MCST-MVS | | | 85.75 6 | 86.99 10 | 84.31 3 | 94.07 1 | 92.80 4 | 88.15 5 | 79.10 1 | 85.66 20 | 70.72 27 | 76.50 29 | 80.45 17 | 82.17 2 | 88.35 2 | 87.49 3 | 91.63 2 | 97.65 3 |
|
HPM-MVS++ | | | 85.64 7 | 88.43 4 | 82.39 9 | 92.65 2 | 90.24 23 | 85.83 13 | 74.21 8 | 90.68 7 | 75.63 15 | 86.77 10 | 84.15 5 | 78.68 12 | 86.33 7 | 85.26 9 | 87.32 51 | 95.60 18 |
|
CNVR-MVS | | | 85.96 5 | 87.58 8 | 84.06 5 | 92.58 3 | 92.40 7 | 87.62 7 | 77.77 3 | 88.44 11 | 75.93 14 | 79.49 22 | 81.97 13 | 81.65 3 | 87.04 6 | 86.58 4 | 88.79 18 | 97.18 7 |
|
NCCC | | | 84.16 13 | 85.46 18 | 82.64 8 | 92.34 4 | 90.57 20 | 86.57 10 | 76.51 5 | 86.85 17 | 72.91 20 | 77.20 28 | 78.69 23 | 79.09 11 | 84.64 18 | 84.88 14 | 88.44 27 | 95.41 21 |
|
DPE-MVS | | | 87.60 3 | 90.44 3 | 84.29 4 | 92.09 5 | 93.44 2 | 88.69 2 | 75.11 6 | 93.06 4 | 80.80 4 | 94.23 2 | 86.70 2 | 81.44 4 | 84.84 16 | 83.52 25 | 87.64 45 | 97.28 5 |
|
DVP-MVS | | | 88.07 1 | 90.73 1 | 84.97 1 | 91.98 6 | 95.01 1 | 87.86 6 | 76.88 4 | 93.90 1 | 85.15 1 | 90.11 5 | 86.90 1 | 79.46 8 | 86.26 9 | 84.67 16 | 88.50 26 | 98.25 2 |
|
CSCG | | | 82.90 17 | 84.52 20 | 81.02 15 | 91.85 7 | 93.43 3 | 87.14 8 | 74.01 11 | 81.96 30 | 76.14 12 | 70.84 34 | 82.49 9 | 69.71 58 | 82.32 38 | 85.18 11 | 87.26 54 | 95.40 22 |
|
SMA-MVS | | | 85.24 9 | 88.27 6 | 81.72 12 | 91.74 8 | 90.71 17 | 86.71 9 | 73.16 16 | 90.56 8 | 74.33 16 | 83.07 15 | 85.88 3 | 77.16 16 | 86.28 8 | 85.58 6 | 87.23 55 | 95.77 15 |
|
DPM-MVS | | | 85.41 8 | 86.72 13 | 83.89 7 | 91.66 9 | 91.92 11 | 90.49 1 | 78.09 2 | 86.90 15 | 73.95 17 | 74.52 31 | 82.01 12 | 79.29 9 | 90.24 1 | 90.65 1 | 89.86 6 | 90.78 69 |
|
QAPM | | | 77.50 42 | 77.43 47 | 77.59 33 | 91.52 10 | 92.00 10 | 81.41 37 | 70.63 24 | 66.22 71 | 58.05 68 | 54.70 77 | 71.79 41 | 74.49 30 | 82.46 34 | 82.04 33 | 89.46 11 | 92.79 50 |
|
APDe-MVS | | | 86.37 4 | 88.41 5 | 84.00 6 | 91.43 11 | 91.83 12 | 88.34 3 | 74.67 7 | 91.19 5 | 81.76 3 | 91.13 3 | 81.94 14 | 80.07 5 | 83.38 24 | 82.58 31 | 87.69 43 | 96.78 10 |
|
3Dnovator | | 70.49 5 | 78.42 36 | 76.77 53 | 80.35 17 | 91.43 11 | 90.27 22 | 81.84 33 | 70.79 23 | 72.10 53 | 71.95 21 | 50.02 95 | 67.86 53 | 77.47 15 | 82.89 28 | 84.24 18 | 88.61 22 | 89.99 78 |
|
DeepC-MVS_fast | | 75.41 2 | 81.69 21 | 82.10 30 | 81.20 14 | 91.04 13 | 87.81 47 | 83.42 24 | 74.04 10 | 83.77 24 | 71.09 25 | 66.88 44 | 72.44 35 | 79.48 7 | 85.08 13 | 84.97 13 | 88.12 37 | 93.78 39 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SteuartSystems-ACMMP | | | 82.51 18 | 85.35 19 | 79.20 23 | 90.25 14 | 89.39 30 | 84.79 18 | 70.95 22 | 82.86 26 | 68.32 35 | 86.44 11 | 77.19 24 | 73.07 36 | 83.63 23 | 83.64 22 | 87.82 38 | 94.34 31 |
Skip Steuart: Steuart Systems R&D Blog. |
HFP-MVS | | | 82.48 19 | 84.12 21 | 80.56 16 | 90.15 15 | 87.55 50 | 84.28 20 | 69.67 31 | 85.22 21 | 77.95 10 | 84.69 13 | 75.94 27 | 75.04 25 | 81.85 45 | 81.17 49 | 86.30 72 | 92.40 52 |
|
DeepPCF-MVS | | 76.94 1 | 83.08 16 | 87.77 7 | 77.60 32 | 90.11 16 | 90.96 16 | 78.48 51 | 72.63 19 | 93.10 3 | 65.84 39 | 80.67 20 | 81.55 15 | 74.80 27 | 85.94 11 | 85.39 8 | 83.75 135 | 96.77 11 |
|
OpenMVS | | 67.62 8 | 74.92 56 | 73.91 67 | 76.09 41 | 90.10 17 | 90.38 21 | 78.01 55 | 66.35 51 | 66.09 73 | 62.80 46 | 46.33 118 | 64.55 63 | 71.77 46 | 79.92 60 | 80.88 56 | 87.52 47 | 89.20 87 |
|
MAR-MVS | | | 77.19 45 | 78.37 45 | 75.81 43 | 89.87 18 | 90.58 19 | 79.33 50 | 65.56 57 | 77.62 46 | 58.33 66 | 59.24 67 | 67.98 51 | 74.83 26 | 82.37 37 | 83.12 27 | 86.95 61 | 87.67 103 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
TSAR-MVS + ACMM | | | 81.59 23 | 85.84 17 | 76.63 36 | 89.82 19 | 86.53 60 | 86.32 12 | 66.72 49 | 85.96 19 | 65.43 40 | 88.98 8 | 82.29 10 | 67.57 76 | 82.06 42 | 81.33 46 | 83.93 133 | 93.75 40 |
|
train_agg | | | 83.35 15 | 86.93 12 | 79.17 24 | 89.70 20 | 88.41 38 | 85.60 16 | 72.89 18 | 86.31 18 | 66.58 38 | 90.48 4 | 82.24 11 | 73.06 37 | 83.10 27 | 82.64 30 | 87.21 59 | 95.30 23 |
|
abl_6 | | | | | 79.06 26 | 89.68 21 | 92.14 9 | 77.70 58 | 69.68 30 | 86.87 16 | 71.88 22 | 74.29 32 | 80.06 19 | 76.56 19 | | | 88.84 17 | 95.82 13 |
|
APD-MVS | | | 84.83 10 | 87.00 9 | 82.30 10 | 89.61 22 | 89.21 31 | 86.51 11 | 73.64 13 | 90.98 6 | 77.99 9 | 89.89 6 | 80.04 20 | 79.18 10 | 82.00 43 | 81.37 45 | 86.88 63 | 95.49 20 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMMP_NAP | | | 83.54 14 | 86.37 15 | 80.25 18 | 89.57 23 | 90.10 25 | 85.27 17 | 71.66 20 | 87.38 12 | 73.08 19 | 84.23 14 | 80.16 18 | 75.31 23 | 84.85 15 | 83.64 22 | 86.57 67 | 94.21 35 |
|
MSP-MVS | | | 87.87 2 | 90.57 2 | 84.73 2 | 89.38 24 | 91.60 14 | 88.24 4 | 74.15 9 | 93.55 2 | 82.28 2 | 94.99 1 | 83.21 7 | 85.96 1 | 87.67 4 | 84.67 16 | 88.32 29 | 98.29 1 |
|
AdaColmap | | | 76.23 49 | 73.55 69 | 79.35 22 | 89.38 24 | 85.00 73 | 79.99 47 | 73.04 17 | 76.60 48 | 71.17 24 | 55.18 75 | 57.99 96 | 77.87 13 | 76.82 85 | 76.82 86 | 84.67 120 | 86.45 110 |
|
3Dnovator+ | | 70.16 6 | 77.87 39 | 77.29 49 | 78.55 27 | 89.25 26 | 88.32 40 | 80.09 45 | 67.95 41 | 74.89 52 | 71.83 23 | 52.05 89 | 70.68 45 | 76.27 21 | 82.27 39 | 82.04 33 | 85.92 81 | 90.77 70 |
|
CDPH-MVS | | | 79.39 33 | 82.13 29 | 76.19 40 | 89.22 27 | 88.34 39 | 84.20 21 | 71.00 21 | 79.67 40 | 56.97 72 | 77.77 25 | 72.24 39 | 68.50 70 | 81.33 49 | 82.74 28 | 87.23 55 | 92.84 48 |
|
SD-MVS | | | 84.31 12 | 86.96 11 | 81.22 13 | 88.98 28 | 88.68 35 | 85.65 14 | 73.85 12 | 89.09 10 | 79.63 5 | 87.34 9 | 84.84 4 | 73.71 32 | 82.66 31 | 81.60 42 | 85.48 98 | 94.51 29 |
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 |
MP-MVS | | | 80.94 24 | 83.49 23 | 77.96 29 | 88.48 29 | 88.16 42 | 82.82 29 | 69.34 33 | 80.79 36 | 69.67 31 | 82.35 17 | 77.13 25 | 71.60 48 | 80.97 54 | 80.96 54 | 85.87 84 | 94.06 36 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
ACMMPR | | | 80.62 26 | 82.98 25 | 77.87 31 | 88.41 30 | 87.05 56 | 83.02 26 | 69.18 34 | 83.91 23 | 68.35 34 | 82.89 16 | 73.64 32 | 72.16 43 | 80.78 55 | 81.13 51 | 86.10 77 | 91.43 59 |
|
MSLP-MVS++ | | | 78.57 35 | 77.33 48 | 80.02 19 | 88.39 31 | 84.79 74 | 84.62 19 | 66.17 53 | 75.96 49 | 78.40 7 | 61.59 57 | 71.47 42 | 73.54 35 | 78.43 71 | 78.88 68 | 88.97 15 | 90.18 77 |
|
PGM-MVS | | | 79.42 32 | 81.84 31 | 76.60 37 | 88.38 32 | 86.69 58 | 82.97 28 | 65.75 55 | 80.39 37 | 64.94 41 | 81.95 19 | 72.11 40 | 71.41 49 | 80.45 56 | 80.55 59 | 86.18 74 | 90.76 71 |
|
EPNet | | | 79.28 34 | 82.25 27 | 75.83 42 | 88.31 33 | 90.14 24 | 79.43 49 | 68.07 40 | 81.76 32 | 61.26 54 | 77.26 27 | 70.08 47 | 70.06 56 | 82.43 36 | 82.00 35 | 87.82 38 | 92.09 54 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DELS-MVS | | | 79.49 28 | 79.84 37 | 79.08 25 | 88.26 34 | 92.49 5 | 84.12 22 | 70.63 24 | 65.27 78 | 69.60 33 | 61.29 59 | 66.50 57 | 72.75 39 | 88.07 3 | 88.03 2 | 89.13 13 | 97.22 6 |
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 |
zzz-MVS | | | 81.65 22 | 83.10 24 | 79.97 20 | 88.14 35 | 87.62 49 | 83.96 23 | 69.90 28 | 86.92 14 | 77.67 11 | 72.47 33 | 78.74 22 | 74.13 31 | 81.59 48 | 81.15 50 | 86.01 80 | 93.19 45 |
|
TSAR-MVS + MP. | | | 84.39 11 | 86.58 14 | 81.83 11 | 88.09 36 | 86.47 61 | 85.63 15 | 73.62 14 | 90.13 9 | 79.24 6 | 89.67 7 | 82.99 8 | 77.72 14 | 81.22 50 | 80.92 55 | 86.68 66 | 94.66 28 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
X-MVS | | | 78.16 38 | 80.55 35 | 75.38 45 | 87.99 37 | 86.27 64 | 81.05 41 | 68.98 35 | 78.33 42 | 61.07 56 | 75.25 30 | 72.27 36 | 67.52 77 | 80.03 59 | 80.52 60 | 85.66 95 | 91.20 63 |
|
DeepC-MVS | | 74.46 3 | 80.30 27 | 81.05 33 | 79.42 21 | 87.42 38 | 88.50 37 | 83.23 25 | 73.27 15 | 82.78 27 | 71.01 26 | 62.86 54 | 69.93 48 | 74.80 27 | 84.30 19 | 84.20 19 | 86.79 65 | 94.77 25 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | | | | 86.96 39 | | | | | | | 70.61 46 | | | | | |
|
CP-MVS | | | 79.44 29 | 81.51 32 | 77.02 35 | 86.95 40 | 85.96 68 | 82.00 31 | 68.44 39 | 81.82 31 | 67.39 36 | 77.43 26 | 73.68 31 | 71.62 47 | 79.56 63 | 79.58 62 | 85.73 88 | 92.51 51 |
|
MVS_111021_HR | | | 77.42 43 | 78.40 44 | 76.28 38 | 86.95 40 | 90.68 18 | 77.41 60 | 70.56 27 | 66.21 72 | 62.48 49 | 66.17 47 | 63.98 64 | 72.08 44 | 82.87 29 | 83.15 26 | 88.24 32 | 95.71 16 |
|
CANet | | | 80.90 25 | 82.93 26 | 78.53 28 | 86.83 42 | 92.26 8 | 81.19 39 | 66.95 46 | 81.60 33 | 69.90 30 | 66.93 43 | 74.80 29 | 76.79 17 | 84.68 17 | 84.77 15 | 89.50 10 | 95.50 19 |
|
CHOSEN 1792x2688 | | | 72.55 67 | 71.98 76 | 73.22 57 | 86.57 43 | 92.41 6 | 75.63 67 | 66.77 48 | 62.08 84 | 52.32 84 | 30.27 183 | 50.74 125 | 66.14 80 | 86.22 10 | 85.41 7 | 91.90 1 | 96.75 12 |
|
SR-MVS | | | | | | 86.33 44 | | | 67.54 43 | | | | 80.78 16 | | | | | |
|
PHI-MVS | | | 79.43 30 | 84.06 22 | 74.04 53 | 86.15 45 | 91.57 15 | 80.85 43 | 68.90 37 | 82.22 29 | 51.81 87 | 78.10 24 | 74.28 30 | 70.39 55 | 84.01 22 | 84.00 20 | 86.14 76 | 94.24 33 |
|
ACMMP | | | 77.61 41 | 79.59 38 | 75.30 46 | 85.87 46 | 85.58 69 | 81.42 36 | 67.38 45 | 79.38 41 | 62.61 47 | 78.53 23 | 65.79 59 | 68.80 69 | 78.56 70 | 78.50 73 | 85.75 85 | 90.80 68 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
HQP-MVS | | | 78.26 37 | 80.91 34 | 75.17 47 | 85.67 47 | 84.33 80 | 83.01 27 | 69.38 32 | 79.88 39 | 55.83 73 | 79.85 21 | 64.90 62 | 70.81 51 | 82.46 34 | 81.78 38 | 86.30 72 | 93.18 46 |
|
OPM-MVS | | | 72.74 66 | 70.93 85 | 74.85 50 | 85.30 48 | 84.34 79 | 82.82 29 | 69.79 29 | 49.96 130 | 55.39 79 | 54.09 84 | 60.14 84 | 70.04 57 | 80.38 58 | 79.43 63 | 85.74 87 | 88.20 99 |
|
MS-PatchMatch | | | 70.34 81 | 69.00 94 | 71.91 66 | 85.20 49 | 85.35 70 | 77.84 57 | 61.77 89 | 58.01 98 | 55.40 78 | 41.26 136 | 58.34 93 | 61.69 102 | 81.70 47 | 78.29 74 | 89.56 9 | 80.02 153 |
|
MVS_0304 | | | 79.43 30 | 82.20 28 | 76.20 39 | 84.22 50 | 91.79 13 | 81.82 34 | 63.81 67 | 76.83 47 | 61.71 52 | 66.37 46 | 75.52 28 | 76.38 20 | 85.54 12 | 85.03 12 | 89.28 12 | 94.32 32 |
|
PCF-MVS | | 70.85 4 | 75.73 51 | 76.55 56 | 74.78 51 | 83.67 51 | 88.04 46 | 81.47 35 | 70.62 26 | 69.24 65 | 57.52 70 | 60.59 63 | 69.18 49 | 70.65 53 | 77.11 81 | 77.65 80 | 84.75 118 | 94.01 37 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
ACMM | | 66.70 10 | 70.42 77 | 68.49 98 | 72.67 61 | 82.85 52 | 77.76 135 | 77.70 58 | 64.76 62 | 64.61 79 | 60.74 60 | 49.29 96 | 53.97 115 | 65.86 81 | 74.97 101 | 75.57 102 | 84.13 132 | 83.29 135 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XVS | | | | | | 82.43 53 | 86.27 64 | 75.70 65 | | | 61.07 56 | | 72.27 36 | | | | 85.67 92 | |
|
X-MVStestdata | | | | | | 82.43 53 | 86.27 64 | 75.70 65 | | | 61.07 56 | | 72.27 36 | | | | 85.67 92 | |
|
PVSNet_BlendedMVS | | | 76.84 47 | 78.47 42 | 74.95 48 | 82.37 55 | 89.90 27 | 75.45 71 | 65.45 58 | 74.99 50 | 70.66 28 | 63.07 52 | 58.27 94 | 67.60 74 | 84.24 20 | 81.70 40 | 88.18 33 | 97.10 8 |
|
PVSNet_Blended | | | 76.84 47 | 78.47 42 | 74.95 48 | 82.37 55 | 89.90 27 | 75.45 71 | 65.45 58 | 74.99 50 | 70.66 28 | 63.07 52 | 58.27 94 | 67.60 74 | 84.24 20 | 81.70 40 | 88.18 33 | 97.10 8 |
|
CLD-MVS | | | 77.36 44 | 77.29 49 | 77.45 34 | 82.21 57 | 88.11 43 | 81.92 32 | 68.96 36 | 77.97 44 | 69.62 32 | 62.08 55 | 59.44 87 | 73.57 34 | 81.75 46 | 81.27 48 | 88.41 28 | 90.39 74 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
LGP-MVS_train | | | 72.02 70 | 73.18 72 | 70.67 73 | 82.13 58 | 80.26 113 | 79.58 48 | 63.04 74 | 70.09 59 | 51.98 85 | 65.06 48 | 55.62 107 | 62.49 99 | 75.97 93 | 76.32 93 | 84.80 117 | 88.93 90 |
|
MSDG | | | 65.57 107 | 61.57 144 | 70.24 75 | 82.02 59 | 76.47 144 | 74.46 84 | 68.73 38 | 56.52 103 | 50.33 95 | 38.47 149 | 41.10 147 | 62.42 100 | 72.12 134 | 72.94 135 | 83.47 138 | 73.37 175 |
|
IB-MVS | | 64.48 11 | 69.02 85 | 68.97 95 | 69.09 83 | 81.75 60 | 89.01 33 | 64.50 142 | 64.91 61 | 56.65 102 | 62.59 48 | 47.89 102 | 45.23 136 | 51.99 146 | 69.18 161 | 81.88 37 | 88.77 19 | 92.93 47 |
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 |
canonicalmvs | | | 77.65 40 | 79.59 38 | 75.39 44 | 81.52 61 | 89.83 29 | 81.32 38 | 60.74 101 | 80.05 38 | 66.72 37 | 68.43 39 | 65.09 60 | 74.72 29 | 78.87 67 | 82.73 29 | 87.32 51 | 92.16 53 |
|
CPTT-MVS | | | 75.43 52 | 77.13 51 | 73.44 55 | 81.43 62 | 82.55 91 | 80.96 42 | 64.35 63 | 77.95 45 | 61.39 53 | 69.20 37 | 70.94 44 | 69.38 65 | 73.89 114 | 73.32 128 | 83.14 145 | 92.06 55 |
|
DWT-MVSNet_training | | | 72.81 65 | 73.98 66 | 71.45 67 | 81.26 63 | 86.37 62 | 72.08 91 | 59.82 106 | 69.13 67 | 58.15 67 | 54.71 76 | 61.33 77 | 67.81 73 | 76.86 84 | 78.63 69 | 89.59 8 | 90.86 66 |
|
EPNet_dtu | | | 66.17 103 | 70.13 90 | 61.54 134 | 81.04 64 | 77.39 139 | 68.87 120 | 62.50 82 | 69.78 60 | 33.51 169 | 63.77 51 | 56.22 102 | 37.65 182 | 72.20 133 | 72.18 143 | 85.69 91 | 79.38 155 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMP | | 68.86 7 | 72.15 69 | 72.25 74 | 72.03 64 | 80.96 65 | 80.87 107 | 77.93 56 | 64.13 65 | 69.29 63 | 60.79 59 | 64.04 50 | 53.54 117 | 63.91 89 | 73.74 117 | 75.27 104 | 84.45 125 | 88.98 89 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
HyFIR lowres test | | | 68.39 89 | 68.28 100 | 68.52 86 | 80.85 66 | 88.11 43 | 71.08 106 | 58.09 114 | 54.87 117 | 47.80 105 | 27.55 189 | 55.80 105 | 64.97 84 | 79.11 65 | 79.14 66 | 88.31 30 | 93.35 42 |
|
LS3D | | | 64.54 116 | 62.14 140 | 67.34 95 | 80.85 66 | 75.79 150 | 69.99 112 | 65.87 54 | 60.77 88 | 44.35 117 | 42.43 130 | 45.95 135 | 65.01 83 | 69.88 156 | 68.69 166 | 77.97 182 | 71.43 182 |
|
CNLPA | | | 71.37 75 | 70.27 89 | 72.66 62 | 80.79 68 | 81.33 101 | 71.07 107 | 65.75 55 | 82.36 28 | 64.80 42 | 42.46 129 | 56.49 101 | 72.70 40 | 73.00 125 | 70.52 159 | 80.84 167 | 85.76 118 |
|
TSAR-MVS + GP. | | | 82.27 20 | 85.98 16 | 77.94 30 | 80.72 69 | 88.25 41 | 81.12 40 | 67.71 42 | 87.10 13 | 73.31 18 | 85.23 12 | 83.68 6 | 76.64 18 | 80.43 57 | 81.47 44 | 88.15 35 | 95.66 17 |
|
baseline1 | | | 71.47 72 | 72.02 75 | 70.82 71 | 80.56 70 | 84.51 76 | 76.61 64 | 66.93 47 | 56.22 106 | 48.66 100 | 55.40 74 | 60.43 81 | 62.55 98 | 83.35 25 | 80.99 52 | 89.60 7 | 83.28 136 |
|
PLC | | 64.00 12 | 68.54 87 | 66.66 109 | 70.74 72 | 80.28 71 | 74.88 156 | 72.64 90 | 63.70 69 | 69.26 64 | 55.71 75 | 47.24 109 | 55.31 109 | 70.42 54 | 72.05 136 | 70.67 157 | 81.66 161 | 77.19 161 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
OMC-MVS | | | 74.03 59 | 75.82 59 | 71.95 65 | 79.56 72 | 80.98 105 | 75.35 73 | 63.21 72 | 84.48 22 | 61.83 51 | 61.54 58 | 66.89 55 | 69.41 64 | 76.60 86 | 74.07 118 | 82.34 155 | 86.15 113 |
|
CostFormer | | | 72.18 68 | 73.90 68 | 70.18 76 | 79.47 73 | 86.19 67 | 76.94 63 | 48.62 171 | 66.07 74 | 60.40 61 | 54.14 83 | 65.82 58 | 67.98 71 | 75.84 94 | 76.41 91 | 87.67 44 | 92.83 49 |
|
MVS_111021_LR | | | 74.26 58 | 75.95 58 | 72.27 63 | 79.43 74 | 85.04 72 | 72.71 89 | 65.27 60 | 70.92 56 | 63.58 45 | 69.32 36 | 60.31 83 | 69.43 63 | 77.01 82 | 77.15 83 | 83.22 142 | 91.93 57 |
|
MVS_Test | | | 75.22 53 | 76.69 54 | 73.51 54 | 79.30 75 | 88.82 34 | 80.06 46 | 58.74 109 | 69.77 61 | 57.50 71 | 59.78 66 | 61.35 75 | 75.31 23 | 82.07 41 | 83.60 24 | 90.13 5 | 91.41 61 |
|
casdiffmvs | | | 75.20 54 | 75.69 60 | 74.63 52 | 79.26 76 | 89.07 32 | 78.47 52 | 63.59 70 | 67.05 69 | 63.79 44 | 55.72 73 | 60.32 82 | 73.58 33 | 82.16 40 | 81.78 38 | 89.08 14 | 93.72 41 |
|
PVSNet_Blended_VisFu | | | 71.76 71 | 73.54 70 | 69.69 77 | 79.01 77 | 87.16 55 | 72.05 92 | 61.80 88 | 56.46 104 | 59.66 62 | 53.88 85 | 62.48 67 | 59.08 121 | 81.17 51 | 78.90 67 | 86.53 69 | 94.74 26 |
|
ACMH | | 59.42 14 | 61.59 141 | 59.22 160 | 64.36 111 | 78.92 78 | 78.26 129 | 67.65 126 | 67.48 44 | 39.81 170 | 30.98 175 | 38.25 151 | 34.59 179 | 61.37 106 | 70.55 150 | 73.47 124 | 79.74 174 | 79.59 154 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FC-MVSNet-train | | | 68.83 86 | 68.29 99 | 69.47 78 | 78.35 79 | 79.94 114 | 64.72 141 | 66.38 50 | 54.96 114 | 54.51 81 | 56.75 70 | 47.91 131 | 66.91 78 | 75.57 98 | 75.75 98 | 85.92 81 | 87.12 105 |
|
EIA-MVS | | | 73.48 61 | 76.05 57 | 70.47 74 | 78.12 80 | 87.21 54 | 71.78 95 | 60.63 102 | 69.66 62 | 55.56 77 | 64.86 49 | 60.69 79 | 69.53 61 | 77.35 80 | 78.59 70 | 87.22 57 | 94.01 37 |
|
ETV-MVS | | | 76.16 50 | 79.12 40 | 72.71 60 | 78.08 81 | 87.54 51 | 74.15 85 | 61.37 93 | 70.55 58 | 59.25 64 | 68.75 38 | 67.36 54 | 75.64 22 | 81.94 44 | 81.90 36 | 87.78 41 | 95.79 14 |
|
Effi-MVS+ | | | 70.42 77 | 71.23 82 | 69.47 78 | 78.04 82 | 85.24 71 | 75.57 69 | 58.88 107 | 59.56 92 | 48.47 101 | 52.73 88 | 54.94 110 | 69.69 59 | 78.34 73 | 77.06 84 | 86.18 74 | 90.73 72 |
|
Anonymous202405211 | | | | 66.35 113 | | 78.00 83 | 84.41 78 | 74.85 75 | 63.18 73 | 51.00 126 | | 31.37 180 | 53.73 116 | 69.67 60 | 76.28 88 | 76.84 85 | 83.21 144 | 90.85 67 |
|
CS-MVS | | | 75.18 55 | 78.59 41 | 71.20 68 | 77.74 84 | 87.69 48 | 73.93 86 | 58.81 108 | 69.17 66 | 55.73 74 | 67.86 40 | 66.89 55 | 72.87 38 | 82.50 32 | 81.29 47 | 88.15 35 | 94.71 27 |
|
thres100view900 | | | 67.14 101 | 66.09 115 | 68.38 88 | 77.70 85 | 83.84 84 | 74.52 81 | 66.33 52 | 49.16 134 | 43.40 122 | 43.24 121 | 41.34 143 | 62.59 97 | 79.31 64 | 75.92 97 | 85.73 88 | 89.81 79 |
|
tfpn200view9 | | | 65.90 105 | 64.96 119 | 67.00 96 | 77.70 85 | 81.58 97 | 71.71 97 | 62.94 78 | 49.16 134 | 43.40 122 | 43.24 121 | 41.34 143 | 61.42 104 | 76.24 89 | 74.63 110 | 84.84 113 | 88.52 96 |
|
DCV-MVSNet | | | 69.13 84 | 69.07 93 | 69.21 80 | 77.65 87 | 77.52 137 | 74.68 76 | 57.85 119 | 54.92 115 | 55.34 80 | 55.74 72 | 55.56 108 | 66.35 79 | 75.05 100 | 76.56 89 | 83.35 139 | 88.13 100 |
|
Anonymous20231211 | | | 68.44 88 | 66.37 112 | 70.86 70 | 77.58 88 | 83.49 85 | 75.15 74 | 61.89 86 | 52.54 123 | 58.50 65 | 28.89 185 | 56.78 100 | 69.29 66 | 74.96 103 | 76.61 87 | 82.73 148 | 91.36 62 |
|
UA-Net | | | 64.62 113 | 68.23 101 | 60.42 139 | 77.53 89 | 81.38 100 | 60.08 166 | 57.47 125 | 47.01 141 | 44.75 115 | 60.68 61 | 71.32 43 | 41.84 177 | 73.27 120 | 72.25 142 | 80.83 168 | 71.68 180 |
|
thres200 | | | 65.58 106 | 64.74 121 | 66.56 97 | 77.52 90 | 81.61 95 | 73.44 88 | 62.95 76 | 46.23 146 | 42.45 129 | 42.76 123 | 41.18 145 | 58.12 125 | 76.24 89 | 75.59 101 | 84.89 111 | 89.58 82 |
|
ACMH+ | | 60.36 13 | 61.16 142 | 58.38 162 | 64.42 110 | 77.37 91 | 74.35 161 | 68.45 121 | 62.81 80 | 45.86 148 | 38.48 146 | 35.71 167 | 37.35 163 | 59.81 114 | 67.24 166 | 69.80 163 | 79.58 175 | 78.32 159 |
|
TAPA-MVS | | 67.10 9 | 71.45 73 | 73.47 71 | 69.10 82 | 77.04 92 | 80.78 108 | 73.81 87 | 62.10 83 | 80.80 35 | 51.28 88 | 60.91 60 | 63.80 66 | 67.98 71 | 74.59 105 | 72.42 140 | 82.37 154 | 80.97 150 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
IS_MVSNet | | | 67.29 99 | 71.98 76 | 61.82 132 | 76.92 93 | 84.32 81 | 65.90 140 | 58.22 112 | 55.75 110 | 39.22 142 | 54.51 80 | 62.47 68 | 45.99 167 | 78.83 68 | 78.52 72 | 84.70 119 | 89.47 84 |
|
CANet_DTU | | | 72.84 64 | 76.63 55 | 68.43 87 | 76.81 94 | 86.62 59 | 75.54 70 | 54.71 153 | 72.06 54 | 43.54 120 | 67.11 42 | 58.46 91 | 72.40 41 | 81.13 53 | 80.82 57 | 87.57 46 | 90.21 76 |
|
tpm cat1 | | | 67.47 97 | 67.05 107 | 67.98 89 | 76.63 95 | 81.51 99 | 74.49 83 | 47.65 176 | 61.18 86 | 61.12 55 | 42.51 128 | 53.02 120 | 64.74 87 | 70.11 155 | 71.50 146 | 83.22 142 | 89.49 83 |
|
DI_MVS_plusplus_trai | | | 73.94 60 | 74.85 63 | 72.88 59 | 76.57 96 | 86.80 57 | 80.41 44 | 61.47 91 | 62.35 83 | 59.44 63 | 47.91 101 | 68.12 50 | 72.24 42 | 82.84 30 | 81.50 43 | 87.15 60 | 94.42 30 |
|
thres400 | | | 65.18 111 | 64.44 123 | 66.04 98 | 76.40 97 | 82.63 89 | 71.52 99 | 64.27 64 | 44.93 152 | 40.69 137 | 41.86 133 | 40.79 149 | 58.12 125 | 77.67 75 | 74.64 109 | 85.26 101 | 88.56 95 |
|
tpmrst | | | 67.15 100 | 68.12 102 | 66.03 99 | 76.21 98 | 80.98 105 | 71.27 101 | 45.05 182 | 60.69 89 | 50.63 93 | 46.95 114 | 54.15 114 | 65.30 82 | 71.80 138 | 71.77 144 | 87.72 42 | 90.48 73 |
|
gg-mvs-nofinetune | | | 62.34 130 | 66.19 114 | 57.86 155 | 76.15 99 | 88.61 36 | 71.18 104 | 41.24 199 | 25.74 201 | 13.16 202 | 22.91 196 | 63.97 65 | 54.52 141 | 85.06 14 | 85.25 10 | 90.92 3 | 91.78 58 |
|
baseline | | | 72.89 63 | 74.46 65 | 71.07 69 | 75.99 100 | 87.50 52 | 74.57 77 | 60.49 103 | 70.72 57 | 57.60 69 | 60.63 62 | 60.97 78 | 70.79 52 | 75.27 99 | 76.33 92 | 86.94 62 | 89.79 81 |
|
EPMVS | | | 66.21 102 | 67.49 105 | 64.73 106 | 75.81 101 | 84.20 82 | 68.94 119 | 44.37 186 | 61.55 85 | 48.07 104 | 49.21 98 | 54.87 111 | 62.88 95 | 71.82 137 | 71.40 150 | 88.28 31 | 79.37 156 |
|
baseline2 | | | 71.22 76 | 73.01 73 | 69.13 81 | 75.76 102 | 86.34 63 | 71.23 102 | 62.78 81 | 62.62 81 | 52.85 83 | 57.32 69 | 54.31 112 | 63.27 94 | 79.74 61 | 79.31 64 | 88.89 16 | 91.43 59 |
|
EPP-MVSNet | | | 67.58 95 | 71.10 83 | 63.48 118 | 75.71 103 | 83.35 86 | 66.85 133 | 57.83 120 | 53.02 122 | 41.15 134 | 55.82 71 | 67.89 52 | 56.01 136 | 74.40 107 | 72.92 136 | 83.33 140 | 90.30 75 |
|
diffmvs | | | 74.32 57 | 75.42 61 | 73.04 58 | 75.60 104 | 87.27 53 | 78.20 53 | 62.96 75 | 68.66 68 | 61.89 50 | 59.79 65 | 59.84 85 | 71.80 45 | 78.30 74 | 79.87 61 | 87.80 40 | 94.23 34 |
|
thres600view7 | | | 63.77 121 | 63.14 129 | 64.51 108 | 75.49 105 | 81.61 95 | 69.59 115 | 62.95 76 | 43.96 155 | 38.90 144 | 41.09 137 | 40.24 154 | 55.25 139 | 76.24 89 | 71.54 145 | 84.89 111 | 87.30 104 |
|
dps | | | 64.08 118 | 63.22 128 | 65.08 103 | 75.27 106 | 79.65 117 | 66.68 135 | 46.63 180 | 56.94 100 | 55.67 76 | 43.96 120 | 43.63 140 | 64.00 88 | 69.50 160 | 69.82 161 | 82.25 156 | 79.02 157 |
|
MVSTER | | | 76.92 46 | 79.92 36 | 73.42 56 | 74.98 107 | 82.97 87 | 78.15 54 | 63.41 71 | 78.02 43 | 64.41 43 | 67.54 41 | 72.80 34 | 71.05 50 | 83.29 26 | 83.73 21 | 88.53 25 | 91.12 64 |
|
TSAR-MVS + COLMAP | | | 73.09 62 | 76.86 52 | 68.71 84 | 74.97 108 | 82.49 92 | 74.51 82 | 61.83 87 | 83.16 25 | 49.31 99 | 82.22 18 | 51.62 122 | 68.94 68 | 78.76 69 | 75.52 103 | 82.67 150 | 84.23 128 |
|
tpm | | | 64.85 112 | 66.02 116 | 63.48 118 | 74.52 109 | 78.38 128 | 70.98 108 | 44.99 184 | 51.61 125 | 43.28 124 | 47.66 104 | 53.18 118 | 60.57 108 | 70.58 149 | 71.30 153 | 86.54 68 | 89.45 85 |
|
SCA | | | 63.90 120 | 66.67 108 | 60.66 137 | 73.75 110 | 71.78 171 | 59.87 167 | 43.66 187 | 61.13 87 | 45.03 113 | 51.64 90 | 59.45 86 | 57.92 127 | 70.96 144 | 70.80 155 | 83.71 136 | 80.92 151 |
|
Vis-MVSNet (Re-imp) | | | 62.25 133 | 68.74 96 | 54.68 170 | 73.70 111 | 78.74 124 | 56.51 175 | 57.49 124 | 55.22 112 | 26.86 181 | 54.56 79 | 61.35 75 | 31.06 184 | 73.10 122 | 74.90 106 | 82.49 152 | 83.31 134 |
|
Fast-Effi-MVS+ | | | 67.59 94 | 67.56 104 | 67.62 92 | 73.67 112 | 81.14 104 | 71.12 105 | 54.79 152 | 58.88 94 | 50.61 94 | 46.70 116 | 47.05 132 | 69.12 67 | 76.06 92 | 76.44 90 | 86.43 70 | 86.65 108 |
|
IterMVS-LS | | | 66.08 104 | 66.56 111 | 65.51 100 | 73.67 112 | 74.88 156 | 70.89 109 | 53.55 158 | 50.42 128 | 48.32 103 | 50.59 93 | 55.66 106 | 61.83 101 | 73.93 113 | 74.42 114 | 84.82 116 | 86.01 115 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchmatchNet | | | 65.43 109 | 67.71 103 | 62.78 124 | 73.49 114 | 82.83 88 | 66.42 138 | 45.40 181 | 60.40 90 | 45.27 111 | 49.22 97 | 57.60 98 | 60.01 113 | 70.61 147 | 71.38 151 | 86.08 78 | 81.91 147 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
COLMAP_ROB | | 51.17 15 | 55.13 168 | 52.90 180 | 57.73 157 | 73.47 115 | 67.21 183 | 62.13 158 | 55.82 137 | 47.83 138 | 34.39 165 | 31.60 179 | 34.24 180 | 44.90 171 | 63.88 180 | 62.52 187 | 75.67 187 | 63.02 196 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Effi-MVS+-dtu | | | 64.58 114 | 64.08 124 | 65.16 102 | 73.04 116 | 75.17 155 | 70.68 111 | 56.23 134 | 54.12 120 | 44.71 116 | 47.42 105 | 51.10 123 | 63.82 90 | 68.08 164 | 66.32 175 | 82.47 153 | 86.38 111 |
|
thisisatest0530 | | | 68.38 90 | 70.98 84 | 65.35 101 | 72.61 117 | 84.42 77 | 68.21 123 | 57.98 115 | 59.77 91 | 50.80 92 | 54.63 78 | 58.48 90 | 57.92 127 | 76.99 83 | 77.47 81 | 84.60 121 | 85.07 121 |
|
EG-PatchMatch MVS | | | 58.73 158 | 58.03 165 | 59.55 144 | 72.32 118 | 80.49 110 | 63.44 153 | 55.55 142 | 32.49 190 | 38.31 147 | 28.87 186 | 37.22 164 | 42.84 175 | 74.30 111 | 75.70 99 | 84.84 113 | 77.14 162 |
|
TransMVSNet (Re) | | | 57.83 161 | 56.90 168 | 58.91 150 | 72.26 119 | 74.69 159 | 63.57 152 | 61.42 92 | 32.30 191 | 32.65 170 | 33.97 173 | 35.96 173 | 39.17 180 | 73.84 116 | 72.84 137 | 84.37 126 | 74.69 168 |
|
CMPMVS | | 43.63 17 | 57.67 164 | 55.43 172 | 60.28 140 | 72.01 120 | 79.00 122 | 62.77 157 | 53.23 160 | 41.77 162 | 45.42 110 | 30.74 182 | 39.03 156 | 53.01 144 | 64.81 175 | 64.65 181 | 75.26 189 | 68.03 187 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
NR-MVSNet | | | 61.08 144 | 62.09 141 | 59.90 141 | 71.96 121 | 75.87 148 | 63.60 151 | 61.96 84 | 49.31 132 | 27.95 178 | 42.76 123 | 33.85 183 | 48.82 156 | 74.35 109 | 74.05 119 | 85.13 103 | 84.45 125 |
|
tttt0517 | | | 67.99 93 | 70.61 86 | 64.94 104 | 71.94 122 | 83.96 83 | 67.62 127 | 57.98 115 | 59.30 93 | 49.90 97 | 54.50 81 | 57.98 97 | 57.92 127 | 76.48 87 | 77.47 81 | 84.24 128 | 84.58 124 |
|
PMMVS | | | 70.37 80 | 75.06 62 | 64.90 105 | 71.46 123 | 81.88 93 | 64.10 144 | 55.64 140 | 71.31 55 | 46.69 106 | 70.69 35 | 58.56 88 | 69.53 61 | 79.03 66 | 75.63 100 | 81.96 158 | 88.32 98 |
|
test-LLR | | | 68.23 91 | 71.61 80 | 64.28 112 | 71.37 124 | 81.32 102 | 63.98 147 | 61.03 95 | 58.62 95 | 42.96 125 | 52.74 86 | 61.65 73 | 57.74 130 | 75.64 96 | 78.09 78 | 88.61 22 | 93.21 43 |
|
test0.0.03 1 | | | 57.35 165 | 59.89 157 | 54.38 172 | 71.37 124 | 73.45 164 | 52.71 180 | 61.03 95 | 46.11 147 | 26.33 182 | 41.73 134 | 44.08 138 | 29.72 186 | 71.43 142 | 70.90 154 | 85.10 104 | 71.56 181 |
|
tfpnnormal | | | 58.97 155 | 56.48 170 | 61.89 131 | 71.27 126 | 76.21 147 | 66.65 136 | 61.76 90 | 32.90 189 | 36.41 157 | 27.83 188 | 29.14 194 | 50.64 153 | 73.06 123 | 73.05 134 | 84.58 123 | 83.15 139 |
|
Fast-Effi-MVS+-dtu | | | 63.05 126 | 64.72 122 | 61.11 135 | 71.21 127 | 76.81 143 | 70.72 110 | 43.13 191 | 52.51 124 | 35.34 163 | 46.55 117 | 46.36 133 | 61.40 105 | 71.57 141 | 71.44 148 | 84.84 113 | 87.79 102 |
|
MDTV_nov1_ep13 | | | 65.21 110 | 67.28 106 | 62.79 123 | 70.91 128 | 81.72 94 | 69.28 118 | 49.50 170 | 58.08 97 | 43.94 119 | 50.50 94 | 56.02 103 | 58.86 122 | 70.72 146 | 73.37 126 | 84.24 128 | 80.52 152 |
|
FMVSNet3 | | | 70.41 79 | 71.89 78 | 68.68 85 | 70.89 129 | 79.42 120 | 75.63 67 | 60.97 97 | 65.32 75 | 51.06 89 | 47.37 106 | 62.05 69 | 64.90 85 | 82.49 33 | 82.27 32 | 88.64 21 | 84.34 127 |
|
Vis-MVSNet | | | 65.53 108 | 69.83 91 | 60.52 138 | 70.80 130 | 84.59 75 | 66.37 139 | 55.47 144 | 48.40 137 | 40.62 138 | 57.67 68 | 58.43 92 | 45.37 170 | 77.49 76 | 76.24 94 | 84.47 124 | 85.99 116 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CDS-MVSNet | | | 64.22 117 | 65.89 117 | 62.28 130 | 70.05 131 | 80.59 109 | 69.91 114 | 57.98 115 | 43.53 156 | 46.58 107 | 48.22 100 | 50.76 124 | 46.45 164 | 75.68 95 | 76.08 95 | 82.70 149 | 86.34 112 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
UGNet | | | 67.57 96 | 71.69 79 | 62.76 125 | 69.88 132 | 82.58 90 | 66.43 137 | 58.64 110 | 54.71 118 | 51.87 86 | 61.74 56 | 62.01 72 | 45.46 169 | 74.78 104 | 74.99 105 | 84.24 128 | 91.02 65 |
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 |
GA-MVS | | | 64.55 115 | 65.76 118 | 63.12 120 | 69.68 133 | 81.56 98 | 69.59 115 | 58.16 113 | 45.23 151 | 35.58 162 | 47.01 113 | 41.82 142 | 59.41 117 | 79.62 62 | 78.54 71 | 86.32 71 | 86.56 109 |
|
GBi-Net | | | 69.21 82 | 70.40 87 | 67.81 90 | 69.49 134 | 78.65 125 | 74.54 78 | 60.97 97 | 65.32 75 | 51.06 89 | 47.37 106 | 62.05 69 | 63.43 91 | 77.49 76 | 78.22 75 | 87.37 48 | 83.73 130 |
|
test1 | | | 69.21 82 | 70.40 87 | 67.81 90 | 69.49 134 | 78.65 125 | 74.54 78 | 60.97 97 | 65.32 75 | 51.06 89 | 47.37 106 | 62.05 69 | 63.43 91 | 77.49 76 | 78.22 75 | 87.37 48 | 83.73 130 |
|
FMVSNet2 | | | 68.06 92 | 68.57 97 | 67.45 94 | 69.49 134 | 78.65 125 | 74.54 78 | 60.23 105 | 56.29 105 | 49.64 98 | 42.13 132 | 57.08 99 | 63.43 91 | 81.15 52 | 80.99 52 | 87.37 48 | 83.73 130 |
|
UniMVSNet_NR-MVSNet | | | 62.30 132 | 63.51 127 | 60.89 136 | 69.48 137 | 77.83 133 | 64.07 145 | 63.94 66 | 50.03 129 | 31.17 173 | 44.82 119 | 41.12 146 | 51.37 149 | 71.02 143 | 74.81 108 | 85.30 100 | 84.95 122 |
|
gm-plane-assit | | | 54.99 170 | 57.99 166 | 51.49 178 | 69.27 138 | 54.42 202 | 32.32 205 | 42.59 192 | 21.18 205 | 13.71 200 | 23.61 193 | 43.84 139 | 60.21 112 | 87.09 5 | 86.55 5 | 90.81 4 | 89.28 86 |
|
PatchMatch-RL | | | 62.22 136 | 60.69 150 | 64.01 113 | 68.74 139 | 75.75 151 | 59.27 168 | 60.35 104 | 56.09 107 | 53.80 82 | 47.06 112 | 36.45 168 | 64.80 86 | 68.22 163 | 67.22 170 | 77.10 184 | 74.02 170 |
|
CR-MVSNet | | | 62.31 131 | 64.75 120 | 59.47 145 | 68.63 140 | 71.29 173 | 67.53 128 | 43.18 189 | 55.83 108 | 41.40 131 | 41.04 138 | 55.85 104 | 57.29 133 | 72.76 128 | 73.27 130 | 78.77 179 | 83.23 137 |
|
TranMVSNet+NR-MVSNet | | | 60.38 148 | 61.30 146 | 59.30 147 | 68.34 141 | 75.57 154 | 63.38 154 | 63.78 68 | 46.74 143 | 27.73 179 | 42.56 127 | 36.84 166 | 47.66 159 | 70.36 152 | 74.59 111 | 84.91 110 | 82.46 142 |
|
v8 | | | 63.44 124 | 62.58 136 | 64.43 109 | 68.28 142 | 78.07 130 | 71.82 94 | 54.85 150 | 46.70 144 | 45.20 112 | 39.40 146 | 40.91 148 | 60.54 109 | 72.85 127 | 74.39 115 | 85.92 81 | 85.76 118 |
|
v2v482 | | | 63.68 122 | 62.85 134 | 64.65 107 | 68.01 143 | 80.46 111 | 71.90 93 | 57.60 122 | 44.26 153 | 42.82 127 | 39.80 145 | 38.62 159 | 61.56 103 | 73.06 123 | 74.86 107 | 86.03 79 | 88.90 92 |
|
pm-mvs1 | | | 59.21 154 | 59.58 159 | 58.77 151 | 67.97 144 | 77.07 142 | 64.12 143 | 57.20 127 | 34.73 186 | 36.86 153 | 35.34 169 | 40.54 153 | 43.34 174 | 74.32 110 | 73.30 129 | 83.13 146 | 81.77 148 |
|
v10 | | | 63.00 127 | 62.22 139 | 63.90 116 | 67.88 145 | 77.78 134 | 71.59 98 | 54.34 154 | 45.37 150 | 42.76 128 | 38.53 148 | 38.93 157 | 61.05 107 | 74.39 108 | 74.52 113 | 85.75 85 | 86.04 114 |
|
v1144 | | | 63.00 127 | 62.39 138 | 63.70 117 | 67.72 146 | 80.27 112 | 71.23 102 | 56.40 131 | 42.51 158 | 40.81 136 | 38.12 153 | 37.73 160 | 60.42 111 | 74.46 106 | 74.55 112 | 85.64 96 | 89.12 88 |
|
UniMVSNet (Re) | | | 60.62 146 | 62.93 133 | 57.92 154 | 67.64 147 | 77.90 132 | 61.75 160 | 61.24 94 | 49.83 131 | 29.80 177 | 42.57 126 | 40.62 152 | 43.36 173 | 70.49 151 | 73.27 130 | 83.76 134 | 85.81 117 |
|
RPMNet | | | 58.63 159 | 62.80 135 | 53.76 174 | 67.59 148 | 71.29 173 | 54.60 178 | 38.13 201 | 55.83 108 | 35.70 161 | 41.58 135 | 53.04 119 | 47.89 158 | 66.10 168 | 67.38 168 | 78.65 181 | 84.40 126 |
|
v148 | | | 62.00 138 | 61.19 147 | 62.96 121 | 67.46 149 | 79.49 119 | 67.87 124 | 57.66 121 | 42.30 159 | 45.02 114 | 38.20 152 | 38.89 158 | 54.77 140 | 69.83 157 | 72.60 139 | 84.96 107 | 87.01 106 |
|
IterMVS | | | 61.87 139 | 63.55 126 | 59.90 141 | 67.29 150 | 72.20 168 | 67.34 131 | 48.56 172 | 47.48 140 | 37.86 151 | 47.07 111 | 48.27 128 | 54.08 142 | 72.12 134 | 73.71 121 | 84.30 127 | 83.99 129 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1192 | | | 62.25 133 | 61.64 143 | 62.96 121 | 66.88 151 | 79.72 116 | 69.96 113 | 55.77 138 | 41.58 163 | 39.42 140 | 37.05 158 | 35.96 173 | 60.50 110 | 74.30 111 | 74.09 117 | 85.24 102 | 88.76 93 |
|
DU-MVS | | | 60.87 145 | 61.82 142 | 59.76 143 | 66.69 152 | 75.87 148 | 64.07 145 | 61.96 84 | 49.31 132 | 31.17 173 | 42.76 123 | 36.95 165 | 51.37 149 | 69.67 158 | 73.20 133 | 83.30 141 | 84.95 122 |
|
Baseline_NR-MVSNet | | | 59.47 152 | 60.28 153 | 58.54 152 | 66.69 152 | 73.90 162 | 61.63 161 | 62.90 79 | 49.15 136 | 26.87 180 | 35.18 171 | 37.62 161 | 48.20 157 | 69.67 158 | 73.61 122 | 84.92 108 | 82.82 140 |
|
IterMVS-SCA-FT | | | 60.21 149 | 62.97 131 | 57.00 162 | 66.64 154 | 71.84 169 | 67.53 128 | 46.93 179 | 47.56 139 | 36.77 156 | 46.85 115 | 48.21 129 | 52.51 145 | 70.36 152 | 72.40 141 | 71.63 197 | 83.53 133 |
|
v144192 | | | 62.05 137 | 61.46 145 | 62.73 127 | 66.59 155 | 79.87 115 | 69.30 117 | 55.88 136 | 41.50 165 | 39.41 141 | 37.23 156 | 36.45 168 | 59.62 115 | 72.69 130 | 73.51 123 | 85.61 97 | 88.93 90 |
|
v1921920 | | | 61.66 140 | 61.10 148 | 62.31 129 | 66.32 156 | 79.57 118 | 68.41 122 | 55.49 143 | 41.03 166 | 38.69 145 | 36.64 164 | 35.27 176 | 59.60 116 | 73.23 121 | 73.41 125 | 85.37 99 | 88.51 97 |
|
TESTMET0.1,1 | | | 67.38 98 | 71.61 80 | 62.45 128 | 66.05 157 | 81.32 102 | 63.98 147 | 55.36 145 | 58.62 95 | 42.96 125 | 52.74 86 | 61.65 73 | 57.74 130 | 75.64 96 | 78.09 78 | 88.61 22 | 93.21 43 |
|
pmmvs4 | | | 63.14 125 | 62.46 137 | 63.94 115 | 66.03 158 | 76.40 145 | 66.82 134 | 57.60 122 | 56.74 101 | 50.26 96 | 40.81 140 | 37.51 162 | 59.26 119 | 71.75 139 | 71.48 147 | 83.68 137 | 82.53 141 |
|
PatchT | | | 60.46 147 | 63.85 125 | 56.51 164 | 65.95 159 | 75.68 152 | 47.34 188 | 41.39 196 | 53.89 121 | 41.40 131 | 37.84 154 | 50.30 126 | 57.29 133 | 72.76 128 | 73.27 130 | 85.67 92 | 83.23 137 |
|
v1240 | | | 61.09 143 | 60.55 152 | 61.72 133 | 65.92 160 | 79.28 121 | 67.16 132 | 54.91 149 | 39.79 171 | 38.10 148 | 36.08 166 | 34.64 178 | 59.15 120 | 72.86 126 | 73.36 127 | 85.10 104 | 87.84 101 |
|
ADS-MVSNet | | | 58.40 160 | 59.16 161 | 57.52 158 | 65.80 161 | 74.57 160 | 60.26 164 | 40.17 200 | 50.51 127 | 38.01 149 | 40.11 144 | 44.72 137 | 59.36 118 | 64.91 173 | 66.55 173 | 81.53 162 | 72.72 178 |
|
FMVSNet1 | | | 63.48 123 | 63.07 130 | 63.97 114 | 65.31 162 | 76.37 146 | 71.77 96 | 57.90 118 | 43.32 157 | 45.66 109 | 35.06 172 | 49.43 127 | 58.57 123 | 77.49 76 | 78.22 75 | 84.59 122 | 81.60 149 |
|
testgi | | | 48.51 189 | 50.53 187 | 46.16 190 | 64.78 163 | 67.15 184 | 41.54 198 | 54.81 151 | 29.12 196 | 17.03 192 | 32.07 178 | 31.98 186 | 20.15 199 | 65.26 172 | 67.00 172 | 78.67 180 | 61.10 200 |
|
LTVRE_ROB | | 47.26 16 | 49.41 187 | 49.91 190 | 48.82 182 | 64.76 164 | 69.79 176 | 49.05 184 | 47.12 178 | 20.36 207 | 16.52 194 | 36.65 163 | 26.96 197 | 50.76 152 | 60.47 183 | 63.16 185 | 64.73 200 | 72.00 179 |
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 |
Anonymous20231206 | | | 52.23 179 | 52.80 181 | 51.56 177 | 64.70 165 | 69.41 177 | 51.01 182 | 58.60 111 | 36.63 178 | 22.44 188 | 21.80 198 | 31.42 189 | 30.52 185 | 66.79 167 | 67.83 167 | 82.10 157 | 75.73 164 |
|
thisisatest0515 | | | 59.37 153 | 60.68 151 | 57.84 156 | 64.39 166 | 75.65 153 | 58.56 171 | 53.86 156 | 41.55 164 | 42.12 130 | 40.40 142 | 39.59 155 | 47.09 162 | 71.69 140 | 73.79 120 | 81.02 166 | 82.08 146 |
|
USDC | | | 59.69 151 | 60.03 156 | 59.28 148 | 64.04 167 | 71.84 169 | 63.15 156 | 55.36 145 | 54.90 116 | 35.02 164 | 48.34 99 | 29.79 193 | 58.16 124 | 70.60 148 | 71.33 152 | 79.99 172 | 73.42 174 |
|
WR-MVS | | | 51.02 181 | 54.56 174 | 46.90 188 | 63.84 168 | 69.23 178 | 44.78 195 | 56.38 132 | 38.19 175 | 14.19 198 | 37.38 155 | 36.82 167 | 22.39 195 | 60.14 184 | 66.20 177 | 79.81 173 | 73.95 172 |
|
our_test_3 | | | | | | 63.32 169 | 71.07 175 | 55.90 176 | | | | | | | | | | |
|
test20.03 | | | 47.23 192 | 48.69 192 | 45.53 192 | 63.28 170 | 64.39 190 | 41.01 199 | 56.93 130 | 29.16 195 | 15.21 197 | 23.90 192 | 30.76 192 | 17.51 202 | 64.63 176 | 65.26 178 | 79.21 178 | 62.71 197 |
|
UniMVSNet_ETH3D | | | 57.83 161 | 56.46 171 | 59.43 146 | 63.24 171 | 73.22 165 | 67.70 125 | 55.58 141 | 36.17 181 | 36.84 154 | 32.64 175 | 35.14 177 | 51.50 148 | 65.81 169 | 69.81 162 | 81.73 160 | 82.44 144 |
|
pmmvs6 | | | 54.20 175 | 53.54 177 | 54.97 168 | 63.22 172 | 72.98 166 | 60.17 165 | 52.32 165 | 26.77 200 | 34.30 166 | 23.29 195 | 36.23 170 | 40.33 179 | 68.77 162 | 68.76 165 | 79.47 177 | 78.00 160 |
|
v7n | | | 57.04 166 | 56.64 169 | 57.52 158 | 62.85 173 | 74.75 158 | 61.76 159 | 51.80 166 | 35.58 185 | 36.02 160 | 32.33 177 | 33.61 184 | 50.16 154 | 67.73 165 | 70.34 160 | 82.51 151 | 82.12 145 |
|
pmmvs5 | | | 59.72 150 | 60.24 154 | 59.11 149 | 62.77 174 | 77.33 140 | 63.17 155 | 54.00 155 | 40.21 169 | 37.23 152 | 40.41 141 | 35.99 172 | 51.75 147 | 72.55 132 | 72.74 138 | 85.72 90 | 82.45 143 |
|
CVMVSNet | | | 54.92 172 | 58.16 163 | 51.13 179 | 62.61 175 | 68.44 180 | 55.45 177 | 52.38 164 | 42.28 160 | 21.45 189 | 47.10 110 | 46.10 134 | 37.96 181 | 64.42 178 | 63.81 182 | 76.92 185 | 75.01 167 |
|
TAMVS | | | 58.86 156 | 60.91 149 | 56.47 165 | 62.38 176 | 77.57 136 | 58.97 170 | 52.98 161 | 38.76 174 | 36.17 158 | 42.26 131 | 47.94 130 | 46.45 164 | 70.23 154 | 70.79 156 | 81.86 159 | 78.82 158 |
|
DTE-MVSNet | | | 49.82 185 | 51.92 185 | 47.37 187 | 61.75 177 | 64.38 191 | 45.89 194 | 57.33 126 | 36.11 182 | 12.79 203 | 36.87 160 | 31.93 188 | 25.73 193 | 58.01 186 | 65.22 179 | 80.75 169 | 70.93 184 |
|
PEN-MVS | | | 51.04 180 | 52.94 179 | 48.82 182 | 61.45 178 | 66.00 186 | 48.68 185 | 57.20 127 | 36.87 177 | 15.36 196 | 36.98 159 | 32.72 185 | 28.77 190 | 57.63 188 | 66.37 174 | 81.44 163 | 74.00 171 |
|
V42 | | | 62.86 129 | 62.97 131 | 62.74 126 | 60.84 179 | 78.99 123 | 71.46 100 | 57.13 129 | 46.85 142 | 44.28 118 | 38.87 147 | 40.73 151 | 57.63 132 | 72.60 131 | 74.14 116 | 85.09 106 | 88.63 94 |
|
MDTV_nov1_ep13_2view | | | 54.47 174 | 54.61 173 | 54.30 173 | 60.50 180 | 73.82 163 | 57.92 172 | 43.38 188 | 39.43 173 | 32.51 171 | 33.23 174 | 34.05 181 | 47.26 161 | 62.36 181 | 66.21 176 | 84.24 128 | 73.19 176 |
|
MVS-HIRNet | | | 53.86 176 | 53.02 178 | 54.85 169 | 60.30 181 | 72.36 167 | 44.63 196 | 42.20 194 | 39.45 172 | 43.47 121 | 21.66 199 | 34.00 182 | 55.47 137 | 65.42 171 | 67.16 171 | 83.02 147 | 71.08 183 |
|
CHOSEN 280x420 | | | 62.23 135 | 66.57 110 | 57.17 161 | 59.88 182 | 68.92 179 | 61.20 163 | 42.28 193 | 54.17 119 | 39.57 139 | 47.78 103 | 64.97 61 | 62.68 96 | 73.85 115 | 69.52 164 | 77.43 183 | 86.75 107 |
|
TinyColmap | | | 52.66 178 | 50.09 189 | 55.65 166 | 59.72 183 | 64.02 193 | 57.15 174 | 52.96 162 | 40.28 168 | 32.51 171 | 32.42 176 | 20.97 204 | 56.65 135 | 63.95 179 | 65.15 180 | 74.91 190 | 63.87 194 |
|
FC-MVSNet-test | | | 47.24 191 | 54.37 175 | 38.93 197 | 59.49 184 | 58.25 200 | 34.48 204 | 53.36 159 | 45.66 149 | 6.66 208 | 50.62 92 | 42.02 141 | 16.62 203 | 58.39 185 | 61.21 189 | 62.99 201 | 64.40 193 |
|
test-mter | | | 64.06 119 | 69.24 92 | 58.01 153 | 59.07 185 | 77.40 138 | 59.13 169 | 48.11 174 | 55.64 111 | 39.18 143 | 51.56 91 | 58.54 89 | 55.38 138 | 73.52 119 | 76.00 96 | 87.22 57 | 92.05 56 |
|
WR-MVS_H | | | 49.62 186 | 52.63 182 | 46.11 191 | 58.80 186 | 67.58 182 | 46.14 193 | 54.94 147 | 36.51 179 | 13.63 201 | 36.75 162 | 35.67 175 | 22.10 196 | 56.43 192 | 62.76 186 | 81.06 165 | 72.73 177 |
|
CP-MVSNet | | | 50.57 182 | 52.60 183 | 48.21 185 | 58.77 187 | 65.82 187 | 48.17 186 | 56.29 133 | 37.41 176 | 16.59 193 | 37.14 157 | 31.95 187 | 29.21 187 | 56.60 191 | 63.71 183 | 80.22 170 | 75.56 165 |
|
PS-CasMVS | | | 50.17 183 | 52.02 184 | 48.02 186 | 58.60 188 | 65.54 188 | 48.04 187 | 56.19 135 | 36.42 180 | 16.42 195 | 35.68 168 | 31.33 190 | 28.85 189 | 56.42 193 | 63.54 184 | 80.01 171 | 75.18 166 |
|
SixPastTwentyTwo | | | 49.11 188 | 49.22 191 | 48.99 181 | 58.54 189 | 64.14 192 | 47.18 189 | 47.75 175 | 31.15 193 | 24.42 184 | 41.01 139 | 26.55 198 | 44.04 172 | 54.76 196 | 58.70 193 | 71.99 196 | 68.21 185 |
|
TDRefinement | | | 52.70 177 | 51.02 186 | 54.66 171 | 57.41 190 | 65.06 189 | 61.47 162 | 54.94 147 | 44.03 154 | 33.93 167 | 30.13 184 | 27.57 196 | 46.17 166 | 61.86 182 | 62.48 188 | 74.01 193 | 66.06 190 |
|
pmmvs-eth3d | | | 55.20 167 | 53.95 176 | 56.65 163 | 57.34 191 | 67.77 181 | 57.54 173 | 53.74 157 | 40.93 167 | 41.09 135 | 31.19 181 | 29.10 195 | 49.07 155 | 65.54 170 | 67.28 169 | 81.14 164 | 75.81 163 |
|
FPMVS | | | 39.11 198 | 36.39 200 | 42.28 193 | 55.97 192 | 45.94 205 | 46.23 192 | 41.57 195 | 35.73 184 | 22.61 186 | 23.46 194 | 19.82 206 | 28.32 191 | 43.57 200 | 40.67 202 | 58.96 203 | 45.54 203 |
|
MIMVSNet | | | 57.78 163 | 59.71 158 | 55.53 167 | 54.79 193 | 77.10 141 | 63.89 149 | 45.02 183 | 46.59 145 | 36.79 155 | 28.36 187 | 40.77 150 | 45.84 168 | 74.97 101 | 76.58 88 | 86.87 64 | 73.60 173 |
|
N_pmnet | | | 47.67 190 | 47.00 194 | 48.45 184 | 54.72 194 | 62.78 194 | 46.95 190 | 51.25 167 | 36.01 183 | 26.09 183 | 26.59 191 | 25.93 201 | 35.50 183 | 55.67 195 | 59.01 191 | 76.22 186 | 63.04 195 |
|
anonymousdsp | | | 54.99 170 | 57.24 167 | 52.36 175 | 53.82 195 | 71.75 172 | 51.49 181 | 48.14 173 | 33.74 187 | 33.66 168 | 38.34 150 | 36.13 171 | 47.54 160 | 64.53 177 | 70.60 158 | 79.53 176 | 85.59 120 |
|
new-patchmatchnet | | | 42.21 195 | 42.97 196 | 41.33 195 | 53.05 196 | 59.89 197 | 39.38 200 | 49.61 169 | 28.26 198 | 12.10 204 | 22.17 197 | 21.54 203 | 19.22 200 | 50.96 198 | 56.04 196 | 74.61 192 | 61.92 198 |
|
FMVSNet5 | | | 58.86 156 | 60.24 154 | 57.25 160 | 52.66 197 | 66.25 185 | 63.77 150 | 52.86 163 | 57.85 99 | 37.92 150 | 36.12 165 | 52.22 121 | 51.37 149 | 70.88 145 | 71.43 149 | 84.92 108 | 66.91 189 |
|
ET-MVSNet_ETH3D | | | 71.38 74 | 74.70 64 | 67.51 93 | 51.61 198 | 88.06 45 | 77.29 61 | 60.95 100 | 63.61 80 | 48.36 102 | 66.60 45 | 60.67 80 | 79.55 6 | 73.56 118 | 80.58 58 | 87.30 53 | 89.80 80 |
|
ambc | | | | 42.30 197 | | 50.36 199 | 49.51 204 | 35.47 203 | | 32.04 192 | 23.53 185 | 17.36 202 | 8.95 211 | 29.06 188 | 64.88 174 | 56.26 195 | 61.29 202 | 67.12 188 |
|
EU-MVSNet | | | 44.84 193 | 47.85 193 | 41.32 196 | 49.26 200 | 56.59 201 | 43.07 197 | 47.64 177 | 33.03 188 | 13.82 199 | 36.78 161 | 30.99 191 | 24.37 194 | 53.80 197 | 55.57 197 | 69.78 198 | 68.21 185 |
|
RPSCF | | | 55.07 169 | 58.06 164 | 51.57 176 | 48.87 201 | 58.95 198 | 53.68 179 | 41.26 198 | 62.42 82 | 45.88 108 | 54.38 82 | 54.26 113 | 53.75 143 | 57.15 189 | 53.53 199 | 66.01 199 | 65.75 191 |
|
PMVS | | 27.44 18 | 32.08 200 | 29.07 202 | 35.60 199 | 48.33 202 | 24.79 208 | 26.97 207 | 41.34 197 | 20.45 206 | 22.50 187 | 17.11 204 | 18.64 207 | 20.44 198 | 41.99 202 | 38.06 203 | 54.02 205 | 42.44 204 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PM-MVS | | | 50.11 184 | 50.38 188 | 49.80 180 | 47.23 203 | 62.08 196 | 50.91 183 | 44.84 185 | 41.90 161 | 36.10 159 | 35.22 170 | 26.05 200 | 46.83 163 | 57.64 187 | 55.42 198 | 72.90 194 | 74.32 169 |
|
pmmvs3 | | | 41.86 196 | 42.29 198 | 41.36 194 | 39.80 204 | 52.66 203 | 38.93 202 | 35.85 205 | 23.40 204 | 20.22 191 | 19.30 200 | 20.84 205 | 40.56 178 | 55.98 194 | 58.79 192 | 72.80 195 | 65.03 192 |
|
MDA-MVSNet-bldmvs | | | 44.15 194 | 42.27 199 | 46.34 189 | 38.34 205 | 62.31 195 | 46.28 191 | 55.74 139 | 29.83 194 | 20.98 190 | 27.11 190 | 16.45 209 | 41.98 176 | 41.11 203 | 57.47 194 | 74.72 191 | 61.65 199 |
|
MIMVSNet1 | | | 40.84 197 | 43.46 195 | 37.79 198 | 32.14 206 | 58.92 199 | 39.24 201 | 50.83 168 | 27.00 199 | 11.29 205 | 16.76 205 | 26.53 199 | 17.75 201 | 57.14 190 | 61.12 190 | 75.46 188 | 56.78 201 |
|
Gipuma | | | 24.91 201 | 24.61 203 | 25.26 202 | 31.47 207 | 21.59 209 | 18.06 208 | 37.53 202 | 25.43 202 | 10.03 206 | 4.18 210 | 4.25 213 | 14.85 204 | 43.20 201 | 47.03 200 | 39.62 207 | 26.55 208 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
E-PMN | | | 15.08 203 | 11.65 206 | 19.08 203 | 28.73 208 | 12.31 212 | 6.95 213 | 36.87 204 | 10.71 210 | 3.63 211 | 5.13 207 | 2.22 216 | 13.81 206 | 11.34 208 | 18.50 207 | 24.49 209 | 21.32 209 |
|
EMVS | | | 14.40 204 | 10.71 207 | 18.70 204 | 28.15 209 | 12.09 213 | 7.06 212 | 36.89 203 | 11.00 209 | 3.56 212 | 4.95 208 | 2.27 215 | 13.91 205 | 10.13 209 | 16.06 208 | 22.63 210 | 18.51 210 |
|
new_pmnet | | | 33.19 199 | 35.52 201 | 30.47 200 | 27.55 210 | 45.31 206 | 29.29 206 | 30.92 206 | 29.00 197 | 9.88 207 | 18.77 201 | 17.64 208 | 26.77 192 | 44.07 199 | 45.98 201 | 58.41 204 | 47.87 202 |
|
PMMVS2 | | | 20.45 202 | 22.31 204 | 18.27 205 | 20.52 211 | 26.73 207 | 14.85 210 | 28.43 208 | 13.69 208 | 0.79 213 | 10.35 206 | 9.10 210 | 3.83 209 | 27.64 205 | 32.87 204 | 41.17 206 | 35.81 205 |
|
tmp_tt | | | | | 16.09 206 | 13.07 212 | 8.12 214 | 13.61 211 | 2.08 210 | 55.09 113 | 30.10 176 | 40.26 143 | 22.83 202 | 5.35 208 | 29.91 204 | 25.25 206 | 32.33 208 | |
|
MVE | | 15.98 19 | 14.37 205 | 16.36 205 | 12.04 207 | 7.72 213 | 20.24 210 | 5.90 214 | 29.05 207 | 8.28 211 | 3.92 210 | 4.72 209 | 2.42 214 | 9.57 207 | 18.89 207 | 31.46 205 | 16.07 212 | 28.53 207 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
GG-mvs-BLEND | | | 54.54 173 | 77.58 46 | 27.67 201 | 0.03 214 | 90.09 26 | 77.20 62 | 0.02 211 | 66.83 70 | 0.05 214 | 59.90 64 | 73.33 33 | 0.04 210 | 78.40 72 | 79.30 65 | 88.65 20 | 95.20 24 |
|
sosnet-low-res | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 215 | 0.00 217 | 0.00 218 | 0.00 213 | 0.00 214 | 0.00 215 | 0.00 213 | 0.00 218 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 214 | 0.00 213 |
|
sosnet | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 215 | 0.00 217 | 0.00 218 | 0.00 213 | 0.00 214 | 0.00 215 | 0.00 213 | 0.00 218 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 214 | 0.00 213 |
|
testmvs | | | 0.05 206 | 0.08 208 | 0.01 208 | 0.00 215 | 0.01 215 | 0.03 216 | 0.01 212 | 0.05 212 | 0.00 215 | 0.14 212 | 0.01 217 | 0.03 212 | 0.05 210 | 0.05 209 | 0.01 213 | 0.24 212 |
|
test123 | | | 0.05 206 | 0.08 208 | 0.01 208 | 0.00 215 | 0.01 215 | 0.01 217 | 0.00 213 | 0.05 212 | 0.00 215 | 0.16 211 | 0.00 218 | 0.04 210 | 0.02 211 | 0.05 209 | 0.00 214 | 0.26 211 |
|
test_part1 | | | | | | | | | | | | | | | | | | 97.29 4 |
|
MTAPA | | | | | | | | | | | 78.32 8 | | 79.42 21 | | | | | |
|
MTMP | | | | | | | | | | | 76.04 13 | | 76.65 26 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.17 215 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 81.60 33 | | | | | | | | |
|
Patchmtry | | | | | | | 78.06 131 | 67.53 128 | 43.18 189 | | 41.40 131 | | | | | | | |
|
DeepMVS_CX | | | | | | | 19.81 211 | 17.01 209 | 10.02 209 | 23.61 203 | 5.85 209 | 17.21 203 | 8.03 212 | 21.13 197 | 22.60 206 | | 21.42 211 | 30.01 206 |
|