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.

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 41	99.89 1	99.75 35	99.56 48	99.02 10	99.88 3	99.85 26	99.18 6	99.96 19	99.22 31	99.92 12	99.90 1

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 44	99.41 74	99.58 100	99.49 105	99.02 10	99.88 3	99.80 65	99.00 18	99.94 42	99.45 15	99.92 12	99.84 12

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 80	99.39 182	98.91 29	99.78 23	99.85 26	99.36 2	99.94 42	98.84 66	99.88 35	99.82 32

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 64	99.78 36	99.14 101	99.60 91	99.45 152	99.01 13	99.90 1	99.83 37	98.98 19	99.93 57	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 64	99.78 36	99.15 100	99.61 89	99.45 152	99.01 13	99.89 2	99.82 44	99.01 12	99.92 65	99.56 5	99.95 6	99.85 8

Regformer-3		99.57 6	99.53 5	99.68 52	99.76 44	99.29 85	99.58 100	99.44 160	99.01 13	99.87 6	99.80 65	98.97 20	99.91 74	99.44 16	99.92 12	99.83 23

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 82	99.52 61	99.49 143	99.49 105	98.94 26	99.83 12	99.76 88	99.01 12	99.94 42	99.15 38	99.87 39	99.80 42

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 14	99.59 93	99.51 85	98.62 49	99.79 19	99.83 37	99.28 3	99.97 11	98.48 109	99.90 25	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
Regformer-1		99.53 9	99.47 8	99.72 49	99.71 82	99.44 71	99.49 143	99.46 140	98.95 24	99.83 12	99.76 88	99.01 12	99.93 57	99.17 36	99.87 39	99.80 42

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 8	99.69 46	99.68 19	98.98 19	99.37 110	99.74 98	98.81 36	99.94 42	98.79 72	99.86 49	99.84 12

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 66	99.47 131	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 57	99.90 25	99.83 23

HPM-MVS_fast		99.51 12	99.40 14	99.85 19	99.91 1	99.79 19	99.76 28	99.56 48	97.72 135	99.76 29	99.75 93	99.13 7	99.92 65	99.07 44	99.92 12	99.85 8

zzz-MVS		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 196	99.47 131	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 57	99.90 25	99.83 23

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 66	99.67 22	98.15 80	99.68 38	99.69 115	99.06 9	99.96 19	98.69 83	99.87 39	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 66	99.67 22	98.15 80	99.67 44	99.69 115	98.95 26	99.96 19	98.69 83	99.87 39	99.84 12

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 37	99.63 109	99.59 49	99.36 196	99.46 140	99.07 9	99.79 19	99.82 44	98.85 33	99.92 65	98.68 85	99.87 39	99.82 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 15	99.65 76	99.66 25	98.13 82	99.66 49	99.68 120	98.96 21	99.96 19	98.62 91	99.87 39	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 19	99.76 44	99.83 8	99.63 80	99.54 62	98.36 65	99.79 19	99.82 44	98.86 32	99.95 33	98.62 91	99.81 69	99.78 50

DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 76	99.40 76	99.05 270	99.66 25	99.14 6	99.57 68	99.80 65	98.46 62	99.94 42	99.57 4	99.84 58	99.60 105
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
SMA-MVS		99.47 20	99.34 24	99.86 13	99.73 72	99.85 6	99.56 113	99.50 99	97.61 144	99.84 8	99.82 44	99.28 3	99.91 74	98.79 72	99.91 17	99.81 36

ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 152	99.48 115	98.05 98	99.76 29	99.86 22	98.82 35	99.93 57	98.82 71	99.91 17	99.84 12

MSLP-MVS++		99.46 22	99.47 8	99.44 99	99.60 119	99.16 97	99.41 176	99.71 13	98.98 19	99.45 92	99.78 78	99.19 5	99.54 210	99.28 27	99.84 58	99.63 101

PGM-MVS		99.45 23	99.31 32	99.86 13	99.87 15	99.78 23	99.58 100	99.65 30	97.84 121	99.71 32	99.80 65	99.12 8	99.97 11	98.33 122	99.87 39	99.83 23

CP-MVS		99.45 23	99.32 27	99.85 19	99.83 28	99.75 24	99.69 46	99.52 76	98.07 93	99.53 79	99.63 142	98.93 28	99.97 11	98.74 76	99.91 17	99.83 23

ACMMP		99.45 23	99.32 27	99.82 26	99.89 8	99.67 35	99.62 83	99.69 18	98.12 84	99.63 54	99.84 35	98.73 49	99.96 19	98.55 104	99.83 64	99.81 36
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
abl_6		99.44 26	99.31 32	99.83 24	99.85 23	99.75 24	99.66 66	99.59 38	98.13 82	99.82 15	99.81 54	98.60 57	99.96 19	98.46 112	99.88 35	99.79 46

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 46	99.48 115	98.12 84	99.50 84	99.75 93	98.78 39	99.97 11	98.57 98	99.89 33	99.83 23

#test#		99.43 28	99.29 37	99.86 13	99.87 15	99.80 15	99.55 119	99.67 22	97.83 122	99.68 38	99.69 115	99.06 9	99.96 19	98.39 115	99.87 39	99.84 12

MCST-MVS		99.43 28	99.30 34	99.82 26	99.79 35	99.74 27	99.29 214	99.40 179	98.79 40	99.52 81	99.62 147	98.91 29	99.90 87	98.64 88	99.75 80	99.82 32

UA-Net		99.42 30	99.29 37	99.80 31	99.62 113	99.55 54	99.50 135	99.70 15	98.79 40	99.77 24	99.96 1	97.45 94	99.96 19	98.92 55	99.90 25	99.89 2

HPM-MVS		99.42 30	99.28 39	99.83 24	99.90 3	99.72 28	99.81 15	99.54 62	97.59 145	99.68 38	99.63 142	98.91 29	99.94 42	98.58 96	99.91 17	99.84 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.42 30	99.30 34	99.78 35	99.62 113	99.71 29	99.26 228	99.52 76	98.82 35	99.39 106	99.71 106	98.96 21	99.85 113	98.59 95	99.80 71	99.77 52

HSP-MVS		99.41 33	99.26 44	99.85 19	99.89 8	99.80 15	99.67 57	99.37 195	98.70 45	99.77 24	99.49 191	98.21 76	99.95 33	98.46 112	99.77 77	99.81 36

SD-MVS		99.41 33	99.52 6	99.05 147	99.74 67	99.68 33	99.46 155	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 333	98.72 80	99.93 11	99.77 52

MVS_111021_LR		99.41 33	99.33 26	99.65 59	99.77 41	99.51 63	98.94 300	99.85 6	98.82 35	99.65 52	99.74 98	98.51 59	99.80 143	98.83 68	99.89 33	99.64 97

MVS_111021_HR		99.41 33	99.32 27	99.66 55	99.72 76	99.47 67	98.95 298	99.85 6	98.82 35	99.54 78	99.73 101	98.51 59	99.74 161	98.91 56	99.88 35	99.77 52

HPM-MVS++		99.39 37	99.23 46	99.87 6	99.75 56	99.84 7	99.43 165	99.51 85	98.68 47	99.27 136	99.53 178	98.64 55	99.96 19	98.44 114	99.80 71	99.79 46

MP-MVS-pluss		99.37 38	99.20 47	99.88 4	99.90 3	99.87 2	99.30 210	99.52 76	97.18 182	99.60 61	99.79 73	98.79 38	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		99.36 39	99.36 19	99.36 106	99.67 93	98.61 186	99.07 264	99.33 216	99.00 17	99.82 15	99.81 54	99.06 9	99.84 119	99.09 42	99.42 109	99.65 91

PVSNet_Blended_VisFu		99.36 39	99.28 39	99.61 68	99.86 20	99.07 107	99.47 152	99.93 2	97.66 142	99.71 32	99.86 22	97.73 89	99.96 19	99.47 13	99.82 68	99.79 46

NCCC		99.34 41	99.19 48	99.79 34	99.61 117	99.65 40	99.30 210	99.48 115	98.86 31	99.21 158	99.63 142	98.72 50	99.90 87	98.25 126	99.63 103	99.80 42

MP-MVS		99.33 42	99.15 51	99.87 6	99.88 11	99.82 13	99.66 66	99.46 140	98.09 89	99.48 88	99.74 98	98.29 73	99.96 19	97.93 150	99.87 39	99.82 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PS-MVSNAJ		99.32 43	99.32 27	99.30 115	99.57 124	98.94 135	98.97 292	99.46 140	98.92 28	99.71 32	99.24 260	99.01 12	99.98 5	99.35 18	99.66 98	98.97 183

CSCG		99.32 43	99.32 27	99.32 111	99.85 23	98.29 204	99.71 42	99.66 25	98.11 86	99.41 101	99.80 65	98.37 70	99.96 19	98.99 50	99.96 5	99.72 72

ESAPD		99.31 45	99.13 53	99.87 6	99.81 32	99.83 8	99.37 190	99.48 115	97.97 108	99.77 24	99.78 78	98.96 21	99.95 33	97.15 214	99.84 58	99.83 23

PHI-MVS		99.30 46	99.17 50	99.70 51	99.56 127	99.52 61	99.58 100	99.80 8	97.12 188	99.62 57	99.73 101	98.58 58	99.90 87	98.61 93	99.91 17	99.68 84

DeepC-MVS	98.35 2	99.30 46	99.19 48	99.64 64	99.82 29	99.23 92	99.62 83	99.55 55	98.94 26	99.63 54	99.95 2	95.82 142	99.94 42	99.37 17	99.97 3	99.73 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v1_base_debu		99.29 48	99.27 41	99.34 107	99.63 109	98.97 127	99.12 253	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

xiu_mvs_v1_base		99.29 48	99.27 41	99.34 107	99.63 109	98.97 127	99.12 253	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

xiu_mvs_v1_base_debi		99.29 48	99.27 41	99.34 107	99.63 109	98.97 127	99.12 253	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

APD-MVS		99.27 51	99.08 58	99.84 23	99.75 56	99.79 19	99.50 135	99.50 99	97.16 184	99.77 24	99.82 44	98.78 39	99.94 42	97.56 185	99.86 49	99.80 42
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 51	99.12 55	99.74 45	99.18 202	99.75 24	99.56 113	99.57 44	98.45 59	99.49 87	99.85 26	97.77 88	99.94 42	98.33 122	99.84 58	99.52 120

xiu_mvs_v2_base		99.26 53	99.25 45	99.29 118	99.53 129	98.91 140	99.02 279	99.45 152	98.80 39	99.71 32	99.26 257	98.94 27	99.98 5	99.34 22	99.23 120	98.98 182

CANet		99.25 54	99.14 52	99.59 70	99.41 153	99.16 97	99.35 200	99.57 44	98.82 35	99.51 83	99.61 151	96.46 122	99.95 33	99.59 2	99.98 2	99.65 91

3Dnovator	97.25 9	99.24 55	99.05 60	99.81 29	99.12 215	99.66 37	99.84 9	99.74 10	99.09 8	98.92 207	99.90 7	95.94 137	99.98 5	98.95 53	99.92 12	99.79 46

test_prior3		99.21 56	99.05 60	99.68 52	99.67 93	99.48 65	98.96 294	99.56 48	98.34 66	99.01 192	99.52 183	98.68 52	99.83 126	97.96 147	99.74 82	99.74 61

CHOSEN 1792x2688		99.19 57	99.10 57	99.45 96	99.89 8	98.52 193	99.39 183	99.94 1	98.73 44	99.11 174	99.89 10	95.50 149	99.94 42	99.50 8	99.97 3	99.89 2

F-COLMAP		99.19 57	99.04 63	99.64 64	99.78 36	99.27 88	99.42 172	99.54 62	97.29 173	99.41 101	99.59 156	98.42 67	99.93 57	98.19 128	99.69 93	99.73 66

3Dnovator+	97.12 13	99.18 59	98.97 73	99.82 26	99.17 207	99.68 33	99.81 15	99.51 85	99.20 4	98.72 229	99.89 10	95.68 146	99.97 11	98.86 64	99.86 49	99.81 36

MVSFormer		99.17 60	99.12 55	99.29 118	99.51 132	98.94 135	99.88 1	99.46 140	97.55 150	99.80 17	99.65 131	97.39 95	99.28 253	99.03 46	99.85 53	99.65 91

sss		99.17 60	99.05 60	99.53 81	99.62 113	98.97 127	99.36 196	99.62 31	97.83 122	99.67 44	99.65 131	97.37 98	99.95 33	99.19 33	99.19 123	99.68 84

DP-MVS		99.16 62	98.95 77	99.78 35	99.77 41	99.53 58	99.41 176	99.50 99	97.03 203	99.04 189	99.88 14	97.39 95	99.92 65	98.66 86	99.90 25	99.87 4

CNLPA		99.14 63	98.99 70	99.59 70	99.58 122	99.41 74	99.16 246	99.44 160	98.45 59	99.19 164	99.49 191	98.08 80	99.89 95	97.73 169	99.75 80	99.48 131

CDPH-MVS		99.13 64	98.91 81	99.80 31	99.75 56	99.71 29	99.15 249	99.41 172	96.60 230	99.60 61	99.55 168	98.83 34	99.90 87	97.48 193	99.83 64	99.78 50

jason		99.13 64	99.03 65	99.45 96	99.46 144	98.87 143	99.12 253	99.26 243	98.03 101	99.79 19	99.65 131	97.02 105	99.85 113	99.02 48	99.90 25	99.65 91
jason: jason.
lupinMVS		99.13 64	99.01 69	99.46 95	99.51 132	98.94 135	99.05 270	99.16 254	97.86 117	99.80 17	99.56 165	97.39 95	99.86 107	98.94 54	99.85 53	99.58 111

EPP-MVSNet		99.13 64	98.99 70	99.53 81	99.65 105	99.06 108	99.81 15	99.33 216	97.43 161	99.60 61	99.88 14	97.14 102	99.84 119	99.13 39	98.94 142	99.69 80

MG-MVS		99.13 64	99.02 68	99.45 96	99.57 124	98.63 181	99.07 264	99.34 208	98.99 18	99.61 59	99.82 44	97.98 83	99.87 104	97.00 223	99.80 71	99.85 8

CHOSEN 280x420		99.12 69	99.13 53	99.08 143	99.66 103	97.89 220	98.43 330	99.71 13	98.88 30	99.62 57	99.76 88	96.63 118	99.70 185	99.46 14	99.99 1	99.66 88

DP-MVS Recon		99.12 69	98.95 77	99.65 59	99.74 67	99.70 31	99.27 220	99.57 44	96.40 248	99.42 99	99.68 120	98.75 47	99.80 143	97.98 146	99.72 86	99.44 141

Vis-MVSNet		99.12 69	98.97 73	99.56 76	99.78 36	99.10 104	99.68 55	99.66 25	98.49 56	99.86 7	99.87 19	94.77 189	99.84 119	99.19 33	99.41 110	99.74 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 69	99.08 58	99.24 128	99.46 144	98.55 188	99.51 130	99.46 140	98.09 89	99.45 92	99.82 44	98.34 71	99.51 211	98.70 81	98.93 143	99.67 87

VNet		99.11 73	98.90 82	99.73 47	99.52 130	99.56 52	99.41 176	99.39 182	99.01 13	99.74 31	99.78 78	95.56 147	99.92 65	99.52 7	98.18 184	99.72 72

CPTT-MVS		99.11 73	98.90 82	99.74 45	99.80 34	99.46 68	99.59 93	99.49 105	97.03 203	99.63 54	99.69 115	97.27 100	99.96 19	97.82 158	99.84 58	99.81 36

HyFIR lowres test		99.11 73	98.92 79	99.65 59	99.90 3	99.37 77	99.02 279	99.91 3	97.67 141	99.59 64	99.75 93	95.90 139	99.73 169	99.53 6	99.02 135	99.86 5

MVS_Test		99.10 76	98.97 73	99.48 90	99.49 139	99.14 101	99.67 57	99.34 208	97.31 171	99.58 65	99.76 88	97.65 91	99.82 135	98.87 61	99.07 132	99.46 138

1121		99.09 77	98.87 86	99.75 40	99.74 67	99.60 47	99.27 220	99.48 115	96.82 217	99.25 144	99.65 131	98.38 68	99.93 57	97.53 188	99.67 97	99.73 66

CDS-MVSNet		99.09 77	99.03 65	99.25 125	99.42 150	98.73 171	99.45 156	99.46 140	98.11 86	99.46 91	99.77 85	98.01 82	99.37 230	98.70 81	98.92 145	99.66 88
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		99.08 79	98.97 73	99.42 103	99.76 44	98.79 166	98.78 312	99.91 3	96.74 219	99.67 44	99.49 191	97.53 92	99.88 102	98.98 51	99.85 53	99.60 105

OMC-MVS		99.08 79	99.04 63	99.20 132	99.67 93	98.22 207	99.28 217	99.52 76	98.07 93	99.66 49	99.81 54	97.79 87	99.78 154	97.79 161	99.81 69	99.60 105

MVS_0304		99.06 81	98.86 89	99.66 55	99.51 132	99.36 78	99.22 237	99.51 85	98.95 24	99.58 65	99.65 131	93.74 231	99.98 5	99.66 1	99.95 6	99.64 97

WTY-MVS		99.06 81	98.88 85	99.61 68	99.62 113	99.16 97	99.37 190	99.56 48	98.04 99	99.53 79	99.62 147	96.84 110	99.94 42	98.85 65	98.49 168	99.72 72

IS-MVSNet		99.05 83	98.87 86	99.57 74	99.73 72	99.32 81	99.75 35	99.20 250	98.02 102	99.56 69	99.86 22	96.54 120	99.67 190	98.09 136	99.13 126	99.73 66

PAPM_NR		99.04 84	98.84 92	99.66 55	99.74 67	99.44 71	99.39 183	99.38 188	97.70 138	99.28 132	99.28 254	98.34 71	99.85 113	96.96 227	99.45 107	99.69 80

API-MVS		99.04 84	99.03 65	99.06 145	99.40 158	99.31 84	99.55 119	99.56 48	98.54 53	99.33 121	99.39 223	98.76 44	99.78 154	96.98 225	99.78 75	98.07 310

mvs_anonymous		99.03 86	98.99 70	99.16 135	99.38 161	98.52 193	99.51 130	99.38 188	97.79 127	99.38 108	99.81 54	97.30 99	99.45 215	99.35 18	98.99 137	99.51 125

train_agg		99.02 87	98.77 99	99.77 37	99.67 93	99.65 40	99.05 270	99.41 172	96.28 254	98.95 203	99.49 191	98.76 44	99.91 74	97.63 178	99.72 86	99.75 56

canonicalmvs		99.02 87	98.86 89	99.51 87	99.42 150	99.32 81	99.80 19	99.48 115	98.63 48	99.31 123	98.81 295	97.09 103	99.75 160	99.27 29	97.90 206	99.47 135

PLC	97.94 4	99.02 87	98.85 91	99.53 81	99.66 103	99.01 120	99.24 232	99.52 76	96.85 214	99.27 136	99.48 197	98.25 75	99.91 74	97.76 165	99.62 104	99.65 91
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
agg_prior1		99.01 90	98.76 101	99.76 39	99.67 93	99.62 43	98.99 285	99.40 179	96.26 257	98.87 213	99.49 191	98.77 42	99.91 74	97.69 175	99.72 86	99.75 56

AdaColmap		99.01 90	98.80 96	99.66 55	99.56 127	99.54 55	99.18 244	99.70 15	98.18 79	99.35 117	99.63 142	96.32 127	99.90 87	97.48 193	99.77 77	99.55 113

1112_ss		98.98 92	98.77 99	99.59 70	99.68 92	99.02 118	99.25 230	99.48 115	97.23 179	99.13 170	99.58 159	96.93 109	99.90 87	98.87 61	98.78 155	99.84 12

MSDG		98.98 92	98.80 96	99.53 81	99.76 44	99.19 94	98.75 315	99.55 55	97.25 176	99.47 89	99.77 85	97.82 86	99.87 104	96.93 230	99.90 25	99.54 115

CANet_DTU		98.97 94	98.87 86	99.25 125	99.33 170	98.42 202	99.08 263	99.30 225	99.16 5	99.43 96	99.75 93	95.27 155	99.97 11	98.56 101	99.95 6	99.36 149

agg_prior3		98.97 94	98.71 105	99.75 40	99.67 93	99.60 47	99.04 275	99.41 172	95.93 277	98.87 213	99.48 197	98.61 56	99.91 74	97.63 178	99.72 86	99.75 56

114514_t		98.93 96	98.67 109	99.72 49	99.85 23	99.53 58	99.62 83	99.59 38	92.65 323	99.71 32	99.78 78	98.06 81	99.90 87	98.84 66	99.91 17	99.74 61

PS-MVSNAJss		98.92 97	98.92 79	98.90 176	98.78 282	98.53 190	99.78 22	99.54 62	98.07 93	99.00 199	99.76 88	99.01 12	99.37 230	99.13 39	97.23 238	98.81 201

Test_1112_low_res		98.89 98	98.66 112	99.57 74	99.69 89	98.95 132	99.03 276	99.47 131	96.98 205	99.15 169	99.23 261	96.77 114	99.89 95	98.83 68	98.78 155	99.86 5

AllTest		98.87 99	98.72 103	99.31 112	99.86 20	98.48 198	99.56 113	99.61 32	97.85 119	99.36 114	99.85 26	95.95 135	99.85 113	96.66 249	99.83 64	99.59 109

UGNet		98.87 99	98.69 107	99.40 104	99.22 194	98.72 173	99.44 160	99.68 19	99.24 3	99.18 166	99.42 212	92.74 246	99.96 19	99.34 22	99.94 10	99.53 119
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
Vis-MVSNet (Re-imp)		98.87 99	98.72 103	99.31 112	99.71 82	98.88 142	99.80 19	99.44 160	97.91 115	99.36 114	99.78 78	95.49 150	99.43 224	97.91 151	99.11 127	99.62 103

mvs-test1		98.86 102	98.84 92	98.89 178	99.33 170	97.77 231	99.44 160	99.30 225	98.47 57	99.10 177	99.43 210	96.78 112	99.95 33	98.73 78	99.02 135	98.96 189

EPNet		98.86 102	98.71 105	99.30 115	97.20 330	98.18 208	99.62 83	98.91 284	99.28 2	98.63 247	99.81 54	95.96 134	99.99 1	99.24 30	99.72 86	99.73 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 102	98.80 96	99.03 148	99.76 44	98.79 166	99.28 217	99.91 3	97.42 163	99.67 44	99.37 228	97.53 92	99.88 102	98.98 51	97.29 237	98.42 298

ab-mvs		98.86 102	98.63 114	99.54 77	99.64 106	99.19 94	99.44 160	99.54 62	97.77 129	99.30 124	99.81 54	94.20 212	99.93 57	99.17 36	98.82 152	99.49 129

MAR-MVS		98.86 102	98.63 114	99.54 77	99.37 163	99.66 37	99.45 156	99.54 62	96.61 228	99.01 192	99.40 219	97.09 103	99.86 107	97.68 177	99.53 106	99.10 164
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
COLMAP_ROB	97.56 6	98.86 102	98.75 102	99.17 134	99.88 11	98.53 190	99.34 203	99.59 38	97.55 150	98.70 236	99.89 10	95.83 141	99.90 87	98.10 135	99.90 25	99.08 169
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HY-MVS	97.30 7	98.85 108	98.64 113	99.47 93	99.42 150	99.08 106	99.62 83	99.36 196	97.39 166	99.28 132	99.68 120	96.44 124	99.92 65	98.37 118	98.22 180	99.40 146

PVSNet	96.02 17	98.85 108	98.84 92	98.89 178	99.73 72	97.28 238	98.32 334	99.60 35	97.86 117	99.50 84	99.57 163	96.75 115	99.86 107	98.56 101	99.70 92	99.54 115

PatchMatch-RL		98.84 110	98.62 117	99.52 85	99.71 82	99.28 86	99.06 268	99.77 9	97.74 133	99.50 84	99.53 178	95.41 151	99.84 119	97.17 213	99.64 101	99.44 141

Effi-MVS+		98.81 111	98.59 122	99.48 90	99.46 144	99.12 103	98.08 340	99.50 99	97.50 155	99.38 108	99.41 215	96.37 126	99.81 139	99.11 41	98.54 165	99.51 125

alignmvs		98.81 111	98.56 124	99.58 73	99.43 149	99.42 73	99.51 130	98.96 277	98.61 50	99.35 117	98.92 286	94.78 185	99.77 156	99.35 18	98.11 200	99.54 115

DeepPCF-MVS	98.18 3	98.81 111	99.37 17	97.12 304	99.60 119	91.75 332	98.61 323	99.44 160	99.35 1	99.83 12	99.85 26	98.70 51	99.81 139	99.02 48	99.91 17	99.81 36

PMMVS		98.80 114	98.62 117	99.34 107	99.27 187	98.70 174	98.76 314	99.31 223	97.34 168	99.21 158	99.07 273	97.20 101	99.82 135	98.56 101	98.87 149	99.52 120

Effi-MVS+-dtu		98.78 115	98.89 84	98.47 232	99.33 170	96.91 263	99.57 106	99.30 225	98.47 57	99.41 101	98.99 280	96.78 112	99.74 161	98.73 78	99.38 111	98.74 213

FIs		98.78 115	98.63 114	99.23 130	99.18 202	99.54 55	99.83 12	99.59 38	98.28 70	98.79 223	99.81 54	96.75 115	99.37 230	99.08 43	96.38 253	98.78 204

Fast-Effi-MVS+-dtu		98.77 117	98.83 95	98.60 218	99.41 153	96.99 257	99.52 126	99.49 105	98.11 86	99.24 149	99.34 242	96.96 108	99.79 146	97.95 149	99.45 107	99.02 178

FC-MVSNet-test		98.75 118	98.62 117	99.15 137	99.08 223	99.45 70	99.86 8	99.60 35	98.23 75	98.70 236	99.82 44	96.80 111	99.22 268	99.07 44	96.38 253	98.79 203

XVG-OURS		98.73 119	98.68 108	98.88 185	99.70 87	97.73 233	98.92 301	99.55 55	98.52 55	99.45 92	99.84 35	95.27 155	99.91 74	98.08 140	98.84 151	99.00 179

diffmvs		98.72 120	98.49 126	99.43 102	99.48 142	99.19 94	99.62 83	99.42 169	95.58 283	99.37 110	99.67 124	96.14 132	99.74 161	98.14 133	98.96 140	99.37 148

Fast-Effi-MVS+		98.70 121	98.43 128	99.51 87	99.51 132	99.28 86	99.52 126	99.47 131	96.11 271	99.01 192	99.34 242	96.20 131	99.84 119	97.88 153	98.82 152	99.39 147

XVG-OURS-SEG-HR		98.69 122	98.62 117	98.89 178	99.71 82	97.74 232	99.12 253	99.54 62	98.44 62	99.42 99	99.71 106	94.20 212	99.92 65	98.54 106	98.90 147	99.00 179

1314		98.68 123	98.54 125	99.11 142	98.89 265	98.65 179	99.27 220	99.49 105	96.89 212	97.99 282	99.56 165	97.72 90	99.83 126	97.74 168	99.27 119	98.84 199

EI-MVSNet		98.67 124	98.67 109	98.68 213	99.35 166	97.97 216	99.50 135	99.38 188	96.93 209	99.20 161	99.83 37	97.87 84	99.36 234	98.38 117	97.56 218	98.71 217

test_djsdf		98.67 124	98.57 123	98.98 154	98.70 293	98.91 140	99.88 1	99.46 140	97.55 150	99.22 156	99.88 14	95.73 145	99.28 253	99.03 46	97.62 213	98.75 210

QAPM		98.67 124	98.30 137	99.80 31	99.20 197	99.67 35	99.77 25	99.72 11	94.74 292	98.73 228	99.90 7	95.78 143	99.98 5	96.96 227	99.88 35	99.76 55

nrg030		98.64 127	98.42 129	99.28 120	99.05 229	99.69 32	99.81 15	99.46 140	98.04 99	99.01 192	99.82 44	96.69 117	99.38 226	99.34 22	94.59 294	98.78 204

PAPR		98.63 128	98.34 133	99.51 87	99.40 158	99.03 117	98.80 310	99.36 196	96.33 250	99.00 199	99.12 271	98.46 62	99.84 119	95.23 279	99.37 115	99.66 88

CVMVSNet		98.57 129	98.67 109	98.30 247	99.35 166	95.59 292	99.50 135	99.55 55	98.60 51	99.39 106	99.83 37	94.48 203	99.45 215	98.75 75	98.56 164	99.85 8

MVSTER		98.49 130	98.32 135	99.00 152	99.35 166	99.02 118	99.54 122	99.38 188	97.41 164	99.20 161	99.73 101	93.86 226	99.36 234	98.87 61	97.56 218	98.62 268

OpenMVS	96.50 16	98.47 131	98.12 145	99.52 85	99.04 230	99.53 58	99.82 13	99.72 11	94.56 298	98.08 276	99.88 14	94.73 192	99.98 5	97.47 195	99.76 79	99.06 174

IterMVS-LS		98.46 132	98.42 129	98.58 220	99.59 121	98.00 214	99.37 190	99.43 168	96.94 208	99.07 183	99.59 156	97.87 84	99.03 289	98.32 124	95.62 268	98.71 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 133	98.28 138	98.94 160	98.50 309	98.96 131	99.77 25	99.50 99	97.07 199	98.87 213	99.77 85	94.76 190	99.28 253	98.66 86	97.60 214	98.57 289

jajsoiax		98.43 134	98.28 138	98.88 185	98.60 304	98.43 200	99.82 13	99.53 72	98.19 76	98.63 247	99.80 65	93.22 236	99.44 220	99.22 31	97.50 223	98.77 207

BH-untuned		98.42 135	98.36 131	98.59 219	99.49 139	96.70 269	99.27 220	99.13 258	97.24 178	98.80 222	99.38 224	95.75 144	99.74 161	97.07 220	99.16 124	99.33 152

BH-RMVSNet		98.41 136	98.08 149	99.40 104	99.41 153	98.83 150	99.30 210	98.77 298	97.70 138	98.94 205	99.65 131	92.91 242	99.74 161	96.52 253	99.55 105	99.64 97

mvs_tets		98.40 137	98.23 140	98.91 172	98.67 297	98.51 195	99.66 66	99.53 72	98.19 76	98.65 245	99.81 54	92.75 244	99.44 220	99.31 25	97.48 227	98.77 207

XXY-MVS		98.38 138	98.09 148	99.24 128	99.26 189	99.32 81	99.56 113	99.55 55	97.45 160	98.71 230	99.83 37	93.23 235	99.63 201	98.88 57	96.32 255	98.76 209

ACMM	97.58 5	98.37 139	98.34 133	98.48 230	99.41 153	97.10 246	99.56 113	99.45 152	98.53 54	99.04 189	99.85 26	93.00 238	99.71 179	98.74 76	97.45 228	98.64 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn1000		98.33 140	98.02 154	99.25 125	99.78 36	98.73 171	99.70 43	97.55 345	97.48 156	99.69 37	99.53 178	92.37 268	99.85 113	97.82 158	98.26 179	99.16 160

tpmrst		98.33 140	98.48 127	97.90 280	99.16 209	94.78 310	99.31 208	99.11 259	97.27 174	99.45 92	99.59 156	95.33 152	99.84 119	98.48 109	98.61 158	99.09 168

PatchmatchNet		98.31 142	98.36 131	98.19 263	99.16 209	95.32 300	99.27 220	98.92 281	97.37 167	99.37 110	99.58 159	94.90 177	99.70 185	97.43 199	99.21 121	99.54 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20240521		98.30 143	98.00 156	99.18 133	98.98 240	99.46 68	99.78 22	99.49 105	96.91 211	98.00 281	99.25 258	96.51 121	99.38 226	98.15 132	94.95 283	98.71 217

VPA-MVSNet		98.29 144	97.95 161	99.30 115	99.16 209	99.54 55	99.50 135	99.58 43	98.27 71	99.35 117	99.37 228	92.53 261	99.65 194	99.35 18	94.46 295	98.72 215

UniMVSNet (Re)		98.29 144	98.00 156	99.13 141	99.00 235	99.36 78	99.49 143	99.51 85	97.95 110	98.97 202	99.13 268	96.30 128	99.38 226	98.36 120	93.34 311	98.66 254

HQP_MVS		98.27 146	98.22 141	98.44 237	99.29 182	96.97 259	99.39 183	99.47 131	98.97 22	99.11 174	99.61 151	92.71 248	99.69 188	97.78 162	97.63 211	98.67 243

thresconf0.02		98.24 147	97.89 168	99.27 121	99.76 44	99.04 110	99.67 57	97.71 337	97.10 192	99.55 72	99.54 171	92.70 250	99.79 146	96.90 233	98.12 194	98.97 183

tfpn_n400		98.24 147	97.89 168	99.27 121	99.76 44	99.04 110	99.67 57	97.71 337	97.10 192	99.55 72	99.54 171	92.70 250	99.79 146	96.90 233	98.12 194	98.97 183

tfpnconf		98.24 147	97.89 168	99.27 121	99.76 44	99.04 110	99.67 57	97.71 337	97.10 192	99.55 72	99.54 171	92.70 250	99.79 146	96.90 233	98.12 194	98.97 183

tfpnview11		98.24 147	97.89 168	99.27 121	99.76 44	99.04 110	99.67 57	97.71 337	97.10 192	99.55 72	99.54 171	92.70 250	99.79 146	96.90 233	98.12 194	98.97 183

UniMVSNet_NR-MVSNet		98.22 151	97.97 159	98.96 157	98.92 259	98.98 124	99.48 148	99.53 72	97.76 130	98.71 230	99.46 205	96.43 125	99.22 268	98.57 98	92.87 317	98.69 227

LPG-MVS_test		98.22 151	98.13 144	98.49 228	99.33 170	97.05 252	99.58 100	99.55 55	97.46 157	99.24 149	99.83 37	92.58 259	99.72 173	98.09 136	97.51 221	98.68 232

RPSCF		98.22 151	98.62 117	96.99 305	99.82 29	91.58 333	99.72 40	99.44 160	96.61 228	99.66 49	99.89 10	95.92 138	99.82 135	97.46 196	99.10 129	99.57 112

conf0.01		98.21 154	97.89 168	99.15 137	99.76 44	99.04 110	99.67 57	97.71 337	97.10 192	99.55 72	99.54 171	92.70 250	99.79 146	96.90 233	98.12 194	98.61 277

conf0.002		98.21 154	97.89 168	99.15 137	99.76 44	99.04 110	99.67 57	97.71 337	97.10 192	99.55 72	99.54 171	92.70 250	99.79 146	96.90 233	98.12 194	98.61 277

ADS-MVSNet		98.20 156	98.08 149	98.56 223	99.33 170	96.48 276	99.23 233	99.15 255	96.24 259	99.10 177	99.67 124	94.11 217	99.71 179	96.81 240	99.05 133	99.48 131

OPM-MVS		98.19 157	98.10 146	98.45 234	98.88 266	97.07 250	99.28 217	99.38 188	98.57 52	99.22 156	99.81 54	92.12 271	99.66 192	98.08 140	97.54 220	98.61 277

tfpn_ndepth		98.17 158	97.84 176	99.15 137	99.75 56	98.76 170	99.61 89	97.39 347	96.92 210	99.61 59	99.38 224	92.19 270	99.86 107	97.57 183	98.13 192	98.82 200

CR-MVSNet		98.17 158	97.93 163	98.87 189	99.18 202	98.49 196	99.22 237	99.33 216	96.96 206	99.56 69	99.38 224	94.33 208	99.00 293	94.83 286	98.58 161	99.14 161

Patchmatch-test1		98.16 160	98.14 143	98.22 260	99.30 179	95.55 293	99.07 264	98.97 275	97.57 148	99.43 96	99.60 154	92.72 247	99.60 204	97.38 201	99.20 122	99.50 128

CLD-MVS		98.16 160	98.10 146	98.33 244	99.29 182	96.82 266	98.75 315	99.44 160	97.83 122	99.13 170	99.55 168	92.92 240	99.67 190	98.32 124	97.69 210	98.48 294
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		98.13 162	97.90 164	98.81 200	98.61 303	98.87 143	98.99 285	99.21 249	96.44 243	99.06 187	99.58 159	95.90 139	99.11 281	97.18 212	96.11 258	98.46 297

WR-MVS_H		98.13 162	97.87 175	98.90 176	99.02 233	98.84 147	99.70 43	99.59 38	97.27 174	98.40 259	99.19 264	95.53 148	99.23 265	98.34 121	93.78 308	98.61 277

v1neww		98.12 164	97.84 176	98.93 163	98.97 244	98.81 159	99.66 66	99.35 200	96.49 235	99.29 128	99.37 228	95.02 167	99.32 244	97.73 169	94.73 286	98.67 243

v7new		98.12 164	97.84 176	98.93 163	98.97 244	98.81 159	99.66 66	99.35 200	96.49 235	99.29 128	99.37 228	95.02 167	99.32 244	97.73 169	94.73 286	98.67 243

v6		98.12 164	97.84 176	98.94 160	98.94 252	98.83 150	99.66 66	99.34 208	96.49 235	99.30 124	99.37 228	94.95 171	99.34 240	97.77 164	94.74 285	98.67 243

ACMH	97.28 8	98.10 167	97.99 158	98.44 237	99.41 153	96.96 261	99.60 91	99.56 48	98.09 89	98.15 273	99.91 5	90.87 294	99.70 185	98.88 57	97.45 228	98.67 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		98.09 168	97.78 183	99.01 150	98.97 244	99.24 91	99.67 57	99.46 140	97.25 176	98.48 256	99.64 138	93.79 227	99.06 285	98.63 89	94.10 302	98.74 213

DU-MVS		98.08 169	97.79 181	98.96 157	98.87 269	98.98 124	99.41 176	99.45 152	97.87 116	98.71 230	99.50 188	94.82 182	99.22 268	98.57 98	92.87 317	98.68 232

divwei89l23v2f112		98.06 170	97.78 183	98.91 172	98.90 262	98.77 169	99.57 106	99.35 200	96.45 242	99.24 149	99.37 228	94.92 175	99.27 256	97.50 191	94.71 290	98.68 232

v2v482		98.06 170	97.77 187	98.92 168	98.90 262	98.82 157	99.57 106	99.36 196	96.65 225	99.19 164	99.35 239	94.20 212	99.25 262	97.72 173	94.97 281	98.69 227

V42		98.06 170	97.79 181	98.86 193	98.98 240	98.84 147	99.69 46	99.34 208	96.53 234	99.30 124	99.37 228	94.67 195	99.32 244	97.57 183	94.66 291	98.42 298

test-LLR		98.06 170	97.90 164	98.55 225	98.79 278	97.10 246	98.67 319	97.75 334	97.34 168	98.61 250	98.85 291	94.45 204	99.45 215	97.25 206	99.38 111	99.10 164

WR-MVS		98.06 170	97.73 194	99.06 145	98.86 272	99.25 90	99.19 243	99.35 200	97.30 172	98.66 239	99.43 210	93.94 222	99.21 272	98.58 96	94.28 298	98.71 217

ACMP	97.20 11	98.06 170	97.94 162	98.45 234	99.37 163	97.01 255	99.44 160	99.49 105	97.54 153	98.45 257	99.79 73	91.95 272	99.72 173	97.91 151	97.49 226	98.62 268
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1141		98.05 176	97.76 190	98.91 172	98.91 261	98.78 168	99.57 106	99.35 200	96.41 247	99.23 154	99.36 235	94.93 174	99.27 256	97.38 201	94.72 288	98.68 232

v7		98.05 176	97.78 183	98.87 189	98.99 236	98.67 176	99.64 78	99.34 208	96.31 253	99.29 128	99.51 186	94.78 185	99.27 256	97.03 221	95.15 277	98.66 254

v1		98.05 176	97.76 190	98.93 163	98.92 259	98.80 164	99.57 106	99.35 200	96.39 249	99.28 132	99.36 235	94.86 180	99.32 244	97.38 201	94.72 288	98.68 232

EPNet_dtu		98.03 179	97.96 160	98.23 258	98.27 314	95.54 295	99.23 233	98.75 299	99.02 10	97.82 287	99.71 106	96.11 133	99.48 212	93.04 317	99.65 100	99.69 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 179	97.76 190	98.84 197	99.39 160	98.98 124	99.40 182	99.38 188	96.67 224	99.07 183	99.28 254	92.93 239	98.98 295	97.10 217	96.65 246	98.56 290

ADS-MVSNet2		98.02 181	98.07 151	97.87 281	99.33 170	95.19 304	99.23 233	99.08 262	96.24 259	99.10 177	99.67 124	94.11 217	98.93 304	96.81 240	99.05 133	99.48 131

HQP-MVS		98.02 181	97.90 164	98.37 242	99.19 199	96.83 264	98.98 289	99.39 182	98.24 72	98.66 239	99.40 219	92.47 263	99.64 196	97.19 210	97.58 216	98.64 259

LTVRE_ROB	97.16 12	98.02 181	97.90 164	98.40 240	99.23 192	96.80 267	99.70 43	99.60 35	97.12 188	98.18 272	99.70 109	91.73 282	99.72 173	98.39 115	97.45 228	98.68 232
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
PatchFormer-LS_test		98.01 184	98.05 152	97.87 281	99.15 212	94.76 311	99.42 172	98.93 279	97.12 188	98.84 219	98.59 307	93.74 231	99.80 143	98.55 104	98.17 190	99.06 174

BH-w/o		98.00 185	97.89 168	98.32 245	99.35 166	96.20 285	99.01 283	98.90 286	96.42 245	98.38 260	99.00 279	95.26 157	99.72 173	96.06 261	98.61 158	99.03 176

v1144		97.98 186	97.69 197	98.85 196	98.87 269	98.66 178	99.54 122	99.35 200	96.27 256	99.23 154	99.35 239	94.67 195	99.23 265	96.73 244	95.16 276	98.68 232

EU-MVSNet		97.98 186	98.03 153	97.81 287	98.72 290	96.65 272	99.66 66	99.66 25	98.09 89	98.35 263	99.82 44	95.25 158	98.01 325	97.41 200	95.30 273	98.78 204

tpmvs		97.98 186	98.02 154	97.84 284	99.04 230	94.73 312	99.31 208	99.20 250	96.10 275	98.76 226	99.42 212	94.94 172	99.81 139	96.97 226	98.45 169	98.97 183

view600		97.97 189	97.66 199	98.89 178	99.75 56	97.81 226	99.69 46	98.80 294	98.02 102	99.25 144	98.88 287	91.95 272	99.89 95	94.36 295	98.29 175	98.96 189

view800		97.97 189	97.66 199	98.89 178	99.75 56	97.81 226	99.69 46	98.80 294	98.02 102	99.25 144	98.88 287	91.95 272	99.89 95	94.36 295	98.29 175	98.96 189

conf0.05thres1000		97.97 189	97.66 199	98.89 178	99.75 56	97.81 226	99.69 46	98.80 294	98.02 102	99.25 144	98.88 287	91.95 272	99.89 95	94.36 295	98.29 175	98.96 189

tfpn		97.97 189	97.66 199	98.89 178	99.75 56	97.81 226	99.69 46	98.80 294	98.02 102	99.25 144	98.88 287	91.95 272	99.89 95	94.36 295	98.29 175	98.96 189

NR-MVSNet		97.97 189	97.61 207	99.02 149	98.87 269	99.26 89	99.47 152	99.42 169	97.63 143	97.08 298	99.50 188	95.07 165	99.13 278	97.86 155	93.59 309	98.68 232

v8		97.95 194	97.63 206	98.93 163	98.95 249	98.81 159	99.80 19	99.41 172	96.03 276	99.10 177	99.42 212	94.92 175	99.30 250	96.94 229	94.08 303	98.66 254

Patchmatch-test		97.93 195	97.65 204	98.77 206	99.18 202	97.07 250	99.03 276	99.14 257	96.16 266	98.74 227	99.57 163	94.56 199	99.72 173	93.36 312	99.11 127	99.52 120

PS-CasMVS		97.93 195	97.59 209	98.95 159	98.99 236	99.06 108	99.68 55	99.52 76	97.13 186	98.31 265	99.68 120	92.44 267	99.05 286	98.51 107	94.08 303	98.75 210

TranMVSNet+NR-MVSNet		97.93 195	97.66 199	98.76 208	98.78 282	98.62 183	99.65 76	99.49 105	97.76 130	98.49 255	99.60 154	94.23 211	98.97 302	98.00 145	92.90 315	98.70 222

v144192		97.92 198	97.60 208	98.87 189	98.83 275	98.65 179	99.55 119	99.34 208	96.20 262	99.32 122	99.40 219	94.36 207	99.26 261	96.37 258	95.03 280	98.70 222

ACMH+	97.24 10	97.92 198	97.78 183	98.32 245	99.46 144	96.68 271	99.56 113	99.54 62	98.41 63	97.79 289	99.87 19	90.18 301	99.66 192	98.05 144	97.18 241	98.62 268

LFMVS		97.90 200	97.35 242	99.54 77	99.52 130	99.01 120	99.39 183	98.24 326	97.10 192	99.65 52	99.79 73	84.79 337	99.91 74	99.28 27	98.38 172	99.69 80

OurMVSNet-221017-0		97.88 201	97.77 187	98.19 263	98.71 292	96.53 274	99.88 1	99.00 272	97.79 127	98.78 224	99.94 3	91.68 283	99.35 237	97.21 208	96.99 244	98.69 227

v7n		97.87 202	97.52 212	98.92 168	98.76 286	98.58 187	99.84 9	99.46 140	96.20 262	98.91 208	99.70 109	94.89 178	99.44 220	96.03 262	93.89 307	98.75 210

thres600view7		97.86 203	97.51 214	98.92 168	99.72 76	97.95 219	99.59 93	98.74 302	97.94 111	99.27 136	98.62 302	91.75 278	99.86 107	93.73 308	98.19 183	98.96 189

v10		97.85 204	97.52 212	98.86 193	98.99 236	98.67 176	99.75 35	99.41 172	95.70 281	98.98 201	99.41 215	94.75 191	99.23 265	96.01 263	94.63 293	98.67 243

GA-MVS		97.85 204	97.47 221	99.00 152	99.38 161	97.99 215	98.57 325	99.15 255	97.04 202	98.90 210	99.30 251	89.83 303	99.38 226	96.70 246	98.33 173	99.62 103

tfpnnormal		97.84 206	97.47 221	98.98 154	99.20 197	99.22 93	99.64 78	99.61 32	96.32 251	98.27 268	99.70 109	93.35 234	99.44 220	95.69 269	95.40 271	98.27 305

VPNet		97.84 206	97.44 230	99.01 150	99.21 195	98.94 135	99.48 148	99.57 44	98.38 64	99.28 132	99.73 101	88.89 311	99.39 225	99.19 33	93.27 312	98.71 217

LCM-MVSNet-Re		97.83 208	98.15 142	96.87 309	99.30 179	92.25 331	99.59 93	98.26 325	97.43 161	96.20 307	99.13 268	96.27 129	98.73 309	98.17 130	98.99 137	99.64 97

XVG-ACMP-BASELINE		97.83 208	97.71 196	98.20 262	99.11 217	96.33 281	99.41 176	99.52 76	98.06 97	99.05 188	99.50 188	89.64 305	99.73 169	97.73 169	97.38 234	98.53 291

IterMVS		97.83 208	97.77 187	98.02 271	99.58 122	96.27 283	99.02 279	99.48 115	97.22 180	98.71 230	99.70 109	92.75 244	99.13 278	97.46 196	96.00 261	98.67 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS		97.82 211	97.65 204	98.35 243	98.88 266	95.98 287	99.49 143	94.71 353	97.57 148	99.26 140	99.48 197	92.46 266	99.71 179	97.87 154	99.08 131	99.35 150

tfpn111		97.81 212	97.49 218	98.78 205	99.72 76	97.86 222	99.59 93	98.74 302	97.93 112	99.26 140	98.62 302	91.75 278	99.86 107	93.57 309	98.18 184	98.61 277

MVP-Stereo		97.81 212	97.75 193	97.99 274	97.53 323	96.60 273	98.96 294	98.85 290	97.22 180	97.23 295	99.36 235	95.28 154	99.46 214	95.51 273	99.78 75	97.92 320
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 212	97.44 230	98.91 172	98.88 266	98.68 175	99.51 130	99.34 208	96.18 264	99.20 161	99.34 242	94.03 220	99.36 234	95.32 278	95.18 275	98.69 227

v1921920		97.80 215	97.45 224	98.84 197	98.80 276	98.53 190	99.52 126	99.34 208	96.15 268	99.24 149	99.47 201	93.98 221	99.29 252	95.40 276	95.13 278	98.69 227

V4		97.80 215	97.51 214	98.67 215	98.79 278	98.63 181	99.87 4	99.44 160	95.87 278	99.01 192	99.46 205	94.52 202	99.33 241	96.64 252	93.97 305	98.05 311

v148		97.79 217	97.55 210	98.50 227	98.74 287	97.72 234	99.54 122	99.33 216	96.26 257	98.90 210	99.51 186	94.68 194	99.14 275	97.83 157	93.15 314	98.63 266

v52		97.79 217	97.50 216	98.66 216	98.80 276	98.62 183	99.87 4	99.44 160	95.87 278	99.01 192	99.46 205	94.44 206	99.33 241	96.65 251	93.96 306	98.05 311

conf200view11		97.78 219	97.45 224	98.77 206	99.72 76	97.86 222	99.59 93	98.74 302	97.93 112	99.26 140	98.62 302	91.75 278	99.83 126	93.22 313	98.18 184	98.61 277

thres400		97.77 220	97.38 238	98.92 168	99.69 89	97.96 217	99.50 135	98.73 311	97.83 122	99.17 167	98.45 311	91.67 284	99.83 126	93.22 313	98.18 184	98.96 189

thres100view900		97.76 221	97.45 224	98.69 212	99.72 76	97.86 222	99.59 93	98.74 302	97.93 112	99.26 140	98.62 302	91.75 278	99.83 126	93.22 313	98.18 184	98.37 302

PEN-MVS		97.76 221	97.44 230	98.72 210	98.77 285	98.54 189	99.78 22	99.51 85	97.06 201	98.29 267	99.64 138	92.63 258	98.89 305	98.09 136	93.16 313	98.72 215

Baseline_NR-MVSNet		97.76 221	97.45 224	98.68 213	99.09 222	98.29 204	99.41 176	98.85 290	95.65 282	98.63 247	99.67 124	94.82 182	99.10 283	98.07 142	92.89 316	98.64 259

TR-MVS		97.76 221	97.41 235	98.82 199	99.06 226	97.87 221	98.87 306	98.56 320	96.63 227	98.68 238	99.22 262	92.49 262	99.65 194	95.40 276	97.79 208	98.95 196

Patchmtry		97.75 225	97.40 236	98.81 200	99.10 220	98.87 143	99.11 259	99.33 216	94.83 290	98.81 221	99.38 224	94.33 208	99.02 290	96.10 260	95.57 269	98.53 291

dp		97.75 225	97.80 180	97.59 295	99.10 220	93.71 322	99.32 205	98.88 288	96.48 241	99.08 182	99.55 168	92.67 257	99.82 135	96.52 253	98.58 161	99.24 157

TAPA-MVS	97.07 15	97.74 227	97.34 245	98.94 160	99.70 87	97.53 235	99.25 230	99.51 85	91.90 327	99.30 124	99.63 142	98.78 39	99.64 196	88.09 334	99.87 39	99.65 91
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 228	97.35 242	98.88 185	99.47 143	97.12 245	99.34 203	98.85 290	98.19 76	99.67 44	99.85 26	82.98 341	99.92 65	99.49 12	98.32 174	99.60 105

MIMVSNet		97.73 228	97.45 224	98.57 221	99.45 148	97.50 236	99.02 279	98.98 274	96.11 271	99.41 101	99.14 267	90.28 297	98.74 308	95.74 267	98.93 143	99.47 135

tfpn200view9		97.72 230	97.38 238	98.72 210	99.69 89	97.96 217	99.50 135	98.73 311	97.83 122	99.17 167	98.45 311	91.67 284	99.83 126	93.22 313	98.18 184	98.37 302

CostFormer		97.72 230	97.73 194	97.71 292	99.15 212	94.02 318	99.54 122	99.02 271	94.67 293	99.04 189	99.35 239	92.35 269	99.77 156	98.50 108	97.94 205	99.34 151

FMVSNet2		97.72 230	97.36 240	98.80 202	99.51 132	98.84 147	99.45 156	99.42 169	96.49 235	98.86 218	99.29 253	90.26 298	98.98 295	96.44 255	96.56 249	98.58 288

test0.0.03 1		97.71 233	97.42 234	98.56 223	98.41 312	97.82 225	98.78 312	98.63 316	97.34 168	98.05 280	98.98 283	94.45 204	98.98 295	95.04 282	97.15 242	98.89 197

v1240		97.69 234	97.32 248	98.79 203	98.85 273	98.43 200	99.48 148	99.36 196	96.11 271	99.27 136	99.36 235	93.76 229	99.24 264	94.46 292	95.23 274	98.70 222

cascas		97.69 234	97.43 233	98.48 230	98.60 304	97.30 237	98.18 339	99.39 182	92.96 320	98.41 258	98.78 298	93.77 228	99.27 256	98.16 131	98.61 158	98.86 198

pm-mvs1		97.68 236	97.28 252	98.88 185	99.06 226	98.62 183	99.50 135	99.45 152	96.32 251	97.87 285	99.79 73	92.47 263	99.35 237	97.54 187	93.54 310	98.67 243

GBi-Net		97.68 236	97.48 219	98.29 248	99.51 132	97.26 240	99.43 165	99.48 115	96.49 235	99.07 183	99.32 248	90.26 298	98.98 295	97.10 217	96.65 246	98.62 268

test1		97.68 236	97.48 219	98.29 248	99.51 132	97.26 240	99.43 165	99.48 115	96.49 235	99.07 183	99.32 248	90.26 298	98.98 295	97.10 217	96.65 246	98.62 268

tpm		97.67 239	97.55 210	98.03 269	99.02 233	95.01 307	99.43 165	98.54 321	96.44 243	99.12 172	99.34 242	91.83 277	99.60 204	97.75 167	96.46 251	99.48 131

PCF-MVS	97.08 14	97.66 240	97.06 260	99.47 93	99.61 117	99.09 105	98.04 341	99.25 245	91.24 330	98.51 253	99.70 109	94.55 200	99.91 74	92.76 320	99.85 53	99.42 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
our_test_3		97.65 241	97.68 198	97.55 296	98.62 301	94.97 308	98.84 308	99.30 225	96.83 216	98.19 271	99.34 242	97.01 106	99.02 290	95.00 283	96.01 260	98.64 259

testgi		97.65 241	97.50 216	98.13 266	99.36 165	96.45 277	99.42 172	99.48 115	97.76 130	97.87 285	99.45 208	91.09 291	98.81 307	94.53 290	98.52 166	99.13 163

thres200		97.61 243	97.28 252	98.62 217	99.64 106	98.03 213	99.26 228	98.74 302	97.68 140	99.09 181	98.32 313	91.66 286	99.81 139	92.88 319	98.22 180	98.03 314

PAPM		97.59 244	97.09 259	99.07 144	99.06 226	98.26 206	98.30 335	99.10 260	94.88 289	98.08 276	99.34 242	96.27 129	99.64 196	89.87 328	98.92 145	99.31 153

VDDNet		97.55 245	97.02 261	99.16 135	99.49 139	98.12 212	99.38 188	99.30 225	95.35 285	99.68 38	99.90 7	82.62 343	99.93 57	99.31 25	98.13 192	99.42 144

TESTMET0.1,1		97.55 245	97.27 254	98.40 240	98.93 257	96.53 274	98.67 319	97.61 344	96.96 206	98.64 246	99.28 254	88.63 317	99.45 215	97.30 205	99.38 111	99.21 158

DWT-MVSNet_test		97.53 247	97.40 236	97.93 277	99.03 232	94.86 309	99.57 106	98.63 316	96.59 232	98.36 262	98.79 296	89.32 307	99.74 161	98.14 133	98.16 191	99.20 159

pmmvs5		97.52 248	97.30 250	98.16 265	98.57 306	96.73 268	99.27 220	98.90 286	96.14 269	98.37 261	99.53 178	91.54 288	99.14 275	97.51 190	95.87 263	98.63 266

v748		97.52 248	97.23 255	98.41 239	98.69 294	97.23 243	99.87 4	99.45 152	95.72 280	98.51 253	99.53 178	94.13 216	99.30 250	96.78 242	92.39 321	98.70 222

LF4IMVS		97.52 248	97.46 223	97.70 293	98.98 240	95.55 293	99.29 214	98.82 293	98.07 93	98.66 239	99.64 138	89.97 302	99.61 203	97.01 222	96.68 245	97.94 318

DTE-MVSNet		97.51 251	97.19 257	98.46 233	98.63 300	98.13 211	99.84 9	99.48 115	96.68 223	97.97 283	99.67 124	92.92 240	98.56 311	96.88 239	92.60 320	98.70 222

SixPastTwentyTwo		97.50 252	97.33 247	98.03 269	98.65 298	96.23 284	99.77 25	98.68 314	97.14 185	97.90 284	99.93 4	90.45 296	99.18 274	97.00 223	96.43 252	98.67 243

JIA-IIPM		97.50 252	97.02 261	98.93 163	98.73 288	97.80 230	99.30 210	98.97 275	91.73 328	98.91 208	94.86 344	95.10 164	99.71 179	97.58 181	97.98 204	99.28 155

ppachtmachnet_test		97.49 254	97.45 224	97.61 294	98.62 301	95.24 301	98.80 310	99.46 140	96.11 271	98.22 269	99.62 147	96.45 123	98.97 302	93.77 307	95.97 262	98.61 277

test-mter		97.49 254	97.13 258	98.55 225	98.79 278	97.10 246	98.67 319	97.75 334	96.65 225	98.61 250	98.85 291	88.23 322	99.45 215	97.25 206	99.38 111	99.10 164

DI_MVS_plusplus_test		97.45 256	96.79 265	99.44 99	97.76 321	99.04 110	99.21 240	98.61 318	97.74 133	94.01 323	98.83 293	87.38 328	99.83 126	98.63 89	98.90 147	99.44 141

test_normal		97.44 257	96.77 267	99.44 99	97.75 322	99.00 122	99.10 261	98.64 315	97.71 136	93.93 326	98.82 294	87.39 327	99.83 126	98.61 93	98.97 139	99.49 129

tpm2		97.44 257	97.34 245	97.74 291	99.15 212	94.36 315	99.45 156	98.94 278	93.45 318	98.90 210	99.44 209	91.35 289	99.59 206	97.31 204	98.07 201	99.29 154

tpm cat1		97.39 259	97.36 240	97.50 299	99.17 207	93.73 320	99.43 165	99.31 223	91.27 329	98.71 230	99.08 272	94.31 210	99.77 156	96.41 257	98.50 167	99.00 179

tpmp4_e23		97.34 260	97.29 251	97.52 297	99.25 191	93.73 320	99.58 100	99.19 253	94.00 309	98.20 270	99.41 215	90.74 295	99.74 161	97.13 216	98.07 201	99.07 173

USDC		97.34 260	97.20 256	97.75 290	99.07 224	95.20 303	98.51 328	99.04 269	97.99 107	98.31 265	99.86 22	89.02 309	99.55 209	95.67 271	97.36 235	98.49 293

MVS		97.28 262	96.55 269	99.48 90	98.78 282	98.95 132	99.27 220	99.39 182	83.53 343	98.08 276	99.54 171	96.97 107	99.87 104	94.23 303	99.16 124	99.63 101

DSMNet-mixed		97.25 263	97.35 242	96.95 307	97.84 319	93.61 324	99.57 106	96.63 349	96.13 270	98.87 213	98.61 306	94.59 198	97.70 333	95.08 281	98.86 150	99.55 113

MS-PatchMatch		97.24 264	97.32 248	96.99 305	98.45 311	93.51 325	98.82 309	99.32 222	97.41 164	98.13 274	99.30 251	88.99 310	99.56 207	95.68 270	99.80 71	97.90 321

TransMVSNet (Re)		97.15 265	96.58 268	98.86 193	99.12 215	98.85 146	99.49 143	98.91 284	95.48 284	97.16 297	99.80 65	93.38 233	99.11 281	94.16 305	91.73 322	98.62 268

TinyColmap		97.12 266	96.89 263	97.83 285	99.07 224	95.52 296	98.57 325	98.74 302	97.58 147	97.81 288	99.79 73	88.16 323	99.56 207	95.10 280	97.21 239	98.39 301

K. test v3		97.10 267	96.79 265	98.01 272	98.72 290	96.33 281	99.87 4	97.05 348	97.59 145	96.16 308	99.80 65	88.71 313	99.04 287	96.69 247	96.55 250	98.65 257

LP		97.04 268	96.80 264	97.77 289	98.90 262	95.23 302	98.97 292	99.06 267	94.02 308	98.09 275	99.41 215	93.88 224	98.82 306	90.46 326	98.42 171	99.26 156

PatchT		97.03 269	96.44 270	98.79 203	98.99 236	98.34 203	99.16 246	99.07 265	92.13 324	99.52 81	97.31 337	94.54 201	98.98 295	88.54 332	98.73 157	99.03 176

FMVSNet1		96.84 270	96.36 271	98.29 248	99.32 177	97.26 240	99.43 165	99.48 115	95.11 287	98.55 252	99.32 248	83.95 340	98.98 295	95.81 266	96.26 256	98.62 268

test_0402		96.64 271	96.24 272	97.85 283	98.85 273	96.43 278	99.44 160	99.26 243	93.52 315	96.98 301	99.52 183	88.52 318	99.20 273	92.58 322	97.50 223	97.93 319

RPMNet		96.61 272	95.85 280	98.87 189	99.18 202	98.49 196	99.22 237	99.08 262	88.72 339	99.56 69	97.38 335	94.08 219	99.00 293	86.87 339	98.58 161	99.14 161

X-MVStestdata		96.55 273	95.45 294	99.87 6	99.85 23	99.83 8	99.69 46	99.68 19	98.98 19	99.37 110	64.01 359	98.81 36	99.94 42	98.79 72	99.86 49	99.84 12

pmmvs6		96.53 274	96.09 275	97.82 286	98.69 294	95.47 297	99.37 190	99.47 131	93.46 317	97.41 292	99.78 78	87.06 329	99.33 241	96.92 231	92.70 319	98.65 257

UnsupCasMVSNet_eth		96.44 275	96.12 274	97.40 301	98.65 298	95.65 290	99.36 196	99.51 85	97.13 186	96.04 311	98.99 280	88.40 320	98.17 314	96.71 245	90.27 325	98.40 300

FMVSNet5		96.43 276	96.19 273	97.15 302	99.11 217	95.89 289	99.32 205	99.52 76	94.47 302	98.34 264	99.07 273	87.54 326	97.07 336	92.61 321	95.72 266	98.47 295

v18		96.42 277	95.80 284	98.26 251	98.95 249	98.82 157	99.76 28	99.28 237	94.58 295	94.12 318	97.70 322	95.22 160	98.16 315	94.83 286	87.80 332	97.79 329

v17		96.42 277	95.81 282	98.25 255	98.94 252	98.80 164	99.76 28	99.28 237	94.57 296	94.18 317	97.71 321	95.23 159	98.16 315	94.86 284	87.73 334	97.80 324

v16		96.39 279	95.76 285	98.26 251	98.96 247	98.81 159	99.76 28	99.28 237	94.57 296	94.10 319	97.70 322	95.04 166	98.16 315	94.70 288	87.77 333	97.80 324

new_pmnet		96.38 280	96.03 276	97.41 300	98.13 317	95.16 306	99.05 270	99.20 250	93.94 310	97.39 293	98.79 296	91.61 287	99.04 287	90.43 327	95.77 265	98.05 311

v15		96.28 281	95.62 287	98.25 255	98.94 252	98.83 150	99.76 28	99.29 230	94.52 300	94.02 322	97.61 329	95.02 167	98.13 319	94.53 290	86.92 337	97.80 324

V14		96.26 282	95.60 288	98.26 251	98.94 252	98.83 150	99.76 28	99.29 230	94.49 301	93.96 324	97.66 325	94.99 170	98.13 319	94.41 293	86.90 338	97.80 324

V9		96.25 283	95.58 289	98.26 251	98.94 252	98.83 150	99.75 35	99.29 230	94.45 303	93.96 324	97.62 328	94.94 172	98.14 318	94.40 294	86.87 339	97.81 322

v13		96.24 284	95.58 289	98.25 255	98.98 240	98.83 150	99.75 35	99.29 230	94.35 305	93.89 327	97.60 330	95.17 162	98.11 321	94.27 302	86.86 340	97.81 322

v12		96.24 284	95.58 289	98.23 258	98.96 247	98.81 159	99.76 28	99.29 230	94.42 304	93.85 328	97.60 330	95.12 163	98.09 322	94.32 299	86.85 341	97.80 324

v11		96.23 286	95.57 292	98.21 261	98.93 257	98.83 150	99.72 40	99.29 230	94.29 306	94.05 321	97.64 327	94.88 179	98.04 323	92.89 318	88.43 330	97.77 330

Anonymous20231206		96.22 287	96.03 276	96.79 311	97.31 328	94.14 317	99.63 80	99.08 262	96.17 265	97.04 299	99.06 275	93.94 222	97.76 332	86.96 338	95.06 279	98.47 295

IB-MVS	95.67 18	96.22 287	95.44 295	98.57 221	99.21 195	96.70 269	98.65 322	97.74 336	96.71 221	97.27 294	98.54 309	86.03 331	99.92 65	98.47 111	86.30 342	99.10 164
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
gg-mvs-nofinetune		96.17 289	95.32 296	98.73 209	98.79 278	98.14 210	99.38 188	94.09 354	91.07 332	98.07 279	91.04 350	89.62 306	99.35 237	96.75 243	99.09 130	98.68 232

test20.03		96.12 290	95.96 279	96.63 312	97.44 324	95.45 298	99.51 130	99.38 188	96.55 233	96.16 308	99.25 258	93.76 229	96.17 341	87.35 337	94.22 300	98.27 305

PVSNet_0	94.43 19	96.09 291	95.47 293	97.94 276	99.31 178	94.34 316	97.81 342	99.70 15	97.12 188	97.46 291	98.75 299	89.71 304	99.79 146	97.69 175	81.69 346	99.68 84

EG-PatchMatch MVS		95.97 292	95.69 286	96.81 310	97.78 320	92.79 328	99.16 246	98.93 279	96.16 266	94.08 320	99.22 262	82.72 342	99.47 213	95.67 271	97.50 223	98.17 308

Patchmatch-RL test		95.84 293	95.81 282	95.95 316	95.61 333	90.57 334	98.24 336	98.39 322	95.10 288	95.20 313	98.67 301	94.78 185	97.77 331	96.28 259	90.02 326	99.51 125

MVS-HIRNet		95.75 294	95.16 298	97.51 298	99.30 179	93.69 323	98.88 305	95.78 350	85.09 342	98.78 224	92.65 346	91.29 290	99.37 230	94.85 285	99.85 53	99.46 138

testpf		95.66 295	96.02 278	94.58 319	98.35 313	92.32 330	97.25 347	97.91 333	92.83 321	97.03 300	98.99 280	88.69 314	98.61 310	95.72 268	97.40 232	92.80 346

MIMVSNet1		95.51 296	95.04 299	96.92 308	97.38 325	95.60 291	99.52 126	99.50 99	93.65 313	96.97 302	99.17 265	85.28 335	96.56 340	88.36 333	95.55 270	98.60 284

MDA-MVSNet_test_wron		95.45 297	94.60 302	98.01 272	98.16 316	97.21 244	99.11 259	99.24 246	93.49 316	80.73 348	98.98 283	93.02 237	98.18 313	94.22 304	94.45 296	98.64 259

TDRefinement		95.42 298	94.57 303	97.97 275	89.83 349	96.11 286	99.48 148	98.75 299	96.74 219	96.68 303	99.88 14	88.65 316	99.71 179	98.37 118	82.74 345	98.09 309

YYNet1		95.36 299	94.51 304	97.92 278	97.89 318	97.10 246	99.10 261	99.23 247	93.26 319	80.77 347	99.04 277	92.81 243	98.02 324	94.30 300	94.18 301	98.64 259

pmmvs-eth3d		95.34 300	94.73 301	97.15 302	95.53 335	95.94 288	99.35 200	99.10 260	95.13 286	93.55 329	97.54 333	88.15 324	97.91 327	94.58 289	89.69 328	97.61 333

Test4		95.05 301	93.67 309	99.22 131	96.07 332	98.94 135	99.20 242	99.27 242	97.71 136	89.96 341	97.59 332	66.18 349	99.25 262	98.06 143	98.96 140	99.47 135

MDA-MVSNet-bldmvs		94.96 302	93.98 307	97.92 278	98.24 315	97.27 239	99.15 249	99.33 216	93.80 312	80.09 349	99.03 278	88.31 321	97.86 329	93.49 311	94.36 297	98.62 268

N_pmnet		94.95 303	95.83 281	92.31 327	98.47 310	79.33 350	99.12 253	92.81 359	93.87 311	97.68 290	99.13 268	93.87 225	99.01 292	91.38 324	96.19 257	98.59 285

testus		94.61 304	95.30 297	92.54 326	96.44 331	84.18 342	98.36 331	99.03 270	94.18 307	96.49 304	98.57 308	88.74 312	95.09 345	87.41 336	98.45 169	98.36 304

new-patchmatchnet		94.48 305	94.08 306	95.67 317	95.08 337	92.41 329	99.18 244	99.28 237	94.55 299	93.49 330	97.37 336	87.86 325	97.01 337	91.57 323	88.36 331	97.61 333

testing_2		94.44 306	92.93 312	98.98 154	94.16 340	99.00 122	99.42 172	99.28 237	96.60 230	84.86 343	96.84 338	70.91 346	99.27 256	98.23 127	96.08 259	98.68 232

OpenMVS_ROB	92.34 20	94.38 307	93.70 308	96.41 315	97.38 325	93.17 326	99.06 268	98.75 299	86.58 340	94.84 316	98.26 315	81.53 344	99.32 244	89.01 331	97.87 207	96.76 337

CMPMVS	69.68 23	94.13 308	94.90 300	91.84 328	97.24 329	80.01 349	98.52 327	99.48 115	89.01 337	91.99 335	99.67 124	85.67 333	99.13 278	95.44 274	97.03 243	96.39 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 309	93.25 311	96.60 313	94.76 338	94.49 313	98.92 301	98.18 329	89.66 334	96.48 305	98.06 316	86.28 330	97.33 335	89.68 329	87.20 336	97.97 317

test2356		94.07 310	94.46 305	92.89 324	95.18 336	86.13 340	97.60 345	99.06 267	93.61 314	96.15 310	98.28 314	85.60 334	93.95 347	86.68 340	98.00 203	98.59 285

UnsupCasMVSNet_bld		93.53 311	92.51 313	96.58 314	97.38 325	93.82 319	98.24 336	99.48 115	91.10 331	93.10 331	96.66 339	74.89 345	98.37 312	94.03 306	87.71 335	97.56 335

PM-MVS		92.96 312	92.23 314	95.14 318	95.61 333	89.98 336	99.37 190	98.21 327	94.80 291	95.04 315	97.69 324	65.06 350	97.90 328	94.30 300	89.98 327	97.54 336

test1235678		92.91 313	93.30 310	91.71 330	93.14 343	83.01 344	98.75 315	98.58 319	92.80 322	92.45 333	97.91 318	88.51 319	93.54 348	82.26 344	95.35 272	98.59 285

1111		92.30 314	92.21 315	92.55 325	93.30 341	86.27 338	99.15 249	98.74 302	91.94 325	90.85 338	97.82 319	84.18 338	95.21 343	79.65 346	94.27 299	96.19 340

test12356		91.74 315	92.19 316	90.37 333	91.22 345	82.41 345	98.61 323	98.28 324	90.66 333	91.82 336	97.92 317	84.90 336	92.61 349	81.64 345	94.66 291	96.09 341

Gipuma		90.99 316	90.15 317	93.51 321	98.73 288	90.12 335	93.98 351	99.45 152	79.32 346	92.28 334	94.91 343	69.61 347	97.98 326	87.42 335	95.67 267	92.45 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		90.69 317	89.39 318	94.58 319	94.25 339	88.18 337	99.29 214	99.07 265	82.45 345	92.95 332	97.65 326	63.96 352	97.79 330	89.27 330	85.63 343	97.77 330

testmv		87.91 318	87.80 319	88.24 334	87.68 352	77.50 352	99.07 264	97.66 343	89.27 335	86.47 342	96.22 341	68.35 348	92.49 351	76.63 350	88.82 329	94.72 344

PMMVS2		86.87 319	85.37 322	91.35 332	90.21 348	83.80 343	98.89 304	97.45 346	83.13 344	91.67 337	95.03 342	48.49 356	94.70 346	85.86 341	77.62 347	95.54 342

LCM-MVSNet		86.80 320	85.22 323	91.53 331	87.81 351	80.96 348	98.23 338	98.99 273	71.05 349	90.13 340	96.51 340	48.45 357	96.88 338	90.51 325	85.30 344	96.76 337

FPMVS		84.93 321	85.65 321	82.75 341	86.77 353	63.39 359	98.35 333	98.92 281	74.11 348	83.39 345	98.98 283	50.85 355	92.40 352	84.54 342	94.97 281	92.46 347

.test1245		83.42 322	86.17 320	75.15 344	93.30 341	86.27 338	99.15 249	98.74 302	91.94 325	90.85 338	97.82 319	84.18 338	95.21 343	79.65 346	39.90 356	43.98 357

no-one		83.04 323	80.12 325	91.79 329	89.44 350	85.65 341	99.32 205	98.32 323	89.06 336	79.79 351	89.16 352	44.86 358	96.67 339	84.33 343	46.78 354	93.05 345

tmp_tt		82.80 324	81.52 324	86.66 335	66.61 360	68.44 358	92.79 353	97.92 331	68.96 351	80.04 350	99.85 26	85.77 332	96.15 342	97.86 155	43.89 355	95.39 343

E-PMN		80.61 325	79.88 326	82.81 340	90.75 347	76.38 354	97.69 343	95.76 351	66.44 353	83.52 344	92.25 347	62.54 353	87.16 356	68.53 354	61.40 350	84.89 355

EMVS		80.02 326	79.22 327	82.43 342	91.19 346	76.40 353	97.55 346	92.49 361	66.36 354	83.01 346	91.27 348	64.63 351	85.79 357	65.82 355	60.65 351	85.08 354

PNet_i23d		79.43 327	77.68 328	84.67 337	86.18 354	71.69 357	96.50 349	93.68 355	75.17 347	71.33 352	91.18 349	32.18 361	90.62 353	78.57 349	74.34 348	91.71 350

ANet_high		77.30 328	74.86 330	84.62 338	75.88 358	77.61 351	97.63 344	93.15 358	88.81 338	64.27 354	89.29 351	36.51 359	83.93 358	75.89 351	52.31 353	92.33 349

MVE	76.82 21	76.91 329	74.31 331	84.70 336	85.38 356	76.05 355	96.88 348	93.17 357	67.39 352	71.28 353	89.01 353	21.66 366	87.69 355	71.74 353	72.29 349	90.35 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 330	74.97 329	79.01 343	70.98 359	55.18 360	93.37 352	98.21 327	65.08 355	61.78 356	93.83 345	21.74 365	92.53 350	78.59 348	91.12 324	89.34 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		74.42 331	71.19 332	84.14 339	76.16 357	74.29 356	96.00 350	92.57 360	69.57 350	63.84 355	87.49 354	21.98 363	88.86 354	75.56 352	57.50 352	89.26 353

pcd1.5k->3k		40.85 332	43.49 334	32.93 346	98.95 249	0.00 364	0.00 355	99.53 72	0.00 359	0.00 360	0.27 361	95.32 153	0.00 362	0.00 359	97.30 236	98.80 202

wuyk23d		40.18 333	41.29 336	36.84 345	86.18 354	49.12 361	79.73 354	22.81 363	27.64 356	25.46 359	28.45 360	21.98 363	48.89 359	55.80 356	23.56 359	12.51 359

testmvs		39.17 334	43.78 333	25.37 348	36.04 362	16.84 363	98.36 331	26.56 362	20.06 357	38.51 358	67.32 355	29.64 362	15.30 361	37.59 357	39.90 356	43.98 357

test123		39.01 335	42.50 335	28.53 347	39.17 361	20.91 362	98.75 315	19.17 364	19.83 358	38.57 357	66.67 356	33.16 360	15.42 360	37.50 358	29.66 358	49.26 356

cdsmvs_eth3d_5k		24.64 336	32.85 337	0.00 349	0.00 363	0.00 364	0.00 355	99.51 85	0.00 359	0.00 360	99.56 165	96.58 119	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.30 337	11.06 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	99.58 159	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		8.27 338	11.03 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.27 361	99.01 12	0.00 362	0.00 359	0.00 360	0.00 360

sosnet-low-res		0.02 339	0.03 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.27 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.02 339	0.03 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.27 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.02 339	0.03 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.27 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.02 339	0.03 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.27 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.02 339	0.03 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.27 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																99.52 120

test_part3							99.37 190		97.97 108		99.78 78		99.95 33	97.15 214

test_part2					99.81 32	99.83 8				99.77 24

test_part1								99.48 115				98.96 21			99.84 58	99.83 23

sam_mvs1												94.86 180				99.52 120

sam_mvs												94.72 193

semantic-postprocess				98.06 268	99.57 124	96.36 280		99.49 105	97.18 182	98.71 230	99.72 105	92.70 250	99.14 275	97.44 198	95.86 264	98.67 243

ambc				93.06 323	92.68 344	82.36 346	98.47 329	98.73 311		95.09 314	97.41 334	55.55 354	99.10 283	96.42 256	91.32 323	97.71 332

MTGPA								99.47 131

test_post1							99.23 233				65.14 358	94.18 215	99.71 179	97.58 181

test_post											65.99 357	94.65 197	99.73 169

patchmatchnet-post											98.70 300	94.79 184	99.74 161

GG-mvs-BLEND				98.45 234	98.55 307	98.16 209	99.43 165	93.68 355		97.23 295	98.46 310	89.30 308	99.22 268	95.43 275	98.22 180	97.98 316

MTMP								98.88 288

gm-plane-assit					98.54 308	92.96 327			94.65 294		99.15 266		99.64 196	97.56 185

test9_res														97.49 192	99.72 86	99.75 56

TEST9					99.67 93	99.65 40	99.05 270	99.41 172	96.22 261	98.95 203	99.49 191	98.77 42	99.91 74

test_8					99.67 93	99.61 45	99.03 276	99.41 172	96.28 254	98.93 206	99.48 197	98.76 44	99.91 74

agg_prior2														97.21 208	99.73 85	99.75 56

agg_prior					99.67 93	99.62 43		99.40 179		98.87 213			99.91 74

TestCases				99.31 112	99.86 20	98.48 198		99.61 32	97.85 119	99.36 114	99.85 26	95.95 135	99.85 113	96.66 249	99.83 64	99.59 109

test_prior4						99.56 52	98.99 285

test_prior2							98.96 294		98.34 66	99.01 192	99.52 183	98.68 52		97.96 147	99.74 82

test_prior				99.68 52	99.67 93	99.48 65		99.56 48					99.83 126			99.74 61

旧先验2							98.96 294		96.70 222	99.47 89			99.94 42	98.19 128

新几何2							99.01 283

新几何1				99.75 40	99.75 56	99.59 49		99.54 62	96.76 218	99.29 128	99.64 138	98.43 64	99.94 42	96.92 231	99.66 98	99.72 72

旧先验1					99.74 67	99.59 49		99.54 62			99.69 115	98.47 61			99.68 96	99.73 66

无先验							98.99 285	99.51 85	96.89 212				99.93 57	97.53 188		99.72 72

原ACMM2							98.95 298

原ACMM1				99.65 59	99.73 72	99.33 80		99.47 131	97.46 157	99.12 172	99.66 130	98.67 54	99.91 74	97.70 174	99.69 93	99.71 79

test222					99.75 56	99.49 64	98.91 303	99.49 105	96.42 245	99.34 120	99.65 131	98.28 74			99.69 93	99.72 72

testdata2													99.95 33	96.67 248

segment_acmp												98.96 21

testdata				99.54 77	99.75 56	98.95 132		99.51 85	97.07 199	99.43 96	99.70 109	98.87 31	99.94 42	97.76 165	99.64 101	99.72 72

testdata1							98.85 307		98.32 69

test12				99.75 40	99.64 106	99.61 45		99.29 230		99.21 158		98.38 68	99.89 95		99.74 82	99.74 61

plane_prior7					99.29 182	97.03 254

plane_prior6					99.27 187	96.98 258						92.71 248

plane_prior5								99.47 131					99.69 188	97.78 162	97.63 211	98.67 243

plane_prior4											99.61 151

plane_prior3						97.00 256			98.69 46	99.11 174

plane_prior2							99.39 183		98.97 22

plane_prior1					99.26 189

plane_prior						96.97 259	99.21 240		98.45 59						97.60 214

n2								0.00 365

nn								0.00 365

door-mid								98.05 330

lessismore_v0				97.79 288	98.69 294	95.44 299		94.75 352		95.71 312	99.87 19	88.69 314	99.32 244	95.89 264	94.93 284	98.62 268

LGP-MVS_train				98.49 228	99.33 170	97.05 252		99.55 55	97.46 157	99.24 149	99.83 37	92.58 259	99.72 173	98.09 136	97.51 221	98.68 232

test11								99.35 200

door								97.92 331

HQP5-MVS						96.83 264

HQP-NCC					99.19 199		98.98 289		98.24 72	98.66 239

ACMP_Plane					99.19 199		98.98 289		98.24 72	98.66 239

BP-MVS														97.19 210

HQP4-MVS										98.66 239			99.64 196			98.64 259

HQP3-MVS								99.39 182							97.58 216

HQP2-MVS												92.47 263

NP-MVS					99.23 192	96.92 262					99.40 219

MDTV_nov1_ep13_2view						95.18 305	99.35 200		96.84 215	99.58 65		95.19 161		97.82 158		99.46 138

MDTV_nov1_ep13			98.32 135		99.11 217	94.44 314	99.27 220	98.74 302	97.51 154	99.40 105	99.62 147	94.78 185	99.76 159	97.59 180	98.81 154

ACMMP++_ref															97.19 240

ACMMP++															97.43 231

Test By Simon												98.75 47

ITE_SJBPF				98.08 267	99.29 182	96.37 279		98.92 281	98.34 66	98.83 220	99.75 93	91.09 291	99.62 202	95.82 265	97.40 232	98.25 307

DeepMVS_CX				93.34 322	99.29 182	82.27 347		99.22 248	85.15 341	96.33 306	99.05 276	90.97 293	99.73 169	93.57 309	97.77 209	98.01 315

High-resolution multi-view benchmark