LCM-MVSNet | | | 86.90 1 | 88.67 1 | 81.57 25 | 91.50 1 | 63.30 126 | 84.80 34 | 87.77 10 | 86.18 1 | 96.26 1 | 96.06 1 | 90.32 1 | 84.49 69 | 68.08 95 | 97.05 1 | 96.93 1 |
|
TDRefinement | | | 86.32 2 | 86.33 2 | 86.29 1 | 88.64 33 | 81.19 5 | 88.84 4 | 90.72 1 | 78.27 9 | 87.95 15 | 92.53 13 | 79.37 13 | 84.79 66 | 74.51 51 | 96.15 2 | 92.88 7 |
|
abl_6 | | | 84.92 3 | 85.70 3 | 82.57 17 | 86.72 49 | 79.27 8 | 87.56 7 | 86.08 28 | 77.48 13 | 88.12 14 | 91.53 28 | 81.18 8 | 84.31 75 | 78.12 24 | 94.47 37 | 84.15 125 |
|
test1172 | | | 84.85 4 | 85.39 5 | 83.21 3 | 88.34 38 | 80.50 6 | 85.12 30 | 85.22 43 | 81.06 3 | 87.20 28 | 90.28 70 | 79.20 14 | 85.58 48 | 78.04 27 | 94.08 56 | 83.55 137 |
|
SR-MVS-dyc-post | | | 84.75 5 | 85.26 7 | 83.21 3 | 86.19 55 | 79.18 9 | 87.23 9 | 86.27 23 | 77.51 11 | 87.65 19 | 90.73 46 | 79.20 14 | 85.58 48 | 78.11 25 | 94.46 38 | 84.89 94 |
|
HPM-MVS_fast | | | 84.59 6 | 85.10 8 | 83.06 6 | 88.60 34 | 75.83 27 | 86.27 25 | 86.89 18 | 73.69 25 | 86.17 36 | 91.70 24 | 78.23 20 | 85.20 58 | 79.45 13 | 94.91 25 | 88.15 45 |
|
SR-MVS | | | 84.51 7 | 85.27 6 | 82.25 21 | 88.52 35 | 77.71 17 | 86.81 17 | 85.25 42 | 77.42 15 | 86.15 37 | 90.24 71 | 81.69 5 | 85.94 33 | 77.77 29 | 93.58 67 | 83.09 153 |
|
ACMMP |  | | 84.22 8 | 84.84 10 | 82.35 20 | 89.23 23 | 76.66 26 | 87.65 6 | 85.89 31 | 71.03 45 | 85.85 41 | 90.58 50 | 78.77 17 | 85.78 41 | 79.37 16 | 95.17 17 | 84.62 106 |
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 |
LTVRE_ROB | | 75.46 1 | 84.22 8 | 84.98 9 | 81.94 23 | 84.82 78 | 75.40 30 | 91.60 3 | 87.80 8 | 73.52 26 | 88.90 11 | 93.06 6 | 71.39 75 | 81.53 121 | 81.53 3 | 92.15 89 | 88.91 37 |
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 |
HPM-MVS |  | | 84.12 10 | 84.63 11 | 82.60 15 | 88.21 39 | 74.40 36 | 85.24 29 | 87.21 16 | 70.69 48 | 85.14 54 | 90.42 58 | 78.99 16 | 86.62 14 | 80.83 6 | 94.93 24 | 86.79 64 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
CP-MVS | | | 84.12 10 | 84.55 12 | 82.80 12 | 89.42 18 | 79.74 7 | 88.19 5 | 84.43 64 | 71.96 42 | 84.70 62 | 90.56 51 | 77.12 26 | 86.18 27 | 79.24 18 | 95.36 13 | 82.49 172 |
|
mPP-MVS | | | 84.01 12 | 84.39 13 | 82.88 8 | 90.65 4 | 81.38 4 | 87.08 13 | 82.79 91 | 72.41 36 | 85.11 55 | 90.85 42 | 76.65 30 | 84.89 63 | 79.30 17 | 94.63 34 | 82.35 175 |
|
COLMAP_ROB |  | 72.78 3 | 83.75 13 | 84.11 17 | 82.68 14 | 82.97 110 | 74.39 37 | 87.18 11 | 88.18 7 | 78.98 7 | 86.11 39 | 91.47 30 | 79.70 12 | 85.76 42 | 66.91 110 | 95.46 11 | 87.89 47 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
ACMMPR | | | 83.62 14 | 83.93 19 | 82.69 13 | 89.78 11 | 77.51 22 | 87.01 15 | 84.19 73 | 70.23 49 | 84.49 64 | 90.67 49 | 75.15 43 | 86.37 19 | 79.58 11 | 94.26 51 | 84.18 124 |
|
APD-MVS_3200maxsize | | | 83.57 15 | 84.33 14 | 81.31 33 | 82.83 113 | 73.53 45 | 85.50 28 | 87.45 14 | 74.11 21 | 86.45 34 | 90.52 54 | 80.02 11 | 84.48 70 | 77.73 30 | 94.34 49 | 85.93 75 |
|
region2R | | | 83.54 16 | 83.86 21 | 82.58 16 | 89.82 10 | 77.53 20 | 87.06 14 | 84.23 72 | 70.19 51 | 83.86 73 | 90.72 48 | 75.20 42 | 86.27 22 | 79.41 15 | 94.25 52 | 83.95 128 |
|
XVS | | | 83.51 17 | 83.73 22 | 82.85 10 | 89.43 16 | 77.61 18 | 86.80 18 | 84.66 58 | 72.71 29 | 82.87 82 | 90.39 62 | 73.86 56 | 86.31 20 | 78.84 20 | 94.03 57 | 84.64 104 |
|
LPG-MVS_test | | | 83.47 18 | 84.33 14 | 80.90 38 | 87.00 43 | 70.41 66 | 82.04 60 | 86.35 20 | 69.77 53 | 87.75 16 | 91.13 35 | 81.83 3 | 86.20 25 | 77.13 37 | 95.96 5 | 86.08 71 |
|
HFP-MVS | | | 83.39 19 | 84.03 18 | 81.48 27 | 89.25 21 | 75.69 28 | 87.01 15 | 84.27 68 | 70.23 49 | 84.47 65 | 90.43 56 | 76.79 27 | 85.94 33 | 79.58 11 | 94.23 53 | 82.82 162 |
|
MTAPA | | | 83.19 20 | 83.87 20 | 81.13 35 | 91.16 2 | 78.16 14 | 84.87 32 | 80.63 128 | 72.08 39 | 84.93 56 | 90.79 43 | 74.65 49 | 84.42 72 | 80.98 4 | 94.75 29 | 80.82 200 |
|
MP-MVS |  | | 83.19 20 | 83.54 26 | 82.14 22 | 90.54 5 | 79.00 11 | 86.42 23 | 83.59 82 | 71.31 43 | 81.26 103 | 90.96 39 | 74.57 51 | 84.69 67 | 78.41 22 | 94.78 28 | 82.74 166 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
ZNCC-MVS | | | 83.12 22 | 83.68 23 | 81.45 29 | 89.14 26 | 73.28 47 | 86.32 24 | 85.97 30 | 67.39 64 | 84.02 71 | 90.39 62 | 74.73 48 | 86.46 16 | 80.73 7 | 94.43 42 | 84.60 109 |
|
PGM-MVS | | | 83.07 23 | 83.25 33 | 82.54 18 | 89.57 14 | 77.21 24 | 82.04 60 | 85.40 39 | 67.96 62 | 84.91 60 | 90.88 40 | 75.59 38 | 86.57 15 | 78.16 23 | 94.71 32 | 83.82 129 |
|
SteuartSystems-ACMMP | | | 83.07 23 | 83.64 24 | 81.35 31 | 85.14 74 | 71.00 60 | 85.53 27 | 84.78 52 | 70.91 46 | 85.64 44 | 90.41 59 | 75.55 40 | 87.69 3 | 79.75 8 | 95.08 20 | 85.36 84 |
Skip Steuart: Steuart Systems R&D Blog. |
zzz-MVS | | | 83.01 25 | 83.63 25 | 81.13 35 | 91.16 2 | 78.16 14 | 82.72 56 | 80.63 128 | 72.08 39 | 84.93 56 | 90.79 43 | 74.65 49 | 84.42 72 | 80.98 4 | 94.75 29 | 80.82 200 |
|
APDe-MVS | | | 82.88 26 | 84.14 16 | 79.08 57 | 84.80 80 | 66.72 96 | 86.54 21 | 85.11 45 | 72.00 41 | 86.65 33 | 91.75 23 | 78.20 21 | 87.04 9 | 77.93 28 | 94.32 50 | 83.47 142 |
|
GST-MVS | | | 82.79 27 | 83.27 32 | 81.34 32 | 88.99 28 | 73.29 46 | 85.94 26 | 85.13 44 | 68.58 60 | 84.14 70 | 90.21 73 | 73.37 61 | 86.41 17 | 79.09 19 | 93.98 60 | 84.30 123 |
|
ACMP | | 69.50 8 | 82.64 28 | 83.38 29 | 80.40 43 | 86.50 51 | 69.44 74 | 82.30 57 | 86.08 28 | 66.80 69 | 86.70 32 | 89.99 76 | 81.64 6 | 85.95 32 | 74.35 52 | 96.11 3 | 85.81 77 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MP-MVS-pluss | | | 82.54 29 | 83.46 28 | 79.76 47 | 88.88 32 | 68.44 83 | 81.57 63 | 86.33 22 | 63.17 110 | 85.38 51 | 91.26 34 | 76.33 32 | 84.67 68 | 83.30 1 | 94.96 23 | 86.17 70 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
#test# | | | 82.40 30 | 82.71 41 | 81.48 27 | 89.25 21 | 75.69 28 | 84.47 36 | 84.27 68 | 64.45 94 | 84.47 65 | 90.43 56 | 76.79 27 | 85.94 33 | 76.01 39 | 94.23 53 | 82.82 162 |
|
ACMMP_NAP | | | 82.33 31 | 83.28 31 | 79.46 53 | 89.28 19 | 69.09 81 | 83.62 45 | 84.98 48 | 64.77 91 | 83.97 72 | 91.02 38 | 75.53 41 | 85.93 36 | 82.00 2 | 94.36 47 | 83.35 147 |
|
SMA-MVS |  | | 82.12 32 | 82.68 42 | 80.43 42 | 88.90 31 | 69.52 72 | 85.12 30 | 84.76 53 | 63.53 104 | 84.23 69 | 91.47 30 | 72.02 67 | 87.16 7 | 79.74 10 | 94.36 47 | 84.61 107 |
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 |
ACMM | | 69.25 9 | 82.11 33 | 83.31 30 | 78.49 68 | 88.17 40 | 73.96 39 | 83.11 52 | 84.52 63 | 66.40 74 | 87.45 23 | 89.16 94 | 81.02 9 | 80.52 144 | 74.27 53 | 95.73 7 | 80.98 196 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
DPE-MVS |  | | 82.00 34 | 83.02 36 | 78.95 62 | 85.36 71 | 67.25 90 | 82.91 53 | 84.98 48 | 73.52 26 | 85.43 50 | 90.03 75 | 76.37 31 | 86.97 11 | 74.56 50 | 94.02 59 | 82.62 168 |
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
SED-MVS | | | 81.78 35 | 83.48 27 | 76.67 91 | 86.12 59 | 61.06 140 | 83.62 45 | 84.72 55 | 72.61 32 | 87.38 25 | 89.70 81 | 77.48 24 | 85.89 39 | 75.29 45 | 94.39 43 | 83.08 154 |
|
PMVS |  | 70.70 6 | 81.70 36 | 83.15 34 | 77.36 85 | 90.35 6 | 82.82 2 | 82.15 58 | 79.22 152 | 74.08 22 | 87.16 30 | 91.97 19 | 84.80 2 | 76.97 205 | 64.98 121 | 93.61 65 | 72.28 287 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
UA-Net | | | 81.56 37 | 82.28 44 | 79.40 54 | 88.91 30 | 69.16 79 | 84.67 35 | 80.01 142 | 75.34 17 | 79.80 120 | 94.91 2 | 69.79 87 | 80.25 149 | 72.63 63 | 94.46 38 | 88.78 41 |
|
CPTT-MVS | | | 81.51 38 | 81.76 47 | 80.76 40 | 89.20 24 | 78.75 12 | 86.48 22 | 82.03 101 | 68.80 56 | 80.92 108 | 88.52 106 | 72.00 68 | 82.39 106 | 74.80 47 | 93.04 73 | 81.14 191 |
|
DVP-MVS++ | | | 81.24 39 | 82.74 40 | 76.76 90 | 83.14 103 | 60.90 144 | 91.64 1 | 85.49 35 | 74.03 23 | 84.93 56 | 90.38 64 | 66.82 114 | 85.90 37 | 77.43 33 | 90.78 120 | 83.49 139 |
|
ACMH+ | | 66.64 10 | 81.20 40 | 82.48 43 | 77.35 86 | 81.16 136 | 62.39 130 | 80.51 69 | 87.80 8 | 73.02 28 | 87.57 21 | 91.08 37 | 80.28 10 | 82.44 105 | 64.82 122 | 96.10 4 | 87.21 58 |
|
testtj | | | 81.19 41 | 81.70 49 | 79.67 51 | 83.95 93 | 69.77 71 | 83.58 48 | 84.63 60 | 72.13 38 | 82.85 84 | 88.36 111 | 75.00 46 | 86.79 12 | 71.99 72 | 92.84 76 | 82.44 173 |
|
DVP-MVS |  | | 81.15 42 | 83.12 35 | 75.24 113 | 86.16 57 | 60.78 146 | 83.77 43 | 80.58 131 | 72.48 34 | 85.83 42 | 90.41 59 | 78.57 18 | 85.69 44 | 75.86 42 | 94.39 43 | 79.24 225 |
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 |
APD-MVS |  | | 81.13 43 | 81.73 48 | 79.36 55 | 84.47 85 | 70.53 65 | 83.85 41 | 83.70 80 | 69.43 55 | 83.67 75 | 88.96 100 | 75.89 36 | 86.41 17 | 72.62 64 | 92.95 74 | 81.14 191 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
3Dnovator+ | | 73.19 2 | 81.08 44 | 80.48 57 | 82.87 9 | 81.41 133 | 72.03 50 | 84.38 37 | 86.23 26 | 77.28 16 | 80.65 111 | 90.18 74 | 59.80 180 | 87.58 4 | 73.06 60 | 91.34 102 | 89.01 33 |
|
DeepC-MVS | | 72.44 4 | 81.00 45 | 80.83 56 | 81.50 26 | 86.70 50 | 70.03 70 | 82.06 59 | 87.00 17 | 59.89 134 | 80.91 109 | 90.53 52 | 72.19 65 | 88.56 1 | 73.67 57 | 94.52 36 | 85.92 76 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
OPM-MVS | | | 80.99 46 | 81.63 51 | 79.07 58 | 86.86 48 | 69.39 75 | 79.41 86 | 84.00 78 | 65.64 78 | 85.54 48 | 89.28 87 | 76.32 33 | 83.47 87 | 74.03 54 | 93.57 68 | 84.35 120 |
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
LS3D | | | 80.99 46 | 80.85 55 | 81.41 30 | 78.37 169 | 71.37 56 | 87.45 8 | 85.87 32 | 77.48 13 | 81.98 92 | 89.95 78 | 69.14 91 | 85.26 54 | 66.15 111 | 91.24 104 | 87.61 51 |
|
SF-MVS | | | 80.72 48 | 81.80 46 | 77.48 82 | 82.03 124 | 64.40 116 | 83.41 50 | 88.46 6 | 65.28 85 | 84.29 67 | 89.18 91 | 73.73 59 | 83.22 92 | 76.01 39 | 93.77 62 | 84.81 100 |
|
XVG-ACMP-BASELINE | | | 80.54 49 | 81.06 53 | 78.98 61 | 87.01 42 | 72.91 48 | 80.23 78 | 85.56 34 | 66.56 73 | 85.64 44 | 89.57 83 | 69.12 92 | 80.55 143 | 72.51 65 | 93.37 69 | 83.48 141 |
|
MSP-MVS | | | 80.49 50 | 79.67 65 | 82.96 7 | 89.70 12 | 77.46 23 | 87.16 12 | 85.10 46 | 64.94 90 | 81.05 105 | 88.38 110 | 57.10 207 | 87.10 8 | 79.75 8 | 83.87 228 | 84.31 121 |
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 |
PEN-MVS | | | 80.46 51 | 82.91 37 | 73.11 141 | 89.83 9 | 39.02 305 | 77.06 117 | 82.61 95 | 80.04 5 | 90.60 6 | 92.85 9 | 74.93 47 | 85.21 57 | 63.15 141 | 95.15 18 | 95.09 2 |
|
PS-CasMVS | | | 80.41 52 | 82.86 39 | 73.07 142 | 89.93 7 | 39.21 302 | 77.15 115 | 81.28 114 | 79.74 6 | 90.87 4 | 92.73 11 | 75.03 45 | 84.93 62 | 63.83 132 | 95.19 16 | 95.07 3 |
|
DTE-MVSNet | | | 80.35 53 | 82.89 38 | 72.74 153 | 89.84 8 | 37.34 320 | 77.16 114 | 81.81 104 | 80.45 4 | 90.92 3 | 92.95 7 | 74.57 51 | 86.12 30 | 63.65 133 | 94.68 33 | 94.76 6 |
|
xxxxxxxxxxxxxcwj | | | 80.31 54 | 80.94 54 | 78.42 70 | 87.00 43 | 67.23 91 | 79.24 87 | 88.61 5 | 56.65 167 | 84.29 67 | 89.18 91 | 73.73 59 | 83.22 92 | 76.01 39 | 93.77 62 | 84.81 100 |
|
SD-MVS | | | 80.28 55 | 81.55 52 | 76.47 96 | 83.57 97 | 67.83 87 | 83.39 51 | 85.35 41 | 64.42 95 | 86.14 38 | 87.07 127 | 74.02 55 | 80.97 134 | 77.70 31 | 92.32 87 | 80.62 208 |
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 |
WR-MVS_H | | | 80.22 56 | 82.17 45 | 74.39 120 | 89.46 15 | 42.69 279 | 78.24 102 | 82.24 98 | 78.21 10 | 89.57 9 | 92.10 18 | 68.05 103 | 85.59 47 | 66.04 113 | 95.62 9 | 94.88 5 |
|
HPM-MVS++ |  | | 79.89 57 | 79.80 63 | 80.18 45 | 89.02 27 | 78.44 13 | 83.49 49 | 80.18 139 | 64.71 93 | 78.11 140 | 88.39 109 | 65.46 129 | 83.14 94 | 77.64 32 | 91.20 105 | 78.94 228 |
|
XVG-OURS-SEG-HR | | | 79.62 58 | 79.99 61 | 78.49 68 | 86.46 52 | 74.79 34 | 77.15 115 | 85.39 40 | 66.73 70 | 80.39 115 | 88.85 102 | 74.43 54 | 78.33 185 | 74.73 49 | 85.79 200 | 82.35 175 |
|
XVG-OURS | | | 79.51 59 | 79.82 62 | 78.58 67 | 86.11 62 | 74.96 33 | 76.33 125 | 84.95 50 | 66.89 66 | 82.75 86 | 88.99 99 | 66.82 114 | 78.37 183 | 74.80 47 | 90.76 123 | 82.40 174 |
|
CP-MVSNet | | | 79.48 60 | 81.65 50 | 72.98 145 | 89.66 13 | 39.06 304 | 76.76 119 | 80.46 133 | 78.91 8 | 90.32 7 | 91.70 24 | 68.49 98 | 84.89 63 | 63.40 138 | 95.12 19 | 95.01 4 |
|
OMC-MVS | | | 79.41 61 | 78.79 70 | 81.28 34 | 80.62 140 | 70.71 64 | 80.91 66 | 84.76 53 | 62.54 114 | 81.77 95 | 86.65 144 | 71.46 73 | 83.53 86 | 67.95 100 | 92.44 84 | 89.60 23 |
|
v7n | | | 79.37 62 | 80.41 58 | 76.28 98 | 78.67 168 | 55.81 181 | 79.22 89 | 82.51 97 | 70.72 47 | 87.54 22 | 92.44 14 | 68.00 105 | 81.34 122 | 72.84 61 | 91.72 91 | 91.69 10 |
|
ETH3D-3000-0.1 | | | 79.14 63 | 79.80 63 | 77.16 89 | 80.67 139 | 64.57 113 | 80.26 76 | 87.60 12 | 60.74 127 | 82.47 89 | 88.03 119 | 71.73 70 | 81.81 117 | 73.12 59 | 93.61 65 | 85.09 89 |
|
TSAR-MVS + MP. | | | 79.05 64 | 78.81 69 | 79.74 48 | 88.94 29 | 67.52 88 | 86.61 20 | 81.38 112 | 51.71 226 | 77.15 152 | 91.42 33 | 65.49 128 | 87.20 6 | 79.44 14 | 87.17 185 | 84.51 115 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
mvs_tets | | | 78.93 65 | 78.67 73 | 79.72 49 | 84.81 79 | 73.93 40 | 80.65 68 | 76.50 190 | 51.98 224 | 87.40 24 | 91.86 21 | 76.09 35 | 78.53 175 | 68.58 90 | 90.20 129 | 86.69 66 |
|
test_djsdf | | | 78.88 66 | 78.27 76 | 80.70 41 | 81.42 132 | 71.24 58 | 83.98 39 | 75.72 196 | 52.27 219 | 87.37 27 | 92.25 16 | 68.04 104 | 80.56 141 | 72.28 68 | 91.15 107 | 90.32 20 |
|
HQP_MVS | | | 78.77 67 | 78.78 71 | 78.72 64 | 85.18 72 | 65.18 108 | 82.74 54 | 85.49 35 | 65.45 80 | 78.23 137 | 89.11 95 | 60.83 170 | 86.15 28 | 71.09 74 | 90.94 112 | 84.82 98 |
|
anonymousdsp | | | 78.60 68 | 77.80 80 | 81.00 37 | 78.01 175 | 74.34 38 | 80.09 79 | 76.12 192 | 50.51 241 | 89.19 10 | 90.88 40 | 71.45 74 | 77.78 197 | 73.38 58 | 90.60 125 | 90.90 16 |
|
OurMVSNet-221017-0 | | | 78.57 69 | 78.53 75 | 78.67 65 | 80.48 141 | 64.16 117 | 80.24 77 | 82.06 100 | 61.89 118 | 88.77 12 | 93.32 4 | 57.15 205 | 82.60 104 | 70.08 80 | 92.80 78 | 89.25 27 |
|
jajsoiax | | | 78.51 70 | 78.16 78 | 79.59 52 | 84.65 82 | 73.83 42 | 80.42 71 | 76.12 192 | 51.33 232 | 87.19 29 | 91.51 29 | 73.79 58 | 78.44 179 | 68.27 93 | 90.13 134 | 86.49 68 |
|
CNVR-MVS | | | 78.49 71 | 78.59 74 | 78.16 73 | 85.86 66 | 67.40 89 | 78.12 105 | 81.50 108 | 63.92 99 | 77.51 149 | 86.56 148 | 68.43 100 | 84.82 65 | 73.83 55 | 91.61 96 | 82.26 178 |
|
DeepPCF-MVS | | 71.07 5 | 78.48 72 | 77.14 87 | 82.52 19 | 84.39 89 | 77.04 25 | 76.35 123 | 84.05 76 | 56.66 166 | 80.27 116 | 85.31 175 | 68.56 97 | 87.03 10 | 67.39 105 | 91.26 103 | 83.50 138 |
|
DP-MVS | | | 78.44 73 | 79.29 67 | 75.90 103 | 81.86 127 | 65.33 106 | 79.05 91 | 84.63 60 | 74.83 20 | 80.41 114 | 86.27 155 | 71.68 71 | 83.45 88 | 62.45 145 | 92.40 85 | 78.92 229 |
|
ETH3D cwj APD-0.16 | | | 78.38 74 | 78.72 72 | 77.38 84 | 80.09 144 | 66.16 101 | 79.08 90 | 86.13 27 | 57.55 157 | 80.93 107 | 87.76 122 | 71.98 69 | 82.73 102 | 72.11 71 | 92.83 77 | 83.25 149 |
|
NCCC | | | 78.25 75 | 78.04 79 | 78.89 63 | 85.61 68 | 69.45 73 | 79.80 83 | 80.99 123 | 65.77 77 | 75.55 182 | 86.25 157 | 67.42 108 | 85.42 50 | 70.10 79 | 90.88 118 | 81.81 184 |
|
test_0402 | | | 78.17 76 | 79.48 66 | 74.24 123 | 83.50 98 | 59.15 162 | 72.52 162 | 74.60 209 | 75.34 17 | 88.69 13 | 91.81 22 | 75.06 44 | 82.37 107 | 65.10 119 | 88.68 160 | 81.20 189 |
|
AllTest | | | 77.66 77 | 77.43 82 | 78.35 71 | 79.19 157 | 70.81 61 | 78.60 95 | 88.64 3 | 65.37 83 | 80.09 118 | 88.17 115 | 70.33 81 | 78.43 180 | 55.60 197 | 90.90 116 | 85.81 77 |
|
PS-MVSNAJss | | | 77.54 78 | 77.35 84 | 78.13 75 | 84.88 77 | 66.37 99 | 78.55 96 | 79.59 147 | 53.48 210 | 86.29 35 | 92.43 15 | 62.39 152 | 80.25 149 | 67.90 101 | 90.61 124 | 87.77 48 |
|
ACMH | | 63.62 14 | 77.50 79 | 80.11 60 | 69.68 192 | 79.61 148 | 56.28 177 | 78.81 92 | 83.62 81 | 63.41 108 | 87.14 31 | 90.23 72 | 76.11 34 | 73.32 244 | 67.58 102 | 94.44 41 | 79.44 223 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CDPH-MVS | | | 77.33 80 | 77.06 88 | 78.14 74 | 84.21 90 | 63.98 119 | 76.07 128 | 83.45 83 | 54.20 198 | 77.68 148 | 87.18 123 | 69.98 84 | 85.37 51 | 68.01 97 | 92.72 82 | 85.08 91 |
|
DeepC-MVS_fast | | 69.89 7 | 77.17 81 | 76.33 92 | 79.70 50 | 83.90 95 | 67.94 85 | 80.06 81 | 83.75 79 | 56.73 165 | 74.88 190 | 85.32 174 | 65.54 127 | 87.79 2 | 65.61 117 | 91.14 108 | 83.35 147 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DROMVSNet | | | 77.08 82 | 77.39 83 | 76.14 101 | 76.86 195 | 56.87 175 | 80.32 75 | 87.52 13 | 63.45 106 | 74.66 196 | 84.52 182 | 69.87 86 | 84.94 61 | 69.76 84 | 89.59 145 | 86.60 67 |
|
test_part1 | | | 76.97 83 | 78.21 77 | 73.25 138 | 77.87 177 | 45.76 255 | 78.27 101 | 87.26 15 | 66.69 71 | 85.31 52 | 91.43 32 | 55.95 216 | 84.24 77 | 65.71 115 | 95.43 12 | 89.75 22 |
|
X-MVStestdata | | | 76.81 84 | 74.79 105 | 82.85 10 | 89.43 16 | 77.61 18 | 86.80 18 | 84.66 58 | 72.71 29 | 82.87 82 | 9.95 373 | 73.86 56 | 86.31 20 | 78.84 20 | 94.03 57 | 84.64 104 |
|
UniMVSNet_ETH3D | | | 76.74 85 | 79.02 68 | 69.92 191 | 89.27 20 | 43.81 267 | 74.47 149 | 71.70 229 | 72.33 37 | 85.50 49 | 93.65 3 | 77.98 22 | 76.88 208 | 54.60 207 | 91.64 94 | 89.08 31 |
|
test_prior3 | | | 76.71 86 | 77.19 85 | 75.27 111 | 82.15 122 | 59.85 155 | 75.57 132 | 84.33 66 | 58.92 142 | 76.53 171 | 86.78 135 | 67.83 106 | 83.39 89 | 69.81 82 | 92.76 80 | 82.58 169 |
|
CS-MVS | | | 76.42 87 | 76.03 94 | 77.57 79 | 77.07 186 | 63.35 124 | 80.80 67 | 82.77 92 | 60.39 130 | 74.11 204 | 83.29 202 | 69.71 88 | 81.97 114 | 69.28 87 | 86.87 187 | 87.38 55 |
|
train_agg | | | 76.38 88 | 76.55 89 | 75.86 104 | 85.47 69 | 69.32 77 | 76.42 121 | 78.69 161 | 54.00 203 | 76.97 155 | 86.74 138 | 66.60 117 | 81.10 128 | 72.50 66 | 91.56 97 | 77.15 248 |
|
TranMVSNet+NR-MVSNet | | | 76.13 89 | 77.66 81 | 71.56 167 | 84.61 83 | 42.57 281 | 70.98 188 | 78.29 170 | 68.67 59 | 83.04 79 | 89.26 88 | 72.99 63 | 80.75 140 | 55.58 200 | 95.47 10 | 91.35 11 |
|
agg_prior1 | | | 75.89 90 | 76.41 90 | 74.31 122 | 84.44 87 | 66.02 102 | 76.12 127 | 78.62 164 | 54.40 192 | 76.95 157 | 86.85 132 | 66.44 120 | 80.34 146 | 72.45 67 | 91.42 100 | 76.57 252 |
|
CS-MVS-test | | | 75.88 91 | 75.24 103 | 77.78 77 | 77.30 185 | 63.59 122 | 77.02 118 | 86.83 19 | 60.82 126 | 74.17 202 | 79.23 249 | 65.42 130 | 82.29 110 | 69.48 86 | 86.87 187 | 87.47 53 |
|
SixPastTwentyTwo | | | 75.77 92 | 76.34 91 | 74.06 126 | 81.69 130 | 54.84 186 | 76.47 120 | 75.49 198 | 64.10 98 | 87.73 18 | 92.24 17 | 50.45 241 | 81.30 124 | 67.41 104 | 91.46 99 | 86.04 73 |
|
RPSCF | | | 75.76 93 | 74.37 111 | 79.93 46 | 74.81 219 | 77.53 20 | 77.53 109 | 79.30 151 | 59.44 137 | 78.88 129 | 89.80 80 | 71.26 76 | 73.09 246 | 57.45 180 | 80.89 262 | 89.17 30 |
|
ETH3 D test6400 | | | 75.73 94 | 76.00 95 | 74.92 114 | 81.75 128 | 56.93 173 | 78.31 99 | 84.60 62 | 52.83 215 | 77.15 152 | 85.14 177 | 68.59 96 | 84.03 78 | 65.44 118 | 90.20 129 | 83.82 129 |
|
v10 | | | 75.69 95 | 76.20 93 | 74.16 124 | 74.44 230 | 48.69 222 | 75.84 131 | 82.93 90 | 59.02 141 | 85.92 40 | 89.17 93 | 58.56 190 | 82.74 101 | 70.73 76 | 89.14 155 | 91.05 13 |
|
Anonymous20231211 | | | 75.54 96 | 77.19 85 | 70.59 176 | 77.67 182 | 45.70 257 | 74.73 145 | 80.19 138 | 68.80 56 | 82.95 81 | 92.91 8 | 66.26 121 | 76.76 211 | 58.41 176 | 92.77 79 | 89.30 26 |
|
Effi-MVS+-dtu | | | 75.43 97 | 72.28 147 | 84.91 2 | 77.05 187 | 83.58 1 | 78.47 97 | 77.70 177 | 57.68 152 | 74.89 189 | 78.13 266 | 64.80 136 | 84.26 76 | 56.46 189 | 85.32 207 | 86.88 63 |
|
Regformer-2 | | | 75.32 98 | 74.47 109 | 77.88 76 | 74.22 232 | 66.65 97 | 72.77 160 | 77.54 179 | 68.47 61 | 80.44 113 | 72.08 314 | 70.60 80 | 80.97 134 | 70.08 80 | 84.02 226 | 86.01 74 |
|
F-COLMAP | | | 75.29 99 | 73.99 116 | 79.18 56 | 81.73 129 | 71.90 51 | 81.86 62 | 82.98 88 | 59.86 135 | 72.27 227 | 84.00 189 | 64.56 139 | 83.07 97 | 51.48 228 | 87.19 184 | 82.56 171 |
|
HQP-MVS | | | 75.24 100 | 75.01 104 | 75.94 102 | 82.37 117 | 58.80 165 | 77.32 111 | 84.12 74 | 59.08 138 | 71.58 235 | 85.96 167 | 58.09 195 | 85.30 53 | 67.38 106 | 89.16 152 | 83.73 135 |
|
TAPA-MVS | | 65.27 12 | 75.16 101 | 74.29 113 | 77.77 78 | 74.86 218 | 68.08 84 | 77.89 106 | 84.04 77 | 55.15 180 | 76.19 178 | 83.39 196 | 66.91 112 | 80.11 154 | 60.04 165 | 90.14 133 | 85.13 88 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
IS-MVSNet | | | 75.10 102 | 75.42 102 | 74.15 125 | 79.23 155 | 48.05 231 | 79.43 84 | 78.04 173 | 70.09 52 | 79.17 127 | 88.02 120 | 53.04 226 | 83.60 84 | 58.05 178 | 93.76 64 | 90.79 17 |
|
v8 | | | 75.07 103 | 75.64 99 | 73.35 135 | 73.42 242 | 47.46 240 | 75.20 136 | 81.45 110 | 60.05 132 | 85.64 44 | 89.26 88 | 58.08 197 | 81.80 118 | 69.71 85 | 87.97 168 | 90.79 17 |
|
UniMVSNet (Re) | | | 75.00 104 | 75.48 101 | 73.56 133 | 83.14 103 | 47.92 233 | 70.41 196 | 81.04 122 | 63.67 102 | 79.54 122 | 86.37 153 | 62.83 146 | 81.82 116 | 57.10 183 | 95.25 15 | 90.94 15 |
|
PHI-MVS | | | 74.92 105 | 74.36 112 | 76.61 92 | 76.40 198 | 62.32 131 | 80.38 72 | 83.15 86 | 54.16 200 | 73.23 216 | 80.75 227 | 62.19 155 | 83.86 80 | 68.02 96 | 90.92 115 | 83.65 136 |
|
DU-MVS | | | 74.91 106 | 75.57 100 | 72.93 147 | 83.50 98 | 45.79 253 | 69.47 204 | 80.14 140 | 65.22 86 | 81.74 97 | 87.08 125 | 61.82 158 | 81.07 130 | 56.21 192 | 94.98 21 | 91.93 8 |
|
UniMVSNet_NR-MVSNet | | | 74.90 107 | 75.65 98 | 72.64 156 | 83.04 108 | 45.79 253 | 69.26 207 | 78.81 158 | 66.66 72 | 81.74 97 | 86.88 131 | 63.26 144 | 81.07 130 | 56.21 192 | 94.98 21 | 91.05 13 |
|
nrg030 | | | 74.87 108 | 75.99 96 | 71.52 168 | 74.90 217 | 49.88 218 | 74.10 153 | 82.58 96 | 54.55 191 | 83.50 77 | 89.21 90 | 71.51 72 | 75.74 220 | 61.24 152 | 92.34 86 | 88.94 36 |
|
Vis-MVSNet |  | | 74.85 109 | 74.56 107 | 75.72 105 | 81.63 131 | 64.64 112 | 76.35 123 | 79.06 154 | 62.85 112 | 73.33 214 | 88.41 108 | 62.54 150 | 79.59 160 | 63.94 131 | 82.92 238 | 82.94 158 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Regformer-4 | | | 74.64 110 | 73.67 122 | 77.55 80 | 74.74 221 | 64.49 115 | 72.91 158 | 75.42 200 | 67.45 63 | 80.24 117 | 72.07 316 | 68.98 93 | 80.19 153 | 70.29 78 | 80.91 260 | 87.98 46 |
|
MSLP-MVS++ | | | 74.48 111 | 75.78 97 | 70.59 176 | 84.66 81 | 62.40 129 | 78.65 94 | 84.24 71 | 60.55 129 | 77.71 147 | 81.98 214 | 63.12 145 | 77.64 199 | 62.95 142 | 88.14 163 | 71.73 292 |
|
Regformer-1 | | | 74.28 112 | 73.63 124 | 76.21 100 | 74.22 232 | 64.12 118 | 72.77 160 | 75.46 199 | 66.86 68 | 79.27 125 | 72.08 314 | 69.29 90 | 78.74 171 | 68.73 89 | 84.02 226 | 85.77 82 |
|
AdaColmap |  | | 74.22 113 | 74.56 107 | 73.20 139 | 81.95 125 | 60.97 142 | 79.43 84 | 80.90 124 | 65.57 79 | 72.54 224 | 81.76 218 | 70.98 78 | 85.26 54 | 47.88 258 | 90.00 135 | 73.37 275 |
|
CSCG | | | 74.12 114 | 74.39 110 | 73.33 136 | 79.35 152 | 61.66 136 | 77.45 110 | 81.98 102 | 62.47 116 | 79.06 128 | 80.19 236 | 61.83 157 | 78.79 170 | 59.83 167 | 87.35 178 | 79.54 222 |
|
PAPM_NR | | | 73.91 115 | 74.16 114 | 73.16 140 | 81.90 126 | 53.50 196 | 81.28 64 | 81.40 111 | 66.17 75 | 73.30 215 | 83.31 201 | 59.96 176 | 83.10 96 | 58.45 175 | 81.66 254 | 82.87 160 |
|
EPP-MVSNet | | | 73.86 116 | 73.38 128 | 75.31 110 | 78.19 171 | 53.35 198 | 80.45 70 | 77.32 183 | 65.11 88 | 76.47 173 | 86.80 133 | 49.47 245 | 83.77 81 | 53.89 216 | 92.72 82 | 88.81 40 |
|
mvs-test1 | | | 73.81 117 | 70.69 169 | 83.18 5 | 77.05 187 | 81.39 3 | 75.39 134 | 77.70 177 | 57.68 152 | 71.19 245 | 74.72 291 | 64.80 136 | 83.66 83 | 56.46 189 | 81.19 258 | 84.50 116 |
|
RRT_MVS | | | 73.80 118 | 71.19 164 | 81.60 24 | 71.04 265 | 70.33 68 | 78.78 93 | 74.91 206 | 56.96 161 | 77.83 144 | 85.56 172 | 32.82 328 | 87.39 5 | 71.16 73 | 91.68 93 | 87.07 62 |
|
K. test v3 | | | 73.67 119 | 73.61 125 | 73.87 128 | 79.78 146 | 55.62 184 | 74.69 147 | 62.04 298 | 66.16 76 | 84.76 61 | 93.23 5 | 49.47 245 | 80.97 134 | 65.66 116 | 86.67 193 | 85.02 93 |
|
NR-MVSNet | | | 73.62 120 | 74.05 115 | 72.33 162 | 83.50 98 | 43.71 268 | 65.65 262 | 77.32 183 | 64.32 96 | 75.59 181 | 87.08 125 | 62.45 151 | 81.34 122 | 54.90 203 | 95.63 8 | 91.93 8 |
|
DP-MVS Recon | | | 73.57 121 | 72.69 141 | 76.23 99 | 82.85 112 | 63.39 123 | 74.32 150 | 82.96 89 | 57.75 151 | 70.35 252 | 81.98 214 | 64.34 141 | 84.41 74 | 49.69 242 | 89.95 137 | 80.89 198 |
|
CNLPA | | | 73.44 122 | 73.03 137 | 74.66 115 | 78.27 170 | 75.29 31 | 75.99 129 | 78.49 166 | 65.39 82 | 75.67 180 | 83.22 206 | 61.23 166 | 66.77 301 | 53.70 218 | 85.33 206 | 81.92 183 |
|
MCST-MVS | | | 73.42 123 | 73.34 130 | 73.63 132 | 81.28 134 | 59.17 161 | 74.80 143 | 83.13 87 | 45.50 279 | 72.84 219 | 83.78 192 | 65.15 133 | 80.99 132 | 64.54 123 | 89.09 157 | 80.73 205 |
|
v1192 | | | 73.40 124 | 73.42 126 | 73.32 137 | 74.65 227 | 48.67 223 | 72.21 165 | 81.73 105 | 52.76 216 | 81.85 93 | 84.56 181 | 57.12 206 | 82.24 112 | 68.58 90 | 87.33 179 | 89.06 32 |
|
114514_t | | | 73.40 124 | 73.33 131 | 73.64 131 | 84.15 92 | 57.11 172 | 78.20 103 | 80.02 141 | 43.76 293 | 72.55 223 | 86.07 165 | 64.00 142 | 83.35 91 | 60.14 163 | 91.03 111 | 80.45 211 |
|
FC-MVSNet-test | | | 73.32 126 | 74.78 106 | 68.93 205 | 79.21 156 | 36.57 322 | 71.82 176 | 79.54 149 | 57.63 156 | 82.57 88 | 90.38 64 | 59.38 183 | 78.99 166 | 57.91 179 | 94.56 35 | 91.23 12 |
|
v1144 | | | 73.29 127 | 73.39 127 | 73.01 143 | 74.12 236 | 48.11 230 | 72.01 168 | 81.08 121 | 53.83 207 | 81.77 95 | 84.68 179 | 58.07 198 | 81.91 115 | 68.10 94 | 86.86 189 | 88.99 35 |
|
GeoE | | | 73.14 128 | 73.77 121 | 71.26 171 | 78.09 173 | 52.64 201 | 74.32 150 | 79.56 148 | 56.32 170 | 76.35 176 | 83.36 200 | 70.76 79 | 77.96 193 | 63.32 139 | 81.84 249 | 83.18 152 |
|
baseline | | | 73.10 129 | 73.96 117 | 70.51 178 | 71.46 263 | 46.39 251 | 72.08 166 | 84.40 65 | 55.95 173 | 76.62 167 | 86.46 151 | 67.20 109 | 78.03 192 | 64.22 126 | 87.27 182 | 87.11 61 |
|
h-mvs33 | | | 73.08 130 | 71.61 157 | 77.48 82 | 83.89 96 | 72.89 49 | 70.47 194 | 71.12 244 | 54.28 194 | 77.89 141 | 83.41 195 | 49.04 248 | 80.98 133 | 63.62 134 | 90.77 122 | 78.58 232 |
|
TSAR-MVS + GP. | | | 73.08 130 | 71.60 158 | 77.54 81 | 78.99 164 | 70.73 63 | 74.96 138 | 69.38 255 | 60.73 128 | 74.39 200 | 78.44 260 | 57.72 202 | 82.78 100 | 60.16 162 | 89.60 144 | 79.11 227 |
|
v1240 | | | 73.06 132 | 73.14 133 | 72.84 150 | 74.74 221 | 47.27 243 | 71.88 175 | 81.11 118 | 51.80 225 | 82.28 91 | 84.21 186 | 56.22 214 | 82.34 108 | 68.82 88 | 87.17 185 | 88.91 37 |
|
casdiffmvs | | | 73.06 132 | 73.84 118 | 70.72 174 | 71.32 264 | 46.71 247 | 70.93 189 | 84.26 70 | 55.62 176 | 77.46 150 | 87.10 124 | 67.09 110 | 77.81 195 | 63.95 129 | 86.83 190 | 87.64 50 |
|
IterMVS-LS | | | 73.01 134 | 73.12 135 | 72.66 155 | 73.79 239 | 49.90 215 | 71.63 177 | 78.44 167 | 58.22 146 | 80.51 112 | 86.63 145 | 58.15 194 | 79.62 158 | 62.51 143 | 88.20 162 | 88.48 43 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CANet | | | 73.00 135 | 71.84 152 | 76.48 95 | 75.82 207 | 61.28 138 | 74.81 141 | 80.37 136 | 63.17 110 | 62.43 306 | 80.50 231 | 61.10 168 | 85.16 60 | 64.00 128 | 84.34 222 | 83.01 157 |
|
v144192 | | | 72.99 136 | 73.06 136 | 72.77 151 | 74.58 228 | 47.48 239 | 71.90 174 | 80.44 134 | 51.57 228 | 81.46 101 | 84.11 188 | 58.04 199 | 82.12 113 | 67.98 98 | 87.47 175 | 88.70 42 |
|
MVS_111021_HR | | | 72.98 137 | 72.97 139 | 72.99 144 | 80.82 137 | 65.47 105 | 68.81 214 | 72.77 220 | 57.67 154 | 75.76 179 | 82.38 211 | 71.01 77 | 77.17 203 | 61.38 151 | 86.15 196 | 76.32 253 |
|
v1921920 | | | 72.96 138 | 72.98 138 | 72.89 149 | 74.67 224 | 47.58 238 | 71.92 173 | 80.69 127 | 51.70 227 | 81.69 99 | 83.89 190 | 56.58 212 | 82.25 111 | 68.34 92 | 87.36 177 | 88.82 39 |
|
CLD-MVS | | | 72.88 139 | 72.36 146 | 74.43 119 | 77.03 189 | 54.30 190 | 68.77 217 | 83.43 84 | 52.12 221 | 76.79 165 | 74.44 295 | 69.54 89 | 83.91 79 | 55.88 195 | 93.25 72 | 85.09 89 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Regformer-3 | | | 72.86 140 | 72.28 147 | 74.62 116 | 74.74 221 | 60.18 152 | 72.91 158 | 71.76 228 | 64.74 92 | 78.42 135 | 72.07 316 | 67.00 111 | 76.28 215 | 67.97 99 | 80.91 260 | 87.39 54 |
|
EI-MVSNet-Vis-set | | | 72.78 141 | 71.87 151 | 75.54 108 | 74.77 220 | 59.02 163 | 72.24 164 | 71.56 232 | 63.92 99 | 78.59 131 | 71.59 322 | 66.22 122 | 78.60 173 | 67.58 102 | 80.32 268 | 89.00 34 |
|
ETV-MVS | | | 72.72 142 | 72.16 150 | 74.38 121 | 76.90 193 | 55.95 178 | 73.34 156 | 84.67 57 | 62.04 117 | 72.19 230 | 70.81 326 | 65.90 125 | 85.24 56 | 58.64 173 | 84.96 214 | 81.95 182 |
|
PCF-MVS | | 63.80 13 | 72.70 143 | 71.69 154 | 75.72 105 | 78.10 172 | 60.01 154 | 73.04 157 | 81.50 108 | 45.34 283 | 79.66 121 | 84.35 185 | 65.15 133 | 82.65 103 | 48.70 250 | 89.38 151 | 84.50 116 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
EI-MVSNet-UG-set | | | 72.63 144 | 71.68 155 | 75.47 109 | 74.67 224 | 58.64 168 | 72.02 167 | 71.50 233 | 63.53 104 | 78.58 133 | 71.39 325 | 65.98 123 | 78.53 175 | 67.30 108 | 80.18 270 | 89.23 28 |
|
Anonymous20240529 | | | 72.56 145 | 73.79 120 | 68.86 207 | 76.89 194 | 45.21 259 | 68.80 216 | 77.25 185 | 67.16 65 | 76.89 160 | 90.44 55 | 65.95 124 | 74.19 240 | 50.75 234 | 90.00 135 | 87.18 60 |
|
FIs | | | 72.56 145 | 73.80 119 | 68.84 208 | 78.74 167 | 37.74 316 | 71.02 187 | 79.83 143 | 56.12 171 | 80.88 110 | 89.45 85 | 58.18 192 | 78.28 186 | 56.63 185 | 93.36 70 | 90.51 19 |
|
v2v482 | | | 72.55 147 | 72.58 143 | 72.43 159 | 72.92 254 | 46.72 246 | 71.41 180 | 79.13 153 | 55.27 178 | 81.17 104 | 85.25 176 | 55.41 217 | 81.13 127 | 67.25 109 | 85.46 202 | 89.43 25 |
|
hse-mvs2 | | | 72.32 148 | 70.66 170 | 77.31 87 | 83.10 107 | 71.77 52 | 69.19 209 | 71.45 235 | 54.28 194 | 77.89 141 | 78.26 262 | 49.04 248 | 79.23 162 | 63.62 134 | 89.13 156 | 80.92 197 |
|
canonicalmvs | | | 72.29 149 | 73.38 128 | 69.04 200 | 74.23 231 | 47.37 241 | 73.93 154 | 83.18 85 | 54.36 193 | 76.61 168 | 81.64 220 | 72.03 66 | 75.34 224 | 57.12 182 | 87.28 181 | 84.40 118 |
|
Effi-MVS+ | | | 72.10 150 | 72.28 147 | 71.58 166 | 74.21 235 | 50.33 211 | 74.72 146 | 82.73 93 | 62.62 113 | 70.77 248 | 76.83 276 | 69.96 85 | 80.97 134 | 60.20 160 | 78.43 287 | 83.45 144 |
|
MVS_111021_LR | | | 72.10 150 | 71.82 153 | 72.95 146 | 79.53 150 | 73.90 41 | 70.45 195 | 66.64 268 | 56.87 162 | 76.81 164 | 81.76 218 | 68.78 94 | 71.76 264 | 61.81 146 | 83.74 230 | 73.18 277 |
|
pmmvs6 | | | 71.82 152 | 73.66 123 | 66.31 237 | 75.94 206 | 42.01 283 | 66.99 244 | 72.53 223 | 63.45 106 | 76.43 174 | 92.78 10 | 72.95 64 | 69.69 278 | 51.41 229 | 90.46 126 | 87.22 57 |
|
PLC |  | 62.01 16 | 71.79 153 | 70.28 172 | 76.33 97 | 80.31 143 | 68.63 82 | 78.18 104 | 81.24 115 | 54.57 190 | 67.09 280 | 80.63 229 | 59.44 181 | 81.74 120 | 46.91 265 | 84.17 223 | 78.63 230 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
VDDNet | | | 71.60 154 | 73.13 134 | 67.02 230 | 86.29 53 | 41.11 288 | 69.97 198 | 66.50 269 | 68.72 58 | 74.74 192 | 91.70 24 | 59.90 177 | 75.81 218 | 48.58 252 | 91.72 91 | 84.15 125 |
|
3Dnovator | | 65.95 11 | 71.50 155 | 71.22 163 | 72.34 161 | 73.16 246 | 63.09 127 | 78.37 98 | 78.32 168 | 57.67 154 | 72.22 229 | 84.61 180 | 54.77 218 | 78.47 177 | 60.82 158 | 81.07 259 | 75.45 259 |
|
WR-MVS | | | 71.20 156 | 72.48 144 | 67.36 225 | 84.98 76 | 35.70 330 | 64.43 277 | 68.66 259 | 65.05 89 | 81.49 100 | 86.43 152 | 57.57 203 | 76.48 213 | 50.36 238 | 93.32 71 | 89.90 21 |
|
V42 | | | 71.06 157 | 70.83 167 | 71.72 165 | 67.25 299 | 47.14 244 | 65.94 256 | 80.35 137 | 51.35 231 | 83.40 78 | 83.23 204 | 59.25 184 | 78.80 169 | 65.91 114 | 80.81 264 | 89.23 28 |
|
FMVSNet1 | | | 71.06 157 | 72.48 144 | 66.81 231 | 77.65 183 | 40.68 293 | 71.96 170 | 73.03 215 | 61.14 122 | 79.45 124 | 90.36 67 | 60.44 172 | 75.20 226 | 50.20 239 | 88.05 165 | 84.54 111 |
|
dcpmvs_2 | | | 71.02 159 | 72.65 142 | 66.16 238 | 76.06 205 | 50.49 210 | 71.97 169 | 79.36 150 | 50.34 242 | 82.81 85 | 83.63 193 | 64.38 140 | 67.27 295 | 61.54 150 | 83.71 232 | 80.71 207 |
|
API-MVS | | | 70.97 160 | 71.51 160 | 69.37 194 | 75.20 212 | 55.94 179 | 80.99 65 | 76.84 187 | 62.48 115 | 71.24 243 | 77.51 272 | 61.51 162 | 80.96 138 | 52.04 224 | 85.76 201 | 71.22 297 |
|
VDD-MVS | | | 70.81 161 | 71.44 161 | 68.91 206 | 79.07 162 | 46.51 248 | 67.82 230 | 70.83 248 | 61.23 121 | 74.07 206 | 88.69 103 | 59.86 178 | 75.62 221 | 51.11 231 | 90.28 128 | 84.61 107 |
|
EG-PatchMatch MVS | | | 70.70 162 | 70.88 166 | 70.16 184 | 82.64 116 | 58.80 165 | 71.48 178 | 73.64 213 | 54.98 181 | 76.55 169 | 81.77 217 | 61.10 168 | 78.94 167 | 54.87 204 | 80.84 263 | 72.74 282 |
|
Baseline_NR-MVSNet | | | 70.62 163 | 73.19 132 | 62.92 269 | 76.97 190 | 34.44 338 | 68.84 212 | 70.88 247 | 60.25 131 | 79.50 123 | 90.53 52 | 61.82 158 | 69.11 282 | 54.67 206 | 95.27 14 | 85.22 85 |
|
alignmvs | | | 70.54 164 | 71.00 165 | 69.15 199 | 73.50 240 | 48.04 232 | 69.85 201 | 79.62 144 | 53.94 206 | 76.54 170 | 82.00 213 | 59.00 186 | 74.68 233 | 57.32 181 | 87.21 183 | 84.72 102 |
|
MG-MVS | | | 70.47 165 | 71.34 162 | 67.85 219 | 79.26 154 | 40.42 297 | 74.67 148 | 75.15 205 | 58.41 145 | 68.74 269 | 88.14 118 | 56.08 215 | 83.69 82 | 59.90 166 | 81.71 253 | 79.43 224 |
|
AUN-MVS | | | 70.22 166 | 67.88 202 | 77.22 88 | 82.96 111 | 71.61 53 | 69.08 210 | 71.39 236 | 49.17 255 | 71.70 233 | 78.07 267 | 37.62 313 | 79.21 163 | 61.81 146 | 89.15 154 | 80.82 200 |
|
UGNet | | | 70.20 167 | 69.05 182 | 73.65 130 | 76.24 200 | 63.64 120 | 75.87 130 | 72.53 223 | 61.48 120 | 60.93 316 | 86.14 161 | 52.37 230 | 77.12 204 | 50.67 235 | 85.21 208 | 80.17 216 |
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 |
PVSNet_Blended_VisFu | | | 70.04 168 | 68.88 185 | 73.53 134 | 82.71 114 | 63.62 121 | 74.81 141 | 81.95 103 | 48.53 261 | 67.16 279 | 79.18 252 | 51.42 236 | 78.38 182 | 54.39 211 | 79.72 277 | 78.60 231 |
|
Fast-Effi-MVS+-dtu | | | 70.00 169 | 68.74 189 | 73.77 129 | 73.47 241 | 64.53 114 | 71.36 181 | 78.14 172 | 55.81 175 | 68.84 268 | 74.71 292 | 65.36 131 | 75.75 219 | 52.00 225 | 79.00 281 | 81.03 193 |
|
DPM-MVS | | | 69.98 170 | 69.22 181 | 72.26 163 | 82.69 115 | 58.82 164 | 70.53 193 | 81.23 116 | 47.79 268 | 64.16 294 | 80.21 234 | 51.32 237 | 83.12 95 | 60.14 163 | 84.95 215 | 74.83 265 |
|
MVSFormer | | | 69.93 171 | 69.03 183 | 72.63 157 | 74.93 215 | 59.19 159 | 83.98 39 | 75.72 196 | 52.27 219 | 63.53 302 | 76.74 277 | 43.19 279 | 80.56 141 | 72.28 68 | 78.67 285 | 78.14 240 |
|
MVS_Test | | | 69.84 172 | 70.71 168 | 67.24 226 | 67.49 298 | 43.25 275 | 69.87 200 | 81.22 117 | 52.69 217 | 71.57 238 | 86.68 141 | 62.09 156 | 74.51 235 | 66.05 112 | 78.74 283 | 83.96 127 |
|
c3_l | | | 69.82 173 | 69.89 174 | 69.61 193 | 66.24 307 | 43.48 271 | 68.12 227 | 79.61 146 | 51.43 230 | 77.72 146 | 80.18 237 | 54.61 221 | 78.15 191 | 63.62 134 | 87.50 174 | 87.20 59 |
|
TransMVSNet (Re) | | | 69.62 174 | 71.63 156 | 63.57 258 | 76.51 197 | 35.93 328 | 65.75 261 | 71.29 239 | 61.05 123 | 75.02 187 | 89.90 79 | 65.88 126 | 70.41 276 | 49.79 241 | 89.48 147 | 84.38 119 |
|
EI-MVSNet | | | 69.61 175 | 69.01 184 | 71.41 170 | 73.94 237 | 49.90 215 | 71.31 183 | 71.32 237 | 58.22 146 | 75.40 185 | 70.44 328 | 58.16 193 | 75.85 216 | 62.51 143 | 79.81 274 | 88.48 43 |
|
Gipuma |  | | 69.55 176 | 72.83 140 | 59.70 293 | 63.63 327 | 53.97 192 | 80.08 80 | 75.93 194 | 64.24 97 | 73.49 211 | 88.93 101 | 57.89 201 | 62.46 318 | 59.75 168 | 91.55 98 | 62.67 346 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
tttt0517 | | | 69.46 177 | 67.79 203 | 74.46 117 | 75.34 210 | 52.72 200 | 75.05 137 | 63.27 289 | 54.69 187 | 78.87 130 | 84.37 184 | 26.63 357 | 81.15 126 | 63.95 129 | 87.93 169 | 89.51 24 |
|
eth_miper_zixun_eth | | | 69.42 178 | 68.73 190 | 71.50 169 | 67.99 292 | 46.42 249 | 67.58 232 | 78.81 158 | 50.72 239 | 78.13 139 | 80.34 233 | 50.15 243 | 80.34 146 | 60.18 161 | 84.65 216 | 87.74 49 |
|
BH-untuned | | | 69.39 179 | 69.46 176 | 69.18 198 | 77.96 176 | 56.88 174 | 68.47 224 | 77.53 180 | 56.77 164 | 77.79 145 | 79.63 243 | 60.30 174 | 80.20 152 | 46.04 271 | 80.65 265 | 70.47 302 |
|
v148 | | | 69.38 180 | 69.39 177 | 69.36 195 | 69.14 285 | 44.56 262 | 68.83 213 | 72.70 221 | 54.79 185 | 78.59 131 | 84.12 187 | 54.69 219 | 76.74 212 | 59.40 170 | 82.20 244 | 86.79 64 |
|
1121 | | | 69.23 181 | 68.26 194 | 72.12 164 | 88.36 37 | 71.40 55 | 68.59 219 | 62.06 296 | 43.80 292 | 74.75 191 | 86.18 158 | 52.92 227 | 76.85 209 | 54.47 208 | 83.27 236 | 68.12 319 |
|
PAPR | | | 69.20 182 | 68.66 191 | 70.82 173 | 75.15 214 | 47.77 235 | 75.31 135 | 81.11 118 | 49.62 252 | 66.33 282 | 79.27 248 | 61.53 161 | 82.96 98 | 48.12 256 | 81.50 256 | 81.74 185 |
|
QAPM | | | 69.18 183 | 69.26 179 | 68.94 204 | 71.61 262 | 52.58 202 | 80.37 73 | 78.79 160 | 49.63 251 | 73.51 210 | 85.14 177 | 53.66 224 | 79.12 164 | 55.11 202 | 75.54 303 | 75.11 263 |
|
LCM-MVSNet-Re | | | 69.10 184 | 71.57 159 | 61.70 277 | 70.37 274 | 34.30 340 | 61.45 300 | 79.62 144 | 56.81 163 | 89.59 8 | 88.16 117 | 68.44 99 | 72.94 247 | 42.30 288 | 87.33 179 | 77.85 245 |
|
EPNet | | | 69.10 184 | 67.32 208 | 74.46 117 | 68.33 289 | 61.27 139 | 77.56 108 | 63.57 287 | 60.95 124 | 56.62 332 | 82.75 207 | 51.53 235 | 81.24 125 | 54.36 212 | 90.20 129 | 80.88 199 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DELS-MVS | | | 68.83 186 | 68.31 192 | 70.38 179 | 70.55 273 | 48.31 226 | 63.78 284 | 82.13 99 | 54.00 203 | 68.96 265 | 75.17 287 | 58.95 187 | 80.06 155 | 58.55 174 | 82.74 240 | 82.76 164 |
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 |
Fast-Effi-MVS+ | | | 68.81 187 | 68.30 193 | 70.35 180 | 74.66 226 | 48.61 224 | 66.06 255 | 78.32 168 | 50.62 240 | 71.48 241 | 75.54 283 | 68.75 95 | 79.59 160 | 50.55 237 | 78.73 284 | 82.86 161 |
|
OpenMVS |  | 62.51 15 | 68.76 188 | 68.75 188 | 68.78 209 | 70.56 272 | 53.91 193 | 78.29 100 | 77.35 182 | 48.85 258 | 70.22 254 | 83.52 194 | 52.65 229 | 76.93 206 | 55.31 201 | 81.99 246 | 75.49 258 |
|
VPA-MVSNet | | | 68.71 189 | 70.37 171 | 63.72 256 | 76.13 202 | 38.06 314 | 64.10 279 | 71.48 234 | 56.60 169 | 74.10 205 | 88.31 112 | 64.78 138 | 69.72 277 | 47.69 260 | 90.15 132 | 83.37 146 |
|
BH-RMVSNet | | | 68.69 190 | 68.20 198 | 70.14 185 | 76.40 198 | 53.90 194 | 64.62 274 | 73.48 214 | 58.01 148 | 73.91 208 | 81.78 216 | 59.09 185 | 78.22 187 | 48.59 251 | 77.96 292 | 78.31 235 |
|
EIA-MVS | | | 68.59 191 | 67.16 210 | 72.90 148 | 75.18 213 | 55.64 183 | 69.39 205 | 81.29 113 | 52.44 218 | 64.53 290 | 70.69 327 | 60.33 173 | 82.30 109 | 54.27 213 | 76.31 298 | 80.75 204 |
|
pm-mvs1 | | | 68.40 192 | 69.85 175 | 64.04 254 | 73.10 250 | 39.94 299 | 64.61 275 | 70.50 249 | 55.52 177 | 73.97 207 | 89.33 86 | 63.91 143 | 68.38 286 | 49.68 243 | 88.02 166 | 83.81 131 |
|
miper_ehance_all_eth | | | 68.36 193 | 68.16 199 | 68.98 202 | 65.14 318 | 43.34 273 | 67.07 243 | 78.92 157 | 49.11 256 | 76.21 177 | 77.72 269 | 53.48 225 | 77.92 194 | 61.16 154 | 84.59 218 | 85.68 83 |
|
GBi-Net | | | 68.30 194 | 68.79 186 | 66.81 231 | 73.14 247 | 40.68 293 | 71.96 170 | 73.03 215 | 54.81 182 | 74.72 193 | 90.36 67 | 48.63 254 | 75.20 226 | 47.12 262 | 85.37 203 | 84.54 111 |
|
test1 | | | 68.30 194 | 68.79 186 | 66.81 231 | 73.14 247 | 40.68 293 | 71.96 170 | 73.03 215 | 54.81 182 | 74.72 193 | 90.36 67 | 48.63 254 | 75.20 226 | 47.12 262 | 85.37 203 | 84.54 111 |
|
DIV-MVS_self_test | | | 68.27 196 | 68.26 194 | 68.29 214 | 64.98 319 | 43.67 269 | 65.89 257 | 74.67 207 | 50.04 247 | 76.86 162 | 82.43 209 | 48.74 252 | 75.38 222 | 60.94 156 | 89.81 140 | 85.81 77 |
|
cl____ | | | 68.26 197 | 68.26 194 | 68.29 214 | 64.98 319 | 43.67 269 | 65.89 257 | 74.67 207 | 50.04 247 | 76.86 162 | 82.42 210 | 48.74 252 | 75.38 222 | 60.92 157 | 89.81 140 | 85.80 81 |
|
TinyColmap | | | 67.98 198 | 69.28 178 | 64.08 252 | 67.98 293 | 46.82 245 | 70.04 197 | 75.26 203 | 53.05 212 | 77.36 151 | 86.79 134 | 59.39 182 | 72.59 254 | 45.64 273 | 88.01 167 | 72.83 280 |
|
xiu_mvs_v1_base_debu | | | 67.87 199 | 67.07 211 | 70.26 181 | 79.13 159 | 61.90 133 | 67.34 236 | 71.25 240 | 47.98 264 | 67.70 273 | 74.19 301 | 61.31 163 | 72.62 251 | 56.51 186 | 78.26 289 | 76.27 254 |
|
xiu_mvs_v1_base | | | 67.87 199 | 67.07 211 | 70.26 181 | 79.13 159 | 61.90 133 | 67.34 236 | 71.25 240 | 47.98 264 | 67.70 273 | 74.19 301 | 61.31 163 | 72.62 251 | 56.51 186 | 78.26 289 | 76.27 254 |
|
xiu_mvs_v1_base_debi | | | 67.87 199 | 67.07 211 | 70.26 181 | 79.13 159 | 61.90 133 | 67.34 236 | 71.25 240 | 47.98 264 | 67.70 273 | 74.19 301 | 61.31 163 | 72.62 251 | 56.51 186 | 78.26 289 | 76.27 254 |
|
MAR-MVS | | | 67.72 202 | 66.16 217 | 72.40 160 | 74.45 229 | 64.99 111 | 74.87 139 | 77.50 181 | 48.67 259 | 65.78 286 | 68.58 346 | 57.01 209 | 77.79 196 | 46.68 268 | 81.92 247 | 74.42 268 |
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 |
IterMVS-SCA-FT | | | 67.68 203 | 66.07 218 | 72.49 158 | 73.34 244 | 58.20 170 | 63.80 283 | 65.55 274 | 48.10 263 | 76.91 159 | 82.64 208 | 45.20 266 | 78.84 168 | 61.20 153 | 77.89 293 | 80.44 212 |
|
LF4IMVS | | | 67.50 204 | 67.31 209 | 68.08 217 | 58.86 351 | 61.93 132 | 71.43 179 | 75.90 195 | 44.67 288 | 72.42 226 | 80.20 235 | 57.16 204 | 70.44 274 | 58.99 172 | 86.12 197 | 71.88 290 |
|
FMVSNet2 | | | 67.48 205 | 68.21 197 | 65.29 243 | 73.14 247 | 38.94 306 | 68.81 214 | 71.21 243 | 54.81 182 | 76.73 166 | 86.48 150 | 48.63 254 | 74.60 234 | 47.98 257 | 86.11 198 | 82.35 175 |
|
MSDG | | | 67.47 206 | 67.48 207 | 67.46 224 | 70.70 269 | 54.69 188 | 66.90 247 | 78.17 171 | 60.88 125 | 70.41 251 | 74.76 289 | 61.22 167 | 73.18 245 | 47.38 261 | 76.87 295 | 74.49 267 |
|
diffmvs | | | 67.42 207 | 67.50 206 | 67.20 227 | 62.26 332 | 45.21 259 | 64.87 271 | 77.04 186 | 48.21 262 | 71.74 232 | 79.70 242 | 58.40 191 | 71.17 268 | 64.99 120 | 80.27 269 | 85.22 85 |
|
cl22 | | | 67.14 208 | 66.51 215 | 69.03 201 | 63.20 328 | 43.46 272 | 66.88 248 | 76.25 191 | 49.22 254 | 74.48 198 | 77.88 268 | 45.49 265 | 77.40 201 | 60.64 159 | 84.59 218 | 86.24 69 |
|
ANet_high | | | 67.08 209 | 69.94 173 | 58.51 302 | 57.55 356 | 27.09 365 | 58.43 319 | 76.80 188 | 63.56 103 | 82.40 90 | 91.93 20 | 59.82 179 | 64.98 309 | 50.10 240 | 88.86 159 | 83.46 143 |
|
LFMVS | | | 67.06 210 | 67.89 201 | 64.56 248 | 78.02 174 | 38.25 311 | 70.81 192 | 59.60 304 | 65.18 87 | 71.06 246 | 86.56 148 | 43.85 275 | 75.22 225 | 46.35 269 | 89.63 143 | 80.21 215 |
|
thisisatest0530 | | | 67.05 211 | 65.16 224 | 72.73 154 | 73.10 250 | 50.55 209 | 71.26 185 | 63.91 285 | 50.22 244 | 74.46 199 | 80.75 227 | 26.81 356 | 80.25 149 | 59.43 169 | 86.50 194 | 87.37 56 |
|
bset_n11_16_dypcd | | | 66.91 212 | 65.84 220 | 70.12 186 | 72.95 253 | 53.54 195 | 63.64 285 | 68.65 260 | 48.54 260 | 72.54 224 | 74.28 298 | 40.58 296 | 78.54 174 | 63.52 137 | 87.82 170 | 78.29 236 |
|
MIMVSNet1 | | | 66.57 213 | 69.23 180 | 58.59 301 | 81.26 135 | 37.73 317 | 64.06 280 | 57.62 310 | 57.02 160 | 78.40 136 | 90.75 45 | 62.65 147 | 58.10 331 | 41.77 293 | 89.58 146 | 79.95 217 |
|
tfpnnormal | | | 66.48 214 | 67.93 200 | 62.16 275 | 73.40 243 | 36.65 321 | 63.45 287 | 64.99 277 | 55.97 172 | 72.82 220 | 87.80 121 | 57.06 208 | 69.10 283 | 48.31 255 | 87.54 172 | 80.72 206 |
|
KD-MVS_self_test | | | 66.38 215 | 67.51 205 | 62.97 267 | 61.76 334 | 34.39 339 | 58.11 321 | 75.30 202 | 50.84 238 | 77.12 154 | 85.42 173 | 56.84 210 | 69.44 279 | 51.07 232 | 91.16 106 | 85.08 91 |
|
Anonymous202405211 | | | 66.02 216 | 66.89 214 | 63.43 261 | 74.22 232 | 38.14 312 | 59.00 316 | 66.13 270 | 63.33 109 | 69.76 259 | 85.95 168 | 51.88 231 | 70.50 273 | 44.23 280 | 87.52 173 | 81.64 186 |
|
miper_enhance_ethall | | | 65.86 217 | 65.05 232 | 68.28 216 | 61.62 336 | 42.62 280 | 64.74 272 | 77.97 174 | 42.52 302 | 73.42 213 | 72.79 311 | 49.66 244 | 77.68 198 | 58.12 177 | 84.59 218 | 84.54 111 |
|
RRT_test8_iter05 | | | 65.80 218 | 65.13 227 | 67.80 222 | 67.02 302 | 40.85 292 | 67.13 242 | 75.33 201 | 49.73 249 | 72.69 221 | 81.32 221 | 24.45 367 | 77.37 202 | 61.69 149 | 86.82 191 | 85.18 87 |
|
RPMNet | | | 65.77 219 | 65.08 231 | 67.84 220 | 66.37 304 | 48.24 228 | 70.93 189 | 86.27 23 | 54.66 188 | 61.35 310 | 86.77 137 | 33.29 325 | 85.67 46 | 55.93 194 | 70.17 333 | 69.62 311 |
|
VPNet | | | 65.58 220 | 67.56 204 | 59.65 294 | 79.72 147 | 30.17 357 | 60.27 310 | 62.14 293 | 54.19 199 | 71.24 243 | 86.63 145 | 58.80 188 | 67.62 291 | 44.17 281 | 90.87 119 | 81.18 190 |
|
PVSNet_BlendedMVS | | | 65.38 221 | 64.30 233 | 68.61 210 | 69.81 278 | 49.36 219 | 65.60 264 | 78.96 155 | 45.50 279 | 59.98 319 | 78.61 258 | 51.82 232 | 78.20 188 | 44.30 278 | 84.11 224 | 78.27 237 |
|
TAMVS | | | 65.31 222 | 63.75 238 | 69.97 190 | 82.23 121 | 59.76 157 | 66.78 249 | 63.37 288 | 45.20 284 | 69.79 258 | 79.37 247 | 47.42 260 | 72.17 257 | 34.48 336 | 85.15 210 | 77.99 244 |
|
test_yl | | | 65.11 223 | 65.09 229 | 65.18 244 | 70.59 270 | 40.86 290 | 63.22 292 | 72.79 218 | 57.91 149 | 68.88 266 | 79.07 255 | 42.85 282 | 74.89 230 | 45.50 274 | 84.97 211 | 79.81 218 |
|
DCV-MVSNet | | | 65.11 223 | 65.09 229 | 65.18 244 | 70.59 270 | 40.86 290 | 63.22 292 | 72.79 218 | 57.91 149 | 68.88 266 | 79.07 255 | 42.85 282 | 74.89 230 | 45.50 274 | 84.97 211 | 79.81 218 |
|
mvs_anonymous | | | 65.08 225 | 65.49 221 | 63.83 255 | 63.79 325 | 37.60 318 | 66.52 252 | 69.82 253 | 43.44 297 | 73.46 212 | 86.08 164 | 58.79 189 | 71.75 265 | 51.90 226 | 75.63 302 | 82.15 179 |
|
FMVSNet3 | | | 65.00 226 | 65.16 224 | 64.52 249 | 69.47 281 | 37.56 319 | 66.63 250 | 70.38 250 | 51.55 229 | 74.72 193 | 83.27 203 | 37.89 312 | 74.44 236 | 47.12 262 | 85.37 203 | 81.57 187 |
|
ECVR-MVS |  | | 64.82 227 | 65.22 222 | 63.60 257 | 78.80 165 | 31.14 354 | 66.97 245 | 56.47 320 | 54.23 196 | 69.94 256 | 88.68 104 | 37.23 314 | 74.81 232 | 45.28 276 | 89.41 149 | 84.86 96 |
|
BH-w/o | | | 64.81 228 | 64.29 234 | 66.36 236 | 76.08 204 | 54.71 187 | 65.61 263 | 75.23 204 | 50.10 246 | 71.05 247 | 71.86 321 | 54.33 222 | 79.02 165 | 38.20 314 | 76.14 299 | 65.36 334 |
|
EGC-MVSNET | | | 64.77 229 | 61.17 260 | 75.60 107 | 86.90 47 | 74.47 35 | 84.04 38 | 68.62 261 | 0.60 375 | 1.13 377 | 91.61 27 | 65.32 132 | 74.15 241 | 64.01 127 | 88.28 161 | 78.17 239 |
|
test1111 | | | 64.62 230 | 65.19 223 | 62.93 268 | 79.01 163 | 29.91 358 | 65.45 265 | 54.41 328 | 54.09 201 | 71.47 242 | 88.48 107 | 37.02 315 | 74.29 239 | 46.83 267 | 89.94 138 | 84.58 110 |
|
cascas | | | 64.59 231 | 62.77 249 | 70.05 188 | 75.27 211 | 50.02 214 | 61.79 299 | 71.61 230 | 42.46 303 | 63.68 300 | 68.89 343 | 49.33 247 | 80.35 145 | 47.82 259 | 84.05 225 | 79.78 220 |
|
TR-MVS | | | 64.59 231 | 63.54 241 | 67.73 223 | 75.75 209 | 50.83 208 | 63.39 288 | 70.29 251 | 49.33 253 | 71.55 239 | 74.55 293 | 50.94 238 | 78.46 178 | 40.43 300 | 75.69 301 | 73.89 272 |
|
PM-MVS | | | 64.49 233 | 63.61 240 | 67.14 229 | 76.68 196 | 75.15 32 | 68.49 223 | 42.85 359 | 51.17 235 | 77.85 143 | 80.51 230 | 45.76 261 | 66.31 304 | 52.83 223 | 76.35 297 | 59.96 352 |
|
jason | | | 64.47 234 | 62.84 248 | 69.34 197 | 76.91 192 | 59.20 158 | 67.15 241 | 65.67 271 | 35.29 338 | 65.16 288 | 76.74 277 | 44.67 270 | 70.68 270 | 54.74 205 | 79.28 280 | 78.14 240 |
jason: jason. |
xiu_mvs_v2_base | | | 64.43 235 | 63.96 236 | 65.85 242 | 77.72 181 | 51.32 206 | 63.63 286 | 72.31 226 | 45.06 287 | 61.70 307 | 69.66 336 | 62.56 148 | 73.93 243 | 49.06 247 | 73.91 315 | 72.31 286 |
|
pmmvs-eth3d | | | 64.41 236 | 63.27 244 | 67.82 221 | 75.81 208 | 60.18 152 | 69.49 203 | 62.05 297 | 38.81 323 | 74.13 203 | 82.23 212 | 43.76 276 | 68.65 284 | 42.53 287 | 80.63 267 | 74.63 266 |
|
CDS-MVSNet | | | 64.33 237 | 62.66 250 | 69.35 196 | 80.44 142 | 58.28 169 | 65.26 267 | 65.66 272 | 44.36 289 | 67.30 278 | 75.54 283 | 43.27 278 | 71.77 263 | 37.68 317 | 84.44 221 | 78.01 243 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PS-MVSNAJ | | | 64.27 238 | 63.73 239 | 65.90 241 | 77.82 179 | 51.42 205 | 63.33 289 | 72.33 225 | 45.09 286 | 61.60 308 | 68.04 347 | 62.39 152 | 73.95 242 | 49.07 246 | 73.87 316 | 72.34 285 |
|
ab-mvs | | | 64.11 239 | 65.13 227 | 61.05 284 | 71.99 260 | 38.03 315 | 67.59 231 | 68.79 258 | 49.08 257 | 65.32 287 | 86.26 156 | 58.02 200 | 66.85 299 | 39.33 304 | 79.79 276 | 78.27 237 |
|
CANet_DTU | | | 64.04 240 | 63.83 237 | 64.66 247 | 68.39 286 | 42.97 277 | 73.45 155 | 74.50 210 | 52.05 223 | 54.78 339 | 75.44 286 | 43.99 274 | 70.42 275 | 53.49 220 | 78.41 288 | 80.59 209 |
|
VNet | | | 64.01 241 | 65.15 226 | 60.57 288 | 73.28 245 | 35.61 331 | 57.60 323 | 67.08 266 | 54.61 189 | 66.76 281 | 83.37 198 | 56.28 213 | 66.87 297 | 42.19 289 | 85.20 209 | 79.23 226 |
|
Anonymous20240521 | | | 63.55 242 | 66.07 218 | 55.99 309 | 66.18 309 | 44.04 266 | 68.77 217 | 68.80 257 | 46.99 273 | 72.57 222 | 85.84 169 | 39.87 300 | 50.22 340 | 53.40 222 | 92.23 88 | 73.71 274 |
|
lupinMVS | | | 63.36 243 | 61.49 258 | 68.97 203 | 74.93 215 | 59.19 159 | 65.80 260 | 64.52 282 | 34.68 343 | 63.53 302 | 74.25 299 | 43.19 279 | 70.62 271 | 53.88 217 | 78.67 285 | 77.10 249 |
|
ET-MVSNet_ETH3D | | | 63.32 244 | 60.69 266 | 71.20 172 | 70.15 277 | 55.66 182 | 65.02 270 | 64.32 283 | 43.28 301 | 68.99 264 | 72.05 320 | 25.46 363 | 78.19 190 | 54.16 215 | 82.80 239 | 79.74 221 |
|
MVSTER | | | 63.29 245 | 61.60 257 | 68.36 212 | 59.77 348 | 46.21 252 | 60.62 307 | 71.32 237 | 41.83 305 | 75.40 185 | 79.12 253 | 30.25 349 | 75.85 216 | 56.30 191 | 79.81 274 | 83.03 156 |
|
OpenMVS_ROB |  | 54.93 17 | 63.23 246 | 63.28 243 | 63.07 265 | 69.81 278 | 45.34 258 | 68.52 222 | 67.14 265 | 43.74 294 | 70.61 250 | 79.22 250 | 47.90 258 | 72.66 250 | 48.75 249 | 73.84 317 | 71.21 298 |
|
IterMVS | | | 63.12 247 | 62.48 251 | 65.02 246 | 66.34 306 | 52.86 199 | 63.81 282 | 62.25 292 | 46.57 276 | 71.51 240 | 80.40 232 | 44.60 271 | 66.82 300 | 51.38 230 | 75.47 304 | 75.38 261 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
HyFIR lowres test | | | 63.01 248 | 60.47 267 | 70.61 175 | 83.04 108 | 54.10 191 | 59.93 312 | 72.24 227 | 33.67 348 | 69.00 263 | 75.63 282 | 38.69 306 | 76.93 206 | 36.60 325 | 75.45 305 | 80.81 203 |
|
GA-MVS | | | 62.91 249 | 61.66 254 | 66.66 235 | 67.09 301 | 44.49 263 | 61.18 304 | 69.36 256 | 51.33 232 | 69.33 261 | 74.47 294 | 36.83 316 | 74.94 229 | 50.60 236 | 74.72 310 | 80.57 210 |
|
PVSNet_Blended | | | 62.90 250 | 61.64 255 | 66.69 234 | 69.81 278 | 49.36 219 | 61.23 303 | 78.96 155 | 42.04 304 | 59.98 319 | 68.86 344 | 51.82 232 | 78.20 188 | 44.30 278 | 77.77 294 | 72.52 283 |
|
USDC | | | 62.80 251 | 63.10 246 | 61.89 276 | 65.19 315 | 43.30 274 | 67.42 235 | 74.20 211 | 35.80 337 | 72.25 228 | 84.48 183 | 45.67 263 | 71.95 262 | 37.95 316 | 84.97 211 | 70.42 304 |
|
Vis-MVSNet (Re-imp) | | | 62.74 252 | 63.21 245 | 61.34 282 | 72.19 258 | 31.56 351 | 67.31 240 | 53.87 329 | 53.60 209 | 69.88 257 | 83.37 198 | 40.52 297 | 70.98 269 | 41.40 294 | 86.78 192 | 81.48 188 |
|
patch_mono-2 | | | 62.73 253 | 64.08 235 | 58.68 300 | 70.36 275 | 55.87 180 | 60.84 306 | 64.11 284 | 41.23 309 | 64.04 295 | 78.22 263 | 60.00 175 | 48.80 342 | 54.17 214 | 83.71 232 | 71.37 294 |
|
D2MVS | | | 62.58 254 | 61.05 262 | 67.20 227 | 63.85 324 | 47.92 233 | 56.29 326 | 69.58 254 | 39.32 318 | 70.07 255 | 78.19 264 | 34.93 321 | 72.68 249 | 53.44 221 | 83.74 230 | 81.00 195 |
|
MVS_0304 | | | 62.51 255 | 62.27 252 | 63.25 262 | 69.39 282 | 48.47 225 | 64.05 281 | 62.48 291 | 59.69 136 | 54.10 344 | 81.04 225 | 45.71 262 | 66.31 304 | 41.38 295 | 82.58 242 | 74.96 264 |
|
CL-MVSNet_self_test | | | 62.44 256 | 63.40 242 | 59.55 295 | 72.34 257 | 32.38 347 | 56.39 325 | 64.84 278 | 51.21 234 | 67.46 277 | 81.01 226 | 50.75 239 | 63.51 316 | 38.47 312 | 88.12 164 | 82.75 165 |
|
MDA-MVSNet-bldmvs | | | 62.34 257 | 61.73 253 | 64.16 250 | 61.64 335 | 49.90 215 | 48.11 347 | 57.24 316 | 53.31 211 | 80.95 106 | 79.39 246 | 49.00 250 | 61.55 322 | 45.92 272 | 80.05 271 | 81.03 193 |
|
miper_lstm_enhance | | | 61.97 258 | 61.63 256 | 62.98 266 | 60.04 344 | 45.74 256 | 47.53 349 | 70.95 245 | 44.04 290 | 73.06 217 | 78.84 257 | 39.72 301 | 60.33 324 | 55.82 196 | 84.64 217 | 82.88 159 |
|
wuyk23d | | | 61.97 258 | 66.25 216 | 49.12 329 | 58.19 355 | 60.77 148 | 66.32 253 | 52.97 336 | 55.93 174 | 90.62 5 | 86.91 130 | 73.07 62 | 35.98 369 | 20.63 371 | 91.63 95 | 50.62 360 |
|
thres600view7 | | | 61.82 260 | 61.38 259 | 63.12 264 | 71.81 261 | 34.93 335 | 64.64 273 | 56.99 317 | 54.78 186 | 70.33 253 | 79.74 241 | 32.07 335 | 72.42 256 | 38.61 310 | 83.46 234 | 82.02 180 |
|
PAPM | | | 61.79 261 | 60.37 268 | 66.05 239 | 76.09 203 | 41.87 284 | 69.30 206 | 76.79 189 | 40.64 315 | 53.80 345 | 79.62 244 | 44.38 272 | 82.92 99 | 29.64 354 | 73.11 319 | 73.36 276 |
|
MVP-Stereo | | | 61.56 262 | 59.22 274 | 68.58 211 | 79.28 153 | 60.44 150 | 69.20 208 | 71.57 231 | 43.58 296 | 56.42 333 | 78.37 261 | 39.57 303 | 76.46 214 | 34.86 335 | 60.16 357 | 68.86 318 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
CMPMVS |  | 48.73 20 | 61.54 263 | 60.89 263 | 63.52 259 | 61.08 339 | 51.55 204 | 68.07 228 | 68.00 264 | 33.88 345 | 65.87 284 | 81.25 223 | 37.91 311 | 67.71 289 | 49.32 245 | 82.60 241 | 71.31 296 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test2506 | | | 61.23 264 | 60.85 264 | 62.38 273 | 78.80 165 | 27.88 364 | 67.33 239 | 37.42 372 | 54.23 196 | 67.55 276 | 88.68 104 | 17.87 376 | 74.39 237 | 46.33 270 | 89.41 149 | 84.86 96 |
|
thres100view900 | | | 61.17 265 | 61.09 261 | 61.39 281 | 72.14 259 | 35.01 334 | 65.42 266 | 56.99 317 | 55.23 179 | 70.71 249 | 79.90 239 | 32.07 335 | 72.09 258 | 35.61 332 | 81.73 250 | 77.08 250 |
|
Patchmtry | | | 60.91 266 | 63.01 247 | 54.62 312 | 66.10 310 | 26.27 368 | 67.47 234 | 56.40 321 | 54.05 202 | 72.04 231 | 86.66 142 | 33.19 326 | 60.17 325 | 43.69 282 | 87.45 176 | 77.42 246 |
|
EU-MVSNet | | | 60.82 267 | 60.80 265 | 60.86 287 | 68.37 287 | 41.16 287 | 72.27 163 | 68.27 263 | 26.96 366 | 69.08 262 | 75.71 281 | 32.09 334 | 67.44 292 | 55.59 199 | 78.90 282 | 73.97 270 |
|
pmmvs4 | | | 60.78 268 | 59.04 276 | 66.00 240 | 73.06 252 | 57.67 171 | 64.53 276 | 60.22 302 | 36.91 332 | 65.96 283 | 77.27 273 | 39.66 302 | 68.54 285 | 38.87 307 | 74.89 309 | 71.80 291 |
|
thres400 | | | 60.77 269 | 59.97 270 | 63.15 263 | 70.78 267 | 35.35 332 | 63.27 290 | 57.47 311 | 53.00 213 | 68.31 270 | 77.09 274 | 32.45 332 | 72.09 258 | 35.61 332 | 81.73 250 | 82.02 180 |
|
MVS | | | 60.62 270 | 59.97 270 | 62.58 271 | 68.13 291 | 47.28 242 | 68.59 219 | 73.96 212 | 32.19 352 | 59.94 321 | 68.86 344 | 50.48 240 | 77.64 199 | 41.85 292 | 75.74 300 | 62.83 344 |
|
thisisatest0515 | | | 60.48 271 | 57.86 285 | 68.34 213 | 67.25 299 | 46.42 249 | 60.58 308 | 62.14 293 | 40.82 313 | 63.58 301 | 69.12 339 | 26.28 359 | 78.34 184 | 48.83 248 | 82.13 245 | 80.26 214 |
|
tfpn200view9 | | | 60.35 272 | 59.97 270 | 61.51 279 | 70.78 267 | 35.35 332 | 63.27 290 | 57.47 311 | 53.00 213 | 68.31 270 | 77.09 274 | 32.45 332 | 72.09 258 | 35.61 332 | 81.73 250 | 77.08 250 |
|
ppachtmachnet_test | | | 60.26 273 | 59.61 273 | 62.20 274 | 67.70 296 | 44.33 264 | 58.18 320 | 60.96 301 | 40.75 314 | 65.80 285 | 72.57 312 | 41.23 289 | 63.92 313 | 46.87 266 | 82.42 243 | 78.33 234 |
|
Patchmatch-RL test | | | 59.95 274 | 59.12 275 | 62.44 272 | 72.46 256 | 54.61 189 | 59.63 313 | 47.51 352 | 41.05 312 | 74.58 197 | 74.30 297 | 31.06 343 | 65.31 306 | 51.61 227 | 79.85 273 | 67.39 322 |
|
1314 | | | 59.83 275 | 58.86 278 | 62.74 270 | 65.71 312 | 44.78 261 | 68.59 219 | 72.63 222 | 33.54 350 | 61.05 314 | 67.29 352 | 43.62 277 | 71.26 267 | 49.49 244 | 67.84 344 | 72.19 288 |
|
IB-MVS | | 49.67 18 | 59.69 276 | 56.96 291 | 67.90 218 | 68.19 290 | 50.30 212 | 61.42 301 | 65.18 276 | 47.57 270 | 55.83 336 | 67.15 353 | 23.77 368 | 79.60 159 | 43.56 284 | 79.97 272 | 73.79 273 |
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 |
1112_ss | | | 59.48 277 | 58.99 277 | 60.96 286 | 77.84 178 | 42.39 282 | 61.42 301 | 68.45 262 | 37.96 327 | 59.93 322 | 67.46 349 | 45.11 268 | 65.07 308 | 40.89 298 | 71.81 324 | 75.41 260 |
|
FPMVS | | | 59.43 278 | 60.07 269 | 57.51 305 | 77.62 184 | 71.52 54 | 62.33 296 | 50.92 341 | 57.40 158 | 69.40 260 | 80.00 238 | 39.14 304 | 61.92 321 | 37.47 320 | 66.36 346 | 39.09 369 |
|
CVMVSNet | | | 59.21 279 | 58.44 282 | 61.51 279 | 73.94 237 | 47.76 236 | 71.31 183 | 64.56 281 | 26.91 367 | 60.34 318 | 70.44 328 | 36.24 318 | 67.65 290 | 53.57 219 | 68.66 341 | 69.12 316 |
|
CR-MVSNet | | | 58.96 280 | 58.49 281 | 60.36 290 | 66.37 304 | 48.24 228 | 70.93 189 | 56.40 321 | 32.87 351 | 61.35 310 | 86.66 142 | 33.19 326 | 63.22 317 | 48.50 253 | 70.17 333 | 69.62 311 |
|
EPNet_dtu | | | 58.93 281 | 58.52 280 | 60.16 292 | 67.91 294 | 47.70 237 | 69.97 198 | 58.02 308 | 49.73 249 | 47.28 360 | 73.02 310 | 38.14 308 | 62.34 319 | 36.57 326 | 85.99 199 | 70.43 303 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Test_1112_low_res | | | 58.78 282 | 58.69 279 | 59.04 298 | 79.41 151 | 38.13 313 | 57.62 322 | 66.98 267 | 34.74 341 | 59.62 323 | 77.56 271 | 42.92 281 | 63.65 315 | 38.66 309 | 70.73 330 | 75.35 262 |
|
PatchMatch-RL | | | 58.68 283 | 57.72 286 | 61.57 278 | 76.21 201 | 73.59 44 | 61.83 298 | 49.00 348 | 47.30 272 | 61.08 312 | 68.97 341 | 50.16 242 | 59.01 328 | 36.06 331 | 68.84 340 | 52.10 359 |
|
SCA | | | 58.57 284 | 58.04 284 | 60.17 291 | 70.17 276 | 41.07 289 | 65.19 268 | 53.38 334 | 43.34 300 | 61.00 315 | 73.48 305 | 45.20 266 | 69.38 280 | 40.34 301 | 70.31 332 | 70.05 306 |
|
CHOSEN 1792x2688 | | | 58.09 285 | 56.30 296 | 63.45 260 | 79.95 145 | 50.93 207 | 54.07 334 | 65.59 273 | 28.56 363 | 61.53 309 | 74.33 296 | 41.09 292 | 66.52 303 | 33.91 339 | 67.69 345 | 72.92 279 |
|
HY-MVS | | 49.31 19 | 57.96 286 | 57.59 287 | 59.10 297 | 66.85 303 | 36.17 325 | 65.13 269 | 65.39 275 | 39.24 320 | 54.69 341 | 78.14 265 | 44.28 273 | 67.18 296 | 33.75 340 | 70.79 329 | 73.95 271 |
|
baseline1 | | | 57.82 287 | 58.36 283 | 56.19 308 | 69.17 284 | 30.76 356 | 62.94 294 | 55.21 324 | 46.04 278 | 63.83 298 | 78.47 259 | 41.20 290 | 63.68 314 | 39.44 303 | 68.99 339 | 74.13 269 |
|
thres200 | | | 57.55 288 | 57.02 290 | 59.17 296 | 67.89 295 | 34.93 335 | 58.91 318 | 57.25 315 | 50.24 243 | 64.01 296 | 71.46 324 | 32.49 331 | 71.39 266 | 31.31 346 | 79.57 278 | 71.19 299 |
|
CostFormer | | | 57.35 289 | 56.14 297 | 60.97 285 | 63.76 326 | 38.43 308 | 67.50 233 | 60.22 302 | 37.14 331 | 59.12 324 | 76.34 279 | 32.78 329 | 71.99 261 | 39.12 306 | 69.27 338 | 72.47 284 |
|
our_test_3 | | | 56.46 290 | 56.51 294 | 56.30 307 | 67.70 296 | 39.66 301 | 55.36 331 | 52.34 339 | 40.57 316 | 63.85 297 | 69.91 335 | 40.04 299 | 58.22 330 | 43.49 285 | 75.29 308 | 71.03 301 |
|
tpm2 | | | 56.12 291 | 54.64 304 | 60.55 289 | 66.24 307 | 36.01 326 | 68.14 226 | 56.77 319 | 33.60 349 | 58.25 327 | 75.52 285 | 30.25 349 | 74.33 238 | 33.27 341 | 69.76 337 | 71.32 295 |
|
tpmvs | | | 55.84 292 | 55.45 302 | 57.01 306 | 60.33 343 | 33.20 345 | 65.89 257 | 59.29 306 | 47.52 271 | 56.04 334 | 73.60 304 | 31.05 344 | 68.06 288 | 40.64 299 | 64.64 349 | 69.77 309 |
|
gg-mvs-nofinetune | | | 55.75 293 | 56.75 293 | 52.72 318 | 62.87 329 | 28.04 363 | 68.92 211 | 41.36 367 | 71.09 44 | 50.80 352 | 92.63 12 | 20.74 371 | 66.86 298 | 29.97 352 | 72.41 321 | 63.25 343 |
|
test20.03 | | | 55.74 294 | 57.51 288 | 50.42 322 | 59.89 347 | 32.09 349 | 50.63 341 | 49.01 347 | 50.11 245 | 65.07 289 | 83.23 204 | 45.61 264 | 48.11 345 | 30.22 350 | 83.82 229 | 71.07 300 |
|
MS-PatchMatch | | | 55.59 295 | 54.89 303 | 57.68 304 | 69.18 283 | 49.05 221 | 61.00 305 | 62.93 290 | 35.98 335 | 58.36 326 | 68.93 342 | 36.71 317 | 66.59 302 | 37.62 319 | 63.30 353 | 57.39 355 |
|
baseline2 | | | 55.57 296 | 52.74 310 | 64.05 253 | 65.26 314 | 44.11 265 | 62.38 295 | 54.43 327 | 39.03 321 | 51.21 350 | 67.35 351 | 33.66 324 | 72.45 255 | 37.14 322 | 64.22 351 | 75.60 257 |
|
XXY-MVS | | | 55.19 297 | 57.40 289 | 48.56 331 | 64.45 322 | 34.84 337 | 51.54 340 | 53.59 331 | 38.99 322 | 63.79 299 | 79.43 245 | 56.59 211 | 45.57 349 | 36.92 324 | 71.29 326 | 65.25 335 |
|
FMVSNet5 | | | 55.08 298 | 55.54 301 | 53.71 313 | 65.80 311 | 33.50 344 | 56.22 327 | 52.50 338 | 43.72 295 | 61.06 313 | 83.38 197 | 25.46 363 | 54.87 334 | 30.11 351 | 81.64 255 | 72.75 281 |
|
PatchmatchNet |  | | 54.60 299 | 54.27 305 | 55.59 310 | 65.17 317 | 39.08 303 | 66.92 246 | 51.80 340 | 39.89 317 | 58.39 325 | 73.12 309 | 31.69 337 | 58.33 329 | 43.01 286 | 58.38 363 | 69.38 314 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MIMVSNet | | | 54.39 300 | 56.12 298 | 49.20 327 | 72.57 255 | 30.91 355 | 59.98 311 | 48.43 350 | 41.66 306 | 55.94 335 | 83.86 191 | 41.19 291 | 50.42 339 | 26.05 361 | 75.38 306 | 66.27 330 |
|
Anonymous20231206 | | | 54.13 301 | 55.82 299 | 49.04 330 | 70.89 266 | 35.96 327 | 51.73 339 | 50.87 342 | 34.86 339 | 62.49 305 | 79.22 250 | 42.52 285 | 44.29 357 | 27.95 359 | 81.88 248 | 66.88 326 |
|
JIA-IIPM | | | 54.03 302 | 51.62 314 | 61.25 283 | 59.14 350 | 55.21 185 | 59.10 315 | 47.72 351 | 50.85 237 | 50.31 356 | 85.81 170 | 20.10 373 | 63.97 312 | 36.16 330 | 55.41 368 | 64.55 341 |
|
tpm cat1 | | | 54.02 303 | 52.63 311 | 58.19 303 | 64.85 321 | 39.86 300 | 66.26 254 | 57.28 314 | 32.16 353 | 56.90 330 | 70.39 330 | 32.75 330 | 65.30 307 | 34.29 337 | 58.79 360 | 69.41 313 |
|
testgi | | | 54.00 304 | 56.86 292 | 45.45 339 | 58.20 354 | 25.81 369 | 49.05 343 | 49.50 346 | 45.43 282 | 67.84 272 | 81.17 224 | 51.81 234 | 43.20 361 | 29.30 355 | 79.41 279 | 67.34 324 |
|
PatchT | | | 53.35 305 | 56.47 295 | 43.99 345 | 64.19 323 | 17.46 376 | 59.15 314 | 43.10 358 | 52.11 222 | 54.74 340 | 86.95 129 | 29.97 352 | 49.98 341 | 43.62 283 | 74.40 313 | 64.53 342 |
|
DWT-MVSNet_test | | | 53.04 306 | 51.12 319 | 58.77 299 | 61.23 337 | 38.67 307 | 62.16 297 | 57.74 309 | 38.24 324 | 51.76 349 | 59.07 363 | 21.36 370 | 67.40 293 | 44.80 277 | 63.76 352 | 70.25 305 |
|
new-patchmatchnet | | | 52.89 307 | 55.76 300 | 44.26 344 | 59.94 346 | 6.31 379 | 37.36 366 | 50.76 343 | 41.10 310 | 64.28 293 | 79.82 240 | 44.77 269 | 48.43 344 | 36.24 329 | 87.61 171 | 78.03 242 |
|
YYNet1 | | | 52.58 308 | 53.50 307 | 49.85 323 | 54.15 370 | 36.45 324 | 40.53 360 | 46.55 354 | 38.09 326 | 75.52 183 | 73.31 308 | 41.08 293 | 43.88 358 | 41.10 296 | 71.14 328 | 69.21 315 |
|
MDA-MVSNet_test_wron | | | 52.57 309 | 53.49 308 | 49.81 324 | 54.24 369 | 36.47 323 | 40.48 361 | 46.58 353 | 38.13 325 | 75.47 184 | 73.32 307 | 41.05 294 | 43.85 359 | 40.98 297 | 71.20 327 | 69.10 317 |
|
pmmvs5 | | | 52.49 310 | 52.58 312 | 52.21 320 | 54.99 367 | 32.38 347 | 55.45 330 | 53.84 330 | 32.15 354 | 55.49 338 | 74.81 288 | 38.08 309 | 57.37 332 | 34.02 338 | 74.40 313 | 66.88 326 |
|
UnsupCasMVSNet_eth | | | 52.26 311 | 53.29 309 | 49.16 328 | 55.08 366 | 33.67 343 | 50.03 342 | 58.79 307 | 37.67 328 | 63.43 304 | 74.75 290 | 41.82 287 | 45.83 348 | 38.59 311 | 59.42 359 | 67.98 321 |
|
N_pmnet | | | 52.06 312 | 51.11 320 | 54.92 311 | 59.64 349 | 71.03 59 | 37.42 365 | 61.62 300 | 33.68 347 | 57.12 328 | 72.10 313 | 37.94 310 | 31.03 371 | 29.13 358 | 71.35 325 | 62.70 345 |
|
KD-MVS_2432*1600 | | | 52.05 313 | 51.58 315 | 53.44 314 | 52.11 372 | 31.20 352 | 44.88 356 | 64.83 279 | 41.53 307 | 64.37 291 | 70.03 333 | 15.61 380 | 64.20 310 | 36.25 327 | 74.61 311 | 64.93 338 |
|
miper_refine_blended | | | 52.05 313 | 51.58 315 | 53.44 314 | 52.11 372 | 31.20 352 | 44.88 356 | 64.83 279 | 41.53 307 | 64.37 291 | 70.03 333 | 15.61 380 | 64.20 310 | 36.25 327 | 74.61 311 | 64.93 338 |
|
PVSNet | | 43.83 21 | 51.56 315 | 51.17 318 | 52.73 317 | 68.34 288 | 38.27 310 | 48.22 346 | 53.56 332 | 36.41 333 | 54.29 342 | 64.94 356 | 34.60 322 | 54.20 337 | 30.34 349 | 69.87 335 | 65.71 333 |
|
tpm | | | 50.60 316 | 52.42 313 | 45.14 341 | 65.18 316 | 26.29 367 | 60.30 309 | 43.50 357 | 37.41 329 | 57.01 329 | 79.09 254 | 30.20 351 | 42.32 362 | 32.77 343 | 66.36 346 | 66.81 328 |
|
test-LLR | | | 50.43 317 | 50.69 322 | 49.64 325 | 60.76 340 | 41.87 284 | 53.18 336 | 45.48 355 | 43.41 298 | 49.41 357 | 60.47 361 | 29.22 354 | 44.73 355 | 42.09 290 | 72.14 322 | 62.33 348 |
|
tpmrst | | | 50.15 318 | 51.38 317 | 46.45 336 | 56.05 361 | 24.77 370 | 64.40 278 | 49.98 344 | 36.14 334 | 53.32 346 | 69.59 337 | 35.16 320 | 48.69 343 | 39.24 305 | 58.51 362 | 65.89 331 |
|
UnsupCasMVSNet_bld | | | 50.01 319 | 51.03 321 | 46.95 332 | 58.61 352 | 32.64 346 | 48.31 345 | 53.27 335 | 34.27 344 | 60.47 317 | 71.53 323 | 41.40 288 | 47.07 346 | 30.68 348 | 60.78 356 | 61.13 350 |
|
WTY-MVS | | | 49.39 320 | 50.31 323 | 46.62 335 | 61.22 338 | 32.00 350 | 46.61 352 | 49.77 345 | 33.87 346 | 54.12 343 | 69.55 338 | 41.96 286 | 45.40 351 | 31.28 347 | 64.42 350 | 62.47 347 |
|
ADS-MVSNet2 | | | 48.76 321 | 47.25 329 | 53.29 316 | 55.90 363 | 40.54 296 | 47.34 350 | 54.99 326 | 31.41 359 | 50.48 353 | 72.06 318 | 31.23 340 | 54.26 336 | 25.93 362 | 55.93 365 | 65.07 336 |
|
test-mter | | | 48.56 322 | 48.20 327 | 49.64 325 | 60.76 340 | 41.87 284 | 53.18 336 | 45.48 355 | 31.91 357 | 49.41 357 | 60.47 361 | 18.34 374 | 44.73 355 | 42.09 290 | 72.14 322 | 62.33 348 |
|
Patchmatch-test | | | 47.93 323 | 49.96 324 | 41.84 348 | 57.42 357 | 24.26 371 | 48.75 344 | 41.49 366 | 39.30 319 | 56.79 331 | 73.48 305 | 30.48 348 | 33.87 370 | 29.29 356 | 72.61 320 | 67.39 322 |
|
test0.0.03 1 | | | 47.72 324 | 48.31 326 | 45.93 337 | 55.53 365 | 29.39 359 | 46.40 353 | 41.21 368 | 43.41 298 | 55.81 337 | 67.65 348 | 29.22 354 | 43.77 360 | 25.73 364 | 69.87 335 | 64.62 340 |
|
sss | | | 47.59 325 | 48.32 325 | 45.40 340 | 56.73 360 | 33.96 341 | 45.17 355 | 48.51 349 | 32.11 356 | 52.37 348 | 65.79 354 | 40.39 298 | 41.91 365 | 31.85 344 | 61.97 354 | 60.35 351 |
|
pmmvs3 | | | 46.71 326 | 45.09 333 | 51.55 321 | 56.76 359 | 48.25 227 | 55.78 329 | 39.53 371 | 24.13 370 | 50.35 355 | 63.40 357 | 15.90 379 | 51.08 338 | 29.29 356 | 70.69 331 | 55.33 358 |
|
EPMVS | | | 45.74 327 | 46.53 330 | 43.39 346 | 54.14 371 | 22.33 373 | 55.02 332 | 35.00 375 | 34.69 342 | 51.09 351 | 70.20 332 | 25.92 361 | 42.04 364 | 37.19 321 | 55.50 367 | 65.78 332 |
|
MVS-HIRNet | | | 45.53 328 | 47.29 328 | 40.24 351 | 62.29 331 | 26.82 366 | 56.02 328 | 37.41 373 | 29.74 362 | 43.69 369 | 81.27 222 | 33.96 323 | 55.48 333 | 24.46 367 | 56.79 364 | 38.43 370 |
|
TESTMET0.1,1 | | | 45.17 329 | 44.93 334 | 45.89 338 | 56.02 362 | 38.31 309 | 53.18 336 | 41.94 365 | 27.85 364 | 44.86 365 | 56.47 364 | 17.93 375 | 41.50 366 | 38.08 315 | 68.06 342 | 57.85 354 |
|
E-PMN | | | 45.17 329 | 45.36 332 | 44.60 343 | 50.07 374 | 42.75 278 | 38.66 363 | 42.29 363 | 46.39 277 | 39.55 370 | 51.15 368 | 26.00 360 | 45.37 352 | 37.68 317 | 76.41 296 | 45.69 366 |
|
PMMVS | | | 44.69 331 | 43.95 338 | 46.92 333 | 50.05 375 | 53.47 197 | 48.08 348 | 42.40 361 | 22.36 371 | 44.01 368 | 53.05 366 | 42.60 284 | 45.49 350 | 31.69 345 | 61.36 355 | 41.79 367 |
|
ADS-MVSNet | | | 44.62 332 | 45.58 331 | 41.73 349 | 55.90 363 | 20.83 374 | 47.34 350 | 39.94 370 | 31.41 359 | 50.48 353 | 72.06 318 | 31.23 340 | 39.31 367 | 25.93 362 | 55.93 365 | 65.07 336 |
|
EMVS | | | 44.61 333 | 44.45 337 | 45.10 342 | 48.91 376 | 43.00 276 | 37.92 364 | 41.10 369 | 46.75 275 | 38.00 372 | 48.43 370 | 26.42 358 | 46.27 347 | 37.11 323 | 75.38 306 | 46.03 365 |
|
dp | | | 44.09 334 | 44.88 335 | 41.72 350 | 58.53 353 | 23.18 372 | 54.70 333 | 42.38 362 | 34.80 340 | 44.25 367 | 65.61 355 | 24.48 366 | 44.80 354 | 29.77 353 | 49.42 370 | 57.18 356 |
|
DSMNet-mixed | | | 43.18 335 | 44.66 336 | 38.75 353 | 54.75 368 | 28.88 362 | 57.06 324 | 27.42 378 | 13.47 372 | 47.27 361 | 77.67 270 | 38.83 305 | 39.29 368 | 25.32 366 | 60.12 358 | 48.08 362 |
|
CHOSEN 280x420 | | | 41.62 336 | 39.89 341 | 46.80 334 | 61.81 333 | 51.59 203 | 33.56 367 | 35.74 374 | 27.48 365 | 37.64 373 | 53.53 365 | 23.24 369 | 42.09 363 | 27.39 360 | 58.64 361 | 46.72 364 |
|
PVSNet_0 | | 36.71 22 | 41.12 337 | 40.78 340 | 42.14 347 | 59.97 345 | 40.13 298 | 40.97 359 | 42.24 364 | 30.81 361 | 44.86 365 | 49.41 369 | 40.70 295 | 45.12 353 | 23.15 368 | 34.96 372 | 41.16 368 |
|
PMMVS2 | | | 37.74 338 | 40.87 339 | 28.36 355 | 42.41 378 | 5.35 380 | 24.61 368 | 27.75 377 | 32.15 354 | 47.85 359 | 70.27 331 | 35.85 319 | 29.51 372 | 19.08 372 | 67.85 343 | 50.22 361 |
|
new_pmnet | | | 37.55 339 | 39.80 342 | 30.79 354 | 56.83 358 | 16.46 377 | 39.35 362 | 30.65 376 | 25.59 368 | 45.26 363 | 61.60 360 | 24.54 365 | 28.02 373 | 21.60 369 | 52.80 369 | 47.90 363 |
|
MVE |  | 27.91 23 | 36.69 340 | 35.64 343 | 39.84 352 | 43.37 377 | 35.85 329 | 19.49 369 | 24.61 379 | 24.68 369 | 39.05 371 | 62.63 359 | 38.67 307 | 27.10 374 | 21.04 370 | 47.25 371 | 56.56 357 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test_method | | | 19.26 341 | 19.12 345 | 19.71 356 | 9.09 380 | 1.91 382 | 7.79 371 | 53.44 333 | 1.42 374 | 10.27 376 | 35.80 371 | 17.42 377 | 25.11 375 | 12.44 373 | 24.38 374 | 32.10 371 |
|
cdsmvs_eth3d_5k | | | 17.71 342 | 23.62 344 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 70.17 252 | 0.00 379 | 0.00 380 | 74.25 299 | 68.16 102 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
tmp_tt | | | 11.98 343 | 14.73 346 | 3.72 358 | 2.28 381 | 4.62 381 | 19.44 370 | 14.50 381 | 0.47 376 | 21.55 374 | 9.58 374 | 25.78 362 | 4.57 377 | 11.61 374 | 27.37 373 | 1.96 373 |
|
ab-mvs-re | | | 5.62 344 | 7.50 347 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 67.46 349 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
pcd_1.5k_mvsjas | | | 5.20 345 | 6.93 348 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 62.39 152 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
test123 | | | 4.43 346 | 5.78 349 | 0.39 360 | 0.97 382 | 0.28 383 | 46.33 354 | 0.45 383 | 0.31 377 | 0.62 378 | 1.50 377 | 0.61 383 | 0.11 379 | 0.56 376 | 0.63 376 | 0.77 375 |
|
testmvs | | | 4.06 347 | 5.28 350 | 0.41 359 | 0.64 383 | 0.16 384 | 42.54 358 | 0.31 384 | 0.26 378 | 0.50 379 | 1.40 378 | 0.77 382 | 0.17 378 | 0.56 376 | 0.55 377 | 0.90 374 |
|
test_blank | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
uanet_test | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
DCPMVS | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
sosnet-low-res | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
sosnet | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
uncertanet | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
Regformer | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
uanet | | | 0.00 348 | 0.00 351 | 0.00 361 | 0.00 384 | 0.00 385 | 0.00 372 | 0.00 385 | 0.00 379 | 0.00 380 | 0.00 379 | 0.00 384 | 0.00 380 | 0.00 378 | 0.00 378 | 0.00 376 |
|
FOURS1 | | | | | | 89.19 25 | 77.84 16 | 91.64 1 | 89.11 2 | 84.05 2 | 91.57 2 | | | | | | |
|
MSC_two_6792asdad | | | | | 79.02 59 | 83.14 103 | 67.03 93 | | 80.75 125 | | | | | 86.24 23 | 77.27 35 | 94.85 26 | 83.78 132 |
|
PC_three_1452 | | | | | | | | | | 46.98 274 | 81.83 94 | 86.28 154 | 66.55 119 | 84.47 71 | 63.31 140 | 90.78 120 | 83.49 139 |
|
No_MVS | | | | | 79.02 59 | 83.14 103 | 67.03 93 | | 80.75 125 | | | | | 86.24 23 | 77.27 35 | 94.85 26 | 83.78 132 |
|
test_one_0601 | | | | | | 85.84 67 | 61.45 137 | | 85.63 33 | 75.27 19 | 85.62 47 | 90.38 64 | 76.72 29 | | | | |
|
eth-test2 | | | | | | 0.00 384 | | | | | | | | | | | |
|
eth-test | | | | | | 0.00 384 | | | | | | | | | | | |
|
ZD-MVS | | | | | | 83.91 94 | 69.36 76 | | 81.09 120 | 58.91 144 | 82.73 87 | 89.11 95 | 75.77 37 | 86.63 13 | 72.73 62 | 92.93 75 | |
|
RE-MVS-def | | | | 85.50 4 | | 86.19 55 | 79.18 9 | 87.23 9 | 86.27 23 | 77.51 11 | 87.65 19 | 90.73 46 | 81.38 7 | | 78.11 25 | 94.46 38 | 84.89 94 |
|
IU-MVS | | | | | | 86.12 59 | 60.90 144 | | 80.38 135 | 45.49 281 | 81.31 102 | | | | 75.64 44 | 94.39 43 | 84.65 103 |
|
OPU-MVS | | | | | 78.65 66 | 83.44 101 | 66.85 95 | 83.62 45 | | | | 86.12 162 | 66.82 114 | 86.01 31 | 61.72 148 | 89.79 142 | 83.08 154 |
|
test_241102_TWO | | | | | | | | | 84.80 51 | 72.61 32 | 84.93 56 | 89.70 81 | 77.73 23 | 85.89 39 | 75.29 45 | 94.22 55 | 83.25 149 |
|
test_241102_ONE | | | | | | 86.12 59 | 61.06 140 | | 84.72 55 | 72.64 31 | 87.38 25 | 89.47 84 | 77.48 24 | 85.74 43 | | | |
|
9.14 | | | | 80.22 59 | | 80.68 138 | | 80.35 74 | 87.69 11 | 59.90 133 | 83.00 80 | 88.20 114 | 74.57 51 | 81.75 119 | 73.75 56 | 93.78 61 | |
|
save fliter | | | | | | 87.00 43 | 67.23 91 | 79.24 87 | 77.94 175 | 56.65 167 | | | | | | | |
|
test_0728_THIRD | | | | | | | | | | 74.03 23 | 85.83 42 | 90.41 59 | 75.58 39 | 85.69 44 | 77.43 33 | 94.74 31 | 84.31 121 |
|
test_0728_SECOND | | | | | 76.57 93 | 86.20 54 | 60.57 149 | 83.77 43 | 85.49 35 | | | | | 85.90 37 | 75.86 42 | 94.39 43 | 83.25 149 |
|
test0726 | | | | | | 86.16 57 | 60.78 146 | 83.81 42 | 85.10 46 | 72.48 34 | 85.27 53 | 89.96 77 | 78.57 18 | | | | |
|
GSMVS | | | | | | | | | | | | | | | | | 70.05 306 |
|
test_part2 | | | | | | 85.90 63 | 66.44 98 | | | | 84.61 63 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 31.41 338 | | | | 70.05 306 |
|
sam_mvs | | | | | | | | | | | | | 31.21 342 | | | | |
|
ambc | | | | | 70.10 187 | 77.74 180 | 50.21 213 | 74.28 152 | 77.93 176 | | 79.26 126 | 88.29 113 | 54.11 223 | 79.77 157 | 64.43 124 | 91.10 109 | 80.30 213 |
|
MTGPA |  | | | | | | | | 80.63 128 | | | | | | | | |
|
test_post1 | | | | | | | | 66.63 250 | | | | 2.08 375 | 30.66 347 | 59.33 327 | 40.34 301 | | |
|
test_post | | | | | | | | | | | | 1.99 376 | 30.91 345 | 54.76 335 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 68.99 340 | 31.32 339 | 69.38 280 | | | |
|
GG-mvs-BLEND | | | | | 52.24 319 | 60.64 342 | 29.21 361 | 69.73 202 | 42.41 360 | | 45.47 362 | 52.33 367 | 20.43 372 | 68.16 287 | 25.52 365 | 65.42 348 | 59.36 353 |
|
MTMP | | | | | | | | 84.83 33 | 19.26 380 | | | | | | | | |
|
gm-plane-assit | | | | | | 62.51 330 | 33.91 342 | | | 37.25 330 | | 62.71 358 | | 72.74 248 | 38.70 308 | | |
|
test9_res | | | | | | | | | | | | | | | 72.12 70 | 91.37 101 | 77.40 247 |
|
TEST9 | | | | | | 85.47 69 | 69.32 77 | 76.42 121 | 78.69 161 | 53.73 208 | 76.97 155 | 86.74 138 | 66.84 113 | 81.10 128 | | | |
|
test_8 | | | | | | 85.09 75 | 67.89 86 | 76.26 126 | 78.66 163 | 54.00 203 | 76.89 160 | 86.72 140 | 66.60 117 | 80.89 139 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 70.70 77 | 90.93 114 | 78.55 233 |
|
agg_prior | | | | | | 84.44 87 | 66.02 102 | | 78.62 164 | | 76.95 157 | | | 80.34 146 | | | |
|
TestCases | | | | | 78.35 71 | 79.19 157 | 70.81 61 | | 88.64 3 | 65.37 83 | 80.09 118 | 88.17 115 | 70.33 81 | 78.43 180 | 55.60 197 | 90.90 116 | 85.81 77 |
|
test_prior4 | | | | | | | 70.14 69 | 77.57 107 | | | | | | | | | |
|
test_prior2 | | | | | | | | 75.57 132 | | 58.92 142 | 76.53 171 | 86.78 135 | 67.83 106 | | 69.81 82 | 92.76 80 | |
|
test_prior | | | | | 75.27 111 | 82.15 122 | 59.85 155 | | 84.33 66 | | | | | 83.39 89 | | | 82.58 169 |
|
旧先验2 | | | | | | | | 71.17 186 | | 45.11 285 | 78.54 134 | | | 61.28 323 | 59.19 171 | | |
|
新几何2 | | | | | | | | 71.33 182 | | | | | | | | | |
|
新几何1 | | | | | 69.99 189 | 88.37 36 | 71.34 57 | | 62.08 295 | 43.85 291 | 74.99 188 | 86.11 163 | 52.85 228 | 70.57 272 | 50.99 233 | 83.23 237 | 68.05 320 |
|
旧先验1 | | | | | | 84.55 84 | 60.36 151 | | 63.69 286 | | | 87.05 128 | 54.65 220 | | | 83.34 235 | 69.66 310 |
|
无先验 | | | | | | | | 74.82 140 | 70.94 246 | 47.75 269 | | | | 76.85 209 | 54.47 208 | | 72.09 289 |
|
原ACMM2 | | | | | | | | 74.78 144 | | | | | | | | | |
|
原ACMM1 | | | | | 73.90 127 | 85.90 63 | 65.15 110 | | 81.67 106 | 50.97 236 | 74.25 201 | 86.16 160 | 61.60 160 | 83.54 85 | 56.75 184 | 91.08 110 | 73.00 278 |
|
test222 | | | | | | 87.30 41 | 69.15 80 | 67.85 229 | 59.59 305 | 41.06 311 | 73.05 218 | 85.72 171 | 48.03 257 | | | 80.65 265 | 66.92 325 |
|
testdata2 | | | | | | | | | | | | | | 67.30 294 | 48.34 254 | | |
|
segment_acmp | | | | | | | | | | | | | 68.30 101 | | | | |
|
testdata | | | | | 64.13 251 | 85.87 65 | 63.34 125 | | 61.80 299 | 47.83 267 | 76.42 175 | 86.60 147 | 48.83 251 | 62.31 320 | 54.46 210 | 81.26 257 | 66.74 329 |
|
testdata1 | | | | | | | | 68.34 225 | | 57.24 159 | | | | | | | |
|
test12 | | | | | 76.51 94 | 82.28 120 | 60.94 143 | | 81.64 107 | | 73.60 209 | | 64.88 135 | 85.19 59 | | 90.42 127 | 83.38 145 |
|
plane_prior7 | | | | | | 85.18 72 | 66.21 100 | | | | | | | | | | |
|
plane_prior6 | | | | | | 84.18 91 | 65.31 107 | | | | | | 60.83 170 | | | | |
|
plane_prior5 | | | | | | | | | 85.49 35 | | | | | 86.15 28 | 71.09 74 | 90.94 112 | 84.82 98 |
|
plane_prior4 | | | | | | | | | | | | 89.11 95 | | | | | |
|
plane_prior3 | | | | | | | 65.67 104 | | | 63.82 101 | 78.23 137 | | | | | | |
|
plane_prior2 | | | | | | | | 82.74 54 | | 65.45 80 | | | | | | | |
|
plane_prior1 | | | | | | 84.46 86 | | | | | | | | | | | |
|
plane_prior | | | | | | | 65.18 108 | 80.06 81 | | 61.88 119 | | | | | | 89.91 139 | |
|
n2 | | | | | | | | | 0.00 385 | | | | | | | | |
|
nn | | | | | | | | | 0.00 385 | | | | | | | | |
|
door-mid | | | | | | | | | 55.02 325 | | | | | | | | |
|
lessismore_v0 | | | | | 72.75 152 | 79.60 149 | 56.83 176 | | 57.37 313 | | 83.80 74 | 89.01 98 | 47.45 259 | 78.74 171 | 64.39 125 | 86.49 195 | 82.69 167 |
|
LGP-MVS_train | | | | | 80.90 38 | 87.00 43 | 70.41 66 | | 86.35 20 | 69.77 53 | 87.75 16 | 91.13 35 | 81.83 3 | 86.20 25 | 77.13 37 | 95.96 5 | 86.08 71 |
|
test11 | | | | | | | | | 82.71 94 | | | | | | | | |
|
door | | | | | | | | | 52.91 337 | | | | | | | | |
|
HQP5-MVS | | | | | | | 58.80 165 | | | | | | | | | | |
|
HQP-NCC | | | | | | 82.37 117 | | 77.32 111 | | 59.08 138 | 71.58 235 | | | | | | |
|
ACMP_Plane | | | | | | 82.37 117 | | 77.32 111 | | 59.08 138 | 71.58 235 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.38 106 | | |
|
HQP4-MVS | | | | | | | | | | | 71.59 234 | | | 85.31 52 | | | 83.74 134 |
|
HQP3-MVS | | | | | | | | | 84.12 74 | | | | | | | 89.16 152 | |
|
HQP2-MVS | | | | | | | | | | | | | 58.09 195 | | | | |
|
NP-MVS | | | | | | 83.34 102 | 63.07 128 | | | | | 85.97 166 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 18.41 375 | 53.74 335 | | 31.57 358 | 44.89 364 | | 29.90 353 | | 32.93 342 | | 71.48 293 |
|
MDTV_nov1_ep13 | | | | 54.05 306 | | 65.54 313 | 29.30 360 | 59.00 316 | 55.22 323 | 35.96 336 | 52.44 347 | 75.98 280 | 30.77 346 | 59.62 326 | 38.21 313 | 73.33 318 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 89.47 148 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.96 90 | |
|
Test By Simon | | | | | | | | | | | | | 62.56 148 | | | | |
|
ITE_SJBPF | | | | | 80.35 44 | 76.94 191 | 73.60 43 | | 80.48 132 | 66.87 67 | 83.64 76 | 86.18 158 | 70.25 83 | 79.90 156 | 61.12 155 | 88.95 158 | 87.56 52 |
|
DeepMVS_CX |  | | | | 11.83 357 | 15.51 379 | 13.86 378 | | 11.25 382 | 5.76 373 | 20.85 375 | 26.46 372 | 17.06 378 | 9.22 376 | 9.69 375 | 13.82 375 | 12.42 372 |
|