test_241102_ONE | | | | | | 95.30 2 | 70.98 62 | | 94.06 12 | 77.17 49 | 93.10 1 | 95.39 8 | 82.99 1 | 97.27 6 | | | |
|
test0726 | | | | | | 95.27 3 | 71.25 57 | 93.60 4 | 94.11 7 | 77.33 45 | 92.81 2 | 95.79 3 | 80.98 5 | | | | |
|
test_241102_TWO | | | | | | | | | 94.06 12 | 77.24 47 | 92.78 3 | 95.72 6 | 81.26 4 | 97.44 2 | 89.07 6 | 96.58 2 | 94.26 28 |
|
SMA-MVS | | | 89.08 5 | 89.23 5 | 88.61 2 | 94.25 24 | 73.73 7 | 92.40 18 | 93.63 21 | 74.77 101 | 92.29 4 | 95.97 2 | 74.28 29 | 97.24 7 | 88.58 8 | 96.91 1 | 94.87 7 |
|
DPE-MVS | | | 89.48 3 | 89.98 2 | 88.01 11 | 94.80 7 | 72.69 29 | 91.59 35 | 94.10 9 | 75.90 80 | 92.29 4 | 95.66 7 | 81.67 2 | 97.38 5 | 87.44 15 | 96.34 8 | 93.95 41 |
|
DVP-MVS | | | 89.60 1 | 90.35 1 | 87.33 40 | 95.27 3 | 71.25 57 | 93.49 5 | 92.73 57 | 77.33 45 | 92.12 6 | 95.78 4 | 80.98 5 | 97.40 3 | 89.08 4 | 96.41 5 | 93.33 72 |
|
test_0728_THIRD | | | | | | | | | | 78.38 32 | 92.12 6 | 95.78 4 | 81.46 3 | 97.40 3 | 89.42 2 | 96.57 3 | 94.67 13 |
|
test_part2 | | | | | | 95.06 5 | 72.65 30 | | | | 91.80 8 | | | | | | |
|
MSP-MVS | | | 89.51 2 | 89.91 3 | 88.30 6 | 94.28 23 | 73.46 15 | 92.90 12 | 94.11 7 | 80.27 12 | 91.35 9 | 94.16 35 | 78.35 8 | 96.77 18 | 89.59 1 | 94.22 56 | 94.67 13 |
|
APDe-MVS | | | 89.15 4 | 89.63 4 | 87.73 24 | 94.49 15 | 71.69 53 | 93.83 2 | 93.96 15 | 75.70 85 | 91.06 10 | 96.03 1 | 76.84 10 | 97.03 11 | 89.09 3 | 95.65 25 | 94.47 20 |
|
SD-MVS | | | 88.06 13 | 88.50 12 | 86.71 52 | 92.60 64 | 72.71 27 | 91.81 34 | 93.19 36 | 77.87 33 | 90.32 11 | 94.00 40 | 74.83 22 | 93.78 132 | 87.63 12 | 94.27 55 | 93.65 59 |
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 |
DeepPCF-MVS | | 80.84 1 | 88.10 11 | 88.56 11 | 86.73 51 | 92.24 66 | 69.03 96 | 89.57 80 | 93.39 31 | 77.53 42 | 89.79 12 | 94.12 37 | 78.98 7 | 96.58 30 | 85.66 22 | 95.72 22 | 94.58 16 |
|
xxxxxxxxxxxxxcwj | | | 88.46 9 | 88.74 9 | 87.64 32 | 92.78 56 | 71.95 47 | 92.40 18 | 94.74 2 | 75.71 82 | 89.16 13 | 95.10 10 | 75.65 16 | 96.19 39 | 87.07 16 | 96.01 10 | 94.79 8 |
|
SF-MVS | | | 88.46 9 | 88.74 9 | 87.64 32 | 92.78 56 | 71.95 47 | 92.40 18 | 94.74 2 | 75.71 82 | 89.16 13 | 95.10 10 | 75.65 16 | 96.19 39 | 87.07 16 | 96.01 10 | 94.79 8 |
|
TSAR-MVS + MP. | | | 88.02 16 | 88.11 15 | 87.72 26 | 93.68 38 | 72.13 44 | 91.41 40 | 92.35 71 | 74.62 105 | 88.90 15 | 93.85 43 | 75.75 15 | 96.00 47 | 87.80 10 | 94.63 45 | 95.04 3 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
ETH3D-3000-0.1 | | | 88.09 12 | 88.29 13 | 87.50 36 | 92.76 58 | 71.89 51 | 91.43 39 | 94.70 4 | 74.47 107 | 88.86 16 | 94.61 18 | 75.23 19 | 95.84 51 | 86.62 21 | 95.92 14 | 94.78 10 |
|
APD-MVS | | | 87.44 24 | 87.52 22 | 87.19 42 | 94.24 25 | 72.39 39 | 91.86 33 | 92.83 53 | 73.01 135 | 88.58 17 | 94.52 19 | 73.36 34 | 96.49 31 | 84.26 38 | 95.01 35 | 92.70 92 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
9.14 | | | | 88.26 14 | | 92.84 55 | | 91.52 38 | 94.75 1 | 73.93 119 | 88.57 18 | 94.67 16 | 75.57 18 | 95.79 53 | 86.77 18 | 95.76 21 | |
|
testtj | | | 87.78 18 | 87.78 19 | 87.77 22 | 94.55 13 | 72.47 36 | 92.23 27 | 93.49 26 | 74.75 102 | 88.33 19 | 94.43 26 | 73.27 36 | 97.02 12 | 84.18 41 | 94.84 41 | 93.82 49 |
|
ACMMP_NAP | | | 88.05 15 | 88.08 16 | 87.94 14 | 93.70 36 | 73.05 20 | 90.86 47 | 93.59 22 | 76.27 76 | 88.14 20 | 95.09 12 | 71.06 53 | 96.67 22 | 87.67 11 | 96.37 7 | 94.09 33 |
|
SteuartSystems-ACMMP | | | 88.72 8 | 88.86 8 | 88.32 5 | 92.14 68 | 72.96 23 | 93.73 3 | 93.67 20 | 80.19 14 | 88.10 21 | 94.80 13 | 73.76 33 | 97.11 9 | 87.51 13 | 95.82 17 | 94.90 6 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 88.93 7 | 89.13 7 | 88.33 4 | 94.77 8 | 73.82 6 | 90.51 54 | 93.00 43 | 80.90 9 | 88.06 22 | 94.06 39 | 76.43 11 | 96.84 15 | 88.48 9 | 95.99 12 | 94.34 24 |
|
canonicalmvs | | | 85.91 49 | 85.87 50 | 86.04 65 | 89.84 103 | 69.44 94 | 90.45 59 | 93.00 43 | 76.70 65 | 88.01 23 | 91.23 90 | 73.28 35 | 93.91 126 | 81.50 62 | 88.80 106 | 94.77 11 |
|
HPM-MVS++ | | | 89.02 6 | 89.15 6 | 88.63 1 | 95.01 6 | 76.03 1 | 92.38 22 | 92.85 52 | 80.26 13 | 87.78 24 | 94.27 30 | 75.89 14 | 96.81 17 | 87.45 14 | 96.44 4 | 93.05 83 |
|
alignmvs | | | 85.48 54 | 85.32 55 | 85.96 66 | 89.51 111 | 69.47 91 | 89.74 76 | 92.47 65 | 76.17 77 | 87.73 25 | 91.46 87 | 70.32 61 | 93.78 132 | 81.51 61 | 88.95 103 | 94.63 15 |
|
ETH3 D test6400 | | | 87.50 23 | 87.44 24 | 87.70 29 | 93.71 35 | 71.75 52 | 90.62 52 | 94.05 14 | 70.80 163 | 87.59 26 | 93.51 47 | 77.57 9 | 96.63 25 | 83.31 45 | 95.77 19 | 94.72 12 |
|
ETH3D cwj APD-0.16 | | | 87.31 30 | 87.27 26 | 87.44 38 | 91.60 75 | 72.45 38 | 90.02 68 | 94.37 5 | 71.76 148 | 87.28 27 | 94.27 30 | 75.18 20 | 96.08 43 | 85.16 25 | 95.77 19 | 93.80 52 |
|
旧先验2 | | | | | | | | 86.56 170 | | 58.10 305 | 87.04 28 | | | 88.98 259 | 74.07 122 | | |
|
Regformer-2 | | | 86.63 41 | 86.53 40 | 86.95 46 | 89.33 117 | 71.24 59 | 88.43 111 | 92.05 80 | 82.50 1 | 86.88 29 | 90.09 115 | 74.45 24 | 95.61 57 | 84.38 36 | 90.63 85 | 94.01 38 |
|
SR-MVS | | | 86.73 37 | 86.67 38 | 86.91 47 | 94.11 31 | 72.11 45 | 92.37 23 | 92.56 64 | 74.50 106 | 86.84 30 | 94.65 17 | 67.31 87 | 95.77 54 | 84.80 32 | 92.85 64 | 92.84 90 |
|
Regformer-1 | | | 86.41 45 | 86.33 41 | 86.64 53 | 89.33 117 | 70.93 66 | 88.43 111 | 91.39 109 | 82.14 3 | 86.65 31 | 90.09 115 | 74.39 27 | 95.01 83 | 83.97 43 | 90.63 85 | 93.97 40 |
|
MP-MVS-pluss | | | 87.67 20 | 87.72 20 | 87.54 34 | 93.64 39 | 72.04 46 | 89.80 74 | 93.50 25 | 75.17 96 | 86.34 32 | 95.29 9 | 70.86 54 | 96.00 47 | 88.78 7 | 96.04 9 | 94.58 16 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
APD-MVS_3200maxsize | | | 85.97 48 | 85.88 49 | 86.22 61 | 92.69 60 | 69.53 89 | 91.93 32 | 92.99 45 | 73.54 127 | 85.94 33 | 94.51 22 | 65.80 103 | 95.61 57 | 83.04 52 | 92.51 69 | 93.53 66 |
|
zzz-MVS | | | 87.53 22 | 87.41 25 | 87.90 18 | 94.18 28 | 74.25 2 | 90.23 63 | 92.02 81 | 79.45 19 | 85.88 34 | 94.80 13 | 68.07 78 | 96.21 37 | 86.69 19 | 95.34 29 | 93.23 75 |
|
MTAPA | | | 87.23 31 | 87.00 32 | 87.90 18 | 94.18 28 | 74.25 2 | 86.58 169 | 92.02 81 | 79.45 19 | 85.88 34 | 94.80 13 | 68.07 78 | 96.21 37 | 86.69 19 | 95.34 29 | 93.23 75 |
|
TSAR-MVS + GP. | | | 85.71 52 | 85.33 54 | 86.84 48 | 91.34 77 | 72.50 34 | 89.07 92 | 87.28 217 | 76.41 69 | 85.80 36 | 90.22 113 | 74.15 32 | 95.37 70 | 81.82 60 | 91.88 71 | 92.65 96 |
|
NCCC | | | 88.06 13 | 88.01 17 | 88.24 7 | 94.41 19 | 73.62 8 | 91.22 44 | 92.83 53 | 81.50 6 | 85.79 37 | 93.47 50 | 73.02 39 | 97.00 13 | 84.90 28 | 94.94 37 | 94.10 32 |
|
testdata | | | | | 79.97 219 | 90.90 84 | 64.21 189 | | 84.71 242 | 59.27 298 | 85.40 38 | 92.91 61 | 62.02 145 | 89.08 257 | 68.95 170 | 91.37 77 | 86.63 269 |
|
Regformer-4 | | | 85.68 53 | 85.45 52 | 86.35 57 | 88.95 134 | 69.67 86 | 88.29 121 | 91.29 111 | 81.73 5 | 85.36 39 | 90.01 117 | 72.62 41 | 95.35 71 | 83.28 48 | 87.57 119 | 94.03 36 |
|
abl_6 | | | 85.23 59 | 84.95 62 | 86.07 64 | 92.23 67 | 70.48 73 | 90.80 49 | 92.08 79 | 73.51 128 | 85.26 40 | 94.16 35 | 62.75 131 | 95.92 50 | 82.46 58 | 91.30 79 | 91.81 120 |
|
ZNCC-MVS | | | 87.94 17 | 87.85 18 | 88.20 8 | 94.39 21 | 73.33 17 | 93.03 10 | 93.81 18 | 76.81 59 | 85.24 41 | 94.32 29 | 71.76 48 | 96.93 14 | 85.53 24 | 95.79 18 | 94.32 25 |
|
PHI-MVS | | | 86.43 43 | 86.17 46 | 87.24 41 | 90.88 85 | 70.96 63 | 92.27 26 | 94.07 11 | 72.45 138 | 85.22 42 | 91.90 75 | 69.47 69 | 96.42 32 | 83.28 48 | 95.94 13 | 94.35 23 |
|
Regformer-3 | | | 85.23 59 | 85.07 59 | 85.70 68 | 88.95 134 | 69.01 98 | 88.29 121 | 89.91 150 | 80.95 8 | 85.01 43 | 90.01 117 | 72.45 42 | 94.19 112 | 82.50 57 | 87.57 119 | 93.90 44 |
|
TEST9 | | | | | | 93.26 45 | 72.96 23 | 88.75 102 | 91.89 90 | 68.44 213 | 85.00 44 | 93.10 56 | 74.36 28 | 95.41 65 | | | |
|
train_agg | | | 86.43 43 | 86.20 44 | 87.13 44 | 93.26 45 | 72.96 23 | 88.75 102 | 91.89 90 | 68.69 209 | 85.00 44 | 93.10 56 | 74.43 25 | 95.41 65 | 84.97 27 | 95.71 23 | 93.02 85 |
|
HFP-MVS | | | 87.58 21 | 87.47 23 | 87.94 14 | 94.58 11 | 73.54 12 | 93.04 8 | 93.24 33 | 76.78 61 | 84.91 46 | 94.44 24 | 70.78 55 | 96.61 26 | 84.53 34 | 94.89 39 | 93.66 54 |
|
#test# | | | 87.33 29 | 87.13 31 | 87.94 14 | 94.58 11 | 73.54 12 | 92.34 24 | 93.24 33 | 75.23 93 | 84.91 46 | 94.44 24 | 70.78 55 | 96.61 26 | 83.75 44 | 94.89 39 | 93.66 54 |
|
test_prior3 | | | 86.73 37 | 86.86 37 | 86.33 58 | 92.61 62 | 69.59 87 | 88.85 98 | 92.97 48 | 75.41 89 | 84.91 46 | 93.54 45 | 74.28 29 | 95.48 61 | 83.31 45 | 95.86 15 | 93.91 42 |
|
test_prior2 | | | | | | | | 88.85 98 | | 75.41 89 | 84.91 46 | 93.54 45 | 74.28 29 | | 83.31 45 | 95.86 15 | |
|
test_8 | | | | | | 93.13 47 | 72.57 33 | 88.68 106 | 91.84 93 | 68.69 209 | 84.87 50 | 93.10 56 | 74.43 25 | 95.16 75 | | | |
|
MCST-MVS | | | 87.37 28 | 87.25 28 | 87.73 24 | 94.53 14 | 72.46 37 | 89.82 72 | 93.82 17 | 73.07 133 | 84.86 51 | 92.89 62 | 76.22 12 | 96.33 33 | 84.89 30 | 95.13 34 | 94.40 21 |
|
GST-MVS | | | 87.42 26 | 87.26 27 | 87.89 21 | 94.12 30 | 72.97 22 | 92.39 21 | 93.43 29 | 76.89 57 | 84.68 52 | 93.99 41 | 70.67 58 | 96.82 16 | 84.18 41 | 95.01 35 | 93.90 44 |
|
ACMMPR | | | 87.44 24 | 87.23 29 | 88.08 10 | 94.64 9 | 73.59 9 | 93.04 8 | 93.20 35 | 76.78 61 | 84.66 53 | 94.52 19 | 68.81 76 | 96.65 23 | 84.53 34 | 94.90 38 | 94.00 39 |
|
CDPH-MVS | | | 85.76 51 | 85.29 57 | 87.17 43 | 93.49 42 | 71.08 60 | 88.58 109 | 92.42 69 | 68.32 214 | 84.61 54 | 93.48 48 | 72.32 43 | 96.15 42 | 79.00 79 | 95.43 27 | 94.28 27 |
|
UA-Net | | | 85.08 63 | 84.96 61 | 85.45 70 | 92.07 69 | 68.07 123 | 89.78 75 | 90.86 123 | 82.48 2 | 84.60 55 | 93.20 54 | 69.35 70 | 95.22 73 | 71.39 147 | 90.88 83 | 93.07 82 |
|
region2R | | | 87.42 26 | 87.20 30 | 88.09 9 | 94.63 10 | 73.55 10 | 93.03 10 | 93.12 38 | 76.73 64 | 84.45 56 | 94.52 19 | 69.09 73 | 96.70 21 | 84.37 37 | 94.83 42 | 94.03 36 |
|
agg_prior1 | | | 86.22 47 | 86.09 48 | 86.62 54 | 92.85 53 | 71.94 49 | 88.59 108 | 91.78 96 | 68.96 204 | 84.41 57 | 93.18 55 | 74.94 21 | 94.93 84 | 84.75 33 | 95.33 31 | 93.01 86 |
|
agg_prior | | | | | | 92.85 53 | 71.94 49 | | 91.78 96 | | 84.41 57 | | | 94.93 84 | | | |
|
VDD-MVS | | | 83.01 85 | 82.36 86 | 84.96 84 | 91.02 82 | 66.40 148 | 88.91 95 | 88.11 197 | 77.57 38 | 84.39 59 | 93.29 53 | 52.19 228 | 93.91 126 | 77.05 99 | 88.70 108 | 94.57 18 |
|
casdiffmvs | | | 85.11 62 | 85.14 58 | 85.01 82 | 87.20 190 | 65.77 160 | 87.75 136 | 92.83 53 | 77.84 34 | 84.36 60 | 92.38 68 | 72.15 45 | 93.93 125 | 81.27 64 | 90.48 87 | 95.33 1 |
|
MSLP-MVS++ | | | 85.43 56 | 85.76 51 | 84.45 99 | 91.93 71 | 70.24 74 | 90.71 50 | 92.86 51 | 77.46 44 | 84.22 61 | 92.81 66 | 67.16 89 | 92.94 169 | 80.36 72 | 94.35 53 | 90.16 167 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 36 | 86.62 39 | 87.76 23 | 93.52 41 | 72.37 40 | 91.26 41 | 93.04 39 | 76.62 66 | 84.22 61 | 93.36 52 | 71.44 51 | 96.76 19 | 80.82 68 | 95.33 31 | 94.16 30 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ETV-MVS | | | 84.90 66 | 84.67 65 | 85.59 69 | 89.39 115 | 68.66 112 | 88.74 104 | 92.64 62 | 79.97 17 | 84.10 63 | 85.71 228 | 69.32 71 | 95.38 67 | 80.82 68 | 91.37 77 | 92.72 91 |
|
VNet | | | 82.21 92 | 82.41 84 | 81.62 184 | 90.82 86 | 60.93 239 | 84.47 218 | 89.78 152 | 76.36 74 | 84.07 64 | 91.88 76 | 64.71 111 | 90.26 237 | 70.68 152 | 88.89 104 | 93.66 54 |
|
baseline | | | 84.93 64 | 84.98 60 | 84.80 91 | 87.30 188 | 65.39 167 | 87.30 147 | 92.88 50 | 77.62 36 | 84.04 65 | 92.26 69 | 71.81 47 | 93.96 119 | 81.31 63 | 90.30 89 | 95.03 4 |
|
PGM-MVS | | | 86.68 39 | 86.27 43 | 87.90 18 | 94.22 26 | 73.38 16 | 90.22 65 | 93.04 39 | 75.53 87 | 83.86 66 | 94.42 27 | 67.87 82 | 96.64 24 | 82.70 55 | 94.57 47 | 93.66 54 |
|
MP-MVS | | | 87.71 19 | 87.64 21 | 87.93 17 | 94.36 22 | 73.88 4 | 92.71 17 | 92.65 61 | 77.57 38 | 83.84 67 | 94.40 28 | 72.24 44 | 96.28 35 | 85.65 23 | 95.30 33 | 93.62 61 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
HPM-MVS | | | 87.11 33 | 86.98 33 | 87.50 36 | 93.88 33 | 72.16 43 | 92.19 28 | 93.33 32 | 76.07 79 | 83.81 68 | 93.95 42 | 69.77 67 | 96.01 46 | 85.15 26 | 94.66 44 | 94.32 25 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
CP-MVS | | | 87.11 33 | 86.92 34 | 87.68 31 | 94.20 27 | 73.86 5 | 93.98 1 | 92.82 56 | 76.62 66 | 83.68 69 | 94.46 23 | 67.93 80 | 95.95 49 | 84.20 40 | 94.39 51 | 93.23 75 |
|
XVS | | | 87.18 32 | 86.91 35 | 88.00 12 | 94.42 17 | 73.33 17 | 92.78 13 | 92.99 45 | 79.14 21 | 83.67 70 | 94.17 34 | 67.45 85 | 96.60 28 | 83.06 50 | 94.50 48 | 94.07 34 |
|
X-MVStestdata | | | 80.37 133 | 77.83 166 | 88.00 12 | 94.42 17 | 73.33 17 | 92.78 13 | 92.99 45 | 79.14 21 | 83.67 70 | 12.47 346 | 67.45 85 | 96.60 28 | 83.06 50 | 94.50 48 | 94.07 34 |
|
DELS-MVS | | | 85.41 57 | 85.30 56 | 85.77 67 | 88.49 150 | 67.93 125 | 85.52 199 | 93.44 28 | 78.70 28 | 83.63 72 | 89.03 142 | 74.57 23 | 95.71 56 | 80.26 74 | 94.04 57 | 93.66 54 |
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 |
LFMVS | | | 81.82 100 | 81.23 100 | 83.57 128 | 91.89 72 | 63.43 207 | 89.84 71 | 81.85 281 | 77.04 54 | 83.21 73 | 93.10 56 | 52.26 227 | 93.43 149 | 71.98 142 | 89.95 96 | 93.85 46 |
|
VDDNet | | | 81.52 105 | 80.67 107 | 84.05 114 | 90.44 91 | 64.13 191 | 89.73 77 | 85.91 234 | 71.11 159 | 83.18 74 | 93.48 48 | 50.54 251 | 93.49 146 | 73.40 130 | 88.25 115 | 94.54 19 |
|
CSCG | | | 86.41 45 | 86.19 45 | 87.07 45 | 92.91 52 | 72.48 35 | 90.81 48 | 93.56 23 | 73.95 117 | 83.16 75 | 91.07 96 | 75.94 13 | 95.19 74 | 79.94 76 | 94.38 52 | 93.55 64 |
|
nrg030 | | | 83.88 69 | 83.53 70 | 84.96 84 | 86.77 198 | 69.28 95 | 90.46 58 | 92.67 59 | 74.79 100 | 82.95 76 | 91.33 89 | 72.70 40 | 93.09 163 | 80.79 70 | 79.28 220 | 92.50 99 |
|
EI-MVSNet-Vis-set | | | 84.19 68 | 83.81 69 | 85.31 72 | 88.18 159 | 67.85 126 | 87.66 138 | 89.73 155 | 80.05 16 | 82.95 76 | 89.59 127 | 70.74 57 | 94.82 92 | 80.66 71 | 84.72 154 | 93.28 74 |
|
MVS_Test | | | 83.15 81 | 83.06 76 | 83.41 133 | 86.86 194 | 63.21 211 | 86.11 182 | 92.00 84 | 74.31 110 | 82.87 78 | 89.44 135 | 70.03 63 | 93.21 154 | 77.39 96 | 88.50 113 | 93.81 50 |
|
DPM-MVS | | | 84.93 64 | 84.29 68 | 86.84 48 | 90.20 95 | 73.04 21 | 87.12 151 | 93.04 39 | 69.80 182 | 82.85 79 | 91.22 91 | 73.06 38 | 96.02 45 | 76.72 104 | 94.63 45 | 91.46 129 |
|
DeepC-MVS | | 79.81 2 | 87.08 35 | 86.88 36 | 87.69 30 | 91.16 79 | 72.32 42 | 90.31 61 | 93.94 16 | 77.12 51 | 82.82 80 | 94.23 33 | 72.13 46 | 97.09 10 | 84.83 31 | 95.37 28 | 93.65 59 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | 86.67 40 | 86.32 42 | 87.72 26 | 94.41 19 | 73.55 10 | 92.74 15 | 92.22 74 | 76.87 58 | 82.81 81 | 94.25 32 | 66.44 94 | 96.24 36 | 82.88 54 | 94.28 54 | 93.38 69 |
|
test12 | | | | | 86.80 50 | 92.63 61 | 70.70 71 | | 91.79 95 | | 82.71 82 | | 71.67 49 | 96.16 41 | | 94.50 48 | 93.54 65 |
|
HPM-MVS_fast | | | 85.35 58 | 84.95 62 | 86.57 56 | 93.69 37 | 70.58 72 | 92.15 30 | 91.62 100 | 73.89 120 | 82.67 83 | 94.09 38 | 62.60 132 | 95.54 60 | 80.93 66 | 92.93 62 | 93.57 63 |
|
CS-MVS | | | 84.76 67 | 84.61 66 | 85.22 77 | 89.66 105 | 66.43 147 | 90.23 63 | 93.56 23 | 76.52 68 | 82.59 84 | 85.93 222 | 70.41 59 | 95.80 52 | 79.93 77 | 92.68 67 | 93.42 68 |
|
Effi-MVS+ | | | 83.62 74 | 83.08 75 | 85.24 75 | 88.38 155 | 67.45 131 | 88.89 96 | 89.15 172 | 75.50 88 | 82.27 85 | 88.28 161 | 69.61 68 | 94.45 102 | 77.81 91 | 87.84 117 | 93.84 48 |
|
EI-MVSNet-UG-set | | | 83.81 70 | 83.38 72 | 85.09 80 | 87.87 168 | 67.53 130 | 87.44 144 | 89.66 156 | 79.74 18 | 82.23 86 | 89.41 136 | 70.24 62 | 94.74 95 | 79.95 75 | 83.92 162 | 92.99 87 |
|
MVS_111021_HR | | | 85.14 61 | 84.75 64 | 86.32 60 | 91.65 74 | 72.70 28 | 85.98 184 | 90.33 138 | 76.11 78 | 82.08 87 | 91.61 82 | 71.36 52 | 94.17 114 | 81.02 65 | 92.58 68 | 92.08 113 |
|
diffmvs | | | 82.10 93 | 81.88 94 | 82.76 167 | 83.00 257 | 63.78 197 | 83.68 235 | 89.76 153 | 72.94 136 | 82.02 88 | 89.85 120 | 65.96 102 | 90.79 231 | 82.38 59 | 87.30 126 | 93.71 53 |
|
xiu_mvs_v1_base_debu | | | 80.80 121 | 79.72 124 | 84.03 116 | 87.35 183 | 70.19 77 | 85.56 192 | 88.77 186 | 69.06 200 | 81.83 89 | 88.16 164 | 50.91 245 | 92.85 171 | 78.29 88 | 87.56 121 | 89.06 206 |
|
xiu_mvs_v1_base | | | 80.80 121 | 79.72 124 | 84.03 116 | 87.35 183 | 70.19 77 | 85.56 192 | 88.77 186 | 69.06 200 | 81.83 89 | 88.16 164 | 50.91 245 | 92.85 171 | 78.29 88 | 87.56 121 | 89.06 206 |
|
xiu_mvs_v1_base_debi | | | 80.80 121 | 79.72 124 | 84.03 116 | 87.35 183 | 70.19 77 | 85.56 192 | 88.77 186 | 69.06 200 | 81.83 89 | 88.16 164 | 50.91 245 | 92.85 171 | 78.29 88 | 87.56 121 | 89.06 206 |
|
æ–°å‡ ä½•1 | | | | | 83.42 131 | 93.13 47 | 70.71 70 | | 85.48 236 | 57.43 310 | 81.80 92 | 91.98 73 | 63.28 120 | 92.27 188 | 64.60 207 | 92.99 61 | 87.27 253 |
|
test_yl | | | 81.17 110 | 80.47 111 | 83.24 139 | 89.13 129 | 63.62 198 | 86.21 178 | 89.95 148 | 72.43 141 | 81.78 93 | 89.61 125 | 57.50 188 | 93.58 140 | 70.75 150 | 86.90 131 | 92.52 97 |
|
DCV-MVSNet | | | 81.17 110 | 80.47 111 | 83.24 139 | 89.13 129 | 63.62 198 | 86.21 178 | 89.95 148 | 72.43 141 | 81.78 93 | 89.61 125 | 57.50 188 | 93.58 140 | 70.75 150 | 86.90 131 | 92.52 97 |
|
1121 | | | 80.84 116 | 79.77 122 | 84.05 114 | 93.11 49 | 70.78 69 | 84.66 212 | 85.42 237 | 57.37 311 | 81.76 95 | 92.02 72 | 63.41 118 | 94.12 115 | 67.28 182 | 92.93 62 | 87.26 254 |
|
MG-MVS | | | 83.41 77 | 83.45 71 | 83.28 136 | 92.74 59 | 62.28 225 | 88.17 126 | 89.50 160 | 75.22 94 | 81.49 96 | 92.74 67 | 66.75 90 | 95.11 77 | 72.85 136 | 91.58 74 | 92.45 100 |
|
CANet | | | 86.45 42 | 86.10 47 | 87.51 35 | 90.09 97 | 70.94 65 | 89.70 78 | 92.59 63 | 81.78 4 | 81.32 97 | 91.43 88 | 70.34 60 | 97.23 8 | 84.26 38 | 93.36 60 | 94.37 22 |
|
MVSFormer | | | 82.85 86 | 82.05 90 | 85.24 75 | 87.35 183 | 70.21 75 | 90.50 55 | 90.38 134 | 68.55 211 | 81.32 97 | 89.47 130 | 61.68 147 | 93.46 147 | 78.98 80 | 90.26 90 | 92.05 114 |
|
lupinMVS | | | 81.39 108 | 80.27 116 | 84.76 92 | 87.35 183 | 70.21 75 | 85.55 195 | 86.41 226 | 62.85 269 | 81.32 97 | 88.61 151 | 61.68 147 | 92.24 191 | 78.41 86 | 90.26 90 | 91.83 118 |
|
xiu_mvs_v2_base | | | 81.69 101 | 81.05 103 | 83.60 126 | 89.15 128 | 68.03 124 | 84.46 220 | 90.02 146 | 70.67 167 | 81.30 100 | 86.53 212 | 63.17 124 | 94.19 112 | 75.60 113 | 88.54 111 | 88.57 228 |
|
PS-MVSNAJ | | | 81.69 101 | 81.02 104 | 83.70 125 | 89.51 111 | 68.21 121 | 84.28 226 | 90.09 145 | 70.79 164 | 81.26 101 | 85.62 232 | 63.15 125 | 94.29 104 | 75.62 112 | 88.87 105 | 88.59 227 |
|
原ACMM1 | | | | | 84.35 103 | 93.01 51 | 68.79 102 | | 92.44 66 | 63.96 261 | 81.09 102 | 91.57 83 | 66.06 99 | 95.45 63 | 67.19 185 | 94.82 43 | 88.81 221 |
|
jason | | | 81.39 108 | 80.29 115 | 84.70 93 | 86.63 200 | 69.90 82 | 85.95 185 | 86.77 222 | 63.24 263 | 81.07 103 | 89.47 130 | 61.08 161 | 92.15 193 | 78.33 87 | 90.07 95 | 92.05 114 |
jason: jason. |
OPM-MVS | | | 83.50 75 | 82.95 78 | 85.14 78 | 88.79 142 | 70.95 64 | 89.13 91 | 91.52 103 | 77.55 41 | 80.96 104 | 91.75 77 | 60.71 165 | 94.50 101 | 79.67 78 | 86.51 138 | 89.97 183 |
|
Vis-MVSNet | | | 83.46 76 | 82.80 81 | 85.43 71 | 90.25 94 | 68.74 106 | 90.30 62 | 90.13 144 | 76.33 75 | 80.87 105 | 92.89 62 | 61.00 162 | 94.20 111 | 72.45 140 | 90.97 81 | 93.35 71 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
ACMMP | | | 85.89 50 | 85.39 53 | 87.38 39 | 93.59 40 | 72.63 31 | 92.74 15 | 93.18 37 | 76.78 61 | 80.73 106 | 93.82 44 | 64.33 112 | 96.29 34 | 82.67 56 | 90.69 84 | 93.23 75 |
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 |
Anonymous20240529 | | | 80.19 137 | 78.89 142 | 84.10 111 | 90.60 88 | 64.75 179 | 88.95 94 | 90.90 121 | 65.97 237 | 80.59 107 | 91.17 93 | 49.97 256 | 93.73 138 | 69.16 168 | 82.70 182 | 93.81 50 |
|
MVS_111021_LR | | | 82.61 89 | 82.11 88 | 84.11 110 | 88.82 139 | 71.58 54 | 85.15 202 | 86.16 231 | 74.69 103 | 80.47 108 | 91.04 97 | 62.29 139 | 90.55 235 | 80.33 73 | 90.08 94 | 90.20 166 |
|
VPA-MVSNet | | | 80.60 127 | 80.55 109 | 80.76 206 | 88.07 163 | 60.80 242 | 86.86 159 | 91.58 102 | 75.67 86 | 80.24 109 | 89.45 134 | 63.34 119 | 90.25 238 | 70.51 154 | 79.22 221 | 91.23 133 |
|
Anonymous202405211 | | | 78.25 176 | 77.01 183 | 81.99 178 | 91.03 81 | 60.67 243 | 84.77 210 | 83.90 253 | 70.65 169 | 80.00 110 | 91.20 92 | 41.08 310 | 91.43 214 | 65.21 200 | 85.26 149 | 93.85 46 |
|
test222 | | | | | | 91.50 76 | 68.26 119 | 84.16 227 | 83.20 267 | 54.63 322 | 79.74 111 | 91.63 81 | 58.97 178 | | | 91.42 76 | 86.77 265 |
|
OMC-MVS | | | 82.69 87 | 81.97 93 | 84.85 88 | 88.75 144 | 67.42 132 | 87.98 129 | 90.87 122 | 74.92 99 | 79.72 112 | 91.65 79 | 62.19 142 | 93.96 119 | 75.26 115 | 86.42 139 | 93.16 80 |
|
CPTT-MVS | | | 83.73 71 | 83.33 73 | 84.92 87 | 93.28 44 | 70.86 68 | 92.09 31 | 90.38 134 | 68.75 208 | 79.57 113 | 92.83 64 | 60.60 169 | 93.04 167 | 80.92 67 | 91.56 75 | 90.86 143 |
|
IS-MVSNet | | | 83.15 81 | 82.81 80 | 84.18 109 | 89.94 101 | 63.30 209 | 91.59 35 | 88.46 194 | 79.04 25 | 79.49 114 | 92.16 70 | 65.10 108 | 94.28 105 | 67.71 177 | 91.86 72 | 94.95 5 |
|
PS-MVSNAJss | | | 82.07 95 | 81.31 98 | 84.34 104 | 86.51 201 | 67.27 136 | 89.27 84 | 91.51 104 | 71.75 149 | 79.37 115 | 90.22 113 | 63.15 125 | 94.27 106 | 77.69 92 | 82.36 185 | 91.49 127 |
|
EPP-MVSNet | | | 83.40 78 | 83.02 77 | 84.57 95 | 90.13 96 | 64.47 185 | 92.32 25 | 90.73 125 | 74.45 109 | 79.35 116 | 91.10 94 | 69.05 75 | 95.12 76 | 72.78 137 | 87.22 127 | 94.13 31 |
|
DP-MVS Recon | | | 83.11 83 | 82.09 89 | 86.15 62 | 94.44 16 | 70.92 67 | 88.79 100 | 92.20 75 | 70.53 170 | 79.17 117 | 91.03 99 | 64.12 114 | 96.03 44 | 68.39 174 | 90.14 92 | 91.50 126 |
|
ab-mvs | | | 79.51 147 | 78.97 141 | 81.14 198 | 88.46 152 | 60.91 240 | 83.84 233 | 89.24 169 | 70.36 172 | 79.03 118 | 88.87 145 | 63.23 123 | 90.21 239 | 65.12 201 | 82.57 183 | 92.28 106 |
|
EIA-MVS | | | 83.31 80 | 82.80 81 | 84.82 89 | 89.59 107 | 65.59 162 | 88.21 124 | 92.68 58 | 74.66 104 | 78.96 119 | 86.42 214 | 69.06 74 | 95.26 72 | 75.54 114 | 90.09 93 | 93.62 61 |
|
PVSNet_Blended_VisFu | | | 82.62 88 | 81.83 95 | 84.96 84 | 90.80 87 | 69.76 84 | 88.74 104 | 91.70 99 | 69.39 190 | 78.96 119 | 88.46 156 | 65.47 105 | 94.87 91 | 74.42 118 | 88.57 109 | 90.24 165 |
|
HQP_MVS | | | 83.64 73 | 83.14 74 | 85.14 78 | 90.08 98 | 68.71 108 | 91.25 42 | 92.44 66 | 79.12 23 | 78.92 121 | 91.00 100 | 60.42 171 | 95.38 67 | 78.71 82 | 86.32 140 | 91.33 130 |
|
plane_prior3 | | | | | | | 68.60 113 | | | 78.44 30 | 78.92 121 | | | | | | |
|
EI-MVSNet | | | 80.52 130 | 79.98 118 | 82.12 174 | 84.28 229 | 63.19 213 | 86.41 172 | 88.95 182 | 74.18 114 | 78.69 123 | 87.54 178 | 66.62 91 | 92.43 182 | 72.57 139 | 80.57 204 | 90.74 147 |
|
MVSTER | | | 79.01 161 | 77.88 165 | 82.38 172 | 83.07 254 | 64.80 178 | 84.08 232 | 88.95 182 | 69.01 203 | 78.69 123 | 87.17 189 | 54.70 208 | 92.43 182 | 74.69 117 | 80.57 204 | 89.89 186 |
|
API-MVS | | | 81.99 97 | 81.23 100 | 84.26 107 | 90.94 83 | 70.18 80 | 91.10 45 | 89.32 164 | 71.51 155 | 78.66 125 | 88.28 161 | 65.26 106 | 95.10 80 | 64.74 206 | 91.23 80 | 87.51 247 |
|
UniMVSNet (Re) | | | 81.60 104 | 81.11 102 | 83.09 146 | 88.38 155 | 64.41 186 | 87.60 139 | 93.02 42 | 78.42 31 | 78.56 126 | 88.16 164 | 69.78 66 | 93.26 153 | 69.58 164 | 76.49 246 | 91.60 122 |
|
MAR-MVS | | | 81.84 99 | 80.70 106 | 85.27 74 | 91.32 78 | 71.53 55 | 89.82 72 | 90.92 120 | 69.77 183 | 78.50 127 | 86.21 218 | 62.36 138 | 94.52 100 | 65.36 199 | 92.05 70 | 89.77 191 |
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 |
Fast-Effi-MVS+ | | | 80.81 119 | 79.92 119 | 83.47 129 | 88.85 136 | 64.51 182 | 85.53 197 | 89.39 162 | 70.79 164 | 78.49 128 | 85.06 244 | 67.54 84 | 93.58 140 | 67.03 188 | 86.58 136 | 92.32 104 |
|
FIs | | | 82.07 95 | 82.42 83 | 81.04 201 | 88.80 141 | 58.34 263 | 88.26 123 | 93.49 26 | 76.93 56 | 78.47 129 | 91.04 97 | 69.92 65 | 92.34 187 | 69.87 161 | 84.97 151 | 92.44 101 |
|
UniMVSNet_NR-MVSNet | | | 81.88 98 | 81.54 97 | 82.92 155 | 88.46 152 | 63.46 205 | 87.13 150 | 92.37 70 | 80.19 14 | 78.38 130 | 89.14 138 | 71.66 50 | 93.05 165 | 70.05 158 | 76.46 247 | 92.25 107 |
|
DU-MVS | | | 81.12 112 | 80.52 110 | 82.90 156 | 87.80 171 | 63.46 205 | 87.02 154 | 91.87 92 | 79.01 26 | 78.38 130 | 89.07 140 | 65.02 109 | 93.05 165 | 70.05 158 | 76.46 247 | 92.20 109 |
|
CLD-MVS | | | 82.31 91 | 81.65 96 | 84.29 106 | 88.47 151 | 67.73 129 | 85.81 190 | 92.35 71 | 75.78 81 | 78.33 132 | 86.58 209 | 64.01 115 | 94.35 103 | 76.05 108 | 87.48 124 | 90.79 144 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
VPNet | | | 78.69 168 | 78.66 145 | 78.76 242 | 88.31 157 | 55.72 301 | 84.45 221 | 86.63 224 | 76.79 60 | 78.26 133 | 90.55 107 | 59.30 176 | 89.70 247 | 66.63 189 | 77.05 237 | 90.88 142 |
|
V42 | | | 79.38 154 | 78.24 157 | 82.83 159 | 81.10 293 | 65.50 164 | 85.55 195 | 89.82 151 | 71.57 154 | 78.21 134 | 86.12 220 | 60.66 167 | 93.18 158 | 75.64 111 | 75.46 264 | 89.81 190 |
|
BH-RMVSNet | | | 79.61 145 | 78.44 151 | 83.14 144 | 89.38 116 | 65.93 155 | 84.95 207 | 87.15 218 | 73.56 126 | 78.19 135 | 89.79 121 | 56.67 197 | 93.36 150 | 59.53 246 | 86.74 134 | 90.13 169 |
|
v2v482 | | | 80.23 135 | 79.29 135 | 83.05 149 | 83.62 241 | 64.14 190 | 87.04 153 | 89.97 147 | 73.61 124 | 78.18 136 | 87.22 186 | 61.10 160 | 93.82 129 | 76.11 107 | 76.78 244 | 91.18 134 |
|
PVSNet_BlendedMVS | | | 80.60 127 | 80.02 117 | 82.36 173 | 88.85 136 | 65.40 165 | 86.16 180 | 92.00 84 | 69.34 192 | 78.11 137 | 86.09 221 | 66.02 100 | 94.27 106 | 71.52 144 | 82.06 187 | 87.39 249 |
|
PVSNet_Blended | | | 80.98 113 | 80.34 113 | 82.90 156 | 88.85 136 | 65.40 165 | 84.43 222 | 92.00 84 | 67.62 217 | 78.11 137 | 85.05 245 | 66.02 100 | 94.27 106 | 71.52 144 | 89.50 99 | 89.01 211 |
|
v1144 | | | 80.03 139 | 79.03 139 | 83.01 151 | 83.78 239 | 64.51 182 | 87.11 152 | 90.57 129 | 71.96 147 | 78.08 139 | 86.20 219 | 61.41 152 | 93.94 122 | 74.93 116 | 77.23 234 | 90.60 152 |
|
TranMVSNet+NR-MVSNet | | | 80.84 116 | 80.31 114 | 82.42 171 | 87.85 169 | 62.33 223 | 87.74 137 | 91.33 110 | 80.55 11 | 77.99 140 | 89.86 119 | 65.23 107 | 92.62 176 | 67.05 187 | 75.24 271 | 92.30 105 |
|
Baseline_NR-MVSNet | | | 78.15 181 | 78.33 155 | 77.61 260 | 85.79 207 | 56.21 297 | 86.78 163 | 85.76 235 | 73.60 125 | 77.93 141 | 87.57 176 | 65.02 109 | 88.99 258 | 67.14 186 | 75.33 268 | 87.63 244 |
|
TR-MVS | | | 77.44 197 | 76.18 201 | 81.20 196 | 88.24 158 | 63.24 210 | 84.61 216 | 86.40 227 | 67.55 218 | 77.81 142 | 86.48 213 | 54.10 213 | 93.15 159 | 57.75 264 | 82.72 181 | 87.20 255 |
|
v1192 | | | 79.59 146 | 78.43 152 | 83.07 148 | 83.55 243 | 64.52 181 | 86.93 157 | 90.58 128 | 70.83 162 | 77.78 143 | 85.90 223 | 59.15 177 | 93.94 122 | 73.96 123 | 77.19 236 | 90.76 145 |
|
PCF-MVS | | 73.52 7 | 80.38 132 | 78.84 143 | 85.01 82 | 87.71 175 | 68.99 99 | 83.65 236 | 91.46 108 | 63.00 266 | 77.77 144 | 90.28 110 | 66.10 97 | 95.09 81 | 61.40 232 | 88.22 116 | 90.94 141 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
WR-MVS | | | 79.49 148 | 79.22 137 | 80.27 215 | 88.79 142 | 58.35 262 | 85.06 204 | 88.61 192 | 78.56 29 | 77.65 145 | 88.34 159 | 63.81 117 | 90.66 234 | 64.98 204 | 77.22 235 | 91.80 121 |
|
XVG-OURS | | | 80.41 131 | 79.23 136 | 83.97 120 | 85.64 210 | 69.02 97 | 83.03 248 | 90.39 133 | 71.09 160 | 77.63 146 | 91.49 86 | 54.62 210 | 91.35 216 | 75.71 110 | 83.47 170 | 91.54 124 |
|
v144192 | | | 79.47 149 | 78.37 153 | 82.78 165 | 83.35 245 | 63.96 193 | 86.96 155 | 90.36 137 | 69.99 178 | 77.50 147 | 85.67 230 | 60.66 167 | 93.77 134 | 74.27 120 | 76.58 245 | 90.62 150 |
|
v1921920 | | | 79.22 156 | 78.03 160 | 82.80 162 | 83.30 247 | 63.94 194 | 86.80 161 | 90.33 138 | 69.91 180 | 77.48 148 | 85.53 233 | 58.44 181 | 93.75 136 | 73.60 125 | 76.85 241 | 90.71 148 |
|
thisisatest0530 | | | 79.40 152 | 77.76 170 | 84.31 105 | 87.69 177 | 65.10 175 | 87.36 145 | 84.26 249 | 70.04 177 | 77.42 149 | 88.26 163 | 49.94 257 | 94.79 94 | 70.20 156 | 84.70 155 | 93.03 84 |
|
FC-MVSNet-test | | | 81.52 105 | 82.02 91 | 80.03 218 | 88.42 154 | 55.97 299 | 87.95 131 | 93.42 30 | 77.10 52 | 77.38 150 | 90.98 102 | 69.96 64 | 91.79 204 | 68.46 173 | 84.50 156 | 92.33 103 |
|
v1240 | | | 78.99 162 | 77.78 168 | 82.64 168 | 83.21 249 | 63.54 202 | 86.62 168 | 90.30 140 | 69.74 186 | 77.33 151 | 85.68 229 | 57.04 195 | 93.76 135 | 73.13 134 | 76.92 238 | 90.62 150 |
|
PAPM_NR | | | 83.02 84 | 82.41 84 | 84.82 89 | 92.47 65 | 66.37 149 | 87.93 133 | 91.80 94 | 73.82 121 | 77.32 152 | 90.66 105 | 67.90 81 | 94.90 88 | 70.37 155 | 89.48 100 | 93.19 79 |
|
ACMM | | 73.20 8 | 80.78 124 | 79.84 121 | 83.58 127 | 89.31 122 | 68.37 116 | 89.99 69 | 91.60 101 | 70.28 174 | 77.25 153 | 89.66 123 | 53.37 219 | 93.53 145 | 74.24 121 | 82.85 178 | 88.85 219 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
HQP4-MVS | | | | | | | | | | | 77.24 154 | | | 95.11 77 | | | 91.03 137 |
|
HQP-NCC | | | | | | 89.33 117 | | 89.17 86 | | 76.41 69 | 77.23 155 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 117 | | 89.17 86 | | 76.41 69 | 77.23 155 | | | | | | |
|
HQP-MVS | | | 82.61 89 | 82.02 91 | 84.37 101 | 89.33 117 | 66.98 140 | 89.17 86 | 92.19 76 | 76.41 69 | 77.23 155 | 90.23 112 | 60.17 174 | 95.11 77 | 77.47 94 | 85.99 145 | 91.03 137 |
|
TAPA-MVS | | 73.13 9 | 79.15 157 | 77.94 162 | 82.79 164 | 89.59 107 | 62.99 218 | 88.16 127 | 91.51 104 | 65.77 238 | 77.14 158 | 91.09 95 | 60.91 163 | 93.21 154 | 50.26 296 | 87.05 129 | 92.17 111 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PAPR | | | 81.66 103 | 80.89 105 | 83.99 119 | 90.27 93 | 64.00 192 | 86.76 165 | 91.77 98 | 68.84 207 | 77.13 159 | 89.50 128 | 67.63 83 | 94.88 90 | 67.55 179 | 88.52 112 | 93.09 81 |
|
UniMVSNet_ETH3D | | | 79.10 159 | 78.24 157 | 81.70 183 | 86.85 195 | 60.24 249 | 87.28 148 | 88.79 185 | 74.25 112 | 76.84 160 | 90.53 108 | 49.48 262 | 91.56 210 | 67.98 175 | 82.15 186 | 93.29 73 |
|
EPNet | | | 83.72 72 | 82.92 79 | 86.14 63 | 84.22 231 | 69.48 90 | 91.05 46 | 85.27 238 | 81.30 7 | 76.83 161 | 91.65 79 | 66.09 98 | 95.56 59 | 76.00 109 | 93.85 58 | 93.38 69 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
baseline1 | | | 76.98 204 | 76.75 193 | 77.66 258 | 88.13 160 | 55.66 303 | 85.12 203 | 81.89 279 | 73.04 134 | 76.79 162 | 88.90 143 | 62.43 137 | 87.78 276 | 63.30 214 | 71.18 300 | 89.55 198 |
|
tttt0517 | | | 79.40 152 | 77.91 163 | 83.90 123 | 88.10 162 | 63.84 195 | 88.37 118 | 84.05 251 | 71.45 156 | 76.78 163 | 89.12 139 | 49.93 259 | 94.89 89 | 70.18 157 | 83.18 174 | 92.96 88 |
|
TAMVS | | | 78.89 165 | 77.51 176 | 83.03 150 | 87.80 171 | 67.79 128 | 84.72 211 | 85.05 241 | 67.63 216 | 76.75 164 | 87.70 172 | 62.25 140 | 90.82 230 | 58.53 257 | 87.13 128 | 90.49 157 |
|
XVG-OURS-SEG-HR | | | 80.81 119 | 79.76 123 | 83.96 121 | 85.60 211 | 68.78 103 | 83.54 241 | 90.50 131 | 70.66 168 | 76.71 165 | 91.66 78 | 60.69 166 | 91.26 218 | 76.94 100 | 81.58 192 | 91.83 118 |
|
3Dnovator+ | | 77.84 4 | 85.48 54 | 84.47 67 | 88.51 3 | 91.08 80 | 73.49 14 | 93.18 7 | 93.78 19 | 80.79 10 | 76.66 166 | 93.37 51 | 60.40 173 | 96.75 20 | 77.20 97 | 93.73 59 | 95.29 2 |
|
LPG-MVS_test | | | 82.08 94 | 81.27 99 | 84.50 97 | 89.23 125 | 68.76 104 | 90.22 65 | 91.94 88 | 75.37 91 | 76.64 167 | 91.51 84 | 54.29 211 | 94.91 86 | 78.44 84 | 83.78 163 | 89.83 188 |
|
LGP-MVS_train | | | | | 84.50 97 | 89.23 125 | 68.76 104 | | 91.94 88 | 75.37 91 | 76.64 167 | 91.51 84 | 54.29 211 | 94.91 86 | 78.44 84 | 83.78 163 | 89.83 188 |
|
tfpn200view9 | | | 76.42 213 | 75.37 211 | 79.55 231 | 89.13 129 | 57.65 275 | 85.17 200 | 83.60 256 | 73.41 129 | 76.45 169 | 86.39 215 | 52.12 229 | 91.95 199 | 48.33 304 | 83.75 165 | 89.07 204 |
|
thres400 | | | 76.50 210 | 75.37 211 | 79.86 221 | 89.13 129 | 57.65 275 | 85.17 200 | 83.60 256 | 73.41 129 | 76.45 169 | 86.39 215 | 52.12 229 | 91.95 199 | 48.33 304 | 83.75 165 | 90.00 179 |
|
HyFIR lowres test | | | 77.53 196 | 75.40 209 | 83.94 122 | 89.59 107 | 66.62 144 | 80.36 270 | 88.64 191 | 56.29 317 | 76.45 169 | 85.17 241 | 57.64 186 | 93.28 152 | 61.34 234 | 83.10 176 | 91.91 116 |
|
mvs-test1 | | | 80.88 114 | 79.40 131 | 85.29 73 | 85.13 219 | 69.75 85 | 89.28 83 | 88.10 198 | 74.99 97 | 76.44 172 | 86.72 198 | 57.27 191 | 94.26 110 | 73.53 126 | 83.18 174 | 91.87 117 |
|
CDS-MVSNet | | | 79.07 160 | 77.70 172 | 83.17 143 | 87.60 178 | 68.23 120 | 84.40 224 | 86.20 230 | 67.49 219 | 76.36 173 | 86.54 211 | 61.54 150 | 90.79 231 | 61.86 228 | 87.33 125 | 90.49 157 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
thres100view900 | | | 76.50 210 | 75.55 206 | 79.33 232 | 89.52 110 | 56.99 283 | 85.83 189 | 83.23 265 | 73.94 118 | 76.32 174 | 87.12 190 | 51.89 236 | 91.95 199 | 48.33 304 | 83.75 165 | 89.07 204 |
|
thres600view7 | | | 76.50 210 | 75.44 207 | 79.68 225 | 89.40 114 | 57.16 280 | 85.53 197 | 83.23 265 | 73.79 122 | 76.26 175 | 87.09 191 | 51.89 236 | 91.89 202 | 48.05 309 | 83.72 168 | 90.00 179 |
|
UGNet | | | 80.83 118 | 79.59 127 | 84.54 96 | 88.04 164 | 68.09 122 | 89.42 81 | 88.16 196 | 76.95 55 | 76.22 176 | 89.46 132 | 49.30 265 | 93.94 122 | 68.48 172 | 90.31 88 | 91.60 122 |
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 |
test_djsdf | | | 80.30 134 | 79.32 134 | 83.27 137 | 83.98 236 | 65.37 168 | 90.50 55 | 90.38 134 | 68.55 211 | 76.19 177 | 88.70 147 | 56.44 198 | 93.46 147 | 78.98 80 | 80.14 211 | 90.97 140 |
|
v148 | | | 78.72 167 | 77.80 167 | 81.47 188 | 82.73 266 | 61.96 229 | 86.30 176 | 88.08 200 | 73.26 131 | 76.18 178 | 85.47 235 | 62.46 136 | 92.36 186 | 71.92 143 | 73.82 284 | 90.09 173 |
|
WTY-MVS | | | 75.65 224 | 75.68 204 | 75.57 279 | 86.40 202 | 56.82 285 | 77.92 295 | 82.40 274 | 65.10 245 | 76.18 178 | 87.72 171 | 63.13 128 | 80.90 311 | 60.31 240 | 81.96 188 | 89.00 213 |
|
mvs_anonymous | | | 79.42 151 | 79.11 138 | 80.34 213 | 84.45 228 | 57.97 269 | 82.59 250 | 87.62 210 | 67.40 221 | 76.17 180 | 88.56 154 | 68.47 77 | 89.59 248 | 70.65 153 | 86.05 144 | 93.47 67 |
|
Anonymous20231211 | | | 78.97 163 | 77.69 173 | 82.81 161 | 90.54 89 | 64.29 188 | 90.11 67 | 91.51 104 | 65.01 248 | 76.16 181 | 88.13 168 | 50.56 250 | 93.03 168 | 69.68 163 | 77.56 232 | 91.11 136 |
|
thisisatest0515 | | | 77.33 200 | 75.38 210 | 83.18 142 | 85.27 215 | 63.80 196 | 82.11 255 | 83.27 264 | 65.06 246 | 75.91 182 | 83.84 258 | 49.54 261 | 94.27 106 | 67.24 184 | 86.19 142 | 91.48 128 |
|
CANet_DTU | | | 80.61 126 | 79.87 120 | 82.83 159 | 85.60 211 | 63.17 214 | 87.36 145 | 88.65 190 | 76.37 73 | 75.88 183 | 88.44 157 | 53.51 218 | 93.07 164 | 73.30 131 | 89.74 98 | 92.25 107 |
|
thres200 | | | 75.55 225 | 74.47 221 | 78.82 241 | 87.78 174 | 57.85 272 | 83.07 247 | 83.51 259 | 72.44 140 | 75.84 184 | 84.42 250 | 52.08 231 | 91.75 205 | 47.41 311 | 83.64 169 | 86.86 263 |
|
CHOSEN 1792x2688 | | | 77.63 195 | 75.69 203 | 83.44 130 | 89.98 100 | 68.58 114 | 78.70 288 | 87.50 213 | 56.38 316 | 75.80 185 | 86.84 194 | 58.67 179 | 91.40 215 | 61.58 231 | 85.75 148 | 90.34 162 |
|
AdaColmap | | | 80.58 129 | 79.42 130 | 84.06 113 | 93.09 50 | 68.91 101 | 89.36 82 | 88.97 181 | 69.27 193 | 75.70 186 | 89.69 122 | 57.20 194 | 95.77 54 | 63.06 216 | 88.41 114 | 87.50 248 |
|
cl_fuxian | | | 78.75 166 | 77.91 163 | 81.26 194 | 82.89 262 | 61.56 234 | 84.09 231 | 89.13 174 | 69.97 179 | 75.56 187 | 84.29 252 | 66.36 95 | 92.09 195 | 73.47 129 | 75.48 263 | 90.12 170 |
|
miper_ehance_all_eth | | | 78.59 171 | 77.76 170 | 81.08 200 | 82.66 268 | 61.56 234 | 83.65 236 | 89.15 172 | 68.87 206 | 75.55 188 | 83.79 260 | 66.49 93 | 92.03 196 | 73.25 132 | 76.39 249 | 89.64 194 |
|
miper_enhance_ethall | | | 77.87 190 | 76.86 187 | 80.92 203 | 81.65 282 | 61.38 236 | 82.68 249 | 88.98 179 | 65.52 242 | 75.47 189 | 82.30 277 | 65.76 104 | 92.00 198 | 72.95 135 | 76.39 249 | 89.39 200 |
|
3Dnovator | | 76.31 5 | 83.38 79 | 82.31 87 | 86.59 55 | 87.94 167 | 72.94 26 | 90.64 51 | 92.14 78 | 77.21 48 | 75.47 189 | 92.83 64 | 58.56 180 | 94.72 96 | 73.24 133 | 92.71 66 | 92.13 112 |
|
jajsoiax | | | 79.29 155 | 77.96 161 | 83.27 137 | 84.68 225 | 66.57 146 | 89.25 85 | 90.16 143 | 69.20 197 | 75.46 191 | 89.49 129 | 45.75 288 | 93.13 161 | 76.84 102 | 80.80 200 | 90.11 171 |
|
IterMVS-LS | | | 80.06 138 | 79.38 132 | 82.11 175 | 85.89 206 | 63.20 212 | 86.79 162 | 89.34 163 | 74.19 113 | 75.45 192 | 86.72 198 | 66.62 91 | 92.39 184 | 72.58 138 | 76.86 240 | 90.75 146 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
BH-untuned | | | 79.47 149 | 78.60 146 | 82.05 176 | 89.19 127 | 65.91 156 | 86.07 183 | 88.52 193 | 72.18 143 | 75.42 193 | 87.69 173 | 61.15 159 | 93.54 144 | 60.38 239 | 86.83 133 | 86.70 267 |
|
mvs_tets | | | 79.13 158 | 77.77 169 | 83.22 141 | 84.70 224 | 66.37 149 | 89.17 86 | 90.19 142 | 69.38 191 | 75.40 194 | 89.46 132 | 44.17 294 | 93.15 159 | 76.78 103 | 80.70 202 | 90.14 168 |
|
HY-MVS | | 69.67 12 | 77.95 187 | 77.15 181 | 80.36 212 | 87.57 182 | 60.21 250 | 83.37 243 | 87.78 208 | 66.11 233 | 75.37 195 | 87.06 193 | 63.27 121 | 90.48 236 | 61.38 233 | 82.43 184 | 90.40 161 |
|
GBi-Net | | | 78.40 173 | 77.40 177 | 81.40 190 | 87.60 178 | 63.01 215 | 88.39 115 | 89.28 165 | 71.63 151 | 75.34 196 | 87.28 182 | 54.80 204 | 91.11 221 | 62.72 217 | 79.57 214 | 90.09 173 |
|
test1 | | | 78.40 173 | 77.40 177 | 81.40 190 | 87.60 178 | 63.01 215 | 88.39 115 | 89.28 165 | 71.63 151 | 75.34 196 | 87.28 182 | 54.80 204 | 91.11 221 | 62.72 217 | 79.57 214 | 90.09 173 |
|
FMVSNet3 | | | 77.88 189 | 76.85 188 | 80.97 202 | 86.84 196 | 62.36 222 | 86.52 171 | 88.77 186 | 71.13 158 | 75.34 196 | 86.66 205 | 54.07 214 | 91.10 224 | 62.72 217 | 79.57 214 | 89.45 199 |
|
CostFormer | | | 75.24 230 | 73.90 227 | 79.27 233 | 82.65 269 | 58.27 264 | 80.80 265 | 82.73 272 | 61.57 280 | 75.33 199 | 83.13 267 | 55.52 200 | 91.07 227 | 64.98 204 | 78.34 227 | 88.45 230 |
|
FMVSNet2 | | | 78.20 179 | 77.21 180 | 81.20 196 | 87.60 178 | 62.89 219 | 87.47 143 | 89.02 177 | 71.63 151 | 75.29 200 | 87.28 182 | 54.80 204 | 91.10 224 | 62.38 221 | 79.38 218 | 89.61 195 |
|
v8 | | | 79.97 141 | 79.02 140 | 82.80 162 | 84.09 233 | 64.50 184 | 87.96 130 | 90.29 141 | 74.13 116 | 75.24 201 | 86.81 195 | 62.88 130 | 93.89 128 | 74.39 119 | 75.40 266 | 90.00 179 |
|
anonymousdsp | | | 78.60 170 | 77.15 181 | 82.98 153 | 80.51 299 | 67.08 138 | 87.24 149 | 89.53 159 | 65.66 240 | 75.16 202 | 87.19 188 | 52.52 221 | 92.25 189 | 77.17 98 | 79.34 219 | 89.61 195 |
|
QAPM | | | 80.88 114 | 79.50 129 | 85.03 81 | 88.01 166 | 68.97 100 | 91.59 35 | 92.00 84 | 66.63 229 | 75.15 203 | 92.16 70 | 57.70 185 | 95.45 63 | 63.52 210 | 88.76 107 | 90.66 149 |
|
v10 | | | 79.74 144 | 78.67 144 | 82.97 154 | 84.06 234 | 64.95 176 | 87.88 135 | 90.62 127 | 73.11 132 | 75.11 204 | 86.56 210 | 61.46 151 | 94.05 118 | 73.68 124 | 75.55 261 | 89.90 185 |
|
Vis-MVSNet (Re-imp) | | | 78.36 175 | 78.45 150 | 78.07 253 | 88.64 146 | 51.78 318 | 86.70 166 | 79.63 302 | 74.14 115 | 75.11 204 | 90.83 103 | 61.29 156 | 89.75 245 | 58.10 261 | 91.60 73 | 92.69 94 |
|
cl-mvsnet2 | | | 78.07 183 | 77.01 183 | 81.23 195 | 82.37 275 | 61.83 231 | 83.55 240 | 87.98 202 | 68.96 204 | 75.06 206 | 83.87 256 | 61.40 153 | 91.88 203 | 73.53 126 | 76.39 249 | 89.98 182 |
|
ACMP | | 74.13 6 | 81.51 107 | 80.57 108 | 84.36 102 | 89.42 113 | 68.69 111 | 89.97 70 | 91.50 107 | 74.46 108 | 75.04 207 | 90.41 109 | 53.82 216 | 94.54 98 | 77.56 93 | 82.91 177 | 89.86 187 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
Effi-MVS+-dtu | | | 80.03 139 | 78.57 148 | 84.42 100 | 85.13 219 | 68.74 106 | 88.77 101 | 88.10 198 | 74.99 97 | 74.97 208 | 83.49 264 | 57.27 191 | 93.36 150 | 73.53 126 | 80.88 198 | 91.18 134 |
|
XXY-MVS | | | 75.41 228 | 75.56 205 | 74.96 284 | 83.59 242 | 57.82 273 | 80.59 269 | 83.87 254 | 66.54 230 | 74.93 209 | 88.31 160 | 63.24 122 | 80.09 314 | 62.16 224 | 76.85 241 | 86.97 261 |
|
eth_miper_zixun_eth | | | 77.92 188 | 76.69 194 | 81.61 186 | 83.00 257 | 61.98 228 | 83.15 244 | 89.20 171 | 69.52 189 | 74.86 210 | 84.35 251 | 61.76 146 | 92.56 179 | 71.50 146 | 72.89 289 | 90.28 164 |
|
GA-MVS | | | 76.87 206 | 75.17 214 | 81.97 179 | 82.75 265 | 62.58 220 | 81.44 264 | 86.35 229 | 72.16 145 | 74.74 211 | 82.89 269 | 46.20 283 | 92.02 197 | 68.85 171 | 81.09 196 | 91.30 132 |
|
sss | | | 73.60 243 | 73.64 230 | 73.51 294 | 82.80 264 | 55.01 305 | 76.12 301 | 81.69 282 | 62.47 274 | 74.68 212 | 85.85 226 | 57.32 190 | 78.11 321 | 60.86 237 | 80.93 197 | 87.39 249 |
|
BH-w/o | | | 78.21 178 | 77.33 179 | 80.84 204 | 88.81 140 | 65.13 174 | 84.87 208 | 87.85 207 | 69.75 184 | 74.52 213 | 84.74 248 | 61.34 154 | 93.11 162 | 58.24 260 | 85.84 147 | 84.27 296 |
|
FMVSNet1 | | | 77.44 197 | 76.12 202 | 81.40 190 | 86.81 197 | 63.01 215 | 88.39 115 | 89.28 165 | 70.49 171 | 74.39 214 | 87.28 182 | 49.06 268 | 91.11 221 | 60.91 236 | 78.52 223 | 90.09 173 |
|
cl-mvsnet_ | | | 77.72 192 | 76.76 191 | 80.58 208 | 82.49 272 | 60.48 246 | 83.09 245 | 87.87 205 | 69.22 195 | 74.38 215 | 85.22 240 | 62.10 143 | 91.53 211 | 71.09 148 | 75.41 265 | 89.73 193 |
|
cl-mvsnet1 | | | 77.72 192 | 76.76 191 | 80.58 208 | 82.48 273 | 60.48 246 | 83.09 245 | 87.86 206 | 69.22 195 | 74.38 215 | 85.24 239 | 62.10 143 | 91.53 211 | 71.09 148 | 75.40 266 | 89.74 192 |
|
114514_t | | | 80.68 125 | 79.51 128 | 84.20 108 | 94.09 32 | 67.27 136 | 89.64 79 | 91.11 117 | 58.75 303 | 74.08 217 | 90.72 104 | 58.10 182 | 95.04 82 | 69.70 162 | 89.42 101 | 90.30 163 |
|
WR-MVS_H | | | 78.51 172 | 78.49 149 | 78.56 245 | 88.02 165 | 56.38 294 | 88.43 111 | 92.67 59 | 77.14 50 | 73.89 218 | 87.55 177 | 66.25 96 | 89.24 254 | 58.92 252 | 73.55 286 | 90.06 177 |
|
tpm2 | | | 73.26 247 | 71.46 249 | 78.63 243 | 83.34 246 | 56.71 288 | 80.65 268 | 80.40 295 | 56.63 315 | 73.55 219 | 82.02 281 | 51.80 238 | 91.24 219 | 56.35 273 | 78.42 226 | 87.95 237 |
|
CP-MVSNet | | | 78.22 177 | 78.34 154 | 77.84 255 | 87.83 170 | 54.54 307 | 87.94 132 | 91.17 115 | 77.65 35 | 73.48 220 | 88.49 155 | 62.24 141 | 88.43 268 | 62.19 223 | 74.07 279 | 90.55 155 |
|
pm-mvs1 | | | 77.25 201 | 76.68 195 | 78.93 239 | 84.22 231 | 58.62 261 | 86.41 172 | 88.36 195 | 71.37 157 | 73.31 221 | 88.01 169 | 61.22 158 | 89.15 256 | 64.24 208 | 73.01 288 | 89.03 210 |
|
PS-CasMVS | | | 78.01 186 | 78.09 159 | 77.77 257 | 87.71 175 | 54.39 309 | 88.02 128 | 91.22 112 | 77.50 43 | 73.26 222 | 88.64 150 | 60.73 164 | 88.41 269 | 61.88 227 | 73.88 283 | 90.53 156 |
|
CVMVSNet | | | 72.99 251 | 72.58 240 | 74.25 291 | 84.28 229 | 50.85 324 | 86.41 172 | 83.45 262 | 44.56 332 | 73.23 223 | 87.54 178 | 49.38 263 | 85.70 290 | 65.90 195 | 78.44 225 | 86.19 274 |
|
PEN-MVS | | | 77.73 191 | 77.69 173 | 77.84 255 | 87.07 193 | 53.91 311 | 87.91 134 | 91.18 114 | 77.56 40 | 73.14 224 | 88.82 146 | 61.23 157 | 89.17 255 | 59.95 242 | 72.37 291 | 90.43 159 |
|
1112_ss | | | 77.40 199 | 76.43 198 | 80.32 214 | 89.11 133 | 60.41 248 | 83.65 236 | 87.72 209 | 62.13 277 | 73.05 225 | 86.72 198 | 62.58 134 | 89.97 242 | 62.11 226 | 80.80 200 | 90.59 154 |
|
tpm | | | 72.37 257 | 71.71 248 | 74.35 290 | 82.19 277 | 52.00 316 | 79.22 282 | 77.29 314 | 64.56 252 | 72.95 226 | 83.68 263 | 51.35 241 | 83.26 304 | 58.33 259 | 75.80 257 | 87.81 241 |
|
cascas | | | 76.72 208 | 74.64 217 | 82.99 152 | 85.78 208 | 65.88 157 | 82.33 253 | 89.21 170 | 60.85 285 | 72.74 227 | 81.02 288 | 47.28 275 | 93.75 136 | 67.48 180 | 85.02 150 | 89.34 201 |
|
CR-MVSNet | | | 73.37 244 | 71.27 252 | 79.67 226 | 81.32 291 | 65.19 172 | 75.92 303 | 80.30 296 | 59.92 292 | 72.73 228 | 81.19 284 | 52.50 222 | 86.69 282 | 59.84 243 | 77.71 229 | 87.11 259 |
|
RPMNet | | | 71.62 259 | 68.94 267 | 79.67 226 | 81.32 291 | 65.19 172 | 75.92 303 | 78.30 308 | 57.60 309 | 72.73 228 | 76.45 319 | 52.30 226 | 86.69 282 | 48.14 308 | 77.71 229 | 87.11 259 |
|
DTE-MVSNet | | | 76.99 203 | 76.80 189 | 77.54 262 | 86.24 203 | 53.06 315 | 87.52 141 | 90.66 126 | 77.08 53 | 72.50 230 | 88.67 149 | 60.48 170 | 89.52 249 | 57.33 268 | 70.74 302 | 90.05 178 |
|
Test_1112_low_res | | | 76.40 214 | 75.44 207 | 79.27 233 | 89.28 123 | 58.09 265 | 81.69 259 | 87.07 219 | 59.53 296 | 72.48 231 | 86.67 204 | 61.30 155 | 89.33 252 | 60.81 238 | 80.15 210 | 90.41 160 |
|
v7n | | | 78.97 163 | 77.58 175 | 83.14 144 | 83.45 244 | 65.51 163 | 88.32 119 | 91.21 113 | 73.69 123 | 72.41 232 | 86.32 217 | 57.93 183 | 93.81 130 | 69.18 167 | 75.65 259 | 90.11 171 |
|
SCA | | | 74.22 237 | 72.33 243 | 79.91 220 | 84.05 235 | 62.17 226 | 79.96 275 | 79.29 304 | 66.30 232 | 72.38 233 | 80.13 296 | 51.95 234 | 88.60 266 | 59.25 248 | 77.67 231 | 88.96 215 |
|
CNLPA | | | 78.08 182 | 76.79 190 | 81.97 179 | 90.40 92 | 71.07 61 | 87.59 140 | 84.55 245 | 66.03 236 | 72.38 233 | 89.64 124 | 57.56 187 | 86.04 288 | 59.61 245 | 83.35 171 | 88.79 222 |
|
NR-MVSNet | | | 80.23 135 | 79.38 132 | 82.78 165 | 87.80 171 | 63.34 208 | 86.31 175 | 91.09 118 | 79.01 26 | 72.17 235 | 89.07 140 | 67.20 88 | 92.81 175 | 66.08 194 | 75.65 259 | 92.20 109 |
|
OpenMVS | | 72.83 10 | 79.77 143 | 78.33 155 | 84.09 112 | 85.17 216 | 69.91 81 | 90.57 53 | 90.97 119 | 66.70 225 | 72.17 235 | 91.91 74 | 54.70 208 | 93.96 119 | 61.81 229 | 90.95 82 | 88.41 232 |
|
MVS | | | 78.19 180 | 76.99 185 | 81.78 181 | 85.66 209 | 66.99 139 | 84.66 212 | 90.47 132 | 55.08 321 | 72.02 237 | 85.27 238 | 63.83 116 | 94.11 117 | 66.10 193 | 89.80 97 | 84.24 297 |
|
XVG-ACMP-BASELINE | | | 76.11 218 | 74.27 224 | 81.62 184 | 83.20 250 | 64.67 180 | 83.60 239 | 89.75 154 | 69.75 184 | 71.85 238 | 87.09 191 | 32.78 331 | 92.11 194 | 69.99 160 | 80.43 207 | 88.09 236 |
|
PatchmatchNet | | | 73.12 249 | 71.33 251 | 78.49 248 | 83.18 251 | 60.85 241 | 79.63 277 | 78.57 306 | 64.13 257 | 71.73 239 | 79.81 301 | 51.20 243 | 85.97 289 | 57.40 267 | 76.36 252 | 88.66 225 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpmrst | | | 72.39 255 | 72.13 244 | 73.18 296 | 80.54 298 | 49.91 327 | 79.91 276 | 79.08 305 | 63.11 264 | 71.69 240 | 79.95 298 | 55.32 201 | 82.77 306 | 65.66 198 | 73.89 282 | 86.87 262 |
|
PatchFormer-LS_test | | | 74.50 233 | 73.05 237 | 78.86 240 | 82.95 260 | 59.55 256 | 81.65 260 | 82.30 275 | 67.44 220 | 71.62 241 | 78.15 310 | 52.34 225 | 88.92 263 | 65.05 203 | 75.90 256 | 88.12 235 |
|
TransMVSNet (Re) | | | 75.39 229 | 74.56 219 | 77.86 254 | 85.50 213 | 57.10 282 | 86.78 163 | 86.09 233 | 72.17 144 | 71.53 242 | 87.34 181 | 63.01 129 | 89.31 253 | 56.84 271 | 61.83 322 | 87.17 256 |
|
Fast-Effi-MVS+-dtu | | | 78.02 185 | 76.49 197 | 82.62 169 | 83.16 253 | 66.96 142 | 86.94 156 | 87.45 215 | 72.45 138 | 71.49 243 | 84.17 253 | 54.79 207 | 91.58 209 | 67.61 178 | 80.31 208 | 89.30 202 |
|
PAPM | | | 77.68 194 | 76.40 199 | 81.51 187 | 87.29 189 | 61.85 230 | 83.78 234 | 89.59 157 | 64.74 250 | 71.23 244 | 88.70 147 | 62.59 133 | 93.66 139 | 52.66 286 | 87.03 130 | 89.01 211 |
|
tfpnnormal | | | 74.39 234 | 73.16 235 | 78.08 252 | 86.10 205 | 58.05 266 | 84.65 215 | 87.53 212 | 70.32 173 | 71.22 245 | 85.63 231 | 54.97 203 | 89.86 243 | 43.03 325 | 75.02 272 | 86.32 271 |
|
RPSCF | | | 73.23 248 | 71.46 249 | 78.54 246 | 82.50 271 | 59.85 251 | 82.18 254 | 82.84 271 | 58.96 300 | 71.15 246 | 89.41 136 | 45.48 290 | 84.77 296 | 58.82 254 | 71.83 296 | 91.02 139 |
|
DWT-MVSNet_test | | | 73.70 242 | 71.86 246 | 79.21 235 | 82.91 261 | 58.94 258 | 82.34 252 | 82.17 276 | 65.21 243 | 71.05 247 | 78.31 307 | 44.21 293 | 90.17 240 | 63.29 215 | 77.28 233 | 88.53 229 |
|
DI_MVS_plusplus_test | | | 79.89 142 | 78.58 147 | 83.85 124 | 82.89 262 | 65.32 169 | 86.12 181 | 89.55 158 | 69.64 187 | 70.55 248 | 85.82 227 | 57.24 193 | 93.81 130 | 76.85 101 | 88.55 110 | 92.41 102 |
|
PatchT | | | 68.46 283 | 67.85 278 | 70.29 307 | 80.70 296 | 43.93 336 | 72.47 316 | 74.88 321 | 60.15 290 | 70.55 248 | 76.57 318 | 49.94 257 | 81.59 309 | 50.58 292 | 74.83 274 | 85.34 285 |
|
IterMVS-SCA-FT | | | 75.43 227 | 73.87 228 | 80.11 217 | 82.69 267 | 64.85 177 | 81.57 262 | 83.47 261 | 69.16 198 | 70.49 250 | 84.15 254 | 51.95 234 | 88.15 271 | 69.23 166 | 72.14 294 | 87.34 251 |
|
miper_lstm_enhance | | | 74.11 238 | 73.11 236 | 77.13 268 | 80.11 302 | 59.62 253 | 72.23 317 | 86.92 221 | 66.76 224 | 70.40 251 | 82.92 268 | 56.93 196 | 82.92 305 | 69.06 169 | 72.63 290 | 88.87 218 |
|
gg-mvs-nofinetune | | | 69.95 274 | 67.96 276 | 75.94 275 | 83.07 254 | 54.51 308 | 77.23 298 | 70.29 331 | 63.11 264 | 70.32 252 | 62.33 333 | 43.62 296 | 88.69 265 | 53.88 281 | 87.76 118 | 84.62 295 |
|
DP-MVS | | | 76.78 207 | 74.57 218 | 83.42 131 | 93.29 43 | 69.46 93 | 88.55 110 | 83.70 255 | 63.98 260 | 70.20 253 | 88.89 144 | 54.01 215 | 94.80 93 | 46.66 313 | 81.88 190 | 86.01 279 |
|
pmmvs6 | | | 74.69 232 | 73.39 231 | 78.61 244 | 81.38 288 | 57.48 278 | 86.64 167 | 87.95 203 | 64.99 249 | 70.18 254 | 86.61 206 | 50.43 252 | 89.52 249 | 62.12 225 | 70.18 304 | 88.83 220 |
|
PVSNet | | 64.34 18 | 72.08 258 | 70.87 257 | 75.69 277 | 86.21 204 | 56.44 292 | 74.37 313 | 80.73 289 | 62.06 278 | 70.17 255 | 82.23 279 | 42.86 300 | 83.31 303 | 54.77 278 | 84.45 158 | 87.32 252 |
|
1314 | | | 76.53 209 | 75.30 213 | 80.21 216 | 83.93 237 | 62.32 224 | 84.66 212 | 88.81 184 | 60.23 289 | 70.16 256 | 84.07 255 | 55.30 202 | 90.73 233 | 67.37 181 | 83.21 173 | 87.59 246 |
|
Patchmtry | | | 70.74 265 | 69.16 265 | 75.49 281 | 80.72 295 | 54.07 310 | 74.94 312 | 80.30 296 | 58.34 304 | 70.01 257 | 81.19 284 | 52.50 222 | 86.54 284 | 53.37 283 | 71.09 301 | 85.87 282 |
|
EPMVS | | | 69.02 279 | 68.16 273 | 71.59 299 | 79.61 310 | 49.80 329 | 77.40 297 | 66.93 338 | 62.82 270 | 70.01 257 | 79.05 303 | 45.79 286 | 77.86 323 | 56.58 272 | 75.26 270 | 87.13 258 |
|
IterMVS | | | 74.29 235 | 72.94 238 | 78.35 249 | 81.53 285 | 63.49 204 | 81.58 261 | 82.49 273 | 68.06 215 | 69.99 259 | 83.69 262 | 51.66 240 | 85.54 291 | 65.85 196 | 71.64 297 | 86.01 279 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
test-LLR | | | 72.94 252 | 72.43 241 | 74.48 288 | 81.35 289 | 58.04 267 | 78.38 289 | 77.46 312 | 66.66 226 | 69.95 260 | 79.00 305 | 48.06 271 | 79.24 315 | 66.13 191 | 84.83 152 | 86.15 275 |
|
test-mter | | | 71.41 261 | 70.39 260 | 74.48 288 | 81.35 289 | 58.04 267 | 78.38 289 | 77.46 312 | 60.32 288 | 69.95 260 | 79.00 305 | 36.08 327 | 79.24 315 | 66.13 191 | 84.83 152 | 86.15 275 |
|
pmmvs4 | | | 74.03 240 | 71.91 245 | 80.39 211 | 81.96 279 | 68.32 117 | 81.45 263 | 82.14 277 | 59.32 297 | 69.87 262 | 85.13 242 | 52.40 224 | 88.13 272 | 60.21 241 | 74.74 275 | 84.73 293 |
|
PLC | | 70.83 11 | 78.05 184 | 76.37 200 | 83.08 147 | 91.88 73 | 67.80 127 | 88.19 125 | 89.46 161 | 64.33 256 | 69.87 262 | 88.38 158 | 53.66 217 | 93.58 140 | 58.86 253 | 82.73 180 | 87.86 240 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
LTVRE_ROB | | 69.57 13 | 76.25 216 | 74.54 220 | 81.41 189 | 88.60 147 | 64.38 187 | 79.24 281 | 89.12 175 | 70.76 166 | 69.79 264 | 87.86 170 | 49.09 267 | 93.20 156 | 56.21 274 | 80.16 209 | 86.65 268 |
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 |
LS3D | | | 76.95 205 | 74.82 216 | 83.37 134 | 90.45 90 | 67.36 135 | 89.15 90 | 86.94 220 | 61.87 279 | 69.52 265 | 90.61 106 | 51.71 239 | 94.53 99 | 46.38 316 | 86.71 135 | 88.21 234 |
|
IB-MVS | | 68.01 15 | 75.85 221 | 73.36 232 | 83.31 135 | 84.76 223 | 66.03 152 | 83.38 242 | 85.06 240 | 70.21 176 | 69.40 266 | 81.05 287 | 45.76 287 | 94.66 97 | 65.10 202 | 75.49 262 | 89.25 203 |
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 |
PatchMatch-RL | | | 72.38 256 | 70.90 255 | 76.80 271 | 88.60 147 | 67.38 134 | 79.53 278 | 76.17 318 | 62.75 271 | 69.36 267 | 82.00 282 | 45.51 289 | 84.89 295 | 53.62 282 | 80.58 203 | 78.12 327 |
|
MDTV_nov1_ep13 | | | | 69.97 262 | | 83.18 251 | 53.48 313 | 77.10 299 | 80.18 299 | 60.45 286 | 69.33 268 | 80.44 293 | 48.89 269 | 86.90 281 | 51.60 289 | 78.51 224 | |
|
D2MVS | | | 74.82 231 | 73.21 234 | 79.64 228 | 79.81 306 | 62.56 221 | 80.34 271 | 87.35 216 | 64.37 255 | 68.86 269 | 82.66 273 | 46.37 280 | 90.10 241 | 67.91 176 | 81.24 195 | 86.25 272 |
|
PMMVS | | | 69.34 277 | 68.67 268 | 71.35 303 | 75.67 326 | 62.03 227 | 75.17 307 | 73.46 326 | 50.00 330 | 68.68 270 | 79.05 303 | 52.07 232 | 78.13 320 | 61.16 235 | 82.77 179 | 73.90 331 |
|
Patchmatch-RL test | | | 70.24 271 | 67.78 281 | 77.61 260 | 77.43 319 | 59.57 255 | 71.16 319 | 70.33 330 | 62.94 268 | 68.65 271 | 72.77 326 | 50.62 249 | 85.49 292 | 69.58 164 | 66.58 315 | 87.77 242 |
|
MS-PatchMatch | | | 73.83 241 | 72.67 239 | 77.30 265 | 83.87 238 | 66.02 153 | 81.82 256 | 84.66 243 | 61.37 283 | 68.61 272 | 82.82 271 | 47.29 274 | 88.21 270 | 59.27 247 | 84.32 159 | 77.68 328 |
|
tpm cat1 | | | 70.57 267 | 68.31 271 | 77.35 264 | 82.41 274 | 57.95 270 | 78.08 293 | 80.22 298 | 52.04 327 | 68.54 273 | 77.66 314 | 52.00 233 | 87.84 275 | 51.77 287 | 72.07 295 | 86.25 272 |
|
TESTMET0.1,1 | | | 69.89 275 | 69.00 266 | 72.55 297 | 79.27 314 | 56.85 284 | 78.38 289 | 74.71 324 | 57.64 308 | 68.09 274 | 77.19 316 | 37.75 322 | 76.70 326 | 63.92 209 | 84.09 161 | 84.10 300 |
|
MIMVSNet | | | 70.69 266 | 69.30 263 | 74.88 285 | 84.52 226 | 56.35 295 | 75.87 305 | 79.42 303 | 64.59 251 | 67.76 275 | 82.41 275 | 41.10 309 | 81.54 310 | 46.64 315 | 81.34 193 | 86.75 266 |
|
ACMH+ | | 68.96 14 | 76.01 219 | 74.01 225 | 82.03 177 | 88.60 147 | 65.31 170 | 88.86 97 | 87.55 211 | 70.25 175 | 67.75 276 | 87.47 180 | 41.27 308 | 93.19 157 | 58.37 258 | 75.94 255 | 87.60 245 |
|
LCM-MVSNet-Re | | | 77.05 202 | 76.94 186 | 77.36 263 | 87.20 190 | 51.60 319 | 80.06 273 | 80.46 294 | 75.20 95 | 67.69 277 | 86.72 198 | 62.48 135 | 88.98 259 | 63.44 212 | 89.25 102 | 91.51 125 |
|
ITE_SJBPF | | | | | 78.22 250 | 81.77 281 | 60.57 244 | | 83.30 263 | 69.25 194 | 67.54 278 | 87.20 187 | 36.33 326 | 87.28 280 | 54.34 279 | 74.62 276 | 86.80 264 |
|
pmmvs5 | | | 71.55 260 | 70.20 261 | 75.61 278 | 77.83 317 | 56.39 293 | 81.74 258 | 80.89 286 | 57.76 307 | 67.46 279 | 84.49 249 | 49.26 266 | 85.32 294 | 57.08 270 | 75.29 269 | 85.11 289 |
|
MVP-Stereo | | | 76.12 217 | 74.46 222 | 81.13 199 | 85.37 214 | 69.79 83 | 84.42 223 | 87.95 203 | 65.03 247 | 67.46 279 | 85.33 237 | 53.28 220 | 91.73 207 | 58.01 262 | 83.27 172 | 81.85 316 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
test_0402 | | | 72.79 253 | 70.44 258 | 79.84 222 | 88.13 160 | 65.99 154 | 85.93 186 | 84.29 247 | 65.57 241 | 67.40 281 | 85.49 234 | 46.92 277 | 92.61 177 | 35.88 334 | 74.38 278 | 80.94 319 |
|
GG-mvs-BLEND | | | | | 75.38 282 | 81.59 284 | 55.80 300 | 79.32 280 | 69.63 333 | | 67.19 282 | 73.67 325 | 43.24 297 | 88.90 264 | 50.41 293 | 84.50 156 | 81.45 318 |
|
tpmvs | | | 71.09 263 | 69.29 264 | 76.49 272 | 82.04 278 | 56.04 298 | 78.92 286 | 81.37 285 | 64.05 258 | 67.18 283 | 78.28 308 | 49.74 260 | 89.77 244 | 49.67 299 | 72.37 291 | 83.67 302 |
|
OurMVSNet-221017-0 | | | 74.26 236 | 72.42 242 | 79.80 223 | 83.76 240 | 59.59 254 | 85.92 187 | 86.64 223 | 66.39 231 | 66.96 284 | 87.58 175 | 39.46 315 | 91.60 208 | 65.76 197 | 69.27 306 | 88.22 233 |
|
baseline2 | | | 75.70 223 | 73.83 229 | 81.30 193 | 83.26 248 | 61.79 232 | 82.57 251 | 80.65 290 | 66.81 223 | 66.88 285 | 83.42 265 | 57.86 184 | 92.19 192 | 63.47 211 | 79.57 214 | 89.91 184 |
|
MVS_0304 | | | 72.48 254 | 70.89 256 | 77.24 266 | 82.20 276 | 59.68 252 | 84.11 229 | 83.49 260 | 67.10 222 | 66.87 286 | 80.59 292 | 35.00 330 | 87.40 278 | 59.07 251 | 79.58 213 | 84.63 294 |
|
F-COLMAP | | | 76.38 215 | 74.33 223 | 82.50 170 | 89.28 123 | 66.95 143 | 88.41 114 | 89.03 176 | 64.05 258 | 66.83 287 | 88.61 151 | 46.78 278 | 92.89 170 | 57.48 265 | 78.55 222 | 87.67 243 |
|
ACMH | | 67.68 16 | 75.89 220 | 73.93 226 | 81.77 182 | 88.71 145 | 66.61 145 | 88.62 107 | 89.01 178 | 69.81 181 | 66.78 288 | 86.70 203 | 41.95 307 | 91.51 213 | 55.64 275 | 78.14 228 | 87.17 256 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
test0.0.03 1 | | | 68.00 284 | 67.69 282 | 68.90 312 | 77.55 318 | 47.43 331 | 75.70 306 | 72.95 328 | 66.66 226 | 66.56 289 | 82.29 278 | 48.06 271 | 75.87 330 | 44.97 322 | 74.51 277 | 83.41 304 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 341 | 75.16 308 | | 55.10 320 | 66.53 290 | | 49.34 264 | | 53.98 280 | | 87.94 238 |
|
ET-MVSNet_ETH3D | | | 78.63 169 | 76.63 196 | 84.64 94 | 86.73 199 | 69.47 91 | 85.01 205 | 84.61 244 | 69.54 188 | 66.51 291 | 86.59 207 | 50.16 254 | 91.75 205 | 76.26 106 | 84.24 160 | 92.69 94 |
|
EU-MVSNet | | | 68.53 282 | 67.61 283 | 71.31 304 | 78.51 316 | 47.01 333 | 84.47 218 | 84.27 248 | 42.27 333 | 66.44 292 | 84.79 247 | 40.44 313 | 83.76 299 | 58.76 255 | 68.54 311 | 83.17 306 |
|
EPNet_dtu | | | 75.46 226 | 74.86 215 | 77.23 267 | 82.57 270 | 54.60 306 | 86.89 158 | 83.09 268 | 71.64 150 | 66.25 293 | 85.86 225 | 55.99 199 | 88.04 273 | 54.92 277 | 86.55 137 | 89.05 209 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Anonymous20231206 | | | 68.60 280 | 67.80 280 | 71.02 305 | 80.23 301 | 50.75 325 | 78.30 292 | 80.47 293 | 56.79 314 | 66.11 294 | 82.63 274 | 46.35 281 | 78.95 317 | 43.62 324 | 75.70 258 | 83.36 305 |
|
SixPastTwentyTwo | | | 73.37 244 | 71.26 253 | 79.70 224 | 85.08 221 | 57.89 271 | 85.57 191 | 83.56 258 | 71.03 161 | 65.66 295 | 85.88 224 | 42.10 305 | 92.57 178 | 59.11 250 | 63.34 321 | 88.65 226 |
|
MSDG | | | 73.36 246 | 70.99 254 | 80.49 210 | 84.51 227 | 65.80 158 | 80.71 267 | 86.13 232 | 65.70 239 | 65.46 296 | 83.74 261 | 44.60 291 | 90.91 229 | 51.13 291 | 76.89 239 | 84.74 292 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 268 | 68.19 272 | 77.65 259 | 80.26 300 | 59.41 257 | 85.01 205 | 82.96 270 | 58.76 302 | 65.43 297 | 82.33 276 | 37.63 323 | 91.23 220 | 45.34 321 | 76.03 254 | 82.32 313 |
|
ppachtmachnet_test | | | 70.04 273 | 67.34 284 | 78.14 251 | 79.80 307 | 61.13 237 | 79.19 283 | 80.59 291 | 59.16 299 | 65.27 298 | 79.29 302 | 46.75 279 | 87.29 279 | 49.33 300 | 66.72 313 | 86.00 281 |
|
ADS-MVSNet2 | | | 66.20 296 | 63.33 297 | 74.82 286 | 79.92 304 | 58.75 260 | 67.55 330 | 75.19 320 | 53.37 324 | 65.25 299 | 75.86 320 | 42.32 303 | 80.53 313 | 41.57 328 | 68.91 308 | 85.18 286 |
|
ADS-MVSNet | | | 64.36 300 | 62.88 301 | 68.78 314 | 79.92 304 | 47.17 332 | 67.55 330 | 71.18 329 | 53.37 324 | 65.25 299 | 75.86 320 | 42.32 303 | 73.99 337 | 41.57 328 | 68.91 308 | 85.18 286 |
|
testgi | | | 66.67 292 | 66.53 288 | 67.08 317 | 75.62 327 | 41.69 339 | 75.93 302 | 76.50 317 | 66.11 233 | 65.20 301 | 86.59 207 | 35.72 328 | 74.71 334 | 43.71 323 | 73.38 287 | 84.84 291 |
|
PM-MVS | | | 66.41 294 | 64.14 294 | 73.20 295 | 73.92 329 | 56.45 291 | 78.97 285 | 64.96 342 | 63.88 262 | 64.72 302 | 80.24 295 | 19.84 342 | 83.44 302 | 66.24 190 | 64.52 320 | 79.71 324 |
|
JIA-IIPM | | | 66.32 295 | 62.82 302 | 76.82 270 | 77.09 322 | 61.72 233 | 65.34 333 | 75.38 319 | 58.04 306 | 64.51 303 | 62.32 334 | 42.05 306 | 86.51 285 | 51.45 290 | 69.22 307 | 82.21 314 |
|
ambc | | | | | 75.24 283 | 73.16 333 | 50.51 326 | 63.05 337 | 87.47 214 | | 64.28 304 | 77.81 313 | 17.80 343 | 89.73 246 | 57.88 263 | 60.64 325 | 85.49 283 |
|
EG-PatchMatch MVS | | | 74.04 239 | 71.82 247 | 80.71 207 | 84.92 222 | 67.42 132 | 85.86 188 | 88.08 200 | 66.04 235 | 64.22 305 | 83.85 257 | 35.10 329 | 92.56 179 | 57.44 266 | 80.83 199 | 82.16 315 |
|
dp | | | 66.80 290 | 65.43 290 | 70.90 306 | 79.74 309 | 48.82 330 | 75.12 310 | 74.77 322 | 59.61 294 | 64.08 306 | 77.23 315 | 42.89 299 | 80.72 312 | 48.86 302 | 66.58 315 | 83.16 307 |
|
pmmvs-eth3d | | | 70.50 269 | 67.83 279 | 78.52 247 | 77.37 320 | 66.18 151 | 81.82 256 | 81.51 283 | 58.90 301 | 63.90 307 | 80.42 294 | 42.69 301 | 86.28 287 | 58.56 256 | 65.30 318 | 83.11 308 |
|
COLMAP_ROB | | 66.92 17 | 73.01 250 | 70.41 259 | 80.81 205 | 87.13 192 | 65.63 161 | 88.30 120 | 84.19 250 | 62.96 267 | 63.80 308 | 87.69 173 | 38.04 321 | 92.56 179 | 46.66 313 | 74.91 273 | 84.24 297 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
FMVSNet5 | | | 69.50 276 | 67.96 276 | 74.15 292 | 82.97 259 | 55.35 304 | 80.01 274 | 82.12 278 | 62.56 273 | 63.02 309 | 81.53 283 | 36.92 324 | 81.92 308 | 48.42 303 | 74.06 280 | 85.17 288 |
|
test20.03 | | | 67.45 287 | 66.95 286 | 68.94 311 | 75.48 328 | 44.84 335 | 77.50 296 | 77.67 311 | 66.66 226 | 63.01 310 | 83.80 259 | 47.02 276 | 78.40 319 | 42.53 327 | 68.86 310 | 83.58 303 |
|
K. test v3 | | | 71.19 262 | 68.51 269 | 79.21 235 | 83.04 256 | 57.78 274 | 84.35 225 | 76.91 316 | 72.90 137 | 62.99 311 | 82.86 270 | 39.27 316 | 91.09 226 | 61.65 230 | 52.66 334 | 88.75 223 |
|
our_test_3 | | | 69.14 278 | 67.00 285 | 75.57 279 | 79.80 307 | 58.80 259 | 77.96 294 | 77.81 310 | 59.55 295 | 62.90 312 | 78.25 309 | 47.43 273 | 83.97 298 | 51.71 288 | 67.58 312 | 83.93 301 |
|
CHOSEN 280x420 | | | 66.51 293 | 64.71 292 | 71.90 298 | 81.45 286 | 63.52 203 | 57.98 338 | 68.95 337 | 53.57 323 | 62.59 313 | 76.70 317 | 46.22 282 | 75.29 333 | 55.25 276 | 79.68 212 | 76.88 330 |
|
USDC | | | 70.33 270 | 68.37 270 | 76.21 274 | 80.60 297 | 56.23 296 | 79.19 283 | 86.49 225 | 60.89 284 | 61.29 314 | 85.47 235 | 31.78 335 | 89.47 251 | 53.37 283 | 76.21 253 | 82.94 312 |
|
lessismore_v0 | | | | | 78.97 238 | 81.01 294 | 57.15 281 | | 65.99 339 | | 61.16 315 | 82.82 271 | 39.12 317 | 91.34 217 | 59.67 244 | 46.92 337 | 88.43 231 |
|
UnsupCasMVSNet_eth | | | 67.33 288 | 65.99 289 | 71.37 301 | 73.48 331 | 51.47 321 | 75.16 308 | 85.19 239 | 65.20 244 | 60.78 316 | 80.93 291 | 42.35 302 | 77.20 325 | 57.12 269 | 53.69 333 | 85.44 284 |
|
testing_2 | | | 75.73 222 | 73.34 233 | 82.89 158 | 77.37 320 | 65.22 171 | 84.10 230 | 90.54 130 | 69.09 199 | 60.46 317 | 81.15 286 | 40.48 312 | 92.84 174 | 76.36 105 | 80.54 206 | 90.60 152 |
|
AllTest | | | 70.96 264 | 68.09 275 | 79.58 229 | 85.15 217 | 63.62 198 | 84.58 217 | 79.83 300 | 62.31 275 | 60.32 318 | 86.73 196 | 32.02 333 | 88.96 261 | 50.28 294 | 71.57 298 | 86.15 275 |
|
TestCases | | | | | 79.58 229 | 85.15 217 | 63.62 198 | | 79.83 300 | 62.31 275 | 60.32 318 | 86.73 196 | 32.02 333 | 88.96 261 | 50.28 294 | 71.57 298 | 86.15 275 |
|
Patchmatch-test | | | 64.82 299 | 63.24 298 | 69.57 309 | 79.42 312 | 49.82 328 | 63.49 336 | 69.05 336 | 51.98 328 | 59.95 320 | 80.13 296 | 50.91 245 | 70.98 339 | 40.66 330 | 73.57 285 | 87.90 239 |
|
MIMVSNet1 | | | 68.58 281 | 66.78 287 | 73.98 293 | 80.07 303 | 51.82 317 | 80.77 266 | 84.37 246 | 64.40 254 | 59.75 321 | 82.16 280 | 36.47 325 | 83.63 301 | 42.73 326 | 70.33 303 | 86.48 270 |
|
LF4IMVS | | | 64.02 301 | 62.19 303 | 69.50 310 | 70.90 337 | 53.29 314 | 76.13 300 | 77.18 315 | 52.65 326 | 58.59 322 | 80.98 289 | 23.55 339 | 76.52 327 | 53.06 285 | 66.66 314 | 78.68 326 |
|
PVSNet_0 | | 57.27 20 | 61.67 304 | 59.27 306 | 68.85 313 | 79.61 310 | 57.44 279 | 68.01 329 | 73.44 327 | 55.93 318 | 58.54 323 | 70.41 329 | 44.58 292 | 77.55 324 | 47.01 312 | 35.91 338 | 71.55 333 |
|
TDRefinement | | | 67.49 285 | 64.34 293 | 76.92 269 | 73.47 332 | 61.07 238 | 84.86 209 | 82.98 269 | 59.77 293 | 58.30 324 | 85.13 242 | 26.06 337 | 87.89 274 | 47.92 310 | 60.59 326 | 81.81 317 |
|
UnsupCasMVSNet_bld | | | 63.70 302 | 61.53 305 | 70.21 308 | 73.69 330 | 51.39 322 | 72.82 315 | 81.89 279 | 55.63 319 | 57.81 325 | 71.80 328 | 38.67 318 | 78.61 318 | 49.26 301 | 52.21 335 | 80.63 320 |
|
DSMNet-mixed | | | 57.77 307 | 56.90 308 | 60.38 321 | 67.70 339 | 35.61 342 | 69.18 325 | 53.97 345 | 32.30 342 | 57.49 326 | 79.88 299 | 40.39 314 | 68.57 341 | 38.78 332 | 72.37 291 | 76.97 329 |
|
N_pmnet | | | 52.79 310 | 53.26 310 | 51.40 326 | 78.99 315 | 7.68 352 | 69.52 323 | 3.89 353 | 51.63 329 | 57.01 327 | 74.98 323 | 40.83 311 | 65.96 342 | 37.78 333 | 64.67 319 | 80.56 322 |
|
new-patchmatchnet | | | 61.73 303 | 61.73 304 | 61.70 320 | 72.74 336 | 24.50 349 | 69.16 326 | 78.03 309 | 61.40 281 | 56.72 328 | 75.53 322 | 38.42 319 | 76.48 328 | 45.95 318 | 57.67 328 | 84.13 299 |
|
CMPMVS | | 51.72 21 | 70.19 272 | 68.16 273 | 76.28 273 | 73.15 334 | 57.55 277 | 79.47 279 | 83.92 252 | 48.02 331 | 56.48 329 | 84.81 246 | 43.13 298 | 86.42 286 | 62.67 220 | 81.81 191 | 84.89 290 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |