ESAPD | | | 87.78 1 | 90.56 1 | 84.53 1 | 92.88 2 | 93.82 1 | 88.95 1 | 76.05 4 | 92.95 3 | 80.32 2 | 93.12 2 | 86.87 1 | 80.88 4 | 85.54 10 | 84.01 18 | 88.09 33 | 97.62 2 |
|
MCST-MVS | | | 85.75 5 | 86.99 9 | 84.31 2 | 94.07 1 | 92.80 3 | 88.15 3 | 79.10 1 | 85.66 18 | 70.72 25 | 76.50 28 | 80.45 14 | 82.17 2 | 88.35 1 | 87.49 2 | 91.63 2 | 97.65 1 |
|
CNVR-MVS | | | 85.96 4 | 87.58 7 | 84.06 3 | 92.58 4 | 92.40 6 | 87.62 4 | 77.77 2 | 88.44 10 | 75.93 12 | 79.49 21 | 81.97 11 | 81.65 3 | 87.04 5 | 86.58 3 | 88.79 14 | 97.18 4 |
|
APDe-MVS | | | 86.37 3 | 88.41 4 | 84.00 4 | 91.43 9 | 91.83 10 | 88.34 2 | 74.67 5 | 91.19 4 | 81.76 1 | 91.13 3 | 81.94 12 | 80.07 5 | 83.38 23 | 82.58 30 | 87.69 37 | 96.78 7 |
|
HSP-MVS | | | 86.82 2 | 89.95 2 | 83.16 5 | 89.38 22 | 91.60 12 | 85.63 12 | 74.15 7 | 94.20 1 | 75.52 14 | 94.99 1 | 83.21 6 | 85.96 1 | 87.67 3 | 85.88 6 | 88.32 25 | 92.13 47 |
|
NCCC | | | 84.16 11 | 85.46 16 | 82.64 6 | 92.34 5 | 90.57 18 | 86.57 6 | 76.51 3 | 86.85 15 | 72.91 17 | 77.20 27 | 78.69 20 | 79.09 8 | 84.64 16 | 84.88 14 | 88.44 23 | 95.41 17 |
|
HPM-MVS++ | | | 85.64 6 | 88.43 3 | 82.39 7 | 92.65 3 | 90.24 21 | 85.83 10 | 74.21 6 | 90.68 6 | 75.63 13 | 86.77 9 | 84.15 4 | 78.68 9 | 86.33 6 | 85.26 9 | 87.32 44 | 95.60 14 |
|
APD-MVS | | | 84.83 8 | 87.00 8 | 82.30 8 | 89.61 20 | 89.21 30 | 86.51 7 | 73.64 11 | 90.98 5 | 77.99 7 | 89.89 5 | 80.04 17 | 79.18 7 | 82.00 39 | 81.37 42 | 86.88 53 | 95.49 16 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
TSAR-MVS + MP. | | | 84.39 9 | 86.58 12 | 81.83 9 | 88.09 34 | 86.47 53 | 85.63 12 | 73.62 12 | 90.13 8 | 79.24 4 | 89.67 6 | 82.99 7 | 77.72 11 | 81.22 45 | 80.92 50 | 86.68 56 | 94.66 23 |
|
SMA-MVS | | | 84.91 7 | 87.95 5 | 81.36 10 | 91.75 7 | 90.84 15 | 86.35 8 | 73.36 13 | 90.22 7 | 72.81 18 | 80.70 18 | 85.67 2 | 76.69 15 | 86.06 8 | 86.14 5 | 87.20 50 | 96.05 10 |
|
SD-MVS | | | 84.31 10 | 86.96 10 | 81.22 11 | 88.98 26 | 88.68 33 | 85.65 11 | 73.85 10 | 89.09 9 | 79.63 3 | 87.34 8 | 84.84 3 | 73.71 29 | 82.66 29 | 81.60 39 | 85.48 107 | 94.51 24 |
|
DeepC-MVS_fast | | 75.41 2 | 81.69 19 | 82.10 28 | 81.20 12 | 91.04 11 | 87.81 44 | 83.42 22 | 74.04 8 | 83.77 22 | 71.09 23 | 66.88 40 | 72.44 32 | 79.48 6 | 85.08 12 | 84.97 13 | 88.12 32 | 93.78 32 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CSCG | | | 82.90 15 | 84.52 18 | 81.02 13 | 91.85 6 | 93.43 2 | 87.14 5 | 74.01 9 | 81.96 28 | 76.14 10 | 70.84 32 | 82.49 8 | 69.71 52 | 82.32 35 | 85.18 11 | 87.26 46 | 95.40 18 |
|
HFP-MVS | | | 82.48 17 | 84.12 19 | 80.56 14 | 90.15 13 | 87.55 46 | 84.28 18 | 69.67 29 | 85.22 19 | 77.95 8 | 84.69 12 | 75.94 24 | 75.04 22 | 81.85 40 | 81.17 45 | 86.30 63 | 92.40 45 |
|
3Dnovator | | 70.49 5 | 78.42 34 | 76.77 50 | 80.35 15 | 91.43 9 | 90.27 20 | 81.84 31 | 70.79 21 | 72.10 52 | 71.95 19 | 50.02 84 | 67.86 50 | 77.47 12 | 82.89 26 | 84.24 16 | 88.61 18 | 89.99 69 |
|
ACMMP_Plus | | | 83.54 12 | 86.37 13 | 80.25 16 | 89.57 21 | 90.10 23 | 85.27 15 | 71.66 18 | 87.38 11 | 73.08 16 | 84.23 13 | 80.16 15 | 75.31 20 | 84.85 14 | 83.64 22 | 86.57 57 | 94.21 29 |
|
MSLP-MVS++ | | | 78.57 33 | 77.33 45 | 80.02 17 | 88.39 29 | 84.79 67 | 84.62 17 | 66.17 50 | 75.96 48 | 78.40 5 | 61.59 52 | 71.47 39 | 73.54 31 | 78.43 66 | 78.88 61 | 88.97 12 | 90.18 68 |
|
zzz-MVS | | | 81.65 20 | 83.10 22 | 79.97 18 | 88.14 33 | 87.62 45 | 83.96 21 | 69.90 26 | 86.92 13 | 77.67 9 | 72.47 31 | 78.74 19 | 74.13 28 | 81.59 43 | 81.15 46 | 86.01 74 | 93.19 37 |
|
DeepC-MVS | | 74.46 3 | 80.30 25 | 81.05 31 | 79.42 19 | 87.42 36 | 88.50 35 | 83.23 23 | 73.27 14 | 82.78 25 | 71.01 24 | 62.86 48 | 69.93 45 | 74.80 24 | 84.30 17 | 84.20 17 | 86.79 55 | 94.77 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | | | 76.23 48 | 73.55 61 | 79.35 20 | 89.38 22 | 85.00 66 | 79.99 46 | 73.04 15 | 76.60 47 | 71.17 22 | 55.18 64 | 57.99 84 | 77.87 10 | 76.82 77 | 76.82 75 | 84.67 135 | 86.45 112 |
|
SteuartSystems-ACMMP | | | 82.51 16 | 85.35 17 | 79.20 21 | 90.25 12 | 89.39 29 | 84.79 16 | 70.95 20 | 82.86 24 | 68.32 33 | 86.44 10 | 77.19 21 | 73.07 33 | 83.63 21 | 83.64 22 | 87.82 34 | 94.34 26 |
Skip Steuart: Steuart Systems R&D Blog. |
train_agg | | | 83.35 13 | 86.93 11 | 79.17 22 | 89.70 18 | 88.41 36 | 85.60 14 | 72.89 16 | 86.31 16 | 66.58 36 | 90.48 4 | 82.24 10 | 73.06 34 | 83.10 25 | 82.64 29 | 87.21 49 | 95.30 19 |
|
DELS-MVS | | | 79.49 26 | 79.84 35 | 79.08 23 | 88.26 32 | 92.49 4 | 84.12 20 | 70.63 22 | 65.27 71 | 69.60 31 | 61.29 54 | 66.50 53 | 72.75 35 | 88.07 2 | 88.03 1 | 89.13 11 | 97.22 3 |
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 |
abl_6 | | | | | 79.06 24 | 89.68 19 | 92.14 8 | 77.70 56 | 69.68 28 | 86.87 14 | 71.88 20 | 74.29 30 | 80.06 16 | 76.56 17 | | | 88.84 13 | 95.82 11 |
|
3Dnovator+ | | 70.16 6 | 77.87 37 | 77.29 46 | 78.55 25 | 89.25 24 | 88.32 38 | 80.09 44 | 67.95 39 | 74.89 51 | 71.83 21 | 52.05 77 | 70.68 42 | 76.27 19 | 82.27 36 | 82.04 32 | 85.92 80 | 90.77 60 |
|
CANet | | | 80.90 23 | 82.93 24 | 78.53 26 | 86.83 40 | 92.26 7 | 81.19 37 | 66.95 43 | 81.60 31 | 69.90 28 | 66.93 39 | 74.80 26 | 76.79 14 | 84.68 15 | 84.77 15 | 89.50 8 | 95.50 15 |
|
MP-MVS | | | 80.94 22 | 83.49 21 | 77.96 27 | 88.48 27 | 88.16 40 | 82.82 27 | 69.34 31 | 80.79 34 | 69.67 29 | 82.35 15 | 77.13 22 | 71.60 43 | 80.97 49 | 80.96 49 | 85.87 87 | 94.06 30 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
TSAR-MVS + GP. | | | 82.27 18 | 85.98 14 | 77.94 28 | 80.72 67 | 88.25 39 | 81.12 38 | 67.71 40 | 87.10 12 | 73.31 15 | 85.23 11 | 83.68 5 | 76.64 16 | 80.43 52 | 81.47 41 | 88.15 31 | 95.66 13 |
|
ACMMPR | | | 80.62 24 | 82.98 23 | 77.87 29 | 88.41 28 | 87.05 48 | 83.02 24 | 69.18 32 | 83.91 21 | 68.35 32 | 82.89 14 | 73.64 29 | 72.16 39 | 80.78 50 | 81.13 47 | 86.10 68 | 91.43 53 |
|
DeepPCF-MVS | | 76.94 1 | 83.08 14 | 87.77 6 | 77.60 30 | 90.11 14 | 90.96 14 | 78.48 50 | 72.63 17 | 93.10 2 | 65.84 37 | 80.67 19 | 81.55 13 | 74.80 24 | 85.94 9 | 85.39 8 | 83.75 152 | 96.77 8 |
|
QAPM | | | 77.50 40 | 77.43 44 | 77.59 31 | 91.52 8 | 92.00 9 | 81.41 35 | 70.63 22 | 66.22 64 | 58.05 64 | 54.70 66 | 71.79 38 | 74.49 27 | 82.46 31 | 82.04 32 | 89.46 9 | 92.79 43 |
|
CLD-MVS | | | 77.36 42 | 77.29 46 | 77.45 32 | 82.21 54 | 88.11 41 | 81.92 30 | 68.96 34 | 77.97 42 | 69.62 30 | 62.08 50 | 59.44 76 | 73.57 30 | 81.75 41 | 81.27 44 | 88.41 24 | 90.39 65 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CP-MVS | | | 79.44 27 | 81.51 30 | 77.02 33 | 86.95 38 | 85.96 60 | 82.00 29 | 68.44 37 | 81.82 29 | 67.39 34 | 77.43 25 | 73.68 28 | 71.62 42 | 79.56 57 | 79.58 56 | 85.73 97 | 92.51 44 |
|
TSAR-MVS + ACMM | | | 81.59 21 | 85.84 15 | 76.63 34 | 89.82 17 | 86.53 52 | 86.32 9 | 66.72 46 | 85.96 17 | 65.43 38 | 88.98 7 | 82.29 9 | 67.57 69 | 82.06 38 | 81.33 43 | 83.93 150 | 93.75 33 |
|
PGM-MVS | | | 79.42 30 | 81.84 29 | 76.60 35 | 88.38 30 | 86.69 50 | 82.97 26 | 65.75 52 | 80.39 35 | 64.94 39 | 81.95 17 | 72.11 37 | 71.41 44 | 80.45 51 | 80.55 53 | 86.18 65 | 90.76 61 |
|
MVS_111021_HR | | | 77.42 41 | 78.40 41 | 76.28 36 | 86.95 38 | 90.68 16 | 77.41 58 | 70.56 25 | 66.21 65 | 62.48 46 | 66.17 42 | 63.98 60 | 72.08 40 | 82.87 27 | 83.15 25 | 88.24 28 | 95.71 12 |
|
MVS_0304 | | | 79.43 28 | 82.20 26 | 76.20 37 | 84.22 47 | 91.79 11 | 81.82 32 | 63.81 65 | 76.83 46 | 61.71 48 | 66.37 41 | 75.52 25 | 76.38 18 | 85.54 10 | 85.03 12 | 89.28 10 | 94.32 27 |
|
CDPH-MVS | | | 79.39 31 | 82.13 27 | 76.19 38 | 89.22 25 | 88.34 37 | 84.20 19 | 71.00 19 | 79.67 38 | 56.97 67 | 77.77 24 | 72.24 36 | 68.50 61 | 81.33 44 | 82.74 27 | 87.23 47 | 92.84 41 |
|
OpenMVS | | 67.62 8 | 74.92 52 | 73.91 59 | 76.09 39 | 90.10 15 | 90.38 19 | 78.01 53 | 66.35 48 | 66.09 66 | 62.80 43 | 46.33 109 | 64.55 59 | 71.77 41 | 79.92 55 | 80.88 51 | 87.52 40 | 89.20 76 |
|
EPNet | | | 79.28 32 | 82.25 25 | 75.83 40 | 88.31 31 | 90.14 22 | 79.43 48 | 68.07 38 | 81.76 30 | 61.26 50 | 77.26 26 | 70.08 44 | 70.06 50 | 82.43 33 | 82.00 34 | 87.82 34 | 92.09 48 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MAR-MVS | | | 77.19 44 | 78.37 42 | 75.81 41 | 89.87 16 | 90.58 17 | 79.33 49 | 65.56 54 | 77.62 45 | 58.33 62 | 59.24 60 | 67.98 48 | 74.83 23 | 82.37 34 | 83.12 26 | 86.95 52 | 87.67 103 |
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 |
canonicalmvs | | | 77.65 38 | 79.59 36 | 75.39 42 | 81.52 58 | 89.83 27 | 81.32 36 | 60.74 100 | 80.05 36 | 66.72 35 | 68.43 36 | 65.09 56 | 74.72 26 | 78.87 61 | 82.73 28 | 87.32 44 | 92.16 46 |
|
X-MVS | | | 78.16 36 | 80.55 33 | 75.38 43 | 87.99 35 | 86.27 55 | 81.05 39 | 68.98 33 | 78.33 40 | 61.07 52 | 75.25 29 | 72.27 33 | 67.52 70 | 80.03 54 | 80.52 54 | 85.66 104 | 91.20 55 |
|
ACMMP | | | 77.61 39 | 79.59 36 | 75.30 44 | 85.87 43 | 85.58 62 | 81.42 34 | 67.38 42 | 79.38 39 | 62.61 44 | 78.53 22 | 65.79 55 | 68.80 60 | 78.56 65 | 78.50 65 | 85.75 93 | 90.80 59 |
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 |
HQP-MVS | | | 78.26 35 | 80.91 32 | 75.17 45 | 85.67 44 | 84.33 70 | 83.01 25 | 69.38 30 | 79.88 37 | 55.83 68 | 79.85 20 | 64.90 58 | 70.81 46 | 82.46 31 | 81.78 36 | 86.30 63 | 93.18 38 |
|
PVSNet_BlendedMVS | | | 76.84 46 | 78.47 39 | 74.95 46 | 82.37 52 | 89.90 25 | 75.45 69 | 65.45 55 | 74.99 49 | 70.66 26 | 63.07 46 | 58.27 82 | 67.60 67 | 84.24 18 | 81.70 37 | 88.18 29 | 97.10 5 |
|
PVSNet_Blended | | | 76.84 46 | 78.47 39 | 74.95 46 | 82.37 52 | 89.90 25 | 75.45 69 | 65.45 55 | 74.99 49 | 70.66 26 | 63.07 46 | 58.27 82 | 67.60 67 | 84.24 18 | 81.70 37 | 88.18 29 | 97.10 5 |
|
OPM-MVS | | | 72.74 59 | 70.93 75 | 74.85 48 | 85.30 45 | 84.34 69 | 82.82 27 | 69.79 27 | 49.96 117 | 55.39 72 | 54.09 71 | 60.14 75 | 70.04 51 | 80.38 53 | 79.43 57 | 85.74 96 | 88.20 100 |
|
PCF-MVS | | 70.85 4 | 75.73 49 | 76.55 53 | 74.78 49 | 83.67 48 | 88.04 43 | 81.47 33 | 70.62 24 | 69.24 61 | 57.52 65 | 60.59 57 | 69.18 46 | 70.65 47 | 77.11 74 | 77.65 72 | 84.75 133 | 94.01 31 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
casdiffmvs | | | 77.30 43 | 79.53 38 | 74.70 50 | 81.15 61 | 89.49 28 | 80.82 42 | 60.74 100 | 77.66 44 | 59.79 59 | 62.47 49 | 66.82 52 | 77.17 13 | 83.43 22 | 83.84 20 | 88.53 21 | 93.02 39 |
|
PHI-MVS | | | 79.43 28 | 84.06 20 | 74.04 51 | 86.15 42 | 91.57 13 | 80.85 41 | 68.90 35 | 82.22 27 | 51.81 79 | 78.10 23 | 74.28 27 | 70.39 49 | 84.01 20 | 84.00 19 | 86.14 67 | 94.24 28 |
|
MVS_Test | | | 75.22 51 | 76.69 51 | 73.51 52 | 79.30 73 | 88.82 32 | 80.06 45 | 58.74 111 | 69.77 58 | 57.50 66 | 59.78 59 | 61.35 71 | 75.31 20 | 82.07 37 | 83.60 24 | 90.13 5 | 91.41 54 |
|
CPTT-MVS | | | 75.43 50 | 77.13 48 | 73.44 53 | 81.43 59 | 82.55 79 | 80.96 40 | 64.35 60 | 77.95 43 | 61.39 49 | 69.20 35 | 70.94 41 | 69.38 57 | 73.89 111 | 73.32 137 | 83.14 163 | 92.06 49 |
|
MVSTER | | | 76.92 45 | 79.92 34 | 73.42 54 | 74.98 104 | 82.97 75 | 78.15 51 | 63.41 68 | 78.02 41 | 64.41 41 | 67.54 37 | 72.80 31 | 71.05 45 | 83.29 24 | 83.73 21 | 88.53 21 | 91.12 56 |
|
CHOSEN 1792x2688 | | | 72.55 60 | 71.98 66 | 73.22 55 | 86.57 41 | 92.41 5 | 75.63 65 | 66.77 45 | 62.08 77 | 52.32 76 | 30.27 202 | 50.74 111 | 66.14 72 | 86.22 7 | 85.41 7 | 91.90 1 | 96.75 9 |
|
DI_MVS_plusplus_trai | | | 73.94 55 | 74.85 57 | 72.88 56 | 76.57 93 | 86.80 49 | 80.41 43 | 61.47 90 | 62.35 76 | 59.44 61 | 47.91 92 | 68.12 47 | 72.24 38 | 82.84 28 | 81.50 40 | 87.15 51 | 94.42 25 |
|
ACMM | | 66.70 10 | 70.42 67 | 68.49 87 | 72.67 57 | 82.85 49 | 77.76 141 | 77.70 56 | 64.76 59 | 64.61 73 | 60.74 56 | 49.29 86 | 53.97 101 | 65.86 73 | 74.97 96 | 75.57 90 | 84.13 148 | 83.29 138 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CNLPA | | | 71.37 66 | 70.27 78 | 72.66 58 | 80.79 66 | 81.33 95 | 71.07 113 | 65.75 52 | 82.36 26 | 64.80 40 | 42.46 126 | 56.49 87 | 72.70 36 | 73.00 123 | 70.52 172 | 80.84 183 | 85.76 121 |
|
MVS_111021_LR | | | 74.26 53 | 75.95 54 | 72.27 59 | 79.43 72 | 85.04 65 | 72.71 81 | 65.27 57 | 70.92 55 | 63.58 42 | 69.32 34 | 60.31 74 | 69.43 55 | 77.01 75 | 77.15 73 | 83.22 159 | 91.93 51 |
|
ACMP | | 68.86 7 | 72.15 62 | 72.25 65 | 72.03 60 | 80.96 63 | 80.87 101 | 77.93 54 | 64.13 62 | 69.29 59 | 60.79 55 | 64.04 44 | 53.54 102 | 63.91 81 | 73.74 115 | 75.27 92 | 84.45 140 | 88.98 79 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
OMC-MVS | | | 74.03 54 | 75.82 55 | 71.95 61 | 79.56 70 | 80.98 99 | 75.35 71 | 63.21 69 | 84.48 20 | 61.83 47 | 61.54 53 | 66.89 51 | 69.41 56 | 76.60 79 | 74.07 126 | 82.34 172 | 86.15 116 |
|
MS-PatchMatch | | | 70.34 72 | 69.00 83 | 71.91 62 | 85.20 46 | 85.35 63 | 77.84 55 | 61.77 88 | 58.01 88 | 55.40 71 | 41.26 133 | 58.34 81 | 61.69 92 | 81.70 42 | 78.29 66 | 89.56 7 | 80.02 163 |
|
Anonymous20240521 | | | 69.94 73 | 68.36 88 | 71.79 63 | 80.33 68 | 86.03 59 | 78.05 52 | 66.83 44 | 56.01 97 | 46.20 98 | 39.00 155 | 47.74 118 | 73.45 32 | 78.84 62 | 80.47 55 | 85.87 87 | 90.42 64 |
|
DWT-MVSNet_training | | | 72.81 58 | 73.98 58 | 71.45 64 | 81.26 60 | 86.37 54 | 72.08 84 | 59.82 108 | 69.13 62 | 58.15 63 | 54.71 65 | 61.33 73 | 67.81 66 | 76.86 76 | 78.63 62 | 89.59 6 | 90.86 58 |
|
PLC | | 64.00 12 | 68.54 79 | 66.66 99 | 70.74 65 | 80.28 69 | 74.88 164 | 72.64 82 | 63.70 67 | 69.26 60 | 55.71 69 | 47.24 101 | 55.31 95 | 70.42 48 | 72.05 149 | 70.67 170 | 81.66 177 | 77.19 172 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
LGP-MVS_train | | | 72.02 63 | 73.18 64 | 70.67 66 | 82.13 55 | 80.26 116 | 79.58 47 | 63.04 72 | 70.09 56 | 51.98 77 | 65.06 43 | 55.62 94 | 62.49 89 | 75.97 88 | 76.32 81 | 84.80 132 | 88.93 80 |
|
MSDG | | | 65.57 100 | 61.57 149 | 70.24 67 | 82.02 56 | 76.47 153 | 74.46 78 | 68.73 36 | 56.52 93 | 50.33 86 | 38.47 161 | 41.10 141 | 62.42 90 | 72.12 147 | 72.94 145 | 83.47 155 | 73.37 188 |
|
CostFormer | | | 72.18 61 | 73.90 60 | 70.18 68 | 79.47 71 | 86.19 58 | 76.94 60 | 48.62 192 | 66.07 67 | 60.40 57 | 54.14 70 | 65.82 54 | 67.98 64 | 75.84 89 | 76.41 80 | 87.67 38 | 92.83 42 |
|
PVSNet_Blended_VisFu | | | 71.76 64 | 73.54 62 | 69.69 69 | 79.01 74 | 87.16 47 | 72.05 85 | 61.80 87 | 56.46 94 | 59.66 60 | 53.88 72 | 62.48 63 | 59.08 131 | 81.17 46 | 78.90 60 | 86.53 60 | 94.74 22 |
|
diffmvs | | | 70.35 71 | 70.96 74 | 69.63 70 | 75.47 102 | 85.71 61 | 75.66 64 | 57.37 131 | 64.97 72 | 54.69 73 | 53.06 73 | 54.37 98 | 68.01 63 | 74.96 98 | 76.54 78 | 86.57 57 | 87.12 106 |
|
Effi-MVS+ | | | 70.42 67 | 71.23 72 | 69.47 71 | 78.04 78 | 85.24 64 | 75.57 67 | 58.88 110 | 59.56 83 | 48.47 90 | 52.73 76 | 54.94 96 | 69.69 53 | 78.34 68 | 77.06 74 | 86.18 65 | 90.73 62 |
|
FC-MVSNet-train | | | 68.83 78 | 68.29 89 | 69.47 71 | 78.35 76 | 79.94 117 | 64.72 152 | 66.38 47 | 54.96 104 | 54.51 74 | 56.75 62 | 47.91 117 | 66.91 71 | 75.57 93 | 75.75 86 | 85.92 80 | 87.12 106 |
|
tpmp4_e23 | | | 69.38 74 | 69.47 81 | 69.28 73 | 78.20 77 | 82.35 81 | 75.92 61 | 49.20 190 | 64.15 74 | 59.96 58 | 47.93 91 | 55.77 92 | 68.06 62 | 73.05 122 | 74.53 105 | 84.34 142 | 88.50 98 |
|
TAPA-MVS | | 67.10 9 | 71.45 65 | 73.47 63 | 69.10 74 | 77.04 88 | 80.78 102 | 73.81 79 | 62.10 82 | 80.80 33 | 51.28 80 | 60.91 55 | 63.80 62 | 67.98 64 | 74.59 100 | 72.42 151 | 82.37 171 | 80.97 158 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
IB-MVS | | 64.48 11 | 69.02 77 | 68.97 84 | 69.09 75 | 81.75 57 | 89.01 31 | 64.50 153 | 64.91 58 | 56.65 92 | 62.59 45 | 47.89 93 | 45.23 124 | 51.99 154 | 69.18 175 | 81.88 35 | 88.77 15 | 92.93 40 |
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 |
TSAR-MVS + COLMAP | | | 73.09 56 | 76.86 49 | 68.71 76 | 74.97 105 | 82.49 80 | 74.51 76 | 61.83 86 | 83.16 23 | 49.31 89 | 82.22 16 | 51.62 108 | 68.94 59 | 78.76 64 | 75.52 91 | 82.67 167 | 84.23 130 |
|
FMVSNet3 | | | 70.41 69 | 71.89 68 | 68.68 77 | 70.89 130 | 79.42 125 | 75.63 65 | 60.97 96 | 65.32 68 | 51.06 81 | 47.37 98 | 62.05 65 | 64.90 77 | 82.49 30 | 82.27 31 | 88.64 17 | 84.34 129 |
|
HyFIR lowres test | | | 68.39 80 | 68.28 90 | 68.52 78 | 80.85 64 | 88.11 41 | 71.08 112 | 58.09 116 | 54.87 106 | 47.80 93 | 27.55 207 | 55.80 91 | 64.97 76 | 79.11 59 | 79.14 59 | 88.31 26 | 93.35 34 |
|
CANet_DTU | | | 72.84 57 | 76.63 52 | 68.43 79 | 76.81 91 | 86.62 51 | 75.54 68 | 54.71 159 | 72.06 53 | 43.54 115 | 67.11 38 | 58.46 79 | 72.40 37 | 81.13 48 | 80.82 52 | 87.57 39 | 90.21 67 |
|
thres100view900 | | | 67.14 90 | 66.09 105 | 68.38 80 | 77.70 81 | 83.84 73 | 74.52 75 | 66.33 49 | 49.16 122 | 43.40 120 | 43.24 116 | 41.34 134 | 62.59 88 | 79.31 58 | 75.92 85 | 85.73 97 | 89.81 70 |
|
tpm cat1 | | | 67.47 86 | 67.05 98 | 67.98 81 | 76.63 92 | 81.51 93 | 74.49 77 | 47.65 197 | 61.18 79 | 61.12 51 | 42.51 125 | 53.02 105 | 64.74 79 | 70.11 167 | 71.50 158 | 83.22 159 | 89.49 72 |
|
GBi-Net | | | 69.21 75 | 70.40 76 | 67.81 82 | 69.49 135 | 78.65 130 | 74.54 72 | 60.97 96 | 65.32 68 | 51.06 81 | 47.37 98 | 62.05 65 | 63.43 83 | 77.49 70 | 78.22 67 | 87.37 41 | 83.73 133 |
|
test1 | | | 69.21 75 | 70.40 76 | 67.81 82 | 69.49 135 | 78.65 130 | 74.54 72 | 60.97 96 | 65.32 68 | 51.06 81 | 47.37 98 | 62.05 65 | 63.43 83 | 77.49 70 | 78.22 67 | 87.37 41 | 83.73 133 |
|
Fast-Effi-MVS+ | | | 67.59 83 | 67.56 94 | 67.62 84 | 73.67 110 | 81.14 98 | 71.12 110 | 54.79 158 | 58.88 84 | 50.61 85 | 46.70 107 | 47.05 119 | 69.12 58 | 76.06 87 | 76.44 79 | 86.43 61 | 86.65 110 |
|
FMVSNet2 | | | 68.06 82 | 68.57 86 | 67.45 85 | 69.49 135 | 78.65 130 | 74.54 72 | 60.23 107 | 56.29 95 | 49.64 88 | 42.13 129 | 57.08 86 | 63.43 83 | 81.15 47 | 80.99 48 | 87.37 41 | 83.73 133 |
|
LS3D | | | 64.54 109 | 62.14 142 | 67.34 86 | 80.85 64 | 75.79 159 | 69.99 122 | 65.87 51 | 60.77 80 | 44.35 111 | 42.43 127 | 45.95 122 | 65.01 75 | 69.88 170 | 68.69 180 | 77.97 201 | 71.43 198 |
|
conf0.002 | | | 67.12 91 | 67.13 97 | 67.11 87 | 77.95 79 | 82.11 82 | 71.71 94 | 63.06 70 | 49.16 122 | 43.43 117 | 47.76 95 | 48.79 114 | 61.42 94 | 76.61 78 | 76.55 77 | 85.07 117 | 88.92 82 |
|
conf0.01 | | | 66.60 92 | 66.18 103 | 67.09 88 | 77.90 80 | 82.02 83 | 71.71 94 | 63.05 71 | 49.16 122 | 43.41 119 | 46.23 110 | 45.78 123 | 61.42 94 | 76.55 80 | 74.63 99 | 85.04 118 | 88.87 84 |
|
tfpn200view9 | | | 65.90 97 | 64.96 110 | 67.00 89 | 77.70 81 | 81.58 89 | 71.71 94 | 62.94 76 | 49.16 122 | 43.40 120 | 43.24 116 | 41.34 134 | 61.42 94 | 76.24 82 | 74.63 99 | 84.84 126 | 88.52 94 |
|
tfpn111 | | | 66.52 93 | 66.12 104 | 66.98 90 | 77.70 81 | 81.58 89 | 71.71 94 | 62.94 76 | 49.16 122 | 43.28 122 | 51.38 79 | 41.34 134 | 61.42 94 | 76.24 82 | 74.63 99 | 84.84 126 | 88.52 94 |
|
conf200view11 | | | 65.89 98 | 64.96 110 | 66.98 90 | 77.70 81 | 81.58 89 | 71.71 94 | 62.94 76 | 49.16 122 | 43.28 122 | 43.24 116 | 41.34 134 | 61.42 94 | 76.24 82 | 74.63 99 | 84.84 126 | 88.52 94 |
|
thres200 | | | 65.58 99 | 64.74 113 | 66.56 92 | 77.52 86 | 81.61 87 | 73.44 80 | 62.95 74 | 46.23 143 | 42.45 137 | 42.76 120 | 41.18 139 | 58.12 135 | 76.24 82 | 75.59 89 | 84.89 123 | 89.58 71 |
|
thres400 | | | 65.18 104 | 64.44 115 | 66.04 93 | 76.40 94 | 82.63 77 | 71.52 104 | 64.27 61 | 44.93 151 | 40.69 145 | 41.86 130 | 40.79 148 | 58.12 135 | 77.67 69 | 74.64 98 | 85.26 110 | 88.56 93 |
|
tpmrst | | | 67.15 89 | 68.12 92 | 66.03 94 | 76.21 95 | 80.98 99 | 71.27 106 | 45.05 204 | 60.69 81 | 50.63 84 | 46.95 106 | 54.15 100 | 65.30 74 | 71.80 152 | 71.77 155 | 87.72 36 | 90.48 63 |
|
IterMVS-LS | | | 66.08 96 | 66.56 101 | 65.51 95 | 73.67 110 | 74.88 164 | 70.89 116 | 53.55 166 | 50.42 115 | 48.32 91 | 50.59 82 | 55.66 93 | 61.83 91 | 73.93 110 | 74.42 112 | 84.82 131 | 86.01 118 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+-dtu | | | 64.58 107 | 64.08 116 | 65.16 96 | 73.04 115 | 75.17 163 | 70.68 118 | 56.23 141 | 54.12 109 | 44.71 110 | 47.42 97 | 51.10 109 | 63.82 82 | 68.08 179 | 66.32 192 | 82.47 170 | 86.38 114 |
|
dps | | | 64.08 112 | 63.22 125 | 65.08 97 | 75.27 103 | 79.65 121 | 66.68 144 | 46.63 202 | 56.94 90 | 55.67 70 | 43.96 112 | 43.63 130 | 64.00 80 | 69.50 174 | 69.82 175 | 82.25 173 | 79.02 167 |
|
v6 | | | 64.09 111 | 63.40 121 | 64.90 98 | 68.28 146 | 80.78 102 | 71.85 88 | 57.64 123 | 46.73 138 | 45.18 105 | 39.40 146 | 40.89 145 | 60.54 109 | 72.86 128 | 74.40 113 | 85.92 80 | 88.72 89 |
|
PMMVS | | | 70.37 70 | 75.06 56 | 64.90 98 | 71.46 124 | 81.88 84 | 64.10 155 | 55.64 147 | 71.31 54 | 46.69 96 | 70.69 33 | 58.56 77 | 69.53 54 | 79.03 60 | 75.63 88 | 81.96 175 | 88.32 99 |
|
v1neww | | | 64.08 112 | 63.38 122 | 64.89 100 | 68.27 148 | 80.77 104 | 71.84 89 | 57.65 121 | 46.66 140 | 45.10 106 | 39.40 146 | 40.86 146 | 60.57 106 | 72.86 128 | 74.40 113 | 85.92 80 | 88.71 90 |
|
v7new | | | 64.08 112 | 63.38 122 | 64.89 100 | 68.27 148 | 80.77 104 | 71.84 89 | 57.65 121 | 46.66 140 | 45.10 106 | 39.40 146 | 40.86 146 | 60.57 106 | 72.86 128 | 74.40 113 | 85.92 80 | 88.71 90 |
|
v18 | | | 63.31 126 | 62.02 144 | 64.81 102 | 68.48 142 | 73.38 173 | 72.14 83 | 54.28 161 | 48.99 129 | 47.21 94 | 39.56 145 | 41.20 138 | 60.80 103 | 72.89 127 | 74.46 111 | 85.96 79 | 83.64 136 |
|
EPMVS | | | 66.21 94 | 67.49 95 | 64.73 103 | 75.81 98 | 84.20 72 | 68.94 130 | 44.37 208 | 61.55 78 | 48.07 92 | 49.21 88 | 54.87 97 | 62.88 86 | 71.82 151 | 71.40 162 | 88.28 27 | 79.37 166 |
|
v16 | | | 63.12 128 | 61.78 146 | 64.68 104 | 68.45 143 | 73.29 174 | 71.86 87 | 54.12 162 | 48.36 131 | 47.00 95 | 39.30 150 | 41.01 142 | 60.67 104 | 72.83 133 | 74.40 113 | 86.01 74 | 83.24 140 |
|
view600 | | | 63.91 117 | 63.27 124 | 64.66 105 | 75.57 100 | 81.73 85 | 69.71 125 | 63.04 72 | 43.97 154 | 39.18 151 | 41.09 134 | 40.24 156 | 55.38 145 | 76.28 81 | 72.04 154 | 85.08 116 | 87.52 104 |
|
v2v482 | | | 63.68 119 | 62.85 131 | 64.65 106 | 68.01 156 | 80.46 113 | 71.90 86 | 57.60 124 | 44.26 152 | 42.82 135 | 39.80 144 | 38.62 172 | 61.56 93 | 73.06 120 | 74.86 96 | 86.03 73 | 88.90 83 |
|
v17 | | | 62.99 133 | 61.70 147 | 64.51 107 | 68.40 144 | 73.28 175 | 71.80 92 | 54.11 163 | 47.87 132 | 46.14 99 | 39.29 151 | 41.01 142 | 60.60 105 | 72.81 134 | 74.39 118 | 85.99 77 | 83.25 139 |
|
thres600view7 | | | 63.77 118 | 63.14 126 | 64.51 107 | 75.49 101 | 81.61 87 | 69.59 126 | 62.95 74 | 43.96 155 | 38.90 153 | 41.09 134 | 40.24 156 | 55.25 147 | 76.24 82 | 71.54 157 | 84.89 123 | 87.30 105 |
|
v8 | | | 63.44 125 | 62.58 138 | 64.43 109 | 68.28 146 | 78.07 136 | 71.82 91 | 54.85 156 | 46.70 139 | 45.20 104 | 39.40 146 | 40.91 144 | 60.54 109 | 72.85 132 | 74.39 118 | 85.92 80 | 85.76 121 |
|
ACMH+ | | 60.36 13 | 61.16 155 | 58.38 177 | 64.42 110 | 77.37 87 | 74.35 169 | 68.45 132 | 62.81 80 | 45.86 145 | 38.48 156 | 35.71 183 | 37.35 177 | 59.81 120 | 67.24 181 | 69.80 176 | 79.58 193 | 78.32 170 |
|
ACMH | | 59.42 14 | 61.59 154 | 59.22 175 | 64.36 111 | 78.92 75 | 78.26 134 | 67.65 136 | 67.48 41 | 39.81 182 | 30.98 191 | 38.25 164 | 34.59 195 | 61.37 100 | 70.55 162 | 73.47 133 | 79.74 192 | 79.59 164 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v1141 | | | 63.48 122 | 62.75 136 | 64.32 112 | 68.13 150 | 80.69 108 | 71.69 101 | 57.43 128 | 43.66 160 | 42.83 134 | 39.02 153 | 39.74 162 | 59.95 116 | 72.94 124 | 74.49 108 | 85.86 89 | 88.75 87 |
|
divwei89l23v2f112 | | | 63.48 122 | 62.76 135 | 64.32 112 | 68.13 150 | 80.68 109 | 71.71 94 | 57.43 128 | 43.69 158 | 42.84 132 | 39.01 154 | 39.75 161 | 59.94 117 | 72.93 125 | 74.49 108 | 85.86 89 | 88.75 87 |
|
v1 | | | 63.49 121 | 62.77 134 | 64.32 112 | 68.13 150 | 80.70 107 | 71.70 100 | 57.43 128 | 43.69 158 | 42.89 131 | 39.03 152 | 39.77 160 | 59.93 118 | 72.93 125 | 74.48 110 | 85.86 89 | 88.77 85 |
|
v7 | | | 63.61 120 | 63.02 128 | 64.29 115 | 67.88 160 | 80.32 114 | 71.60 102 | 56.63 137 | 45.37 147 | 42.84 132 | 38.54 159 | 38.91 170 | 61.05 101 | 74.39 103 | 74.52 106 | 85.75 93 | 89.10 78 |
|
test-LLR | | | 68.23 81 | 71.61 70 | 64.28 116 | 71.37 125 | 81.32 96 | 63.98 159 | 61.03 93 | 58.62 85 | 42.96 127 | 52.74 74 | 61.65 69 | 57.74 137 | 75.64 91 | 78.09 70 | 88.61 18 | 93.21 35 |
|
PatchMatch-RL | | | 62.22 143 | 60.69 156 | 64.01 117 | 68.74 140 | 75.75 160 | 59.27 184 | 60.35 103 | 56.09 96 | 53.80 75 | 47.06 104 | 36.45 182 | 64.80 78 | 68.22 178 | 67.22 187 | 77.10 203 | 74.02 183 |
|
FMVSNet1 | | | 63.48 122 | 63.07 127 | 63.97 118 | 65.31 179 | 76.37 155 | 71.77 93 | 57.90 118 | 43.32 163 | 45.66 101 | 35.06 188 | 49.43 113 | 58.57 133 | 77.49 70 | 78.22 67 | 84.59 137 | 81.60 156 |
|
pmmvs4 | | | 63.14 127 | 62.46 139 | 63.94 119 | 66.03 174 | 76.40 154 | 66.82 143 | 57.60 124 | 56.74 91 | 50.26 87 | 40.81 138 | 37.51 176 | 59.26 129 | 71.75 153 | 71.48 159 | 83.68 153 | 82.53 146 |
|
v10 | | | 63.00 131 | 62.22 141 | 63.90 120 | 67.88 160 | 77.78 140 | 71.59 103 | 54.34 160 | 45.37 147 | 42.76 136 | 38.53 160 | 38.93 169 | 61.05 101 | 74.39 103 | 74.52 106 | 85.75 93 | 86.04 117 |
|
v1144 | | | 63.00 131 | 62.39 140 | 63.70 121 | 67.72 163 | 80.27 115 | 71.23 108 | 56.40 138 | 42.51 169 | 40.81 144 | 38.12 168 | 37.73 174 | 60.42 112 | 74.46 101 | 74.55 104 | 85.64 105 | 89.12 77 |
|
v15 | | | 62.07 144 | 60.70 155 | 63.67 122 | 68.09 153 | 73.00 176 | 71.27 106 | 53.41 167 | 43.70 157 | 43.43 117 | 38.77 157 | 39.83 158 | 59.87 119 | 72.74 137 | 74.25 120 | 85.98 78 | 82.61 145 |
|
V14 | | | 61.96 147 | 60.56 157 | 63.59 123 | 68.06 154 | 72.93 179 | 71.10 111 | 53.33 169 | 43.47 162 | 43.28 122 | 38.59 158 | 39.78 159 | 59.76 121 | 72.65 139 | 74.19 121 | 86.01 74 | 82.32 150 |
|
V9 | | | 61.85 149 | 60.42 160 | 63.51 124 | 68.02 155 | 72.85 180 | 70.91 115 | 53.24 170 | 43.25 164 | 43.27 126 | 38.41 162 | 39.73 163 | 59.60 123 | 72.55 141 | 74.13 124 | 86.04 72 | 82.04 152 |
|
tpm | | | 64.85 105 | 66.02 106 | 63.48 125 | 74.52 107 | 78.38 133 | 70.98 114 | 44.99 206 | 51.61 113 | 43.28 122 | 47.66 96 | 53.18 103 | 60.57 106 | 70.58 161 | 71.30 167 | 86.54 59 | 89.45 74 |
|
EPP-MVSNet | | | 67.58 84 | 71.10 73 | 63.48 125 | 75.71 99 | 83.35 74 | 66.85 142 | 57.83 119 | 53.02 111 | 41.15 142 | 55.82 63 | 67.89 49 | 56.01 143 | 74.40 102 | 72.92 146 | 83.33 157 | 90.30 66 |
|
view800 | | | 63.02 130 | 62.69 137 | 63.39 127 | 74.79 106 | 80.76 106 | 67.83 135 | 61.93 85 | 43.16 165 | 37.78 162 | 40.43 139 | 39.73 163 | 53.16 152 | 75.01 95 | 73.32 137 | 84.87 125 | 86.43 113 |
|
v12 | | | 61.70 151 | 60.27 162 | 63.38 128 | 68.00 157 | 72.76 181 | 70.63 119 | 53.14 172 | 43.01 166 | 42.95 130 | 38.25 164 | 39.64 165 | 59.48 125 | 72.47 143 | 74.05 127 | 86.06 71 | 81.71 155 |
|
v13 | | | 61.60 153 | 60.13 165 | 63.31 129 | 67.95 159 | 72.67 183 | 70.51 120 | 53.05 173 | 42.80 167 | 42.96 127 | 38.10 169 | 39.57 166 | 59.31 128 | 72.36 144 | 73.98 129 | 86.10 68 | 81.40 157 |
|
v11 | | | 61.74 150 | 60.47 159 | 63.22 130 | 67.83 162 | 72.72 182 | 70.31 121 | 52.95 176 | 42.75 168 | 41.89 138 | 38.16 167 | 38.49 173 | 60.40 113 | 74.35 105 | 74.40 113 | 85.92 80 | 82.39 149 |
|
GA-MVS | | | 64.55 108 | 65.76 108 | 63.12 131 | 69.68 134 | 81.56 92 | 69.59 126 | 58.16 115 | 45.23 149 | 35.58 174 | 47.01 105 | 41.82 133 | 59.41 126 | 79.62 56 | 78.54 63 | 86.32 62 | 86.56 111 |
|
v1192 | | | 62.25 140 | 61.64 148 | 62.96 132 | 66.88 168 | 79.72 120 | 69.96 123 | 55.77 145 | 41.58 176 | 39.42 148 | 37.05 174 | 35.96 187 | 60.50 111 | 74.30 108 | 74.09 125 | 85.24 111 | 88.76 86 |
|
v148 | | | 62.00 146 | 61.19 152 | 62.96 132 | 67.46 166 | 79.49 123 | 67.87 134 | 57.66 120 | 42.30 171 | 45.02 108 | 38.20 166 | 38.89 171 | 54.77 148 | 69.83 171 | 72.60 150 | 84.96 119 | 87.01 108 |
|
MDTV_nov1_ep13 | | | 65.21 103 | 67.28 96 | 62.79 134 | 70.91 129 | 81.72 86 | 69.28 129 | 49.50 187 | 58.08 87 | 43.94 114 | 50.50 83 | 56.02 89 | 58.86 132 | 70.72 158 | 73.37 135 | 84.24 144 | 80.52 159 |
|
PatchmatchNet | | | 65.43 102 | 67.71 93 | 62.78 135 | 73.49 112 | 82.83 76 | 66.42 147 | 45.40 203 | 60.40 82 | 45.27 103 | 49.22 87 | 57.60 85 | 60.01 115 | 70.61 159 | 71.38 165 | 86.08 70 | 81.91 153 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
UGNet | | | 67.57 85 | 71.69 69 | 62.76 136 | 69.88 133 | 82.58 78 | 66.43 146 | 58.64 112 | 54.71 107 | 51.87 78 | 61.74 51 | 62.01 68 | 45.46 182 | 74.78 99 | 74.99 94 | 84.24 144 | 91.02 57 |
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 |
V42 | | | 62.86 135 | 62.97 129 | 62.74 137 | 60.84 195 | 78.99 128 | 71.46 105 | 57.13 135 | 46.85 136 | 44.28 112 | 38.87 156 | 40.73 150 | 57.63 139 | 72.60 140 | 74.14 123 | 85.09 115 | 88.63 92 |
|
v144192 | | | 62.05 145 | 61.46 150 | 62.73 138 | 66.59 171 | 79.87 118 | 69.30 128 | 55.88 143 | 41.50 177 | 39.41 149 | 37.23 172 | 36.45 182 | 59.62 122 | 72.69 138 | 73.51 132 | 85.61 106 | 88.93 80 |
|
thresconf0.02 | | | 63.92 116 | 65.18 109 | 62.46 139 | 75.91 97 | 80.65 110 | 67.51 139 | 63.86 64 | 45.00 150 | 33.32 184 | 51.38 79 | 51.68 107 | 48.34 166 | 75.49 94 | 75.13 93 | 85.84 92 | 76.91 174 |
|
TESTMET0.1,1 | | | 67.38 87 | 71.61 70 | 62.45 140 | 66.05 173 | 81.32 96 | 63.98 159 | 55.36 151 | 58.62 85 | 42.96 127 | 52.74 74 | 61.65 69 | 57.74 137 | 75.64 91 | 78.09 70 | 88.61 18 | 93.21 35 |
|
v1921920 | | | 61.66 152 | 61.10 153 | 62.31 141 | 66.32 172 | 79.57 122 | 68.41 133 | 55.49 149 | 41.03 178 | 38.69 155 | 36.64 180 | 35.27 193 | 59.60 123 | 73.23 118 | 73.41 134 | 85.37 108 | 88.51 97 |
|
CDS-MVSNet | | | 64.22 110 | 65.89 107 | 62.28 142 | 70.05 132 | 80.59 111 | 69.91 124 | 57.98 117 | 43.53 161 | 46.58 97 | 48.22 90 | 50.76 110 | 46.45 176 | 75.68 90 | 76.08 83 | 82.70 166 | 86.34 115 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tfpn | | | 62.54 136 | 62.79 133 | 62.25 143 | 74.16 108 | 79.86 119 | 66.07 149 | 60.97 96 | 42.43 170 | 36.41 166 | 39.88 143 | 43.76 129 | 51.25 159 | 73.85 112 | 74.17 122 | 84.67 135 | 85.57 124 |
|
tfpn_ndepth | | | 62.95 134 | 63.75 118 | 62.02 144 | 76.89 90 | 79.48 124 | 64.09 156 | 60.98 95 | 49.48 119 | 38.73 154 | 49.92 85 | 44.79 125 | 47.37 171 | 71.91 150 | 71.66 156 | 84.07 149 | 79.00 168 |
|
tfpnnormal | | | 58.97 167 | 56.48 185 | 61.89 145 | 71.27 127 | 76.21 156 | 66.65 145 | 61.76 89 | 32.90 208 | 36.41 166 | 27.83 206 | 29.14 211 | 50.64 161 | 73.06 120 | 73.05 144 | 84.58 138 | 83.15 143 |
|
IS_MVSNet | | | 67.29 88 | 71.98 66 | 61.82 146 | 76.92 89 | 84.32 71 | 65.90 150 | 58.22 114 | 55.75 100 | 39.22 150 | 54.51 68 | 62.47 64 | 45.99 179 | 78.83 63 | 78.52 64 | 84.70 134 | 89.47 73 |
|
v1240 | | | 61.09 156 | 60.55 158 | 61.72 147 | 65.92 176 | 79.28 126 | 67.16 141 | 54.91 155 | 39.79 183 | 38.10 158 | 36.08 182 | 34.64 194 | 59.15 130 | 72.86 128 | 73.36 136 | 85.10 113 | 87.84 101 |
|
EPNet_dtu | | | 66.17 95 | 70.13 79 | 61.54 148 | 81.04 62 | 77.39 145 | 68.87 131 | 62.50 81 | 69.78 57 | 33.51 183 | 63.77 45 | 56.22 88 | 37.65 199 | 72.20 145 | 72.18 153 | 85.69 100 | 79.38 165 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
conf0.05thres1000 | | | 60.33 162 | 59.42 172 | 61.40 149 | 73.15 114 | 78.25 135 | 65.29 151 | 60.30 104 | 36.61 194 | 35.75 172 | 33.25 190 | 39.23 167 | 50.35 162 | 72.18 146 | 72.67 149 | 83.57 154 | 83.74 132 |
|
Fast-Effi-MVS+-dtu | | | 63.05 129 | 64.72 114 | 61.11 150 | 71.21 128 | 76.81 152 | 70.72 117 | 43.13 212 | 52.51 112 | 35.34 175 | 46.55 108 | 46.36 120 | 61.40 99 | 71.57 154 | 71.44 160 | 84.84 126 | 87.79 102 |
|
UniMVSNet_NR-MVSNet | | | 62.30 139 | 63.51 120 | 60.89 151 | 69.48 138 | 77.83 139 | 64.07 157 | 63.94 63 | 50.03 116 | 31.17 189 | 44.82 111 | 41.12 140 | 51.37 156 | 71.02 156 | 74.81 97 | 85.30 109 | 84.95 125 |
|
Vis-MVSNet | | | 65.53 101 | 69.83 80 | 60.52 152 | 70.80 131 | 84.59 68 | 66.37 148 | 55.47 150 | 48.40 130 | 40.62 146 | 57.67 61 | 58.43 80 | 45.37 183 | 77.49 70 | 76.24 82 | 84.47 139 | 85.99 119 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
UA-Net | | | 64.62 106 | 68.23 91 | 60.42 153 | 77.53 85 | 81.38 94 | 60.08 181 | 57.47 127 | 47.01 135 | 44.75 109 | 60.68 56 | 71.32 40 | 41.84 190 | 73.27 117 | 72.25 152 | 80.83 184 | 71.68 196 |
|
CMPMVS | | 43.63 17 | 57.67 179 | 55.43 186 | 60.28 154 | 72.01 122 | 79.00 127 | 62.77 169 | 53.23 171 | 41.77 175 | 45.42 102 | 30.74 201 | 39.03 168 | 53.01 153 | 64.81 189 | 64.65 198 | 75.26 209 | 68.03 204 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
NR-MVSNet | | | 61.08 157 | 62.09 143 | 59.90 155 | 71.96 123 | 75.87 157 | 63.60 163 | 61.96 83 | 49.31 120 | 27.95 198 | 42.76 120 | 33.85 199 | 48.82 165 | 74.35 105 | 74.05 127 | 85.13 112 | 84.45 127 |
|
IterMVS | | | 61.87 148 | 63.55 119 | 59.90 155 | 67.29 167 | 72.20 185 | 67.34 140 | 48.56 193 | 47.48 134 | 37.86 161 | 47.07 103 | 48.27 115 | 54.08 150 | 72.12 147 | 73.71 130 | 84.30 143 | 83.99 131 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DU-MVS | | | 60.87 158 | 61.82 145 | 59.76 157 | 66.69 169 | 75.87 157 | 64.07 157 | 61.96 83 | 49.31 120 | 31.17 189 | 42.76 120 | 36.95 179 | 51.37 156 | 69.67 172 | 73.20 143 | 83.30 158 | 84.95 125 |
|
EG-PatchMatch MVS | | | 58.73 171 | 58.03 180 | 59.55 158 | 72.32 120 | 80.49 112 | 63.44 165 | 55.55 148 | 32.49 209 | 38.31 157 | 28.87 204 | 37.22 178 | 42.84 188 | 74.30 108 | 75.70 87 | 84.84 126 | 77.14 173 |
|
CR-MVSNet | | | 62.31 138 | 64.75 112 | 59.47 159 | 68.63 141 | 71.29 190 | 67.53 137 | 43.18 210 | 55.83 98 | 41.40 139 | 41.04 136 | 55.85 90 | 57.29 140 | 72.76 135 | 73.27 140 | 78.77 198 | 83.23 141 |
|
TranMVSNet+NR-MVSNet | | | 60.38 161 | 61.30 151 | 59.30 160 | 68.34 145 | 75.57 162 | 63.38 166 | 63.78 66 | 46.74 137 | 27.73 199 | 42.56 124 | 36.84 180 | 47.66 169 | 70.36 165 | 74.59 103 | 84.91 122 | 82.46 147 |
|
USDC | | | 59.69 164 | 60.03 166 | 59.28 161 | 64.04 183 | 71.84 188 | 63.15 168 | 55.36 151 | 54.90 105 | 35.02 178 | 48.34 89 | 29.79 210 | 58.16 134 | 70.60 160 | 71.33 166 | 79.99 190 | 73.42 187 |
|
pmmvs5 | | | 59.72 163 | 60.24 163 | 59.11 162 | 62.77 189 | 77.33 146 | 63.17 167 | 54.00 164 | 40.21 181 | 37.23 163 | 40.41 140 | 35.99 186 | 51.75 155 | 72.55 141 | 72.74 148 | 85.72 99 | 82.45 148 |
|
TransMVSNet (Re) | | | 57.83 177 | 56.90 183 | 58.91 163 | 72.26 121 | 74.69 167 | 63.57 164 | 61.42 91 | 32.30 210 | 32.65 186 | 33.97 189 | 35.96 187 | 39.17 197 | 73.84 114 | 72.84 147 | 84.37 141 | 74.69 181 |
|
pm-mvs1 | | | 59.21 166 | 59.58 171 | 58.77 164 | 67.97 158 | 77.07 151 | 64.12 154 | 57.20 133 | 34.73 202 | 36.86 164 | 35.34 185 | 40.54 155 | 43.34 187 | 74.32 107 | 73.30 139 | 83.13 164 | 81.77 154 |
|
Baseline_NR-MVSNet | | | 59.47 165 | 60.28 161 | 58.54 165 | 66.69 169 | 73.90 170 | 61.63 173 | 62.90 79 | 49.15 128 | 26.87 200 | 35.18 187 | 37.62 175 | 48.20 167 | 69.67 172 | 73.61 131 | 84.92 120 | 82.82 144 |
|
tfpnview11 | | | 58.92 168 | 59.60 170 | 58.13 166 | 72.99 116 | 77.11 149 | 60.48 176 | 60.37 102 | 42.10 173 | 29.10 195 | 43.45 113 | 40.72 151 | 41.67 191 | 70.53 163 | 70.43 173 | 84.17 147 | 72.85 190 |
|
test-mter | | | 64.06 115 | 69.24 82 | 58.01 167 | 59.07 202 | 77.40 144 | 59.13 185 | 48.11 195 | 55.64 101 | 39.18 151 | 51.56 78 | 58.54 78 | 55.38 145 | 73.52 116 | 76.00 84 | 87.22 48 | 92.05 50 |
|
UniMVSNet (Re) | | | 60.62 159 | 62.93 130 | 57.92 168 | 67.64 164 | 77.90 138 | 61.75 172 | 61.24 92 | 49.83 118 | 29.80 193 | 42.57 123 | 40.62 154 | 43.36 186 | 70.49 164 | 73.27 140 | 83.76 151 | 85.81 120 |
|
tfpn_n400 | | | 58.64 172 | 59.27 173 | 57.89 169 | 72.83 117 | 77.26 147 | 60.35 177 | 60.29 105 | 39.77 184 | 29.10 195 | 43.45 113 | 40.72 151 | 41.61 192 | 70.06 168 | 71.39 163 | 83.17 161 | 72.26 193 |
|
tfpnconf | | | 58.64 172 | 59.27 173 | 57.89 169 | 72.83 117 | 77.26 147 | 60.35 177 | 60.29 105 | 39.77 184 | 29.10 195 | 43.45 113 | 40.72 151 | 41.61 192 | 70.06 168 | 71.39 163 | 83.17 161 | 72.26 193 |
|
gg-mvs-nofinetune | | | 62.34 137 | 66.19 102 | 57.86 171 | 76.15 96 | 88.61 34 | 71.18 109 | 41.24 221 | 25.74 222 | 13.16 226 | 22.91 217 | 63.97 61 | 54.52 149 | 85.06 13 | 85.25 10 | 90.92 3 | 91.78 52 |
|
COLMAP_ROB | | 51.17 15 | 55.13 184 | 52.90 197 | 57.73 172 | 73.47 113 | 67.21 200 | 62.13 170 | 55.82 144 | 47.83 133 | 34.39 179 | 31.60 199 | 34.24 196 | 44.90 184 | 63.88 196 | 62.52 206 | 75.67 207 | 63.02 215 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
v7n | | | 57.04 181 | 56.64 184 | 57.52 173 | 62.85 188 | 74.75 166 | 61.76 171 | 51.80 180 | 35.58 201 | 36.02 171 | 32.33 194 | 33.61 200 | 50.16 163 | 67.73 180 | 70.34 174 | 82.51 168 | 82.12 151 |
|
ADS-MVSNet | | | 58.40 175 | 59.16 176 | 57.52 173 | 65.80 177 | 74.57 168 | 60.26 179 | 40.17 222 | 50.51 114 | 38.01 159 | 40.11 142 | 44.72 126 | 59.36 127 | 64.91 187 | 66.55 190 | 81.53 178 | 72.72 192 |
|
FMVSNet5 | | | 58.86 169 | 60.24 163 | 57.25 175 | 52.66 219 | 66.25 202 | 63.77 162 | 52.86 177 | 57.85 89 | 37.92 160 | 36.12 181 | 52.22 106 | 51.37 156 | 70.88 157 | 71.43 161 | 84.92 120 | 66.91 206 |
|
CHOSEN 280x420 | | | 62.23 142 | 66.57 100 | 57.17 176 | 59.88 199 | 68.92 196 | 61.20 175 | 42.28 214 | 54.17 108 | 39.57 147 | 47.78 94 | 64.97 57 | 62.68 87 | 73.85 112 | 69.52 177 | 77.43 202 | 86.75 109 |
|
pmmvs-eth3d | | | 55.20 182 | 53.95 193 | 56.65 177 | 57.34 208 | 67.77 198 | 57.54 189 | 53.74 165 | 40.93 179 | 41.09 143 | 31.19 200 | 29.10 212 | 49.07 164 | 65.54 184 | 67.28 186 | 81.14 181 | 75.81 175 |
|
PatchT | | | 60.46 160 | 63.85 117 | 56.51 178 | 65.95 175 | 75.68 161 | 47.34 206 | 41.39 217 | 53.89 110 | 41.40 139 | 37.84 170 | 50.30 112 | 57.29 140 | 72.76 135 | 73.27 140 | 85.67 101 | 83.23 141 |
|
tfpn1000 | | | 58.35 176 | 59.96 167 | 56.47 179 | 72.78 119 | 77.51 143 | 56.66 191 | 59.16 109 | 43.74 156 | 29.76 194 | 42.79 119 | 42.49 131 | 37.04 200 | 68.92 176 | 68.98 178 | 83.45 156 | 75.25 178 |
|
TAMVS | | | 58.86 169 | 60.91 154 | 56.47 179 | 62.38 191 | 77.57 142 | 58.97 186 | 52.98 174 | 38.76 188 | 36.17 169 | 42.26 128 | 47.94 116 | 46.45 176 | 70.23 166 | 70.79 169 | 81.86 176 | 78.82 169 |
|
v748 | | | 55.19 183 | 54.63 189 | 55.85 181 | 61.44 194 | 72.97 178 | 58.72 187 | 51.62 181 | 34.48 204 | 36.39 168 | 32.09 195 | 33.05 201 | 45.48 181 | 61.85 199 | 67.87 183 | 81.45 179 | 80.08 162 |
|
TinyColmap | | | 52.66 196 | 50.09 206 | 55.65 182 | 59.72 200 | 64.02 210 | 57.15 190 | 52.96 175 | 40.28 180 | 32.51 187 | 32.42 193 | 20.97 225 | 56.65 142 | 63.95 193 | 65.15 197 | 74.91 210 | 63.87 212 |
|
MIMVSNet | | | 57.78 178 | 59.71 169 | 55.53 183 | 54.79 210 | 77.10 150 | 63.89 161 | 45.02 205 | 46.59 142 | 36.79 165 | 28.36 205 | 40.77 149 | 45.84 180 | 74.97 96 | 76.58 76 | 86.87 54 | 73.60 186 |
|
pmmvs6 | | | 54.20 193 | 53.54 194 | 54.97 184 | 63.22 187 | 72.98 177 | 60.17 180 | 52.32 179 | 26.77 221 | 34.30 180 | 23.29 216 | 36.23 184 | 40.33 195 | 68.77 177 | 68.76 179 | 79.47 195 | 78.00 171 |
|
MVS-HIRNet | | | 53.86 194 | 53.02 195 | 54.85 185 | 60.30 198 | 72.36 184 | 44.63 214 | 42.20 215 | 39.45 186 | 43.47 116 | 21.66 220 | 34.00 198 | 55.47 144 | 65.42 185 | 67.16 188 | 83.02 165 | 71.08 199 |
|
Vis-MVSNet (Re-imp) | | | 62.25 140 | 68.74 85 | 54.68 186 | 73.70 109 | 78.74 129 | 56.51 192 | 57.49 126 | 55.22 102 | 26.86 201 | 54.56 67 | 61.35 71 | 31.06 202 | 73.10 119 | 74.90 95 | 82.49 169 | 83.31 137 |
|
TDRefinement | | | 52.70 195 | 51.02 203 | 54.66 187 | 57.41 207 | 65.06 206 | 61.47 174 | 54.94 153 | 44.03 153 | 33.93 181 | 30.13 203 | 27.57 213 | 46.17 178 | 61.86 198 | 62.48 207 | 74.01 213 | 66.06 208 |
|
test0.0.03 1 | | | 57.35 180 | 59.89 168 | 54.38 188 | 71.37 125 | 73.45 172 | 52.71 197 | 61.03 93 | 46.11 144 | 26.33 202 | 41.73 131 | 44.08 127 | 29.72 205 | 71.43 155 | 70.90 168 | 85.10 113 | 71.56 197 |
|
V4 | | | 54.78 190 | 55.14 188 | 54.37 189 | 54.07 214 | 72.13 186 | 59.83 183 | 49.39 188 | 34.46 205 | 35.11 176 | 31.64 197 | 35.72 190 | 47.22 173 | 63.90 194 | 67.92 181 | 80.59 186 | 80.23 160 |
|
v52 | | | 54.79 189 | 55.15 187 | 54.36 190 | 54.07 214 | 72.13 186 | 59.84 182 | 49.39 188 | 34.50 203 | 35.08 177 | 31.63 198 | 35.74 189 | 47.21 174 | 63.90 194 | 67.92 181 | 80.59 186 | 80.23 160 |
|
MDTV_nov1_ep13_2view | | | 54.47 192 | 54.61 190 | 54.30 191 | 60.50 196 | 73.82 171 | 57.92 188 | 43.38 209 | 39.43 187 | 32.51 187 | 33.23 191 | 34.05 197 | 47.26 172 | 62.36 197 | 66.21 193 | 84.24 144 | 73.19 189 |
|
RPMNet | | | 58.63 174 | 62.80 132 | 53.76 192 | 67.59 165 | 71.29 190 | 54.60 195 | 38.13 225 | 55.83 98 | 35.70 173 | 41.58 132 | 53.04 104 | 47.89 168 | 66.10 183 | 67.38 185 | 78.65 200 | 84.40 128 |
|
anonymousdsp | | | 54.99 186 | 57.24 182 | 52.36 193 | 53.82 216 | 71.75 189 | 51.49 198 | 48.14 194 | 33.74 206 | 33.66 182 | 38.34 163 | 36.13 185 | 47.54 170 | 64.53 191 | 70.60 171 | 79.53 194 | 85.59 123 |
|
RPSCF | | | 55.07 185 | 58.06 179 | 51.57 194 | 48.87 226 | 58.95 216 | 53.68 196 | 41.26 220 | 62.42 75 | 45.88 100 | 54.38 69 | 54.26 99 | 53.75 151 | 57.15 208 | 53.53 224 | 66.01 223 | 65.75 209 |
|
Anonymous20231206 | | | 52.23 197 | 52.80 198 | 51.56 195 | 64.70 182 | 69.41 194 | 51.01 199 | 58.60 113 | 36.63 193 | 22.44 208 | 21.80 219 | 31.42 206 | 30.52 203 | 66.79 182 | 67.83 184 | 82.10 174 | 75.73 176 |
|
gm-plane-assit | | | 54.99 186 | 57.99 181 | 51.49 196 | 69.27 139 | 54.42 222 | 32.32 226 | 42.59 213 | 21.18 228 | 13.71 224 | 23.61 213 | 43.84 128 | 60.21 114 | 87.09 4 | 86.55 4 | 90.81 4 | 89.28 75 |
|
CVMVSNet | | | 54.92 188 | 58.16 178 | 51.13 197 | 62.61 190 | 68.44 197 | 55.45 194 | 52.38 178 | 42.28 172 | 21.45 209 | 47.10 102 | 46.10 121 | 37.96 198 | 64.42 192 | 63.81 200 | 76.92 205 | 75.01 180 |
|
LP | | | 48.21 208 | 46.65 214 | 50.03 198 | 60.39 197 | 63.86 211 | 48.73 202 | 38.71 224 | 35.60 200 | 32.99 185 | 23.31 215 | 24.95 221 | 40.07 196 | 57.73 205 | 61.56 208 | 79.29 196 | 59.51 221 |
|
PM-MVS | | | 50.11 202 | 50.38 205 | 49.80 199 | 47.23 228 | 62.08 214 | 50.91 200 | 44.84 207 | 41.90 174 | 36.10 170 | 35.22 186 | 26.05 219 | 46.83 175 | 57.64 206 | 55.42 223 | 72.90 214 | 74.32 182 |
|
SixPastTwentyTwo | | | 49.11 206 | 49.22 208 | 48.99 200 | 58.54 206 | 64.14 209 | 47.18 207 | 47.75 196 | 31.15 212 | 24.42 204 | 41.01 137 | 26.55 215 | 44.04 185 | 54.76 218 | 58.70 213 | 71.99 217 | 68.21 202 |
|
PEN-MVS | | | 51.04 198 | 52.94 196 | 48.82 201 | 61.45 193 | 66.00 203 | 48.68 203 | 57.20 133 | 36.87 192 | 15.36 220 | 36.98 175 | 32.72 202 | 28.77 209 | 57.63 207 | 66.37 191 | 81.44 180 | 74.00 184 |
|
LTVRE_ROB | | 47.26 16 | 49.41 205 | 49.91 207 | 48.82 201 | 64.76 181 | 69.79 193 | 49.05 201 | 47.12 199 | 20.36 230 | 16.52 218 | 36.65 179 | 26.96 214 | 50.76 160 | 60.47 201 | 63.16 203 | 64.73 224 | 72.00 195 |
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 |
N_pmnet | | | 47.67 209 | 47.00 213 | 48.45 203 | 54.72 211 | 62.78 212 | 46.95 208 | 51.25 182 | 36.01 198 | 26.09 203 | 26.59 210 | 25.93 220 | 35.50 201 | 55.67 214 | 59.01 211 | 76.22 206 | 63.04 214 |
|
CP-MVSNet | | | 50.57 200 | 52.60 200 | 48.21 204 | 58.77 204 | 65.82 204 | 48.17 204 | 56.29 140 | 37.41 190 | 16.59 217 | 37.14 173 | 31.95 204 | 29.21 206 | 56.60 210 | 63.71 201 | 80.22 188 | 75.56 177 |
|
PS-CasMVS | | | 50.17 201 | 52.02 201 | 48.02 205 | 58.60 205 | 65.54 205 | 48.04 205 | 56.19 142 | 36.42 196 | 16.42 219 | 35.68 184 | 31.33 207 | 28.85 208 | 56.42 212 | 63.54 202 | 80.01 189 | 75.18 179 |
|
Anonymous20231211 | | | 43.05 216 | 39.25 221 | 47.48 206 | 54.29 213 | 57.29 220 | 41.10 218 | 49.06 191 | 17.67 233 | 44.00 113 | 10.63 232 | 16.79 231 | 25.15 216 | 52.14 220 | 56.70 218 | 71.30 218 | 63.51 213 |
|
DTE-MVSNet | | | 49.82 203 | 51.92 202 | 47.37 207 | 61.75 192 | 64.38 208 | 45.89 212 | 57.33 132 | 36.11 197 | 12.79 227 | 36.87 176 | 31.93 205 | 25.73 214 | 58.01 204 | 65.22 196 | 80.75 185 | 70.93 200 |
|
WR-MVS | | | 51.02 199 | 54.56 191 | 46.90 208 | 63.84 184 | 69.23 195 | 44.78 213 | 56.38 139 | 38.19 189 | 14.19 222 | 37.38 171 | 36.82 181 | 22.39 219 | 60.14 202 | 66.20 194 | 79.81 191 | 73.95 185 |
|
MDA-MVSNet-bldmvs | | | 44.15 214 | 42.27 220 | 46.34 209 | 38.34 231 | 62.31 213 | 46.28 209 | 55.74 146 | 29.83 214 | 20.98 210 | 27.11 209 | 16.45 232 | 41.98 189 | 41.11 230 | 57.47 215 | 74.72 211 | 61.65 219 |
|
testgi | | | 48.51 207 | 50.53 204 | 46.16 210 | 64.78 180 | 67.15 201 | 41.54 217 | 54.81 157 | 29.12 216 | 17.03 215 | 32.07 196 | 31.98 203 | 20.15 223 | 65.26 186 | 67.00 189 | 78.67 199 | 61.10 220 |
|
WR-MVS_H | | | 49.62 204 | 52.63 199 | 46.11 211 | 58.80 203 | 67.58 199 | 46.14 211 | 54.94 153 | 36.51 195 | 13.63 225 | 36.75 178 | 35.67 191 | 22.10 220 | 56.43 211 | 62.76 204 | 81.06 182 | 72.73 191 |
|
test20.03 | | | 47.23 211 | 48.69 209 | 45.53 212 | 63.28 186 | 64.39 207 | 41.01 219 | 56.93 136 | 29.16 215 | 15.21 221 | 23.90 212 | 30.76 209 | 17.51 228 | 64.63 190 | 65.26 195 | 79.21 197 | 62.71 216 |
|
test2356 | | | 46.29 212 | 47.37 211 | 45.03 213 | 54.38 212 | 57.99 219 | 42.03 216 | 50.32 184 | 30.78 213 | 16.65 216 | 27.40 208 | 23.70 222 | 29.86 204 | 61.20 200 | 64.31 199 | 76.93 204 | 66.22 207 |
|
FPMVS | | | 39.11 221 | 36.39 225 | 42.28 214 | 55.97 209 | 45.94 231 | 46.23 210 | 41.57 216 | 35.73 199 | 22.61 206 | 23.46 214 | 19.82 227 | 28.32 212 | 43.57 226 | 40.67 229 | 58.96 227 | 45.54 226 |
|
pmmvs3 | | | 41.86 219 | 42.29 219 | 41.36 215 | 39.80 229 | 52.66 224 | 38.93 222 | 35.85 231 | 23.40 225 | 20.22 211 | 19.30 221 | 20.84 226 | 40.56 194 | 55.98 213 | 58.79 212 | 72.80 215 | 65.03 210 |
|
new-patchmatchnet | | | 42.21 218 | 42.97 217 | 41.33 216 | 53.05 218 | 59.89 215 | 39.38 220 | 49.61 186 | 28.26 218 | 12.10 228 | 22.17 218 | 21.54 224 | 19.22 224 | 50.96 223 | 56.04 221 | 74.61 212 | 61.92 218 |
|
EU-MVSNet | | | 44.84 213 | 47.85 210 | 41.32 217 | 49.26 223 | 56.59 221 | 43.07 215 | 47.64 198 | 33.03 207 | 13.82 223 | 36.78 177 | 30.99 208 | 24.37 217 | 53.80 219 | 55.57 222 | 69.78 219 | 68.21 202 |
|
testus | | | 42.30 217 | 43.69 215 | 40.67 218 | 53.21 217 | 53.50 223 | 31.81 227 | 49.96 185 | 27.06 219 | 11.55 229 | 25.67 211 | 19.00 228 | 25.20 215 | 55.34 215 | 62.59 205 | 72.31 216 | 62.69 217 |
|
testpf | | | 43.39 215 | 47.17 212 | 38.98 219 | 65.58 178 | 47.38 230 | 36.09 223 | 31.67 232 | 36.97 191 | 19.47 212 | 33.01 192 | 35.62 192 | 23.61 218 | 50.86 224 | 56.08 220 | 57.48 229 | 70.27 201 |
|
FC-MVSNet-test | | | 47.24 210 | 54.37 192 | 38.93 220 | 59.49 201 | 58.25 218 | 34.48 225 | 53.36 168 | 45.66 146 | 6.66 235 | 50.62 81 | 42.02 132 | 16.62 229 | 58.39 203 | 61.21 209 | 62.99 225 | 64.40 211 |
|
1111 | | | 38.93 222 | 38.98 222 | 38.86 221 | 50.10 221 | 50.42 225 | 29.52 228 | 38.00 226 | 22.67 226 | 17.99 213 | 17.40 223 | 26.26 217 | 28.72 210 | 54.86 216 | 58.20 214 | 68.82 222 | 43.08 229 |
|
MIMVSNet1 | | | 40.84 220 | 43.46 216 | 37.79 222 | 32.14 233 | 58.92 217 | 39.24 221 | 50.83 183 | 27.00 220 | 11.29 230 | 16.76 230 | 26.53 216 | 17.75 227 | 57.14 209 | 61.12 210 | 75.46 208 | 56.78 224 |
|
testmv | | | 37.40 223 | 37.95 223 | 36.76 223 | 48.97 224 | 49.33 228 | 28.65 231 | 46.74 200 | 18.34 231 | 7.68 233 | 16.80 228 | 14.47 233 | 19.18 225 | 51.72 221 | 56.93 216 | 69.36 220 | 58.09 222 |
|
test1235678 | | | 37.40 223 | 37.94 224 | 36.76 223 | 48.97 224 | 49.30 229 | 28.65 231 | 46.73 201 | 18.33 232 | 7.68 233 | 16.79 229 | 14.46 234 | 19.18 225 | 51.72 221 | 56.92 217 | 69.36 220 | 58.07 223 |
|
PMVS | | 27.44 18 | 32.08 226 | 29.07 228 | 35.60 225 | 48.33 227 | 24.79 236 | 26.97 233 | 41.34 218 | 20.45 229 | 22.50 207 | 17.11 227 | 18.64 229 | 20.44 222 | 41.99 229 | 38.06 230 | 54.02 232 | 42.44 230 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new_pmnet | | | 33.19 225 | 35.52 226 | 30.47 226 | 27.55 237 | 45.31 232 | 29.29 230 | 30.92 233 | 29.00 217 | 9.88 232 | 18.77 222 | 17.64 230 | 26.77 213 | 44.07 225 | 45.98 227 | 58.41 228 | 47.87 225 |
|
test12356 | | | 29.92 227 | 31.49 227 | 28.08 227 | 38.46 230 | 37.74 234 | 21.36 234 | 40.17 222 | 16.83 234 | 5.61 237 | 15.66 231 | 11.48 235 | 6.60 235 | 42.01 228 | 51.23 225 | 56.29 230 | 45.52 227 |
|
GG-mvs-BLEND | | | 54.54 191 | 77.58 43 | 27.67 228 | 0.03 241 | 90.09 24 | 77.20 59 | 0.02 238 | 66.83 63 | 0.05 243 | 59.90 58 | 73.33 30 | 0.04 238 | 78.40 67 | 79.30 58 | 88.65 16 | 95.20 20 |
|
no-one | | | 26.96 228 | 26.51 229 | 27.49 229 | 37.87 232 | 39.14 233 | 17.12 236 | 41.31 219 | 12.02 236 | 3.68 239 | 8.04 234 | 8.42 238 | 10.67 233 | 28.11 232 | 45.96 228 | 54.27 231 | 43.89 228 |
|
.test1245 | | | 25.86 229 | 24.56 231 | 27.39 230 | 50.10 221 | 50.42 225 | 29.52 228 | 38.00 226 | 22.67 226 | 17.99 213 | 17.40 223 | 26.26 217 | 28.72 210 | 54.86 216 | 0.05 236 | 0.01 240 | 0.24 238 |
|
Gipuma | | | 24.91 230 | 24.61 230 | 25.26 231 | 31.47 234 | 21.59 237 | 18.06 235 | 37.53 228 | 25.43 223 | 10.03 231 | 4.18 238 | 4.25 240 | 14.85 230 | 43.20 227 | 47.03 226 | 39.62 234 | 26.55 234 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
E-PMN | | | 15.08 232 | 11.65 234 | 19.08 232 | 28.73 235 | 12.31 240 | 6.95 241 | 36.87 230 | 10.71 238 | 3.63 240 | 5.13 235 | 2.22 243 | 13.81 232 | 11.34 236 | 18.50 234 | 24.49 236 | 21.32 235 |
|
EMVS | | | 14.40 233 | 10.71 235 | 18.70 233 | 28.15 236 | 12.09 241 | 7.06 240 | 36.89 229 | 11.00 237 | 3.56 241 | 4.95 236 | 2.27 242 | 13.91 231 | 10.13 237 | 16.06 235 | 22.63 237 | 18.51 236 |
|
PMMVS2 | | | 20.45 231 | 22.31 232 | 18.27 234 | 20.52 238 | 26.73 235 | 14.85 238 | 28.43 235 | 13.69 235 | 0.79 242 | 10.35 233 | 9.10 236 | 3.83 237 | 27.64 233 | 32.87 231 | 41.17 233 | 35.81 231 |
|
tmp_tt | | | | | 16.09 235 | 13.07 239 | 8.12 242 | 13.61 239 | 2.08 237 | 55.09 103 | 30.10 192 | 40.26 141 | 22.83 223 | 5.35 236 | 29.91 231 | 25.25 233 | 32.33 235 | |
|
MVE | | 15.98 19 | 14.37 234 | 16.36 233 | 12.04 236 | 7.72 240 | 20.24 238 | 5.90 242 | 29.05 234 | 8.28 239 | 3.92 238 | 4.72 237 | 2.42 241 | 9.57 234 | 18.89 235 | 31.46 232 | 16.07 239 | 28.53 233 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 0.05 235 | 0.08 236 | 0.01 237 | 0.00 242 | 0.01 243 | 0.03 244 | 0.01 239 | 0.05 240 | 0.00 244 | 0.14 240 | 0.01 244 | 0.03 240 | 0.05 238 | 0.05 236 | 0.01 240 | 0.24 238 |
|
test123 | | | 0.05 235 | 0.08 236 | 0.01 237 | 0.00 242 | 0.01 243 | 0.01 245 | 0.00 240 | 0.05 240 | 0.00 244 | 0.16 239 | 0.00 245 | 0.04 238 | 0.02 239 | 0.05 236 | 0.00 242 | 0.26 237 |
|
sosnet-low-res | | | 0.00 237 | 0.00 238 | 0.00 239 | 0.00 242 | 0.00 245 | 0.00 246 | 0.00 240 | 0.00 242 | 0.00 244 | 0.00 241 | 0.00 245 | 0.00 241 | 0.00 240 | 0.00 239 | 0.00 242 | 0.00 240 |
|
sosnet | | | 0.00 237 | 0.00 238 | 0.00 239 | 0.00 242 | 0.00 245 | 0.00 246 | 0.00 240 | 0.00 242 | 0.00 244 | 0.00 241 | 0.00 245 | 0.00 241 | 0.00 240 | 0.00 239 | 0.00 242 | 0.00 240 |
|
our_test_3 | | | | | | 63.32 185 | 71.07 192 | 55.90 193 | | | | | | | | | | |
|
ambc | | | | 42.30 218 | | 50.36 220 | 49.51 227 | 35.47 224 | | 32.04 211 | 23.53 205 | 17.36 225 | 8.95 237 | 29.06 207 | 64.88 188 | 56.26 219 | 61.29 226 | 67.12 205 |
|
MTAPA | | | | | | | | | | | 78.32 6 | | 79.42 18 | | | | | |
|
MTMP | | | | | | | | | | | 76.04 11 | | 76.65 23 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.17 243 | | | | | | | | | | |
|
XVS | | | | | | 82.43 50 | 86.27 55 | 75.70 62 | | | 61.07 52 | | 72.27 33 | | | | 85.67 101 | |
|
X-MVStestdata | | | | | | 82.43 50 | 86.27 55 | 75.70 62 | | | 61.07 52 | | 72.27 33 | | | | 85.67 101 | |
|
mPP-MVS | | | | | | 86.96 37 | | | | | | | 70.61 43 | | | | | |
|
NP-MVS | | | | | | | | | | 81.60 31 | | | | | | | | |
|
Patchmtry | | | | | | | 78.06 137 | 67.53 137 | 43.18 210 | | 41.40 139 | | | | | | | |
|
DeepMVS_CX | | | | | | | 19.81 239 | 17.01 237 | 10.02 236 | 23.61 224 | 5.85 236 | 17.21 226 | 8.03 239 | 21.13 221 | 22.60 234 | | 21.42 238 | 30.01 232 |
|