DVP-MVS++ | | | 95.98 1 | 96.36 1 | 94.82 28 | 97.78 51 | 86.00 47 | 98.29 1 | 97.49 5 | 90.75 17 | 97.62 5 | 98.06 6 | 92.59 2 | 99.61 3 | 95.64 6 | 99.02 12 | 98.86 10 |
|
SED-MVS | | | 95.91 2 | 96.28 2 | 94.80 30 | 98.77 5 | 85.99 49 | 97.13 14 | 97.44 14 | 90.31 26 | 97.71 1 | 98.07 4 | 92.31 4 | 99.58 8 | 95.66 4 | 99.13 3 | 98.84 13 |
|
DVP-MVS |  | | 95.67 3 | 96.02 3 | 94.64 36 | 98.78 3 | 85.93 52 | 97.09 16 | 96.73 76 | 90.27 29 | 97.04 10 | 98.05 8 | 91.47 8 | 99.55 14 | 95.62 8 | 99.08 7 | 98.45 34 |
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025 |
DPE-MVS |  | | 95.57 4 | 95.67 4 | 95.25 9 | 98.36 25 | 87.28 15 | 95.56 82 | 97.51 4 | 89.13 58 | 97.14 8 | 97.91 11 | 91.64 7 | 99.62 1 | 94.61 14 | 99.17 2 | 98.86 10 |
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
APDe-MVS | | | 95.46 5 | 95.64 5 | 94.91 19 | 98.26 28 | 86.29 43 | 97.46 6 | 97.40 19 | 89.03 61 | 96.20 16 | 98.10 2 | 89.39 16 | 99.34 32 | 95.88 3 | 99.03 11 | 99.10 4 |
|
MSP-MVS | | | 95.42 6 | 95.56 6 | 94.98 17 | 98.49 17 | 86.52 33 | 96.91 25 | 97.47 10 | 91.73 8 | 96.10 17 | 96.69 53 | 89.90 12 | 99.30 38 | 94.70 12 | 98.04 63 | 99.13 2 |
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025 |
CNVR-MVS | | | 95.40 7 | 95.37 7 | 95.50 7 | 98.11 36 | 88.51 7 | 95.29 92 | 96.96 50 | 92.09 4 | 95.32 22 | 97.08 36 | 89.49 15 | 99.33 35 | 95.10 11 | 98.85 19 | 98.66 19 |
|
SMA-MVS |  | | 95.20 8 | 95.07 10 | 95.59 5 | 98.14 35 | 88.48 8 | 96.26 45 | 97.28 29 | 85.90 138 | 97.67 3 | 98.10 2 | 88.41 20 | 99.56 10 | 94.66 13 | 99.19 1 | 98.71 18 |
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology |
SteuartSystems-ACMMP | | | 95.20 8 | 95.32 9 | 94.85 23 | 96.99 72 | 86.33 39 | 97.33 7 | 97.30 27 | 91.38 10 | 95.39 21 | 97.46 17 | 88.98 19 | 99.40 28 | 94.12 18 | 98.89 18 | 98.82 15 |
Skip Steuart: Steuart Systems R&D Blog. |
HPM-MVS++ |  | | 95.14 10 | 94.91 12 | 95.83 4 | 98.25 29 | 89.65 4 | 95.92 63 | 96.96 50 | 91.75 7 | 94.02 35 | 96.83 48 | 88.12 24 | 99.55 14 | 93.41 29 | 98.94 16 | 98.28 48 |
|
SF-MVS | | | 94.97 11 | 94.90 13 | 95.20 10 | 97.84 47 | 87.76 9 | 96.65 34 | 97.48 9 | 87.76 101 | 95.71 19 | 97.70 13 | 88.28 23 | 99.35 31 | 93.89 22 | 98.78 25 | 98.48 28 |
|
SD-MVS | | | 94.96 12 | 95.33 8 | 93.88 54 | 97.25 69 | 86.69 25 | 96.19 48 | 97.11 41 | 90.42 25 | 96.95 12 | 97.27 25 | 89.53 14 | 96.91 234 | 94.38 16 | 98.85 19 | 98.03 68 |
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 |
TSAR-MVS + MP. | | | 94.85 13 | 94.94 11 | 94.58 39 | 98.25 29 | 86.33 39 | 96.11 53 | 96.62 85 | 88.14 89 | 96.10 17 | 96.96 42 | 89.09 18 | 98.94 73 | 94.48 15 | 98.68 35 | 98.48 28 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
NCCC | | | 94.81 14 | 94.69 15 | 95.17 12 | 97.83 48 | 87.46 14 | 95.66 76 | 96.93 54 | 92.34 2 | 93.94 36 | 96.58 63 | 87.74 27 | 99.44 27 | 92.83 37 | 98.40 50 | 98.62 20 |
|
ACMMP_NAP | | | 94.74 15 | 94.56 16 | 95.28 8 | 98.02 41 | 87.70 10 | 95.68 74 | 97.34 21 | 88.28 82 | 95.30 23 | 97.67 14 | 85.90 43 | 99.54 18 | 93.91 21 | 98.95 15 | 98.60 21 |
|
HFP-MVS | | | 94.52 16 | 94.40 19 | 94.86 22 | 98.61 10 | 86.81 22 | 96.94 20 | 97.34 21 | 88.63 71 | 93.65 40 | 97.21 29 | 86.10 41 | 99.49 24 | 92.35 48 | 98.77 27 | 98.30 45 |
|
ZNCC-MVS | | | 94.47 17 | 94.28 23 | 95.03 14 | 98.52 15 | 86.96 17 | 96.85 28 | 97.32 25 | 88.24 83 | 93.15 50 | 97.04 39 | 86.17 40 | 99.62 1 | 92.40 46 | 98.81 22 | 98.52 24 |
|
XVS | | | 94.45 18 | 94.32 20 | 94.85 23 | 98.54 13 | 86.60 31 | 96.93 22 | 97.19 33 | 90.66 22 | 92.85 57 | 97.16 34 | 85.02 54 | 99.49 24 | 91.99 61 | 98.56 46 | 98.47 31 |
|
MCST-MVS | | | 94.45 18 | 94.20 28 | 95.19 11 | 98.46 19 | 87.50 13 | 95.00 112 | 97.12 39 | 87.13 111 | 92.51 70 | 96.30 70 | 89.24 17 | 99.34 32 | 93.46 26 | 98.62 42 | 98.73 16 |
|
region2R | | | 94.43 20 | 94.27 25 | 94.92 18 | 98.65 8 | 86.67 27 | 96.92 24 | 97.23 32 | 88.60 73 | 93.58 42 | 97.27 25 | 85.22 50 | 99.54 18 | 92.21 51 | 98.74 29 | 98.56 23 |
|
ACMMPR | | | 94.43 20 | 94.28 23 | 94.91 19 | 98.63 9 | 86.69 25 | 96.94 20 | 97.32 25 | 88.63 71 | 93.53 45 | 97.26 27 | 85.04 53 | 99.54 18 | 92.35 48 | 98.78 25 | 98.50 25 |
|
MTAPA | | | 94.42 22 | 94.22 26 | 95.00 16 | 98.42 21 | 86.95 18 | 94.36 157 | 96.97 48 | 91.07 11 | 93.14 51 | 97.56 15 | 84.30 62 | 99.56 10 | 93.43 27 | 98.75 28 | 98.47 31 |
|
CP-MVS | | | 94.34 23 | 94.21 27 | 94.74 34 | 98.39 23 | 86.64 29 | 97.60 4 | 97.24 30 | 88.53 75 | 92.73 64 | 97.23 28 | 85.20 51 | 99.32 36 | 92.15 54 | 98.83 21 | 98.25 53 |
|
MP-MVS |  | | 94.25 24 | 94.07 32 | 94.77 32 | 98.47 18 | 86.31 41 | 96.71 31 | 96.98 47 | 89.04 60 | 91.98 79 | 97.19 31 | 85.43 48 | 99.56 10 | 92.06 60 | 98.79 23 | 98.44 35 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
APD-MVS |  | | 94.24 25 | 94.07 32 | 94.75 33 | 98.06 39 | 86.90 20 | 95.88 64 | 96.94 53 | 85.68 144 | 95.05 25 | 97.18 32 | 87.31 33 | 99.07 51 | 91.90 67 | 98.61 44 | 98.28 48 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
SR-MVS | | | 94.23 26 | 94.17 30 | 94.43 44 | 98.21 32 | 85.78 59 | 96.40 39 | 96.90 57 | 88.20 87 | 94.33 29 | 97.40 20 | 84.75 59 | 99.03 56 | 93.35 30 | 97.99 64 | 98.48 28 |
|
GST-MVS | | | 94.21 27 | 93.97 35 | 94.90 21 | 98.41 22 | 86.82 21 | 96.54 36 | 97.19 33 | 88.24 83 | 93.26 47 | 96.83 48 | 85.48 47 | 99.59 7 | 91.43 73 | 98.40 50 | 98.30 45 |
|
MP-MVS-pluss | | | 94.21 27 | 94.00 34 | 94.85 23 | 98.17 33 | 86.65 28 | 94.82 123 | 97.17 37 | 86.26 130 | 92.83 59 | 97.87 12 | 85.57 46 | 99.56 10 | 94.37 17 | 98.92 17 | 98.34 40 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
DeepPCF-MVS | | 89.96 1 | 94.20 29 | 94.77 14 | 92.49 101 | 96.52 87 | 80.00 204 | 94.00 181 | 97.08 42 | 90.05 33 | 95.65 20 | 97.29 24 | 89.66 13 | 98.97 70 | 93.95 20 | 98.71 30 | 98.50 25 |
|
CS-MVS | | | 94.12 30 | 94.44 18 | 93.17 68 | 96.55 84 | 83.08 115 | 97.63 3 | 96.95 52 | 91.71 9 | 93.50 46 | 96.21 73 | 85.61 44 | 98.24 123 | 93.64 24 | 98.17 56 | 98.19 56 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 31 | 93.79 38 | 94.80 30 | 97.48 61 | 86.78 23 | 95.65 78 | 96.89 58 | 89.40 50 | 92.81 60 | 96.97 41 | 85.37 49 | 99.24 41 | 90.87 83 | 98.69 33 | 98.38 39 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CS-MVS-test | | | 94.02 32 | 94.29 22 | 93.24 65 | 96.69 78 | 83.24 108 | 97.49 5 | 96.92 55 | 92.14 3 | 92.90 55 | 95.77 94 | 85.02 54 | 98.33 118 | 93.03 34 | 98.62 42 | 98.13 60 |
|
HPM-MVS |  | | 94.02 32 | 93.88 36 | 94.43 44 | 98.39 23 | 85.78 59 | 97.25 10 | 97.07 43 | 86.90 119 | 92.62 67 | 96.80 52 | 84.85 58 | 99.17 45 | 92.43 44 | 98.65 40 | 98.33 41 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
mPP-MVS | | | 93.99 34 | 93.78 39 | 94.63 37 | 98.50 16 | 85.90 56 | 96.87 26 | 96.91 56 | 88.70 69 | 91.83 88 | 97.17 33 | 83.96 66 | 99.55 14 | 91.44 72 | 98.64 41 | 98.43 36 |
|
PGM-MVS | | | 93.96 35 | 93.72 41 | 94.68 35 | 98.43 20 | 86.22 44 | 95.30 90 | 97.78 1 | 87.45 107 | 93.26 47 | 97.33 23 | 84.62 60 | 99.51 22 | 90.75 85 | 98.57 45 | 98.32 44 |
|
PHI-MVS | | | 93.89 36 | 93.65 44 | 94.62 38 | 96.84 75 | 86.43 36 | 96.69 32 | 97.49 5 | 85.15 158 | 93.56 44 | 96.28 71 | 85.60 45 | 99.31 37 | 92.45 43 | 98.79 23 | 98.12 62 |
|
SR-MVS-dyc-post | | | 93.82 37 | 93.82 37 | 93.82 56 | 97.92 43 | 84.57 73 | 96.28 43 | 96.76 72 | 87.46 105 | 93.75 38 | 97.43 18 | 84.24 63 | 99.01 61 | 92.73 38 | 97.80 70 | 97.88 75 |
|
APD-MVS_3200maxsize | | | 93.78 38 | 93.77 40 | 93.80 58 | 97.92 43 | 84.19 85 | 96.30 41 | 96.87 60 | 86.96 115 | 93.92 37 | 97.47 16 | 83.88 67 | 98.96 72 | 92.71 41 | 97.87 68 | 98.26 52 |
|
patch_mono-2 | | | 93.74 39 | 94.32 20 | 92.01 117 | 97.54 57 | 78.37 242 | 93.40 204 | 97.19 33 | 88.02 91 | 94.99 26 | 97.21 29 | 88.35 21 | 98.44 109 | 94.07 19 | 98.09 61 | 99.23 1 |
|
MSLP-MVS++ | | | 93.72 40 | 94.08 31 | 92.65 93 | 97.31 65 | 83.43 103 | 95.79 68 | 97.33 23 | 90.03 34 | 93.58 42 | 96.96 42 | 84.87 57 | 97.76 161 | 92.19 53 | 98.66 38 | 96.76 119 |
|
TSAR-MVS + GP. | | | 93.66 41 | 93.41 45 | 94.41 46 | 96.59 82 | 86.78 23 | 94.40 150 | 93.93 235 | 89.77 42 | 94.21 30 | 95.59 101 | 87.35 32 | 98.61 95 | 92.72 40 | 96.15 98 | 97.83 79 |
|
CANet | | | 93.54 42 | 93.20 49 | 94.55 40 | 95.65 117 | 85.73 61 | 94.94 115 | 96.69 81 | 91.89 6 | 90.69 104 | 95.88 88 | 81.99 90 | 99.54 18 | 93.14 33 | 97.95 66 | 98.39 37 |
|
dcpmvs_2 | | | 93.49 43 | 94.19 29 | 91.38 151 | 97.69 54 | 76.78 274 | 94.25 160 | 96.29 101 | 88.33 79 | 94.46 27 | 96.88 45 | 88.07 25 | 98.64 91 | 93.62 25 | 98.09 61 | 98.73 16 |
|
MVS_111021_HR | | | 93.45 44 | 93.31 46 | 93.84 55 | 96.99 72 | 84.84 68 | 93.24 216 | 97.24 30 | 88.76 68 | 91.60 93 | 95.85 89 | 86.07 42 | 98.66 89 | 91.91 65 | 98.16 57 | 98.03 68 |
|
train_agg | | | 93.44 45 | 93.08 50 | 94.52 41 | 97.53 58 | 86.49 34 | 94.07 173 | 96.78 69 | 81.86 228 | 92.77 61 | 96.20 74 | 87.63 29 | 99.12 49 | 92.14 55 | 98.69 33 | 97.94 71 |
|
DROMVSNet | | | 93.44 45 | 93.71 42 | 92.63 94 | 95.21 131 | 82.43 136 | 97.27 9 | 96.71 79 | 90.57 24 | 92.88 56 | 95.80 92 | 83.16 71 | 98.16 129 | 93.68 23 | 98.14 58 | 97.31 96 |
|
DELS-MVS | | | 93.43 47 | 93.25 47 | 93.97 51 | 95.42 124 | 85.04 67 | 93.06 223 | 97.13 38 | 90.74 19 | 91.84 86 | 95.09 117 | 86.32 39 | 99.21 43 | 91.22 74 | 98.45 48 | 97.65 84 |
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 |
HPM-MVS_fast | | | 93.40 48 | 93.22 48 | 93.94 53 | 98.36 25 | 84.83 69 | 97.15 13 | 96.80 68 | 85.77 141 | 92.47 71 | 97.13 35 | 82.38 79 | 99.07 51 | 90.51 90 | 98.40 50 | 97.92 74 |
|
DeepC-MVS | | 88.79 3 | 93.31 49 | 92.99 52 | 94.26 49 | 96.07 102 | 85.83 57 | 94.89 118 | 96.99 46 | 89.02 63 | 89.56 118 | 97.37 22 | 82.51 78 | 99.38 29 | 92.20 52 | 98.30 53 | 97.57 89 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
canonicalmvs | | | 93.27 50 | 92.75 56 | 94.85 23 | 95.70 116 | 87.66 11 | 96.33 40 | 96.41 95 | 90.00 35 | 94.09 33 | 94.60 138 | 82.33 81 | 98.62 94 | 92.40 46 | 92.86 159 | 98.27 50 |
|
ACMMP |  | | 93.24 51 | 92.88 54 | 94.30 48 | 98.09 38 | 85.33 65 | 96.86 27 | 97.45 13 | 88.33 79 | 90.15 113 | 97.03 40 | 81.44 93 | 99.51 22 | 90.85 84 | 95.74 101 | 98.04 67 |
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 |
CSCG | | | 93.23 52 | 93.05 51 | 93.76 59 | 98.04 40 | 84.07 87 | 96.22 47 | 97.37 20 | 84.15 174 | 90.05 114 | 95.66 98 | 87.77 26 | 99.15 48 | 89.91 93 | 98.27 54 | 98.07 64 |
|
alignmvs | | | 93.08 53 | 92.50 60 | 94.81 29 | 95.62 119 | 87.61 12 | 95.99 59 | 96.07 119 | 89.77 42 | 94.12 32 | 94.87 123 | 80.56 99 | 98.66 89 | 92.42 45 | 93.10 155 | 98.15 59 |
|
EI-MVSNet-Vis-set | | | 93.01 54 | 92.92 53 | 93.29 63 | 95.01 138 | 83.51 102 | 94.48 142 | 95.77 141 | 90.87 13 | 92.52 69 | 96.67 55 | 84.50 61 | 99.00 65 | 91.99 61 | 94.44 130 | 97.36 95 |
|
casdiffmvs_mvg |  | | 92.96 55 | 92.83 55 | 93.35 62 | 94.59 161 | 83.40 105 | 95.00 112 | 96.34 99 | 90.30 28 | 92.05 77 | 96.05 82 | 83.43 69 | 98.15 130 | 92.07 57 | 95.67 102 | 98.49 27 |
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
UA-Net | | | 92.83 56 | 92.54 59 | 93.68 60 | 96.10 100 | 84.71 71 | 95.66 76 | 96.39 96 | 91.92 5 | 93.22 49 | 96.49 66 | 83.16 71 | 98.87 77 | 84.47 159 | 95.47 107 | 97.45 94 |
|
CDPH-MVS | | | 92.83 56 | 92.30 62 | 94.44 42 | 97.79 49 | 86.11 46 | 94.06 175 | 96.66 82 | 80.09 255 | 92.77 61 | 96.63 60 | 86.62 36 | 99.04 55 | 87.40 121 | 98.66 38 | 98.17 58 |
|
ETV-MVS | | | 92.74 58 | 92.66 57 | 92.97 78 | 95.20 132 | 84.04 89 | 95.07 108 | 96.51 91 | 90.73 20 | 92.96 54 | 91.19 257 | 84.06 64 | 98.34 116 | 91.72 69 | 96.54 92 | 96.54 128 |
|
EI-MVSNet-UG-set | | | 92.74 58 | 92.62 58 | 93.12 70 | 94.86 149 | 83.20 110 | 94.40 150 | 95.74 144 | 90.71 21 | 92.05 77 | 96.60 62 | 84.00 65 | 98.99 67 | 91.55 70 | 93.63 140 | 97.17 103 |
|
DPM-MVS | | | 92.58 60 | 91.74 68 | 95.08 13 | 96.19 95 | 89.31 5 | 92.66 233 | 96.56 90 | 83.44 192 | 91.68 92 | 95.04 118 | 86.60 38 | 98.99 67 | 85.60 145 | 97.92 67 | 96.93 115 |
|
casdiffmvs |  | | 92.51 61 | 92.43 61 | 92.74 88 | 94.41 173 | 81.98 146 | 94.54 140 | 96.23 108 | 89.57 46 | 91.96 81 | 96.17 78 | 82.58 77 | 98.01 148 | 90.95 81 | 95.45 109 | 98.23 54 |
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
MVS_111021_LR | | | 92.47 62 | 92.29 63 | 92.98 77 | 95.99 106 | 84.43 82 | 93.08 221 | 96.09 117 | 88.20 87 | 91.12 100 | 95.72 97 | 81.33 95 | 97.76 161 | 91.74 68 | 97.37 77 | 96.75 120 |
|
3Dnovator+ | | 87.14 4 | 92.42 63 | 91.37 71 | 95.55 6 | 95.63 118 | 88.73 6 | 97.07 18 | 96.77 71 | 90.84 14 | 84.02 242 | 96.62 61 | 75.95 150 | 99.34 32 | 87.77 115 | 97.68 73 | 98.59 22 |
|
baseline | | | 92.39 64 | 92.29 63 | 92.69 92 | 94.46 170 | 81.77 151 | 94.14 166 | 96.27 103 | 89.22 54 | 91.88 84 | 96.00 83 | 82.35 80 | 97.99 150 | 91.05 76 | 95.27 114 | 98.30 45 |
|
VNet | | | 92.24 65 | 91.91 66 | 93.24 65 | 96.59 82 | 83.43 103 | 94.84 122 | 96.44 93 | 89.19 56 | 94.08 34 | 95.90 87 | 77.85 134 | 98.17 128 | 88.90 103 | 93.38 149 | 98.13 60 |
|
CPTT-MVS | | | 91.99 66 | 91.80 67 | 92.55 98 | 98.24 31 | 81.98 146 | 96.76 30 | 96.49 92 | 81.89 227 | 90.24 109 | 96.44 68 | 78.59 123 | 98.61 95 | 89.68 94 | 97.85 69 | 97.06 107 |
|
EIA-MVS | | | 91.95 67 | 91.94 65 | 91.98 121 | 95.16 133 | 80.01 203 | 95.36 85 | 96.73 76 | 88.44 76 | 89.34 122 | 92.16 223 | 83.82 68 | 98.45 108 | 89.35 97 | 97.06 80 | 97.48 92 |
|
DP-MVS Recon | | | 91.95 67 | 91.28 73 | 93.96 52 | 98.33 27 | 85.92 54 | 94.66 134 | 96.66 82 | 82.69 209 | 90.03 115 | 95.82 91 | 82.30 82 | 99.03 56 | 84.57 157 | 96.48 95 | 96.91 116 |
|
EPNet | | | 91.79 69 | 91.02 79 | 94.10 50 | 90.10 311 | 85.25 66 | 96.03 58 | 92.05 282 | 92.83 1 | 87.39 157 | 95.78 93 | 79.39 114 | 99.01 61 | 88.13 111 | 97.48 75 | 98.05 66 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MG-MVS | | | 91.77 70 | 91.70 69 | 92.00 120 | 97.08 71 | 80.03 202 | 93.60 198 | 95.18 182 | 87.85 99 | 90.89 102 | 96.47 67 | 82.06 88 | 98.36 113 | 85.07 149 | 97.04 81 | 97.62 85 |
|
Vis-MVSNet |  | | 91.75 71 | 91.23 74 | 93.29 63 | 95.32 126 | 83.78 94 | 96.14 51 | 95.98 125 | 89.89 36 | 90.45 106 | 96.58 63 | 75.09 162 | 98.31 121 | 84.75 155 | 96.90 84 | 97.78 82 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
3Dnovator | | 86.66 5 | 91.73 72 | 90.82 83 | 94.44 42 | 94.59 161 | 86.37 38 | 97.18 12 | 97.02 45 | 89.20 55 | 84.31 238 | 96.66 56 | 73.74 186 | 99.17 45 | 86.74 131 | 97.96 65 | 97.79 81 |
|
EPP-MVSNet | | | 91.70 73 | 91.56 70 | 92.13 116 | 95.88 109 | 80.50 187 | 97.33 7 | 95.25 178 | 86.15 134 | 89.76 117 | 95.60 100 | 83.42 70 | 98.32 120 | 87.37 123 | 93.25 152 | 97.56 90 |
|
MVSFormer | | | 91.68 74 | 91.30 72 | 92.80 84 | 93.86 194 | 83.88 92 | 95.96 61 | 95.90 132 | 84.66 169 | 91.76 89 | 94.91 121 | 77.92 131 | 97.30 205 | 89.64 95 | 97.11 78 | 97.24 99 |
|
Effi-MVS+ | | | 91.59 75 | 91.11 76 | 93.01 76 | 94.35 177 | 83.39 106 | 94.60 136 | 95.10 186 | 87.10 112 | 90.57 105 | 93.10 195 | 81.43 94 | 98.07 144 | 89.29 99 | 94.48 128 | 97.59 88 |
|
IS-MVSNet | | | 91.43 76 | 91.09 78 | 92.46 102 | 95.87 111 | 81.38 163 | 96.95 19 | 93.69 246 | 89.72 44 | 89.50 120 | 95.98 84 | 78.57 124 | 97.77 160 | 83.02 177 | 96.50 94 | 98.22 55 |
|
PVSNet_Blended_VisFu | | | 91.38 77 | 90.91 81 | 92.80 84 | 96.39 90 | 83.17 111 | 94.87 120 | 96.66 82 | 83.29 196 | 89.27 123 | 94.46 142 | 80.29 101 | 99.17 45 | 87.57 119 | 95.37 110 | 96.05 146 |
|
diffmvs |  | | 91.37 78 | 91.23 74 | 91.77 136 | 93.09 216 | 80.27 190 | 92.36 242 | 95.52 161 | 87.03 114 | 91.40 97 | 94.93 120 | 80.08 103 | 97.44 189 | 92.13 56 | 94.56 125 | 97.61 86 |
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
MVS_Test | | | 91.31 79 | 91.11 76 | 91.93 125 | 94.37 174 | 80.14 195 | 93.46 203 | 95.80 139 | 86.46 126 | 91.35 98 | 93.77 174 | 82.21 84 | 98.09 141 | 87.57 119 | 94.95 117 | 97.55 91 |
|
OMC-MVS | | | 91.23 80 | 90.62 85 | 93.08 72 | 96.27 93 | 84.07 87 | 93.52 200 | 95.93 128 | 86.95 116 | 89.51 119 | 96.13 80 | 78.50 125 | 98.35 115 | 85.84 143 | 92.90 158 | 96.83 118 |
|
PAPM_NR | | | 91.22 81 | 90.78 84 | 92.52 100 | 97.60 56 | 81.46 160 | 94.37 156 | 96.24 107 | 86.39 128 | 87.41 154 | 94.80 129 | 82.06 88 | 98.48 102 | 82.80 183 | 95.37 110 | 97.61 86 |
|
PS-MVSNAJ | | | 91.18 82 | 90.92 80 | 91.96 123 | 95.26 129 | 82.60 135 | 92.09 253 | 95.70 146 | 86.27 129 | 91.84 86 | 92.46 213 | 79.70 109 | 98.99 67 | 89.08 101 | 95.86 100 | 94.29 217 |
|
xiu_mvs_v2_base | | | 91.13 83 | 90.89 82 | 91.86 130 | 94.97 141 | 82.42 137 | 92.24 247 | 95.64 153 | 86.11 137 | 91.74 91 | 93.14 193 | 79.67 112 | 98.89 76 | 89.06 102 | 95.46 108 | 94.28 218 |
|
nrg030 | | | 91.08 84 | 90.39 86 | 93.17 68 | 93.07 217 | 86.91 19 | 96.41 37 | 96.26 104 | 88.30 81 | 88.37 137 | 94.85 126 | 82.19 85 | 97.64 172 | 91.09 75 | 82.95 270 | 94.96 181 |
|
lupinMVS | | | 90.92 85 | 90.21 89 | 93.03 75 | 93.86 194 | 83.88 92 | 92.81 230 | 93.86 239 | 79.84 258 | 91.76 89 | 94.29 148 | 77.92 131 | 98.04 146 | 90.48 91 | 97.11 78 | 97.17 103 |
|
h-mvs33 | | | 90.80 86 | 90.15 92 | 92.75 87 | 96.01 104 | 82.66 132 | 95.43 84 | 95.53 160 | 89.80 38 | 93.08 52 | 95.64 99 | 75.77 151 | 99.00 65 | 92.07 57 | 78.05 325 | 96.60 124 |
|
jason | | | 90.80 86 | 90.10 93 | 92.90 81 | 93.04 219 | 83.53 101 | 93.08 221 | 94.15 228 | 80.22 252 | 91.41 96 | 94.91 121 | 76.87 138 | 97.93 155 | 90.28 92 | 96.90 84 | 97.24 99 |
jason: jason. |
VDD-MVS | | | 90.74 88 | 89.92 100 | 93.20 67 | 96.27 93 | 83.02 117 | 95.73 71 | 93.86 239 | 88.42 78 | 92.53 68 | 96.84 47 | 62.09 297 | 98.64 91 | 90.95 81 | 92.62 162 | 97.93 73 |
|
PVSNet_Blended | | | 90.73 89 | 90.32 88 | 91.98 121 | 96.12 97 | 81.25 165 | 92.55 237 | 96.83 64 | 82.04 221 | 89.10 125 | 92.56 211 | 81.04 97 | 98.85 81 | 86.72 133 | 95.91 99 | 95.84 153 |
|
test_yl | | | 90.69 90 | 90.02 98 | 92.71 89 | 95.72 114 | 82.41 139 | 94.11 168 | 95.12 184 | 85.63 146 | 91.49 94 | 94.70 132 | 74.75 166 | 98.42 111 | 86.13 138 | 92.53 163 | 97.31 96 |
|
DCV-MVSNet | | | 90.69 90 | 90.02 98 | 92.71 89 | 95.72 114 | 82.41 139 | 94.11 168 | 95.12 184 | 85.63 146 | 91.49 94 | 94.70 132 | 74.75 166 | 98.42 111 | 86.13 138 | 92.53 163 | 97.31 96 |
|
API-MVS | | | 90.66 92 | 90.07 94 | 92.45 103 | 96.36 91 | 84.57 73 | 96.06 57 | 95.22 181 | 82.39 212 | 89.13 124 | 94.27 151 | 80.32 100 | 98.46 105 | 80.16 230 | 96.71 89 | 94.33 214 |
|
xiu_mvs_v1_base_debu | | | 90.64 93 | 90.05 95 | 92.40 104 | 93.97 191 | 84.46 79 | 93.32 206 | 95.46 163 | 85.17 155 | 92.25 72 | 94.03 156 | 70.59 221 | 98.57 98 | 90.97 78 | 94.67 120 | 94.18 219 |
|
xiu_mvs_v1_base | | | 90.64 93 | 90.05 95 | 92.40 104 | 93.97 191 | 84.46 79 | 93.32 206 | 95.46 163 | 85.17 155 | 92.25 72 | 94.03 156 | 70.59 221 | 98.57 98 | 90.97 78 | 94.67 120 | 94.18 219 |
|
xiu_mvs_v1_base_debi | | | 90.64 93 | 90.05 95 | 92.40 104 | 93.97 191 | 84.46 79 | 93.32 206 | 95.46 163 | 85.17 155 | 92.25 72 | 94.03 156 | 70.59 221 | 98.57 98 | 90.97 78 | 94.67 120 | 94.18 219 |
|
HQP_MVS | | | 90.60 96 | 90.19 90 | 91.82 133 | 94.70 157 | 82.73 128 | 95.85 65 | 96.22 109 | 90.81 15 | 86.91 166 | 94.86 124 | 74.23 174 | 98.12 131 | 88.15 109 | 89.99 186 | 94.63 193 |
|
FIs | | | 90.51 97 | 90.35 87 | 90.99 172 | 93.99 190 | 80.98 173 | 95.73 71 | 97.54 3 | 89.15 57 | 86.72 171 | 94.68 134 | 81.83 92 | 97.24 213 | 85.18 148 | 88.31 220 | 94.76 191 |
|
MAR-MVS | | | 90.30 98 | 89.37 110 | 93.07 74 | 96.61 81 | 84.48 78 | 95.68 74 | 95.67 148 | 82.36 214 | 87.85 145 | 92.85 200 | 76.63 144 | 98.80 85 | 80.01 231 | 96.68 90 | 95.91 149 |
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 |
FC-MVSNet-test | | | 90.27 99 | 90.18 91 | 90.53 183 | 93.71 200 | 79.85 209 | 95.77 69 | 97.59 2 | 89.31 52 | 86.27 180 | 94.67 135 | 81.93 91 | 97.01 228 | 84.26 161 | 88.09 224 | 94.71 192 |
|
CANet_DTU | | | 90.26 100 | 89.41 109 | 92.81 83 | 93.46 208 | 83.01 118 | 93.48 201 | 94.47 215 | 89.43 49 | 87.76 149 | 94.23 152 | 70.54 225 | 99.03 56 | 84.97 150 | 96.39 96 | 96.38 131 |
|
OPM-MVS | | | 90.12 101 | 89.56 104 | 91.82 133 | 93.14 214 | 83.90 91 | 94.16 165 | 95.74 144 | 88.96 64 | 87.86 144 | 95.43 105 | 72.48 202 | 97.91 156 | 88.10 113 | 90.18 185 | 93.65 253 |
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
LFMVS | | | 90.08 102 | 89.13 116 | 92.95 79 | 96.71 77 | 82.32 141 | 96.08 54 | 89.91 331 | 86.79 120 | 92.15 76 | 96.81 50 | 62.60 295 | 98.34 116 | 87.18 125 | 93.90 136 | 98.19 56 |
|
GeoE | | | 90.05 103 | 89.43 108 | 91.90 129 | 95.16 133 | 80.37 189 | 95.80 67 | 94.65 212 | 83.90 179 | 87.55 153 | 94.75 131 | 78.18 129 | 97.62 174 | 81.28 210 | 93.63 140 | 97.71 83 |
|
PAPR | | | 90.02 104 | 89.27 115 | 92.29 112 | 95.78 112 | 80.95 175 | 92.68 232 | 96.22 109 | 81.91 225 | 86.66 172 | 93.75 176 | 82.23 83 | 98.44 109 | 79.40 241 | 94.79 118 | 97.48 92 |
|
PVSNet_BlendedMVS | | | 89.98 105 | 89.70 102 | 90.82 176 | 96.12 97 | 81.25 165 | 93.92 186 | 96.83 64 | 83.49 191 | 89.10 125 | 92.26 221 | 81.04 97 | 98.85 81 | 86.72 133 | 87.86 228 | 92.35 298 |
|
PS-MVSNAJss | | | 89.97 106 | 89.62 103 | 91.02 169 | 91.90 249 | 80.85 178 | 95.26 95 | 95.98 125 | 86.26 130 | 86.21 181 | 94.29 148 | 79.70 109 | 97.65 169 | 88.87 104 | 88.10 222 | 94.57 198 |
|
mvsmamba | | | 89.96 107 | 89.50 105 | 91.33 154 | 92.90 226 | 81.82 149 | 96.68 33 | 92.37 270 | 89.03 61 | 87.00 162 | 94.85 126 | 73.05 194 | 97.65 169 | 91.03 77 | 88.63 211 | 94.51 203 |
|
XVG-OURS-SEG-HR | | | 89.95 108 | 89.45 106 | 91.47 148 | 94.00 189 | 81.21 168 | 91.87 256 | 96.06 121 | 85.78 140 | 88.55 133 | 95.73 96 | 74.67 170 | 97.27 209 | 88.71 105 | 89.64 195 | 95.91 149 |
|
UGNet | | | 89.95 108 | 88.95 120 | 92.95 79 | 94.51 167 | 83.31 107 | 95.70 73 | 95.23 179 | 89.37 51 | 87.58 151 | 93.94 164 | 64.00 286 | 98.78 86 | 83.92 166 | 96.31 97 | 96.74 121 |
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 |
UniMVSNet_NR-MVSNet | | | 89.92 110 | 89.29 113 | 91.81 135 | 93.39 209 | 83.72 95 | 94.43 148 | 97.12 39 | 89.80 38 | 86.46 174 | 93.32 184 | 83.16 71 | 97.23 214 | 84.92 151 | 81.02 299 | 94.49 208 |
|
AdaColmap |  | | 89.89 111 | 89.07 117 | 92.37 107 | 97.41 62 | 83.03 116 | 94.42 149 | 95.92 129 | 82.81 207 | 86.34 179 | 94.65 136 | 73.89 182 | 99.02 59 | 80.69 221 | 95.51 105 | 95.05 176 |
|
hse-mvs2 | | | 89.88 112 | 89.34 111 | 91.51 145 | 94.83 151 | 81.12 170 | 93.94 184 | 93.91 238 | 89.80 38 | 93.08 52 | 93.60 178 | 75.77 151 | 97.66 168 | 92.07 57 | 77.07 332 | 95.74 158 |
|
UniMVSNet (Re) | | | 89.80 113 | 89.07 117 | 92.01 117 | 93.60 204 | 84.52 76 | 94.78 126 | 97.47 10 | 89.26 53 | 86.44 177 | 92.32 218 | 82.10 86 | 97.39 201 | 84.81 154 | 80.84 303 | 94.12 223 |
|
HQP-MVS | | | 89.80 113 | 89.28 114 | 91.34 153 | 94.17 180 | 81.56 154 | 94.39 152 | 96.04 122 | 88.81 65 | 85.43 205 | 93.97 163 | 73.83 184 | 97.96 152 | 87.11 128 | 89.77 193 | 94.50 206 |
|
FA-MVS(test-final) | | | 89.66 115 | 88.91 122 | 91.93 125 | 94.57 164 | 80.27 190 | 91.36 267 | 94.74 209 | 84.87 163 | 89.82 116 | 92.61 210 | 74.72 169 | 98.47 104 | 83.97 165 | 93.53 143 | 97.04 109 |
|
VPA-MVSNet | | | 89.62 116 | 88.96 119 | 91.60 141 | 93.86 194 | 82.89 123 | 95.46 83 | 97.33 23 | 87.91 94 | 88.43 136 | 93.31 185 | 74.17 177 | 97.40 198 | 87.32 124 | 82.86 275 | 94.52 201 |
|
WTY-MVS | | | 89.60 117 | 88.92 121 | 91.67 139 | 95.47 123 | 81.15 169 | 92.38 241 | 94.78 207 | 83.11 199 | 89.06 127 | 94.32 146 | 78.67 122 | 96.61 248 | 81.57 207 | 90.89 179 | 97.24 99 |
|
Vis-MVSNet (Re-imp) | | | 89.59 118 | 89.44 107 | 90.03 210 | 95.74 113 | 75.85 286 | 95.61 80 | 90.80 317 | 87.66 104 | 87.83 146 | 95.40 106 | 76.79 140 | 96.46 261 | 78.37 246 | 96.73 88 | 97.80 80 |
|
VDDNet | | | 89.56 119 | 88.49 136 | 92.76 86 | 95.07 137 | 82.09 143 | 96.30 41 | 93.19 253 | 81.05 247 | 91.88 84 | 96.86 46 | 61.16 308 | 98.33 118 | 88.43 108 | 92.49 165 | 97.84 78 |
|
114514_t | | | 89.51 120 | 88.50 134 | 92.54 99 | 98.11 36 | 81.99 145 | 95.16 103 | 96.36 98 | 70.19 347 | 85.81 186 | 95.25 110 | 76.70 142 | 98.63 93 | 82.07 195 | 96.86 87 | 97.00 112 |
|
QAPM | | | 89.51 120 | 88.15 145 | 93.59 61 | 94.92 145 | 84.58 72 | 96.82 29 | 96.70 80 | 78.43 280 | 83.41 257 | 96.19 77 | 73.18 193 | 99.30 38 | 77.11 262 | 96.54 92 | 96.89 117 |
|
CLD-MVS | | | 89.47 122 | 88.90 123 | 91.18 159 | 94.22 179 | 82.07 144 | 92.13 251 | 96.09 117 | 87.90 95 | 85.37 211 | 92.45 214 | 74.38 172 | 97.56 177 | 87.15 126 | 90.43 181 | 93.93 232 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
LPG-MVS_test | | | 89.45 123 | 88.90 123 | 91.12 161 | 94.47 168 | 81.49 158 | 95.30 90 | 96.14 114 | 86.73 122 | 85.45 202 | 95.16 114 | 69.89 231 | 98.10 133 | 87.70 117 | 89.23 202 | 93.77 246 |
|
CDS-MVSNet | | | 89.45 123 | 88.51 133 | 92.29 112 | 93.62 203 | 83.61 100 | 93.01 224 | 94.68 211 | 81.95 223 | 87.82 147 | 93.24 189 | 78.69 121 | 96.99 229 | 80.34 227 | 93.23 153 | 96.28 134 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
iter_conf_final | | | 89.42 125 | 88.69 127 | 91.60 141 | 95.12 136 | 82.93 121 | 95.75 70 | 92.14 279 | 87.32 109 | 87.12 161 | 94.07 154 | 67.09 261 | 97.55 178 | 90.61 87 | 89.01 206 | 94.32 215 |
|
Fast-Effi-MVS+ | | | 89.41 126 | 88.64 128 | 91.71 138 | 94.74 153 | 80.81 179 | 93.54 199 | 95.10 186 | 83.11 199 | 86.82 170 | 90.67 274 | 79.74 108 | 97.75 164 | 80.51 225 | 93.55 142 | 96.57 126 |
|
ab-mvs | | | 89.41 126 | 88.35 138 | 92.60 95 | 95.15 135 | 82.65 133 | 92.20 249 | 95.60 155 | 83.97 178 | 88.55 133 | 93.70 177 | 74.16 178 | 98.21 127 | 82.46 188 | 89.37 198 | 96.94 114 |
|
XVG-OURS | | | 89.40 128 | 88.70 126 | 91.52 144 | 94.06 183 | 81.46 160 | 91.27 269 | 96.07 119 | 86.14 135 | 88.89 129 | 95.77 94 | 68.73 251 | 97.26 211 | 87.39 122 | 89.96 188 | 95.83 154 |
|
test_vis1_n_1920 | | | 89.39 129 | 89.84 101 | 88.04 268 | 92.97 223 | 72.64 316 | 94.71 131 | 96.03 124 | 86.18 133 | 91.94 83 | 96.56 65 | 61.63 300 | 95.74 293 | 93.42 28 | 95.11 116 | 95.74 158 |
|
mvs_anonymous | | | 89.37 130 | 89.32 112 | 89.51 233 | 93.47 207 | 74.22 299 | 91.65 263 | 94.83 203 | 82.91 205 | 85.45 202 | 93.79 172 | 81.23 96 | 96.36 267 | 86.47 135 | 94.09 133 | 97.94 71 |
|
DU-MVS | | | 89.34 131 | 88.50 134 | 91.85 132 | 93.04 219 | 83.72 95 | 94.47 145 | 96.59 87 | 89.50 47 | 86.46 174 | 93.29 187 | 77.25 136 | 97.23 214 | 84.92 151 | 81.02 299 | 94.59 196 |
|
TAMVS | | | 89.21 132 | 88.29 142 | 91.96 123 | 93.71 200 | 82.62 134 | 93.30 210 | 94.19 226 | 82.22 216 | 87.78 148 | 93.94 164 | 78.83 118 | 96.95 231 | 77.70 255 | 92.98 157 | 96.32 132 |
|
ACMM | | 84.12 9 | 89.14 133 | 88.48 137 | 91.12 161 | 94.65 160 | 81.22 167 | 95.31 88 | 96.12 116 | 85.31 154 | 85.92 185 | 94.34 144 | 70.19 229 | 98.06 145 | 85.65 144 | 88.86 209 | 94.08 227 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
test1111 | | | 89.10 134 | 88.64 128 | 90.48 189 | 95.53 122 | 74.97 292 | 96.08 54 | 84.89 353 | 88.13 90 | 90.16 112 | 96.65 57 | 63.29 291 | 98.10 133 | 86.14 136 | 96.90 84 | 98.39 37 |
|
EI-MVSNet | | | 89.10 134 | 88.86 125 | 89.80 222 | 91.84 251 | 78.30 244 | 93.70 195 | 95.01 189 | 85.73 142 | 87.15 159 | 95.28 108 | 79.87 106 | 97.21 216 | 83.81 168 | 87.36 234 | 93.88 235 |
|
ECVR-MVS |  | | 89.09 136 | 88.53 132 | 90.77 178 | 95.62 119 | 75.89 285 | 96.16 49 | 84.22 355 | 87.89 97 | 90.20 110 | 96.65 57 | 63.19 293 | 98.10 133 | 85.90 141 | 96.94 82 | 98.33 41 |
|
RRT_MVS | | | 89.09 136 | 88.62 131 | 90.49 187 | 92.85 227 | 79.65 213 | 96.41 37 | 94.41 218 | 88.22 85 | 85.50 198 | 94.77 130 | 69.36 239 | 97.31 204 | 89.33 98 | 86.73 241 | 94.51 203 |
|
CNLPA | | | 89.07 138 | 87.98 149 | 92.34 108 | 96.87 74 | 84.78 70 | 94.08 172 | 93.24 251 | 81.41 238 | 84.46 228 | 95.13 116 | 75.57 158 | 96.62 245 | 77.21 260 | 93.84 138 | 95.61 163 |
|
PLC |  | 84.53 7 | 89.06 139 | 88.03 148 | 92.15 115 | 97.27 68 | 82.69 131 | 94.29 158 | 95.44 168 | 79.71 260 | 84.01 243 | 94.18 153 | 76.68 143 | 98.75 87 | 77.28 259 | 93.41 148 | 95.02 177 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
test_djsdf | | | 89.03 140 | 88.64 128 | 90.21 200 | 90.74 296 | 79.28 225 | 95.96 61 | 95.90 132 | 84.66 169 | 85.33 213 | 92.94 199 | 74.02 180 | 97.30 205 | 89.64 95 | 88.53 213 | 94.05 229 |
|
HY-MVS | | 83.01 12 | 89.03 140 | 87.94 151 | 92.29 112 | 94.86 149 | 82.77 124 | 92.08 254 | 94.49 214 | 81.52 237 | 86.93 164 | 92.79 206 | 78.32 128 | 98.23 124 | 79.93 232 | 90.55 180 | 95.88 151 |
|
ACMP | | 84.23 8 | 89.01 142 | 88.35 138 | 90.99 172 | 94.73 154 | 81.27 164 | 95.07 108 | 95.89 134 | 86.48 125 | 83.67 250 | 94.30 147 | 69.33 240 | 97.99 150 | 87.10 130 | 88.55 212 | 93.72 250 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
sss | | | 88.93 143 | 88.26 144 | 90.94 175 | 94.05 184 | 80.78 180 | 91.71 260 | 95.38 172 | 81.55 236 | 88.63 132 | 93.91 168 | 75.04 163 | 95.47 304 | 82.47 187 | 91.61 170 | 96.57 126 |
|
iter_conf05 | | | 88.85 144 | 88.08 147 | 91.17 160 | 94.27 178 | 81.64 153 | 95.18 100 | 92.15 278 | 86.23 132 | 87.28 158 | 94.07 154 | 63.89 289 | 97.55 178 | 90.63 86 | 89.00 207 | 94.32 215 |
|
TranMVSNet+NR-MVSNet | | | 88.84 145 | 87.95 150 | 91.49 146 | 92.68 231 | 83.01 118 | 94.92 117 | 96.31 100 | 89.88 37 | 85.53 195 | 93.85 171 | 76.63 144 | 96.96 230 | 81.91 199 | 79.87 316 | 94.50 206 |
|
CHOSEN 1792x2688 | | | 88.84 145 | 87.69 154 | 92.30 111 | 96.14 96 | 81.42 162 | 90.01 293 | 95.86 136 | 74.52 319 | 87.41 154 | 93.94 164 | 75.46 159 | 98.36 113 | 80.36 226 | 95.53 104 | 97.12 106 |
|
MVSTER | | | 88.84 145 | 88.29 142 | 90.51 186 | 92.95 224 | 80.44 188 | 93.73 192 | 95.01 189 | 84.66 169 | 87.15 159 | 93.12 194 | 72.79 198 | 97.21 216 | 87.86 114 | 87.36 234 | 93.87 236 |
|
OpenMVS |  | 83.78 11 | 88.74 148 | 87.29 164 | 93.08 72 | 92.70 230 | 85.39 64 | 96.57 35 | 96.43 94 | 78.74 275 | 80.85 286 | 96.07 81 | 69.64 235 | 99.01 61 | 78.01 253 | 96.65 91 | 94.83 188 |
|
thisisatest0530 | | | 88.67 149 | 87.61 156 | 91.86 130 | 94.87 148 | 80.07 198 | 94.63 135 | 89.90 332 | 84.00 177 | 88.46 135 | 93.78 173 | 66.88 265 | 98.46 105 | 83.30 173 | 92.65 161 | 97.06 107 |
|
Effi-MVS+-dtu | | | 88.65 150 | 88.35 138 | 89.54 230 | 93.33 210 | 76.39 280 | 94.47 145 | 94.36 220 | 87.70 102 | 85.43 205 | 89.56 296 | 73.45 189 | 97.26 211 | 85.57 146 | 91.28 172 | 94.97 178 |
|
tttt0517 | | | 88.61 151 | 87.78 153 | 91.11 164 | 94.96 142 | 77.81 257 | 95.35 86 | 89.69 335 | 85.09 160 | 88.05 142 | 94.59 139 | 66.93 263 | 98.48 102 | 83.27 174 | 92.13 168 | 97.03 110 |
|
BH-untuned | | | 88.60 152 | 88.13 146 | 90.01 213 | 95.24 130 | 78.50 238 | 93.29 211 | 94.15 228 | 84.75 167 | 84.46 228 | 93.40 181 | 75.76 153 | 97.40 198 | 77.59 256 | 94.52 127 | 94.12 223 |
|
NR-MVSNet | | | 88.58 153 | 87.47 160 | 91.93 125 | 93.04 219 | 84.16 86 | 94.77 127 | 96.25 106 | 89.05 59 | 80.04 300 | 93.29 187 | 79.02 117 | 97.05 226 | 81.71 206 | 80.05 313 | 94.59 196 |
|
1112_ss | | | 88.42 154 | 87.33 163 | 91.72 137 | 94.92 145 | 80.98 173 | 92.97 226 | 94.54 213 | 78.16 286 | 83.82 246 | 93.88 169 | 78.78 120 | 97.91 156 | 79.45 237 | 89.41 197 | 96.26 135 |
|
WR-MVS | | | 88.38 155 | 87.67 155 | 90.52 185 | 93.30 211 | 80.18 193 | 93.26 213 | 95.96 127 | 88.57 74 | 85.47 201 | 92.81 204 | 76.12 146 | 96.91 234 | 81.24 211 | 82.29 279 | 94.47 211 |
|
BH-RMVSNet | | | 88.37 156 | 87.48 159 | 91.02 169 | 95.28 127 | 79.45 217 | 92.89 228 | 93.07 255 | 85.45 151 | 86.91 166 | 94.84 128 | 70.35 226 | 97.76 161 | 73.97 290 | 94.59 124 | 95.85 152 |
|
IterMVS-LS | | | 88.36 157 | 87.91 152 | 89.70 226 | 93.80 197 | 78.29 245 | 93.73 192 | 95.08 188 | 85.73 142 | 84.75 220 | 91.90 237 | 79.88 105 | 96.92 233 | 83.83 167 | 82.51 276 | 93.89 233 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
X-MVStestdata | | | 88.31 158 | 86.13 204 | 94.85 23 | 98.54 13 | 86.60 31 | 96.93 22 | 97.19 33 | 90.66 22 | 92.85 57 | 23.41 374 | 85.02 54 | 99.49 24 | 91.99 61 | 98.56 46 | 98.47 31 |
|
LCM-MVSNet-Re | | | 88.30 159 | 88.32 141 | 88.27 260 | 94.71 156 | 72.41 321 | 93.15 217 | 90.98 312 | 87.77 100 | 79.25 308 | 91.96 235 | 78.35 127 | 95.75 292 | 83.04 176 | 95.62 103 | 96.65 123 |
|
jajsoiax | | | 88.24 160 | 87.50 158 | 90.48 189 | 90.89 290 | 80.14 195 | 95.31 88 | 95.65 152 | 84.97 162 | 84.24 239 | 94.02 159 | 65.31 280 | 97.42 191 | 88.56 106 | 88.52 214 | 93.89 233 |
|
VPNet | | | 88.20 161 | 87.47 160 | 90.39 194 | 93.56 205 | 79.46 216 | 94.04 176 | 95.54 159 | 88.67 70 | 86.96 163 | 94.58 140 | 69.33 240 | 97.15 218 | 84.05 164 | 80.53 308 | 94.56 199 |
|
TAPA-MVS | | 84.62 6 | 88.16 162 | 87.01 172 | 91.62 140 | 96.64 80 | 80.65 182 | 94.39 152 | 96.21 112 | 76.38 299 | 86.19 182 | 95.44 103 | 79.75 107 | 98.08 143 | 62.75 347 | 95.29 112 | 96.13 139 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
baseline1 | | | 88.10 163 | 87.28 165 | 90.57 181 | 94.96 142 | 80.07 198 | 94.27 159 | 91.29 305 | 86.74 121 | 87.41 154 | 94.00 161 | 76.77 141 | 96.20 272 | 80.77 219 | 79.31 321 | 95.44 165 |
|
Anonymous20240529 | | | 88.09 164 | 86.59 188 | 92.58 97 | 96.53 86 | 81.92 148 | 95.99 59 | 95.84 137 | 74.11 323 | 89.06 127 | 95.21 113 | 61.44 303 | 98.81 84 | 83.67 171 | 87.47 231 | 97.01 111 |
|
HyFIR lowres test | | | 88.09 164 | 86.81 176 | 91.93 125 | 96.00 105 | 80.63 183 | 90.01 293 | 95.79 140 | 73.42 329 | 87.68 150 | 92.10 229 | 73.86 183 | 97.96 152 | 80.75 220 | 91.70 169 | 97.19 102 |
|
mvs_tets | | | 88.06 166 | 87.28 165 | 90.38 196 | 90.94 286 | 79.88 207 | 95.22 97 | 95.66 150 | 85.10 159 | 84.21 240 | 93.94 164 | 63.53 290 | 97.40 198 | 88.50 107 | 88.40 218 | 93.87 236 |
|
F-COLMAP | | | 87.95 167 | 86.80 177 | 91.40 150 | 96.35 92 | 80.88 177 | 94.73 129 | 95.45 166 | 79.65 261 | 82.04 274 | 94.61 137 | 71.13 212 | 98.50 101 | 76.24 271 | 91.05 177 | 94.80 190 |
|
LS3D | | | 87.89 168 | 86.32 198 | 92.59 96 | 96.07 102 | 82.92 122 | 95.23 96 | 94.92 196 | 75.66 306 | 82.89 264 | 95.98 84 | 72.48 202 | 99.21 43 | 68.43 321 | 95.23 115 | 95.64 162 |
|
anonymousdsp | | | 87.84 169 | 87.09 168 | 90.12 206 | 89.13 323 | 80.54 186 | 94.67 133 | 95.55 157 | 82.05 219 | 83.82 246 | 92.12 226 | 71.47 210 | 97.15 218 | 87.15 126 | 87.80 230 | 92.67 287 |
|
v2v482 | | | 87.84 169 | 87.06 169 | 90.17 202 | 90.99 282 | 79.23 228 | 94.00 181 | 95.13 183 | 84.87 163 | 85.53 195 | 92.07 232 | 74.45 171 | 97.45 187 | 84.71 156 | 81.75 287 | 93.85 239 |
|
WR-MVS_H | | | 87.80 171 | 87.37 162 | 89.10 241 | 93.23 212 | 78.12 248 | 95.61 80 | 97.30 27 | 87.90 95 | 83.72 248 | 92.01 234 | 79.65 113 | 96.01 280 | 76.36 268 | 80.54 307 | 93.16 272 |
|
AUN-MVS | | | 87.78 172 | 86.54 190 | 91.48 147 | 94.82 152 | 81.05 171 | 93.91 188 | 93.93 235 | 83.00 202 | 86.93 164 | 93.53 179 | 69.50 237 | 97.67 166 | 86.14 136 | 77.12 331 | 95.73 160 |
|
PCF-MVS | | 84.11 10 | 87.74 173 | 86.08 208 | 92.70 91 | 94.02 185 | 84.43 82 | 89.27 303 | 95.87 135 | 73.62 328 | 84.43 230 | 94.33 145 | 78.48 126 | 98.86 79 | 70.27 307 | 94.45 129 | 94.81 189 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Anonymous202405211 | | | 87.68 174 | 86.13 204 | 92.31 110 | 96.66 79 | 80.74 181 | 94.87 120 | 91.49 300 | 80.47 251 | 89.46 121 | 95.44 103 | 54.72 335 | 98.23 124 | 82.19 193 | 89.89 190 | 97.97 70 |
|
V42 | | | 87.68 174 | 86.86 174 | 90.15 204 | 90.58 302 | 80.14 195 | 94.24 162 | 95.28 177 | 83.66 185 | 85.67 189 | 91.33 252 | 74.73 168 | 97.41 196 | 84.43 160 | 81.83 285 | 92.89 282 |
|
thres600view7 | | | 87.65 176 | 86.67 183 | 90.59 180 | 96.08 101 | 78.72 231 | 94.88 119 | 91.58 296 | 87.06 113 | 88.08 140 | 92.30 219 | 68.91 248 | 98.10 133 | 70.05 314 | 91.10 173 | 94.96 181 |
|
XXY-MVS | | | 87.65 176 | 86.85 175 | 90.03 210 | 92.14 241 | 80.60 185 | 93.76 191 | 95.23 179 | 82.94 204 | 84.60 223 | 94.02 159 | 74.27 173 | 95.49 303 | 81.04 213 | 83.68 263 | 94.01 231 |
|
Test_1112_low_res | | | 87.65 176 | 86.51 191 | 91.08 165 | 94.94 144 | 79.28 225 | 91.77 258 | 94.30 222 | 76.04 304 | 83.51 255 | 92.37 216 | 77.86 133 | 97.73 165 | 78.69 245 | 89.13 204 | 96.22 136 |
|
thres100view900 | | | 87.63 179 | 86.71 181 | 90.38 196 | 96.12 97 | 78.55 235 | 95.03 111 | 91.58 296 | 87.15 110 | 88.06 141 | 92.29 220 | 68.91 248 | 98.10 133 | 70.13 311 | 91.10 173 | 94.48 209 |
|
CP-MVSNet | | | 87.63 179 | 87.26 167 | 88.74 250 | 93.12 215 | 76.59 277 | 95.29 92 | 96.58 88 | 88.43 77 | 83.49 256 | 92.98 198 | 75.28 160 | 95.83 288 | 78.97 243 | 81.15 295 | 93.79 241 |
|
thres400 | | | 87.62 181 | 86.64 184 | 90.57 181 | 95.99 106 | 78.64 233 | 94.58 137 | 91.98 286 | 86.94 117 | 88.09 138 | 91.77 239 | 69.18 245 | 98.10 133 | 70.13 311 | 91.10 173 | 94.96 181 |
|
v1144 | | | 87.61 182 | 86.79 178 | 90.06 209 | 91.01 281 | 79.34 221 | 93.95 183 | 95.42 171 | 83.36 195 | 85.66 190 | 91.31 255 | 74.98 164 | 97.42 191 | 83.37 172 | 82.06 281 | 93.42 262 |
|
bld_raw_dy_0_64 | | | 87.60 183 | 86.73 179 | 90.21 200 | 91.72 255 | 80.26 192 | 95.09 107 | 88.61 340 | 85.68 144 | 85.55 192 | 94.38 143 | 63.93 288 | 96.66 242 | 87.73 116 | 87.84 229 | 93.72 250 |
|
tfpn200view9 | | | 87.58 184 | 86.64 184 | 90.41 193 | 95.99 106 | 78.64 233 | 94.58 137 | 91.98 286 | 86.94 117 | 88.09 138 | 91.77 239 | 69.18 245 | 98.10 133 | 70.13 311 | 91.10 173 | 94.48 209 |
|
BH-w/o | | | 87.57 185 | 87.05 170 | 89.12 240 | 94.90 147 | 77.90 253 | 92.41 239 | 93.51 248 | 82.89 206 | 83.70 249 | 91.34 251 | 75.75 154 | 97.07 224 | 75.49 276 | 93.49 145 | 92.39 296 |
|
UniMVSNet_ETH3D | | | 87.53 186 | 86.37 195 | 91.00 171 | 92.44 234 | 78.96 230 | 94.74 128 | 95.61 154 | 84.07 176 | 85.36 212 | 94.52 141 | 59.78 316 | 97.34 203 | 82.93 178 | 87.88 227 | 96.71 122 |
|
ET-MVSNet_ETH3D | | | 87.51 187 | 85.91 216 | 92.32 109 | 93.70 202 | 83.93 90 | 92.33 244 | 90.94 313 | 84.16 173 | 72.09 346 | 92.52 212 | 69.90 230 | 95.85 287 | 89.20 100 | 88.36 219 | 97.17 103 |
|
1314 | | | 87.51 187 | 86.57 189 | 90.34 198 | 92.42 235 | 79.74 211 | 92.63 234 | 95.35 176 | 78.35 281 | 80.14 297 | 91.62 246 | 74.05 179 | 97.15 218 | 81.05 212 | 93.53 143 | 94.12 223 |
|
v8 | | | 87.50 189 | 86.71 181 | 89.89 216 | 91.37 268 | 79.40 218 | 94.50 141 | 95.38 172 | 84.81 166 | 83.60 253 | 91.33 252 | 76.05 147 | 97.42 191 | 82.84 181 | 80.51 310 | 92.84 284 |
|
Fast-Effi-MVS+-dtu | | | 87.44 190 | 86.72 180 | 89.63 228 | 92.04 245 | 77.68 262 | 94.03 177 | 93.94 234 | 85.81 139 | 82.42 268 | 91.32 254 | 70.33 227 | 97.06 225 | 80.33 228 | 90.23 184 | 94.14 222 |
|
MVS | | | 87.44 190 | 86.10 207 | 91.44 149 | 92.61 232 | 83.62 99 | 92.63 234 | 95.66 150 | 67.26 351 | 81.47 278 | 92.15 224 | 77.95 130 | 98.22 126 | 79.71 234 | 95.48 106 | 92.47 293 |
|
FE-MVS | | | 87.40 192 | 86.02 210 | 91.57 143 | 94.56 165 | 79.69 212 | 90.27 283 | 93.72 245 | 80.57 250 | 88.80 130 | 91.62 246 | 65.32 279 | 98.59 97 | 74.97 284 | 94.33 132 | 96.44 129 |
|
FMVSNet3 | | | 87.40 192 | 86.11 206 | 91.30 155 | 93.79 199 | 83.64 98 | 94.20 164 | 94.81 205 | 83.89 180 | 84.37 231 | 91.87 238 | 68.45 254 | 96.56 253 | 78.23 250 | 85.36 248 | 93.70 252 |
|
test_fmvs1 | | | 87.34 194 | 87.56 157 | 86.68 300 | 90.59 301 | 71.80 325 | 94.01 179 | 94.04 233 | 78.30 282 | 91.97 80 | 95.22 111 | 56.28 328 | 93.71 327 | 92.89 36 | 94.71 119 | 94.52 201 |
|
thisisatest0515 | | | 87.33 195 | 85.99 211 | 91.37 152 | 93.49 206 | 79.55 214 | 90.63 279 | 89.56 338 | 80.17 253 | 87.56 152 | 90.86 268 | 67.07 262 | 98.28 122 | 81.50 208 | 93.02 156 | 96.29 133 |
|
PS-CasMVS | | | 87.32 196 | 86.88 173 | 88.63 253 | 92.99 222 | 76.33 282 | 95.33 87 | 96.61 86 | 88.22 85 | 83.30 261 | 93.07 196 | 73.03 196 | 95.79 291 | 78.36 247 | 81.00 301 | 93.75 248 |
|
GBi-Net | | | 87.26 197 | 85.98 212 | 91.08 165 | 94.01 186 | 83.10 112 | 95.14 104 | 94.94 192 | 83.57 187 | 84.37 231 | 91.64 242 | 66.59 270 | 96.34 268 | 78.23 250 | 85.36 248 | 93.79 241 |
|
test1 | | | 87.26 197 | 85.98 212 | 91.08 165 | 94.01 186 | 83.10 112 | 95.14 104 | 94.94 192 | 83.57 187 | 84.37 231 | 91.64 242 | 66.59 270 | 96.34 268 | 78.23 250 | 85.36 248 | 93.79 241 |
|
v1192 | | | 87.25 199 | 86.33 197 | 90.00 214 | 90.76 295 | 79.04 229 | 93.80 189 | 95.48 162 | 82.57 210 | 85.48 200 | 91.18 259 | 73.38 192 | 97.42 191 | 82.30 191 | 82.06 281 | 93.53 256 |
|
v10 | | | 87.25 199 | 86.38 194 | 89.85 217 | 91.19 274 | 79.50 215 | 94.48 142 | 95.45 166 | 83.79 183 | 83.62 252 | 91.19 257 | 75.13 161 | 97.42 191 | 81.94 198 | 80.60 305 | 92.63 289 |
|
DP-MVS | | | 87.25 199 | 85.36 231 | 92.90 81 | 97.65 55 | 83.24 108 | 94.81 124 | 92.00 284 | 74.99 314 | 81.92 276 | 95.00 119 | 72.66 199 | 99.05 53 | 66.92 332 | 92.33 166 | 96.40 130 |
|
miper_ehance_all_eth | | | 87.22 202 | 86.62 187 | 89.02 244 | 92.13 242 | 77.40 267 | 90.91 275 | 94.81 205 | 81.28 241 | 84.32 236 | 90.08 285 | 79.26 115 | 96.62 245 | 83.81 168 | 82.94 271 | 93.04 277 |
|
test2506 | | | 87.21 203 | 86.28 200 | 90.02 212 | 95.62 119 | 73.64 304 | 96.25 46 | 71.38 374 | 87.89 97 | 90.45 106 | 96.65 57 | 55.29 333 | 98.09 141 | 86.03 140 | 96.94 82 | 98.33 41 |
|
thres200 | | | 87.21 203 | 86.24 202 | 90.12 206 | 95.36 125 | 78.53 236 | 93.26 213 | 92.10 280 | 86.42 127 | 88.00 143 | 91.11 263 | 69.24 244 | 98.00 149 | 69.58 315 | 91.04 178 | 93.83 240 |
|
v144192 | | | 87.19 205 | 86.35 196 | 89.74 223 | 90.64 299 | 78.24 246 | 93.92 186 | 95.43 169 | 81.93 224 | 85.51 197 | 91.05 265 | 74.21 176 | 97.45 187 | 82.86 180 | 81.56 289 | 93.53 256 |
|
FMVSNet2 | | | 87.19 205 | 85.82 218 | 91.30 155 | 94.01 186 | 83.67 97 | 94.79 125 | 94.94 192 | 83.57 187 | 83.88 245 | 92.05 233 | 66.59 270 | 96.51 256 | 77.56 257 | 85.01 251 | 93.73 249 |
|
c3_l | | | 87.14 207 | 86.50 192 | 89.04 243 | 92.20 239 | 77.26 268 | 91.22 271 | 94.70 210 | 82.01 222 | 84.34 235 | 90.43 278 | 78.81 119 | 96.61 248 | 83.70 170 | 81.09 296 | 93.25 267 |
|
Baseline_NR-MVSNet | | | 87.07 208 | 86.63 186 | 88.40 256 | 91.44 263 | 77.87 255 | 94.23 163 | 92.57 267 | 84.12 175 | 85.74 188 | 92.08 230 | 77.25 136 | 96.04 277 | 82.29 192 | 79.94 314 | 91.30 316 |
|
v148 | | | 87.04 209 | 86.32 198 | 89.21 237 | 90.94 286 | 77.26 268 | 93.71 194 | 94.43 216 | 84.84 165 | 84.36 234 | 90.80 271 | 76.04 148 | 97.05 226 | 82.12 194 | 79.60 318 | 93.31 264 |
|
test_fmvs1_n | | | 87.03 210 | 87.04 171 | 86.97 292 | 89.74 319 | 71.86 323 | 94.55 139 | 94.43 216 | 78.47 278 | 91.95 82 | 95.50 102 | 51.16 345 | 93.81 325 | 93.02 35 | 94.56 125 | 95.26 171 |
|
v1921920 | | | 86.97 211 | 86.06 209 | 89.69 227 | 90.53 305 | 78.11 249 | 93.80 189 | 95.43 169 | 81.90 226 | 85.33 213 | 91.05 265 | 72.66 199 | 97.41 196 | 82.05 196 | 81.80 286 | 93.53 256 |
|
tt0805 | | | 86.92 212 | 85.74 224 | 90.48 189 | 92.22 238 | 79.98 205 | 95.63 79 | 94.88 199 | 83.83 182 | 84.74 221 | 92.80 205 | 57.61 324 | 97.67 166 | 85.48 147 | 84.42 255 | 93.79 241 |
|
miper_enhance_ethall | | | 86.90 213 | 86.18 203 | 89.06 242 | 91.66 260 | 77.58 264 | 90.22 289 | 94.82 204 | 79.16 267 | 84.48 227 | 89.10 299 | 79.19 116 | 96.66 242 | 84.06 163 | 82.94 271 | 92.94 280 |
|
v7n | | | 86.81 214 | 85.76 222 | 89.95 215 | 90.72 297 | 79.25 227 | 95.07 108 | 95.92 129 | 84.45 172 | 82.29 269 | 90.86 268 | 72.60 201 | 97.53 181 | 79.42 240 | 80.52 309 | 93.08 276 |
|
PEN-MVS | | | 86.80 215 | 86.27 201 | 88.40 256 | 92.32 237 | 75.71 288 | 95.18 100 | 96.38 97 | 87.97 92 | 82.82 265 | 93.15 192 | 73.39 191 | 95.92 283 | 76.15 272 | 79.03 323 | 93.59 254 |
|
cl22 | | | 86.78 216 | 85.98 212 | 89.18 239 | 92.34 236 | 77.62 263 | 90.84 276 | 94.13 230 | 81.33 240 | 83.97 244 | 90.15 283 | 73.96 181 | 96.60 250 | 84.19 162 | 82.94 271 | 93.33 263 |
|
v1240 | | | 86.78 216 | 85.85 217 | 89.56 229 | 90.45 306 | 77.79 258 | 93.61 197 | 95.37 174 | 81.65 232 | 85.43 205 | 91.15 261 | 71.50 209 | 97.43 190 | 81.47 209 | 82.05 283 | 93.47 260 |
|
TR-MVS | | | 86.78 216 | 85.76 222 | 89.82 219 | 94.37 174 | 78.41 240 | 92.47 238 | 92.83 260 | 81.11 246 | 86.36 178 | 92.40 215 | 68.73 251 | 97.48 184 | 73.75 293 | 89.85 192 | 93.57 255 |
|
PatchMatch-RL | | | 86.77 219 | 85.54 225 | 90.47 192 | 95.88 109 | 82.71 130 | 90.54 280 | 92.31 273 | 79.82 259 | 84.32 236 | 91.57 250 | 68.77 250 | 96.39 264 | 73.16 295 | 93.48 147 | 92.32 299 |
|
PAPM | | | 86.68 220 | 85.39 229 | 90.53 183 | 93.05 218 | 79.33 224 | 89.79 296 | 94.77 208 | 78.82 272 | 81.95 275 | 93.24 189 | 76.81 139 | 97.30 205 | 66.94 330 | 93.16 154 | 94.95 184 |
|
pm-mvs1 | | | 86.61 221 | 85.54 225 | 89.82 219 | 91.44 263 | 80.18 193 | 95.28 94 | 94.85 201 | 83.84 181 | 81.66 277 | 92.62 209 | 72.45 204 | 96.48 258 | 79.67 235 | 78.06 324 | 92.82 285 |
|
GA-MVS | | | 86.61 221 | 85.27 233 | 90.66 179 | 91.33 271 | 78.71 232 | 90.40 282 | 93.81 242 | 85.34 153 | 85.12 215 | 89.57 295 | 61.25 305 | 97.11 222 | 80.99 216 | 89.59 196 | 96.15 137 |
|
Anonymous20231211 | | | 86.59 223 | 85.13 235 | 90.98 174 | 96.52 87 | 81.50 156 | 96.14 51 | 96.16 113 | 73.78 326 | 83.65 251 | 92.15 224 | 63.26 292 | 97.37 202 | 82.82 182 | 81.74 288 | 94.06 228 |
|
test_vis1_n | | | 86.56 224 | 86.49 193 | 86.78 299 | 88.51 328 | 72.69 313 | 94.68 132 | 93.78 243 | 79.55 262 | 90.70 103 | 95.31 107 | 48.75 350 | 93.28 333 | 93.15 32 | 93.99 134 | 94.38 213 |
|
DIV-MVS_self_test | | | 86.53 225 | 85.78 219 | 88.75 248 | 92.02 247 | 76.45 279 | 90.74 277 | 94.30 222 | 81.83 230 | 83.34 259 | 90.82 270 | 75.75 154 | 96.57 251 | 81.73 205 | 81.52 291 | 93.24 268 |
|
cl____ | | | 86.52 226 | 85.78 219 | 88.75 248 | 92.03 246 | 76.46 278 | 90.74 277 | 94.30 222 | 81.83 230 | 83.34 259 | 90.78 272 | 75.74 156 | 96.57 251 | 81.74 204 | 81.54 290 | 93.22 269 |
|
eth_miper_zixun_eth | | | 86.50 227 | 85.77 221 | 88.68 251 | 91.94 248 | 75.81 287 | 90.47 281 | 94.89 197 | 82.05 219 | 84.05 241 | 90.46 277 | 75.96 149 | 96.77 238 | 82.76 184 | 79.36 320 | 93.46 261 |
|
baseline2 | | | 86.50 227 | 85.39 229 | 89.84 218 | 91.12 278 | 76.70 275 | 91.88 255 | 88.58 341 | 82.35 215 | 79.95 301 | 90.95 267 | 73.42 190 | 97.63 173 | 80.27 229 | 89.95 189 | 95.19 173 |
|
EPNet_dtu | | | 86.49 229 | 85.94 215 | 88.14 265 | 90.24 309 | 72.82 311 | 94.11 168 | 92.20 276 | 86.66 124 | 79.42 307 | 92.36 217 | 73.52 187 | 95.81 290 | 71.26 301 | 93.66 139 | 95.80 156 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
cascas | | | 86.43 230 | 84.98 238 | 90.80 177 | 92.10 244 | 80.92 176 | 90.24 287 | 95.91 131 | 73.10 332 | 83.57 254 | 88.39 310 | 65.15 281 | 97.46 186 | 84.90 153 | 91.43 171 | 94.03 230 |
|
SCA | | | 86.32 231 | 85.18 234 | 89.73 225 | 92.15 240 | 76.60 276 | 91.12 272 | 91.69 293 | 83.53 190 | 85.50 198 | 88.81 303 | 66.79 266 | 96.48 258 | 76.65 265 | 90.35 183 | 96.12 140 |
|
LTVRE_ROB | | 82.13 13 | 86.26 232 | 84.90 241 | 90.34 198 | 94.44 172 | 81.50 156 | 92.31 246 | 94.89 197 | 83.03 201 | 79.63 305 | 92.67 207 | 69.69 234 | 97.79 159 | 71.20 302 | 86.26 244 | 91.72 308 |
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 |
DTE-MVSNet | | | 86.11 233 | 85.48 227 | 87.98 269 | 91.65 261 | 74.92 293 | 94.93 116 | 95.75 143 | 87.36 108 | 82.26 270 | 93.04 197 | 72.85 197 | 95.82 289 | 74.04 289 | 77.46 329 | 93.20 270 |
|
XVG-ACMP-BASELINE | | | 86.00 234 | 84.84 243 | 89.45 234 | 91.20 273 | 78.00 250 | 91.70 261 | 95.55 157 | 85.05 161 | 82.97 263 | 92.25 222 | 54.49 336 | 97.48 184 | 82.93 178 | 87.45 233 | 92.89 282 |
|
MVP-Stereo | | | 85.97 235 | 84.86 242 | 89.32 235 | 90.92 288 | 82.19 142 | 92.11 252 | 94.19 226 | 78.76 274 | 78.77 311 | 91.63 245 | 68.38 255 | 96.56 253 | 75.01 283 | 93.95 135 | 89.20 341 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
D2MVS | | | 85.90 236 | 85.09 236 | 88.35 258 | 90.79 293 | 77.42 266 | 91.83 257 | 95.70 146 | 80.77 249 | 80.08 299 | 90.02 286 | 66.74 268 | 96.37 265 | 81.88 200 | 87.97 226 | 91.26 317 |
|
test-LLR | | | 85.87 237 | 85.41 228 | 87.25 285 | 90.95 284 | 71.67 327 | 89.55 297 | 89.88 333 | 83.41 193 | 84.54 225 | 87.95 317 | 67.25 258 | 95.11 309 | 81.82 201 | 93.37 150 | 94.97 178 |
|
FMVSNet1 | | | 85.85 238 | 84.11 252 | 91.08 165 | 92.81 228 | 83.10 112 | 95.14 104 | 94.94 192 | 81.64 233 | 82.68 266 | 91.64 242 | 59.01 320 | 96.34 268 | 75.37 278 | 83.78 260 | 93.79 241 |
|
PatchmatchNet |  | | 85.85 238 | 84.70 245 | 89.29 236 | 91.76 254 | 75.54 289 | 88.49 315 | 91.30 304 | 81.63 234 | 85.05 216 | 88.70 307 | 71.71 206 | 96.24 271 | 74.61 287 | 89.05 205 | 96.08 143 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
CostFormer | | | 85.77 240 | 84.94 240 | 88.26 261 | 91.16 277 | 72.58 319 | 89.47 301 | 91.04 311 | 76.26 302 | 86.45 176 | 89.97 288 | 70.74 219 | 96.86 237 | 82.35 190 | 87.07 239 | 95.34 170 |
|
PMMVS | | | 85.71 241 | 84.96 239 | 87.95 270 | 88.90 326 | 77.09 270 | 88.68 313 | 90.06 327 | 72.32 338 | 86.47 173 | 90.76 273 | 72.15 205 | 94.40 315 | 81.78 203 | 93.49 145 | 92.36 297 |
|
PVSNet | | 78.82 18 | 85.55 242 | 84.65 246 | 88.23 263 | 94.72 155 | 71.93 322 | 87.12 330 | 92.75 263 | 78.80 273 | 84.95 218 | 90.53 276 | 64.43 285 | 96.71 241 | 74.74 285 | 93.86 137 | 96.06 145 |
|
IterMVS-SCA-FT | | | 85.45 243 | 84.53 249 | 88.18 264 | 91.71 257 | 76.87 273 | 90.19 290 | 92.65 266 | 85.40 152 | 81.44 279 | 90.54 275 | 66.79 266 | 95.00 312 | 81.04 213 | 81.05 297 | 92.66 288 |
|
pmmvs4 | | | 85.43 244 | 83.86 257 | 90.16 203 | 90.02 314 | 82.97 120 | 90.27 283 | 92.67 265 | 75.93 305 | 80.73 287 | 91.74 241 | 71.05 213 | 95.73 294 | 78.85 244 | 83.46 267 | 91.78 307 |
|
mvsany_test1 | | | 85.42 245 | 85.30 232 | 85.77 310 | 87.95 339 | 75.41 291 | 87.61 327 | 80.97 363 | 76.82 296 | 88.68 131 | 95.83 90 | 77.44 135 | 90.82 351 | 85.90 141 | 86.51 242 | 91.08 325 |
|
ACMH | | 80.38 17 | 85.36 246 | 83.68 259 | 90.39 194 | 94.45 171 | 80.63 183 | 94.73 129 | 94.85 201 | 82.09 218 | 77.24 319 | 92.65 208 | 60.01 314 | 97.58 175 | 72.25 299 | 84.87 252 | 92.96 279 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OurMVSNet-221017-0 | | | 85.35 247 | 84.64 247 | 87.49 279 | 90.77 294 | 72.59 318 | 94.01 179 | 94.40 219 | 84.72 168 | 79.62 306 | 93.17 191 | 61.91 299 | 96.72 239 | 81.99 197 | 81.16 293 | 93.16 272 |
|
CR-MVSNet | | | 85.35 247 | 83.76 258 | 90.12 206 | 90.58 302 | 79.34 221 | 85.24 342 | 91.96 288 | 78.27 283 | 85.55 192 | 87.87 320 | 71.03 214 | 95.61 295 | 73.96 291 | 89.36 199 | 95.40 167 |
|
tpmrst | | | 85.35 247 | 84.99 237 | 86.43 302 | 90.88 291 | 67.88 348 | 88.71 312 | 91.43 302 | 80.13 254 | 86.08 184 | 88.80 305 | 73.05 194 | 96.02 279 | 82.48 186 | 83.40 269 | 95.40 167 |
|
miper_lstm_enhance | | | 85.27 250 | 84.59 248 | 87.31 282 | 91.28 272 | 74.63 294 | 87.69 324 | 94.09 232 | 81.20 245 | 81.36 281 | 89.85 291 | 74.97 165 | 94.30 318 | 81.03 215 | 79.84 317 | 93.01 278 |
|
IB-MVS | | 80.51 15 | 85.24 251 | 83.26 263 | 91.19 158 | 92.13 242 | 79.86 208 | 91.75 259 | 91.29 305 | 83.28 197 | 80.66 289 | 88.49 309 | 61.28 304 | 98.46 105 | 80.99 216 | 79.46 319 | 95.25 172 |
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 |
CHOSEN 280x420 | | | 85.15 252 | 83.99 255 | 88.65 252 | 92.47 233 | 78.40 241 | 79.68 362 | 92.76 262 | 74.90 316 | 81.41 280 | 89.59 294 | 69.85 233 | 95.51 300 | 79.92 233 | 95.29 112 | 92.03 303 |
|
RPSCF | | | 85.07 253 | 84.27 250 | 87.48 280 | 92.91 225 | 70.62 337 | 91.69 262 | 92.46 268 | 76.20 303 | 82.67 267 | 95.22 111 | 63.94 287 | 97.29 208 | 77.51 258 | 85.80 246 | 94.53 200 |
|
MS-PatchMatch | | | 85.05 254 | 84.16 251 | 87.73 273 | 91.42 266 | 78.51 237 | 91.25 270 | 93.53 247 | 77.50 289 | 80.15 296 | 91.58 248 | 61.99 298 | 95.51 300 | 75.69 275 | 94.35 131 | 89.16 342 |
|
ACMH+ | | 81.04 14 | 85.05 254 | 83.46 262 | 89.82 219 | 94.66 159 | 79.37 219 | 94.44 147 | 94.12 231 | 82.19 217 | 78.04 314 | 92.82 203 | 58.23 322 | 97.54 180 | 73.77 292 | 82.90 274 | 92.54 290 |
|
IterMVS | | | 84.88 256 | 83.98 256 | 87.60 275 | 91.44 263 | 76.03 284 | 90.18 291 | 92.41 269 | 83.24 198 | 81.06 285 | 90.42 279 | 66.60 269 | 94.28 319 | 79.46 236 | 80.98 302 | 92.48 292 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MSDG | | | 84.86 257 | 83.09 265 | 90.14 205 | 93.80 197 | 80.05 200 | 89.18 306 | 93.09 254 | 78.89 270 | 78.19 312 | 91.91 236 | 65.86 278 | 97.27 209 | 68.47 320 | 88.45 216 | 93.11 274 |
|
tpm | | | 84.73 258 | 84.02 254 | 86.87 297 | 90.33 307 | 68.90 344 | 89.06 308 | 89.94 330 | 80.85 248 | 85.75 187 | 89.86 290 | 68.54 253 | 95.97 281 | 77.76 254 | 84.05 259 | 95.75 157 |
|
tfpnnormal | | | 84.72 259 | 83.23 264 | 89.20 238 | 92.79 229 | 80.05 200 | 94.48 142 | 95.81 138 | 82.38 213 | 81.08 284 | 91.21 256 | 69.01 247 | 96.95 231 | 61.69 349 | 80.59 306 | 90.58 331 |
|
CVMVSNet | | | 84.69 260 | 84.79 244 | 84.37 321 | 91.84 251 | 64.92 357 | 93.70 195 | 91.47 301 | 66.19 353 | 86.16 183 | 95.28 108 | 67.18 260 | 93.33 332 | 80.89 218 | 90.42 182 | 94.88 186 |
|
test-mter | | | 84.54 261 | 83.64 260 | 87.25 285 | 90.95 284 | 71.67 327 | 89.55 297 | 89.88 333 | 79.17 266 | 84.54 225 | 87.95 317 | 55.56 330 | 95.11 309 | 81.82 201 | 93.37 150 | 94.97 178 |
|
TransMVSNet (Re) | | | 84.43 262 | 83.06 266 | 88.54 254 | 91.72 255 | 78.44 239 | 95.18 100 | 92.82 261 | 82.73 208 | 79.67 304 | 92.12 226 | 73.49 188 | 95.96 282 | 71.10 306 | 68.73 353 | 91.21 319 |
|
pmmvs5 | | | 84.21 263 | 82.84 270 | 88.34 259 | 88.95 325 | 76.94 272 | 92.41 239 | 91.91 290 | 75.63 307 | 80.28 294 | 91.18 259 | 64.59 284 | 95.57 296 | 77.09 263 | 83.47 266 | 92.53 291 |
|
tpm2 | | | 84.08 264 | 82.94 267 | 87.48 280 | 91.39 267 | 71.27 329 | 89.23 305 | 90.37 321 | 71.95 340 | 84.64 222 | 89.33 297 | 67.30 257 | 96.55 255 | 75.17 280 | 87.09 238 | 94.63 193 |
|
test_fmvs2 | | | 83.98 265 | 84.03 253 | 83.83 326 | 87.16 341 | 67.53 351 | 93.93 185 | 92.89 258 | 77.62 288 | 86.89 169 | 93.53 179 | 47.18 355 | 92.02 344 | 90.54 88 | 86.51 242 | 91.93 305 |
|
COLMAP_ROB |  | 80.39 16 | 83.96 266 | 82.04 273 | 89.74 223 | 95.28 127 | 79.75 210 | 94.25 160 | 92.28 274 | 75.17 312 | 78.02 315 | 93.77 174 | 58.60 321 | 97.84 158 | 65.06 340 | 85.92 245 | 91.63 310 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
RPMNet | | | 83.95 267 | 81.53 277 | 91.21 157 | 90.58 302 | 79.34 221 | 85.24 342 | 96.76 72 | 71.44 342 | 85.55 192 | 82.97 350 | 70.87 217 | 98.91 75 | 61.01 351 | 89.36 199 | 95.40 167 |
|
SixPastTwentyTwo | | | 83.91 268 | 82.90 268 | 86.92 294 | 90.99 282 | 70.67 336 | 93.48 201 | 91.99 285 | 85.54 149 | 77.62 318 | 92.11 228 | 60.59 310 | 96.87 236 | 76.05 273 | 77.75 326 | 93.20 270 |
|
EPMVS | | | 83.90 269 | 82.70 271 | 87.51 277 | 90.23 310 | 72.67 314 | 88.62 314 | 81.96 361 | 81.37 239 | 85.01 217 | 88.34 311 | 66.31 273 | 94.45 314 | 75.30 279 | 87.12 237 | 95.43 166 |
|
TESTMET0.1,1 | | | 83.74 270 | 82.85 269 | 86.42 303 | 89.96 315 | 71.21 331 | 89.55 297 | 87.88 343 | 77.41 290 | 83.37 258 | 87.31 325 | 56.71 326 | 93.65 329 | 80.62 223 | 92.85 160 | 94.40 212 |
|
MVS_0304 | | | 83.46 271 | 81.92 274 | 88.10 266 | 90.63 300 | 77.49 265 | 93.26 213 | 93.75 244 | 80.04 256 | 80.44 293 | 87.24 327 | 47.94 352 | 95.55 297 | 75.79 274 | 88.16 221 | 91.26 317 |
|
pmmvs6 | | | 83.42 272 | 81.60 276 | 88.87 246 | 88.01 337 | 77.87 255 | 94.96 114 | 94.24 225 | 74.67 318 | 78.80 310 | 91.09 264 | 60.17 313 | 96.49 257 | 77.06 264 | 75.40 337 | 92.23 301 |
|
AllTest | | | 83.42 272 | 81.39 278 | 89.52 231 | 95.01 138 | 77.79 258 | 93.12 218 | 90.89 315 | 77.41 290 | 76.12 327 | 93.34 182 | 54.08 338 | 97.51 182 | 68.31 322 | 84.27 257 | 93.26 265 |
|
tpmvs | | | 83.35 274 | 82.07 272 | 87.20 289 | 91.07 280 | 71.00 334 | 88.31 318 | 91.70 292 | 78.91 269 | 80.49 292 | 87.18 328 | 69.30 243 | 97.08 223 | 68.12 325 | 83.56 265 | 93.51 259 |
|
USDC | | | 82.76 275 | 81.26 280 | 87.26 284 | 91.17 275 | 74.55 295 | 89.27 303 | 93.39 250 | 78.26 284 | 75.30 332 | 92.08 230 | 54.43 337 | 96.63 244 | 71.64 300 | 85.79 247 | 90.61 328 |
|
Patchmtry | | | 82.71 276 | 80.93 282 | 88.06 267 | 90.05 313 | 76.37 281 | 84.74 347 | 91.96 288 | 72.28 339 | 81.32 282 | 87.87 320 | 71.03 214 | 95.50 302 | 68.97 317 | 80.15 312 | 92.32 299 |
|
PatchT | | | 82.68 277 | 81.27 279 | 86.89 296 | 90.09 312 | 70.94 335 | 84.06 349 | 90.15 324 | 74.91 315 | 85.63 191 | 83.57 346 | 69.37 238 | 94.87 313 | 65.19 337 | 88.50 215 | 94.84 187 |
|
MIMVSNet | | | 82.59 278 | 80.53 283 | 88.76 247 | 91.51 262 | 78.32 243 | 86.57 333 | 90.13 325 | 79.32 263 | 80.70 288 | 88.69 308 | 52.98 342 | 93.07 337 | 66.03 335 | 88.86 209 | 94.90 185 |
|
test0.0.03 1 | | | 82.41 279 | 81.69 275 | 84.59 319 | 88.23 334 | 72.89 310 | 90.24 287 | 87.83 344 | 83.41 193 | 79.86 302 | 89.78 292 | 67.25 258 | 88.99 359 | 65.18 338 | 83.42 268 | 91.90 306 |
|
EG-PatchMatch MVS | | | 82.37 280 | 80.34 286 | 88.46 255 | 90.27 308 | 79.35 220 | 92.80 231 | 94.33 221 | 77.14 294 | 73.26 343 | 90.18 282 | 47.47 354 | 96.72 239 | 70.25 308 | 87.32 236 | 89.30 339 |
|
tpm cat1 | | | 81.96 281 | 80.27 287 | 87.01 291 | 91.09 279 | 71.02 333 | 87.38 328 | 91.53 299 | 66.25 352 | 80.17 295 | 86.35 334 | 68.22 256 | 96.15 275 | 69.16 316 | 82.29 279 | 93.86 238 |
|
our_test_3 | | | 81.93 282 | 80.46 285 | 86.33 304 | 88.46 331 | 73.48 306 | 88.46 316 | 91.11 307 | 76.46 297 | 76.69 323 | 88.25 313 | 66.89 264 | 94.36 316 | 68.75 318 | 79.08 322 | 91.14 321 |
|
ppachtmachnet_test | | | 81.84 283 | 80.07 291 | 87.15 290 | 88.46 331 | 74.43 298 | 89.04 309 | 92.16 277 | 75.33 310 | 77.75 316 | 88.99 300 | 66.20 274 | 95.37 305 | 65.12 339 | 77.60 327 | 91.65 309 |
|
gg-mvs-nofinetune | | | 81.77 284 | 79.37 297 | 88.99 245 | 90.85 292 | 77.73 261 | 86.29 334 | 79.63 366 | 74.88 317 | 83.19 262 | 69.05 364 | 60.34 311 | 96.11 276 | 75.46 277 | 94.64 123 | 93.11 274 |
|
CL-MVSNet_self_test | | | 81.74 285 | 80.53 283 | 85.36 313 | 85.96 347 | 72.45 320 | 90.25 285 | 93.07 255 | 81.24 243 | 79.85 303 | 87.29 326 | 70.93 216 | 92.52 340 | 66.95 329 | 69.23 349 | 91.11 323 |
|
Patchmatch-RL test | | | 81.67 286 | 79.96 292 | 86.81 298 | 85.42 352 | 71.23 330 | 82.17 356 | 87.50 347 | 78.47 278 | 77.19 320 | 82.50 351 | 70.81 218 | 93.48 330 | 82.66 185 | 72.89 341 | 95.71 161 |
|
ADS-MVSNet2 | | | 81.66 287 | 79.71 295 | 87.50 278 | 91.35 269 | 74.19 300 | 83.33 352 | 88.48 342 | 72.90 334 | 82.24 271 | 85.77 338 | 64.98 282 | 93.20 335 | 64.57 341 | 83.74 261 | 95.12 174 |
|
K. test v3 | | | 81.59 288 | 80.15 290 | 85.91 309 | 89.89 317 | 69.42 343 | 92.57 236 | 87.71 345 | 85.56 148 | 73.44 342 | 89.71 293 | 55.58 329 | 95.52 299 | 77.17 261 | 69.76 347 | 92.78 286 |
|
ADS-MVSNet | | | 81.56 289 | 79.78 293 | 86.90 295 | 91.35 269 | 71.82 324 | 83.33 352 | 89.16 339 | 72.90 334 | 82.24 271 | 85.77 338 | 64.98 282 | 93.76 326 | 64.57 341 | 83.74 261 | 95.12 174 |
|
FMVSNet5 | | | 81.52 290 | 79.60 296 | 87.27 283 | 91.17 275 | 77.95 251 | 91.49 265 | 92.26 275 | 76.87 295 | 76.16 326 | 87.91 319 | 51.67 343 | 92.34 341 | 67.74 326 | 81.16 293 | 91.52 311 |
|
dp | | | 81.47 291 | 80.23 288 | 85.17 316 | 89.92 316 | 65.49 355 | 86.74 331 | 90.10 326 | 76.30 301 | 81.10 283 | 87.12 329 | 62.81 294 | 95.92 283 | 68.13 324 | 79.88 315 | 94.09 226 |
|
Patchmatch-test | | | 81.37 292 | 79.30 298 | 87.58 276 | 90.92 288 | 74.16 301 | 80.99 358 | 87.68 346 | 70.52 346 | 76.63 324 | 88.81 303 | 71.21 211 | 92.76 339 | 60.01 355 | 86.93 240 | 95.83 154 |
|
EU-MVSNet | | | 81.32 293 | 80.95 281 | 82.42 332 | 88.50 330 | 63.67 358 | 93.32 206 | 91.33 303 | 64.02 356 | 80.57 291 | 92.83 202 | 61.21 307 | 92.27 342 | 76.34 269 | 80.38 311 | 91.32 315 |
|
test_0402 | | | 81.30 294 | 79.17 302 | 87.67 274 | 93.19 213 | 78.17 247 | 92.98 225 | 91.71 291 | 75.25 311 | 76.02 329 | 90.31 280 | 59.23 318 | 96.37 265 | 50.22 363 | 83.63 264 | 88.47 348 |
|
JIA-IIPM | | | 81.04 295 | 78.98 305 | 87.25 285 | 88.64 327 | 73.48 306 | 81.75 357 | 89.61 337 | 73.19 331 | 82.05 273 | 73.71 361 | 66.07 277 | 95.87 286 | 71.18 304 | 84.60 254 | 92.41 295 |
|
Anonymous20231206 | | | 81.03 296 | 79.77 294 | 84.82 318 | 87.85 340 | 70.26 339 | 91.42 266 | 92.08 281 | 73.67 327 | 77.75 316 | 89.25 298 | 62.43 296 | 93.08 336 | 61.50 350 | 82.00 284 | 91.12 322 |
|
pmmvs-eth3d | | | 80.97 297 | 78.72 306 | 87.74 272 | 84.99 354 | 79.97 206 | 90.11 292 | 91.65 294 | 75.36 309 | 73.51 341 | 86.03 335 | 59.45 317 | 93.96 324 | 75.17 280 | 72.21 342 | 89.29 340 |
|
testgi | | | 80.94 298 | 80.20 289 | 83.18 327 | 87.96 338 | 66.29 352 | 91.28 268 | 90.70 319 | 83.70 184 | 78.12 313 | 92.84 201 | 51.37 344 | 90.82 351 | 63.34 344 | 82.46 277 | 92.43 294 |
|
CMPMVS |  | 59.16 21 | 80.52 299 | 79.20 301 | 84.48 320 | 83.98 355 | 67.63 350 | 89.95 295 | 93.84 241 | 64.79 355 | 66.81 356 | 91.14 262 | 57.93 323 | 95.17 307 | 76.25 270 | 88.10 222 | 90.65 327 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Anonymous20240521 | | | 80.44 300 | 79.21 300 | 84.11 324 | 85.75 350 | 67.89 347 | 92.86 229 | 93.23 252 | 75.61 308 | 75.59 331 | 87.47 324 | 50.03 346 | 94.33 317 | 71.14 305 | 81.21 292 | 90.12 333 |
|
LF4IMVS | | | 80.37 301 | 79.07 304 | 84.27 323 | 86.64 343 | 69.87 342 | 89.39 302 | 91.05 310 | 76.38 299 | 74.97 334 | 90.00 287 | 47.85 353 | 94.25 320 | 74.55 288 | 80.82 304 | 88.69 346 |
|
KD-MVS_self_test | | | 80.20 302 | 79.24 299 | 83.07 328 | 85.64 351 | 65.29 356 | 91.01 274 | 93.93 235 | 78.71 276 | 76.32 325 | 86.40 333 | 59.20 319 | 92.93 338 | 72.59 297 | 69.35 348 | 91.00 326 |
|
UnsupCasMVSNet_eth | | | 80.07 303 | 78.27 307 | 85.46 312 | 85.24 353 | 72.63 317 | 88.45 317 | 94.87 200 | 82.99 203 | 71.64 349 | 88.07 316 | 56.34 327 | 91.75 347 | 73.48 294 | 63.36 360 | 92.01 304 |
|
test20.03 | | | 79.95 304 | 79.08 303 | 82.55 330 | 85.79 349 | 67.74 349 | 91.09 273 | 91.08 308 | 81.23 244 | 74.48 338 | 89.96 289 | 61.63 300 | 90.15 353 | 60.08 353 | 76.38 333 | 89.76 334 |
|
TDRefinement | | | 79.81 305 | 77.34 309 | 87.22 288 | 79.24 364 | 75.48 290 | 93.12 218 | 92.03 283 | 76.45 298 | 75.01 333 | 91.58 248 | 49.19 349 | 96.44 262 | 70.22 310 | 69.18 350 | 89.75 335 |
|
TinyColmap | | | 79.76 306 | 77.69 308 | 85.97 306 | 91.71 257 | 73.12 308 | 89.55 297 | 90.36 322 | 75.03 313 | 72.03 347 | 90.19 281 | 46.22 356 | 96.19 274 | 63.11 345 | 81.03 298 | 88.59 347 |
|
OpenMVS_ROB |  | 74.94 19 | 79.51 307 | 77.03 314 | 86.93 293 | 87.00 342 | 76.23 283 | 92.33 244 | 90.74 318 | 68.93 349 | 74.52 337 | 88.23 314 | 49.58 348 | 96.62 245 | 57.64 357 | 84.29 256 | 87.94 350 |
|
MIMVSNet1 | | | 79.38 308 | 77.28 310 | 85.69 311 | 86.35 344 | 73.67 303 | 91.61 264 | 92.75 263 | 78.11 287 | 72.64 345 | 88.12 315 | 48.16 351 | 91.97 346 | 60.32 352 | 77.49 328 | 91.43 314 |
|
YYNet1 | | | 79.22 309 | 77.20 311 | 85.28 315 | 88.20 336 | 72.66 315 | 85.87 336 | 90.05 329 | 74.33 321 | 62.70 358 | 87.61 322 | 66.09 276 | 92.03 343 | 66.94 330 | 72.97 340 | 91.15 320 |
|
MDA-MVSNet_test_wron | | | 79.21 310 | 77.19 312 | 85.29 314 | 88.22 335 | 72.77 312 | 85.87 336 | 90.06 327 | 74.34 320 | 62.62 359 | 87.56 323 | 66.14 275 | 91.99 345 | 66.90 333 | 73.01 339 | 91.10 324 |
|
MDA-MVSNet-bldmvs | | | 78.85 311 | 76.31 316 | 86.46 301 | 89.76 318 | 73.88 302 | 88.79 311 | 90.42 320 | 79.16 267 | 59.18 360 | 88.33 312 | 60.20 312 | 94.04 321 | 62.00 348 | 68.96 351 | 91.48 313 |
|
KD-MVS_2432*1600 | | | 78.50 312 | 76.02 319 | 85.93 307 | 86.22 345 | 74.47 296 | 84.80 345 | 92.33 271 | 79.29 264 | 76.98 321 | 85.92 336 | 53.81 340 | 93.97 322 | 67.39 327 | 57.42 365 | 89.36 337 |
|
miper_refine_blended | | | 78.50 312 | 76.02 319 | 85.93 307 | 86.22 345 | 74.47 296 | 84.80 345 | 92.33 271 | 79.29 264 | 76.98 321 | 85.92 336 | 53.81 340 | 93.97 322 | 67.39 327 | 57.42 365 | 89.36 337 |
|
PM-MVS | | | 78.11 314 | 76.12 318 | 84.09 325 | 83.54 357 | 70.08 340 | 88.97 310 | 85.27 352 | 79.93 257 | 74.73 336 | 86.43 332 | 34.70 363 | 93.48 330 | 79.43 239 | 72.06 343 | 88.72 345 |
|
test_vis1_rt | | | 77.96 315 | 76.46 315 | 82.48 331 | 85.89 348 | 71.74 326 | 90.25 285 | 78.89 367 | 71.03 345 | 71.30 350 | 81.35 353 | 42.49 359 | 91.05 350 | 84.55 158 | 82.37 278 | 84.65 353 |
|
test_fmvs3 | | | 77.67 316 | 77.16 313 | 79.22 336 | 79.52 363 | 61.14 362 | 92.34 243 | 91.64 295 | 73.98 324 | 78.86 309 | 86.59 330 | 27.38 367 | 87.03 361 | 88.12 112 | 75.97 335 | 89.50 336 |
|
PVSNet_0 | | 73.20 20 | 77.22 317 | 74.83 322 | 84.37 321 | 90.70 298 | 71.10 332 | 83.09 354 | 89.67 336 | 72.81 336 | 73.93 340 | 83.13 348 | 60.79 309 | 93.70 328 | 68.54 319 | 50.84 368 | 88.30 349 |
|
DSMNet-mixed | | | 76.94 318 | 76.29 317 | 78.89 337 | 83.10 358 | 56.11 372 | 87.78 322 | 79.77 365 | 60.65 359 | 75.64 330 | 88.71 306 | 61.56 302 | 88.34 360 | 60.07 354 | 89.29 201 | 92.21 302 |
|
new-patchmatchnet | | | 76.41 319 | 75.17 321 | 80.13 334 | 82.65 360 | 59.61 364 | 87.66 325 | 91.08 308 | 78.23 285 | 69.85 352 | 83.22 347 | 54.76 334 | 91.63 349 | 64.14 343 | 64.89 358 | 89.16 342 |
|
UnsupCasMVSNet_bld | | | 76.23 320 | 73.27 323 | 85.09 317 | 83.79 356 | 72.92 309 | 85.65 339 | 93.47 249 | 71.52 341 | 68.84 354 | 79.08 356 | 49.77 347 | 93.21 334 | 66.81 334 | 60.52 362 | 89.13 344 |
|
mvsany_test3 | | | 74.95 321 | 73.26 324 | 80.02 335 | 74.61 366 | 63.16 360 | 85.53 340 | 78.42 368 | 74.16 322 | 74.89 335 | 86.46 331 | 36.02 362 | 89.09 358 | 82.39 189 | 66.91 354 | 87.82 351 |
|
MVS-HIRNet | | | 73.70 322 | 72.20 325 | 78.18 340 | 91.81 253 | 56.42 371 | 82.94 355 | 82.58 359 | 55.24 361 | 68.88 353 | 66.48 365 | 55.32 332 | 95.13 308 | 58.12 356 | 88.42 217 | 83.01 356 |
|
new_pmnet | | | 72.15 323 | 70.13 327 | 78.20 339 | 82.95 359 | 65.68 353 | 83.91 350 | 82.40 360 | 62.94 358 | 64.47 357 | 79.82 355 | 42.85 358 | 86.26 363 | 57.41 358 | 74.44 338 | 82.65 358 |
|
test_f | | | 71.95 324 | 70.87 326 | 75.21 343 | 74.21 368 | 59.37 365 | 85.07 344 | 85.82 349 | 65.25 354 | 70.42 351 | 83.13 348 | 23.62 368 | 82.93 368 | 78.32 248 | 71.94 344 | 83.33 355 |
|
pmmvs3 | | | 71.81 325 | 68.71 328 | 81.11 333 | 75.86 365 | 70.42 338 | 86.74 331 | 83.66 356 | 58.95 360 | 68.64 355 | 80.89 354 | 36.93 361 | 89.52 356 | 63.10 346 | 63.59 359 | 83.39 354 |
|
APD_test1 | | | 69.04 326 | 66.26 330 | 77.36 342 | 80.51 361 | 62.79 361 | 85.46 341 | 83.51 357 | 54.11 363 | 59.14 361 | 84.79 342 | 23.40 370 | 89.61 355 | 55.22 359 | 70.24 346 | 79.68 361 |
|
N_pmnet | | | 68.89 327 | 68.44 329 | 70.23 347 | 89.07 324 | 28.79 381 | 88.06 319 | 19.50 382 | 69.47 348 | 71.86 348 | 84.93 340 | 61.24 306 | 91.75 347 | 54.70 360 | 77.15 330 | 90.15 332 |
|
LCM-MVSNet | | | 66.00 328 | 62.16 333 | 77.51 341 | 64.51 376 | 58.29 366 | 83.87 351 | 90.90 314 | 48.17 365 | 54.69 362 | 73.31 362 | 16.83 376 | 86.75 362 | 65.47 336 | 61.67 361 | 87.48 352 |
|
test_vis3_rt | | | 65.12 329 | 62.60 331 | 72.69 345 | 71.44 369 | 60.71 363 | 87.17 329 | 65.55 375 | 63.80 357 | 53.22 363 | 65.65 367 | 14.54 377 | 89.44 357 | 76.65 265 | 65.38 356 | 67.91 366 |
|
FPMVS | | | 64.63 330 | 62.55 332 | 70.88 346 | 70.80 370 | 56.71 367 | 84.42 348 | 84.42 354 | 51.78 364 | 49.57 364 | 81.61 352 | 23.49 369 | 81.48 369 | 40.61 370 | 76.25 334 | 74.46 362 |
|
EGC-MVSNET | | | 61.97 331 | 56.37 335 | 78.77 338 | 89.63 321 | 73.50 305 | 89.12 307 | 82.79 358 | 0.21 379 | 1.24 380 | 84.80 341 | 39.48 360 | 90.04 354 | 44.13 365 | 75.94 336 | 72.79 363 |
|
PMMVS2 | | | 59.60 332 | 56.40 334 | 69.21 350 | 68.83 373 | 46.58 376 | 73.02 367 | 77.48 371 | 55.07 362 | 49.21 365 | 72.95 363 | 17.43 375 | 80.04 370 | 49.32 364 | 44.33 370 | 80.99 360 |
|
testf1 | | | 59.54 333 | 56.11 336 | 69.85 348 | 69.28 371 | 56.61 369 | 80.37 360 | 76.55 372 | 42.58 368 | 45.68 367 | 75.61 357 | 11.26 378 | 84.18 365 | 43.20 367 | 60.44 363 | 68.75 364 |
|
APD_test2 | | | 59.54 333 | 56.11 336 | 69.85 348 | 69.28 371 | 56.61 369 | 80.37 360 | 76.55 372 | 42.58 368 | 45.68 367 | 75.61 357 | 11.26 378 | 84.18 365 | 43.20 367 | 60.44 363 | 68.75 364 |
|
ANet_high | | | 58.88 335 | 54.22 339 | 72.86 344 | 56.50 379 | 56.67 368 | 80.75 359 | 86.00 348 | 73.09 333 | 37.39 371 | 64.63 368 | 22.17 371 | 79.49 371 | 43.51 366 | 23.96 373 | 82.43 359 |
|
Gipuma |  | | 57.99 336 | 54.91 338 | 67.24 351 | 88.51 328 | 65.59 354 | 52.21 370 | 90.33 323 | 43.58 367 | 42.84 370 | 51.18 371 | 20.29 373 | 85.07 364 | 34.77 371 | 70.45 345 | 51.05 370 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS |  | 47.18 22 | 52.22 337 | 48.46 341 | 63.48 352 | 45.72 381 | 46.20 377 | 73.41 366 | 78.31 369 | 41.03 370 | 30.06 373 | 65.68 366 | 6.05 380 | 83.43 367 | 30.04 372 | 65.86 355 | 60.80 367 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test_method | | | 50.52 338 | 48.47 340 | 56.66 354 | 52.26 380 | 18.98 383 | 41.51 372 | 81.40 362 | 10.10 374 | 44.59 369 | 75.01 360 | 28.51 365 | 68.16 372 | 53.54 361 | 49.31 369 | 82.83 357 |
|
MVE |  | 39.65 23 | 43.39 339 | 38.59 345 | 57.77 353 | 56.52 378 | 48.77 375 | 55.38 369 | 58.64 379 | 29.33 373 | 28.96 374 | 52.65 370 | 4.68 381 | 64.62 375 | 28.11 373 | 33.07 371 | 59.93 368 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 43.23 340 | 42.29 342 | 46.03 356 | 65.58 375 | 37.41 378 | 73.51 365 | 64.62 376 | 33.99 371 | 28.47 375 | 47.87 372 | 19.90 374 | 67.91 373 | 22.23 374 | 24.45 372 | 32.77 371 |
|
EMVS | | | 42.07 341 | 41.12 343 | 44.92 357 | 63.45 377 | 35.56 380 | 73.65 364 | 63.48 377 | 33.05 372 | 26.88 376 | 45.45 373 | 21.27 372 | 67.14 374 | 19.80 375 | 23.02 374 | 32.06 372 |
|
tmp_tt | | | 35.64 342 | 39.24 344 | 24.84 358 | 14.87 382 | 23.90 382 | 62.71 368 | 51.51 381 | 6.58 376 | 36.66 372 | 62.08 369 | 44.37 357 | 30.34 378 | 52.40 362 | 22.00 375 | 20.27 373 |
|
cdsmvs_eth3d_5k | | | 22.14 343 | 29.52 346 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 95.76 142 | 0.00 380 | 0.00 381 | 94.29 148 | 75.66 157 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
wuyk23d | | | 21.27 344 | 20.48 347 | 23.63 359 | 68.59 374 | 36.41 379 | 49.57 371 | 6.85 383 | 9.37 375 | 7.89 377 | 4.46 379 | 4.03 382 | 31.37 377 | 17.47 376 | 16.07 376 | 3.12 374 |
|
testmvs | | | 8.92 345 | 11.52 348 | 1.12 361 | 1.06 383 | 0.46 385 | 86.02 335 | 0.65 384 | 0.62 377 | 2.74 378 | 9.52 377 | 0.31 384 | 0.45 380 | 2.38 377 | 0.39 377 | 2.46 376 |
|
test123 | | | 8.76 346 | 11.22 349 | 1.39 360 | 0.85 384 | 0.97 384 | 85.76 338 | 0.35 385 | 0.54 378 | 2.45 379 | 8.14 378 | 0.60 383 | 0.48 379 | 2.16 378 | 0.17 378 | 2.71 375 |
|
ab-mvs-re | | | 7.82 347 | 10.43 350 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 93.88 169 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
pcd_1.5k_mvsjas | | | 6.64 348 | 8.86 351 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 79.70 109 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
test_blank | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uanet_test | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
DCPMVS | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
sosnet-low-res | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
sosnet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uncertanet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
Regformer | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uanet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
FOURS1 | | | | | | 98.86 1 | 85.54 63 | 98.29 1 | 97.49 5 | 89.79 41 | 96.29 15 | | | | | | |
|
MSC_two_6792asdad | | | | | 96.52 1 | 97.78 51 | 90.86 1 | | 96.85 61 | | | | | 99.61 3 | 96.03 1 | 99.06 9 | 99.07 5 |
|
PC_three_1452 | | | | | | | | | | 82.47 211 | 97.09 9 | 97.07 38 | 92.72 1 | 98.04 146 | 92.70 42 | 99.02 12 | 98.86 10 |
|
No_MVS | | | | | 96.52 1 | 97.78 51 | 90.86 1 | | 96.85 61 | | | | | 99.61 3 | 96.03 1 | 99.06 9 | 99.07 5 |
|
test_one_0601 | | | | | | 98.58 11 | 85.83 57 | | 97.44 14 | 91.05 12 | 96.78 13 | 98.06 6 | 91.45 11 | | | | |
|
eth-test2 | | | | | | 0.00 385 | | | | | | | | | | | |
|
eth-test | | | | | | 0.00 385 | | | | | | | | | | | |
|
ZD-MVS | | | | | | 98.15 34 | 86.62 30 | | 97.07 43 | 83.63 186 | 94.19 31 | 96.91 44 | 87.57 31 | 99.26 40 | 91.99 61 | 98.44 49 | |
|
RE-MVS-def | | | | 93.68 43 | | 97.92 43 | 84.57 73 | 96.28 43 | 96.76 72 | 87.46 105 | 93.75 38 | 97.43 18 | 82.94 74 | | 92.73 38 | 97.80 70 | 97.88 75 |
|
IU-MVS | | | | | | 98.77 5 | 86.00 47 | | 96.84 63 | 81.26 242 | 97.26 7 | | | | 95.50 10 | 99.13 3 | 99.03 7 |
|
OPU-MVS | | | | | 96.21 3 | 98.00 42 | 90.85 3 | 97.13 14 | | | | 97.08 36 | 92.59 2 | 98.94 73 | 92.25 50 | 98.99 14 | 98.84 13 |
|
test_241102_TWO | | | | | | | | | 97.44 14 | 90.31 26 | 97.62 5 | 98.07 4 | 91.46 10 | 99.58 8 | 95.66 4 | 99.12 6 | 98.98 9 |
|
test_241102_ONE | | | | | | 98.77 5 | 85.99 49 | | 97.44 14 | 90.26 31 | 97.71 1 | 97.96 10 | 92.31 4 | 99.38 29 | | | |
|
9.14 | | | | 94.47 17 | | 97.79 49 | | 96.08 54 | 97.44 14 | 86.13 136 | 95.10 24 | 97.40 20 | 88.34 22 | 99.22 42 | 93.25 31 | 98.70 32 | |
|
save fliter | | | | | | 97.85 46 | 85.63 62 | 95.21 98 | 96.82 66 | 89.44 48 | | | | | | | |
|
test_0728_THIRD | | | | | | | | | | 90.75 17 | 97.04 10 | 98.05 8 | 92.09 6 | 99.55 14 | 95.64 6 | 99.13 3 | 99.13 2 |
|
test_0728_SECOND | | | | | 95.01 15 | 98.79 2 | 86.43 36 | 97.09 16 | 97.49 5 | | | | | 99.61 3 | 95.62 8 | 99.08 7 | 98.99 8 |
|
test0726 | | | | | | 98.78 3 | 85.93 52 | 97.19 11 | 97.47 10 | 90.27 29 | 97.64 4 | 98.13 1 | 91.47 8 | | | | |
|
GSMVS | | | | | | | | | | | | | | | | | 96.12 140 |
|
test_part2 | | | | | | 98.55 12 | 87.22 16 | | | | 96.40 14 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 207 | | | | 96.12 140 |
|
sam_mvs | | | | | | | | | | | | | 70.60 220 | | | | |
|
ambc | | | | | 83.06 329 | 79.99 362 | 63.51 359 | 77.47 363 | 92.86 259 | | 74.34 339 | 84.45 343 | 28.74 364 | 95.06 311 | 73.06 296 | 68.89 352 | 90.61 328 |
|
MTGPA |  | | | | | | | | 96.97 48 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 320 | | | | 9.81 376 | 69.31 242 | 95.53 298 | 76.65 265 | | |
|
test_post | | | | | | | | | | | | 10.29 375 | 70.57 224 | 95.91 285 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 345 | 71.53 208 | 96.48 258 | | | |
|
GG-mvs-BLEND | | | | | 87.94 271 | 89.73 320 | 77.91 252 | 87.80 321 | 78.23 370 | | 80.58 290 | 83.86 344 | 59.88 315 | 95.33 306 | 71.20 302 | 92.22 167 | 90.60 330 |
|
MTMP | | | | | | | | 96.16 49 | 60.64 378 | | | | | | | | |
|
gm-plane-assit | | | | | | 89.60 322 | 68.00 346 | | | 77.28 293 | | 88.99 300 | | 97.57 176 | 79.44 238 | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 65 | 98.71 30 | 98.07 64 |
|
TEST9 | | | | | | 97.53 58 | 86.49 34 | 94.07 173 | 96.78 69 | 81.61 235 | 92.77 61 | 96.20 74 | 87.71 28 | 99.12 49 | | | |
|
test_8 | | | | | | 97.49 60 | 86.30 42 | 94.02 178 | 96.76 72 | 81.86 228 | 92.70 65 | 96.20 74 | 87.63 29 | 99.02 59 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 88 | 98.68 35 | 98.27 50 |
|
agg_prior | | | | | | 97.38 63 | 85.92 54 | | 96.72 78 | | 92.16 75 | | | 98.97 70 | | | |
|
TestCases | | | | | 89.52 231 | 95.01 138 | 77.79 258 | | 90.89 315 | 77.41 290 | 76.12 327 | 93.34 182 | 54.08 338 | 97.51 182 | 68.31 322 | 84.27 257 | 93.26 265 |
|
test_prior4 | | | | | | | 85.96 51 | 94.11 168 | | | | | | | | | |
|
test_prior2 | | | | | | | | 94.12 167 | | 87.67 103 | 92.63 66 | 96.39 69 | 86.62 36 | | 91.50 71 | 98.67 37 | |
|
test_prior | | | | | 93.82 56 | 97.29 67 | 84.49 77 | | 96.88 59 | | | | | 98.87 77 | | | 98.11 63 |
|
旧先验2 | | | | | | | | 93.36 205 | | 71.25 343 | 94.37 28 | | | 97.13 221 | 86.74 131 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 93.11 220 | | | | | | | | | |
|
æ–°å‡ ä½•1 | | | | | 93.10 71 | 97.30 66 | 84.35 84 | | 95.56 156 | 71.09 344 | 91.26 99 | 96.24 72 | 82.87 75 | 98.86 79 | 79.19 242 | 98.10 60 | 96.07 144 |
|
旧先验1 | | | | | | 96.79 76 | 81.81 150 | | 95.67 148 | | | 96.81 50 | 86.69 35 | | | 97.66 74 | 96.97 113 |
|
æ— å…ˆéªŒ | | | | | | | | 93.28 212 | 96.26 104 | 73.95 325 | | | | 99.05 53 | 80.56 224 | | 96.59 125 |
|
原ACMM2 | | | | | | | | 92.94 227 | | | | | | | | | |
|
原ACMM1 | | | | | 92.01 117 | 97.34 64 | 81.05 171 | | 96.81 67 | 78.89 270 | 90.45 106 | 95.92 86 | 82.65 76 | 98.84 83 | 80.68 222 | 98.26 55 | 96.14 138 |
|
test222 | | | | | | 96.55 84 | 81.70 152 | 92.22 248 | 95.01 189 | 68.36 350 | 90.20 110 | 96.14 79 | 80.26 102 | | | 97.80 70 | 96.05 146 |
|
testdata2 | | | | | | | | | | | | | | 98.75 87 | 78.30 249 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 34 | | | | |
|
testdata | | | | | 90.49 187 | 96.40 89 | 77.89 254 | | 95.37 174 | 72.51 337 | 93.63 41 | 96.69 53 | 82.08 87 | 97.65 169 | 83.08 175 | 97.39 76 | 95.94 148 |
|
testdata1 | | | | | | | | 92.15 250 | | 87.94 93 | | | | | | | |
|
test12 | | | | | 94.34 47 | 97.13 70 | 86.15 45 | | 96.29 101 | | 91.04 101 | | 85.08 52 | 99.01 61 | | 98.13 59 | 97.86 77 |
|
plane_prior7 | | | | | | 94.70 157 | 82.74 127 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 166 | 82.75 125 | | | | | | 74.23 174 | | | | |
|
plane_prior5 | | | | | | | | | 96.22 109 | | | | | 98.12 131 | 88.15 109 | 89.99 186 | 94.63 193 |
|
plane_prior4 | | | | | | | | | | | | 94.86 124 | | | | | |
|
plane_prior3 | | | | | | | 82.75 125 | | | 90.26 31 | 86.91 166 | | | | | | |
|
plane_prior2 | | | | | | | | 95.85 65 | | 90.81 15 | | | | | | | |
|
plane_prior1 | | | | | | 94.59 161 | | | | | | | | | | | |
|
plane_prior | | | | | | | 82.73 128 | 95.21 98 | | 89.66 45 | | | | | | 89.88 191 | |
|
n2 | | | | | | | | | 0.00 386 | | | | | | | | |
|
nn | | | | | | | | | 0.00 386 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 350 | | | | | | | | |
|
lessismore_v0 | | | | | 86.04 305 | 88.46 331 | 68.78 345 | | 80.59 364 | | 73.01 344 | 90.11 284 | 55.39 331 | 96.43 263 | 75.06 282 | 65.06 357 | 92.90 281 |
|
LGP-MVS_train | | | | | 91.12 161 | 94.47 168 | 81.49 158 | | 96.14 114 | 86.73 122 | 85.45 202 | 95.16 114 | 69.89 231 | 98.10 133 | 87.70 117 | 89.23 202 | 93.77 246 |
|
test11 | | | | | | | | | 96.57 89 | | | | | | | | |
|
door | | | | | | | | | 85.33 351 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 154 | | | | | | | | | | |
|
HQP-NCC | | | | | | 94.17 180 | | 94.39 152 | | 88.81 65 | 85.43 205 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 180 | | 94.39 152 | | 88.81 65 | 85.43 205 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 128 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 205 | | | 97.96 152 | | | 94.51 203 |
|
HQP3-MVS | | | | | | | | | 96.04 122 | | | | | | | 89.77 193 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 184 | | | | |
|
NP-MVS | | | | | | 94.37 174 | 82.42 137 | | | | | 93.98 162 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 373 | 87.62 326 | | 73.32 330 | 84.59 224 | | 70.33 227 | | 74.65 286 | | 95.50 164 |
|
MDTV_nov1_ep13 | | | | 83.56 261 | | 91.69 259 | 69.93 341 | 87.75 323 | 91.54 298 | 78.60 277 | 84.86 219 | 88.90 302 | 69.54 236 | 96.03 278 | 70.25 308 | 88.93 208 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 231 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 225 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 104 | | | | |
|
ITE_SJBPF | | | | | 88.24 262 | 91.88 250 | 77.05 271 | | 92.92 257 | 85.54 149 | 80.13 298 | 93.30 186 | 57.29 325 | 96.20 272 | 72.46 298 | 84.71 253 | 91.49 312 |
|
DeepMVS_CX |  | | | | 56.31 355 | 74.23 367 | 51.81 374 | | 56.67 380 | 44.85 366 | 48.54 366 | 75.16 359 | 27.87 366 | 58.74 376 | 40.92 369 | 52.22 367 | 58.39 369 |
|