This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CS-MVS99.50 1299.48 1099.54 8299.76 5699.42 8599.90 199.55 6598.56 7199.78 3599.70 14298.65 6599.79 16699.65 999.78 9099.41 174
CS-MVS-test99.49 1499.48 1099.54 8299.78 4799.30 9699.89 299.58 4998.56 7199.73 4899.69 15298.55 7099.82 15299.69 699.85 5599.48 159
RRT_MVS98.70 13598.66 12498.83 20798.90 29098.45 20199.89 299.28 26597.76 16398.94 22999.92 1096.98 12699.25 28299.28 4797.00 26698.80 225
mvsmamba98.92 10698.87 10099.08 15799.07 26899.16 11199.88 499.51 10398.15 11799.40 13699.89 2097.12 11999.33 26899.38 3297.40 25498.73 239
MVSFormer99.17 6899.12 6499.29 13699.51 15698.94 15199.88 499.46 16997.55 18499.80 2899.65 17097.39 11099.28 27799.03 6999.85 5599.65 113
test_djsdf98.67 14098.57 14198.98 17298.70 31998.91 15599.88 499.46 16997.55 18499.22 17999.88 2695.73 16999.28 27799.03 6997.62 23098.75 234
OurMVSNet-221017-097.88 21897.77 20998.19 27098.71 31896.53 29099.88 499.00 30197.79 16098.78 25399.94 491.68 29199.35 26597.21 25196.99 26798.69 251
DROMVSNet99.44 2999.39 1999.58 7599.56 14499.49 7899.88 499.58 4998.38 8699.73 4899.69 15298.20 9099.70 20299.64 1099.82 7699.54 142
DVP-MVS++99.59 399.50 899.88 599.51 15699.88 899.87 999.51 10398.99 3399.88 1199.81 7699.27 599.96 2298.85 9699.80 8399.81 47
FOURS199.91 199.93 199.87 999.56 5799.10 1699.81 25
K. test v397.10 28396.79 28498.01 28298.72 31696.33 29799.87 997.05 36297.59 17996.16 34299.80 8988.71 32699.04 31396.69 28396.55 27398.65 273
FC-MVSNet-test98.75 13198.62 13299.15 15499.08 26799.45 8399.86 1299.60 4198.23 10598.70 26599.82 6396.80 13199.22 28899.07 6796.38 27698.79 226
v7n97.87 22097.52 23498.92 18298.76 31298.58 18599.84 1399.46 16996.20 29498.91 23399.70 14294.89 19799.44 24496.03 29793.89 32798.75 234
DTE-MVSNet97.51 26797.19 27598.46 24598.63 32598.13 21699.84 1399.48 14296.68 25697.97 31399.67 16492.92 25698.56 34196.88 27692.60 34198.70 247
3Dnovator97.25 999.24 6299.05 7199.81 3699.12 25899.66 5399.84 1399.74 1099.09 2098.92 23299.90 1695.94 16099.98 898.95 7799.92 1399.79 60
FIs98.78 12898.63 12799.23 14599.18 24599.54 7099.83 1699.59 4498.28 9798.79 25299.81 7696.75 13499.37 25899.08 6696.38 27698.78 227
test_fmvs392.10 32691.77 32993.08 34296.19 36086.25 36399.82 1798.62 34196.65 25995.19 34996.90 35955.05 37495.93 37096.63 28790.92 34997.06 358
jajsoiax98.43 15398.28 15998.88 19398.60 32998.43 20399.82 1799.53 8498.19 11198.63 27699.80 8993.22 25299.44 24499.22 5397.50 24298.77 230
OpenMVScopyleft96.50 1698.47 15098.12 16999.52 9699.04 27599.53 7399.82 1799.72 1194.56 33198.08 30699.88 2694.73 20999.98 897.47 23899.76 9699.06 206
nrg03098.64 14398.42 14999.28 13899.05 27499.69 4799.81 2099.46 16998.04 13799.01 21799.82 6396.69 13699.38 25399.34 3994.59 31698.78 227
HPM-MVScopyleft99.42 3499.28 4699.83 3299.90 499.72 4299.81 2099.54 7397.59 17999.68 6099.63 18298.91 3499.94 5798.58 13699.91 1899.84 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet99.13 7598.99 8399.53 9099.65 11499.06 12899.81 2099.33 24197.43 19899.60 9099.88 2697.14 11899.84 13599.13 6098.94 17299.69 99
3Dnovator+97.12 1399.18 6698.97 8799.82 3399.17 25199.68 4899.81 2099.51 10399.20 898.72 25899.89 2095.68 17299.97 1498.86 9499.86 4899.81 47
FA-MVS(test-final)98.75 13198.53 14499.41 11499.55 14899.05 13099.80 2499.01 30096.59 26899.58 9499.59 19695.39 17999.90 10197.78 20599.49 12999.28 187
bld_raw_dy_0_6498.69 13798.58 14098.99 17098.88 29398.96 14399.80 2499.41 19997.91 14799.32 15699.87 3295.70 17199.31 27499.09 6497.27 25998.71 242
GeoE98.85 12098.62 13299.53 9099.61 12999.08 12599.80 2499.51 10397.10 22999.31 15899.78 10695.23 18899.77 17398.21 17099.03 16799.75 74
canonicalmvs99.02 9798.86 10399.51 9899.42 18599.32 9299.80 2499.48 14298.63 6699.31 15898.81 33197.09 12199.75 17999.27 5097.90 22299.47 165
v897.95 21097.63 22698.93 18098.95 28798.81 16899.80 2499.41 19996.03 30899.10 20399.42 24994.92 19599.30 27596.94 27194.08 32598.66 271
Vis-MVSNet (Re-imp)98.87 11098.72 11599.31 12899.71 8798.88 15799.80 2499.44 18897.91 14799.36 14899.78 10695.49 17799.43 24897.91 19399.11 15899.62 125
Anonymous2024052196.20 29995.89 30197.13 31797.72 34894.96 32899.79 3099.29 26393.01 34597.20 33099.03 31689.69 31998.36 34591.16 35196.13 28198.07 335
PS-MVSNAJss98.92 10698.92 9398.90 18898.78 30898.53 18999.78 3199.54 7398.07 13199.00 22199.76 11999.01 1899.37 25899.13 6097.23 26098.81 224
PEN-MVS97.76 23897.44 24898.72 21898.77 31198.54 18899.78 3199.51 10397.06 23398.29 29999.64 17692.63 26998.89 33598.09 17993.16 33498.72 240
anonymousdsp98.44 15298.28 15998.94 17898.50 33498.96 14399.77 3399.50 12297.07 23198.87 24199.77 11394.76 20799.28 27798.66 12397.60 23198.57 301
SixPastTwentyTwo97.50 26897.33 26598.03 27998.65 32396.23 30099.77 3398.68 33997.14 22297.90 31499.93 690.45 30999.18 29697.00 26596.43 27598.67 263
QAPM98.67 14098.30 15899.80 3899.20 24099.67 5199.77 3399.72 1194.74 32898.73 25799.90 1695.78 16799.98 896.96 26999.88 3799.76 73
test_vis3_rt87.04 33285.81 33590.73 34893.99 37081.96 36999.76 3690.23 38192.81 34781.35 36991.56 36940.06 37899.07 31094.27 32788.23 35691.15 369
dcpmvs_299.23 6399.58 298.16 27299.83 3594.68 33299.76 3699.52 8999.07 2399.98 499.88 2698.56 6999.93 7099.67 899.98 299.87 17
HPM-MVS_fast99.51 1199.40 1899.85 2599.91 199.79 3099.76 3699.56 5797.72 16899.76 4399.75 12299.13 1299.92 8099.07 6799.92 1399.85 22
v1097.85 22397.52 23498.86 20198.99 28098.67 17699.75 3999.41 19995.70 31298.98 22399.41 25394.75 20899.23 28596.01 29894.63 31598.67 263
APDe-MVS99.66 199.57 399.92 199.77 5399.89 499.75 3999.56 5799.02 2699.88 1199.85 4299.18 1099.96 2299.22 5399.92 1399.90 4
IS-MVSNet99.05 9398.87 10099.57 7799.73 7899.32 9299.75 3999.20 27898.02 14099.56 9899.86 3796.54 14099.67 20998.09 17999.13 15799.73 83
test_vis1_n97.92 21497.44 24899.34 12199.53 15098.08 21899.74 4299.49 13099.15 10100.00 199.94 479.51 36299.98 899.88 299.76 9699.97 2
test_fmvs1_n98.41 15698.14 16699.21 14699.82 3797.71 24199.74 4299.49 13099.32 499.99 299.95 285.32 34999.97 1499.82 399.84 6399.96 3
tttt051798.42 15498.14 16699.28 13899.66 10898.38 20699.74 4296.85 36397.68 17299.79 3099.74 12791.39 29999.89 11198.83 10299.56 12499.57 138
test_fmvs297.25 27897.30 26897.09 31999.43 18393.31 34999.73 4598.87 31998.83 5299.28 16499.80 8984.45 35299.66 21297.88 19597.45 24898.30 324
baseline99.15 7199.02 7899.53 9099.66 10899.14 11799.72 4699.48 14298.35 9199.42 12799.84 5296.07 15399.79 16699.51 1999.14 15699.67 106
RPSCF98.22 17098.62 13296.99 32099.82 3791.58 35799.72 4699.44 18896.61 26499.66 6999.89 2095.92 16199.82 15297.46 23999.10 16199.57 138
CSCG99.32 4999.32 3299.32 12799.85 2598.29 20899.71 4899.66 2698.11 12399.41 13199.80 8998.37 8399.96 2298.99 7399.96 799.72 89
WR-MVS_H98.13 18197.87 20098.90 18899.02 27798.84 16299.70 4999.59 4497.27 21198.40 29299.19 30095.53 17599.23 28598.34 16293.78 32898.61 295
LTVRE_ROB97.16 1298.02 19897.90 19598.40 25399.23 23396.80 28199.70 4999.60 4197.12 22598.18 30399.70 14291.73 29099.72 19098.39 15697.45 24898.68 256
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_f91.90 32791.26 33193.84 33995.52 36785.92 36499.69 5198.53 34595.31 31793.87 35596.37 36255.33 37398.27 34695.70 30490.98 34897.32 357
XVS99.53 999.42 1599.87 1199.85 2599.83 1699.69 5199.68 1998.98 3699.37 14499.74 12798.81 4499.94 5798.79 10799.86 4899.84 26
X-MVStestdata96.55 29195.45 30899.87 1199.85 2599.83 1699.69 5199.68 1998.98 3699.37 14464.01 37898.81 4499.94 5798.79 10799.86 4899.84 26
V4298.06 18897.79 20498.86 20198.98 28398.84 16299.69 5199.34 23496.53 27099.30 16099.37 26494.67 21299.32 27197.57 22894.66 31498.42 316
mPP-MVS99.44 2999.30 4099.86 2099.88 1199.79 3099.69 5199.48 14298.12 12199.50 11099.75 12298.78 4799.97 1498.57 13999.89 3499.83 35
CP-MVS99.45 2599.32 3299.85 2599.83 3599.75 3999.69 5199.52 8998.07 13199.53 10599.63 18298.93 3399.97 1498.74 11199.91 1899.83 35
FE-MVS98.48 14998.17 16399.40 11599.54 14998.96 14399.68 5798.81 32495.54 31499.62 8499.70 14293.82 24199.93 7097.35 24599.46 13099.32 184
PS-CasMVS97.93 21197.59 22998.95 17798.99 28099.06 12899.68 5799.52 8997.13 22398.31 29799.68 15892.44 27899.05 31298.51 14794.08 32598.75 234
Vis-MVSNetpermissive99.12 8198.97 8799.56 7999.78 4799.10 12199.68 5799.66 2698.49 7799.86 1699.87 3294.77 20699.84 13599.19 5599.41 13499.74 78
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_vis1_n_192098.63 14498.40 15199.31 12899.86 2097.94 22999.67 6099.62 3399.43 199.99 299.91 1187.29 342100.00 199.92 199.92 1399.98 1
EIA-MVS99.18 6699.09 6899.45 10899.49 16799.18 10899.67 6099.53 8497.66 17599.40 13699.44 24598.10 9499.81 15798.94 7899.62 12099.35 180
MSP-MVS99.42 3499.27 4899.88 599.89 899.80 2799.67 6099.50 12298.70 6399.77 3899.49 23198.21 8999.95 4898.46 15399.77 9399.88 12
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
MVS_Test99.10 8898.97 8799.48 10299.49 16799.14 11799.67 6099.34 23497.31 20899.58 9499.76 11997.65 10699.82 15298.87 8999.07 16499.46 167
CP-MVSNet98.09 18597.78 20799.01 16698.97 28599.24 10399.67 6099.46 16997.25 21398.48 28899.64 17693.79 24299.06 31198.63 12694.10 32498.74 237
MTAPA99.52 1099.39 1999.89 499.90 499.86 1399.66 6599.47 16098.79 5899.68 6099.81 7698.43 7899.97 1498.88 8699.90 2599.83 35
HFP-MVS99.49 1499.37 2299.86 2099.87 1599.80 2799.66 6599.67 2298.15 11799.68 6099.69 15299.06 1699.96 2298.69 11999.87 4099.84 26
mvs_tets98.40 15998.23 16198.91 18698.67 32298.51 19599.66 6599.53 8498.19 11198.65 27499.81 7692.75 26099.44 24499.31 4297.48 24698.77 230
EU-MVSNet97.98 20598.03 18197.81 29798.72 31696.65 28699.66 6599.66 2698.09 12698.35 29599.82 6395.25 18798.01 35297.41 24395.30 30398.78 227
ACMMPR99.49 1499.36 2499.86 2099.87 1599.79 3099.66 6599.67 2298.15 11799.67 6499.69 15298.95 2799.96 2298.69 11999.87 4099.84 26
MP-MVScopyleft99.33 4899.15 6199.87 1199.88 1199.82 2299.66 6599.46 16998.09 12699.48 11499.74 12798.29 8699.96 2297.93 19299.87 4099.82 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
region2R99.48 1899.35 2699.87 1199.88 1199.80 2799.65 7199.66 2698.13 12099.66 6999.68 15898.96 2499.96 2298.62 12799.87 4099.84 26
TranMVSNet+NR-MVSNet97.93 21197.66 22298.76 21698.78 30898.62 18199.65 7199.49 13097.76 16398.49 28799.60 19494.23 22798.97 32998.00 18892.90 33698.70 247
mvsany_test393.77 32393.45 32594.74 33795.78 36388.01 36299.64 7398.25 34898.28 9794.31 35397.97 35168.89 36698.51 34397.50 23490.37 35097.71 349
ZNCC-MVS99.47 2199.33 3099.87 1199.87 1599.81 2599.64 7399.67 2298.08 13099.55 10299.64 17698.91 3499.96 2298.72 11499.90 2599.82 40
tfpnnormal97.84 22697.47 24098.98 17299.20 24099.22 10599.64 7399.61 3696.32 28598.27 30099.70 14293.35 24999.44 24495.69 30595.40 30198.27 326
casdiffmvs_mvgpermissive99.15 7199.02 7899.55 8199.66 10899.09 12299.64 7399.56 5798.26 10099.45 11899.87 3296.03 15599.81 15799.54 1599.15 15599.73 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
iter_conf_final98.71 13498.61 13898.99 17099.49 16798.96 14399.63 7799.41 19998.19 11199.39 13999.77 11394.82 19999.38 25399.30 4597.52 23898.64 275
SR-MVS-dyc-post99.45 2599.31 3899.85 2599.76 5699.82 2299.63 7799.52 8998.38 8699.76 4399.82 6398.53 7199.95 4898.61 13099.81 7999.77 68
RE-MVS-def99.34 2899.76 5699.82 2299.63 7799.52 8998.38 8699.76 4399.82 6398.75 5498.61 13099.81 7999.77 68
TSAR-MVS + MP.99.58 499.50 899.81 3699.91 199.66 5399.63 7799.39 21098.91 4699.78 3599.85 4299.36 299.94 5798.84 9999.88 3799.82 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous2023120696.22 29796.03 29796.79 32797.31 35494.14 33999.63 7799.08 29296.17 29797.04 33499.06 31393.94 23797.76 35886.96 36595.06 30898.47 309
APD-MVS_3200maxsize99.48 1899.35 2699.85 2599.76 5699.83 1699.63 7799.54 7398.36 9099.79 3099.82 6398.86 3899.95 4898.62 12799.81 7999.78 66
test072699.85 2599.89 499.62 8399.50 12299.10 1699.86 1699.82 6398.94 29
EPNet98.86 11398.71 11799.30 13397.20 35698.18 21299.62 8398.91 31399.28 698.63 27699.81 7695.96 15799.99 299.24 5299.72 10499.73 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t98.93 10598.67 12199.72 5299.85 2599.53 7399.62 8399.59 4492.65 34899.71 5499.78 10698.06 9699.90 10198.84 9999.91 1899.74 78
HY-MVS97.30 798.85 12098.64 12699.47 10599.42 18599.08 12599.62 8399.36 22597.39 20399.28 16499.68 15896.44 14499.92 8098.37 15998.22 20899.40 176
ACMMPcopyleft99.45 2599.32 3299.82 3399.89 899.67 5199.62 8399.69 1898.12 12199.63 8099.84 5298.73 5799.96 2298.55 14599.83 7299.81 47
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
DeepC-MVS98.35 299.30 5199.19 5899.64 6499.82 3799.23 10499.62 8399.55 6598.94 4299.63 8099.95 295.82 16699.94 5799.37 3499.97 599.73 83
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set99.58 499.56 599.64 6499.78 4799.15 11699.61 8999.45 18099.01 2899.89 1099.82 6399.01 1899.92 8099.56 1499.95 899.85 22
test250696.81 28796.65 28597.29 31499.74 7192.21 35599.60 9085.06 38299.13 1299.77 3899.93 687.82 34099.85 12999.38 3299.38 13599.80 56
SED-MVS99.61 299.52 699.88 599.84 3199.90 299.60 9099.48 14299.08 2199.91 799.81 7699.20 799.96 2298.91 8399.85 5599.79 60
OPU-MVS99.64 6499.56 14499.72 4299.60 9099.70 14299.27 599.42 24998.24 16999.80 8399.79 60
GST-MVS99.40 4199.24 5399.85 2599.86 2099.79 3099.60 9099.67 2297.97 14299.63 8099.68 15898.52 7299.95 4898.38 15799.86 4899.81 47
EI-MVSNet-UG-set99.58 499.57 399.64 6499.78 4799.14 11799.60 9099.45 18099.01 2899.90 999.83 5698.98 2399.93 7099.59 1199.95 899.86 19
ACMH97.28 898.10 18497.99 18598.44 24999.41 18896.96 27599.60 9099.56 5798.09 12698.15 30499.91 1190.87 30699.70 20298.88 8697.45 24898.67 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ECVR-MVScopyleft98.04 19498.05 17998.00 28499.74 7194.37 33699.59 9694.98 37299.13 1299.66 6999.93 690.67 30899.84 13599.40 3199.38 13599.80 56
SR-MVS99.43 3299.29 4499.86 2099.75 6499.83 1699.59 9699.62 3398.21 10899.73 4899.79 10098.68 6199.96 2298.44 15499.77 9399.79 60
thres100view90097.76 23897.45 24398.69 22099.72 8297.86 23399.59 9698.74 33197.93 14599.26 17298.62 33791.75 28899.83 14693.22 33898.18 21398.37 322
thres600view797.86 22297.51 23698.92 18299.72 8297.95 22799.59 9698.74 33197.94 14499.27 16898.62 33791.75 28899.86 12393.73 33398.19 21298.96 217
LCM-MVSNet-Re97.83 22898.15 16596.87 32599.30 21692.25 35499.59 9698.26 34797.43 19896.20 34199.13 30696.27 14998.73 34098.17 17598.99 17099.64 120
baseline198.31 16497.95 19099.38 11999.50 16598.74 17199.59 9698.93 30898.41 8499.14 19599.60 19494.59 21599.79 16698.48 14993.29 33299.61 127
SteuartSystems-ACMMP99.54 899.42 1599.87 1199.82 3799.81 2599.59 9699.51 10398.62 6799.79 3099.83 5699.28 499.97 1498.48 14999.90 2599.84 26
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CPTT-MVS99.11 8598.90 9699.74 4999.80 4499.46 8299.59 9699.49 13097.03 23599.63 8099.69 15297.27 11699.96 2297.82 20299.84 6399.81 47
test111198.04 19498.11 17097.83 29499.74 7193.82 34199.58 10495.40 37199.12 1499.65 7599.93 690.73 30799.84 13599.43 3099.38 13599.82 40
PGM-MVS99.45 2599.31 3899.86 2099.87 1599.78 3699.58 10499.65 3197.84 15499.71 5499.80 8999.12 1399.97 1498.33 16399.87 4099.83 35
LPG-MVS_test98.22 17098.13 16898.49 23899.33 20897.05 26499.58 10499.55 6597.46 19299.24 17499.83 5692.58 27099.72 19098.09 17997.51 24098.68 256
PHI-MVS99.30 5199.17 6099.70 5399.56 14499.52 7699.58 10499.80 897.12 22599.62 8499.73 13398.58 6799.90 10198.61 13099.91 1899.68 103
SF-MVS99.38 4399.24 5399.79 4199.79 4599.68 4899.57 10899.54 7397.82 15999.71 5499.80 8998.95 2799.93 7098.19 17299.84 6399.74 78
DVP-MVScopyleft99.57 799.47 1299.88 599.85 2599.89 499.57 10899.37 22499.10 1699.81 2599.80 8998.94 2999.96 2298.93 8099.86 4899.81 47
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_SECOND99.91 299.84 3199.89 499.57 10899.51 10399.96 2298.93 8099.86 4899.88 12
Effi-MVS+-dtu98.78 12898.89 9898.47 24499.33 20896.91 27799.57 10899.30 25998.47 7899.41 13198.99 32096.78 13299.74 18098.73 11399.38 13598.74 237
v2v48298.06 18897.77 20998.92 18298.90 29098.82 16699.57 10899.36 22596.65 25999.19 18899.35 27094.20 22899.25 28297.72 21494.97 31098.69 251
DSMNet-mixed97.25 27897.35 26096.95 32397.84 34493.61 34799.57 10896.63 36796.13 30298.87 24198.61 33994.59 21597.70 35995.08 31798.86 17999.55 140
KD-MVS_self_test95.00 31394.34 31896.96 32297.07 35995.39 31999.56 11499.44 18895.11 32097.13 33297.32 35791.86 28697.27 36290.35 35481.23 36698.23 330
ETV-MVS99.26 5899.21 5699.40 11599.46 17799.30 9699.56 11499.52 8998.52 7599.44 12399.27 29098.41 8199.86 12399.10 6399.59 12299.04 207
SMA-MVScopyleft99.44 2999.30 4099.85 2599.73 7899.83 1699.56 11499.47 16097.45 19599.78 3599.82 6399.18 1099.91 9098.79 10799.89 3499.81 47
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
AllTest98.87 11098.72 11599.31 12899.86 2098.48 19999.56 11499.61 3697.85 15299.36 14899.85 4295.95 15899.85 12996.66 28599.83 7299.59 133
casdiffmvspermissive99.13 7598.98 8699.56 7999.65 11499.16 11199.56 11499.50 12298.33 9499.41 13199.86 3795.92 16199.83 14699.45 2999.16 15299.70 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XXY-MVS98.38 16098.09 17499.24 14399.26 22799.32 9299.56 11499.55 6597.45 19598.71 25999.83 5693.23 25099.63 22598.88 8696.32 27898.76 232
ACMH+97.24 1097.92 21497.78 20798.32 26099.46 17796.68 28599.56 11499.54 7398.41 8497.79 31999.87 3290.18 31599.66 21298.05 18797.18 26398.62 286
ACMM97.58 598.37 16198.34 15498.48 24099.41 18897.10 25899.56 11499.45 18098.53 7499.04 21499.85 4293.00 25499.71 19698.74 11197.45 24898.64 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D99.27 5699.12 6499.74 4999.18 24599.75 3999.56 11499.57 5298.45 8099.49 11399.85 4297.77 10399.94 5798.33 16399.84 6399.52 148
test_fmvs198.88 10998.79 11199.16 15199.69 9597.61 24399.55 12399.49 13099.32 499.98 499.91 1191.41 29899.96 2299.82 399.92 1399.90 4
v14419297.92 21497.60 22898.87 19798.83 30398.65 17899.55 12399.34 23496.20 29499.32 15699.40 25694.36 22399.26 28196.37 29395.03 30998.70 247
iter_conf0598.55 14798.44 14798.87 19799.34 20698.60 18499.55 12399.42 19698.21 10899.37 14499.77 11393.55 24699.38 25399.30 4597.48 24698.63 283
API-MVS99.04 9499.03 7599.06 16099.40 19399.31 9599.55 12399.56 5798.54 7399.33 15599.39 26098.76 5199.78 17196.98 26799.78 9098.07 335
APD_test195.87 30496.49 28994.00 33899.53 15084.01 36599.54 12799.32 25195.91 31097.99 31199.85 4285.49 34899.88 11691.96 34898.84 18198.12 333
thisisatest053098.35 16298.03 18199.31 12899.63 11998.56 18699.54 12796.75 36597.53 18899.73 4899.65 17091.25 30299.89 11198.62 12799.56 12499.48 159
MTMP99.54 12798.88 317
v114497.98 20597.69 21998.85 20498.87 29798.66 17799.54 12799.35 23096.27 28999.23 17899.35 27094.67 21299.23 28596.73 28095.16 30698.68 256
v14897.79 23697.55 23098.50 23798.74 31397.72 23899.54 12799.33 24196.26 29098.90 23599.51 22594.68 21199.14 29897.83 20193.15 33598.63 283
CostFormer97.72 24797.73 21697.71 30199.15 25694.02 34099.54 12799.02 29994.67 32999.04 21499.35 27092.35 28099.77 17398.50 14897.94 22199.34 182
MVSTER98.49 14898.32 15699.00 16899.35 20299.02 13299.54 12799.38 21697.41 20199.20 18599.73 13393.86 24099.36 26298.87 8997.56 23598.62 286
patch_mono-299.26 5899.62 198.16 27299.81 4194.59 33399.52 13499.64 3299.33 399.73 4899.90 1699.00 2299.99 299.69 699.98 299.89 6
Fast-Effi-MVS+-dtu98.77 13098.83 10798.60 22499.41 18896.99 27199.52 13499.49 13098.11 12399.24 17499.34 27396.96 12899.79 16697.95 19199.45 13199.02 210
Fast-Effi-MVS+98.70 13598.43 14899.51 9899.51 15699.28 9899.52 13499.47 16096.11 30399.01 21799.34 27396.20 15199.84 13597.88 19598.82 18399.39 177
v192192097.80 23597.45 24398.84 20598.80 30498.53 18999.52 13499.34 23496.15 30099.24 17499.47 23993.98 23699.29 27695.40 31295.13 30798.69 251
MIMVSNet195.51 30895.04 31296.92 32497.38 35195.60 31099.52 13499.50 12293.65 33996.97 33699.17 30185.28 35096.56 36788.36 36195.55 29898.60 298
UniMVSNet_ETH3D97.32 27696.81 28398.87 19799.40 19397.46 24699.51 13999.53 8495.86 31198.54 28499.77 11382.44 35899.66 21298.68 12197.52 23899.50 157
alignmvs98.81 12498.56 14299.58 7599.43 18399.42 8599.51 13998.96 30698.61 6899.35 15198.92 32894.78 20399.77 17399.35 3598.11 21899.54 142
v119297.81 23397.44 24898.91 18698.88 29398.68 17599.51 13999.34 23496.18 29699.20 18599.34 27394.03 23599.36 26295.32 31495.18 30598.69 251
test20.0396.12 30195.96 29996.63 32897.44 35095.45 31799.51 13999.38 21696.55 26996.16 34299.25 29393.76 24496.17 36887.35 36494.22 32298.27 326
mvs_anonymous99.03 9698.99 8399.16 15199.38 19798.52 19399.51 13999.38 21697.79 16099.38 14299.81 7697.30 11499.45 23999.35 3598.99 17099.51 154
TAMVS99.12 8199.08 6999.24 14399.46 17798.55 18799.51 13999.46 16998.09 12699.45 11899.82 6398.34 8499.51 23598.70 11698.93 17399.67 106
test_yl98.86 11398.63 12799.54 8299.49 16799.18 10899.50 14599.07 29598.22 10699.61 8799.51 22595.37 18099.84 13598.60 13398.33 20299.59 133
DCV-MVSNet98.86 11398.63 12799.54 8299.49 16799.18 10899.50 14599.07 29598.22 10699.61 8799.51 22595.37 18099.84 13598.60 13398.33 20299.59 133
tfpn200view997.72 24797.38 25698.72 21899.69 9597.96 22599.50 14598.73 33697.83 15599.17 19298.45 34291.67 29299.83 14693.22 33898.18 21398.37 322
UA-Net99.42 3499.29 4499.80 3899.62 12599.55 6899.50 14599.70 1598.79 5899.77 3899.96 197.45 10999.96 2298.92 8299.90 2599.89 6
pm-mvs197.68 25497.28 27098.88 19399.06 27198.62 18199.50 14599.45 18096.32 28597.87 31599.79 10092.47 27499.35 26597.54 23193.54 33098.67 263
EI-MVSNet98.67 14098.67 12198.68 22199.35 20297.97 22399.50 14599.38 21696.93 24499.20 18599.83 5697.87 9999.36 26298.38 15797.56 23598.71 242
CVMVSNet98.57 14698.67 12198.30 26299.35 20295.59 31199.50 14599.55 6598.60 6999.39 13999.83 5694.48 22099.45 23998.75 11098.56 19599.85 22
VPA-MVSNet98.29 16797.95 19099.30 13399.16 25399.54 7099.50 14599.58 4998.27 9999.35 15199.37 26492.53 27299.65 21799.35 3594.46 31798.72 240
thres40097.77 23797.38 25698.92 18299.69 9597.96 22599.50 14598.73 33697.83 15599.17 19298.45 34291.67 29299.83 14693.22 33898.18 21398.96 217
APD-MVScopyleft99.27 5699.08 6999.84 3199.75 6499.79 3099.50 14599.50 12297.16 22199.77 3899.82 6398.78 4799.94 5797.56 22999.86 4899.80 56
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_vis1_rt95.81 30695.65 30596.32 33299.67 10091.35 35899.49 15596.74 36698.25 10195.24 34798.10 34974.96 36399.90 10199.53 1698.85 18097.70 351
TransMVSNet (Re)97.15 28196.58 28698.86 20199.12 25898.85 16199.49 15598.91 31395.48 31597.16 33199.80 8993.38 24899.11 30694.16 33091.73 34398.62 286
UniMVSNet (Re)98.29 16798.00 18499.13 15599.00 27999.36 9099.49 15599.51 10397.95 14398.97 22599.13 30696.30 14899.38 25398.36 16193.34 33198.66 271
EPMVS97.82 23197.65 22398.35 25798.88 29395.98 30499.49 15594.71 37497.57 18299.26 17299.48 23692.46 27799.71 19697.87 19799.08 16399.35 180
Anonymous2023121197.88 21897.54 23398.90 18899.71 8798.53 18999.48 15999.57 5294.16 33498.81 24899.68 15893.23 25099.42 24998.84 9994.42 31998.76 232
v124097.69 25297.32 26698.79 21398.85 30198.43 20399.48 15999.36 22596.11 30399.27 16899.36 26793.76 24499.24 28494.46 32495.23 30498.70 247
VPNet97.84 22697.44 24899.01 16699.21 23898.94 15199.48 15999.57 5298.38 8699.28 16499.73 13388.89 32599.39 25199.19 5593.27 33398.71 242
UniMVSNet_NR-MVSNet98.22 17097.97 18798.96 17598.92 28998.98 13699.48 15999.53 8497.76 16398.71 25999.46 24396.43 14599.22 28898.57 13992.87 33898.69 251
TDRefinement95.42 31094.57 31697.97 28689.83 37596.11 30399.48 15998.75 32896.74 25296.68 33799.88 2688.65 32999.71 19698.37 15982.74 36498.09 334
ACMMP_NAP99.47 2199.34 2899.88 599.87 1599.86 1399.47 16499.48 14298.05 13699.76 4399.86 3798.82 4399.93 7098.82 10699.91 1899.84 26
NR-MVSNet97.97 20897.61 22799.02 16598.87 29799.26 10199.47 16499.42 19697.63 17797.08 33399.50 22895.07 19199.13 30197.86 19893.59 32998.68 256
PVSNet_Blended_VisFu99.36 4599.28 4699.61 7099.86 2099.07 12799.47 16499.93 297.66 17599.71 5499.86 3797.73 10499.96 2299.47 2799.82 7699.79 60
SD-MVS99.41 3899.52 699.05 16299.74 7199.68 4899.46 16799.52 8999.11 1599.88 1199.91 1199.43 197.70 35998.72 11499.93 1299.77 68
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
tt080597.97 20897.77 20998.57 22999.59 13696.61 28899.45 16899.08 29298.21 10898.88 23899.80 8988.66 32899.70 20298.58 13697.72 22699.39 177
tpm297.44 27397.34 26397.74 30099.15 25694.36 33799.45 16898.94 30793.45 34398.90 23599.44 24591.35 30099.59 22997.31 24698.07 21999.29 186
FMVSNet297.72 24797.36 25898.80 21299.51 15698.84 16299.45 16899.42 19696.49 27298.86 24599.29 28590.26 31198.98 32296.44 29096.56 27298.58 300
CDS-MVSNet99.09 8999.03 7599.25 14199.42 18598.73 17299.45 16899.46 16998.11 12399.46 11799.77 11398.01 9799.37 25898.70 11698.92 17599.66 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS98.86 11398.63 12799.54 8299.37 19999.66 5399.45 16899.54 7396.61 26499.01 21799.40 25697.09 12199.86 12397.68 21999.53 12799.10 195
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
UGNet98.87 11098.69 11999.40 11599.22 23698.72 17399.44 17399.68 1999.24 799.18 19199.42 24992.74 26299.96 2299.34 3999.94 1199.53 147
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
ab-mvs98.86 11398.63 12799.54 8299.64 11699.19 10699.44 17399.54 7397.77 16299.30 16099.81 7694.20 22899.93 7099.17 5898.82 18399.49 158
test_040296.64 29096.24 29397.85 29298.85 30196.43 29499.44 17399.26 26893.52 34096.98 33599.52 22288.52 33199.20 29592.58 34797.50 24297.93 346
ACMP97.20 1198.06 18897.94 19298.45 24699.37 19997.01 26999.44 17399.49 13097.54 18798.45 28999.79 10091.95 28499.72 19097.91 19397.49 24598.62 286
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND98.45 24698.55 33298.16 21399.43 17793.68 37697.23 32898.46 34189.30 32299.22 28895.43 31198.22 20897.98 343
HPM-MVS++copyleft99.39 4299.23 5599.87 1199.75 6499.84 1599.43 17799.51 10398.68 6599.27 16899.53 21998.64 6699.96 2298.44 15499.80 8399.79 60
tpm cat197.39 27497.36 25897.50 30999.17 25193.73 34399.43 17799.31 25591.27 35298.71 25999.08 31094.31 22699.77 17396.41 29298.50 19899.00 211
tpm97.67 25797.55 23098.03 27999.02 27795.01 32699.43 17798.54 34496.44 27999.12 19899.34 27391.83 28799.60 22897.75 21096.46 27499.48 159
GBi-Net97.68 25497.48 23898.29 26399.51 15697.26 25399.43 17799.48 14296.49 27299.07 20899.32 28090.26 31198.98 32297.10 26096.65 26998.62 286
test197.68 25497.48 23898.29 26399.51 15697.26 25399.43 17799.48 14296.49 27299.07 20899.32 28090.26 31198.98 32297.10 26096.65 26998.62 286
FMVSNet196.84 28696.36 29198.29 26399.32 21497.26 25399.43 17799.48 14295.11 32098.55 28399.32 28083.95 35498.98 32295.81 30196.26 27998.62 286
testgi97.65 25997.50 23798.13 27699.36 20196.45 29399.42 18499.48 14297.76 16397.87 31599.45 24491.09 30398.81 33694.53 32398.52 19799.13 194
F-COLMAP99.19 6499.04 7399.64 6499.78 4799.27 10099.42 18499.54 7397.29 21099.41 13199.59 19698.42 8099.93 7098.19 17299.69 10999.73 83
Anonymous20240521198.30 16697.98 18699.26 14099.57 14098.16 21399.41 18698.55 34396.03 30899.19 18899.74 12791.87 28599.92 8099.16 5998.29 20799.70 97
MSLP-MVS++99.46 2399.47 1299.44 11299.60 13499.16 11199.41 18699.71 1398.98 3699.45 11899.78 10699.19 999.54 23499.28 4799.84 6399.63 123
VNet99.11 8598.90 9699.73 5199.52 15499.56 6699.41 18699.39 21099.01 2899.74 4799.78 10695.56 17499.92 8099.52 1898.18 21399.72 89
baseline297.87 22097.55 23098.82 20899.18 24598.02 22099.41 18696.58 36896.97 23896.51 33899.17 30193.43 24799.57 23097.71 21599.03 16798.86 221
DU-MVS98.08 18797.79 20498.96 17598.87 29798.98 13699.41 18699.45 18097.87 14998.71 25999.50 22894.82 19999.22 28898.57 13992.87 33898.68 256
Baseline_NR-MVSNet97.76 23897.45 24398.68 22199.09 26598.29 20899.41 18698.85 32095.65 31398.63 27699.67 16494.82 19999.10 30898.07 18692.89 33798.64 275
XVG-ACMP-BASELINE97.83 22897.71 21898.20 26999.11 26096.33 29799.41 18699.52 8998.06 13599.05 21399.50 22889.64 32099.73 18697.73 21297.38 25698.53 303
DP-MVS99.16 7098.95 9199.78 4399.77 5399.53 7399.41 18699.50 12297.03 23599.04 21499.88 2697.39 11099.92 8098.66 12399.90 2599.87 17
9.1499.10 6699.72 8299.40 19499.51 10397.53 18899.64 7999.78 10698.84 4199.91 9097.63 22099.82 76
D2MVS98.41 15698.50 14598.15 27599.26 22796.62 28799.40 19499.61 3697.71 16998.98 22399.36 26796.04 15499.67 20998.70 11697.41 25398.15 332
Anonymous2024052998.09 18597.68 22099.34 12199.66 10898.44 20299.40 19499.43 19493.67 33899.22 17999.89 2090.23 31499.93 7099.26 5198.33 20299.66 109
FMVSNet398.03 19697.76 21398.84 20599.39 19698.98 13699.40 19499.38 21696.67 25799.07 20899.28 28792.93 25598.98 32297.10 26096.65 26998.56 302
LFMVS97.90 21797.35 26099.54 8299.52 15499.01 13499.39 19898.24 34997.10 22999.65 7599.79 10084.79 35199.91 9099.28 4798.38 20199.69 99
HQP_MVS98.27 16998.22 16298.44 24999.29 22096.97 27399.39 19899.47 16098.97 3999.11 20099.61 19192.71 26599.69 20797.78 20597.63 22898.67 263
plane_prior299.39 19898.97 39
CHOSEN 1792x268899.19 6499.10 6699.45 10899.89 898.52 19399.39 19899.94 198.73 6199.11 20099.89 2095.50 17699.94 5799.50 2099.97 599.89 6
PAPM_NR99.04 9498.84 10599.66 5599.74 7199.44 8499.39 19899.38 21697.70 17099.28 16499.28 28798.34 8499.85 12996.96 26999.45 13199.69 99
gg-mvs-nofinetune96.17 30095.32 31098.73 21798.79 30598.14 21599.38 20394.09 37591.07 35598.07 30991.04 37189.62 32199.35 26596.75 27999.09 16298.68 256
VDDNet97.55 26397.02 28099.16 15199.49 16798.12 21799.38 20399.30 25995.35 31699.68 6099.90 1682.62 35799.93 7099.31 4298.13 21799.42 172
pmmvs696.53 29296.09 29697.82 29698.69 32095.47 31699.37 20599.47 16093.46 34297.41 32499.78 10687.06 34399.33 26896.92 27492.70 34098.65 273
PM-MVS92.96 32592.23 32895.14 33695.61 36489.98 36199.37 20598.21 35094.80 32795.04 35197.69 35265.06 36797.90 35594.30 32589.98 35397.54 355
WTY-MVS99.06 9298.88 9999.61 7099.62 12599.16 11199.37 20599.56 5798.04 13799.53 10599.62 18796.84 13099.94 5798.85 9698.49 19999.72 89
IterMVS-LS98.46 15198.42 14998.58 22899.59 13698.00 22199.37 20599.43 19496.94 24399.07 20899.59 19697.87 9999.03 31598.32 16595.62 29698.71 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs3397.70 25197.28 27098.97 17499.70 9297.27 25199.36 20999.45 18098.94 4299.66 6999.64 17694.93 19399.99 299.48 2584.36 36199.65 113
DPE-MVScopyleft99.46 2399.32 3299.91 299.78 4799.88 899.36 20999.51 10398.73 6199.88 1199.84 5298.72 5899.96 2298.16 17699.87 4099.88 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UnsupCasMVSNet_eth96.44 29496.12 29597.40 31198.65 32395.65 30999.36 20999.51 10397.13 22396.04 34498.99 32088.40 33298.17 34896.71 28190.27 35198.40 319
sss99.17 6899.05 7199.53 9099.62 12598.97 13999.36 20999.62 3397.83 15599.67 6499.65 17097.37 11399.95 4899.19 5599.19 15199.68 103
DeepC-MVS_fast98.69 199.49 1499.39 1999.77 4599.63 11999.59 6299.36 20999.46 16999.07 2399.79 3099.82 6398.85 3999.92 8098.68 12199.87 4099.82 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet99.25 6199.14 6299.59 7299.41 18899.16 11199.35 21499.57 5298.82 5399.51 10999.61 19196.46 14299.95 4899.59 1199.98 299.65 113
pmmvs-eth3d95.34 31294.73 31497.15 31595.53 36695.94 30599.35 21499.10 28995.13 31893.55 35697.54 35388.15 33697.91 35494.58 32289.69 35497.61 352
MDTV_nov1_ep13_2view95.18 32499.35 21496.84 24899.58 9495.19 18997.82 20299.46 167
VDD-MVS97.73 24597.35 26098.88 19399.47 17697.12 25799.34 21798.85 32098.19 11199.67 6499.85 4282.98 35599.92 8099.49 2498.32 20699.60 129
COLMAP_ROBcopyleft97.56 698.86 11398.75 11499.17 15099.88 1198.53 18999.34 21799.59 4497.55 18498.70 26599.89 2095.83 16599.90 10198.10 17899.90 2599.08 200
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EGC-MVSNET82.80 33677.86 34297.62 30397.91 34296.12 30299.33 21999.28 2658.40 37925.05 38099.27 29084.11 35399.33 26889.20 35798.22 20897.42 356
FMVSNet596.43 29596.19 29497.15 31599.11 26095.89 30699.32 22099.52 8994.47 33398.34 29699.07 31187.54 34197.07 36392.61 34695.72 29498.47 309
dp97.75 24297.80 20397.59 30599.10 26393.71 34499.32 22098.88 31796.48 27699.08 20799.55 21092.67 26899.82 15296.52 28898.58 19299.24 189
tpmvs97.98 20598.02 18397.84 29399.04 27594.73 33199.31 22299.20 27896.10 30798.76 25599.42 24994.94 19299.81 15796.97 26898.45 20098.97 215
tpmrst98.33 16398.48 14697.90 29099.16 25394.78 33099.31 22299.11 28897.27 21199.45 11899.59 19695.33 18299.84 13598.48 14998.61 18999.09 199
MP-MVS-pluss99.37 4499.20 5799.88 599.90 499.87 1299.30 22499.52 8997.18 21999.60 9099.79 10098.79 4699.95 4898.83 10299.91 1899.83 35
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC99.34 4799.19 5899.79 4199.61 12999.65 5699.30 22499.48 14298.86 4899.21 18299.63 18298.72 5899.90 10198.25 16899.63 11999.80 56
JIA-IIPM97.50 26897.02 28098.93 18098.73 31497.80 23599.30 22498.97 30491.73 35198.91 23394.86 36595.10 19099.71 19697.58 22497.98 22099.28 187
BH-RMVSNet98.41 15698.08 17599.40 11599.41 18898.83 16599.30 22498.77 32797.70 17098.94 22999.65 17092.91 25899.74 18096.52 28899.55 12699.64 120
MCST-MVS99.43 3299.30 4099.82 3399.79 4599.74 4199.29 22899.40 20798.79 5899.52 10799.62 18798.91 3499.90 10198.64 12599.75 9899.82 40
LF4IMVS97.52 26597.46 24297.70 30298.98 28395.55 31299.29 22898.82 32398.07 13198.66 26899.64 17689.97 31699.61 22797.01 26496.68 26897.94 345
hse-mvs297.50 26897.14 27698.59 22599.49 16797.05 26499.28 23099.22 27498.94 4299.66 6999.42 24994.93 19399.65 21799.48 2583.80 36399.08 200
OPM-MVS98.19 17498.10 17198.45 24698.88 29397.07 26299.28 23099.38 21698.57 7099.22 17999.81 7692.12 28199.66 21298.08 18397.54 23798.61 295
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvspermissive99.14 7399.02 7899.51 9899.61 12998.96 14399.28 23099.49 13098.46 7999.72 5399.71 13896.50 14199.88 11699.31 4299.11 15899.67 106
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS98.86 11398.80 10899.03 16499.76 5698.79 16999.28 23099.91 397.42 20099.67 6499.37 26497.53 10799.88 11698.98 7497.29 25898.42 316
OMC-MVS99.08 9099.04 7399.20 14799.67 10098.22 21199.28 23099.52 8998.07 13199.66 6999.81 7697.79 10299.78 17197.79 20499.81 7999.60 129
AUN-MVS96.88 28596.31 29298.59 22599.48 17597.04 26799.27 23599.22 27497.44 19798.51 28599.41 25391.97 28399.66 21297.71 21583.83 36299.07 205
pmmvs597.52 26597.30 26898.16 27298.57 33196.73 28299.27 23598.90 31596.14 30198.37 29499.53 21991.54 29799.14 29897.51 23395.87 28998.63 283
131498.68 13998.54 14399.11 15698.89 29298.65 17899.27 23599.49 13096.89 24597.99 31199.56 20797.72 10599.83 14697.74 21199.27 14698.84 223
MVS97.28 27796.55 28799.48 10298.78 30898.95 14899.27 23599.39 21083.53 36598.08 30699.54 21596.97 12799.87 12094.23 32899.16 15299.63 123
BH-untuned98.42 15498.36 15298.59 22599.49 16796.70 28399.27 23599.13 28797.24 21598.80 25099.38 26195.75 16899.74 18097.07 26399.16 15299.33 183
MDTV_nov1_ep1398.32 15699.11 26094.44 33599.27 23598.74 33197.51 19099.40 13699.62 18794.78 20399.76 17797.59 22398.81 185
DP-MVS Recon99.12 8198.95 9199.65 5999.74 7199.70 4699.27 23599.57 5296.40 28399.42 12799.68 15898.75 5499.80 16397.98 18999.72 10499.44 170
PatchmatchNetpermissive98.31 16498.36 15298.19 27099.16 25395.32 32099.27 23598.92 31097.37 20499.37 14499.58 20094.90 19699.70 20297.43 24299.21 14999.54 142
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres20097.61 26197.28 27098.62 22399.64 11698.03 21999.26 24398.74 33197.68 17299.09 20698.32 34691.66 29499.81 15792.88 34298.22 20898.03 338
CNVR-MVS99.42 3499.30 4099.78 4399.62 12599.71 4499.26 24399.52 8998.82 5399.39 13999.71 13898.96 2499.85 12998.59 13599.80 8399.77 68
1112_ss98.98 10198.77 11299.59 7299.68 9999.02 13299.25 24599.48 14297.23 21699.13 19699.58 20096.93 12999.90 10198.87 8998.78 18699.84 26
TAPA-MVS97.07 1597.74 24497.34 26398.94 17899.70 9297.53 24499.25 24599.51 10391.90 35099.30 16099.63 18298.78 4799.64 22088.09 36299.87 4099.65 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLCcopyleft97.94 499.02 9798.85 10499.53 9099.66 10899.01 13499.24 24799.52 8996.85 24799.27 16899.48 23698.25 8899.91 9097.76 20899.62 12099.65 113
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post199.23 24865.14 37794.18 23199.71 19697.58 224
ADS-MVSNet298.02 19898.07 17897.87 29199.33 20895.19 32399.23 24899.08 29296.24 29199.10 20399.67 16494.11 23298.93 33296.81 27799.05 16599.48 159
ADS-MVSNet98.20 17398.08 17598.56 23299.33 20896.48 29299.23 24899.15 28496.24 29199.10 20399.67 16494.11 23299.71 19696.81 27799.05 16599.48 159
EPNet_dtu98.03 19697.96 18898.23 26898.27 33895.54 31499.23 24898.75 32899.02 2697.82 31799.71 13896.11 15299.48 23693.04 34199.65 11699.69 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet98.17 17797.93 19398.87 19799.18 24598.49 19799.22 25299.33 24196.96 23999.56 9899.38 26194.33 22499.00 32094.83 32198.58 19299.14 192
RPMNet96.72 28995.90 30099.19 14899.18 24598.49 19799.22 25299.52 8988.72 36199.56 9897.38 35594.08 23499.95 4886.87 36698.58 19299.14 192
plane_prior96.97 27399.21 25498.45 8097.60 231
WR-MVS98.06 18897.73 21699.06 16098.86 30099.25 10299.19 25599.35 23097.30 20998.66 26899.43 24793.94 23799.21 29398.58 13694.28 32198.71 242
new-patchmatchnet94.48 31994.08 31995.67 33595.08 36892.41 35399.18 25699.28 26594.55 33293.49 35797.37 35687.86 33997.01 36491.57 34988.36 35597.61 352
AdaColmapbinary99.01 10098.80 10899.66 5599.56 14499.54 7099.18 25699.70 1598.18 11599.35 15199.63 18296.32 14799.90 10197.48 23699.77 9399.55 140
EG-PatchMatch MVS95.97 30395.69 30496.81 32697.78 34592.79 35299.16 25898.93 30896.16 29894.08 35499.22 29682.72 35699.47 23795.67 30797.50 24298.17 331
PatchT97.03 28496.44 29098.79 21398.99 28098.34 20799.16 25899.07 29592.13 34999.52 10797.31 35894.54 21998.98 32288.54 36098.73 18899.03 208
CNLPA99.14 7398.99 8399.59 7299.58 13899.41 8799.16 25899.44 18898.45 8099.19 18899.49 23198.08 9599.89 11197.73 21299.75 9899.48 159
MDA-MVSNet-bldmvs94.96 31493.98 32097.92 28898.24 33997.27 25199.15 26199.33 24193.80 33780.09 37299.03 31688.31 33397.86 35693.49 33694.36 32098.62 286
CDPH-MVS99.13 7598.91 9599.80 3899.75 6499.71 4499.15 26199.41 19996.60 26699.60 9099.55 21098.83 4299.90 10197.48 23699.83 7299.78 66
save fliter99.76 5699.59 6299.14 26399.40 20799.00 31
testf190.42 33090.68 33289.65 35097.78 34573.97 37799.13 26498.81 32489.62 35791.80 36198.93 32662.23 37098.80 33786.61 36791.17 34596.19 362
APD_test290.42 33090.68 33289.65 35097.78 34573.97 37799.13 26498.81 32489.62 35791.80 36198.93 32662.23 37098.80 33786.61 36791.17 34596.19 362
xiu_mvs_v1_base_debu99.29 5399.27 4899.34 12199.63 11998.97 13999.12 26699.51 10398.86 4899.84 1899.47 23998.18 9199.99 299.50 2099.31 14399.08 200
xiu_mvs_v1_base99.29 5399.27 4899.34 12199.63 11998.97 13999.12 26699.51 10398.86 4899.84 1899.47 23998.18 9199.99 299.50 2099.31 14399.08 200
xiu_mvs_v1_base_debi99.29 5399.27 4899.34 12199.63 11998.97 13999.12 26699.51 10398.86 4899.84 1899.47 23998.18 9199.99 299.50 2099.31 14399.08 200
XVG-OURS-SEG-HR98.69 13798.62 13298.89 19199.71 8797.74 23699.12 26699.54 7398.44 8399.42 12799.71 13894.20 22899.92 8098.54 14698.90 17799.00 211
jason99.13 7599.03 7599.45 10899.46 17798.87 15899.12 26699.26 26898.03 13999.79 3099.65 17097.02 12499.85 12999.02 7199.90 2599.65 113
jason: jason.
N_pmnet94.95 31595.83 30292.31 34498.47 33579.33 37299.12 26692.81 37993.87 33697.68 32099.13 30693.87 23999.01 31991.38 35096.19 28098.59 299
MDA-MVSNet_test_wron95.45 30994.60 31598.01 28298.16 34097.21 25699.11 27299.24 27293.49 34180.73 37198.98 32393.02 25398.18 34794.22 32994.45 31898.64 275
Patchmtry97.75 24297.40 25598.81 21099.10 26398.87 15899.11 27299.33 24194.83 32698.81 24899.38 26194.33 22499.02 31796.10 29595.57 29798.53 303
YYNet195.36 31194.51 31797.92 28897.89 34397.10 25899.10 27499.23 27393.26 34480.77 37099.04 31592.81 25998.02 35194.30 32594.18 32398.64 275
CANet_DTU98.97 10398.87 10099.25 14199.33 20898.42 20599.08 27599.30 25999.16 999.43 12499.75 12295.27 18499.97 1498.56 14299.95 899.36 179
SCA98.19 17498.16 16498.27 26799.30 21695.55 31299.07 27698.97 30497.57 18299.43 12499.57 20492.72 26399.74 18097.58 22499.20 15099.52 148
TSAR-MVS + GP.99.36 4599.36 2499.36 12099.67 10098.61 18399.07 27699.33 24199.00 3199.82 2499.81 7699.06 1699.84 13599.09 6499.42 13399.65 113
MG-MVS99.13 7599.02 7899.45 10899.57 14098.63 18099.07 27699.34 23498.99 3399.61 8799.82 6397.98 9899.87 12097.00 26599.80 8399.85 22
PatchMatch-RL98.84 12398.62 13299.52 9699.71 8799.28 9899.06 27999.77 997.74 16799.50 11099.53 21995.41 17899.84 13597.17 25899.64 11799.44 170
OpenMVS_ROBcopyleft92.34 2094.38 32093.70 32496.41 33197.38 35193.17 35099.06 27998.75 32886.58 36294.84 35298.26 34781.53 35999.32 27189.01 35897.87 22396.76 359
TEST999.67 10099.65 5699.05 28199.41 19996.22 29398.95 22799.49 23198.77 5099.91 90
train_agg99.02 9798.77 11299.77 4599.67 10099.65 5699.05 28199.41 19996.28 28798.95 22799.49 23198.76 5199.91 9097.63 22099.72 10499.75 74
lupinMVS99.13 7599.01 8299.46 10799.51 15698.94 15199.05 28199.16 28397.86 15099.80 2899.56 20797.39 11099.86 12398.94 7899.85 5599.58 137
DELS-MVS99.48 1899.42 1599.65 5999.72 8299.40 8899.05 28199.66 2699.14 1199.57 9799.80 8998.46 7699.94 5799.57 1399.84 6399.60 129
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
new_pmnet96.38 29696.03 29797.41 31098.13 34195.16 32599.05 28199.20 27893.94 33597.39 32598.79 33291.61 29699.04 31390.43 35395.77 29198.05 337
MVS_030496.79 28896.52 28897.59 30599.22 23694.92 32999.04 28699.59 4496.49 27298.43 29098.99 32080.48 36199.39 25197.15 25999.27 14698.47 309
Patchmatch-test97.93 21197.65 22398.77 21599.18 24597.07 26299.03 28799.14 28696.16 29898.74 25699.57 20494.56 21799.72 19093.36 33799.11 15899.52 148
test_899.67 10099.61 6099.03 28799.41 19996.28 28798.93 23199.48 23698.76 5199.91 90
Test_1112_low_res98.89 10898.66 12499.57 7799.69 9598.95 14899.03 28799.47 16096.98 23799.15 19499.23 29596.77 13399.89 11198.83 10298.78 18699.86 19
IterMVS-SCA-FT97.82 23197.75 21498.06 27899.57 14096.36 29699.02 29099.49 13097.18 21998.71 25999.72 13792.72 26399.14 29897.44 24195.86 29098.67 263
xiu_mvs_v2_base99.26 5899.25 5299.29 13699.53 15098.91 15599.02 29099.45 18098.80 5799.71 5499.26 29298.94 2999.98 899.34 3999.23 14898.98 214
MIMVSNet97.73 24597.45 24398.57 22999.45 18297.50 24599.02 29098.98 30396.11 30399.41 13199.14 30590.28 31098.74 33995.74 30398.93 17399.47 165
IterMVS97.83 22897.77 20998.02 28199.58 13896.27 29999.02 29099.48 14297.22 21798.71 25999.70 14292.75 26099.13 30197.46 23996.00 28498.67 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test99.11 8598.92 9399.65 5999.90 499.37 8999.02 29099.91 397.67 17499.59 9399.75 12295.90 16399.73 18699.53 1699.02 16999.86 19
新几何299.01 295
BH-w/o98.00 20397.89 19998.32 26099.35 20296.20 30199.01 29598.90 31596.42 28198.38 29399.00 31995.26 18699.72 19096.06 29698.61 18999.03 208
test_prior499.56 6698.99 297
无先验98.99 29799.51 10396.89 24599.93 7097.53 23299.72 89
pmmvs498.13 18197.90 19598.81 21098.61 32898.87 15898.99 29799.21 27796.44 27999.06 21299.58 20095.90 16399.11 30697.18 25796.11 28298.46 313
HQP-NCC99.19 24298.98 30098.24 10298.66 268
ACMP_Plane99.19 24298.98 30098.24 10298.66 268
HQP-MVS98.02 19897.90 19598.37 25699.19 24296.83 27898.98 30099.39 21098.24 10298.66 26899.40 25692.47 27499.64 22097.19 25597.58 23398.64 275
PS-MVSNAJ99.32 4999.32 3299.30 13399.57 14098.94 15198.97 30399.46 16998.92 4599.71 5499.24 29499.01 1899.98 899.35 3599.66 11498.97 215
MVP-Stereo97.81 23397.75 21497.99 28597.53 34996.60 28998.96 30498.85 32097.22 21797.23 32899.36 26795.28 18399.46 23895.51 30999.78 9097.92 347
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior298.96 30498.34 9299.01 21799.52 22298.68 6197.96 19099.74 101
旧先验298.96 30496.70 25599.47 11599.94 5798.19 172
原ACMM298.95 307
MVS_111021_HR99.41 3899.32 3299.66 5599.72 8299.47 8198.95 30799.85 698.82 5399.54 10399.73 13398.51 7399.74 18098.91 8399.88 3799.77 68
mvsany_test199.50 1299.46 1499.62 6999.61 12999.09 12298.94 30999.48 14299.10 1699.96 699.91 1198.85 3999.96 2299.72 599.58 12399.82 40
MVS_111021_LR99.41 3899.33 3099.65 5999.77 5399.51 7798.94 30999.85 698.82 5399.65 7599.74 12798.51 7399.80 16398.83 10299.89 3499.64 120
pmmvs394.09 32293.25 32696.60 32994.76 36994.49 33498.92 31198.18 35289.66 35696.48 33998.06 35086.28 34497.33 36189.68 35687.20 35897.97 344
XVG-OURS98.73 13398.68 12098.88 19399.70 9297.73 23798.92 31199.55 6598.52 7599.45 11899.84 5295.27 18499.91 9098.08 18398.84 18199.00 211
test22299.75 6499.49 7898.91 31399.49 13096.42 28199.34 15499.65 17098.28 8799.69 10999.72 89
PMMVS286.87 33385.37 33791.35 34790.21 37483.80 36698.89 31497.45 36183.13 36691.67 36395.03 36348.49 37694.70 37185.86 36977.62 36895.54 364
miper_lstm_enhance98.00 20397.91 19498.28 26699.34 20697.43 24798.88 31599.36 22596.48 27698.80 25099.55 21095.98 15698.91 33397.27 24895.50 30098.51 305
MVS-HIRNet95.75 30795.16 31197.51 30899.30 21693.69 34598.88 31595.78 36985.09 36498.78 25392.65 36791.29 30199.37 25894.85 32099.85 5599.46 167
TR-MVS97.76 23897.41 25498.82 20899.06 27197.87 23198.87 31798.56 34296.63 26398.68 26799.22 29692.49 27399.65 21795.40 31297.79 22498.95 219
testdata198.85 31898.32 95
ET-MVSNet_ETH3D96.49 29395.64 30699.05 16299.53 15098.82 16698.84 31997.51 36097.63 17784.77 36599.21 29992.09 28298.91 33398.98 7492.21 34299.41 174
our_test_397.65 25997.68 22097.55 30798.62 32694.97 32798.84 31999.30 25996.83 25098.19 30299.34 27397.01 12599.02 31795.00 31996.01 28398.64 275
MS-PatchMatch97.24 28097.32 26696.99 32098.45 33693.51 34898.82 32199.32 25197.41 20198.13 30599.30 28388.99 32499.56 23195.68 30699.80 8397.90 348
c3_l98.12 18398.04 18098.38 25599.30 21697.69 24298.81 32299.33 24196.67 25798.83 24699.34 27397.11 12098.99 32197.58 22495.34 30298.48 307
ppachtmachnet_test97.49 27197.45 24397.61 30498.62 32695.24 32198.80 32399.46 16996.11 30398.22 30199.62 18796.45 14398.97 32993.77 33295.97 28898.61 295
PAPR98.63 14498.34 15499.51 9899.40 19399.03 13198.80 32399.36 22596.33 28499.00 22199.12 30998.46 7699.84 13595.23 31599.37 14299.66 109
test0.0.03 197.71 25097.42 25398.56 23298.41 33797.82 23498.78 32598.63 34097.34 20598.05 31098.98 32394.45 22198.98 32295.04 31897.15 26498.89 220
PVSNet_Blended99.08 9098.97 8799.42 11399.76 5698.79 16998.78 32599.91 396.74 25299.67 6499.49 23197.53 10799.88 11698.98 7499.85 5599.60 129
PMMVS98.80 12798.62 13299.34 12199.27 22598.70 17498.76 32799.31 25597.34 20599.21 18299.07 31197.20 11799.82 15298.56 14298.87 17899.52 148
test12339.01 34542.50 34728.53 36039.17 38320.91 38498.75 32819.17 38519.83 37838.57 37766.67 37533.16 38015.42 37937.50 37829.66 37749.26 374
MSDG98.98 10198.80 10899.53 9099.76 5699.19 10698.75 32899.55 6597.25 21399.47 11599.77 11397.82 10199.87 12096.93 27299.90 2599.54 142
CLD-MVS98.16 17898.10 17198.33 25899.29 22096.82 28098.75 32899.44 18897.83 15599.13 19699.55 21092.92 25699.67 20998.32 16597.69 22798.48 307
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_ehance_all_eth98.18 17698.10 17198.41 25199.23 23397.72 23898.72 33199.31 25596.60 26698.88 23899.29 28597.29 11599.13 30197.60 22295.99 28598.38 321
cl____98.01 20197.84 20298.55 23499.25 23197.97 22398.71 33299.34 23496.47 27898.59 28299.54 21595.65 17399.21 29397.21 25195.77 29198.46 313
DIV-MVS_self_test98.01 20197.85 20198.48 24099.24 23297.95 22798.71 33299.35 23096.50 27198.60 28199.54 21595.72 17099.03 31597.21 25195.77 29198.46 313
test-LLR98.06 18897.90 19598.55 23498.79 30597.10 25898.67 33497.75 35697.34 20598.61 27998.85 32994.45 22199.45 23997.25 24999.38 13599.10 195
TESTMET0.1,197.55 26397.27 27398.40 25398.93 28896.53 29098.67 33497.61 35996.96 23998.64 27599.28 28788.63 33099.45 23997.30 24799.38 13599.21 191
test-mter97.49 27197.13 27798.55 23498.79 30597.10 25898.67 33497.75 35696.65 25998.61 27998.85 32988.23 33499.45 23997.25 24999.38 13599.10 195
IB-MVS95.67 1896.22 29795.44 30998.57 22999.21 23896.70 28398.65 33797.74 35896.71 25497.27 32798.54 34086.03 34599.92 8098.47 15286.30 35999.10 195
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
DPM-MVS98.95 10498.71 11799.66 5599.63 11999.55 6898.64 33899.10 28997.93 14599.42 12799.55 21098.67 6399.80 16395.80 30299.68 11299.61 127
thisisatest051598.14 18097.79 20499.19 14899.50 16598.50 19698.61 33996.82 36496.95 24199.54 10399.43 24791.66 29499.86 12398.08 18399.51 12899.22 190
DeepPCF-MVS98.18 398.81 12499.37 2297.12 31899.60 13491.75 35698.61 33999.44 18899.35 299.83 2399.85 4298.70 6099.81 15799.02 7199.91 1899.81 47
cl2297.85 22397.64 22598.48 24099.09 26597.87 23198.60 34199.33 24197.11 22898.87 24199.22 29692.38 27999.17 29798.21 17095.99 28598.42 316
GA-MVS97.85 22397.47 24099.00 16899.38 19797.99 22298.57 34299.15 28497.04 23498.90 23599.30 28389.83 31799.38 25396.70 28298.33 20299.62 125
TinyColmap97.12 28296.89 28297.83 29499.07 26895.52 31598.57 34298.74 33197.58 18197.81 31899.79 10088.16 33599.56 23195.10 31697.21 26198.39 320
eth_miper_zixun_eth98.05 19397.96 18898.33 25899.26 22797.38 24898.56 34499.31 25596.65 25998.88 23899.52 22296.58 13899.12 30597.39 24495.53 29998.47 309
CMPMVSbinary69.68 2394.13 32194.90 31391.84 34597.24 35580.01 37198.52 34599.48 14289.01 35991.99 36099.67 16485.67 34799.13 30195.44 31097.03 26596.39 361
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC97.34 27597.20 27497.75 29999.07 26895.20 32298.51 34699.04 29897.99 14198.31 29799.86 3789.02 32399.55 23395.67 30797.36 25798.49 306
ambc93.06 34392.68 37182.36 36798.47 34798.73 33695.09 35097.41 35455.55 37299.10 30896.42 29191.32 34497.71 349
miper_enhance_ethall98.16 17898.08 17598.41 25198.96 28697.72 23898.45 34899.32 25196.95 24198.97 22599.17 30197.06 12399.22 28897.86 19895.99 28598.29 325
CHOSEN 280x42099.12 8199.13 6399.08 15799.66 10897.89 23098.43 34999.71 1398.88 4799.62 8499.76 11996.63 13799.70 20299.46 2899.99 199.66 109
testmvs39.17 34443.78 34625.37 36136.04 38416.84 38598.36 35026.56 38320.06 37738.51 37867.32 37429.64 38115.30 38037.59 37739.90 37643.98 375
FPMVS84.93 33585.65 33682.75 35686.77 37763.39 38198.35 35198.92 31074.11 36883.39 36798.98 32350.85 37592.40 37384.54 37094.97 31092.46 366
KD-MVS_2432*160094.62 31693.72 32297.31 31297.19 35795.82 30798.34 35299.20 27895.00 32397.57 32198.35 34487.95 33798.10 34992.87 34377.00 36998.01 339
miper_refine_blended94.62 31693.72 32297.31 31297.19 35795.82 30798.34 35299.20 27895.00 32397.57 32198.35 34487.95 33798.10 34992.87 34377.00 36998.01 339
CL-MVSNet_self_test94.49 31893.97 32196.08 33396.16 36193.67 34698.33 35499.38 21695.13 31897.33 32698.15 34892.69 26796.57 36688.67 35979.87 36797.99 342
PVSNet96.02 1798.85 12098.84 10598.89 19199.73 7897.28 25098.32 35599.60 4197.86 15099.50 11099.57 20496.75 13499.86 12398.56 14299.70 10899.54 142
PAPM97.59 26297.09 27899.07 15999.06 27198.26 21098.30 35699.10 28994.88 32598.08 30699.34 27396.27 14999.64 22089.87 35598.92 17599.31 185
Patchmatch-RL test95.84 30595.81 30395.95 33495.61 36490.57 35998.24 35798.39 34695.10 32295.20 34898.67 33694.78 20397.77 35796.28 29490.02 35299.51 154
UnsupCasMVSNet_bld93.53 32492.51 32796.58 33097.38 35193.82 34198.24 35799.48 14291.10 35493.10 35896.66 36074.89 36498.37 34494.03 33187.71 35797.56 354
LCM-MVSNet86.80 33485.22 33891.53 34687.81 37680.96 37098.23 35998.99 30271.05 36990.13 36496.51 36148.45 37796.88 36590.51 35285.30 36096.76 359
cascas97.69 25297.43 25298.48 24098.60 32997.30 24998.18 36099.39 21092.96 34698.41 29198.78 33393.77 24399.27 28098.16 17698.61 18998.86 221
Effi-MVS+98.81 12498.59 13999.48 10299.46 17799.12 12098.08 36199.50 12297.50 19199.38 14299.41 25396.37 14699.81 15799.11 6298.54 19699.51 154
PCF-MVS97.08 1497.66 25897.06 27999.47 10599.61 12999.09 12298.04 36299.25 27091.24 35398.51 28599.70 14294.55 21899.91 9092.76 34599.85 5599.42 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_094.43 1996.09 30295.47 30797.94 28799.31 21594.34 33897.81 36399.70 1597.12 22597.46 32398.75 33489.71 31899.79 16697.69 21881.69 36599.68 103
E-PMN80.61 33879.88 34082.81 35590.75 37376.38 37597.69 36495.76 37066.44 37383.52 36692.25 36862.54 36987.16 37568.53 37461.40 37284.89 373
ANet_high77.30 34074.86 34484.62 35475.88 38077.61 37397.63 36593.15 37888.81 36064.27 37589.29 37236.51 37983.93 37775.89 37252.31 37492.33 368
EMVS80.02 33979.22 34182.43 35791.19 37276.40 37497.55 36692.49 38066.36 37483.01 36891.27 37064.63 36885.79 37665.82 37560.65 37385.08 372
MVEpermissive76.82 2176.91 34174.31 34584.70 35385.38 37976.05 37696.88 36793.17 37767.39 37271.28 37489.01 37321.66 38487.69 37471.74 37372.29 37190.35 370
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method91.10 32891.36 33090.31 34995.85 36273.72 37994.89 36899.25 27068.39 37195.82 34599.02 31880.50 36098.95 33193.64 33494.89 31398.25 328
Gipumacopyleft90.99 32990.15 33493.51 34098.73 31490.12 36093.98 36999.45 18079.32 36792.28 35994.91 36469.61 36597.98 35387.42 36395.67 29592.45 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVScopyleft70.75 2275.98 34274.97 34379.01 35870.98 38155.18 38293.37 37098.21 35065.08 37561.78 37693.83 36621.74 38392.53 37278.59 37191.12 34789.34 371
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt82.80 33681.52 33986.66 35266.61 38268.44 38092.79 37197.92 35468.96 37080.04 37399.85 4285.77 34696.15 36997.86 19843.89 37595.39 365
wuyk23d40.18 34341.29 34836.84 35986.18 37849.12 38379.73 37222.81 38427.64 37625.46 37928.45 37921.98 38248.89 37855.80 37623.56 37812.51 376
test_blank0.13 3490.17 3520.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3811.57 3800.00 3850.00 3810.00 3790.00 3790.00 377
uanet_test0.02 3500.03 3530.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 3810.00 3850.00 3810.00 3790.00 3790.00 377
DCPMVS0.02 3500.03 3530.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 3810.00 3850.00 3810.00 3790.00 3790.00 377
cdsmvs_eth3d_5k24.64 34632.85 3490.00 3620.00 3850.00 3860.00 37399.51 1030.00 3800.00 38199.56 20796.58 1380.00 3810.00 3790.00 3790.00 377
pcd_1.5k_mvsjas8.27 34811.03 3510.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 38199.01 180.00 3810.00 3790.00 3790.00 377
sosnet-low-res0.02 3500.03 3530.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 3810.00 3850.00 3810.00 3790.00 3790.00 377
sosnet0.02 3500.03 3530.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 3810.00 3850.00 3810.00 3790.00 3790.00 377
uncertanet0.02 3500.03 3530.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 3810.00 3850.00 3810.00 3790.00 3790.00 377
Regformer0.02 3500.03 3530.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 3810.00 3850.00 3810.00 3790.00 3790.00 377
ab-mvs-re8.30 34711.06 3500.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 38199.58 2000.00 3850.00 3810.00 3790.00 3790.00 377
uanet0.02 3500.03 3530.00 3620.00 3850.00 3860.00 3730.00 3860.00 3800.00 3810.27 3810.00 3850.00 3810.00 3790.00 3790.00 377
MSC_two_6792asdad99.87 1199.51 15699.76 3799.33 24199.96 2298.87 8999.84 6399.89 6
PC_three_145298.18 11599.84 1899.70 14299.31 398.52 34298.30 16799.80 8399.81 47
No_MVS99.87 1199.51 15699.76 3799.33 24199.96 2298.87 8999.84 6399.89 6
test_one_060199.81 4199.88 899.49 13098.97 3999.65 7599.81 7699.09 14
eth-test20.00 385
eth-test0.00 385
ZD-MVS99.71 8799.79 3099.61 3696.84 24899.56 9899.54 21598.58 6799.96 2296.93 27299.75 98
IU-MVS99.84 3199.88 899.32 25198.30 9699.84 1898.86 9499.85 5599.89 6
test_241102_TWO99.48 14299.08 2199.88 1199.81 7698.94 2999.96 2298.91 8399.84 6399.88 12
test_241102_ONE99.84 3199.90 299.48 14299.07 2399.91 799.74 12799.20 799.76 177
test_0728_THIRD98.99 3399.81 2599.80 8999.09 1499.96 2298.85 9699.90 2599.88 12
GSMVS99.52 148
test_part299.81 4199.83 1699.77 38
sam_mvs194.86 19899.52 148
sam_mvs94.72 210
MTGPAbinary99.47 160
test_post65.99 37694.65 21499.73 186
patchmatchnet-post98.70 33594.79 20299.74 180
gm-plane-assit98.54 33392.96 35194.65 33099.15 30499.64 22097.56 229
test9_res97.49 23599.72 10499.75 74
agg_prior297.21 25199.73 10399.75 74
agg_prior99.67 10099.62 5999.40 20798.87 24199.91 90
TestCases99.31 12899.86 2098.48 19999.61 3697.85 15299.36 14899.85 4295.95 15899.85 12996.66 28599.83 7299.59 133
test_prior99.68 5499.67 10099.48 8099.56 5799.83 14699.74 78
新几何199.75 4799.75 6499.59 6299.54 7396.76 25199.29 16399.64 17698.43 7899.94 5796.92 27499.66 11499.72 89
旧先验199.74 7199.59 6299.54 7399.69 15298.47 7599.68 11299.73 83
原ACMM199.65 5999.73 7899.33 9199.47 16097.46 19299.12 19899.66 16998.67 6399.91 9097.70 21799.69 10999.71 96
testdata299.95 4896.67 284
segment_acmp98.96 24
testdata99.54 8299.75 6498.95 14899.51 10397.07 23199.43 12499.70 14298.87 3799.94 5797.76 20899.64 11799.72 89
test1299.75 4799.64 11699.61 6099.29 26399.21 18298.38 8299.89 11199.74 10199.74 78
plane_prior799.29 22097.03 268
plane_prior699.27 22596.98 27292.71 265
plane_prior599.47 16099.69 20797.78 20597.63 22898.67 263
plane_prior499.61 191
plane_prior397.00 27098.69 6499.11 200
plane_prior199.26 227
n20.00 386
nn0.00 386
door-mid98.05 353
lessismore_v097.79 29898.69 32095.44 31894.75 37395.71 34699.87 3288.69 32799.32 27195.89 29994.93 31298.62 286
LGP-MVS_train98.49 23899.33 20897.05 26499.55 6597.46 19299.24 17499.83 5692.58 27099.72 19098.09 17997.51 24098.68 256
test1199.35 230
door97.92 354
HQP5-MVS96.83 278
BP-MVS97.19 255
HQP4-MVS98.66 26899.64 22098.64 275
HQP3-MVS99.39 21097.58 233
HQP2-MVS92.47 274
NP-MVS99.23 23396.92 27699.40 256
ACMMP++_ref97.19 262
ACMMP++97.43 252
Test By Simon98.75 54
ITE_SJBPF98.08 27799.29 22096.37 29598.92 31098.34 9298.83 24699.75 12291.09 30399.62 22695.82 30097.40 25498.25 328
DeepMVS_CXcopyleft93.34 34199.29 22082.27 36899.22 27485.15 36396.33 34099.05 31490.97 30599.73 18693.57 33597.77 22598.01 339