DVP-MVS | | | 77.54 1 | 84.41 1 | 69.54 3 | 79.93 1 | 86.08 1 | 77.20 5 | 60.31 2 | 88.62 1 | 62.54 1 | 86.67 3 | 83.77 1 | 58.04 30 | 75.84 5 | 75.69 6 | 79.21 18 | 94.17 1 |
|
MCST-MVS | | | 74.06 6 | 77.71 11 | 69.79 2 | 78.95 2 | 81.99 5 | 76.33 7 | 62.16 1 | 75.89 18 | 52.96 22 | 64.37 28 | 73.30 18 | 65.66 3 | 77.49 2 | 77.43 3 | 82.67 1 | 93.51 3 |
|
CSCG | | | 72.98 9 | 76.86 13 | 68.46 5 | 78.23 3 | 81.74 7 | 77.26 4 | 60.00 3 | 75.61 21 | 59.06 3 | 62.72 30 | 77.42 5 | 56.63 42 | 74.24 9 | 77.18 4 | 79.56 13 | 89.13 18 |
|
DPE-MVS | | | 75.74 3 | 82.82 3 | 67.49 8 | 77.07 4 | 82.01 4 | 77.05 6 | 57.70 8 | 86.55 4 | 55.44 12 | 90.50 2 | 82.52 2 | 60.33 16 | 72.99 13 | 72.98 14 | 77.33 44 | 92.19 6 |
|
APDe-MVS | | | 74.59 5 | 80.23 4 | 68.01 7 | 76.51 5 | 80.20 12 | 77.39 3 | 58.18 6 | 85.31 5 | 56.84 9 | 84.89 4 | 76.08 10 | 60.66 14 | 71.85 24 | 71.76 19 | 78.47 27 | 91.49 9 |
|
DPM-MVS | | | 74.63 4 | 78.53 8 | 70.07 1 | 76.10 6 | 82.56 3 | 79.30 1 | 59.89 4 | 80.49 10 | 57.75 7 | 66.98 22 | 76.16 9 | 65.95 2 | 79.35 1 | 78.47 1 | 81.45 5 | 85.71 43 |
|
3Dnovator | | 58.39 4 | 65.97 32 | 66.85 48 | 64.94 12 | 73.72 7 | 79.03 17 | 67.73 36 | 54.25 22 | 61.52 50 | 52.79 24 | 42.27 89 | 60.73 50 | 62.01 7 | 71.29 26 | 71.75 20 | 79.12 20 | 81.34 84 |
|
MAR-MVS | | | 66.85 27 | 69.81 36 | 63.39 21 | 73.56 8 | 80.51 11 | 69.87 26 | 51.51 34 | 67.78 41 | 46.44 41 | 51.09 62 | 61.60 47 | 60.38 15 | 72.67 20 | 73.61 12 | 78.59 24 | 81.44 81 |
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 |
SteuartSystems-ACMMP | | | 69.78 17 | 74.76 17 | 63.98 16 | 73.45 9 | 78.56 21 | 73.13 14 | 55.24 18 | 70.68 31 | 48.93 34 | 70.43 17 | 69.10 25 | 54.00 54 | 72.78 19 | 72.98 14 | 79.14 19 | 88.74 21 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 73.87 7 | 78.60 7 | 68.35 6 | 73.32 10 | 81.97 6 | 76.19 8 | 59.29 5 | 80.12 11 | 56.70 10 | 67.09 21 | 76.48 7 | 64.26 5 | 75.88 4 | 75.75 5 | 80.32 8 | 92.93 4 |
|
QAPM | | | 65.47 35 | 67.82 40 | 62.72 24 | 72.56 11 | 81.17 10 | 67.43 39 | 55.38 17 | 56.07 62 | 43.29 58 | 43.60 84 | 65.38 33 | 59.10 25 | 72.20 21 | 70.76 31 | 78.56 25 | 85.59 46 |
|
CHOSEN 1792x2688 | | | 62.48 54 | 64.06 59 | 60.64 37 | 72.50 12 | 84.18 2 | 62.43 64 | 53.77 25 | 47.90 87 | 39.85 79 | 25.15 177 | 44.76 109 | 53.72 55 | 77.29 3 | 77.61 2 | 81.60 4 | 91.53 8 |
|
NCCC | | | 71.36 13 | 75.44 15 | 66.60 9 | 72.46 13 | 79.18 16 | 74.16 10 | 57.83 7 | 76.93 16 | 54.19 17 | 63.47 29 | 71.08 23 | 61.30 12 | 73.56 11 | 73.70 11 | 79.69 12 | 90.19 11 |
|
OpenMVS | | 55.62 8 | 62.57 50 | 63.76 60 | 61.19 34 | 72.13 14 | 78.84 19 | 64.42 55 | 50.51 43 | 56.44 59 | 45.67 46 | 36.88 117 | 56.51 61 | 56.66 41 | 68.28 54 | 68.96 47 | 77.73 39 | 80.44 91 |
|
MSP-MVS | | | 76.38 2 | 82.99 2 | 68.68 4 | 71.93 15 | 78.65 20 | 77.61 2 | 55.44 15 | 88.04 2 | 60.25 2 | 92.24 1 | 77.08 6 | 69.84 1 | 75.48 6 | 75.69 6 | 76.99 51 | 93.75 2 |
|
HPM-MVS++ | | | 72.44 10 | 78.73 6 | 65.11 11 | 71.88 16 | 77.31 29 | 71.98 17 | 55.67 13 | 83.11 8 | 53.59 19 | 75.90 9 | 78.49 4 | 61.00 13 | 73.99 10 | 73.31 13 | 76.55 54 | 88.97 19 |
|
SMA-MVS | | | 73.31 8 | 79.53 5 | 66.05 10 | 71.25 17 | 80.13 13 | 74.99 9 | 56.09 11 | 84.14 6 | 54.48 15 | 73.74 13 | 80.23 3 | 61.43 10 | 74.96 7 | 74.09 10 | 78.08 34 | 89.42 15 |
|
MVS_111021_HR | | | 64.66 39 | 67.11 46 | 61.80 29 | 71.04 18 | 77.91 25 | 62.75 63 | 54.78 20 | 51.43 75 | 47.54 40 | 53.77 53 | 54.85 65 | 56.84 37 | 70.59 29 | 71.50 22 | 77.86 37 | 89.70 13 |
|
AdaColmap | | | 62.79 48 | 62.63 64 | 62.98 23 | 70.82 19 | 72.90 64 | 67.84 35 | 54.09 24 | 65.14 45 | 50.71 28 | 41.78 91 | 47.64 96 | 60.17 19 | 67.41 60 | 66.83 64 | 74.28 97 | 76.69 107 |
|
abl_6 | | | | | 63.79 20 | 70.80 20 | 81.22 9 | 65.26 54 | 53.25 27 | 77.02 15 | 53.02 21 | 65.14 27 | 73.74 16 | 60.30 17 | | | 80.13 9 | 90.27 10 |
|
MS-PatchMatch | | | 61.41 61 | 61.88 72 | 60.85 35 | 70.57 21 | 75.98 36 | 66.29 48 | 46.91 78 | 50.56 77 | 48.28 38 | 36.30 120 | 51.64 72 | 50.95 77 | 72.89 16 | 70.65 32 | 82.13 3 | 75.17 116 |
|
APD-MVS | | | 71.86 11 | 77.91 10 | 64.80 13 | 70.39 22 | 75.69 40 | 74.02 11 | 56.14 10 | 83.59 7 | 52.92 23 | 84.67 5 | 73.46 17 | 59.30 24 | 69.47 38 | 69.66 39 | 76.02 60 | 88.84 20 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
train_agg | | | 70.74 14 | 76.53 14 | 63.98 16 | 70.33 23 | 75.16 44 | 72.33 16 | 55.78 12 | 75.74 19 | 50.41 31 | 80.08 8 | 73.15 19 | 57.75 34 | 71.96 23 | 70.94 29 | 77.25 48 | 88.69 22 |
|
ACMMP_NAP | | | 71.50 12 | 77.27 12 | 64.77 14 | 69.64 24 | 79.26 14 | 73.53 12 | 54.73 21 | 79.32 13 | 54.23 16 | 74.81 10 | 74.61 14 | 59.40 23 | 73.00 12 | 72.17 17 | 77.10 50 | 87.72 28 |
|
TSAR-MVS + ACMM | | | 65.95 33 | 72.83 20 | 57.93 53 | 69.35 25 | 65.85 114 | 73.36 13 | 39.84 140 | 76.00 17 | 48.69 37 | 82.54 7 | 75.03 13 | 49.38 90 | 65.33 73 | 63.42 101 | 66.94 160 | 81.67 78 |
|
TSAR-MVS + MP. | | | 70.28 15 | 75.09 16 | 64.66 15 | 69.34 26 | 64.61 122 | 72.60 15 | 56.29 9 | 80.73 9 | 58.36 5 | 84.56 6 | 75.22 12 | 55.37 49 | 69.11 46 | 69.45 41 | 75.97 62 | 81.97 73 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
SD-MVS | | | 68.30 19 | 72.58 21 | 63.31 22 | 69.24 27 | 67.85 95 | 70.81 23 | 53.65 26 | 79.64 12 | 58.52 4 | 74.31 11 | 75.37 11 | 53.52 60 | 65.63 70 | 63.56 99 | 74.13 102 | 81.73 77 |
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 |
DELS-MVS | | | 67.36 21 | 70.34 35 | 63.89 19 | 69.12 28 | 81.55 8 | 70.82 22 | 55.02 19 | 53.38 70 | 48.83 35 | 56.45 44 | 59.35 52 | 60.05 21 | 74.93 8 | 74.78 8 | 79.51 14 | 91.95 7 |
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 |
CDPH-MVS | | | 67.03 26 | 71.64 27 | 61.65 31 | 69.10 29 | 76.84 33 | 71.35 21 | 55.42 16 | 67.02 42 | 42.83 60 | 65.27 26 | 64.60 35 | 53.16 63 | 69.70 37 | 71.40 23 | 78.02 36 | 86.67 35 |
|
HFP-MVS | | | 68.75 18 | 72.84 19 | 63.98 16 | 68.87 30 | 75.09 45 | 71.87 18 | 51.22 35 | 73.50 25 | 58.17 6 | 68.05 20 | 68.67 26 | 57.79 33 | 70.49 31 | 69.23 43 | 75.98 61 | 84.84 50 |
|
MSLP-MVS++ | | | 61.81 59 | 62.19 69 | 61.37 33 | 68.33 31 | 63.08 136 | 70.75 24 | 38.89 146 | 63.96 48 | 57.51 8 | 48.59 70 | 61.66 46 | 53.67 58 | 62.04 109 | 59.92 144 | 79.03 21 | 76.08 110 |
|
CLD-MVS | | | 64.69 38 | 67.25 43 | 61.69 30 | 68.22 32 | 78.33 22 | 63.09 60 | 47.59 69 | 69.64 35 | 53.98 18 | 54.87 50 | 53.94 68 | 57.87 31 | 72.79 17 | 71.34 24 | 79.40 16 | 69.87 150 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HQP-MVS | | | 67.22 24 | 72.08 24 | 61.56 32 | 66.76 33 | 73.58 57 | 71.41 19 | 52.98 28 | 69.92 34 | 43.85 54 | 70.58 16 | 58.75 54 | 56.76 39 | 72.90 15 | 71.88 18 | 77.57 40 | 86.94 34 |
|
OPM-MVS | | | 61.59 60 | 62.30 68 | 60.76 36 | 66.53 34 | 73.35 59 | 71.41 19 | 54.18 23 | 40.82 115 | 41.57 72 | 45.70 79 | 54.84 66 | 54.43 53 | 69.92 35 | 69.19 44 | 76.45 55 | 82.25 67 |
|
MP-MVS | | | 67.34 22 | 73.08 18 | 60.64 37 | 66.20 35 | 76.62 34 | 69.22 29 | 50.92 37 | 70.07 32 | 48.81 36 | 69.66 18 | 70.12 24 | 53.68 57 | 68.41 51 | 69.13 45 | 74.98 81 | 87.53 30 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
zzz-MVS | | | 67.78 20 | 72.46 22 | 62.33 27 | 66.09 36 | 74.21 50 | 70.05 25 | 51.54 33 | 77.27 14 | 54.61 14 | 60.30 37 | 71.51 22 | 56.73 40 | 69.19 44 | 68.63 51 | 74.96 82 | 86.11 40 |
|
3Dnovator+ | | 55.76 7 | 62.70 49 | 65.10 55 | 59.90 44 | 65.89 37 | 72.15 69 | 62.94 62 | 49.82 46 | 62.77 49 | 49.06 33 | 43.62 83 | 61.47 49 | 58.60 28 | 68.51 50 | 66.75 65 | 73.08 123 | 80.40 92 |
|
PGM-MVS | | | 65.35 36 | 70.43 34 | 59.43 47 | 65.78 38 | 73.75 54 | 69.41 27 | 48.18 62 | 68.80 38 | 45.37 47 | 65.88 25 | 64.04 37 | 52.68 69 | 68.94 47 | 68.68 50 | 75.18 76 | 82.93 64 |
|
MSDG | | | 52.58 111 | 51.40 142 | 53.95 79 | 65.48 39 | 64.31 130 | 61.44 69 | 44.02 105 | 44.17 96 | 32.92 108 | 30.40 155 | 31.81 162 | 46.35 108 | 62.13 107 | 62.55 111 | 73.49 113 | 64.41 164 |
|
ACMMPR | | | 66.20 31 | 71.51 30 | 60.00 43 | 65.34 40 | 74.04 52 | 69.39 28 | 50.92 37 | 71.97 29 | 46.04 43 | 66.79 23 | 65.68 30 | 53.07 64 | 68.93 48 | 69.12 46 | 75.21 75 | 84.05 57 |
|
DWT-MVSNet_training | | | 61.22 62 | 63.52 62 | 58.53 49 | 65.00 41 | 76.55 35 | 59.50 82 | 48.22 61 | 51.79 73 | 42.14 67 | 47.85 73 | 50.21 80 | 55.46 48 | 66.16 67 | 67.92 57 | 80.85 6 | 84.14 56 |
|
X-MVS | | | 63.53 44 | 68.62 38 | 57.60 57 | 64.77 42 | 73.06 61 | 65.82 50 | 50.53 42 | 65.77 44 | 42.02 68 | 58.20 40 | 63.42 40 | 47.83 101 | 68.25 55 | 68.50 52 | 74.61 92 | 83.16 63 |
|
CANet | | | 67.21 25 | 71.83 26 | 61.83 28 | 64.51 43 | 79.25 15 | 66.72 45 | 48.73 53 | 68.49 39 | 50.63 30 | 61.40 33 | 66.47 29 | 61.44 9 | 69.31 43 | 69.90 35 | 78.94 22 | 88.00 25 |
|
SR-MVS | | | | | | 63.74 44 | | | 48.51 58 | | | | 73.80 15 | | | | | |
|
FC-MVSNet-train | | | 55.68 84 | 57.00 100 | 54.13 78 | 63.37 45 | 66.16 110 | 46.77 152 | 52.14 31 | 42.36 106 | 37.67 85 | 48.50 71 | 41.42 125 | 51.28 73 | 61.58 114 | 63.22 103 | 73.56 111 | 75.76 113 |
|
ACMH | | 47.82 13 | 50.10 131 | 49.60 151 | 50.69 99 | 63.36 46 | 66.99 103 | 56.83 98 | 52.13 32 | 31.06 165 | 17.74 173 | 28.22 167 | 26.24 186 | 45.17 116 | 60.88 123 | 63.80 97 | 68.91 149 | 70.00 149 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CostFormer | | | 62.45 55 | 65.68 53 | 58.67 48 | 63.29 47 | 77.65 26 | 67.62 37 | 38.42 149 | 54.04 67 | 46.00 44 | 48.27 72 | 57.89 57 | 56.97 36 | 67.03 63 | 67.79 59 | 79.74 10 | 87.09 33 |
|
ACMM | | 53.73 9 | 57.91 71 | 58.27 90 | 57.49 58 | 63.10 48 | 66.45 108 | 65.65 51 | 49.02 50 | 53.69 68 | 42.67 64 | 36.41 119 | 46.07 105 | 50.38 80 | 64.74 79 | 64.63 88 | 74.14 101 | 75.91 111 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
mPP-MVS | | | | | | 63.08 49 | | | | | | | 62.34 43 | | | | | |
|
PHI-MVS | | | 65.17 37 | 72.07 25 | 57.11 64 | 63.02 50 | 77.35 28 | 67.04 42 | 48.14 64 | 68.03 40 | 37.56 86 | 66.00 24 | 65.39 32 | 53.19 62 | 70.68 28 | 70.57 33 | 73.72 109 | 86.46 39 |
|
DeepC-MVS_fast | | 60.18 3 | 66.84 28 | 70.69 33 | 62.36 26 | 62.76 51 | 73.21 60 | 67.96 33 | 52.31 29 | 72.26 28 | 51.03 25 | 56.50 43 | 64.26 36 | 63.37 6 | 71.64 25 | 70.85 30 | 76.70 53 | 86.10 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
XVS | | | | | | 62.70 52 | 73.06 61 | 61.80 66 | | | 42.02 68 | | 63.42 40 | | | | 74.68 90 | |
|
X-MVStestdata | | | | | | 62.70 52 | 73.06 61 | 61.80 66 | | | 42.02 68 | | 63.42 40 | | | | 74.68 90 | |
|
ACMMP | | | 63.27 45 | 67.85 39 | 57.93 53 | 62.64 54 | 72.30 68 | 68.23 31 | 48.77 52 | 66.50 43 | 43.05 59 | 62.07 31 | 57.84 58 | 49.98 82 | 66.58 65 | 66.46 71 | 74.93 83 | 83.17 61 |
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 |
MVS_0304 | | | 66.31 30 | 71.61 28 | 60.14 41 | 62.59 55 | 78.98 18 | 67.13 41 | 45.75 90 | 64.35 47 | 45.23 48 | 60.69 35 | 67.67 28 | 61.73 8 | 71.09 27 | 71.03 27 | 78.41 30 | 87.44 31 |
|
baseline1 | | | 57.21 77 | 60.53 78 | 53.33 81 | 62.50 56 | 69.86 81 | 57.33 94 | 50.59 40 | 43.39 98 | 30.00 123 | 48.60 69 | 51.09 76 | 42.36 133 | 69.38 41 | 68.03 54 | 77.20 49 | 73.39 123 |
|
DeepPCF-MVS | | 62.48 1 | 70.07 16 | 78.36 9 | 60.39 40 | 62.38 57 | 76.96 32 | 65.54 52 | 52.23 30 | 87.46 3 | 49.07 32 | 74.05 12 | 76.19 8 | 59.01 26 | 72.79 17 | 71.61 21 | 74.13 102 | 89.49 14 |
|
CP-MVS | | | 64.37 41 | 69.48 37 | 58.39 50 | 62.21 58 | 71.81 71 | 67.27 40 | 49.51 47 | 69.40 37 | 45.76 45 | 60.41 36 | 64.96 34 | 51.84 71 | 67.33 61 | 67.57 60 | 73.78 108 | 84.89 49 |
|
canonicalmvs | | | 65.55 34 | 70.75 32 | 59.49 46 | 62.11 59 | 78.26 24 | 66.52 46 | 43.82 109 | 71.54 30 | 47.84 39 | 61.30 34 | 61.68 45 | 58.48 29 | 67.56 57 | 69.67 38 | 78.16 33 | 85.25 48 |
|
LGP-MVS_train | | | 59.69 65 | 62.59 65 | 56.31 68 | 61.94 60 | 68.15 94 | 66.90 43 | 48.15 63 | 59.75 53 | 38.47 82 | 50.38 65 | 48.34 93 | 46.87 106 | 65.39 72 | 64.93 82 | 75.51 71 | 81.21 86 |
|
Effi-MVS+ | | | 59.63 66 | 61.78 75 | 57.12 63 | 61.56 61 | 71.63 72 | 63.61 58 | 47.59 69 | 47.18 88 | 37.79 83 | 45.29 80 | 49.93 82 | 56.27 43 | 67.45 58 | 67.06 62 | 75.91 63 | 83.93 58 |
|
HyFIR lowres test | | | 57.12 78 | 59.11 80 | 54.80 74 | 61.55 62 | 77.55 27 | 59.02 85 | 45.00 94 | 41.84 112 | 33.93 103 | 22.44 184 | 49.16 88 | 51.02 76 | 68.39 52 | 68.71 49 | 78.26 32 | 85.70 44 |
|
TSAR-MVS + GP. | | | 66.77 29 | 72.21 23 | 60.44 39 | 61.23 63 | 70.00 79 | 64.26 57 | 47.79 66 | 72.98 26 | 56.32 11 | 71.35 15 | 72.33 20 | 55.68 47 | 65.49 71 | 66.66 66 | 77.35 43 | 86.62 36 |
|
ACMP | | 56.21 5 | 59.78 64 | 61.81 74 | 57.41 59 | 61.15 64 | 68.88 89 | 65.98 49 | 48.85 51 | 58.56 56 | 44.19 52 | 48.89 68 | 46.31 102 | 48.56 95 | 63.61 92 | 64.49 91 | 75.75 67 | 81.91 74 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
Anonymous202405211 | | | | 56.81 103 | | 60.91 65 | 73.48 58 | 59.82 79 | 48.68 55 | 39.26 121 | | 24.00 180 | 46.77 100 | 50.73 79 | 65.28 75 | 65.72 73 | 75.37 74 | 83.17 61 |
|
casdiffmvs | | | 63.87 42 | 67.08 47 | 60.12 42 | 60.90 66 | 78.29 23 | 67.91 34 | 48.01 65 | 55.89 64 | 44.97 49 | 50.45 64 | 56.94 60 | 59.54 22 | 70.17 34 | 69.81 36 | 79.41 15 | 87.99 26 |
|
IB-MVS | | 53.15 10 | 57.33 75 | 59.02 82 | 55.37 72 | 60.83 67 | 77.11 30 | 54.51 114 | 50.10 45 | 43.22 99 | 42.82 62 | 40.50 96 | 37.61 132 | 44.67 121 | 59.27 141 | 69.81 36 | 79.29 17 | 85.59 46 |
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 |
tpm cat1 | | | 57.41 74 | 58.26 91 | 56.42 67 | 60.80 68 | 72.56 66 | 64.35 56 | 38.43 148 | 49.18 83 | 46.36 42 | 36.69 118 | 43.50 114 | 54.47 51 | 61.39 117 | 62.64 109 | 74.11 104 | 81.81 75 |
|
gg-mvs-nofinetune | | | 50.82 125 | 55.83 108 | 44.97 138 | 60.63 69 | 75.69 40 | 53.40 121 | 34.48 173 | 20.05 199 | 6.93 193 | 18.27 192 | 52.70 69 | 33.57 152 | 70.50 30 | 72.93 16 | 80.84 7 | 80.68 90 |
|
EPNet | | | 64.39 40 | 70.93 31 | 56.77 66 | 60.58 70 | 75.77 37 | 59.28 84 | 50.58 41 | 69.93 33 | 40.73 75 | 68.59 19 | 61.60 47 | 53.72 55 | 68.65 49 | 68.07 53 | 75.75 67 | 83.87 59 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CS-MVS | | | 62.84 46 | 67.51 41 | 57.38 60 | 60.49 71 | 75.48 43 | 62.36 65 | 43.72 111 | 53.40 69 | 41.28 73 | 57.32 42 | 58.61 56 | 57.80 32 | 69.85 36 | 69.49 40 | 78.64 23 | 88.45 23 |
|
MVS_Test | | | 63.75 43 | 67.24 44 | 59.68 45 | 60.01 72 | 76.99 31 | 68.13 32 | 45.17 92 | 57.45 57 | 43.74 55 | 53.07 56 | 56.16 63 | 61.33 11 | 70.27 32 | 71.11 26 | 79.72 11 | 85.63 45 |
|
DCV-MVSNet | | | 56.80 80 | 58.96 83 | 54.28 76 | 59.96 73 | 66.74 106 | 60.37 76 | 44.87 96 | 41.01 114 | 36.81 89 | 47.57 74 | 47.87 95 | 48.23 98 | 64.41 82 | 65.17 79 | 75.45 72 | 79.95 94 |
|
tpmrst | | | 57.23 76 | 59.08 81 | 55.06 73 | 59.91 74 | 70.65 76 | 60.71 73 | 35.38 167 | 47.91 86 | 42.58 65 | 39.78 100 | 45.45 107 | 54.44 52 | 62.19 106 | 62.82 106 | 77.37 42 | 84.73 51 |
|
dps | | | 52.84 108 | 52.92 129 | 52.74 85 | 59.89 75 | 69.49 85 | 54.47 115 | 37.38 155 | 42.49 105 | 39.53 80 | 35.33 122 | 32.71 155 | 51.83 72 | 60.45 128 | 61.12 130 | 73.33 117 | 68.86 156 |
|
DeepC-MVS | | 60.65 2 | 67.33 23 | 71.52 29 | 62.44 25 | 59.79 76 | 74.84 46 | 68.89 30 | 55.56 14 | 73.91 24 | 53.50 20 | 55.00 49 | 65.63 31 | 60.08 20 | 71.99 22 | 71.33 25 | 76.85 52 | 87.94 27 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Anonymous20231211 | | | 56.40 81 | 57.00 100 | 55.70 71 | 59.78 77 | 72.49 67 | 61.29 72 | 46.83 80 | 40.50 116 | 40.46 76 | 22.12 186 | 49.73 83 | 51.07 75 | 64.39 83 | 65.30 78 | 74.74 86 | 84.44 54 |
|
baseline2 | | | 59.20 68 | 61.72 76 | 56.27 69 | 59.61 78 | 74.12 51 | 58.65 87 | 49.42 48 | 48.10 85 | 40.12 78 | 49.10 67 | 44.15 111 | 51.24 74 | 66.65 64 | 67.88 58 | 78.56 25 | 82.06 69 |
|
EG-PatchMatch MVS | | | 50.23 128 | 50.89 145 | 49.47 113 | 59.54 79 | 70.88 73 | 52.46 129 | 44.01 106 | 26.22 186 | 31.91 112 | 24.97 178 | 31.45 166 | 33.48 154 | 64.79 78 | 66.51 70 | 75.40 73 | 71.39 139 |
|
tpm | | | 54.94 87 | 57.86 96 | 51.54 92 | 59.48 80 | 67.04 102 | 58.34 88 | 34.60 171 | 41.93 111 | 34.41 98 | 42.40 88 | 47.14 98 | 49.07 93 | 61.46 115 | 61.67 125 | 73.31 118 | 83.39 60 |
|
Effi-MVS+-dtu | | | 53.63 98 | 54.85 115 | 52.20 89 | 59.32 81 | 61.33 147 | 56.42 105 | 40.24 138 | 43.84 97 | 34.22 100 | 39.49 105 | 46.18 104 | 53.00 67 | 58.72 147 | 57.49 153 | 69.99 145 | 76.91 106 |
|
EIA-MVS | | | 60.56 63 | 64.29 58 | 56.20 70 | 59.14 82 | 72.68 65 | 59.55 81 | 43.56 113 | 51.78 74 | 41.01 74 | 55.47 46 | 51.93 71 | 55.87 44 | 65.01 76 | 66.57 67 | 78.06 35 | 86.60 38 |
|
PCF-MVS | | 55.99 6 | 62.31 56 | 66.60 49 | 57.32 62 | 59.12 83 | 73.68 56 | 67.53 38 | 48.71 54 | 61.35 51 | 42.83 60 | 51.33 61 | 63.48 39 | 53.48 61 | 65.64 69 | 64.87 83 | 72.22 128 | 85.83 42 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PVSNet_BlendedMVS | | | 62.53 51 | 66.37 50 | 58.05 51 | 58.17 84 | 75.70 38 | 61.30 70 | 48.67 56 | 58.67 54 | 50.93 26 | 55.43 47 | 49.39 85 | 53.01 65 | 69.46 39 | 66.55 68 | 76.24 58 | 89.39 16 |
|
PVSNet_Blended | | | 62.53 51 | 66.37 50 | 58.05 51 | 58.17 84 | 75.70 38 | 61.30 70 | 48.67 56 | 58.67 54 | 50.93 26 | 55.43 47 | 49.39 85 | 53.01 65 | 69.46 39 | 66.55 68 | 76.24 58 | 89.39 16 |
|
TransMVSNet (Re) | | | 47.46 144 | 48.94 156 | 45.74 133 | 57.96 86 | 64.29 131 | 48.26 142 | 48.47 59 | 26.33 185 | 19.33 165 | 29.45 164 | 31.28 169 | 25.31 179 | 63.05 97 | 62.70 107 | 75.10 79 | 65.47 162 |
|
DI_MVS_plusplus_trai | | | 61.86 58 | 65.26 54 | 57.90 56 | 57.93 87 | 74.51 48 | 66.30 47 | 46.49 85 | 49.96 79 | 41.62 71 | 42.69 87 | 61.77 44 | 58.74 27 | 70.25 33 | 69.32 42 | 76.31 57 | 88.30 24 |
|
ETV-MVS | | | 62.81 47 | 67.50 42 | 57.34 61 | 57.83 88 | 74.47 49 | 61.55 68 | 43.46 114 | 53.12 71 | 43.49 56 | 58.46 39 | 57.16 59 | 60.22 18 | 69.37 42 | 69.98 34 | 77.47 41 | 89.91 12 |
|
NR-MVSNet | | | 48.84 138 | 51.76 137 | 45.44 135 | 57.66 89 | 60.64 149 | 47.39 146 | 47.63 67 | 37.26 132 | 13.31 178 | 37.31 114 | 29.64 176 | 33.53 153 | 63.52 93 | 62.09 118 | 73.10 122 | 71.89 135 |
|
diffmvs | | | 62.30 57 | 66.05 52 | 57.92 55 | 57.08 90 | 75.60 42 | 66.90 43 | 47.06 77 | 55.45 66 | 43.37 57 | 53.45 55 | 55.60 64 | 57.21 35 | 66.57 66 | 68.00 55 | 75.89 65 | 87.70 29 |
|
PVSNet_Blended_VisFu | | | 58.56 70 | 62.33 67 | 54.16 77 | 56.90 91 | 73.92 53 | 57.72 90 | 46.16 88 | 44.23 95 | 42.73 63 | 46.26 76 | 51.06 77 | 46.28 109 | 67.99 56 | 65.38 77 | 75.18 76 | 87.44 31 |
|
ACMH+ | | 47.85 12 | 49.13 137 | 48.86 157 | 49.44 114 | 56.75 92 | 62.01 143 | 56.62 103 | 47.55 71 | 37.49 131 | 23.98 146 | 26.68 172 | 29.46 177 | 43.12 128 | 57.45 154 | 58.85 148 | 68.62 151 | 70.05 148 |
|
EPMVS | | | 54.07 92 | 56.06 106 | 51.75 91 | 56.74 93 | 70.80 74 | 55.32 110 | 34.20 177 | 46.46 91 | 36.59 90 | 40.38 98 | 42.55 117 | 49.77 86 | 61.25 121 | 60.90 133 | 77.86 37 | 70.08 147 |
|
gm-plane-assit | | | 45.41 156 | 48.03 160 | 42.34 154 | 56.49 94 | 40.48 199 | 24.54 203 | 34.15 180 | 14.44 205 | 6.59 194 | 17.82 193 | 35.32 143 | 49.82 85 | 72.93 14 | 74.11 9 | 82.47 2 | 81.12 87 |
|
Fast-Effi-MVS+-dtu | | | 52.47 112 | 55.89 107 | 48.48 120 | 56.25 95 | 65.07 121 | 58.75 86 | 23.79 200 | 41.27 113 | 27.07 136 | 37.95 112 | 41.34 126 | 50.85 78 | 62.90 102 | 62.34 115 | 74.17 100 | 80.37 93 |
|
MVS_111021_LR | | | 57.06 79 | 60.60 77 | 52.93 83 | 56.25 95 | 65.14 120 | 55.16 112 | 41.21 132 | 52.32 72 | 44.89 50 | 53.92 52 | 49.27 87 | 52.16 70 | 61.46 115 | 60.54 137 | 67.92 153 | 81.53 80 |
|
thres100view900 | | | 52.33 114 | 53.91 120 | 50.48 102 | 56.10 97 | 67.79 96 | 56.18 107 | 49.18 49 | 35.86 144 | 25.22 142 | 34.74 127 | 34.10 151 | 42.41 132 | 64.45 81 | 62.62 110 | 73.81 107 | 77.85 102 |
|
tfpn200view9 | | | 50.91 123 | 52.45 134 | 49.11 117 | 56.10 97 | 64.53 125 | 53.06 124 | 47.31 74 | 35.86 144 | 25.22 142 | 34.74 127 | 34.10 151 | 41.08 135 | 60.84 124 | 61.37 127 | 71.90 131 | 75.70 114 |
|
SCA | | | 50.88 124 | 53.70 122 | 47.59 124 | 55.99 99 | 55.81 171 | 43.14 166 | 33.45 183 | 45.16 92 | 37.14 88 | 41.83 90 | 43.82 113 | 44.43 123 | 60.37 129 | 60.02 142 | 71.38 133 | 68.90 155 |
|
thres200 | | | 50.76 126 | 52.52 132 | 48.70 119 | 55.98 100 | 64.60 123 | 55.29 111 | 47.34 72 | 33.91 151 | 24.36 145 | 34.33 131 | 33.90 153 | 37.27 142 | 60.84 124 | 62.41 114 | 71.99 129 | 77.63 103 |
|
IS_MVSNet | | | 51.53 118 | 57.98 94 | 44.01 144 | 55.96 101 | 66.16 110 | 47.65 145 | 42.84 126 | 39.82 119 | 19.09 168 | 44.97 81 | 50.28 79 | 27.20 175 | 63.43 95 | 63.84 96 | 71.33 134 | 77.33 104 |
|
PatchmatchNet | | | 53.37 102 | 55.62 110 | 50.75 97 | 55.93 102 | 70.54 77 | 51.39 133 | 36.41 160 | 44.85 93 | 37.26 87 | 39.40 107 | 42.54 118 | 47.83 101 | 60.29 131 | 60.88 135 | 75.69 69 | 70.87 141 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
LS3D | | | 49.59 133 | 49.75 150 | 49.40 115 | 55.88 103 | 59.86 155 | 56.31 106 | 45.33 91 | 48.57 84 | 28.32 131 | 31.54 147 | 36.81 137 | 46.27 110 | 57.17 155 | 55.88 164 | 64.29 168 | 58.42 181 |
|
IterMVS-LS | | | 53.36 103 | 55.65 109 | 50.68 100 | 55.34 104 | 59.04 158 | 55.00 113 | 39.98 139 | 38.72 124 | 33.22 107 | 44.52 82 | 47.05 99 | 49.63 88 | 61.82 112 | 61.77 120 | 70.92 137 | 76.61 109 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UA-Net | | | 47.19 147 | 53.02 128 | 40.38 163 | 55.31 105 | 60.02 154 | 38.41 176 | 38.68 147 | 36.42 138 | 22.47 154 | 51.95 58 | 58.72 55 | 25.62 178 | 54.11 168 | 53.40 173 | 61.79 181 | 56.51 184 |
|
MVSTER | | | 62.51 53 | 67.22 45 | 57.02 65 | 55.05 106 | 69.23 87 | 63.02 61 | 46.88 79 | 61.11 52 | 43.95 53 | 59.20 38 | 58.86 53 | 56.80 38 | 69.13 45 | 70.98 28 | 76.41 56 | 82.04 70 |
|
thres400 | | | 50.39 127 | 52.22 135 | 48.26 121 | 55.02 107 | 66.32 109 | 52.97 125 | 48.33 60 | 32.68 155 | 22.94 150 | 33.21 138 | 33.38 154 | 37.27 142 | 62.74 103 | 61.38 126 | 73.04 124 | 75.81 112 |
|
CPTT-MVS | | | 59.54 67 | 64.47 57 | 53.79 80 | 54.99 108 | 67.63 98 | 65.48 53 | 44.59 99 | 64.81 46 | 37.74 84 | 51.55 59 | 59.90 51 | 49.77 86 | 61.83 111 | 61.26 129 | 70.18 142 | 84.31 55 |
|
EPP-MVSNet | | | 52.91 107 | 58.91 84 | 45.91 131 | 54.99 108 | 68.84 90 | 49.27 139 | 42.71 127 | 37.53 130 | 20.20 160 | 46.09 77 | 56.19 62 | 36.90 144 | 61.37 118 | 60.90 133 | 71.41 132 | 81.41 82 |
|
baseline | | | 58.65 69 | 61.99 70 | 54.75 75 | 54.70 110 | 71.85 70 | 60.20 77 | 43.91 107 | 55.99 63 | 40.13 77 | 53.50 54 | 50.91 78 | 55.76 45 | 61.29 119 | 61.73 121 | 73.83 106 | 78.68 100 |
|
Fast-Effi-MVS+ | | | 55.73 83 | 58.26 91 | 52.76 84 | 54.33 111 | 68.19 93 | 57.05 95 | 34.66 169 | 46.92 89 | 38.96 81 | 40.53 95 | 41.55 123 | 55.69 46 | 65.31 74 | 65.99 72 | 75.90 64 | 79.34 96 |
|
MDTV_nov1_ep13 | | | 52.99 106 | 55.59 111 | 49.95 109 | 54.08 112 | 70.69 75 | 56.47 104 | 38.42 149 | 42.78 101 | 30.19 122 | 39.56 104 | 43.31 116 | 45.78 111 | 60.07 136 | 62.11 117 | 74.74 86 | 70.62 142 |
|
thres600view7 | | | 48.44 140 | 50.23 148 | 46.35 129 | 54.05 113 | 64.60 123 | 50.18 136 | 47.34 72 | 31.73 161 | 20.74 158 | 32.28 144 | 32.62 157 | 33.79 151 | 60.84 124 | 56.11 162 | 71.99 129 | 73.40 122 |
|
EPNet_dtu | | | 49.85 132 | 56.99 102 | 41.52 159 | 52.79 114 | 57.06 165 | 41.44 170 | 43.13 121 | 56.13 61 | 19.24 167 | 52.11 57 | 48.38 92 | 22.14 182 | 58.19 149 | 58.38 149 | 70.35 140 | 68.71 157 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tfpnnormal | | | 46.61 152 | 46.82 165 | 46.37 128 | 52.70 115 | 62.31 140 | 50.39 135 | 47.17 75 | 25.74 188 | 21.80 155 | 23.13 182 | 24.15 194 | 33.45 155 | 60.28 132 | 60.77 136 | 72.70 126 | 71.39 139 |
|
CANet_DTU | | | 57.87 72 | 63.63 61 | 51.15 94 | 52.18 116 | 70.20 78 | 58.14 89 | 37.32 156 | 56.49 58 | 31.06 117 | 57.38 41 | 50.05 81 | 53.67 58 | 64.98 77 | 65.04 81 | 74.57 93 | 81.29 85 |
|
v8 | | | 53.77 96 | 54.82 116 | 52.54 87 | 52.12 117 | 66.95 105 | 60.56 75 | 43.23 120 | 37.17 136 | 35.35 93 | 34.96 126 | 37.50 134 | 49.51 89 | 63.67 91 | 64.59 89 | 74.48 94 | 78.91 99 |
|
v10 | | | 53.44 101 | 54.40 118 | 52.31 88 | 52.08 118 | 66.99 103 | 59.68 80 | 43.41 115 | 35.90 142 | 34.30 99 | 33.98 133 | 35.56 140 | 50.10 81 | 64.39 83 | 64.67 87 | 74.32 95 | 79.30 97 |
|
v1144 | | | 53.47 100 | 54.65 117 | 52.10 90 | 51.93 119 | 69.81 82 | 59.32 83 | 44.77 98 | 33.21 154 | 32.52 109 | 33.55 136 | 34.34 150 | 49.29 91 | 64.58 80 | 64.81 86 | 74.74 86 | 82.27 66 |
|
Vis-MVSNet (Re-imp) | | | 44.31 162 | 51.67 139 | 35.72 176 | 51.82 120 | 55.24 175 | 34.57 182 | 41.63 131 | 39.10 122 | 8.84 191 | 45.93 78 | 46.63 101 | 14.45 192 | 54.09 169 | 57.03 156 | 63.00 176 | 63.65 168 |
|
v2v482 | | | 54.00 93 | 55.12 112 | 52.69 86 | 51.73 121 | 69.42 86 | 60.65 74 | 45.09 93 | 34.56 147 | 33.73 106 | 35.29 123 | 35.36 142 | 49.92 83 | 64.05 89 | 65.16 80 | 75.00 80 | 81.98 72 |
|
test-LLR | | | 54.62 90 | 58.66 86 | 49.89 110 | 51.68 122 | 65.89 112 | 47.88 143 | 46.35 86 | 42.51 103 | 29.84 124 | 41.41 92 | 48.87 89 | 45.20 114 | 62.91 100 | 64.43 92 | 78.43 28 | 84.62 52 |
|
test0.0.03 1 | | | 43.07 169 | 46.95 164 | 38.54 168 | 51.68 122 | 58.77 161 | 35.28 178 | 46.35 86 | 32.05 159 | 12.44 180 | 28.53 166 | 35.52 141 | 14.40 193 | 57.12 157 | 56.93 157 | 71.11 135 | 59.69 175 |
|
GA-MVS | | | 53.77 96 | 56.41 105 | 50.70 98 | 51.63 124 | 69.96 80 | 57.55 92 | 44.39 100 | 34.31 148 | 27.15 134 | 40.99 94 | 36.40 138 | 47.65 103 | 67.45 58 | 67.16 61 | 75.83 66 | 78.60 101 |
|
UniMVSNet_NR-MVSNet | | | 49.56 134 | 53.04 127 | 45.49 134 | 51.59 125 | 64.42 129 | 46.97 150 | 51.01 36 | 37.87 126 | 16.42 174 | 39.87 99 | 34.91 146 | 33.43 156 | 59.59 139 | 62.70 107 | 73.52 112 | 71.94 132 |
|
thisisatest0530 | | | 53.61 99 | 57.22 98 | 49.40 115 | 51.30 126 | 68.22 92 | 52.72 128 | 43.34 118 | 42.72 102 | 35.31 94 | 43.57 85 | 44.14 112 | 44.37 124 | 63.00 98 | 64.86 84 | 69.34 148 | 74.00 117 |
|
v1192 | | | 52.69 109 | 53.86 121 | 51.31 93 | 51.22 127 | 69.76 83 | 57.37 93 | 44.39 100 | 32.21 157 | 31.39 116 | 32.41 143 | 32.44 158 | 49.19 92 | 64.25 85 | 64.17 94 | 74.31 96 | 81.81 75 |
|
v144192 | | | 52.43 113 | 53.63 123 | 51.03 95 | 51.06 128 | 69.60 84 | 56.94 97 | 44.84 97 | 32.15 158 | 30.88 118 | 32.45 142 | 32.71 155 | 48.36 96 | 62.98 99 | 63.52 100 | 74.10 105 | 82.02 71 |
|
v1921920 | | | 51.95 115 | 53.19 125 | 50.51 101 | 50.82 129 | 69.14 88 | 55.45 109 | 44.34 104 | 31.53 162 | 30.53 120 | 31.96 145 | 31.67 163 | 48.31 97 | 63.12 96 | 63.28 102 | 73.59 110 | 81.60 79 |
|
FMVSNet3 | | | 55.66 85 | 59.68 79 | 50.96 96 | 50.59 130 | 66.49 107 | 57.57 91 | 46.61 82 | 49.30 80 | 28.77 128 | 39.61 101 | 51.42 73 | 43.85 126 | 68.29 53 | 68.80 48 | 78.35 31 | 73.86 118 |
|
TranMVSNet+NR-MVSNet | | | 48.06 143 | 51.36 143 | 44.21 142 | 50.38 131 | 62.09 142 | 47.28 147 | 50.88 39 | 36.11 139 | 13.25 179 | 37.51 113 | 31.60 165 | 30.70 166 | 59.34 140 | 62.53 112 | 72.81 125 | 70.31 144 |
|
tttt0517 | | | 53.05 105 | 56.73 104 | 48.76 118 | 50.35 132 | 67.51 99 | 51.96 132 | 43.34 118 | 42.00 110 | 33.88 104 | 43.19 86 | 43.49 115 | 44.37 124 | 62.58 105 | 64.86 84 | 68.67 150 | 73.46 120 |
|
v1240 | | | 51.42 119 | 52.66 131 | 49.97 108 | 50.31 133 | 68.70 91 | 54.05 118 | 43.85 108 | 30.78 166 | 30.22 121 | 31.43 148 | 31.03 170 | 47.98 99 | 62.62 104 | 63.16 104 | 73.40 115 | 80.93 88 |
|
v148 | | | 51.72 116 | 53.15 126 | 50.05 106 | 50.15 134 | 67.51 99 | 56.98 96 | 42.85 125 | 32.60 156 | 32.41 111 | 33.88 134 | 34.71 147 | 44.45 122 | 61.06 122 | 63.00 105 | 73.45 114 | 79.24 98 |
|
PatchT | | | 48.11 142 | 51.27 144 | 44.43 139 | 50.13 135 | 61.58 145 | 33.59 183 | 32.92 185 | 40.38 117 | 31.74 113 | 30.60 154 | 36.93 136 | 45.00 118 | 58.80 144 | 61.11 131 | 73.19 120 | 69.47 151 |
|
CNLPA | | | 54.00 93 | 57.08 99 | 50.40 103 | 49.83 136 | 61.75 144 | 53.47 120 | 37.27 157 | 74.55 22 | 44.85 51 | 33.58 135 | 45.42 108 | 52.94 68 | 58.89 143 | 53.66 172 | 64.06 169 | 71.68 137 |
|
our_test_3 | | | | | | 49.68 137 | 61.50 146 | 45.84 158 | | | | | | | | | | |
|
pmmvs4 | | | 51.28 120 | 52.50 133 | 49.85 111 | 49.54 138 | 63.02 137 | 52.83 127 | 43.41 115 | 44.65 94 | 35.71 92 | 34.38 130 | 32.25 159 | 45.14 117 | 60.21 135 | 60.03 141 | 72.44 127 | 72.98 129 |
|
GBi-Net | | | 54.66 88 | 58.42 88 | 50.26 104 | 49.36 139 | 65.81 115 | 56.80 99 | 46.61 82 | 49.30 80 | 28.77 128 | 39.61 101 | 51.42 73 | 42.71 129 | 64.25 85 | 65.54 74 | 77.32 45 | 73.03 126 |
|
test1 | | | 54.66 88 | 58.42 88 | 50.26 104 | 49.36 139 | 65.81 115 | 56.80 99 | 46.61 82 | 49.30 80 | 28.77 128 | 39.61 101 | 51.42 73 | 42.71 129 | 64.25 85 | 65.54 74 | 77.32 45 | 73.03 126 |
|
FMVSNet2 | | | 53.94 95 | 57.29 97 | 50.03 107 | 49.36 139 | 65.81 115 | 56.80 99 | 45.95 89 | 43.13 100 | 28.04 132 | 35.68 121 | 48.18 94 | 42.71 129 | 67.23 62 | 67.95 56 | 77.32 45 | 73.03 126 |
|
pm-mvs1 | | | 46.14 153 | 49.34 155 | 42.41 153 | 48.93 142 | 62.22 141 | 44.98 160 | 42.68 128 | 27.66 178 | 20.76 157 | 29.88 160 | 34.96 145 | 26.41 177 | 60.03 137 | 60.42 138 | 70.70 139 | 70.20 145 |
|
Anonymous20231206 | | | 40.63 174 | 43.29 176 | 37.53 172 | 48.88 143 | 55.81 171 | 34.99 179 | 44.98 95 | 28.16 175 | 10.16 188 | 17.26 197 | 27.50 182 | 18.28 186 | 54.00 170 | 55.07 167 | 67.85 154 | 65.23 163 |
|
ADS-MVSNet | | | 45.39 157 | 46.42 166 | 44.19 143 | 48.74 144 | 57.52 164 | 43.91 164 | 31.93 186 | 35.89 143 | 27.11 135 | 30.12 156 | 32.06 161 | 45.30 112 | 53.13 174 | 55.19 166 | 68.15 152 | 61.07 174 |
|
v7n | | | 47.22 146 | 48.38 158 | 45.87 132 | 48.20 145 | 63.58 132 | 50.69 134 | 40.93 136 | 26.60 184 | 26.44 138 | 26.52 173 | 29.65 175 | 38.19 140 | 58.22 148 | 60.23 140 | 70.79 138 | 73.83 119 |
|
Vis-MVSNet | | | 51.13 121 | 58.04 93 | 43.06 150 | 47.68 146 | 67.71 97 | 49.10 140 | 39.09 145 | 37.75 128 | 22.57 152 | 51.03 63 | 48.78 91 | 32.42 161 | 62.12 108 | 61.80 119 | 67.49 157 | 77.12 105 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS-HIRNet | | | 43.98 164 | 43.63 175 | 44.39 141 | 47.66 147 | 59.31 157 | 32.66 189 | 33.88 182 | 30.15 169 | 33.75 105 | 16.82 198 | 28.39 180 | 45.25 113 | 53.92 172 | 55.00 168 | 73.16 121 | 61.80 171 |
|
IterMVS | | | 50.23 128 | 53.27 124 | 46.68 127 | 47.59 148 | 60.58 151 | 53.10 123 | 36.62 159 | 36.07 140 | 25.89 139 | 39.42 106 | 40.05 129 | 43.65 127 | 60.22 134 | 61.35 128 | 73.23 119 | 75.23 115 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 49.25 135 | 53.97 119 | 43.75 146 | 47.53 149 | 64.53 125 | 48.59 141 | 42.27 129 | 33.77 152 | 26.64 137 | 40.46 97 | 42.26 120 | 30.01 168 | 61.77 113 | 61.71 122 | 67.48 158 | 73.28 125 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
testgi | | | 34.51 189 | 37.42 190 | 31.12 191 | 47.37 150 | 50.34 181 | 24.38 204 | 41.21 132 | 20.32 197 | 5.64 199 | 20.56 187 | 26.55 185 | 8.06 204 | 49.28 180 | 52.65 174 | 60.05 183 | 42.23 201 |
|
CR-MVSNet | | | 48.82 139 | 51.85 136 | 45.29 136 | 46.74 151 | 55.95 169 | 52.06 130 | 34.21 175 | 42.17 107 | 31.74 113 | 32.92 140 | 42.53 119 | 45.00 118 | 58.80 144 | 61.11 131 | 61.99 180 | 69.47 151 |
|
IterMVS-SCA-FT | | | 45.87 154 | 51.55 141 | 39.24 166 | 46.22 152 | 59.43 156 | 52.89 126 | 31.93 186 | 36.01 141 | 23.68 147 | 38.86 109 | 39.88 131 | 39.05 137 | 56.25 161 | 58.17 150 | 41.70 201 | 72.25 131 |
|
MDTV_nov1_ep13_2view | | | 44.44 160 | 45.75 169 | 42.91 151 | 46.13 153 | 63.43 134 | 46.53 155 | 34.20 177 | 29.08 173 | 19.95 163 | 26.23 174 | 27.89 181 | 35.88 146 | 53.36 173 | 56.43 160 | 74.74 86 | 63.86 167 |
|
test20.03 | | | 36.00 185 | 38.92 185 | 32.60 184 | 45.92 154 | 50.99 179 | 28.05 199 | 43.69 112 | 21.62 193 | 6.03 195 | 17.61 195 | 25.91 188 | 8.34 203 | 51.26 176 | 52.60 175 | 63.58 171 | 52.46 191 |
|
UniMVSNet (Re) | | | 46.89 150 | 51.65 140 | 41.34 160 | 45.60 155 | 62.71 138 | 44.05 163 | 47.10 76 | 37.24 134 | 13.55 177 | 36.90 116 | 34.54 149 | 26.76 176 | 57.56 151 | 59.90 145 | 70.98 136 | 72.69 130 |
|
FMVSNet1 | | | 50.14 130 | 52.78 130 | 47.06 125 | 45.56 156 | 63.56 133 | 54.22 116 | 43.74 110 | 34.10 150 | 25.37 141 | 29.79 161 | 42.06 121 | 38.70 138 | 64.25 85 | 65.54 74 | 74.75 85 | 70.18 146 |
|
RPMNet | | | 43.70 166 | 48.17 159 | 38.48 169 | 45.52 157 | 55.95 169 | 37.66 177 | 26.63 198 | 42.17 107 | 25.47 140 | 29.59 163 | 37.61 132 | 33.87 150 | 50.85 178 | 52.02 177 | 61.75 182 | 69.00 154 |
|
thisisatest0515 | | | 46.88 151 | 49.57 152 | 43.74 147 | 45.33 158 | 60.46 152 | 46.19 156 | 41.06 135 | 30.34 168 | 29.73 126 | 32.50 141 | 31.63 164 | 35.43 147 | 58.75 146 | 61.71 122 | 64.70 167 | 71.59 138 |
|
TESTMET0.1,1 | | | 53.30 104 | 58.66 86 | 47.04 126 | 44.94 159 | 65.89 112 | 47.88 143 | 35.95 163 | 42.51 103 | 29.84 124 | 41.41 92 | 48.87 89 | 45.20 114 | 62.91 100 | 64.43 92 | 78.43 28 | 84.62 52 |
|
V42 | | | 52.63 110 | 55.08 113 | 49.76 112 | 44.93 160 | 67.49 101 | 60.19 78 | 42.13 130 | 37.21 135 | 34.08 102 | 34.57 129 | 37.30 135 | 47.29 104 | 63.48 94 | 64.15 95 | 69.96 146 | 81.38 83 |
|
TAMVS | | | 44.27 163 | 49.35 154 | 38.35 170 | 44.74 161 | 61.04 148 | 39.07 174 | 31.82 188 | 29.95 170 | 18.34 171 | 33.55 136 | 39.94 130 | 30.01 168 | 56.85 158 | 57.58 152 | 66.13 162 | 66.54 159 |
|
pmmvs6 | | | 41.90 173 | 44.01 174 | 39.43 165 | 44.45 162 | 58.77 161 | 41.92 168 | 39.22 144 | 21.74 192 | 19.08 169 | 17.40 196 | 31.33 168 | 24.28 181 | 55.94 162 | 56.67 158 | 67.60 156 | 66.24 160 |
|
DU-MVS | | | 47.33 145 | 50.86 146 | 43.20 149 | 44.43 163 | 60.64 149 | 46.97 150 | 47.63 67 | 37.26 132 | 16.42 174 | 37.31 114 | 31.39 167 | 33.43 156 | 57.53 152 | 59.98 143 | 70.35 140 | 71.94 132 |
|
Baseline_NR-MVSNet | | | 47.14 148 | 50.83 147 | 42.84 152 | 44.43 163 | 63.31 135 | 44.50 162 | 50.36 44 | 37.71 129 | 11.25 184 | 30.84 151 | 32.09 160 | 30.96 164 | 57.53 152 | 63.73 98 | 75.53 70 | 70.60 143 |
|
PMMVS | | | 55.74 82 | 62.68 63 | 47.64 123 | 44.34 165 | 65.58 118 | 47.22 149 | 37.96 152 | 56.43 60 | 34.11 101 | 61.51 32 | 47.41 97 | 54.55 50 | 65.88 68 | 62.49 113 | 67.67 155 | 79.48 95 |
|
CMPMVS | | 33.64 16 | 44.39 161 | 46.41 167 | 42.03 155 | 44.21 166 | 56.50 167 | 46.73 153 | 26.48 199 | 34.20 149 | 35.14 95 | 24.22 179 | 34.64 148 | 40.52 136 | 56.50 160 | 56.07 163 | 59.12 185 | 62.74 170 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
WR-MVS | | | 37.61 178 | 42.15 177 | 32.31 187 | 43.64 167 | 51.85 176 | 29.39 195 | 43.35 117 | 27.65 179 | 4.40 202 | 29.90 159 | 29.80 174 | 10.46 196 | 46.73 184 | 51.98 178 | 62.60 178 | 57.16 182 |
|
pmmvs5 | | | 47.02 149 | 50.02 149 | 43.51 148 | 43.48 168 | 62.65 139 | 47.24 148 | 37.78 154 | 30.59 167 | 24.80 144 | 35.26 124 | 30.43 171 | 34.36 149 | 59.05 142 | 60.28 139 | 73.40 115 | 71.92 134 |
|
LTVRE_ROB | | 32.83 17 | 35.10 187 | 37.46 189 | 32.35 186 | 43.12 169 | 49.99 183 | 28.52 197 | 33.23 184 | 12.73 206 | 8.18 192 | 27.71 170 | 21.34 197 | 32.64 160 | 46.92 183 | 48.11 186 | 48.41 198 | 55.45 187 |
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 |
PLC | | 44.22 14 | 49.14 136 | 51.75 138 | 46.10 130 | 42.78 170 | 55.60 174 | 53.11 122 | 34.46 174 | 55.69 65 | 32.47 110 | 34.16 132 | 41.45 124 | 48.91 94 | 57.13 156 | 54.09 170 | 64.84 165 | 64.10 165 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
pmmvs-eth3d | | | 44.67 159 | 45.27 171 | 43.98 145 | 42.56 171 | 55.72 173 | 44.97 161 | 40.81 137 | 31.96 160 | 29.13 127 | 26.09 175 | 25.27 191 | 36.69 145 | 55.13 165 | 56.62 159 | 69.68 147 | 66.12 161 |
|
OMC-MVS | | | 55.48 86 | 61.85 73 | 48.04 122 | 41.55 172 | 60.32 153 | 56.80 99 | 31.78 189 | 75.67 20 | 42.30 66 | 51.52 60 | 54.15 67 | 49.91 84 | 60.28 132 | 57.59 151 | 65.91 163 | 73.42 121 |
|
DTE-MVSNet | | | 36.91 181 | 40.44 181 | 32.79 183 | 40.74 173 | 47.55 189 | 30.71 193 | 44.39 100 | 27.03 182 | 4.32 203 | 30.88 150 | 25.99 187 | 12.73 194 | 45.58 186 | 50.80 180 | 63.86 170 | 55.23 188 |
|
UGNet | | | 51.04 122 | 58.79 85 | 42.00 156 | 40.59 174 | 65.32 119 | 46.65 154 | 39.26 143 | 39.90 118 | 27.30 133 | 54.12 51 | 52.03 70 | 30.93 165 | 59.85 138 | 59.62 146 | 67.23 159 | 80.70 89 |
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 |
PEN-MVS | | | 38.23 177 | 41.72 178 | 34.15 178 | 40.56 175 | 50.07 182 | 33.17 186 | 44.35 103 | 27.64 180 | 5.54 200 | 30.84 151 | 26.67 184 | 14.99 190 | 45.64 185 | 52.38 176 | 66.29 161 | 58.83 178 |
|
ambc | | | | 35.52 195 | | 38.36 176 | 40.40 200 | 28.38 198 | | 25.20 189 | 14.87 176 | 13.22 202 | 7.54 210 | 19.34 185 | 55.63 163 | 47.79 189 | 47.91 199 | 58.89 177 |
|
UniMVSNet_ETH3D | | | 43.97 165 | 46.01 168 | 41.59 157 | 38.31 177 | 56.20 168 | 49.69 137 | 38.18 151 | 28.18 174 | 19.88 164 | 27.82 169 | 30.20 172 | 33.41 158 | 54.18 167 | 56.30 161 | 70.05 144 | 69.17 153 |
|
MIMVSNet | | | 45.62 155 | 49.56 153 | 41.02 161 | 38.17 178 | 64.43 128 | 49.48 138 | 35.43 166 | 36.53 137 | 20.06 162 | 22.58 183 | 35.16 144 | 28.75 173 | 61.97 110 | 62.20 116 | 74.20 98 | 64.07 166 |
|
PatchMatch-RL | | | 43.37 167 | 44.93 172 | 41.56 158 | 37.94 179 | 51.70 177 | 40.02 172 | 35.75 164 | 39.04 123 | 30.71 119 | 35.14 125 | 27.43 183 | 46.58 107 | 51.99 175 | 50.55 181 | 58.38 187 | 58.64 179 |
|
WR-MVS_H | | | 36.29 183 | 40.35 183 | 31.55 189 | 37.80 180 | 49.94 184 | 30.57 194 | 41.11 134 | 26.90 183 | 4.14 204 | 30.72 153 | 28.85 178 | 10.45 197 | 42.47 194 | 47.99 188 | 65.24 164 | 55.54 186 |
|
CP-MVSNet | | | 37.09 180 | 40.62 180 | 32.99 180 | 37.56 181 | 48.25 187 | 32.75 187 | 43.05 122 | 27.88 177 | 5.93 196 | 31.27 149 | 25.82 189 | 15.09 188 | 43.37 192 | 48.82 182 | 63.54 173 | 58.90 176 |
|
PS-CasMVS | | | 36.84 182 | 40.23 184 | 32.89 181 | 37.44 182 | 48.09 188 | 32.68 188 | 42.97 124 | 27.36 181 | 5.89 197 | 30.08 158 | 25.48 190 | 14.96 191 | 43.28 193 | 48.71 183 | 63.39 174 | 58.63 180 |
|
FC-MVSNet-test | | | 30.97 195 | 37.38 191 | 23.49 198 | 37.42 183 | 33.68 203 | 19.43 206 | 39.27 142 | 31.37 164 | 1.67 210 | 38.56 111 | 28.85 178 | 6.06 207 | 41.40 195 | 43.80 195 | 37.10 203 | 44.03 200 |
|
N_pmnet | | | 34.09 191 | 35.74 194 | 32.17 188 | 37.25 184 | 43.17 196 | 32.26 191 | 35.57 165 | 26.22 186 | 10.60 187 | 20.44 188 | 19.38 200 | 20.20 184 | 44.59 189 | 47.00 192 | 57.13 190 | 49.35 198 |
|
SixPastTwentyTwo | | | 36.11 184 | 37.80 188 | 34.13 179 | 37.13 185 | 46.72 191 | 34.58 181 | 34.96 168 | 21.20 195 | 11.66 181 | 29.15 165 | 19.88 199 | 29.77 170 | 44.93 187 | 48.34 185 | 56.67 191 | 54.41 190 |
|
new-patchmatchnet | | | 30.47 196 | 32.80 199 | 27.75 194 | 36.81 186 | 43.98 194 | 24.85 202 | 39.29 141 | 20.52 196 | 4.06 205 | 15.94 199 | 16.05 203 | 9.57 198 | 41.32 196 | 42.05 197 | 51.94 197 | 49.74 197 |
|
TAPA-MVS | | 47.92 11 | 51.66 117 | 57.88 95 | 44.40 140 | 36.46 187 | 58.42 163 | 53.82 119 | 30.83 190 | 69.51 36 | 34.97 96 | 46.90 75 | 49.67 84 | 46.99 105 | 58.00 150 | 54.64 169 | 63.33 175 | 68.00 158 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
COLMAP_ROB | | 34.79 15 | 38.65 176 | 40.72 179 | 36.23 175 | 36.41 188 | 49.22 186 | 45.51 159 | 27.60 196 | 37.81 127 | 20.54 159 | 23.37 181 | 24.25 193 | 28.11 174 | 51.02 177 | 48.55 184 | 59.22 184 | 50.82 195 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
anonymousdsp | | | 43.03 170 | 47.19 162 | 38.18 171 | 36.00 189 | 56.92 166 | 38.44 175 | 34.56 172 | 24.22 190 | 22.53 153 | 29.69 162 | 29.92 173 | 35.21 148 | 53.96 171 | 58.98 147 | 62.32 179 | 76.66 108 |
|
CVMVSNet | | | 38.91 175 | 44.49 173 | 32.40 185 | 34.57 190 | 47.20 190 | 34.81 180 | 34.20 177 | 31.45 163 | 8.95 190 | 38.86 109 | 36.38 139 | 24.30 180 | 47.77 182 | 46.94 193 | 57.59 189 | 62.85 169 |
|
USDC | | | 42.80 171 | 45.57 170 | 39.58 164 | 34.55 191 | 51.13 178 | 42.61 167 | 36.21 161 | 39.59 120 | 23.65 148 | 33.13 139 | 20.87 198 | 37.86 141 | 55.35 164 | 57.16 155 | 62.61 177 | 61.75 172 |
|
TSAR-MVS + COLMAP | | | 54.37 91 | 62.43 66 | 44.98 137 | 34.33 192 | 58.94 160 | 54.11 117 | 34.15 180 | 74.06 23 | 34.57 97 | 71.63 14 | 42.03 122 | 47.88 100 | 61.26 120 | 57.33 154 | 64.83 166 | 71.74 136 |
|
test-mter | | | 48.31 141 | 55.04 114 | 40.45 162 | 34.12 193 | 59.02 159 | 41.77 169 | 28.05 194 | 38.43 125 | 22.67 151 | 39.35 108 | 44.40 110 | 41.88 134 | 60.30 130 | 61.68 124 | 74.20 98 | 82.12 68 |
|
CHOSEN 280x420 | | | 42.39 172 | 47.40 161 | 36.54 174 | 33.56 194 | 39.66 202 | 40.67 171 | 26.88 197 | 34.66 146 | 18.03 172 | 30.09 157 | 45.59 106 | 44.82 120 | 54.46 166 | 54.00 171 | 55.28 194 | 73.32 124 |
|
EU-MVSNet | | | 33.00 193 | 36.49 193 | 28.92 192 | 33.10 195 | 42.86 197 | 29.32 196 | 35.99 162 | 22.94 191 | 5.83 198 | 25.29 176 | 24.43 192 | 15.21 187 | 41.22 197 | 41.65 198 | 54.08 195 | 57.01 183 |
|
ET-MVSNet_ETH3D | | | 57.84 73 | 61.91 71 | 53.09 82 | 32.91 196 | 74.53 47 | 63.51 59 | 46.80 81 | 46.52 90 | 36.14 91 | 56.00 45 | 46.20 103 | 64.41 4 | 60.75 127 | 66.99 63 | 74.79 84 | 82.35 65 |
|
TinyColmap | | | 37.18 179 | 37.37 192 | 36.95 173 | 31.17 197 | 45.21 193 | 39.71 173 | 34.65 170 | 29.83 171 | 20.20 160 | 18.54 191 | 13.72 206 | 38.27 139 | 50.33 179 | 51.57 179 | 57.71 188 | 52.42 192 |
|
FMVSNet5 | | | 43.29 168 | 47.07 163 | 38.87 167 | 30.46 198 | 50.99 179 | 45.87 157 | 37.19 158 | 42.17 107 | 19.32 166 | 26.77 171 | 40.51 127 | 30.26 167 | 56.82 159 | 55.81 165 | 70.10 143 | 56.46 185 |
|
FPMVS | | | 26.87 198 | 28.19 200 | 25.32 196 | 27.09 199 | 29.49 205 | 32.28 190 | 17.79 205 | 28.09 176 | 11.33 182 | 19.38 190 | 14.69 204 | 20.88 183 | 35.11 200 | 32.82 201 | 42.56 200 | 37.75 202 |
|
MDA-MVSNet-bldmvs | | | 34.31 190 | 34.11 196 | 34.54 177 | 24.73 200 | 49.66 185 | 33.42 185 | 43.03 123 | 21.59 194 | 11.10 185 | 19.81 189 | 12.68 207 | 31.41 163 | 35.59 199 | 48.05 187 | 63.56 172 | 51.39 194 |
|
pmmvs3 | | | 31.22 194 | 33.62 197 | 28.43 193 | 22.82 201 | 40.26 201 | 26.40 200 | 22.05 203 | 16.89 203 | 10.99 186 | 14.72 200 | 16.26 202 | 29.70 171 | 44.82 188 | 47.39 190 | 58.61 186 | 54.98 189 |
|
PM-MVS | | | 34.96 188 | 38.17 187 | 31.22 190 | 22.78 202 | 40.82 198 | 33.56 184 | 23.61 201 | 29.16 172 | 21.43 156 | 28.00 168 | 21.43 196 | 31.90 162 | 44.33 190 | 42.12 196 | 54.07 196 | 61.34 173 |
|
PMVS | | 18.18 18 | 21.95 199 | 22.85 201 | 20.90 200 | 21.92 203 | 14.78 207 | 19.95 205 | 17.31 206 | 15.69 204 | 11.32 183 | 13.70 201 | 13.91 205 | 15.02 189 | 34.92 201 | 31.72 202 | 39.85 202 | 35.20 203 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
E-PMN | | | 10.66 203 | 8.30 206 | 13.42 203 | 19.91 204 | 7.87 211 | 4.30 212 | 29.47 192 | 8.37 210 | 1.70 209 | 3.67 207 | 1.29 216 | 9.12 200 | 8.98 208 | 13.59 206 | 16.03 208 | 14.30 209 |
|
MIMVSNet1 | | | 29.60 197 | 33.37 198 | 25.20 197 | 19.52 205 | 43.94 195 | 26.29 201 | 37.92 153 | 19.95 200 | 3.79 206 | 12.64 204 | 21.99 195 | 7.70 205 | 43.83 191 | 46.32 194 | 55.97 193 | 44.92 199 |
|
EMVS | | | 10.15 204 | 7.67 207 | 13.05 204 | 19.22 206 | 7.77 212 | 4.48 210 | 29.34 193 | 8.65 209 | 1.67 210 | 3.55 208 | 1.36 215 | 9.15 199 | 8.15 209 | 11.79 208 | 14.44 209 | 12.43 210 |
|
TDRefinement | | | 35.76 186 | 38.23 186 | 32.88 182 | 19.09 207 | 46.04 192 | 43.29 165 | 29.49 191 | 33.49 153 | 19.04 170 | 22.29 185 | 17.82 201 | 29.69 172 | 48.60 181 | 47.24 191 | 56.65 192 | 52.12 193 |
|
Gipuma | | | 17.16 201 | 17.83 203 | 16.36 201 | 18.76 208 | 12.15 210 | 11.97 208 | 27.78 195 | 17.94 202 | 4.86 201 | 2.53 210 | 2.73 213 | 8.90 201 | 34.32 202 | 36.09 200 | 25.92 206 | 19.06 206 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
RPSCF | | | 33.61 192 | 40.43 182 | 25.65 195 | 16.00 209 | 32.41 204 | 31.73 192 | 13.33 208 | 50.13 78 | 23.12 149 | 31.56 146 | 40.09 128 | 32.73 159 | 41.14 198 | 37.05 199 | 36.99 204 | 50.63 196 |
|
PMMVS2 | | | 12.25 202 | 14.17 204 | 10.00 205 | 11.39 210 | 14.35 208 | 8.21 209 | 19.29 204 | 9.31 207 | 0.19 213 | 7.38 206 | 6.19 211 | 1.10 209 | 19.26 204 | 21.13 205 | 19.85 207 | 21.56 205 |
|
new_pmnet | | | 19.10 200 | 22.71 202 | 14.89 202 | 10.93 211 | 24.08 206 | 14.22 207 | 13.94 207 | 18.68 201 | 2.93 207 | 12.84 203 | 11.27 208 | 11.94 195 | 30.57 203 | 30.58 203 | 35.38 205 | 30.93 204 |
|
MVE | | 10.35 19 | 9.76 205 | 11.08 205 | 8.22 206 | 4.43 212 | 13.04 209 | 3.36 213 | 23.57 202 | 5.74 211 | 1.76 208 | 3.09 209 | 1.75 214 | 6.78 206 | 12.78 206 | 23.04 204 | 9.44 210 | 18.09 207 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 4.41 207 | 2.56 213 | 1.81 214 | 2.61 214 | 0.27 210 | 20.12 198 | 9.81 189 | 17.69 194 | 9.04 209 | 1.96 208 | 12.88 205 | 12.11 207 | 9.23 211 | |
|
GG-mvs-BLEND | | | 44.87 158 | 64.59 56 | 21.86 199 | 0.01 214 | 73.70 55 | 55.99 108 | 0.01 211 | 50.70 76 | 0.01 214 | 49.18 66 | 63.61 38 | 0.01 210 | 63.83 90 | 64.50 90 | 75.13 78 | 86.62 36 |
|
sosnet-low-res | | | 0.00 208 | 0.00 210 | 0.00 208 | 0.00 215 | 0.00 215 | 0.00 216 | 0.00 212 | 0.00 214 | 0.00 215 | 0.00 213 | 0.00 217 | 0.00 212 | 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 | 0.00 216 | 0.00 212 | 0.00 214 | 0.00 215 | 0.00 213 | 0.00 217 | 0.00 212 | 0.00 211 | 0.00 211 | 0.00 213 | 0.00 213 |
|
testmvs | | | 0.01 206 | 0.01 208 | 0.00 208 | 0.00 215 | 0.00 215 | 0.00 216 | 0.00 212 | 0.01 212 | 0.00 215 | 0.02 211 | 0.00 217 | 0.00 212 | 0.01 210 | 0.01 209 | 0.00 213 | 0.03 211 |
|
test123 | | | 0.01 206 | 0.01 208 | 0.00 208 | 0.00 215 | 0.00 215 | 0.00 216 | 0.00 212 | 0.01 212 | 0.00 215 | 0.02 211 | 0.00 217 | 0.01 210 | 0.00 211 | 0.01 209 | 0.00 213 | 0.03 211 |
|
test_part1 | | | | | | | | | | | | | | | | | | 92.54 5 |
|
MTAPA | | | | | | | | | | | 54.82 13 | | 71.98 21 | | | | | |
|
MTMP | | | | | | | | | | | 50.64 29 | | 68.31 27 | | | | | |
|
Patchmatch-RL test | | | | | | | | 0.69 215 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 72.62 27 | | | | | | | | |
|
Patchmtry | | | | | | | 64.49 127 | 52.06 130 | 34.21 175 | | 31.74 113 | | | | | | | |
|
DeepMVS_CX | | | | | | | 5.87 213 | 4.32 211 | 1.74 209 | 9.04 208 | 1.30 212 | 7.97 205 | 3.16 212 | 8.56 202 | 9.74 207 | | 6.30 212 | 14.51 208 |
|