High-res multi-view results

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

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) 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	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 2	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

pmmvs6		99.07 4	99.24 4	98.56 48	99.81 2	96.38 62	98.87 9	99.30 27	99.01 16	99.63 11	99.66 3	99.27 2	99.68 122	97.75 50	99.89 26	99.62 36

testf1		98.57 17	98.45 29	98.93 18	99.79 3	98.78 2	97.69 80	99.42 22	97.69 63	98.92 51	98.77 78	97.80 25	99.25 264	96.27 98	99.69 78	98.76 219

APD_test2		98.57 17	98.45 29	98.93 18	99.79 3	98.78 2	97.69 80	99.42 22	97.69 63	98.92 51	98.77 78	97.80 25	99.25 264	96.27 98	99.69 78	98.76 219

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 42	99.77 5	96.34 65	99.18 5	99.20 35	99.67 2	99.73 3	99.65 5	99.15 3	99.86 24	97.22 67	99.92 16	99.77 12

OurMVSNet-221017-0		98.61 16	98.61 24	98.63 44	99.77 5	96.35 64	99.17 6	99.05 66	98.05 47	99.61 13	99.52 7	93.72 189	99.88 20	98.72 24	99.88 27	99.65 33

Gipuma		98.07 47	98.31 35	97.36 146	99.76 7	96.28 68	98.51 27	99.10 52	98.76 23	96.79 222	99.34 25	96.61 89	98.82 318	96.38 94	99.50 139	96.98 345
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		98.51 23	98.45 29	98.67 40	99.72 8	96.71 50	98.76 12	98.89 103	98.49 31	99.38 22	99.14 46	95.44 141	99.84 30	96.47 91	99.80 51	99.47 79

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 37	99.71 9	96.99 44	99.69 2	99.57 14	99.02 15	99.62 12	99.36 21	98.53 9	99.52 181	98.58 29	99.95 5	99.66 30
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
mvs_tets		98.90 5	98.94 6	98.75 31	99.69 10	96.48 60	98.54 23	99.22 32	96.23 120	99.71 4	99.48 10	98.77 7	99.93 3	98.89 17	99.95 5	99.84 5

PS-MVSNAJss		98.53 22	98.63 20	98.21 78	99.68 11	94.82 129	98.10 56	99.21 33	96.91 92	99.75 2	99.45 13	95.82 124	99.92 5	98.80 19	99.96 4	99.89 1

RRT_MVS		97.95 58	97.79 73	98.43 57	99.67 12	95.56 93	98.86 10	96.73 303	97.99 49	99.15 36	99.35 23	89.84 267	99.90 14	98.64 26	99.90 24	99.82 6

jajsoiax		98.77 9	98.79 12	98.74 34	99.66 13	96.48 60	98.45 31	99.12 49	95.83 146	99.67 7	99.37 19	98.25 13	99.92 5	98.77 20	99.94 8	99.82 6

v7n		98.73 11	98.99 5	97.95 98	99.64 14	94.20 155	98.67 15	99.14 47	99.08 10	99.42 20	99.23 33	96.53 93	99.91 13	99.27 5	99.93 11	99.73 22

test_djsdf		98.73 11	98.74 16	98.69 39	99.63 15	96.30 67	98.67 15	99.02 75	96.50 108	99.32 26	99.44 14	97.43 39	99.92 5	98.73 22	99.95 5	99.86 2

anonymousdsp		98.72 14	98.63 20	98.99 10	99.62 16	97.29 37	98.65 19	99.19 37	95.62 155	99.35 25	99.37 19	97.38 41	99.90 14	98.59 28	99.91 19	99.77 12

APD_test1		97.95 58	97.68 85	98.75 31	99.60 17	98.60 5	97.21 111	99.08 58	96.57 106	98.07 140	98.38 118	96.22 113	99.14 282	94.71 193	99.31 195	98.52 245

FOURS1					99.59 18	98.20 7	99.03 7	99.25 31	98.96 18	98.87 56

PEN-MVS		98.75 10	98.85 10	98.44 55	99.58 19	95.67 90	98.45 31	99.15 44	99.33 5	99.30 27	99.00 55	97.27 46	99.92 5	97.64 56	99.92 16	99.75 19

EGC-MVSNET		83.08 370	77.93 373	98.53 50	99.57 20	97.55 26	98.33 38	98.57 179	4.71 406	10.38 407	98.90 69	95.60 136	99.50 186	95.69 128	99.61 98	98.55 242

Baseline_NR-MVSNet		97.72 90	97.79 73	97.50 131	99.56 21	93.29 186	95.44 223	98.86 114	98.20 42	98.37 101	99.24 32	94.69 160	99.55 173	95.98 114	99.79 53	99.65 33

SixPastTwentyTwo		97.49 108	97.57 100	97.26 153	99.56 21	92.33 208	98.28 42	96.97 292	98.30 38	99.45 18	99.35 23	88.43 284	99.89 18	98.01 39	99.76 58	99.54 53

tt0805		97.44 112	97.56 101	97.11 162	99.55 23	96.36 63	98.66 18	95.66 318	98.31 36	97.09 205	95.45 325	97.17 52	98.50 352	98.67 25	97.45 332	96.48 365

PS-CasMVS		98.73 11	98.85 10	98.39 61	99.55 23	95.47 102	98.49 28	99.13 48	99.22 8	99.22 33	98.96 61	97.35 42	99.92 5	97.79 48	99.93 11	99.79 10

DTE-MVSNet		98.79 8	98.86 8	98.59 46	99.55 23	96.12 72	98.48 30	99.10 52	99.36 4	99.29 28	99.06 52	97.27 46	99.93 3	97.71 52	99.91 19	99.70 26

HPM-MVS_fast		98.32 30	98.13 40	98.88 23	99.54 26	97.48 30	98.35 35	99.03 73	95.88 142	97.88 159	98.22 145	98.15 16	99.74 76	96.50 90	99.62 92	99.42 97

TDRefinement		98.90 5	98.86 8	99.02 6	99.54 26	98.06 8	99.34 4	99.44 20	98.85 21	99.00 46	99.20 35	97.42 40	99.59 159	97.21 68	99.76 58	99.40 100

mvsmamba		98.16 37	98.06 47	98.44 55	99.53 28	95.87 81	98.70 13	98.94 97	97.71 61	98.85 57	99.10 48	91.35 243	99.83 32	98.47 30	99.90 24	99.64 35

pm-mvs1		98.47 24	98.67 18	97.86 103	99.52 29	94.58 139	98.28 42	99.00 84	97.57 67	99.27 29	99.22 34	98.32 12	99.50 186	97.09 74	99.75 65	99.50 62

TransMVSNet (Re)		98.38 28	98.67 18	97.51 127	99.51 30	93.39 184	98.20 51	98.87 111	98.23 40	99.48 16	99.27 30	98.47 11	99.55 173	96.52 89	99.53 125	99.60 37

WR-MVS_H		98.65 15	98.62 22	98.75 31	99.51 30	96.61 56	98.55 22	99.17 39	99.05 13	99.17 35	98.79 75	95.47 139	99.89 18	97.95 41	99.91 19	99.75 19

PMVS	89.60 17	96.71 157	96.97 136	95.95 231	99.51 30	97.81 16	97.42 102	97.49 274	97.93 50	95.95 267	98.58 96	96.88 75	96.91 389	89.59 314	99.36 177	93.12 394
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss		97.69 92	97.36 114	98.70 38	99.50 33	96.84 47	95.38 230	98.99 87	92.45 268	98.11 133	98.31 124	97.25 49	99.77 56	96.60 86	99.62 92	99.48 76
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		98.16 37	98.37 33	97.56 122	99.49 34	93.10 191	98.35 35	99.21 33	98.43 32	98.89 54	98.83 74	94.30 174	99.81 36	97.87 43	99.91 19	99.77 12

VPNet		97.26 124	97.49 109	96.59 198	99.47 35	90.58 250	96.27 165	98.53 181	97.77 54	98.46 93	98.41 114	94.59 165	99.68 122	94.61 194	99.29 198	99.52 58

CP-MVSNet		98.42 26	98.46 27	98.30 68	99.46 36	95.22 118	98.27 44	98.84 121	99.05 13	99.01 44	98.65 91	95.37 142	99.90 14	97.57 57	99.91 19	99.77 12

XXY-MVS		97.54 105	97.70 81	97.07 167	99.46 36	92.21 213	97.22 110	99.00 84	94.93 187	98.58 81	98.92 65	97.31 44	99.41 219	94.44 199	99.43 163	99.59 38

MTAPA		98.14 39	97.84 66	99.06 3	99.44 38	97.90 12	97.25 107	98.73 149	97.69 63	97.90 157	97.96 176	95.81 128	99.82 34	96.13 104	99.61 98	99.45 85

SteuartSystems-ACMMP		98.02 50	97.76 78	98.79 29	99.43 39	97.21 41	97.15 113	98.90 102	96.58 103	98.08 138	97.87 186	97.02 62	99.76 61	95.25 158	99.59 103	99.40 100
Skip Steuart: Steuart Systems R&D Blog.
ACMH	93.61 9	98.44 25	98.76 13	97.51 127	99.43 39	93.54 178	98.23 46	99.05 66	97.40 79	99.37 23	99.08 51	98.79 6	99.47 196	97.74 51	99.71 74	99.50 62
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 43	97.83 69	98.92 21	99.42 41	97.46 31	98.57 20	99.05 66	95.43 166	97.41 184	97.50 216	97.98 19	99.79 44	95.58 138	99.57 108	99.50 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SDMVSNet		97.97 52	98.26 39	97.11 162	99.41 42	92.21 213	96.92 126	98.60 174	98.58 28	98.78 64	99.39 16	97.80 25	99.62 149	94.98 180	99.86 31	99.52 58

sd_testset		97.97 52	98.12 41	97.51 127	99.41 42	93.44 181	97.96 62	98.25 213	98.58 28	98.78 64	99.39 16	98.21 14	99.56 168	92.65 250	99.86 31	99.52 58

K. test v3		96.44 171	96.28 177	96.95 174	99.41 42	91.53 232	97.65 83	90.31 383	98.89 20	98.93 50	99.36 21	84.57 318	99.92 5	97.81 46	99.56 111	99.39 104

VDDNet		96.98 136	96.84 144	97.41 143	99.40 45	93.26 188	97.94 64	95.31 329	99.26 7	98.39 100	99.18 39	87.85 293	99.62 149	95.13 170	99.09 225	99.35 114

test_fmvsmconf0.01_n		98.57 17	98.74 16	98.06 88	99.39 46	94.63 136	96.70 143	99.82 1	95.44 165	99.64 10	99.52 7	98.96 4	99.74 76	99.38 3	99.86 31	99.81 8

ACMH+	93.58 10	98.23 36	98.31 35	97.98 97	99.39 46	95.22 118	97.55 91	99.20 35	98.21 41	99.25 31	98.51 104	98.21 14	99.40 221	94.79 186	99.72 71	99.32 115

TSAR-MVS + MP.		97.42 114	97.23 122	98.00 95	99.38 48	95.00 125	97.63 85	98.20 221	93.00 252	98.16 128	98.06 166	95.89 119	99.72 87	95.67 130	99.10 224	99.28 127
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
FIs		97.93 65	98.07 45	97.48 135	99.38 48	92.95 194	98.03 61	99.11 50	98.04 48	98.62 76	98.66 88	93.75 188	99.78 47	97.23 66	99.84 40	99.73 22

lessismore_v0				97.05 168	99.36 50	92.12 218		84.07 401		98.77 68	98.98 58	85.36 312	99.74 76	97.34 65	99.37 174	99.30 120

Anonymous20240521		97.07 129	97.51 106	95.76 239	99.35 51	88.18 291	97.78 72	98.40 197	97.11 87	98.34 107	99.04 53	89.58 269	99.79 44	98.09 36	99.93 11	99.30 120

ACMMP_NAP		97.89 72	97.63 93	98.67 40	99.35 51	96.84 47	96.36 160	98.79 137	95.07 180	97.88 159	98.35 120	97.24 50	99.72 87	96.05 107	99.58 105	99.45 85

Vis-MVSNet		98.27 33	98.34 34	98.07 86	99.33 53	95.21 120	98.04 59	99.46 18	97.32 82	97.82 166	99.11 47	96.75 83	99.86 24	97.84 45	99.36 177	99.15 151
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high		98.31 31	98.94 6	96.41 211	99.33 53	89.64 261	97.92 66	99.56 16	99.27 6	99.66 9	99.50 9	97.67 31	99.83 32	97.55 58	99.98 2	99.77 12

ZNCC-MVS		97.92 66	97.62 95	98.83 25	99.32 55	97.24 39	97.45 98	98.84 121	95.76 148	96.93 217	97.43 220	97.26 48	99.79 44	96.06 105	99.53 125	99.45 85

MP-MVS		97.64 96	97.18 124	99.00 9	99.32 55	97.77 17	97.49 97	98.73 149	96.27 117	95.59 282	97.75 197	96.30 108	99.78 47	93.70 231	99.48 146	99.45 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SSC-MVS		95.92 191	97.03 133	92.58 352	99.28 57	78.39 388	96.68 144	95.12 331	98.90 19	99.11 39	98.66 88	91.36 242	99.68 122	95.00 177	99.16 214	99.67 28

PVSNet_Blended_VisFu		95.95 190	95.80 199	96.42 209	99.28 57	90.62 249	95.31 237	99.08 58	88.40 328	96.97 215	98.17 150	92.11 229	99.78 47	93.64 232	99.21 207	98.86 208

tfpnnormal		97.72 90	97.97 55	96.94 175	99.26 59	92.23 212	97.83 71	98.45 188	98.25 39	99.13 38	98.66 88	96.65 86	99.69 117	93.92 223	99.62 92	98.91 197

MSP-MVS		97.45 111	96.92 141	99.03 5	99.26 59	97.70 18	97.66 82	98.89 103	95.65 153	98.51 85	96.46 286	92.15 227	99.81 36	95.14 168	98.58 279	99.58 39
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
testgi		96.07 184	96.50 168	94.80 288	99.26 59	87.69 306	95.96 193	98.58 178	95.08 179	98.02 146	96.25 296	97.92 20	97.60 382	88.68 328	98.74 262	99.11 164

IS-MVSNet		96.93 138	96.68 153	97.70 113	99.25 62	94.00 161	98.57 20	96.74 301	98.36 34	98.14 131	97.98 175	88.23 286	99.71 102	93.10 246	99.72 71	99.38 106

DVP-MVS		97.78 85	97.65 88	98.16 79	99.24 63	95.51 97	96.74 137	98.23 216	95.92 139	98.40 98	98.28 133	97.06 58	99.71 102	95.48 143	99.52 130	99.26 132
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
test0726					99.24 63	95.51 97	96.89 128	98.89 103	95.92 139	98.64 74	98.31 124	97.06 58

test_0728_SECOND				98.25 73	99.23 65	95.49 101	96.74 137	98.89 103					99.75 67	95.48 143	99.52 130	99.53 56

GST-MVS		97.82 81	97.49 109	98.81 27	99.23 65	97.25 38	97.16 112	98.79 137	95.96 136	97.53 173	97.40 222	96.93 69	99.77 56	95.04 174	99.35 182	99.42 97

ACMMP		98.05 48	97.75 80	98.93 18	99.23 65	97.60 22	98.09 57	98.96 94	95.75 150	97.91 156	98.06 166	96.89 73	99.76 61	95.32 155	99.57 108	99.43 96
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
KD-MVS_self_test		97.86 76	98.07 45	97.25 154	99.22 68	92.81 196	97.55 91	98.94 97	97.10 88	98.85 57	98.88 71	95.03 152	99.67 128	97.39 64	99.65 87	99.26 132

SED-MVS		97.94 62	97.90 59	98.07 86	99.22 68	95.35 108	96.79 134	98.83 127	96.11 126	99.08 40	98.24 140	97.87 23	99.72 87	95.44 147	99.51 135	99.14 154

IU-MVS					99.22 68	95.40 103		98.14 234	85.77 356	98.36 104				95.23 160	99.51 135	99.49 70

test_241102_ONE					99.22 68	95.35 108		98.83 127	96.04 131	99.08 40	98.13 153	97.87 23	99.33 245

nrg030		98.54 21	98.62 22	98.32 65	99.22 68	95.66 91	97.90 67	99.08 58	98.31 36	99.02 43	98.74 81	97.68 30	99.61 156	97.77 49	99.85 38	99.70 26

region2R		97.92 66	97.59 98	98.92 21	99.22 68	97.55 26	97.60 86	98.84 121	96.00 134	97.22 189	97.62 207	96.87 77	99.76 61	95.48 143	99.43 163	99.46 81

mPP-MVS		97.91 69	97.53 104	99.04 4	99.22 68	97.87 14	97.74 78	98.78 141	96.04 131	97.10 200	97.73 200	96.53 93	99.78 47	95.16 165	99.50 139	99.46 81

WB-MVS		95.50 207	96.62 155	92.11 361	99.21 75	77.26 396	96.12 179	95.40 328	98.62 26	98.84 59	98.26 138	91.08 246	99.50 186	93.37 236	98.70 267	99.58 39

COLMAP_ROB	94.48 6	98.25 35	98.11 42	98.64 43	99.21 75	97.35 35	97.96 62	99.16 40	98.34 35	98.78 64	98.52 102	97.32 43	99.45 203	94.08 215	99.67 84	99.13 156
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		97.95 58	97.62 95	98.94 15	99.20 77	97.56 25	97.59 88	98.83 127	96.05 129	97.46 182	97.63 206	96.77 82	99.76 61	95.61 135	99.46 151	99.49 70

PGM-MVS		97.88 73	97.52 105	98.96 13	99.20 77	97.62 21	97.09 118	99.06 62	95.45 163	97.55 172	97.94 179	97.11 53	99.78 47	94.77 189	99.46 151	99.48 76

test_0402		97.84 77	97.97 55	97.47 136	99.19 79	94.07 158	96.71 142	98.73 149	98.66 25	98.56 82	98.41 114	96.84 79	99.69 117	94.82 184	99.81 48	98.64 232

EPP-MVSNet		96.84 144	96.58 159	97.65 117	99.18 80	93.78 170	98.68 14	96.34 306	97.91 51	97.30 186	98.06 166	88.46 283	99.85 27	93.85 225	99.40 171	99.32 115

fmvsm_s_conf0.1_n_a		97.80 83	98.01 52	97.18 157	99.17 81	92.51 204	96.57 148	99.15 44	93.68 226	98.89 54	99.30 28	96.42 102	99.37 234	99.03 13	99.83 43	99.66 30

test_fmvsmconf0.1_n		98.41 27	98.54 25	98.03 93	99.16 82	94.61 137	96.18 173	99.73 3	95.05 181	99.60 14	99.34 25	98.68 8	99.72 87	99.21 7	99.85 38	99.76 17

XVG-ACMP-BASELINE		97.58 103	97.28 119	98.49 52	99.16 82	96.90 46	96.39 155	98.98 90	95.05 181	98.06 141	98.02 170	95.86 120	99.56 168	94.37 204	99.64 89	99.00 180

CHOSEN 1792x2688		94.10 271	93.41 280	96.18 221	99.16 82	90.04 255	92.15 348	98.68 161	79.90 388	96.22 256	97.83 188	87.92 292	99.42 210	89.18 320	99.65 87	99.08 169

HFP-MVS		97.94 62	97.64 91	98.83 25	99.15 85	97.50 29	97.59 88	98.84 121	96.05 129	97.49 177	97.54 212	97.07 57	99.70 110	95.61 135	99.46 151	99.30 120

XVS		97.96 54	97.63 93	98.94 15	99.15 85	97.66 19	97.77 73	98.83 127	97.42 75	96.32 249	97.64 205	96.49 96	99.72 87	95.66 131	99.37 174	99.45 85

X-MVStestdata		92.86 302	90.83 329	98.94 15	99.15 85	97.66 19	97.77 73	98.83 127	97.42 75	96.32 249	36.50 404	96.49 96	99.72 87	95.66 131	99.37 174	99.45 85

LPG-MVS_test		97.94 62	97.67 86	98.74 34	99.15 85	97.02 42	97.09 118	99.02 75	95.15 176	98.34 107	98.23 142	97.91 21	99.70 110	94.41 201	99.73 67	99.50 62

LGP-MVS_train				98.74 34	99.15 85	97.02 42		99.02 75	95.15 176	98.34 107	98.23 142	97.91 21	99.70 110	94.41 201	99.73 67	99.50 62

RPSCF		97.87 74	97.51 106	98.95 14	99.15 85	98.43 6	97.56 90	99.06 62	96.19 123	98.48 90	98.70 85	94.72 159	99.24 268	94.37 204	99.33 190	99.17 148

ACMM	93.33 11	98.05 48	97.79 73	98.85 24	99.15 85	97.55 26	96.68 144	98.83 127	95.21 172	98.36 104	98.13 153	98.13 18	99.62 149	96.04 108	99.54 121	99.39 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		97.95 58	98.08 44	97.56 122	99.14 92	93.67 172	98.23 46	98.66 166	97.41 78	99.00 46	99.19 36	95.47 139	99.73 82	95.83 123	99.76 58	99.30 120

Vis-MVSNet (Re-imp)		95.11 228	94.85 231	95.87 236	99.12 93	89.17 269	97.54 96	94.92 334	96.50 108	96.58 236	97.27 236	83.64 324	99.48 194	88.42 331	99.67 84	98.97 185

dcpmvs_2		97.12 127	97.99 54	94.51 302	99.11 94	84.00 358	97.75 76	99.65 9	97.38 80	99.14 37	98.42 113	95.16 148	99.96 2	95.52 139	99.78 56	99.58 39

OPM-MVS		97.54 105	97.25 120	98.41 59	99.11 94	96.61 56	95.24 241	98.46 187	94.58 199	98.10 135	98.07 161	97.09 56	99.39 225	95.16 165	99.44 155	99.21 140
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UA-Net		98.88 7	98.76 13	99.22 2	99.11 94	97.89 13	99.47 3	99.32 25	99.08 10	97.87 162	99.67 2	96.47 98	99.92 5	97.88 42	99.98 2	99.85 3

fmvsm_s_conf0.1_n		97.73 88	98.02 51	96.85 182	99.09 97	91.43 236	96.37 159	99.11 50	94.19 209	99.01 44	99.25 31	96.30 108	99.38 228	99.00 14	99.88 27	99.73 22

AllTest		97.20 126	96.92 141	98.06 88	99.08 98	96.16 70	97.14 115	99.16 40	94.35 204	97.78 167	98.07 161	95.84 121	99.12 286	91.41 272	99.42 166	98.91 197

TestCases				98.06 88	99.08 98	96.16 70		99.16 40	94.35 204	97.78 167	98.07 161	95.84 121	99.12 286	91.41 272	99.42 166	98.91 197

TranMVSNet+NR-MVSNet		98.33 29	98.30 37	98.43 57	99.07 100	95.87 81	96.73 141	99.05 66	98.67 24	98.84 59	98.45 110	97.58 36	99.88 20	96.45 92	99.86 31	99.54 53

test1111		94.53 257	94.81 235	93.72 321	99.06 101	81.94 373	98.31 39	83.87 402	96.37 113	98.49 88	99.17 42	81.49 333	99.73 82	96.64 84	99.86 31	99.49 70

VPA-MVSNet		98.27 33	98.46 27	97.70 113	99.06 101	93.80 168	97.76 75	99.00 84	98.40 33	99.07 42	98.98 58	96.89 73	99.75 67	97.19 71	99.79 53	99.55 52

114514_t		93.96 276	93.22 284	96.19 220	99.06 101	90.97 243	95.99 189	98.94 97	73.88 400	93.43 340	96.93 257	92.38 225	99.37 234	89.09 321	99.28 199	98.25 275

EG-PatchMatch MVS		97.69 92	97.79 73	97.40 144	99.06 101	93.52 179	95.96 193	98.97 93	94.55 200	98.82 61	98.76 80	97.31 44	99.29 256	97.20 70	99.44 155	99.38 106

test_one_0601					99.05 105	95.50 100		98.87 111	97.21 86	98.03 145	98.30 128	96.93 69

ACMP	92.54 13	97.47 110	97.10 127	98.55 49	99.04 106	96.70 51	96.24 170	98.89 103	93.71 223	97.97 151	97.75 197	97.44 38	99.63 144	93.22 243	99.70 77	99.32 115
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_fmvsmvis_n_1920		98.08 45	98.47 26	96.93 176	99.03 107	93.29 186	96.32 163	99.65 9	95.59 157	99.71 4	99.01 54	97.66 32	99.60 158	99.44 2	99.83 43	97.90 305

test_part2					99.03 107	96.07 74				98.08 138

XVG-OURS-SEG-HR		97.38 116	97.07 130	98.30 68	99.01 109	97.41 34	94.66 268	99.02 75	95.20 173	98.15 130	97.52 214	98.83 5	98.43 357	94.87 182	96.41 356	99.07 171

XVG-OURS		97.12 127	96.74 150	98.26 70	98.99 110	97.45 32	93.82 303	99.05 66	95.19 174	98.32 111	97.70 202	95.22 147	98.41 358	94.27 208	98.13 298	98.93 193

CP-MVS		97.92 66	97.56 101	98.99 10	98.99 110	97.82 15	97.93 65	98.96 94	96.11 126	96.89 220	97.45 218	96.85 78	99.78 47	95.19 161	99.63 91	99.38 106

test2506		89.86 342	89.16 347	91.97 362	98.95 112	76.83 397	98.54 23	61.07 411	96.20 121	97.07 206	99.16 43	55.19 405	99.69 117	96.43 93	99.83 43	99.38 106

ECVR-MVS		94.37 263	94.48 253	94.05 317	98.95 112	83.10 363	98.31 39	82.48 404	96.20 121	98.23 120	99.16 43	81.18 336	99.66 134	95.95 115	99.83 43	99.38 106

CSCG		97.40 115	97.30 117	97.69 115	98.95 112	94.83 128	97.28 106	98.99 87	96.35 116	98.13 132	95.95 311	95.99 117	99.66 134	94.36 206	99.73 67	98.59 238

test_fmvsmconf_n		98.30 32	98.41 32	97.99 96	98.94 115	94.60 138	96.00 188	99.64 12	94.99 184	99.43 19	99.18 39	98.51 10	99.71 102	99.13 10	99.84 40	99.67 28

SF-MVS		97.60 100	97.39 112	98.22 75	98.93 116	95.69 88	97.05 120	99.10 52	95.32 169	97.83 165	97.88 185	96.44 101	99.72 87	94.59 198	99.39 172	99.25 136

HyFIR lowres test		93.72 282	92.65 299	96.91 179	98.93 116	91.81 229	91.23 367	98.52 182	82.69 376	96.46 243	96.52 284	80.38 341	99.90 14	90.36 304	98.79 257	99.03 176

fmvsm_l_conf0.5_n_a		97.60 100	97.76 78	97.11 162	98.92 118	92.28 210	95.83 202	99.32 25	93.22 240	98.91 53	98.49 105	96.31 107	99.64 140	99.07 12	99.76 58	99.40 100

fmvsm_l_conf0.5_n		97.68 94	97.81 71	97.27 151	98.92 118	92.71 201	95.89 199	99.41 24	93.36 234	99.00 46	98.44 112	96.46 100	99.65 136	99.09 11	99.76 58	99.45 85

PM-MVS		97.36 120	97.10 127	98.14 82	98.91 120	96.77 49	96.20 172	98.63 172	93.82 220	98.54 83	98.33 122	93.98 181	99.05 297	95.99 113	99.45 154	98.61 237

CPTT-MVS		96.69 158	96.08 185	98.49 52	98.89 121	96.64 55	97.25 107	98.77 142	92.89 258	96.01 266	97.13 243	92.23 226	99.67 128	92.24 256	99.34 185	99.17 148

test_fmvsm_n_1920		98.08 45	98.29 38	97.43 140	98.88 122	93.95 163	96.17 177	99.57 14	95.66 152	99.52 15	98.71 84	97.04 60	99.64 140	99.21 7	99.87 29	98.69 228

patch_mono-2		96.59 163	96.93 139	95.55 250	98.88 122	87.12 317	94.47 273	99.30 27	94.12 212	96.65 234	98.41 114	94.98 155	99.87 22	95.81 125	99.78 56	99.66 30

GeoE		97.75 87	97.70 81	97.89 101	98.88 122	94.53 140	97.10 117	98.98 90	95.75 150	97.62 170	97.59 209	97.61 35	99.77 56	96.34 96	99.44 155	99.36 112

DPE-MVS		97.64 96	97.35 115	98.50 51	98.85 125	96.18 69	95.21 243	98.99 87	95.84 145	98.78 64	98.08 159	96.84 79	99.81 36	93.98 221	99.57 108	99.52 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		97.48 109	97.11 126	98.60 45	98.83 126	96.67 53	96.74 137	98.73 149	91.61 281	98.48 90	98.36 119	96.53 93	99.68 122	95.17 163	99.54 121	99.45 85
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
SR-MVS-dyc-post		98.14 39	97.84 66	99.02 6	98.81 127	98.05 9	97.55 91	98.86 114	97.77 54	98.20 122	98.07 161	96.60 91	99.76 61	95.49 140	99.20 208	99.26 132

RE-MVS-def			97.88 64		98.81 127	98.05 9	97.55 91	98.86 114	97.77 54	98.20 122	98.07 161	96.94 67		95.49 140	99.20 208	99.26 132

fmvsm_s_conf0.5_n_a		97.65 95	97.83 69	97.13 161	98.80 129	92.51 204	96.25 169	99.06 62	93.67 227	98.64 74	99.00 55	96.23 112	99.36 237	98.99 15	99.80 51	99.53 56

UniMVSNet (Re)		97.83 78	97.65 88	98.35 64	98.80 129	95.86 83	95.92 197	99.04 72	97.51 72	98.22 121	97.81 192	94.68 162	99.78 47	97.14 72	99.75 65	99.41 99

fmvsm_s_conf0.5_n		97.62 98	97.89 62	96.80 186	98.79 131	91.44 235	96.14 178	99.06 62	94.19 209	98.82 61	98.98 58	96.22 113	99.38 228	98.98 16	99.86 31	99.58 39

Anonymous20231211		98.55 20	98.76 13	97.94 99	98.79 131	94.37 147	98.84 11	99.15 44	99.37 3	99.67 7	99.43 15	95.61 135	99.72 87	98.12 34	99.86 31	99.73 22

APD-MVS_3200maxsize		98.13 42	97.90 59	98.79 29	98.79 131	97.31 36	97.55 91	98.92 100	97.72 59	98.25 118	98.13 153	97.10 54	99.75 67	95.44 147	99.24 206	99.32 115

DeepC-MVS	95.41 4	97.82 81	97.70 81	98.16 79	98.78 134	95.72 86	96.23 171	99.02 75	93.92 219	98.62 76	98.99 57	97.69 29	99.62 149	96.18 103	99.87 29	99.15 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		98.00 51	97.66 87	99.01 8	98.77 135	97.93 11	97.38 103	98.83 127	97.32 82	98.06 141	97.85 187	96.65 86	99.77 56	95.00 177	99.11 222	99.32 115

MCST-MVS		96.24 178	95.80 199	97.56 122	98.75 136	94.13 157	94.66 268	98.17 227	90.17 304	96.21 257	96.10 305	95.14 149	99.43 208	94.13 214	98.85 251	99.13 156

DU-MVS		97.79 84	97.60 97	98.36 63	98.73 137	95.78 84	95.65 213	98.87 111	97.57 67	98.31 113	97.83 188	94.69 160	99.85 27	97.02 77	99.71 74	99.46 81

NR-MVSNet		97.96 54	97.86 65	98.26 70	98.73 137	95.54 95	98.14 54	98.73 149	97.79 53	99.42 20	97.83 188	94.40 172	99.78 47	95.91 118	99.76 58	99.46 81

Anonymous20231206		95.27 220	95.06 222	95.88 235	98.72 139	89.37 266	95.70 207	97.85 252	88.00 334	96.98 214	97.62 207	91.95 234	99.34 243	89.21 319	99.53 125	98.94 189

APDe-MVS		98.14 39	98.03 50	98.47 54	98.72 139	96.04 75	98.07 58	99.10 52	95.96 136	98.59 80	98.69 86	96.94 67	99.81 36	96.64 84	99.58 105	99.57 46
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
UniMVSNet_NR-MVSNet		97.83 78	97.65 88	98.37 62	98.72 139	95.78 84	95.66 211	99.02 75	98.11 44	98.31 113	97.69 203	94.65 164	99.85 27	97.02 77	99.71 74	99.48 76

tttt0517		93.31 295	92.56 302	95.57 247	98.71 142	87.86 300	97.44 99	87.17 396	95.79 147	97.47 181	96.84 263	64.12 392	99.81 36	96.20 101	99.32 192	99.02 179

v8		97.60 100	98.06 47	96.23 217	98.71 142	89.44 265	97.43 101	98.82 135	97.29 84	98.74 70	99.10 48	93.86 184	99.68 122	98.61 27	99.94 8	99.56 50

HQP_MVS		96.66 160	96.33 176	97.68 116	98.70 144	94.29 150	96.50 151	98.75 146	96.36 114	96.16 260	96.77 269	91.91 237	99.46 199	92.59 252	99.20 208	99.28 127

plane_prior7					98.70 144	94.67 134

Anonymous20240529		97.96 54	98.04 49	97.71 112	98.69 146	94.28 153	97.86 69	98.31 210	98.79 22	99.23 32	98.86 73	95.76 130	99.61 156	95.49 140	99.36 177	99.23 138

VDD-MVS		97.37 118	97.25 120	97.74 110	98.69 146	94.50 143	97.04 121	95.61 322	98.59 27	98.51 85	98.72 82	92.54 219	99.58 161	96.02 110	99.49 142	99.12 161

EC-MVSNet		97.90 71	97.94 58	97.79 107	98.66 148	95.14 121	98.31 39	99.66 8	97.57 67	95.95 267	97.01 253	96.99 64	99.82 34	97.66 55	99.64 89	98.39 256

HPM-MVS++		96.99 133	96.38 173	98.81 27	98.64 149	97.59 23	95.97 191	98.20 221	95.51 161	95.06 294	96.53 282	94.10 178	99.70 110	94.29 207	99.15 215	99.13 156

ab-mvs		96.59 163	96.59 158	96.60 197	98.64 149	92.21 213	98.35 35	97.67 263	94.45 201	96.99 212	98.79 75	94.96 156	99.49 191	90.39 303	99.07 228	98.08 285

F-COLMAP		95.30 219	94.38 258	98.05 92	98.64 149	96.04 75	95.61 217	98.66 166	89.00 319	93.22 344	96.40 290	92.90 205	99.35 241	87.45 346	97.53 327	98.77 218

ITE_SJBPF				97.85 104	98.64 149	96.66 54		98.51 184	95.63 154	97.22 189	97.30 235	95.52 137	98.55 348	90.97 282	98.90 244	98.34 264

test_fmvs3		97.38 116	97.56 101	96.84 184	98.63 153	92.81 196	97.60 86	99.61 13	90.87 292	98.76 69	99.66 3	94.03 180	97.90 376	99.24 6	99.68 82	99.81 8

v148		96.58 165	96.97 136	95.42 257	98.63 153	87.57 307	95.09 247	97.90 249	95.91 141	98.24 119	97.96 176	93.42 194	99.39 225	96.04 108	99.52 130	99.29 126

UnsupCasMVSNet_bld		94.72 246	94.26 260	96.08 225	98.62 155	90.54 253	93.38 317	98.05 245	90.30 301	97.02 210	96.80 268	89.54 270	99.16 280	88.44 330	96.18 362	98.56 240

DP-MVS		97.87 74	97.89 62	97.81 106	98.62 155	94.82 129	97.13 116	98.79 137	98.98 17	98.74 70	98.49 105	95.80 129	99.49 191	95.04 174	99.44 155	99.11 164

v10		97.55 104	97.97 55	96.31 215	98.60 157	89.64 261	97.44 99	99.02 75	96.60 101	98.72 72	99.16 43	93.48 193	99.72 87	98.76 21	99.92 16	99.58 39

Test_1112_low_res		93.53 290	92.86 291	95.54 251	98.60 157	88.86 278	92.75 330	98.69 159	82.66 377	92.65 358	96.92 259	84.75 316	99.56 168	90.94 283	97.76 313	98.19 280

V42		97.04 130	97.16 125	96.68 195	98.59 159	91.05 240	96.33 162	98.36 202	94.60 196	97.99 147	98.30 128	93.32 195	99.62 149	97.40 63	99.53 125	99.38 106

1112_ss		94.12 270	93.42 279	96.23 217	98.59 159	90.85 244	94.24 281	98.85 118	85.49 357	92.97 350	94.94 333	86.01 306	99.64 140	91.78 268	97.92 306	98.20 279

v2v482		96.78 151	97.06 131	95.95 231	98.57 161	88.77 281	95.36 231	98.26 212	95.18 175	97.85 164	98.23 142	92.58 215	99.63 144	97.80 47	99.69 78	99.45 85

casdiffmvs_mvg		97.83 78	98.11 42	97.00 173	98.57 161	92.10 221	95.97 191	99.18 38	97.67 66	99.00 46	98.48 109	97.64 33	99.50 186	96.96 79	99.54 121	99.40 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WR-MVS		96.90 141	96.81 146	97.16 158	98.56 163	92.20 216	94.33 276	98.12 236	97.34 81	98.20 122	97.33 233	92.81 206	99.75 67	94.79 186	99.81 48	99.54 53

test_vis1_n_1920		95.77 197	96.41 171	93.85 318	98.55 164	84.86 348	95.91 198	99.71 4	92.72 262	97.67 169	98.90 69	87.44 296	98.73 327	97.96 40	98.85 251	97.96 301

APD-MVS		97.00 132	96.53 165	98.41 59	98.55 164	96.31 66	96.32 163	98.77 142	92.96 257	97.44 183	97.58 211	95.84 121	99.74 76	91.96 261	99.35 182	99.19 145
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmatch-RL test		94.66 250	94.49 252	95.19 265	98.54 166	88.91 276	92.57 336	98.74 148	91.46 284	98.32 111	97.75 197	77.31 356	98.81 320	96.06 105	99.61 98	97.85 309

9.14			96.69 152		98.53 167		96.02 186	98.98 90	93.23 239	97.18 194	97.46 217	96.47 98	99.62 149	92.99 247	99.32 192

CS-MVS-test		97.91 69	97.84 66	98.14 82	98.52 168	96.03 77	98.38 34	99.67 6	98.11 44	95.50 284	96.92 259	96.81 81	99.87 22	96.87 82	99.76 58	98.51 246

baseline		97.44 112	97.78 77	96.43 208	98.52 168	90.75 248	96.84 129	99.03 73	96.51 107	97.86 163	98.02 170	96.67 85	99.36 237	97.09 74	99.47 148	99.19 145

casdiffmvs		97.50 107	97.81 71	96.56 202	98.51 170	91.04 241	95.83 202	99.09 57	97.23 85	98.33 110	98.30 128	97.03 61	99.37 234	96.58 88	99.38 173	99.28 127
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS-LS		96.92 139	97.29 118	95.79 238	98.51 170	88.13 294	95.10 246	98.66 166	96.99 89	98.46 93	98.68 87	92.55 217	99.74 76	96.91 80	99.79 53	99.50 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		95.55 206	95.13 217	96.80 186	98.51 170	93.99 162	94.60 270	98.69 159	90.20 303	95.78 276	96.21 298	92.73 210	98.98 306	90.58 298	98.86 250	97.42 333

h-mvs33		96.29 176	95.63 206	98.26 70	98.50 173	96.11 73	96.90 127	97.09 287	96.58 103	97.21 191	98.19 147	84.14 320	99.78 47	95.89 119	96.17 363	98.89 201

test20.03		96.58 165	96.61 157	96.48 206	98.49 174	91.72 230	95.68 210	97.69 262	96.81 95	98.27 117	97.92 182	94.18 177	98.71 330	90.78 289	99.66 86	99.00 180

plane_prior1					98.49 174

save fliter					98.48 176	94.71 131	94.53 272	98.41 195	95.02 183

MDA-MVSNet-bldmvs		95.69 199	95.67 203	95.74 240	98.48 176	88.76 282	92.84 327	97.25 279	96.00 134	97.59 171	97.95 178	91.38 241	99.46 199	93.16 245	96.35 358	98.99 183

UnsupCasMVSNet_eth		95.91 192	95.73 202	96.44 207	98.48 176	91.52 233	95.31 237	98.45 188	95.76 148	97.48 179	97.54 212	89.53 272	98.69 333	94.43 200	94.61 381	99.13 156

CS-MVS		98.09 44	98.01 52	98.32 65	98.45 179	96.69 52	98.52 26	99.69 5	98.07 46	96.07 263	97.19 241	96.88 75	99.86 24	97.50 60	99.73 67	98.41 253

test_vis3_rt		97.04 130	96.98 135	97.23 156	98.44 180	95.88 80	96.82 131	99.67 6	90.30 301	99.27 29	99.33 27	94.04 179	96.03 395	97.14 72	97.83 310	99.78 11

ZD-MVS					98.43 181	95.94 79		98.56 180	90.72 294	96.66 232	97.07 247	95.02 153	99.74 76	91.08 279	98.93 242

thisisatest0530		92.71 305	91.76 313	95.56 249	98.42 182	88.23 289	96.03 185	87.35 395	94.04 216	96.56 238	95.47 324	64.03 393	99.77 56	94.78 188	99.11 222	98.68 231

v1144		96.84 144	97.08 129	96.13 224	98.42 182	89.28 268	95.41 227	98.67 164	94.21 207	97.97 151	98.31 124	93.06 200	99.65 136	98.06 38	99.62 92	99.45 85

plane_prior6					98.38 184	94.37 147						91.91 237

FPMVS		89.92 341	88.63 349	93.82 319	98.37 185	96.94 45	91.58 357	93.34 351	88.00 334	90.32 379	97.10 246	70.87 383	91.13 403	71.91 401	96.16 364	93.39 393

PAPM_NR		94.61 253	94.17 265	95.96 229	98.36 186	91.23 238	95.93 196	97.95 246	92.98 253	93.42 341	94.43 346	90.53 253	98.38 361	87.60 341	96.29 360	98.27 273

MVS_111021_HR		96.73 154	96.54 164	97.27 151	98.35 187	93.66 175	93.42 315	98.36 202	94.74 190	96.58 236	96.76 271	96.54 92	98.99 304	94.87 182	99.27 201	99.15 151

TAMVS		95.49 208	94.94 224	97.16 158	98.31 188	93.41 183	95.07 250	96.82 297	91.09 290	97.51 175	97.82 191	89.96 264	99.42 210	88.42 331	99.44 155	98.64 232

OMC-MVS		96.48 169	96.00 188	97.91 100	98.30 189	96.01 78	94.86 260	98.60 174	91.88 277	97.18 194	97.21 240	96.11 115	99.04 298	90.49 302	99.34 185	98.69 228

新几何1				97.25 154	98.29 190	94.70 133		97.73 260	77.98 394	94.83 301	96.67 275	92.08 231	99.45 203	88.17 335	98.65 273	97.61 324

jason		94.39 262	94.04 268	95.41 259	98.29 190	87.85 302	92.74 332	96.75 300	85.38 361	95.29 289	96.15 300	88.21 287	99.65 136	94.24 209	99.34 185	98.74 221
jason: jason.
v1192		96.83 147	97.06 131	96.15 223	98.28 192	89.29 267	95.36 231	98.77 142	93.73 222	98.11 133	98.34 121	93.02 204	99.67 128	98.35 32	99.58 105	99.50 62

CDPH-MVS		95.45 213	94.65 241	97.84 105	98.28 192	94.96 126	93.73 307	98.33 206	85.03 364	95.44 285	96.60 278	95.31 144	99.44 206	90.01 308	99.13 218	99.11 164

MVS_111021_LR		96.82 148	96.55 162	97.62 119	98.27 194	95.34 110	93.81 305	98.33 206	94.59 198	96.56 238	96.63 277	96.61 89	98.73 327	94.80 185	99.34 185	98.78 215

CLD-MVS		95.47 211	95.07 220	96.69 194	98.27 194	92.53 203	91.36 361	98.67 164	91.22 289	95.78 276	94.12 349	95.65 134	98.98 306	90.81 287	99.72 71	98.57 239
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous202405211		96.34 175	95.98 190	97.43 140	98.25 196	93.85 166	96.74 137	94.41 339	97.72 59	98.37 101	98.03 169	87.15 299	99.53 178	94.06 216	99.07 228	98.92 196

pmmvs-eth3d		96.49 168	96.18 181	97.42 142	98.25 196	94.29 150	94.77 264	98.07 243	89.81 309	97.97 151	98.33 122	93.11 199	99.08 294	95.46 146	99.84 40	98.89 201

v144192		96.69 158	96.90 143	96.03 226	98.25 196	88.92 275	95.49 221	98.77 142	93.05 250	98.09 136	98.29 132	92.51 222	99.70 110	98.11 35	99.56 111	99.47 79

ambc				96.56 202	98.23 199	91.68 231	97.88 68	98.13 235		98.42 96	98.56 99	94.22 176	99.04 298	94.05 218	99.35 182	98.95 187

test_cas_vis1_n_1920		95.34 216	95.67 203	94.35 308	98.21 200	86.83 323	95.61 217	99.26 30	90.45 299	98.17 127	98.96 61	84.43 319	98.31 366	96.74 83	99.17 213	97.90 305

thres100view900		91.76 322	91.26 322	93.26 330	98.21 200	84.50 352	96.39 155	90.39 380	96.87 93	96.33 248	93.08 359	73.44 376	99.42 210	78.85 390	97.74 314	95.85 372

v1921920		96.72 155	96.96 138	95.99 227	98.21 200	88.79 280	95.42 225	98.79 137	93.22 240	98.19 126	98.26 138	92.68 211	99.70 110	98.34 33	99.55 118	99.49 70

thres600view7		92.03 318	91.43 315	93.82 319	98.19 203	84.61 351	96.27 165	90.39 380	96.81 95	96.37 247	93.11 355	73.44 376	99.49 191	80.32 385	97.95 305	97.36 334

PatchMatch-RL		94.61 253	93.81 273	97.02 172	98.19 203	95.72 86	93.66 308	97.23 280	88.17 332	94.94 299	95.62 320	91.43 240	98.57 345	87.36 347	97.68 320	96.76 358

LF4IMVS		96.07 184	95.63 206	97.36 146	98.19 203	95.55 94	95.44 223	98.82 135	92.29 271	95.70 280	96.55 280	92.63 214	98.69 333	91.75 270	99.33 190	97.85 309

test_vis1_n		95.67 201	95.89 196	95.03 274	98.18 206	89.89 258	96.94 125	99.28 29	88.25 331	98.20 122	98.92 65	86.69 303	97.19 384	97.70 54	98.82 255	98.00 299

v1240		96.74 152	97.02 134	95.91 234	98.18 206	88.52 283	95.39 229	98.88 109	93.15 248	98.46 93	98.40 117	92.80 207	99.71 102	98.45 31	99.49 142	99.49 70

TAPA-MVS	93.32 12	94.93 235	94.23 261	97.04 170	98.18 206	94.51 141	95.22 242	98.73 149	81.22 383	96.25 255	95.95 311	93.80 187	98.98 306	89.89 310	98.87 248	97.62 323
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.17 209	93.24 189	92.74 332	97.61 272	75.17 398	94.65 304	96.69 274	90.96 249			98.66 271	97.66 320

MIMVSNet		93.42 292	92.86 291	95.10 271	98.17 209	88.19 290	98.13 55	93.69 344	92.07 272	95.04 297	98.21 146	80.95 339	99.03 301	81.42 382	98.06 301	98.07 287

原ACMM1				96.58 199	98.16 211	92.12 218		98.15 233	85.90 354	93.49 337	96.43 287	92.47 223	99.38 228	87.66 340	98.62 275	98.23 276

testdata				95.70 243	98.16 211	90.58 250		97.72 261	80.38 386	95.62 281	97.02 251	92.06 232	98.98 306	89.06 323	98.52 281	97.54 328

test_fmvs1_n		95.21 222	95.28 211	94.99 277	98.15 213	89.13 273	96.81 132	99.43 21	86.97 344	97.21 191	98.92 65	83.00 328	97.13 385	98.09 36	98.94 240	98.72 224

MVP-Stereo		95.69 199	95.28 211	96.92 177	98.15 213	93.03 192	95.64 216	98.20 221	90.39 300	96.63 235	97.73 200	91.63 239	99.10 292	91.84 266	97.31 336	98.63 234
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		97.37 118	97.70 81	96.35 212	98.14 215	95.13 122	96.54 150	98.92 100	95.94 138	99.19 34	98.08 159	97.74 28	95.06 396	95.24 159	99.54 121	98.87 207
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
EU-MVSNet		94.25 264	94.47 254	93.60 324	98.14 215	82.60 368	97.24 109	92.72 358	85.08 362	98.48 90	98.94 63	82.59 331	98.76 325	97.47 62	99.53 125	99.44 95

NP-MVS					98.14 215	93.72 171					95.08 329

LCM-MVSNet-Re		97.33 121	97.33 116	97.32 148	98.13 218	93.79 169	96.99 123	99.65 9	96.74 97	99.47 17	98.93 64	96.91 72	99.84 30	90.11 306	99.06 231	98.32 265

3Dnovator+	96.13 3	97.73 88	97.59 98	98.15 81	98.11 219	95.60 92	98.04 59	98.70 158	98.13 43	96.93 217	98.45 110	95.30 145	99.62 149	95.64 133	98.96 237	99.24 137

VNet		96.84 144	96.83 145	96.88 180	98.06 220	92.02 223	96.35 161	97.57 273	97.70 62	97.88 159	97.80 193	92.40 224	99.54 176	94.73 191	98.96 237	99.08 169

LFMVS		95.32 218	94.88 230	96.62 196	98.03 221	91.47 234	97.65 83	90.72 379	99.11 9	97.89 158	98.31 124	79.20 344	99.48 194	93.91 224	99.12 221	98.93 193

tfpn200view9		91.55 324	91.00 324	93.21 334	98.02 222	84.35 354	95.70 207	90.79 377	96.26 118	95.90 272	92.13 375	73.62 373	99.42 210	78.85 390	97.74 314	95.85 372

thres400		91.68 323	91.00 324	93.71 322	98.02 222	84.35 354	95.70 207	90.79 377	96.26 118	95.90 272	92.13 375	73.62 373	99.42 210	78.85 390	97.74 314	97.36 334

OPU-MVS				97.64 118	98.01 224	95.27 113	96.79 134				97.35 231	96.97 65	98.51 351	91.21 278	99.25 203	99.14 154

xiu_mvs_v1_base_debu		95.62 203	95.96 191	94.60 296	98.01 224	88.42 284	93.99 295	98.21 218	92.98 253	95.91 269	94.53 341	96.39 103	99.72 87	95.43 150	98.19 295	95.64 376

xiu_mvs_v1_base		95.62 203	95.96 191	94.60 296	98.01 224	88.42 284	93.99 295	98.21 218	92.98 253	95.91 269	94.53 341	96.39 103	99.72 87	95.43 150	98.19 295	95.64 376

xiu_mvs_v1_base_debi		95.62 203	95.96 191	94.60 296	98.01 224	88.42 284	93.99 295	98.21 218	92.98 253	95.91 269	94.53 341	96.39 103	99.72 87	95.43 150	98.19 295	95.64 376

CNVR-MVS		96.92 139	96.55 162	98.03 93	98.00 228	95.54 95	94.87 259	98.17 227	94.60 196	96.38 246	97.05 249	95.67 133	99.36 237	95.12 171	99.08 226	99.19 145

PLC	91.02 16	94.05 274	92.90 290	97.51 127	98.00 228	95.12 123	94.25 280	98.25 213	86.17 350	91.48 371	95.25 327	91.01 247	99.19 274	85.02 367	96.69 351	98.22 277
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		96.99 133	96.80 147	97.56 122	97.96 230	93.67 172	98.23 46	98.66 166	95.59 157	97.99 147	99.19 36	89.51 273	99.73 82	94.60 195	99.44 155	99.30 120

test1		96.99 133	96.80 147	97.56 122	97.96 230	93.67 172	98.23 46	98.66 166	95.59 157	97.99 147	99.19 36	89.51 273	99.73 82	94.60 195	99.44 155	99.30 120

FMVSNet2		96.72 155	96.67 154	96.87 181	97.96 230	91.88 226	97.15 113	98.06 244	95.59 157	98.50 87	98.62 94	89.51 273	99.65 136	94.99 179	99.60 101	99.07 171

BH-untuned		94.69 247	94.75 238	94.52 301	97.95 233	87.53 308	94.07 292	97.01 290	93.99 217	97.10 200	95.65 318	92.65 213	98.95 311	87.60 341	96.74 349	97.09 340

DPM-MVS		93.68 284	92.77 297	96.42 209	97.91 234	92.54 202	91.17 368	97.47 276	84.99 366	93.08 348	94.74 337	89.90 265	99.00 302	87.54 343	98.09 300	97.72 318

MVS_0304		96.62 162	96.40 172	97.28 150	97.91 234	92.30 209	96.47 153	89.74 388	97.52 71	95.38 288	98.63 93	92.76 208	99.81 36	99.28 4	99.93 11	99.75 19

QAPM		95.88 193	95.57 208	96.80 186	97.90 236	91.84 228	98.18 53	98.73 149	88.41 327	96.42 244	98.13 153	94.73 158	99.75 67	88.72 326	98.94 240	98.81 212

TinyColmap		96.00 189	96.34 175	94.96 279	97.90 236	87.91 299	94.13 290	98.49 185	94.41 202	98.16 128	97.76 194	96.29 110	98.68 336	90.52 299	99.42 166	98.30 269

test_fmvs2		96.38 174	96.45 169	96.16 222	97.85 238	91.30 237	96.81 132	99.45 19	89.24 315	98.49 88	99.38 18	88.68 280	97.62 381	98.83 18	99.32 192	99.57 46

HQP-NCC					97.85 238		94.26 277		93.18 244	92.86 352

ACMP_Plane					97.85 238		94.26 277		93.18 244	92.86 352

N_pmnet		95.18 224	94.23 261	98.06 88	97.85 238	96.55 58	92.49 338	91.63 369	89.34 313	98.09 136	97.41 221	90.33 258	99.06 296	91.58 271	99.31 195	98.56 240

HQP-MVS		95.17 226	94.58 249	96.92 177	97.85 238	92.47 206	94.26 277	98.43 191	93.18 244	92.86 352	95.08 329	90.33 258	99.23 270	90.51 300	98.74 262	99.05 175

hse-mvs2		95.77 197	95.09 219	97.79 107	97.84 243	95.51 97	95.66 211	95.43 327	96.58 103	97.21 191	96.16 299	84.14 320	99.54 176	95.89 119	96.92 340	98.32 265

TEST9					97.84 243	95.23 115	93.62 309	98.39 198	86.81 345	93.78 325	95.99 307	94.68 162	99.52 181

train_agg		95.46 212	94.66 240	97.88 102	97.84 243	95.23 115	93.62 309	98.39 198	87.04 341	93.78 325	95.99 307	94.58 166	99.52 181	91.76 269	98.90 244	98.89 201

MSLP-MVS++		96.42 173	96.71 151	95.57 247	97.82 246	90.56 252	95.71 206	98.84 121	94.72 191	96.71 229	97.39 226	94.91 157	98.10 374	95.28 156	99.02 233	98.05 294

test_8					97.81 247	95.07 124	93.54 312	98.38 200	87.04 341	93.71 329	95.96 310	94.58 166	99.52 181

NCCC		96.52 167	95.99 189	98.10 85	97.81 247	95.68 89	95.00 255	98.20 221	95.39 167	95.40 287	96.36 292	93.81 186	99.45 203	93.55 234	98.42 287	99.17 148

WTY-MVS		93.55 289	93.00 289	95.19 265	97.81 247	87.86 300	93.89 301	96.00 311	89.02 318	94.07 318	95.44 326	86.27 304	99.33 245	87.69 339	96.82 346	98.39 256

CNLPA		95.04 231	94.47 254	96.75 190	97.81 247	95.25 114	94.12 291	97.89 250	94.41 202	94.57 305	95.69 316	90.30 261	98.35 364	86.72 353	98.76 260	96.64 360

AUN-MVS		93.95 278	92.69 298	97.74 110	97.80 251	95.38 105	95.57 220	95.46 326	91.26 288	92.64 359	96.10 305	74.67 367	99.55 173	93.72 230	96.97 339	98.30 269

EIA-MVS		96.04 186	95.77 201	96.85 182	97.80 251	92.98 193	96.12 179	99.16 40	94.65 194	93.77 327	91.69 380	95.68 132	99.67 128	94.18 211	98.85 251	97.91 304

agg_prior					97.80 251	94.96 126		98.36 202		93.49 337			99.53 178

旧先验1					97.80 251	93.87 165		97.75 259			97.04 250	93.57 191			98.68 268	98.72 224

PCF-MVS	89.43 18	92.12 315	90.64 332	96.57 201	97.80 251	93.48 180	89.88 386	98.45 188	74.46 399	96.04 265	95.68 317	90.71 252	99.31 249	73.73 398	99.01 235	96.91 349
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior				97.46 137	97.79 256	94.26 154		98.42 194					99.34 243			98.79 214

PVSNet_BlendedMVS		95.02 234	94.93 226	95.27 261	97.79 256	87.40 312	94.14 289	98.68 161	88.94 320	94.51 307	98.01 172	93.04 201	99.30 252	89.77 312	99.49 142	99.11 164

PVSNet_Blended		93.96 276	93.65 275	94.91 280	97.79 256	87.40 312	91.43 360	98.68 161	84.50 371	94.51 307	94.48 345	93.04 201	99.30 252	89.77 312	98.61 276	98.02 297

USDC		94.56 255	94.57 251	94.55 300	97.78 259	86.43 328	92.75 330	98.65 171	85.96 352	96.91 219	97.93 181	90.82 250	98.74 326	90.71 294	99.59 103	98.47 250

alignmvs		96.01 188	95.52 209	97.50 131	97.77 260	94.71 131	96.07 182	96.84 295	97.48 73	96.78 226	94.28 348	85.50 311	99.40 221	96.22 100	98.73 265	98.40 254

ETV-MVS		96.13 183	95.90 195	96.82 185	97.76 261	93.89 164	95.40 228	98.95 96	95.87 143	95.58 283	91.00 386	96.36 106	99.72 87	93.36 237	98.83 254	96.85 352

D2MVS		95.18 224	95.17 215	95.21 264	97.76 261	87.76 305	94.15 287	97.94 247	89.77 310	96.99 212	97.68 204	87.45 295	99.14 282	95.03 176	99.81 48	98.74 221

DVP-MVS++		97.96 54	97.90 59	98.12 84	97.75 263	95.40 103	99.03 7	98.89 103	96.62 99	98.62 76	98.30 128	96.97 65	99.75 67	95.70 126	99.25 203	99.21 140

MSC_two_6792asdad				98.22 75	97.75 263	95.34 110		98.16 231					99.75 67	95.87 121	99.51 135	99.57 46

No_MVS				98.22 75	97.75 263	95.34 110		98.16 231					99.75 67	95.87 121	99.51 135	99.57 46

TSAR-MVS + GP.		96.47 170	96.12 182	97.49 134	97.74 266	95.23 115	94.15 287	96.90 294	93.26 238	98.04 144	96.70 273	94.41 171	98.89 313	94.77 189	99.14 216	98.37 258

3Dnovator	96.53 2	97.61 99	97.64 91	97.50 131	97.74 266	93.65 176	98.49 28	98.88 109	96.86 94	97.11 199	98.55 100	95.82 124	99.73 82	95.94 116	99.42 166	99.13 156

MM		96.87 143	96.62 155	97.62 119	97.72 268	93.30 185	96.39 155	92.61 361	97.90 52	96.76 227	98.64 92	90.46 255	99.81 36	99.16 9	99.94 8	99.76 17

sss		94.22 265	93.72 274	95.74 240	97.71 269	89.95 257	93.84 302	96.98 291	88.38 329	93.75 328	95.74 315	87.94 288	98.89 313	91.02 281	98.10 299	98.37 258

DeepC-MVS_fast	94.34 7	96.74 152	96.51 167	97.44 139	97.69 270	94.15 156	96.02 186	98.43 191	93.17 247	97.30 186	97.38 228	95.48 138	99.28 258	93.74 228	99.34 185	98.88 205
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IterMVS-SCA-FT		95.86 194	96.19 180	94.85 285	97.68 271	85.53 336	92.42 343	97.63 271	96.99 89	98.36 104	98.54 101	87.94 288	99.75 67	97.07 76	99.08 226	99.27 131

MVSFormer		96.14 182	96.36 174	95.49 253	97.68 271	87.81 303	98.67 15	99.02 75	96.50 108	94.48 309	96.15 300	86.90 300	99.92 5	98.73 22	99.13 218	98.74 221

lupinMVS		93.77 279	93.28 282	95.24 262	97.68 271	87.81 303	92.12 349	96.05 309	84.52 370	94.48 309	95.06 331	86.90 300	99.63 144	93.62 233	99.13 218	98.27 273

Fast-Effi-MVS+		95.49 208	95.07 220	96.75 190	97.67 274	92.82 195	94.22 283	98.60 174	91.61 281	93.42 341	92.90 362	96.73 84	99.70 110	92.60 251	97.89 309	97.74 317

testing3		89.72 344	88.26 353	94.10 316	97.66 275	84.30 356	94.80 261	88.25 393	94.66 193	95.07 293	92.51 370	41.15 411	99.43 208	91.81 267	98.44 286	98.55 242

canonicalmvs		97.23 125	97.21 123	97.30 149	97.65 276	94.39 145	97.84 70	99.05 66	97.42 75	96.68 230	93.85 352	97.63 34	99.33 245	96.29 97	98.47 284	98.18 281

CDS-MVSNet		94.88 238	94.12 266	97.14 160	97.64 277	93.57 177	93.96 299	97.06 289	90.05 306	96.30 252	96.55 280	86.10 305	99.47 196	90.10 307	99.31 195	98.40 254
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs5		94.63 252	94.34 259	95.50 252	97.63 278	88.34 287	94.02 293	97.13 285	87.15 340	95.22 291	97.15 242	87.50 294	99.27 261	93.99 220	99.26 202	98.88 205

test_f		95.82 196	95.88 197	95.66 244	97.61 279	93.21 190	95.61 217	98.17 227	86.98 343	98.42 96	99.47 11	90.46 255	94.74 398	97.71 52	98.45 285	99.03 176

test12				97.46 137	97.61 279	94.07 158		97.78 258		93.57 335		93.31 196	99.42 210		98.78 258	98.89 201

PMMVS2		93.66 285	94.07 267	92.45 356	97.57 281	80.67 381	86.46 394	96.00 311	93.99 217	97.10 200	97.38 228	89.90 265	97.82 378	88.76 325	99.47 148	98.86 208

BH-RMVSNet		94.56 255	94.44 257	94.91 280	97.57 281	87.44 311	93.78 306	96.26 307	93.69 225	96.41 245	96.50 285	92.10 230	99.00 302	85.96 355	97.71 317	98.31 267

PVSNet	86.72 19	91.10 329	90.97 326	91.49 365	97.56 283	78.04 390	87.17 393	94.60 337	84.65 369	92.34 363	92.20 374	87.37 297	98.47 355	85.17 366	97.69 319	97.96 301

DELS-MVS		96.17 181	96.23 178	95.99 227	97.55 284	90.04 255	92.38 346	98.52 182	94.13 211	96.55 240	97.06 248	94.99 154	99.58 161	95.62 134	99.28 199	98.37 258
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
IterMVS		95.42 214	95.83 198	94.20 313	97.52 285	83.78 360	92.41 344	97.47 276	95.49 162	98.06 141	98.49 105	87.94 288	99.58 161	96.02 110	99.02 233	99.23 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FA-MVS(test-final)		94.91 236	94.89 229	94.99 277	97.51 286	88.11 296	98.27 44	95.20 330	92.40 270	96.68 230	98.60 95	83.44 325	99.28 258	93.34 238	98.53 280	97.59 326

CL-MVSNet_self_test		95.04 231	94.79 237	95.82 237	97.51 286	89.79 259	91.14 369	96.82 297	93.05 250	96.72 228	96.40 290	90.82 250	99.16 280	91.95 262	98.66 271	98.50 248

new-patchmatchnet		95.67 201	96.58 159	92.94 343	97.48 288	80.21 383	92.96 325	98.19 226	94.83 188	98.82 61	98.79 75	93.31 196	99.51 185	95.83 123	99.04 232	99.12 161

MDA-MVSNet_test_wron		94.73 242	94.83 234	94.42 305	97.48 288	85.15 343	90.28 380	95.87 315	92.52 265	97.48 179	97.76 194	91.92 236	99.17 279	93.32 239	96.80 348	98.94 189

PHI-MVS		96.96 137	96.53 165	98.25 73	97.48 288	96.50 59	96.76 136	98.85 118	93.52 229	96.19 259	96.85 262	95.94 118	99.42 210	93.79 227	99.43 163	98.83 210

DeepPCF-MVS	94.58 5	96.90 141	96.43 170	98.31 67	97.48 288	97.23 40	92.56 337	98.60 174	92.84 259	98.54 83	97.40 222	96.64 88	98.78 322	94.40 203	99.41 170	98.93 193

thres200		91.00 331	90.42 335	92.77 348	97.47 292	83.98 359	94.01 294	91.18 375	95.12 178	95.44 285	91.21 384	73.93 369	99.31 249	77.76 393	97.63 324	95.01 383

YYNet1		94.73 242	94.84 232	94.41 306	97.47 292	85.09 345	90.29 379	95.85 316	92.52 265	97.53 173	97.76 194	91.97 233	99.18 275	93.31 240	96.86 343	98.95 187

Effi-MVS+		96.19 180	96.01 187	96.71 192	97.43 294	92.19 217	96.12 179	99.10 52	95.45 163	93.33 343	94.71 338	97.23 51	99.56 168	93.21 244	97.54 326	98.37 258

pmmvs4		94.82 240	94.19 264	96.70 193	97.42 295	92.75 200	92.09 351	96.76 299	86.80 346	95.73 279	97.22 239	89.28 276	98.89 313	93.28 241	99.14 216	98.46 252

mvsany_test3		96.21 179	95.93 194	97.05 168	97.40 296	94.33 149	95.76 205	94.20 341	89.10 316	99.36 24	99.60 6	93.97 182	97.85 377	95.40 154	98.63 274	98.99 183

MSDG		95.33 217	95.13 217	95.94 233	97.40 296	91.85 227	91.02 372	98.37 201	95.30 170	96.31 251	95.99 307	94.51 169	98.38 361	89.59 314	97.65 323	97.60 325

EI-MVSNet-Vis-set		97.32 122	97.39 112	97.11 162	97.36 298	92.08 222	95.34 234	97.65 267	97.74 57	98.29 116	98.11 157	95.05 150	99.68 122	97.50 60	99.50 139	99.56 50

PS-MVSNAJ		94.10 271	94.47 254	93.00 340	97.35 299	84.88 347	91.86 354	97.84 254	91.96 275	94.17 314	92.50 371	95.82 124	99.71 102	91.27 275	97.48 329	94.40 387

diffmvs		96.04 186	96.23 178	95.46 255	97.35 299	88.03 297	93.42 315	99.08 58	94.09 215	96.66 232	96.93 257	93.85 185	99.29 256	96.01 112	98.67 269	99.06 173
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EI-MVSNet-UG-set		97.32 122	97.40 111	97.09 166	97.34 301	92.01 224	95.33 235	97.65 267	97.74 57	98.30 115	98.14 151	95.04 151	99.69 117	97.55 58	99.52 130	99.58 39

baseline1		93.14 299	92.64 300	94.62 295	97.34 301	87.20 316	96.67 146	93.02 353	94.71 192	96.51 241	95.83 314	81.64 332	98.60 344	90.00 309	88.06 398	98.07 287

AdaColmap		95.11 228	94.62 245	96.58 199	97.33 303	94.45 144	94.92 257	98.08 239	93.15 248	93.98 323	95.53 323	94.34 173	99.10 292	85.69 358	98.61 276	96.20 370

xiu_mvs_v2_base		94.22 265	94.63 244	92.99 341	97.32 304	84.84 349	92.12 349	97.84 254	91.96 275	94.17 314	93.43 353	96.07 116	99.71 102	91.27 275	97.48 329	94.42 386

OpenMVS_ROB	91.80 14	93.64 287	93.05 285	95.42 257	97.31 305	91.21 239	95.08 249	96.68 304	81.56 380	96.88 221	96.41 288	90.44 257	99.25 264	85.39 363	97.67 321	95.80 374

EI-MVSNet		96.63 161	96.93 139	95.74 240	97.26 306	88.13 294	95.29 239	97.65 267	96.99 89	97.94 154	98.19 147	92.55 217	99.58 161	96.91 80	99.56 111	99.50 62

CVMVSNet		92.33 311	92.79 294	90.95 368	97.26 306	75.84 400	95.29 239	92.33 363	81.86 378	96.27 253	98.19 147	81.44 334	98.46 356	94.23 210	98.29 292	98.55 242

FE-MVS		92.95 301	92.22 305	95.11 269	97.21 308	88.33 288	98.54 23	93.66 347	89.91 308	96.21 257	98.14 151	70.33 385	99.50 186	87.79 337	98.24 294	97.51 329

Fast-Effi-MVS+-dtu		96.44 171	96.12 182	97.39 145	97.18 309	94.39 145	95.46 222	98.73 149	96.03 133	94.72 302	94.92 335	96.28 111	99.69 117	93.81 226	97.98 303	98.09 284

dmvs_re		92.08 317	91.27 320	94.51 302	97.16 310	92.79 199	95.65 213	92.64 360	94.11 213	92.74 355	90.98 387	83.41 326	94.44 400	80.72 384	94.07 384	96.29 368

OpenMVS	94.22 8	95.48 210	95.20 213	96.32 214	97.16 310	91.96 225	97.74 78	98.84 121	87.26 338	94.36 311	98.01 172	93.95 183	99.67 128	90.70 295	98.75 261	97.35 336

BH-w/o		92.14 314	91.94 308	92.73 349	97.13 312	85.30 339	92.46 340	95.64 319	89.33 314	94.21 313	92.74 366	89.60 268	98.24 369	81.68 381	94.66 380	94.66 385

MG-MVS		94.08 273	94.00 269	94.32 309	97.09 313	85.89 333	93.19 323	95.96 313	92.52 265	94.93 300	97.51 215	89.54 270	98.77 323	87.52 345	97.71 317	98.31 267

thisisatest0515		90.43 334	89.18 346	94.17 315	97.07 314	85.44 337	89.75 387	87.58 394	88.28 330	93.69 331	91.72 379	65.27 391	99.58 161	90.59 297	98.67 269	97.50 331

MVS-HIRNet		88.40 356	90.20 337	82.99 385	97.01 315	60.04 410	93.11 324	85.61 400	84.45 372	88.72 391	99.09 50	84.72 317	98.23 370	82.52 379	96.59 354	90.69 400

GA-MVS		92.83 303	92.15 307	94.87 284	96.97 316	87.27 315	90.03 381	96.12 308	91.83 278	94.05 319	94.57 339	76.01 363	98.97 310	92.46 255	97.34 335	98.36 263

test_yl		94.40 260	94.00 269	95.59 245	96.95 317	89.52 263	94.75 265	95.55 324	96.18 124	96.79 222	96.14 302	81.09 337	99.18 275	90.75 290	97.77 311	98.07 287

DCV-MVSNet		94.40 260	94.00 269	95.59 245	96.95 317	89.52 263	94.75 265	95.55 324	96.18 124	96.79 222	96.14 302	81.09 337	99.18 275	90.75 290	97.77 311	98.07 287

MVS_Test		96.27 177	96.79 149	94.73 292	96.94 319	86.63 325	96.18 173	98.33 206	94.94 185	96.07 263	98.28 133	95.25 146	99.26 262	97.21 68	97.90 308	98.30 269

MAR-MVS		94.21 267	93.03 287	97.76 109	96.94 319	97.44 33	96.97 124	97.15 284	87.89 336	92.00 366	92.73 367	92.14 228	99.12 286	83.92 372	97.51 328	96.73 359
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
Effi-MVS+-dtu		96.81 149	96.09 184	98.99 10	96.90 321	98.69 4	96.42 154	98.09 238	95.86 144	95.15 292	95.54 322	94.26 175	99.81 36	94.06 216	98.51 283	98.47 250

MS-PatchMatch		94.83 239	94.91 228	94.57 299	96.81 322	87.10 318	94.23 282	97.34 278	88.74 323	97.14 196	97.11 245	91.94 235	98.23 370	92.99 247	97.92 306	98.37 258

dmvs_testset		87.30 365	86.99 362	88.24 381	96.71 323	77.48 393	94.68 267	86.81 398	92.64 264	89.61 386	87.01 400	85.91 307	93.12 401	61.04 405	88.49 397	94.13 388

UGNet		96.81 149	96.56 161	97.58 121	96.64 324	93.84 167	97.75 76	97.12 286	96.47 111	93.62 332	98.88 71	93.22 198	99.53 178	95.61 135	99.69 78	99.36 112
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
API-MVS		95.09 230	95.01 223	95.31 260	96.61 325	94.02 160	96.83 130	97.18 283	95.60 156	95.79 274	94.33 347	94.54 168	98.37 363	85.70 357	98.52 281	93.52 391

iter_conf05_11		93.77 279	93.29 281	95.24 262	96.54 326	89.14 272	91.55 358	95.02 332	90.16 305	93.21 345	93.94 350	87.37 297	99.56 168	92.24 256	99.56 111	97.03 343

bld_raw_dy_0_64		95.16 227	95.16 216	95.15 268	96.54 326	89.06 274	96.63 147	99.54 17	89.68 311	98.72 72	94.50 344	88.64 281	99.38 228	92.24 256	99.93 11	97.03 343

PAPM		87.64 361	85.84 368	93.04 337	96.54 326	84.99 346	88.42 392	95.57 323	79.52 389	83.82 400	93.05 361	80.57 340	98.41 358	62.29 404	92.79 388	95.71 375

FMVSNet3		95.26 221	94.94 224	96.22 219	96.53 329	90.06 254	95.99 189	97.66 265	94.11 213	97.99 147	97.91 183	80.22 342	99.63 144	94.60 195	99.44 155	98.96 186

HY-MVS	91.43 15	92.58 306	91.81 311	94.90 282	96.49 330	88.87 277	97.31 104	94.62 336	85.92 353	90.50 377	96.84 263	85.05 313	99.40 221	83.77 375	95.78 369	96.43 366

TR-MVS		92.54 307	92.20 306	93.57 325	96.49 330	86.66 324	93.51 313	94.73 335	89.96 307	94.95 298	93.87 351	90.24 263	98.61 342	81.18 383	94.88 378	95.45 380

ET-MVSNet_ETH3D		91.12 328	89.67 340	95.47 254	96.41 332	89.15 271	91.54 359	90.23 384	89.07 317	86.78 399	92.84 364	69.39 387	99.44 206	94.16 212	96.61 353	97.82 311

CANet		95.86 194	95.65 205	96.49 205	96.41 332	90.82 245	94.36 275	98.41 195	94.94 185	92.62 361	96.73 272	92.68 211	99.71 102	95.12 171	99.60 101	98.94 189

mvs_anonymous		95.36 215	96.07 186	93.21 334	96.29 334	81.56 375	94.60 270	97.66 265	93.30 237	96.95 216	98.91 68	93.03 203	99.38 228	96.60 86	97.30 337	98.69 228

SCA		93.38 294	93.52 278	92.96 342	96.24 335	81.40 377	93.24 321	94.00 342	91.58 283	94.57 305	96.97 254	87.94 288	99.42 210	89.47 316	97.66 322	98.06 291

LS3D		97.77 86	97.50 108	98.57 47	96.24 335	97.58 24	98.45 31	98.85 118	98.58 28	97.51 175	97.94 179	95.74 131	99.63 144	95.19 161	98.97 236	98.51 246

new_pmnet		92.34 310	91.69 314	94.32 309	96.23 337	89.16 270	92.27 347	92.88 355	84.39 373	95.29 289	96.35 293	85.66 309	96.74 393	84.53 370	97.56 325	97.05 341

MVE	73.61 22	86.48 368	85.92 367	88.18 382	96.23 337	85.28 341	81.78 400	75.79 406	86.01 351	82.53 402	91.88 377	92.74 209	87.47 405	71.42 402	94.86 379	91.78 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
c3_l		95.20 223	95.32 210	94.83 287	96.19 339	86.43 328	91.83 355	98.35 205	93.47 231	97.36 185	97.26 237	88.69 279	99.28 258	95.41 153	99.36 177	98.78 215

DSMNet-mixed		92.19 313	91.83 310	93.25 331	96.18 340	83.68 361	96.27 165	93.68 346	76.97 397	92.54 362	99.18 39	89.20 278	98.55 348	83.88 373	98.60 278	97.51 329

miper_lstm_enhance		94.81 241	94.80 236	94.85 285	96.16 341	86.45 327	91.14 369	98.20 221	93.49 230	97.03 209	97.37 230	84.97 315	99.26 262	95.28 156	99.56 111	98.83 210

our_test_3		94.20 269	94.58 249	93.07 336	96.16 341	81.20 378	90.42 378	96.84 295	90.72 294	97.14 196	97.13 243	90.47 254	99.11 289	94.04 219	98.25 293	98.91 197

ppachtmachnet_test		94.49 259	94.84 232	93.46 327	96.16 341	82.10 370	90.59 376	97.48 275	90.53 298	97.01 211	97.59 209	91.01 247	99.36 237	93.97 222	99.18 212	98.94 189

ETVMVS		87.62 362	85.75 369	93.22 333	96.15 344	83.26 362	92.94 326	90.37 382	91.39 285	90.37 378	88.45 396	51.93 408	98.64 339	73.76 397	96.38 357	97.75 316

Patchmatch-test		93.60 288	93.25 283	94.63 294	96.14 345	87.47 309	96.04 184	94.50 338	93.57 228	96.47 242	96.97 254	76.50 359	98.61 342	90.67 296	98.41 288	97.81 313

iter_conf05		93.65 286	93.05 285	95.46 255	96.13 346	87.45 310	95.95 195	98.22 217	92.66 263	97.04 208	97.89 184	63.52 394	99.72 87	96.19 102	99.82 47	99.21 140

wuyk23d		93.25 297	95.20 213	87.40 384	96.07 347	95.38 105	97.04 121	94.97 333	95.33 168	99.70 6	98.11 157	98.14 17	91.94 402	77.76 393	99.68 82	74.89 402

eth_miper_zixun_eth		94.89 237	94.93 226	94.75 291	95.99 348	86.12 331	91.35 362	98.49 185	93.40 232	97.12 198	97.25 238	86.87 302	99.35 241	95.08 173	98.82 255	98.78 215

test_fmvs1		94.51 258	94.60 246	94.26 312	95.91 349	87.92 298	95.35 233	99.02 75	86.56 348	96.79 222	98.52 102	82.64 330	97.00 388	97.87 43	98.71 266	97.88 307

testing91		89.67 345	88.55 350	93.04 337	95.90 350	81.80 374	92.71 334	93.71 343	93.71 223	90.18 381	90.15 392	57.11 397	99.22 272	87.17 350	96.32 359	98.12 283

CANet_DTU		94.65 251	94.21 263	95.96 229	95.90 350	89.68 260	93.92 300	97.83 256	93.19 243	90.12 382	95.64 319	88.52 282	99.57 167	93.27 242	99.47 148	98.62 235

testing11		88.93 351	87.63 359	92.80 347	95.87 352	81.49 376	92.48 339	91.54 370	91.62 280	88.27 393	90.24 390	55.12 406	99.11 289	87.30 348	96.28 361	97.81 313

DIV-MVS_self_test		94.73 242	94.64 242	95.01 275	95.86 353	87.00 319	91.33 363	98.08 239	93.34 235	97.10 200	97.34 232	84.02 322	99.31 249	95.15 167	99.55 118	98.72 224

cl____		94.73 242	94.64 242	95.01 275	95.85 354	87.00 319	91.33 363	98.08 239	93.34 235	97.10 200	97.33 233	84.01 323	99.30 252	95.14 168	99.56 111	98.71 227

MVSTER		94.21 267	93.93 272	95.05 273	95.83 355	86.46 326	95.18 244	97.65 267	92.41 269	97.94 154	98.00 174	72.39 378	99.58 161	96.36 95	99.56 111	99.12 161

FMVSNet5		93.39 293	92.35 303	96.50 204	95.83 355	90.81 247	97.31 104	98.27 211	92.74 261	96.27 253	98.28 133	62.23 395	99.67 128	90.86 285	99.36 177	99.03 176

testing222		87.35 364	85.50 371	92.93 344	95.79 357	82.83 364	92.40 345	90.10 386	92.80 260	88.87 390	89.02 395	48.34 409	98.70 331	75.40 396	96.74 349	97.27 338

testing99		89.21 349	88.04 355	92.70 350	95.78 358	81.00 380	92.65 335	92.03 364	93.20 242	89.90 385	90.08 394	55.25 403	99.14 282	87.54 343	95.95 365	97.97 300

miper_ehance_all_eth		94.69 247	94.70 239	94.64 293	95.77 359	86.22 330	91.32 365	98.24 215	91.67 279	97.05 207	96.65 276	88.39 285	99.22 272	94.88 181	98.34 289	98.49 249

test_vis1_rt		94.03 275	93.65 275	95.17 267	95.76 360	93.42 182	93.97 298	98.33 206	84.68 368	93.17 346	95.89 313	92.53 221	94.79 397	93.50 235	94.97 377	97.31 337

PVSNet_0	81.89 21	84.49 369	83.21 372	88.34 380	95.76 360	74.97 403	83.49 397	92.70 359	78.47 393	87.94 394	86.90 401	83.38 327	96.63 394	73.44 399	66.86 405	93.40 392

PAPR		92.22 312	91.27 320	95.07 272	95.73 362	88.81 279	91.97 352	97.87 251	85.80 355	90.91 373	92.73 367	91.16 244	98.33 365	79.48 387	95.76 370	98.08 285

baseline2		89.65 346	88.44 352	93.25 331	95.62 363	82.71 365	93.82 303	85.94 399	88.89 321	87.35 397	92.54 369	71.23 381	99.33 245	86.01 354	94.60 382	97.72 318

CHOSEN 280x420		89.98 339	89.19 345	92.37 357	95.60 364	81.13 379	86.22 395	97.09 287	81.44 382	87.44 396	93.15 354	73.99 368	99.47 196	88.69 327	99.07 228	96.52 364

ADS-MVSNet2		91.47 326	90.51 334	94.36 307	95.51 365	85.63 334	95.05 252	95.70 317	83.46 374	92.69 356	96.84 263	79.15 345	99.41 219	85.66 359	90.52 392	98.04 295

ADS-MVSNet		90.95 332	90.26 336	93.04 337	95.51 365	82.37 369	95.05 252	93.41 350	83.46 374	92.69 356	96.84 263	79.15 345	98.70 331	85.66 359	90.52 392	98.04 295

CR-MVSNet		93.29 296	92.79 294	94.78 290	95.44 367	88.15 292	96.18 173	97.20 281	84.94 367	94.10 316	98.57 97	77.67 351	99.39 225	95.17 163	95.81 366	96.81 356

RPMNet		94.68 249	94.60 246	94.90 282	95.44 367	88.15 292	96.18 173	98.86 114	97.43 74	94.10 316	98.49 105	79.40 343	99.76 61	95.69 128	95.81 366	96.81 356

1314		92.38 309	92.30 304	92.64 351	95.42 369	85.15 343	95.86 200	96.97 292	85.40 360	90.62 374	93.06 360	91.12 245	97.80 379	86.74 352	95.49 374	94.97 384

tpm		91.08 330	90.85 328	91.75 364	95.33 370	78.09 389	95.03 254	91.27 374	88.75 322	93.53 336	97.40 222	71.24 380	99.30 252	91.25 277	93.87 385	97.87 308

UWE-MVS		87.57 363	86.72 365	90.13 374	95.21 371	73.56 404	91.94 353	83.78 403	88.73 324	93.00 349	92.87 363	55.22 404	99.25 264	81.74 380	97.96 304	97.59 326

Syy-MVS		92.09 316	91.80 312	92.93 344	95.19 372	82.65 366	92.46 340	91.35 371	90.67 296	91.76 369	87.61 398	85.64 310	98.50 352	94.73 191	96.84 344	97.65 321

myMVS_eth3d		87.16 367	85.61 370	91.82 363	95.19 372	79.32 385	92.46 340	91.35 371	90.67 296	91.76 369	87.61 398	41.96 410	98.50 352	82.66 378	96.84 344	97.65 321

IB-MVS	85.98 20	88.63 354	86.95 364	93.68 323	95.12 374	84.82 350	90.85 373	90.17 385	87.55 337	88.48 392	91.34 383	58.01 396	99.59 159	87.24 349	93.80 386	96.63 362
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
PatchT		93.75 281	93.57 277	94.29 311	95.05 375	87.32 314	96.05 183	92.98 354	97.54 70	94.25 312	98.72 82	75.79 364	99.24 268	95.92 117	95.81 366	96.32 367

tpm2		88.47 355	87.69 358	90.79 369	94.98 376	77.34 394	95.09 247	91.83 367	77.51 396	89.40 387	96.41 288	67.83 389	98.73 327	83.58 377	92.60 390	96.29 368

WB-MVSnew		91.50 325	91.29 318	92.14 360	94.85 377	80.32 382	93.29 320	88.77 391	88.57 326	94.03 320	92.21 373	92.56 216	98.28 368	80.21 386	97.08 338	97.81 313

Patchmtry		95.03 233	94.59 248	96.33 213	94.83 378	90.82 245	96.38 158	97.20 281	96.59 102	97.49 177	98.57 97	77.67 351	99.38 228	92.95 249	99.62 92	98.80 213

MVS		90.02 337	89.20 344	92.47 355	94.71 379	86.90 321	95.86 200	96.74 301	64.72 402	90.62 374	92.77 365	92.54 219	98.39 360	79.30 388	95.56 373	92.12 395

CostFormer		89.75 343	89.25 341	91.26 367	94.69 380	78.00 391	95.32 236	91.98 366	81.50 381	90.55 376	96.96 256	71.06 382	98.89 313	88.59 329	92.63 389	96.87 350

PatchmatchNet		91.98 319	91.87 309	92.30 358	94.60 381	79.71 384	95.12 245	93.59 349	89.52 312	93.61 333	97.02 251	77.94 349	99.18 275	90.84 286	94.57 383	98.01 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		88.01 359	87.33 360	90.05 375	94.48 382	76.28 399	94.47 273	94.35 340	73.84 401	89.26 388	95.61 321	73.64 372	98.30 367	84.13 371	86.20 400	95.57 379

MDTV_nov1_ep13			91.28 319		94.31 383	73.51 405	94.80 261	93.16 352	86.75 347	93.45 339	97.40 222	76.37 360	98.55 348	88.85 324	96.43 355

cl22		93.25 297	92.84 293	94.46 304	94.30 384	86.00 332	91.09 371	96.64 305	90.74 293	95.79 274	96.31 294	78.24 348	98.77 323	94.15 213	98.34 289	98.62 235

cascas		91.89 320	91.35 317	93.51 326	94.27 385	85.60 335	88.86 391	98.61 173	79.32 390	92.16 365	91.44 382	89.22 277	98.12 373	90.80 288	97.47 331	96.82 355

test-LLR		89.97 340	89.90 338	90.16 372	94.24 386	74.98 401	89.89 383	89.06 389	92.02 273	89.97 383	90.77 388	73.92 370	98.57 345	91.88 264	97.36 333	96.92 347

test-mter		87.92 360	87.17 361	90.16 372	94.24 386	74.98 401	89.89 383	89.06 389	86.44 349	89.97 383	90.77 388	54.96 407	98.57 345	91.88 264	97.36 333	96.92 347

pmmvs3		90.00 338	88.90 348	93.32 328	94.20 388	85.34 338	91.25 366	92.56 362	78.59 392	93.82 324	95.17 328	67.36 390	98.69 333	89.08 322	98.03 302	95.92 371

tpmrst		90.31 335	90.61 333	89.41 376	94.06 389	72.37 407	95.06 251	93.69 344	88.01 333	92.32 364	96.86 261	77.45 353	98.82 318	91.04 280	87.01 399	97.04 342

mvsany_test1		93.47 291	93.03 287	94.79 289	94.05 390	92.12 218	90.82 374	90.01 387	85.02 365	97.26 188	98.28 133	93.57 191	97.03 386	92.51 254	95.75 371	95.23 382

test0.0.03 1		90.11 336	89.21 343	92.83 346	93.89 391	86.87 322	91.74 356	88.74 392	92.02 273	94.71 303	91.14 385	73.92 370	94.48 399	83.75 376	92.94 387	97.16 339

JIA-IIPM		91.79 321	90.69 331	95.11 269	93.80 392	90.98 242	94.16 286	91.78 368	96.38 112	90.30 380	99.30 28	72.02 379	98.90 312	88.28 333	90.17 394	95.45 380

miper_enhance_ethall		93.14 299	92.78 296	94.20 313	93.65 393	85.29 340	89.97 382	97.85 252	85.05 363	96.15 262	94.56 340	85.74 308	99.14 282	93.74 228	98.34 289	98.17 282

TESTMET0.1,1		87.20 366	86.57 366	89.07 377	93.62 394	72.84 406	89.89 383	87.01 397	85.46 359	89.12 389	90.20 391	56.00 402	97.72 380	90.91 284	96.92 340	96.64 360

CMPMVS	73.10 23	92.74 304	91.39 316	96.77 189	93.57 395	94.67 134	94.21 284	97.67 263	80.36 387	93.61 333	96.60 278	82.85 329	97.35 383	84.86 368	98.78 258	98.29 272
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
E-PMN		89.52 347	89.78 339	88.73 378	93.14 396	77.61 392	83.26 398	92.02 365	94.82 189	93.71 329	93.11 355	75.31 365	96.81 390	85.81 356	96.81 347	91.77 397

PMMVS		92.39 308	91.08 323	96.30 216	93.12 397	92.81 196	90.58 377	95.96 313	79.17 391	91.85 368	92.27 372	90.29 262	98.66 338	89.85 311	96.68 352	97.43 332

EMVS		89.06 350	89.22 342	88.61 379	93.00 398	77.34 394	82.91 399	90.92 376	94.64 195	92.63 360	91.81 378	76.30 361	97.02 387	83.83 374	96.90 342	91.48 398

dp		88.08 358	88.05 354	88.16 383	92.85 399	68.81 409	94.17 285	92.88 355	85.47 358	91.38 372	96.14 302	68.87 388	98.81 320	86.88 351	83.80 402	96.87 350

gg-mvs-nofinetune		88.28 357	86.96 363	92.23 359	92.84 400	84.44 353	98.19 52	74.60 407	99.08 10	87.01 398	99.47 11	56.93 398	98.23 370	78.91 389	95.61 372	94.01 389

tpmvs		90.79 333	90.87 327	90.57 371	92.75 401	76.30 398	95.79 204	93.64 348	91.04 291	91.91 367	96.26 295	77.19 357	98.86 317	89.38 318	89.85 395	96.56 363

EPMVS		89.26 348	88.55 350	91.39 366	92.36 402	79.11 387	95.65 213	79.86 405	88.60 325	93.12 347	96.53 282	70.73 384	98.10 374	90.75 290	89.32 396	96.98 345

gm-plane-assit					91.79 403	71.40 408			81.67 379		90.11 393		98.99 304	84.86 368

GG-mvs-BLEND				90.60 370	91.00 404	84.21 357	98.23 46	72.63 410		82.76 401	84.11 402	56.14 401	96.79 391	72.20 400	92.09 391	90.78 399

DeepMVS_CX				77.17 386	90.94 405	85.28 341		74.08 409	52.51 403	80.87 404	88.03 397	75.25 366	70.63 406	59.23 406	84.94 401	75.62 401

EPNet_dtu		91.39 327	90.75 330	93.31 329	90.48 406	82.61 367	94.80 261	92.88 355	93.39 233	81.74 403	94.90 336	81.36 335	99.11 289	88.28 333	98.87 248	98.21 278
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*1600		88.93 351	87.74 356	92.49 353	88.04 407	81.99 371	89.63 388	95.62 320	91.35 286	95.06 294	93.11 355	56.58 399	98.63 340	85.19 364	95.07 375	96.85 352

miper_refine_blended		88.93 351	87.74 356	92.49 353	88.04 407	81.99 371	89.63 388	95.62 320	91.35 286	95.06 294	93.11 355	56.58 399	98.63 340	85.19 364	95.07 375	96.85 352

EPNet		93.72 282	92.62 301	97.03 171	87.61 409	92.25 211	96.27 165	91.28 373	96.74 97	87.65 395	97.39 226	85.00 314	99.64 140	92.14 259	99.48 146	99.20 144
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_method		66.88 371	66.13 374	69.11 387	62.68 410	25.73 413	49.76 401	96.04 310	14.32 405	64.27 406	91.69 380	73.45 375	88.05 404	76.06 395	66.94 404	93.54 390

tmp_tt		57.23 372	62.50 375	41.44 388	34.77 411	49.21 412	83.93 396	60.22 412	15.31 404	71.11 405	79.37 403	70.09 386	44.86 407	64.76 403	82.93 403	30.25 403

test123		12.59 374	15.49 377	3.87 389	6.07 412	2.55 414	90.75 375	2.59 414	2.52 407	5.20 409	13.02 406	4.96 412	1.85 409	5.20 407	9.09 406	7.23 404

testmvs		12.33 375	15.23 378	3.64 390	5.77 413	2.23 415	88.99 390	3.62 413	2.30 408	5.29 408	13.09 405	4.52 413	1.95 408	5.16 408	8.32 407	6.75 405

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

eth-test2					0.00 414

eth-test					0.00 414

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		24.22 373	32.30 376	0.00 391	0.00 414	0.00 416	0.00 402	98.10 237	0.00 409	0.00 410	95.06 331	97.54 37	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		7.98 376	10.65 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	95.82 124	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		7.91 377	10.55 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	94.94 333	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						79.32 385								85.41 362

PC_three_1452									87.24 339	98.37 101	97.44 219	97.00 63	96.78 392	92.01 260	99.25 203	99.21 140

test_241102_TWO								98.83 127	96.11 126	98.62 76	98.24 140	96.92 71	99.72 87	95.44 147	99.49 142	99.49 70

test_0728_THIRD									96.62 99	98.40 98	98.28 133	97.10 54	99.71 102	95.70 126	99.62 92	99.58 39

GSMVS																98.06 291

sam_mvs1												77.80 350				98.06 291

sam_mvs												77.38 354

MTGPA								98.73 149

test_post1							94.98 256				10.37 408	76.21 362	99.04 298	89.47 316

test_post											10.87 407	76.83 358	99.07 295

patchmatchnet-post											96.84 263	77.36 355	99.42 210

MTMP							96.55 149	74.60 407

test9_res														91.29 274	98.89 247	99.00 180

agg_prior2														90.34 305	98.90 244	99.10 168

test_prior4						95.38 105	93.61 311

test_prior2							93.33 319		94.21 207	94.02 321	96.25 296	93.64 190		91.90 263	98.96 237

旧先验2							93.35 318		77.95 395	95.77 278			98.67 337	90.74 293

新几何2							93.43 314

无先验							93.20 322	97.91 248	80.78 384				99.40 221	87.71 338		97.94 303

原ACMM2							92.82 328

testdata2													99.46 199	87.84 336

segment_acmp												95.34 143

testdata1							92.77 329		93.78 221

plane_prior5								98.75 146					99.46 199	92.59 252	99.20 208	99.28 127

plane_prior4											96.77 269

plane_prior3						94.51 141			95.29 171	96.16 260

plane_prior2							96.50 151		96.36 114

plane_prior						94.29 150	95.42 225		94.31 206						98.93 242

n2								0.00 415

nn								0.00 415

door-mid								98.17 227

test11								98.08 239

door								97.81 257

HQP5-MVS						92.47 206

BP-MVS														90.51 300

HQP4-MVS										92.87 351			99.23 270			99.06 173

HQP3-MVS								98.43 191							98.74 262

HQP2-MVS												90.33 258

MDTV_nov1_ep13_2view						57.28 411	94.89 258		80.59 385	94.02 321		78.66 347		85.50 361		97.82 311

ACMMP++_ref															99.52 130

ACMMP++															99.55 118

Test By Simon												94.51 169

High-resolution multi-view benchmark