LTVRE_ROB | | 86.10 1 | 93.04 3 | 93.44 2 | 91.82 23 | 93.73 66 | 85.72 33 | 96.79 1 | 95.51 8 | 88.86 14 | 95.63 8 | 96.99 8 | 84.81 70 | 93.16 143 | 91.10 1 | 97.53 77 | 96.58 33 |
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 |
MP-MVS-pluss | | | 90.81 29 | 91.08 36 | 89.99 51 | 95.97 14 | 79.88 74 | 88.13 99 | 94.51 21 | 75.79 143 | 92.94 46 | 94.96 48 | 88.36 29 | 95.01 68 | 90.70 2 | 98.40 20 | 95.09 66 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
ACMMP_NAP | | | 90.65 31 | 91.07 38 | 89.42 64 | 95.93 16 | 79.54 79 | 89.95 63 | 93.68 56 | 77.65 119 | 91.97 70 | 94.89 50 | 88.38 28 | 95.45 51 | 89.27 3 | 97.87 54 | 93.27 133 |
|
ZNCC-MVS | | | 91.26 23 | 91.34 29 | 91.01 35 | 95.73 21 | 83.05 54 | 92.18 29 | 94.22 27 | 80.14 89 | 91.29 81 | 93.97 94 | 87.93 40 | 95.87 19 | 88.65 4 | 97.96 48 | 94.12 100 |
|
zzz-MVS | | | 91.27 22 | 91.26 33 | 91.29 29 | 96.59 4 | 86.29 19 | 88.94 86 | 91.81 122 | 84.07 40 | 92.00 68 | 94.40 72 | 86.63 54 | 95.28 58 | 88.59 5 | 98.31 24 | 92.30 174 |
|
MTAPA | | | 91.52 16 | 91.60 20 | 91.29 29 | 96.59 4 | 86.29 19 | 92.02 31 | 91.81 122 | 84.07 40 | 92.00 68 | 94.40 72 | 86.63 54 | 95.28 58 | 88.59 5 | 98.31 24 | 92.30 174 |
|
HPM-MVS |  | | 92.13 9 | 92.20 11 | 91.91 17 | 95.58 26 | 84.67 43 | 93.51 8 | 94.85 16 | 82.88 57 | 91.77 73 | 93.94 101 | 90.55 13 | 95.73 31 | 88.50 7 | 98.23 29 | 95.33 57 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
MSP-MVS | | | 89.08 65 | 88.16 78 | 91.83 21 | 95.76 18 | 86.14 24 | 92.75 17 | 93.90 46 | 78.43 112 | 89.16 125 | 92.25 148 | 72.03 217 | 96.36 2 | 88.21 8 | 90.93 258 | 92.98 144 |
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025 |
HPM-MVS_fast | | | 92.50 5 | 92.54 6 | 92.37 6 | 95.93 16 | 85.81 32 | 92.99 13 | 94.23 26 | 85.21 34 | 92.51 58 | 95.13 45 | 90.65 10 | 95.34 55 | 88.06 9 | 98.15 36 | 95.95 44 |
|
SMA-MVS |  | | 90.31 38 | 90.48 49 | 89.83 53 | 95.31 31 | 79.52 80 | 90.98 46 | 93.24 77 | 75.37 150 | 92.84 50 | 95.28 39 | 85.58 66 | 96.09 7 | 87.92 10 | 97.76 60 | 93.88 108 |
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 |
HFP-MVS | | | 91.30 21 | 91.39 25 | 91.02 33 | 95.43 29 | 84.66 44 | 92.58 22 | 93.29 73 | 81.99 66 | 91.47 76 | 93.96 97 | 88.35 30 | 95.56 40 | 87.74 11 | 97.74 62 | 92.85 148 |
|
ACMMPR | | | 91.49 17 | 91.35 28 | 91.92 16 | 95.74 20 | 85.88 29 | 92.58 22 | 93.25 76 | 81.99 66 | 91.40 78 | 94.17 85 | 87.51 44 | 95.87 19 | 87.74 11 | 97.76 60 | 93.99 103 |
|
anonymousdsp | | | 89.73 53 | 88.88 70 | 92.27 9 | 89.82 179 | 86.67 17 | 90.51 52 | 90.20 170 | 69.87 222 | 95.06 11 | 96.14 21 | 84.28 75 | 93.07 148 | 87.68 13 | 96.34 117 | 97.09 22 |
|
TSAR-MVS + MP. | | | 88.14 77 | 87.82 82 | 89.09 70 | 95.72 22 | 76.74 116 | 92.49 25 | 91.19 139 | 67.85 244 | 86.63 170 | 94.84 52 | 79.58 136 | 95.96 13 | 87.62 14 | 94.50 183 | 94.56 80 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
SteuartSystems-ACMMP | | | 91.16 26 | 91.36 26 | 90.55 42 | 93.91 61 | 80.97 68 | 91.49 39 | 93.48 63 | 82.82 58 | 92.60 57 | 93.97 94 | 88.19 33 | 96.29 4 | 87.61 15 | 98.20 33 | 94.39 89 |
Skip Steuart: Steuart Systems R&D Blog. |
region2R | | | 91.44 20 | 91.30 32 | 91.87 19 | 95.75 19 | 85.90 28 | 92.63 21 | 93.30 72 | 81.91 68 | 90.88 89 | 94.21 81 | 87.75 41 | 95.87 19 | 87.60 16 | 97.71 64 | 93.83 110 |
|
APDe-MVS | | | 91.22 24 | 91.92 13 | 89.14 69 | 92.97 85 | 78.04 93 | 92.84 16 | 94.14 35 | 83.33 51 | 93.90 25 | 95.73 28 | 88.77 26 | 96.41 1 | 87.60 16 | 97.98 45 | 92.98 144 |
|
abl_6 | | | 93.02 4 | 93.16 4 | 92.60 4 | 94.73 44 | 88.99 7 | 93.26 12 | 94.19 30 | 89.11 12 | 94.43 16 | 95.27 40 | 91.86 3 | 95.09 65 | 87.54 18 | 98.02 41 | 93.71 118 |
|
MSC_two_6792asdad | | | | | 88.81 73 | 91.55 136 | 77.99 94 | | 91.01 143 | | | | | 96.05 8 | 87.45 19 | 98.17 34 | 92.40 169 |
|
No_MVS | | | | | 88.81 73 | 91.55 136 | 77.99 94 | | 91.01 143 | | | | | 96.05 8 | 87.45 19 | 98.17 34 | 92.40 169 |
|
DVP-MVS++ | | | 90.07 42 | 91.09 35 | 87.00 103 | 91.55 136 | 72.64 146 | 96.19 2 | 94.10 38 | 85.33 32 | 93.49 39 | 94.64 61 | 81.12 120 | 95.88 17 | 87.41 21 | 95.94 134 | 92.48 165 |
|
test_0728_THIRD | | | | | | | | | | 85.33 32 | 93.75 31 | 94.65 58 | 87.44 45 | 95.78 28 | 87.41 21 | 98.21 31 | 92.98 144 |
|
XVS | | | 91.54 15 | 91.36 26 | 92.08 10 | 95.64 24 | 86.25 21 | 92.64 19 | 93.33 67 | 85.07 35 | 89.99 102 | 94.03 91 | 86.57 56 | 95.80 25 | 87.35 23 | 97.62 68 | 94.20 94 |
|
X-MVStestdata | | | 85.04 127 | 82.70 171 | 92.08 10 | 95.64 24 | 86.25 21 | 92.64 19 | 93.33 67 | 85.07 35 | 89.99 102 | 16.05 375 | 86.57 56 | 95.80 25 | 87.35 23 | 97.62 68 | 94.20 94 |
|
ACMMP |  | | 91.91 12 | 91.87 17 | 92.03 13 | 95.53 27 | 85.91 27 | 93.35 11 | 94.16 31 | 82.52 61 | 92.39 61 | 94.14 87 | 89.15 23 | 95.62 36 | 87.35 23 | 98.24 28 | 94.56 80 |
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 |
CP-MVS | | | 91.67 14 | 91.58 21 | 91.96 14 | 95.29 32 | 87.62 12 | 93.38 9 | 93.36 65 | 83.16 53 | 91.06 84 | 94.00 93 | 88.26 32 | 95.71 32 | 87.28 26 | 98.39 21 | 92.55 162 |
|
mPP-MVS | | | 91.69 13 | 91.47 24 | 92.37 6 | 96.04 13 | 88.48 10 | 92.72 18 | 92.60 101 | 83.09 54 | 91.54 75 | 94.25 80 | 87.67 43 | 95.51 46 | 87.21 27 | 98.11 37 | 93.12 139 |
|
SR-MVS-dyc-post | | | 92.41 6 | 92.41 7 | 92.39 5 | 94.13 55 | 88.95 8 | 92.87 14 | 94.16 31 | 88.75 16 | 93.79 29 | 94.43 68 | 88.83 24 | 95.51 46 | 87.16 28 | 97.60 70 | 92.73 153 |
|
RE-MVS-def | | | | 92.61 5 | | 94.13 55 | 88.95 8 | 92.87 14 | 94.16 31 | 88.75 16 | 93.79 29 | 94.43 68 | 90.64 11 | | 87.16 28 | 97.60 70 | 92.73 153 |
|
test1172 | | | 92.40 7 | 92.41 7 | 92.37 6 | 94.68 45 | 89.04 6 | 91.98 32 | 93.62 57 | 90.14 11 | 93.63 36 | 94.16 86 | 88.83 24 | 95.51 46 | 87.11 30 | 97.54 76 | 92.54 163 |
|
GST-MVS | | | 90.96 28 | 91.01 39 | 90.82 38 | 95.45 28 | 82.73 57 | 91.75 37 | 93.74 52 | 80.98 79 | 91.38 79 | 93.80 104 | 87.20 48 | 95.80 25 | 87.10 31 | 97.69 65 | 93.93 106 |
|
SR-MVS | | | 92.23 8 | 92.34 9 | 91.91 17 | 94.89 39 | 87.85 11 | 92.51 24 | 93.87 49 | 88.20 21 | 93.24 43 | 94.02 92 | 90.15 17 | 95.67 34 | 86.82 32 | 97.34 83 | 92.19 182 |
|
test_low_dy_conf_001 | | | 87.67 88 | 86.99 96 | 89.72 55 | 93.02 82 | 78.17 92 | 91.15 44 | 89.33 187 | 69.89 221 | 92.70 55 | 95.39 37 | 66.77 243 | 94.23 95 | 86.77 33 | 97.90 52 | 96.76 27 |
|
bld_raw_conf005 | | | 88.83 70 | 88.48 75 | 89.85 52 | 92.53 96 | 76.54 117 | 91.30 40 | 93.28 75 | 74.96 154 | 93.26 42 | 96.02 23 | 70.41 225 | 95.63 35 | 86.73 34 | 97.87 54 | 97.39 15 |
|
APD-MVS_3200maxsize | | | 92.05 10 | 92.24 10 | 91.48 24 | 93.02 82 | 85.17 36 | 92.47 26 | 95.05 15 | 87.65 24 | 93.21 44 | 94.39 74 | 90.09 18 | 95.08 66 | 86.67 35 | 97.60 70 | 94.18 96 |
|
DVP-MVS |  | | 90.06 43 | 91.32 30 | 86.29 117 | 94.16 53 | 72.56 150 | 90.54 50 | 91.01 143 | 83.61 47 | 93.75 31 | 94.65 58 | 89.76 19 | 95.78 28 | 86.42 36 | 97.97 46 | 90.55 225 |
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 |
test_0728_SECOND | | | | | 86.79 107 | 94.25 49 | 72.45 154 | 90.54 50 | 94.10 38 | | | | | 95.88 17 | 86.42 36 | 97.97 46 | 92.02 186 |
|
PGM-MVS | | | 91.20 25 | 90.95 42 | 91.93 15 | 95.67 23 | 85.85 30 | 90.00 60 | 93.90 46 | 80.32 86 | 91.74 74 | 94.41 71 | 88.17 34 | 95.98 11 | 86.37 38 | 97.99 43 | 93.96 105 |
|
MP-MVS |  | | 91.14 27 | 90.91 43 | 91.83 21 | 96.18 11 | 86.88 16 | 92.20 28 | 93.03 87 | 82.59 60 | 88.52 136 | 94.37 75 | 86.74 53 | 95.41 53 | 86.32 39 | 98.21 31 | 93.19 137 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MVSFormer | | | 82.23 180 | 81.57 190 | 84.19 163 | 85.54 258 | 69.26 185 | 91.98 32 | 90.08 173 | 71.54 201 | 76.23 305 | 85.07 289 | 58.69 289 | 94.27 90 | 86.26 40 | 88.77 283 | 89.03 251 |
|
test_djsdf | | | 89.62 54 | 89.01 66 | 91.45 25 | 92.36 101 | 82.98 55 | 91.98 32 | 90.08 173 | 71.54 201 | 94.28 21 | 96.54 13 | 81.57 115 | 94.27 90 | 86.26 40 | 96.49 110 | 97.09 22 |
|
v7n | | | 90.13 40 | 90.96 41 | 87.65 97 | 91.95 117 | 71.06 171 | 89.99 62 | 93.05 84 | 86.53 28 | 94.29 19 | 96.27 17 | 82.69 91 | 94.08 104 | 86.25 42 | 97.63 67 | 97.82 8 |
|
SD-MVS | | | 88.96 67 | 89.88 53 | 86.22 120 | 91.63 129 | 77.07 112 | 89.82 66 | 93.77 51 | 78.90 105 | 92.88 47 | 92.29 146 | 86.11 62 | 90.22 227 | 86.24 43 | 97.24 86 | 91.36 205 |
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 |
HPM-MVS++ |  | | 88.93 68 | 88.45 76 | 90.38 45 | 94.92 37 | 85.85 30 | 89.70 67 | 91.27 136 | 78.20 114 | 86.69 169 | 92.28 147 | 80.36 130 | 95.06 67 | 86.17 44 | 96.49 110 | 90.22 230 |
|
TDRefinement | | | 93.52 2 | 93.39 3 | 93.88 1 | 95.94 15 | 90.26 3 | 95.70 4 | 96.46 2 | 90.58 8 | 92.86 49 | 96.29 16 | 88.16 35 | 94.17 100 | 86.07 45 | 98.48 18 | 97.22 20 |
|
SED-MVS | | | 90.46 37 | 91.64 19 | 86.93 104 | 94.18 50 | 72.65 144 | 90.47 53 | 93.69 54 | 83.77 44 | 94.11 23 | 94.27 76 | 90.28 15 | 95.84 23 | 86.03 46 | 97.92 49 | 92.29 176 |
|
test_241102_TWO | | | | | | | | | 93.71 53 | 83.77 44 | 93.49 39 | 94.27 76 | 89.27 22 | 95.84 23 | 86.03 46 | 97.82 56 | 92.04 185 |
|
UA-Net | | | 91.49 17 | 91.53 22 | 91.39 26 | 94.98 36 | 82.95 56 | 93.52 7 | 92.79 96 | 88.22 20 | 88.53 135 | 97.64 2 | 83.45 84 | 94.55 85 | 86.02 48 | 98.60 13 | 96.67 30 |
|
mvsmamba | | | 87.87 82 | 87.23 91 | 89.78 54 | 92.31 105 | 76.51 121 | 91.09 45 | 91.87 118 | 72.61 187 | 92.16 64 | 95.23 43 | 66.01 247 | 95.59 38 | 86.02 48 | 97.78 58 | 97.24 18 |
|
IU-MVS | | | | | | 94.18 50 | 72.64 146 | | 90.82 148 | 56.98 319 | 89.67 113 | | | | 85.78 50 | 97.92 49 | 93.28 132 |
|
xxxxxxxxxxxxxcwj | | | 89.04 66 | 89.13 64 | 88.79 75 | 93.75 64 | 77.44 104 | 86.31 128 | 95.27 12 | 70.80 209 | 92.28 62 | 93.80 104 | 86.89 51 | 94.64 79 | 85.52 51 | 97.51 78 | 94.30 92 |
|
SF-MVS | | | 90.27 39 | 90.80 45 | 88.68 80 | 92.86 89 | 77.09 111 | 91.19 43 | 95.74 5 | 81.38 74 | 92.28 62 | 93.80 104 | 86.89 51 | 94.64 79 | 85.52 51 | 97.51 78 | 94.30 92 |
|
LPG-MVS_test | | | 91.47 19 | 91.68 18 | 90.82 38 | 94.75 42 | 81.69 61 | 90.00 60 | 94.27 23 | 82.35 63 | 93.67 34 | 94.82 53 | 91.18 5 | 95.52 44 | 85.36 53 | 98.73 7 | 95.23 62 |
|
LGP-MVS_train | | | | | 90.82 38 | 94.75 42 | 81.69 61 | | 94.27 23 | 82.35 63 | 93.67 34 | 94.82 53 | 91.18 5 | 95.52 44 | 85.36 53 | 98.73 7 | 95.23 62 |
|
LCM-MVSNet | | | 95.70 1 | 96.40 1 | 93.61 2 | 98.67 1 | 85.39 34 | 95.54 5 | 97.36 1 | 96.97 1 | 99.04 1 | 99.05 1 | 96.61 1 | 95.92 14 | 85.07 55 | 99.27 1 | 99.54 1 |
|
OurMVSNet-221017-0 | | | 90.01 46 | 89.74 55 | 90.83 37 | 93.16 80 | 80.37 71 | 91.91 35 | 93.11 80 | 81.10 77 | 95.32 10 | 97.24 5 | 72.94 205 | 94.85 73 | 85.07 55 | 97.78 58 | 97.26 17 |
|
ACMM | | 79.39 9 | 90.65 31 | 90.99 40 | 89.63 59 | 95.03 35 | 83.53 49 | 89.62 72 | 93.35 66 | 79.20 101 | 93.83 28 | 93.60 111 | 90.81 8 | 92.96 150 | 85.02 57 | 98.45 19 | 92.41 168 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
RRT_MVS | | | 88.30 75 | 87.83 81 | 89.70 56 | 93.62 69 | 75.70 127 | 92.36 27 | 89.06 192 | 77.34 122 | 93.63 36 | 95.83 26 | 65.40 251 | 95.90 15 | 85.01 58 | 98.23 29 | 97.49 13 |
|
#test# | | | 90.49 36 | 90.31 51 | 91.02 33 | 95.43 29 | 84.66 44 | 90.65 48 | 93.29 73 | 77.00 127 | 91.47 76 | 93.96 97 | 88.35 30 | 95.56 40 | 84.88 59 | 97.74 62 | 92.85 148 |
|
3Dnovator+ | | 83.92 2 | 89.97 49 | 89.66 56 | 90.92 36 | 91.27 145 | 81.66 64 | 91.25 41 | 94.13 36 | 88.89 13 | 88.83 130 | 94.26 79 | 77.55 153 | 95.86 22 | 84.88 59 | 95.87 138 | 95.24 61 |
|
OPM-MVS | | | 89.80 51 | 89.97 52 | 89.27 66 | 94.76 41 | 79.86 75 | 86.76 121 | 92.78 97 | 78.78 107 | 92.51 58 | 93.64 110 | 88.13 36 | 93.84 114 | 84.83 61 | 97.55 73 | 94.10 101 |
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
CNVR-MVS | | | 87.81 86 | 87.68 84 | 88.21 90 | 92.87 87 | 77.30 109 | 85.25 141 | 91.23 137 | 77.31 124 | 87.07 160 | 91.47 167 | 82.94 89 | 94.71 76 | 84.67 62 | 96.27 121 | 92.62 160 |
|
XVG-OURS-SEG-HR | | | 89.59 55 | 89.37 61 | 90.28 47 | 94.47 46 | 85.95 26 | 86.84 117 | 93.91 45 | 80.07 90 | 86.75 166 | 93.26 114 | 93.64 2 | 90.93 205 | 84.60 63 | 90.75 263 | 93.97 104 |
|
DPE-MVS |  | | 90.53 35 | 91.08 36 | 88.88 71 | 93.38 74 | 78.65 88 | 89.15 83 | 94.05 40 | 84.68 38 | 93.90 25 | 94.11 89 | 88.13 36 | 96.30 3 | 84.51 64 | 97.81 57 | 91.70 197 |
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
mvs_tets | | | 89.78 52 | 89.27 63 | 91.30 28 | 93.51 70 | 84.79 41 | 89.89 65 | 90.63 153 | 70.00 220 | 94.55 15 | 96.67 11 | 87.94 39 | 93.59 125 | 84.27 65 | 95.97 131 | 95.52 52 |
|
DeepC-MVS | | 82.31 4 | 89.15 63 | 89.08 65 | 89.37 65 | 93.64 68 | 79.07 83 | 88.54 95 | 94.20 28 | 73.53 169 | 89.71 111 | 94.82 53 | 85.09 67 | 95.77 30 | 84.17 66 | 98.03 40 | 93.26 134 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
jajsoiax | | | 89.41 58 | 88.81 72 | 91.19 32 | 93.38 74 | 84.72 42 | 89.70 67 | 90.29 167 | 69.27 225 | 94.39 17 | 96.38 15 | 86.02 64 | 93.52 129 | 83.96 67 | 95.92 136 | 95.34 56 |
|
v10 | | | 86.54 100 | 87.10 93 | 84.84 146 | 88.16 210 | 63.28 232 | 86.64 124 | 92.20 109 | 75.42 149 | 92.81 52 | 94.50 64 | 74.05 190 | 94.06 105 | 83.88 68 | 96.28 119 | 97.17 21 |
|
XVG-OURS | | | 89.18 62 | 88.83 71 | 90.23 48 | 94.28 48 | 86.11 25 | 85.91 131 | 93.60 60 | 80.16 88 | 89.13 126 | 93.44 112 | 83.82 79 | 90.98 203 | 83.86 69 | 95.30 159 | 93.60 125 |
|
Regformer-4 | | | 86.41 102 | 85.71 120 | 88.52 81 | 84.27 275 | 77.57 101 | 84.07 160 | 88.00 210 | 82.82 58 | 89.84 108 | 85.48 277 | 82.06 104 | 92.77 156 | 83.83 70 | 91.04 252 | 95.22 64 |
|
9.14 | | | | 89.29 62 | | 91.84 125 | | 88.80 90 | 95.32 11 | 75.14 152 | 91.07 83 | 92.89 125 | 87.27 46 | 93.78 116 | 83.69 71 | 97.55 73 | |
|
Regformer-2 | | | 86.74 98 | 86.08 112 | 88.73 76 | 84.18 279 | 79.20 81 | 83.52 179 | 89.33 187 | 83.33 51 | 89.92 106 | 85.07 289 | 83.23 87 | 93.16 143 | 83.39 72 | 92.72 223 | 93.83 110 |
|
ETH3D-3000-0.1 | | | 88.85 69 | 88.96 69 | 88.52 81 | 91.94 119 | 77.27 110 | 88.71 92 | 95.26 13 | 76.08 134 | 90.66 92 | 92.69 132 | 84.48 73 | 93.83 115 | 83.38 73 | 97.48 80 | 94.47 84 |
|
ACMH | | 76.49 14 | 89.34 60 | 91.14 34 | 83.96 167 | 92.50 98 | 70.36 176 | 89.55 73 | 93.84 50 | 81.89 69 | 94.70 13 | 95.44 36 | 90.69 9 | 88.31 263 | 83.33 74 | 98.30 26 | 93.20 136 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v8 | | | 86.22 107 | 86.83 101 | 84.36 157 | 87.82 214 | 62.35 247 | 86.42 127 | 91.33 134 | 76.78 129 | 92.73 54 | 94.48 66 | 73.41 199 | 93.72 118 | 83.10 75 | 95.41 152 | 97.01 24 |
|
PS-MVSNAJss | | | 88.31 74 | 87.90 80 | 89.56 62 | 93.31 76 | 77.96 96 | 87.94 102 | 91.97 115 | 70.73 211 | 94.19 22 | 96.67 11 | 76.94 162 | 94.57 83 | 83.07 76 | 96.28 119 | 96.15 36 |
|
CPTT-MVS | | | 89.39 59 | 88.98 68 | 90.63 41 | 95.09 34 | 86.95 15 | 92.09 30 | 92.30 107 | 79.74 92 | 87.50 152 | 92.38 141 | 81.42 117 | 93.28 138 | 83.07 76 | 97.24 86 | 91.67 198 |
|
SixPastTwentyTwo | | | 87.20 91 | 87.45 88 | 86.45 113 | 92.52 97 | 69.19 188 | 87.84 104 | 88.05 208 | 81.66 71 | 94.64 14 | 96.53 14 | 65.94 248 | 94.75 75 | 83.02 78 | 96.83 98 | 95.41 54 |
|
ACMP | | 79.16 10 | 90.54 34 | 90.60 48 | 90.35 46 | 94.36 47 | 80.98 67 | 89.16 82 | 94.05 40 | 79.03 104 | 92.87 48 | 93.74 108 | 90.60 12 | 95.21 62 | 82.87 79 | 98.76 4 | 94.87 70 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v1240 | | | 84.30 143 | 84.51 145 | 83.65 174 | 87.65 219 | 61.26 257 | 82.85 201 | 91.54 127 | 67.94 242 | 90.68 91 | 90.65 193 | 71.71 219 | 93.64 120 | 82.84 80 | 94.78 176 | 96.07 39 |
|
XVG-ACMP-BASELINE | | | 89.98 47 | 89.84 54 | 90.41 44 | 94.91 38 | 84.50 46 | 89.49 77 | 93.98 42 | 79.68 93 | 92.09 66 | 93.89 102 | 83.80 80 | 93.10 147 | 82.67 81 | 98.04 38 | 93.64 123 |
|
DROMVSNet | | | 88.01 79 | 88.32 77 | 87.09 102 | 89.28 186 | 72.03 160 | 90.31 56 | 96.31 3 | 80.88 80 | 85.12 197 | 89.67 213 | 84.47 74 | 95.46 50 | 82.56 82 | 96.26 122 | 93.77 116 |
|
CS-MVS | | | 88.14 77 | 87.67 85 | 89.54 63 | 89.56 181 | 79.18 82 | 90.47 53 | 94.77 18 | 79.37 99 | 84.32 212 | 89.33 218 | 83.87 77 | 94.53 86 | 82.45 83 | 94.89 173 | 94.90 68 |
|
v1192 | | | 84.57 135 | 84.69 140 | 84.21 161 | 87.75 216 | 62.88 237 | 83.02 196 | 91.43 130 | 69.08 228 | 89.98 104 | 90.89 184 | 72.70 209 | 93.62 124 | 82.41 84 | 94.97 170 | 96.13 37 |
|
Regformer-1 | | | 86.00 110 | 85.50 124 | 87.49 98 | 84.18 279 | 76.90 114 | 83.52 179 | 87.94 212 | 82.18 65 | 89.19 124 | 85.07 289 | 82.28 100 | 91.89 179 | 82.40 85 | 92.72 223 | 93.69 119 |
|
v1921920 | | | 84.23 147 | 84.37 149 | 83.79 170 | 87.64 220 | 61.71 252 | 82.91 199 | 91.20 138 | 67.94 242 | 90.06 99 | 90.34 198 | 72.04 216 | 93.59 125 | 82.32 86 | 94.91 171 | 96.07 39 |
|
APD-MVS |  | | 89.54 56 | 89.63 57 | 89.26 67 | 92.57 94 | 81.34 66 | 90.19 58 | 93.08 83 | 80.87 81 | 91.13 82 | 93.19 115 | 86.22 61 | 95.97 12 | 82.23 87 | 97.18 88 | 90.45 227 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
EI-MVSNet-Vis-set | | | 85.12 124 | 84.53 144 | 86.88 105 | 84.01 282 | 72.76 143 | 83.91 168 | 85.18 250 | 80.44 83 | 88.75 131 | 85.49 276 | 80.08 132 | 91.92 177 | 82.02 88 | 90.85 261 | 95.97 42 |
|
ZD-MVS | | | | | | 92.22 109 | 80.48 70 | | 91.85 119 | 71.22 206 | 90.38 94 | 92.98 120 | 86.06 63 | 96.11 6 | 81.99 89 | 96.75 101 | |
|
Regformer-3 | | | 85.06 126 | 84.67 141 | 86.22 120 | 84.27 275 | 73.43 138 | 84.07 160 | 85.26 248 | 80.77 82 | 88.62 134 | 85.48 277 | 80.56 128 | 90.39 223 | 81.99 89 | 91.04 252 | 94.85 74 |
|
EI-MVSNet-UG-set | | | 85.04 127 | 84.44 146 | 86.85 106 | 83.87 285 | 72.52 152 | 83.82 170 | 85.15 251 | 80.27 87 | 88.75 131 | 85.45 280 | 79.95 134 | 91.90 178 | 81.92 91 | 90.80 262 | 96.13 37 |
|
v144192 | | | 84.24 146 | 84.41 147 | 83.71 173 | 87.59 221 | 61.57 253 | 82.95 198 | 91.03 142 | 67.82 245 | 89.80 109 | 90.49 196 | 73.28 202 | 93.51 130 | 81.88 92 | 94.89 173 | 96.04 41 |
|
v1144 | | | 84.54 138 | 84.72 138 | 84.00 165 | 87.67 218 | 62.55 243 | 82.97 197 | 90.93 146 | 70.32 216 | 89.80 109 | 90.99 179 | 73.50 196 | 93.48 131 | 81.69 93 | 94.65 181 | 95.97 42 |
|
ETH3D cwj APD-0.16 | | | 87.83 85 | 87.62 86 | 88.47 83 | 91.21 146 | 78.20 90 | 87.26 110 | 94.54 20 | 72.05 197 | 88.89 127 | 92.31 145 | 83.86 78 | 94.24 93 | 81.59 94 | 96.87 95 | 92.97 147 |
|
testtj | | | 89.51 57 | 89.48 60 | 89.59 61 | 92.26 106 | 80.80 69 | 90.14 59 | 93.54 61 | 83.37 50 | 90.57 93 | 92.55 137 | 84.99 68 | 96.15 5 | 81.26 95 | 96.61 105 | 91.83 193 |
|
train_agg | | | 85.98 112 | 85.28 127 | 88.07 92 | 92.34 102 | 79.70 77 | 83.94 165 | 90.32 161 | 65.79 258 | 84.49 208 | 90.97 180 | 81.93 108 | 93.63 121 | 81.21 96 | 96.54 108 | 90.88 214 |
|
NCCC | | | 87.36 89 | 86.87 100 | 88.83 72 | 92.32 104 | 78.84 86 | 86.58 125 | 91.09 141 | 78.77 108 | 84.85 203 | 90.89 184 | 80.85 123 | 95.29 56 | 81.14 97 | 95.32 156 | 92.34 172 |
|
agg_prior1 | | | 85.72 115 | 85.20 128 | 87.28 101 | 91.58 133 | 77.69 99 | 83.69 175 | 90.30 164 | 66.29 255 | 84.32 212 | 91.07 177 | 82.13 102 | 93.18 141 | 81.02 98 | 96.36 116 | 90.98 210 |
|
v2v482 | | | 84.09 150 | 84.24 151 | 83.62 175 | 87.13 229 | 61.40 254 | 82.71 204 | 89.71 179 | 72.19 196 | 89.55 119 | 91.41 168 | 70.70 224 | 93.20 140 | 81.02 98 | 93.76 197 | 96.25 35 |
|
WR-MVS_H | | | 89.91 50 | 91.31 31 | 85.71 133 | 96.32 10 | 62.39 245 | 89.54 75 | 93.31 70 | 90.21 10 | 95.57 9 | 95.66 31 | 81.42 117 | 95.90 15 | 80.94 100 | 98.80 3 | 98.84 5 |
|
LS3D | | | 90.60 33 | 90.34 50 | 91.38 27 | 89.03 191 | 84.23 47 | 93.58 6 | 94.68 19 | 90.65 7 | 90.33 96 | 93.95 100 | 84.50 72 | 95.37 54 | 80.87 101 | 95.50 151 | 94.53 83 |
|
test9_res | | | | | | | | | | | | | | | 80.83 102 | 96.45 113 | 90.57 223 |
|
HQP_MVS | | | 87.75 87 | 87.43 89 | 88.70 79 | 93.45 71 | 76.42 122 | 89.45 78 | 93.61 58 | 79.44 97 | 86.55 171 | 92.95 123 | 74.84 180 | 95.22 60 | 80.78 103 | 95.83 139 | 94.46 85 |
|
plane_prior5 | | | | | | | | | 93.61 58 | | | | | 95.22 60 | 80.78 103 | 95.83 139 | 94.46 85 |
|
PHI-MVS | | | 86.38 103 | 85.81 117 | 88.08 91 | 88.44 204 | 77.34 107 | 89.35 81 | 93.05 84 | 73.15 180 | 84.76 204 | 87.70 243 | 78.87 140 | 94.18 98 | 80.67 105 | 96.29 118 | 92.73 153 |
|
K. test v3 | | | 85.14 123 | 84.73 136 | 86.37 114 | 91.13 151 | 69.63 181 | 85.45 139 | 76.68 306 | 84.06 42 | 92.44 60 | 96.99 8 | 62.03 268 | 94.65 78 | 80.58 106 | 93.24 208 | 94.83 76 |
|
Vis-MVSNet |  | | 86.86 95 | 86.58 103 | 87.72 95 | 92.09 113 | 77.43 106 | 87.35 109 | 92.09 111 | 78.87 106 | 84.27 218 | 94.05 90 | 78.35 145 | 93.65 119 | 80.54 107 | 91.58 246 | 92.08 184 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
test_part1 | | | 87.15 92 | 87.82 82 | 85.15 142 | 88.88 195 | 63.04 235 | 87.98 100 | 94.85 16 | 82.52 61 | 93.61 38 | 95.73 28 | 67.51 237 | 95.71 32 | 80.48 108 | 98.83 2 | 96.69 29 |
|
V42 | | | 83.47 165 | 83.37 162 | 83.75 172 | 83.16 291 | 63.33 231 | 81.31 233 | 90.23 169 | 69.51 224 | 90.91 88 | 90.81 187 | 74.16 188 | 92.29 169 | 80.06 109 | 90.22 269 | 95.62 50 |
|
MVS_Test | | | 82.47 177 | 83.22 163 | 80.22 237 | 82.62 297 | 57.75 296 | 82.54 210 | 91.96 116 | 71.16 207 | 82.89 236 | 92.52 139 | 77.41 154 | 90.50 220 | 80.04 110 | 87.84 296 | 92.40 169 |
|
COLMAP_ROB |  | 83.01 3 | 91.97 11 | 91.95 12 | 92.04 12 | 93.68 67 | 86.15 23 | 93.37 10 | 95.10 14 | 90.28 9 | 92.11 65 | 95.03 47 | 89.75 21 | 94.93 70 | 79.95 111 | 98.27 27 | 95.04 67 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
test_0402 | | | 88.65 71 | 89.58 59 | 85.88 129 | 92.55 95 | 72.22 158 | 84.01 163 | 89.44 185 | 88.63 18 | 94.38 18 | 95.77 27 | 86.38 60 | 93.59 125 | 79.84 112 | 95.21 160 | 91.82 194 |
|
EGC-MVSNET | | | 74.79 271 | 69.99 305 | 89.19 68 | 94.89 39 | 87.00 14 | 91.89 36 | 86.28 234 | 1.09 376 | 2.23 378 | 95.98 24 | 81.87 112 | 89.48 244 | 79.76 113 | 95.96 132 | 91.10 208 |
|
nrg030 | | | 87.85 84 | 88.49 74 | 85.91 127 | 90.07 175 | 69.73 179 | 87.86 103 | 94.20 28 | 74.04 163 | 92.70 55 | 94.66 57 | 85.88 65 | 91.50 186 | 79.72 114 | 97.32 84 | 96.50 34 |
|
agg_prior2 | | | | | | | | | | | | | | | 79.68 115 | 96.16 124 | 90.22 230 |
|
DeepPCF-MVS | | 81.24 5 | 87.28 90 | 86.21 110 | 90.49 43 | 91.48 140 | 84.90 39 | 83.41 184 | 92.38 106 | 70.25 217 | 89.35 123 | 90.68 191 | 82.85 90 | 94.57 83 | 79.55 116 | 95.95 133 | 92.00 187 |
|
test_prior3 | | | 86.31 104 | 86.31 107 | 86.32 115 | 90.59 164 | 71.99 161 | 83.37 185 | 92.85 93 | 75.43 147 | 84.58 206 | 91.57 163 | 81.92 110 | 94.17 100 | 79.54 117 | 96.97 92 | 92.80 150 |
|
test_prior2 | | | | | | | | 83.37 185 | | 75.43 147 | 84.58 206 | 91.57 163 | 81.92 110 | | 79.54 117 | 96.97 92 | |
|
lessismore_v0 | | | | | 85.95 126 | 91.10 152 | 70.99 172 | | 70.91 344 | | 91.79 72 | 94.42 70 | 61.76 269 | 92.93 152 | 79.52 119 | 93.03 214 | 93.93 106 |
|
PS-CasMVS | | | 90.06 43 | 91.92 13 | 84.47 154 | 96.56 7 | 58.83 289 | 89.04 84 | 92.74 98 | 91.40 5 | 96.12 4 | 96.06 22 | 87.23 47 | 95.57 39 | 79.42 120 | 98.74 6 | 99.00 2 |
|
tttt0517 | | | 81.07 195 | 79.58 216 | 85.52 136 | 88.99 193 | 66.45 208 | 87.03 115 | 75.51 314 | 73.76 167 | 88.32 142 | 90.20 202 | 37.96 365 | 94.16 103 | 79.36 121 | 95.13 163 | 95.93 45 |
|
DTE-MVSNet | | | 89.98 47 | 91.91 15 | 84.21 161 | 96.51 8 | 57.84 294 | 88.93 87 | 92.84 95 | 91.92 3 | 96.16 3 | 96.23 18 | 86.95 50 | 95.99 10 | 79.05 122 | 98.57 15 | 98.80 6 |
|
CP-MVSNet | | | 89.27 61 | 90.91 43 | 84.37 155 | 96.34 9 | 58.61 291 | 88.66 94 | 92.06 112 | 90.78 6 | 95.67 7 | 95.17 44 | 81.80 113 | 95.54 43 | 79.00 123 | 98.69 10 | 98.95 4 |
|
ambc | | | | | 82.98 188 | 90.55 166 | 64.86 217 | 88.20 97 | 89.15 190 | | 89.40 122 | 93.96 97 | 71.67 220 | 91.38 194 | 78.83 124 | 96.55 107 | 92.71 156 |
|
PEN-MVS | | | 90.03 45 | 91.88 16 | 84.48 153 | 96.57 6 | 58.88 286 | 88.95 85 | 93.19 78 | 91.62 4 | 96.01 6 | 96.16 20 | 87.02 49 | 95.60 37 | 78.69 125 | 98.72 9 | 98.97 3 |
|
baseline | | | 85.20 122 | 85.93 114 | 83.02 187 | 86.30 247 | 62.37 246 | 84.55 151 | 93.96 43 | 74.48 160 | 87.12 156 | 92.03 151 | 82.30 98 | 91.94 176 | 78.39 126 | 94.21 189 | 94.74 77 |
|
DeepC-MVS_fast | | 80.27 8 | 86.23 106 | 85.65 122 | 87.96 94 | 91.30 143 | 76.92 113 | 87.19 111 | 91.99 114 | 70.56 212 | 84.96 199 | 90.69 190 | 80.01 133 | 95.14 63 | 78.37 127 | 95.78 144 | 91.82 194 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMH+ | | 77.89 11 | 90.73 30 | 91.50 23 | 88.44 85 | 93.00 84 | 76.26 124 | 89.65 71 | 95.55 7 | 87.72 23 | 93.89 27 | 94.94 49 | 91.62 4 | 93.44 133 | 78.35 128 | 98.76 4 | 95.61 51 |
|
MCST-MVS | | | 84.36 140 | 83.93 156 | 85.63 134 | 91.59 130 | 71.58 168 | 83.52 179 | 92.13 110 | 61.82 286 | 83.96 222 | 89.75 212 | 79.93 135 | 93.46 132 | 78.33 129 | 94.34 187 | 91.87 192 |
|
3Dnovator | | 80.37 7 | 84.80 131 | 84.71 139 | 85.06 144 | 86.36 245 | 74.71 131 | 88.77 91 | 90.00 175 | 75.65 145 | 84.96 199 | 93.17 116 | 74.06 189 | 91.19 197 | 78.28 130 | 91.09 250 | 89.29 245 |
|
h-mvs33 | | | 84.25 145 | 82.76 170 | 88.72 77 | 91.82 127 | 82.60 58 | 84.00 164 | 84.98 257 | 71.27 203 | 86.70 167 | 90.55 195 | 63.04 264 | 93.92 110 | 78.26 131 | 94.20 190 | 89.63 237 |
|
hse-mvs2 | | | 83.47 165 | 81.81 185 | 88.47 83 | 91.03 153 | 82.27 59 | 82.61 205 | 83.69 263 | 71.27 203 | 86.70 167 | 86.05 270 | 63.04 264 | 92.41 163 | 78.26 131 | 93.62 203 | 90.71 218 |
|
c3_l | | | 81.64 189 | 81.59 189 | 81.79 213 | 80.86 313 | 59.15 283 | 78.61 272 | 90.18 171 | 68.36 235 | 87.20 154 | 87.11 256 | 69.39 227 | 91.62 184 | 78.16 133 | 94.43 186 | 94.60 79 |
|
IterMVS-LS | | | 84.73 132 | 84.98 132 | 83.96 167 | 87.35 224 | 63.66 227 | 83.25 189 | 89.88 177 | 76.06 135 | 89.62 115 | 92.37 144 | 73.40 201 | 92.52 161 | 78.16 133 | 94.77 178 | 95.69 47 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 82.61 174 | 82.42 178 | 83.20 184 | 83.25 289 | 63.66 227 | 83.50 182 | 85.07 252 | 76.06 135 | 86.55 171 | 85.10 286 | 73.41 199 | 90.25 224 | 78.15 135 | 90.67 265 | 95.68 48 |
|
GeoE | | | 85.45 119 | 85.81 117 | 84.37 155 | 90.08 173 | 67.07 202 | 85.86 134 | 91.39 133 | 72.33 193 | 87.59 150 | 90.25 201 | 84.85 69 | 92.37 165 | 78.00 136 | 91.94 240 | 93.66 120 |
|
diffmvs | | | 80.40 208 | 80.48 203 | 80.17 238 | 79.02 333 | 60.04 272 | 77.54 286 | 90.28 168 | 66.65 253 | 82.40 241 | 87.33 251 | 73.50 196 | 87.35 272 | 77.98 137 | 89.62 274 | 93.13 138 |
|
OMC-MVS | | | 88.19 76 | 87.52 87 | 90.19 49 | 91.94 119 | 81.68 63 | 87.49 108 | 93.17 79 | 76.02 137 | 88.64 133 | 91.22 171 | 84.24 76 | 93.37 136 | 77.97 138 | 97.03 91 | 95.52 52 |
|
casdiffmvs | | | 85.21 121 | 85.85 116 | 83.31 181 | 86.17 252 | 62.77 239 | 83.03 195 | 93.93 44 | 74.69 157 | 88.21 143 | 92.68 133 | 82.29 99 | 91.89 179 | 77.87 139 | 93.75 199 | 95.27 60 |
|
CS-MVS-test | | | 87.00 93 | 86.43 105 | 88.71 78 | 89.46 182 | 77.46 103 | 89.42 80 | 95.73 6 | 77.87 117 | 81.64 257 | 87.25 252 | 82.43 95 | 94.53 86 | 77.65 140 | 96.46 112 | 94.14 99 |
|
DP-MVS | | | 88.60 72 | 89.01 66 | 87.36 100 | 91.30 143 | 77.50 102 | 87.55 106 | 92.97 90 | 87.95 22 | 89.62 115 | 92.87 126 | 84.56 71 | 93.89 111 | 77.65 140 | 96.62 104 | 90.70 219 |
|
PMVS |  | 80.48 6 | 90.08 41 | 90.66 47 | 88.34 88 | 96.71 3 | 92.97 1 | 90.31 56 | 89.57 183 | 88.51 19 | 90.11 98 | 95.12 46 | 90.98 7 | 88.92 253 | 77.55 142 | 97.07 90 | 83.13 320 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MSLP-MVS++ | | | 85.00 129 | 86.03 113 | 81.90 207 | 91.84 125 | 71.56 169 | 86.75 122 | 93.02 88 | 75.95 140 | 87.12 156 | 89.39 216 | 77.98 147 | 89.40 249 | 77.46 143 | 94.78 176 | 84.75 297 |
|
IterMVS-SCA-FT | | | 80.64 203 | 79.41 217 | 84.34 158 | 83.93 283 | 69.66 180 | 76.28 302 | 81.09 282 | 72.43 188 | 86.47 177 | 90.19 203 | 60.46 274 | 93.15 145 | 77.45 144 | 86.39 308 | 90.22 230 |
|
CDPH-MVS | | | 86.17 109 | 85.54 123 | 88.05 93 | 92.25 107 | 75.45 128 | 83.85 169 | 92.01 113 | 65.91 257 | 86.19 178 | 91.75 161 | 83.77 81 | 94.98 69 | 77.43 145 | 96.71 102 | 93.73 117 |
|
BP-MVS | | | | | | | | | | | | | | | 77.30 146 | | |
|
HQP-MVS | | | 84.61 134 | 84.06 153 | 86.27 118 | 91.19 147 | 70.66 173 | 84.77 145 | 92.68 99 | 73.30 175 | 80.55 271 | 90.17 205 | 72.10 213 | 94.61 81 | 77.30 146 | 94.47 184 | 93.56 127 |
|
MVS_111021_LR | | | 84.28 144 | 83.76 158 | 85.83 131 | 89.23 188 | 83.07 53 | 80.99 239 | 83.56 265 | 72.71 185 | 86.07 182 | 89.07 224 | 81.75 114 | 86.19 290 | 77.11 148 | 93.36 204 | 88.24 257 |
|
CANet | | | 83.79 158 | 82.85 169 | 86.63 109 | 86.17 252 | 72.21 159 | 83.76 173 | 91.43 130 | 77.24 125 | 74.39 321 | 87.45 248 | 75.36 174 | 95.42 52 | 77.03 149 | 92.83 219 | 92.25 180 |
|
dcpmvs_2 | | | 84.23 147 | 85.14 129 | 81.50 215 | 88.61 199 | 61.98 251 | 82.90 200 | 93.11 80 | 68.66 234 | 92.77 53 | 92.39 140 | 78.50 142 | 87.63 269 | 76.99 150 | 92.30 228 | 94.90 68 |
|
Anonymous20231211 | | | 88.40 73 | 89.62 58 | 84.73 149 | 90.46 167 | 65.27 214 | 88.86 88 | 93.02 88 | 87.15 25 | 93.05 45 | 97.10 6 | 82.28 100 | 92.02 175 | 76.70 151 | 97.99 43 | 96.88 26 |
|
iter_conf05 | | | 78.81 226 | 77.35 239 | 83.21 183 | 82.98 295 | 60.75 267 | 84.09 159 | 88.34 202 | 63.12 277 | 84.25 220 | 89.48 215 | 31.41 372 | 94.51 88 | 76.64 152 | 95.83 139 | 94.38 90 |
|
MVS_111021_HR | | | 84.63 133 | 84.34 150 | 85.49 138 | 90.18 172 | 75.86 126 | 79.23 264 | 87.13 222 | 73.35 172 | 85.56 192 | 89.34 217 | 83.60 83 | 90.50 220 | 76.64 152 | 94.05 193 | 90.09 235 |
|
RPSCF | | | 88.00 80 | 86.93 99 | 91.22 31 | 90.08 173 | 89.30 5 | 89.68 69 | 91.11 140 | 79.26 100 | 89.68 112 | 94.81 56 | 82.44 94 | 87.74 267 | 76.54 154 | 88.74 285 | 96.61 32 |
|
DIV-MVS_self_test | | | 80.43 206 | 80.23 206 | 81.02 224 | 79.99 321 | 59.25 280 | 77.07 292 | 87.02 227 | 67.38 246 | 86.19 178 | 89.22 219 | 63.09 262 | 90.16 229 | 76.32 155 | 95.80 142 | 93.66 120 |
|
cl____ | | | 80.42 207 | 80.23 206 | 81.02 224 | 79.99 321 | 59.25 280 | 77.07 292 | 87.02 227 | 67.37 247 | 86.18 180 | 89.21 220 | 63.08 263 | 90.16 229 | 76.31 156 | 95.80 142 | 93.65 122 |
|
AUN-MVS | | | 81.18 194 | 78.78 223 | 88.39 86 | 90.93 155 | 82.14 60 | 82.51 211 | 83.67 264 | 64.69 272 | 80.29 274 | 85.91 273 | 51.07 320 | 92.38 164 | 76.29 157 | 93.63 202 | 90.65 222 |
|
Gipuma |  | | 84.44 139 | 86.33 106 | 78.78 254 | 84.20 278 | 73.57 137 | 89.55 73 | 90.44 157 | 84.24 39 | 84.38 210 | 94.89 50 | 76.35 171 | 80.40 327 | 76.14 158 | 96.80 100 | 82.36 328 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
miper_ehance_all_eth | | | 80.34 210 | 80.04 213 | 81.24 220 | 79.82 323 | 58.95 285 | 77.66 283 | 89.66 180 | 65.75 261 | 85.99 186 | 85.11 285 | 68.29 234 | 91.42 191 | 76.03 159 | 92.03 236 | 93.33 130 |
|
alignmvs | | | 83.94 156 | 83.98 155 | 83.80 169 | 87.80 215 | 67.88 198 | 84.54 153 | 91.42 132 | 73.27 178 | 88.41 139 | 87.96 238 | 72.33 212 | 90.83 210 | 76.02 160 | 94.11 191 | 92.69 157 |
|
ETH3 D test6400 | | | 85.09 125 | 84.87 134 | 85.75 132 | 90.80 159 | 69.34 183 | 85.90 132 | 93.31 70 | 65.43 264 | 86.11 181 | 89.95 207 | 80.92 122 | 94.86 72 | 75.90 161 | 95.57 149 | 93.05 141 |
|
PC_three_1452 | | | | | | | | | | 58.96 305 | 90.06 99 | 91.33 169 | 80.66 126 | 93.03 149 | 75.78 162 | 95.94 134 | 92.48 165 |
|
canonicalmvs | | | 85.50 117 | 86.14 111 | 83.58 176 | 87.97 211 | 67.13 201 | 87.55 106 | 94.32 22 | 73.44 171 | 88.47 137 | 87.54 246 | 86.45 58 | 91.06 202 | 75.76 163 | 93.76 197 | 92.54 163 |
|
CSCG | | | 86.26 105 | 86.47 104 | 85.60 135 | 90.87 157 | 74.26 134 | 87.98 100 | 91.85 119 | 80.35 85 | 89.54 121 | 88.01 237 | 79.09 138 | 92.13 171 | 75.51 164 | 95.06 167 | 90.41 228 |
|
thisisatest0530 | | | 79.07 221 | 77.33 240 | 84.26 160 | 87.13 229 | 64.58 219 | 83.66 177 | 75.95 309 | 68.86 231 | 85.22 196 | 87.36 250 | 38.10 363 | 93.57 128 | 75.47 165 | 94.28 188 | 94.62 78 |
|
TSAR-MVS + GP. | | | 83.95 155 | 82.69 172 | 87.72 95 | 89.27 187 | 81.45 65 | 83.72 174 | 81.58 280 | 74.73 156 | 85.66 189 | 86.06 269 | 72.56 211 | 92.69 158 | 75.44 166 | 95.21 160 | 89.01 253 |
|
cl22 | | | 78.97 222 | 78.21 232 | 81.24 220 | 77.74 337 | 59.01 284 | 77.46 289 | 87.13 222 | 65.79 258 | 84.32 212 | 85.10 286 | 58.96 288 | 90.88 209 | 75.36 167 | 92.03 236 | 93.84 109 |
|
eth_miper_zixun_eth | | | 80.84 199 | 80.22 208 | 82.71 195 | 81.41 305 | 60.98 263 | 77.81 281 | 90.14 172 | 67.31 248 | 86.95 163 | 87.24 253 | 64.26 254 | 92.31 167 | 75.23 168 | 91.61 244 | 94.85 74 |
|
v148 | | | 82.31 178 | 82.48 177 | 81.81 212 | 85.59 257 | 59.66 276 | 81.47 231 | 86.02 238 | 72.85 183 | 88.05 144 | 90.65 193 | 70.73 223 | 90.91 207 | 75.15 169 | 91.79 241 | 94.87 70 |
|
FC-MVSNet-test | | | 85.93 113 | 87.05 95 | 82.58 198 | 92.25 107 | 56.44 305 | 85.75 135 | 93.09 82 | 77.33 123 | 91.94 71 | 94.65 58 | 74.78 182 | 93.41 135 | 75.11 170 | 98.58 14 | 97.88 7 |
|
UniMVSNet (Re) | | | 86.87 94 | 86.98 98 | 86.55 111 | 93.11 81 | 68.48 192 | 83.80 172 | 92.87 92 | 80.37 84 | 89.61 117 | 91.81 159 | 77.72 150 | 94.18 98 | 75.00 171 | 98.53 16 | 96.99 25 |
|
OPU-MVS | | | | | 88.27 89 | 91.89 121 | 77.83 97 | 90.47 53 | | | | 91.22 171 | 81.12 120 | 94.68 77 | 74.48 172 | 95.35 154 | 92.29 176 |
|
DELS-MVS | | | 81.44 191 | 81.25 192 | 82.03 205 | 84.27 275 | 62.87 238 | 76.47 300 | 92.49 103 | 70.97 208 | 81.64 257 | 83.83 302 | 75.03 177 | 92.70 157 | 74.29 173 | 92.22 234 | 90.51 226 |
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 |
Effi-MVS+ | | | 83.90 157 | 84.01 154 | 83.57 177 | 87.22 227 | 65.61 213 | 86.55 126 | 92.40 104 | 78.64 110 | 81.34 262 | 84.18 300 | 83.65 82 | 92.93 152 | 74.22 174 | 87.87 295 | 92.17 183 |
|
UniMVSNet_NR-MVSNet | | | 86.84 96 | 87.06 94 | 86.17 124 | 92.86 89 | 67.02 203 | 82.55 209 | 91.56 126 | 83.08 55 | 90.92 86 | 91.82 158 | 78.25 146 | 93.99 106 | 74.16 175 | 98.35 22 | 97.49 13 |
|
DU-MVS | | | 86.80 97 | 86.99 96 | 86.21 122 | 93.24 78 | 67.02 203 | 83.16 193 | 92.21 108 | 81.73 70 | 90.92 86 | 91.97 152 | 77.20 156 | 93.99 106 | 74.16 175 | 98.35 22 | 97.61 10 |
|
LF4IMVS | | | 82.75 173 | 81.93 184 | 85.19 140 | 82.08 298 | 80.15 73 | 85.53 138 | 88.76 195 | 68.01 239 | 85.58 191 | 87.75 242 | 71.80 218 | 86.85 279 | 74.02 177 | 93.87 196 | 88.58 256 |
|
FIs | | | 85.35 120 | 86.27 108 | 82.60 197 | 91.86 122 | 57.31 298 | 85.10 143 | 93.05 84 | 75.83 142 | 91.02 85 | 93.97 94 | 73.57 195 | 92.91 154 | 73.97 178 | 98.02 41 | 97.58 12 |
|
IS-MVSNet | | | 86.66 99 | 86.82 102 | 86.17 124 | 92.05 115 | 66.87 205 | 91.21 42 | 88.64 197 | 86.30 30 | 89.60 118 | 92.59 134 | 69.22 229 | 94.91 71 | 73.89 179 | 97.89 53 | 96.72 28 |
|
EU-MVSNet | | | 75.12 265 | 74.43 267 | 77.18 280 | 83.11 293 | 59.48 278 | 85.71 137 | 82.43 273 | 39.76 370 | 85.64 190 | 88.76 227 | 44.71 351 | 87.88 266 | 73.86 180 | 85.88 312 | 84.16 303 |
|
ETV-MVS | | | 84.31 142 | 83.91 157 | 85.52 136 | 88.58 200 | 70.40 175 | 84.50 155 | 93.37 64 | 78.76 109 | 84.07 221 | 78.72 346 | 80.39 129 | 95.13 64 | 73.82 181 | 92.98 216 | 91.04 209 |
|
Anonymous20240521 | | | 80.18 215 | 81.25 192 | 76.95 282 | 83.15 292 | 60.84 265 | 82.46 212 | 85.99 239 | 68.76 232 | 86.78 164 | 93.73 109 | 59.13 286 | 77.44 334 | 73.71 182 | 97.55 73 | 92.56 161 |
|
MVSTER | | | 77.09 245 | 75.70 256 | 81.25 218 | 75.27 357 | 61.08 259 | 77.49 288 | 85.07 252 | 60.78 297 | 86.55 171 | 88.68 229 | 43.14 355 | 90.25 224 | 73.69 183 | 90.67 265 | 92.42 167 |
|
ITE_SJBPF | | | | | 90.11 50 | 90.72 161 | 84.97 38 | | 90.30 164 | 81.56 72 | 90.02 101 | 91.20 173 | 82.40 96 | 90.81 211 | 73.58 184 | 94.66 180 | 94.56 80 |
|
RPMNet | | | 78.88 224 | 78.28 231 | 80.68 231 | 79.58 324 | 62.64 241 | 82.58 207 | 94.16 31 | 74.80 155 | 75.72 311 | 92.59 134 | 48.69 326 | 95.56 40 | 73.48 185 | 82.91 335 | 83.85 307 |
|
EG-PatchMatch MVS | | | 84.08 151 | 84.11 152 | 83.98 166 | 92.22 109 | 72.61 149 | 82.20 223 | 87.02 227 | 72.63 186 | 88.86 128 | 91.02 178 | 78.52 141 | 91.11 200 | 73.41 186 | 91.09 250 | 88.21 258 |
|
patch_mono-2 | | | 78.89 223 | 79.39 218 | 77.41 279 | 84.78 265 | 68.11 195 | 75.60 307 | 83.11 267 | 60.96 295 | 79.36 282 | 89.89 210 | 75.18 176 | 72.97 345 | 73.32 187 | 92.30 228 | 91.15 207 |
|
miper_lstm_enhance | | | 76.45 255 | 76.10 252 | 77.51 277 | 76.72 345 | 60.97 264 | 64.69 352 | 85.04 254 | 63.98 274 | 83.20 232 | 88.22 234 | 56.67 301 | 78.79 332 | 73.22 188 | 93.12 211 | 92.78 152 |
|
xiu_mvs_v1_base_debu | | | 80.84 199 | 80.14 210 | 82.93 190 | 88.31 205 | 71.73 164 | 79.53 255 | 87.17 219 | 65.43 264 | 79.59 279 | 82.73 317 | 76.94 162 | 90.14 232 | 73.22 188 | 88.33 287 | 86.90 276 |
|
xiu_mvs_v1_base | | | 80.84 199 | 80.14 210 | 82.93 190 | 88.31 205 | 71.73 164 | 79.53 255 | 87.17 219 | 65.43 264 | 79.59 279 | 82.73 317 | 76.94 162 | 90.14 232 | 73.22 188 | 88.33 287 | 86.90 276 |
|
xiu_mvs_v1_base_debi | | | 80.84 199 | 80.14 210 | 82.93 190 | 88.31 205 | 71.73 164 | 79.53 255 | 87.17 219 | 65.43 264 | 79.59 279 | 82.73 317 | 76.94 162 | 90.14 232 | 73.22 188 | 88.33 287 | 86.90 276 |
|
TranMVSNet+NR-MVSNet | | | 87.86 83 | 88.76 73 | 85.18 141 | 94.02 58 | 64.13 224 | 84.38 156 | 91.29 135 | 84.88 37 | 92.06 67 | 93.84 103 | 86.45 58 | 93.73 117 | 73.22 188 | 98.66 11 | 97.69 9 |
|
TAPA-MVS | | 77.73 12 | 85.71 116 | 84.83 135 | 88.37 87 | 88.78 197 | 79.72 76 | 87.15 113 | 93.50 62 | 69.17 226 | 85.80 188 | 89.56 214 | 80.76 124 | 92.13 171 | 73.21 193 | 95.51 150 | 93.25 135 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
miper_enhance_ethall | | | 77.83 237 | 76.93 244 | 80.51 232 | 76.15 350 | 58.01 293 | 75.47 311 | 88.82 193 | 58.05 311 | 83.59 227 | 80.69 331 | 64.41 253 | 91.20 196 | 73.16 194 | 92.03 236 | 92.33 173 |
|
旧先验2 | | | | | | | | 81.73 226 | | 56.88 320 | 86.54 176 | | | 84.90 303 | 72.81 195 | | |
|
114514_t | | | 83.10 171 | 82.54 176 | 84.77 148 | 92.90 86 | 69.10 190 | 86.65 123 | 90.62 154 | 54.66 328 | 81.46 259 | 90.81 187 | 76.98 161 | 94.38 89 | 72.62 196 | 96.18 123 | 90.82 216 |
|
UniMVSNet_ETH3D | | | 89.12 64 | 90.72 46 | 84.31 159 | 97.00 2 | 64.33 223 | 89.67 70 | 88.38 201 | 88.84 15 | 94.29 19 | 97.57 3 | 90.48 14 | 91.26 195 | 72.57 197 | 97.65 66 | 97.34 16 |
|
NR-MVSNet | | | 86.00 110 | 86.22 109 | 85.34 139 | 93.24 78 | 64.56 220 | 82.21 221 | 90.46 156 | 80.99 78 | 88.42 138 | 91.97 152 | 77.56 152 | 93.85 112 | 72.46 198 | 98.65 12 | 97.61 10 |
|
Baseline_NR-MVSNet | | | 84.00 154 | 85.90 115 | 78.29 265 | 91.47 141 | 53.44 324 | 82.29 217 | 87.00 230 | 79.06 103 | 89.55 119 | 95.72 30 | 77.20 156 | 86.14 291 | 72.30 199 | 98.51 17 | 95.28 59 |
|
Effi-MVS+-dtu | | | 85.82 114 | 83.38 161 | 93.14 3 | 87.13 229 | 91.15 2 | 87.70 105 | 88.42 199 | 74.57 158 | 83.56 228 | 85.65 274 | 78.49 143 | 94.21 96 | 72.04 200 | 92.88 218 | 94.05 102 |
|
mvs-test1 | | | 84.55 136 | 82.12 180 | 91.84 20 | 87.13 229 | 89.54 4 | 85.05 144 | 88.42 199 | 74.57 158 | 80.60 268 | 82.98 310 | 78.49 143 | 93.98 108 | 72.04 200 | 89.77 272 | 92.00 187 |
|
PM-MVS | | | 80.20 214 | 79.00 221 | 83.78 171 | 88.17 209 | 86.66 18 | 81.31 233 | 66.81 358 | 69.64 223 | 88.33 141 | 90.19 203 | 64.58 252 | 83.63 313 | 71.99 202 | 90.03 270 | 81.06 345 |
|
EIA-MVS | | | 82.19 181 | 81.23 194 | 85.10 143 | 87.95 212 | 69.17 189 | 83.22 192 | 93.33 67 | 70.42 213 | 78.58 289 | 79.77 343 | 77.29 155 | 94.20 97 | 71.51 203 | 88.96 281 | 91.93 191 |
|
DPM-MVS | | | 80.10 217 | 79.18 220 | 82.88 193 | 90.71 162 | 69.74 178 | 78.87 268 | 90.84 147 | 60.29 301 | 75.64 313 | 85.92 272 | 67.28 238 | 93.11 146 | 71.24 204 | 91.79 241 | 85.77 287 |
|
OpenMVS |  | 76.72 13 | 81.98 186 | 82.00 183 | 81.93 206 | 84.42 271 | 68.22 194 | 88.50 96 | 89.48 184 | 66.92 250 | 81.80 254 | 91.86 154 | 72.59 210 | 90.16 229 | 71.19 205 | 91.25 249 | 87.40 270 |
|
AllTest | | | 87.97 81 | 87.40 90 | 89.68 57 | 91.59 130 | 83.40 50 | 89.50 76 | 95.44 9 | 79.47 95 | 88.00 145 | 93.03 118 | 82.66 92 | 91.47 187 | 70.81 206 | 96.14 125 | 94.16 97 |
|
TestCases | | | | | 89.68 57 | 91.59 130 | 83.40 50 | | 95.44 9 | 79.47 95 | 88.00 145 | 93.03 118 | 82.66 92 | 91.47 187 | 70.81 206 | 96.14 125 | 94.16 97 |
|
ET-MVSNet_ETH3D | | | 75.28 262 | 72.77 282 | 82.81 194 | 83.03 294 | 68.11 195 | 77.09 291 | 76.51 307 | 60.67 299 | 77.60 298 | 80.52 335 | 38.04 364 | 91.15 199 | 70.78 208 | 90.68 264 | 89.17 246 |
|
EPP-MVSNet | | | 85.47 118 | 85.04 131 | 86.77 108 | 91.52 139 | 69.37 182 | 91.63 38 | 87.98 211 | 81.51 73 | 87.05 161 | 91.83 157 | 66.18 246 | 95.29 56 | 70.75 209 | 96.89 94 | 95.64 49 |
|
jason | | | 77.42 242 | 75.75 255 | 82.43 203 | 87.10 233 | 69.27 184 | 77.99 278 | 81.94 277 | 51.47 346 | 77.84 294 | 85.07 289 | 60.32 276 | 89.00 251 | 70.74 210 | 89.27 278 | 89.03 251 |
jason: jason. |
MG-MVS | | | 80.32 211 | 80.94 197 | 78.47 261 | 88.18 208 | 52.62 331 | 82.29 217 | 85.01 256 | 72.01 199 | 79.24 285 | 92.54 138 | 69.36 228 | 93.36 137 | 70.65 211 | 89.19 279 | 89.45 239 |
|
QAPM | | | 82.59 175 | 82.59 175 | 82.58 198 | 86.44 240 | 66.69 206 | 89.94 64 | 90.36 160 | 67.97 241 | 84.94 201 | 92.58 136 | 72.71 208 | 92.18 170 | 70.63 212 | 87.73 297 | 88.85 254 |
|
CVMVSNet | | | 72.62 287 | 71.41 296 | 76.28 292 | 83.25 289 | 60.34 270 | 83.50 182 | 79.02 295 | 37.77 371 | 76.33 303 | 85.10 286 | 49.60 325 | 87.41 271 | 70.54 213 | 77.54 357 | 81.08 343 |
|
pmmvs6 | | | 86.52 101 | 88.06 79 | 81.90 207 | 92.22 109 | 62.28 248 | 84.66 149 | 89.15 190 | 83.54 49 | 89.85 107 | 97.32 4 | 88.08 38 | 86.80 280 | 70.43 214 | 97.30 85 | 96.62 31 |
|
D2MVS | | | 76.84 248 | 75.67 257 | 80.34 235 | 80.48 319 | 62.16 250 | 73.50 323 | 84.80 260 | 57.61 315 | 82.24 243 | 87.54 246 | 51.31 319 | 87.65 268 | 70.40 215 | 93.19 210 | 91.23 206 |
|
PAPM_NR | | | 83.23 168 | 83.19 165 | 83.33 180 | 90.90 156 | 65.98 210 | 88.19 98 | 90.78 149 | 78.13 116 | 80.87 266 | 87.92 241 | 73.49 198 | 92.42 162 | 70.07 216 | 88.40 286 | 91.60 200 |
|
lupinMVS | | | 76.37 256 | 74.46 266 | 82.09 204 | 85.54 258 | 69.26 185 | 76.79 294 | 80.77 285 | 50.68 352 | 76.23 305 | 82.82 315 | 58.69 289 | 88.94 252 | 69.85 217 | 88.77 283 | 88.07 259 |
|
PVSNet_Blended_VisFu | | | 81.55 190 | 80.49 202 | 84.70 151 | 91.58 133 | 73.24 141 | 84.21 157 | 91.67 125 | 62.86 279 | 80.94 264 | 87.16 254 | 67.27 239 | 92.87 155 | 69.82 218 | 88.94 282 | 87.99 262 |
|
Patchmatch-RL test | | | 74.48 273 | 73.68 272 | 76.89 285 | 84.83 264 | 66.54 207 | 72.29 329 | 69.16 351 | 57.70 313 | 86.76 165 | 86.33 264 | 45.79 340 | 82.59 316 | 69.63 219 | 90.65 267 | 81.54 336 |
|
EPNet | | | 80.37 209 | 78.41 230 | 86.23 119 | 76.75 344 | 73.28 139 | 87.18 112 | 77.45 301 | 76.24 133 | 68.14 344 | 88.93 226 | 65.41 250 | 93.85 112 | 69.47 220 | 96.12 127 | 91.55 202 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CLD-MVS | | | 83.18 169 | 82.64 173 | 84.79 147 | 89.05 190 | 67.82 199 | 77.93 279 | 92.52 102 | 68.33 236 | 85.07 198 | 81.54 327 | 82.06 104 | 92.96 150 | 69.35 221 | 97.91 51 | 93.57 126 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
原ACMM1 | | | | | 84.60 152 | 92.81 92 | 74.01 135 | | 91.50 128 | 62.59 280 | 82.73 238 | 90.67 192 | 76.53 169 | 94.25 92 | 69.24 222 | 95.69 147 | 85.55 288 |
|
VDD-MVS | | | 84.23 147 | 84.58 143 | 83.20 184 | 91.17 150 | 65.16 216 | 83.25 189 | 84.97 258 | 79.79 91 | 87.18 155 | 94.27 76 | 74.77 183 | 90.89 208 | 69.24 222 | 96.54 108 | 93.55 129 |
|
CANet_DTU | | | 77.81 239 | 77.05 242 | 80.09 239 | 81.37 306 | 59.90 274 | 83.26 188 | 88.29 204 | 69.16 227 | 67.83 347 | 83.72 303 | 60.93 271 | 89.47 245 | 69.22 224 | 89.70 273 | 90.88 214 |
|
Anonymous20240529 | | | 86.20 108 | 87.13 92 | 83.42 179 | 90.19 171 | 64.55 221 | 84.55 151 | 90.71 150 | 85.85 31 | 89.94 105 | 95.24 42 | 82.13 102 | 90.40 222 | 69.19 225 | 96.40 115 | 95.31 58 |
|
FMVSNet1 | | | 84.55 136 | 85.45 125 | 81.85 209 | 90.27 170 | 61.05 260 | 86.83 118 | 88.27 205 | 78.57 111 | 89.66 114 | 95.64 32 | 75.43 173 | 90.68 215 | 69.09 226 | 95.33 155 | 93.82 112 |
|
UGNet | | | 82.78 172 | 81.64 187 | 86.21 122 | 86.20 251 | 76.24 125 | 86.86 116 | 85.68 242 | 77.07 126 | 73.76 324 | 92.82 127 | 69.64 226 | 91.82 182 | 69.04 227 | 93.69 200 | 90.56 224 |
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 |
ANet_high | | | 83.17 170 | 85.68 121 | 75.65 296 | 81.24 307 | 45.26 363 | 79.94 250 | 92.91 91 | 83.83 43 | 91.33 80 | 96.88 10 | 80.25 131 | 85.92 293 | 68.89 228 | 95.89 137 | 95.76 46 |
|
Fast-Effi-MVS+-dtu | | | 82.54 176 | 81.41 191 | 85.90 128 | 85.60 256 | 76.53 120 | 83.07 194 | 89.62 182 | 73.02 182 | 79.11 286 | 83.51 305 | 80.74 125 | 90.24 226 | 68.76 229 | 89.29 276 | 90.94 212 |
|
pm-mvs1 | | | 83.69 159 | 84.95 133 | 79.91 240 | 90.04 177 | 59.66 276 | 82.43 213 | 87.44 215 | 75.52 146 | 87.85 147 | 95.26 41 | 81.25 119 | 85.65 297 | 68.74 230 | 96.04 128 | 94.42 88 |
|
CR-MVSNet | | | 74.00 277 | 73.04 280 | 76.85 286 | 79.58 324 | 62.64 241 | 82.58 207 | 76.90 303 | 50.50 353 | 75.72 311 | 92.38 141 | 48.07 328 | 84.07 309 | 68.72 231 | 82.91 335 | 83.85 307 |
|
KD-MVS_self_test | | | 81.93 187 | 83.14 166 | 78.30 264 | 84.75 266 | 52.75 328 | 80.37 245 | 89.42 186 | 70.24 218 | 90.26 97 | 93.39 113 | 74.55 187 | 86.77 281 | 68.61 232 | 96.64 103 | 95.38 55 |
|
IterMVS | | | 76.91 247 | 76.34 250 | 78.64 257 | 80.91 311 | 64.03 225 | 76.30 301 | 79.03 294 | 64.88 271 | 83.11 233 | 89.16 221 | 59.90 280 | 84.46 306 | 68.61 232 | 85.15 318 | 87.42 269 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
testdata | | | | | 79.54 247 | 92.87 87 | 72.34 155 | | 80.14 289 | 59.91 303 | 85.47 194 | 91.75 161 | 67.96 236 | 85.24 299 | 68.57 234 | 92.18 235 | 81.06 345 |
|
mvs_anonymous | | | 78.13 235 | 78.76 224 | 76.23 294 | 79.24 330 | 50.31 347 | 78.69 270 | 84.82 259 | 61.60 290 | 83.09 235 | 92.82 127 | 73.89 192 | 87.01 274 | 68.33 235 | 86.41 307 | 91.37 204 |
|
WR-MVS | | | 83.56 162 | 84.40 148 | 81.06 223 | 93.43 73 | 54.88 316 | 78.67 271 | 85.02 255 | 81.24 75 | 90.74 90 | 91.56 165 | 72.85 206 | 91.08 201 | 68.00 236 | 98.04 38 | 97.23 19 |
|
TransMVSNet (Re) | | | 84.02 153 | 85.74 119 | 78.85 253 | 91.00 154 | 55.20 315 | 82.29 217 | 87.26 218 | 79.65 94 | 88.38 140 | 95.52 35 | 83.00 88 | 86.88 278 | 67.97 237 | 96.60 106 | 94.45 87 |
|
无先验 | | | | | | | | 82.81 202 | 85.62 243 | 58.09 310 | | | | 91.41 192 | 67.95 238 | | 84.48 298 |
|
1121 | | | 80.86 198 | 79.81 215 | 84.02 164 | 93.93 60 | 78.70 87 | 81.64 228 | 80.18 288 | 55.43 325 | 83.67 225 | 91.15 174 | 71.29 221 | 91.41 192 | 67.95 238 | 93.06 213 | 81.96 331 |
|
Fast-Effi-MVS+ | | | 81.04 196 | 80.57 199 | 82.46 202 | 87.50 222 | 63.22 233 | 78.37 275 | 89.63 181 | 68.01 239 | 81.87 250 | 82.08 322 | 82.31 97 | 92.65 159 | 67.10 240 | 88.30 291 | 91.51 203 |
|
FMVSNet2 | | | 81.31 192 | 81.61 188 | 80.41 234 | 86.38 242 | 58.75 290 | 83.93 167 | 86.58 232 | 72.43 188 | 87.65 149 | 92.98 120 | 63.78 258 | 90.22 227 | 66.86 241 | 93.92 195 | 92.27 178 |
|
GA-MVS | | | 75.83 259 | 74.61 263 | 79.48 248 | 81.87 300 | 59.25 280 | 73.42 324 | 82.88 269 | 68.68 233 | 79.75 278 | 81.80 324 | 50.62 322 | 89.46 246 | 66.85 242 | 85.64 313 | 89.72 236 |
|
CNLPA | | | 83.55 163 | 83.10 167 | 84.90 145 | 89.34 185 | 83.87 48 | 84.54 153 | 88.77 194 | 79.09 102 | 83.54 229 | 88.66 230 | 74.87 179 | 81.73 321 | 66.84 243 | 92.29 230 | 89.11 247 |
|
tfpnnormal | | | 81.79 188 | 82.95 168 | 78.31 263 | 88.93 194 | 55.40 311 | 80.83 242 | 82.85 270 | 76.81 128 | 85.90 187 | 94.14 87 | 74.58 186 | 86.51 285 | 66.82 244 | 95.68 148 | 93.01 143 |
|
VPA-MVSNet | | | 83.47 165 | 84.73 136 | 79.69 244 | 90.29 169 | 57.52 297 | 81.30 235 | 88.69 196 | 76.29 131 | 87.58 151 | 94.44 67 | 80.60 127 | 87.20 273 | 66.60 245 | 96.82 99 | 94.34 91 |
|
VDDNet | | | 84.35 141 | 85.39 126 | 81.25 218 | 95.13 33 | 59.32 279 | 85.42 140 | 81.11 281 | 86.41 29 | 87.41 153 | 96.21 19 | 73.61 194 | 90.61 218 | 66.33 246 | 96.85 96 | 93.81 115 |
|
DP-MVS Recon | | | 84.05 152 | 83.22 163 | 86.52 112 | 91.73 128 | 75.27 129 | 83.23 191 | 92.40 104 | 72.04 198 | 82.04 247 | 88.33 233 | 77.91 149 | 93.95 109 | 66.17 247 | 95.12 165 | 90.34 229 |
|
GBi-Net | | | 82.02 184 | 82.07 181 | 81.85 209 | 86.38 242 | 61.05 260 | 86.83 118 | 88.27 205 | 72.43 188 | 86.00 183 | 95.64 32 | 63.78 258 | 90.68 215 | 65.95 248 | 93.34 205 | 93.82 112 |
|
test1 | | | 82.02 184 | 82.07 181 | 81.85 209 | 86.38 242 | 61.05 260 | 86.83 118 | 88.27 205 | 72.43 188 | 86.00 183 | 95.64 32 | 63.78 258 | 90.68 215 | 65.95 248 | 93.34 205 | 93.82 112 |
|
FMVSNet3 | | | 78.80 227 | 78.55 227 | 79.57 246 | 82.89 296 | 56.89 303 | 81.76 225 | 85.77 241 | 69.04 229 | 86.00 183 | 90.44 197 | 51.75 318 | 90.09 235 | 65.95 248 | 93.34 205 | 91.72 196 |
|
新几何1 | | | | | 82.95 189 | 93.96 59 | 78.56 89 | | 80.24 287 | 55.45 324 | 83.93 223 | 91.08 176 | 71.19 222 | 88.33 262 | 65.84 251 | 93.07 212 | 81.95 332 |
|
F-COLMAP | | | 84.97 130 | 83.42 160 | 89.63 59 | 92.39 100 | 83.40 50 | 88.83 89 | 91.92 117 | 73.19 179 | 80.18 277 | 89.15 222 | 77.04 160 | 93.28 138 | 65.82 252 | 92.28 231 | 92.21 181 |
|
ppachtmachnet_test | | | 74.73 272 | 74.00 270 | 76.90 284 | 80.71 316 | 56.89 303 | 71.53 332 | 78.42 296 | 58.24 309 | 79.32 284 | 82.92 314 | 57.91 295 | 84.26 308 | 65.60 253 | 91.36 248 | 89.56 238 |
|
API-MVS | | | 82.28 179 | 82.61 174 | 81.30 217 | 86.29 248 | 69.79 177 | 88.71 92 | 87.67 214 | 78.42 113 | 82.15 246 | 84.15 301 | 77.98 147 | 91.59 185 | 65.39 254 | 92.75 220 | 82.51 327 |
|
test1111 | | | 78.53 231 | 78.85 222 | 77.56 276 | 92.22 109 | 47.49 356 | 82.61 205 | 69.24 350 | 72.43 188 | 85.28 195 | 94.20 82 | 51.91 316 | 90.07 236 | 65.36 255 | 96.45 113 | 95.11 65 |
|
thisisatest0515 | | | 73.00 285 | 70.52 299 | 80.46 233 | 81.45 304 | 59.90 274 | 73.16 327 | 74.31 321 | 57.86 312 | 76.08 308 | 77.78 349 | 37.60 366 | 92.12 173 | 65.00 256 | 91.45 247 | 89.35 242 |
|
cascas | | | 76.29 257 | 74.81 262 | 80.72 230 | 84.47 268 | 62.94 236 | 73.89 321 | 87.34 216 | 55.94 322 | 75.16 318 | 76.53 357 | 63.97 256 | 91.16 198 | 65.00 256 | 90.97 257 | 88.06 260 |
|
test2506 | | | 74.12 276 | 73.39 276 | 76.28 292 | 91.85 123 | 44.20 366 | 84.06 162 | 48.20 378 | 72.30 194 | 81.90 249 | 94.20 82 | 27.22 379 | 89.77 241 | 64.81 258 | 96.02 129 | 94.87 70 |
|
MDA-MVSNet-bldmvs | | | 77.47 241 | 76.90 245 | 79.16 251 | 79.03 332 | 64.59 218 | 66.58 349 | 75.67 312 | 73.15 180 | 88.86 128 | 88.99 225 | 66.94 240 | 81.23 323 | 64.71 259 | 88.22 292 | 91.64 199 |
|
OpenMVS_ROB |  | 70.19 17 | 77.77 240 | 77.46 236 | 78.71 256 | 84.39 272 | 61.15 258 | 81.18 237 | 82.52 271 | 62.45 283 | 83.34 230 | 87.37 249 | 66.20 245 | 88.66 259 | 64.69 260 | 85.02 319 | 86.32 280 |
|
PS-MVSNAJ | | | 77.04 246 | 76.53 248 | 78.56 258 | 87.09 234 | 61.40 254 | 75.26 312 | 87.13 222 | 61.25 291 | 74.38 322 | 77.22 354 | 76.94 162 | 90.94 204 | 64.63 261 | 84.83 324 | 83.35 315 |
|
xiu_mvs_v2_base | | | 77.19 244 | 76.75 246 | 78.52 259 | 87.01 235 | 61.30 256 | 75.55 310 | 87.12 225 | 61.24 292 | 74.45 320 | 78.79 345 | 77.20 156 | 90.93 205 | 64.62 262 | 84.80 325 | 83.32 316 |
|
PatchT | | | 70.52 301 | 72.76 283 | 63.79 342 | 79.38 328 | 33.53 376 | 77.63 284 | 65.37 360 | 73.61 168 | 71.77 332 | 92.79 130 | 44.38 352 | 75.65 341 | 64.53 263 | 85.37 315 | 82.18 329 |
|
LFMVS | | | 80.15 216 | 80.56 200 | 78.89 252 | 89.19 189 | 55.93 307 | 85.22 142 | 73.78 326 | 82.96 56 | 84.28 217 | 92.72 131 | 57.38 298 | 90.07 236 | 63.80 264 | 95.75 145 | 90.68 220 |
|
ECVR-MVS |  | | 78.44 232 | 78.63 226 | 77.88 272 | 91.85 123 | 48.95 350 | 83.68 176 | 69.91 348 | 72.30 194 | 84.26 219 | 94.20 82 | 51.89 317 | 89.82 240 | 63.58 265 | 96.02 129 | 94.87 70 |
|
1314 | | | 73.22 282 | 72.56 287 | 75.20 298 | 80.41 320 | 57.84 294 | 81.64 228 | 85.36 245 | 51.68 345 | 73.10 327 | 76.65 356 | 61.45 270 | 85.19 300 | 63.54 266 | 79.21 351 | 82.59 323 |
|
testdata2 | | | | | | | | | | | | | | 86.43 287 | 63.52 267 | | |
|
Patchmtry | | | 76.56 253 | 77.46 236 | 73.83 305 | 79.37 329 | 46.60 360 | 82.41 214 | 76.90 303 | 73.81 166 | 85.56 192 | 92.38 141 | 48.07 328 | 83.98 310 | 63.36 268 | 95.31 158 | 90.92 213 |
|
MSDG | | | 80.06 218 | 79.99 214 | 80.25 236 | 83.91 284 | 68.04 197 | 77.51 287 | 89.19 189 | 77.65 119 | 81.94 248 | 83.45 307 | 76.37 170 | 86.31 288 | 63.31 269 | 86.59 305 | 86.41 279 |
|
BH-RMVSNet | | | 80.53 204 | 80.22 208 | 81.49 216 | 87.19 228 | 66.21 209 | 77.79 282 | 86.23 235 | 74.21 162 | 83.69 224 | 88.50 231 | 73.25 203 | 90.75 212 | 63.18 270 | 87.90 294 | 87.52 268 |
|
test_yl | | | 78.71 229 | 78.51 228 | 79.32 249 | 84.32 273 | 58.84 287 | 78.38 273 | 85.33 246 | 75.99 138 | 82.49 239 | 86.57 260 | 58.01 292 | 90.02 238 | 62.74 271 | 92.73 221 | 89.10 248 |
|
DCV-MVSNet | | | 78.71 229 | 78.51 228 | 79.32 249 | 84.32 273 | 58.84 287 | 78.38 273 | 85.33 246 | 75.99 138 | 82.49 239 | 86.57 260 | 58.01 292 | 90.02 238 | 62.74 271 | 92.73 221 | 89.10 248 |
|
TinyColmap | | | 81.25 193 | 82.34 179 | 77.99 270 | 85.33 260 | 60.68 268 | 82.32 216 | 88.33 203 | 71.26 205 | 86.97 162 | 92.22 150 | 77.10 159 | 86.98 277 | 62.37 273 | 95.17 162 | 86.31 281 |
|
Anonymous202405211 | | | 80.51 205 | 81.19 195 | 78.49 260 | 88.48 202 | 57.26 299 | 76.63 297 | 82.49 272 | 81.21 76 | 84.30 216 | 92.24 149 | 67.99 235 | 86.24 289 | 62.22 274 | 95.13 163 | 91.98 190 |
|
our_test_3 | | | 71.85 293 | 71.59 293 | 72.62 311 | 80.71 316 | 53.78 321 | 69.72 339 | 71.71 342 | 58.80 306 | 78.03 291 | 80.51 336 | 56.61 302 | 78.84 331 | 62.20 275 | 86.04 311 | 85.23 291 |
|
pmmvs-eth3d | | | 78.42 233 | 77.04 243 | 82.57 200 | 87.44 223 | 74.41 133 | 80.86 241 | 79.67 291 | 55.68 323 | 84.69 205 | 90.31 200 | 60.91 272 | 85.42 298 | 62.20 275 | 91.59 245 | 87.88 265 |
|
CMPMVS |  | 59.41 20 | 75.12 265 | 73.57 273 | 79.77 241 | 75.84 352 | 67.22 200 | 81.21 236 | 82.18 274 | 50.78 350 | 76.50 301 | 87.66 244 | 55.20 310 | 82.99 315 | 62.17 277 | 90.64 268 | 89.09 250 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MIMVSNet1 | | | 83.63 161 | 84.59 142 | 80.74 228 | 94.06 57 | 62.77 239 | 82.72 203 | 84.53 261 | 77.57 121 | 90.34 95 | 95.92 25 | 76.88 168 | 85.83 295 | 61.88 278 | 97.42 81 | 93.62 124 |
|
BH-untuned | | | 80.96 197 | 80.99 196 | 80.84 227 | 88.55 201 | 68.23 193 | 80.33 246 | 88.46 198 | 72.79 184 | 86.55 171 | 86.76 259 | 74.72 184 | 91.77 183 | 61.79 279 | 88.99 280 | 82.52 326 |
|
AdaColmap |  | | 83.66 160 | 83.69 159 | 83.57 177 | 90.05 176 | 72.26 157 | 86.29 130 | 90.00 175 | 78.19 115 | 81.65 256 | 87.16 254 | 83.40 85 | 94.24 93 | 61.69 280 | 94.76 179 | 84.21 302 |
|
VPNet | | | 80.25 212 | 81.68 186 | 75.94 295 | 92.46 99 | 47.98 354 | 76.70 296 | 81.67 279 | 73.45 170 | 84.87 202 | 92.82 127 | 74.66 185 | 86.51 285 | 61.66 281 | 96.85 96 | 93.33 130 |
|
MAR-MVS | | | 80.24 213 | 78.74 225 | 84.73 149 | 86.87 239 | 78.18 91 | 85.75 135 | 87.81 213 | 65.67 263 | 77.84 294 | 78.50 347 | 73.79 193 | 90.53 219 | 61.59 282 | 90.87 260 | 85.49 290 |
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 |
PLC |  | 73.85 16 | 82.09 183 | 80.31 204 | 87.45 99 | 90.86 158 | 80.29 72 | 85.88 133 | 90.65 152 | 68.17 238 | 76.32 304 | 86.33 264 | 73.12 204 | 92.61 160 | 61.40 283 | 90.02 271 | 89.44 240 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
test-LLR | | | 67.21 316 | 66.74 320 | 68.63 328 | 76.45 348 | 55.21 313 | 67.89 343 | 67.14 355 | 62.43 284 | 65.08 356 | 72.39 362 | 43.41 353 | 69.37 351 | 61.00 284 | 84.89 322 | 81.31 338 |
|
test-mter | | | 65.00 325 | 63.79 328 | 68.63 328 | 76.45 348 | 55.21 313 | 67.89 343 | 67.14 355 | 50.98 349 | 65.08 356 | 72.39 362 | 28.27 377 | 69.37 351 | 61.00 284 | 84.89 322 | 81.31 338 |
|
PatchmatchNet |  | | 69.71 309 | 68.83 310 | 72.33 314 | 77.66 339 | 53.60 322 | 79.29 260 | 69.99 347 | 57.66 314 | 72.53 329 | 82.93 313 | 46.45 332 | 80.08 329 | 60.91 286 | 72.09 363 | 83.31 317 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MVS_0304 | | | 78.17 234 | 77.23 241 | 80.99 226 | 84.13 281 | 69.07 191 | 81.39 232 | 80.81 284 | 76.28 132 | 67.53 349 | 89.11 223 | 62.87 266 | 86.77 281 | 60.90 287 | 92.01 239 | 87.13 273 |
|
PVSNet_BlendedMVS | | | 78.80 227 | 77.84 234 | 81.65 214 | 84.43 269 | 63.41 229 | 79.49 258 | 90.44 157 | 61.70 289 | 75.43 314 | 87.07 257 | 69.11 230 | 91.44 189 | 60.68 288 | 92.24 232 | 90.11 234 |
|
PVSNet_Blended | | | 76.49 254 | 75.40 258 | 79.76 242 | 84.43 269 | 63.41 229 | 75.14 313 | 90.44 157 | 57.36 317 | 75.43 314 | 78.30 348 | 69.11 230 | 91.44 189 | 60.68 288 | 87.70 298 | 84.42 300 |
|
VNet | | | 79.31 220 | 80.27 205 | 76.44 289 | 87.92 213 | 53.95 320 | 75.58 309 | 84.35 262 | 74.39 161 | 82.23 244 | 90.72 189 | 72.84 207 | 84.39 307 | 60.38 290 | 93.98 194 | 90.97 211 |
|
LCM-MVSNet-Re | | | 83.48 164 | 85.06 130 | 78.75 255 | 85.94 255 | 55.75 310 | 80.05 248 | 94.27 23 | 76.47 130 | 96.09 5 | 94.54 63 | 83.31 86 | 89.75 243 | 59.95 291 | 94.89 173 | 90.75 217 |
|
YYNet1 | | | 70.06 305 | 70.44 300 | 68.90 325 | 73.76 362 | 53.42 325 | 58.99 363 | 67.20 354 | 58.42 308 | 87.10 158 | 85.39 282 | 59.82 281 | 67.32 358 | 59.79 292 | 83.50 331 | 85.96 283 |
|
MDA-MVSNet_test_wron | | | 70.05 306 | 70.44 300 | 68.88 326 | 73.84 361 | 53.47 323 | 58.93 364 | 67.28 353 | 58.43 307 | 87.09 159 | 85.40 281 | 59.80 282 | 67.25 359 | 59.66 293 | 83.54 330 | 85.92 285 |
|
PAPR | | | 78.84 225 | 78.10 233 | 81.07 222 | 85.17 261 | 60.22 271 | 82.21 221 | 90.57 155 | 62.51 281 | 75.32 316 | 84.61 296 | 74.99 178 | 92.30 168 | 59.48 294 | 88.04 293 | 90.68 220 |
|
IB-MVS | | 62.13 19 | 71.64 295 | 68.97 309 | 79.66 245 | 80.80 315 | 62.26 249 | 73.94 320 | 76.90 303 | 63.27 276 | 68.63 343 | 76.79 355 | 33.83 370 | 91.84 181 | 59.28 295 | 87.26 300 | 84.88 295 |
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 |
PCF-MVS | | 74.62 15 | 82.15 182 | 80.92 198 | 85.84 130 | 89.43 183 | 72.30 156 | 80.53 243 | 91.82 121 | 57.36 317 | 87.81 148 | 89.92 209 | 77.67 151 | 93.63 121 | 58.69 296 | 95.08 166 | 91.58 201 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
1112_ss | | | 74.82 270 | 73.74 271 | 78.04 269 | 89.57 180 | 60.04 272 | 76.49 299 | 87.09 226 | 54.31 329 | 73.66 325 | 79.80 341 | 60.25 277 | 86.76 283 | 58.37 297 | 84.15 328 | 87.32 271 |
|
tpmvs | | | 70.16 303 | 69.56 307 | 71.96 315 | 74.71 360 | 48.13 352 | 79.63 253 | 75.45 315 | 65.02 270 | 70.26 338 | 81.88 323 | 45.34 346 | 85.68 296 | 58.34 298 | 75.39 360 | 82.08 330 |
|
UnsupCasMVSNet_eth | | | 71.63 296 | 72.30 289 | 69.62 322 | 76.47 347 | 52.70 330 | 70.03 338 | 80.97 283 | 59.18 304 | 79.36 282 | 88.21 235 | 60.50 273 | 69.12 354 | 58.33 299 | 77.62 356 | 87.04 274 |
|
tpmrst | | | 66.28 322 | 66.69 321 | 65.05 340 | 72.82 368 | 39.33 371 | 78.20 276 | 70.69 345 | 53.16 335 | 67.88 346 | 80.36 337 | 48.18 327 | 74.75 343 | 58.13 300 | 70.79 365 | 81.08 343 |
|
test_post1 | | | | | | | | 78.85 269 | | | | 3.13 376 | 45.19 348 | 80.13 328 | 58.11 301 | | |
|
SCA | | | 73.32 280 | 72.57 286 | 75.58 297 | 81.62 302 | 55.86 308 | 78.89 267 | 71.37 343 | 61.73 287 | 74.93 319 | 83.42 308 | 60.46 274 | 87.01 274 | 58.11 301 | 82.63 339 | 83.88 304 |
|
pmmvs4 | | | 74.92 268 | 72.98 281 | 80.73 229 | 84.95 262 | 71.71 167 | 76.23 303 | 77.59 300 | 52.83 336 | 77.73 297 | 86.38 262 | 56.35 304 | 84.97 302 | 57.72 303 | 87.05 302 | 85.51 289 |
|
Vis-MVSNet (Re-imp) | | | 77.82 238 | 77.79 235 | 77.92 271 | 88.82 196 | 51.29 341 | 83.28 187 | 71.97 338 | 74.04 163 | 82.23 244 | 89.78 211 | 57.38 298 | 89.41 248 | 57.22 304 | 95.41 152 | 93.05 141 |
|
ab-mvs | | | 79.67 219 | 80.56 200 | 76.99 281 | 88.48 202 | 56.93 301 | 84.70 148 | 86.06 237 | 68.95 230 | 80.78 267 | 93.08 117 | 75.30 175 | 84.62 305 | 56.78 305 | 90.90 259 | 89.43 241 |
|
baseline1 | | | 73.26 281 | 73.54 274 | 72.43 313 | 84.92 263 | 47.79 355 | 79.89 251 | 74.00 322 | 65.93 256 | 78.81 288 | 86.28 267 | 56.36 303 | 81.63 322 | 56.63 306 | 79.04 352 | 87.87 266 |
|
Test_1112_low_res | | | 73.90 278 | 73.08 279 | 76.35 290 | 90.35 168 | 55.95 306 | 73.40 325 | 86.17 236 | 50.70 351 | 73.14 326 | 85.94 271 | 58.31 291 | 85.90 294 | 56.51 307 | 83.22 332 | 87.20 272 |
|
TESTMET0.1,1 | | | 61.29 332 | 60.32 337 | 64.19 341 | 72.06 369 | 51.30 340 | 67.89 343 | 62.09 363 | 45.27 362 | 60.65 365 | 69.01 365 | 27.93 378 | 64.74 367 | 56.31 308 | 81.65 342 | 76.53 352 |
|
XXY-MVS | | | 74.44 275 | 76.19 251 | 69.21 324 | 84.61 267 | 52.43 332 | 71.70 331 | 77.18 302 | 60.73 298 | 80.60 268 | 90.96 182 | 75.44 172 | 69.35 353 | 56.13 309 | 88.33 287 | 85.86 286 |
|
MDTV_nov1_ep13 | | | | 68.29 314 | | 78.03 336 | 43.87 367 | 74.12 319 | 72.22 336 | 52.17 340 | 67.02 350 | 85.54 275 | 45.36 345 | 80.85 325 | 55.73 310 | 84.42 327 | |
|
E-PMN | | | 61.59 331 | 61.62 333 | 61.49 347 | 66.81 375 | 55.40 311 | 53.77 366 | 60.34 368 | 66.80 252 | 58.90 369 | 65.50 368 | 40.48 360 | 66.12 364 | 55.72 311 | 86.25 309 | 62.95 366 |
|
MVS | | | 73.21 283 | 72.59 285 | 75.06 300 | 80.97 310 | 60.81 266 | 81.64 228 | 85.92 240 | 46.03 361 | 71.68 333 | 77.54 350 | 68.47 233 | 89.77 241 | 55.70 312 | 85.39 314 | 74.60 356 |
|
TR-MVS | | | 76.77 250 | 75.79 254 | 79.72 243 | 86.10 254 | 65.79 212 | 77.14 290 | 83.02 268 | 65.20 269 | 81.40 260 | 82.10 321 | 66.30 244 | 90.73 214 | 55.57 313 | 85.27 316 | 82.65 322 |
|
EPMVS | | | 62.47 327 | 62.63 331 | 62.01 344 | 70.63 372 | 38.74 372 | 74.76 315 | 52.86 375 | 53.91 331 | 67.71 348 | 80.01 339 | 39.40 361 | 66.60 362 | 55.54 314 | 68.81 370 | 80.68 347 |
|
MS-PatchMatch | | | 70.93 299 | 70.22 302 | 73.06 309 | 81.85 301 | 62.50 244 | 73.82 322 | 77.90 298 | 52.44 339 | 75.92 309 | 81.27 328 | 55.67 307 | 81.75 320 | 55.37 315 | 77.70 355 | 74.94 355 |
|
CL-MVSNet_self_test | | | 76.81 249 | 77.38 238 | 75.12 299 | 86.90 237 | 51.34 339 | 73.20 326 | 80.63 286 | 68.30 237 | 81.80 254 | 88.40 232 | 66.92 241 | 80.90 324 | 55.35 316 | 94.90 172 | 93.12 139 |
|
new-patchmatchnet | | | 70.10 304 | 73.37 277 | 60.29 350 | 81.23 308 | 16.95 380 | 59.54 360 | 74.62 317 | 62.93 278 | 80.97 263 | 87.93 240 | 62.83 267 | 71.90 348 | 55.24 317 | 95.01 169 | 92.00 187 |
|
CostFormer | | | 69.98 307 | 68.68 312 | 73.87 304 | 77.14 341 | 50.72 345 | 79.26 261 | 74.51 319 | 51.94 344 | 70.97 337 | 84.75 294 | 45.16 349 | 87.49 270 | 55.16 318 | 79.23 350 | 83.40 314 |
|
thres600view7 | | | 75.97 258 | 75.35 260 | 77.85 274 | 87.01 235 | 51.84 337 | 80.45 244 | 73.26 330 | 75.20 151 | 83.10 234 | 86.31 266 | 45.54 341 | 89.05 250 | 55.03 319 | 92.24 232 | 92.66 158 |
|
EMVS | | | 61.10 334 | 60.81 335 | 61.99 345 | 65.96 376 | 55.86 308 | 53.10 367 | 58.97 370 | 67.06 249 | 56.89 372 | 63.33 369 | 40.98 358 | 67.03 360 | 54.79 320 | 86.18 310 | 63.08 365 |
|
USDC | | | 76.63 251 | 76.73 247 | 76.34 291 | 83.46 287 | 57.20 300 | 80.02 249 | 88.04 209 | 52.14 342 | 83.65 226 | 91.25 170 | 63.24 261 | 86.65 284 | 54.66 321 | 94.11 191 | 85.17 292 |
|
CDS-MVSNet | | | 77.32 243 | 75.40 258 | 83.06 186 | 89.00 192 | 72.48 153 | 77.90 280 | 82.17 275 | 60.81 296 | 78.94 287 | 83.49 306 | 59.30 284 | 88.76 258 | 54.64 322 | 92.37 227 | 87.93 264 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
gm-plane-assit | | | | | | 75.42 356 | 44.97 365 | | | 52.17 340 | | 72.36 364 | | 87.90 265 | 54.10 323 | | |
|
PatchMatch-RL | | | 74.48 273 | 73.22 278 | 78.27 266 | 87.70 217 | 85.26 35 | 75.92 305 | 70.09 346 | 64.34 273 | 76.09 307 | 81.25 329 | 65.87 249 | 78.07 333 | 53.86 324 | 83.82 329 | 71.48 359 |
|
EPNet_dtu | | | 72.87 286 | 71.33 297 | 77.49 278 | 77.72 338 | 60.55 269 | 82.35 215 | 75.79 310 | 66.49 254 | 58.39 371 | 81.06 330 | 53.68 313 | 85.98 292 | 53.55 325 | 92.97 217 | 85.95 284 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
JIA-IIPM | | | 69.41 310 | 66.64 322 | 77.70 275 | 73.19 364 | 71.24 170 | 75.67 306 | 65.56 359 | 70.42 213 | 65.18 355 | 92.97 122 | 33.64 371 | 83.06 314 | 53.52 326 | 69.61 369 | 78.79 350 |
|
baseline2 | | | 69.77 308 | 66.89 318 | 78.41 262 | 79.51 326 | 58.09 292 | 76.23 303 | 69.57 349 | 57.50 316 | 64.82 359 | 77.45 352 | 46.02 335 | 88.44 260 | 53.08 327 | 77.83 354 | 88.70 255 |
|
KD-MVS_2432*1600 | | | 66.87 318 | 65.81 323 | 70.04 318 | 67.50 373 | 47.49 356 | 62.56 356 | 79.16 292 | 61.21 293 | 77.98 292 | 80.61 332 | 25.29 381 | 82.48 317 | 53.02 328 | 84.92 320 | 80.16 348 |
|
miper_refine_blended | | | 66.87 318 | 65.81 323 | 70.04 318 | 67.50 373 | 47.49 356 | 62.56 356 | 79.16 292 | 61.21 293 | 77.98 292 | 80.61 332 | 25.29 381 | 82.48 317 | 53.02 328 | 84.92 320 | 80.16 348 |
|
BH-w/o | | | 76.57 252 | 76.07 253 | 78.10 268 | 86.88 238 | 65.92 211 | 77.63 284 | 86.33 233 | 65.69 262 | 80.89 265 | 79.95 340 | 68.97 232 | 90.74 213 | 53.01 330 | 85.25 317 | 77.62 351 |
|
pmmvs5 | | | 70.73 300 | 70.07 303 | 72.72 310 | 77.03 343 | 52.73 329 | 74.14 318 | 75.65 313 | 50.36 354 | 72.17 331 | 85.37 283 | 55.42 309 | 80.67 326 | 52.86 331 | 87.59 299 | 84.77 296 |
|
tpm | | | 67.95 314 | 68.08 315 | 67.55 332 | 78.74 335 | 43.53 368 | 75.60 307 | 67.10 357 | 54.92 327 | 72.23 330 | 88.10 236 | 42.87 356 | 75.97 339 | 52.21 332 | 80.95 346 | 83.15 319 |
|
MVP-Stereo | | | 75.81 260 | 73.51 275 | 82.71 195 | 89.35 184 | 73.62 136 | 80.06 247 | 85.20 249 | 60.30 300 | 73.96 323 | 87.94 239 | 57.89 296 | 89.45 247 | 52.02 333 | 74.87 361 | 85.06 294 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
thres100view900 | | | 75.45 261 | 75.05 261 | 76.66 288 | 87.27 225 | 51.88 336 | 81.07 238 | 73.26 330 | 75.68 144 | 83.25 231 | 86.37 263 | 45.54 341 | 88.80 254 | 51.98 334 | 90.99 254 | 89.31 243 |
|
tfpn200view9 | | | 74.86 269 | 74.23 268 | 76.74 287 | 86.24 249 | 52.12 333 | 79.24 262 | 73.87 324 | 73.34 173 | 81.82 252 | 84.60 297 | 46.02 335 | 88.80 254 | 51.98 334 | 90.99 254 | 89.31 243 |
|
thres400 | | | 75.14 263 | 74.23 268 | 77.86 273 | 86.24 249 | 52.12 333 | 79.24 262 | 73.87 324 | 73.34 173 | 81.82 252 | 84.60 297 | 46.02 335 | 88.80 254 | 51.98 334 | 90.99 254 | 92.66 158 |
|
HyFIR lowres test | | | 75.12 265 | 72.66 284 | 82.50 201 | 91.44 142 | 65.19 215 | 72.47 328 | 87.31 217 | 46.79 358 | 80.29 274 | 84.30 299 | 52.70 315 | 92.10 174 | 51.88 337 | 86.73 304 | 90.22 230 |
|
TAMVS | | | 78.08 236 | 76.36 249 | 83.23 182 | 90.62 163 | 72.87 142 | 79.08 265 | 80.01 290 | 61.72 288 | 81.35 261 | 86.92 258 | 63.96 257 | 88.78 257 | 50.61 338 | 93.01 215 | 88.04 261 |
|
sss | | | 66.92 317 | 67.26 317 | 65.90 336 | 77.23 340 | 51.10 344 | 64.79 351 | 71.72 341 | 52.12 343 | 70.13 339 | 80.18 338 | 57.96 294 | 65.36 366 | 50.21 339 | 81.01 345 | 81.25 340 |
|
FPMVS | | | 72.29 291 | 72.00 290 | 73.14 308 | 88.63 198 | 85.00 37 | 74.65 317 | 67.39 352 | 71.94 200 | 77.80 296 | 87.66 244 | 50.48 323 | 75.83 340 | 49.95 340 | 79.51 347 | 58.58 370 |
|
tpm cat1 | | | 66.76 320 | 65.21 326 | 71.42 316 | 77.09 342 | 50.62 346 | 78.01 277 | 73.68 328 | 44.89 363 | 68.64 342 | 79.00 344 | 45.51 343 | 82.42 319 | 49.91 341 | 70.15 366 | 81.23 342 |
|
CHOSEN 1792x2688 | | | 72.45 288 | 70.56 298 | 78.13 267 | 90.02 178 | 63.08 234 | 68.72 341 | 83.16 266 | 42.99 367 | 75.92 309 | 85.46 279 | 57.22 300 | 85.18 301 | 49.87 342 | 81.67 340 | 86.14 282 |
|
HY-MVS | | 64.64 18 | 73.03 284 | 72.47 288 | 74.71 301 | 83.36 288 | 54.19 318 | 82.14 224 | 81.96 276 | 56.76 321 | 69.57 341 | 86.21 268 | 60.03 278 | 84.83 304 | 49.58 343 | 82.65 337 | 85.11 293 |
|
MDTV_nov1_ep13_2view | | | | | | | 27.60 379 | 70.76 334 | | 46.47 360 | 61.27 363 | | 45.20 347 | | 49.18 344 | | 83.75 309 |
|
PMMVS | | | 61.65 330 | 60.38 336 | 65.47 339 | 65.40 377 | 69.26 185 | 63.97 354 | 61.73 366 | 36.80 372 | 60.11 366 | 68.43 366 | 59.42 283 | 66.35 363 | 48.97 345 | 78.57 353 | 60.81 367 |
|
WTY-MVS | | | 67.91 315 | 68.35 313 | 66.58 335 | 80.82 314 | 48.12 353 | 65.96 350 | 72.60 333 | 53.67 332 | 71.20 335 | 81.68 326 | 58.97 287 | 69.06 355 | 48.57 346 | 81.67 340 | 82.55 324 |
|
UnsupCasMVSNet_bld | | | 69.21 311 | 69.68 306 | 67.82 331 | 79.42 327 | 51.15 342 | 67.82 346 | 75.79 310 | 54.15 330 | 77.47 299 | 85.36 284 | 59.26 285 | 70.64 350 | 48.46 347 | 79.35 349 | 81.66 334 |
|
tpm2 | | | 68.45 313 | 66.83 319 | 73.30 307 | 78.93 334 | 48.50 351 | 79.76 252 | 71.76 340 | 47.50 357 | 69.92 340 | 83.60 304 | 42.07 357 | 88.40 261 | 48.44 348 | 79.51 347 | 83.01 321 |
|
Patchmatch-test | | | 65.91 323 | 67.38 316 | 61.48 348 | 75.51 354 | 43.21 369 | 68.84 340 | 63.79 362 | 62.48 282 | 72.80 328 | 83.42 308 | 44.89 350 | 59.52 370 | 48.27 349 | 86.45 306 | 81.70 333 |
|
FMVSNet5 | | | 72.10 292 | 71.69 292 | 73.32 306 | 81.57 303 | 53.02 327 | 76.77 295 | 78.37 297 | 63.31 275 | 76.37 302 | 91.85 155 | 36.68 367 | 78.98 330 | 47.87 350 | 92.45 226 | 87.95 263 |
|
dp | | | 60.70 336 | 60.29 338 | 61.92 346 | 72.04 370 | 38.67 373 | 70.83 333 | 64.08 361 | 51.28 347 | 60.75 364 | 77.28 353 | 36.59 368 | 71.58 349 | 47.41 351 | 62.34 372 | 75.52 354 |
|
N_pmnet | | | 70.20 302 | 68.80 311 | 74.38 303 | 80.91 311 | 84.81 40 | 59.12 362 | 76.45 308 | 55.06 326 | 75.31 317 | 82.36 320 | 55.74 306 | 54.82 371 | 47.02 352 | 87.24 301 | 83.52 311 |
|
thres200 | | | 72.34 290 | 71.55 295 | 74.70 302 | 83.48 286 | 51.60 338 | 75.02 314 | 73.71 327 | 70.14 219 | 78.56 290 | 80.57 334 | 46.20 333 | 88.20 264 | 46.99 353 | 89.29 276 | 84.32 301 |
|
test20.03 | | | 73.75 279 | 74.59 265 | 71.22 317 | 81.11 309 | 51.12 343 | 70.15 337 | 72.10 337 | 70.42 213 | 80.28 276 | 91.50 166 | 64.21 255 | 74.72 344 | 46.96 354 | 94.58 182 | 87.82 267 |
|
pmmvs3 | | | 62.47 327 | 60.02 339 | 69.80 321 | 71.58 371 | 64.00 226 | 70.52 335 | 58.44 371 | 39.77 369 | 66.05 351 | 75.84 358 | 27.10 380 | 72.28 346 | 46.15 355 | 84.77 326 | 73.11 357 |
|
testgi | | | 72.36 289 | 74.61 263 | 65.59 337 | 80.56 318 | 42.82 370 | 68.29 342 | 73.35 329 | 66.87 251 | 81.84 251 | 89.93 208 | 72.08 215 | 66.92 361 | 46.05 356 | 92.54 225 | 87.01 275 |
|
PVSNet | | 58.17 21 | 66.41 321 | 65.63 325 | 68.75 327 | 81.96 299 | 49.88 349 | 62.19 358 | 72.51 335 | 51.03 348 | 68.04 345 | 75.34 360 | 50.84 321 | 74.77 342 | 45.82 357 | 82.96 333 | 81.60 335 |
|
gg-mvs-nofinetune | | | 68.96 312 | 69.11 308 | 68.52 330 | 76.12 351 | 45.32 362 | 83.59 178 | 55.88 373 | 86.68 26 | 64.62 360 | 97.01 7 | 30.36 374 | 83.97 311 | 44.78 358 | 82.94 334 | 76.26 353 |
|
Anonymous20231206 | | | 71.38 297 | 71.88 291 | 69.88 320 | 86.31 246 | 54.37 317 | 70.39 336 | 74.62 317 | 52.57 338 | 76.73 300 | 88.76 227 | 59.94 279 | 72.06 347 | 44.35 359 | 93.23 209 | 83.23 318 |
|
CHOSEN 280x420 | | | 59.08 337 | 56.52 342 | 66.76 334 | 76.51 346 | 64.39 222 | 49.62 368 | 59.00 369 | 43.86 365 | 55.66 373 | 68.41 367 | 35.55 369 | 68.21 357 | 43.25 360 | 76.78 359 | 67.69 364 |
|
ADS-MVSNet2 | | | 65.87 324 | 63.64 329 | 72.55 312 | 73.16 365 | 56.92 302 | 67.10 347 | 74.81 316 | 49.74 355 | 66.04 352 | 82.97 311 | 46.71 330 | 77.26 335 | 42.29 361 | 69.96 367 | 83.46 312 |
|
ADS-MVSNet | | | 61.90 329 | 62.19 332 | 61.03 349 | 73.16 365 | 36.42 374 | 67.10 347 | 61.75 365 | 49.74 355 | 66.04 352 | 82.97 311 | 46.71 330 | 63.21 368 | 42.29 361 | 69.96 367 | 83.46 312 |
|
DSMNet-mixed | | | 60.98 335 | 61.61 334 | 59.09 352 | 72.88 367 | 45.05 364 | 74.70 316 | 46.61 379 | 26.20 373 | 65.34 354 | 90.32 199 | 55.46 308 | 63.12 369 | 41.72 363 | 81.30 344 | 69.09 363 |
|
MIMVSNet | | | 71.09 298 | 71.59 293 | 69.57 323 | 87.23 226 | 50.07 348 | 78.91 266 | 71.83 339 | 60.20 302 | 71.26 334 | 91.76 160 | 55.08 311 | 76.09 338 | 41.06 364 | 87.02 303 | 82.54 325 |
|
test0.0.03 1 | | | 64.66 326 | 64.36 327 | 65.57 338 | 75.03 359 | 46.89 359 | 64.69 352 | 61.58 367 | 62.43 284 | 71.18 336 | 77.54 350 | 43.41 353 | 68.47 356 | 40.75 365 | 82.65 337 | 81.35 337 |
|
PAPM | | | 71.77 294 | 70.06 304 | 76.92 283 | 86.39 241 | 53.97 319 | 76.62 298 | 86.62 231 | 53.44 333 | 63.97 361 | 84.73 295 | 57.79 297 | 92.34 166 | 39.65 366 | 81.33 343 | 84.45 299 |
|
MVS-HIRNet | | | 61.16 333 | 62.92 330 | 55.87 353 | 79.09 331 | 35.34 375 | 71.83 330 | 57.98 372 | 46.56 359 | 59.05 368 | 91.14 175 | 49.95 324 | 76.43 337 | 38.74 367 | 71.92 364 | 55.84 371 |
|
GG-mvs-BLEND | | | | | 67.16 333 | 73.36 363 | 46.54 361 | 84.15 158 | 55.04 374 | | 58.64 370 | 61.95 371 | 29.93 375 | 83.87 312 | 38.71 368 | 76.92 358 | 71.07 360 |
|
new_pmnet | | | 55.69 339 | 57.66 341 | 49.76 355 | 75.47 355 | 30.59 377 | 59.56 359 | 51.45 376 | 43.62 366 | 62.49 362 | 75.48 359 | 40.96 359 | 49.15 374 | 37.39 369 | 72.52 362 | 69.55 362 |
|
PVSNet_0 | | 51.08 22 | 56.10 338 | 54.97 343 | 59.48 351 | 75.12 358 | 53.28 326 | 55.16 365 | 61.89 364 | 44.30 364 | 59.16 367 | 62.48 370 | 54.22 312 | 65.91 365 | 35.40 370 | 47.01 373 | 59.25 369 |
|
wuyk23d | | | 75.13 264 | 79.30 219 | 62.63 343 | 75.56 353 | 75.18 130 | 80.89 240 | 73.10 332 | 75.06 153 | 94.76 12 | 95.32 38 | 87.73 42 | 52.85 372 | 34.16 371 | 97.11 89 | 59.85 368 |
|
MVE |  | 40.22 23 | 51.82 341 | 50.47 344 | 55.87 353 | 62.66 379 | 51.91 335 | 31.61 371 | 39.28 380 | 40.65 368 | 50.76 374 | 74.98 361 | 56.24 305 | 44.67 375 | 33.94 372 | 64.11 371 | 71.04 361 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PMMVS2 | | | 55.64 340 | 59.27 340 | 44.74 356 | 64.30 378 | 12.32 381 | 40.60 369 | 49.79 377 | 53.19 334 | 65.06 358 | 84.81 293 | 53.60 314 | 49.76 373 | 32.68 373 | 89.41 275 | 72.15 358 |
|
test_method | | | 30.46 342 | 29.60 345 | 33.06 357 | 17.99 381 | 3.84 383 | 13.62 372 | 73.92 323 | 2.79 375 | 18.29 377 | 53.41 372 | 28.53 376 | 43.25 376 | 22.56 374 | 35.27 375 | 52.11 372 |
|
tmp_tt | | | 20.25 344 | 24.50 347 | 7.49 359 | 4.47 382 | 8.70 382 | 34.17 370 | 25.16 382 | 1.00 377 | 32.43 376 | 18.49 374 | 39.37 362 | 9.21 378 | 21.64 375 | 43.75 374 | 4.57 374 |
|
DeepMVS_CX |  | | | | 24.13 358 | 32.95 380 | 29.49 378 | | 21.63 383 | 12.07 374 | 37.95 375 | 45.07 373 | 30.84 373 | 19.21 377 | 17.94 376 | 33.06 376 | 23.69 373 |
|
test123 | | | 6.27 347 | 8.08 350 | 0.84 360 | 1.11 384 | 0.57 384 | 62.90 355 | 0.82 384 | 0.54 378 | 1.07 380 | 2.75 379 | 1.26 383 | 0.30 379 | 1.04 377 | 1.26 378 | 1.66 375 |
|
testmvs | | | 5.91 348 | 7.65 351 | 0.72 361 | 1.20 383 | 0.37 385 | 59.14 361 | 0.67 385 | 0.49 379 | 1.11 379 | 2.76 378 | 0.94 384 | 0.24 380 | 1.02 378 | 1.47 377 | 1.55 376 |
|
test_blank | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uanet_test | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
DCPMVS | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
cdsmvs_eth3d_5k | | | 20.81 343 | 27.75 346 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 85.44 244 | 0.00 380 | 0.00 381 | 82.82 315 | 81.46 116 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
pcd_1.5k_mvsjas | | | 6.41 346 | 8.55 349 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 76.94 162 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
sosnet-low-res | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
sosnet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uncertanet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
Regformer | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
ab-mvs-re | | | 6.65 345 | 8.87 348 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 79.80 341 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
uanet | | | 0.00 349 | 0.00 352 | 0.00 362 | 0.00 385 | 0.00 386 | 0.00 373 | 0.00 386 | 0.00 380 | 0.00 381 | 0.00 380 | 0.00 385 | 0.00 381 | 0.00 379 | 0.00 379 | 0.00 377 |
|
FOURS1 | | | | | | 96.08 12 | 87.41 13 | 96.19 2 | 95.83 4 | 92.95 2 | 96.57 2 | | | | | | |
|
test_one_0601 | | | | | | 93.85 63 | 73.27 140 | | 94.11 37 | 86.57 27 | 93.47 41 | 94.64 61 | 88.42 27 | | | | |
|
eth-test2 | | | | | | 0.00 385 | | | | | | | | | | | |
|
eth-test | | | | | | 0.00 385 | | | | | | | | | | | |
|
test_241102_ONE | | | | | | 94.18 50 | 72.65 144 | | 93.69 54 | 83.62 46 | 94.11 23 | 93.78 107 | 90.28 15 | 95.50 49 | | | |
|
save fliter | | | | | | 93.75 64 | 77.44 104 | 86.31 128 | 89.72 178 | 70.80 209 | | | | | | | |
|
test0726 | | | | | | 94.16 53 | 72.56 150 | 90.63 49 | 93.90 46 | 83.61 47 | 93.75 31 | 94.49 65 | 89.76 19 | | | | |
|
GSMVS | | | | | | | | | | | | | | | | | 83.88 304 |
|
test_part2 | | | | | | 93.86 62 | 77.77 98 | | | | 92.84 50 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 46.11 334 | | | | 83.88 304 |
|
sam_mvs | | | | | | | | | | | | | 45.92 339 | | | | |
|
MTGPA |  | | | | | | | | 91.81 122 | | | | | | | | |
|
test_post | | | | | | | | | | | | 3.10 377 | 45.43 344 | 77.22 336 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 81.71 325 | 45.93 338 | 87.01 274 | | | |
|
MTMP | | | | | | | | 90.66 47 | 33.14 381 | | | | | | | | |
|
TEST9 | | | | | | 92.34 102 | 79.70 77 | 83.94 165 | 90.32 161 | 65.41 268 | 84.49 208 | 90.97 180 | 82.03 106 | 93.63 121 | | | |
|
test_8 | | | | | | 92.09 113 | 78.87 85 | 83.82 170 | 90.31 163 | 65.79 258 | 84.36 211 | 90.96 182 | 81.93 108 | 93.44 133 | | | |
|
agg_prior | | | | | | 91.58 133 | 77.69 99 | | 90.30 164 | | 84.32 212 | | | 93.18 141 | | | |
|
test_prior4 | | | | | | | 78.97 84 | 84.59 150 | | | | | | | | | |
|
test_prior | | | | | 86.32 115 | 90.59 164 | 71.99 161 | | 92.85 93 | | | | | 94.17 100 | | | 92.80 150 |
|
新几何2 | | | | | | | | 81.72 227 | | | | | | | | | |
|
旧先验1 | | | | | | 91.97 116 | 71.77 163 | | 81.78 278 | | | 91.84 156 | 73.92 191 | | | 93.65 201 | 83.61 310 |
|
原ACMM2 | | | | | | | | 82.26 220 | | | | | | | | | |
|
test222 | | | | | | 93.31 76 | 76.54 117 | 79.38 259 | 77.79 299 | 52.59 337 | 82.36 242 | 90.84 186 | 66.83 242 | | | 91.69 243 | 81.25 340 |
|
segment_acmp | | | | | | | | | | | | | 81.94 107 | | | | |
|
testdata1 | | | | | | | | 79.62 254 | | 73.95 165 | | | | | | | |
|
test12 | | | | | 86.57 110 | 90.74 160 | 72.63 148 | | 90.69 151 | | 82.76 237 | | 79.20 137 | 94.80 74 | | 95.32 156 | 92.27 178 |
|
plane_prior7 | | | | | | 93.45 71 | 77.31 108 | | | | | | | | | | |
|
plane_prior6 | | | | | | 92.61 93 | 76.54 117 | | | | | | 74.84 180 | | | | |
|
plane_prior4 | | | | | | | | | | | | 92.95 123 | | | | | |
|
plane_prior3 | | | | | | | 76.85 115 | | | 77.79 118 | 86.55 171 | | | | | | |
|
plane_prior2 | | | | | | | | 89.45 78 | | 79.44 97 | | | | | | | |
|
plane_prior1 | | | | | | 92.83 91 | | | | | | | | | | | |
|
plane_prior | | | | | | | 76.42 122 | 87.15 113 | | 75.94 141 | | | | | | 95.03 168 | |
|
n2 | | | | | | | | | 0.00 386 | | | | | | | | |
|
nn | | | | | | | | | 0.00 386 | | | | | | | | |
|
door-mid | | | | | | | | | 74.45 320 | | | | | | | | |
|
test11 | | | | | | | | | 91.46 129 | | | | | | | | |
|
door | | | | | | | | | 72.57 334 | | | | | | | | |
|
HQP5-MVS | | | | | | | 70.66 173 | | | | | | | | | | |
|
HQP-NCC | | | | | | 91.19 147 | | 84.77 145 | | 73.30 175 | 80.55 271 | | | | | | |
|
ACMP_Plane | | | | | | 91.19 147 | | 84.77 145 | | 73.30 175 | 80.55 271 | | | | | | |
|
HQP4-MVS | | | | | | | | | | | 80.56 270 | | | 94.61 81 | | | 93.56 127 |
|
HQP3-MVS | | | | | | | | | 92.68 99 | | | | | | | 94.47 184 | |
|
HQP2-MVS | | | | | | | | | | | | | 72.10 213 | | | | |
|
NP-MVS | | | | | | 91.95 117 | 74.55 132 | | | | | 90.17 205 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 95.74 146 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 97.35 82 | |
|
Test By Simon | | | | | | | | | | | | | 79.09 138 | | | | |
|