SED-MVS | | | 95.91 2 | 96.28 2 | 94.80 36 | 98.77 5 | 85.99 58 | 97.13 13 | 97.44 14 | 90.31 31 | 97.71 1 | 98.07 4 | 92.31 4 | 99.58 8 | 95.66 4 | 99.13 3 | 98.84 13 |
|
test_241102_ONE | | | | | | 98.77 5 | 85.99 58 | | 97.44 14 | 90.26 35 | 97.71 1 | 97.96 10 | 92.31 4 | 99.38 32 | | | |
|
SMA-MVS |  | | 95.20 8 | 95.07 10 | 95.59 5 | 98.14 38 | 88.48 8 | 96.26 44 | 97.28 31 | 85.90 145 | 97.67 3 | 98.10 2 | 88.41 20 | 99.56 10 | 94.66 13 | 99.19 1 | 98.71 17 |
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 |
test0726 | | | | | | 98.78 3 | 85.93 61 | 97.19 10 | 97.47 10 | 90.27 33 | 97.64 4 | 98.13 1 | 91.47 8 | | | | |
|
DVP-MVS++ | | | 95.98 1 | 96.36 1 | 94.82 34 | 97.78 57 | 86.00 56 | 98.29 1 | 97.49 5 | 90.75 22 | 97.62 5 | 98.06 6 | 92.59 2 | 99.61 3 | 95.64 6 | 99.02 12 | 98.86 10 |
|
test_241102_TWO | | | | | | | | | 97.44 14 | 90.31 31 | 97.62 5 | 98.07 4 | 91.46 10 | 99.58 8 | 95.66 4 | 99.12 6 | 98.98 9 |
|
IU-MVS | | | | | | 98.77 5 | 86.00 56 | | 96.84 69 | 81.26 252 | 97.26 7 | | | | 95.50 10 | 99.13 3 | 99.03 7 |
|
DPE-MVS |  | | 95.57 4 | 95.67 4 | 95.25 9 | 98.36 27 | 87.28 17 | 95.56 83 | 97.51 4 | 89.13 63 | 97.14 8 | 97.91 11 | 91.64 7 | 99.62 1 | 94.61 14 | 99.17 2 | 98.86 10 |
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
PC_three_1452 | | | | | | | | | | 82.47 220 | 97.09 9 | 97.07 45 | 92.72 1 | 98.04 158 | 92.70 45 | 99.02 12 | 98.86 10 |
|
DVP-MVS |  | | 95.67 3 | 96.02 3 | 94.64 43 | 98.78 3 | 85.93 61 | 97.09 15 | 96.73 83 | 90.27 33 | 97.04 10 | 98.05 8 | 91.47 8 | 99.55 15 | 95.62 8 | 99.08 7 | 98.45 37 |
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_THIRD | | | | | | | | | | 90.75 22 | 97.04 10 | 98.05 8 | 92.09 6 | 99.55 15 | 95.64 6 | 99.13 3 | 99.13 2 |
|
SD-MVS | | | 94.96 12 | 95.33 8 | 93.88 65 | 97.25 79 | 86.69 32 | 96.19 48 | 97.11 46 | 90.42 30 | 96.95 12 | 97.27 29 | 89.53 14 | 96.91 247 | 94.38 16 | 98.85 19 | 98.03 77 |
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 |
test_one_0601 | | | | | | 98.58 12 | 85.83 67 | | 97.44 14 | 91.05 17 | 96.78 13 | 98.06 6 | 91.45 11 | | | | |
|
test_part2 | | | | | | 98.55 13 | 87.22 18 | | | | 96.40 14 | | | | | | |
|
FOURS1 | | | | | | 98.86 1 | 85.54 74 | 98.29 1 | 97.49 5 | 89.79 45 | 96.29 15 | | | | | | |
|
APDe-MVS | | | 95.46 5 | 95.64 5 | 94.91 24 | 98.26 30 | 86.29 51 | 97.46 5 | 97.40 20 | 89.03 66 | 96.20 16 | 98.10 2 | 89.39 16 | 99.34 36 | 95.88 3 | 99.03 11 | 99.10 4 |
|
MSP-MVS | | | 95.42 6 | 95.56 6 | 94.98 21 | 98.49 18 | 86.52 40 | 96.91 24 | 97.47 10 | 91.73 9 | 96.10 17 | 96.69 62 | 89.90 12 | 99.30 42 | 94.70 12 | 98.04 72 | 99.13 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 |
TSAR-MVS + MP. | | | 94.85 13 | 94.94 11 | 94.58 46 | 98.25 31 | 86.33 47 | 96.11 55 | 96.62 95 | 88.14 92 | 96.10 17 | 96.96 50 | 89.09 18 | 98.94 87 | 94.48 15 | 98.68 39 | 98.48 29 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
xxxxxxxxxxxxxcwj | | | 94.65 16 | 94.70 15 | 94.48 50 | 97.85 50 | 85.63 72 | 95.21 101 | 95.47 177 | 89.44 52 | 95.71 19 | 97.70 13 | 88.28 23 | 99.35 34 | 93.89 22 | 98.78 25 | 98.48 29 |
|
SF-MVS | | | 94.97 11 | 94.90 13 | 95.20 10 | 97.84 52 | 87.76 10 | 96.65 32 | 97.48 9 | 87.76 105 | 95.71 19 | 97.70 13 | 88.28 23 | 99.35 34 | 93.89 22 | 98.78 25 | 98.48 29 |
|
DeepPCF-MVS | | 89.96 1 | 94.20 36 | 94.77 14 | 92.49 116 | 96.52 98 | 80.00 217 | 94.00 187 | 97.08 47 | 90.05 37 | 95.65 21 | 97.29 28 | 89.66 13 | 98.97 83 | 93.95 20 | 98.71 34 | 98.50 27 |
|
SteuartSystems-ACMMP | | | 95.20 8 | 95.32 9 | 94.85 29 | 96.99 82 | 86.33 47 | 97.33 6 | 97.30 29 | 91.38 13 | 95.39 22 | 97.46 19 | 88.98 19 | 99.40 31 | 94.12 18 | 98.89 18 | 98.82 15 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 95.40 7 | 95.37 7 | 95.50 7 | 98.11 39 | 88.51 7 | 95.29 95 | 96.96 57 | 92.09 3 | 95.32 23 | 97.08 43 | 89.49 15 | 99.33 39 | 95.10 11 | 98.85 19 | 98.66 19 |
|
ACMMP_NAP | | | 94.74 15 | 94.56 17 | 95.28 8 | 98.02 45 | 87.70 12 | 95.68 76 | 97.34 22 | 88.28 86 | 95.30 24 | 97.67 15 | 85.90 53 | 99.54 19 | 93.91 21 | 98.95 15 | 98.60 22 |
|
ETH3D-3000-0.1 | | | 94.61 17 | 94.44 20 | 95.12 13 | 97.70 60 | 87.71 11 | 95.98 63 | 97.44 14 | 86.67 131 | 95.25 25 | 97.31 27 | 87.73 29 | 99.24 47 | 93.11 38 | 98.76 30 | 98.40 40 |
|
9.14 | | | | 94.47 18 | | 97.79 54 | | 96.08 56 | 97.44 14 | 86.13 143 | 95.10 26 | 97.40 23 | 88.34 22 | 99.22 49 | 93.25 35 | 98.70 36 | |
|
APD-MVS |  | | 94.24 31 | 94.07 37 | 94.75 39 | 98.06 43 | 86.90 23 | 95.88 67 | 96.94 59 | 85.68 151 | 95.05 27 | 97.18 38 | 87.31 35 | 99.07 61 | 91.90 71 | 98.61 49 | 98.28 53 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
patch_mono-2 | | | 93.74 48 | 94.32 22 | 92.01 134 | 97.54 63 | 78.37 252 | 93.40 210 | 97.19 38 | 88.02 94 | 94.99 28 | 97.21 34 | 88.35 21 | 98.44 122 | 94.07 19 | 98.09 70 | 99.23 1 |
|
testtj | | | 94.39 26 | 94.18 32 | 95.00 18 | 98.24 33 | 86.77 30 | 96.16 49 | 97.23 35 | 87.28 116 | 94.85 29 | 97.04 46 | 86.99 41 | 99.52 23 | 91.54 77 | 98.33 60 | 98.71 17 |
|
Regformer-2 | | | 94.33 28 | 94.22 28 | 94.68 41 | 95.54 134 | 86.75 31 | 94.57 143 | 96.70 88 | 91.84 6 | 94.41 30 | 96.56 74 | 87.19 38 | 99.13 57 | 93.50 27 | 97.65 85 | 98.16 65 |
|
旧先验2 | | | | | | | | 93.36 211 | | 71.25 346 | 94.37 31 | | | 97.13 231 | 86.74 136 | | |
|
SR-MVS | | | 94.23 32 | 94.17 33 | 94.43 53 | 98.21 35 | 85.78 69 | 96.40 38 | 96.90 62 | 88.20 90 | 94.33 32 | 97.40 23 | 84.75 69 | 99.03 67 | 93.35 32 | 97.99 73 | 98.48 29 |
|
Regformer-1 | | | 94.22 33 | 94.13 35 | 94.51 49 | 95.54 134 | 86.36 46 | 94.57 143 | 96.44 104 | 91.69 11 | 94.32 33 | 96.56 74 | 87.05 40 | 99.03 67 | 93.35 32 | 97.65 85 | 98.15 66 |
|
TSAR-MVS + GP. | | | 93.66 51 | 93.41 56 | 94.41 55 | 96.59 92 | 86.78 28 | 94.40 155 | 93.93 247 | 89.77 46 | 94.21 34 | 95.59 112 | 87.35 34 | 98.61 110 | 92.72 43 | 96.15 113 | 97.83 91 |
|
ZD-MVS | | | | | | 98.15 37 | 86.62 37 | | 97.07 48 | 83.63 194 | 94.19 35 | 96.91 52 | 87.57 33 | 99.26 46 | 91.99 63 | 98.44 55 | |
|
test1172 | | | 93.97 41 | 94.07 37 | 93.66 74 | 98.11 39 | 83.45 120 | 96.26 44 | 96.84 69 | 88.33 83 | 94.19 35 | 97.43 20 | 84.24 74 | 99.01 73 | 93.26 34 | 97.98 74 | 98.52 25 |
|
alignmvs | | | 93.08 67 | 92.50 73 | 94.81 35 | 95.62 131 | 87.61 14 | 95.99 61 | 96.07 130 | 89.77 46 | 94.12 37 | 94.87 131 | 80.56 112 | 98.66 105 | 92.42 48 | 93.10 163 | 98.15 66 |
|
canonicalmvs | | | 93.27 63 | 92.75 69 | 94.85 29 | 95.70 128 | 87.66 13 | 96.33 39 | 96.41 107 | 90.00 39 | 94.09 38 | 94.60 144 | 82.33 94 | 98.62 109 | 92.40 49 | 92.86 168 | 98.27 55 |
|
VNet | | | 92.24 78 | 91.91 79 | 93.24 79 | 96.59 92 | 83.43 121 | 94.84 126 | 96.44 104 | 89.19 61 | 94.08 39 | 95.90 100 | 77.85 148 | 98.17 141 | 88.90 108 | 93.38 157 | 98.13 68 |
|
HPM-MVS++ |  | | 95.14 10 | 94.91 12 | 95.83 4 | 98.25 31 | 89.65 4 | 95.92 66 | 96.96 57 | 91.75 8 | 94.02 40 | 96.83 55 | 88.12 25 | 99.55 15 | 93.41 31 | 98.94 16 | 98.28 53 |
|
NCCC | | | 94.81 14 | 94.69 16 | 95.17 12 | 97.83 53 | 87.46 16 | 95.66 78 | 96.93 60 | 92.34 2 | 93.94 41 | 96.58 72 | 87.74 28 | 99.44 30 | 92.83 40 | 98.40 57 | 98.62 21 |
|
APD-MVS_3200maxsize | | | 93.78 47 | 93.77 48 | 93.80 71 | 97.92 47 | 84.19 101 | 96.30 40 | 96.87 66 | 86.96 122 | 93.92 42 | 97.47 18 | 83.88 79 | 98.96 86 | 92.71 44 | 97.87 78 | 98.26 57 |
|
ETH3D cwj APD-0.16 | | | 93.91 43 | 93.53 54 | 95.06 15 | 96.76 87 | 87.78 9 | 94.92 120 | 97.21 37 | 84.33 181 | 93.89 43 | 97.09 42 | 87.20 37 | 99.29 44 | 91.90 71 | 98.44 55 | 98.12 69 |
|
ETH3 D test6400 | | | 93.64 52 | 93.22 59 | 94.92 22 | 97.79 54 | 86.84 24 | 95.31 90 | 97.26 32 | 82.67 218 | 93.81 44 | 96.29 82 | 87.29 36 | 99.27 45 | 89.87 98 | 98.67 41 | 98.65 20 |
|
SR-MVS-dyc-post | | | 93.82 46 | 93.82 44 | 93.82 67 | 97.92 47 | 84.57 86 | 96.28 42 | 96.76 79 | 87.46 112 | 93.75 45 | 97.43 20 | 84.24 74 | 99.01 73 | 92.73 41 | 97.80 80 | 97.88 87 |
|
RE-MVS-def | | | | 93.68 51 | | 97.92 47 | 84.57 86 | 96.28 42 | 96.76 79 | 87.46 112 | 93.75 45 | 97.43 20 | 82.94 86 | | 92.73 41 | 97.80 80 | 97.88 87 |
|
Regformer-4 | | | 93.91 43 | 93.81 45 | 94.19 60 | 95.36 139 | 85.47 75 | 94.68 135 | 96.41 107 | 91.60 12 | 93.75 45 | 96.71 60 | 85.95 52 | 99.10 60 | 93.21 36 | 96.65 104 | 98.01 79 |
|
HFP-MVS | | | 94.52 18 | 94.40 21 | 94.86 27 | 98.61 10 | 86.81 26 | 96.94 19 | 97.34 22 | 88.63 75 | 93.65 48 | 97.21 34 | 86.10 49 | 99.49 26 | 92.35 51 | 98.77 28 | 98.30 49 |
|
#test# | | | 94.32 29 | 94.14 34 | 94.86 27 | 98.61 10 | 86.81 26 | 96.43 35 | 97.34 22 | 87.51 111 | 93.65 48 | 97.21 34 | 86.10 49 | 99.49 26 | 91.68 75 | 98.77 28 | 98.30 49 |
|
testdata | | | | | 90.49 199 | 96.40 100 | 77.89 264 | | 95.37 189 | 72.51 340 | 93.63 50 | 96.69 62 | 82.08 100 | 97.65 183 | 83.08 178 | 97.39 88 | 95.94 160 |
|
Regformer-3 | | | 93.68 50 | 93.64 53 | 93.81 70 | 95.36 139 | 84.61 84 | 94.68 135 | 95.83 150 | 91.27 14 | 93.60 51 | 96.71 60 | 85.75 54 | 98.86 94 | 92.87 39 | 96.65 104 | 97.96 81 |
|
region2R | | | 94.43 23 | 94.27 27 | 94.92 22 | 98.65 8 | 86.67 34 | 96.92 23 | 97.23 35 | 88.60 77 | 93.58 52 | 97.27 29 | 85.22 61 | 99.54 19 | 92.21 54 | 98.74 33 | 98.56 24 |
|
MSLP-MVS++ | | | 93.72 49 | 94.08 36 | 92.65 108 | 97.31 73 | 83.43 121 | 95.79 71 | 97.33 25 | 90.03 38 | 93.58 52 | 96.96 50 | 84.87 67 | 97.76 174 | 92.19 56 | 98.66 44 | 96.76 131 |
|
PHI-MVS | | | 93.89 45 | 93.65 52 | 94.62 45 | 96.84 85 | 86.43 43 | 96.69 31 | 97.49 5 | 85.15 167 | 93.56 54 | 96.28 83 | 85.60 56 | 99.31 41 | 92.45 46 | 98.79 23 | 98.12 69 |
|
ACMMPR | | | 94.43 23 | 94.28 25 | 94.91 24 | 98.63 9 | 86.69 32 | 96.94 19 | 97.32 27 | 88.63 75 | 93.53 55 | 97.26 31 | 85.04 64 | 99.54 19 | 92.35 51 | 98.78 25 | 98.50 27 |
|
CS-MVS-test | | | 93.62 53 | 93.88 42 | 92.86 96 | 96.59 92 | 82.12 157 | 96.43 35 | 96.57 99 | 91.76 7 | 93.52 56 | 94.41 149 | 83.85 80 | 98.24 135 | 93.62 25 | 98.17 64 | 98.21 61 |
|
CS-MVS | | | 94.05 37 | 94.45 19 | 92.84 97 | 96.57 95 | 82.09 158 | 97.63 3 | 96.97 53 | 91.71 10 | 93.51 57 | 96.22 86 | 85.65 55 | 98.24 135 | 93.60 26 | 98.17 64 | 98.20 62 |
|
GST-MVS | | | 94.21 34 | 93.97 41 | 94.90 26 | 98.41 24 | 86.82 25 | 96.54 34 | 97.19 38 | 88.24 87 | 93.26 58 | 96.83 55 | 85.48 58 | 99.59 7 | 91.43 81 | 98.40 57 | 98.30 49 |
|
PGM-MVS | | | 93.96 42 | 93.72 49 | 94.68 41 | 98.43 21 | 86.22 52 | 95.30 93 | 97.78 1 | 87.45 114 | 93.26 58 | 97.33 26 | 84.62 70 | 99.51 24 | 90.75 92 | 98.57 50 | 98.32 48 |
|
UA-Net | | | 92.83 69 | 92.54 72 | 93.68 73 | 96.10 111 | 84.71 83 | 95.66 78 | 96.39 109 | 91.92 4 | 93.22 60 | 96.49 76 | 83.16 83 | 98.87 91 | 84.47 163 | 95.47 121 | 97.45 106 |
|
abl_6 | | | 93.18 66 | 93.05 63 | 93.57 76 | 97.52 66 | 84.27 100 | 95.53 84 | 96.67 91 | 87.85 102 | 93.20 61 | 97.22 33 | 80.35 113 | 99.18 52 | 91.91 68 | 97.21 90 | 97.26 111 |
|
ZNCC-MVS | | | 94.47 19 | 94.28 25 | 95.03 16 | 98.52 16 | 86.96 19 | 96.85 27 | 97.32 27 | 88.24 87 | 93.15 62 | 97.04 46 | 86.17 48 | 99.62 1 | 92.40 49 | 98.81 22 | 98.52 25 |
|
zzz-MVS | | | 94.47 19 | 94.30 24 | 95.00 18 | 98.42 22 | 86.95 20 | 95.06 113 | 96.97 53 | 91.07 15 | 93.14 63 | 97.56 16 | 84.30 72 | 99.56 10 | 93.43 29 | 98.75 31 | 98.47 33 |
|
MTAPA | | | 94.42 25 | 94.22 28 | 95.00 18 | 98.42 22 | 86.95 20 | 94.36 163 | 96.97 53 | 91.07 15 | 93.14 63 | 97.56 16 | 84.30 72 | 99.56 10 | 93.43 29 | 98.75 31 | 98.47 33 |
|
h-mvs33 | | | 90.80 99 | 90.15 105 | 92.75 102 | 96.01 115 | 82.66 146 | 95.43 86 | 95.53 173 | 89.80 42 | 93.08 65 | 95.64 110 | 75.77 164 | 99.00 78 | 92.07 60 | 78.05 330 | 96.60 136 |
|
hse-mvs2 | | | 89.88 125 | 89.34 123 | 91.51 159 | 94.83 166 | 81.12 185 | 93.94 190 | 93.91 250 | 89.80 42 | 93.08 65 | 93.60 184 | 75.77 164 | 97.66 181 | 92.07 60 | 77.07 337 | 95.74 170 |
|
ETV-MVS | | | 92.74 71 | 92.66 70 | 92.97 91 | 95.20 148 | 84.04 105 | 95.07 110 | 96.51 102 | 90.73 25 | 92.96 67 | 91.19 261 | 84.06 76 | 98.34 129 | 91.72 74 | 96.54 107 | 96.54 140 |
|
DROMVSNet | | | 93.44 57 | 93.71 50 | 92.63 109 | 95.21 147 | 82.43 150 | 97.27 8 | 96.71 87 | 90.57 29 | 92.88 68 | 95.80 104 | 83.16 83 | 98.16 142 | 93.68 24 | 98.14 67 | 97.31 108 |
|
XVS | | | 94.45 21 | 94.32 22 | 94.85 29 | 98.54 14 | 86.60 38 | 96.93 21 | 97.19 38 | 90.66 27 | 92.85 69 | 97.16 40 | 85.02 65 | 99.49 26 | 91.99 63 | 98.56 51 | 98.47 33 |
|
X-MVStestdata | | | 88.31 169 | 86.13 212 | 94.85 29 | 98.54 14 | 86.60 38 | 96.93 21 | 97.19 38 | 90.66 27 | 92.85 69 | 23.41 372 | 85.02 65 | 99.49 26 | 91.99 63 | 98.56 51 | 98.47 33 |
|
MP-MVS-pluss | | | 94.21 34 | 94.00 40 | 94.85 29 | 98.17 36 | 86.65 35 | 94.82 127 | 97.17 42 | 86.26 139 | 92.83 71 | 97.87 12 | 85.57 57 | 99.56 10 | 94.37 17 | 98.92 17 | 98.34 44 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
DeepC-MVS_fast | | 89.43 2 | 94.04 38 | 93.79 46 | 94.80 36 | 97.48 68 | 86.78 28 | 95.65 80 | 96.89 63 | 89.40 55 | 92.81 72 | 96.97 49 | 85.37 60 | 99.24 47 | 90.87 90 | 98.69 37 | 98.38 43 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TEST9 | | | | | | 97.53 64 | 86.49 41 | 94.07 180 | 96.78 76 | 81.61 245 | 92.77 73 | 96.20 88 | 87.71 30 | 99.12 58 | | | |
|
train_agg | | | 93.44 57 | 93.08 62 | 94.52 48 | 97.53 64 | 86.49 41 | 94.07 180 | 96.78 76 | 81.86 238 | 92.77 73 | 96.20 88 | 87.63 31 | 99.12 58 | 92.14 58 | 98.69 37 | 97.94 82 |
|
CDPH-MVS | | | 92.83 69 | 92.30 75 | 94.44 51 | 97.79 54 | 86.11 54 | 94.06 182 | 96.66 92 | 80.09 265 | 92.77 73 | 96.63 69 | 86.62 43 | 99.04 66 | 87.40 126 | 98.66 44 | 98.17 64 |
|
CP-MVS | | | 94.34 27 | 94.21 30 | 94.74 40 | 98.39 25 | 86.64 36 | 97.60 4 | 97.24 33 | 88.53 79 | 92.73 76 | 97.23 32 | 85.20 62 | 99.32 40 | 92.15 57 | 98.83 21 | 98.25 58 |
|
test_8 | | | | | | 97.49 67 | 86.30 50 | 94.02 185 | 96.76 79 | 81.86 238 | 92.70 77 | 96.20 88 | 87.63 31 | 99.02 71 | | | |
|
test_prior3 | | | 93.60 54 | 93.53 54 | 93.82 67 | 97.29 75 | 84.49 90 | 94.12 173 | 96.88 64 | 87.67 108 | 92.63 78 | 96.39 79 | 86.62 43 | 98.87 91 | 91.50 78 | 98.67 41 | 98.11 71 |
|
test_prior2 | | | | | | | | 94.12 173 | | 87.67 108 | 92.63 78 | 96.39 79 | 86.62 43 | | 91.50 78 | 98.67 41 | |
|
HPM-MVS |  | | 94.02 39 | 93.88 42 | 94.43 53 | 98.39 25 | 85.78 69 | 97.25 9 | 97.07 48 | 86.90 126 | 92.62 80 | 96.80 59 | 84.85 68 | 99.17 53 | 92.43 47 | 98.65 46 | 98.33 45 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
VDD-MVS | | | 90.74 101 | 89.92 113 | 93.20 80 | 96.27 104 | 83.02 132 | 95.73 73 | 93.86 251 | 88.42 82 | 92.53 81 | 96.84 54 | 62.09 308 | 98.64 107 | 90.95 88 | 92.62 171 | 97.93 84 |
|
EI-MVSNet-Vis-set | | | 93.01 68 | 92.92 67 | 93.29 77 | 95.01 153 | 83.51 119 | 94.48 147 | 95.77 154 | 90.87 18 | 92.52 82 | 96.67 64 | 84.50 71 | 99.00 78 | 91.99 63 | 94.44 141 | 97.36 107 |
|
MCST-MVS | | | 94.45 21 | 94.20 31 | 95.19 11 | 98.46 20 | 87.50 15 | 95.00 115 | 97.12 44 | 87.13 118 | 92.51 83 | 96.30 81 | 89.24 17 | 99.34 36 | 93.46 28 | 98.62 48 | 98.73 16 |
|
HPM-MVS_fast | | | 93.40 60 | 93.22 59 | 93.94 64 | 98.36 27 | 84.83 81 | 97.15 12 | 96.80 75 | 85.77 148 | 92.47 84 | 97.13 41 | 82.38 92 | 99.07 61 | 90.51 94 | 98.40 57 | 97.92 85 |
|
xiu_mvs_v1_base_debu | | | 90.64 106 | 90.05 108 | 92.40 119 | 93.97 202 | 84.46 93 | 93.32 212 | 95.46 178 | 85.17 164 | 92.25 85 | 94.03 160 | 70.59 234 | 98.57 112 | 90.97 85 | 94.67 132 | 94.18 227 |
|
xiu_mvs_v1_base | | | 90.64 106 | 90.05 108 | 92.40 119 | 93.97 202 | 84.46 93 | 93.32 212 | 95.46 178 | 85.17 164 | 92.25 85 | 94.03 160 | 70.59 234 | 98.57 112 | 90.97 85 | 94.67 132 | 94.18 227 |
|
xiu_mvs_v1_base_debi | | | 90.64 106 | 90.05 108 | 92.40 119 | 93.97 202 | 84.46 93 | 93.32 212 | 95.46 178 | 85.17 164 | 92.25 85 | 94.03 160 | 70.59 234 | 98.57 112 | 90.97 85 | 94.67 132 | 94.18 227 |
|
agg_prior1 | | | 93.29 62 | 92.97 66 | 94.26 58 | 97.38 70 | 85.92 63 | 93.92 191 | 96.72 85 | 81.96 232 | 92.16 88 | 96.23 85 | 87.85 26 | 98.97 83 | 91.95 67 | 98.55 53 | 97.90 86 |
|
agg_prior | | | | | | 97.38 70 | 85.92 63 | | 96.72 85 | | 92.16 88 | | | 98.97 83 | | | |
|
LFMVS | | | 90.08 116 | 89.13 129 | 92.95 92 | 96.71 88 | 82.32 155 | 96.08 56 | 89.91 338 | 86.79 127 | 92.15 90 | 96.81 57 | 62.60 305 | 98.34 129 | 87.18 130 | 93.90 145 | 98.19 63 |
|
EI-MVSNet-UG-set | | | 92.74 71 | 92.62 71 | 93.12 83 | 94.86 164 | 83.20 126 | 94.40 155 | 95.74 157 | 90.71 26 | 92.05 91 | 96.60 71 | 84.00 77 | 98.99 80 | 91.55 76 | 93.63 149 | 97.17 116 |
|
MP-MVS |  | | 94.25 30 | 94.07 37 | 94.77 38 | 98.47 19 | 86.31 49 | 96.71 30 | 96.98 52 | 89.04 65 | 91.98 92 | 97.19 37 | 85.43 59 | 99.56 10 | 92.06 62 | 98.79 23 | 98.44 38 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
casdiffmvs | | | 92.51 74 | 92.43 74 | 92.74 103 | 94.41 185 | 81.98 162 | 94.54 145 | 96.23 119 | 89.57 50 | 91.96 93 | 96.17 92 | 82.58 90 | 98.01 161 | 90.95 88 | 95.45 123 | 98.23 59 |
|
VDDNet | | | 89.56 131 | 88.49 146 | 92.76 101 | 95.07 152 | 82.09 158 | 96.30 40 | 93.19 263 | 81.05 257 | 91.88 94 | 96.86 53 | 61.16 318 | 98.33 131 | 88.43 114 | 92.49 174 | 97.84 90 |
|
baseline | | | 92.39 77 | 92.29 76 | 92.69 107 | 94.46 182 | 81.77 166 | 94.14 172 | 96.27 114 | 89.22 59 | 91.88 94 | 96.00 96 | 82.35 93 | 97.99 163 | 91.05 84 | 95.27 128 | 98.30 49 |
|
PS-MVSNAJ | | | 91.18 95 | 90.92 93 | 91.96 140 | 95.26 145 | 82.60 149 | 92.09 259 | 95.70 159 | 86.27 138 | 91.84 96 | 92.46 218 | 79.70 123 | 98.99 80 | 89.08 106 | 95.86 115 | 94.29 225 |
|
DELS-MVS | | | 93.43 59 | 93.25 58 | 93.97 62 | 95.42 138 | 85.04 79 | 93.06 229 | 97.13 43 | 90.74 24 | 91.84 96 | 95.09 125 | 86.32 47 | 99.21 50 | 91.22 82 | 98.45 54 | 97.65 96 |
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 |
mPP-MVS | | | 93.99 40 | 93.78 47 | 94.63 44 | 98.50 17 | 85.90 66 | 96.87 25 | 96.91 61 | 88.70 73 | 91.83 98 | 97.17 39 | 83.96 78 | 99.55 15 | 91.44 80 | 98.64 47 | 98.43 39 |
|
MVSFormer | | | 91.68 87 | 91.30 85 | 92.80 99 | 93.86 205 | 83.88 108 | 95.96 64 | 95.90 144 | 84.66 177 | 91.76 99 | 94.91 129 | 77.92 145 | 97.30 215 | 89.64 100 | 97.11 91 | 97.24 112 |
|
lupinMVS | | | 90.92 98 | 90.21 102 | 93.03 88 | 93.86 205 | 83.88 108 | 92.81 236 | 93.86 251 | 79.84 268 | 91.76 99 | 94.29 154 | 77.92 145 | 98.04 158 | 90.48 95 | 97.11 91 | 97.17 116 |
|
xiu_mvs_v2_base | | | 91.13 96 | 90.89 95 | 91.86 146 | 94.97 156 | 82.42 151 | 92.24 253 | 95.64 166 | 86.11 144 | 91.74 101 | 93.14 198 | 79.67 126 | 98.89 90 | 89.06 107 | 95.46 122 | 94.28 226 |
|
DPM-MVS | | | 92.58 73 | 91.74 81 | 95.08 14 | 96.19 106 | 89.31 5 | 92.66 239 | 96.56 101 | 83.44 200 | 91.68 102 | 95.04 126 | 86.60 46 | 98.99 80 | 85.60 150 | 97.92 77 | 96.93 127 |
|
MVS_111021_HR | | | 93.45 56 | 93.31 57 | 93.84 66 | 96.99 82 | 84.84 80 | 93.24 222 | 97.24 33 | 88.76 72 | 91.60 103 | 95.85 102 | 86.07 51 | 98.66 105 | 91.91 68 | 98.16 66 | 98.03 77 |
|
test_yl | | | 90.69 103 | 90.02 111 | 92.71 104 | 95.72 126 | 82.41 153 | 94.11 175 | 95.12 199 | 85.63 152 | 91.49 104 | 94.70 138 | 74.75 179 | 98.42 124 | 86.13 143 | 92.53 172 | 97.31 108 |
|
DCV-MVSNet | | | 90.69 103 | 90.02 111 | 92.71 104 | 95.72 126 | 82.41 153 | 94.11 175 | 95.12 199 | 85.63 152 | 91.49 104 | 94.70 138 | 74.75 179 | 98.42 124 | 86.13 143 | 92.53 172 | 97.31 108 |
|
jason | | | 90.80 99 | 90.10 106 | 92.90 94 | 93.04 231 | 83.53 118 | 93.08 227 | 94.15 241 | 80.22 262 | 91.41 106 | 94.91 129 | 76.87 151 | 97.93 168 | 90.28 96 | 96.90 97 | 97.24 112 |
jason: jason. |
diffmvs | | | 91.37 91 | 91.23 87 | 91.77 152 | 93.09 228 | 80.27 205 | 92.36 249 | 95.52 174 | 87.03 121 | 91.40 107 | 94.93 128 | 80.08 117 | 97.44 200 | 92.13 59 | 94.56 137 | 97.61 98 |
|
MVS_Test | | | 91.31 92 | 91.11 89 | 91.93 142 | 94.37 186 | 80.14 208 | 93.46 209 | 95.80 152 | 86.46 134 | 91.35 108 | 93.77 179 | 82.21 97 | 98.09 153 | 87.57 124 | 94.95 130 | 97.55 103 |
|
新几何1 | | | | | 93.10 84 | 97.30 74 | 84.35 99 | | 95.56 169 | 71.09 347 | 91.26 109 | 96.24 84 | 82.87 88 | 98.86 94 | 79.19 245 | 98.10 69 | 96.07 156 |
|
1121 | | | 90.42 111 | 89.49 117 | 93.20 80 | 97.27 77 | 84.46 93 | 92.63 240 | 95.51 175 | 71.01 348 | 91.20 110 | 96.21 87 | 82.92 87 | 99.05 63 | 80.56 226 | 98.07 71 | 96.10 154 |
|
MVS_111021_LR | | | 92.47 75 | 92.29 76 | 92.98 90 | 95.99 117 | 84.43 97 | 93.08 227 | 96.09 128 | 88.20 90 | 91.12 111 | 95.72 108 | 81.33 108 | 97.76 174 | 91.74 73 | 97.37 89 | 96.75 132 |
|
test12 | | | | | 94.34 56 | 97.13 80 | 86.15 53 | | 96.29 113 | | 91.04 112 | | 85.08 63 | 99.01 73 | | 98.13 68 | 97.86 89 |
|
MG-MVS | | | 91.77 83 | 91.70 82 | 92.00 137 | 97.08 81 | 80.03 215 | 93.60 204 | 95.18 197 | 87.85 102 | 90.89 113 | 96.47 77 | 82.06 101 | 98.36 126 | 85.07 154 | 97.04 94 | 97.62 97 |
|
CANet | | | 93.54 55 | 93.20 61 | 94.55 47 | 95.65 129 | 85.73 71 | 94.94 118 | 96.69 90 | 91.89 5 | 90.69 114 | 95.88 101 | 81.99 103 | 99.54 19 | 93.14 37 | 97.95 76 | 98.39 41 |
|
Effi-MVS+ | | | 91.59 88 | 91.11 89 | 93.01 89 | 94.35 189 | 83.39 123 | 94.60 140 | 95.10 201 | 87.10 119 | 90.57 115 | 93.10 200 | 81.43 107 | 98.07 156 | 89.29 104 | 94.48 139 | 97.59 100 |
|
test2506 | | | 87.21 211 | 86.28 208 | 90.02 222 | 95.62 131 | 73.64 314 | 96.25 46 | 71.38 373 | 87.89 100 | 90.45 116 | 96.65 66 | 55.29 342 | 98.09 153 | 86.03 145 | 96.94 95 | 98.33 45 |
|
原ACMM1 | | | | | 92.01 134 | 97.34 72 | 81.05 186 | | 96.81 74 | 78.89 279 | 90.45 116 | 95.92 99 | 82.65 89 | 98.84 99 | 80.68 224 | 98.26 63 | 96.14 149 |
|
Vis-MVSNet |  | | 91.75 84 | 91.23 87 | 93.29 77 | 95.32 142 | 83.78 111 | 96.14 52 | 95.98 136 | 89.89 40 | 90.45 116 | 96.58 72 | 75.09 175 | 98.31 133 | 84.75 160 | 96.90 97 | 97.78 94 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CPTT-MVS | | | 91.99 79 | 91.80 80 | 92.55 113 | 98.24 33 | 81.98 162 | 96.76 29 | 96.49 103 | 81.89 237 | 90.24 119 | 96.44 78 | 78.59 137 | 98.61 110 | 89.68 99 | 97.85 79 | 97.06 120 |
|
ECVR-MVS |  | | 89.09 147 | 88.53 142 | 90.77 189 | 95.62 131 | 75.89 295 | 96.16 49 | 84.22 360 | 87.89 100 | 90.20 120 | 96.65 66 | 63.19 303 | 98.10 145 | 85.90 146 | 96.94 95 | 98.33 45 |
|
test222 | | | | | | 96.55 96 | 81.70 167 | 92.22 254 | 95.01 204 | 68.36 353 | 90.20 120 | 96.14 93 | 80.26 116 | | | 97.80 80 | 96.05 158 |
|
test1111 | | | 89.10 145 | 88.64 139 | 90.48 200 | 95.53 136 | 74.97 301 | 96.08 56 | 84.89 358 | 88.13 93 | 90.16 122 | 96.65 66 | 63.29 301 | 98.10 145 | 86.14 141 | 96.90 97 | 98.39 41 |
|
ACMMP |  | | 93.24 64 | 92.88 68 | 94.30 57 | 98.09 42 | 85.33 77 | 96.86 26 | 97.45 13 | 88.33 83 | 90.15 123 | 97.03 48 | 81.44 106 | 99.51 24 | 90.85 91 | 95.74 116 | 98.04 76 |
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 |
CSCG | | | 93.23 65 | 93.05 63 | 93.76 72 | 98.04 44 | 84.07 103 | 96.22 47 | 97.37 21 | 84.15 183 | 90.05 124 | 95.66 109 | 87.77 27 | 99.15 56 | 89.91 97 | 98.27 62 | 98.07 73 |
|
DP-MVS Recon | | | 91.95 80 | 91.28 86 | 93.96 63 | 98.33 29 | 85.92 63 | 94.66 138 | 96.66 92 | 82.69 217 | 90.03 125 | 95.82 103 | 82.30 95 | 99.03 67 | 84.57 162 | 96.48 110 | 96.91 128 |
|
EPP-MVSNet | | | 91.70 86 | 91.56 83 | 92.13 132 | 95.88 121 | 80.50 202 | 97.33 6 | 95.25 193 | 86.15 141 | 89.76 126 | 95.60 111 | 83.42 82 | 98.32 132 | 87.37 128 | 93.25 160 | 97.56 102 |
|
DeepC-MVS | | 88.79 3 | 93.31 61 | 92.99 65 | 94.26 58 | 96.07 113 | 85.83 67 | 94.89 122 | 96.99 51 | 89.02 67 | 89.56 127 | 97.37 25 | 82.51 91 | 99.38 32 | 92.20 55 | 98.30 61 | 97.57 101 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
OMC-MVS | | | 91.23 93 | 90.62 98 | 93.08 85 | 96.27 104 | 84.07 103 | 93.52 206 | 95.93 140 | 86.95 123 | 89.51 128 | 96.13 94 | 78.50 139 | 98.35 128 | 85.84 147 | 92.90 167 | 96.83 130 |
|
IS-MVSNet | | | 91.43 89 | 91.09 91 | 92.46 117 | 95.87 123 | 81.38 178 | 96.95 18 | 93.69 256 | 89.72 48 | 89.50 129 | 95.98 97 | 78.57 138 | 97.77 173 | 83.02 180 | 96.50 109 | 98.22 60 |
|
Anonymous202405211 | | | 87.68 185 | 86.13 212 | 92.31 126 | 96.66 89 | 80.74 196 | 94.87 124 | 91.49 305 | 80.47 261 | 89.46 130 | 95.44 113 | 54.72 344 | 98.23 137 | 82.19 195 | 89.89 199 | 97.97 80 |
|
EIA-MVS | | | 91.95 80 | 91.94 78 | 91.98 138 | 95.16 149 | 80.01 216 | 95.36 87 | 96.73 83 | 88.44 80 | 89.34 131 | 92.16 228 | 83.82 81 | 98.45 121 | 89.35 103 | 97.06 93 | 97.48 104 |
|
PVSNet_Blended_VisFu | | | 91.38 90 | 90.91 94 | 92.80 99 | 96.39 101 | 83.17 127 | 94.87 124 | 96.66 92 | 83.29 204 | 89.27 132 | 94.46 148 | 80.29 115 | 99.17 53 | 87.57 124 | 95.37 124 | 96.05 158 |
|
API-MVS | | | 90.66 105 | 90.07 107 | 92.45 118 | 96.36 102 | 84.57 86 | 96.06 59 | 95.22 196 | 82.39 221 | 89.13 133 | 94.27 157 | 80.32 114 | 98.46 118 | 80.16 233 | 96.71 102 | 94.33 224 |
|
PVSNet_BlendedMVS | | | 89.98 119 | 89.70 114 | 90.82 187 | 96.12 108 | 81.25 180 | 93.92 191 | 96.83 71 | 83.49 199 | 89.10 134 | 92.26 226 | 81.04 110 | 98.85 97 | 86.72 138 | 87.86 234 | 92.35 306 |
|
PVSNet_Blended | | | 90.73 102 | 90.32 101 | 91.98 138 | 96.12 108 | 81.25 180 | 92.55 244 | 96.83 71 | 82.04 230 | 89.10 134 | 92.56 216 | 81.04 110 | 98.85 97 | 86.72 138 | 95.91 114 | 95.84 165 |
|
Anonymous20240529 | | | 88.09 175 | 86.59 196 | 92.58 112 | 96.53 97 | 81.92 164 | 95.99 61 | 95.84 149 | 74.11 327 | 89.06 136 | 95.21 121 | 61.44 313 | 98.81 100 | 83.67 174 | 87.47 236 | 97.01 123 |
|
WTY-MVS | | | 89.60 129 | 88.92 134 | 91.67 155 | 95.47 137 | 81.15 184 | 92.38 248 | 94.78 222 | 83.11 207 | 89.06 136 | 94.32 152 | 78.67 136 | 96.61 260 | 81.57 209 | 90.89 188 | 97.24 112 |
|
XVG-OURS | | | 89.40 140 | 88.70 138 | 91.52 158 | 94.06 194 | 81.46 175 | 91.27 275 | 96.07 130 | 86.14 142 | 88.89 138 | 95.77 106 | 68.73 263 | 97.26 221 | 87.39 127 | 89.96 197 | 95.83 166 |
|
sss | | | 88.93 154 | 88.26 154 | 90.94 186 | 94.05 195 | 80.78 195 | 91.71 267 | 95.38 187 | 81.55 246 | 88.63 139 | 93.91 172 | 75.04 176 | 95.47 315 | 82.47 190 | 91.61 179 | 96.57 138 |
|
XVG-OURS-SEG-HR | | | 89.95 121 | 89.45 118 | 91.47 162 | 94.00 200 | 81.21 183 | 91.87 262 | 96.06 132 | 85.78 147 | 88.55 140 | 95.73 107 | 74.67 182 | 97.27 219 | 88.71 111 | 89.64 204 | 95.91 161 |
|
ab-mvs | | | 89.41 138 | 88.35 148 | 92.60 110 | 95.15 151 | 82.65 147 | 92.20 255 | 95.60 168 | 83.97 187 | 88.55 140 | 93.70 183 | 74.16 190 | 98.21 140 | 82.46 191 | 89.37 207 | 96.94 126 |
|
thisisatest0530 | | | 88.67 160 | 87.61 167 | 91.86 146 | 94.87 163 | 80.07 211 | 94.63 139 | 89.90 339 | 84.00 186 | 88.46 142 | 93.78 178 | 66.88 277 | 98.46 118 | 83.30 176 | 92.65 170 | 97.06 120 |
|
VPA-MVSNet | | | 89.62 128 | 88.96 132 | 91.60 157 | 93.86 205 | 82.89 137 | 95.46 85 | 97.33 25 | 87.91 97 | 88.43 143 | 93.31 190 | 74.17 189 | 97.40 209 | 87.32 129 | 82.86 279 | 94.52 215 |
|
nrg030 | | | 91.08 97 | 90.39 99 | 93.17 82 | 93.07 229 | 86.91 22 | 96.41 37 | 96.26 115 | 88.30 85 | 88.37 144 | 94.85 134 | 82.19 98 | 97.64 185 | 91.09 83 | 82.95 274 | 94.96 193 |
|
tfpn200view9 | | | 87.58 194 | 86.64 192 | 90.41 203 | 95.99 117 | 78.64 243 | 94.58 141 | 91.98 292 | 86.94 124 | 88.09 145 | 91.77 244 | 69.18 257 | 98.10 145 | 70.13 312 | 91.10 182 | 94.48 220 |
|
thres400 | | | 87.62 192 | 86.64 192 | 90.57 193 | 95.99 117 | 78.64 243 | 94.58 141 | 91.98 292 | 86.94 124 | 88.09 145 | 91.77 244 | 69.18 257 | 98.10 145 | 70.13 312 | 91.10 182 | 94.96 193 |
|
thres600view7 | | | 87.65 187 | 86.67 191 | 90.59 192 | 96.08 112 | 78.72 241 | 94.88 123 | 91.58 301 | 87.06 120 | 88.08 147 | 92.30 224 | 68.91 260 | 98.10 145 | 70.05 315 | 91.10 182 | 94.96 193 |
|
thres100view900 | | | 87.63 190 | 86.71 189 | 90.38 206 | 96.12 108 | 78.55 245 | 95.03 114 | 91.58 301 | 87.15 117 | 88.06 148 | 92.29 225 | 68.91 260 | 98.10 145 | 70.13 312 | 91.10 182 | 94.48 220 |
|
tttt0517 | | | 88.61 162 | 87.78 163 | 91.11 175 | 94.96 157 | 77.81 267 | 95.35 88 | 89.69 342 | 85.09 169 | 88.05 149 | 94.59 145 | 66.93 275 | 98.48 116 | 83.27 177 | 92.13 177 | 97.03 122 |
|
thres200 | | | 87.21 211 | 86.24 210 | 90.12 216 | 95.36 139 | 78.53 246 | 93.26 219 | 92.10 286 | 86.42 136 | 88.00 150 | 91.11 267 | 69.24 256 | 98.00 162 | 69.58 316 | 91.04 187 | 93.83 249 |
|
OPM-MVS | | | 90.12 115 | 89.56 116 | 91.82 149 | 93.14 226 | 83.90 107 | 94.16 171 | 95.74 157 | 88.96 68 | 87.86 151 | 95.43 115 | 72.48 214 | 97.91 169 | 88.10 119 | 90.18 194 | 93.65 260 |
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
MAR-MVS | | | 90.30 112 | 89.37 122 | 93.07 87 | 96.61 91 | 84.48 92 | 95.68 76 | 95.67 161 | 82.36 223 | 87.85 152 | 92.85 206 | 76.63 157 | 98.80 101 | 80.01 234 | 96.68 103 | 95.91 161 |
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 |
Vis-MVSNet (Re-imp) | | | 89.59 130 | 89.44 119 | 90.03 220 | 95.74 125 | 75.85 296 | 95.61 81 | 90.80 323 | 87.66 110 | 87.83 153 | 95.40 116 | 76.79 153 | 96.46 273 | 78.37 250 | 96.73 101 | 97.80 92 |
|
CDS-MVSNet | | | 89.45 135 | 88.51 143 | 92.29 128 | 93.62 214 | 83.61 117 | 93.01 230 | 94.68 225 | 81.95 233 | 87.82 154 | 93.24 194 | 78.69 135 | 96.99 242 | 80.34 230 | 93.23 161 | 96.28 145 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TAMVS | | | 89.21 143 | 88.29 152 | 91.96 140 | 93.71 211 | 82.62 148 | 93.30 216 | 94.19 239 | 82.22 225 | 87.78 155 | 93.94 168 | 78.83 132 | 96.95 244 | 77.70 258 | 92.98 166 | 96.32 143 |
|
CANet_DTU | | | 90.26 114 | 89.41 121 | 92.81 98 | 93.46 219 | 83.01 133 | 93.48 207 | 94.47 229 | 89.43 54 | 87.76 156 | 94.23 158 | 70.54 238 | 99.03 67 | 84.97 155 | 96.39 111 | 96.38 142 |
|
HyFIR lowres test | | | 88.09 175 | 86.81 185 | 91.93 142 | 96.00 116 | 80.63 198 | 90.01 297 | 95.79 153 | 73.42 332 | 87.68 157 | 92.10 234 | 73.86 195 | 97.96 165 | 80.75 222 | 91.70 178 | 97.19 115 |
|
UGNet | | | 89.95 121 | 88.95 133 | 92.95 92 | 94.51 179 | 83.31 124 | 95.70 75 | 95.23 194 | 89.37 56 | 87.58 158 | 93.94 168 | 64.00 298 | 98.78 102 | 83.92 169 | 96.31 112 | 96.74 133 |
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 |
thisisatest0515 | | | 87.33 203 | 85.99 218 | 91.37 165 | 93.49 217 | 79.55 224 | 90.63 285 | 89.56 345 | 80.17 263 | 87.56 159 | 90.86 272 | 67.07 274 | 98.28 134 | 81.50 210 | 93.02 165 | 96.29 144 |
|
GeoE | | | 90.05 117 | 89.43 120 | 91.90 145 | 95.16 149 | 80.37 204 | 95.80 70 | 94.65 226 | 83.90 188 | 87.55 160 | 94.75 137 | 78.18 143 | 97.62 187 | 81.28 212 | 93.63 149 | 97.71 95 |
|
baseline1 | | | 88.10 174 | 87.28 175 | 90.57 193 | 94.96 157 | 80.07 211 | 94.27 166 | 91.29 310 | 86.74 128 | 87.41 161 | 94.00 165 | 76.77 154 | 96.20 284 | 80.77 221 | 79.31 326 | 95.44 178 |
|
CHOSEN 1792x2688 | | | 88.84 156 | 87.69 164 | 92.30 127 | 96.14 107 | 81.42 177 | 90.01 297 | 95.86 148 | 74.52 324 | 87.41 161 | 93.94 168 | 75.46 172 | 98.36 126 | 80.36 229 | 95.53 118 | 97.12 119 |
|
PAPM_NR | | | 91.22 94 | 90.78 97 | 92.52 115 | 97.60 62 | 81.46 175 | 94.37 162 | 96.24 118 | 86.39 137 | 87.41 161 | 94.80 136 | 82.06 101 | 98.48 116 | 82.80 186 | 95.37 124 | 97.61 98 |
|
EPNet | | | 91.79 82 | 91.02 92 | 94.10 61 | 90.10 320 | 85.25 78 | 96.03 60 | 92.05 288 | 92.83 1 | 87.39 164 | 95.78 105 | 79.39 128 | 99.01 73 | 88.13 118 | 97.48 87 | 98.05 75 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EI-MVSNet | | | 89.10 145 | 88.86 137 | 89.80 232 | 91.84 261 | 78.30 254 | 93.70 201 | 95.01 204 | 85.73 149 | 87.15 165 | 95.28 117 | 79.87 120 | 97.21 226 | 83.81 171 | 87.36 239 | 93.88 244 |
|
MVSTER | | | 88.84 156 | 88.29 152 | 90.51 198 | 92.95 236 | 80.44 203 | 93.73 198 | 95.01 204 | 84.66 177 | 87.15 165 | 93.12 199 | 72.79 210 | 97.21 226 | 87.86 120 | 87.36 239 | 93.87 245 |
|
RRT_MVS | | | 88.86 155 | 87.68 165 | 92.39 122 | 92.02 256 | 86.09 55 | 94.38 161 | 94.94 207 | 85.45 158 | 87.14 167 | 93.84 176 | 65.88 290 | 97.11 232 | 88.73 110 | 86.77 246 | 93.98 240 |
|
mvs-test1 | | | 89.45 135 | 89.14 128 | 90.38 206 | 93.33 221 | 77.63 273 | 94.95 117 | 94.36 232 | 87.70 106 | 87.10 168 | 92.81 210 | 73.45 201 | 98.03 160 | 85.57 151 | 93.04 164 | 95.48 176 |
|
VPNet | | | 88.20 172 | 87.47 170 | 90.39 204 | 93.56 216 | 79.46 226 | 94.04 183 | 95.54 172 | 88.67 74 | 86.96 169 | 94.58 146 | 69.33 252 | 97.15 228 | 84.05 168 | 80.53 313 | 94.56 213 |
|
AUN-MVS | | | 87.78 183 | 86.54 198 | 91.48 161 | 94.82 167 | 81.05 186 | 93.91 194 | 93.93 247 | 83.00 210 | 86.93 170 | 93.53 185 | 69.50 250 | 97.67 180 | 86.14 141 | 77.12 336 | 95.73 171 |
|
HY-MVS | | 83.01 12 | 89.03 150 | 87.94 161 | 92.29 128 | 94.86 164 | 82.77 138 | 92.08 260 | 94.49 228 | 81.52 247 | 86.93 170 | 92.79 212 | 78.32 142 | 98.23 137 | 79.93 235 | 90.55 189 | 95.88 163 |
|
HQP_MVS | | | 90.60 109 | 90.19 103 | 91.82 149 | 94.70 172 | 82.73 142 | 95.85 68 | 96.22 120 | 90.81 20 | 86.91 172 | 94.86 132 | 74.23 186 | 98.12 143 | 88.15 116 | 89.99 195 | 94.63 206 |
|
plane_prior3 | | | | | | | 82.75 139 | | | 90.26 35 | 86.91 172 | | | | | | |
|
BH-RMVSNet | | | 88.37 167 | 87.48 169 | 91.02 180 | 95.28 143 | 79.45 227 | 92.89 234 | 93.07 265 | 85.45 158 | 86.91 172 | 94.84 135 | 70.35 239 | 97.76 174 | 73.97 291 | 94.59 136 | 95.85 164 |
|
Fast-Effi-MVS+ | | | 89.41 138 | 88.64 139 | 91.71 154 | 94.74 168 | 80.81 194 | 93.54 205 | 95.10 201 | 83.11 207 | 86.82 175 | 90.67 279 | 79.74 122 | 97.75 177 | 80.51 228 | 93.55 151 | 96.57 138 |
|
FIs | | | 90.51 110 | 90.35 100 | 90.99 183 | 93.99 201 | 80.98 188 | 95.73 73 | 97.54 3 | 89.15 62 | 86.72 176 | 94.68 140 | 81.83 105 | 97.24 223 | 85.18 153 | 88.31 226 | 94.76 203 |
|
PAPR | | | 90.02 118 | 89.27 127 | 92.29 128 | 95.78 124 | 80.95 190 | 92.68 238 | 96.22 120 | 81.91 235 | 86.66 177 | 93.75 181 | 82.23 96 | 98.44 122 | 79.40 244 | 94.79 131 | 97.48 104 |
|
PMMVS | | | 85.71 248 | 84.96 245 | 87.95 281 | 88.90 334 | 77.09 281 | 88.68 318 | 90.06 334 | 72.32 341 | 86.47 178 | 90.76 277 | 72.15 217 | 94.40 326 | 81.78 205 | 93.49 153 | 92.36 305 |
|
UniMVSNet_NR-MVSNet | | | 89.92 123 | 89.29 125 | 91.81 151 | 93.39 220 | 83.72 112 | 94.43 153 | 97.12 44 | 89.80 42 | 86.46 179 | 93.32 189 | 83.16 83 | 97.23 224 | 84.92 156 | 81.02 303 | 94.49 219 |
|
DU-MVS | | | 89.34 142 | 88.50 144 | 91.85 148 | 93.04 231 | 83.72 112 | 94.47 150 | 96.59 97 | 89.50 51 | 86.46 179 | 93.29 192 | 77.25 149 | 97.23 224 | 84.92 156 | 81.02 303 | 94.59 210 |
|
CostFormer | | | 85.77 247 | 84.94 246 | 88.26 273 | 91.16 287 | 72.58 328 | 89.47 305 | 91.04 316 | 76.26 307 | 86.45 181 | 89.97 293 | 70.74 232 | 96.86 250 | 82.35 192 | 87.07 244 | 95.34 183 |
|
UniMVSNet (Re) | | | 89.80 126 | 89.07 130 | 92.01 134 | 93.60 215 | 84.52 89 | 94.78 130 | 97.47 10 | 89.26 58 | 86.44 182 | 92.32 223 | 82.10 99 | 97.39 212 | 84.81 159 | 80.84 307 | 94.12 231 |
|
TR-MVS | | | 86.78 224 | 85.76 229 | 89.82 229 | 94.37 186 | 78.41 250 | 92.47 245 | 92.83 269 | 81.11 256 | 86.36 183 | 92.40 220 | 68.73 263 | 97.48 195 | 73.75 294 | 89.85 201 | 93.57 262 |
|
AdaColmap |  | | 89.89 124 | 89.07 130 | 92.37 123 | 97.41 69 | 83.03 131 | 94.42 154 | 95.92 141 | 82.81 215 | 86.34 184 | 94.65 142 | 73.89 194 | 99.02 71 | 80.69 223 | 95.51 119 | 95.05 188 |
|
FC-MVSNet-test | | | 90.27 113 | 90.18 104 | 90.53 195 | 93.71 211 | 79.85 221 | 95.77 72 | 97.59 2 | 89.31 57 | 86.27 185 | 94.67 141 | 81.93 104 | 97.01 241 | 84.26 165 | 88.09 230 | 94.71 204 |
|
PS-MVSNAJss | | | 89.97 120 | 89.62 115 | 91.02 180 | 91.90 259 | 80.85 193 | 95.26 98 | 95.98 136 | 86.26 139 | 86.21 186 | 94.29 154 | 79.70 123 | 97.65 183 | 88.87 109 | 88.10 228 | 94.57 212 |
|
TAPA-MVS | | 84.62 6 | 88.16 173 | 87.01 181 | 91.62 156 | 96.64 90 | 80.65 197 | 94.39 157 | 96.21 123 | 76.38 304 | 86.19 187 | 95.44 113 | 79.75 121 | 98.08 155 | 62.75 348 | 95.29 126 | 96.13 150 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CVMVSNet | | | 84.69 267 | 84.79 250 | 84.37 328 | 91.84 261 | 64.92 362 | 93.70 201 | 91.47 306 | 66.19 356 | 86.16 188 | 95.28 117 | 67.18 272 | 93.33 341 | 80.89 220 | 90.42 191 | 94.88 198 |
|
tpmrst | | | 85.35 253 | 84.99 243 | 86.43 310 | 90.88 301 | 67.88 354 | 88.71 317 | 91.43 307 | 80.13 264 | 86.08 189 | 88.80 310 | 73.05 207 | 96.02 291 | 82.48 189 | 83.40 273 | 95.40 180 |
|
ACMM | | 84.12 9 | 89.14 144 | 88.48 147 | 91.12 172 | 94.65 175 | 81.22 182 | 95.31 90 | 96.12 127 | 85.31 162 | 85.92 190 | 94.34 150 | 70.19 242 | 98.06 157 | 85.65 149 | 88.86 216 | 94.08 235 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
114514_t | | | 89.51 132 | 88.50 144 | 92.54 114 | 98.11 39 | 81.99 161 | 95.16 106 | 96.36 111 | 70.19 350 | 85.81 191 | 95.25 119 | 76.70 155 | 98.63 108 | 82.07 197 | 96.86 100 | 97.00 124 |
|
tpm | | | 84.73 265 | 84.02 259 | 86.87 307 | 90.33 316 | 68.90 350 | 89.06 312 | 89.94 337 | 80.85 258 | 85.75 192 | 89.86 295 | 68.54 265 | 95.97 293 | 77.76 257 | 84.05 263 | 95.75 169 |
|
Baseline_NR-MVSNet | | | 87.07 216 | 86.63 194 | 88.40 268 | 91.44 272 | 77.87 265 | 94.23 169 | 92.57 276 | 84.12 184 | 85.74 193 | 92.08 235 | 77.25 149 | 96.04 289 | 82.29 194 | 79.94 319 | 91.30 323 |
|
test_part1 | | | 89.00 153 | 87.99 158 | 92.04 133 | 95.94 120 | 83.81 110 | 96.14 52 | 96.05 133 | 86.44 135 | 85.69 194 | 93.73 182 | 71.57 220 | 97.66 181 | 85.80 148 | 80.54 311 | 94.66 205 |
|
V42 | | | 87.68 185 | 86.86 183 | 90.15 214 | 90.58 311 | 80.14 208 | 94.24 168 | 95.28 192 | 83.66 193 | 85.67 195 | 91.33 256 | 74.73 181 | 97.41 207 | 84.43 164 | 81.83 289 | 92.89 290 |
|
v1144 | | | 87.61 193 | 86.79 187 | 90.06 219 | 91.01 291 | 79.34 231 | 93.95 189 | 95.42 186 | 83.36 203 | 85.66 196 | 91.31 259 | 74.98 177 | 97.42 202 | 83.37 175 | 82.06 285 | 93.42 269 |
|
PatchT | | | 82.68 283 | 81.27 285 | 86.89 306 | 90.09 321 | 70.94 341 | 84.06 349 | 90.15 331 | 74.91 320 | 85.63 197 | 83.57 349 | 69.37 251 | 94.87 324 | 65.19 338 | 88.50 221 | 94.84 199 |
|
CR-MVSNet | | | 85.35 253 | 83.76 263 | 90.12 216 | 90.58 311 | 79.34 231 | 85.24 343 | 91.96 294 | 78.27 290 | 85.55 198 | 87.87 325 | 71.03 227 | 95.61 306 | 73.96 292 | 89.36 208 | 95.40 180 |
|
RPMNet | | | 83.95 273 | 81.53 283 | 91.21 169 | 90.58 311 | 79.34 231 | 85.24 343 | 96.76 79 | 71.44 345 | 85.55 198 | 82.97 352 | 70.87 230 | 98.91 89 | 61.01 352 | 89.36 208 | 95.40 180 |
|
v2v482 | | | 87.84 180 | 87.06 179 | 90.17 212 | 90.99 292 | 79.23 238 | 94.00 187 | 95.13 198 | 84.87 172 | 85.53 200 | 92.07 237 | 74.45 183 | 97.45 198 | 84.71 161 | 81.75 291 | 93.85 248 |
|
TranMVSNet+NR-MVSNet | | | 88.84 156 | 87.95 160 | 91.49 160 | 92.68 241 | 83.01 133 | 94.92 120 | 96.31 112 | 89.88 41 | 85.53 200 | 93.85 175 | 76.63 157 | 96.96 243 | 81.91 201 | 79.87 321 | 94.50 217 |
|
v144192 | | | 87.19 213 | 86.35 204 | 89.74 233 | 90.64 309 | 78.24 256 | 93.92 191 | 95.43 184 | 81.93 234 | 85.51 202 | 91.05 269 | 74.21 188 | 97.45 198 | 82.86 183 | 81.56 293 | 93.53 263 |
|
SCA | | | 86.32 238 | 85.18 240 | 89.73 235 | 92.15 249 | 76.60 286 | 91.12 278 | 91.69 299 | 83.53 198 | 85.50 203 | 88.81 308 | 66.79 278 | 96.48 270 | 76.65 268 | 90.35 192 | 96.12 151 |
|
bset_n11_16_dypcd | | | 86.83 221 | 85.55 231 | 90.65 191 | 88.22 342 | 81.70 167 | 88.88 315 | 90.42 326 | 85.26 163 | 85.49 204 | 90.69 278 | 67.11 273 | 97.02 240 | 89.51 102 | 84.39 259 | 93.23 276 |
|
v1192 | | | 87.25 207 | 86.33 205 | 90.00 224 | 90.76 305 | 79.04 239 | 93.80 195 | 95.48 176 | 82.57 219 | 85.48 205 | 91.18 263 | 73.38 205 | 97.42 202 | 82.30 193 | 82.06 285 | 93.53 263 |
|
WR-MVS | | | 88.38 166 | 87.67 166 | 90.52 197 | 93.30 223 | 80.18 206 | 93.26 219 | 95.96 138 | 88.57 78 | 85.47 206 | 92.81 210 | 76.12 159 | 96.91 247 | 81.24 213 | 82.29 282 | 94.47 222 |
|
mvs_anonymous | | | 89.37 141 | 89.32 124 | 89.51 243 | 93.47 218 | 74.22 308 | 91.65 270 | 94.83 218 | 82.91 213 | 85.45 207 | 93.79 177 | 81.23 109 | 96.36 279 | 86.47 140 | 94.09 143 | 97.94 82 |
|
LPG-MVS_test | | | 89.45 135 | 88.90 135 | 91.12 172 | 94.47 180 | 81.49 173 | 95.30 93 | 96.14 125 | 86.73 129 | 85.45 207 | 95.16 122 | 69.89 244 | 98.10 145 | 87.70 122 | 89.23 211 | 93.77 254 |
|
LGP-MVS_train | | | | | 91.12 172 | 94.47 180 | 81.49 173 | | 96.14 125 | 86.73 129 | 85.45 207 | 95.16 122 | 69.89 244 | 98.10 145 | 87.70 122 | 89.23 211 | 93.77 254 |
|
Effi-MVS+-dtu | | | 88.65 161 | 88.35 148 | 89.54 240 | 93.33 221 | 76.39 290 | 94.47 150 | 94.36 232 | 87.70 106 | 85.43 210 | 89.56 301 | 73.45 201 | 97.26 221 | 85.57 151 | 91.28 181 | 94.97 190 |
|
v1240 | | | 86.78 224 | 85.85 224 | 89.56 239 | 90.45 315 | 77.79 268 | 93.61 203 | 95.37 189 | 81.65 242 | 85.43 210 | 91.15 265 | 71.50 222 | 97.43 201 | 81.47 211 | 82.05 287 | 93.47 267 |
|
HQP-NCC | | | | | | 94.17 191 | | 94.39 157 | | 88.81 69 | 85.43 210 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 191 | | 94.39 157 | | 88.81 69 | 85.43 210 | | | | | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 210 | | | 97.96 165 | | | 94.51 216 |
|
HQP-MVS | | | 89.80 126 | 89.28 126 | 91.34 166 | 94.17 191 | 81.56 169 | 94.39 157 | 96.04 134 | 88.81 69 | 85.43 210 | 93.97 167 | 73.83 196 | 97.96 165 | 87.11 133 | 89.77 202 | 94.50 217 |
|
CLD-MVS | | | 89.47 134 | 88.90 135 | 91.18 171 | 94.22 190 | 82.07 160 | 92.13 257 | 96.09 128 | 87.90 98 | 85.37 216 | 92.45 219 | 74.38 184 | 97.56 190 | 87.15 131 | 90.43 190 | 93.93 241 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
UniMVSNet_ETH3D | | | 87.53 196 | 86.37 202 | 91.00 182 | 92.44 244 | 78.96 240 | 94.74 132 | 95.61 167 | 84.07 185 | 85.36 217 | 94.52 147 | 59.78 327 | 97.34 214 | 82.93 181 | 87.88 233 | 96.71 134 |
|
v1921920 | | | 86.97 218 | 86.06 217 | 89.69 237 | 90.53 314 | 78.11 259 | 93.80 195 | 95.43 184 | 81.90 236 | 85.33 218 | 91.05 269 | 72.66 211 | 97.41 207 | 82.05 198 | 81.80 290 | 93.53 263 |
|
test_djsdf | | | 89.03 150 | 88.64 139 | 90.21 211 | 90.74 306 | 79.28 235 | 95.96 64 | 95.90 144 | 84.66 177 | 85.33 218 | 92.94 204 | 74.02 192 | 97.30 215 | 89.64 100 | 88.53 219 | 94.05 237 |
|
GA-MVS | | | 86.61 229 | 85.27 239 | 90.66 190 | 91.33 281 | 78.71 242 | 90.40 288 | 93.81 254 | 85.34 161 | 85.12 220 | 89.57 300 | 61.25 315 | 97.11 232 | 80.99 218 | 89.59 205 | 96.15 148 |
|
PatchmatchNet |  | | 85.85 245 | 84.70 251 | 89.29 246 | 91.76 264 | 75.54 299 | 88.49 320 | 91.30 309 | 81.63 244 | 85.05 221 | 88.70 312 | 71.71 218 | 96.24 283 | 74.61 288 | 89.05 214 | 96.08 155 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EPMVS | | | 83.90 275 | 82.70 277 | 87.51 288 | 90.23 319 | 72.67 324 | 88.62 319 | 81.96 365 | 81.37 249 | 85.01 222 | 88.34 316 | 66.31 285 | 94.45 325 | 75.30 281 | 87.12 242 | 95.43 179 |
|
PVSNet | | 78.82 18 | 85.55 249 | 84.65 252 | 88.23 275 | 94.72 170 | 71.93 331 | 87.12 333 | 92.75 272 | 78.80 282 | 84.95 223 | 90.53 281 | 64.43 297 | 96.71 254 | 74.74 286 | 93.86 146 | 96.06 157 |
|
MDTV_nov1_ep13 | | | | 83.56 267 | | 91.69 268 | 69.93 347 | 87.75 328 | 91.54 303 | 78.60 286 | 84.86 224 | 88.90 307 | 69.54 249 | 96.03 290 | 70.25 309 | 88.93 215 | |
|
IterMVS-LS | | | 88.36 168 | 87.91 162 | 89.70 236 | 93.80 208 | 78.29 255 | 93.73 198 | 95.08 203 | 85.73 149 | 84.75 225 | 91.90 242 | 79.88 119 | 96.92 246 | 83.83 170 | 82.51 280 | 93.89 242 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpm2 | | | 84.08 271 | 82.94 273 | 87.48 291 | 91.39 277 | 71.27 335 | 89.23 309 | 90.37 328 | 71.95 343 | 84.64 226 | 89.33 302 | 67.30 269 | 96.55 267 | 75.17 282 | 87.09 243 | 94.63 206 |
|
XXY-MVS | | | 87.65 187 | 86.85 184 | 90.03 220 | 92.14 250 | 80.60 200 | 93.76 197 | 95.23 194 | 82.94 212 | 84.60 227 | 94.02 163 | 74.27 185 | 95.49 314 | 81.04 215 | 83.68 267 | 94.01 239 |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 371 | 87.62 331 | | 73.32 333 | 84.59 228 | | 70.33 240 | | 74.65 287 | | 95.50 175 |
|
test-LLR | | | 85.87 244 | 85.41 235 | 87.25 296 | 90.95 294 | 71.67 333 | 89.55 301 | 89.88 340 | 83.41 201 | 84.54 229 | 87.95 322 | 67.25 270 | 95.11 320 | 81.82 203 | 93.37 158 | 94.97 190 |
|
test-mter | | | 84.54 268 | 83.64 266 | 87.25 296 | 90.95 294 | 71.67 333 | 89.55 301 | 89.88 340 | 79.17 275 | 84.54 229 | 87.95 322 | 55.56 339 | 95.11 320 | 81.82 203 | 93.37 158 | 94.97 190 |
|
miper_enhance_ethall | | | 86.90 219 | 86.18 211 | 89.06 252 | 91.66 269 | 77.58 275 | 90.22 293 | 94.82 219 | 79.16 276 | 84.48 231 | 89.10 304 | 79.19 130 | 96.66 255 | 84.06 167 | 82.94 275 | 92.94 288 |
|
BH-untuned | | | 88.60 163 | 88.13 156 | 90.01 223 | 95.24 146 | 78.50 248 | 93.29 217 | 94.15 241 | 84.75 175 | 84.46 232 | 93.40 186 | 75.76 166 | 97.40 209 | 77.59 259 | 94.52 138 | 94.12 231 |
|
CNLPA | | | 89.07 148 | 87.98 159 | 92.34 124 | 96.87 84 | 84.78 82 | 94.08 179 | 93.24 261 | 81.41 248 | 84.46 232 | 95.13 124 | 75.57 171 | 96.62 257 | 77.21 263 | 93.84 147 | 95.61 174 |
|
PCF-MVS | | 84.11 10 | 87.74 184 | 86.08 216 | 92.70 106 | 94.02 196 | 84.43 97 | 89.27 307 | 95.87 147 | 73.62 331 | 84.43 234 | 94.33 151 | 78.48 140 | 98.86 94 | 70.27 308 | 94.45 140 | 94.81 201 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
GBi-Net | | | 87.26 205 | 85.98 219 | 91.08 176 | 94.01 197 | 83.10 128 | 95.14 107 | 94.94 207 | 83.57 195 | 84.37 235 | 91.64 247 | 66.59 282 | 96.34 280 | 78.23 253 | 85.36 252 | 93.79 250 |
|
test1 | | | 87.26 205 | 85.98 219 | 91.08 176 | 94.01 197 | 83.10 128 | 95.14 107 | 94.94 207 | 83.57 195 | 84.37 235 | 91.64 247 | 66.59 282 | 96.34 280 | 78.23 253 | 85.36 252 | 93.79 250 |
|
FMVSNet3 | | | 87.40 202 | 86.11 214 | 91.30 167 | 93.79 210 | 83.64 115 | 94.20 170 | 94.81 220 | 83.89 189 | 84.37 235 | 91.87 243 | 68.45 266 | 96.56 265 | 78.23 253 | 85.36 252 | 93.70 259 |
|
v148 | | | 87.04 217 | 86.32 206 | 89.21 247 | 90.94 296 | 77.26 279 | 93.71 200 | 94.43 230 | 84.84 173 | 84.36 238 | 90.80 275 | 76.04 161 | 97.05 238 | 82.12 196 | 79.60 323 | 93.31 271 |
|
c3_l | | | 87.14 215 | 86.50 200 | 89.04 253 | 92.20 248 | 77.26 279 | 91.22 277 | 94.70 224 | 82.01 231 | 84.34 239 | 90.43 283 | 78.81 133 | 96.61 260 | 83.70 173 | 81.09 300 | 93.25 274 |
|
miper_ehance_all_eth | | | 87.22 210 | 86.62 195 | 89.02 254 | 92.13 251 | 77.40 278 | 90.91 281 | 94.81 220 | 81.28 251 | 84.32 240 | 90.08 290 | 79.26 129 | 96.62 257 | 83.81 171 | 82.94 275 | 93.04 285 |
|
PatchMatch-RL | | | 86.77 227 | 85.54 232 | 90.47 202 | 95.88 121 | 82.71 144 | 90.54 286 | 92.31 281 | 79.82 269 | 84.32 240 | 91.57 254 | 68.77 262 | 96.39 276 | 73.16 296 | 93.48 155 | 92.32 307 |
|
3Dnovator | | 86.66 5 | 91.73 85 | 90.82 96 | 94.44 51 | 94.59 176 | 86.37 45 | 97.18 11 | 97.02 50 | 89.20 60 | 84.31 242 | 96.66 65 | 73.74 198 | 99.17 53 | 86.74 136 | 97.96 75 | 97.79 93 |
|
RRT_test8_iter05 | | | 86.90 219 | 86.36 203 | 88.52 266 | 93.00 234 | 73.27 318 | 94.32 164 | 95.96 138 | 85.50 157 | 84.26 243 | 92.86 205 | 60.76 320 | 97.70 179 | 88.32 115 | 82.29 282 | 94.60 209 |
|
jajsoiax | | | 88.24 171 | 87.50 168 | 90.48 200 | 90.89 300 | 80.14 208 | 95.31 90 | 95.65 165 | 84.97 171 | 84.24 244 | 94.02 163 | 65.31 292 | 97.42 202 | 88.56 112 | 88.52 220 | 93.89 242 |
|
mvs_tets | | | 88.06 177 | 87.28 175 | 90.38 206 | 90.94 296 | 79.88 219 | 95.22 100 | 95.66 163 | 85.10 168 | 84.21 245 | 93.94 168 | 63.53 300 | 97.40 209 | 88.50 113 | 88.40 224 | 93.87 245 |
|
eth_miper_zixun_eth | | | 86.50 234 | 85.77 228 | 88.68 262 | 91.94 258 | 75.81 297 | 90.47 287 | 94.89 213 | 82.05 228 | 84.05 246 | 90.46 282 | 75.96 162 | 96.77 251 | 82.76 187 | 79.36 325 | 93.46 268 |
|
3Dnovator+ | | 87.14 4 | 92.42 76 | 91.37 84 | 95.55 6 | 95.63 130 | 88.73 6 | 97.07 17 | 96.77 78 | 90.84 19 | 84.02 247 | 96.62 70 | 75.95 163 | 99.34 36 | 87.77 121 | 97.68 83 | 98.59 23 |
|
PLC |  | 84.53 7 | 89.06 149 | 88.03 157 | 92.15 131 | 97.27 77 | 82.69 145 | 94.29 165 | 95.44 183 | 79.71 270 | 84.01 248 | 94.18 159 | 76.68 156 | 98.75 103 | 77.28 262 | 93.41 156 | 95.02 189 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
cl22 | | | 86.78 224 | 85.98 219 | 89.18 249 | 92.34 246 | 77.62 274 | 90.84 282 | 94.13 243 | 81.33 250 | 83.97 249 | 90.15 288 | 73.96 193 | 96.60 262 | 84.19 166 | 82.94 275 | 93.33 270 |
|
FMVSNet2 | | | 87.19 213 | 85.82 225 | 91.30 167 | 94.01 197 | 83.67 114 | 94.79 129 | 94.94 207 | 83.57 195 | 83.88 250 | 92.05 238 | 66.59 282 | 96.51 268 | 77.56 260 | 85.01 255 | 93.73 257 |
|
DWT-MVSNet_test | | | 84.95 262 | 83.68 264 | 88.77 257 | 91.43 275 | 73.75 312 | 91.74 266 | 90.98 317 | 80.66 260 | 83.84 251 | 87.36 330 | 62.44 306 | 97.11 232 | 78.84 248 | 85.81 249 | 95.46 177 |
|
anonymousdsp | | | 87.84 180 | 87.09 178 | 90.12 216 | 89.13 331 | 80.54 201 | 94.67 137 | 95.55 170 | 82.05 228 | 83.82 252 | 92.12 231 | 71.47 223 | 97.15 228 | 87.15 131 | 87.80 235 | 92.67 295 |
|
1112_ss | | | 88.42 165 | 87.33 173 | 91.72 153 | 94.92 160 | 80.98 188 | 92.97 232 | 94.54 227 | 78.16 293 | 83.82 252 | 93.88 173 | 78.78 134 | 97.91 169 | 79.45 240 | 89.41 206 | 96.26 146 |
|
WR-MVS_H | | | 87.80 182 | 87.37 172 | 89.10 251 | 93.23 224 | 78.12 258 | 95.61 81 | 97.30 29 | 87.90 98 | 83.72 254 | 92.01 239 | 79.65 127 | 96.01 292 | 76.36 270 | 80.54 311 | 93.16 280 |
|
BH-w/o | | | 87.57 195 | 87.05 180 | 89.12 250 | 94.90 162 | 77.90 263 | 92.41 246 | 93.51 258 | 82.89 214 | 83.70 255 | 91.34 255 | 75.75 167 | 97.07 236 | 75.49 278 | 93.49 153 | 92.39 304 |
|
ACMP | | 84.23 8 | 89.01 152 | 88.35 148 | 90.99 183 | 94.73 169 | 81.27 179 | 95.07 110 | 95.89 146 | 86.48 133 | 83.67 256 | 94.30 153 | 69.33 252 | 97.99 163 | 87.10 135 | 88.55 218 | 93.72 258 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
Anonymous20231211 | | | 86.59 231 | 85.13 241 | 90.98 185 | 96.52 98 | 81.50 171 | 96.14 52 | 96.16 124 | 73.78 329 | 83.65 257 | 92.15 229 | 63.26 302 | 97.37 213 | 82.82 185 | 81.74 292 | 94.06 236 |
|
v10 | | | 87.25 207 | 86.38 201 | 89.85 227 | 91.19 284 | 79.50 225 | 94.48 147 | 95.45 181 | 83.79 191 | 83.62 258 | 91.19 261 | 75.13 174 | 97.42 202 | 81.94 200 | 80.60 309 | 92.63 297 |
|
v8 | | | 87.50 199 | 86.71 189 | 89.89 226 | 91.37 278 | 79.40 228 | 94.50 146 | 95.38 187 | 84.81 174 | 83.60 259 | 91.33 256 | 76.05 160 | 97.42 202 | 82.84 184 | 80.51 315 | 92.84 292 |
|
cascas | | | 86.43 237 | 84.98 244 | 90.80 188 | 92.10 253 | 80.92 191 | 90.24 291 | 95.91 143 | 73.10 335 | 83.57 260 | 88.39 315 | 65.15 293 | 97.46 197 | 84.90 158 | 91.43 180 | 94.03 238 |
|
Test_1112_low_res | | | 87.65 187 | 86.51 199 | 91.08 176 | 94.94 159 | 79.28 235 | 91.77 264 | 94.30 235 | 76.04 309 | 83.51 261 | 92.37 221 | 77.86 147 | 97.73 178 | 78.69 249 | 89.13 213 | 96.22 147 |
|
CP-MVSNet | | | 87.63 190 | 87.26 177 | 88.74 261 | 93.12 227 | 76.59 287 | 95.29 95 | 96.58 98 | 88.43 81 | 83.49 262 | 92.98 203 | 75.28 173 | 95.83 300 | 78.97 246 | 81.15 299 | 93.79 250 |
|
QAPM | | | 89.51 132 | 88.15 155 | 93.59 75 | 94.92 160 | 84.58 85 | 96.82 28 | 96.70 88 | 78.43 288 | 83.41 263 | 96.19 91 | 73.18 206 | 99.30 42 | 77.11 265 | 96.54 107 | 96.89 129 |
|
TESTMET0.1,1 | | | 83.74 276 | 82.85 275 | 86.42 311 | 89.96 324 | 71.21 337 | 89.55 301 | 87.88 349 | 77.41 296 | 83.37 264 | 87.31 331 | 56.71 336 | 93.65 338 | 80.62 225 | 92.85 169 | 94.40 223 |
|
cl____ | | | 86.52 233 | 85.78 226 | 88.75 259 | 92.03 255 | 76.46 288 | 90.74 283 | 94.30 235 | 81.83 240 | 83.34 265 | 90.78 276 | 75.74 169 | 96.57 263 | 81.74 206 | 81.54 294 | 93.22 277 |
|
DIV-MVS_self_test | | | 86.53 232 | 85.78 226 | 88.75 259 | 92.02 256 | 76.45 289 | 90.74 283 | 94.30 235 | 81.83 240 | 83.34 265 | 90.82 274 | 75.75 167 | 96.57 263 | 81.73 207 | 81.52 295 | 93.24 275 |
|
PS-CasMVS | | | 87.32 204 | 86.88 182 | 88.63 264 | 92.99 235 | 76.33 292 | 95.33 89 | 96.61 96 | 88.22 89 | 83.30 267 | 93.07 201 | 73.03 208 | 95.79 303 | 78.36 251 | 81.00 305 | 93.75 256 |
|
gg-mvs-nofinetune | | | 81.77 290 | 79.37 303 | 88.99 255 | 90.85 302 | 77.73 271 | 86.29 337 | 79.63 369 | 74.88 322 | 83.19 268 | 69.05 363 | 60.34 322 | 96.11 288 | 75.46 279 | 94.64 135 | 93.11 282 |
|
XVG-ACMP-BASELINE | | | 86.00 241 | 84.84 249 | 89.45 244 | 91.20 283 | 78.00 260 | 91.70 268 | 95.55 170 | 85.05 170 | 82.97 269 | 92.25 227 | 54.49 345 | 97.48 195 | 82.93 181 | 87.45 238 | 92.89 290 |
|
LS3D | | | 87.89 179 | 86.32 206 | 92.59 111 | 96.07 113 | 82.92 136 | 95.23 99 | 94.92 212 | 75.66 311 | 82.89 270 | 95.98 97 | 72.48 214 | 99.21 50 | 68.43 322 | 95.23 129 | 95.64 173 |
|
PEN-MVS | | | 86.80 223 | 86.27 209 | 88.40 268 | 92.32 247 | 75.71 298 | 95.18 104 | 96.38 110 | 87.97 95 | 82.82 271 | 93.15 197 | 73.39 204 | 95.92 295 | 76.15 274 | 79.03 328 | 93.59 261 |
|
FMVSNet1 | | | 85.85 245 | 84.11 258 | 91.08 176 | 92.81 238 | 83.10 128 | 95.14 107 | 94.94 207 | 81.64 243 | 82.68 272 | 91.64 247 | 59.01 331 | 96.34 280 | 75.37 280 | 83.78 264 | 93.79 250 |
|
RPSCF | | | 85.07 259 | 84.27 256 | 87.48 291 | 92.91 237 | 70.62 343 | 91.69 269 | 92.46 277 | 76.20 308 | 82.67 273 | 95.22 120 | 63.94 299 | 97.29 218 | 77.51 261 | 85.80 250 | 94.53 214 |
|
Fast-Effi-MVS+-dtu | | | 87.44 200 | 86.72 188 | 89.63 238 | 92.04 254 | 77.68 272 | 94.03 184 | 93.94 246 | 85.81 146 | 82.42 274 | 91.32 258 | 70.33 240 | 97.06 237 | 80.33 231 | 90.23 193 | 94.14 230 |
|
v7n | | | 86.81 222 | 85.76 229 | 89.95 225 | 90.72 307 | 79.25 237 | 95.07 110 | 95.92 141 | 84.45 180 | 82.29 275 | 90.86 272 | 72.60 213 | 97.53 192 | 79.42 243 | 80.52 314 | 93.08 284 |
|
DTE-MVSNet | | | 86.11 240 | 85.48 234 | 87.98 280 | 91.65 270 | 74.92 302 | 94.93 119 | 95.75 156 | 87.36 115 | 82.26 276 | 93.04 202 | 72.85 209 | 95.82 301 | 74.04 290 | 77.46 334 | 93.20 278 |
|
ADS-MVSNet2 | | | 81.66 293 | 79.71 301 | 87.50 289 | 91.35 279 | 74.19 309 | 83.33 352 | 88.48 348 | 72.90 337 | 82.24 277 | 85.77 342 | 64.98 294 | 93.20 343 | 64.57 342 | 83.74 265 | 95.12 186 |
|
ADS-MVSNet | | | 81.56 295 | 79.78 299 | 86.90 305 | 91.35 279 | 71.82 332 | 83.33 352 | 89.16 346 | 72.90 337 | 82.24 277 | 85.77 342 | 64.98 294 | 93.76 336 | 64.57 342 | 83.74 265 | 95.12 186 |
|
JIA-IIPM | | | 81.04 301 | 78.98 311 | 87.25 296 | 88.64 335 | 73.48 316 | 81.75 357 | 89.61 344 | 73.19 334 | 82.05 279 | 73.71 360 | 66.07 289 | 95.87 298 | 71.18 305 | 84.60 258 | 92.41 303 |
|
F-COLMAP | | | 87.95 178 | 86.80 186 | 91.40 164 | 96.35 103 | 80.88 192 | 94.73 133 | 95.45 181 | 79.65 271 | 82.04 280 | 94.61 143 | 71.13 225 | 98.50 115 | 76.24 273 | 91.05 186 | 94.80 202 |
|
PAPM | | | 86.68 228 | 85.39 236 | 90.53 195 | 93.05 230 | 79.33 234 | 89.79 300 | 94.77 223 | 78.82 281 | 81.95 281 | 93.24 194 | 76.81 152 | 97.30 215 | 66.94 331 | 93.16 162 | 94.95 196 |
|
DP-MVS | | | 87.25 207 | 85.36 238 | 92.90 94 | 97.65 61 | 83.24 125 | 94.81 128 | 92.00 290 | 74.99 319 | 81.92 282 | 95.00 127 | 72.66 211 | 99.05 63 | 66.92 333 | 92.33 175 | 96.40 141 |
|
pm-mvs1 | | | 86.61 229 | 85.54 232 | 89.82 229 | 91.44 272 | 80.18 206 | 95.28 97 | 94.85 216 | 83.84 190 | 81.66 283 | 92.62 215 | 72.45 216 | 96.48 270 | 79.67 238 | 78.06 329 | 92.82 293 |
|
MVS | | | 87.44 200 | 86.10 215 | 91.44 163 | 92.61 242 | 83.62 116 | 92.63 240 | 95.66 163 | 67.26 354 | 81.47 284 | 92.15 229 | 77.95 144 | 98.22 139 | 79.71 237 | 95.48 120 | 92.47 301 |
|
IterMVS-SCA-FT | | | 85.45 250 | 84.53 255 | 88.18 276 | 91.71 266 | 76.87 284 | 90.19 294 | 92.65 275 | 85.40 160 | 81.44 285 | 90.54 280 | 66.79 278 | 95.00 323 | 81.04 215 | 81.05 301 | 92.66 296 |
|
CHOSEN 280x420 | | | 85.15 258 | 83.99 260 | 88.65 263 | 92.47 243 | 78.40 251 | 79.68 360 | 92.76 271 | 74.90 321 | 81.41 286 | 89.59 299 | 69.85 246 | 95.51 311 | 79.92 236 | 95.29 126 | 92.03 311 |
|
miper_lstm_enhance | | | 85.27 256 | 84.59 254 | 87.31 293 | 91.28 282 | 74.63 303 | 87.69 329 | 94.09 245 | 81.20 255 | 81.36 287 | 89.85 296 | 74.97 178 | 94.30 329 | 81.03 217 | 79.84 322 | 93.01 286 |
|
Patchmtry | | | 82.71 282 | 80.93 288 | 88.06 279 | 90.05 322 | 76.37 291 | 84.74 347 | 91.96 294 | 72.28 342 | 81.32 288 | 87.87 325 | 71.03 227 | 95.50 313 | 68.97 318 | 80.15 317 | 92.32 307 |
|
dp | | | 81.47 297 | 80.23 294 | 85.17 323 | 89.92 325 | 65.49 360 | 86.74 334 | 90.10 333 | 76.30 306 | 81.10 289 | 87.12 335 | 62.81 304 | 95.92 295 | 68.13 325 | 79.88 320 | 94.09 234 |
|
tfpnnormal | | | 84.72 266 | 83.23 270 | 89.20 248 | 92.79 239 | 80.05 213 | 94.48 147 | 95.81 151 | 82.38 222 | 81.08 290 | 91.21 260 | 69.01 259 | 96.95 244 | 61.69 350 | 80.59 310 | 90.58 337 |
|
IterMVS | | | 84.88 263 | 83.98 261 | 87.60 286 | 91.44 272 | 76.03 294 | 90.18 295 | 92.41 278 | 83.24 206 | 81.06 291 | 90.42 284 | 66.60 281 | 94.28 330 | 79.46 239 | 80.98 306 | 92.48 300 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
OpenMVS |  | 83.78 11 | 88.74 159 | 87.29 174 | 93.08 85 | 92.70 240 | 85.39 76 | 96.57 33 | 96.43 106 | 78.74 284 | 80.85 292 | 96.07 95 | 69.64 248 | 99.01 73 | 78.01 256 | 96.65 104 | 94.83 200 |
|
pmmvs4 | | | 85.43 251 | 83.86 262 | 90.16 213 | 90.02 323 | 82.97 135 | 90.27 289 | 92.67 274 | 75.93 310 | 80.73 293 | 91.74 246 | 71.05 226 | 95.73 305 | 78.85 247 | 83.46 271 | 91.78 314 |
|
MIMVSNet | | | 82.59 284 | 80.53 289 | 88.76 258 | 91.51 271 | 78.32 253 | 86.57 336 | 90.13 332 | 79.32 272 | 80.70 294 | 88.69 313 | 52.98 351 | 93.07 345 | 66.03 336 | 88.86 216 | 94.90 197 |
|
IB-MVS | | 80.51 15 | 85.24 257 | 83.26 269 | 91.19 170 | 92.13 251 | 79.86 220 | 91.75 265 | 91.29 310 | 83.28 205 | 80.66 295 | 88.49 314 | 61.28 314 | 98.46 118 | 80.99 218 | 79.46 324 | 95.25 184 |
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 |
GG-mvs-BLEND | | | | | 87.94 282 | 89.73 328 | 77.91 262 | 87.80 326 | 78.23 371 | | 80.58 296 | 83.86 347 | 59.88 326 | 95.33 317 | 71.20 303 | 92.22 176 | 90.60 336 |
|
EU-MVSNet | | | 81.32 299 | 80.95 287 | 82.42 337 | 88.50 337 | 63.67 363 | 93.32 212 | 91.33 308 | 64.02 358 | 80.57 297 | 92.83 208 | 61.21 317 | 92.27 350 | 76.34 271 | 80.38 316 | 91.32 322 |
|
tpmvs | | | 83.35 280 | 82.07 278 | 87.20 300 | 91.07 290 | 71.00 340 | 88.31 323 | 91.70 298 | 78.91 278 | 80.49 298 | 87.18 334 | 69.30 255 | 97.08 235 | 68.12 326 | 83.56 269 | 93.51 266 |
|
MVS_0304 | | | 83.46 277 | 81.92 280 | 88.10 278 | 90.63 310 | 77.49 276 | 93.26 219 | 93.75 255 | 80.04 266 | 80.44 299 | 87.24 333 | 47.94 359 | 95.55 308 | 75.79 276 | 88.16 227 | 91.26 324 |
|
pmmvs5 | | | 84.21 270 | 82.84 276 | 88.34 271 | 88.95 333 | 76.94 283 | 92.41 246 | 91.91 296 | 75.63 312 | 80.28 300 | 91.18 263 | 64.59 296 | 95.57 307 | 77.09 266 | 83.47 270 | 92.53 299 |
|
tpm cat1 | | | 81.96 287 | 80.27 293 | 87.01 302 | 91.09 289 | 71.02 339 | 87.38 332 | 91.53 304 | 66.25 355 | 80.17 301 | 86.35 338 | 68.22 268 | 96.15 287 | 69.16 317 | 82.29 282 | 93.86 247 |
|
MS-PatchMatch | | | 85.05 260 | 84.16 257 | 87.73 284 | 91.42 276 | 78.51 247 | 91.25 276 | 93.53 257 | 77.50 295 | 80.15 302 | 91.58 252 | 61.99 309 | 95.51 311 | 75.69 277 | 94.35 142 | 89.16 347 |
|
1314 | | | 87.51 197 | 86.57 197 | 90.34 209 | 92.42 245 | 79.74 223 | 92.63 240 | 95.35 191 | 78.35 289 | 80.14 303 | 91.62 251 | 74.05 191 | 97.15 228 | 81.05 214 | 93.53 152 | 94.12 231 |
|
ITE_SJBPF | | | | | 88.24 274 | 91.88 260 | 77.05 282 | | 92.92 267 | 85.54 155 | 80.13 304 | 93.30 191 | 57.29 335 | 96.20 284 | 72.46 299 | 84.71 257 | 91.49 319 |
|
D2MVS | | | 85.90 243 | 85.09 242 | 88.35 270 | 90.79 303 | 77.42 277 | 91.83 263 | 95.70 159 | 80.77 259 | 80.08 305 | 90.02 291 | 66.74 280 | 96.37 277 | 81.88 202 | 87.97 232 | 91.26 324 |
|
NR-MVSNet | | | 88.58 164 | 87.47 170 | 91.93 142 | 93.04 231 | 84.16 102 | 94.77 131 | 96.25 117 | 89.05 64 | 80.04 306 | 93.29 192 | 79.02 131 | 97.05 238 | 81.71 208 | 80.05 318 | 94.59 210 |
|
baseline2 | | | 86.50 234 | 85.39 236 | 89.84 228 | 91.12 288 | 76.70 285 | 91.88 261 | 88.58 347 | 82.35 224 | 79.95 307 | 90.95 271 | 73.42 203 | 97.63 186 | 80.27 232 | 89.95 198 | 95.19 185 |
|
test0.0.03 1 | | | 82.41 285 | 81.69 281 | 84.59 326 | 88.23 341 | 72.89 321 | 90.24 291 | 87.83 350 | 83.41 201 | 79.86 308 | 89.78 297 | 67.25 270 | 88.99 361 | 65.18 339 | 83.42 272 | 91.90 313 |
|
CL-MVSNet_self_test | | | 81.74 291 | 80.53 289 | 85.36 320 | 85.96 353 | 72.45 329 | 90.25 290 | 93.07 265 | 81.24 253 | 79.85 309 | 87.29 332 | 70.93 229 | 92.52 348 | 66.95 330 | 69.23 351 | 91.11 330 |
|
TransMVSNet (Re) | | | 84.43 269 | 83.06 272 | 88.54 265 | 91.72 265 | 78.44 249 | 95.18 104 | 92.82 270 | 82.73 216 | 79.67 310 | 92.12 231 | 73.49 200 | 95.96 294 | 71.10 307 | 68.73 355 | 91.21 326 |
|
LTVRE_ROB | | 82.13 13 | 86.26 239 | 84.90 247 | 90.34 209 | 94.44 184 | 81.50 171 | 92.31 252 | 94.89 213 | 83.03 209 | 79.63 311 | 92.67 213 | 69.69 247 | 97.79 172 | 71.20 303 | 86.26 247 | 91.72 315 |
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 |
OurMVSNet-221017-0 | | | 85.35 253 | 84.64 253 | 87.49 290 | 90.77 304 | 72.59 327 | 94.01 186 | 94.40 231 | 84.72 176 | 79.62 312 | 93.17 196 | 61.91 310 | 96.72 252 | 81.99 199 | 81.16 297 | 93.16 280 |
|
EPNet_dtu | | | 86.49 236 | 85.94 222 | 88.14 277 | 90.24 318 | 72.82 322 | 94.11 175 | 92.20 284 | 86.66 132 | 79.42 313 | 92.36 222 | 73.52 199 | 95.81 302 | 71.26 302 | 93.66 148 | 95.80 168 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
LCM-MVSNet-Re | | | 88.30 170 | 88.32 151 | 88.27 272 | 94.71 171 | 72.41 330 | 93.15 223 | 90.98 317 | 87.77 104 | 79.25 314 | 91.96 240 | 78.35 141 | 95.75 304 | 83.04 179 | 95.62 117 | 96.65 135 |
|
pmmvs6 | | | 83.42 278 | 81.60 282 | 88.87 256 | 88.01 345 | 77.87 265 | 94.96 116 | 94.24 238 | 74.67 323 | 78.80 315 | 91.09 268 | 60.17 324 | 96.49 269 | 77.06 267 | 75.40 341 | 92.23 309 |
|
MVP-Stereo | | | 85.97 242 | 84.86 248 | 89.32 245 | 90.92 298 | 82.19 156 | 92.11 258 | 94.19 239 | 78.76 283 | 78.77 316 | 91.63 250 | 68.38 267 | 96.56 265 | 75.01 285 | 93.95 144 | 89.20 346 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
MSDG | | | 84.86 264 | 83.09 271 | 90.14 215 | 93.80 208 | 80.05 213 | 89.18 310 | 93.09 264 | 78.89 279 | 78.19 317 | 91.91 241 | 65.86 291 | 97.27 219 | 68.47 321 | 88.45 222 | 93.11 282 |
|
testgi | | | 80.94 304 | 80.20 295 | 83.18 333 | 87.96 346 | 66.29 357 | 91.28 274 | 90.70 325 | 83.70 192 | 78.12 318 | 92.84 207 | 51.37 353 | 90.82 357 | 63.34 345 | 82.46 281 | 92.43 302 |
|
ACMH+ | | 81.04 14 | 85.05 260 | 83.46 268 | 89.82 229 | 94.66 174 | 79.37 229 | 94.44 152 | 94.12 244 | 82.19 226 | 78.04 319 | 92.82 209 | 58.23 333 | 97.54 191 | 73.77 293 | 82.90 278 | 92.54 298 |
|
COLMAP_ROB |  | 80.39 16 | 83.96 272 | 82.04 279 | 89.74 233 | 95.28 143 | 79.75 222 | 94.25 167 | 92.28 282 | 75.17 317 | 78.02 320 | 93.77 179 | 58.60 332 | 97.84 171 | 65.06 341 | 85.92 248 | 91.63 317 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
ppachtmachnet_test | | | 81.84 289 | 80.07 297 | 87.15 301 | 88.46 338 | 74.43 307 | 89.04 313 | 92.16 285 | 75.33 315 | 77.75 321 | 88.99 305 | 66.20 286 | 95.37 316 | 65.12 340 | 77.60 332 | 91.65 316 |
|
Anonymous20231206 | | | 81.03 302 | 79.77 300 | 84.82 325 | 87.85 347 | 70.26 345 | 91.42 273 | 92.08 287 | 73.67 330 | 77.75 321 | 89.25 303 | 62.43 307 | 93.08 344 | 61.50 351 | 82.00 288 | 91.12 329 |
|
SixPastTwentyTwo | | | 83.91 274 | 82.90 274 | 86.92 304 | 90.99 292 | 70.67 342 | 93.48 207 | 91.99 291 | 85.54 155 | 77.62 323 | 92.11 233 | 60.59 321 | 96.87 249 | 76.05 275 | 77.75 331 | 93.20 278 |
|
ACMH | | 80.38 17 | 85.36 252 | 83.68 264 | 90.39 204 | 94.45 183 | 80.63 198 | 94.73 133 | 94.85 216 | 82.09 227 | 77.24 324 | 92.65 214 | 60.01 325 | 97.58 188 | 72.25 300 | 84.87 256 | 92.96 287 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Patchmatch-RL test | | | 81.67 292 | 79.96 298 | 86.81 308 | 85.42 357 | 71.23 336 | 82.17 356 | 87.50 353 | 78.47 287 | 77.19 325 | 82.50 353 | 70.81 231 | 93.48 339 | 82.66 188 | 72.89 345 | 95.71 172 |
|
KD-MVS_2432*1600 | | | 78.50 318 | 76.02 323 | 85.93 315 | 86.22 351 | 74.47 305 | 84.80 345 | 92.33 279 | 79.29 273 | 76.98 326 | 85.92 340 | 53.81 349 | 93.97 333 | 67.39 328 | 57.42 363 | 89.36 342 |
|
miper_refine_blended | | | 78.50 318 | 76.02 323 | 85.93 315 | 86.22 351 | 74.47 305 | 84.80 345 | 92.33 279 | 79.29 273 | 76.98 326 | 85.92 340 | 53.81 349 | 93.97 333 | 67.39 328 | 57.42 363 | 89.36 342 |
|
our_test_3 | | | 81.93 288 | 80.46 291 | 86.33 312 | 88.46 338 | 73.48 316 | 88.46 321 | 91.11 312 | 76.46 302 | 76.69 328 | 88.25 318 | 66.89 276 | 94.36 327 | 68.75 319 | 79.08 327 | 91.14 328 |
|
Patchmatch-test | | | 81.37 298 | 79.30 304 | 87.58 287 | 90.92 298 | 74.16 310 | 80.99 358 | 87.68 352 | 70.52 349 | 76.63 329 | 88.81 308 | 71.21 224 | 92.76 347 | 60.01 356 | 86.93 245 | 95.83 166 |
|
KD-MVS_self_test | | | 80.20 308 | 79.24 305 | 83.07 334 | 85.64 356 | 65.29 361 | 91.01 280 | 93.93 247 | 78.71 285 | 76.32 330 | 86.40 337 | 59.20 330 | 92.93 346 | 72.59 298 | 69.35 350 | 91.00 332 |
|
FMVSNet5 | | | 81.52 296 | 79.60 302 | 87.27 294 | 91.17 285 | 77.95 261 | 91.49 272 | 92.26 283 | 76.87 301 | 76.16 331 | 87.91 324 | 51.67 352 | 92.34 349 | 67.74 327 | 81.16 297 | 91.52 318 |
|
AllTest | | | 83.42 278 | 81.39 284 | 89.52 241 | 95.01 153 | 77.79 268 | 93.12 224 | 90.89 321 | 77.41 296 | 76.12 332 | 93.34 187 | 54.08 347 | 97.51 193 | 68.31 323 | 84.27 261 | 93.26 272 |
|
TestCases | | | | | 89.52 241 | 95.01 153 | 77.79 268 | | 90.89 321 | 77.41 296 | 76.12 332 | 93.34 187 | 54.08 347 | 97.51 193 | 68.31 323 | 84.27 261 | 93.26 272 |
|
test_0402 | | | 81.30 300 | 79.17 308 | 87.67 285 | 93.19 225 | 78.17 257 | 92.98 231 | 91.71 297 | 75.25 316 | 76.02 334 | 90.31 285 | 59.23 329 | 96.37 277 | 50.22 363 | 83.63 268 | 88.47 353 |
|
DSMNet-mixed | | | 76.94 322 | 76.29 321 | 78.89 340 | 83.10 363 | 56.11 370 | 87.78 327 | 79.77 368 | 60.65 360 | 75.64 335 | 88.71 311 | 61.56 312 | 88.34 362 | 60.07 355 | 89.29 210 | 92.21 310 |
|
Anonymous20240521 | | | 80.44 306 | 79.21 306 | 84.11 331 | 85.75 355 | 67.89 353 | 92.86 235 | 93.23 262 | 75.61 313 | 75.59 336 | 87.47 329 | 50.03 354 | 94.33 328 | 71.14 306 | 81.21 296 | 90.12 339 |
|
USDC | | | 82.76 281 | 81.26 286 | 87.26 295 | 91.17 285 | 74.55 304 | 89.27 307 | 93.39 260 | 78.26 291 | 75.30 337 | 92.08 235 | 54.43 346 | 96.63 256 | 71.64 301 | 85.79 251 | 90.61 334 |
|
TDRefinement | | | 79.81 311 | 77.34 315 | 87.22 299 | 79.24 367 | 75.48 300 | 93.12 224 | 92.03 289 | 76.45 303 | 75.01 338 | 91.58 252 | 49.19 357 | 96.44 274 | 70.22 311 | 69.18 352 | 89.75 341 |
|
LF4IMVS | | | 80.37 307 | 79.07 310 | 84.27 330 | 86.64 349 | 69.87 348 | 89.39 306 | 91.05 315 | 76.38 304 | 74.97 339 | 90.00 292 | 47.85 360 | 94.25 331 | 74.55 289 | 80.82 308 | 88.69 351 |
|
PM-MVS | | | 78.11 320 | 76.12 322 | 84.09 332 | 83.54 362 | 70.08 346 | 88.97 314 | 85.27 357 | 79.93 267 | 74.73 340 | 86.43 336 | 34.70 367 | 93.48 339 | 79.43 242 | 72.06 347 | 88.72 350 |
|
OpenMVS_ROB |  | 74.94 19 | 79.51 313 | 77.03 319 | 86.93 303 | 87.00 348 | 76.23 293 | 92.33 250 | 90.74 324 | 68.93 352 | 74.52 341 | 88.23 319 | 49.58 356 | 96.62 257 | 57.64 358 | 84.29 260 | 87.94 355 |
|
test20.03 | | | 79.95 310 | 79.08 309 | 82.55 336 | 85.79 354 | 67.74 355 | 91.09 279 | 91.08 313 | 81.23 254 | 74.48 342 | 89.96 294 | 61.63 311 | 90.15 358 | 60.08 354 | 76.38 338 | 89.76 340 |
|
ambc | | | | | 83.06 335 | 79.99 366 | 63.51 364 | 77.47 361 | 92.86 268 | | 74.34 343 | 84.45 346 | 28.74 368 | 95.06 322 | 73.06 297 | 68.89 354 | 90.61 334 |
|
PVSNet_0 | | 73.20 20 | 77.22 321 | 74.83 326 | 84.37 328 | 90.70 308 | 71.10 338 | 83.09 354 | 89.67 343 | 72.81 339 | 73.93 344 | 83.13 351 | 60.79 319 | 93.70 337 | 68.54 320 | 50.84 366 | 88.30 354 |
|
pmmvs-eth3d | | | 80.97 303 | 78.72 312 | 87.74 283 | 84.99 359 | 79.97 218 | 90.11 296 | 91.65 300 | 75.36 314 | 73.51 345 | 86.03 339 | 59.45 328 | 93.96 335 | 75.17 282 | 72.21 346 | 89.29 345 |
|
K. test v3 | | | 81.59 294 | 80.15 296 | 85.91 317 | 89.89 326 | 69.42 349 | 92.57 243 | 87.71 351 | 85.56 154 | 73.44 346 | 89.71 298 | 55.58 338 | 95.52 310 | 77.17 264 | 69.76 349 | 92.78 294 |
|
EG-PatchMatch MVS | | | 82.37 286 | 80.34 292 | 88.46 267 | 90.27 317 | 79.35 230 | 92.80 237 | 94.33 234 | 77.14 300 | 73.26 347 | 90.18 287 | 47.47 361 | 96.72 252 | 70.25 309 | 87.32 241 | 89.30 344 |
|
lessismore_v0 | | | | | 86.04 313 | 88.46 338 | 68.78 351 | | 80.59 367 | | 73.01 348 | 90.11 289 | 55.39 340 | 96.43 275 | 75.06 284 | 65.06 357 | 92.90 289 |
|
MIMVSNet1 | | | 79.38 314 | 77.28 316 | 85.69 318 | 86.35 350 | 73.67 313 | 91.61 271 | 92.75 272 | 78.11 294 | 72.64 349 | 88.12 320 | 48.16 358 | 91.97 353 | 60.32 353 | 77.49 333 | 91.43 321 |
|
ET-MVSNet_ETH3D | | | 87.51 197 | 85.91 223 | 92.32 125 | 93.70 213 | 83.93 106 | 92.33 250 | 90.94 319 | 84.16 182 | 72.09 350 | 92.52 217 | 69.90 243 | 95.85 299 | 89.20 105 | 88.36 225 | 97.17 116 |
|
TinyColmap | | | 79.76 312 | 77.69 314 | 85.97 314 | 91.71 266 | 73.12 319 | 89.55 301 | 90.36 329 | 75.03 318 | 72.03 351 | 90.19 286 | 46.22 362 | 96.19 286 | 63.11 346 | 81.03 302 | 88.59 352 |
|
N_pmnet | | | 68.89 328 | 68.44 331 | 70.23 347 | 89.07 332 | 28.79 379 | 88.06 324 | 19.50 380 | 69.47 351 | 71.86 352 | 84.93 344 | 61.24 316 | 91.75 354 | 54.70 360 | 77.15 335 | 90.15 338 |
|
UnsupCasMVSNet_eth | | | 80.07 309 | 78.27 313 | 85.46 319 | 85.24 358 | 72.63 326 | 88.45 322 | 94.87 215 | 82.99 211 | 71.64 353 | 88.07 321 | 56.34 337 | 91.75 354 | 73.48 295 | 63.36 360 | 92.01 312 |
|
new-patchmatchnet | | | 76.41 323 | 75.17 325 | 80.13 339 | 82.65 365 | 59.61 365 | 87.66 330 | 91.08 313 | 78.23 292 | 69.85 354 | 83.22 350 | 54.76 343 | 91.63 356 | 64.14 344 | 64.89 358 | 89.16 347 |
|
MVS-HIRNet | | | 73.70 325 | 72.20 328 | 78.18 343 | 91.81 263 | 56.42 369 | 82.94 355 | 82.58 363 | 55.24 362 | 68.88 355 | 66.48 364 | 55.32 341 | 95.13 319 | 58.12 357 | 88.42 223 | 83.01 358 |
|
UnsupCasMVSNet_bld | | | 76.23 324 | 73.27 327 | 85.09 324 | 83.79 361 | 72.92 320 | 85.65 342 | 93.47 259 | 71.52 344 | 68.84 356 | 79.08 357 | 49.77 355 | 93.21 342 | 66.81 335 | 60.52 362 | 89.13 349 |
|
pmmvs3 | | | 71.81 327 | 68.71 330 | 81.11 338 | 75.86 368 | 70.42 344 | 86.74 334 | 83.66 361 | 58.95 361 | 68.64 357 | 80.89 355 | 36.93 366 | 89.52 360 | 63.10 347 | 63.59 359 | 83.39 357 |
|
CMPMVS |  | 59.16 21 | 80.52 305 | 79.20 307 | 84.48 327 | 83.98 360 | 67.63 356 | 89.95 299 | 93.84 253 | 64.79 357 | 66.81 358 | 91.14 266 | 57.93 334 | 95.17 318 | 76.25 272 | 88.10 228 | 90.65 333 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
new_pmnet | | | 72.15 326 | 70.13 329 | 78.20 342 | 82.95 364 | 65.68 358 | 83.91 350 | 82.40 364 | 62.94 359 | 64.47 359 | 79.82 356 | 42.85 364 | 86.26 364 | 57.41 359 | 74.44 342 | 82.65 360 |
|
YYNet1 | | | 79.22 315 | 77.20 317 | 85.28 322 | 88.20 344 | 72.66 325 | 85.87 339 | 90.05 336 | 74.33 326 | 62.70 360 | 87.61 327 | 66.09 288 | 92.03 351 | 66.94 331 | 72.97 344 | 91.15 327 |
|
MDA-MVSNet_test_wron | | | 79.21 316 | 77.19 318 | 85.29 321 | 88.22 342 | 72.77 323 | 85.87 339 | 90.06 334 | 74.34 325 | 62.62 361 | 87.56 328 | 66.14 287 | 91.99 352 | 66.90 334 | 73.01 343 | 91.10 331 |
|
MDA-MVSNet-bldmvs | | | 78.85 317 | 76.31 320 | 86.46 309 | 89.76 327 | 73.88 311 | 88.79 316 | 90.42 326 | 79.16 276 | 59.18 362 | 88.33 317 | 60.20 323 | 94.04 332 | 62.00 349 | 68.96 353 | 91.48 320 |
|
LCM-MVSNet | | | 66.00 329 | 62.16 333 | 77.51 344 | 64.51 374 | 58.29 366 | 83.87 351 | 90.90 320 | 48.17 365 | 54.69 363 | 73.31 361 | 16.83 377 | 86.75 363 | 65.47 337 | 61.67 361 | 87.48 356 |
|
FPMVS | | | 64.63 330 | 62.55 332 | 70.88 346 | 70.80 370 | 56.71 367 | 84.42 348 | 84.42 359 | 51.78 364 | 49.57 364 | 81.61 354 | 23.49 371 | 81.48 367 | 40.61 368 | 76.25 339 | 74.46 363 |
|
PMMVS2 | | | 59.60 332 | 56.40 334 | 69.21 348 | 68.83 371 | 46.58 374 | 73.02 365 | 77.48 372 | 55.07 363 | 49.21 365 | 72.95 362 | 17.43 376 | 80.04 368 | 49.32 364 | 44.33 368 | 80.99 362 |
|
DeepMVS_CX |  | | | | 56.31 353 | 74.23 369 | 51.81 372 | | 56.67 378 | 44.85 366 | 48.54 366 | 75.16 358 | 27.87 370 | 58.74 374 | 40.92 367 | 52.22 365 | 58.39 367 |
|
test_method | | | 50.52 336 | 48.47 338 | 56.66 352 | 52.26 378 | 18.98 381 | 41.51 370 | 81.40 366 | 10.10 372 | 44.59 367 | 75.01 359 | 28.51 369 | 68.16 370 | 53.54 361 | 49.31 367 | 82.83 359 |
|
Gipuma |  | | 57.99 334 | 54.91 336 | 67.24 349 | 88.51 336 | 65.59 359 | 52.21 368 | 90.33 330 | 43.58 367 | 42.84 368 | 51.18 369 | 20.29 374 | 85.07 365 | 34.77 369 | 70.45 348 | 51.05 368 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
ANet_high | | | 58.88 333 | 54.22 337 | 72.86 345 | 56.50 377 | 56.67 368 | 80.75 359 | 86.00 354 | 73.09 336 | 37.39 369 | 64.63 366 | 22.17 372 | 79.49 369 | 43.51 366 | 23.96 371 | 82.43 361 |
|
tmp_tt | | | 35.64 340 | 39.24 342 | 24.84 356 | 14.87 380 | 23.90 380 | 62.71 366 | 51.51 379 | 6.58 374 | 36.66 370 | 62.08 367 | 44.37 363 | 30.34 376 | 52.40 362 | 22.00 373 | 20.27 371 |
|
PMVS |  | 47.18 22 | 52.22 335 | 48.46 339 | 63.48 350 | 45.72 379 | 46.20 375 | 73.41 364 | 78.31 370 | 41.03 368 | 30.06 371 | 65.68 365 | 6.05 378 | 83.43 366 | 30.04 370 | 65.86 356 | 60.80 365 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE |  | 39.65 23 | 43.39 337 | 38.59 343 | 57.77 351 | 56.52 376 | 48.77 373 | 55.38 367 | 58.64 377 | 29.33 371 | 28.96 372 | 52.65 368 | 4.68 379 | 64.62 373 | 28.11 371 | 33.07 369 | 59.93 366 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 43.23 338 | 42.29 340 | 46.03 354 | 65.58 373 | 37.41 376 | 73.51 363 | 64.62 374 | 33.99 369 | 28.47 373 | 47.87 370 | 19.90 375 | 67.91 371 | 22.23 372 | 24.45 370 | 32.77 369 |
|
EMVS | | | 42.07 339 | 41.12 341 | 44.92 355 | 63.45 375 | 35.56 378 | 73.65 362 | 63.48 375 | 33.05 370 | 26.88 374 | 45.45 371 | 21.27 373 | 67.14 372 | 19.80 373 | 23.02 372 | 32.06 370 |
|
wuyk23d | | | 21.27 342 | 20.48 345 | 23.63 357 | 68.59 372 | 36.41 377 | 49.57 369 | 6.85 381 | 9.37 373 | 7.89 375 | 4.46 377 | 4.03 380 | 31.37 375 | 17.47 374 | 16.07 374 | 3.12 372 |
|
testmvs | | | 8.92 343 | 11.52 346 | 1.12 359 | 1.06 381 | 0.46 383 | 86.02 338 | 0.65 382 | 0.62 375 | 2.74 376 | 9.52 375 | 0.31 382 | 0.45 378 | 2.38 375 | 0.39 375 | 2.46 374 |
|
test123 | | | 8.76 344 | 11.22 347 | 1.39 358 | 0.85 382 | 0.97 382 | 85.76 341 | 0.35 383 | 0.54 376 | 2.45 377 | 8.14 376 | 0.60 381 | 0.48 377 | 2.16 376 | 0.17 376 | 2.71 373 |
|
EGC-MVSNET | | | 61.97 331 | 56.37 335 | 78.77 341 | 89.63 329 | 73.50 315 | 89.12 311 | 82.79 362 | 0.21 377 | 1.24 378 | 84.80 345 | 39.48 365 | 90.04 359 | 44.13 365 | 75.94 340 | 72.79 364 |
|
test_blank | | | 0.00 347 | 0.00 350 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
uanet_test | | | 0.00 347 | 0.00 350 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
cdsmvs_eth3d_5k | | | 22.14 341 | 29.52 344 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 95.76 155 | 0.00 378 | 0.00 379 | 94.29 154 | 75.66 170 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
pcd_1.5k_mvsjas | | | 6.64 346 | 8.86 349 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 79.70 123 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
sosnet-low-res | | | 0.00 347 | 0.00 350 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
sosnet | | | 0.00 347 | 0.00 350 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
uncertanet | | | 0.00 347 | 0.00 350 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
Regformer | | | 0.00 347 | 0.00 350 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
ab-mvs-re | | | 7.82 345 | 10.43 348 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 93.88 173 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
uanet | | | 0.00 347 | 0.00 350 | 0.00 360 | 0.00 383 | 0.00 384 | 0.00 371 | 0.00 384 | 0.00 378 | 0.00 379 | 0.00 378 | 0.00 383 | 0.00 379 | 0.00 377 | 0.00 377 | 0.00 375 |
|
MSC_two_6792asdad | | | | | 96.52 1 | 97.78 57 | 90.86 1 | | 96.85 67 | | | | | 99.61 3 | 96.03 1 | 99.06 9 | 99.07 5 |
|
No_MVS | | | | | 96.52 1 | 97.78 57 | 90.86 1 | | 96.85 67 | | | | | 99.61 3 | 96.03 1 | 99.06 9 | 99.07 5 |
|
eth-test2 | | | | | | 0.00 383 | | | | | | | | | | | |
|
eth-test | | | | | | 0.00 383 | | | | | | | | | | | |
|
OPU-MVS | | | | | 96.21 3 | 98.00 46 | 90.85 3 | 97.13 13 | | | | 97.08 43 | 92.59 2 | 98.94 87 | 92.25 53 | 98.99 14 | 98.84 13 |
|
save fliter | | | | | | 97.85 50 | 85.63 72 | 95.21 101 | 96.82 73 | 89.44 52 | | | | | | | |
|
test_0728_SECOND | | | | | 95.01 17 | 98.79 2 | 86.43 43 | 97.09 15 | 97.49 5 | | | | | 99.61 3 | 95.62 8 | 99.08 7 | 98.99 8 |
|
GSMVS | | | | | | | | | | | | | | | | | 96.12 151 |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 219 | | | | 96.12 151 |
|
sam_mvs | | | | | | | | | | | | | 70.60 233 | | | | |
|
MTGPA |  | | | | | | | | 96.97 53 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 325 | | | | 9.81 374 | 69.31 254 | 95.53 309 | 76.65 268 | | |
|
test_post | | | | | | | | | | | | 10.29 373 | 70.57 237 | 95.91 297 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 348 | 71.53 221 | 96.48 270 | | | |
|
MTMP | | | | | | | | 96.16 49 | 60.64 376 | | | | | | | | |
|
gm-plane-assit | | | | | | 89.60 330 | 68.00 352 | | | 77.28 299 | | 88.99 305 | | 97.57 189 | 79.44 241 | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 68 | 98.71 34 | 98.07 73 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 93 | 98.68 39 | 98.27 55 |
|
test_prior4 | | | | | | | 85.96 60 | 94.11 175 | | | | | | | | | |
|
test_prior | | | | | 93.82 67 | 97.29 75 | 84.49 90 | | 96.88 64 | | | | | 98.87 91 | | | 98.11 71 |
|
新几何2 | | | | | | | | 93.11 226 | | | | | | | | | |
|
旧先验1 | | | | | | 96.79 86 | 81.81 165 | | 95.67 161 | | | 96.81 57 | 86.69 42 | | | 97.66 84 | 96.97 125 |
|
无先验 | | | | | | | | 93.28 218 | 96.26 115 | 73.95 328 | | | | 99.05 63 | 80.56 226 | | 96.59 137 |
|
原ACMM2 | | | | | | | | 92.94 233 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.75 103 | 78.30 252 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 39 | | | | |
|
testdata1 | | | | | | | | 92.15 256 | | 87.94 96 | | | | | | | |
|
plane_prior7 | | | | | | 94.70 172 | 82.74 141 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 178 | 82.75 139 | | | | | | 74.23 186 | | | | |
|
plane_prior5 | | | | | | | | | 96.22 120 | | | | | 98.12 143 | 88.15 116 | 89.99 195 | 94.63 206 |
|
plane_prior4 | | | | | | | | | | | | 94.86 132 | | | | | |
|
plane_prior2 | | | | | | | | 95.85 68 | | 90.81 20 | | | | | | | |
|
plane_prior1 | | | | | | 94.59 176 | | | | | | | | | | | |
|
plane_prior | | | | | | | 82.73 142 | 95.21 101 | | 89.66 49 | | | | | | 89.88 200 | |
|
n2 | | | | | | | | | 0.00 384 | | | | | | | | |
|
nn | | | | | | | | | 0.00 384 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 355 | | | | | | | | |
|
test11 | | | | | | | | | 96.57 99 | | | | | | | | |
|
door | | | | | | | | | 85.33 356 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 169 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 133 | | |
|
HQP3-MVS | | | | | | | | | 96.04 134 | | | | | | | 89.77 202 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 196 | | | | |
|
NP-MVS | | | | | | 94.37 186 | 82.42 151 | | | | | 93.98 166 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 236 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 231 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 118 | | | | |
|