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 38	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

Regformer-4		99.59 2	99.54 4	99.73 45	99.76 41	99.41 70	99.58 88	99.49 104	99.02 10	99.88 3	99.80 64	99.00 17	99.94 40	99.45 15	99.92 12	99.84 12

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 27	99.91 1	99.66 34	99.63 72	99.39 178	98.91 29	99.78 22	99.85 26	99.36 2	99.94 40	98.84 66	99.88 34	99.82 30

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 62	99.78 34	99.14 97	99.60 82	99.45 148	99.01 13	99.90 1	99.83 37	98.98 18	99.93 55	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 62	99.78 34	99.15 96	99.61 81	99.45 148	99.01 13	99.89 2	99.82 44	99.01 11	99.92 63	99.56 5	99.95 6	99.85 8

Regformer-3		99.57 6	99.53 5	99.68 50	99.76 41	99.29 81	99.58 88	99.44 156	99.01 13	99.87 6	99.80 64	98.97 19	99.91 72	99.44 16	99.92 12	99.83 23

Regformer-2		99.54 7	99.47 8	99.75 38	99.71 68	99.52 58	99.49 130	99.49 104	98.94 26	99.83 11	99.76 85	99.01 11	99.94 40	99.15 38	99.87 38	99.80 39

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 11	99.59 84	99.51 85	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
Regformer-1		99.53 9	99.47 8	99.72 47	99.71 68	99.44 67	99.49 130	99.46 137	98.95 24	99.83 11	99.76 85	99.01 11	99.93 55	99.17 36	99.87 38	99.80 39

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	99.74 95	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 58	99.47 127	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

HPM-MVS_fast		99.51 12	99.40 14	99.85 17	99.91 1	99.79 16	99.76 27	99.56 48	97.72 130	99.76 26	99.75 90	99.13 6	99.92 63	99.07 44	99.92 12	99.85 8

MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 181	99.47 127	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

HFP-MVS		99.49 13	99.37 17	99.86 12	99.87 15	99.80 12	99.66 58	99.67 22	98.15 80	99.68 34	99.69 112	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 12	99.87 15	99.79 16	99.66 58	99.67 22	98.15 80	99.67 40	99.69 112	98.95 23	99.96 19	98.69 82	99.87 38	99.84 12

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 35	99.63 95	99.59 46	99.36 181	99.46 137	99.07 9	99.79 18	99.82 44	98.85 30	99.92 63	98.68 84	99.87 38	99.82 30
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 12	99.65 68	99.66 25	98.13 82	99.66 45	99.68 117	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 17	99.76 41	99.83 7	99.63 72	99.54 62	98.36 65	99.79 18	99.82 44	98.86 29	99.95 33	98.62 90	99.81 66	99.78 47

DELS-MVS		99.48 17	99.42 11	99.65 57	99.72 65	99.40 72	99.05 255	99.66 25	99.14 6	99.57 63	99.80 64	98.46 59	99.94 40	99.57 4	99.84 57	99.60 102
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
ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 139	99.48 113	98.05 98	99.76 26	99.86 22	98.82 32	99.93 55	98.82 71	99.91 17	99.84 12

MSLP-MVS++		99.46 21	99.47 8	99.44 97	99.60 105	99.16 93	99.41 163	99.71 13	98.98 19	99.45 81	99.78 77	99.19 4	99.54 196	99.28 27	99.84 57	99.63 98

PGM-MVS		99.45 22	99.31 31	99.86 12	99.87 15	99.78 20	99.58 88	99.65 30	97.84 116	99.71 29	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

CP-MVS		99.45 22	99.32 26	99.85 17	99.83 28	99.75 21	99.69 44	99.52 76	98.07 93	99.53 68	99.63 139	98.93 25	99.97 11	98.74 75	99.91 17	99.83 23

ACMMP		99.45 22	99.32 26	99.82 24	99.89 8	99.67 32	99.62 75	99.69 18	98.12 84	99.63 50	99.84 35	98.73 46	99.96 19	98.55 103	99.83 61	99.81 34
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 25	99.31 31	99.83 22	99.85 23	99.75 21	99.66 58	99.59 38	98.13 82	99.82 14	99.81 53	98.60 54	99.96 19	98.46 111	99.88 34	99.79 43

mPP-MVS		99.44 25	99.30 33	99.86 12	99.88 11	99.79 16	99.69 44	99.48 113	98.12 84	99.50 73	99.75 90	98.78 36	99.97 11	98.57 97	99.89 32	99.83 23

#test#		99.43 27	99.29 36	99.86 12	99.87 15	99.80 12	99.55 106	99.67 22	97.83 117	99.68 34	99.69 112	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

MCST-MVS		99.43 27	99.30 33	99.82 24	99.79 33	99.74 24	99.29 199	99.40 175	98.79 40	99.52 70	99.62 144	98.91 26	99.90 84	98.64 87	99.75 77	99.82 30

UA-Net		99.42 29	99.29 36	99.80 29	99.62 99	99.55 51	99.50 122	99.70 15	98.79 40	99.77 23	99.96 1	97.45 91	99.96 19	98.92 55	99.90 24	99.89 2

HPM-MVS		99.42 29	99.28 38	99.83 22	99.90 3	99.72 25	99.81 15	99.54 62	97.59 139	99.68 34	99.63 139	98.91 26	99.94 40	98.58 95	99.91 17	99.84 12

CNVR-MVS		99.42 29	99.30 33	99.78 33	99.62 99	99.71 26	99.26 213	99.52 76	98.82 35	99.39 95	99.71 103	98.96 20	99.85 108	98.59 94	99.80 68	99.77 49

HSP-MVS		99.41 32	99.26 43	99.85 17	99.89 8	99.80 12	99.67 55	99.37 191	98.70 45	99.77 23	99.49 180	98.21 73	99.95 33	98.46 111	99.77 74	99.81 34

SD-MVS		99.41 32	99.52 6	99.05 136	99.74 57	99.68 30	99.46 142	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 316	98.72 79	99.93 11	99.77 49

MVS_111021_LR		99.41 32	99.33 25	99.65 57	99.77 38	99.51 60	98.94 285	99.85 6	98.82 35	99.65 48	99.74 95	98.51 56	99.80 135	98.83 68	99.89 32	99.64 94

MVS_111021_HR		99.41 32	99.32 26	99.66 53	99.72 65	99.47 64	98.95 283	99.85 6	98.82 35	99.54 67	99.73 98	98.51 56	99.74 147	98.91 56	99.88 34	99.77 49

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 47	99.84 6	99.43 152	99.51 85	98.68 47	99.27 125	99.53 168	98.64 52	99.96 19	98.44 113	99.80 68	99.79 43

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 195	99.52 76	97.18 175	99.60 56	99.79 72	98.79 35	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 38	99.36 19	99.36 104	99.67 79	98.61 174	99.07 249	99.33 212	99.00 17	99.82 14	99.81 53	99.06 8	99.84 113	99.09 42	99.42 106	99.65 88

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 66	99.86 20	99.07 103	99.47 139	99.93 2	97.66 137	99.71 29	99.86 22	97.73 86	99.96 19	99.47 13	99.82 65	99.79 43

NCCC		99.34 40	99.19 47	99.79 32	99.61 103	99.65 37	99.30 195	99.48 113	98.86 31	99.21 144	99.63 139	98.72 47	99.90 84	98.25 125	99.63 100	99.80 39

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 10	99.66 58	99.46 137	98.09 89	99.48 77	99.74 95	98.29 70	99.96 19	97.93 148	99.87 38	99.82 30

PS-MVSNAJ		99.32 42	99.32 26	99.30 113	99.57 110	98.94 125	98.97 277	99.46 137	98.92 28	99.71 29	99.24 246	99.01 11	99.98 5	99.35 18	99.66 95	98.97 177

CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 192	99.71 41	99.66 25	98.11 86	99.41 90	99.80 64	98.37 67	99.96 19	98.99 50	99.96 5	99.72 69

PHI-MVS		99.30 44	99.17 49	99.70 49	99.56 113	99.52 58	99.58 88	99.80 8	97.12 181	99.62 53	99.73 98	98.58 55	99.90 84	98.61 92	99.91 17	99.68 81

DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 88	99.62 75	99.55 55	98.94 26	99.63 50	99.95 2	95.82 136	99.94 40	99.37 17	99.97 3	99.73 63
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 46	99.27 40	99.34 105	99.63 95	98.97 117	99.12 238	99.51 85	98.86 31	99.84 8	99.47 190	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base		99.29 46	99.27 40	99.34 105	99.63 95	98.97 117	99.12 238	99.51 85	98.86 31	99.84 8	99.47 190	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base_debi		99.29 46	99.27 40	99.34 105	99.63 95	98.97 117	99.12 238	99.51 85	98.86 31	99.84 8	99.47 190	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

APD-MVS		99.27 49	99.08 56	99.84 21	99.75 47	99.79 16	99.50 122	99.50 99	97.16 177	99.77 23	99.82 44	98.78 36	99.94 40	97.56 181	99.86 48	99.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 49	99.12 53	99.74 43	99.18 188	99.75 21	99.56 101	99.57 44	98.45 59	99.49 76	99.85 26	97.77 85	99.94 40	98.33 121	99.84 57	99.52 117

xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 115	98.91 130	99.02 264	99.45 148	98.80 39	99.71 29	99.26 244	98.94 24	99.98 5	99.34 22	99.23 117	98.98 176

CANet		99.25 52	99.14 51	99.59 68	99.41 139	99.16 93	99.35 185	99.57 44	98.82 35	99.51 72	99.61 147	96.46 117	99.95 33	99.59 2	99.98 2	99.65 88

3Dnovator	97.25 9	99.24 53	99.05 58	99.81 27	99.12 201	99.66 34	99.84 9	99.74 10	99.09 8	98.92 193	99.90 7	95.94 131	99.98 5	98.95 53	99.92 12	99.79 43

test_prior3		99.21 54	99.05 58	99.68 50	99.67 79	99.48 62	98.96 279	99.56 48	98.34 66	99.01 178	99.52 172	98.68 49	99.83 120	97.96 145	99.74 79	99.74 58

CHOSEN 1792x2688		99.19 55	99.10 55	99.45 94	99.89 8	98.52 181	99.39 170	99.94 1	98.73 44	99.11 160	99.89 10	95.50 143	99.94 40	99.50 8	99.97 3	99.89 2

F-COLMAP		99.19 55	99.04 61	99.64 62	99.78 34	99.27 84	99.42 159	99.54 62	97.29 166	99.41 90	99.59 152	98.42 64	99.93 55	98.19 127	99.69 90	99.73 63

3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 193	99.68 30	99.81 15	99.51 85	99.20 4	98.72 215	99.89 10	95.68 140	99.97 11	98.86 64	99.86 48	99.81 34

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 118	98.94 125	99.88 1	99.46 137	97.55 144	99.80 16	99.65 128	97.39 92	99.28 238	99.03 46	99.85 52	99.65 88

sss		99.17 58	99.05 58	99.53 79	99.62 99	98.97 117	99.36 181	99.62 31	97.83 117	99.67 40	99.65 128	97.37 95	99.95 33	99.19 33	99.19 120	99.68 81

DP-MVS		99.16 60	98.95 75	99.78 33	99.77 38	99.53 55	99.41 163	99.50 99	97.03 190	99.04 175	99.88 14	97.39 92	99.92 63	98.66 85	99.90 24	99.87 4

CNLPA		99.14 61	98.99 68	99.59 68	99.58 108	99.41 70	99.16 231	99.44 156	98.45 59	99.19 150	99.49 180	98.08 77	99.89 92	97.73 166	99.75 77	99.48 126

CDPH-MVS		99.13 62	98.91 79	99.80 29	99.75 47	99.71 26	99.15 234	99.41 168	96.60 214	99.60 56	99.55 164	98.83 31	99.90 84	97.48 189	99.83 61	99.78 47

jason		99.13 62	99.03 63	99.45 94	99.46 130	98.87 133	99.12 238	99.26 238	98.03 101	99.79 18	99.65 128	97.02 102	99.85 108	99.02 48	99.90 24	99.65 88
jason: jason.
lupinMVS		99.13 62	99.01 67	99.46 93	99.51 118	98.94 125	99.05 255	99.16 249	97.86 112	99.80 16	99.56 161	97.39 92	99.86 104	98.94 54	99.85 52	99.58 108

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 91	99.06 104	99.81 15	99.33 212	97.43 154	99.60 56	99.88 14	97.14 99	99.84 113	99.13 39	98.94 139	99.69 77

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 110	98.63 169	99.07 249	99.34 204	98.99 18	99.61 55	99.82 44	97.98 80	99.87 101	97.00 217	99.80 68	99.85 8

CHOSEN 280x420		99.12 67	99.13 52	99.08 132	99.66 89	97.89 208	98.43 313	99.71 13	98.88 30	99.62 53	99.76 85	96.63 114	99.70 171	99.46 14	99.99 1	99.66 85

DP-MVS Recon		99.12 67	98.95 75	99.65 57	99.74 57	99.70 28	99.27 205	99.57 44	96.40 232	99.42 88	99.68 117	98.75 44	99.80 135	97.98 144	99.72 83	99.44 136

Vis-MVSNet		99.12 67	98.97 71	99.56 74	99.78 34	99.10 100	99.68 53	99.66 25	98.49 56	99.86 7	99.87 19	94.77 183	99.84 113	99.19 33	99.41 107	99.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 67	99.08 56	99.24 121	99.46 130	98.55 176	99.51 117	99.46 137	98.09 89	99.45 81	99.82 44	98.34 68	99.51 197	98.70 80	98.93 140	99.67 84

VNet		99.11 71	98.90 80	99.73 45	99.52 116	99.56 49	99.41 163	99.39 178	99.01 13	99.74 28	99.78 77	95.56 141	99.92 63	99.52 7	98.18 180	99.72 69

CPTT-MVS		99.11 71	98.90 80	99.74 43	99.80 32	99.46 65	99.59 84	99.49 104	97.03 190	99.63 50	99.69 112	97.27 97	99.96 19	97.82 156	99.84 57	99.81 34

HyFIR lowres test		99.11 71	98.92 77	99.65 57	99.90 3	99.37 73	99.02 264	99.91 3	97.67 136	99.59 59	99.75 90	95.90 133	99.73 155	99.53 6	99.02 132	99.86 5

MVS_Test		99.10 74	98.97 71	99.48 88	99.49 125	99.14 97	99.67 55	99.34 204	97.31 164	99.58 60	99.76 85	97.65 88	99.82 127	98.87 61	99.07 129	99.46 133

1121		99.09 75	98.87 84	99.75 38	99.74 57	99.60 44	99.27 205	99.48 113	96.82 201	99.25 130	99.65 128	98.38 65	99.93 55	97.53 184	99.67 94	99.73 63

CDS-MVSNet		99.09 75	99.03 63	99.25 119	99.42 136	98.73 160	99.45 143	99.46 137	98.11 86	99.46 80	99.77 82	98.01 79	99.37 215	98.70 80	98.92 142	99.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 41	98.79 156	98.78 295	99.91 3	96.74 203	99.67 40	99.49 180	97.53 89	99.88 99	98.98 51	99.85 52	99.60 102

OMC-MVS		99.08 77	99.04 61	99.20 125	99.67 79	98.22 195	99.28 202	99.52 76	98.07 93	99.66 45	99.81 53	97.79 84	99.78 140	97.79 158	99.81 66	99.60 102

MVS_0304		99.06 79	98.86 87	99.66 53	99.51 118	99.36 74	99.22 222	99.51 85	98.95 24	99.58 60	99.65 128	93.74 225	99.98 5	99.66 1	99.95 6	99.64 94

WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 99	99.16 93	99.37 177	99.56 48	98.04 99	99.53 68	99.62 144	96.84 106	99.94 40	98.85 65	98.49 165	99.72 69

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 62	99.32 77	99.75 34	99.20 245	98.02 102	99.56 64	99.86 22	96.54 116	99.67 176	98.09 134	99.13 123	99.73 63

PAPM_NR		99.04 82	98.84 90	99.66 53	99.74 57	99.44 67	99.39 170	99.38 184	97.70 133	99.28 121	99.28 241	98.34 68	99.85 108	96.96 221	99.45 104	99.69 77

API-MVS		99.04 82	99.03 63	99.06 134	99.40 144	99.31 80	99.55 106	99.56 48	98.54 53	99.33 110	99.39 212	98.76 41	99.78 140	96.98 219	99.78 72	98.07 291

mvs_anonymous		99.03 84	98.99 68	99.16 127	99.38 147	98.52 181	99.51 117	99.38 184	97.79 122	99.38 97	99.81 53	97.30 96	99.45 201	99.35 18	98.99 134	99.51 120

train_agg		99.02 85	98.77 97	99.77 35	99.67 79	99.65 37	99.05 255	99.41 168	96.28 238	98.95 189	99.49 180	98.76 41	99.91 72	97.63 175	99.72 83	99.75 53

canonicalmvs		99.02 85	98.86 87	99.51 85	99.42 136	99.32 77	99.80 19	99.48 113	98.63 48	99.31 112	98.81 281	97.09 100	99.75 146	99.27 29	97.90 192	99.47 130

PLC	97.94 4	99.02 85	98.85 89	99.53 79	99.66 89	99.01 110	99.24 217	99.52 76	96.85 199	99.27 125	99.48 186	98.25 72	99.91 72	97.76 162	99.62 101	99.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
agg_prior1		99.01 88	98.76 99	99.76 37	99.67 79	99.62 40	98.99 270	99.40 175	96.26 241	98.87 199	99.49 180	98.77 39	99.91 72	97.69 172	99.72 83	99.75 53

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 113	99.54 52	99.18 229	99.70 15	98.18 79	99.35 106	99.63 139	96.32 121	99.90 84	97.48 189	99.77 74	99.55 110

1112_ss		98.98 90	98.77 97	99.59 68	99.68 78	99.02 108	99.25 215	99.48 113	97.23 172	99.13 156	99.58 155	96.93 105	99.90 84	98.87 61	98.78 152	99.84 12

MSDG		98.98 90	98.80 94	99.53 79	99.76 41	99.19 90	98.75 298	99.55 55	97.25 169	99.47 78	99.77 82	97.82 83	99.87 101	96.93 224	99.90 24	99.54 112

CANet_DTU		98.97 92	98.87 84	99.25 119	99.33 156	98.42 190	99.08 248	99.30 221	99.16 5	99.43 85	99.75 90	95.27 149	99.97 11	98.56 100	99.95 6	99.36 144

agg_prior3		98.97 92	98.71 103	99.75 38	99.67 79	99.60 44	99.04 260	99.41 168	95.93 260	98.87 199	99.48 186	98.61 53	99.91 72	97.63 175	99.72 83	99.75 53

114514_t		98.93 94	98.67 107	99.72 47	99.85 23	99.53 55	99.62 75	99.59 38	92.65 306	99.71 29	99.78 77	98.06 78	99.90 84	98.84 66	99.91 17	99.74 58

PS-MVSNAJss		98.92 95	98.92 77	98.90 165	98.78 267	98.53 178	99.78 22	99.54 62	98.07 93	99.00 185	99.76 85	99.01 11	99.37 215	99.13 39	97.23 224	98.81 190

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 75	98.95 122	99.03 261	99.47 127	96.98 192	99.15 155	99.23 247	96.77 110	99.89 92	98.83 68	98.78 152	99.86 5

AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 186	99.56 101	99.61 32	97.85 114	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

UGNet		98.87 97	98.69 105	99.40 102	99.22 180	98.72 161	99.44 147	99.68 19	99.24 3	99.18 152	99.42 201	92.74 240	99.96 19	99.34 22	99.94 10	99.53 116
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 97	98.72 101	99.31 110	99.71 68	98.88 132	99.80 19	99.44 156	97.91 110	99.36 103	99.78 77	95.49 144	99.43 210	97.91 149	99.11 124	99.62 100

mvs-test1		98.86 100	98.84 90	98.89 167	99.33 156	97.77 216	99.44 147	99.30 221	98.47 57	99.10 163	99.43 199	96.78 108	99.95 33	98.73 77	99.02 132	98.96 179

EPNet		98.86 100	98.71 103	99.30 113	97.20 313	98.18 196	99.62 75	98.91 279	99.28 2	98.63 233	99.81 53	95.96 128	99.99 1	99.24 30	99.72 83	99.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 137	99.76 41	98.79 156	99.28 202	99.91 3	97.42 156	99.67 40	99.37 216	97.53 89	99.88 99	98.98 51	97.29 223	98.42 280

ab-mvs		98.86 100	98.63 112	99.54 75	99.64 92	99.19 90	99.44 147	99.54 62	97.77 124	99.30 113	99.81 53	94.20 206	99.93 55	99.17 36	98.82 149	99.49 124

MAR-MVS		98.86 100	98.63 112	99.54 75	99.37 149	99.66 34	99.45 143	99.54 62	96.61 212	99.01 178	99.40 208	97.09 100	99.86 104	97.68 174	99.53 103	99.10 158
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	98.86 100	98.75 100	99.17 126	99.88 11	98.53 178	99.34 188	99.59 38	97.55 144	98.70 222	99.89 10	95.83 135	99.90 84	98.10 133	99.90 24	99.08 163
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	98.85 106	98.64 111	99.47 91	99.42 136	99.08 102	99.62 75	99.36 192	97.39 159	99.28 121	99.68 117	96.44 118	99.92 63	98.37 117	98.22 176	99.40 141

PVSNet	96.02 17	98.85 106	98.84 90	98.89 167	99.73 62	97.28 223	98.32 317	99.60 35	97.86 112	99.50 73	99.57 159	96.75 111	99.86 104	98.56 100	99.70 89	99.54 112

PatchMatch-RL		98.84 108	98.62 115	99.52 83	99.71 68	99.28 82	99.06 253	99.77 9	97.74 128	99.50 73	99.53 168	95.41 145	99.84 113	97.17 209	99.64 98	99.44 136

Effi-MVS+		98.81 109	98.59 120	99.48 88	99.46 130	99.12 99	98.08 323	99.50 99	97.50 149	99.38 97	99.41 204	96.37 120	99.81 131	99.11 41	98.54 162	99.51 120

alignmvs		98.81 109	98.56 122	99.58 71	99.43 135	99.42 69	99.51 117	98.96 272	98.61 50	99.35 106	98.92 272	94.78 179	99.77 142	99.35 18	98.11 186	99.54 112

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 288	99.60 105	91.75 315	98.61 306	99.44 156	99.35 1	99.83 11	99.85 26	98.70 48	99.81 131	99.02 48	99.91 17	99.81 34

PMMVS		98.80 112	98.62 115	99.34 105	99.27 173	98.70 162	98.76 297	99.31 219	97.34 161	99.21 144	99.07 259	97.20 98	99.82 127	98.56 100	98.87 146	99.52 117

Effi-MVS+-dtu		98.78 113	98.89 82	98.47 218	99.33 156	96.91 248	99.57 94	99.30 221	98.47 57	99.41 90	98.99 266	96.78 108	99.74 147	98.73 77	99.38 108	98.74 202

FIs		98.78 113	98.63 112	99.23 123	99.18 188	99.54 52	99.83 12	99.59 38	98.28 70	98.79 209	99.81 53	96.75 111	99.37 215	99.08 43	96.38 239	98.78 193

Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 204	99.41 139	96.99 242	99.52 113	99.49 104	98.11 86	99.24 135	99.34 230	96.96 104	99.79 138	97.95 147	99.45 104	99.02 172

FC-MVSNet-test		98.75 116	98.62 115	99.15 129	99.08 209	99.45 66	99.86 8	99.60 35	98.23 75	98.70 222	99.82 44	96.80 107	99.22 253	99.07 44	96.38 239	98.79 192

XVG-OURS		98.73 117	98.68 106	98.88 174	99.70 73	97.73 218	98.92 286	99.55 55	98.52 55	99.45 81	99.84 35	95.27 149	99.91 72	98.08 138	98.84 148	99.00 173

diffmvs		98.72 118	98.49 124	99.43 100	99.48 128	99.19 90	99.62 75	99.42 165	95.58 266	99.37 99	99.67 121	96.14 126	99.74 147	98.14 131	98.96 137	99.37 143

Fast-Effi-MVS+		98.70 119	98.43 126	99.51 85	99.51 118	99.28 82	99.52 113	99.47 127	96.11 255	99.01 178	99.34 230	96.20 125	99.84 113	97.88 151	98.82 149	99.39 142

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 167	99.71 68	97.74 217	99.12 238	99.54 62	98.44 62	99.42 88	99.71 103	94.20 206	99.92 63	98.54 105	98.90 144	99.00 173

1314		98.68 121	98.54 123	99.11 131	98.89 250	98.65 167	99.27 205	99.49 104	96.89 197	97.99 265	99.56 161	97.72 87	99.83 120	97.74 165	99.27 116	98.84 189

EI-MVSNet		98.67 122	98.67 107	98.68 199	99.35 152	97.97 204	99.50 122	99.38 184	96.93 196	99.20 147	99.83 37	97.87 81	99.36 219	98.38 116	97.56 204	98.71 206

test_djsdf		98.67 122	98.57 121	98.98 143	98.70 278	98.91 130	99.88 1	99.46 137	97.55 144	99.22 142	99.88 14	95.73 139	99.28 238	99.03 46	97.62 199	98.75 199

QAPM		98.67 122	98.30 135	99.80 29	99.20 183	99.67 32	99.77 24	99.72 11	94.74 275	98.73 214	99.90 7	95.78 137	99.98 5	96.96 221	99.88 34	99.76 52

nrg030		98.64 125	98.42 127	99.28 118	99.05 215	99.69 29	99.81 15	99.46 137	98.04 99	99.01 178	99.82 44	96.69 113	99.38 212	99.34 22	94.59 277	98.78 193

PAPR		98.63 126	98.34 131	99.51 85	99.40 144	99.03 107	98.80 294	99.36 192	96.33 234	99.00 185	99.12 257	98.46 59	99.84 113	95.23 267	99.37 112	99.66 85

CVMVSNet		98.57 127	98.67 107	98.30 233	99.35 152	95.59 277	99.50 122	99.55 55	98.60 51	99.39 95	99.83 37	94.48 197	99.45 201	98.75 74	98.56 161	99.85 8

MVSTER		98.49 128	98.32 133	99.00 141	99.35 152	99.02 108	99.54 109	99.38 184	97.41 157	99.20 147	99.73 98	93.86 220	99.36 219	98.87 61	97.56 204	98.62 255

OpenMVS	96.50 16	98.47 129	98.12 143	99.52 83	99.04 216	99.53 55	99.82 13	99.72 11	94.56 281	98.08 260	99.88 14	94.73 186	99.98 5	97.47 191	99.76 76	99.06 168

IterMVS-LS		98.46 130	98.42 127	98.58 206	99.59 107	98.00 202	99.37 177	99.43 164	96.94 195	99.07 169	99.59 152	97.87 81	99.03 274	98.32 123	95.62 252	98.71 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 131	98.28 136	98.94 149	98.50 292	98.96 121	99.77 24	99.50 99	97.07 186	98.87 199	99.77 82	94.76 184	99.28 238	98.66 85	97.60 200	98.57 271

jajsoiax		98.43 132	98.28 136	98.88 174	98.60 287	98.43 188	99.82 13	99.53 72	98.19 76	98.63 233	99.80 64	93.22 230	99.44 206	99.22 31	97.50 209	98.77 196

BH-untuned		98.42 133	98.36 129	98.59 205	99.49 125	96.70 254	99.27 205	99.13 253	97.24 171	98.80 208	99.38 213	95.75 138	99.74 147	97.07 214	99.16 121	99.33 147

BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 139	98.83 140	99.30 195	98.77 293	97.70 133	98.94 191	99.65 128	92.91 236	99.74 147	96.52 241	99.55 102	99.64 94

mvs_tets		98.40 135	98.23 138	98.91 161	98.67 282	98.51 183	99.66 58	99.53 72	98.19 76	98.65 231	99.81 53	92.75 238	99.44 206	99.31 25	97.48 213	98.77 196

XXY-MVS		98.38 136	98.09 146	99.24 121	99.26 175	99.32 77	99.56 101	99.55 55	97.45 153	98.71 216	99.83 37	93.23 229	99.63 187	98.88 57	96.32 241	98.76 198

ACMM	97.58 5	98.37 137	98.34 131	98.48 216	99.41 139	97.10 231	99.56 101	99.45 148	98.53 54	99.04 175	99.85 26	93.00 232	99.71 165	98.74 75	97.45 214	98.64 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		98.33 138	98.48 125	97.90 266	99.16 195	94.78 293	99.31 193	99.11 254	97.27 167	99.45 81	99.59 152	95.33 146	99.84 113	98.48 108	98.61 155	99.09 162

PatchmatchNet		98.31 139	98.36 129	98.19 249	99.16 195	95.32 285	99.27 205	98.92 276	97.37 160	99.37 99	99.58 155	94.90 171	99.70 171	97.43 195	99.21 118	99.54 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		98.29 140	97.95 157	99.30 113	99.16 195	99.54 52	99.50 122	99.58 43	98.27 71	99.35 106	99.37 216	92.53 249	99.65 180	99.35 18	94.46 278	98.72 204

UniMVSNet (Re)		98.29 140	98.00 153	99.13 130	99.00 221	99.36 74	99.49 130	99.51 85	97.95 108	98.97 188	99.13 254	96.30 122	99.38 212	98.36 119	93.34 294	98.66 242

HQP_MVS		98.27 142	98.22 139	98.44 223	99.29 168	96.97 244	99.39 170	99.47 127	98.97 22	99.11 160	99.61 147	92.71 242	99.69 174	97.78 159	97.63 197	98.67 231

UniMVSNet_NR-MVSNet		98.22 143	97.97 155	98.96 146	98.92 244	98.98 114	99.48 135	99.53 72	97.76 125	98.71 216	99.46 194	96.43 119	99.22 253	98.57 97	92.87 300	98.69 215

LPG-MVS_test		98.22 143	98.13 142	98.49 214	99.33 156	97.05 237	99.58 88	99.55 55	97.46 150	99.24 135	99.83 37	92.58 247	99.72 159	98.09 134	97.51 207	98.68 220

RPSCF		98.22 143	98.62 115	96.99 289	99.82 29	91.58 316	99.72 39	99.44 156	96.61 212	99.66 45	99.89 10	95.92 132	99.82 127	97.46 192	99.10 126	99.57 109

ADS-MVSNet		98.20 146	98.08 147	98.56 209	99.33 156	96.48 261	99.23 218	99.15 250	96.24 243	99.10 163	99.67 121	94.11 211	99.71 165	96.81 228	99.05 130	99.48 126

OPM-MVS		98.19 147	98.10 144	98.45 220	98.88 251	97.07 235	99.28 202	99.38 184	98.57 52	99.22 142	99.81 53	92.12 257	99.66 178	98.08 138	97.54 206	98.61 264

CR-MVSNet		98.17 148	97.93 159	98.87 178	99.18 188	98.49 184	99.22 222	99.33 212	96.96 193	99.56 64	99.38 213	94.33 202	99.00 277	94.83 273	98.58 158	99.14 155

Patchmatch-test1		98.16 149	98.14 141	98.22 246	99.30 165	95.55 278	99.07 249	98.97 270	97.57 142	99.43 85	99.60 150	92.72 241	99.60 190	97.38 197	99.20 119	99.50 123

CLD-MVS		98.16 149	98.10 144	98.33 230	99.29 168	96.82 251	98.75 298	99.44 156	97.83 117	99.13 156	99.55 164	92.92 234	99.67 176	98.32 123	97.69 196	98.48 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		98.13 151	97.90 160	98.81 189	98.61 286	98.87 133	98.99 270	99.21 244	96.44 227	99.06 173	99.58 155	95.90 133	99.11 266	97.18 208	96.11 244	98.46 279

WR-MVS_H		98.13 151	97.87 165	98.90 165	99.02 219	98.84 137	99.70 42	99.59 38	97.27 167	98.40 245	99.19 250	95.53 142	99.23 250	98.34 120	93.78 291	98.61 264

v1neww		98.12 153	97.84 166	98.93 152	98.97 229	98.81 149	99.66 58	99.35 196	96.49 219	99.29 117	99.37 216	95.02 161	99.32 229	97.73 166	94.73 269	98.67 231

v7new		98.12 153	97.84 166	98.93 152	98.97 229	98.81 149	99.66 58	99.35 196	96.49 219	99.29 117	99.37 216	95.02 161	99.32 229	97.73 166	94.73 269	98.67 231

v6		98.12 153	97.84 166	98.94 149	98.94 237	98.83 140	99.66 58	99.34 204	96.49 219	99.30 113	99.37 216	94.95 165	99.34 225	97.77 161	94.74 268	98.67 231

ACMH	97.28 8	98.10 156	97.99 154	98.44 223	99.41 139	96.96 246	99.60 82	99.56 48	98.09 89	98.15 257	99.91 5	90.87 277	99.70 171	98.88 57	97.45 214	98.67 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		98.09 157	97.78 172	99.01 139	98.97 229	99.24 87	99.67 55	99.46 137	97.25 169	98.48 242	99.64 135	93.79 221	99.06 270	98.63 88	94.10 285	98.74 202

DU-MVS		98.08 158	97.79 170	98.96 146	98.87 254	98.98 114	99.41 163	99.45 148	97.87 111	98.71 216	99.50 177	94.82 176	99.22 253	98.57 97	92.87 300	98.68 220

divwei89l23v2f112		98.06 159	97.78 172	98.91 161	98.90 247	98.77 159	99.57 94	99.35 196	96.45 226	99.24 135	99.37 216	94.92 169	99.27 241	97.50 187	94.71 273	98.68 220

v2v482		98.06 159	97.77 176	98.92 157	98.90 247	98.82 147	99.57 94	99.36 192	96.65 209	99.19 150	99.35 227	94.20 206	99.25 247	97.72 170	94.97 265	98.69 215

V42		98.06 159	97.79 170	98.86 182	98.98 226	98.84 137	99.69 44	99.34 204	96.53 218	99.30 113	99.37 216	94.67 189	99.32 229	97.57 180	94.66 274	98.42 280

test-LLR		98.06 159	97.90 160	98.55 211	98.79 263	97.10 231	98.67 302	97.75 326	97.34 161	98.61 236	98.85 277	94.45 198	99.45 201	97.25 202	99.38 108	99.10 158

WR-MVS		98.06 159	97.73 183	99.06 134	98.86 257	99.25 86	99.19 228	99.35 196	97.30 165	98.66 225	99.43 199	93.94 216	99.21 257	98.58 95	94.28 281	98.71 206

ACMP	97.20 11	98.06 159	97.94 158	98.45 220	99.37 149	97.01 240	99.44 147	99.49 104	97.54 147	98.45 243	99.79 72	91.95 258	99.72 159	97.91 149	97.49 212	98.62 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1141		98.05 165	97.76 179	98.91 161	98.91 246	98.78 158	99.57 94	99.35 196	96.41 231	99.23 140	99.36 223	94.93 168	99.27 241	97.38 197	94.72 271	98.68 220

v7		98.05 165	97.78 172	98.87 178	98.99 222	98.67 164	99.64 70	99.34 204	96.31 237	99.29 117	99.51 175	94.78 179	99.27 241	97.03 215	95.15 261	98.66 242

v1		98.05 165	97.76 179	98.93 152	98.92 244	98.80 154	99.57 94	99.35 196	96.39 233	99.28 121	99.36 223	94.86 174	99.32 229	97.38 197	94.72 271	98.68 220

EPNet_dtu		98.03 168	97.96 156	98.23 244	98.27 297	95.54 280	99.23 218	98.75 294	99.02 10	97.82 270	99.71 103	96.11 127	99.48 198	93.04 300	99.65 97	99.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 168	97.76 179	98.84 186	99.39 146	98.98 114	99.40 169	99.38 184	96.67 208	99.07 169	99.28 241	92.93 233	98.98 279	97.10 211	96.65 232	98.56 272

ADS-MVSNet2		98.02 170	98.07 149	97.87 267	99.33 156	95.19 288	99.23 218	99.08 257	96.24 243	99.10 163	99.67 121	94.11 211	98.93 287	96.81 228	99.05 130	99.48 126

HQP-MVS		98.02 170	97.90 160	98.37 228	99.19 185	96.83 249	98.98 274	99.39 178	98.24 72	98.66 225	99.40 208	92.47 251	99.64 182	97.19 206	97.58 202	98.64 247

LTVRE_ROB	97.16 12	98.02 170	97.90 160	98.40 226	99.23 178	96.80 252	99.70 42	99.60 35	97.12 181	98.18 256	99.70 106	91.73 265	99.72 159	98.39 114	97.45 214	98.68 220
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		98.01 173	98.05 150	97.87 267	99.15 198	94.76 294	99.42 159	98.93 274	97.12 181	98.84 205	98.59 290	93.74 225	99.80 135	98.55 103	98.17 183	99.06 168

BH-w/o		98.00 174	97.89 164	98.32 231	99.35 152	96.20 270	99.01 268	98.90 281	96.42 229	98.38 246	99.00 265	95.26 151	99.72 159	96.06 249	98.61 155	99.03 170

v1144		97.98 175	97.69 186	98.85 185	98.87 254	98.66 166	99.54 109	99.35 196	96.27 240	99.23 140	99.35 227	94.67 189	99.23 250	96.73 232	95.16 260	98.68 220

EU-MVSNet		97.98 175	98.03 151	97.81 273	98.72 275	96.65 257	99.66 58	99.66 25	98.09 89	98.35 249	99.82 44	95.25 152	98.01 308	97.41 196	95.30 257	98.78 193

tpmvs		97.98 175	98.02 152	97.84 270	99.04 216	94.73 295	99.31 193	99.20 245	96.10 258	98.76 212	99.42 201	94.94 166	99.81 131	96.97 220	98.45 166	98.97 177

view600		97.97 178	97.66 187	98.89 167	99.75 47	97.81 211	99.69 44	98.80 289	98.02 102	99.25 130	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

view800		97.97 178	97.66 187	98.89 167	99.75 47	97.81 211	99.69 44	98.80 289	98.02 102	99.25 130	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

conf0.05thres1000		97.97 178	97.66 187	98.89 167	99.75 47	97.81 211	99.69 44	98.80 289	98.02 102	99.25 130	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

tfpn		97.97 178	97.66 187	98.89 167	99.75 47	97.81 211	99.69 44	98.80 289	98.02 102	99.25 130	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

NR-MVSNet		97.97 178	97.61 195	99.02 138	98.87 254	99.26 85	99.47 139	99.42 165	97.63 138	97.08 281	99.50 177	95.07 159	99.13 263	97.86 153	93.59 292	98.68 220

v8		97.95 183	97.63 194	98.93 152	98.95 234	98.81 149	99.80 19	99.41 168	96.03 259	99.10 163	99.42 201	94.92 169	99.30 235	96.94 223	94.08 286	98.66 242

Patchmatch-test		97.93 184	97.65 192	98.77 194	99.18 188	97.07 235	99.03 261	99.14 252	96.16 250	98.74 213	99.57 159	94.56 193	99.72 159	93.36 297	99.11 124	99.52 117

PS-CasMVS		97.93 184	97.59 197	98.95 148	98.99 222	99.06 104	99.68 53	99.52 76	97.13 179	98.31 251	99.68 117	92.44 255	99.05 271	98.51 106	94.08 286	98.75 199

TranMVSNet+NR-MVSNet		97.93 184	97.66 187	98.76 195	98.78 267	98.62 171	99.65 68	99.49 104	97.76 125	98.49 241	99.60 150	94.23 205	98.97 286	98.00 143	92.90 298	98.70 210

v144192		97.92 187	97.60 196	98.87 178	98.83 260	98.65 167	99.55 106	99.34 204	96.20 246	99.32 111	99.40 208	94.36 201	99.26 246	96.37 246	95.03 264	98.70 210

ACMH+	97.24 10	97.92 187	97.78 172	98.32 231	99.46 130	96.68 256	99.56 101	99.54 62	98.41 63	97.79 272	99.87 19	90.18 284	99.66 178	98.05 142	97.18 227	98.62 255

LFMVS		97.90 189	97.35 226	99.54 75	99.52 116	99.01 110	99.39 170	98.24 318	97.10 185	99.65 48	99.79 72	84.79 320	99.91 72	99.28 27	98.38 169	99.69 77

OurMVSNet-221017-0		97.88 190	97.77 176	98.19 249	98.71 277	96.53 259	99.88 1	99.00 267	97.79 122	98.78 210	99.94 3	91.68 266	99.35 222	97.21 204	96.99 230	98.69 215

v7n		97.87 191	97.52 200	98.92 157	98.76 271	98.58 175	99.84 9	99.46 137	96.20 246	98.91 194	99.70 106	94.89 172	99.44 206	96.03 250	93.89 290	98.75 199

thres600view7		97.86 192	97.51 202	98.92 157	99.72 65	97.95 207	99.59 84	98.74 297	97.94 109	99.27 125	98.62 288	91.75 264	99.86 104	93.73 294	98.19 179	98.96 179

v10		97.85 193	97.52 200	98.86 182	98.99 222	98.67 164	99.75 34	99.41 168	95.70 264	98.98 187	99.41 204	94.75 185	99.23 250	96.01 251	94.63 276	98.67 231

GA-MVS		97.85 193	97.47 208	99.00 141	99.38 147	97.99 203	98.57 308	99.15 250	97.04 189	98.90 196	99.30 238	89.83 286	99.38 212	96.70 234	98.33 170	99.62 100

tfpnnormal		97.84 195	97.47 208	98.98 143	99.20 183	99.22 89	99.64 70	99.61 32	96.32 235	98.27 254	99.70 106	93.35 228	99.44 206	95.69 257	95.40 255	98.27 286

VPNet		97.84 195	97.44 214	99.01 139	99.21 181	98.94 125	99.48 135	99.57 44	98.38 64	99.28 121	99.73 98	88.89 294	99.39 211	99.19 33	93.27 295	98.71 206

LCM-MVSNet-Re		97.83 197	98.15 140	96.87 293	99.30 165	92.25 314	99.59 84	98.26 317	97.43 154	96.20 290	99.13 254	96.27 123	98.73 292	98.17 129	98.99 134	99.64 94

XVG-ACMP-BASELINE		97.83 197	97.71 185	98.20 248	99.11 203	96.33 266	99.41 163	99.52 76	98.06 97	99.05 174	99.50 177	89.64 288	99.73 155	97.73 166	97.38 220	98.53 273

IterMVS		97.83 197	97.77 176	98.02 257	99.58 108	96.27 268	99.02 264	99.48 113	97.22 173	98.71 216	99.70 106	92.75 238	99.13 263	97.46 192	96.00 246	98.67 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS		97.82 200	97.65 192	98.35 229	98.88 251	95.98 272	99.49 130	94.71 337	97.57 142	99.26 129	99.48 186	92.46 254	99.71 165	97.87 152	99.08 128	99.35 145

MVP-Stereo		97.81 201	97.75 182	97.99 260	97.53 306	96.60 258	98.96 279	98.85 285	97.22 173	97.23 278	99.36 223	95.28 148	99.46 200	95.51 261	99.78 72	97.92 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 201	97.44 214	98.91 161	98.88 251	98.68 163	99.51 117	99.34 204	96.18 248	99.20 147	99.34 230	94.03 214	99.36 219	95.32 266	95.18 259	98.69 215

v1921920		97.80 203	97.45 211	98.84 186	98.80 261	98.53 178	99.52 113	99.34 204	96.15 252	99.24 135	99.47 190	93.98 215	99.29 237	95.40 264	95.13 262	98.69 215

V4		97.80 203	97.51 202	98.67 201	98.79 263	98.63 169	99.87 4	99.44 156	95.87 261	99.01 178	99.46 194	94.52 196	99.33 226	96.64 240	93.97 288	98.05 292

v148		97.79 205	97.55 198	98.50 213	98.74 272	97.72 219	99.54 109	99.33 212	96.26 241	98.90 196	99.51 175	94.68 188	99.14 260	97.83 155	93.15 297	98.63 253

v52		97.79 205	97.50 204	98.66 202	98.80 261	98.62 171	99.87 4	99.44 156	95.87 261	99.01 178	99.46 194	94.44 200	99.33 226	96.65 239	93.96 289	98.05 292

thres400		97.77 207	97.38 222	98.92 157	99.69 75	97.96 205	99.50 122	98.73 303	97.83 117	99.17 153	98.45 294	91.67 267	99.83 120	93.22 298	98.18 180	98.96 179

PEN-MVS		97.76 208	97.44 214	98.72 197	98.77 270	98.54 177	99.78 22	99.51 85	97.06 188	98.29 253	99.64 135	92.63 246	98.89 288	98.09 134	93.16 296	98.72 204

Baseline_NR-MVSNet		97.76 208	97.45 211	98.68 199	99.09 208	98.29 192	99.41 163	98.85 285	95.65 265	98.63 233	99.67 121	94.82 176	99.10 268	98.07 140	92.89 299	98.64 247

TR-MVS		97.76 208	97.41 219	98.82 188	99.06 212	97.87 209	98.87 291	98.56 312	96.63 211	98.68 224	99.22 248	92.49 250	99.65 180	95.40 264	97.79 194	98.95 186

Patchmtry		97.75 211	97.40 220	98.81 189	99.10 206	98.87 133	99.11 244	99.33 212	94.83 273	98.81 207	99.38 213	94.33 202	99.02 275	96.10 248	95.57 253	98.53 273

dp		97.75 211	97.80 169	97.59 280	99.10 206	93.71 305	99.32 190	98.88 283	96.48 225	99.08 168	99.55 164	92.67 245	99.82 127	96.52 241	98.58 158	99.24 152

TAPA-MVS	97.07 15	97.74 213	97.34 229	98.94 149	99.70 73	97.53 220	99.25 215	99.51 85	91.90 310	99.30 113	99.63 139	98.78 36	99.64 182	88.09 317	99.87 38	99.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 214	97.35 226	98.88 174	99.47 129	97.12 230	99.34 188	98.85 285	98.19 76	99.67 40	99.85 26	82.98 324	99.92 63	99.49 12	98.32 171	99.60 102

MIMVSNet		97.73 214	97.45 211	98.57 207	99.45 134	97.50 221	99.02 264	98.98 269	96.11 255	99.41 90	99.14 253	90.28 280	98.74 291	95.74 255	98.93 140	99.47 130

tfpn200view9		97.72 216	97.38 222	98.72 197	99.69 75	97.96 205	99.50 122	98.73 303	97.83 117	99.17 153	98.45 294	91.67 267	99.83 120	93.22 298	98.18 180	98.37 284

CostFormer		97.72 216	97.73 183	97.71 278	99.15 198	94.02 301	99.54 109	99.02 266	94.67 276	99.04 175	99.35 227	92.35 256	99.77 142	98.50 107	97.94 191	99.34 146

FMVSNet2		97.72 216	97.36 224	98.80 191	99.51 118	98.84 137	99.45 143	99.42 165	96.49 219	98.86 204	99.29 240	90.26 281	98.98 279	96.44 243	96.56 235	98.58 270

test0.0.03 1		97.71 219	97.42 218	98.56 209	98.41 295	97.82 210	98.78 295	98.63 308	97.34 161	98.05 264	98.98 269	94.45 198	98.98 279	95.04 270	97.15 228	98.89 187

v1240		97.69 220	97.32 232	98.79 192	98.85 258	98.43 188	99.48 135	99.36 192	96.11 255	99.27 125	99.36 223	93.76 223	99.24 249	94.46 279	95.23 258	98.70 210

cascas		97.69 220	97.43 217	98.48 216	98.60 287	97.30 222	98.18 322	99.39 178	92.96 303	98.41 244	98.78 284	93.77 222	99.27 241	98.16 130	98.61 155	98.86 188

pm-mvs1		97.68 222	97.28 236	98.88 174	99.06 212	98.62 171	99.50 122	99.45 148	96.32 235	97.87 268	99.79 72	92.47 251	99.35 222	97.54 183	93.54 293	98.67 231

GBi-Net		97.68 222	97.48 206	98.29 234	99.51 118	97.26 225	99.43 152	99.48 113	96.49 219	99.07 169	99.32 235	90.26 281	98.98 279	97.10 211	96.65 232	98.62 255

test1		97.68 222	97.48 206	98.29 234	99.51 118	97.26 225	99.43 152	99.48 113	96.49 219	99.07 169	99.32 235	90.26 281	98.98 279	97.10 211	96.65 232	98.62 255

tpm		97.67 225	97.55 198	98.03 255	99.02 219	95.01 291	99.43 152	98.54 313	96.44 227	99.12 158	99.34 230	91.83 263	99.60 190	97.75 164	96.46 237	99.48 126

PCF-MVS	97.08 14	97.66 226	97.06 244	99.47 91	99.61 103	99.09 101	98.04 324	99.25 240	91.24 313	98.51 239	99.70 106	94.55 194	99.91 72	92.76 303	99.85 52	99.42 139
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testgi		97.65 227	97.50 204	98.13 252	99.36 151	96.45 262	99.42 159	99.48 113	97.76 125	97.87 268	99.45 197	91.09 274	98.81 290	94.53 277	98.52 163	99.13 157

thres200		97.61 228	97.28 236	98.62 203	99.64 92	98.03 201	99.26 213	98.74 297	97.68 135	99.09 167	98.32 296	91.66 269	99.81 131	92.88 302	98.22 176	98.03 295

PAPM		97.59 229	97.09 243	99.07 133	99.06 212	98.26 194	98.30 318	99.10 255	94.88 272	98.08 260	99.34 230	96.27 123	99.64 182	89.87 311	98.92 142	99.31 148

VDDNet		97.55 230	97.02 245	99.16 127	99.49 125	98.12 200	99.38 175	99.30 221	95.35 268	99.68 34	99.90 7	82.62 326	99.93 55	99.31 25	98.13 185	99.42 139

TESTMET0.1,1		97.55 230	97.27 238	98.40 226	98.93 242	96.53 259	98.67 302	97.61 330	96.96 193	98.64 232	99.28 241	88.63 300	99.45 201	97.30 201	99.38 108	99.21 153

DWT-MVSNet_test		97.53 232	97.40 220	97.93 263	99.03 218	94.86 292	99.57 94	98.63 308	96.59 216	98.36 248	98.79 282	89.32 290	99.74 147	98.14 131	98.16 184	99.20 154

pmmvs5		97.52 233	97.30 234	98.16 251	98.57 289	96.73 253	99.27 205	98.90 281	96.14 253	98.37 247	99.53 168	91.54 271	99.14 260	97.51 186	95.87 247	98.63 253

v748		97.52 233	97.23 239	98.41 225	98.69 279	97.23 228	99.87 4	99.45 148	95.72 263	98.51 239	99.53 168	94.13 210	99.30 235	96.78 230	92.39 304	98.70 210

LF4IMVS		97.52 233	97.46 210	97.70 279	98.98 226	95.55 278	99.29 199	98.82 288	98.07 93	98.66 225	99.64 135	89.97 285	99.61 189	97.01 216	96.68 231	97.94 299

DTE-MVSNet		97.51 236	97.19 241	98.46 219	98.63 285	98.13 199	99.84 9	99.48 113	96.68 207	97.97 266	99.67 121	92.92 234	98.56 294	96.88 227	92.60 303	98.70 210

SixPastTwentyTwo		97.50 237	97.33 231	98.03 255	98.65 283	96.23 269	99.77 24	98.68 306	97.14 178	97.90 267	99.93 4	90.45 279	99.18 259	97.00 217	96.43 238	98.67 231

JIA-IIPM		97.50 237	97.02 245	98.93 152	98.73 273	97.80 215	99.30 195	98.97 270	91.73 311	98.91 194	94.86 327	95.10 158	99.71 165	97.58 178	97.98 190	99.28 150

test-mter		97.49 239	97.13 242	98.55 211	98.79 263	97.10 231	98.67 302	97.75 326	96.65 209	98.61 236	98.85 277	88.23 305	99.45 201	97.25 202	99.38 108	99.10 158

DI_MVS_plusplus_test		97.45 240	96.79 249	99.44 97	97.76 304	99.04 106	99.21 225	98.61 310	97.74 128	94.01 306	98.83 279	87.38 311	99.83 120	98.63 88	98.90 144	99.44 136

test_normal		97.44 241	96.77 251	99.44 97	97.75 305	99.00 112	99.10 246	98.64 307	97.71 131	93.93 309	98.82 280	87.39 310	99.83 120	98.61 92	98.97 136	99.49 124

tpm2		97.44 241	97.34 229	97.74 277	99.15 198	94.36 298	99.45 143	98.94 273	93.45 301	98.90 196	99.44 198	91.35 272	99.59 192	97.31 200	98.07 187	99.29 149

tpm cat1		97.39 243	97.36 224	97.50 283	99.17 193	93.73 303	99.43 152	99.31 219	91.27 312	98.71 216	99.08 258	94.31 204	99.77 142	96.41 245	98.50 164	99.00 173

tpmp4_e23		97.34 244	97.29 235	97.52 281	99.25 177	93.73 303	99.58 88	99.19 248	94.00 292	98.20 255	99.41 204	90.74 278	99.74 147	97.13 210	98.07 187	99.07 167

USDC		97.34 244	97.20 240	97.75 276	99.07 210	95.20 287	98.51 311	99.04 264	97.99 107	98.31 251	99.86 22	89.02 292	99.55 195	95.67 259	97.36 221	98.49 275

MVS		97.28 246	96.55 253	99.48 88	98.78 267	98.95 122	99.27 205	99.39 178	83.53 326	98.08 260	99.54 167	96.97 103	99.87 101	94.23 290	99.16 121	99.63 98

DSMNet-mixed		97.25 247	97.35 226	96.95 291	97.84 302	93.61 307	99.57 94	96.63 333	96.13 254	98.87 199	98.61 289	94.59 192	97.70 316	95.08 269	98.86 147	99.55 110

MS-PatchMatch		97.24 248	97.32 232	96.99 289	98.45 294	93.51 308	98.82 293	99.32 218	97.41 157	98.13 258	99.30 238	88.99 293	99.56 193	95.68 258	99.80 68	97.90 302

TransMVSNet (Re)		97.15 249	96.58 252	98.86 182	99.12 201	98.85 136	99.49 130	98.91 279	95.48 267	97.16 280	99.80 64	93.38 227	99.11 266	94.16 292	91.73 305	98.62 255

TinyColmap		97.12 250	96.89 247	97.83 271	99.07 210	95.52 281	98.57 308	98.74 297	97.58 141	97.81 271	99.79 72	88.16 306	99.56 193	95.10 268	97.21 225	98.39 283

K. test v3		97.10 251	96.79 249	98.01 258	98.72 275	96.33 266	99.87 4	97.05 332	97.59 139	96.16 291	99.80 64	88.71 296	99.04 272	96.69 235	96.55 236	98.65 245

LP		97.04 252	96.80 248	97.77 275	98.90 247	95.23 286	98.97 277	99.06 262	94.02 291	98.09 259	99.41 204	93.88 218	98.82 289	90.46 309	98.42 168	99.26 151

PatchT		97.03 253	96.44 254	98.79 192	98.99 222	98.34 191	99.16 231	99.07 260	92.13 307	99.52 70	97.31 320	94.54 195	98.98 279	88.54 315	98.73 154	99.03 170

FMVSNet1		96.84 254	96.36 255	98.29 234	99.32 163	97.26 225	99.43 152	99.48 113	95.11 270	98.55 238	99.32 235	83.95 323	98.98 279	95.81 254	96.26 242	98.62 255

test_0402		96.64 255	96.24 256	97.85 269	98.85 258	96.43 263	99.44 147	99.26 238	93.52 298	96.98 284	99.52 172	88.52 301	99.20 258	92.58 305	97.50 209	97.93 300

RPMNet		96.61 256	95.85 264	98.87 178	99.18 188	98.49 184	99.22 222	99.08 257	88.72 322	99.56 64	97.38 318	94.08 213	99.00 277	86.87 322	98.58 158	99.14 155

X-MVStestdata		96.55 257	95.45 278	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	64.01 342	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

pmmvs6		96.53 258	96.09 259	97.82 272	98.69 279	95.47 282	99.37 177	99.47 127	93.46 300	97.41 275	99.78 77	87.06 312	99.33 226	96.92 225	92.70 302	98.65 245

UnsupCasMVSNet_eth		96.44 259	96.12 258	97.40 285	98.65 283	95.65 275	99.36 181	99.51 85	97.13 179	96.04 294	98.99 266	88.40 303	98.17 297	96.71 233	90.27 308	98.40 282

FMVSNet5		96.43 260	96.19 257	97.15 286	99.11 203	95.89 274	99.32 190	99.52 76	94.47 285	98.34 250	99.07 259	87.54 309	97.07 319	92.61 304	95.72 250	98.47 277

v18		96.42 261	95.80 268	98.26 237	98.95 234	98.82 147	99.76 27	99.28 232	94.58 278	94.12 301	97.70 305	95.22 154	98.16 298	94.83 273	87.80 315	97.79 310

v17		96.42 261	95.81 266	98.25 241	98.94 237	98.80 154	99.76 27	99.28 232	94.57 279	94.18 300	97.71 304	95.23 153	98.16 298	94.86 271	87.73 317	97.80 305

v16		96.39 263	95.76 269	98.26 237	98.96 232	98.81 149	99.76 27	99.28 232	94.57 279	94.10 302	97.70 305	95.04 160	98.16 298	94.70 275	87.77 316	97.80 305

new_pmnet		96.38 264	96.03 260	97.41 284	98.13 300	95.16 290	99.05 255	99.20 245	93.94 293	97.39 276	98.79 282	91.61 270	99.04 272	90.43 310	95.77 249	98.05 292

v15		96.28 265	95.62 271	98.25 241	98.94 237	98.83 140	99.76 27	99.29 225	94.52 283	94.02 305	97.61 312	95.02 161	98.13 302	94.53 277	86.92 320	97.80 305

V14		96.26 266	95.60 272	98.26 237	98.94 237	98.83 140	99.76 27	99.29 225	94.49 284	93.96 307	97.66 308	94.99 164	98.13 302	94.41 280	86.90 321	97.80 305

V9		96.25 267	95.58 273	98.26 237	98.94 237	98.83 140	99.75 34	99.29 225	94.45 286	93.96 307	97.62 311	94.94 166	98.14 301	94.40 281	86.87 322	97.81 303

v13		96.24 268	95.58 273	98.25 241	98.98 226	98.83 140	99.75 34	99.29 225	94.35 288	93.89 310	97.60 313	95.17 156	98.11 304	94.27 289	86.86 323	97.81 303

v12		96.24 268	95.58 273	98.23 244	98.96 232	98.81 149	99.76 27	99.29 225	94.42 287	93.85 311	97.60 313	95.12 157	98.09 305	94.32 286	86.85 324	97.80 305

v11		96.23 270	95.57 276	98.21 247	98.93 242	98.83 140	99.72 39	99.29 225	94.29 289	94.05 304	97.64 310	94.88 173	98.04 306	92.89 301	88.43 313	97.77 311

Anonymous20231206		96.22 271	96.03 260	96.79 295	97.31 311	94.14 300	99.63 72	99.08 257	96.17 249	97.04 282	99.06 261	93.94 216	97.76 315	86.96 321	95.06 263	98.47 277

IB-MVS	95.67 18	96.22 271	95.44 279	98.57 207	99.21 181	96.70 254	98.65 305	97.74 328	96.71 205	97.27 277	98.54 292	86.03 314	99.92 63	98.47 110	86.30 325	99.10 158
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		96.17 273	95.32 280	98.73 196	98.79 263	98.14 198	99.38 175	94.09 338	91.07 315	98.07 263	91.04 333	89.62 289	99.35 222	96.75 231	99.09 127	98.68 220

test20.03		96.12 274	95.96 263	96.63 296	97.44 307	95.45 283	99.51 117	99.38 184	96.55 217	96.16 291	99.25 245	93.76 223	96.17 324	87.35 320	94.22 283	98.27 286

PVSNet_0	94.43 19	96.09 275	95.47 277	97.94 262	99.31 164	94.34 299	97.81 325	99.70 15	97.12 181	97.46 274	98.75 285	89.71 287	99.79 138	97.69 172	81.69 329	99.68 81

EG-PatchMatch MVS		95.97 276	95.69 270	96.81 294	97.78 303	92.79 311	99.16 231	98.93 274	96.16 250	94.08 303	99.22 248	82.72 325	99.47 199	95.67 259	97.50 209	98.17 289

Patchmatch-RL test		95.84 277	95.81 266	95.95 300	95.61 316	90.57 317	98.24 319	98.39 314	95.10 271	95.20 296	98.67 287	94.78 179	97.77 314	96.28 247	90.02 309	99.51 120

MVS-HIRNet		95.75 278	95.16 282	97.51 282	99.30 165	93.69 306	98.88 290	95.78 334	85.09 325	98.78 210	92.65 329	91.29 273	99.37 215	94.85 272	99.85 52	99.46 133

testpf		95.66 279	96.02 262	94.58 303	98.35 296	92.32 313	97.25 330	97.91 325	92.83 304	97.03 283	98.99 266	88.69 297	98.61 293	95.72 256	97.40 218	92.80 327

MIMVSNet1		95.51 280	95.04 283	96.92 292	97.38 308	95.60 276	99.52 113	99.50 99	93.65 296	96.97 285	99.17 251	85.28 318	96.56 323	88.36 316	95.55 254	98.60 266

MDA-MVSNet_test_wron		95.45 281	94.60 286	98.01 258	98.16 299	97.21 229	99.11 244	99.24 241	93.49 299	80.73 331	98.98 269	93.02 231	98.18 296	94.22 291	94.45 279	98.64 247

TDRefinement		95.42 282	94.57 287	97.97 261	89.83 332	96.11 271	99.48 135	98.75 294	96.74 203	96.68 286	99.88 14	88.65 299	99.71 165	98.37 117	82.74 328	98.09 290

YYNet1		95.36 283	94.51 288	97.92 264	97.89 301	97.10 231	99.10 246	99.23 242	93.26 302	80.77 330	99.04 263	92.81 237	98.02 307	94.30 287	94.18 284	98.64 247

pmmvs-eth3d		95.34 284	94.73 285	97.15 286	95.53 318	95.94 273	99.35 185	99.10 255	95.13 269	93.55 312	97.54 316	88.15 307	97.91 310	94.58 276	89.69 311	97.61 314

Test4		95.05 285	93.67 293	99.22 124	96.07 315	98.94 125	99.20 227	99.27 237	97.71 131	89.96 324	97.59 315	66.18 332	99.25 247	98.06 141	98.96 137	99.47 130

MDA-MVSNet-bldmvs		94.96 286	93.98 291	97.92 264	98.24 298	97.27 224	99.15 234	99.33 212	93.80 295	80.09 332	99.03 264	88.31 304	97.86 312	93.49 296	94.36 280	98.62 255

N_pmnet		94.95 287	95.83 265	92.31 311	98.47 293	79.33 333	99.12 238	92.81 343	93.87 294	97.68 273	99.13 254	93.87 219	99.01 276	91.38 307	96.19 243	98.59 267

testus		94.61 288	95.30 281	92.54 310	96.44 314	84.18 325	98.36 314	99.03 265	94.18 290	96.49 287	98.57 291	88.74 295	95.09 328	87.41 319	98.45 166	98.36 285

new-patchmatchnet		94.48 289	94.08 290	95.67 301	95.08 320	92.41 312	99.18 229	99.28 232	94.55 282	93.49 313	97.37 319	87.86 308	97.01 320	91.57 306	88.36 314	97.61 314

testing_2		94.44 290	92.93 296	98.98 143	94.16 323	99.00 112	99.42 159	99.28 232	96.60 214	84.86 326	96.84 321	70.91 329	99.27 241	98.23 126	96.08 245	98.68 220

OpenMVS_ROB	92.34 20	94.38 291	93.70 292	96.41 299	97.38 308	93.17 309	99.06 253	98.75 294	86.58 323	94.84 299	98.26 298	81.53 327	99.32 229	89.01 314	97.87 193	96.76 318

CMPMVS	69.68 23	94.13 292	94.90 284	91.84 312	97.24 312	80.01 332	98.52 310	99.48 113	89.01 320	91.99 318	99.67 121	85.67 316	99.13 263	95.44 262	97.03 229	96.39 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 293	93.25 295	96.60 297	94.76 321	94.49 296	98.92 286	98.18 321	89.66 317	96.48 288	98.06 299	86.28 313	97.33 318	89.68 312	87.20 319	97.97 298

test2356		94.07 294	94.46 289	92.89 308	95.18 319	86.13 323	97.60 328	99.06 262	93.61 297	96.15 293	98.28 297	85.60 317	93.95 330	86.68 323	98.00 189	98.59 267

UnsupCasMVSNet_bld		93.53 295	92.51 297	96.58 298	97.38 308	93.82 302	98.24 319	99.48 113	91.10 314	93.10 314	96.66 322	74.89 328	98.37 295	94.03 293	87.71 318	97.56 316

PM-MVS		92.96 296	92.23 298	95.14 302	95.61 316	89.98 319	99.37 177	98.21 319	94.80 274	95.04 298	97.69 307	65.06 333	97.90 311	94.30 287	89.98 310	97.54 317

test1235678		92.91 297	93.30 294	91.71 314	93.14 326	83.01 327	98.75 298	98.58 311	92.80 305	92.45 316	97.91 301	88.51 302	93.54 331	82.26 327	95.35 256	98.59 267

1111		92.30 298	92.21 299	92.55 309	93.30 324	86.27 321	99.15 234	98.74 297	91.94 308	90.85 321	97.82 302	84.18 321	95.21 326	79.65 329	94.27 282	96.19 321

test12356		91.74 299	92.19 300	90.37 317	91.22 328	82.41 328	98.61 306	98.28 316	90.66 316	91.82 319	97.92 300	84.90 319	92.61 332	81.64 328	94.66 274	96.09 322

Gipuma		90.99 300	90.15 301	93.51 305	98.73 273	90.12 318	93.98 334	99.45 148	79.32 329	92.28 317	94.91 326	69.61 330	97.98 309	87.42 318	95.67 251	92.45 329
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		90.69 301	89.39 302	94.58 303	94.25 322	88.18 320	99.29 199	99.07 260	82.45 328	92.95 315	97.65 309	63.96 335	97.79 313	89.27 313	85.63 326	97.77 311

testmv		87.91 302	87.80 303	88.24 318	87.68 335	77.50 335	99.07 249	97.66 329	89.27 318	86.47 325	96.22 324	68.35 331	92.49 334	76.63 333	88.82 312	94.72 325

PMMVS2		86.87 303	85.37 306	91.35 316	90.21 331	83.80 326	98.89 289	97.45 331	83.13 327	91.67 320	95.03 325	48.49 339	94.70 329	85.86 324	77.62 330	95.54 323

LCM-MVSNet		86.80 304	85.22 307	91.53 315	87.81 334	80.96 331	98.23 321	98.99 268	71.05 332	90.13 323	96.51 323	48.45 340	96.88 321	90.51 308	85.30 327	96.76 318

FPMVS		84.93 305	85.65 305	82.75 325	86.77 336	63.39 342	98.35 316	98.92 276	74.11 331	83.39 328	98.98 269	50.85 338	92.40 335	84.54 325	94.97 265	92.46 328

.test1245		83.42 306	86.17 304	75.15 328	93.30 324	86.27 321	99.15 234	98.74 297	91.94 308	90.85 321	97.82 302	84.18 321	95.21 326	79.65 329	39.90 339	43.98 338

no-one		83.04 307	80.12 309	91.79 313	89.44 333	85.65 324	99.32 190	98.32 315	89.06 319	79.79 334	89.16 335	44.86 341	96.67 322	84.33 326	46.78 337	93.05 326

tmp_tt		82.80 308	81.52 308	86.66 319	66.61 343	68.44 341	92.79 336	97.92 323	68.96 334	80.04 333	99.85 26	85.77 315	96.15 325	97.86 153	43.89 338	95.39 324

E-PMN		80.61 309	79.88 310	82.81 324	90.75 330	76.38 337	97.69 326	95.76 335	66.44 336	83.52 327	92.25 330	62.54 336	87.16 339	68.53 337	61.40 333	84.89 336

EMVS		80.02 310	79.22 311	82.43 326	91.19 329	76.40 336	97.55 329	92.49 345	66.36 337	83.01 329	91.27 331	64.63 334	85.79 340	65.82 338	60.65 334	85.08 335

PNet_i23d		79.43 311	77.68 312	84.67 321	86.18 337	71.69 340	96.50 332	93.68 339	75.17 330	71.33 335	91.18 332	32.18 344	90.62 336	78.57 332	74.34 331	91.71 331

ANet_high		77.30 312	74.86 314	84.62 322	75.88 341	77.61 334	97.63 327	93.15 342	88.81 321	64.27 337	89.29 334	36.51 342	83.93 341	75.89 334	52.31 336	92.33 330

MVE	76.82 21	76.91 313	74.31 315	84.70 320	85.38 339	76.05 338	96.88 331	93.17 341	67.39 335	71.28 336	89.01 336	21.66 349	87.69 338	71.74 336	72.29 332	90.35 332
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 314	74.97 313	79.01 327	70.98 342	55.18 343	93.37 335	98.21 319	65.08 338	61.78 339	93.83 328	21.74 348	92.53 333	78.59 331	91.12 307	89.34 333
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		74.42 315	71.19 316	84.14 323	76.16 340	74.29 339	96.00 333	92.57 344	69.57 333	63.84 338	87.49 337	21.98 346	88.86 337	75.56 335	57.50 335	89.26 334

pcd1.5k->3k		40.85 316	43.49 318	32.93 330	98.95 234	0.00 347	0.00 338	99.53 72	0.00 342	0.00 343	0.27 344	95.32 147	0.00 345	0.00 342	97.30 222	98.80 191

wuyk23d		40.18 317	41.29 320	36.84 329	86.18 337	49.12 344	79.73 337	22.81 347	27.64 339	25.46 342	28.45 343	21.98 346	48.89 342	55.80 339	23.56 342	12.51 340

testmvs		39.17 318	43.78 317	25.37 332	36.04 345	16.84 346	98.36 314	26.56 346	20.06 340	38.51 341	67.32 338	29.64 345	15.30 344	37.59 340	39.90 339	43.98 338

test123		39.01 319	42.50 319	28.53 331	39.17 344	20.91 345	98.75 298	19.17 348	19.83 341	38.57 340	66.67 339	33.16 343	15.42 343	37.50 341	29.66 341	49.26 337

cdsmvs_eth3d_5k		24.64 320	32.85 321	0.00 333	0.00 346	0.00 347	0.00 338	99.51 85	0.00 342	0.00 343	99.56 161	96.58 115	0.00 345	0.00 342	0.00 343	0.00 341

ab-mvs-re		8.30 321	11.06 322	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	99.58 155	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

pcd_1.5k_mvsjas		8.27 322	11.03 323	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.27 344	99.01 11	0.00 345	0.00 342	0.00 343	0.00 341

sosnet-low-res		0.02 323	0.03 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.27 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

sosnet		0.02 323	0.03 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.27 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uncertanet		0.02 323	0.03 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.27 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

Regformer		0.02 323	0.03 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.27 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uanet		0.02 323	0.03 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.27 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

ESAPD								99.47 127

sam_mvs1												94.86 174

sam_mvs												94.72 187

semantic-postprocess				98.06 254	99.57 110	96.36 265		99.49 104	97.18 175	98.71 216	99.72 102	92.70 244	99.14 260	97.44 194	95.86 248	98.67 231

ambc				93.06 307	92.68 327	82.36 329	98.47 312	98.73 303		95.09 297	97.41 317	55.55 337	99.10 268	96.42 244	91.32 306	97.71 313

MTGPA								99.47 127

test_post1							99.23 218				65.14 341	94.18 209	99.71 165	97.58 178

test_post											65.99 340	94.65 191	99.73 155

patchmatchnet-post											98.70 286	94.79 178	99.74 147

GG-mvs-BLEND				98.45 220	98.55 290	98.16 197	99.43 152	93.68 339		97.23 278	98.46 293	89.30 291	99.22 253	95.43 263	98.22 176	97.98 297

MTMP								98.88 283

gm-plane-assit					98.54 291	92.96 310			94.65 277		99.15 252		99.64 182	97.56 181

test9_res														97.49 188	99.72 83	99.75 53

TEST9					99.67 79	99.65 37	99.05 255	99.41 168	96.22 245	98.95 189	99.49 180	98.77 39	99.91 72

test_8					99.67 79	99.61 42	99.03 261	99.41 168	96.28 238	98.93 192	99.48 186	98.76 41	99.91 72

agg_prior2														97.21 204	99.73 82	99.75 53

agg_prior					99.67 79	99.62 40		99.40 175		98.87 199			99.91 72

TestCases				99.31 110	99.86 20	98.48 186		99.61 32	97.85 114	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

test_prior4						99.56 49	98.99 270

test_prior2							98.96 279		98.34 66	99.01 178	99.52 172	98.68 49		97.96 145	99.74 79

test_prior				99.68 50	99.67 79	99.48 62		99.56 48					99.83 120			99.74 58

旧先验2							98.96 279		96.70 206	99.47 78			99.94 40	98.19 127

新几何2							99.01 268

新几何1				99.75 38	99.75 47	99.59 46		99.54 62	96.76 202	99.29 117	99.64 135	98.43 61	99.94 40	96.92 225	99.66 95	99.72 69

旧先验1					99.74 57	99.59 46		99.54 62			99.69 112	98.47 58			99.68 93	99.73 63

无先验							98.99 270	99.51 85	96.89 197				99.93 55	97.53 184		99.72 69

原ACMM2							98.95 283

原ACMM1				99.65 57	99.73 62	99.33 76		99.47 127	97.46 150	99.12 158	99.66 127	98.67 51	99.91 72	97.70 171	99.69 90	99.71 76

test222					99.75 47	99.49 61	98.91 288	99.49 104	96.42 229	99.34 109	99.65 128	98.28 71			99.69 90	99.72 69

testdata2													99.95 33	96.67 236

segment_acmp												98.96 20

testdata				99.54 75	99.75 47	98.95 122		99.51 85	97.07 186	99.43 85	99.70 106	98.87 28	99.94 40	97.76 162	99.64 98	99.72 69

testdata1							98.85 292		98.32 69

test12				99.75 38	99.64 92	99.61 42		99.29 225		99.21 144		98.38 65	99.89 92		99.74 79	99.74 58

plane_prior7					99.29 168	97.03 239

plane_prior6					99.27 173	96.98 243						92.71 242

plane_prior5								99.47 127					99.69 174	97.78 159	97.63 197	98.67 231

plane_prior4											99.61 147

plane_prior3						97.00 241			98.69 46	99.11 160

plane_prior2							99.39 170		98.97 22

plane_prior1					99.26 175

plane_prior						96.97 244	99.21 225		98.45 59						97.60 200

n2								0.00 349

nn								0.00 349

door-mid								98.05 322

lessismore_v0				97.79 274	98.69 279	95.44 284		94.75 336		95.71 295	99.87 19	88.69 297	99.32 229	95.89 252	94.93 267	98.62 255

LGP-MVS_train				98.49 214	99.33 156	97.05 237		99.55 55	97.46 150	99.24 135	99.83 37	92.58 247	99.72 159	98.09 134	97.51 207	98.68 220

test11								99.35 196

door								97.92 323

HQP5-MVS						96.83 249

HQP-NCC					99.19 185		98.98 274		98.24 72	98.66 225

ACMP_Plane					99.19 185		98.98 274		98.24 72	98.66 225

BP-MVS														97.19 206

HQP4-MVS										98.66 225			99.64 182			98.64 247

HQP3-MVS								99.39 178							97.58 202

HQP2-MVS												92.47 251

NP-MVS					99.23 178	96.92 247					99.40 208

MDTV_nov1_ep13_2view						95.18 289	99.35 185		96.84 200	99.58 60		95.19 155		97.82 156		99.46 133

MDTV_nov1_ep13			98.32 133		99.11 203	94.44 297	99.27 205	98.74 297	97.51 148	99.40 94	99.62 144	94.78 179	99.76 145	97.59 177	98.81 151

ACMMP++_ref															97.19 226

ACMMP++															97.43 217

Test By Simon												98.75 44

ITE_SJBPF				98.08 253	99.29 168	96.37 264		98.92 276	98.34 66	98.83 206	99.75 90	91.09 274	99.62 188	95.82 253	97.40 218	98.25 288

DeepMVS_CX				93.34 306	99.29 168	82.27 330		99.22 243	85.15 324	96.33 289	99.05 262	90.97 276	99.73 155	93.57 295	97.77 195	98.01 296

High-resolution multi-view benchmark