Regformer-2 | | | 86.63 44 | 86.53 43 | 86.95 51 | 89.33 139 | 71.24 67 | 88.43 126 | 92.05 93 | 82.50 1 | 86.88 36 | 90.09 134 | 74.45 31 | 95.61 63 | 84.38 43 | 90.63 103 | 94.01 51 |
|
UA-Net | | | 85.08 71 | 84.96 70 | 85.45 80 | 92.07 81 | 68.07 136 | 89.78 88 | 90.86 137 | 82.48 2 | 84.60 69 | 93.20 63 | 69.35 79 | 95.22 85 | 71.39 168 | 90.88 101 | 93.07 96 |
|
Regformer-1 | | | 86.41 48 | 86.33 46 | 86.64 58 | 89.33 139 | 70.93 75 | 88.43 126 | 91.39 123 | 82.14 3 | 86.65 38 | 90.09 134 | 74.39 34 | 95.01 97 | 83.97 52 | 90.63 103 | 93.97 53 |
|
CANet | | | 86.45 45 | 86.10 54 | 87.51 40 | 90.09 114 | 70.94 74 | 89.70 91 | 92.59 72 | 81.78 4 | 81.32 113 | 91.43 104 | 70.34 69 | 97.23 12 | 84.26 46 | 93.36 71 | 94.37 35 |
|
Regformer-4 | | | 85.68 60 | 85.45 61 | 86.35 62 | 88.95 157 | 69.67 99 | 88.29 136 | 91.29 125 | 81.73 5 | 85.36 50 | 90.01 138 | 72.62 49 | 95.35 83 | 83.28 60 | 87.57 136 | 94.03 49 |
|
NCCC | | | 88.06 14 | 88.01 18 | 88.24 10 | 94.41 24 | 73.62 11 | 91.22 54 | 92.83 61 | 81.50 6 | 85.79 46 | 93.47 59 | 73.02 47 | 97.00 17 | 84.90 33 | 94.94 44 | 94.10 45 |
|
EPNet | | | 83.72 79 | 82.92 88 | 86.14 69 | 84.22 260 | 69.48 103 | 91.05 57 | 85.27 259 | 81.30 7 | 76.83 186 | 91.65 95 | 66.09 109 | 95.56 65 | 76.00 130 | 93.85 67 | 93.38 83 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Regformer-3 | | | 85.23 67 | 85.07 68 | 85.70 78 | 88.95 157 | 69.01 111 | 88.29 136 | 89.91 162 | 80.95 8 | 85.01 54 | 90.01 138 | 72.45 50 | 94.19 130 | 82.50 73 | 87.57 136 | 93.90 57 |
|
CNVR-MVS | | | 88.93 9 | 89.13 9 | 88.33 7 | 94.77 12 | 73.82 9 | 90.51 66 | 93.00 47 | 80.90 9 | 88.06 28 | 94.06 46 | 76.43 17 | 96.84 20 | 88.48 14 | 95.99 19 | 94.34 37 |
|
3Dnovator+ | | 77.84 4 | 85.48 62 | 84.47 76 | 88.51 6 | 91.08 93 | 73.49 17 | 93.18 11 | 93.78 21 | 80.79 10 | 76.66 191 | 93.37 60 | 60.40 190 | 96.75 25 | 77.20 117 | 93.73 69 | 95.29 3 |
|
TranMVSNet+NR-MVSNet | | | 80.84 129 | 80.31 128 | 82.42 189 | 87.85 194 | 62.33 247 | 87.74 156 | 91.33 124 | 80.55 11 | 77.99 164 | 89.86 140 | 65.23 118 | 92.62 195 | 67.05 212 | 75.24 292 | 92.30 123 |
|
MSP-MVS | | | 89.51 4 | 89.91 5 | 88.30 9 | 94.28 32 | 73.46 18 | 92.90 16 | 94.11 8 | 80.27 12 | 91.35 14 | 94.16 41 | 78.35 13 | 96.77 23 | 89.59 3 | 94.22 65 | 94.67 24 |
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 |
HPM-MVS++ |  | | 89.02 8 | 89.15 8 | 88.63 4 | 95.01 9 | 76.03 1 | 92.38 26 | 92.85 60 | 80.26 13 | 87.78 30 | 94.27 36 | 75.89 20 | 96.81 22 | 87.45 19 | 96.44 9 | 93.05 97 |
|
UniMVSNet_NR-MVSNet | | | 81.88 108 | 81.54 108 | 82.92 173 | 88.46 177 | 63.46 229 | 87.13 170 | 92.37 79 | 80.19 14 | 78.38 152 | 89.14 161 | 71.66 58 | 93.05 185 | 70.05 179 | 76.46 268 | 92.25 125 |
|
SteuartSystems-ACMMP | | | 88.72 10 | 88.86 10 | 88.32 8 | 92.14 80 | 72.96 26 | 93.73 5 | 93.67 23 | 80.19 14 | 88.10 27 | 94.80 16 | 73.76 41 | 97.11 13 | 87.51 18 | 95.82 24 | 94.90 10 |
Skip Steuart: Steuart Systems R&D Blog. |
EI-MVSNet-Vis-set | | | 84.19 75 | 83.81 78 | 85.31 82 | 88.18 184 | 67.85 139 | 87.66 157 | 89.73 167 | 80.05 16 | 82.95 93 | 89.59 149 | 70.74 66 | 94.82 107 | 80.66 90 | 84.72 172 | 93.28 88 |
|
ETV-MVS | | | 84.90 74 | 84.67 74 | 85.59 79 | 89.39 136 | 68.66 125 | 88.74 119 | 92.64 71 | 79.97 17 | 84.10 79 | 85.71 253 | 69.32 80 | 95.38 79 | 80.82 86 | 91.37 95 | 92.72 107 |
|
EI-MVSNet-UG-set | | | 83.81 77 | 83.38 81 | 85.09 89 | 87.87 193 | 67.53 147 | 87.44 163 | 89.66 169 | 79.74 18 | 82.23 102 | 89.41 158 | 70.24 71 | 94.74 110 | 79.95 94 | 83.92 182 | 92.99 102 |
|
CS-MVS | | | 86.69 41 | 86.95 36 | 85.90 75 | 90.76 102 | 67.57 146 | 92.83 17 | 93.30 36 | 79.67 19 | 84.57 70 | 92.27 83 | 71.47 59 | 95.02 96 | 84.24 48 | 93.46 70 | 95.13 4 |
|
zzz-MVS | | | 87.53 24 | 87.41 27 | 87.90 21 | 94.18 37 | 74.25 5 | 90.23 76 | 92.02 94 | 79.45 20 | 85.88 43 | 94.80 16 | 68.07 88 | 96.21 43 | 86.69 24 | 95.34 36 | 93.23 89 |
|
MTAPA | | | 87.23 33 | 87.00 34 | 87.90 21 | 94.18 37 | 74.25 5 | 86.58 189 | 92.02 94 | 79.45 20 | 85.88 43 | 94.80 16 | 68.07 88 | 96.21 43 | 86.69 24 | 95.34 36 | 93.23 89 |
|
DROMVSNet | | | 86.01 53 | 86.38 45 | 84.91 97 | 89.31 144 | 66.27 170 | 92.32 30 | 93.63 24 | 79.37 22 | 84.17 78 | 91.88 91 | 69.04 85 | 95.43 74 | 83.93 53 | 93.77 68 | 93.01 100 |
|
XVS | | | 87.18 34 | 86.91 38 | 88.00 15 | 94.42 22 | 73.33 20 | 92.78 18 | 92.99 49 | 79.14 23 | 83.67 86 | 94.17 40 | 67.45 95 | 96.60 34 | 83.06 62 | 94.50 57 | 94.07 47 |
|
X-MVStestdata | | | 80.37 147 | 77.83 184 | 88.00 15 | 94.42 22 | 73.33 20 | 92.78 18 | 92.99 49 | 79.14 23 | 83.67 86 | 12.47 374 | 67.45 95 | 96.60 34 | 83.06 62 | 94.50 57 | 94.07 47 |
|
HQP_MVS | | | 83.64 81 | 83.14 83 | 85.14 87 | 90.08 115 | 68.71 121 | 91.25 52 | 92.44 75 | 79.12 25 | 78.92 141 | 91.00 118 | 60.42 188 | 95.38 79 | 78.71 102 | 86.32 157 | 91.33 151 |
|
plane_prior2 | | | | | | | | 91.25 52 | | 79.12 25 | | | | | | | |
|
IS-MVSNet | | | 83.15 89 | 82.81 89 | 84.18 122 | 89.94 122 | 63.30 233 | 91.59 44 | 88.46 210 | 79.04 27 | 79.49 134 | 92.16 85 | 65.10 119 | 94.28 122 | 67.71 202 | 91.86 90 | 94.95 7 |
|
DU-MVS | | | 81.12 125 | 80.52 124 | 82.90 174 | 87.80 197 | 63.46 229 | 87.02 174 | 91.87 105 | 79.01 28 | 78.38 152 | 89.07 163 | 65.02 120 | 93.05 185 | 70.05 179 | 76.46 268 | 92.20 127 |
|
NR-MVSNet | | | 80.23 150 | 79.38 146 | 82.78 182 | 87.80 197 | 63.34 232 | 86.31 196 | 91.09 132 | 79.01 28 | 72.17 262 | 89.07 163 | 67.20 98 | 92.81 194 | 66.08 219 | 75.65 279 | 92.20 127 |
|
CS-MVS-test | | | 86.29 50 | 86.48 44 | 85.71 77 | 91.02 95 | 67.21 155 | 92.36 28 | 93.78 21 | 78.97 30 | 83.51 89 | 91.20 109 | 70.65 68 | 95.15 88 | 81.96 77 | 94.89 46 | 94.77 21 |
|
DELS-MVS | | | 85.41 65 | 85.30 65 | 85.77 76 | 88.49 175 | 67.93 138 | 85.52 221 | 93.44 31 | 78.70 31 | 83.63 88 | 89.03 165 | 74.57 29 | 95.71 62 | 80.26 93 | 94.04 66 | 93.66 68 |
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 |
WR-MVS | | | 79.49 164 | 79.22 153 | 80.27 236 | 88.79 165 | 58.35 285 | 85.06 226 | 88.61 208 | 78.56 32 | 77.65 169 | 88.34 184 | 63.81 131 | 90.66 256 | 64.98 228 | 77.22 256 | 91.80 140 |
|
plane_prior3 | | | | | | | 68.60 126 | | | 78.44 33 | 78.92 141 | | | | | | |
|
UniMVSNet (Re) | | | 81.60 117 | 81.11 113 | 83.09 164 | 88.38 180 | 64.41 210 | 87.60 158 | 93.02 46 | 78.42 34 | 78.56 148 | 88.16 190 | 69.78 75 | 93.26 172 | 69.58 186 | 76.49 267 | 91.60 142 |
|
DVP-MVS++ | | | 90.23 1 | 91.01 1 | 87.89 24 | 94.34 29 | 71.25 63 | 95.06 1 | 94.23 5 | 78.38 35 | 92.78 4 | 95.74 6 | 82.45 3 | 97.49 3 | 89.42 4 | 96.68 2 | 94.95 7 |
|
test_0728_THIRD | | | | | | | | | | 78.38 35 | 92.12 9 | 95.78 4 | 81.46 7 | 97.40 7 | 89.42 4 | 96.57 7 | 94.67 24 |
|
test_one_0601 | | | | | | 95.07 7 | 71.46 61 | | 94.14 7 | 78.27 37 | 92.05 11 | 95.74 6 | 80.83 11 | | | | |
|
SD-MVS | | | 88.06 14 | 88.50 13 | 86.71 57 | 92.60 76 | 72.71 30 | 91.81 42 | 93.19 40 | 77.87 38 | 90.32 17 | 94.00 48 | 74.83 28 | 93.78 149 | 87.63 17 | 94.27 64 | 93.65 73 |
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 |
casdiffmvs | | | 85.11 70 | 85.14 67 | 85.01 91 | 87.20 218 | 65.77 182 | 87.75 155 | 92.83 61 | 77.84 39 | 84.36 75 | 92.38 82 | 72.15 53 | 93.93 143 | 81.27 82 | 90.48 105 | 95.33 2 |
|
CP-MVSNet | | | 78.22 195 | 78.34 172 | 77.84 274 | 87.83 196 | 54.54 330 | 87.94 149 | 91.17 129 | 77.65 40 | 73.48 247 | 88.49 180 | 62.24 156 | 88.43 289 | 62.19 246 | 74.07 300 | 90.55 179 |
|
plane_prior | | | | | | | 68.71 121 | 90.38 73 | | 77.62 41 | | | | | | 86.16 160 | |
|
baseline | | | 84.93 72 | 84.98 69 | 84.80 102 | 87.30 216 | 65.39 191 | 87.30 167 | 92.88 58 | 77.62 41 | 84.04 81 | 92.26 84 | 71.81 55 | 93.96 137 | 81.31 81 | 90.30 107 | 95.03 6 |
|
VDD-MVS | | | 83.01 94 | 82.36 95 | 84.96 93 | 91.02 95 | 66.40 167 | 88.91 109 | 88.11 213 | 77.57 43 | 84.39 74 | 93.29 62 | 52.19 246 | 93.91 144 | 77.05 119 | 88.70 126 | 94.57 29 |
|
MP-MVS |  | | 87.71 21 | 87.64 23 | 87.93 20 | 94.36 28 | 73.88 7 | 92.71 22 | 92.65 70 | 77.57 43 | 83.84 83 | 94.40 34 | 72.24 52 | 96.28 41 | 85.65 28 | 95.30 40 | 93.62 76 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
PEN-MVS | | | 77.73 209 | 77.69 191 | 77.84 274 | 87.07 221 | 53.91 335 | 87.91 151 | 91.18 128 | 77.56 45 | 73.14 251 | 88.82 171 | 61.23 174 | 89.17 276 | 59.95 265 | 72.37 315 | 90.43 183 |
|
OPM-MVS | | | 83.50 83 | 82.95 87 | 85.14 87 | 88.79 165 | 70.95 73 | 89.13 104 | 91.52 117 | 77.55 46 | 80.96 120 | 91.75 93 | 60.71 182 | 94.50 118 | 79.67 96 | 86.51 155 | 89.97 207 |
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
DeepPCF-MVS | | 80.84 1 | 88.10 12 | 88.56 12 | 86.73 56 | 92.24 78 | 69.03 109 | 89.57 93 | 93.39 34 | 77.53 47 | 89.79 18 | 94.12 43 | 78.98 12 | 96.58 36 | 85.66 27 | 95.72 29 | 94.58 27 |
|
PS-CasMVS | | | 78.01 204 | 78.09 177 | 77.77 276 | 87.71 201 | 54.39 332 | 88.02 145 | 91.22 126 | 77.50 48 | 73.26 249 | 88.64 175 | 60.73 181 | 88.41 290 | 61.88 250 | 73.88 304 | 90.53 180 |
|
MSLP-MVS++ | | | 85.43 64 | 85.76 58 | 84.45 112 | 91.93 83 | 70.24 86 | 90.71 62 | 92.86 59 | 77.46 49 | 84.22 76 | 92.81 76 | 67.16 99 | 92.94 189 | 80.36 91 | 94.35 62 | 90.16 191 |
|
DVP-MVS |  | | 89.60 3 | 90.35 3 | 87.33 45 | 95.27 5 | 71.25 63 | 93.49 9 | 92.73 65 | 77.33 50 | 92.12 9 | 95.78 4 | 80.98 9 | 97.40 7 | 89.08 7 | 96.41 12 | 93.33 86 |
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 | | | | | | 95.27 5 | 71.25 63 | 93.60 6 | 94.11 8 | 77.33 50 | 92.81 3 | 95.79 3 | 80.98 9 | | | | |
|
SED-MVS | | | 90.08 2 | 90.85 2 | 87.77 26 | 95.30 2 | 70.98 70 | 93.57 7 | 94.06 12 | 77.24 52 | 93.10 1 | 95.72 8 | 82.99 1 | 97.44 5 | 89.07 9 | 96.63 4 | 94.88 11 |
|
test_241102_TWO | | | | | | | | | 94.06 12 | 77.24 52 | 92.78 4 | 95.72 8 | 81.26 8 | 97.44 5 | 89.07 9 | 96.58 6 | 94.26 41 |
|
3Dnovator | | 76.31 5 | 83.38 87 | 82.31 96 | 86.59 60 | 87.94 192 | 72.94 29 | 90.64 63 | 92.14 91 | 77.21 54 | 75.47 215 | 92.83 74 | 58.56 197 | 94.72 111 | 73.24 155 | 92.71 77 | 92.13 130 |
|
test_part1 | | | 82.78 96 | 82.08 100 | 84.89 98 | 90.66 103 | 66.97 160 | 90.96 58 | 92.93 57 | 77.19 55 | 80.53 124 | 90.04 136 | 63.44 132 | 95.39 78 | 76.04 129 | 76.90 260 | 92.31 122 |
|
test_241102_ONE | | | | | | 95.30 2 | 70.98 70 | | 94.06 12 | 77.17 56 | 93.10 1 | 95.39 11 | 82.99 1 | 97.27 10 | | | |
|
WR-MVS_H | | | 78.51 190 | 78.49 167 | 78.56 264 | 88.02 190 | 56.38 317 | 88.43 126 | 92.67 67 | 77.14 57 | 73.89 244 | 87.55 204 | 66.25 107 | 89.24 275 | 58.92 275 | 73.55 307 | 90.06 201 |
|
DeepC-MVS | | 79.81 2 | 87.08 37 | 86.88 39 | 87.69 36 | 91.16 92 | 72.32 47 | 90.31 74 | 93.94 17 | 77.12 58 | 82.82 97 | 94.23 39 | 72.13 54 | 97.09 14 | 84.83 36 | 95.37 35 | 93.65 73 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
FC-MVSNet-test | | | 81.52 118 | 82.02 102 | 80.03 240 | 88.42 179 | 55.97 322 | 87.95 148 | 93.42 33 | 77.10 59 | 77.38 174 | 90.98 120 | 69.96 73 | 91.79 226 | 68.46 198 | 84.50 174 | 92.33 120 |
|
DTE-MVSNet | | | 76.99 223 | 76.80 208 | 77.54 281 | 86.24 231 | 53.06 343 | 87.52 160 | 90.66 139 | 77.08 60 | 72.50 257 | 88.67 174 | 60.48 187 | 89.52 270 | 57.33 291 | 70.74 326 | 90.05 202 |
|
LFMVS | | | 81.82 110 | 81.23 111 | 83.57 146 | 91.89 84 | 63.43 231 | 89.84 84 | 81.85 305 | 77.04 61 | 83.21 90 | 93.10 65 | 52.26 245 | 93.43 168 | 71.98 163 | 89.95 114 | 93.85 59 |
|
UGNet | | | 80.83 131 | 79.59 141 | 84.54 108 | 88.04 189 | 68.09 135 | 89.42 94 | 88.16 212 | 76.95 62 | 76.22 202 | 89.46 154 | 49.30 283 | 93.94 140 | 68.48 197 | 90.31 106 | 91.60 142 |
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 |
FIs | | | 82.07 105 | 82.42 92 | 81.04 220 | 88.80 164 | 58.34 286 | 88.26 138 | 93.49 29 | 76.93 63 | 78.47 151 | 91.04 115 | 69.92 74 | 92.34 208 | 69.87 183 | 84.97 169 | 92.44 119 |
|
GST-MVS | | | 87.42 28 | 87.26 29 | 87.89 24 | 94.12 39 | 72.97 25 | 92.39 25 | 93.43 32 | 76.89 64 | 84.68 64 | 93.99 50 | 70.67 67 | 96.82 21 | 84.18 50 | 95.01 42 | 93.90 57 |
|
mPP-MVS | | | 86.67 43 | 86.32 47 | 87.72 32 | 94.41 24 | 73.55 13 | 92.74 20 | 92.22 87 | 76.87 65 | 82.81 98 | 94.25 38 | 66.44 104 | 96.24 42 | 82.88 66 | 94.28 63 | 93.38 83 |
|
ZNCC-MVS | | | 87.94 18 | 87.85 20 | 88.20 11 | 94.39 26 | 73.33 20 | 93.03 14 | 93.81 20 | 76.81 66 | 85.24 52 | 94.32 35 | 71.76 56 | 96.93 18 | 85.53 29 | 95.79 25 | 94.32 38 |
|
VPNet | | | 78.69 186 | 78.66 164 | 78.76 261 | 88.31 182 | 55.72 324 | 84.45 243 | 86.63 243 | 76.79 67 | 78.26 156 | 90.55 126 | 59.30 193 | 89.70 268 | 66.63 214 | 77.05 258 | 90.88 167 |
|
HFP-MVS | | | 87.58 23 | 87.47 25 | 87.94 17 | 94.58 16 | 73.54 15 | 93.04 12 | 93.24 37 | 76.78 68 | 84.91 58 | 94.44 30 | 70.78 64 | 96.61 32 | 84.53 41 | 94.89 46 | 93.66 68 |
|
ACMMPR | | | 87.44 26 | 87.23 31 | 88.08 13 | 94.64 13 | 73.59 12 | 93.04 12 | 93.20 39 | 76.78 68 | 84.66 67 | 94.52 23 | 68.81 86 | 96.65 29 | 84.53 41 | 94.90 45 | 94.00 52 |
|
ACMMP |  | | 85.89 56 | 85.39 62 | 87.38 44 | 93.59 51 | 72.63 34 | 92.74 20 | 93.18 41 | 76.78 68 | 80.73 122 | 93.82 53 | 64.33 125 | 96.29 40 | 82.67 72 | 90.69 102 | 93.23 89 |
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 |
region2R | | | 87.42 28 | 87.20 32 | 88.09 12 | 94.63 14 | 73.55 13 | 93.03 14 | 93.12 42 | 76.73 71 | 84.45 71 | 94.52 23 | 69.09 82 | 96.70 26 | 84.37 44 | 94.83 51 | 94.03 49 |
|
canonicalmvs | | | 85.91 55 | 85.87 57 | 86.04 72 | 89.84 124 | 69.44 107 | 90.45 72 | 93.00 47 | 76.70 72 | 88.01 29 | 91.23 107 | 73.28 43 | 93.91 144 | 81.50 80 | 88.80 124 | 94.77 21 |
|
CP-MVS | | | 87.11 35 | 86.92 37 | 87.68 37 | 94.20 36 | 73.86 8 | 93.98 3 | 92.82 64 | 76.62 73 | 83.68 85 | 94.46 27 | 67.93 90 | 95.95 56 | 84.20 49 | 94.39 60 | 93.23 89 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 38 | 86.62 42 | 87.76 29 | 93.52 52 | 72.37 44 | 91.26 50 | 93.04 43 | 76.62 73 | 84.22 76 | 93.36 61 | 71.44 60 | 96.76 24 | 80.82 86 | 95.33 38 | 94.16 43 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + GP. | | | 85.71 59 | 85.33 63 | 86.84 53 | 91.34 90 | 72.50 37 | 89.07 105 | 87.28 233 | 76.41 75 | 85.80 45 | 90.22 132 | 74.15 39 | 95.37 82 | 81.82 78 | 91.88 87 | 92.65 112 |
|
HQP-NCC | | | | | | 89.33 139 | | 89.17 99 | | 76.41 75 | 77.23 179 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 139 | | 89.17 99 | | 76.41 75 | 77.23 179 | | | | | | |
|
HQP-MVS | | | 82.61 99 | 82.02 102 | 84.37 114 | 89.33 139 | 66.98 158 | 89.17 99 | 92.19 89 | 76.41 75 | 77.23 179 | 90.23 131 | 60.17 191 | 95.11 90 | 77.47 114 | 85.99 163 | 91.03 162 |
|
CANet_DTU | | | 80.61 140 | 79.87 134 | 82.83 176 | 85.60 239 | 63.17 238 | 87.36 164 | 88.65 206 | 76.37 79 | 75.88 209 | 88.44 182 | 53.51 236 | 93.07 184 | 73.30 153 | 89.74 116 | 92.25 125 |
|
VNet | | | 82.21 102 | 82.41 93 | 81.62 202 | 90.82 100 | 60.93 263 | 84.47 240 | 89.78 164 | 76.36 80 | 84.07 80 | 91.88 91 | 64.71 124 | 90.26 259 | 70.68 173 | 88.89 122 | 93.66 68 |
|
Vis-MVSNet |  | | 83.46 84 | 82.80 90 | 85.43 81 | 90.25 111 | 68.74 119 | 90.30 75 | 90.13 156 | 76.33 81 | 80.87 121 | 92.89 72 | 61.00 179 | 94.20 129 | 72.45 162 | 90.97 99 | 93.35 85 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
ACMMP_NAP | | | 88.05 16 | 88.08 17 | 87.94 17 | 93.70 47 | 73.05 23 | 90.86 59 | 93.59 26 | 76.27 82 | 88.14 26 | 95.09 15 | 71.06 62 | 96.67 28 | 87.67 16 | 96.37 14 | 94.09 46 |
|
alignmvs | | | 85.48 62 | 85.32 64 | 85.96 74 | 89.51 131 | 69.47 104 | 89.74 89 | 92.47 74 | 76.17 83 | 87.73 32 | 91.46 103 | 70.32 70 | 93.78 149 | 81.51 79 | 88.95 121 | 94.63 26 |
|
MVS_111021_HR | | | 85.14 69 | 84.75 73 | 86.32 65 | 91.65 87 | 72.70 31 | 85.98 204 | 90.33 150 | 76.11 84 | 82.08 103 | 91.61 98 | 71.36 61 | 94.17 132 | 81.02 83 | 92.58 79 | 92.08 131 |
|
HPM-MVS |  | | 87.11 35 | 86.98 35 | 87.50 41 | 93.88 43 | 72.16 49 | 92.19 34 | 93.33 35 | 76.07 85 | 83.81 84 | 93.95 51 | 69.77 76 | 96.01 52 | 85.15 31 | 94.66 53 | 94.32 38 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
h-mvs33 | | | 83.15 89 | 82.19 97 | 86.02 73 | 90.56 105 | 70.85 78 | 88.15 143 | 89.16 184 | 76.02 86 | 84.67 65 | 91.39 105 | 61.54 165 | 95.50 69 | 82.71 69 | 75.48 283 | 91.72 141 |
|
hse-mvs2 | | | 81.72 111 | 80.94 117 | 84.07 126 | 88.72 168 | 67.68 144 | 85.87 208 | 87.26 234 | 76.02 86 | 84.67 65 | 88.22 189 | 61.54 165 | 93.48 164 | 82.71 69 | 73.44 309 | 91.06 160 |
|
DPE-MVS |  | | 89.48 5 | 89.98 4 | 88.01 14 | 94.80 11 | 72.69 32 | 91.59 44 | 94.10 10 | 75.90 88 | 92.29 7 | 95.66 10 | 81.67 6 | 97.38 9 | 87.44 20 | 96.34 15 | 93.95 54 |
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
CLD-MVS | | | 82.31 101 | 81.65 107 | 84.29 119 | 88.47 176 | 67.73 142 | 85.81 212 | 92.35 80 | 75.78 89 | 78.33 154 | 86.58 236 | 64.01 128 | 94.35 120 | 76.05 128 | 87.48 141 | 90.79 169 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
SF-MVS | | | 88.46 11 | 88.74 11 | 87.64 38 | 92.78 69 | 71.95 53 | 92.40 23 | 94.74 2 | 75.71 90 | 89.16 19 | 95.10 13 | 75.65 22 | 96.19 45 | 87.07 21 | 96.01 17 | 94.79 18 |
|
testdata1 | | | | | | | | 84.14 251 | | 75.71 90 | | | | | | | |
|
APDe-MVS | | | 89.15 6 | 89.63 6 | 87.73 30 | 94.49 20 | 71.69 58 | 93.83 4 | 93.96 16 | 75.70 92 | 91.06 16 | 96.03 1 | 76.84 15 | 97.03 15 | 89.09 6 | 95.65 32 | 94.47 31 |
|
VPA-MVSNet | | | 80.60 141 | 80.55 123 | 80.76 226 | 88.07 188 | 60.80 266 | 86.86 179 | 91.58 116 | 75.67 93 | 80.24 127 | 89.45 156 | 63.34 134 | 90.25 260 | 70.51 175 | 79.22 242 | 91.23 155 |
|
PGM-MVS | | | 86.68 42 | 86.27 48 | 87.90 21 | 94.22 35 | 73.38 19 | 90.22 77 | 93.04 43 | 75.53 94 | 83.86 82 | 94.42 33 | 67.87 92 | 96.64 30 | 82.70 71 | 94.57 56 | 93.66 68 |
|
Effi-MVS+ | | | 83.62 82 | 83.08 84 | 85.24 85 | 88.38 180 | 67.45 148 | 88.89 110 | 89.15 185 | 75.50 95 | 82.27 101 | 88.28 186 | 69.61 77 | 94.45 119 | 77.81 111 | 87.84 134 | 93.84 61 |
|
test_prior3 | | | 86.73 39 | 86.86 40 | 86.33 63 | 92.61 74 | 69.59 100 | 88.85 112 | 92.97 54 | 75.41 96 | 84.91 58 | 93.54 54 | 74.28 36 | 95.48 70 | 83.31 57 | 95.86 22 | 93.91 55 |
|
test_prior2 | | | | | | | | 88.85 112 | | 75.41 96 | 84.91 58 | 93.54 54 | 74.28 36 | | 83.31 57 | 95.86 22 | |
|
LPG-MVS_test | | | 82.08 104 | 81.27 110 | 84.50 109 | 89.23 148 | 68.76 117 | 90.22 77 | 91.94 101 | 75.37 98 | 76.64 192 | 91.51 100 | 54.29 229 | 94.91 100 | 78.44 104 | 83.78 183 | 89.83 212 |
|
LGP-MVS_train | | | | | 84.50 109 | 89.23 148 | 68.76 117 | | 91.94 101 | 75.37 98 | 76.64 192 | 91.51 100 | 54.29 229 | 94.91 100 | 78.44 104 | 83.78 183 | 89.83 212 |
|
#test# | | | 87.33 31 | 87.13 33 | 87.94 17 | 94.58 16 | 73.54 15 | 92.34 29 | 93.24 37 | 75.23 100 | 84.91 58 | 94.44 30 | 70.78 64 | 96.61 32 | 83.75 55 | 94.89 46 | 93.66 68 |
|
MG-MVS | | | 83.41 85 | 83.45 80 | 83.28 154 | 92.74 71 | 62.28 249 | 88.17 141 | 89.50 172 | 75.22 101 | 81.49 112 | 92.74 80 | 66.75 100 | 95.11 90 | 72.85 158 | 91.58 92 | 92.45 118 |
|
LCM-MVSNet-Re | | | 77.05 222 | 76.94 205 | 77.36 282 | 87.20 218 | 51.60 348 | 80.06 296 | 80.46 318 | 75.20 102 | 67.69 302 | 86.72 225 | 62.48 150 | 88.98 280 | 63.44 236 | 89.25 120 | 91.51 145 |
|
MP-MVS-pluss | | | 87.67 22 | 87.72 22 | 87.54 39 | 93.64 50 | 72.04 52 | 89.80 87 | 93.50 28 | 75.17 103 | 86.34 41 | 95.29 12 | 70.86 63 | 96.00 53 | 88.78 12 | 96.04 16 | 94.58 27 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
test1172 | | | 86.20 52 | 86.22 49 | 86.12 70 | 93.95 42 | 69.89 95 | 91.79 43 | 92.28 82 | 75.07 104 | 86.40 40 | 94.58 22 | 65.00 122 | 95.56 65 | 84.34 45 | 92.60 78 | 92.90 104 |
|
test1111 | | | 79.43 167 | 79.18 155 | 80.15 238 | 89.99 120 | 53.31 341 | 87.33 166 | 77.05 341 | 75.04 105 | 80.23 128 | 92.77 79 | 48.97 288 | 92.33 209 | 68.87 193 | 92.40 83 | 94.81 17 |
|
Effi-MVS+-dtu | | | 80.03 154 | 78.57 166 | 84.42 113 | 85.13 247 | 68.74 119 | 88.77 116 | 88.10 214 | 74.99 106 | 74.97 234 | 83.49 289 | 57.27 209 | 93.36 169 | 73.53 148 | 80.88 220 | 91.18 156 |
|
mvs-test1 | | | 80.88 127 | 79.40 145 | 85.29 83 | 85.13 247 | 69.75 98 | 89.28 96 | 88.10 214 | 74.99 106 | 76.44 197 | 86.72 225 | 57.27 209 | 94.26 127 | 73.53 148 | 83.18 195 | 91.87 136 |
|
OMC-MVS | | | 82.69 97 | 81.97 104 | 84.85 99 | 88.75 167 | 67.42 149 | 87.98 146 | 90.87 136 | 74.92 108 | 79.72 132 | 91.65 95 | 62.19 157 | 93.96 137 | 75.26 137 | 86.42 156 | 93.16 94 |
|
test2506 | | | 77.30 219 | 76.49 216 | 79.74 246 | 90.08 115 | 52.02 344 | 87.86 154 | 63.10 371 | 74.88 109 | 80.16 129 | 92.79 77 | 38.29 344 | 92.35 207 | 68.74 195 | 92.50 81 | 94.86 14 |
|
ECVR-MVS |  | | 79.61 160 | 79.26 151 | 80.67 228 | 90.08 115 | 54.69 328 | 87.89 152 | 77.44 338 | 74.88 109 | 80.27 126 | 92.79 77 | 48.96 289 | 92.45 201 | 68.55 196 | 92.50 81 | 94.86 14 |
|
nrg030 | | | 83.88 76 | 83.53 79 | 84.96 93 | 86.77 226 | 69.28 108 | 90.46 71 | 92.67 67 | 74.79 111 | 82.95 93 | 91.33 106 | 72.70 48 | 93.09 183 | 80.79 88 | 79.28 241 | 92.50 115 |
|
SMA-MVS |  | | 89.08 7 | 89.23 7 | 88.61 5 | 94.25 33 | 73.73 10 | 92.40 23 | 93.63 24 | 74.77 112 | 92.29 7 | 95.97 2 | 74.28 36 | 97.24 11 | 88.58 13 | 96.91 1 | 94.87 13 |
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 |
testtj | | | 87.78 20 | 87.78 21 | 87.77 26 | 94.55 18 | 72.47 39 | 92.23 33 | 93.49 29 | 74.75 113 | 88.33 25 | 94.43 32 | 73.27 44 | 97.02 16 | 84.18 50 | 94.84 49 | 93.82 62 |
|
MVS_111021_LR | | | 82.61 99 | 82.11 98 | 84.11 123 | 88.82 162 | 71.58 59 | 85.15 224 | 86.16 250 | 74.69 114 | 80.47 125 | 91.04 115 | 62.29 154 | 90.55 257 | 80.33 92 | 90.08 112 | 90.20 190 |
|
EIA-MVS | | | 83.31 88 | 82.80 90 | 84.82 100 | 89.59 127 | 65.59 184 | 88.21 139 | 92.68 66 | 74.66 115 | 78.96 139 | 86.42 241 | 69.06 83 | 95.26 84 | 75.54 135 | 90.09 111 | 93.62 76 |
|
mvsmamba | | | 81.69 113 | 80.74 119 | 84.56 107 | 87.45 210 | 66.72 163 | 91.26 50 | 85.89 254 | 74.66 115 | 78.23 157 | 90.56 125 | 54.33 228 | 94.91 100 | 80.73 89 | 83.54 190 | 92.04 134 |
|
TSAR-MVS + MP. | | | 88.02 17 | 88.11 16 | 87.72 32 | 93.68 49 | 72.13 50 | 91.41 49 | 92.35 80 | 74.62 117 | 88.90 21 | 93.85 52 | 75.75 21 | 96.00 53 | 87.80 15 | 94.63 54 | 95.04 5 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
SR-MVS | | | 86.73 39 | 86.67 41 | 86.91 52 | 94.11 40 | 72.11 51 | 92.37 27 | 92.56 73 | 74.50 118 | 86.84 37 | 94.65 20 | 67.31 97 | 95.77 60 | 84.80 37 | 92.85 75 | 92.84 106 |
|
FOURS1 | | | | | | 95.00 10 | 72.39 42 | 95.06 1 | 93.84 18 | 74.49 119 | 91.30 15 | | | | | | |
|
ETH3D-3000-0.1 | | | 88.09 13 | 88.29 14 | 87.50 41 | 92.76 70 | 71.89 56 | 91.43 48 | 94.70 3 | 74.47 120 | 88.86 22 | 94.61 21 | 75.23 25 | 95.84 58 | 86.62 26 | 95.92 21 | 94.78 20 |
|
ACMP | | 74.13 6 | 81.51 120 | 80.57 122 | 84.36 115 | 89.42 134 | 68.69 124 | 89.97 82 | 91.50 121 | 74.46 121 | 75.04 233 | 90.41 128 | 53.82 234 | 94.54 115 | 77.56 113 | 82.91 198 | 89.86 211 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
EPP-MVSNet | | | 83.40 86 | 83.02 86 | 84.57 106 | 90.13 113 | 64.47 208 | 92.32 30 | 90.73 138 | 74.45 122 | 79.35 136 | 91.10 112 | 69.05 84 | 95.12 89 | 72.78 159 | 87.22 144 | 94.13 44 |
|
xxxxxxxxxxxxxcwj | | | 87.88 19 | 87.92 19 | 87.77 26 | 93.80 44 | 72.35 45 | 90.47 69 | 89.69 168 | 74.31 123 | 89.16 19 | 95.10 13 | 75.65 22 | 96.19 45 | 87.07 21 | 96.01 17 | 94.79 18 |
|
save fliter | | | | | | 93.80 44 | 72.35 45 | 90.47 69 | 91.17 129 | 74.31 123 | | | | | | | |
|
MVS_Test | | | 83.15 89 | 83.06 85 | 83.41 151 | 86.86 222 | 63.21 235 | 86.11 202 | 92.00 97 | 74.31 123 | 82.87 95 | 89.44 157 | 70.03 72 | 93.21 173 | 77.39 116 | 88.50 130 | 93.81 63 |
|
UniMVSNet_ETH3D | | | 79.10 177 | 78.24 175 | 81.70 201 | 86.85 223 | 60.24 274 | 87.28 168 | 88.79 199 | 74.25 126 | 76.84 185 | 90.53 127 | 49.48 280 | 91.56 232 | 67.98 200 | 82.15 207 | 93.29 87 |
|
IterMVS-LS | | | 80.06 153 | 79.38 146 | 82.11 193 | 85.89 234 | 63.20 236 | 86.79 182 | 89.34 175 | 74.19 127 | 75.45 218 | 86.72 225 | 66.62 101 | 92.39 204 | 72.58 160 | 76.86 262 | 90.75 171 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 80.52 144 | 79.98 132 | 82.12 192 | 84.28 258 | 63.19 237 | 86.41 193 | 88.95 195 | 74.18 128 | 78.69 144 | 87.54 205 | 66.62 101 | 92.43 202 | 72.57 161 | 80.57 226 | 90.74 172 |
|
Vis-MVSNet (Re-imp) | | | 78.36 193 | 78.45 168 | 78.07 272 | 88.64 171 | 51.78 347 | 86.70 186 | 79.63 326 | 74.14 129 | 75.11 230 | 90.83 121 | 61.29 173 | 89.75 266 | 58.10 284 | 91.60 91 | 92.69 110 |
|
v8 | | | 79.97 157 | 79.02 158 | 82.80 179 | 84.09 262 | 64.50 207 | 87.96 147 | 90.29 153 | 74.13 130 | 75.24 227 | 86.81 222 | 62.88 145 | 93.89 146 | 74.39 141 | 75.40 287 | 90.00 203 |
|
CSCG | | | 86.41 48 | 86.19 51 | 87.07 50 | 92.91 65 | 72.48 38 | 90.81 60 | 93.56 27 | 73.95 131 | 83.16 92 | 91.07 114 | 75.94 19 | 95.19 86 | 79.94 95 | 94.38 61 | 93.55 79 |
|
thres100view900 | | | 76.50 230 | 75.55 227 | 79.33 254 | 89.52 130 | 56.99 306 | 85.83 211 | 83.23 291 | 73.94 132 | 76.32 200 | 87.12 217 | 51.89 254 | 91.95 220 | 48.33 332 | 83.75 185 | 89.07 228 |
|
9.14 | | | | 88.26 15 | | 92.84 68 | | 91.52 47 | 94.75 1 | 73.93 133 | 88.57 24 | 94.67 19 | 75.57 24 | 95.79 59 | 86.77 23 | 95.76 28 | |
|
HPM-MVS_fast | | | 85.35 66 | 84.95 71 | 86.57 61 | 93.69 48 | 70.58 84 | 92.15 36 | 91.62 114 | 73.89 134 | 82.67 100 | 94.09 44 | 62.60 147 | 95.54 68 | 80.93 84 | 92.93 73 | 93.57 78 |
|
RRT_MVS | | | 80.35 148 | 79.22 153 | 83.74 142 | 87.63 204 | 65.46 188 | 91.08 56 | 88.92 197 | 73.82 135 | 76.44 197 | 90.03 137 | 49.05 287 | 94.25 128 | 76.84 121 | 79.20 243 | 91.51 145 |
|
PAPM_NR | | | 83.02 93 | 82.41 93 | 84.82 100 | 92.47 77 | 66.37 168 | 87.93 150 | 91.80 108 | 73.82 135 | 77.32 176 | 90.66 123 | 67.90 91 | 94.90 103 | 70.37 176 | 89.48 118 | 93.19 93 |
|
thres600view7 | | | 76.50 230 | 75.44 228 | 79.68 248 | 89.40 135 | 57.16 303 | 85.53 219 | 83.23 291 | 73.79 137 | 76.26 201 | 87.09 218 | 51.89 254 | 91.89 223 | 48.05 337 | 83.72 188 | 90.00 203 |
|
v7n | | | 78.97 181 | 77.58 194 | 83.14 162 | 83.45 274 | 65.51 185 | 88.32 134 | 91.21 127 | 73.69 138 | 72.41 259 | 86.32 244 | 57.93 200 | 93.81 148 | 69.18 189 | 75.65 279 | 90.11 195 |
|
dcpmvs_2 | | | 85.63 61 | 86.15 53 | 84.06 127 | 91.71 86 | 64.94 199 | 86.47 192 | 91.87 105 | 73.63 139 | 86.60 39 | 93.02 70 | 76.57 16 | 91.87 225 | 83.36 56 | 92.15 84 | 95.35 1 |
|
v2v482 | | | 80.23 150 | 79.29 150 | 83.05 167 | 83.62 270 | 64.14 214 | 87.04 173 | 89.97 159 | 73.61 140 | 78.18 160 | 87.22 213 | 61.10 177 | 93.82 147 | 76.11 127 | 76.78 265 | 91.18 156 |
|
Baseline_NR-MVSNet | | | 78.15 199 | 78.33 173 | 77.61 279 | 85.79 235 | 56.21 320 | 86.78 183 | 85.76 255 | 73.60 141 | 77.93 165 | 87.57 203 | 65.02 120 | 88.99 279 | 67.14 211 | 75.33 289 | 87.63 266 |
|
BH-RMVSNet | | | 79.61 160 | 78.44 169 | 83.14 162 | 89.38 137 | 65.93 175 | 84.95 229 | 87.15 236 | 73.56 142 | 78.19 159 | 89.79 142 | 56.67 214 | 93.36 169 | 59.53 269 | 86.74 151 | 90.13 193 |
|
APD-MVS_3200maxsize | | | 85.97 54 | 85.88 56 | 86.22 67 | 92.69 72 | 69.53 102 | 91.93 38 | 92.99 49 | 73.54 143 | 85.94 42 | 94.51 26 | 65.80 114 | 95.61 63 | 83.04 64 | 92.51 80 | 93.53 81 |
|
SR-MVS-dyc-post | | | 85.77 57 | 85.61 59 | 86.23 66 | 93.06 62 | 70.63 82 | 91.88 39 | 92.27 83 | 73.53 144 | 85.69 47 | 94.45 28 | 65.00 122 | 95.56 65 | 82.75 67 | 91.87 88 | 92.50 115 |
|
RE-MVS-def | | | | 85.48 60 | | 93.06 62 | 70.63 82 | 91.88 39 | 92.27 83 | 73.53 144 | 85.69 47 | 94.45 28 | 63.87 129 | | 82.75 67 | 91.87 88 | 92.50 115 |
|
abl_6 | | | 85.23 67 | 84.95 71 | 86.07 71 | 92.23 79 | 70.48 85 | 90.80 61 | 92.08 92 | 73.51 146 | 85.26 51 | 94.16 41 | 62.75 146 | 95.92 57 | 82.46 74 | 91.30 97 | 91.81 139 |
|
tfpn200view9 | | | 76.42 233 | 75.37 232 | 79.55 253 | 89.13 152 | 57.65 298 | 85.17 222 | 83.60 282 | 73.41 147 | 76.45 194 | 86.39 242 | 52.12 247 | 91.95 220 | 48.33 332 | 83.75 185 | 89.07 228 |
|
thres400 | | | 76.50 230 | 75.37 232 | 79.86 243 | 89.13 152 | 57.65 298 | 85.17 222 | 83.60 282 | 73.41 147 | 76.45 194 | 86.39 242 | 52.12 247 | 91.95 220 | 48.33 332 | 83.75 185 | 90.00 203 |
|
v148 | | | 78.72 185 | 77.80 185 | 81.47 206 | 82.73 293 | 61.96 253 | 86.30 197 | 88.08 216 | 73.26 149 | 76.18 204 | 85.47 260 | 62.46 151 | 92.36 206 | 71.92 164 | 73.82 305 | 90.09 197 |
|
iter_conf_final | | | 80.63 139 | 79.35 148 | 84.46 111 | 89.36 138 | 67.70 143 | 89.85 83 | 84.49 269 | 73.19 150 | 78.30 155 | 88.94 166 | 45.98 305 | 94.56 113 | 79.59 97 | 84.48 176 | 91.11 158 |
|
v10 | | | 79.74 159 | 78.67 163 | 82.97 172 | 84.06 263 | 64.95 198 | 87.88 153 | 90.62 140 | 73.11 151 | 75.11 230 | 86.56 237 | 61.46 168 | 94.05 136 | 73.68 146 | 75.55 281 | 89.90 209 |
|
MCST-MVS | | | 87.37 30 | 87.25 30 | 87.73 30 | 94.53 19 | 72.46 40 | 89.82 85 | 93.82 19 | 73.07 152 | 84.86 63 | 92.89 72 | 76.22 18 | 96.33 39 | 84.89 35 | 95.13 41 | 94.40 34 |
|
baseline1 | | | 76.98 224 | 76.75 212 | 77.66 277 | 88.13 185 | 55.66 325 | 85.12 225 | 81.89 303 | 73.04 153 | 76.79 187 | 88.90 168 | 62.43 152 | 87.78 297 | 63.30 238 | 71.18 324 | 89.55 221 |
|
APD-MVS |  | | 87.44 26 | 87.52 24 | 87.19 47 | 94.24 34 | 72.39 42 | 91.86 41 | 92.83 61 | 73.01 154 | 88.58 23 | 94.52 23 | 73.36 42 | 96.49 37 | 84.26 46 | 95.01 42 | 92.70 108 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
diffmvs | | | 82.10 103 | 81.88 105 | 82.76 184 | 83.00 287 | 63.78 221 | 83.68 257 | 89.76 165 | 72.94 155 | 82.02 104 | 89.85 141 | 65.96 113 | 90.79 253 | 82.38 75 | 87.30 143 | 93.71 67 |
|
K. test v3 | | | 71.19 280 | 68.51 288 | 79.21 257 | 83.04 286 | 57.78 297 | 84.35 247 | 76.91 342 | 72.90 156 | 62.99 339 | 82.86 295 | 39.27 339 | 91.09 248 | 61.65 253 | 52.66 361 | 88.75 247 |
|
Fast-Effi-MVS+-dtu | | | 78.02 203 | 76.49 216 | 82.62 186 | 83.16 283 | 66.96 161 | 86.94 176 | 87.45 231 | 72.45 157 | 71.49 269 | 84.17 278 | 54.79 224 | 91.58 231 | 67.61 203 | 80.31 229 | 89.30 226 |
|
PHI-MVS | | | 86.43 46 | 86.17 52 | 87.24 46 | 90.88 99 | 70.96 72 | 92.27 32 | 94.07 11 | 72.45 157 | 85.22 53 | 91.90 90 | 69.47 78 | 96.42 38 | 83.28 60 | 95.94 20 | 94.35 36 |
|
thres200 | | | 75.55 244 | 74.47 242 | 78.82 260 | 87.78 200 | 57.85 295 | 83.07 270 | 83.51 285 | 72.44 159 | 75.84 210 | 84.42 275 | 52.08 249 | 91.75 227 | 47.41 339 | 83.64 189 | 86.86 287 |
|
test_yl | | | 81.17 123 | 80.47 125 | 83.24 157 | 89.13 152 | 63.62 222 | 86.21 199 | 89.95 160 | 72.43 160 | 81.78 109 | 89.61 147 | 57.50 206 | 93.58 157 | 70.75 171 | 86.90 148 | 92.52 113 |
|
DCV-MVSNet | | | 81.17 123 | 80.47 125 | 83.24 157 | 89.13 152 | 63.62 222 | 86.21 199 | 89.95 160 | 72.43 160 | 81.78 109 | 89.61 147 | 57.50 206 | 93.58 157 | 70.75 171 | 86.90 148 | 92.52 113 |
|
BH-untuned | | | 79.47 165 | 78.60 165 | 82.05 194 | 89.19 150 | 65.91 176 | 86.07 203 | 88.52 209 | 72.18 162 | 75.42 219 | 87.69 200 | 61.15 176 | 93.54 161 | 60.38 262 | 86.83 150 | 86.70 291 |
|
TransMVSNet (Re) | | | 75.39 248 | 74.56 240 | 77.86 273 | 85.50 241 | 57.10 305 | 86.78 183 | 86.09 252 | 72.17 163 | 71.53 268 | 87.34 208 | 63.01 144 | 89.31 274 | 56.84 295 | 61.83 349 | 87.17 279 |
|
GA-MVS | | | 76.87 226 | 75.17 235 | 81.97 197 | 82.75 292 | 62.58 244 | 81.44 285 | 86.35 248 | 72.16 164 | 74.74 237 | 82.89 294 | 46.20 304 | 92.02 218 | 68.85 194 | 81.09 218 | 91.30 154 |
|
v1144 | | | 80.03 154 | 79.03 157 | 83.01 169 | 83.78 268 | 64.51 205 | 87.11 172 | 90.57 142 | 71.96 165 | 78.08 163 | 86.20 246 | 61.41 169 | 93.94 140 | 74.93 138 | 77.23 255 | 90.60 177 |
|
ETH3D cwj APD-0.16 | | | 87.31 32 | 87.27 28 | 87.44 43 | 91.60 88 | 72.45 41 | 90.02 80 | 94.37 4 | 71.76 166 | 87.28 34 | 94.27 36 | 75.18 26 | 96.08 49 | 85.16 30 | 95.77 26 | 93.80 65 |
|
PS-MVSNAJss | | | 82.07 105 | 81.31 109 | 84.34 117 | 86.51 229 | 67.27 153 | 89.27 97 | 91.51 118 | 71.75 167 | 79.37 135 | 90.22 132 | 63.15 140 | 94.27 123 | 77.69 112 | 82.36 206 | 91.49 148 |
|
EPNet_dtu | | | 75.46 245 | 74.86 236 | 77.23 286 | 82.57 297 | 54.60 329 | 86.89 178 | 83.09 294 | 71.64 168 | 66.25 320 | 85.86 251 | 55.99 216 | 88.04 294 | 54.92 302 | 86.55 154 | 89.05 233 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
GBi-Net | | | 78.40 191 | 77.40 196 | 81.40 208 | 87.60 205 | 63.01 239 | 88.39 130 | 89.28 177 | 71.63 169 | 75.34 222 | 87.28 209 | 54.80 221 | 91.11 243 | 62.72 240 | 79.57 235 | 90.09 197 |
|
test1 | | | 78.40 191 | 77.40 196 | 81.40 208 | 87.60 205 | 63.01 239 | 88.39 130 | 89.28 177 | 71.63 169 | 75.34 222 | 87.28 209 | 54.80 221 | 91.11 243 | 62.72 240 | 79.57 235 | 90.09 197 |
|
FMVSNet2 | | | 78.20 197 | 77.21 199 | 81.20 215 | 87.60 205 | 62.89 243 | 87.47 162 | 89.02 190 | 71.63 169 | 75.29 226 | 87.28 209 | 54.80 221 | 91.10 246 | 62.38 244 | 79.38 239 | 89.61 219 |
|
iter_conf05 | | | 80.00 156 | 78.70 162 | 83.91 139 | 87.84 195 | 65.83 178 | 88.84 114 | 84.92 264 | 71.61 172 | 78.70 143 | 88.94 166 | 43.88 317 | 94.56 113 | 79.28 98 | 84.28 179 | 91.33 151 |
|
patch_mono-2 | | | 83.65 80 | 84.54 75 | 80.99 221 | 90.06 119 | 65.83 178 | 84.21 249 | 88.74 204 | 71.60 173 | 85.01 54 | 92.44 81 | 74.51 30 | 83.50 326 | 82.15 76 | 92.15 84 | 93.64 75 |
|
V42 | | | 79.38 171 | 78.24 175 | 82.83 176 | 81.10 320 | 65.50 186 | 85.55 217 | 89.82 163 | 71.57 174 | 78.21 158 | 86.12 247 | 60.66 184 | 93.18 178 | 75.64 132 | 75.46 285 | 89.81 214 |
|
API-MVS | | | 81.99 107 | 81.23 111 | 84.26 120 | 90.94 97 | 70.18 92 | 91.10 55 | 89.32 176 | 71.51 175 | 78.66 146 | 88.28 186 | 65.26 117 | 95.10 93 | 64.74 230 | 91.23 98 | 87.51 270 |
|
tttt0517 | | | 79.40 169 | 77.91 181 | 83.90 140 | 88.10 187 | 63.84 219 | 88.37 133 | 84.05 277 | 71.45 176 | 76.78 188 | 89.12 162 | 49.93 277 | 94.89 104 | 70.18 178 | 83.18 195 | 92.96 103 |
|
pm-mvs1 | | | 77.25 221 | 76.68 214 | 78.93 259 | 84.22 260 | 58.62 284 | 86.41 193 | 88.36 211 | 71.37 177 | 73.31 248 | 88.01 196 | 61.22 175 | 89.15 277 | 64.24 232 | 73.01 312 | 89.03 234 |
|
GeoE | | | 81.71 112 | 81.01 116 | 83.80 141 | 89.51 131 | 64.45 209 | 88.97 107 | 88.73 205 | 71.27 178 | 78.63 147 | 89.76 143 | 66.32 106 | 93.20 175 | 69.89 182 | 86.02 162 | 93.74 66 |
|
FMVSNet3 | | | 77.88 207 | 76.85 207 | 80.97 222 | 86.84 224 | 62.36 246 | 86.52 191 | 88.77 200 | 71.13 179 | 75.34 222 | 86.66 232 | 54.07 232 | 91.10 246 | 62.72 240 | 79.57 235 | 89.45 222 |
|
VDDNet | | | 81.52 118 | 80.67 121 | 84.05 129 | 90.44 108 | 64.13 215 | 89.73 90 | 85.91 253 | 71.11 180 | 83.18 91 | 93.48 57 | 50.54 269 | 93.49 163 | 73.40 152 | 88.25 132 | 94.54 30 |
|
XVG-OURS | | | 80.41 145 | 79.23 152 | 83.97 136 | 85.64 238 | 69.02 110 | 83.03 271 | 90.39 145 | 71.09 181 | 77.63 170 | 91.49 102 | 54.62 227 | 91.35 238 | 75.71 131 | 83.47 191 | 91.54 144 |
|
SixPastTwentyTwo | | | 73.37 262 | 71.26 272 | 79.70 247 | 85.08 249 | 57.89 294 | 85.57 213 | 83.56 284 | 71.03 182 | 65.66 322 | 85.88 250 | 42.10 329 | 92.57 197 | 59.11 273 | 63.34 347 | 88.65 250 |
|
ZD-MVS | | | | | | 94.38 27 | 72.22 48 | | 92.67 67 | 70.98 183 | 87.75 31 | 94.07 45 | 74.01 40 | 96.70 26 | 84.66 39 | 94.84 49 | |
|
v1192 | | | 79.59 162 | 78.43 170 | 83.07 166 | 83.55 272 | 64.52 204 | 86.93 177 | 90.58 141 | 70.83 184 | 77.78 167 | 85.90 249 | 59.15 194 | 93.94 140 | 73.96 145 | 77.19 257 | 90.76 170 |
|
ETH3 D test6400 | | | 87.50 25 | 87.44 26 | 87.70 35 | 93.71 46 | 71.75 57 | 90.62 64 | 94.05 15 | 70.80 185 | 87.59 33 | 93.51 56 | 77.57 14 | 96.63 31 | 83.31 57 | 95.77 26 | 94.72 23 |
|
Fast-Effi-MVS+ | | | 80.81 132 | 79.92 133 | 83.47 147 | 88.85 159 | 64.51 205 | 85.53 219 | 89.39 174 | 70.79 186 | 78.49 150 | 85.06 269 | 67.54 94 | 93.58 157 | 67.03 213 | 86.58 153 | 92.32 121 |
|
PS-MVSNAJ | | | 81.69 113 | 81.02 115 | 83.70 143 | 89.51 131 | 68.21 134 | 84.28 248 | 90.09 157 | 70.79 186 | 81.26 117 | 85.62 257 | 63.15 140 | 94.29 121 | 75.62 133 | 88.87 123 | 88.59 251 |
|
LTVRE_ROB | | 69.57 13 | 76.25 236 | 74.54 241 | 81.41 207 | 88.60 172 | 64.38 211 | 79.24 304 | 89.12 188 | 70.76 188 | 69.79 289 | 87.86 197 | 49.09 285 | 93.20 175 | 56.21 299 | 80.16 230 | 86.65 292 |
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 |
xiu_mvs_v2_base | | | 81.69 113 | 81.05 114 | 83.60 144 | 89.15 151 | 68.03 137 | 84.46 242 | 90.02 158 | 70.67 189 | 81.30 116 | 86.53 239 | 63.17 139 | 94.19 130 | 75.60 134 | 88.54 128 | 88.57 252 |
|
XVG-OURS-SEG-HR | | | 80.81 132 | 79.76 137 | 83.96 137 | 85.60 239 | 68.78 116 | 83.54 263 | 90.50 143 | 70.66 190 | 76.71 190 | 91.66 94 | 60.69 183 | 91.26 240 | 76.94 120 | 81.58 214 | 91.83 137 |
|
Anonymous202405211 | | | 78.25 194 | 77.01 202 | 81.99 196 | 91.03 94 | 60.67 268 | 84.77 232 | 83.90 279 | 70.65 191 | 80.00 130 | 91.20 109 | 41.08 334 | 91.43 236 | 65.21 225 | 85.26 167 | 93.85 59 |
|
DP-MVS Recon | | | 83.11 92 | 82.09 99 | 86.15 68 | 94.44 21 | 70.92 76 | 88.79 115 | 92.20 88 | 70.53 192 | 79.17 137 | 91.03 117 | 64.12 127 | 96.03 50 | 68.39 199 | 90.14 110 | 91.50 147 |
|
FMVSNet1 | | | 77.44 215 | 76.12 222 | 81.40 208 | 86.81 225 | 63.01 239 | 88.39 130 | 89.28 177 | 70.49 193 | 74.39 240 | 87.28 209 | 49.06 286 | 91.11 243 | 60.91 259 | 78.52 245 | 90.09 197 |
|
ab-mvs | | | 79.51 163 | 78.97 159 | 81.14 217 | 88.46 177 | 60.91 264 | 83.84 255 | 89.24 181 | 70.36 194 | 79.03 138 | 88.87 170 | 63.23 138 | 90.21 261 | 65.12 226 | 82.57 204 | 92.28 124 |
|
tfpnnormal | | | 74.39 252 | 73.16 255 | 78.08 271 | 86.10 233 | 58.05 289 | 84.65 237 | 87.53 228 | 70.32 195 | 71.22 271 | 85.63 256 | 54.97 220 | 89.86 264 | 43.03 353 | 75.02 293 | 86.32 295 |
|
ACMM | | 73.20 8 | 80.78 137 | 79.84 135 | 83.58 145 | 89.31 144 | 68.37 129 | 89.99 81 | 91.60 115 | 70.28 196 | 77.25 177 | 89.66 145 | 53.37 237 | 93.53 162 | 74.24 143 | 82.85 199 | 88.85 243 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 68.96 14 | 76.01 239 | 74.01 246 | 82.03 195 | 88.60 172 | 65.31 193 | 88.86 111 | 87.55 227 | 70.25 197 | 67.75 301 | 87.47 207 | 41.27 332 | 93.19 177 | 58.37 281 | 75.94 276 | 87.60 267 |
|
IB-MVS | | 68.01 15 | 75.85 241 | 73.36 253 | 83.31 153 | 84.76 252 | 66.03 172 | 83.38 264 | 85.06 261 | 70.21 198 | 69.40 291 | 81.05 312 | 45.76 309 | 94.66 112 | 65.10 227 | 75.49 282 | 89.25 227 |
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 |
thisisatest0530 | | | 79.40 169 | 77.76 188 | 84.31 118 | 87.69 203 | 65.10 197 | 87.36 164 | 84.26 275 | 70.04 199 | 77.42 173 | 88.26 188 | 49.94 275 | 94.79 109 | 70.20 177 | 84.70 173 | 93.03 98 |
|
v144192 | | | 79.47 165 | 78.37 171 | 82.78 182 | 83.35 275 | 63.96 217 | 86.96 175 | 90.36 149 | 69.99 200 | 77.50 171 | 85.67 255 | 60.66 184 | 93.77 151 | 74.27 142 | 76.58 266 | 90.62 175 |
|
c3_l | | | 78.75 184 | 77.91 181 | 81.26 212 | 82.89 290 | 61.56 258 | 84.09 253 | 89.13 187 | 69.97 201 | 75.56 213 | 84.29 277 | 66.36 105 | 92.09 216 | 73.47 151 | 75.48 283 | 90.12 194 |
|
v1921920 | | | 79.22 173 | 78.03 178 | 82.80 179 | 83.30 277 | 63.94 218 | 86.80 181 | 90.33 150 | 69.91 202 | 77.48 172 | 85.53 258 | 58.44 198 | 93.75 153 | 73.60 147 | 76.85 263 | 90.71 173 |
|
ACMH | | 67.68 16 | 75.89 240 | 73.93 247 | 81.77 200 | 88.71 169 | 66.61 165 | 88.62 122 | 89.01 191 | 69.81 203 | 66.78 313 | 86.70 230 | 41.95 331 | 91.51 235 | 55.64 300 | 78.14 250 | 87.17 279 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
DPM-MVS | | | 84.93 72 | 84.29 77 | 86.84 53 | 90.20 112 | 73.04 24 | 87.12 171 | 93.04 43 | 69.80 204 | 82.85 96 | 91.22 108 | 73.06 46 | 96.02 51 | 76.72 125 | 94.63 54 | 91.46 150 |
|
MAR-MVS | | | 81.84 109 | 80.70 120 | 85.27 84 | 91.32 91 | 71.53 60 | 89.82 85 | 90.92 134 | 69.77 205 | 78.50 149 | 86.21 245 | 62.36 153 | 94.52 117 | 65.36 224 | 92.05 86 | 89.77 215 |
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 |
XVG-ACMP-BASELINE | | | 76.11 238 | 74.27 245 | 81.62 202 | 83.20 280 | 64.67 203 | 83.60 261 | 89.75 166 | 69.75 206 | 71.85 265 | 87.09 218 | 32.78 357 | 92.11 215 | 69.99 181 | 80.43 228 | 88.09 258 |
|
BH-w/o | | | 78.21 196 | 77.33 198 | 80.84 224 | 88.81 163 | 65.13 196 | 84.87 230 | 87.85 223 | 69.75 206 | 74.52 239 | 84.74 273 | 61.34 171 | 93.11 182 | 58.24 283 | 85.84 165 | 84.27 321 |
|
v1240 | | | 78.99 180 | 77.78 186 | 82.64 185 | 83.21 279 | 63.54 226 | 86.62 188 | 90.30 152 | 69.74 208 | 77.33 175 | 85.68 254 | 57.04 212 | 93.76 152 | 73.13 156 | 76.92 259 | 90.62 175 |
|
ET-MVSNet_ETH3D | | | 78.63 187 | 76.63 215 | 84.64 105 | 86.73 227 | 69.47 104 | 85.01 227 | 84.61 267 | 69.54 209 | 66.51 318 | 86.59 234 | 50.16 272 | 91.75 227 | 76.26 126 | 84.24 180 | 92.69 110 |
|
eth_miper_zixun_eth | | | 77.92 206 | 76.69 213 | 81.61 204 | 83.00 287 | 61.98 252 | 83.15 267 | 89.20 183 | 69.52 210 | 74.86 236 | 84.35 276 | 61.76 161 | 92.56 198 | 71.50 167 | 72.89 313 | 90.28 188 |
|
PVSNet_Blended_VisFu | | | 82.62 98 | 81.83 106 | 84.96 93 | 90.80 101 | 69.76 97 | 88.74 119 | 91.70 113 | 69.39 211 | 78.96 139 | 88.46 181 | 65.47 116 | 94.87 106 | 74.42 140 | 88.57 127 | 90.24 189 |
|
mvs_tets | | | 79.13 176 | 77.77 187 | 83.22 159 | 84.70 253 | 66.37 168 | 89.17 99 | 90.19 154 | 69.38 212 | 75.40 220 | 89.46 154 | 44.17 315 | 93.15 179 | 76.78 123 | 80.70 224 | 90.14 192 |
|
PVSNet_BlendedMVS | | | 80.60 141 | 80.02 131 | 82.36 191 | 88.85 159 | 65.40 189 | 86.16 201 | 92.00 97 | 69.34 213 | 78.11 161 | 86.09 248 | 66.02 111 | 94.27 123 | 71.52 165 | 82.06 208 | 87.39 272 |
|
AdaColmap |  | | 80.58 143 | 79.42 144 | 84.06 127 | 93.09 61 | 68.91 114 | 89.36 95 | 88.97 194 | 69.27 214 | 75.70 212 | 89.69 144 | 57.20 211 | 95.77 60 | 63.06 239 | 88.41 131 | 87.50 271 |
|
ITE_SJBPF | | | | | 78.22 269 | 81.77 308 | 60.57 269 | | 83.30 289 | 69.25 215 | 67.54 303 | 87.20 214 | 36.33 350 | 87.28 301 | 54.34 304 | 74.62 297 | 86.80 288 |
|
cl____ | | | 77.72 210 | 76.76 210 | 80.58 229 | 82.49 299 | 60.48 271 | 83.09 268 | 87.87 221 | 69.22 216 | 74.38 241 | 85.22 265 | 62.10 158 | 91.53 233 | 71.09 169 | 75.41 286 | 89.73 217 |
|
DIV-MVS_self_test | | | 77.72 210 | 76.76 210 | 80.58 229 | 82.48 300 | 60.48 271 | 83.09 268 | 87.86 222 | 69.22 216 | 74.38 241 | 85.24 264 | 62.10 158 | 91.53 233 | 71.09 169 | 75.40 287 | 89.74 216 |
|
bld_raw_dy_0_64 | | | 77.29 220 | 75.98 223 | 81.22 214 | 85.04 250 | 65.47 187 | 88.14 144 | 77.56 335 | 69.20 218 | 73.77 245 | 89.40 160 | 42.24 328 | 88.85 285 | 76.78 123 | 81.64 213 | 89.33 225 |
|
jajsoiax | | | 79.29 172 | 77.96 179 | 83.27 155 | 84.68 254 | 66.57 166 | 89.25 98 | 90.16 155 | 69.20 218 | 75.46 217 | 89.49 151 | 45.75 310 | 93.13 181 | 76.84 121 | 80.80 222 | 90.11 195 |
|
IterMVS-SCA-FT | | | 75.43 246 | 73.87 249 | 80.11 239 | 82.69 294 | 64.85 200 | 81.57 283 | 83.47 287 | 69.16 220 | 70.49 275 | 84.15 279 | 51.95 252 | 88.15 292 | 69.23 188 | 72.14 318 | 87.34 274 |
|
CL-MVSNet_self_test | | | 72.37 275 | 71.46 267 | 75.09 303 | 79.49 339 | 53.53 337 | 80.76 288 | 85.01 263 | 69.12 221 | 70.51 274 | 82.05 306 | 57.92 201 | 84.13 321 | 52.27 312 | 66.00 341 | 87.60 267 |
|
AUN-MVS | | | 79.21 174 | 77.60 193 | 84.05 129 | 88.71 169 | 67.61 145 | 85.84 210 | 87.26 234 | 69.08 222 | 77.23 179 | 88.14 194 | 53.20 239 | 93.47 165 | 75.50 136 | 73.45 308 | 91.06 160 |
|
xiu_mvs_v1_base_debu | | | 80.80 134 | 79.72 138 | 84.03 132 | 87.35 211 | 70.19 89 | 85.56 214 | 88.77 200 | 69.06 223 | 81.83 105 | 88.16 190 | 50.91 263 | 92.85 191 | 78.29 108 | 87.56 138 | 89.06 230 |
|
xiu_mvs_v1_base | | | 80.80 134 | 79.72 138 | 84.03 132 | 87.35 211 | 70.19 89 | 85.56 214 | 88.77 200 | 69.06 223 | 81.83 105 | 88.16 190 | 50.91 263 | 92.85 191 | 78.29 108 | 87.56 138 | 89.06 230 |
|
xiu_mvs_v1_base_debi | | | 80.80 134 | 79.72 138 | 84.03 132 | 87.35 211 | 70.19 89 | 85.56 214 | 88.77 200 | 69.06 223 | 81.83 105 | 88.16 190 | 50.91 263 | 92.85 191 | 78.29 108 | 87.56 138 | 89.06 230 |
|
MVSTER | | | 79.01 179 | 77.88 183 | 82.38 190 | 83.07 284 | 64.80 201 | 84.08 254 | 88.95 195 | 69.01 226 | 78.69 144 | 87.17 216 | 54.70 225 | 92.43 202 | 74.69 139 | 80.57 226 | 89.89 210 |
|
cl22 | | | 78.07 201 | 77.01 202 | 81.23 213 | 82.37 302 | 61.83 255 | 83.55 262 | 87.98 218 | 68.96 227 | 75.06 232 | 83.87 281 | 61.40 170 | 91.88 224 | 73.53 148 | 76.39 270 | 89.98 206 |
|
agg_prior1 | | | 86.22 51 | 86.09 55 | 86.62 59 | 92.85 66 | 71.94 54 | 88.59 123 | 91.78 110 | 68.96 227 | 84.41 72 | 93.18 64 | 74.94 27 | 94.93 98 | 84.75 38 | 95.33 38 | 93.01 100 |
|
miper_ehance_all_eth | | | 78.59 189 | 77.76 188 | 81.08 219 | 82.66 295 | 61.56 258 | 83.65 258 | 89.15 185 | 68.87 229 | 75.55 214 | 83.79 285 | 66.49 103 | 92.03 217 | 73.25 154 | 76.39 270 | 89.64 218 |
|
PAPR | | | 81.66 116 | 80.89 118 | 83.99 135 | 90.27 110 | 64.00 216 | 86.76 185 | 91.77 112 | 68.84 230 | 77.13 184 | 89.50 150 | 67.63 93 | 94.88 105 | 67.55 204 | 88.52 129 | 93.09 95 |
|
CPTT-MVS | | | 83.73 78 | 83.33 82 | 84.92 96 | 93.28 55 | 70.86 77 | 92.09 37 | 90.38 146 | 68.75 231 | 79.57 133 | 92.83 74 | 60.60 186 | 93.04 187 | 80.92 85 | 91.56 93 | 90.86 168 |
|
train_agg | | | 86.43 46 | 86.20 50 | 87.13 49 | 93.26 56 | 72.96 26 | 88.75 117 | 91.89 103 | 68.69 232 | 85.00 56 | 93.10 65 | 74.43 32 | 95.41 76 | 84.97 32 | 95.71 30 | 93.02 99 |
|
test_8 | | | | | | 93.13 58 | 72.57 36 | 88.68 121 | 91.84 107 | 68.69 232 | 84.87 62 | 93.10 65 | 74.43 32 | 95.16 87 | | | |
|
MVSFormer | | | 82.85 95 | 82.05 101 | 85.24 85 | 87.35 211 | 70.21 87 | 90.50 67 | 90.38 146 | 68.55 234 | 81.32 113 | 89.47 152 | 61.68 162 | 93.46 166 | 78.98 100 | 90.26 108 | 92.05 132 |
|
test_djsdf | | | 80.30 149 | 79.32 149 | 83.27 155 | 83.98 265 | 65.37 192 | 90.50 67 | 90.38 146 | 68.55 234 | 76.19 203 | 88.70 172 | 56.44 215 | 93.46 166 | 78.98 100 | 80.14 232 | 90.97 165 |
|
TEST9 | | | | | | 93.26 56 | 72.96 26 | 88.75 117 | 91.89 103 | 68.44 236 | 85.00 56 | 93.10 65 | 74.36 35 | 95.41 76 | | | |
|
CDPH-MVS | | | 85.76 58 | 85.29 66 | 87.17 48 | 93.49 53 | 71.08 68 | 88.58 124 | 92.42 78 | 68.32 237 | 84.61 68 | 93.48 57 | 72.32 51 | 96.15 48 | 79.00 99 | 95.43 34 | 94.28 40 |
|
PC_three_1452 | | | | | | | | | | 68.21 238 | 92.02 12 | 94.00 48 | 82.09 5 | 95.98 55 | 84.58 40 | 96.68 2 | 94.95 7 |
|
IterMVS | | | 74.29 253 | 72.94 257 | 78.35 268 | 81.53 312 | 63.49 228 | 81.58 282 | 82.49 299 | 68.06 239 | 69.99 284 | 83.69 287 | 51.66 258 | 85.54 311 | 65.85 221 | 71.64 321 | 86.01 303 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
TAMVS | | | 78.89 183 | 77.51 195 | 83.03 168 | 87.80 197 | 67.79 141 | 84.72 233 | 85.05 262 | 67.63 240 | 76.75 189 | 87.70 199 | 62.25 155 | 90.82 252 | 58.53 280 | 87.13 145 | 90.49 181 |
|
PVSNet_Blended | | | 80.98 126 | 80.34 127 | 82.90 174 | 88.85 159 | 65.40 189 | 84.43 244 | 92.00 97 | 67.62 241 | 78.11 161 | 85.05 270 | 66.02 111 | 94.27 123 | 71.52 165 | 89.50 117 | 89.01 235 |
|
TR-MVS | | | 77.44 215 | 76.18 221 | 81.20 215 | 88.24 183 | 63.24 234 | 84.61 238 | 86.40 246 | 67.55 242 | 77.81 166 | 86.48 240 | 54.10 231 | 93.15 179 | 57.75 287 | 82.72 202 | 87.20 278 |
|
CDS-MVSNet | | | 79.07 178 | 77.70 190 | 83.17 161 | 87.60 205 | 68.23 133 | 84.40 246 | 86.20 249 | 67.49 243 | 76.36 199 | 86.54 238 | 61.54 165 | 90.79 253 | 61.86 251 | 87.33 142 | 90.49 181 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
mvs_anonymous | | | 79.42 168 | 79.11 156 | 80.34 234 | 84.45 257 | 57.97 292 | 82.59 273 | 87.62 226 | 67.40 244 | 76.17 206 | 88.56 179 | 68.47 87 | 89.59 269 | 70.65 174 | 86.05 161 | 93.47 82 |
|
MVS_0304 | | | 72.48 272 | 70.89 275 | 77.24 285 | 82.20 303 | 59.68 277 | 84.11 252 | 83.49 286 | 67.10 245 | 66.87 311 | 80.59 318 | 35.00 354 | 87.40 299 | 59.07 274 | 79.58 234 | 84.63 319 |
|
IU-MVS | | | | | | 95.30 2 | 71.25 63 | | 92.95 56 | 66.81 246 | 92.39 6 | | | | 88.94 11 | 96.63 4 | 94.85 16 |
|
baseline2 | | | 75.70 242 | 73.83 250 | 81.30 211 | 83.26 278 | 61.79 256 | 82.57 274 | 80.65 314 | 66.81 246 | 66.88 310 | 83.42 290 | 57.86 202 | 92.19 213 | 63.47 235 | 79.57 235 | 89.91 208 |
|
miper_lstm_enhance | | | 74.11 256 | 73.11 256 | 77.13 287 | 80.11 329 | 59.62 278 | 72.23 341 | 86.92 240 | 66.76 248 | 70.40 276 | 82.92 293 | 56.93 213 | 82.92 330 | 69.06 191 | 72.63 314 | 88.87 242 |
|
OpenMVS |  | 72.83 10 | 79.77 158 | 78.33 173 | 84.09 125 | 85.17 244 | 69.91 93 | 90.57 65 | 90.97 133 | 66.70 249 | 72.17 262 | 91.91 89 | 54.70 225 | 93.96 137 | 61.81 252 | 90.95 100 | 88.41 255 |
|
test-LLR | | | 72.94 270 | 72.43 260 | 74.48 308 | 81.35 316 | 58.04 290 | 78.38 312 | 77.46 336 | 66.66 250 | 69.95 285 | 79.00 331 | 48.06 292 | 79.24 342 | 66.13 216 | 84.83 170 | 86.15 299 |
|
test20.03 | | | 67.45 306 | 66.95 307 | 68.94 335 | 75.48 355 | 44.84 365 | 77.50 320 | 77.67 334 | 66.66 250 | 63.01 338 | 83.80 284 | 47.02 297 | 78.40 346 | 42.53 355 | 68.86 334 | 83.58 328 |
|
test0.0.03 1 | | | 68.00 304 | 67.69 301 | 68.90 336 | 77.55 346 | 47.43 360 | 75.70 330 | 72.95 354 | 66.66 250 | 66.56 314 | 82.29 303 | 48.06 292 | 75.87 357 | 44.97 350 | 74.51 298 | 83.41 329 |
|
QAPM | | | 80.88 127 | 79.50 143 | 85.03 90 | 88.01 191 | 68.97 113 | 91.59 44 | 92.00 97 | 66.63 253 | 75.15 229 | 92.16 85 | 57.70 203 | 95.45 72 | 63.52 234 | 88.76 125 | 90.66 174 |
|
XXY-MVS | | | 75.41 247 | 75.56 226 | 74.96 304 | 83.59 271 | 57.82 296 | 80.59 291 | 83.87 280 | 66.54 254 | 74.93 235 | 88.31 185 | 63.24 137 | 80.09 341 | 62.16 247 | 76.85 263 | 86.97 285 |
|
OurMVSNet-221017-0 | | | 74.26 254 | 72.42 261 | 79.80 245 | 83.76 269 | 59.59 279 | 85.92 207 | 86.64 242 | 66.39 255 | 66.96 309 | 87.58 202 | 39.46 338 | 91.60 230 | 65.76 222 | 69.27 330 | 88.22 256 |
|
SCA | | | 74.22 255 | 72.33 262 | 79.91 242 | 84.05 264 | 62.17 250 | 79.96 298 | 79.29 328 | 66.30 256 | 72.38 260 | 80.13 322 | 51.95 252 | 88.60 287 | 59.25 271 | 77.67 253 | 88.96 239 |
|
testgi | | | 66.67 311 | 66.53 309 | 67.08 342 | 75.62 354 | 41.69 369 | 75.93 326 | 76.50 343 | 66.11 257 | 65.20 328 | 86.59 234 | 35.72 352 | 74.71 361 | 43.71 351 | 73.38 310 | 84.84 316 |
|
HY-MVS | | 69.67 12 | 77.95 205 | 77.15 200 | 80.36 233 | 87.57 209 | 60.21 275 | 83.37 265 | 87.78 224 | 66.11 257 | 75.37 221 | 87.06 220 | 63.27 136 | 90.48 258 | 61.38 256 | 82.43 205 | 90.40 185 |
|
EG-PatchMatch MVS | | | 74.04 257 | 71.82 265 | 80.71 227 | 84.92 251 | 67.42 149 | 85.86 209 | 88.08 216 | 66.04 259 | 64.22 332 | 83.85 282 | 35.10 353 | 92.56 198 | 57.44 289 | 80.83 221 | 82.16 340 |
|
CNLPA | | | 78.08 200 | 76.79 209 | 81.97 197 | 90.40 109 | 71.07 69 | 87.59 159 | 84.55 268 | 66.03 260 | 72.38 260 | 89.64 146 | 57.56 205 | 86.04 308 | 59.61 268 | 83.35 192 | 88.79 246 |
|
Anonymous20240529 | | | 80.19 152 | 78.89 160 | 84.10 124 | 90.60 104 | 64.75 202 | 88.95 108 | 90.90 135 | 65.97 261 | 80.59 123 | 91.17 111 | 49.97 274 | 93.73 155 | 69.16 190 | 82.70 203 | 93.81 63 |
|
TAPA-MVS | | 73.13 9 | 79.15 175 | 77.94 180 | 82.79 181 | 89.59 127 | 62.99 242 | 88.16 142 | 91.51 118 | 65.77 262 | 77.14 183 | 91.09 113 | 60.91 180 | 93.21 173 | 50.26 324 | 87.05 146 | 92.17 129 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MSDG | | | 73.36 264 | 70.99 273 | 80.49 231 | 84.51 256 | 65.80 180 | 80.71 289 | 86.13 251 | 65.70 263 | 65.46 323 | 83.74 286 | 44.60 313 | 90.91 251 | 51.13 317 | 76.89 261 | 84.74 317 |
|
anonymousdsp | | | 78.60 188 | 77.15 200 | 82.98 171 | 80.51 326 | 67.08 156 | 87.24 169 | 89.53 171 | 65.66 264 | 75.16 228 | 87.19 215 | 52.52 240 | 92.25 211 | 77.17 118 | 79.34 240 | 89.61 219 |
|
test_0402 | | | 72.79 271 | 70.44 278 | 79.84 244 | 88.13 185 | 65.99 174 | 85.93 206 | 84.29 273 | 65.57 265 | 67.40 306 | 85.49 259 | 46.92 298 | 92.61 196 | 35.88 362 | 74.38 299 | 80.94 346 |
|
miper_enhance_ethall | | | 77.87 208 | 76.86 206 | 80.92 223 | 81.65 309 | 61.38 260 | 82.68 272 | 88.98 192 | 65.52 266 | 75.47 215 | 82.30 302 | 65.76 115 | 92.00 219 | 72.95 157 | 76.39 270 | 89.39 223 |
|
UnsupCasMVSNet_eth | | | 67.33 307 | 65.99 310 | 71.37 325 | 73.48 362 | 51.47 350 | 75.16 332 | 85.19 260 | 65.20 267 | 60.78 345 | 80.93 317 | 42.35 324 | 77.20 352 | 57.12 292 | 53.69 360 | 85.44 308 |
|
WTY-MVS | | | 75.65 243 | 75.68 225 | 75.57 298 | 86.40 230 | 56.82 308 | 77.92 319 | 82.40 300 | 65.10 268 | 76.18 204 | 87.72 198 | 63.13 143 | 80.90 338 | 60.31 263 | 81.96 209 | 89.00 237 |
|
thisisatest0515 | | | 77.33 218 | 75.38 231 | 83.18 160 | 85.27 243 | 63.80 220 | 82.11 277 | 83.27 290 | 65.06 269 | 75.91 208 | 83.84 283 | 49.54 279 | 94.27 123 | 67.24 209 | 86.19 159 | 91.48 149 |
|
MVP-Stereo | | | 76.12 237 | 74.46 243 | 81.13 218 | 85.37 242 | 69.79 96 | 84.42 245 | 87.95 219 | 65.03 270 | 67.46 304 | 85.33 262 | 53.28 238 | 91.73 229 | 58.01 285 | 83.27 193 | 81.85 341 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Anonymous20231211 | | | 78.97 181 | 77.69 191 | 82.81 178 | 90.54 106 | 64.29 212 | 90.11 79 | 91.51 118 | 65.01 271 | 76.16 207 | 88.13 195 | 50.56 268 | 93.03 188 | 69.68 185 | 77.56 254 | 91.11 158 |
|
pmmvs6 | | | 74.69 251 | 73.39 252 | 78.61 263 | 81.38 315 | 57.48 301 | 86.64 187 | 87.95 219 | 64.99 272 | 70.18 279 | 86.61 233 | 50.43 270 | 89.52 270 | 62.12 248 | 70.18 328 | 88.83 244 |
|
PAPM | | | 77.68 212 | 76.40 219 | 81.51 205 | 87.29 217 | 61.85 254 | 83.78 256 | 89.59 170 | 64.74 273 | 71.23 270 | 88.70 172 | 62.59 148 | 93.66 156 | 52.66 311 | 87.03 147 | 89.01 235 |
|
MIMVSNet | | | 70.69 284 | 69.30 283 | 74.88 305 | 84.52 255 | 56.35 318 | 75.87 329 | 79.42 327 | 64.59 274 | 67.76 300 | 82.41 300 | 41.10 333 | 81.54 335 | 46.64 343 | 81.34 215 | 86.75 290 |
|
tpm | | | 72.37 275 | 71.71 266 | 74.35 310 | 82.19 304 | 52.00 345 | 79.22 305 | 77.29 339 | 64.56 275 | 72.95 253 | 83.68 288 | 51.35 259 | 83.26 329 | 58.33 282 | 75.80 277 | 87.81 263 |
|
MDA-MVSNet-bldmvs | | | 66.68 310 | 63.66 318 | 75.75 295 | 79.28 341 | 60.56 270 | 73.92 338 | 78.35 331 | 64.43 276 | 50.13 363 | 79.87 326 | 44.02 316 | 83.67 324 | 46.10 345 | 56.86 356 | 83.03 335 |
|
MIMVSNet1 | | | 68.58 301 | 66.78 308 | 73.98 313 | 80.07 330 | 51.82 346 | 80.77 287 | 84.37 270 | 64.40 277 | 59.75 349 | 82.16 305 | 36.47 349 | 83.63 325 | 42.73 354 | 70.33 327 | 86.48 294 |
|
D2MVS | | | 74.82 250 | 73.21 254 | 79.64 250 | 79.81 333 | 62.56 245 | 80.34 294 | 87.35 232 | 64.37 278 | 68.86 294 | 82.66 298 | 46.37 301 | 90.10 262 | 67.91 201 | 81.24 217 | 86.25 296 |
|
PLC |  | 70.83 11 | 78.05 202 | 76.37 220 | 83.08 165 | 91.88 85 | 67.80 140 | 88.19 140 | 89.46 173 | 64.33 279 | 69.87 287 | 88.38 183 | 53.66 235 | 93.58 157 | 58.86 276 | 82.73 201 | 87.86 262 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PatchmatchNet |  | | 73.12 267 | 71.33 270 | 78.49 267 | 83.18 281 | 60.85 265 | 79.63 300 | 78.57 330 | 64.13 280 | 71.73 266 | 79.81 327 | 51.20 261 | 85.97 309 | 57.40 290 | 76.36 273 | 88.66 249 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
KD-MVS_2432*1600 | | | 66.22 315 | 63.89 316 | 73.21 316 | 75.47 356 | 53.42 339 | 70.76 346 | 84.35 271 | 64.10 281 | 66.52 316 | 78.52 334 | 34.55 355 | 84.98 315 | 50.40 320 | 50.33 364 | 81.23 344 |
|
miper_refine_blended | | | 66.22 315 | 63.89 316 | 73.21 316 | 75.47 356 | 53.42 339 | 70.76 346 | 84.35 271 | 64.10 281 | 66.52 316 | 78.52 334 | 34.55 355 | 84.98 315 | 50.40 320 | 50.33 364 | 81.23 344 |
|
tpmvs | | | 71.09 281 | 69.29 284 | 76.49 291 | 82.04 305 | 56.04 321 | 78.92 309 | 81.37 309 | 64.05 283 | 67.18 308 | 78.28 336 | 49.74 278 | 89.77 265 | 49.67 327 | 72.37 315 | 83.67 327 |
|
F-COLMAP | | | 76.38 235 | 74.33 244 | 82.50 188 | 89.28 146 | 66.95 162 | 88.41 129 | 89.03 189 | 64.05 283 | 66.83 312 | 88.61 176 | 46.78 299 | 92.89 190 | 57.48 288 | 78.55 244 | 87.67 265 |
|
DP-MVS | | | 76.78 227 | 74.57 239 | 83.42 149 | 93.29 54 | 69.46 106 | 88.55 125 | 83.70 281 | 63.98 285 | 70.20 278 | 88.89 169 | 54.01 233 | 94.80 108 | 46.66 341 | 81.88 211 | 86.01 303 |
|
原ACMM1 | | | | | 84.35 116 | 93.01 64 | 68.79 115 | | 92.44 75 | 63.96 286 | 81.09 118 | 91.57 99 | 66.06 110 | 95.45 72 | 67.19 210 | 94.82 52 | 88.81 245 |
|
PM-MVS | | | 66.41 313 | 64.14 315 | 73.20 318 | 73.92 360 | 56.45 314 | 78.97 308 | 64.96 369 | 63.88 287 | 64.72 329 | 80.24 321 | 19.84 369 | 83.44 327 | 66.24 215 | 64.52 345 | 79.71 351 |
|
jason | | | 81.39 121 | 80.29 129 | 84.70 104 | 86.63 228 | 69.90 94 | 85.95 205 | 86.77 241 | 63.24 288 | 81.07 119 | 89.47 152 | 61.08 178 | 92.15 214 | 78.33 107 | 90.07 113 | 92.05 132 |
jason: jason. |
KD-MVS_self_test | | | 68.81 298 | 67.59 303 | 72.46 321 | 74.29 359 | 45.45 363 | 77.93 318 | 87.00 238 | 63.12 289 | 63.99 334 | 78.99 333 | 42.32 325 | 84.77 318 | 56.55 297 | 64.09 346 | 87.16 281 |
|
gg-mvs-nofinetune | | | 69.95 292 | 67.96 295 | 75.94 294 | 83.07 284 | 54.51 331 | 77.23 322 | 70.29 357 | 63.11 290 | 70.32 277 | 62.33 360 | 43.62 318 | 88.69 286 | 53.88 306 | 87.76 135 | 84.62 320 |
|
tpmrst | | | 72.39 273 | 72.13 263 | 73.18 319 | 80.54 325 | 49.91 356 | 79.91 299 | 79.08 329 | 63.11 290 | 71.69 267 | 79.95 324 | 55.32 218 | 82.77 331 | 65.66 223 | 73.89 303 | 86.87 286 |
|
PCF-MVS | | 73.52 7 | 80.38 146 | 78.84 161 | 85.01 91 | 87.71 201 | 68.99 112 | 83.65 258 | 91.46 122 | 63.00 292 | 77.77 168 | 90.28 129 | 66.10 108 | 95.09 94 | 61.40 255 | 88.22 133 | 90.94 166 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
COLMAP_ROB |  | 66.92 17 | 73.01 268 | 70.41 279 | 80.81 225 | 87.13 220 | 65.63 183 | 88.30 135 | 84.19 276 | 62.96 293 | 63.80 336 | 87.69 200 | 38.04 345 | 92.56 198 | 46.66 341 | 74.91 294 | 84.24 322 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Patchmatch-RL test | | | 70.24 289 | 67.78 300 | 77.61 279 | 77.43 347 | 59.57 280 | 71.16 343 | 70.33 356 | 62.94 294 | 68.65 296 | 72.77 354 | 50.62 267 | 85.49 312 | 69.58 186 | 66.58 339 | 87.77 264 |
|
lupinMVS | | | 81.39 121 | 80.27 130 | 84.76 103 | 87.35 211 | 70.21 87 | 85.55 217 | 86.41 245 | 62.85 295 | 81.32 113 | 88.61 176 | 61.68 162 | 92.24 212 | 78.41 106 | 90.26 108 | 91.83 137 |
|
EPMVS | | | 69.02 297 | 68.16 292 | 71.59 323 | 79.61 337 | 49.80 358 | 77.40 321 | 66.93 365 | 62.82 296 | 70.01 282 | 79.05 329 | 45.79 308 | 77.86 350 | 56.58 296 | 75.26 291 | 87.13 282 |
|
PatchMatch-RL | | | 72.38 274 | 70.90 274 | 76.80 290 | 88.60 172 | 67.38 151 | 79.53 301 | 76.17 344 | 62.75 297 | 69.36 292 | 82.00 308 | 45.51 311 | 84.89 317 | 53.62 307 | 80.58 225 | 78.12 354 |
|
gm-plane-assit | | | | | | 81.40 314 | 53.83 336 | | | 62.72 298 | | 80.94 315 | | 92.39 204 | 63.40 237 | | |
|
FMVSNet5 | | | 69.50 294 | 67.96 295 | 74.15 312 | 82.97 289 | 55.35 326 | 80.01 297 | 82.12 302 | 62.56 299 | 63.02 337 | 81.53 309 | 36.92 348 | 81.92 333 | 48.42 331 | 74.06 301 | 85.17 313 |
|
sss | | | 73.60 260 | 73.64 251 | 73.51 315 | 82.80 291 | 55.01 327 | 76.12 325 | 81.69 306 | 62.47 300 | 74.68 238 | 85.85 252 | 57.32 208 | 78.11 348 | 60.86 260 | 80.93 219 | 87.39 272 |
|
AllTest | | | 70.96 282 | 68.09 294 | 79.58 251 | 85.15 245 | 63.62 222 | 84.58 239 | 79.83 324 | 62.31 301 | 60.32 346 | 86.73 223 | 32.02 358 | 88.96 282 | 50.28 322 | 71.57 322 | 86.15 299 |
|
TestCases | | | | | 79.58 251 | 85.15 245 | 63.62 222 | | 79.83 324 | 62.31 301 | 60.32 346 | 86.73 223 | 32.02 358 | 88.96 282 | 50.28 322 | 71.57 322 | 86.15 299 |
|
1112_ss | | | 77.40 217 | 76.43 218 | 80.32 235 | 89.11 156 | 60.41 273 | 83.65 258 | 87.72 225 | 62.13 303 | 73.05 252 | 86.72 225 | 62.58 149 | 89.97 263 | 62.11 249 | 80.80 222 | 90.59 178 |
|
PVSNet | | 64.34 18 | 72.08 277 | 70.87 276 | 75.69 296 | 86.21 232 | 56.44 315 | 74.37 337 | 80.73 313 | 62.06 304 | 70.17 280 | 82.23 304 | 42.86 322 | 83.31 328 | 54.77 303 | 84.45 177 | 87.32 275 |
|
LS3D | | | 76.95 225 | 74.82 237 | 83.37 152 | 90.45 107 | 67.36 152 | 89.15 103 | 86.94 239 | 61.87 305 | 69.52 290 | 90.61 124 | 51.71 257 | 94.53 116 | 46.38 344 | 86.71 152 | 88.21 257 |
|
CostFormer | | | 75.24 249 | 73.90 248 | 79.27 255 | 82.65 296 | 58.27 287 | 80.80 286 | 82.73 298 | 61.57 306 | 75.33 225 | 83.13 292 | 55.52 217 | 91.07 249 | 64.98 228 | 78.34 249 | 88.45 253 |
|
new-patchmatchnet | | | 61.73 324 | 61.73 326 | 61.70 345 | 72.74 366 | 24.50 379 | 69.16 352 | 78.03 332 | 61.40 307 | 56.72 356 | 75.53 350 | 38.42 342 | 76.48 355 | 45.95 346 | 57.67 355 | 84.13 324 |
|
ANet_high | | | 50.57 334 | 46.10 337 | 63.99 343 | 48.67 377 | 39.13 370 | 70.99 345 | 80.85 311 | 61.39 308 | 31.18 368 | 57.70 365 | 17.02 371 | 73.65 365 | 31.22 364 | 15.89 374 | 79.18 352 |
|
MS-PatchMatch | | | 73.83 259 | 72.67 258 | 77.30 284 | 83.87 267 | 66.02 173 | 81.82 278 | 84.66 266 | 61.37 309 | 68.61 297 | 82.82 296 | 47.29 295 | 88.21 291 | 59.27 270 | 84.32 178 | 77.68 355 |
|
USDC | | | 70.33 288 | 68.37 289 | 76.21 293 | 80.60 324 | 56.23 319 | 79.19 306 | 86.49 244 | 60.89 310 | 61.29 343 | 85.47 260 | 31.78 360 | 89.47 272 | 53.37 308 | 76.21 274 | 82.94 337 |
|
cascas | | | 76.72 228 | 74.64 238 | 82.99 170 | 85.78 236 | 65.88 177 | 82.33 275 | 89.21 182 | 60.85 311 | 72.74 254 | 81.02 313 | 47.28 296 | 93.75 153 | 67.48 205 | 85.02 168 | 89.34 224 |
|
MDTV_nov1_ep13 | | | | 69.97 282 | | 83.18 281 | 53.48 338 | 77.10 323 | 80.18 323 | 60.45 312 | 69.33 293 | 80.44 319 | 48.89 290 | 86.90 302 | 51.60 315 | 78.51 246 | |
|
TinyColmap | | | 67.30 308 | 64.81 312 | 74.76 307 | 81.92 307 | 56.68 312 | 80.29 295 | 81.49 308 | 60.33 313 | 56.27 358 | 83.22 291 | 24.77 364 | 87.66 298 | 45.52 347 | 69.47 329 | 79.95 350 |
|
test-mter | | | 71.41 279 | 70.39 280 | 74.48 308 | 81.35 316 | 58.04 290 | 78.38 312 | 77.46 336 | 60.32 314 | 69.95 285 | 79.00 331 | 36.08 351 | 79.24 342 | 66.13 216 | 84.83 170 | 86.15 299 |
|
1314 | | | 76.53 229 | 75.30 234 | 80.21 237 | 83.93 266 | 62.32 248 | 84.66 234 | 88.81 198 | 60.23 315 | 70.16 281 | 84.07 280 | 55.30 219 | 90.73 255 | 67.37 206 | 83.21 194 | 87.59 269 |
|
PatchT | | | 68.46 303 | 67.85 297 | 70.29 331 | 80.70 323 | 43.93 366 | 72.47 340 | 74.88 347 | 60.15 316 | 70.55 273 | 76.57 345 | 49.94 275 | 81.59 334 | 50.58 318 | 74.83 295 | 85.34 309 |
|
无先验 | | | | | | | | 87.48 161 | 88.98 192 | 60.00 317 | | | | 94.12 133 | 67.28 207 | | 88.97 238 |
|
CR-MVSNet | | | 73.37 262 | 71.27 271 | 79.67 249 | 81.32 318 | 65.19 194 | 75.92 327 | 80.30 320 | 59.92 318 | 72.73 255 | 81.19 310 | 52.50 241 | 86.69 303 | 59.84 266 | 77.71 251 | 87.11 283 |
|
TDRefinement | | | 67.49 305 | 64.34 314 | 76.92 288 | 73.47 363 | 61.07 262 | 84.86 231 | 82.98 295 | 59.77 319 | 58.30 352 | 85.13 267 | 26.06 363 | 87.89 295 | 47.92 338 | 60.59 353 | 81.81 342 |
|
dp | | | 66.80 309 | 65.43 311 | 70.90 330 | 79.74 336 | 48.82 359 | 75.12 334 | 74.77 348 | 59.61 320 | 64.08 333 | 77.23 342 | 42.89 321 | 80.72 339 | 48.86 330 | 66.58 339 | 83.16 332 |
|
our_test_3 | | | 69.14 296 | 67.00 306 | 75.57 298 | 79.80 334 | 58.80 282 | 77.96 317 | 77.81 333 | 59.55 321 | 62.90 340 | 78.25 337 | 47.43 294 | 83.97 322 | 51.71 314 | 67.58 336 | 83.93 326 |
|
Test_1112_low_res | | | 76.40 234 | 75.44 228 | 79.27 255 | 89.28 146 | 58.09 288 | 81.69 281 | 87.07 237 | 59.53 322 | 72.48 258 | 86.67 231 | 61.30 172 | 89.33 273 | 60.81 261 | 80.15 231 | 90.41 184 |
|
pmmvs4 | | | 74.03 258 | 71.91 264 | 80.39 232 | 81.96 306 | 68.32 130 | 81.45 284 | 82.14 301 | 59.32 323 | 69.87 287 | 85.13 267 | 52.40 243 | 88.13 293 | 60.21 264 | 74.74 296 | 84.73 318 |
|
testdata | | | | | 79.97 241 | 90.90 98 | 64.21 213 | | 84.71 265 | 59.27 324 | 85.40 49 | 92.91 71 | 62.02 160 | 89.08 278 | 68.95 192 | 91.37 95 | 86.63 293 |
|
ppachtmachnet_test | | | 70.04 291 | 67.34 304 | 78.14 270 | 79.80 334 | 61.13 261 | 79.19 306 | 80.59 315 | 59.16 325 | 65.27 325 | 79.29 328 | 46.75 300 | 87.29 300 | 49.33 328 | 66.72 337 | 86.00 305 |
|
RPSCF | | | 73.23 266 | 71.46 267 | 78.54 265 | 82.50 298 | 59.85 276 | 82.18 276 | 82.84 297 | 58.96 326 | 71.15 272 | 89.41 158 | 45.48 312 | 84.77 318 | 58.82 277 | 71.83 320 | 91.02 164 |
|
pmmvs-eth3d | | | 70.50 287 | 67.83 298 | 78.52 266 | 77.37 348 | 66.18 171 | 81.82 278 | 81.51 307 | 58.90 327 | 63.90 335 | 80.42 320 | 42.69 323 | 86.28 307 | 58.56 279 | 65.30 343 | 83.11 333 |
|
OpenMVS_ROB |  | 64.09 19 | 70.56 286 | 68.19 291 | 77.65 278 | 80.26 327 | 59.41 281 | 85.01 227 | 82.96 296 | 58.76 328 | 65.43 324 | 82.33 301 | 37.63 347 | 91.23 242 | 45.34 349 | 76.03 275 | 82.32 338 |
|
114514_t | | | 80.68 138 | 79.51 142 | 84.20 121 | 94.09 41 | 67.27 153 | 89.64 92 | 91.11 131 | 58.75 329 | 74.08 243 | 90.72 122 | 58.10 199 | 95.04 95 | 69.70 184 | 89.42 119 | 90.30 187 |
|
Patchmtry | | | 70.74 283 | 69.16 285 | 75.49 300 | 80.72 322 | 54.07 334 | 74.94 336 | 80.30 320 | 58.34 330 | 70.01 282 | 81.19 310 | 52.50 241 | 86.54 304 | 53.37 308 | 71.09 325 | 85.87 306 |
|
Anonymous20240521 | | | 68.80 299 | 67.22 305 | 73.55 314 | 74.33 358 | 54.11 333 | 83.18 266 | 85.61 256 | 58.15 331 | 61.68 342 | 80.94 315 | 30.71 361 | 81.27 337 | 57.00 294 | 73.34 311 | 85.28 310 |
|
旧先验2 | | | | | | | | 86.56 190 | | 58.10 332 | 87.04 35 | | | 88.98 280 | 74.07 144 | | |
|
JIA-IIPM | | | 66.32 314 | 62.82 324 | 76.82 289 | 77.09 349 | 61.72 257 | 65.34 359 | 75.38 345 | 58.04 333 | 64.51 330 | 62.32 361 | 42.05 330 | 86.51 305 | 51.45 316 | 69.22 331 | 82.21 339 |
|
pmmvs5 | | | 71.55 278 | 70.20 281 | 75.61 297 | 77.83 345 | 56.39 316 | 81.74 280 | 80.89 310 | 57.76 334 | 67.46 304 | 84.49 274 | 49.26 284 | 85.32 314 | 57.08 293 | 75.29 290 | 85.11 314 |
|
TESTMET0.1,1 | | | 69.89 293 | 69.00 286 | 72.55 320 | 79.27 342 | 56.85 307 | 78.38 312 | 74.71 350 | 57.64 335 | 68.09 299 | 77.19 343 | 37.75 346 | 76.70 353 | 63.92 233 | 84.09 181 | 84.10 325 |
|
RPMNet | | | 73.51 261 | 70.49 277 | 82.58 187 | 81.32 318 | 65.19 194 | 75.92 327 | 92.27 83 | 57.60 336 | 72.73 255 | 76.45 346 | 52.30 244 | 95.43 74 | 48.14 336 | 77.71 251 | 87.11 283 |
|
新几何1 | | | | | 83.42 149 | 93.13 58 | 70.71 80 | | 85.48 257 | 57.43 337 | 81.80 108 | 91.98 88 | 63.28 135 | 92.27 210 | 64.60 231 | 92.99 72 | 87.27 276 |
|
1121 | | | 80.84 129 | 79.77 136 | 84.05 129 | 93.11 60 | 70.78 79 | 84.66 234 | 85.42 258 | 57.37 338 | 81.76 111 | 92.02 87 | 63.41 133 | 94.12 133 | 67.28 207 | 92.93 73 | 87.26 277 |
|
YYNet1 | | | 65.03 318 | 62.91 322 | 71.38 324 | 75.85 352 | 56.60 313 | 69.12 353 | 74.66 351 | 57.28 339 | 54.12 359 | 77.87 339 | 45.85 307 | 74.48 362 | 49.95 325 | 61.52 351 | 83.05 334 |
|
MDA-MVSNet_test_wron | | | 65.03 318 | 62.92 321 | 71.37 325 | 75.93 351 | 56.73 309 | 69.09 354 | 74.73 349 | 57.28 339 | 54.03 360 | 77.89 338 | 45.88 306 | 74.39 363 | 49.89 326 | 61.55 350 | 82.99 336 |
|
Anonymous20231206 | | | 68.60 300 | 67.80 299 | 71.02 329 | 80.23 328 | 50.75 354 | 78.30 315 | 80.47 317 | 56.79 341 | 66.11 321 | 82.63 299 | 46.35 302 | 78.95 344 | 43.62 352 | 75.70 278 | 83.36 330 |
|
tpm2 | | | 73.26 265 | 71.46 267 | 78.63 262 | 83.34 276 | 56.71 311 | 80.65 290 | 80.40 319 | 56.63 342 | 73.55 246 | 82.02 307 | 51.80 256 | 91.24 241 | 56.35 298 | 78.42 248 | 87.95 259 |
|
CHOSEN 1792x2688 | | | 77.63 213 | 75.69 224 | 83.44 148 | 89.98 121 | 68.58 127 | 78.70 311 | 87.50 229 | 56.38 343 | 75.80 211 | 86.84 221 | 58.67 196 | 91.40 237 | 61.58 254 | 85.75 166 | 90.34 186 |
|
HyFIR lowres test | | | 77.53 214 | 75.40 230 | 83.94 138 | 89.59 127 | 66.62 164 | 80.36 293 | 88.64 207 | 56.29 344 | 76.45 194 | 85.17 266 | 57.64 204 | 93.28 171 | 61.34 257 | 83.10 197 | 91.91 135 |
|
PVSNet_0 | | 57.27 20 | 61.67 325 | 59.27 328 | 68.85 337 | 79.61 337 | 57.44 302 | 68.01 355 | 73.44 353 | 55.93 345 | 58.54 351 | 70.41 357 | 44.58 314 | 77.55 351 | 47.01 340 | 35.91 367 | 71.55 360 |
|
UnsupCasMVSNet_bld | | | 63.70 323 | 61.53 327 | 70.21 332 | 73.69 361 | 51.39 351 | 72.82 339 | 81.89 303 | 55.63 346 | 57.81 353 | 71.80 356 | 38.67 341 | 78.61 345 | 49.26 329 | 52.21 362 | 80.63 347 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 371 | 75.16 332 | | 55.10 347 | 66.53 315 | | 49.34 282 | | 53.98 305 | | 87.94 260 |
|
MVS | | | 78.19 198 | 76.99 204 | 81.78 199 | 85.66 237 | 66.99 157 | 84.66 234 | 90.47 144 | 55.08 348 | 72.02 264 | 85.27 263 | 63.83 130 | 94.11 135 | 66.10 218 | 89.80 115 | 84.24 322 |
|
test222 | | | | | | 91.50 89 | 68.26 132 | 84.16 250 | 83.20 293 | 54.63 349 | 79.74 131 | 91.63 97 | 58.97 195 | | | 91.42 94 | 86.77 289 |
|
CHOSEN 280x420 | | | 66.51 312 | 64.71 313 | 71.90 322 | 81.45 313 | 63.52 227 | 57.98 364 | 68.95 363 | 53.57 350 | 62.59 341 | 76.70 344 | 46.22 303 | 75.29 360 | 55.25 301 | 79.68 233 | 76.88 357 |
|
ADS-MVSNet2 | | | 66.20 317 | 63.33 319 | 74.82 306 | 79.92 331 | 58.75 283 | 67.55 356 | 75.19 346 | 53.37 351 | 65.25 326 | 75.86 347 | 42.32 325 | 80.53 340 | 41.57 356 | 68.91 332 | 85.18 311 |
|
ADS-MVSNet | | | 64.36 321 | 62.88 323 | 68.78 338 | 79.92 331 | 47.17 361 | 67.55 356 | 71.18 355 | 53.37 351 | 65.25 326 | 75.86 347 | 42.32 325 | 73.99 364 | 41.57 356 | 68.91 332 | 85.18 311 |
|
LF4IMVS | | | 64.02 322 | 62.19 325 | 69.50 334 | 70.90 367 | 53.29 342 | 76.13 324 | 77.18 340 | 52.65 353 | 58.59 350 | 80.98 314 | 23.55 366 | 76.52 354 | 53.06 310 | 66.66 338 | 78.68 353 |
|
tpm cat1 | | | 70.57 285 | 68.31 290 | 77.35 283 | 82.41 301 | 57.95 293 | 78.08 316 | 80.22 322 | 52.04 354 | 68.54 298 | 77.66 341 | 52.00 251 | 87.84 296 | 51.77 313 | 72.07 319 | 86.25 296 |
|
Patchmatch-test | | | 64.82 320 | 63.24 320 | 69.57 333 | 79.42 340 | 49.82 357 | 63.49 362 | 69.05 362 | 51.98 355 | 59.95 348 | 80.13 322 | 50.91 263 | 70.98 366 | 40.66 358 | 73.57 306 | 87.90 261 |
|
N_pmnet | | | 52.79 331 | 53.26 332 | 51.40 351 | 78.99 343 | 7.68 382 | 69.52 349 | 3.89 382 | 51.63 356 | 57.01 355 | 74.98 351 | 40.83 335 | 65.96 369 | 37.78 361 | 64.67 344 | 80.56 349 |
|
PMMVS | | | 69.34 295 | 68.67 287 | 71.35 327 | 75.67 353 | 62.03 251 | 75.17 331 | 73.46 352 | 50.00 357 | 68.68 295 | 79.05 329 | 52.07 250 | 78.13 347 | 61.16 258 | 82.77 200 | 73.90 358 |
|
CMPMVS |  | 51.72 21 | 70.19 290 | 68.16 292 | 76.28 292 | 73.15 365 | 57.55 300 | 79.47 302 | 83.92 278 | 48.02 358 | 56.48 357 | 84.81 271 | 43.13 320 | 86.42 306 | 62.67 243 | 81.81 212 | 84.89 315 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
CVMVSNet | | | 72.99 269 | 72.58 259 | 74.25 311 | 84.28 258 | 50.85 353 | 86.41 193 | 83.45 288 | 44.56 359 | 73.23 250 | 87.54 205 | 49.38 281 | 85.70 310 | 65.90 220 | 78.44 247 | 86.19 298 |
|
EU-MVSNet | | | 68.53 302 | 67.61 302 | 71.31 328 | 78.51 344 | 47.01 362 | 84.47 240 | 84.27 274 | 42.27 360 | 66.44 319 | 84.79 272 | 40.44 336 | 83.76 323 | 58.76 278 | 68.54 335 | 83.17 331 |
|
FPMVS | | | 53.68 330 | 51.64 333 | 59.81 347 | 65.08 370 | 51.03 352 | 69.48 350 | 69.58 360 | 41.46 361 | 40.67 365 | 72.32 355 | 16.46 372 | 70.00 367 | 24.24 369 | 65.42 342 | 58.40 366 |
|
pmmvs3 | | | 57.79 327 | 54.26 331 | 68.37 339 | 64.02 371 | 56.72 310 | 75.12 334 | 65.17 367 | 40.20 362 | 52.93 361 | 69.86 358 | 20.36 368 | 75.48 359 | 45.45 348 | 55.25 359 | 72.90 359 |
|
new_pmnet | | | 50.91 333 | 50.29 334 | 52.78 350 | 68.58 368 | 34.94 374 | 63.71 361 | 56.63 373 | 39.73 363 | 44.95 364 | 65.47 359 | 21.93 367 | 58.48 370 | 34.98 363 | 56.62 357 | 64.92 362 |
|
MVS-HIRNet | | | 59.14 326 | 57.67 329 | 63.57 344 | 81.65 309 | 43.50 367 | 71.73 342 | 65.06 368 | 39.59 364 | 51.43 362 | 57.73 364 | 38.34 343 | 82.58 332 | 39.53 359 | 73.95 302 | 64.62 363 |
|
PMMVS2 | | | 40.82 337 | 38.86 340 | 46.69 352 | 53.84 373 | 16.45 380 | 48.61 367 | 49.92 375 | 37.49 365 | 31.67 367 | 60.97 363 | 8.14 379 | 56.42 371 | 28.42 366 | 30.72 369 | 67.19 361 |
|
LCM-MVSNet | | | 54.25 329 | 49.68 335 | 67.97 340 | 53.73 374 | 45.28 364 | 66.85 358 | 80.78 312 | 35.96 366 | 39.45 366 | 62.23 362 | 8.70 378 | 78.06 349 | 48.24 335 | 51.20 363 | 80.57 348 |
|
PMVS |  | 37.38 22 | 44.16 336 | 40.28 339 | 55.82 348 | 40.82 379 | 42.54 368 | 65.12 360 | 63.99 370 | 34.43 367 | 24.48 370 | 57.12 366 | 3.92 380 | 76.17 356 | 17.10 372 | 55.52 358 | 48.75 367 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Gipuma |  | | 45.18 335 | 41.86 338 | 55.16 349 | 77.03 350 | 51.52 349 | 32.50 370 | 80.52 316 | 32.46 368 | 27.12 369 | 35.02 370 | 9.52 377 | 75.50 358 | 22.31 370 | 60.21 354 | 38.45 369 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
DSMNet-mixed | | | 57.77 328 | 56.90 330 | 60.38 346 | 67.70 369 | 35.61 372 | 69.18 351 | 53.97 374 | 32.30 369 | 57.49 354 | 79.88 325 | 40.39 337 | 68.57 368 | 38.78 360 | 72.37 315 | 76.97 356 |
|
E-PMN | | | 31.77 338 | 30.64 341 | 35.15 355 | 52.87 375 | 27.67 376 | 57.09 365 | 47.86 376 | 24.64 370 | 16.40 375 | 33.05 371 | 11.23 375 | 54.90 372 | 14.46 374 | 18.15 372 | 22.87 371 |
|
EMVS | | | 30.81 340 | 29.65 342 | 34.27 356 | 50.96 376 | 25.95 378 | 56.58 366 | 46.80 377 | 24.01 371 | 15.53 376 | 30.68 372 | 12.47 374 | 54.43 373 | 12.81 375 | 17.05 373 | 22.43 372 |
|
MVE |  | 26.22 23 | 30.37 341 | 25.89 345 | 43.81 353 | 44.55 378 | 35.46 373 | 28.87 371 | 39.07 378 | 18.20 372 | 18.58 374 | 40.18 369 | 2.68 381 | 47.37 375 | 17.07 373 | 23.78 371 | 48.60 368 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX |  | | | | 27.40 357 | 40.17 380 | 26.90 377 | | 24.59 381 | 17.44 373 | 23.95 371 | 48.61 368 | 9.77 376 | 26.48 376 | 18.06 371 | 24.47 370 | 28.83 370 |
|
wuyk23d | | | 16.82 344 | 15.94 347 | 19.46 358 | 58.74 372 | 31.45 375 | 39.22 368 | 3.74 383 | 6.84 374 | 6.04 377 | 2.70 377 | 1.27 382 | 24.29 377 | 10.54 376 | 14.40 376 | 2.63 374 |
|
test_method | | | 31.52 339 | 29.28 343 | 38.23 354 | 27.03 381 | 6.50 383 | 20.94 372 | 62.21 372 | 4.05 375 | 22.35 373 | 52.50 367 | 13.33 373 | 47.58 374 | 27.04 368 | 34.04 368 | 60.62 364 |
|
tmp_tt | | | 18.61 343 | 21.40 346 | 10.23 359 | 4.82 382 | 10.11 381 | 34.70 369 | 30.74 380 | 1.48 376 | 23.91 372 | 26.07 373 | 28.42 362 | 13.41 378 | 27.12 367 | 15.35 375 | 7.17 373 |
|
EGC-MVSNET | | | 52.07 332 | 47.05 336 | 67.14 341 | 83.51 273 | 60.71 267 | 80.50 292 | 67.75 364 | 0.07 377 | 0.43 378 | 75.85 349 | 24.26 365 | 81.54 335 | 28.82 365 | 62.25 348 | 59.16 365 |
|
testmvs | | | 6.04 347 | 8.02 350 | 0.10 361 | 0.08 383 | 0.03 385 | 69.74 348 | 0.04 384 | 0.05 378 | 0.31 379 | 1.68 378 | 0.02 384 | 0.04 379 | 0.24 377 | 0.02 377 | 0.25 376 |
|
test123 | | | 6.12 346 | 8.11 349 | 0.14 360 | 0.06 384 | 0.09 384 | 71.05 344 | 0.03 385 | 0.04 379 | 0.25 380 | 1.30 379 | 0.05 383 | 0.03 380 | 0.21 378 | 0.01 378 | 0.29 375 |
|
test_blank | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uanet_test | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
DCPMVS | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
cdsmvs_eth3d_5k | | | 19.96 342 | 26.61 344 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 89.26 180 | 0.00 380 | 0.00 381 | 88.61 176 | 61.62 164 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
pcd_1.5k_mvsjas | | | 5.26 348 | 7.02 351 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 63.15 140 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
sosnet-low-res | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
sosnet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uncertanet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
Regformer | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
ab-mvs-re | | | 7.23 345 | 9.64 348 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 86.72 225 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uanet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
MSC_two_6792asdad | | | | | 89.16 1 | 94.34 29 | 75.53 2 | | 92.99 49 | | | | | 97.53 1 | 89.67 1 | 96.44 9 | 94.41 32 |
|
No_MVS | | | | | 89.16 1 | 94.34 29 | 75.53 2 | | 92.99 49 | | | | | 97.53 1 | 89.67 1 | 96.44 9 | 94.41 32 |
|
eth-test2 | | | | | | 0.00 385 | | | | | | | | | | | |
|
eth-test | | | | | | 0.00 385 | | | | | | | | | | | |
|
OPU-MVS | | | | | 89.06 3 | 94.62 15 | 75.42 4 | 93.57 7 | | | | 94.02 47 | 82.45 3 | 96.87 19 | 83.77 54 | 96.48 8 | 94.88 11 |
|
test_0728_SECOND | | | | | 87.71 34 | 95.34 1 | 71.43 62 | 93.49 9 | 94.23 5 | | | | | 97.49 3 | 89.08 7 | 96.41 12 | 94.21 42 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 239 |
|
test_part2 | | | | | | 95.06 8 | 72.65 33 | | | | 91.80 13 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 260 | | | | 88.96 239 |
|
sam_mvs | | | | | | | | | | | | | 50.01 273 | | | | |
|
ambc | | | | | 75.24 302 | 73.16 364 | 50.51 355 | 63.05 363 | 87.47 230 | | 64.28 331 | 77.81 340 | 17.80 370 | 89.73 267 | 57.88 286 | 60.64 352 | 85.49 307 |
|
MTGPA |  | | | | | | | | 92.02 94 | | | | | | | | |
|
test_post1 | | | | | | | | 78.90 310 | | | | 5.43 376 | 48.81 291 | 85.44 313 | 59.25 271 | | |
|
test_post | | | | | | | | | | | | 5.46 375 | 50.36 271 | 84.24 320 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 352 | 51.12 262 | 88.60 287 | | | |
|
GG-mvs-BLEND | | | | | 75.38 301 | 81.59 311 | 55.80 323 | 79.32 303 | 69.63 359 | | 67.19 307 | 73.67 353 | 43.24 319 | 88.90 284 | 50.41 319 | 84.50 174 | 81.45 343 |
|
MTMP | | | | | | | | 92.18 35 | 32.83 379 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 84.90 33 | 95.70 31 | 92.87 105 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 65 | 95.45 33 | 92.70 108 |
|
agg_prior | | | | | | 92.85 66 | 71.94 54 | | 91.78 110 | | 84.41 72 | | | 94.93 98 | | | |
|
test_prior4 | | | | | | | 72.60 35 | 89.01 106 | | | | | | | | | |
|
test_prior | | | | | 86.33 63 | 92.61 74 | 69.59 100 | | 92.97 54 | | | | | 95.48 70 | | | 93.91 55 |
|
新几何2 | | | | | | | | 86.29 198 | | | | | | | | | |
|
旧先验1 | | | | | | 91.96 82 | 65.79 181 | | 86.37 247 | | | 93.08 69 | 69.31 81 | | | 92.74 76 | 88.74 248 |
|
原ACMM2 | | | | | | | | 86.86 179 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 91.01 250 | 62.37 245 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 45 | | | | |
|
test12 | | | | | 86.80 55 | 92.63 73 | 70.70 81 | | 91.79 109 | | 82.71 99 | | 71.67 57 | 96.16 47 | | 94.50 57 | 93.54 80 |
|
plane_prior7 | | | | | | 90.08 115 | 68.51 128 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 124 | 68.70 123 | | | | | | 60.42 188 | | | | |
|
plane_prior5 | | | | | | | | | 92.44 75 | | | | | 95.38 79 | 78.71 102 | 86.32 157 | 91.33 151 |
|
plane_prior4 | | | | | | | | | | | | 91.00 118 | | | | | |
|
plane_prior1 | | | | | | 89.90 123 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 386 | | | | | | | | |
|
nn | | | | | | | | | 0.00 386 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 358 | | | | | | | | |
|
lessismore_v0 | | | | | 78.97 258 | 81.01 321 | 57.15 304 | | 65.99 366 | | 61.16 344 | 82.82 296 | 39.12 340 | 91.34 239 | 59.67 267 | 46.92 366 | 88.43 254 |
|
test11 | | | | | | | | | 92.23 86 | | | | | | | | |
|
door | | | | | | | | | 69.44 361 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 158 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 114 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 178 | | | 95.11 90 | | | 91.03 162 |
|
HQP3-MVS | | | | | | | | | 92.19 89 | | | | | | | 85.99 163 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 191 | | | | |
|
NP-MVS | | | | | | 89.62 126 | 68.32 130 | | | | | 90.24 130 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 210 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 216 | |
|
Test By Simon | | | | | | | | | | | | | 64.33 125 | | | | |
|