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		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 10	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 11	100.00 1	99.85 19

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 19	99.34 15	99.69 4	99.58 54	99.90 2	99.86 18	99.78 8	99.58 6	99.95 23	99.00 62	99.95 32	99.78 33

pmmvs6		99.67 3	99.70 3	99.60 11	99.90 4	99.27 22	99.53 7	99.76 28	99.64 15	99.84 20	99.83 3	99.50 8	99.87 101	99.36 38	99.92 55	99.64 64

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 21	99.85 17	99.11 59	99.90 1	99.78 26	99.63 17	99.78 26	99.67 25	99.48 9	99.81 179	99.30 43	99.97 20	99.77 35
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		99.63 5	99.67 5	99.49 48	99.88 9	98.61 92	99.34 20	99.71 33	99.27 58	99.90 12	99.74 13	99.68 4	99.97 4	99.55 29	99.99 5	99.88 14

jajsoiax		99.58 6	99.61 8	99.48 51	99.87 12	98.61 92	99.28 37	99.66 44	99.09 82	99.89 15	99.68 20	99.53 7	99.97 4	99.50 32	99.99 5	99.87 16

test_fmvsmconf0.01_n		99.57 7	99.63 7	99.36 64	99.87 12	98.13 132	98.08 160	99.95 1	99.45 36	99.98 2	99.75 11	99.80 1	99.97 4	99.82 8	99.99 5	99.99 1

ANet_high		99.57 7	99.67 5	99.28 86	99.89 6	98.09 136	99.14 54	99.93 4	99.82 3	99.93 6	99.81 5	99.17 18	99.94 36	99.31 41	100.00 1	99.82 25

v7n		99.53 9	99.57 9	99.41 60	99.88 9	98.54 100	99.45 10	99.61 50	99.66 13	99.68 39	99.66 27	98.44 59	99.95 23	99.73 19	99.96 25	99.75 43

test_djsdf		99.52 10	99.51 11	99.53 34	99.86 15	98.74 82	99.39 17	99.56 68	99.11 72	99.70 35	99.73 15	99.00 22	99.97 4	99.26 44	99.98 12	99.89 11

anonymousdsp		99.51 11	99.47 16	99.62 6	99.88 9	99.08 63	99.34 20	99.69 36	98.93 97	99.65 45	99.72 16	98.93 26	99.95 23	99.11 53	100.00 1	99.82 25

test_fmvsmconf0.1_n		99.49 12	99.54 10	99.34 73	99.78 26	98.11 133	97.77 204	99.90 9	99.33 50	99.97 3	99.66 27	99.71 3	99.96 12	99.79 13	99.99 5	99.96 5

UA-Net		99.47 13	99.40 20	99.70 2	99.49 116	99.29 19	99.80 3	99.72 32	99.82 3	99.04 143	99.81 5	98.05 89	99.96 12	98.85 70	99.99 5	99.86 18

PS-MVSNAJss		99.46 14	99.49 12	99.35 70	99.90 4	98.15 129	99.20 45	99.65 45	99.48 32	99.92 8	99.71 17	98.07 86	99.96 12	99.53 30	100.00 1	99.93 8

test_fmvsmconf_n		99.44 15	99.48 14	99.31 83	99.64 71	98.10 135	97.68 215	99.84 18	99.29 56	99.92 8	99.57 42	99.60 5	99.96 12	99.74 18	99.98 12	99.89 11

pm-mvs1		99.44 15	99.48 14	99.33 78	99.80 23	98.63 89	99.29 33	99.63 46	99.30 55	99.65 45	99.60 39	99.16 20	99.82 166	99.07 56	99.83 93	99.56 98

TransMVSNet (Re)		99.44 15	99.47 16	99.36 64	99.80 23	98.58 95	99.27 39	99.57 61	99.39 43	99.75 30	99.62 34	99.17 18	99.83 156	99.06 57	99.62 187	99.66 59

DTE-MVSNet		99.43 18	99.35 23	99.66 4	99.71 48	99.30 17	99.31 27	99.51 84	99.64 15	99.56 53	99.46 66	98.23 71	99.97 4	98.78 73	99.93 44	99.72 46

TDRefinement		99.42 19	99.38 21	99.55 23	99.76 32	99.33 16	99.68 5	99.71 33	99.38 44	99.53 60	99.61 37	98.64 43	99.80 186	98.24 107	99.84 86	99.52 119

PEN-MVS		99.41 20	99.34 25	99.62 6	99.73 39	99.14 52	99.29 33	99.54 77	99.62 20	99.56 53	99.42 74	98.16 82	99.96 12	98.78 73	99.93 44	99.77 35

nrg030		99.40 21	99.35 23	99.54 27	99.58 78	99.13 55	98.98 72	99.48 95	99.68 11	99.46 71	99.26 101	98.62 46	99.73 239	99.17 52	99.92 55	99.76 39

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 48	99.10 60	99.29 33	99.53 80	99.53 29	99.46 71	99.41 77	98.23 71	99.95 23	98.89 69	99.95 32	99.81 28

MIMVSNet1		99.38 23	99.32 28	99.55 23	99.86 15	99.19 37	99.41 13	99.59 52	99.59 23	99.71 33	99.57 42	97.12 155	99.90 65	99.21 49	99.87 78	99.54 109

OurMVSNet-221017-0		99.37 24	99.31 30	99.53 34	99.91 3	98.98 65	99.63 6	99.58 54	99.44 38	99.78 26	99.76 10	96.39 195	99.92 51	99.44 36	99.92 55	99.68 55

Vis-MVSNet		99.34 25	99.36 22	99.27 89	99.73 39	98.26 118	99.17 50	99.78 26	99.11 72	99.27 108	99.48 64	98.82 31	99.95 23	98.94 65	99.93 44	99.59 81
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsm_n_1920		99.33 26	99.45 18	98.99 136	99.57 82	97.73 178	97.93 181	99.83 20	99.22 61	99.93 6	99.30 95	99.42 10	99.96 12	99.85 5	99.99 5	99.29 214

WR-MVS_H		99.33 26	99.22 40	99.65 5	99.71 48	99.24 25	99.32 23	99.55 72	99.46 35	99.50 67	99.34 88	97.30 144	99.93 41	98.90 67	99.93 44	99.77 35

VPA-MVSNet		99.30 28	99.30 32	99.28 86	99.49 116	98.36 114	99.00 69	99.45 107	99.63 17	99.52 62	99.44 71	98.25 69	99.88 84	99.09 55	99.84 86	99.62 68

sd_testset		99.28 29	99.31 30	99.19 102	99.68 59	98.06 145	99.41 13	99.30 167	99.69 9	99.63 48	99.68 20	99.25 14	99.96 12	97.25 162	99.92 55	99.57 92

Anonymous20231211		99.27 30	99.27 35	99.26 91	99.29 159	98.18 126	99.49 8	99.51 84	99.70 8	99.80 24	99.68 20	96.84 170	99.83 156	99.21 49	99.91 63	99.77 35

FC-MVSNet-test		99.27 30	99.25 38	99.34 73	99.77 29	98.37 111	99.30 32	99.57 61	99.61 22	99.40 83	99.50 59	97.12 155	99.85 122	99.02 61	99.94 40	99.80 29

test_fmvsmvis_n_1920		99.26 32	99.49 12	98.54 204	99.66 65	96.97 219	98.00 174	99.85 15	99.24 60	99.92 8	99.50 59	99.39 11	99.95 23	99.89 3	99.98 12	98.71 308

testf1		99.25 33	99.16 45	99.51 43	99.89 6	99.63 3	98.71 92	99.69 36	98.90 99	99.43 76	99.35 84	98.86 28	99.67 266	97.81 134	99.81 100	99.24 224

APD_test2		99.25 33	99.16 45	99.51 43	99.89 6	99.63 3	98.71 92	99.69 36	98.90 99	99.43 76	99.35 84	98.86 28	99.67 266	97.81 134	99.81 100	99.24 224

KD-MVS_self_test		99.25 33	99.18 42	99.44 57	99.63 75	99.06 64	98.69 94	99.54 77	99.31 53	99.62 51	99.53 54	97.36 142	99.86 110	99.24 48	99.71 154	99.39 177

ACMH	96.65 7	99.25 33	99.24 39	99.26 91	99.72 45	98.38 109	99.07 62	99.55 72	98.30 133	99.65 45	99.45 70	99.22 15	99.76 222	98.44 98	99.77 124	99.64 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvsmamba		99.24 37	99.15 50	99.49 48	99.83 20	98.85 74	99.41 13	99.55 72	99.54 27	99.40 83	99.52 57	95.86 222	99.91 60	99.32 40	99.95 32	99.70 52

SDMVSNet		99.23 38	99.32 28	98.96 140	99.68 59	97.35 197	98.84 84	99.48 95	99.69 9	99.63 48	99.68 20	99.03 21	99.96 12	97.97 125	99.92 55	99.57 92

fmvsm_l_conf0.5_n		99.21 39	99.28 34	99.02 133	99.64 71	97.28 201	97.82 197	99.76 28	98.73 107	99.82 21	99.09 140	98.81 32	99.95 23	99.86 4	99.96 25	99.83 22

CP-MVSNet		99.21 39	99.09 55	99.56 21	99.65 66	98.96 70	99.13 55	99.34 147	99.42 41	99.33 97	99.26 101	97.01 163	99.94 36	98.74 77	99.93 44	99.79 30

fmvsm_l_conf0.5_n_a		99.19 41	99.27 35	98.94 143	99.65 66	97.05 215	97.80 200	99.76 28	98.70 110	99.78 26	99.11 134	98.79 34	99.95 23	99.85 5	99.96 25	99.83 22

fmvsm_s_conf0.1_n_a		99.17 42	99.30 32	98.80 161	99.75 36	96.59 233	97.97 180	99.86 13	98.22 141	99.88 17	99.71 17	98.59 49	99.84 139	99.73 19	99.98 12	99.98 2

TranMVSNet+NR-MVSNet		99.17 42	99.07 58	99.46 56	99.37 147	98.87 73	98.39 131	99.42 120	99.42 41	99.36 92	99.06 141	98.38 62	99.95 23	98.34 103	99.90 70	99.57 92

FMVSNet1		99.17 42	99.17 43	99.17 103	99.55 94	98.24 120	99.20 45	99.44 111	99.21 63	99.43 76	99.55 48	97.82 105	99.86 110	98.42 100	99.89 74	99.41 165

fmvsm_s_conf0.1_n		99.16 45	99.33 26	98.64 182	99.71 48	96.10 244	97.87 192	99.85 15	98.56 122	99.90 12	99.68 20	98.69 41	99.85 122	99.72 21	99.98 12	99.97 3

test_vis3_rt		99.14 46	99.17 43	99.07 121	99.78 26	98.38 109	98.92 76	99.94 2	97.80 174	99.91 11	99.67 25	97.15 154	98.91 381	99.76 16	99.56 210	99.92 9

FIs		99.14 46	99.09 55	99.29 84	99.70 55	98.28 117	99.13 55	99.52 83	99.48 32	99.24 117	99.41 77	96.79 176	99.82 166	98.69 82	99.88 75	99.76 39

XXY-MVS		99.14 46	99.15 50	99.10 115	99.76 32	97.74 176	98.85 82	99.62 47	98.48 125	99.37 90	99.49 63	98.75 36	99.86 110	98.20 110	99.80 110	99.71 47

CS-MVS		99.13 49	99.10 54	99.24 96	99.06 213	99.15 47	99.36 19	99.88 11	99.36 48	98.21 246	98.46 264	98.68 42	99.93 41	99.03 60	99.85 82	98.64 317

CS-MVS-test		99.13 49	99.09 55	99.26 91	99.13 198	98.97 66	99.31 27	99.88 11	99.44 38	98.16 249	98.51 256	98.64 43	99.93 41	98.91 66	99.85 82	98.88 285

test_fmvs3		99.12 51	99.41 19	98.25 231	99.76 32	95.07 282	99.05 65	99.94 2	97.78 176	99.82 21	99.84 2	98.56 52	99.71 247	99.96 1	99.96 25	99.97 3

casdiffmvs_mvg		99.12 51	99.16 45	98.99 136	99.43 135	97.73 178	98.00 174	99.62 47	99.22 61	99.55 55	99.22 110	98.93 26	99.75 229	98.66 84	99.81 100	99.50 124
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_a		99.10 53	99.20 41	98.78 167	99.55 94	96.59 233	97.79 201	99.82 22	98.21 142	99.81 23	99.53 54	98.46 58	99.84 139	99.70 22	99.97 20	99.90 10

fmvsm_s_conf0.5_n		99.09 54	99.26 37	98.61 190	99.55 94	96.09 247	97.74 209	99.81 23	98.55 123	99.85 19	99.55 48	98.60 48	99.84 139	99.69 24	99.98 12	99.89 11

RRT_MVS		99.09 54	98.94 67	99.55 23	99.87 12	98.82 78	99.48 9	98.16 317	99.49 31	99.59 52	99.65 30	94.79 256	99.95 23	99.45 35	99.96 25	99.88 14

EC-MVSNet		99.09 54	99.05 59	99.20 100	99.28 160	98.93 71	99.24 41	99.84 18	99.08 84	98.12 254	98.37 272	98.72 38	99.90 65	99.05 58	99.77 124	98.77 302

ACMH+	96.62 9	99.08 57	99.00 62	99.33 78	99.71 48	98.83 76	98.60 102	99.58 54	99.11 72	99.53 60	99.18 117	98.81 32	99.67 266	96.71 211	99.77 124	99.50 124

bld_raw_dy_0_64		99.07 58	99.00 62	99.29 84	99.85 17	98.18 126	99.11 58	99.40 123	99.33 50	99.38 87	99.44 71	95.21 239	99.97 4	99.31 41	99.98 12	99.73 45

GeoE		99.05 59	98.99 65	99.25 94	99.44 130	98.35 115	98.73 89	99.56 68	98.42 126	98.91 167	98.81 210	98.94 25	99.91 60	98.35 102	99.73 142	99.49 128

Gipuma		99.03 60	99.16 45	98.64 182	99.94 2	98.51 102	99.32 23	99.75 31	99.58 25	98.60 212	99.62 34	98.22 74	99.51 326	97.70 142	99.73 142	97.89 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v8		99.01 61	99.16 45	98.57 196	99.47 125	96.31 241	98.90 77	99.47 102	99.03 88	99.52 62	99.57 42	96.93 166	99.81 179	99.60 25	99.98 12	99.60 75

HPM-MVS_fast		99.01 61	98.82 78	99.57 16	99.71 48	99.35 12	99.00 69	99.50 86	97.33 218	98.94 164	98.86 199	98.75 36	99.82 166	97.53 149	99.71 154	99.56 98

APDe-MVS		98.99 63	98.79 81	99.60 11	99.21 174	99.15 47	98.87 79	99.48 95	97.57 192	99.35 94	99.24 106	97.83 102	99.89 75	97.88 131	99.70 159	99.75 43
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
EG-PatchMatch MVS		98.99 63	99.01 61	98.94 143	99.50 109	97.47 190	98.04 167	99.59 52	98.15 153	99.40 83	99.36 83	98.58 51	99.76 222	98.78 73	99.68 167	99.59 81

COLMAP_ROB	96.50 10	98.99 63	98.85 76	99.41 60	99.58 78	99.10 60	98.74 86	99.56 68	99.09 82	99.33 97	99.19 114	98.40 61	99.72 246	95.98 257	99.76 135	99.42 162
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		98.98 66	98.86 75	99.36 64	99.82 22	98.55 97	97.47 242	99.57 61	99.37 45	99.21 120	99.61 37	96.76 179	99.83 156	98.06 118	99.83 93	99.71 47

v10		98.97 67	99.11 52	98.55 201	99.44 130	96.21 243	98.90 77	99.55 72	98.73 107	99.48 68	99.60 39	96.63 186	99.83 156	99.70 22	99.99 5	99.61 74

DeepC-MVS	97.60 4	98.97 67	98.93 68	99.10 115	99.35 152	97.98 152	98.01 173	99.46 104	97.56 194	99.54 56	99.50 59	98.97 23	99.84 139	98.06 118	99.92 55	99.49 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		98.96 69	99.02 60	98.76 171	99.38 141	97.26 203	98.49 119	99.50 86	98.86 102	99.19 122	99.06 141	98.23 71	99.69 254	98.71 80	99.76 135	99.33 203

casdiffmvs		98.95 70	99.00 62	98.81 159	99.38 141	97.33 198	97.82 197	99.57 61	99.17 70	99.35 94	99.17 121	98.35 66	99.69 254	98.46 97	99.73 142	99.41 165
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NR-MVSNet		98.95 70	98.82 78	99.36 64	99.16 191	98.72 87	99.22 42	99.20 198	99.10 79	99.72 31	98.76 218	96.38 197	99.86 110	98.00 123	99.82 96	99.50 124

Anonymous20240529		98.93 72	98.87 72	99.12 111	99.19 181	98.22 125	99.01 67	98.99 247	99.25 59	99.54 56	99.37 80	97.04 159	99.80 186	97.89 128	99.52 222	99.35 196

DP-MVS		98.93 72	98.81 80	99.28 86	99.21 174	98.45 106	98.46 124	99.33 152	99.63 17	99.48 68	99.15 127	97.23 150	99.75 229	97.17 165	99.66 178	99.63 67

SED-MVS		98.91 74	98.72 88	99.49 48	99.49 116	99.17 39	98.10 158	99.31 159	98.03 157	99.66 42	99.02 153	98.36 63	99.88 84	96.91 187	99.62 187	99.41 165

ACMM	96.08 12	98.91 74	98.73 86	99.48 51	99.55 94	99.14 52	98.07 162	99.37 132	97.62 186	99.04 143	98.96 175	98.84 30	99.79 199	97.43 153	99.65 179	99.49 128
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		98.90 76	98.70 93	99.51 43	98.43 318	99.15 47	99.43 11	99.32 154	98.17 149	99.26 112	99.02 153	98.18 78	99.88 84	97.07 175	99.45 236	99.49 128

tfpnnormal		98.90 76	98.90 71	98.91 148	99.67 63	97.82 169	99.00 69	99.44 111	99.45 36	99.51 66	99.24 106	98.20 77	99.86 110	95.92 259	99.69 162	99.04 257

MTAPA		98.88 78	98.64 102	99.61 9	99.67 63	99.36 11	98.43 127	99.20 198	98.83 106	98.89 170	98.90 189	96.98 165	99.92 51	97.16 166	99.70 159	99.56 98

mvsany_test3		98.87 79	98.92 69	98.74 178	99.38 141	96.94 223	98.58 104	99.10 225	96.49 266	99.96 4	99.81 5	98.18 78	99.45 337	98.97 64	99.79 115	99.83 22

VPNet		98.87 79	98.83 77	99.01 134	99.70 55	97.62 185	98.43 127	99.35 141	99.47 34	99.28 106	99.05 148	96.72 182	99.82 166	98.09 116	99.36 247	99.59 81

UniMVSNet (Re)		98.87 79	98.71 90	99.35 70	99.24 167	98.73 85	97.73 211	99.38 128	98.93 97	99.12 128	98.73 221	96.77 177	99.86 110	98.63 87	99.80 110	99.46 147

UniMVSNet_NR-MVSNet		98.86 82	98.68 96	99.40 62	99.17 189	98.74 82	97.68 215	99.40 123	99.14 71	99.06 136	98.59 248	96.71 183	99.93 41	98.57 90	99.77 124	99.53 116

APD-MVS_3200maxsize		98.84 83	98.61 109	99.53 34	99.19 181	99.27 22	98.49 119	99.33 152	98.64 111	99.03 146	98.98 170	97.89 99	99.85 122	96.54 227	99.42 240	99.46 147

APD_test1		98.83 84	98.66 99	99.34 73	99.78 26	99.47 6	98.42 129	99.45 107	98.28 138	98.98 150	99.19 114	97.76 108	99.58 305	96.57 219	99.55 213	98.97 269

PM-MVS		98.82 85	98.72 88	99.12 111	99.64 71	98.54 100	97.98 177	99.68 41	97.62 186	99.34 96	99.18 117	97.54 127	99.77 216	97.79 136	99.74 139	99.04 257

DU-MVS		98.82 85	98.63 103	99.39 63	99.16 191	98.74 82	97.54 234	99.25 187	98.84 105	99.06 136	98.76 218	96.76 179	99.93 41	98.57 90	99.77 124	99.50 124

SR-MVS-dyc-post		98.81 87	98.55 114	99.57 16	99.20 178	99.38 8	98.48 122	99.30 167	98.64 111	98.95 157	98.96 175	97.49 136	99.86 110	96.56 223	99.39 243	99.45 151

3Dnovator	98.27 2	98.81 87	98.73 86	99.05 128	98.76 266	97.81 171	99.25 40	99.30 167	98.57 120	98.55 221	99.33 90	97.95 97	99.90 65	97.16 166	99.67 173	99.44 155

HPM-MVS		98.79 89	98.53 117	99.59 15	99.65 66	99.29 19	99.16 51	99.43 117	96.74 257	98.61 210	98.38 271	98.62 46	99.87 101	96.47 231	99.67 173	99.59 81
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		98.79 89	98.54 116	99.54 27	99.73 39	99.16 43	98.23 143	99.31 159	97.92 165	98.90 168	98.90 189	98.00 92	99.88 84	96.15 250	99.72 149	99.58 87
Skip Steuart: Steuart Systems R&D Blog.
dcpmvs_2		98.78 91	99.11 52	97.78 263	99.56 90	93.67 327	99.06 63	99.86 13	99.50 30	99.66 42	99.26 101	97.21 152	99.99 2	98.00 123	99.91 63	99.68 55

V42		98.78 91	98.78 82	98.76 171	99.44 130	97.04 216	98.27 140	99.19 202	97.87 169	99.25 116	99.16 123	96.84 170	99.78 210	99.21 49	99.84 86	99.46 147

test20.03		98.78 91	98.77 83	98.78 167	99.46 126	97.20 208	97.78 202	99.24 192	99.04 87	99.41 80	98.90 189	97.65 115	99.76 222	97.70 142	99.79 115	99.39 177

DVP-MVS		98.77 94	98.52 118	99.52 39	99.50 109	99.21 28	98.02 170	98.84 271	97.97 160	99.08 134	99.02 153	97.61 121	99.88 84	96.99 181	99.63 184	99.48 138
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0402		98.76 95	98.71 90	98.93 145	99.56 90	98.14 131	98.45 126	99.34 147	99.28 57	98.95 157	98.91 186	98.34 67	99.79 199	95.63 274	99.91 63	98.86 287

ACMMP_NAP		98.75 96	98.48 126	99.57 16	99.58 78	99.29 19	97.82 197	99.25 187	96.94 248	98.78 189	99.12 133	98.02 90	99.84 139	97.13 171	99.67 173	99.59 81

SixPastTwentyTwo		98.75 96	98.62 105	99.16 106	99.83 20	97.96 156	99.28 37	98.20 314	99.37 45	99.70 35	99.65 30	92.65 297	99.93 41	99.04 59	99.84 86	99.60 75

ACMMP		98.75 96	98.50 121	99.52 39	99.56 90	99.16 43	98.87 79	99.37 132	97.16 238	98.82 186	99.01 162	97.71 111	99.87 101	96.29 242	99.69 162	99.54 109
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
XVS		98.72 99	98.45 131	99.53 34	99.46 126	99.21 28	98.65 96	99.34 147	98.62 115	97.54 294	98.63 242	97.50 133	99.83 156	96.79 200	99.53 219	99.56 98

SSC-MVS		98.71 100	98.74 84	98.62 187	99.72 45	96.08 249	98.74 86	98.64 295	99.74 6	99.67 41	99.24 106	94.57 260	99.95 23	99.11 53	99.24 267	99.82 25

SR-MVS		98.71 100	98.43 134	99.57 16	99.18 188	99.35 12	98.36 134	99.29 175	98.29 136	98.88 174	98.85 202	97.53 129	99.87 101	96.14 251	99.31 255	99.48 138

HFP-MVS		98.71 100	98.44 133	99.51 43	99.49 116	99.16 43	98.52 111	99.31 159	97.47 202	98.58 216	98.50 260	97.97 96	99.85 122	96.57 219	99.59 198	99.53 116

LPG-MVS_test		98.71 100	98.46 130	99.47 54	99.57 82	98.97 66	98.23 143	99.48 95	96.60 262	99.10 132	99.06 141	98.71 39	99.83 156	95.58 277	99.78 120	99.62 68

test_fmvs2		98.70 104	98.97 66	97.89 256	99.54 99	94.05 309	98.55 107	99.92 6	96.78 255	99.72 31	99.78 8	96.60 187	99.67 266	99.91 2	99.90 70	99.94 7

ACMMPR		98.70 104	98.42 136	99.54 27	99.52 104	99.14 52	98.52 111	99.31 159	97.47 202	98.56 219	98.54 252	97.75 109	99.88 84	96.57 219	99.59 198	99.58 87

CP-MVS		98.70 104	98.42 136	99.52 39	99.36 148	99.12 57	98.72 90	99.36 136	97.54 197	98.30 241	98.40 268	97.86 101	99.89 75	96.53 228	99.72 149	99.56 98

tt0805		98.69 107	98.62 105	98.90 151	99.75 36	99.30 17	99.15 53	96.97 347	98.86 102	98.87 178	97.62 325	98.63 45	98.96 378	99.41 37	98.29 334	98.45 327

Anonymous20240521		98.69 107	98.87 72	98.16 239	99.77 29	95.11 281	99.08 59	99.44 111	99.34 49	99.33 97	99.55 48	94.10 274	99.94 36	99.25 46	99.96 25	99.42 162

region2R		98.69 107	98.40 138	99.54 27	99.53 102	99.17 39	98.52 111	99.31 159	97.46 207	98.44 231	98.51 256	97.83 102	99.88 84	96.46 232	99.58 203	99.58 87

EI-MVSNet-UG-set		98.69 107	98.71 90	98.62 187	99.10 202	96.37 238	97.23 258	98.87 262	99.20 65	99.19 122	98.99 166	97.30 144	99.85 122	98.77 76	99.79 115	99.65 63

3Dnovator+	97.89 3	98.69 107	98.51 119	99.24 96	98.81 261	98.40 107	99.02 66	99.19 202	98.99 91	98.07 258	99.28 97	97.11 157	99.84 139	96.84 198	99.32 253	99.47 145

ZNCC-MVS		98.68 112	98.40 138	99.54 27	99.57 82	99.21 28	98.46 124	99.29 175	97.28 224	98.11 255	98.39 269	98.00 92	99.87 101	96.86 197	99.64 181	99.55 105

EI-MVSNet-Vis-set		98.68 112	98.70 93	98.63 186	99.09 205	96.40 237	97.23 258	98.86 267	99.20 65	99.18 126	98.97 172	97.29 146	99.85 122	98.72 79	99.78 120	99.64 64

CSCG		98.68 112	98.50 121	99.20 100	99.45 129	98.63 89	98.56 106	99.57 61	97.87 169	98.85 179	98.04 300	97.66 114	99.84 139	96.72 209	99.81 100	99.13 246

test_f		98.67 115	98.87 72	98.05 248	99.72 45	95.59 260	98.51 116	99.81 23	96.30 275	99.78 26	99.82 4	96.14 204	98.63 386	99.82 8	99.93 44	99.95 6

PGM-MVS		98.66 116	98.37 144	99.55 23	99.53 102	99.18 38	98.23 143	99.49 93	97.01 245	98.69 199	98.88 196	98.00 92	99.89 75	95.87 263	99.59 198	99.58 87

GBi-Net		98.65 117	98.47 128	99.17 103	98.90 241	98.24 120	99.20 45	99.44 111	98.59 117	98.95 157	99.55 48	94.14 270	99.86 110	97.77 137	99.69 162	99.41 165

test1		98.65 117	98.47 128	99.17 103	98.90 241	98.24 120	99.20 45	99.44 111	98.59 117	98.95 157	99.55 48	94.14 270	99.86 110	97.77 137	99.69 162	99.41 165

LCM-MVSNet-Re		98.64 119	98.48 126	99.11 113	98.85 252	98.51 102	98.49 119	99.83 20	98.37 127	99.69 37	99.46 66	98.21 76	99.92 51	94.13 314	99.30 258	98.91 281

mPP-MVS		98.64 119	98.34 148	99.54 27	99.54 99	99.17 39	98.63 98	99.24 192	97.47 202	98.09 257	98.68 230	97.62 120	99.89 75	96.22 245	99.62 187	99.57 92

TSAR-MVS + MP.		98.63 121	98.49 125	99.06 127	99.64 71	97.90 160	98.51 116	98.94 249	96.96 246	99.24 117	98.89 195	97.83 102	99.81 179	96.88 194	99.49 232	99.48 138
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
LS3D		98.63 121	98.38 143	99.36 64	97.25 374	99.38 8	99.12 57	99.32 154	99.21 63	98.44 231	98.88 196	97.31 143	99.80 186	96.58 217	99.34 251	98.92 278

RPSCF		98.62 123	98.36 145	99.42 58	99.65 66	99.42 7	98.55 107	99.57 61	97.72 180	98.90 168	99.26 101	96.12 206	99.52 322	95.72 270	99.71 154	99.32 205

GST-MVS		98.61 124	98.30 153	99.52 39	99.51 106	99.20 34	98.26 141	99.25 187	97.44 210	98.67 201	98.39 269	97.68 112	99.85 122	96.00 255	99.51 224	99.52 119

v1192		98.60 125	98.66 99	98.41 218	99.27 162	95.88 254	97.52 236	99.36 136	97.41 211	99.33 97	99.20 113	96.37 198	99.82 166	99.57 27	99.92 55	99.55 105

v1144		98.60 125	98.66 99	98.41 218	99.36 148	95.90 253	97.58 230	99.34 147	97.51 198	99.27 108	99.15 127	96.34 200	99.80 186	99.47 34	99.93 44	99.51 121

DPE-MVS		98.59 127	98.26 158	99.57 16	99.27 162	99.15 47	97.01 270	99.39 126	97.67 182	99.44 75	98.99 166	97.53 129	99.89 75	95.40 281	99.68 167	99.66 59
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		98.57 128	98.23 161	99.60 11	99.69 57	99.35 12	97.16 265	99.38 128	94.87 314	98.97 154	98.99 166	98.01 91	99.88 84	97.29 159	99.70 159	99.58 87
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 129	98.32 152	99.25 94	99.41 138	98.73 85	97.13 267	99.18 206	97.10 241	98.75 195	98.92 185	98.18 78	99.65 282	96.68 213	99.56 210	99.37 186
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VDD-MVS		98.56 129	98.39 141	99.07 121	99.13 198	98.07 142	98.59 103	97.01 345	99.59 23	99.11 129	99.27 99	94.82 251	99.79 199	98.34 103	99.63 184	99.34 198

v2v482		98.56 129	98.62 105	98.37 222	99.42 136	95.81 257	97.58 230	99.16 213	97.90 167	99.28 106	99.01 162	95.98 216	99.79 199	99.33 39	99.90 70	99.51 121

XVG-ACMP-BASELINE		98.56 129	98.34 148	99.22 99	99.54 99	98.59 94	97.71 212	99.46 104	97.25 227	98.98 150	98.99 166	97.54 127	99.84 139	95.88 260	99.74 139	99.23 226

v1240		98.55 133	98.62 105	98.32 225	99.22 172	95.58 262	97.51 238	99.45 107	97.16 238	99.45 74	99.24 106	96.12 206	99.85 122	99.60 25	99.88 75	99.55 105

IterMVS-LS		98.55 133	98.70 93	98.09 241	99.48 123	94.73 290	97.22 261	99.39 126	98.97 93	99.38 87	99.31 94	96.00 212	99.93 41	98.58 88	99.97 20	99.60 75
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 135	98.57 113	98.45 213	99.21 174	95.98 251	97.63 223	99.36 136	97.15 240	99.32 103	99.18 117	95.84 223	99.84 139	99.50 32	99.91 63	99.54 109

v1921920		98.54 135	98.60 110	98.38 221	99.20 178	95.76 259	97.56 232	99.36 136	97.23 233	99.38 87	99.17 121	96.02 210	99.84 139	99.57 27	99.90 70	99.54 109

SF-MVS		98.53 137	98.27 157	99.32 80	99.31 155	98.75 81	98.19 147	99.41 121	96.77 256	98.83 183	98.90 189	97.80 106	99.82 166	95.68 273	99.52 222	99.38 184

XVG-OURS		98.53 137	98.34 148	99.11 113	99.50 109	98.82 78	95.97 320	99.50 86	97.30 222	99.05 141	98.98 170	99.35 12	99.32 356	95.72 270	99.68 167	99.18 238

UGNet		98.53 137	98.45 131	98.79 164	97.94 346	96.96 221	99.08 59	98.54 299	99.10 79	96.82 331	99.47 65	96.55 189	99.84 139	98.56 93	99.94 40	99.55 105
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
WB-MVS		98.52 140	98.55 114	98.43 216	99.65 66	95.59 260	98.52 111	98.77 282	99.65 14	99.52 62	99.00 165	94.34 266	99.93 41	98.65 85	98.83 311	99.76 39

patch_mono-2		98.51 141	98.63 103	98.17 237	99.38 141	94.78 287	97.36 248	99.69 36	98.16 152	98.49 227	99.29 96	97.06 158	99.97 4	98.29 106	99.91 63	99.76 39

XVG-OURS-SEG-HR		98.49 142	98.28 155	99.14 109	99.49 116	98.83 76	96.54 294	99.48 95	97.32 220	99.11 129	98.61 246	99.33 13	99.30 359	96.23 244	98.38 331	99.28 216

FMVSNet2		98.49 142	98.40 138	98.75 174	98.90 241	97.14 214	98.61 101	99.13 220	98.59 117	99.19 122	99.28 97	94.14 270	99.82 166	97.97 125	99.80 110	99.29 214

pmmvs-eth3d		98.47 144	98.34 148	98.86 153	99.30 158	97.76 174	97.16 265	99.28 178	95.54 296	99.42 79	99.19 114	97.27 147	99.63 288	97.89 128	99.97 20	99.20 231

MP-MVS		98.46 145	98.09 176	99.54 27	99.57 82	99.22 27	98.50 118	99.19 202	97.61 189	97.58 290	98.66 235	97.40 140	99.88 84	94.72 295	99.60 194	99.54 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.45 146	98.60 110	98.00 251	99.44 130	94.98 283	97.44 244	99.06 230	98.30 133	99.32 103	98.97 172	96.65 185	99.62 290	98.37 101	99.85 82	99.39 177

AllTest		98.44 147	98.20 163	99.16 106	99.50 109	98.55 97	98.25 142	99.58 54	96.80 253	98.88 174	99.06 141	97.65 115	99.57 307	94.45 302	99.61 192	99.37 186

VNet		98.42 148	98.30 153	98.79 164	98.79 265	97.29 200	98.23 143	98.66 292	99.31 53	98.85 179	98.80 211	94.80 254	99.78 210	98.13 113	99.13 284	99.31 209

ab-mvs		98.41 149	98.36 145	98.59 193	99.19 181	97.23 204	99.32 23	98.81 276	97.66 183	98.62 208	99.40 79	96.82 173	99.80 186	95.88 260	99.51 224	98.75 305

ACMP	95.32 15	98.41 149	98.09 176	99.36 64	99.51 106	98.79 80	97.68 215	99.38 128	95.76 291	98.81 188	98.82 208	98.36 63	99.82 166	94.75 292	99.77 124	99.48 138
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n_1920		98.40 151	98.92 69	96.81 325	99.74 38	90.76 371	98.15 152	99.91 7	98.33 130	99.89 15	99.55 48	95.07 244	99.88 84	99.76 16	99.93 44	99.79 30

SMA-MVS		98.40 151	98.03 183	99.51 43	99.16 191	99.21 28	98.05 165	99.22 195	94.16 330	98.98 150	99.10 137	97.52 131	99.79 199	96.45 233	99.64 181	99.53 116
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
MSP-MVS		98.40 151	98.00 185	99.61 9	99.57 82	99.25 24	98.57 105	99.35 141	97.55 196	99.31 105	97.71 318	94.61 259	99.88 84	96.14 251	99.19 276	99.70 52
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
SD-MVS		98.40 151	98.68 96	97.54 287	98.96 229	97.99 149	97.88 189	99.36 136	98.20 146	99.63 48	99.04 150	98.76 35	95.33 398	96.56 223	99.74 139	99.31 209
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
EI-MVSNet		98.40 151	98.51 119	98.04 249	99.10 202	94.73 290	97.20 262	98.87 262	98.97 93	99.06 136	99.02 153	96.00 212	99.80 186	98.58 88	99.82 96	99.60 75

WR-MVS		98.40 151	98.19 165	99.03 131	99.00 222	97.65 182	96.85 280	98.94 249	98.57 120	98.89 170	98.50 260	95.60 228	99.85 122	97.54 148	99.85 82	99.59 81

new-patchmatchnet		98.35 157	98.74 84	97.18 306	99.24 167	92.23 352	96.42 301	99.48 95	98.30 133	99.69 37	99.53 54	97.44 138	99.82 166	98.84 71	99.77 124	99.49 128

canonicalmvs		98.34 158	98.26 158	98.58 194	98.46 315	97.82 169	98.96 73	99.46 104	99.19 69	97.46 301	95.46 376	98.59 49	99.46 336	98.08 117	98.71 319	98.46 325

test_cas_vis1_n_1920		98.33 159	98.68 96	97.27 303	99.69 57	92.29 350	98.03 168	99.85 15	97.62 186	99.96 4	99.62 34	93.98 275	99.74 234	99.52 31	99.86 81	99.79 30

testgi		98.32 160	98.39 141	98.13 240	99.57 82	95.54 263	97.78 202	99.49 93	97.37 215	99.19 122	97.65 322	98.96 24	99.49 328	96.50 230	98.99 300	99.34 198

DeepPCF-MVS	96.93 5	98.32 160	98.01 184	99.23 98	98.39 323	98.97 66	95.03 354	99.18 206	96.88 251	99.33 97	98.78 214	98.16 82	99.28 363	96.74 206	99.62 187	99.44 155

test_vis1_n		98.31 162	98.50 121	97.73 272	99.76 32	94.17 307	98.68 95	99.91 7	96.31 273	99.79 25	99.57 42	92.85 294	99.42 342	99.79 13	99.84 86	99.60 75

MVS_111021_LR		98.30 163	98.12 174	98.83 156	99.16 191	98.03 147	96.09 317	99.30 167	97.58 191	98.10 256	98.24 283	98.25 69	99.34 353	96.69 212	99.65 179	99.12 247

EPP-MVSNet		98.30 163	98.04 182	99.07 121	99.56 90	97.83 166	99.29 33	98.07 321	99.03 88	98.59 214	99.13 131	92.16 302	99.90 65	96.87 195	99.68 167	99.49 128

DeepC-MVS_fast	96.85 6	98.30 163	98.15 171	98.75 174	98.61 295	97.23 204	97.76 207	99.09 227	97.31 221	98.75 195	98.66 235	97.56 125	99.64 285	96.10 254	99.55 213	99.39 177
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 166	97.95 188	99.34 73	98.44 317	99.16 43	98.12 155	99.38 128	96.01 284	98.06 259	98.43 266	97.80 106	99.67 266	95.69 272	99.58 203	99.20 231

Fast-Effi-MVS+-dtu		98.27 167	98.09 176	98.81 159	98.43 318	98.11 133	97.61 226	99.50 86	98.64 111	97.39 306	97.52 330	98.12 85	99.95 23	96.90 192	98.71 319	98.38 332

DELS-MVS		98.27 167	98.20 163	98.48 210	98.86 249	96.70 231	95.60 337	99.20 198	97.73 178	98.45 230	98.71 224	97.50 133	99.82 166	98.21 109	99.59 198	98.93 277
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
Effi-MVS+-dtu		98.26 169	97.90 194	99.35 70	98.02 343	99.49 5	98.02 170	99.16 213	98.29 136	97.64 285	97.99 302	96.44 194	99.95 23	96.66 214	98.93 307	98.60 320

MVSFormer		98.26 169	98.43 134	97.77 264	98.88 247	93.89 321	99.39 17	99.56 68	99.11 72	98.16 249	98.13 290	93.81 278	99.97 4	99.26 44	99.57 207	99.43 159

MVS_111021_HR		98.25 171	98.08 179	98.75 174	99.09 205	97.46 191	95.97 320	99.27 181	97.60 190	97.99 264	98.25 282	98.15 84	99.38 348	96.87 195	99.57 207	99.42 162

TAMVS		98.24 172	98.05 181	98.80 161	99.07 209	97.18 210	97.88 189	98.81 276	96.66 261	99.17 127	99.21 111	94.81 253	99.77 216	96.96 185	99.88 75	99.44 155

diffmvs		98.22 173	98.24 160	98.17 237	99.00 222	95.44 268	96.38 303	99.58 54	97.79 175	98.53 224	98.50 260	96.76 179	99.74 234	97.95 127	99.64 181	99.34 198
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Anonymous20231206		98.21 174	98.21 162	98.20 235	99.51 106	95.43 269	98.13 153	99.32 154	96.16 278	98.93 165	98.82 208	96.00 212	99.83 156	97.32 158	99.73 142	99.36 192

VDDNet		98.21 174	97.95 188	99.01 134	99.58 78	97.74 176	99.01 67	97.29 340	99.67 12	98.97 154	99.50 59	90.45 315	99.80 186	97.88 131	99.20 273	99.48 138

IS-MVSNet		98.19 176	97.90 194	99.08 119	99.57 82	97.97 153	99.31 27	98.32 309	99.01 90	98.98 150	99.03 152	91.59 307	99.79 199	95.49 279	99.80 110	99.48 138

MVS_Test		98.18 177	98.36 145	97.67 274	98.48 313	94.73 290	98.18 148	99.02 241	97.69 181	98.04 262	99.11 134	97.22 151	99.56 310	98.57 90	98.90 309	98.71 308

TSAR-MVS + GP.		98.18 177	97.98 186	98.77 170	98.71 275	97.88 161	96.32 306	98.66 292	96.33 271	99.23 119	98.51 256	97.48 137	99.40 344	97.16 166	99.46 234	99.02 260

CNVR-MVS		98.17 179	97.87 197	99.07 121	98.67 287	98.24 120	97.01 270	98.93 251	97.25 227	97.62 286	98.34 276	97.27 147	99.57 307	96.42 234	99.33 252	99.39 177

PVSNet_Blended_VisFu		98.17 179	98.15 171	98.22 234	99.73 39	95.15 278	97.36 248	99.68 41	94.45 324	98.99 149	99.27 99	96.87 169	99.94 36	97.13 171	99.91 63	99.57 92

MVS_0304		98.10 181	97.88 196	98.76 171	98.82 258	96.50 235	97.90 186	91.35 391	99.56 26	98.32 240	99.13 131	96.06 208	99.93 41	99.84 7	99.97 20	99.85 19

HPM-MVS++		98.10 181	97.64 214	99.48 51	99.09 205	99.13 55	97.52 236	98.75 286	97.46 207	96.90 326	97.83 313	96.01 211	99.84 139	95.82 267	99.35 249	99.46 147

APD-MVS		98.10 181	97.67 209	99.42 58	99.11 200	98.93 71	97.76 207	99.28 178	94.97 311	98.72 198	98.77 216	97.04 159	99.85 122	93.79 324	99.54 215	99.49 128
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_fmvs1_n		98.09 184	98.28 155	97.52 289	99.68 59	93.47 330	98.63 98	99.93 4	95.41 303	99.68 39	99.64 32	91.88 306	99.48 331	99.82 8	99.87 78	99.62 68

MVP-Stereo		98.08 185	97.92 192	98.57 196	98.96 229	96.79 227	97.90 186	99.18 206	96.41 269	98.46 229	98.95 179	95.93 219	99.60 297	96.51 229	98.98 302	99.31 209
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PMMVS2		98.07 186	98.08 179	98.04 249	99.41 138	94.59 296	94.59 368	99.40 123	97.50 199	98.82 186	98.83 205	96.83 172	99.84 139	97.50 151	99.81 100	99.71 47

ETV-MVS		98.03 187	97.86 198	98.56 200	98.69 284	98.07 142	97.51 238	99.50 86	98.10 154	97.50 298	95.51 373	98.41 60	99.88 84	96.27 243	99.24 267	97.71 363

Effi-MVS+		98.02 188	97.82 200	98.62 187	98.53 309	97.19 209	97.33 250	99.68 41	97.30 222	96.68 335	97.46 334	98.56 52	99.80 186	96.63 215	98.20 337	98.86 287

MSLP-MVS++		98.02 188	98.14 173	97.64 278	98.58 302	95.19 277	97.48 240	99.23 194	97.47 202	97.90 268	98.62 244	97.04 159	98.81 384	97.55 146	99.41 241	98.94 276

EIA-MVS		98.00 190	97.74 204	98.80 161	98.72 272	98.09 136	98.05 165	99.60 51	97.39 213	96.63 337	95.55 372	97.68 112	99.80 186	96.73 208	99.27 262	98.52 323

MCST-MVS		98.00 190	97.63 215	99.10 115	99.24 167	98.17 128	96.89 279	98.73 289	95.66 292	97.92 266	97.70 320	97.17 153	99.66 277	96.18 249	99.23 269	99.47 145

K. test v3		98.00 190	97.66 212	99.03 131	99.79 25	97.56 186	99.19 49	92.47 385	99.62 20	99.52 62	99.66 27	89.61 320	99.96 12	99.25 46	99.81 100	99.56 98

HQP_MVS		97.99 193	97.67 209	98.93 145	99.19 181	97.65 182	97.77 204	99.27 181	98.20 146	97.79 277	97.98 303	94.90 247	99.70 250	94.42 304	99.51 224	99.45 151

MDA-MVSNet-bldmvs		97.94 194	97.91 193	98.06 246	99.44 130	94.96 284	96.63 292	99.15 218	98.35 128	98.83 183	99.11 134	94.31 267	99.85 122	96.60 216	98.72 317	99.37 186

Anonymous202405211		97.90 195	97.50 222	99.08 119	98.90 241	98.25 119	98.53 110	96.16 361	98.87 101	99.11 129	98.86 199	90.40 316	99.78 210	97.36 156	99.31 255	99.19 236

LF4IMVS		97.90 195	97.69 208	98.52 206	99.17 189	97.66 181	97.19 264	99.47 102	96.31 273	97.85 273	98.20 287	96.71 183	99.52 322	94.62 296	99.72 149	98.38 332

UnsupCasMVSNet_eth		97.89 197	97.60 217	98.75 174	99.31 155	97.17 211	97.62 224	99.35 141	98.72 109	98.76 194	98.68 230	92.57 298	99.74 234	97.76 141	95.60 384	99.34 198

TinyColmap		97.89 197	97.98 186	97.60 280	98.86 249	94.35 302	96.21 311	99.44 111	97.45 209	99.06 136	98.88 196	97.99 95	99.28 363	94.38 308	99.58 203	99.18 238

OMC-MVS		97.88 199	97.49 223	99.04 130	98.89 246	98.63 89	96.94 274	99.25 187	95.02 309	98.53 224	98.51 256	97.27 147	99.47 334	93.50 331	99.51 224	99.01 261

CANet		97.87 200	97.76 202	98.19 236	97.75 354	95.51 265	96.76 285	99.05 233	97.74 177	96.93 320	98.21 286	95.59 229	99.89 75	97.86 133	99.93 44	99.19 236

xiu_mvs_v1_base_debu		97.86 201	98.17 167	96.92 318	98.98 226	93.91 318	96.45 298	99.17 210	97.85 171	98.41 234	97.14 346	98.47 55	99.92 51	98.02 120	99.05 290	96.92 375

xiu_mvs_v1_base		97.86 201	98.17 167	96.92 318	98.98 226	93.91 318	96.45 298	99.17 210	97.85 171	98.41 234	97.14 346	98.47 55	99.92 51	98.02 120	99.05 290	96.92 375

xiu_mvs_v1_base_debi		97.86 201	98.17 167	96.92 318	98.98 226	93.91 318	96.45 298	99.17 210	97.85 171	98.41 234	97.14 346	98.47 55	99.92 51	98.02 120	99.05 290	96.92 375

NCCC		97.86 201	97.47 226	99.05 128	98.61 295	98.07 142	96.98 272	98.90 257	97.63 185	97.04 317	97.93 308	95.99 215	99.66 277	95.31 282	98.82 313	99.43 159

PMVS	91.26 20	97.86 201	97.94 190	97.65 276	99.71 48	97.94 158	98.52 111	98.68 291	98.99 91	97.52 296	99.35 84	97.41 139	98.18 390	91.59 358	99.67 173	96.82 378
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IterMVS-SCA-FT		97.85 206	98.18 166	96.87 321	99.27 162	91.16 367	95.53 339	99.25 187	99.10 79	99.41 80	99.35 84	93.10 287	99.96 12	98.65 85	99.94 40	99.49 128

D2MVS		97.84 207	97.84 199	97.83 259	99.14 196	94.74 289	96.94 274	98.88 260	95.84 289	98.89 170	98.96 175	94.40 264	99.69 254	97.55 146	99.95 32	99.05 253

CPTT-MVS		97.84 207	97.36 231	99.27 89	99.31 155	98.46 105	98.29 138	99.27 181	94.90 313	97.83 274	98.37 272	94.90 247	99.84 139	93.85 323	99.54 215	99.51 121

mvs_anonymous		97.83 209	98.16 170	96.87 321	98.18 335	91.89 354	97.31 252	98.90 257	97.37 215	98.83 183	99.46 66	96.28 201	99.79 199	98.90 67	98.16 341	98.95 272

h-mvs33		97.77 210	97.33 234	99.10 115	99.21 174	97.84 165	98.35 135	98.57 298	99.11 72	98.58 216	99.02 153	88.65 329	99.96 12	98.11 114	96.34 376	99.49 128

test_vis1_rt		97.75 211	97.72 207	97.83 259	98.81 261	96.35 239	97.30 253	99.69 36	94.61 318	97.87 270	98.05 299	96.26 202	98.32 389	98.74 77	98.18 338	98.82 290

IterMVS		97.73 212	98.11 175	96.57 329	99.24 167	90.28 372	95.52 341	99.21 196	98.86 102	99.33 97	99.33 90	93.11 286	99.94 36	98.49 96	99.94 40	99.48 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_fmvs1		97.72 213	97.94 190	97.07 312	98.66 292	92.39 347	97.68 215	99.81 23	95.20 307	99.54 56	99.44 71	91.56 308	99.41 343	99.78 15	99.77 124	99.40 174

MSDG		97.71 214	97.52 221	98.28 230	98.91 240	96.82 226	94.42 371	99.37 132	97.65 184	98.37 239	98.29 281	97.40 140	99.33 355	94.09 315	99.22 270	98.68 315

CDS-MVSNet		97.69 215	97.35 232	98.69 179	98.73 270	97.02 218	96.92 278	98.75 286	95.89 288	98.59 214	98.67 232	92.08 304	99.74 234	96.72 209	99.81 100	99.32 205
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 216	97.75 203	97.45 295	98.23 333	93.78 324	97.29 254	98.84 271	96.10 280	98.64 205	98.65 237	96.04 209	99.36 349	96.84 198	99.14 282	99.20 231

Fast-Effi-MVS+		97.67 217	97.38 229	98.57 196	98.71 275	97.43 194	97.23 258	99.45 107	94.82 315	96.13 350	96.51 354	98.52 54	99.91 60	96.19 247	98.83 311	98.37 334

EU-MVSNet		97.66 218	98.50 121	95.13 358	99.63 75	85.84 388	98.35 135	98.21 313	98.23 140	99.54 56	99.46 66	95.02 245	99.68 263	98.24 107	99.87 78	99.87 16

pmmvs5		97.64 219	97.49 223	98.08 244	99.14 196	95.12 280	96.70 289	99.05 233	93.77 337	98.62 208	98.83 205	93.23 283	99.75 229	98.33 105	99.76 135	99.36 192

N_pmnet		97.63 220	97.17 240	98.99 136	99.27 162	97.86 163	95.98 319	93.41 382	95.25 305	99.47 70	98.90 189	95.63 227	99.85 122	96.91 187	99.73 142	99.27 217

mvsany_test1		97.60 221	97.54 219	97.77 264	97.72 355	95.35 271	95.36 346	97.13 343	94.13 331	99.71 33	99.33 90	97.93 98	99.30 359	97.60 145	98.94 306	98.67 316

YYNet1		97.60 221	97.67 209	97.39 299	99.04 217	93.04 337	95.27 347	98.38 308	97.25 227	98.92 166	98.95 179	95.48 234	99.73 239	96.99 181	98.74 315	99.41 165

MDA-MVSNet_test_wron		97.60 221	97.66 212	97.41 298	99.04 217	93.09 333	95.27 347	98.42 305	97.26 226	98.88 174	98.95 179	95.43 235	99.73 239	97.02 178	98.72 317	99.41 165

pmmvs4		97.58 224	97.28 235	98.51 207	98.84 253	96.93 224	95.40 345	98.52 301	93.60 339	98.61 210	98.65 237	95.10 243	99.60 297	96.97 184	99.79 115	98.99 265

PVSNet_BlendedMVS		97.55 225	97.53 220	97.60 280	98.92 237	93.77 325	96.64 291	99.43 117	94.49 320	97.62 286	99.18 117	96.82 173	99.67 266	94.73 293	99.93 44	99.36 192

ppachtmachnet_test		97.50 226	97.74 204	96.78 327	98.70 279	91.23 366	94.55 369	99.05 233	96.36 270	99.21 120	98.79 213	96.39 195	99.78 210	96.74 206	99.82 96	99.34 198

FMVSNet3		97.50 226	97.24 237	98.29 229	98.08 341	95.83 256	97.86 194	98.91 256	97.89 168	98.95 157	98.95 179	87.06 335	99.81 179	97.77 137	99.69 162	99.23 226

CHOSEN 1792x2688		97.49 228	97.14 244	98.54 204	99.68 59	96.09 247	96.50 296	99.62 47	91.58 362	98.84 182	98.97 172	92.36 299	99.88 84	96.76 204	99.95 32	99.67 58

CLD-MVS		97.49 228	97.16 241	98.48 210	99.07 209	97.03 217	94.71 361	99.21 196	94.46 322	98.06 259	97.16 344	97.57 124	99.48 331	94.46 301	99.78 120	98.95 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		97.46 230	97.07 245	98.64 182	98.73 270	97.33 198	97.45 243	97.64 333	99.11 72	98.58 216	97.98 303	88.65 329	99.79 199	98.11 114	97.39 360	98.81 294

Vis-MVSNet (Re-imp)		97.46 230	97.16 241	98.34 224	99.55 94	96.10 244	98.94 74	98.44 304	98.32 132	98.16 249	98.62 244	88.76 325	99.73 239	93.88 321	99.79 115	99.18 238

jason		97.45 232	97.35 232	97.76 267	99.24 167	93.93 317	95.86 328	98.42 305	94.24 328	98.50 226	98.13 290	94.82 251	99.91 60	97.22 163	99.73 142	99.43 159
jason: jason.
CL-MVSNet_self_test		97.44 233	97.22 238	98.08 244	98.57 304	95.78 258	94.30 374	98.79 279	96.58 264	98.60 212	98.19 288	94.74 258	99.64 285	96.41 235	98.84 310	98.82 290

DSMNet-mixed		97.42 234	97.60 217	96.87 321	99.15 195	91.46 358	98.54 109	99.12 221	92.87 350	97.58 290	99.63 33	96.21 203	99.90 65	95.74 269	99.54 215	99.27 217

USDC		97.41 235	97.40 227	97.44 296	98.94 231	93.67 327	95.17 350	99.53 80	94.03 334	98.97 154	99.10 137	95.29 237	99.34 353	95.84 266	99.73 142	99.30 212

our_test_3		97.39 236	97.73 206	96.34 333	98.70 279	89.78 374	94.61 367	98.97 248	96.50 265	99.04 143	98.85 202	95.98 216	99.84 139	97.26 161	99.67 173	99.41 165

c3_l		97.36 237	97.37 230	97.31 300	98.09 340	93.25 332	95.01 355	99.16 213	97.05 242	98.77 192	98.72 223	92.88 292	99.64 285	96.93 186	99.76 135	99.05 253

alignmvs		97.35 238	96.88 255	98.78 167	98.54 307	98.09 136	97.71 212	97.69 330	99.20 65	97.59 289	95.90 367	88.12 334	99.55 313	98.18 111	98.96 304	98.70 311

Patchmtry		97.35 238	96.97 249	98.50 209	97.31 373	96.47 236	98.18 148	98.92 254	98.95 96	98.78 189	99.37 80	85.44 350	99.85 122	95.96 258	99.83 93	99.17 242

DP-MVS Recon		97.33 240	96.92 252	98.57 196	99.09 205	97.99 149	96.79 282	99.35 141	93.18 344	97.71 281	98.07 298	95.00 246	99.31 357	93.97 317	99.13 284	98.42 331

QAPM		97.31 241	96.81 262	98.82 157	98.80 264	97.49 189	99.06 63	99.19 202	90.22 374	97.69 283	99.16 123	96.91 167	99.90 65	90.89 370	99.41 241	99.07 251

UnsupCasMVSNet_bld		97.30 242	96.92 252	98.45 213	99.28 160	96.78 230	96.20 312	99.27 181	95.42 300	98.28 243	98.30 280	93.16 285	99.71 247	94.99 287	97.37 361	98.87 286

F-COLMAP		97.30 242	96.68 269	99.14 109	99.19 181	98.39 108	97.27 257	99.30 167	92.93 348	96.62 338	98.00 301	95.73 225	99.68 263	92.62 347	98.46 330	99.35 196

1112_ss		97.29 244	96.86 256	98.58 194	99.34 154	96.32 240	96.75 286	99.58 54	93.14 345	96.89 327	97.48 332	92.11 303	99.86 110	96.91 187	99.54 215	99.57 92

CANet_DTU		97.26 245	97.06 246	97.84 258	97.57 362	94.65 294	96.19 313	98.79 279	97.23 233	95.14 371	98.24 283	93.22 284	99.84 139	97.34 157	99.84 86	99.04 257

Patchmatch-RL test		97.26 245	97.02 248	97.99 252	99.52 104	95.53 264	96.13 316	99.71 33	97.47 202	99.27 108	99.16 123	84.30 359	99.62 290	97.89 128	99.77 124	98.81 294

CDPH-MVS		97.26 245	96.66 272	99.07 121	99.00 222	98.15 129	96.03 318	99.01 244	91.21 368	97.79 277	97.85 312	96.89 168	99.69 254	92.75 344	99.38 246	99.39 177

PatchMatch-RL		97.24 248	96.78 263	98.61 190	99.03 220	97.83 166	96.36 304	99.06 230	93.49 342	97.36 308	97.78 314	95.75 224	99.49 328	93.44 332	98.77 314	98.52 323

eth_miper_zixun_eth		97.23 249	97.25 236	97.17 307	98.00 344	92.77 341	94.71 361	99.18 206	97.27 225	98.56 219	98.74 220	91.89 305	99.69 254	97.06 177	99.81 100	99.05 253

sss		97.21 250	96.93 250	98.06 246	98.83 255	95.22 276	96.75 286	98.48 303	94.49 320	97.27 309	97.90 309	92.77 295	99.80 186	96.57 219	99.32 253	99.16 245

LFMVS		97.20 251	96.72 266	98.64 182	98.72 272	96.95 222	98.93 75	94.14 380	99.74 6	98.78 189	99.01 162	84.45 356	99.73 239	97.44 152	99.27 262	99.25 221

HyFIR lowres test		97.19 252	96.60 277	98.96 140	99.62 77	97.28 201	95.17 350	99.50 86	94.21 329	99.01 147	98.32 279	86.61 338	99.99 2	97.10 173	99.84 86	99.60 75

miper_lstm_enhance		97.18 253	97.16 241	97.25 305	98.16 336	92.85 339	95.15 352	99.31 159	97.25 227	98.74 197	98.78 214	90.07 317	99.78 210	97.19 164	99.80 110	99.11 248

CNLPA		97.17 254	96.71 267	98.55 201	98.56 305	98.05 146	96.33 305	98.93 251	96.91 250	97.06 316	97.39 337	94.38 265	99.45 337	91.66 355	99.18 278	98.14 341

xiu_mvs_v2_base		97.16 255	97.49 223	96.17 339	98.54 307	92.46 345	95.45 343	98.84 271	97.25 227	97.48 300	96.49 355	98.31 68	99.90 65	96.34 239	98.68 322	96.15 386

AdaColmap		97.14 256	96.71 267	98.46 212	98.34 325	97.80 172	96.95 273	98.93 251	95.58 295	96.92 321	97.66 321	95.87 221	99.53 318	90.97 367	99.14 282	98.04 346

iter_conf_final		97.10 257	96.65 274	98.45 213	98.53 309	96.08 249	98.30 137	99.11 223	98.10 154	98.85 179	98.95 179	79.38 380	99.87 101	98.68 83	99.91 63	99.40 174

train_agg		97.10 257	96.45 282	99.07 121	98.71 275	98.08 140	95.96 322	99.03 238	91.64 360	95.85 356	97.53 328	96.47 192	99.76 222	93.67 325	99.16 279	99.36 192

OpenMVS	96.65 7	97.09 259	96.68 269	98.32 225	98.32 326	97.16 212	98.86 81	99.37 132	89.48 378	96.29 349	99.15 127	96.56 188	99.90 65	92.90 338	99.20 273	97.89 351

PS-MVSNAJ		97.08 260	97.39 228	96.16 341	98.56 305	92.46 345	95.24 349	98.85 270	97.25 227	97.49 299	95.99 364	98.07 86	99.90 65	96.37 236	98.67 323	96.12 387

miper_ehance_all_eth		97.06 261	97.03 247	97.16 309	97.83 351	93.06 334	94.66 364	99.09 227	95.99 285	98.69 199	98.45 265	92.73 296	99.61 296	96.79 200	99.03 294	98.82 290

lupinMVS		97.06 261	96.86 256	97.65 276	98.88 247	93.89 321	95.48 342	97.97 323	93.53 340	98.16 249	97.58 326	93.81 278	99.91 60	96.77 203	99.57 207	99.17 242

API-MVS		97.04 263	96.91 254	97.42 297	97.88 350	98.23 124	98.18 148	98.50 302	97.57 192	97.39 306	96.75 351	96.77 177	99.15 372	90.16 373	99.02 297	94.88 392

cl____		97.02 264	96.83 259	97.58 282	97.82 352	94.04 311	94.66 364	99.16 213	97.04 243	98.63 206	98.71 224	88.68 328	99.69 254	97.00 179	99.81 100	99.00 264

DIV-MVS_self_test		97.02 264	96.84 258	97.58 282	97.82 352	94.03 312	94.66 364	99.16 213	97.04 243	98.63 206	98.71 224	88.69 326	99.69 254	97.00 179	99.81 100	99.01 261

RPMNet		97.02 264	96.93 250	97.30 301	97.71 357	94.22 303	98.11 156	99.30 167	99.37 45	96.91 323	99.34 88	86.72 337	99.87 101	97.53 149	97.36 363	97.81 356

HQP-MVS		97.00 267	96.49 281	98.55 201	98.67 287	96.79 227	96.29 307	99.04 236	96.05 281	95.55 362	96.84 349	93.84 276	99.54 316	92.82 341	99.26 265	99.32 205

FA-MVS(test-final)		96.99 268	96.82 260	97.50 291	98.70 279	94.78 287	99.34 20	96.99 346	95.07 308	98.48 228	99.33 90	88.41 332	99.65 282	96.13 253	98.92 308	98.07 345

new_pmnet		96.99 268	96.76 264	97.67 274	98.72 272	94.89 285	95.95 324	98.20 314	92.62 353	98.55 221	98.54 252	94.88 250	99.52 322	93.96 318	99.44 239	98.59 322

Test_1112_low_res		96.99 268	96.55 279	98.31 227	99.35 152	95.47 267	95.84 331	99.53 80	91.51 364	96.80 332	98.48 263	91.36 309	99.83 156	96.58 217	99.53 219	99.62 68

PVSNet_Blended		96.88 271	96.68 269	97.47 294	98.92 237	93.77 325	94.71 361	99.43 117	90.98 370	97.62 286	97.36 340	96.82 173	99.67 266	94.73 293	99.56 210	98.98 266

MVSTER		96.86 272	96.55 279	97.79 262	97.91 348	94.21 305	97.56 232	98.87 262	97.49 201	99.06 136	99.05 148	80.72 372	99.80 186	98.44 98	99.82 96	99.37 186

BH-untuned		96.83 273	96.75 265	97.08 310	98.74 269	93.33 331	96.71 288	98.26 311	96.72 258	98.44 231	97.37 339	95.20 240	99.47 334	91.89 353	97.43 359	98.44 329

BH-RMVSNet		96.83 273	96.58 278	97.58 282	98.47 314	94.05 309	96.67 290	97.36 336	96.70 260	97.87 270	97.98 303	95.14 242	99.44 339	90.47 372	98.58 328	99.25 221

PAPM_NR		96.82 275	96.32 285	98.30 228	99.07 209	96.69 232	97.48 240	98.76 283	95.81 290	96.61 339	96.47 357	94.12 273	99.17 370	90.82 371	97.78 353	99.06 252

MG-MVS		96.77 276	96.61 275	97.26 304	98.31 327	93.06 334	95.93 325	98.12 320	96.45 268	97.92 266	98.73 221	93.77 280	99.39 346	91.19 366	99.04 293	99.33 203

test_yl		96.69 277	96.29 286	97.90 254	98.28 328	95.24 274	97.29 254	97.36 336	98.21 142	98.17 247	97.86 310	86.27 340	99.55 313	94.87 290	98.32 332	98.89 282

DCV-MVSNet		96.69 277	96.29 286	97.90 254	98.28 328	95.24 274	97.29 254	97.36 336	98.21 142	98.17 247	97.86 310	86.27 340	99.55 313	94.87 290	98.32 332	98.89 282

WTY-MVS		96.67 279	96.27 288	97.87 257	98.81 261	94.61 295	96.77 284	97.92 325	94.94 312	97.12 312	97.74 317	91.11 311	99.82 166	93.89 320	98.15 342	99.18 238

PatchT		96.65 280	96.35 283	97.54 287	97.40 370	95.32 272	97.98 177	96.64 355	99.33 50	96.89 327	99.42 74	84.32 358	99.81 179	97.69 144	97.49 356	97.48 369

TAPA-MVS	96.21 11	96.63 281	95.95 292	98.65 181	98.93 233	98.09 136	96.93 276	99.28 178	83.58 391	98.13 253	97.78 314	96.13 205	99.40 344	93.52 329	99.29 260	98.45 327
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 282	96.25 289	97.71 273	99.04 217	94.66 293	99.16 51	96.92 351	97.23 233	97.87 270	99.10 137	86.11 344	99.65 282	91.65 356	99.21 272	98.82 290

Patchmatch-test		96.55 283	96.34 284	97.17 307	98.35 324	93.06 334	98.40 130	97.79 326	97.33 218	98.41 234	98.67 232	83.68 363	99.69 254	95.16 285	99.31 255	98.77 302

iter_conf05		96.54 284	96.07 290	97.92 253	97.90 349	94.50 297	97.87 192	99.14 219	97.73 178	98.89 170	98.95 179	75.75 390	99.87 101	98.50 95	99.92 55	99.40 174

PMMVS		96.51 285	95.98 291	98.09 241	97.53 365	95.84 255	94.92 357	98.84 271	91.58 362	96.05 354	95.58 371	95.68 226	99.66 277	95.59 276	98.09 345	98.76 304

PLC	94.65 16	96.51 285	95.73 296	98.85 154	98.75 268	97.91 159	96.42 301	99.06 230	90.94 371	95.59 359	97.38 338	94.41 263	99.59 301	90.93 368	98.04 351	99.05 253
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 287	95.77 294	98.69 179	99.48 123	97.43 194	97.84 196	99.55 72	81.42 393	96.51 343	98.58 249	95.53 230	99.67 266	93.41 333	99.58 203	98.98 266

test1111		96.49 288	96.82 260	95.52 352	99.42 136	87.08 385	99.22 42	87.14 397	99.11 72	99.46 71	99.58 41	88.69 326	99.86 110	98.80 72	99.95 32	99.62 68

MAR-MVS		96.47 289	95.70 297	98.79 164	97.92 347	99.12 57	98.28 139	98.60 297	92.16 358	95.54 365	96.17 362	94.77 257	99.52 322	89.62 375	98.23 335	97.72 362
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
ECVR-MVS		96.42 290	96.61 275	95.85 344	99.38 141	88.18 381	99.22 42	86.00 399	99.08 84	99.36 92	99.57 42	88.47 331	99.82 166	98.52 94	99.95 32	99.54 109

SCA		96.41 291	96.66 272	95.67 348	98.24 331	88.35 379	95.85 330	96.88 352	96.11 279	97.67 284	98.67 232	93.10 287	99.85 122	94.16 310	99.22 270	98.81 294

DPM-MVS		96.32 292	95.59 302	98.51 207	98.76 266	97.21 207	94.54 370	98.26 311	91.94 359	96.37 347	97.25 342	93.06 289	99.43 340	91.42 361	98.74 315	98.89 282

CMPMVS	75.91 23	96.29 293	95.44 307	98.84 155	96.25 391	98.69 88	97.02 269	99.12 221	88.90 381	97.83 274	98.86 199	89.51 321	98.90 382	91.92 352	99.51 224	98.92 278
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		96.28 294	95.95 292	97.28 302	97.71 357	94.22 303	98.11 156	98.92 254	92.31 356	96.91 323	99.37 80	85.44 350	99.81 179	97.39 155	97.36 363	97.81 356

CVMVSNet		96.25 295	97.21 239	93.38 375	99.10 202	80.56 402	97.20 262	98.19 316	96.94 248	99.00 148	99.02 153	89.50 322	99.80 186	96.36 238	99.59 198	99.78 33

AUN-MVS		96.24 296	95.45 306	98.60 192	98.70 279	97.22 206	97.38 246	97.65 331	95.95 286	95.53 366	97.96 307	82.11 371	99.79 199	96.31 240	97.44 358	98.80 299

EPNet		96.14 297	95.44 307	98.25 231	90.76 401	95.50 266	97.92 183	94.65 372	98.97 93	92.98 388	98.85 202	89.12 324	99.87 101	95.99 256	99.68 167	99.39 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		96.06 298	97.62 216	91.38 378	98.65 294	98.57 96	98.85 82	96.95 349	96.86 252	99.90 12	99.16 123	99.18 17	98.40 388	89.23 377	99.77 124	77.18 396

Syy-MVS		96.04 299	95.56 303	97.49 292	97.10 377	94.48 298	96.18 314	96.58 356	95.65 293	94.77 374	92.29 394	91.27 310	99.36 349	98.17 112	98.05 349	98.63 318

miper_enhance_ethall		96.01 300	95.74 295	96.81 325	96.41 389	92.27 351	93.69 383	98.89 259	91.14 369	98.30 241	97.35 341	90.58 314	99.58 305	96.31 240	99.03 294	98.60 320

FMVSNet5		96.01 300	95.20 316	98.41 218	97.53 365	96.10 244	98.74 86	99.50 86	97.22 236	98.03 263	99.04 150	69.80 394	99.88 84	97.27 160	99.71 154	99.25 221

dmvs_re		95.98 302	95.39 310	97.74 270	98.86 249	97.45 192	98.37 133	95.69 368	97.95 162	96.56 340	95.95 365	90.70 313	97.68 392	88.32 379	96.13 380	98.11 342

baseline1		95.96 303	95.44 307	97.52 289	98.51 312	93.99 315	98.39 131	96.09 363	98.21 142	98.40 238	97.76 316	86.88 336	99.63 288	95.42 280	89.27 396	98.95 272

HY-MVS	95.94 13	95.90 304	95.35 312	97.55 286	97.95 345	94.79 286	98.81 85	96.94 350	92.28 357	95.17 370	98.57 250	89.90 319	99.75 229	91.20 365	97.33 365	98.10 343

GA-MVS		95.86 305	95.32 313	97.49 292	98.60 297	94.15 308	93.83 381	97.93 324	95.49 298	96.68 335	97.42 336	83.21 364	99.30 359	96.22 245	98.55 329	99.01 261

OpenMVS_ROB	95.38 14	95.84 306	95.18 317	97.81 261	98.41 322	97.15 213	97.37 247	98.62 296	83.86 390	98.65 204	98.37 272	94.29 268	99.68 263	88.41 378	98.62 326	96.60 381

cl22		95.79 307	95.39 310	96.98 315	96.77 384	92.79 340	94.40 372	98.53 300	94.59 319	97.89 269	98.17 289	82.82 368	99.24 365	96.37 236	99.03 294	98.92 278

1314		95.74 308	95.60 301	96.17 339	97.53 365	92.75 342	98.07 162	98.31 310	91.22 367	94.25 379	96.68 352	95.53 230	99.03 374	91.64 357	97.18 366	96.74 379

PVSNet	93.40 17	95.67 309	95.70 297	95.57 351	98.83 255	88.57 377	92.50 388	97.72 328	92.69 352	96.49 346	96.44 358	93.72 281	99.43 340	93.61 326	99.28 261	98.71 308

FE-MVS		95.66 310	94.95 322	97.77 264	98.53 309	95.28 273	99.40 16	96.09 363	93.11 346	97.96 265	99.26 101	79.10 382	99.77 216	92.40 350	98.71 319	98.27 336

tttt0517		95.64 311	94.98 320	97.64 278	99.36 148	93.81 323	98.72 90	90.47 393	98.08 156	98.67 201	98.34 276	73.88 392	99.92 51	97.77 137	99.51 224	99.20 231

PatchmatchNet		95.58 312	95.67 299	95.30 357	97.34 372	87.32 384	97.65 221	96.65 354	95.30 304	97.07 315	98.69 228	84.77 353	99.75 229	94.97 288	98.64 324	98.83 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 313	95.12 318	96.86 324	97.54 364	93.94 316	96.49 297	96.53 358	94.36 327	97.03 318	96.61 353	94.26 269	99.16 371	86.91 383	96.31 377	97.47 370

JIA-IIPM		95.52 314	95.03 319	97.00 313	96.85 382	94.03 312	96.93 276	95.82 366	99.20 65	94.63 377	99.71 17	83.09 365	99.60 297	94.42 304	94.64 388	97.36 372

CHOSEN 280x420		95.51 315	95.47 304	95.65 350	98.25 330	88.27 380	93.25 385	98.88 260	93.53 340	94.65 376	97.15 345	86.17 342	99.93 41	97.41 154	99.93 44	98.73 307

ADS-MVSNet2		95.43 316	94.98 320	96.76 328	98.14 337	91.74 355	97.92 183	97.76 327	90.23 372	96.51 343	98.91 186	85.61 347	99.85 122	92.88 339	96.90 369	98.69 312

PAPR		95.29 317	94.47 326	97.75 268	97.50 369	95.14 279	94.89 358	98.71 290	91.39 366	95.35 369	95.48 375	94.57 260	99.14 373	84.95 386	97.37 361	98.97 269

thisisatest0530		95.27 318	94.45 327	97.74 270	99.19 181	94.37 301	97.86 194	90.20 394	97.17 237	98.22 245	97.65 322	73.53 393	99.90 65	96.90 192	99.35 249	98.95 272

ADS-MVSNet		95.24 319	94.93 323	96.18 338	98.14 337	90.10 373	97.92 183	97.32 339	90.23 372	96.51 343	98.91 186	85.61 347	99.74 234	92.88 339	96.90 369	98.69 312

BH-w/o		95.13 320	94.89 324	95.86 343	98.20 334	91.31 362	95.65 335	97.37 335	93.64 338	96.52 342	95.70 370	93.04 290	99.02 375	88.10 380	95.82 383	97.24 373

tpmrst		95.07 321	95.46 305	93.91 368	97.11 376	84.36 395	97.62 224	96.96 348	94.98 310	96.35 348	98.80 211	85.46 349	99.59 301	95.60 275	96.23 378	97.79 359

pmmvs3		95.03 322	94.40 328	96.93 317	97.70 359	92.53 344	95.08 353	97.71 329	88.57 382	97.71 281	98.08 297	79.39 379	99.82 166	96.19 247	99.11 288	98.43 330

tpmvs		95.02 323	95.25 314	94.33 364	96.39 390	85.87 387	98.08 160	96.83 353	95.46 299	95.51 367	98.69 228	85.91 345	99.53 318	94.16 310	96.23 378	97.58 367

EPNet_dtu		94.93 324	94.78 325	95.38 356	93.58 398	87.68 383	96.78 283	95.69 368	97.35 217	89.14 395	98.09 296	88.15 333	99.49 328	94.95 289	99.30 258	98.98 266
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		94.79 325	94.33 331	96.15 342	96.02 394	92.36 349	92.34 390	99.26 186	85.34 389	95.08 372	94.96 383	92.96 291	98.53 387	94.41 307	98.59 327	97.56 368

tpm		94.67 326	94.34 330	95.66 349	97.68 361	88.42 378	97.88 189	94.90 371	94.46 322	96.03 355	98.56 251	78.66 383	99.79 199	95.88 260	95.01 387	98.78 301

test0.0.03 1		94.51 327	93.69 336	96.99 314	96.05 392	93.61 329	94.97 356	93.49 381	96.17 276	97.57 292	94.88 384	82.30 369	99.01 377	93.60 327	94.17 391	98.37 334

thres600view7		94.45 328	93.83 334	96.29 335	99.06 213	91.53 357	97.99 176	94.24 378	98.34 129	97.44 303	95.01 380	79.84 375	99.67 266	84.33 387	98.23 335	97.66 364

PCF-MVS	92.86 18	94.36 329	93.00 346	98.42 217	98.70 279	97.56 186	93.16 386	99.11 223	79.59 394	97.55 293	97.43 335	92.19 301	99.73 239	79.85 395	99.45 236	97.97 350
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		94.32 330	92.59 348	99.53 34	99.46 126	99.21 28	98.65 96	99.34 147	98.62 115	97.54 294	45.85 397	97.50 133	99.83 156	96.79 200	99.53 219	99.56 98

MVS-HIRNet		94.32 330	95.62 300	90.42 379	98.46 315	75.36 403	96.29 307	89.13 396	95.25 305	95.38 368	99.75 11	92.88 292	99.19 369	94.07 316	99.39 243	96.72 380

ET-MVSNet_ETH3D		94.30 332	93.21 342	97.58 282	98.14 337	94.47 299	94.78 360	93.24 384	94.72 316	89.56 394	95.87 368	78.57 385	99.81 179	96.91 187	97.11 368	98.46 325

thres100view900		94.19 333	93.67 337	95.75 347	99.06 213	91.35 361	98.03 168	94.24 378	98.33 130	97.40 305	94.98 382	79.84 375	99.62 290	83.05 389	98.08 346	96.29 382

E-PMN		94.17 334	94.37 329	93.58 372	96.86 381	85.71 390	90.11 392	97.07 344	98.17 149	97.82 276	97.19 343	84.62 355	98.94 379	89.77 374	97.68 355	96.09 388

thres400		94.14 335	93.44 339	96.24 337	98.93 233	91.44 359	97.60 227	94.29 376	97.94 163	97.10 313	94.31 388	79.67 377	99.62 290	83.05 389	98.08 346	97.66 364

thisisatest0515		94.12 336	93.16 343	96.97 316	98.60 297	92.90 338	93.77 382	90.61 392	94.10 332	96.91 323	95.87 368	74.99 391	99.80 186	94.52 299	99.12 287	98.20 338

tfpn200view9		94.03 337	93.44 339	95.78 346	98.93 233	91.44 359	97.60 227	94.29 376	97.94 163	97.10 313	94.31 388	79.67 377	99.62 290	83.05 389	98.08 346	96.29 382

CostFormer		93.97 338	93.78 335	94.51 363	97.53 365	85.83 389	97.98 177	95.96 365	89.29 380	94.99 373	98.63 242	78.63 384	99.62 290	94.54 298	96.50 374	98.09 344

test-LLR		93.90 339	93.85 333	94.04 366	96.53 386	84.62 393	94.05 378	92.39 386	96.17 276	94.12 381	95.07 378	82.30 369	99.67 266	95.87 263	98.18 338	97.82 354

EMVS		93.83 340	94.02 332	93.23 376	96.83 383	84.96 391	89.77 393	96.32 360	97.92 165	97.43 304	96.36 361	86.17 342	98.93 380	87.68 381	97.73 354	95.81 389

baseline2		93.73 341	92.83 347	96.42 332	97.70 359	91.28 364	96.84 281	89.77 395	93.96 336	92.44 389	95.93 366	79.14 381	99.77 216	92.94 337	96.76 373	98.21 337

thres200		93.72 342	93.14 344	95.46 355	98.66 292	91.29 363	96.61 293	94.63 373	97.39 213	96.83 330	93.71 390	79.88 374	99.56 310	82.40 392	98.13 343	95.54 391

EPMVS		93.72 342	93.27 341	95.09 360	96.04 393	87.76 382	98.13 153	85.01 400	94.69 317	96.92 321	98.64 240	78.47 387	99.31 357	95.04 286	96.46 375	98.20 338

testing3		93.51 344	92.09 352	97.75 268	98.60 297	94.40 300	97.32 251	95.26 370	97.56 194	96.79 333	95.50 374	53.57 404	99.77 216	95.26 283	98.97 303	99.08 249

dp		93.47 345	93.59 338	93.13 377	96.64 385	81.62 401	97.66 219	96.42 359	92.80 351	96.11 351	98.64 240	78.55 386	99.59 301	93.31 334	92.18 395	98.16 340

FPMVS		93.44 346	92.23 350	97.08 310	99.25 166	97.86 163	95.61 336	97.16 342	92.90 349	93.76 387	98.65 237	75.94 389	95.66 396	79.30 396	97.49 356	97.73 361

tpm cat1		93.29 347	93.13 345	93.75 370	97.39 371	84.74 392	97.39 245	97.65 331	83.39 392	94.16 380	98.41 267	82.86 367	99.39 346	91.56 359	95.35 386	97.14 374

MVS		93.19 348	92.09 352	96.50 331	96.91 380	94.03 312	98.07 162	98.06 322	68.01 395	94.56 378	96.48 356	95.96 218	99.30 359	83.84 388	96.89 371	96.17 384

tpm2		93.09 349	92.58 349	94.62 362	97.56 363	86.53 386	97.66 219	95.79 367	86.15 387	94.07 383	98.23 285	75.95 388	99.53 318	90.91 369	96.86 372	97.81 356

dmvs_testset		92.94 350	92.21 351	95.13 358	98.59 300	90.99 368	97.65 221	92.09 388	96.95 247	94.00 384	93.55 391	92.34 300	96.97 395	72.20 398	92.52 393	97.43 371

KD-MVS_2432*1600		92.87 351	91.99 354	95.51 353	91.37 399	89.27 375	94.07 376	98.14 318	95.42 300	97.25 310	96.44 358	67.86 396	99.24 365	91.28 363	96.08 381	98.02 347

miper_refine_blended		92.87 351	91.99 354	95.51 353	91.37 399	89.27 375	94.07 376	98.14 318	95.42 300	97.25 310	96.44 358	67.86 396	99.24 365	91.28 363	96.08 381	98.02 347

MVE	83.40 22	92.50 353	91.92 356	94.25 365	98.83 255	91.64 356	92.71 387	83.52 401	95.92 287	86.46 398	95.46 376	95.20 240	95.40 397	80.51 394	98.64 324	95.73 390
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		92.39 354	91.89 357	93.89 369	99.38 141	82.28 399	99.32 23	66.03 405	99.08 84	98.77 192	99.57 42	66.26 400	99.84 139	98.71 80	99.95 32	99.54 109

gg-mvs-nofinetune		92.37 355	91.20 360	95.85 344	95.80 395	92.38 348	99.31 27	81.84 402	99.75 5	91.83 391	99.74 13	68.29 395	99.02 375	87.15 382	97.12 367	96.16 385

test-mter		92.33 356	91.76 359	94.04 366	96.53 386	84.62 393	94.05 378	92.39 386	94.00 335	94.12 381	95.07 378	65.63 402	99.67 266	95.87 263	98.18 338	97.82 354

IB-MVS	91.63 19	92.24 357	90.90 361	96.27 336	97.22 375	91.24 365	94.36 373	93.33 383	92.37 355	92.24 390	94.58 387	66.20 401	99.89 75	93.16 336	94.63 389	97.66 364
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
TESTMET0.1,1		92.19 358	91.77 358	93.46 373	96.48 388	82.80 398	94.05 378	91.52 390	94.45 324	94.00 384	94.88 384	66.65 399	99.56 310	95.78 268	98.11 344	98.02 347

myMVS_eth3d		91.92 359	90.45 362	96.30 334	97.10 377	90.90 369	96.18 314	96.58 356	95.65 293	94.77 374	92.29 394	53.88 403	99.36 349	89.59 376	98.05 349	98.63 318

PAPM		91.88 360	90.34 363	96.51 330	98.06 342	92.56 343	92.44 389	97.17 341	86.35 386	90.38 393	96.01 363	86.61 338	99.21 368	70.65 399	95.43 385	97.75 360

PVSNet_0	89.98 21	91.15 361	90.30 364	93.70 371	97.72 355	84.34 396	90.24 391	97.42 334	90.20 375	93.79 386	93.09 392	90.90 312	98.89 383	86.57 384	72.76 398	97.87 353

EGC-MVSNET		85.24 362	80.54 365	99.34 73	99.77 29	99.20 34	99.08 59	99.29 175	12.08 399	20.84 400	99.42 74	97.55 126	99.85 122	97.08 174	99.72 149	98.96 271

test_method		79.78 363	79.50 366	80.62 380	80.21 402	45.76 406	70.82 394	98.41 307	31.08 398	80.89 399	97.71 318	84.85 352	97.37 393	91.51 360	80.03 397	98.75 305

tmp_tt		78.77 364	78.73 367	78.90 381	58.45 403	74.76 405	94.20 375	78.26 404	39.16 397	86.71 397	92.82 393	80.50 373	75.19 400	86.16 385	92.29 394	86.74 395

cdsmvs_eth3d_5k		24.66 365	32.88 368	0.00 384	0.00 406	0.00 409	0.00 395	99.10 225	0.00 402	0.00 403	97.58 326	99.21 16	0.00 403	0.00 402	0.00 401	0.00 399

testmvs		17.12 366	20.53 369	6.87 383	12.05 404	4.20 408	93.62 384	6.73 406	4.62 401	10.41 401	24.33 398	8.28 406	3.56 402	9.69 401	15.07 399	12.86 398

test123		17.04 367	20.11 370	7.82 382	10.25 405	4.91 407	94.80 359	4.47 407	4.93 400	10.00 402	24.28 399	9.69 405	3.64 401	10.14 400	12.43 400	14.92 397

pcd_1.5k_mvsjas		8.17 368	10.90 371	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	98.07 86	0.00 403	0.00 402	0.00 401	0.00 399

ab-mvs-re		8.12 369	10.83 372	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	97.48 332	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

test_blank		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet_test		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

DCPMVS		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

sosnet-low-res		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

sosnet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uncertanet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

Regformer		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

MM				98.91 148		96.97 219	97.89 188	94.44 374	99.54 27	98.95 157	99.14 130	93.50 282	99.92 51	99.80 12	99.96 25	99.85 19

WAC-MVS						90.90 369								91.37 362

FOURS1					99.73 39	99.67 2	99.43 11	99.54 77	99.43 40	99.26 112

MSC_two_6792asdad				99.32 80	98.43 318	98.37 111		98.86 267					99.89 75	97.14 169	99.60 194	99.71 47

PC_three_1452									93.27 343	99.40 83	98.54 252	98.22 74	97.00 394	95.17 284	99.45 236	99.49 128

No_MVS				99.32 80	98.43 318	98.37 111		98.86 267					99.89 75	97.14 169	99.60 194	99.71 47

test_one_0601					99.39 140	99.20 34		99.31 159	98.49 124	98.66 203	99.02 153	97.64 118

eth-test2					0.00 406

eth-test					0.00 406

ZD-MVS					99.01 221	98.84 75		99.07 229	94.10 332	98.05 261	98.12 292	96.36 199	99.86 110	92.70 346	99.19 276

RE-MVS-def			98.58 112		99.20 178	99.38 8	98.48 122	99.30 167	98.64 111	98.95 157	98.96 175	97.75 109		96.56 223	99.39 243	99.45 151

IU-MVS					99.49 116	99.15 47		98.87 262	92.97 347	99.41 80				96.76 204	99.62 187	99.66 59

OPU-MVS				98.82 157	98.59 300	98.30 116	98.10 158				98.52 255	98.18 78	98.75 385	94.62 296	99.48 233	99.41 165

test_241102_TWO								99.30 167	98.03 157	99.26 112	99.02 153	97.51 132	99.88 84	96.91 187	99.60 194	99.66 59

test_241102_ONE					99.49 116	99.17 39		99.31 159	97.98 159	99.66 42	98.90 189	98.36 63	99.48 331

9.14			97.78 201		99.07 209		97.53 235	99.32 154	95.53 297	98.54 223	98.70 227	97.58 123	99.76 222	94.32 309	99.46 234

save fliter					99.11 200	97.97 153	96.53 295	99.02 241	98.24 139

test_0728_THIRD									98.17 149	99.08 134	99.02 153	97.89 99	99.88 84	97.07 175	99.71 154	99.70 52

test_0728_SECOND				99.60 11	99.50 109	99.23 26	98.02 170	99.32 154					99.88 84	96.99 181	99.63 184	99.68 55

test0726					99.50 109	99.21 28	98.17 151	99.35 141	97.97 160	99.26 112	99.06 141	97.61 121

GSMVS																98.81 294

test_part2					99.36 148	99.10 60				99.05 141

sam_mvs1												84.74 354				98.81 294

sam_mvs												84.29 360

ambc				98.24 233	98.82 258	95.97 252	98.62 100	99.00 246		99.27 108	99.21 111	96.99 164	99.50 327	96.55 226	99.50 231	99.26 220

MTGPA								99.20 198

test_post1							97.59 229				20.48 401	83.07 366	99.66 277	94.16 310

test_post											21.25 400	83.86 362	99.70 250

patchmatchnet-post											98.77 216	84.37 357	99.85 122

GG-mvs-BLEND				94.76 361	94.54 397	92.13 353	99.31 27	80.47 403		88.73 396	91.01 396	67.59 398	98.16 391	82.30 393	94.53 390	93.98 393

MTMP							97.93 181	91.91 389

gm-plane-assit					94.83 396	81.97 400			88.07 384		94.99 381		99.60 297	91.76 354

test9_res														93.28 335	99.15 281	99.38 184

TEST9					98.71 275	98.08 140	95.96 322	99.03 238	91.40 365	95.85 356	97.53 328	96.52 190	99.76 222

test_8					98.67 287	98.01 148	95.91 327	99.02 241	91.64 360	95.79 358	97.50 331	96.47 192	99.76 222

agg_prior2														92.50 349	99.16 279	99.37 186

agg_prior					98.68 286	97.99 149		99.01 244		95.59 359			99.77 216

TestCases				99.16 106	99.50 109	98.55 97		99.58 54	96.80 253	98.88 174	99.06 141	97.65 115	99.57 307	94.45 302	99.61 192	99.37 186

test_prior4						97.97 153	95.86 328

test_prior2							95.74 333		96.48 267	96.11 351	97.63 324	95.92 220		94.16 310	99.20 273

test_prior				98.95 142	98.69 284	97.95 157		99.03 238					99.59 301			99.30 212

旧先验2							95.76 332		88.56 383	97.52 296			99.66 277	94.48 300

新几何2							95.93 325

新几何1				98.91 148	98.94 231	97.76 174		98.76 283	87.58 385	96.75 334	98.10 294	94.80 254	99.78 210	92.73 345	99.00 299	99.20 231

旧先验1					98.82 258	97.45 192		98.76 283			98.34 276	95.50 233			99.01 298	99.23 226

无先验							95.74 333	98.74 288	89.38 379				99.73 239	92.38 351		99.22 230

原ACMM2							95.53 339

原ACMM1				98.35 223	98.90 241	96.25 242		98.83 275	92.48 354	96.07 353	98.10 294	95.39 236	99.71 247	92.61 348	98.99 300	99.08 249

test222					98.92 237	96.93 224	95.54 338	98.78 281	85.72 388	96.86 329	98.11 293	94.43 262			99.10 289	99.23 226

testdata2													99.79 199	92.80 343

segment_acmp												97.02 162

testdata				98.09 241	98.93 233	95.40 270		98.80 278	90.08 376	97.45 302	98.37 272	95.26 238	99.70 250	93.58 328	98.95 305	99.17 242

testdata1							95.44 344		96.32 272

test12				98.93 145	98.58 302	97.83 166		98.66 292		96.53 341		95.51 232	99.69 254		99.13 284	99.27 217

plane_prior7					99.19 181	97.87 162

plane_prior6					98.99 225	97.70 180						94.90 247

plane_prior5								99.27 181					99.70 250	94.42 304	99.51 224	99.45 151

plane_prior4											97.98 303

plane_prior3						97.78 173			97.41 211	97.79 277

plane_prior2							97.77 204		98.20 146

plane_prior1					99.05 216

plane_prior						97.65 182	97.07 268		96.72 258						99.36 247

n2								0.00 408

nn								0.00 408

door-mid								99.57 61

lessismore_v0				98.97 139	99.73 39	97.53 188		86.71 398		99.37 90	99.52 57	89.93 318	99.92 51	98.99 63	99.72 149	99.44 155

LGP-MVS_train				99.47 54	99.57 82	98.97 66		99.48 95	96.60 262	99.10 132	99.06 141	98.71 39	99.83 156	95.58 277	99.78 120	99.62 68

test11								98.87 262

door								99.41 121

HQP5-MVS						96.79 227

HQP-NCC					98.67 287		96.29 307		96.05 281	95.55 362

ACMP_Plane					98.67 287		96.29 307		96.05 281	95.55 362

BP-MVS														92.82 341

HQP4-MVS										95.56 361			99.54 316			99.32 205

HQP3-MVS								99.04 236							99.26 265

HQP2-MVS												93.84 276

NP-MVS					98.84 253	97.39 196					96.84 349

MDTV_nov1_ep13_2view						74.92 404	97.69 214		90.06 377	97.75 280		85.78 346		93.52 329		98.69 312

MDTV_nov1_ep13			95.22 315		97.06 379	83.20 397	97.74 209	96.16 361	94.37 326	96.99 319	98.83 205	83.95 361	99.53 318	93.90 319	97.95 352

ACMMP++_ref															99.77 124

ACMMP++															99.68 167

Test By Simon												96.52 190

ITE_SJBPF				98.87 152	99.22 172	98.48 104		99.35 141	97.50 199	98.28 243	98.60 247	97.64 118	99.35 352	93.86 322	99.27 262	98.79 300

DeepMVS_CX				93.44 374	98.24 331	94.21 305		94.34 375	64.28 396	91.34 392	94.87 386	89.45 323	92.77 399	77.54 397	93.14 392	93.35 394

High-resolution multi-view benchmark