| test_fmvsm_n_1920 | | | 97.55 16 | 97.89 3 | 96.53 105 | 98.41 85 | 91.73 130 | 98.01 66 | 99.02 1 | 96.37 13 | 99.30 7 | 98.92 23 | 92.39 44 | 99.79 46 | 99.16 14 | 99.46 46 | 98.08 224 |
|
| PGM-MVS | | | 96.81 58 | 96.53 69 | 97.65 47 | 99.35 25 | 93.53 65 | 97.65 129 | 98.98 2 | 92.22 173 | 97.14 76 | 98.44 64 | 91.17 71 | 99.85 21 | 94.35 159 | 99.46 46 | 99.57 36 |
|
| MVS_111021_HR | | | 96.68 69 | 96.58 68 | 96.99 84 | 98.46 79 | 92.31 110 | 96.20 301 | 98.90 3 | 94.30 86 | 95.86 134 | 97.74 140 | 92.33 45 | 99.38 136 | 96.04 96 | 99.42 56 | 99.28 77 |
|
| test_fmvsmconf_n | | | 97.49 21 | 97.56 16 | 97.29 64 | 97.44 165 | 92.37 107 | 97.91 85 | 98.88 4 | 95.83 19 | 98.92 23 | 99.05 14 | 91.45 61 | 99.80 40 | 99.12 16 | 99.46 46 | 99.69 14 |
|
| lecture | | | 97.58 15 | 97.63 12 | 97.43 58 | 99.37 19 | 92.93 86 | 98.86 7 | 98.85 5 | 95.27 34 | 98.65 36 | 98.90 25 | 91.97 52 | 99.80 40 | 97.63 38 | 99.21 83 | 99.57 36 |
|
| ACMMP |  | | 96.27 86 | 95.93 89 | 97.28 66 | 99.24 33 | 92.62 98 | 98.25 40 | 98.81 6 | 92.99 140 | 94.56 178 | 98.39 68 | 88.96 102 | 99.85 21 | 94.57 153 | 97.63 163 | 99.36 72 |
| 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 87 | 96.19 85 | 96.39 124 | 98.23 105 | 91.35 153 | 96.24 298 | 98.79 7 | 93.99 95 | 95.80 136 | 97.65 150 | 89.92 91 | 99.24 149 | 95.87 100 | 99.20 88 | 98.58 168 |
|
| patch_mono-2 | | | 96.83 57 | 97.44 24 | 95.01 221 | 99.05 45 | 85.39 361 | 96.98 213 | 98.77 8 | 94.70 66 | 97.99 51 | 98.66 43 | 93.61 21 | 99.91 1 | 97.67 37 | 99.50 40 | 99.72 13 |
|
| fmvsm_s_conf0.5_n | | | 96.85 54 | 97.13 31 | 96.04 149 | 98.07 120 | 90.28 202 | 97.97 77 | 98.76 9 | 94.93 48 | 98.84 28 | 99.06 12 | 88.80 106 | 99.65 79 | 99.06 18 | 98.63 123 | 98.18 209 |
|
| fmvsm_l_conf0.5_n | | | 97.65 9 | 97.75 8 | 97.34 61 | 98.21 106 | 92.75 92 | 97.83 98 | 98.73 10 | 95.04 45 | 99.30 7 | 98.84 36 | 93.34 24 | 99.78 49 | 99.32 7 | 99.13 98 | 99.50 52 |
|
| fmvsm_s_conf0.5_n_a | | | 96.75 62 | 96.93 46 | 96.20 140 | 97.64 151 | 90.72 184 | 98.00 67 | 98.73 10 | 94.55 73 | 98.91 24 | 99.08 8 | 88.22 118 | 99.63 88 | 98.91 21 | 98.37 136 | 98.25 204 |
|
| fmvsm_s_conf0.5_n_10 | | | 97.29 31 | 97.40 26 | 96.97 86 | 98.24 100 | 91.96 126 | 97.89 88 | 98.72 12 | 96.77 7 | 99.46 3 | 99.06 12 | 87.78 127 | 99.84 26 | 99.40 4 | 99.27 75 | 99.12 92 |
|
| fmvsm_l_conf0.5_n_9 | | | 97.59 13 | 97.79 6 | 96.97 86 | 98.28 94 | 91.49 144 | 97.61 138 | 98.71 13 | 97.10 5 | 99.70 1 | 98.93 22 | 90.95 76 | 99.77 52 | 99.35 6 | 99.53 33 | 99.65 20 |
|
| FC-MVSNet-test | | | 93.94 179 | 93.57 171 | 95.04 219 | 95.48 312 | 91.45 149 | 98.12 55 | 98.71 13 | 93.37 122 | 90.23 292 | 96.70 218 | 87.66 129 | 97.85 343 | 91.49 223 | 90.39 332 | 95.83 318 |
|
| UniMVSNet (Re) | | | 93.31 206 | 92.55 219 | 95.61 189 | 95.39 318 | 93.34 71 | 97.39 172 | 98.71 13 | 93.14 135 | 90.10 301 | 94.83 320 | 87.71 128 | 98.03 316 | 91.67 221 | 83.99 406 | 95.46 337 |
|
| MED-MVS test | | | | | 98.00 23 | 99.56 1 | 94.50 35 | 98.69 11 | 98.70 16 | 93.45 118 | 98.73 30 | 98.53 51 | | 99.86 9 | 97.40 50 | 99.58 23 | 99.65 20 |
|
| MED-MVS | | | 97.91 4 | 97.88 4 | 98.00 23 | 99.56 1 | 94.50 35 | 98.69 11 | 98.70 16 | 94.23 87 | 98.73 30 | 98.53 51 | 95.46 7 | 99.86 9 | 97.40 50 | 99.58 23 | 99.65 20 |
|
| TestfortrainingZip a | | | 97.92 3 | 97.70 10 | 98.58 3 | 99.56 1 | 96.08 5 | 98.69 11 | 98.70 16 | 93.45 118 | 98.73 30 | 98.53 51 | 95.46 7 | 99.86 9 | 96.63 69 | 99.58 23 | 99.80 1 |
|
| fmvsm_l_conf0.5_n_a | | | 97.63 11 | 97.76 7 | 97.26 68 | 98.25 99 | 92.59 100 | 97.81 103 | 98.68 19 | 94.93 48 | 99.24 10 | 98.87 31 | 93.52 22 | 99.79 46 | 99.32 7 | 99.21 83 | 99.40 66 |
|
| FIs | | | 94.09 170 | 93.70 167 | 95.27 208 | 95.70 301 | 92.03 122 | 98.10 56 | 98.68 19 | 93.36 124 | 90.39 289 | 96.70 218 | 87.63 132 | 97.94 334 | 92.25 201 | 90.50 331 | 95.84 317 |
|
| WR-MVS_H | | | 92.00 263 | 91.35 260 | 93.95 289 | 95.09 345 | 89.47 238 | 98.04 63 | 98.68 19 | 91.46 204 | 88.34 352 | 94.68 327 | 85.86 170 | 97.56 372 | 85.77 347 | 84.24 404 | 94.82 382 |
|
| fmvsm_s_conf0.5_n_4 | | | 96.75 62 | 97.07 34 | 95.79 173 | 97.76 142 | 89.57 231 | 97.66 128 | 98.66 22 | 95.36 30 | 99.03 16 | 98.90 25 | 88.39 114 | 99.73 61 | 99.17 13 | 98.66 121 | 98.08 224 |
|
| VPA-MVSNet | | | 93.24 208 | 92.48 224 | 95.51 195 | 95.70 301 | 92.39 106 | 97.86 91 | 98.66 22 | 92.30 170 | 92.09 250 | 95.37 295 | 80.49 286 | 98.40 270 | 93.95 165 | 85.86 377 | 95.75 326 |
|
| fmvsm_l_conf0.5_n_3 | | | 97.64 10 | 97.60 13 | 97.79 34 | 98.14 113 | 93.94 56 | 97.93 83 | 98.65 24 | 96.70 8 | 99.38 5 | 99.07 11 | 89.92 91 | 99.81 35 | 99.16 14 | 99.43 53 | 99.61 30 |
|
| fmvsm_s_conf0.5_n_3 | | | 97.15 36 | 97.36 28 | 96.52 107 | 97.98 126 | 91.19 161 | 97.84 95 | 98.65 24 | 97.08 6 | 99.25 9 | 99.10 6 | 87.88 125 | 99.79 46 | 99.32 7 | 99.18 90 | 98.59 167 |
|
| fmvsm_s_conf0.5_n_8 | | | 97.32 28 | 97.48 23 | 96.85 88 | 98.28 94 | 91.07 169 | 97.76 108 | 98.62 26 | 97.53 2 | 99.20 12 | 99.12 5 | 88.24 117 | 99.81 35 | 99.41 3 | 99.17 91 | 99.67 15 |
|
| fmvsm_s_conf0.5_n_2 | | | 96.62 70 | 96.82 55 | 96.02 151 | 97.98 126 | 90.43 194 | 97.50 153 | 98.59 27 | 96.59 10 | 99.31 6 | 99.08 8 | 84.47 200 | 99.75 58 | 99.37 5 | 98.45 133 | 97.88 237 |
|
| UniMVSNet_NR-MVSNet | | | 93.37 204 | 92.67 213 | 95.47 201 | 95.34 324 | 92.83 89 | 97.17 196 | 98.58 28 | 92.98 145 | 90.13 297 | 95.80 271 | 88.37 116 | 97.85 343 | 91.71 218 | 83.93 407 | 95.73 328 |
|
| CSCG | | | 96.05 90 | 95.91 90 | 96.46 117 | 99.24 33 | 90.47 191 | 98.30 33 | 98.57 29 | 89.01 298 | 93.97 199 | 97.57 160 | 92.62 40 | 99.76 54 | 94.66 147 | 99.27 75 | 99.15 87 |
|
| fmvsm_s_conf0.5_n_9 | | | 97.33 27 | 97.57 15 | 96.62 101 | 98.43 82 | 90.32 201 | 97.80 104 | 98.53 30 | 97.24 4 | 99.62 2 | 99.14 2 | 88.65 109 | 99.80 40 | 99.54 1 | 99.15 95 | 99.74 9 |
|
| fmvsm_s_conf0.5_n_6 | | | 97.08 39 | 97.17 30 | 96.81 89 | 97.28 170 | 91.73 130 | 97.75 110 | 98.50 31 | 94.86 52 | 99.22 11 | 98.78 40 | 89.75 94 | 99.76 54 | 99.10 17 | 99.29 73 | 98.94 121 |
|
| MSLP-MVS++ | | | 96.94 48 | 97.06 35 | 96.59 102 | 98.72 64 | 91.86 128 | 97.67 125 | 98.49 32 | 94.66 69 | 97.24 72 | 98.41 67 | 92.31 47 | 98.94 195 | 96.61 71 | 99.46 46 | 98.96 114 |
|
| HyFIR lowres test | | | 93.66 191 | 92.92 201 | 95.87 162 | 98.24 100 | 89.88 217 | 94.58 377 | 98.49 32 | 85.06 396 | 93.78 202 | 95.78 275 | 82.86 235 | 98.67 244 | 91.77 216 | 95.71 230 | 99.07 100 |
|
| CHOSEN 1792x2688 | | | 94.15 165 | 93.51 177 | 96.06 147 | 98.27 96 | 89.38 243 | 95.18 363 | 98.48 34 | 85.60 386 | 93.76 203 | 97.11 193 | 83.15 225 | 99.61 90 | 91.33 226 | 98.72 119 | 99.19 83 |
|
| fmvsm_s_conf0.5_n_7 | | | 96.45 77 | 96.80 57 | 95.37 204 | 97.29 169 | 88.38 276 | 97.23 190 | 98.47 35 | 95.14 39 | 98.43 41 | 99.09 7 | 87.58 133 | 99.72 65 | 98.80 25 | 99.21 83 | 98.02 228 |
|
| fmvsm_s_conf0.5_n_5 | | | 97.00 45 | 96.97 43 | 97.09 79 | 97.58 161 | 92.56 101 | 97.68 124 | 98.47 35 | 94.02 93 | 98.90 25 | 98.89 28 | 88.94 103 | 99.78 49 | 99.18 12 | 99.03 107 | 98.93 125 |
|
| PHI-MVS | | | 96.77 60 | 96.46 76 | 97.71 45 | 98.40 86 | 94.07 52 | 98.21 47 | 98.45 37 | 89.86 270 | 97.11 78 | 98.01 104 | 92.52 42 | 99.69 73 | 96.03 97 | 99.53 33 | 99.36 72 |
|
| fmvsm_s_conf0.1_n | | | 96.58 73 | 96.77 60 | 96.01 154 | 96.67 228 | 90.25 203 | 97.91 85 | 98.38 38 | 94.48 77 | 98.84 28 | 99.14 2 | 88.06 120 | 99.62 89 | 98.82 23 | 98.60 125 | 98.15 213 |
|
| PVSNet_BlendedMVS | | | 94.06 171 | 93.92 161 | 94.47 256 | 98.27 96 | 89.46 240 | 96.73 246 | 98.36 39 | 90.17 262 | 94.36 184 | 95.24 303 | 88.02 121 | 99.58 98 | 93.44 179 | 90.72 327 | 94.36 402 |
|
| PVSNet_Blended | | | 94.87 140 | 94.56 139 | 95.81 169 | 98.27 96 | 89.46 240 | 95.47 345 | 98.36 39 | 88.84 307 | 94.36 184 | 96.09 260 | 88.02 121 | 99.58 98 | 93.44 179 | 98.18 145 | 98.40 189 |
|
| 3Dnovator | | 91.36 5 | 95.19 126 | 94.44 148 | 97.44 57 | 96.56 239 | 93.36 70 | 98.65 16 | 98.36 39 | 94.12 90 | 89.25 331 | 98.06 98 | 82.20 252 | 99.77 52 | 93.41 181 | 99.32 71 | 99.18 84 |
|
| FOURS1 | | | | | | 99.55 4 | 93.34 71 | 99.29 1 | 98.35 42 | 94.98 46 | 98.49 39 | | | | | | |
|
| DPE-MVS |  | | 97.86 6 | 97.65 11 | 98.47 6 | 99.17 38 | 95.78 8 | 97.21 193 | 98.35 42 | 95.16 38 | 98.71 35 | 98.80 38 | 95.05 12 | 99.89 3 | 96.70 68 | 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 17 | 97.39 27 | 98.00 23 | 99.21 36 | 94.50 35 | 97.75 110 | 98.34 44 | 94.23 87 | 98.15 46 | 98.53 51 | 93.32 27 | 99.84 26 | 97.40 50 | 99.58 23 | 99.65 20 |
|
| fmvsm_s_conf0.1_n_a | | | 96.40 79 | 96.47 73 | 96.16 142 | 95.48 312 | 90.69 185 | 97.91 85 | 98.33 45 | 94.07 91 | 98.93 20 | 99.14 2 | 87.44 141 | 99.61 90 | 98.63 26 | 98.32 138 | 98.18 209 |
|
| HFP-MVS | | | 97.14 37 | 96.92 47 | 97.83 30 | 99.42 10 | 94.12 50 | 98.52 20 | 98.32 46 | 93.21 127 | 97.18 73 | 98.29 84 | 92.08 49 | 99.83 31 | 95.63 113 | 99.59 19 | 99.54 45 |
|
| ACMMPR | | | 97.07 41 | 96.84 51 | 97.79 34 | 99.44 9 | 93.88 57 | 98.52 20 | 98.31 47 | 93.21 127 | 97.15 75 | 98.33 78 | 91.35 65 | 99.86 9 | 95.63 113 | 99.59 19 | 99.62 27 |
|
| test_fmvsmvis_n_1920 | | | 96.70 65 | 96.84 51 | 96.31 129 | 96.62 230 | 91.73 130 | 97.98 71 | 98.30 48 | 96.19 14 | 96.10 124 | 98.95 20 | 89.42 95 | 99.76 54 | 98.90 22 | 99.08 102 | 97.43 264 |
|
| APDe-MVS |  | | 97.82 7 | 97.73 9 | 98.08 19 | 99.15 39 | 94.82 29 | 98.81 8 | 98.30 48 | 94.76 64 | 98.30 43 | 98.90 25 | 93.77 19 | 99.68 75 | 97.93 29 | 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 6 | 95.36 14 | 98.31 32 | 98.29 50 | 94.92 50 | 98.99 18 | 98.92 23 | 95.08 10 | | | | |
|
| MSP-MVS | | | 97.59 13 | 97.54 17 | 97.73 42 | 99.40 14 | 93.77 61 | 98.53 19 | 98.29 50 | 95.55 27 | 98.56 38 | 97.81 132 | 93.90 17 | 99.65 79 | 96.62 70 | 99.21 83 | 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 67 | 95.39 12 | 99.29 1 | 98.28 52 | 94.78 61 | 98.93 20 | 98.87 31 | 96.04 2 | 99.86 9 | 97.45 46 | 99.58 23 | 99.59 32 |
|
| test_0728_SECOND | | | | | 98.51 5 | 99.45 6 | 95.93 6 | 98.21 47 | 98.28 52 | | | | | 99.86 9 | 97.52 42 | 99.67 6 | 99.75 7 |
|
| CP-MVS | | | 97.02 43 | 96.81 56 | 97.64 49 | 99.33 26 | 93.54 64 | 98.80 9 | 98.28 52 | 92.99 140 | 96.45 111 | 98.30 83 | 91.90 53 | 99.85 21 | 95.61 115 | 99.68 4 | 99.54 45 |
|
| test_fmvsmconf0.1_n | | | 97.09 38 | 97.06 35 | 97.19 73 | 95.67 303 | 92.21 114 | 97.95 80 | 98.27 55 | 95.78 23 | 98.40 42 | 99.00 16 | 89.99 89 | 99.78 49 | 99.06 18 | 99.41 59 | 99.59 32 |
|
| SED-MVS | | | 98.05 2 | 97.99 1 | 98.24 11 | 99.42 10 | 95.30 18 | 98.25 40 | 98.27 55 | 95.13 40 | 99.19 13 | 98.89 28 | 95.54 5 | 99.85 21 | 97.52 42 | 99.66 10 | 99.56 40 |
|
| test_241102_TWO | | | | | | | | | 98.27 55 | 95.13 40 | 98.93 20 | 98.89 28 | 94.99 13 | 99.85 21 | 97.52 42 | 99.65 13 | 99.74 9 |
|
| test_241102_ONE | | | | | | 99.42 10 | 95.30 18 | | 98.27 55 | 95.09 43 | 99.19 13 | 98.81 37 | 95.54 5 | 99.65 79 | | | |
|
| SF-MVS | | | 97.39 24 | 97.13 31 | 98.17 16 | 99.02 48 | 95.28 20 | 98.23 44 | 98.27 55 | 92.37 167 | 98.27 44 | 98.65 45 | 93.33 25 | 99.72 65 | 96.49 75 | 99.52 35 | 99.51 49 |
|
| SteuartSystems-ACMMP | | | 97.62 12 | 97.53 18 | 97.87 28 | 98.39 88 | 94.25 44 | 98.43 27 | 98.27 55 | 95.34 32 | 98.11 47 | 98.56 47 | 94.53 14 | 99.71 67 | 96.57 73 | 99.62 17 | 99.65 20 |
| Skip Steuart: Steuart Systems R&D Blog. |
| test_one_0601 | | | | | | 99.32 27 | 95.20 21 | | 98.25 61 | 95.13 40 | 98.48 40 | 98.87 31 | 95.16 9 | | | | |
|
| PVSNet_Blended_VisFu | | | 95.27 116 | 94.91 125 | 96.38 125 | 98.20 107 | 90.86 177 | 97.27 184 | 98.25 61 | 90.21 261 | 94.18 192 | 97.27 182 | 87.48 140 | 99.73 61 | 93.53 176 | 97.77 161 | 98.55 170 |
|
| region2R | | | 97.07 41 | 96.84 51 | 97.77 38 | 99.46 5 | 93.79 59 | 98.52 20 | 98.24 63 | 93.19 130 | 97.14 76 | 98.34 75 | 91.59 60 | 99.87 7 | 95.46 119 | 99.59 19 | 99.64 25 |
|
| PS-CasMVS | | | 91.55 283 | 90.84 284 | 93.69 306 | 94.96 349 | 88.28 279 | 97.84 95 | 98.24 63 | 91.46 204 | 88.04 363 | 95.80 271 | 79.67 302 | 97.48 380 | 87.02 327 | 84.54 401 | 95.31 351 |
|
| DU-MVS | | | 92.90 226 | 92.04 235 | 95.49 198 | 94.95 350 | 92.83 89 | 97.16 197 | 98.24 63 | 93.02 139 | 90.13 297 | 95.71 278 | 83.47 217 | 97.85 343 | 91.71 218 | 83.93 407 | 95.78 322 |
|
| 9.14 | | | | 96.75 61 | | 98.93 56 | | 97.73 115 | 98.23 66 | 91.28 213 | 97.88 55 | 98.44 64 | 93.00 29 | 99.65 79 | 95.76 106 | 99.47 45 | |
|
| reproduce_model | | | 97.51 20 | 97.51 20 | 97.50 54 | 98.99 52 | 93.01 82 | 97.79 106 | 98.21 67 | 95.73 24 | 97.99 51 | 99.03 15 | 92.63 39 | 99.82 33 | 97.80 31 | 99.42 56 | 99.67 15 |
|
| D2MVS | | | 91.30 300 | 90.95 278 | 92.35 354 | 94.71 365 | 85.52 355 | 96.18 303 | 98.21 67 | 88.89 305 | 86.60 392 | 93.82 376 | 79.92 298 | 97.95 332 | 89.29 276 | 90.95 324 | 93.56 417 |
|
| reproduce-ours | | | 97.53 18 | 97.51 20 | 97.60 51 | 98.97 53 | 93.31 73 | 97.71 120 | 98.20 69 | 95.80 21 | 97.88 55 | 98.98 18 | 92.91 30 | 99.81 35 | 97.68 33 | 99.43 53 | 99.67 15 |
|
| our_new_method | | | 97.53 18 | 97.51 20 | 97.60 51 | 98.97 53 | 93.31 73 | 97.71 120 | 98.20 69 | 95.80 21 | 97.88 55 | 98.98 18 | 92.91 30 | 99.81 35 | 97.68 33 | 99.43 53 | 99.67 15 |
|
| SDMVSNet | | | 94.17 163 | 93.61 170 | 95.86 165 | 98.09 116 | 91.37 151 | 97.35 176 | 98.20 69 | 93.18 132 | 91.79 258 | 97.28 180 | 79.13 310 | 98.93 196 | 94.61 150 | 92.84 290 | 97.28 272 |
|
| XVS | | | 97.18 34 | 96.96 45 | 97.81 32 | 99.38 17 | 94.03 54 | 98.59 17 | 98.20 69 | 94.85 53 | 96.59 99 | 98.29 84 | 91.70 56 | 99.80 40 | 95.66 108 | 99.40 61 | 99.62 27 |
|
| X-MVStestdata | | | 91.71 272 | 89.67 338 | 97.81 32 | 99.38 17 | 94.03 54 | 98.59 17 | 98.20 69 | 94.85 53 | 96.59 99 | 32.69 479 | 91.70 56 | 99.80 40 | 95.66 108 | 99.40 61 | 99.62 27 |
|
| ACMMP_NAP | | | 97.20 33 | 96.86 49 | 98.23 12 | 99.09 40 | 95.16 23 | 97.60 139 | 98.19 74 | 92.82 154 | 97.93 54 | 98.74 42 | 91.60 59 | 99.86 9 | 96.26 80 | 99.52 35 | 99.67 15 |
|
| CP-MVSNet | | | 91.89 268 | 91.24 267 | 93.82 298 | 95.05 346 | 88.57 269 | 97.82 100 | 98.19 74 | 91.70 193 | 88.21 358 | 95.76 276 | 81.96 257 | 97.52 378 | 87.86 302 | 84.65 395 | 95.37 347 |
|
| ZNCC-MVS | | | 96.96 46 | 96.67 64 | 97.85 29 | 99.37 19 | 94.12 50 | 98.49 24 | 98.18 76 | 92.64 161 | 96.39 113 | 98.18 91 | 91.61 58 | 99.88 4 | 95.59 118 | 99.55 30 | 99.57 36 |
|
| SMA-MVS |  | | 97.35 25 | 97.03 40 | 98.30 9 | 99.06 44 | 95.42 11 | 97.94 81 | 98.18 76 | 90.57 252 | 98.85 27 | 98.94 21 | 93.33 25 | 99.83 31 | 96.72 66 | 99.68 4 | 99.63 26 |
| 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 | | | 91.20 305 | 90.44 301 | 93.48 317 | 94.49 373 | 87.91 294 | 97.76 108 | 98.18 76 | 91.29 210 | 87.78 367 | 95.74 277 | 80.35 289 | 97.33 391 | 85.46 351 | 82.96 417 | 95.19 362 |
|
| DELS-MVS | | | 96.61 71 | 96.38 80 | 97.30 63 | 97.79 140 | 93.19 78 | 95.96 315 | 98.18 76 | 95.23 35 | 95.87 133 | 97.65 150 | 91.45 61 | 99.70 72 | 95.87 100 | 99.44 52 | 99.00 109 |
| 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 | | | 89.70 357 | 88.40 363 | 93.60 310 | 95.15 341 | 90.10 206 | 97.56 144 | 98.16 80 | 87.28 359 | 86.16 398 | 94.63 331 | 77.57 338 | 98.05 312 | 74.48 438 | 84.59 399 | 92.65 430 |
|
| VNet | | | 95.89 98 | 95.45 101 | 97.21 71 | 98.07 120 | 92.94 85 | 97.50 153 | 98.15 81 | 93.87 99 | 97.52 62 | 97.61 156 | 85.29 184 | 99.53 112 | 95.81 105 | 95.27 243 | 99.16 85 |
|
| DeepPCF-MVS | | 93.97 1 | 96.61 71 | 97.09 33 | 95.15 212 | 98.09 116 | 86.63 327 | 96.00 313 | 98.15 81 | 95.43 28 | 97.95 53 | 98.56 47 | 93.40 23 | 99.36 137 | 96.77 63 | 99.48 44 | 99.45 59 |
|
| SD-MVS | | | 97.41 23 | 97.53 18 | 97.06 82 | 98.57 78 | 94.46 38 | 97.92 84 | 98.14 83 | 94.82 57 | 99.01 17 | 98.55 49 | 94.18 16 | 97.41 387 | 96.94 58 | 99.64 14 | 99.32 74 |
| 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 54 | 96.52 70 | 97.82 31 | 99.36 23 | 94.14 49 | 98.29 34 | 98.13 84 | 92.72 157 | 96.70 91 | 98.06 98 | 91.35 65 | 99.86 9 | 94.83 136 | 99.28 74 | 99.47 58 |
|
| UA-Net | | | 95.95 95 | 95.53 97 | 97.20 72 | 97.67 147 | 92.98 84 | 97.65 129 | 98.13 84 | 94.81 59 | 96.61 97 | 98.35 72 | 88.87 104 | 99.51 117 | 90.36 251 | 97.35 174 | 99.11 94 |
|
| QAPM | | | 93.45 202 | 92.27 229 | 96.98 85 | 96.77 221 | 92.62 98 | 98.39 29 | 98.12 86 | 84.50 404 | 88.27 356 | 97.77 136 | 82.39 249 | 99.81 35 | 85.40 352 | 98.81 115 | 98.51 175 |
|
| Vis-MVSNet |  | | 95.23 121 | 94.81 127 | 96.51 111 | 97.18 175 | 91.58 141 | 98.26 39 | 98.12 86 | 94.38 84 | 94.90 167 | 98.15 93 | 82.28 250 | 98.92 198 | 91.45 225 | 98.58 127 | 99.01 106 |
| Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
| OpenMVS |  | 89.19 12 | 92.86 229 | 91.68 250 | 96.40 122 | 95.34 324 | 92.73 94 | 98.27 37 | 98.12 86 | 84.86 399 | 85.78 400 | 97.75 137 | 78.89 320 | 99.74 59 | 87.50 317 | 98.65 122 | 96.73 289 |
|
| TranMVSNet+NR-MVSNet | | | 92.50 238 | 91.63 251 | 95.14 213 | 94.76 361 | 92.07 119 | 97.53 150 | 98.11 89 | 92.90 151 | 89.56 319 | 96.12 255 | 83.16 224 | 97.60 370 | 89.30 275 | 83.20 416 | 95.75 326 |
|
| CPTT-MVS | | | 95.57 108 | 95.19 112 | 96.70 92 | 99.27 31 | 91.48 146 | 98.33 31 | 98.11 89 | 87.79 344 | 95.17 161 | 98.03 101 | 87.09 148 | 99.61 90 | 93.51 177 | 99.42 56 | 99.02 103 |
|
| APD-MVS |  | | 96.95 47 | 96.60 66 | 98.01 21 | 99.03 47 | 94.93 28 | 97.72 118 | 98.10 91 | 91.50 202 | 98.01 50 | 98.32 80 | 92.33 45 | 99.58 98 | 94.85 133 | 99.51 38 | 99.53 48 |
| Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
| mPP-MVS | | | 96.86 52 | 96.60 66 | 97.64 49 | 99.40 14 | 93.44 66 | 98.50 23 | 98.09 92 | 93.27 126 | 95.95 131 | 98.33 78 | 91.04 73 | 99.88 4 | 95.20 122 | 99.57 29 | 99.60 31 |
|
| ZD-MVS | | | | | | 99.05 45 | 94.59 33 | | 98.08 93 | 89.22 291 | 97.03 81 | 98.10 94 | 92.52 42 | 99.65 79 | 94.58 152 | 99.31 72 | |
|
| MTGPA |  | | | | | | | | 98.08 93 | | | | | | | | |
|
| MTAPA | | | 97.08 39 | 96.78 59 | 97.97 27 | 99.37 19 | 94.42 40 | 97.24 186 | 98.08 93 | 95.07 44 | 96.11 123 | 98.59 46 | 90.88 79 | 99.90 2 | 96.18 92 | 99.50 40 | 99.58 35 |
|
| CNVR-MVS | | | 97.68 8 | 97.44 24 | 98.37 8 | 98.90 59 | 95.86 7 | 97.27 184 | 98.08 93 | 95.81 20 | 97.87 58 | 98.31 81 | 94.26 15 | 99.68 75 | 97.02 57 | 99.49 43 | 99.57 36 |
|
| DP-MVS Recon | | | 95.68 103 | 95.12 116 | 97.37 60 | 99.19 37 | 94.19 46 | 97.03 204 | 98.08 93 | 88.35 325 | 95.09 163 | 97.65 150 | 89.97 90 | 99.48 124 | 92.08 210 | 98.59 126 | 98.44 186 |
|
| SR-MVS | | | 97.01 44 | 96.86 49 | 97.47 56 | 99.09 40 | 93.27 75 | 97.98 71 | 98.07 98 | 93.75 102 | 97.45 64 | 98.48 61 | 91.43 63 | 99.59 95 | 96.22 83 | 99.27 75 | 99.54 45 |
|
| MCST-MVS | | | 97.18 34 | 96.84 51 | 98.20 15 | 99.30 29 | 95.35 16 | 97.12 200 | 98.07 98 | 93.54 112 | 96.08 125 | 97.69 145 | 93.86 18 | 99.71 67 | 96.50 74 | 99.39 63 | 99.55 43 |
|
| NR-MVSNet | | | 92.34 247 | 91.27 266 | 95.53 194 | 94.95 350 | 93.05 81 | 97.39 172 | 98.07 98 | 92.65 159 | 84.46 411 | 95.71 278 | 85.00 191 | 97.77 354 | 89.71 263 | 83.52 413 | 95.78 322 |
|
| MP-MVS-pluss | | | 96.70 65 | 96.27 83 | 97.98 26 | 99.23 35 | 94.71 30 | 96.96 215 | 98.06 101 | 90.67 242 | 95.55 147 | 98.78 40 | 91.07 72 | 99.86 9 | 96.58 72 | 99.55 30 | 99.38 70 |
| MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
| APD-MVS_3200maxsize | | | 96.81 58 | 96.71 63 | 97.12 76 | 99.01 51 | 92.31 110 | 97.98 71 | 98.06 101 | 93.11 136 | 97.44 65 | 98.55 49 | 90.93 77 | 99.55 108 | 96.06 93 | 99.25 80 | 99.51 49 |
|
| MP-MVS |  | | 96.77 60 | 96.45 77 | 97.72 43 | 99.39 16 | 93.80 58 | 98.41 28 | 98.06 101 | 93.37 122 | 95.54 149 | 98.34 75 | 90.59 83 | 99.88 4 | 94.83 136 | 99.54 32 | 99.49 54 |
| Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
| HPM-MVS_fast | | | 96.51 74 | 96.27 83 | 97.22 70 | 99.32 27 | 92.74 93 | 98.74 10 | 98.06 101 | 90.57 252 | 96.77 88 | 98.35 72 | 90.21 86 | 99.53 112 | 94.80 140 | 99.63 16 | 99.38 70 |
|
| HPM-MVS |  | | 96.69 67 | 96.45 77 | 97.40 59 | 99.36 23 | 93.11 80 | 98.87 6 | 98.06 101 | 91.17 221 | 96.40 112 | 97.99 107 | 90.99 74 | 99.58 98 | 95.61 115 | 99.61 18 | 99.49 54 |
| Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
| sss | | | 94.51 154 | 93.80 163 | 96.64 94 | 97.07 181 | 91.97 124 | 96.32 290 | 98.06 101 | 88.94 303 | 94.50 181 | 96.78 213 | 84.60 197 | 99.27 147 | 91.90 211 | 96.02 220 | 98.68 161 |
|
| DeepC-MVS | | 93.07 3 | 96.06 89 | 95.66 94 | 97.29 64 | 97.96 128 | 93.17 79 | 97.30 182 | 98.06 101 | 93.92 97 | 93.38 218 | 98.66 43 | 86.83 150 | 99.73 61 | 95.60 117 | 99.22 82 | 98.96 114 |
| 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 29 | 97.03 40 | 98.11 18 | 98.77 62 | 95.06 26 | 97.34 177 | 98.04 108 | 95.96 15 | 97.09 79 | 97.88 119 | 93.18 28 | 99.71 67 | 95.84 104 | 99.17 91 | 99.56 40 |
|
| DeepC-MVS_fast | | 93.89 2 | 96.93 49 | 96.64 65 | 97.78 36 | 98.64 73 | 94.30 41 | 97.41 167 | 98.04 108 | 94.81 59 | 96.59 99 | 98.37 70 | 91.24 68 | 99.64 87 | 95.16 124 | 99.52 35 | 99.42 65 |
| 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 51 | 96.80 57 | 97.11 78 | 99.02 48 | 92.34 108 | 97.98 71 | 98.03 110 | 93.52 115 | 97.43 67 | 98.51 56 | 91.40 64 | 99.56 106 | 96.05 94 | 99.26 78 | 99.43 63 |
|
| RE-MVS-def | | | | 96.72 62 | | 99.02 48 | 92.34 108 | 97.98 71 | 98.03 110 | 93.52 115 | 97.43 67 | 98.51 56 | 90.71 81 | | 96.05 94 | 99.26 78 | 99.43 63 |
|
| RPMNet | | | 88.98 363 | 87.05 377 | 94.77 240 | 94.45 375 | 87.19 311 | 90.23 453 | 98.03 110 | 77.87 452 | 92.40 236 | 87.55 459 | 80.17 293 | 99.51 117 | 68.84 459 | 93.95 276 | 97.60 257 |
|
| save fliter | | | | | | 98.91 58 | 94.28 42 | 97.02 206 | 98.02 113 | 95.35 31 | | | | | | | |
|
| TEST9 | | | | | | 98.70 65 | 94.19 46 | 96.41 276 | 98.02 113 | 88.17 329 | 96.03 126 | 97.56 162 | 92.74 36 | 99.59 95 | | | |
|
| train_agg | | | 96.30 85 | 95.83 93 | 97.72 43 | 98.70 65 | 94.19 46 | 96.41 276 | 98.02 113 | 88.58 316 | 96.03 126 | 97.56 162 | 92.73 37 | 99.59 95 | 95.04 126 | 99.37 67 | 99.39 68 |
|
| test_8 | | | | | | 98.67 67 | 94.06 53 | 96.37 284 | 98.01 116 | 88.58 316 | 95.98 130 | 97.55 164 | 92.73 37 | 99.58 98 | | | |
|
| fmvsm_s_conf0.5_n_11 | | | 97.30 29 | 97.59 14 | 96.43 119 | 98.42 83 | 91.37 151 | 98.04 63 | 98.00 117 | 97.30 3 | 99.45 4 | 99.21 1 | 89.28 97 | 99.80 40 | 99.27 10 | 99.35 69 | 98.12 216 |
|
| agg_prior | | | | | | 98.67 67 | 93.79 59 | | 98.00 117 | | 95.68 143 | | | 99.57 105 | | | |
|
| test_prior | | | | | 97.23 69 | 98.67 67 | 92.99 83 | | 98.00 117 | | | | | 99.41 132 | | | 99.29 75 |
|
| WR-MVS | | | 92.34 247 | 91.53 255 | 94.77 240 | 95.13 343 | 90.83 178 | 96.40 280 | 97.98 120 | 91.88 188 | 89.29 328 | 95.54 289 | 82.50 245 | 97.80 350 | 89.79 262 | 85.27 386 | 95.69 329 |
|
| HPM-MVS++ |  | | 97.34 26 | 96.97 43 | 98.47 6 | 99.08 42 | 96.16 4 | 97.55 149 | 97.97 121 | 95.59 25 | 96.61 97 | 97.89 116 | 92.57 41 | 99.84 26 | 95.95 99 | 99.51 38 | 99.40 66 |
|
| CANet | | | 96.39 80 | 96.02 88 | 97.50 54 | 97.62 154 | 93.38 68 | 97.02 206 | 97.96 122 | 95.42 29 | 94.86 168 | 97.81 132 | 87.38 143 | 99.82 33 | 96.88 60 | 99.20 88 | 99.29 75 |
|
| 114514_t | | | 93.95 178 | 93.06 195 | 96.63 98 | 99.07 43 | 91.61 138 | 97.46 164 | 97.96 122 | 77.99 450 | 93.00 227 | 97.57 160 | 86.14 166 | 99.33 139 | 89.22 279 | 99.15 95 | 98.94 121 |
|
| IU-MVS | | | | | | 99.42 10 | 95.39 12 | | 97.94 124 | 90.40 259 | 98.94 19 | | | | 97.41 49 | 99.66 10 | 99.74 9 |
|
| MSC_two_6792asdad | | | | | 98.86 1 | 98.67 67 | 96.94 1 | | 97.93 125 | | | | | 99.86 9 | 97.68 33 | 99.67 6 | 99.77 3 |
|
| No_MVS | | | | | 98.86 1 | 98.67 67 | 96.94 1 | | 97.93 125 | | | | | 99.86 9 | 97.68 33 | 99.67 6 | 99.77 3 |
|
| fmvsm_s_conf0.1_n_2 | | | 96.33 84 | 96.44 79 | 96.00 155 | 97.30 168 | 90.37 200 | 97.53 150 | 97.92 127 | 96.52 11 | 99.14 15 | 99.08 8 | 83.21 222 | 99.74 59 | 99.22 11 | 98.06 150 | 97.88 237 |
|
| Anonymous20231211 | | | 90.63 329 | 89.42 345 | 94.27 270 | 98.24 100 | 89.19 255 | 98.05 62 | 97.89 128 | 79.95 442 | 88.25 357 | 94.96 312 | 72.56 379 | 98.13 295 | 89.70 264 | 85.14 388 | 95.49 333 |
|
| 原ACMM1 | | | | | 96.38 125 | 98.59 75 | 91.09 168 | | 97.89 128 | 87.41 355 | 95.22 160 | 97.68 146 | 90.25 85 | 99.54 110 | 87.95 301 | 99.12 100 | 98.49 178 |
|
| CDPH-MVS | | | 95.97 94 | 95.38 106 | 97.77 38 | 98.93 56 | 94.44 39 | 96.35 285 | 97.88 130 | 86.98 363 | 96.65 95 | 97.89 116 | 91.99 51 | 99.47 125 | 92.26 199 | 99.46 46 | 99.39 68 |
|
| test11 | | | | | | | | | 97.88 130 | | | | | | | | |
|
| EIA-MVS | | | 95.53 109 | 95.47 100 | 95.71 184 | 97.06 184 | 89.63 227 | 97.82 100 | 97.87 132 | 93.57 108 | 93.92 200 | 95.04 309 | 90.61 82 | 98.95 193 | 94.62 149 | 98.68 120 | 98.54 171 |
|
| CS-MVS | | | 96.86 52 | 97.06 35 | 96.26 135 | 98.16 112 | 91.16 166 | 99.09 3 | 97.87 132 | 95.30 33 | 97.06 80 | 98.03 101 | 91.72 54 | 98.71 237 | 97.10 55 | 99.17 91 | 98.90 130 |
|
| 无先验 | | | | | | | | 95.79 326 | 97.87 132 | 83.87 412 | | | | 99.65 79 | 87.68 311 | | 98.89 136 |
|
| 3Dnovator+ | | 91.43 4 | 95.40 110 | 94.48 146 | 98.16 17 | 96.90 201 | 95.34 17 | 98.48 25 | 97.87 132 | 94.65 70 | 88.53 348 | 98.02 103 | 83.69 213 | 99.71 67 | 93.18 185 | 98.96 110 | 99.44 61 |
|
| VPNet | | | 92.23 255 | 91.31 263 | 94.99 223 | 95.56 308 | 90.96 172 | 97.22 192 | 97.86 136 | 92.96 146 | 90.96 280 | 96.62 230 | 75.06 359 | 98.20 289 | 91.90 211 | 83.65 412 | 95.80 320 |
|
| test_vis1_n_1920 | | | 94.17 163 | 94.58 138 | 92.91 338 | 97.42 166 | 82.02 409 | 97.83 98 | 97.85 137 | 94.68 67 | 98.10 48 | 98.49 58 | 70.15 398 | 99.32 141 | 97.91 30 | 98.82 114 | 97.40 266 |
|
| DVP-MVS |  | | 97.91 4 | 97.81 5 | 98.22 14 | 99.45 6 | 95.36 14 | 98.21 47 | 97.85 137 | 94.92 50 | 98.73 30 | 98.87 31 | 95.08 10 | 99.84 26 | 97.52 42 | 99.67 6 | 99.48 56 |
| 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 22 | 97.33 29 | 97.69 46 | 99.25 32 | 94.24 45 | 98.07 60 | 97.85 137 | 93.72 103 | 98.57 37 | 98.35 72 | 93.69 20 | 99.40 133 | 97.06 56 | 99.46 46 | 99.44 61 |
| 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 50 | 97.04 39 | 96.45 118 | 98.29 93 | 91.66 137 | 99.03 4 | 97.85 137 | 95.84 18 | 96.90 83 | 97.97 109 | 91.24 68 | 98.75 227 | 96.92 59 | 99.33 70 | 98.94 121 |
|
| test_fmvsmconf0.01_n | | | 96.15 88 | 95.85 92 | 97.03 83 | 92.66 426 | 91.83 129 | 97.97 77 | 97.84 141 | 95.57 26 | 97.53 61 | 99.00 16 | 84.20 206 | 99.76 54 | 98.82 23 | 99.08 102 | 99.48 56 |
|
| GDP-MVS | | | 95.62 105 | 95.13 114 | 97.09 79 | 96.79 214 | 93.26 76 | 97.89 88 | 97.83 142 | 93.58 107 | 96.80 85 | 97.82 130 | 83.06 229 | 99.16 161 | 94.40 156 | 97.95 156 | 98.87 140 |
|
| balanced_conf03 | | | 96.84 56 | 96.89 48 | 96.68 93 | 97.63 153 | 92.22 113 | 98.17 53 | 97.82 143 | 94.44 79 | 98.23 45 | 97.36 175 | 90.97 75 | 99.22 151 | 97.74 32 | 99.66 10 | 98.61 164 |
|
| AdaColmap |  | | 94.34 158 | 93.68 168 | 96.31 129 | 98.59 75 | 91.68 136 | 96.59 265 | 97.81 144 | 89.87 269 | 92.15 246 | 97.06 196 | 83.62 216 | 99.54 110 | 89.34 274 | 98.07 149 | 97.70 250 |
|
| MVSMamba_PlusPlus | | | 96.51 74 | 96.48 72 | 96.59 102 | 98.07 120 | 91.97 124 | 98.14 54 | 97.79 145 | 90.43 257 | 97.34 70 | 97.52 165 | 91.29 67 | 99.19 154 | 98.12 28 | 99.64 14 | 98.60 165 |
|
| KinetiMVS | | | 95.26 117 | 94.75 132 | 96.79 90 | 96.99 194 | 92.05 120 | 97.82 100 | 97.78 146 | 94.77 63 | 96.46 109 | 97.70 143 | 80.62 283 | 99.34 138 | 92.37 198 | 98.28 140 | 98.97 111 |
|
| mamv4 | | | 94.66 151 | 96.10 87 | 90.37 408 | 98.01 123 | 73.41 459 | 96.82 232 | 97.78 146 | 89.95 268 | 94.52 179 | 97.43 170 | 92.91 30 | 99.09 174 | 98.28 27 | 99.16 94 | 98.60 165 |
|
| ETV-MVS | | | 96.02 91 | 95.89 91 | 96.40 122 | 97.16 176 | 92.44 105 | 97.47 162 | 97.77 148 | 94.55 73 | 96.48 107 | 94.51 337 | 91.23 70 | 98.92 198 | 95.65 111 | 98.19 144 | 97.82 245 |
|
| 新几何1 | | | | | 97.32 62 | 98.60 74 | 93.59 63 | | 97.75 149 | 81.58 433 | 95.75 138 | 97.85 124 | 90.04 88 | 99.67 77 | 86.50 333 | 99.13 98 | 98.69 160 |
|
| 旧先验1 | | | | | | 98.38 89 | 93.38 68 | | 97.75 149 | | | 98.09 96 | 92.30 48 | | | 99.01 108 | 99.16 85 |
|
| EC-MVSNet | | | 96.42 78 | 96.47 73 | 96.26 135 | 97.01 192 | 91.52 143 | 98.89 5 | 97.75 149 | 94.42 80 | 96.64 96 | 97.68 146 | 89.32 96 | 98.60 253 | 97.45 46 | 99.11 101 | 98.67 162 |
|
| EI-MVSNet-Vis-set | | | 96.51 74 | 96.47 73 | 96.63 98 | 98.24 100 | 91.20 160 | 96.89 223 | 97.73 152 | 94.74 65 | 96.49 106 | 98.49 58 | 90.88 79 | 99.58 98 | 96.44 76 | 98.32 138 | 99.13 89 |
|
| PAPM_NR | | | 95.01 131 | 94.59 137 | 96.26 135 | 98.89 60 | 90.68 186 | 97.24 186 | 97.73 152 | 91.80 189 | 92.93 232 | 96.62 230 | 89.13 100 | 99.14 166 | 89.21 280 | 97.78 160 | 98.97 111 |
|
| Anonymous20240529 | | | 91.98 264 | 90.73 291 | 95.73 182 | 98.14 113 | 89.40 242 | 97.99 68 | 97.72 154 | 79.63 444 | 93.54 211 | 97.41 172 | 69.94 400 | 99.56 106 | 91.04 233 | 91.11 320 | 98.22 206 |
|
| CHOSEN 280x420 | | | 93.12 214 | 92.72 212 | 94.34 264 | 96.71 227 | 87.27 307 | 90.29 452 | 97.72 154 | 86.61 370 | 91.34 269 | 95.29 297 | 84.29 205 | 98.41 269 | 93.25 183 | 98.94 111 | 97.35 269 |
|
| EI-MVSNet-UG-set | | | 96.34 83 | 96.30 82 | 96.47 115 | 98.20 107 | 90.93 174 | 96.86 226 | 97.72 154 | 94.67 68 | 96.16 122 | 98.46 62 | 90.43 84 | 99.58 98 | 96.23 82 | 97.96 155 | 98.90 130 |
|
| LS3D | | | 93.57 195 | 92.61 217 | 96.47 115 | 97.59 157 | 91.61 138 | 97.67 125 | 97.72 154 | 85.17 394 | 90.29 291 | 98.34 75 | 84.60 197 | 99.73 61 | 83.85 375 | 98.27 141 | 98.06 226 |
|
| PAPR | | | 94.18 162 | 93.42 184 | 96.48 114 | 97.64 151 | 91.42 150 | 95.55 340 | 97.71 158 | 88.99 300 | 92.34 242 | 95.82 270 | 89.19 98 | 99.11 169 | 86.14 339 | 97.38 172 | 98.90 130 |
|
| UGNet | | | 94.04 173 | 93.28 187 | 96.31 129 | 96.85 206 | 91.19 161 | 97.88 90 | 97.68 159 | 94.40 82 | 93.00 227 | 96.18 250 | 73.39 376 | 99.61 90 | 91.72 217 | 98.46 132 | 98.13 214 |
| 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 | | | | | 95.46 202 | 98.18 111 | 88.90 262 | | 97.66 160 | 82.73 424 | 97.03 81 | 98.07 97 | 90.06 87 | 98.85 205 | 89.67 265 | 98.98 109 | 98.64 163 |
|
| test12 | | | | | 97.65 47 | 98.46 79 | 94.26 43 | | 97.66 160 | | 95.52 150 | | 90.89 78 | 99.46 126 | | 99.25 80 | 99.22 82 |
|
| DTE-MVSNet | | | 90.56 330 | 89.75 336 | 93.01 334 | 93.95 388 | 87.25 308 | 97.64 133 | 97.65 162 | 90.74 237 | 87.12 380 | 95.68 281 | 79.97 297 | 97.00 404 | 83.33 376 | 81.66 423 | 94.78 389 |
|
| TAPA-MVS | | 90.10 7 | 92.30 250 | 91.22 269 | 95.56 191 | 98.33 91 | 89.60 229 | 96.79 238 | 97.65 162 | 81.83 430 | 91.52 264 | 97.23 185 | 87.94 123 | 98.91 200 | 71.31 453 | 98.37 136 | 98.17 212 |
| Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
| sd_testset | | | 93.10 215 | 92.45 225 | 95.05 217 | 98.09 116 | 89.21 252 | 96.89 223 | 97.64 164 | 93.18 132 | 91.79 258 | 97.28 180 | 75.35 358 | 98.65 247 | 88.99 285 | 92.84 290 | 97.28 272 |
|
| test_cas_vis1_n_1920 | | | 94.48 156 | 94.55 142 | 94.28 269 | 96.78 219 | 86.45 333 | 97.63 135 | 97.64 164 | 93.32 125 | 97.68 60 | 98.36 71 | 73.75 374 | 99.08 177 | 96.73 65 | 99.05 104 | 97.31 271 |
|
| NormalMVS | | | 96.36 82 | 96.11 86 | 97.12 76 | 99.37 19 | 92.90 87 | 97.99 68 | 97.63 166 | 95.92 16 | 96.57 102 | 97.93 111 | 85.34 182 | 99.50 120 | 94.99 129 | 99.21 83 | 98.97 111 |
|
| Elysia | | | 94.00 175 | 93.12 192 | 96.64 94 | 96.08 287 | 92.72 95 | 97.50 153 | 97.63 166 | 91.15 223 | 94.82 169 | 97.12 191 | 74.98 361 | 99.06 183 | 90.78 238 | 98.02 151 | 98.12 216 |
|
| StellarMVS | | | 94.00 175 | 93.12 192 | 96.64 94 | 96.08 287 | 92.72 95 | 97.50 153 | 97.63 166 | 91.15 223 | 94.82 169 | 97.12 191 | 74.98 361 | 99.06 183 | 90.78 238 | 98.02 151 | 98.12 216 |
|
| cdsmvs_eth3d_5k | | | 23.24 448 | 30.99 450 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 97.63 166 | 0.00 485 | 0.00 486 | 96.88 209 | 84.38 202 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| DPM-MVS | | | 95.69 102 | 94.92 124 | 98.01 21 | 98.08 119 | 95.71 10 | 95.27 356 | 97.62 170 | 90.43 257 | 95.55 147 | 97.07 195 | 91.72 54 | 99.50 120 | 89.62 267 | 98.94 111 | 98.82 146 |
|
| sasdasda | | | 96.02 91 | 95.45 101 | 97.75 40 | 97.59 157 | 95.15 24 | 98.28 35 | 97.60 171 | 94.52 75 | 96.27 117 | 96.12 255 | 87.65 130 | 99.18 157 | 96.20 88 | 94.82 252 | 98.91 127 |
|
| canonicalmvs | | | 96.02 91 | 95.45 101 | 97.75 40 | 97.59 157 | 95.15 24 | 98.28 35 | 97.60 171 | 94.52 75 | 96.27 117 | 96.12 255 | 87.65 130 | 99.18 157 | 96.20 88 | 94.82 252 | 98.91 127 |
|
| test222 | | | | | | 98.24 100 | 92.21 114 | 95.33 351 | 97.60 171 | 79.22 446 | 95.25 158 | 97.84 126 | 88.80 106 | | | 99.15 95 | 98.72 157 |
|
| cascas | | | 91.20 305 | 90.08 318 | 94.58 250 | 94.97 348 | 89.16 256 | 93.65 417 | 97.59 174 | 79.90 443 | 89.40 323 | 92.92 402 | 75.36 357 | 98.36 277 | 92.14 204 | 94.75 255 | 96.23 299 |
|
| E2 | | | 95.20 123 | 95.00 121 | 95.79 173 | 96.79 214 | 89.66 224 | 96.82 232 | 97.58 175 | 92.35 168 | 95.28 156 | 97.83 128 | 86.68 152 | 98.76 221 | 94.79 143 | 96.92 193 | 98.95 118 |
|
| E3 | | | 95.20 123 | 95.00 121 | 95.79 173 | 96.77 221 | 89.66 224 | 96.82 232 | 97.58 175 | 92.35 168 | 95.28 156 | 97.83 128 | 86.69 151 | 98.76 221 | 94.79 143 | 96.92 193 | 98.95 118 |
|
| h-mvs33 | | | 94.15 165 | 93.52 176 | 96.04 149 | 97.81 139 | 90.22 204 | 97.62 137 | 97.58 175 | 95.19 36 | 96.74 89 | 97.45 167 | 83.67 214 | 99.61 90 | 95.85 102 | 79.73 430 | 98.29 202 |
|
| MGCFI-Net | | | 95.94 96 | 95.40 105 | 97.56 53 | 97.59 157 | 94.62 32 | 98.21 47 | 97.57 178 | 94.41 81 | 96.17 121 | 96.16 253 | 87.54 135 | 99.17 159 | 96.19 90 | 94.73 257 | 98.91 127 |
|
| MVSFormer | | | 95.37 111 | 95.16 113 | 95.99 156 | 96.34 263 | 91.21 158 | 98.22 45 | 97.57 178 | 91.42 206 | 96.22 119 | 97.32 176 | 86.20 164 | 97.92 337 | 94.07 162 | 99.05 104 | 98.85 142 |
|
| test_djsdf | | | 93.07 217 | 92.76 207 | 94.00 283 | 93.49 405 | 88.70 266 | 98.22 45 | 97.57 178 | 91.42 206 | 90.08 303 | 95.55 288 | 82.85 236 | 97.92 337 | 94.07 162 | 91.58 311 | 95.40 344 |
|
| OMC-MVS | | | 95.09 128 | 94.70 133 | 96.25 138 | 98.46 79 | 91.28 154 | 96.43 272 | 97.57 178 | 92.04 184 | 94.77 173 | 97.96 110 | 87.01 149 | 99.09 174 | 91.31 227 | 96.77 198 | 98.36 193 |
|
| E4 | | | 95.09 128 | 94.86 126 | 95.77 176 | 96.58 234 | 89.56 232 | 96.85 227 | 97.56 182 | 92.50 162 | 95.03 164 | 97.86 122 | 86.03 167 | 98.78 215 | 94.71 146 | 96.65 207 | 98.96 114 |
|
| viewcassd2359sk11 | | | 95.26 117 | 95.09 118 | 95.80 170 | 96.95 198 | 89.72 223 | 96.80 237 | 97.56 182 | 92.21 175 | 95.37 154 | 97.80 134 | 87.17 147 | 98.77 219 | 94.82 138 | 97.10 187 | 98.90 130 |
|
| PS-MVSNAJss | | | 93.74 188 | 93.51 177 | 94.44 258 | 93.91 390 | 89.28 250 | 97.75 110 | 97.56 182 | 92.50 162 | 89.94 305 | 96.54 233 | 88.65 109 | 98.18 292 | 93.83 171 | 90.90 325 | 95.86 314 |
|
| casdiffmvs_mvg |  | | 95.81 101 | 95.57 95 | 96.51 111 | 96.87 203 | 91.49 144 | 97.50 153 | 97.56 182 | 93.99 95 | 95.13 162 | 97.92 114 | 87.89 124 | 98.78 215 | 95.97 98 | 97.33 175 | 99.26 79 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| E3new | | | 95.28 115 | 95.11 117 | 95.80 170 | 97.03 189 | 89.76 221 | 96.78 242 | 97.54 186 | 92.06 183 | 95.40 153 | 97.75 137 | 87.49 139 | 98.76 221 | 94.85 133 | 97.10 187 | 98.88 138 |
|
| jajsoiax | | | 92.42 243 | 91.89 243 | 94.03 282 | 93.33 413 | 88.50 273 | 97.73 115 | 97.53 187 | 92.00 186 | 88.85 340 | 96.50 235 | 75.62 356 | 98.11 299 | 93.88 169 | 91.56 312 | 95.48 334 |
|
| mvs_tets | | | 92.31 249 | 91.76 246 | 93.94 291 | 93.41 410 | 88.29 278 | 97.63 135 | 97.53 187 | 92.04 184 | 88.76 343 | 96.45 237 | 74.62 366 | 98.09 304 | 93.91 167 | 91.48 313 | 95.45 339 |
|
| dcpmvs_2 | | | 96.37 81 | 97.05 38 | 94.31 267 | 98.96 55 | 84.11 382 | 97.56 144 | 97.51 189 | 93.92 97 | 97.43 67 | 98.52 55 | 92.75 35 | 99.32 141 | 97.32 54 | 99.50 40 | 99.51 49 |
|
| HQP_MVS | | | 93.78 187 | 93.43 182 | 94.82 233 | 96.21 267 | 89.99 210 | 97.74 113 | 97.51 189 | 94.85 53 | 91.34 269 | 96.64 223 | 81.32 269 | 98.60 253 | 93.02 191 | 92.23 299 | 95.86 314 |
|
| plane_prior5 | | | | | | | | | 97.51 189 | | | | | 98.60 253 | 93.02 191 | 92.23 299 | 95.86 314 |
|
| viewmanbaseed2359cas | | | 95.24 120 | 95.02 120 | 95.91 159 | 96.87 203 | 89.98 212 | 96.82 232 | 97.49 192 | 92.26 171 | 95.47 151 | 97.82 130 | 86.47 157 | 98.69 239 | 94.80 140 | 97.20 183 | 99.06 101 |
|
| reproduce_monomvs | | | 91.30 300 | 91.10 273 | 91.92 368 | 96.82 211 | 82.48 403 | 97.01 209 | 97.49 192 | 94.64 71 | 88.35 351 | 95.27 300 | 70.53 393 | 98.10 300 | 95.20 122 | 84.60 398 | 95.19 362 |
|
| viewmacassd2359aftdt | | | 95.07 130 | 94.80 128 | 95.87 162 | 96.53 244 | 89.84 218 | 96.90 222 | 97.48 194 | 92.44 164 | 95.36 155 | 97.89 116 | 85.23 185 | 98.68 241 | 94.40 156 | 97.00 191 | 99.09 96 |
|
| PS-MVSNAJ | | | 95.37 111 | 95.33 108 | 95.49 198 | 97.35 167 | 90.66 187 | 95.31 353 | 97.48 194 | 93.85 100 | 96.51 105 | 95.70 280 | 88.65 109 | 99.65 79 | 94.80 140 | 98.27 141 | 96.17 303 |
|
| API-MVS | | | 94.84 142 | 94.49 145 | 95.90 160 | 97.90 134 | 92.00 123 | 97.80 104 | 97.48 194 | 89.19 292 | 94.81 171 | 96.71 216 | 88.84 105 | 99.17 159 | 88.91 287 | 98.76 118 | 96.53 292 |
|
| MG-MVS | | | 95.61 106 | 95.38 106 | 96.31 129 | 98.42 83 | 90.53 189 | 96.04 310 | 97.48 194 | 93.47 117 | 95.67 144 | 98.10 94 | 89.17 99 | 99.25 148 | 91.27 228 | 98.77 117 | 99.13 89 |
|
| MAR-MVS | | | 94.22 161 | 93.46 179 | 96.51 111 | 98.00 125 | 92.19 117 | 97.67 125 | 97.47 198 | 88.13 333 | 93.00 227 | 95.84 268 | 84.86 195 | 99.51 117 | 87.99 300 | 98.17 146 | 97.83 244 |
| 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 | | | 92.98 221 | 92.53 221 | 94.32 265 | 96.12 282 | 89.20 253 | 95.28 354 | 97.47 198 | 92.66 158 | 89.90 306 | 95.62 284 | 80.58 284 | 98.40 270 | 92.73 196 | 92.40 297 | 95.38 346 |
| Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
| UniMVSNet_ETH3D | | | 91.34 298 | 90.22 314 | 94.68 244 | 94.86 357 | 87.86 295 | 97.23 190 | 97.46 200 | 87.99 334 | 89.90 306 | 96.92 207 | 66.35 428 | 98.23 286 | 90.30 252 | 90.99 323 | 97.96 231 |
|
| nrg030 | | | 94.05 172 | 93.31 186 | 96.27 134 | 95.22 335 | 94.59 33 | 98.34 30 | 97.46 200 | 92.93 147 | 91.21 278 | 96.64 223 | 87.23 146 | 98.22 287 | 94.99 129 | 85.80 378 | 95.98 313 |
|
| XVG-OURS | | | 93.72 189 | 93.35 185 | 94.80 238 | 97.07 181 | 88.61 267 | 94.79 372 | 97.46 200 | 91.97 187 | 93.99 197 | 97.86 122 | 81.74 263 | 98.88 202 | 92.64 197 | 92.67 295 | 96.92 284 |
|
| LPG-MVS_test | | | 92.94 224 | 92.56 218 | 94.10 277 | 96.16 277 | 88.26 280 | 97.65 129 | 97.46 200 | 91.29 210 | 90.12 299 | 97.16 188 | 79.05 313 | 98.73 231 | 92.25 201 | 91.89 307 | 95.31 351 |
|
| LGP-MVS_train | | | | | 94.10 277 | 96.16 277 | 88.26 280 | | 97.46 200 | 91.29 210 | 90.12 299 | 97.16 188 | 79.05 313 | 98.73 231 | 92.25 201 | 91.89 307 | 95.31 351 |
|
| MVS | | | 91.71 272 | 90.44 301 | 95.51 195 | 95.20 337 | 91.59 140 | 96.04 310 | 97.45 205 | 73.44 460 | 87.36 376 | 95.60 285 | 85.42 181 | 99.10 171 | 85.97 344 | 97.46 167 | 95.83 318 |
|
| XVG-OURS-SEG-HR | | | 93.86 184 | 93.55 172 | 94.81 235 | 97.06 184 | 88.53 272 | 95.28 354 | 97.45 205 | 91.68 194 | 94.08 196 | 97.68 146 | 82.41 248 | 98.90 201 | 93.84 170 | 92.47 296 | 96.98 280 |
|
| baseline | | | 95.58 107 | 95.42 104 | 96.08 145 | 96.78 219 | 90.41 195 | 97.16 197 | 97.45 205 | 93.69 106 | 95.65 145 | 97.85 124 | 87.29 144 | 98.68 241 | 95.66 108 | 97.25 181 | 99.13 89 |
|
| ab-mvs | | | 93.57 195 | 92.55 219 | 96.64 94 | 97.28 170 | 91.96 126 | 95.40 347 | 97.45 205 | 89.81 274 | 93.22 224 | 96.28 246 | 79.62 304 | 99.46 126 | 90.74 241 | 93.11 287 | 98.50 176 |
|
| xiu_mvs_v2_base | | | 95.32 114 | 95.29 109 | 95.40 203 | 97.22 172 | 90.50 190 | 95.44 346 | 97.44 209 | 93.70 105 | 96.46 109 | 96.18 250 | 88.59 113 | 99.53 112 | 94.79 143 | 97.81 159 | 96.17 303 |
|
| 1314 | | | 92.81 233 | 92.03 236 | 95.14 213 | 95.33 327 | 89.52 237 | 96.04 310 | 97.44 209 | 87.72 348 | 86.25 397 | 95.33 296 | 83.84 211 | 98.79 214 | 89.26 277 | 97.05 190 | 97.11 278 |
|
| casdiffmvs |  | | 95.64 104 | 95.49 98 | 96.08 145 | 96.76 225 | 90.45 192 | 97.29 183 | 97.44 209 | 94.00 94 | 95.46 152 | 97.98 108 | 87.52 138 | 98.73 231 | 95.64 112 | 97.33 175 | 99.08 98 |
| 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 | | | 94.76 148 | 94.68 134 | 95.01 221 | 96.76 225 | 87.41 303 | 96.38 282 | 97.43 212 | 92.65 159 | 94.52 179 | 97.75 137 | 85.55 179 | 98.81 211 | 94.36 158 | 96.69 204 | 98.82 146 |
|
| XXY-MVS | | | 92.16 257 | 91.23 268 | 94.95 229 | 94.75 362 | 90.94 173 | 97.47 162 | 97.43 212 | 89.14 293 | 88.90 336 | 96.43 238 | 79.71 301 | 98.24 285 | 89.56 268 | 87.68 359 | 95.67 330 |
|
| anonymousdsp | | | 92.16 257 | 91.55 254 | 93.97 287 | 92.58 428 | 89.55 234 | 97.51 152 | 97.42 214 | 89.42 286 | 88.40 350 | 94.84 319 | 80.66 282 | 97.88 342 | 91.87 213 | 91.28 317 | 94.48 397 |
|
| Effi-MVS+ | | | 94.93 136 | 94.45 147 | 96.36 127 | 96.61 231 | 91.47 147 | 96.41 276 | 97.41 215 | 91.02 229 | 94.50 181 | 95.92 264 | 87.53 136 | 98.78 215 | 93.89 168 | 96.81 197 | 98.84 145 |
|
| RRT-MVS | | | 94.51 154 | 94.35 151 | 94.98 225 | 96.40 257 | 86.55 330 | 97.56 144 | 97.41 215 | 93.19 130 | 94.93 166 | 97.04 197 | 79.12 311 | 99.30 145 | 96.19 90 | 97.32 177 | 99.09 96 |
|
| HQP3-MVS | | | | | | | | | 97.39 217 | | | | | | | 92.10 304 | |
|
| HQP-MVS | | | 93.19 211 | 92.74 210 | 94.54 253 | 95.86 293 | 89.33 246 | 96.65 256 | 97.39 217 | 93.55 109 | 90.14 293 | 95.87 266 | 80.95 273 | 98.50 263 | 92.13 207 | 92.10 304 | 95.78 322 |
|
| PLC |  | 91.00 6 | 94.11 169 | 93.43 182 | 96.13 143 | 98.58 77 | 91.15 167 | 96.69 252 | 97.39 217 | 87.29 358 | 91.37 268 | 96.71 216 | 88.39 114 | 99.52 116 | 87.33 320 | 97.13 186 | 97.73 248 |
| Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
| diffmvs_AUTHOR | | | 95.33 113 | 95.27 110 | 95.50 197 | 96.37 261 | 89.08 258 | 96.08 308 | 97.38 220 | 93.09 138 | 96.53 104 | 97.74 140 | 86.45 158 | 98.68 241 | 96.32 78 | 97.48 166 | 98.75 153 |
|
| v7n | | | 90.76 322 | 89.86 329 | 93.45 319 | 93.54 402 | 87.60 301 | 97.70 123 | 97.37 221 | 88.85 306 | 87.65 369 | 94.08 367 | 81.08 272 | 98.10 300 | 84.68 361 | 83.79 411 | 94.66 394 |
|
| UnsupCasMVSNet_eth | | | 85.99 400 | 84.45 404 | 90.62 404 | 89.97 446 | 82.40 406 | 93.62 418 | 97.37 221 | 89.86 270 | 78.59 450 | 92.37 412 | 65.25 438 | 95.35 439 | 82.27 390 | 70.75 458 | 94.10 408 |
|
| viewdifsd2359ckpt13 | | | 94.87 140 | 94.52 143 | 95.90 160 | 96.88 202 | 90.19 205 | 96.92 219 | 97.36 223 | 91.26 214 | 94.65 175 | 97.46 166 | 85.79 173 | 98.64 248 | 93.64 174 | 96.76 199 | 98.88 138 |
|
| ACMM | | 89.79 8 | 92.96 222 | 92.50 223 | 94.35 262 | 96.30 265 | 88.71 265 | 97.58 140 | 97.36 223 | 91.40 208 | 90.53 286 | 96.65 222 | 79.77 300 | 98.75 227 | 91.24 229 | 91.64 309 | 95.59 332 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| xiu_mvs_v1_base_debu | | | 95.01 131 | 94.76 129 | 95.75 179 | 96.58 234 | 91.71 133 | 96.25 295 | 97.35 225 | 92.99 140 | 96.70 91 | 96.63 227 | 82.67 240 | 99.44 129 | 96.22 83 | 97.46 167 | 96.11 309 |
|
| xiu_mvs_v1_base | | | 95.01 131 | 94.76 129 | 95.75 179 | 96.58 234 | 91.71 133 | 96.25 295 | 97.35 225 | 92.99 140 | 96.70 91 | 96.63 227 | 82.67 240 | 99.44 129 | 96.22 83 | 97.46 167 | 96.11 309 |
|
| xiu_mvs_v1_base_debi | | | 95.01 131 | 94.76 129 | 95.75 179 | 96.58 234 | 91.71 133 | 96.25 295 | 97.35 225 | 92.99 140 | 96.70 91 | 96.63 227 | 82.67 240 | 99.44 129 | 96.22 83 | 97.46 167 | 96.11 309 |
|
| diffmvs |  | | 95.25 119 | 95.13 114 | 95.63 187 | 96.43 256 | 89.34 245 | 95.99 314 | 97.35 225 | 92.83 153 | 96.31 115 | 97.37 174 | 86.44 159 | 98.67 244 | 96.26 80 | 97.19 184 | 98.87 140 |
| 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 | | | 94.71 150 | 94.02 159 | 96.79 90 | 97.71 145 | 92.05 120 | 96.59 265 | 97.35 225 | 90.61 248 | 94.64 176 | 96.93 204 | 86.41 160 | 99.39 134 | 91.20 230 | 94.71 258 | 98.94 121 |
|
| viewdifsd2359ckpt09 | | | 94.81 145 | 94.37 150 | 96.12 144 | 96.91 199 | 90.75 183 | 96.94 216 | 97.31 230 | 90.51 255 | 94.31 186 | 97.38 173 | 85.70 175 | 98.71 237 | 93.54 175 | 96.75 200 | 98.90 130 |
|
| SSM_0407 | | | 94.54 153 | 94.12 158 | 95.80 170 | 96.79 214 | 90.38 197 | 96.79 238 | 97.29 231 | 91.24 215 | 93.68 204 | 97.60 157 | 85.03 189 | 98.67 244 | 92.14 204 | 96.51 210 | 98.35 195 |
|
| SSM_0404 | | | 94.73 149 | 94.31 153 | 95.98 157 | 97.05 186 | 90.90 176 | 97.01 209 | 97.29 231 | 91.24 215 | 94.17 193 | 97.60 157 | 85.03 189 | 98.76 221 | 92.14 204 | 97.30 178 | 98.29 202 |
|
| F-COLMAP | | | 93.58 193 | 92.98 199 | 95.37 204 | 98.40 86 | 88.98 260 | 97.18 195 | 97.29 231 | 87.75 347 | 90.49 287 | 97.10 194 | 85.21 186 | 99.50 120 | 86.70 330 | 96.72 203 | 97.63 252 |
|
| VortexMVS | | | 92.88 228 | 92.64 214 | 93.58 312 | 96.58 234 | 87.53 302 | 96.93 218 | 97.28 234 | 92.78 156 | 89.75 311 | 94.99 310 | 82.73 239 | 97.76 355 | 94.60 151 | 88.16 354 | 95.46 337 |
|
| XVG-ACMP-BASELINE | | | 90.93 318 | 90.21 315 | 93.09 332 | 94.31 381 | 85.89 348 | 95.33 351 | 97.26 235 | 91.06 228 | 89.38 324 | 95.44 294 | 68.61 411 | 98.60 253 | 89.46 270 | 91.05 321 | 94.79 387 |
|
| PCF-MVS | | 89.48 11 | 91.56 282 | 89.95 326 | 96.36 127 | 96.60 232 | 92.52 103 | 92.51 437 | 97.26 235 | 79.41 445 | 88.90 336 | 96.56 232 | 84.04 210 | 99.55 108 | 77.01 429 | 97.30 178 | 97.01 279 |
| Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
| ACMP | | 89.59 10 | 92.62 237 | 92.14 232 | 94.05 280 | 96.40 257 | 88.20 283 | 97.36 175 | 97.25 237 | 91.52 201 | 88.30 354 | 96.64 223 | 78.46 325 | 98.72 236 | 91.86 214 | 91.48 313 | 95.23 358 |
| Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
| icg_test_0407_2 | | | 93.58 193 | 93.46 179 | 93.94 291 | 96.19 271 | 86.16 342 | 93.73 412 | 97.24 238 | 91.54 197 | 93.50 213 | 97.04 197 | 85.64 177 | 96.91 407 | 90.68 243 | 95.59 234 | 98.76 149 |
|
| IMVS_0407 | | | 93.94 179 | 93.75 165 | 94.49 255 | 96.19 271 | 86.16 342 | 96.35 285 | 97.24 238 | 91.54 197 | 93.50 213 | 97.04 197 | 85.64 177 | 98.54 260 | 90.68 243 | 95.59 234 | 98.76 149 |
|
| IMVS_0404 | | | 92.44 241 | 91.92 241 | 94.00 283 | 96.19 271 | 86.16 342 | 93.84 409 | 97.24 238 | 91.54 197 | 88.17 360 | 97.04 197 | 76.96 343 | 97.09 398 | 90.68 243 | 95.59 234 | 98.76 149 |
|
| IMVS_0403 | | | 93.98 177 | 93.79 164 | 94.55 252 | 96.19 271 | 86.16 342 | 96.35 285 | 97.24 238 | 91.54 197 | 93.59 208 | 97.04 197 | 85.86 170 | 98.73 231 | 90.68 243 | 95.59 234 | 98.76 149 |
|
| OPM-MVS | | | 93.28 207 | 92.76 207 | 94.82 233 | 94.63 368 | 90.77 181 | 96.65 256 | 97.18 242 | 93.72 103 | 91.68 262 | 97.26 183 | 79.33 308 | 98.63 250 | 92.13 207 | 92.28 298 | 95.07 365 |
| Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
| PatchMatch-RL | | | 92.90 226 | 92.02 237 | 95.56 191 | 98.19 109 | 90.80 179 | 95.27 356 | 97.18 242 | 87.96 335 | 91.86 257 | 95.68 281 | 80.44 287 | 98.99 191 | 84.01 370 | 97.54 165 | 96.89 285 |
|
| alignmvs | | | 95.87 100 | 95.23 111 | 97.78 36 | 97.56 163 | 95.19 22 | 97.86 91 | 97.17 244 | 94.39 83 | 96.47 108 | 96.40 240 | 85.89 169 | 99.20 153 | 96.21 87 | 95.11 248 | 98.95 118 |
|
| MVS_Test | | | 94.89 138 | 94.62 136 | 95.68 185 | 96.83 209 | 89.55 234 | 96.70 250 | 97.17 244 | 91.17 221 | 95.60 146 | 96.11 259 | 87.87 126 | 98.76 221 | 93.01 193 | 97.17 185 | 98.72 157 |
|
| Fast-Effi-MVS+ | | | 93.46 199 | 92.75 209 | 95.59 190 | 96.77 221 | 90.03 207 | 96.81 236 | 97.13 246 | 88.19 328 | 91.30 272 | 94.27 355 | 86.21 163 | 98.63 250 | 87.66 312 | 96.46 216 | 98.12 216 |
|
| EI-MVSNet | | | 93.03 219 | 92.88 203 | 93.48 317 | 95.77 299 | 86.98 316 | 96.44 270 | 97.12 247 | 90.66 244 | 91.30 272 | 97.64 153 | 86.56 154 | 98.05 312 | 89.91 258 | 90.55 329 | 95.41 341 |
|
| MVSTER | | | 93.20 210 | 92.81 206 | 94.37 261 | 96.56 239 | 89.59 230 | 97.06 203 | 97.12 247 | 91.24 215 | 91.30 272 | 95.96 262 | 82.02 256 | 98.05 312 | 93.48 178 | 90.55 329 | 95.47 336 |
|
| viewmambaseed2359dif | | | 94.28 159 | 94.14 156 | 94.71 243 | 96.21 267 | 86.97 317 | 95.93 317 | 97.11 249 | 89.00 299 | 95.00 165 | 97.70 143 | 86.02 168 | 98.59 257 | 93.71 173 | 96.59 209 | 98.57 169 |
|
| test_yl | | | 94.78 146 | 94.23 154 | 96.43 119 | 97.74 143 | 91.22 156 | 96.85 227 | 97.10 250 | 91.23 218 | 95.71 140 | 96.93 204 | 84.30 203 | 99.31 143 | 93.10 186 | 95.12 246 | 98.75 153 |
|
| DCV-MVSNet | | | 94.78 146 | 94.23 154 | 96.43 119 | 97.74 143 | 91.22 156 | 96.85 227 | 97.10 250 | 91.23 218 | 95.71 140 | 96.93 204 | 84.30 203 | 99.31 143 | 93.10 186 | 95.12 246 | 98.75 153 |
|
| LTVRE_ROB | | 88.41 13 | 90.99 314 | 89.92 328 | 94.19 271 | 96.18 275 | 89.55 234 | 96.31 291 | 97.09 252 | 87.88 338 | 85.67 401 | 95.91 265 | 78.79 321 | 98.57 258 | 81.50 393 | 89.98 334 | 94.44 400 |
| 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 | | | 93.46 199 | 93.23 189 | 94.17 272 | 96.12 282 | 85.42 357 | 96.43 272 | 97.08 253 | 92.91 148 | 94.21 189 | 98.00 105 | 80.82 279 | 98.74 229 | 94.41 155 | 89.05 343 | 98.34 199 |
|
| test_fmvs1_n | | | 92.73 235 | 92.88 203 | 92.29 358 | 96.08 287 | 81.05 417 | 97.98 71 | 97.08 253 | 90.72 239 | 96.79 87 | 98.18 91 | 63.07 442 | 98.45 267 | 97.62 40 | 98.42 135 | 97.36 267 |
|
| v10 | | | 91.04 312 | 90.23 312 | 93.49 316 | 94.12 384 | 88.16 286 | 97.32 180 | 97.08 253 | 88.26 327 | 88.29 355 | 94.22 360 | 82.17 253 | 97.97 324 | 86.45 334 | 84.12 405 | 94.33 403 |
|
| viewdifsd2359ckpt11 | | | 93.46 199 | 93.22 190 | 94.17 272 | 96.11 284 | 85.42 357 | 96.43 272 | 97.07 256 | 92.91 148 | 94.20 190 | 98.00 105 | 80.82 279 | 98.73 231 | 94.42 154 | 89.04 345 | 98.34 199 |
|
| mamba_0408 | | | 93.70 190 | 92.99 196 | 95.83 167 | 96.79 214 | 90.38 197 | 88.69 462 | 97.07 256 | 90.96 231 | 93.68 204 | 97.31 178 | 84.97 192 | 98.76 221 | 90.95 234 | 96.51 210 | 98.35 195 |
|
| SSM_04072 | | | 93.51 198 | 92.99 196 | 95.05 217 | 96.79 214 | 90.38 197 | 88.69 462 | 97.07 256 | 90.96 231 | 93.68 204 | 97.31 178 | 84.97 192 | 96.42 418 | 90.95 234 | 96.51 210 | 98.35 195 |
|
| v144192 | | | 91.06 311 | 90.28 308 | 93.39 320 | 93.66 399 | 87.23 310 | 96.83 231 | 97.07 256 | 87.43 354 | 89.69 314 | 94.28 354 | 81.48 266 | 98.00 319 | 87.18 324 | 84.92 394 | 94.93 373 |
|
| v1192 | | | 91.07 310 | 90.23 312 | 93.58 312 | 93.70 396 | 87.82 297 | 96.73 246 | 97.07 256 | 87.77 345 | 89.58 317 | 94.32 352 | 80.90 277 | 97.97 324 | 86.52 332 | 85.48 381 | 94.95 369 |
|
| v8 | | | 91.29 302 | 90.53 300 | 93.57 314 | 94.15 383 | 88.12 287 | 97.34 177 | 97.06 261 | 88.99 300 | 88.32 353 | 94.26 357 | 83.08 227 | 98.01 318 | 87.62 314 | 83.92 409 | 94.57 396 |
|
| mvs_anonymous | | | 93.82 185 | 93.74 166 | 94.06 279 | 96.44 255 | 85.41 359 | 95.81 324 | 97.05 262 | 89.85 272 | 90.09 302 | 96.36 242 | 87.44 141 | 97.75 357 | 93.97 164 | 96.69 204 | 99.02 103 |
|
| IterMVS-LS | | | 92.29 251 | 91.94 240 | 93.34 322 | 96.25 266 | 86.97 317 | 96.57 268 | 97.05 262 | 90.67 242 | 89.50 322 | 94.80 322 | 86.59 153 | 97.64 365 | 89.91 258 | 86.11 376 | 95.40 344 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| v1921920 | | | 90.85 320 | 90.03 323 | 93.29 324 | 93.55 401 | 86.96 319 | 96.74 245 | 97.04 264 | 87.36 356 | 89.52 321 | 94.34 349 | 80.23 292 | 97.97 324 | 86.27 335 | 85.21 387 | 94.94 371 |
|
| CDS-MVSNet | | | 94.14 168 | 93.54 173 | 95.93 158 | 96.18 275 | 91.46 148 | 96.33 289 | 97.04 264 | 88.97 302 | 93.56 209 | 96.51 234 | 87.55 134 | 97.89 341 | 89.80 261 | 95.95 222 | 98.44 186 |
| Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
| SSC-MVS3.2 | | | 89.74 356 | 89.26 349 | 91.19 393 | 95.16 338 | 80.29 428 | 94.53 379 | 97.03 266 | 91.79 190 | 88.86 339 | 94.10 364 | 69.94 400 | 97.82 347 | 85.29 353 | 86.66 372 | 95.45 339 |
|
| v1144 | | | 91.37 295 | 90.60 296 | 93.68 307 | 93.89 391 | 88.23 282 | 96.84 230 | 97.03 266 | 88.37 324 | 89.69 314 | 94.39 344 | 82.04 255 | 97.98 321 | 87.80 304 | 85.37 383 | 94.84 379 |
|
| v1240 | | | 90.70 326 | 89.85 330 | 93.23 326 | 93.51 404 | 86.80 320 | 96.61 262 | 97.02 268 | 87.16 361 | 89.58 317 | 94.31 353 | 79.55 305 | 97.98 321 | 85.52 350 | 85.44 382 | 94.90 376 |
|
| EPP-MVSNet | | | 95.22 122 | 95.04 119 | 95.76 177 | 97.49 164 | 89.56 232 | 98.67 15 | 97.00 269 | 90.69 240 | 94.24 188 | 97.62 155 | 89.79 93 | 98.81 211 | 93.39 182 | 96.49 214 | 98.92 126 |
|
| V42 | | | 91.58 281 | 90.87 280 | 93.73 302 | 94.05 387 | 88.50 273 | 97.32 180 | 96.97 270 | 88.80 312 | 89.71 312 | 94.33 350 | 82.54 244 | 98.05 312 | 89.01 284 | 85.07 390 | 94.64 395 |
|
| test_fmvs1 | | | 93.21 209 | 93.53 174 | 92.25 361 | 96.55 241 | 81.20 416 | 97.40 171 | 96.96 271 | 90.68 241 | 96.80 85 | 98.04 100 | 69.25 406 | 98.40 270 | 97.58 41 | 98.50 128 | 97.16 277 |
|
| FMVSNet2 | | | 91.31 299 | 90.08 318 | 94.99 223 | 96.51 248 | 92.21 114 | 97.41 167 | 96.95 272 | 88.82 309 | 88.62 345 | 94.75 324 | 73.87 370 | 97.42 386 | 85.20 356 | 88.55 351 | 95.35 348 |
|
| ACMH | | 87.59 16 | 90.53 331 | 89.42 345 | 93.87 296 | 96.21 267 | 87.92 292 | 97.24 186 | 96.94 273 | 88.45 322 | 83.91 421 | 96.27 247 | 71.92 382 | 98.62 252 | 84.43 364 | 89.43 340 | 95.05 367 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| GBi-Net | | | 91.35 296 | 90.27 309 | 94.59 246 | 96.51 248 | 91.18 163 | 97.50 153 | 96.93 274 | 88.82 309 | 89.35 325 | 94.51 337 | 73.87 370 | 97.29 393 | 86.12 340 | 88.82 346 | 95.31 351 |
|
| test1 | | | 91.35 296 | 90.27 309 | 94.59 246 | 96.51 248 | 91.18 163 | 97.50 153 | 96.93 274 | 88.82 309 | 89.35 325 | 94.51 337 | 73.87 370 | 97.29 393 | 86.12 340 | 88.82 346 | 95.31 351 |
|
| FMVSNet3 | | | 91.78 270 | 90.69 294 | 95.03 220 | 96.53 244 | 92.27 112 | 97.02 206 | 96.93 274 | 89.79 275 | 89.35 325 | 94.65 330 | 77.01 341 | 97.47 381 | 86.12 340 | 88.82 346 | 95.35 348 |
|
| FMVSNet1 | | | 89.88 351 | 88.31 364 | 94.59 246 | 95.41 317 | 91.18 163 | 97.50 153 | 96.93 274 | 86.62 369 | 87.41 374 | 94.51 337 | 65.94 433 | 97.29 393 | 83.04 379 | 87.43 362 | 95.31 351 |
|
| GeoE | | | 93.89 182 | 93.28 187 | 95.72 183 | 96.96 197 | 89.75 222 | 98.24 43 | 96.92 278 | 89.47 283 | 92.12 248 | 97.21 186 | 84.42 201 | 98.39 275 | 87.71 307 | 96.50 213 | 99.01 106 |
|
| SymmetryMVS | | | 95.94 96 | 95.54 96 | 97.15 74 | 97.85 136 | 92.90 87 | 97.99 68 | 96.91 279 | 95.92 16 | 96.57 102 | 97.93 111 | 85.34 182 | 99.50 120 | 94.99 129 | 96.39 217 | 99.05 102 |
|
| miper_enhance_ethall | | | 91.54 285 | 91.01 276 | 93.15 330 | 95.35 323 | 87.07 315 | 93.97 401 | 96.90 280 | 86.79 367 | 89.17 332 | 93.43 396 | 86.55 155 | 97.64 365 | 89.97 257 | 86.93 367 | 94.74 391 |
|
| eth_miper_zixun_eth | | | 91.02 313 | 90.59 297 | 92.34 356 | 95.33 327 | 84.35 378 | 94.10 398 | 96.90 280 | 88.56 318 | 88.84 341 | 94.33 350 | 84.08 208 | 97.60 370 | 88.77 290 | 84.37 403 | 95.06 366 |
|
| TAMVS | | | 94.01 174 | 93.46 179 | 95.64 186 | 96.16 277 | 90.45 192 | 96.71 249 | 96.89 282 | 89.27 290 | 93.46 216 | 96.92 207 | 87.29 144 | 97.94 334 | 88.70 292 | 95.74 228 | 98.53 172 |
|
| miper_ehance_all_eth | | | 91.59 279 | 91.13 272 | 92.97 336 | 95.55 309 | 86.57 328 | 94.47 382 | 96.88 283 | 87.77 345 | 88.88 338 | 94.01 369 | 86.22 162 | 97.54 374 | 89.49 269 | 86.93 367 | 94.79 387 |
|
| v2v482 | | | 91.59 279 | 90.85 283 | 93.80 299 | 93.87 392 | 88.17 285 | 96.94 216 | 96.88 283 | 89.54 280 | 89.53 320 | 94.90 316 | 81.70 264 | 98.02 317 | 89.25 278 | 85.04 392 | 95.20 359 |
|
| CNLPA | | | 94.28 159 | 93.53 174 | 96.52 107 | 98.38 89 | 92.55 102 | 96.59 265 | 96.88 283 | 90.13 265 | 91.91 254 | 97.24 184 | 85.21 186 | 99.09 174 | 87.64 313 | 97.83 158 | 97.92 234 |
|
| PAPM | | | 91.52 286 | 90.30 307 | 95.20 210 | 95.30 330 | 89.83 219 | 93.38 423 | 96.85 286 | 86.26 377 | 88.59 346 | 95.80 271 | 84.88 194 | 98.15 294 | 75.67 434 | 95.93 223 | 97.63 252 |
|
| c3_l | | | 91.38 293 | 90.89 279 | 92.88 340 | 95.58 307 | 86.30 336 | 94.68 374 | 96.84 287 | 88.17 329 | 88.83 342 | 94.23 358 | 85.65 176 | 97.47 381 | 89.36 273 | 84.63 396 | 94.89 377 |
|
| pm-mvs1 | | | 90.72 325 | 89.65 340 | 93.96 288 | 94.29 382 | 89.63 227 | 97.79 106 | 96.82 288 | 89.07 295 | 86.12 399 | 95.48 293 | 78.61 323 | 97.78 352 | 86.97 328 | 81.67 422 | 94.46 398 |
|
| test_vis1_n | | | 92.37 246 | 92.26 230 | 92.72 346 | 94.75 362 | 82.64 399 | 98.02 65 | 96.80 289 | 91.18 220 | 97.77 59 | 97.93 111 | 58.02 452 | 98.29 283 | 97.63 38 | 98.21 143 | 97.23 275 |
|
| CMPMVS |  | 62.92 21 | 85.62 405 | 84.92 399 | 87.74 431 | 89.14 451 | 73.12 461 | 94.17 396 | 96.80 289 | 73.98 457 | 73.65 459 | 94.93 314 | 66.36 427 | 97.61 369 | 83.95 372 | 91.28 317 | 92.48 435 |
| M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
| MS-PatchMatch | | | 90.27 338 | 89.77 334 | 91.78 377 | 94.33 379 | 84.72 375 | 95.55 340 | 96.73 291 | 86.17 379 | 86.36 396 | 95.28 299 | 71.28 387 | 97.80 350 | 84.09 369 | 98.14 147 | 92.81 427 |
|
| Effi-MVS+-dtu | | | 93.08 216 | 93.21 191 | 92.68 349 | 96.02 290 | 83.25 392 | 97.14 199 | 96.72 292 | 93.85 100 | 91.20 279 | 93.44 393 | 83.08 227 | 98.30 282 | 91.69 220 | 95.73 229 | 96.50 294 |
|
| TSAR-MVS + GP. | | | 96.69 67 | 96.49 71 | 97.27 67 | 98.31 92 | 93.39 67 | 96.79 238 | 96.72 292 | 94.17 89 | 97.44 65 | 97.66 149 | 92.76 34 | 99.33 139 | 96.86 62 | 97.76 162 | 99.08 98 |
|
| 1112_ss | | | 93.37 204 | 92.42 226 | 96.21 139 | 97.05 186 | 90.99 170 | 96.31 291 | 96.72 292 | 86.87 366 | 89.83 309 | 96.69 220 | 86.51 156 | 99.14 166 | 88.12 297 | 93.67 281 | 98.50 176 |
|
| PVSNet | | 86.66 18 | 92.24 254 | 91.74 249 | 93.73 302 | 97.77 141 | 83.69 389 | 92.88 432 | 96.72 292 | 87.91 337 | 93.00 227 | 94.86 318 | 78.51 324 | 99.05 186 | 86.53 331 | 97.45 171 | 98.47 181 |
|
| miper_lstm_enhance | | | 90.50 334 | 90.06 322 | 91.83 373 | 95.33 327 | 83.74 386 | 93.86 407 | 96.70 296 | 87.56 352 | 87.79 366 | 93.81 377 | 83.45 219 | 96.92 406 | 87.39 318 | 84.62 397 | 94.82 382 |
|
| v148 | | | 90.99 314 | 90.38 303 | 92.81 343 | 93.83 393 | 85.80 349 | 96.78 242 | 96.68 297 | 89.45 285 | 88.75 344 | 93.93 373 | 82.96 233 | 97.82 347 | 87.83 303 | 83.25 414 | 94.80 385 |
|
| ACMH+ | | 87.92 14 | 90.20 342 | 89.18 351 | 93.25 325 | 96.48 251 | 86.45 333 | 96.99 212 | 96.68 297 | 88.83 308 | 84.79 410 | 96.22 249 | 70.16 397 | 98.53 261 | 84.42 365 | 88.04 355 | 94.77 390 |
|
| CANet_DTU | | | 94.37 157 | 93.65 169 | 96.55 104 | 96.46 254 | 92.13 118 | 96.21 299 | 96.67 299 | 94.38 84 | 93.53 212 | 97.03 202 | 79.34 307 | 99.71 67 | 90.76 240 | 98.45 133 | 97.82 245 |
|
| cl____ | | | 90.96 317 | 90.32 305 | 92.89 339 | 95.37 321 | 86.21 339 | 94.46 384 | 96.64 300 | 87.82 341 | 88.15 361 | 94.18 361 | 82.98 231 | 97.54 374 | 87.70 308 | 85.59 379 | 94.92 375 |
|
| HY-MVS | | 89.66 9 | 93.87 183 | 92.95 200 | 96.63 98 | 97.10 180 | 92.49 104 | 95.64 337 | 96.64 300 | 89.05 297 | 93.00 227 | 95.79 274 | 85.77 174 | 99.45 128 | 89.16 283 | 94.35 260 | 97.96 231 |
|
| Test_1112_low_res | | | 92.84 231 | 91.84 244 | 95.85 166 | 97.04 188 | 89.97 214 | 95.53 342 | 96.64 300 | 85.38 389 | 89.65 316 | 95.18 304 | 85.86 170 | 99.10 171 | 87.70 308 | 93.58 286 | 98.49 178 |
|
| DIV-MVS_self_test | | | 90.97 316 | 90.33 304 | 92.88 340 | 95.36 322 | 86.19 341 | 94.46 384 | 96.63 303 | 87.82 341 | 88.18 359 | 94.23 358 | 82.99 230 | 97.53 376 | 87.72 305 | 85.57 380 | 94.93 373 |
|
| Fast-Effi-MVS+-dtu | | | 92.29 251 | 91.99 238 | 93.21 328 | 95.27 331 | 85.52 355 | 97.03 204 | 96.63 303 | 92.09 181 | 89.11 334 | 95.14 306 | 80.33 290 | 98.08 305 | 87.54 316 | 94.74 256 | 96.03 312 |
|
| UnsupCasMVSNet_bld | | | 82.13 422 | 79.46 427 | 90.14 411 | 88.00 459 | 82.47 404 | 90.89 450 | 96.62 305 | 78.94 447 | 75.61 454 | 84.40 465 | 56.63 455 | 96.31 420 | 77.30 426 | 66.77 466 | 91.63 446 |
|
| cl22 | | | 91.21 304 | 90.56 299 | 93.14 331 | 96.09 286 | 86.80 320 | 94.41 386 | 96.58 306 | 87.80 343 | 88.58 347 | 93.99 371 | 80.85 278 | 97.62 368 | 89.87 260 | 86.93 367 | 94.99 368 |
|
| jason | | | 94.84 142 | 94.39 149 | 96.18 141 | 95.52 310 | 90.93 174 | 96.09 307 | 96.52 307 | 89.28 289 | 96.01 129 | 97.32 176 | 84.70 196 | 98.77 219 | 95.15 125 | 98.91 113 | 98.85 142 |
| jason: jason. |
| tt0805 | | | 91.09 309 | 90.07 321 | 94.16 275 | 95.61 305 | 88.31 277 | 97.56 144 | 96.51 308 | 89.56 279 | 89.17 332 | 95.64 283 | 67.08 425 | 98.38 276 | 91.07 232 | 88.44 352 | 95.80 320 |
|
| AUN-MVS | | | 91.76 271 | 90.75 289 | 94.81 235 | 97.00 193 | 88.57 269 | 96.65 256 | 96.49 309 | 89.63 277 | 92.15 246 | 96.12 255 | 78.66 322 | 98.50 263 | 90.83 236 | 79.18 433 | 97.36 267 |
|
| hse-mvs2 | | | 93.45 202 | 92.99 196 | 94.81 235 | 97.02 191 | 88.59 268 | 96.69 252 | 96.47 310 | 95.19 36 | 96.74 89 | 96.16 253 | 83.67 214 | 98.48 266 | 95.85 102 | 79.13 434 | 97.35 269 |
|
| SD_0403 | | | 90.01 346 | 90.02 324 | 89.96 414 | 95.65 304 | 76.76 449 | 95.76 328 | 96.46 311 | 90.58 251 | 86.59 393 | 96.29 245 | 82.12 254 | 94.78 443 | 73.00 448 | 93.76 279 | 98.35 195 |
|
| EG-PatchMatch MVS | | | 87.02 386 | 85.44 391 | 91.76 379 | 92.67 425 | 85.00 369 | 96.08 308 | 96.45 312 | 83.41 420 | 79.52 444 | 93.49 390 | 57.10 454 | 97.72 359 | 79.34 417 | 90.87 326 | 92.56 432 |
|
| KD-MVS_self_test | | | 85.95 401 | 84.95 398 | 88.96 425 | 89.55 450 | 79.11 443 | 95.13 364 | 96.42 313 | 85.91 382 | 84.07 419 | 90.48 435 | 70.03 399 | 94.82 442 | 80.04 409 | 72.94 455 | 92.94 425 |
|
| FE-MVSNET2 | | | 86.36 394 | 84.68 403 | 91.39 387 | 87.67 461 | 86.47 332 | 96.21 299 | 96.41 314 | 87.87 339 | 79.31 446 | 89.64 443 | 65.29 437 | 95.58 434 | 82.42 388 | 77.28 440 | 92.14 443 |
|
| pmmvs6 | | | 87.81 378 | 86.19 386 | 92.69 348 | 91.32 438 | 86.30 336 | 97.34 177 | 96.41 314 | 80.59 441 | 84.05 420 | 94.37 346 | 67.37 420 | 97.67 362 | 84.75 360 | 79.51 432 | 94.09 410 |
|
| PMMVS | | | 92.86 229 | 92.34 227 | 94.42 260 | 94.92 353 | 86.73 323 | 94.53 379 | 96.38 316 | 84.78 401 | 94.27 187 | 95.12 308 | 83.13 226 | 98.40 270 | 91.47 224 | 96.49 214 | 98.12 216 |
|
| RPSCF | | | 90.75 323 | 90.86 281 | 90.42 407 | 96.84 207 | 76.29 452 | 95.61 338 | 96.34 317 | 83.89 410 | 91.38 267 | 97.87 120 | 76.45 347 | 98.78 215 | 87.16 325 | 92.23 299 | 96.20 301 |
|
| BP-MVS1 | | | 95.89 98 | 95.49 98 | 97.08 81 | 96.67 228 | 93.20 77 | 98.08 58 | 96.32 318 | 94.56 72 | 96.32 114 | 97.84 126 | 84.07 209 | 99.15 163 | 96.75 64 | 98.78 116 | 98.90 130 |
|
| MSDG | | | 91.42 291 | 90.24 311 | 94.96 228 | 97.15 178 | 88.91 261 | 93.69 415 | 96.32 318 | 85.72 385 | 86.93 389 | 96.47 236 | 80.24 291 | 98.98 192 | 80.57 406 | 95.05 249 | 96.98 280 |
|
| WBMVS | | | 90.69 328 | 89.99 325 | 92.81 343 | 96.48 251 | 85.00 369 | 95.21 361 | 96.30 320 | 89.46 284 | 89.04 335 | 94.05 368 | 72.45 380 | 97.82 347 | 89.46 270 | 87.41 364 | 95.61 331 |
|
| OurMVSNet-221017-0 | | | 90.51 333 | 90.19 316 | 91.44 385 | 93.41 410 | 81.25 414 | 96.98 213 | 96.28 321 | 91.68 194 | 86.55 394 | 96.30 244 | 74.20 369 | 97.98 321 | 88.96 286 | 87.40 365 | 95.09 364 |
|
| MVP-Stereo | | | 90.74 324 | 90.08 318 | 92.71 347 | 93.19 415 | 88.20 283 | 95.86 321 | 96.27 322 | 86.07 380 | 84.86 409 | 94.76 323 | 77.84 336 | 97.75 357 | 83.88 374 | 98.01 153 | 92.17 442 |
| Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
| lupinMVS | | | 94.99 135 | 94.56 139 | 96.29 133 | 96.34 263 | 91.21 158 | 95.83 323 | 96.27 322 | 88.93 304 | 96.22 119 | 96.88 209 | 86.20 164 | 98.85 205 | 95.27 121 | 99.05 104 | 98.82 146 |
|
| BH-untuned | | | 92.94 224 | 92.62 216 | 93.92 295 | 97.22 172 | 86.16 342 | 96.40 280 | 96.25 324 | 90.06 266 | 89.79 310 | 96.17 252 | 83.19 223 | 98.35 278 | 87.19 323 | 97.27 180 | 97.24 274 |
|
| CL-MVSNet_self_test | | | 86.31 396 | 85.15 395 | 89.80 416 | 88.83 454 | 81.74 412 | 93.93 404 | 96.22 325 | 86.67 368 | 85.03 407 | 90.80 433 | 78.09 332 | 94.50 444 | 74.92 437 | 71.86 457 | 93.15 423 |
|
| IS-MVSNet | | | 94.90 137 | 94.52 143 | 96.05 148 | 97.67 147 | 90.56 188 | 98.44 26 | 96.22 325 | 93.21 127 | 93.99 197 | 97.74 140 | 85.55 179 | 98.45 267 | 89.98 256 | 97.86 157 | 99.14 88 |
|
| FA-MVS(test-final) | | | 93.52 197 | 92.92 201 | 95.31 207 | 96.77 221 | 88.54 271 | 94.82 371 | 96.21 327 | 89.61 278 | 94.20 190 | 95.25 302 | 83.24 221 | 99.14 166 | 90.01 255 | 96.16 219 | 98.25 204 |
|
| GA-MVS | | | 91.38 293 | 90.31 306 | 94.59 246 | 94.65 367 | 87.62 300 | 94.34 389 | 96.19 328 | 90.73 238 | 90.35 290 | 93.83 374 | 71.84 383 | 97.96 328 | 87.22 322 | 93.61 284 | 98.21 207 |
|
| LuminaMVS | | | 94.89 138 | 94.35 151 | 96.53 105 | 95.48 312 | 92.80 91 | 96.88 225 | 96.18 329 | 92.85 152 | 95.92 132 | 96.87 211 | 81.44 267 | 98.83 208 | 96.43 77 | 97.10 187 | 97.94 233 |
|
| IterMVS-SCA-FT | | | 90.31 336 | 89.81 332 | 91.82 374 | 95.52 310 | 84.20 381 | 94.30 392 | 96.15 330 | 90.61 248 | 87.39 375 | 94.27 355 | 75.80 353 | 96.44 417 | 87.34 319 | 86.88 371 | 94.82 382 |
|
| IterMVS | | | 90.15 344 | 89.67 338 | 91.61 381 | 95.48 312 | 83.72 387 | 94.33 390 | 96.12 331 | 89.99 267 | 87.31 378 | 94.15 363 | 75.78 355 | 96.27 421 | 86.97 328 | 86.89 370 | 94.83 380 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| DP-MVS | | | 92.76 234 | 91.51 258 | 96.52 107 | 98.77 62 | 90.99 170 | 97.38 174 | 96.08 332 | 82.38 426 | 89.29 328 | 97.87 120 | 83.77 212 | 99.69 73 | 81.37 399 | 96.69 204 | 98.89 136 |
|
| pmmvs4 | | | 90.93 318 | 89.85 330 | 94.17 272 | 93.34 412 | 90.79 180 | 94.60 376 | 96.02 333 | 84.62 402 | 87.45 372 | 95.15 305 | 81.88 261 | 97.45 383 | 87.70 308 | 87.87 357 | 94.27 407 |
|
| ppachtmachnet_test | | | 88.35 373 | 87.29 372 | 91.53 382 | 92.45 431 | 83.57 390 | 93.75 411 | 95.97 334 | 84.28 405 | 85.32 406 | 94.18 361 | 79.00 319 | 96.93 405 | 75.71 433 | 84.99 393 | 94.10 408 |
|
| Anonymous20240521 | | | 86.42 393 | 85.44 391 | 89.34 423 | 90.33 443 | 79.79 434 | 96.73 246 | 95.92 335 | 83.71 415 | 83.25 425 | 91.36 430 | 63.92 440 | 96.01 422 | 78.39 421 | 85.36 384 | 92.22 440 |
|
| ITE_SJBPF | | | | | 92.43 352 | 95.34 324 | 85.37 362 | | 95.92 335 | 91.47 203 | 87.75 368 | 96.39 241 | 71.00 389 | 97.96 328 | 82.36 389 | 89.86 336 | 93.97 413 |
|
| test_fmvs2 | | | 89.77 355 | 89.93 327 | 89.31 424 | 93.68 398 | 76.37 451 | 97.64 133 | 95.90 337 | 89.84 273 | 91.49 265 | 96.26 248 | 58.77 450 | 97.10 397 | 94.65 148 | 91.13 319 | 94.46 398 |
|
| USDC | | | 88.94 364 | 87.83 369 | 92.27 359 | 94.66 366 | 84.96 371 | 93.86 407 | 95.90 337 | 87.34 357 | 83.40 423 | 95.56 287 | 67.43 419 | 98.19 291 | 82.64 387 | 89.67 338 | 93.66 416 |
|
| COLMAP_ROB |  | 87.81 15 | 90.40 335 | 89.28 348 | 93.79 300 | 97.95 129 | 87.13 314 | 96.92 219 | 95.89 339 | 82.83 423 | 86.88 391 | 97.18 187 | 73.77 373 | 99.29 146 | 78.44 420 | 93.62 283 | 94.95 369 |
| Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
| VDD-MVS | | | 93.82 185 | 93.08 194 | 96.02 151 | 97.88 135 | 89.96 215 | 97.72 118 | 95.85 340 | 92.43 165 | 95.86 134 | 98.44 64 | 68.42 415 | 99.39 134 | 96.31 79 | 94.85 250 | 98.71 159 |
|
| VDDNet | | | 93.05 218 | 92.07 233 | 96.02 151 | 96.84 207 | 90.39 196 | 98.08 58 | 95.85 340 | 86.22 378 | 95.79 137 | 98.46 62 | 67.59 418 | 99.19 154 | 94.92 132 | 94.85 250 | 98.47 181 |
|
| mvsmamba | | | 94.57 152 | 94.14 156 | 95.87 162 | 97.03 189 | 89.93 216 | 97.84 95 | 95.85 340 | 91.34 209 | 94.79 172 | 96.80 212 | 80.67 281 | 98.81 211 | 94.85 133 | 98.12 148 | 98.85 142 |
|
| Vis-MVSNet (Re-imp) | | | 94.15 165 | 93.88 162 | 94.95 229 | 97.61 155 | 87.92 292 | 98.10 56 | 95.80 343 | 92.22 173 | 93.02 226 | 97.45 167 | 84.53 199 | 97.91 340 | 88.24 296 | 97.97 154 | 99.02 103 |
|
| MM | | | 97.29 31 | 96.98 42 | 98.23 12 | 98.01 123 | 95.03 27 | 98.07 60 | 95.76 344 | 97.78 1 | 97.52 62 | 98.80 38 | 88.09 119 | 99.86 9 | 99.44 2 | 99.37 67 | 99.80 1 |
|
| KD-MVS_2432*1600 | | | 84.81 411 | 82.64 414 | 91.31 388 | 91.07 440 | 85.34 363 | 91.22 445 | 95.75 345 | 85.56 387 | 83.09 426 | 90.21 438 | 67.21 421 | 95.89 424 | 77.18 427 | 62.48 470 | 92.69 428 |
|
| miper_refine_blended | | | 84.81 411 | 82.64 414 | 91.31 388 | 91.07 440 | 85.34 363 | 91.22 445 | 95.75 345 | 85.56 387 | 83.09 426 | 90.21 438 | 67.21 421 | 95.89 424 | 77.18 427 | 62.48 470 | 92.69 428 |
|
| FE-MVS | | | 92.05 262 | 91.05 274 | 95.08 216 | 96.83 209 | 87.93 291 | 93.91 406 | 95.70 347 | 86.30 375 | 94.15 194 | 94.97 311 | 76.59 345 | 99.21 152 | 84.10 368 | 96.86 195 | 98.09 223 |
|
| tpm cat1 | | | 88.36 372 | 87.21 375 | 91.81 375 | 95.13 343 | 80.55 423 | 92.58 436 | 95.70 347 | 74.97 456 | 87.45 372 | 91.96 423 | 78.01 335 | 98.17 293 | 80.39 408 | 88.74 349 | 96.72 290 |
|
| our_test_3 | | | 88.78 368 | 87.98 368 | 91.20 392 | 92.45 431 | 82.53 401 | 93.61 419 | 95.69 349 | 85.77 384 | 84.88 408 | 93.71 379 | 79.99 296 | 96.78 413 | 79.47 414 | 86.24 373 | 94.28 406 |
|
| BH-w/o | | | 92.14 259 | 91.75 247 | 93.31 323 | 96.99 194 | 85.73 352 | 95.67 332 | 95.69 349 | 88.73 314 | 89.26 330 | 94.82 321 | 82.97 232 | 98.07 309 | 85.26 355 | 96.32 218 | 96.13 308 |
|
| CR-MVSNet | | | 90.82 321 | 89.77 334 | 93.95 289 | 94.45 375 | 87.19 311 | 90.23 453 | 95.68 351 | 86.89 365 | 92.40 236 | 92.36 415 | 80.91 275 | 97.05 400 | 81.09 403 | 93.95 276 | 97.60 257 |
|
| Patchmtry | | | 88.64 370 | 87.25 373 | 92.78 345 | 94.09 385 | 86.64 324 | 89.82 457 | 95.68 351 | 80.81 438 | 87.63 370 | 92.36 415 | 80.91 275 | 97.03 401 | 78.86 418 | 85.12 389 | 94.67 393 |
|
| testing91 | | | 91.90 267 | 91.02 275 | 94.53 254 | 96.54 242 | 86.55 330 | 95.86 321 | 95.64 353 | 91.77 191 | 91.89 255 | 93.47 392 | 69.94 400 | 98.86 203 | 90.23 254 | 93.86 278 | 98.18 209 |
|
| BH-RMVSNet | | | 92.72 236 | 91.97 239 | 94.97 227 | 97.16 176 | 87.99 290 | 96.15 305 | 95.60 354 | 90.62 247 | 91.87 256 | 97.15 190 | 78.41 326 | 98.57 258 | 83.16 377 | 97.60 164 | 98.36 193 |
|
| PVSNet_0 | | 82.17 19 | 85.46 406 | 83.64 409 | 90.92 396 | 95.27 331 | 79.49 439 | 90.55 451 | 95.60 354 | 83.76 414 | 83.00 428 | 89.95 440 | 71.09 388 | 97.97 324 | 82.75 385 | 60.79 472 | 95.31 351 |
|
| guyue | | | 95.17 127 | 94.96 123 | 95.82 168 | 96.97 196 | 89.65 226 | 97.56 144 | 95.58 356 | 94.82 57 | 95.72 139 | 97.42 171 | 82.90 234 | 98.84 207 | 96.71 67 | 96.93 192 | 98.96 114 |
|
| SCA | | | 91.84 269 | 91.18 271 | 93.83 297 | 95.59 306 | 84.95 372 | 94.72 373 | 95.58 356 | 90.82 234 | 92.25 244 | 93.69 381 | 75.80 353 | 98.10 300 | 86.20 337 | 95.98 221 | 98.45 183 |
|
| MonoMVSNet | | | 91.92 265 | 91.77 245 | 92.37 353 | 92.94 419 | 83.11 395 | 97.09 202 | 95.55 358 | 92.91 148 | 90.85 282 | 94.55 334 | 81.27 271 | 96.52 416 | 93.01 193 | 87.76 358 | 97.47 263 |
|
| AllTest | | | 90.23 340 | 88.98 354 | 93.98 285 | 97.94 130 | 86.64 324 | 96.51 269 | 95.54 359 | 85.38 389 | 85.49 403 | 96.77 214 | 70.28 395 | 99.15 163 | 80.02 410 | 92.87 288 | 96.15 306 |
|
| TestCases | | | | | 93.98 285 | 97.94 130 | 86.64 324 | | 95.54 359 | 85.38 389 | 85.49 403 | 96.77 214 | 70.28 395 | 99.15 163 | 80.02 410 | 92.87 288 | 96.15 306 |
|
| mmtdpeth | | | 89.70 357 | 88.96 355 | 91.90 370 | 95.84 298 | 84.42 377 | 97.46 164 | 95.53 361 | 90.27 260 | 94.46 183 | 90.50 434 | 69.74 404 | 98.95 193 | 97.39 53 | 69.48 461 | 92.34 436 |
|
| tpmvs | | | 89.83 354 | 89.15 352 | 91.89 371 | 94.92 353 | 80.30 427 | 93.11 428 | 95.46 362 | 86.28 376 | 88.08 362 | 92.65 405 | 80.44 287 | 98.52 262 | 81.47 395 | 89.92 335 | 96.84 286 |
|
| pmmvs5 | | | 89.86 353 | 88.87 358 | 92.82 342 | 92.86 421 | 86.23 338 | 96.26 294 | 95.39 363 | 84.24 406 | 87.12 380 | 94.51 337 | 74.27 368 | 97.36 390 | 87.61 315 | 87.57 360 | 94.86 378 |
|
| PatchmatchNet |  | | 91.91 266 | 91.35 260 | 93.59 311 | 95.38 319 | 84.11 382 | 93.15 427 | 95.39 363 | 89.54 280 | 92.10 249 | 93.68 383 | 82.82 237 | 98.13 295 | 84.81 359 | 95.32 242 | 98.52 173 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
| tpmrst | | | 91.44 290 | 91.32 262 | 91.79 376 | 95.15 341 | 79.20 442 | 93.42 422 | 95.37 365 | 88.55 319 | 93.49 215 | 93.67 384 | 82.49 246 | 98.27 284 | 90.41 249 | 89.34 341 | 97.90 235 |
|
| Anonymous20231206 | | | 87.09 385 | 86.14 387 | 89.93 415 | 91.22 439 | 80.35 425 | 96.11 306 | 95.35 366 | 83.57 417 | 84.16 415 | 93.02 400 | 73.54 375 | 95.61 432 | 72.16 450 | 86.14 375 | 93.84 415 |
|
| MIMVSNet1 | | | 84.93 409 | 83.05 411 | 90.56 405 | 89.56 449 | 84.84 374 | 95.40 347 | 95.35 366 | 83.91 409 | 80.38 440 | 92.21 420 | 57.23 453 | 93.34 457 | 70.69 456 | 82.75 420 | 93.50 418 |
|
| TDRefinement | | | 86.53 389 | 84.76 401 | 91.85 372 | 82.23 473 | 84.25 379 | 96.38 282 | 95.35 366 | 84.97 398 | 84.09 418 | 94.94 313 | 65.76 434 | 98.34 281 | 84.60 363 | 74.52 451 | 92.97 424 |
|
| TR-MVS | | | 91.48 289 | 90.59 297 | 94.16 275 | 96.40 257 | 87.33 304 | 95.67 332 | 95.34 369 | 87.68 349 | 91.46 266 | 95.52 290 | 76.77 344 | 98.35 278 | 82.85 382 | 93.61 284 | 96.79 288 |
|
| EPNet_dtu | | | 91.71 272 | 91.28 265 | 92.99 335 | 93.76 395 | 83.71 388 | 96.69 252 | 95.28 370 | 93.15 134 | 87.02 385 | 95.95 263 | 83.37 220 | 97.38 389 | 79.46 415 | 96.84 196 | 97.88 237 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| FMVSNet5 | | | 87.29 382 | 85.79 389 | 91.78 377 | 94.80 360 | 87.28 306 | 95.49 344 | 95.28 370 | 84.09 408 | 83.85 422 | 91.82 424 | 62.95 443 | 94.17 448 | 78.48 419 | 85.34 385 | 93.91 414 |
|
| MDTV_nov1_ep13 | | | | 90.76 287 | | 95.22 335 | 80.33 426 | 93.03 430 | 95.28 370 | 88.14 332 | 92.84 233 | 93.83 374 | 81.34 268 | 98.08 305 | 82.86 380 | 94.34 261 | |
|
| LF4IMVS | | | 87.94 376 | 87.25 373 | 89.98 413 | 92.38 433 | 80.05 433 | 94.38 387 | 95.25 373 | 87.59 351 | 84.34 412 | 94.74 325 | 64.31 439 | 97.66 364 | 84.83 358 | 87.45 361 | 92.23 439 |
|
| TransMVSNet (Re) | | | 88.94 364 | 87.56 370 | 93.08 333 | 94.35 378 | 88.45 275 | 97.73 115 | 95.23 374 | 87.47 353 | 84.26 414 | 95.29 297 | 79.86 299 | 97.33 391 | 79.44 416 | 74.44 452 | 93.45 420 |
|
| test20.03 | | | 86.14 399 | 85.40 393 | 88.35 426 | 90.12 444 | 80.06 432 | 95.90 320 | 95.20 375 | 88.59 315 | 81.29 435 | 93.62 386 | 71.43 386 | 92.65 461 | 71.26 454 | 81.17 425 | 92.34 436 |
|
| new-patchmatchnet | | | 83.18 418 | 81.87 421 | 87.11 434 | 86.88 464 | 75.99 453 | 93.70 413 | 95.18 376 | 85.02 397 | 77.30 453 | 88.40 452 | 65.99 432 | 93.88 453 | 74.19 442 | 70.18 459 | 91.47 451 |
|
| MDA-MVSNet_test_wron | | | 85.87 403 | 84.23 406 | 90.80 402 | 92.38 433 | 82.57 400 | 93.17 425 | 95.15 377 | 82.15 427 | 67.65 465 | 92.33 418 | 78.20 328 | 95.51 436 | 77.33 424 | 79.74 429 | 94.31 405 |
|
| YYNet1 | | | 85.87 403 | 84.23 406 | 90.78 403 | 92.38 433 | 82.46 405 | 93.17 425 | 95.14 378 | 82.12 428 | 67.69 463 | 92.36 415 | 78.16 331 | 95.50 437 | 77.31 425 | 79.73 430 | 94.39 401 |
|
| Baseline_NR-MVSNet | | | 91.20 305 | 90.62 295 | 92.95 337 | 93.83 393 | 88.03 289 | 97.01 209 | 95.12 379 | 88.42 323 | 89.70 313 | 95.13 307 | 83.47 217 | 97.44 384 | 89.66 266 | 83.24 415 | 93.37 421 |
|
| thres200 | | | 92.23 255 | 91.39 259 | 94.75 242 | 97.61 155 | 89.03 259 | 96.60 264 | 95.09 380 | 92.08 182 | 93.28 221 | 94.00 370 | 78.39 327 | 99.04 189 | 81.26 402 | 94.18 267 | 96.19 302 |
|
| ADS-MVSNet | | | 89.89 350 | 88.68 360 | 93.53 315 | 95.86 293 | 84.89 373 | 90.93 448 | 95.07 381 | 83.23 421 | 91.28 275 | 91.81 425 | 79.01 317 | 97.85 343 | 79.52 412 | 91.39 315 | 97.84 242 |
|
| pmmvs-eth3d | | | 86.22 397 | 84.45 404 | 91.53 382 | 88.34 458 | 87.25 308 | 94.47 382 | 95.01 382 | 83.47 418 | 79.51 445 | 89.61 444 | 69.75 403 | 95.71 429 | 83.13 378 | 76.73 444 | 91.64 445 |
|
| Anonymous202405211 | | | 92.07 261 | 90.83 285 | 95.76 177 | 98.19 109 | 88.75 264 | 97.58 140 | 95.00 383 | 86.00 381 | 93.64 207 | 97.45 167 | 66.24 430 | 99.53 112 | 90.68 243 | 92.71 293 | 99.01 106 |
|
| MDA-MVSNet-bldmvs | | | 85.00 408 | 82.95 413 | 91.17 394 | 93.13 417 | 83.33 391 | 94.56 378 | 95.00 383 | 84.57 403 | 65.13 469 | 92.65 405 | 70.45 394 | 95.85 426 | 73.57 445 | 77.49 439 | 94.33 403 |
|
| ambc | | | | | 86.56 437 | 83.60 470 | 70.00 464 | 85.69 469 | 94.97 385 | | 80.60 439 | 88.45 451 | 37.42 471 | 96.84 410 | 82.69 386 | 75.44 449 | 92.86 426 |
|
| testgi | | | 87.97 375 | 87.21 375 | 90.24 410 | 92.86 421 | 80.76 418 | 96.67 255 | 94.97 385 | 91.74 192 | 85.52 402 | 95.83 269 | 62.66 445 | 94.47 446 | 76.25 431 | 88.36 353 | 95.48 334 |
|
| myMVS_eth3d28 | | | 91.52 286 | 90.97 277 | 93.17 329 | 96.91 199 | 83.24 393 | 95.61 338 | 94.96 387 | 92.24 172 | 91.98 252 | 93.28 397 | 69.31 405 | 98.40 270 | 88.71 291 | 95.68 231 | 97.88 237 |
|
| dp | | | 88.90 366 | 88.26 366 | 90.81 400 | 94.58 371 | 76.62 450 | 92.85 433 | 94.93 388 | 85.12 395 | 90.07 304 | 93.07 399 | 75.81 352 | 98.12 298 | 80.53 407 | 87.42 363 | 97.71 249 |
|
| test_fmvs3 | | | 83.21 417 | 83.02 412 | 83.78 441 | 86.77 465 | 68.34 467 | 96.76 244 | 94.91 389 | 86.49 371 | 84.14 417 | 89.48 445 | 36.04 472 | 91.73 463 | 91.86 214 | 80.77 427 | 91.26 453 |
|
| test_0402 | | | 86.46 392 | 84.79 400 | 91.45 384 | 95.02 347 | 85.55 354 | 96.29 293 | 94.89 390 | 80.90 435 | 82.21 431 | 93.97 372 | 68.21 416 | 97.29 393 | 62.98 464 | 88.68 350 | 91.51 448 |
|
| tfpn200view9 | | | 92.38 245 | 91.52 256 | 94.95 229 | 97.85 136 | 89.29 248 | 97.41 167 | 94.88 391 | 92.19 178 | 93.27 222 | 94.46 342 | 78.17 329 | 99.08 177 | 81.40 396 | 94.08 271 | 96.48 295 |
|
| CVMVSNet | | | 91.23 303 | 91.75 247 | 89.67 417 | 95.77 299 | 74.69 454 | 96.44 270 | 94.88 391 | 85.81 383 | 92.18 245 | 97.64 153 | 79.07 312 | 95.58 434 | 88.06 299 | 95.86 226 | 98.74 156 |
|
| thres400 | | | 92.42 243 | 91.52 256 | 95.12 215 | 97.85 136 | 89.29 248 | 97.41 167 | 94.88 391 | 92.19 178 | 93.27 222 | 94.46 342 | 78.17 329 | 99.08 177 | 81.40 396 | 94.08 271 | 96.98 280 |
|
| tt0320 | | | 85.39 407 | 83.12 410 | 92.19 363 | 93.44 409 | 85.79 350 | 96.19 302 | 94.87 394 | 71.19 463 | 82.92 429 | 91.76 427 | 58.43 451 | 96.81 411 | 81.03 404 | 78.26 438 | 93.98 412 |
|
| EPNet | | | 95.20 123 | 94.56 139 | 97.14 75 | 92.80 423 | 92.68 97 | 97.85 94 | 94.87 394 | 96.64 9 | 92.46 235 | 97.80 134 | 86.23 161 | 99.65 79 | 93.72 172 | 98.62 124 | 99.10 95 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| testing99 | | | 91.62 277 | 90.72 292 | 94.32 265 | 96.48 251 | 86.11 347 | 95.81 324 | 94.76 396 | 91.55 196 | 91.75 260 | 93.44 393 | 68.55 413 | 98.82 209 | 90.43 248 | 93.69 280 | 98.04 227 |
|
| sc_t1 | | | 86.48 391 | 84.10 408 | 93.63 308 | 93.45 408 | 85.76 351 | 96.79 238 | 94.71 397 | 73.06 461 | 86.45 395 | 94.35 347 | 55.13 458 | 97.95 332 | 84.38 366 | 78.55 437 | 97.18 276 |
|
| SixPastTwentyTwo | | | 89.15 362 | 88.54 362 | 90.98 395 | 93.49 405 | 80.28 429 | 96.70 250 | 94.70 398 | 90.78 235 | 84.15 416 | 95.57 286 | 71.78 384 | 97.71 360 | 84.63 362 | 85.07 390 | 94.94 371 |
|
| thres100view900 | | | 92.43 242 | 91.58 253 | 94.98 225 | 97.92 132 | 89.37 244 | 97.71 120 | 94.66 399 | 92.20 176 | 93.31 220 | 94.90 316 | 78.06 333 | 99.08 177 | 81.40 396 | 94.08 271 | 96.48 295 |
|
| thres600view7 | | | 92.49 240 | 91.60 252 | 95.18 211 | 97.91 133 | 89.47 238 | 97.65 129 | 94.66 399 | 92.18 180 | 93.33 219 | 94.91 315 | 78.06 333 | 99.10 171 | 81.61 392 | 94.06 275 | 96.98 280 |
|
| PatchT | | | 88.87 367 | 87.42 371 | 93.22 327 | 94.08 386 | 85.10 367 | 89.51 458 | 94.64 401 | 81.92 429 | 92.36 239 | 88.15 455 | 80.05 295 | 97.01 403 | 72.43 449 | 93.65 282 | 97.54 260 |
|
| baseline1 | | | 92.82 232 | 91.90 242 | 95.55 193 | 97.20 174 | 90.77 181 | 97.19 194 | 94.58 402 | 92.20 176 | 92.36 239 | 96.34 243 | 84.16 207 | 98.21 288 | 89.20 281 | 83.90 410 | 97.68 251 |
|
| AstraMVS | | | 94.82 144 | 94.64 135 | 95.34 206 | 96.36 262 | 88.09 288 | 97.58 140 | 94.56 403 | 94.98 46 | 95.70 142 | 97.92 114 | 81.93 260 | 98.93 196 | 96.87 61 | 95.88 224 | 98.99 110 |
|
| UBG | | | 91.55 283 | 90.76 287 | 93.94 291 | 96.52 247 | 85.06 368 | 95.22 359 | 94.54 404 | 90.47 256 | 91.98 252 | 92.71 404 | 72.02 381 | 98.74 229 | 88.10 298 | 95.26 244 | 98.01 229 |
|
| Gipuma |  | | 67.86 438 | 65.41 440 | 75.18 454 | 92.66 426 | 73.45 458 | 66.50 476 | 94.52 405 | 53.33 474 | 57.80 475 | 66.07 475 | 30.81 474 | 89.20 467 | 48.15 473 | 78.88 436 | 62.90 475 |
| S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
| testing11 | | | 91.68 275 | 90.75 289 | 94.47 256 | 96.53 244 | 86.56 329 | 95.76 328 | 94.51 406 | 91.10 227 | 91.24 277 | 93.59 387 | 68.59 412 | 98.86 203 | 91.10 231 | 94.29 263 | 98.00 230 |
|
| CostFormer | | | 91.18 308 | 90.70 293 | 92.62 350 | 94.84 358 | 81.76 411 | 94.09 399 | 94.43 407 | 84.15 407 | 92.72 234 | 93.77 378 | 79.43 306 | 98.20 289 | 90.70 242 | 92.18 302 | 97.90 235 |
|
| tpm2 | | | 89.96 347 | 89.21 350 | 92.23 362 | 94.91 355 | 81.25 414 | 93.78 410 | 94.42 408 | 80.62 440 | 91.56 263 | 93.44 393 | 76.44 348 | 97.94 334 | 85.60 349 | 92.08 306 | 97.49 261 |
|
| testing3-2 | | | 92.10 260 | 92.05 234 | 92.27 359 | 97.71 145 | 79.56 436 | 97.42 166 | 94.41 409 | 93.53 113 | 93.22 224 | 95.49 291 | 69.16 407 | 99.11 169 | 93.25 183 | 94.22 265 | 98.13 214 |
|
| MGCNet | | | 96.74 64 | 96.31 81 | 98.02 20 | 96.87 203 | 94.65 31 | 97.58 140 | 94.39 410 | 96.47 12 | 97.16 74 | 98.39 68 | 87.53 136 | 99.87 7 | 98.97 20 | 99.41 59 | 99.55 43 |
|
| JIA-IIPM | | | 88.26 374 | 87.04 378 | 91.91 369 | 93.52 403 | 81.42 413 | 89.38 459 | 94.38 411 | 80.84 437 | 90.93 281 | 80.74 467 | 79.22 309 | 97.92 337 | 82.76 384 | 91.62 310 | 96.38 298 |
|
| dmvs_re | | | 90.21 341 | 89.50 343 | 92.35 354 | 95.47 316 | 85.15 365 | 95.70 331 | 94.37 412 | 90.94 233 | 88.42 349 | 93.57 388 | 74.63 365 | 95.67 431 | 82.80 383 | 89.57 339 | 96.22 300 |
|
| Patchmatch-test | | | 89.42 360 | 87.99 367 | 93.70 305 | 95.27 331 | 85.11 366 | 88.98 460 | 94.37 412 | 81.11 434 | 87.10 383 | 93.69 381 | 82.28 250 | 97.50 379 | 74.37 440 | 94.76 254 | 98.48 180 |
|
| LCM-MVSNet | | | 72.55 431 | 69.39 435 | 82.03 443 | 70.81 483 | 65.42 472 | 90.12 455 | 94.36 414 | 55.02 473 | 65.88 467 | 81.72 466 | 24.16 480 | 89.96 464 | 74.32 441 | 68.10 464 | 90.71 456 |
|
| ADS-MVSNet2 | | | 89.45 359 | 88.59 361 | 92.03 366 | 95.86 293 | 82.26 407 | 90.93 448 | 94.32 415 | 83.23 421 | 91.28 275 | 91.81 425 | 79.01 317 | 95.99 423 | 79.52 412 | 91.39 315 | 97.84 242 |
|
| mvs5depth | | | 86.53 389 | 85.08 396 | 90.87 397 | 88.74 456 | 82.52 402 | 91.91 441 | 94.23 416 | 86.35 374 | 87.11 382 | 93.70 380 | 66.52 426 | 97.76 355 | 81.37 399 | 75.80 446 | 92.31 438 |
|
| EU-MVSNet | | | 88.72 369 | 88.90 357 | 88.20 428 | 93.15 416 | 74.21 456 | 96.63 261 | 94.22 417 | 85.18 393 | 87.32 377 | 95.97 261 | 76.16 350 | 94.98 441 | 85.27 354 | 86.17 374 | 95.41 341 |
|
| tt0320-xc | | | 84.83 410 | 82.33 418 | 92.31 357 | 93.66 399 | 86.20 340 | 96.17 304 | 94.06 418 | 71.26 462 | 82.04 433 | 92.22 419 | 55.07 459 | 96.72 414 | 81.49 394 | 75.04 450 | 94.02 411 |
|
| MIMVSNet | | | 88.50 371 | 86.76 381 | 93.72 304 | 94.84 358 | 87.77 298 | 91.39 443 | 94.05 419 | 86.41 373 | 87.99 364 | 92.59 408 | 63.27 441 | 95.82 428 | 77.44 423 | 92.84 290 | 97.57 259 |
|
| OpenMVS_ROB |  | 81.14 20 | 84.42 413 | 82.28 419 | 90.83 398 | 90.06 445 | 84.05 384 | 95.73 330 | 94.04 420 | 73.89 459 | 80.17 443 | 91.53 429 | 59.15 449 | 97.64 365 | 66.92 462 | 89.05 343 | 90.80 455 |
|
| TinyColmap | | | 86.82 387 | 85.35 394 | 91.21 390 | 94.91 355 | 82.99 397 | 93.94 403 | 94.02 421 | 83.58 416 | 81.56 434 | 94.68 327 | 62.34 446 | 98.13 295 | 75.78 432 | 87.35 366 | 92.52 434 |
|
| ETVMVS | | | 90.52 332 | 89.14 353 | 94.67 245 | 96.81 213 | 87.85 296 | 95.91 319 | 93.97 422 | 89.71 276 | 92.34 242 | 92.48 410 | 65.41 436 | 97.96 328 | 81.37 399 | 94.27 264 | 98.21 207 |
|
| IB-MVS | | 87.33 17 | 89.91 348 | 88.28 365 | 94.79 239 | 95.26 334 | 87.70 299 | 95.12 365 | 93.95 423 | 89.35 288 | 87.03 384 | 92.49 409 | 70.74 392 | 99.19 154 | 89.18 282 | 81.37 424 | 97.49 261 |
| 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 | | | 87.13 384 | 87.02 379 | 87.47 432 | 95.16 338 | 73.21 460 | 95.00 367 | 93.93 424 | 88.55 319 | 86.96 386 | 91.99 421 | 75.90 351 | 94.00 450 | 61.59 466 | 94.11 268 | 95.20 359 |
|
| myMVS_eth3d | | | 87.18 383 | 86.38 384 | 89.58 418 | 95.16 338 | 79.53 437 | 95.00 367 | 93.93 424 | 88.55 319 | 86.96 386 | 91.99 421 | 56.23 456 | 94.00 450 | 75.47 436 | 94.11 268 | 95.20 359 |
|
| testing222 | | | 90.31 336 | 88.96 355 | 94.35 262 | 96.54 242 | 87.29 305 | 95.50 343 | 93.84 426 | 90.97 230 | 91.75 260 | 92.96 401 | 62.18 447 | 98.00 319 | 82.86 380 | 94.08 271 | 97.76 247 |
|
| test_f | | | 80.57 424 | 79.62 426 | 83.41 442 | 83.38 471 | 67.80 469 | 93.57 420 | 93.72 427 | 80.80 439 | 77.91 452 | 87.63 458 | 33.40 473 | 92.08 462 | 87.14 326 | 79.04 435 | 90.34 457 |
|
| LCM-MVSNet-Re | | | 92.50 238 | 92.52 222 | 92.44 351 | 96.82 211 | 81.89 410 | 96.92 219 | 93.71 428 | 92.41 166 | 84.30 413 | 94.60 332 | 85.08 188 | 97.03 401 | 91.51 222 | 97.36 173 | 98.40 189 |
|
| tpm | | | 90.25 339 | 89.74 337 | 91.76 379 | 93.92 389 | 79.73 435 | 93.98 400 | 93.54 429 | 88.28 326 | 91.99 251 | 93.25 398 | 77.51 339 | 97.44 384 | 87.30 321 | 87.94 356 | 98.12 216 |
|
| ET-MVSNet_ETH3D | | | 91.49 288 | 90.11 317 | 95.63 187 | 96.40 257 | 91.57 142 | 95.34 350 | 93.48 430 | 90.60 250 | 75.58 455 | 95.49 291 | 80.08 294 | 96.79 412 | 94.25 160 | 89.76 337 | 98.52 173 |
|
| LFMVS | | | 93.60 192 | 92.63 215 | 96.52 107 | 98.13 115 | 91.27 155 | 97.94 81 | 93.39 431 | 90.57 252 | 96.29 116 | 98.31 81 | 69.00 408 | 99.16 161 | 94.18 161 | 95.87 225 | 99.12 92 |
|
| MVStest1 | | | 82.38 421 | 80.04 425 | 89.37 421 | 87.63 462 | 82.83 398 | 95.03 366 | 93.37 432 | 73.90 458 | 73.50 460 | 94.35 347 | 62.89 444 | 93.25 459 | 73.80 443 | 65.92 467 | 92.04 444 |
|
| FE-MVSNET | | | 83.85 414 | 81.97 420 | 89.51 419 | 87.19 463 | 83.19 394 | 95.21 361 | 93.17 433 | 83.45 419 | 78.90 448 | 89.05 448 | 65.46 435 | 93.84 454 | 69.71 458 | 75.56 448 | 91.51 448 |
|
| Patchmatch-RL test | | | 87.38 381 | 86.24 385 | 90.81 400 | 88.74 456 | 78.40 446 | 88.12 467 | 93.17 433 | 87.11 362 | 82.17 432 | 89.29 446 | 81.95 258 | 95.60 433 | 88.64 293 | 77.02 441 | 98.41 188 |
|
| ttmdpeth | | | 85.91 402 | 84.76 401 | 89.36 422 | 89.14 451 | 80.25 430 | 95.66 335 | 93.16 435 | 83.77 413 | 83.39 424 | 95.26 301 | 66.24 430 | 95.26 440 | 80.65 405 | 75.57 447 | 92.57 431 |
|
| test-LLR | | | 91.42 291 | 91.19 270 | 92.12 364 | 94.59 369 | 80.66 420 | 94.29 393 | 92.98 436 | 91.11 225 | 90.76 284 | 92.37 412 | 79.02 315 | 98.07 309 | 88.81 288 | 96.74 201 | 97.63 252 |
|
| test-mter | | | 90.19 343 | 89.54 342 | 92.12 364 | 94.59 369 | 80.66 420 | 94.29 393 | 92.98 436 | 87.68 349 | 90.76 284 | 92.37 412 | 67.67 417 | 98.07 309 | 88.81 288 | 96.74 201 | 97.63 252 |
|
| WB-MVSnew | | | 89.88 351 | 89.56 341 | 90.82 399 | 94.57 372 | 83.06 396 | 95.65 336 | 92.85 438 | 87.86 340 | 90.83 283 | 94.10 364 | 79.66 303 | 96.88 408 | 76.34 430 | 94.19 266 | 92.54 433 |
|
| testing3 | | | 87.67 379 | 86.88 380 | 90.05 412 | 96.14 280 | 80.71 419 | 97.10 201 | 92.85 438 | 90.15 264 | 87.54 371 | 94.55 334 | 55.70 457 | 94.10 449 | 73.77 444 | 94.10 270 | 95.35 348 |
|
| test_method | | | 66.11 439 | 64.89 441 | 69.79 457 | 72.62 481 | 35.23 489 | 65.19 477 | 92.83 440 | 20.35 479 | 65.20 468 | 88.08 456 | 43.14 469 | 82.70 474 | 73.12 447 | 63.46 469 | 91.45 452 |
|
| test0.0.03 1 | | | 89.37 361 | 88.70 359 | 91.41 386 | 92.47 430 | 85.63 353 | 95.22 359 | 92.70 441 | 91.11 225 | 86.91 390 | 93.65 385 | 79.02 315 | 93.19 460 | 78.00 422 | 89.18 342 | 95.41 341 |
|
| new_pmnet | | | 82.89 419 | 81.12 424 | 88.18 429 | 89.63 448 | 80.18 431 | 91.77 442 | 92.57 442 | 76.79 454 | 75.56 456 | 88.23 454 | 61.22 448 | 94.48 445 | 71.43 452 | 82.92 418 | 89.87 458 |
|
| mvsany_test1 | | | 93.93 181 | 93.98 160 | 93.78 301 | 94.94 352 | 86.80 320 | 94.62 375 | 92.55 443 | 88.77 313 | 96.85 84 | 98.49 58 | 88.98 101 | 98.08 305 | 95.03 127 | 95.62 233 | 96.46 297 |
|
| thisisatest0515 | | | 92.29 251 | 91.30 264 | 95.25 209 | 96.60 232 | 88.90 262 | 94.36 388 | 92.32 444 | 87.92 336 | 93.43 217 | 94.57 333 | 77.28 340 | 99.00 190 | 89.42 272 | 95.86 226 | 97.86 241 |
|
| thisisatest0530 | | | 93.03 219 | 92.21 231 | 95.49 198 | 97.07 181 | 89.11 257 | 97.49 161 | 92.19 445 | 90.16 263 | 94.09 195 | 96.41 239 | 76.43 349 | 99.05 186 | 90.38 250 | 95.68 231 | 98.31 201 |
|
| tttt0517 | | | 92.96 222 | 92.33 228 | 94.87 232 | 97.11 179 | 87.16 313 | 97.97 77 | 92.09 446 | 90.63 246 | 93.88 201 | 97.01 203 | 76.50 346 | 99.06 183 | 90.29 253 | 95.45 240 | 98.38 191 |
|
| K. test v3 | | | 87.64 380 | 86.75 382 | 90.32 409 | 93.02 418 | 79.48 440 | 96.61 262 | 92.08 447 | 90.66 244 | 80.25 442 | 94.09 366 | 67.21 421 | 96.65 415 | 85.96 345 | 80.83 426 | 94.83 380 |
|
| TESTMET0.1,1 | | | 90.06 345 | 89.42 345 | 91.97 367 | 94.41 377 | 80.62 422 | 94.29 393 | 91.97 448 | 87.28 359 | 90.44 288 | 92.47 411 | 68.79 409 | 97.67 362 | 88.50 295 | 96.60 208 | 97.61 256 |
|
| PM-MVS | | | 83.48 416 | 81.86 422 | 88.31 427 | 87.83 460 | 77.59 448 | 93.43 421 | 91.75 449 | 86.91 364 | 80.63 438 | 89.91 441 | 44.42 468 | 95.84 427 | 85.17 357 | 76.73 444 | 91.50 450 |
|
| baseline2 | | | 91.63 276 | 90.86 281 | 93.94 291 | 94.33 379 | 86.32 335 | 95.92 318 | 91.64 450 | 89.37 287 | 86.94 388 | 94.69 326 | 81.62 265 | 98.69 239 | 88.64 293 | 94.57 259 | 96.81 287 |
|
| APD_test1 | | | 79.31 426 | 77.70 429 | 84.14 440 | 89.11 453 | 69.07 466 | 92.36 440 | 91.50 451 | 69.07 465 | 73.87 458 | 92.63 407 | 39.93 470 | 94.32 447 | 70.54 457 | 80.25 428 | 89.02 460 |
|
| FPMVS | | | 71.27 432 | 69.85 434 | 75.50 453 | 74.64 478 | 59.03 478 | 91.30 444 | 91.50 451 | 58.80 470 | 57.92 474 | 88.28 453 | 29.98 476 | 85.53 473 | 53.43 471 | 82.84 419 | 81.95 466 |
|
| door | | | | | | | | | 91.13 453 | | | | | | | | |
|
| door-mid | | | | | | | | | 91.06 454 | | | | | | | | |
|
| EGC-MVSNET | | | 68.77 437 | 63.01 443 | 86.07 439 | 92.49 429 | 82.24 408 | 93.96 402 | 90.96 455 | 0.71 484 | 2.62 485 | 90.89 432 | 53.66 460 | 93.46 455 | 57.25 469 | 84.55 400 | 82.51 465 |
|
| mvsany_test3 | | | 83.59 415 | 82.44 417 | 87.03 435 | 83.80 468 | 73.82 457 | 93.70 413 | 90.92 456 | 86.42 372 | 82.51 430 | 90.26 437 | 46.76 467 | 95.71 429 | 90.82 237 | 76.76 443 | 91.57 447 |
|
| pmmvs3 | | | 79.97 425 | 77.50 430 | 87.39 433 | 82.80 472 | 79.38 441 | 92.70 435 | 90.75 457 | 70.69 464 | 78.66 449 | 87.47 460 | 51.34 463 | 93.40 456 | 73.39 446 | 69.65 460 | 89.38 459 |
|
| UWE-MVS | | | 89.91 348 | 89.48 344 | 91.21 390 | 95.88 292 | 78.23 447 | 94.91 370 | 90.26 458 | 89.11 294 | 92.35 241 | 94.52 336 | 68.76 410 | 97.96 328 | 83.95 372 | 95.59 234 | 97.42 265 |
|
| DSMNet-mixed | | | 86.34 395 | 86.12 388 | 87.00 436 | 89.88 447 | 70.43 462 | 94.93 369 | 90.08 459 | 77.97 451 | 85.42 405 | 92.78 403 | 74.44 367 | 93.96 452 | 74.43 439 | 95.14 245 | 96.62 291 |
|
| MVS-HIRNet | | | 82.47 420 | 81.21 423 | 86.26 438 | 95.38 319 | 69.21 465 | 88.96 461 | 89.49 460 | 66.28 467 | 80.79 437 | 74.08 472 | 68.48 414 | 97.39 388 | 71.93 451 | 95.47 239 | 92.18 441 |
|
| WB-MVS | | | 76.77 428 | 76.63 431 | 77.18 448 | 85.32 466 | 56.82 480 | 94.53 379 | 89.39 461 | 82.66 425 | 71.35 461 | 89.18 447 | 75.03 360 | 88.88 468 | 35.42 477 | 66.79 465 | 85.84 462 |
|
| test1111 | | | 93.19 211 | 92.82 205 | 94.30 268 | 97.58 161 | 84.56 376 | 98.21 47 | 89.02 462 | 93.53 113 | 94.58 177 | 98.21 88 | 72.69 377 | 99.05 186 | 93.06 189 | 98.48 131 | 99.28 77 |
|
| SSC-MVS | | | 76.05 429 | 75.83 432 | 76.72 452 | 84.77 467 | 56.22 481 | 94.32 391 | 88.96 463 | 81.82 431 | 70.52 462 | 88.91 449 | 74.79 364 | 88.71 469 | 33.69 478 | 64.71 468 | 85.23 463 |
|
| ECVR-MVS |  | | 93.19 211 | 92.73 211 | 94.57 251 | 97.66 149 | 85.41 359 | 98.21 47 | 88.23 464 | 93.43 120 | 94.70 174 | 98.21 88 | 72.57 378 | 99.07 181 | 93.05 190 | 98.49 129 | 99.25 80 |
|
| EPMVS | | | 90.70 326 | 89.81 332 | 93.37 321 | 94.73 364 | 84.21 380 | 93.67 416 | 88.02 465 | 89.50 282 | 92.38 238 | 93.49 390 | 77.82 337 | 97.78 352 | 86.03 343 | 92.68 294 | 98.11 222 |
|
| ANet_high | | | 63.94 441 | 59.58 444 | 77.02 449 | 61.24 485 | 66.06 470 | 85.66 470 | 87.93 466 | 78.53 449 | 42.94 477 | 71.04 474 | 25.42 479 | 80.71 476 | 52.60 472 | 30.83 478 | 84.28 464 |
|
| PMMVS2 | | | 70.19 433 | 66.92 437 | 80.01 444 | 76.35 477 | 65.67 471 | 86.22 468 | 87.58 467 | 64.83 469 | 62.38 470 | 80.29 469 | 26.78 478 | 88.49 471 | 63.79 463 | 54.07 474 | 85.88 461 |
|
| lessismore_v0 | | | | | 90.45 406 | 91.96 436 | 79.09 444 | | 87.19 468 | | 80.32 441 | 94.39 344 | 66.31 429 | 97.55 373 | 84.00 371 | 76.84 442 | 94.70 392 |
|
| PMVS |  | 53.92 22 | 58.58 442 | 55.40 445 | 68.12 458 | 51.00 486 | 48.64 483 | 78.86 473 | 87.10 469 | 46.77 475 | 35.84 481 | 74.28 471 | 8.76 484 | 86.34 472 | 42.07 475 | 73.91 453 | 69.38 472 |
| Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
| UWE-MVS-28 | | | 86.81 388 | 86.41 383 | 88.02 430 | 92.87 420 | 74.60 455 | 95.38 349 | 86.70 470 | 88.17 329 | 87.28 379 | 94.67 329 | 70.83 391 | 93.30 458 | 67.45 460 | 94.31 262 | 96.17 303 |
|
| test_vis1_rt | | | 86.16 398 | 85.06 397 | 89.46 420 | 93.47 407 | 80.46 424 | 96.41 276 | 86.61 471 | 85.22 392 | 79.15 447 | 88.64 450 | 52.41 462 | 97.06 399 | 93.08 188 | 90.57 328 | 90.87 454 |
|
| testf1 | | | 69.31 435 | 66.76 438 | 76.94 450 | 78.61 475 | 61.93 474 | 88.27 465 | 86.11 472 | 55.62 471 | 59.69 471 | 85.31 463 | 20.19 482 | 89.32 465 | 57.62 467 | 69.44 462 | 79.58 467 |
|
| APD_test2 | | | 69.31 435 | 66.76 438 | 76.94 450 | 78.61 475 | 61.93 474 | 88.27 465 | 86.11 472 | 55.62 471 | 59.69 471 | 85.31 463 | 20.19 482 | 89.32 465 | 57.62 467 | 69.44 462 | 79.58 467 |
|
| gg-mvs-nofinetune | | | 87.82 377 | 85.61 390 | 94.44 258 | 94.46 374 | 89.27 251 | 91.21 447 | 84.61 474 | 80.88 436 | 89.89 308 | 74.98 470 | 71.50 385 | 97.53 376 | 85.75 348 | 97.21 182 | 96.51 293 |
|
| dmvs_testset | | | 81.38 423 | 82.60 416 | 77.73 447 | 91.74 437 | 51.49 482 | 93.03 430 | 84.21 475 | 89.07 295 | 78.28 451 | 91.25 431 | 76.97 342 | 88.53 470 | 56.57 470 | 82.24 421 | 93.16 422 |
|
| GG-mvs-BLEND | | | | | 93.62 309 | 93.69 397 | 89.20 253 | 92.39 439 | 83.33 476 | | 87.98 365 | 89.84 442 | 71.00 389 | 96.87 409 | 82.08 391 | 95.40 241 | 94.80 385 |
|
| MTMP | | | | | | | | 97.86 91 | 82.03 477 | | | | | | | | |
|
| DeepMVS_CX |  | | | | 74.68 455 | 90.84 442 | 64.34 473 | | 81.61 478 | 65.34 468 | 67.47 466 | 88.01 457 | 48.60 466 | 80.13 477 | 62.33 465 | 73.68 454 | 79.58 467 |
|
| E-PMN | | | 53.28 443 | 52.56 447 | 55.43 461 | 74.43 479 | 47.13 484 | 83.63 472 | 76.30 479 | 42.23 476 | 42.59 478 | 62.22 477 | 28.57 477 | 74.40 478 | 31.53 479 | 31.51 477 | 44.78 476 |
|
| test2506 | | | 91.60 278 | 90.78 286 | 94.04 281 | 97.66 149 | 83.81 385 | 98.27 37 | 75.53 480 | 93.43 120 | 95.23 159 | 98.21 88 | 67.21 421 | 99.07 181 | 93.01 193 | 98.49 129 | 99.25 80 |
|
| EMVS | | | 52.08 445 | 51.31 448 | 54.39 462 | 72.62 481 | 45.39 486 | 83.84 471 | 75.51 481 | 41.13 477 | 40.77 479 | 59.65 478 | 30.08 475 | 73.60 479 | 28.31 481 | 29.90 479 | 44.18 477 |
|
| test_vis3_rt | | | 72.73 430 | 70.55 433 | 79.27 445 | 80.02 474 | 68.13 468 | 93.92 405 | 74.30 482 | 76.90 453 | 58.99 473 | 73.58 473 | 20.29 481 | 95.37 438 | 84.16 367 | 72.80 456 | 74.31 470 |
|
| MVE |  | 50.73 23 | 53.25 444 | 48.81 449 | 66.58 460 | 65.34 484 | 57.50 479 | 72.49 475 | 70.94 483 | 40.15 478 | 39.28 480 | 63.51 476 | 6.89 486 | 73.48 480 | 38.29 476 | 42.38 476 | 68.76 474 |
| Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
| tmp_tt | | | 51.94 446 | 53.82 446 | 46.29 463 | 33.73 487 | 45.30 487 | 78.32 474 | 67.24 484 | 18.02 480 | 50.93 476 | 87.05 461 | 52.99 461 | 53.11 482 | 70.76 455 | 25.29 480 | 40.46 478 |
|
| kuosan | | | 65.27 440 | 64.66 442 | 67.11 459 | 83.80 468 | 61.32 477 | 88.53 464 | 60.77 485 | 68.22 466 | 67.67 464 | 80.52 468 | 49.12 465 | 70.76 481 | 29.67 480 | 53.64 475 | 69.26 473 |
|
| dongtai | | | 69.99 434 | 69.33 436 | 71.98 456 | 88.78 455 | 61.64 476 | 89.86 456 | 59.93 486 | 75.67 455 | 74.96 457 | 85.45 462 | 50.19 464 | 81.66 475 | 43.86 474 | 55.27 473 | 72.63 471 |
|
| N_pmnet | | | 78.73 427 | 78.71 428 | 78.79 446 | 92.80 423 | 46.50 485 | 94.14 397 | 43.71 487 | 78.61 448 | 80.83 436 | 91.66 428 | 74.94 363 | 96.36 419 | 67.24 461 | 84.45 402 | 93.50 418 |
|
| wuyk23d | | | 25.11 447 | 24.57 451 | 26.74 464 | 73.98 480 | 39.89 488 | 57.88 478 | 9.80 488 | 12.27 481 | 10.39 482 | 6.97 484 | 7.03 485 | 36.44 483 | 25.43 482 | 17.39 481 | 3.89 481 |
|
| testmvs | | | 13.36 449 | 16.33 452 | 4.48 466 | 5.04 488 | 2.26 491 | 93.18 424 | 3.28 489 | 2.70 482 | 8.24 483 | 21.66 480 | 2.29 488 | 2.19 484 | 7.58 483 | 2.96 482 | 9.00 480 |
|
| test123 | | | 13.04 450 | 15.66 453 | 5.18 465 | 4.51 489 | 3.45 490 | 92.50 438 | 1.81 490 | 2.50 483 | 7.58 484 | 20.15 481 | 3.67 487 | 2.18 485 | 7.13 484 | 1.07 483 | 9.90 479 |
|
| mmdepth | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| monomultidepth | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| test_blank | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| uanet_test | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| DCPMVS | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| pcd_1.5k_mvsjas | | | 7.39 452 | 9.85 455 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 88.65 109 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| sosnet-low-res | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| sosnet | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| uncertanet | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| Regformer | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| n2 | | | | | | | | | 0.00 491 | | | | | | | | |
|
| nn | | | | | | | | | 0.00 491 | | | | | | | | |
|
| ab-mvs-re | | | 8.06 451 | 10.74 454 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 96.69 220 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| uanet | | | 0.00 453 | 0.00 456 | 0.00 467 | 0.00 490 | 0.00 492 | 0.00 479 | 0.00 491 | 0.00 485 | 0.00 486 | 0.00 485 | 0.00 489 | 0.00 486 | 0.00 485 | 0.00 484 | 0.00 482 |
|
| TestfortrainingZip | | | | | | | | 98.69 11 | | | | | | | | | |
|
| WAC-MVS | | | | | | | 79.53 437 | | | | | | | | 75.56 435 | | |
|
| PC_three_1452 | | | | | | | | | | 90.77 236 | 98.89 26 | 98.28 86 | 96.24 1 | 98.35 278 | 95.76 106 | 99.58 23 | 99.59 32 |
|
| eth-test2 | | | | | | 0.00 490 | | | | | | | | | | | |
|
| eth-test | | | | | | 0.00 490 | | | | | | | | | | | |
|
| OPU-MVS | | | | | 98.55 4 | 98.82 61 | 96.86 3 | 98.25 40 | | | | 98.26 87 | 96.04 2 | 99.24 149 | 95.36 120 | 99.59 19 | 99.56 40 |
|
| test_0728_THIRD | | | | | | | | | | 94.78 61 | 98.73 30 | 98.87 31 | 95.87 4 | 99.84 26 | 97.45 46 | 99.72 2 | 99.77 3 |
|
| GSMVS | | | | | | | | | | | | | | | | | 98.45 183 |
|
| test_part2 | | | | | | 99.28 30 | 95.74 9 | | | | 98.10 48 | | | | | | |
|
| sam_mvs1 | | | | | | | | | | | | | 82.76 238 | | | | 98.45 183 |
|
| sam_mvs | | | | | | | | | | | | | 81.94 259 | | | | |
|
| test_post1 | | | | | | | | 92.81 434 | | | | 16.58 483 | 80.53 285 | 97.68 361 | 86.20 337 | | |
|
| test_post | | | | | | | | | | | | 17.58 482 | 81.76 262 | 98.08 305 | | | |
|
| patchmatchnet-post | | | | | | | | | | | | 90.45 436 | 82.65 243 | 98.10 300 | | | |
|
| gm-plane-assit | | | | | | 93.22 414 | 78.89 445 | | | 84.82 400 | | 93.52 389 | | 98.64 248 | 87.72 305 | | |
|
| test9_res | | | | | | | | | | | | | | | 94.81 139 | 99.38 64 | 99.45 59 |
|
| agg_prior2 | | | | | | | | | | | | | | | 93.94 166 | 99.38 64 | 99.50 52 |
|
| test_prior4 | | | | | | | 93.66 62 | 96.42 275 | | | | | | | | | |
|
| test_prior2 | | | | | | | | 96.35 285 | | 92.80 155 | 96.03 126 | 97.59 159 | 92.01 50 | | 95.01 128 | 99.38 64 | |
|
| 旧先验2 | | | | | | | | 95.94 316 | | 81.66 432 | 97.34 70 | | | 98.82 209 | 92.26 199 | | |
|
| 新几何2 | | | | | | | | 95.79 326 | | | | | | | | | |
|
| 原ACMM2 | | | | | | | | 95.67 332 | | | | | | | | | |
|
| testdata2 | | | | | | | | | | | | | | 99.67 77 | 85.96 345 | | |
|
| segment_acmp | | | | | | | | | | | | | 92.89 33 | | | | |
|
| testdata1 | | | | | | | | 95.26 358 | | 93.10 137 | | | | | | | |
|
| plane_prior7 | | | | | | 96.21 267 | 89.98 212 | | | | | | | | | | |
|
| plane_prior6 | | | | | | 96.10 285 | 90.00 208 | | | | | | 81.32 269 | | | | |
|
| plane_prior4 | | | | | | | | | | | | 96.64 223 | | | | | |
|
| plane_prior3 | | | | | | | 90.00 208 | | | 94.46 78 | 91.34 269 | | | | | | |
|
| plane_prior2 | | | | | | | | 97.74 113 | | 94.85 53 | | | | | | | |
|
| plane_prior1 | | | | | | 96.14 280 | | | | | | | | | | | |
|
| plane_prior | | | | | | | 89.99 210 | 97.24 186 | | 94.06 92 | | | | | | 92.16 303 | |
|
| HQP5-MVS | | | | | | | 89.33 246 | | | | | | | | | | |
|
| HQP-NCC | | | | | | 95.86 293 | | 96.65 256 | | 93.55 109 | 90.14 293 | | | | | | |
|
| ACMP_Plane | | | | | | 95.86 293 | | 96.65 256 | | 93.55 109 | 90.14 293 | | | | | | |
|
| BP-MVS | | | | | | | | | | | | | | | 92.13 207 | | |
|
| HQP4-MVS | | | | | | | | | | | 90.14 293 | | | 98.50 263 | | | 95.78 322 |
|
| HQP2-MVS | | | | | | | | | | | | | 80.95 273 | | | | |
|
| NP-MVS | | | | | | 95.99 291 | 89.81 220 | | | | | 95.87 266 | | | | | |
|
| MDTV_nov1_ep13_2view | | | | | | | 70.35 463 | 93.10 429 | | 83.88 411 | 93.55 210 | | 82.47 247 | | 86.25 336 | | 98.38 191 |
|
| ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 333 | |
|
| ACMMP++ | | | | | | | | | | | | | | | | 91.02 322 | |
|
| Test By Simon | | | | | | | | | | | | | 88.73 108 | | | | |
|