MTAPA | | | | | | | | | | | 65.14 1 | | 80.20 14 | | | | | |
|
zzz-MVS | | | 74.25 16 | 77.97 20 | 69.91 11 | 73.43 19 | 74.06 39 | 79.69 14 | 56.44 14 | 80.74 10 | 64.98 2 | 68.72 26 | 79.98 15 | 62.92 10 | 78.24 14 | 77.77 12 | 81.99 25 | 86.30 18 |
|
HFP-MVS | | | 74.87 11 | 78.86 16 | 70.21 7 | 73.99 16 | 77.91 14 | 80.36 11 | 56.63 12 | 78.41 16 | 64.27 3 | 74.54 16 | 77.75 23 | 62.96 9 | 78.70 9 | 77.82 10 | 83.02 8 | 86.91 16 |
|
CSCG | | | 74.68 12 | 79.22 12 | 69.40 14 | 75.69 8 | 80.01 5 | 79.12 19 | 52.83 36 | 79.34 14 | 63.99 4 | 70.49 21 | 82.02 8 | 60.35 27 | 77.48 22 | 77.22 16 | 84.38 1 | 87.97 11 |
|
SMA-MVS | | | 77.34 3 | 82.65 3 | 71.13 3 | 75.33 10 | 80.39 3 | 82.14 3 | 58.49 3 | 84.51 4 | 63.89 5 | 78.09 11 | 83.76 2 | 63.31 7 | 81.19 1 | 80.62 1 | 83.60 4 | 90.03 3 |
|
ACMMP_Plus | | | 76.15 5 | 81.17 5 | 70.30 6 | 74.09 15 | 79.47 6 | 81.59 6 | 57.09 10 | 81.38 7 | 63.89 5 | 79.02 9 | 80.48 13 | 62.24 14 | 80.05 4 | 79.12 4 | 82.94 10 | 88.64 5 |
|
HPM-MVS++ | | | 76.01 6 | 80.47 8 | 70.81 4 | 76.60 4 | 74.96 31 | 80.18 12 | 58.36 4 | 81.96 6 | 63.50 7 | 78.80 10 | 82.53 7 | 64.40 4 | 78.74 8 | 78.84 5 | 81.81 27 | 87.46 13 |
|
CNVR-MVS | | | 75.62 8 | 79.91 10 | 70.61 5 | 75.76 6 | 78.82 10 | 81.66 5 | 57.12 9 | 79.77 13 | 63.04 8 | 70.69 20 | 81.15 10 | 62.99 8 | 80.23 3 | 79.54 3 | 83.11 7 | 89.16 4 |
|
SD-MVS | | | 74.43 13 | 78.87 14 | 69.26 16 | 74.39 14 | 73.70 41 | 79.06 20 | 55.24 22 | 81.04 8 | 62.71 9 | 80.18 8 | 82.61 6 | 61.70 18 | 75.43 36 | 73.92 39 | 82.44 17 | 85.22 28 |
|
TSAR-MVS + MP. | | | 75.22 10 | 80.06 9 | 69.56 13 | 74.61 13 | 72.74 45 | 80.59 8 | 55.70 20 | 80.80 9 | 62.65 10 | 86.25 3 | 82.92 5 | 62.07 16 | 76.89 25 | 75.66 27 | 81.77 29 | 85.19 29 |
|
MTMP | | | | | | | | | | | 62.63 11 | | 78.04 21 | | | | | |
|
ESAPD | | | 78.19 1 | 83.74 1 | 71.72 1 | 79.01 1 | 81.38 1 | 83.23 2 | 58.63 2 | 83.92 5 | 62.44 12 | 87.06 2 | 85.82 1 | 64.54 3 | 79.39 5 | 77.99 8 | 82.44 17 | 90.61 1 |
|
NCCC | | | 74.27 15 | 77.83 21 | 70.13 9 | 75.70 7 | 77.41 18 | 80.51 10 | 57.09 10 | 78.25 17 | 62.28 13 | 65.54 33 | 78.26 20 | 62.18 15 | 79.13 6 | 78.51 6 | 83.01 9 | 87.68 12 |
|
DeepC-MVS | | 66.32 2 | 73.85 19 | 78.10 19 | 68.90 18 | 67.92 44 | 79.31 7 | 78.16 24 | 59.28 1 | 78.24 18 | 61.13 14 | 67.36 32 | 76.10 27 | 63.40 6 | 79.11 7 | 78.41 7 | 83.52 5 | 88.16 9 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
APDe-MVS | | | 77.58 2 | 82.93 2 | 71.35 2 | 77.86 2 | 80.55 2 | 83.38 1 | 57.61 7 | 85.57 1 | 61.11 15 | 86.10 4 | 82.98 4 | 64.76 2 | 78.29 12 | 76.78 19 | 83.40 6 | 90.20 2 |
|
HSP-MVS | | | 76.78 4 | 82.44 4 | 70.19 8 | 75.26 11 | 80.22 4 | 80.59 8 | 57.85 6 | 84.79 3 | 60.84 16 | 88.54 1 | 83.43 3 | 66.24 1 | 78.21 15 | 76.47 21 | 80.34 38 | 85.43 27 |
|
CLD-MVS | | | 67.02 45 | 71.57 38 | 61.71 47 | 71.01 29 | 74.81 33 | 71.62 47 | 38.91 166 | 71.86 36 | 60.70 17 | 64.97 35 | 67.88 53 | 51.88 94 | 76.77 28 | 74.98 32 | 76.11 95 | 69.75 120 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MP-MVS | | | 74.31 14 | 78.87 14 | 68.99 17 | 73.49 18 | 78.56 11 | 79.25 18 | 56.51 13 | 75.33 23 | 60.69 18 | 75.30 15 | 79.12 18 | 61.81 17 | 77.78 19 | 77.93 9 | 82.18 23 | 88.06 10 |
|
MSLP-MVS++ | | | 68.17 39 | 70.72 44 | 65.19 34 | 69.41 37 | 70.64 51 | 74.99 36 | 45.76 67 | 70.20 42 | 60.17 19 | 56.42 62 | 73.01 38 | 61.14 20 | 72.80 47 | 70.54 51 | 79.70 43 | 81.42 46 |
|
MCST-MVS | | | 73.67 21 | 77.39 22 | 69.33 15 | 76.26 5 | 78.19 13 | 78.77 21 | 54.54 25 | 75.33 23 | 59.99 20 | 67.96 28 | 79.23 17 | 62.43 13 | 78.00 16 | 75.71 26 | 84.02 2 | 87.30 14 |
|
SteuartSystems-ACMMP | | | 75.23 9 | 79.60 11 | 70.13 9 | 76.81 3 | 78.92 8 | 81.74 4 | 57.99 5 | 75.30 25 | 59.83 21 | 75.69 14 | 78.45 19 | 60.48 25 | 80.58 2 | 79.77 2 | 83.94 3 | 88.52 6 |
Skip Steuart: Steuart Systems R&D Blog. |
APD-MVS | | | 75.80 7 | 80.90 7 | 69.86 12 | 75.42 9 | 78.48 12 | 81.43 7 | 57.44 8 | 80.45 11 | 59.32 22 | 85.28 5 | 80.82 12 | 63.96 5 | 76.89 25 | 76.08 24 | 81.58 33 | 88.30 8 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
train_agg | | | 73.89 18 | 78.25 18 | 68.80 19 | 75.25 12 | 72.27 47 | 79.75 13 | 56.05 17 | 74.87 28 | 58.97 23 | 81.83 7 | 79.76 16 | 61.05 22 | 77.39 23 | 76.01 25 | 81.71 30 | 85.61 25 |
|
CP-MVS | | | 72.63 23 | 76.95 24 | 67.59 22 | 70.67 30 | 75.53 29 | 77.95 26 | 56.01 18 | 75.65 22 | 58.82 24 | 69.16 25 | 76.48 25 | 60.46 26 | 77.66 20 | 77.20 17 | 81.65 31 | 86.97 15 |
|
3Dnovator+ | | 62.63 4 | 69.51 31 | 72.62 35 | 65.88 32 | 68.21 43 | 76.47 25 | 73.50 44 | 52.74 37 | 70.85 39 | 58.65 25 | 55.97 64 | 69.95 45 | 61.11 21 | 76.80 27 | 75.09 28 | 81.09 36 | 83.23 40 |
|
DeepC-MVS_fast | | 65.08 3 | 72.00 25 | 76.11 25 | 67.21 24 | 68.93 40 | 77.46 16 | 76.54 30 | 54.35 26 | 74.92 27 | 58.64 26 | 65.18 34 | 74.04 37 | 62.62 11 | 77.92 17 | 77.02 18 | 82.16 24 | 86.21 19 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMMPR | | | 73.79 20 | 78.41 17 | 68.40 20 | 72.35 23 | 77.79 15 | 79.32 16 | 56.38 15 | 77.67 20 | 58.30 27 | 74.16 17 | 76.66 24 | 61.40 19 | 78.32 11 | 77.80 11 | 82.68 14 | 86.51 17 |
|
DeepPCF-MVS | | 66.49 1 | 74.25 16 | 80.97 6 | 66.41 27 | 67.75 46 | 78.87 9 | 75.61 34 | 54.16 28 | 84.86 2 | 58.22 28 | 77.94 12 | 81.01 11 | 62.52 12 | 78.34 10 | 77.38 13 | 80.16 41 | 88.40 7 |
|
PGM-MVS | | | 72.89 22 | 77.13 23 | 67.94 21 | 72.47 22 | 77.25 19 | 79.27 17 | 54.63 24 | 73.71 30 | 57.95 29 | 72.38 18 | 75.33 29 | 60.75 23 | 78.25 13 | 77.36 15 | 82.57 16 | 85.62 24 |
|
AdaColmap | | | 67.89 41 | 68.85 51 | 66.77 25 | 73.73 17 | 74.30 38 | 75.28 35 | 53.58 31 | 70.24 41 | 57.59 30 | 51.19 83 | 59.19 76 | 60.74 24 | 75.33 38 | 73.72 41 | 79.69 45 | 77.96 60 |
|
TSAR-MVS + GP. | | | 69.71 30 | 73.92 32 | 64.80 38 | 68.27 42 | 70.56 52 | 71.90 46 | 50.75 46 | 71.38 37 | 57.46 31 | 68.68 27 | 75.42 28 | 60.10 28 | 73.47 44 | 73.99 38 | 80.32 39 | 83.97 34 |
|
CNLPA | | | 62.78 56 | 66.31 56 | 58.65 55 | 58.47 83 | 68.41 60 | 65.98 86 | 41.22 151 | 78.02 19 | 56.04 32 | 46.65 122 | 59.50 75 | 57.50 38 | 69.67 65 | 65.27 122 | 72.70 136 | 76.67 69 |
|
ACMP | | 61.42 5 | 68.72 38 | 71.37 39 | 65.64 33 | 69.06 39 | 74.45 37 | 75.88 33 | 53.30 32 | 68.10 45 | 55.74 33 | 61.53 49 | 62.29 64 | 56.97 43 | 74.70 40 | 74.23 37 | 82.88 11 | 84.31 31 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMMP | | | 71.57 26 | 75.84 26 | 66.59 26 | 70.30 34 | 76.85 24 | 78.46 23 | 53.95 29 | 73.52 31 | 55.56 34 | 70.13 22 | 71.36 42 | 58.55 34 | 77.00 24 | 76.23 23 | 82.71 13 | 85.81 23 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
MAR-MVS | | | 68.04 40 | 70.74 43 | 64.90 37 | 71.68 27 | 76.33 26 | 74.63 39 | 50.48 50 | 63.81 51 | 55.52 35 | 54.88 69 | 69.90 46 | 57.39 39 | 75.42 37 | 74.79 33 | 79.71 42 | 80.03 50 |
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 | | | 72.56 24 | 79.07 13 | 64.96 36 | 73.24 20 | 73.16 44 | 78.50 22 | 48.80 59 | 79.34 14 | 55.32 36 | 85.04 6 | 81.49 9 | 58.57 33 | 75.06 39 | 73.75 40 | 75.35 103 | 85.61 25 |
|
OMC-MVS | | | 65.16 50 | 71.35 40 | 57.94 61 | 52.95 159 | 68.82 57 | 69.00 51 | 38.28 173 | 79.89 12 | 55.20 37 | 62.76 42 | 68.31 50 | 56.14 49 | 71.30 54 | 68.70 64 | 76.06 97 | 79.67 51 |
|
MVS_111021_HR | | | 67.62 42 | 70.39 45 | 64.39 39 | 69.77 36 | 70.45 53 | 71.44 49 | 51.72 42 | 60.77 58 | 55.06 38 | 62.14 46 | 66.40 56 | 58.13 36 | 76.13 30 | 74.79 33 | 80.19 40 | 82.04 44 |
|
3Dnovator | | 60.86 6 | 66.99 46 | 70.32 46 | 63.11 44 | 66.63 49 | 74.52 34 | 71.56 48 | 45.76 67 | 67.37 47 | 55.00 39 | 54.31 73 | 68.19 51 | 58.49 35 | 73.97 43 | 73.63 42 | 81.22 35 | 80.23 49 |
|
HQP-MVS | | | 70.88 29 | 75.02 29 | 66.05 30 | 71.69 26 | 74.47 36 | 77.51 27 | 53.17 33 | 72.89 32 | 54.88 40 | 70.03 23 | 70.48 44 | 57.26 40 | 76.02 31 | 75.01 31 | 81.78 28 | 86.21 19 |
|
CANet | | | 68.77 36 | 73.01 33 | 63.83 42 | 68.30 41 | 75.19 30 | 73.73 43 | 47.90 60 | 63.86 50 | 54.84 41 | 67.51 30 | 74.36 35 | 57.62 37 | 74.22 42 | 73.57 43 | 80.56 37 | 82.36 41 |
|
ACMM | | 60.30 7 | 67.58 43 | 68.82 52 | 66.13 29 | 70.59 31 | 72.01 49 | 76.54 30 | 54.26 27 | 65.64 49 | 54.78 42 | 50.35 85 | 61.72 67 | 58.74 32 | 75.79 34 | 75.03 29 | 81.88 26 | 81.17 47 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
abl_6 | | | | | 64.36 40 | 70.08 35 | 77.45 17 | 72.88 45 | 50.15 51 | 71.31 38 | 54.77 43 | 62.79 41 | 77.99 22 | 56.80 45 | | | 81.50 34 | 83.91 35 |
|
XVS | | | | | | 70.49 32 | 76.96 21 | 74.36 40 | | | 54.48 44 | | 74.47 32 | | | | 82.24 19 | |
|
X-MVStestdata | | | | | | 70.49 32 | 76.96 21 | 74.36 40 | | | 54.48 44 | | 74.47 32 | | | | 82.24 19 | |
|
X-MVS | | | 71.18 28 | 75.66 28 | 65.96 31 | 71.71 25 | 76.96 21 | 77.26 28 | 55.88 19 | 72.75 33 | 54.48 44 | 64.39 37 | 74.47 32 | 54.19 56 | 77.84 18 | 77.37 14 | 82.21 21 | 85.85 22 |
|
PCF-MVS | | 59.98 8 | 67.32 44 | 71.04 42 | 62.97 45 | 64.77 57 | 74.49 35 | 74.78 38 | 49.54 53 | 67.44 46 | 54.39 47 | 58.35 57 | 72.81 39 | 55.79 52 | 71.54 52 | 69.24 59 | 78.57 51 | 83.41 38 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_0304 | | | 69.49 32 | 73.96 31 | 64.28 41 | 67.92 44 | 76.13 27 | 74.90 37 | 47.60 61 | 63.29 53 | 54.09 48 | 67.44 31 | 76.35 26 | 59.53 30 | 75.81 33 | 75.03 29 | 81.62 32 | 83.70 37 |
|
OPM-MVS | | | 69.33 33 | 71.05 41 | 67.32 23 | 72.34 24 | 75.70 28 | 79.57 15 | 56.34 16 | 55.21 64 | 53.81 49 | 59.51 53 | 68.96 47 | 59.67 29 | 77.61 21 | 76.44 22 | 82.19 22 | 83.88 36 |
|
MVS_111021_LR | | | 63.05 54 | 66.43 55 | 59.10 54 | 61.33 69 | 63.77 102 | 65.87 87 | 43.58 105 | 60.20 59 | 53.70 50 | 62.09 47 | 62.38 63 | 55.84 51 | 70.24 62 | 68.08 68 | 74.30 108 | 78.28 59 |
|
CDPH-MVS | | | 71.47 27 | 75.82 27 | 66.41 27 | 72.97 21 | 77.15 20 | 78.14 25 | 54.71 23 | 69.88 43 | 53.07 51 | 70.98 19 | 74.83 31 | 56.95 44 | 76.22 29 | 76.57 20 | 82.62 15 | 85.09 30 |
|
PVSNet_BlendedMVS | | | 61.63 60 | 64.82 64 | 57.91 63 | 57.21 124 | 67.55 67 | 63.47 104 | 46.08 65 | 54.72 65 | 52.46 52 | 58.59 55 | 60.73 69 | 51.82 95 | 70.46 59 | 65.20 124 | 76.44 84 | 76.50 75 |
|
PVSNet_Blended | | | 61.63 60 | 64.82 64 | 57.91 63 | 57.21 124 | 67.55 67 | 63.47 104 | 46.08 65 | 54.72 65 | 52.46 52 | 58.59 55 | 60.73 69 | 51.82 95 | 70.46 59 | 65.20 124 | 76.44 84 | 76.50 75 |
|
LGP-MVS_train | | | 68.87 35 | 72.03 37 | 65.18 35 | 69.33 38 | 74.03 40 | 76.67 29 | 53.88 30 | 68.46 44 | 52.05 54 | 63.21 39 | 63.89 59 | 56.31 46 | 75.99 32 | 74.43 35 | 82.83 12 | 84.18 32 |
|
PHI-MVS | | | 69.27 34 | 74.84 30 | 62.76 46 | 66.83 48 | 74.83 32 | 73.88 42 | 49.32 55 | 70.61 40 | 50.93 55 | 69.62 24 | 74.84 30 | 57.25 41 | 75.53 35 | 74.32 36 | 78.35 55 | 84.17 33 |
|
PVSNet_Blended_VisFu | | | 63.65 52 | 66.92 53 | 59.83 52 | 60.03 75 | 73.44 43 | 66.33 80 | 48.95 57 | 52.20 78 | 50.81 56 | 56.07 63 | 60.25 72 | 53.56 61 | 73.23 46 | 70.01 56 | 79.30 47 | 83.24 39 |
|
CPTT-MVS | | | 68.76 37 | 73.01 33 | 63.81 43 | 65.42 55 | 73.66 42 | 76.39 32 | 52.08 38 | 72.61 34 | 50.33 57 | 60.73 50 | 72.65 40 | 59.43 31 | 73.32 45 | 72.12 45 | 79.19 49 | 85.99 21 |
|
OpenMVS | | 57.13 9 | 62.81 55 | 65.75 59 | 59.39 53 | 66.47 51 | 69.52 55 | 64.26 100 | 43.07 121 | 61.34 57 | 50.19 58 | 47.29 119 | 64.41 58 | 54.60 55 | 70.18 63 | 68.62 66 | 77.73 57 | 78.89 54 |
|
MVS_Test | | | 62.40 58 | 66.23 57 | 57.94 61 | 59.77 78 | 64.77 98 | 66.50 79 | 41.76 141 | 57.26 62 | 49.33 59 | 62.68 43 | 67.47 55 | 53.50 64 | 68.57 74 | 66.25 92 | 76.77 72 | 76.58 72 |
|
QAPM | | | 65.27 49 | 69.49 50 | 60.35 49 | 65.43 54 | 72.20 48 | 65.69 89 | 47.23 62 | 63.46 52 | 49.14 60 | 53.56 74 | 71.04 43 | 57.01 42 | 72.60 48 | 71.41 48 | 77.62 59 | 82.14 43 |
|
DELS-MVS | | | 65.87 47 | 70.30 47 | 60.71 48 | 64.05 64 | 72.68 46 | 70.90 50 | 45.43 71 | 57.49 61 | 49.05 61 | 64.43 36 | 68.66 48 | 55.11 54 | 74.31 41 | 73.02 44 | 79.70 43 | 81.51 45 |
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 |
TAPA-MVS | | 54.74 10 | 60.85 62 | 66.61 54 | 54.12 101 | 47.38 187 | 65.33 91 | 65.35 92 | 36.51 184 | 75.16 26 | 48.82 62 | 54.70 70 | 63.51 60 | 53.31 68 | 68.36 75 | 64.97 127 | 73.37 121 | 74.27 105 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
tpmp4_e23 | | | 56.84 110 | 57.14 129 | 56.49 91 | 62.45 66 | 62.05 117 | 67.57 54 | 41.56 146 | 54.17 67 | 48.57 63 | 49.18 89 | 46.54 147 | 50.44 99 | 61.93 164 | 58.82 178 | 68.34 171 | 67.28 138 |
|
canonicalmvs | | | 65.62 48 | 72.06 36 | 58.11 57 | 63.94 65 | 71.05 50 | 64.49 98 | 43.18 116 | 74.08 29 | 47.35 64 | 64.17 38 | 71.97 41 | 51.17 97 | 71.87 50 | 70.74 49 | 78.51 53 | 80.56 48 |
|
tpm cat1 | | | 53.30 132 | 53.41 160 | 53.17 108 | 58.16 84 | 59.15 152 | 63.73 103 | 38.27 174 | 50.73 82 | 46.98 65 | 45.57 137 | 44.00 172 | 49.20 103 | 55.90 200 | 54.02 199 | 62.65 193 | 64.50 167 |
|
DI_MVS_plusplus_trai | | | 61.88 59 | 65.17 63 | 58.06 58 | 60.05 74 | 65.26 93 | 66.03 84 | 44.22 83 | 55.75 63 | 46.73 66 | 54.64 71 | 68.12 52 | 54.13 58 | 69.13 67 | 66.66 84 | 77.18 65 | 76.61 70 |
|
Effi-MVS+ | | | 63.28 53 | 65.96 58 | 60.17 50 | 64.26 61 | 68.06 61 | 68.78 52 | 45.71 69 | 54.08 68 | 46.64 67 | 55.92 65 | 63.13 62 | 55.94 50 | 70.38 61 | 71.43 47 | 79.68 46 | 78.70 55 |
|
v18 | | | 58.68 81 | 60.20 89 | 56.90 80 | 57.26 122 | 63.28 111 | 66.58 78 | 42.42 136 | 48.86 95 | 46.37 68 | 49.01 95 | 53.05 97 | 52.74 78 | 67.40 110 | 65.52 118 | 76.02 99 | 74.28 104 |
|
v16 | | | 58.71 78 | 60.20 89 | 56.97 71 | 57.35 109 | 63.36 110 | 66.67 76 | 42.49 133 | 48.69 101 | 46.36 69 | 48.87 99 | 52.92 103 | 52.82 73 | 67.57 100 | 65.58 116 | 76.15 94 | 74.38 101 |
|
TSAR-MVS + COLMAP | | | 62.65 57 | 69.90 48 | 54.19 100 | 46.31 191 | 66.73 76 | 65.49 91 | 41.36 149 | 76.57 21 | 46.31 70 | 76.80 13 | 56.68 84 | 53.27 69 | 69.50 66 | 66.65 85 | 72.40 142 | 76.36 77 |
|
EPNet | | | 65.14 51 | 69.54 49 | 60.00 51 | 66.61 50 | 67.67 66 | 67.53 55 | 55.32 21 | 62.67 55 | 46.22 71 | 67.74 29 | 65.93 57 | 48.07 110 | 72.17 49 | 72.12 45 | 76.28 87 | 78.47 57 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
diffmvs | | | 59.53 66 | 64.04 69 | 54.26 99 | 55.09 139 | 59.86 142 | 64.80 95 | 39.55 165 | 58.39 60 | 46.21 72 | 60.48 51 | 67.82 54 | 49.27 102 | 63.53 151 | 63.32 147 | 70.64 157 | 74.89 87 |
|
v17 | | | 58.69 79 | 60.19 92 | 56.94 73 | 57.38 104 | 63.37 109 | 66.67 76 | 42.47 135 | 48.52 105 | 46.10 73 | 48.90 98 | 53.00 98 | 52.84 71 | 67.58 99 | 65.60 112 | 76.19 92 | 74.38 101 |
|
v6 | | | 58.89 72 | 60.54 82 | 56.96 72 | 57.34 111 | 66.13 85 | 66.71 71 | 42.84 123 | 47.85 109 | 45.80 74 | 49.04 91 | 52.95 100 | 52.79 74 | 67.53 102 | 65.59 113 | 76.26 88 | 74.73 89 |
|
v8 | | | 58.88 73 | 60.57 81 | 56.92 77 | 57.35 109 | 65.69 90 | 66.69 75 | 42.64 131 | 47.89 108 | 45.77 75 | 49.04 91 | 52.98 99 | 52.77 77 | 67.51 105 | 65.57 117 | 76.26 88 | 75.30 85 |
|
v1neww | | | 58.88 73 | 60.54 82 | 56.94 73 | 57.33 113 | 66.13 85 | 66.70 73 | 42.84 123 | 47.84 110 | 45.74 76 | 49.02 93 | 52.93 101 | 52.78 75 | 67.53 102 | 65.59 113 | 76.26 88 | 74.73 89 |
|
v7new | | | 58.88 73 | 60.54 82 | 56.94 73 | 57.33 113 | 66.13 85 | 66.70 73 | 42.84 123 | 47.84 110 | 45.74 76 | 49.02 93 | 52.93 101 | 52.78 75 | 67.53 102 | 65.59 113 | 76.26 88 | 74.73 89 |
|
v7 | | | 59.19 70 | 60.62 79 | 57.53 66 | 57.96 86 | 67.19 72 | 67.09 60 | 44.28 82 | 46.84 115 | 45.45 78 | 48.19 111 | 51.06 117 | 53.62 60 | 67.84 92 | 66.59 87 | 76.79 69 | 76.60 71 |
|
v10 | | | 59.17 71 | 60.60 80 | 57.50 67 | 57.95 87 | 66.73 76 | 67.09 60 | 44.11 84 | 46.85 114 | 45.42 79 | 48.18 113 | 51.07 116 | 53.63 59 | 67.84 92 | 66.59 87 | 76.79 69 | 76.92 67 |
|
Fast-Effi-MVS+ | | | 60.36 63 | 63.35 70 | 56.87 83 | 58.70 80 | 65.86 89 | 65.08 93 | 37.11 179 | 53.00 75 | 45.36 80 | 52.12 79 | 56.07 89 | 56.27 47 | 71.28 55 | 69.42 58 | 78.71 50 | 75.69 81 |
|
v2v482 | | | 58.69 79 | 60.12 96 | 57.03 70 | 57.16 126 | 66.05 88 | 67.17 58 | 43.52 107 | 46.33 120 | 45.19 81 | 49.46 88 | 51.02 118 | 52.51 85 | 67.30 112 | 66.03 95 | 76.61 80 | 74.62 96 |
|
v15 | | | 58.43 93 | 59.75 101 | 56.88 82 | 57.45 100 | 63.44 107 | 66.84 66 | 42.65 130 | 46.24 122 | 45.07 82 | 48.68 103 | 52.07 107 | 52.63 83 | 67.84 92 | 65.70 106 | 76.65 76 | 74.31 103 |
|
V9 | | | 58.45 89 | 59.75 101 | 56.92 77 | 57.51 97 | 63.49 105 | 66.86 63 | 42.73 128 | 46.07 126 | 45.05 83 | 48.45 106 | 51.99 109 | 52.66 81 | 68.04 90 | 65.75 102 | 76.72 74 | 74.50 98 |
|
V14 | | | 58.44 90 | 59.75 101 | 56.90 80 | 57.48 99 | 63.46 106 | 66.85 65 | 42.68 129 | 46.16 123 | 45.03 84 | 48.57 104 | 52.04 108 | 52.65 82 | 67.93 91 | 65.72 105 | 76.69 75 | 74.40 100 |
|
v13 | | | 58.44 90 | 59.72 105 | 56.94 73 | 57.55 91 | 63.51 103 | 66.86 63 | 42.81 126 | 45.90 129 | 44.98 85 | 48.17 114 | 51.87 111 | 52.68 79 | 68.20 80 | 65.78 100 | 76.78 71 | 74.63 95 |
|
v12 | | | 58.44 90 | 59.74 104 | 56.92 77 | 57.54 93 | 63.50 104 | 66.84 66 | 42.77 127 | 45.96 127 | 44.95 86 | 48.31 107 | 51.94 110 | 52.67 80 | 68.14 83 | 65.75 102 | 76.75 73 | 74.55 97 |
|
CMPMVS | | 37.70 17 | 49.24 164 | 52.71 164 | 45.19 166 | 45.97 193 | 51.23 189 | 47.44 182 | 29.31 211 | 43.04 154 | 44.69 87 | 34.45 197 | 48.35 128 | 43.64 129 | 62.59 155 | 59.82 173 | 60.08 198 | 69.48 126 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
v1 | | | 58.56 82 | 60.06 97 | 56.83 85 | 57.36 106 | 66.19 82 | 66.80 68 | 43.10 120 | 45.87 131 | 44.68 88 | 48.73 100 | 51.83 113 | 52.38 87 | 67.45 108 | 65.65 107 | 76.63 77 | 74.66 92 |
|
CostFormer | | | 56.57 111 | 59.13 114 | 53.60 102 | 57.52 96 | 61.12 129 | 66.94 62 | 35.95 186 | 53.44 69 | 44.68 88 | 55.87 66 | 54.44 92 | 48.21 108 | 60.37 172 | 58.33 181 | 68.27 173 | 70.33 118 |
|
v1141 | | | 58.56 82 | 60.05 98 | 56.81 86 | 57.36 106 | 66.18 83 | 66.80 68 | 43.11 118 | 45.87 131 | 44.60 90 | 48.71 101 | 51.83 113 | 52.38 87 | 67.46 106 | 65.64 110 | 76.63 77 | 74.66 92 |
|
divwei89l23v2f112 | | | 58.56 82 | 60.05 98 | 56.81 86 | 57.36 106 | 66.18 83 | 66.80 68 | 43.11 118 | 45.89 130 | 44.60 90 | 48.71 101 | 51.84 112 | 52.38 87 | 67.45 108 | 65.65 107 | 76.63 77 | 74.66 92 |
|
v11 | | | 58.19 99 | 59.47 110 | 56.70 88 | 57.54 93 | 63.42 108 | 66.28 82 | 42.49 133 | 45.62 135 | 44.59 92 | 48.16 115 | 50.78 121 | 52.84 71 | 67.80 96 | 65.76 101 | 76.49 83 | 74.76 88 |
|
PLC | | 52.09 14 | 59.21 69 | 62.47 72 | 55.41 96 | 53.24 158 | 64.84 97 | 64.47 99 | 40.41 159 | 65.92 48 | 44.53 93 | 46.19 130 | 55.69 90 | 55.33 53 | 68.24 79 | 65.30 121 | 74.50 106 | 71.09 112 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
v1144 | | | 58.88 73 | 60.16 93 | 57.39 68 | 58.03 85 | 67.26 70 | 67.14 59 | 44.46 81 | 45.17 137 | 44.33 94 | 47.81 116 | 49.92 126 | 53.20 70 | 67.77 97 | 66.62 86 | 77.15 66 | 76.58 72 |
|
IB-MVS | | 54.11 11 | 58.36 95 | 60.70 78 | 55.62 94 | 58.67 81 | 68.02 62 | 61.56 107 | 43.15 117 | 46.09 124 | 44.06 95 | 44.24 145 | 50.99 120 | 48.71 105 | 66.70 121 | 70.33 52 | 77.60 60 | 78.50 56 |
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 |
MSDG | | | 58.46 88 | 58.97 116 | 57.85 65 | 66.27 53 | 66.23 81 | 67.72 53 | 42.33 137 | 53.43 70 | 43.68 96 | 43.39 153 | 45.35 154 | 49.75 100 | 68.66 72 | 67.77 71 | 77.38 63 | 67.96 133 |
|
v1192 | | | 58.51 85 | 59.66 106 | 57.17 69 | 57.82 88 | 67.72 64 | 66.21 83 | 44.83 77 | 44.15 144 | 43.49 97 | 46.68 121 | 47.94 130 | 53.55 62 | 67.39 111 | 66.51 89 | 77.13 67 | 77.20 65 |
|
tpm | | | 48.82 168 | 51.27 179 | 45.96 163 | 54.10 153 | 47.35 200 | 56.05 137 | 30.23 209 | 46.70 116 | 43.21 98 | 52.54 78 | 47.55 136 | 37.28 161 | 54.11 205 | 50.50 210 | 54.90 211 | 60.12 186 |
|
v144192 | | | 58.23 98 | 59.40 112 | 56.87 83 | 57.56 90 | 66.89 74 | 65.70 88 | 45.01 76 | 44.06 145 | 42.88 99 | 46.61 123 | 48.09 129 | 53.49 65 | 66.94 117 | 65.90 98 | 76.61 80 | 77.29 63 |
|
DWT-MVSNet_training | | | 53.80 127 | 54.31 152 | 53.21 106 | 57.65 89 | 59.04 153 | 60.65 113 | 40.11 162 | 46.35 119 | 42.77 100 | 49.07 90 | 41.07 187 | 51.06 98 | 58.62 182 | 58.96 177 | 67.00 181 | 67.06 139 |
|
v1921920 | | | 57.89 102 | 59.02 115 | 56.58 90 | 57.55 91 | 66.66 79 | 64.72 97 | 44.70 79 | 43.55 148 | 42.73 101 | 46.17 131 | 46.93 144 | 53.51 63 | 66.78 120 | 65.75 102 | 76.29 86 | 77.28 64 |
|
V42 | | | 56.97 107 | 60.14 94 | 53.28 105 | 48.16 182 | 62.78 116 | 66.30 81 | 37.93 175 | 47.44 112 | 42.68 102 | 48.19 111 | 52.59 105 | 51.90 93 | 67.46 106 | 65.94 97 | 72.72 130 | 76.55 74 |
|
v1240 | | | 57.55 103 | 58.63 118 | 56.29 92 | 57.30 119 | 66.48 80 | 63.77 102 | 44.56 80 | 42.77 164 | 42.48 103 | 45.64 136 | 46.28 149 | 53.46 66 | 66.32 128 | 65.80 99 | 76.16 93 | 77.13 66 |
|
v148 | | | 55.58 117 | 57.61 127 | 53.20 107 | 54.59 148 | 61.86 118 | 61.18 110 | 38.70 171 | 44.30 143 | 42.25 104 | 47.53 117 | 50.24 125 | 48.73 104 | 65.15 145 | 62.61 157 | 73.79 114 | 71.61 110 |
|
MS-PatchMatch | | | 58.19 99 | 60.20 89 | 55.85 93 | 65.17 56 | 64.16 100 | 64.82 94 | 41.48 148 | 50.95 80 | 42.17 105 | 45.38 138 | 56.42 85 | 48.08 109 | 68.30 76 | 66.70 83 | 73.39 120 | 69.46 128 |
|
Effi-MVS+-dtu | | | 60.34 64 | 62.32 73 | 58.03 60 | 64.31 59 | 67.44 69 | 65.99 85 | 42.26 138 | 49.55 86 | 42.00 106 | 48.92 97 | 59.79 74 | 56.27 47 | 68.07 86 | 67.03 77 | 77.35 64 | 75.45 83 |
|
EG-PatchMatch MVS | | | 56.98 106 | 58.24 122 | 55.50 95 | 64.66 58 | 68.62 58 | 61.48 109 | 43.63 104 | 38.44 197 | 41.44 107 | 38.05 189 | 46.18 151 | 43.95 127 | 71.71 51 | 70.61 50 | 77.87 56 | 74.08 106 |
|
MVSTER | | | 57.19 104 | 61.11 77 | 52.62 112 | 50.82 175 | 58.79 155 | 61.55 108 | 37.86 176 | 48.81 97 | 41.31 108 | 57.43 61 | 52.10 106 | 48.60 106 | 68.19 82 | 66.75 82 | 75.56 101 | 75.68 82 |
|
LS3D | | | 60.20 65 | 61.70 74 | 58.45 56 | 64.18 62 | 67.77 63 | 67.19 57 | 48.84 58 | 61.67 56 | 41.27 109 | 45.89 133 | 51.81 115 | 54.18 57 | 68.78 69 | 66.50 90 | 75.03 104 | 69.48 126 |
|
PatchmatchNet | | | 49.92 161 | 51.29 178 | 48.32 146 | 51.83 169 | 51.86 187 | 53.38 161 | 37.63 178 | 47.90 107 | 40.83 110 | 48.54 105 | 45.30 155 | 45.19 124 | 56.86 189 | 53.99 201 | 61.08 197 | 54.57 202 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
pmmvs4 | | | 54.66 125 | 56.07 134 | 53.00 109 | 54.63 145 | 57.08 166 | 60.43 117 | 44.10 85 | 51.69 79 | 40.55 111 | 46.55 126 | 44.79 164 | 45.95 121 | 62.54 156 | 63.66 142 | 72.36 144 | 66.20 148 |
|
dps | | | 50.42 156 | 51.20 182 | 49.51 127 | 55.88 131 | 56.07 168 | 53.73 155 | 38.89 167 | 43.66 146 | 40.36 112 | 45.66 135 | 37.63 204 | 45.23 123 | 59.05 175 | 56.18 184 | 62.94 192 | 60.16 185 |
|
IterMVS-LS | | | 58.30 96 | 61.39 75 | 54.71 98 | 59.92 77 | 58.40 159 | 59.42 119 | 43.64 102 | 48.71 99 | 40.25 113 | 57.53 60 | 58.55 78 | 52.15 92 | 65.42 144 | 65.34 120 | 72.85 127 | 75.77 79 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpmrst | | | 48.08 175 | 49.88 190 | 45.98 162 | 52.71 160 | 48.11 198 | 53.62 158 | 33.70 197 | 48.70 100 | 39.74 114 | 48.96 96 | 46.23 150 | 40.29 144 | 50.14 214 | 49.28 212 | 55.80 208 | 57.71 193 |
|
CR-MVSNet | | | 50.47 155 | 52.61 165 | 47.98 151 | 49.03 181 | 52.94 181 | 48.27 176 | 38.86 168 | 44.41 140 | 39.59 115 | 44.34 144 | 44.65 167 | 46.63 118 | 58.97 177 | 60.31 170 | 65.48 184 | 62.66 174 |
|
Patchmtry | | | | | | | 47.61 199 | 48.27 176 | 38.86 168 | | 39.59 115 | | | | | | | |
|
PatchT | | | 48.08 175 | 51.03 183 | 44.64 170 | 42.96 204 | 50.12 192 | 40.36 211 | 35.09 188 | 43.17 153 | 39.59 115 | 42.00 175 | 39.96 194 | 46.63 118 | 58.97 177 | 60.31 170 | 63.21 191 | 62.66 174 |
|
CHOSEN 1792x2688 | | | 55.85 114 | 58.01 123 | 53.33 104 | 57.26 122 | 62.82 115 | 63.29 106 | 41.55 147 | 46.65 117 | 38.34 118 | 34.55 196 | 53.50 94 | 52.43 86 | 67.10 115 | 67.56 74 | 67.13 178 | 73.92 107 |
|
MVS-HIRNet | | | 42.24 200 | 41.15 213 | 43.51 176 | 44.06 203 | 40.74 214 | 35.77 218 | 35.35 187 | 35.38 205 | 38.34 118 | 25.63 215 | 38.55 200 | 43.48 131 | 50.77 211 | 47.03 220 | 64.07 188 | 49.98 212 |
|
v7n | | | 55.67 115 | 57.46 128 | 53.59 103 | 56.06 130 | 65.29 92 | 61.06 112 | 43.26 115 | 40.17 184 | 37.99 120 | 40.79 180 | 45.27 156 | 47.09 114 | 67.67 98 | 66.21 93 | 76.08 96 | 76.82 68 |
|
MDTV_nov1_ep13 | | | 50.32 158 | 52.43 168 | 47.86 153 | 49.87 178 | 54.70 175 | 58.10 123 | 34.29 192 | 45.59 136 | 37.71 121 | 47.44 118 | 47.42 137 | 41.86 137 | 58.07 185 | 55.21 192 | 65.34 186 | 58.56 191 |
|
PatchMatch-RL | | | 50.11 160 | 51.56 175 | 48.43 144 | 46.23 192 | 51.94 186 | 50.21 169 | 38.62 172 | 46.62 118 | 37.51 122 | 42.43 169 | 39.38 196 | 52.24 91 | 60.98 168 | 59.56 174 | 65.76 183 | 60.01 187 |
|
HyFIR lowres test | | | 56.87 109 | 58.60 119 | 54.84 97 | 56.62 129 | 69.27 56 | 64.77 96 | 42.21 139 | 45.66 134 | 37.50 123 | 33.08 198 | 57.47 83 | 53.33 67 | 65.46 143 | 67.94 69 | 74.60 105 | 71.35 111 |
|
CANet_DTU | | | 58.88 73 | 64.68 66 | 52.12 115 | 55.77 132 | 66.75 75 | 63.92 101 | 37.04 180 | 53.32 71 | 37.45 124 | 59.81 52 | 61.81 66 | 44.43 126 | 68.25 77 | 67.47 75 | 74.12 111 | 75.33 84 |
|
pmmvs-eth3d | | | 51.33 150 | 52.25 170 | 50.26 122 | 50.82 175 | 54.65 176 | 56.03 138 | 43.45 112 | 43.51 149 | 37.20 125 | 39.20 187 | 39.04 198 | 42.28 135 | 61.85 165 | 62.78 154 | 71.78 151 | 64.72 165 |
|
GA-MVS | | | 55.67 115 | 58.33 120 | 52.58 113 | 55.23 137 | 63.09 112 | 61.08 111 | 40.15 161 | 42.95 155 | 37.02 126 | 52.61 77 | 47.68 133 | 47.51 112 | 65.92 136 | 65.35 119 | 74.49 107 | 70.68 116 |
|
V4 | | | 53.60 128 | 56.73 133 | 49.93 125 | 45.54 194 | 61.64 121 | 60.65 113 | 36.99 181 | 38.74 195 | 36.33 127 | 39.64 184 | 47.12 141 | 47.05 115 | 66.89 118 | 65.64 110 | 73.04 125 | 77.48 61 |
|
v52 | | | 53.60 128 | 56.74 132 | 49.93 125 | 45.54 194 | 61.64 121 | 60.65 113 | 36.99 181 | 38.75 193 | 36.32 128 | 39.64 184 | 47.13 140 | 47.05 115 | 66.89 118 | 65.65 107 | 73.04 125 | 77.48 61 |
|
USDC | | | 51.11 151 | 53.71 156 | 48.08 150 | 44.76 197 | 55.99 169 | 53.01 162 | 40.90 153 | 52.49 76 | 36.14 129 | 44.67 143 | 33.66 211 | 43.27 133 | 63.23 152 | 61.10 163 | 70.39 160 | 64.82 164 |
|
ACMH+ | | 53.71 12 | 59.26 68 | 60.28 86 | 58.06 58 | 64.17 63 | 68.46 59 | 67.51 56 | 50.93 45 | 52.46 77 | 35.83 130 | 40.83 179 | 45.12 157 | 52.32 90 | 69.88 64 | 69.00 62 | 77.59 61 | 76.21 78 |
|
RPSCF | | | 46.41 188 | 54.42 150 | 37.06 205 | 25.70 233 | 45.14 210 | 45.39 196 | 20.81 227 | 62.79 54 | 35.10 131 | 44.92 142 | 55.60 91 | 43.56 130 | 56.12 197 | 52.45 206 | 51.80 217 | 63.91 170 |
|
Fast-Effi-MVS+-dtu | | | 56.30 112 | 59.29 113 | 52.82 111 | 58.64 82 | 64.89 96 | 65.56 90 | 32.89 204 | 45.80 133 | 35.04 132 | 45.89 133 | 54.14 93 | 49.41 101 | 67.16 114 | 66.45 91 | 75.37 102 | 70.69 115 |
|
FC-MVSNet-train | | | 58.40 94 | 63.15 71 | 52.85 110 | 64.29 60 | 61.84 119 | 55.98 139 | 46.47 63 | 53.06 73 | 34.96 133 | 61.95 48 | 56.37 87 | 39.49 145 | 68.67 71 | 68.36 67 | 75.92 100 | 71.81 109 |
|
PMMVS | | | 49.20 166 | 54.28 153 | 43.28 179 | 34.13 219 | 45.70 209 | 48.98 173 | 26.09 222 | 46.31 121 | 34.92 134 | 55.22 67 | 53.47 95 | 47.48 113 | 59.43 174 | 59.04 176 | 68.05 174 | 60.77 182 |
|
v748 | | | 52.93 133 | 55.29 142 | 50.19 123 | 51.90 168 | 61.31 127 | 56.54 134 | 40.05 163 | 39.12 191 | 34.82 135 | 39.93 183 | 43.83 173 | 43.66 128 | 64.26 149 | 63.32 147 | 74.15 110 | 75.28 86 |
|
IterMVS | | | 53.45 131 | 57.12 130 | 49.17 131 | 49.23 179 | 60.93 130 | 59.05 121 | 34.63 190 | 44.53 139 | 33.22 136 | 51.09 84 | 51.01 119 | 48.38 107 | 62.43 157 | 60.79 167 | 70.54 159 | 69.05 131 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UGNet | | | 57.03 105 | 65.25 62 | 47.44 156 | 46.54 190 | 66.73 76 | 56.30 135 | 43.28 114 | 50.06 83 | 32.99 137 | 62.57 44 | 63.26 61 | 33.31 177 | 68.25 77 | 67.58 73 | 72.20 146 | 78.29 58 |
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 |
COLMAP_ROB | | 46.52 15 | 51.99 144 | 54.86 147 | 48.63 143 | 49.13 180 | 61.73 120 | 60.53 116 | 36.57 183 | 53.14 72 | 32.95 138 | 37.10 190 | 38.68 199 | 40.49 142 | 65.72 139 | 63.08 150 | 72.11 147 | 64.60 166 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
anonymousdsp | | | 52.84 134 | 57.78 125 | 47.06 157 | 40.24 210 | 58.95 154 | 53.70 156 | 33.54 199 | 36.51 204 | 32.69 139 | 43.88 147 | 45.40 153 | 47.97 111 | 67.17 113 | 70.28 53 | 74.22 109 | 82.29 42 |
|
test-LLR | | | 49.28 163 | 50.29 186 | 48.10 149 | 55.26 135 | 47.16 201 | 49.52 170 | 43.48 110 | 39.22 189 | 31.98 140 | 43.65 151 | 47.93 131 | 41.29 140 | 56.80 190 | 55.36 190 | 67.08 179 | 61.94 177 |
|
TESTMET0.1,1 | | | 46.09 191 | 50.29 186 | 41.18 191 | 36.91 215 | 47.16 201 | 49.52 170 | 20.32 228 | 39.22 189 | 31.98 140 | 43.65 151 | 47.93 131 | 41.29 140 | 56.80 190 | 55.36 190 | 67.08 179 | 61.94 177 |
|
TinyColmap | | | 47.08 184 | 47.56 198 | 46.52 161 | 42.35 206 | 53.44 180 | 51.77 166 | 40.70 157 | 43.44 151 | 31.92 142 | 29.78 206 | 23.72 226 | 45.04 125 | 61.99 163 | 59.54 175 | 67.35 177 | 61.03 181 |
|
RPMNet | | | 46.41 188 | 48.72 193 | 43.72 175 | 47.77 185 | 52.94 181 | 46.02 193 | 33.92 194 | 44.41 140 | 31.82 143 | 36.89 191 | 37.42 205 | 37.41 155 | 53.88 206 | 54.02 199 | 65.37 185 | 61.47 179 |
|
UA-Net | | | 58.50 86 | 64.68 66 | 51.30 117 | 66.97 47 | 67.13 73 | 53.68 157 | 45.65 70 | 49.51 88 | 31.58 144 | 62.91 40 | 68.47 49 | 35.85 167 | 68.20 80 | 67.28 76 | 74.03 112 | 69.24 130 |
|
TDRefinement | | | 49.31 162 | 52.44 167 | 45.67 165 | 30.44 225 | 59.42 146 | 59.24 120 | 39.78 164 | 48.76 98 | 31.20 145 | 35.73 193 | 29.90 215 | 42.81 134 | 64.24 150 | 62.59 158 | 70.55 158 | 66.43 144 |
|
GBi-Net | | | 55.20 120 | 60.25 87 | 49.31 128 | 52.42 161 | 61.44 123 | 57.03 129 | 44.04 87 | 49.18 91 | 30.47 146 | 48.28 108 | 58.19 79 | 38.22 150 | 68.05 87 | 66.96 78 | 73.69 116 | 69.65 121 |
|
test1 | | | 55.20 120 | 60.25 87 | 49.31 128 | 52.42 161 | 61.44 123 | 57.03 129 | 44.04 87 | 49.18 91 | 30.47 146 | 48.28 108 | 58.19 79 | 38.22 150 | 68.05 87 | 66.96 78 | 73.69 116 | 69.65 121 |
|
FMVSNet3 | | | 54.78 124 | 59.58 108 | 49.17 131 | 52.37 164 | 61.31 127 | 56.72 133 | 44.04 87 | 49.18 91 | 30.47 146 | 48.28 108 | 58.19 79 | 38.09 153 | 65.48 142 | 65.20 124 | 73.31 122 | 69.45 129 |
|
FMVSNet2 | | | 55.04 123 | 59.95 100 | 49.31 128 | 52.42 161 | 61.44 123 | 57.03 129 | 44.08 86 | 49.55 86 | 30.40 149 | 46.89 120 | 58.84 77 | 38.22 150 | 67.07 116 | 66.21 93 | 73.69 116 | 69.65 121 |
|
Vis-MVSNet | | | 58.48 87 | 65.70 60 | 50.06 124 | 53.40 157 | 67.20 71 | 60.24 118 | 43.32 113 | 48.83 96 | 30.23 150 | 62.38 45 | 61.61 68 | 40.35 143 | 71.03 57 | 69.77 57 | 72.82 129 | 79.11 53 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
PM-MVS | | | 44.55 195 | 48.13 196 | 40.37 194 | 32.85 223 | 46.82 205 | 46.11 192 | 29.28 212 | 40.48 182 | 29.99 151 | 39.98 182 | 34.39 210 | 41.80 138 | 56.08 198 | 53.88 203 | 62.19 195 | 65.31 158 |
|
LP | | | 40.79 204 | 41.99 210 | 39.38 197 | 40.98 208 | 46.49 207 | 42.14 207 | 33.66 198 | 35.37 206 | 29.89 152 | 29.30 209 | 27.81 217 | 32.74 178 | 52.55 207 | 52.19 207 | 56.87 206 | 50.23 211 |
|
MDTV_nov1_ep13_2view | | | 47.62 179 | 49.72 191 | 45.18 167 | 48.05 183 | 53.70 179 | 54.90 150 | 33.80 196 | 39.90 186 | 29.79 153 | 38.85 188 | 41.89 180 | 39.17 146 | 58.99 176 | 55.55 189 | 65.34 186 | 59.17 189 |
|
EPMVS | | | 44.66 194 | 47.86 197 | 40.92 192 | 47.97 184 | 44.70 211 | 47.58 181 | 33.27 200 | 48.11 106 | 29.58 154 | 49.65 86 | 44.38 170 | 34.65 170 | 51.71 209 | 47.90 216 | 52.49 216 | 48.57 216 |
|
CDS-MVSNet | | | 52.42 138 | 57.06 131 | 47.02 158 | 53.92 155 | 58.30 161 | 55.50 142 | 46.47 63 | 42.52 167 | 29.38 155 | 49.50 87 | 52.85 104 | 28.49 192 | 66.70 121 | 66.89 81 | 68.34 171 | 62.63 176 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
FMVSNet1 | | | 54.08 126 | 58.68 117 | 48.71 142 | 50.90 174 | 61.35 126 | 56.73 132 | 43.94 91 | 45.91 128 | 29.32 156 | 42.72 166 | 56.26 88 | 37.70 154 | 68.05 87 | 66.96 78 | 73.69 116 | 69.50 125 |
|
ACMH | | 52.42 13 | 58.24 97 | 59.56 109 | 56.70 88 | 66.34 52 | 69.59 54 | 66.71 71 | 49.12 56 | 46.08 125 | 28.90 157 | 42.67 167 | 41.20 186 | 52.60 84 | 71.39 53 | 70.28 53 | 76.51 82 | 75.72 80 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ADS-MVSNet | | | 40.67 206 | 43.38 208 | 37.50 204 | 44.36 199 | 39.79 217 | 42.09 208 | 32.67 206 | 44.34 142 | 28.87 158 | 40.76 181 | 40.37 192 | 30.22 185 | 48.34 224 | 45.87 222 | 46.81 225 | 44.21 220 |
|
pmmvs5 | | | 47.07 185 | 51.02 184 | 42.46 184 | 45.18 196 | 51.47 188 | 48.23 178 | 33.09 203 | 38.17 200 | 28.62 159 | 46.60 124 | 43.48 175 | 30.74 184 | 58.28 183 | 58.63 180 | 68.92 169 | 60.48 183 |
|
test-mter | | | 45.30 192 | 50.37 185 | 39.38 197 | 33.65 221 | 46.99 203 | 47.59 180 | 18.59 230 | 38.75 193 | 28.00 160 | 43.28 156 | 46.82 146 | 41.50 139 | 57.28 188 | 55.78 187 | 66.93 182 | 63.70 171 |
|
thres100view900 | | | 52.04 142 | 54.81 148 | 48.80 140 | 57.31 115 | 59.33 147 | 55.30 149 | 42.92 122 | 42.85 158 | 27.81 161 | 43.00 162 | 45.06 159 | 36.99 162 | 64.74 147 | 63.51 144 | 72.47 141 | 65.21 161 |
|
tfpn200view9 | | | 52.53 135 | 55.51 136 | 49.06 133 | 57.31 115 | 60.24 132 | 55.42 144 | 43.77 93 | 42.85 158 | 27.81 161 | 43.00 162 | 45.06 159 | 37.32 156 | 66.38 123 | 64.54 129 | 72.71 133 | 66.54 141 |
|
tfpn111 | | | 52.44 137 | 55.38 139 | 49.01 135 | 57.31 115 | 60.24 132 | 55.42 144 | 43.77 93 | 42.85 158 | 27.51 163 | 42.03 173 | 45.06 159 | 37.32 156 | 66.38 123 | 64.54 129 | 72.71 133 | 66.54 141 |
|
conf200view11 | | | 52.51 136 | 55.51 136 | 49.01 135 | 57.31 115 | 60.24 132 | 55.42 144 | 43.77 93 | 42.85 158 | 27.51 163 | 43.00 162 | 45.06 159 | 37.32 156 | 66.38 123 | 64.54 129 | 72.71 133 | 66.54 141 |
|
conf0.01 | | | 52.02 143 | 54.62 149 | 49.00 137 | 57.30 119 | 60.17 137 | 55.42 144 | 43.76 96 | 42.85 158 | 27.49 165 | 43.12 159 | 39.71 195 | 37.32 156 | 66.26 131 | 64.54 129 | 72.72 130 | 65.66 156 |
|
conf0.002 | | | 51.76 148 | 54.13 154 | 49.00 137 | 57.28 121 | 60.15 138 | 55.42 144 | 43.75 98 | 42.85 158 | 27.49 165 | 43.13 158 | 37.12 207 | 37.32 156 | 66.23 132 | 64.17 136 | 72.72 130 | 65.24 160 |
|
thres200 | | | 52.39 139 | 55.37 141 | 48.90 139 | 57.39 103 | 60.18 135 | 55.60 141 | 43.73 99 | 42.93 156 | 27.41 167 | 43.35 154 | 45.09 158 | 36.61 165 | 66.36 126 | 63.92 141 | 72.66 137 | 65.78 154 |
|
EPNet_dtu | | | 52.05 141 | 58.26 121 | 44.81 169 | 54.10 153 | 50.09 193 | 52.01 165 | 40.82 156 | 53.03 74 | 27.41 167 | 54.90 68 | 57.96 82 | 26.72 197 | 62.97 153 | 62.70 156 | 67.78 175 | 66.19 149 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPP-MVSNet | | | 59.39 67 | 65.45 61 | 52.32 114 | 60.96 71 | 67.70 65 | 58.42 122 | 44.75 78 | 49.71 85 | 27.23 169 | 59.03 54 | 62.20 65 | 43.34 132 | 70.71 58 | 69.13 60 | 79.25 48 | 79.63 52 |
|
thres400 | | | 52.38 140 | 55.51 136 | 48.74 141 | 57.49 98 | 60.10 139 | 55.45 143 | 43.54 106 | 42.90 157 | 26.72 170 | 43.34 155 | 45.03 163 | 36.61 165 | 66.20 133 | 64.53 133 | 72.66 137 | 66.43 144 |
|
IS_MVSNet | | | 57.95 101 | 64.26 68 | 50.60 119 | 61.62 68 | 65.25 94 | 57.18 128 | 45.42 72 | 50.79 81 | 26.49 171 | 57.81 59 | 60.05 73 | 34.51 171 | 71.24 56 | 70.20 55 | 78.36 54 | 74.44 99 |
|
tfpnnormal | | | 50.16 159 | 52.19 171 | 47.78 154 | 56.86 128 | 58.37 160 | 54.15 153 | 44.01 90 | 38.35 199 | 25.94 172 | 36.10 192 | 37.89 201 | 34.50 172 | 65.93 135 | 63.42 145 | 71.26 154 | 65.28 159 |
|
view600 | | | 51.96 145 | 55.13 144 | 48.27 147 | 57.41 102 | 60.05 140 | 54.74 151 | 43.64 102 | 42.57 166 | 25.88 173 | 43.11 160 | 44.48 168 | 35.34 168 | 66.27 129 | 63.61 143 | 72.61 140 | 65.80 153 |
|
pm-mvs1 | | | 51.02 152 | 55.55 135 | 45.73 164 | 54.16 152 | 58.52 157 | 50.92 167 | 42.56 132 | 40.32 183 | 25.67 174 | 43.66 150 | 50.34 124 | 30.06 186 | 65.85 137 | 63.97 140 | 70.99 156 | 66.21 147 |
|
thres600view7 | | | 51.91 147 | 55.14 143 | 48.14 148 | 57.43 101 | 60.18 135 | 54.60 152 | 43.73 99 | 42.61 165 | 25.20 175 | 43.10 161 | 44.47 169 | 35.19 169 | 66.36 126 | 63.28 149 | 72.66 137 | 66.01 151 |
|
view800 | | | 51.55 149 | 54.89 146 | 47.66 155 | 57.37 105 | 59.77 144 | 53.62 158 | 43.72 101 | 42.22 168 | 24.94 176 | 42.80 165 | 43.81 174 | 33.94 175 | 66.09 134 | 64.38 135 | 72.39 143 | 65.14 163 |
|
TransMVSNet (Re) | | | 51.92 146 | 55.38 139 | 47.88 152 | 60.95 72 | 59.90 141 | 53.95 154 | 45.14 74 | 39.47 188 | 24.85 177 | 43.87 148 | 46.51 148 | 29.15 188 | 67.55 101 | 65.23 123 | 73.26 124 | 65.16 162 |
|
ambc | | | | 45.54 204 | | 50.66 177 | 52.63 184 | 40.99 210 | | 38.36 198 | 24.67 178 | 22.62 220 | 13.94 233 | 29.14 189 | 65.71 140 | 58.06 182 | 58.60 202 | 67.43 135 |
|
pmmvs6 | | | 48.35 171 | 51.64 173 | 44.51 172 | 51.92 167 | 57.94 163 | 49.44 172 | 42.17 140 | 34.45 207 | 24.62 179 | 28.87 211 | 46.90 145 | 29.07 190 | 64.60 148 | 63.08 150 | 69.83 162 | 65.68 155 |
|
UniMVSNet_NR-MVSNet | | | 56.94 108 | 61.14 76 | 52.05 116 | 60.02 76 | 65.21 95 | 57.44 126 | 52.93 35 | 49.37 89 | 24.31 180 | 54.62 72 | 50.54 122 | 39.04 147 | 68.69 70 | 68.84 63 | 78.53 52 | 70.72 113 |
|
DU-MVS | | | 55.41 118 | 59.59 107 | 50.54 121 | 54.60 146 | 62.97 113 | 57.44 126 | 51.80 40 | 48.62 103 | 24.31 180 | 51.99 80 | 47.00 143 | 39.04 147 | 68.11 84 | 67.75 72 | 76.03 98 | 70.72 113 |
|
tfpn | | | 50.58 154 | 53.65 158 | 47.00 159 | 57.34 111 | 59.31 148 | 52.41 163 | 43.76 96 | 41.81 172 | 23.86 182 | 42.49 168 | 37.80 202 | 32.63 180 | 65.68 141 | 64.02 139 | 71.99 149 | 64.41 168 |
|
conf0.05thres1000 | | | 50.64 153 | 53.84 155 | 46.92 160 | 57.02 127 | 59.29 149 | 52.29 164 | 43.80 92 | 39.84 187 | 23.81 183 | 39.26 186 | 43.14 177 | 32.52 181 | 65.74 138 | 64.04 137 | 72.05 148 | 65.53 157 |
|
MIMVSNet | | | 43.79 197 | 48.53 194 | 38.27 201 | 41.46 207 | 48.97 196 | 50.81 168 | 32.88 205 | 44.55 138 | 22.07 184 | 32.05 199 | 47.15 139 | 24.76 200 | 58.73 179 | 56.09 186 | 57.63 205 | 52.14 205 |
|
FMVSNet5 | | | 40.96 202 | 45.81 202 | 35.29 209 | 34.30 218 | 44.55 212 | 47.28 183 | 28.84 213 | 40.76 180 | 21.62 185 | 29.85 205 | 42.44 178 | 24.77 199 | 57.53 187 | 55.00 193 | 54.93 210 | 50.56 210 |
|
thresconf0.02 | | | 48.17 174 | 51.22 181 | 44.60 171 | 55.14 138 | 55.73 170 | 48.95 174 | 41.35 150 | 43.43 152 | 21.23 186 | 42.03 173 | 37.25 206 | 31.19 183 | 62.33 160 | 60.61 169 | 69.76 163 | 57.17 195 |
|
tfpn_ndepth | | | 48.34 172 | 52.27 169 | 43.76 174 | 54.35 150 | 56.46 167 | 47.24 184 | 40.92 152 | 43.45 150 | 21.04 187 | 41.16 178 | 43.22 176 | 28.90 191 | 61.57 166 | 60.65 168 | 70.12 161 | 59.34 188 |
|
UniMVSNet (Re) | | | 55.15 122 | 60.39 85 | 49.03 134 | 55.31 134 | 64.59 99 | 55.77 140 | 50.63 47 | 48.66 102 | 20.95 188 | 51.47 82 | 50.40 123 | 34.41 173 | 67.81 95 | 67.89 70 | 77.11 68 | 71.88 108 |
|
NR-MVSNet | | | 55.35 119 | 59.46 111 | 50.56 120 | 61.33 69 | 62.97 113 | 57.91 125 | 51.80 40 | 48.62 103 | 20.59 189 | 51.99 80 | 44.73 165 | 34.10 174 | 68.58 73 | 68.64 65 | 77.66 58 | 70.67 117 |
|
TranMVSNet+NR-MVSNet | | | 55.87 113 | 60.14 94 | 50.88 118 | 59.46 79 | 63.82 101 | 57.93 124 | 52.98 34 | 48.94 94 | 20.52 190 | 52.87 76 | 47.33 138 | 36.81 164 | 69.12 68 | 69.03 61 | 77.56 62 | 69.89 119 |
|
TAMVS | | | 44.02 196 | 49.18 192 | 37.99 203 | 47.03 189 | 45.97 208 | 45.04 197 | 28.47 214 | 39.11 192 | 20.23 191 | 43.22 157 | 48.52 127 | 28.49 192 | 58.15 184 | 57.95 183 | 58.71 200 | 51.36 207 |
|
SixPastTwentyTwo | | | 47.55 183 | 50.25 188 | 44.41 173 | 47.30 188 | 54.31 178 | 47.81 179 | 40.36 160 | 33.76 208 | 19.93 192 | 43.75 149 | 32.77 213 | 42.07 136 | 59.82 173 | 60.94 166 | 68.98 168 | 66.37 146 |
|
PMVS | | 27.84 18 | 33.81 216 | 35.28 221 | 32.09 213 | 34.13 219 | 24.81 232 | 32.51 222 | 26.48 221 | 26.41 222 | 19.37 193 | 23.76 218 | 24.02 225 | 25.18 198 | 50.78 210 | 47.24 219 | 54.89 212 | 49.95 213 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
FPMVS | | | 38.36 211 | 40.41 214 | 35.97 206 | 38.92 212 | 39.85 216 | 45.50 195 | 25.79 223 | 41.13 178 | 18.70 194 | 30.10 204 | 24.56 221 | 31.86 182 | 49.42 219 | 46.80 221 | 55.04 209 | 51.03 208 |
|
tfpn_n400 | | | 47.56 181 | 51.56 175 | 42.90 182 | 54.91 141 | 55.28 173 | 46.21 188 | 41.59 143 | 41.51 175 | 18.54 195 | 42.25 170 | 41.54 183 | 27.12 194 | 62.41 158 | 61.02 164 | 69.05 166 | 56.90 197 |
|
tfpnconf | | | 47.56 181 | 51.56 175 | 42.90 182 | 54.91 141 | 55.28 173 | 46.21 188 | 41.59 143 | 41.51 175 | 18.54 195 | 42.25 170 | 41.54 183 | 27.12 194 | 62.41 158 | 61.02 164 | 69.05 166 | 56.90 197 |
|
tfpnview11 | | | 47.58 180 | 51.57 174 | 42.92 181 | 54.94 140 | 55.30 172 | 46.21 188 | 41.58 145 | 42.10 169 | 18.54 195 | 42.25 170 | 41.54 183 | 27.12 194 | 62.29 161 | 61.12 162 | 69.15 165 | 56.40 199 |
|
CHOSEN 280x420 | | | 40.80 203 | 45.05 205 | 35.84 208 | 32.95 222 | 29.57 229 | 44.98 198 | 23.71 225 | 37.54 202 | 18.42 198 | 31.36 202 | 47.07 142 | 46.41 120 | 56.71 192 | 54.65 197 | 48.55 222 | 58.47 192 |
|
MDA-MVSNet-bldmvs | | | 41.36 201 | 43.15 209 | 39.27 199 | 28.74 227 | 52.68 183 | 44.95 199 | 40.84 155 | 32.89 210 | 18.13 199 | 31.61 201 | 22.09 228 | 38.97 149 | 50.45 213 | 56.11 185 | 64.01 189 | 56.23 200 |
|
Baseline_NR-MVSNet | | | 53.50 130 | 57.89 124 | 48.37 145 | 54.60 146 | 59.25 151 | 56.10 136 | 51.84 39 | 49.32 90 | 17.92 200 | 45.38 138 | 47.68 133 | 36.93 163 | 68.11 84 | 65.95 96 | 72.84 128 | 69.57 124 |
|
pmmvs3 | | | 35.10 214 | 38.47 215 | 31.17 214 | 26.37 232 | 40.47 215 | 34.51 221 | 18.09 231 | 24.75 223 | 16.88 201 | 23.05 219 | 26.69 219 | 32.69 179 | 50.73 212 | 51.60 208 | 58.46 203 | 51.98 206 |
|
tfpn1000 | | | 46.75 187 | 51.24 180 | 41.51 189 | 54.39 149 | 55.60 171 | 43.85 202 | 40.90 153 | 41.82 171 | 16.71 202 | 41.26 177 | 41.58 182 | 23.96 201 | 60.76 169 | 60.27 172 | 69.26 164 | 57.42 194 |
|
testpf | | | 34.85 215 | 36.16 220 | 33.31 211 | 47.49 186 | 35.56 225 | 36.85 216 | 32.31 207 | 23.08 226 | 15.63 203 | 29.39 208 | 29.48 216 | 19.62 210 | 41.38 227 | 41.07 226 | 47.95 223 | 53.18 203 |
|
test0.0.03 1 | | | 43.15 198 | 46.95 199 | 38.72 200 | 55.26 135 | 50.56 190 | 42.48 206 | 43.48 110 | 38.16 201 | 15.11 204 | 35.07 195 | 44.69 166 | 16.47 214 | 55.95 199 | 54.34 198 | 59.54 199 | 49.87 214 |
|
CVMVSNet | | | 46.38 190 | 52.01 172 | 39.81 195 | 42.40 205 | 50.26 191 | 46.15 191 | 37.68 177 | 40.03 185 | 15.09 205 | 46.56 125 | 47.56 135 | 33.72 176 | 56.50 194 | 55.65 188 | 63.80 190 | 67.53 134 |
|
LTVRE_ROB | | 44.17 16 | 47.06 186 | 50.15 189 | 43.44 177 | 51.39 171 | 58.42 158 | 42.90 205 | 43.51 108 | 22.27 229 | 14.85 206 | 41.94 176 | 34.57 209 | 45.43 122 | 62.28 162 | 62.77 155 | 62.56 194 | 68.83 132 |
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 | | | 42.28 199 | 45.89 201 | 38.07 202 | 51.96 166 | 48.98 195 | 43.66 204 | 38.81 170 | 38.74 195 | 14.32 207 | 26.74 213 | 40.90 188 | 20.94 207 | 56.64 193 | 54.67 196 | 58.71 200 | 54.59 201 |
|
Vis-MVSNet (Re-imp) | | | 50.37 157 | 57.73 126 | 41.80 188 | 57.53 95 | 54.35 177 | 45.70 194 | 45.24 73 | 49.80 84 | 13.43 208 | 58.23 58 | 56.42 85 | 20.11 209 | 62.96 154 | 63.36 146 | 68.76 170 | 58.96 190 |
|
tmp_tt | | | | | 5.40 234 | 3.97 237 | 2.35 239 | 3.26 239 | 0.44 235 | 17.56 231 | 12.09 209 | 11.48 231 | 7.14 236 | 1.98 234 | 15.68 233 | 15.49 233 | 10.69 236 | |
|
gg-mvs-nofinetune | | | 49.07 167 | 52.56 166 | 45.00 168 | 61.99 67 | 59.78 143 | 53.55 160 | 41.63 142 | 31.62 214 | 12.08 210 | 29.56 207 | 53.28 96 | 29.57 187 | 66.27 129 | 64.49 134 | 71.19 155 | 62.92 173 |
|
gm-plane-assit | | | 44.74 193 | 45.95 200 | 43.33 178 | 60.88 73 | 46.79 206 | 36.97 215 | 32.24 208 | 24.15 225 | 11.79 211 | 29.26 210 | 32.97 212 | 46.64 117 | 65.09 146 | 62.95 152 | 71.45 153 | 60.42 184 |
|
1111 | | | 31.35 220 | 33.52 224 | 28.83 217 | 44.28 201 | 32.44 226 | 31.71 223 | 33.25 201 | 27.87 218 | 10.92 212 | 22.18 221 | 24.05 223 | 15.89 216 | 49.03 222 | 44.09 223 | 36.94 230 | 34.96 226 |
|
.test1245 | | | 22.44 228 | 22.23 229 | 22.67 226 | 44.28 201 | 32.44 226 | 31.71 223 | 33.25 201 | 27.87 218 | 10.92 212 | 22.18 221 | 24.05 223 | 15.89 216 | 49.03 222 | 0.01 234 | 0.00 238 | 0.06 236 |
|
test2356 | | | 33.40 217 | 36.53 218 | 29.76 215 | 37.51 214 | 38.39 220 | 34.68 220 | 27.35 216 | 27.88 217 | 10.61 214 | 25.54 216 | 24.44 222 | 17.15 213 | 49.99 216 | 48.32 214 | 51.24 218 | 41.16 224 |
|
CP-MVSNet | | | 48.37 170 | 53.53 159 | 42.34 185 | 51.35 172 | 58.01 162 | 46.56 186 | 50.54 48 | 41.62 174 | 10.61 214 | 46.53 127 | 40.68 191 | 23.18 203 | 58.71 180 | 61.83 159 | 71.81 150 | 67.36 137 |
|
PS-CasMVS | | | 48.18 173 | 53.25 163 | 42.27 186 | 51.26 173 | 57.94 163 | 46.51 187 | 50.52 49 | 41.30 177 | 10.56 216 | 45.35 140 | 40.34 193 | 23.04 205 | 58.66 181 | 61.79 160 | 71.74 152 | 67.38 136 |
|
EU-MVSNet | | | 40.63 207 | 45.65 203 | 34.78 210 | 39.11 211 | 46.94 204 | 40.02 212 | 34.03 193 | 33.50 209 | 10.37 217 | 35.57 194 | 37.80 202 | 23.65 202 | 51.90 208 | 50.21 211 | 61.49 196 | 63.62 172 |
|
PEN-MVS | | | 49.21 165 | 54.32 151 | 43.24 180 | 54.33 151 | 59.26 150 | 47.04 185 | 51.37 44 | 41.67 173 | 9.97 218 | 46.22 129 | 41.80 181 | 22.97 206 | 60.52 170 | 64.03 138 | 73.73 115 | 66.75 140 |
|
test20.03 | | | 40.38 208 | 44.20 206 | 35.92 207 | 53.73 156 | 49.05 194 | 38.54 213 | 43.49 109 | 32.55 211 | 9.54 219 | 27.88 212 | 39.12 197 | 12.24 225 | 56.28 195 | 54.69 195 | 57.96 204 | 49.83 215 |
|
N_pmnet | | | 32.67 219 | 36.85 217 | 27.79 220 | 40.55 209 | 32.13 228 | 35.80 217 | 26.79 220 | 37.24 203 | 9.10 220 | 32.02 200 | 30.94 214 | 16.30 215 | 47.22 225 | 41.21 225 | 38.21 228 | 37.21 225 |
|
Gipuma | | | 25.87 225 | 26.91 228 | 24.66 225 | 28.98 226 | 20.17 233 | 20.46 232 | 34.62 191 | 29.55 216 | 9.10 220 | 4.91 236 | 5.31 238 | 15.76 218 | 49.37 220 | 49.10 213 | 39.03 227 | 29.95 229 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
testgi | | | 38.71 210 | 43.64 207 | 32.95 212 | 52.30 165 | 48.63 197 | 35.59 219 | 35.05 189 | 31.58 215 | 9.03 222 | 30.29 203 | 40.75 190 | 11.19 230 | 55.30 201 | 53.47 204 | 54.53 213 | 45.48 218 |
|
WR-MVS | | | 48.78 169 | 55.06 145 | 41.45 190 | 55.50 133 | 60.40 131 | 43.77 203 | 49.99 52 | 41.92 170 | 8.10 223 | 45.24 141 | 45.56 152 | 17.47 211 | 61.57 166 | 64.60 128 | 73.85 113 | 66.14 150 |
|
DTE-MVSNet | | | 48.03 177 | 53.28 162 | 41.91 187 | 54.64 144 | 57.50 165 | 44.63 201 | 51.66 43 | 41.02 179 | 7.97 224 | 46.26 128 | 40.90 188 | 20.24 208 | 60.45 171 | 62.89 153 | 72.33 145 | 63.97 169 |
|
WR-MVS_H | | | 47.65 178 | 53.67 157 | 40.63 193 | 51.45 170 | 59.74 145 | 44.71 200 | 49.37 54 | 40.69 181 | 7.61 225 | 46.04 132 | 44.34 171 | 17.32 212 | 57.79 186 | 61.18 161 | 73.30 123 | 65.86 152 |
|
Anonymous20231211 | | | 40.75 205 | 41.57 211 | 39.80 196 | 54.71 143 | 52.32 185 | 41.42 209 | 45.09 75 | 24.45 224 | 6.80 226 | 14.58 228 | 23.43 227 | 23.08 204 | 56.20 196 | 58.74 179 | 67.68 176 | 61.31 180 |
|
MIMVSNet1 | | | 35.51 213 | 41.41 212 | 28.63 218 | 27.53 229 | 43.36 213 | 38.09 214 | 33.82 195 | 32.01 212 | 6.77 227 | 21.63 223 | 35.43 208 | 11.97 227 | 55.05 203 | 53.99 201 | 53.59 215 | 48.36 217 |
|
new-patchmatchnet | | | 33.24 218 | 37.20 216 | 28.62 219 | 44.32 200 | 38.26 222 | 29.68 228 | 36.05 185 | 31.97 213 | 6.33 228 | 26.59 214 | 27.33 218 | 11.12 231 | 50.08 215 | 41.05 227 | 44.23 226 | 45.15 219 |
|
testus | | | 31.33 221 | 36.31 219 | 25.52 224 | 37.55 213 | 38.40 219 | 25.87 229 | 23.58 226 | 26.46 221 | 5.97 229 | 24.15 217 | 24.92 220 | 12.44 224 | 49.14 221 | 48.21 215 | 47.73 224 | 42.86 221 |
|
testmv | | | 30.97 222 | 34.42 222 | 26.95 221 | 36.49 216 | 37.38 223 | 29.80 226 | 27.28 217 | 22.34 227 | 4.72 230 | 20.63 225 | 20.64 229 | 13.22 222 | 49.86 218 | 47.74 217 | 50.20 219 | 42.36 222 |
|
test1235678 | | | 30.97 222 | 34.42 222 | 26.95 221 | 36.49 216 | 37.38 223 | 29.79 227 | 27.28 217 | 22.33 228 | 4.72 230 | 20.62 226 | 20.64 229 | 13.22 222 | 49.87 217 | 47.74 217 | 50.20 219 | 42.36 222 |
|
new_pmnet | | | 23.19 227 | 28.17 227 | 17.37 228 | 17.03 234 | 24.92 231 | 19.66 233 | 16.16 233 | 27.05 220 | 4.42 232 | 20.77 224 | 19.20 231 | 12.19 226 | 37.71 229 | 36.38 228 | 34.77 231 | 31.17 228 |
|
no-one | | | 29.19 224 | 31.89 225 | 26.05 223 | 30.96 224 | 38.33 221 | 21.54 230 | 29.86 210 | 15.84 233 | 3.56 233 | 11.28 232 | 13.03 234 | 14.44 221 | 38.96 228 | 52.83 205 | 55.96 207 | 52.92 204 |
|
E-PMN | | | 15.09 230 | 13.19 232 | 17.30 229 | 27.80 228 | 12.62 236 | 7.81 236 | 27.54 215 | 14.62 235 | 3.19 234 | 6.89 233 | 2.52 241 | 15.09 219 | 15.93 232 | 20.22 231 | 22.38 232 | 19.53 232 |
|
EMVS | | | 14.49 231 | 12.45 233 | 16.87 231 | 27.02 230 | 12.56 237 | 8.13 235 | 27.19 219 | 15.05 234 | 3.14 235 | 6.69 234 | 2.67 240 | 15.08 220 | 14.60 234 | 18.05 232 | 20.67 233 | 17.56 234 |
|
FC-MVSNet-test | | | 39.65 209 | 48.35 195 | 29.49 216 | 44.43 198 | 39.28 218 | 30.23 225 | 40.44 158 | 43.59 147 | 3.12 236 | 53.00 75 | 42.03 179 | 10.02 232 | 55.09 202 | 54.77 194 | 48.66 221 | 50.71 209 |
|
MVE | | 12.28 19 | 13.53 232 | 15.72 231 | 10.96 233 | 7.39 236 | 15.71 235 | 6.05 237 | 23.73 224 | 10.29 237 | 3.01 237 | 5.77 235 | 3.41 239 | 11.91 228 | 20.11 231 | 29.79 229 | 13.67 235 | 24.98 230 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test12356 | | | 23.91 226 | 28.47 226 | 18.60 227 | 26.80 231 | 28.30 230 | 20.92 231 | 19.76 229 | 19.89 230 | 2.88 238 | 18.48 227 | 16.57 232 | 4.05 233 | 42.34 226 | 41.93 224 | 37.21 229 | 31.75 227 |
|
DeepMVS_CX | | | | | | | 6.95 238 | 5.98 238 | 2.25 234 | 11.73 236 | 2.07 239 | 11.85 230 | 5.43 237 | 11.75 229 | 11.40 235 | | 8.10 237 | 18.38 233 |
|
PMMVS2 | | | 15.84 229 | 19.68 230 | 11.35 232 | 15.74 235 | 16.95 234 | 13.31 234 | 17.64 232 | 16.08 232 | 0.36 240 | 13.12 229 | 11.47 235 | 1.69 235 | 28.82 230 | 27.24 230 | 19.38 234 | 24.09 231 |
|
GG-mvs-BLEND | | | 36.62 212 | 53.39 161 | 17.06 230 | 0.01 238 | 58.61 156 | 48.63 175 | 0.01 236 | 47.13 113 | 0.02 241 | 43.98 146 | 60.64 71 | 0.03 236 | 54.92 204 | 51.47 209 | 53.64 214 | 56.99 196 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 242 | 0.00 237 | 0.00 240 | 0.00 242 | 0.00 239 | 0.00 242 | 0.00 239 | 0.00 237 | 0.00 237 | 0.00 238 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 242 | 0.00 237 | 0.00 240 | 0.00 242 | 0.00 239 | 0.00 242 | 0.00 239 | 0.00 237 | 0.00 237 | 0.00 238 | 0.00 238 |
|
testmvs | | | 0.01 233 | 0.02 234 | 0.00 235 | 0.00 239 | 0.00 240 | 0.01 241 | 0.00 237 | 0.01 238 | 0.00 242 | 0.03 238 | 0.00 242 | 0.01 237 | 0.01 236 | 0.01 234 | 0.00 238 | 0.06 236 |
|
test123 | | | 0.01 233 | 0.02 234 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 242 | 0.00 237 | 0.01 238 | 0.00 242 | 0.04 237 | 0.00 242 | 0.01 237 | 0.00 237 | 0.01 234 | 0.00 238 | 0.07 235 |
|
Patchmatch-RL test | | | | | | | | 1.04 240 | | | | | | | | | | |
|
mPP-MVS | | | | | | 71.67 28 | | | | | | | 74.36 35 | | | | | |
|
NP-MVS | | | | | | | | | | 72.00 35 | | | | | | | | |
|