | 62.28 145 | 70.05 136 | 80.59 114 | 69.91 128 | 57.98 121 | 43.53 165 | 46.58 101 | 48.22 93 | 50.76 115 | 46.45 180 | 75.68 93 | 76.08 87 | 82.70 170 | 86.34 118 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
UGNet | | | 67.57 88 | 71.69 72 | 62.76 139 | 69.88 137 | 82.58 81 | 66.43 150 | 58.64 115 | 54.71 109 | 51.87 82 | 61.74 53 | 62.01 69 | 45.46 186 | 74.78 103 | 74.99 98 | 84.24 145 | 91.02 59 |
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 |
GA-MVS | | | 64.55 111 | 65.76 112 | 63.12 134 | 69.68 138 | 81.56 95 | 69.59 130 | 58.16 119 | 45.23 153 | 35.58 176 | 47.01 108 | 41.82 137 | 59.41 130 | 79.62 58 | 78.54 65 | 86.32 64 | 86.56 114 |
|
GBi-Net | | | 69.21 76 | 70.40 78 | 67.81 85 | 69.49 139 | 78.65 133 | 74.54 76 | 60.97 99 | 65.32 71 | 51.06 85 | 47.37 101 | 62.05 66 | 63.43 87 | 77.49 71 | 78.22 69 | 87.37 43 | 83.73 136 |
|
test1 | | | 69.21 76 | 70.40 78 | 67.81 85 | 69.49 139 | 78.65 133 | 74.54 76 | 60.97 99 | 65.32 71 | 51.06 85 | 47.37 101 | 62.05 66 | 63.43 87 | 77.49 71 | 78.22 69 | 87.37 43 | 83.73 136 |
|
FMVSNet2 | | | 68.06 85 | 68.57 89 | 67.45 88 | 69.49 139 | 78.65 133 | 74.54 76 | 60.23 110 | 56.29 97 | 49.64 92 | 42.13 132 | 57.08 88 | 63.43 87 | 81.15 48 | 80.99 50 | 87.37 43 | 83.73 136 |
|
UniMVSNet_NR-MVSNet | | | 62.30 142 | 63.51 124 | 60.89 154 | 69.48 142 | 77.83 142 | 64.07 161 | 63.94 63 | 50.03 120 | 31.17 191 | 44.82 114 | 41.12 144 | 51.37 160 | 71.02 160 | 74.81 101 | 85.30 110 | 84.95 128 |
|
gm-plane-assit | | | 54.99 189 | 57.99 185 | 51.49 199 | 69.27 143 | 54.42 225 | 32.32 229 | 42.59 216 | 21.18 232 | 13.71 226 | 23.61 217 | 43.84 132 | 60.21 118 | 87.09 4 | 86.55 4 | 90.81 4 | 89.28 78 |
|
PatchMatch-RL | | | 62.22 146 | 60.69 160 | 64.01 120 | 68.74 144 | 75.75 164 | 59.27 188 | 60.35 106 | 56.09 98 | 53.80 79 | 47.06 107 | 36.45 186 | 64.80 82 | 68.22 182 | 67.22 191 | 77.10 207 | 74.02 186 |
|
CR-MVSNet | | | 62.31 141 | 64.75 116 | 59.47 162 | 68.63 145 | 71.29 194 | 67.53 141 | 43.18 213 | 55.83 99 | 41.40 141 | 41.04 139 | 55.85 94 | 57.29 144 | 72.76 139 | 73.27 144 | 78.77 202 | 83.23 144 |
|
v18 | | | 63.31 129 | 62.02 148 | 64.81 105 | 68.48 146 | 73.38 177 | 72.14 87 | 54.28 165 | 48.99 133 | 47.21 98 | 39.56 148 | 41.20 142 | 60.80 107 | 72.89 131 | 74.46 115 | 85.96 81 | 83.64 139 |
|
v16 | | | 63.12 131 | 61.78 150 | 64.68 107 | 68.45 147 | 73.29 178 | 71.86 91 | 54.12 166 | 48.36 135 | 47.00 99 | 39.30 153 | 41.01 146 | 60.67 108 | 72.83 137 | 74.40 117 | 86.01 76 | 83.24 143 |
|
v17 | | | 62.99 136 | 61.70 151 | 64.51 110 | 68.40 148 | 73.28 179 | 71.80 96 | 54.11 167 | 47.87 136 | 46.14 102 | 39.29 154 | 41.01 146 | 60.60 109 | 72.81 138 | 74.39 122 | 85.99 79 | 83.25 142 |
|
TranMVSNet+NR-MVSNet | | | 60.38 164 | 61.30 155 | 59.30 163 | 68.34 149 | 75.57 166 | 63.38 170 | 63.78 66 | 46.74 141 | 27.73 201 | 42.56 127 | 36.84 184 | 47.66 173 | 70.36 169 | 74.59 107 | 84.91 123 | 82.46 150 |
|
v8 | | | 63.44 128 | 62.58 142 | 64.43 112 | 68.28 150 | 78.07 139 | 71.82 95 | 54.85 160 | 46.70 143 | 45.20 107 | 39.40 149 | 40.91 148 | 60.54 113 | 72.85 136 | 74.39 122 | 85.92 82 | 85.76 124 |
|
v6 | | | 64.09 114 | 63.40 125 | 64.90 101 | 68.28 150 | 80.78 105 | 71.85 92 | 57.64 128 | 46.73 142 | 45.18 108 | 39.40 149 | 40.89 149 | 60.54 113 | 72.86 132 | 74.40 117 | 85.92 82 | 88.72 92 |
|
v1neww | | | 64.08 115 | 63.38 126 | 64.89 103 | 68.27 152 | 80.77 107 | 71.84 93 | 57.65 126 | 46.66 144 | 45.10 109 | 39.40 149 | 40.86 150 | 60.57 110 | 72.86 132 | 74.40 117 | 85.92 82 | 88.71 93 |
|
v7new | | | 64.08 115 | 63.38 126 | 64.89 103 | 68.27 152 | 80.77 107 | 71.84 93 | 57.65 126 | 46.66 144 | 45.10 109 | 39.40 149 | 40.86 150 | 60.57 110 | 72.86 132 | 74.40 117 | 85.92 82 | 88.71 93 |
|
v1141 | | | 63.48 125 | 62.75 140 | 64.32 115 | 68.13 154 | 80.69 111 | 71.69 105 | 57.43 133 | 43.66 164 | 42.83 136 | 39.02 156 | 39.74 166 | 59.95 120 | 72.94 128 | 74.49 112 | 85.86 90 | 88.75 90 |
|
divwei89l23v2f112 | | | 63.48 125 | 62.76 139 | 64.32 115 | 68.13 154 | 80.68 112 | 71.71 98 | 57.43 133 | 43.69 162 | 42.84 134 | 39.01 157 | 39.75 165 | 59.94 121 | 72.93 129 | 74.49 112 | 85.86 90 | 88.75 90 |
|
v1 | | | 63.49 124 | 62.77 138 | 64.32 115 | 68.13 154 | 80.70 110 | 71.70 104 | 57.43 133 | 43.69 162 | 42.89 133 | 39.03 155 | 39.77 164 | 59.93 122 | 72.93 129 | 74.48 114 | 85.86 90 | 88.77 88 |
|
v15 | | | 62.07 147 | 60.70 159 | 63.67 125 | 68.09 157 | 73.00 180 | 71.27 110 | 53.41 171 | 43.70 161 | 43.43 119 | 38.77 159 | 39.83 162 | 59.87 123 | 72.74 141 | 74.25 124 | 85.98 80 | 82.61 148 |
|
V14 | | | 61.96 150 | 60.56 161 | 63.59 126 | 68.06 158 | 72.93 183 | 71.10 115 | 53.33 173 | 43.47 166 | 43.28 124 | 38.59 160 | 39.78 163 | 59.76 125 | 72.65 143 | 74.19 125 | 86.01 76 | 82.32 153 |
|
V9 | | | 61.85 152 | 60.42 164 | 63.51 127 | 68.02 159 | 72.85 184 | 70.91 119 | 53.24 174 | 43.25 168 | 43.27 128 | 38.41 164 | 39.73 167 | 59.60 127 | 72.55 145 | 74.13 128 | 86.04 74 | 82.04 155 |
|
v2v482 | | | 63.68 122 | 62.85 135 | 64.65 109 | 68.01 160 | 80.46 116 | 71.90 90 | 57.60 129 | 44.26 156 | 42.82 137 | 39.80 147 | 38.62 176 | 61.56 97 | 73.06 124 | 74.86 100 | 86.03 75 | 88.90 86 |
|
v12 | | | 61.70 154 | 60.27 166 | 63.38 131 | 68.00 161 | 72.76 185 | 70.63 123 | 53.14 176 | 43.01 170 | 42.95 132 | 38.25 166 | 39.64 169 | 59.48 129 | 72.47 147 | 74.05 131 | 86.06 73 | 81.71 158 |
|
pm-mvs1 | | | 59.21 169 | 59.58 175 | 58.77 167 | 67.97 162 | 77.07 155 | 64.12 158 | 57.20 137 | 34.73 206 | 36.86 166 | 35.34 187 | 40.54 159 | 43.34 191 | 74.32 111 | 73.30 143 | 83.13 167 | 81.77 157 |
|
v13 | | | 61.60 156 | 60.13 169 | 63.31 132 | 67.95 163 | 72.67 187 | 70.51 124 | 53.05 177 | 42.80 171 | 42.96 129 | 38.10 171 | 39.57 170 | 59.31 132 | 72.36 148 | 73.98 133 | 86.10 70 | 81.40 160 |
|
v7 | | | 63.61 123 | 63.02 132 | 64.29 118 | 67.88 164 | 80.32 117 | 71.60 106 | 56.63 141 | 45.37 151 | 42.84 134 | 38.54 161 | 38.91 174 | 61.05 105 | 74.39 107 | 74.52 110 | 85.75 94 | 89.10 81 |
|
v10 | | | 63.00 134 | 62.22 145 | 63.90 123 | 67.88 164 | 77.78 143 | 71.59 107 | 54.34 164 | 45.37 151 | 42.76 138 | 38.53 162 | 38.93 173 | 61.05 105 | 74.39 107 | 74.52 110 | 85.75 94 | 86.04 120 |
|
v11 | | | 61.74 153 | 60.47 163 | 63.22 133 | 67.83 166 | 72.72 186 | 70.31 125 | 52.95 180 | 42.75 172 | 41.89 140 | 38.16 169 | 38.49 177 | 60.40 117 | 74.35 109 | 74.40 117 | 85.92 82 | 82.39 152 |
|
v1144 | | | 63.00 134 | 62.39 144 | 63.70 124 | 67.72 167 | 80.27 118 | 71.23 112 | 56.40 142 | 42.51 173 | 40.81 146 | 38.12 170 | 37.73 178 | 60.42 116 | 74.46 105 | 74.55 108 | 85.64 106 | 89.12 80 |
|
UniMVSNet (Re) | | | 60.62 162 | 62.93 134 | 57.92 171 | 67.64 168 | 77.90 141 | 61.75 176 | 61.24 95 | 49.83 122 | 29.80 195 | 42.57 126 | 40.62 158 | 43.36 190 | 70.49 168 | 73.27 144 | 83.76 152 | 85.81 123 |
|
RPMNet | | | 58.63 177 | 62.80 136 | 53.76 195 | 67.59 169 | 71.29 194 | 54.60 199 | 38.13 228 | 55.83 99 | 35.70 175 | 41.58 135 | 53.04 109 | 47.89 172 | 66.10 187 | 67.38 189 | 78.65 204 | 84.40 131 |
|
v148 | | | 62.00 149 | 61.19 156 | 62.96 135 | 67.46 170 | 79.49 126 | 67.87 138 | 57.66 125 | 42.30 175 | 45.02 111 | 38.20 168 | 38.89 175 | 54.77 152 | 69.83 175 | 72.60 154 | 84.96 120 | 87.01 111 |
|
IterMVS | | | 61.87 151 | 63.55 123 | 59.90 158 | 67.29 171 | 72.20 189 | 67.34 144 | 48.56 196 | 47.48 138 | 37.86 163 | 47.07 106 | 48.27 120 | 54.08 154 | 72.12 151 | 73.71 134 | 84.30 144 | 83.99 134 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1192 | | | 62.25 143 | 61.64 152 | 62.96 135 | 66.88 172 | 79.72 123 | 69.96 127 | 55.77 149 | 41.58 180 | 39.42 150 | 37.05 176 | 35.96 191 | 60.50 115 | 74.30 112 | 74.09 129 | 85.24 112 | 88.76 89 |
|
DU-MVS | | | 60.87 161 | 61.82 149 | 59.76 160 | 66.69 173 | 75.87 161 | 64.07 161 | 61.96 84 | 49.31 124 | 31.17 191 | 42.76 123 | 36.95 183 | 51.37 160 | 69.67 176 | 73.20 147 | 83.30 160 | 84.95 128 |
|
Baseline_NR-MVSNet | | | 59.47 168 | 60.28 165 | 58.54 168 | 66.69 173 | 73.90 174 | 61.63 177 | 62.90 80 | 49.15 132 | 26.87 202 | 35.18 189 | 37.62 179 | 48.20 171 | 69.67 176 | 73.61 135 | 84.92 121 | 82.82 147 |
|
v144192 | | | 62.05 148 | 61.46 154 | 62.73 141 | 66.59 175 | 79.87 121 | 69.30 132 | 55.88 147 | 41.50 181 | 39.41 151 | 37.23 174 | 36.45 186 | 59.62 126 | 72.69 142 | 73.51 136 | 85.61 107 | 88.93 83 |
|
v1921920 | | | 61.66 155 | 61.10 157 | 62.31 144 | 66.32 176 | 79.57 125 | 68.41 137 | 55.49 153 | 41.03 182 | 38.69 157 | 36.64 182 | 35.27 197 | 59.60 127 | 73.23 122 | 73.41 138 | 85.37 109 | 88.51 100 |
|
TESTMET0.1,1 | | | 67.38 90 | 71.61 73 | 62.45 143 | 66.05 177 | 81.32 99 | 63.98 163 | 55.36 155 | 58.62 87 | 42.96 129 | 52.74 77 | 61.65 70 | 57.74 141 | 75.64 94 | 78.09 72 | 88.61 20 | 93.21 36 |
|
pmmvs4 | | | 63.14 130 | 62.46 143 | 63.94 122 | 66.03 178 | 76.40 158 | 66.82 147 | 57.60 129 | 56.74 93 | 50.26 91 | 40.81 141 | 37.51 180 | 59.26 133 | 71.75 157 | 71.48 163 | 83.68 154 | 82.53 149 |
|
PatchT | | | 60.46 163 | 63.85 121 | 56.51 181 | 65.95 179 | 75.68 165 | 47.34 210 | 41.39 220 | 53.89 112 | 41.40 141 | 37.84 172 | 50.30 117 | 57.29 144 | 72.76 139 | 73.27 144 | 85.67 102 | 83.23 144 |
|
v1240 | | | 61.09 159 | 60.55 162 | 61.72 150 | 65.92 180 | 79.28 129 | 67.16 145 | 54.91 159 | 39.79 187 | 38.10 160 | 36.08 184 | 34.64 198 | 59.15 134 | 72.86 132 | 73.36 140 | 85.10 114 | 87.84 105 |
|
ADS-MVSNet | | | 58.40 178 | 59.16 180 | 57.52 176 | 65.80 181 | 74.57 172 | 60.26 183 | 40.17 225 | 50.51 118 | 38.01 161 | 40.11 145 | 44.72 130 | 59.36 131 | 64.91 191 | 66.55 194 | 81.53 182 | 72.72 195 |
|
testpf | | | 43.39 218 | 47.17 216 | 38.98 221 | 65.58 182 | 47.38 233 | 36.09 226 | 31.67 235 | 36.97 195 | 19.47 214 | 33.01 194 | 35.62 196 | 23.61 221 | 50.86 227 | 56.08 223 | 57.48 232 | 70.27 204 |
|
FMVSNet1 | | | 63.48 125 | 63.07 131 | 63.97 121 | 65.31 183 | 76.37 159 | 71.77 97 | 57.90 122 | 43.32 167 | 45.66 104 | 35.06 190 | 49.43 118 | 58.57 137 | 77.49 71 | 78.22 69 | 84.59 138 | 81.60 159 |
|
testgi | | | 48.51 210 | 50.53 208 | 46.16 212 | 64.78 184 | 67.15 205 | 41.54 221 | 54.81 161 | 29.12 220 | 17.03 217 | 32.07 198 | 31.98 207 | 20.15 226 | 65.26 190 | 67.00 193 | 78.67 203 | 61.10 222 |
|
LTVRE_ROB | | 47.26 16 | 49.41 208 | 49.91 211 | 48.82 204 | 64.76 185 | 69.79 197 | 49.05 205 | 47.12 202 | 20.36 234 | 16.52 220 | 36.65 181 | 26.96 218 | 50.76 164 | 60.47 205 | 63.16 207 | 64.73 227 | 72.00 198 |
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 |
Anonymous20231206 | | | 52.23 200 | 52.80 202 | 51.56 198 | 64.70 186 | 69.41 198 | 51.01 203 | 58.60 116 | 36.63 197 | 22.44 210 | 21.80 223 | 31.42 210 | 30.52 207 | 66.79 186 | 67.83 188 | 82.10 178 | 75.73 179 |
|
USDC | | | 59.69 167 | 60.03 170 | 59.28 164 | 64.04 187 | 71.84 192 | 63.15 172 | 55.36 155 | 54.90 107 | 35.02 180 | 48.34 92 | 29.79 214 | 58.16 138 | 70.60 164 | 71.33 170 | 79.99 194 | 73.42 190 |
|
WR-MVS | | | 51.02 202 | 54.56 195 | 46.90 210 | 63.84 188 | 69.23 199 | 44.78 217 | 56.38 143 | 38.19 193 | 14.19 224 | 37.38 173 | 36.82 185 | 22.39 222 | 60.14 206 | 66.20 198 | 79.81 195 | 73.95 188 |
|
our_test_3 | | | | | | 63.32 189 | 71.07 196 | 55.90 197 | | | | | | | | | | |
|
test20.03 | | | 47.23 214 | 48.69 213 | 45.53 214 | 63.28 190 | 64.39 211 | 41.01 222 | 56.93 140 | 29.16 219 | 15.21 223 | 23.90 216 | 30.76 213 | 17.51 231 | 64.63 194 | 65.26 199 | 79.21 201 | 62.71 218 |
|
pmmvs6 | | | 54.20 196 | 53.54 198 | 54.97 187 | 63.22 191 | 72.98 181 | 60.17 184 | 52.32 183 | 26.77 225 | 34.30 182 | 23.29 220 | 36.23 188 | 40.33 199 | 68.77 181 | 68.76 183 | 79.47 199 | 78.00 174 |
|
v7n | | | 57.04 184 | 56.64 188 | 57.52 176 | 62.85 192 | 74.75 170 | 61.76 175 | 51.80 184 | 35.58 205 | 36.02 173 | 32.33 196 | 33.61 204 | 50.16 167 | 67.73 184 | 70.34 178 | 82.51 172 | 82.12 154 |
|
pmmvs5 | | | 59.72 166 | 60.24 167 | 59.11 165 | 62.77 193 | 77.33 150 | 63.17 171 | 54.00 168 | 40.21 185 | 37.23 165 | 40.41 143 | 35.99 190 | 51.75 159 | 72.55 145 | 72.74 152 | 85.72 100 | 82.45 151 |
|
CVMVSNet | | | 54.92 191 | 58.16 182 | 51.13 200 | 62.61 194 | 68.44 201 | 55.45 198 | 52.38 182 | 42.28 176 | 21.45 211 | 47.10 105 | 46.10 125 | 37.96 202 | 64.42 196 | 63.81 204 | 76.92 209 | 75.01 183 |
|
TAMVS | | | 58.86 172 | 60.91 158 | 56.47 182 | 62.38 195 | 77.57 145 | 58.97 190 | 52.98 178 | 38.76 192 | 36.17 171 | 42.26 131 | 47.94 121 | 46.45 180 | 70.23 170 | 70.79 173 | 81.86 180 | 78.82 172 |
|
DTE-MVSNet | | | 49.82 206 | 51.92 206 | 47.37 209 | 61.75 196 | 64.38 212 | 45.89 216 | 57.33 136 | 36.11 201 | 12.79 229 | 36.87 178 | 31.93 209 | 25.73 218 | 58.01 208 | 65.22 200 | 80.75 189 | 70.93 203 |
|
PEN-MVS | | | 51.04 201 | 52.94 200 | 48.82 204 | 61.45 197 | 66.00 207 | 48.68 207 | 57.20 137 | 36.87 196 | 15.36 222 | 36.98 177 | 32.72 206 | 28.77 213 | 57.63 211 | 66.37 195 | 81.44 184 | 74.00 187 |
|
v748 | | | 55.19 186 | 54.63 193 | 55.85 184 | 61.44 198 | 72.97 182 | 58.72 191 | 51.62 185 | 34.48 208 | 36.39 170 | 32.09 197 | 33.05 205 | 45.48 185 | 61.85 203 | 67.87 187 | 81.45 183 | 80.08 165 |
|
V42 | | | 62.86 138 | 62.97 133 | 62.74 140 | 60.84 199 | 78.99 131 | 71.46 109 | 57.13 139 | 46.85 140 | 44.28 115 | 38.87 158 | 40.73 154 | 57.63 143 | 72.60 144 | 74.14 127 | 85.09 116 | 88.63 95 |
|
MDTV_nov1_ep13_2view | | | 54.47 195 | 54.61 194 | 54.30 194 | 60.50 200 | 73.82 175 | 57.92 192 | 43.38 212 | 39.43 191 | 32.51 189 | 33.23 193 | 34.05 201 | 47.26 176 | 62.36 201 | 66.21 197 | 84.24 145 | 73.19 192 |
|
LP | | | 48.21 211 | 46.65 218 | 50.03 201 | 60.39 201 | 63.86 215 | 48.73 206 | 38.71 227 | 35.60 204 | 32.99 187 | 23.31 219 | 24.95 225 | 40.07 200 | 57.73 209 | 61.56 212 | 79.29 200 | 59.51 223 |
|
MVS-HIRNet | | | 53.86 197 | 53.02 199 | 54.85 188 | 60.30 202 | 72.36 188 | 44.63 218 | 42.20 218 | 39.45 190 | 43.47 118 | 21.66 224 | 34.00 202 | 55.47 148 | 65.42 189 | 67.16 192 | 83.02 168 | 71.08 202 |
|
CHOSEN 280x420 | | | 62.23 145 | 66.57 102 | 57.17 179 | 59.88 203 | 68.92 200 | 61.20 179 | 42.28 217 | 54.17 110 | 39.57 149 | 47.78 97 | 64.97 58 | 62.68 91 | 73.85 116 | 69.52 181 | 77.43 206 | 86.75 112 |
|
TinyColmap | | | 52.66 199 | 50.09 210 | 55.65 185 | 59.72 204 | 64.02 214 | 57.15 194 | 52.96 179 | 40.28 184 | 32.51 189 | 32.42 195 | 20.97 229 | 56.65 146 | 63.95 197 | 65.15 201 | 74.91 214 | 63.87 215 |
|
FC-MVSNet-test | | | 47.24 213 | 54.37 196 | 38.93 222 | 59.49 205 | 58.25 222 | 34.48 228 | 53.36 172 | 45.66 150 | 6.66 237 | 50.62 84 | 42.02 136 | 16.62 232 | 58.39 207 | 61.21 213 | 62.99 228 | 64.40 214 |
|
test-mter | | | 64.06 118 | 69.24 84 | 58.01 170 | 59.07 206 | 77.40 148 | 59.13 189 | 48.11 198 | 55.64 102 | 39.18 153 | 51.56 81 | 58.54 80 | 55.38 149 | 73.52 120 | 76.00 88 | 87.22 51 | 92.05 51 |
|
WR-MVS_H | | | 49.62 207 | 52.63 203 | 46.11 213 | 58.80 207 | 67.58 203 | 46.14 215 | 54.94 157 | 36.51 199 | 13.63 227 | 36.75 180 | 35.67 195 | 22.10 223 | 56.43 215 | 62.76 208 | 81.06 186 | 72.73 194 |
|
CP-MVSNet | | | 50.57 203 | 52.60 204 | 48.21 207 | 58.77 208 | 65.82 208 | 48.17 208 | 56.29 144 | 37.41 194 | 16.59 219 | 37.14 175 | 31.95 208 | 29.21 210 | 56.60 214 | 63.71 205 | 80.22 192 | 75.56 180 |
|
PS-CasMVS | | | 50.17 204 | 52.02 205 | 48.02 208 | 58.60 209 | 65.54 209 | 48.04 209 | 56.19 146 | 36.42 200 | 16.42 221 | 35.68 186 | 31.33 211 | 28.85 212 | 56.42 216 | 63.54 206 | 80.01 193 | 75.18 182 |
|
SixPastTwentyTwo | | | 49.11 209 | 49.22 212 | 48.99 203 | 58.54 210 | 64.14 213 | 47.18 211 | 47.75 199 | 31.15 216 | 24.42 206 | 41.01 140 | 26.55 219 | 44.04 189 | 54.76 222 | 58.70 217 | 71.99 221 | 68.21 205 |
|
TDRefinement | | | 52.70 198 | 51.02 207 | 54.66 190 | 57.41 211 | 65.06 210 | 61.47 178 | 54.94 157 | 44.03 157 | 33.93 183 | 30.13 206 | 27.57 217 | 46.17 182 | 61.86 202 | 62.48 211 | 74.01 217 | 66.06 211 |
|
pmmvs-eth3d | | | 55.20 185 | 53.95 197 | 56.65 180 | 57.34 212 | 67.77 202 | 57.54 193 | 53.74 169 | 40.93 183 | 41.09 145 | 31.19 203 | 29.10 216 | 49.07 168 | 65.54 188 | 67.28 190 | 81.14 185 | 75.81 178 |
|
FPMVS | | | 39.11 223 | 36.39 228 | 42.28 216 | 55.97 213 | 45.94 234 | 46.23 214 | 41.57 219 | 35.73 203 | 22.61 208 | 23.46 218 | 19.82 231 | 28.32 216 | 43.57 229 | 40.67 232 | 58.96 230 | 45.54 228 |
|
MIMVSNet | | | 57.78 181 | 59.71 173 | 55.53 186 | 54.79 214 | 77.10 154 | 63.89 165 | 45.02 208 | 46.59 146 | 36.79 167 | 28.36 209 | 40.77 153 | 45.84 184 | 74.97 100 | 76.58 80 | 86.87 56 | 73.60 189 |
|
N_pmnet | | | 47.67 212 | 47.00 217 | 48.45 206 | 54.72 215 | 62.78 216 | 46.95 212 | 51.25 186 | 36.01 202 | 26.09 205 | 26.59 214 | 25.93 224 | 35.50 205 | 55.67 218 | 59.01 215 | 76.22 210 | 63.04 216 |
|
test2356 | | | 46.29 215 | 47.37 215 | 45.03 215 | 54.38 216 | 57.99 223 | 42.03 220 | 50.32 188 | 30.78 217 | 16.65 218 | 27.40 212 | 23.70 226 | 29.86 208 | 61.20 204 | 64.31 203 | 76.93 208 | 66.22 210 |
|
v52 | | | 54.79 192 | 55.15 191 | 54.36 193 | 54.07 217 | 72.13 190 | 59.84 186 | 49.39 192 | 34.50 207 | 35.08 179 | 31.63 200 | 35.74 193 | 47.21 178 | 63.90 198 | 67.92 185 | 80.59 190 | 80.23 163 |
|
V4 | | | 54.78 193 | 55.14 192 | 54.37 192 | 54.07 217 | 72.13 190 | 59.83 187 | 49.39 192 | 34.46 209 | 35.11 178 | 31.64 199 | 35.72 194 | 47.22 177 | 63.90 198 | 67.92 185 | 80.59 190 | 80.23 163 |
|
anonymousdsp | | | 54.99 189 | 57.24 186 | 52.36 196 | 53.82 219 | 71.75 193 | 51.49 202 | 48.14 197 | 33.74 210 | 33.66 184 | 38.34 165 | 36.13 189 | 47.54 174 | 64.53 195 | 70.60 175 | 79.53 198 | 85.59 126 |
|
testus | | | 42.30 219 | 43.69 219 | 40.67 220 | 53.21 220 | 53.50 226 | 31.81 230 | 49.96 189 | 27.06 223 | 11.55 231 | 25.67 215 | 19.00 232 | 25.20 219 | 55.34 219 | 62.59 209 | 72.31 220 | 62.69 219 |
|
new-patchmatchnet | | | 42.21 220 | 42.97 221 | 41.33 218 | 53.05 221 | 59.89 219 | 39.38 223 | 49.61 190 | 28.26 222 | 12.10 230 | 22.17 222 | 21.54 228 | 19.22 227 | 50.96 226 | 56.04 224 | 74.61 216 | 61.92 220 |
|
FMVSNet5 | | | 58.86 172 | 60.24 167 | 57.25 178 | 52.66 222 | 66.25 206 | 63.77 166 | 52.86 181 | 57.85 91 | 37.92 162 | 36.12 183 | 52.22 111 | 51.37 160 | 70.88 161 | 71.43 165 | 84.92 121 | 66.91 209 |
|
ambc | | | | 42.30 222 | | 50.36 223 | 49.51 230 | 35.47 227 | | 32.04 215 | 23.53 207 | 17.36 229 | 8.95 240 | 29.06 211 | 64.88 192 | 56.26 222 | 61.29 229 | 67.12 208 |
|
1111 | | | 38.93 224 | 38.98 225 | 38.86 223 | 50.10 224 | 50.42 228 | 29.52 231 | 38.00 229 | 22.67 230 | 17.99 215 | 17.40 227 | 26.26 221 | 28.72 214 | 54.86 220 | 58.20 218 | 68.82 225 | 43.08 231 |
|
.test1245 | | | 25.86 231 | 24.56 234 | 27.39 232 | 50.10 224 | 50.42 228 | 29.52 231 | 38.00 229 | 22.67 230 | 17.99 215 | 17.40 227 | 26.26 221 | 28.72 214 | 54.86 220 | 0.05 239 | 0.01 243 | 0.24 240 |
|
EU-MVSNet | | | 44.84 216 | 47.85 214 | 41.32 219 | 49.26 226 | 56.59 224 | 43.07 219 | 47.64 201 | 33.03 211 | 13.82 225 | 36.78 179 | 30.99 212 | 24.37 220 | 53.80 223 | 55.57 225 | 69.78 222 | 68.21 205 |
|
testmv | | | 37.40 225 | 37.95 226 | 36.76 225 | 48.97 227 | 49.33 231 | 28.65 234 | 46.74 203 | 18.34 235 | 7.68 235 | 16.80 232 | 14.47 236 | 19.18 228 | 51.72 224 | 56.93 220 | 69.36 223 | 58.09 224 |
|
test1235678 | | | 37.40 225 | 37.94 227 | 36.76 225 | 48.97 227 | 49.30 232 | 28.65 234 | 46.73 204 | 18.33 236 | 7.68 235 | 16.79 233 | 14.46 237 | 19.18 228 | 51.72 224 | 56.92 221 | 69.36 223 | 58.07 225 |
|
RPSCF | | | 55.07 188 | 58.06 183 | 51.57 197 | 48.87 229 | 58.95 220 | 53.68 200 | 41.26 223 | 62.42 77 | 45.88 103 | 54.38 73 | 54.26 103 | 53.75 155 | 57.15 212 | 53.53 227 | 66.01 226 | 65.75 212 |
|
PMVS | | 27.44 18 | 32.08 228 | 29.07 231 | 35.60 227 | 48.33 230 | 24.79 239 | 26.97 236 | 41.34 221 | 20.45 233 | 22.50 209 | 17.11 231 | 18.64 233 | 20.44 225 | 41.99 232 | 38.06 233 | 54.02 235 | 42.44 232 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PM-MVS | | | 50.11 205 | 50.38 209 | 49.80 202 | 47.23 231 | 62.08 218 | 50.91 204 | 44.84 210 | 41.90 178 | 36.10 172 | 35.22 188 | 26.05 223 | 46.83 179 | 57.64 210 | 55.42 226 | 72.90 218 | 74.32 185 |
|
pmmvs3 | | | 41.86 221 | 42.29 223 | 41.36 217 | 39.80 232 | 52.66 227 | 38.93 225 | 35.85 234 | 23.40 229 | 20.22 213 | 19.30 225 | 20.84 230 | 40.56 198 | 55.98 217 | 58.79 216 | 72.80 219 | 65.03 213 |
|
test12356 | | | 29.92 229 | 31.49 230 | 28.08 229 | 38.46 233 | 37.74 237 | 21.36 237 | 40.17 225 | 16.83 237 | 5.61 239 | 15.66 235 | 11.48 238 | 6.60 238 | 42.01 231 | 51.23 228 | 56.29 233 | 45.52 229 |
|
MDA-MVSNet-bldmvs | | | 44.15 217 | 42.27 224 | 46.34 211 | 38.34 234 | 62.31 217 | 46.28 213 | 55.74 150 | 29.83 218 | 20.98 212 | 27.11 213 | 16.45 235 | 41.98 193 | 41.11 233 | 57.47 219 | 74.72 215 | 61.65 221 |
|
no-one | | | 26.96 230 | 26.51 232 | 27.49 231 | 37.87 235 | 39.14 236 | 17.12 239 | 41.31 222 | 12.02 239 | 3.68 241 | 8.04 237 | 8.42 241 | 10.67 236 | 28.11 235 | 45.96 231 | 54.27 234 | 43.89 230 |
|
MIMVSNet1 | | | 40.84 222 | 43.46 220 | 37.79 224 | 32.14 236 | 58.92 221 | 39.24 224 | 50.83 187 | 27.00 224 | 11.29 232 | 16.76 234 | 26.53 220 | 17.75 230 | 57.14 213 | 61.12 214 | 75.46 212 | 56.78 226 |
|
Gipuma | | | 24.91 232 | 24.61 233 | 25.26 233 | 31.47 237 | 21.59 240 | 18.06 238 | 37.53 231 | 25.43 227 | 10.03 233 | 4.18 241 | 4.25 243 | 14.85 233 | 43.20 230 | 47.03 229 | 39.62 237 | 26.55 236 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
E-PMN | | | 15.08 234 | 11.65 237 | 19.08 234 | 28.73 238 | 12.31 243 | 6.95 244 | 36.87 233 | 10.71 241 | 3.63 242 | 5.13 238 | 2.22 246 | 13.81 235 | 11.34 239 | 18.50 237 | 24.49 239 | 21.32 237 |
|
EMVS | | | 14.40 235 | 10.71 238 | 18.70 235 | 28.15 239 | 12.09 244 | 7.06 243 | 36.89 232 | 11.00 240 | 3.56 243 | 4.95 239 | 2.27 245 | 13.91 234 | 10.13 240 | 16.06 238 | 22.63 240 | 18.51 238 |
|
new_pmnet | | | 33.19 227 | 35.52 229 | 30.47 228 | 27.55 240 | 45.31 235 | 29.29 233 | 30.92 236 | 29.00 221 | 9.88 234 | 18.77 226 | 17.64 234 | 26.77 217 | 44.07 228 | 45.98 230 | 58.41 231 | 47.87 227 |
|
PMMVS2 | | | 20.45 233 | 22.31 235 | 18.27 236 | 20.52 241 | 26.73 238 | 14.85 241 | 28.43 238 | 13.69 238 | 0.79 244 | 10.35 236 | 9.10 239 | 3.83 240 | 27.64 236 | 32.87 234 | 41.17 236 | 35.81 233 |
|
tmp_tt | | | | | 16.09 237 | 13.07 242 | 8.12 245 | 13.61 242 | 2.08 240 | 55.09 104 | 30.10 194 | 40.26 144 | 22.83 227 | 5.35 239 | 29.91 234 | 25.25 236 | 32.33 238 | |
|
MVE | | 15.98 19 | 14.37 236 | 16.36 236 | 12.04 238 | 7.72 243 | 20.24 241 | 5.90 245 | 29.05 237 | 8.28 242 | 3.92 240 | 4.72 240 | 2.42 244 | 9.57 237 | 18.89 238 | 31.46 235 | 16.07 242 | 28.53 235 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
GG-mvs-BLEND | | | 54.54 194 | 77.58 43 | 27.67 230 | 0.03 244 | 90.09 26 | 77.20 61 | 0.02 241 | 66.83 65 | 0.05 245 | 59.90 60 | 73.33 31 | 0.04 241 | 78.40 68 | 79.30 59 | 88.65 18 | 95.20 20 |
|
sosnet-low-res | | | 0.00 239 | 0.00 241 | 0.00 241 | 0.00 245 | 0.00 248 | 0.00 249 | 0.00 243 | 0.00 245 | 0.00 246 | 0.00 244 | 0.00 248 | 0.00 244 | 0.00 243 | 0.00 242 | 0.00 245 | 0.00 242 |
|
sosnet | | | 0.00 239 | 0.00 241 | 0.00 241 | 0.00 245 | 0.00 248 | 0.00 249 | 0.00 243 | 0.00 245 | 0.00 246 | 0.00 244 | 0.00 248 | 0.00 244 | 0.00 243 | 0.00 242 | 0.00 245 | 0.00 242 |
|
testmvs | | | 0.05 237 | 0.08 239 | 0.01 239 | 0.00 245 | 0.01 246 | 0.03 247 | 0.01 242 | 0.05 243 | 0.00 246 | 0.14 243 | 0.01 247 | 0.03 243 | 0.05 241 | 0.05 239 | 0.01 243 | 0.24 240 |
|
test123 | | | 0.05 237 | 0.08 239 | 0.01 239 | 0.00 245 | 0.01 246 | 0.01 248 | 0.00 243 | 0.05 243 | 0.00 246 | 0.16 242 | 0.00 248 | 0.04 241 | 0.02 242 | 0.05 239 | 0.00 245 | 0.26 239 |
|
MTAPA | | | | | | | | | | | 78.32 7 | | 79.42 19 | | | | | |
|
MTMP | | | | | | | | | | | 76.04 12 | | 76.65 24 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.17 246 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 81.60 32 | | | | | | | | |
|
Patchmtry | | | | | | | 78.06 140 | 67.53 141 | 43.18 213 | | 41.40 141 | | | | | | | |
|
DeepMVS_CX | | | | | | | 19.81 242 | 17.01 240 | 10.02 239 | 23.61 228 | 5.85 238 | 17.21 230 | 8.03 242 | 21.13 224 | 22.60 237 | | 21.42 241 | 30.01 234 |
|