ESAPD | | | 76.46 1 | 83.32 1 | 68.47 2 | 78.83 2 | 83.07 2 | 77.86 1 | 58.75 4 | 86.89 3 | 56.64 8 | 89.08 2 | 83.11 1 | 60.69 12 | 74.28 7 | 74.11 8 | 78.06 29 | 92.67 3 |
|
APDe-MVS | | | 74.59 2 | 80.23 3 | 68.01 5 | 76.51 4 | 80.20 11 | 77.39 2 | 58.18 5 | 85.31 4 | 56.84 6 | 84.89 3 | 76.08 8 | 60.66 13 | 71.85 22 | 71.76 17 | 78.47 22 | 91.49 6 |
|
HSP-MVS | | | 74.54 3 | 81.12 2 | 66.86 6 | 71.93 13 | 78.65 18 | 72.60 12 | 55.44 13 | 89.94 1 | 54.35 12 | 92.24 1 | 77.08 5 | 69.84 1 | 75.48 4 | 75.01 5 | 76.99 43 | 79.45 91 |
|
MCST-MVS | | | 74.06 4 | 77.71 9 | 69.79 1 | 78.95 1 | 81.99 3 | 76.33 4 | 62.16 1 | 75.89 16 | 52.96 20 | 64.37 26 | 73.30 15 | 65.66 2 | 77.49 1 | 77.43 2 | 82.67 1 | 93.51 1 |
|
CNVR-MVS | | | 73.87 5 | 78.60 6 | 68.35 4 | 73.32 8 | 81.97 4 | 76.19 5 | 59.29 3 | 80.12 9 | 56.70 7 | 67.09 20 | 76.48 6 | 64.26 3 | 75.88 3 | 75.75 4 | 80.32 7 | 92.93 2 |
|
SMA-MVS | | | 73.13 6 | 79.49 4 | 65.71 8 | 71.08 15 | 80.36 10 | 74.71 6 | 56.31 7 | 83.60 5 | 53.34 18 | 72.81 12 | 80.52 2 | 60.83 11 | 74.88 6 | 74.53 7 | 77.96 31 | 89.72 9 |
|
CSCG | | | 72.98 7 | 76.86 11 | 68.46 3 | 78.23 3 | 81.74 5 | 77.26 3 | 60.00 2 | 75.61 19 | 59.06 1 | 62.72 28 | 77.42 4 | 56.63 36 | 74.24 8 | 77.18 3 | 79.56 12 | 89.13 14 |
|
HPM-MVS++ | | | 72.44 8 | 78.73 5 | 65.11 9 | 71.88 14 | 77.31 26 | 71.98 15 | 55.67 11 | 83.11 7 | 53.59 16 | 75.90 8 | 78.49 3 | 61.00 10 | 73.99 9 | 73.31 12 | 76.55 46 | 88.97 15 |
|
APD-MVS | | | 71.86 9 | 77.91 8 | 64.80 11 | 70.39 20 | 75.69 37 | 74.02 8 | 56.14 9 | 83.59 6 | 52.92 21 | 84.67 4 | 73.46 14 | 59.30 20 | 69.47 34 | 69.66 35 | 76.02 53 | 88.84 16 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMMP_Plus | | | 71.50 10 | 77.27 10 | 64.77 12 | 69.64 22 | 79.26 12 | 73.53 9 | 54.73 19 | 79.32 11 | 54.23 13 | 74.81 9 | 74.61 12 | 59.40 19 | 73.00 11 | 72.17 15 | 77.10 42 | 87.72 22 |
|
NCCC | | | 71.36 11 | 75.44 13 | 66.60 7 | 72.46 11 | 79.18 14 | 74.16 7 | 57.83 6 | 76.93 14 | 54.19 14 | 63.47 27 | 71.08 20 | 61.30 9 | 73.56 10 | 73.70 10 | 79.69 11 | 90.19 8 |
|
train_agg | | | 70.74 12 | 76.53 12 | 63.98 14 | 70.33 21 | 75.16 39 | 72.33 14 | 55.78 10 | 75.74 17 | 50.41 29 | 80.08 7 | 73.15 16 | 57.75 28 | 71.96 21 | 70.94 27 | 77.25 41 | 88.69 18 |
|
TSAR-MVS + MP. | | | 70.28 13 | 75.09 14 | 64.66 13 | 69.34 24 | 64.61 118 | 72.60 12 | 56.29 8 | 80.73 8 | 58.36 3 | 84.56 5 | 75.22 10 | 55.37 41 | 69.11 40 | 69.45 36 | 75.97 55 | 81.97 65 |
|
DeepPCF-MVS | | 62.48 1 | 70.07 14 | 78.36 7 | 60.39 38 | 62.38 54 | 76.96 29 | 65.54 49 | 52.23 28 | 87.46 2 | 49.07 30 | 74.05 11 | 76.19 7 | 59.01 22 | 72.79 15 | 71.61 19 | 74.13 107 | 89.49 11 |
|
SteuartSystems-ACMMP | | | 69.78 15 | 74.76 15 | 63.98 14 | 73.45 7 | 78.56 19 | 73.13 11 | 55.24 16 | 70.68 29 | 48.93 32 | 70.43 16 | 69.10 22 | 54.00 47 | 72.78 17 | 72.98 13 | 79.14 16 | 88.74 17 |
Skip Steuart: Steuart Systems R&D Blog. |
HFP-MVS | | | 68.75 16 | 72.84 17 | 63.98 14 | 68.87 28 | 75.09 40 | 71.87 16 | 51.22 33 | 73.50 23 | 58.17 4 | 68.05 19 | 68.67 23 | 57.79 27 | 70.49 29 | 69.23 38 | 75.98 54 | 84.84 41 |
|
SD-MVS | | | 68.30 17 | 72.58 19 | 63.31 20 | 69.24 25 | 67.85 90 | 70.81 21 | 53.65 24 | 79.64 10 | 58.52 2 | 74.31 10 | 75.37 9 | 53.52 53 | 65.63 62 | 63.56 102 | 74.13 107 | 81.73 72 |
|
zzz-MVS | | | 67.78 18 | 72.46 20 | 62.33 25 | 66.09 34 | 74.21 43 | 70.05 23 | 51.54 31 | 77.27 12 | 54.61 11 | 60.30 35 | 71.51 19 | 56.73 34 | 69.19 38 | 68.63 46 | 74.96 73 | 86.11 32 |
|
DELS-MVS | | | 67.36 19 | 70.34 33 | 63.89 17 | 69.12 26 | 81.55 6 | 70.82 20 | 55.02 17 | 53.38 65 | 48.83 33 | 56.45 40 | 59.35 50 | 60.05 18 | 74.93 5 | 74.78 6 | 79.51 13 | 91.95 4 |
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 |
MP-MVS | | | 67.34 20 | 73.08 16 | 60.64 35 | 66.20 33 | 76.62 31 | 69.22 27 | 50.92 35 | 70.07 30 | 48.81 34 | 69.66 17 | 70.12 21 | 53.68 50 | 68.41 45 | 69.13 40 | 74.98 72 | 87.53 23 |
|
DeepC-MVS | | 60.65 2 | 67.33 21 | 71.52 27 | 62.44 23 | 59.79 69 | 74.84 41 | 68.89 28 | 55.56 12 | 73.91 22 | 53.50 17 | 55.00 43 | 65.63 28 | 60.08 17 | 71.99 20 | 71.33 23 | 76.85 44 | 87.94 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
HQP-MVS | | | 67.22 22 | 72.08 22 | 61.56 30 | 66.76 31 | 73.58 50 | 71.41 17 | 52.98 26 | 69.92 32 | 43.85 52 | 70.58 15 | 58.75 52 | 56.76 33 | 72.90 13 | 71.88 16 | 77.57 35 | 86.94 27 |
|
CANet | | | 67.21 23 | 71.83 24 | 61.83 26 | 64.51 41 | 79.25 13 | 66.72 41 | 48.73 49 | 68.49 37 | 50.63 28 | 61.40 31 | 66.47 26 | 61.44 7 | 69.31 37 | 69.90 32 | 78.94 19 | 88.00 20 |
|
CDPH-MVS | | | 67.03 24 | 71.64 25 | 61.65 29 | 69.10 27 | 76.84 30 | 71.35 19 | 55.42 14 | 67.02 40 | 42.83 56 | 65.27 24 | 64.60 32 | 53.16 56 | 69.70 33 | 71.40 21 | 78.02 30 | 86.67 28 |
|
MAR-MVS | | | 66.85 25 | 69.81 34 | 63.39 19 | 73.56 6 | 80.51 9 | 69.87 24 | 51.51 32 | 67.78 39 | 46.44 39 | 51.09 54 | 61.60 45 | 60.38 14 | 72.67 18 | 73.61 11 | 78.59 20 | 81.44 76 |
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 |
DeepC-MVS_fast | | 60.18 3 | 66.84 26 | 70.69 31 | 62.36 24 | 62.76 49 | 73.21 53 | 67.96 31 | 52.31 27 | 72.26 26 | 51.03 23 | 56.50 39 | 64.26 33 | 63.37 4 | 71.64 23 | 70.85 28 | 76.70 45 | 86.10 33 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + GP. | | | 66.77 27 | 72.21 21 | 60.44 37 | 61.23 60 | 70.00 69 | 64.26 55 | 47.79 59 | 72.98 24 | 56.32 9 | 71.35 14 | 72.33 17 | 55.68 39 | 65.49 63 | 66.66 57 | 77.35 37 | 86.62 29 |
|
MVS_0304 | | | 66.31 28 | 71.61 26 | 60.14 39 | 62.59 53 | 78.98 16 | 67.13 38 | 45.75 87 | 64.35 45 | 45.23 47 | 60.69 33 | 67.67 25 | 61.73 6 | 71.09 25 | 71.03 25 | 78.41 25 | 87.44 24 |
|
ACMMPR | | | 66.20 29 | 71.51 28 | 60.00 40 | 65.34 38 | 74.04 45 | 69.39 26 | 50.92 35 | 71.97 27 | 46.04 42 | 66.79 21 | 65.68 27 | 53.07 57 | 68.93 42 | 69.12 41 | 75.21 66 | 84.05 48 |
|
3Dnovator | | 58.39 4 | 65.97 30 | 66.85 43 | 64.94 10 | 73.72 5 | 79.03 15 | 67.73 33 | 54.25 20 | 61.52 48 | 52.79 22 | 42.27 76 | 60.73 48 | 62.01 5 | 71.29 24 | 71.75 18 | 79.12 17 | 81.34 80 |
|
TSAR-MVS + ACMM | | | 65.95 31 | 72.83 18 | 57.93 51 | 69.35 23 | 65.85 107 | 73.36 10 | 39.84 153 | 76.00 15 | 48.69 35 | 82.54 6 | 75.03 11 | 49.38 87 | 65.33 65 | 63.42 104 | 66.94 176 | 81.67 73 |
|
canonicalmvs | | | 65.55 32 | 70.75 30 | 59.49 43 | 62.11 56 | 78.26 21 | 66.52 42 | 43.82 118 | 71.54 28 | 47.84 37 | 61.30 32 | 61.68 43 | 58.48 25 | 67.56 51 | 69.67 34 | 78.16 28 | 85.25 39 |
|
QAPM | | | 65.47 33 | 67.82 38 | 62.72 22 | 72.56 9 | 81.17 8 | 67.43 36 | 55.38 15 | 56.07 61 | 43.29 54 | 43.60 73 | 65.38 30 | 59.10 21 | 72.20 19 | 70.76 29 | 78.56 21 | 85.59 37 |
|
PGM-MVS | | | 65.35 34 | 70.43 32 | 59.43 44 | 65.78 36 | 73.75 47 | 69.41 25 | 48.18 56 | 68.80 36 | 45.37 46 | 65.88 23 | 64.04 34 | 52.68 62 | 68.94 41 | 68.68 45 | 75.18 67 | 82.93 54 |
|
PHI-MVS | | | 65.17 35 | 72.07 23 | 57.11 59 | 63.02 48 | 77.35 25 | 67.04 39 | 48.14 58 | 68.03 38 | 37.56 78 | 66.00 22 | 65.39 29 | 53.19 55 | 70.68 26 | 70.57 31 | 73.72 113 | 86.46 31 |
|
CLD-MVS | | | 64.69 36 | 67.25 39 | 61.69 28 | 68.22 30 | 78.33 20 | 63.09 57 | 47.59 63 | 69.64 33 | 53.98 15 | 54.87 44 | 53.94 62 | 57.87 26 | 72.79 15 | 71.34 22 | 79.40 14 | 69.87 159 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MVS_111021_HR | | | 64.66 37 | 67.11 42 | 61.80 27 | 71.04 16 | 77.91 22 | 62.75 60 | 54.78 18 | 51.43 69 | 47.54 38 | 53.77 47 | 54.85 59 | 56.84 31 | 70.59 27 | 71.50 20 | 77.86 32 | 89.70 10 |
|
EPNet | | | 64.39 38 | 70.93 29 | 56.77 61 | 60.58 65 | 75.77 34 | 59.28 89 | 50.58 38 | 69.93 31 | 40.73 70 | 68.59 18 | 61.60 45 | 53.72 48 | 68.65 43 | 68.07 48 | 75.75 59 | 83.87 50 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CP-MVS | | | 64.37 39 | 69.48 35 | 58.39 47 | 62.21 55 | 71.81 61 | 67.27 37 | 49.51 44 | 69.40 35 | 45.76 44 | 60.41 34 | 64.96 31 | 51.84 64 | 67.33 55 | 67.57 52 | 73.78 112 | 84.89 40 |
|
MVS_Test | | | 63.75 40 | 67.24 40 | 59.68 42 | 60.01 66 | 76.99 28 | 68.13 30 | 45.17 92 | 57.45 55 | 43.74 53 | 53.07 48 | 56.16 58 | 61.33 8 | 70.27 30 | 71.11 24 | 79.72 10 | 85.63 36 |
|
X-MVS | | | 63.53 41 | 68.62 36 | 57.60 54 | 64.77 40 | 73.06 54 | 65.82 46 | 50.53 39 | 65.77 42 | 42.02 65 | 58.20 37 | 63.42 37 | 47.83 106 | 68.25 49 | 68.50 47 | 74.61 89 | 83.16 53 |
|
ACMMP | | | 63.27 42 | 67.85 37 | 57.93 51 | 62.64 52 | 72.30 59 | 68.23 29 | 48.77 48 | 66.50 41 | 43.05 55 | 62.07 29 | 57.84 55 | 49.98 73 | 66.58 58 | 66.46 61 | 74.93 77 | 83.17 52 |
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 |
AdaColmap | | | 62.79 43 | 62.63 58 | 62.98 21 | 70.82 17 | 72.90 57 | 67.84 32 | 54.09 22 | 65.14 43 | 50.71 26 | 41.78 77 | 47.64 85 | 60.17 16 | 67.41 54 | 66.83 55 | 74.28 102 | 76.69 105 |
|
3Dnovator+ | | 55.76 7 | 62.70 44 | 65.10 50 | 59.90 41 | 65.89 35 | 72.15 60 | 62.94 59 | 49.82 43 | 62.77 47 | 49.06 31 | 43.62 72 | 61.47 47 | 58.60 24 | 68.51 44 | 66.75 56 | 73.08 127 | 80.40 88 |
|
OpenMVS | | 55.62 8 | 62.57 45 | 63.76 54 | 61.19 32 | 72.13 12 | 78.84 17 | 64.42 53 | 50.51 40 | 56.44 58 | 45.67 45 | 36.88 104 | 56.51 56 | 56.66 35 | 68.28 48 | 68.96 42 | 77.73 34 | 80.44 87 |
|
PVSNet_BlendedMVS | | | 62.53 46 | 66.37 45 | 58.05 49 | 58.17 74 | 75.70 35 | 61.30 65 | 48.67 51 | 58.67 52 | 50.93 24 | 55.43 41 | 49.39 75 | 53.01 58 | 69.46 35 | 66.55 58 | 76.24 51 | 89.39 12 |
|
PVSNet_Blended | | | 62.53 46 | 66.37 45 | 58.05 49 | 58.17 74 | 75.70 35 | 61.30 65 | 48.67 51 | 58.67 52 | 50.93 24 | 55.43 41 | 49.39 75 | 53.01 58 | 69.46 35 | 66.55 58 | 76.24 51 | 89.39 12 |
|
MVSTER | | | 62.51 48 | 67.22 41 | 57.02 60 | 55.05 98 | 69.23 83 | 63.02 58 | 46.88 77 | 61.11 50 | 43.95 51 | 59.20 36 | 58.86 51 | 56.80 32 | 69.13 39 | 70.98 26 | 76.41 48 | 82.04 59 |
|
CHOSEN 1792x2688 | | | 62.48 49 | 64.06 53 | 60.64 35 | 72.50 10 | 84.18 1 | 62.43 61 | 53.77 23 | 47.90 80 | 39.85 71 | 25.15 194 | 44.76 97 | 53.72 48 | 77.29 2 | 77.61 1 | 81.60 4 | 91.53 5 |
|
CostFormer | | | 62.45 50 | 65.68 47 | 58.67 45 | 63.29 45 | 77.65 23 | 67.62 34 | 38.42 165 | 54.04 63 | 46.00 43 | 48.27 62 | 57.89 54 | 56.97 30 | 67.03 57 | 67.79 51 | 79.74 9 | 87.09 26 |
|
PCF-MVS | | 55.99 6 | 62.31 51 | 66.60 44 | 57.32 57 | 59.12 73 | 73.68 49 | 67.53 35 | 48.71 50 | 61.35 49 | 42.83 56 | 51.33 53 | 63.48 36 | 53.48 54 | 65.64 61 | 64.87 71 | 72.22 134 | 85.83 34 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DI_MVS_plusplus_trai | | | 61.86 52 | 65.26 48 | 57.90 53 | 57.93 77 | 74.51 42 | 66.30 43 | 46.49 82 | 49.96 73 | 41.62 68 | 42.69 74 | 61.77 42 | 58.74 23 | 70.25 31 | 69.32 37 | 76.31 49 | 88.30 19 |
|
MSLP-MVS++ | | | 61.81 53 | 62.19 63 | 61.37 31 | 68.33 29 | 63.08 136 | 70.75 22 | 38.89 162 | 63.96 46 | 57.51 5 | 48.59 60 | 61.66 44 | 53.67 51 | 62.04 113 | 59.92 149 | 79.03 18 | 76.08 108 |
|
OPM-MVS | | | 61.59 54 | 62.30 62 | 60.76 34 | 66.53 32 | 73.35 51 | 71.41 17 | 54.18 21 | 40.82 102 | 41.57 69 | 45.70 68 | 54.84 60 | 54.43 46 | 69.92 32 | 69.19 39 | 76.45 47 | 82.25 57 |
|
MS-PatchMatch | | | 61.41 55 | 61.88 64 | 60.85 33 | 70.57 19 | 75.98 33 | 66.29 44 | 46.91 76 | 50.56 71 | 48.28 36 | 36.30 111 | 51.64 65 | 50.95 68 | 72.89 14 | 70.65 30 | 82.13 3 | 75.17 122 |
|
DWT-MVSNet_training | | | 61.22 56 | 63.52 56 | 58.53 46 | 65.00 39 | 76.55 32 | 59.50 87 | 48.22 55 | 51.79 68 | 42.14 64 | 47.85 63 | 50.21 71 | 55.46 40 | 66.16 59 | 67.92 50 | 80.85 5 | 84.14 47 |
|
diffmvs | | | 61.00 57 | 65.21 49 | 56.08 64 | 55.72 95 | 74.18 44 | 64.62 52 | 43.25 124 | 56.71 56 | 42.71 60 | 50.01 57 | 62.20 41 | 54.45 44 | 64.56 72 | 64.30 96 | 75.75 59 | 84.53 45 |
|
ACMP | | 56.21 5 | 59.78 58 | 61.81 66 | 57.41 56 | 61.15 61 | 68.88 85 | 65.98 45 | 48.85 47 | 58.56 54 | 44.19 50 | 48.89 59 | 46.31 91 | 48.56 98 | 63.61 97 | 64.49 93 | 75.75 59 | 81.91 66 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
tpmp4_e23 | | | 59.70 59 | 61.03 68 | 58.14 48 | 63.70 42 | 73.33 52 | 65.69 47 | 39.53 155 | 52.56 66 | 46.23 41 | 41.59 78 | 47.46 86 | 57.38 29 | 65.01 67 | 65.89 63 | 76.31 49 | 81.36 79 |
|
LGP-MVS_train | | | 59.69 60 | 62.59 59 | 56.31 63 | 61.94 57 | 68.15 89 | 66.90 40 | 48.15 57 | 59.75 51 | 38.47 74 | 50.38 56 | 48.34 83 | 46.87 111 | 65.39 64 | 64.93 70 | 75.51 64 | 81.21 82 |
|
Effi-MVS+ | | | 59.63 61 | 61.78 67 | 57.12 58 | 61.56 58 | 71.63 62 | 63.61 56 | 47.59 63 | 47.18 81 | 37.79 75 | 45.29 69 | 49.93 73 | 56.27 37 | 67.45 52 | 67.06 54 | 75.91 56 | 83.93 49 |
|
CPTT-MVS | | | 59.54 62 | 64.47 52 | 53.79 71 | 54.99 100 | 67.63 93 | 65.48 50 | 44.59 108 | 64.81 44 | 37.74 76 | 51.55 51 | 59.90 49 | 49.77 78 | 61.83 115 | 61.26 134 | 70.18 153 | 84.31 46 |
|
PVSNet_Blended_VisFu | | | 58.56 63 | 62.33 61 | 54.16 68 | 56.90 79 | 73.92 46 | 57.72 95 | 46.16 85 | 44.23 86 | 42.73 59 | 46.26 65 | 51.06 69 | 46.28 114 | 67.99 50 | 65.38 67 | 75.18 67 | 87.44 24 |
|
ACMM | | 53.73 9 | 57.91 64 | 58.27 80 | 57.49 55 | 63.10 46 | 66.45 101 | 65.65 48 | 49.02 46 | 53.69 64 | 42.67 61 | 36.41 108 | 46.07 93 | 50.38 70 | 64.74 70 | 64.63 82 | 74.14 106 | 75.91 112 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CANet_DTU | | | 57.87 65 | 63.63 55 | 51.15 99 | 52.18 115 | 70.20 68 | 58.14 94 | 37.32 171 | 56.49 57 | 31.06 120 | 57.38 38 | 50.05 72 | 53.67 51 | 64.98 68 | 65.04 69 | 74.57 90 | 81.29 81 |
|
tpm cat1 | | | 57.41 66 | 58.26 81 | 56.42 62 | 60.80 63 | 72.56 58 | 64.35 54 | 38.43 164 | 49.18 77 | 46.36 40 | 36.69 106 | 43.50 99 | 54.47 43 | 61.39 121 | 62.64 113 | 74.11 109 | 81.81 70 |
|
IB-MVS | | 53.15 10 | 57.33 67 | 59.02 73 | 55.37 65 | 60.83 62 | 77.11 27 | 54.51 118 | 50.10 42 | 43.22 89 | 42.82 58 | 40.50 83 | 37.61 118 | 44.67 126 | 59.27 149 | 69.81 33 | 79.29 15 | 85.59 37 |
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 |
tpmrst | | | 57.23 68 | 59.08 72 | 55.06 66 | 59.91 67 | 70.65 66 | 60.71 68 | 35.38 186 | 47.91 79 | 42.58 62 | 39.78 87 | 45.45 95 | 54.44 45 | 62.19 110 | 62.82 109 | 77.37 36 | 84.73 42 |
|
HyFIR lowres test | | | 57.12 69 | 59.11 71 | 54.80 67 | 61.55 59 | 77.55 24 | 59.02 90 | 45.00 94 | 41.84 100 | 33.93 99 | 22.44 201 | 49.16 78 | 51.02 67 | 68.39 46 | 68.71 44 | 78.26 27 | 85.70 35 |
|
MVS_111021_LR | | | 57.06 70 | 60.60 69 | 52.93 72 | 56.25 85 | 65.14 113 | 55.16 116 | 41.21 146 | 52.32 67 | 44.89 48 | 53.92 46 | 49.27 77 | 52.16 63 | 61.46 119 | 60.54 143 | 67.92 165 | 81.53 75 |
|
PMMVS | | | 55.74 71 | 62.68 57 | 47.64 130 | 44.34 179 | 65.58 111 | 47.22 158 | 37.96 167 | 56.43 59 | 34.11 96 | 61.51 30 | 47.41 87 | 54.55 42 | 65.88 60 | 62.49 117 | 67.67 168 | 79.48 90 |
|
Fast-Effi-MVS+ | | | 55.73 72 | 58.26 81 | 52.76 77 | 54.33 103 | 68.19 88 | 57.05 99 | 34.66 188 | 46.92 82 | 38.96 73 | 40.53 82 | 41.55 107 | 55.69 38 | 65.31 66 | 65.99 62 | 75.90 57 | 79.34 92 |
|
FC-MVSNet-train | | | 55.68 73 | 57.00 89 | 54.13 69 | 63.37 43 | 66.16 103 | 46.77 161 | 52.14 29 | 42.36 95 | 37.67 77 | 48.50 61 | 41.42 109 | 51.28 66 | 61.58 118 | 63.22 106 | 73.56 115 | 75.76 114 |
|
FMVSNet3 | | | 55.66 74 | 59.68 70 | 50.96 101 | 50.59 146 | 66.49 100 | 57.57 96 | 46.61 78 | 49.30 74 | 28.77 129 | 39.61 88 | 51.42 66 | 43.85 128 | 68.29 47 | 68.80 43 | 78.35 26 | 73.86 126 |
|
OMC-MVS | | | 55.48 75 | 61.85 65 | 48.04 129 | 41.55 188 | 60.32 164 | 56.80 103 | 31.78 211 | 75.67 18 | 42.30 63 | 51.52 52 | 54.15 61 | 49.91 75 | 60.28 138 | 57.59 160 | 65.91 179 | 73.42 128 |
|
tpm | | | 54.94 76 | 57.86 86 | 51.54 96 | 59.48 71 | 67.04 96 | 58.34 93 | 34.60 190 | 41.93 99 | 34.41 92 | 42.40 75 | 47.14 88 | 49.07 90 | 61.46 119 | 61.67 127 | 73.31 122 | 83.39 51 |
|
GBi-Net | | | 54.66 77 | 58.42 78 | 50.26 109 | 49.36 153 | 65.81 108 | 56.80 103 | 46.61 78 | 49.30 74 | 28.77 129 | 39.61 88 | 51.42 66 | 42.71 131 | 64.25 77 | 65.54 64 | 77.32 38 | 73.03 132 |
|
test1 | | | 54.66 77 | 58.42 78 | 50.26 109 | 49.36 153 | 65.81 108 | 56.80 103 | 46.61 78 | 49.30 74 | 28.77 129 | 39.61 88 | 51.42 66 | 42.71 131 | 64.25 77 | 65.54 64 | 77.32 38 | 73.03 132 |
|
test-LLR | | | 54.62 79 | 58.66 76 | 49.89 115 | 51.68 135 | 65.89 105 | 47.88 152 | 46.35 83 | 42.51 92 | 29.84 126 | 41.41 79 | 48.87 79 | 45.20 119 | 62.91 105 | 64.43 94 | 78.43 23 | 84.62 43 |
|
TSAR-MVS + COLMAP | | | 54.37 80 | 62.43 60 | 44.98 145 | 34.33 213 | 58.94 172 | 54.11 121 | 34.15 199 | 74.06 21 | 34.57 91 | 71.63 13 | 42.03 106 | 47.88 105 | 61.26 123 | 57.33 165 | 64.83 184 | 71.74 142 |
|
v1neww | | | 54.32 81 | 55.60 98 | 52.81 74 | 52.11 117 | 69.43 80 | 60.57 72 | 44.86 97 | 37.13 126 | 35.34 87 | 34.95 120 | 37.46 124 | 49.53 83 | 63.69 90 | 64.59 83 | 74.47 93 | 81.99 61 |
|
v7new | | | 54.32 81 | 55.60 98 | 52.81 74 | 52.11 117 | 69.43 80 | 60.57 72 | 44.86 97 | 37.13 126 | 35.34 87 | 34.95 120 | 37.46 124 | 49.53 83 | 63.69 90 | 64.59 83 | 74.47 93 | 81.99 61 |
|
v6 | | | 54.32 81 | 55.61 97 | 52.82 73 | 52.11 117 | 69.44 79 | 60.57 72 | 44.86 97 | 37.15 125 | 35.40 85 | 34.96 118 | 37.47 123 | 49.52 85 | 63.68 93 | 64.59 83 | 74.47 93 | 81.99 61 |
|
EPMVS | | | 54.07 84 | 56.06 92 | 51.75 93 | 56.74 81 | 70.80 64 | 55.32 114 | 34.20 196 | 46.46 83 | 36.59 82 | 40.38 85 | 42.55 101 | 49.77 78 | 61.25 124 | 60.90 138 | 77.86 32 | 70.08 155 |
|
v2v482 | | | 54.00 85 | 55.12 102 | 52.69 80 | 51.73 133 | 69.42 82 | 60.65 70 | 45.09 93 | 34.56 143 | 33.73 103 | 35.29 114 | 35.36 143 | 49.92 74 | 64.05 81 | 65.16 68 | 75.00 71 | 81.98 64 |
|
CNLPA | | | 54.00 85 | 57.08 88 | 50.40 108 | 49.83 151 | 61.75 153 | 53.47 124 | 37.27 172 | 74.55 20 | 44.85 49 | 33.58 143 | 45.42 96 | 52.94 61 | 58.89 151 | 53.66 186 | 64.06 186 | 71.68 143 |
|
v7 | | | 53.99 87 | 55.20 101 | 52.57 82 | 52.08 120 | 69.46 78 | 59.68 83 | 44.92 96 | 35.90 132 | 34.33 93 | 33.99 137 | 35.55 141 | 50.08 72 | 64.38 76 | 64.67 79 | 74.32 98 | 82.47 55 |
|
FMVSNet2 | | | 53.94 88 | 57.29 87 | 50.03 112 | 49.36 153 | 65.81 108 | 56.80 103 | 45.95 86 | 43.13 90 | 28.04 133 | 35.68 112 | 48.18 84 | 42.71 131 | 67.23 56 | 67.95 49 | 77.32 38 | 73.03 132 |
|
v1141 | | | 53.78 89 | 55.02 105 | 52.33 85 | 51.89 125 | 69.75 73 | 60.24 77 | 44.81 102 | 34.04 151 | 33.34 107 | 34.57 127 | 36.17 138 | 48.76 93 | 63.90 82 | 64.81 74 | 74.94 74 | 81.87 69 |
|
divwei89l23v2f112 | | | 53.78 89 | 55.02 105 | 52.33 85 | 51.89 125 | 69.74 75 | 60.25 76 | 44.82 101 | 34.06 149 | 33.33 108 | 34.56 130 | 36.18 136 | 48.75 94 | 63.90 82 | 64.82 72 | 74.94 74 | 81.88 67 |
|
v1 | | | 53.78 89 | 55.02 105 | 52.34 84 | 51.89 125 | 69.75 73 | 60.24 77 | 44.81 102 | 34.05 150 | 33.39 106 | 34.57 127 | 36.18 136 | 48.75 94 | 63.90 82 | 64.82 72 | 74.94 74 | 81.88 67 |
|
v8 | | | 53.77 92 | 54.82 109 | 52.54 83 | 52.12 116 | 66.95 99 | 60.56 75 | 43.23 125 | 37.17 124 | 35.35 86 | 34.96 118 | 37.50 122 | 49.51 86 | 63.67 95 | 64.59 83 | 74.48 92 | 78.91 95 |
|
GA-MVS | | | 53.77 92 | 56.41 91 | 50.70 103 | 51.63 137 | 69.96 70 | 57.55 97 | 44.39 109 | 34.31 145 | 27.15 135 | 40.99 81 | 36.40 130 | 47.65 108 | 67.45 52 | 67.16 53 | 75.83 58 | 78.60 96 |
|
Effi-MVS+-dtu | | | 53.63 94 | 54.85 108 | 52.20 88 | 59.32 72 | 61.33 157 | 56.42 109 | 40.24 151 | 43.84 88 | 34.22 95 | 39.49 92 | 46.18 92 | 53.00 60 | 58.72 154 | 57.49 162 | 69.99 155 | 76.91 102 |
|
v18 | | | 53.63 94 | 54.35 112 | 52.80 76 | 52.25 112 | 62.94 138 | 60.80 67 | 42.78 132 | 39.23 107 | 36.81 80 | 35.07 117 | 37.78 117 | 49.82 76 | 63.69 90 | 64.65 81 | 74.32 98 | 77.07 101 |
|
v16 | | | 53.50 96 | 54.17 113 | 52.73 79 | 52.24 113 | 62.90 139 | 60.67 69 | 42.67 135 | 38.72 110 | 36.70 81 | 34.84 123 | 37.59 120 | 49.69 80 | 63.72 87 | 64.68 77 | 74.39 96 | 76.72 103 |
|
v1144 | | | 53.47 97 | 54.65 110 | 52.10 89 | 51.93 122 | 69.81 71 | 59.32 88 | 44.77 105 | 33.21 159 | 32.52 112 | 33.55 144 | 34.34 153 | 49.29 88 | 64.58 71 | 64.81 74 | 74.74 82 | 82.27 56 |
|
v10 | | | 53.44 98 | 54.40 111 | 52.31 87 | 52.08 120 | 66.99 97 | 59.68 83 | 43.41 121 | 35.90 132 | 34.30 94 | 33.98 138 | 35.56 140 | 50.10 71 | 64.39 75 | 64.67 79 | 74.32 98 | 79.30 93 |
|
v17 | | | 53.40 99 | 54.09 114 | 52.60 81 | 52.22 114 | 62.90 139 | 60.64 71 | 42.66 136 | 38.24 113 | 36.04 83 | 34.85 122 | 37.59 120 | 49.63 81 | 63.70 89 | 64.68 77 | 74.39 96 | 76.70 104 |
|
PatchmatchNet | | | 53.37 100 | 55.62 96 | 50.75 102 | 55.93 93 | 70.54 67 | 51.39 138 | 36.41 175 | 44.85 84 | 37.26 79 | 39.40 94 | 42.54 102 | 47.83 106 | 60.29 137 | 60.88 140 | 75.69 62 | 70.87 149 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
IterMVS-LS | | | 53.36 101 | 55.65 95 | 50.68 105 | 55.34 96 | 59.04 170 | 55.00 117 | 39.98 152 | 38.72 110 | 33.22 110 | 44.52 71 | 47.05 89 | 49.63 81 | 61.82 116 | 61.77 124 | 70.92 147 | 76.61 107 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
TESTMET0.1,1 | | | 53.30 102 | 58.66 76 | 47.04 132 | 44.94 173 | 65.89 105 | 47.88 152 | 35.95 180 | 42.51 92 | 29.84 126 | 41.41 79 | 48.87 79 | 45.20 119 | 62.91 105 | 64.43 94 | 78.43 23 | 84.62 43 |
|
MDTV_nov1_ep13 | | | 52.99 103 | 55.59 100 | 49.95 114 | 54.08 105 | 70.69 65 | 56.47 108 | 38.42 165 | 42.78 91 | 30.19 125 | 39.56 91 | 43.31 100 | 45.78 116 | 60.07 142 | 62.11 121 | 74.74 82 | 70.62 150 |
|
EPP-MVSNet | | | 52.91 104 | 58.91 74 | 45.91 138 | 54.99 100 | 68.84 86 | 49.27 144 | 42.71 133 | 37.53 118 | 20.20 170 | 46.09 66 | 56.19 57 | 36.90 148 | 61.37 122 | 60.90 138 | 71.41 143 | 81.41 77 |
|
dps | | | 52.84 105 | 52.92 129 | 52.74 78 | 59.89 68 | 69.49 77 | 54.47 119 | 37.38 170 | 42.49 94 | 39.53 72 | 35.33 113 | 32.71 161 | 51.83 65 | 60.45 135 | 61.12 135 | 73.33 121 | 68.86 163 |
|
v1192 | | | 52.69 106 | 53.86 117 | 51.31 98 | 51.22 139 | 69.76 72 | 57.37 98 | 44.39 109 | 32.21 163 | 31.39 119 | 32.41 150 | 32.44 165 | 49.19 89 | 64.25 77 | 64.17 97 | 74.31 101 | 81.81 70 |
|
V42 | | | 52.63 107 | 55.08 103 | 49.76 117 | 44.93 174 | 67.49 95 | 60.19 79 | 42.13 139 | 37.21 123 | 34.08 97 | 34.57 127 | 37.30 126 | 47.29 109 | 63.48 100 | 64.15 98 | 69.96 156 | 81.38 78 |
|
v15 | | | 52.60 108 | 53.17 123 | 51.94 90 | 51.92 123 | 62.76 141 | 60.04 80 | 42.19 138 | 34.35 144 | 33.96 98 | 34.37 132 | 36.40 130 | 48.73 96 | 63.75 86 | 64.59 83 | 74.79 78 | 75.97 111 |
|
MSDG | | | 52.58 109 | 51.40 146 | 53.95 70 | 65.48 37 | 64.31 128 | 61.44 64 | 44.02 115 | 44.17 87 | 32.92 111 | 30.40 168 | 31.81 171 | 46.35 113 | 62.13 111 | 62.55 115 | 73.49 117 | 64.41 173 |
|
V14 | | | 52.51 110 | 53.06 125 | 51.87 91 | 51.91 124 | 62.70 143 | 59.93 81 | 42.13 139 | 34.16 147 | 33.88 100 | 34.22 134 | 36.35 133 | 48.62 97 | 63.72 87 | 64.59 83 | 74.77 79 | 75.69 119 |
|
Fast-Effi-MVS+-dtu | | | 52.47 111 | 55.89 93 | 48.48 127 | 56.25 85 | 65.07 114 | 58.75 92 | 23.79 225 | 41.27 101 | 27.07 137 | 37.95 98 | 41.34 110 | 50.85 69 | 62.90 107 | 62.34 119 | 74.17 105 | 80.37 89 |
|
v144192 | | | 52.43 112 | 53.63 119 | 51.03 100 | 51.06 140 | 69.60 76 | 56.94 101 | 44.84 100 | 32.15 164 | 30.88 121 | 32.45 149 | 32.71 161 | 48.36 100 | 62.98 104 | 63.52 103 | 74.10 110 | 82.02 60 |
|
V9 | | | 52.42 113 | 52.94 128 | 51.81 92 | 51.89 125 | 62.64 145 | 59.81 82 | 42.08 141 | 33.97 152 | 33.85 101 | 34.05 136 | 36.28 134 | 48.49 99 | 63.68 93 | 64.59 83 | 74.76 80 | 75.41 120 |
|
thres100view900 | | | 52.33 114 | 53.91 116 | 50.48 107 | 56.10 87 | 67.79 91 | 56.18 111 | 49.18 45 | 35.86 135 | 25.22 143 | 34.74 124 | 34.10 154 | 42.41 134 | 64.45 74 | 62.62 114 | 73.81 111 | 77.85 97 |
|
v12 | | | 52.31 115 | 52.81 131 | 51.72 94 | 51.87 129 | 62.56 146 | 59.66 85 | 42.02 142 | 33.77 154 | 33.70 104 | 33.88 139 | 36.20 135 | 48.36 100 | 63.64 96 | 64.56 90 | 74.73 85 | 75.08 123 |
|
v11 | | | 52.23 116 | 52.83 130 | 51.53 97 | 51.73 133 | 62.49 147 | 58.82 91 | 41.81 144 | 33.53 157 | 33.23 109 | 33.73 141 | 35.10 146 | 49.07 90 | 64.49 73 | 64.71 76 | 74.49 91 | 75.75 115 |
|
v13 | | | 52.22 117 | 52.69 133 | 51.67 95 | 51.85 131 | 62.48 148 | 59.57 86 | 41.97 143 | 33.61 156 | 33.69 105 | 33.71 142 | 36.11 139 | 48.23 103 | 63.57 98 | 64.55 91 | 74.72 86 | 74.77 124 |
|
v1921920 | | | 51.95 118 | 53.19 122 | 50.51 106 | 50.82 145 | 69.14 84 | 55.45 113 | 44.34 113 | 31.53 169 | 30.53 123 | 31.96 153 | 31.67 172 | 48.31 102 | 63.12 102 | 63.28 105 | 73.59 114 | 81.60 74 |
|
v148 | | | 51.72 119 | 53.15 124 | 50.05 111 | 50.15 149 | 67.51 94 | 56.98 100 | 42.85 130 | 32.60 162 | 32.41 114 | 33.88 139 | 34.71 150 | 44.45 127 | 61.06 125 | 63.00 108 | 73.45 118 | 79.24 94 |
|
TAPA-MVS | | 47.92 11 | 51.66 120 | 57.88 85 | 44.40 149 | 36.46 207 | 58.42 179 | 53.82 123 | 30.83 213 | 69.51 34 | 34.97 90 | 46.90 64 | 49.67 74 | 46.99 110 | 58.00 157 | 54.64 183 | 63.33 192 | 68.00 166 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
conf0.002 | | | 51.63 121 | 53.79 118 | 49.12 121 | 56.33 83 | 64.84 116 | 53.05 129 | 47.38 67 | 35.86 135 | 24.83 145 | 37.86 99 | 38.15 115 | 41.08 136 | 61.04 127 | 62.70 110 | 72.05 135 | 76.06 109 |
|
IS_MVSNet | | | 51.53 122 | 57.98 84 | 44.01 153 | 55.96 92 | 66.16 103 | 47.65 154 | 42.84 131 | 39.82 105 | 19.09 177 | 44.97 70 | 50.28 70 | 27.20 187 | 63.43 101 | 63.84 99 | 71.33 144 | 77.33 99 |
|
v1240 | | | 51.42 123 | 52.66 135 | 49.97 113 | 50.31 148 | 68.70 87 | 54.05 122 | 43.85 117 | 30.78 174 | 30.22 124 | 31.43 157 | 31.03 181 | 47.98 104 | 62.62 109 | 63.16 107 | 73.40 119 | 80.93 84 |
|
conf0.01 | | | 51.32 124 | 53.22 121 | 49.11 122 | 56.29 84 | 64.78 117 | 53.05 129 | 47.37 68 | 35.86 135 | 24.83 145 | 36.85 105 | 36.64 129 | 41.08 136 | 61.01 128 | 61.37 129 | 72.03 136 | 76.01 110 |
|
pmmvs4 | | | 51.28 125 | 52.50 137 | 49.85 116 | 49.54 152 | 63.02 137 | 52.83 134 | 43.41 121 | 44.65 85 | 35.71 84 | 34.38 131 | 32.25 166 | 45.14 122 | 60.21 141 | 60.03 147 | 72.44 133 | 72.98 135 |
|
Vis-MVSNet | | | 51.13 126 | 58.04 83 | 43.06 162 | 47.68 161 | 67.71 92 | 49.10 147 | 39.09 161 | 37.75 116 | 22.57 160 | 51.03 55 | 48.78 81 | 32.42 167 | 62.12 112 | 61.80 123 | 67.49 170 | 77.12 100 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
UGNet | | | 51.04 127 | 58.79 75 | 42.00 168 | 40.59 190 | 65.32 112 | 46.65 163 | 39.26 159 | 39.90 104 | 27.30 134 | 54.12 45 | 52.03 64 | 30.93 172 | 59.85 145 | 59.62 151 | 67.23 172 | 80.70 85 |
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 |
tfpn111 | | | 51.00 128 | 52.68 134 | 49.04 124 | 56.10 87 | 64.52 123 | 53.05 129 | 47.31 71 | 35.86 135 | 24.79 148 | 36.35 109 | 34.10 154 | 41.08 136 | 60.84 130 | 61.37 129 | 71.90 139 | 75.70 116 |
|
tfpn200view9 | | | 50.91 129 | 52.45 138 | 49.11 122 | 56.10 87 | 64.53 121 | 53.06 128 | 47.31 71 | 35.86 135 | 25.22 143 | 34.74 124 | 34.10 154 | 41.08 136 | 60.84 130 | 61.37 129 | 71.90 139 | 75.70 116 |
|
conf200view11 | | | 50.87 130 | 52.45 138 | 49.04 124 | 56.10 87 | 64.52 123 | 53.05 129 | 47.31 71 | 35.86 135 | 24.79 148 | 34.74 124 | 34.10 154 | 41.08 136 | 60.84 130 | 61.37 129 | 71.90 139 | 75.70 116 |
|
gg-mvs-nofinetune | | | 50.82 131 | 55.83 94 | 44.97 146 | 60.63 64 | 75.69 37 | 53.40 125 | 34.48 192 | 20.05 217 | 6.93 214 | 18.27 210 | 52.70 63 | 33.57 159 | 70.50 28 | 72.93 14 | 80.84 6 | 80.68 86 |
|
thres200 | | | 50.76 132 | 52.52 136 | 48.70 126 | 55.98 91 | 64.60 119 | 55.29 115 | 47.34 69 | 33.91 153 | 24.36 152 | 34.33 133 | 33.90 158 | 37.27 146 | 60.84 130 | 62.41 118 | 71.99 137 | 77.63 98 |
|
thres400 | | | 50.39 133 | 52.22 140 | 48.26 128 | 55.02 99 | 66.32 102 | 52.97 133 | 48.33 54 | 32.68 161 | 22.94 158 | 33.21 146 | 33.38 160 | 37.27 146 | 62.74 108 | 61.38 128 | 73.04 128 | 75.81 113 |
|
EG-PatchMatch MVS | | | 50.23 134 | 50.89 149 | 49.47 118 | 59.54 70 | 70.88 63 | 52.46 135 | 44.01 116 | 26.22 198 | 31.91 115 | 24.97 195 | 31.45 174 | 33.48 161 | 64.79 69 | 66.51 60 | 75.40 65 | 71.39 144 |
|
IterMVS | | | 50.23 134 | 53.27 120 | 46.68 134 | 47.59 163 | 60.58 162 | 53.10 127 | 36.62 174 | 36.07 131 | 25.89 140 | 39.42 93 | 40.05 113 | 43.65 129 | 60.22 140 | 61.35 133 | 73.23 123 | 75.23 121 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
FMVSNet1 | | | 50.14 136 | 52.78 132 | 47.06 131 | 45.56 170 | 63.56 132 | 54.22 120 | 43.74 119 | 34.10 148 | 25.37 142 | 29.79 175 | 42.06 105 | 38.70 142 | 64.25 77 | 65.54 64 | 74.75 81 | 70.18 154 |
|
ACMH | | 47.82 13 | 50.10 137 | 49.60 158 | 50.69 104 | 63.36 44 | 66.99 97 | 56.83 102 | 52.13 30 | 31.06 172 | 17.74 185 | 28.22 181 | 26.24 200 | 45.17 121 | 60.88 129 | 63.80 100 | 68.91 161 | 70.00 157 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EPNet_dtu | | | 49.85 138 | 56.99 90 | 41.52 172 | 52.79 109 | 57.06 181 | 41.44 180 | 43.13 126 | 56.13 60 | 19.24 176 | 52.11 49 | 48.38 82 | 22.14 197 | 58.19 156 | 58.38 157 | 70.35 151 | 68.71 164 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
LS3D | | | 49.59 139 | 49.75 157 | 49.40 120 | 55.88 94 | 59.86 168 | 56.31 110 | 45.33 90 | 48.57 78 | 28.32 132 | 31.54 155 | 36.81 128 | 46.27 115 | 57.17 163 | 55.88 175 | 64.29 185 | 58.42 192 |
|
UniMVSNet_NR-MVSNet | | | 49.56 140 | 53.04 126 | 45.49 141 | 51.59 138 | 64.42 127 | 46.97 159 | 51.01 34 | 37.87 114 | 16.42 187 | 39.87 86 | 34.91 148 | 33.43 163 | 59.59 146 | 62.70 110 | 73.52 116 | 71.94 137 |
|
CDS-MVSNet | | | 49.25 141 | 53.97 115 | 43.75 155 | 47.53 164 | 64.53 121 | 48.59 148 | 42.27 137 | 33.77 154 | 26.64 138 | 40.46 84 | 42.26 104 | 30.01 175 | 61.77 117 | 61.71 125 | 67.48 171 | 73.28 131 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PLC | | 44.22 14 | 49.14 142 | 51.75 143 | 46.10 137 | 42.78 186 | 55.60 188 | 53.11 126 | 34.46 193 | 55.69 62 | 32.47 113 | 34.16 135 | 41.45 108 | 48.91 92 | 57.13 164 | 54.09 184 | 64.84 183 | 64.10 174 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMH+ | | 47.85 12 | 49.13 143 | 48.86 165 | 49.44 119 | 56.75 80 | 62.01 152 | 56.62 107 | 47.55 65 | 37.49 119 | 23.98 154 | 26.68 185 | 29.46 190 | 43.12 130 | 57.45 162 | 58.85 155 | 68.62 163 | 70.05 156 |
|
view600 | | | 48.85 144 | 50.58 153 | 46.82 133 | 54.19 104 | 64.94 115 | 50.81 139 | 47.53 66 | 31.78 167 | 21.59 164 | 32.31 151 | 32.63 163 | 34.28 156 | 61.06 125 | 57.41 163 | 72.54 131 | 73.96 125 |
|
NR-MVSNet | | | 48.84 145 | 51.76 142 | 45.44 142 | 57.66 78 | 60.64 160 | 47.39 155 | 47.63 61 | 37.26 120 | 13.31 194 | 37.31 101 | 29.64 189 | 33.53 160 | 63.52 99 | 62.09 122 | 73.10 126 | 71.89 140 |
|
CR-MVSNet | | | 48.82 146 | 51.85 141 | 45.29 143 | 46.74 166 | 55.95 184 | 52.06 136 | 34.21 194 | 42.17 96 | 31.74 116 | 32.92 148 | 42.53 103 | 45.00 123 | 58.80 152 | 61.11 136 | 61.99 197 | 69.47 160 |
|
thres600view7 | | | 48.44 147 | 50.23 154 | 46.35 136 | 54.05 106 | 64.60 119 | 50.18 142 | 47.34 69 | 31.73 168 | 20.74 167 | 32.28 152 | 32.62 164 | 33.79 158 | 60.84 130 | 56.11 173 | 71.99 137 | 73.40 129 |
|
test-mter | | | 48.31 148 | 55.04 104 | 40.45 175 | 34.12 214 | 59.02 171 | 41.77 179 | 28.05 217 | 38.43 112 | 22.67 159 | 39.35 95 | 44.40 98 | 41.88 135 | 60.30 136 | 61.68 126 | 74.20 103 | 82.12 58 |
|
PatchT | | | 48.11 149 | 51.27 148 | 44.43 148 | 50.13 150 | 61.58 154 | 33.59 198 | 32.92 205 | 40.38 103 | 31.74 116 | 30.60 167 | 36.93 127 | 45.00 123 | 58.80 152 | 61.11 136 | 73.19 124 | 69.47 160 |
|
TranMVSNet+NR-MVSNet | | | 48.06 150 | 51.36 147 | 44.21 151 | 50.38 147 | 62.09 151 | 47.28 156 | 50.88 37 | 36.11 130 | 13.25 195 | 37.51 100 | 31.60 173 | 30.70 173 | 59.34 148 | 62.53 116 | 72.81 129 | 70.31 152 |
|
thresconf0.02 | | | 47.89 151 | 50.76 152 | 44.54 147 | 53.86 107 | 63.96 130 | 46.23 165 | 47.72 60 | 33.00 160 | 17.08 186 | 36.35 109 | 37.80 116 | 29.86 177 | 60.01 144 | 60.57 142 | 72.49 132 | 63.62 178 |
|
view800 | | | 47.68 152 | 49.78 156 | 45.24 144 | 53.39 108 | 63.19 135 | 48.13 151 | 46.57 81 | 30.98 173 | 20.25 169 | 31.52 156 | 31.90 170 | 31.52 169 | 59.37 147 | 59.61 152 | 71.56 142 | 71.89 140 |
|
TransMVSNet (Re) | | | 47.46 153 | 48.94 164 | 45.74 140 | 57.96 76 | 64.29 129 | 48.26 149 | 48.47 53 | 26.33 197 | 19.33 174 | 29.45 178 | 31.28 179 | 25.31 191 | 63.05 103 | 62.70 110 | 75.10 70 | 65.47 170 |
|
DU-MVS | | | 47.33 154 | 50.86 150 | 43.20 160 | 44.43 177 | 60.64 160 | 46.97 159 | 47.63 61 | 37.26 120 | 16.42 187 | 37.31 101 | 31.39 175 | 33.43 163 | 57.53 160 | 59.98 148 | 70.35 151 | 71.94 137 |
|
v7n | | | 47.22 155 | 48.38 166 | 45.87 139 | 48.20 160 | 63.58 131 | 50.69 140 | 40.93 149 | 26.60 196 | 26.44 139 | 26.52 186 | 29.65 188 | 38.19 144 | 58.22 155 | 60.23 146 | 70.79 148 | 73.83 127 |
|
UA-Net | | | 47.19 156 | 53.02 127 | 40.38 176 | 55.31 97 | 60.02 167 | 38.41 187 | 38.68 163 | 36.42 129 | 22.47 162 | 51.95 50 | 58.72 53 | 25.62 190 | 54.11 179 | 53.40 188 | 61.79 198 | 56.51 196 |
|
Baseline_NR-MVSNet | | | 47.14 157 | 50.83 151 | 42.84 164 | 44.43 177 | 63.31 134 | 44.50 172 | 50.36 41 | 37.71 117 | 11.25 201 | 30.84 164 | 32.09 167 | 30.96 171 | 57.53 160 | 63.73 101 | 75.53 63 | 70.60 151 |
|
pmmvs5 | | | 47.02 158 | 50.02 155 | 43.51 157 | 43.48 184 | 62.65 144 | 47.24 157 | 37.78 169 | 30.59 176 | 24.80 147 | 35.26 115 | 30.43 185 | 34.36 155 | 59.05 150 | 60.28 145 | 73.40 119 | 71.92 139 |
|
UniMVSNet (Re) | | | 46.89 159 | 51.65 145 | 41.34 173 | 45.60 169 | 62.71 142 | 44.05 173 | 47.10 75 | 37.24 122 | 13.55 193 | 36.90 103 | 34.54 152 | 26.76 188 | 57.56 159 | 59.90 150 | 70.98 146 | 72.69 136 |
|
tfpn | | | 46.62 160 | 49.07 163 | 43.75 155 | 52.70 110 | 61.49 156 | 45.65 167 | 45.68 88 | 30.25 178 | 18.84 180 | 30.87 163 | 33.67 159 | 29.22 182 | 57.80 158 | 59.49 153 | 70.44 150 | 69.95 158 |
|
tfpnnormal | | | 46.61 161 | 46.82 174 | 46.37 135 | 52.70 110 | 62.31 149 | 50.39 141 | 47.17 74 | 25.74 201 | 21.80 163 | 23.13 199 | 24.15 209 | 33.45 162 | 60.28 138 | 60.77 141 | 72.70 130 | 71.39 144 |
|
tfpn_ndepth | | | 46.53 162 | 49.41 160 | 43.18 161 | 54.66 102 | 61.56 155 | 42.25 177 | 45.66 89 | 35.68 141 | 18.31 183 | 36.55 107 | 34.84 149 | 28.88 183 | 55.45 174 | 57.01 168 | 69.32 159 | 64.78 172 |
|
pm-mvs1 | | | 46.14 163 | 49.34 162 | 42.41 165 | 48.93 156 | 62.22 150 | 44.98 170 | 42.68 134 | 27.66 189 | 20.76 166 | 29.88 174 | 34.96 147 | 26.41 189 | 60.03 143 | 60.42 144 | 70.70 149 | 70.20 153 |
|
MIMVSNet | | | 45.62 164 | 49.56 159 | 41.02 174 | 38.17 197 | 64.43 126 | 49.48 143 | 35.43 185 | 36.53 128 | 20.06 172 | 22.58 200 | 35.16 145 | 28.75 185 | 61.97 114 | 62.20 120 | 74.20 103 | 64.07 175 |
|
gm-plane-assit | | | 45.41 165 | 48.03 168 | 42.34 166 | 56.49 82 | 40.48 216 | 24.54 221 | 34.15 199 | 14.44 226 | 6.59 215 | 17.82 211 | 35.32 144 | 49.82 76 | 72.93 12 | 74.11 8 | 82.47 2 | 81.12 83 |
|
ADS-MVSNet | | | 45.39 166 | 46.42 177 | 44.19 152 | 48.74 159 | 57.52 180 | 43.91 174 | 31.93 209 | 35.89 134 | 27.11 136 | 30.12 170 | 32.06 168 | 45.30 117 | 53.13 188 | 55.19 179 | 68.15 164 | 61.07 184 |
|
conf0.05thres1000 | | | 45.26 167 | 46.99 172 | 43.24 159 | 51.87 129 | 60.52 163 | 45.17 169 | 45.24 91 | 27.06 193 | 18.60 181 | 26.24 187 | 31.23 180 | 28.82 184 | 56.88 166 | 58.52 156 | 69.71 157 | 68.50 165 |
|
v748 | | | 44.90 168 | 46.14 179 | 43.46 158 | 45.37 172 | 60.89 159 | 48.15 150 | 39.42 156 | 25.81 200 | 24.36 152 | 25.90 190 | 28.48 193 | 34.44 154 | 53.39 186 | 57.35 164 | 69.00 160 | 71.14 148 |
|
GG-mvs-BLEND | | | 44.87 169 | 64.59 51 | 21.86 223 | 0.01 238 | 73.70 48 | 55.99 112 | 0.01 236 | 50.70 70 | 0.01 241 | 49.18 58 | 63.61 35 | 0.01 236 | 63.83 85 | 64.50 92 | 75.13 69 | 86.62 29 |
|
pmmvs-eth3d | | | 44.67 170 | 45.27 183 | 43.98 154 | 42.56 187 | 55.72 187 | 44.97 171 | 40.81 150 | 31.96 166 | 29.13 128 | 26.09 189 | 25.27 206 | 36.69 149 | 55.13 176 | 56.62 171 | 69.68 158 | 66.12 169 |
|
MDTV_nov1_ep13_2view | | | 44.44 171 | 45.75 180 | 42.91 163 | 46.13 167 | 63.43 133 | 46.53 164 | 34.20 196 | 29.08 185 | 19.95 173 | 26.23 188 | 27.89 195 | 35.88 152 | 53.36 187 | 56.43 172 | 74.74 82 | 63.86 176 |
|
v52 | | | 44.41 172 | 46.76 175 | 41.67 169 | 38.93 194 | 60.06 165 | 49.26 145 | 36.02 177 | 25.57 202 | 24.73 151 | 25.66 191 | 31.34 176 | 35.93 150 | 55.52 172 | 57.99 158 | 65.14 181 | 71.21 146 |
|
V4 | | | 44.41 172 | 46.76 175 | 41.67 169 | 38.93 194 | 60.05 166 | 49.26 145 | 36.02 177 | 25.55 203 | 24.75 150 | 25.66 191 | 31.33 177 | 35.93 150 | 55.52 172 | 57.99 158 | 65.14 181 | 71.21 146 |
|
CMPMVS | | 33.64 16 | 44.39 174 | 46.41 178 | 42.03 167 | 44.21 181 | 56.50 183 | 46.73 162 | 26.48 222 | 34.20 146 | 35.14 89 | 24.22 196 | 34.64 151 | 40.52 141 | 56.50 169 | 56.07 174 | 59.12 204 | 62.74 180 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Vis-MVSNet (Re-imp) | | | 44.31 175 | 51.67 144 | 35.72 193 | 51.82 132 | 55.24 189 | 34.57 197 | 41.63 145 | 39.10 108 | 8.84 209 | 45.93 67 | 46.63 90 | 14.45 212 | 54.09 180 | 57.03 167 | 63.00 193 | 63.65 177 |
|
TAMVS | | | 44.27 176 | 49.35 161 | 38.35 186 | 44.74 175 | 61.04 158 | 39.07 184 | 31.82 210 | 29.95 180 | 18.34 182 | 33.55 144 | 39.94 114 | 30.01 175 | 56.85 167 | 57.58 161 | 66.13 178 | 66.54 167 |
|
MVS-HIRNet | | | 43.98 177 | 43.63 190 | 44.39 150 | 47.66 162 | 59.31 169 | 32.66 204 | 33.88 201 | 30.15 179 | 33.75 102 | 16.82 217 | 28.39 194 | 45.25 118 | 53.92 185 | 55.00 182 | 73.16 125 | 61.80 181 |
|
RPMNet | | | 43.70 178 | 48.17 167 | 38.48 185 | 45.52 171 | 55.95 184 | 37.66 190 | 26.63 221 | 42.17 96 | 25.47 141 | 29.59 177 | 37.61 118 | 33.87 157 | 50.85 193 | 52.02 193 | 61.75 199 | 69.00 162 |
|
PatchMatch-RL | | | 43.37 179 | 44.93 187 | 41.56 171 | 37.94 198 | 51.70 191 | 40.02 182 | 35.75 181 | 39.04 109 | 30.71 122 | 35.14 116 | 27.43 197 | 46.58 112 | 51.99 189 | 50.55 198 | 58.38 206 | 58.64 190 |
|
FMVSNet5 | | | 43.29 180 | 47.07 171 | 38.87 182 | 30.46 220 | 50.99 194 | 45.87 166 | 37.19 173 | 42.17 96 | 19.32 175 | 26.77 184 | 40.51 111 | 30.26 174 | 56.82 168 | 55.81 176 | 70.10 154 | 56.46 198 |
|
test0.0.03 1 | | | 43.07 181 | 46.95 173 | 38.54 184 | 51.68 135 | 58.77 173 | 35.28 192 | 46.35 83 | 32.05 165 | 12.44 196 | 28.53 180 | 35.52 142 | 14.40 213 | 57.12 165 | 56.93 169 | 71.11 145 | 59.69 185 |
|
anonymousdsp | | | 43.03 182 | 47.19 170 | 38.18 187 | 36.00 210 | 56.92 182 | 38.44 186 | 34.56 191 | 24.22 206 | 22.53 161 | 29.69 176 | 29.92 186 | 35.21 153 | 53.96 182 | 58.98 154 | 62.32 196 | 76.66 106 |
|
USDC | | | 42.80 183 | 45.57 181 | 39.58 178 | 34.55 212 | 51.13 193 | 42.61 176 | 36.21 176 | 39.59 106 | 23.65 156 | 33.13 147 | 20.87 215 | 37.86 145 | 55.35 175 | 57.16 166 | 62.61 194 | 61.75 182 |
|
tfpnview11 | | | 42.71 184 | 45.29 182 | 39.71 177 | 51.06 140 | 58.61 177 | 38.47 185 | 44.80 104 | 30.44 177 | 13.60 190 | 31.25 159 | 30.97 182 | 22.40 194 | 54.20 178 | 55.04 181 | 67.90 166 | 56.51 196 |
|
tfpn_n400 | | | 42.55 185 | 45.11 184 | 39.55 179 | 50.95 142 | 58.68 175 | 38.40 188 | 44.75 106 | 29.29 182 | 13.60 190 | 31.25 159 | 30.97 182 | 22.38 195 | 53.96 182 | 55.66 177 | 67.20 173 | 56.00 199 |
|
tfpnconf | | | 42.55 185 | 45.11 184 | 39.55 179 | 50.95 142 | 58.68 175 | 38.40 188 | 44.75 106 | 29.29 182 | 13.60 190 | 31.25 159 | 30.97 182 | 22.38 195 | 53.96 182 | 55.66 177 | 67.20 173 | 56.00 199 |
|
CHOSEN 280x420 | | | 42.39 187 | 47.40 169 | 36.54 191 | 33.56 215 | 39.66 219 | 40.67 181 | 26.88 220 | 34.66 142 | 18.03 184 | 30.09 171 | 45.59 94 | 44.82 125 | 54.46 177 | 54.00 185 | 55.28 213 | 73.32 130 |
|
pmmvs6 | | | 41.90 188 | 44.01 189 | 39.43 181 | 44.45 176 | 58.77 173 | 41.92 178 | 39.22 160 | 21.74 209 | 19.08 178 | 17.40 214 | 31.33 177 | 24.28 193 | 55.94 170 | 56.67 170 | 67.60 169 | 66.24 168 |
|
tfpn1000 | | | 41.76 189 | 45.01 186 | 37.96 188 | 50.95 142 | 58.44 178 | 34.94 194 | 44.09 114 | 30.68 175 | 12.08 197 | 30.14 169 | 31.96 169 | 18.67 201 | 51.96 190 | 53.45 187 | 67.05 175 | 58.40 193 |
|
Anonymous20231206 | | | 40.63 190 | 43.29 191 | 37.53 189 | 48.88 158 | 55.81 186 | 34.99 193 | 44.98 95 | 28.16 186 | 10.16 205 | 17.26 215 | 27.50 196 | 18.28 202 | 54.00 181 | 55.07 180 | 67.85 167 | 65.23 171 |
|
CVMVSNet | | | 38.91 191 | 44.49 188 | 32.40 202 | 34.57 211 | 47.20 205 | 34.81 195 | 34.20 196 | 31.45 170 | 8.95 208 | 38.86 96 | 36.38 132 | 24.30 192 | 47.77 199 | 46.94 212 | 57.59 208 | 62.85 179 |
|
COLMAP_ROB | | 34.79 15 | 38.65 192 | 40.72 194 | 36.23 192 | 36.41 208 | 49.22 201 | 45.51 168 | 27.60 219 | 37.81 115 | 20.54 168 | 23.37 198 | 24.25 208 | 28.11 186 | 51.02 192 | 48.55 202 | 59.22 203 | 50.82 212 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PEN-MVS | | | 38.23 193 | 41.72 193 | 34.15 195 | 40.56 191 | 50.07 197 | 33.17 201 | 44.35 112 | 27.64 191 | 5.54 221 | 30.84 164 | 26.67 198 | 14.99 210 | 45.64 202 | 52.38 192 | 66.29 177 | 58.83 188 |
|
LP | | | 38.21 194 | 37.72 204 | 38.79 183 | 44.07 182 | 51.16 192 | 35.54 191 | 31.37 212 | 25.38 204 | 23.73 155 | 18.64 208 | 18.03 219 | 29.31 181 | 47.85 198 | 52.63 190 | 68.71 162 | 50.34 214 |
|
WR-MVS | | | 37.61 195 | 42.15 192 | 32.31 204 | 43.64 183 | 51.85 190 | 29.39 210 | 43.35 123 | 27.65 190 | 4.40 223 | 29.90 173 | 29.80 187 | 10.46 220 | 46.73 201 | 51.98 194 | 62.60 195 | 57.16 194 |
|
TinyColmap | | | 37.18 196 | 37.37 208 | 36.95 190 | 31.17 219 | 45.21 209 | 39.71 183 | 34.65 189 | 29.83 181 | 20.20 170 | 18.54 209 | 13.72 226 | 38.27 143 | 50.33 194 | 51.57 196 | 57.71 207 | 52.42 208 |
|
CP-MVSNet | | | 37.09 197 | 40.62 195 | 32.99 197 | 37.56 200 | 48.25 202 | 32.75 202 | 43.05 127 | 27.88 188 | 5.93 217 | 31.27 158 | 25.82 203 | 15.09 208 | 43.37 211 | 48.82 200 | 63.54 190 | 58.90 186 |
|
DTE-MVSNet | | | 36.91 198 | 40.44 196 | 32.79 200 | 40.74 189 | 47.55 204 | 30.71 208 | 44.39 109 | 27.03 194 | 4.32 224 | 30.88 162 | 25.99 201 | 12.73 215 | 45.58 203 | 50.80 197 | 63.86 187 | 55.23 203 |
|
PS-CasMVS | | | 36.84 199 | 40.23 199 | 32.89 198 | 37.44 201 | 48.09 203 | 32.68 203 | 42.97 129 | 27.36 192 | 5.89 218 | 30.08 172 | 25.48 205 | 14.96 211 | 43.28 212 | 48.71 201 | 63.39 191 | 58.63 191 |
|
WR-MVS_H | | | 36.29 200 | 40.35 198 | 31.55 207 | 37.80 199 | 49.94 199 | 30.57 209 | 41.11 148 | 26.90 195 | 4.14 225 | 30.72 166 | 28.85 191 | 10.45 221 | 42.47 215 | 47.99 206 | 65.24 180 | 55.54 201 |
|
SixPastTwentyTwo | | | 36.11 201 | 37.80 203 | 34.13 196 | 37.13 204 | 46.72 206 | 34.58 196 | 34.96 187 | 21.20 213 | 11.66 198 | 29.15 179 | 19.88 216 | 29.77 178 | 44.93 204 | 48.34 203 | 56.67 210 | 54.41 205 |
|
test20.03 | | | 36.00 202 | 38.92 200 | 32.60 201 | 45.92 168 | 50.99 194 | 28.05 215 | 43.69 120 | 21.62 211 | 6.03 216 | 17.61 213 | 25.91 202 | 8.34 227 | 51.26 191 | 52.60 191 | 63.58 188 | 52.46 207 |
|
TDRefinement | | | 35.76 203 | 38.23 201 | 32.88 199 | 19.09 231 | 46.04 208 | 43.29 175 | 29.49 214 | 33.49 158 | 19.04 179 | 22.29 202 | 17.82 220 | 29.69 180 | 48.60 197 | 47.24 210 | 56.65 211 | 52.12 210 |
|
LTVRE_ROB | | 32.83 17 | 35.10 204 | 37.46 205 | 32.35 203 | 43.12 185 | 49.99 198 | 28.52 213 | 33.23 204 | 12.73 230 | 8.18 210 | 27.71 183 | 21.34 212 | 32.64 166 | 46.92 200 | 48.11 204 | 48.41 220 | 55.45 202 |
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 |
PM-MVS | | | 34.96 205 | 38.17 202 | 31.22 208 | 22.78 225 | 40.82 215 | 33.56 199 | 23.61 226 | 29.16 184 | 21.43 165 | 28.00 182 | 21.43 211 | 31.90 168 | 44.33 209 | 42.12 219 | 54.07 215 | 61.34 183 |
|
testgi | | | 34.51 206 | 37.42 206 | 31.12 209 | 47.37 165 | 50.34 196 | 24.38 222 | 41.21 146 | 20.32 215 | 5.64 220 | 20.56 203 | 26.55 199 | 8.06 228 | 49.28 195 | 52.65 189 | 60.05 201 | 42.23 222 |
|
MDA-MVSNet-bldmvs | | | 34.31 207 | 34.11 214 | 34.54 194 | 24.73 223 | 49.66 200 | 33.42 200 | 43.03 128 | 21.59 212 | 11.10 202 | 19.81 205 | 12.68 228 | 31.41 170 | 35.59 223 | 48.05 205 | 63.56 189 | 51.39 211 |
|
test2356 | | | 34.09 208 | 36.84 209 | 30.87 210 | 36.25 209 | 43.59 212 | 27.92 216 | 35.44 184 | 21.73 210 | 6.94 213 | 19.31 207 | 18.23 218 | 17.77 203 | 49.28 195 | 51.58 195 | 60.94 200 | 54.17 206 |
|
N_pmnet | | | 34.09 208 | 35.74 211 | 32.17 205 | 37.25 203 | 43.17 213 | 32.26 206 | 35.57 183 | 26.22 198 | 10.60 204 | 20.44 204 | 19.38 217 | 20.20 199 | 44.59 208 | 47.00 211 | 57.13 209 | 49.35 216 |
|
RPSCF | | | 33.61 210 | 40.43 197 | 25.65 217 | 16.00 233 | 32.41 227 | 31.73 207 | 13.33 233 | 50.13 72 | 23.12 157 | 31.56 154 | 40.09 112 | 32.73 165 | 41.14 221 | 37.05 223 | 36.99 228 | 50.63 213 |
|
EU-MVSNet | | | 33.00 211 | 36.49 210 | 28.92 211 | 33.10 216 | 42.86 214 | 29.32 212 | 35.99 179 | 22.94 207 | 5.83 219 | 25.29 193 | 24.43 207 | 15.21 207 | 41.22 220 | 41.65 221 | 54.08 214 | 57.01 195 |
|
Anonymous20231211 | | | 32.12 212 | 32.10 219 | 32.15 206 | 44.26 180 | 46.14 207 | 29.39 210 | 39.72 154 | 14.08 229 | 3.70 228 | 7.94 230 | 13.30 227 | 16.12 206 | 43.07 213 | 47.88 207 | 59.72 202 | 52.28 209 |
|
testpf | | | 31.84 213 | 34.86 213 | 28.32 213 | 48.89 157 | 32.91 226 | 26.53 217 | 25.77 224 | 21.99 208 | 10.05 206 | 23.39 197 | 25.55 204 | 14.07 214 | 39.23 222 | 42.32 218 | 44.58 224 | 58.65 189 |
|
pmmvs3 | | | 31.22 214 | 33.62 215 | 28.43 212 | 22.82 224 | 40.26 218 | 26.40 218 | 22.05 228 | 16.89 224 | 10.99 203 | 14.72 219 | 16.26 221 | 29.70 179 | 44.82 205 | 47.39 209 | 58.61 205 | 54.98 204 |
|
FC-MVSNet-test | | | 30.97 215 | 37.38 207 | 23.49 222 | 37.42 202 | 33.68 225 | 19.43 228 | 39.27 158 | 31.37 171 | 1.67 236 | 38.56 97 | 28.85 191 | 6.06 232 | 41.40 218 | 43.80 217 | 37.10 227 | 44.03 221 |
|
new-patchmatchnet | | | 30.47 216 | 32.80 217 | 27.75 215 | 36.81 206 | 43.98 210 | 24.85 220 | 39.29 157 | 20.52 214 | 4.06 226 | 15.94 218 | 16.05 222 | 9.57 222 | 41.32 219 | 42.05 220 | 51.94 218 | 49.74 215 |
|
MIMVSNet1 | | | 29.60 217 | 33.37 216 | 25.20 221 | 19.52 229 | 43.94 211 | 26.29 219 | 37.92 168 | 19.95 218 | 3.79 227 | 12.64 227 | 21.99 210 | 7.70 230 | 43.83 210 | 46.32 214 | 55.97 212 | 44.92 220 |
|
testus | | | 29.45 218 | 32.20 218 | 26.23 216 | 37.01 205 | 37.90 220 | 17.56 229 | 35.70 182 | 18.23 220 | 3.39 229 | 17.04 216 | 14.78 223 | 11.78 217 | 42.48 214 | 49.38 199 | 51.92 219 | 45.62 219 |
|
1111 | | | 29.41 219 | 30.75 220 | 27.85 214 | 39.46 192 | 37.63 221 | 22.26 223 | 32.15 207 | 17.93 222 | 7.92 211 | 13.48 221 | 20.98 213 | 17.30 204 | 44.76 206 | 46.51 213 | 47.99 221 | 33.96 226 |
|
testmv | | | 27.97 220 | 29.98 221 | 25.62 218 | 32.54 217 | 36.86 223 | 20.53 225 | 33.33 202 | 14.11 227 | 2.64 231 | 12.76 225 | 11.77 229 | 11.07 218 | 42.34 216 | 45.44 215 | 53.60 216 | 46.60 217 |
|
test1235678 | | | 27.96 221 | 29.97 222 | 25.62 218 | 32.54 217 | 36.83 224 | 20.53 225 | 33.33 202 | 14.10 228 | 2.64 231 | 12.75 226 | 11.76 230 | 11.07 218 | 42.34 216 | 45.43 216 | 53.60 216 | 46.59 218 |
|
FPMVS | | | 26.87 222 | 28.19 223 | 25.32 220 | 27.09 221 | 29.49 229 | 32.28 205 | 17.79 230 | 28.09 187 | 11.33 199 | 19.38 206 | 14.69 224 | 20.88 198 | 35.11 224 | 32.82 226 | 42.56 225 | 37.75 224 |
|
PMVS | | 18.18 18 | 21.95 223 | 22.85 224 | 20.90 225 | 21.92 227 | 14.78 232 | 19.95 227 | 17.31 231 | 15.69 225 | 11.32 200 | 13.70 220 | 13.91 225 | 15.02 209 | 34.92 225 | 31.72 227 | 39.85 226 | 35.20 225 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
no-one | | | 21.91 224 | 22.52 227 | 21.20 224 | 25.97 222 | 30.78 228 | 13.29 232 | 32.75 206 | 9.08 233 | 1.84 233 | 6.18 232 | 7.00 235 | 8.03 229 | 25.56 229 | 40.16 222 | 45.29 223 | 38.83 223 |
|
test12356 | | | 20.09 225 | 22.80 225 | 16.93 227 | 22.59 226 | 24.43 230 | 13.32 231 | 25.93 223 | 12.67 231 | 1.58 238 | 11.53 228 | 9.25 232 | 2.29 233 | 33.15 227 | 37.05 223 | 35.85 229 | 31.54 227 |
|
.test1245 | | | 19.53 226 | 19.26 228 | 19.85 226 | 39.46 192 | 37.63 221 | 22.26 223 | 32.15 207 | 17.93 222 | 7.92 211 | 13.48 221 | 20.98 213 | 17.30 204 | 44.76 206 | 0.01 234 | 0.00 238 | 0.03 235 |
|
new_pmnet | | | 19.10 227 | 22.71 226 | 14.89 229 | 10.93 235 | 24.08 231 | 14.22 230 | 13.94 232 | 18.68 219 | 2.93 230 | 12.84 224 | 11.27 231 | 11.94 216 | 30.57 228 | 30.58 228 | 35.38 230 | 30.93 228 |
|
Gipuma | | | 17.16 228 | 17.83 229 | 16.36 228 | 18.76 232 | 12.15 235 | 11.97 233 | 27.78 218 | 17.94 221 | 4.86 222 | 2.53 236 | 2.73 238 | 8.90 225 | 34.32 226 | 36.09 225 | 25.92 231 | 19.06 230 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMMVS2 | | | 12.25 229 | 14.17 230 | 10.00 232 | 11.39 234 | 14.35 233 | 8.21 234 | 19.29 229 | 9.31 232 | 0.19 240 | 7.38 231 | 6.19 236 | 1.10 235 | 19.26 230 | 21.13 230 | 19.85 232 | 21.56 229 |
|
E-PMN | | | 10.66 230 | 8.30 232 | 13.42 230 | 19.91 228 | 7.87 236 | 4.30 237 | 29.47 215 | 8.37 236 | 1.70 235 | 3.67 233 | 1.29 241 | 9.12 224 | 8.98 234 | 13.59 231 | 16.03 233 | 14.30 233 |
|
EMVS | | | 10.15 231 | 7.67 233 | 13.05 231 | 19.22 230 | 7.77 237 | 4.48 235 | 29.34 216 | 8.65 235 | 1.67 236 | 3.55 234 | 1.36 240 | 9.15 223 | 8.15 235 | 11.79 233 | 14.44 234 | 12.43 234 |
|
MVE | | 10.35 19 | 9.76 232 | 11.08 231 | 8.22 233 | 4.43 236 | 13.04 234 | 3.36 238 | 23.57 227 | 5.74 237 | 1.76 234 | 3.09 235 | 1.75 239 | 6.78 231 | 12.78 232 | 23.04 229 | 9.44 235 | 18.09 231 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 0.01 233 | 0.01 234 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 241 | 0.00 237 | 0.01 238 | 0.00 242 | 0.02 237 | 0.00 242 | 0.00 238 | 0.01 236 | 0.01 234 | 0.00 238 | 0.03 235 |
|
test123 | | | 0.01 233 | 0.01 234 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 241 | 0.00 237 | 0.01 238 | 0.00 242 | 0.02 237 | 0.00 242 | 0.01 236 | 0.00 237 | 0.01 234 | 0.00 238 | 0.03 235 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 241 | 0.00 237 | 0.00 240 | 0.00 242 | 0.00 239 | 0.00 242 | 0.00 238 | 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 | 0.00 241 | 0.00 237 | 0.00 240 | 0.00 242 | 0.00 239 | 0.00 242 | 0.00 238 | 0.00 237 | 0.00 237 | 0.00 238 | 0.00 238 |
|
ambc | | | | 35.52 212 | | 38.36 196 | 40.40 217 | 28.38 214 | | 25.20 205 | 14.87 189 | 13.22 223 | 7.54 234 | 19.34 200 | 55.63 171 | 47.79 208 | 47.91 222 | 58.89 187 |
|
MTAPA | | | | | | | | | | | 54.82 10 | | 71.98 18 | | | | | |
|
MTMP | | | | | | | | | | | 50.64 27 | | 68.31 24 | | | | | |
|
Patchmatch-RL test | | | | | | | | 0.69 240 | | | | | | | | | | |
|
tmp_tt | | | | | 4.41 234 | 2.56 237 | 1.81 239 | 2.61 239 | 0.27 235 | 20.12 216 | 9.81 207 | 17.69 212 | 9.04 233 | 1.96 234 | 12.88 231 | 12.11 232 | 9.23 236 | |
|
XVS | | | | | | 62.70 50 | 73.06 54 | 61.80 62 | | | 42.02 65 | | 63.42 37 | | | | 74.68 87 | |
|
X-MVStestdata | | | | | | 62.70 50 | 73.06 54 | 61.80 62 | | | 42.02 65 | | 63.42 37 | | | | 74.68 87 | |
|
abl_6 | | | | | 63.79 18 | 70.80 18 | 81.22 7 | 65.26 51 | 53.25 25 | 77.02 13 | 53.02 19 | 65.14 25 | 73.74 13 | 60.30 15 | | | 80.13 8 | 90.27 7 |
|
mPP-MVS | | | | | | 63.08 47 | | | | | | | 62.34 40 | | | | | |
|
NP-MVS | | | | | | | | | | 72.62 25 | | | | | | | | |
|
Patchmtry | | | | | | | 64.49 125 | 52.06 136 | 34.21 194 | | 31.74 116 | | | | | | | |
|
DeepMVS_CX | | | | | | | 5.87 238 | 4.32 236 | 1.74 234 | 9.04 234 | 1.30 239 | 7.97 229 | 3.16 237 | 8.56 226 | 9.74 233 | | 6.30 237 | 14.51 232 |
|