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