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 14	99.69 4	99.58 26	99.90 2	99.86 7	99.78 5	99.58 3	99.95 14	99.00 32	99.95 15	99.78 14

pmmvs6		99.67 3	99.70 3	99.60 13	99.90 4	99.27 20	99.53 7	99.76 6	99.64 12	99.84 8	99.83 2	99.50 5	99.87 80	99.36 14	99.92 33	99.64 39

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 24	99.85 13	99.11 55	99.90 1	99.78 4	99.63 14	99.78 10	99.67 16	99.48 6	99.81 156	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 48	99.88 7	98.61 87	99.34 13	99.71 9	99.27 42	99.90 4	99.74 8	99.68 2	99.97 3	99.55 8	99.99 5	99.88 3

jajsoiax		99.58 6	99.61 7	99.48 50	99.87 10	98.61 87	99.28 27	99.66 16	99.09 62	99.89 6	99.68 14	99.53 4	99.97 3	99.50 10	99.99 5	99.87 4

ANet_high		99.57 7	99.67 5	99.28 79	99.89 6	98.09 125	99.14 40	99.93 1	99.82 3	99.93 2	99.81 3	99.17 12	99.94 22	99.31 16	100.00 1	99.82 9

v7n		99.53 8	99.57 8	99.41 60	99.88 7	98.54 95	99.45 9	99.61 21	99.66 11	99.68 19	99.66 17	98.44 38	99.95 14	99.73 2	99.96 14	99.75 22

test_djsdf		99.52 9	99.51 9	99.53 36	99.86 11	98.74 76	99.39 11	99.56 40	99.11 55	99.70 15	99.73 10	99.00 15	99.97 3	99.26 18	99.98 9	99.89 2

anonymousdsp		99.51 10	99.47 12	99.62 6	99.88 7	99.08 59	99.34 13	99.69 12	98.93 76	99.65 22	99.72 11	98.93 18	99.95 14	99.11 26	100.00 1	99.82 9

UA-Net		99.47 11	99.40 14	99.70 2	99.49 83	99.29 17	99.80 3	99.72 8	99.82 3	99.04 110	99.81 3	98.05 66	99.96 8	98.85 40	99.99 5	99.86 6

PS-MVSNAJss		99.46 12	99.49 10	99.35 69	99.90 4	98.15 121	99.20 32	99.65 17	99.48 24	99.92 3	99.71 12	98.07 63	99.96 8	99.53 9	100.00 1	99.93 1

pm-mvs1		99.44 13	99.48 11	99.33 74	99.80 17	98.63 84	99.29 23	99.63 18	99.30 40	99.65 22	99.60 25	99.16 14	99.82 143	99.07 28	99.83 61	99.56 71

TransMVSNet (Re)		99.44 13	99.47 12	99.36 64	99.80 17	98.58 90	99.27 29	99.57 33	99.39 32	99.75 12	99.62 21	99.17 12	99.83 133	99.06 29	99.62 151	99.66 34

DTE-MVSNet		99.43 15	99.35 17	99.66 4	99.71 29	99.30 16	99.31 18	99.51 54	99.64 12	99.56 28	99.46 42	98.23 51	99.97 3	98.78 43	99.93 24	99.72 25

TDRefinement		99.42 16	99.38 15	99.55 26	99.76 21	99.33 15	99.68 5	99.71 9	99.38 33	99.53 33	99.61 23	98.64 27	99.80 165	98.24 71	99.84 55	99.52 93

PEN-MVS		99.41 17	99.34 19	99.62 6	99.73 23	99.14 48	99.29 23	99.54 47	99.62 17	99.56 28	99.42 48	98.16 59	99.96 8	98.78 43	99.93 24	99.77 16

nrg030		99.40 18	99.35 17	99.54 29	99.58 50	99.13 51	98.98 54	99.48 66	99.68 9	99.46 43	99.26 68	98.62 28	99.73 217	99.17 25	99.92 33	99.76 20

PS-CasMVS		99.40 18	99.33 20	99.62 6	99.71 29	99.10 56	99.29 23	99.53 50	99.53 23	99.46 43	99.41 50	98.23 51	99.95 14	98.89 38	99.95 15	99.81 11

MIMVSNet1		99.38 20	99.32 21	99.55 26	99.86 11	99.19 34	99.41 10	99.59 24	99.59 20	99.71 14	99.57 27	97.12 133	99.90 46	99.21 22	99.87 51	99.54 83

OurMVSNet-221017-0		99.37 21	99.31 22	99.53 36	99.91 3	98.98 61	99.63 6	99.58 26	99.44 29	99.78 10	99.76 6	96.39 175	99.92 33	99.44 13	99.92 33	99.68 31

Vis-MVSNet		99.34 22	99.36 16	99.27 82	99.73 23	98.26 109	99.17 37	99.78 4	99.11 55	99.27 72	99.48 40	98.82 20	99.95 14	98.94 34	99.93 24	99.59 55
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 29	99.24 23	99.32 15	99.55 43	99.46 27	99.50 39	99.34 59	97.30 122	99.93 26	98.90 36	99.93 24	99.77 16

VPA-MVSNet		99.30 24	99.30 23	99.28 79	99.49 83	98.36 106	99.00 51	99.45 77	99.63 14	99.52 35	99.44 47	98.25 49	99.88 64	99.09 27	99.84 55	99.62 44

Anonymous20231211		99.27 25	99.27 24	99.26 85	99.29 120	98.18 118	99.49 8	99.51 54	99.70 8	99.80 9	99.68 14	96.84 148	99.83 133	99.21 22	99.91 39	99.77 16

FC-MVSNet-test		99.27 25	99.25 25	99.34 72	99.77 20	98.37 105	99.30 22	99.57 33	99.61 19	99.40 52	99.50 35	97.12 133	99.85 101	99.02 31	99.94 20	99.80 12

DIV-MVS_2432*1600		99.25 27	99.18 28	99.44 56	99.63 47	99.06 60	98.69 70	99.54 47	99.31 38	99.62 27	99.53 32	97.36 120	99.86 87	99.24 21	99.71 115	99.39 147

ACMH	96.65 7	99.25 27	99.24 26	99.26 85	99.72 28	98.38 104	99.07 45	99.55 43	98.30 106	99.65 22	99.45 46	99.22 9	99.76 202	98.44 63	99.77 89	99.64 39
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.21 29	99.09 34	99.56 24	99.65 42	98.96 65	99.13 41	99.34 116	99.42 30	99.33 62	99.26 68	97.01 140	99.94 22	98.74 48	99.93 24	99.79 13

TranMVSNet+NR-MVSNet		99.17 30	99.07 36	99.46 55	99.37 108	98.87 67	98.39 102	99.42 88	99.42 30	99.36 58	99.06 100	98.38 41	99.95 14	98.34 68	99.90 43	99.57 66

FMVSNet1		99.17 30	99.17 29	99.17 93	99.55 64	98.24 111	99.20 32	99.44 80	99.21 44	99.43 47	99.55 29	97.82 82	99.86 87	98.42 65	99.89 47	99.41 138

FIs		99.14 32	99.09 34	99.29 77	99.70 35	98.28 108	99.13 41	99.52 53	99.48 24	99.24 79	99.41 50	96.79 154	99.82 143	98.69 51	99.88 48	99.76 20

XXY-MVS		99.14 32	99.15 32	99.10 105	99.76 21	97.74 168	98.85 63	99.62 19	98.48 99	99.37 56	99.49 38	98.75 23	99.86 87	98.20 74	99.80 76	99.71 26

ACMH+	96.62 9	99.08 34	99.00 39	99.33 74	99.71 29	98.83 70	98.60 76	99.58 26	99.11 55	99.53 33	99.18 79	98.81 21	99.67 244	96.71 166	99.77 89	99.50 100

Gipuma		99.03 35	99.16 30	98.64 168	99.94 2	98.51 97	99.32 15	99.75 7	99.58 22	98.60 177	99.62 21	98.22 54	99.51 300	97.70 102	99.73 105	97.89 309
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v8		99.01 36	99.16 30	98.57 180	99.47 93	96.31 225	98.90 58	99.47 72	99.03 65	99.52 35	99.57 27	96.93 144	99.81 156	99.60 4	99.98 9	99.60 49

HPM-MVS_fast		99.01 36	98.82 47	99.57 18	99.71 29	99.35 11	99.00 51	99.50 56	97.33 184	98.94 132	98.86 157	98.75 23	99.82 143	97.53 108	99.71 115	99.56 71

APDe-MVS		98.99 38	98.79 50	99.60 13	99.21 134	99.15 45	98.87 60	99.48 66	97.57 158	99.35 59	99.24 71	97.83 79	99.89 55	97.88 91	99.70 120	99.75 22

abl_6		98.99 38	98.78 51	99.61 9	99.45 97	99.46 3	98.60 76	99.50 56	98.59 93	99.24 79	99.04 110	98.54 33	99.89 55	96.45 188	99.62 151	99.50 100

EG-PatchMatch MVS		98.99 38	99.01 38	98.94 132	99.50 76	97.47 181	98.04 136	99.59 24	98.15 123	99.40 52	99.36 56	98.58 31	99.76 202	98.78 43	99.68 131	99.59 55

COLMAP_ROB	96.50 10	98.99 38	98.85 45	99.41 60	99.58 50	99.10 56	98.74 66	99.56 40	99.09 62	99.33 62	99.19 77	98.40 40	99.72 225	95.98 214	99.76 98	99.42 136
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 42	98.86 44	99.36 64	99.82 16	98.55 92	97.47 198	99.57 33	99.37 34	99.21 83	99.61 23	96.76 157	99.83 133	98.06 80	99.83 61	99.71 26

v10		98.97 43	99.11 33	98.55 185	99.44 99	96.21 227	98.90 58	99.55 43	98.73 85	99.48 40	99.60 25	96.63 164	99.83 133	99.70 3	99.99 5	99.61 48

DeepC-MVS	97.60 4	98.97 43	98.93 41	99.10 105	99.35 113	97.98 142	98.01 142	99.46 74	97.56 160	99.54 30	99.50 35	98.97 16	99.84 118	98.06 80	99.92 33	99.49 104
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 45	99.02 37	98.76 159	99.38 106	97.26 191	98.49 91	99.50 56	98.86 79	99.19 85	99.06 100	98.23 51	99.69 232	98.71 50	99.76 98	99.33 175

casdiffmvs		98.95 46	99.00 39	98.81 149	99.38 106	97.33 187	97.82 160	99.57 33	99.17 52	99.35 59	99.17 83	98.35 45	99.69 232	98.46 62	99.73 105	99.41 138

NR-MVSNet		98.95 46	98.82 47	99.36 64	99.16 151	98.72 81	99.22 31	99.20 166	99.10 59	99.72 13	98.76 178	96.38 177	99.86 87	98.00 85	99.82 64	99.50 100

Anonymous20240529		98.93 48	98.87 43	99.12 101	99.19 140	98.22 116	99.01 49	98.99 221	99.25 43	99.54 30	99.37 53	97.04 136	99.80 165	97.89 88	99.52 186	99.35 167

DP-MVS		98.93 48	98.81 49	99.28 79	99.21 134	98.45 101	98.46 96	99.33 121	99.63 14	99.48 40	99.15 89	97.23 130	99.75 209	97.17 123	99.66 142	99.63 43

SED-MVS		98.91 50	98.72 57	99.49 48	99.49 83	99.17 36	98.10 127	99.31 128	98.03 127	99.66 20	99.02 114	98.36 42	99.88 64	96.91 142	99.62 151	99.41 138

ACMM	96.08 12	98.91 50	98.73 55	99.48 50	99.55 64	99.14 48	98.07 130	99.37 100	97.62 153	99.04 110	98.96 132	98.84 19	99.79 178	97.43 112	99.65 143	99.49 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		98.90 52	98.90 42	98.91 136	99.67 39	97.82 160	99.00 51	99.44 80	99.45 28	99.51 38	99.24 71	98.20 56	99.86 87	95.92 216	99.69 126	99.04 230

MTAPA		98.88 53	98.64 69	99.61 9	99.67 39	99.36 9	98.43 99	99.20 166	98.83 82	98.89 138	98.90 144	96.98 142	99.92 33	97.16 124	99.70 120	99.56 71

VPNet		98.87 54	98.83 46	99.01 125	99.70 35	97.62 176	98.43 99	99.35 110	99.47 26	99.28 70	99.05 107	96.72 160	99.82 143	98.09 78	99.36 214	99.59 55

UniMVSNet (Re)		98.87 54	98.71 59	99.35 69	99.24 127	98.73 79	97.73 170	99.38 96	98.93 76	99.12 92	98.73 181	96.77 155	99.86 87	98.63 53	99.80 76	99.46 120

UniMVSNet_NR-MVSNet		98.86 56	98.68 64	99.40 62	99.17 149	98.74 76	97.68 174	99.40 92	99.14 53	99.06 103	98.59 211	96.71 161	99.93 26	98.57 56	99.77 89	99.53 89

APD-MVS_3200maxsize		98.84 57	98.61 74	99.53 36	99.19 140	99.27 20	98.49 91	99.33 121	98.64 87	99.03 113	98.98 127	97.89 76	99.85 101	96.54 182	99.42 205	99.46 120

PM-MVS		98.82 58	98.72 57	99.12 101	99.64 45	98.54 95	97.98 145	99.68 13	97.62 153	99.34 61	99.18 79	97.54 102	99.77 195	97.79 94	99.74 102	99.04 230

DU-MVS		98.82 58	98.63 70	99.39 63	99.16 151	98.74 76	97.54 190	99.25 155	98.84 81	99.06 103	98.76 178	96.76 157	99.93 26	98.57 56	99.77 89	99.50 100

SR-MVS-dyc-post		98.81 60	98.55 80	99.57 18	99.20 137	99.38 5	98.48 94	99.30 137	98.64 87	98.95 126	98.96 132	97.49 111	99.86 87	96.56 178	99.39 209	99.45 124

3Dnovator	98.27 2	98.81 60	98.73 55	99.05 118	98.76 232	97.81 162	99.25 30	99.30 137	98.57 97	98.55 185	99.33 61	97.95 75	99.90 46	97.16 124	99.67 137	99.44 129

zzz-MVS		98.79 62	98.52 83	99.61 9	99.67 39	99.36 9	97.33 206	99.20 166	98.83 82	98.89 138	98.90 144	96.98 142	99.92 33	97.16 124	99.70 120	99.56 71

HPM-MVS		98.79 62	98.53 82	99.59 17	99.65 42	99.29 17	99.16 38	99.43 85	96.74 222	98.61 175	98.38 234	98.62 28	99.87 80	96.47 186	99.67 137	99.59 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		98.79 62	98.54 81	99.54 29	99.73 23	99.16 40	98.23 112	99.31 128	97.92 134	98.90 135	98.90 144	98.00 69	99.88 64	96.15 208	99.72 111	99.58 61
Skip Steuart: Steuart Systems R&D Blog.
V42		98.78 65	98.78 51	98.76 159	99.44 99	97.04 204	98.27 109	99.19 171	97.87 138	99.25 78	99.16 85	96.84 148	99.78 189	99.21 22	99.84 55	99.46 120

test20.03		98.78 65	98.77 53	98.78 156	99.46 94	97.20 197	97.78 162	99.24 160	99.04 64	99.41 49	98.90 144	97.65 92	99.76 202	97.70 102	99.79 81	99.39 147

DVP-MVS		98.77 67	98.52 83	99.52 41	99.50 76	99.21 26	98.02 139	98.84 244	97.97 130	99.08 100	99.02 114	97.61 97	99.88 64	96.99 136	99.63 148	99.48 110
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 68	98.49 90	99.57 18	99.18 147	99.37 8	98.39 102	99.31 128	98.43 100	98.90 135	98.88 153	97.49 111	99.86 87	96.43 190	99.37 213	99.48 110

test_0402		98.76 68	98.71 59	98.93 133	99.56 61	98.14 123	98.45 98	99.34 116	99.28 41	98.95 126	98.91 141	98.34 46	99.79 178	95.63 233	99.91 39	98.86 258

ACMMP_NAP		98.75 70	98.48 92	99.57 18	99.58 50	99.29 17	97.82 160	99.25 155	96.94 214	98.78 156	99.12 93	98.02 67	99.84 118	97.13 128	99.67 137	99.59 55

SixPastTwentyTwo		98.75 70	98.62 71	99.16 96	99.83 15	97.96 147	99.28 27	98.20 286	99.37 34	99.70 15	99.65 19	92.65 270	99.93 26	99.04 30	99.84 55	99.60 49

ACMMP		98.75 70	98.50 87	99.52 41	99.56 61	99.16 40	98.87 60	99.37 100	97.16 204	98.82 153	99.01 120	97.71 88	99.87 80	96.29 200	99.69 126	99.54 83
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 73	98.68 64	98.89 139	99.02 182	97.22 194	97.17 221	99.06 201	99.21 44	99.17 90	98.85 160	97.45 114	99.86 87	98.48 61	99.70 120	99.60 49

XVS		98.72 74	98.45 98	99.53 36	99.46 94	99.21 26	98.65 71	99.34 116	98.62 91	97.54 253	98.63 204	97.50 108	99.83 133	96.79 155	99.53 183	99.56 71

SR-MVS		98.71 75	98.43 102	99.57 18	99.18 147	99.35 11	98.36 105	99.29 144	98.29 109	98.88 142	98.85 160	97.53 104	99.87 80	96.14 209	99.31 222	99.48 110

HFP-MVS		98.71 75	98.44 100	99.51 45	99.49 83	99.16 40	98.52 85	99.31 128	97.47 167	98.58 181	98.50 221	97.97 73	99.85 101	96.57 175	99.59 161	99.53 89

LPG-MVS_test		98.71 75	98.46 96	99.47 53	99.57 54	98.97 62	98.23 112	99.48 66	96.60 227	99.10 97	99.06 100	98.71 25	99.83 133	95.58 236	99.78 85	99.62 44

ACMMPR		98.70 78	98.42 104	99.54 29	99.52 71	99.14 48	98.52 85	99.31 128	97.47 167	98.56 183	98.54 215	97.75 86	99.88 64	96.57 175	99.59 161	99.58 61

CP-MVS		98.70 78	98.42 104	99.52 41	99.36 109	99.12 53	98.72 68	99.36 104	97.54 162	98.30 203	98.40 230	97.86 78	99.89 55	96.53 183	99.72 111	99.56 71

region2R		98.69 80	98.40 106	99.54 29	99.53 69	99.17 36	98.52 85	99.31 128	97.46 172	98.44 193	98.51 218	97.83 79	99.88 64	96.46 187	99.58 167	99.58 61

EI-MVSNet-UG-set		98.69 80	98.71 59	98.62 172	99.10 163	96.37 222	97.23 213	98.87 237	99.20 47	99.19 85	98.99 123	97.30 122	99.85 101	98.77 46	99.79 81	99.65 38

3Dnovator+	97.89 3	98.69 80	98.51 85	99.24 88	98.81 227	98.40 102	99.02 48	99.19 171	98.99 68	98.07 218	99.28 64	97.11 135	99.84 118	96.84 153	99.32 220	99.47 118

ZNCC-MVS		98.68 83	98.40 106	99.54 29	99.57 54	99.21 26	98.46 96	99.29 144	97.28 190	98.11 215	98.39 232	98.00 69	99.87 80	96.86 152	99.64 145	99.55 79

EI-MVSNet-Vis-set		98.68 83	98.70 62	98.63 170	99.09 166	96.40 221	97.23 213	98.86 242	99.20 47	99.18 89	98.97 129	97.29 124	99.85 101	98.72 49	99.78 85	99.64 39

CSCG		98.68 83	98.50 87	99.20 91	99.45 97	98.63 84	98.56 81	99.57 33	97.87 138	98.85 146	98.04 263	97.66 91	99.84 118	96.72 164	99.81 68	99.13 219

PGM-MVS		98.66 86	98.37 112	99.55 26	99.53 69	99.18 35	98.23 112	99.49 64	97.01 212	98.69 165	98.88 153	98.00 69	99.89 55	95.87 220	99.59 161	99.58 61

GBi-Net		98.65 87	98.47 94	99.17 93	98.90 206	98.24 111	99.20 32	99.44 80	98.59 93	98.95 126	99.55 29	94.14 245	99.86 87	97.77 96	99.69 126	99.41 138

test1		98.65 87	98.47 94	99.17 93	98.90 206	98.24 111	99.20 32	99.44 80	98.59 93	98.95 126	99.55 29	94.14 245	99.86 87	97.77 96	99.69 126	99.41 138

LCM-MVSNet-Re		98.64 89	98.48 92	99.11 103	98.85 217	98.51 97	98.49 91	99.83 3	98.37 101	99.69 17	99.46 42	98.21 55	99.92 33	94.13 273	99.30 225	98.91 253

mPP-MVS		98.64 89	98.34 116	99.54 29	99.54 67	99.17 36	98.63 73	99.24 160	97.47 167	98.09 217	98.68 190	97.62 96	99.89 55	96.22 203	99.62 151	99.57 66

TSAR-MVS + MP.		98.63 91	98.49 90	99.06 116	99.64 45	97.90 152	98.51 89	98.94 224	96.96 213	99.24 79	98.89 152	97.83 79	99.81 156	96.88 149	99.49 197	99.48 110
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 91	98.38 111	99.36 64	97.25 335	99.38 5	99.12 43	99.32 123	99.21 44	98.44 193	98.88 153	97.31 121	99.80 165	96.58 173	99.34 218	98.92 250

RPSCF		98.62 93	98.36 113	99.42 57	99.65 42	99.42 4	98.55 82	99.57 33	97.72 147	98.90 135	99.26 68	96.12 184	99.52 296	95.72 227	99.71 115	99.32 177

GST-MVS		98.61 94	98.30 121	99.52 41	99.51 73	99.20 32	98.26 110	99.25 155	97.44 175	98.67 167	98.39 232	97.68 89	99.85 101	96.00 212	99.51 189	99.52 93

Regformer-3		98.61 94	98.61 74	98.63 170	99.02 182	96.53 219	97.17 221	98.84 244	99.13 54	99.10 97	98.85 160	97.24 129	99.79 178	98.41 66	99.70 120	99.57 66

v1192		98.60 96	98.66 67	98.41 201	99.27 122	95.88 234	97.52 192	99.36 104	97.41 177	99.33 62	99.20 76	96.37 178	99.82 143	99.57 6	99.92 33	99.55 79

v1144		98.60 96	98.66 67	98.41 201	99.36 109	95.90 233	97.58 186	99.34 116	97.51 163	99.27 72	99.15 89	96.34 180	99.80 165	99.47 12	99.93 24	99.51 96

Regformer-2		98.60 96	98.46 96	99.02 124	98.85 217	97.71 170	96.91 237	99.09 197	98.98 70	99.01 114	98.64 200	97.37 119	99.84 118	97.75 101	99.57 171	99.52 93

DPE-MVS		98.59 99	98.26 125	99.57 18	99.27 122	99.15 45	97.01 228	99.39 94	97.67 149	99.44 46	98.99 123	97.53 104	99.89 55	95.40 240	99.68 131	99.66 34

MP-MVS-pluss		98.57 100	98.23 129	99.60 13	99.69 37	99.35 11	97.16 223	99.38 96	94.87 275	98.97 123	98.99 123	98.01 68	99.88 64	97.29 118	99.70 120	99.58 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 101	98.32 120	99.25 87	99.41 104	98.73 79	97.13 225	99.18 175	97.10 207	98.75 161	98.92 140	98.18 57	99.65 257	96.68 168	99.56 176	99.37 157

VDD-MVS		98.56 101	98.39 109	99.07 111	99.13 158	98.07 131	98.59 78	97.01 315	99.59 20	99.11 94	99.27 66	94.82 228	99.79 178	98.34 68	99.63 148	99.34 169

v2v482		98.56 101	98.62 71	98.37 205	99.42 103	95.81 237	97.58 186	99.16 184	97.90 136	99.28 70	99.01 120	95.98 193	99.79 178	99.33 15	99.90 43	99.51 96

XVG-ACMP-BASELINE		98.56 101	98.34 116	99.22 90	99.54 67	98.59 89	97.71 171	99.46 74	97.25 193	98.98 120	98.99 123	97.54 102	99.84 118	95.88 217	99.74 102	99.23 199

Regformer-1		98.55 105	98.44 100	98.87 141	98.85 217	97.29 188	96.91 237	98.99 221	98.97 71	98.99 118	98.64 200	97.26 128	99.81 156	97.79 94	99.57 171	99.51 96

v1240		98.55 105	98.62 71	98.32 208	99.22 132	95.58 240	97.51 194	99.45 77	97.16 204	99.45 45	99.24 71	96.12 184	99.85 101	99.60 4	99.88 48	99.55 79

IterMVS-LS		98.55 105	98.70 62	98.09 222	99.48 91	94.73 263	97.22 216	99.39 94	98.97 71	99.38 54	99.31 63	96.00 189	99.93 26	98.58 54	99.97 11	99.60 49
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 108	98.57 79	98.45 198	99.21 134	95.98 231	97.63 179	99.36 104	97.15 206	99.32 67	99.18 79	95.84 200	99.84 118	99.50 10	99.91 39	99.54 83

v1921920		98.54 108	98.60 76	98.38 204	99.20 137	95.76 239	97.56 188	99.36 104	97.23 199	99.38 54	99.17 83	96.02 187	99.84 118	99.57 6	99.90 43	99.54 83

SF-MVS		98.53 110	98.27 124	99.32 76	99.31 116	98.75 75	98.19 117	99.41 89	96.77 221	98.83 149	98.90 144	97.80 83	99.82 143	95.68 230	99.52 186	99.38 154

XVG-OURS		98.53 110	98.34 116	99.11 103	99.50 76	98.82 72	95.97 281	99.50 56	97.30 188	99.05 108	98.98 127	99.35 7	99.32 325	95.72 227	99.68 131	99.18 211

UGNet		98.53 110	98.45 98	98.79 153	97.94 307	96.96 207	99.08 44	98.54 272	99.10 59	96.82 291	99.47 41	96.55 167	99.84 118	98.56 59	99.94 20	99.55 79
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
#test#		98.50 113	98.16 138	99.51 45	99.49 83	99.16 40	98.03 137	99.31 128	96.30 239	98.58 181	98.50 221	97.97 73	99.85 101	95.68 230	99.59 161	99.53 89

XVG-OURS-SEG-HR		98.49 114	98.28 123	99.14 99	99.49 83	98.83 70	96.54 255	99.48 66	97.32 186	99.11 94	98.61 209	99.33 8	99.30 328	96.23 202	98.38 298	99.28 189

FMVSNet2		98.49 114	98.40 106	98.75 161	98.90 206	97.14 203	98.61 75	99.13 191	98.59 93	99.19 85	99.28 64	94.14 245	99.82 143	97.97 86	99.80 76	99.29 188

pmmvs-eth3d		98.47 116	98.34 116	98.86 143	99.30 119	97.76 165	97.16 223	99.28 146	95.54 259	99.42 48	99.19 77	97.27 125	99.63 262	97.89 88	99.97 11	99.20 204

MP-MVS		98.46 117	98.09 145	99.54 29	99.57 54	99.22 25	98.50 90	99.19 171	97.61 155	97.58 249	98.66 195	97.40 117	99.88 64	94.72 253	99.60 159	99.54 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.45 118	98.60 76	98.00 231	99.44 99	94.98 258	97.44 200	99.06 201	98.30 106	99.32 67	98.97 129	96.65 163	99.62 264	98.37 67	99.85 53	99.39 147

xxxxxxxxxxxxxcwj		98.44 119	98.24 127	99.06 116	99.11 159	97.97 143	96.53 256	99.54 47	98.24 112	98.83 149	98.90 144	97.80 83	99.82 143	95.68 230	99.52 186	99.38 154

AllTest		98.44 119	98.20 131	99.16 96	99.50 76	98.55 92	98.25 111	99.58 26	96.80 219	98.88 142	99.06 100	97.65 92	99.57 281	94.45 260	99.61 157	99.37 157

VNet		98.42 121	98.30 121	98.79 153	98.79 231	97.29 188	98.23 112	98.66 267	99.31 38	98.85 146	98.80 171	94.80 231	99.78 189	98.13 76	99.13 254	99.31 181

ab-mvs		98.41 122	98.36 113	98.59 176	99.19 140	97.23 192	99.32 15	98.81 250	97.66 150	98.62 173	99.40 52	96.82 151	99.80 165	95.88 217	99.51 189	98.75 273

ACMP	95.32 15	98.41 122	98.09 145	99.36 64	99.51 73	98.79 74	97.68 174	99.38 96	95.76 256	98.81 155	98.82 169	98.36 42	99.82 143	94.75 250	99.77 89	99.48 110
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		98.40 124	98.03 152	99.51 45	99.16 151	99.21 26	98.05 134	99.22 163	94.16 291	98.98 120	99.10 97	97.52 106	99.79 178	96.45 188	99.64 145	99.53 89
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		98.40 124	98.00 154	99.61 9	99.57 54	99.25 22	98.57 80	99.35 110	97.55 161	99.31 69	97.71 281	94.61 235	99.88 64	96.14 209	99.19 243	99.70 29
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		98.40 124	98.68 64	97.54 258	98.96 193	97.99 138	97.88 153	99.36 104	98.20 118	99.63 25	99.04 110	98.76 22	95.33 358	96.56 178	99.74 102	99.31 181
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		98.40 124	98.51 85	98.04 229	99.10 163	94.73 263	97.20 217	98.87 237	98.97 71	99.06 103	99.02 114	96.00 189	99.80 165	98.58 54	99.82 64	99.60 49

WR-MVS		98.40 124	98.19 133	99.03 121	99.00 185	97.65 173	96.85 240	98.94 224	98.57 97	98.89 138	98.50 221	95.60 206	99.85 101	97.54 107	99.85 53	99.59 55

new-patchmatchnet		98.35 129	98.74 54	97.18 273	99.24 127	92.23 316	96.42 264	99.48 66	98.30 106	99.69 17	99.53 32	97.44 115	99.82 143	98.84 41	99.77 89	99.49 104

canonicalmvs		98.34 130	98.26 125	98.58 177	98.46 278	97.82 160	98.96 55	99.46 74	99.19 51	97.46 260	95.46 338	98.59 30	99.46 309	98.08 79	98.71 287	98.46 287

testgi		98.32 131	98.39 109	98.13 221	99.57 54	95.54 241	97.78 162	99.49 64	97.37 181	99.19 85	97.65 284	98.96 17	99.49 302	96.50 185	98.99 272	99.34 169

DeepPCF-MVS	96.93 5	98.32 131	98.01 153	99.23 89	98.39 283	98.97 62	95.03 316	99.18 175	96.88 217	99.33 62	98.78 174	98.16 59	99.28 331	96.74 161	99.62 151	99.44 129

MVS_111021_LR		98.30 133	98.12 143	98.83 146	99.16 151	98.03 136	96.09 278	99.30 137	97.58 157	98.10 216	98.24 246	98.25 49	99.34 322	96.69 167	99.65 143	99.12 220

EPP-MVSNet		98.30 133	98.04 151	99.07 111	99.56 61	97.83 157	99.29 23	98.07 292	99.03 65	98.59 179	99.13 92	92.16 274	99.90 46	96.87 150	99.68 131	99.49 104

DeepC-MVS_fast	96.85 6	98.30 133	98.15 140	98.75 161	98.61 261	97.23 192	97.76 167	99.09 197	97.31 187	98.75 161	98.66 195	97.56 101	99.64 259	96.10 211	99.55 178	99.39 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 136	97.95 157	99.34 72	98.44 280	99.16 40	98.12 124	99.38 96	96.01 248	98.06 219	98.43 228	97.80 83	99.67 244	95.69 229	99.58 167	99.20 204

Fast-Effi-MVS+-dtu		98.27 137	98.09 145	98.81 149	98.43 281	98.11 124	97.61 182	99.50 56	98.64 87	97.39 265	97.52 292	98.12 62	99.95 14	96.90 147	98.71 287	98.38 293

DELS-MVS		98.27 137	98.20 131	98.48 195	98.86 215	96.70 216	95.60 300	99.20 166	97.73 146	98.45 192	98.71 184	97.50 108	99.82 143	98.21 73	99.59 161	98.93 249
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		98.26 139	97.90 162	99.35 69	98.02 303	99.49 2	98.02 139	99.16 184	98.29 109	97.64 244	97.99 265	96.44 173	99.95 14	96.66 169	98.93 277	98.60 282

MVSFormer		98.26 139	98.43 102	97.77 240	98.88 212	93.89 289	99.39 11	99.56 40	99.11 55	98.16 210	98.13 253	93.81 251	99.97 3	99.26 18	99.57 171	99.43 133

MVS_111021_HR		98.25 141	98.08 148	98.75 161	99.09 166	97.46 182	95.97 281	99.27 149	97.60 156	97.99 224	98.25 245	98.15 61	99.38 319	96.87 150	99.57 171	99.42 136

TAMVS		98.24 142	98.05 150	98.80 151	99.07 170	97.18 199	97.88 153	98.81 250	96.66 226	99.17 90	99.21 74	94.81 230	99.77 195	96.96 140	99.88 48	99.44 129

diffmvs		98.22 143	98.24 127	98.17 220	99.00 185	95.44 246	96.38 266	99.58 26	97.79 144	98.53 188	98.50 221	96.76 157	99.74 213	97.95 87	99.64 145	99.34 169

Anonymous20231206		98.21 144	98.21 130	98.20 218	99.51 73	95.43 247	98.13 122	99.32 123	96.16 242	98.93 133	98.82 169	96.00 189	99.83 133	97.32 117	99.73 105	99.36 163

VDDNet		98.21 144	97.95 157	99.01 125	99.58 50	97.74 168	99.01 49	97.29 311	99.67 10	98.97 123	99.50 35	90.45 283	99.80 165	97.88 91	99.20 239	99.48 110

IS-MVSNet		98.19 146	97.90 162	99.08 108	99.57 54	97.97 143	99.31 18	98.32 281	99.01 67	98.98 120	99.03 113	91.59 278	99.79 178	95.49 238	99.80 76	99.48 110

MVS_Test		98.18 147	98.36 113	97.67 245	98.48 276	94.73 263	98.18 118	99.02 214	97.69 148	98.04 222	99.11 95	97.22 131	99.56 284	98.57 56	98.90 278	98.71 275

TSAR-MVS + GP.		98.18 147	97.98 155	98.77 158	98.71 241	97.88 153	96.32 269	98.66 267	96.33 236	99.23 82	98.51 218	97.48 113	99.40 315	97.16 124	99.46 201	99.02 233

CNVR-MVS		98.17 149	97.87 164	99.07 111	98.67 254	98.24 111	97.01 228	98.93 226	97.25 193	97.62 245	98.34 239	97.27 125	99.57 281	96.42 191	99.33 219	99.39 147

PVSNet_Blended_VisFu		98.17 149	98.15 140	98.22 217	99.73 23	95.15 255	97.36 204	99.68 13	94.45 284	98.99 118	99.27 66	96.87 147	99.94 22	97.13 128	99.91 39	99.57 66

HPM-MVS++		98.10 151	97.64 180	99.48 50	99.09 166	99.13 51	97.52 192	98.75 260	97.46 172	96.90 286	97.83 275	96.01 188	99.84 118	95.82 224	99.35 216	99.46 120

APD-MVS		98.10 151	97.67 175	99.42 57	99.11 159	98.93 66	97.76 167	99.28 146	94.97 272	98.72 164	98.77 176	97.04 136	99.85 101	93.79 284	99.54 179	99.49 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVP-Stereo		98.08 153	97.92 160	98.57 180	98.96 193	96.79 212	97.90 152	99.18 175	96.41 234	98.46 191	98.95 136	95.93 196	99.60 271	96.51 184	98.98 274	99.31 181
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PMMVS2		98.07 154	98.08 148	98.04 229	99.41 104	94.59 269	94.59 330	99.40 92	97.50 164	98.82 153	98.83 166	96.83 150	99.84 118	97.50 110	99.81 68	99.71 26

ETH3D-3000-0.1		98.03 155	97.62 182	99.29 77	99.11 159	98.80 73	97.47 198	99.32 123	95.54 259	98.43 196	98.62 206	96.61 165	99.77 195	93.95 278	99.49 197	99.30 184

ETV-MVS		98.03 155	97.86 165	98.56 184	98.69 249	98.07 131	97.51 194	99.50 56	98.10 124	97.50 257	95.51 336	98.41 39	99.88 64	96.27 201	99.24 234	97.71 322

Effi-MVS+		98.02 157	97.82 167	98.62 172	98.53 273	97.19 198	97.33 206	99.68 13	97.30 188	96.68 294	97.46 297	98.56 32	99.80 165	96.63 171	98.20 303	98.86 258

MSLP-MVS++		98.02 157	98.14 142	97.64 249	98.58 266	95.19 254	97.48 196	99.23 162	97.47 167	97.90 227	98.62 206	97.04 136	98.81 350	97.55 105	99.41 206	98.94 248

EIA-MVS		98.00 159	97.74 171	98.80 151	98.72 238	98.09 125	98.05 134	99.60 23	97.39 179	96.63 296	95.55 335	97.68 89	99.80 165	96.73 163	99.27 229	98.52 285

MCST-MVS		98.00 159	97.63 181	99.10 105	99.24 127	98.17 120	96.89 239	98.73 263	95.66 257	97.92 225	97.70 282	97.17 132	99.66 252	96.18 207	99.23 235	99.47 118

K. test v3		98.00 159	97.66 178	99.03 121	99.79 19	97.56 177	99.19 36	92.47 349	99.62 17	99.52 35	99.66 17	89.61 288	99.96 8	99.25 20	99.81 68	99.56 71

HQP_MVS		97.99 162	97.67 175	98.93 133	99.19 140	97.65 173	97.77 165	99.27 149	98.20 118	97.79 235	97.98 266	94.90 224	99.70 228	94.42 262	99.51 189	99.45 124

MDA-MVSNet-bldmvs		97.94 163	97.91 161	98.06 227	99.44 99	94.96 259	96.63 253	99.15 190	98.35 102	98.83 149	99.11 95	94.31 242	99.85 101	96.60 172	98.72 285	99.37 157

test_part1		97.91 164	97.46 195	99.27 82	98.80 229	98.18 118	99.07 45	99.36 104	99.75 5	99.63 25	99.49 38	82.20 335	99.89 55	98.87 39	99.95 15	99.74 24

Anonymous202405211		97.90 165	97.50 189	99.08 108	98.90 206	98.25 110	98.53 84	96.16 327	98.87 78	99.11 94	98.86 157	90.40 284	99.78 189	97.36 115	99.31 222	99.19 209

LF4IMVS		97.90 165	97.69 174	98.52 189	99.17 149	97.66 172	97.19 220	99.47 72	96.31 238	97.85 231	98.20 250	96.71 161	99.52 296	94.62 254	99.72 111	98.38 293

UnsupCasMVSNet_eth		97.89 167	97.60 184	98.75 161	99.31 116	97.17 200	97.62 180	99.35 110	98.72 86	98.76 160	98.68 190	92.57 271	99.74 213	97.76 100	95.60 345	99.34 169

TinyColmap		97.89 167	97.98 155	97.60 251	98.86 215	94.35 272	96.21 274	99.44 80	97.45 174	99.06 103	98.88 153	97.99 72	99.28 331	94.38 266	99.58 167	99.18 211

OMC-MVS		97.88 169	97.49 190	99.04 120	98.89 211	98.63 84	96.94 232	99.25 155	95.02 270	98.53 188	98.51 218	97.27 125	99.47 307	93.50 292	99.51 189	99.01 234

CANet		97.87 170	97.76 169	98.19 219	97.75 315	95.51 243	96.76 246	99.05 205	97.74 145	96.93 280	98.21 249	95.59 207	99.89 55	97.86 93	99.93 24	99.19 209

xiu_mvs_v1_base_debu		97.86 171	98.17 135	96.92 284	98.98 190	93.91 286	96.45 261	99.17 181	97.85 140	98.41 197	97.14 310	98.47 35	99.92 33	98.02 82	99.05 261	96.92 335

xiu_mvs_v1_base		97.86 171	98.17 135	96.92 284	98.98 190	93.91 286	96.45 261	99.17 181	97.85 140	98.41 197	97.14 310	98.47 35	99.92 33	98.02 82	99.05 261	96.92 335

xiu_mvs_v1_base_debi		97.86 171	98.17 135	96.92 284	98.98 190	93.91 286	96.45 261	99.17 181	97.85 140	98.41 197	97.14 310	98.47 35	99.92 33	98.02 82	99.05 261	96.92 335

NCCC		97.86 171	97.47 194	99.05 118	98.61 261	98.07 131	96.98 230	98.90 232	97.63 152	97.04 277	97.93 270	95.99 192	99.66 252	95.31 241	98.82 281	99.43 133

PMVS	91.26 20	97.86 171	97.94 159	97.65 247	99.71 29	97.94 150	98.52 85	98.68 266	98.99 68	97.52 255	99.35 57	97.41 116	98.18 354	91.59 321	99.67 137	96.82 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IterMVS-SCA-FT		97.85 176	98.18 134	96.87 287	99.27 122	91.16 331	95.53 302	99.25 155	99.10 59	99.41 49	99.35 57	93.10 261	99.96 8	98.65 52	99.94 20	99.49 104

D2MVS		97.84 177	97.84 166	97.83 237	99.14 156	94.74 262	96.94 232	98.88 235	95.84 253	98.89 138	98.96 132	94.40 240	99.69 232	97.55 105	99.95 15	99.05 226

CPTT-MVS		97.84 177	97.36 200	99.27 82	99.31 116	98.46 100	98.29 107	99.27 149	94.90 274	97.83 232	98.37 236	94.90 224	99.84 118	93.85 283	99.54 179	99.51 96

mvs-test1		97.83 179	97.48 193	98.89 139	98.02 303	99.20 32	97.20 217	99.16 184	98.29 109	96.46 306	97.17 307	96.44 173	99.92 33	96.66 169	97.90 316	97.54 329

mvs_anonymous		97.83 179	98.16 138	96.87 287	98.18 295	91.89 318	97.31 208	98.90 232	97.37 181	98.83 149	99.46 42	96.28 181	99.79 178	98.90 36	98.16 306	98.95 244

CS-MVS		97.82 181	97.59 186	98.52 189	98.76 232	98.04 135	98.20 116	99.61 21	97.10 207	96.02 317	94.87 348	98.27 48	99.84 118	96.31 197	99.17 246	97.69 323

testtj		97.79 182	97.25 205	99.42 57	99.03 180	98.85 68	97.78 162	99.18 175	95.83 254	98.12 214	98.50 221	95.50 211	99.86 87	92.23 314	99.07 260	99.54 83

IterMVS		97.73 183	98.11 144	96.57 294	99.24 127	90.28 332	95.52 304	99.21 164	98.86 79	99.33 62	99.33 61	93.11 260	99.94 22	98.49 60	99.94 20	99.48 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		97.71 184	97.52 188	98.28 213	98.91 205	96.82 211	94.42 333	99.37 100	97.65 151	98.37 202	98.29 244	97.40 117	99.33 324	94.09 274	99.22 236	98.68 281

CDS-MVSNet		97.69 185	97.35 201	98.69 165	98.73 237	97.02 206	96.92 236	98.75 260	95.89 252	98.59 179	98.67 192	92.08 276	99.74 213	96.72 164	99.81 68	99.32 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 186	97.75 170	97.45 263	98.23 293	93.78 292	97.29 209	98.84 244	96.10 244	98.64 170	98.65 197	96.04 186	99.36 320	96.84 153	99.14 251	99.20 204

Fast-Effi-MVS+		97.67 187	97.38 198	98.57 180	98.71 241	97.43 184	97.23 213	99.45 77	94.82 276	96.13 310	96.51 318	98.52 34	99.91 43	96.19 205	98.83 280	98.37 295

EU-MVSNet		97.66 188	98.50 87	95.13 321	99.63 47	85.84 348	98.35 106	98.21 285	98.23 114	99.54 30	99.46 42	95.02 222	99.68 241	98.24 71	99.87 51	99.87 4

MVS_0304		97.64 189	97.35 201	98.52 189	97.87 311	96.69 217	98.59 78	98.05 294	97.44 175	93.74 348	98.85 160	93.69 255	99.88 64	98.11 77	99.81 68	98.98 239

pmmvs5		97.64 189	97.49 190	98.08 225	99.14 156	95.12 257	96.70 250	99.05 205	93.77 297	98.62 173	98.83 166	93.23 257	99.75 209	98.33 70	99.76 98	99.36 163

N_pmnet		97.63 191	97.17 210	98.99 127	99.27 122	97.86 155	95.98 280	93.41 346	95.25 268	99.47 42	98.90 144	95.63 205	99.85 101	96.91 142	99.73 105	99.27 191

YYNet1		97.60 192	97.67 175	97.39 267	99.04 177	93.04 303	95.27 309	98.38 280	97.25 193	98.92 134	98.95 136	95.48 213	99.73 217	96.99 136	98.74 283	99.41 138

MDA-MVSNet_test_wron		97.60 192	97.66 178	97.41 266	99.04 177	93.09 299	95.27 309	98.42 278	97.26 192	98.88 142	98.95 136	95.43 214	99.73 217	97.02 133	98.72 285	99.41 138

pmmvs4		97.58 194	97.28 204	98.51 192	98.84 220	96.93 209	95.40 308	98.52 274	93.60 299	98.61 175	98.65 197	95.10 221	99.60 271	96.97 139	99.79 81	98.99 238

ETH3D cwj APD-0.16		97.55 195	97.00 218	99.19 92	98.51 274	98.64 83	96.85 240	99.13 191	94.19 290	97.65 243	98.40 230	95.78 201	99.81 156	93.37 295	99.16 247	99.12 220

PVSNet_BlendedMVS		97.55 195	97.53 187	97.60 251	98.92 202	93.77 293	96.64 252	99.43 85	94.49 280	97.62 245	99.18 79	96.82 151	99.67 244	94.73 251	99.93 24	99.36 163

ppachtmachnet_test		97.50 197	97.74 171	96.78 292	98.70 245	91.23 330	94.55 331	99.05 205	96.36 235	99.21 83	98.79 173	96.39 175	99.78 189	96.74 161	99.82 64	99.34 169

FMVSNet3		97.50 197	97.24 207	98.29 212	98.08 301	95.83 236	97.86 156	98.91 231	97.89 137	98.95 126	98.95 136	87.06 299	99.81 156	97.77 96	99.69 126	99.23 199

CHOSEN 1792x2688		97.49 199	97.14 214	98.54 188	99.68 38	96.09 230	96.50 259	99.62 19	91.58 322	98.84 148	98.97 129	92.36 272	99.88 64	96.76 159	99.95 15	99.67 33

CLD-MVS		97.49 199	97.16 211	98.48 195	99.07 170	97.03 205	94.71 323	99.21 164	94.46 282	98.06 219	97.16 308	97.57 100	99.48 305	94.46 259	99.78 85	98.95 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_prior3		97.48 201	97.00 218	98.95 130	98.69 249	97.95 148	95.74 295	99.03 210	96.48 231	96.11 311	97.63 286	95.92 197	99.59 275	94.16 268	99.20 239	99.30 184

Vis-MVSNet (Re-imp)		97.46 202	97.16 211	98.34 207	99.55 64	96.10 228	98.94 56	98.44 277	98.32 105	98.16 210	98.62 206	88.76 294	99.73 217	93.88 281	99.79 81	99.18 211

jason		97.45 203	97.35 201	97.76 241	99.24 127	93.93 285	95.86 289	98.42 278	94.24 288	98.50 190	98.13 253	94.82 228	99.91 43	97.22 121	99.73 105	99.43 133
jason: jason.
CL-MVSNet_2432*1600		97.44 204	97.22 208	98.08 225	98.57 268	95.78 238	94.30 336	98.79 253	96.58 229	98.60 177	98.19 251	94.74 234	99.64 259	96.41 192	98.84 279	98.82 261

DSMNet-mixed		97.42 205	97.60 184	96.87 287	99.15 155	91.46 322	98.54 83	99.12 193	92.87 308	97.58 249	99.63 20	96.21 182	99.90 46	95.74 226	99.54 179	99.27 191

USDC		97.41 206	97.40 196	97.44 264	98.94 196	93.67 295	95.17 312	99.53 50	94.03 294	98.97 123	99.10 97	95.29 216	99.34 322	95.84 223	99.73 105	99.30 184

our_test_3		97.39 207	97.73 173	96.34 298	98.70 245	89.78 334	94.61 329	98.97 223	96.50 230	99.04 110	98.85 160	95.98 193	99.84 118	97.26 120	99.67 137	99.41 138

cl_fuxian		97.36 208	97.37 199	97.31 268	98.09 300	93.25 298	95.01 317	99.16 184	97.05 209	98.77 159	98.72 183	92.88 266	99.64 259	96.93 141	99.76 98	99.05 226

alignmvs		97.35 209	96.88 226	98.78 156	98.54 271	98.09 125	97.71 171	97.69 302	99.20 47	97.59 248	95.90 330	88.12 298	99.55 287	98.18 75	98.96 275	98.70 277

Patchmtry		97.35 209	96.97 220	98.50 194	97.31 334	96.47 220	98.18 118	98.92 229	98.95 75	98.78 156	99.37 53	85.44 314	99.85 101	95.96 215	99.83 61	99.17 215

DP-MVS Recon		97.33 211	96.92 223	98.57 180	99.09 166	97.99 138	96.79 243	99.35 110	93.18 303	97.71 239	98.07 262	95.00 223	99.31 326	93.97 276	99.13 254	98.42 292

QAPM		97.31 212	96.81 231	98.82 147	98.80 229	97.49 180	99.06 47	99.19 171	90.22 334	97.69 241	99.16 85	96.91 145	99.90 46	90.89 331	99.41 206	99.07 224

UnsupCasMVSNet_bld		97.30 213	96.92 223	98.45 198	99.28 121	96.78 215	96.20 275	99.27 149	95.42 264	98.28 205	98.30 243	93.16 259	99.71 226	94.99 245	97.37 324	98.87 257

F-COLMAP		97.30 213	96.68 238	99.14 99	99.19 140	98.39 103	97.27 212	99.30 137	92.93 306	96.62 297	98.00 264	95.73 203	99.68 241	92.62 309	98.46 297	99.35 167

1112_ss		97.29 215	96.86 227	98.58 177	99.34 115	96.32 224	96.75 247	99.58 26	93.14 304	96.89 287	97.48 295	92.11 275	99.86 87	96.91 142	99.54 179	99.57 66

CANet_DTU		97.26 216	97.06 215	97.84 236	97.57 322	94.65 267	96.19 276	98.79 253	97.23 199	95.14 334	98.24 246	93.22 258	99.84 118	97.34 116	99.84 55	99.04 230

Patchmatch-RL test		97.26 216	97.02 217	97.99 232	99.52 71	95.53 242	96.13 277	99.71 9	97.47 167	99.27 72	99.16 85	84.30 322	99.62 264	97.89 88	99.77 89	98.81 264

CDPH-MVS		97.26 216	96.66 241	99.07 111	99.00 185	98.15 121	96.03 279	99.01 217	91.21 328	97.79 235	97.85 274	96.89 146	99.69 232	92.75 306	99.38 212	99.39 147

PatchMatch-RL		97.24 219	96.78 232	98.61 174	99.03 180	97.83 157	96.36 267	99.06 201	93.49 302	97.36 267	97.78 277	95.75 202	99.49 302	93.44 293	98.77 282	98.52 285

eth_miper_zixun_eth		97.23 220	97.25 205	97.17 274	98.00 305	92.77 307	94.71 323	99.18 175	97.27 191	98.56 183	98.74 180	91.89 277	99.69 232	97.06 132	99.81 68	99.05 226

sss		97.21 221	96.93 221	98.06 227	98.83 222	95.22 253	96.75 247	98.48 276	94.49 280	97.27 268	97.90 271	92.77 268	99.80 165	96.57 175	99.32 220	99.16 218

LFMVS		97.20 222	96.72 235	98.64 168	98.72 238	96.95 208	98.93 57	94.14 343	99.74 7	98.78 156	99.01 120	84.45 319	99.73 217	97.44 111	99.27 229	99.25 195

HyFIR lowres test		97.19 223	96.60 244	98.96 129	99.62 49	97.28 190	95.17 312	99.50 56	94.21 289	99.01 114	98.32 242	86.61 302	99.99 2	97.10 130	99.84 55	99.60 49

miper_lstm_enhance		97.18 224	97.16 211	97.25 272	98.16 296	92.85 305	95.15 314	99.31 128	97.25 193	98.74 163	98.78 174	90.07 285	99.78 189	97.19 122	99.80 76	99.11 222

CNLPA		97.17 225	96.71 236	98.55 185	98.56 269	98.05 134	96.33 268	98.93 226	96.91 216	97.06 276	97.39 300	94.38 241	99.45 311	91.66 318	99.18 245	98.14 301

xiu_mvs_v2_base		97.16 226	97.49 190	96.17 303	98.54 271	92.46 311	95.45 306	98.84 244	97.25 193	97.48 259	96.49 319	98.31 47	99.90 46	96.34 196	98.68 289	96.15 346

AdaColmap		97.14 227	96.71 236	98.46 197	98.34 285	97.80 163	96.95 231	98.93 226	95.58 258	96.92 281	97.66 283	95.87 199	99.53 292	90.97 328	99.14 251	98.04 304

train_agg		97.10 228	96.45 251	99.07 111	98.71 241	98.08 129	95.96 283	99.03 210	91.64 320	95.85 318	97.53 290	96.47 171	99.76 202	93.67 286	99.16 247	99.36 163

OpenMVS	96.65 7	97.09 229	96.68 238	98.32 208	98.32 286	97.16 201	98.86 62	99.37 100	89.48 338	96.29 309	99.15 89	96.56 166	99.90 46	92.90 300	99.20 239	97.89 309

PS-MVSNAJ		97.08 230	97.39 197	96.16 305	98.56 269	92.46 311	95.24 311	98.85 243	97.25 193	97.49 258	95.99 328	98.07 63	99.90 46	96.37 193	98.67 290	96.12 347

RRT_MVS		97.07 231	96.57 246	98.58 177	95.89 355	96.33 223	97.36 204	98.77 256	97.85 140	99.08 100	99.12 93	82.30 332	99.96 8	98.82 42	99.90 43	99.45 124

miper_ehance_all_eth		97.06 232	97.03 216	97.16 276	97.83 312	93.06 300	94.66 326	99.09 197	95.99 249	98.69 165	98.45 227	92.73 269	99.61 270	96.79 155	99.03 265	98.82 261

agg_prior1		97.06 232	96.40 252	99.03 121	98.68 252	97.99 138	95.76 293	99.01 217	91.73 319	95.59 321	97.50 293	96.49 170	99.77 195	93.71 285	99.14 251	99.34 169

lupinMVS		97.06 232	96.86 227	97.65 247	98.88 212	93.89 289	95.48 305	97.97 295	93.53 300	98.16 210	97.58 288	93.81 251	99.91 43	96.77 158	99.57 171	99.17 215

API-MVS		97.04 235	96.91 225	97.42 265	97.88 310	98.23 115	98.18 118	98.50 275	97.57 158	97.39 265	96.75 315	96.77 155	99.15 340	90.16 334	99.02 268	94.88 352

cl-mvsnet_		97.02 236	96.83 230	97.58 253	97.82 313	94.04 279	94.66 326	99.16 184	97.04 210	98.63 171	98.71 184	88.68 296	99.69 232	97.00 134	99.81 68	99.00 237

cl-mvsnet1		97.02 236	96.84 229	97.58 253	97.82 313	94.03 280	94.66 326	99.16 184	97.04 210	98.63 171	98.71 184	88.69 295	99.69 232	97.00 134	99.81 68	99.01 234

RPMNet		97.02 236	96.93 221	97.30 269	97.71 317	94.22 273	98.11 125	99.30 137	99.37 34	96.91 283	99.34 59	86.72 301	99.87 80	97.53 108	97.36 326	97.81 315

HQP-MVS		97.00 239	96.49 250	98.55 185	98.67 254	96.79 212	96.29 270	99.04 208	96.05 245	95.55 325	96.84 313	93.84 249	99.54 290	92.82 303	99.26 232	99.32 177

bset_n11_16_dypcd		96.99 240	96.56 247	98.27 214	99.00 185	95.25 250	92.18 353	94.05 344	98.75 84	99.01 114	98.38 234	88.98 293	99.93 26	98.77 46	99.92 33	99.64 39

new_pmnet		96.99 240	96.76 233	97.67 245	98.72 238	94.89 260	95.95 285	98.20 286	92.62 311	98.55 185	98.54 215	94.88 227	99.52 296	93.96 277	99.44 204	98.59 284

Test_1112_low_res		96.99 240	96.55 248	98.31 210	99.35 113	95.47 245	95.84 292	99.53 50	91.51 324	96.80 292	98.48 226	91.36 279	99.83 133	96.58 173	99.53 183	99.62 44

PVSNet_Blended		96.88 243	96.68 238	97.47 262	98.92 202	93.77 293	94.71 323	99.43 85	90.98 330	97.62 245	97.36 303	96.82 151	99.67 244	94.73 251	99.56 176	98.98 239

MVSTER		96.86 244	96.55 248	97.79 239	97.91 309	94.21 275	97.56 188	98.87 237	97.49 166	99.06 103	99.05 107	80.72 337	99.80 165	98.44 63	99.82 64	99.37 157

BH-untuned		96.83 245	96.75 234	97.08 277	98.74 236	93.33 297	96.71 249	98.26 283	96.72 223	98.44 193	97.37 302	95.20 218	99.47 307	91.89 316	97.43 323	98.44 290

BH-RMVSNet		96.83 245	96.58 245	97.58 253	98.47 277	94.05 278	96.67 251	97.36 307	96.70 225	97.87 229	97.98 266	95.14 220	99.44 312	90.47 333	98.58 295	99.25 195

PAPM_NR		96.82 247	96.32 255	98.30 211	99.07 170	96.69 217	97.48 196	98.76 257	95.81 255	96.61 298	96.47 321	94.12 248	99.17 338	90.82 332	97.78 317	99.06 225

MG-MVS		96.77 248	96.61 243	97.26 271	98.31 287	93.06 300	95.93 286	98.12 291	96.45 233	97.92 225	98.73 181	93.77 253	99.39 317	91.19 327	99.04 264	99.33 175

1121		96.73 249	96.00 260	98.91 136	98.95 195	97.76 165	98.07 130	98.73 263	87.65 346	96.54 299	98.13 253	94.52 237	99.73 217	92.38 312	99.02 268	99.24 198

test_yl		96.69 250	96.29 256	97.90 233	98.28 288	95.24 251	97.29 209	97.36 307	98.21 115	98.17 208	97.86 272	86.27 304	99.55 287	94.87 248	98.32 299	98.89 254

DCV-MVSNet		96.69 250	96.29 256	97.90 233	98.28 288	95.24 251	97.29 209	97.36 307	98.21 115	98.17 208	97.86 272	86.27 304	99.55 287	94.87 248	98.32 299	98.89 254

WTY-MVS		96.67 252	96.27 258	97.87 235	98.81 227	94.61 268	96.77 245	97.92 297	94.94 273	97.12 271	97.74 280	91.11 280	99.82 143	93.89 280	98.15 307	99.18 211

PatchT		96.65 253	96.35 253	97.54 258	97.40 330	95.32 249	97.98 145	96.64 323	99.33 37	96.89 287	99.42 48	84.32 321	99.81 156	97.69 104	97.49 320	97.48 330

TAPA-MVS	96.21 11	96.63 254	95.95 262	98.65 167	98.93 198	98.09 125	96.93 234	99.28 146	83.58 353	98.13 213	97.78 277	96.13 183	99.40 315	93.52 290	99.29 227	98.45 289
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 255	96.25 259	97.71 244	99.04 177	94.66 266	99.16 38	96.92 319	97.23 199	97.87 229	99.10 97	86.11 308	99.65 257	91.65 319	99.21 238	98.82 261

Patchmatch-test		96.55 256	96.34 254	97.17 274	98.35 284	93.06 300	98.40 101	97.79 298	97.33 184	98.41 197	98.67 192	83.68 326	99.69 232	95.16 242	99.31 222	98.77 271

PMMVS		96.51 257	95.98 261	98.09 222	97.53 325	95.84 235	94.92 319	98.84 244	91.58 322	96.05 315	95.58 334	95.68 204	99.66 252	95.59 235	98.09 310	98.76 272

PLC	94.65 16	96.51 257	95.73 266	98.85 144	98.75 235	97.91 151	96.42 264	99.06 201	90.94 331	95.59 321	97.38 301	94.41 239	99.59 275	90.93 329	98.04 314	99.05 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 259	95.77 264	98.69 165	99.48 91	97.43 184	97.84 158	99.55 43	81.42 355	96.51 302	98.58 212	95.53 208	99.67 244	93.41 294	99.58 167	98.98 239

MAR-MVS		96.47 260	95.70 267	98.79 153	97.92 308	99.12 53	98.28 108	98.60 271	92.16 317	95.54 328	96.17 326	94.77 233	99.52 296	89.62 336	98.23 301	97.72 321
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		96.46 261	95.59 272	99.08 108	98.88 212	98.21 117	96.53 256	99.18 175	88.87 342	97.08 274	97.79 276	93.64 256	99.77 195	88.92 338	99.40 208	99.28 189

SCA		96.41 262	96.66 241	95.67 311	98.24 291	88.35 339	95.85 291	96.88 320	96.11 243	97.67 242	98.67 192	93.10 261	99.85 101	94.16 268	99.22 236	98.81 264

DPM-MVS		96.32 263	95.59 272	98.51 192	98.76 232	97.21 196	94.54 332	98.26 283	91.94 318	96.37 307	97.25 305	93.06 263	99.43 313	91.42 323	98.74 283	98.89 254

CMPMVS	75.91 23	96.29 264	95.44 277	98.84 145	96.25 351	98.69 82	97.02 227	99.12 193	88.90 341	97.83 232	98.86 157	89.51 289	98.90 348	91.92 315	99.51 189	98.92 250
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		96.28 265	95.95 262	97.28 270	97.71 317	94.22 273	98.11 125	98.92 229	92.31 314	96.91 283	99.37 53	85.44 314	99.81 156	97.39 114	97.36 326	97.81 315

CVMVSNet		96.25 266	97.21 209	93.38 337	99.10 163	80.56 361	97.20 217	98.19 288	96.94 214	99.00 117	99.02 114	89.50 290	99.80 165	96.36 195	99.59 161	99.78 14

AUN-MVS		96.24 267	95.45 276	98.60 175	98.70 245	97.22 194	97.38 202	97.65 303	95.95 250	95.53 329	97.96 269	82.11 336	99.79 178	96.31 197	97.44 322	98.80 268

EPNet		96.14 268	95.44 277	98.25 215	90.76 362	95.50 244	97.92 149	94.65 336	98.97 71	92.98 349	98.85 160	89.12 292	99.87 80	95.99 213	99.68 131	99.39 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		96.06 269	97.62 182	91.38 340	98.65 260	98.57 91	98.85 63	96.95 317	96.86 218	99.90 4	99.16 85	99.18 11	98.40 353	89.23 337	99.77 89	77.18 356

miper_enhance_ethall		96.01 270	95.74 265	96.81 291	96.41 349	92.27 315	93.69 345	98.89 234	91.14 329	98.30 203	97.35 304	90.58 282	99.58 280	96.31 197	99.03 265	98.60 282

FMVSNet5		96.01 270	95.20 285	98.41 201	97.53 325	96.10 228	98.74 66	99.50 56	97.22 202	98.03 223	99.04 110	69.80 357	99.88 64	97.27 119	99.71 115	99.25 195

baseline1		95.96 272	95.44 277	97.52 260	98.51 274	93.99 283	98.39 102	96.09 329	98.21 115	98.40 201	97.76 279	86.88 300	99.63 262	95.42 239	89.27 357	98.95 244

HY-MVS	95.94 13	95.90 273	95.35 281	97.55 257	97.95 306	94.79 261	98.81 65	96.94 318	92.28 315	95.17 333	98.57 213	89.90 287	99.75 209	91.20 326	97.33 328	98.10 302

GA-MVS		95.86 274	95.32 282	97.49 261	98.60 263	94.15 277	93.83 343	97.93 296	95.49 262	96.68 294	97.42 299	83.21 327	99.30 328	96.22 203	98.55 296	99.01 234

OpenMVS_ROB	95.38 14	95.84 275	95.18 286	97.81 238	98.41 282	97.15 202	97.37 203	98.62 270	83.86 352	98.65 169	98.37 236	94.29 243	99.68 241	88.41 339	98.62 293	96.60 341

cl-mvsnet2		95.79 276	95.39 280	96.98 281	96.77 343	92.79 306	94.40 334	98.53 273	94.59 279	97.89 228	98.17 252	82.82 331	99.24 333	96.37 193	99.03 265	98.92 250

1314		95.74 277	95.60 271	96.17 303	97.53 325	92.75 308	98.07 130	98.31 282	91.22 327	94.25 340	96.68 316	95.53 208	99.03 342	91.64 320	97.18 329	96.74 339

PVSNet	93.40 17	95.67 278	95.70 267	95.57 314	98.83 222	88.57 337	92.50 350	97.72 300	92.69 310	96.49 305	96.44 322	93.72 254	99.43 313	93.61 287	99.28 228	98.71 275

tttt0517		95.64 279	94.98 290	97.64 249	99.36 109	93.81 291	98.72 68	90.47 355	98.08 125	98.67 167	98.34 239	73.88 354	99.92 33	97.77 96	99.51 189	99.20 204

PatchmatchNet		95.58 280	95.67 269	95.30 320	97.34 332	87.32 343	97.65 178	96.65 322	95.30 267	97.07 275	98.69 188	84.77 316	99.75 209	94.97 246	98.64 291	98.83 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 281	95.12 288	96.86 290	97.54 324	93.94 284	96.49 260	96.53 324	94.36 287	97.03 278	96.61 317	94.26 244	99.16 339	86.91 343	96.31 339	97.47 331

JIA-IIPM		95.52 282	95.03 289	97.00 279	96.85 341	94.03 280	96.93 234	95.82 331	99.20 47	94.63 338	99.71 12	83.09 328	99.60 271	94.42 262	94.64 349	97.36 332

CHOSEN 280x420		95.51 283	95.47 274	95.65 313	98.25 290	88.27 340	93.25 347	98.88 235	93.53 300	94.65 337	97.15 309	86.17 306	99.93 26	97.41 113	99.93 24	98.73 274

ADS-MVSNet2		95.43 284	94.98 290	96.76 293	98.14 297	91.74 319	97.92 149	97.76 299	90.23 332	96.51 302	98.91 141	85.61 311	99.85 101	92.88 301	96.90 332	98.69 278

PAPR		95.29 285	94.47 295	97.75 242	97.50 329	95.14 256	94.89 320	98.71 265	91.39 326	95.35 332	95.48 337	94.57 236	99.14 341	84.95 346	97.37 324	98.97 243

thisisatest0530		95.27 286	94.45 296	97.74 243	99.19 140	94.37 271	97.86 156	90.20 356	97.17 203	98.22 207	97.65 284	73.53 355	99.90 46	96.90 147	99.35 216	98.95 244

ADS-MVSNet		95.24 287	94.93 292	96.18 302	98.14 297	90.10 333	97.92 149	97.32 310	90.23 332	96.51 302	98.91 141	85.61 311	99.74 213	92.88 301	96.90 332	98.69 278

RRT_test8_iter05		95.24 287	95.13 287	95.57 314	97.32 333	87.02 345	97.99 143	99.41 89	98.06 126	99.12 92	99.05 107	66.85 362	99.85 101	98.93 35	99.47 200	99.84 8

BH-w/o		95.13 289	94.89 293	95.86 307	98.20 294	91.31 326	95.65 298	97.37 306	93.64 298	96.52 301	95.70 333	93.04 264	99.02 343	88.10 340	95.82 344	97.24 333

tpmrst		95.07 290	95.46 275	93.91 331	97.11 337	84.36 355	97.62 180	96.96 316	94.98 271	96.35 308	98.80 171	85.46 313	99.59 275	95.60 234	96.23 340	97.79 318

pmmvs3		95.03 291	94.40 297	96.93 283	97.70 319	92.53 310	95.08 315	97.71 301	88.57 343	97.71 239	98.08 261	79.39 344	99.82 143	96.19 205	99.11 258	98.43 291

tpmvs		95.02 292	95.25 283	94.33 327	96.39 350	85.87 347	98.08 129	96.83 321	95.46 263	95.51 330	98.69 188	85.91 309	99.53 292	94.16 268	96.23 340	97.58 327

EPNet_dtu		94.93 293	94.78 294	95.38 319	93.58 359	87.68 342	96.78 244	95.69 333	97.35 183	89.14 357	98.09 260	88.15 297	99.49 302	94.95 247	99.30 225	98.98 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		94.79 294	94.33 300	96.15 306	96.02 354	92.36 314	92.34 352	99.26 154	85.34 351	95.08 335	94.96 345	92.96 265	98.53 352	94.41 265	98.59 294	97.56 328

tpm		94.67 295	94.34 299	95.66 312	97.68 321	88.42 338	97.88 153	94.90 335	94.46 282	96.03 316	98.56 214	78.66 346	99.79 178	95.88 217	95.01 348	98.78 270

test0.0.03 1		94.51 296	93.69 305	96.99 280	96.05 352	93.61 296	94.97 318	93.49 345	96.17 240	97.57 251	94.88 346	82.30 332	99.01 345	93.60 288	94.17 353	98.37 295

thres600view7		94.45 297	93.83 303	96.29 299	99.06 174	91.53 321	97.99 143	94.24 341	98.34 103	97.44 262	95.01 342	79.84 340	99.67 244	84.33 347	98.23 301	97.66 324

PCF-MVS	92.86 18	94.36 298	93.00 315	98.42 200	98.70 245	97.56 177	93.16 348	99.11 195	79.59 356	97.55 252	97.43 298	92.19 273	99.73 217	79.85 355	99.45 203	97.97 308
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		94.32 299	92.59 317	99.53 36	99.46 94	99.21 26	98.65 71	99.34 116	98.62 91	97.54 253	45.85 358	97.50 108	99.83 133	96.79 155	99.53 183	99.56 71

MVS-HIRNet		94.32 299	95.62 270	90.42 341	98.46 278	75.36 362	96.29 270	89.13 358	95.25 268	95.38 331	99.75 7	92.88 266	99.19 337	94.07 275	99.39 209	96.72 340

ET-MVSNet_ETH3D		94.30 301	93.21 311	97.58 253	98.14 297	94.47 270	94.78 322	93.24 348	94.72 277	89.56 356	95.87 331	78.57 348	99.81 156	96.91 142	97.11 331	98.46 287

thres100view900		94.19 302	93.67 306	95.75 310	99.06 174	91.35 325	98.03 137	94.24 341	98.33 104	97.40 264	94.98 344	79.84 340	99.62 264	83.05 349	98.08 311	96.29 342

E-PMN		94.17 303	94.37 298	93.58 334	96.86 340	85.71 350	90.11 355	97.07 314	98.17 121	97.82 234	97.19 306	84.62 318	98.94 346	89.77 335	97.68 319	96.09 348

thres400		94.14 304	93.44 308	96.24 301	98.93 198	91.44 323	97.60 183	94.29 339	97.94 132	97.10 272	94.31 351	79.67 342	99.62 264	83.05 349	98.08 311	97.66 324

thisisatest0515		94.12 305	93.16 312	96.97 282	98.60 263	92.90 304	93.77 344	90.61 354	94.10 292	96.91 283	95.87 331	74.99 353	99.80 165	94.52 257	99.12 257	98.20 298

tfpn200view9		94.03 306	93.44 308	95.78 309	98.93 198	91.44 323	97.60 183	94.29 339	97.94 132	97.10 272	94.31 351	79.67 342	99.62 264	83.05 349	98.08 311	96.29 342

CostFormer		93.97 307	93.78 304	94.51 326	97.53 325	85.83 349	97.98 145	95.96 330	89.29 340	94.99 336	98.63 204	78.63 347	99.62 264	94.54 256	96.50 337	98.09 303

test-LLR		93.90 308	93.85 302	94.04 329	96.53 345	84.62 353	94.05 340	92.39 350	96.17 240	94.12 342	95.07 340	82.30 332	99.67 244	95.87 220	98.18 304	97.82 313

EMVS		93.83 309	94.02 301	93.23 338	96.83 342	84.96 351	89.77 356	96.32 326	97.92 134	97.43 263	96.36 325	86.17 306	98.93 347	87.68 341	97.73 318	95.81 349

baseline2		93.73 310	92.83 316	96.42 297	97.70 319	91.28 328	96.84 242	89.77 357	93.96 296	92.44 351	95.93 329	79.14 345	99.77 195	92.94 299	96.76 336	98.21 297

thres200		93.72 311	93.14 313	95.46 318	98.66 259	91.29 327	96.61 254	94.63 337	97.39 179	96.83 290	93.71 354	79.88 339	99.56 284	82.40 352	98.13 308	95.54 351

EPMVS		93.72 311	93.27 310	95.09 322	96.04 353	87.76 341	98.13 122	85.01 360	94.69 278	96.92 281	98.64 200	78.47 350	99.31 326	95.04 243	96.46 338	98.20 298

dp		93.47 313	93.59 307	93.13 339	96.64 344	81.62 360	97.66 176	96.42 325	92.80 309	96.11 311	98.64 200	78.55 349	99.59 275	93.31 296	92.18 356	98.16 300

FPMVS		93.44 314	92.23 319	97.08 277	99.25 126	97.86 155	95.61 299	97.16 313	92.90 307	93.76 347	98.65 197	75.94 352	95.66 356	79.30 356	97.49 320	97.73 320

tpm cat1		93.29 315	93.13 314	93.75 332	97.39 331	84.74 352	97.39 201	97.65 303	83.39 354	94.16 341	98.41 229	82.86 330	99.39 317	91.56 322	95.35 347	97.14 334

MVS		93.19 316	92.09 320	96.50 296	96.91 339	94.03 280	98.07 130	98.06 293	68.01 357	94.56 339	96.48 320	95.96 195	99.30 328	83.84 348	96.89 334	96.17 344

tpm2		93.09 317	92.58 318	94.62 325	97.56 323	86.53 346	97.66 176	95.79 332	86.15 349	94.07 344	98.23 248	75.95 351	99.53 292	90.91 330	96.86 335	97.81 315

KD-MVS_2432*1600		92.87 318	91.99 322	95.51 316	91.37 360	89.27 335	94.07 338	98.14 289	95.42 264	97.25 269	96.44 322	67.86 359	99.24 333	91.28 324	96.08 342	98.02 305

miper_refine_blended		92.87 318	91.99 322	95.51 316	91.37 360	89.27 335	94.07 338	98.14 289	95.42 264	97.25 269	96.44 322	67.86 359	99.24 333	91.28 324	96.08 342	98.02 305

DWT-MVSNet_test		92.75 320	92.05 321	94.85 323	96.48 347	87.21 344	97.83 159	94.99 334	92.22 316	92.72 350	94.11 353	70.75 356	99.46 309	95.01 244	94.33 352	97.87 311

MVE	83.40 22	92.50 321	91.92 324	94.25 328	98.83 222	91.64 320	92.71 349	83.52 361	95.92 251	86.46 360	95.46 338	95.20 218	95.40 357	80.51 354	98.64 291	95.73 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
gg-mvs-nofinetune		92.37 322	91.20 327	95.85 308	95.80 356	92.38 313	99.31 18	81.84 362	99.75 5	91.83 353	99.74 8	68.29 358	99.02 343	87.15 342	97.12 330	96.16 345

test-mter		92.33 323	91.76 326	94.04 329	96.53 345	84.62 353	94.05 340	92.39 350	94.00 295	94.12 342	95.07 340	65.63 365	99.67 244	95.87 220	98.18 304	97.82 313

IB-MVS	91.63 19	92.24 324	90.90 328	96.27 300	97.22 336	91.24 329	94.36 335	93.33 347	92.37 313	92.24 352	94.58 350	66.20 364	99.89 55	93.16 298	94.63 350	97.66 324
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		92.19 325	91.77 325	93.46 335	96.48 347	82.80 358	94.05 340	91.52 353	94.45 284	94.00 345	94.88 346	66.65 363	99.56 284	95.78 225	98.11 309	98.02 305

PAPM		91.88 326	90.34 329	96.51 295	98.06 302	92.56 309	92.44 351	97.17 312	86.35 348	90.38 355	96.01 327	86.61 302	99.21 336	70.65 358	95.43 346	97.75 319

PVSNet_0	89.98 21	91.15 327	90.30 330	93.70 333	97.72 316	84.34 356	90.24 354	97.42 305	90.20 335	93.79 346	93.09 355	90.90 281	98.89 349	86.57 344	72.76 358	97.87 311

tmp_tt		78.77 328	78.73 331	78.90 342	58.45 363	74.76 364	94.20 337	78.26 364	39.16 359	86.71 359	92.82 356	80.50 338	75.19 360	86.16 345	92.29 355	86.74 355

cdsmvs_eth3d_5k		24.66 329	32.88 332	0.00 345	0.00 366	0.00 367	0.00 357	99.10 196	0.00 362	0.00 363	97.58 288	99.21 10	0.00 363	0.00 361	0.00 361	0.00 359

testmvs		17.12 330	20.53 333	6.87 344	12.05 364	4.20 366	93.62 346	6.73 365	4.62 361	10.41 361	24.33 359	8.28 367	3.56 362	9.69 360	15.07 359	12.86 358

test123		17.04 331	20.11 334	7.82 343	10.25 365	4.91 365	94.80 321	4.47 366	4.93 360	10.00 362	24.28 360	9.69 366	3.64 361	10.14 359	12.43 360	14.92 357

pcd_1.5k_mvsjas		8.17 332	10.90 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	98.07 63	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.12 333	10.83 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	97.48 295	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					99.01 184	98.84 69		99.07 200	94.10 292	98.05 221	98.12 256	96.36 179	99.86 87	92.70 308	99.19 243

RE-MVS-def			98.58 78		99.20 137	99.38 5	98.48 94	99.30 137	98.64 87	98.95 126	98.96 132	97.75 86		96.56 178	99.39 209	99.45 124

IU-MVS					99.49 83	99.15 45		98.87 237	92.97 305	99.41 49				96.76 159	99.62 151	99.66 34

OPU-MVS				98.82 147	98.59 265	98.30 107	98.10 127				98.52 217	98.18 57	98.75 351	94.62 254	99.48 199	99.41 138

test_241102_TWO								99.30 137	98.03 127	99.26 76	99.02 114	97.51 107	99.88 64	96.91 142	99.60 159	99.66 34

test_241102_ONE					99.49 83	99.17 36		99.31 128	97.98 129	99.66 20	98.90 144	98.36 42	99.48 305

9.14			97.78 168		99.07 170		97.53 191	99.32 123	95.53 261	98.54 187	98.70 187	97.58 99	99.76 202	94.32 267	99.46 201

save fliter					99.11 159	97.97 143	96.53 256	99.02 214	98.24 112

test_0728_THIRD									98.17 121	99.08 100	99.02 114	97.89 76	99.88 64	97.07 131	99.71 115	99.70 29

test_0728_SECOND				99.60 13	99.50 76	99.23 24	98.02 139	99.32 123					99.88 64	96.99 136	99.63 148	99.68 31

test0726					99.50 76	99.21 26	98.17 121	99.35 110	97.97 130	99.26 76	99.06 100	97.61 97

GSMVS																98.81 264

test_part2					99.36 109	99.10 56				99.05 108

sam_mvs1												84.74 317				98.81 264

sam_mvs												84.29 323

ambc				98.24 216	98.82 225	95.97 232	98.62 74	99.00 220		99.27 72	99.21 74	96.99 141	99.50 301	96.55 181	99.50 196	99.26 194

MTGPA								99.20 166

test_post1							97.59 185				20.48 362	83.07 329	99.66 252	94.16 268

test_post											21.25 361	83.86 325	99.70 228

patchmatchnet-post											98.77 176	84.37 320	99.85 101

GG-mvs-BLEND				94.76 324	94.54 358	92.13 317	99.31 18	80.47 363		88.73 358	91.01 357	67.59 361	98.16 355	82.30 353	94.53 351	93.98 353

MTMP							97.93 148	91.91 352

gm-plane-assit					94.83 357	81.97 359			88.07 345		94.99 343		99.60 271	91.76 317

test9_res														93.28 297	99.15 250	99.38 154

TEST9					98.71 241	98.08 129	95.96 283	99.03 210	91.40 325	95.85 318	97.53 290	96.52 168	99.76 202

test_8					98.67 254	98.01 137	95.91 288	99.02 214	91.64 320	95.79 320	97.50 293	96.47 171	99.76 202

agg_prior2														92.50 311	99.16 247	99.37 157

agg_prior					98.68 252	97.99 138		99.01 217		95.59 321			99.77 195

TestCases				99.16 96	99.50 76	98.55 92		99.58 26	96.80 219	98.88 142	99.06 100	97.65 92	99.57 281	94.45 260	99.61 157	99.37 157

test_prior4						97.97 143	95.86 289

test_prior2							95.74 295		96.48 231	96.11 311	97.63 286	95.92 197		94.16 268	99.20 239

test_prior				98.95 130	98.69 249	97.95 148		99.03 210					99.59 275			99.30 184

旧先验2							95.76 293		88.56 344	97.52 255			99.66 252	94.48 258

新几何2							95.93 286

新几何1				98.91 136	98.94 196	97.76 165		98.76 257	87.58 347	96.75 293	98.10 258	94.80 231	99.78 189	92.73 307	99.00 271	99.20 204

旧先验1					98.82 225	97.45 183		98.76 257			98.34 239	95.50 211			99.01 270	99.23 199

无先验							95.74 295	98.74 262	89.38 339				99.73 217	92.38 312		99.22 203

原ACMM2							95.53 302

原ACMM1				98.35 206	98.90 206	96.25 226		98.83 249	92.48 312	96.07 314	98.10 258	95.39 215	99.71 226	92.61 310	98.99 272	99.08 223

test222					98.92 202	96.93 209	95.54 301	98.78 255	85.72 350	96.86 289	98.11 257	94.43 238			99.10 259	99.23 199

testdata2													99.79 178	92.80 305

segment_acmp												97.02 139

testdata				98.09 222	98.93 198	95.40 248		98.80 252	90.08 336	97.45 261	98.37 236	95.26 217	99.70 228	93.58 289	98.95 276	99.17 215

testdata1							95.44 307		96.32 237

test12				98.93 133	98.58 266	97.83 157		98.66 267		96.53 300		95.51 210	99.69 232		99.13 254	99.27 191

plane_prior7					99.19 140	97.87 154

plane_prior6					98.99 189	97.70 171						94.90 224

plane_prior5								99.27 149					99.70 228	94.42 262	99.51 189	99.45 124

plane_prior4											97.98 266

plane_prior3						97.78 164			97.41 177	97.79 235

plane_prior2							97.77 165		98.20 118

plane_prior1					99.05 176

plane_prior						97.65 173	97.07 226		96.72 223						99.36 214

n2								0.00 367

nn								0.00 367

door-mid								99.57 33

lessismore_v0				98.97 128	99.73 23	97.53 179		86.71 359		99.37 56	99.52 34	89.93 286	99.92 33	98.99 33	99.72 111	99.44 129

LGP-MVS_train				99.47 53	99.57 54	98.97 62		99.48 66	96.60 227	99.10 97	99.06 100	98.71 25	99.83 133	95.58 236	99.78 85	99.62 44

test11								98.87 237

door								99.41 89

HQP5-MVS						96.79 212

HQP-NCC					98.67 254		96.29 270		96.05 245	95.55 325

ACMP_Plane					98.67 254		96.29 270		96.05 245	95.55 325

BP-MVS														92.82 303

HQP4-MVS										95.56 324			99.54 290			99.32 177

HQP3-MVS								99.04 208							99.26 232

HQP2-MVS												93.84 249

NP-MVS					98.84 220	97.39 186					96.84 313

MDTV_nov1_ep13_2view						74.92 363	97.69 173		90.06 337	97.75 238		85.78 310		93.52 290		98.69 278

MDTV_nov1_ep13			95.22 284		97.06 338	83.20 357	97.74 169	96.16 327	94.37 286	96.99 279	98.83 166	83.95 324	99.53 292	93.90 279	97.95 315

ACMMP++_ref															99.77 89

ACMMP++															99.68 131

Test By Simon												96.52 168

ITE_SJBPF				98.87 141	99.22 132	98.48 99		99.35 110	97.50 164	98.28 205	98.60 210	97.64 95	99.35 321	93.86 282	99.27 229	98.79 269

DeepMVS_CX				93.44 336	98.24 291	94.21 275		94.34 338	64.28 358	91.34 354	94.87 348	89.45 291	92.77 359	77.54 357	93.14 354	93.35 354

High-resolution multi-view benchmark