| test_fmvsm_n_1920 | | | 97.55 14 | 97.89 3 | 96.53 100 | 98.41 80 | 91.73 125 | 98.01 61 | 99.02 1 | 96.37 11 | 99.30 5 | 98.92 21 | 92.39 41 | 99.79 40 | 99.16 12 | 99.46 42 | 98.08 206 |
|
| PGM-MVS | | | 96.81 53 | 96.53 64 | 97.65 43 | 99.35 22 | 93.53 61 | 97.65 122 | 98.98 2 | 92.22 159 | 97.14 70 | 98.44 58 | 91.17 68 | 99.85 18 | 94.35 143 | 99.46 42 | 99.57 32 |
|
| MVS_111021_HR | | | 96.68 64 | 96.58 63 | 96.99 80 | 98.46 75 | 92.31 106 | 96.20 282 | 98.90 3 | 94.30 84 | 95.86 127 | 97.74 125 | 92.33 42 | 99.38 130 | 96.04 89 | 99.42 52 | 99.28 73 |
|
| test_fmvsmconf_n | | | 97.49 18 | 97.56 13 | 97.29 60 | 97.44 159 | 92.37 103 | 97.91 80 | 98.88 4 | 95.83 17 | 98.92 21 | 99.05 12 | 91.45 58 | 99.80 35 | 99.12 14 | 99.46 42 | 99.69 13 |
|
| lecture | | | 97.58 13 | 97.63 10 | 97.43 54 | 99.37 16 | 92.93 82 | 98.86 7 | 98.85 5 | 95.27 32 | 98.65 31 | 98.90 23 | 91.97 49 | 99.80 35 | 97.63 36 | 99.21 77 | 99.57 32 |
|
| ACMMP |  | | 96.27 81 | 95.93 84 | 97.28 62 | 99.24 30 | 92.62 94 | 98.25 36 | 98.81 6 | 92.99 133 | 94.56 164 | 98.39 62 | 88.96 98 | 99.85 18 | 94.57 141 | 97.63 157 | 99.36 68 |
| 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 82 | 96.19 80 | 96.39 118 | 98.23 99 | 91.35 147 | 96.24 280 | 98.79 7 | 93.99 91 | 95.80 129 | 97.65 134 | 89.92 88 | 99.24 143 | 95.87 93 | 99.20 82 | 98.58 153 |
|
| patch_mono-2 | | | 96.83 52 | 97.44 21 | 95.01 206 | 99.05 41 | 85.39 342 | 96.98 206 | 98.77 8 | 94.70 64 | 97.99 45 | 98.66 41 | 93.61 19 | 99.91 1 | 97.67 35 | 99.50 36 | 99.72 12 |
|
| fmvsm_s_conf0.5_n | | | 96.85 49 | 97.13 26 | 96.04 142 | 98.07 114 | 90.28 195 | 97.97 72 | 98.76 9 | 94.93 46 | 98.84 26 | 99.06 11 | 88.80 102 | 99.65 73 | 99.06 16 | 98.63 117 | 98.18 192 |
|
| fmvsm_l_conf0.5_n | | | 97.65 7 | 97.75 7 | 97.34 57 | 98.21 100 | 92.75 88 | 97.83 92 | 98.73 10 | 95.04 43 | 99.30 5 | 98.84 34 | 93.34 22 | 99.78 43 | 99.32 6 | 99.13 92 | 99.50 48 |
|
| fmvsm_s_conf0.5_n_a | | | 96.75 57 | 96.93 41 | 96.20 134 | 97.64 145 | 90.72 177 | 98.00 62 | 98.73 10 | 94.55 71 | 98.91 22 | 99.08 7 | 88.22 114 | 99.63 82 | 98.91 19 | 98.37 130 | 98.25 187 |
|
| fmvsm_l_conf0.5_n_9 | | | 97.59 11 | 97.79 5 | 96.97 82 | 98.28 89 | 91.49 139 | 97.61 131 | 98.71 12 | 97.10 4 | 99.70 1 | 98.93 20 | 90.95 73 | 99.77 46 | 99.35 5 | 99.53 29 | 99.65 19 |
|
| FC-MVSNet-test | | | 93.94 164 | 93.57 156 | 95.04 204 | 95.48 294 | 91.45 144 | 98.12 51 | 98.71 12 | 93.37 116 | 90.23 274 | 96.70 200 | 87.66 124 | 97.85 325 | 91.49 205 | 90.39 316 | 95.83 300 |
|
| UniMVSNet (Re) | | | 93.31 189 | 92.55 202 | 95.61 175 | 95.39 300 | 93.34 67 | 97.39 165 | 98.71 12 | 93.14 129 | 90.10 283 | 94.83 302 | 87.71 123 | 98.03 298 | 91.67 203 | 83.99 388 | 95.46 319 |
|
| fmvsm_l_conf0.5_n_a | | | 97.63 9 | 97.76 6 | 97.26 64 | 98.25 94 | 92.59 96 | 97.81 97 | 98.68 15 | 94.93 46 | 99.24 8 | 98.87 29 | 93.52 20 | 99.79 40 | 99.32 6 | 99.21 77 | 99.40 62 |
|
| FIs | | | 94.09 155 | 93.70 152 | 95.27 193 | 95.70 283 | 92.03 118 | 98.10 52 | 98.68 15 | 93.36 118 | 90.39 271 | 96.70 200 | 87.63 127 | 97.94 316 | 92.25 183 | 90.50 315 | 95.84 299 |
|
| WR-MVS_H | | | 92.00 246 | 91.35 243 | 93.95 271 | 95.09 327 | 89.47 224 | 98.04 59 | 98.68 15 | 91.46 188 | 88.34 334 | 94.68 309 | 85.86 159 | 97.56 354 | 85.77 329 | 84.24 386 | 94.82 364 |
|
| fmvsm_s_conf0.5_n_4 | | | 96.75 57 | 97.07 29 | 95.79 162 | 97.76 136 | 89.57 218 | 97.66 121 | 98.66 18 | 95.36 28 | 99.03 14 | 98.90 23 | 88.39 110 | 99.73 55 | 99.17 11 | 98.66 115 | 98.08 206 |
|
| VPA-MVSNet | | | 93.24 191 | 92.48 207 | 95.51 181 | 95.70 283 | 92.39 102 | 97.86 85 | 98.66 18 | 92.30 156 | 92.09 232 | 95.37 277 | 80.49 269 | 98.40 252 | 93.95 149 | 85.86 359 | 95.75 308 |
|
| fmvsm_l_conf0.5_n_3 | | | 97.64 8 | 97.60 11 | 97.79 30 | 98.14 107 | 93.94 52 | 97.93 78 | 98.65 20 | 96.70 6 | 99.38 3 | 99.07 10 | 89.92 88 | 99.81 30 | 99.16 12 | 99.43 49 | 99.61 26 |
|
| fmvsm_s_conf0.5_n_3 | | | 97.15 31 | 97.36 23 | 96.52 102 | 97.98 120 | 91.19 155 | 97.84 89 | 98.65 20 | 97.08 5 | 99.25 7 | 99.10 5 | 87.88 121 | 99.79 40 | 99.32 6 | 99.18 84 | 98.59 152 |
|
| fmvsm_s_conf0.5_n_8 | | | 97.32 25 | 97.48 20 | 96.85 83 | 98.28 89 | 91.07 163 | 97.76 102 | 98.62 22 | 97.53 2 | 99.20 10 | 99.12 4 | 88.24 113 | 99.81 30 | 99.41 3 | 99.17 85 | 99.67 14 |
|
| fmvsm_s_conf0.5_n_2 | | | 96.62 65 | 96.82 50 | 96.02 144 | 97.98 120 | 90.43 187 | 97.50 146 | 98.59 23 | 96.59 8 | 99.31 4 | 99.08 7 | 84.47 185 | 99.75 52 | 99.37 4 | 98.45 127 | 97.88 219 |
|
| UniMVSNet_NR-MVSNet | | | 93.37 187 | 92.67 196 | 95.47 186 | 95.34 306 | 92.83 85 | 97.17 189 | 98.58 24 | 92.98 138 | 90.13 279 | 95.80 253 | 88.37 112 | 97.85 325 | 91.71 200 | 83.93 389 | 95.73 310 |
|
| CSCG | | | 96.05 85 | 95.91 85 | 96.46 112 | 99.24 30 | 90.47 184 | 98.30 29 | 98.57 25 | 89.01 280 | 93.97 181 | 97.57 144 | 92.62 37 | 99.76 48 | 94.66 135 | 99.27 70 | 99.15 83 |
|
| fmvsm_s_conf0.5_n_9 | | | 97.33 24 | 97.57 12 | 96.62 96 | 98.43 78 | 90.32 194 | 97.80 98 | 98.53 26 | 97.24 3 | 99.62 2 | 99.14 1 | 88.65 105 | 99.80 35 | 99.54 1 | 99.15 89 | 99.74 8 |
|
| fmvsm_s_conf0.5_n_6 | | | 97.08 34 | 97.17 25 | 96.81 84 | 97.28 164 | 91.73 125 | 97.75 104 | 98.50 27 | 94.86 50 | 99.22 9 | 98.78 38 | 89.75 91 | 99.76 48 | 99.10 15 | 99.29 68 | 98.94 112 |
|
| MSLP-MVS++ | | | 96.94 43 | 97.06 30 | 96.59 97 | 98.72 60 | 91.86 123 | 97.67 118 | 98.49 28 | 94.66 67 | 97.24 66 | 98.41 61 | 92.31 44 | 98.94 189 | 96.61 65 | 99.46 42 | 98.96 108 |
|
| HyFIR lowres test | | | 93.66 176 | 92.92 184 | 95.87 154 | 98.24 95 | 89.88 209 | 94.58 356 | 98.49 28 | 85.06 377 | 93.78 184 | 95.78 257 | 82.86 220 | 98.67 227 | 91.77 198 | 95.71 214 | 99.07 94 |
|
| CHOSEN 1792x2688 | | | 94.15 150 | 93.51 162 | 96.06 140 | 98.27 91 | 89.38 229 | 95.18 342 | 98.48 30 | 85.60 367 | 93.76 185 | 97.11 175 | 83.15 210 | 99.61 84 | 91.33 208 | 98.72 113 | 99.19 79 |
|
| fmvsm_s_conf0.5_n_7 | | | 96.45 72 | 96.80 52 | 95.37 189 | 97.29 163 | 88.38 261 | 97.23 183 | 98.47 31 | 95.14 37 | 98.43 36 | 99.09 6 | 87.58 128 | 99.72 59 | 98.80 23 | 99.21 77 | 98.02 210 |
|
| fmvsm_s_conf0.5_n_5 | | | 97.00 40 | 96.97 38 | 97.09 75 | 97.58 155 | 92.56 97 | 97.68 117 | 98.47 31 | 94.02 89 | 98.90 23 | 98.89 26 | 88.94 99 | 99.78 43 | 99.18 10 | 99.03 101 | 98.93 116 |
|
| PHI-MVS | | | 96.77 55 | 96.46 71 | 97.71 41 | 98.40 81 | 94.07 48 | 98.21 43 | 98.45 33 | 89.86 252 | 97.11 72 | 98.01 98 | 92.52 39 | 99.69 67 | 96.03 90 | 99.53 29 | 99.36 68 |
|
| fmvsm_s_conf0.1_n | | | 96.58 68 | 96.77 55 | 96.01 147 | 96.67 215 | 90.25 196 | 97.91 80 | 98.38 34 | 94.48 75 | 98.84 26 | 99.14 1 | 88.06 116 | 99.62 83 | 98.82 21 | 98.60 119 | 98.15 196 |
|
| PVSNet_BlendedMVS | | | 94.06 156 | 93.92 146 | 94.47 240 | 98.27 91 | 89.46 226 | 96.73 231 | 98.36 35 | 90.17 244 | 94.36 169 | 95.24 285 | 88.02 117 | 99.58 92 | 93.44 161 | 90.72 311 | 94.36 384 |
|
| PVSNet_Blended | | | 94.87 128 | 94.56 126 | 95.81 160 | 98.27 91 | 89.46 226 | 95.47 325 | 98.36 35 | 88.84 289 | 94.36 169 | 96.09 242 | 88.02 117 | 99.58 92 | 93.44 161 | 98.18 139 | 98.40 174 |
|
| 3Dnovator | | 91.36 5 | 95.19 116 | 94.44 134 | 97.44 53 | 96.56 225 | 93.36 66 | 98.65 12 | 98.36 35 | 94.12 86 | 89.25 313 | 98.06 92 | 82.20 237 | 99.77 46 | 93.41 163 | 99.32 66 | 99.18 80 |
|
| FOURS1 | | | | | | 99.55 1 | 93.34 67 | 99.29 1 | 98.35 38 | 94.98 44 | 98.49 34 | | | | | | |
|
| DPE-MVS |  | | 97.86 4 | 97.65 9 | 98.47 5 | 99.17 34 | 95.78 7 | 97.21 186 | 98.35 38 | 95.16 36 | 98.71 30 | 98.80 36 | 95.05 10 | 99.89 3 | 96.70 63 | 99.73 1 | 99.73 11 |
| Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
| fmvsm_s_conf0.1_n_a | | | 96.40 74 | 96.47 68 | 96.16 136 | 95.48 294 | 90.69 178 | 97.91 80 | 98.33 40 | 94.07 87 | 98.93 18 | 99.14 1 | 87.44 135 | 99.61 84 | 98.63 24 | 98.32 132 | 98.18 192 |
|
| HFP-MVS | | | 97.14 32 | 96.92 42 | 97.83 26 | 99.42 7 | 94.12 46 | 98.52 16 | 98.32 41 | 93.21 121 | 97.18 67 | 98.29 78 | 92.08 46 | 99.83 26 | 95.63 106 | 99.59 19 | 99.54 41 |
|
| ACMMPR | | | 97.07 36 | 96.84 46 | 97.79 30 | 99.44 6 | 93.88 53 | 98.52 16 | 98.31 42 | 93.21 121 | 97.15 69 | 98.33 72 | 91.35 62 | 99.86 9 | 95.63 106 | 99.59 19 | 99.62 23 |
|
| test_fmvsmvis_n_1920 | | | 96.70 60 | 96.84 46 | 96.31 123 | 96.62 217 | 91.73 125 | 97.98 66 | 98.30 43 | 96.19 12 | 96.10 117 | 98.95 18 | 89.42 92 | 99.76 48 | 98.90 20 | 99.08 96 | 97.43 246 |
|
| APDe-MVS |  | | 97.82 5 | 97.73 8 | 98.08 18 | 99.15 35 | 94.82 28 | 98.81 8 | 98.30 43 | 94.76 62 | 98.30 38 | 98.90 23 | 93.77 17 | 99.68 69 | 97.93 27 | 99.69 3 | 99.75 6 |
| Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition |
| test0726 | | | | | | 99.45 3 | 95.36 13 | 98.31 28 | 98.29 45 | 94.92 48 | 98.99 16 | 98.92 21 | 95.08 8 | | | | |
|
| MSP-MVS | | | 97.59 11 | 97.54 14 | 97.73 38 | 99.40 11 | 93.77 57 | 98.53 15 | 98.29 45 | 95.55 25 | 98.56 33 | 97.81 120 | 93.90 15 | 99.65 73 | 96.62 64 | 99.21 77 | 99.77 2 |
| 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 9 | 98.67 63 | 95.39 11 | 99.29 1 | 98.28 47 | 94.78 59 | 98.93 18 | 98.87 29 | 96.04 2 | 99.86 9 | 97.45 44 | 99.58 23 | 99.59 28 |
|
| test_0728_SECOND | | | | | 98.51 4 | 99.45 3 | 95.93 5 | 98.21 43 | 98.28 47 | | | | | 99.86 9 | 97.52 40 | 99.67 6 | 99.75 6 |
|
| CP-MVS | | | 97.02 38 | 96.81 51 | 97.64 45 | 99.33 23 | 93.54 60 | 98.80 9 | 98.28 47 | 92.99 133 | 96.45 104 | 98.30 77 | 91.90 50 | 99.85 18 | 95.61 108 | 99.68 4 | 99.54 41 |
|
| test_fmvsmconf0.1_n | | | 97.09 33 | 97.06 30 | 97.19 69 | 95.67 285 | 92.21 110 | 97.95 75 | 98.27 50 | 95.78 21 | 98.40 37 | 99.00 14 | 89.99 86 | 99.78 43 | 99.06 16 | 99.41 55 | 99.59 28 |
|
| SED-MVS | | | 98.05 2 | 97.99 1 | 98.24 10 | 99.42 7 | 95.30 17 | 98.25 36 | 98.27 50 | 95.13 38 | 99.19 11 | 98.89 26 | 95.54 5 | 99.85 18 | 97.52 40 | 99.66 10 | 99.56 36 |
|
| test_241102_TWO | | | | | | | | | 98.27 50 | 95.13 38 | 98.93 18 | 98.89 26 | 94.99 11 | 99.85 18 | 97.52 40 | 99.65 13 | 99.74 8 |
|
| test_241102_ONE | | | | | | 99.42 7 | 95.30 17 | | 98.27 50 | 95.09 41 | 99.19 11 | 98.81 35 | 95.54 5 | 99.65 73 | | | |
|
| SF-MVS | | | 97.39 21 | 97.13 26 | 98.17 15 | 99.02 44 | 95.28 19 | 98.23 40 | 98.27 50 | 92.37 155 | 98.27 39 | 98.65 43 | 93.33 23 | 99.72 59 | 96.49 69 | 99.52 31 | 99.51 45 |
|
| SteuartSystems-ACMMP | | | 97.62 10 | 97.53 15 | 97.87 24 | 98.39 83 | 94.25 40 | 98.43 23 | 98.27 50 | 95.34 30 | 98.11 41 | 98.56 45 | 94.53 12 | 99.71 61 | 96.57 67 | 99.62 17 | 99.65 19 |
| Skip Steuart: Steuart Systems R&D Blog. |
| test_one_0601 | | | | | | 99.32 24 | 95.20 20 | | 98.25 56 | 95.13 38 | 98.48 35 | 98.87 29 | 95.16 7 | | | | |
|
| PVSNet_Blended_VisFu | | | 95.27 109 | 94.91 115 | 96.38 119 | 98.20 101 | 90.86 171 | 97.27 177 | 98.25 56 | 90.21 243 | 94.18 174 | 97.27 164 | 87.48 134 | 99.73 55 | 93.53 158 | 97.77 155 | 98.55 155 |
|
| region2R | | | 97.07 36 | 96.84 46 | 97.77 34 | 99.46 2 | 93.79 55 | 98.52 16 | 98.24 58 | 93.19 124 | 97.14 70 | 98.34 69 | 91.59 57 | 99.87 7 | 95.46 112 | 99.59 19 | 99.64 21 |
|
| PS-CasMVS | | | 91.55 266 | 90.84 267 | 93.69 288 | 94.96 331 | 88.28 264 | 97.84 89 | 98.24 58 | 91.46 188 | 88.04 345 | 95.80 253 | 79.67 285 | 97.48 362 | 87.02 309 | 84.54 383 | 95.31 333 |
|
| DU-MVS | | | 92.90 209 | 92.04 218 | 95.49 183 | 94.95 332 | 92.83 85 | 97.16 190 | 98.24 58 | 93.02 132 | 90.13 279 | 95.71 260 | 83.47 202 | 97.85 325 | 91.71 200 | 83.93 389 | 95.78 304 |
|
| 9.14 | | | | 96.75 56 | | 98.93 52 | | 97.73 108 | 98.23 61 | 91.28 197 | 97.88 49 | 98.44 58 | 93.00 26 | 99.65 73 | 95.76 99 | 99.47 41 | |
|
| reproduce_model | | | 97.51 17 | 97.51 17 | 97.50 50 | 98.99 48 | 93.01 78 | 97.79 100 | 98.21 62 | 95.73 22 | 97.99 45 | 99.03 13 | 92.63 36 | 99.82 28 | 97.80 29 | 99.42 52 | 99.67 14 |
|
| D2MVS | | | 91.30 283 | 90.95 261 | 92.35 336 | 94.71 347 | 85.52 338 | 96.18 284 | 98.21 62 | 88.89 287 | 86.60 374 | 93.82 358 | 79.92 281 | 97.95 314 | 89.29 258 | 90.95 308 | 93.56 399 |
|
| reproduce-ours | | | 97.53 15 | 97.51 17 | 97.60 47 | 98.97 49 | 93.31 69 | 97.71 113 | 98.20 64 | 95.80 19 | 97.88 49 | 98.98 16 | 92.91 27 | 99.81 30 | 97.68 31 | 99.43 49 | 99.67 14 |
|
| our_new_method | | | 97.53 15 | 97.51 17 | 97.60 47 | 98.97 49 | 93.31 69 | 97.71 113 | 98.20 64 | 95.80 19 | 97.88 49 | 98.98 16 | 92.91 27 | 99.81 30 | 97.68 31 | 99.43 49 | 99.67 14 |
|
| SDMVSNet | | | 94.17 148 | 93.61 155 | 95.86 156 | 98.09 110 | 91.37 146 | 97.35 169 | 98.20 64 | 93.18 126 | 91.79 240 | 97.28 162 | 79.13 293 | 98.93 190 | 94.61 138 | 92.84 274 | 97.28 254 |
|
| XVS | | | 97.18 29 | 96.96 40 | 97.81 28 | 99.38 14 | 94.03 50 | 98.59 13 | 98.20 64 | 94.85 51 | 96.59 93 | 98.29 78 | 91.70 53 | 99.80 35 | 95.66 101 | 99.40 57 | 99.62 23 |
|
| X-MVStestdata | | | 91.71 255 | 89.67 321 | 97.81 28 | 99.38 14 | 94.03 50 | 98.59 13 | 98.20 64 | 94.85 51 | 96.59 93 | 32.69 459 | 91.70 53 | 99.80 35 | 95.66 101 | 99.40 57 | 99.62 23 |
|
| ACMMP_NAP | | | 97.20 28 | 96.86 44 | 98.23 11 | 99.09 36 | 95.16 22 | 97.60 132 | 98.19 69 | 92.82 145 | 97.93 48 | 98.74 40 | 91.60 56 | 99.86 9 | 96.26 73 | 99.52 31 | 99.67 14 |
|
| CP-MVSNet | | | 91.89 251 | 91.24 250 | 93.82 280 | 95.05 328 | 88.57 254 | 97.82 94 | 98.19 69 | 91.70 177 | 88.21 340 | 95.76 258 | 81.96 242 | 97.52 360 | 87.86 284 | 84.65 377 | 95.37 329 |
|
| ZNCC-MVS | | | 96.96 41 | 96.67 59 | 97.85 25 | 99.37 16 | 94.12 46 | 98.49 20 | 98.18 71 | 92.64 151 | 96.39 106 | 98.18 85 | 91.61 55 | 99.88 4 | 95.59 111 | 99.55 26 | 99.57 32 |
|
| SMA-MVS |  | | 97.35 22 | 97.03 35 | 98.30 8 | 99.06 40 | 95.42 10 | 97.94 76 | 98.18 71 | 90.57 235 | 98.85 25 | 98.94 19 | 93.33 23 | 99.83 26 | 96.72 61 | 99.68 4 | 99.63 22 |
| 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 | | | 91.20 288 | 90.44 284 | 93.48 299 | 94.49 355 | 87.91 279 | 97.76 102 | 98.18 71 | 91.29 194 | 87.78 349 | 95.74 259 | 80.35 272 | 97.33 373 | 85.46 333 | 82.96 399 | 95.19 344 |
|
| DELS-MVS | | | 96.61 66 | 96.38 75 | 97.30 59 | 97.79 134 | 93.19 74 | 95.96 295 | 98.18 71 | 95.23 33 | 95.87 126 | 97.65 134 | 91.45 58 | 99.70 66 | 95.87 93 | 99.44 48 | 99.00 103 |
| 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 | | | 89.70 340 | 88.40 346 | 93.60 292 | 95.15 323 | 90.10 198 | 97.56 137 | 98.16 75 | 87.28 340 | 86.16 380 | 94.63 313 | 77.57 321 | 98.05 294 | 74.48 419 | 84.59 381 | 92.65 412 |
|
| VNet | | | 95.89 93 | 95.45 96 | 97.21 67 | 98.07 114 | 92.94 81 | 97.50 146 | 98.15 76 | 93.87 95 | 97.52 56 | 97.61 140 | 85.29 170 | 99.53 106 | 95.81 98 | 95.27 227 | 99.16 81 |
|
| DeepPCF-MVS | | 93.97 1 | 96.61 66 | 97.09 28 | 95.15 197 | 98.09 110 | 86.63 311 | 96.00 293 | 98.15 76 | 95.43 26 | 97.95 47 | 98.56 45 | 93.40 21 | 99.36 131 | 96.77 58 | 99.48 40 | 99.45 55 |
|
| SD-MVS | | | 97.41 20 | 97.53 15 | 97.06 78 | 98.57 74 | 94.46 34 | 97.92 79 | 98.14 78 | 94.82 55 | 99.01 15 | 98.55 47 | 94.18 14 | 97.41 369 | 96.94 53 | 99.64 14 | 99.32 70 |
| 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 49 | 96.52 65 | 97.82 27 | 99.36 20 | 94.14 45 | 98.29 30 | 98.13 79 | 92.72 148 | 96.70 85 | 98.06 92 | 91.35 62 | 99.86 9 | 94.83 128 | 99.28 69 | 99.47 54 |
|
| UA-Net | | | 95.95 90 | 95.53 92 | 97.20 68 | 97.67 141 | 92.98 80 | 97.65 122 | 98.13 79 | 94.81 57 | 96.61 91 | 98.35 66 | 88.87 100 | 99.51 111 | 90.36 233 | 97.35 167 | 99.11 89 |
|
| QAPM | | | 93.45 185 | 92.27 212 | 96.98 81 | 96.77 210 | 92.62 94 | 98.39 25 | 98.12 81 | 84.50 385 | 88.27 338 | 97.77 123 | 82.39 234 | 99.81 30 | 85.40 334 | 98.81 109 | 98.51 160 |
|
| Vis-MVSNet |  | | 95.23 113 | 94.81 116 | 96.51 106 | 97.18 169 | 91.58 136 | 98.26 35 | 98.12 81 | 94.38 82 | 94.90 154 | 98.15 87 | 82.28 235 | 98.92 192 | 91.45 207 | 98.58 121 | 99.01 100 |
| Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
| OpenMVS |  | 89.19 12 | 92.86 212 | 91.68 233 | 96.40 116 | 95.34 306 | 92.73 90 | 98.27 33 | 98.12 81 | 84.86 380 | 85.78 382 | 97.75 124 | 78.89 303 | 99.74 53 | 87.50 299 | 98.65 116 | 96.73 271 |
|
| TranMVSNet+NR-MVSNet | | | 92.50 221 | 91.63 234 | 95.14 198 | 94.76 343 | 92.07 115 | 97.53 143 | 98.11 84 | 92.90 142 | 89.56 301 | 96.12 237 | 83.16 209 | 97.60 352 | 89.30 257 | 83.20 398 | 95.75 308 |
|
| CPTT-MVS | | | 95.57 103 | 95.19 106 | 96.70 87 | 99.27 28 | 91.48 141 | 98.33 27 | 98.11 84 | 87.79 325 | 95.17 149 | 98.03 95 | 87.09 141 | 99.61 84 | 93.51 159 | 99.42 52 | 99.02 97 |
|
| APD-MVS |  | | 96.95 42 | 96.60 61 | 98.01 20 | 99.03 43 | 94.93 27 | 97.72 111 | 98.10 86 | 91.50 186 | 98.01 44 | 98.32 74 | 92.33 42 | 99.58 92 | 94.85 126 | 99.51 34 | 99.53 44 |
| Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
| mPP-MVS | | | 96.86 47 | 96.60 61 | 97.64 45 | 99.40 11 | 93.44 62 | 98.50 19 | 98.09 87 | 93.27 120 | 95.95 124 | 98.33 72 | 91.04 70 | 99.88 4 | 95.20 115 | 99.57 25 | 99.60 27 |
|
| ZD-MVS | | | | | | 99.05 41 | 94.59 32 | | 98.08 88 | 89.22 273 | 97.03 75 | 98.10 88 | 92.52 39 | 99.65 73 | 94.58 140 | 99.31 67 | |
|
| MTGPA |  | | | | | | | | 98.08 88 | | | | | | | | |
|
| MTAPA | | | 97.08 34 | 96.78 54 | 97.97 23 | 99.37 16 | 94.42 36 | 97.24 179 | 98.08 88 | 95.07 42 | 96.11 116 | 98.59 44 | 90.88 76 | 99.90 2 | 96.18 85 | 99.50 36 | 99.58 31 |
|
| CNVR-MVS | | | 97.68 6 | 97.44 21 | 98.37 7 | 98.90 55 | 95.86 6 | 97.27 177 | 98.08 88 | 95.81 18 | 97.87 52 | 98.31 75 | 94.26 13 | 99.68 69 | 97.02 52 | 99.49 39 | 99.57 32 |
|
| DP-MVS Recon | | | 95.68 98 | 95.12 110 | 97.37 56 | 99.19 33 | 94.19 42 | 97.03 197 | 98.08 88 | 88.35 307 | 95.09 151 | 97.65 134 | 89.97 87 | 99.48 118 | 92.08 192 | 98.59 120 | 98.44 171 |
|
| SR-MVS | | | 97.01 39 | 96.86 44 | 97.47 52 | 99.09 36 | 93.27 71 | 97.98 66 | 98.07 93 | 93.75 98 | 97.45 58 | 98.48 55 | 91.43 60 | 99.59 89 | 96.22 76 | 99.27 70 | 99.54 41 |
|
| MCST-MVS | | | 97.18 29 | 96.84 46 | 98.20 14 | 99.30 26 | 95.35 15 | 97.12 193 | 98.07 93 | 93.54 108 | 96.08 118 | 97.69 129 | 93.86 16 | 99.71 61 | 96.50 68 | 99.39 59 | 99.55 39 |
|
| NR-MVSNet | | | 92.34 230 | 91.27 249 | 95.53 180 | 94.95 332 | 93.05 77 | 97.39 165 | 98.07 93 | 92.65 150 | 84.46 393 | 95.71 260 | 85.00 176 | 97.77 336 | 89.71 245 | 83.52 395 | 95.78 304 |
|
| MP-MVS-pluss | | | 96.70 60 | 96.27 78 | 97.98 22 | 99.23 32 | 94.71 29 | 96.96 208 | 98.06 96 | 90.67 225 | 95.55 140 | 98.78 38 | 91.07 69 | 99.86 9 | 96.58 66 | 99.55 26 | 99.38 66 |
| MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
| APD-MVS_3200maxsize | | | 96.81 53 | 96.71 58 | 97.12 72 | 99.01 47 | 92.31 106 | 97.98 66 | 98.06 96 | 93.11 130 | 97.44 59 | 98.55 47 | 90.93 74 | 99.55 102 | 96.06 86 | 99.25 74 | 99.51 45 |
|
| MP-MVS |  | | 96.77 55 | 96.45 72 | 97.72 39 | 99.39 13 | 93.80 54 | 98.41 24 | 98.06 96 | 93.37 116 | 95.54 142 | 98.34 69 | 90.59 80 | 99.88 4 | 94.83 128 | 99.54 28 | 99.49 50 |
| Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
| HPM-MVS_fast | | | 96.51 69 | 96.27 78 | 97.22 66 | 99.32 24 | 92.74 89 | 98.74 10 | 98.06 96 | 90.57 235 | 96.77 82 | 98.35 66 | 90.21 83 | 99.53 106 | 94.80 131 | 99.63 16 | 99.38 66 |
|
| HPM-MVS |  | | 96.69 62 | 96.45 72 | 97.40 55 | 99.36 20 | 93.11 76 | 98.87 6 | 98.06 96 | 91.17 204 | 96.40 105 | 97.99 99 | 90.99 71 | 99.58 92 | 95.61 108 | 99.61 18 | 99.49 50 |
| Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
| sss | | | 94.51 139 | 93.80 148 | 96.64 89 | 97.07 175 | 91.97 120 | 96.32 272 | 98.06 96 | 88.94 285 | 94.50 166 | 96.78 195 | 84.60 182 | 99.27 141 | 91.90 193 | 96.02 204 | 98.68 146 |
|
| DeepC-MVS | | 93.07 3 | 96.06 84 | 95.66 89 | 97.29 60 | 97.96 122 | 93.17 75 | 97.30 175 | 98.06 96 | 93.92 93 | 93.38 200 | 98.66 41 | 86.83 143 | 99.73 55 | 95.60 110 | 99.22 76 | 98.96 108 |
| 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 26 | 97.03 35 | 98.11 17 | 98.77 58 | 95.06 25 | 97.34 170 | 98.04 103 | 95.96 13 | 97.09 73 | 97.88 110 | 93.18 25 | 99.71 61 | 95.84 97 | 99.17 85 | 99.56 36 |
|
| DeepC-MVS_fast | | 93.89 2 | 96.93 44 | 96.64 60 | 97.78 32 | 98.64 69 | 94.30 37 | 97.41 160 | 98.04 103 | 94.81 57 | 96.59 93 | 98.37 64 | 91.24 65 | 99.64 81 | 95.16 117 | 99.52 31 | 99.42 61 |
| 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 46 | 96.80 52 | 97.11 74 | 99.02 44 | 92.34 104 | 97.98 66 | 98.03 105 | 93.52 111 | 97.43 61 | 98.51 50 | 91.40 61 | 99.56 100 | 96.05 87 | 99.26 72 | 99.43 59 |
|
| RE-MVS-def | | | | 96.72 57 | | 99.02 44 | 92.34 104 | 97.98 66 | 98.03 105 | 93.52 111 | 97.43 61 | 98.51 50 | 90.71 78 | | 96.05 87 | 99.26 72 | 99.43 59 |
|
| RPMNet | | | 88.98 346 | 87.05 360 | 94.77 224 | 94.45 357 | 87.19 295 | 90.23 432 | 98.03 105 | 77.87 432 | 92.40 218 | 87.55 439 | 80.17 276 | 99.51 111 | 68.84 439 | 93.95 260 | 97.60 239 |
|
| save fliter | | | | | | 98.91 54 | 94.28 38 | 97.02 199 | 98.02 108 | 95.35 29 | | | | | | | |
|
| TEST9 | | | | | | 98.70 61 | 94.19 42 | 96.41 259 | 98.02 108 | 88.17 311 | 96.03 119 | 97.56 146 | 92.74 33 | 99.59 89 | | | |
|
| train_agg | | | 96.30 80 | 95.83 88 | 97.72 39 | 98.70 61 | 94.19 42 | 96.41 259 | 98.02 108 | 88.58 298 | 96.03 119 | 97.56 146 | 92.73 34 | 99.59 89 | 95.04 119 | 99.37 63 | 99.39 64 |
|
| test_8 | | | | | | 98.67 63 | 94.06 49 | 96.37 266 | 98.01 111 | 88.58 298 | 95.98 123 | 97.55 148 | 92.73 34 | 99.58 92 | | | |
|
| agg_prior | | | | | | 98.67 63 | 93.79 55 | | 98.00 112 | | 95.68 136 | | | 99.57 99 | | | |
|
| test_prior | | | | | 97.23 65 | 98.67 63 | 92.99 79 | | 98.00 112 | | | | | 99.41 126 | | | 99.29 71 |
|
| WR-MVS | | | 92.34 230 | 91.53 238 | 94.77 224 | 95.13 325 | 90.83 172 | 96.40 263 | 97.98 114 | 91.88 172 | 89.29 310 | 95.54 271 | 82.50 230 | 97.80 332 | 89.79 244 | 85.27 368 | 95.69 311 |
|
| HPM-MVS++ |  | | 97.34 23 | 96.97 38 | 98.47 5 | 99.08 38 | 96.16 4 | 97.55 142 | 97.97 115 | 95.59 23 | 96.61 91 | 97.89 108 | 92.57 38 | 99.84 23 | 95.95 92 | 99.51 34 | 99.40 62 |
|
| CANet | | | 96.39 75 | 96.02 83 | 97.50 50 | 97.62 148 | 93.38 64 | 97.02 199 | 97.96 116 | 95.42 27 | 94.86 155 | 97.81 120 | 87.38 137 | 99.82 28 | 96.88 55 | 99.20 82 | 99.29 71 |
|
| 114514_t | | | 93.95 163 | 93.06 178 | 96.63 93 | 99.07 39 | 91.61 133 | 97.46 157 | 97.96 116 | 77.99 430 | 93.00 209 | 97.57 144 | 86.14 156 | 99.33 133 | 89.22 261 | 99.15 89 | 98.94 112 |
|
| IU-MVS | | | | | | 99.42 7 | 95.39 11 | | 97.94 118 | 90.40 241 | 98.94 17 | | | | 97.41 47 | 99.66 10 | 99.74 8 |
|
| MSC_two_6792asdad | | | | | 98.86 1 | 98.67 63 | 96.94 1 | | 97.93 119 | | | | | 99.86 9 | 97.68 31 | 99.67 6 | 99.77 2 |
|
| No_MVS | | | | | 98.86 1 | 98.67 63 | 96.94 1 | | 97.93 119 | | | | | 99.86 9 | 97.68 31 | 99.67 6 | 99.77 2 |
|
| fmvsm_s_conf0.1_n_2 | | | 96.33 79 | 96.44 74 | 96.00 148 | 97.30 162 | 90.37 193 | 97.53 143 | 97.92 121 | 96.52 9 | 99.14 13 | 99.08 7 | 83.21 207 | 99.74 53 | 99.22 9 | 98.06 144 | 97.88 219 |
|
| Anonymous20231211 | | | 90.63 312 | 89.42 328 | 94.27 254 | 98.24 95 | 89.19 241 | 98.05 58 | 97.89 122 | 79.95 422 | 88.25 339 | 94.96 294 | 72.56 362 | 98.13 277 | 89.70 246 | 85.14 370 | 95.49 315 |
|
| 原ACMM1 | | | | | 96.38 119 | 98.59 71 | 91.09 162 | | 97.89 122 | 87.41 336 | 95.22 148 | 97.68 130 | 90.25 82 | 99.54 104 | 87.95 283 | 99.12 94 | 98.49 163 |
|
| CDPH-MVS | | | 95.97 89 | 95.38 101 | 97.77 34 | 98.93 52 | 94.44 35 | 96.35 267 | 97.88 124 | 86.98 344 | 96.65 89 | 97.89 108 | 91.99 48 | 99.47 119 | 92.26 181 | 99.46 42 | 99.39 64 |
|
| test11 | | | | | | | | | 97.88 124 | | | | | | | | |
|
| EIA-MVS | | | 95.53 104 | 95.47 95 | 95.71 170 | 97.06 178 | 89.63 214 | 97.82 94 | 97.87 126 | 93.57 104 | 93.92 182 | 95.04 291 | 90.61 79 | 98.95 187 | 94.62 137 | 98.68 114 | 98.54 156 |
|
| CS-MVS | | | 96.86 47 | 97.06 30 | 96.26 129 | 98.16 106 | 91.16 160 | 99.09 3 | 97.87 126 | 95.30 31 | 97.06 74 | 98.03 95 | 91.72 51 | 98.71 223 | 97.10 50 | 99.17 85 | 98.90 121 |
|
| 无先验 | | | | | | | | 95.79 306 | 97.87 126 | 83.87 393 | | | | 99.65 73 | 87.68 293 | | 98.89 125 |
|
| 3Dnovator+ | | 91.43 4 | 95.40 105 | 94.48 132 | 98.16 16 | 96.90 192 | 95.34 16 | 98.48 21 | 97.87 126 | 94.65 68 | 88.53 330 | 98.02 97 | 83.69 198 | 99.71 61 | 93.18 167 | 98.96 104 | 99.44 57 |
|
| VPNet | | | 92.23 238 | 91.31 246 | 94.99 207 | 95.56 290 | 90.96 166 | 97.22 185 | 97.86 130 | 92.96 139 | 90.96 262 | 96.62 212 | 75.06 342 | 98.20 271 | 91.90 193 | 83.65 394 | 95.80 302 |
|
| test_vis1_n_1920 | | | 94.17 148 | 94.58 125 | 92.91 320 | 97.42 160 | 82.02 389 | 97.83 92 | 97.85 131 | 94.68 65 | 98.10 42 | 98.49 52 | 70.15 381 | 99.32 135 | 97.91 28 | 98.82 108 | 97.40 248 |
|
| DVP-MVS |  | | 97.91 3 | 97.81 4 | 98.22 13 | 99.45 3 | 95.36 13 | 98.21 43 | 97.85 131 | 94.92 48 | 98.73 28 | 98.87 29 | 95.08 8 | 99.84 23 | 97.52 40 | 99.67 6 | 99.48 52 |
| 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 19 | 97.33 24 | 97.69 42 | 99.25 29 | 94.24 41 | 98.07 56 | 97.85 131 | 93.72 99 | 98.57 32 | 98.35 66 | 93.69 18 | 99.40 127 | 97.06 51 | 99.46 42 | 99.44 57 |
| 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 45 | 97.04 34 | 96.45 113 | 98.29 88 | 91.66 132 | 99.03 4 | 97.85 131 | 95.84 16 | 96.90 77 | 97.97 101 | 91.24 65 | 98.75 215 | 96.92 54 | 99.33 65 | 98.94 112 |
|
| test_fmvsmconf0.01_n | | | 96.15 83 | 95.85 87 | 97.03 79 | 92.66 408 | 91.83 124 | 97.97 72 | 97.84 135 | 95.57 24 | 97.53 55 | 99.00 14 | 84.20 191 | 99.76 48 | 98.82 21 | 99.08 96 | 99.48 52 |
|
| GDP-MVS | | | 95.62 100 | 95.13 108 | 97.09 75 | 96.79 204 | 93.26 72 | 97.89 83 | 97.83 136 | 93.58 103 | 96.80 79 | 97.82 118 | 83.06 214 | 99.16 155 | 94.40 142 | 97.95 150 | 98.87 127 |
|
| balanced_conf03 | | | 96.84 51 | 96.89 43 | 96.68 88 | 97.63 147 | 92.22 109 | 98.17 49 | 97.82 137 | 94.44 77 | 98.23 40 | 97.36 157 | 90.97 72 | 99.22 145 | 97.74 30 | 99.66 10 | 98.61 149 |
|
| AdaColmap |  | | 94.34 143 | 93.68 153 | 96.31 123 | 98.59 71 | 91.68 131 | 96.59 250 | 97.81 138 | 89.87 251 | 92.15 228 | 97.06 178 | 83.62 201 | 99.54 104 | 89.34 256 | 98.07 143 | 97.70 232 |
|
| MVSMamba_PlusPlus | | | 96.51 69 | 96.48 67 | 96.59 97 | 98.07 114 | 91.97 120 | 98.14 50 | 97.79 139 | 90.43 239 | 97.34 64 | 97.52 149 | 91.29 64 | 99.19 148 | 98.12 26 | 99.64 14 | 98.60 150 |
|
| KinetiMVS | | | 95.26 110 | 94.75 120 | 96.79 85 | 96.99 187 | 92.05 116 | 97.82 94 | 97.78 140 | 94.77 61 | 96.46 102 | 97.70 127 | 80.62 266 | 99.34 132 | 92.37 180 | 98.28 134 | 98.97 105 |
|
| mamv4 | | | 94.66 136 | 96.10 82 | 90.37 389 | 98.01 117 | 73.41 439 | 96.82 221 | 97.78 140 | 89.95 250 | 94.52 165 | 97.43 153 | 92.91 27 | 99.09 168 | 98.28 25 | 99.16 88 | 98.60 150 |
|
| ETV-MVS | | | 96.02 86 | 95.89 86 | 96.40 116 | 97.16 170 | 92.44 101 | 97.47 155 | 97.77 142 | 94.55 71 | 96.48 100 | 94.51 319 | 91.23 67 | 98.92 192 | 95.65 104 | 98.19 138 | 97.82 227 |
|
| 新几何1 | | | | | 97.32 58 | 98.60 70 | 93.59 59 | | 97.75 143 | 81.58 413 | 95.75 131 | 97.85 114 | 90.04 85 | 99.67 71 | 86.50 315 | 99.13 92 | 98.69 145 |
|
| 旧先验1 | | | | | | 98.38 84 | 93.38 64 | | 97.75 143 | | | 98.09 90 | 92.30 45 | | | 99.01 102 | 99.16 81 |
|
| EC-MVSNet | | | 96.42 73 | 96.47 68 | 96.26 129 | 97.01 185 | 91.52 138 | 98.89 5 | 97.75 143 | 94.42 78 | 96.64 90 | 97.68 130 | 89.32 93 | 98.60 235 | 97.45 44 | 99.11 95 | 98.67 147 |
|
| EI-MVSNet-Vis-set | | | 96.51 69 | 96.47 68 | 96.63 93 | 98.24 95 | 91.20 154 | 96.89 213 | 97.73 146 | 94.74 63 | 96.49 99 | 98.49 52 | 90.88 76 | 99.58 92 | 96.44 70 | 98.32 132 | 99.13 85 |
|
| PAPM_NR | | | 95.01 119 | 94.59 124 | 96.26 129 | 98.89 56 | 90.68 179 | 97.24 179 | 97.73 146 | 91.80 173 | 92.93 214 | 96.62 212 | 89.13 96 | 99.14 160 | 89.21 262 | 97.78 154 | 98.97 105 |
|
| Anonymous20240529 | | | 91.98 247 | 90.73 274 | 95.73 168 | 98.14 107 | 89.40 228 | 97.99 63 | 97.72 148 | 79.63 424 | 93.54 193 | 97.41 155 | 69.94 383 | 99.56 100 | 91.04 215 | 91.11 304 | 98.22 189 |
|
| CHOSEN 280x420 | | | 93.12 197 | 92.72 195 | 94.34 248 | 96.71 214 | 87.27 291 | 90.29 431 | 97.72 148 | 86.61 351 | 91.34 251 | 95.29 279 | 84.29 190 | 98.41 251 | 93.25 165 | 98.94 105 | 97.35 251 |
|
| EI-MVSNet-UG-set | | | 96.34 78 | 96.30 77 | 96.47 110 | 98.20 101 | 90.93 168 | 96.86 216 | 97.72 148 | 94.67 66 | 96.16 115 | 98.46 56 | 90.43 81 | 99.58 92 | 96.23 75 | 97.96 149 | 98.90 121 |
|
| LS3D | | | 93.57 180 | 92.61 200 | 96.47 110 | 97.59 151 | 91.61 133 | 97.67 118 | 97.72 148 | 85.17 375 | 90.29 273 | 98.34 69 | 84.60 182 | 99.73 55 | 83.85 357 | 98.27 135 | 98.06 208 |
|
| PAPR | | | 94.18 147 | 93.42 169 | 96.48 109 | 97.64 145 | 91.42 145 | 95.55 320 | 97.71 152 | 88.99 282 | 92.34 224 | 95.82 252 | 89.19 94 | 99.11 163 | 86.14 321 | 97.38 165 | 98.90 121 |
|
| UGNet | | | 94.04 158 | 93.28 172 | 96.31 123 | 96.85 196 | 91.19 155 | 97.88 84 | 97.68 153 | 94.40 80 | 93.00 209 | 96.18 232 | 73.39 359 | 99.61 84 | 91.72 199 | 98.46 126 | 98.13 197 |
| 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 | | | | | 95.46 187 | 98.18 105 | 88.90 247 | | 97.66 154 | 82.73 404 | 97.03 75 | 98.07 91 | 90.06 84 | 98.85 199 | 89.67 247 | 98.98 103 | 98.64 148 |
|
| test12 | | | | | 97.65 43 | 98.46 75 | 94.26 39 | | 97.66 154 | | 95.52 143 | | 90.89 75 | 99.46 120 | | 99.25 74 | 99.22 78 |
|
| DTE-MVSNet | | | 90.56 313 | 89.75 319 | 93.01 316 | 93.95 370 | 87.25 292 | 97.64 126 | 97.65 156 | 90.74 220 | 87.12 362 | 95.68 263 | 79.97 280 | 97.00 386 | 83.33 358 | 81.66 405 | 94.78 371 |
|
| TAPA-MVS | | 90.10 7 | 92.30 233 | 91.22 252 | 95.56 177 | 98.33 86 | 89.60 216 | 96.79 224 | 97.65 156 | 81.83 410 | 91.52 246 | 97.23 167 | 87.94 119 | 98.91 194 | 71.31 434 | 98.37 130 | 98.17 195 |
| Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
| sd_testset | | | 93.10 198 | 92.45 208 | 95.05 202 | 98.09 110 | 89.21 238 | 96.89 213 | 97.64 158 | 93.18 126 | 91.79 240 | 97.28 162 | 75.35 341 | 98.65 230 | 88.99 267 | 92.84 274 | 97.28 254 |
|
| test_cas_vis1_n_1920 | | | 94.48 141 | 94.55 129 | 94.28 253 | 96.78 208 | 86.45 316 | 97.63 128 | 97.64 158 | 93.32 119 | 97.68 54 | 98.36 65 | 73.75 357 | 99.08 171 | 96.73 60 | 99.05 98 | 97.31 253 |
|
| NormalMVS | | | 96.36 77 | 96.11 81 | 97.12 72 | 99.37 16 | 92.90 83 | 97.99 63 | 97.63 160 | 95.92 14 | 96.57 96 | 97.93 103 | 85.34 168 | 99.50 114 | 94.99 122 | 99.21 77 | 98.97 105 |
|
| Elysia | | | 94.00 160 | 93.12 175 | 96.64 89 | 96.08 269 | 92.72 91 | 97.50 146 | 97.63 160 | 91.15 206 | 94.82 156 | 97.12 173 | 74.98 344 | 99.06 177 | 90.78 220 | 98.02 145 | 98.12 199 |
|
| StellarMVS | | | 94.00 160 | 93.12 175 | 96.64 89 | 96.08 269 | 92.72 91 | 97.50 146 | 97.63 160 | 91.15 206 | 94.82 156 | 97.12 173 | 74.98 344 | 99.06 177 | 90.78 220 | 98.02 145 | 98.12 199 |
|
| cdsmvs_eth3d_5k | | | 23.24 429 | 30.99 431 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 97.63 160 | 0.00 465 | 0.00 466 | 96.88 191 | 84.38 187 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| DPM-MVS | | | 95.69 97 | 94.92 114 | 98.01 20 | 98.08 113 | 95.71 9 | 95.27 336 | 97.62 164 | 90.43 239 | 95.55 140 | 97.07 177 | 91.72 51 | 99.50 114 | 89.62 249 | 98.94 105 | 98.82 133 |
|
| sasdasda | | | 96.02 86 | 95.45 96 | 97.75 36 | 97.59 151 | 95.15 23 | 98.28 31 | 97.60 165 | 94.52 73 | 96.27 110 | 96.12 237 | 87.65 125 | 99.18 151 | 96.20 81 | 94.82 236 | 98.91 118 |
|
| canonicalmvs | | | 96.02 86 | 95.45 96 | 97.75 36 | 97.59 151 | 95.15 23 | 98.28 31 | 97.60 165 | 94.52 73 | 96.27 110 | 96.12 237 | 87.65 125 | 99.18 151 | 96.20 81 | 94.82 236 | 98.91 118 |
|
| test222 | | | | | | 98.24 95 | 92.21 110 | 95.33 331 | 97.60 165 | 79.22 426 | 95.25 146 | 97.84 116 | 88.80 102 | | | 99.15 89 | 98.72 142 |
|
| cascas | | | 91.20 288 | 90.08 301 | 94.58 234 | 94.97 330 | 89.16 242 | 93.65 396 | 97.59 168 | 79.90 423 | 89.40 305 | 92.92 384 | 75.36 340 | 98.36 259 | 92.14 186 | 94.75 239 | 96.23 281 |
|
| h-mvs33 | | | 94.15 150 | 93.52 161 | 96.04 142 | 97.81 133 | 90.22 197 | 97.62 130 | 97.58 169 | 95.19 34 | 96.74 83 | 97.45 150 | 83.67 199 | 99.61 84 | 95.85 95 | 79.73 412 | 98.29 185 |
|
| MGCFI-Net | | | 95.94 91 | 95.40 100 | 97.56 49 | 97.59 151 | 94.62 31 | 98.21 43 | 97.57 170 | 94.41 79 | 96.17 114 | 96.16 235 | 87.54 130 | 99.17 153 | 96.19 83 | 94.73 241 | 98.91 118 |
|
| MVSFormer | | | 95.37 106 | 95.16 107 | 95.99 149 | 96.34 247 | 91.21 152 | 98.22 41 | 97.57 170 | 91.42 190 | 96.22 112 | 97.32 158 | 86.20 154 | 97.92 319 | 94.07 146 | 99.05 98 | 98.85 129 |
|
| test_djsdf | | | 93.07 200 | 92.76 190 | 94.00 265 | 93.49 387 | 88.70 251 | 98.22 41 | 97.57 170 | 91.42 190 | 90.08 285 | 95.55 270 | 82.85 221 | 97.92 319 | 94.07 146 | 91.58 295 | 95.40 326 |
|
| OMC-MVS | | | 95.09 118 | 94.70 121 | 96.25 132 | 98.46 75 | 91.28 148 | 96.43 257 | 97.57 170 | 92.04 168 | 94.77 160 | 97.96 102 | 87.01 142 | 99.09 168 | 91.31 209 | 96.77 186 | 98.36 178 |
|
| PS-MVSNAJss | | | 93.74 173 | 93.51 162 | 94.44 242 | 93.91 372 | 89.28 236 | 97.75 104 | 97.56 174 | 92.50 152 | 89.94 287 | 96.54 215 | 88.65 105 | 98.18 274 | 93.83 155 | 90.90 309 | 95.86 296 |
|
| casdiffmvs_mvg |  | | 95.81 96 | 95.57 90 | 96.51 106 | 96.87 193 | 91.49 139 | 97.50 146 | 97.56 174 | 93.99 91 | 95.13 150 | 97.92 106 | 87.89 120 | 98.78 208 | 95.97 91 | 97.33 168 | 99.26 75 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| jajsoiax | | | 92.42 226 | 91.89 226 | 94.03 264 | 93.33 395 | 88.50 258 | 97.73 108 | 97.53 176 | 92.00 170 | 88.85 322 | 96.50 217 | 75.62 339 | 98.11 281 | 93.88 153 | 91.56 296 | 95.48 316 |
|
| mvs_tets | | | 92.31 232 | 91.76 229 | 93.94 273 | 93.41 392 | 88.29 263 | 97.63 128 | 97.53 176 | 92.04 168 | 88.76 325 | 96.45 219 | 74.62 349 | 98.09 286 | 93.91 151 | 91.48 297 | 95.45 321 |
|
| dcpmvs_2 | | | 96.37 76 | 97.05 33 | 94.31 251 | 98.96 51 | 84.11 363 | 97.56 137 | 97.51 178 | 93.92 93 | 97.43 61 | 98.52 49 | 92.75 32 | 99.32 135 | 97.32 49 | 99.50 36 | 99.51 45 |
|
| HQP_MVS | | | 93.78 172 | 93.43 167 | 94.82 217 | 96.21 251 | 89.99 202 | 97.74 106 | 97.51 178 | 94.85 51 | 91.34 251 | 96.64 205 | 81.32 254 | 98.60 235 | 93.02 173 | 92.23 283 | 95.86 296 |
|
| plane_prior5 | | | | | | | | | 97.51 178 | | | | | 98.60 235 | 93.02 173 | 92.23 283 | 95.86 296 |
|
| viewmanbaseed2359cas | | | 95.24 112 | 95.02 112 | 95.91 152 | 96.87 193 | 89.98 204 | 96.82 221 | 97.49 181 | 92.26 157 | 95.47 144 | 97.82 118 | 86.47 148 | 98.69 224 | 94.80 131 | 97.20 176 | 99.06 95 |
|
| reproduce_monomvs | | | 91.30 283 | 91.10 256 | 91.92 350 | 96.82 201 | 82.48 383 | 97.01 202 | 97.49 181 | 94.64 69 | 88.35 333 | 95.27 282 | 70.53 376 | 98.10 282 | 95.20 115 | 84.60 380 | 95.19 344 |
|
| PS-MVSNAJ | | | 95.37 106 | 95.33 103 | 95.49 183 | 97.35 161 | 90.66 180 | 95.31 333 | 97.48 183 | 93.85 96 | 96.51 98 | 95.70 262 | 88.65 105 | 99.65 73 | 94.80 131 | 98.27 135 | 96.17 285 |
|
| API-MVS | | | 94.84 129 | 94.49 131 | 95.90 153 | 97.90 128 | 92.00 119 | 97.80 98 | 97.48 183 | 89.19 274 | 94.81 158 | 96.71 198 | 88.84 101 | 99.17 153 | 88.91 269 | 98.76 112 | 96.53 274 |
|
| MG-MVS | | | 95.61 101 | 95.38 101 | 96.31 123 | 98.42 79 | 90.53 182 | 96.04 290 | 97.48 183 | 93.47 113 | 95.67 137 | 98.10 88 | 89.17 95 | 99.25 142 | 91.27 210 | 98.77 111 | 99.13 85 |
|
| MAR-MVS | | | 94.22 146 | 93.46 164 | 96.51 106 | 98.00 119 | 92.19 113 | 97.67 118 | 97.47 186 | 88.13 315 | 93.00 209 | 95.84 250 | 84.86 180 | 99.51 111 | 87.99 282 | 98.17 140 | 97.83 226 |
| 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 | | | 92.98 204 | 92.53 204 | 94.32 249 | 96.12 266 | 89.20 239 | 95.28 334 | 97.47 186 | 92.66 149 | 89.90 288 | 95.62 266 | 80.58 267 | 98.40 252 | 92.73 178 | 92.40 281 | 95.38 328 |
| Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
| UniMVSNet_ETH3D | | | 91.34 281 | 90.22 297 | 94.68 228 | 94.86 339 | 87.86 280 | 97.23 183 | 97.46 188 | 87.99 316 | 89.90 288 | 96.92 189 | 66.35 411 | 98.23 268 | 90.30 234 | 90.99 307 | 97.96 213 |
|
| nrg030 | | | 94.05 157 | 93.31 171 | 96.27 128 | 95.22 317 | 94.59 32 | 98.34 26 | 97.46 188 | 92.93 140 | 91.21 260 | 96.64 205 | 87.23 140 | 98.22 269 | 94.99 122 | 85.80 360 | 95.98 295 |
|
| XVG-OURS | | | 93.72 174 | 93.35 170 | 94.80 222 | 97.07 175 | 88.61 252 | 94.79 351 | 97.46 188 | 91.97 171 | 93.99 179 | 97.86 113 | 81.74 248 | 98.88 196 | 92.64 179 | 92.67 279 | 96.92 266 |
|
| LPG-MVS_test | | | 92.94 207 | 92.56 201 | 94.10 259 | 96.16 261 | 88.26 265 | 97.65 122 | 97.46 188 | 91.29 194 | 90.12 281 | 97.16 170 | 79.05 296 | 98.73 218 | 92.25 183 | 91.89 291 | 95.31 333 |
|
| LGP-MVS_train | | | | | 94.10 259 | 96.16 261 | 88.26 265 | | 97.46 188 | 91.29 194 | 90.12 281 | 97.16 170 | 79.05 296 | 98.73 218 | 92.25 183 | 91.89 291 | 95.31 333 |
|
| MVS | | | 91.71 255 | 90.44 284 | 95.51 181 | 95.20 319 | 91.59 135 | 96.04 290 | 97.45 193 | 73.44 440 | 87.36 358 | 95.60 267 | 85.42 167 | 99.10 165 | 85.97 326 | 97.46 160 | 95.83 300 |
|
| XVG-OURS-SEG-HR | | | 93.86 169 | 93.55 157 | 94.81 219 | 97.06 178 | 88.53 257 | 95.28 334 | 97.45 193 | 91.68 178 | 94.08 178 | 97.68 130 | 82.41 233 | 98.90 195 | 93.84 154 | 92.47 280 | 96.98 262 |
|
| baseline | | | 95.58 102 | 95.42 99 | 96.08 138 | 96.78 208 | 90.41 188 | 97.16 190 | 97.45 193 | 93.69 102 | 95.65 138 | 97.85 114 | 87.29 138 | 98.68 226 | 95.66 101 | 97.25 174 | 99.13 85 |
|
| ab-mvs | | | 93.57 180 | 92.55 202 | 96.64 89 | 97.28 164 | 91.96 122 | 95.40 327 | 97.45 193 | 89.81 256 | 93.22 206 | 96.28 228 | 79.62 287 | 99.46 120 | 90.74 223 | 93.11 271 | 98.50 161 |
|
| xiu_mvs_v2_base | | | 95.32 108 | 95.29 104 | 95.40 188 | 97.22 166 | 90.50 183 | 95.44 326 | 97.44 197 | 93.70 101 | 96.46 102 | 96.18 232 | 88.59 109 | 99.53 106 | 94.79 134 | 97.81 153 | 96.17 285 |
|
| 1314 | | | 92.81 216 | 92.03 219 | 95.14 198 | 95.33 309 | 89.52 223 | 96.04 290 | 97.44 197 | 87.72 329 | 86.25 379 | 95.33 278 | 83.84 196 | 98.79 207 | 89.26 259 | 97.05 181 | 97.11 260 |
|
| casdiffmvs |  | | 95.64 99 | 95.49 93 | 96.08 138 | 96.76 213 | 90.45 185 | 97.29 176 | 97.44 197 | 94.00 90 | 95.46 145 | 97.98 100 | 87.52 133 | 98.73 218 | 95.64 105 | 97.33 168 | 99.08 92 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| XXY-MVS | | | 92.16 240 | 91.23 251 | 94.95 213 | 94.75 344 | 90.94 167 | 97.47 155 | 97.43 200 | 89.14 275 | 88.90 318 | 96.43 220 | 79.71 284 | 98.24 267 | 89.56 250 | 87.68 341 | 95.67 312 |
|
| anonymousdsp | | | 92.16 240 | 91.55 237 | 93.97 269 | 92.58 410 | 89.55 220 | 97.51 145 | 97.42 201 | 89.42 268 | 88.40 332 | 94.84 301 | 80.66 265 | 97.88 324 | 91.87 195 | 91.28 301 | 94.48 379 |
|
| Effi-MVS+ | | | 94.93 124 | 94.45 133 | 96.36 121 | 96.61 218 | 91.47 142 | 96.41 259 | 97.41 202 | 91.02 212 | 94.50 166 | 95.92 246 | 87.53 131 | 98.78 208 | 93.89 152 | 96.81 185 | 98.84 132 |
|
| RRT-MVS | | | 94.51 139 | 94.35 136 | 94.98 209 | 96.40 242 | 86.55 314 | 97.56 137 | 97.41 202 | 93.19 124 | 94.93 153 | 97.04 179 | 79.12 294 | 99.30 139 | 96.19 83 | 97.32 170 | 99.09 91 |
|
| HQP3-MVS | | | | | | | | | 97.39 204 | | | | | | | 92.10 288 | |
|
| HQP-MVS | | | 93.19 194 | 92.74 193 | 94.54 237 | 95.86 275 | 89.33 232 | 96.65 241 | 97.39 204 | 93.55 105 | 90.14 275 | 95.87 248 | 80.95 258 | 98.50 245 | 92.13 189 | 92.10 288 | 95.78 304 |
|
| PLC |  | 91.00 6 | 94.11 154 | 93.43 167 | 96.13 137 | 98.58 73 | 91.15 161 | 96.69 237 | 97.39 204 | 87.29 339 | 91.37 250 | 96.71 198 | 88.39 110 | 99.52 110 | 87.33 302 | 97.13 179 | 97.73 230 |
| Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
| v7n | | | 90.76 305 | 89.86 312 | 93.45 301 | 93.54 384 | 87.60 286 | 97.70 116 | 97.37 207 | 88.85 288 | 87.65 351 | 94.08 349 | 81.08 257 | 98.10 282 | 84.68 343 | 83.79 393 | 94.66 376 |
|
| UnsupCasMVSNet_eth | | | 85.99 382 | 84.45 386 | 90.62 385 | 89.97 428 | 82.40 386 | 93.62 397 | 97.37 207 | 89.86 252 | 78.59 430 | 92.37 394 | 65.25 419 | 95.35 420 | 82.27 371 | 70.75 438 | 94.10 390 |
|
| ACMM | | 89.79 8 | 92.96 205 | 92.50 206 | 94.35 246 | 96.30 249 | 88.71 250 | 97.58 133 | 97.36 209 | 91.40 192 | 90.53 268 | 96.65 204 | 79.77 283 | 98.75 215 | 91.24 211 | 91.64 293 | 95.59 314 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| xiu_mvs_v1_base_debu | | | 95.01 119 | 94.76 117 | 95.75 165 | 96.58 221 | 91.71 128 | 96.25 277 | 97.35 210 | 92.99 133 | 96.70 85 | 96.63 209 | 82.67 225 | 99.44 123 | 96.22 76 | 97.46 160 | 96.11 291 |
|
| xiu_mvs_v1_base | | | 95.01 119 | 94.76 117 | 95.75 165 | 96.58 221 | 91.71 128 | 96.25 277 | 97.35 210 | 92.99 133 | 96.70 85 | 96.63 209 | 82.67 225 | 99.44 123 | 96.22 76 | 97.46 160 | 96.11 291 |
|
| xiu_mvs_v1_base_debi | | | 95.01 119 | 94.76 117 | 95.75 165 | 96.58 221 | 91.71 128 | 96.25 277 | 97.35 210 | 92.99 133 | 96.70 85 | 96.63 209 | 82.67 225 | 99.44 123 | 96.22 76 | 97.46 160 | 96.11 291 |
|
| diffmvs |  | | 95.25 111 | 95.13 108 | 95.63 173 | 96.43 241 | 89.34 231 | 95.99 294 | 97.35 210 | 92.83 144 | 96.31 108 | 97.37 156 | 86.44 149 | 98.67 227 | 96.26 73 | 97.19 177 | 98.87 127 |
| 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 | | | 94.71 135 | 94.02 144 | 96.79 85 | 97.71 139 | 92.05 116 | 96.59 250 | 97.35 210 | 90.61 231 | 94.64 162 | 96.93 186 | 86.41 150 | 99.39 128 | 91.20 212 | 94.71 242 | 98.94 112 |
|
| mamba_test_0407 | | | 94.54 138 | 94.12 143 | 95.80 161 | 96.79 204 | 90.38 190 | 96.79 224 | 97.29 215 | 91.24 198 | 93.68 186 | 97.60 141 | 85.03 174 | 98.67 227 | 92.14 186 | 96.51 194 | 98.35 180 |
|
| mamba_0404 | | | 94.73 134 | 94.31 138 | 95.98 150 | 97.05 180 | 90.90 170 | 97.01 202 | 97.29 215 | 91.24 198 | 94.17 175 | 97.60 141 | 85.03 174 | 98.76 212 | 92.14 186 | 97.30 171 | 98.29 185 |
|
| F-COLMAP | | | 93.58 178 | 92.98 182 | 95.37 189 | 98.40 81 | 88.98 245 | 97.18 188 | 97.29 215 | 87.75 328 | 90.49 269 | 97.10 176 | 85.21 171 | 99.50 114 | 86.70 312 | 96.72 189 | 97.63 234 |
|
| VortexMVS | | | 92.88 211 | 92.64 197 | 93.58 294 | 96.58 221 | 87.53 287 | 96.93 210 | 97.28 218 | 92.78 147 | 89.75 293 | 94.99 292 | 82.73 224 | 97.76 337 | 94.60 139 | 88.16 336 | 95.46 319 |
|
| XVG-ACMP-BASELINE | | | 90.93 301 | 90.21 298 | 93.09 314 | 94.31 363 | 85.89 331 | 95.33 331 | 97.26 219 | 91.06 211 | 89.38 306 | 95.44 276 | 68.61 394 | 98.60 235 | 89.46 252 | 91.05 305 | 94.79 369 |
|
| PCF-MVS | | 89.48 11 | 91.56 265 | 89.95 309 | 96.36 121 | 96.60 219 | 92.52 99 | 92.51 416 | 97.26 219 | 79.41 425 | 88.90 318 | 96.56 214 | 84.04 195 | 99.55 102 | 77.01 410 | 97.30 171 | 97.01 261 |
| Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
| ACMP | | 89.59 10 | 92.62 220 | 92.14 215 | 94.05 262 | 96.40 242 | 88.20 268 | 97.36 168 | 97.25 221 | 91.52 185 | 88.30 336 | 96.64 205 | 78.46 308 | 98.72 222 | 91.86 196 | 91.48 297 | 95.23 340 |
| Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
| icg_test_0407_2 | | | 93.58 178 | 93.46 164 | 93.94 273 | 96.19 255 | 86.16 325 | 93.73 391 | 97.24 222 | 91.54 181 | 93.50 195 | 97.04 179 | 85.64 164 | 96.91 389 | 90.68 225 | 95.59 218 | 98.76 135 |
|
| icg_test_0407 | | | 93.94 164 | 93.75 150 | 94.49 239 | 96.19 255 | 86.16 325 | 96.35 267 | 97.24 222 | 91.54 181 | 93.50 195 | 97.04 179 | 85.64 164 | 98.54 242 | 90.68 225 | 95.59 218 | 98.76 135 |
|
| ICG_test_0404 | | | 92.44 224 | 91.92 224 | 94.00 265 | 96.19 255 | 86.16 325 | 93.84 388 | 97.24 222 | 91.54 181 | 88.17 342 | 97.04 179 | 76.96 326 | 97.09 380 | 90.68 225 | 95.59 218 | 98.76 135 |
|
| icg_test_0403 | | | 93.98 162 | 93.79 149 | 94.55 236 | 96.19 255 | 86.16 325 | 96.35 267 | 97.24 222 | 91.54 181 | 93.59 190 | 97.04 179 | 85.86 159 | 98.73 218 | 90.68 225 | 95.59 218 | 98.76 135 |
|
| OPM-MVS | | | 93.28 190 | 92.76 190 | 94.82 217 | 94.63 350 | 90.77 175 | 96.65 241 | 97.18 226 | 93.72 99 | 91.68 244 | 97.26 165 | 79.33 291 | 98.63 232 | 92.13 189 | 92.28 282 | 95.07 347 |
| Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
| PatchMatch-RL | | | 92.90 209 | 92.02 220 | 95.56 177 | 98.19 103 | 90.80 173 | 95.27 336 | 97.18 226 | 87.96 317 | 91.86 239 | 95.68 263 | 80.44 270 | 98.99 185 | 84.01 352 | 97.54 159 | 96.89 267 |
|
| alignmvs | | | 95.87 95 | 95.23 105 | 97.78 32 | 97.56 157 | 95.19 21 | 97.86 85 | 97.17 228 | 94.39 81 | 96.47 101 | 96.40 222 | 85.89 158 | 99.20 147 | 96.21 80 | 95.11 232 | 98.95 111 |
|
| MVS_Test | | | 94.89 126 | 94.62 123 | 95.68 171 | 96.83 199 | 89.55 220 | 96.70 235 | 97.17 228 | 91.17 204 | 95.60 139 | 96.11 241 | 87.87 122 | 98.76 212 | 93.01 175 | 97.17 178 | 98.72 142 |
|
| Fast-Effi-MVS+ | | | 93.46 184 | 92.75 192 | 95.59 176 | 96.77 210 | 90.03 199 | 96.81 223 | 97.13 230 | 88.19 310 | 91.30 254 | 94.27 337 | 86.21 153 | 98.63 232 | 87.66 294 | 96.46 200 | 98.12 199 |
|
| EI-MVSNet | | | 93.03 202 | 92.88 186 | 93.48 299 | 95.77 281 | 86.98 300 | 96.44 255 | 97.12 231 | 90.66 227 | 91.30 254 | 97.64 137 | 86.56 145 | 98.05 294 | 89.91 240 | 90.55 313 | 95.41 323 |
|
| MVSTER | | | 93.20 193 | 92.81 189 | 94.37 245 | 96.56 225 | 89.59 217 | 97.06 196 | 97.12 231 | 91.24 198 | 91.30 254 | 95.96 244 | 82.02 241 | 98.05 294 | 93.48 160 | 90.55 313 | 95.47 318 |
|
| viewmambaseed2359dif | | | 94.28 144 | 94.14 141 | 94.71 227 | 96.21 251 | 86.97 301 | 95.93 297 | 97.11 233 | 89.00 281 | 95.00 152 | 97.70 127 | 86.02 157 | 98.59 239 | 93.71 157 | 96.59 193 | 98.57 154 |
|
| test_yl | | | 94.78 132 | 94.23 139 | 96.43 114 | 97.74 137 | 91.22 150 | 96.85 217 | 97.10 234 | 91.23 201 | 95.71 133 | 96.93 186 | 84.30 188 | 99.31 137 | 93.10 168 | 95.12 230 | 98.75 139 |
|
| DCV-MVSNet | | | 94.78 132 | 94.23 139 | 96.43 114 | 97.74 137 | 91.22 150 | 96.85 217 | 97.10 234 | 91.23 201 | 95.71 133 | 96.93 186 | 84.30 188 | 99.31 137 | 93.10 168 | 95.12 230 | 98.75 139 |
|
| LTVRE_ROB | | 88.41 13 | 90.99 297 | 89.92 311 | 94.19 255 | 96.18 259 | 89.55 220 | 96.31 273 | 97.09 236 | 87.88 320 | 85.67 383 | 95.91 247 | 78.79 304 | 98.57 240 | 81.50 374 | 89.98 318 | 94.44 382 |
| 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 |
| test_fmvs1_n | | | 92.73 218 | 92.88 186 | 92.29 340 | 96.08 269 | 81.05 397 | 97.98 66 | 97.08 237 | 90.72 222 | 96.79 81 | 98.18 85 | 63.07 423 | 98.45 249 | 97.62 38 | 98.42 129 | 97.36 249 |
|
| v10 | | | 91.04 295 | 90.23 295 | 93.49 298 | 94.12 366 | 88.16 271 | 97.32 173 | 97.08 237 | 88.26 309 | 88.29 337 | 94.22 342 | 82.17 238 | 97.97 306 | 86.45 316 | 84.12 387 | 94.33 385 |
|
| mamba_0408 | | | 93.70 175 | 92.99 179 | 95.83 158 | 96.79 204 | 90.38 190 | 88.69 441 | 97.07 239 | 90.96 214 | 93.68 186 | 97.31 160 | 84.97 177 | 98.76 212 | 90.95 216 | 96.51 194 | 98.35 180 |
|
| mamba_test_0407_2 | | | 93.51 183 | 92.99 179 | 95.05 202 | 96.79 204 | 90.38 190 | 88.69 441 | 97.07 239 | 90.96 214 | 93.68 186 | 97.31 160 | 84.97 177 | 96.42 400 | 90.95 216 | 96.51 194 | 98.35 180 |
|
| v144192 | | | 91.06 294 | 90.28 291 | 93.39 302 | 93.66 381 | 87.23 294 | 96.83 220 | 97.07 239 | 87.43 335 | 89.69 296 | 94.28 336 | 81.48 251 | 98.00 301 | 87.18 306 | 84.92 376 | 94.93 355 |
|
| v1192 | | | 91.07 293 | 90.23 295 | 93.58 294 | 93.70 378 | 87.82 282 | 96.73 231 | 97.07 239 | 87.77 326 | 89.58 299 | 94.32 334 | 80.90 262 | 97.97 306 | 86.52 314 | 85.48 363 | 94.95 351 |
|
| v8 | | | 91.29 285 | 90.53 283 | 93.57 296 | 94.15 365 | 88.12 272 | 97.34 170 | 97.06 243 | 88.99 282 | 88.32 335 | 94.26 339 | 83.08 212 | 98.01 300 | 87.62 296 | 83.92 391 | 94.57 378 |
|
| mvs_anonymous | | | 93.82 170 | 93.74 151 | 94.06 261 | 96.44 240 | 85.41 340 | 95.81 304 | 97.05 244 | 89.85 254 | 90.09 284 | 96.36 224 | 87.44 135 | 97.75 339 | 93.97 148 | 96.69 190 | 99.02 97 |
|
| IterMVS-LS | | | 92.29 234 | 91.94 223 | 93.34 304 | 96.25 250 | 86.97 301 | 96.57 253 | 97.05 244 | 90.67 225 | 89.50 304 | 94.80 304 | 86.59 144 | 97.64 347 | 89.91 240 | 86.11 358 | 95.40 326 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| v1921920 | | | 90.85 303 | 90.03 306 | 93.29 306 | 93.55 383 | 86.96 303 | 96.74 230 | 97.04 246 | 87.36 337 | 89.52 303 | 94.34 331 | 80.23 275 | 97.97 306 | 86.27 317 | 85.21 369 | 94.94 353 |
|
| CDS-MVSNet | | | 94.14 153 | 93.54 158 | 95.93 151 | 96.18 259 | 91.46 143 | 96.33 271 | 97.04 246 | 88.97 284 | 93.56 191 | 96.51 216 | 87.55 129 | 97.89 323 | 89.80 243 | 95.95 206 | 98.44 171 |
| Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
| SSC-MVS3.2 | | | 89.74 339 | 89.26 332 | 91.19 374 | 95.16 320 | 80.29 408 | 94.53 358 | 97.03 248 | 91.79 174 | 88.86 321 | 94.10 346 | 69.94 383 | 97.82 329 | 85.29 335 | 86.66 354 | 95.45 321 |
|
| v1144 | | | 91.37 278 | 90.60 279 | 93.68 289 | 93.89 373 | 88.23 267 | 96.84 219 | 97.03 248 | 88.37 306 | 89.69 296 | 94.39 326 | 82.04 240 | 97.98 303 | 87.80 286 | 85.37 365 | 94.84 361 |
|
| v1240 | | | 90.70 309 | 89.85 313 | 93.23 308 | 93.51 386 | 86.80 304 | 96.61 247 | 97.02 250 | 87.16 342 | 89.58 299 | 94.31 335 | 79.55 288 | 97.98 303 | 85.52 332 | 85.44 364 | 94.90 358 |
|
| EPP-MVSNet | | | 95.22 114 | 95.04 111 | 95.76 163 | 97.49 158 | 89.56 219 | 98.67 11 | 97.00 251 | 90.69 223 | 94.24 172 | 97.62 139 | 89.79 90 | 98.81 205 | 93.39 164 | 96.49 198 | 98.92 117 |
|
| V42 | | | 91.58 264 | 90.87 263 | 93.73 284 | 94.05 369 | 88.50 258 | 97.32 173 | 96.97 252 | 88.80 294 | 89.71 294 | 94.33 332 | 82.54 229 | 98.05 294 | 89.01 266 | 85.07 372 | 94.64 377 |
|
| test_fmvs1 | | | 93.21 192 | 93.53 159 | 92.25 343 | 96.55 227 | 81.20 396 | 97.40 164 | 96.96 253 | 90.68 224 | 96.80 79 | 98.04 94 | 69.25 389 | 98.40 252 | 97.58 39 | 98.50 122 | 97.16 259 |
|
| FMVSNet2 | | | 91.31 282 | 90.08 301 | 94.99 207 | 96.51 233 | 92.21 110 | 97.41 160 | 96.95 254 | 88.82 291 | 88.62 327 | 94.75 306 | 73.87 353 | 97.42 368 | 85.20 338 | 88.55 333 | 95.35 330 |
|
| ACMH | | 87.59 16 | 90.53 314 | 89.42 328 | 93.87 278 | 96.21 251 | 87.92 277 | 97.24 179 | 96.94 255 | 88.45 304 | 83.91 403 | 96.27 229 | 71.92 365 | 98.62 234 | 84.43 346 | 89.43 324 | 95.05 349 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| GBi-Net | | | 91.35 279 | 90.27 292 | 94.59 230 | 96.51 233 | 91.18 157 | 97.50 146 | 96.93 256 | 88.82 291 | 89.35 307 | 94.51 319 | 73.87 353 | 97.29 375 | 86.12 322 | 88.82 328 | 95.31 333 |
|
| test1 | | | 91.35 279 | 90.27 292 | 94.59 230 | 96.51 233 | 91.18 157 | 97.50 146 | 96.93 256 | 88.82 291 | 89.35 307 | 94.51 319 | 73.87 353 | 97.29 375 | 86.12 322 | 88.82 328 | 95.31 333 |
|
| FMVSNet3 | | | 91.78 253 | 90.69 277 | 95.03 205 | 96.53 230 | 92.27 108 | 97.02 199 | 96.93 256 | 89.79 257 | 89.35 307 | 94.65 312 | 77.01 324 | 97.47 363 | 86.12 322 | 88.82 328 | 95.35 330 |
|
| FMVSNet1 | | | 89.88 334 | 88.31 347 | 94.59 230 | 95.41 299 | 91.18 157 | 97.50 146 | 96.93 256 | 86.62 350 | 87.41 356 | 94.51 319 | 65.94 416 | 97.29 375 | 83.04 361 | 87.43 344 | 95.31 333 |
|
| GeoE | | | 93.89 167 | 93.28 172 | 95.72 169 | 96.96 190 | 89.75 212 | 98.24 39 | 96.92 260 | 89.47 265 | 92.12 230 | 97.21 168 | 84.42 186 | 98.39 257 | 87.71 289 | 96.50 197 | 99.01 100 |
|
| SymmetryMVS | | | 95.94 91 | 95.54 91 | 97.15 70 | 97.85 130 | 92.90 83 | 97.99 63 | 96.91 261 | 95.92 14 | 96.57 96 | 97.93 103 | 85.34 168 | 99.50 114 | 94.99 122 | 96.39 201 | 99.05 96 |
|
| miper_enhance_ethall | | | 91.54 268 | 91.01 259 | 93.15 312 | 95.35 305 | 87.07 299 | 93.97 380 | 96.90 262 | 86.79 348 | 89.17 314 | 93.43 378 | 86.55 146 | 97.64 347 | 89.97 239 | 86.93 349 | 94.74 373 |
|
| eth_miper_zixun_eth | | | 91.02 296 | 90.59 280 | 92.34 338 | 95.33 309 | 84.35 359 | 94.10 377 | 96.90 262 | 88.56 300 | 88.84 323 | 94.33 332 | 84.08 193 | 97.60 352 | 88.77 272 | 84.37 385 | 95.06 348 |
|
| TAMVS | | | 94.01 159 | 93.46 164 | 95.64 172 | 96.16 261 | 90.45 185 | 96.71 234 | 96.89 264 | 89.27 272 | 93.46 198 | 96.92 189 | 87.29 138 | 97.94 316 | 88.70 274 | 95.74 212 | 98.53 157 |
|
| miper_ehance_all_eth | | | 91.59 262 | 91.13 255 | 92.97 318 | 95.55 291 | 86.57 312 | 94.47 361 | 96.88 265 | 87.77 326 | 88.88 320 | 94.01 351 | 86.22 152 | 97.54 356 | 89.49 251 | 86.93 349 | 94.79 369 |
|
| v2v482 | | | 91.59 262 | 90.85 266 | 93.80 281 | 93.87 374 | 88.17 270 | 96.94 209 | 96.88 265 | 89.54 262 | 89.53 302 | 94.90 298 | 81.70 249 | 98.02 299 | 89.25 260 | 85.04 374 | 95.20 341 |
|
| CNLPA | | | 94.28 144 | 93.53 159 | 96.52 102 | 98.38 84 | 92.55 98 | 96.59 250 | 96.88 265 | 90.13 247 | 91.91 236 | 97.24 166 | 85.21 171 | 99.09 168 | 87.64 295 | 97.83 152 | 97.92 216 |
|
| PAPM | | | 91.52 269 | 90.30 290 | 95.20 195 | 95.30 312 | 89.83 210 | 93.38 402 | 96.85 268 | 86.26 358 | 88.59 328 | 95.80 253 | 84.88 179 | 98.15 276 | 75.67 415 | 95.93 207 | 97.63 234 |
|
| c3_l | | | 91.38 276 | 90.89 262 | 92.88 322 | 95.58 289 | 86.30 319 | 94.68 353 | 96.84 269 | 88.17 311 | 88.83 324 | 94.23 340 | 85.65 163 | 97.47 363 | 89.36 255 | 84.63 378 | 94.89 359 |
|
| pm-mvs1 | | | 90.72 308 | 89.65 323 | 93.96 270 | 94.29 364 | 89.63 214 | 97.79 100 | 96.82 270 | 89.07 277 | 86.12 381 | 95.48 275 | 78.61 306 | 97.78 334 | 86.97 310 | 81.67 404 | 94.46 380 |
|
| test_vis1_n | | | 92.37 229 | 92.26 213 | 92.72 328 | 94.75 344 | 82.64 379 | 98.02 60 | 96.80 271 | 91.18 203 | 97.77 53 | 97.93 103 | 58.02 433 | 98.29 265 | 97.63 36 | 98.21 137 | 97.23 257 |
|
| CMPMVS |  | 62.92 21 | 85.62 387 | 84.92 382 | 87.74 411 | 89.14 433 | 73.12 441 | 94.17 375 | 96.80 271 | 73.98 437 | 73.65 439 | 94.93 296 | 66.36 410 | 97.61 351 | 83.95 354 | 91.28 301 | 92.48 417 |
| M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
| MS-PatchMatch | | | 90.27 321 | 89.77 317 | 91.78 359 | 94.33 361 | 84.72 356 | 95.55 320 | 96.73 273 | 86.17 360 | 86.36 378 | 95.28 281 | 71.28 370 | 97.80 332 | 84.09 351 | 98.14 141 | 92.81 409 |
|
| Effi-MVS+-dtu | | | 93.08 199 | 93.21 174 | 92.68 331 | 96.02 272 | 83.25 373 | 97.14 192 | 96.72 274 | 93.85 96 | 91.20 261 | 93.44 375 | 83.08 212 | 98.30 264 | 91.69 202 | 95.73 213 | 96.50 276 |
|
| TSAR-MVS + GP. | | | 96.69 62 | 96.49 66 | 97.27 63 | 98.31 87 | 93.39 63 | 96.79 224 | 96.72 274 | 94.17 85 | 97.44 59 | 97.66 133 | 92.76 31 | 99.33 133 | 96.86 57 | 97.76 156 | 99.08 92 |
|
| 1112_ss | | | 93.37 187 | 92.42 209 | 96.21 133 | 97.05 180 | 90.99 164 | 96.31 273 | 96.72 274 | 86.87 347 | 89.83 291 | 96.69 202 | 86.51 147 | 99.14 160 | 88.12 279 | 93.67 265 | 98.50 161 |
|
| PVSNet | | 86.66 18 | 92.24 237 | 91.74 232 | 93.73 284 | 97.77 135 | 83.69 370 | 92.88 411 | 96.72 274 | 87.91 319 | 93.00 209 | 94.86 300 | 78.51 307 | 99.05 180 | 86.53 313 | 97.45 164 | 98.47 166 |
|
| miper_lstm_enhance | | | 90.50 317 | 90.06 305 | 91.83 355 | 95.33 309 | 83.74 367 | 93.86 386 | 96.70 278 | 87.56 333 | 87.79 348 | 93.81 359 | 83.45 204 | 96.92 388 | 87.39 300 | 84.62 379 | 94.82 364 |
|
| v148 | | | 90.99 297 | 90.38 286 | 92.81 325 | 93.83 375 | 85.80 332 | 96.78 228 | 96.68 279 | 89.45 267 | 88.75 326 | 93.93 355 | 82.96 218 | 97.82 329 | 87.83 285 | 83.25 396 | 94.80 367 |
|
| ACMH+ | | 87.92 14 | 90.20 325 | 89.18 334 | 93.25 307 | 96.48 236 | 86.45 316 | 96.99 205 | 96.68 279 | 88.83 290 | 84.79 392 | 96.22 231 | 70.16 380 | 98.53 243 | 84.42 347 | 88.04 337 | 94.77 372 |
|
| CANet_DTU | | | 94.37 142 | 93.65 154 | 96.55 99 | 96.46 239 | 92.13 114 | 96.21 281 | 96.67 281 | 94.38 82 | 93.53 194 | 97.03 184 | 79.34 290 | 99.71 61 | 90.76 222 | 98.45 127 | 97.82 227 |
|
| cl____ | | | 90.96 300 | 90.32 288 | 92.89 321 | 95.37 303 | 86.21 322 | 94.46 363 | 96.64 282 | 87.82 322 | 88.15 343 | 94.18 343 | 82.98 216 | 97.54 356 | 87.70 290 | 85.59 361 | 94.92 357 |
|
| HY-MVS | | 89.66 9 | 93.87 168 | 92.95 183 | 96.63 93 | 97.10 174 | 92.49 100 | 95.64 317 | 96.64 282 | 89.05 279 | 93.00 209 | 95.79 256 | 85.77 162 | 99.45 122 | 89.16 265 | 94.35 244 | 97.96 213 |
|
| Test_1112_low_res | | | 92.84 214 | 91.84 227 | 95.85 157 | 97.04 182 | 89.97 206 | 95.53 322 | 96.64 282 | 85.38 370 | 89.65 298 | 95.18 286 | 85.86 159 | 99.10 165 | 87.70 290 | 93.58 270 | 98.49 163 |
|
| DIV-MVS_self_test | | | 90.97 299 | 90.33 287 | 92.88 322 | 95.36 304 | 86.19 324 | 94.46 363 | 96.63 285 | 87.82 322 | 88.18 341 | 94.23 340 | 82.99 215 | 97.53 358 | 87.72 287 | 85.57 362 | 94.93 355 |
|
| Fast-Effi-MVS+-dtu | | | 92.29 234 | 91.99 221 | 93.21 310 | 95.27 313 | 85.52 338 | 97.03 197 | 96.63 285 | 92.09 166 | 89.11 316 | 95.14 288 | 80.33 273 | 98.08 287 | 87.54 298 | 94.74 240 | 96.03 294 |
|
| UnsupCasMVSNet_bld | | | 82.13 403 | 79.46 408 | 90.14 392 | 88.00 441 | 82.47 384 | 90.89 429 | 96.62 287 | 78.94 427 | 75.61 434 | 84.40 445 | 56.63 436 | 96.31 402 | 77.30 407 | 66.77 446 | 91.63 427 |
|
| cl22 | | | 91.21 287 | 90.56 282 | 93.14 313 | 96.09 268 | 86.80 304 | 94.41 365 | 96.58 288 | 87.80 324 | 88.58 329 | 93.99 353 | 80.85 263 | 97.62 350 | 89.87 242 | 86.93 349 | 94.99 350 |
|
| jason | | | 94.84 129 | 94.39 135 | 96.18 135 | 95.52 292 | 90.93 168 | 96.09 288 | 96.52 289 | 89.28 271 | 96.01 122 | 97.32 158 | 84.70 181 | 98.77 211 | 95.15 118 | 98.91 107 | 98.85 129 |
| jason: jason. |
| tt0805 | | | 91.09 292 | 90.07 304 | 94.16 257 | 95.61 287 | 88.31 262 | 97.56 137 | 96.51 290 | 89.56 261 | 89.17 314 | 95.64 265 | 67.08 408 | 98.38 258 | 91.07 214 | 88.44 334 | 95.80 302 |
|
| AUN-MVS | | | 91.76 254 | 90.75 272 | 94.81 219 | 97.00 186 | 88.57 254 | 96.65 241 | 96.49 291 | 89.63 259 | 92.15 228 | 96.12 237 | 78.66 305 | 98.50 245 | 90.83 218 | 79.18 415 | 97.36 249 |
|
| hse-mvs2 | | | 93.45 185 | 92.99 179 | 94.81 219 | 97.02 184 | 88.59 253 | 96.69 237 | 96.47 292 | 95.19 34 | 96.74 83 | 96.16 235 | 83.67 199 | 98.48 248 | 95.85 95 | 79.13 416 | 97.35 251 |
|
| SD_0403 | | | 90.01 329 | 90.02 307 | 89.96 395 | 95.65 286 | 76.76 429 | 95.76 308 | 96.46 293 | 90.58 234 | 86.59 375 | 96.29 227 | 82.12 239 | 94.78 424 | 73.00 429 | 93.76 263 | 98.35 180 |
|
| EG-PatchMatch MVS | | | 87.02 369 | 85.44 374 | 91.76 361 | 92.67 407 | 85.00 350 | 96.08 289 | 96.45 294 | 83.41 400 | 79.52 426 | 93.49 372 | 57.10 435 | 97.72 341 | 79.34 398 | 90.87 310 | 92.56 414 |
|
| KD-MVS_self_test | | | 85.95 383 | 84.95 381 | 88.96 405 | 89.55 432 | 79.11 423 | 95.13 343 | 96.42 295 | 85.91 363 | 84.07 401 | 90.48 417 | 70.03 382 | 94.82 423 | 80.04 390 | 72.94 435 | 92.94 407 |
|
| pmmvs6 | | | 87.81 361 | 86.19 369 | 92.69 330 | 91.32 420 | 86.30 319 | 97.34 170 | 96.41 296 | 80.59 421 | 84.05 402 | 94.37 328 | 67.37 403 | 97.67 344 | 84.75 342 | 79.51 414 | 94.09 392 |
|
| PMMVS | | | 92.86 212 | 92.34 210 | 94.42 244 | 94.92 335 | 86.73 307 | 94.53 358 | 96.38 297 | 84.78 382 | 94.27 171 | 95.12 290 | 83.13 211 | 98.40 252 | 91.47 206 | 96.49 198 | 98.12 199 |
|
| RPSCF | | | 90.75 306 | 90.86 264 | 90.42 388 | 96.84 197 | 76.29 432 | 95.61 318 | 96.34 298 | 83.89 391 | 91.38 249 | 97.87 111 | 76.45 330 | 98.78 208 | 87.16 307 | 92.23 283 | 96.20 283 |
|
| BP-MVS1 | | | 95.89 93 | 95.49 93 | 97.08 77 | 96.67 215 | 93.20 73 | 98.08 54 | 96.32 299 | 94.56 70 | 96.32 107 | 97.84 116 | 84.07 194 | 99.15 157 | 96.75 59 | 98.78 110 | 98.90 121 |
|
| MSDG | | | 91.42 274 | 90.24 294 | 94.96 212 | 97.15 172 | 88.91 246 | 93.69 394 | 96.32 299 | 85.72 366 | 86.93 371 | 96.47 218 | 80.24 274 | 98.98 186 | 80.57 387 | 95.05 233 | 96.98 262 |
|
| WBMVS | | | 90.69 311 | 89.99 308 | 92.81 325 | 96.48 236 | 85.00 350 | 95.21 341 | 96.30 301 | 89.46 266 | 89.04 317 | 94.05 350 | 72.45 363 | 97.82 329 | 89.46 252 | 87.41 346 | 95.61 313 |
|
| OurMVSNet-221017-0 | | | 90.51 316 | 90.19 299 | 91.44 367 | 93.41 392 | 81.25 394 | 96.98 206 | 96.28 302 | 91.68 178 | 86.55 376 | 96.30 226 | 74.20 352 | 97.98 303 | 88.96 268 | 87.40 347 | 95.09 346 |
|
| MVP-Stereo | | | 90.74 307 | 90.08 301 | 92.71 329 | 93.19 397 | 88.20 268 | 95.86 301 | 96.27 303 | 86.07 361 | 84.86 391 | 94.76 305 | 77.84 319 | 97.75 339 | 83.88 356 | 98.01 147 | 92.17 424 |
| Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
| lupinMVS | | | 94.99 123 | 94.56 126 | 96.29 127 | 96.34 247 | 91.21 152 | 95.83 303 | 96.27 303 | 88.93 286 | 96.22 112 | 96.88 191 | 86.20 154 | 98.85 199 | 95.27 114 | 99.05 98 | 98.82 133 |
|
| BH-untuned | | | 92.94 207 | 92.62 199 | 93.92 277 | 97.22 166 | 86.16 325 | 96.40 263 | 96.25 305 | 90.06 248 | 89.79 292 | 96.17 234 | 83.19 208 | 98.35 260 | 87.19 305 | 97.27 173 | 97.24 256 |
|
| CL-MVSNet_self_test | | | 86.31 378 | 85.15 378 | 89.80 397 | 88.83 436 | 81.74 392 | 93.93 383 | 96.22 306 | 86.67 349 | 85.03 389 | 90.80 415 | 78.09 315 | 94.50 425 | 74.92 418 | 71.86 437 | 93.15 405 |
|
| IS-MVSNet | | | 94.90 125 | 94.52 130 | 96.05 141 | 97.67 141 | 90.56 181 | 98.44 22 | 96.22 306 | 93.21 121 | 93.99 179 | 97.74 125 | 85.55 166 | 98.45 249 | 89.98 238 | 97.86 151 | 99.14 84 |
|
| FA-MVS(test-final) | | | 93.52 182 | 92.92 184 | 95.31 192 | 96.77 210 | 88.54 256 | 94.82 350 | 96.21 308 | 89.61 260 | 94.20 173 | 95.25 284 | 83.24 206 | 99.14 160 | 90.01 237 | 96.16 203 | 98.25 187 |
|
| GA-MVS | | | 91.38 276 | 90.31 289 | 94.59 230 | 94.65 349 | 87.62 285 | 94.34 368 | 96.19 309 | 90.73 221 | 90.35 272 | 93.83 356 | 71.84 366 | 97.96 310 | 87.22 304 | 93.61 268 | 98.21 190 |
|
| LuminaMVS | | | 94.89 126 | 94.35 136 | 96.53 100 | 95.48 294 | 92.80 87 | 96.88 215 | 96.18 310 | 92.85 143 | 95.92 125 | 96.87 193 | 81.44 252 | 98.83 202 | 96.43 71 | 97.10 180 | 97.94 215 |
|
| IterMVS-SCA-FT | | | 90.31 319 | 89.81 315 | 91.82 356 | 95.52 292 | 84.20 362 | 94.30 371 | 96.15 311 | 90.61 231 | 87.39 357 | 94.27 337 | 75.80 336 | 96.44 399 | 87.34 301 | 86.88 353 | 94.82 364 |
|
| IterMVS | | | 90.15 327 | 89.67 321 | 91.61 363 | 95.48 294 | 83.72 368 | 94.33 369 | 96.12 312 | 89.99 249 | 87.31 360 | 94.15 345 | 75.78 338 | 96.27 403 | 86.97 310 | 86.89 352 | 94.83 362 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| DP-MVS | | | 92.76 217 | 91.51 241 | 96.52 102 | 98.77 58 | 90.99 164 | 97.38 167 | 96.08 313 | 82.38 406 | 89.29 310 | 97.87 111 | 83.77 197 | 99.69 67 | 81.37 380 | 96.69 190 | 98.89 125 |
|
| pmmvs4 | | | 90.93 301 | 89.85 313 | 94.17 256 | 93.34 394 | 90.79 174 | 94.60 355 | 96.02 314 | 84.62 383 | 87.45 354 | 95.15 287 | 81.88 246 | 97.45 365 | 87.70 290 | 87.87 339 | 94.27 389 |
|
| ppachtmachnet_test | | | 88.35 356 | 87.29 355 | 91.53 364 | 92.45 413 | 83.57 371 | 93.75 390 | 95.97 315 | 84.28 386 | 85.32 388 | 94.18 343 | 79.00 302 | 96.93 387 | 75.71 414 | 84.99 375 | 94.10 390 |
|
| Anonymous20240521 | | | 86.42 376 | 85.44 374 | 89.34 403 | 90.33 425 | 79.79 414 | 96.73 231 | 95.92 316 | 83.71 396 | 83.25 407 | 91.36 412 | 63.92 421 | 96.01 404 | 78.39 402 | 85.36 366 | 92.22 422 |
|
| ITE_SJBPF | | | | | 92.43 334 | 95.34 306 | 85.37 343 | | 95.92 316 | 91.47 187 | 87.75 350 | 96.39 223 | 71.00 372 | 97.96 310 | 82.36 370 | 89.86 320 | 93.97 395 |
|
| test_fmvs2 | | | 89.77 338 | 89.93 310 | 89.31 404 | 93.68 380 | 76.37 431 | 97.64 126 | 95.90 318 | 89.84 255 | 91.49 247 | 96.26 230 | 58.77 431 | 97.10 379 | 94.65 136 | 91.13 303 | 94.46 380 |
|
| USDC | | | 88.94 347 | 87.83 352 | 92.27 341 | 94.66 348 | 84.96 352 | 93.86 386 | 95.90 318 | 87.34 338 | 83.40 405 | 95.56 269 | 67.43 402 | 98.19 273 | 82.64 369 | 89.67 322 | 93.66 398 |
|
| COLMAP_ROB |  | 87.81 15 | 90.40 318 | 89.28 331 | 93.79 282 | 97.95 123 | 87.13 298 | 96.92 211 | 95.89 320 | 82.83 403 | 86.88 373 | 97.18 169 | 73.77 356 | 99.29 140 | 78.44 401 | 93.62 267 | 94.95 351 |
| Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
| VDD-MVS | | | 93.82 170 | 93.08 177 | 96.02 144 | 97.88 129 | 89.96 207 | 97.72 111 | 95.85 321 | 92.43 153 | 95.86 127 | 98.44 58 | 68.42 398 | 99.39 128 | 96.31 72 | 94.85 234 | 98.71 144 |
|
| VDDNet | | | 93.05 201 | 92.07 216 | 96.02 144 | 96.84 197 | 90.39 189 | 98.08 54 | 95.85 321 | 86.22 359 | 95.79 130 | 98.46 56 | 67.59 401 | 99.19 148 | 94.92 125 | 94.85 234 | 98.47 166 |
|
| mvsmamba | | | 94.57 137 | 94.14 141 | 95.87 154 | 97.03 183 | 89.93 208 | 97.84 89 | 95.85 321 | 91.34 193 | 94.79 159 | 96.80 194 | 80.67 264 | 98.81 205 | 94.85 126 | 98.12 142 | 98.85 129 |
|
| Vis-MVSNet (Re-imp) | | | 94.15 150 | 93.88 147 | 94.95 213 | 97.61 149 | 87.92 277 | 98.10 52 | 95.80 324 | 92.22 159 | 93.02 208 | 97.45 150 | 84.53 184 | 97.91 322 | 88.24 278 | 97.97 148 | 99.02 97 |
|
| MM | | | 97.29 27 | 96.98 37 | 98.23 11 | 98.01 117 | 95.03 26 | 98.07 56 | 95.76 325 | 97.78 1 | 97.52 56 | 98.80 36 | 88.09 115 | 99.86 9 | 99.44 2 | 99.37 63 | 99.80 1 |
|
| KD-MVS_2432*1600 | | | 84.81 393 | 82.64 396 | 91.31 369 | 91.07 422 | 85.34 344 | 91.22 424 | 95.75 326 | 85.56 368 | 83.09 408 | 90.21 420 | 67.21 404 | 95.89 406 | 77.18 408 | 62.48 450 | 92.69 410 |
|
| miper_refine_blended | | | 84.81 393 | 82.64 396 | 91.31 369 | 91.07 422 | 85.34 344 | 91.22 424 | 95.75 326 | 85.56 368 | 83.09 408 | 90.21 420 | 67.21 404 | 95.89 406 | 77.18 408 | 62.48 450 | 92.69 410 |
|
| FE-MVS | | | 92.05 245 | 91.05 257 | 95.08 201 | 96.83 199 | 87.93 276 | 93.91 385 | 95.70 328 | 86.30 356 | 94.15 176 | 94.97 293 | 76.59 328 | 99.21 146 | 84.10 350 | 96.86 183 | 98.09 205 |
|
| tpm cat1 | | | 88.36 355 | 87.21 358 | 91.81 357 | 95.13 325 | 80.55 403 | 92.58 415 | 95.70 328 | 74.97 436 | 87.45 354 | 91.96 405 | 78.01 318 | 98.17 275 | 80.39 389 | 88.74 331 | 96.72 272 |
|
| our_test_3 | | | 88.78 351 | 87.98 351 | 91.20 373 | 92.45 413 | 82.53 381 | 93.61 398 | 95.69 330 | 85.77 365 | 84.88 390 | 93.71 361 | 79.99 279 | 96.78 395 | 79.47 395 | 86.24 355 | 94.28 388 |
|
| BH-w/o | | | 92.14 242 | 91.75 230 | 93.31 305 | 96.99 187 | 85.73 335 | 95.67 312 | 95.69 330 | 88.73 296 | 89.26 312 | 94.82 303 | 82.97 217 | 98.07 291 | 85.26 337 | 96.32 202 | 96.13 290 |
|
| CR-MVSNet | | | 90.82 304 | 89.77 317 | 93.95 271 | 94.45 357 | 87.19 295 | 90.23 432 | 95.68 332 | 86.89 346 | 92.40 218 | 92.36 397 | 80.91 260 | 97.05 382 | 81.09 384 | 93.95 260 | 97.60 239 |
|
| Patchmtry | | | 88.64 353 | 87.25 356 | 92.78 327 | 94.09 367 | 86.64 308 | 89.82 436 | 95.68 332 | 80.81 418 | 87.63 352 | 92.36 397 | 80.91 260 | 97.03 383 | 78.86 399 | 85.12 371 | 94.67 375 |
|
| testing91 | | | 91.90 250 | 91.02 258 | 94.53 238 | 96.54 228 | 86.55 314 | 95.86 301 | 95.64 334 | 91.77 175 | 91.89 237 | 93.47 374 | 69.94 383 | 98.86 197 | 90.23 236 | 93.86 262 | 98.18 192 |
|
| BH-RMVSNet | | | 92.72 219 | 91.97 222 | 94.97 211 | 97.16 170 | 87.99 275 | 96.15 286 | 95.60 335 | 90.62 230 | 91.87 238 | 97.15 172 | 78.41 309 | 98.57 240 | 83.16 359 | 97.60 158 | 98.36 178 |
|
| PVSNet_0 | | 82.17 19 | 85.46 388 | 83.64 391 | 90.92 377 | 95.27 313 | 79.49 419 | 90.55 430 | 95.60 335 | 83.76 395 | 83.00 410 | 89.95 422 | 71.09 371 | 97.97 306 | 82.75 367 | 60.79 452 | 95.31 333 |
|
| guyue | | | 95.17 117 | 94.96 113 | 95.82 159 | 96.97 189 | 89.65 213 | 97.56 137 | 95.58 337 | 94.82 55 | 95.72 132 | 97.42 154 | 82.90 219 | 98.84 201 | 96.71 62 | 96.93 182 | 98.96 108 |
|
| SCA | | | 91.84 252 | 91.18 254 | 93.83 279 | 95.59 288 | 84.95 353 | 94.72 352 | 95.58 337 | 90.82 217 | 92.25 226 | 93.69 363 | 75.80 336 | 98.10 282 | 86.20 319 | 95.98 205 | 98.45 168 |
|
| MonoMVSNet | | | 91.92 248 | 91.77 228 | 92.37 335 | 92.94 401 | 83.11 375 | 97.09 195 | 95.55 339 | 92.91 141 | 90.85 264 | 94.55 316 | 81.27 256 | 96.52 398 | 93.01 175 | 87.76 340 | 97.47 245 |
|
| AllTest | | | 90.23 323 | 88.98 337 | 93.98 267 | 97.94 124 | 86.64 308 | 96.51 254 | 95.54 340 | 85.38 370 | 85.49 385 | 96.77 196 | 70.28 378 | 99.15 157 | 80.02 391 | 92.87 272 | 96.15 288 |
|
| TestCases | | | | | 93.98 267 | 97.94 124 | 86.64 308 | | 95.54 340 | 85.38 370 | 85.49 385 | 96.77 196 | 70.28 378 | 99.15 157 | 80.02 391 | 92.87 272 | 96.15 288 |
|
| mmtdpeth | | | 89.70 340 | 88.96 338 | 91.90 352 | 95.84 280 | 84.42 358 | 97.46 157 | 95.53 342 | 90.27 242 | 94.46 168 | 90.50 416 | 69.74 387 | 98.95 187 | 97.39 48 | 69.48 441 | 92.34 418 |
|
| tpmvs | | | 89.83 337 | 89.15 335 | 91.89 353 | 94.92 335 | 80.30 407 | 93.11 407 | 95.46 343 | 86.28 357 | 88.08 344 | 92.65 387 | 80.44 270 | 98.52 244 | 81.47 376 | 89.92 319 | 96.84 268 |
|
| pmmvs5 | | | 89.86 336 | 88.87 341 | 92.82 324 | 92.86 403 | 86.23 321 | 96.26 276 | 95.39 344 | 84.24 387 | 87.12 362 | 94.51 319 | 74.27 351 | 97.36 372 | 87.61 297 | 87.57 342 | 94.86 360 |
|
| PatchmatchNet |  | | 91.91 249 | 91.35 243 | 93.59 293 | 95.38 301 | 84.11 363 | 93.15 406 | 95.39 344 | 89.54 262 | 92.10 231 | 93.68 365 | 82.82 222 | 98.13 277 | 84.81 341 | 95.32 226 | 98.52 158 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
| tpmrst | | | 91.44 273 | 91.32 245 | 91.79 358 | 95.15 323 | 79.20 422 | 93.42 401 | 95.37 346 | 88.55 301 | 93.49 197 | 93.67 366 | 82.49 231 | 98.27 266 | 90.41 231 | 89.34 325 | 97.90 217 |
|
| Anonymous20231206 | | | 87.09 368 | 86.14 370 | 89.93 396 | 91.22 421 | 80.35 405 | 96.11 287 | 95.35 347 | 83.57 398 | 84.16 397 | 93.02 382 | 73.54 358 | 95.61 414 | 72.16 431 | 86.14 357 | 93.84 397 |
|
| MIMVSNet1 | | | 84.93 391 | 83.05 393 | 90.56 386 | 89.56 431 | 84.84 355 | 95.40 327 | 95.35 347 | 83.91 390 | 80.38 422 | 92.21 402 | 57.23 434 | 93.34 437 | 70.69 437 | 82.75 402 | 93.50 400 |
|
| TDRefinement | | | 86.53 372 | 84.76 384 | 91.85 354 | 82.23 453 | 84.25 360 | 96.38 265 | 95.35 347 | 84.97 379 | 84.09 400 | 94.94 295 | 65.76 417 | 98.34 263 | 84.60 345 | 74.52 431 | 92.97 406 |
|
| TR-MVS | | | 91.48 272 | 90.59 280 | 94.16 257 | 96.40 242 | 87.33 288 | 95.67 312 | 95.34 350 | 87.68 330 | 91.46 248 | 95.52 272 | 76.77 327 | 98.35 260 | 82.85 364 | 93.61 268 | 96.79 270 |
|
| EPNet_dtu | | | 91.71 255 | 91.28 248 | 92.99 317 | 93.76 377 | 83.71 369 | 96.69 237 | 95.28 351 | 93.15 128 | 87.02 367 | 95.95 245 | 83.37 205 | 97.38 371 | 79.46 396 | 96.84 184 | 97.88 219 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| FMVSNet5 | | | 87.29 365 | 85.79 372 | 91.78 359 | 94.80 342 | 87.28 290 | 95.49 324 | 95.28 351 | 84.09 389 | 83.85 404 | 91.82 406 | 62.95 424 | 94.17 429 | 78.48 400 | 85.34 367 | 93.91 396 |
|
| MDTV_nov1_ep13 | | | | 90.76 270 | | 95.22 317 | 80.33 406 | 93.03 409 | 95.28 351 | 88.14 314 | 92.84 215 | 93.83 356 | 81.34 253 | 98.08 287 | 82.86 362 | 94.34 245 | |
|
| LF4IMVS | | | 87.94 359 | 87.25 356 | 89.98 394 | 92.38 415 | 80.05 413 | 94.38 366 | 95.25 354 | 87.59 332 | 84.34 394 | 94.74 307 | 64.31 420 | 97.66 346 | 84.83 340 | 87.45 343 | 92.23 421 |
|
| TransMVSNet (Re) | | | 88.94 347 | 87.56 353 | 93.08 315 | 94.35 360 | 88.45 260 | 97.73 108 | 95.23 355 | 87.47 334 | 84.26 396 | 95.29 279 | 79.86 282 | 97.33 373 | 79.44 397 | 74.44 432 | 93.45 402 |
|
| test20.03 | | | 86.14 381 | 85.40 376 | 88.35 406 | 90.12 426 | 80.06 412 | 95.90 300 | 95.20 356 | 88.59 297 | 81.29 417 | 93.62 368 | 71.43 369 | 92.65 441 | 71.26 435 | 81.17 407 | 92.34 418 |
|
| new-patchmatchnet | | | 83.18 399 | 81.87 402 | 87.11 414 | 86.88 444 | 75.99 433 | 93.70 392 | 95.18 357 | 85.02 378 | 77.30 433 | 88.40 432 | 65.99 415 | 93.88 434 | 74.19 423 | 70.18 439 | 91.47 431 |
|
| MDA-MVSNet_test_wron | | | 85.87 385 | 84.23 388 | 90.80 383 | 92.38 415 | 82.57 380 | 93.17 404 | 95.15 358 | 82.15 407 | 67.65 445 | 92.33 400 | 78.20 311 | 95.51 417 | 77.33 405 | 79.74 411 | 94.31 387 |
|
| YYNet1 | | | 85.87 385 | 84.23 388 | 90.78 384 | 92.38 415 | 82.46 385 | 93.17 404 | 95.14 359 | 82.12 408 | 67.69 443 | 92.36 397 | 78.16 314 | 95.50 418 | 77.31 406 | 79.73 412 | 94.39 383 |
|
| Baseline_NR-MVSNet | | | 91.20 288 | 90.62 278 | 92.95 319 | 93.83 375 | 88.03 274 | 97.01 202 | 95.12 360 | 88.42 305 | 89.70 295 | 95.13 289 | 83.47 202 | 97.44 366 | 89.66 248 | 83.24 397 | 93.37 403 |
|
| thres200 | | | 92.23 238 | 91.39 242 | 94.75 226 | 97.61 149 | 89.03 244 | 96.60 249 | 95.09 361 | 92.08 167 | 93.28 203 | 94.00 352 | 78.39 310 | 99.04 183 | 81.26 383 | 94.18 251 | 96.19 284 |
|
| ADS-MVSNet | | | 89.89 333 | 88.68 343 | 93.53 297 | 95.86 275 | 84.89 354 | 90.93 427 | 95.07 362 | 83.23 401 | 91.28 257 | 91.81 407 | 79.01 300 | 97.85 325 | 79.52 393 | 91.39 299 | 97.84 224 |
|
| pmmvs-eth3d | | | 86.22 379 | 84.45 386 | 91.53 364 | 88.34 440 | 87.25 292 | 94.47 361 | 95.01 363 | 83.47 399 | 79.51 427 | 89.61 425 | 69.75 386 | 95.71 411 | 83.13 360 | 76.73 425 | 91.64 426 |
|
| Anonymous202405211 | | | 92.07 244 | 90.83 268 | 95.76 163 | 98.19 103 | 88.75 249 | 97.58 133 | 95.00 364 | 86.00 362 | 93.64 189 | 97.45 150 | 66.24 413 | 99.53 106 | 90.68 225 | 92.71 277 | 99.01 100 |
|
| MDA-MVSNet-bldmvs | | | 85.00 390 | 82.95 395 | 91.17 375 | 93.13 399 | 83.33 372 | 94.56 357 | 95.00 364 | 84.57 384 | 65.13 449 | 92.65 387 | 70.45 377 | 95.85 408 | 73.57 426 | 77.49 421 | 94.33 385 |
|
| ambc | | | | | 86.56 417 | 83.60 450 | 70.00 444 | 85.69 448 | 94.97 366 | | 80.60 421 | 88.45 431 | 37.42 452 | 96.84 392 | 82.69 368 | 75.44 429 | 92.86 408 |
|
| testgi | | | 87.97 358 | 87.21 358 | 90.24 391 | 92.86 403 | 80.76 398 | 96.67 240 | 94.97 366 | 91.74 176 | 85.52 384 | 95.83 251 | 62.66 426 | 94.47 427 | 76.25 412 | 88.36 335 | 95.48 316 |
|
| myMVS_eth3d28 | | | 91.52 269 | 90.97 260 | 93.17 311 | 96.91 191 | 83.24 374 | 95.61 318 | 94.96 368 | 92.24 158 | 91.98 234 | 93.28 379 | 69.31 388 | 98.40 252 | 88.71 273 | 95.68 215 | 97.88 219 |
|
| dp | | | 88.90 349 | 88.26 349 | 90.81 381 | 94.58 353 | 76.62 430 | 92.85 412 | 94.93 369 | 85.12 376 | 90.07 286 | 93.07 381 | 75.81 335 | 98.12 280 | 80.53 388 | 87.42 345 | 97.71 231 |
|
| test_fmvs3 | | | 83.21 398 | 83.02 394 | 83.78 421 | 86.77 445 | 68.34 447 | 96.76 229 | 94.91 370 | 86.49 352 | 84.14 399 | 89.48 426 | 36.04 453 | 91.73 443 | 91.86 196 | 80.77 409 | 91.26 433 |
|
| test_0402 | | | 86.46 375 | 84.79 383 | 91.45 366 | 95.02 329 | 85.55 337 | 96.29 275 | 94.89 371 | 80.90 415 | 82.21 413 | 93.97 354 | 68.21 399 | 97.29 375 | 62.98 444 | 88.68 332 | 91.51 429 |
|
| tfpn200view9 | | | 92.38 228 | 91.52 239 | 94.95 213 | 97.85 130 | 89.29 234 | 97.41 160 | 94.88 372 | 92.19 163 | 93.27 204 | 94.46 324 | 78.17 312 | 99.08 171 | 81.40 377 | 94.08 255 | 96.48 277 |
|
| CVMVSNet | | | 91.23 286 | 91.75 230 | 89.67 398 | 95.77 281 | 74.69 434 | 96.44 255 | 94.88 372 | 85.81 364 | 92.18 227 | 97.64 137 | 79.07 295 | 95.58 416 | 88.06 281 | 95.86 210 | 98.74 141 |
|
| thres400 | | | 92.42 226 | 91.52 239 | 95.12 200 | 97.85 130 | 89.29 234 | 97.41 160 | 94.88 372 | 92.19 163 | 93.27 204 | 94.46 324 | 78.17 312 | 99.08 171 | 81.40 377 | 94.08 255 | 96.98 262 |
|
| tt0320 | | | 85.39 389 | 83.12 392 | 92.19 345 | 93.44 391 | 85.79 333 | 96.19 283 | 94.87 375 | 71.19 443 | 82.92 411 | 91.76 409 | 58.43 432 | 96.81 393 | 81.03 385 | 78.26 420 | 93.98 394 |
|
| EPNet | | | 95.20 115 | 94.56 126 | 97.14 71 | 92.80 405 | 92.68 93 | 97.85 88 | 94.87 375 | 96.64 7 | 92.46 217 | 97.80 122 | 86.23 151 | 99.65 73 | 93.72 156 | 98.62 118 | 99.10 90 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| testing99 | | | 91.62 260 | 90.72 275 | 94.32 249 | 96.48 236 | 86.11 330 | 95.81 304 | 94.76 377 | 91.55 180 | 91.75 242 | 93.44 375 | 68.55 396 | 98.82 203 | 90.43 230 | 93.69 264 | 98.04 209 |
|
| sc_t1 | | | 86.48 374 | 84.10 390 | 93.63 290 | 93.45 390 | 85.76 334 | 96.79 224 | 94.71 378 | 73.06 441 | 86.45 377 | 94.35 329 | 55.13 439 | 97.95 314 | 84.38 348 | 78.55 419 | 97.18 258 |
|
| SixPastTwentyTwo | | | 89.15 345 | 88.54 345 | 90.98 376 | 93.49 387 | 80.28 409 | 96.70 235 | 94.70 379 | 90.78 218 | 84.15 398 | 95.57 268 | 71.78 367 | 97.71 342 | 84.63 344 | 85.07 372 | 94.94 353 |
|
| thres100view900 | | | 92.43 225 | 91.58 236 | 94.98 209 | 97.92 126 | 89.37 230 | 97.71 113 | 94.66 380 | 92.20 161 | 93.31 202 | 94.90 298 | 78.06 316 | 99.08 171 | 81.40 377 | 94.08 255 | 96.48 277 |
|
| thres600view7 | | | 92.49 223 | 91.60 235 | 95.18 196 | 97.91 127 | 89.47 224 | 97.65 122 | 94.66 380 | 92.18 165 | 93.33 201 | 94.91 297 | 78.06 316 | 99.10 165 | 81.61 373 | 94.06 259 | 96.98 262 |
|
| PatchT | | | 88.87 350 | 87.42 354 | 93.22 309 | 94.08 368 | 85.10 348 | 89.51 437 | 94.64 382 | 81.92 409 | 92.36 221 | 88.15 435 | 80.05 278 | 97.01 385 | 72.43 430 | 93.65 266 | 97.54 242 |
|
| baseline1 | | | 92.82 215 | 91.90 225 | 95.55 179 | 97.20 168 | 90.77 175 | 97.19 187 | 94.58 383 | 92.20 161 | 92.36 221 | 96.34 225 | 84.16 192 | 98.21 270 | 89.20 263 | 83.90 392 | 97.68 233 |
|
| AstraMVS | | | 94.82 131 | 94.64 122 | 95.34 191 | 96.36 246 | 88.09 273 | 97.58 133 | 94.56 384 | 94.98 44 | 95.70 135 | 97.92 106 | 81.93 245 | 98.93 190 | 96.87 56 | 95.88 208 | 98.99 104 |
|
| UBG | | | 91.55 266 | 90.76 270 | 93.94 273 | 96.52 232 | 85.06 349 | 95.22 339 | 94.54 385 | 90.47 238 | 91.98 234 | 92.71 386 | 72.02 364 | 98.74 217 | 88.10 280 | 95.26 228 | 98.01 211 |
|
| Gipuma |  | | 67.86 419 | 65.41 421 | 75.18 434 | 92.66 408 | 73.45 438 | 66.50 455 | 94.52 386 | 53.33 454 | 57.80 455 | 66.07 455 | 30.81 455 | 89.20 447 | 48.15 453 | 78.88 418 | 62.90 455 |
| S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
| testing11 | | | 91.68 258 | 90.75 272 | 94.47 240 | 96.53 230 | 86.56 313 | 95.76 308 | 94.51 387 | 91.10 210 | 91.24 259 | 93.59 369 | 68.59 395 | 98.86 197 | 91.10 213 | 94.29 247 | 98.00 212 |
|
| CostFormer | | | 91.18 291 | 90.70 276 | 92.62 332 | 94.84 340 | 81.76 391 | 94.09 378 | 94.43 388 | 84.15 388 | 92.72 216 | 93.77 360 | 79.43 289 | 98.20 271 | 90.70 224 | 92.18 286 | 97.90 217 |
|
| tpm2 | | | 89.96 330 | 89.21 333 | 92.23 344 | 94.91 337 | 81.25 394 | 93.78 389 | 94.42 389 | 80.62 420 | 91.56 245 | 93.44 375 | 76.44 331 | 97.94 316 | 85.60 331 | 92.08 290 | 97.49 243 |
|
| testing3-2 | | | 92.10 243 | 92.05 217 | 92.27 341 | 97.71 139 | 79.56 416 | 97.42 159 | 94.41 390 | 93.53 109 | 93.22 206 | 95.49 273 | 69.16 390 | 99.11 163 | 93.25 165 | 94.22 249 | 98.13 197 |
|
| MVS_0304 | | | 96.74 59 | 96.31 76 | 98.02 19 | 96.87 193 | 94.65 30 | 97.58 133 | 94.39 391 | 96.47 10 | 97.16 68 | 98.39 62 | 87.53 131 | 99.87 7 | 98.97 18 | 99.41 55 | 99.55 39 |
|
| JIA-IIPM | | | 88.26 357 | 87.04 361 | 91.91 351 | 93.52 385 | 81.42 393 | 89.38 438 | 94.38 392 | 80.84 417 | 90.93 263 | 80.74 447 | 79.22 292 | 97.92 319 | 82.76 366 | 91.62 294 | 96.38 280 |
|
| dmvs_re | | | 90.21 324 | 89.50 326 | 92.35 336 | 95.47 298 | 85.15 346 | 95.70 311 | 94.37 393 | 90.94 216 | 88.42 331 | 93.57 370 | 74.63 348 | 95.67 413 | 82.80 365 | 89.57 323 | 96.22 282 |
|
| Patchmatch-test | | | 89.42 343 | 87.99 350 | 93.70 287 | 95.27 313 | 85.11 347 | 88.98 439 | 94.37 393 | 81.11 414 | 87.10 365 | 93.69 363 | 82.28 235 | 97.50 361 | 74.37 421 | 94.76 238 | 98.48 165 |
|
| LCM-MVSNet | | | 72.55 412 | 69.39 416 | 82.03 423 | 70.81 463 | 65.42 452 | 90.12 434 | 94.36 395 | 55.02 453 | 65.88 447 | 81.72 446 | 24.16 461 | 89.96 444 | 74.32 422 | 68.10 444 | 90.71 436 |
|
| ADS-MVSNet2 | | | 89.45 342 | 88.59 344 | 92.03 348 | 95.86 275 | 82.26 387 | 90.93 427 | 94.32 396 | 83.23 401 | 91.28 257 | 91.81 407 | 79.01 300 | 95.99 405 | 79.52 393 | 91.39 299 | 97.84 224 |
|
| mvs5depth | | | 86.53 372 | 85.08 379 | 90.87 378 | 88.74 438 | 82.52 382 | 91.91 420 | 94.23 397 | 86.35 355 | 87.11 364 | 93.70 362 | 66.52 409 | 97.76 337 | 81.37 380 | 75.80 427 | 92.31 420 |
|
| EU-MVSNet | | | 88.72 352 | 88.90 340 | 88.20 408 | 93.15 398 | 74.21 436 | 96.63 246 | 94.22 398 | 85.18 374 | 87.32 359 | 95.97 243 | 76.16 333 | 94.98 422 | 85.27 336 | 86.17 356 | 95.41 323 |
|
| tt0320-xc | | | 84.83 392 | 82.33 400 | 92.31 339 | 93.66 381 | 86.20 323 | 96.17 285 | 94.06 399 | 71.26 442 | 82.04 415 | 92.22 401 | 55.07 440 | 96.72 396 | 81.49 375 | 75.04 430 | 94.02 393 |
|
| MIMVSNet | | | 88.50 354 | 86.76 364 | 93.72 286 | 94.84 340 | 87.77 283 | 91.39 422 | 94.05 400 | 86.41 354 | 87.99 346 | 92.59 390 | 63.27 422 | 95.82 410 | 77.44 404 | 92.84 274 | 97.57 241 |
|
| OpenMVS_ROB |  | 81.14 20 | 84.42 395 | 82.28 401 | 90.83 379 | 90.06 427 | 84.05 365 | 95.73 310 | 94.04 401 | 73.89 439 | 80.17 425 | 91.53 411 | 59.15 430 | 97.64 347 | 66.92 442 | 89.05 327 | 90.80 435 |
|
| TinyColmap | | | 86.82 370 | 85.35 377 | 91.21 371 | 94.91 337 | 82.99 377 | 93.94 382 | 94.02 402 | 83.58 397 | 81.56 416 | 94.68 309 | 62.34 427 | 98.13 277 | 75.78 413 | 87.35 348 | 92.52 416 |
|
| ETVMVS | | | 90.52 315 | 89.14 336 | 94.67 229 | 96.81 203 | 87.85 281 | 95.91 299 | 93.97 403 | 89.71 258 | 92.34 224 | 92.48 392 | 65.41 418 | 97.96 310 | 81.37 380 | 94.27 248 | 98.21 190 |
|
| IB-MVS | | 87.33 17 | 89.91 331 | 88.28 348 | 94.79 223 | 95.26 316 | 87.70 284 | 95.12 344 | 93.95 404 | 89.35 270 | 87.03 366 | 92.49 391 | 70.74 375 | 99.19 148 | 89.18 264 | 81.37 406 | 97.49 243 |
| 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 | | | 87.13 367 | 87.02 362 | 87.47 412 | 95.16 320 | 73.21 440 | 95.00 346 | 93.93 405 | 88.55 301 | 86.96 368 | 91.99 403 | 75.90 334 | 94.00 431 | 61.59 446 | 94.11 252 | 95.20 341 |
|
| myMVS_eth3d | | | 87.18 366 | 86.38 367 | 89.58 399 | 95.16 320 | 79.53 417 | 95.00 346 | 93.93 405 | 88.55 301 | 86.96 368 | 91.99 403 | 56.23 437 | 94.00 431 | 75.47 417 | 94.11 252 | 95.20 341 |
|
| testing222 | | | 90.31 319 | 88.96 338 | 94.35 246 | 96.54 228 | 87.29 289 | 95.50 323 | 93.84 407 | 90.97 213 | 91.75 242 | 92.96 383 | 62.18 428 | 98.00 301 | 82.86 362 | 94.08 255 | 97.76 229 |
|
| test_f | | | 80.57 405 | 79.62 407 | 83.41 422 | 83.38 451 | 67.80 449 | 93.57 399 | 93.72 408 | 80.80 419 | 77.91 432 | 87.63 438 | 33.40 454 | 92.08 442 | 87.14 308 | 79.04 417 | 90.34 437 |
|
| LCM-MVSNet-Re | | | 92.50 221 | 92.52 205 | 92.44 333 | 96.82 201 | 81.89 390 | 96.92 211 | 93.71 409 | 92.41 154 | 84.30 395 | 94.60 314 | 85.08 173 | 97.03 383 | 91.51 204 | 97.36 166 | 98.40 174 |
|
| tpm | | | 90.25 322 | 89.74 320 | 91.76 361 | 93.92 371 | 79.73 415 | 93.98 379 | 93.54 410 | 88.28 308 | 91.99 233 | 93.25 380 | 77.51 322 | 97.44 366 | 87.30 303 | 87.94 338 | 98.12 199 |
|
| ET-MVSNet_ETH3D | | | 91.49 271 | 90.11 300 | 95.63 173 | 96.40 242 | 91.57 137 | 95.34 330 | 93.48 411 | 90.60 233 | 75.58 435 | 95.49 273 | 80.08 277 | 96.79 394 | 94.25 144 | 89.76 321 | 98.52 158 |
|
| LFMVS | | | 93.60 177 | 92.63 198 | 96.52 102 | 98.13 109 | 91.27 149 | 97.94 76 | 93.39 412 | 90.57 235 | 96.29 109 | 98.31 75 | 69.00 391 | 99.16 155 | 94.18 145 | 95.87 209 | 99.12 88 |
|
| MVStest1 | | | 82.38 402 | 80.04 406 | 89.37 401 | 87.63 443 | 82.83 378 | 95.03 345 | 93.37 413 | 73.90 438 | 73.50 440 | 94.35 329 | 62.89 425 | 93.25 439 | 73.80 424 | 65.92 447 | 92.04 425 |
|
| Patchmatch-RL test | | | 87.38 364 | 86.24 368 | 90.81 381 | 88.74 438 | 78.40 426 | 88.12 446 | 93.17 414 | 87.11 343 | 82.17 414 | 89.29 427 | 81.95 243 | 95.60 415 | 88.64 275 | 77.02 422 | 98.41 173 |
|
| ttmdpeth | | | 85.91 384 | 84.76 384 | 89.36 402 | 89.14 433 | 80.25 410 | 95.66 315 | 93.16 415 | 83.77 394 | 83.39 406 | 95.26 283 | 66.24 413 | 95.26 421 | 80.65 386 | 75.57 428 | 92.57 413 |
|
| test-LLR | | | 91.42 274 | 91.19 253 | 92.12 346 | 94.59 351 | 80.66 400 | 94.29 372 | 92.98 416 | 91.11 208 | 90.76 266 | 92.37 394 | 79.02 298 | 98.07 291 | 88.81 270 | 96.74 187 | 97.63 234 |
|
| test-mter | | | 90.19 326 | 89.54 325 | 92.12 346 | 94.59 351 | 80.66 400 | 94.29 372 | 92.98 416 | 87.68 330 | 90.76 266 | 92.37 394 | 67.67 400 | 98.07 291 | 88.81 270 | 96.74 187 | 97.63 234 |
|
| WB-MVSnew | | | 89.88 334 | 89.56 324 | 90.82 380 | 94.57 354 | 83.06 376 | 95.65 316 | 92.85 418 | 87.86 321 | 90.83 265 | 94.10 346 | 79.66 286 | 96.88 390 | 76.34 411 | 94.19 250 | 92.54 415 |
|
| testing3 | | | 87.67 362 | 86.88 363 | 90.05 393 | 96.14 264 | 80.71 399 | 97.10 194 | 92.85 418 | 90.15 246 | 87.54 353 | 94.55 316 | 55.70 438 | 94.10 430 | 73.77 425 | 94.10 254 | 95.35 330 |
|
| test_method | | | 66.11 420 | 64.89 422 | 69.79 437 | 72.62 461 | 35.23 469 | 65.19 456 | 92.83 420 | 20.35 459 | 65.20 448 | 88.08 436 | 43.14 450 | 82.70 454 | 73.12 428 | 63.46 449 | 91.45 432 |
|
| test0.0.03 1 | | | 89.37 344 | 88.70 342 | 91.41 368 | 92.47 412 | 85.63 336 | 95.22 339 | 92.70 421 | 91.11 208 | 86.91 372 | 93.65 367 | 79.02 298 | 93.19 440 | 78.00 403 | 89.18 326 | 95.41 323 |
|
| new_pmnet | | | 82.89 400 | 81.12 405 | 88.18 409 | 89.63 430 | 80.18 411 | 91.77 421 | 92.57 422 | 76.79 434 | 75.56 436 | 88.23 434 | 61.22 429 | 94.48 426 | 71.43 433 | 82.92 400 | 89.87 438 |
|
| mvsany_test1 | | | 93.93 166 | 93.98 145 | 93.78 283 | 94.94 334 | 86.80 304 | 94.62 354 | 92.55 423 | 88.77 295 | 96.85 78 | 98.49 52 | 88.98 97 | 98.08 287 | 95.03 120 | 95.62 217 | 96.46 279 |
|
| thisisatest0515 | | | 92.29 234 | 91.30 247 | 95.25 194 | 96.60 219 | 88.90 247 | 94.36 367 | 92.32 424 | 87.92 318 | 93.43 199 | 94.57 315 | 77.28 323 | 99.00 184 | 89.42 254 | 95.86 210 | 97.86 223 |
|
| thisisatest0530 | | | 93.03 202 | 92.21 214 | 95.49 183 | 97.07 175 | 89.11 243 | 97.49 154 | 92.19 425 | 90.16 245 | 94.09 177 | 96.41 221 | 76.43 332 | 99.05 180 | 90.38 232 | 95.68 215 | 98.31 184 |
|
| tttt0517 | | | 92.96 205 | 92.33 211 | 94.87 216 | 97.11 173 | 87.16 297 | 97.97 72 | 92.09 426 | 90.63 229 | 93.88 183 | 97.01 185 | 76.50 329 | 99.06 177 | 90.29 235 | 95.45 224 | 98.38 176 |
|
| K. test v3 | | | 87.64 363 | 86.75 365 | 90.32 390 | 93.02 400 | 79.48 420 | 96.61 247 | 92.08 427 | 90.66 227 | 80.25 424 | 94.09 348 | 67.21 404 | 96.65 397 | 85.96 327 | 80.83 408 | 94.83 362 |
|
| TESTMET0.1,1 | | | 90.06 328 | 89.42 328 | 91.97 349 | 94.41 359 | 80.62 402 | 94.29 372 | 91.97 428 | 87.28 340 | 90.44 270 | 92.47 393 | 68.79 392 | 97.67 344 | 88.50 277 | 96.60 192 | 97.61 238 |
|
| PM-MVS | | | 83.48 397 | 81.86 403 | 88.31 407 | 87.83 442 | 77.59 428 | 93.43 400 | 91.75 429 | 86.91 345 | 80.63 420 | 89.91 423 | 44.42 449 | 95.84 409 | 85.17 339 | 76.73 425 | 91.50 430 |
|
| baseline2 | | | 91.63 259 | 90.86 264 | 93.94 273 | 94.33 361 | 86.32 318 | 95.92 298 | 91.64 430 | 89.37 269 | 86.94 370 | 94.69 308 | 81.62 250 | 98.69 224 | 88.64 275 | 94.57 243 | 96.81 269 |
|
| APD_test1 | | | 79.31 407 | 77.70 410 | 84.14 420 | 89.11 435 | 69.07 446 | 92.36 419 | 91.50 431 | 69.07 445 | 73.87 438 | 92.63 389 | 39.93 451 | 94.32 428 | 70.54 438 | 80.25 410 | 89.02 440 |
|
| FPMVS | | | 71.27 413 | 69.85 415 | 75.50 433 | 74.64 458 | 59.03 458 | 91.30 423 | 91.50 431 | 58.80 450 | 57.92 454 | 88.28 433 | 29.98 457 | 85.53 453 | 53.43 451 | 82.84 401 | 81.95 446 |
|
| door | | | | | | | | | 91.13 433 | | | | | | | | |
|
| door-mid | | | | | | | | | 91.06 434 | | | | | | | | |
|
| EGC-MVSNET | | | 68.77 418 | 63.01 424 | 86.07 419 | 92.49 411 | 82.24 388 | 93.96 381 | 90.96 435 | 0.71 464 | 2.62 465 | 90.89 414 | 53.66 441 | 93.46 435 | 57.25 449 | 84.55 382 | 82.51 445 |
|
| mvsany_test3 | | | 83.59 396 | 82.44 399 | 87.03 415 | 83.80 448 | 73.82 437 | 93.70 392 | 90.92 436 | 86.42 353 | 82.51 412 | 90.26 419 | 46.76 448 | 95.71 411 | 90.82 219 | 76.76 424 | 91.57 428 |
|
| pmmvs3 | | | 79.97 406 | 77.50 411 | 87.39 413 | 82.80 452 | 79.38 421 | 92.70 414 | 90.75 437 | 70.69 444 | 78.66 429 | 87.47 440 | 51.34 444 | 93.40 436 | 73.39 427 | 69.65 440 | 89.38 439 |
|
| UWE-MVS | | | 89.91 331 | 89.48 327 | 91.21 371 | 95.88 274 | 78.23 427 | 94.91 349 | 90.26 438 | 89.11 276 | 92.35 223 | 94.52 318 | 68.76 393 | 97.96 310 | 83.95 354 | 95.59 218 | 97.42 247 |
|
| DSMNet-mixed | | | 86.34 377 | 86.12 371 | 87.00 416 | 89.88 429 | 70.43 442 | 94.93 348 | 90.08 439 | 77.97 431 | 85.42 387 | 92.78 385 | 74.44 350 | 93.96 433 | 74.43 420 | 95.14 229 | 96.62 273 |
|
| MVS-HIRNet | | | 82.47 401 | 81.21 404 | 86.26 418 | 95.38 301 | 69.21 445 | 88.96 440 | 89.49 440 | 66.28 447 | 80.79 419 | 74.08 452 | 68.48 397 | 97.39 370 | 71.93 432 | 95.47 223 | 92.18 423 |
|
| WB-MVS | | | 76.77 409 | 76.63 412 | 77.18 428 | 85.32 446 | 56.82 460 | 94.53 358 | 89.39 441 | 82.66 405 | 71.35 441 | 89.18 428 | 75.03 343 | 88.88 448 | 35.42 457 | 66.79 445 | 85.84 442 |
|
| test1111 | | | 93.19 194 | 92.82 188 | 94.30 252 | 97.58 155 | 84.56 357 | 98.21 43 | 89.02 442 | 93.53 109 | 94.58 163 | 98.21 82 | 72.69 360 | 99.05 180 | 93.06 171 | 98.48 125 | 99.28 73 |
|
| SSC-MVS | | | 76.05 410 | 75.83 413 | 76.72 432 | 84.77 447 | 56.22 461 | 94.32 370 | 88.96 443 | 81.82 411 | 70.52 442 | 88.91 429 | 74.79 347 | 88.71 449 | 33.69 458 | 64.71 448 | 85.23 443 |
|
| ECVR-MVS |  | | 93.19 194 | 92.73 194 | 94.57 235 | 97.66 143 | 85.41 340 | 98.21 43 | 88.23 444 | 93.43 114 | 94.70 161 | 98.21 82 | 72.57 361 | 99.07 175 | 93.05 172 | 98.49 123 | 99.25 76 |
|
| EPMVS | | | 90.70 309 | 89.81 315 | 93.37 303 | 94.73 346 | 84.21 361 | 93.67 395 | 88.02 445 | 89.50 264 | 92.38 220 | 93.49 372 | 77.82 320 | 97.78 334 | 86.03 325 | 92.68 278 | 98.11 204 |
|
| ANet_high | | | 63.94 422 | 59.58 425 | 77.02 429 | 61.24 465 | 66.06 450 | 85.66 449 | 87.93 446 | 78.53 429 | 42.94 457 | 71.04 454 | 25.42 460 | 80.71 456 | 52.60 452 | 30.83 458 | 84.28 444 |
|
| PMMVS2 | | | 70.19 414 | 66.92 418 | 80.01 424 | 76.35 457 | 65.67 451 | 86.22 447 | 87.58 447 | 64.83 449 | 62.38 450 | 80.29 449 | 26.78 459 | 88.49 451 | 63.79 443 | 54.07 454 | 85.88 441 |
|
| lessismore_v0 | | | | | 90.45 387 | 91.96 418 | 79.09 424 | | 87.19 448 | | 80.32 423 | 94.39 326 | 66.31 412 | 97.55 355 | 84.00 353 | 76.84 423 | 94.70 374 |
|
| PMVS |  | 53.92 22 | 58.58 423 | 55.40 426 | 68.12 438 | 51.00 466 | 48.64 463 | 78.86 452 | 87.10 449 | 46.77 455 | 35.84 461 | 74.28 451 | 8.76 465 | 86.34 452 | 42.07 455 | 73.91 433 | 69.38 452 |
| Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
| UWE-MVS-28 | | | 86.81 371 | 86.41 366 | 88.02 410 | 92.87 402 | 74.60 435 | 95.38 329 | 86.70 450 | 88.17 311 | 87.28 361 | 94.67 311 | 70.83 374 | 93.30 438 | 67.45 440 | 94.31 246 | 96.17 285 |
|
| test_vis1_rt | | | 86.16 380 | 85.06 380 | 89.46 400 | 93.47 389 | 80.46 404 | 96.41 259 | 86.61 451 | 85.22 373 | 79.15 428 | 88.64 430 | 52.41 443 | 97.06 381 | 93.08 170 | 90.57 312 | 90.87 434 |
|
| testf1 | | | 69.31 416 | 66.76 419 | 76.94 430 | 78.61 455 | 61.93 454 | 88.27 444 | 86.11 452 | 55.62 451 | 59.69 451 | 85.31 443 | 20.19 463 | 89.32 445 | 57.62 447 | 69.44 442 | 79.58 447 |
|
| APD_test2 | | | 69.31 416 | 66.76 419 | 76.94 430 | 78.61 455 | 61.93 454 | 88.27 444 | 86.11 452 | 55.62 451 | 59.69 451 | 85.31 443 | 20.19 463 | 89.32 445 | 57.62 447 | 69.44 442 | 79.58 447 |
|
| gg-mvs-nofinetune | | | 87.82 360 | 85.61 373 | 94.44 242 | 94.46 356 | 89.27 237 | 91.21 426 | 84.61 454 | 80.88 416 | 89.89 290 | 74.98 450 | 71.50 368 | 97.53 358 | 85.75 330 | 97.21 175 | 96.51 275 |
|
| dmvs_testset | | | 81.38 404 | 82.60 398 | 77.73 427 | 91.74 419 | 51.49 462 | 93.03 409 | 84.21 455 | 89.07 277 | 78.28 431 | 91.25 413 | 76.97 325 | 88.53 450 | 56.57 450 | 82.24 403 | 93.16 404 |
|
| GG-mvs-BLEND | | | | | 93.62 291 | 93.69 379 | 89.20 239 | 92.39 418 | 83.33 456 | | 87.98 347 | 89.84 424 | 71.00 372 | 96.87 391 | 82.08 372 | 95.40 225 | 94.80 367 |
|
| MTMP | | | | | | | | 97.86 85 | 82.03 457 | | | | | | | | |
|
| DeepMVS_CX |  | | | | 74.68 435 | 90.84 424 | 64.34 453 | | 81.61 458 | 65.34 448 | 67.47 446 | 88.01 437 | 48.60 447 | 80.13 457 | 62.33 445 | 73.68 434 | 79.58 447 |
|
| E-PMN | | | 53.28 424 | 52.56 428 | 55.43 441 | 74.43 459 | 47.13 464 | 83.63 451 | 76.30 459 | 42.23 456 | 42.59 458 | 62.22 457 | 28.57 458 | 74.40 458 | 31.53 459 | 31.51 457 | 44.78 456 |
|
| test2506 | | | 91.60 261 | 90.78 269 | 94.04 263 | 97.66 143 | 83.81 366 | 98.27 33 | 75.53 460 | 93.43 114 | 95.23 147 | 98.21 82 | 67.21 404 | 99.07 175 | 93.01 175 | 98.49 123 | 99.25 76 |
|
| EMVS | | | 52.08 426 | 51.31 429 | 54.39 442 | 72.62 461 | 45.39 466 | 83.84 450 | 75.51 461 | 41.13 457 | 40.77 459 | 59.65 458 | 30.08 456 | 73.60 459 | 28.31 461 | 29.90 459 | 44.18 457 |
|
| test_vis3_rt | | | 72.73 411 | 70.55 414 | 79.27 425 | 80.02 454 | 68.13 448 | 93.92 384 | 74.30 462 | 76.90 433 | 58.99 453 | 73.58 453 | 20.29 462 | 95.37 419 | 84.16 349 | 72.80 436 | 74.31 450 |
|
| MVE |  | 50.73 23 | 53.25 425 | 48.81 430 | 66.58 440 | 65.34 464 | 57.50 459 | 72.49 454 | 70.94 463 | 40.15 458 | 39.28 460 | 63.51 456 | 6.89 467 | 73.48 460 | 38.29 456 | 42.38 456 | 68.76 454 |
| Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
| tmp_tt | | | 51.94 427 | 53.82 427 | 46.29 443 | 33.73 467 | 45.30 467 | 78.32 453 | 67.24 464 | 18.02 460 | 50.93 456 | 87.05 441 | 52.99 442 | 53.11 462 | 70.76 436 | 25.29 460 | 40.46 458 |
|
| kuosan | | | 65.27 421 | 64.66 423 | 67.11 439 | 83.80 448 | 61.32 457 | 88.53 443 | 60.77 465 | 68.22 446 | 67.67 444 | 80.52 448 | 49.12 446 | 70.76 461 | 29.67 460 | 53.64 455 | 69.26 453 |
|
| dongtai | | | 69.99 415 | 69.33 417 | 71.98 436 | 88.78 437 | 61.64 456 | 89.86 435 | 59.93 466 | 75.67 435 | 74.96 437 | 85.45 442 | 50.19 445 | 81.66 455 | 43.86 454 | 55.27 453 | 72.63 451 |
|
| N_pmnet | | | 78.73 408 | 78.71 409 | 78.79 426 | 92.80 405 | 46.50 465 | 94.14 376 | 43.71 467 | 78.61 428 | 80.83 418 | 91.66 410 | 74.94 346 | 96.36 401 | 67.24 441 | 84.45 384 | 93.50 400 |
|
| wuyk23d | | | 25.11 428 | 24.57 432 | 26.74 444 | 73.98 460 | 39.89 468 | 57.88 457 | 9.80 468 | 12.27 461 | 10.39 462 | 6.97 464 | 7.03 466 | 36.44 463 | 25.43 462 | 17.39 461 | 3.89 461 |
|
| testmvs | | | 13.36 430 | 16.33 433 | 4.48 446 | 5.04 468 | 2.26 471 | 93.18 403 | 3.28 469 | 2.70 462 | 8.24 463 | 21.66 460 | 2.29 469 | 2.19 464 | 7.58 463 | 2.96 462 | 9.00 460 |
|
| test123 | | | 13.04 431 | 15.66 434 | 5.18 445 | 4.51 469 | 3.45 470 | 92.50 417 | 1.81 470 | 2.50 463 | 7.58 464 | 20.15 461 | 3.67 468 | 2.18 465 | 7.13 464 | 1.07 463 | 9.90 459 |
|
| mmdepth | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| monomultidepth | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| test_blank | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| uanet_test | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| DCPMVS | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| pcd_1.5k_mvsjas | | | 7.39 433 | 9.85 436 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 88.65 105 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| sosnet-low-res | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| sosnet | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| uncertanet | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| Regformer | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| n2 | | | | | | | | | 0.00 471 | | | | | | | | |
|
| nn | | | | | | | | | 0.00 471 | | | | | | | | |
|
| ab-mvs-re | | | 8.06 432 | 10.74 435 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 96.69 202 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| uanet | | | 0.00 434 | 0.00 437 | 0.00 447 | 0.00 470 | 0.00 472 | 0.00 458 | 0.00 471 | 0.00 465 | 0.00 466 | 0.00 465 | 0.00 470 | 0.00 466 | 0.00 465 | 0.00 464 | 0.00 462 |
|
| WAC-MVS | | | | | | | 79.53 417 | | | | | | | | 75.56 416 | | |
|
| PC_three_1452 | | | | | | | | | | 90.77 219 | 98.89 24 | 98.28 80 | 96.24 1 | 98.35 260 | 95.76 99 | 99.58 23 | 99.59 28 |
|
| eth-test2 | | | | | | 0.00 470 | | | | | | | | | | | |
|
| eth-test | | | | | | 0.00 470 | | | | | | | | | | | |
|
| OPU-MVS | | | | | 98.55 3 | 98.82 57 | 96.86 3 | 98.25 36 | | | | 98.26 81 | 96.04 2 | 99.24 143 | 95.36 113 | 99.59 19 | 99.56 36 |
|
| test_0728_THIRD | | | | | | | | | | 94.78 59 | 98.73 28 | 98.87 29 | 95.87 4 | 99.84 23 | 97.45 44 | 99.72 2 | 99.77 2 |
|
| GSMVS | | | | | | | | | | | | | | | | | 98.45 168 |
|
| test_part2 | | | | | | 99.28 27 | 95.74 8 | | | | 98.10 42 | | | | | | |
|
| sam_mvs1 | | | | | | | | | | | | | 82.76 223 | | | | 98.45 168 |
|
| sam_mvs | | | | | | | | | | | | | 81.94 244 | | | | |
|
| test_post1 | | | | | | | | 92.81 413 | | | | 16.58 463 | 80.53 268 | 97.68 343 | 86.20 319 | | |
|
| test_post | | | | | | | | | | | | 17.58 462 | 81.76 247 | 98.08 287 | | | |
|
| patchmatchnet-post | | | | | | | | | | | | 90.45 418 | 82.65 228 | 98.10 282 | | | |
|
| gm-plane-assit | | | | | | 93.22 396 | 78.89 425 | | | 84.82 381 | | 93.52 371 | | 98.64 231 | 87.72 287 | | |
|
| test9_res | | | | | | | | | | | | | | | 94.81 130 | 99.38 60 | 99.45 55 |
|
| agg_prior2 | | | | | | | | | | | | | | | 93.94 150 | 99.38 60 | 99.50 48 |
|
| test_prior4 | | | | | | | 93.66 58 | 96.42 258 | | | | | | | | | |
|
| test_prior2 | | | | | | | | 96.35 267 | | 92.80 146 | 96.03 119 | 97.59 143 | 92.01 47 | | 95.01 121 | 99.38 60 | |
|
| 旧先验2 | | | | | | | | 95.94 296 | | 81.66 412 | 97.34 64 | | | 98.82 203 | 92.26 181 | | |
|
| 新几何2 | | | | | | | | 95.79 306 | | | | | | | | | |
|
| 原ACMM2 | | | | | | | | 95.67 312 | | | | | | | | | |
|
| testdata2 | | | | | | | | | | | | | | 99.67 71 | 85.96 327 | | |
|
| segment_acmp | | | | | | | | | | | | | 92.89 30 | | | | |
|
| testdata1 | | | | | | | | 95.26 338 | | 93.10 131 | | | | | | | |
|
| plane_prior7 | | | | | | 96.21 251 | 89.98 204 | | | | | | | | | | |
|
| plane_prior6 | | | | | | 96.10 267 | 90.00 200 | | | | | | 81.32 254 | | | | |
|
| plane_prior4 | | | | | | | | | | | | 96.64 205 | | | | | |
|
| plane_prior3 | | | | | | | 90.00 200 | | | 94.46 76 | 91.34 251 | | | | | | |
|
| plane_prior2 | | | | | | | | 97.74 106 | | 94.85 51 | | | | | | | |
|
| plane_prior1 | | | | | | 96.14 264 | | | | | | | | | | | |
|
| plane_prior | | | | | | | 89.99 202 | 97.24 179 | | 94.06 88 | | | | | | 92.16 287 | |
|
| HQP5-MVS | | | | | | | 89.33 232 | | | | | | | | | | |
|
| HQP-NCC | | | | | | 95.86 275 | | 96.65 241 | | 93.55 105 | 90.14 275 | | | | | | |
|
| ACMP_Plane | | | | | | 95.86 275 | | 96.65 241 | | 93.55 105 | 90.14 275 | | | | | | |
|
| BP-MVS | | | | | | | | | | | | | | | 92.13 189 | | |
|
| HQP4-MVS | | | | | | | | | | | 90.14 275 | | | 98.50 245 | | | 95.78 304 |
|
| HQP2-MVS | | | | | | | | | | | | | 80.95 258 | | | | |
|
| NP-MVS | | | | | | 95.99 273 | 89.81 211 | | | | | 95.87 248 | | | | | |
|
| MDTV_nov1_ep13_2view | | | | | | | 70.35 443 | 93.10 408 | | 83.88 392 | 93.55 192 | | 82.47 232 | | 86.25 318 | | 98.38 176 |
|
| ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 317 | |
|
| ACMMP++ | | | | | | | | | | | | | | | | 91.02 306 | |
|
| Test By Simon | | | | | | | | | | | | | 88.73 104 | | | | |
|