| test_fmvsm_n_1920 | | | 97.55 15 | 97.89 3 | 96.53 103 | 98.41 82 | 91.73 128 | 98.01 63 | 99.02 1 | 96.37 12 | 99.30 6 | 98.92 22 | 92.39 43 | 99.79 43 | 99.16 13 | 99.46 44 | 98.08 217 |
|
| PGM-MVS | | | 96.81 56 | 96.53 67 | 97.65 45 | 99.35 23 | 93.53 63 | 97.65 126 | 98.98 2 | 92.22 167 | 97.14 73 | 98.44 61 | 91.17 70 | 99.85 19 | 94.35 152 | 99.46 44 | 99.57 34 |
|
| MVS_111021_HR | | | 96.68 67 | 96.58 66 | 96.99 82 | 98.46 77 | 92.31 108 | 96.20 293 | 98.90 3 | 94.30 85 | 95.86 131 | 97.74 133 | 92.33 44 | 99.38 133 | 96.04 93 | 99.42 54 | 99.28 75 |
|
| test_fmvsmconf_n | | | 97.49 20 | 97.56 14 | 97.29 62 | 97.44 162 | 92.37 105 | 97.91 82 | 98.88 4 | 95.83 18 | 98.92 22 | 99.05 13 | 91.45 60 | 99.80 38 | 99.12 15 | 99.46 44 | 99.69 14 |
|
| lecture | | | 97.58 14 | 97.63 11 | 97.43 56 | 99.37 17 | 92.93 84 | 98.86 7 | 98.85 5 | 95.27 33 | 98.65 33 | 98.90 24 | 91.97 51 | 99.80 38 | 97.63 37 | 99.21 80 | 99.57 34 |
|
| ACMMP |  | | 96.27 84 | 95.93 87 | 97.28 64 | 99.24 31 | 92.62 96 | 98.25 38 | 98.81 6 | 92.99 137 | 94.56 171 | 98.39 65 | 88.96 100 | 99.85 19 | 94.57 146 | 97.63 160 | 99.36 70 |
| 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 |
| MVS_111021_LR | | | 96.24 85 | 96.19 83 | 96.39 121 | 98.23 102 | 91.35 150 | 96.24 291 | 98.79 7 | 93.99 93 | 95.80 133 | 97.65 143 | 89.92 90 | 99.24 146 | 95.87 97 | 99.20 85 | 98.58 162 |
|
| patch_mono-2 | | | 96.83 55 | 97.44 22 | 95.01 214 | 99.05 43 | 85.39 353 | 96.98 210 | 98.77 8 | 94.70 65 | 97.99 48 | 98.66 42 | 93.61 20 | 99.91 1 | 97.67 36 | 99.50 38 | 99.72 13 |
|
| fmvsm_s_conf0.5_n | | | 96.85 52 | 97.13 29 | 96.04 146 | 98.07 117 | 90.28 199 | 97.97 74 | 98.76 9 | 94.93 47 | 98.84 27 | 99.06 11 | 88.80 104 | 99.65 76 | 99.06 17 | 98.63 120 | 98.18 203 |
|
| fmvsm_l_conf0.5_n | | | 97.65 8 | 97.75 7 | 97.34 59 | 98.21 103 | 92.75 90 | 97.83 95 | 98.73 10 | 95.04 44 | 99.30 6 | 98.84 35 | 93.34 23 | 99.78 46 | 99.32 7 | 99.13 95 | 99.50 50 |
|
| fmvsm_s_conf0.5_n_a | | | 96.75 60 | 96.93 44 | 96.20 137 | 97.64 148 | 90.72 181 | 98.00 64 | 98.73 10 | 94.55 72 | 98.91 23 | 99.08 7 | 88.22 116 | 99.63 85 | 98.91 20 | 98.37 133 | 98.25 198 |
|
| fmvsm_s_conf0.5_n_10 | | | 97.29 29 | 97.40 24 | 96.97 84 | 98.24 97 | 91.96 124 | 97.89 85 | 98.72 12 | 96.77 6 | 99.46 3 | 99.06 11 | 87.78 125 | 99.84 24 | 99.40 4 | 99.27 72 | 99.12 90 |
|
| fmvsm_l_conf0.5_n_9 | | | 97.59 12 | 97.79 5 | 96.97 84 | 98.28 91 | 91.49 142 | 97.61 135 | 98.71 13 | 97.10 4 | 99.70 1 | 98.93 21 | 90.95 75 | 99.77 49 | 99.35 6 | 99.53 31 | 99.65 20 |
|
| FC-MVSNet-test | | | 93.94 173 | 93.57 165 | 95.04 212 | 95.48 305 | 91.45 147 | 98.12 53 | 98.71 13 | 93.37 119 | 90.23 285 | 96.70 211 | 87.66 127 | 97.85 336 | 91.49 216 | 90.39 325 | 95.83 311 |
|
| UniMVSNet (Re) | | | 93.31 200 | 92.55 213 | 95.61 182 | 95.39 311 | 93.34 69 | 97.39 169 | 98.71 13 | 93.14 132 | 90.10 294 | 94.83 313 | 87.71 126 | 98.03 309 | 91.67 214 | 83.99 399 | 95.46 330 |
|
| TestfortrainingZip a | | | 97.92 3 | 97.70 9 | 98.58 3 | 99.56 1 | 96.08 5 | 98.69 11 | 98.70 16 | 93.45 116 | 98.73 29 | 98.53 50 | 95.46 7 | 99.86 9 | 96.63 66 | 99.58 23 | 99.80 1 |
|
| fmvsm_l_conf0.5_n_a | | | 97.63 10 | 97.76 6 | 97.26 66 | 98.25 96 | 92.59 98 | 97.81 100 | 98.68 17 | 94.93 47 | 99.24 9 | 98.87 30 | 93.52 21 | 99.79 43 | 99.32 7 | 99.21 80 | 99.40 64 |
|
| FIs | | | 94.09 164 | 93.70 161 | 95.27 201 | 95.70 294 | 92.03 120 | 98.10 54 | 98.68 17 | 93.36 121 | 90.39 282 | 96.70 211 | 87.63 130 | 97.94 327 | 92.25 194 | 90.50 324 | 95.84 310 |
|
| WR-MVS_H | | | 92.00 257 | 91.35 254 | 93.95 282 | 95.09 338 | 89.47 231 | 98.04 61 | 98.68 17 | 91.46 197 | 88.34 345 | 94.68 320 | 85.86 164 | 97.56 365 | 85.77 340 | 84.24 397 | 94.82 375 |
|
| fmvsm_s_conf0.5_n_4 | | | 96.75 60 | 97.07 32 | 95.79 169 | 97.76 139 | 89.57 225 | 97.66 125 | 98.66 20 | 95.36 29 | 99.03 15 | 98.90 24 | 88.39 112 | 99.73 58 | 99.17 12 | 98.66 118 | 98.08 217 |
|
| VPA-MVSNet | | | 93.24 202 | 92.48 218 | 95.51 188 | 95.70 294 | 92.39 104 | 97.86 88 | 98.66 20 | 92.30 164 | 92.09 243 | 95.37 288 | 80.49 280 | 98.40 263 | 93.95 158 | 85.86 370 | 95.75 319 |
|
| fmvsm_l_conf0.5_n_3 | | | 97.64 9 | 97.60 12 | 97.79 32 | 98.14 110 | 93.94 54 | 97.93 80 | 98.65 22 | 96.70 7 | 99.38 4 | 99.07 10 | 89.92 90 | 99.81 33 | 99.16 13 | 99.43 51 | 99.61 28 |
|
| fmvsm_s_conf0.5_n_3 | | | 97.15 34 | 97.36 26 | 96.52 105 | 97.98 123 | 91.19 158 | 97.84 92 | 98.65 22 | 97.08 5 | 99.25 8 | 99.10 5 | 87.88 123 | 99.79 43 | 99.32 7 | 99.18 87 | 98.59 161 |
|
| fmvsm_s_conf0.5_n_8 | | | 97.32 27 | 97.48 21 | 96.85 86 | 98.28 91 | 91.07 166 | 97.76 105 | 98.62 24 | 97.53 2 | 99.20 11 | 99.12 4 | 88.24 115 | 99.81 33 | 99.41 3 | 99.17 88 | 99.67 15 |
|
| fmvsm_s_conf0.5_n_2 | | | 96.62 68 | 96.82 53 | 96.02 148 | 97.98 123 | 90.43 191 | 97.50 150 | 98.59 25 | 96.59 9 | 99.31 5 | 99.08 7 | 84.47 194 | 99.75 55 | 99.37 5 | 98.45 130 | 97.88 230 |
|
| UniMVSNet_NR-MVSNet | | | 93.37 198 | 92.67 207 | 95.47 194 | 95.34 317 | 92.83 87 | 97.17 193 | 98.58 26 | 92.98 142 | 90.13 290 | 95.80 264 | 88.37 114 | 97.85 336 | 91.71 211 | 83.93 400 | 95.73 321 |
|
| CSCG | | | 96.05 88 | 95.91 88 | 96.46 115 | 99.24 31 | 90.47 188 | 98.30 31 | 98.57 27 | 89.01 291 | 93.97 192 | 97.57 153 | 92.62 39 | 99.76 51 | 94.66 140 | 99.27 72 | 99.15 85 |
|
| fmvsm_s_conf0.5_n_9 | | | 97.33 26 | 97.57 13 | 96.62 99 | 98.43 80 | 90.32 198 | 97.80 101 | 98.53 28 | 97.24 3 | 99.62 2 | 99.14 1 | 88.65 107 | 99.80 38 | 99.54 1 | 99.15 92 | 99.74 9 |
|
| fmvsm_s_conf0.5_n_6 | | | 97.08 37 | 97.17 28 | 96.81 87 | 97.28 167 | 91.73 128 | 97.75 107 | 98.50 29 | 94.86 51 | 99.22 10 | 98.78 39 | 89.75 93 | 99.76 51 | 99.10 16 | 99.29 70 | 98.94 116 |
|
| MSLP-MVS++ | | | 96.94 46 | 97.06 33 | 96.59 100 | 98.72 62 | 91.86 126 | 97.67 122 | 98.49 30 | 94.66 68 | 97.24 69 | 98.41 64 | 92.31 46 | 98.94 192 | 96.61 68 | 99.46 44 | 98.96 112 |
|
| HyFIR lowres test | | | 93.66 185 | 92.92 195 | 95.87 159 | 98.24 97 | 89.88 214 | 94.58 369 | 98.49 30 | 85.06 388 | 93.78 195 | 95.78 268 | 82.86 229 | 98.67 237 | 91.77 209 | 95.71 223 | 99.07 98 |
|
| CHOSEN 1792x2688 | | | 94.15 159 | 93.51 171 | 96.06 144 | 98.27 93 | 89.38 236 | 95.18 355 | 98.48 32 | 85.60 378 | 93.76 196 | 97.11 186 | 83.15 219 | 99.61 87 | 91.33 219 | 98.72 116 | 99.19 81 |
|
| fmvsm_s_conf0.5_n_7 | | | 96.45 75 | 96.80 55 | 95.37 197 | 97.29 166 | 88.38 269 | 97.23 187 | 98.47 33 | 95.14 38 | 98.43 38 | 99.09 6 | 87.58 131 | 99.72 62 | 98.80 24 | 99.21 80 | 98.02 221 |
|
| fmvsm_s_conf0.5_n_5 | | | 97.00 43 | 96.97 41 | 97.09 77 | 97.58 158 | 92.56 99 | 97.68 121 | 98.47 33 | 94.02 91 | 98.90 24 | 98.89 27 | 88.94 101 | 99.78 46 | 99.18 11 | 99.03 104 | 98.93 120 |
|
| PHI-MVS | | | 96.77 58 | 96.46 74 | 97.71 43 | 98.40 83 | 94.07 50 | 98.21 45 | 98.45 35 | 89.86 263 | 97.11 75 | 98.01 101 | 92.52 41 | 99.69 70 | 96.03 94 | 99.53 31 | 99.36 70 |
|
| fmvsm_s_conf0.1_n | | | 96.58 71 | 96.77 58 | 96.01 151 | 96.67 222 | 90.25 200 | 97.91 82 | 98.38 36 | 94.48 76 | 98.84 27 | 99.14 1 | 88.06 118 | 99.62 86 | 98.82 22 | 98.60 122 | 98.15 207 |
|
| PVSNet_BlendedMVS | | | 94.06 165 | 93.92 155 | 94.47 249 | 98.27 93 | 89.46 233 | 96.73 239 | 98.36 37 | 90.17 255 | 94.36 177 | 95.24 296 | 88.02 119 | 99.58 95 | 93.44 172 | 90.72 320 | 94.36 395 |
|
| PVSNet_Blended | | | 94.87 134 | 94.56 133 | 95.81 166 | 98.27 93 | 89.46 233 | 95.47 337 | 98.36 37 | 88.84 300 | 94.36 177 | 96.09 253 | 88.02 119 | 99.58 95 | 93.44 172 | 98.18 142 | 98.40 183 |
|
| 3Dnovator | | 91.36 5 | 95.19 121 | 94.44 142 | 97.44 55 | 96.56 232 | 93.36 68 | 98.65 14 | 98.36 37 | 94.12 88 | 89.25 324 | 98.06 95 | 82.20 246 | 99.77 49 | 93.41 174 | 99.32 68 | 99.18 82 |
|
| FOURS1 | | | | | | 99.55 2 | 93.34 69 | 99.29 1 | 98.35 40 | 94.98 45 | 98.49 36 | | | | | | |
|
| DPE-MVS |  | | 97.86 5 | 97.65 10 | 98.47 6 | 99.17 36 | 95.78 8 | 97.21 190 | 98.35 40 | 95.16 37 | 98.71 32 | 98.80 37 | 95.05 11 | 99.89 3 | 96.70 65 | 99.73 1 | 99.73 12 |
| Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
| ME-MVS | | | 97.54 16 | 97.39 25 | 98.00 23 | 99.21 34 | 94.50 35 | 97.75 107 | 98.34 42 | 94.23 86 | 98.15 43 | 98.53 50 | 93.32 26 | 99.84 24 | 97.40 49 | 99.58 23 | 99.65 20 |
|
| fmvsm_s_conf0.1_n_a | | | 96.40 77 | 96.47 71 | 96.16 139 | 95.48 305 | 90.69 182 | 97.91 82 | 98.33 43 | 94.07 89 | 98.93 19 | 99.14 1 | 87.44 138 | 99.61 87 | 98.63 25 | 98.32 135 | 98.18 203 |
|
| HFP-MVS | | | 97.14 35 | 96.92 45 | 97.83 28 | 99.42 8 | 94.12 48 | 98.52 18 | 98.32 44 | 93.21 124 | 97.18 70 | 98.29 81 | 92.08 48 | 99.83 29 | 95.63 110 | 99.59 19 | 99.54 43 |
|
| ACMMPR | | | 97.07 39 | 96.84 49 | 97.79 32 | 99.44 7 | 93.88 55 | 98.52 18 | 98.31 45 | 93.21 124 | 97.15 72 | 98.33 75 | 91.35 64 | 99.86 9 | 95.63 110 | 99.59 19 | 99.62 25 |
|
| test_fmvsmvis_n_1920 | | | 96.70 63 | 96.84 49 | 96.31 126 | 96.62 224 | 91.73 128 | 97.98 68 | 98.30 46 | 96.19 13 | 96.10 121 | 98.95 19 | 89.42 94 | 99.76 51 | 98.90 21 | 99.08 99 | 97.43 257 |
|
| APDe-MVS |  | | 97.82 6 | 97.73 8 | 98.08 19 | 99.15 37 | 94.82 29 | 98.81 8 | 98.30 46 | 94.76 63 | 98.30 40 | 98.90 24 | 93.77 18 | 99.68 72 | 97.93 28 | 99.69 3 | 99.75 7 |
| Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition |
| test0726 | | | | | | 99.45 4 | 95.36 14 | 98.31 30 | 98.29 48 | 94.92 49 | 98.99 17 | 98.92 22 | 95.08 9 | | | | |
|
| MSP-MVS | | | 97.59 12 | 97.54 15 | 97.73 40 | 99.40 12 | 93.77 59 | 98.53 17 | 98.29 48 | 95.55 26 | 98.56 35 | 97.81 126 | 93.90 16 | 99.65 76 | 96.62 67 | 99.21 80 | 99.77 3 |
| 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 |
| DVP-MVS++ | | | 98.06 1 | 97.99 1 | 98.28 10 | 98.67 65 | 95.39 12 | 99.29 1 | 98.28 50 | 94.78 60 | 98.93 19 | 98.87 30 | 96.04 2 | 99.86 9 | 97.45 45 | 99.58 23 | 99.59 30 |
|
| test_0728_SECOND | | | | | 98.51 5 | 99.45 4 | 95.93 6 | 98.21 45 | 98.28 50 | | | | | 99.86 9 | 97.52 41 | 99.67 6 | 99.75 7 |
|
| CP-MVS | | | 97.02 41 | 96.81 54 | 97.64 47 | 99.33 24 | 93.54 62 | 98.80 9 | 98.28 50 | 92.99 137 | 96.45 108 | 98.30 80 | 91.90 52 | 99.85 19 | 95.61 112 | 99.68 4 | 99.54 43 |
|
| test_fmvsmconf0.1_n | | | 97.09 36 | 97.06 33 | 97.19 71 | 95.67 296 | 92.21 112 | 97.95 77 | 98.27 53 | 95.78 22 | 98.40 39 | 99.00 15 | 89.99 88 | 99.78 46 | 99.06 17 | 99.41 57 | 99.59 30 |
|
| SED-MVS | | | 98.05 2 | 97.99 1 | 98.24 11 | 99.42 8 | 95.30 18 | 98.25 38 | 98.27 53 | 95.13 39 | 99.19 12 | 98.89 27 | 95.54 5 | 99.85 19 | 97.52 41 | 99.66 10 | 99.56 38 |
|
| test_241102_TWO | | | | | | | | | 98.27 53 | 95.13 39 | 98.93 19 | 98.89 27 | 94.99 12 | 99.85 19 | 97.52 41 | 99.65 13 | 99.74 9 |
|
| test_241102_ONE | | | | | | 99.42 8 | 95.30 18 | | 98.27 53 | 95.09 42 | 99.19 12 | 98.81 36 | 95.54 5 | 99.65 76 | | | |
|
| SF-MVS | | | 97.39 23 | 97.13 29 | 98.17 16 | 99.02 46 | 95.28 20 | 98.23 42 | 98.27 53 | 92.37 163 | 98.27 41 | 98.65 44 | 93.33 24 | 99.72 62 | 96.49 72 | 99.52 33 | 99.51 47 |
|
| SteuartSystems-ACMMP | | | 97.62 11 | 97.53 16 | 97.87 26 | 98.39 85 | 94.25 42 | 98.43 25 | 98.27 53 | 95.34 31 | 98.11 44 | 98.56 46 | 94.53 13 | 99.71 64 | 96.57 70 | 99.62 17 | 99.65 20 |
| Skip Steuart: Steuart Systems R&D Blog. |
| test_one_0601 | | | | | | 99.32 25 | 95.20 21 | | 98.25 59 | 95.13 39 | 98.48 37 | 98.87 30 | 95.16 8 | | | | |
|
| PVSNet_Blended_VisFu | | | 95.27 113 | 94.91 120 | 96.38 122 | 98.20 104 | 90.86 174 | 97.27 181 | 98.25 59 | 90.21 254 | 94.18 185 | 97.27 175 | 87.48 137 | 99.73 58 | 93.53 169 | 97.77 158 | 98.55 164 |
|
| region2R | | | 97.07 39 | 96.84 49 | 97.77 36 | 99.46 3 | 93.79 57 | 98.52 18 | 98.24 61 | 93.19 127 | 97.14 73 | 98.34 72 | 91.59 59 | 99.87 7 | 95.46 116 | 99.59 19 | 99.64 23 |
|
| PS-CasMVS | | | 91.55 277 | 90.84 278 | 93.69 299 | 94.96 342 | 88.28 272 | 97.84 92 | 98.24 61 | 91.46 197 | 88.04 356 | 95.80 264 | 79.67 296 | 97.48 373 | 87.02 320 | 84.54 394 | 95.31 344 |
|
| DU-MVS | | | 92.90 220 | 92.04 229 | 95.49 191 | 94.95 343 | 92.83 87 | 97.16 194 | 98.24 61 | 93.02 136 | 90.13 290 | 95.71 271 | 83.47 211 | 97.85 336 | 91.71 211 | 83.93 400 | 95.78 315 |
|
| 9.14 | | | | 96.75 59 | | 98.93 54 | | 97.73 112 | 98.23 64 | 91.28 206 | 97.88 52 | 98.44 61 | 93.00 28 | 99.65 76 | 95.76 103 | 99.47 43 | |
|
| reproduce_model | | | 97.51 19 | 97.51 18 | 97.50 52 | 98.99 50 | 93.01 80 | 97.79 103 | 98.21 65 | 95.73 23 | 97.99 48 | 99.03 14 | 92.63 38 | 99.82 31 | 97.80 30 | 99.42 54 | 99.67 15 |
|
| D2MVS | | | 91.30 294 | 90.95 272 | 92.35 347 | 94.71 358 | 85.52 347 | 96.18 295 | 98.21 65 | 88.89 298 | 86.60 385 | 93.82 369 | 79.92 292 | 97.95 325 | 89.29 269 | 90.95 317 | 93.56 410 |
|
| reproduce-ours | | | 97.53 17 | 97.51 18 | 97.60 49 | 98.97 51 | 93.31 71 | 97.71 117 | 98.20 67 | 95.80 20 | 97.88 52 | 98.98 17 | 92.91 29 | 99.81 33 | 97.68 32 | 99.43 51 | 99.67 15 |
|
| our_new_method | | | 97.53 17 | 97.51 18 | 97.60 49 | 98.97 51 | 93.31 71 | 97.71 117 | 98.20 67 | 95.80 20 | 97.88 52 | 98.98 17 | 92.91 29 | 99.81 33 | 97.68 32 | 99.43 51 | 99.67 15 |
|
| SDMVSNet | | | 94.17 157 | 93.61 164 | 95.86 162 | 98.09 113 | 91.37 149 | 97.35 173 | 98.20 67 | 93.18 129 | 91.79 251 | 97.28 173 | 79.13 304 | 98.93 193 | 94.61 143 | 92.84 283 | 97.28 265 |
|
| XVS | | | 97.18 32 | 96.96 43 | 97.81 30 | 99.38 15 | 94.03 52 | 98.59 15 | 98.20 67 | 94.85 52 | 96.59 96 | 98.29 81 | 91.70 55 | 99.80 38 | 95.66 105 | 99.40 59 | 99.62 25 |
|
| X-MVStestdata | | | 91.71 266 | 89.67 332 | 97.81 30 | 99.38 15 | 94.03 52 | 98.59 15 | 98.20 67 | 94.85 52 | 96.59 96 | 32.69 471 | 91.70 55 | 99.80 38 | 95.66 105 | 99.40 59 | 99.62 25 |
|
| ACMMP_NAP | | | 97.20 31 | 96.86 47 | 98.23 12 | 99.09 38 | 95.16 23 | 97.60 136 | 98.19 72 | 92.82 151 | 97.93 51 | 98.74 41 | 91.60 58 | 99.86 9 | 96.26 77 | 99.52 33 | 99.67 15 |
|
| CP-MVSNet | | | 91.89 262 | 91.24 261 | 93.82 291 | 95.05 339 | 88.57 262 | 97.82 97 | 98.19 72 | 91.70 186 | 88.21 351 | 95.76 269 | 81.96 251 | 97.52 371 | 87.86 295 | 84.65 388 | 95.37 340 |
|
| ZNCC-MVS | | | 96.96 44 | 96.67 62 | 97.85 27 | 99.37 17 | 94.12 48 | 98.49 22 | 98.18 74 | 92.64 158 | 96.39 110 | 98.18 88 | 91.61 57 | 99.88 4 | 95.59 115 | 99.55 28 | 99.57 34 |
|
| SMA-MVS |  | | 97.35 24 | 97.03 38 | 98.30 9 | 99.06 42 | 95.42 11 | 97.94 78 | 98.18 74 | 90.57 245 | 98.85 26 | 98.94 20 | 93.33 24 | 99.83 29 | 96.72 63 | 99.68 4 | 99.63 24 |
| 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 |
| PEN-MVS | | | 91.20 299 | 90.44 295 | 93.48 310 | 94.49 366 | 87.91 287 | 97.76 105 | 98.18 74 | 91.29 203 | 87.78 360 | 95.74 270 | 80.35 283 | 97.33 384 | 85.46 344 | 82.96 410 | 95.19 355 |
|
| DELS-MVS | | | 96.61 69 | 96.38 78 | 97.30 61 | 97.79 137 | 93.19 76 | 95.96 307 | 98.18 74 | 95.23 34 | 95.87 130 | 97.65 143 | 91.45 60 | 99.70 69 | 95.87 97 | 99.44 50 | 99.00 107 |
| 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 |
| tfpnnormal | | | 89.70 351 | 88.40 357 | 93.60 303 | 95.15 334 | 90.10 203 | 97.56 141 | 98.16 78 | 87.28 351 | 86.16 391 | 94.63 324 | 77.57 332 | 98.05 305 | 74.48 430 | 84.59 392 | 92.65 423 |
|
| VNet | | | 95.89 96 | 95.45 99 | 97.21 69 | 98.07 117 | 92.94 83 | 97.50 150 | 98.15 79 | 93.87 97 | 97.52 59 | 97.61 149 | 85.29 178 | 99.53 109 | 95.81 102 | 95.27 236 | 99.16 83 |
|
| DeepPCF-MVS | | 93.97 1 | 96.61 69 | 97.09 31 | 95.15 205 | 98.09 113 | 86.63 320 | 96.00 305 | 98.15 79 | 95.43 27 | 97.95 50 | 98.56 46 | 93.40 22 | 99.36 134 | 96.77 60 | 99.48 42 | 99.45 57 |
|
| SD-MVS | | | 97.41 22 | 97.53 16 | 97.06 80 | 98.57 76 | 94.46 36 | 97.92 81 | 98.14 81 | 94.82 56 | 99.01 16 | 98.55 48 | 94.18 15 | 97.41 380 | 96.94 55 | 99.64 14 | 99.32 72 |
| 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 |
| GST-MVS | | | 96.85 52 | 96.52 68 | 97.82 29 | 99.36 21 | 94.14 47 | 98.29 32 | 98.13 82 | 92.72 154 | 96.70 88 | 98.06 95 | 91.35 64 | 99.86 9 | 94.83 132 | 99.28 71 | 99.47 56 |
|
| UA-Net | | | 95.95 93 | 95.53 95 | 97.20 70 | 97.67 144 | 92.98 82 | 97.65 126 | 98.13 82 | 94.81 58 | 96.61 94 | 98.35 69 | 88.87 102 | 99.51 114 | 90.36 244 | 97.35 171 | 99.11 92 |
|
| QAPM | | | 93.45 196 | 92.27 223 | 96.98 83 | 96.77 216 | 92.62 96 | 98.39 27 | 98.12 84 | 84.50 396 | 88.27 349 | 97.77 130 | 82.39 243 | 99.81 33 | 85.40 345 | 98.81 112 | 98.51 169 |
|
| Vis-MVSNet |  | | 95.23 118 | 94.81 121 | 96.51 109 | 97.18 172 | 91.58 139 | 98.26 37 | 98.12 84 | 94.38 83 | 94.90 160 | 98.15 90 | 82.28 244 | 98.92 195 | 91.45 218 | 98.58 124 | 99.01 104 |
| Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
| OpenMVS |  | 89.19 12 | 92.86 223 | 91.68 244 | 96.40 119 | 95.34 317 | 92.73 92 | 98.27 35 | 98.12 84 | 84.86 391 | 85.78 393 | 97.75 131 | 78.89 314 | 99.74 56 | 87.50 310 | 98.65 119 | 96.73 282 |
|
| TranMVSNet+NR-MVSNet | | | 92.50 232 | 91.63 245 | 95.14 206 | 94.76 354 | 92.07 117 | 97.53 147 | 98.11 87 | 92.90 148 | 89.56 312 | 96.12 248 | 83.16 218 | 97.60 363 | 89.30 268 | 83.20 409 | 95.75 319 |
|
| CPTT-MVS | | | 95.57 106 | 95.19 110 | 96.70 90 | 99.27 29 | 91.48 144 | 98.33 29 | 98.11 87 | 87.79 336 | 95.17 155 | 98.03 98 | 87.09 145 | 99.61 87 | 93.51 170 | 99.42 54 | 99.02 101 |
|
| APD-MVS |  | | 96.95 45 | 96.60 64 | 98.01 21 | 99.03 45 | 94.93 28 | 97.72 115 | 98.10 89 | 91.50 195 | 98.01 47 | 98.32 77 | 92.33 44 | 99.58 95 | 94.85 130 | 99.51 36 | 99.53 46 |
| Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
| mPP-MVS | | | 96.86 50 | 96.60 64 | 97.64 47 | 99.40 12 | 93.44 64 | 98.50 21 | 98.09 90 | 93.27 123 | 95.95 128 | 98.33 75 | 91.04 72 | 99.88 4 | 95.20 119 | 99.57 27 | 99.60 29 |
|
| ZD-MVS | | | | | | 99.05 43 | 94.59 33 | | 98.08 91 | 89.22 284 | 97.03 78 | 98.10 91 | 92.52 41 | 99.65 76 | 94.58 145 | 99.31 69 | |
|
| MTGPA |  | | | | | | | | 98.08 91 | | | | | | | | |
|
| MTAPA | | | 97.08 37 | 96.78 57 | 97.97 25 | 99.37 17 | 94.42 38 | 97.24 183 | 98.08 91 | 95.07 43 | 96.11 120 | 98.59 45 | 90.88 78 | 99.90 2 | 96.18 89 | 99.50 38 | 99.58 33 |
|
| CNVR-MVS | | | 97.68 7 | 97.44 22 | 98.37 8 | 98.90 57 | 95.86 7 | 97.27 181 | 98.08 91 | 95.81 19 | 97.87 55 | 98.31 78 | 94.26 14 | 99.68 72 | 97.02 54 | 99.49 41 | 99.57 34 |
|
| DP-MVS Recon | | | 95.68 101 | 95.12 114 | 97.37 58 | 99.19 35 | 94.19 44 | 97.03 201 | 98.08 91 | 88.35 318 | 95.09 157 | 97.65 143 | 89.97 89 | 99.48 121 | 92.08 203 | 98.59 123 | 98.44 180 |
|
| SR-MVS | | | 97.01 42 | 96.86 47 | 97.47 54 | 99.09 38 | 93.27 73 | 97.98 68 | 98.07 96 | 93.75 100 | 97.45 61 | 98.48 58 | 91.43 62 | 99.59 92 | 96.22 80 | 99.27 72 | 99.54 43 |
|
| MCST-MVS | | | 97.18 32 | 96.84 49 | 98.20 15 | 99.30 27 | 95.35 16 | 97.12 197 | 98.07 96 | 93.54 110 | 96.08 122 | 97.69 138 | 93.86 17 | 99.71 64 | 96.50 71 | 99.39 61 | 99.55 41 |
|
| NR-MVSNet | | | 92.34 241 | 91.27 260 | 95.53 187 | 94.95 343 | 93.05 79 | 97.39 169 | 98.07 96 | 92.65 156 | 84.46 404 | 95.71 271 | 85.00 185 | 97.77 347 | 89.71 256 | 83.52 406 | 95.78 315 |
|
| MP-MVS-pluss | | | 96.70 63 | 96.27 81 | 97.98 24 | 99.23 33 | 94.71 30 | 96.96 212 | 98.06 99 | 90.67 235 | 95.55 144 | 98.78 39 | 91.07 71 | 99.86 9 | 96.58 69 | 99.55 28 | 99.38 68 |
| MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
| APD-MVS_3200maxsize | | | 96.81 56 | 96.71 61 | 97.12 74 | 99.01 49 | 92.31 108 | 97.98 68 | 98.06 99 | 93.11 133 | 97.44 62 | 98.55 48 | 90.93 76 | 99.55 105 | 96.06 90 | 99.25 77 | 99.51 47 |
|
| MP-MVS |  | | 96.77 58 | 96.45 75 | 97.72 41 | 99.39 14 | 93.80 56 | 98.41 26 | 98.06 99 | 93.37 119 | 95.54 146 | 98.34 72 | 90.59 82 | 99.88 4 | 94.83 132 | 99.54 30 | 99.49 52 |
| Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
| HPM-MVS_fast | | | 96.51 72 | 96.27 81 | 97.22 68 | 99.32 25 | 92.74 91 | 98.74 10 | 98.06 99 | 90.57 245 | 96.77 85 | 98.35 69 | 90.21 85 | 99.53 109 | 94.80 136 | 99.63 16 | 99.38 68 |
|
| HPM-MVS |  | | 96.69 65 | 96.45 75 | 97.40 57 | 99.36 21 | 93.11 78 | 98.87 6 | 98.06 99 | 91.17 214 | 96.40 109 | 97.99 104 | 90.99 73 | 99.58 95 | 95.61 112 | 99.61 18 | 99.49 52 |
| Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
| sss | | | 94.51 148 | 93.80 157 | 96.64 92 | 97.07 178 | 91.97 122 | 96.32 283 | 98.06 99 | 88.94 296 | 94.50 174 | 96.78 206 | 84.60 191 | 99.27 144 | 91.90 204 | 96.02 213 | 98.68 155 |
|
| DeepC-MVS | | 93.07 3 | 96.06 87 | 95.66 92 | 97.29 62 | 97.96 125 | 93.17 77 | 97.30 179 | 98.06 99 | 93.92 95 | 93.38 211 | 98.66 42 | 86.83 147 | 99.73 58 | 95.60 114 | 99.22 79 | 98.96 112 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| NCCC | | | 97.30 28 | 97.03 38 | 98.11 18 | 98.77 60 | 95.06 26 | 97.34 174 | 98.04 106 | 95.96 14 | 97.09 76 | 97.88 116 | 93.18 27 | 99.71 64 | 95.84 101 | 99.17 88 | 99.56 38 |
|
| DeepC-MVS_fast | | 93.89 2 | 96.93 47 | 96.64 63 | 97.78 34 | 98.64 71 | 94.30 39 | 97.41 164 | 98.04 106 | 94.81 58 | 96.59 96 | 98.37 67 | 91.24 67 | 99.64 84 | 95.16 121 | 99.52 33 | 99.42 63 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| SR-MVS-dyc-post | | | 96.88 49 | 96.80 55 | 97.11 76 | 99.02 46 | 92.34 106 | 97.98 68 | 98.03 108 | 93.52 113 | 97.43 64 | 98.51 53 | 91.40 63 | 99.56 103 | 96.05 91 | 99.26 75 | 99.43 61 |
|
| RE-MVS-def | | | | 96.72 60 | | 99.02 46 | 92.34 106 | 97.98 68 | 98.03 108 | 93.52 113 | 97.43 64 | 98.51 53 | 90.71 80 | | 96.05 91 | 99.26 75 | 99.43 61 |
|
| RPMNet | | | 88.98 357 | 87.05 371 | 94.77 233 | 94.45 368 | 87.19 304 | 90.23 445 | 98.03 108 | 77.87 444 | 92.40 229 | 87.55 451 | 80.17 287 | 99.51 114 | 68.84 451 | 93.95 269 | 97.60 250 |
|
| save fliter | | | | | | 98.91 56 | 94.28 40 | 97.02 203 | 98.02 111 | 95.35 30 | | | | | | | |
|
| TEST9 | | | | | | 98.70 63 | 94.19 44 | 96.41 269 | 98.02 111 | 88.17 322 | 96.03 123 | 97.56 155 | 92.74 35 | 99.59 92 | | | |
|
| train_agg | | | 96.30 83 | 95.83 91 | 97.72 41 | 98.70 63 | 94.19 44 | 96.41 269 | 98.02 111 | 88.58 309 | 96.03 123 | 97.56 155 | 92.73 36 | 99.59 92 | 95.04 123 | 99.37 65 | 99.39 66 |
|
| test_8 | | | | | | 98.67 65 | 94.06 51 | 96.37 277 | 98.01 114 | 88.58 309 | 95.98 127 | 97.55 157 | 92.73 36 | 99.58 95 | | | |
|
| agg_prior | | | | | | 98.67 65 | 93.79 57 | | 98.00 115 | | 95.68 140 | | | 99.57 102 | | | |
|
| test_prior | | | | | 97.23 67 | 98.67 65 | 92.99 81 | | 98.00 115 | | | | | 99.41 129 | | | 99.29 73 |
|
| WR-MVS | | | 92.34 241 | 91.53 249 | 94.77 233 | 95.13 336 | 90.83 175 | 96.40 273 | 97.98 117 | 91.88 181 | 89.29 321 | 95.54 282 | 82.50 239 | 97.80 343 | 89.79 255 | 85.27 379 | 95.69 322 |
|
| HPM-MVS++ |  | | 97.34 25 | 96.97 41 | 98.47 6 | 99.08 40 | 96.16 4 | 97.55 146 | 97.97 118 | 95.59 24 | 96.61 94 | 97.89 113 | 92.57 40 | 99.84 24 | 95.95 96 | 99.51 36 | 99.40 64 |
|
| CANet | | | 96.39 78 | 96.02 86 | 97.50 52 | 97.62 151 | 93.38 66 | 97.02 203 | 97.96 119 | 95.42 28 | 94.86 161 | 97.81 126 | 87.38 140 | 99.82 31 | 96.88 57 | 99.20 85 | 99.29 73 |
|
| 114514_t | | | 93.95 172 | 93.06 189 | 96.63 96 | 99.07 41 | 91.61 136 | 97.46 161 | 97.96 119 | 77.99 442 | 93.00 220 | 97.57 153 | 86.14 161 | 99.33 136 | 89.22 272 | 99.15 92 | 98.94 116 |
|
| IU-MVS | | | | | | 99.42 8 | 95.39 12 | | 97.94 121 | 90.40 252 | 98.94 18 | | | | 97.41 48 | 99.66 10 | 99.74 9 |
|
| MSC_two_6792asdad | | | | | 98.86 1 | 98.67 65 | 96.94 1 | | 97.93 122 | | | | | 99.86 9 | 97.68 32 | 99.67 6 | 99.77 3 |
|
| No_MVS | | | | | 98.86 1 | 98.67 65 | 96.94 1 | | 97.93 122 | | | | | 99.86 9 | 97.68 32 | 99.67 6 | 99.77 3 |
|
| fmvsm_s_conf0.1_n_2 | | | 96.33 82 | 96.44 77 | 96.00 152 | 97.30 165 | 90.37 197 | 97.53 147 | 97.92 124 | 96.52 10 | 99.14 14 | 99.08 7 | 83.21 216 | 99.74 56 | 99.22 10 | 98.06 147 | 97.88 230 |
|
| Anonymous20231211 | | | 90.63 323 | 89.42 339 | 94.27 263 | 98.24 97 | 89.19 248 | 98.05 60 | 97.89 125 | 79.95 434 | 88.25 350 | 94.96 305 | 72.56 373 | 98.13 288 | 89.70 257 | 85.14 381 | 95.49 326 |
|
| 原ACMM1 | | | | | 96.38 122 | 98.59 73 | 91.09 165 | | 97.89 125 | 87.41 347 | 95.22 154 | 97.68 139 | 90.25 84 | 99.54 107 | 87.95 294 | 99.12 97 | 98.49 172 |
|
| CDPH-MVS | | | 95.97 92 | 95.38 104 | 97.77 36 | 98.93 54 | 94.44 37 | 96.35 278 | 97.88 127 | 86.98 355 | 96.65 92 | 97.89 113 | 91.99 50 | 99.47 122 | 92.26 192 | 99.46 44 | 99.39 66 |
|
| test11 | | | | | | | | | 97.88 127 | | | | | | | | |
|
| EIA-MVS | | | 95.53 107 | 95.47 98 | 95.71 177 | 97.06 181 | 89.63 221 | 97.82 97 | 97.87 129 | 93.57 106 | 93.92 193 | 95.04 302 | 90.61 81 | 98.95 190 | 94.62 142 | 98.68 117 | 98.54 165 |
|
| CS-MVS | | | 96.86 50 | 97.06 33 | 96.26 132 | 98.16 109 | 91.16 163 | 99.09 3 | 97.87 129 | 95.30 32 | 97.06 77 | 98.03 98 | 91.72 53 | 98.71 230 | 97.10 52 | 99.17 88 | 98.90 125 |
|
| 无先验 | | | | | | | | 95.79 318 | 97.87 129 | 83.87 404 | | | | 99.65 76 | 87.68 304 | | 98.89 131 |
|
| 3Dnovator+ | | 91.43 4 | 95.40 108 | 94.48 140 | 98.16 17 | 96.90 197 | 95.34 17 | 98.48 23 | 97.87 129 | 94.65 69 | 88.53 341 | 98.02 100 | 83.69 207 | 99.71 64 | 93.18 178 | 98.96 107 | 99.44 59 |
|
| VPNet | | | 92.23 249 | 91.31 257 | 94.99 216 | 95.56 301 | 90.96 169 | 97.22 189 | 97.86 133 | 92.96 143 | 90.96 273 | 96.62 223 | 75.06 353 | 98.20 282 | 91.90 204 | 83.65 405 | 95.80 313 |
|
| test_vis1_n_1920 | | | 94.17 157 | 94.58 132 | 92.91 331 | 97.42 163 | 82.02 401 | 97.83 95 | 97.85 134 | 94.68 66 | 98.10 45 | 98.49 55 | 70.15 392 | 99.32 138 | 97.91 29 | 98.82 111 | 97.40 259 |
|
| DVP-MVS |  | | 97.91 4 | 97.81 4 | 98.22 14 | 99.45 4 | 95.36 14 | 98.21 45 | 97.85 134 | 94.92 49 | 98.73 29 | 98.87 30 | 95.08 9 | 99.84 24 | 97.52 41 | 99.67 6 | 99.48 54 |
| 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 |
| TSAR-MVS + MP. | | | 97.42 21 | 97.33 27 | 97.69 44 | 99.25 30 | 94.24 43 | 98.07 58 | 97.85 134 | 93.72 101 | 98.57 34 | 98.35 69 | 93.69 19 | 99.40 130 | 97.06 53 | 99.46 44 | 99.44 59 |
| Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
| SPE-MVS-test | | | 96.89 48 | 97.04 37 | 96.45 116 | 98.29 90 | 91.66 135 | 99.03 4 | 97.85 134 | 95.84 17 | 96.90 80 | 97.97 106 | 91.24 67 | 98.75 220 | 96.92 56 | 99.33 67 | 98.94 116 |
|
| test_fmvsmconf0.01_n | | | 96.15 86 | 95.85 90 | 97.03 81 | 92.66 419 | 91.83 127 | 97.97 74 | 97.84 138 | 95.57 25 | 97.53 58 | 99.00 15 | 84.20 200 | 99.76 51 | 98.82 22 | 99.08 99 | 99.48 54 |
|
| GDP-MVS | | | 95.62 103 | 95.13 112 | 97.09 77 | 96.79 210 | 93.26 74 | 97.89 85 | 97.83 139 | 93.58 105 | 96.80 82 | 97.82 124 | 83.06 223 | 99.16 158 | 94.40 149 | 97.95 153 | 98.87 134 |
|
| balanced_conf03 | | | 96.84 54 | 96.89 46 | 96.68 91 | 97.63 150 | 92.22 111 | 98.17 51 | 97.82 140 | 94.44 78 | 98.23 42 | 97.36 168 | 90.97 74 | 99.22 148 | 97.74 31 | 99.66 10 | 98.61 158 |
|
| AdaColmap |  | | 94.34 152 | 93.68 162 | 96.31 126 | 98.59 73 | 91.68 134 | 96.59 258 | 97.81 141 | 89.87 262 | 92.15 239 | 97.06 189 | 83.62 210 | 99.54 107 | 89.34 267 | 98.07 146 | 97.70 243 |
|
| MVSMamba_PlusPlus | | | 96.51 72 | 96.48 70 | 96.59 100 | 98.07 117 | 91.97 122 | 98.14 52 | 97.79 142 | 90.43 250 | 97.34 67 | 97.52 158 | 91.29 66 | 99.19 151 | 98.12 27 | 99.64 14 | 98.60 159 |
|
| KinetiMVS | | | 95.26 114 | 94.75 126 | 96.79 88 | 96.99 190 | 92.05 118 | 97.82 97 | 97.78 143 | 94.77 62 | 96.46 106 | 97.70 136 | 80.62 277 | 99.34 135 | 92.37 191 | 98.28 137 | 98.97 109 |
|
| mamv4 | | | 94.66 145 | 96.10 85 | 90.37 400 | 98.01 120 | 73.41 451 | 96.82 228 | 97.78 143 | 89.95 261 | 94.52 172 | 97.43 163 | 92.91 29 | 99.09 171 | 98.28 26 | 99.16 91 | 98.60 159 |
|
| ETV-MVS | | | 96.02 89 | 95.89 89 | 96.40 119 | 97.16 173 | 92.44 103 | 97.47 159 | 97.77 145 | 94.55 72 | 96.48 104 | 94.51 330 | 91.23 69 | 98.92 195 | 95.65 108 | 98.19 141 | 97.82 238 |
|
| 新几何1 | | | | | 97.32 60 | 98.60 72 | 93.59 61 | | 97.75 146 | 81.58 425 | 95.75 135 | 97.85 120 | 90.04 87 | 99.67 74 | 86.50 326 | 99.13 95 | 98.69 154 |
|
| 旧先验1 | | | | | | 98.38 86 | 93.38 66 | | 97.75 146 | | | 98.09 93 | 92.30 47 | | | 99.01 105 | 99.16 83 |
|
| EC-MVSNet | | | 96.42 76 | 96.47 71 | 96.26 132 | 97.01 188 | 91.52 141 | 98.89 5 | 97.75 146 | 94.42 79 | 96.64 93 | 97.68 139 | 89.32 95 | 98.60 246 | 97.45 45 | 99.11 98 | 98.67 156 |
|
| EI-MVSNet-Vis-set | | | 96.51 72 | 96.47 71 | 96.63 96 | 98.24 97 | 91.20 157 | 96.89 220 | 97.73 149 | 94.74 64 | 96.49 103 | 98.49 55 | 90.88 78 | 99.58 95 | 96.44 73 | 98.32 135 | 99.13 87 |
|
| PAPM_NR | | | 95.01 125 | 94.59 131 | 96.26 132 | 98.89 58 | 90.68 183 | 97.24 183 | 97.73 149 | 91.80 182 | 92.93 225 | 96.62 223 | 89.13 98 | 99.14 163 | 89.21 273 | 97.78 157 | 98.97 109 |
|
| Anonymous20240529 | | | 91.98 258 | 90.73 285 | 95.73 175 | 98.14 110 | 89.40 235 | 97.99 65 | 97.72 151 | 79.63 436 | 93.54 204 | 97.41 165 | 69.94 394 | 99.56 103 | 91.04 226 | 91.11 313 | 98.22 200 |
|
| CHOSEN 280x420 | | | 93.12 208 | 92.72 206 | 94.34 257 | 96.71 221 | 87.27 300 | 90.29 444 | 97.72 151 | 86.61 362 | 91.34 262 | 95.29 290 | 84.29 199 | 98.41 262 | 93.25 176 | 98.94 108 | 97.35 262 |
|
| EI-MVSNet-UG-set | | | 96.34 81 | 96.30 80 | 96.47 113 | 98.20 104 | 90.93 171 | 96.86 223 | 97.72 151 | 94.67 67 | 96.16 119 | 98.46 59 | 90.43 83 | 99.58 95 | 96.23 79 | 97.96 152 | 98.90 125 |
|
| LS3D | | | 93.57 189 | 92.61 211 | 96.47 113 | 97.59 154 | 91.61 136 | 97.67 122 | 97.72 151 | 85.17 386 | 90.29 284 | 98.34 72 | 84.60 191 | 99.73 58 | 83.85 368 | 98.27 138 | 98.06 219 |
|
| PAPR | | | 94.18 156 | 93.42 178 | 96.48 112 | 97.64 148 | 91.42 148 | 95.55 332 | 97.71 155 | 88.99 293 | 92.34 235 | 95.82 263 | 89.19 96 | 99.11 166 | 86.14 332 | 97.38 169 | 98.90 125 |
|
| UGNet | | | 94.04 167 | 93.28 181 | 96.31 126 | 96.85 202 | 91.19 158 | 97.88 87 | 97.68 156 | 94.40 81 | 93.00 220 | 96.18 243 | 73.39 370 | 99.61 87 | 91.72 210 | 98.46 129 | 98.13 208 |
| 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 |
| testdata | | | | | 95.46 195 | 98.18 108 | 88.90 255 | | 97.66 157 | 82.73 416 | 97.03 78 | 98.07 94 | 90.06 86 | 98.85 202 | 89.67 258 | 98.98 106 | 98.64 157 |
|
| test12 | | | | | 97.65 45 | 98.46 77 | 94.26 41 | | 97.66 157 | | 95.52 147 | | 90.89 77 | 99.46 123 | | 99.25 77 | 99.22 80 |
|
| DTE-MVSNet | | | 90.56 324 | 89.75 330 | 93.01 327 | 93.95 381 | 87.25 301 | 97.64 130 | 97.65 159 | 90.74 230 | 87.12 373 | 95.68 274 | 79.97 291 | 97.00 397 | 83.33 369 | 81.66 416 | 94.78 382 |
|
| TAPA-MVS | | 90.10 7 | 92.30 244 | 91.22 263 | 95.56 184 | 98.33 88 | 89.60 223 | 96.79 232 | 97.65 159 | 81.83 422 | 91.52 257 | 97.23 178 | 87.94 121 | 98.91 197 | 71.31 445 | 98.37 133 | 98.17 206 |
| Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
| sd_testset | | | 93.10 209 | 92.45 219 | 95.05 210 | 98.09 113 | 89.21 245 | 96.89 220 | 97.64 161 | 93.18 129 | 91.79 251 | 97.28 173 | 75.35 352 | 98.65 240 | 88.99 278 | 92.84 283 | 97.28 265 |
|
| test_cas_vis1_n_1920 | | | 94.48 150 | 94.55 136 | 94.28 262 | 96.78 214 | 86.45 325 | 97.63 132 | 97.64 161 | 93.32 122 | 97.68 57 | 98.36 68 | 73.75 368 | 99.08 174 | 96.73 62 | 99.05 101 | 97.31 264 |
|
| NormalMVS | | | 96.36 80 | 96.11 84 | 97.12 74 | 99.37 17 | 92.90 85 | 97.99 65 | 97.63 163 | 95.92 15 | 96.57 99 | 97.93 108 | 85.34 176 | 99.50 117 | 94.99 126 | 99.21 80 | 98.97 109 |
|
| Elysia | | | 94.00 169 | 93.12 186 | 96.64 92 | 96.08 280 | 92.72 93 | 97.50 150 | 97.63 163 | 91.15 216 | 94.82 162 | 97.12 184 | 74.98 355 | 99.06 180 | 90.78 231 | 98.02 148 | 98.12 210 |
|
| StellarMVS | | | 94.00 169 | 93.12 186 | 96.64 92 | 96.08 280 | 92.72 93 | 97.50 150 | 97.63 163 | 91.15 216 | 94.82 162 | 97.12 184 | 74.98 355 | 99.06 180 | 90.78 231 | 98.02 148 | 98.12 210 |
|
| cdsmvs_eth3d_5k | | | 23.24 441 | 30.99 443 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 97.63 163 | 0.00 477 | 0.00 478 | 96.88 202 | 84.38 196 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| DPM-MVS | | | 95.69 100 | 94.92 119 | 98.01 21 | 98.08 116 | 95.71 10 | 95.27 348 | 97.62 167 | 90.43 250 | 95.55 144 | 97.07 188 | 91.72 53 | 99.50 117 | 89.62 260 | 98.94 108 | 98.82 140 |
|
| sasdasda | | | 96.02 89 | 95.45 99 | 97.75 38 | 97.59 154 | 95.15 24 | 98.28 33 | 97.60 168 | 94.52 74 | 96.27 114 | 96.12 248 | 87.65 128 | 99.18 154 | 96.20 85 | 94.82 245 | 98.91 122 |
|
| canonicalmvs | | | 96.02 89 | 95.45 99 | 97.75 38 | 97.59 154 | 95.15 24 | 98.28 33 | 97.60 168 | 94.52 74 | 96.27 114 | 96.12 248 | 87.65 128 | 99.18 154 | 96.20 85 | 94.82 245 | 98.91 122 |
|
| test222 | | | | | | 98.24 97 | 92.21 112 | 95.33 343 | 97.60 168 | 79.22 438 | 95.25 152 | 97.84 122 | 88.80 104 | | | 99.15 92 | 98.72 151 |
|
| cascas | | | 91.20 299 | 90.08 312 | 94.58 243 | 94.97 341 | 89.16 249 | 93.65 409 | 97.59 171 | 79.90 435 | 89.40 316 | 92.92 395 | 75.36 351 | 98.36 270 | 92.14 197 | 94.75 248 | 96.23 292 |
|
| h-mvs33 | | | 94.15 159 | 93.52 170 | 96.04 146 | 97.81 136 | 90.22 201 | 97.62 134 | 97.58 172 | 95.19 35 | 96.74 86 | 97.45 160 | 83.67 208 | 99.61 87 | 95.85 99 | 79.73 423 | 98.29 196 |
|
| MGCFI-Net | | | 95.94 94 | 95.40 103 | 97.56 51 | 97.59 154 | 94.62 32 | 98.21 45 | 97.57 173 | 94.41 80 | 96.17 118 | 96.16 246 | 87.54 133 | 99.17 156 | 96.19 87 | 94.73 250 | 98.91 122 |
|
| MVSFormer | | | 95.37 109 | 95.16 111 | 95.99 153 | 96.34 256 | 91.21 155 | 98.22 43 | 97.57 173 | 91.42 199 | 96.22 116 | 97.32 169 | 86.20 159 | 97.92 330 | 94.07 155 | 99.05 101 | 98.85 136 |
|
| test_djsdf | | | 93.07 211 | 92.76 201 | 94.00 276 | 93.49 398 | 88.70 259 | 98.22 43 | 97.57 173 | 91.42 199 | 90.08 296 | 95.55 281 | 82.85 230 | 97.92 330 | 94.07 155 | 91.58 304 | 95.40 337 |
|
| OMC-MVS | | | 95.09 123 | 94.70 127 | 96.25 135 | 98.46 77 | 91.28 151 | 96.43 265 | 97.57 173 | 92.04 177 | 94.77 166 | 97.96 107 | 87.01 146 | 99.09 171 | 91.31 220 | 96.77 192 | 98.36 187 |
|
| viewcassd2359sk11 | | | 95.26 114 | 95.09 115 | 95.80 167 | 96.95 194 | 89.72 219 | 96.80 231 | 97.56 177 | 92.21 169 | 95.37 150 | 97.80 128 | 87.17 144 | 98.77 215 | 94.82 134 | 97.10 184 | 98.90 125 |
|
| PS-MVSNAJss | | | 93.74 182 | 93.51 171 | 94.44 251 | 93.91 383 | 89.28 243 | 97.75 107 | 97.56 177 | 92.50 159 | 89.94 298 | 96.54 226 | 88.65 107 | 98.18 285 | 93.83 164 | 90.90 318 | 95.86 307 |
|
| casdiffmvs_mvg |  | | 95.81 99 | 95.57 93 | 96.51 109 | 96.87 199 | 91.49 142 | 97.50 150 | 97.56 177 | 93.99 93 | 95.13 156 | 97.92 111 | 87.89 122 | 98.78 212 | 95.97 95 | 97.33 172 | 99.26 77 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| jajsoiax | | | 92.42 237 | 91.89 237 | 94.03 275 | 93.33 406 | 88.50 266 | 97.73 112 | 97.53 180 | 92.00 179 | 88.85 333 | 96.50 228 | 75.62 350 | 98.11 292 | 93.88 162 | 91.56 305 | 95.48 327 |
|
| mvs_tets | | | 92.31 243 | 91.76 240 | 93.94 284 | 93.41 403 | 88.29 271 | 97.63 132 | 97.53 180 | 92.04 177 | 88.76 336 | 96.45 230 | 74.62 360 | 98.09 297 | 93.91 160 | 91.48 306 | 95.45 332 |
|
| dcpmvs_2 | | | 96.37 79 | 97.05 36 | 94.31 260 | 98.96 53 | 84.11 374 | 97.56 141 | 97.51 182 | 93.92 95 | 97.43 64 | 98.52 52 | 92.75 34 | 99.32 138 | 97.32 51 | 99.50 38 | 99.51 47 |
|
| HQP_MVS | | | 93.78 181 | 93.43 176 | 94.82 226 | 96.21 260 | 89.99 207 | 97.74 110 | 97.51 182 | 94.85 52 | 91.34 262 | 96.64 216 | 81.32 263 | 98.60 246 | 93.02 184 | 92.23 292 | 95.86 307 |
|
| plane_prior5 | | | | | | | | | 97.51 182 | | | | | 98.60 246 | 93.02 184 | 92.23 292 | 95.86 307 |
|
| viewmanbaseed2359cas | | | 95.24 117 | 95.02 117 | 95.91 156 | 96.87 199 | 89.98 209 | 96.82 228 | 97.49 185 | 92.26 165 | 95.47 148 | 97.82 124 | 86.47 152 | 98.69 232 | 94.80 136 | 97.20 180 | 99.06 99 |
|
| reproduce_monomvs | | | 91.30 294 | 91.10 267 | 91.92 361 | 96.82 207 | 82.48 395 | 97.01 206 | 97.49 185 | 94.64 70 | 88.35 344 | 95.27 293 | 70.53 387 | 98.10 293 | 95.20 119 | 84.60 391 | 95.19 355 |
|
| viewmacassd2359aftdt | | | 95.07 124 | 94.80 122 | 95.87 159 | 96.53 237 | 89.84 215 | 96.90 219 | 97.48 187 | 92.44 160 | 95.36 151 | 97.89 113 | 85.23 179 | 98.68 234 | 94.40 149 | 97.00 187 | 99.09 94 |
|
| PS-MVSNAJ | | | 95.37 109 | 95.33 106 | 95.49 191 | 97.35 164 | 90.66 184 | 95.31 345 | 97.48 187 | 93.85 98 | 96.51 102 | 95.70 273 | 88.65 107 | 99.65 76 | 94.80 136 | 98.27 138 | 96.17 296 |
|
| API-MVS | | | 94.84 136 | 94.49 139 | 95.90 157 | 97.90 131 | 92.00 121 | 97.80 101 | 97.48 187 | 89.19 285 | 94.81 164 | 96.71 209 | 88.84 103 | 99.17 156 | 88.91 280 | 98.76 115 | 96.53 285 |
|
| MG-MVS | | | 95.61 104 | 95.38 104 | 96.31 126 | 98.42 81 | 90.53 186 | 96.04 302 | 97.48 187 | 93.47 115 | 95.67 141 | 98.10 91 | 89.17 97 | 99.25 145 | 91.27 221 | 98.77 114 | 99.13 87 |
|
| MAR-MVS | | | 94.22 155 | 93.46 173 | 96.51 109 | 98.00 122 | 92.19 115 | 97.67 122 | 97.47 191 | 88.13 326 | 93.00 220 | 95.84 261 | 84.86 189 | 99.51 114 | 87.99 293 | 98.17 143 | 97.83 237 |
| 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 |
| CLD-MVS | | | 92.98 215 | 92.53 215 | 94.32 258 | 96.12 275 | 89.20 246 | 95.28 346 | 97.47 191 | 92.66 155 | 89.90 299 | 95.62 277 | 80.58 278 | 98.40 263 | 92.73 189 | 92.40 290 | 95.38 339 |
| Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
| UniMVSNet_ETH3D | | | 91.34 292 | 90.22 308 | 94.68 237 | 94.86 350 | 87.86 288 | 97.23 187 | 97.46 193 | 87.99 327 | 89.90 299 | 96.92 200 | 66.35 422 | 98.23 279 | 90.30 245 | 90.99 316 | 97.96 224 |
|
| nrg030 | | | 94.05 166 | 93.31 180 | 96.27 131 | 95.22 328 | 94.59 33 | 98.34 28 | 97.46 193 | 92.93 144 | 91.21 271 | 96.64 216 | 87.23 143 | 98.22 280 | 94.99 126 | 85.80 371 | 95.98 306 |
|
| XVG-OURS | | | 93.72 183 | 93.35 179 | 94.80 231 | 97.07 178 | 88.61 260 | 94.79 364 | 97.46 193 | 91.97 180 | 93.99 190 | 97.86 119 | 81.74 257 | 98.88 199 | 92.64 190 | 92.67 288 | 96.92 277 |
|
| LPG-MVS_test | | | 92.94 218 | 92.56 212 | 94.10 270 | 96.16 270 | 88.26 273 | 97.65 126 | 97.46 193 | 91.29 203 | 90.12 292 | 97.16 181 | 79.05 307 | 98.73 224 | 92.25 194 | 91.89 300 | 95.31 344 |
|
| LGP-MVS_train | | | | | 94.10 270 | 96.16 270 | 88.26 273 | | 97.46 193 | 91.29 203 | 90.12 292 | 97.16 181 | 79.05 307 | 98.73 224 | 92.25 194 | 91.89 300 | 95.31 344 |
|
| MVS | | | 91.71 266 | 90.44 295 | 95.51 188 | 95.20 330 | 91.59 138 | 96.04 302 | 97.45 198 | 73.44 452 | 87.36 369 | 95.60 278 | 85.42 175 | 99.10 168 | 85.97 337 | 97.46 164 | 95.83 311 |
|
| XVG-OURS-SEG-HR | | | 93.86 178 | 93.55 166 | 94.81 228 | 97.06 181 | 88.53 265 | 95.28 346 | 97.45 198 | 91.68 187 | 94.08 189 | 97.68 139 | 82.41 242 | 98.90 198 | 93.84 163 | 92.47 289 | 96.98 273 |
|
| baseline | | | 95.58 105 | 95.42 102 | 96.08 142 | 96.78 214 | 90.41 192 | 97.16 194 | 97.45 198 | 93.69 104 | 95.65 142 | 97.85 120 | 87.29 141 | 98.68 234 | 95.66 105 | 97.25 178 | 99.13 87 |
|
| ab-mvs | | | 93.57 189 | 92.55 213 | 96.64 92 | 97.28 167 | 91.96 124 | 95.40 339 | 97.45 198 | 89.81 267 | 93.22 217 | 96.28 239 | 79.62 298 | 99.46 123 | 90.74 234 | 93.11 280 | 98.50 170 |
|
| xiu_mvs_v2_base | | | 95.32 112 | 95.29 107 | 95.40 196 | 97.22 169 | 90.50 187 | 95.44 338 | 97.44 202 | 93.70 103 | 96.46 106 | 96.18 243 | 88.59 111 | 99.53 109 | 94.79 139 | 97.81 156 | 96.17 296 |
|
| 1314 | | | 92.81 227 | 92.03 230 | 95.14 206 | 95.33 320 | 89.52 230 | 96.04 302 | 97.44 202 | 87.72 340 | 86.25 390 | 95.33 289 | 83.84 205 | 98.79 211 | 89.26 270 | 97.05 186 | 97.11 271 |
|
| casdiffmvs |  | | 95.64 102 | 95.49 96 | 96.08 142 | 96.76 219 | 90.45 189 | 97.29 180 | 97.44 202 | 94.00 92 | 95.46 149 | 97.98 105 | 87.52 136 | 98.73 224 | 95.64 109 | 97.33 172 | 99.08 96 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| viewdifsd2359ckpt07 | | | 94.76 142 | 94.68 128 | 95.01 214 | 96.76 219 | 87.41 296 | 96.38 275 | 97.43 205 | 92.65 156 | 94.52 172 | 97.75 131 | 85.55 173 | 98.81 208 | 94.36 151 | 96.69 198 | 98.82 140 |
|
| XXY-MVS | | | 92.16 251 | 91.23 262 | 94.95 222 | 94.75 355 | 90.94 170 | 97.47 159 | 97.43 205 | 89.14 286 | 88.90 329 | 96.43 231 | 79.71 295 | 98.24 278 | 89.56 261 | 87.68 352 | 95.67 323 |
|
| anonymousdsp | | | 92.16 251 | 91.55 248 | 93.97 280 | 92.58 421 | 89.55 227 | 97.51 149 | 97.42 207 | 89.42 279 | 88.40 343 | 94.84 312 | 80.66 276 | 97.88 335 | 91.87 206 | 91.28 310 | 94.48 390 |
|
| Effi-MVS+ | | | 94.93 130 | 94.45 141 | 96.36 124 | 96.61 225 | 91.47 145 | 96.41 269 | 97.41 208 | 91.02 222 | 94.50 174 | 95.92 257 | 87.53 134 | 98.78 212 | 93.89 161 | 96.81 191 | 98.84 139 |
|
| RRT-MVS | | | 94.51 148 | 94.35 145 | 94.98 218 | 96.40 250 | 86.55 323 | 97.56 141 | 97.41 208 | 93.19 127 | 94.93 159 | 97.04 190 | 79.12 305 | 99.30 142 | 96.19 87 | 97.32 174 | 99.09 94 |
|
| HQP3-MVS | | | | | | | | | 97.39 210 | | | | | | | 92.10 297 | |
|
| HQP-MVS | | | 93.19 205 | 92.74 204 | 94.54 246 | 95.86 286 | 89.33 239 | 96.65 249 | 97.39 210 | 93.55 107 | 90.14 286 | 95.87 259 | 80.95 267 | 98.50 256 | 92.13 200 | 92.10 297 | 95.78 315 |
|
| PLC |  | 91.00 6 | 94.11 163 | 93.43 176 | 96.13 140 | 98.58 75 | 91.15 164 | 96.69 245 | 97.39 210 | 87.29 350 | 91.37 261 | 96.71 209 | 88.39 112 | 99.52 113 | 87.33 313 | 97.13 183 | 97.73 241 |
| Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
| diffmvs_AUTHOR | | | 95.33 111 | 95.27 108 | 95.50 190 | 96.37 254 | 89.08 251 | 96.08 300 | 97.38 213 | 93.09 135 | 96.53 101 | 97.74 133 | 86.45 153 | 98.68 234 | 96.32 75 | 97.48 163 | 98.75 147 |
|
| v7n | | | 90.76 316 | 89.86 323 | 93.45 312 | 93.54 395 | 87.60 294 | 97.70 120 | 97.37 214 | 88.85 299 | 87.65 362 | 94.08 360 | 81.08 266 | 98.10 293 | 84.68 354 | 83.79 404 | 94.66 387 |
|
| UnsupCasMVSNet_eth | | | 85.99 393 | 84.45 397 | 90.62 396 | 89.97 439 | 82.40 398 | 93.62 410 | 97.37 214 | 89.86 263 | 78.59 442 | 92.37 405 | 65.25 431 | 95.35 431 | 82.27 382 | 70.75 450 | 94.10 401 |
|
| viewdifsd2359ckpt13 | | | 94.87 134 | 94.52 137 | 95.90 157 | 96.88 198 | 90.19 202 | 96.92 216 | 97.36 216 | 91.26 207 | 94.65 168 | 97.46 159 | 85.79 167 | 98.64 241 | 93.64 167 | 96.76 193 | 98.88 133 |
|
| ACMM | | 89.79 8 | 92.96 216 | 92.50 217 | 94.35 255 | 96.30 258 | 88.71 258 | 97.58 137 | 97.36 216 | 91.40 201 | 90.53 279 | 96.65 215 | 79.77 294 | 98.75 220 | 91.24 222 | 91.64 302 | 95.59 325 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| xiu_mvs_v1_base_debu | | | 95.01 125 | 94.76 123 | 95.75 172 | 96.58 228 | 91.71 131 | 96.25 288 | 97.35 218 | 92.99 137 | 96.70 88 | 96.63 220 | 82.67 234 | 99.44 126 | 96.22 80 | 97.46 164 | 96.11 302 |
|
| xiu_mvs_v1_base | | | 95.01 125 | 94.76 123 | 95.75 172 | 96.58 228 | 91.71 131 | 96.25 288 | 97.35 218 | 92.99 137 | 96.70 88 | 96.63 220 | 82.67 234 | 99.44 126 | 96.22 80 | 97.46 164 | 96.11 302 |
|
| xiu_mvs_v1_base_debi | | | 95.01 125 | 94.76 123 | 95.75 172 | 96.58 228 | 91.71 131 | 96.25 288 | 97.35 218 | 92.99 137 | 96.70 88 | 96.63 220 | 82.67 234 | 99.44 126 | 96.22 80 | 97.46 164 | 96.11 302 |
|
| diffmvs |  | | 95.25 116 | 95.13 112 | 95.63 180 | 96.43 249 | 89.34 238 | 95.99 306 | 97.35 218 | 92.83 150 | 96.31 112 | 97.37 167 | 86.44 154 | 98.67 237 | 96.26 77 | 97.19 181 | 98.87 134 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| WTY-MVS | | | 94.71 144 | 94.02 153 | 96.79 88 | 97.71 142 | 92.05 118 | 96.59 258 | 97.35 218 | 90.61 241 | 94.64 169 | 96.93 197 | 86.41 155 | 99.39 131 | 91.20 223 | 94.71 251 | 98.94 116 |
|
| viewdifsd2359ckpt09 | | | 94.81 139 | 94.37 144 | 96.12 141 | 96.91 195 | 90.75 180 | 96.94 213 | 97.31 223 | 90.51 248 | 94.31 179 | 97.38 166 | 85.70 169 | 98.71 230 | 93.54 168 | 96.75 194 | 98.90 125 |
|
| SSM_0407 | | | 94.54 147 | 94.12 152 | 95.80 167 | 96.79 210 | 90.38 194 | 96.79 232 | 97.29 224 | 91.24 208 | 93.68 197 | 97.60 150 | 85.03 183 | 98.67 237 | 92.14 197 | 96.51 203 | 98.35 189 |
|
| SSM_0404 | | | 94.73 143 | 94.31 147 | 95.98 154 | 97.05 183 | 90.90 173 | 97.01 206 | 97.29 224 | 91.24 208 | 94.17 186 | 97.60 150 | 85.03 183 | 98.76 217 | 92.14 197 | 97.30 175 | 98.29 196 |
|
| F-COLMAP | | | 93.58 187 | 92.98 193 | 95.37 197 | 98.40 83 | 88.98 253 | 97.18 192 | 97.29 224 | 87.75 339 | 90.49 280 | 97.10 187 | 85.21 180 | 99.50 117 | 86.70 323 | 96.72 197 | 97.63 245 |
|
| VortexMVS | | | 92.88 222 | 92.64 208 | 93.58 305 | 96.58 228 | 87.53 295 | 96.93 215 | 97.28 227 | 92.78 153 | 89.75 304 | 94.99 303 | 82.73 233 | 97.76 348 | 94.60 144 | 88.16 347 | 95.46 330 |
|
| XVG-ACMP-BASELINE | | | 90.93 312 | 90.21 309 | 93.09 325 | 94.31 374 | 85.89 340 | 95.33 343 | 97.26 228 | 91.06 221 | 89.38 317 | 95.44 287 | 68.61 405 | 98.60 246 | 89.46 263 | 91.05 314 | 94.79 380 |
|
| PCF-MVS | | 89.48 11 | 91.56 276 | 89.95 320 | 96.36 124 | 96.60 226 | 92.52 101 | 92.51 429 | 97.26 228 | 79.41 437 | 88.90 329 | 96.56 225 | 84.04 204 | 99.55 105 | 77.01 421 | 97.30 175 | 97.01 272 |
| Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
| ACMP | | 89.59 10 | 92.62 231 | 92.14 226 | 94.05 273 | 96.40 250 | 88.20 276 | 97.36 172 | 97.25 230 | 91.52 194 | 88.30 347 | 96.64 216 | 78.46 319 | 98.72 229 | 91.86 207 | 91.48 306 | 95.23 351 |
| Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
| icg_test_0407_2 | | | 93.58 187 | 93.46 173 | 93.94 284 | 96.19 264 | 86.16 334 | 93.73 404 | 97.24 231 | 91.54 190 | 93.50 206 | 97.04 190 | 85.64 171 | 96.91 400 | 90.68 236 | 95.59 227 | 98.76 143 |
|
| IMVS_0407 | | | 93.94 173 | 93.75 159 | 94.49 248 | 96.19 264 | 86.16 334 | 96.35 278 | 97.24 231 | 91.54 190 | 93.50 206 | 97.04 190 | 85.64 171 | 98.54 253 | 90.68 236 | 95.59 227 | 98.76 143 |
|
| IMVS_0404 | | | 92.44 235 | 91.92 235 | 94.00 276 | 96.19 264 | 86.16 334 | 93.84 401 | 97.24 231 | 91.54 190 | 88.17 353 | 97.04 190 | 76.96 337 | 97.09 391 | 90.68 236 | 95.59 227 | 98.76 143 |
|
| IMVS_0403 | | | 93.98 171 | 93.79 158 | 94.55 245 | 96.19 264 | 86.16 334 | 96.35 278 | 97.24 231 | 91.54 190 | 93.59 201 | 97.04 190 | 85.86 164 | 98.73 224 | 90.68 236 | 95.59 227 | 98.76 143 |
|
| OPM-MVS | | | 93.28 201 | 92.76 201 | 94.82 226 | 94.63 361 | 90.77 178 | 96.65 249 | 97.18 235 | 93.72 101 | 91.68 255 | 97.26 176 | 79.33 302 | 98.63 243 | 92.13 200 | 92.28 291 | 95.07 358 |
| Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
| PatchMatch-RL | | | 92.90 220 | 92.02 231 | 95.56 184 | 98.19 106 | 90.80 176 | 95.27 348 | 97.18 235 | 87.96 328 | 91.86 250 | 95.68 274 | 80.44 281 | 98.99 188 | 84.01 363 | 97.54 162 | 96.89 278 |
|
| alignmvs | | | 95.87 98 | 95.23 109 | 97.78 34 | 97.56 160 | 95.19 22 | 97.86 88 | 97.17 237 | 94.39 82 | 96.47 105 | 96.40 233 | 85.89 163 | 99.20 150 | 96.21 84 | 95.11 241 | 98.95 115 |
|
| MVS_Test | | | 94.89 132 | 94.62 130 | 95.68 178 | 96.83 205 | 89.55 227 | 96.70 243 | 97.17 237 | 91.17 214 | 95.60 143 | 96.11 252 | 87.87 124 | 98.76 217 | 93.01 186 | 97.17 182 | 98.72 151 |
|
| Fast-Effi-MVS+ | | | 93.46 193 | 92.75 203 | 95.59 183 | 96.77 216 | 90.03 204 | 96.81 230 | 97.13 239 | 88.19 321 | 91.30 265 | 94.27 348 | 86.21 158 | 98.63 243 | 87.66 305 | 96.46 209 | 98.12 210 |
|
| EI-MVSNet | | | 93.03 213 | 92.88 197 | 93.48 310 | 95.77 292 | 86.98 309 | 96.44 263 | 97.12 240 | 90.66 237 | 91.30 265 | 97.64 146 | 86.56 149 | 98.05 305 | 89.91 251 | 90.55 322 | 95.41 334 |
|
| MVSTER | | | 93.20 204 | 92.81 200 | 94.37 254 | 96.56 232 | 89.59 224 | 97.06 200 | 97.12 240 | 91.24 208 | 91.30 265 | 95.96 255 | 82.02 250 | 98.05 305 | 93.48 171 | 90.55 322 | 95.47 329 |
|
| viewmambaseed2359dif | | | 94.28 153 | 94.14 150 | 94.71 236 | 96.21 260 | 86.97 310 | 95.93 309 | 97.11 242 | 89.00 292 | 95.00 158 | 97.70 136 | 86.02 162 | 98.59 250 | 93.71 166 | 96.59 202 | 98.57 163 |
|
| test_yl | | | 94.78 140 | 94.23 148 | 96.43 117 | 97.74 140 | 91.22 153 | 96.85 224 | 97.10 243 | 91.23 211 | 95.71 137 | 96.93 197 | 84.30 197 | 99.31 140 | 93.10 179 | 95.12 239 | 98.75 147 |
|
| DCV-MVSNet | | | 94.78 140 | 94.23 148 | 96.43 117 | 97.74 140 | 91.22 153 | 96.85 224 | 97.10 243 | 91.23 211 | 95.71 137 | 96.93 197 | 84.30 197 | 99.31 140 | 93.10 179 | 95.12 239 | 98.75 147 |
|
| LTVRE_ROB | | 88.41 13 | 90.99 308 | 89.92 322 | 94.19 264 | 96.18 268 | 89.55 227 | 96.31 284 | 97.09 245 | 87.88 331 | 85.67 394 | 95.91 258 | 78.79 315 | 98.57 251 | 81.50 385 | 89.98 327 | 94.44 393 |
| 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 |
| viewmsd2359difaftdt | | | 93.46 193 | 93.23 183 | 94.17 265 | 96.12 275 | 85.42 349 | 96.43 265 | 97.08 246 | 92.91 145 | 94.21 182 | 98.00 102 | 80.82 273 | 98.74 222 | 94.41 148 | 89.05 336 | 98.34 193 |
|
| test_fmvs1_n | | | 92.73 229 | 92.88 197 | 92.29 351 | 96.08 280 | 81.05 409 | 97.98 68 | 97.08 246 | 90.72 232 | 96.79 84 | 98.18 88 | 63.07 435 | 98.45 260 | 97.62 39 | 98.42 132 | 97.36 260 |
|
| v10 | | | 91.04 306 | 90.23 306 | 93.49 309 | 94.12 377 | 88.16 279 | 97.32 177 | 97.08 246 | 88.26 320 | 88.29 348 | 94.22 353 | 82.17 247 | 97.97 317 | 86.45 327 | 84.12 398 | 94.33 396 |
|
| viewdifsd2359ckpt11 | | | 93.46 193 | 93.22 184 | 94.17 265 | 96.11 277 | 85.42 349 | 96.43 265 | 97.07 249 | 92.91 145 | 94.20 183 | 98.00 102 | 80.82 273 | 98.73 224 | 94.42 147 | 89.04 338 | 98.34 193 |
|
| mamba_0408 | | | 93.70 184 | 92.99 190 | 95.83 164 | 96.79 210 | 90.38 194 | 88.69 454 | 97.07 249 | 90.96 224 | 93.68 197 | 97.31 171 | 84.97 186 | 98.76 217 | 90.95 227 | 96.51 203 | 98.35 189 |
|
| SSM_04072 | | | 93.51 192 | 92.99 190 | 95.05 210 | 96.79 210 | 90.38 194 | 88.69 454 | 97.07 249 | 90.96 224 | 93.68 197 | 97.31 171 | 84.97 186 | 96.42 411 | 90.95 227 | 96.51 203 | 98.35 189 |
|
| v144192 | | | 91.06 305 | 90.28 302 | 93.39 313 | 93.66 392 | 87.23 303 | 96.83 227 | 97.07 249 | 87.43 346 | 89.69 307 | 94.28 347 | 81.48 260 | 98.00 312 | 87.18 317 | 84.92 387 | 94.93 366 |
|
| v1192 | | | 91.07 304 | 90.23 306 | 93.58 305 | 93.70 389 | 87.82 290 | 96.73 239 | 97.07 249 | 87.77 337 | 89.58 310 | 94.32 345 | 80.90 271 | 97.97 317 | 86.52 325 | 85.48 374 | 94.95 362 |
|
| v8 | | | 91.29 296 | 90.53 294 | 93.57 307 | 94.15 376 | 88.12 280 | 97.34 174 | 97.06 254 | 88.99 293 | 88.32 346 | 94.26 350 | 83.08 221 | 98.01 311 | 87.62 307 | 83.92 402 | 94.57 389 |
|
| mvs_anonymous | | | 93.82 179 | 93.74 160 | 94.06 272 | 96.44 248 | 85.41 351 | 95.81 316 | 97.05 255 | 89.85 265 | 90.09 295 | 96.36 235 | 87.44 138 | 97.75 350 | 93.97 157 | 96.69 198 | 99.02 101 |
|
| IterMVS-LS | | | 92.29 245 | 91.94 234 | 93.34 315 | 96.25 259 | 86.97 310 | 96.57 261 | 97.05 255 | 90.67 235 | 89.50 315 | 94.80 315 | 86.59 148 | 97.64 358 | 89.91 251 | 86.11 369 | 95.40 337 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| v1921920 | | | 90.85 314 | 90.03 317 | 93.29 317 | 93.55 394 | 86.96 312 | 96.74 238 | 97.04 257 | 87.36 348 | 89.52 314 | 94.34 342 | 80.23 286 | 97.97 317 | 86.27 328 | 85.21 380 | 94.94 364 |
|
| CDS-MVSNet | | | 94.14 162 | 93.54 167 | 95.93 155 | 96.18 268 | 91.46 146 | 96.33 282 | 97.04 257 | 88.97 295 | 93.56 202 | 96.51 227 | 87.55 132 | 97.89 334 | 89.80 254 | 95.95 215 | 98.44 180 |
| Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
| SSC-MVS3.2 | | | 89.74 350 | 89.26 343 | 91.19 385 | 95.16 331 | 80.29 420 | 94.53 371 | 97.03 259 | 91.79 183 | 88.86 332 | 94.10 357 | 69.94 394 | 97.82 340 | 85.29 346 | 86.66 365 | 95.45 332 |
|
| v1144 | | | 91.37 289 | 90.60 290 | 93.68 300 | 93.89 384 | 88.23 275 | 96.84 226 | 97.03 259 | 88.37 317 | 89.69 307 | 94.39 337 | 82.04 249 | 97.98 314 | 87.80 297 | 85.37 376 | 94.84 372 |
|
| v1240 | | | 90.70 320 | 89.85 324 | 93.23 319 | 93.51 397 | 86.80 313 | 96.61 255 | 97.02 261 | 87.16 353 | 89.58 310 | 94.31 346 | 79.55 299 | 97.98 314 | 85.52 343 | 85.44 375 | 94.90 369 |
|
| EPP-MVSNet | | | 95.22 119 | 95.04 116 | 95.76 170 | 97.49 161 | 89.56 226 | 98.67 13 | 97.00 262 | 90.69 233 | 94.24 181 | 97.62 148 | 89.79 92 | 98.81 208 | 93.39 175 | 96.49 207 | 98.92 121 |
|
| V42 | | | 91.58 275 | 90.87 274 | 93.73 295 | 94.05 380 | 88.50 266 | 97.32 177 | 96.97 263 | 88.80 305 | 89.71 305 | 94.33 343 | 82.54 238 | 98.05 305 | 89.01 277 | 85.07 383 | 94.64 388 |
|
| test_fmvs1 | | | 93.21 203 | 93.53 168 | 92.25 354 | 96.55 234 | 81.20 408 | 97.40 168 | 96.96 264 | 90.68 234 | 96.80 82 | 98.04 97 | 69.25 400 | 98.40 263 | 97.58 40 | 98.50 125 | 97.16 270 |
|
| FMVSNet2 | | | 91.31 293 | 90.08 312 | 94.99 216 | 96.51 241 | 92.21 112 | 97.41 164 | 96.95 265 | 88.82 302 | 88.62 338 | 94.75 317 | 73.87 364 | 97.42 379 | 85.20 349 | 88.55 344 | 95.35 341 |
|
| ACMH | | 87.59 16 | 90.53 325 | 89.42 339 | 93.87 289 | 96.21 260 | 87.92 285 | 97.24 183 | 96.94 266 | 88.45 315 | 83.91 414 | 96.27 240 | 71.92 376 | 98.62 245 | 84.43 357 | 89.43 333 | 95.05 360 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| GBi-Net | | | 91.35 290 | 90.27 303 | 94.59 239 | 96.51 241 | 91.18 160 | 97.50 150 | 96.93 267 | 88.82 302 | 89.35 318 | 94.51 330 | 73.87 364 | 97.29 386 | 86.12 333 | 88.82 339 | 95.31 344 |
|
| test1 | | | 91.35 290 | 90.27 303 | 94.59 239 | 96.51 241 | 91.18 160 | 97.50 150 | 96.93 267 | 88.82 302 | 89.35 318 | 94.51 330 | 73.87 364 | 97.29 386 | 86.12 333 | 88.82 339 | 95.31 344 |
|
| FMVSNet3 | | | 91.78 264 | 90.69 288 | 95.03 213 | 96.53 237 | 92.27 110 | 97.02 203 | 96.93 267 | 89.79 268 | 89.35 318 | 94.65 323 | 77.01 335 | 97.47 374 | 86.12 333 | 88.82 339 | 95.35 341 |
|
| FMVSNet1 | | | 89.88 345 | 88.31 358 | 94.59 239 | 95.41 310 | 91.18 160 | 97.50 150 | 96.93 267 | 86.62 361 | 87.41 367 | 94.51 330 | 65.94 427 | 97.29 386 | 83.04 372 | 87.43 355 | 95.31 344 |
|
| GeoE | | | 93.89 176 | 93.28 181 | 95.72 176 | 96.96 193 | 89.75 218 | 98.24 41 | 96.92 271 | 89.47 276 | 92.12 241 | 97.21 179 | 84.42 195 | 98.39 268 | 87.71 300 | 96.50 206 | 99.01 104 |
|
| SymmetryMVS | | | 95.94 94 | 95.54 94 | 97.15 72 | 97.85 133 | 92.90 85 | 97.99 65 | 96.91 272 | 95.92 15 | 96.57 99 | 97.93 108 | 85.34 176 | 99.50 117 | 94.99 126 | 96.39 210 | 99.05 100 |
|
| miper_enhance_ethall | | | 91.54 279 | 91.01 270 | 93.15 323 | 95.35 316 | 87.07 308 | 93.97 393 | 96.90 273 | 86.79 359 | 89.17 325 | 93.43 389 | 86.55 150 | 97.64 358 | 89.97 250 | 86.93 360 | 94.74 384 |
|
| eth_miper_zixun_eth | | | 91.02 307 | 90.59 291 | 92.34 349 | 95.33 320 | 84.35 370 | 94.10 390 | 96.90 273 | 88.56 311 | 88.84 334 | 94.33 343 | 84.08 202 | 97.60 363 | 88.77 283 | 84.37 396 | 95.06 359 |
|
| TAMVS | | | 94.01 168 | 93.46 173 | 95.64 179 | 96.16 270 | 90.45 189 | 96.71 242 | 96.89 275 | 89.27 283 | 93.46 209 | 96.92 200 | 87.29 141 | 97.94 327 | 88.70 285 | 95.74 221 | 98.53 166 |
|
| miper_ehance_all_eth | | | 91.59 273 | 91.13 266 | 92.97 329 | 95.55 302 | 86.57 321 | 94.47 374 | 96.88 276 | 87.77 337 | 88.88 331 | 94.01 362 | 86.22 157 | 97.54 367 | 89.49 262 | 86.93 360 | 94.79 380 |
|
| v2v482 | | | 91.59 273 | 90.85 277 | 93.80 292 | 93.87 385 | 88.17 278 | 96.94 213 | 96.88 276 | 89.54 273 | 89.53 313 | 94.90 309 | 81.70 258 | 98.02 310 | 89.25 271 | 85.04 385 | 95.20 352 |
|
| CNLPA | | | 94.28 153 | 93.53 168 | 96.52 105 | 98.38 86 | 92.55 100 | 96.59 258 | 96.88 276 | 90.13 258 | 91.91 247 | 97.24 177 | 85.21 180 | 99.09 171 | 87.64 306 | 97.83 155 | 97.92 227 |
|
| PAPM | | | 91.52 280 | 90.30 301 | 95.20 203 | 95.30 323 | 89.83 216 | 93.38 415 | 96.85 279 | 86.26 369 | 88.59 339 | 95.80 264 | 84.88 188 | 98.15 287 | 75.67 426 | 95.93 216 | 97.63 245 |
|
| c3_l | | | 91.38 287 | 90.89 273 | 92.88 333 | 95.58 300 | 86.30 328 | 94.68 366 | 96.84 280 | 88.17 322 | 88.83 335 | 94.23 351 | 85.65 170 | 97.47 374 | 89.36 266 | 84.63 389 | 94.89 370 |
|
| pm-mvs1 | | | 90.72 319 | 89.65 334 | 93.96 281 | 94.29 375 | 89.63 221 | 97.79 103 | 96.82 281 | 89.07 288 | 86.12 392 | 95.48 286 | 78.61 317 | 97.78 345 | 86.97 321 | 81.67 415 | 94.46 391 |
|
| test_vis1_n | | | 92.37 240 | 92.26 224 | 92.72 339 | 94.75 355 | 82.64 391 | 98.02 62 | 96.80 282 | 91.18 213 | 97.77 56 | 97.93 108 | 58.02 445 | 98.29 276 | 97.63 37 | 98.21 140 | 97.23 268 |
|
| CMPMVS |  | 62.92 21 | 85.62 398 | 84.92 393 | 87.74 423 | 89.14 444 | 73.12 453 | 94.17 388 | 96.80 282 | 73.98 449 | 73.65 451 | 94.93 307 | 66.36 421 | 97.61 362 | 83.95 365 | 91.28 310 | 92.48 428 |
| M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
| MS-PatchMatch | | | 90.27 332 | 89.77 328 | 91.78 370 | 94.33 372 | 84.72 367 | 95.55 332 | 96.73 284 | 86.17 371 | 86.36 389 | 95.28 292 | 71.28 381 | 97.80 343 | 84.09 362 | 98.14 144 | 92.81 420 |
|
| Effi-MVS+-dtu | | | 93.08 210 | 93.21 185 | 92.68 342 | 96.02 283 | 83.25 384 | 97.14 196 | 96.72 285 | 93.85 98 | 91.20 272 | 93.44 386 | 83.08 221 | 98.30 275 | 91.69 213 | 95.73 222 | 96.50 287 |
|
| TSAR-MVS + GP. | | | 96.69 65 | 96.49 69 | 97.27 65 | 98.31 89 | 93.39 65 | 96.79 232 | 96.72 285 | 94.17 87 | 97.44 62 | 97.66 142 | 92.76 33 | 99.33 136 | 96.86 59 | 97.76 159 | 99.08 96 |
|
| 1112_ss | | | 93.37 198 | 92.42 220 | 96.21 136 | 97.05 183 | 90.99 167 | 96.31 284 | 96.72 285 | 86.87 358 | 89.83 302 | 96.69 213 | 86.51 151 | 99.14 163 | 88.12 290 | 93.67 274 | 98.50 170 |
|
| PVSNet | | 86.66 18 | 92.24 248 | 91.74 243 | 93.73 295 | 97.77 138 | 83.69 381 | 92.88 424 | 96.72 285 | 87.91 330 | 93.00 220 | 94.86 311 | 78.51 318 | 99.05 183 | 86.53 324 | 97.45 168 | 98.47 175 |
|
| miper_lstm_enhance | | | 90.50 328 | 90.06 316 | 91.83 366 | 95.33 320 | 83.74 378 | 93.86 399 | 96.70 289 | 87.56 344 | 87.79 359 | 93.81 370 | 83.45 213 | 96.92 399 | 87.39 311 | 84.62 390 | 94.82 375 |
|
| v148 | | | 90.99 308 | 90.38 297 | 92.81 336 | 93.83 386 | 85.80 341 | 96.78 236 | 96.68 290 | 89.45 278 | 88.75 337 | 93.93 366 | 82.96 227 | 97.82 340 | 87.83 296 | 83.25 407 | 94.80 378 |
|
| ACMH+ | | 87.92 14 | 90.20 336 | 89.18 345 | 93.25 318 | 96.48 244 | 86.45 325 | 96.99 209 | 96.68 290 | 88.83 301 | 84.79 403 | 96.22 242 | 70.16 391 | 98.53 254 | 84.42 358 | 88.04 348 | 94.77 383 |
|
| CANet_DTU | | | 94.37 151 | 93.65 163 | 96.55 102 | 96.46 247 | 92.13 116 | 96.21 292 | 96.67 292 | 94.38 83 | 93.53 205 | 97.03 195 | 79.34 301 | 99.71 64 | 90.76 233 | 98.45 130 | 97.82 238 |
|
| cl____ | | | 90.96 311 | 90.32 299 | 92.89 332 | 95.37 314 | 86.21 331 | 94.46 376 | 96.64 293 | 87.82 333 | 88.15 354 | 94.18 354 | 82.98 225 | 97.54 367 | 87.70 301 | 85.59 372 | 94.92 368 |
|
| HY-MVS | | 89.66 9 | 93.87 177 | 92.95 194 | 96.63 96 | 97.10 177 | 92.49 102 | 95.64 329 | 96.64 293 | 89.05 290 | 93.00 220 | 95.79 267 | 85.77 168 | 99.45 125 | 89.16 276 | 94.35 253 | 97.96 224 |
|
| Test_1112_low_res | | | 92.84 225 | 91.84 238 | 95.85 163 | 97.04 185 | 89.97 211 | 95.53 334 | 96.64 293 | 85.38 381 | 89.65 309 | 95.18 297 | 85.86 164 | 99.10 168 | 87.70 301 | 93.58 279 | 98.49 172 |
|
| DIV-MVS_self_test | | | 90.97 310 | 90.33 298 | 92.88 333 | 95.36 315 | 86.19 333 | 94.46 376 | 96.63 296 | 87.82 333 | 88.18 352 | 94.23 351 | 82.99 224 | 97.53 369 | 87.72 298 | 85.57 373 | 94.93 366 |
|
| Fast-Effi-MVS+-dtu | | | 92.29 245 | 91.99 232 | 93.21 321 | 95.27 324 | 85.52 347 | 97.03 201 | 96.63 296 | 92.09 175 | 89.11 327 | 95.14 299 | 80.33 284 | 98.08 298 | 87.54 309 | 94.74 249 | 96.03 305 |
|
| UnsupCasMVSNet_bld | | | 82.13 415 | 79.46 420 | 90.14 403 | 88.00 452 | 82.47 396 | 90.89 442 | 96.62 298 | 78.94 439 | 75.61 446 | 84.40 457 | 56.63 448 | 96.31 413 | 77.30 418 | 66.77 458 | 91.63 438 |
|
| cl22 | | | 91.21 298 | 90.56 293 | 93.14 324 | 96.09 279 | 86.80 313 | 94.41 378 | 96.58 299 | 87.80 335 | 88.58 340 | 93.99 364 | 80.85 272 | 97.62 361 | 89.87 253 | 86.93 360 | 94.99 361 |
|
| jason | | | 94.84 136 | 94.39 143 | 96.18 138 | 95.52 303 | 90.93 171 | 96.09 299 | 96.52 300 | 89.28 282 | 96.01 126 | 97.32 169 | 84.70 190 | 98.77 215 | 95.15 122 | 98.91 110 | 98.85 136 |
| jason: jason. |
| tt0805 | | | 91.09 303 | 90.07 315 | 94.16 268 | 95.61 298 | 88.31 270 | 97.56 141 | 96.51 301 | 89.56 272 | 89.17 325 | 95.64 276 | 67.08 419 | 98.38 269 | 91.07 225 | 88.44 345 | 95.80 313 |
|
| AUN-MVS | | | 91.76 265 | 90.75 283 | 94.81 228 | 97.00 189 | 88.57 262 | 96.65 249 | 96.49 302 | 89.63 270 | 92.15 239 | 96.12 248 | 78.66 316 | 98.50 256 | 90.83 229 | 79.18 426 | 97.36 260 |
|
| hse-mvs2 | | | 93.45 196 | 92.99 190 | 94.81 228 | 97.02 187 | 88.59 261 | 96.69 245 | 96.47 303 | 95.19 35 | 96.74 86 | 96.16 246 | 83.67 208 | 98.48 259 | 95.85 99 | 79.13 427 | 97.35 262 |
|
| SD_0403 | | | 90.01 340 | 90.02 318 | 89.96 406 | 95.65 297 | 76.76 441 | 95.76 320 | 96.46 304 | 90.58 244 | 86.59 386 | 96.29 238 | 82.12 248 | 94.78 435 | 73.00 440 | 93.76 272 | 98.35 189 |
|
| EG-PatchMatch MVS | | | 87.02 380 | 85.44 385 | 91.76 372 | 92.67 418 | 85.00 361 | 96.08 300 | 96.45 305 | 83.41 412 | 79.52 437 | 93.49 383 | 57.10 447 | 97.72 352 | 79.34 409 | 90.87 319 | 92.56 425 |
|
| KD-MVS_self_test | | | 85.95 394 | 84.95 392 | 88.96 417 | 89.55 443 | 79.11 435 | 95.13 356 | 96.42 306 | 85.91 374 | 84.07 412 | 90.48 428 | 70.03 393 | 94.82 434 | 80.04 401 | 72.94 447 | 92.94 418 |
|
| pmmvs6 | | | 87.81 372 | 86.19 380 | 92.69 341 | 91.32 431 | 86.30 328 | 97.34 174 | 96.41 307 | 80.59 433 | 84.05 413 | 94.37 339 | 67.37 414 | 97.67 355 | 84.75 353 | 79.51 425 | 94.09 403 |
|
| PMMVS | | | 92.86 223 | 92.34 221 | 94.42 253 | 94.92 346 | 86.73 316 | 94.53 371 | 96.38 308 | 84.78 393 | 94.27 180 | 95.12 301 | 83.13 220 | 98.40 263 | 91.47 217 | 96.49 207 | 98.12 210 |
|
| RPSCF | | | 90.75 317 | 90.86 275 | 90.42 399 | 96.84 203 | 76.29 444 | 95.61 330 | 96.34 309 | 83.89 402 | 91.38 260 | 97.87 117 | 76.45 341 | 98.78 212 | 87.16 318 | 92.23 292 | 96.20 294 |
|
| BP-MVS1 | | | 95.89 96 | 95.49 96 | 97.08 79 | 96.67 222 | 93.20 75 | 98.08 56 | 96.32 310 | 94.56 71 | 96.32 111 | 97.84 122 | 84.07 203 | 99.15 160 | 96.75 61 | 98.78 113 | 98.90 125 |
|
| MSDG | | | 91.42 285 | 90.24 305 | 94.96 221 | 97.15 175 | 88.91 254 | 93.69 407 | 96.32 310 | 85.72 377 | 86.93 382 | 96.47 229 | 80.24 285 | 98.98 189 | 80.57 398 | 95.05 242 | 96.98 273 |
|
| WBMVS | | | 90.69 322 | 89.99 319 | 92.81 336 | 96.48 244 | 85.00 361 | 95.21 353 | 96.30 312 | 89.46 277 | 89.04 328 | 94.05 361 | 72.45 374 | 97.82 340 | 89.46 263 | 87.41 357 | 95.61 324 |
|
| OurMVSNet-221017-0 | | | 90.51 327 | 90.19 310 | 91.44 378 | 93.41 403 | 81.25 406 | 96.98 210 | 96.28 313 | 91.68 187 | 86.55 387 | 96.30 237 | 74.20 363 | 97.98 314 | 88.96 279 | 87.40 358 | 95.09 357 |
|
| MVP-Stereo | | | 90.74 318 | 90.08 312 | 92.71 340 | 93.19 408 | 88.20 276 | 95.86 313 | 96.27 314 | 86.07 372 | 84.86 402 | 94.76 316 | 77.84 330 | 97.75 350 | 83.88 367 | 98.01 150 | 92.17 435 |
| Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
| lupinMVS | | | 94.99 129 | 94.56 133 | 96.29 130 | 96.34 256 | 91.21 155 | 95.83 315 | 96.27 314 | 88.93 297 | 96.22 116 | 96.88 202 | 86.20 159 | 98.85 202 | 95.27 118 | 99.05 101 | 98.82 140 |
|
| BH-untuned | | | 92.94 218 | 92.62 210 | 93.92 288 | 97.22 169 | 86.16 334 | 96.40 273 | 96.25 316 | 90.06 259 | 89.79 303 | 96.17 245 | 83.19 217 | 98.35 271 | 87.19 316 | 97.27 177 | 97.24 267 |
|
| CL-MVSNet_self_test | | | 86.31 389 | 85.15 389 | 89.80 408 | 88.83 447 | 81.74 404 | 93.93 396 | 96.22 317 | 86.67 360 | 85.03 400 | 90.80 426 | 78.09 326 | 94.50 436 | 74.92 429 | 71.86 449 | 93.15 416 |
|
| IS-MVSNet | | | 94.90 131 | 94.52 137 | 96.05 145 | 97.67 144 | 90.56 185 | 98.44 24 | 96.22 317 | 93.21 124 | 93.99 190 | 97.74 133 | 85.55 173 | 98.45 260 | 89.98 249 | 97.86 154 | 99.14 86 |
|
| FA-MVS(test-final) | | | 93.52 191 | 92.92 195 | 95.31 200 | 96.77 216 | 88.54 264 | 94.82 363 | 96.21 319 | 89.61 271 | 94.20 183 | 95.25 295 | 83.24 215 | 99.14 163 | 90.01 248 | 96.16 212 | 98.25 198 |
|
| GA-MVS | | | 91.38 287 | 90.31 300 | 94.59 239 | 94.65 360 | 87.62 293 | 94.34 381 | 96.19 320 | 90.73 231 | 90.35 283 | 93.83 367 | 71.84 377 | 97.96 321 | 87.22 315 | 93.61 277 | 98.21 201 |
|
| LuminaMVS | | | 94.89 132 | 94.35 145 | 96.53 103 | 95.48 305 | 92.80 89 | 96.88 222 | 96.18 321 | 92.85 149 | 95.92 129 | 96.87 204 | 81.44 261 | 98.83 205 | 96.43 74 | 97.10 184 | 97.94 226 |
|
| IterMVS-SCA-FT | | | 90.31 330 | 89.81 326 | 91.82 367 | 95.52 303 | 84.20 373 | 94.30 384 | 96.15 322 | 90.61 241 | 87.39 368 | 94.27 348 | 75.80 347 | 96.44 410 | 87.34 312 | 86.88 364 | 94.82 375 |
|
| IterMVS | | | 90.15 338 | 89.67 332 | 91.61 374 | 95.48 305 | 83.72 379 | 94.33 382 | 96.12 323 | 89.99 260 | 87.31 371 | 94.15 356 | 75.78 349 | 96.27 414 | 86.97 321 | 86.89 363 | 94.83 373 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| DP-MVS | | | 92.76 228 | 91.51 252 | 96.52 105 | 98.77 60 | 90.99 167 | 97.38 171 | 96.08 324 | 82.38 418 | 89.29 321 | 97.87 117 | 83.77 206 | 99.69 70 | 81.37 391 | 96.69 198 | 98.89 131 |
|
| pmmvs4 | | | 90.93 312 | 89.85 324 | 94.17 265 | 93.34 405 | 90.79 177 | 94.60 368 | 96.02 325 | 84.62 394 | 87.45 365 | 95.15 298 | 81.88 255 | 97.45 376 | 87.70 301 | 87.87 350 | 94.27 400 |
|
| ppachtmachnet_test | | | 88.35 367 | 87.29 366 | 91.53 375 | 92.45 424 | 83.57 382 | 93.75 403 | 95.97 326 | 84.28 397 | 85.32 399 | 94.18 354 | 79.00 313 | 96.93 398 | 75.71 425 | 84.99 386 | 94.10 401 |
|
| Anonymous20240521 | | | 86.42 387 | 85.44 385 | 89.34 415 | 90.33 436 | 79.79 426 | 96.73 239 | 95.92 327 | 83.71 407 | 83.25 418 | 91.36 423 | 63.92 433 | 96.01 415 | 78.39 413 | 85.36 377 | 92.22 433 |
|
| ITE_SJBPF | | | | | 92.43 345 | 95.34 317 | 85.37 354 | | 95.92 327 | 91.47 196 | 87.75 361 | 96.39 234 | 71.00 383 | 97.96 321 | 82.36 381 | 89.86 329 | 93.97 406 |
|
| test_fmvs2 | | | 89.77 349 | 89.93 321 | 89.31 416 | 93.68 391 | 76.37 443 | 97.64 130 | 95.90 329 | 89.84 266 | 91.49 258 | 96.26 241 | 58.77 443 | 97.10 390 | 94.65 141 | 91.13 312 | 94.46 391 |
|
| USDC | | | 88.94 358 | 87.83 363 | 92.27 352 | 94.66 359 | 84.96 363 | 93.86 399 | 95.90 329 | 87.34 349 | 83.40 416 | 95.56 280 | 67.43 413 | 98.19 284 | 82.64 380 | 89.67 331 | 93.66 409 |
|
| COLMAP_ROB |  | 87.81 15 | 90.40 329 | 89.28 342 | 93.79 293 | 97.95 126 | 87.13 307 | 96.92 216 | 95.89 331 | 82.83 415 | 86.88 384 | 97.18 180 | 73.77 367 | 99.29 143 | 78.44 412 | 93.62 276 | 94.95 362 |
| Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
| VDD-MVS | | | 93.82 179 | 93.08 188 | 96.02 148 | 97.88 132 | 89.96 212 | 97.72 115 | 95.85 332 | 92.43 161 | 95.86 131 | 98.44 61 | 68.42 409 | 99.39 131 | 96.31 76 | 94.85 243 | 98.71 153 |
|
| VDDNet | | | 93.05 212 | 92.07 227 | 96.02 148 | 96.84 203 | 90.39 193 | 98.08 56 | 95.85 332 | 86.22 370 | 95.79 134 | 98.46 59 | 67.59 412 | 99.19 151 | 94.92 129 | 94.85 243 | 98.47 175 |
|
| mvsmamba | | | 94.57 146 | 94.14 150 | 95.87 159 | 97.03 186 | 89.93 213 | 97.84 92 | 95.85 332 | 91.34 202 | 94.79 165 | 96.80 205 | 80.67 275 | 98.81 208 | 94.85 130 | 98.12 145 | 98.85 136 |
|
| Vis-MVSNet (Re-imp) | | | 94.15 159 | 93.88 156 | 94.95 222 | 97.61 152 | 87.92 285 | 98.10 54 | 95.80 335 | 92.22 167 | 93.02 219 | 97.45 160 | 84.53 193 | 97.91 333 | 88.24 289 | 97.97 151 | 99.02 101 |
|
| MM | | | 97.29 29 | 96.98 40 | 98.23 12 | 98.01 120 | 95.03 27 | 98.07 58 | 95.76 336 | 97.78 1 | 97.52 59 | 98.80 37 | 88.09 117 | 99.86 9 | 99.44 2 | 99.37 65 | 99.80 1 |
|
| KD-MVS_2432*1600 | | | 84.81 404 | 82.64 407 | 91.31 380 | 91.07 433 | 85.34 355 | 91.22 437 | 95.75 337 | 85.56 379 | 83.09 419 | 90.21 431 | 67.21 415 | 95.89 417 | 77.18 419 | 62.48 462 | 92.69 421 |
|
| miper_refine_blended | | | 84.81 404 | 82.64 407 | 91.31 380 | 91.07 433 | 85.34 355 | 91.22 437 | 95.75 337 | 85.56 379 | 83.09 419 | 90.21 431 | 67.21 415 | 95.89 417 | 77.18 419 | 62.48 462 | 92.69 421 |
|
| FE-MVS | | | 92.05 256 | 91.05 268 | 95.08 209 | 96.83 205 | 87.93 284 | 93.91 398 | 95.70 339 | 86.30 367 | 94.15 187 | 94.97 304 | 76.59 339 | 99.21 149 | 84.10 361 | 96.86 189 | 98.09 216 |
|
| tpm cat1 | | | 88.36 366 | 87.21 369 | 91.81 368 | 95.13 336 | 80.55 415 | 92.58 428 | 95.70 339 | 74.97 448 | 87.45 365 | 91.96 416 | 78.01 329 | 98.17 286 | 80.39 400 | 88.74 342 | 96.72 283 |
|
| our_test_3 | | | 88.78 362 | 87.98 362 | 91.20 384 | 92.45 424 | 82.53 393 | 93.61 411 | 95.69 341 | 85.77 376 | 84.88 401 | 93.71 372 | 79.99 290 | 96.78 406 | 79.47 406 | 86.24 366 | 94.28 399 |
|
| BH-w/o | | | 92.14 253 | 91.75 241 | 93.31 316 | 96.99 190 | 85.73 344 | 95.67 324 | 95.69 341 | 88.73 307 | 89.26 323 | 94.82 314 | 82.97 226 | 98.07 302 | 85.26 348 | 96.32 211 | 96.13 301 |
|
| CR-MVSNet | | | 90.82 315 | 89.77 328 | 93.95 282 | 94.45 368 | 87.19 304 | 90.23 445 | 95.68 343 | 86.89 357 | 92.40 229 | 92.36 408 | 80.91 269 | 97.05 393 | 81.09 395 | 93.95 269 | 97.60 250 |
|
| Patchmtry | | | 88.64 364 | 87.25 367 | 92.78 338 | 94.09 378 | 86.64 317 | 89.82 449 | 95.68 343 | 80.81 430 | 87.63 363 | 92.36 408 | 80.91 269 | 97.03 394 | 78.86 410 | 85.12 382 | 94.67 386 |
|
| testing91 | | | 91.90 261 | 91.02 269 | 94.53 247 | 96.54 235 | 86.55 323 | 95.86 313 | 95.64 345 | 91.77 184 | 91.89 248 | 93.47 385 | 69.94 394 | 98.86 200 | 90.23 247 | 93.86 271 | 98.18 203 |
|
| BH-RMVSNet | | | 92.72 230 | 91.97 233 | 94.97 220 | 97.16 173 | 87.99 283 | 96.15 297 | 95.60 346 | 90.62 240 | 91.87 249 | 97.15 183 | 78.41 320 | 98.57 251 | 83.16 370 | 97.60 161 | 98.36 187 |
|
| PVSNet_0 | | 82.17 19 | 85.46 399 | 83.64 402 | 90.92 388 | 95.27 324 | 79.49 431 | 90.55 443 | 95.60 346 | 83.76 406 | 83.00 421 | 89.95 433 | 71.09 382 | 97.97 317 | 82.75 378 | 60.79 464 | 95.31 344 |
|
| guyue | | | 95.17 122 | 94.96 118 | 95.82 165 | 96.97 192 | 89.65 220 | 97.56 141 | 95.58 348 | 94.82 56 | 95.72 136 | 97.42 164 | 82.90 228 | 98.84 204 | 96.71 64 | 96.93 188 | 98.96 112 |
|
| SCA | | | 91.84 263 | 91.18 265 | 93.83 290 | 95.59 299 | 84.95 364 | 94.72 365 | 95.58 348 | 90.82 227 | 92.25 237 | 93.69 374 | 75.80 347 | 98.10 293 | 86.20 330 | 95.98 214 | 98.45 177 |
|
| MonoMVSNet | | | 91.92 259 | 91.77 239 | 92.37 346 | 92.94 412 | 83.11 387 | 97.09 199 | 95.55 350 | 92.91 145 | 90.85 275 | 94.55 327 | 81.27 265 | 96.52 409 | 93.01 186 | 87.76 351 | 97.47 256 |
|
| AllTest | | | 90.23 334 | 88.98 348 | 93.98 278 | 97.94 127 | 86.64 317 | 96.51 262 | 95.54 351 | 85.38 381 | 85.49 396 | 96.77 207 | 70.28 389 | 99.15 160 | 80.02 402 | 92.87 281 | 96.15 299 |
|
| TestCases | | | | | 93.98 278 | 97.94 127 | 86.64 317 | | 95.54 351 | 85.38 381 | 85.49 396 | 96.77 207 | 70.28 389 | 99.15 160 | 80.02 402 | 92.87 281 | 96.15 299 |
|
| mmtdpeth | | | 89.70 351 | 88.96 349 | 91.90 363 | 95.84 291 | 84.42 369 | 97.46 161 | 95.53 353 | 90.27 253 | 94.46 176 | 90.50 427 | 69.74 398 | 98.95 190 | 97.39 50 | 69.48 453 | 92.34 429 |
|
| tpmvs | | | 89.83 348 | 89.15 346 | 91.89 364 | 94.92 346 | 80.30 419 | 93.11 420 | 95.46 354 | 86.28 368 | 88.08 355 | 92.65 398 | 80.44 281 | 98.52 255 | 81.47 387 | 89.92 328 | 96.84 279 |
|
| pmmvs5 | | | 89.86 347 | 88.87 352 | 92.82 335 | 92.86 414 | 86.23 330 | 96.26 287 | 95.39 355 | 84.24 398 | 87.12 373 | 94.51 330 | 74.27 362 | 97.36 383 | 87.61 308 | 87.57 353 | 94.86 371 |
|
| PatchmatchNet |  | | 91.91 260 | 91.35 254 | 93.59 304 | 95.38 312 | 84.11 374 | 93.15 419 | 95.39 355 | 89.54 273 | 92.10 242 | 93.68 376 | 82.82 231 | 98.13 288 | 84.81 352 | 95.32 235 | 98.52 167 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
| tpmrst | | | 91.44 284 | 91.32 256 | 91.79 369 | 95.15 334 | 79.20 434 | 93.42 414 | 95.37 357 | 88.55 312 | 93.49 208 | 93.67 377 | 82.49 240 | 98.27 277 | 90.41 242 | 89.34 334 | 97.90 228 |
|
| Anonymous20231206 | | | 87.09 379 | 86.14 381 | 89.93 407 | 91.22 432 | 80.35 417 | 96.11 298 | 95.35 358 | 83.57 409 | 84.16 408 | 93.02 393 | 73.54 369 | 95.61 425 | 72.16 442 | 86.14 368 | 93.84 408 |
|
| MIMVSNet1 | | | 84.93 402 | 83.05 404 | 90.56 397 | 89.56 442 | 84.84 366 | 95.40 339 | 95.35 358 | 83.91 401 | 80.38 433 | 92.21 413 | 57.23 446 | 93.34 449 | 70.69 448 | 82.75 413 | 93.50 411 |
|
| TDRefinement | | | 86.53 383 | 84.76 395 | 91.85 365 | 82.23 465 | 84.25 371 | 96.38 275 | 95.35 358 | 84.97 390 | 84.09 411 | 94.94 306 | 65.76 428 | 98.34 274 | 84.60 356 | 74.52 443 | 92.97 417 |
|
| TR-MVS | | | 91.48 283 | 90.59 291 | 94.16 268 | 96.40 250 | 87.33 297 | 95.67 324 | 95.34 361 | 87.68 341 | 91.46 259 | 95.52 283 | 76.77 338 | 98.35 271 | 82.85 375 | 93.61 277 | 96.79 281 |
|
| EPNet_dtu | | | 91.71 266 | 91.28 259 | 92.99 328 | 93.76 388 | 83.71 380 | 96.69 245 | 95.28 362 | 93.15 131 | 87.02 378 | 95.95 256 | 83.37 214 | 97.38 382 | 79.46 407 | 96.84 190 | 97.88 230 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| FMVSNet5 | | | 87.29 376 | 85.79 383 | 91.78 370 | 94.80 353 | 87.28 299 | 95.49 336 | 95.28 362 | 84.09 400 | 83.85 415 | 91.82 417 | 62.95 436 | 94.17 440 | 78.48 411 | 85.34 378 | 93.91 407 |
|
| MDTV_nov1_ep13 | | | | 90.76 281 | | 95.22 328 | 80.33 418 | 93.03 422 | 95.28 362 | 88.14 325 | 92.84 226 | 93.83 367 | 81.34 262 | 98.08 298 | 82.86 373 | 94.34 254 | |
|
| LF4IMVS | | | 87.94 370 | 87.25 367 | 89.98 405 | 92.38 426 | 80.05 425 | 94.38 379 | 95.25 365 | 87.59 343 | 84.34 405 | 94.74 318 | 64.31 432 | 97.66 357 | 84.83 351 | 87.45 354 | 92.23 432 |
|
| TransMVSNet (Re) | | | 88.94 358 | 87.56 364 | 93.08 326 | 94.35 371 | 88.45 268 | 97.73 112 | 95.23 366 | 87.47 345 | 84.26 407 | 95.29 290 | 79.86 293 | 97.33 384 | 79.44 408 | 74.44 444 | 93.45 413 |
|
| test20.03 | | | 86.14 392 | 85.40 387 | 88.35 418 | 90.12 437 | 80.06 424 | 95.90 312 | 95.20 367 | 88.59 308 | 81.29 428 | 93.62 379 | 71.43 380 | 92.65 453 | 71.26 446 | 81.17 418 | 92.34 429 |
|
| new-patchmatchnet | | | 83.18 411 | 81.87 414 | 87.11 426 | 86.88 456 | 75.99 445 | 93.70 405 | 95.18 368 | 85.02 389 | 77.30 445 | 88.40 444 | 65.99 426 | 93.88 445 | 74.19 434 | 70.18 451 | 91.47 443 |
|
| MDA-MVSNet_test_wron | | | 85.87 396 | 84.23 399 | 90.80 394 | 92.38 426 | 82.57 392 | 93.17 417 | 95.15 369 | 82.15 419 | 67.65 457 | 92.33 411 | 78.20 322 | 95.51 428 | 77.33 416 | 79.74 422 | 94.31 398 |
|
| YYNet1 | | | 85.87 396 | 84.23 399 | 90.78 395 | 92.38 426 | 82.46 397 | 93.17 417 | 95.14 370 | 82.12 420 | 67.69 455 | 92.36 408 | 78.16 325 | 95.50 429 | 77.31 417 | 79.73 423 | 94.39 394 |
|
| Baseline_NR-MVSNet | | | 91.20 299 | 90.62 289 | 92.95 330 | 93.83 386 | 88.03 282 | 97.01 206 | 95.12 371 | 88.42 316 | 89.70 306 | 95.13 300 | 83.47 211 | 97.44 377 | 89.66 259 | 83.24 408 | 93.37 414 |
|
| thres200 | | | 92.23 249 | 91.39 253 | 94.75 235 | 97.61 152 | 89.03 252 | 96.60 257 | 95.09 372 | 92.08 176 | 93.28 214 | 94.00 363 | 78.39 321 | 99.04 186 | 81.26 394 | 94.18 260 | 96.19 295 |
|
| ADS-MVSNet | | | 89.89 344 | 88.68 354 | 93.53 308 | 95.86 286 | 84.89 365 | 90.93 440 | 95.07 373 | 83.23 413 | 91.28 268 | 91.81 418 | 79.01 311 | 97.85 336 | 79.52 404 | 91.39 308 | 97.84 235 |
|
| pmmvs-eth3d | | | 86.22 390 | 84.45 397 | 91.53 375 | 88.34 451 | 87.25 301 | 94.47 374 | 95.01 374 | 83.47 410 | 79.51 438 | 89.61 436 | 69.75 397 | 95.71 422 | 83.13 371 | 76.73 436 | 91.64 437 |
|
| Anonymous202405211 | | | 92.07 255 | 90.83 279 | 95.76 170 | 98.19 106 | 88.75 257 | 97.58 137 | 95.00 375 | 86.00 373 | 93.64 200 | 97.45 160 | 66.24 424 | 99.53 109 | 90.68 236 | 92.71 286 | 99.01 104 |
|
| MDA-MVSNet-bldmvs | | | 85.00 401 | 82.95 406 | 91.17 386 | 93.13 410 | 83.33 383 | 94.56 370 | 95.00 375 | 84.57 395 | 65.13 461 | 92.65 398 | 70.45 388 | 95.85 419 | 73.57 437 | 77.49 432 | 94.33 396 |
|
| ambc | | | | | 86.56 429 | 83.60 462 | 70.00 456 | 85.69 461 | 94.97 377 | | 80.60 432 | 88.45 443 | 37.42 464 | 96.84 403 | 82.69 379 | 75.44 441 | 92.86 419 |
|
| testgi | | | 87.97 369 | 87.21 369 | 90.24 402 | 92.86 414 | 80.76 410 | 96.67 248 | 94.97 377 | 91.74 185 | 85.52 395 | 95.83 262 | 62.66 438 | 94.47 438 | 76.25 423 | 88.36 346 | 95.48 327 |
|
| myMVS_eth3d28 | | | 91.52 280 | 90.97 271 | 93.17 322 | 96.91 195 | 83.24 385 | 95.61 330 | 94.96 379 | 92.24 166 | 91.98 245 | 93.28 390 | 69.31 399 | 98.40 263 | 88.71 284 | 95.68 224 | 97.88 230 |
|
| dp | | | 88.90 360 | 88.26 360 | 90.81 392 | 94.58 364 | 76.62 442 | 92.85 425 | 94.93 380 | 85.12 387 | 90.07 297 | 93.07 392 | 75.81 346 | 98.12 291 | 80.53 399 | 87.42 356 | 97.71 242 |
|
| test_fmvs3 | | | 83.21 410 | 83.02 405 | 83.78 433 | 86.77 457 | 68.34 459 | 96.76 237 | 94.91 381 | 86.49 363 | 84.14 410 | 89.48 437 | 36.04 465 | 91.73 455 | 91.86 207 | 80.77 420 | 91.26 445 |
|
| test_0402 | | | 86.46 386 | 84.79 394 | 91.45 377 | 95.02 340 | 85.55 346 | 96.29 286 | 94.89 382 | 80.90 427 | 82.21 424 | 93.97 365 | 68.21 410 | 97.29 386 | 62.98 456 | 88.68 343 | 91.51 440 |
|
| tfpn200view9 | | | 92.38 239 | 91.52 250 | 94.95 222 | 97.85 133 | 89.29 241 | 97.41 164 | 94.88 383 | 92.19 172 | 93.27 215 | 94.46 335 | 78.17 323 | 99.08 174 | 81.40 388 | 94.08 264 | 96.48 288 |
|
| CVMVSNet | | | 91.23 297 | 91.75 241 | 89.67 409 | 95.77 292 | 74.69 446 | 96.44 263 | 94.88 383 | 85.81 375 | 92.18 238 | 97.64 146 | 79.07 306 | 95.58 427 | 88.06 292 | 95.86 219 | 98.74 150 |
|
| thres400 | | | 92.42 237 | 91.52 250 | 95.12 208 | 97.85 133 | 89.29 241 | 97.41 164 | 94.88 383 | 92.19 172 | 93.27 215 | 94.46 335 | 78.17 323 | 99.08 174 | 81.40 388 | 94.08 264 | 96.98 273 |
|
| tt0320 | | | 85.39 400 | 83.12 403 | 92.19 356 | 93.44 402 | 85.79 342 | 96.19 294 | 94.87 386 | 71.19 455 | 82.92 422 | 91.76 420 | 58.43 444 | 96.81 404 | 81.03 396 | 78.26 431 | 93.98 405 |
|
| EPNet | | | 95.20 120 | 94.56 133 | 97.14 73 | 92.80 416 | 92.68 95 | 97.85 91 | 94.87 386 | 96.64 8 | 92.46 228 | 97.80 128 | 86.23 156 | 99.65 76 | 93.72 165 | 98.62 121 | 99.10 93 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| testing99 | | | 91.62 271 | 90.72 286 | 94.32 258 | 96.48 244 | 86.11 339 | 95.81 316 | 94.76 388 | 91.55 189 | 91.75 253 | 93.44 386 | 68.55 407 | 98.82 206 | 90.43 241 | 93.69 273 | 98.04 220 |
|
| sc_t1 | | | 86.48 385 | 84.10 401 | 93.63 301 | 93.45 401 | 85.76 343 | 96.79 232 | 94.71 389 | 73.06 453 | 86.45 388 | 94.35 340 | 55.13 451 | 97.95 325 | 84.38 359 | 78.55 430 | 97.18 269 |
|
| SixPastTwentyTwo | | | 89.15 356 | 88.54 356 | 90.98 387 | 93.49 398 | 80.28 421 | 96.70 243 | 94.70 390 | 90.78 228 | 84.15 409 | 95.57 279 | 71.78 378 | 97.71 353 | 84.63 355 | 85.07 383 | 94.94 364 |
|
| thres100view900 | | | 92.43 236 | 91.58 247 | 94.98 218 | 97.92 129 | 89.37 237 | 97.71 117 | 94.66 391 | 92.20 170 | 93.31 213 | 94.90 309 | 78.06 327 | 99.08 174 | 81.40 388 | 94.08 264 | 96.48 288 |
|
| thres600view7 | | | 92.49 234 | 91.60 246 | 95.18 204 | 97.91 130 | 89.47 231 | 97.65 126 | 94.66 391 | 92.18 174 | 93.33 212 | 94.91 308 | 78.06 327 | 99.10 168 | 81.61 384 | 94.06 268 | 96.98 273 |
|
| PatchT | | | 88.87 361 | 87.42 365 | 93.22 320 | 94.08 379 | 85.10 359 | 89.51 450 | 94.64 393 | 81.92 421 | 92.36 232 | 88.15 447 | 80.05 289 | 97.01 396 | 72.43 441 | 93.65 275 | 97.54 253 |
|
| baseline1 | | | 92.82 226 | 91.90 236 | 95.55 186 | 97.20 171 | 90.77 178 | 97.19 191 | 94.58 394 | 92.20 170 | 92.36 232 | 96.34 236 | 84.16 201 | 98.21 281 | 89.20 274 | 83.90 403 | 97.68 244 |
|
| AstraMVS | | | 94.82 138 | 94.64 129 | 95.34 199 | 96.36 255 | 88.09 281 | 97.58 137 | 94.56 395 | 94.98 45 | 95.70 139 | 97.92 111 | 81.93 254 | 98.93 193 | 96.87 58 | 95.88 217 | 98.99 108 |
|
| UBG | | | 91.55 277 | 90.76 281 | 93.94 284 | 96.52 240 | 85.06 360 | 95.22 351 | 94.54 396 | 90.47 249 | 91.98 245 | 92.71 397 | 72.02 375 | 98.74 222 | 88.10 291 | 95.26 237 | 98.01 222 |
|
| Gipuma |  | | 67.86 431 | 65.41 433 | 75.18 446 | 92.66 419 | 73.45 450 | 66.50 468 | 94.52 397 | 53.33 466 | 57.80 467 | 66.07 467 | 30.81 467 | 89.20 459 | 48.15 465 | 78.88 429 | 62.90 467 |
| S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
| testing11 | | | 91.68 269 | 90.75 283 | 94.47 249 | 96.53 237 | 86.56 322 | 95.76 320 | 94.51 398 | 91.10 220 | 91.24 270 | 93.59 380 | 68.59 406 | 98.86 200 | 91.10 224 | 94.29 256 | 98.00 223 |
|
| CostFormer | | | 91.18 302 | 90.70 287 | 92.62 343 | 94.84 351 | 81.76 403 | 94.09 391 | 94.43 399 | 84.15 399 | 92.72 227 | 93.77 371 | 79.43 300 | 98.20 282 | 90.70 235 | 92.18 295 | 97.90 228 |
|
| tpm2 | | | 89.96 341 | 89.21 344 | 92.23 355 | 94.91 348 | 81.25 406 | 93.78 402 | 94.42 400 | 80.62 432 | 91.56 256 | 93.44 386 | 76.44 342 | 97.94 327 | 85.60 342 | 92.08 299 | 97.49 254 |
|
| testing3-2 | | | 92.10 254 | 92.05 228 | 92.27 352 | 97.71 142 | 79.56 428 | 97.42 163 | 94.41 401 | 93.53 111 | 93.22 217 | 95.49 284 | 69.16 401 | 99.11 166 | 93.25 176 | 94.22 258 | 98.13 208 |
|
| MGCNet | | | 96.74 62 | 96.31 79 | 98.02 20 | 96.87 199 | 94.65 31 | 97.58 137 | 94.39 402 | 96.47 11 | 97.16 71 | 98.39 65 | 87.53 134 | 99.87 7 | 98.97 19 | 99.41 57 | 99.55 41 |
|
| JIA-IIPM | | | 88.26 368 | 87.04 372 | 91.91 362 | 93.52 396 | 81.42 405 | 89.38 451 | 94.38 403 | 80.84 429 | 90.93 274 | 80.74 459 | 79.22 303 | 97.92 330 | 82.76 377 | 91.62 303 | 96.38 291 |
|
| dmvs_re | | | 90.21 335 | 89.50 337 | 92.35 347 | 95.47 309 | 85.15 357 | 95.70 323 | 94.37 404 | 90.94 226 | 88.42 342 | 93.57 381 | 74.63 359 | 95.67 424 | 82.80 376 | 89.57 332 | 96.22 293 |
|
| Patchmatch-test | | | 89.42 354 | 87.99 361 | 93.70 298 | 95.27 324 | 85.11 358 | 88.98 452 | 94.37 404 | 81.11 426 | 87.10 376 | 93.69 374 | 82.28 244 | 97.50 372 | 74.37 432 | 94.76 247 | 98.48 174 |
|
| LCM-MVSNet | | | 72.55 424 | 69.39 428 | 82.03 435 | 70.81 475 | 65.42 464 | 90.12 447 | 94.36 406 | 55.02 465 | 65.88 459 | 81.72 458 | 24.16 473 | 89.96 456 | 74.32 433 | 68.10 456 | 90.71 448 |
|
| ADS-MVSNet2 | | | 89.45 353 | 88.59 355 | 92.03 359 | 95.86 286 | 82.26 399 | 90.93 440 | 94.32 407 | 83.23 413 | 91.28 268 | 91.81 418 | 79.01 311 | 95.99 416 | 79.52 404 | 91.39 308 | 97.84 235 |
|
| mvs5depth | | | 86.53 383 | 85.08 390 | 90.87 389 | 88.74 449 | 82.52 394 | 91.91 433 | 94.23 408 | 86.35 366 | 87.11 375 | 93.70 373 | 66.52 420 | 97.76 348 | 81.37 391 | 75.80 438 | 92.31 431 |
|
| EU-MVSNet | | | 88.72 363 | 88.90 351 | 88.20 420 | 93.15 409 | 74.21 448 | 96.63 254 | 94.22 409 | 85.18 385 | 87.32 370 | 95.97 254 | 76.16 344 | 94.98 433 | 85.27 347 | 86.17 367 | 95.41 334 |
|
| tt0320-xc | | | 84.83 403 | 82.33 411 | 92.31 350 | 93.66 392 | 86.20 332 | 96.17 296 | 94.06 410 | 71.26 454 | 82.04 426 | 92.22 412 | 55.07 452 | 96.72 407 | 81.49 386 | 75.04 442 | 94.02 404 |
|
| MIMVSNet | | | 88.50 365 | 86.76 375 | 93.72 297 | 94.84 351 | 87.77 291 | 91.39 435 | 94.05 411 | 86.41 365 | 87.99 357 | 92.59 401 | 63.27 434 | 95.82 421 | 77.44 415 | 92.84 283 | 97.57 252 |
|
| OpenMVS_ROB |  | 81.14 20 | 84.42 406 | 82.28 412 | 90.83 390 | 90.06 438 | 84.05 376 | 95.73 322 | 94.04 412 | 73.89 451 | 80.17 436 | 91.53 422 | 59.15 442 | 97.64 358 | 66.92 454 | 89.05 336 | 90.80 447 |
|
| TinyColmap | | | 86.82 381 | 85.35 388 | 91.21 382 | 94.91 348 | 82.99 389 | 93.94 395 | 94.02 413 | 83.58 408 | 81.56 427 | 94.68 320 | 62.34 439 | 98.13 288 | 75.78 424 | 87.35 359 | 92.52 427 |
|
| ETVMVS | | | 90.52 326 | 89.14 347 | 94.67 238 | 96.81 209 | 87.85 289 | 95.91 311 | 93.97 414 | 89.71 269 | 92.34 235 | 92.48 403 | 65.41 430 | 97.96 321 | 81.37 391 | 94.27 257 | 98.21 201 |
|
| IB-MVS | | 87.33 17 | 89.91 342 | 88.28 359 | 94.79 232 | 95.26 327 | 87.70 292 | 95.12 357 | 93.95 415 | 89.35 281 | 87.03 377 | 92.49 402 | 70.74 386 | 99.19 151 | 89.18 275 | 81.37 417 | 97.49 254 |
| 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 |
| Syy-MVS | | | 87.13 378 | 87.02 373 | 87.47 424 | 95.16 331 | 73.21 452 | 95.00 359 | 93.93 416 | 88.55 312 | 86.96 379 | 91.99 414 | 75.90 345 | 94.00 442 | 61.59 458 | 94.11 261 | 95.20 352 |
|
| myMVS_eth3d | | | 87.18 377 | 86.38 378 | 89.58 410 | 95.16 331 | 79.53 429 | 95.00 359 | 93.93 416 | 88.55 312 | 86.96 379 | 91.99 414 | 56.23 449 | 94.00 442 | 75.47 428 | 94.11 261 | 95.20 352 |
|
| testing222 | | | 90.31 330 | 88.96 349 | 94.35 255 | 96.54 235 | 87.29 298 | 95.50 335 | 93.84 418 | 90.97 223 | 91.75 253 | 92.96 394 | 62.18 440 | 98.00 312 | 82.86 373 | 94.08 264 | 97.76 240 |
|
| test_f | | | 80.57 417 | 79.62 419 | 83.41 434 | 83.38 463 | 67.80 461 | 93.57 412 | 93.72 419 | 80.80 431 | 77.91 444 | 87.63 450 | 33.40 466 | 92.08 454 | 87.14 319 | 79.04 428 | 90.34 449 |
|
| LCM-MVSNet-Re | | | 92.50 232 | 92.52 216 | 92.44 344 | 96.82 207 | 81.89 402 | 96.92 216 | 93.71 420 | 92.41 162 | 84.30 406 | 94.60 325 | 85.08 182 | 97.03 394 | 91.51 215 | 97.36 170 | 98.40 183 |
|
| tpm | | | 90.25 333 | 89.74 331 | 91.76 372 | 93.92 382 | 79.73 427 | 93.98 392 | 93.54 421 | 88.28 319 | 91.99 244 | 93.25 391 | 77.51 333 | 97.44 377 | 87.30 314 | 87.94 349 | 98.12 210 |
|
| ET-MVSNet_ETH3D | | | 91.49 282 | 90.11 311 | 95.63 180 | 96.40 250 | 91.57 140 | 95.34 342 | 93.48 422 | 90.60 243 | 75.58 447 | 95.49 284 | 80.08 288 | 96.79 405 | 94.25 153 | 89.76 330 | 98.52 167 |
|
| LFMVS | | | 93.60 186 | 92.63 209 | 96.52 105 | 98.13 112 | 91.27 152 | 97.94 78 | 93.39 423 | 90.57 245 | 96.29 113 | 98.31 78 | 69.00 402 | 99.16 158 | 94.18 154 | 95.87 218 | 99.12 90 |
|
| MVStest1 | | | 82.38 414 | 80.04 418 | 89.37 413 | 87.63 454 | 82.83 390 | 95.03 358 | 93.37 424 | 73.90 450 | 73.50 452 | 94.35 340 | 62.89 437 | 93.25 451 | 73.80 435 | 65.92 459 | 92.04 436 |
|
| FE-MVSNET | | | 83.85 407 | 81.97 413 | 89.51 411 | 87.19 455 | 83.19 386 | 95.21 353 | 93.17 425 | 83.45 411 | 78.90 440 | 89.05 440 | 65.46 429 | 93.84 446 | 69.71 450 | 75.56 440 | 91.51 440 |
|
| Patchmatch-RL test | | | 87.38 375 | 86.24 379 | 90.81 392 | 88.74 449 | 78.40 438 | 88.12 459 | 93.17 425 | 87.11 354 | 82.17 425 | 89.29 438 | 81.95 252 | 95.60 426 | 88.64 286 | 77.02 433 | 98.41 182 |
|
| ttmdpeth | | | 85.91 395 | 84.76 395 | 89.36 414 | 89.14 444 | 80.25 422 | 95.66 327 | 93.16 427 | 83.77 405 | 83.39 417 | 95.26 294 | 66.24 424 | 95.26 432 | 80.65 397 | 75.57 439 | 92.57 424 |
|
| test-LLR | | | 91.42 285 | 91.19 264 | 92.12 357 | 94.59 362 | 80.66 412 | 94.29 385 | 92.98 428 | 91.11 218 | 90.76 277 | 92.37 405 | 79.02 309 | 98.07 302 | 88.81 281 | 96.74 195 | 97.63 245 |
|
| test-mter | | | 90.19 337 | 89.54 336 | 92.12 357 | 94.59 362 | 80.66 412 | 94.29 385 | 92.98 428 | 87.68 341 | 90.76 277 | 92.37 405 | 67.67 411 | 98.07 302 | 88.81 281 | 96.74 195 | 97.63 245 |
|
| WB-MVSnew | | | 89.88 345 | 89.56 335 | 90.82 391 | 94.57 365 | 83.06 388 | 95.65 328 | 92.85 430 | 87.86 332 | 90.83 276 | 94.10 357 | 79.66 297 | 96.88 401 | 76.34 422 | 94.19 259 | 92.54 426 |
|
| testing3 | | | 87.67 373 | 86.88 374 | 90.05 404 | 96.14 273 | 80.71 411 | 97.10 198 | 92.85 430 | 90.15 257 | 87.54 364 | 94.55 327 | 55.70 450 | 94.10 441 | 73.77 436 | 94.10 263 | 95.35 341 |
|
| test_method | | | 66.11 432 | 64.89 434 | 69.79 449 | 72.62 473 | 35.23 481 | 65.19 469 | 92.83 432 | 20.35 471 | 65.20 460 | 88.08 448 | 43.14 462 | 82.70 466 | 73.12 439 | 63.46 461 | 91.45 444 |
|
| test0.0.03 1 | | | 89.37 355 | 88.70 353 | 91.41 379 | 92.47 423 | 85.63 345 | 95.22 351 | 92.70 433 | 91.11 218 | 86.91 383 | 93.65 378 | 79.02 309 | 93.19 452 | 78.00 414 | 89.18 335 | 95.41 334 |
|
| new_pmnet | | | 82.89 412 | 81.12 417 | 88.18 421 | 89.63 441 | 80.18 423 | 91.77 434 | 92.57 434 | 76.79 446 | 75.56 448 | 88.23 446 | 61.22 441 | 94.48 437 | 71.43 444 | 82.92 411 | 89.87 450 |
|
| mvsany_test1 | | | 93.93 175 | 93.98 154 | 93.78 294 | 94.94 345 | 86.80 313 | 94.62 367 | 92.55 435 | 88.77 306 | 96.85 81 | 98.49 55 | 88.98 99 | 98.08 298 | 95.03 124 | 95.62 226 | 96.46 290 |
|
| thisisatest0515 | | | 92.29 245 | 91.30 258 | 95.25 202 | 96.60 226 | 88.90 255 | 94.36 380 | 92.32 436 | 87.92 329 | 93.43 210 | 94.57 326 | 77.28 334 | 99.00 187 | 89.42 265 | 95.86 219 | 97.86 234 |
|
| thisisatest0530 | | | 93.03 213 | 92.21 225 | 95.49 191 | 97.07 178 | 89.11 250 | 97.49 158 | 92.19 437 | 90.16 256 | 94.09 188 | 96.41 232 | 76.43 343 | 99.05 183 | 90.38 243 | 95.68 224 | 98.31 195 |
|
| tttt0517 | | | 92.96 216 | 92.33 222 | 94.87 225 | 97.11 176 | 87.16 306 | 97.97 74 | 92.09 438 | 90.63 239 | 93.88 194 | 97.01 196 | 76.50 340 | 99.06 180 | 90.29 246 | 95.45 233 | 98.38 185 |
|
| K. test v3 | | | 87.64 374 | 86.75 376 | 90.32 401 | 93.02 411 | 79.48 432 | 96.61 255 | 92.08 439 | 90.66 237 | 80.25 435 | 94.09 359 | 67.21 415 | 96.65 408 | 85.96 338 | 80.83 419 | 94.83 373 |
|
| TESTMET0.1,1 | | | 90.06 339 | 89.42 339 | 91.97 360 | 94.41 370 | 80.62 414 | 94.29 385 | 91.97 440 | 87.28 351 | 90.44 281 | 92.47 404 | 68.79 403 | 97.67 355 | 88.50 288 | 96.60 201 | 97.61 249 |
|
| PM-MVS | | | 83.48 409 | 81.86 415 | 88.31 419 | 87.83 453 | 77.59 440 | 93.43 413 | 91.75 441 | 86.91 356 | 80.63 431 | 89.91 434 | 44.42 461 | 95.84 420 | 85.17 350 | 76.73 436 | 91.50 442 |
|
| baseline2 | | | 91.63 270 | 90.86 275 | 93.94 284 | 94.33 372 | 86.32 327 | 95.92 310 | 91.64 442 | 89.37 280 | 86.94 381 | 94.69 319 | 81.62 259 | 98.69 232 | 88.64 286 | 94.57 252 | 96.81 280 |
|
| APD_test1 | | | 79.31 419 | 77.70 422 | 84.14 432 | 89.11 446 | 69.07 458 | 92.36 432 | 91.50 443 | 69.07 457 | 73.87 450 | 92.63 400 | 39.93 463 | 94.32 439 | 70.54 449 | 80.25 421 | 89.02 452 |
|
| FPMVS | | | 71.27 425 | 69.85 427 | 75.50 445 | 74.64 470 | 59.03 470 | 91.30 436 | 91.50 443 | 58.80 462 | 57.92 466 | 88.28 445 | 29.98 469 | 85.53 465 | 53.43 463 | 82.84 412 | 81.95 458 |
|
| door | | | | | | | | | 91.13 445 | | | | | | | | |
|
| door-mid | | | | | | | | | 91.06 446 | | | | | | | | |
|
| EGC-MVSNET | | | 68.77 430 | 63.01 436 | 86.07 431 | 92.49 422 | 82.24 400 | 93.96 394 | 90.96 447 | 0.71 476 | 2.62 477 | 90.89 425 | 53.66 453 | 93.46 447 | 57.25 461 | 84.55 393 | 82.51 457 |
|
| mvsany_test3 | | | 83.59 408 | 82.44 410 | 87.03 427 | 83.80 460 | 73.82 449 | 93.70 405 | 90.92 448 | 86.42 364 | 82.51 423 | 90.26 430 | 46.76 460 | 95.71 422 | 90.82 230 | 76.76 435 | 91.57 439 |
|
| pmmvs3 | | | 79.97 418 | 77.50 423 | 87.39 425 | 82.80 464 | 79.38 433 | 92.70 427 | 90.75 449 | 70.69 456 | 78.66 441 | 87.47 452 | 51.34 456 | 93.40 448 | 73.39 438 | 69.65 452 | 89.38 451 |
|
| UWE-MVS | | | 89.91 342 | 89.48 338 | 91.21 382 | 95.88 285 | 78.23 439 | 94.91 362 | 90.26 450 | 89.11 287 | 92.35 234 | 94.52 329 | 68.76 404 | 97.96 321 | 83.95 365 | 95.59 227 | 97.42 258 |
|
| DSMNet-mixed | | | 86.34 388 | 86.12 382 | 87.00 428 | 89.88 440 | 70.43 454 | 94.93 361 | 90.08 451 | 77.97 443 | 85.42 398 | 92.78 396 | 74.44 361 | 93.96 444 | 74.43 431 | 95.14 238 | 96.62 284 |
|
| MVS-HIRNet | | | 82.47 413 | 81.21 416 | 86.26 430 | 95.38 312 | 69.21 457 | 88.96 453 | 89.49 452 | 66.28 459 | 80.79 430 | 74.08 464 | 68.48 408 | 97.39 381 | 71.93 443 | 95.47 232 | 92.18 434 |
|
| WB-MVS | | | 76.77 421 | 76.63 424 | 77.18 440 | 85.32 458 | 56.82 472 | 94.53 371 | 89.39 453 | 82.66 417 | 71.35 453 | 89.18 439 | 75.03 354 | 88.88 460 | 35.42 469 | 66.79 457 | 85.84 454 |
|
| test1111 | | | 93.19 205 | 92.82 199 | 94.30 261 | 97.58 158 | 84.56 368 | 98.21 45 | 89.02 454 | 93.53 111 | 94.58 170 | 98.21 85 | 72.69 371 | 99.05 183 | 93.06 182 | 98.48 128 | 99.28 75 |
|
| SSC-MVS | | | 76.05 422 | 75.83 425 | 76.72 444 | 84.77 459 | 56.22 473 | 94.32 383 | 88.96 455 | 81.82 423 | 70.52 454 | 88.91 441 | 74.79 358 | 88.71 461 | 33.69 470 | 64.71 460 | 85.23 455 |
|
| ECVR-MVS |  | | 93.19 205 | 92.73 205 | 94.57 244 | 97.66 146 | 85.41 351 | 98.21 45 | 88.23 456 | 93.43 117 | 94.70 167 | 98.21 85 | 72.57 372 | 99.07 178 | 93.05 183 | 98.49 126 | 99.25 78 |
|
| EPMVS | | | 90.70 320 | 89.81 326 | 93.37 314 | 94.73 357 | 84.21 372 | 93.67 408 | 88.02 457 | 89.50 275 | 92.38 231 | 93.49 383 | 77.82 331 | 97.78 345 | 86.03 336 | 92.68 287 | 98.11 215 |
|
| ANet_high | | | 63.94 434 | 59.58 437 | 77.02 441 | 61.24 477 | 66.06 462 | 85.66 462 | 87.93 458 | 78.53 441 | 42.94 469 | 71.04 466 | 25.42 472 | 80.71 468 | 52.60 464 | 30.83 470 | 84.28 456 |
|
| PMMVS2 | | | 70.19 426 | 66.92 430 | 80.01 436 | 76.35 469 | 65.67 463 | 86.22 460 | 87.58 459 | 64.83 461 | 62.38 462 | 80.29 461 | 26.78 471 | 88.49 463 | 63.79 455 | 54.07 466 | 85.88 453 |
|
| lessismore_v0 | | | | | 90.45 398 | 91.96 429 | 79.09 436 | | 87.19 460 | | 80.32 434 | 94.39 337 | 66.31 423 | 97.55 366 | 84.00 364 | 76.84 434 | 94.70 385 |
|
| PMVS |  | 53.92 22 | 58.58 435 | 55.40 438 | 68.12 450 | 51.00 478 | 48.64 475 | 78.86 465 | 87.10 461 | 46.77 467 | 35.84 473 | 74.28 463 | 8.76 477 | 86.34 464 | 42.07 467 | 73.91 445 | 69.38 464 |
| Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
| UWE-MVS-28 | | | 86.81 382 | 86.41 377 | 88.02 422 | 92.87 413 | 74.60 447 | 95.38 341 | 86.70 462 | 88.17 322 | 87.28 372 | 94.67 322 | 70.83 385 | 93.30 450 | 67.45 452 | 94.31 255 | 96.17 296 |
|
| test_vis1_rt | | | 86.16 391 | 85.06 391 | 89.46 412 | 93.47 400 | 80.46 416 | 96.41 269 | 86.61 463 | 85.22 384 | 79.15 439 | 88.64 442 | 52.41 455 | 97.06 392 | 93.08 181 | 90.57 321 | 90.87 446 |
|
| testf1 | | | 69.31 428 | 66.76 431 | 76.94 442 | 78.61 467 | 61.93 466 | 88.27 457 | 86.11 464 | 55.62 463 | 59.69 463 | 85.31 455 | 20.19 475 | 89.32 457 | 57.62 459 | 69.44 454 | 79.58 459 |
|
| APD_test2 | | | 69.31 428 | 66.76 431 | 76.94 442 | 78.61 467 | 61.93 466 | 88.27 457 | 86.11 464 | 55.62 463 | 59.69 463 | 85.31 455 | 20.19 475 | 89.32 457 | 57.62 459 | 69.44 454 | 79.58 459 |
|
| gg-mvs-nofinetune | | | 87.82 371 | 85.61 384 | 94.44 251 | 94.46 367 | 89.27 244 | 91.21 439 | 84.61 466 | 80.88 428 | 89.89 301 | 74.98 462 | 71.50 379 | 97.53 369 | 85.75 341 | 97.21 179 | 96.51 286 |
|
| dmvs_testset | | | 81.38 416 | 82.60 409 | 77.73 439 | 91.74 430 | 51.49 474 | 93.03 422 | 84.21 467 | 89.07 288 | 78.28 443 | 91.25 424 | 76.97 336 | 88.53 462 | 56.57 462 | 82.24 414 | 93.16 415 |
|
| GG-mvs-BLEND | | | | | 93.62 302 | 93.69 390 | 89.20 246 | 92.39 431 | 83.33 468 | | 87.98 358 | 89.84 435 | 71.00 383 | 96.87 402 | 82.08 383 | 95.40 234 | 94.80 378 |
|
| MTMP | | | | | | | | 97.86 88 | 82.03 469 | | | | | | | | |
|
| DeepMVS_CX |  | | | | 74.68 447 | 90.84 435 | 64.34 465 | | 81.61 470 | 65.34 460 | 67.47 458 | 88.01 449 | 48.60 459 | 80.13 469 | 62.33 457 | 73.68 446 | 79.58 459 |
|
| E-PMN | | | 53.28 436 | 52.56 440 | 55.43 453 | 74.43 471 | 47.13 476 | 83.63 464 | 76.30 471 | 42.23 468 | 42.59 470 | 62.22 469 | 28.57 470 | 74.40 470 | 31.53 471 | 31.51 469 | 44.78 468 |
|
| test2506 | | | 91.60 272 | 90.78 280 | 94.04 274 | 97.66 146 | 83.81 377 | 98.27 35 | 75.53 472 | 93.43 117 | 95.23 153 | 98.21 85 | 67.21 415 | 99.07 178 | 93.01 186 | 98.49 126 | 99.25 78 |
|
| EMVS | | | 52.08 438 | 51.31 441 | 54.39 454 | 72.62 473 | 45.39 478 | 83.84 463 | 75.51 473 | 41.13 469 | 40.77 471 | 59.65 470 | 30.08 468 | 73.60 471 | 28.31 473 | 29.90 471 | 44.18 469 |
|
| test_vis3_rt | | | 72.73 423 | 70.55 426 | 79.27 437 | 80.02 466 | 68.13 460 | 93.92 397 | 74.30 474 | 76.90 445 | 58.99 465 | 73.58 465 | 20.29 474 | 95.37 430 | 84.16 360 | 72.80 448 | 74.31 462 |
|
| MVE |  | 50.73 23 | 53.25 437 | 48.81 442 | 66.58 452 | 65.34 476 | 57.50 471 | 72.49 467 | 70.94 475 | 40.15 470 | 39.28 472 | 63.51 468 | 6.89 479 | 73.48 472 | 38.29 468 | 42.38 468 | 68.76 466 |
| Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
| tmp_tt | | | 51.94 439 | 53.82 439 | 46.29 455 | 33.73 479 | 45.30 479 | 78.32 466 | 67.24 476 | 18.02 472 | 50.93 468 | 87.05 453 | 52.99 454 | 53.11 474 | 70.76 447 | 25.29 472 | 40.46 470 |
|
| kuosan | | | 65.27 433 | 64.66 435 | 67.11 451 | 83.80 460 | 61.32 469 | 88.53 456 | 60.77 477 | 68.22 458 | 67.67 456 | 80.52 460 | 49.12 458 | 70.76 473 | 29.67 472 | 53.64 467 | 69.26 465 |
|
| dongtai | | | 69.99 427 | 69.33 429 | 71.98 448 | 88.78 448 | 61.64 468 | 89.86 448 | 59.93 478 | 75.67 447 | 74.96 449 | 85.45 454 | 50.19 457 | 81.66 467 | 43.86 466 | 55.27 465 | 72.63 463 |
|
| N_pmnet | | | 78.73 420 | 78.71 421 | 78.79 438 | 92.80 416 | 46.50 477 | 94.14 389 | 43.71 479 | 78.61 440 | 80.83 429 | 91.66 421 | 74.94 357 | 96.36 412 | 67.24 453 | 84.45 395 | 93.50 411 |
|
| wuyk23d | | | 25.11 440 | 24.57 444 | 26.74 456 | 73.98 472 | 39.89 480 | 57.88 470 | 9.80 480 | 12.27 473 | 10.39 474 | 6.97 476 | 7.03 478 | 36.44 475 | 25.43 474 | 17.39 473 | 3.89 473 |
|
| testmvs | | | 13.36 442 | 16.33 445 | 4.48 458 | 5.04 480 | 2.26 483 | 93.18 416 | 3.28 481 | 2.70 474 | 8.24 475 | 21.66 472 | 2.29 481 | 2.19 476 | 7.58 475 | 2.96 474 | 9.00 472 |
|
| test123 | | | 13.04 443 | 15.66 446 | 5.18 457 | 4.51 481 | 3.45 482 | 92.50 430 | 1.81 482 | 2.50 475 | 7.58 476 | 20.15 473 | 3.67 480 | 2.18 477 | 7.13 476 | 1.07 475 | 9.90 471 |
|
| mmdepth | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| monomultidepth | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| test_blank | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| uanet_test | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| DCPMVS | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| pcd_1.5k_mvsjas | | | 7.39 445 | 9.85 448 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 88.65 107 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| sosnet-low-res | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| sosnet | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| uncertanet | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| Regformer | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| n2 | | | | | | | | | 0.00 483 | | | | | | | | |
|
| nn | | | | | | | | | 0.00 483 | | | | | | | | |
|
| ab-mvs-re | | | 8.06 444 | 10.74 447 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 96.69 213 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| uanet | | | 0.00 446 | 0.00 449 | 0.00 459 | 0.00 482 | 0.00 484 | 0.00 471 | 0.00 483 | 0.00 477 | 0.00 478 | 0.00 477 | 0.00 482 | 0.00 478 | 0.00 477 | 0.00 476 | 0.00 474 |
|
| TestfortrainingZip | | | | | | | | 98.69 11 | | | | | | | | | |
|
| WAC-MVS | | | | | | | 79.53 429 | | | | | | | | 75.56 427 | | |
|
| PC_three_1452 | | | | | | | | | | 90.77 229 | 98.89 25 | 98.28 83 | 96.24 1 | 98.35 271 | 95.76 103 | 99.58 23 | 99.59 30 |
|
| eth-test2 | | | | | | 0.00 482 | | | | | | | | | | | |
|
| eth-test | | | | | | 0.00 482 | | | | | | | | | | | |
|
| OPU-MVS | | | | | 98.55 4 | 98.82 59 | 96.86 3 | 98.25 38 | | | | 98.26 84 | 96.04 2 | 99.24 146 | 95.36 117 | 99.59 19 | 99.56 38 |
|
| test_0728_THIRD | | | | | | | | | | 94.78 60 | 98.73 29 | 98.87 30 | 95.87 4 | 99.84 24 | 97.45 45 | 99.72 2 | 99.77 3 |
|
| GSMVS | | | | | | | | | | | | | | | | | 98.45 177 |
|
| test_part2 | | | | | | 99.28 28 | 95.74 9 | | | | 98.10 45 | | | | | | |
|
| sam_mvs1 | | | | | | | | | | | | | 82.76 232 | | | | 98.45 177 |
|
| sam_mvs | | | | | | | | | | | | | 81.94 253 | | | | |
|
| test_post1 | | | | | | | | 92.81 426 | | | | 16.58 475 | 80.53 279 | 97.68 354 | 86.20 330 | | |
|
| test_post | | | | | | | | | | | | 17.58 474 | 81.76 256 | 98.08 298 | | | |
|
| patchmatchnet-post | | | | | | | | | | | | 90.45 429 | 82.65 237 | 98.10 293 | | | |
|
| gm-plane-assit | | | | | | 93.22 407 | 78.89 437 | | | 84.82 392 | | 93.52 382 | | 98.64 241 | 87.72 298 | | |
|
| test9_res | | | | | | | | | | | | | | | 94.81 135 | 99.38 62 | 99.45 57 |
|
| agg_prior2 | | | | | | | | | | | | | | | 93.94 159 | 99.38 62 | 99.50 50 |
|
| test_prior4 | | | | | | | 93.66 60 | 96.42 268 | | | | | | | | | |
|
| test_prior2 | | | | | | | | 96.35 278 | | 92.80 152 | 96.03 123 | 97.59 152 | 92.01 49 | | 95.01 125 | 99.38 62 | |
|
| 旧先验2 | | | | | | | | 95.94 308 | | 81.66 424 | 97.34 67 | | | 98.82 206 | 92.26 192 | | |
|
| 新几何2 | | | | | | | | 95.79 318 | | | | | | | | | |
|
| 原ACMM2 | | | | | | | | 95.67 324 | | | | | | | | | |
|
| testdata2 | | | | | | | | | | | | | | 99.67 74 | 85.96 338 | | |
|
| segment_acmp | | | | | | | | | | | | | 92.89 32 | | | | |
|
| testdata1 | | | | | | | | 95.26 350 | | 93.10 134 | | | | | | | |
|
| plane_prior7 | | | | | | 96.21 260 | 89.98 209 | | | | | | | | | | |
|
| plane_prior6 | | | | | | 96.10 278 | 90.00 205 | | | | | | 81.32 263 | | | | |
|
| plane_prior4 | | | | | | | | | | | | 96.64 216 | | | | | |
|
| plane_prior3 | | | | | | | 90.00 205 | | | 94.46 77 | 91.34 262 | | | | | | |
|
| plane_prior2 | | | | | | | | 97.74 110 | | 94.85 52 | | | | | | | |
|
| plane_prior1 | | | | | | 96.14 273 | | | | | | | | | | | |
|
| plane_prior | | | | | | | 89.99 207 | 97.24 183 | | 94.06 90 | | | | | | 92.16 296 | |
|
| HQP5-MVS | | | | | | | 89.33 239 | | | | | | | | | | |
|
| HQP-NCC | | | | | | 95.86 286 | | 96.65 249 | | 93.55 107 | 90.14 286 | | | | | | |
|
| ACMP_Plane | | | | | | 95.86 286 | | 96.65 249 | | 93.55 107 | 90.14 286 | | | | | | |
|
| BP-MVS | | | | | | | | | | | | | | | 92.13 200 | | |
|
| HQP4-MVS | | | | | | | | | | | 90.14 286 | | | 98.50 256 | | | 95.78 315 |
|
| HQP2-MVS | | | | | | | | | | | | | 80.95 267 | | | | |
|
| NP-MVS | | | | | | 95.99 284 | 89.81 217 | | | | | 95.87 259 | | | | | |
|
| MDTV_nov1_ep13_2view | | | | | | | 70.35 455 | 93.10 421 | | 83.88 403 | 93.55 203 | | 82.47 241 | | 86.25 329 | | 98.38 185 |
|
| ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 326 | |
|
| ACMMP++ | | | | | | | | | | | | | | | | 91.02 315 | |
|
| Test By Simon | | | | | | | | | | | | | 88.73 106 | | | | |
|