MG-MVS | | | 87.11 33 | 86.27 44 | 89.62 8 | 97.79 1 | 76.27 4 | 94.96 44 | 94.49 41 | 78.74 67 | 83.87 68 | 92.94 117 | 64.34 83 | 96.94 103 | 75.19 141 | 94.09 40 | 95.66 47 |
|
MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 4 | 97.66 2 | 73.37 9 | 97.13 2 | 95.58 8 | 89.33 1 | 85.77 44 | 96.26 27 | 72.84 27 | 99.38 1 | 92.64 6 | 95.93 10 | 97.08 9 |
|
OPU-MVS | | | | | 89.97 3 | 97.52 3 | 73.15 13 | 96.89 4 | | | | 97.00 9 | 83.82 2 | 99.15 2 | 95.72 1 | 97.63 3 | 97.62 2 |
|
DVP-MVS++ | | | 90.53 3 | 91.09 4 | 88.87 14 | 97.31 4 | 69.91 37 | 93.96 69 | 94.37 49 | 72.48 166 | 92.07 6 | 96.85 12 | 83.82 2 | 99.15 2 | 91.53 16 | 97.42 4 | 97.55 4 |
|
MSC_two_6792asdad | | | | | 89.60 9 | 97.31 4 | 73.22 11 | | 95.05 21 | | | | | 99.07 13 | 92.01 12 | 94.77 25 | 96.51 20 |
|
No_MVS | | | | | 89.60 9 | 97.31 4 | 73.22 11 | | 95.05 21 | | | | | 99.07 13 | 92.01 12 | 94.77 25 | 96.51 20 |
|
DP-MVS Recon | | | 82.73 104 | 81.65 111 | 85.98 82 | 97.31 4 | 67.06 108 | 95.15 35 | 91.99 139 | 69.08 244 | 76.50 142 | 93.89 99 | 54.48 195 | 98.20 36 | 70.76 181 | 85.66 136 | 92.69 157 |
|
testtj | | | 86.62 43 | 86.66 42 | 86.50 66 | 96.95 8 | 65.70 143 | 94.41 51 | 93.45 82 | 67.74 254 | 86.19 39 | 96.39 23 | 64.38 82 | 97.91 46 | 87.33 49 | 93.14 58 | 95.90 42 |
|
ETH3 D test6400 | | | 90.27 6 | 90.44 8 | 89.75 6 | 96.82 9 | 74.33 7 | 95.89 16 | 94.80 29 | 77.13 89 | 89.13 22 | 97.38 2 | 74.49 18 | 98.48 28 | 92.32 11 | 95.98 8 | 96.46 25 |
|
CNVR-MVS | | | 90.32 5 | 90.89 6 | 88.61 19 | 96.76 10 | 70.65 26 | 96.47 12 | 94.83 26 | 84.83 9 | 89.07 23 | 96.80 15 | 70.86 35 | 99.06 15 | 92.64 6 | 95.71 11 | 96.12 35 |
|
ZD-MVS | | | | | | 96.63 11 | 65.50 150 | | 93.50 80 | 70.74 221 | 85.26 53 | 95.19 58 | 64.92 77 | 97.29 78 | 87.51 45 | 93.01 59 | |
|
NCCC | | | 89.07 14 | 89.46 14 | 87.91 24 | 96.60 12 | 69.05 55 | 96.38 13 | 94.64 36 | 84.42 10 | 86.74 34 | 96.20 28 | 66.56 60 | 98.76 22 | 89.03 34 | 94.56 33 | 95.92 41 |
|
IU-MVS | | | | | | 96.46 13 | 69.91 37 | | 95.18 14 | 80.75 37 | 95.28 1 | | | | 92.34 8 | 95.36 14 | 96.47 24 |
|
SED-MVS | | | 89.94 8 | 90.36 9 | 88.70 16 | 96.45 14 | 69.38 47 | 96.89 4 | 94.44 43 | 71.65 196 | 92.11 4 | 97.21 5 | 76.79 9 | 99.11 6 | 92.34 8 | 95.36 14 | 97.62 2 |
|
test_241102_ONE | | | | | | 96.45 14 | 69.38 47 | | 94.44 43 | 71.65 196 | 92.11 4 | 97.05 8 | 76.79 9 | 99.11 6 | | | |
|
test_0728_SECOND | | | | | 88.70 16 | 96.45 14 | 70.43 29 | 96.64 8 | 94.37 49 | | | | | 99.15 2 | 91.91 14 | 94.90 21 | 96.51 20 |
|
DVP-MVS |  | | 89.41 12 | 89.73 13 | 88.45 21 | 96.40 17 | 69.99 33 | 96.64 8 | 94.52 39 | 71.92 182 | 90.55 16 | 96.93 10 | 73.77 22 | 99.08 11 | 91.91 14 | 94.90 21 | 96.29 30 |
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 |
test0726 | | | | | | 96.40 17 | 69.99 33 | 96.76 6 | 94.33 51 | 71.92 182 | 91.89 8 | 97.11 7 | 73.77 22 | | | | |
|
AdaColmap |  | | 78.94 169 | 77.00 186 | 84.76 124 | 96.34 19 | 65.86 139 | 92.66 126 | 87.97 287 | 62.18 298 | 70.56 201 | 92.37 133 | 43.53 282 | 97.35 74 | 64.50 240 | 82.86 155 | 91.05 193 |
|
test_one_0601 | | | | | | 96.32 20 | 69.74 42 | | 94.18 55 | 71.42 208 | 90.67 15 | 96.85 12 | 74.45 19 | | | | |
|
test_part2 | | | | | | 96.29 21 | 68.16 78 | | | | 90.78 13 | | | | | | |
|
DPE-MVS |  | | 88.77 15 | 89.21 15 | 87.45 36 | 96.26 22 | 67.56 92 | 94.17 55 | 94.15 57 | 68.77 247 | 90.74 14 | 97.27 3 | 76.09 12 | 98.49 27 | 90.58 23 | 94.91 20 | 96.30 29 |
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
MAR-MVS | | | 84.18 80 | 83.43 80 | 86.44 68 | 96.25 23 | 65.93 138 | 94.28 54 | 94.27 53 | 74.41 123 | 79.16 112 | 95.61 40 | 53.99 200 | 98.88 20 | 69.62 192 | 93.26 56 | 94.50 100 |
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 |
API-MVS | | | 82.28 111 | 80.53 128 | 87.54 34 | 96.13 24 | 70.59 27 | 93.63 88 | 91.04 186 | 65.72 272 | 75.45 152 | 92.83 123 | 56.11 176 | 98.89 19 | 64.10 242 | 89.75 103 | 93.15 146 |
|
APDe-MVS | | | 87.54 25 | 87.84 22 | 86.65 59 | 96.07 25 | 66.30 129 | 94.84 46 | 93.78 64 | 69.35 237 | 88.39 25 | 96.34 25 | 67.74 50 | 97.66 58 | 90.62 22 | 93.44 53 | 96.01 39 |
|
patch_mono-2 | | | 89.71 10 | 90.99 5 | 85.85 88 | 96.04 26 | 63.70 198 | 95.04 41 | 95.19 13 | 86.74 5 | 91.53 11 | 95.15 59 | 73.86 21 | 97.58 63 | 93.38 3 | 92.00 75 | 96.28 32 |
|
PAPR | | | 85.15 62 | 84.47 67 | 87.18 41 | 96.02 27 | 68.29 72 | 91.85 159 | 93.00 104 | 76.59 97 | 79.03 113 | 95.00 62 | 61.59 114 | 97.61 62 | 78.16 125 | 89.00 107 | 95.63 48 |
|
APD-MVS |  | | 85.93 52 | 85.99 49 | 85.76 92 | 95.98 28 | 65.21 155 | 93.59 90 | 92.58 120 | 66.54 265 | 86.17 40 | 95.88 33 | 63.83 89 | 97.00 95 | 86.39 58 | 92.94 60 | 95.06 75 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS_fast | | 79.48 2 | 87.95 20 | 88.00 20 | 87.79 27 | 95.86 29 | 68.32 71 | 95.74 20 | 94.11 59 | 83.82 12 | 83.49 69 | 96.19 29 | 64.53 81 | 98.44 30 | 83.42 81 | 94.88 24 | 96.61 14 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DP-MVS | | | 69.90 275 | 66.48 281 | 80.14 241 | 95.36 30 | 62.93 215 | 89.56 237 | 76.11 349 | 50.27 348 | 57.69 317 | 85.23 226 | 39.68 293 | 95.73 143 | 33.35 359 | 71.05 241 | 81.78 325 |
|
114514_t | | | 79.17 165 | 77.67 172 | 83.68 156 | 95.32 31 | 65.53 149 | 92.85 117 | 91.60 159 | 63.49 285 | 67.92 237 | 90.63 158 | 46.65 263 | 95.72 147 | 67.01 215 | 83.54 153 | 89.79 205 |
|
Regformer-1 | | | 87.24 30 | 87.60 27 | 86.15 79 | 95.14 32 | 65.83 141 | 93.95 72 | 95.12 16 | 82.11 22 | 84.25 61 | 95.73 36 | 67.88 48 | 98.35 33 | 85.60 63 | 88.64 111 | 94.26 105 |
|
Regformer-2 | | | 87.00 37 | 87.43 30 | 85.71 95 | 95.14 32 | 64.73 169 | 93.95 72 | 94.95 23 | 81.69 27 | 84.03 66 | 95.73 36 | 67.35 52 | 98.19 37 | 85.40 65 | 88.64 111 | 94.20 107 |
|
HPM-MVS++ |  | | 89.37 13 | 89.95 12 | 87.64 29 | 95.10 34 | 68.23 76 | 95.24 32 | 94.49 41 | 82.43 19 | 88.90 24 | 96.35 24 | 71.89 34 | 98.63 24 | 88.76 36 | 96.40 6 | 96.06 36 |
|
CSCG | | | 86.87 38 | 86.26 45 | 88.72 15 | 95.05 35 | 70.79 25 | 93.83 83 | 95.33 11 | 68.48 251 | 77.63 128 | 94.35 86 | 73.04 25 | 98.45 29 | 84.92 70 | 93.71 49 | 96.92 11 |
|
dcpmvs_2 | | | 87.37 28 | 87.55 28 | 86.85 49 | 95.04 36 | 68.20 77 | 90.36 217 | 90.66 195 | 79.37 52 | 81.20 86 | 93.67 102 | 74.73 15 | 96.55 118 | 90.88 21 | 92.00 75 | 95.82 44 |
|
LFMVS | | | 84.34 75 | 82.73 96 | 89.18 13 | 94.76 37 | 73.25 10 | 94.99 43 | 91.89 145 | 71.90 184 | 82.16 78 | 93.49 107 | 47.98 255 | 97.05 90 | 82.55 89 | 84.82 141 | 97.25 7 |
|
CDPH-MVS | | | 85.71 56 | 85.46 57 | 86.46 67 | 94.75 38 | 67.19 104 | 93.89 77 | 92.83 109 | 70.90 216 | 83.09 72 | 95.28 50 | 63.62 93 | 97.36 73 | 80.63 105 | 94.18 39 | 94.84 85 |
|
test_prior3 | | | 87.38 27 | 87.70 24 | 86.42 69 | 94.71 39 | 67.35 99 | 95.10 37 | 93.10 100 | 75.40 110 | 85.25 54 | 95.61 40 | 67.94 45 | 96.84 107 | 87.47 46 | 94.77 25 | 95.05 76 |
|
test_prior | | | | | 86.42 69 | 94.71 39 | 67.35 99 | | 93.10 100 | | | | | 96.84 107 | | | 95.05 76 |
|
test12 | | | | | 87.09 44 | 94.60 41 | 68.86 59 | | 92.91 106 | | 82.67 76 | | 65.44 70 | 97.55 64 | | 93.69 50 | 94.84 85 |
|
test_yl | | | 84.28 76 | 83.16 87 | 87.64 29 | 94.52 42 | 69.24 50 | 95.78 17 | 95.09 19 | 69.19 241 | 81.09 88 | 92.88 121 | 57.00 162 | 97.44 68 | 81.11 103 | 81.76 163 | 96.23 33 |
|
DCV-MVSNet | | | 84.28 76 | 83.16 87 | 87.64 29 | 94.52 42 | 69.24 50 | 95.78 17 | 95.09 19 | 69.19 241 | 81.09 88 | 92.88 121 | 57.00 162 | 97.44 68 | 81.11 103 | 81.76 163 | 96.23 33 |
|
CANet | | | 89.61 11 | 89.99 11 | 88.46 20 | 94.39 44 | 69.71 43 | 96.53 11 | 93.78 64 | 86.89 4 | 89.68 19 | 95.78 34 | 65.94 64 | 99.10 9 | 92.99 4 | 93.91 44 | 96.58 17 |
|
test_8 | | | | | | 94.19 45 | 67.19 104 | 94.15 59 | 93.42 85 | 71.87 187 | 85.38 51 | 95.35 46 | 68.19 41 | 96.95 102 | | | |
|
TEST9 | | | | | | 94.18 46 | 67.28 101 | 94.16 56 | 93.51 78 | 71.75 194 | 85.52 48 | 95.33 47 | 68.01 44 | 97.27 82 | | | |
|
train_agg | | | 87.21 31 | 87.42 31 | 86.60 60 | 94.18 46 | 67.28 101 | 94.16 56 | 93.51 78 | 71.87 187 | 85.52 48 | 95.33 47 | 68.19 41 | 97.27 82 | 89.09 31 | 94.90 21 | 95.25 69 |
|
Regformer-3 | | | 85.80 54 | 85.92 50 | 85.46 102 | 94.17 48 | 65.09 162 | 92.95 112 | 95.11 17 | 81.13 33 | 81.68 81 | 95.04 60 | 65.82 66 | 98.32 34 | 83.02 83 | 84.36 145 | 92.97 152 |
|
Regformer-4 | | | 85.45 58 | 85.69 55 | 84.73 125 | 94.17 48 | 63.23 207 | 92.95 112 | 94.83 26 | 80.66 38 | 81.29 84 | 95.04 60 | 65.12 72 | 98.08 40 | 82.74 85 | 84.36 145 | 92.88 156 |
|
agg_prior1 | | | 87.02 36 | 87.26 33 | 86.28 76 | 94.16 50 | 66.97 112 | 94.08 63 | 93.31 88 | 71.85 189 | 84.49 59 | 95.39 45 | 68.91 38 | 96.75 111 | 88.84 35 | 94.32 38 | 95.13 72 |
|
agg_prior | | | | | | 94.16 50 | 66.97 112 | | 93.31 88 | | 84.49 59 | | | 96.75 111 | | | |
|
PAPM_NR | | | 82.97 101 | 81.84 108 | 86.37 72 | 94.10 52 | 66.76 118 | 87.66 272 | 92.84 108 | 69.96 230 | 74.07 164 | 93.57 105 | 63.10 102 | 97.50 66 | 70.66 183 | 90.58 96 | 94.85 82 |
|
FOURS1 | | | | | | 93.95 53 | 61.77 236 | 93.96 69 | 91.92 142 | 62.14 299 | 86.57 35 | | | | | | |
|
VNet | | | 86.20 47 | 85.65 56 | 87.84 26 | 93.92 54 | 69.99 33 | 95.73 22 | 95.94 6 | 78.43 69 | 86.00 42 | 93.07 114 | 58.22 148 | 97.00 95 | 85.22 67 | 84.33 148 | 96.52 19 |
|
9.14 | | | | 87.63 25 | | 93.86 55 | | 94.41 51 | 94.18 55 | 72.76 160 | 86.21 38 | 96.51 19 | 66.64 58 | 97.88 49 | 90.08 25 | 94.04 41 | |
|
xxxxxxxxxxxxxcwj | | | 87.14 32 | 87.19 34 | 86.99 47 | 93.84 56 | 67.89 84 | 95.05 39 | 84.72 316 | 78.19 71 | 86.25 36 | 96.44 21 | 66.98 54 | 97.79 51 | 88.68 37 | 94.56 33 | 95.28 64 |
|
save fliter | | | | | | 93.84 56 | 67.89 84 | 95.05 39 | 92.66 115 | 78.19 71 | | | | | | | |
|
PVSNet_BlendedMVS | | | 83.38 93 | 83.43 80 | 83.22 168 | 93.76 58 | 67.53 94 | 94.06 64 | 93.61 74 | 79.13 58 | 81.00 91 | 85.14 227 | 63.19 100 | 97.29 78 | 87.08 52 | 73.91 218 | 84.83 289 |
|
PVSNet_Blended | | | 86.73 42 | 86.86 40 | 86.31 75 | 93.76 58 | 67.53 94 | 96.33 14 | 93.61 74 | 82.34 20 | 81.00 91 | 93.08 112 | 63.19 100 | 97.29 78 | 87.08 52 | 91.38 86 | 94.13 113 |
|
HFP-MVS | | | 84.73 68 | 84.40 70 | 85.72 93 | 93.75 60 | 65.01 163 | 93.50 94 | 93.19 94 | 72.19 176 | 79.22 110 | 94.93 66 | 59.04 142 | 97.67 55 | 81.55 96 | 92.21 69 | 94.49 101 |
|
#test# | | | 84.98 65 | 84.74 66 | 85.72 93 | 93.75 60 | 65.01 163 | 94.09 62 | 93.19 94 | 73.55 145 | 79.22 110 | 94.93 66 | 59.04 142 | 97.67 55 | 82.66 86 | 92.21 69 | 94.49 101 |
|
Anonymous202405211 | | | 77.96 190 | 75.33 206 | 85.87 86 | 93.73 62 | 64.52 171 | 94.85 45 | 85.36 311 | 62.52 296 | 76.11 143 | 90.18 167 | 29.43 344 | 97.29 78 | 68.51 203 | 77.24 201 | 95.81 45 |
|
SD-MVS | | | 87.49 26 | 87.49 29 | 87.50 35 | 93.60 63 | 68.82 61 | 93.90 76 | 92.63 118 | 76.86 92 | 87.90 27 | 95.76 35 | 66.17 61 | 97.63 60 | 89.06 32 | 91.48 84 | 96.05 37 |
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 |
ETH3D-3000-0.1 | | | 87.61 24 | 87.89 21 | 86.75 55 | 93.58 64 | 67.21 103 | 94.31 53 | 94.14 58 | 72.92 157 | 87.13 30 | 96.62 17 | 67.81 49 | 97.94 43 | 90.13 24 | 94.42 36 | 95.09 74 |
|
ACMMPR | | | 84.37 73 | 84.06 72 | 85.28 109 | 93.56 65 | 64.37 181 | 93.50 94 | 93.15 97 | 72.19 176 | 78.85 119 | 94.86 70 | 56.69 169 | 97.45 67 | 81.55 96 | 92.20 71 | 94.02 120 |
|
region2R | | | 84.36 74 | 84.03 73 | 85.36 107 | 93.54 66 | 64.31 183 | 93.43 97 | 92.95 105 | 72.16 179 | 78.86 118 | 94.84 71 | 56.97 164 | 97.53 65 | 81.38 100 | 92.11 73 | 94.24 106 |
|
TSAR-MVS + GP. | | | 87.96 19 | 88.37 18 | 86.70 58 | 93.51 67 | 65.32 152 | 95.15 35 | 93.84 63 | 78.17 73 | 85.93 43 | 94.80 72 | 75.80 13 | 98.21 35 | 89.38 27 | 88.78 108 | 96.59 15 |
|
PHI-MVS | | | 86.83 40 | 86.85 41 | 86.78 54 | 93.47 68 | 65.55 148 | 95.39 29 | 95.10 18 | 71.77 193 | 85.69 47 | 96.52 18 | 62.07 109 | 98.77 21 | 86.06 61 | 95.60 12 | 96.03 38 |
|
SR-MVS | | | 82.81 103 | 82.58 99 | 83.50 162 | 93.35 69 | 61.16 247 | 92.23 140 | 91.28 173 | 64.48 278 | 81.27 85 | 95.28 50 | 53.71 204 | 95.86 137 | 82.87 84 | 88.77 109 | 93.49 136 |
|
iter_conf05 | | | 83.27 95 | 82.70 97 | 84.98 117 | 93.32 70 | 71.84 16 | 94.16 56 | 81.76 336 | 82.74 16 | 73.83 167 | 88.40 186 | 72.77 28 | 94.61 186 | 82.10 91 | 75.21 211 | 88.48 221 |
|
EPNet | | | 87.84 22 | 88.38 17 | 86.23 77 | 93.30 71 | 66.05 133 | 95.26 31 | 94.84 25 | 87.09 3 | 88.06 26 | 94.53 77 | 66.79 57 | 97.34 75 | 83.89 78 | 91.68 80 | 95.29 62 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test1172 | | | 81.90 119 | 81.83 109 | 82.13 196 | 93.23 72 | 57.52 303 | 91.61 171 | 90.98 188 | 64.32 280 | 80.20 99 | 95.00 62 | 51.26 225 | 95.61 151 | 81.73 95 | 88.13 115 | 93.26 142 |
|
XVS | | | 83.87 86 | 83.47 78 | 85.05 114 | 93.22 73 | 63.78 193 | 92.92 115 | 92.66 115 | 73.99 131 | 78.18 123 | 94.31 89 | 55.25 183 | 97.41 70 | 79.16 115 | 91.58 82 | 93.95 122 |
|
X-MVStestdata | | | 76.86 203 | 74.13 223 | 85.05 114 | 93.22 73 | 63.78 193 | 92.92 115 | 92.66 115 | 73.99 131 | 78.18 123 | 10.19 378 | 55.25 183 | 97.41 70 | 79.16 115 | 91.58 82 | 93.95 122 |
|
SMA-MVS |  | | 88.14 16 | 88.29 19 | 87.67 28 | 93.21 75 | 68.72 63 | 93.85 79 | 94.03 60 | 74.18 128 | 91.74 9 | 96.67 16 | 65.61 69 | 98.42 32 | 89.24 30 | 96.08 7 | 95.88 43 |
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 |
原ACMM1 | | | | | 84.42 136 | 93.21 75 | 64.27 186 | | 93.40 87 | 65.39 273 | 79.51 107 | 92.50 127 | 58.11 150 | 96.69 113 | 65.27 237 | 93.96 42 | 92.32 167 |
|
MVS_111021_HR | | | 86.19 48 | 85.80 53 | 87.37 37 | 93.17 77 | 69.79 40 | 93.99 68 | 93.76 67 | 79.08 60 | 78.88 117 | 93.99 97 | 62.25 108 | 98.15 38 | 85.93 62 | 91.15 90 | 94.15 112 |
|
CP-MVS | | | 83.71 90 | 83.40 83 | 84.65 129 | 93.14 78 | 63.84 191 | 94.59 48 | 92.28 126 | 71.03 214 | 77.41 131 | 94.92 68 | 55.21 186 | 96.19 124 | 81.32 101 | 90.70 94 | 93.91 124 |
|
DELS-MVS | | | 90.05 7 | 90.09 10 | 89.94 4 | 93.14 78 | 73.88 8 | 97.01 3 | 94.40 47 | 88.32 2 | 85.71 46 | 94.91 69 | 74.11 20 | 98.91 17 | 87.26 51 | 95.94 9 | 97.03 10 |
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 |
ZNCC-MVS | | | 85.33 60 | 85.08 61 | 86.06 80 | 93.09 80 | 65.65 145 | 93.89 77 | 93.41 86 | 73.75 139 | 79.94 101 | 94.68 75 | 60.61 123 | 98.03 41 | 82.63 88 | 93.72 48 | 94.52 98 |
|
DeepPCF-MVS | | 81.17 1 | 89.72 9 | 91.38 3 | 84.72 127 | 93.00 81 | 58.16 293 | 96.72 7 | 94.41 45 | 86.50 6 | 90.25 18 | 97.83 1 | 75.46 14 | 98.67 23 | 92.78 5 | 95.49 13 | 97.32 6 |
|
PLC |  | 68.80 14 | 75.23 231 | 73.68 230 | 79.86 250 | 92.93 82 | 58.68 287 | 90.64 210 | 88.30 278 | 60.90 307 | 64.43 274 | 90.53 159 | 42.38 286 | 94.57 189 | 56.52 280 | 76.54 205 | 86.33 257 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MSP-MVS | | | 90.38 4 | 91.87 1 | 85.88 85 | 92.83 83 | 64.03 190 | 93.06 105 | 94.33 51 | 82.19 21 | 93.65 3 | 96.15 30 | 85.89 1 | 97.19 84 | 91.02 20 | 97.75 1 | 96.43 26 |
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 |
mPP-MVS | | | 82.96 102 | 82.44 102 | 84.52 133 | 92.83 83 | 62.92 217 | 92.76 118 | 91.85 148 | 71.52 204 | 75.61 150 | 94.24 91 | 53.48 208 | 96.99 98 | 78.97 118 | 90.73 93 | 93.64 133 |
|
GST-MVS | | | 84.63 71 | 84.29 71 | 85.66 97 | 92.82 85 | 65.27 153 | 93.04 107 | 93.13 98 | 73.20 149 | 78.89 114 | 94.18 93 | 59.41 138 | 97.85 50 | 81.45 98 | 92.48 68 | 93.86 127 |
|
WTY-MVS | | | 86.32 45 | 85.81 52 | 87.85 25 | 92.82 85 | 69.37 49 | 95.20 33 | 95.25 12 | 82.71 17 | 81.91 79 | 94.73 73 | 67.93 47 | 97.63 60 | 79.55 111 | 82.25 159 | 96.54 18 |
|
PGM-MVS | | | 83.25 96 | 82.70 97 | 84.92 119 | 92.81 87 | 64.07 189 | 90.44 213 | 92.20 133 | 71.28 209 | 77.23 134 | 94.43 80 | 55.17 187 | 97.31 77 | 79.33 114 | 91.38 86 | 93.37 137 |
|
EI-MVSNet-Vis-set | | | 83.77 88 | 83.67 75 | 84.06 146 | 92.79 88 | 63.56 203 | 91.76 164 | 94.81 28 | 79.65 49 | 77.87 125 | 94.09 94 | 63.35 98 | 97.90 47 | 79.35 113 | 79.36 177 | 90.74 195 |
|
SF-MVS | | | 87.03 35 | 87.09 36 | 86.84 50 | 92.70 89 | 67.45 98 | 93.64 87 | 93.76 67 | 70.78 220 | 86.25 36 | 96.44 21 | 66.98 54 | 97.79 51 | 88.68 37 | 94.56 33 | 95.28 64 |
|
MVSTER | | | 82.47 108 | 82.05 105 | 83.74 152 | 92.68 90 | 69.01 56 | 91.90 156 | 93.21 91 | 79.83 44 | 72.14 187 | 85.71 222 | 74.72 16 | 94.72 182 | 75.72 138 | 72.49 230 | 87.50 233 |
|
iter_conf_final | | | 81.74 122 | 80.93 121 | 84.18 143 | 92.66 91 | 69.10 53 | 92.94 114 | 82.80 334 | 79.01 62 | 74.85 156 | 88.40 186 | 61.83 112 | 94.61 186 | 79.36 112 | 76.52 206 | 88.83 213 |
|
CS-MVS-test | | | 86.14 49 | 87.01 37 | 83.52 159 | 92.63 92 | 59.36 279 | 95.49 26 | 91.92 142 | 80.09 42 | 85.46 50 | 95.53 43 | 61.82 113 | 95.77 141 | 86.77 56 | 93.37 54 | 95.41 53 |
|
MP-MVS |  | | 85.02 63 | 84.97 63 | 85.17 113 | 92.60 93 | 64.27 186 | 93.24 100 | 92.27 127 | 73.13 151 | 79.63 106 | 94.43 80 | 61.90 110 | 97.17 85 | 85.00 68 | 92.56 65 | 94.06 118 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
thres200 | | | 79.66 156 | 78.33 160 | 83.66 158 | 92.54 94 | 65.82 142 | 93.06 105 | 96.31 3 | 74.90 120 | 73.30 170 | 88.66 181 | 59.67 134 | 95.61 151 | 47.84 313 | 78.67 184 | 89.56 210 |
|
test_part1 | | | 79.63 157 | 77.86 171 | 84.93 118 | 92.50 95 | 71.43 18 | 94.15 59 | 91.08 183 | 72.51 165 | 70.66 200 | 84.98 229 | 59.84 131 | 95.07 171 | 72.07 170 | 62.94 295 | 88.30 227 |
|
APD-MVS_3200maxsize | | | 81.64 123 | 81.32 114 | 82.59 181 | 92.36 96 | 58.74 286 | 91.39 180 | 91.01 187 | 63.35 286 | 79.72 105 | 94.62 76 | 51.82 218 | 96.14 126 | 79.71 109 | 87.93 117 | 92.89 155 |
|
新几何1 | | | | | 84.73 125 | 92.32 97 | 64.28 185 | | 91.46 165 | 59.56 317 | 79.77 104 | 92.90 119 | 56.95 165 | 96.57 116 | 63.40 246 | 92.91 61 | 93.34 138 |
|
1121 | | | 81.25 128 | 80.05 133 | 84.87 122 | 92.30 98 | 64.31 183 | 87.91 267 | 91.39 167 | 59.44 318 | 79.94 101 | 92.91 118 | 57.09 158 | 97.01 93 | 66.63 217 | 92.81 63 | 93.29 141 |
|
EI-MVSNet-UG-set | | | 83.14 98 | 82.96 90 | 83.67 157 | 92.28 99 | 63.19 209 | 91.38 182 | 94.68 34 | 79.22 55 | 76.60 140 | 93.75 100 | 62.64 104 | 97.76 53 | 78.07 126 | 78.01 188 | 90.05 203 |
|
HPM-MVS |  | | 83.25 96 | 82.95 91 | 84.17 144 | 92.25 100 | 62.88 219 | 90.91 198 | 91.86 146 | 70.30 226 | 77.12 135 | 93.96 98 | 56.75 167 | 96.28 122 | 82.04 92 | 91.34 88 | 93.34 138 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
HY-MVS | | 76.49 5 | 84.28 76 | 83.36 85 | 87.02 46 | 92.22 101 | 67.74 88 | 84.65 291 | 94.50 40 | 79.15 57 | 82.23 77 | 87.93 197 | 66.88 56 | 96.94 103 | 80.53 106 | 82.20 160 | 96.39 28 |
|
tfpn200view9 | | | 78.79 174 | 77.43 177 | 82.88 173 | 92.21 102 | 64.49 172 | 92.05 148 | 96.28 4 | 73.48 146 | 71.75 192 | 88.26 191 | 60.07 129 | 95.32 164 | 45.16 323 | 77.58 193 | 88.83 213 |
|
thres400 | | | 78.68 177 | 77.43 177 | 82.43 183 | 92.21 102 | 64.49 172 | 92.05 148 | 96.28 4 | 73.48 146 | 71.75 192 | 88.26 191 | 60.07 129 | 95.32 164 | 45.16 323 | 77.58 193 | 87.48 234 |
|
PS-MVSNAJ | | | 88.14 16 | 87.61 26 | 89.71 7 | 92.06 104 | 76.72 1 | 95.75 19 | 93.26 90 | 83.86 11 | 89.55 20 | 96.06 31 | 53.55 205 | 97.89 48 | 91.10 18 | 93.31 55 | 94.54 96 |
|
SR-MVS-dyc-post | | | 81.06 133 | 80.70 124 | 82.15 194 | 92.02 105 | 58.56 288 | 90.90 199 | 90.45 198 | 62.76 292 | 78.89 114 | 94.46 78 | 51.26 225 | 95.61 151 | 78.77 121 | 86.77 127 | 92.28 169 |
|
RE-MVS-def | | | | 80.48 129 | | 92.02 105 | 58.56 288 | 90.90 199 | 90.45 198 | 62.76 292 | 78.89 114 | 94.46 78 | 49.30 242 | | 78.77 121 | 86.77 127 | 92.28 169 |
|
MSLP-MVS++ | | | 86.27 46 | 85.91 51 | 87.35 38 | 92.01 107 | 68.97 58 | 95.04 41 | 92.70 112 | 79.04 61 | 81.50 83 | 96.50 20 | 58.98 144 | 96.78 109 | 83.49 80 | 93.93 43 | 96.29 30 |
|
CS-MVS | | | 85.80 54 | 86.65 43 | 83.27 167 | 92.00 108 | 58.92 284 | 95.31 30 | 91.86 146 | 79.97 43 | 84.82 56 | 95.40 44 | 62.26 107 | 95.51 160 | 86.11 60 | 92.08 74 | 95.37 56 |
|
ETH3D cwj APD-0.16 | | | 87.06 34 | 87.18 35 | 86.71 56 | 91.99 109 | 67.48 97 | 92.97 110 | 94.21 54 | 71.48 207 | 85.72 45 | 96.32 26 | 68.13 43 | 98.00 42 | 89.06 32 | 94.70 31 | 94.65 92 |
|
旧先验1 | | | | | | 91.94 110 | 60.74 257 | | 91.50 163 | | | 94.36 82 | 65.23 71 | | | 91.84 77 | 94.55 94 |
|
thres600view7 | | | 78.00 188 | 76.66 190 | 82.03 202 | 91.93 111 | 63.69 199 | 91.30 187 | 96.33 1 | 72.43 169 | 70.46 203 | 87.89 198 | 60.31 124 | 94.92 177 | 42.64 335 | 76.64 204 | 87.48 234 |
|
LS3D | | | 69.17 279 | 66.40 282 | 77.50 282 | 91.92 112 | 56.12 314 | 85.12 288 | 80.37 342 | 46.96 355 | 56.50 321 | 87.51 203 | 37.25 313 | 93.71 228 | 32.52 364 | 79.40 176 | 82.68 315 |
|
GG-mvs-BLEND | | | | | 86.53 65 | 91.91 113 | 69.67 45 | 75.02 343 | 94.75 31 | | 78.67 121 | 90.85 154 | 77.91 7 | 94.56 191 | 72.25 166 | 93.74 47 | 95.36 57 |
|
thres100view900 | | | 78.37 183 | 77.01 185 | 82.46 182 | 91.89 114 | 63.21 208 | 91.19 193 | 96.33 1 | 72.28 174 | 70.45 204 | 87.89 198 | 60.31 124 | 95.32 164 | 45.16 323 | 77.58 193 | 88.83 213 |
|
zzz-MVS | | | 84.73 68 | 84.47 67 | 85.50 100 | 91.89 114 | 65.16 157 | 91.55 172 | 92.23 128 | 75.32 112 | 80.53 95 | 95.21 56 | 56.06 177 | 97.16 86 | 84.86 71 | 92.55 66 | 94.18 108 |
|
MTAPA | | | 83.91 85 | 83.38 84 | 85.50 100 | 91.89 114 | 65.16 157 | 81.75 311 | 92.23 128 | 75.32 112 | 80.53 95 | 95.21 56 | 56.06 177 | 97.16 86 | 84.86 71 | 92.55 66 | 94.18 108 |
|
canonicalmvs | | | 86.85 39 | 86.25 46 | 88.66 18 | 91.80 117 | 71.92 15 | 93.54 92 | 91.71 154 | 80.26 41 | 87.55 28 | 95.25 54 | 63.59 95 | 96.93 105 | 88.18 39 | 84.34 147 | 97.11 8 |
|
TSAR-MVS + MP. | | | 88.11 18 | 88.64 16 | 86.54 64 | 91.73 118 | 68.04 80 | 90.36 217 | 93.55 77 | 82.89 14 | 91.29 12 | 92.89 120 | 72.27 31 | 96.03 133 | 87.99 40 | 94.77 25 | 95.54 51 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
ACMMP |  | | 81.49 124 | 80.67 125 | 83.93 149 | 91.71 119 | 62.90 218 | 92.13 142 | 92.22 132 | 71.79 192 | 71.68 194 | 93.49 107 | 50.32 231 | 96.96 101 | 78.47 123 | 84.22 152 | 91.93 176 |
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 |
BH-RMVSNet | | | 79.46 162 | 77.65 173 | 84.89 120 | 91.68 120 | 65.66 144 | 93.55 91 | 88.09 284 | 72.93 156 | 73.37 169 | 91.12 151 | 46.20 270 | 96.12 127 | 56.28 282 | 85.61 137 | 92.91 154 |
|
baseline1 | | | 81.84 120 | 81.03 120 | 84.28 142 | 91.60 121 | 66.62 121 | 91.08 195 | 91.66 157 | 81.87 25 | 74.86 155 | 91.67 145 | 69.98 37 | 94.92 177 | 71.76 173 | 64.75 283 | 91.29 190 |
|
ACMMP_NAP | | | 86.05 50 | 85.80 53 | 86.80 53 | 91.58 122 | 67.53 94 | 91.79 161 | 93.49 81 | 74.93 119 | 84.61 57 | 95.30 49 | 59.42 137 | 97.92 45 | 86.13 59 | 94.92 19 | 94.94 81 |
|
MVS_Test | | | 84.16 81 | 83.20 86 | 87.05 45 | 91.56 123 | 69.82 39 | 89.99 231 | 92.05 136 | 77.77 79 | 82.84 73 | 86.57 211 | 63.93 88 | 96.09 128 | 74.91 147 | 89.18 106 | 95.25 69 |
|
HPM-MVS_fast | | | 80.25 145 | 79.55 145 | 82.33 187 | 91.55 124 | 59.95 269 | 91.32 186 | 89.16 250 | 65.23 276 | 74.71 157 | 93.07 114 | 47.81 257 | 95.74 142 | 74.87 149 | 88.23 113 | 91.31 189 |
|
CPTT-MVS | | | 79.59 158 | 79.16 153 | 80.89 230 | 91.54 125 | 59.80 271 | 92.10 144 | 88.54 275 | 60.42 310 | 72.96 172 | 93.28 109 | 48.27 251 | 92.80 252 | 78.89 120 | 86.50 132 | 90.06 202 |
|
CNLPA | | | 74.31 241 | 72.30 247 | 80.32 235 | 91.49 126 | 61.66 239 | 90.85 203 | 80.72 340 | 56.67 332 | 63.85 279 | 90.64 156 | 46.75 262 | 90.84 296 | 53.79 290 | 75.99 209 | 88.47 223 |
|
MP-MVS-pluss | | | 85.24 61 | 85.13 60 | 85.56 99 | 91.42 127 | 65.59 147 | 91.54 173 | 92.51 122 | 74.56 122 | 80.62 94 | 95.64 39 | 59.15 141 | 97.00 95 | 86.94 54 | 93.80 45 | 94.07 117 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
gg-mvs-nofinetune | | | 77.18 201 | 74.31 219 | 85.80 90 | 91.42 127 | 68.36 70 | 71.78 345 | 94.72 32 | 49.61 349 | 77.12 135 | 45.92 366 | 77.41 8 | 93.98 220 | 67.62 210 | 93.16 57 | 95.05 76 |
|
xiu_mvs_v2_base | | | 87.92 21 | 87.38 32 | 89.55 12 | 91.41 129 | 76.43 3 | 95.74 20 | 93.12 99 | 83.53 13 | 89.55 20 | 95.95 32 | 53.45 209 | 97.68 54 | 91.07 19 | 92.62 64 | 94.54 96 |
|
EIA-MVS | | | 84.84 67 | 84.88 64 | 84.69 128 | 91.30 130 | 62.36 226 | 93.85 79 | 92.04 137 | 79.45 50 | 79.33 109 | 94.28 90 | 62.42 106 | 96.35 121 | 80.05 108 | 91.25 89 | 95.38 55 |
|
alignmvs | | | 87.28 29 | 86.97 38 | 88.24 23 | 91.30 130 | 71.14 23 | 95.61 24 | 93.56 76 | 79.30 53 | 87.07 33 | 95.25 54 | 68.43 39 | 96.93 105 | 87.87 41 | 84.33 148 | 96.65 13 |
|
EPMVS | | | 78.49 182 | 75.98 198 | 86.02 81 | 91.21 132 | 69.68 44 | 80.23 324 | 91.20 174 | 75.25 114 | 72.48 182 | 78.11 308 | 54.65 191 | 93.69 229 | 57.66 278 | 83.04 154 | 94.69 88 |
|
FMVSNet3 | | | 77.73 194 | 76.04 197 | 82.80 174 | 91.20 133 | 68.99 57 | 91.87 157 | 91.99 139 | 73.35 148 | 67.04 250 | 83.19 250 | 56.62 170 | 92.14 275 | 59.80 270 | 69.34 250 | 87.28 241 |
|
Anonymous20240529 | | | 76.84 206 | 74.15 222 | 84.88 121 | 91.02 134 | 64.95 166 | 93.84 82 | 91.09 181 | 53.57 339 | 73.00 171 | 87.42 204 | 35.91 322 | 97.32 76 | 69.14 198 | 72.41 232 | 92.36 165 |
|
tpmvs | | | 72.88 255 | 69.76 267 | 82.22 192 | 90.98 135 | 67.05 109 | 78.22 336 | 88.30 278 | 63.10 290 | 64.35 275 | 74.98 330 | 55.09 188 | 94.27 202 | 43.25 329 | 69.57 249 | 85.34 283 |
|
MVS | | | 84.66 70 | 82.86 94 | 90.06 2 | 90.93 136 | 74.56 6 | 87.91 267 | 95.54 9 | 68.55 249 | 72.35 186 | 94.71 74 | 59.78 133 | 98.90 18 | 81.29 102 | 94.69 32 | 96.74 12 |
|
PVSNet | | 73.49 8 | 80.05 149 | 78.63 157 | 84.31 140 | 90.92 137 | 64.97 165 | 92.47 134 | 91.05 185 | 79.18 56 | 72.43 184 | 90.51 160 | 37.05 318 | 94.06 213 | 68.06 205 | 86.00 134 | 93.90 126 |
|
3Dnovator+ | | 73.60 7 | 82.10 116 | 80.60 127 | 86.60 60 | 90.89 138 | 66.80 117 | 95.20 33 | 93.44 84 | 74.05 130 | 67.42 245 | 92.49 129 | 49.46 240 | 97.65 59 | 70.80 180 | 91.68 80 | 95.33 58 |
|
VDD-MVS | | | 83.06 99 | 81.81 110 | 86.81 52 | 90.86 139 | 67.70 89 | 95.40 28 | 91.50 163 | 75.46 107 | 81.78 80 | 92.34 134 | 40.09 292 | 97.13 88 | 86.85 55 | 82.04 161 | 95.60 49 |
|
BH-w/o | | | 80.49 142 | 79.30 150 | 84.05 147 | 90.83 140 | 64.36 182 | 93.60 89 | 89.42 240 | 74.35 125 | 69.09 219 | 90.15 168 | 55.23 185 | 95.61 151 | 64.61 239 | 86.43 133 | 92.17 174 |
|
ET-MVSNet_ETH3D | | | 84.01 83 | 83.15 89 | 86.58 62 | 90.78 141 | 70.89 24 | 94.74 47 | 94.62 37 | 81.44 30 | 58.19 311 | 93.64 103 | 73.64 24 | 92.35 272 | 82.66 86 | 78.66 185 | 96.50 23 |
|
Anonymous20231211 | | | 73.08 250 | 70.39 261 | 81.13 220 | 90.62 142 | 63.33 205 | 91.40 178 | 90.06 220 | 51.84 343 | 64.46 273 | 80.67 285 | 36.49 320 | 94.07 212 | 63.83 244 | 64.17 288 | 85.98 269 |
|
TR-MVS | | | 78.77 175 | 77.37 182 | 82.95 172 | 90.49 143 | 60.88 251 | 93.67 86 | 90.07 217 | 70.08 229 | 74.51 158 | 91.37 149 | 45.69 272 | 95.70 148 | 60.12 268 | 80.32 172 | 92.29 168 |
|
SteuartSystems-ACMMP | | | 86.82 41 | 86.90 39 | 86.58 62 | 90.42 144 | 66.38 126 | 96.09 15 | 93.87 62 | 77.73 80 | 84.01 67 | 95.66 38 | 63.39 97 | 97.94 43 | 87.40 48 | 93.55 52 | 95.42 52 |
Skip Steuart: Steuart Systems R&D Blog. |
TAPA-MVS | | 70.22 12 | 74.94 235 | 73.53 231 | 79.17 263 | 90.40 145 | 52.07 334 | 89.19 249 | 89.61 235 | 62.69 294 | 70.07 209 | 92.67 125 | 48.89 249 | 94.32 199 | 38.26 349 | 79.97 173 | 91.12 192 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
mvs_anonymous | | | 81.36 126 | 79.99 136 | 85.46 102 | 90.39 146 | 68.40 69 | 86.88 282 | 90.61 197 | 74.41 123 | 70.31 207 | 84.67 234 | 63.79 90 | 92.32 273 | 73.13 155 | 85.70 135 | 95.67 46 |
|
CANet_DTU | | | 84.09 82 | 83.52 76 | 85.81 89 | 90.30 147 | 66.82 115 | 91.87 157 | 89.01 259 | 85.27 7 | 86.09 41 | 93.74 101 | 47.71 258 | 96.98 99 | 77.90 128 | 89.78 102 | 93.65 132 |
|
Fast-Effi-MVS+ | | | 81.14 130 | 80.01 135 | 84.51 134 | 90.24 148 | 65.86 139 | 94.12 61 | 89.15 251 | 73.81 138 | 75.37 153 | 88.26 191 | 57.26 156 | 94.53 194 | 66.97 216 | 84.92 140 | 93.15 146 |
|
ETV-MVS | | | 86.01 51 | 86.11 48 | 85.70 96 | 90.21 149 | 67.02 111 | 93.43 97 | 91.92 142 | 81.21 32 | 84.13 65 | 94.07 96 | 60.93 120 | 95.63 149 | 89.28 29 | 89.81 100 | 94.46 103 |
|
tpmrst | | | 80.57 139 | 79.14 154 | 84.84 123 | 90.10 150 | 68.28 73 | 81.70 312 | 89.72 233 | 77.63 84 | 75.96 144 | 79.54 300 | 64.94 76 | 92.71 255 | 75.43 139 | 77.28 200 | 93.55 134 |
|
PVSNet_Blended_VisFu | | | 83.97 84 | 83.50 77 | 85.39 106 | 90.02 151 | 66.59 123 | 93.77 84 | 91.73 152 | 77.43 88 | 77.08 137 | 89.81 173 | 63.77 91 | 96.97 100 | 79.67 110 | 88.21 114 | 92.60 160 |
|
UGNet | | | 79.87 153 | 78.68 156 | 83.45 164 | 89.96 152 | 61.51 241 | 92.13 142 | 90.79 190 | 76.83 94 | 78.85 119 | 86.33 214 | 38.16 304 | 96.17 125 | 67.93 207 | 87.17 122 | 92.67 158 |
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 |
CHOSEN 1792x2688 | | | 84.98 65 | 83.45 79 | 89.57 11 | 89.94 153 | 75.14 5 | 92.07 147 | 92.32 125 | 81.87 25 | 75.68 147 | 88.27 190 | 60.18 126 | 98.60 25 | 80.46 107 | 90.27 99 | 94.96 80 |
|
abl_6 | | | 79.82 154 | 79.20 152 | 81.70 208 | 89.85 154 | 58.34 290 | 88.47 260 | 90.07 217 | 62.56 295 | 77.71 127 | 93.08 112 | 47.65 259 | 96.78 109 | 77.94 127 | 85.45 138 | 89.99 204 |
|
BH-untuned | | | 78.68 177 | 77.08 183 | 83.48 163 | 89.84 155 | 63.74 195 | 92.70 122 | 88.59 273 | 71.57 202 | 66.83 253 | 88.65 182 | 51.75 220 | 95.39 162 | 59.03 273 | 84.77 142 | 91.32 188 |
|
test222 | | | | | | 89.77 156 | 61.60 240 | 89.55 238 | 89.42 240 | 56.83 331 | 77.28 133 | 92.43 131 | 52.76 212 | | | 91.14 91 | 93.09 148 |
|
PMMVS | | | 81.98 118 | 82.04 106 | 81.78 204 | 89.76 157 | 56.17 313 | 91.13 194 | 90.69 192 | 77.96 75 | 80.09 100 | 93.57 105 | 46.33 268 | 94.99 173 | 81.41 99 | 87.46 120 | 94.17 110 |
|
DPM-MVS | | | 90.70 2 | 90.52 7 | 91.24 1 | 89.68 158 | 76.68 2 | 97.29 1 | 95.35 10 | 82.87 15 | 91.58 10 | 97.22 4 | 79.93 5 | 99.10 9 | 83.12 82 | 97.64 2 | 97.94 1 |
|
QAPM | | | 79.95 152 | 77.39 181 | 87.64 29 | 89.63 159 | 71.41 19 | 93.30 99 | 93.70 71 | 65.34 275 | 67.39 247 | 91.75 143 | 47.83 256 | 98.96 16 | 57.71 277 | 89.81 100 | 92.54 162 |
|
3Dnovator | | 73.91 6 | 82.69 107 | 80.82 122 | 88.31 22 | 89.57 160 | 71.26 20 | 92.60 128 | 94.39 48 | 78.84 64 | 67.89 239 | 92.48 130 | 48.42 250 | 98.52 26 | 68.80 202 | 94.40 37 | 95.15 71 |
|
Effi-MVS+ | | | 83.82 87 | 82.76 95 | 86.99 47 | 89.56 161 | 69.40 46 | 91.35 184 | 86.12 305 | 72.59 162 | 83.22 71 | 92.81 124 | 59.60 135 | 96.01 135 | 81.76 94 | 87.80 118 | 95.56 50 |
|
PatchmatchNet |  | | 77.46 197 | 74.63 212 | 85.96 83 | 89.55 162 | 70.35 30 | 79.97 328 | 89.55 236 | 72.23 175 | 70.94 198 | 76.91 319 | 57.03 160 | 92.79 253 | 54.27 288 | 81.17 167 | 94.74 87 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PatchMatch-RL | | | 72.06 261 | 69.98 262 | 78.28 273 | 89.51 163 | 55.70 317 | 83.49 298 | 83.39 330 | 61.24 306 | 63.72 280 | 82.76 252 | 34.77 325 | 93.03 240 | 53.37 293 | 77.59 192 | 86.12 266 |
|
thisisatest0515 | | | 83.41 92 | 82.49 101 | 86.16 78 | 89.46 164 | 68.26 74 | 93.54 92 | 94.70 33 | 74.31 126 | 75.75 145 | 90.92 152 | 72.62 29 | 96.52 119 | 69.64 190 | 81.50 165 | 93.71 130 |
|
h-mvs33 | | | 83.01 100 | 82.56 100 | 84.35 139 | 89.34 165 | 62.02 231 | 92.72 120 | 93.76 67 | 81.45 28 | 82.73 74 | 92.25 136 | 60.11 127 | 97.13 88 | 87.69 43 | 62.96 294 | 93.91 124 |
|
DROMVSNet | | | 84.53 72 | 85.04 62 | 83.01 171 | 89.34 165 | 61.37 244 | 94.42 50 | 91.09 181 | 77.91 77 | 83.24 70 | 94.20 92 | 58.37 147 | 95.40 161 | 85.35 66 | 91.41 85 | 92.27 172 |
|
UA-Net | | | 80.02 150 | 79.65 141 | 81.11 221 | 89.33 167 | 57.72 298 | 86.33 285 | 89.00 260 | 77.44 87 | 81.01 90 | 89.15 178 | 59.33 139 | 95.90 136 | 61.01 262 | 84.28 150 | 89.73 207 |
|
dp | | | 75.01 234 | 72.09 249 | 83.76 151 | 89.28 168 | 66.22 132 | 79.96 329 | 89.75 228 | 71.16 211 | 67.80 241 | 77.19 316 | 51.81 219 | 92.54 264 | 50.39 299 | 71.44 239 | 92.51 163 |
|
sss | | | 82.71 106 | 82.38 103 | 83.73 154 | 89.25 169 | 59.58 274 | 92.24 139 | 94.89 24 | 77.96 75 | 79.86 103 | 92.38 132 | 56.70 168 | 97.05 90 | 77.26 131 | 80.86 170 | 94.55 94 |
|
MVSFormer | | | 83.75 89 | 82.88 93 | 86.37 72 | 89.24 170 | 71.18 21 | 89.07 251 | 90.69 192 | 65.80 270 | 87.13 30 | 94.34 87 | 64.99 74 | 92.67 258 | 72.83 158 | 91.80 78 | 95.27 66 |
|
lupinMVS | | | 87.74 23 | 87.77 23 | 87.63 33 | 89.24 170 | 71.18 21 | 96.57 10 | 92.90 107 | 82.70 18 | 87.13 30 | 95.27 52 | 64.99 74 | 95.80 138 | 89.34 28 | 91.80 78 | 95.93 40 |
|
IB-MVS | | 77.80 4 | 82.18 112 | 80.46 130 | 87.35 38 | 89.14 172 | 70.28 31 | 95.59 25 | 95.17 15 | 78.85 63 | 70.19 208 | 85.82 220 | 70.66 36 | 97.67 55 | 72.19 169 | 66.52 270 | 94.09 115 |
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 |
MDTV_nov1_ep13 | | | | 72.61 243 | | 89.06 173 | 68.48 67 | 80.33 322 | 90.11 216 | 71.84 190 | 71.81 191 | 75.92 327 | 53.01 211 | 93.92 223 | 48.04 310 | 73.38 220 | |
|
testdata | | | | | 81.34 214 | 89.02 174 | 57.72 298 | | 89.84 226 | 58.65 322 | 85.32 52 | 94.09 94 | 57.03 160 | 93.28 236 | 69.34 195 | 90.56 97 | 93.03 150 |
|
CostFormer | | | 82.33 110 | 81.15 115 | 85.86 87 | 89.01 175 | 68.46 68 | 82.39 309 | 93.01 102 | 75.59 105 | 80.25 98 | 81.57 269 | 72.03 33 | 94.96 174 | 79.06 117 | 77.48 197 | 94.16 111 |
|
GeoE | | | 78.90 170 | 77.43 177 | 83.29 166 | 88.95 176 | 62.02 231 | 92.31 136 | 86.23 303 | 70.24 227 | 71.34 197 | 89.27 176 | 54.43 196 | 94.04 216 | 63.31 247 | 80.81 171 | 93.81 129 |
|
GBi-Net | | | 75.65 224 | 73.83 227 | 81.10 222 | 88.85 177 | 65.11 159 | 90.01 228 | 90.32 204 | 70.84 217 | 67.04 250 | 80.25 292 | 48.03 252 | 91.54 289 | 59.80 270 | 69.34 250 | 86.64 250 |
|
test1 | | | 75.65 224 | 73.83 227 | 81.10 222 | 88.85 177 | 65.11 159 | 90.01 228 | 90.32 204 | 70.84 217 | 67.04 250 | 80.25 292 | 48.03 252 | 91.54 289 | 59.80 270 | 69.34 250 | 86.64 250 |
|
FMVSNet2 | | | 76.07 214 | 74.01 225 | 82.26 191 | 88.85 177 | 67.66 90 | 91.33 185 | 91.61 158 | 70.84 217 | 65.98 258 | 82.25 258 | 48.03 252 | 92.00 280 | 58.46 275 | 68.73 256 | 87.10 244 |
|
DeepC-MVS | | 77.85 3 | 85.52 57 | 85.24 59 | 86.37 72 | 88.80 180 | 66.64 120 | 92.15 141 | 93.68 72 | 81.07 34 | 76.91 138 | 93.64 103 | 62.59 105 | 98.44 30 | 85.50 64 | 92.84 62 | 94.03 119 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
EPP-MVSNet | | | 81.79 121 | 81.52 112 | 82.61 180 | 88.77 181 | 60.21 266 | 93.02 109 | 93.66 73 | 68.52 250 | 72.90 174 | 90.39 163 | 72.19 32 | 94.96 174 | 74.93 146 | 79.29 179 | 92.67 158 |
|
1112_ss | | | 80.56 140 | 79.83 139 | 82.77 175 | 88.65 182 | 60.78 253 | 92.29 137 | 88.36 277 | 72.58 163 | 72.46 183 | 94.95 64 | 65.09 73 | 93.42 235 | 66.38 223 | 77.71 190 | 94.10 114 |
|
tpm cat1 | | | 75.30 230 | 72.21 248 | 84.58 132 | 88.52 183 | 67.77 87 | 78.16 337 | 88.02 285 | 61.88 303 | 68.45 232 | 76.37 323 | 60.65 121 | 94.03 218 | 53.77 291 | 74.11 215 | 91.93 176 |
|
LCM-MVSNet-Re | | | 72.93 253 | 71.84 251 | 76.18 299 | 88.49 184 | 48.02 351 | 80.07 327 | 70.17 363 | 73.96 134 | 52.25 333 | 80.09 295 | 49.98 235 | 88.24 321 | 67.35 211 | 84.23 151 | 92.28 169 |
|
Vis-MVSNet |  | | 80.92 136 | 79.98 137 | 83.74 152 | 88.48 185 | 61.80 235 | 93.44 96 | 88.26 282 | 73.96 134 | 77.73 126 | 91.76 142 | 49.94 236 | 94.76 179 | 65.84 229 | 90.37 98 | 94.65 92 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Vis-MVSNet (Re-imp) | | | 79.24 164 | 79.57 142 | 78.24 275 | 88.46 186 | 52.29 333 | 90.41 215 | 89.12 253 | 74.24 127 | 69.13 218 | 91.91 140 | 65.77 67 | 90.09 308 | 59.00 274 | 88.09 116 | 92.33 166 |
|
ab-mvs | | | 80.18 146 | 78.31 161 | 85.80 90 | 88.44 187 | 65.49 151 | 83.00 306 | 92.67 114 | 71.82 191 | 77.36 132 | 85.01 228 | 54.50 192 | 96.59 114 | 76.35 136 | 75.63 210 | 95.32 60 |
|
gm-plane-assit | | | | | | 88.42 188 | 67.04 110 | | | 78.62 68 | | 91.83 141 | | 97.37 72 | 76.57 134 | | |
|
MVS_111021_LR | | | 82.02 117 | 81.52 112 | 83.51 161 | 88.42 188 | 62.88 219 | 89.77 235 | 88.93 261 | 76.78 95 | 75.55 151 | 93.10 110 | 50.31 232 | 95.38 163 | 83.82 79 | 87.02 123 | 92.26 173 |
|
test2506 | | | 83.29 94 | 82.92 92 | 84.37 138 | 88.39 190 | 63.18 210 | 92.01 150 | 91.35 169 | 77.66 82 | 78.49 122 | 91.42 146 | 64.58 80 | 95.09 170 | 73.19 154 | 89.23 104 | 94.85 82 |
|
ECVR-MVS |  | | 81.29 127 | 80.38 131 | 84.01 148 | 88.39 190 | 61.96 233 | 92.56 133 | 86.79 297 | 77.66 82 | 76.63 139 | 91.42 146 | 46.34 267 | 95.24 168 | 74.36 151 | 89.23 104 | 94.85 82 |
|
baseline | | | 85.01 64 | 84.44 69 | 86.71 56 | 88.33 192 | 68.73 62 | 90.24 222 | 91.82 150 | 81.05 35 | 81.18 87 | 92.50 127 | 63.69 92 | 96.08 130 | 84.45 73 | 86.71 129 | 95.32 60 |
|
tpm2 | | | 79.80 155 | 77.95 168 | 85.34 108 | 88.28 193 | 68.26 74 | 81.56 314 | 91.42 166 | 70.11 228 | 77.59 130 | 80.50 287 | 67.40 51 | 94.26 204 | 67.34 212 | 77.35 198 | 93.51 135 |
|
thisisatest0530 | | | 81.15 129 | 80.07 132 | 84.39 137 | 88.26 194 | 65.63 146 | 91.40 178 | 94.62 37 | 71.27 210 | 70.93 199 | 89.18 177 | 72.47 30 | 96.04 132 | 65.62 232 | 76.89 203 | 91.49 181 |
|
casdiffmvs | | | 85.37 59 | 84.87 65 | 86.84 50 | 88.25 195 | 69.07 54 | 93.04 107 | 91.76 151 | 81.27 31 | 80.84 93 | 92.07 138 | 64.23 84 | 96.06 131 | 84.98 69 | 87.43 121 | 95.39 54 |
|
Test_1112_low_res | | | 79.56 159 | 78.60 158 | 82.43 183 | 88.24 196 | 60.39 263 | 92.09 145 | 87.99 286 | 72.10 180 | 71.84 190 | 87.42 204 | 64.62 79 | 93.04 239 | 65.80 230 | 77.30 199 | 93.85 128 |
|
PAPM | | | 85.89 53 | 85.46 57 | 87.18 41 | 88.20 197 | 72.42 14 | 92.41 135 | 92.77 110 | 82.11 22 | 80.34 97 | 93.07 114 | 68.27 40 | 95.02 172 | 78.39 124 | 93.59 51 | 94.09 115 |
|
TESTMET0.1,1 | | | 82.41 109 | 81.98 107 | 83.72 155 | 88.08 198 | 63.74 195 | 92.70 122 | 93.77 66 | 79.30 53 | 77.61 129 | 87.57 202 | 58.19 149 | 94.08 211 | 73.91 153 | 86.68 130 | 93.33 140 |
|
ADS-MVSNet2 | | | 66.90 298 | 63.44 303 | 77.26 288 | 88.06 199 | 60.70 258 | 68.01 355 | 75.56 352 | 57.57 324 | 64.48 271 | 69.87 347 | 38.68 298 | 84.10 342 | 40.87 340 | 67.89 262 | 86.97 245 |
|
ADS-MVSNet | | | 68.54 286 | 64.38 299 | 81.03 226 | 88.06 199 | 66.90 114 | 68.01 355 | 84.02 323 | 57.57 324 | 64.48 271 | 69.87 347 | 38.68 298 | 89.21 314 | 40.87 340 | 67.89 262 | 86.97 245 |
|
EPNet_dtu | | | 78.80 173 | 79.26 151 | 77.43 284 | 88.06 199 | 49.71 346 | 91.96 155 | 91.95 141 | 77.67 81 | 76.56 141 | 91.28 150 | 58.51 146 | 90.20 306 | 56.37 281 | 80.95 169 | 92.39 164 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
miper_enhance_ethall | | | 78.86 171 | 77.97 167 | 81.54 210 | 88.00 202 | 65.17 156 | 91.41 176 | 89.15 251 | 75.19 116 | 68.79 226 | 83.98 242 | 67.17 53 | 92.82 250 | 72.73 161 | 65.30 274 | 86.62 254 |
|
IS-MVSNet | | | 80.14 147 | 79.41 147 | 82.33 187 | 87.91 203 | 60.08 268 | 91.97 154 | 88.27 280 | 72.90 158 | 71.44 196 | 91.73 144 | 61.44 115 | 93.66 230 | 62.47 255 | 86.53 131 | 93.24 143 |
|
CLD-MVS | | | 82.73 104 | 82.35 104 | 83.86 150 | 87.90 204 | 67.65 91 | 95.45 27 | 92.18 135 | 85.06 8 | 72.58 179 | 92.27 135 | 52.46 215 | 95.78 139 | 84.18 74 | 79.06 180 | 88.16 228 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
test1111 | | | 80.84 137 | 80.02 134 | 83.33 165 | 87.87 205 | 60.76 255 | 92.62 127 | 86.86 296 | 77.86 78 | 75.73 146 | 91.39 148 | 46.35 266 | 94.70 185 | 72.79 160 | 88.68 110 | 94.52 98 |
|
HyFIR lowres test | | | 81.03 134 | 79.56 143 | 85.43 104 | 87.81 206 | 68.11 79 | 90.18 223 | 90.01 222 | 70.65 222 | 72.95 173 | 86.06 218 | 63.61 94 | 94.50 196 | 75.01 145 | 79.75 175 | 93.67 131 |
|
1314 | | | 80.70 138 | 78.95 155 | 85.94 84 | 87.77 207 | 67.56 92 | 87.91 267 | 92.55 121 | 72.17 178 | 67.44 244 | 93.09 111 | 50.27 233 | 97.04 92 | 71.68 175 | 87.64 119 | 93.23 144 |
|
cl22 | | | 77.94 191 | 76.78 188 | 81.42 212 | 87.57 208 | 64.93 167 | 90.67 208 | 88.86 264 | 72.45 168 | 67.63 243 | 82.68 254 | 64.07 85 | 92.91 248 | 71.79 171 | 65.30 274 | 86.44 256 |
|
HQP-NCC | | | | | | 87.54 209 | | 94.06 64 | | 79.80 45 | 74.18 160 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 209 | | 94.06 64 | | 79.80 45 | 74.18 160 | | | | | | |
|
HQP-MVS | | | 81.14 130 | 80.64 126 | 82.64 179 | 87.54 209 | 63.66 201 | 94.06 64 | 91.70 155 | 79.80 45 | 74.18 160 | 90.30 165 | 51.63 222 | 95.61 151 | 77.63 129 | 78.90 181 | 88.63 218 |
|
NP-MVS | | | | | | 87.41 212 | 63.04 211 | | | | | 90.30 165 | | | | | |
|
diffmvs | | | 84.28 76 | 83.83 74 | 85.61 98 | 87.40 213 | 68.02 81 | 90.88 201 | 89.24 245 | 80.54 39 | 81.64 82 | 92.52 126 | 59.83 132 | 94.52 195 | 87.32 50 | 85.11 139 | 94.29 104 |
|
baseline2 | | | 83.68 91 | 83.42 82 | 84.48 135 | 87.37 214 | 66.00 135 | 90.06 226 | 95.93 7 | 79.71 48 | 69.08 220 | 90.39 163 | 77.92 6 | 96.28 122 | 78.91 119 | 81.38 166 | 91.16 191 |
|
plane_prior6 | | | | | | 87.23 215 | 62.32 227 | | | | | | 50.66 229 | | | | |
|
tttt0517 | | | 79.50 160 | 78.53 159 | 82.41 186 | 87.22 216 | 61.43 243 | 89.75 236 | 94.76 30 | 69.29 239 | 67.91 238 | 88.06 196 | 72.92 26 | 95.63 149 | 62.91 251 | 73.90 219 | 90.16 201 |
|
plane_prior1 | | | | | | 87.15 217 | | | | | | | | | | | |
|
cascas | | | 78.18 186 | 75.77 201 | 85.41 105 | 87.14 218 | 69.11 52 | 92.96 111 | 91.15 178 | 66.71 264 | 70.47 202 | 86.07 217 | 37.49 312 | 96.48 120 | 70.15 186 | 79.80 174 | 90.65 196 |
|
CHOSEN 280x420 | | | 77.35 199 | 76.95 187 | 78.55 270 | 87.07 219 | 62.68 223 | 69.71 351 | 82.95 332 | 68.80 246 | 71.48 195 | 87.27 207 | 66.03 63 | 84.00 345 | 76.47 135 | 82.81 157 | 88.95 212 |
|
HQP_MVS | | | 80.34 144 | 79.75 140 | 82.12 197 | 86.94 220 | 62.42 224 | 93.13 103 | 91.31 170 | 78.81 65 | 72.53 180 | 89.14 179 | 50.66 229 | 95.55 157 | 76.74 132 | 78.53 186 | 88.39 224 |
|
plane_prior7 | | | | | | 86.94 220 | 61.51 241 | | | | | | | | | | |
|
test-LLR | | | 80.10 148 | 79.56 143 | 81.72 206 | 86.93 222 | 61.17 245 | 92.70 122 | 91.54 160 | 71.51 205 | 75.62 148 | 86.94 208 | 53.83 201 | 92.38 269 | 72.21 167 | 84.76 143 | 91.60 179 |
|
test-mter | | | 79.96 151 | 79.38 149 | 81.72 206 | 86.93 222 | 61.17 245 | 92.70 122 | 91.54 160 | 73.85 136 | 75.62 148 | 86.94 208 | 49.84 238 | 92.38 269 | 72.21 167 | 84.76 143 | 91.60 179 |
|
SCA | | | 75.82 222 | 72.76 240 | 85.01 116 | 86.63 224 | 70.08 32 | 81.06 318 | 89.19 248 | 71.60 201 | 70.01 210 | 77.09 317 | 45.53 273 | 90.25 301 | 60.43 265 | 73.27 221 | 94.68 89 |
|
AUN-MVS | | | 78.37 183 | 77.43 177 | 81.17 217 | 86.60 225 | 57.45 305 | 89.46 242 | 91.16 176 | 74.11 129 | 74.40 159 | 90.49 161 | 55.52 182 | 94.57 189 | 74.73 150 | 60.43 319 | 91.48 182 |
|
hse-mvs2 | | | 81.12 132 | 81.11 119 | 81.16 218 | 86.52 226 | 57.48 304 | 89.40 243 | 91.16 176 | 81.45 28 | 82.73 74 | 90.49 161 | 60.11 127 | 94.58 188 | 87.69 43 | 60.41 320 | 91.41 184 |
|
xiu_mvs_v1_base_debu | | | 82.16 113 | 81.12 116 | 85.26 110 | 86.42 227 | 68.72 63 | 92.59 130 | 90.44 201 | 73.12 152 | 84.20 62 | 94.36 82 | 38.04 306 | 95.73 143 | 84.12 75 | 86.81 124 | 91.33 185 |
|
xiu_mvs_v1_base | | | 82.16 113 | 81.12 116 | 85.26 110 | 86.42 227 | 68.72 63 | 92.59 130 | 90.44 201 | 73.12 152 | 84.20 62 | 94.36 82 | 38.04 306 | 95.73 143 | 84.12 75 | 86.81 124 | 91.33 185 |
|
xiu_mvs_v1_base_debi | | | 82.16 113 | 81.12 116 | 85.26 110 | 86.42 227 | 68.72 63 | 92.59 130 | 90.44 201 | 73.12 152 | 84.20 62 | 94.36 82 | 38.04 306 | 95.73 143 | 84.12 75 | 86.81 124 | 91.33 185 |
|
F-COLMAP | | | 70.66 268 | 68.44 273 | 77.32 286 | 86.37 230 | 55.91 315 | 88.00 265 | 86.32 300 | 56.94 330 | 57.28 319 | 88.07 195 | 33.58 329 | 92.49 266 | 51.02 297 | 68.37 258 | 83.55 298 |
|
CDS-MVSNet | | | 81.43 125 | 80.74 123 | 83.52 159 | 86.26 231 | 64.45 175 | 92.09 145 | 90.65 196 | 75.83 104 | 73.95 166 | 89.81 173 | 63.97 87 | 92.91 248 | 71.27 177 | 82.82 156 | 93.20 145 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
VDDNet | | | 80.50 141 | 78.26 162 | 87.21 40 | 86.19 232 | 69.79 40 | 94.48 49 | 91.31 170 | 60.42 310 | 79.34 108 | 90.91 153 | 38.48 302 | 96.56 117 | 82.16 90 | 81.05 168 | 95.27 66 |
|
jason | | | 86.40 44 | 86.17 47 | 87.11 43 | 86.16 233 | 70.54 28 | 95.71 23 | 92.19 134 | 82.00 24 | 84.58 58 | 94.34 87 | 61.86 111 | 95.53 159 | 87.76 42 | 90.89 92 | 95.27 66 |
jason: jason. |
PCF-MVS | | 73.15 9 | 79.29 163 | 77.63 174 | 84.29 141 | 86.06 234 | 65.96 137 | 87.03 278 | 91.10 180 | 69.86 232 | 69.79 215 | 90.64 156 | 57.54 155 | 96.59 114 | 64.37 241 | 82.29 158 | 90.32 199 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MS-PatchMatch | | | 77.90 193 | 76.50 191 | 82.12 197 | 85.99 235 | 69.95 36 | 91.75 166 | 92.70 112 | 73.97 133 | 62.58 290 | 84.44 237 | 41.11 289 | 95.78 139 | 63.76 245 | 92.17 72 | 80.62 333 |
|
FIs | | | 79.47 161 | 79.41 147 | 79.67 254 | 85.95 236 | 59.40 276 | 91.68 168 | 93.94 61 | 78.06 74 | 68.96 223 | 88.28 189 | 66.61 59 | 91.77 284 | 66.20 226 | 74.99 212 | 87.82 230 |
|
VPA-MVSNet | | | 79.03 166 | 78.00 166 | 82.11 200 | 85.95 236 | 64.48 174 | 93.22 102 | 94.66 35 | 75.05 118 | 74.04 165 | 84.95 230 | 52.17 217 | 93.52 232 | 74.90 148 | 67.04 266 | 88.32 226 |
|
tpm | | | 78.58 180 | 77.03 184 | 83.22 168 | 85.94 238 | 64.56 170 | 83.21 304 | 91.14 179 | 78.31 70 | 73.67 168 | 79.68 298 | 64.01 86 | 92.09 278 | 66.07 227 | 71.26 240 | 93.03 150 |
|
OpenMVS |  | 70.45 11 | 78.54 181 | 75.92 199 | 86.41 71 | 85.93 239 | 71.68 17 | 92.74 119 | 92.51 122 | 66.49 266 | 64.56 270 | 91.96 139 | 43.88 281 | 98.10 39 | 54.61 286 | 90.65 95 | 89.44 211 |
|
OMC-MVS | | | 78.67 179 | 77.91 170 | 80.95 228 | 85.76 240 | 57.40 306 | 88.49 259 | 88.67 270 | 73.85 136 | 72.43 184 | 92.10 137 | 49.29 243 | 94.55 192 | 72.73 161 | 77.89 189 | 90.91 194 |
|
miper_ehance_all_eth | | | 77.60 195 | 76.44 192 | 81.09 225 | 85.70 241 | 64.41 179 | 90.65 209 | 88.64 272 | 72.31 172 | 67.37 248 | 82.52 255 | 64.77 78 | 92.64 262 | 70.67 182 | 65.30 274 | 86.24 260 |
|
KD-MVS_2432*1600 | | | 69.03 281 | 66.37 283 | 77.01 291 | 85.56 242 | 61.06 248 | 81.44 315 | 90.25 210 | 67.27 260 | 58.00 314 | 76.53 321 | 54.49 193 | 87.63 328 | 48.04 310 | 35.77 365 | 82.34 318 |
|
miper_refine_blended | | | 69.03 281 | 66.37 283 | 77.01 291 | 85.56 242 | 61.06 248 | 81.44 315 | 90.25 210 | 67.27 260 | 58.00 314 | 76.53 321 | 54.49 193 | 87.63 328 | 48.04 310 | 35.77 365 | 82.34 318 |
|
EI-MVSNet | | | 78.97 168 | 78.22 163 | 81.25 215 | 85.33 244 | 62.73 222 | 89.53 240 | 93.21 91 | 72.39 171 | 72.14 187 | 90.13 169 | 60.99 118 | 94.72 182 | 67.73 209 | 72.49 230 | 86.29 258 |
|
CVMVSNet | | | 74.04 243 | 74.27 220 | 73.33 316 | 85.33 244 | 43.94 362 | 89.53 240 | 88.39 276 | 54.33 338 | 70.37 205 | 90.13 169 | 49.17 245 | 84.05 343 | 61.83 259 | 79.36 177 | 91.99 175 |
|
ACMH | | 63.93 17 | 68.62 284 | 64.81 292 | 80.03 244 | 85.22 246 | 63.25 206 | 87.72 270 | 84.66 318 | 60.83 308 | 51.57 336 | 79.43 301 | 27.29 349 | 94.96 174 | 41.76 336 | 64.84 281 | 81.88 323 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
cl____ | | | 76.07 214 | 74.67 210 | 80.28 237 | 85.15 247 | 61.76 237 | 90.12 224 | 88.73 268 | 71.16 211 | 65.43 261 | 81.57 269 | 61.15 116 | 92.95 243 | 66.54 220 | 62.17 301 | 86.13 265 |
|
DIV-MVS_self_test | | | 76.07 214 | 74.67 210 | 80.28 237 | 85.14 248 | 61.75 238 | 90.12 224 | 88.73 268 | 71.16 211 | 65.42 262 | 81.60 268 | 61.15 116 | 92.94 247 | 66.54 220 | 62.16 303 | 86.14 263 |
|
TAMVS | | | 80.37 143 | 79.45 146 | 83.13 170 | 85.14 248 | 63.37 204 | 91.23 189 | 90.76 191 | 74.81 121 | 72.65 177 | 88.49 183 | 60.63 122 | 92.95 243 | 69.41 194 | 81.95 162 | 93.08 149 |
|
MSDG | | | 69.54 277 | 65.73 286 | 80.96 227 | 85.11 250 | 63.71 197 | 84.19 293 | 83.28 331 | 56.95 329 | 54.50 324 | 84.03 240 | 31.50 337 | 96.03 133 | 42.87 333 | 69.13 253 | 83.14 308 |
|
c3_l | | | 76.83 207 | 75.47 205 | 80.93 229 | 85.02 251 | 64.18 188 | 90.39 216 | 88.11 283 | 71.66 195 | 66.65 255 | 81.64 267 | 63.58 96 | 92.56 263 | 69.31 196 | 62.86 296 | 86.04 267 |
|
ACMP | | 71.68 10 | 75.58 227 | 74.23 221 | 79.62 256 | 84.97 252 | 59.64 272 | 90.80 205 | 89.07 256 | 70.39 225 | 62.95 286 | 87.30 206 | 38.28 303 | 93.87 225 | 72.89 157 | 71.45 238 | 85.36 282 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
FC-MVSNet-test | | | 77.99 189 | 78.08 165 | 77.70 279 | 84.89 253 | 55.51 318 | 90.27 220 | 93.75 70 | 76.87 91 | 66.80 254 | 87.59 201 | 65.71 68 | 90.23 305 | 62.89 252 | 73.94 217 | 87.37 237 |
|
PVSNet_0 | | 68.08 15 | 71.81 262 | 68.32 275 | 82.27 189 | 84.68 254 | 62.31 228 | 88.68 256 | 90.31 207 | 75.84 103 | 57.93 316 | 80.65 286 | 37.85 309 | 94.19 207 | 69.94 188 | 29.05 370 | 90.31 200 |
|
eth_miper_zixun_eth | | | 75.96 220 | 74.40 218 | 80.66 231 | 84.66 255 | 63.02 212 | 89.28 245 | 88.27 280 | 71.88 186 | 65.73 259 | 81.65 266 | 59.45 136 | 92.81 251 | 68.13 204 | 60.53 317 | 86.14 263 |
|
WR-MVS | | | 76.76 208 | 75.74 202 | 79.82 251 | 84.60 256 | 62.27 229 | 92.60 128 | 92.51 122 | 76.06 101 | 67.87 240 | 85.34 225 | 56.76 166 | 90.24 304 | 62.20 256 | 63.69 293 | 86.94 247 |
|
ACMH+ | | 65.35 16 | 67.65 293 | 64.55 295 | 76.96 293 | 84.59 257 | 57.10 309 | 88.08 264 | 80.79 339 | 58.59 323 | 53.00 330 | 81.09 281 | 26.63 351 | 92.95 243 | 46.51 317 | 61.69 310 | 80.82 330 |
|
VPNet | | | 78.82 172 | 77.53 176 | 82.70 177 | 84.52 258 | 66.44 125 | 93.93 74 | 92.23 128 | 80.46 40 | 72.60 178 | 88.38 188 | 49.18 244 | 93.13 238 | 72.47 165 | 63.97 291 | 88.55 220 |
|
IterMVS-LS | | | 76.49 210 | 75.18 208 | 80.43 234 | 84.49 259 | 62.74 221 | 90.64 210 | 88.80 265 | 72.40 170 | 65.16 264 | 81.72 265 | 60.98 119 | 92.27 274 | 67.74 208 | 64.65 285 | 86.29 258 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UniMVSNet_NR-MVSNet | | | 78.15 187 | 77.55 175 | 79.98 245 | 84.46 260 | 60.26 264 | 92.25 138 | 93.20 93 | 77.50 86 | 68.88 224 | 86.61 210 | 66.10 62 | 92.13 276 | 66.38 223 | 62.55 297 | 87.54 232 |
|
FMVSNet5 | | | 68.04 290 | 65.66 288 | 75.18 304 | 84.43 261 | 57.89 295 | 83.54 297 | 86.26 302 | 61.83 304 | 53.64 329 | 73.30 335 | 37.15 316 | 85.08 339 | 48.99 305 | 61.77 306 | 82.56 317 |
|
MVS-HIRNet | | | 60.25 323 | 55.55 329 | 74.35 309 | 84.37 262 | 56.57 312 | 71.64 346 | 74.11 355 | 34.44 366 | 45.54 355 | 42.24 369 | 31.11 340 | 89.81 309 | 40.36 343 | 76.10 208 | 76.67 354 |
|
LPG-MVS_test | | | 75.82 222 | 74.58 214 | 79.56 258 | 84.31 263 | 59.37 277 | 90.44 213 | 89.73 231 | 69.49 235 | 64.86 265 | 88.42 184 | 38.65 300 | 94.30 200 | 72.56 163 | 72.76 226 | 85.01 287 |
|
LGP-MVS_train | | | | | 79.56 258 | 84.31 263 | 59.37 277 | | 89.73 231 | 69.49 235 | 64.86 265 | 88.42 184 | 38.65 300 | 94.30 200 | 72.56 163 | 72.76 226 | 85.01 287 |
|
ACMM | | 69.62 13 | 74.34 240 | 72.73 241 | 79.17 263 | 84.25 265 | 57.87 296 | 90.36 217 | 89.93 223 | 63.17 289 | 65.64 260 | 86.04 219 | 37.79 310 | 94.10 209 | 65.89 228 | 71.52 237 | 85.55 279 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UniMVSNet (Re) | | | 77.58 196 | 76.78 188 | 79.98 245 | 84.11 266 | 60.80 252 | 91.76 164 | 93.17 96 | 76.56 98 | 69.93 214 | 84.78 233 | 63.32 99 | 92.36 271 | 64.89 238 | 62.51 299 | 86.78 249 |
|
test_0402 | | | 64.54 308 | 61.09 314 | 74.92 305 | 84.10 267 | 60.75 256 | 87.95 266 | 79.71 344 | 52.03 342 | 52.41 332 | 77.20 315 | 32.21 335 | 91.64 286 | 23.14 368 | 61.03 313 | 72.36 360 |
|
LTVRE_ROB | | 59.60 19 | 66.27 301 | 63.54 302 | 74.45 308 | 84.00 268 | 51.55 336 | 67.08 358 | 83.53 327 | 58.78 321 | 54.94 323 | 80.31 290 | 34.54 326 | 93.23 237 | 40.64 342 | 68.03 260 | 78.58 349 |
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 |
miper_lstm_enhance | | | 73.05 251 | 71.73 253 | 77.03 290 | 83.80 269 | 58.32 291 | 81.76 310 | 88.88 262 | 69.80 233 | 61.01 296 | 78.23 307 | 57.19 157 | 87.51 330 | 65.34 236 | 59.53 322 | 85.27 285 |
|
Patchmatch-test | | | 65.86 303 | 60.94 315 | 80.62 232 | 83.75 270 | 58.83 285 | 58.91 365 | 75.26 354 | 44.50 361 | 50.95 340 | 77.09 317 | 58.81 145 | 87.90 324 | 35.13 355 | 64.03 289 | 95.12 73 |
|
nrg030 | | | 80.93 135 | 79.86 138 | 84.13 145 | 83.69 271 | 68.83 60 | 93.23 101 | 91.20 174 | 75.55 106 | 75.06 154 | 88.22 194 | 63.04 103 | 94.74 181 | 81.88 93 | 66.88 267 | 88.82 216 |
|
GA-MVS | | | 78.33 185 | 76.23 195 | 84.65 129 | 83.65 272 | 66.30 129 | 91.44 174 | 90.14 215 | 76.01 102 | 70.32 206 | 84.02 241 | 42.50 285 | 94.72 182 | 70.98 178 | 77.00 202 | 92.94 153 |
|
FMVSNet1 | | | 72.71 258 | 69.91 265 | 81.10 222 | 83.60 273 | 65.11 159 | 90.01 228 | 90.32 204 | 63.92 282 | 63.56 281 | 80.25 292 | 36.35 321 | 91.54 289 | 54.46 287 | 66.75 268 | 86.64 250 |
|
OPM-MVS | | | 79.00 167 | 78.09 164 | 81.73 205 | 83.52 274 | 63.83 192 | 91.64 170 | 90.30 208 | 76.36 100 | 71.97 189 | 89.93 172 | 46.30 269 | 95.17 169 | 75.10 142 | 77.70 191 | 86.19 262 |
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
tfpnnormal | | | 70.10 272 | 67.36 278 | 78.32 272 | 83.45 275 | 60.97 250 | 88.85 253 | 92.77 110 | 64.85 277 | 60.83 298 | 78.53 304 | 43.52 283 | 93.48 233 | 31.73 365 | 61.70 309 | 80.52 334 |
|
Effi-MVS+-dtu | | | 76.14 213 | 75.28 207 | 78.72 269 | 83.22 276 | 55.17 320 | 89.87 232 | 87.78 288 | 75.42 108 | 67.98 235 | 81.43 271 | 45.08 276 | 92.52 265 | 75.08 143 | 71.63 235 | 88.48 221 |
|
mvs-test1 | | | 78.74 176 | 77.95 168 | 81.14 219 | 83.22 276 | 57.13 308 | 93.96 69 | 87.78 288 | 75.42 108 | 72.68 176 | 90.80 155 | 45.08 276 | 94.54 193 | 75.08 143 | 77.49 196 | 91.74 178 |
|
CR-MVSNet | | | 73.79 247 | 70.82 259 | 82.70 177 | 83.15 278 | 67.96 82 | 70.25 348 | 84.00 324 | 73.67 143 | 69.97 212 | 72.41 338 | 57.82 152 | 89.48 312 | 52.99 294 | 73.13 222 | 90.64 197 |
|
RPMNet | | | 70.42 271 | 65.68 287 | 84.63 131 | 83.15 278 | 67.96 82 | 70.25 348 | 90.45 198 | 46.83 357 | 69.97 212 | 65.10 355 | 56.48 173 | 95.30 167 | 35.79 354 | 73.13 222 | 90.64 197 |
|
mvsmamba | | | 76.85 205 | 75.71 203 | 80.25 239 | 83.07 280 | 59.16 281 | 91.44 174 | 80.64 341 | 76.84 93 | 67.95 236 | 86.33 214 | 46.17 271 | 94.24 205 | 76.06 137 | 72.92 225 | 87.36 238 |
|
DU-MVS | | | 76.86 203 | 75.84 200 | 79.91 247 | 82.96 281 | 60.26 264 | 91.26 188 | 91.54 160 | 76.46 99 | 68.88 224 | 86.35 212 | 56.16 174 | 92.13 276 | 66.38 223 | 62.55 297 | 87.35 239 |
|
NR-MVSNet | | | 76.05 217 | 74.59 213 | 80.44 233 | 82.96 281 | 62.18 230 | 90.83 204 | 91.73 152 | 77.12 90 | 60.96 297 | 86.35 212 | 59.28 140 | 91.80 283 | 60.74 263 | 61.34 312 | 87.35 239 |
|
XXY-MVS | | | 77.94 191 | 76.44 192 | 82.43 183 | 82.60 283 | 64.44 176 | 92.01 150 | 91.83 149 | 73.59 144 | 70.00 211 | 85.82 220 | 54.43 196 | 94.76 179 | 69.63 191 | 68.02 261 | 88.10 229 |
|
TranMVSNet+NR-MVSNet | | | 75.86 221 | 74.52 216 | 79.89 248 | 82.44 284 | 60.64 260 | 91.37 183 | 91.37 168 | 76.63 96 | 67.65 242 | 86.21 216 | 52.37 216 | 91.55 288 | 61.84 258 | 60.81 315 | 87.48 234 |
|
bld_raw_conf005 | | | 75.32 229 | 73.81 229 | 79.87 249 | 82.38 285 | 58.28 292 | 90.86 202 | 78.04 346 | 75.22 115 | 66.60 256 | 85.65 223 | 38.81 297 | 94.20 206 | 74.18 152 | 72.54 229 | 87.14 243 |
|
RRT_MVS | | | 74.44 239 | 72.97 238 | 78.84 268 | 82.36 286 | 57.66 300 | 89.83 234 | 88.79 267 | 70.61 223 | 64.58 269 | 84.89 231 | 39.24 295 | 92.65 261 | 70.11 187 | 66.34 271 | 86.21 261 |
|
IterMVS | | | 72.65 260 | 70.83 258 | 78.09 277 | 82.17 287 | 62.96 214 | 87.64 273 | 86.28 301 | 71.56 203 | 60.44 299 | 78.85 303 | 45.42 275 | 86.66 334 | 63.30 248 | 61.83 305 | 84.65 291 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Patchmtry | | | 67.53 295 | 63.93 300 | 78.34 271 | 82.12 288 | 64.38 180 | 68.72 352 | 84.00 324 | 48.23 354 | 59.24 304 | 72.41 338 | 57.82 152 | 89.27 313 | 46.10 320 | 56.68 332 | 81.36 326 |
|
PatchT | | | 69.11 280 | 65.37 291 | 80.32 235 | 82.07 289 | 63.68 200 | 67.96 357 | 87.62 290 | 50.86 346 | 69.37 216 | 65.18 354 | 57.09 158 | 88.53 318 | 41.59 338 | 66.60 269 | 88.74 217 |
|
MIMVSNet | | | 71.64 263 | 68.44 273 | 81.23 216 | 81.97 290 | 64.44 176 | 73.05 344 | 88.80 265 | 69.67 234 | 64.59 268 | 74.79 331 | 32.79 331 | 87.82 325 | 53.99 289 | 76.35 207 | 91.42 183 |
|
MVP-Stereo | | | 77.12 202 | 76.23 195 | 79.79 252 | 81.72 291 | 66.34 128 | 89.29 244 | 90.88 189 | 70.56 224 | 62.01 293 | 82.88 251 | 49.34 241 | 94.13 208 | 65.55 234 | 93.80 45 | 78.88 346 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
IterMVS-SCA-FT | | | 71.55 265 | 69.97 263 | 76.32 297 | 81.48 292 | 60.67 259 | 87.64 273 | 85.99 306 | 66.17 268 | 59.50 303 | 78.88 302 | 45.53 273 | 83.65 347 | 62.58 254 | 61.93 304 | 84.63 292 |
|
COLMAP_ROB |  | 57.96 20 | 62.98 316 | 59.65 318 | 72.98 319 | 81.44 293 | 53.00 330 | 83.75 296 | 75.53 353 | 48.34 353 | 48.81 346 | 81.40 273 | 24.14 354 | 90.30 300 | 32.95 361 | 60.52 318 | 75.65 356 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
JIA-IIPM | | | 66.06 302 | 62.45 309 | 76.88 294 | 81.42 294 | 54.45 325 | 57.49 366 | 88.67 270 | 49.36 350 | 63.86 278 | 46.86 365 | 56.06 177 | 90.25 301 | 49.53 303 | 68.83 254 | 85.95 270 |
|
WR-MVS_H | | | 70.59 269 | 69.94 264 | 72.53 322 | 81.03 295 | 51.43 337 | 87.35 276 | 92.03 138 | 67.38 259 | 60.23 300 | 80.70 283 | 55.84 181 | 83.45 349 | 46.33 319 | 58.58 327 | 82.72 312 |
|
test_low_dy_conf_001 | | | 74.54 238 | 73.33 234 | 78.16 276 | 80.91 296 | 55.04 321 | 89.24 246 | 89.06 257 | 69.33 238 | 64.21 276 | 85.60 224 | 39.35 294 | 92.96 242 | 71.28 176 | 71.03 242 | 86.49 255 |
|
Fast-Effi-MVS+-dtu | | | 75.04 233 | 73.37 233 | 80.07 243 | 80.86 297 | 59.52 275 | 91.20 192 | 85.38 310 | 71.90 184 | 65.20 263 | 84.84 232 | 41.46 288 | 92.97 241 | 66.50 222 | 72.96 224 | 87.73 231 |
|
Baseline_NR-MVSNet | | | 73.99 244 | 72.83 239 | 77.48 283 | 80.78 298 | 59.29 280 | 91.79 161 | 84.55 319 | 68.85 245 | 68.99 222 | 80.70 283 | 56.16 174 | 92.04 279 | 62.67 253 | 60.98 314 | 81.11 327 |
|
CP-MVSNet | | | 70.50 270 | 69.91 265 | 72.26 325 | 80.71 299 | 51.00 340 | 87.23 277 | 90.30 208 | 67.84 253 | 59.64 302 | 82.69 253 | 50.23 234 | 82.30 357 | 51.28 296 | 59.28 323 | 83.46 302 |
|
v8 | | | 75.35 228 | 73.26 235 | 81.61 209 | 80.67 300 | 66.82 115 | 89.54 239 | 89.27 244 | 71.65 196 | 63.30 284 | 80.30 291 | 54.99 189 | 94.06 213 | 67.33 213 | 62.33 300 | 83.94 295 |
|
PS-MVSNAJss | | | 77.26 200 | 76.31 194 | 80.13 242 | 80.64 301 | 59.16 281 | 90.63 212 | 91.06 184 | 72.80 159 | 68.58 230 | 84.57 236 | 53.55 205 | 93.96 221 | 72.97 156 | 71.96 234 | 87.27 242 |
|
TransMVSNet (Re) | | | 70.07 273 | 67.66 277 | 77.31 287 | 80.62 302 | 59.13 283 | 91.78 163 | 84.94 315 | 65.97 269 | 60.08 301 | 80.44 288 | 50.78 228 | 91.87 281 | 48.84 306 | 45.46 354 | 80.94 329 |
|
v2v482 | | | 77.42 198 | 75.65 204 | 82.73 176 | 80.38 303 | 67.13 107 | 91.85 159 | 90.23 212 | 75.09 117 | 69.37 216 | 83.39 248 | 53.79 203 | 94.44 197 | 71.77 172 | 65.00 280 | 86.63 253 |
|
MVS_0304 | | | 68.99 283 | 67.23 280 | 74.28 311 | 80.36 304 | 52.54 331 | 87.01 280 | 86.36 299 | 59.89 316 | 66.22 257 | 73.56 334 | 24.25 353 | 88.03 323 | 57.34 279 | 70.11 245 | 82.27 320 |
|
PS-CasMVS | | | 69.86 276 | 69.13 270 | 72.07 328 | 80.35 305 | 50.57 342 | 87.02 279 | 89.75 228 | 67.27 260 | 59.19 306 | 82.28 257 | 46.58 264 | 82.24 358 | 50.69 298 | 59.02 324 | 83.39 304 |
|
v10 | | | 74.77 236 | 72.54 245 | 81.46 211 | 80.33 306 | 66.71 119 | 89.15 250 | 89.08 255 | 70.94 215 | 63.08 285 | 79.86 296 | 52.52 214 | 94.04 216 | 65.70 231 | 62.17 301 | 83.64 297 |
|
test0.0.03 1 | | | 72.76 256 | 72.71 242 | 72.88 320 | 80.25 307 | 47.99 352 | 91.22 190 | 89.45 238 | 71.51 205 | 62.51 291 | 87.66 200 | 53.83 201 | 85.06 340 | 50.16 300 | 67.84 264 | 85.58 277 |
|
v1144 | | | 76.73 209 | 74.88 209 | 82.27 189 | 80.23 308 | 66.60 122 | 91.68 168 | 90.21 214 | 73.69 141 | 69.06 221 | 81.89 262 | 52.73 213 | 94.40 198 | 69.21 197 | 65.23 277 | 85.80 273 |
|
v148 | | | 76.19 212 | 74.47 217 | 81.36 213 | 80.05 309 | 64.44 176 | 91.75 166 | 90.23 212 | 73.68 142 | 67.13 249 | 80.84 282 | 55.92 180 | 93.86 227 | 68.95 200 | 61.73 308 | 85.76 276 |
|
v1192 | | | 75.98 219 | 73.92 226 | 82.15 194 | 79.73 310 | 66.24 131 | 91.22 190 | 89.75 228 | 72.67 161 | 68.49 231 | 81.42 272 | 49.86 237 | 94.27 202 | 67.08 214 | 65.02 279 | 85.95 270 |
|
AllTest | | | 61.66 318 | 58.06 322 | 72.46 323 | 79.57 311 | 51.42 338 | 80.17 325 | 68.61 365 | 51.25 344 | 45.88 351 | 81.23 275 | 19.86 363 | 86.58 335 | 38.98 346 | 57.01 330 | 79.39 342 |
|
TestCases | | | | | 72.46 323 | 79.57 311 | 51.42 338 | | 68.61 365 | 51.25 344 | 45.88 351 | 81.23 275 | 19.86 363 | 86.58 335 | 38.98 346 | 57.01 330 | 79.39 342 |
|
MDA-MVSNet-bldmvs | | | 61.54 320 | 57.70 324 | 73.05 318 | 79.53 313 | 57.00 310 | 83.08 305 | 81.23 337 | 57.57 324 | 34.91 366 | 72.45 337 | 32.79 331 | 86.26 337 | 35.81 353 | 41.95 358 | 75.89 355 |
|
v144192 | | | 76.05 217 | 74.03 224 | 82.12 197 | 79.50 314 | 66.55 124 | 91.39 180 | 89.71 234 | 72.30 173 | 68.17 233 | 81.33 274 | 51.75 220 | 94.03 218 | 67.94 206 | 64.19 287 | 85.77 274 |
|
v1921920 | | | 75.63 226 | 73.49 232 | 82.06 201 | 79.38 315 | 66.35 127 | 91.07 197 | 89.48 237 | 71.98 181 | 67.99 234 | 81.22 277 | 49.16 246 | 93.90 224 | 66.56 219 | 64.56 286 | 85.92 272 |
|
PEN-MVS | | | 69.46 278 | 68.56 272 | 72.17 327 | 79.27 316 | 49.71 346 | 86.90 281 | 89.24 245 | 67.24 263 | 59.08 307 | 82.51 256 | 47.23 261 | 83.54 348 | 48.42 308 | 57.12 328 | 83.25 305 |
|
v1240 | | | 75.21 232 | 72.98 237 | 81.88 203 | 79.20 317 | 66.00 135 | 90.75 207 | 89.11 254 | 71.63 200 | 67.41 246 | 81.22 277 | 47.36 260 | 93.87 225 | 65.46 235 | 64.72 284 | 85.77 274 |
|
pmmvs4 | | | 73.92 245 | 71.81 252 | 80.25 239 | 79.17 318 | 65.24 154 | 87.43 275 | 87.26 294 | 67.64 258 | 63.46 282 | 83.91 243 | 48.96 248 | 91.53 292 | 62.94 250 | 65.49 273 | 83.96 294 |
|
D2MVS | | | 73.80 246 | 72.02 250 | 79.15 265 | 79.15 319 | 62.97 213 | 88.58 258 | 90.07 217 | 72.94 155 | 59.22 305 | 78.30 305 | 42.31 287 | 92.70 257 | 65.59 233 | 72.00 233 | 81.79 324 |
|
V42 | | | 76.46 211 | 74.55 215 | 82.19 193 | 79.14 320 | 67.82 86 | 90.26 221 | 89.42 240 | 73.75 139 | 68.63 229 | 81.89 262 | 51.31 224 | 94.09 210 | 71.69 174 | 64.84 281 | 84.66 290 |
|
pm-mvs1 | | | 72.89 254 | 71.09 257 | 78.26 274 | 79.10 321 | 57.62 301 | 90.80 205 | 89.30 243 | 67.66 256 | 62.91 287 | 81.78 264 | 49.11 247 | 92.95 243 | 60.29 267 | 58.89 325 | 84.22 293 |
|
our_test_3 | | | 68.29 288 | 64.69 294 | 79.11 266 | 78.92 322 | 64.85 168 | 88.40 262 | 85.06 313 | 60.32 312 | 52.68 331 | 76.12 325 | 40.81 290 | 89.80 311 | 44.25 328 | 55.65 333 | 82.67 316 |
|
ppachtmachnet_test | | | 67.72 292 | 63.70 301 | 79.77 253 | 78.92 322 | 66.04 134 | 88.68 256 | 82.90 333 | 60.11 314 | 55.45 322 | 75.96 326 | 39.19 296 | 90.55 297 | 39.53 344 | 52.55 342 | 82.71 313 |
|
TinyColmap | | | 60.32 322 | 56.42 328 | 72.00 329 | 78.78 324 | 53.18 329 | 78.36 335 | 75.64 351 | 52.30 341 | 41.59 362 | 75.82 328 | 14.76 368 | 88.35 320 | 35.84 352 | 54.71 338 | 74.46 357 |
|
SixPastTwentyTwo | | | 64.92 306 | 61.78 313 | 74.34 310 | 78.74 325 | 49.76 345 | 83.42 301 | 79.51 345 | 62.86 291 | 50.27 341 | 77.35 312 | 30.92 341 | 90.49 299 | 45.89 321 | 47.06 351 | 82.78 309 |
|
EG-PatchMatch MVS | | | 68.55 285 | 65.41 290 | 77.96 278 | 78.69 326 | 62.93 215 | 89.86 233 | 89.17 249 | 60.55 309 | 50.27 341 | 77.73 311 | 22.60 358 | 94.06 213 | 47.18 316 | 72.65 228 | 76.88 353 |
|
pmmvs5 | | | 73.35 249 | 71.52 254 | 78.86 267 | 78.64 327 | 60.61 261 | 91.08 195 | 86.90 295 | 67.69 255 | 63.32 283 | 83.64 244 | 44.33 280 | 90.53 298 | 62.04 257 | 66.02 272 | 85.46 280 |
|
UniMVSNet_ETH3D | | | 72.74 257 | 70.53 260 | 79.36 260 | 78.62 328 | 56.64 311 | 85.01 289 | 89.20 247 | 63.77 284 | 64.84 267 | 84.44 237 | 34.05 327 | 91.86 282 | 63.94 243 | 70.89 243 | 89.57 209 |
|
XVG-OURS | | | 74.25 242 | 72.46 246 | 79.63 255 | 78.45 329 | 57.59 302 | 80.33 322 | 87.39 291 | 63.86 283 | 68.76 227 | 89.62 175 | 40.50 291 | 91.72 285 | 69.00 199 | 74.25 214 | 89.58 208 |
|
XVG-OURS-SEG-HR | | | 74.70 237 | 73.08 236 | 79.57 257 | 78.25 330 | 57.33 307 | 80.49 320 | 87.32 292 | 63.22 288 | 68.76 227 | 90.12 171 | 44.89 278 | 91.59 287 | 70.55 184 | 74.09 216 | 89.79 205 |
|
MDA-MVSNet_test_wron | | | 63.78 313 | 60.16 316 | 74.64 306 | 78.15 331 | 60.41 262 | 83.49 298 | 84.03 322 | 56.17 335 | 39.17 364 | 71.59 344 | 37.22 314 | 83.24 352 | 42.87 333 | 48.73 348 | 80.26 337 |
|
YYNet1 | | | 63.76 314 | 60.14 317 | 74.62 307 | 78.06 332 | 60.19 267 | 83.46 300 | 83.99 326 | 56.18 334 | 39.25 363 | 71.56 345 | 37.18 315 | 83.34 350 | 42.90 332 | 48.70 349 | 80.32 336 |
|
DTE-MVSNet | | | 68.46 287 | 67.33 279 | 71.87 330 | 77.94 333 | 49.00 349 | 86.16 286 | 88.58 274 | 66.36 267 | 58.19 311 | 82.21 259 | 46.36 265 | 83.87 346 | 44.97 326 | 55.17 335 | 82.73 311 |
|
USDC | | | 67.43 297 | 64.51 296 | 76.19 298 | 77.94 333 | 55.29 319 | 78.38 334 | 85.00 314 | 73.17 150 | 48.36 347 | 80.37 289 | 21.23 360 | 92.48 267 | 52.15 295 | 64.02 290 | 80.81 331 |
|
bld_raw_dy_0_64 | | | 71.59 264 | 69.71 268 | 77.22 289 | 77.82 335 | 58.12 294 | 87.71 271 | 73.66 356 | 68.01 252 | 61.90 295 | 84.29 239 | 33.68 328 | 88.43 319 | 69.91 189 | 70.43 244 | 85.11 286 |
|
jajsoiax | | | 73.05 251 | 71.51 255 | 77.67 280 | 77.46 336 | 54.83 322 | 88.81 254 | 90.04 221 | 69.13 243 | 62.85 288 | 83.51 246 | 31.16 339 | 92.75 254 | 70.83 179 | 69.80 246 | 85.43 281 |
|
mvs_tets | | | 72.71 258 | 71.11 256 | 77.52 281 | 77.41 337 | 54.52 324 | 88.45 261 | 89.76 227 | 68.76 248 | 62.70 289 | 83.26 249 | 29.49 343 | 92.71 255 | 70.51 185 | 69.62 248 | 85.34 283 |
|
N_pmnet | | | 50.55 331 | 49.11 334 | 54.88 349 | 77.17 338 | 4.02 385 | 84.36 292 | 2.00 384 | 48.59 351 | 45.86 353 | 68.82 349 | 32.22 334 | 82.80 354 | 31.58 366 | 51.38 344 | 77.81 351 |
|
test_djsdf | | | 73.76 248 | 72.56 244 | 77.39 285 | 77.00 339 | 53.93 326 | 89.07 251 | 90.69 192 | 65.80 270 | 63.92 277 | 82.03 261 | 43.14 284 | 92.67 258 | 72.83 158 | 68.53 257 | 85.57 278 |
|
OpenMVS_ROB |  | 61.12 18 | 66.39 300 | 62.92 306 | 76.80 295 | 76.51 340 | 57.77 297 | 89.22 247 | 83.41 329 | 55.48 336 | 53.86 328 | 77.84 310 | 26.28 352 | 93.95 222 | 34.90 356 | 68.76 255 | 78.68 348 |
|
v7n | | | 71.31 266 | 68.65 271 | 79.28 261 | 76.40 341 | 60.77 254 | 86.71 283 | 89.45 238 | 64.17 281 | 58.77 310 | 78.24 306 | 44.59 279 | 93.54 231 | 57.76 276 | 61.75 307 | 83.52 300 |
|
K. test v3 | | | 63.09 315 | 59.61 319 | 73.53 315 | 76.26 342 | 49.38 348 | 83.27 302 | 77.15 348 | 64.35 279 | 47.77 349 | 72.32 340 | 28.73 345 | 87.79 326 | 49.93 302 | 36.69 364 | 83.41 303 |
|
RPSCF | | | 64.24 310 | 61.98 312 | 71.01 332 | 76.10 343 | 45.00 359 | 75.83 341 | 75.94 350 | 46.94 356 | 58.96 308 | 84.59 235 | 31.40 338 | 82.00 359 | 47.76 314 | 60.33 321 | 86.04 267 |
|
OurMVSNet-221017-0 | | | 64.68 307 | 62.17 311 | 72.21 326 | 76.08 344 | 47.35 355 | 80.67 319 | 81.02 338 | 56.19 333 | 51.60 335 | 79.66 299 | 27.05 350 | 88.56 317 | 53.60 292 | 53.63 340 | 80.71 332 |
|
Anonymous20231206 | | | 67.53 295 | 65.78 285 | 72.79 321 | 74.95 345 | 47.59 354 | 88.23 263 | 87.32 292 | 61.75 305 | 58.07 313 | 77.29 314 | 37.79 310 | 87.29 332 | 42.91 331 | 63.71 292 | 83.48 301 |
|
EGC-MVSNET | | | 42.35 334 | 38.09 337 | 55.11 348 | 74.57 346 | 46.62 357 | 71.63 347 | 55.77 374 | 0.04 379 | 0.24 380 | 62.70 358 | 14.24 369 | 74.91 365 | 17.59 371 | 46.06 353 | 43.80 368 |
|
ITE_SJBPF | | | | | 70.43 333 | 74.44 347 | 47.06 356 | | 77.32 347 | 60.16 313 | 54.04 327 | 83.53 245 | 23.30 357 | 84.01 344 | 43.07 330 | 61.58 311 | 80.21 339 |
|
EU-MVSNet | | | 64.01 311 | 63.01 305 | 67.02 342 | 74.40 348 | 38.86 371 | 83.27 302 | 86.19 304 | 45.11 359 | 54.27 325 | 81.15 280 | 36.91 319 | 80.01 362 | 48.79 307 | 57.02 329 | 82.19 322 |
|
XVG-ACMP-BASELINE | | | 68.04 290 | 65.53 289 | 75.56 301 | 74.06 349 | 52.37 332 | 78.43 333 | 85.88 307 | 62.03 300 | 58.91 309 | 81.21 279 | 20.38 362 | 91.15 295 | 60.69 264 | 68.18 259 | 83.16 307 |
|
CL-MVSNet_self_test | | | 69.92 274 | 68.09 276 | 75.41 302 | 73.25 350 | 55.90 316 | 90.05 227 | 89.90 224 | 69.96 230 | 61.96 294 | 76.54 320 | 51.05 227 | 87.64 327 | 49.51 304 | 50.59 346 | 82.70 314 |
|
anonymousdsp | | | 71.14 267 | 69.37 269 | 76.45 296 | 72.95 351 | 54.71 323 | 84.19 293 | 88.88 262 | 61.92 302 | 62.15 292 | 79.77 297 | 38.14 305 | 91.44 294 | 68.90 201 | 67.45 265 | 83.21 306 |
|
lessismore_v0 | | | | | 73.72 314 | 72.93 352 | 47.83 353 | | 61.72 373 | | 45.86 353 | 73.76 333 | 28.63 347 | 89.81 309 | 47.75 315 | 31.37 369 | 83.53 299 |
|
pmmvs6 | | | 67.57 294 | 64.76 293 | 76.00 300 | 72.82 353 | 53.37 328 | 88.71 255 | 86.78 298 | 53.19 340 | 57.58 318 | 78.03 309 | 35.33 324 | 92.41 268 | 55.56 284 | 54.88 337 | 82.21 321 |
|
testgi | | | 64.48 309 | 62.87 307 | 69.31 335 | 71.24 354 | 40.62 366 | 85.49 287 | 79.92 343 | 65.36 274 | 54.18 326 | 83.49 247 | 23.74 356 | 84.55 341 | 41.60 337 | 60.79 316 | 82.77 310 |
|
Patchmatch-RL test | | | 68.17 289 | 64.49 297 | 79.19 262 | 71.22 355 | 53.93 326 | 70.07 350 | 71.54 362 | 69.22 240 | 56.79 320 | 62.89 357 | 56.58 171 | 88.61 315 | 69.53 193 | 52.61 341 | 95.03 79 |
|
Gipuma |  | | 34.91 339 | 31.44 342 | 45.30 354 | 70.99 356 | 39.64 370 | 19.85 374 | 72.56 359 | 20.10 372 | 16.16 374 | 21.47 375 | 5.08 379 | 71.16 367 | 13.07 373 | 43.70 357 | 25.08 372 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
UnsupCasMVSNet_eth | | | 65.79 304 | 63.10 304 | 73.88 312 | 70.71 357 | 50.29 344 | 81.09 317 | 89.88 225 | 72.58 163 | 49.25 345 | 74.77 332 | 32.57 333 | 87.43 331 | 55.96 283 | 41.04 360 | 83.90 296 |
|
CMPMVS |  | 48.56 21 | 66.77 299 | 64.41 298 | 73.84 313 | 70.65 358 | 50.31 343 | 77.79 338 | 85.73 309 | 45.54 358 | 44.76 356 | 82.14 260 | 35.40 323 | 90.14 307 | 63.18 249 | 74.54 213 | 81.07 328 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test20.03 | | | 63.83 312 | 62.65 308 | 67.38 341 | 70.58 359 | 39.94 367 | 86.57 284 | 84.17 321 | 63.29 287 | 51.86 334 | 77.30 313 | 37.09 317 | 82.47 355 | 38.87 348 | 54.13 339 | 79.73 340 |
|
MIMVSNet1 | | | 60.16 324 | 57.33 325 | 68.67 336 | 69.71 360 | 44.13 361 | 78.92 331 | 84.21 320 | 55.05 337 | 44.63 357 | 71.85 342 | 23.91 355 | 81.54 361 | 32.63 363 | 55.03 336 | 80.35 335 |
|
pmmvs-eth3d | | | 65.53 305 | 62.32 310 | 75.19 303 | 69.39 361 | 59.59 273 | 82.80 307 | 83.43 328 | 62.52 296 | 51.30 338 | 72.49 336 | 32.86 330 | 87.16 333 | 55.32 285 | 50.73 345 | 78.83 347 |
|
UnsupCasMVSNet_bld | | | 61.60 319 | 57.71 323 | 73.29 317 | 68.73 362 | 51.64 335 | 78.61 332 | 89.05 258 | 57.20 328 | 46.11 350 | 61.96 359 | 28.70 346 | 88.60 316 | 50.08 301 | 38.90 362 | 79.63 341 |
|
Anonymous20240521 | | | 62.09 317 | 59.08 320 | 71.10 331 | 67.19 363 | 48.72 350 | 83.91 295 | 85.23 312 | 50.38 347 | 47.84 348 | 71.22 346 | 20.74 361 | 85.51 338 | 46.47 318 | 58.75 326 | 79.06 345 |
|
KD-MVS_self_test | | | 60.87 321 | 58.60 321 | 67.68 339 | 66.13 364 | 39.93 368 | 75.63 342 | 84.70 317 | 57.32 327 | 49.57 344 | 68.45 350 | 29.55 342 | 82.87 353 | 48.09 309 | 47.94 350 | 80.25 338 |
|
new-patchmatchnet | | | 59.30 326 | 56.48 327 | 67.79 338 | 65.86 365 | 44.19 360 | 82.47 308 | 81.77 335 | 59.94 315 | 43.65 360 | 66.20 353 | 27.67 348 | 81.68 360 | 39.34 345 | 41.40 359 | 77.50 352 |
|
PM-MVS | | | 59.40 325 | 56.59 326 | 67.84 337 | 63.63 366 | 41.86 363 | 76.76 339 | 63.22 371 | 59.01 320 | 51.07 339 | 72.27 341 | 11.72 371 | 83.25 351 | 61.34 260 | 50.28 347 | 78.39 350 |
|
DSMNet-mixed | | | 56.78 327 | 54.44 330 | 63.79 344 | 63.21 367 | 29.44 375 | 64.43 360 | 64.10 370 | 42.12 363 | 51.32 337 | 71.60 343 | 31.76 336 | 75.04 364 | 36.23 351 | 65.20 278 | 86.87 248 |
|
new_pmnet | | | 49.31 332 | 46.44 335 | 57.93 346 | 62.84 368 | 40.74 365 | 68.47 354 | 62.96 372 | 36.48 365 | 35.09 365 | 57.81 361 | 14.97 367 | 72.18 366 | 32.86 362 | 46.44 352 | 60.88 366 |
|
LF4IMVS | | | 54.01 330 | 52.12 331 | 59.69 345 | 62.41 369 | 39.91 369 | 68.59 353 | 68.28 367 | 42.96 362 | 44.55 358 | 75.18 329 | 14.09 370 | 68.39 368 | 41.36 339 | 51.68 343 | 70.78 361 |
|
ambc | | | | | 69.61 334 | 61.38 370 | 41.35 364 | 49.07 369 | 85.86 308 | | 50.18 343 | 66.40 352 | 10.16 372 | 88.14 322 | 45.73 322 | 44.20 355 | 79.32 344 |
|
TDRefinement | | | 55.28 329 | 51.58 332 | 66.39 343 | 59.53 371 | 46.15 358 | 76.23 340 | 72.80 358 | 44.60 360 | 42.49 361 | 76.28 324 | 15.29 366 | 82.39 356 | 33.20 360 | 43.75 356 | 70.62 362 |
|
pmmvs3 | | | 55.51 328 | 51.50 333 | 67.53 340 | 57.90 372 | 50.93 341 | 80.37 321 | 73.66 356 | 40.63 364 | 44.15 359 | 64.75 356 | 16.30 365 | 78.97 363 | 44.77 327 | 40.98 361 | 72.69 358 |
|
test_method | | | 38.59 337 | 35.16 340 | 48.89 353 | 54.33 373 | 21.35 380 | 45.32 370 | 53.71 375 | 7.41 376 | 28.74 367 | 51.62 363 | 8.70 375 | 52.87 374 | 33.73 357 | 32.89 368 | 72.47 359 |
|
DeepMVS_CX |  | | | | 34.71 357 | 51.45 374 | 24.73 379 | | 28.48 383 | 31.46 367 | 17.49 373 | 52.75 362 | 5.80 378 | 42.60 378 | 18.18 370 | 19.42 371 | 36.81 370 |
|
FPMVS | | | 45.64 333 | 43.10 336 | 53.23 351 | 51.42 375 | 36.46 372 | 64.97 359 | 71.91 360 | 29.13 368 | 27.53 368 | 61.55 360 | 9.83 373 | 65.01 372 | 16.00 372 | 55.58 334 | 58.22 367 |
|
wuyk23d | | | 11.30 346 | 10.95 349 | 12.33 361 | 48.05 376 | 19.89 381 | 25.89 373 | 1.92 385 | 3.58 377 | 3.12 379 | 1.37 379 | 0.64 384 | 15.77 380 | 6.23 378 | 7.77 378 | 1.35 376 |
|
PMMVS2 | | | 37.93 338 | 33.61 341 | 50.92 352 | 46.31 377 | 24.76 378 | 60.55 364 | 50.05 376 | 28.94 369 | 20.93 370 | 47.59 364 | 4.41 381 | 65.13 371 | 25.14 367 | 18.55 372 | 62.87 365 |
|
E-PMN | | | 24.61 341 | 24.00 345 | 26.45 358 | 43.74 378 | 18.44 382 | 60.86 362 | 39.66 378 | 15.11 373 | 9.53 377 | 22.10 374 | 6.52 377 | 46.94 376 | 8.31 376 | 10.14 374 | 13.98 374 |
|
EMVS | | | 23.76 343 | 23.20 347 | 25.46 359 | 41.52 379 | 16.90 383 | 60.56 363 | 38.79 381 | 14.62 374 | 8.99 378 | 20.24 377 | 7.35 376 | 45.82 377 | 7.25 377 | 9.46 375 | 13.64 375 |
|
LCM-MVSNet | | | 40.54 335 | 35.79 338 | 54.76 350 | 36.92 380 | 30.81 374 | 51.41 367 | 69.02 364 | 22.07 370 | 24.63 369 | 45.37 367 | 4.56 380 | 65.81 370 | 33.67 358 | 34.50 367 | 67.67 363 |
|
ANet_high | | | 40.27 336 | 35.20 339 | 55.47 347 | 34.74 381 | 34.47 373 | 63.84 361 | 71.56 361 | 48.42 352 | 18.80 372 | 41.08 370 | 9.52 374 | 64.45 373 | 20.18 369 | 8.66 377 | 67.49 364 |
|
MVE |  | 24.84 23 | 24.35 342 | 19.77 348 | 38.09 356 | 34.56 382 | 26.92 377 | 26.57 372 | 38.87 380 | 11.73 375 | 11.37 376 | 27.44 372 | 1.37 383 | 50.42 375 | 11.41 374 | 14.60 373 | 36.93 369 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PMVS |  | 26.43 22 | 31.84 340 | 28.16 343 | 42.89 355 | 25.87 383 | 27.58 376 | 50.92 368 | 49.78 377 | 21.37 371 | 14.17 375 | 40.81 371 | 2.01 382 | 66.62 369 | 9.61 375 | 38.88 363 | 34.49 371 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 22.26 344 | 23.75 346 | 17.80 360 | 5.23 384 | 12.06 384 | 35.26 371 | 39.48 379 | 2.82 378 | 18.94 371 | 44.20 368 | 22.23 359 | 24.64 379 | 36.30 350 | 9.31 376 | 16.69 373 |
|
testmvs | | | 7.23 348 | 9.62 351 | 0.06 363 | 0.04 385 | 0.02 387 | 84.98 290 | 0.02 386 | 0.03 380 | 0.18 381 | 1.21 380 | 0.01 386 | 0.02 381 | 0.14 379 | 0.01 379 | 0.13 378 |
|
test123 | | | 6.92 349 | 9.21 352 | 0.08 362 | 0.03 386 | 0.05 386 | 81.65 313 | 0.01 387 | 0.02 381 | 0.14 382 | 0.85 381 | 0.03 385 | 0.02 381 | 0.12 380 | 0.00 380 | 0.16 377 |
|
test_blank | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
eth-test2 | | | | | | 0.00 387 | | | | | | | | | | | |
|
eth-test | | | | | | 0.00 387 | | | | | | | | | | | |
|
uanet_test | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
DCPMVS | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
cdsmvs_eth3d_5k | | | 19.86 345 | 26.47 344 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 93.45 82 | 0.00 382 | 0.00 383 | 95.27 52 | 49.56 239 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
pcd_1.5k_mvsjas | | | 4.46 350 | 5.95 353 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 53.55 205 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
sosnet-low-res | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
sosnet | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
uncertanet | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
Regformer | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
ab-mvs-re | | | 7.91 347 | 10.55 350 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 94.95 64 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
uanet | | | 0.00 351 | 0.00 354 | 0.00 364 | 0.00 387 | 0.00 388 | 0.00 375 | 0.00 388 | 0.00 382 | 0.00 383 | 0.00 382 | 0.00 387 | 0.00 383 | 0.00 381 | 0.00 380 | 0.00 379 |
|
PC_three_1452 | | | | | | | | | | 80.91 36 | 94.07 2 | 96.83 14 | 83.57 4 | 99.12 5 | 95.70 2 | 97.42 4 | 97.55 4 |
|
test_241102_TWO | | | | | | | | | 94.41 45 | 71.65 196 | 92.07 6 | 97.21 5 | 74.58 17 | 99.11 6 | 92.34 8 | 95.36 14 | 96.59 15 |
|
test_0728_THIRD | | | | | | | | | | 72.48 166 | 90.55 16 | 96.93 10 | 76.24 11 | 99.08 11 | 91.53 16 | 94.99 17 | 96.43 26 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.68 89 |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 151 | | | | 94.68 89 |
|
sam_mvs | | | | | | | | | | | | | 54.91 190 | | | | |
|
MTGPA |  | | | | | | | | 92.23 128 | | | | | | | | |
|
test_post1 | | | | | | | | 78.95 330 | | | | 20.70 376 | 53.05 210 | 91.50 293 | 60.43 265 | | |
|
test_post | | | | | | | | | | | | 23.01 373 | 56.49 172 | 92.67 258 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 351 | 57.62 154 | 90.25 301 | | | |
|
MTMP | | | | | | | | 93.77 84 | 32.52 382 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 26 | 94.96 18 | 95.29 62 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 57 | 94.75 30 | 95.33 58 |
|
test_prior4 | | | | | | | 67.18 106 | 93.92 75 | | | | | | | | | |
|
test_prior2 | | | | | | | | 95.10 37 | | 75.40 110 | 85.25 54 | 95.61 40 | 67.94 45 | | 87.47 46 | 94.77 25 | |
|
旧先验2 | | | | | | | | 92.00 153 | | 59.37 319 | 87.54 29 | | | 93.47 234 | 75.39 140 | | |
|
新几何2 | | | | | | | | 91.41 176 | | | | | | | | | |
|
无先验 | | | | | | | | 92.71 121 | 92.61 119 | 62.03 300 | | | | 97.01 93 | 66.63 217 | | 93.97 121 |
|
原ACMM2 | | | | | | | | 92.01 150 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 96.09 128 | 61.26 261 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 64 | | | | |
|
testdata1 | | | | | | | | 89.21 248 | | 77.55 85 | | | | | | | |
|
plane_prior5 | | | | | | | | | 91.31 170 | | | | | 95.55 157 | 76.74 132 | 78.53 186 | 88.39 224 |
|
plane_prior4 | | | | | | | | | | | | 89.14 179 | | | | | |
|
plane_prior3 | | | | | | | 61.95 234 | | | 79.09 59 | 72.53 180 | | | | | | |
|
plane_prior2 | | | | | | | | 93.13 103 | | 78.81 65 | | | | | | | |
|
plane_prior | | | | | | | 62.42 224 | 93.85 79 | | 79.38 51 | | | | | | 78.80 183 | |
|
n2 | | | | | | | | | 0.00 388 | | | | | | | | |
|
nn | | | | | | | | | 0.00 388 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 369 | | | | | | | | |
|
test11 | | | | | | | | | 93.01 102 | | | | | | | | |
|
door | | | | | | | | | 66.57 368 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 201 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 129 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 160 | | | 95.61 151 | | | 88.63 218 |
|
HQP3-MVS | | | | | | | | | 91.70 155 | | | | | | | 78.90 181 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 222 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 270 | 80.13 326 | | 67.65 257 | 72.79 175 | | 54.33 198 | | 59.83 269 | | 92.58 161 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 235 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 247 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 199 | | | | |
|