DVP-MVS | | | 78.77 1 | 84.89 1 | 71.62 1 | 78.04 1 | 82.05 1 | 81.64 7 | 57.96 5 | 87.53 1 | 66.64 1 | 88.77 1 | 86.31 1 | 63.16 8 | 79.99 5 | 78.56 6 | 82.31 19 | 91.03 1 |
|
APDe-MVS | | | 77.58 4 | 82.93 4 | 71.35 3 | 77.86 2 | 80.55 3 | 83.38 1 | 57.61 8 | 85.57 3 | 61.11 18 | 86.10 5 | 82.98 5 | 64.76 2 | 78.29 13 | 76.78 21 | 83.40 6 | 90.20 4 |
|
DPE-MVS | | | 78.11 2 | 83.84 2 | 71.42 2 | 77.82 3 | 81.32 2 | 82.92 3 | 57.81 7 | 84.04 6 | 63.19 10 | 88.63 2 | 86.00 2 | 64.52 3 | 78.71 9 | 77.63 14 | 82.26 20 | 90.57 3 |
|
SteuartSystems-ACMMP | | | 75.23 10 | 79.60 12 | 70.13 10 | 76.81 4 | 78.92 9 | 81.74 5 | 57.99 4 | 75.30 27 | 59.83 23 | 75.69 15 | 78.45 21 | 60.48 26 | 80.58 2 | 79.77 2 | 83.94 3 | 88.52 9 |
Skip Steuart: Steuart Systems R&D Blog. |
HPM-MVS++ | | | 76.01 7 | 80.47 9 | 70.81 6 | 76.60 5 | 74.96 33 | 80.18 14 | 58.36 2 | 81.96 7 | 63.50 9 | 78.80 11 | 82.53 8 | 64.40 4 | 78.74 8 | 78.84 5 | 81.81 29 | 87.46 16 |
|
MCST-MVS | | | 73.67 22 | 77.39 23 | 69.33 16 | 76.26 6 | 78.19 14 | 78.77 23 | 54.54 28 | 75.33 25 | 59.99 22 | 67.96 29 | 79.23 19 | 62.43 14 | 78.00 17 | 75.71 28 | 84.02 2 | 87.30 17 |
|
CNVR-MVS | | | 75.62 9 | 79.91 11 | 70.61 7 | 75.76 7 | 78.82 11 | 81.66 6 | 57.12 10 | 79.77 14 | 63.04 11 | 70.69 21 | 81.15 12 | 62.99 9 | 80.23 3 | 79.54 3 | 83.11 7 | 89.16 7 |
|
NCCC | | | 74.27 16 | 77.83 22 | 70.13 10 | 75.70 8 | 77.41 20 | 80.51 12 | 57.09 11 | 78.25 18 | 62.28 15 | 65.54 34 | 78.26 22 | 62.18 16 | 79.13 6 | 78.51 8 | 83.01 9 | 87.68 15 |
|
CSCG | | | 74.68 13 | 79.22 13 | 69.40 15 | 75.69 9 | 80.01 6 | 79.12 21 | 52.83 39 | 79.34 15 | 63.99 7 | 70.49 22 | 82.02 9 | 60.35 28 | 77.48 23 | 77.22 18 | 84.38 1 | 87.97 14 |
|
DPM-MVS | | | 72.80 24 | 75.90 27 | 69.19 18 | 75.51 10 | 77.68 17 | 81.62 8 | 54.83 24 | 75.96 23 | 62.06 16 | 63.96 40 | 76.58 27 | 58.55 35 | 76.66 30 | 76.77 22 | 82.60 16 | 83.68 40 |
|
SMA-MVS | | | 77.32 5 | 82.51 5 | 71.26 4 | 75.43 11 | 80.19 5 | 82.22 4 | 58.26 3 | 84.83 5 | 64.36 5 | 78.19 12 | 83.46 3 | 63.61 6 | 81.00 1 | 80.28 1 | 83.66 4 | 89.62 5 |
|
APD-MVS | | | 75.80 8 | 80.90 8 | 69.86 13 | 75.42 12 | 78.48 13 | 81.43 10 | 57.44 9 | 80.45 12 | 59.32 24 | 85.28 6 | 80.82 14 | 63.96 5 | 76.89 26 | 76.08 26 | 81.58 35 | 88.30 11 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MSP-MVS | | | 77.82 3 | 83.46 3 | 71.24 5 | 75.26 13 | 80.22 4 | 82.95 2 | 57.85 6 | 85.90 2 | 64.79 4 | 88.54 3 | 83.43 4 | 66.24 1 | 78.21 16 | 78.56 6 | 80.34 41 | 89.39 6 |
|
train_agg | | | 73.89 19 | 78.25 19 | 68.80 21 | 75.25 14 | 72.27 49 | 79.75 15 | 56.05 18 | 74.87 30 | 58.97 25 | 81.83 8 | 79.76 18 | 61.05 23 | 77.39 24 | 76.01 27 | 81.71 32 | 85.61 28 |
|
TSAR-MVS + MP. | | | 75.22 11 | 80.06 10 | 69.56 14 | 74.61 15 | 72.74 47 | 80.59 11 | 55.70 21 | 80.80 10 | 62.65 13 | 86.25 4 | 82.92 6 | 62.07 17 | 76.89 26 | 75.66 29 | 81.77 31 | 85.19 31 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
SD-MVS | | | 74.43 14 | 78.87 15 | 69.26 17 | 74.39 16 | 73.70 43 | 79.06 22 | 55.24 23 | 81.04 9 | 62.71 12 | 80.18 9 | 82.61 7 | 61.70 19 | 75.43 38 | 73.92 41 | 82.44 18 | 85.22 30 |
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 |
ACMMP_NAP | | | 76.15 6 | 81.17 6 | 70.30 8 | 74.09 17 | 79.47 7 | 81.59 9 | 57.09 11 | 81.38 8 | 63.89 8 | 79.02 10 | 80.48 15 | 62.24 15 | 80.05 4 | 79.12 4 | 82.94 10 | 88.64 8 |
|
HFP-MVS | | | 74.87 12 | 78.86 17 | 70.21 9 | 73.99 18 | 77.91 15 | 80.36 13 | 56.63 13 | 78.41 17 | 64.27 6 | 74.54 17 | 77.75 25 | 62.96 10 | 78.70 10 | 77.82 11 | 83.02 8 | 86.91 19 |
|
AdaColmap | | | 67.89 43 | 68.85 53 | 66.77 27 | 73.73 19 | 74.30 40 | 75.28 37 | 53.58 34 | 70.24 43 | 57.59 32 | 51.19 96 | 59.19 85 | 60.74 25 | 75.33 40 | 73.72 43 | 79.69 50 | 77.96 64 |
|
MP-MVS | | | 74.31 15 | 78.87 15 | 68.99 19 | 73.49 20 | 78.56 12 | 79.25 20 | 56.51 14 | 75.33 25 | 60.69 20 | 75.30 16 | 79.12 20 | 61.81 18 | 77.78 20 | 77.93 10 | 82.18 25 | 88.06 13 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
zzz-MVS | | | 74.25 17 | 77.97 21 | 69.91 12 | 73.43 21 | 74.06 41 | 79.69 16 | 56.44 15 | 80.74 11 | 64.98 3 | 68.72 27 | 79.98 17 | 62.92 11 | 78.24 15 | 77.77 13 | 81.99 27 | 86.30 21 |
|
TSAR-MVS + ACMM | | | 72.56 26 | 79.07 14 | 64.96 38 | 73.24 22 | 73.16 46 | 78.50 24 | 48.80 62 | 79.34 15 | 55.32 38 | 85.04 7 | 81.49 11 | 58.57 34 | 75.06 41 | 73.75 42 | 75.35 98 | 85.61 28 |
|
CDPH-MVS | | | 71.47 29 | 75.82 29 | 66.41 29 | 72.97 23 | 77.15 22 | 78.14 27 | 54.71 25 | 69.88 45 | 53.07 53 | 70.98 20 | 74.83 34 | 56.95 48 | 76.22 31 | 76.57 23 | 82.62 15 | 85.09 32 |
|
PGM-MVS | | | 72.89 23 | 77.13 24 | 67.94 23 | 72.47 24 | 77.25 21 | 79.27 19 | 54.63 27 | 73.71 32 | 57.95 31 | 72.38 19 | 75.33 32 | 60.75 24 | 78.25 14 | 77.36 17 | 82.57 17 | 85.62 27 |
|
ACMMPR | | | 73.79 21 | 78.41 18 | 68.40 22 | 72.35 25 | 77.79 16 | 79.32 18 | 56.38 16 | 77.67 21 | 58.30 29 | 74.16 18 | 76.66 26 | 61.40 20 | 78.32 12 | 77.80 12 | 82.68 14 | 86.51 20 |
|
OPM-MVS | | | 69.33 35 | 71.05 43 | 67.32 25 | 72.34 26 | 75.70 30 | 79.57 17 | 56.34 17 | 55.21 67 | 53.81 51 | 59.51 59 | 68.96 51 | 59.67 30 | 77.61 22 | 76.44 24 | 82.19 24 | 83.88 38 |
|
X-MVS | | | 71.18 30 | 75.66 30 | 65.96 33 | 71.71 27 | 76.96 23 | 77.26 30 | 55.88 20 | 72.75 35 | 54.48 46 | 64.39 38 | 74.47 35 | 54.19 62 | 77.84 19 | 77.37 16 | 82.21 23 | 85.85 25 |
|
HQP-MVS | | | 70.88 31 | 75.02 31 | 66.05 32 | 71.69 28 | 74.47 38 | 77.51 29 | 53.17 36 | 72.89 34 | 54.88 42 | 70.03 24 | 70.48 47 | 57.26 44 | 76.02 33 | 75.01 33 | 81.78 30 | 86.21 22 |
|
MAR-MVS | | | 68.04 42 | 70.74 45 | 64.90 39 | 71.68 29 | 76.33 28 | 74.63 41 | 50.48 53 | 63.81 53 | 55.52 37 | 54.88 78 | 69.90 49 | 57.39 42 | 75.42 39 | 74.79 35 | 79.71 47 | 80.03 53 |
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 |
mPP-MVS | | | | | | 71.67 30 | | | | | | | 74.36 38 | | | | | |
|
SR-MVS | | | | | | 71.46 31 | | | 54.67 26 | | | | 81.54 10 | | | | | |
|
CLD-MVS | | | 67.02 47 | 71.57 40 | 61.71 49 | 71.01 32 | 74.81 35 | 71.62 49 | 38.91 151 | 71.86 38 | 60.70 19 | 64.97 36 | 67.88 58 | 51.88 85 | 76.77 29 | 74.98 34 | 76.11 88 | 69.75 117 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CP-MVS | | | 72.63 25 | 76.95 25 | 67.59 24 | 70.67 33 | 75.53 31 | 77.95 28 | 56.01 19 | 75.65 24 | 58.82 26 | 69.16 26 | 76.48 28 | 60.46 27 | 77.66 21 | 77.20 19 | 81.65 33 | 86.97 18 |
|
ACMM | | 60.30 7 | 67.58 45 | 68.82 54 | 66.13 31 | 70.59 34 | 72.01 51 | 76.54 32 | 54.26 30 | 65.64 51 | 54.78 44 | 50.35 99 | 61.72 73 | 58.74 33 | 75.79 36 | 75.03 31 | 81.88 28 | 81.17 50 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XVS | | | | | | 70.49 35 | 76.96 23 | 74.36 42 | | | 54.48 46 | | 74.47 35 | | | | 82.24 21 | |
|
X-MVStestdata | | | | | | 70.49 35 | 76.96 23 | 74.36 42 | | | 54.48 46 | | 74.47 35 | | | | 82.24 21 | |
|
ACMMP | | | 71.57 28 | 75.84 28 | 66.59 28 | 70.30 37 | 76.85 26 | 78.46 25 | 53.95 32 | 73.52 33 | 55.56 36 | 70.13 23 | 71.36 45 | 58.55 35 | 77.00 25 | 76.23 25 | 82.71 13 | 85.81 26 |
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 |
abl_6 | | | | | 64.36 42 | 70.08 38 | 77.45 19 | 72.88 47 | 50.15 54 | 71.31 40 | 54.77 45 | 62.79 45 | 77.99 24 | 56.80 49 | | | 81.50 36 | 83.91 37 |
|
MVS_111021_HR | | | 67.62 44 | 70.39 47 | 64.39 41 | 69.77 39 | 70.45 55 | 71.44 51 | 51.72 45 | 60.77 61 | 55.06 40 | 62.14 50 | 66.40 60 | 58.13 38 | 76.13 32 | 74.79 35 | 80.19 44 | 82.04 47 |
|
MSLP-MVS++ | | | 68.17 41 | 70.72 46 | 65.19 36 | 69.41 40 | 70.64 53 | 74.99 38 | 45.76 72 | 70.20 44 | 60.17 21 | 56.42 70 | 73.01 41 | 61.14 21 | 72.80 51 | 70.54 54 | 79.70 48 | 81.42 49 |
|
LGP-MVS_train | | | 68.87 37 | 72.03 39 | 65.18 37 | 69.33 41 | 74.03 42 | 76.67 31 | 53.88 33 | 68.46 46 | 52.05 56 | 63.21 42 | 63.89 63 | 56.31 52 | 75.99 34 | 74.43 37 | 82.83 12 | 84.18 34 |
|
ACMP | | 61.42 5 | 68.72 40 | 71.37 41 | 65.64 35 | 69.06 42 | 74.45 39 | 75.88 35 | 53.30 35 | 68.10 47 | 55.74 35 | 61.53 53 | 62.29 69 | 56.97 47 | 74.70 42 | 74.23 39 | 82.88 11 | 84.31 33 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
DeepC-MVS_fast | | 65.08 3 | 72.00 27 | 76.11 26 | 67.21 26 | 68.93 43 | 77.46 18 | 76.54 32 | 54.35 29 | 74.92 29 | 58.64 28 | 65.18 35 | 74.04 40 | 62.62 12 | 77.92 18 | 77.02 20 | 82.16 26 | 86.21 22 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 68.77 38 | 73.01 35 | 63.83 44 | 68.30 44 | 75.19 32 | 73.73 45 | 47.90 63 | 63.86 52 | 54.84 43 | 67.51 31 | 74.36 38 | 57.62 40 | 74.22 44 | 73.57 45 | 80.56 40 | 82.36 44 |
|
TSAR-MVS + GP. | | | 69.71 32 | 73.92 34 | 64.80 40 | 68.27 45 | 70.56 54 | 71.90 48 | 50.75 49 | 71.38 39 | 57.46 33 | 68.68 28 | 75.42 31 | 60.10 29 | 73.47 48 | 73.99 40 | 80.32 42 | 83.97 36 |
|
3Dnovator+ | | 62.63 4 | 69.51 33 | 72.62 37 | 65.88 34 | 68.21 46 | 76.47 27 | 73.50 46 | 52.74 40 | 70.85 41 | 58.65 27 | 55.97 72 | 69.95 48 | 61.11 22 | 76.80 28 | 75.09 30 | 81.09 39 | 83.23 43 |
|
MVS_0304 | | | 69.49 34 | 73.96 33 | 64.28 43 | 67.92 47 | 76.13 29 | 74.90 39 | 47.60 64 | 63.29 55 | 54.09 50 | 67.44 32 | 76.35 29 | 59.53 31 | 75.81 35 | 75.03 31 | 81.62 34 | 83.70 39 |
|
DeepC-MVS | | 66.32 2 | 73.85 20 | 78.10 20 | 68.90 20 | 67.92 47 | 79.31 8 | 78.16 26 | 59.28 1 | 78.24 19 | 61.13 17 | 67.36 33 | 76.10 30 | 63.40 7 | 79.11 7 | 78.41 9 | 83.52 5 | 88.16 12 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepPCF-MVS | | 66.49 1 | 74.25 17 | 80.97 7 | 66.41 29 | 67.75 49 | 78.87 10 | 75.61 36 | 54.16 31 | 84.86 4 | 58.22 30 | 77.94 13 | 81.01 13 | 62.52 13 | 78.34 11 | 77.38 15 | 80.16 45 | 88.40 10 |
|
UA-Net | | | 58.50 83 | 64.68 72 | 51.30 112 | 66.97 50 | 67.13 78 | 53.68 151 | 45.65 75 | 49.51 97 | 31.58 140 | 62.91 44 | 68.47 53 | 35.85 161 | 68.20 89 | 67.28 86 | 74.03 108 | 69.24 127 |
|
PHI-MVS | | | 69.27 36 | 74.84 32 | 62.76 48 | 66.83 51 | 74.83 34 | 73.88 44 | 49.32 58 | 70.61 42 | 50.93 57 | 69.62 25 | 74.84 33 | 57.25 45 | 75.53 37 | 74.32 38 | 78.35 61 | 84.17 35 |
|
3Dnovator | | 60.86 6 | 66.99 48 | 70.32 48 | 63.11 46 | 66.63 52 | 74.52 36 | 71.56 50 | 45.76 72 | 67.37 49 | 55.00 41 | 54.31 83 | 68.19 55 | 58.49 37 | 73.97 45 | 73.63 44 | 81.22 38 | 80.23 52 |
|
EPNet | | | 65.14 53 | 69.54 51 | 60.00 54 | 66.61 53 | 67.67 70 | 67.53 59 | 55.32 22 | 62.67 57 | 46.22 73 | 67.74 30 | 65.93 61 | 48.07 104 | 72.17 53 | 72.12 47 | 76.28 84 | 78.47 61 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
OpenMVS | | 57.13 9 | 62.81 60 | 65.75 64 | 59.39 57 | 66.47 54 | 69.52 57 | 64.26 90 | 43.07 119 | 61.34 60 | 50.19 60 | 47.29 117 | 64.41 62 | 54.60 61 | 70.18 69 | 68.62 71 | 77.73 63 | 78.89 57 |
|
ACMH | | 52.42 13 | 58.24 90 | 59.56 107 | 56.70 79 | 66.34 55 | 69.59 56 | 66.71 67 | 49.12 59 | 46.08 131 | 28.90 153 | 42.67 161 | 41.20 176 | 52.60 78 | 71.39 57 | 70.28 56 | 76.51 80 | 75.72 85 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MSDG | | | 58.46 85 | 58.97 113 | 57.85 71 | 66.27 56 | 66.23 87 | 67.72 58 | 42.33 124 | 53.43 74 | 43.68 85 | 43.39 152 | 45.35 154 | 49.75 93 | 68.66 80 | 67.77 79 | 77.38 69 | 67.96 132 |
|
QAPM | | | 65.27 51 | 69.49 52 | 60.35 52 | 65.43 57 | 72.20 50 | 65.69 79 | 47.23 65 | 63.46 54 | 49.14 63 | 53.56 84 | 71.04 46 | 57.01 46 | 72.60 52 | 71.41 50 | 77.62 65 | 82.14 46 |
|
CPTT-MVS | | | 68.76 39 | 73.01 35 | 63.81 45 | 65.42 58 | 73.66 44 | 76.39 34 | 52.08 41 | 72.61 36 | 50.33 59 | 60.73 54 | 72.65 43 | 59.43 32 | 73.32 49 | 72.12 47 | 79.19 54 | 85.99 24 |
|
MS-PatchMatch | | | 58.19 92 | 60.20 96 | 55.85 83 | 65.17 59 | 64.16 103 | 64.82 85 | 41.48 132 | 50.95 87 | 42.17 95 | 45.38 137 | 56.42 96 | 48.08 103 | 68.30 85 | 66.70 94 | 73.39 116 | 69.46 125 |
|
PCF-MVS | | 59.98 8 | 67.32 46 | 71.04 44 | 62.97 47 | 64.77 60 | 74.49 37 | 74.78 40 | 49.54 56 | 67.44 48 | 54.39 49 | 58.35 63 | 72.81 42 | 55.79 58 | 71.54 56 | 69.24 63 | 78.57 56 | 83.41 41 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
EG-PatchMatch MVS | | | 56.98 99 | 58.24 120 | 55.50 85 | 64.66 61 | 68.62 60 | 61.48 99 | 43.63 104 | 38.44 182 | 41.44 97 | 38.05 172 | 46.18 149 | 43.95 122 | 71.71 55 | 70.61 53 | 77.87 62 | 74.08 97 |
|
Effi-MVS+-dtu | | | 60.34 71 | 62.32 80 | 58.03 66 | 64.31 62 | 67.44 74 | 65.99 74 | 42.26 125 | 49.55 95 | 42.00 96 | 48.92 107 | 59.79 83 | 56.27 53 | 68.07 93 | 67.03 87 | 77.35 70 | 75.45 88 |
|
FC-MVSNet-train | | | 58.40 86 | 63.15 78 | 52.85 105 | 64.29 63 | 61.84 114 | 55.98 135 | 46.47 68 | 53.06 77 | 34.96 129 | 61.95 52 | 56.37 98 | 39.49 142 | 68.67 79 | 68.36 73 | 75.92 92 | 71.81 105 |
|
Effi-MVS+ | | | 63.28 57 | 65.96 63 | 60.17 53 | 64.26 64 | 68.06 64 | 68.78 54 | 45.71 74 | 54.08 71 | 46.64 71 | 55.92 73 | 63.13 67 | 55.94 56 | 70.38 67 | 71.43 49 | 79.68 51 | 78.70 59 |
|
CS-MVS | | | 63.45 56 | 65.72 65 | 60.80 50 | 64.20 65 | 67.86 66 | 68.46 56 | 43.50 109 | 53.86 72 | 49.90 61 | 56.44 69 | 60.45 80 | 57.27 43 | 73.56 47 | 70.13 59 | 81.45 37 | 77.73 66 |
|
LS3D | | | 60.20 72 | 61.70 81 | 58.45 62 | 64.18 66 | 67.77 67 | 67.19 61 | 48.84 61 | 61.67 59 | 41.27 100 | 45.89 132 | 51.81 113 | 54.18 63 | 68.78 77 | 66.50 101 | 75.03 100 | 69.48 123 |
|
ACMH+ | | 53.71 12 | 59.26 75 | 60.28 93 | 58.06 64 | 64.17 67 | 68.46 61 | 67.51 60 | 50.93 48 | 52.46 83 | 35.83 126 | 40.83 166 | 45.12 158 | 52.32 81 | 69.88 70 | 69.00 67 | 77.59 67 | 76.21 82 |
|
DELS-MVS | | | 65.87 49 | 70.30 49 | 60.71 51 | 64.05 68 | 72.68 48 | 70.90 52 | 45.43 76 | 57.49 64 | 49.05 65 | 64.43 37 | 68.66 52 | 55.11 60 | 74.31 43 | 73.02 46 | 79.70 48 | 81.51 48 |
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 |
canonicalmvs | | | 65.62 50 | 72.06 38 | 58.11 63 | 63.94 69 | 71.05 52 | 64.49 88 | 43.18 117 | 74.08 31 | 47.35 67 | 64.17 39 | 71.97 44 | 51.17 88 | 71.87 54 | 70.74 52 | 78.51 59 | 80.56 51 |
|
EIA-MVS | | | 61.53 68 | 63.79 76 | 58.89 60 | 63.82 70 | 67.61 71 | 65.35 82 | 42.15 128 | 49.98 92 | 45.66 76 | 57.47 67 | 56.62 95 | 56.59 51 | 70.91 62 | 69.15 64 | 79.78 46 | 74.80 91 |
|
Anonymous202405211 | | | | 60.60 89 | | 63.44 71 | 66.71 84 | 61.00 104 | 47.23 65 | 50.62 90 | | 36.85 175 | 60.63 79 | 43.03 130 | 69.17 74 | 67.72 81 | 75.41 95 | 72.54 102 |
|
ETV-MVS | | | 63.10 58 | 66.18 62 | 59.51 56 | 62.37 72 | 67.42 75 | 67.80 57 | 42.86 121 | 54.77 68 | 45.90 74 | 60.15 56 | 57.01 93 | 57.83 39 | 73.78 46 | 71.30 51 | 80.27 43 | 78.82 58 |
|
gg-mvs-nofinetune | | | 49.07 157 | 52.56 157 | 45.00 158 | 61.99 73 | 59.78 133 | 53.55 153 | 41.63 130 | 31.62 198 | 12.08 192 | 29.56 192 | 53.28 108 | 29.57 176 | 66.27 123 | 64.49 128 | 71.19 145 | 62.92 163 |
|
DCV-MVSNet | | | 59.49 73 | 64.00 75 | 54.23 90 | 61.81 74 | 64.33 102 | 61.42 100 | 43.77 98 | 52.85 80 | 38.94 114 | 55.62 75 | 62.15 71 | 43.24 129 | 69.39 73 | 67.66 82 | 76.22 86 | 75.97 83 |
|
casdiffmvs | | | 64.09 54 | 68.13 55 | 59.37 58 | 61.81 74 | 68.32 63 | 68.48 55 | 44.45 87 | 61.95 58 | 49.12 64 | 63.04 43 | 69.67 50 | 53.83 65 | 70.46 64 | 66.06 106 | 78.55 57 | 77.43 67 |
|
Anonymous20231211 | | | 57.71 95 | 60.79 87 | 54.13 92 | 61.68 76 | 65.81 90 | 60.81 105 | 43.70 102 | 51.97 85 | 39.67 109 | 34.82 180 | 63.59 64 | 43.31 127 | 68.55 83 | 66.63 97 | 75.59 93 | 74.13 96 |
|
IS_MVSNet | | | 57.95 93 | 64.26 74 | 50.60 114 | 61.62 77 | 65.25 96 | 57.18 123 | 45.42 77 | 50.79 88 | 26.49 164 | 57.81 65 | 60.05 82 | 34.51 165 | 71.24 60 | 70.20 58 | 78.36 60 | 74.44 93 |
|
NR-MVSNet | | | 55.35 114 | 59.46 108 | 50.56 115 | 61.33 78 | 62.97 108 | 57.91 120 | 51.80 43 | 48.62 112 | 20.59 176 | 51.99 92 | 44.73 164 | 34.10 168 | 68.58 81 | 68.64 70 | 77.66 64 | 70.67 114 |
|
MVS_111021_LR | | | 63.05 59 | 66.43 59 | 59.10 59 | 61.33 78 | 63.77 105 | 65.87 76 | 43.58 105 | 60.20 62 | 53.70 52 | 62.09 51 | 62.38 68 | 55.84 57 | 70.24 68 | 68.08 74 | 74.30 105 | 78.28 63 |
|
EPP-MVSNet | | | 59.39 74 | 65.45 67 | 52.32 109 | 60.96 80 | 67.70 69 | 58.42 117 | 44.75 83 | 49.71 94 | 27.23 161 | 59.03 60 | 62.20 70 | 43.34 126 | 70.71 63 | 69.13 65 | 79.25 53 | 79.63 55 |
|
TransMVSNet (Re) | | | 51.92 139 | 55.38 137 | 47.88 144 | 60.95 81 | 59.90 132 | 53.95 148 | 45.14 79 | 39.47 176 | 24.85 168 | 43.87 147 | 46.51 145 | 29.15 177 | 67.55 103 | 65.23 119 | 73.26 121 | 65.16 154 |
|
gm-plane-assit | | | 44.74 177 | 45.95 185 | 43.33 166 | 60.88 82 | 46.79 191 | 36.97 199 | 32.24 189 | 24.15 204 | 11.79 193 | 29.26 193 | 32.97 198 | 46.64 109 | 65.09 137 | 62.95 141 | 71.45 142 | 60.42 174 |
|
baseline1 | | | 54.48 122 | 58.69 114 | 49.57 120 | 60.63 83 | 58.29 149 | 55.70 137 | 44.95 81 | 49.20 100 | 29.62 149 | 54.77 79 | 54.75 103 | 35.29 162 | 67.15 111 | 64.08 130 | 71.21 144 | 62.58 167 |
|
DI_MVS_plusplus_trai | | | 61.88 64 | 65.17 69 | 58.06 64 | 60.05 84 | 65.26 94 | 66.03 73 | 44.22 88 | 55.75 66 | 46.73 69 | 54.64 81 | 68.12 56 | 54.13 64 | 69.13 75 | 66.66 95 | 77.18 72 | 76.61 75 |
|
PVSNet_Blended_VisFu | | | 63.65 55 | 66.92 56 | 59.83 55 | 60.03 85 | 73.44 45 | 66.33 70 | 48.95 60 | 52.20 84 | 50.81 58 | 56.07 71 | 60.25 81 | 53.56 67 | 73.23 50 | 70.01 60 | 79.30 52 | 83.24 42 |
|
UniMVSNet_NR-MVSNet | | | 56.94 101 | 61.14 84 | 52.05 111 | 60.02 86 | 65.21 97 | 57.44 121 | 52.93 38 | 49.37 98 | 24.31 171 | 54.62 82 | 50.54 119 | 39.04 144 | 68.69 78 | 68.84 68 | 78.53 58 | 70.72 110 |
|
IterMVS-LS | | | 58.30 89 | 61.39 83 | 54.71 88 | 59.92 87 | 58.40 146 | 59.42 111 | 43.64 103 | 48.71 109 | 40.25 107 | 57.53 66 | 58.55 87 | 52.15 83 | 65.42 135 | 65.34 116 | 72.85 123 | 75.77 84 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MVS_Test | | | 62.40 63 | 66.23 61 | 57.94 67 | 59.77 88 | 64.77 100 | 66.50 69 | 41.76 129 | 57.26 65 | 49.33 62 | 62.68 47 | 67.47 59 | 53.50 70 | 68.57 82 | 66.25 103 | 76.77 77 | 76.58 76 |
|
TranMVSNet+NR-MVSNet | | | 55.87 108 | 60.14 98 | 50.88 113 | 59.46 89 | 63.82 104 | 57.93 119 | 52.98 37 | 48.94 104 | 20.52 177 | 52.87 86 | 47.33 136 | 36.81 158 | 69.12 76 | 69.03 66 | 77.56 68 | 69.89 116 |
|
Fast-Effi-MVS+ | | | 60.36 70 | 63.35 77 | 56.87 77 | 58.70 90 | 65.86 89 | 65.08 84 | 37.11 164 | 53.00 79 | 45.36 78 | 52.12 91 | 56.07 100 | 56.27 53 | 71.28 59 | 69.42 62 | 78.71 55 | 75.69 86 |
|
IB-MVS | | 54.11 11 | 58.36 88 | 60.70 88 | 55.62 84 | 58.67 91 | 68.02 65 | 61.56 97 | 43.15 118 | 46.09 130 | 44.06 84 | 44.24 144 | 50.99 118 | 48.71 98 | 66.70 117 | 70.33 55 | 77.60 66 | 78.50 60 |
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 |
Fast-Effi-MVS+-dtu | | | 56.30 106 | 59.29 110 | 52.82 106 | 58.64 92 | 64.89 98 | 65.56 80 | 32.89 186 | 45.80 133 | 35.04 128 | 45.89 132 | 54.14 105 | 49.41 94 | 67.16 110 | 66.45 102 | 75.37 97 | 70.69 112 |
|
CNLPA | | | 62.78 61 | 66.31 60 | 58.65 61 | 58.47 93 | 68.41 62 | 65.98 75 | 41.22 135 | 78.02 20 | 56.04 34 | 46.65 120 | 59.50 84 | 57.50 41 | 69.67 71 | 65.27 118 | 72.70 129 | 76.67 74 |
|
tpm cat1 | | | 53.30 128 | 53.41 150 | 53.17 102 | 58.16 94 | 59.15 139 | 63.73 93 | 38.27 159 | 50.73 89 | 46.98 68 | 45.57 136 | 44.00 170 | 49.20 95 | 55.90 184 | 54.02 183 | 62.65 175 | 64.50 158 |
|
v1144 | | | 58.88 78 | 60.16 97 | 57.39 73 | 58.03 95 | 67.26 76 | 67.14 63 | 44.46 86 | 45.17 136 | 44.33 83 | 47.81 114 | 49.92 123 | 53.20 76 | 67.77 99 | 66.62 98 | 77.15 73 | 76.58 76 |
|
v10 | | | 59.17 77 | 60.60 89 | 57.50 72 | 57.95 96 | 66.73 81 | 67.09 65 | 44.11 89 | 46.85 123 | 45.42 77 | 48.18 113 | 51.07 115 | 53.63 66 | 67.84 97 | 66.59 99 | 76.79 76 | 76.92 72 |
|
v1192 | | | 58.51 82 | 59.66 103 | 57.17 74 | 57.82 97 | 67.72 68 | 66.21 72 | 44.83 82 | 44.15 144 | 43.49 86 | 46.68 119 | 47.94 127 | 53.55 68 | 67.39 106 | 66.51 100 | 77.13 74 | 77.20 70 |
|
DWT-MVSNet_training | | | 53.80 125 | 54.31 145 | 53.21 99 | 57.65 98 | 59.04 140 | 60.65 106 | 40.11 147 | 46.35 127 | 42.77 89 | 49.07 104 | 41.07 177 | 51.06 89 | 58.62 166 | 58.96 162 | 67.00 162 | 67.06 137 |
|
v144192 | | | 58.23 91 | 59.40 109 | 56.87 77 | 57.56 99 | 66.89 79 | 65.70 77 | 45.01 80 | 44.06 145 | 42.88 88 | 46.61 121 | 48.09 126 | 53.49 71 | 66.94 115 | 65.90 110 | 76.61 78 | 77.29 68 |
|
v1921920 | | | 57.89 94 | 59.02 112 | 56.58 80 | 57.55 100 | 66.66 85 | 64.72 87 | 44.70 84 | 43.55 148 | 42.73 90 | 46.17 129 | 46.93 140 | 53.51 69 | 66.78 116 | 65.75 112 | 76.29 83 | 77.28 69 |
|
Vis-MVSNet (Re-imp) | | | 50.37 147 | 57.73 126 | 41.80 173 | 57.53 101 | 54.35 163 | 45.70 181 | 45.24 78 | 49.80 93 | 13.43 190 | 58.23 64 | 56.42 96 | 20.11 192 | 62.96 143 | 63.36 137 | 68.76 153 | 58.96 179 |
|
CostFormer | | | 56.57 104 | 59.13 111 | 53.60 95 | 57.52 102 | 61.12 121 | 66.94 66 | 35.95 169 | 53.44 73 | 44.68 81 | 55.87 74 | 54.44 104 | 48.21 101 | 60.37 156 | 58.33 164 | 68.27 155 | 70.33 115 |
|
thres400 | | | 52.38 134 | 55.51 135 | 48.74 131 | 57.49 103 | 60.10 131 | 55.45 140 | 43.54 106 | 42.90 155 | 26.72 163 | 43.34 154 | 45.03 162 | 36.61 159 | 66.20 125 | 64.53 127 | 72.66 130 | 66.43 140 |
|
thres600view7 | | | 51.91 140 | 55.14 139 | 48.14 140 | 57.43 104 | 60.18 129 | 54.60 146 | 43.73 100 | 42.61 159 | 25.20 167 | 43.10 157 | 44.47 167 | 35.19 163 | 66.36 120 | 63.28 138 | 72.66 130 | 66.01 147 |
|
thres200 | | | 52.39 133 | 55.37 138 | 48.90 129 | 57.39 105 | 60.18 129 | 55.60 138 | 43.73 100 | 42.93 154 | 27.41 159 | 43.35 153 | 45.09 159 | 36.61 159 | 66.36 120 | 63.92 133 | 72.66 130 | 65.78 149 |
|
baseline2 | | | 55.89 107 | 57.82 123 | 53.64 94 | 57.36 106 | 61.09 122 | 59.75 110 | 40.45 142 | 47.38 121 | 41.26 101 | 51.23 95 | 46.90 141 | 48.11 102 | 65.63 132 | 64.38 129 | 74.90 101 | 68.16 131 |
|
v8 | | | 58.88 78 | 60.57 91 | 56.92 76 | 57.35 107 | 65.69 91 | 66.69 68 | 42.64 122 | 47.89 118 | 45.77 75 | 49.04 105 | 52.98 109 | 52.77 77 | 67.51 104 | 65.57 113 | 76.26 85 | 75.30 90 |
|
thres100view900 | | | 52.04 137 | 54.81 142 | 48.80 130 | 57.31 108 | 59.33 136 | 55.30 142 | 42.92 120 | 42.85 156 | 27.81 157 | 43.00 158 | 45.06 160 | 36.99 156 | 64.74 138 | 63.51 135 | 72.47 133 | 65.21 153 |
|
tfpn200view9 | | | 52.53 131 | 55.51 135 | 49.06 127 | 57.31 108 | 60.24 128 | 55.42 141 | 43.77 98 | 42.85 156 | 27.81 157 | 43.00 158 | 45.06 160 | 37.32 154 | 66.38 119 | 64.54 126 | 72.71 128 | 66.54 139 |
|
v1240 | | | 57.55 96 | 58.63 116 | 56.29 81 | 57.30 110 | 66.48 86 | 63.77 92 | 44.56 85 | 42.77 158 | 42.48 92 | 45.64 135 | 46.28 147 | 53.46 72 | 66.32 122 | 65.80 111 | 76.16 87 | 77.13 71 |
|
CHOSEN 1792x2688 | | | 55.85 109 | 58.01 121 | 53.33 97 | 57.26 111 | 62.82 110 | 63.29 96 | 41.55 131 | 46.65 125 | 38.34 115 | 34.55 181 | 53.50 106 | 52.43 80 | 67.10 112 | 67.56 84 | 67.13 159 | 73.92 99 |
|
PVSNet_BlendedMVS | | | 61.63 66 | 64.82 70 | 57.91 69 | 57.21 112 | 67.55 72 | 63.47 94 | 46.08 70 | 54.72 69 | 52.46 54 | 58.59 61 | 60.73 76 | 51.82 86 | 70.46 64 | 65.20 120 | 76.44 81 | 76.50 79 |
|
PVSNet_Blended | | | 61.63 66 | 64.82 70 | 57.91 69 | 57.21 112 | 67.55 72 | 63.47 94 | 46.08 70 | 54.72 69 | 52.46 54 | 58.59 61 | 60.73 76 | 51.82 86 | 70.46 64 | 65.20 120 | 76.44 81 | 76.50 79 |
|
v2v482 | | | 58.69 81 | 60.12 100 | 57.03 75 | 57.16 114 | 66.05 88 | 67.17 62 | 43.52 107 | 46.33 128 | 45.19 79 | 49.46 103 | 51.02 116 | 52.51 79 | 67.30 107 | 66.03 107 | 76.61 78 | 74.62 92 |
|
tfpnnormal | | | 50.16 149 | 52.19 161 | 47.78 146 | 56.86 115 | 58.37 147 | 54.15 147 | 44.01 95 | 38.35 184 | 25.94 165 | 36.10 176 | 37.89 190 | 34.50 166 | 65.93 127 | 63.42 136 | 71.26 143 | 65.28 152 |
|
diffmvs | | | 61.64 65 | 66.55 58 | 55.90 82 | 56.63 116 | 63.71 106 | 67.13 64 | 41.27 134 | 59.49 63 | 46.70 70 | 63.93 41 | 68.01 57 | 50.46 90 | 67.30 107 | 65.51 114 | 73.24 122 | 77.87 65 |
|
HyFIR lowres test | | | 56.87 102 | 58.60 117 | 54.84 87 | 56.62 117 | 69.27 58 | 64.77 86 | 42.21 126 | 45.66 134 | 37.50 121 | 33.08 183 | 57.47 92 | 53.33 73 | 65.46 134 | 67.94 75 | 74.60 102 | 71.35 107 |
|
v7n | | | 55.67 110 | 57.46 128 | 53.59 96 | 56.06 118 | 65.29 93 | 61.06 103 | 43.26 116 | 40.17 173 | 37.99 118 | 40.79 167 | 45.27 157 | 47.09 108 | 67.67 101 | 66.21 104 | 76.08 89 | 76.82 73 |
|
dps | | | 50.42 146 | 51.20 167 | 49.51 121 | 55.88 119 | 56.07 159 | 53.73 149 | 38.89 152 | 43.66 146 | 40.36 106 | 45.66 134 | 37.63 192 | 45.23 117 | 59.05 159 | 56.18 168 | 62.94 174 | 60.16 175 |
|
CANet_DTU | | | 58.88 78 | 64.68 72 | 52.12 110 | 55.77 120 | 66.75 80 | 63.92 91 | 37.04 165 | 53.32 75 | 37.45 122 | 59.81 57 | 61.81 72 | 44.43 121 | 68.25 86 | 67.47 85 | 74.12 107 | 75.33 89 |
|
WR-MVS | | | 48.78 159 | 55.06 140 | 41.45 174 | 55.50 121 | 60.40 127 | 43.77 189 | 49.99 55 | 41.92 162 | 8.10 202 | 45.24 140 | 45.56 152 | 17.47 193 | 61.57 151 | 64.60 125 | 73.85 109 | 66.14 146 |
|
UniMVSNet (Re) | | | 55.15 118 | 60.39 92 | 49.03 128 | 55.31 122 | 64.59 101 | 55.77 136 | 50.63 50 | 48.66 111 | 20.95 175 | 51.47 94 | 50.40 120 | 34.41 167 | 67.81 98 | 67.89 76 | 77.11 75 | 71.88 104 |
|
test-LLR | | | 49.28 153 | 50.29 171 | 48.10 141 | 55.26 123 | 47.16 186 | 49.52 162 | 43.48 111 | 39.22 177 | 31.98 136 | 43.65 150 | 47.93 128 | 41.29 137 | 56.80 175 | 55.36 174 | 67.08 160 | 61.94 168 |
|
test0.0.03 1 | | | 43.15 182 | 46.95 184 | 38.72 182 | 55.26 123 | 50.56 175 | 42.48 192 | 43.48 111 | 38.16 186 | 15.11 186 | 35.07 179 | 44.69 165 | 16.47 195 | 55.95 183 | 54.34 182 | 59.54 182 | 49.87 196 |
|
GA-MVS | | | 55.67 110 | 58.33 118 | 52.58 108 | 55.23 125 | 63.09 107 | 61.08 102 | 40.15 146 | 42.95 153 | 37.02 124 | 52.61 88 | 47.68 131 | 47.51 106 | 65.92 128 | 65.35 115 | 74.49 104 | 70.68 113 |
|
DTE-MVSNet | | | 48.03 165 | 53.28 152 | 41.91 172 | 54.64 126 | 57.50 155 | 44.63 188 | 51.66 46 | 41.02 168 | 7.97 203 | 46.26 126 | 40.90 178 | 20.24 191 | 60.45 155 | 62.89 142 | 72.33 136 | 63.97 159 |
|
pmmvs4 | | | 54.66 121 | 56.07 132 | 53.00 103 | 54.63 127 | 57.08 157 | 60.43 108 | 44.10 90 | 51.69 86 | 40.55 104 | 46.55 124 | 44.79 163 | 45.95 114 | 62.54 145 | 63.66 134 | 72.36 135 | 66.20 144 |
|
DU-MVS | | | 55.41 113 | 59.59 104 | 50.54 116 | 54.60 128 | 62.97 108 | 57.44 121 | 51.80 43 | 48.62 112 | 24.31 171 | 51.99 92 | 47.00 139 | 39.04 144 | 68.11 91 | 67.75 80 | 76.03 91 | 70.72 110 |
|
Baseline_NR-MVSNet | | | 53.50 126 | 57.89 122 | 48.37 138 | 54.60 128 | 59.25 138 | 56.10 131 | 51.84 42 | 49.32 99 | 17.92 184 | 45.38 137 | 47.68 131 | 36.93 157 | 68.11 91 | 65.95 108 | 72.84 124 | 69.57 121 |
|
v148 | | | 55.58 112 | 57.61 127 | 53.20 100 | 54.59 130 | 61.86 113 | 61.18 101 | 38.70 156 | 44.30 143 | 42.25 94 | 47.53 115 | 50.24 122 | 48.73 97 | 65.15 136 | 62.61 146 | 73.79 110 | 71.61 106 |
|
PEN-MVS | | | 49.21 155 | 54.32 144 | 43.24 168 | 54.33 131 | 59.26 137 | 47.04 175 | 51.37 47 | 41.67 164 | 9.97 197 | 46.22 127 | 41.80 175 | 22.97 189 | 60.52 154 | 64.03 131 | 73.73 111 | 66.75 138 |
|
pm-mvs1 | | | 51.02 143 | 55.55 134 | 45.73 153 | 54.16 132 | 58.52 144 | 50.92 158 | 42.56 123 | 40.32 172 | 25.67 166 | 43.66 149 | 50.34 121 | 30.06 175 | 65.85 129 | 63.97 132 | 70.99 146 | 66.21 143 |
|
EPNet_dtu | | | 52.05 136 | 58.26 119 | 44.81 159 | 54.10 133 | 50.09 178 | 52.01 156 | 40.82 138 | 53.03 78 | 27.41 159 | 54.90 77 | 57.96 91 | 26.72 182 | 62.97 142 | 62.70 145 | 67.78 157 | 66.19 145 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tpm | | | 48.82 158 | 51.27 166 | 45.96 152 | 54.10 133 | 47.35 185 | 56.05 132 | 30.23 190 | 46.70 124 | 43.21 87 | 52.54 89 | 47.55 134 | 37.28 155 | 54.11 189 | 50.50 192 | 54.90 192 | 60.12 176 |
|
CDS-MVSNet | | | 52.42 132 | 57.06 130 | 47.02 149 | 53.92 135 | 58.30 148 | 55.50 139 | 46.47 68 | 42.52 160 | 29.38 151 | 49.50 102 | 52.85 110 | 28.49 180 | 66.70 117 | 66.89 91 | 68.34 154 | 62.63 166 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
test20.03 | | | 40.38 190 | 44.20 191 | 35.92 189 | 53.73 136 | 49.05 179 | 38.54 197 | 43.49 110 | 32.55 195 | 9.54 198 | 27.88 195 | 39.12 186 | 12.24 200 | 56.28 180 | 54.69 179 | 57.96 187 | 49.83 197 |
|
Vis-MVSNet | | | 58.48 84 | 65.70 66 | 50.06 119 | 53.40 137 | 67.20 77 | 60.24 109 | 43.32 114 | 48.83 106 | 30.23 146 | 62.38 49 | 61.61 74 | 40.35 140 | 71.03 61 | 69.77 61 | 72.82 125 | 79.11 56 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
PLC | | 52.09 14 | 59.21 76 | 62.47 79 | 55.41 86 | 53.24 138 | 64.84 99 | 64.47 89 | 40.41 144 | 65.92 50 | 44.53 82 | 46.19 128 | 55.69 101 | 55.33 59 | 68.24 88 | 65.30 117 | 74.50 103 | 71.09 108 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
thisisatest0530 | | | 56.68 103 | 59.68 102 | 53.19 101 | 52.97 139 | 60.96 124 | 59.41 112 | 40.51 140 | 48.26 115 | 41.06 102 | 52.67 87 | 46.30 146 | 49.78 91 | 67.66 102 | 67.83 77 | 75.39 96 | 74.07 98 |
|
OMC-MVS | | | 65.16 52 | 71.35 42 | 57.94 67 | 52.95 140 | 68.82 59 | 69.00 53 | 38.28 158 | 79.89 13 | 55.20 39 | 62.76 46 | 68.31 54 | 56.14 55 | 71.30 58 | 68.70 69 | 76.06 90 | 79.67 54 |
|
tpmrst | | | 48.08 163 | 49.88 175 | 45.98 151 | 52.71 141 | 48.11 183 | 53.62 152 | 33.70 182 | 48.70 110 | 39.74 108 | 48.96 106 | 46.23 148 | 40.29 141 | 50.14 197 | 49.28 194 | 55.80 189 | 57.71 182 |
|
tttt0517 | | | 56.53 105 | 59.59 104 | 52.95 104 | 52.66 142 | 60.99 123 | 59.21 114 | 40.51 140 | 47.89 118 | 40.40 105 | 52.50 90 | 46.04 150 | 49.78 91 | 67.75 100 | 67.83 77 | 75.15 99 | 74.17 95 |
|
GBi-Net | | | 55.20 115 | 60.25 94 | 49.31 122 | 52.42 143 | 61.44 116 | 57.03 124 | 44.04 92 | 49.18 101 | 30.47 142 | 48.28 109 | 58.19 88 | 38.22 147 | 68.05 94 | 66.96 88 | 73.69 112 | 69.65 118 |
|
test1 | | | 55.20 115 | 60.25 94 | 49.31 122 | 52.42 143 | 61.44 116 | 57.03 124 | 44.04 92 | 49.18 101 | 30.47 142 | 48.28 109 | 58.19 88 | 38.22 147 | 68.05 94 | 66.96 88 | 73.69 112 | 69.65 118 |
|
FMVSNet2 | | | 55.04 119 | 59.95 101 | 49.31 122 | 52.42 143 | 61.44 116 | 57.03 124 | 44.08 91 | 49.55 95 | 30.40 145 | 46.89 118 | 58.84 86 | 38.22 147 | 67.07 113 | 66.21 104 | 73.69 112 | 69.65 118 |
|
FMVSNet3 | | | 54.78 120 | 59.58 106 | 49.17 125 | 52.37 146 | 61.31 120 | 56.72 129 | 44.04 92 | 49.18 101 | 30.47 142 | 48.28 109 | 58.19 88 | 38.09 150 | 65.48 133 | 65.20 120 | 73.31 119 | 69.45 126 |
|
testgi | | | 38.71 192 | 43.64 192 | 32.95 193 | 52.30 147 | 48.63 182 | 35.59 202 | 35.05 172 | 31.58 199 | 9.03 201 | 30.29 188 | 40.75 180 | 11.19 205 | 55.30 185 | 53.47 188 | 54.53 194 | 45.48 200 |
|
Anonymous20231206 | | | 42.28 183 | 45.89 186 | 38.07 184 | 51.96 148 | 48.98 180 | 43.66 190 | 38.81 155 | 38.74 181 | 14.32 189 | 26.74 196 | 40.90 178 | 20.94 190 | 56.64 178 | 54.67 180 | 58.71 183 | 54.59 186 |
|
pmmvs6 | | | 48.35 161 | 51.64 163 | 44.51 161 | 51.92 149 | 57.94 153 | 49.44 164 | 42.17 127 | 34.45 191 | 24.62 170 | 28.87 194 | 46.90 141 | 29.07 179 | 64.60 139 | 63.08 139 | 69.83 150 | 65.68 150 |
|
PatchmatchNet | | | 49.92 151 | 51.29 165 | 48.32 139 | 51.83 150 | 51.86 172 | 53.38 154 | 37.63 163 | 47.90 117 | 40.83 103 | 48.54 108 | 45.30 155 | 45.19 118 | 56.86 174 | 53.99 185 | 61.08 180 | 54.57 187 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
WR-MVS_H | | | 47.65 166 | 53.67 148 | 40.63 177 | 51.45 151 | 59.74 134 | 44.71 187 | 49.37 57 | 40.69 170 | 7.61 204 | 46.04 130 | 44.34 169 | 17.32 194 | 57.79 171 | 61.18 151 | 73.30 120 | 65.86 148 |
|
LTVRE_ROB | | 44.17 16 | 47.06 171 | 50.15 174 | 43.44 165 | 51.39 152 | 58.42 145 | 42.90 191 | 43.51 108 | 22.27 205 | 14.85 188 | 41.94 165 | 34.57 195 | 45.43 115 | 62.28 148 | 62.77 144 | 62.56 177 | 68.83 129 |
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 |
CP-MVSNet | | | 48.37 160 | 53.53 149 | 42.34 170 | 51.35 153 | 58.01 152 | 46.56 176 | 50.54 51 | 41.62 165 | 10.61 194 | 46.53 125 | 40.68 181 | 23.18 187 | 58.71 164 | 61.83 148 | 71.81 139 | 67.36 136 |
|
PS-CasMVS | | | 48.18 162 | 53.25 153 | 42.27 171 | 51.26 154 | 57.94 153 | 46.51 177 | 50.52 52 | 41.30 166 | 10.56 195 | 45.35 139 | 40.34 183 | 23.04 188 | 58.66 165 | 61.79 149 | 71.74 141 | 67.38 135 |
|
our_test_3 | | | | | | 51.15 155 | 57.31 156 | 55.12 143 | | | | | | | | | | |
|
SCA | | | 50.99 144 | 53.22 154 | 48.40 137 | 51.07 156 | 56.78 158 | 50.25 160 | 39.05 150 | 48.31 114 | 41.38 98 | 49.54 101 | 46.70 144 | 46.00 113 | 58.31 167 | 56.28 167 | 62.65 175 | 56.60 184 |
|
UniMVSNet_ETH3D | | | 52.62 130 | 55.98 133 | 48.70 133 | 51.04 157 | 60.71 126 | 56.87 127 | 46.74 67 | 42.52 160 | 26.96 162 | 42.50 162 | 45.95 151 | 37.87 151 | 66.22 124 | 65.15 123 | 72.74 126 | 68.78 130 |
|
IterMVS-SCA-FT | | | 52.18 135 | 57.75 125 | 45.68 154 | 51.01 158 | 62.06 112 | 55.10 144 | 34.75 173 | 44.85 137 | 32.86 134 | 51.13 97 | 51.22 114 | 48.74 96 | 62.47 146 | 61.51 150 | 51.61 199 | 71.02 109 |
|
FMVSNet1 | | | 54.08 123 | 58.68 115 | 48.71 132 | 50.90 159 | 61.35 119 | 56.73 128 | 43.94 96 | 45.91 132 | 29.32 152 | 42.72 160 | 56.26 99 | 37.70 152 | 68.05 94 | 66.96 88 | 73.69 112 | 69.50 122 |
|
pmmvs-eth3d | | | 51.33 141 | 52.25 160 | 50.26 118 | 50.82 160 | 54.65 162 | 56.03 133 | 43.45 113 | 43.51 149 | 37.20 123 | 39.20 170 | 39.04 187 | 42.28 132 | 61.85 150 | 62.78 143 | 71.78 140 | 64.72 156 |
|
MVSTER | | | 57.19 97 | 61.11 85 | 52.62 107 | 50.82 160 | 58.79 142 | 61.55 98 | 37.86 161 | 48.81 107 | 41.31 99 | 57.43 68 | 52.10 112 | 48.60 99 | 68.19 90 | 66.75 93 | 75.56 94 | 75.68 87 |
|
ambc | | | | 45.54 189 | | 50.66 162 | 52.63 170 | 40.99 194 | | 38.36 183 | 24.67 169 | 22.62 201 | 13.94 209 | 29.14 178 | 65.71 131 | 58.06 165 | 58.60 185 | 67.43 134 |
|
thisisatest0515 | | | 53.85 124 | 56.84 131 | 50.37 117 | 50.25 163 | 58.17 150 | 55.99 134 | 39.90 148 | 41.88 163 | 38.16 117 | 45.91 131 | 45.30 155 | 44.58 120 | 66.15 126 | 66.89 91 | 73.36 118 | 73.57 101 |
|
baseline | | | 55.19 117 | 60.88 86 | 48.55 135 | 49.87 164 | 58.10 151 | 58.70 116 | 34.75 173 | 52.82 81 | 39.48 113 | 60.18 55 | 60.86 75 | 45.41 116 | 61.05 152 | 60.74 155 | 63.10 173 | 72.41 103 |
|
MDTV_nov1_ep13 | | | 50.32 148 | 52.43 159 | 47.86 145 | 49.87 164 | 54.70 161 | 58.10 118 | 34.29 177 | 45.59 135 | 37.71 119 | 47.44 116 | 47.42 135 | 41.86 134 | 58.07 170 | 55.21 176 | 65.34 167 | 58.56 180 |
|
IterMVS | | | 53.45 127 | 57.12 129 | 49.17 125 | 49.23 166 | 60.93 125 | 59.05 115 | 34.63 175 | 44.53 139 | 33.22 131 | 51.09 98 | 51.01 117 | 48.38 100 | 62.43 147 | 60.79 154 | 70.54 148 | 69.05 128 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
COLMAP_ROB | | 46.52 15 | 51.99 138 | 54.86 141 | 48.63 134 | 49.13 167 | 61.73 115 | 60.53 107 | 36.57 166 | 53.14 76 | 32.95 133 | 37.10 173 | 38.68 188 | 40.49 139 | 65.72 130 | 63.08 139 | 72.11 138 | 64.60 157 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
CR-MVSNet | | | 50.47 145 | 52.61 156 | 47.98 143 | 49.03 168 | 52.94 167 | 48.27 167 | 38.86 153 | 44.41 140 | 39.59 110 | 44.34 143 | 44.65 166 | 46.63 110 | 58.97 161 | 60.31 156 | 65.48 165 | 62.66 164 |
|
V42 | | | 56.97 100 | 60.14 98 | 53.28 98 | 48.16 169 | 62.78 111 | 66.30 71 | 37.93 160 | 47.44 120 | 42.68 91 | 48.19 112 | 52.59 111 | 51.90 84 | 67.46 105 | 65.94 109 | 72.72 127 | 76.55 78 |
|
MDTV_nov1_ep13_2view | | | 47.62 167 | 49.72 176 | 45.18 157 | 48.05 170 | 53.70 165 | 54.90 145 | 33.80 181 | 39.90 175 | 29.79 148 | 38.85 171 | 41.89 174 | 39.17 143 | 58.99 160 | 55.55 173 | 65.34 167 | 59.17 178 |
|
EPMVS | | | 44.66 178 | 47.86 182 | 40.92 176 | 47.97 171 | 44.70 195 | 47.58 172 | 33.27 184 | 48.11 116 | 29.58 150 | 49.65 100 | 44.38 168 | 34.65 164 | 51.71 192 | 47.90 196 | 52.49 197 | 48.57 198 |
|
RPMNet | | | 46.41 172 | 48.72 178 | 43.72 163 | 47.77 172 | 52.94 167 | 46.02 180 | 33.92 179 | 44.41 140 | 31.82 139 | 36.89 174 | 37.42 193 | 37.41 153 | 53.88 190 | 54.02 183 | 65.37 166 | 61.47 170 |
|
TAPA-MVS | | 54.74 10 | 60.85 69 | 66.61 57 | 54.12 93 | 47.38 173 | 65.33 92 | 65.35 82 | 36.51 167 | 75.16 28 | 48.82 66 | 54.70 80 | 63.51 65 | 53.31 74 | 68.36 84 | 64.97 124 | 73.37 117 | 74.27 94 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
SixPastTwentyTwo | | | 47.55 168 | 50.25 173 | 44.41 162 | 47.30 174 | 54.31 164 | 47.81 170 | 40.36 145 | 33.76 192 | 19.93 179 | 43.75 148 | 32.77 199 | 42.07 133 | 59.82 157 | 60.94 153 | 68.98 151 | 66.37 142 |
|
TAMVS | | | 44.02 180 | 49.18 177 | 37.99 185 | 47.03 175 | 45.97 192 | 45.04 184 | 28.47 194 | 39.11 179 | 20.23 178 | 43.22 156 | 48.52 124 | 28.49 180 | 58.15 169 | 57.95 166 | 58.71 183 | 51.36 190 |
|
UGNet | | | 57.03 98 | 65.25 68 | 47.44 147 | 46.54 176 | 66.73 81 | 56.30 130 | 43.28 115 | 50.06 91 | 32.99 132 | 62.57 48 | 63.26 66 | 33.31 170 | 68.25 86 | 67.58 83 | 72.20 137 | 78.29 62 |
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 |
TSAR-MVS + COLMAP | | | 62.65 62 | 69.90 50 | 54.19 91 | 46.31 177 | 66.73 81 | 65.49 81 | 41.36 133 | 76.57 22 | 46.31 72 | 76.80 14 | 56.68 94 | 53.27 75 | 69.50 72 | 66.65 96 | 72.40 134 | 76.36 81 |
|
PatchMatch-RL | | | 50.11 150 | 51.56 164 | 48.43 136 | 46.23 178 | 51.94 171 | 50.21 161 | 38.62 157 | 46.62 126 | 37.51 120 | 42.43 163 | 39.38 185 | 52.24 82 | 60.98 153 | 59.56 159 | 65.76 164 | 60.01 177 |
|
CMPMVS | | 37.70 17 | 49.24 154 | 52.71 155 | 45.19 156 | 45.97 179 | 51.23 174 | 47.44 173 | 29.31 191 | 43.04 152 | 44.69 80 | 34.45 182 | 48.35 125 | 43.64 123 | 62.59 144 | 59.82 158 | 60.08 181 | 69.48 123 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
pmmvs5 | | | 47.07 170 | 51.02 169 | 42.46 169 | 45.18 180 | 51.47 173 | 48.23 169 | 33.09 185 | 38.17 185 | 28.62 155 | 46.60 122 | 43.48 171 | 30.74 173 | 58.28 168 | 58.63 163 | 68.92 152 | 60.48 173 |
|
USDC | | | 51.11 142 | 53.71 147 | 48.08 142 | 44.76 181 | 55.99 160 | 53.01 155 | 40.90 136 | 52.49 82 | 36.14 125 | 44.67 142 | 33.66 197 | 43.27 128 | 63.23 141 | 61.10 152 | 70.39 149 | 64.82 155 |
|
FC-MVSNet-test | | | 39.65 191 | 48.35 180 | 29.49 196 | 44.43 182 | 39.28 202 | 30.23 205 | 40.44 143 | 43.59 147 | 3.12 210 | 53.00 85 | 42.03 173 | 10.02 207 | 55.09 186 | 54.77 178 | 48.66 200 | 50.71 192 |
|
ADS-MVSNet | | | 40.67 188 | 43.38 193 | 37.50 186 | 44.36 183 | 39.79 201 | 42.09 193 | 32.67 188 | 44.34 142 | 28.87 154 | 40.76 168 | 40.37 182 | 30.22 174 | 48.34 201 | 45.87 200 | 46.81 202 | 44.21 202 |
|
new-patchmatchnet | | | 33.24 198 | 37.20 199 | 28.62 198 | 44.32 184 | 38.26 203 | 29.68 206 | 36.05 168 | 31.97 197 | 6.33 206 | 26.59 197 | 27.33 202 | 11.12 206 | 50.08 198 | 41.05 202 | 44.23 203 | 45.15 201 |
|
MVS-HIRNet | | | 42.24 184 | 41.15 196 | 43.51 164 | 44.06 185 | 40.74 198 | 35.77 201 | 35.35 170 | 35.38 190 | 38.34 115 | 25.63 198 | 38.55 189 | 43.48 125 | 50.77 194 | 47.03 198 | 64.07 169 | 49.98 194 |
|
PatchT | | | 48.08 163 | 51.03 168 | 44.64 160 | 42.96 186 | 50.12 177 | 40.36 195 | 35.09 171 | 43.17 151 | 39.59 110 | 42.00 164 | 39.96 184 | 46.63 110 | 58.97 161 | 60.31 156 | 63.21 172 | 62.66 164 |
|
CVMVSNet | | | 46.38 174 | 52.01 162 | 39.81 179 | 42.40 187 | 50.26 176 | 46.15 178 | 37.68 162 | 40.03 174 | 15.09 187 | 46.56 123 | 47.56 133 | 33.72 169 | 56.50 179 | 55.65 172 | 63.80 171 | 67.53 133 |
|
TinyColmap | | | 47.08 169 | 47.56 183 | 46.52 150 | 42.35 188 | 53.44 166 | 51.77 157 | 40.70 139 | 43.44 150 | 31.92 138 | 29.78 191 | 23.72 206 | 45.04 119 | 61.99 149 | 59.54 160 | 67.35 158 | 61.03 171 |
|
ET-MVSNet_ETH3D | | | 58.38 87 | 61.57 82 | 54.67 89 | 42.15 189 | 65.26 94 | 65.70 77 | 43.82 97 | 48.84 105 | 42.34 93 | 59.76 58 | 47.76 130 | 56.68 50 | 67.02 114 | 68.60 72 | 77.33 71 | 73.73 100 |
|
MIMVSNet | | | 43.79 181 | 48.53 179 | 38.27 183 | 41.46 190 | 48.97 181 | 50.81 159 | 32.88 187 | 44.55 138 | 22.07 173 | 32.05 184 | 47.15 137 | 24.76 185 | 58.73 163 | 56.09 170 | 57.63 188 | 52.14 188 |
|
N_pmnet | | | 32.67 199 | 36.85 200 | 27.79 199 | 40.55 191 | 32.13 204 | 35.80 200 | 26.79 197 | 37.24 188 | 9.10 199 | 32.02 185 | 30.94 200 | 16.30 196 | 47.22 202 | 41.21 201 | 38.21 205 | 37.21 203 |
|
anonymousdsp | | | 52.84 129 | 57.78 124 | 47.06 148 | 40.24 192 | 58.95 141 | 53.70 150 | 33.54 183 | 36.51 189 | 32.69 135 | 43.88 146 | 45.40 153 | 47.97 105 | 67.17 109 | 70.28 56 | 74.22 106 | 82.29 45 |
|
EU-MVSNet | | | 40.63 189 | 45.65 188 | 34.78 192 | 39.11 193 | 46.94 189 | 40.02 196 | 34.03 178 | 33.50 193 | 10.37 196 | 35.57 178 | 37.80 191 | 23.65 186 | 51.90 191 | 50.21 193 | 61.49 179 | 63.62 162 |
|
FPMVS | | | 38.36 193 | 40.41 197 | 35.97 188 | 38.92 194 | 39.85 200 | 45.50 182 | 25.79 200 | 41.13 167 | 18.70 181 | 30.10 189 | 24.56 204 | 31.86 172 | 49.42 199 | 46.80 199 | 55.04 190 | 51.03 191 |
|
TESTMET0.1,1 | | | 46.09 175 | 50.29 171 | 41.18 175 | 36.91 195 | 47.16 186 | 49.52 162 | 20.32 204 | 39.22 177 | 31.98 136 | 43.65 150 | 47.93 128 | 41.29 137 | 56.80 175 | 55.36 174 | 67.08 160 | 61.94 168 |
|
FMVSNet5 | | | 40.96 186 | 45.81 187 | 35.29 191 | 34.30 196 | 44.55 196 | 47.28 174 | 28.84 193 | 40.76 169 | 21.62 174 | 29.85 190 | 42.44 172 | 24.77 184 | 57.53 172 | 55.00 177 | 54.93 191 | 50.56 193 |
|
PMMVS | | | 49.20 156 | 54.28 146 | 43.28 167 | 34.13 197 | 45.70 193 | 48.98 165 | 26.09 199 | 46.31 129 | 34.92 130 | 55.22 76 | 53.47 107 | 47.48 107 | 59.43 158 | 59.04 161 | 68.05 156 | 60.77 172 |
|
PMVS | | 27.84 18 | 33.81 197 | 35.28 201 | 32.09 194 | 34.13 197 | 24.81 207 | 32.51 204 | 26.48 198 | 26.41 202 | 19.37 180 | 23.76 199 | 24.02 205 | 25.18 183 | 50.78 193 | 47.24 197 | 54.89 193 | 49.95 195 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test-mter | | | 45.30 176 | 50.37 170 | 39.38 180 | 33.65 199 | 46.99 188 | 47.59 171 | 18.59 205 | 38.75 180 | 28.00 156 | 43.28 155 | 46.82 143 | 41.50 136 | 57.28 173 | 55.78 171 | 66.93 163 | 63.70 161 |
|
CHOSEN 280x420 | | | 40.80 187 | 45.05 190 | 35.84 190 | 32.95 200 | 29.57 205 | 44.98 185 | 23.71 202 | 37.54 187 | 18.42 182 | 31.36 187 | 47.07 138 | 46.41 112 | 56.71 177 | 54.65 181 | 48.55 201 | 58.47 181 |
|
PM-MVS | | | 44.55 179 | 48.13 181 | 40.37 178 | 32.85 201 | 46.82 190 | 46.11 179 | 29.28 192 | 40.48 171 | 29.99 147 | 39.98 169 | 34.39 196 | 41.80 135 | 56.08 182 | 53.88 187 | 62.19 178 | 65.31 151 |
|
TDRefinement | | | 49.31 152 | 52.44 158 | 45.67 155 | 30.44 202 | 59.42 135 | 59.24 113 | 39.78 149 | 48.76 108 | 31.20 141 | 35.73 177 | 29.90 201 | 42.81 131 | 64.24 140 | 62.59 147 | 70.55 147 | 66.43 140 |
|
Gipuma | | | 25.87 200 | 26.91 203 | 24.66 200 | 28.98 203 | 20.17 208 | 20.46 207 | 34.62 176 | 29.55 200 | 9.10 199 | 4.91 210 | 5.31 213 | 15.76 197 | 49.37 200 | 49.10 195 | 39.03 204 | 29.95 205 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MDA-MVSNet-bldmvs | | | 41.36 185 | 43.15 194 | 39.27 181 | 28.74 204 | 52.68 169 | 44.95 186 | 40.84 137 | 32.89 194 | 18.13 183 | 31.61 186 | 22.09 207 | 38.97 146 | 50.45 196 | 56.11 169 | 64.01 170 | 56.23 185 |
|
E-PMN | | | 15.09 203 | 13.19 206 | 17.30 202 | 27.80 205 | 12.62 211 | 7.81 211 | 27.54 195 | 14.62 209 | 3.19 208 | 6.89 207 | 2.52 216 | 15.09 198 | 15.93 206 | 20.22 206 | 22.38 207 | 19.53 208 |
|
MIMVSNet1 | | | 35.51 195 | 41.41 195 | 28.63 197 | 27.53 206 | 43.36 197 | 38.09 198 | 33.82 180 | 32.01 196 | 6.77 205 | 21.63 202 | 35.43 194 | 11.97 202 | 55.05 187 | 53.99 185 | 53.59 196 | 48.36 199 |
|
EMVS | | | 14.49 204 | 12.45 207 | 16.87 204 | 27.02 207 | 12.56 212 | 8.13 210 | 27.19 196 | 15.05 208 | 3.14 209 | 6.69 208 | 2.67 215 | 15.08 199 | 14.60 208 | 18.05 207 | 20.67 208 | 17.56 210 |
|
pmmvs3 | | | 35.10 196 | 38.47 198 | 31.17 195 | 26.37 208 | 40.47 199 | 34.51 203 | 18.09 206 | 24.75 203 | 16.88 185 | 23.05 200 | 26.69 203 | 32.69 171 | 50.73 195 | 51.60 190 | 58.46 186 | 51.98 189 |
|
RPSCF | | | 46.41 172 | 54.42 143 | 37.06 187 | 25.70 209 | 45.14 194 | 45.39 183 | 20.81 203 | 62.79 56 | 35.10 127 | 44.92 141 | 55.60 102 | 43.56 124 | 56.12 181 | 52.45 189 | 51.80 198 | 63.91 160 |
|
new_pmnet | | | 23.19 201 | 28.17 202 | 17.37 201 | 17.03 210 | 24.92 206 | 19.66 208 | 16.16 208 | 27.05 201 | 4.42 207 | 20.77 203 | 19.20 208 | 12.19 201 | 37.71 203 | 36.38 203 | 34.77 206 | 31.17 204 |
|
PMMVS2 | | | 15.84 202 | 19.68 204 | 11.35 205 | 15.74 211 | 16.95 209 | 13.31 209 | 17.64 207 | 16.08 207 | 0.36 213 | 13.12 204 | 11.47 210 | 1.69 209 | 28.82 204 | 27.24 205 | 19.38 209 | 24.09 207 |
|
MVE | | 12.28 19 | 13.53 205 | 15.72 205 | 10.96 206 | 7.39 212 | 15.71 210 | 6.05 212 | 23.73 201 | 10.29 211 | 3.01 211 | 5.77 209 | 3.41 214 | 11.91 203 | 20.11 205 | 29.79 204 | 13.67 210 | 24.98 206 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 5.40 207 | 3.97 213 | 2.35 214 | 3.26 214 | 0.44 210 | 17.56 206 | 12.09 191 | 11.48 206 | 7.14 211 | 1.98 208 | 15.68 207 | 15.49 208 | 10.69 211 | |
|
GG-mvs-BLEND | | | 36.62 194 | 53.39 151 | 17.06 203 | 0.01 214 | 58.61 143 | 48.63 166 | 0.01 211 | 47.13 122 | 0.02 214 | 43.98 145 | 60.64 78 | 0.03 210 | 54.92 188 | 51.47 191 | 53.64 195 | 56.99 183 |
|
sosnet-low-res | | | 0.00 208 | 0.00 210 | 0.00 208 | 0.00 215 | 0.00 215 | 0.00 217 | 0.00 212 | 0.00 214 | 0.00 215 | 0.00 213 | 0.00 217 | 0.00 213 | 0.00 211 | 0.00 211 | 0.00 213 | 0.00 213 |
|
sosnet | | | 0.00 208 | 0.00 210 | 0.00 208 | 0.00 215 | 0.00 215 | 0.00 217 | 0.00 212 | 0.00 214 | 0.00 215 | 0.00 213 | 0.00 217 | 0.00 213 | 0.00 211 | 0.00 211 | 0.00 213 | 0.00 213 |
|
testmvs | | | 0.01 206 | 0.02 208 | 0.00 208 | 0.00 215 | 0.00 215 | 0.01 216 | 0.00 212 | 0.01 212 | 0.00 215 | 0.03 212 | 0.00 217 | 0.01 211 | 0.01 210 | 0.01 209 | 0.00 213 | 0.06 212 |
|
test123 | | | 0.01 206 | 0.02 208 | 0.00 208 | 0.00 215 | 0.00 215 | 0.00 217 | 0.00 212 | 0.01 212 | 0.00 215 | 0.04 211 | 0.00 217 | 0.01 211 | 0.00 211 | 0.01 209 | 0.00 213 | 0.07 211 |
|
test_part1 | | | | | | | | | | | | | | | | | | 90.62 2 |
|
MTAPA | | | | | | | | | | | 65.14 2 | | 80.20 16 | | | | | |
|
MTMP | | | | | | | | | | | 62.63 14 | | 78.04 23 | | | | | |
|
Patchmatch-RL test | | | | | | | | 1.04 215 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 72.00 37 | | | | | | | | |
|
Patchmtry | | | | | | | 47.61 184 | 48.27 167 | 38.86 153 | | 39.59 110 | | | | | | | |
|
DeepMVS_CX | | | | | | | 6.95 213 | 5.98 213 | 2.25 209 | 11.73 210 | 2.07 212 | 11.85 205 | 5.43 212 | 11.75 204 | 11.40 209 | | 8.10 212 | 18.38 209 |
|