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 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 1	100.00 1	99.85 7

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 14	99.34 15	99.69 4	99.58 30	99.90 2	99.86 7	99.78 5	99.58 3	99.95 17	99.00 36	99.95 16	99.78 14

pmmvs6		99.67 3	99.70 3	99.60 13	99.90 4	99.27 21	99.53 7	99.76 10	99.64 12	99.84 8	99.83 2	99.50 5	99.87 91	99.36 14	99.92 37	99.64 43

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 24	99.85 13	99.11 61	99.90 1	99.78 8	99.63 14	99.78 10	99.67 16	99.48 6	99.81 170	99.30 17	99.97 11	99.77 16
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 49	99.88 7	98.61 94	99.34 16	99.71 13	99.27 47	99.90 4	99.74 8	99.68 2	99.97 4	99.55 8	99.99 5	99.88 3

jajsoiax		99.58 6	99.61 7	99.48 51	99.87 10	98.61 94	99.28 31	99.66 21	99.09 70	99.89 6	99.68 14	99.53 4	99.97 4	99.50 10	99.99 5	99.87 4

ANet_high		99.57 7	99.67 5	99.28 83	99.89 6	98.09 135	99.14 47	99.93 1	99.82 3	99.93 2	99.81 3	99.17 12	99.94 25	99.31 16	100.00 1	99.82 9

v7n		99.53 8	99.57 8	99.41 61	99.88 7	98.54 102	99.45 9	99.61 26	99.66 11	99.68 19	99.66 17	98.44 41	99.95 17	99.73 2	99.96 14	99.75 23

test_djsdf		99.52 9	99.51 9	99.53 36	99.86 11	98.74 83	99.39 13	99.56 44	99.11 60	99.70 15	99.73 10	99.00 15	99.97 4	99.26 18	99.98 9	99.89 2

anonymousdsp		99.51 10	99.47 12	99.62 6	99.88 7	99.08 65	99.34 16	99.69 16	98.93 87	99.65 23	99.72 11	98.93 19	99.95 17	99.11 27	100.00 1	99.82 9

UA-Net		99.47 11	99.40 14	99.70 2	99.49 87	99.29 18	99.80 3	99.72 12	99.82 3	99.04 117	99.81 3	98.05 70	99.96 10	98.85 44	99.99 5	99.86 6

PS-MVSNAJss		99.46 12	99.49 10	99.35 70	99.90 4	98.15 131	99.20 39	99.65 22	99.48 25	99.92 3	99.71 12	98.07 67	99.96 10	99.53 9	100.00 1	99.93 1

pm-mvs1		99.44 13	99.48 11	99.33 76	99.80 17	98.63 91	99.29 27	99.63 23	99.30 45	99.65 23	99.60 25	99.16 14	99.82 156	99.07 29	99.83 69	99.56 76

TransMVSNet (Re)		99.44 13	99.47 12	99.36 65	99.80 17	98.58 97	99.27 33	99.57 37	99.39 34	99.75 12	99.62 21	99.17 12	99.83 146	99.06 30	99.62 162	99.66 38

DTE-MVSNet		99.43 15	99.35 17	99.66 4	99.71 32	99.30 17	99.31 22	99.51 60	99.64 12	99.56 29	99.46 46	98.23 53	99.97 4	98.78 48	99.93 28	99.72 26

TDRefinement		99.42 16	99.38 15	99.55 26	99.76 23	99.33 16	99.68 5	99.71 13	99.38 35	99.53 34	99.61 23	98.64 30	99.80 179	98.24 80	99.84 63	99.52 100

PEN-MVS		99.41 17	99.34 19	99.62 6	99.73 25	99.14 53	99.29 27	99.54 52	99.62 17	99.56 29	99.42 52	98.16 63	99.96 10	98.78 48	99.93 28	99.77 16

nrg030		99.40 18	99.35 17	99.54 29	99.58 53	99.13 57	98.98 64	99.48 72	99.68 9	99.46 44	99.26 74	98.62 31	99.73 232	99.17 26	99.92 37	99.76 20

PS-CasMVS		99.40 18	99.33 20	99.62 6	99.71 32	99.10 62	99.29 27	99.53 56	99.53 23	99.46 44	99.41 55	98.23 53	99.95 17	98.89 42	99.95 16	99.81 11

MIMVSNet1		99.38 20	99.32 21	99.55 26	99.86 11	99.19 37	99.41 12	99.59 28	99.59 20	99.71 14	99.57 28	97.12 141	99.90 53	99.21 23	99.87 57	99.54 88

OurMVSNet-221017-0		99.37 21	99.31 22	99.53 36	99.91 3	98.98 67	99.63 6	99.58 30	99.44 30	99.78 10	99.76 6	96.39 184	99.92 39	99.44 13	99.92 37	99.68 34

Vis-MVSNet		99.34 22	99.36 16	99.27 86	99.73 25	98.26 119	99.17 44	99.78 8	99.11 60	99.27 77	99.48 44	98.82 21	99.95 17	98.94 38	99.93 28	99.59 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
WR-MVS_H		99.33 23	99.22 27	99.65 5	99.71 32	99.24 24	99.32 18	99.55 48	99.46 28	99.50 40	99.34 64	97.30 129	99.93 30	98.90 40	99.93 28	99.77 16

VPA-MVSNet		99.30 24	99.30 23	99.28 83	99.49 87	98.36 115	99.00 61	99.45 83	99.63 14	99.52 36	99.44 51	98.25 51	99.88 74	99.09 28	99.84 63	99.62 48

Anonymous20231211		99.27 25	99.27 24	99.26 89	99.29 130	98.18 128	99.49 8	99.51 60	99.70 8	99.80 9	99.68 14	96.84 157	99.83 146	99.21 23	99.91 43	99.77 16

FC-MVSNet-test		99.27 25	99.25 25	99.34 73	99.77 20	98.37 112	99.30 26	99.57 37	99.61 19	99.40 54	99.50 39	97.12 141	99.85 113	99.02 35	99.94 24	99.80 12

KD-MVS_self_test		99.25 27	99.18 28	99.44 57	99.63 50	99.06 66	98.69 82	99.54 52	99.31 43	99.62 28	99.53 36	97.36 127	99.86 98	99.24 22	99.71 126	99.39 159

ACMH	96.65 7	99.25 27	99.24 26	99.26 89	99.72 31	98.38 111	99.07 54	99.55 48	98.30 119	99.65 23	99.45 50	99.22 9	99.76 217	98.44 70	99.77 97	99.64 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.21 29	99.09 36	99.56 24	99.65 45	98.96 71	99.13 48	99.34 123	99.42 32	99.33 66	99.26 74	97.01 149	99.94 25	98.74 53	99.93 28	99.79 13

TranMVSNet+NR-MVSNet		99.17 30	99.07 38	99.46 56	99.37 118	98.87 74	98.39 114	99.42 95	99.42 32	99.36 61	99.06 107	98.38 44	99.95 17	98.34 76	99.90 49	99.57 71

FMVSNet1		99.17 30	99.17 29	99.17 101	99.55 68	98.24 121	99.20 39	99.44 86	99.21 49	99.43 49	99.55 32	97.82 87	99.86 98	98.42 72	99.89 53	99.41 150

FIs		99.14 32	99.09 36	99.29 81	99.70 38	98.28 118	99.13 48	99.52 59	99.48 25	99.24 86	99.41 55	96.79 163	99.82 156	98.69 57	99.88 54	99.76 20

XXY-MVS		99.14 32	99.15 32	99.10 113	99.76 23	97.74 178	98.85 73	99.62 24	98.48 111	99.37 59	99.49 42	98.75 24	99.86 98	98.20 83	99.80 84	99.71 27

CS-MVS		99.13 34	99.10 35	99.24 94	99.07 183	99.14 53	99.36 15	99.88 3	99.36 40	98.20 221	98.46 239	98.66 29	99.93 30	99.03 34	99.85 59	98.65 299

DROMVSNet		99.09 35	99.05 39	99.20 98	99.28 131	98.93 72	99.24 35	99.84 5	99.08 72	98.12 229	98.37 249	98.72 26	99.90 53	99.05 31	99.77 97	98.77 285

ACMH+	96.62 9	99.08 36	99.00 42	99.33 76	99.71 32	98.83 77	98.60 88	99.58 30	99.11 60	99.53 34	99.18 86	98.81 22	99.67 259	96.71 182	99.77 97	99.50 107

GeoE		99.05 37	98.99 44	99.25 91	99.44 103	98.35 116	98.73 78	99.56 44	98.42 113	98.91 142	98.81 181	98.94 18	99.91 49	98.35 75	99.73 114	99.49 111

Gipuma		99.03 38	99.16 30	98.64 177	99.94 2	98.51 104	99.32 18	99.75 11	99.58 22	98.60 187	99.62 21	98.22 56	99.51 315	97.70 114	99.73 114	97.89 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v8		99.01 39	99.16 30	98.57 190	99.47 97	96.31 236	98.90 68	99.47 78	99.03 76	99.52 36	99.57 28	96.93 153	99.81 170	99.60 4	99.98 9	99.60 54

HPM-MVS_fast		99.01 39	98.82 52	99.57 18	99.71 32	99.35 12	99.00 61	99.50 62	97.33 199	98.94 139	98.86 167	98.75 24	99.82 156	97.53 120	99.71 126	99.56 76

APDe-MVS		98.99 41	98.79 56	99.60 13	99.21 145	99.15 48	98.87 70	99.48 72	97.57 173	99.35 63	99.24 78	97.83 84	99.89 63	97.88 103	99.70 131	99.75 23

abl_6		98.99 41	98.78 57	99.61 9	99.45 101	99.46 4	98.60 88	99.50 62	98.59 104	99.24 86	99.04 117	98.54 36	99.89 63	96.45 204	99.62 162	99.50 107

EG-PatchMatch MVS		98.99 41	99.01 41	98.94 141	99.50 80	97.47 191	98.04 148	99.59 28	98.15 138	99.40 54	99.36 61	98.58 34	99.76 217	98.78 48	99.68 142	99.59 60

COLMAP_ROB	96.50 10	98.99 41	98.85 50	99.41 61	99.58 53	99.10 62	98.74 76	99.56 44	99.09 70	99.33 66	99.19 84	98.40 43	99.72 240	95.98 229	99.76 107	99.42 147
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 45	98.86 49	99.36 65	99.82 16	98.55 99	97.47 210	99.57 37	99.37 36	99.21 90	99.61 23	96.76 166	99.83 146	98.06 91	99.83 69	99.71 27

v10		98.97 46	99.11 33	98.55 195	99.44 103	96.21 238	98.90 68	99.55 48	98.73 96	99.48 41	99.60 25	96.63 173	99.83 146	99.70 3	99.99 5	99.61 53

DeepC-MVS	97.60 4	98.97 46	98.93 45	99.10 113	99.35 123	97.98 151	98.01 154	99.46 80	97.56 175	99.54 31	99.50 39	98.97 16	99.84 131	98.06 91	99.92 37	99.49 111
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 48	99.02 40	98.76 168	99.38 113	97.26 202	98.49 103	99.50 62	98.86 90	99.19 92	99.06 107	98.23 53	99.69 247	98.71 55	99.76 107	99.33 187

casdiffmvs		98.95 49	99.00 42	98.81 158	99.38 113	97.33 197	97.82 172	99.57 37	99.17 57	99.35 63	99.17 90	98.35 48	99.69 247	98.46 69	99.73 114	99.41 150

NR-MVSNet		98.95 49	98.82 52	99.36 65	99.16 163	98.72 88	99.22 36	99.20 176	99.10 67	99.72 13	98.76 189	96.38 186	99.86 98	98.00 96	99.82 72	99.50 107

Anonymous20240529		98.93 51	98.87 47	99.12 109	99.19 152	98.22 126	99.01 59	98.99 231	99.25 48	99.54 31	99.37 58	97.04 145	99.80 179	97.89 100	99.52 199	99.35 179

DP-MVS		98.93 51	98.81 54	99.28 83	99.21 145	98.45 108	98.46 108	99.33 128	99.63 14	99.48 41	99.15 96	97.23 137	99.75 224	97.17 135	99.66 153	99.63 47

CS-MVS-test		98.92 53	98.81 54	99.25 91	99.08 182	99.15 48	98.71 81	99.79 7	99.37 36	98.20 221	97.38 317	97.86 82	99.93 30	99.04 32	99.85 59	98.67 298

SED-MVS		98.91 54	98.72 63	99.49 49	99.49 87	99.17 39	98.10 139	99.31 136	98.03 142	99.66 20	99.02 121	98.36 45	99.88 74	96.91 158	99.62 162	99.41 150

ACMM	96.08 12	98.91 54	98.73 61	99.48 51	99.55 68	99.14 53	98.07 142	99.37 107	97.62 168	99.04 117	98.96 142	98.84 20	99.79 192	97.43 124	99.65 154	99.49 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		98.90 56	98.70 68	99.51 45	98.43 295	99.15 48	99.43 10	99.32 130	98.17 134	99.26 81	99.02 121	98.18 60	99.88 74	97.07 146	99.45 216	99.49 111

tfpnnormal		98.90 56	98.90 46	98.91 145	99.67 42	97.82 170	99.00 61	99.44 86	99.45 29	99.51 39	99.24 78	98.20 59	99.86 98	95.92 231	99.69 137	99.04 242

MTAPA		98.88 58	98.64 76	99.61 9	99.67 42	99.36 10	98.43 111	99.20 176	98.83 93	98.89 146	98.90 154	96.98 151	99.92 39	97.16 136	99.70 131	99.56 76

VPNet		98.87 59	98.83 51	99.01 134	99.70 38	97.62 186	98.43 111	99.35 117	99.47 27	99.28 75	99.05 114	96.72 169	99.82 156	98.09 89	99.36 229	99.59 60

UniMVSNet (Re)		98.87 59	98.71 65	99.35 70	99.24 138	98.73 86	97.73 182	99.38 103	98.93 87	99.12 99	98.73 192	96.77 164	99.86 98	98.63 59	99.80 84	99.46 131

UniMVSNet_NR-MVSNet		98.86 61	98.68 71	99.40 63	99.17 161	98.74 83	97.68 186	99.40 99	99.14 58	99.06 110	98.59 222	96.71 170	99.93 30	98.57 62	99.77 97	99.53 96

APD-MVS_3200maxsize		98.84 62	98.61 82	99.53 36	99.19 152	99.27 21	98.49 103	99.33 128	98.64 98	99.03 120	98.98 137	97.89 80	99.85 113	96.54 198	99.42 220	99.46 131

PM-MVS		98.82 63	98.72 63	99.12 109	99.64 48	98.54 102	97.98 157	99.68 18	97.62 168	99.34 65	99.18 86	97.54 109	99.77 210	97.79 106	99.74 111	99.04 242

DU-MVS		98.82 63	98.63 77	99.39 64	99.16 163	98.74 83	97.54 202	99.25 165	98.84 92	99.06 110	98.76 189	96.76 166	99.93 30	98.57 62	99.77 97	99.50 107

SR-MVS-dyc-post		98.81 65	98.55 88	99.57 18	99.20 149	99.38 6	98.48 106	99.30 146	98.64 98	98.95 133	98.96 142	97.49 118	99.86 98	96.56 194	99.39 224	99.45 135

3Dnovator	98.27 2	98.81 65	98.73 61	99.05 127	98.76 246	97.81 172	99.25 34	99.30 146	98.57 108	98.55 197	99.33 66	97.95 79	99.90 53	97.16 136	99.67 148	99.44 140

zzz-MVS		98.79 67	98.52 91	99.61 9	99.67 42	99.36 10	97.33 220	99.20 176	98.83 93	98.89 146	98.90 154	96.98 151	99.92 39	97.16 136	99.70 131	99.56 76

HPM-MVS		98.79 67	98.53 90	99.59 17	99.65 45	99.29 18	99.16 45	99.43 92	96.74 236	98.61 185	98.38 247	98.62 31	99.87 91	96.47 202	99.67 148	99.59 60
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		98.79 67	98.54 89	99.54 29	99.73 25	99.16 43	98.23 125	99.31 136	97.92 149	98.90 143	98.90 154	98.00 73	99.88 74	96.15 223	99.72 121	99.58 66
Skip Steuart: Steuart Systems R&D Blog.
dcpmvs_2		98.78 70	99.11 33	97.78 251	99.56 64	93.67 308	99.06 56	99.86 4	99.50 24	99.66 20	99.26 74	97.21 139	99.99 2	98.00 96	99.91 43	99.68 34

V42		98.78 70	98.78 57	98.76 168	99.44 103	97.04 215	98.27 122	99.19 181	97.87 153	99.25 85	99.16 92	96.84 157	99.78 204	99.21 23	99.84 63	99.46 131

test20.03		98.78 70	98.77 59	98.78 165	99.46 98	97.20 208	97.78 174	99.24 170	99.04 75	99.41 51	98.90 154	97.65 97	99.76 217	97.70 114	99.79 89	99.39 159

DVP-MVS		98.77 73	98.52 91	99.52 41	99.50 80	99.21 27	98.02 151	98.84 256	97.97 145	99.08 107	99.02 121	97.61 103	99.88 74	96.99 152	99.63 159	99.48 121
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
test1172		98.76 74	98.49 98	99.57 18	99.18 159	99.37 9	98.39 114	99.31 136	98.43 112	98.90 143	98.88 163	97.49 118	99.86 98	96.43 206	99.37 228	99.48 121

test_0402		98.76 74	98.71 65	98.93 142	99.56 64	98.14 133	98.45 110	99.34 123	99.28 46	98.95 133	98.91 151	98.34 49	99.79 192	95.63 248	99.91 43	98.86 271

ACMMP_NAP		98.75 76	98.48 100	99.57 18	99.58 53	99.29 18	97.82 172	99.25 165	96.94 228	98.78 164	99.12 100	98.02 71	99.84 131	97.13 142	99.67 148	99.59 60

SixPastTwentyTwo		98.75 76	98.62 79	99.16 104	99.83 15	97.96 156	99.28 31	98.20 300	99.37 36	99.70 15	99.65 19	92.65 280	99.93 30	99.04 32	99.84 63	99.60 54

ACMMP		98.75 76	98.50 95	99.52 41	99.56 64	99.16 43	98.87 70	99.37 107	97.16 219	98.82 161	99.01 130	97.71 93	99.87 91	96.29 215	99.69 137	99.54 88
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
Regformer-4		98.73 79	98.68 71	98.89 148	99.02 196	97.22 205	97.17 235	99.06 211	99.21 49	99.17 97	98.85 170	97.45 121	99.86 98	98.48 68	99.70 131	99.60 54

XVS		98.72 80	98.45 106	99.53 36	99.46 98	99.21 27	98.65 83	99.34 123	98.62 102	97.54 269	98.63 215	97.50 115	99.83 146	96.79 171	99.53 196	99.56 76

SR-MVS		98.71 81	98.43 110	99.57 18	99.18 159	99.35 12	98.36 117	99.29 153	98.29 122	98.88 150	98.85 170	97.53 111	99.87 91	96.14 224	99.31 237	99.48 121

HFP-MVS		98.71 81	98.44 108	99.51 45	99.49 87	99.16 43	98.52 97	99.31 136	97.47 182	98.58 191	98.50 233	97.97 77	99.85 113	96.57 191	99.59 174	99.53 96

LPG-MVS_test		98.71 81	98.46 104	99.47 54	99.57 57	98.97 68	98.23 125	99.48 72	96.60 241	99.10 104	99.06 107	98.71 27	99.83 146	95.58 251	99.78 93	99.62 48

ACMMPR		98.70 84	98.42 112	99.54 29	99.52 75	99.14 53	98.52 97	99.31 136	97.47 182	98.56 195	98.54 226	97.75 91	99.88 74	96.57 191	99.59 174	99.58 66

CP-MVS		98.70 84	98.42 112	99.52 41	99.36 119	99.12 59	98.72 79	99.36 111	97.54 177	98.30 216	98.40 243	97.86 82	99.89 63	96.53 199	99.72 121	99.56 76

Anonymous20240521		98.69 86	98.87 47	98.16 231	99.77 20	95.11 269	99.08 51	99.44 86	99.34 41	99.33 66	99.55 32	94.10 258	99.94 25	99.25 20	99.96 14	99.42 147

region2R		98.69 86	98.40 114	99.54 29	99.53 73	99.17 39	98.52 97	99.31 136	97.46 187	98.44 206	98.51 230	97.83 84	99.88 74	96.46 203	99.58 180	99.58 66

EI-MVSNet-UG-set		98.69 86	98.71 65	98.62 182	99.10 175	96.37 233	97.23 227	98.87 247	99.20 52	99.19 92	98.99 133	97.30 129	99.85 113	98.77 51	99.79 89	99.65 42

3Dnovator+	97.89 3	98.69 86	98.51 93	99.24 94	98.81 241	98.40 109	99.02 58	99.19 181	98.99 79	98.07 234	99.28 70	97.11 143	99.84 131	96.84 169	99.32 235	99.47 129

ZNCC-MVS		98.68 90	98.40 114	99.54 29	99.57 57	99.21 27	98.46 108	99.29 153	97.28 205	98.11 231	98.39 245	98.00 73	99.87 91	96.86 168	99.64 156	99.55 84

EI-MVSNet-Vis-set		98.68 90	98.70 68	98.63 180	99.09 178	96.40 232	97.23 227	98.86 252	99.20 52	99.18 96	98.97 139	97.29 131	99.85 113	98.72 54	99.78 93	99.64 43

CSCG		98.68 90	98.50 95	99.20 98	99.45 101	98.63 91	98.56 93	99.57 37	97.87 153	98.85 154	98.04 277	97.66 96	99.84 131	96.72 180	99.81 76	99.13 231

PGM-MVS		98.66 93	98.37 120	99.55 26	99.53 73	99.18 38	98.23 125	99.49 70	97.01 226	98.69 174	98.88 163	98.00 73	99.89 63	95.87 235	99.59 174	99.58 66

GBi-Net		98.65 94	98.47 102	99.17 101	98.90 220	98.24 121	99.20 39	99.44 86	98.59 104	98.95 133	99.55 32	94.14 254	99.86 98	97.77 108	99.69 137	99.41 150

test1		98.65 94	98.47 102	99.17 101	98.90 220	98.24 121	99.20 39	99.44 86	98.59 104	98.95 133	99.55 32	94.14 254	99.86 98	97.77 108	99.69 137	99.41 150

LCM-MVSNet-Re		98.64 96	98.48 100	99.11 111	98.85 231	98.51 104	98.49 103	99.83 6	98.37 114	99.69 17	99.46 46	98.21 58	99.92 39	94.13 289	99.30 240	98.91 266

mPP-MVS		98.64 96	98.34 124	99.54 29	99.54 71	99.17 39	98.63 85	99.24 170	97.47 182	98.09 233	98.68 201	97.62 102	99.89 63	96.22 218	99.62 162	99.57 71

TSAR-MVS + MP.		98.63 98	98.49 98	99.06 125	99.64 48	97.90 161	98.51 101	98.94 234	96.96 227	99.24 86	98.89 162	97.83 84	99.81 170	96.88 165	99.49 210	99.48 121
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 98	98.38 119	99.36 65	97.25 352	99.38 6	99.12 50	99.32 130	99.21 49	98.44 206	98.88 163	97.31 128	99.80 179	96.58 189	99.34 233	98.92 263

RPSCF		98.62 100	98.36 121	99.42 58	99.65 45	99.42 5	98.55 94	99.57 37	97.72 162	98.90 143	99.26 74	96.12 193	99.52 311	95.72 242	99.71 126	99.32 189

GST-MVS		98.61 101	98.30 129	99.52 41	99.51 77	99.20 33	98.26 123	99.25 165	97.44 190	98.67 176	98.39 245	97.68 94	99.85 113	96.00 227	99.51 202	99.52 100

Regformer-3		98.61 101	98.61 82	98.63 180	99.02 196	96.53 230	97.17 235	98.84 256	99.13 59	99.10 104	98.85 170	97.24 136	99.79 192	98.41 73	99.70 131	99.57 71

v1192		98.60 103	98.66 74	98.41 210	99.27 133	95.88 245	97.52 204	99.36 111	97.41 192	99.33 66	99.20 83	96.37 187	99.82 156	99.57 6	99.92 37	99.55 84

v1144		98.60 103	98.66 74	98.41 210	99.36 119	95.90 244	97.58 198	99.34 123	97.51 178	99.27 77	99.15 96	96.34 189	99.80 179	99.47 12	99.93 28	99.51 103

Regformer-2		98.60 103	98.46 104	99.02 133	98.85 231	97.71 180	96.91 251	99.09 207	98.98 81	99.01 121	98.64 211	97.37 126	99.84 131	97.75 113	99.57 184	99.52 100

DPE-MVS		98.59 106	98.26 133	99.57 18	99.27 133	99.15 48	97.01 242	99.39 101	97.67 164	99.44 48	98.99 133	97.53 111	99.89 63	95.40 255	99.68 142	99.66 38
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		98.57 107	98.23 137	99.60 13	99.69 40	99.35 12	97.16 237	99.38 103	94.87 289	98.97 130	98.99 133	98.01 72	99.88 74	97.29 130	99.70 131	99.58 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 108	98.32 128	99.25 91	99.41 110	98.73 86	97.13 239	99.18 185	97.10 222	98.75 170	98.92 150	98.18 60	99.65 272	96.68 184	99.56 189	99.37 169
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VDD-MVS		98.56 108	98.39 117	99.07 120	99.13 170	98.07 141	98.59 90	97.01 330	99.59 20	99.11 101	99.27 72	94.82 237	99.79 192	98.34 76	99.63 159	99.34 181

v2v482		98.56 108	98.62 79	98.37 214	99.42 108	95.81 248	97.58 198	99.16 194	97.90 151	99.28 75	99.01 130	95.98 202	99.79 192	99.33 15	99.90 49	99.51 103

XVG-ACMP-BASELINE		98.56 108	98.34 124	99.22 97	99.54 71	98.59 96	97.71 183	99.46 80	97.25 208	98.98 127	98.99 133	97.54 109	99.84 131	95.88 232	99.74 111	99.23 211

Regformer-1		98.55 112	98.44 108	98.87 150	98.85 231	97.29 199	96.91 251	98.99 231	98.97 82	98.99 125	98.64 211	97.26 135	99.81 170	97.79 106	99.57 184	99.51 103

v1240		98.55 112	98.62 79	98.32 217	99.22 143	95.58 251	97.51 206	99.45 83	97.16 219	99.45 47	99.24 78	96.12 193	99.85 113	99.60 4	99.88 54	99.55 84

IterMVS-LS		98.55 112	98.70 68	98.09 233	99.48 95	94.73 276	97.22 230	99.39 101	98.97 82	99.38 57	99.31 68	96.00 198	99.93 30	98.58 60	99.97 11	99.60 54
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 115	98.57 87	98.45 207	99.21 145	95.98 242	97.63 191	99.36 111	97.15 221	99.32 72	99.18 86	95.84 209	99.84 131	99.50 10	99.91 43	99.54 88

v1921920		98.54 115	98.60 84	98.38 213	99.20 149	95.76 250	97.56 200	99.36 111	97.23 214	99.38 57	99.17 90	96.02 196	99.84 131	99.57 6	99.90 49	99.54 88

SF-MVS		98.53 117	98.27 132	99.32 78	99.31 126	98.75 82	98.19 129	99.41 96	96.77 235	98.83 157	98.90 154	97.80 88	99.82 156	95.68 245	99.52 199	99.38 166

XVG-OURS		98.53 117	98.34 124	99.11 111	99.50 80	98.82 79	95.97 295	99.50 62	97.30 203	99.05 115	98.98 137	99.35 7	99.32 340	95.72 242	99.68 142	99.18 223

UGNet		98.53 117	98.45 106	98.79 162	97.94 324	96.96 218	99.08 51	98.54 285	99.10 67	96.82 307	99.47 45	96.55 176	99.84 131	98.56 65	99.94 24	99.55 84
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
patch_mono-2		98.51 120	98.63 77	98.17 229	99.38 113	94.78 274	97.36 217	99.69 16	98.16 137	98.49 203	99.29 69	97.06 144	99.97 4	98.29 79	99.91 43	99.76 20

#test#		98.50 121	98.16 146	99.51 45	99.49 87	99.16 43	98.03 149	99.31 136	96.30 253	98.58 191	98.50 233	97.97 77	99.85 113	95.68 245	99.59 174	99.53 96

XVG-OURS-SEG-HR		98.49 122	98.28 131	99.14 107	99.49 87	98.83 77	96.54 269	99.48 72	97.32 201	99.11 101	98.61 220	99.33 8	99.30 343	96.23 217	98.38 312	99.28 201

FMVSNet2		98.49 122	98.40 114	98.75 170	98.90 220	97.14 214	98.61 87	99.13 201	98.59 104	99.19 92	99.28 70	94.14 254	99.82 156	97.97 98	99.80 84	99.29 200

pmmvs-eth3d		98.47 124	98.34 124	98.86 152	99.30 129	97.76 175	97.16 237	99.28 156	95.54 273	99.42 50	99.19 84	97.27 132	99.63 277	97.89 100	99.97 11	99.20 216

MP-MVS		98.46 125	98.09 153	99.54 29	99.57 57	99.22 26	98.50 102	99.19 181	97.61 170	97.58 265	98.66 206	97.40 124	99.88 74	94.72 269	99.60 170	99.54 88
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.45 126	98.60 84	98.00 242	99.44 103	94.98 270	97.44 213	99.06 211	98.30 119	99.32 72	98.97 139	96.65 172	99.62 279	98.37 74	99.85 59	99.39 159

xxxxxxxxxxxxxcwj		98.44 127	98.24 135	99.06 125	99.11 171	97.97 152	96.53 270	99.54 52	98.24 125	98.83 157	98.90 154	97.80 88	99.82 156	95.68 245	99.52 199	99.38 166

AllTest		98.44 127	98.20 139	99.16 104	99.50 80	98.55 99	98.25 124	99.58 30	96.80 233	98.88 150	99.06 107	97.65 97	99.57 296	94.45 276	99.61 168	99.37 169

VNet		98.42 129	98.30 129	98.79 162	98.79 245	97.29 199	98.23 125	98.66 279	99.31 43	98.85 154	98.80 182	94.80 240	99.78 204	98.13 85	99.13 268	99.31 193

ab-mvs		98.41 130	98.36 121	98.59 186	99.19 152	97.23 203	99.32 18	98.81 262	97.66 165	98.62 183	99.40 57	96.82 160	99.80 179	95.88 232	99.51 202	98.75 288

ACMP	95.32 15	98.41 130	98.09 153	99.36 65	99.51 77	98.79 81	97.68 186	99.38 103	95.76 270	98.81 163	98.82 179	98.36 45	99.82 156	94.75 266	99.77 97	99.48 121
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		98.40 132	98.03 160	99.51 45	99.16 163	99.21 27	98.05 146	99.22 173	94.16 305	98.98 127	99.10 104	97.52 113	99.79 192	96.45 204	99.64 156	99.53 96
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		98.40 132	98.00 162	99.61 9	99.57 57	99.25 23	98.57 92	99.35 117	97.55 176	99.31 74	97.71 296	94.61 244	99.88 74	96.14 224	99.19 258	99.70 32
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		98.40 132	98.68 71	97.54 270	98.96 207	97.99 147	97.88 165	99.36 111	98.20 131	99.63 26	99.04 117	98.76 23	95.33 375	96.56 194	99.74 111	99.31 193
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		98.40 132	98.51 93	98.04 240	99.10 175	94.73 276	97.20 231	98.87 247	98.97 82	99.06 110	99.02 121	96.00 198	99.80 179	98.58 60	99.82 72	99.60 54

WR-MVS		98.40 132	98.19 141	99.03 130	99.00 199	97.65 183	96.85 254	98.94 234	98.57 108	98.89 146	98.50 233	95.60 215	99.85 113	97.54 119	99.85 59	99.59 60

new-patchmatchnet		98.35 137	98.74 60	97.18 285	99.24 138	92.23 330	96.42 278	99.48 72	98.30 119	99.69 17	99.53 36	97.44 122	99.82 156	98.84 45	99.77 97	99.49 111

canonicalmvs		98.34 138	98.26 133	98.58 187	98.46 292	97.82 170	98.96 65	99.46 80	99.19 56	97.46 276	95.46 355	98.59 33	99.46 324	98.08 90	98.71 301	98.46 305

testgi		98.32 139	98.39 117	98.13 232	99.57 57	95.54 252	97.78 174	99.49 70	97.37 196	99.19 92	97.65 300	98.96 17	99.49 317	96.50 201	98.99 286	99.34 181

DeepPCF-MVS	96.93 5	98.32 139	98.01 161	99.23 96	98.39 300	98.97 68	95.03 330	99.18 185	96.88 231	99.33 66	98.78 185	98.16 63	99.28 346	96.74 177	99.62 162	99.44 140

MVS_111021_LR		98.30 141	98.12 151	98.83 155	99.16 163	98.03 145	96.09 292	99.30 146	97.58 172	98.10 232	98.24 260	98.25 51	99.34 337	96.69 183	99.65 154	99.12 232

EPP-MVSNet		98.30 141	98.04 159	99.07 120	99.56 64	97.83 167	99.29 27	98.07 306	99.03 76	98.59 189	99.13 99	92.16 284	99.90 53	96.87 166	99.68 142	99.49 111

DeepC-MVS_fast	96.85 6	98.30 141	98.15 148	98.75 170	98.61 275	97.23 203	97.76 179	99.09 207	97.31 202	98.75 170	98.66 206	97.56 107	99.64 274	96.10 226	99.55 191	99.39 159
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 144	97.95 165	99.34 73	98.44 294	99.16 43	98.12 136	99.38 103	96.01 262	98.06 235	98.43 241	97.80 88	99.67 259	95.69 244	99.58 180	99.20 216

Fast-Effi-MVS+-dtu		98.27 145	98.09 153	98.81 158	98.43 295	98.11 134	97.61 194	99.50 62	98.64 98	97.39 281	97.52 308	98.12 66	99.95 17	96.90 163	98.71 301	98.38 311

DELS-MVS		98.27 145	98.20 139	98.48 204	98.86 229	96.70 227	95.60 314	99.20 176	97.73 161	98.45 205	98.71 195	97.50 115	99.82 156	98.21 82	99.59 174	98.93 262
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		98.26 147	97.90 170	99.35 70	98.02 320	99.49 3	98.02 151	99.16 194	98.29 122	97.64 260	97.99 279	96.44 182	99.95 17	96.66 185	98.93 291	98.60 300

MVSFormer		98.26 147	98.43 110	97.77 252	98.88 226	93.89 302	99.39 13	99.56 44	99.11 60	98.16 225	98.13 267	93.81 261	99.97 4	99.26 18	99.57 184	99.43 144

MVS_111021_HR		98.25 149	98.08 156	98.75 170	99.09 178	97.46 192	95.97 295	99.27 159	97.60 171	97.99 240	98.25 259	98.15 65	99.38 334	96.87 166	99.57 184	99.42 147

TAMVS		98.24 150	98.05 158	98.80 160	99.07 183	97.18 210	97.88 165	98.81 262	96.66 240	99.17 97	99.21 81	94.81 239	99.77 210	96.96 156	99.88 54	99.44 140

diffmvs		98.22 151	98.24 135	98.17 229	99.00 199	95.44 257	96.38 280	99.58 30	97.79 159	98.53 200	98.50 233	96.76 166	99.74 228	97.95 99	99.64 156	99.34 181

Anonymous20231206		98.21 152	98.21 138	98.20 227	99.51 77	95.43 258	98.13 134	99.32 130	96.16 256	98.93 140	98.82 179	96.00 198	99.83 146	97.32 129	99.73 114	99.36 175

VDDNet		98.21 152	97.95 165	99.01 134	99.58 53	97.74 178	99.01 59	97.29 326	99.67 10	98.97 130	99.50 39	90.45 293	99.80 179	97.88 103	99.20 254	99.48 121

IS-MVSNet		98.19 154	97.90 170	99.08 117	99.57 57	97.97 152	99.31 22	98.32 295	99.01 78	98.98 127	99.03 120	91.59 288	99.79 192	95.49 253	99.80 84	99.48 121

MVS_Test		98.18 155	98.36 121	97.67 257	98.48 290	94.73 276	98.18 130	99.02 224	97.69 163	98.04 238	99.11 102	97.22 138	99.56 299	98.57 62	98.90 292	98.71 291

TSAR-MVS + GP.		98.18 155	97.98 163	98.77 167	98.71 255	97.88 162	96.32 283	98.66 279	96.33 250	99.23 89	98.51 230	97.48 120	99.40 330	97.16 136	99.46 214	99.02 245

CNVR-MVS		98.17 157	97.87 172	99.07 120	98.67 268	98.24 121	97.01 242	98.93 236	97.25 208	97.62 261	98.34 253	97.27 132	99.57 296	96.42 207	99.33 234	99.39 159

PVSNet_Blended_VisFu		98.17 157	98.15 148	98.22 226	99.73 25	95.15 266	97.36 217	99.68 18	94.45 298	98.99 125	99.27 72	96.87 156	99.94 25	97.13 142	99.91 43	99.57 71

HPM-MVS++		98.10 159	97.64 188	99.48 51	99.09 178	99.13 57	97.52 204	98.75 272	97.46 187	96.90 302	97.83 290	96.01 197	99.84 131	95.82 239	99.35 231	99.46 131

APD-MVS		98.10 159	97.67 183	99.42 58	99.11 171	98.93 72	97.76 179	99.28 156	94.97 286	98.72 173	98.77 187	97.04 145	99.85 113	93.79 300	99.54 192	99.49 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVP-Stereo		98.08 161	97.92 168	98.57 190	98.96 207	96.79 223	97.90 164	99.18 185	96.41 248	98.46 204	98.95 146	95.93 205	99.60 286	96.51 200	98.98 288	99.31 193
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PMMVS2		98.07 162	98.08 156	98.04 240	99.41 110	94.59 282	94.59 344	99.40 99	97.50 179	98.82 161	98.83 176	96.83 159	99.84 131	97.50 122	99.81 76	99.71 27

ETH3D-3000-0.1		98.03 163	97.62 190	99.29 81	99.11 171	98.80 80	97.47 210	99.32 130	95.54 273	98.43 209	98.62 217	96.61 174	99.77 210	93.95 294	99.49 210	99.30 196

ETV-MVS		98.03 163	97.86 173	98.56 194	98.69 263	98.07 141	97.51 206	99.50 62	98.10 139	97.50 273	95.51 353	98.41 42	99.88 74	96.27 216	99.24 249	97.71 340

Effi-MVS+		98.02 165	97.82 175	98.62 182	98.53 287	97.19 209	97.33 220	99.68 18	97.30 203	96.68 310	97.46 313	98.56 35	99.80 179	96.63 187	98.20 317	98.86 271

MSLP-MVS++		98.02 165	98.14 150	97.64 261	98.58 280	95.19 265	97.48 208	99.23 172	97.47 182	97.90 243	98.62 217	97.04 145	98.81 365	97.55 117	99.41 221	98.94 261

EIA-MVS		98.00 167	97.74 179	98.80 160	98.72 252	98.09 135	98.05 146	99.60 27	97.39 194	96.63 312	95.55 352	97.68 94	99.80 179	96.73 179	99.27 244	98.52 303

MCST-MVS		98.00 167	97.63 189	99.10 113	99.24 138	98.17 130	96.89 253	98.73 275	95.66 271	97.92 241	97.70 298	97.17 140	99.66 267	96.18 222	99.23 250	99.47 129

K. test v3		98.00 167	97.66 186	99.03 130	99.79 19	97.56 187	99.19 43	92.47 364	99.62 17	99.52 36	99.66 17	89.61 298	99.96 10	99.25 20	99.81 76	99.56 76

HQP_MVS		97.99 170	97.67 183	98.93 142	99.19 152	97.65 183	97.77 177	99.27 159	98.20 131	97.79 251	97.98 280	94.90 233	99.70 243	94.42 278	99.51 202	99.45 135

MDA-MVSNet-bldmvs		97.94 171	97.91 169	98.06 238	99.44 103	94.96 271	96.63 267	99.15 200	98.35 115	98.83 157	99.11 102	94.31 251	99.85 113	96.60 188	98.72 299	99.37 169

test_part1		97.91 172	97.46 202	99.27 86	98.80 243	98.18 128	99.07 54	99.36 111	99.75 5	99.63 26	99.49 42	82.20 350	99.89 63	98.87 43	99.95 16	99.74 25

Anonymous202405211		97.90 173	97.50 196	99.08 117	98.90 220	98.25 120	98.53 96	96.16 342	98.87 89	99.11 101	98.86 167	90.40 294	99.78 204	97.36 127	99.31 237	99.19 221

LF4IMVS		97.90 173	97.69 182	98.52 199	99.17 161	97.66 182	97.19 234	99.47 78	96.31 252	97.85 247	98.20 264	96.71 170	99.52 311	94.62 270	99.72 121	98.38 311

UnsupCasMVSNet_eth		97.89 175	97.60 192	98.75 170	99.31 126	97.17 211	97.62 192	99.35 117	98.72 97	98.76 169	98.68 201	92.57 281	99.74 228	97.76 112	95.60 361	99.34 181

TinyColmap		97.89 175	97.98 163	97.60 263	98.86 229	94.35 285	96.21 288	99.44 86	97.45 189	99.06 110	98.88 163	97.99 76	99.28 346	94.38 282	99.58 180	99.18 223

OMC-MVS		97.88 177	97.49 197	99.04 129	98.89 225	98.63 91	96.94 246	99.25 165	95.02 284	98.53 200	98.51 230	97.27 132	99.47 322	93.50 308	99.51 202	99.01 246

CANet		97.87 178	97.76 177	98.19 228	97.75 332	95.51 254	96.76 260	99.05 215	97.74 160	96.93 296	98.21 263	95.59 216	99.89 63	97.86 105	99.93 28	99.19 221

xiu_mvs_v1_base_debu		97.86 179	98.17 143	96.92 296	98.98 204	93.91 299	96.45 275	99.17 191	97.85 155	98.41 210	97.14 327	98.47 38	99.92 39	98.02 93	99.05 275	96.92 352

xiu_mvs_v1_base		97.86 179	98.17 143	96.92 296	98.98 204	93.91 299	96.45 275	99.17 191	97.85 155	98.41 210	97.14 327	98.47 38	99.92 39	98.02 93	99.05 275	96.92 352

xiu_mvs_v1_base_debi		97.86 179	98.17 143	96.92 296	98.98 204	93.91 299	96.45 275	99.17 191	97.85 155	98.41 210	97.14 327	98.47 38	99.92 39	98.02 93	99.05 275	96.92 352

NCCC		97.86 179	97.47 201	99.05 127	98.61 275	98.07 141	96.98 244	98.90 242	97.63 167	97.04 293	97.93 285	95.99 201	99.66 267	95.31 256	98.82 295	99.43 144

PMVS	91.26 20	97.86 179	97.94 167	97.65 259	99.71 32	97.94 159	98.52 97	98.68 278	98.99 79	97.52 271	99.35 62	97.41 123	98.18 369	91.59 337	99.67 148	96.82 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IterMVS-SCA-FT		97.85 184	98.18 142	96.87 299	99.27 133	91.16 345	95.53 316	99.25 165	99.10 67	99.41 51	99.35 62	93.10 271	99.96 10	98.65 58	99.94 24	99.49 111

D2MVS		97.84 185	97.84 174	97.83 248	99.14 168	94.74 275	96.94 246	98.88 245	95.84 267	98.89 146	98.96 142	94.40 249	99.69 247	97.55 117	99.95 16	99.05 238

CPTT-MVS		97.84 185	97.36 207	99.27 86	99.31 126	98.46 107	98.29 120	99.27 159	94.90 288	97.83 248	98.37 249	94.90 233	99.84 131	93.85 299	99.54 192	99.51 103

mvs-test1		97.83 187	97.48 200	98.89 148	98.02 320	99.20 33	97.20 231	99.16 194	98.29 122	96.46 322	97.17 324	96.44 182	99.92 39	96.66 185	97.90 330	97.54 346

mvs_anonymous		97.83 187	98.16 146	96.87 299	98.18 312	91.89 332	97.31 222	98.90 242	97.37 196	98.83 157	99.46 46	96.28 190	99.79 192	98.90 40	98.16 320	98.95 257

testtj		97.79 189	97.25 213	99.42 58	99.03 194	98.85 75	97.78 174	99.18 185	95.83 268	98.12 229	98.50 233	95.50 220	99.86 98	92.23 330	99.07 274	99.54 88

h-mvs33		97.77 190	97.33 211	99.10 113	99.21 145	97.84 166	98.35 118	98.57 284	99.11 60	98.58 191	99.02 121	88.65 308	99.96 10	98.11 86	96.34 354	99.49 111

IterMVS		97.73 191	98.11 152	96.57 306	99.24 138	90.28 346	95.52 318	99.21 174	98.86 90	99.33 66	99.33 66	93.11 270	99.94 25	98.49 67	99.94 24	99.48 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		97.71 192	97.52 195	98.28 222	98.91 219	96.82 222	94.42 347	99.37 107	97.65 166	98.37 215	98.29 258	97.40 124	99.33 339	94.09 290	99.22 251	98.68 297

CDS-MVSNet		97.69 193	97.35 208	98.69 174	98.73 250	97.02 217	96.92 250	98.75 272	95.89 266	98.59 189	98.67 203	92.08 286	99.74 228	96.72 180	99.81 76	99.32 189
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 194	97.75 178	97.45 275	98.23 310	93.78 305	97.29 223	98.84 256	96.10 258	98.64 180	98.65 208	96.04 195	99.36 335	96.84 169	99.14 265	99.20 216

Fast-Effi-MVS+		97.67 195	97.38 205	98.57 190	98.71 255	97.43 194	97.23 227	99.45 83	94.82 290	96.13 326	96.51 335	98.52 37	99.91 49	96.19 220	98.83 294	98.37 313

EU-MVSNet		97.66 196	98.50 95	95.13 335	99.63 50	85.84 364	98.35 118	98.21 299	98.23 127	99.54 31	99.46 46	95.02 231	99.68 256	98.24 80	99.87 57	99.87 4

MVS_0304		97.64 197	97.35 208	98.52 199	97.87 328	96.69 228	98.59 90	98.05 308	97.44 190	93.74 363	98.85 170	93.69 265	99.88 74	98.11 86	99.81 76	98.98 251

pmmvs5		97.64 197	97.49 197	98.08 236	99.14 168	95.12 268	96.70 264	99.05 215	93.77 311	98.62 183	98.83 176	93.23 267	99.75 224	98.33 78	99.76 107	99.36 175

N_pmnet		97.63 199	97.17 218	98.99 136	99.27 133	97.86 164	95.98 294	93.41 361	95.25 282	99.47 43	98.90 154	95.63 214	99.85 113	96.91 158	99.73 114	99.27 203

YYNet1		97.60 200	97.67 183	97.39 279	99.04 191	93.04 317	95.27 323	98.38 294	97.25 208	98.92 141	98.95 146	95.48 222	99.73 232	96.99 152	98.74 297	99.41 150

MDA-MVSNet_test_wron		97.60 200	97.66 186	97.41 278	99.04 191	93.09 313	95.27 323	98.42 291	97.26 207	98.88 150	98.95 146	95.43 223	99.73 232	97.02 149	98.72 299	99.41 150

pmmvs4		97.58 202	97.28 212	98.51 201	98.84 234	96.93 220	95.40 322	98.52 287	93.60 313	98.61 185	98.65 208	95.10 230	99.60 286	96.97 155	99.79 89	98.99 250

ETH3D cwj APD-0.16		97.55 203	97.00 227	99.19 100	98.51 288	98.64 90	96.85 254	99.13 201	94.19 304	97.65 259	98.40 243	95.78 210	99.81 170	93.37 311	99.16 261	99.12 232

PVSNet_BlendedMVS		97.55 203	97.53 194	97.60 263	98.92 216	93.77 306	96.64 266	99.43 92	94.49 294	97.62 261	99.18 86	96.82 160	99.67 259	94.73 267	99.93 28	99.36 175

ppachtmachnet_test		97.50 205	97.74 179	96.78 304	98.70 259	91.23 344	94.55 345	99.05 215	96.36 249	99.21 90	98.79 184	96.39 184	99.78 204	96.74 177	99.82 72	99.34 181

FMVSNet3		97.50 205	97.24 215	98.29 221	98.08 318	95.83 247	97.86 168	98.91 241	97.89 152	98.95 133	98.95 146	87.06 313	99.81 170	97.77 108	99.69 137	99.23 211

CHOSEN 1792x2688		97.49 207	97.14 222	98.54 198	99.68 41	96.09 241	96.50 273	99.62 24	91.58 337	98.84 156	98.97 139	92.36 282	99.88 74	96.76 175	99.95 16	99.67 37

CLD-MVS		97.49 207	97.16 219	98.48 204	99.07 183	97.03 216	94.71 337	99.21 174	94.46 296	98.06 235	97.16 325	97.57 106	99.48 320	94.46 275	99.78 93	98.95 257
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_prior3		97.48 209	97.00 227	98.95 139	98.69 263	97.95 157	95.74 309	99.03 220	96.48 245	96.11 327	97.63 302	95.92 206	99.59 290	94.16 284	99.20 254	99.30 196

hse-mvs2		97.46 210	97.07 223	98.64 177	98.73 250	97.33 197	97.45 212	97.64 319	99.11 60	98.58 191	97.98 280	88.65 308	99.79 192	98.11 86	97.39 338	98.81 277

Vis-MVSNet (Re-imp)		97.46 210	97.16 219	98.34 216	99.55 68	96.10 239	98.94 66	98.44 290	98.32 118	98.16 225	98.62 217	88.76 304	99.73 232	93.88 297	99.79 89	99.18 223

jason		97.45 212	97.35 208	97.76 253	99.24 138	93.93 298	95.86 303	98.42 291	94.24 302	98.50 202	98.13 267	94.82 237	99.91 49	97.22 133	99.73 114	99.43 144
jason: jason.
CL-MVSNet_self_test		97.44 213	97.22 216	98.08 236	98.57 282	95.78 249	94.30 350	98.79 265	96.58 243	98.60 187	98.19 265	94.74 243	99.64 274	96.41 208	98.84 293	98.82 274

DSMNet-mixed		97.42 214	97.60 192	96.87 299	99.15 167	91.46 336	98.54 95	99.12 203	92.87 323	97.58 265	99.63 20	96.21 191	99.90 53	95.74 241	99.54 192	99.27 203

USDC		97.41 215	97.40 203	97.44 276	98.94 210	93.67 308	95.17 326	99.53 56	94.03 308	98.97 130	99.10 104	95.29 225	99.34 337	95.84 238	99.73 114	99.30 196

our_test_3		97.39 216	97.73 181	96.34 310	98.70 259	89.78 348	94.61 343	98.97 233	96.50 244	99.04 117	98.85 170	95.98 202	99.84 131	97.26 132	99.67 148	99.41 150

c3_l		97.36 217	97.37 206	97.31 280	98.09 317	93.25 312	95.01 331	99.16 194	97.05 223	98.77 167	98.72 194	92.88 276	99.64 274	96.93 157	99.76 107	99.05 238

alignmvs		97.35 218	96.88 235	98.78 165	98.54 285	98.09 135	97.71 183	97.69 316	99.20 52	97.59 264	95.90 347	88.12 312	99.55 302	98.18 84	98.96 289	98.70 293

Patchmtry		97.35 218	96.97 229	98.50 203	97.31 351	96.47 231	98.18 130	98.92 239	98.95 86	98.78 164	99.37 58	85.44 328	99.85 113	95.96 230	99.83 69	99.17 227

DP-MVS Recon		97.33 220	96.92 232	98.57 190	99.09 178	97.99 147	96.79 257	99.35 117	93.18 318	97.71 255	98.07 276	95.00 232	99.31 341	93.97 292	99.13 268	98.42 310

QAPM		97.31 221	96.81 241	98.82 156	98.80 243	97.49 190	99.06 56	99.19 181	90.22 349	97.69 257	99.16 92	96.91 154	99.90 53	90.89 348	99.41 221	99.07 236

UnsupCasMVSNet_bld		97.30 222	96.92 232	98.45 207	99.28 131	96.78 226	96.20 289	99.27 159	95.42 278	98.28 218	98.30 257	93.16 269	99.71 241	94.99 261	97.37 339	98.87 270

F-COLMAP		97.30 222	96.68 248	99.14 107	99.19 152	98.39 110	97.27 226	99.30 146	92.93 321	96.62 313	98.00 278	95.73 212	99.68 256	92.62 325	98.46 311	99.35 179

1112_ss		97.29 224	96.86 236	98.58 187	99.34 125	96.32 235	96.75 261	99.58 30	93.14 319	96.89 303	97.48 311	92.11 285	99.86 98	96.91 158	99.54 192	99.57 71

CANet_DTU		97.26 225	97.06 224	97.84 247	97.57 339	94.65 280	96.19 290	98.79 265	97.23 214	95.14 349	98.24 260	93.22 268	99.84 131	97.34 128	99.84 63	99.04 242

Patchmatch-RL test		97.26 225	97.02 226	97.99 243	99.52 75	95.53 253	96.13 291	99.71 13	97.47 182	99.27 77	99.16 92	84.30 337	99.62 279	97.89 100	99.77 97	98.81 277

CDPH-MVS		97.26 225	96.66 251	99.07 120	99.00 199	98.15 131	96.03 293	99.01 227	91.21 343	97.79 251	97.85 289	96.89 155	99.69 247	92.75 322	99.38 227	99.39 159

PatchMatch-RL		97.24 228	96.78 242	98.61 184	99.03 194	97.83 167	96.36 281	99.06 211	93.49 316	97.36 283	97.78 292	95.75 211	99.49 317	93.44 309	98.77 296	98.52 303

eth_miper_zixun_eth		97.23 229	97.25 213	97.17 286	98.00 322	92.77 321	94.71 337	99.18 185	97.27 206	98.56 195	98.74 191	91.89 287	99.69 247	97.06 148	99.81 76	99.05 238

sss		97.21 230	96.93 230	98.06 238	98.83 236	95.22 264	96.75 261	98.48 289	94.49 294	97.27 284	97.90 286	92.77 278	99.80 179	96.57 191	99.32 235	99.16 230

LFMVS		97.20 231	96.72 245	98.64 177	98.72 252	96.95 219	98.93 67	94.14 358	99.74 7	98.78 164	99.01 130	84.45 334	99.73 232	97.44 123	99.27 244	99.25 207

HyFIR lowres test		97.19 232	96.60 255	98.96 138	99.62 52	97.28 201	95.17 326	99.50 62	94.21 303	99.01 121	98.32 256	86.61 316	99.99 2	97.10 144	99.84 63	99.60 54

miper_lstm_enhance		97.18 233	97.16 219	97.25 284	98.16 313	92.85 319	95.15 328	99.31 136	97.25 208	98.74 172	98.78 185	90.07 295	99.78 204	97.19 134	99.80 84	99.11 234

CNLPA		97.17 234	96.71 246	98.55 195	98.56 283	98.05 144	96.33 282	98.93 236	96.91 230	97.06 292	97.39 316	94.38 250	99.45 326	91.66 334	99.18 260	98.14 319

xiu_mvs_v2_base		97.16 235	97.49 197	96.17 315	98.54 285	92.46 325	95.45 320	98.84 256	97.25 208	97.48 275	96.49 336	98.31 50	99.90 53	96.34 212	98.68 303	96.15 363

AdaColmap		97.14 236	96.71 246	98.46 206	98.34 302	97.80 173	96.95 245	98.93 236	95.58 272	96.92 297	97.66 299	95.87 208	99.53 307	90.97 345	99.14 265	98.04 322

train_agg		97.10 237	96.45 262	99.07 120	98.71 255	98.08 139	95.96 297	99.03 220	91.64 335	95.85 333	97.53 306	96.47 180	99.76 217	93.67 302	99.16 261	99.36 175

OpenMVS	96.65 7	97.09 238	96.68 248	98.32 217	98.32 303	97.16 212	98.86 72	99.37 107	89.48 353	96.29 325	99.15 96	96.56 175	99.90 53	92.90 316	99.20 254	97.89 327

PS-MVSNAJ		97.08 239	97.39 204	96.16 317	98.56 283	92.46 325	95.24 325	98.85 255	97.25 208	97.49 274	95.99 345	98.07 67	99.90 53	96.37 209	98.67 304	96.12 364

RRT_MVS		97.07 240	96.57 257	98.58 187	95.89 372	96.33 234	97.36 217	98.77 268	97.85 155	99.08 107	99.12 100	82.30 347	99.96 10	98.82 46	99.90 49	99.45 135

miper_ehance_all_eth		97.06 241	97.03 225	97.16 288	97.83 329	93.06 314	94.66 340	99.09 207	95.99 263	98.69 174	98.45 240	92.73 279	99.61 285	96.79 171	99.03 279	98.82 274

agg_prior1		97.06 241	96.40 263	99.03 130	98.68 266	97.99 147	95.76 307	99.01 227	91.73 334	95.59 336	97.50 309	96.49 179	99.77 210	93.71 301	99.14 265	99.34 181

lupinMVS		97.06 241	96.86 236	97.65 259	98.88 226	93.89 302	95.48 319	97.97 309	93.53 314	98.16 225	97.58 304	93.81 261	99.91 49	96.77 174	99.57 184	99.17 227

API-MVS		97.04 244	96.91 234	97.42 277	97.88 327	98.23 125	98.18 130	98.50 288	97.57 173	97.39 281	96.75 332	96.77 164	99.15 355	90.16 351	99.02 282	94.88 369

cl____		97.02 245	96.83 239	97.58 265	97.82 330	94.04 292	94.66 340	99.16 194	97.04 224	98.63 181	98.71 195	88.68 307	99.69 247	97.00 150	99.81 76	99.00 249

DIV-MVS_self_test		97.02 245	96.84 238	97.58 265	97.82 330	94.03 293	94.66 340	99.16 194	97.04 224	98.63 181	98.71 195	88.69 305	99.69 247	97.00 150	99.81 76	99.01 246

RPMNet		97.02 245	96.93 230	97.30 281	97.71 334	94.22 286	98.11 137	99.30 146	99.37 36	96.91 299	99.34 64	86.72 315	99.87 91	97.53 120	97.36 341	97.81 333

HQP-MVS		97.00 248	96.49 261	98.55 195	98.67 268	96.79 223	96.29 284	99.04 218	96.05 259	95.55 340	96.84 330	93.84 259	99.54 305	92.82 319	99.26 247	99.32 189

bset_n11_16_dypcd		96.99 249	96.56 258	98.27 223	99.00 199	95.25 261	92.18 367	94.05 359	98.75 95	99.01 121	98.38 247	88.98 303	99.93 30	98.77 51	99.92 37	99.64 43

new_pmnet		96.99 249	96.76 243	97.67 257	98.72 252	94.89 272	95.95 299	98.20 300	92.62 326	98.55 197	98.54 226	94.88 236	99.52 311	93.96 293	99.44 219	98.59 302

Test_1112_low_res		96.99 249	96.55 259	98.31 219	99.35 123	95.47 256	95.84 306	99.53 56	91.51 339	96.80 308	98.48 238	91.36 289	99.83 146	96.58 189	99.53 196	99.62 48

PVSNet_Blended		96.88 252	96.68 248	97.47 274	98.92 216	93.77 306	94.71 337	99.43 92	90.98 345	97.62 261	97.36 320	96.82 160	99.67 259	94.73 267	99.56 189	98.98 251

MVSTER		96.86 253	96.55 259	97.79 250	97.91 326	94.21 288	97.56 200	98.87 247	97.49 181	99.06 110	99.05 114	80.72 352	99.80 179	98.44 70	99.82 72	99.37 169

BH-untuned		96.83 254	96.75 244	97.08 289	98.74 249	93.33 311	96.71 263	98.26 297	96.72 237	98.44 206	97.37 319	95.20 227	99.47 322	91.89 332	97.43 337	98.44 308

BH-RMVSNet		96.83 254	96.58 256	97.58 265	98.47 291	94.05 291	96.67 265	97.36 322	96.70 239	97.87 245	97.98 280	95.14 229	99.44 327	90.47 350	98.58 309	99.25 207

PAPM_NR		96.82 256	96.32 266	98.30 220	99.07 183	96.69 228	97.48 208	98.76 269	95.81 269	96.61 314	96.47 338	94.12 257	99.17 353	90.82 349	97.78 331	99.06 237

MG-MVS		96.77 257	96.61 253	97.26 283	98.31 304	93.06 314	95.93 300	98.12 305	96.45 247	97.92 241	98.73 192	93.77 263	99.39 332	91.19 344	99.04 278	99.33 187

1121		96.73 258	96.00 271	98.91 145	98.95 209	97.76 175	98.07 142	98.73 275	87.65 361	96.54 315	98.13 267	94.52 246	99.73 232	92.38 328	99.02 282	99.24 210

test_yl		96.69 259	96.29 267	97.90 244	98.28 305	95.24 262	97.29 223	97.36 322	98.21 128	98.17 223	97.86 287	86.27 318	99.55 302	94.87 264	98.32 313	98.89 267

DCV-MVSNet		96.69 259	96.29 267	97.90 244	98.28 305	95.24 262	97.29 223	97.36 322	98.21 128	98.17 223	97.86 287	86.27 318	99.55 302	94.87 264	98.32 313	98.89 267

WTY-MVS		96.67 261	96.27 269	97.87 246	98.81 241	94.61 281	96.77 259	97.92 311	94.94 287	97.12 287	97.74 295	91.11 290	99.82 156	93.89 296	98.15 321	99.18 223

PatchT		96.65 262	96.35 264	97.54 270	97.40 347	95.32 260	97.98 157	96.64 338	99.33 42	96.89 303	99.42 52	84.32 336	99.81 170	97.69 116	97.49 334	97.48 347

TAPA-MVS	96.21 11	96.63 263	95.95 273	98.65 176	98.93 212	98.09 135	96.93 248	99.28 156	83.58 368	98.13 228	97.78 292	96.13 192	99.40 330	93.52 306	99.29 242	98.45 307
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 264	96.25 270	97.71 256	99.04 191	94.66 279	99.16 45	96.92 334	97.23 214	97.87 245	99.10 104	86.11 322	99.65 272	91.65 335	99.21 253	98.82 274

Patchmatch-test		96.55 265	96.34 265	97.17 286	98.35 301	93.06 314	98.40 113	97.79 312	97.33 199	98.41 210	98.67 203	83.68 341	99.69 247	95.16 258	99.31 237	98.77 285

PMMVS		96.51 266	95.98 272	98.09 233	97.53 342	95.84 246	94.92 333	98.84 256	91.58 337	96.05 331	95.58 351	95.68 213	99.66 267	95.59 250	98.09 324	98.76 287

PLC	94.65 16	96.51 266	95.73 277	98.85 153	98.75 248	97.91 160	96.42 278	99.06 211	90.94 346	95.59 336	97.38 317	94.41 248	99.59 290	90.93 346	98.04 328	99.05 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 268	95.77 275	98.69 174	99.48 95	97.43 194	97.84 170	99.55 48	81.42 370	96.51 318	98.58 223	95.53 217	99.67 259	93.41 310	99.58 180	98.98 251

test1111		96.49 269	96.82 240	95.52 329	99.42 108	87.08 360	99.22 36	87.14 374	99.11 60	99.46 44	99.58 27	88.69 305	99.86 98	98.80 47	99.95 16	99.62 48

MAR-MVS		96.47 270	95.70 278	98.79 162	97.92 325	99.12 59	98.28 121	98.60 283	92.16 332	95.54 343	96.17 343	94.77 242	99.52 311	89.62 353	98.23 315	97.72 339
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
ETH3 D test6400		96.46 271	95.59 283	99.08 117	98.88 226	98.21 127	96.53 270	99.18 185	88.87 357	97.08 290	97.79 291	93.64 266	99.77 210	88.92 355	99.40 223	99.28 201

ECVR-MVS		96.42 272	96.61 253	95.85 320	99.38 113	88.18 355	99.22 36	86.00 376	99.08 72	99.36 61	99.57 28	88.47 310	99.82 156	98.52 66	99.95 16	99.54 88

SCA		96.41 273	96.66 251	95.67 324	98.24 308	88.35 353	95.85 305	96.88 335	96.11 257	97.67 258	98.67 203	93.10 271	99.85 113	94.16 284	99.22 251	98.81 277

DPM-MVS		96.32 274	95.59 283	98.51 201	98.76 246	97.21 207	94.54 346	98.26 297	91.94 333	96.37 323	97.25 322	93.06 273	99.43 328	91.42 340	98.74 297	98.89 267

CMPMVS	75.91 23	96.29 275	95.44 288	98.84 154	96.25 368	98.69 89	97.02 241	99.12 203	88.90 356	97.83 248	98.86 167	89.51 299	98.90 363	91.92 331	99.51 202	98.92 263
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		96.28 276	95.95 273	97.28 282	97.71 334	94.22 286	98.11 137	98.92 239	92.31 329	96.91 299	99.37 58	85.44 328	99.81 170	97.39 126	97.36 341	97.81 333

CVMVSNet		96.25 277	97.21 217	93.38 352	99.10 175	80.56 378	97.20 231	98.19 302	96.94 228	99.00 124	99.02 121	89.50 300	99.80 179	96.36 211	99.59 174	99.78 14

AUN-MVS		96.24 278	95.45 287	98.60 185	98.70 259	97.22 205	97.38 215	97.65 317	95.95 264	95.53 344	97.96 284	82.11 351	99.79 192	96.31 213	97.44 336	98.80 282

EPNet		96.14 279	95.44 288	98.25 224	90.76 379	95.50 255	97.92 161	94.65 351	98.97 82	92.98 364	98.85 170	89.12 302	99.87 91	95.99 228	99.68 142	99.39 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		96.06 280	97.62 190	91.38 355	98.65 274	98.57 98	98.85 73	96.95 332	96.86 232	99.90 4	99.16 92	99.18 11	98.40 368	89.23 354	99.77 97	77.18 373

miper_enhance_ethall		96.01 281	95.74 276	96.81 303	96.41 366	92.27 329	93.69 359	98.89 244	91.14 344	98.30 216	97.35 321	90.58 292	99.58 295	96.31 213	99.03 279	98.60 300

FMVSNet5		96.01 281	95.20 296	98.41 210	97.53 342	96.10 239	98.74 76	99.50 62	97.22 217	98.03 239	99.04 117	69.80 372	99.88 74	97.27 131	99.71 126	99.25 207

baseline1		95.96 283	95.44 288	97.52 272	98.51 288	93.99 296	98.39 114	96.09 344	98.21 128	98.40 214	97.76 294	86.88 314	99.63 277	95.42 254	89.27 373	98.95 257

HY-MVS	95.94 13	95.90 284	95.35 292	97.55 269	97.95 323	94.79 273	98.81 75	96.94 333	92.28 330	95.17 348	98.57 224	89.90 297	99.75 224	91.20 343	97.33 343	98.10 320

GA-MVS		95.86 285	95.32 293	97.49 273	98.60 277	94.15 290	93.83 357	97.93 310	95.49 276	96.68 310	97.42 315	83.21 342	99.30 343	96.22 218	98.55 310	99.01 246

OpenMVS_ROB	95.38 14	95.84 286	95.18 297	97.81 249	98.41 299	97.15 213	97.37 216	98.62 282	83.86 367	98.65 179	98.37 249	94.29 252	99.68 256	88.41 356	98.62 307	96.60 358

cl22		95.79 287	95.39 291	96.98 293	96.77 360	92.79 320	94.40 348	98.53 286	94.59 293	97.89 244	98.17 266	82.82 346	99.24 348	96.37 209	99.03 279	98.92 263

1314		95.74 288	95.60 282	96.17 315	97.53 342	92.75 322	98.07 142	98.31 296	91.22 342	94.25 355	96.68 333	95.53 217	99.03 357	91.64 336	97.18 344	96.74 356

PVSNet	93.40 17	95.67 289	95.70 278	95.57 327	98.83 236	88.57 351	92.50 364	97.72 314	92.69 325	96.49 321	96.44 339	93.72 264	99.43 328	93.61 303	99.28 243	98.71 291

tttt0517		95.64 290	94.98 301	97.64 261	99.36 119	93.81 304	98.72 79	90.47 370	98.08 140	98.67 176	98.34 253	73.88 369	99.92 39	97.77 108	99.51 202	99.20 216

PatchmatchNet		95.58 291	95.67 280	95.30 334	97.34 349	87.32 358	97.65 190	96.65 337	95.30 281	97.07 291	98.69 199	84.77 331	99.75 224	94.97 262	98.64 305	98.83 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 292	95.12 299	96.86 302	97.54 341	93.94 297	96.49 274	96.53 339	94.36 301	97.03 294	96.61 334	94.26 253	99.16 354	86.91 360	96.31 355	97.47 348

JIA-IIPM		95.52 293	95.03 300	97.00 291	96.85 358	94.03 293	96.93 248	95.82 346	99.20 52	94.63 353	99.71 12	83.09 343	99.60 286	94.42 278	94.64 365	97.36 349

CHOSEN 280x420		95.51 294	95.47 285	95.65 326	98.25 307	88.27 354	93.25 361	98.88 245	93.53 314	94.65 352	97.15 326	86.17 320	99.93 30	97.41 125	99.93 28	98.73 290

ADS-MVSNet2		95.43 295	94.98 301	96.76 305	98.14 314	91.74 333	97.92 161	97.76 313	90.23 347	96.51 318	98.91 151	85.61 325	99.85 113	92.88 317	96.90 347	98.69 294

PAPR		95.29 296	94.47 306	97.75 254	97.50 346	95.14 267	94.89 334	98.71 277	91.39 341	95.35 347	95.48 354	94.57 245	99.14 356	84.95 363	97.37 339	98.97 255

thisisatest0530		95.27 297	94.45 307	97.74 255	99.19 152	94.37 284	97.86 168	90.20 371	97.17 218	98.22 220	97.65 300	73.53 370	99.90 53	96.90 163	99.35 231	98.95 257

ADS-MVSNet		95.24 298	94.93 303	96.18 314	98.14 314	90.10 347	97.92 161	97.32 325	90.23 347	96.51 318	98.91 151	85.61 325	99.74 228	92.88 317	96.90 347	98.69 294

RRT_test8_iter05		95.24 298	95.13 298	95.57 327	97.32 350	87.02 361	97.99 155	99.41 96	98.06 141	99.12 99	99.05 114	66.85 377	99.85 113	98.93 39	99.47 213	99.84 8

BH-w/o		95.13 300	94.89 304	95.86 319	98.20 311	91.31 340	95.65 312	97.37 321	93.64 312	96.52 317	95.70 350	93.04 274	99.02 358	88.10 357	95.82 360	97.24 350

tpmrst		95.07 301	95.46 286	93.91 345	97.11 354	84.36 371	97.62 192	96.96 331	94.98 285	96.35 324	98.80 182	85.46 327	99.59 290	95.60 249	96.23 356	97.79 336

pmmvs3		95.03 302	94.40 308	96.93 295	97.70 336	92.53 324	95.08 329	97.71 315	88.57 358	97.71 255	98.08 275	79.39 359	99.82 156	96.19 220	99.11 272	98.43 309

tpmvs		95.02 303	95.25 294	94.33 341	96.39 367	85.87 363	98.08 141	96.83 336	95.46 277	95.51 345	98.69 199	85.91 323	99.53 307	94.16 284	96.23 356	97.58 344

EPNet_dtu		94.93 304	94.78 305	95.38 333	93.58 376	87.68 357	96.78 258	95.69 348	97.35 198	89.14 372	98.09 274	88.15 311	99.49 317	94.95 263	99.30 240	98.98 251
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		94.79 305	94.33 311	96.15 318	96.02 371	92.36 328	92.34 366	99.26 164	85.34 366	95.08 350	94.96 362	92.96 275	98.53 367	94.41 281	98.59 308	97.56 345

tpm		94.67 306	94.34 310	95.66 325	97.68 338	88.42 352	97.88 165	94.90 350	94.46 296	96.03 332	98.56 225	78.66 361	99.79 192	95.88 232	95.01 364	98.78 284

test0.0.03 1		94.51 307	93.69 316	96.99 292	96.05 369	93.61 310	94.97 332	93.49 360	96.17 254	97.57 267	94.88 363	82.30 347	99.01 360	93.60 304	94.17 369	98.37 313

thres600view7		94.45 308	93.83 314	96.29 311	99.06 188	91.53 335	97.99 155	94.24 356	98.34 116	97.44 278	95.01 359	79.84 355	99.67 259	84.33 364	98.23 315	97.66 341

PCF-MVS	92.86 18	94.36 309	93.00 326	98.42 209	98.70 259	97.56 187	93.16 362	99.11 205	79.59 371	97.55 268	97.43 314	92.19 283	99.73 232	79.85 372	99.45 216	97.97 326
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		94.32 310	92.59 328	99.53 36	99.46 98	99.21 27	98.65 83	99.34 123	98.62 102	97.54 269	45.85 374	97.50 115	99.83 146	96.79 171	99.53 196	99.56 76

MVS-HIRNet		94.32 310	95.62 281	90.42 356	98.46 292	75.36 379	96.29 284	89.13 373	95.25 282	95.38 346	99.75 7	92.88 276	99.19 352	94.07 291	99.39 224	96.72 357

ET-MVSNet_ETH3D		94.30 312	93.21 322	97.58 265	98.14 314	94.47 283	94.78 336	93.24 363	94.72 291	89.56 371	95.87 348	78.57 363	99.81 170	96.91 158	97.11 346	98.46 305

thres100view900		94.19 313	93.67 317	95.75 323	99.06 188	91.35 339	98.03 149	94.24 356	98.33 117	97.40 280	94.98 361	79.84 355	99.62 279	83.05 366	98.08 325	96.29 359

E-PMN		94.17 314	94.37 309	93.58 349	96.86 357	85.71 366	90.11 369	97.07 329	98.17 134	97.82 250	97.19 323	84.62 333	98.94 361	89.77 352	97.68 333	96.09 365

thres400		94.14 315	93.44 319	96.24 313	98.93 212	91.44 337	97.60 195	94.29 354	97.94 147	97.10 288	94.31 367	79.67 357	99.62 279	83.05 366	98.08 325	97.66 341

thisisatest0515		94.12 316	93.16 323	96.97 294	98.60 277	92.90 318	93.77 358	90.61 369	94.10 306	96.91 299	95.87 348	74.99 368	99.80 179	94.52 273	99.12 271	98.20 316

tfpn200view9		94.03 317	93.44 319	95.78 322	98.93 212	91.44 337	97.60 195	94.29 354	97.94 147	97.10 288	94.31 367	79.67 357	99.62 279	83.05 366	98.08 325	96.29 359

CostFormer		93.97 318	93.78 315	94.51 340	97.53 342	85.83 365	97.98 157	95.96 345	89.29 355	94.99 351	98.63 215	78.63 362	99.62 279	94.54 272	96.50 352	98.09 321

test-LLR		93.90 319	93.85 313	94.04 343	96.53 362	84.62 369	94.05 354	92.39 365	96.17 254	94.12 357	95.07 357	82.30 347	99.67 259	95.87 235	98.18 318	97.82 331

EMVS		93.83 320	94.02 312	93.23 353	96.83 359	84.96 367	89.77 370	96.32 341	97.92 149	97.43 279	96.36 342	86.17 320	98.93 362	87.68 358	97.73 332	95.81 366

baseline2		93.73 321	92.83 327	96.42 309	97.70 336	91.28 342	96.84 256	89.77 372	93.96 310	92.44 366	95.93 346	79.14 360	99.77 210	92.94 315	96.76 351	98.21 315

thres200		93.72 322	93.14 324	95.46 332	98.66 273	91.29 341	96.61 268	94.63 352	97.39 194	96.83 306	93.71 370	79.88 354	99.56 299	82.40 369	98.13 322	95.54 368

EPMVS		93.72 322	93.27 321	95.09 336	96.04 370	87.76 356	98.13 134	85.01 377	94.69 292	96.92 297	98.64 211	78.47 365	99.31 341	95.04 259	96.46 353	98.20 316

dp		93.47 324	93.59 318	93.13 354	96.64 361	81.62 377	97.66 188	96.42 340	92.80 324	96.11 327	98.64 211	78.55 364	99.59 290	93.31 312	92.18 372	98.16 318

FPMVS		93.44 325	92.23 330	97.08 289	99.25 137	97.86 164	95.61 313	97.16 328	92.90 322	93.76 362	98.65 208	75.94 367	95.66 373	79.30 373	97.49 334	97.73 338

tpm cat1		93.29 326	93.13 325	93.75 347	97.39 348	84.74 368	97.39 214	97.65 317	83.39 369	94.16 356	98.41 242	82.86 345	99.39 332	91.56 338	95.35 363	97.14 351

MVS		93.19 327	92.09 331	96.50 308	96.91 356	94.03 293	98.07 142	98.06 307	68.01 372	94.56 354	96.48 337	95.96 204	99.30 343	83.84 365	96.89 349	96.17 361

tpm2		93.09 328	92.58 329	94.62 339	97.56 340	86.53 362	97.66 188	95.79 347	86.15 364	94.07 359	98.23 262	75.95 366	99.53 307	90.91 347	96.86 350	97.81 333

KD-MVS_2432*1600		92.87 329	91.99 333	95.51 330	91.37 377	89.27 349	94.07 352	98.14 303	95.42 278	97.25 285	96.44 339	67.86 374	99.24 348	91.28 341	96.08 358	98.02 323

miper_refine_blended		92.87 329	91.99 333	95.51 330	91.37 377	89.27 349	94.07 352	98.14 303	95.42 278	97.25 285	96.44 339	67.86 374	99.24 348	91.28 341	96.08 358	98.02 323

DWT-MVSNet_test		92.75 331	92.05 332	94.85 337	96.48 364	87.21 359	97.83 171	94.99 349	92.22 331	92.72 365	94.11 369	70.75 371	99.46 324	95.01 260	94.33 368	97.87 329

MVE	83.40 22	92.50 332	91.92 335	94.25 342	98.83 236	91.64 334	92.71 363	83.52 378	95.92 265	86.46 375	95.46 355	95.20 227	95.40 374	80.51 371	98.64 305	95.73 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		92.39 333	91.89 336	93.89 346	99.38 113	82.28 375	99.32 18	66.03 382	99.08 72	98.77 167	99.57 28	66.26 379	99.84 131	98.71 55	99.95 16	99.54 88

gg-mvs-nofinetune		92.37 334	91.20 339	95.85 320	95.80 373	92.38 327	99.31 22	81.84 379	99.75 5	91.83 368	99.74 8	68.29 373	99.02 358	87.15 359	97.12 345	96.16 362

test-mter		92.33 335	91.76 338	94.04 343	96.53 362	84.62 369	94.05 354	92.39 365	94.00 309	94.12 357	95.07 357	65.63 381	99.67 259	95.87 235	98.18 318	97.82 331

IB-MVS	91.63 19	92.24 336	90.90 340	96.27 312	97.22 353	91.24 343	94.36 349	93.33 362	92.37 328	92.24 367	94.58 366	66.20 380	99.89 63	93.16 314	94.63 366	97.66 341
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		92.19 337	91.77 337	93.46 350	96.48 364	82.80 374	94.05 354	91.52 368	94.45 298	94.00 360	94.88 363	66.65 378	99.56 299	95.78 240	98.11 323	98.02 323

PAPM		91.88 338	90.34 341	96.51 307	98.06 319	92.56 323	92.44 365	97.17 327	86.35 363	90.38 370	96.01 344	86.61 316	99.21 351	70.65 375	95.43 362	97.75 337

PVSNet_0	89.98 21	91.15 339	90.30 342	93.70 348	97.72 333	84.34 372	90.24 368	97.42 320	90.20 350	93.79 361	93.09 371	90.90 291	98.89 364	86.57 361	72.76 375	97.87 329

EGC-MVSNET		85.24 340	80.54 343	99.34 73	99.77 20	99.20 33	99.08 51	99.29 153	12.08 376	20.84 377	99.42 52	97.55 108	99.85 113	97.08 145	99.72 121	98.96 256

test_method		79.78 341	79.50 344	80.62 357	80.21 380	45.76 382	70.82 371	98.41 293	31.08 375	80.89 376	97.71 296	84.85 330	97.37 371	91.51 339	80.03 374	98.75 288

tmp_tt		78.77 342	78.73 345	78.90 358	58.45 381	74.76 381	94.20 351	78.26 381	39.16 374	86.71 374	92.82 372	80.50 353	75.19 377	86.16 362	92.29 371	86.74 372

cdsmvs_eth3d_5k		24.66 343	32.88 346	0.00 361	0.00 384	0.00 385	0.00 372	99.10 206	0.00 379	0.00 380	97.58 304	99.21 10	0.00 380	0.00 378	0.00 378	0.00 376

testmvs		17.12 344	20.53 347	6.87 360	12.05 382	4.20 384	93.62 360	6.73 383	4.62 378	10.41 378	24.33 375	8.28 383	3.56 379	9.69 377	15.07 376	12.86 375

test123		17.04 345	20.11 348	7.82 359	10.25 383	4.91 383	94.80 335	4.47 384	4.93 377	10.00 379	24.28 376	9.69 382	3.64 378	10.14 376	12.43 377	14.92 374

pcd_1.5k_mvsjas		8.17 346	10.90 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	98.07 67	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		8.12 347	10.83 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	97.48 311	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					99.73 25	99.67 2	99.43 10	99.54 52	99.43 31	99.26 81

MSC_two_6792asdad				99.32 78	98.43 295	98.37 112		98.86 252					99.89 63	97.14 140	99.60 170	99.71 27

PC_three_1452									93.27 317	99.40 54	98.54 226	98.22 56	97.00 372	95.17 257	99.45 216	99.49 111

No_MVS				99.32 78	98.43 295	98.37 112		98.86 252					99.89 63	97.14 140	99.60 170	99.71 27

test_one_0601					99.39 112	99.20 33		99.31 136	98.49 110	98.66 178	99.02 121	97.64 100

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					99.01 198	98.84 76		99.07 210	94.10 306	98.05 237	98.12 270	96.36 188	99.86 98	92.70 324	99.19 258

RE-MVS-def			98.58 86		99.20 149	99.38 6	98.48 106	99.30 146	98.64 98	98.95 133	98.96 142	97.75 91		96.56 194	99.39 224	99.45 135

IU-MVS					99.49 87	99.15 48		98.87 247	92.97 320	99.41 51				96.76 175	99.62 162	99.66 38

OPU-MVS				98.82 156	98.59 279	98.30 117	98.10 139				98.52 229	98.18 60	98.75 366	94.62 270	99.48 212	99.41 150

test_241102_TWO								99.30 146	98.03 142	99.26 81	99.02 121	97.51 114	99.88 74	96.91 158	99.60 170	99.66 38

test_241102_ONE					99.49 87	99.17 39		99.31 136	97.98 144	99.66 20	98.90 154	98.36 45	99.48 320

9.14			97.78 176		99.07 183		97.53 203	99.32 130	95.53 275	98.54 199	98.70 198	97.58 105	99.76 217	94.32 283	99.46 214

save fliter					99.11 171	97.97 152	96.53 270	99.02 224	98.24 125

test_0728_THIRD									98.17 134	99.08 107	99.02 121	97.89 80	99.88 74	97.07 146	99.71 126	99.70 32

test_0728_SECOND				99.60 13	99.50 80	99.23 25	98.02 151	99.32 130					99.88 74	96.99 152	99.63 159	99.68 34

test0726					99.50 80	99.21 27	98.17 133	99.35 117	97.97 145	99.26 81	99.06 107	97.61 103

GSMVS																98.81 277

test_part2					99.36 119	99.10 62				99.05 115

sam_mvs1												84.74 332				98.81 277

sam_mvs												84.29 338

ambc				98.24 225	98.82 239	95.97 243	98.62 86	99.00 230		99.27 77	99.21 81	96.99 150	99.50 316	96.55 197	99.50 209	99.26 206

MTGPA								99.20 176

test_post1							97.59 197				20.48 378	83.07 344	99.66 267	94.16 284

test_post											21.25 377	83.86 340	99.70 243

patchmatchnet-post											98.77 187	84.37 335	99.85 113

GG-mvs-BLEND				94.76 338	94.54 375	92.13 331	99.31 22	80.47 380		88.73 373	91.01 373	67.59 376	98.16 370	82.30 370	94.53 367	93.98 370

MTMP							97.93 160	91.91 367

gm-plane-assit					94.83 374	81.97 376			88.07 360		94.99 360		99.60 286	91.76 333

test9_res														93.28 313	99.15 264	99.38 166

TEST9					98.71 255	98.08 139	95.96 297	99.03 220	91.40 340	95.85 333	97.53 306	96.52 177	99.76 217

test_8					98.67 268	98.01 146	95.91 302	99.02 224	91.64 335	95.79 335	97.50 309	96.47 180	99.76 217

agg_prior2														92.50 327	99.16 261	99.37 169

agg_prior					98.68 266	97.99 147		99.01 227		95.59 336			99.77 210

TestCases				99.16 104	99.50 80	98.55 99		99.58 30	96.80 233	98.88 150	99.06 107	97.65 97	99.57 296	94.45 276	99.61 168	99.37 169

test_prior4						97.97 152	95.86 303

test_prior2							95.74 309		96.48 245	96.11 327	97.63 302	95.92 206		94.16 284	99.20 254

test_prior				98.95 139	98.69 263	97.95 157		99.03 220					99.59 290			99.30 196

旧先验2							95.76 307		88.56 359	97.52 271			99.66 267	94.48 274

新几何2							95.93 300

新几何1				98.91 145	98.94 210	97.76 175		98.76 269	87.58 362	96.75 309	98.10 272	94.80 240	99.78 204	92.73 323	99.00 285	99.20 216

旧先验1					98.82 239	97.45 193		98.76 269			98.34 253	95.50 220			99.01 284	99.23 211

无先验							95.74 309	98.74 274	89.38 354				99.73 232	92.38 328		99.22 215

原ACMM2							95.53 316

原ACMM1				98.35 215	98.90 220	96.25 237		98.83 261	92.48 327	96.07 330	98.10 272	95.39 224	99.71 241	92.61 326	98.99 286	99.08 235

test222					98.92 216	96.93 220	95.54 315	98.78 267	85.72 365	96.86 305	98.11 271	94.43 247			99.10 273	99.23 211

testdata2													99.79 192	92.80 321

segment_acmp												97.02 148

testdata				98.09 233	98.93 212	95.40 259		98.80 264	90.08 351	97.45 277	98.37 249	95.26 226	99.70 243	93.58 305	98.95 290	99.17 227

testdata1							95.44 321		96.32 251

test12				98.93 142	98.58 280	97.83 167		98.66 279		96.53 316		95.51 219	99.69 247		99.13 268	99.27 203

plane_prior7					99.19 152	97.87 163

plane_prior6					98.99 203	97.70 181						94.90 233

plane_prior5								99.27 159					99.70 243	94.42 278	99.51 202	99.45 135

plane_prior4											97.98 280

plane_prior3						97.78 174			97.41 192	97.79 251

plane_prior2							97.77 177		98.20 131

plane_prior1					99.05 190

plane_prior						97.65 183	97.07 240		96.72 237						99.36 229

n2								0.00 385

nn								0.00 385

door-mid								99.57 37

lessismore_v0				98.97 137	99.73 25	97.53 189		86.71 375		99.37 59	99.52 38	89.93 296	99.92 39	98.99 37	99.72 121	99.44 140

LGP-MVS_train				99.47 54	99.57 57	98.97 68		99.48 72	96.60 241	99.10 104	99.06 107	98.71 27	99.83 146	95.58 251	99.78 93	99.62 48

test11								98.87 247

door								99.41 96

HQP5-MVS						96.79 223

HQP-NCC					98.67 268		96.29 284		96.05 259	95.55 340

ACMP_Plane					98.67 268		96.29 284		96.05 259	95.55 340

BP-MVS														92.82 319

HQP4-MVS										95.56 339			99.54 305			99.32 189

HQP3-MVS								99.04 218							99.26 247

HQP2-MVS												93.84 259

NP-MVS					98.84 234	97.39 196					96.84 330

MDTV_nov1_ep13_2view						74.92 380	97.69 185		90.06 352	97.75 254		85.78 324		93.52 306		98.69 294

MDTV_nov1_ep13			95.22 295		97.06 355	83.20 373	97.74 181	96.16 342	94.37 300	96.99 295	98.83 176	83.95 339	99.53 307	93.90 295	97.95 329

ACMMP++_ref															99.77 97

ACMMP++															99.68 142

Test By Simon												96.52 177

ITE_SJBPF				98.87 150	99.22 143	98.48 106		99.35 117	97.50 179	98.28 218	98.60 221	97.64 100	99.35 336	93.86 298	99.27 244	98.79 283

DeepMVS_CX				93.44 351	98.24 308	94.21 288		94.34 353	64.28 373	91.34 369	94.87 365	89.45 301	92.77 376	77.54 374	93.14 370	93.35 371

High-resolution multi-view benchmark