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 |
|
CHOSEN 1792x2688 | | | 62.48 50 | 64.06 54 | 60.64 36 | 72.50 10 | 84.18 1 | 62.43 62 | 53.77 23 | 47.90 81 | 39.85 72 | 25.15 196 | 44.76 98 | 53.72 49 | 77.29 2 | 77.61 1 | 81.60 4 | 91.53 5 |
|
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 4 | 75.88 3 | 75.75 4 | 80.32 8 | 92.93 2 |
|
HSP-MVS | | | 74.54 3 | 81.12 2 | 66.86 6 | 71.93 13 | 78.65 19 | 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 44 | 79.45 93 |
|
DELS-MVS | | | 67.36 19 | 70.34 34 | 63.89 17 | 69.12 26 | 81.55 6 | 70.82 20 | 55.02 17 | 53.38 66 | 48.83 33 | 56.45 40 | 59.35 51 | 60.05 19 | 74.93 5 | 74.78 6 | 79.51 14 | 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 |
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 12 | 74.88 6 | 74.53 7 | 77.96 32 | 89.72 9 |
|
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 13 | 74.28 7 | 74.11 8 | 78.06 30 | 92.67 3 |
|
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 37 | 74.24 8 | 77.18 3 | 79.56 13 | 89.13 14 |
|
HPM-MVS++ | | | 72.44 8 | 78.73 5 | 65.11 9 | 71.88 14 | 77.31 27 | 71.98 15 | 55.67 11 | 83.11 7 | 53.59 16 | 75.90 8 | 78.49 3 | 61.00 11 | 73.99 9 | 73.31 12 | 76.55 47 | 88.97 15 |
|
NCCC | | | 71.36 11 | 75.44 13 | 66.60 7 | 72.46 11 | 79.18 15 | 74.16 7 | 57.83 6 | 76.93 14 | 54.19 14 | 63.47 27 | 71.08 20 | 61.30 10 | 73.56 10 | 73.70 10 | 79.69 12 | 90.19 8 |
|
ACMMP_Plus | | | 71.50 10 | 77.27 10 | 64.77 12 | 69.64 22 | 79.26 13 | 73.53 9 | 54.73 19 | 79.32 11 | 54.23 13 | 74.81 9 | 74.61 12 | 59.40 20 | 73.00 11 | 72.17 15 | 77.10 43 | 87.72 23 |
|
gm-plane-assit | | | 45.41 167 | 48.03 170 | 42.34 168 | 56.49 84 | 40.48 219 | 24.54 224 | 34.15 201 | 14.44 228 | 6.59 218 | 17.82 213 | 35.32 146 | 49.82 78 | 72.93 12 | 74.11 8 | 82.47 2 | 81.12 85 |
|
HQP-MVS | | | 67.22 22 | 72.08 22 | 61.56 31 | 66.76 31 | 73.58 51 | 71.41 17 | 52.98 26 | 69.92 32 | 43.85 53 | 70.58 15 | 58.75 53 | 56.76 34 | 72.90 13 | 71.88 17 | 77.57 36 | 86.94 28 |
|
MS-PatchMatch | | | 61.41 56 | 61.88 65 | 60.85 34 | 70.57 19 | 75.98 34 | 66.29 45 | 46.91 78 | 50.56 72 | 48.28 36 | 36.30 112 | 51.64 66 | 50.95 70 | 72.89 14 | 70.65 31 | 82.13 3 | 75.17 124 |
|
DeepPCF-MVS | | 62.48 1 | 70.07 14 | 78.36 7 | 60.39 39 | 62.38 56 | 76.96 30 | 65.54 50 | 52.23 28 | 87.46 2 | 49.07 30 | 74.05 11 | 76.19 7 | 59.01 23 | 72.79 15 | 71.61 20 | 74.13 109 | 89.49 11 |
|
CLD-MVS | | | 64.69 37 | 67.25 40 | 61.69 29 | 68.22 30 | 78.33 21 | 63.09 58 | 47.59 65 | 69.64 33 | 53.98 15 | 54.87 45 | 53.94 63 | 57.87 27 | 72.79 15 | 71.34 23 | 79.40 15 | 69.87 161 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
SteuartSystems-ACMMP | | | 69.78 15 | 74.76 15 | 63.98 14 | 73.45 7 | 78.56 20 | 73.13 11 | 55.24 16 | 70.68 29 | 48.93 32 | 70.43 16 | 69.10 22 | 54.00 48 | 72.78 17 | 72.98 13 | 79.14 17 | 88.74 17 |
Skip Steuart: Steuart Systems R&D Blog. |
MAR-MVS | | | 66.85 26 | 69.81 35 | 63.39 19 | 73.56 6 | 80.51 9 | 69.87 24 | 51.51 32 | 67.78 39 | 46.44 39 | 51.09 55 | 61.60 46 | 60.38 15 | 72.67 18 | 73.61 11 | 78.59 21 | 81.44 78 |
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 |
casdiffmvs | | | 67.18 24 | 71.12 29 | 62.59 23 | 66.68 32 | 79.98 12 | 69.84 25 | 48.00 60 | 66.52 41 | 46.21 42 | 56.34 41 | 62.22 41 | 64.38 3 | 72.59 19 | 71.96 16 | 80.44 7 | 88.23 20 |
|
QAPM | | | 65.47 34 | 67.82 39 | 62.72 22 | 72.56 9 | 81.17 8 | 67.43 37 | 55.38 15 | 56.07 62 | 43.29 55 | 43.60 74 | 65.38 30 | 59.10 22 | 72.20 20 | 70.76 30 | 78.56 22 | 85.59 38 |
|
DeepC-MVS | | 60.65 2 | 67.33 21 | 71.52 27 | 62.44 24 | 59.79 71 | 74.84 42 | 68.89 29 | 55.56 12 | 73.91 22 | 53.50 17 | 55.00 44 | 65.63 28 | 60.08 18 | 71.99 21 | 71.33 24 | 76.85 45 | 87.94 22 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
train_agg | | | 70.74 12 | 76.53 12 | 63.98 14 | 70.33 21 | 75.16 40 | 72.33 14 | 55.78 10 | 75.74 17 | 50.41 29 | 80.08 7 | 73.15 16 | 57.75 29 | 71.96 22 | 70.94 28 | 77.25 42 | 88.69 18 |
|
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 14 | 71.85 23 | 71.76 18 | 78.47 23 | 91.49 6 |
|
DeepC-MVS_fast | | 60.18 3 | 66.84 27 | 70.69 32 | 62.36 25 | 62.76 50 | 73.21 55 | 67.96 32 | 52.31 27 | 72.26 26 | 51.03 23 | 56.50 39 | 64.26 33 | 63.37 5 | 71.64 24 | 70.85 29 | 76.70 46 | 86.10 34 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
3Dnovator | | 58.39 4 | 65.97 31 | 66.85 44 | 64.94 10 | 73.72 5 | 79.03 16 | 67.73 34 | 54.25 20 | 61.52 49 | 52.79 22 | 42.27 77 | 60.73 49 | 62.01 6 | 71.29 25 | 71.75 19 | 79.12 18 | 81.34 82 |
|
MVS_0304 | | | 66.31 29 | 71.61 26 | 60.14 40 | 62.59 54 | 78.98 17 | 67.13 39 | 45.75 89 | 64.35 46 | 45.23 48 | 60.69 33 | 67.67 25 | 61.73 7 | 71.09 26 | 71.03 26 | 78.41 26 | 87.44 25 |
|
PHI-MVS | | | 65.17 36 | 72.07 23 | 57.11 60 | 63.02 49 | 77.35 26 | 67.04 40 | 48.14 59 | 68.03 38 | 37.56 79 | 66.00 22 | 65.39 29 | 53.19 56 | 70.68 27 | 70.57 32 | 73.72 115 | 86.46 32 |
|
MVS_111021_HR | | | 64.66 38 | 67.11 43 | 61.80 28 | 71.04 16 | 77.91 23 | 62.75 61 | 54.78 18 | 51.43 70 | 47.54 38 | 53.77 48 | 54.85 60 | 56.84 32 | 70.59 28 | 71.50 21 | 77.86 33 | 89.70 10 |
|
gg-mvs-nofinetune | | | 50.82 133 | 55.83 96 | 44.97 148 | 60.63 66 | 75.69 38 | 53.40 127 | 34.48 194 | 20.05 219 | 6.93 217 | 18.27 212 | 52.70 64 | 33.57 161 | 70.50 29 | 72.93 14 | 80.84 6 | 80.68 88 |
|
HFP-MVS | | | 68.75 16 | 72.84 17 | 63.98 14 | 68.87 28 | 75.09 41 | 71.87 16 | 51.22 33 | 73.50 23 | 58.17 4 | 68.05 19 | 68.67 23 | 57.79 28 | 70.49 30 | 69.23 39 | 75.98 56 | 84.84 42 |
|
MVS_Test | | | 63.75 41 | 67.24 41 | 59.68 43 | 60.01 68 | 76.99 29 | 68.13 31 | 45.17 94 | 57.45 56 | 43.74 54 | 53.07 49 | 56.16 59 | 61.33 9 | 70.27 31 | 71.11 25 | 79.72 11 | 85.63 37 |
|
DI_MVS_plusplus_trai | | | 61.86 53 | 65.26 49 | 57.90 54 | 57.93 79 | 74.51 43 | 66.30 44 | 46.49 84 | 49.96 74 | 41.62 69 | 42.69 75 | 61.77 43 | 58.74 24 | 70.25 32 | 69.32 38 | 76.31 50 | 88.30 19 |
|
OPM-MVS | | | 61.59 55 | 62.30 63 | 60.76 35 | 66.53 33 | 73.35 53 | 71.41 17 | 54.18 21 | 40.82 104 | 41.57 70 | 45.70 69 | 54.84 61 | 54.43 47 | 69.92 33 | 69.19 40 | 76.45 48 | 82.25 58 |
|
CDPH-MVS | | | 67.03 25 | 71.64 25 | 61.65 30 | 69.10 27 | 76.84 31 | 71.35 19 | 55.42 14 | 67.02 40 | 42.83 57 | 65.27 24 | 64.60 32 | 53.16 57 | 69.70 34 | 71.40 22 | 78.02 31 | 86.67 29 |
|
APD-MVS | | | 71.86 9 | 77.91 8 | 64.80 11 | 70.39 20 | 75.69 38 | 74.02 8 | 56.14 9 | 83.59 6 | 52.92 21 | 84.67 4 | 73.46 14 | 59.30 21 | 69.47 35 | 69.66 36 | 76.02 55 | 88.84 16 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
PVSNet_BlendedMVS | | | 62.53 47 | 66.37 46 | 58.05 50 | 58.17 76 | 75.70 36 | 61.30 66 | 48.67 52 | 58.67 53 | 50.93 24 | 55.43 42 | 49.39 76 | 53.01 59 | 69.46 36 | 66.55 60 | 76.24 52 | 89.39 12 |
|
PVSNet_Blended | | | 62.53 47 | 66.37 46 | 58.05 50 | 58.17 76 | 75.70 36 | 61.30 66 | 48.67 52 | 58.67 53 | 50.93 24 | 55.43 42 | 49.39 76 | 53.01 59 | 69.46 36 | 66.55 60 | 76.24 52 | 89.39 12 |
|
CANet | | | 67.21 23 | 71.83 24 | 61.83 27 | 64.51 42 | 79.25 14 | 66.72 42 | 48.73 50 | 68.49 37 | 50.63 28 | 61.40 31 | 66.47 26 | 61.44 8 | 69.31 38 | 69.90 33 | 78.94 20 | 88.00 21 |
|
zzz-MVS | | | 67.78 18 | 72.46 20 | 62.33 26 | 66.09 35 | 74.21 44 | 70.05 23 | 51.54 31 | 77.27 12 | 54.61 11 | 60.30 35 | 71.51 19 | 56.73 35 | 69.19 39 | 68.63 47 | 74.96 75 | 86.11 33 |
|
MVSTER | | | 62.51 49 | 67.22 42 | 57.02 61 | 55.05 100 | 69.23 85 | 63.02 59 | 46.88 79 | 61.11 51 | 43.95 52 | 59.20 36 | 58.86 52 | 56.80 33 | 69.13 40 | 70.98 27 | 76.41 49 | 82.04 60 |
|
TSAR-MVS + MP. | | | 70.28 13 | 75.09 14 | 64.66 13 | 69.34 24 | 64.61 120 | 72.60 12 | 56.29 8 | 80.73 8 | 58.36 3 | 84.56 5 | 75.22 10 | 55.37 42 | 69.11 41 | 69.45 37 | 75.97 57 | 81.97 66 |
|
PGM-MVS | | | 65.35 35 | 70.43 33 | 59.43 45 | 65.78 37 | 73.75 48 | 69.41 26 | 48.18 57 | 68.80 36 | 45.37 47 | 65.88 23 | 64.04 34 | 52.68 63 | 68.94 42 | 68.68 46 | 75.18 69 | 82.93 55 |
|
ACMMPR | | | 66.20 30 | 71.51 28 | 60.00 41 | 65.34 39 | 74.04 46 | 69.39 27 | 50.92 35 | 71.97 27 | 46.04 43 | 66.79 21 | 65.68 27 | 53.07 58 | 68.93 43 | 69.12 42 | 75.21 68 | 84.05 49 |
|
EPNet | | | 64.39 39 | 70.93 30 | 56.77 62 | 60.58 67 | 75.77 35 | 59.28 91 | 50.58 39 | 69.93 31 | 40.73 71 | 68.59 18 | 61.60 46 | 53.72 49 | 68.65 44 | 68.07 49 | 75.75 61 | 83.87 51 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
3Dnovator+ | | 55.76 7 | 62.70 45 | 65.10 51 | 59.90 42 | 65.89 36 | 72.15 62 | 62.94 60 | 49.82 44 | 62.77 48 | 49.06 31 | 43.62 73 | 61.47 48 | 58.60 25 | 68.51 45 | 66.75 58 | 73.08 129 | 80.40 90 |
|
MP-MVS | | | 67.34 20 | 73.08 16 | 60.64 36 | 66.20 34 | 76.62 32 | 69.22 28 | 50.92 35 | 70.07 30 | 48.81 34 | 69.66 17 | 70.12 21 | 53.68 51 | 68.41 46 | 69.13 41 | 74.98 74 | 87.53 24 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
HyFIR lowres test | | | 57.12 71 | 59.11 72 | 54.80 69 | 61.55 61 | 77.55 25 | 59.02 92 | 45.00 96 | 41.84 102 | 33.93 101 | 22.44 203 | 49.16 79 | 51.02 69 | 68.39 47 | 68.71 45 | 78.26 28 | 85.70 36 |
|
FMVSNet3 | | | 55.66 76 | 59.68 71 | 50.96 103 | 50.59 148 | 66.49 102 | 57.57 98 | 46.61 80 | 49.30 75 | 28.77 131 | 39.61 89 | 51.42 67 | 43.85 130 | 68.29 48 | 68.80 44 | 78.35 27 | 73.86 128 |
|
OpenMVS | | 55.62 8 | 62.57 46 | 63.76 55 | 61.19 33 | 72.13 12 | 78.84 18 | 64.42 54 | 50.51 41 | 56.44 59 | 45.67 46 | 36.88 105 | 56.51 57 | 56.66 36 | 68.28 49 | 68.96 43 | 77.73 35 | 80.44 89 |
|
X-MVS | | | 63.53 42 | 68.62 37 | 57.60 55 | 64.77 41 | 73.06 56 | 65.82 47 | 50.53 40 | 65.77 43 | 42.02 66 | 58.20 37 | 63.42 37 | 47.83 108 | 68.25 50 | 68.50 48 | 74.61 91 | 83.16 54 |
|
PVSNet_Blended_VisFu | | | 58.56 64 | 62.33 62 | 54.16 70 | 56.90 81 | 73.92 47 | 57.72 97 | 46.16 87 | 44.23 88 | 42.73 60 | 46.26 66 | 51.06 70 | 46.28 116 | 67.99 51 | 65.38 69 | 75.18 69 | 87.44 25 |
|
canonicalmvs | | | 65.55 33 | 70.75 31 | 59.49 44 | 62.11 58 | 78.26 22 | 66.52 43 | 43.82 120 | 71.54 28 | 47.84 37 | 61.30 32 | 61.68 44 | 58.48 26 | 67.56 52 | 69.67 35 | 78.16 29 | 85.25 40 |
|
Effi-MVS+ | | | 59.63 62 | 61.78 68 | 57.12 59 | 61.56 60 | 71.63 64 | 63.61 57 | 47.59 65 | 47.18 82 | 37.79 76 | 45.29 70 | 49.93 74 | 56.27 38 | 67.45 53 | 67.06 56 | 75.91 58 | 83.93 50 |
|
GA-MVS | | | 53.77 94 | 56.41 93 | 50.70 105 | 51.63 139 | 69.96 72 | 57.55 99 | 44.39 111 | 34.31 147 | 27.15 138 | 40.99 82 | 36.40 132 | 47.65 110 | 67.45 53 | 67.16 55 | 75.83 60 | 78.60 98 |
|
AdaColmap | | | 62.79 44 | 62.63 59 | 62.98 21 | 70.82 17 | 72.90 59 | 67.84 33 | 54.09 22 | 65.14 44 | 50.71 26 | 41.78 78 | 47.64 86 | 60.17 17 | 67.41 55 | 66.83 57 | 74.28 104 | 76.69 107 |
|
CP-MVS | | | 64.37 40 | 69.48 36 | 58.39 48 | 62.21 57 | 71.81 63 | 67.27 38 | 49.51 45 | 69.40 35 | 45.76 45 | 60.41 34 | 64.96 31 | 51.84 66 | 67.33 56 | 67.57 53 | 73.78 114 | 84.89 41 |
|
FMVSNet2 | | | 53.94 90 | 57.29 89 | 50.03 114 | 49.36 156 | 65.81 110 | 56.80 105 | 45.95 88 | 43.13 92 | 28.04 136 | 35.68 113 | 48.18 85 | 42.71 133 | 67.23 57 | 67.95 50 | 77.32 39 | 73.03 134 |
|
CostFormer | | | 62.45 51 | 65.68 48 | 58.67 46 | 63.29 46 | 77.65 24 | 67.62 35 | 38.42 167 | 54.04 64 | 46.00 44 | 48.27 63 | 57.89 55 | 56.97 31 | 67.03 58 | 67.79 52 | 79.74 10 | 87.09 27 |
|
ACMMP | | | 63.27 43 | 67.85 38 | 57.93 52 | 62.64 53 | 72.30 61 | 68.23 30 | 48.77 49 | 66.50 42 | 43.05 56 | 62.07 29 | 57.84 56 | 49.98 75 | 66.58 59 | 66.46 63 | 74.93 79 | 83.17 53 |
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 |
Anonymous20240521 | | | 57.48 67 | 58.50 79 | 56.29 65 | 62.43 55 | 73.55 52 | 61.22 68 | 50.86 38 | 45.47 85 | 34.58 92 | 32.14 154 | 42.75 102 | 52.48 64 | 66.50 60 | 67.55 54 | 76.18 54 | 81.54 76 |
|
DWT-MVSNet_training | | | 61.22 57 | 63.52 57 | 58.53 47 | 65.00 40 | 76.55 33 | 59.50 89 | 48.22 56 | 51.79 69 | 42.14 65 | 47.85 64 | 50.21 72 | 55.46 41 | 66.16 61 | 67.92 51 | 80.85 5 | 84.14 48 |
|
PMMVS | | | 55.74 73 | 62.68 58 | 47.64 132 | 44.34 182 | 65.58 113 | 47.22 160 | 37.96 169 | 56.43 60 | 34.11 98 | 61.51 30 | 47.41 88 | 54.55 43 | 65.88 62 | 62.49 119 | 67.67 170 | 79.48 92 |
|
PCF-MVS | | 55.99 6 | 62.31 52 | 66.60 45 | 57.32 58 | 59.12 75 | 73.68 50 | 67.53 36 | 48.71 51 | 61.35 50 | 42.83 57 | 51.33 54 | 63.48 36 | 53.48 55 | 65.64 63 | 64.87 73 | 72.22 136 | 85.83 35 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
SD-MVS | | | 68.30 17 | 72.58 19 | 63.31 20 | 69.24 25 | 67.85 92 | 70.81 21 | 53.65 24 | 79.64 10 | 58.52 2 | 74.31 10 | 75.37 9 | 53.52 54 | 65.63 64 | 63.56 104 | 74.13 109 | 81.73 73 |
|
TSAR-MVS + GP. | | | 66.77 28 | 72.21 21 | 60.44 38 | 61.23 62 | 70.00 71 | 64.26 56 | 47.79 61 | 72.98 24 | 56.32 9 | 71.35 14 | 72.33 17 | 55.68 40 | 65.49 65 | 66.66 59 | 77.35 38 | 86.62 30 |
|
LGP-MVS_train | | | 59.69 61 | 62.59 60 | 56.31 64 | 61.94 59 | 68.15 91 | 66.90 41 | 48.15 58 | 59.75 52 | 38.47 75 | 50.38 57 | 48.34 84 | 46.87 113 | 65.39 66 | 64.93 72 | 75.51 66 | 81.21 84 |
|
TSAR-MVS + ACMM | | | 65.95 32 | 72.83 18 | 57.93 52 | 69.35 23 | 65.85 109 | 73.36 10 | 39.84 155 | 76.00 15 | 48.69 35 | 82.54 6 | 75.03 11 | 49.38 89 | 65.33 67 | 63.42 106 | 66.94 178 | 81.67 74 |
|
Fast-Effi-MVS+ | | | 55.73 74 | 58.26 83 | 52.76 79 | 54.33 105 | 68.19 90 | 57.05 101 | 34.66 190 | 46.92 83 | 38.96 74 | 40.53 83 | 41.55 109 | 55.69 39 | 65.31 68 | 65.99 64 | 75.90 59 | 79.34 94 |
|
tpmp4_e23 | | | 59.70 60 | 61.03 69 | 58.14 49 | 63.70 43 | 73.33 54 | 65.69 48 | 39.53 157 | 52.56 67 | 46.23 41 | 41.59 79 | 47.46 87 | 57.38 30 | 65.01 69 | 65.89 65 | 76.31 50 | 81.36 81 |
|
CANet_DTU | | | 57.87 66 | 63.63 56 | 51.15 101 | 52.18 117 | 70.20 70 | 58.14 96 | 37.32 173 | 56.49 58 | 31.06 122 | 57.38 38 | 50.05 73 | 53.67 52 | 64.98 70 | 65.04 71 | 74.57 92 | 81.29 83 |
|
EG-PatchMatch MVS | | | 50.23 136 | 50.89 151 | 49.47 120 | 59.54 72 | 70.88 65 | 52.46 137 | 44.01 118 | 26.22 200 | 31.91 117 | 24.97 197 | 31.45 176 | 33.48 163 | 64.79 71 | 66.51 62 | 75.40 67 | 71.39 146 |
|
ACMM | | 53.73 9 | 57.91 65 | 58.27 82 | 57.49 56 | 63.10 47 | 66.45 103 | 65.65 49 | 49.02 47 | 53.69 65 | 42.67 62 | 36.41 109 | 46.07 94 | 50.38 72 | 64.74 72 | 64.63 84 | 74.14 108 | 75.91 114 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v1144 | | | 53.47 99 | 54.65 112 | 52.10 91 | 51.93 124 | 69.81 73 | 59.32 90 | 44.77 107 | 33.21 161 | 32.52 114 | 33.55 145 | 34.34 155 | 49.29 90 | 64.58 73 | 64.81 76 | 74.74 84 | 82.27 57 |
|
diffmvs | | | 61.00 58 | 65.21 50 | 56.08 66 | 55.72 97 | 74.18 45 | 64.62 53 | 43.25 126 | 56.71 57 | 42.71 61 | 50.01 58 | 62.20 42 | 54.45 45 | 64.56 74 | 64.30 98 | 75.75 61 | 84.53 46 |
|
v11 | | | 52.23 118 | 52.83 132 | 51.53 99 | 51.73 135 | 62.49 149 | 58.82 93 | 41.81 146 | 33.53 159 | 33.23 111 | 33.73 142 | 35.10 148 | 49.07 92 | 64.49 75 | 64.71 78 | 74.49 93 | 75.75 117 |
|
thres100view900 | | | 52.33 116 | 53.91 118 | 50.48 109 | 56.10 89 | 67.79 93 | 56.18 113 | 49.18 46 | 35.86 137 | 25.22 146 | 34.74 125 | 34.10 156 | 42.41 136 | 64.45 76 | 62.62 116 | 73.81 113 | 77.85 99 |
|
v10 | | | 53.44 100 | 54.40 113 | 52.31 89 | 52.08 122 | 66.99 99 | 59.68 85 | 43.41 123 | 35.90 134 | 34.30 96 | 33.98 139 | 35.56 142 | 50.10 73 | 64.39 77 | 64.67 81 | 74.32 100 | 79.30 95 |
|
v7 | | | 53.99 89 | 55.20 103 | 52.57 84 | 52.08 122 | 69.46 80 | 59.68 85 | 44.92 98 | 35.90 134 | 34.33 95 | 33.99 138 | 35.55 143 | 50.08 74 | 64.38 78 | 64.67 81 | 74.32 100 | 82.47 56 |
|
v1192 | | | 52.69 108 | 53.86 119 | 51.31 100 | 51.22 141 | 69.76 74 | 57.37 100 | 44.39 111 | 32.21 165 | 31.39 121 | 32.41 151 | 32.44 167 | 49.19 91 | 64.25 79 | 64.17 99 | 74.31 103 | 81.81 71 |
|
GBi-Net | | | 54.66 79 | 58.42 80 | 50.26 111 | 49.36 156 | 65.81 110 | 56.80 105 | 46.61 80 | 49.30 75 | 28.77 131 | 39.61 89 | 51.42 67 | 42.71 133 | 64.25 79 | 65.54 66 | 77.32 39 | 73.03 134 |
|
test1 | | | 54.66 79 | 58.42 80 | 50.26 111 | 49.36 156 | 65.81 110 | 56.80 105 | 46.61 80 | 49.30 75 | 28.77 131 | 39.61 89 | 51.42 67 | 42.71 133 | 64.25 79 | 65.54 66 | 77.32 39 | 73.03 134 |
|
FMVSNet1 | | | 50.14 138 | 52.78 134 | 47.06 133 | 45.56 173 | 63.56 134 | 54.22 122 | 43.74 121 | 34.10 150 | 25.37 145 | 29.79 177 | 42.06 107 | 38.70 144 | 64.25 79 | 65.54 66 | 74.75 83 | 70.18 156 |
|
v2v482 | | | 54.00 87 | 55.12 104 | 52.69 82 | 51.73 135 | 69.42 84 | 60.65 72 | 45.09 95 | 34.56 145 | 33.73 105 | 35.29 115 | 35.36 145 | 49.92 76 | 64.05 83 | 65.16 70 | 75.00 73 | 81.98 65 |
|
v1141 | | | 53.78 91 | 55.02 107 | 52.33 87 | 51.89 127 | 69.75 75 | 60.24 79 | 44.81 104 | 34.04 153 | 33.34 109 | 34.57 128 | 36.17 140 | 48.76 95 | 63.90 84 | 64.81 76 | 74.94 76 | 81.87 70 |
|
divwei89l23v2f112 | | | 53.78 91 | 55.02 107 | 52.33 87 | 51.89 127 | 69.74 77 | 60.25 78 | 44.82 103 | 34.06 151 | 33.33 110 | 34.56 131 | 36.18 138 | 48.75 96 | 63.90 84 | 64.82 74 | 74.94 76 | 81.88 68 |
|
v1 | | | 53.78 91 | 55.02 107 | 52.34 86 | 51.89 127 | 69.75 75 | 60.24 79 | 44.81 104 | 34.05 152 | 33.39 108 | 34.57 128 | 36.18 138 | 48.75 96 | 63.90 84 | 64.82 74 | 74.94 76 | 81.88 68 |
|
GG-mvs-BLEND | | | 44.87 171 | 64.59 52 | 21.86 225 | 0.01 241 | 73.70 49 | 55.99 114 | 0.01 238 | 50.70 71 | 0.01 243 | 49.18 59 | 63.61 35 | 0.01 238 | 63.83 87 | 64.50 94 | 75.13 71 | 86.62 30 |
|
v15 | | | 52.60 110 | 53.17 125 | 51.94 92 | 51.92 125 | 62.76 143 | 60.04 82 | 42.19 140 | 34.35 146 | 33.96 100 | 34.37 133 | 36.40 132 | 48.73 98 | 63.75 88 | 64.59 85 | 74.79 80 | 75.97 113 |
|
v16 | | | 53.50 98 | 54.17 115 | 52.73 81 | 52.24 115 | 62.90 141 | 60.67 71 | 42.67 137 | 38.72 112 | 36.70 82 | 34.84 124 | 37.59 122 | 49.69 82 | 63.72 89 | 64.68 79 | 74.39 98 | 76.72 105 |
|
V14 | | | 52.51 112 | 53.06 127 | 51.87 93 | 51.91 126 | 62.70 145 | 59.93 83 | 42.13 141 | 34.16 149 | 33.88 102 | 34.22 135 | 36.35 135 | 48.62 99 | 63.72 89 | 64.59 85 | 74.77 81 | 75.69 121 |
|
v17 | | | 53.40 101 | 54.09 116 | 52.60 83 | 52.22 116 | 62.90 141 | 60.64 73 | 42.66 138 | 38.24 115 | 36.04 84 | 34.85 123 | 37.59 122 | 49.63 83 | 63.70 91 | 64.68 79 | 74.39 98 | 76.70 106 |
|
v1neww | | | 54.32 83 | 55.60 100 | 52.81 76 | 52.11 119 | 69.43 82 | 60.57 74 | 44.86 99 | 37.13 128 | 35.34 88 | 34.95 121 | 37.46 126 | 49.53 85 | 63.69 92 | 64.59 85 | 74.47 95 | 81.99 62 |
|
v7new | | | 54.32 83 | 55.60 100 | 52.81 76 | 52.11 119 | 69.43 82 | 60.57 74 | 44.86 99 | 37.13 128 | 35.34 88 | 34.95 121 | 37.46 126 | 49.53 85 | 63.69 92 | 64.59 85 | 74.47 95 | 81.99 62 |
|
v18 | | | 53.63 96 | 54.35 114 | 52.80 78 | 52.25 114 | 62.94 140 | 60.80 69 | 42.78 134 | 39.23 109 | 36.81 81 | 35.07 118 | 37.78 119 | 49.82 78 | 63.69 92 | 64.65 83 | 74.32 100 | 77.07 103 |
|
v6 | | | 54.32 83 | 55.61 99 | 52.82 75 | 52.11 119 | 69.44 81 | 60.57 74 | 44.86 99 | 37.15 127 | 35.40 86 | 34.96 119 | 37.47 125 | 49.52 87 | 63.68 95 | 64.59 85 | 74.47 95 | 81.99 62 |
|
V9 | | | 52.42 115 | 52.94 130 | 51.81 94 | 51.89 127 | 62.64 147 | 59.81 84 | 42.08 143 | 33.97 154 | 33.85 103 | 34.05 137 | 36.28 136 | 48.49 101 | 63.68 95 | 64.59 85 | 74.76 82 | 75.41 122 |
|
v8 | | | 53.77 94 | 54.82 111 | 52.54 85 | 52.12 118 | 66.95 101 | 60.56 77 | 43.23 127 | 37.17 126 | 35.35 87 | 34.96 119 | 37.50 124 | 49.51 88 | 63.67 97 | 64.59 85 | 74.48 94 | 78.91 97 |
|
v12 | | | 52.31 117 | 52.81 133 | 51.72 96 | 51.87 131 | 62.56 148 | 59.66 87 | 42.02 144 | 33.77 156 | 33.70 106 | 33.88 140 | 36.20 137 | 48.36 102 | 63.64 98 | 64.56 92 | 74.73 87 | 75.08 125 |
|
ACMP | | 56.21 5 | 59.78 59 | 61.81 67 | 57.41 57 | 61.15 63 | 68.88 87 | 65.98 46 | 48.85 48 | 58.56 55 | 44.19 51 | 48.89 60 | 46.31 92 | 48.56 100 | 63.61 99 | 64.49 95 | 75.75 61 | 81.91 67 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v13 | | | 52.22 119 | 52.69 135 | 51.67 97 | 51.85 133 | 62.48 150 | 59.57 88 | 41.97 145 | 33.61 158 | 33.69 107 | 33.71 143 | 36.11 141 | 48.23 105 | 63.57 100 | 64.55 93 | 74.72 88 | 74.77 126 |
|
NR-MVSNet | | | 48.84 147 | 51.76 144 | 45.44 144 | 57.66 80 | 60.64 163 | 47.39 157 | 47.63 63 | 37.26 122 | 13.31 197 | 37.31 102 | 29.64 191 | 33.53 162 | 63.52 101 | 62.09 124 | 73.10 128 | 71.89 142 |
|
V42 | | | 52.63 109 | 55.08 105 | 49.76 119 | 44.93 177 | 67.49 97 | 60.19 81 | 42.13 141 | 37.21 125 | 34.08 99 | 34.57 128 | 37.30 128 | 47.29 111 | 63.48 102 | 64.15 100 | 69.96 158 | 81.38 80 |
|
IS_MVSNet | | | 51.53 124 | 57.98 86 | 44.01 155 | 55.96 94 | 66.16 105 | 47.65 156 | 42.84 133 | 39.82 107 | 19.09 180 | 44.97 71 | 50.28 71 | 27.20 189 | 63.43 103 | 63.84 101 | 71.33 146 | 77.33 101 |
|
v1921920 | | | 51.95 120 | 53.19 124 | 50.51 108 | 50.82 147 | 69.14 86 | 55.45 115 | 44.34 115 | 31.53 171 | 30.53 125 | 31.96 155 | 31.67 174 | 48.31 104 | 63.12 104 | 63.28 107 | 73.59 116 | 81.60 75 |
|
TransMVSNet (Re) | | | 47.46 155 | 48.94 166 | 45.74 142 | 57.96 78 | 64.29 131 | 48.26 151 | 48.47 54 | 26.33 199 | 19.33 177 | 29.45 180 | 31.28 181 | 25.31 193 | 63.05 105 | 62.70 112 | 75.10 72 | 65.47 172 |
|
v144192 | | | 52.43 114 | 53.63 121 | 51.03 102 | 51.06 142 | 69.60 78 | 56.94 103 | 44.84 102 | 32.15 166 | 30.88 123 | 32.45 150 | 32.71 163 | 48.36 102 | 62.98 106 | 63.52 105 | 74.10 112 | 82.02 61 |
|
test-LLR | | | 54.62 81 | 58.66 77 | 49.89 117 | 51.68 137 | 65.89 107 | 47.88 154 | 46.35 85 | 42.51 94 | 29.84 128 | 41.41 80 | 48.87 80 | 45.20 121 | 62.91 107 | 64.43 96 | 78.43 24 | 84.62 44 |
|
TESTMET0.1,1 | | | 53.30 104 | 58.66 77 | 47.04 134 | 44.94 176 | 65.89 107 | 47.88 154 | 35.95 182 | 42.51 94 | 29.84 128 | 41.41 80 | 48.87 80 | 45.20 121 | 62.91 107 | 64.43 96 | 78.43 24 | 84.62 44 |
|
Fast-Effi-MVS+-dtu | | | 52.47 113 | 55.89 95 | 48.48 129 | 56.25 87 | 65.07 116 | 58.75 94 | 23.79 227 | 41.27 103 | 27.07 140 | 37.95 99 | 41.34 112 | 50.85 71 | 62.90 109 | 62.34 121 | 74.17 107 | 80.37 91 |
|
thres400 | | | 50.39 135 | 52.22 142 | 48.26 130 | 55.02 101 | 66.32 104 | 52.97 135 | 48.33 55 | 32.68 163 | 22.94 161 | 33.21 147 | 33.38 162 | 37.27 148 | 62.74 110 | 61.38 130 | 73.04 130 | 75.81 115 |
|
v1240 | | | 51.42 125 | 52.66 137 | 49.97 115 | 50.31 150 | 68.70 89 | 54.05 124 | 43.85 119 | 30.78 176 | 30.22 126 | 31.43 159 | 31.03 183 | 47.98 106 | 62.62 111 | 63.16 109 | 73.40 121 | 80.93 86 |
|
tpmrst | | | 57.23 70 | 59.08 73 | 55.06 68 | 59.91 69 | 70.65 68 | 60.71 70 | 35.38 188 | 47.91 80 | 42.58 63 | 39.78 88 | 45.45 96 | 54.44 46 | 62.19 112 | 62.82 111 | 77.37 37 | 84.73 43 |
|
MSDG | | | 52.58 111 | 51.40 148 | 53.95 72 | 65.48 38 | 64.31 130 | 61.44 65 | 44.02 117 | 44.17 89 | 32.92 113 | 30.40 170 | 31.81 173 | 46.35 115 | 62.13 113 | 62.55 117 | 73.49 119 | 64.41 175 |
|
Vis-MVSNet | | | 51.13 128 | 58.04 85 | 43.06 164 | 47.68 164 | 67.71 94 | 49.10 149 | 39.09 163 | 37.75 118 | 22.57 163 | 51.03 56 | 48.78 82 | 32.42 169 | 62.12 114 | 61.80 125 | 67.49 172 | 77.12 102 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MSLP-MVS++ | | | 61.81 54 | 62.19 64 | 61.37 32 | 68.33 29 | 63.08 138 | 70.75 22 | 38.89 164 | 63.96 47 | 57.51 5 | 48.59 61 | 61.66 45 | 53.67 52 | 62.04 115 | 59.92 151 | 79.03 19 | 76.08 110 |
|
MIMVSNet | | | 45.62 166 | 49.56 161 | 41.02 176 | 38.17 200 | 64.43 128 | 49.48 145 | 35.43 187 | 36.53 130 | 20.06 175 | 22.58 202 | 35.16 147 | 28.75 187 | 61.97 116 | 62.20 122 | 74.20 105 | 64.07 177 |
|
CPTT-MVS | | | 59.54 63 | 64.47 53 | 53.79 73 | 54.99 102 | 67.63 95 | 65.48 51 | 44.59 110 | 64.81 45 | 37.74 77 | 51.55 52 | 59.90 50 | 49.77 80 | 61.83 117 | 61.26 136 | 70.18 155 | 84.31 47 |
|
IterMVS-LS | | | 53.36 103 | 55.65 97 | 50.68 107 | 55.34 98 | 59.04 173 | 55.00 119 | 39.98 154 | 38.72 112 | 33.22 112 | 44.52 72 | 47.05 90 | 49.63 83 | 61.82 118 | 61.77 126 | 70.92 149 | 76.61 109 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 49.25 143 | 53.97 117 | 43.75 157 | 47.53 167 | 64.53 123 | 48.59 150 | 42.27 139 | 33.77 156 | 26.64 141 | 40.46 85 | 42.26 106 | 30.01 177 | 61.77 119 | 61.71 127 | 67.48 173 | 73.28 133 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
FC-MVSNet-train | | | 55.68 75 | 57.00 91 | 54.13 71 | 63.37 44 | 66.16 105 | 46.77 163 | 52.14 29 | 42.36 97 | 37.67 78 | 48.50 62 | 41.42 111 | 51.28 68 | 61.58 120 | 63.22 108 | 73.56 117 | 75.76 116 |
|
tpm | | | 54.94 78 | 57.86 88 | 51.54 98 | 59.48 73 | 67.04 98 | 58.34 95 | 34.60 192 | 41.93 101 | 34.41 94 | 42.40 76 | 47.14 89 | 49.07 92 | 61.46 121 | 61.67 129 | 73.31 124 | 83.39 52 |
|
MVS_111021_LR | | | 57.06 72 | 60.60 70 | 52.93 74 | 56.25 87 | 65.14 115 | 55.16 118 | 41.21 148 | 52.32 68 | 44.89 49 | 53.92 47 | 49.27 78 | 52.16 65 | 61.46 121 | 60.54 145 | 67.92 167 | 81.53 77 |
|
tpm cat1 | | | 57.41 68 | 58.26 83 | 56.42 63 | 60.80 65 | 72.56 60 | 64.35 55 | 38.43 166 | 49.18 78 | 46.36 40 | 36.69 107 | 43.50 100 | 54.47 44 | 61.39 123 | 62.64 115 | 74.11 111 | 81.81 71 |
|
EPP-MVSNet | | | 52.91 106 | 58.91 75 | 45.91 140 | 54.99 102 | 68.84 88 | 49.27 146 | 42.71 135 | 37.53 120 | 20.20 173 | 46.09 67 | 56.19 58 | 36.90 150 | 61.37 124 | 60.90 140 | 71.41 145 | 81.41 79 |
|
TSAR-MVS + COLMAP | | | 54.37 82 | 62.43 61 | 44.98 147 | 34.33 216 | 58.94 175 | 54.11 123 | 34.15 201 | 74.06 21 | 34.57 93 | 71.63 13 | 42.03 108 | 47.88 107 | 61.26 125 | 57.33 167 | 64.83 186 | 71.74 144 |
|
EPMVS | | | 54.07 86 | 56.06 94 | 51.75 95 | 56.74 83 | 70.80 66 | 55.32 116 | 34.20 198 | 46.46 84 | 36.59 83 | 40.38 86 | 42.55 103 | 49.77 80 | 61.25 126 | 60.90 140 | 77.86 33 | 70.08 157 |
|
view600 | | | 48.85 146 | 50.58 155 | 46.82 135 | 54.19 106 | 64.94 117 | 50.81 141 | 47.53 68 | 31.78 169 | 21.59 167 | 32.31 152 | 32.63 165 | 34.28 158 | 61.06 127 | 57.41 165 | 72.54 133 | 73.96 127 |
|
v148 | | | 51.72 121 | 53.15 126 | 50.05 113 | 50.15 151 | 67.51 96 | 56.98 102 | 42.85 132 | 32.60 164 | 32.41 116 | 33.88 140 | 34.71 152 | 44.45 129 | 61.06 127 | 63.00 110 | 73.45 120 | 79.24 96 |
|
conf0.002 | | | 51.63 123 | 53.79 120 | 49.12 123 | 56.33 85 | 64.84 118 | 53.05 131 | 47.38 69 | 35.86 137 | 24.83 148 | 37.86 100 | 38.15 117 | 41.08 138 | 61.04 129 | 62.70 112 | 72.05 137 | 76.06 111 |
|
conf0.01 | | | 51.32 126 | 53.22 123 | 49.11 124 | 56.29 86 | 64.78 119 | 53.05 131 | 47.37 70 | 35.86 137 | 24.83 148 | 36.85 106 | 36.64 131 | 41.08 138 | 61.01 130 | 61.37 131 | 72.03 138 | 76.01 112 |
|
ACMH | | 47.82 13 | 50.10 139 | 49.60 160 | 50.69 106 | 63.36 45 | 66.99 99 | 56.83 104 | 52.13 30 | 31.06 174 | 17.74 188 | 28.22 183 | 26.24 202 | 45.17 123 | 60.88 131 | 63.80 102 | 68.91 163 | 70.00 159 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tfpn111 | | | 51.00 130 | 52.68 136 | 49.04 126 | 56.10 89 | 64.52 125 | 53.05 131 | 47.31 73 | 35.86 137 | 24.79 151 | 36.35 110 | 34.10 156 | 41.08 138 | 60.84 132 | 61.37 131 | 71.90 141 | 75.70 118 |
|
conf200view11 | | | 50.87 132 | 52.45 140 | 49.04 126 | 56.10 89 | 64.52 125 | 53.05 131 | 47.31 73 | 35.86 137 | 24.79 151 | 34.74 125 | 34.10 156 | 41.08 138 | 60.84 132 | 61.37 131 | 71.90 141 | 75.70 118 |
|
tfpn200view9 | | | 50.91 131 | 52.45 140 | 49.11 124 | 56.10 89 | 64.53 123 | 53.06 130 | 47.31 73 | 35.86 137 | 25.22 146 | 34.74 125 | 34.10 156 | 41.08 138 | 60.84 132 | 61.37 131 | 71.90 141 | 75.70 118 |
|
thres600view7 | | | 48.44 149 | 50.23 156 | 46.35 138 | 54.05 108 | 64.60 121 | 50.18 144 | 47.34 71 | 31.73 170 | 20.74 170 | 32.28 153 | 32.62 166 | 33.79 160 | 60.84 132 | 56.11 175 | 71.99 139 | 73.40 131 |
|
thres200 | | | 50.76 134 | 52.52 138 | 48.70 128 | 55.98 93 | 64.60 121 | 55.29 117 | 47.34 71 | 33.91 155 | 24.36 155 | 34.33 134 | 33.90 160 | 37.27 148 | 60.84 132 | 62.41 120 | 71.99 139 | 77.63 100 |
|
dps | | | 52.84 107 | 52.92 131 | 52.74 80 | 59.89 70 | 69.49 79 | 54.47 121 | 37.38 172 | 42.49 96 | 39.53 73 | 35.33 114 | 32.71 163 | 51.83 67 | 60.45 137 | 61.12 137 | 73.33 123 | 68.86 165 |
|
test-mter | | | 48.31 150 | 55.04 106 | 40.45 177 | 34.12 217 | 59.02 174 | 41.77 182 | 28.05 219 | 38.43 114 | 22.67 162 | 39.35 96 | 44.40 99 | 41.88 137 | 60.30 138 | 61.68 128 | 74.20 105 | 82.12 59 |
|
PatchmatchNet | | | 53.37 102 | 55.62 98 | 50.75 104 | 55.93 95 | 70.54 69 | 51.39 140 | 36.41 177 | 44.85 86 | 37.26 80 | 39.40 95 | 42.54 104 | 47.83 108 | 60.29 139 | 60.88 142 | 75.69 64 | 70.87 151 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tfpnnormal | | | 46.61 163 | 46.82 176 | 46.37 137 | 52.70 112 | 62.31 151 | 50.39 143 | 47.17 76 | 25.74 203 | 21.80 166 | 23.13 201 | 24.15 211 | 33.45 164 | 60.28 140 | 60.77 143 | 72.70 132 | 71.39 146 |
|
OMC-MVS | | | 55.48 77 | 61.85 66 | 48.04 131 | 41.55 191 | 60.32 167 | 56.80 105 | 31.78 213 | 75.67 18 | 42.30 64 | 51.52 53 | 54.15 62 | 49.91 77 | 60.28 140 | 57.59 162 | 65.91 181 | 73.42 130 |
|
IterMVS | | | 50.23 136 | 53.27 122 | 46.68 136 | 47.59 166 | 60.58 165 | 53.10 129 | 36.62 176 | 36.07 133 | 25.89 143 | 39.42 94 | 40.05 115 | 43.65 131 | 60.22 142 | 61.35 135 | 73.23 125 | 75.23 123 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
pmmvs4 | | | 51.28 127 | 52.50 139 | 49.85 118 | 49.54 155 | 63.02 139 | 52.83 136 | 43.41 123 | 44.65 87 | 35.71 85 | 34.38 132 | 32.25 168 | 45.14 124 | 60.21 143 | 60.03 149 | 72.44 135 | 72.98 137 |
|
MDTV_nov1_ep13 | | | 52.99 105 | 55.59 102 | 49.95 116 | 54.08 107 | 70.69 67 | 56.47 110 | 38.42 167 | 42.78 93 | 30.19 127 | 39.56 92 | 43.31 101 | 45.78 118 | 60.07 144 | 62.11 123 | 74.74 84 | 70.62 152 |
|
pm-mvs1 | | | 46.14 165 | 49.34 164 | 42.41 167 | 48.93 159 | 62.22 152 | 44.98 173 | 42.68 136 | 27.66 191 | 20.76 169 | 29.88 176 | 34.96 149 | 26.41 191 | 60.03 145 | 60.42 146 | 70.70 151 | 70.20 155 |
|
thresconf0.02 | | | 47.89 153 | 50.76 154 | 44.54 149 | 53.86 109 | 63.96 132 | 46.23 167 | 47.72 62 | 33.00 162 | 17.08 189 | 36.35 110 | 37.80 118 | 29.86 179 | 60.01 146 | 60.57 144 | 72.49 134 | 63.62 180 |
|
UGNet | | | 51.04 129 | 58.79 76 | 42.00 170 | 40.59 193 | 65.32 114 | 46.65 165 | 39.26 161 | 39.90 106 | 27.30 137 | 54.12 46 | 52.03 65 | 30.93 174 | 59.85 147 | 59.62 153 | 67.23 174 | 80.70 87 |
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 |
UniMVSNet_NR-MVSNet | | | 49.56 142 | 53.04 128 | 45.49 143 | 51.59 140 | 64.42 129 | 46.97 161 | 51.01 34 | 37.87 116 | 16.42 190 | 39.87 87 | 34.91 150 | 33.43 165 | 59.59 148 | 62.70 112 | 73.52 118 | 71.94 139 |
|
view800 | | | 47.68 154 | 49.78 158 | 45.24 146 | 53.39 110 | 63.19 137 | 48.13 153 | 46.57 83 | 30.98 175 | 20.25 172 | 31.52 158 | 31.90 172 | 31.52 171 | 59.37 149 | 59.61 154 | 71.56 144 | 71.89 142 |
|
TranMVSNet+NR-MVSNet | | | 48.06 152 | 51.36 149 | 44.21 153 | 50.38 149 | 62.09 153 | 47.28 158 | 50.88 37 | 36.11 132 | 13.25 198 | 37.51 101 | 31.60 175 | 30.70 175 | 59.34 150 | 62.53 118 | 72.81 131 | 70.31 154 |
|
IB-MVS | | 53.15 10 | 57.33 69 | 59.02 74 | 55.37 67 | 60.83 64 | 77.11 28 | 54.51 120 | 50.10 43 | 43.22 91 | 42.82 59 | 40.50 84 | 37.61 120 | 44.67 128 | 59.27 151 | 69.81 34 | 79.29 16 | 85.59 38 |
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 |
pmmvs5 | | | 47.02 160 | 50.02 157 | 43.51 159 | 43.48 187 | 62.65 146 | 47.24 159 | 37.78 171 | 30.59 178 | 24.80 150 | 35.26 116 | 30.43 187 | 34.36 157 | 59.05 152 | 60.28 147 | 73.40 121 | 71.92 141 |
|
CNLPA | | | 54.00 87 | 57.08 90 | 50.40 110 | 49.83 153 | 61.75 155 | 53.47 126 | 37.27 174 | 74.55 20 | 44.85 50 | 33.58 144 | 45.42 97 | 52.94 62 | 58.89 153 | 53.66 188 | 64.06 188 | 71.68 145 |
|
CR-MVSNet | | | 48.82 148 | 51.85 143 | 45.29 145 | 46.74 169 | 55.95 187 | 52.06 138 | 34.21 196 | 42.17 98 | 31.74 118 | 32.92 149 | 42.53 105 | 45.00 125 | 58.80 154 | 61.11 138 | 61.99 199 | 69.47 162 |
|
PatchT | | | 48.11 151 | 51.27 150 | 44.43 150 | 50.13 152 | 61.58 156 | 33.59 201 | 32.92 207 | 40.38 105 | 31.74 118 | 30.60 169 | 36.93 129 | 45.00 125 | 58.80 154 | 61.11 138 | 73.19 126 | 69.47 162 |
|
Effi-MVS+-dtu | | | 53.63 96 | 54.85 110 | 52.20 90 | 59.32 74 | 61.33 160 | 56.42 111 | 40.24 153 | 43.84 90 | 34.22 97 | 39.49 93 | 46.18 93 | 53.00 61 | 58.72 156 | 57.49 164 | 69.99 157 | 76.91 104 |
|
v7n | | | 47.22 157 | 48.38 168 | 45.87 141 | 48.20 163 | 63.58 133 | 50.69 142 | 40.93 151 | 26.60 198 | 26.44 142 | 26.52 188 | 29.65 190 | 38.19 146 | 58.22 157 | 60.23 148 | 70.79 150 | 73.83 129 |
|
EPNet_dtu | | | 49.85 140 | 56.99 92 | 41.52 174 | 52.79 111 | 57.06 184 | 41.44 183 | 43.13 128 | 56.13 61 | 19.24 179 | 52.11 50 | 48.38 83 | 22.14 199 | 58.19 158 | 58.38 159 | 70.35 153 | 68.71 166 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
TAPA-MVS | | 47.92 11 | 51.66 122 | 57.88 87 | 44.40 151 | 36.46 210 | 58.42 182 | 53.82 125 | 30.83 215 | 69.51 34 | 34.97 91 | 46.90 65 | 49.67 75 | 46.99 112 | 58.00 159 | 54.64 185 | 63.33 194 | 68.00 168 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
tfpn | | | 46.62 162 | 49.07 165 | 43.75 157 | 52.70 112 | 61.49 159 | 45.65 170 | 45.68 90 | 30.25 180 | 18.84 183 | 30.87 165 | 33.67 161 | 29.22 184 | 57.80 160 | 59.49 155 | 70.44 152 | 69.95 160 |
|
UniMVSNet (Re) | | | 46.89 161 | 51.65 147 | 41.34 175 | 45.60 172 | 62.71 144 | 44.05 176 | 47.10 77 | 37.24 124 | 13.55 196 | 36.90 104 | 34.54 154 | 26.76 190 | 57.56 161 | 59.90 152 | 70.98 148 | 72.69 138 |
|
DU-MVS | | | 47.33 156 | 50.86 152 | 43.20 162 | 44.43 180 | 60.64 163 | 46.97 161 | 47.63 63 | 37.26 122 | 16.42 190 | 37.31 102 | 31.39 177 | 33.43 165 | 57.53 162 | 59.98 150 | 70.35 153 | 71.94 139 |
|
Baseline_NR-MVSNet | | | 47.14 159 | 50.83 153 | 42.84 166 | 44.43 180 | 63.31 136 | 44.50 175 | 50.36 42 | 37.71 119 | 11.25 204 | 30.84 166 | 32.09 169 | 30.96 173 | 57.53 162 | 63.73 103 | 75.53 65 | 70.60 153 |
|
ACMH+ | | 47.85 12 | 49.13 145 | 48.86 167 | 49.44 121 | 56.75 82 | 62.01 154 | 56.62 109 | 47.55 67 | 37.49 121 | 23.98 157 | 26.68 187 | 29.46 192 | 43.12 132 | 57.45 164 | 58.85 157 | 68.62 165 | 70.05 158 |
|
LS3D | | | 49.59 141 | 49.75 159 | 49.40 122 | 55.88 96 | 59.86 171 | 56.31 112 | 45.33 92 | 48.57 79 | 28.32 135 | 31.54 157 | 36.81 130 | 46.27 117 | 57.17 165 | 55.88 177 | 64.29 187 | 58.42 194 |
|
PLC | | 44.22 14 | 49.14 144 | 51.75 145 | 46.10 139 | 42.78 189 | 55.60 191 | 53.11 128 | 34.46 195 | 55.69 63 | 32.47 115 | 34.16 136 | 41.45 110 | 48.91 94 | 57.13 166 | 54.09 186 | 64.84 185 | 64.10 176 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
test0.0.03 1 | | | 43.07 183 | 46.95 175 | 38.54 186 | 51.68 137 | 58.77 176 | 35.28 195 | 46.35 85 | 32.05 167 | 12.44 199 | 28.53 182 | 35.52 144 | 14.40 215 | 57.12 167 | 56.93 171 | 71.11 147 | 59.69 187 |
|
conf0.05thres1000 | | | 45.26 169 | 46.99 174 | 43.24 161 | 51.87 131 | 60.52 166 | 45.17 172 | 45.24 93 | 27.06 195 | 18.60 184 | 26.24 189 | 31.23 182 | 28.82 186 | 56.88 168 | 58.52 158 | 69.71 159 | 68.50 167 |
|
TAMVS | | | 44.27 178 | 49.35 163 | 38.35 188 | 44.74 178 | 61.04 161 | 39.07 187 | 31.82 212 | 29.95 182 | 18.34 185 | 33.55 145 | 39.94 116 | 30.01 177 | 56.85 169 | 57.58 163 | 66.13 180 | 66.54 169 |
|
FMVSNet5 | | | 43.29 182 | 47.07 173 | 38.87 184 | 30.46 223 | 50.99 197 | 45.87 168 | 37.19 175 | 42.17 98 | 19.32 178 | 26.77 186 | 40.51 113 | 30.26 176 | 56.82 170 | 55.81 178 | 70.10 156 | 56.46 200 |
|
CMPMVS | | 33.64 16 | 44.39 176 | 46.41 180 | 42.03 169 | 44.21 184 | 56.50 186 | 46.73 164 | 26.48 224 | 34.20 148 | 35.14 90 | 24.22 198 | 34.64 153 | 40.52 143 | 56.50 171 | 56.07 176 | 59.12 206 | 62.74 182 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
pmmvs6 | | | 41.90 190 | 44.01 191 | 39.43 183 | 44.45 179 | 58.77 176 | 41.92 181 | 39.22 162 | 21.74 211 | 19.08 181 | 17.40 216 | 31.33 179 | 24.28 195 | 55.94 172 | 56.67 172 | 67.60 171 | 66.24 170 |
|
ambc | | | | 35.52 214 | | 38.36 199 | 40.40 220 | 28.38 217 | | 25.20 207 | 14.87 192 | 13.22 225 | 7.54 236 | 19.34 202 | 55.63 173 | 47.79 210 | 47.91 224 | 58.89 189 |
|
v52 | | | 44.41 174 | 46.76 177 | 41.67 171 | 38.93 197 | 60.06 168 | 49.26 147 | 36.02 179 | 25.57 204 | 24.73 154 | 25.66 193 | 31.34 178 | 35.93 152 | 55.52 174 | 57.99 160 | 65.14 183 | 71.21 148 |
|
V4 | | | 44.41 174 | 46.76 177 | 41.67 171 | 38.93 197 | 60.05 169 | 49.26 147 | 36.02 179 | 25.55 205 | 24.75 153 | 25.66 193 | 31.33 179 | 35.93 152 | 55.52 174 | 57.99 160 | 65.14 183 | 71.21 148 |
|
tfpn_ndepth | | | 46.53 164 | 49.41 162 | 43.18 163 | 54.66 104 | 61.56 157 | 42.25 180 | 45.66 91 | 35.68 143 | 18.31 186 | 36.55 108 | 34.84 151 | 28.88 185 | 55.45 176 | 57.01 170 | 69.32 161 | 64.78 174 |
|
USDC | | | 42.80 185 | 45.57 183 | 39.58 180 | 34.55 215 | 51.13 196 | 42.61 179 | 36.21 178 | 39.59 108 | 23.65 159 | 33.13 148 | 20.87 217 | 37.86 147 | 55.35 177 | 57.16 168 | 62.61 196 | 61.75 184 |
|
pmmvs-eth3d | | | 44.67 172 | 45.27 185 | 43.98 156 | 42.56 190 | 55.72 190 | 44.97 174 | 40.81 152 | 31.96 168 | 29.13 130 | 26.09 191 | 25.27 208 | 36.69 151 | 55.13 178 | 56.62 173 | 69.68 160 | 66.12 171 |
|
CHOSEN 280x420 | | | 42.39 189 | 47.40 171 | 36.54 193 | 33.56 218 | 39.66 222 | 40.67 184 | 26.88 222 | 34.66 144 | 18.03 187 | 30.09 173 | 45.59 95 | 44.82 127 | 54.46 179 | 54.00 187 | 55.28 215 | 73.32 132 |
|
tfpnview11 | | | 42.71 186 | 45.29 184 | 39.71 179 | 51.06 142 | 58.61 180 | 38.47 188 | 44.80 106 | 30.44 179 | 13.60 193 | 31.25 161 | 30.97 184 | 22.40 196 | 54.20 180 | 55.04 183 | 67.90 168 | 56.51 198 |
|
UA-Net | | | 47.19 158 | 53.02 129 | 40.38 178 | 55.31 99 | 60.02 170 | 38.41 190 | 38.68 165 | 36.42 131 | 22.47 165 | 51.95 51 | 58.72 54 | 25.62 192 | 54.11 181 | 53.40 190 | 61.79 200 | 56.51 198 |
|
Vis-MVSNet (Re-imp) | | | 44.31 177 | 51.67 146 | 35.72 196 | 51.82 134 | 55.24 192 | 34.57 200 | 41.63 147 | 39.10 110 | 8.84 212 | 45.93 68 | 46.63 91 | 14.45 214 | 54.09 182 | 57.03 169 | 63.00 195 | 63.65 179 |
|
Anonymous20231206 | | | 40.63 192 | 43.29 193 | 37.53 191 | 48.88 161 | 55.81 189 | 34.99 196 | 44.98 97 | 28.16 188 | 10.16 208 | 17.26 217 | 27.50 198 | 18.28 204 | 54.00 183 | 55.07 182 | 67.85 169 | 65.23 173 |
|
tfpn_n400 | | | 42.55 187 | 45.11 186 | 39.55 181 | 50.95 144 | 58.68 178 | 38.40 191 | 44.75 108 | 29.29 184 | 13.60 193 | 31.25 161 | 30.97 184 | 22.38 197 | 53.96 184 | 55.66 179 | 67.20 175 | 56.00 201 |
|
tfpnconf | | | 42.55 187 | 45.11 186 | 39.55 181 | 50.95 144 | 58.68 178 | 38.40 191 | 44.75 108 | 29.29 184 | 13.60 193 | 31.25 161 | 30.97 184 | 22.38 197 | 53.96 184 | 55.66 179 | 67.20 175 | 56.00 201 |
|
anonymousdsp | | | 43.03 184 | 47.19 172 | 38.18 189 | 36.00 213 | 56.92 185 | 38.44 189 | 34.56 193 | 24.22 208 | 22.53 164 | 29.69 178 | 29.92 188 | 35.21 155 | 53.96 184 | 58.98 156 | 62.32 198 | 76.66 108 |
|
MVS-HIRNet | | | 43.98 179 | 43.63 192 | 44.39 152 | 47.66 165 | 59.31 172 | 32.66 207 | 33.88 203 | 30.15 181 | 33.75 104 | 16.82 219 | 28.39 196 | 45.25 120 | 53.92 187 | 55.00 184 | 73.16 127 | 61.80 183 |
|
v748 | | | 44.90 170 | 46.14 181 | 43.46 160 | 45.37 175 | 60.89 162 | 48.15 152 | 39.42 158 | 25.81 202 | 24.36 155 | 25.90 192 | 28.48 195 | 34.44 156 | 53.39 188 | 57.35 166 | 69.00 162 | 71.14 150 |
|
MDTV_nov1_ep13_2view | | | 44.44 173 | 45.75 182 | 42.91 165 | 46.13 170 | 63.43 135 | 46.53 166 | 34.20 198 | 29.08 187 | 19.95 176 | 26.23 190 | 27.89 197 | 35.88 154 | 53.36 189 | 56.43 174 | 74.74 84 | 63.86 178 |
|
ADS-MVSNet | | | 45.39 168 | 46.42 179 | 44.19 154 | 48.74 162 | 57.52 183 | 43.91 177 | 31.93 211 | 35.89 136 | 27.11 139 | 30.12 172 | 32.06 170 | 45.30 119 | 53.13 190 | 55.19 181 | 68.15 166 | 61.07 186 |
|
PatchMatch-RL | | | 43.37 181 | 44.93 189 | 41.56 173 | 37.94 201 | 51.70 194 | 40.02 185 | 35.75 183 | 39.04 111 | 30.71 124 | 35.14 117 | 27.43 199 | 46.58 114 | 51.99 191 | 50.55 200 | 58.38 208 | 58.64 192 |
|
tfpn1000 | | | 41.76 191 | 45.01 188 | 37.96 190 | 50.95 144 | 58.44 181 | 34.94 197 | 44.09 116 | 30.68 177 | 12.08 200 | 30.14 171 | 31.96 171 | 18.67 203 | 51.96 192 | 53.45 189 | 67.05 177 | 58.40 195 |
|
test20.03 | | | 36.00 204 | 38.92 202 | 32.60 204 | 45.92 171 | 50.99 197 | 28.05 218 | 43.69 122 | 21.62 213 | 6.03 219 | 17.61 215 | 25.91 204 | 8.34 229 | 51.26 193 | 52.60 193 | 63.58 190 | 52.46 209 |
|
COLMAP_ROB | | 34.79 15 | 38.65 194 | 40.72 196 | 36.23 195 | 36.41 211 | 49.22 204 | 45.51 171 | 27.60 221 | 37.81 117 | 20.54 171 | 23.37 200 | 24.25 210 | 28.11 188 | 51.02 194 | 48.55 204 | 59.22 205 | 50.82 214 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
RPMNet | | | 43.70 180 | 48.17 169 | 38.48 187 | 45.52 174 | 55.95 187 | 37.66 193 | 26.63 223 | 42.17 98 | 25.47 144 | 29.59 179 | 37.61 120 | 33.87 159 | 50.85 195 | 52.02 195 | 61.75 201 | 69.00 164 |
|
TinyColmap | | | 37.18 198 | 37.37 210 | 36.95 192 | 31.17 222 | 45.21 212 | 39.71 186 | 34.65 191 | 29.83 183 | 20.20 173 | 18.54 211 | 13.72 228 | 38.27 145 | 50.33 196 | 51.57 198 | 57.71 209 | 52.42 210 |
|
testgi | | | 34.51 208 | 37.42 208 | 31.12 211 | 47.37 168 | 50.34 199 | 24.38 225 | 41.21 148 | 20.32 217 | 5.64 223 | 20.56 205 | 26.55 201 | 8.06 230 | 49.28 197 | 52.65 191 | 60.05 203 | 42.23 224 |
|
test2356 | | | 34.09 210 | 36.84 211 | 30.87 212 | 36.25 212 | 43.59 215 | 27.92 219 | 35.44 186 | 21.73 212 | 6.94 216 | 19.31 209 | 18.23 220 | 17.77 205 | 49.28 197 | 51.58 197 | 60.94 202 | 54.17 208 |
|
TDRefinement | | | 35.76 205 | 38.23 203 | 32.88 202 | 19.09 234 | 46.04 211 | 43.29 178 | 29.49 216 | 33.49 160 | 19.04 182 | 22.29 204 | 17.82 222 | 29.69 182 | 48.60 199 | 47.24 212 | 56.65 213 | 52.12 212 |
|
LP | | | 38.21 196 | 37.72 206 | 38.79 185 | 44.07 185 | 51.16 195 | 35.54 194 | 31.37 214 | 25.38 206 | 23.73 158 | 18.64 210 | 18.03 221 | 29.31 183 | 47.85 200 | 52.63 192 | 68.71 164 | 50.34 216 |
|
CVMVSNet | | | 38.91 193 | 44.49 190 | 32.40 205 | 34.57 214 | 47.20 208 | 34.81 198 | 34.20 198 | 31.45 172 | 8.95 211 | 38.86 97 | 36.38 134 | 24.30 194 | 47.77 201 | 46.94 214 | 57.59 210 | 62.85 181 |
|
LTVRE_ROB | | 32.83 17 | 35.10 206 | 37.46 207 | 32.35 206 | 43.12 188 | 49.99 201 | 28.52 216 | 33.23 206 | 12.73 232 | 8.18 213 | 27.71 185 | 21.34 214 | 32.64 168 | 46.92 202 | 48.11 206 | 48.41 222 | 55.45 204 |
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 |
WR-MVS | | | 37.61 197 | 42.15 194 | 32.31 207 | 43.64 186 | 51.85 193 | 29.39 213 | 43.35 125 | 27.65 192 | 4.40 226 | 29.90 175 | 29.80 189 | 10.46 222 | 46.73 203 | 51.98 196 | 62.60 197 | 57.16 196 |
|
PEN-MVS | | | 38.23 195 | 41.72 195 | 34.15 198 | 40.56 194 | 50.07 200 | 33.17 204 | 44.35 114 | 27.64 193 | 5.54 224 | 30.84 166 | 26.67 200 | 14.99 212 | 45.64 204 | 52.38 194 | 66.29 179 | 58.83 190 |
|
DTE-MVSNet | | | 36.91 200 | 40.44 198 | 32.79 203 | 40.74 192 | 47.55 207 | 30.71 211 | 44.39 111 | 27.03 196 | 4.32 227 | 30.88 164 | 25.99 203 | 12.73 217 | 45.58 205 | 50.80 199 | 63.86 189 | 55.23 205 |
|
SixPastTwentyTwo | | | 36.11 203 | 37.80 205 | 34.13 199 | 37.13 207 | 46.72 209 | 34.58 199 | 34.96 189 | 21.20 215 | 11.66 201 | 29.15 181 | 19.88 218 | 29.77 180 | 44.93 206 | 48.34 205 | 56.67 212 | 54.41 207 |
|
pmmvs3 | | | 31.22 216 | 33.62 217 | 28.43 214 | 22.82 227 | 40.26 221 | 26.40 221 | 22.05 230 | 16.89 226 | 10.99 206 | 14.72 221 | 16.26 223 | 29.70 181 | 44.82 207 | 47.39 211 | 58.61 207 | 54.98 206 |
|
1111 | | | 29.41 221 | 30.75 222 | 27.85 216 | 39.46 195 | 37.63 224 | 22.26 226 | 32.15 209 | 17.93 224 | 7.92 214 | 13.48 223 | 20.98 215 | 17.30 206 | 44.76 208 | 46.51 215 | 47.99 223 | 33.96 228 |
|
.test1245 | | | 19.53 228 | 19.26 230 | 19.85 228 | 39.46 195 | 37.63 224 | 22.26 226 | 32.15 209 | 17.93 224 | 7.92 214 | 13.48 223 | 20.98 215 | 17.30 206 | 44.76 208 | 0.01 236 | 0.00 240 | 0.03 237 |
|
N_pmnet | | | 34.09 210 | 35.74 213 | 32.17 208 | 37.25 206 | 43.17 216 | 32.26 209 | 35.57 185 | 26.22 200 | 10.60 207 | 20.44 206 | 19.38 219 | 20.20 201 | 44.59 210 | 47.00 213 | 57.13 211 | 49.35 218 |
|
PM-MVS | | | 34.96 207 | 38.17 204 | 31.22 210 | 22.78 228 | 40.82 218 | 33.56 202 | 23.61 228 | 29.16 186 | 21.43 168 | 28.00 184 | 21.43 213 | 31.90 170 | 44.33 211 | 42.12 221 | 54.07 217 | 61.34 185 |
|
MIMVSNet1 | | | 29.60 219 | 33.37 218 | 25.20 223 | 19.52 232 | 43.94 214 | 26.29 222 | 37.92 170 | 19.95 220 | 3.79 230 | 12.64 229 | 21.99 212 | 7.70 232 | 43.83 212 | 46.32 216 | 55.97 214 | 44.92 222 |
|
CP-MVSNet | | | 37.09 199 | 40.62 197 | 32.99 200 | 37.56 203 | 48.25 205 | 32.75 205 | 43.05 129 | 27.88 190 | 5.93 220 | 31.27 160 | 25.82 205 | 15.09 210 | 43.37 213 | 48.82 202 | 63.54 192 | 58.90 188 |
|
PS-CasMVS | | | 36.84 201 | 40.23 201 | 32.89 201 | 37.44 204 | 48.09 206 | 32.68 206 | 42.97 131 | 27.36 194 | 5.89 221 | 30.08 174 | 25.48 207 | 14.96 213 | 43.28 214 | 48.71 203 | 63.39 193 | 58.63 193 |
|
Anonymous20231211 | | | 34.02 212 | 32.10 221 | 36.26 194 | 44.26 183 | 46.14 210 | 29.39 213 | 39.72 156 | 14.08 231 | 28.33 134 | 7.94 232 | 13.30 229 | 16.12 208 | 43.07 215 | 47.88 209 | 59.72 204 | 52.28 211 |
|
testus | | | 29.45 220 | 32.20 220 | 26.23 218 | 37.01 208 | 37.90 223 | 17.56 232 | 35.70 184 | 18.23 222 | 3.39 231 | 17.04 218 | 14.78 225 | 11.78 219 | 42.48 216 | 49.38 201 | 51.92 221 | 45.62 221 |
|
WR-MVS_H | | | 36.29 202 | 40.35 200 | 31.55 209 | 37.80 202 | 49.94 202 | 30.57 212 | 41.11 150 | 26.90 197 | 4.14 228 | 30.72 168 | 28.85 193 | 10.45 223 | 42.47 217 | 47.99 208 | 65.24 182 | 55.54 203 |
|
testmv | | | 27.97 222 | 29.98 223 | 25.62 220 | 32.54 220 | 36.86 226 | 20.53 228 | 33.33 204 | 14.11 229 | 2.64 233 | 12.76 227 | 11.77 231 | 11.07 220 | 42.34 218 | 45.44 217 | 53.60 218 | 46.60 219 |
|
test1235678 | | | 27.96 223 | 29.97 224 | 25.62 220 | 32.54 220 | 36.83 227 | 20.53 228 | 33.33 204 | 14.10 230 | 2.64 233 | 12.75 228 | 11.76 232 | 11.07 220 | 42.34 218 | 45.43 218 | 53.60 218 | 46.59 220 |
|
FC-MVSNet-test | | | 30.97 217 | 37.38 209 | 23.49 224 | 37.42 205 | 33.68 228 | 19.43 231 | 39.27 160 | 31.37 173 | 1.67 238 | 38.56 98 | 28.85 193 | 6.06 234 | 41.40 220 | 43.80 219 | 37.10 229 | 44.03 223 |
|
new-patchmatchnet | | | 30.47 218 | 32.80 219 | 27.75 217 | 36.81 209 | 43.98 213 | 24.85 223 | 39.29 159 | 20.52 216 | 4.06 229 | 15.94 220 | 16.05 224 | 9.57 224 | 41.32 221 | 42.05 222 | 51.94 220 | 49.74 217 |
|
EU-MVSNet | | | 33.00 214 | 36.49 212 | 28.92 213 | 33.10 219 | 42.86 217 | 29.32 215 | 35.99 181 | 22.94 209 | 5.83 222 | 25.29 195 | 24.43 209 | 15.21 209 | 41.22 222 | 41.65 223 | 54.08 216 | 57.01 197 |
|
RPSCF | | | 33.61 213 | 40.43 199 | 25.65 219 | 16.00 236 | 32.41 230 | 31.73 210 | 13.33 235 | 50.13 73 | 23.12 160 | 31.56 156 | 40.09 114 | 32.73 167 | 41.14 223 | 37.05 225 | 36.99 230 | 50.63 215 |
|
testpf | | | 31.84 215 | 34.86 215 | 28.32 215 | 48.89 160 | 32.91 229 | 26.53 220 | 25.77 226 | 21.99 210 | 10.05 209 | 23.39 199 | 25.55 206 | 14.07 216 | 39.23 224 | 42.32 220 | 44.58 226 | 58.65 191 |
|
MDA-MVSNet-bldmvs | | | 34.31 209 | 34.11 216 | 34.54 197 | 24.73 226 | 49.66 203 | 33.42 203 | 43.03 130 | 21.59 214 | 11.10 205 | 19.81 207 | 12.68 230 | 31.41 172 | 35.59 225 | 48.05 207 | 63.56 191 | 51.39 213 |
|
FPMVS | | | 26.87 224 | 28.19 225 | 25.32 222 | 27.09 224 | 29.49 232 | 32.28 208 | 17.79 232 | 28.09 189 | 11.33 202 | 19.38 208 | 14.69 226 | 20.88 200 | 35.11 226 | 32.82 228 | 42.56 227 | 37.75 226 |
|
PMVS | | 18.18 18 | 21.95 225 | 22.85 226 | 20.90 227 | 21.92 230 | 14.78 235 | 19.95 230 | 17.31 233 | 15.69 227 | 11.32 203 | 13.70 222 | 13.91 227 | 15.02 211 | 34.92 227 | 31.72 229 | 39.85 228 | 35.20 227 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Gipuma | | | 17.16 230 | 17.83 231 | 16.36 230 | 18.76 235 | 12.15 238 | 11.97 236 | 27.78 220 | 17.94 223 | 4.86 225 | 2.53 238 | 2.73 240 | 8.90 227 | 34.32 228 | 36.09 227 | 25.92 233 | 19.06 232 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test12356 | | | 20.09 227 | 22.80 227 | 16.93 229 | 22.59 229 | 24.43 233 | 13.32 234 | 25.93 225 | 12.67 233 | 1.58 240 | 11.53 230 | 9.25 234 | 2.29 235 | 33.15 229 | 37.05 225 | 35.85 231 | 31.54 229 |
|
new_pmnet | | | 19.10 229 | 22.71 228 | 14.89 231 | 10.93 238 | 24.08 234 | 14.22 233 | 13.94 234 | 18.68 221 | 2.93 232 | 12.84 226 | 11.27 233 | 11.94 218 | 30.57 230 | 30.58 230 | 35.38 232 | 30.93 230 |
|
no-one | | | 21.91 226 | 22.52 229 | 21.20 226 | 25.97 225 | 30.78 231 | 13.29 235 | 32.75 208 | 9.08 235 | 1.84 235 | 6.18 234 | 7.00 237 | 8.03 231 | 25.56 231 | 40.16 224 | 45.29 225 | 38.83 225 |
|
PMMVS2 | | | 12.25 231 | 14.17 232 | 10.00 234 | 11.39 237 | 14.35 236 | 8.21 237 | 19.29 231 | 9.31 234 | 0.19 242 | 7.38 233 | 6.19 238 | 1.10 237 | 19.26 232 | 21.13 232 | 19.85 234 | 21.56 231 |
|
tmp_tt | | | | | 4.41 236 | 2.56 240 | 1.81 242 | 2.61 242 | 0.27 237 | 20.12 218 | 9.81 210 | 17.69 214 | 9.04 235 | 1.96 236 | 12.88 233 | 12.11 234 | 9.23 238 | |
|
MVE | | 10.35 19 | 9.76 234 | 11.08 233 | 8.22 235 | 4.43 239 | 13.04 237 | 3.36 241 | 23.57 229 | 5.74 239 | 1.76 236 | 3.09 237 | 1.75 241 | 6.78 233 | 12.78 234 | 23.04 231 | 9.44 237 | 18.09 233 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | | | 5.87 241 | 4.32 239 | 1.74 236 | 9.04 236 | 1.30 241 | 7.97 231 | 3.16 239 | 8.56 228 | 9.74 235 | | 6.30 239 | 14.51 234 |
|
E-PMN | | | 10.66 232 | 8.30 234 | 13.42 232 | 19.91 231 | 7.87 239 | 4.30 240 | 29.47 217 | 8.37 238 | 1.70 237 | 3.67 235 | 1.29 243 | 9.12 226 | 8.98 236 | 13.59 233 | 16.03 235 | 14.30 235 |
|
EMVS | | | 10.15 233 | 7.67 235 | 13.05 233 | 19.22 233 | 7.77 240 | 4.48 238 | 29.34 218 | 8.65 237 | 1.67 238 | 3.55 236 | 1.36 242 | 9.15 225 | 8.15 237 | 11.79 235 | 14.44 236 | 12.43 236 |
|
testmvs | | | 0.01 235 | 0.01 236 | 0.00 237 | 0.00 242 | 0.00 243 | 0.00 244 | 0.00 239 | 0.01 240 | 0.00 244 | 0.02 239 | 0.00 244 | 0.00 240 | 0.01 238 | 0.01 236 | 0.00 240 | 0.03 237 |
|
sosnet-low-res | | | 0.00 237 | 0.00 238 | 0.00 237 | 0.00 242 | 0.00 243 | 0.00 244 | 0.00 239 | 0.00 242 | 0.00 244 | 0.00 241 | 0.00 244 | 0.00 240 | 0.00 239 | 0.00 239 | 0.00 240 | 0.00 240 |
|
sosnet | | | 0.00 237 | 0.00 238 | 0.00 237 | 0.00 242 | 0.00 243 | 0.00 244 | 0.00 239 | 0.00 242 | 0.00 244 | 0.00 241 | 0.00 244 | 0.00 240 | 0.00 239 | 0.00 239 | 0.00 240 | 0.00 240 |
|
test123 | | | 0.01 235 | 0.01 236 | 0.00 237 | 0.00 242 | 0.00 243 | 0.00 244 | 0.00 239 | 0.01 240 | 0.00 244 | 0.02 239 | 0.00 244 | 0.01 238 | 0.00 239 | 0.01 236 | 0.00 240 | 0.03 237 |
|
our_test_3 | | | | | | 49.68 154 | 61.50 158 | 45.84 169 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 54.82 10 | | 71.98 18 | | | | | |
|
MTMP | | | | | | | | | | | 50.64 27 | | 68.31 24 | | | | | |
|
Patchmatch-RL test | | | | | | | | 0.69 243 | | | | | | | | | | |
|
XVS | | | | | | 62.70 51 | 73.06 56 | 61.80 63 | | | 42.02 66 | | 63.42 37 | | | | 74.68 89 | |
|
X-MVStestdata | | | | | | 62.70 51 | 73.06 56 | 61.80 63 | | | 42.02 66 | | 63.42 37 | | | | 74.68 89 | |
|
abl_6 | | | | | 63.79 18 | 70.80 18 | 81.22 7 | 65.26 52 | 53.25 25 | 77.02 13 | 53.02 19 | 65.14 25 | 73.74 13 | 60.30 16 | | | 80.13 9 | 90.27 7 |
|
mPP-MVS | | | | | | 63.08 48 | | | | | | | 62.34 40 | | | | | |
|
NP-MVS | | | | | | | | | | 72.62 25 | | | | | | | | |
|
Patchmtry | | | | | | | 64.49 127 | 52.06 138 | 34.21 196 | | 31.74 118 | | | | | | | |
|