SMA-MVS | | | 87.56 3 | 90.17 4 | 84.52 4 | 91.71 3 | 90.57 5 | 90.77 4 | 75.19 9 | 90.67 3 | 80.50 8 | 86.59 13 | 88.86 4 | 78.09 12 | 89.92 1 | 89.41 1 | 90.84 7 | 95.19 2 |
|
ACMMP_Plus | | | 86.52 9 | 89.01 7 | 83.62 13 | 90.28 15 | 90.09 9 | 90.32 8 | 74.05 16 | 88.32 10 | 79.74 11 | 87.04 11 | 85.59 18 | 76.97 25 | 89.35 2 | 88.44 4 | 90.35 25 | 94.27 7 |
|
CNVR-MVS | | | 86.36 10 | 88.19 13 | 84.23 7 | 91.33 5 | 89.84 10 | 90.34 7 | 75.56 6 | 87.36 14 | 78.97 13 | 81.19 24 | 86.76 12 | 78.74 7 | 89.30 3 | 88.58 2 | 90.45 22 | 94.33 6 |
|
SteuartSystems-ACMMP | | | 85.99 12 | 88.31 12 | 83.27 17 | 90.73 8 | 89.84 10 | 90.27 9 | 74.31 11 | 84.56 26 | 75.88 25 | 87.32 10 | 85.04 19 | 77.31 20 | 89.01 4 | 88.46 3 | 91.14 3 | 93.96 8 |
Skip Steuart: Steuart Systems R&D Blog. |
ESAPD | | | 88.46 1 | 91.07 1 | 85.41 1 | 91.73 2 | 92.08 1 | 91.91 2 | 76.73 1 | 90.14 4 | 80.33 9 | 92.75 1 | 90.44 1 | 80.73 3 | 88.97 5 | 87.63 9 | 91.01 6 | 95.48 1 |
|
HPM-MVS++ | | | 87.09 5 | 88.92 9 | 84.95 3 | 92.61 1 | 87.91 35 | 90.23 10 | 76.06 3 | 88.85 8 | 81.20 4 | 87.33 9 | 87.93 8 | 79.47 6 | 88.59 6 | 88.23 5 | 90.15 29 | 93.60 16 |
|
DeepC-MVS | | 78.47 2 | 84.81 22 | 86.03 25 | 83.37 15 | 89.29 27 | 90.38 7 | 88.61 22 | 76.50 2 | 86.25 19 | 77.22 20 | 75.12 35 | 80.28 39 | 77.59 18 | 88.39 7 | 88.17 6 | 91.02 5 | 93.66 14 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepPCF-MVS | | 79.04 1 | 85.30 17 | 88.93 8 | 81.06 27 | 88.77 30 | 90.48 6 | 85.46 42 | 73.08 23 | 90.97 2 | 73.77 32 | 84.81 18 | 85.95 15 | 77.43 19 | 88.22 8 | 87.73 7 | 87.85 68 | 94.34 5 |
|
NCCC | | | 85.34 16 | 86.59 21 | 83.88 12 | 91.48 4 | 88.88 21 | 89.79 12 | 75.54 7 | 86.67 17 | 77.94 19 | 76.55 31 | 84.99 20 | 78.07 13 | 88.04 9 | 87.68 8 | 90.46 21 | 93.31 17 |
|
DeepC-MVS_fast | | 78.24 3 | 84.27 25 | 85.50 27 | 82.85 19 | 90.46 14 | 89.24 17 | 87.83 28 | 74.24 13 | 84.88 22 | 76.23 23 | 75.26 34 | 81.05 37 | 77.62 17 | 88.02 10 | 87.62 10 | 90.69 12 | 92.41 24 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
APDe-MVS | | | 88.00 2 | 90.50 2 | 85.08 2 | 90.95 6 | 91.58 4 | 92.03 1 | 75.53 8 | 91.15 1 | 80.10 10 | 92.27 3 | 88.34 7 | 80.80 2 | 88.00 11 | 86.99 15 | 91.09 4 | 95.16 3 |
|
HSP-MVS | | | 87.45 4 | 90.22 3 | 84.22 8 | 90.00 19 | 91.80 3 | 90.59 5 | 75.80 4 | 89.93 5 | 78.35 16 | 92.54 2 | 89.18 3 | 80.89 1 | 87.99 12 | 86.29 26 | 89.70 36 | 93.85 9 |
|
MCST-MVS | | | 85.13 19 | 86.62 20 | 83.39 14 | 90.55 12 | 89.82 12 | 89.29 17 | 73.89 19 | 84.38 27 | 76.03 24 | 79.01 27 | 85.90 16 | 78.47 8 | 87.81 13 | 86.11 29 | 92.11 1 | 93.29 18 |
|
zzz-MVS | | | 85.71 13 | 86.88 19 | 84.34 6 | 90.54 13 | 87.11 39 | 89.77 13 | 74.17 14 | 88.54 9 | 83.08 2 | 78.60 28 | 86.10 14 | 78.11 11 | 87.80 14 | 87.46 11 | 90.35 25 | 92.56 22 |
|
HFP-MVS | | | 86.15 11 | 87.95 14 | 84.06 10 | 90.80 7 | 89.20 19 | 89.62 15 | 74.26 12 | 87.52 11 | 80.63 6 | 86.82 12 | 84.19 24 | 78.22 10 | 87.58 15 | 87.19 13 | 90.81 8 | 93.13 20 |
|
SD-MVS | | | 86.96 6 | 89.45 5 | 84.05 11 | 90.13 16 | 89.23 18 | 89.77 13 | 74.59 10 | 89.17 6 | 80.70 5 | 89.93 7 | 89.67 2 | 78.47 8 | 87.57 16 | 86.79 18 | 90.67 13 | 93.76 12 |
|
ACMMPR | | | 85.52 14 | 87.53 16 | 83.17 18 | 90.13 16 | 89.27 16 | 89.30 16 | 73.97 17 | 86.89 16 | 77.14 21 | 86.09 14 | 83.18 27 | 77.74 16 | 87.42 17 | 87.20 12 | 90.77 9 | 92.63 21 |
|
MP-MVS | | | 85.50 15 | 87.40 17 | 83.28 16 | 90.65 10 | 89.51 15 | 89.16 19 | 74.11 15 | 83.70 29 | 78.06 18 | 85.54 16 | 84.89 22 | 77.31 20 | 87.40 18 | 87.14 14 | 90.41 23 | 93.65 15 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
3Dnovator+ | | 75.73 4 | 82.40 30 | 82.76 35 | 81.97 24 | 88.02 32 | 89.67 13 | 86.60 32 | 71.48 31 | 81.28 38 | 78.18 17 | 64.78 72 | 77.96 46 | 77.13 23 | 87.32 19 | 86.83 17 | 90.41 23 | 91.48 32 |
|
PHI-MVS | | | 82.36 31 | 85.89 26 | 78.24 44 | 86.40 42 | 89.52 14 | 85.52 39 | 69.52 43 | 82.38 35 | 65.67 62 | 81.35 23 | 82.36 28 | 73.07 41 | 87.31 20 | 86.76 19 | 89.24 44 | 91.56 31 |
|
PGM-MVS | | | 84.42 24 | 86.29 24 | 82.23 22 | 90.04 18 | 88.82 23 | 89.23 18 | 71.74 30 | 82.82 32 | 74.61 28 | 84.41 19 | 82.09 29 | 77.03 24 | 87.13 21 | 86.73 20 | 90.73 11 | 92.06 28 |
|
APD-MVS | | | 86.84 8 | 88.91 10 | 84.41 5 | 90.66 9 | 90.10 8 | 90.78 3 | 75.64 5 | 87.38 13 | 78.72 14 | 90.68 6 | 86.82 11 | 80.15 4 | 87.13 21 | 86.45 24 | 90.51 16 | 93.83 10 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
TSAR-MVS + ACMM | | | 85.10 20 | 88.81 11 | 80.77 30 | 89.55 24 | 88.53 29 | 88.59 23 | 72.55 25 | 87.39 12 | 71.90 38 | 90.95 5 | 87.55 9 | 74.57 30 | 87.08 23 | 86.54 22 | 87.47 73 | 93.67 13 |
|
MVS_0304 | | | 81.73 34 | 83.86 31 | 79.26 37 | 86.22 44 | 89.18 20 | 86.41 33 | 67.15 57 | 75.28 51 | 70.75 48 | 74.59 37 | 83.49 26 | 74.42 32 | 87.05 24 | 86.34 25 | 90.58 15 | 91.08 36 |
|
X-MVS | | | 83.23 28 | 85.20 29 | 80.92 29 | 89.71 23 | 88.68 24 | 88.21 27 | 73.60 20 | 82.57 33 | 71.81 41 | 77.07 29 | 81.92 31 | 71.72 51 | 86.98 25 | 86.86 16 | 90.47 18 | 92.36 25 |
|
TSAR-MVS + MP. | | | 86.88 7 | 89.23 6 | 84.14 9 | 89.78 22 | 88.67 27 | 90.59 5 | 73.46 22 | 88.99 7 | 80.52 7 | 91.26 4 | 88.65 5 | 79.91 5 | 86.96 26 | 86.22 27 | 90.59 14 | 93.83 10 |
|
CP-MVS | | | 84.74 23 | 86.43 23 | 82.77 20 | 89.48 25 | 88.13 34 | 88.64 21 | 73.93 18 | 84.92 21 | 76.77 22 | 81.94 22 | 83.50 25 | 77.29 22 | 86.92 27 | 86.49 23 | 90.49 17 | 93.14 19 |
|
CSCG | | | 85.28 18 | 87.68 15 | 82.49 21 | 89.95 20 | 91.99 2 | 88.82 20 | 71.20 32 | 86.41 18 | 79.63 12 | 79.26 25 | 88.36 6 | 73.94 36 | 86.64 28 | 86.67 21 | 91.40 2 | 94.41 4 |
|
DELS-MVS | | | 79.15 49 | 81.07 44 | 76.91 51 | 83.54 56 | 87.31 37 | 84.45 47 | 64.92 73 | 69.98 61 | 69.34 50 | 71.62 48 | 76.26 49 | 69.84 59 | 86.57 29 | 85.90 30 | 89.39 41 | 89.88 44 |
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 |
train_agg | | | 84.86 21 | 87.21 18 | 82.11 23 | 90.59 11 | 85.47 50 | 89.81 11 | 73.55 21 | 83.95 28 | 73.30 33 | 89.84 8 | 87.23 10 | 75.61 28 | 86.47 30 | 85.46 34 | 89.78 32 | 92.06 28 |
|
MVS_111021_HR | | | 80.13 38 | 81.46 41 | 78.58 42 | 85.77 46 | 85.17 54 | 83.45 52 | 69.28 44 | 74.08 57 | 70.31 49 | 74.31 39 | 75.26 53 | 73.13 40 | 86.46 31 | 85.15 37 | 89.53 39 | 89.81 45 |
|
OPM-MVS | | | 79.68 44 | 79.28 53 | 80.15 33 | 87.99 33 | 86.77 42 | 88.52 24 | 72.72 24 | 64.55 84 | 67.65 56 | 67.87 62 | 74.33 56 | 74.31 34 | 86.37 32 | 85.25 36 | 89.73 35 | 89.81 45 |
|
ACMMP | | | 83.42 27 | 85.27 28 | 81.26 26 | 88.47 31 | 88.49 30 | 88.31 26 | 72.09 27 | 83.42 30 | 72.77 36 | 82.65 20 | 78.22 43 | 75.18 29 | 86.24 33 | 85.76 31 | 90.74 10 | 92.13 27 |
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 |
CANet | | | 81.62 35 | 83.41 32 | 79.53 36 | 87.06 37 | 88.59 28 | 85.47 41 | 67.96 53 | 76.59 49 | 74.05 29 | 74.69 36 | 81.98 30 | 72.98 42 | 86.14 34 | 85.47 33 | 89.68 37 | 90.42 42 |
|
CDPH-MVS | | | 82.64 29 | 85.03 30 | 79.86 34 | 89.41 26 | 88.31 31 | 88.32 25 | 71.84 29 | 80.11 41 | 67.47 57 | 82.09 21 | 81.44 35 | 71.85 49 | 85.89 35 | 86.15 28 | 90.24 27 | 91.25 34 |
|
TSAR-MVS + GP. | | | 83.69 26 | 86.58 22 | 80.32 31 | 85.14 49 | 86.96 40 | 84.91 46 | 70.25 36 | 84.71 25 | 73.91 31 | 85.16 17 | 85.63 17 | 77.92 14 | 85.44 36 | 85.71 32 | 89.77 33 | 92.45 23 |
|
MAR-MVS | | | 79.21 47 | 80.32 49 | 77.92 46 | 87.46 34 | 88.15 33 | 83.95 49 | 67.48 56 | 74.28 55 | 68.25 52 | 64.70 73 | 77.04 47 | 72.17 46 | 85.42 37 | 85.00 38 | 88.22 57 | 87.62 58 |
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 |
CLD-MVS | | | 79.35 46 | 81.23 42 | 77.16 50 | 85.01 52 | 86.92 41 | 85.87 36 | 60.89 125 | 80.07 43 | 75.35 27 | 72.96 42 | 73.21 59 | 68.43 67 | 85.41 38 | 84.63 40 | 87.41 74 | 85.44 78 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MSLP-MVS++ | | | 82.09 32 | 82.66 36 | 81.42 25 | 87.03 38 | 87.22 38 | 85.82 37 | 70.04 37 | 80.30 40 | 78.66 15 | 68.67 58 | 81.04 38 | 77.81 15 | 85.19 39 | 84.88 39 | 89.19 46 | 91.31 33 |
|
3Dnovator | | 73.76 5 | 79.75 42 | 80.52 47 | 78.84 40 | 84.94 54 | 87.35 36 | 84.43 48 | 65.54 67 | 78.29 45 | 73.97 30 | 63.00 78 | 75.62 52 | 74.07 35 | 85.00 40 | 85.34 35 | 90.11 30 | 89.04 49 |
|
casdiffmvs | | | 80.04 39 | 82.12 39 | 77.60 47 | 83.27 57 | 84.92 56 | 85.51 40 | 65.45 68 | 80.73 39 | 67.69 55 | 72.68 44 | 78.05 44 | 74.35 33 | 84.82 41 | 83.94 44 | 89.35 42 | 89.71 47 |
|
LGP-MVS_train | | | 79.83 40 | 81.22 43 | 78.22 45 | 86.28 43 | 85.36 53 | 86.76 31 | 69.59 41 | 77.34 46 | 65.14 64 | 75.68 33 | 70.79 67 | 71.37 54 | 84.60 42 | 84.01 42 | 90.18 28 | 90.74 38 |
|
IS_MVSNet | | | 73.33 70 | 77.34 63 | 68.65 114 | 81.29 65 | 83.47 64 | 74.45 124 | 63.58 81 | 65.75 76 | 48.49 149 | 67.11 66 | 70.61 69 | 54.63 169 | 84.51 43 | 83.58 47 | 89.48 40 | 86.34 67 |
|
HQP-MVS | | | 81.19 36 | 83.27 33 | 78.76 41 | 87.40 35 | 85.45 51 | 86.95 30 | 70.47 35 | 81.31 37 | 66.91 60 | 79.24 26 | 76.63 48 | 71.67 52 | 84.43 44 | 83.78 45 | 89.19 46 | 92.05 30 |
|
PVSNet_Blended_VisFu | | | 76.57 57 | 77.90 56 | 75.02 58 | 80.56 72 | 86.58 44 | 79.24 68 | 66.18 61 | 64.81 81 | 68.18 53 | 65.61 67 | 71.45 63 | 67.05 69 | 84.16 45 | 81.80 55 | 88.90 50 | 90.92 37 |
|
ACMM | | 72.26 8 | 78.86 51 | 78.13 55 | 79.71 35 | 86.89 39 | 83.40 65 | 86.02 35 | 70.50 34 | 75.28 51 | 71.49 45 | 63.01 77 | 69.26 77 | 73.57 38 | 84.11 46 | 83.98 43 | 89.76 34 | 87.84 56 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OMC-MVS | | | 80.26 37 | 82.59 37 | 77.54 48 | 83.04 58 | 85.54 49 | 83.25 53 | 65.05 72 | 87.32 15 | 72.42 37 | 72.04 46 | 78.97 41 | 73.30 39 | 83.86 47 | 81.60 57 | 88.15 59 | 88.83 51 |
|
Vis-MVSNet | | | 72.77 74 | 77.20 64 | 67.59 125 | 74.19 153 | 84.01 60 | 76.61 111 | 61.69 116 | 60.62 111 | 50.61 140 | 70.25 52 | 71.31 65 | 55.57 164 | 83.85 48 | 82.28 51 | 86.90 93 | 88.08 54 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
QAPM | | | 78.47 52 | 80.22 50 | 76.43 53 | 85.03 51 | 86.75 43 | 80.62 59 | 66.00 64 | 73.77 58 | 65.35 63 | 65.54 69 | 78.02 45 | 72.69 43 | 83.71 49 | 83.36 49 | 88.87 52 | 90.41 43 |
|
EPNet | | | 79.08 50 | 80.62 45 | 77.28 49 | 88.90 29 | 83.17 68 | 83.65 50 | 72.41 26 | 74.41 54 | 67.15 59 | 76.78 30 | 74.37 55 | 64.43 102 | 83.70 50 | 83.69 46 | 87.15 79 | 88.19 53 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
AdaColmap | | | 79.74 43 | 78.62 54 | 81.05 28 | 89.23 28 | 86.06 47 | 84.95 45 | 71.96 28 | 79.39 44 | 75.51 26 | 63.16 76 | 68.84 83 | 76.51 26 | 83.55 51 | 82.85 50 | 88.13 60 | 86.46 66 |
|
PVSNet_BlendedMVS | | | 76.21 58 | 77.52 60 | 74.69 61 | 79.46 79 | 83.79 62 | 77.50 101 | 64.34 77 | 69.88 62 | 71.88 39 | 68.54 59 | 70.42 70 | 67.05 69 | 83.48 52 | 79.63 84 | 87.89 66 | 86.87 64 |
|
PVSNet_Blended | | | 76.21 58 | 77.52 60 | 74.69 61 | 79.46 79 | 83.79 62 | 77.50 101 | 64.34 77 | 69.88 62 | 71.88 39 | 68.54 59 | 70.42 70 | 67.05 69 | 83.48 52 | 79.63 84 | 87.89 66 | 86.87 64 |
|
canonicalmvs | | | 79.16 48 | 82.37 38 | 75.41 56 | 82.33 63 | 86.38 46 | 80.80 58 | 63.18 83 | 82.90 31 | 67.34 58 | 72.79 43 | 76.07 50 | 69.62 60 | 83.46 54 | 84.41 41 | 89.20 45 | 90.60 40 |
|
ACMP | | 73.23 7 | 79.79 41 | 80.53 46 | 78.94 39 | 85.61 47 | 85.68 48 | 85.61 38 | 69.59 41 | 77.33 47 | 71.00 47 | 74.45 38 | 69.16 78 | 71.88 47 | 83.15 55 | 83.37 48 | 89.92 31 | 90.57 41 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
UA-Net | | | 74.47 65 | 77.80 57 | 70.59 87 | 85.33 48 | 85.40 52 | 73.54 143 | 65.98 65 | 60.65 110 | 56.00 110 | 72.11 45 | 79.15 40 | 54.63 169 | 83.13 56 | 82.25 52 | 88.04 62 | 81.92 126 |
|
TSAR-MVS + COLMAP | | | 78.34 53 | 81.64 40 | 74.48 63 | 80.13 77 | 85.01 55 | 81.73 54 | 65.93 66 | 84.75 24 | 61.68 73 | 85.79 15 | 66.27 90 | 71.39 53 | 82.91 57 | 80.78 65 | 86.01 132 | 85.98 68 |
|
CPTT-MVS | | | 81.77 33 | 83.10 34 | 80.21 32 | 85.93 45 | 86.45 45 | 87.72 29 | 70.98 33 | 82.54 34 | 71.53 44 | 74.23 40 | 81.49 34 | 76.31 27 | 82.85 58 | 81.87 54 | 88.79 53 | 92.26 26 |
|
MVS_111021_LR | | | 78.13 54 | 79.85 52 | 76.13 54 | 81.12 67 | 81.50 78 | 80.28 60 | 65.25 70 | 76.09 50 | 71.32 46 | 76.49 32 | 72.87 60 | 72.21 45 | 82.79 59 | 81.29 59 | 86.59 115 | 87.91 55 |
|
EPP-MVSNet | | | 74.00 68 | 77.41 62 | 70.02 101 | 80.53 73 | 83.91 61 | 74.99 121 | 62.68 101 | 65.06 79 | 49.77 146 | 68.68 57 | 72.09 62 | 63.06 108 | 82.49 60 | 80.73 66 | 89.12 48 | 88.91 50 |
|
OpenMVS | | 70.44 10 | 76.15 60 | 76.82 67 | 75.37 57 | 85.01 52 | 84.79 57 | 78.99 73 | 62.07 111 | 71.27 60 | 67.88 54 | 57.91 102 | 72.36 61 | 70.15 58 | 82.23 61 | 81.41 58 | 88.12 61 | 87.78 57 |
|
Fast-Effi-MVS+ | | | 73.11 72 | 73.66 75 | 72.48 69 | 77.72 101 | 80.88 87 | 78.55 88 | 58.83 163 | 65.19 78 | 60.36 77 | 59.98 86 | 62.42 101 | 71.22 55 | 81.66 62 | 80.61 76 | 88.20 58 | 84.88 89 |
|
TAPA-MVS | | 71.42 9 | 77.69 55 | 80.05 51 | 74.94 59 | 80.68 71 | 84.52 58 | 81.36 55 | 63.14 84 | 84.77 23 | 64.82 66 | 68.72 56 | 75.91 51 | 71.86 48 | 81.62 63 | 79.55 88 | 87.80 69 | 85.24 81 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CANet_DTU | | | 73.29 71 | 76.96 66 | 69.00 110 | 77.04 109 | 82.06 73 | 79.49 66 | 56.30 177 | 67.85 67 | 53.29 124 | 71.12 49 | 70.37 72 | 61.81 121 | 81.59 64 | 80.96 63 | 86.09 126 | 84.73 90 |
|
Effi-MVS+ | | | 75.28 63 | 76.20 69 | 74.20 64 | 81.15 66 | 83.24 66 | 81.11 56 | 63.13 85 | 66.37 70 | 60.27 78 | 64.30 74 | 68.88 82 | 70.93 57 | 81.56 65 | 81.69 56 | 88.61 54 | 87.35 60 |
|
FC-MVSNet-train | | | 72.60 75 | 75.07 73 | 69.71 105 | 81.10 68 | 78.79 116 | 73.74 141 | 65.23 71 | 66.10 73 | 53.34 123 | 70.36 51 | 63.40 98 | 56.92 149 | 81.44 66 | 80.96 63 | 87.93 64 | 84.46 92 |
|
MVSTER | | | 72.06 76 | 74.24 74 | 69.51 106 | 70.39 183 | 75.97 158 | 76.91 107 | 57.36 172 | 64.64 83 | 61.39 75 | 68.86 55 | 63.76 96 | 63.46 105 | 81.44 66 | 79.70 83 | 87.56 72 | 85.31 80 |
|
EG-PatchMatch MVS | | | 67.24 147 | 66.94 154 | 67.60 124 | 78.73 84 | 81.35 79 | 73.28 147 | 59.49 150 | 46.89 209 | 51.42 135 | 43.65 201 | 53.49 156 | 55.50 165 | 81.38 68 | 80.66 73 | 87.15 79 | 81.17 131 |
|
GBi-Net | | | 70.78 83 | 73.37 78 | 67.76 119 | 72.95 163 | 78.00 124 | 75.15 116 | 62.72 96 | 64.13 86 | 51.44 132 | 58.37 96 | 69.02 79 | 57.59 141 | 81.33 69 | 80.72 67 | 86.70 109 | 82.02 120 |
|
test1 | | | 70.78 83 | 73.37 78 | 67.76 119 | 72.95 163 | 78.00 124 | 75.15 116 | 62.72 96 | 64.13 86 | 51.44 132 | 58.37 96 | 69.02 79 | 57.59 141 | 81.33 69 | 80.72 67 | 86.70 109 | 82.02 120 |
|
FMVSNet1 | | | 68.84 119 | 70.47 95 | 66.94 140 | 71.35 180 | 77.68 132 | 74.71 123 | 62.35 110 | 56.93 143 | 49.94 145 | 50.01 183 | 64.59 94 | 57.07 147 | 81.33 69 | 80.72 67 | 86.25 118 | 82.00 123 |
|
Anonymous20240521 | | | 73.65 69 | 75.78 71 | 71.16 74 | 80.19 75 | 79.27 106 | 77.45 103 | 61.68 117 | 66.73 69 | 58.72 85 | 65.31 70 | 69.96 73 | 62.19 113 | 81.29 72 | 80.97 62 | 86.74 107 | 86.91 63 |
|
PCF-MVS | | 73.28 6 | 79.42 45 | 80.41 48 | 78.26 43 | 84.88 55 | 88.17 32 | 86.08 34 | 69.85 38 | 75.23 53 | 68.43 51 | 68.03 61 | 78.38 42 | 71.76 50 | 81.26 73 | 80.65 74 | 88.56 56 | 91.18 35 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
gg-mvs-nofinetune | | | 62.55 179 | 65.05 177 | 59.62 188 | 78.72 85 | 77.61 133 | 70.83 161 | 53.63 183 | 39.71 221 | 22.04 225 | 36.36 214 | 64.32 95 | 47.53 187 | 81.16 74 | 79.03 93 | 85.00 155 | 77.17 165 |
|
Anonymous202405211 | | | | 72.16 85 | | 80.85 70 | 81.85 75 | 76.88 108 | 65.40 69 | 62.89 95 | | 46.35 196 | 67.99 86 | 62.05 115 | 81.15 75 | 80.38 78 | 85.97 136 | 84.50 91 |
|
CNLPA | | | 77.20 56 | 77.54 59 | 76.80 52 | 82.63 60 | 84.31 59 | 79.77 63 | 64.64 74 | 85.17 20 | 73.18 34 | 56.37 110 | 69.81 74 | 74.53 31 | 81.12 76 | 78.69 95 | 86.04 131 | 87.29 62 |
|
UGNet | | | 72.78 73 | 77.67 58 | 67.07 138 | 71.65 175 | 83.24 66 | 75.20 115 | 63.62 80 | 64.93 80 | 56.72 104 | 71.82 47 | 73.30 57 | 49.02 185 | 81.02 77 | 80.70 72 | 86.22 119 | 88.67 52 |
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 |
DI_MVS_plusplus_trai | | | 75.13 64 | 76.12 70 | 73.96 65 | 78.18 87 | 81.55 76 | 80.97 57 | 62.54 105 | 68.59 66 | 65.13 65 | 61.43 79 | 74.81 54 | 69.32 62 | 81.01 78 | 79.59 86 | 87.64 71 | 85.89 69 |
|
FMVSNet2 | | | 70.39 88 | 72.67 82 | 67.72 122 | 72.95 163 | 78.00 124 | 75.15 116 | 62.69 100 | 63.29 91 | 51.25 136 | 55.64 114 | 68.49 85 | 57.59 141 | 80.91 79 | 80.35 79 | 86.70 109 | 82.02 120 |
|
Anonymous20231211 | | | 71.90 77 | 72.48 83 | 71.21 73 | 80.14 76 | 81.53 77 | 76.92 106 | 62.89 88 | 64.46 85 | 58.94 82 | 43.80 200 | 70.98 66 | 62.22 112 | 80.70 80 | 80.19 81 | 86.18 120 | 85.73 70 |
|
ACMH | | 65.37 14 | 70.71 85 | 70.00 97 | 71.54 72 | 82.51 62 | 82.47 72 | 77.78 98 | 68.13 50 | 56.19 159 | 46.06 166 | 54.30 137 | 51.20 185 | 68.68 65 | 80.66 81 | 80.72 67 | 86.07 127 | 84.45 93 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tfpn111 | | | 68.38 123 | 69.23 118 | 67.39 128 | 77.83 93 | 78.93 110 | 74.28 129 | 62.81 89 | 56.64 147 | 46.70 159 | 56.24 111 | 53.47 158 | 56.59 150 | 80.41 82 | 78.43 97 | 86.11 123 | 80.53 137 |
|
conf200view11 | | | 68.11 127 | 68.72 129 | 67.39 128 | 77.83 93 | 78.93 110 | 74.28 129 | 62.81 89 | 56.64 147 | 46.70 159 | 52.65 168 | 53.47 158 | 56.59 150 | 80.41 82 | 78.43 97 | 86.11 123 | 80.53 137 |
|
tfpn200view9 | | | 68.11 127 | 68.72 129 | 67.40 127 | 77.83 93 | 78.93 110 | 74.28 129 | 62.81 89 | 56.64 147 | 46.82 157 | 52.65 168 | 53.47 158 | 56.59 150 | 80.41 82 | 78.43 97 | 86.11 123 | 80.52 139 |
|
thres600view7 | | | 67.68 137 | 68.43 135 | 66.80 142 | 77.90 88 | 78.86 114 | 73.84 138 | 62.75 94 | 56.07 160 | 44.70 174 | 52.85 165 | 52.81 168 | 55.58 163 | 80.41 82 | 77.77 112 | 86.05 129 | 80.28 141 |
|
thres200 | | | 67.98 130 | 68.55 134 | 67.30 133 | 77.89 90 | 78.86 114 | 74.18 136 | 62.75 94 | 56.35 157 | 46.48 164 | 52.98 162 | 53.54 154 | 56.46 155 | 80.41 82 | 77.97 109 | 86.05 129 | 79.78 147 |
|
PLC | | 68.99 11 | 75.68 61 | 75.31 72 | 76.12 55 | 82.94 59 | 81.26 81 | 79.94 62 | 66.10 62 | 77.15 48 | 66.86 61 | 59.13 91 | 68.53 84 | 73.73 37 | 80.38 87 | 79.04 92 | 87.13 83 | 81.68 128 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
view600 | | | 67.63 141 | 68.36 136 | 66.77 143 | 77.84 92 | 78.66 117 | 73.74 141 | 62.62 103 | 56.04 161 | 44.98 171 | 52.86 164 | 52.83 167 | 55.48 166 | 80.36 88 | 77.75 113 | 85.95 138 | 80.02 144 |
|
conf0.01 | | | 67.72 136 | 67.99 141 | 67.39 128 | 77.82 98 | 78.94 108 | 74.28 129 | 62.81 89 | 56.64 147 | 46.70 159 | 53.33 154 | 48.59 198 | 56.59 150 | 80.34 89 | 78.43 97 | 86.16 122 | 79.67 148 |
|
conf0.002 | | | 67.52 144 | 67.64 145 | 67.39 128 | 77.80 100 | 78.94 108 | 74.28 129 | 62.81 89 | 56.64 147 | 46.70 159 | 53.65 150 | 46.28 206 | 56.59 150 | 80.33 90 | 78.37 102 | 86.17 121 | 79.23 152 |
|
LS3D | | | 74.08 67 | 73.39 77 | 74.88 60 | 85.05 50 | 82.62 71 | 79.71 64 | 68.66 47 | 72.82 59 | 58.80 84 | 57.61 103 | 61.31 103 | 71.07 56 | 80.32 91 | 78.87 94 | 86.00 134 | 80.18 142 |
|
view800 | | | 67.35 146 | 68.22 139 | 66.35 147 | 77.83 93 | 78.62 118 | 72.97 149 | 62.58 104 | 55.71 163 | 44.13 175 | 52.69 167 | 52.24 177 | 54.58 171 | 80.27 92 | 78.19 105 | 86.01 132 | 79.79 146 |
|
NR-MVSNet | | | 68.79 120 | 70.56 93 | 66.71 146 | 77.48 104 | 79.54 102 | 73.52 144 | 69.20 45 | 61.20 107 | 39.76 188 | 58.52 93 | 50.11 191 | 51.37 180 | 80.26 93 | 80.71 71 | 88.97 49 | 83.59 102 |
|
thres400 | | | 67.95 131 | 68.62 133 | 67.17 135 | 77.90 88 | 78.59 119 | 74.27 134 | 62.72 96 | 56.34 158 | 45.77 168 | 53.00 161 | 53.35 163 | 56.46 155 | 80.21 94 | 78.43 97 | 85.91 139 | 80.43 140 |
|
MVS_Test | | | 75.37 62 | 77.13 65 | 73.31 67 | 79.07 82 | 81.32 80 | 79.98 61 | 60.12 145 | 69.72 64 | 64.11 68 | 70.53 50 | 73.22 58 | 68.90 63 | 80.14 95 | 79.48 90 | 87.67 70 | 85.50 76 |
|
tfpn | | | 66.58 150 | 67.18 151 | 65.88 149 | 77.82 98 | 78.45 121 | 72.07 154 | 62.52 106 | 55.35 167 | 43.21 179 | 52.54 172 | 46.12 207 | 53.68 172 | 80.02 96 | 78.23 104 | 85.99 135 | 79.55 150 |
|
pm-mvs1 | | | 65.62 154 | 67.42 148 | 63.53 165 | 73.66 159 | 76.39 153 | 69.66 163 | 60.87 126 | 49.73 201 | 43.97 176 | 51.24 179 | 57.00 124 | 48.16 186 | 79.89 97 | 77.84 111 | 84.85 160 | 79.82 145 |
|
gm-plane-assit | | | 57.00 203 | 57.62 210 | 56.28 200 | 76.10 118 | 62.43 217 | 47.62 226 | 46.57 213 | 33.84 229 | 23.24 219 | 37.52 211 | 40.19 218 | 59.61 133 | 79.81 98 | 77.55 118 | 84.55 165 | 72.03 194 |
|
conf0.05thres1000 | | | 66.26 152 | 66.77 156 | 65.66 150 | 77.45 105 | 78.10 122 | 71.85 157 | 62.44 109 | 51.47 193 | 43.00 180 | 47.92 190 | 51.66 183 | 53.40 174 | 79.71 99 | 77.97 109 | 85.82 140 | 80.56 135 |
|
CDS-MVSNet | | | 67.65 139 | 69.83 104 | 65.09 152 | 75.39 126 | 76.55 149 | 74.42 127 | 63.75 79 | 53.55 182 | 49.37 148 | 59.41 89 | 62.45 100 | 44.44 197 | 79.71 99 | 79.82 82 | 83.17 173 | 77.36 164 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TranMVSNet+NR-MVSNet | | | 69.25 115 | 70.81 92 | 67.43 126 | 77.23 107 | 79.46 104 | 73.48 145 | 69.66 39 | 60.43 112 | 39.56 189 | 58.82 92 | 53.48 157 | 55.74 162 | 79.59 101 | 81.21 60 | 88.89 51 | 82.70 116 |
|
TransMVSNet (Re) | | | 64.74 164 | 65.66 170 | 63.66 164 | 77.40 106 | 75.33 163 | 69.86 162 | 62.67 102 | 47.63 207 | 41.21 186 | 50.01 183 | 52.33 173 | 45.31 196 | 79.57 102 | 77.69 115 | 85.49 147 | 77.07 168 |
|
UniMVSNet_NR-MVSNet | | | 70.59 86 | 72.19 84 | 68.72 112 | 77.72 101 | 80.72 88 | 73.81 139 | 69.65 40 | 61.99 99 | 43.23 177 | 60.54 82 | 57.50 114 | 58.57 135 | 79.56 103 | 81.07 61 | 89.34 43 | 83.97 95 |
|
UniMVSNet (Re) | | | 69.53 109 | 71.90 86 | 66.76 144 | 76.42 112 | 80.93 84 | 72.59 151 | 68.03 52 | 61.75 103 | 41.68 185 | 58.34 99 | 57.23 122 | 53.27 176 | 79.53 104 | 80.62 75 | 88.57 55 | 84.90 88 |
|
FMVSNet3 | | | 70.49 87 | 72.90 80 | 67.67 123 | 72.88 166 | 77.98 127 | 74.96 122 | 62.72 96 | 64.13 86 | 51.44 132 | 58.37 96 | 69.02 79 | 57.43 144 | 79.43 105 | 79.57 87 | 86.59 115 | 81.81 127 |
|
Vis-MVSNet (Re-imp) | | | 67.83 134 | 73.52 76 | 61.19 178 | 78.37 86 | 76.72 148 | 66.80 183 | 62.96 86 | 65.50 77 | 34.17 204 | 67.19 65 | 69.68 75 | 39.20 208 | 79.39 106 | 79.44 91 | 85.68 145 | 76.73 171 |
|
DU-MVS | | | 69.63 104 | 70.91 91 | 68.13 118 | 75.99 119 | 79.54 102 | 73.81 139 | 69.20 45 | 61.20 107 | 43.23 177 | 58.52 93 | 53.50 155 | 58.57 135 | 79.22 107 | 80.45 77 | 87.97 63 | 83.97 95 |
|
Baseline_NR-MVSNet | | | 67.53 143 | 68.77 127 | 66.09 148 | 75.99 119 | 74.75 172 | 72.43 152 | 68.41 48 | 61.33 106 | 38.33 193 | 51.31 178 | 54.13 150 | 56.03 158 | 79.22 107 | 78.19 105 | 85.37 149 | 82.45 118 |
|
diffmvs | | | 74.38 66 | 76.65 68 | 71.74 71 | 77.05 108 | 81.86 74 | 79.30 67 | 60.54 130 | 69.54 65 | 62.16 71 | 69.70 53 | 70.74 68 | 66.73 77 | 79.18 109 | 78.14 107 | 84.63 162 | 87.42 59 |
|
MS-PatchMatch | | | 70.17 95 | 70.49 94 | 69.79 103 | 80.98 69 | 77.97 129 | 77.51 100 | 58.95 155 | 62.33 97 | 55.22 114 | 53.14 159 | 65.90 91 | 62.03 116 | 79.08 110 | 77.11 127 | 84.08 167 | 77.91 160 |
|
MSDG | | | 71.52 80 | 69.87 101 | 73.44 66 | 82.21 64 | 79.35 105 | 79.52 65 | 64.59 75 | 66.15 72 | 61.87 72 | 53.21 158 | 56.09 135 | 65.85 99 | 78.94 111 | 78.50 96 | 86.60 114 | 76.85 170 |
|
ACMH+ | | 66.54 13 | 71.36 81 | 70.09 96 | 72.85 68 | 82.59 61 | 81.13 82 | 78.56 87 | 68.04 51 | 61.55 104 | 52.52 130 | 51.50 177 | 54.14 148 | 68.56 66 | 78.85 112 | 79.50 89 | 86.82 100 | 83.94 97 |
|
thres100view900 | | | 67.60 142 | 68.02 140 | 67.12 137 | 77.83 93 | 77.75 131 | 73.90 137 | 62.52 106 | 56.64 147 | 46.82 157 | 52.65 168 | 53.47 158 | 55.92 159 | 78.77 113 | 77.62 116 | 85.72 144 | 79.23 152 |
|
tfpnnormal | | | 64.27 168 | 63.64 187 | 65.02 153 | 75.84 122 | 75.61 160 | 71.24 160 | 62.52 106 | 47.79 206 | 42.97 181 | 42.65 203 | 44.49 211 | 52.66 178 | 78.77 113 | 76.86 130 | 84.88 158 | 79.29 151 |
|
CHOSEN 1792x2688 | | | 69.20 116 | 69.26 117 | 69.13 108 | 76.86 110 | 78.93 110 | 77.27 104 | 60.12 145 | 61.86 101 | 54.42 115 | 42.54 204 | 61.61 102 | 66.91 75 | 78.55 115 | 78.14 107 | 79.23 188 | 83.23 107 |
|
GA-MVS | | | 68.14 126 | 69.17 119 | 66.93 141 | 73.77 158 | 78.50 120 | 74.45 124 | 58.28 168 | 55.11 170 | 48.44 150 | 60.08 84 | 53.99 151 | 61.50 122 | 78.43 116 | 77.57 117 | 85.13 152 | 80.54 136 |
|
v11 | | | 69.37 113 | 68.65 132 | 70.20 98 | 74.87 134 | 76.97 145 | 78.29 95 | 58.55 167 | 56.38 156 | 56.04 109 | 54.02 146 | 54.98 142 | 66.47 81 | 78.30 117 | 76.91 129 | 86.97 91 | 83.02 108 |
|
v7 | | | 70.33 92 | 69.87 101 | 70.88 75 | 74.79 138 | 81.04 83 | 79.22 69 | 60.57 129 | 57.70 134 | 56.65 106 | 54.23 142 | 55.29 140 | 66.95 72 | 78.28 118 | 77.47 119 | 87.12 86 | 85.05 85 |
|
v10 | | | 70.22 94 | 69.76 105 | 70.74 81 | 74.79 138 | 80.30 99 | 79.22 69 | 59.81 148 | 57.71 133 | 56.58 107 | 54.22 144 | 55.31 138 | 66.95 72 | 78.28 118 | 77.47 119 | 87.12 86 | 85.07 84 |
|
v1144 | | | 69.93 103 | 69.36 116 | 70.61 86 | 74.89 131 | 80.93 84 | 79.11 71 | 60.64 127 | 55.97 162 | 55.31 113 | 53.85 149 | 54.14 148 | 66.54 80 | 78.10 120 | 77.44 121 | 87.14 82 | 85.09 83 |
|
v13 | | | 69.52 110 | 68.76 128 | 70.41 94 | 74.88 132 | 77.02 144 | 78.52 92 | 58.86 157 | 56.61 153 | 56.91 98 | 54.00 147 | 56.17 134 | 66.11 94 | 77.93 121 | 76.74 138 | 87.21 77 | 82.83 109 |
|
v12 | | | 69.54 108 | 68.79 126 | 70.41 94 | 74.88 132 | 77.03 142 | 78.54 91 | 58.85 159 | 56.71 145 | 56.87 100 | 54.13 145 | 56.23 133 | 66.15 90 | 77.89 122 | 76.74 138 | 87.17 78 | 82.80 110 |
|
v1192 | | | 69.50 111 | 68.83 124 | 70.29 97 | 74.49 151 | 80.92 86 | 78.55 88 | 60.54 130 | 55.04 171 | 54.21 116 | 52.79 166 | 52.33 173 | 66.92 74 | 77.88 123 | 77.35 124 | 87.04 89 | 85.51 75 |
|
V9 | | | 69.58 107 | 68.83 124 | 70.46 91 | 74.85 135 | 77.04 140 | 78.65 86 | 58.85 159 | 56.83 144 | 57.12 96 | 54.26 140 | 56.31 128 | 66.14 92 | 77.83 124 | 76.76 133 | 87.13 83 | 82.79 112 |
|
V14 | | | 69.59 106 | 68.86 123 | 70.45 93 | 74.83 136 | 77.04 140 | 78.70 85 | 58.83 163 | 56.95 141 | 57.08 97 | 54.41 136 | 56.34 127 | 66.15 90 | 77.77 125 | 76.76 133 | 87.08 88 | 82.74 115 |
|
v7n | | | 67.05 149 | 66.94 154 | 67.17 135 | 72.35 168 | 78.97 107 | 73.26 148 | 58.88 156 | 51.16 194 | 50.90 137 | 48.21 188 | 50.11 191 | 60.96 124 | 77.70 126 | 77.38 122 | 86.68 112 | 85.05 85 |
|
v15 | | | 69.61 105 | 68.88 122 | 70.46 91 | 74.81 137 | 77.03 142 | 78.75 84 | 58.83 163 | 57.06 137 | 57.18 95 | 54.55 135 | 56.37 126 | 66.13 93 | 77.70 126 | 76.76 133 | 87.03 90 | 82.69 117 |
|
pmmvs6 | | | 62.41 182 | 62.88 190 | 61.87 175 | 71.38 179 | 75.18 170 | 67.76 174 | 59.45 152 | 41.64 217 | 42.52 184 | 37.33 212 | 52.91 166 | 46.87 191 | 77.67 128 | 76.26 151 | 83.23 172 | 79.18 154 |
|
v16 | | | 70.07 97 | 69.46 110 | 70.79 79 | 74.74 144 | 77.08 138 | 78.79 81 | 58.86 157 | 59.75 116 | 59.15 81 | 54.87 129 | 57.33 117 | 66.38 83 | 77.61 129 | 76.77 131 | 86.81 105 | 82.79 112 |
|
v17 | | | 70.03 99 | 69.43 115 | 70.72 83 | 74.75 143 | 77.09 137 | 78.78 83 | 58.85 159 | 59.53 119 | 58.72 85 | 54.87 129 | 57.39 116 | 66.38 83 | 77.60 130 | 76.75 136 | 86.83 99 | 82.80 110 |
|
v1neww | | | 70.34 90 | 69.93 99 | 70.82 77 | 74.68 146 | 80.61 90 | 78.80 79 | 60.17 140 | 58.74 124 | 58.10 90 | 55.00 124 | 57.28 120 | 66.33 86 | 77.53 131 | 76.74 138 | 86.82 100 | 83.61 100 |
|
v7new | | | 70.34 90 | 69.93 99 | 70.82 77 | 74.68 146 | 80.61 90 | 78.80 79 | 60.17 140 | 58.74 124 | 58.10 90 | 55.00 124 | 57.28 120 | 66.33 86 | 77.53 131 | 76.74 138 | 86.82 100 | 83.61 100 |
|
v6 | | | 70.35 89 | 69.94 98 | 70.83 76 | 74.68 146 | 80.62 89 | 78.81 78 | 60.16 143 | 58.81 122 | 58.17 89 | 55.01 123 | 57.31 119 | 66.32 88 | 77.53 131 | 76.73 142 | 86.82 100 | 83.62 99 |
|
v8 | | | 70.23 93 | 69.86 103 | 70.67 85 | 74.69 145 | 79.82 101 | 78.79 81 | 59.18 153 | 58.80 123 | 58.20 88 | 55.00 124 | 57.33 117 | 66.31 89 | 77.51 134 | 76.71 146 | 86.82 100 | 83.88 98 |
|
v18 | | | 70.10 96 | 69.52 108 | 70.77 80 | 74.66 149 | 77.06 139 | 78.84 76 | 58.84 162 | 60.01 115 | 59.23 80 | 55.06 122 | 57.47 115 | 66.34 85 | 77.50 135 | 76.75 136 | 86.71 108 | 82.77 114 |
|
V42 | | | 68.76 121 | 69.63 106 | 67.74 121 | 64.93 204 | 78.01 123 | 78.30 94 | 56.48 176 | 58.65 126 | 56.30 108 | 54.26 140 | 57.03 123 | 64.85 101 | 77.47 136 | 77.01 128 | 85.60 146 | 84.96 87 |
|
v1141 | | | 69.96 102 | 69.44 113 | 70.58 89 | 74.78 140 | 80.50 94 | 78.85 74 | 60.30 135 | 56.95 141 | 56.74 103 | 54.68 133 | 56.26 132 | 65.93 96 | 77.38 137 | 76.72 143 | 86.88 96 | 83.57 105 |
|
divwei89l23v2f112 | | | 69.97 100 | 69.44 113 | 70.58 89 | 74.78 140 | 80.50 94 | 78.85 74 | 60.30 135 | 56.97 140 | 56.75 102 | 54.67 134 | 56.27 131 | 65.92 97 | 77.37 138 | 76.72 143 | 86.88 96 | 83.58 104 |
|
v1 | | | 69.97 100 | 69.45 112 | 70.59 87 | 74.78 140 | 80.51 93 | 78.84 76 | 60.30 135 | 56.98 138 | 56.81 101 | 54.69 132 | 56.29 130 | 65.91 98 | 77.37 138 | 76.71 146 | 86.89 95 | 83.59 102 |
|
tfpn_n400 | | | 64.23 169 | 66.05 163 | 62.12 173 | 76.20 115 | 75.24 164 | 67.43 176 | 61.15 121 | 54.04 180 | 36.38 198 | 55.35 118 | 51.89 179 | 46.94 189 | 77.31 140 | 76.15 155 | 84.59 163 | 72.36 192 |
|
tfpnconf | | | 64.23 169 | 66.05 163 | 62.12 173 | 76.20 115 | 75.24 164 | 67.43 176 | 61.15 121 | 54.04 180 | 36.38 198 | 55.35 118 | 51.89 179 | 46.94 189 | 77.31 140 | 76.15 155 | 84.59 163 | 72.36 192 |
|
tfpnview11 | | | 64.33 167 | 66.17 162 | 62.18 171 | 76.25 114 | 75.23 166 | 67.45 175 | 61.16 120 | 55.50 165 | 36.38 198 | 55.35 118 | 51.89 179 | 46.96 188 | 77.28 142 | 76.10 157 | 84.86 159 | 71.85 195 |
|
v2v482 | | | 70.05 98 | 69.46 110 | 70.74 81 | 74.62 150 | 80.32 98 | 79.00 72 | 60.62 128 | 57.41 135 | 56.89 99 | 55.43 117 | 55.14 141 | 66.39 82 | 77.25 143 | 77.14 126 | 86.90 93 | 83.57 105 |
|
v1921920 | | | 69.03 117 | 68.32 137 | 69.86 102 | 74.03 155 | 80.37 97 | 77.55 99 | 60.25 139 | 54.62 174 | 53.59 122 | 52.36 173 | 51.50 184 | 66.75 76 | 77.17 144 | 76.69 148 | 86.96 92 | 85.56 72 |
|
thresconf0.02 | | | 64.77 163 | 65.90 166 | 63.44 166 | 76.37 113 | 75.17 171 | 69.51 165 | 61.28 119 | 56.98 138 | 39.01 191 | 56.24 111 | 48.68 197 | 49.78 183 | 77.13 145 | 75.61 161 | 84.71 161 | 71.53 196 |
|
v144192 | | | 69.34 114 | 68.68 131 | 70.12 99 | 74.06 154 | 80.54 92 | 78.08 97 | 60.54 130 | 54.99 173 | 54.13 117 | 52.92 163 | 52.80 169 | 66.73 77 | 77.13 145 | 76.72 143 | 87.15 79 | 85.63 71 |
|
IterMVS-LS | | | 71.69 79 | 72.82 81 | 70.37 96 | 77.54 103 | 76.34 154 | 75.13 119 | 60.46 133 | 61.53 105 | 57.57 93 | 64.89 71 | 67.33 87 | 66.04 95 | 77.09 147 | 77.37 123 | 85.48 148 | 85.18 82 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+-dtu | | | 71.82 78 | 71.86 87 | 71.78 70 | 78.77 83 | 80.47 96 | 78.55 88 | 61.67 118 | 60.68 109 | 55.49 111 | 58.48 95 | 65.48 92 | 68.85 64 | 76.92 148 | 75.55 163 | 87.35 75 | 85.46 77 |
|
COLMAP_ROB | | 62.73 15 | 67.66 138 | 66.76 157 | 68.70 113 | 80.49 74 | 77.98 127 | 75.29 114 | 62.95 87 | 63.62 89 | 49.96 144 | 47.32 195 | 50.72 188 | 58.57 135 | 76.87 149 | 75.50 164 | 84.94 157 | 75.33 180 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
v1240 | | | 68.64 122 | 67.89 144 | 69.51 106 | 73.89 157 | 80.26 100 | 76.73 109 | 59.97 147 | 53.43 183 | 53.08 125 | 51.82 176 | 50.84 187 | 66.62 79 | 76.79 150 | 76.77 131 | 86.78 106 | 85.34 79 |
|
IB-MVS | | 66.94 12 | 71.21 82 | 71.66 88 | 70.68 84 | 79.18 81 | 82.83 70 | 72.61 150 | 61.77 115 | 59.66 117 | 63.44 70 | 53.26 156 | 59.65 108 | 59.16 134 | 76.78 151 | 82.11 53 | 87.90 65 | 87.33 61 |
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 |
anonymousdsp | | | 65.28 156 | 67.98 142 | 62.13 172 | 58.73 218 | 73.98 176 | 67.10 180 | 50.69 200 | 48.41 204 | 47.66 156 | 54.27 138 | 52.75 170 | 61.45 123 | 76.71 152 | 80.20 80 | 87.13 83 | 89.53 48 |
|
tfpn_ndepth | | | 65.09 160 | 67.12 152 | 62.73 169 | 75.75 124 | 76.23 155 | 68.00 172 | 60.36 134 | 58.16 127 | 40.27 187 | 54.89 128 | 54.22 147 | 46.80 192 | 76.69 153 | 75.66 160 | 85.19 151 | 73.98 188 |
|
USDC | | | 67.36 145 | 67.90 143 | 66.74 145 | 71.72 173 | 75.23 166 | 71.58 158 | 60.28 138 | 67.45 68 | 50.54 141 | 60.93 80 | 45.20 210 | 62.08 114 | 76.56 154 | 74.50 170 | 84.25 166 | 75.38 179 |
|
HyFIR lowres test | | | 69.47 112 | 68.94 121 | 70.09 100 | 76.77 111 | 82.93 69 | 76.63 110 | 60.17 140 | 59.00 121 | 54.03 118 | 40.54 210 | 65.23 93 | 67.89 68 | 76.54 155 | 78.30 103 | 85.03 154 | 80.07 143 |
|
Fast-Effi-MVS+-dtu | | | 68.34 124 | 69.47 109 | 67.01 139 | 75.15 127 | 77.97 129 | 77.12 105 | 55.40 180 | 57.87 128 | 46.68 163 | 56.17 113 | 60.39 104 | 62.36 111 | 76.32 156 | 76.25 152 | 85.35 150 | 81.34 129 |
|
tfpn1000 | | | 63.81 173 | 66.31 159 | 60.90 180 | 75.76 123 | 75.74 159 | 65.14 192 | 60.14 144 | 56.47 154 | 35.99 201 | 55.11 121 | 52.30 175 | 43.42 200 | 76.21 157 | 75.34 165 | 84.97 156 | 73.01 191 |
|
TDRefinement | | | 66.09 153 | 65.03 178 | 67.31 132 | 69.73 187 | 76.75 147 | 75.33 112 | 64.55 76 | 60.28 113 | 49.72 147 | 45.63 198 | 42.83 213 | 60.46 129 | 75.75 158 | 75.95 158 | 84.08 167 | 78.04 159 |
|
v52 | | | 65.23 157 | 66.24 160 | 64.06 160 | 61.94 208 | 76.42 151 | 72.06 155 | 54.30 182 | 49.94 198 | 50.04 143 | 47.41 193 | 52.42 171 | 60.23 131 | 75.71 159 | 76.22 153 | 85.78 141 | 85.56 72 |
|
V4 | | | 65.23 157 | 66.23 161 | 64.06 160 | 61.94 208 | 76.42 151 | 72.05 156 | 54.31 181 | 49.91 200 | 50.06 142 | 47.42 192 | 52.40 172 | 60.24 130 | 75.71 159 | 76.22 153 | 85.78 141 | 85.56 72 |
|
PatchMatch-RL | | | 67.78 135 | 66.65 158 | 69.10 109 | 73.01 162 | 72.69 179 | 68.49 170 | 61.85 114 | 62.93 94 | 60.20 79 | 56.83 109 | 50.42 189 | 69.52 61 | 75.62 161 | 74.46 171 | 81.51 177 | 73.62 189 |
|
EPNet_dtu | | | 68.08 129 | 71.00 90 | 64.67 157 | 79.64 78 | 68.62 194 | 75.05 120 | 63.30 82 | 66.36 71 | 45.27 170 | 67.40 64 | 66.84 89 | 43.64 199 | 75.37 162 | 74.98 169 | 81.15 179 | 77.44 163 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v148 | | | 67.85 133 | 67.53 146 | 68.23 116 | 73.25 161 | 77.57 135 | 74.26 135 | 57.36 172 | 55.70 164 | 57.45 94 | 53.53 151 | 55.42 137 | 61.96 117 | 75.23 163 | 73.92 172 | 85.08 153 | 81.32 130 |
|
ambc | | | | 53.42 213 | | 64.99 203 | 63.36 211 | 49.96 222 | | 47.07 208 | 37.12 196 | 28.97 224 | 16.36 238 | 41.82 202 | 75.10 164 | 67.34 201 | 71.55 215 | 75.72 175 |
|
TinyColmap | | | 62.84 177 | 61.03 202 | 64.96 155 | 69.61 188 | 71.69 182 | 68.48 171 | 59.76 149 | 55.41 166 | 47.69 155 | 47.33 194 | 34.20 223 | 62.76 110 | 74.52 165 | 72.59 179 | 81.44 178 | 71.47 197 |
|
LTVRE_ROB | | 59.44 16 | 61.82 191 | 62.64 193 | 60.87 181 | 72.83 167 | 77.19 136 | 64.37 196 | 58.97 154 | 33.56 230 | 28.00 212 | 52.59 171 | 42.21 214 | 63.93 104 | 74.52 165 | 76.28 150 | 77.15 195 | 82.13 119 |
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 |
PMMVS | | | 65.06 161 | 69.17 119 | 60.26 184 | 55.25 227 | 63.43 210 | 66.71 184 | 43.01 224 | 62.41 96 | 50.64 139 | 69.44 54 | 67.04 88 | 63.29 107 | 74.36 167 | 73.54 174 | 82.68 174 | 73.99 187 |
|
IterMVS | | | 66.36 151 | 68.30 138 | 64.10 159 | 69.48 190 | 74.61 173 | 73.41 146 | 50.79 199 | 57.30 136 | 48.28 151 | 60.64 81 | 59.92 107 | 60.85 128 | 74.14 168 | 72.66 178 | 81.80 176 | 78.82 156 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
WR-MVS | | | 63.03 175 | 67.40 149 | 57.92 194 | 75.14 128 | 77.60 134 | 60.56 207 | 66.10 62 | 54.11 179 | 23.88 216 | 53.94 148 | 53.58 153 | 34.50 212 | 73.93 169 | 77.71 114 | 87.35 75 | 80.94 132 |
|
pmmvs4 | | | 67.89 132 | 67.39 150 | 68.48 115 | 71.60 177 | 73.57 177 | 74.45 124 | 60.98 124 | 64.65 82 | 57.97 92 | 54.95 127 | 51.73 182 | 61.88 118 | 73.78 170 | 75.11 167 | 83.99 169 | 77.91 160 |
|
CHOSEN 280x420 | | | 58.70 200 | 61.88 199 | 54.98 204 | 55.45 226 | 50.55 230 | 64.92 193 | 40.36 226 | 55.21 168 | 38.13 194 | 48.31 187 | 63.76 96 | 63.03 109 | 73.73 171 | 68.58 197 | 68.00 222 | 73.04 190 |
|
v748 | | | 65.12 159 | 65.24 173 | 64.98 154 | 69.77 186 | 76.45 150 | 69.47 166 | 57.06 174 | 49.93 199 | 50.70 138 | 47.87 191 | 49.50 195 | 57.14 146 | 73.64 172 | 75.18 166 | 85.75 143 | 84.14 94 |
|
MIMVSNet | | | 58.52 201 | 61.34 201 | 55.22 203 | 60.76 211 | 67.01 199 | 66.81 182 | 49.02 205 | 56.43 155 | 38.90 192 | 40.59 209 | 54.54 146 | 40.57 207 | 73.16 173 | 71.65 181 | 75.30 204 | 66.00 208 |
|
pmmvs5 | | | 62.37 185 | 64.04 184 | 60.42 182 | 65.03 202 | 71.67 183 | 67.17 179 | 52.70 190 | 50.30 195 | 44.80 172 | 54.23 142 | 51.19 186 | 49.37 184 | 72.88 174 | 73.48 175 | 83.45 170 | 74.55 183 |
|
pmmvs-eth3d | | | 63.52 174 | 62.44 196 | 64.77 156 | 66.82 198 | 70.12 188 | 69.41 167 | 59.48 151 | 54.34 178 | 52.71 126 | 46.24 197 | 44.35 212 | 56.93 148 | 72.37 175 | 73.77 173 | 83.30 171 | 75.91 173 |
|
FMVSNet5 | | | 57.24 202 | 60.02 205 | 53.99 207 | 56.45 222 | 62.74 214 | 65.27 191 | 47.03 212 | 55.14 169 | 39.55 190 | 40.88 207 | 53.42 162 | 41.83 201 | 72.35 176 | 71.10 185 | 73.79 208 | 64.50 211 |
|
TAMVS | | | 59.58 198 | 62.81 192 | 55.81 201 | 66.03 200 | 65.64 204 | 63.86 198 | 48.74 206 | 49.95 197 | 37.07 197 | 54.77 131 | 58.54 111 | 44.44 197 | 72.29 177 | 71.79 180 | 74.70 205 | 66.66 207 |
|
CMPMVS | | 47.78 17 | 62.49 181 | 62.52 194 | 62.46 170 | 70.01 185 | 70.66 187 | 62.97 201 | 51.84 194 | 51.98 189 | 56.71 105 | 42.87 202 | 53.62 152 | 57.80 140 | 72.23 178 | 70.37 187 | 75.45 203 | 75.91 173 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
DTE-MVSNet | | | 61.85 188 | 64.96 179 | 58.22 193 | 74.32 152 | 74.39 174 | 61.01 206 | 67.85 54 | 51.76 192 | 21.91 226 | 53.28 155 | 48.17 199 | 37.74 209 | 72.22 179 | 76.44 149 | 86.52 117 | 78.49 157 |
|
CR-MVSNet | | | 64.83 162 | 65.54 171 | 64.01 162 | 70.64 182 | 69.41 189 | 65.97 188 | 52.74 188 | 57.81 130 | 52.65 127 | 54.27 138 | 56.31 128 | 60.92 125 | 72.20 180 | 73.09 176 | 81.12 180 | 75.69 176 |
|
PatchT | | | 61.97 187 | 64.04 184 | 59.55 189 | 60.49 212 | 67.40 197 | 56.54 214 | 48.65 207 | 56.69 146 | 52.65 127 | 51.10 180 | 52.14 178 | 60.92 125 | 72.20 180 | 73.09 176 | 78.03 191 | 75.69 176 |
|
PEN-MVS | | | 62.96 176 | 65.77 169 | 59.70 187 | 73.98 156 | 75.45 161 | 63.39 200 | 67.61 55 | 52.49 186 | 25.49 215 | 53.39 152 | 49.12 196 | 40.85 206 | 71.94 182 | 77.26 125 | 86.86 98 | 80.72 134 |
|
CVMVSNet | | | 62.55 179 | 65.89 167 | 58.64 192 | 66.95 196 | 69.15 191 | 66.49 187 | 56.29 178 | 52.46 187 | 32.70 205 | 59.27 90 | 58.21 113 | 50.09 182 | 71.77 183 | 71.39 183 | 79.31 187 | 78.99 155 |
|
RPSCF | | | 67.64 140 | 71.25 89 | 63.43 167 | 61.86 210 | 70.73 186 | 67.26 178 | 50.86 198 | 74.20 56 | 58.91 83 | 67.49 63 | 69.33 76 | 64.10 103 | 71.41 184 | 68.45 199 | 77.61 192 | 77.17 165 |
|
CP-MVSNet | | | 62.68 178 | 65.49 172 | 59.40 190 | 71.84 171 | 75.34 162 | 62.87 202 | 67.04 58 | 52.64 185 | 27.19 213 | 53.38 153 | 48.15 200 | 41.40 204 | 71.26 185 | 75.68 159 | 86.07 127 | 82.00 123 |
|
test0.0.03 1 | | | 58.80 199 | 61.58 200 | 55.56 202 | 75.02 129 | 68.45 195 | 59.58 211 | 61.96 112 | 52.74 184 | 29.57 208 | 49.75 186 | 54.56 145 | 31.46 215 | 71.19 186 | 69.77 188 | 75.75 199 | 64.57 210 |
|
FC-MVSNet-test | | | 56.90 204 | 65.20 175 | 47.21 215 | 66.98 195 | 63.20 212 | 49.11 224 | 58.60 166 | 59.38 120 | 11.50 235 | 65.60 68 | 56.68 125 | 24.66 227 | 71.17 187 | 71.36 184 | 72.38 212 | 69.02 203 |
|
PS-CasMVS | | | 62.38 184 | 65.06 176 | 59.25 191 | 71.73 172 | 75.21 169 | 62.77 203 | 66.99 59 | 51.94 191 | 26.96 214 | 52.00 175 | 47.52 203 | 41.06 205 | 71.16 188 | 75.60 162 | 85.97 136 | 81.97 125 |
|
WR-MVS_H | | | 61.83 190 | 65.87 168 | 57.12 197 | 71.72 173 | 76.87 146 | 61.45 205 | 66.19 60 | 51.97 190 | 22.92 223 | 53.13 160 | 52.30 175 | 33.80 213 | 71.03 189 | 75.00 168 | 86.65 113 | 80.78 133 |
|
test-mter | | | 60.84 194 | 64.62 181 | 56.42 199 | 55.99 225 | 64.18 205 | 65.39 190 | 34.23 232 | 54.39 177 | 46.21 165 | 57.40 106 | 59.49 109 | 55.86 160 | 71.02 190 | 69.65 189 | 80.87 182 | 76.20 172 |
|
tpmp4_e23 | | | 68.32 125 | 67.08 153 | 69.76 104 | 77.86 91 | 75.22 168 | 78.37 93 | 56.17 179 | 66.06 74 | 64.27 67 | 57.15 107 | 54.89 143 | 63.40 106 | 70.97 191 | 68.29 200 | 78.46 190 | 77.00 169 |
|
test-LLR | | | 64.42 165 | 64.36 182 | 64.49 158 | 75.02 129 | 63.93 207 | 66.61 185 | 61.96 112 | 54.41 175 | 47.77 153 | 57.46 104 | 60.25 105 | 55.20 167 | 70.80 192 | 69.33 190 | 80.40 183 | 74.38 184 |
|
TESTMET0.1,1 | | | 61.10 193 | 64.36 182 | 57.29 196 | 57.53 220 | 63.93 207 | 66.61 185 | 36.22 230 | 54.41 175 | 47.77 153 | 57.46 104 | 60.25 105 | 55.20 167 | 70.80 192 | 69.33 190 | 80.40 183 | 74.38 184 |
|
GG-mvs-BLEND | | | 46.86 220 | 67.51 147 | 22.75 233 | 0.05 242 | 76.21 156 | 64.69 194 | 0.04 239 | 61.90 100 | 0.09 243 | 55.57 115 | 71.32 64 | 0.08 239 | 70.54 194 | 67.19 203 | 71.58 214 | 69.86 200 |
|
testgi | | | 54.39 209 | 57.86 208 | 50.35 212 | 71.59 178 | 67.24 198 | 54.95 216 | 53.25 185 | 43.36 214 | 23.78 217 | 44.64 199 | 47.87 201 | 24.96 224 | 70.45 195 | 68.66 196 | 73.60 209 | 62.78 215 |
|
Anonymous20231206 | | | 56.36 205 | 57.80 209 | 54.67 205 | 70.08 184 | 66.39 201 | 60.46 208 | 57.54 170 | 49.50 203 | 29.30 209 | 33.86 220 | 46.64 204 | 35.18 211 | 70.44 196 | 68.88 194 | 75.47 202 | 68.88 204 |
|
test20.03 | | | 53.93 210 | 56.28 211 | 51.19 211 | 72.19 170 | 65.83 202 | 53.20 218 | 61.08 123 | 42.74 215 | 22.08 224 | 37.07 213 | 45.76 209 | 24.29 228 | 70.44 196 | 69.04 192 | 74.31 207 | 63.05 214 |
|
CostFormer | | | 68.92 118 | 69.58 107 | 68.15 117 | 75.98 121 | 76.17 157 | 78.22 96 | 51.86 193 | 65.80 75 | 61.56 74 | 63.57 75 | 62.83 99 | 61.85 119 | 70.40 198 | 68.67 195 | 79.42 186 | 79.62 149 |
|
DWT-MVSNet_training | | | 67.24 147 | 65.96 165 | 68.74 111 | 76.15 117 | 74.36 175 | 74.37 128 | 56.66 175 | 61.82 102 | 60.51 76 | 58.23 101 | 49.76 193 | 65.07 100 | 70.04 199 | 70.39 186 | 79.70 185 | 77.11 167 |
|
SixPastTwentyTwo | | | 61.84 189 | 62.45 195 | 61.12 179 | 69.20 191 | 72.20 180 | 62.03 204 | 57.40 171 | 46.54 210 | 38.03 195 | 57.14 108 | 41.72 215 | 58.12 139 | 69.67 200 | 71.58 182 | 81.94 175 | 78.30 158 |
|
dps | | | 64.00 172 | 62.99 189 | 65.18 151 | 73.29 160 | 72.07 181 | 68.98 169 | 53.07 186 | 57.74 132 | 58.41 87 | 55.55 116 | 47.74 202 | 60.89 127 | 69.53 201 | 67.14 204 | 76.44 198 | 71.19 198 |
|
MDTV_nov1_ep13 | | | 64.37 166 | 65.24 173 | 63.37 168 | 68.94 192 | 70.81 185 | 72.40 153 | 50.29 202 | 60.10 114 | 53.91 120 | 60.07 85 | 59.15 110 | 57.21 145 | 69.43 202 | 67.30 202 | 77.47 193 | 69.78 201 |
|
PM-MVS | | | 60.48 195 | 60.94 203 | 59.94 185 | 58.85 217 | 66.83 200 | 64.27 197 | 51.39 196 | 55.03 172 | 48.03 152 | 50.00 185 | 40.79 217 | 58.26 138 | 69.20 203 | 67.13 205 | 78.84 189 | 77.60 162 |
|
MDTV_nov1_ep13_2view | | | 60.16 196 | 60.51 204 | 59.75 186 | 65.39 201 | 69.05 192 | 68.00 172 | 48.29 209 | 51.99 188 | 45.95 167 | 48.01 189 | 49.64 194 | 53.39 175 | 68.83 204 | 66.52 206 | 77.47 193 | 69.55 202 |
|
PatchmatchNet | | | 64.21 171 | 64.65 180 | 63.69 163 | 71.29 181 | 68.66 193 | 69.63 164 | 51.70 195 | 63.04 92 | 53.77 121 | 59.83 88 | 58.34 112 | 60.23 131 | 68.54 205 | 66.06 207 | 75.56 201 | 68.08 205 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MIMVSNet1 | | | 49.27 214 | 53.25 214 | 44.62 219 | 44.61 232 | 61.52 218 | 53.61 217 | 52.18 191 | 41.62 218 | 18.68 228 | 28.14 228 | 41.58 216 | 25.50 222 | 68.46 206 | 69.04 192 | 73.15 210 | 62.37 216 |
|
RPMNet | | | 61.71 192 | 62.88 190 | 60.34 183 | 69.51 189 | 69.41 189 | 63.48 199 | 49.23 203 | 57.81 130 | 45.64 169 | 50.51 181 | 50.12 190 | 53.13 177 | 68.17 207 | 68.49 198 | 81.07 181 | 75.62 178 |
|
tpm | | | 62.41 182 | 63.15 188 | 61.55 177 | 72.24 169 | 63.79 209 | 71.31 159 | 46.12 215 | 57.82 129 | 55.33 112 | 59.90 87 | 54.74 144 | 53.63 173 | 67.24 208 | 64.29 210 | 70.65 217 | 74.25 186 |
|
tpm cat1 | | | 65.41 155 | 63.81 186 | 67.28 134 | 75.61 125 | 72.88 178 | 75.32 113 | 52.85 187 | 62.97 93 | 63.66 69 | 53.24 157 | 53.29 165 | 61.83 120 | 65.54 209 | 64.14 212 | 74.43 206 | 74.60 182 |
|
EU-MVSNet | | | 54.63 207 | 58.69 206 | 49.90 213 | 56.99 221 | 62.70 215 | 56.41 215 | 50.64 201 | 45.95 212 | 23.14 220 | 50.42 182 | 46.51 205 | 36.63 210 | 65.51 210 | 64.85 209 | 75.57 200 | 74.91 181 |
|
EPMVS | | | 60.00 197 | 61.97 198 | 57.71 195 | 68.46 193 | 63.17 213 | 64.54 195 | 48.23 210 | 63.30 90 | 44.72 173 | 60.19 83 | 56.05 136 | 50.85 181 | 65.27 211 | 62.02 217 | 69.44 219 | 63.81 212 |
|
LP | | | 53.62 211 | 53.43 212 | 53.83 208 | 58.51 219 | 62.59 216 | 57.31 213 | 46.04 216 | 47.86 205 | 42.69 183 | 36.08 216 | 36.86 221 | 46.53 193 | 64.38 212 | 64.25 211 | 71.92 213 | 62.00 217 |
|
pmmvs3 | | | 47.65 215 | 49.08 220 | 45.99 217 | 44.61 232 | 54.79 225 | 50.04 221 | 31.95 235 | 33.91 228 | 29.90 207 | 30.37 222 | 33.53 224 | 46.31 194 | 63.50 213 | 63.67 213 | 73.14 211 | 63.77 213 |
|
tpmrst | | | 62.00 186 | 62.35 197 | 61.58 176 | 71.62 176 | 64.14 206 | 69.07 168 | 48.22 211 | 62.21 98 | 53.93 119 | 58.26 100 | 55.30 139 | 55.81 161 | 63.22 214 | 62.62 215 | 70.85 216 | 70.70 199 |
|
MVS-HIRNet | | | 54.41 208 | 52.10 216 | 57.11 198 | 58.99 216 | 56.10 222 | 49.68 223 | 49.10 204 | 46.18 211 | 52.15 131 | 33.18 221 | 46.11 208 | 56.10 157 | 63.19 215 | 59.70 223 | 76.64 197 | 60.25 219 |
|
test2356 | | | 47.20 218 | 48.62 222 | 45.54 218 | 56.38 223 | 54.89 224 | 50.62 220 | 45.08 219 | 38.65 222 | 23.40 218 | 36.23 215 | 31.10 227 | 29.31 218 | 62.76 216 | 62.49 216 | 68.48 221 | 54.23 227 |
|
testus | | | 45.61 222 | 49.06 221 | 41.59 223 | 56.13 224 | 55.28 223 | 43.51 228 | 39.64 228 | 37.74 223 | 18.23 229 | 35.52 219 | 31.28 226 | 24.69 226 | 62.46 217 | 62.90 214 | 67.33 223 | 58.26 223 |
|
ADS-MVSNet | | | 55.94 206 | 58.01 207 | 53.54 210 | 62.48 207 | 58.48 219 | 59.12 212 | 46.20 214 | 59.65 118 | 42.88 182 | 52.34 174 | 53.31 164 | 46.31 194 | 62.00 218 | 60.02 222 | 64.23 228 | 60.24 220 |
|
new-patchmatchnet | | | 46.97 219 | 49.47 219 | 44.05 221 | 62.82 206 | 56.55 221 | 45.35 227 | 52.01 192 | 42.47 216 | 17.04 231 | 35.73 218 | 35.21 222 | 21.84 233 | 61.27 219 | 54.83 228 | 65.26 227 | 60.26 218 |
|
testmv | | | 42.58 224 | 44.36 224 | 40.49 224 | 54.63 228 | 52.76 226 | 41.21 232 | 44.37 221 | 28.83 232 | 12.87 232 | 27.16 229 | 25.03 233 | 23.01 229 | 60.83 220 | 61.13 218 | 66.88 224 | 54.81 225 |
|
test1235678 | | | 42.57 225 | 44.36 224 | 40.49 224 | 54.63 228 | 52.75 227 | 41.21 232 | 44.37 221 | 28.82 233 | 12.87 232 | 27.15 230 | 25.01 234 | 23.01 229 | 60.83 220 | 61.13 218 | 66.88 224 | 54.81 225 |
|
1111 | | | 43.08 223 | 44.02 226 | 41.98 222 | 59.22 214 | 49.27 232 | 41.48 230 | 45.63 217 | 35.01 226 | 23.06 221 | 28.60 226 | 30.15 229 | 27.22 219 | 60.42 222 | 57.97 224 | 55.27 233 | 46.74 230 |
|
.test1245 | | | 30.81 230 | 29.14 233 | 32.77 229 | 59.22 214 | 49.27 232 | 41.48 230 | 45.63 217 | 35.01 226 | 23.06 221 | 28.60 226 | 30.15 229 | 27.22 219 | 60.42 222 | 0.10 237 | 0.01 241 | 0.43 239 |
|
N_pmnet | | | 47.35 217 | 50.13 217 | 44.11 220 | 59.98 213 | 51.64 228 | 51.86 219 | 44.80 220 | 49.58 202 | 20.76 227 | 40.65 208 | 40.05 219 | 29.64 217 | 59.84 224 | 55.15 227 | 57.63 230 | 54.00 228 |
|
Gipuma | | | 36.38 227 | 35.80 231 | 37.07 226 | 45.76 231 | 33.90 237 | 29.81 236 | 48.47 208 | 39.91 220 | 18.02 230 | 8.00 239 | 8.14 241 | 25.14 223 | 59.29 225 | 61.02 220 | 55.19 234 | 40.31 232 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
FPMVS | | | 51.87 213 | 50.00 218 | 54.07 206 | 66.83 197 | 57.25 220 | 60.25 209 | 50.91 197 | 50.25 196 | 34.36 203 | 36.04 217 | 32.02 225 | 41.49 203 | 58.98 226 | 56.07 226 | 70.56 218 | 59.36 221 |
|
PMVS | | 39.38 18 | 46.06 221 | 43.30 227 | 49.28 214 | 62.93 205 | 38.75 236 | 41.88 229 | 53.50 184 | 33.33 231 | 35.46 202 | 28.90 225 | 31.01 228 | 33.04 214 | 58.61 227 | 54.63 229 | 68.86 220 | 57.88 224 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MDA-MVSNet-bldmvs | | | 53.37 212 | 53.01 215 | 53.79 209 | 43.67 235 | 67.95 196 | 59.69 210 | 57.92 169 | 43.69 213 | 32.41 206 | 41.47 205 | 27.89 232 | 52.38 179 | 56.97 228 | 65.99 208 | 76.68 196 | 67.13 206 |
|
test12356 | | | 35.10 229 | 38.50 229 | 31.13 230 | 44.14 234 | 43.70 235 | 32.27 235 | 34.42 231 | 26.51 235 | 9.47 236 | 25.22 232 | 20.34 235 | 10.86 236 | 53.47 229 | 56.15 225 | 55.59 232 | 44.11 231 |
|
new_pmnet | | | 38.40 226 | 42.64 228 | 33.44 228 | 37.54 238 | 45.00 234 | 36.60 234 | 32.72 234 | 40.27 219 | 12.72 234 | 29.89 223 | 28.90 231 | 24.78 225 | 53.17 230 | 52.90 231 | 56.31 231 | 48.34 229 |
|
testpf | | | 47.41 216 | 48.47 223 | 46.18 216 | 66.30 199 | 50.67 229 | 48.15 225 | 42.60 225 | 37.10 225 | 28.75 210 | 40.97 206 | 39.01 220 | 30.82 216 | 52.95 231 | 53.74 230 | 60.46 229 | 64.87 209 |
|
no-one | | | 36.35 228 | 37.59 230 | 34.91 227 | 46.13 230 | 49.89 231 | 27.99 237 | 43.56 223 | 20.91 237 | 7.03 238 | 14.64 235 | 15.50 239 | 18.92 234 | 42.95 232 | 60.20 221 | 65.84 226 | 59.03 222 |
|
PMMVS2 | | | 25.60 231 | 29.75 232 | 20.76 234 | 28.00 239 | 30.93 238 | 23.10 238 | 29.18 236 | 23.14 236 | 1.46 242 | 18.23 234 | 16.54 237 | 5.08 237 | 40.22 233 | 41.40 233 | 37.76 235 | 37.79 234 |
|
tmp_tt | | | | | 14.50 236 | 14.68 240 | 7.17 243 | 10.46 243 | 2.21 238 | 37.73 224 | 28.71 211 | 25.26 231 | 16.98 236 | 4.37 238 | 31.49 234 | 29.77 234 | 26.56 238 | |
|
MVE | | 19.12 19 | 20.47 234 | 23.27 234 | 17.20 235 | 12.66 241 | 25.41 239 | 10.52 242 | 34.14 233 | 14.79 240 | 6.53 241 | 8.79 238 | 4.68 242 | 16.64 235 | 29.49 235 | 41.63 232 | 22.73 239 | 38.11 233 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | | | 18.74 242 | 18.55 239 | 8.02 237 | 26.96 234 | 7.33 237 | 23.81 233 | 13.05 240 | 25.99 221 | 25.17 236 | | 22.45 240 | 36.25 235 |
|
E-PMN | | | 21.77 232 | 18.24 235 | 25.89 231 | 40.22 236 | 19.58 240 | 12.46 241 | 39.87 227 | 18.68 239 | 6.71 239 | 9.57 236 | 4.31 244 | 22.36 232 | 19.89 237 | 27.28 235 | 33.73 236 | 28.34 236 |
|
EMVS | | | 20.98 233 | 17.15 236 | 25.44 232 | 39.51 237 | 19.37 241 | 12.66 240 | 39.59 229 | 19.10 238 | 6.62 240 | 9.27 237 | 4.40 243 | 22.43 231 | 17.99 238 | 24.40 236 | 31.81 237 | 25.53 237 |
|
testmvs | | | 0.09 235 | 0.15 237 | 0.02 237 | 0.01 243 | 0.02 244 | 0.05 245 | 0.01 240 | 0.11 241 | 0.01 244 | 0.26 241 | 0.01 245 | 0.06 241 | 0.10 239 | 0.10 237 | 0.01 241 | 0.43 239 |
|
test123 | | | 0.09 235 | 0.14 238 | 0.02 237 | 0.00 244 | 0.02 244 | 0.02 246 | 0.01 240 | 0.09 242 | 0.00 245 | 0.30 240 | 0.00 246 | 0.08 239 | 0.03 240 | 0.09 239 | 0.01 241 | 0.45 238 |
|
sosnet-low-res | | | 0.00 237 | 0.00 239 | 0.00 239 | 0.00 244 | 0.00 246 | 0.00 247 | 0.00 242 | 0.00 243 | 0.00 245 | 0.00 242 | 0.00 246 | 0.00 242 | 0.00 241 | 0.00 240 | 0.00 244 | 0.00 241 |
|
sosnet | | | 0.00 237 | 0.00 239 | 0.00 239 | 0.00 244 | 0.00 246 | 0.00 247 | 0.00 242 | 0.00 243 | 0.00 245 | 0.00 242 | 0.00 246 | 0.00 242 | 0.00 241 | 0.00 240 | 0.00 244 | 0.00 241 |
|
our_test_3 | | | | | | 67.93 194 | 70.99 184 | 66.89 181 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 83.48 1 | | 86.45 13 | | | | | |
|
MTMP | | | | | | | | | | | 82.66 3 | | 84.91 21 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.85 244 | | | | | | | | | | |
|
XVS | | | | | | 86.63 40 | 88.68 24 | 85.00 43 | | | 71.81 41 | | 81.92 31 | | | | 90.47 18 | |
|
X-MVStestdata | | | | | | 86.63 40 | 88.68 24 | 85.00 43 | | | 71.81 41 | | 81.92 31 | | | | 90.47 18 | |
|
abl_6 | | | | | 79.05 38 | 87.27 36 | 88.85 22 | 83.62 51 | 68.25 49 | 81.68 36 | 72.94 35 | 73.79 41 | 84.45 23 | 72.55 44 | | | 89.66 38 | 90.64 39 |
|
mPP-MVS | | | | | | 89.90 21 | | | | | | | 81.29 36 | | | | | |
|
NP-MVS | | | | | | | | | | 80.10 42 | | | | | | | | |
|
Patchmtry | | | | | | | 65.80 203 | 65.97 188 | 52.74 188 | | 52.65 127 | | | | | | | |
|