SteuartSystems-ACMMP | | | 88.16 1 | 88.22 2 | 87.98 2 | 92.00 14 | 72.76 4 | 92.99 3 | 93.57 4 | 79.38 2 | 87.27 2 | 94.47 1 | 71.72 4 | 96.63 4 | 87.17 1 | 95.44 3 | 94.56 4 |
Skip Steuart: Steuart Systems R&D Blog. |
DeepPCF-MVS | | 80.84 1 | 88.10 2 | 88.56 1 | 86.73 9 | 92.24 13 | 69.03 20 | 89.57 21 | 93.39 6 | 77.53 4 | 89.79 1 | 94.12 4 | 78.98 1 | 96.58 5 | 85.66 2 | 95.72 2 | 94.58 3 |
|
CP-MVS | | | 87.11 3 | 86.92 3 | 87.68 5 | 94.20 2 | 73.86 1 | 93.98 1 | 92.82 9 | 76.62 9 | 83.68 5 | 94.46 2 | 67.93 10 | 95.95 9 | 84.20 4 | 94.39 6 | 93.23 11 |
|
DeepC-MVS | | 79.81 2 | 87.08 4 | 86.88 4 | 87.69 4 | 91.16 18 | 72.32 9 | 90.31 15 | 93.94 2 | 77.12 6 | 82.82 7 | 94.23 3 | 72.13 3 | 97.09 1 | 84.83 3 | 95.37 4 | 93.65 8 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepC-MVS_fast | | 79.65 3 | 86.91 5 | 86.62 5 | 87.76 3 | 93.52 6 | 72.37 8 | 91.26 10 | 93.04 8 | 76.62 9 | 84.22 4 | 93.36 7 | 71.44 5 | 96.76 2 | 80.82 9 | 95.33 5 | 94.16 6 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PHI-MVS | | | 86.43 6 | 86.17 7 | 87.24 7 | 90.88 21 | 70.96 13 | 92.27 6 | 94.07 1 | 72.45 24 | 85.22 3 | 91.90 15 | 69.47 7 | 96.42 6 | 83.28 5 | 95.94 1 | 94.35 5 |
|
CSCG | | | 86.41 7 | 86.19 6 | 87.07 8 | 92.91 10 | 72.48 7 | 90.81 12 | 93.56 5 | 73.95 21 | 83.16 6 | 91.07 20 | 75.94 2 | 95.19 12 | 79.94 11 | 94.38 7 | 93.55 9 |
|
ACMMP | | | 85.89 8 | 85.39 8 | 87.38 6 | 93.59 5 | 72.63 6 | 92.74 4 | 93.18 7 | 76.78 8 | 80.73 11 | 93.82 5 | 64.33 21 | 96.29 7 | 82.67 6 | 90.69 20 | 93.23 11 |
|
3Dnovator+ | | 77.84 4 | 85.48 9 | 84.47 10 | 88.51 1 | 91.08 19 | 73.49 2 | 93.18 2 | 93.78 3 | 80.79 1 | 76.66 35 | 93.37 6 | 60.40 43 | 96.75 3 | 77.20 13 | 93.73 8 | 95.29 1 |
|
MVS_111021_HR | | | 85.14 10 | 84.75 9 | 86.32 11 | 91.65 16 | 72.70 5 | 85.98 48 | 90.33 31 | 76.11 12 | 82.08 8 | 91.61 17 | 71.36 6 | 94.17 32 | 81.02 7 | 92.58 10 | 92.08 21 |
|
CPTT-MVS | | | 83.73 11 | 83.33 12 | 84.92 18 | 93.28 8 | 70.86 15 | 92.09 7 | 90.38 30 | 68.75 47 | 79.57 14 | 92.83 9 | 60.60 42 | 93.04 47 | 80.92 8 | 91.56 15 | 90.86 27 |
|
Vis-MVSNet |  | | 83.46 12 | 82.80 15 | 85.43 13 | 90.25 26 | 68.74 24 | 90.30 16 | 90.13 32 | 76.33 11 | 80.87 10 | 92.89 8 | 61.00 40 | 94.20 31 | 72.45 24 | 90.97 18 | 93.35 10 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MG-MVS | | | 83.41 13 | 83.45 11 | 83.28 36 | 92.74 11 | 62.28 68 | 88.17 36 | 89.50 34 | 75.22 14 | 81.49 9 | 92.74 11 | 66.75 13 | 95.11 14 | 72.85 21 | 91.58 14 | 92.45 17 |
|
EPP-MVSNet | | | 83.40 14 | 83.02 13 | 84.57 21 | 90.13 27 | 64.47 54 | 92.32 5 | 90.73 29 | 74.45 18 | 79.35 16 | 91.10 18 | 69.05 9 | 95.12 13 | 72.78 22 | 87.22 34 | 94.13 7 |
|
3Dnovator | | 76.31 5 | 83.38 15 | 82.31 17 | 86.59 10 | 87.94 53 | 72.94 3 | 90.64 13 | 92.14 13 | 77.21 5 | 75.47 40 | 92.83 9 | 58.56 44 | 94.72 22 | 73.24 19 | 92.71 9 | 92.13 20 |
|
IS-MVSNet | | | 83.15 16 | 82.81 14 | 84.18 27 | 89.94 28 | 63.30 59 | 91.59 8 | 88.46 51 | 79.04 3 | 79.49 15 | 92.16 12 | 65.10 20 | 94.28 28 | 67.71 31 | 91.86 12 | 94.95 2 |
|
DP-MVS Recon | | | 83.11 17 | 82.09 19 | 86.15 12 | 94.44 1 | 70.92 14 | 88.79 26 | 92.20 12 | 70.53 32 | 79.17 17 | 91.03 22 | 64.12 23 | 96.03 8 | 68.39 30 | 90.14 22 | 91.50 23 |
|
PAPM_NR | | | 83.02 18 | 82.41 16 | 84.82 20 | 92.47 12 | 66.37 39 | 87.93 39 | 91.80 17 | 73.82 22 | 77.32 30 | 90.66 25 | 67.90 11 | 94.90 18 | 70.37 27 | 89.48 25 | 93.19 13 |
|
OMC-MVS | | | 82.69 19 | 81.97 20 | 84.85 19 | 88.75 41 | 67.42 32 | 87.98 38 | 90.87 28 | 74.92 15 | 79.72 13 | 91.65 16 | 62.19 34 | 93.96 33 | 75.26 15 | 86.42 41 | 93.16 14 |
|
PVSNet_Blended_VisFu | | | 82.62 20 | 81.83 21 | 84.96 17 | 90.80 22 | 69.76 18 | 88.74 27 | 91.70 19 | 69.39 41 | 78.96 19 | 88.46 40 | 65.47 18 | 94.87 20 | 74.42 17 | 88.57 28 | 90.24 37 |
|
MVS_111021_LR | | | 82.61 21 | 82.11 18 | 84.11 28 | 88.82 39 | 71.58 10 | 85.15 52 | 86.16 64 | 74.69 16 | 80.47 12 | 91.04 21 | 62.29 32 | 90.55 68 | 80.33 10 | 90.08 23 | 90.20 38 |
|
CLD-MVS | | | 82.31 22 | 81.65 22 | 84.29 24 | 88.47 47 | 67.73 31 | 85.81 51 | 92.35 11 | 75.78 13 | 78.33 24 | 86.58 63 | 64.01 24 | 94.35 27 | 76.05 14 | 87.48 32 | 90.79 28 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
API-MVS | | | 81.99 23 | 81.23 23 | 84.26 25 | 90.94 20 | 70.18 16 | 91.10 11 | 89.32 37 | 71.51 29 | 78.66 22 | 88.28 43 | 65.26 19 | 95.10 15 | 64.74 40 | 91.23 16 | 87.51 66 |
|
MAR-MVS | | | 81.84 24 | 80.70 25 | 85.27 14 | 91.32 17 | 71.53 11 | 89.82 19 | 90.92 27 | 69.77 39 | 78.50 23 | 86.21 66 | 62.36 31 | 94.52 26 | 65.36 36 | 92.05 11 | 89.77 45 |
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 |
PAPR | | | 81.66 25 | 80.89 24 | 83.99 31 | 90.27 25 | 64.00 56 | 86.76 43 | 91.77 18 | 68.84 46 | 77.13 33 | 89.50 33 | 67.63 12 | 94.88 19 | 67.55 32 | 88.52 29 | 93.09 15 |
|
ACMP | | 74.13 6 | 81.51 26 | 80.57 26 | 84.36 23 | 89.42 30 | 68.69 25 | 89.97 18 | 91.50 22 | 74.46 17 | 75.04 51 | 90.41 27 | 53.82 61 | 94.54 24 | 77.56 12 | 82.91 46 | 89.86 44 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PVSNet_Blended | | | 80.98 27 | 80.34 27 | 82.90 42 | 88.85 37 | 65.40 47 | 84.43 59 | 92.00 14 | 67.62 50 | 78.11 26 | 85.05 73 | 66.02 16 | 94.27 29 | 71.52 25 | 89.50 24 | 89.01 51 |
|
QAPM | | | 80.88 28 | 79.50 33 | 85.03 15 | 88.01 52 | 68.97 22 | 91.59 8 | 92.00 14 | 66.63 54 | 75.15 49 | 92.16 12 | 57.70 46 | 95.45 11 | 63.52 41 | 88.76 27 | 90.66 30 |
|
UGNet | | | 80.83 29 | 79.59 31 | 84.54 22 | 88.04 51 | 68.09 28 | 89.42 22 | 88.16 52 | 76.95 7 | 76.22 37 | 89.46 34 | 49.30 77 | 93.94 35 | 68.48 29 | 90.31 21 | 91.60 22 |
|
ACMM | | 73.20 8 | 80.78 30 | 79.84 30 | 83.58 32 | 89.31 32 | 68.37 26 | 89.99 17 | 91.60 20 | 70.28 35 | 77.25 31 | 89.66 31 | 53.37 63 | 93.53 40 | 74.24 18 | 82.85 47 | 88.85 54 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
DELS-MVS | | | 80.73 31 | 80.11 28 | 82.61 44 | 84.88 71 | 63.68 57 | 81.16 73 | 91.08 25 | 66.55 55 | 78.93 20 | 85.10 72 | 69.06 8 | 92.86 49 | 73.22 20 | 90.99 17 | 91.27 24 |
|
Deformable NCC | | | 80.68 32 | 79.51 32 | 84.20 26 | 94.09 3 | 67.27 36 | 89.64 20 | 91.11 24 | 58.75 96 | 74.08 56 | 90.72 24 | 58.10 45 | 95.04 17 | 69.70 28 | 89.42 26 | 90.30 36 |
|
PVSNet_BlendedMVS | | | 80.60 33 | 80.02 29 | 82.36 47 | 88.85 37 | 65.40 47 | 86.16 46 | 92.00 14 | 69.34 42 | 78.11 26 | 86.09 67 | 66.02 16 | 94.27 29 | 71.52 25 | 82.06 53 | 87.39 68 |
|
AdaColmap |  | | 80.58 34 | 79.42 34 | 84.06 30 | 93.09 9 | 68.91 23 | 89.36 23 | 88.97 47 | 69.27 43 | 75.70 39 | 89.69 30 | 57.20 49 | 95.77 10 | 63.06 42 | 88.41 30 | 87.50 67 |
|
PCF-MVS | | 73.52 7 | 80.38 35 | 78.84 37 | 85.01 16 | 87.71 55 | 68.99 21 | 83.65 64 | 91.46 23 | 63.00 74 | 77.77 29 | 90.28 28 | 66.10 15 | 95.09 16 | 61.40 52 | 88.22 31 | 90.94 26 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
IterMVS-LS | | | 80.06 36 | 79.38 35 | 82.11 48 | 85.89 67 | 63.20 61 | 86.79 42 | 89.34 36 | 74.19 19 | 75.45 41 | 86.72 57 | 66.62 14 | 92.39 54 | 72.58 23 | 76.86 81 | 90.75 29 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
OpenMVS |  | 72.83 10 | 79.77 37 | 78.33 41 | 84.09 29 | 85.17 69 | 69.91 17 | 90.57 14 | 90.97 26 | 66.70 53 | 72.17 65 | 91.91 14 | 54.70 56 | 93.96 33 | 61.81 51 | 90.95 19 | 88.41 58 |
|
BH-RMVSNet | | | 79.61 38 | 78.44 40 | 83.14 38 | 89.38 31 | 65.93 42 | 84.95 54 | 87.15 58 | 73.56 23 | 78.19 25 | 89.79 29 | 56.67 50 | 93.36 41 | 59.53 62 | 86.74 39 | 90.13 39 |
|
ab-mvs | | | 79.51 39 | 78.97 36 | 81.14 60 | 88.46 48 | 60.91 73 | 83.84 62 | 89.24 41 | 70.36 34 | 79.03 18 | 88.87 37 | 63.23 27 | 90.21 70 | 65.12 37 | 82.57 51 | 92.28 18 |
|
BH-untuned | | | 79.47 40 | 78.60 38 | 82.05 49 | 89.19 35 | 65.91 43 | 86.07 47 | 88.52 50 | 72.18 25 | 75.42 42 | 87.69 47 | 61.15 39 | 93.54 39 | 60.38 58 | 86.83 38 | 86.70 80 |
|
TAPA-MVS | | 73.13 9 | 79.15 41 | 77.94 42 | 82.79 43 | 89.59 29 | 62.99 65 | 88.16 37 | 91.51 21 | 65.77 60 | 77.14 32 | 91.09 19 | 60.91 41 | 93.21 42 | 50.26 93 | 87.05 36 | 92.17 19 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CDS-MVSNet | | | 79.07 42 | 77.70 44 | 83.17 37 | 87.60 56 | 68.23 27 | 84.40 60 | 86.20 63 | 67.49 52 | 76.36 36 | 86.54 64 | 61.54 35 | 90.79 67 | 61.86 50 | 87.33 33 | 90.49 32 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
MVSTER | | | 79.01 43 | 77.88 43 | 82.38 46 | 83.07 80 | 64.80 53 | 84.08 61 | 88.95 48 | 69.01 45 | 78.69 21 | 87.17 55 | 54.70 56 | 92.43 53 | 74.69 16 | 80.57 64 | 89.89 43 |
|
TAMVS | | | 78.89 44 | 77.51 45 | 83.03 40 | 87.80 54 | 67.79 30 | 84.72 57 | 85.05 68 | 67.63 49 | 76.75 34 | 87.70 46 | 62.25 33 | 90.82 66 | 58.53 65 | 87.13 35 | 90.49 32 |
|
GBi-Net | | | 78.40 45 | 77.40 46 | 81.40 55 | 87.60 56 | 63.01 62 | 88.39 31 | 89.28 38 | 71.63 26 | 75.34 44 | 87.28 50 | 54.80 53 | 91.11 59 | 62.72 43 | 79.57 67 | 90.09 40 |
|
test1 | | | 78.40 45 | 77.40 46 | 81.40 55 | 87.60 56 | 63.01 62 | 88.39 31 | 89.28 38 | 71.63 26 | 75.34 44 | 87.28 50 | 54.80 53 | 91.11 59 | 62.72 43 | 79.57 67 | 90.09 40 |
|
Vis-MVSNet (Re-imp) | | | 78.36 47 | 78.45 39 | 78.07 79 | 88.64 43 | 51.78 100 | 86.70 44 | 79.63 96 | 74.14 20 | 75.11 50 | 90.83 23 | 61.29 38 | 89.75 72 | 58.10 69 | 91.60 13 | 92.69 16 |
|
BH-w/o | | | 78.21 48 | 77.33 48 | 80.84 62 | 88.81 40 | 65.13 52 | 84.87 55 | 87.85 54 | 69.75 40 | 74.52 54 | 84.74 75 | 61.34 36 | 93.11 46 | 58.24 68 | 85.84 42 | 84.27 92 |
|
FMVSNet2 | | | 78.20 49 | 77.21 49 | 81.20 58 | 87.60 56 | 62.89 66 | 87.47 41 | 89.02 45 | 71.63 26 | 75.29 48 | 87.28 50 | 54.80 53 | 91.10 62 | 62.38 47 | 79.38 70 | 89.61 47 |
|
CNLPA | | | 78.08 50 | 76.79 52 | 81.97 51 | 90.40 24 | 71.07 12 | 87.59 40 | 84.55 69 | 66.03 59 | 72.38 64 | 89.64 32 | 57.56 47 | 86.04 85 | 59.61 61 | 83.35 45 | 88.79 55 |
|
PLC |  | 70.83 11 | 78.05 51 | 76.37 55 | 83.08 39 | 91.88 15 | 67.80 29 | 88.19 35 | 89.46 35 | 64.33 69 | 69.87 77 | 88.38 41 | 53.66 62 | 93.58 38 | 58.86 63 | 82.73 49 | 87.86 62 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
HY-MVS | | 69.67 12 | 77.95 52 | 77.15 50 | 80.36 66 | 87.57 60 | 60.21 77 | 83.37 67 | 87.78 55 | 66.11 57 | 75.37 43 | 87.06 56 | 63.27 25 | 90.48 69 | 61.38 53 | 82.43 52 | 90.40 35 |
|
FMVSNet3 | | | 77.88 53 | 76.85 51 | 80.97 61 | 86.84 63 | 62.36 67 | 86.52 45 | 88.77 49 | 71.13 30 | 75.34 44 | 86.66 62 | 54.07 59 | 91.10 62 | 62.72 43 | 79.57 67 | 89.45 48 |
|
PAPM | | | 77.68 54 | 76.40 54 | 81.51 53 | 87.29 61 | 61.85 70 | 83.78 63 | 89.59 33 | 64.74 65 | 71.23 68 | 88.70 38 | 62.59 29 | 93.66 37 | 52.66 87 | 87.03 37 | 89.01 51 |
|
TR-MVS | | | 77.44 55 | 76.18 56 | 81.20 58 | 88.24 49 | 63.24 60 | 84.61 58 | 86.40 62 | 67.55 51 | 77.81 28 | 86.48 65 | 54.10 58 | 93.15 45 | 57.75 70 | 82.72 50 | 87.20 71 |
|
FMVSNet1 | | | 77.44 55 | 76.12 57 | 81.40 55 | 86.81 64 | 63.01 62 | 88.39 31 | 89.28 38 | 70.49 33 | 74.39 55 | 87.28 50 | 49.06 79 | 91.11 59 | 60.91 55 | 78.52 72 | 90.09 40 |
|
1112_ss | | | 77.40 57 | 76.43 53 | 80.32 67 | 89.11 36 | 60.41 76 | 83.65 64 | 87.72 56 | 62.13 80 | 73.05 58 | 86.72 57 | 62.58 30 | 89.97 71 | 62.11 49 | 80.80 62 | 90.59 31 |
|
LS3D | | | 76.95 58 | 74.82 61 | 83.37 34 | 90.45 23 | 67.36 35 | 89.15 24 | 86.94 60 | 61.87 82 | 69.52 79 | 90.61 26 | 51.71 70 | 94.53 25 | 46.38 103 | 86.71 40 | 88.21 59 |
|
DP-MVS | | | 76.78 59 | 74.57 63 | 83.42 33 | 93.29 7 | 69.46 19 | 88.55 29 | 83.70 75 | 63.98 72 | 70.20 72 | 88.89 36 | 54.01 60 | 94.80 21 | 46.66 100 | 81.88 56 | 86.01 84 |
|
cascas | | | 76.72 60 | 74.64 62 | 82.99 41 | 85.78 68 | 65.88 44 | 82.33 68 | 89.21 42 | 60.85 86 | 72.74 60 | 81.02 93 | 47.28 81 | 93.75 36 | 67.48 33 | 85.02 43 | 89.34 49 |
|
Test_1112_low_res | | | 76.40 61 | 75.44 60 | 79.27 72 | 89.28 33 | 58.09 82 | 81.69 71 | 87.07 59 | 59.53 93 | 72.48 63 | 86.67 61 | 61.30 37 | 89.33 74 | 60.81 57 | 80.15 66 | 90.41 34 |
|
F-COLMAP | | | 76.38 62 | 74.33 65 | 82.50 45 | 89.28 33 | 66.95 37 | 88.41 30 | 89.03 44 | 64.05 71 | 66.83 89 | 88.61 39 | 46.78 83 | 92.89 48 | 57.48 71 | 78.55 71 | 87.67 64 |
|
LTVRE_ROB | | 69.57 13 | 76.25 63 | 74.54 64 | 81.41 54 | 88.60 44 | 64.38 55 | 79.24 85 | 89.12 43 | 70.76 31 | 69.79 78 | 87.86 44 | 49.09 78 | 93.20 43 | 56.21 77 | 80.16 65 | 86.65 81 |
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 |
ACMH+ | | 68.96 14 | 76.01 64 | 74.01 66 | 82.03 50 | 88.60 44 | 65.31 49 | 88.86 25 | 87.55 57 | 70.25 36 | 67.75 86 | 87.47 49 | 41.27 96 | 93.19 44 | 58.37 66 | 75.94 86 | 87.60 65 |
|
ACMH | | 67.68 16 | 75.89 65 | 73.93 67 | 81.77 52 | 88.71 42 | 66.61 38 | 88.62 28 | 89.01 46 | 69.81 38 | 66.78 90 | 86.70 60 | 41.95 95 | 91.51 56 | 55.64 78 | 78.14 76 | 87.17 72 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
IB-MVS | | 68.01 15 | 75.85 66 | 73.36 70 | 83.31 35 | 84.76 72 | 66.03 40 | 83.38 66 | 85.06 67 | 70.21 37 | 69.40 80 | 81.05 92 | 45.76 85 | 94.66 23 | 65.10 38 | 75.49 88 | 89.25 50 |
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 |
WTY-MVS | | | 75.65 67 | 75.68 58 | 75.57 88 | 86.40 65 | 56.82 87 | 77.92 89 | 82.40 82 | 65.10 64 | 76.18 38 | 87.72 45 | 63.13 28 | 80.90 98 | 60.31 59 | 81.96 54 | 89.00 53 |
|
XXY-MVS | | | 75.41 68 | 75.56 59 | 74.96 90 | 83.59 75 | 57.82 84 | 80.59 78 | 83.87 74 | 66.54 56 | 74.93 52 | 88.31 42 | 63.24 26 | 80.09 100 | 62.16 48 | 76.85 82 | 86.97 76 |
|
CostFormer | | | 75.24 69 | 73.90 68 | 79.27 72 | 82.65 83 | 58.27 81 | 80.80 74 | 82.73 80 | 61.57 83 | 75.33 47 | 83.13 81 | 55.52 51 | 91.07 64 | 64.98 39 | 78.34 75 | 88.45 57 |
|
IterMVS | | | 74.29 70 | 72.94 71 | 78.35 77 | 81.53 90 | 63.49 58 | 81.58 72 | 82.49 81 | 68.06 48 | 69.99 76 | 83.69 78 | 51.66 71 | 85.54 87 | 65.85 34 | 71.64 100 | 86.01 84 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EG-PatchMatch MVS | | | 74.04 71 | 71.82 73 | 80.71 64 | 84.92 70 | 67.42 32 | 85.86 50 | 88.08 53 | 66.04 58 | 64.22 95 | 83.85 76 | 35.10 107 | 92.56 51 | 57.44 72 | 80.83 61 | 82.16 98 |
|
sss | | | 73.60 72 | 73.64 69 | 73.51 96 | 82.80 82 | 55.01 94 | 76.12 93 | 81.69 86 | 62.47 79 | 74.68 53 | 85.85 68 | 57.32 48 | 78.11 104 | 60.86 56 | 80.93 60 | 87.39 68 |
|
tpmp4_e23 | | | 73.45 73 | 71.17 79 | 80.31 68 | 83.55 76 | 59.56 79 | 81.88 70 | 82.33 83 | 57.94 99 | 70.51 71 | 81.62 88 | 51.19 74 | 91.63 55 | 53.96 82 | 77.51 79 | 89.75 46 |
|
CR-MVSNet | | | 73.37 74 | 71.27 78 | 79.67 70 | 81.32 91 | 65.19 50 | 75.92 94 | 80.30 92 | 59.92 90 | 72.73 61 | 81.19 90 | 52.50 64 | 86.69 80 | 59.84 60 | 77.71 77 | 87.11 74 |
|
MSDG | | | 73.36 75 | 70.99 80 | 80.49 65 | 84.51 74 | 65.80 45 | 80.71 76 | 86.13 65 | 65.70 61 | 65.46 92 | 83.74 77 | 44.60 88 | 90.91 65 | 51.13 91 | 76.89 80 | 84.74 91 |
|
tpm2 | | | 73.26 76 | 71.46 75 | 78.63 74 | 83.34 77 | 56.71 88 | 80.65 77 | 80.40 91 | 56.63 101 | 73.55 57 | 82.02 86 | 51.80 69 | 91.24 57 | 56.35 76 | 78.42 74 | 87.95 60 |
|
RPSCF | | | 73.23 77 | 71.46 75 | 78.54 75 | 82.50 84 | 59.85 78 | 82.18 69 | 82.84 79 | 58.96 94 | 71.15 69 | 89.41 35 | 45.48 87 | 84.77 89 | 58.82 64 | 71.83 99 | 91.02 25 |
|
PatchmatchNet |  | | 73.12 78 | 71.33 77 | 78.49 76 | 83.18 78 | 60.85 74 | 79.63 82 | 78.57 99 | 64.13 70 | 71.73 66 | 79.81 99 | 51.20 73 | 85.97 86 | 57.40 73 | 76.36 83 | 88.66 56 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
COLMAP_ROB |  | 66.92 17 | 73.01 79 | 70.41 84 | 80.81 63 | 87.13 62 | 65.63 46 | 88.30 34 | 84.19 72 | 62.96 75 | 63.80 97 | 87.69 47 | 38.04 102 | 92.56 51 | 46.66 100 | 74.91 90 | 84.24 93 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
test_0402 | | | 72.79 80 | 70.44 83 | 79.84 69 | 88.13 50 | 65.99 41 | 85.93 49 | 84.29 71 | 65.57 62 | 67.40 88 | 85.49 69 | 46.92 82 | 92.61 50 | 35.88 112 | 74.38 93 | 80.94 100 |
|
tpmrst | | | 72.39 81 | 72.13 72 | 73.18 97 | 80.54 96 | 49.91 106 | 79.91 81 | 79.08 98 | 63.11 73 | 71.69 67 | 79.95 97 | 55.32 52 | 82.77 93 | 65.66 35 | 73.89 96 | 86.87 77 |
|
PatchMatch-RL | | | 72.38 82 | 70.90 81 | 76.80 84 | 88.60 44 | 67.38 34 | 79.53 83 | 76.17 103 | 62.75 77 | 69.36 81 | 82.00 87 | 45.51 86 | 84.89 88 | 53.62 83 | 80.58 63 | 78.12 106 |
|
tpm | | | 72.37 83 | 71.71 74 | 74.35 93 | 82.19 86 | 52.00 98 | 79.22 86 | 77.29 101 | 64.56 67 | 72.95 59 | 83.68 79 | 51.35 72 | 83.26 92 | 58.33 67 | 75.80 87 | 87.81 63 |
|
PVSNet | | 64.34 18 | 72.08 84 | 70.87 82 | 75.69 87 | 86.21 66 | 56.44 90 | 74.37 102 | 80.73 89 | 62.06 81 | 70.17 73 | 82.23 84 | 42.86 92 | 83.31 91 | 54.77 79 | 84.45 44 | 87.32 70 |
|
RPMNet | | | 71.62 85 | 68.94 88 | 79.67 70 | 81.32 91 | 65.19 50 | 75.92 94 | 78.30 100 | 57.60 100 | 72.73 61 | 76.45 105 | 52.30 66 | 86.69 80 | 48.14 97 | 77.71 77 | 87.11 74 |
|
Patchmtry | | | 70.74 86 | 69.16 87 | 75.49 89 | 80.72 93 | 54.07 95 | 74.94 101 | 80.30 92 | 58.34 97 | 70.01 74 | 81.19 90 | 52.50 64 | 86.54 82 | 53.37 84 | 71.09 101 | 85.87 86 |
|
MIMVSNet | | | 70.69 87 | 69.30 86 | 74.88 91 | 84.52 73 | 56.35 91 | 75.87 96 | 79.42 97 | 64.59 66 | 67.76 85 | 82.41 82 | 41.10 97 | 81.54 97 | 46.64 102 | 81.34 58 | 86.75 79 |
|
tpm cat1 | | | 70.57 88 | 68.31 91 | 77.35 81 | 82.41 85 | 57.95 83 | 78.08 88 | 80.22 94 | 52.04 106 | 68.54 84 | 77.66 102 | 52.00 68 | 87.84 76 | 51.77 88 | 72.07 98 | 86.25 83 |
|
OpenMVS_ROB |  | 64.09 19 | 70.56 89 | 68.19 92 | 77.65 80 | 80.26 97 | 59.41 80 | 85.01 53 | 82.96 78 | 58.76 95 | 65.43 93 | 82.33 83 | 37.63 103 | 91.23 58 | 45.34 105 | 76.03 85 | 82.32 96 |
|
USDC | | | 70.33 90 | 68.37 90 | 76.21 86 | 80.60 95 | 56.23 92 | 79.19 87 | 86.49 61 | 60.89 85 | 61.29 99 | 85.47 70 | 31.78 108 | 89.47 73 | 53.37 84 | 76.21 84 | 82.94 95 |
|
CMPMVS |  | 51.72 21 | 70.19 91 | 68.16 93 | 76.28 85 | 73.15 109 | 57.55 85 | 79.47 84 | 83.92 73 | 48.02 110 | 56.48 108 | 84.81 74 | 43.13 90 | 86.42 84 | 62.67 46 | 81.81 57 | 84.89 90 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
FMVSNet5 | | | 69.50 92 | 67.96 95 | 74.15 94 | 82.97 81 | 55.35 93 | 80.01 80 | 82.12 84 | 62.56 78 | 63.02 98 | 81.53 89 | 36.92 104 | 81.92 95 | 48.42 96 | 74.06 94 | 85.17 89 |
|
PMMVS | | | 69.34 93 | 68.67 89 | 71.35 100 | 75.67 105 | 62.03 69 | 75.17 97 | 73.46 108 | 50.00 108 | 68.68 83 | 79.05 100 | 52.07 67 | 78.13 103 | 61.16 54 | 82.77 48 | 73.90 108 |
|
EPMVS | | | 69.02 94 | 68.16 93 | 71.59 98 | 79.61 100 | 49.80 107 | 77.40 90 | 66.93 112 | 62.82 76 | 70.01 74 | 79.05 100 | 45.79 84 | 77.86 105 | 56.58 75 | 75.26 89 | 87.13 73 |
|
MIMVSNet1 | | | 68.58 95 | 66.78 97 | 73.98 95 | 80.07 98 | 51.82 99 | 80.77 75 | 84.37 70 | 64.40 68 | 59.75 101 | 82.16 85 | 36.47 105 | 83.63 90 | 42.73 106 | 70.33 102 | 86.48 82 |
|
PatchT | | | 68.46 96 | 67.85 96 | 70.29 102 | 80.70 94 | 43.93 111 | 72.47 104 | 74.88 106 | 60.15 89 | 70.55 70 | 76.57 104 | 49.94 75 | 81.59 96 | 50.58 92 | 74.83 91 | 85.34 88 |
|
TDRefinement | | | 67.49 97 | 64.34 101 | 76.92 82 | 73.47 108 | 61.07 72 | 84.86 56 | 82.98 77 | 59.77 91 | 58.30 104 | 85.13 71 | 26.06 109 | 87.89 75 | 47.92 98 | 60.59 110 | 81.81 99 |
|
UnsupCasMVSNet_eth | | | 67.33 98 | 65.99 98 | 71.37 99 | 73.48 107 | 51.47 102 | 75.16 98 | 85.19 66 | 65.20 63 | 60.78 100 | 80.93 95 | 42.35 93 | 77.20 107 | 57.12 74 | 53.69 114 | 85.44 87 |
|
TinyColmap | | | 67.30 99 | 64.81 100 | 74.76 92 | 81.92 87 | 56.68 89 | 80.29 79 | 81.49 87 | 60.33 88 | 56.27 109 | 83.22 80 | 24.77 110 | 87.66 77 | 45.52 104 | 69.47 103 | 79.95 103 |
|
dp | | | 66.80 100 | 65.43 99 | 70.90 101 | 79.74 99 | 48.82 109 | 75.12 100 | 74.77 107 | 59.61 92 | 64.08 96 | 77.23 103 | 42.89 91 | 80.72 99 | 48.86 95 | 66.58 106 | 83.16 94 |
|
JIA-IIPM | | | 66.32 101 | 62.82 102 | 76.82 83 | 77.09 103 | 61.72 71 | 65.34 111 | 75.38 105 | 58.04 98 | 64.51 94 | 62.32 112 | 42.05 94 | 86.51 83 | 51.45 90 | 69.22 104 | 82.21 97 |
|
LF4IMVS | | | 64.02 102 | 62.19 103 | 69.50 104 | 70.90 110 | 53.29 97 | 76.13 92 | 77.18 102 | 52.65 105 | 58.59 102 | 80.98 94 | 23.55 111 | 76.52 108 | 53.06 86 | 66.66 105 | 78.68 105 |
|
UnsupCasMVSNet_bld | | | 63.70 103 | 61.53 104 | 70.21 103 | 73.69 106 | 51.39 103 | 72.82 103 | 81.89 85 | 55.63 103 | 57.81 105 | 71.80 108 | 38.67 100 | 78.61 102 | 49.26 94 | 52.21 115 | 80.63 101 |
|
PVSNet_0 | | 57.27 20 | 61.67 104 | 59.27 105 | 68.85 105 | 79.61 100 | 57.44 86 | 68.01 110 | 73.44 109 | 55.93 102 | 58.54 103 | 70.41 109 | 44.58 89 | 77.55 106 | 47.01 99 | 35.91 116 | 71.55 109 |
|
MVS-HIRNet | | | 59.14 105 | 57.67 106 | 63.57 108 | 81.65 89 | 43.50 112 | 71.73 105 | 65.06 113 | 39.59 113 | 51.43 110 | 57.73 113 | 38.34 101 | 82.58 94 | 39.53 108 | 73.95 95 | 64.62 112 |
|
DSMNet-mixed | | | 57.77 106 | 56.90 107 | 60.38 109 | 67.70 113 | 35.61 116 | 69.18 109 | 53.97 116 | 32.30 118 | 57.49 106 | 79.88 98 | 40.39 99 | 68.57 115 | 38.78 109 | 72.37 97 | 76.97 107 |
|
FPMVS | | | 53.68 107 | 51.64 109 | 59.81 110 | 65.08 114 | 51.03 104 | 69.48 108 | 69.58 110 | 41.46 111 | 40.67 113 | 72.32 107 | 16.46 116 | 70.00 114 | 24.24 117 | 65.42 107 | 58.40 114 |
|
N_pmnet | | | 52.79 108 | 53.26 108 | 51.40 116 | 78.99 102 | 7.68 122 | 69.52 107 | 3.89 121 | 51.63 107 | 57.01 107 | 74.98 106 | 40.83 98 | 65.96 116 | 37.78 110 | 64.67 108 | 80.56 102 |
|
HyFIR lowres test | | | 51.79 109 | 50.01 111 | 57.11 112 | 68.82 111 | 49.21 108 | 60.50 114 | 53.26 117 | 34.52 114 | 43.77 112 | 64.94 111 | 20.34 113 | 71.75 112 | 39.87 107 | 64.06 109 | 50.39 115 |
|
no-one | | | 51.08 110 | 45.79 113 | 66.95 106 | 57.92 117 | 50.49 105 | 59.63 116 | 76.04 104 | 48.04 109 | 31.85 114 | 56.10 116 | 19.12 114 | 80.08 101 | 36.89 111 | 26.52 117 | 70.29 110 |
|
new_pmnet | | | 50.91 111 | 50.29 110 | 52.78 115 | 68.58 112 | 34.94 119 | 63.71 113 | 56.63 115 | 39.73 112 | 44.95 111 | 65.47 110 | 21.93 112 | 58.48 118 | 34.98 113 | 56.62 112 | 64.92 111 |
|
ANet_high | | | 50.57 112 | 46.10 112 | 63.99 107 | 48.67 118 | 39.13 114 | 70.99 106 | 80.85 88 | 61.39 84 | 31.18 115 | 57.70 114 | 17.02 115 | 73.65 111 | 31.22 115 | 15.89 122 | 79.18 104 |
|
Gipuma |  | | 45.18 113 | 41.86 114 | 55.16 114 | 77.03 104 | 51.52 101 | 32.50 119 | 80.52 90 | 32.46 116 | 27.12 116 | 35.02 120 | 9.52 118 | 75.50 110 | 22.31 118 | 60.21 111 | 38.45 119 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS |  | 37.38 22 | 44.16 114 | 40.28 115 | 55.82 113 | 40.82 121 | 42.54 113 | 65.12 112 | 63.99 114 | 34.43 115 | 24.48 117 | 57.12 115 | 3.92 119 | 76.17 109 | 17.10 120 | 55.52 113 | 48.75 116 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
wuykxyi23d | | | 39.76 115 | 33.18 117 | 59.51 111 | 46.98 119 | 44.01 110 | 57.70 117 | 67.74 111 | 24.13 119 | 13.98 121 | 34.33 121 | 1.27 122 | 71.33 113 | 34.23 114 | 18.23 120 | 63.18 113 |
|
PNet_i23d | | | 38.26 116 | 35.42 116 | 46.79 117 | 58.74 115 | 35.48 117 | 59.65 115 | 51.25 118 | 32.45 117 | 23.44 119 | 47.53 118 | 2.04 121 | 58.96 117 | 25.60 116 | 18.09 121 | 45.92 118 |
|
MVE |  | 26.22 23 | 30.37 117 | 25.89 118 | 43.81 118 | 44.55 120 | 35.46 118 | 28.87 120 | 39.07 119 | 18.20 120 | 18.58 120 | 40.18 119 | 2.68 120 | 47.37 119 | 17.07 121 | 23.78 119 | 48.60 117 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 16.82 118 | 15.94 119 | 19.46 120 | 58.74 115 | 31.45 120 | 39.22 118 | 3.74 122 | 6.84 122 | 6.04 122 | 2.70 122 | 1.27 122 | 24.29 121 | 10.54 122 | 14.40 123 | 2.63 121 |
|
ab-mvs-re | | | 7.23 119 | 9.64 120 | 0.00 121 | 0.00 123 | 0.00 123 | 0.00 121 | 0.00 123 | 0.00 123 | 0.00 123 | 86.72 57 | 0.00 124 | 0.00 122 | 0.00 123 | 0.00 124 | 0.00 122 |
|
DeepMVS_CX |  | | | | 27.40 119 | 40.17 122 | 26.90 121 | | 24.59 120 | 17.44 121 | 23.95 118 | 48.61 117 | 9.77 117 | 26.48 120 | 18.06 119 | 24.47 118 | 28.83 120 |
|
ITE_SJBPF | | | | | 78.22 78 | 81.77 88 | 60.57 75 | | 83.30 76 | 69.25 44 | 67.54 87 | 87.20 54 | 36.33 106 | 87.28 78 | 54.34 80 | 74.62 92 | 86.80 78 |
|
Test By Simon | | | | | | | | | | | | | 64.33 21 | | | | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 59 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 55 | |
|
MDTV_nov1_ep13 | | | | 69.97 85 | | 83.18 78 | 53.48 96 | 77.10 91 | 80.18 95 | 60.45 87 | 69.33 82 | 80.44 96 | 48.89 80 | 86.90 79 | 51.60 89 | 78.51 73 | |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 115 | 75.16 98 | | 55.10 104 | 66.53 91 | | 49.34 76 | | 53.98 81 | | 87.94 61 |
|
NCCC | | | | | | 93.72 4 | | | 92.64 10 | | | | | | | | |
|