HPM-MVS++ | | | 94.60 6 | 94.91 8 | 94.24 6 | 97.86 1 | 96.53 29 | 96.14 7 | 92.51 5 | 93.87 12 | 90.76 9 | 93.45 15 | 93.84 3 | 92.62 7 | 95.11 10 | 94.08 17 | 95.58 47 | 97.48 13 |
|
SMA-MVS | | | 94.70 5 | 95.35 5 | 93.93 9 | 97.57 2 | 97.57 6 | 95.98 10 | 91.91 10 | 94.50 5 | 90.35 11 | 93.46 14 | 92.72 10 | 91.89 15 | 95.89 2 | 95.22 1 | 95.88 24 | 98.10 4 |
|
zzz-MVS | | | 93.80 15 | 93.45 23 | 94.20 7 | 97.53 3 | 96.43 33 | 95.88 16 | 91.12 17 | 94.09 10 | 92.74 3 | 87.68 30 | 90.77 22 | 92.04 12 | 94.74 16 | 93.56 27 | 95.91 23 | 96.85 25 |
|
MP-MVS | | | 93.35 18 | 93.59 21 | 93.08 20 | 97.39 4 | 96.82 19 | 95.38 22 | 90.71 21 | 90.82 33 | 88.07 24 | 92.83 18 | 90.29 26 | 91.32 24 | 94.03 26 | 93.19 35 | 95.61 45 | 97.16 18 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
NCCC | | | 93.69 17 | 93.66 20 | 93.72 13 | 97.37 5 | 96.66 26 | 95.93 15 | 92.50 6 | 93.40 16 | 88.35 22 | 87.36 32 | 92.33 12 | 92.18 11 | 94.89 12 | 94.09 16 | 96.00 20 | 96.91 24 |
|
CNVR-MVS | | | 94.37 9 | 94.65 9 | 94.04 8 | 97.29 6 | 97.11 9 | 96.00 9 | 92.43 7 | 93.45 13 | 89.85 16 | 90.92 22 | 93.04 7 | 92.59 8 | 95.77 3 | 94.82 4 | 96.11 18 | 97.42 15 |
|
HFP-MVS | | | 94.02 12 | 94.22 15 | 93.78 11 | 97.25 7 | 96.85 17 | 95.81 17 | 90.94 20 | 94.12 9 | 90.29 13 | 94.09 11 | 89.98 28 | 92.52 9 | 93.94 29 | 93.49 30 | 95.87 26 | 97.10 21 |
|
APD-MVS | | | 94.37 9 | 94.47 13 | 94.26 5 | 97.18 8 | 96.99 13 | 96.53 6 | 92.68 4 | 92.45 21 | 89.96 14 | 94.53 8 | 91.63 17 | 92.89 5 | 94.58 19 | 93.82 21 | 96.31 14 | 97.26 16 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS_fast | | 88.76 1 | 93.10 20 | 93.02 27 | 93.19 19 | 97.13 9 | 96.51 30 | 95.35 23 | 91.19 16 | 93.14 18 | 88.14 23 | 85.26 38 | 89.49 32 | 91.45 20 | 95.17 8 | 95.07 2 | 95.85 29 | 96.48 32 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
APDe-MVS | | | 95.23 3 | 95.69 4 | 94.70 3 | 97.12 10 | 97.81 4 | 97.19 2 | 92.83 2 | 95.06 4 | 90.98 7 | 96.47 2 | 92.77 9 | 93.38 2 | 95.34 7 | 94.21 14 | 96.68 6 | 98.17 3 |
|
ACMMPR | | | 93.72 16 | 93.94 17 | 93.48 15 | 97.07 11 | 96.93 14 | 95.78 18 | 90.66 23 | 93.88 11 | 89.24 18 | 93.53 13 | 89.08 35 | 92.24 10 | 93.89 31 | 93.50 28 | 95.88 24 | 96.73 29 |
|
mPP-MVS | | | | | | 97.06 12 | | | | | | | 88.08 42 | | | | | |
|
ACMMP_NAP | | | 93.94 13 | 94.49 12 | 93.30 17 | 97.03 13 | 97.31 8 | 95.96 11 | 91.30 15 | 93.41 15 | 88.55 21 | 93.00 16 | 90.33 25 | 91.43 23 | 95.53 5 | 94.41 12 | 95.53 49 | 97.47 14 |
|
PGM-MVS | | | 92.76 23 | 93.03 26 | 92.45 25 | 97.03 13 | 96.67 25 | 95.73 20 | 87.92 39 | 90.15 40 | 86.53 33 | 92.97 17 | 88.33 41 | 91.69 18 | 93.62 34 | 93.03 36 | 95.83 30 | 96.41 35 |
|
SteuartSystems-ACMMP | | | 94.06 11 | 94.65 9 | 93.38 16 | 96.97 15 | 97.36 7 | 96.12 8 | 91.78 11 | 92.05 25 | 87.34 27 | 94.42 9 | 90.87 21 | 91.87 16 | 95.47 6 | 94.59 9 | 96.21 16 | 97.77 10 |
Skip Steuart: Steuart Systems R&D Blog. |
DVP-MVS | | | 95.56 1 | 96.26 1 | 94.73 2 | 96.93 16 | 98.19 1 | 96.62 5 | 92.81 3 | 96.15 1 | 91.73 4 | 95.01 4 | 95.31 1 | 93.41 1 | 95.95 1 | 94.77 6 | 96.90 2 | 98.46 1 |
|
X-MVS | | | 92.36 27 | 92.75 28 | 91.90 30 | 96.89 17 | 96.70 22 | 95.25 24 | 90.48 26 | 91.50 30 | 83.95 45 | 88.20 28 | 88.82 37 | 89.11 34 | 93.75 32 | 93.43 31 | 95.75 36 | 96.83 27 |
|
train_agg | | | 92.87 22 | 93.53 22 | 92.09 27 | 96.88 18 | 95.38 46 | 95.94 13 | 90.59 25 | 90.65 35 | 83.65 48 | 94.31 10 | 91.87 16 | 90.30 28 | 93.38 36 | 92.42 43 | 95.17 66 | 96.73 29 |
|
CP-MVS | | | 93.25 19 | 93.26 24 | 93.24 18 | 96.84 19 | 96.51 30 | 95.52 21 | 90.61 24 | 92.37 22 | 88.88 19 | 90.91 23 | 89.52 31 | 91.91 14 | 93.64 33 | 92.78 41 | 95.69 38 | 97.09 22 |
|
MSP-MVS | | | 95.12 4 | 95.83 3 | 94.30 4 | 96.82 20 | 97.94 3 | 96.98 3 | 92.37 8 | 95.40 2 | 90.59 10 | 96.16 3 | 93.71 4 | 92.70 6 | 94.80 14 | 94.77 6 | 96.37 11 | 97.99 7 |
|
DPE-MVS | | | 95.53 2 | 96.13 2 | 94.82 1 | 96.81 21 | 98.05 2 | 97.42 1 | 93.09 1 | 94.31 7 | 91.49 5 | 97.12 1 | 95.03 2 | 93.27 3 | 95.55 4 | 94.58 10 | 96.86 3 | 98.25 2 |
|
MCST-MVS | | | 93.81 14 | 94.06 16 | 93.53 14 | 96.79 22 | 96.85 17 | 95.95 12 | 91.69 13 | 92.20 23 | 87.17 29 | 90.83 24 | 93.41 5 | 91.96 13 | 94.49 22 | 93.50 28 | 97.61 1 | 97.12 20 |
|
SR-MVS | | | | | | 96.58 23 | | | 90.99 19 | | | | 92.40 11 | | | | | |
|
EPNet | | | 89.60 44 | 89.91 43 | 89.24 51 | 96.45 24 | 93.61 72 | 92.95 44 | 88.03 37 | 85.74 57 | 83.36 49 | 87.29 33 | 83.05 59 | 80.98 89 | 92.22 52 | 91.85 47 | 93.69 130 | 95.58 50 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CSCG | | | 92.76 23 | 93.16 25 | 92.29 26 | 96.30 25 | 97.74 5 | 94.67 30 | 88.98 33 | 92.46 20 | 89.73 17 | 86.67 34 | 92.15 14 | 88.69 39 | 92.26 51 | 92.92 39 | 95.40 54 | 97.89 9 |
|
CDPH-MVS | | | 91.14 36 | 92.01 30 | 90.11 40 | 96.18 26 | 96.18 36 | 94.89 28 | 88.80 35 | 88.76 45 | 77.88 81 | 89.18 27 | 87.71 44 | 87.29 57 | 93.13 39 | 93.31 34 | 95.62 43 | 95.84 43 |
|
DeepC-MVS | | 87.86 3 | 92.26 28 | 91.86 31 | 92.73 22 | 96.18 26 | 96.87 16 | 95.19 25 | 91.76 12 | 92.17 24 | 86.58 32 | 81.79 47 | 85.85 47 | 90.88 26 | 94.57 21 | 94.61 8 | 95.80 32 | 97.18 17 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | | | 90.29 40 | 88.38 54 | 92.53 23 | 96.10 28 | 95.19 50 | 92.98 43 | 91.40 14 | 89.08 44 | 88.65 20 | 78.35 68 | 81.44 66 | 91.30 25 | 90.81 79 | 90.21 73 | 94.72 85 | 93.59 84 |
|
MSLP-MVS++ | | | 92.02 31 | 91.40 34 | 92.75 21 | 96.01 29 | 95.88 41 | 93.73 37 | 89.00 31 | 89.89 41 | 90.31 12 | 81.28 53 | 88.85 36 | 91.45 20 | 92.88 44 | 94.24 13 | 96.00 20 | 96.76 28 |
|
3Dnovator+ | | 86.06 4 | 91.60 33 | 90.86 39 | 92.47 24 | 96.00 30 | 96.50 32 | 94.70 29 | 87.83 40 | 90.49 36 | 89.92 15 | 74.68 87 | 89.35 33 | 90.66 27 | 94.02 27 | 94.14 15 | 95.67 40 | 96.85 25 |
|
TSAR-MVS + ACMM | | | 92.97 21 | 94.51 11 | 91.16 34 | 95.88 31 | 96.59 27 | 95.09 26 | 90.45 27 | 93.42 14 | 83.01 50 | 94.68 7 | 90.74 23 | 88.74 37 | 94.75 15 | 93.78 22 | 93.82 125 | 97.63 11 |
|
ACMMP | | | 92.03 30 | 92.16 29 | 91.87 31 | 95.88 31 | 96.55 28 | 94.47 32 | 89.49 30 | 91.71 28 | 85.26 39 | 91.52 21 | 84.48 52 | 90.21 29 | 92.82 45 | 91.63 49 | 95.92 22 | 96.42 34 |
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 |
SD-MVS | | | 94.53 7 | 95.22 6 | 93.73 12 | 95.69 33 | 97.03 11 | 95.77 19 | 91.95 9 | 94.41 6 | 91.35 6 | 94.97 5 | 93.34 6 | 91.80 17 | 94.72 17 | 93.99 18 | 95.82 31 | 98.07 6 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
DPM-MVS | | | 91.72 32 | 91.48 32 | 92.00 28 | 95.53 34 | 95.75 42 | 95.94 13 | 91.07 18 | 91.20 31 | 85.58 38 | 81.63 51 | 90.74 23 | 88.40 42 | 93.40 35 | 93.75 23 | 95.45 53 | 93.85 78 |
|
TSAR-MVS + MP. | | | 94.48 8 | 94.97 7 | 93.90 10 | 95.53 34 | 97.01 12 | 96.69 4 | 90.71 21 | 94.24 8 | 90.92 8 | 94.97 5 | 92.19 13 | 93.03 4 | 94.83 13 | 93.60 25 | 96.51 10 | 97.97 8 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
CPTT-MVS | | | 91.39 34 | 90.95 37 | 91.91 29 | 95.06 36 | 95.24 48 | 95.02 27 | 88.98 33 | 91.02 32 | 86.71 31 | 84.89 40 | 88.58 40 | 91.60 19 | 90.82 78 | 89.67 89 | 94.08 112 | 96.45 33 |
|
CANet | | | 91.33 35 | 91.46 33 | 91.18 33 | 95.01 37 | 96.71 21 | 93.77 35 | 87.39 43 | 87.72 48 | 87.26 28 | 81.77 48 | 89.73 29 | 87.32 56 | 94.43 23 | 93.86 20 | 96.31 14 | 96.02 41 |
|
PHI-MVS | | | 92.05 29 | 93.74 19 | 90.08 41 | 94.96 38 | 97.06 10 | 93.11 42 | 87.71 41 | 90.71 34 | 80.78 64 | 92.40 19 | 91.03 19 | 87.68 50 | 94.32 24 | 94.48 11 | 96.21 16 | 96.16 38 |
|
MAR-MVS | | | 88.39 56 | 88.44 53 | 88.33 62 | 94.90 39 | 95.06 52 | 90.51 61 | 83.59 72 | 85.27 59 | 79.07 72 | 77.13 73 | 82.89 60 | 87.70 48 | 92.19 54 | 92.32 44 | 94.23 109 | 94.20 74 |
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 |
3Dnovator | | 85.17 5 | 90.48 39 | 89.90 44 | 91.16 34 | 94.88 40 | 95.74 43 | 93.82 34 | 85.36 54 | 89.28 42 | 87.81 25 | 74.34 89 | 87.40 45 | 88.56 40 | 93.07 40 | 93.74 24 | 96.53 9 | 95.71 45 |
|
DeepPCF-MVS | | 88.51 2 | 92.64 26 | 94.42 14 | 90.56 38 | 94.84 41 | 96.92 15 | 91.31 58 | 89.61 29 | 95.16 3 | 84.55 43 | 89.91 26 | 91.45 18 | 90.15 30 | 95.12 9 | 94.81 5 | 92.90 144 | 97.58 12 |
|
QAPM | | | 89.49 45 | 89.58 46 | 89.38 49 | 94.73 42 | 95.94 39 | 92.35 46 | 85.00 57 | 85.69 58 | 80.03 68 | 76.97 75 | 87.81 43 | 87.87 47 | 92.18 55 | 92.10 45 | 96.33 12 | 96.40 36 |
|
MVS_111021_HR | | | 90.56 38 | 91.29 36 | 89.70 46 | 94.71 43 | 95.63 44 | 91.81 54 | 86.38 48 | 87.53 49 | 81.29 58 | 87.96 29 | 85.43 49 | 87.69 49 | 93.90 30 | 92.93 38 | 96.33 12 | 95.69 46 |
|
abl_6 | | | | | 90.66 37 | 94.65 44 | 96.27 34 | 92.21 47 | 86.94 45 | 90.23 38 | 86.38 34 | 85.50 37 | 92.96 8 | 88.37 43 | | | 95.40 54 | 95.46 52 |
|
OpenMVS | | 82.53 11 | 87.71 62 | 86.84 69 | 88.73 55 | 94.42 45 | 95.06 52 | 91.02 60 | 83.49 75 | 82.50 72 | 82.24 55 | 67.62 126 | 85.48 48 | 85.56 67 | 91.19 66 | 91.30 52 | 95.67 40 | 94.75 62 |
|
PLC | | 83.76 9 | 88.61 53 | 86.83 70 | 90.70 36 | 94.22 46 | 92.63 88 | 91.50 56 | 87.19 44 | 89.16 43 | 86.87 30 | 75.51 82 | 80.87 68 | 89.98 31 | 90.01 89 | 89.20 98 | 94.41 104 | 90.45 138 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
LS3D | | | 85.96 74 | 84.37 89 | 87.81 64 | 94.13 47 | 93.27 77 | 90.26 65 | 89.00 31 | 84.91 62 | 72.84 102 | 71.74 101 | 72.47 115 | 87.45 54 | 89.53 97 | 89.09 100 | 93.20 140 | 89.60 141 |
|
EPNet_dtu | | | 81.98 106 | 83.82 91 | 79.83 140 | 94.10 48 | 85.97 165 | 87.29 106 | 84.08 65 | 80.61 92 | 59.96 172 | 81.62 52 | 77.19 92 | 62.91 187 | 87.21 118 | 86.38 137 | 90.66 169 | 87.77 158 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_0304 | | | 90.88 37 | 91.35 35 | 90.34 39 | 93.91 49 | 96.79 20 | 94.49 31 | 86.54 47 | 86.57 52 | 82.85 51 | 81.68 50 | 89.70 30 | 87.57 52 | 94.64 18 | 93.93 19 | 96.67 7 | 96.15 39 |
|
OPM-MVS | | | 87.56 64 | 85.80 80 | 89.62 47 | 93.90 50 | 94.09 66 | 94.12 33 | 88.18 36 | 75.40 124 | 77.30 84 | 76.41 76 | 77.93 88 | 88.79 36 | 92.20 53 | 90.82 60 | 95.40 54 | 93.72 82 |
|
DELS-MVS | | | 89.71 43 | 89.68 45 | 89.74 44 | 93.75 51 | 96.22 35 | 93.76 36 | 85.84 50 | 82.53 70 | 85.05 41 | 78.96 65 | 84.24 53 | 84.25 72 | 94.91 11 | 94.91 3 | 95.78 35 | 96.02 41 |
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 |
CNLPA | | | 88.40 54 | 87.00 68 | 90.03 42 | 93.73 52 | 94.28 62 | 89.56 76 | 85.81 51 | 91.87 26 | 87.55 26 | 69.53 116 | 81.49 65 | 89.23 33 | 89.45 98 | 88.59 108 | 94.31 108 | 93.82 79 |
|
HQP-MVS | | | 89.13 48 | 89.58 46 | 88.60 58 | 93.53 53 | 93.67 70 | 93.29 40 | 87.58 42 | 88.53 46 | 75.50 85 | 87.60 31 | 80.32 71 | 87.07 58 | 90.66 84 | 89.95 81 | 94.62 91 | 96.35 37 |
|
OMC-MVS | | | 90.23 41 | 90.40 40 | 90.03 42 | 93.45 54 | 95.29 47 | 91.89 53 | 86.34 49 | 93.25 17 | 84.94 42 | 81.72 49 | 86.65 46 | 88.90 35 | 91.69 59 | 90.27 72 | 94.65 89 | 93.95 76 |
|
ACMM | | 83.27 10 | 87.68 63 | 86.09 76 | 89.54 48 | 93.26 55 | 92.19 94 | 91.43 57 | 86.74 46 | 86.02 55 | 82.85 51 | 75.63 81 | 75.14 98 | 88.41 41 | 90.68 83 | 89.99 78 | 94.59 92 | 92.97 91 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MVS_111021_LR | | | 90.14 42 | 90.89 38 | 89.26 50 | 93.23 56 | 94.05 67 | 90.43 62 | 84.65 59 | 90.16 39 | 84.52 44 | 90.14 25 | 83.80 56 | 87.99 46 | 92.50 49 | 90.92 58 | 94.74 83 | 94.70 64 |
|
XVS | | | | | | 93.11 57 | 96.70 22 | 91.91 51 | | | 83.95 45 | | 88.82 37 | | | | 95.79 33 | |
|
X-MVStestdata | | | | | | 93.11 57 | 96.70 22 | 91.91 51 | | | 83.95 45 | | 88.82 37 | | | | 95.79 33 | |
|
PCF-MVS | | 84.60 6 | 88.66 51 | 87.75 64 | 89.73 45 | 93.06 59 | 96.02 37 | 93.22 41 | 90.00 28 | 82.44 73 | 80.02 69 | 77.96 71 | 85.16 50 | 87.36 55 | 88.54 106 | 88.54 109 | 94.72 85 | 95.61 49 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
TAPA-MVS | | 84.37 7 | 88.91 50 | 88.93 50 | 88.89 52 | 93.00 60 | 94.85 56 | 92.00 50 | 84.84 58 | 91.68 29 | 80.05 67 | 79.77 60 | 84.56 51 | 88.17 45 | 90.11 88 | 89.00 104 | 95.30 61 | 92.57 106 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PVSNet_Blended_VisFu | | | 87.40 66 | 87.80 61 | 86.92 70 | 92.86 61 | 95.40 45 | 88.56 93 | 83.45 78 | 79.55 101 | 82.26 54 | 74.49 88 | 84.03 54 | 79.24 120 | 92.97 43 | 91.53 51 | 95.15 68 | 96.65 31 |
|
UA-Net | | | 86.07 72 | 87.78 62 | 84.06 93 | 92.85 62 | 95.11 51 | 87.73 99 | 84.38 60 | 73.22 144 | 73.18 98 | 79.99 59 | 89.22 34 | 71.47 164 | 93.22 38 | 93.03 36 | 94.76 82 | 90.69 133 |
|
LGP-MVS_train | | | 88.25 58 | 88.55 51 | 87.89 63 | 92.84 63 | 93.66 71 | 93.35 39 | 85.22 56 | 85.77 56 | 74.03 94 | 86.60 35 | 76.29 95 | 86.62 62 | 91.20 65 | 90.58 68 | 95.29 62 | 95.75 44 |
|
TSAR-MVS + COLMAP | | | 88.40 54 | 89.09 49 | 87.60 67 | 92.72 64 | 93.92 69 | 92.21 47 | 85.57 53 | 91.73 27 | 73.72 95 | 91.75 20 | 73.22 113 | 87.64 51 | 91.49 61 | 89.71 88 | 93.73 128 | 91.82 119 |
|
PVSNet_BlendedMVS | | | 88.19 59 | 88.00 59 | 88.42 59 | 92.71 65 | 94.82 57 | 89.08 82 | 83.81 67 | 84.91 62 | 86.38 34 | 79.14 62 | 78.11 86 | 82.66 78 | 93.05 41 | 91.10 53 | 95.86 27 | 94.86 60 |
|
PVSNet_Blended | | | 88.19 59 | 88.00 59 | 88.42 59 | 92.71 65 | 94.82 57 | 89.08 82 | 83.81 67 | 84.91 62 | 86.38 34 | 79.14 62 | 78.11 86 | 82.66 78 | 93.05 41 | 91.10 53 | 95.86 27 | 94.86 60 |
|
ACMP | | 83.90 8 | 88.32 57 | 88.06 57 | 88.62 57 | 92.18 67 | 93.98 68 | 91.28 59 | 85.24 55 | 86.69 51 | 81.23 59 | 85.62 36 | 75.13 99 | 87.01 60 | 89.83 91 | 89.77 86 | 94.79 79 | 95.43 53 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MSDG | | | 83.87 90 | 81.02 112 | 87.19 69 | 92.17 68 | 89.80 123 | 89.15 80 | 85.72 52 | 80.61 92 | 79.24 71 | 66.66 129 | 68.75 130 | 82.69 77 | 87.95 113 | 87.44 118 | 94.19 110 | 85.92 171 |
|
TSAR-MVS + GP. | | | 92.71 25 | 93.91 18 | 91.30 32 | 91.96 69 | 96.00 38 | 93.43 38 | 87.94 38 | 92.53 19 | 86.27 37 | 93.57 12 | 91.94 15 | 91.44 22 | 93.29 37 | 92.89 40 | 96.78 4 | 97.15 19 |
|
EIA-MVS | | | 87.94 61 | 88.05 58 | 87.81 64 | 91.46 70 | 95.00 54 | 88.67 89 | 82.81 83 | 82.53 70 | 80.81 63 | 80.04 58 | 80.20 72 | 87.48 53 | 92.58 48 | 91.61 50 | 95.63 42 | 94.36 68 |
|
CS-MVS | | | 88.97 49 | 89.44 48 | 88.41 61 | 91.45 71 | 95.24 48 | 90.03 67 | 82.43 93 | 84.08 65 | 81.16 60 | 81.02 55 | 83.83 55 | 88.74 37 | 94.25 25 | 92.73 42 | 96.67 7 | 94.95 57 |
|
canonicalmvs | | | 89.36 47 | 89.92 42 | 88.70 56 | 91.38 72 | 95.92 40 | 91.81 54 | 82.61 91 | 90.37 37 | 82.73 53 | 82.09 45 | 79.28 81 | 88.30 44 | 91.17 67 | 93.59 26 | 95.36 57 | 97.04 23 |
|
CLD-MVS | | | 88.66 51 | 88.52 52 | 88.82 53 | 91.37 73 | 94.22 63 | 92.82 45 | 82.08 95 | 88.27 47 | 85.14 40 | 81.86 46 | 78.53 85 | 85.93 66 | 91.17 67 | 90.61 66 | 95.55 48 | 95.00 55 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
ETV-MVS | | | 89.45 46 | 90.00 41 | 88.82 53 | 91.22 74 | 94.29 61 | 90.21 66 | 83.36 81 | 86.48 53 | 81.94 56 | 83.65 41 | 81.98 64 | 89.46 32 | 94.58 19 | 93.37 33 | 96.73 5 | 95.62 48 |
|
CHOSEN 1792x2688 | | | 82.16 104 | 80.91 115 | 83.61 98 | 91.14 75 | 92.01 95 | 89.55 77 | 79.15 127 | 79.87 96 | 70.29 108 | 52.51 189 | 72.56 114 | 81.39 85 | 88.87 104 | 88.17 112 | 90.15 173 | 92.37 113 |
|
IB-MVS | | 79.09 12 | 82.60 101 | 82.19 100 | 83.07 105 | 91.08 76 | 93.55 73 | 80.90 170 | 81.35 100 | 76.56 116 | 80.87 62 | 64.81 143 | 69.97 124 | 68.87 171 | 85.64 144 | 90.06 77 | 95.36 57 | 94.74 63 |
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 |
IS_MVSNet | | | 86.18 71 | 88.18 56 | 83.85 96 | 91.02 77 | 94.72 59 | 87.48 102 | 82.46 92 | 81.05 87 | 70.28 109 | 76.98 74 | 82.20 63 | 76.65 136 | 93.97 28 | 93.38 32 | 95.18 65 | 94.97 56 |
|
HyFIR lowres test | | | 81.62 114 | 79.45 134 | 84.14 92 | 91.00 78 | 93.38 76 | 88.27 94 | 78.19 135 | 76.28 118 | 70.18 110 | 48.78 193 | 73.69 108 | 83.52 74 | 87.05 121 | 87.83 116 | 93.68 131 | 89.15 144 |
|
COLMAP_ROB | | 76.78 15 | 80.50 121 | 78.49 139 | 82.85 106 | 90.96 79 | 89.65 129 | 86.20 123 | 83.40 79 | 77.15 114 | 66.54 127 | 62.27 151 | 65.62 139 | 77.89 128 | 85.23 151 | 84.70 158 | 92.11 151 | 84.83 175 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
CANet_DTU | | | 85.43 77 | 87.72 65 | 82.76 108 | 90.95 80 | 93.01 82 | 89.99 68 | 75.46 159 | 82.67 69 | 64.91 139 | 83.14 43 | 80.09 73 | 80.68 94 | 92.03 57 | 91.03 55 | 94.57 94 | 92.08 114 |
|
FC-MVSNet-train | | | 85.18 79 | 85.31 83 | 85.03 79 | 90.67 81 | 91.62 98 | 87.66 100 | 83.61 70 | 79.75 98 | 74.37 92 | 78.69 66 | 71.21 119 | 78.91 121 | 91.23 63 | 89.96 80 | 94.96 74 | 94.69 65 |
|
baseline1 | | | 84.54 84 | 84.43 88 | 84.67 81 | 90.62 82 | 91.16 101 | 88.63 91 | 83.75 69 | 79.78 97 | 71.16 105 | 75.14 84 | 74.10 103 | 77.84 129 | 91.56 60 | 90.67 65 | 96.04 19 | 88.58 147 |
|
thres600view7 | | | 82.53 103 | 81.02 112 | 84.28 88 | 90.61 83 | 93.05 80 | 88.57 92 | 82.67 87 | 74.12 135 | 68.56 120 | 65.09 140 | 62.13 157 | 80.40 100 | 91.15 69 | 89.02 103 | 94.88 76 | 92.59 104 |
|
thres400 | | | 82.68 100 | 81.15 110 | 84.47 84 | 90.52 84 | 92.89 84 | 88.95 87 | 82.71 85 | 74.33 132 | 69.22 117 | 65.31 137 | 62.61 152 | 80.63 96 | 90.96 76 | 89.50 93 | 94.79 79 | 92.45 112 |
|
EPP-MVSNet | | | 86.55 68 | 87.76 63 | 85.15 78 | 90.52 84 | 94.41 60 | 87.24 108 | 82.32 94 | 81.79 79 | 73.60 96 | 78.57 67 | 82.41 61 | 82.07 83 | 91.23 63 | 90.39 70 | 95.14 69 | 95.48 51 |
|
ACMH | | 78.52 14 | 81.86 108 | 80.45 119 | 83.51 102 | 90.51 86 | 91.22 100 | 85.62 131 | 84.23 62 | 70.29 159 | 62.21 155 | 69.04 120 | 64.05 144 | 84.48 71 | 87.57 116 | 88.45 111 | 94.01 116 | 92.54 108 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Vis-MVSNet (Re-imp) | | | 83.65 93 | 86.81 71 | 79.96 138 | 90.46 87 | 92.71 85 | 84.84 139 | 82.00 96 | 80.93 89 | 62.44 154 | 76.29 77 | 82.32 62 | 65.54 184 | 92.29 50 | 91.66 48 | 94.49 99 | 91.47 128 |
|
thres200 | | | 82.77 99 | 81.25 109 | 84.54 82 | 90.38 88 | 93.05 80 | 89.13 81 | 82.67 87 | 74.40 131 | 69.53 114 | 65.69 135 | 63.03 149 | 80.63 96 | 91.15 69 | 89.42 94 | 94.88 76 | 92.04 116 |
|
MS-PatchMatch | | | 81.79 110 | 81.44 106 | 82.19 115 | 90.35 89 | 89.29 135 | 88.08 97 | 75.36 160 | 77.60 112 | 69.00 118 | 64.37 146 | 78.87 84 | 77.14 135 | 88.03 112 | 85.70 148 | 93.19 141 | 86.24 168 |
|
PatchMatch-RL | | | 83.34 95 | 81.36 107 | 85.65 75 | 90.33 90 | 89.52 131 | 84.36 143 | 81.82 97 | 80.87 91 | 79.29 70 | 74.04 90 | 62.85 151 | 86.05 65 | 88.40 109 | 87.04 125 | 92.04 152 | 86.77 164 |
|
thres100view900 | | | 82.55 102 | 81.01 114 | 84.34 85 | 90.30 91 | 92.27 92 | 89.04 85 | 82.77 84 | 75.14 125 | 69.56 112 | 65.72 133 | 63.13 146 | 79.62 115 | 89.97 90 | 89.26 97 | 94.73 84 | 91.61 126 |
|
tfpn200view9 | | | 82.86 97 | 81.46 105 | 84.48 83 | 90.30 91 | 93.09 79 | 89.05 84 | 82.71 85 | 75.14 125 | 69.56 112 | 65.72 133 | 63.13 146 | 80.38 101 | 91.15 69 | 89.51 92 | 94.91 75 | 92.50 110 |
|
casdiffmvs | | | 87.45 65 | 87.15 67 | 87.79 66 | 90.15 93 | 94.22 63 | 89.96 69 | 83.93 66 | 85.08 60 | 80.91 61 | 75.81 80 | 77.88 89 | 86.08 64 | 91.86 58 | 90.86 59 | 95.74 37 | 94.37 67 |
|
MVS_Test | | | 86.93 67 | 87.24 66 | 86.56 71 | 90.10 94 | 93.47 74 | 90.31 63 | 80.12 113 | 83.55 67 | 78.12 77 | 79.58 61 | 79.80 76 | 85.45 68 | 90.17 87 | 90.59 67 | 95.29 62 | 93.53 85 |
|
ACMH+ | | 79.08 13 | 81.84 109 | 80.06 124 | 83.91 95 | 89.92 95 | 90.62 105 | 86.21 122 | 83.48 77 | 73.88 137 | 65.75 132 | 66.38 130 | 65.30 140 | 84.63 70 | 85.90 141 | 87.25 121 | 93.45 136 | 91.13 131 |
|
Effi-MVS+ | | | 85.33 78 | 85.08 84 | 85.63 76 | 89.69 96 | 93.42 75 | 89.90 70 | 80.31 111 | 79.32 102 | 72.48 104 | 73.52 95 | 74.03 104 | 86.55 63 | 90.99 74 | 89.98 79 | 94.83 78 | 94.27 73 |
|
Anonymous202405211 | | | | 82.75 98 | | 89.58 97 | 92.97 83 | 89.04 85 | 84.13 64 | 78.72 106 | | 57.18 177 | 76.64 94 | 83.13 76 | 89.55 96 | 89.92 82 | 93.38 138 | 94.28 72 |
|
tttt0517 | | | 85.11 81 | 85.81 79 | 84.30 87 | 89.24 98 | 92.68 87 | 87.12 113 | 80.11 114 | 81.98 77 | 74.31 93 | 78.08 70 | 73.57 109 | 79.90 108 | 91.01 73 | 89.58 90 | 95.11 72 | 93.77 80 |
|
DI_MVS_plusplus_trai | | | 86.41 70 | 85.54 82 | 87.42 68 | 89.24 98 | 93.13 78 | 92.16 49 | 82.65 89 | 82.30 74 | 80.75 65 | 68.30 123 | 80.41 70 | 85.01 69 | 90.56 85 | 90.07 76 | 94.70 87 | 94.01 75 |
|
thisisatest0530 | | | 85.15 80 | 85.86 78 | 84.33 86 | 89.19 100 | 92.57 91 | 87.22 109 | 80.11 114 | 82.15 76 | 74.41 91 | 78.15 69 | 73.80 107 | 79.90 108 | 90.99 74 | 89.58 90 | 95.13 70 | 93.75 81 |
|
DCV-MVSNet | | | 85.88 76 | 86.17 74 | 85.54 77 | 89.10 101 | 89.85 121 | 89.34 78 | 80.70 104 | 83.04 68 | 78.08 79 | 76.19 78 | 79.00 82 | 82.42 81 | 89.67 94 | 90.30 71 | 93.63 133 | 95.12 54 |
|
UGNet | | | 85.90 75 | 88.23 55 | 83.18 104 | 88.96 102 | 94.10 65 | 87.52 101 | 83.60 71 | 81.66 80 | 77.90 80 | 80.76 56 | 83.19 58 | 66.70 181 | 91.13 72 | 90.71 64 | 94.39 105 | 96.06 40 |
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 |
Anonymous20231211 | | | 84.42 88 | 83.02 94 | 86.05 73 | 88.85 103 | 92.70 86 | 88.92 88 | 83.40 79 | 79.99 95 | 78.31 76 | 55.83 181 | 78.92 83 | 83.33 75 | 89.06 101 | 89.76 87 | 93.50 135 | 94.90 58 |
|
DWT-MVSNet_training | | | 80.51 120 | 78.05 148 | 83.39 103 | 88.64 104 | 88.33 150 | 86.11 124 | 76.33 152 | 79.65 99 | 78.64 74 | 69.62 114 | 58.89 176 | 80.82 90 | 80.50 182 | 82.03 177 | 89.77 176 | 87.36 160 |
|
MVSTER | | | 86.03 73 | 86.12 75 | 85.93 74 | 88.62 105 | 89.93 119 | 89.33 79 | 79.91 118 | 81.87 78 | 81.35 57 | 81.07 54 | 74.91 100 | 80.66 95 | 92.13 56 | 90.10 75 | 95.68 39 | 92.80 96 |
|
TDRefinement | | | 79.05 139 | 77.05 158 | 81.39 122 | 88.45 106 | 89.00 142 | 86.92 114 | 82.65 89 | 74.21 134 | 64.41 140 | 59.17 169 | 59.16 173 | 74.52 150 | 85.23 151 | 85.09 153 | 91.37 161 | 87.51 159 |
|
IterMVS-LS | | | 83.28 96 | 82.95 96 | 83.65 97 | 88.39 107 | 88.63 146 | 86.80 117 | 78.64 132 | 76.56 116 | 73.43 97 | 72.52 100 | 75.35 97 | 80.81 92 | 86.43 136 | 88.51 110 | 93.84 124 | 92.66 101 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
diffmvs | | | 86.52 69 | 86.76 72 | 86.23 72 | 88.31 108 | 92.63 88 | 89.58 75 | 81.61 99 | 86.14 54 | 80.26 66 | 79.00 64 | 77.27 91 | 83.58 73 | 88.94 102 | 89.06 101 | 94.05 114 | 94.29 69 |
|
Fast-Effi-MVS+ | | | 83.77 92 | 82.98 95 | 84.69 80 | 87.98 109 | 91.87 96 | 88.10 96 | 77.70 141 | 78.10 110 | 73.04 100 | 69.13 118 | 68.51 131 | 86.66 61 | 90.49 86 | 89.85 84 | 94.67 88 | 92.88 93 |
|
gg-mvs-nofinetune | | | 75.64 176 | 77.26 155 | 73.76 181 | 87.92 110 | 92.20 93 | 87.32 105 | 64.67 198 | 51.92 203 | 35.35 206 | 46.44 196 | 77.05 93 | 71.97 161 | 92.64 47 | 91.02 56 | 95.34 59 | 89.53 142 |
|
RPSCF | | | 83.46 94 | 83.36 93 | 83.59 99 | 87.75 111 | 87.35 156 | 84.82 140 | 79.46 123 | 83.84 66 | 78.12 77 | 82.69 44 | 79.87 74 | 82.60 80 | 82.47 175 | 81.13 179 | 88.78 181 | 86.13 169 |
|
Effi-MVS+-dtu | | | 82.05 105 | 81.76 102 | 82.38 112 | 87.72 112 | 90.56 106 | 86.90 116 | 78.05 137 | 73.85 138 | 66.85 126 | 71.29 103 | 71.90 117 | 82.00 84 | 86.64 131 | 85.48 150 | 92.76 146 | 92.58 105 |
|
CostFormer | | | 80.94 117 | 80.21 121 | 81.79 117 | 87.69 113 | 88.58 147 | 87.47 103 | 70.66 174 | 80.02 94 | 77.88 81 | 73.03 96 | 71.40 118 | 78.24 125 | 79.96 185 | 79.63 181 | 88.82 180 | 88.84 145 |
|
baseline2 | | | 82.80 98 | 82.86 97 | 82.73 109 | 87.68 114 | 90.50 107 | 84.92 138 | 78.93 128 | 78.07 111 | 73.06 99 | 75.08 85 | 69.77 125 | 77.31 132 | 88.90 103 | 86.94 126 | 94.50 97 | 90.74 132 |
|
Vis-MVSNet | | | 84.38 89 | 86.68 73 | 81.70 118 | 87.65 115 | 94.89 55 | 88.14 95 | 80.90 103 | 74.48 130 | 68.23 121 | 77.53 72 | 80.72 69 | 69.98 168 | 92.68 46 | 91.90 46 | 95.33 60 | 94.58 66 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
test-LLR | | | 79.47 134 | 79.84 128 | 79.03 144 | 87.47 116 | 82.40 190 | 81.24 167 | 78.05 137 | 73.72 139 | 62.69 151 | 73.76 92 | 74.42 101 | 73.49 155 | 84.61 160 | 82.99 170 | 91.25 163 | 87.01 162 |
|
test0.0.03 1 | | | 76.03 170 | 78.51 138 | 73.12 185 | 87.47 116 | 85.13 176 | 76.32 188 | 78.05 137 | 73.19 146 | 50.98 192 | 70.64 105 | 69.28 128 | 55.53 191 | 85.33 149 | 84.38 162 | 90.39 171 | 81.63 187 |
|
tpmrst | | | 76.55 163 | 75.99 170 | 77.20 158 | 87.32 118 | 83.05 183 | 82.86 153 | 65.62 193 | 78.61 108 | 67.22 125 | 69.19 117 | 65.71 138 | 75.87 140 | 76.75 194 | 75.33 194 | 84.31 198 | 83.28 181 |
|
baseline | | | 84.89 82 | 86.06 77 | 83.52 101 | 87.25 119 | 89.67 128 | 87.76 98 | 75.68 158 | 84.92 61 | 78.40 75 | 80.10 57 | 80.98 67 | 80.20 104 | 86.69 130 | 87.05 124 | 91.86 155 | 92.99 90 |
|
CDS-MVSNet | | | 81.63 113 | 82.09 101 | 81.09 127 | 87.21 120 | 90.28 110 | 87.46 104 | 80.33 110 | 69.06 163 | 70.66 106 | 71.30 102 | 73.87 105 | 67.99 174 | 89.58 95 | 89.87 83 | 92.87 145 | 90.69 133 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm cat1 | | | 77.78 152 | 75.28 178 | 80.70 130 | 87.14 121 | 85.84 167 | 85.81 127 | 70.40 175 | 77.44 113 | 78.80 73 | 63.72 147 | 64.01 145 | 76.55 137 | 75.60 196 | 75.21 195 | 85.51 196 | 85.12 173 |
|
tpm | | | 76.30 169 | 76.05 169 | 76.59 164 | 86.97 122 | 83.01 184 | 83.83 147 | 67.06 189 | 71.83 149 | 63.87 145 | 69.56 115 | 62.88 150 | 73.41 157 | 79.79 186 | 78.59 185 | 84.41 197 | 86.68 165 |
|
EPMVS | | | 77.53 154 | 78.07 146 | 76.90 162 | 86.89 123 | 84.91 177 | 82.18 162 | 66.64 191 | 81.00 88 | 64.11 143 | 72.75 99 | 69.68 126 | 74.42 152 | 79.36 188 | 78.13 187 | 87.14 189 | 80.68 191 |
|
PatchmatchNet | | | 78.67 144 | 78.85 137 | 78.46 152 | 86.85 124 | 86.03 164 | 83.77 148 | 68.11 186 | 80.88 90 | 66.19 129 | 72.90 98 | 73.40 111 | 78.06 126 | 79.25 189 | 77.71 189 | 87.75 186 | 81.75 186 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
SCA | | | 79.51 133 | 80.15 123 | 78.75 147 | 86.58 125 | 87.70 153 | 83.07 152 | 68.53 183 | 81.31 82 | 66.40 128 | 73.83 91 | 75.38 96 | 79.30 119 | 80.49 183 | 79.39 184 | 88.63 183 | 82.96 183 |
|
USDC | | | 80.69 118 | 79.89 127 | 81.62 120 | 86.48 126 | 89.11 140 | 86.53 119 | 78.86 129 | 81.15 86 | 63.48 147 | 72.98 97 | 59.12 175 | 81.16 87 | 87.10 119 | 85.01 154 | 93.23 139 | 84.77 176 |
|
Fast-Effi-MVS+-dtu | | | 79.95 125 | 80.69 116 | 79.08 143 | 86.36 127 | 89.14 139 | 85.85 126 | 72.28 168 | 72.85 147 | 59.32 175 | 70.43 109 | 68.42 132 | 77.57 130 | 86.14 138 | 86.44 136 | 93.11 142 | 91.39 129 |
|
tfpnnormal | | | 77.46 155 | 74.86 180 | 80.49 134 | 86.34 128 | 88.92 143 | 84.33 144 | 81.26 101 | 61.39 191 | 61.70 162 | 51.99 190 | 53.66 194 | 74.84 147 | 88.63 105 | 87.38 120 | 94.50 97 | 92.08 114 |
|
dps | | | 78.02 149 | 75.94 171 | 80.44 135 | 86.06 129 | 86.62 162 | 82.58 154 | 69.98 178 | 75.14 125 | 77.76 83 | 69.08 119 | 59.93 166 | 78.47 123 | 79.47 187 | 77.96 188 | 87.78 185 | 83.40 180 |
|
IterMVS-SCA-FT | | | 79.41 135 | 80.20 122 | 78.49 151 | 85.88 130 | 86.26 163 | 83.95 146 | 71.94 169 | 73.55 142 | 61.94 158 | 70.48 108 | 70.50 121 | 75.23 142 | 85.81 143 | 84.61 160 | 91.99 154 | 90.18 139 |
|
LTVRE_ROB | | 74.41 16 | 75.78 175 | 74.72 181 | 77.02 161 | 85.88 130 | 89.22 136 | 82.44 157 | 77.17 144 | 50.57 204 | 45.45 198 | 65.44 136 | 52.29 196 | 81.25 86 | 85.50 147 | 87.42 119 | 89.94 175 | 92.62 102 |
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 |
EG-PatchMatch MVS | | | 76.40 167 | 75.47 176 | 77.48 157 | 85.86 132 | 90.22 112 | 82.45 156 | 73.96 165 | 59.64 195 | 59.60 174 | 52.75 188 | 62.20 156 | 68.44 173 | 88.23 110 | 87.50 117 | 94.55 95 | 87.78 157 |
|
CR-MVSNet | | | 78.71 143 | 78.86 136 | 78.55 150 | 85.85 133 | 85.15 174 | 82.30 159 | 68.23 184 | 74.71 128 | 65.37 135 | 64.39 145 | 69.59 127 | 77.18 133 | 85.10 156 | 84.87 155 | 92.34 150 | 88.21 151 |
|
GA-MVS | | | 79.52 132 | 79.71 131 | 79.30 142 | 85.68 134 | 90.36 109 | 84.55 141 | 78.44 133 | 70.47 158 | 57.87 180 | 68.52 122 | 61.38 158 | 76.21 138 | 89.40 99 | 87.89 113 | 93.04 143 | 89.96 140 |
|
UniMVSNet_ETH3D | | | 79.24 137 | 76.47 163 | 82.48 111 | 85.66 135 | 90.97 102 | 86.08 125 | 81.63 98 | 64.48 183 | 68.94 119 | 54.47 183 | 57.65 179 | 78.83 122 | 85.20 154 | 88.91 105 | 93.72 129 | 93.60 83 |
|
TransMVSNet (Re) | | | 76.57 162 | 75.16 179 | 78.22 154 | 85.60 136 | 87.24 157 | 82.46 155 | 81.23 102 | 59.80 194 | 59.05 178 | 57.07 178 | 59.14 174 | 66.60 182 | 88.09 111 | 86.82 127 | 94.37 106 | 87.95 156 |
|
RPMNet | | | 77.07 157 | 77.63 153 | 76.42 165 | 85.56 137 | 85.15 174 | 81.37 164 | 65.27 195 | 74.71 128 | 60.29 171 | 63.71 148 | 66.59 136 | 73.64 154 | 82.71 173 | 82.12 175 | 92.38 149 | 88.39 149 |
|
MDTV_nov1_ep13 | | | 79.14 138 | 79.49 133 | 78.74 148 | 85.40 138 | 86.89 160 | 84.32 145 | 70.29 176 | 78.85 105 | 69.42 115 | 75.37 83 | 73.29 112 | 75.64 141 | 80.61 181 | 79.48 183 | 87.36 187 | 81.91 185 |
|
UniMVSNet (Re) | | | 81.22 115 | 81.08 111 | 81.39 122 | 85.35 139 | 91.76 97 | 84.93 137 | 82.88 82 | 76.13 119 | 65.02 138 | 64.94 141 | 63.09 148 | 75.17 144 | 87.71 115 | 89.04 102 | 94.97 73 | 94.88 59 |
|
UniMVSNet_NR-MVSNet | | | 81.87 107 | 81.33 108 | 82.50 110 | 85.31 140 | 91.30 99 | 85.70 128 | 84.25 61 | 75.89 120 | 64.21 141 | 66.95 128 | 64.65 142 | 80.22 102 | 87.07 120 | 89.18 99 | 95.27 64 | 94.29 69 |
|
IterMVS | | | 78.79 142 | 79.71 131 | 77.71 155 | 85.26 141 | 85.91 166 | 84.54 142 | 69.84 180 | 73.38 143 | 61.25 166 | 70.53 107 | 70.35 122 | 74.43 151 | 85.21 153 | 83.80 165 | 90.95 167 | 88.77 146 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
NR-MVSNet | | | 80.25 123 | 79.98 126 | 80.56 133 | 85.20 142 | 90.94 103 | 85.65 130 | 83.58 73 | 75.74 121 | 61.36 165 | 65.30 138 | 56.75 183 | 72.38 160 | 88.46 108 | 88.80 106 | 95.16 67 | 93.87 77 |
|
CMPMVS | | 56.49 17 | 73.84 184 | 71.73 189 | 76.31 168 | 85.20 142 | 85.67 169 | 75.80 189 | 73.23 166 | 62.26 188 | 65.40 134 | 53.40 187 | 59.70 168 | 71.77 163 | 80.25 184 | 79.56 182 | 86.45 192 | 81.28 188 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
TinyColmap | | | 76.73 159 | 73.95 183 | 79.96 138 | 85.16 144 | 85.64 170 | 82.34 158 | 78.19 135 | 70.63 156 | 62.06 157 | 60.69 161 | 49.61 199 | 80.81 92 | 85.12 155 | 83.69 166 | 91.22 165 | 82.27 184 |
|
gm-plane-assit | | | 70.29 188 | 70.65 190 | 69.88 189 | 85.03 145 | 78.50 199 | 58.41 206 | 65.47 194 | 50.39 205 | 40.88 203 | 49.60 192 | 50.11 198 | 75.14 145 | 91.43 62 | 89.78 85 | 94.32 107 | 84.73 177 |
|
FC-MVSNet-test | | | 76.53 164 | 81.62 104 | 70.58 188 | 84.99 146 | 85.73 168 | 74.81 191 | 78.85 130 | 77.00 115 | 39.13 205 | 75.90 79 | 73.50 110 | 54.08 195 | 86.54 133 | 85.99 145 | 91.65 157 | 86.68 165 |
|
DU-MVS | | | 81.20 116 | 80.30 120 | 82.25 113 | 84.98 147 | 90.94 103 | 85.70 128 | 83.58 73 | 75.74 121 | 64.21 141 | 65.30 138 | 59.60 170 | 80.22 102 | 86.89 123 | 89.31 95 | 94.77 81 | 94.29 69 |
|
Baseline_NR-MVSNet | | | 79.84 127 | 78.37 143 | 81.55 121 | 84.98 147 | 86.66 161 | 85.06 135 | 83.49 75 | 75.57 123 | 63.31 148 | 58.22 176 | 60.97 160 | 78.00 127 | 86.89 123 | 87.13 122 | 94.47 100 | 93.15 88 |
|
TranMVSNet+NR-MVSNet | | | 80.52 119 | 79.84 128 | 81.33 124 | 84.92 149 | 90.39 108 | 85.53 133 | 84.22 63 | 74.27 133 | 60.68 170 | 64.93 142 | 59.96 165 | 77.48 131 | 86.75 128 | 89.28 96 | 95.12 71 | 93.29 86 |
|
pm-mvs1 | | | 78.51 147 | 77.75 152 | 79.40 141 | 84.83 150 | 89.30 134 | 83.55 150 | 79.38 124 | 62.64 187 | 63.68 146 | 58.73 174 | 64.68 141 | 70.78 167 | 89.79 92 | 87.84 114 | 94.17 111 | 91.28 130 |
|
testgi | | | 71.92 186 | 74.20 182 | 69.27 190 | 84.58 151 | 83.06 182 | 73.40 193 | 74.39 162 | 64.04 185 | 46.17 197 | 68.90 121 | 57.15 181 | 48.89 199 | 84.07 165 | 83.08 169 | 88.18 184 | 79.09 195 |
|
thisisatest0515 | | | 79.76 129 | 80.59 118 | 78.80 146 | 84.40 152 | 88.91 144 | 79.48 176 | 76.94 147 | 72.29 148 | 67.33 124 | 67.82 125 | 65.99 137 | 70.80 166 | 88.50 107 | 87.84 114 | 93.86 123 | 92.75 99 |
|
FMVSNet3 | | | 84.44 87 | 84.64 87 | 84.21 89 | 84.32 153 | 90.13 114 | 89.85 71 | 80.37 107 | 81.17 83 | 75.50 85 | 69.63 111 | 79.69 78 | 79.62 115 | 89.72 93 | 90.52 69 | 95.59 46 | 91.58 127 |
|
GBi-Net | | | 84.51 85 | 84.80 85 | 84.17 90 | 84.20 154 | 89.95 116 | 89.70 72 | 80.37 107 | 81.17 83 | 75.50 85 | 69.63 111 | 79.69 78 | 79.75 112 | 90.73 80 | 90.72 61 | 95.52 50 | 91.71 121 |
|
test1 | | | 84.51 85 | 84.80 85 | 84.17 90 | 84.20 154 | 89.95 116 | 89.70 72 | 80.37 107 | 81.17 83 | 75.50 85 | 69.63 111 | 79.69 78 | 79.75 112 | 90.73 80 | 90.72 61 | 95.52 50 | 91.71 121 |
|
FMVSNet2 | | | 83.87 90 | 83.73 92 | 84.05 94 | 84.20 154 | 89.95 116 | 89.70 72 | 80.21 112 | 79.17 104 | 74.89 89 | 65.91 131 | 77.49 90 | 79.75 112 | 90.87 77 | 91.00 57 | 95.52 50 | 91.71 121 |
|
WR-MVS | | | 76.63 161 | 78.02 149 | 75.02 175 | 84.14 157 | 89.76 125 | 78.34 183 | 80.64 105 | 69.56 160 | 52.32 187 | 61.26 154 | 61.24 159 | 60.66 188 | 84.45 162 | 87.07 123 | 93.99 117 | 92.77 97 |
|
v8 | | | 79.90 126 | 78.39 142 | 81.66 119 | 83.97 158 | 89.81 122 | 87.16 111 | 77.40 143 | 71.49 150 | 67.71 122 | 61.24 155 | 62.49 153 | 79.83 111 | 85.48 148 | 86.17 140 | 93.89 121 | 92.02 118 |
|
v2v482 | | | 79.84 127 | 78.07 146 | 81.90 116 | 83.75 159 | 90.21 113 | 87.17 110 | 79.85 119 | 70.65 155 | 65.93 131 | 61.93 152 | 60.07 164 | 80.82 90 | 85.25 150 | 86.71 129 | 93.88 122 | 91.70 124 |
|
v10 | | | 79.62 130 | 78.19 144 | 81.28 125 | 83.73 160 | 89.69 127 | 87.27 107 | 76.86 148 | 70.50 157 | 65.46 133 | 60.58 162 | 60.47 162 | 80.44 99 | 86.91 122 | 86.63 132 | 93.93 118 | 92.55 107 |
|
v1144 | | | 79.38 136 | 77.83 150 | 81.18 126 | 83.62 161 | 90.23 111 | 87.15 112 | 78.35 134 | 69.13 162 | 64.02 144 | 60.20 164 | 59.41 171 | 80.14 106 | 86.78 126 | 86.57 133 | 93.81 126 | 92.53 109 |
|
v148 | | | 78.59 145 | 76.84 161 | 80.62 132 | 83.61 162 | 89.16 138 | 83.65 149 | 79.24 126 | 69.38 161 | 69.34 116 | 59.88 166 | 60.41 163 | 75.19 143 | 83.81 166 | 84.63 159 | 92.70 147 | 90.63 135 |
|
SixPastTwentyTwo | | | 76.02 171 | 75.72 173 | 76.36 166 | 83.38 163 | 87.54 154 | 75.50 190 | 76.22 153 | 65.50 180 | 57.05 181 | 70.64 105 | 53.97 193 | 74.54 149 | 80.96 180 | 82.12 175 | 91.44 159 | 89.35 143 |
|
CVMVSNet | | | 76.70 160 | 78.46 140 | 74.64 179 | 83.34 164 | 84.48 178 | 81.83 163 | 74.58 161 | 68.88 164 | 51.23 191 | 69.77 110 | 70.05 123 | 67.49 177 | 84.27 163 | 83.81 164 | 89.38 178 | 87.96 155 |
|
v1192 | | | 78.94 140 | 77.33 154 | 80.82 129 | 83.25 165 | 89.90 120 | 86.91 115 | 77.72 140 | 68.63 166 | 62.61 153 | 59.17 169 | 57.53 180 | 80.62 98 | 86.89 123 | 86.47 135 | 93.79 127 | 92.75 99 |
|
DTE-MVSNet | | | 75.14 178 | 75.44 177 | 74.80 177 | 83.18 166 | 87.19 158 | 78.25 185 | 80.11 114 | 66.05 175 | 48.31 194 | 60.88 159 | 54.67 190 | 64.54 185 | 82.57 174 | 86.17 140 | 94.43 103 | 90.53 137 |
|
PEN-MVS | | | 76.02 171 | 76.07 167 | 75.95 170 | 83.17 167 | 87.97 151 | 79.65 174 | 80.07 117 | 66.57 173 | 51.45 189 | 60.94 158 | 55.47 188 | 66.81 180 | 82.72 172 | 86.80 128 | 94.59 92 | 92.03 117 |
|
TAMVS | | | 76.42 165 | 77.16 157 | 75.56 171 | 83.05 168 | 85.55 171 | 80.58 172 | 71.43 171 | 65.40 182 | 61.04 169 | 67.27 127 | 69.22 129 | 67.99 174 | 84.88 158 | 84.78 157 | 89.28 179 | 83.01 182 |
|
pmmvs4 | | | 79.99 124 | 78.08 145 | 82.22 114 | 83.04 169 | 87.16 159 | 84.95 136 | 78.80 131 | 78.64 107 | 74.53 90 | 64.61 144 | 59.41 171 | 79.45 117 | 84.13 164 | 84.54 161 | 92.53 148 | 88.08 153 |
|
v144192 | | | 78.81 141 | 77.22 156 | 80.67 131 | 82.95 170 | 89.79 124 | 86.40 120 | 77.42 142 | 68.26 168 | 63.13 149 | 59.50 167 | 58.13 177 | 80.08 107 | 85.93 140 | 86.08 142 | 94.06 113 | 92.83 95 |
|
v1921920 | | | 78.57 146 | 76.99 159 | 80.41 136 | 82.93 171 | 89.63 130 | 86.38 121 | 77.14 145 | 68.31 167 | 61.80 161 | 58.89 173 | 56.79 182 | 80.19 105 | 86.50 135 | 86.05 144 | 94.02 115 | 92.76 98 |
|
CHOSEN 280x420 | | | 80.28 122 | 81.66 103 | 78.67 149 | 82.92 172 | 79.24 198 | 85.36 134 | 66.79 190 | 78.11 109 | 70.32 107 | 75.03 86 | 79.87 74 | 81.09 88 | 89.07 100 | 83.16 168 | 85.54 195 | 87.17 161 |
|
WR-MVS_H | | | 75.84 174 | 76.93 160 | 74.57 180 | 82.86 173 | 89.50 132 | 78.34 183 | 79.36 125 | 66.90 171 | 52.51 186 | 60.20 164 | 59.71 167 | 59.73 189 | 83.61 167 | 85.77 147 | 94.65 89 | 92.84 94 |
|
v1240 | | | 78.15 148 | 76.53 162 | 80.04 137 | 82.85 174 | 89.48 133 | 85.61 132 | 76.77 149 | 67.05 170 | 61.18 168 | 58.37 175 | 56.16 186 | 79.89 110 | 86.11 139 | 86.08 142 | 93.92 119 | 92.47 111 |
|
V42 | | | 79.59 131 | 78.43 141 | 80.94 128 | 82.79 175 | 89.71 126 | 86.66 118 | 76.73 150 | 71.38 151 | 67.42 123 | 61.01 157 | 62.30 155 | 78.39 124 | 85.56 146 | 86.48 134 | 93.65 132 | 92.60 103 |
|
CP-MVSNet | | | 76.36 168 | 76.41 164 | 76.32 167 | 82.73 176 | 88.64 145 | 79.39 177 | 79.62 120 | 67.21 169 | 53.70 183 | 60.72 160 | 55.22 189 | 67.91 176 | 83.52 168 | 86.34 138 | 94.55 95 | 93.19 87 |
|
PS-CasMVS | | | 75.90 173 | 75.86 172 | 75.96 169 | 82.59 177 | 88.46 148 | 79.23 180 | 79.56 122 | 66.00 176 | 52.77 185 | 59.48 168 | 54.35 192 | 67.14 179 | 83.37 169 | 86.23 139 | 94.47 100 | 93.10 89 |
|
test20.03 | | | 68.31 191 | 70.05 192 | 66.28 195 | 82.41 178 | 80.84 194 | 67.35 200 | 76.11 155 | 58.44 197 | 40.80 204 | 53.77 186 | 54.54 191 | 42.28 202 | 83.07 170 | 81.96 178 | 88.73 182 | 77.76 197 |
|
FMVSNet1 | | | 81.64 112 | 80.61 117 | 82.84 107 | 82.36 179 | 89.20 137 | 88.67 89 | 79.58 121 | 70.79 154 | 72.63 103 | 58.95 172 | 72.26 116 | 79.34 118 | 90.73 80 | 90.72 61 | 94.47 100 | 91.62 125 |
|
pmmvs6 | | | 74.83 179 | 72.89 186 | 77.09 159 | 82.11 180 | 87.50 155 | 80.88 171 | 76.97 146 | 52.79 202 | 61.91 160 | 46.66 195 | 60.49 161 | 69.28 170 | 86.74 129 | 85.46 151 | 91.39 160 | 90.56 136 |
|
pmmvs5 | | | 76.93 158 | 76.33 165 | 77.62 156 | 81.97 181 | 88.40 149 | 81.32 166 | 74.35 163 | 65.42 181 | 61.42 164 | 63.07 149 | 57.95 178 | 73.23 158 | 85.60 145 | 85.35 152 | 93.41 137 | 88.55 148 |
|
v7n | | | 77.22 156 | 76.23 166 | 78.38 153 | 81.89 182 | 89.10 141 | 82.24 161 | 76.36 151 | 65.96 177 | 61.21 167 | 56.56 179 | 55.79 187 | 75.07 146 | 86.55 132 | 86.68 130 | 93.52 134 | 92.95 92 |
|
our_test_3 | | | | | | 81.81 183 | 83.96 181 | 76.61 187 | | | | | | | | | | |
|
Anonymous20231206 | | | 70.80 187 | 70.59 191 | 71.04 187 | 81.60 184 | 82.49 189 | 74.64 192 | 75.87 157 | 64.17 184 | 49.27 193 | 44.85 199 | 53.59 195 | 54.68 194 | 83.07 170 | 82.34 174 | 90.17 172 | 83.65 179 |
|
ADS-MVSNet | | | 74.53 181 | 75.69 174 | 73.17 184 | 81.57 185 | 80.71 195 | 79.27 179 | 63.03 200 | 79.27 103 | 59.94 173 | 67.86 124 | 68.32 134 | 71.08 165 | 77.33 193 | 76.83 191 | 84.12 200 | 79.53 192 |
|
test-mter | | | 77.79 151 | 80.02 125 | 75.18 174 | 81.18 186 | 82.85 185 | 80.52 173 | 62.03 202 | 73.62 141 | 62.16 156 | 73.55 94 | 73.83 106 | 73.81 153 | 84.67 159 | 83.34 167 | 91.37 161 | 88.31 150 |
|
TESTMET0.1,1 | | | 77.78 152 | 79.84 128 | 75.38 173 | 80.86 187 | 82.40 190 | 81.24 167 | 62.72 201 | 73.72 139 | 62.69 151 | 73.76 92 | 74.42 101 | 73.49 155 | 84.61 160 | 82.99 170 | 91.25 163 | 87.01 162 |
|
MDTV_nov1_ep13_2view | | | 73.21 185 | 72.91 185 | 73.56 183 | 80.01 188 | 84.28 180 | 78.62 181 | 66.43 192 | 68.64 165 | 59.12 176 | 60.39 163 | 59.69 169 | 69.81 169 | 78.82 191 | 77.43 190 | 87.36 187 | 81.11 190 |
|
FPMVS | | | 63.63 196 | 60.08 201 | 67.78 192 | 80.01 188 | 71.50 204 | 72.88 195 | 69.41 182 | 61.82 190 | 53.11 184 | 45.12 198 | 42.11 206 | 50.86 197 | 66.69 201 | 63.84 202 | 80.41 202 | 69.46 203 |
|
anonymousdsp | | | 77.94 150 | 79.00 135 | 76.71 163 | 79.03 190 | 87.83 152 | 79.58 175 | 72.87 167 | 65.80 178 | 58.86 179 | 65.82 132 | 62.48 154 | 75.99 139 | 86.77 127 | 88.66 107 | 93.92 119 | 95.68 47 |
|
N_pmnet | | | 66.85 192 | 66.63 193 | 67.11 194 | 78.73 191 | 74.66 202 | 70.53 197 | 71.07 172 | 66.46 174 | 46.54 196 | 51.68 191 | 51.91 197 | 55.48 192 | 74.68 197 | 72.38 198 | 80.29 203 | 74.65 200 |
|
PMMVS | | | 81.65 111 | 84.05 90 | 78.86 145 | 78.56 192 | 82.63 187 | 83.10 151 | 67.22 188 | 81.39 81 | 70.11 111 | 84.91 39 | 79.74 77 | 82.12 82 | 87.31 117 | 85.70 148 | 92.03 153 | 86.67 167 |
|
PatchT | | | 76.42 165 | 77.81 151 | 74.80 177 | 78.46 193 | 84.30 179 | 71.82 196 | 65.03 197 | 73.89 136 | 65.37 135 | 61.58 153 | 66.70 135 | 77.18 133 | 85.10 156 | 84.87 155 | 90.94 168 | 88.21 151 |
|
MVS-HIRNet | | | 68.83 190 | 66.39 194 | 71.68 186 | 77.58 194 | 75.52 201 | 66.45 201 | 65.05 196 | 62.16 189 | 62.84 150 | 44.76 200 | 56.60 185 | 71.96 162 | 78.04 192 | 75.06 196 | 86.18 194 | 72.56 201 |
|
pmmvs-eth3d | | | 74.32 182 | 71.96 188 | 77.08 160 | 77.33 195 | 82.71 186 | 78.41 182 | 76.02 156 | 66.65 172 | 65.98 130 | 54.23 185 | 49.02 201 | 73.14 159 | 82.37 176 | 82.69 172 | 91.61 158 | 86.05 170 |
|
new-patchmatchnet | | | 63.80 195 | 63.31 197 | 64.37 196 | 76.49 196 | 75.99 200 | 63.73 203 | 70.99 173 | 57.27 198 | 43.08 200 | 45.86 197 | 43.80 203 | 45.13 201 | 73.20 198 | 70.68 201 | 86.80 191 | 76.34 199 |
|
FMVSNet5 | | | 75.50 177 | 76.07 167 | 74.83 176 | 76.16 197 | 81.19 193 | 81.34 165 | 70.21 177 | 73.20 145 | 61.59 163 | 58.97 171 | 68.33 133 | 68.50 172 | 85.87 142 | 85.85 146 | 91.18 166 | 79.11 194 |
|
PM-MVS | | | 74.17 183 | 73.10 184 | 75.41 172 | 76.07 198 | 82.53 188 | 77.56 186 | 71.69 170 | 71.04 152 | 61.92 159 | 61.23 156 | 47.30 202 | 74.82 148 | 81.78 178 | 79.80 180 | 90.42 170 | 88.05 154 |
|
MIMVSNet | | | 74.69 180 | 75.60 175 | 73.62 182 | 76.02 199 | 85.31 173 | 81.21 169 | 67.43 187 | 71.02 153 | 59.07 177 | 54.48 182 | 64.07 143 | 66.14 183 | 86.52 134 | 86.64 131 | 91.83 156 | 81.17 189 |
|
EU-MVSNet | | | 69.98 189 | 72.30 187 | 67.28 193 | 75.67 200 | 79.39 197 | 73.12 194 | 69.94 179 | 63.59 186 | 42.80 201 | 62.93 150 | 56.71 184 | 55.07 193 | 79.13 190 | 78.55 186 | 87.06 190 | 85.82 172 |
|
ET-MVSNet_ETH3D | | | 84.65 83 | 85.58 81 | 83.56 100 | 74.99 201 | 92.62 90 | 90.29 64 | 80.38 106 | 82.16 75 | 73.01 101 | 83.41 42 | 71.10 120 | 87.05 59 | 87.77 114 | 90.17 74 | 95.62 43 | 91.82 119 |
|
PMVS | | 50.48 18 | 55.81 200 | 51.93 202 | 60.33 199 | 72.90 202 | 49.34 208 | 48.78 207 | 69.51 181 | 43.49 207 | 54.25 182 | 36.26 205 | 41.04 208 | 39.71 204 | 65.07 202 | 60.70 203 | 76.85 205 | 67.58 204 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 61.92 198 | | 70.98 203 | 73.54 203 | 63.64 204 | | 60.06 193 | 52.23 188 | 38.44 203 | 19.17 212 | 57.12 190 | 82.33 177 | 75.03 197 | 83.21 201 | 84.89 174 |
|
pmmvs3 | | | 61.89 197 | 61.74 199 | 62.06 198 | 64.30 204 | 70.83 205 | 64.22 202 | 52.14 206 | 48.78 206 | 44.47 199 | 41.67 202 | 41.70 207 | 63.03 186 | 76.06 195 | 76.02 192 | 84.18 199 | 77.14 198 |
|
MDA-MVSNet-bldmvs | | | 66.22 193 | 64.49 196 | 68.24 191 | 61.67 205 | 82.11 192 | 70.07 198 | 76.16 154 | 59.14 196 | 47.94 195 | 54.35 184 | 35.82 209 | 67.33 178 | 64.94 203 | 75.68 193 | 86.30 193 | 79.36 193 |
|
new_pmnet | | | 59.28 198 | 61.47 200 | 56.73 200 | 61.66 206 | 68.29 206 | 59.57 205 | 54.91 203 | 60.83 192 | 34.38 207 | 44.66 201 | 43.65 204 | 49.90 198 | 71.66 199 | 71.56 200 | 79.94 204 | 69.67 202 |
|
Gipuma | | | 49.17 201 | 47.05 203 | 51.65 201 | 59.67 207 | 48.39 209 | 41.98 209 | 63.47 199 | 55.64 201 | 33.33 208 | 14.90 207 | 13.78 213 | 41.34 203 | 69.31 200 | 72.30 199 | 70.11 206 | 55.00 207 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MIMVSNet1 | | | 65.00 194 | 66.24 195 | 63.55 197 | 58.41 208 | 80.01 196 | 69.00 199 | 74.03 164 | 55.81 200 | 41.88 202 | 36.81 204 | 49.48 200 | 47.89 200 | 81.32 179 | 82.40 173 | 90.08 174 | 77.88 196 |
|
EMVS | | | 30.49 205 | 25.44 207 | 36.39 204 | 51.47 209 | 29.89 213 | 20.17 213 | 54.00 205 | 26.49 209 | 12.02 212 | 13.94 210 | 8.84 214 | 34.37 205 | 25.04 209 | 34.37 208 | 46.29 211 | 39.53 210 |
|
E-PMN | | | 31.40 203 | 26.80 206 | 36.78 203 | 51.39 210 | 29.96 212 | 20.20 212 | 54.17 204 | 25.93 210 | 12.75 211 | 14.73 208 | 8.58 215 | 34.10 206 | 27.36 208 | 37.83 207 | 48.07 210 | 43.18 209 |
|
PMMVS2 | | | 41.68 202 | 44.74 204 | 38.10 202 | 46.97 211 | 52.32 207 | 40.63 210 | 48.08 207 | 35.51 208 | 7.36 213 | 26.86 206 | 24.64 211 | 16.72 208 | 55.24 205 | 59.03 204 | 68.85 207 | 59.59 206 |
|
tmp_tt | | | | | 32.73 205 | 43.96 212 | 21.15 214 | 26.71 211 | 8.99 210 | 65.67 179 | 51.39 190 | 56.01 180 | 42.64 205 | 11.76 209 | 56.60 204 | 50.81 206 | 53.55 209 | |
|
MVE | | 30.17 19 | 30.88 204 | 33.52 205 | 27.80 207 | 23.78 213 | 39.16 211 | 18.69 214 | 46.90 208 | 21.88 211 | 15.39 210 | 14.37 209 | 7.31 216 | 24.41 207 | 41.63 207 | 56.22 205 | 37.64 212 | 54.07 208 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
GG-mvs-BLEND | | | 57.56 199 | 82.61 99 | 28.34 206 | 0.22 214 | 90.10 115 | 79.37 178 | 0.14 212 | 79.56 100 | 0.40 214 | 71.25 104 | 83.40 57 | 0.30 212 | 86.27 137 | 83.87 163 | 89.59 177 | 83.83 178 |
|
testmvs | | | 1.03 206 | 1.63 208 | 0.34 208 | 0.09 215 | 0.35 215 | 0.61 216 | 0.16 211 | 1.49 212 | 0.10 215 | 3.15 211 | 0.15 217 | 0.86 211 | 1.32 210 | 1.18 209 | 0.20 213 | 3.76 212 |
|
test123 | | | 0.87 207 | 1.40 209 | 0.25 209 | 0.03 216 | 0.25 216 | 0.35 217 | 0.08 213 | 1.21 213 | 0.05 216 | 2.84 212 | 0.03 218 | 0.89 210 | 0.43 211 | 1.16 210 | 0.13 214 | 3.87 211 |
|
sosnet-low-res | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
|
sosnet | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
|
test_part1 | | | | | | | | | | | | | | | | | | 98.10 4 |
|
MTAPA | | | | | | | | | | | 92.97 2 | | 91.03 19 | | | | | |
|
MTMP | | | | | | | | | | | 93.14 1 | | 90.21 27 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.55 215 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 87.47 50 | | | | | | | | |
|
Patchmtry | | | | | | | 85.54 172 | 82.30 159 | 68.23 184 | | 65.37 135 | | | | | | | |
|
DeepMVS_CX | | | | | | | 48.31 210 | 48.03 208 | 26.08 209 | 56.42 199 | 25.77 209 | 47.51 194 | 31.31 210 | 51.30 196 | 48.49 206 | | 53.61 208 | 61.52 205 |
|