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 1	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 47	99.81 2	96.38 58	98.87 7	99.30 8	99.01 16	99.63 9	99.66 3	99.27 2	99.68 110	97.75 29	99.89 20	99.62 24

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 41	99.77 3	96.34 60	99.18 5	99.20 13	99.67 2	99.73 3	99.65 4	99.15 3	99.86 20	97.22 43	99.92 12	99.77 8

XVG-OURS-SEG-HR		97.38 95	97.07 107	98.30 65	99.01 90	97.41 31	94.66 227	99.02 49	95.20 147	98.15 97	97.52 173	98.83 4	98.43 317	94.87 140	96.41 310	99.07 148

ACMH	93.61 9	98.44 22	98.76 13	97.51 118	99.43 32	93.54 165	98.23 32	99.05 40	97.40 67	99.37 18	99.08 34	98.79 5	99.47 175	97.74 30	99.71 49	99.50 42
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		98.90 5	98.94 6	98.75 31	99.69 8	96.48 56	98.54 18	99.22 10	96.23 99	99.71 4	99.48 7	98.77 6	99.93 2	98.89 3	99.95 5	99.84 5

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 36	99.71 7	96.99 41	99.69 2	99.57 3	99.02 15	99.62 10	99.36 14	98.53 7	99.52 163	98.58 12	99.95 5	99.66 21
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
TransMVSNet (Re)		98.38 25	98.67 17	97.51 118	99.51 22	93.39 169	98.20 37	98.87 81	98.23 34	99.48 12	99.27 19	98.47 8	99.55 155	96.52 63	99.53 93	99.60 25

pm-mvs1		98.47 21	98.67 17	97.86 95	99.52 21	94.58 125	98.28 29	99.00 57	97.57 57	99.27 23	99.22 22	98.32 9	99.50 168	97.09 50	99.75 41	99.50 42

jajsoiax		98.77 9	98.79 12	98.74 33	99.66 10	96.48 56	98.45 23	99.12 25	95.83 124	99.67 6	99.37 12	98.25 10	99.92 4	98.77 5	99.94 8	99.82 6

ACMH+	93.58 10	98.23 32	98.31 29	97.98 88	99.39 37	95.22 104	97.55 72	99.20 13	98.21 35	99.25 24	98.51 70	98.21 11	99.40 199	94.79 144	99.72 46	99.32 94

HPM-MVS_fast		98.32 27	98.13 33	98.88 22	99.54 19	97.48 27	98.35 26	99.03 47	95.88 119	97.88 127	98.22 102	98.15 12	99.74 63	96.50 65	99.62 62	99.42 77

wuyk23d		93.25 261	95.20 183	87.40 331	96.07 300	95.38 94	97.04 97	94.97 294	95.33 142	99.70 5	98.11 112	98.14 13	91.94 347	77.76 339	99.68 55	74.89 346

ACMM	93.33 11	98.05 39	97.79 50	98.85 23	99.15 70	97.55 23	96.68 115	98.83 95	95.21 146	98.36 74	98.13 108	98.13 14	99.62 134	96.04 79	99.54 90	99.39 83
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 37	97.83 48	98.92 20	99.42 34	97.46 28	98.57 15	99.05 40	95.43 140	97.41 153	97.50 175	97.98 15	99.79 38	95.58 102	99.57 78	99.50 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testgi		96.07 160	96.50 141	94.80 256	99.26 47	87.69 270	95.96 153	98.58 149	95.08 153	98.02 113	96.25 255	97.92 16	97.60 337	88.68 283	98.74 231	99.11 141

LPG-MVS_test		97.94 49	97.67 60	98.74 33	99.15 70	97.02 39	97.09 94	99.02 49	95.15 150	98.34 77	98.23 99	97.91 17	99.70 96	94.41 159	99.73 43	99.50 42

LGP-MVS_train				98.74 33	99.15 70	97.02 39		99.02 49	95.15 150	98.34 77	98.23 99	97.91 17	99.70 96	94.41 159	99.73 43	99.50 42

abl_6		98.42 23	98.19 32	99.09 3	99.16 67	98.10 5	97.73 63	99.11 26	97.76 46	98.62 50	98.27 95	97.88 19	99.80 37	95.67 93	99.50 105	99.38 85

SED-MVS		97.94 49	97.90 43	98.07 80	99.22 56	95.35 96	96.79 106	98.83 95	96.11 103	99.08 30	98.24 97	97.87 20	99.72 72	95.44 108	99.51 103	99.14 130

test_241102_ONE					99.22 56	95.35 96		98.83 95	96.04 108	99.08 30	98.13 108	97.87 20	99.33 221

SD-MVS		97.37 96	97.70 57	96.35 194	98.14 177	95.13 107	96.54 118	98.92 71	95.94 115	99.19 27	98.08 114	97.74 22	95.06 345	95.24 119	99.54 90	98.87 183
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
DeepC-MVS	95.41 4	97.82 64	97.70 57	98.16 73	98.78 104	95.72 77	96.23 136	99.02 49	93.92 195	98.62 50	98.99 37	97.69 23	99.62 134	96.18 73	99.87 22	99.15 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		98.54 18	98.62 21	98.32 62	99.22 56	95.66 83	97.90 52	99.08 34	98.31 31	99.02 33	98.74 53	97.68 24	99.61 140	97.77 28	99.85 25	99.70 18

ANet_high		98.31 28	98.94 6	96.41 193	99.33 42	89.64 232	97.92 51	99.56 4	99.27 6	99.66 8	99.50 6	97.67 25	99.83 28	97.55 34	99.98 2	99.77 8

canonicalmvs		97.23 106	97.21 99	97.30 141	97.65 234	94.39 130	97.84 55	99.05 40	97.42 63	96.68 191	93.85 309	97.63 26	99.33 221	96.29 70	98.47 249	98.18 246

TranMVSNet+NR-MVSNet		98.33 26	98.30 31	98.43 54	99.07 84	95.87 73	96.73 113	99.05 40	98.67 22	98.84 39	98.45 74	97.58 27	99.88 18	96.45 67	99.86 23	99.54 35

cdsmvs_eth3d_5k		24.22 321	32.30 323	0.00 337	0.00 356	0.00 357	0.00 348	98.10 205	0.00 352	0.00 353	95.06 289	97.54 28	0.00 354	0.00 351	0.00 351	0.00 350

ACMP	92.54 13	97.47 88	97.10 104	98.55 48	99.04 88	96.70 48	96.24 135	98.89 74	93.71 199	97.97 118	97.75 154	97.44 29	99.63 126	93.22 200	99.70 52	99.32 94
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		98.73 11	98.74 16	98.69 38	99.63 12	96.30 62	98.67 11	99.02 49	96.50 88	99.32 20	99.44 10	97.43 30	99.92 4	98.73 7	99.95 5	99.86 2

TDRefinement		98.90 5	98.86 8	99.02 8	99.54 19	98.06 6	99.34 4	99.44 6	98.85 19	99.00 35	99.20 23	97.42 31	99.59 142	97.21 44	99.76 37	99.40 80

anonymousdsp		98.72 14	98.63 19	98.99 11	99.62 13	97.29 34	98.65 14	99.19 15	95.62 131	99.35 19	99.37 12	97.38 32	99.90 13	98.59 11	99.91 15	99.77 8

PS-CasMVS		98.73 11	98.85 10	98.39 57	99.55 17	95.47 92	98.49 20	99.13 24	99.22 8	99.22 26	98.96 40	97.35 33	99.92 4	97.79 27	99.93 10	99.79 7

COLMAP_ROB	94.48 6	98.25 31	98.11 34	98.64 42	99.21 62	97.35 32	97.96 48	99.16 17	98.34 30	98.78 42	98.52 69	97.32 34	99.45 182	94.08 174	99.67 56	99.13 133
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS		97.69 72	97.79 50	97.40 135	99.06 85	93.52 166	95.96 153	98.97 66	94.55 174	98.82 40	98.76 52	97.31 35	99.29 232	97.20 46	99.44 123	99.38 85

XXY-MVS		97.54 82	97.70 57	97.07 152	99.46 28	92.21 191	97.22 88	99.00 57	94.93 161	98.58 55	98.92 44	97.31 35	99.41 197	94.44 157	99.43 130	99.59 26

PEN-MVS		98.75 10	98.85 10	98.44 53	99.58 14	95.67 82	98.45 23	99.15 21	99.33 5	99.30 21	99.00 36	97.27 37	99.92 4	97.64 32	99.92 12	99.75 13

DTE-MVSNet		98.79 8	98.86 8	98.59 45	99.55 17	96.12 67	98.48 22	99.10 28	99.36 4	99.29 22	99.06 35	97.27 37	99.93 2	97.71 31	99.91 15	99.70 18

ZNCC-MVS		97.92 53	97.62 70	98.83 24	99.32 44	97.24 36	97.45 76	98.84 87	95.76 126	96.93 180	97.43 180	97.26 39	99.79 38	96.06 76	99.53 93	99.45 65

MP-MVS-pluss		97.69 72	97.36 87	98.70 37	99.50 25	96.84 44	95.38 184	98.99 60	92.45 234	98.11 100	98.31 84	97.25 40	99.77 48	96.60 59	99.62 62	99.48 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		97.89 57	97.63 68	98.67 39	99.35 41	96.84 44	96.36 127	98.79 105	95.07 154	97.88 127	98.35 80	97.24 41	99.72 72	96.05 78	99.58 75	99.45 65

Effi-MVS+		96.19 156	96.01 159	96.71 172	97.43 249	92.19 194	96.12 141	99.10 28	95.45 138	93.33 299	94.71 296	97.23 42	99.56 151	93.21 201	97.54 286	98.37 225

PGM-MVS		97.88 58	97.52 78	98.96 14	99.20 63	97.62 18	97.09 94	99.06 38	95.45 138	97.55 139	97.94 133	97.11 43	99.78 40	94.77 147	99.46 118	99.48 55

test_0728_THIRD									96.62 83	98.40 69	98.28 91	97.10 44	99.71 87	95.70 91	99.62 62	99.58 27

APD-MVS_3200maxsize		98.13 36	97.90 43	98.79 29	98.79 102	97.31 33	97.55 72	98.92 71	97.72 50	98.25 88	98.13 108	97.10 44	99.75 56	95.44 108	99.24 176	99.32 94

OPM-MVS		97.54 82	97.25 94	98.41 55	99.11 80	96.61 52	95.24 197	98.46 158	94.58 173	98.10 103	98.07 116	97.09 46	99.39 204	95.16 125	99.44 123	99.21 118

HFP-MVS		97.94 49	97.64 66	98.83 24	99.15 70	97.50 25	97.59 69	98.84 87	96.05 106	97.49 144	97.54 170	97.07 47	99.70 96	95.61 99	99.46 118	99.30 100

#test#		97.62 76	97.22 98	98.83 24	99.15 70	97.50 25	96.81 105	98.84 87	94.25 183	97.49 144	97.54 170	97.07 47	99.70 96	94.37 162	99.46 118	99.30 100

MSP-MVS		97.78 67	97.65 63	98.16 73	99.24 51	95.51 89	96.74 109	98.23 187	95.92 116	98.40 69	98.28 91	97.06 49	99.71 87	95.48 104	99.52 98	99.26 112

test0726					99.24 51	95.51 89	96.89 102	98.89 74	95.92 116	98.64 49	98.31 84	97.06 49

casdiffmvs		97.50 85	97.81 49	96.56 184	98.51 136	91.04 213	95.83 161	99.09 33	97.23 72	98.33 80	98.30 88	97.03 51	99.37 211	96.58 61	99.38 143	99.28 107

SteuartSystems-ACMMP		98.02 41	97.76 54	98.79 29	99.43 32	97.21 38	97.15 90	98.90 73	96.58 86	98.08 106	97.87 142	97.02 52	99.76 52	95.25 118	99.59 73	99.40 80
Skip Steuart: Steuart Systems R&D Blog.
OPU-MVS				97.64 109	98.01 187	95.27 99	96.79 106				97.35 191	96.97 53	98.51 314	91.21 232	99.25 175	99.14 130

APDe-MVS		98.14 34	98.03 39	98.47 52	98.72 109	96.04 69	98.07 44	99.10 28	95.96 113	98.59 54	98.69 57	96.94 54	99.81 31	96.64 58	99.58 75	99.57 31

GST-MVS		97.82 64	97.49 81	98.81 27	99.23 53	97.25 35	97.16 89	98.79 105	95.96 113	97.53 140	97.40 182	96.93 55	99.77 48	95.04 134	99.35 152	99.42 77

test_241102_TWO								98.83 95	96.11 103	98.62 50	98.24 97	96.92 56	99.72 72	95.44 108	99.49 109	99.49 50

LCM-MVSNet-Re		97.33 99	97.33 89	97.32 140	98.13 180	93.79 155	96.99 100	99.65 2	96.74 81	99.47 13	98.93 43	96.91 57	99.84 25	90.11 261	99.06 199	98.32 232

VPA-MVSNet		98.27 29	98.46 24	97.70 104	99.06 85	93.80 154	97.76 59	99.00 57	98.40 28	99.07 32	98.98 38	96.89 58	99.75 56	97.19 47	99.79 33	99.55 34

ACMMP		98.05 39	97.75 55	98.93 19	99.23 53	97.60 19	98.09 43	98.96 67	95.75 128	97.91 123	98.06 119	96.89 58	99.76 52	95.32 115	99.57 78	99.43 76
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
PMVS	89.60 17	96.71 135	96.97 112	95.95 212	99.51 22	97.81 13	97.42 80	97.49 246	97.93 42	95.95 224	98.58 63	96.88 60	96.91 340	89.59 269	99.36 147	93.12 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
region2R		97.92 53	97.59 73	98.92 20	99.22 56	97.55 23	97.60 68	98.84 87	96.00 111	97.22 157	97.62 166	96.87 61	99.76 52	95.48 104	99.43 130	99.46 60

CP-MVS		97.92 53	97.56 76	98.99 11	98.99 91	97.82 12	97.93 50	98.96 67	96.11 103	96.89 183	97.45 179	96.85 62	99.78 40	95.19 121	99.63 61	99.38 85

DPE-MVS		97.64 74	97.35 88	98.50 49	98.85 99	96.18 64	95.21 199	98.99 60	95.84 123	98.78 42	98.08 114	96.84 63	99.81 31	93.98 181	99.57 78	99.52 39

test_0402		97.84 61	97.97 40	97.47 126	99.19 65	94.07 143	96.71 114	98.73 118	98.66 23	98.56 56	98.41 76	96.84 63	99.69 104	94.82 142	99.81 29	98.64 206

ACMMPR		97.95 47	97.62 70	98.94 16	99.20 63	97.56 22	97.59 69	98.83 95	96.05 106	97.46 150	97.63 165	96.77 65	99.76 52	95.61 99	99.46 118	99.49 50

Vis-MVSNet		98.27 29	98.34 28	98.07 80	99.33 42	95.21 106	98.04 45	99.46 5	97.32 69	97.82 135	99.11 31	96.75 66	99.86 20	97.84 24	99.36 147	99.15 127
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Fast-Effi-MVS+		95.49 181	95.07 188	96.75 170	97.67 233	92.82 180	94.22 243	98.60 146	91.61 245	93.42 297	92.90 317	96.73 67	99.70 96	92.60 207	97.89 270	97.74 273

baseline		97.44 90	97.78 53	96.43 190	98.52 135	90.75 221	96.84 103	99.03 47	96.51 87	97.86 131	98.02 123	96.67 68	99.36 213	97.09 50	99.47 115	99.19 120

SR-MVS		98.00 43	97.66 61	99.01 9	98.77 105	97.93 7	97.38 81	98.83 95	97.32 69	98.06 108	97.85 143	96.65 69	99.77 48	95.00 137	99.11 190	99.32 94

tfpnnormal		97.72 70	97.97 40	96.94 158	99.26 47	92.23 190	97.83 56	98.45 159	98.25 33	99.13 29	98.66 59	96.65 69	99.69 104	93.92 183	99.62 62	98.91 174

DeepPCF-MVS	94.58 5	96.90 119	96.43 143	98.31 64	97.48 243	97.23 37	92.56 294	98.60 146	92.84 229	98.54 57	97.40 182	96.64 71	98.78 290	94.40 161	99.41 139	98.93 169

MVS_111021_LR		96.82 126	96.55 135	97.62 110	98.27 158	95.34 98	93.81 264	98.33 178	94.59 172	96.56 196	96.63 237	96.61 72	98.73 295	94.80 143	99.34 155	98.78 192

Gipuma		98.07 38	98.31 29	97.36 138	99.76 5	96.28 63	98.51 19	99.10 28	98.76 21	96.79 185	99.34 17	96.61 72	98.82 286	96.38 68	99.50 105	96.98 295
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		96.73 132	96.54 137	97.27 142	98.35 151	93.66 162	93.42 274	98.36 173	94.74 165	96.58 194	96.76 230	96.54 74	98.99 271	94.87 140	99.27 173	99.15 127

SMA-MVS		97.48 87	97.11 103	98.60 44	98.83 100	96.67 49	96.74 109	98.73 118	91.61 245	98.48 62	98.36 79	96.53 75	99.68 110	95.17 123	99.54 90	99.45 65

v7n		98.73 11	98.99 5	97.95 89	99.64 11	94.20 140	98.67 11	99.14 23	99.08 10	99.42 15	99.23 21	96.53 75	99.91 12	99.27 2	99.93 10	99.73 15

mPP-MVS		97.91 56	97.53 77	99.04 6	99.22 56	97.87 11	97.74 61	98.78 109	96.04 108	97.10 165	97.73 157	96.53 75	99.78 40	95.16 125	99.50 105	99.46 60

XVS		97.96 44	97.63 68	98.94 16	99.15 70	97.66 16	97.77 57	98.83 95	97.42 63	96.32 207	97.64 164	96.49 78	99.72 72	95.66 95	99.37 144	99.45 65

X-MVStestdata		92.86 265	90.83 289	98.94 16	99.15 70	97.66 16	97.77 57	98.83 95	97.42 63	96.32 207	36.50 348	96.49 78	99.72 72	95.66 95	99.37 144	99.45 65

9.14			96.69 127		98.53 134		96.02 147	98.98 63	93.23 212	97.18 159	97.46 178	96.47 80	99.62 134	92.99 204	99.32 164

UA-Net		98.88 7	98.76 13	99.22 2	99.11 80	97.89 10	99.47 3	99.32 7	99.08 10	97.87 130	99.67 2	96.47 80	99.92 4	97.88 22	99.98 2	99.85 3

xxxxxxxxxxxxxcwj		97.24 105	97.03 110	97.89 93	98.48 141	94.71 119	94.53 232	99.07 37	95.02 157	97.83 133	97.88 140	96.44 82	99.72 72	94.59 154	99.39 141	99.25 113

SF-MVS		97.60 78	97.39 85	98.22 71	98.93 94	95.69 79	97.05 96	99.10 28	95.32 143	97.83 133	97.88 140	96.44 82	99.72 72	94.59 154	99.39 141	99.25 113

xiu_mvs_v1_base_debu		95.62 176	95.96 163	94.60 264	98.01 187	88.42 252	93.99 255	98.21 188	92.98 223	95.91 225	94.53 299	96.39 84	99.72 72	95.43 111	98.19 257	95.64 323

xiu_mvs_v1_base		95.62 176	95.96 163	94.60 264	98.01 187	88.42 252	93.99 255	98.21 188	92.98 223	95.91 225	94.53 299	96.39 84	99.72 72	95.43 111	98.19 257	95.64 323

xiu_mvs_v1_base_debi		95.62 176	95.96 163	94.60 264	98.01 187	88.42 252	93.99 255	98.21 188	92.98 223	95.91 225	94.53 299	96.39 84	99.72 72	95.43 111	98.19 257	95.64 323

ETV-MVS		96.13 159	95.90 166	96.82 166	97.76 224	93.89 149	95.40 182	98.95 69	95.87 120	95.58 239	91.00 337	96.36 87	99.72 72	93.36 194	98.83 223	96.85 302

testing_2		97.43 91	97.71 56	96.60 178	98.91 96	90.85 216	96.01 149	98.54 151	94.78 164	98.78 42	98.96 40	96.35 88	99.54 157	97.25 41	99.82 28	99.40 80

MP-MVS		97.64 74	97.18 100	99.00 10	99.32 44	97.77 14	97.49 75	98.73 118	96.27 96	95.59 238	97.75 154	96.30 89	99.78 40	93.70 190	99.48 113	99.45 65
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TinyColmap		96.00 165	96.34 146	94.96 248	97.90 199	87.91 263	94.13 250	98.49 156	94.41 176	98.16 95	97.76 151	96.29 90	98.68 302	90.52 254	99.42 133	98.30 235

Fast-Effi-MVS+-dtu		96.44 148	96.12 154	97.39 136	97.18 266	94.39 130	95.46 176	98.73 118	96.03 110	94.72 253	94.92 293	96.28 91	99.69 104	93.81 186	97.98 265	98.09 248

OMC-MVS		96.48 146	96.00 160	97.91 92	98.30 153	96.01 72	94.86 219	98.60 146	91.88 242	97.18 159	97.21 201	96.11 92	99.04 265	90.49 257	99.34 155	98.69 203

xiu_mvs_v2_base		94.22 233	94.63 210	92.99 299	97.32 260	84.84 309	92.12 302	97.84 224	91.96 240	94.17 267	93.43 310	96.07 93	99.71 87	91.27 229	97.48 289	94.42 332

CS-MVS		95.86 170	95.59 176	96.69 174	97.85 201	93.14 174	96.42 122	99.25 9	94.17 187	93.56 290	90.76 340	96.05 94	99.72 72	93.28 197	98.91 212	97.21 289

CSCG		97.40 94	97.30 90	97.69 106	98.95 93	94.83 114	97.28 84	98.99 60	96.35 95	98.13 99	95.95 270	95.99 95	99.66 120	94.36 165	99.73 43	98.59 212

PHI-MVS		96.96 115	96.53 138	98.25 69	97.48 243	96.50 55	96.76 108	98.85 84	93.52 202	96.19 216	96.85 221	95.94 96	99.42 188	93.79 187	99.43 130	98.83 186

TSAR-MVS + MP.		97.42 92	97.23 97	98.00 87	99.38 38	95.00 110	97.63 67	98.20 191	93.00 222	98.16 95	98.06 119	95.89 97	99.72 72	95.67 93	99.10 192	99.28 107
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
XVG-ACMP-BASELINE		97.58 80	97.28 93	98.49 50	99.16 67	96.90 43	96.39 124	98.98 63	95.05 155	98.06 108	98.02 123	95.86 98	99.56 151	94.37 162	99.64 60	99.00 157

AllTest		97.20 107	96.92 116	98.06 82	99.08 82	96.16 65	97.14 92	99.16 17	94.35 179	97.78 136	98.07 116	95.84 99	99.12 254	91.41 226	99.42 133	98.91 174

TestCases				98.06 82	99.08 82	96.16 65		99.16 17	94.35 179	97.78 136	98.07 116	95.84 99	99.12 254	91.41 226	99.42 133	98.91 174

APD-MVS		97.00 110	96.53 138	98.41 55	98.55 132	96.31 61	96.32 130	98.77 110	92.96 227	97.44 152	97.58 169	95.84 99	99.74 63	91.96 214	99.35 152	99.19 120
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
pcd_1.5k_mvsjas		7.98 324	10.65 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	95.82 102	0.00 354	0.00 351	0.00 351	0.00 350

PS-MVSNAJss		98.53 19	98.63 19	98.21 72	99.68 9	94.82 115	98.10 42	99.21 11	96.91 76	99.75 2	99.45 9	95.82 102	99.92 4	98.80 4	99.96 4	99.89 1

PS-MVSNAJ		94.10 240	94.47 218	93.00 298	97.35 253	84.88 308	91.86 306	97.84 224	91.96 240	94.17 267	92.50 324	95.82 102	99.71 87	91.27 229	97.48 289	94.40 333

3Dnovator	96.53 2	97.61 77	97.64 66	97.50 121	97.74 226	93.65 163	98.49 20	98.88 79	96.86 78	97.11 164	98.55 67	95.82 102	99.73 68	95.94 86	99.42 133	99.13 133

zzz-MVS		98.01 42	97.66 61	99.06 4	99.44 30	97.90 8	95.66 169	98.73 118	97.69 53	97.90 124	97.96 129	95.81 106	99.82 29	96.13 74	99.61 68	99.45 65

MTAPA		98.14 34	97.84 47	99.06 4	99.44 30	97.90 8	97.25 85	98.73 118	97.69 53	97.90 124	97.96 129	95.81 106	99.82 29	96.13 74	99.61 68	99.45 65

DP-MVS		97.87 59	97.89 45	97.81 98	98.62 123	94.82 115	97.13 93	98.79 105	98.98 17	98.74 46	98.49 71	95.80 108	99.49 169	95.04 134	99.44 123	99.11 141

Anonymous20240529		97.96 44	98.04 38	97.71 102	98.69 116	94.28 137	97.86 54	98.31 181	98.79 20	99.23 25	98.86 47	95.76 109	99.61 140	95.49 103	99.36 147	99.23 116

LS3D		97.77 68	97.50 80	98.57 46	96.24 290	97.58 21	98.45 23	98.85 84	98.58 25	97.51 142	97.94 133	95.74 110	99.63 126	95.19 121	98.97 204	98.51 217

EIA-MVS		96.04 162	95.77 170	96.85 164	97.80 213	92.98 178	96.12 141	99.16 17	94.65 168	93.77 280	91.69 332	95.68 111	99.67 115	94.18 170	98.85 221	97.91 266

CNVR-MVS		96.92 117	96.55 135	98.03 86	98.00 191	95.54 87	94.87 218	98.17 197	94.60 170	96.38 204	97.05 210	95.67 112	99.36 213	95.12 131	99.08 194	99.19 120

CLD-MVS		95.47 184	95.07 188	96.69 174	98.27 158	92.53 184	91.36 312	98.67 136	91.22 250	95.78 232	94.12 307	95.65 113	98.98 273	90.81 240	99.72 46	98.57 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		98.55 17	98.76 13	97.94 90	98.79 102	94.37 132	98.84 8	99.15 21	99.37 3	99.67 6	99.43 11	95.61 114	99.72 72	98.12 16	99.86 23	99.73 15

Regformer-2		97.41 93	97.24 96	97.93 91	97.21 264	94.72 118	94.85 220	98.27 182	97.74 47	98.11 100	97.50 175	95.58 115	99.69 104	96.57 62	99.31 166	99.37 90

ITE_SJBPF				97.85 96	98.64 118	96.66 50		98.51 155	95.63 130	97.22 157	97.30 196	95.52 116	98.55 311	90.97 235	98.90 213	98.34 231

Regformer-4		97.53 84	97.47 83	97.71 102	97.35 253	93.91 148	95.26 194	98.14 201	97.97 41	98.34 77	97.89 138	95.49 117	99.71 87	97.41 38	99.42 133	99.51 41

DeepC-MVS_fast	94.34 7	96.74 130	96.51 140	97.44 131	97.69 229	94.15 141	96.02 147	98.43 162	93.17 218	97.30 155	97.38 188	95.48 118	99.28 234	93.74 188	99.34 155	98.88 181
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		98.65 15	98.62 21	98.75 31	99.51 22	96.61 52	98.55 17	99.17 16	99.05 13	99.17 28	98.79 49	95.47 119	99.89 16	97.95 20	99.91 15	99.75 13

FMVSNet1		97.95 47	98.08 35	97.56 113	99.14 78	93.67 159	98.23 32	98.66 138	97.41 66	99.00 35	99.19 24	95.47 119	99.73 68	95.83 89	99.76 37	99.30 100

MIMVSNet1		98.51 20	98.45 26	98.67 39	99.72 6	96.71 47	98.76 9	98.89 74	98.49 26	99.38 17	99.14 30	95.44 121	99.84 25	96.47 66	99.80 32	99.47 58

CP-MVSNet		98.42 23	98.46 24	98.30 65	99.46 28	95.22 104	98.27 31	98.84 87	99.05 13	99.01 34	98.65 61	95.37 122	99.90 13	97.57 33	99.91 15	99.77 8

Regformer-1		97.27 102	97.16 101	97.61 111	97.21 264	93.86 151	94.85 220	98.04 215	97.62 56	98.03 112	97.50 175	95.34 123	99.63 126	96.52 63	99.31 166	99.35 92

segment_acmp												95.34 123

CDPH-MVS		95.45 186	94.65 207	97.84 97	98.28 156	94.96 111	93.73 266	98.33 178	85.03 310	95.44 240	96.60 238	95.31 125	99.44 185	90.01 263	99.13 186	99.11 141

3Dnovator+	96.13 3	97.73 69	97.59 73	98.15 76	98.11 182	95.60 85	98.04 45	98.70 128	98.13 37	96.93 180	98.45 74	95.30 126	99.62 134	95.64 97	98.96 205	99.24 115

MVS_Test		96.27 152	96.79 124	94.73 260	96.94 275	86.63 286	96.18 138	98.33 178	94.94 159	96.07 220	98.28 91	95.25 127	99.26 237	97.21 44	97.90 269	98.30 235

XVG-OURS		97.12 108	96.74 125	98.26 67	98.99 91	97.45 29	93.82 262	99.05 40	95.19 148	98.32 81	97.70 160	95.22 128	98.41 318	94.27 167	98.13 260	98.93 169

MCST-MVS		96.24 153	95.80 168	97.56 113	98.75 106	94.13 142	94.66 227	98.17 197	90.17 259	96.21 215	96.10 264	95.14 129	99.43 187	94.13 173	98.85 221	99.13 133

EI-MVSNet-Vis-set		97.32 100	97.39 85	97.11 149	97.36 252	92.08 197	95.34 187	97.65 238	97.74 47	98.29 86	98.11 112	95.05 130	99.68 110	97.50 36	99.50 105	99.56 32

Regformer-3		97.25 104	97.29 91	97.11 149	97.35 253	92.32 188	95.26 194	97.62 243	97.67 55	98.17 94	97.89 138	95.05 130	99.56 151	97.16 48	99.42 133	99.46 60

EI-MVSNet-UG-set		97.32 100	97.40 84	97.09 151	97.34 257	92.01 199	95.33 188	97.65 238	97.74 47	98.30 85	98.14 107	95.04 132	99.69 104	97.55 34	99.52 98	99.58 27

DELS-MVS		96.17 157	96.23 149	95.99 208	97.55 241	90.04 228	92.38 299	98.52 153	94.13 188	96.55 198	97.06 209	94.99 133	99.58 144	95.62 98	99.28 171	98.37 225
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
ab-mvs		96.59 141	96.59 131	96.60 178	98.64 118	92.21 191	98.35 26	97.67 234	94.45 175	96.99 175	98.79 49	94.96 134	99.49 169	90.39 258	99.07 196	98.08 249

ETH3D-3000-0.1		96.89 121	96.46 142	98.16 73	98.62 123	95.69 79	95.96 153	98.98 63	93.36 207	97.04 171	97.31 195	94.93 135	99.63 126	92.60 207	99.34 155	99.17 123

MSLP-MVS++		96.42 150	96.71 126	95.57 225	97.82 208	90.56 225	95.71 164	98.84 87	94.72 166	96.71 190	97.39 186	94.91 136	98.10 332	95.28 116	99.02 201	98.05 258

QAPM		95.88 169	95.57 177	96.80 167	97.90 199	91.84 203	98.18 39	98.73 118	88.41 275	96.42 202	98.13 108	94.73 137	99.75 56	88.72 281	98.94 209	98.81 188

RPSCF		97.87 59	97.51 79	98.95 15	99.15 70	98.43 3	97.56 71	99.06 38	96.19 100	98.48 62	98.70 56	94.72 138	99.24 240	94.37 162	99.33 162	99.17 123

DU-MVS		97.79 66	97.60 72	98.36 59	98.73 107	95.78 75	95.65 171	98.87 81	97.57 57	98.31 83	97.83 145	94.69 139	99.85 22	97.02 53	99.71 49	99.46 60

Baseline_NR-MVSNet		97.72 70	97.79 50	97.50 121	99.56 15	93.29 170	95.44 177	98.86 83	98.20 36	98.37 72	99.24 20	94.69 139	99.55 155	95.98 85	99.79 33	99.65 22

TEST9					97.84 206	95.23 101	93.62 268	98.39 169	86.81 290	93.78 278	95.99 265	94.68 141	99.52 163

UniMVSNet (Re)		97.83 62	97.65 63	98.35 61	98.80 101	95.86 74	95.92 157	99.04 46	97.51 60	98.22 91	97.81 149	94.68 141	99.78 40	97.14 49	99.75 41	99.41 79

agg_prior1		95.39 188	94.60 212	97.75 100	97.80 213	94.96 111	93.39 276	98.36 173	87.20 286	93.49 292	95.97 268	94.65 143	99.53 159	91.69 223	98.86 219	98.77 195

UniMVSNet_NR-MVSNet		97.83 62	97.65 63	98.37 58	98.72 109	95.78 75	95.66 169	99.02 49	98.11 38	98.31 83	97.69 162	94.65 143	99.85 22	97.02 53	99.71 49	99.48 55

VPNet		97.26 103	97.49 81	96.59 180	99.47 27	90.58 223	96.27 131	98.53 152	97.77 45	98.46 65	98.41 76	94.59 145	99.68 110	94.61 150	99.29 170	99.52 39

train_agg		95.46 185	94.66 206	97.88 94	97.84 206	95.23 101	93.62 268	98.39 169	87.04 288	93.78 278	95.99 265	94.58 146	99.52 163	91.76 221	98.90 213	98.89 178

test_8					97.81 209	95.07 109	93.54 271	98.38 171	87.04 288	93.71 282	95.96 269	94.58 146	99.52 163

API-MVS		95.09 201	95.01 192	95.31 236	96.61 281	94.02 145	96.83 104	97.18 256	95.60 132	95.79 230	94.33 304	94.54 148	98.37 323	85.70 310	98.52 246	93.52 335

Test By Simon												94.51 149

MSDG		95.33 190	95.13 186	95.94 214	97.40 251	91.85 202	91.02 322	98.37 172	95.30 144	96.31 209	95.99 265	94.51 149	98.38 321	89.59 269	97.65 283	97.60 280

TSAR-MVS + GP.		96.47 147	96.12 154	97.49 124	97.74 226	95.23 101	94.15 247	96.90 266	93.26 211	98.04 111	96.70 233	94.41 151	98.89 281	94.77 147	99.14 182	98.37 225

NR-MVSNet		97.96 44	97.86 46	98.26 67	98.73 107	95.54 87	98.14 40	98.73 118	97.79 44	99.42 15	97.83 145	94.40 152	99.78 40	95.91 88	99.76 37	99.46 60

AdaColmap		95.11 199	94.62 211	96.58 181	97.33 259	94.45 129	94.92 216	98.08 208	93.15 219	93.98 276	95.53 282	94.34 153	99.10 259	85.69 311	98.61 242	96.20 317

FC-MVSNet-test		98.16 33	98.37 27	97.56 113	99.49 26	93.10 176	98.35 26	99.21 11	98.43 27	98.89 38	98.83 48	94.30 154	99.81 31	97.87 23	99.91 15	99.77 8

Effi-MVS+-dtu		96.81 127	96.09 156	98.99 11	96.90 277	98.69 2	96.42 122	98.09 206	95.86 121	95.15 246	95.54 281	94.26 155	99.81 31	94.06 175	98.51 248	98.47 219

mvs-test1		96.20 155	95.50 179	98.32 62	96.90 277	98.16 4	95.07 207	98.09 206	95.86 121	93.63 285	94.32 305	94.26 155	99.71 87	94.06 175	97.27 297	97.07 292

ambc				96.56 184	98.23 164	91.68 206	97.88 53	98.13 203		98.42 68	98.56 66	94.22 157	99.04 265	94.05 178	99.35 152	98.95 163

test20.03		96.58 142	96.61 130	96.48 188	98.49 139	91.72 205	95.68 168	97.69 233	96.81 79	98.27 87	97.92 136	94.18 158	98.71 297	90.78 242	99.66 58	99.00 157

HPM-MVS++		96.99 111	96.38 144	98.81 27	98.64 118	97.59 20	95.97 152	98.20 191	95.51 136	95.06 247	96.53 242	94.10 159	99.70 96	94.29 166	99.15 181	99.13 133

testtj		96.69 136	96.13 153	98.36 59	98.46 145	96.02 71	96.44 121	98.70 128	94.26 182	96.79 185	97.13 203	94.07 160	99.75 56	90.53 253	98.80 225	99.31 99

ETH3D cwj APD-0.16		96.23 154	95.61 175	98.09 79	97.91 197	95.65 84	94.94 215	98.74 116	91.31 249	96.02 222	97.08 208	94.05 161	99.69 104	91.51 225	98.94 209	98.93 169

PM-MVS		97.36 98	97.10 104	98.14 77	98.91 96	96.77 46	96.20 137	98.63 144	93.82 196	98.54 57	98.33 82	93.98 162	99.05 264	95.99 84	99.45 122	98.61 211

OpenMVS	94.22 8	95.48 183	95.20 183	96.32 196	97.16 267	91.96 200	97.74 61	98.84 87	87.26 285	94.36 264	98.01 125	93.95 163	99.67 115	90.70 248	98.75 230	97.35 288

v8		97.60 78	98.06 37	96.23 199	98.71 112	89.44 236	97.43 79	98.82 103	97.29 71	98.74 46	99.10 32	93.86 164	99.68 110	98.61 10	99.94 8	99.56 32

diffmvs		96.04 162	96.23 149	95.46 232	97.35 253	88.03 262	93.42 274	99.08 34	94.09 190	96.66 192	96.93 217	93.85 165	99.29 232	96.01 83	98.67 236	99.06 150

NCCC		96.52 144	95.99 161	98.10 78	97.81 209	95.68 81	95.00 213	98.20 191	95.39 141	95.40 242	96.36 251	93.81 166	99.45 182	93.55 193	98.42 250	99.17 123

TAPA-MVS	93.32 12	94.93 205	94.23 225	97.04 154	98.18 170	94.51 126	95.22 198	98.73 118	81.22 327	96.25 213	95.95 270	93.80 167	98.98 273	89.89 265	98.87 217	97.62 278
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FIs		97.93 52	98.07 36	97.48 125	99.38 38	92.95 179	98.03 47	99.11 26	98.04 40	98.62 50	98.66 59	93.75 168	99.78 40	97.23 42	99.84 26	99.73 15

OurMVSNet-221017-0		98.61 16	98.61 23	98.63 43	99.77 3	96.35 59	99.17 6	99.05 40	98.05 39	99.61 11	99.52 5	93.72 169	99.88 18	98.72 9	99.88 21	99.65 22

test_prior3		95.91 167	95.39 180	97.46 128	97.79 218	94.26 138	93.33 279	98.42 165	94.21 184	94.02 273	96.25 255	93.64 170	99.34 218	91.90 215	98.96 205	98.79 190

test_prior2							93.33 279		94.21 184	94.02 273	96.25 255	93.64 170		91.90 215	98.96 205

旧先验1					97.80 213	93.87 150		97.75 229			97.04 211	93.57 172			98.68 235	98.72 200

v10		97.55 81	97.97 40	96.31 197	98.60 126	89.64 232	97.44 77	99.02 49	96.60 84	98.72 48	99.16 29	93.48 173	99.72 72	98.76 6	99.92 12	99.58 27

v148		96.58 142	96.97 112	95.42 233	98.63 122	87.57 271	95.09 204	97.90 219	95.91 118	98.24 90	97.96 129	93.42 174	99.39 204	96.04 79	99.52 98	99.29 106

V42		97.04 109	97.16 101	96.68 176	98.59 128	91.05 212	96.33 129	98.36 173	94.60 170	97.99 114	98.30 88	93.32 175	99.62 134	97.40 39	99.53 93	99.38 85

new-patchmatchnet		95.67 175	96.58 132	92.94 301	97.48 243	80.21 330	92.96 285	98.19 196	94.83 162	98.82 40	98.79 49	93.31 176	99.51 167	95.83 89	99.04 200	99.12 138

test12				97.46 128	97.61 237	94.07 143		97.78 228		93.57 289		93.31 176	99.42 188		98.78 227	98.89 178

UGNet		96.81 127	96.56 134	97.58 112	96.64 280	93.84 153	97.75 60	97.12 259	96.47 91	93.62 286	98.88 46	93.22 178	99.53 159	95.61 99	99.69 53	99.36 91
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
pmmvs-eth3d		96.49 145	96.18 152	97.42 133	98.25 161	94.29 134	94.77 224	98.07 212	89.81 262	97.97 118	98.33 82	93.11 179	99.08 261	95.46 107	99.84 26	98.89 178

v1144		96.84 122	97.08 106	96.13 205	98.42 146	89.28 239	95.41 181	98.67 136	94.21 184	97.97 118	98.31 84	93.06 180	99.65 121	98.06 18	99.62 62	99.45 65

PVSNet_BlendedMVS		95.02 204	94.93 195	95.27 237	97.79 218	87.40 275	94.14 249	98.68 133	88.94 270	94.51 260	98.01 125	93.04 181	99.30 228	89.77 267	99.49 109	99.11 141

PVSNet_Blended		93.96 244	93.65 242	94.91 249	97.79 218	87.40 275	91.43 311	98.68 133	84.50 315	94.51 260	94.48 302	93.04 181	99.30 228	89.77 267	98.61 242	98.02 261

mvs_anonymous		95.36 189	96.07 158	93.21 293	96.29 288	81.56 325	94.60 229	97.66 236	93.30 210	96.95 179	98.91 45	93.03 183	99.38 208	96.60 59	97.30 296	98.69 203

v1192		96.83 125	97.06 108	96.15 204	98.28 156	89.29 238	95.36 185	98.77 110	93.73 198	98.11 100	98.34 81	93.02 184	99.67 115	98.35 14	99.58 75	99.50 42

F-COLMAP		95.30 192	94.38 222	98.05 85	98.64 118	96.04 69	95.61 174	98.66 138	89.00 269	93.22 300	96.40 250	92.90 185	99.35 216	87.45 300	97.53 287	98.77 195

WR-MVS		96.90 119	96.81 121	97.16 146	98.56 131	92.20 193	94.33 236	98.12 204	97.34 68	98.20 92	97.33 193	92.81 186	99.75 56	94.79 144	99.81 29	99.54 35

v1240		96.74 130	97.02 111	95.91 215	98.18 170	88.52 251	95.39 183	98.88 79	93.15 219	98.46 65	98.40 78	92.80 187	99.71 87	98.45 13	99.49 109	99.49 50

MVE	73.61 22	86.48 318	85.92 319	88.18 329	96.23 292	85.28 302	81.78 347	75.79 350	86.01 295	82.53 347	91.88 329	92.74 188	87.47 349	71.42 345	94.86 324	91.78 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DP-MVS Recon		95.55 179	95.13 186	96.80 167	98.51 136	93.99 147	94.60 229	98.69 131	90.20 258	95.78 232	96.21 258	92.73 189	98.98 273	90.58 252	98.86 219	97.42 285

CANet		95.86 170	95.65 173	96.49 187	96.41 286	90.82 218	94.36 235	98.41 167	94.94 159	92.62 312	96.73 231	92.68 190	99.71 87	95.12 131	99.60 71	98.94 165

v1921920		96.72 133	96.96 114	95.99 208	98.21 165	88.79 248	95.42 179	98.79 105	93.22 213	98.19 93	98.26 96	92.68 190	99.70 96	98.34 15	99.55 87	99.49 50

BH-untuned		94.69 218	94.75 204	94.52 269	97.95 196	87.53 272	94.07 252	97.01 262	93.99 192	97.10 165	95.65 277	92.65 192	98.95 278	87.60 296	96.74 304	97.09 291

LF4IMVS		96.07 160	95.63 174	97.36 138	98.19 167	95.55 86	95.44 177	98.82 103	92.29 236	95.70 236	96.55 240	92.63 193	98.69 299	91.75 222	99.33 162	97.85 268

v2v482		96.78 129	97.06 108	95.95 212	98.57 130	88.77 249	95.36 185	98.26 184	95.18 149	97.85 132	98.23 99	92.58 194	99.63 126	97.80 26	99.69 53	99.45 65

EI-MVSNet		96.63 140	96.93 115	95.74 219	97.26 262	88.13 260	95.29 192	97.65 238	96.99 73	97.94 121	98.19 104	92.55 195	99.58 144	96.91 56	99.56 81	99.50 42

IterMVS-LS		96.92 117	97.29 91	95.79 218	98.51 136	88.13 260	95.10 202	98.66 138	96.99 73	98.46 65	98.68 58	92.55 195	99.74 63	96.91 56	99.79 33	99.50 42
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS		97.37 96	97.25 94	97.74 101	98.69 116	94.50 128	97.04 97	95.61 289	98.59 24	98.51 59	98.72 54	92.54 197	99.58 144	96.02 81	99.49 109	99.12 138

MVS		90.02 298	89.20 304	92.47 306	94.71 323	86.90 283	95.86 158	96.74 272	64.72 347	90.62 323	92.77 319	92.54 197	98.39 320	79.30 334	95.56 321	92.12 339

v144192		96.69 136	96.90 118	96.03 207	98.25 161	88.92 243	95.49 175	98.77 110	93.05 221	98.09 104	98.29 90	92.51 199	99.70 96	98.11 17	99.56 81	99.47 58

原ACMM1				96.58 181	98.16 174	92.12 195		98.15 200	85.90 298	93.49 292	96.43 247	92.47 200	99.38 208	87.66 295	98.62 241	98.23 241

VNet		96.84 122	96.83 120	96.88 162	98.06 183	92.02 198	96.35 128	97.57 245	97.70 52	97.88 127	97.80 150	92.40 201	99.54 157	94.73 149	98.96 205	99.08 146

114514_t		93.96 244	93.22 250	96.19 202	99.06 85	90.97 215	95.99 150	98.94 70	73.88 345	93.43 296	96.93 217	92.38 202	99.37 211	89.09 276	99.28 171	98.25 240

CPTT-MVS		96.69 136	96.08 157	98.49 50	98.89 98	96.64 51	97.25 85	98.77 110	92.89 228	96.01 223	97.13 203	92.23 203	99.67 115	92.24 212	99.34 155	99.17 123

DVP-MVS		97.45 89	96.92 116	99.03 7	99.26 47	97.70 15	97.66 64	98.89 74	95.65 129	98.51 59	96.46 246	92.15 204	99.81 31	95.14 128	98.58 245	99.58 27

MAR-MVS		94.21 236	93.03 252	97.76 99	96.94 275	97.44 30	96.97 101	97.15 257	87.89 283	92.00 317	92.73 321	92.14 205	99.12 254	83.92 322	97.51 288	96.73 307
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
PVSNet_Blended_VisFu		95.95 166	95.80 168	96.42 191	99.28 46	90.62 222	95.31 190	99.08 34	88.40 276	96.97 178	98.17 106	92.11 206	99.78 40	93.64 191	99.21 177	98.86 184

BH-RMVSNet		94.56 225	94.44 221	94.91 249	97.57 238	87.44 274	93.78 265	96.26 277	93.69 200	96.41 203	96.50 245	92.10 207	99.00 269	85.96 308	97.71 277	98.31 233

新几何1				97.25 145	98.29 154	94.70 122		97.73 230	77.98 338	94.83 252	96.67 235	92.08 208	99.45 182	88.17 290	98.65 239	97.61 279

testdata				95.70 222	98.16 174	90.58 223		97.72 231	80.38 330	95.62 237	97.02 212	92.06 209	98.98 273	89.06 278	98.52 246	97.54 281

YYNet1		94.73 213	94.84 200	94.41 272	97.47 247	85.09 306	90.29 328	95.85 286	92.52 231	97.53 140	97.76 151	91.97 210	99.18 246	93.31 196	96.86 300	98.95 163

Anonymous20231206		95.27 193	95.06 190	95.88 216	98.72 109	89.37 237	95.70 165	97.85 222	88.00 281	96.98 177	97.62 166	91.95 211	99.34 218	89.21 274	99.53 93	98.94 165

MS-PatchMatch		94.83 209	94.91 197	94.57 267	96.81 279	87.10 280	94.23 242	97.34 251	88.74 273	97.14 161	97.11 206	91.94 212	98.23 328	92.99 204	97.92 267	98.37 225

1121		94.26 231	93.26 248	97.27 142	98.26 160	94.73 117	95.86 158	97.71 232	77.96 339	94.53 259	96.71 232	91.93 213	99.40 199	87.71 292	98.64 240	97.69 276

MDA-MVSNet_test_wron		94.73 213	94.83 202	94.42 271	97.48 243	85.15 304	90.28 329	95.87 285	92.52 231	97.48 147	97.76 151	91.92 214	99.17 250	93.32 195	96.80 303	98.94 165

HQP_MVS		96.66 139	96.33 147	97.68 107	98.70 114	94.29 134	96.50 119	98.75 114	96.36 93	96.16 217	96.77 228	91.91 215	99.46 178	92.59 209	99.20 178	99.28 107

plane_prior6					98.38 148	94.37 132						91.91 215

ETH3 D test6400		94.77 212	93.87 239	97.47 126	98.12 181	93.73 157	94.56 231	98.70 128	85.45 305	94.70 255	95.93 272	91.77 217	99.63 126	86.45 306	99.14 182	99.05 152

MVP-Stereo		95.69 173	95.28 182	96.92 159	98.15 176	93.03 177	95.64 173	98.20 191	90.39 256	96.63 193	97.73 157	91.63 218	99.10 259	91.84 219	97.31 295	98.63 208
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchMatch-RL		94.61 223	93.81 240	97.02 156	98.19 167	95.72 77	93.66 267	97.23 253	88.17 279	94.94 250	95.62 279	91.43 219	98.57 308	87.36 301	97.68 280	96.76 306

MDA-MVSNet-bldmvs		95.69 173	95.67 172	95.74 219	98.48 141	88.76 250	92.84 286	97.25 252	96.00 111	97.59 138	97.95 132	91.38 220	99.46 178	93.16 202	96.35 311	98.99 160

PAPR		92.22 276	91.27 281	95.07 244	95.73 309	88.81 247	91.97 305	97.87 221	85.80 299	90.91 322	92.73 321	91.16 221	98.33 325	79.48 333	95.76 319	98.08 249

1314		92.38 273	92.30 268	92.64 305	95.42 316	85.15 304	95.86 158	96.97 264	85.40 306	90.62 323	93.06 315	91.12 222	97.80 335	86.74 304	95.49 322	94.97 330

ppachtmachnet_test		94.49 227	94.84 200	93.46 287	96.16 296	82.10 324	90.59 325	97.48 248	90.53 255	97.01 174	97.59 168	91.01 223	99.36 213	93.97 182	99.18 180	98.94 165

PLC	91.02 16	94.05 243	92.90 254	97.51 118	98.00 191	95.12 108	94.25 240	98.25 185	86.17 294	91.48 320	95.25 285	91.01 223	99.19 245	85.02 317	96.69 305	98.22 242
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test222					98.17 172	93.24 172	92.74 291	97.61 244	75.17 343	94.65 256	96.69 234	90.96 225			98.66 238	97.66 277

USDC		94.56 225	94.57 216	94.55 268	97.78 222	86.43 289	92.75 289	98.65 143	85.96 296	96.91 182	97.93 135	90.82 226	98.74 294	90.71 247	99.59 73	98.47 219

PCF-MVS	89.43 18	92.12 279	90.64 292	96.57 183	97.80 213	93.48 167	89.88 335	98.45 159	74.46 344	96.04 221	95.68 276	90.71 227	99.31 225	73.73 341	99.01 203	96.91 299
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PAPM_NR		94.61 223	94.17 229	95.96 210	98.36 150	91.23 210	95.93 156	97.95 216	92.98 223	93.42 297	94.43 303	90.53 228	98.38 321	87.60 296	96.29 312	98.27 238

our_test_3		94.20 238	94.58 214	93.07 295	96.16 296	81.20 327	90.42 327	96.84 267	90.72 254	97.14 161	97.13 203	90.47 229	99.11 257	94.04 179	98.25 256	98.91 174

OpenMVS_ROB	91.80 14	93.64 252	93.05 251	95.42 233	97.31 261	91.21 211	95.08 206	96.68 274	81.56 324	96.88 184	96.41 248	90.44 230	99.25 239	85.39 315	97.67 281	95.80 321

HQP2-MVS												90.33 231

N_pmnet		95.18 196	94.23 225	98.06 82	97.85 201	96.55 54	92.49 295	91.63 325	89.34 265	98.09 104	97.41 181	90.33 231	99.06 263	91.58 224	99.31 166	98.56 214

HQP-MVS		95.17 198	94.58 214	96.92 159	97.85 201	92.47 185	94.26 237	98.43 162	93.18 215	92.86 305	95.08 287	90.33 231	99.23 242	90.51 255	98.74 231	99.05 152

CNLPA		95.04 202	94.47 218	96.75 170	97.81 209	95.25 100	94.12 251	97.89 220	94.41 176	94.57 257	95.69 275	90.30 234	98.35 324	86.72 305	98.76 229	96.64 309

PMMVS		92.39 272	91.08 283	96.30 198	93.12 342	92.81 181	90.58 326	95.96 283	79.17 335	91.85 319	92.27 325	90.29 235	98.66 304	89.85 266	96.68 306	97.43 284

TR-MVS		92.54 270	92.20 269	93.57 285	96.49 284	86.66 285	93.51 272	94.73 296	89.96 261	94.95 249	93.87 308	90.24 236	98.61 305	81.18 331	94.88 323	95.45 327

MVS_0304		95.50 180	95.05 191	96.84 165	96.28 289	93.12 175	97.00 99	96.16 278	95.03 156	89.22 334	97.70 160	90.16 237	99.48 172	94.51 156	99.34 155	97.93 265

TAMVS		95.49 181	94.94 193	97.16 146	98.31 152	93.41 168	95.07 207	96.82 269	91.09 251	97.51 142	97.82 148	89.96 238	99.42 188	88.42 286	99.44 123	98.64 206

DPM-MVS		93.68 250	92.77 261	96.42 191	97.91 197	92.54 183	91.17 319	97.47 249	84.99 311	93.08 302	94.74 295	89.90 239	99.00 269	87.54 298	98.09 262	97.72 274

PMMVS2		93.66 251	94.07 231	92.45 307	97.57 238	80.67 329	86.46 341	96.00 281	93.99 192	97.10 165	97.38 188	89.90 239	97.82 334	88.76 280	99.47 115	98.86 184

BH-w/o		92.14 278	91.94 271	92.73 304	97.13 268	85.30 300	92.46 296	95.64 288	89.33 266	94.21 266	92.74 320	89.60 241	98.24 327	81.68 329	94.66 325	94.66 331

UnsupCasMVSNet_bld		94.72 217	94.26 224	96.08 206	98.62 123	90.54 226	93.38 277	98.05 214	90.30 257	97.02 173	96.80 227	89.54 242	99.16 251	88.44 285	96.18 313	98.56 214

MG-MVS		94.08 242	94.00 235	94.32 274	97.09 269	85.89 294	93.19 283	95.96 283	92.52 231	94.93 251	97.51 174	89.54 242	98.77 291	87.52 299	97.71 277	98.31 233

UnsupCasMVSNet_eth		95.91 167	95.73 171	96.44 189	98.48 141	91.52 208	95.31 190	98.45 159	95.76 126	97.48 147	97.54 170	89.53 244	98.69 299	94.43 158	94.61 326	99.13 133

GBi-Net		96.99 111	96.80 122	97.56 113	97.96 193	93.67 159	98.23 32	98.66 138	95.59 133	97.99 114	99.19 24	89.51 245	99.73 68	94.60 151	99.44 123	99.30 100

test1		96.99 111	96.80 122	97.56 113	97.96 193	93.67 159	98.23 32	98.66 138	95.59 133	97.99 114	99.19 24	89.51 245	99.73 68	94.60 151	99.44 123	99.30 100

FMVSNet2		96.72 133	96.67 129	96.87 163	97.96 193	91.88 201	97.15 90	98.06 213	95.59 133	98.50 61	98.62 62	89.51 245	99.65 121	94.99 138	99.60 71	99.07 148

pmmvs4		94.82 210	94.19 228	96.70 173	97.42 250	92.75 182	92.09 304	96.76 270	86.80 291	95.73 235	97.22 200	89.28 248	98.89 281	93.28 197	99.14 182	98.46 221

cascas		91.89 282	91.35 279	93.51 286	94.27 329	85.60 296	88.86 338	98.61 145	79.32 334	92.16 316	91.44 333	89.22 249	98.12 331	90.80 241	97.47 291	96.82 303

DSMNet-mixed		92.19 277	91.83 273	93.25 291	96.18 295	83.68 319	96.27 131	93.68 306	76.97 342	92.54 313	99.18 27	89.20 250	98.55 311	83.88 323	98.60 244	97.51 282

cl_fuxian		95.20 195	95.32 181	94.83 255	96.19 294	86.43 289	91.83 307	98.35 177	93.47 204	97.36 154	97.26 198	88.69 251	99.28 234	95.41 114	99.36 147	98.78 192

CANet_DTU		94.65 221	94.21 227	95.96 210	95.90 302	89.68 231	93.92 259	97.83 226	93.19 214	90.12 329	95.64 278	88.52 252	99.57 150	93.27 199	99.47 115	98.62 209

EPP-MVSNet		96.84 122	96.58 132	97.65 108	99.18 66	93.78 156	98.68 10	96.34 276	97.91 43	97.30 155	98.06 119	88.46 253	99.85 22	93.85 185	99.40 140	99.32 94

SixPastTwentyTwo		97.49 86	97.57 75	97.26 144	99.56 15	92.33 187	98.28 29	96.97 264	98.30 32	99.45 14	99.35 16	88.43 254	99.89 16	98.01 19	99.76 37	99.54 35

miper_ehance_all_eth		94.69 218	94.70 205	94.64 261	95.77 307	86.22 291	91.32 316	98.24 186	91.67 244	97.05 170	96.65 236	88.39 255	99.22 244	94.88 139	98.34 252	98.49 218

IS-MVSNet		96.93 116	96.68 128	97.70 104	99.25 50	94.00 146	98.57 15	96.74 272	98.36 29	98.14 98	97.98 128	88.23 256	99.71 87	93.10 203	99.72 46	99.38 85

jason		94.39 230	94.04 233	95.41 235	98.29 154	87.85 266	92.74 291	96.75 271	85.38 307	95.29 243	96.15 259	88.21 257	99.65 121	94.24 168	99.34 155	98.74 197
jason: jason.
IterMVS-SCA-FT		95.86 170	96.19 151	94.85 253	97.68 230	85.53 297	92.42 297	97.63 242	96.99 73	98.36 74	98.54 68	87.94 258	99.75 56	97.07 52	99.08 194	99.27 111

SCA		93.38 258	93.52 244	92.96 300	96.24 290	81.40 326	93.24 281	94.00 302	91.58 247	94.57 257	96.97 214	87.94 258	99.42 188	89.47 271	97.66 282	98.06 255

sss		94.22 233	93.72 241	95.74 219	97.71 228	89.95 230	93.84 261	96.98 263	88.38 277	93.75 281	95.74 274	87.94 258	98.89 281	91.02 234	98.10 261	98.37 225

IterMVS		95.42 187	95.83 167	94.20 277	97.52 242	83.78 318	92.41 298	97.47 249	95.49 137	98.06 108	98.49 71	87.94 258	99.58 144	96.02 81	99.02 201	99.23 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		94.10 240	93.41 246	96.18 203	99.16 67	90.04 228	92.15 301	98.68 133	79.90 332	96.22 214	97.83 145	87.92 262	99.42 188	89.18 275	99.65 59	99.08 146

VDDNet		96.98 114	96.84 119	97.41 134	99.40 36	93.26 171	97.94 49	95.31 293	99.26 7	98.39 71	99.18 27	87.85 263	99.62 134	95.13 130	99.09 193	99.35 92

pmmvs5		94.63 222	94.34 223	95.50 229	97.63 236	88.34 255	94.02 253	97.13 258	87.15 287	95.22 245	97.15 202	87.50 264	99.27 236	93.99 180	99.26 174	98.88 181

D2MVS		95.18 196	95.17 185	95.21 239	97.76 224	87.76 269	94.15 247	97.94 217	89.77 263	96.99 175	97.68 163	87.45 265	99.14 252	95.03 136	99.81 29	98.74 197

PVSNet	86.72 19	91.10 290	90.97 286	91.49 313	97.56 240	78.04 335	87.17 340	94.60 298	84.65 313	92.34 314	92.20 326	87.37 266	98.47 315	85.17 316	97.69 279	97.96 263

Anonymous202405211		96.34 151	95.98 162	97.43 132	98.25 161	93.85 152	96.74 109	94.41 300	97.72 50	98.37 72	98.03 122	87.15 267	99.53 159	94.06 175	99.07 196	98.92 173

MVSFormer		96.14 158	96.36 145	95.49 230	97.68 230	87.81 267	98.67 11	99.02 49	96.50 88	94.48 262	96.15 259	86.90 268	99.92 4	98.73 7	99.13 186	98.74 197

lupinMVS		93.77 246	93.28 247	95.24 238	97.68 230	87.81 267	92.12 302	96.05 280	84.52 314	94.48 262	95.06 289	86.90 268	99.63 126	93.62 192	99.13 186	98.27 238

eth_miper_zixun_eth		94.89 207	94.93 195	94.75 259	95.99 301	86.12 292	91.35 313	98.49 156	93.40 205	97.12 163	97.25 199	86.87 270	99.35 216	95.08 133	98.82 224	98.78 192

WTY-MVS		93.55 254	93.00 253	95.19 240	97.81 209	87.86 264	93.89 260	96.00 281	89.02 268	94.07 271	95.44 284	86.27 271	99.33 221	87.69 294	96.82 301	98.39 224

CDS-MVSNet		94.88 208	94.12 230	97.14 148	97.64 235	93.57 164	93.96 258	97.06 261	90.05 260	96.30 210	96.55 240	86.10 272	99.47 175	90.10 262	99.31 166	98.40 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1112_ss		94.12 239	93.42 245	96.23 199	98.59 128	90.85 216	94.24 241	98.85 84	85.49 302	92.97 303	94.94 291	86.01 273	99.64 124	91.78 220	97.92 267	98.20 244

miper_enhance_ethall		93.14 263	92.78 260	94.20 277	93.65 336	85.29 301	89.97 331	97.85 222	85.05 309	96.15 219	94.56 298	85.74 274	99.14 252	93.74 188	98.34 252	98.17 247

new_pmnet		92.34 274	91.69 276	94.32 274	96.23 292	89.16 241	92.27 300	92.88 314	84.39 317	95.29 243	96.35 252	85.66 275	96.74 343	84.53 320	97.56 285	97.05 293

alignmvs		96.01 164	95.52 178	97.50 121	97.77 223	94.71 119	96.07 143	96.84 267	97.48 61	96.78 189	94.28 306	85.50 276	99.40 199	96.22 71	98.73 234	98.40 222

lessismore_v0				97.05 153	99.36 40	92.12 195		84.07 348		98.77 45	98.98 38	85.36 277	99.74 63	97.34 40	99.37 144	99.30 100

HY-MVS	91.43 15	92.58 269	91.81 274	94.90 251	96.49 284	88.87 245	97.31 82	94.62 297	85.92 297	90.50 326	96.84 222	85.05 278	99.40 199	83.77 325	95.78 318	96.43 314

EPNet		93.72 248	92.62 264	97.03 155	87.61 352	92.25 189	96.27 131	91.28 327	96.74 81	87.65 340	97.39 186	85.00 279	99.64 124	92.14 213	99.48 113	99.20 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		94.81 211	94.80 203	94.85 253	96.16 296	86.45 288	91.14 320	98.20 191	93.49 203	97.03 172	97.37 190	84.97 280	99.26 237	95.28 116	99.56 81	98.83 186

Test_1112_low_res		93.53 255	92.86 255	95.54 228	98.60 126	88.86 246	92.75 289	98.69 131	82.66 321	92.65 310	96.92 219	84.75 281	99.56 151	90.94 236	97.76 273	98.19 245

MVS-HIRNet		88.40 310	90.20 297	82.99 332	97.01 271	60.04 352	93.11 284	85.61 347	84.45 316	88.72 336	99.09 33	84.72 282	98.23 328	82.52 328	96.59 308	90.69 344

K. test v3		96.44 148	96.28 148	96.95 157	99.41 35	91.53 207	97.65 65	90.31 336	98.89 18	98.93 37	99.36 14	84.57 283	99.92 4	97.81 25	99.56 81	99.39 83

cl-mvsnet1		94.73 213	94.64 208	95.01 246	95.86 303	87.00 281	91.33 314	98.08 208	93.34 208	97.10 165	97.34 192	84.02 284	99.31 225	95.15 127	99.55 87	98.72 200

cl-mvsnet_		94.73 213	94.64 208	95.01 246	95.85 304	87.00 281	91.33 314	98.08 208	93.34 208	97.10 165	97.33 193	84.01 285	99.30 228	95.14 128	99.56 81	98.71 202

Vis-MVSNet (Re-imp)		95.11 199	94.85 199	95.87 217	99.12 79	89.17 240	97.54 74	94.92 295	96.50 88	96.58 194	97.27 197	83.64 286	99.48 172	88.42 286	99.67 56	98.97 161

PVSNet_0	81.89 21	84.49 319	83.21 321	88.34 328	95.76 308	74.97 346	83.49 344	92.70 318	78.47 337	87.94 339	86.90 345	83.38 287	96.63 344	73.44 342	66.86 348	93.40 336

CMPMVS	73.10 23	92.74 267	91.39 278	96.77 169	93.57 338	94.67 123	94.21 244	97.67 234	80.36 331	93.61 287	96.60 238	82.85 288	97.35 338	84.86 318	98.78 227	98.29 237
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		94.25 232	94.47 218	93.60 284	98.14 177	82.60 322	97.24 87	92.72 317	85.08 308	98.48 62	98.94 42	82.59 289	98.76 293	97.47 37	99.53 93	99.44 75

baseline1		93.14 263	92.64 263	94.62 263	97.34 257	87.20 279	96.67 116	93.02 312	94.71 167	96.51 199	95.83 273	81.64 290	98.60 307	90.00 264	88.06 341	98.07 251

CVMVSNet		92.33 275	92.79 258	90.95 317	97.26 262	75.84 343	95.29 192	92.33 320	81.86 322	96.27 211	98.19 104	81.44 291	98.46 316	94.23 169	98.29 255	98.55 216

EPNet_dtu		91.39 288	90.75 290	93.31 289	90.48 351	82.61 321	94.80 222	92.88 314	93.39 206	81.74 348	94.90 294	81.36 292	99.11 257	88.28 288	98.87 217	98.21 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_yl		94.40 228	94.00 235	95.59 223	96.95 273	89.52 234	94.75 225	95.55 291	96.18 101	96.79 185	96.14 261	81.09 293	99.18 246	90.75 243	97.77 271	98.07 251

DCV-MVSNet		94.40 228	94.00 235	95.59 223	96.95 273	89.52 234	94.75 225	95.55 291	96.18 101	96.79 185	96.14 261	81.09 293	99.18 246	90.75 243	97.77 271	98.07 251

MIMVSNet		93.42 256	92.86 255	95.10 243	98.17 172	88.19 257	98.13 41	93.69 304	92.07 237	95.04 248	98.21 103	80.95 295	99.03 268	81.42 330	98.06 263	98.07 251

PAPM		87.64 316	85.84 320	93.04 296	96.54 282	84.99 307	88.42 339	95.57 290	79.52 333	83.82 345	93.05 316	80.57 296	98.41 318	62.29 347	92.79 332	95.71 322

HyFIR lowres test		93.72 248	92.65 262	96.91 161	98.93 94	91.81 204	91.23 318	98.52 153	82.69 320	96.46 201	96.52 244	80.38 297	99.90 13	90.36 259	98.79 226	99.03 154

FMVSNet3		95.26 194	94.94 193	96.22 201	96.53 283	90.06 227	95.99 150	97.66 236	94.11 189	97.99 114	97.91 137	80.22 298	99.63 126	94.60 151	99.44 123	98.96 162

RPMNet		94.22 233	94.03 234	94.78 257	95.44 314	88.15 258	96.18 138	93.73 303	97.43 62	94.10 269	98.49 71	79.40 299	99.39 204	95.69 92	95.81 315	96.81 304

LFMVS		95.32 191	94.88 198	96.62 177	98.03 184	91.47 209	97.65 65	90.72 333	99.11 9	97.89 126	98.31 84	79.20 300	99.48 172	93.91 184	99.12 189	98.93 169

ADS-MVSNet2		91.47 287	90.51 294	94.36 273	95.51 312	85.63 295	95.05 210	95.70 287	83.46 318	92.69 308	96.84 222	79.15 301	99.41 197	85.66 312	90.52 336	98.04 259

ADS-MVSNet		90.95 293	90.26 296	93.04 296	95.51 312	82.37 323	95.05 210	93.41 309	83.46 318	92.69 308	96.84 222	79.15 301	98.70 298	85.66 312	90.52 336	98.04 259

MDTV_nov1_ep13_2view						57.28 353	94.89 217		80.59 329	94.02 273		78.66 303		85.50 314		97.82 270

cl-mvsnet2		93.25 261	92.84 257	94.46 270	94.30 328	86.00 293	91.09 321	96.64 275	90.74 253	95.79 230	96.31 253	78.24 304	98.77 291	94.15 172	98.34 252	98.62 209

PatchmatchNet		91.98 281	91.87 272	92.30 309	94.60 325	79.71 331	95.12 201	93.59 308	89.52 264	93.61 287	97.02 212	77.94 305	99.18 246	90.84 239	94.57 328	98.01 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												77.80 306				98.06 255

CR-MVSNet		93.29 260	92.79 258	94.78 257	95.44 314	88.15 258	96.18 138	97.20 254	84.94 312	94.10 269	98.57 64	77.67 307	99.39 204	95.17 123	95.81 315	96.81 304

Patchmtry		95.03 203	94.59 213	96.33 195	94.83 322	90.82 218	96.38 126	97.20 254	96.59 85	97.49 144	98.57 64	77.67 307	99.38 208	92.95 206	99.62 62	98.80 189

tpmrst		90.31 296	90.61 293	89.41 324	94.06 333	72.37 349	95.06 209	93.69 304	88.01 280	92.32 315	96.86 220	77.45 309	98.82 286	91.04 233	87.01 343	97.04 294

sam_mvs												77.38 310

patchmatchnet-post											96.84 222	77.36 311	99.42 188

Patchmatch-RL test		94.66 220	94.49 217	95.19 240	98.54 133	88.91 244	92.57 293	98.74 116	91.46 248	98.32 81	97.75 154	77.31 312	98.81 288	96.06 76	99.61 68	97.85 268

tpmvs		90.79 294	90.87 287	90.57 320	92.75 346	76.30 341	95.79 162	93.64 307	91.04 252	91.91 318	96.26 254	77.19 313	98.86 285	89.38 273	89.85 339	96.56 312

test_post											10.87 351	76.83 314	99.07 262

Patchmatch-test		93.60 253	93.25 249	94.63 262	96.14 299	87.47 273	96.04 145	94.50 299	93.57 201	96.47 200	96.97 214	76.50 315	98.61 305	90.67 249	98.41 251	97.81 272

MDTV_nov1_ep13			91.28 280		94.31 327	73.51 347	94.80 222	93.16 311	86.75 292	93.45 295	97.40 182	76.37 316	98.55 311	88.85 279	96.43 309

EMVS		89.06 307	89.22 302	88.61 327	93.00 343	77.34 339	82.91 346	90.92 330	94.64 169	92.63 311	91.81 330	76.30 317	97.02 339	83.83 324	96.90 299	91.48 342

test_post1							94.98 214				10.37 352	76.21 318	99.04 265	89.47 271

GA-MVS		92.83 266	92.15 270	94.87 252	96.97 272	87.27 278	90.03 330	96.12 279	91.83 243	94.05 272	94.57 297	76.01 319	98.97 277	92.46 211	97.34 294	98.36 230

PatchT		93.75 247	93.57 243	94.29 276	95.05 320	87.32 277	96.05 144	92.98 313	97.54 59	94.25 265	98.72 54	75.79 320	99.24 240	95.92 87	95.81 315	96.32 315

E-PMN		89.52 305	89.78 299	88.73 326	93.14 341	77.61 337	83.26 345	92.02 321	94.82 163	93.71 282	93.11 312	75.31 321	96.81 341	85.81 309	96.81 302	91.77 341

DeepMVS_CX				77.17 333	90.94 350	85.28 302		74.08 353	52.51 348	80.87 349	88.03 344	75.25 322	70.63 350	59.23 348	84.94 345	75.62 345

CHOSEN 280x420		89.98 300	89.19 305	92.37 308	95.60 311	81.13 328	86.22 342	97.09 260	81.44 326	87.44 341	93.15 311	73.99 323	99.47 175	88.69 282	99.07 196	96.52 313

thres200		91.00 292	90.42 295	92.77 303	97.47 247	83.98 317	94.01 254	91.18 329	95.12 152	95.44 240	91.21 335	73.93 324	99.31 225	77.76 339	97.63 284	95.01 329

test-LLR		89.97 301	89.90 298	90.16 321	94.24 330	74.98 344	89.89 332	89.06 339	92.02 238	89.97 330	90.77 338	73.92 325	98.57 308	91.88 217	97.36 292	96.92 297

test0.0.03 1		90.11 297	89.21 303	92.83 302	93.89 334	86.87 284	91.74 308	88.74 341	92.02 238	94.71 254	91.14 336	73.92 325	94.48 346	83.75 326	92.94 331	97.16 290

tpm cat1		88.01 313	87.33 313	90.05 323	94.48 326	76.28 342	94.47 234	94.35 301	73.84 346	89.26 333	95.61 280	73.64 327	98.30 326	84.13 321	86.20 344	95.57 326

tfpn200view9		91.55 286	91.00 284	93.21 293	98.02 185	84.35 314	95.70 165	90.79 331	96.26 97	95.90 228	92.13 327	73.62 328	99.42 188	78.85 336	97.74 274	95.85 319

thres400		91.68 285	91.00 284	93.71 282	98.02 185	84.35 314	95.70 165	90.79 331	96.26 97	95.90 228	92.13 327	73.62 328	99.42 188	78.85 336	97.74 274	97.36 286

thres100view900		91.76 284	91.26 282	93.26 290	98.21 165	84.50 312	96.39 124	90.39 334	96.87 77	96.33 206	93.08 314	73.44 330	99.42 188	78.85 336	97.74 274	95.85 319

thres600view7		92.03 280	91.43 277	93.82 280	98.19 167	84.61 311	96.27 131	90.39 334	96.81 79	96.37 205	93.11 312	73.44 330	99.49 169	80.32 332	97.95 266	97.36 286

RRT_MVS		94.90 206	94.07 231	97.39 136	93.18 339	93.21 173	95.26 194	97.49 246	93.94 194	98.25 88	97.85 143	72.96 332	99.84 25	97.90 21	99.78 36	99.14 130

MVSTER		94.21 236	93.93 238	95.05 245	95.83 305	86.46 287	95.18 200	97.65 238	92.41 235	97.94 121	98.00 127	72.39 333	99.58 144	96.36 69	99.56 81	99.12 138

JIA-IIPM		91.79 283	90.69 291	95.11 242	93.80 335	90.98 214	94.16 246	91.78 324	96.38 92	90.30 328	99.30 18	72.02 334	98.90 279	88.28 288	90.17 338	95.45 327

tpm		91.08 291	90.85 288	91.75 312	95.33 317	78.09 334	95.03 212	91.27 328	88.75 272	93.53 291	97.40 182	71.24 335	99.30 228	91.25 231	93.87 329	97.87 267

baseline2		89.65 304	88.44 310	93.25 291	95.62 310	82.71 320	93.82 262	85.94 346	88.89 271	87.35 342	92.54 323	71.23 336	99.33 221	86.01 307	94.60 327	97.72 274

CostFormer		89.75 303	89.25 301	91.26 316	94.69 324	78.00 336	95.32 189	91.98 322	81.50 325	90.55 325	96.96 216	71.06 337	98.89 281	88.59 284	92.63 333	96.87 300

FPMVS		89.92 302	88.63 308	93.82 280	98.37 149	96.94 42	91.58 309	93.34 310	88.00 281	90.32 327	97.10 207	70.87 338	91.13 348	71.91 344	96.16 314	93.39 337

EPMVS		89.26 306	88.55 309	91.39 314	92.36 347	79.11 332	95.65 171	79.86 349	88.60 274	93.12 301	96.53 242	70.73 339	98.10 332	90.75 243	89.32 340	96.98 295

tmp_tt		57.23 320	62.50 322	41.44 334	34.77 353	49.21 354	83.93 343	60.22 355	15.31 349	71.11 350	79.37 347	70.09 340	44.86 351	64.76 346	82.93 347	30.25 347

ET-MVSNet_ETH3D		91.12 289	89.67 300	95.47 231	96.41 286	89.15 242	91.54 310	90.23 337	89.07 267	86.78 344	92.84 318	69.39 341	99.44 185	94.16 171	96.61 307	97.82 270

dp		88.08 312	88.05 311	88.16 330	92.85 344	68.81 351	94.17 245	92.88 314	85.47 303	91.38 321	96.14 261	68.87 342	98.81 288	86.88 303	83.80 346	96.87 300

tpm2		88.47 309	87.69 312	90.79 318	94.98 321	77.34 339	95.09 204	91.83 323	77.51 341	89.40 332	96.41 248	67.83 343	98.73 295	83.58 327	92.60 334	96.29 316

pmmvs3		90.00 299	88.90 307	93.32 288	94.20 332	85.34 299	91.25 317	92.56 319	78.59 336	93.82 277	95.17 286	67.36 344	98.69 299	89.08 277	98.03 264	95.92 318

thisisatest0515		90.43 295	89.18 306	94.17 279	97.07 270	85.44 298	89.75 336	87.58 342	88.28 278	93.69 284	91.72 331	65.27 345	99.58 144	90.59 251	98.67 236	97.50 283

tttt0517		93.31 259	92.56 265	95.57 225	98.71 112	87.86 264	97.44 77	87.17 344	95.79 125	97.47 149	96.84 222	64.12 346	99.81 31	96.20 72	99.32 164	99.02 156

thisisatest0530		92.71 268	91.76 275	95.56 227	98.42 146	88.23 256	96.03 146	87.35 343	94.04 191	96.56 196	95.47 283	64.03 347	99.77 48	94.78 146	99.11 190	98.68 205

FMVSNet5		93.39 257	92.35 267	96.50 186	95.83 305	90.81 220	97.31 82	98.27 182	92.74 230	96.27 211	98.28 91	62.23 348	99.67 115	90.86 238	99.36 147	99.03 154

DWT-MVSNet_test		87.92 314	86.77 317	91.39 314	93.18 339	78.62 333	95.10 202	91.42 326	85.58 301	88.00 338	88.73 343	60.60 349	98.90 279	90.60 250	87.70 342	96.65 308

IB-MVS	85.98 20	88.63 308	86.95 316	93.68 283	95.12 319	84.82 310	90.85 323	90.17 338	87.55 284	88.48 337	91.34 334	58.01 350	99.59 142	87.24 302	93.80 330	96.63 311
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
RRT_test8_iter05		92.46 271	92.52 266	92.29 310	95.33 317	77.43 338	95.73 163	98.55 150	94.41 176	97.46 150	97.72 159	57.44 351	99.74 63	96.92 55	99.14 182	99.69 20

gg-mvs-nofinetune		88.28 311	86.96 315	92.23 311	92.84 345	84.44 313	98.19 38	74.60 351	99.08 10	87.01 343	99.47 8	56.93 352	98.23 328	78.91 335	95.61 320	94.01 334

GG-mvs-BLEND				90.60 319	91.00 349	84.21 316	98.23 32	72.63 354		82.76 346	84.11 346	56.14 353	96.79 342	72.20 343	92.09 335	90.78 343

TESTMET0.1,1		87.20 317	86.57 318	89.07 325	93.62 337	72.84 348	89.89 332	87.01 345	85.46 304	89.12 335	90.20 341	56.00 354	97.72 336	90.91 237	96.92 298	96.64 309

test-mter		87.92 314	87.17 314	90.16 321	94.24 330	74.98 344	89.89 332	89.06 339	86.44 293	89.97 330	90.77 338	54.96 355	98.57 308	91.88 217	97.36 292	96.92 297

test123		12.59 322	15.49 324	3.87 335	6.07 354	2.55 355	90.75 324	2.59 357	2.52 350	5.20 352	13.02 350	4.96 356	1.85 353	5.20 349	9.09 349	7.23 348

testmvs		12.33 323	15.23 325	3.64 336	5.77 355	2.23 356	88.99 337	3.62 356	2.30 351	5.29 351	13.09 349	4.52 357	1.95 352	5.16 350	8.32 350	6.75 349

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_part1				0.00 337		0.00 357	0.00 348	98.84 87				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

ab-mvs-re		7.91 325	10.55 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	94.94 291	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

IU-MVS					99.22 56	95.40 93		98.14 201	85.77 300	98.36 74				95.23 120	99.51 103	99.49 50

save fliter					98.48 141	94.71 119	94.53 232	98.41 167	95.02 157

test_0728_SECOND				98.25 69	99.23 53	95.49 91	96.74 109	98.89 74					99.75 56	95.48 104	99.52 98	99.53 38

GSMVS																98.06 255

test_part2					99.03 89	96.07 68				98.08 106

MTGPA								98.73 118

MTMP							96.55 117	74.60 351

gm-plane-assit					91.79 348	71.40 350			81.67 323		90.11 342		98.99 271	84.86 318

test9_res														91.29 228	98.89 216	99.00 157

agg_prior2														90.34 260	98.90 213	99.10 145

agg_prior					97.80 213	94.96 111		98.36 173		93.49 292			99.53 159

test_prior4						95.38 94	93.61 270

test_prior				97.46 128	97.79 218	94.26 138		98.42 165					99.34 218			98.79 190

旧先验2							93.35 278		77.95 340	95.77 234			98.67 303	90.74 246

新几何2							93.43 273

无先验							93.20 282	97.91 218	80.78 328				99.40 199	87.71 292		97.94 264

原ACMM2							92.82 287

testdata2													99.46 178	87.84 291

testdata1							92.77 288		93.78 197

plane_prior7					98.70 114	94.67 123

plane_prior5								98.75 114					99.46 178	92.59 209	99.20 178	99.28 107

plane_prior4											96.77 228

plane_prior3						94.51 126			95.29 145	96.16 217

plane_prior2							96.50 119		96.36 93

plane_prior1					98.49 139

plane_prior						94.29 134	95.42 179		94.31 181						98.93 211

n2								0.00 358

nn								0.00 358

door-mid								98.17 197

test11								98.08 208

door								97.81 227

HQP5-MVS						92.47 185

HQP-NCC					97.85 201		94.26 237		93.18 215	92.86 305

ACMP_Plane					97.85 201		94.26 237		93.18 215	92.86 305

BP-MVS														90.51 255

HQP4-MVS										92.87 304			99.23 242			99.06 150

HQP3-MVS								98.43 162							98.74 231

NP-MVS					98.14 177	93.72 158					95.08 287

ACMMP++_ref															99.52 98

ACMMP++															99.55 87

High-resolution multi-view benchmark