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.

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 70	98.47 2	99.13 54	99.92 5	96.38 22	100.00 1	99.74 13	100.00 1	100.00 1

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 63	98.20 3	99.93 1	99.98 2	96.82 13	100.00 1	99.75 11	100.00 1	99.99 11

ESAPD		99.18 4	98.99 7	99.75 3	99.89 36	99.25 6	99.88 66	98.41 122	96.14 43	99.49 32	99.91 6	97.20 11	100.00 1	99.99 1	99.99 13	99.99 11

MVS		96.60 105	95.56 130	99.72 4	96.85 193	99.22 8	98.31 270	98.94 38	91.57 179	90.90 196	99.61 81	86.66 173	99.96 42	97.36 95	99.88 56	99.99 11

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 74	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 20	100.00 1	100.00 1

MVS_0304		97.52 70	96.79 82	99.69 6	99.59 69	99.30 4	99.97 12	98.01 167	96.99 19	98.84 64	99.79 44	78.90 255	99.96 42	99.74 13	99.32 94	99.81 79

MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 150	99.44 23	97.33 12	99.00 61	99.72 64	94.03 75	99.98 31	98.73 53	100.00 1	100.00 1

CANet		98.27 46	97.82 54	99.63 8	99.72 62	99.10 10	99.98 6	98.51 97	97.00 18	98.52 78	99.71 66	87.80 161	99.95 50	99.75 11	99.38 92	99.83 77

HPM-MVS++		99.07 6	98.88 11	99.63 8	99.90 33	99.02 12	99.95 31	98.56 84	97.56 9	99.44 36	99.85 20	95.38 38	100.00 1	99.31 29	99.99 13	99.87 74

HY-MVS	92.50 7	97.79 63	97.17 71	99.63 8	98.98 94	99.32 3	97.49 291	99.52 18	95.69 56	98.32 87	97.41 185	93.32 91	99.77 91	98.08 77	95.75 162	99.81 79

DeepC-MVS_fast	96.59 1	98.81 18	98.54 23	99.62 11	99.90 33	98.85 20	99.24 194	98.47 103	98.14 4	99.08 55	99.91 6	93.09 97	100.00 1	99.04 39	99.99 13	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WTY-MVS		98.10 53	97.60 59	99.60 12	98.92 100	99.28 5	99.89 64	99.52 18	95.58 58	98.24 92	99.39 96	93.33 90	99.74 100	97.98 82	95.58 165	99.78 83

train_agg		98.88 15	98.65 16	99.59 13	99.92 27	98.92 15	99.96 19	98.43 112	94.35 85	99.71 15	99.86 16	95.94 27	99.85 78	99.69 18	99.98 25	99.99 11

PAPR		98.52 33	98.16 41	99.58 14	99.97 3	98.77 25	99.95 31	98.43 112	95.35 62	98.03 96	99.75 60	94.03 75	99.98 31	98.11 74	99.83 61	99.99 11

SD-MVS		98.92 13	98.70 14	99.56 15	99.70 64	98.73 32	99.94 45	98.34 134	96.38 34	99.81 7	99.76 55	94.59 56	99.98 31	99.84 6	99.96 36	99.97 53

DP-MVS Recon		98.41 40	98.02 46	99.56 15	99.97 3	98.70 34	99.92 52	98.44 107	92.06 169	98.40 84	99.84 34	95.68 32	100.00 1	98.19 70	99.71 72	99.97 53

ACMMP_Plus		98.49 35	98.14 42	99.54 17	99.66 66	98.62 40	99.85 87	98.37 131	94.68 77	99.53 28	99.83 36	92.87 98	100.00 1	98.66 59	99.84 60	99.99 11

agg_prior1		98.88 15	98.66 15	99.54 17	99.93 24	98.77 25	99.96 19	98.43 112	94.63 78	99.63 20	99.85 20	95.79 31	99.85 78	99.72 16	99.99 13	99.99 11

3Dnovator+	91.53 11	96.31 123	95.24 137	99.52 19	96.88 192	98.64 39	99.72 130	98.24 144	95.27 65	88.42 246	98.98 119	82.76 201	99.94 58	97.10 102	99.83 61	99.96 57

APDe-MVS		99.06 8	98.91 10	99.51 20	99.94 14	98.76 31	99.91 56	98.39 126	97.20 14	99.46 34	99.85 20	95.53 36	99.79 89	99.86 5	100.00 1	99.99 11

DELS-MVS		98.54 31	98.22 37	99.50 21	99.15 86	98.65 38	100.00 1	98.58 80	97.70 7	98.21 93	99.24 105	92.58 106	99.94 58	98.63 61	99.94 43	99.92 68
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
MSLP-MVS++		99.13 5	99.01 6	99.49 22	99.94 14	98.46 51	99.98 6	98.86 53	97.10 15	99.80 8	99.94 4	95.92 29	100.00 1	99.51 21	100.00 1	100.00 1

CDPH-MVS		98.65 23	98.36 33	99.49 22	99.94 14	98.73 32	99.87 71	98.33 135	93.97 101	99.76 11	99.87 14	94.99 49	99.75 96	98.55 63	100.00 1	99.98 43

1314		96.84 92	95.96 106	99.48 24	96.74 200	98.52 47	98.31 270	98.86 53	95.82 48	89.91 210	98.98 119	87.49 164	99.96 42	97.80 86	99.73 70	99.96 57

agg_prior3		98.84 17	98.62 18	99.47 25	99.92 27	98.56 45	99.96 19	98.43 112	94.07 95	99.67 18	99.85 20	96.05 23	99.85 78	99.69 18	99.98 25	99.99 11

test_prior3		98.99 11	98.84 12	99.43 26	99.94 14	98.49 49	99.95 31	98.65 67	95.78 50	99.73 13	99.76 55	96.00 25	99.80 87	99.78 9	100.00 1	99.99 11

test_prior				99.43 26	99.94 14	98.49 49		98.65 67					99.80 87			99.99 11

test12				99.43 26	99.74 56	98.56 45		98.40 124		99.65 19		94.76 54	99.75 96		99.98 25	99.99 11

新几何1				99.42 29	99.75 55	98.27 56		98.63 73	92.69 139	99.55 27	99.82 39	94.40 59	100.00 1	91.21 189	99.94 43	99.99 11

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 30	99.74 56	98.67 35	99.77 109	98.38 129	96.73 26	99.88 3	99.74 62	94.89 53	99.59 115	99.80 7	99.98 25	99.97 53

APD-MVS		98.62 24	98.35 34	99.41 30	99.90 33	98.51 48	99.87 71	98.36 132	94.08 94	99.74 12	99.73 63	94.08 73	99.74 100	99.42 26	99.99 13	99.99 11
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
1121		98.03 55	97.57 61	99.40 32	99.74 56	98.21 57	98.31 270	98.62 74	92.78 134	99.53 28	99.83 36	95.08 43	100.00 1	94.36 142	99.92 50	99.99 11

canonicalmvs		97.09 85	96.32 93	99.39 33	98.93 99	98.95 14	99.72 130	97.35 228	94.45 81	97.88 99	99.42 92	86.71 172	99.52 117	98.48 65	93.97 196	99.72 91

MP-MVS-pluss		98.07 54	97.64 57	99.38 34	99.74 56	98.41 52	99.74 119	98.18 151	93.35 119	96.45 126	99.85 20	92.64 105	99.97 40	98.91 46	99.89 54	99.77 84
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Regformer-1		98.79 19	98.60 20	99.36 35	99.85 40	98.34 53	99.87 71	98.52 91	96.05 45	99.41 39	99.79 44	94.93 51	99.76 93	99.07 34	99.90 52	99.99 11

Regformer-2		98.78 20	98.59 21	99.36 35	99.85 40	98.32 54	99.87 71	98.52 91	96.04 46	99.41 39	99.79 44	94.92 52	99.76 93	99.05 35	99.90 52	99.98 43

MPTG		98.33 44	98.00 47	99.30 37	99.85 40	97.93 66	99.80 100	98.28 140	95.76 52	97.18 112	99.88 11	92.74 102	100.00 1	98.67 56	99.88 56	99.99 11

MTAPA		98.29 45	97.96 51	99.30 37	99.85 40	97.93 66	99.39 179	98.28 140	95.76 52	97.18 112	99.88 11	92.74 102	100.00 1	98.67 56	99.88 56	99.99 11

alignmvs		97.81 61	97.33 66	99.25 39	98.77 115	98.66 36	99.99 3	98.44 107	94.40 84	98.41 82	99.47 90	93.65 86	99.42 132	98.57 62	94.26 185	99.67 96

thres200		96.96 87	96.21 96	99.22 40	98.97 95	98.84 21	99.85 87	99.71 5	93.17 122	96.26 131	98.88 127	89.87 138	99.51 118	94.26 146	94.91 171	99.31 148

tfpn200view9		96.79 94	95.99 101	99.19 41	98.94 97	98.82 22	99.78 104	99.71 5	92.86 127	96.02 134	98.87 129	89.33 141	99.50 120	93.84 153	94.57 172	99.27 153

thres100view900		96.74 98	95.92 109	99.18 42	98.90 103	98.77 25	99.74 119	99.71 5	92.59 147	95.84 138	98.86 131	89.25 143	99.50 120	93.84 153	94.57 172	99.27 153

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 42	98.72 116	97.71 71	99.98 6	98.44 107	96.85 20	99.80 8	99.91 6	97.57 4	99.85 78	99.44 25	99.99 13	99.99 11
Skip Steuart: Steuart Systems R&D Blog.
sss		97.57 69	97.03 76	99.18 42	98.37 131	98.04 63	99.73 125	99.38 26	93.46 117	98.76 68	99.06 113	91.21 123	99.89 68	96.33 112	97.01 142	99.62 104

tfpn111		96.69 101	95.87 119	99.16 45	98.90 103	98.77 25	99.74 119	99.71 5	92.59 147	95.84 138	98.86 131	89.25 143	99.50 120	93.44 163	94.50 176	99.20 159

conf200view11		96.73 100	95.92 109	99.16 45	98.90 103	98.77 25	99.74 119	99.71 5	92.59 147	95.84 138	98.86 131	89.25 143	99.50 120	93.84 153	94.57 172	99.20 159

PS-MVSNAJ		98.44 38	98.20 39	99.16 45	98.80 113	98.92 15	99.54 162	98.17 152	97.34 11	99.85 5	99.85 20	91.20 124	99.89 68	99.41 27	99.67 74	98.69 186

thres400		96.78 95	95.99 101	99.16 45	98.94 97	98.82 22	99.78 104	99.71 5	92.86 127	96.02 134	98.87 129	89.33 141	99.50 120	93.84 153	94.57 172	99.16 165

XVS		98.70 22	98.55 22	99.15 49	99.94 14	97.50 79	99.94 45	98.42 120	96.22 39	99.41 39	99.78 49	94.34 63	99.96 42	98.92 44	99.95 39	99.99 11

X-MVStestdata		93.83 173	92.06 195	99.15 49	99.94 14	97.50 79	99.94 45	98.42 120	96.22 39	99.41 39	41.37 355	94.34 63	99.96 42	98.92 44	99.95 39	99.99 11

HFP-MVS		98.56 29	98.37 31	99.14 51	99.96 8	97.43 83	99.95 31	98.61 76	94.77 73	99.31 45	99.85 20	94.22 68	100.00 1	98.70 54	99.98 25	99.98 43

#test#		98.59 27	98.41 26	99.14 51	99.96 8	97.43 83	99.95 31	98.61 76	95.00 68	99.31 45	99.85 20	94.22 68	100.00 1	98.78 51	99.98 25	99.98 43

thres600view7		96.69 101	95.87 119	99.14 51	98.90 103	98.78 24	99.74 119	99.71 5	92.59 147	95.84 138	98.86 131	89.25 143	99.50 120	93.44 163	94.50 176	99.16 165

114514_t		97.41 75	96.83 79	99.14 51	99.51 77	97.83 68	99.89 64	98.27 143	88.48 236	99.06 56	99.66 77	90.30 135	99.64 114	96.32 113	99.97 34	99.96 57

PAPM		98.60 25	98.42 25	99.14 51	96.05 210	98.96 13	99.90 59	99.35 27	96.68 28	98.35 86	99.66 77	96.45 21	98.51 168	99.45 24	99.89 54	99.96 57

VNet		97.21 81	96.57 89	99.13 56	98.97 95	97.82 69	99.03 217	99.21 29	94.31 87	99.18 53	98.88 127	86.26 177	99.89 68	98.93 43	94.32 183	99.69 94

Regformer-3		98.58 28	98.41 26	99.10 57	99.84 45	97.57 75	99.66 143	98.52 91	95.79 49	99.01 59	99.77 51	94.40 59	99.75 96	98.82 49	99.83 61	99.98 43

QAPM		95.40 143	94.17 157	99.10 57	96.92 189	97.71 71	99.40 176	98.68 64	89.31 219	88.94 238	98.89 125	82.48 202	99.96 42	93.12 172	99.83 61	99.62 104

Regformer-4		98.56 29	98.39 29	99.08 59	99.84 45	97.52 77	99.66 143	98.52 91	95.76 52	99.01 59	99.77 51	94.33 65	99.75 96	98.80 50	99.83 61	99.98 43

3Dnovator	91.47 12	96.28 126	95.34 135	99.08 59	96.82 195	97.47 82	99.45 173	98.81 56	95.52 59	89.39 228	99.00 118	81.97 211	99.95 50	97.27 97	99.83 61	99.84 76

view600		96.46 115	95.59 125	99.06 61	98.87 108	98.60 41	99.69 133	99.71 5	92.20 160	95.23 152	98.80 143	89.17 147	99.43 128	92.29 176	94.37 179	99.16 165

view800		96.46 115	95.59 125	99.06 61	98.87 108	98.60 41	99.69 133	99.71 5	92.20 160	95.23 152	98.80 143	89.17 147	99.43 128	92.29 176	94.37 179	99.16 165

conf0.05thres1000		96.46 115	95.59 125	99.06 61	98.87 108	98.60 41	99.69 133	99.71 5	92.20 160	95.23 152	98.80 143	89.17 147	99.43 128	92.29 176	94.37 179	99.16 165

tfpn		96.46 115	95.59 125	99.06 61	98.87 108	98.60 41	99.69 133	99.71 5	92.20 160	95.23 152	98.80 143	89.17 147	99.43 128	92.29 176	94.37 179	99.16 165

region2R		98.54 31	98.37 31	99.05 65	99.96 8	97.18 97	99.96 19	98.55 88	94.87 71	99.45 35	99.85 20	94.07 74	100.00 1	98.67 56	100.00 1	99.98 43

ACMMPR		98.50 34	98.32 35	99.05 65	99.96 8	97.18 97	99.95 31	98.60 78	94.77 73	99.31 45	99.84 34	93.73 84	100.00 1	98.70 54	99.98 25	99.98 43

MP-MVS		98.23 49	97.97 49	99.03 67	99.94 14	97.17 100	99.95 31	98.39 126	94.70 76	98.26 91	99.81 42	91.84 118	100.00 1	98.85 48	99.97 34	99.93 65

PHI-MVS		98.41 40	98.21 38	99.03 67	99.86 39	97.10 101	99.98 6	98.80 58	90.78 203	99.62 22	99.78 49	95.30 39	100.00 1	99.80 7	99.93 48	99.99 11

xiu_mvs_v2_base		98.23 49	97.97 49	99.02 69	98.69 117	98.66 36	99.52 164	98.08 163	97.05 16	99.86 4	99.86 16	90.65 132	99.71 104	99.39 28	98.63 106	98.69 186

MVS_111021_HR		98.72 21	98.62 18	99.01 70	99.36 83	97.18 97	99.93 50	99.90 1	96.81 24	98.67 72	99.77 51	93.92 77	99.89 68	99.27 30	99.94 43	99.96 57

PGM-MVS		98.34 43	98.13 43	98.99 71	99.92 27	97.00 102	99.75 116	99.50 21	93.90 105	99.37 43	99.76 55	93.24 94	100.00 1	97.75 90	99.96 36	99.98 43

mPP-MVS		98.39 42	98.20 39	98.97 72	99.97 3	96.92 106	99.95 31	98.38 129	95.04 67	98.61 76	99.80 43	93.39 89	100.00 1	98.64 60	100.00 1	99.98 43

原ACMM1				98.96 73	99.73 60	96.99 103		98.51 97	94.06 98	99.62 22	99.85 20	94.97 50	99.96 42	95.11 127	99.95 39	99.92 68

CHOSEN 280x420		99.01 10	99.03 5	98.95 74	99.38 82	98.87 19	98.46 260	99.42 25	97.03 17	99.02 58	99.09 111	99.35 1	98.21 197	99.73 15	99.78 68	99.77 84

HSP-MVS		99.07 6	99.11 4	98.95 74	99.93 24	97.24 94	99.95 31	98.32 136	97.50 10	99.52 31	99.88 11	97.43 6	99.71 104	99.50 22	99.98 25	99.89 71

tfpn_ndepth		97.21 81	96.63 86	98.92 76	99.06 87	98.28 55	99.95 31	98.91 42	92.96 126	96.49 124	98.67 151	97.40 7	99.07 138	91.87 185	94.38 178	99.41 133

CNLPA		97.76 64	97.38 64	98.92 76	99.53 74	96.84 107	99.87 71	98.14 158	93.78 109	96.55 123	99.69 71	92.28 109	99.98 31	97.13 100	99.44 90	99.93 65

CP-MVS		98.45 37	98.32 35	98.87 78	99.96 8	96.62 112	99.97 12	98.39 126	94.43 83	98.90 63	99.87 14	94.30 66	100.00 1	99.04 39	99.99 13	99.99 11

TSAR-MVS + GP.		98.60 25	98.51 24	98.86 79	99.73 60	96.63 111	99.97 12	97.92 176	98.07 5	98.76 68	99.55 84	95.00 48	99.94 58	99.91 4	97.68 124	99.99 11

PVSNet_Blended		97.94 57	97.64 57	98.83 80	99.59 69	96.99 103	100.00 1	99.10 30	95.38 61	98.27 89	99.08 112	89.00 152	99.95 50	99.12 32	99.25 96	99.57 114

APD-MVS_3200maxsize		98.25 48	98.08 45	98.78 81	99.81 50	96.60 113	99.82 95	98.30 138	93.95 103	99.37 43	99.77 51	92.84 99	99.76 93	98.95 41	99.92 50	99.97 53

EPNet		98.49 35	98.40 28	98.77 82	99.62 68	96.80 109	99.90 59	99.51 20	97.60 8	99.20 50	99.36 99	93.71 85	99.91 64	97.99 80	98.71 105	99.61 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EI-MVSNet-Vis-set		98.27 46	98.11 44	98.75 83	99.83 48	96.59 114	99.40 176	98.51 97	95.29 64	98.51 79	99.76 55	93.60 88	99.71 104	98.53 64	99.52 85	99.95 62

tfpn1000		96.90 91	96.29 94	98.74 84	99.00 92	98.09 61	99.92 52	98.91 42	92.08 166	95.85 137	98.65 153	97.39 8	98.83 146	90.56 200	94.23 186	99.31 148

PAPM_NR		98.12 52	97.93 52	98.70 85	99.94 14	96.13 130	99.82 95	98.43 112	94.56 79	97.52 105	99.70 68	94.40 59	99.98 31	97.00 104	99.98 25	99.99 11

HPM-MVS_fast		97.80 62	97.50 62	98.68 86	99.79 52	96.42 117	99.88 66	98.16 155	91.75 176	98.94 62	99.54 86	91.82 119	99.65 113	97.62 92	99.99 13	99.99 11

HPM-MVS		97.96 56	97.72 55	98.68 86	99.84 45	96.39 120	99.90 59	98.17 152	92.61 145	98.62 75	99.57 83	91.87 117	99.67 111	98.87 47	99.99 13	99.99 11

abl_6		97.67 67	97.34 65	98.66 88	99.68 65	96.11 134	99.68 138	98.14 158	93.80 108	99.27 48	99.70 68	88.65 157	99.98 31	97.46 93	99.72 71	99.89 71

PVSNet_Blended_VisFu		97.27 78	96.81 80	98.66 88	98.81 112	96.67 110	99.92 52	98.64 70	94.51 80	96.38 130	98.49 162	89.05 151	99.88 74	97.10 102	98.34 110	99.43 131

ACMMP		97.74 65	97.44 63	98.66 88	99.92 27	96.13 130	99.18 198	99.45 22	94.84 72	96.41 129	99.71 66	91.40 121	99.99 27	97.99 80	98.03 120	99.87 74
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
lupinMVS		97.85 60	97.60 59	98.62 91	97.28 180	97.70 73	99.99 3	97.55 205	95.50 60	99.43 37	99.67 75	90.92 130	98.71 154	98.40 67	99.62 77	99.45 128

MVS_Test		96.46 115	95.74 122	98.61 92	98.18 141	97.23 95	99.31 186	97.15 242	91.07 196	98.84 64	97.05 197	88.17 160	98.97 142	94.39 141	97.50 127	99.61 106

CANet_DTU		96.76 96	96.15 97	98.60 93	98.78 114	97.53 76	99.84 90	97.63 197	97.25 13	99.20 50	99.64 79	81.36 225	99.98 31	92.77 174	98.89 100	98.28 189

EI-MVSNet-UG-set		98.14 51	97.99 48	98.60 93	99.80 51	96.27 121	99.36 183	98.50 101	95.21 66	98.30 88	99.75 60	93.29 93	99.73 103	98.37 68	99.30 95	99.81 79

CPTT-MVS		97.64 68	97.32 67	98.58 95	99.97 3	95.77 139	99.96 19	98.35 133	89.90 214	98.36 85	99.79 44	91.18 127	99.99 27	98.37 68	99.99 13	99.99 11

xiu_mvs_v1_base_debu		97.43 71	97.06 72	98.55 96	97.74 166	98.14 58	99.31 186	97.86 183	96.43 31	99.62 22	99.69 71	85.56 183	99.68 108	99.05 35	98.31 112	97.83 195

xiu_mvs_v1_base		97.43 71	97.06 72	98.55 96	97.74 166	98.14 58	99.31 186	97.86 183	96.43 31	99.62 22	99.69 71	85.56 183	99.68 108	99.05 35	98.31 112	97.83 195

xiu_mvs_v1_base_debi		97.43 71	97.06 72	98.55 96	97.74 166	98.14 58	99.31 186	97.86 183	96.43 31	99.62 22	99.69 71	85.56 183	99.68 108	99.05 35	98.31 112	97.83 195

GG-mvs-BLEND				98.54 99	98.21 139	98.01 64	93.87 325	98.52 91		97.92 98	97.92 178	99.02 2	97.94 210	98.17 71	99.58 82	99.67 96

MVS_111021_LR		98.42 39	98.38 30	98.53 100	99.39 81	95.79 138	99.87 71	99.86 2	96.70 27	98.78 67	99.79 44	92.03 114	99.90 65	99.17 31	99.86 59	99.88 73

ab-mvs		94.69 158	93.42 173	98.51 101	98.07 146	96.26 122	96.49 306	98.68 64	90.31 208	94.54 166	97.00 199	76.30 273	99.71 104	95.98 117	93.38 201	99.56 115

AdaColmap		97.23 80	96.80 81	98.51 101	99.99 1	95.60 147	99.09 204	98.84 55	93.32 120	96.74 120	99.72 64	86.04 178	100.00 1	98.01 78	99.43 91	99.94 64

thresconf0.02		96.53 107	95.88 112	98.48 103	98.59 118	97.38 87	99.87 71	98.91 42	91.32 187	95.22 156	98.83 137	96.57 15	98.66 158	89.55 214	94.09 188	99.40 136

tfpn_n400		96.53 107	95.88 112	98.48 103	98.59 118	97.38 87	99.87 71	98.91 42	91.32 187	95.22 156	98.83 137	96.57 15	98.66 158	89.55 214	94.09 188	99.40 136

tfpnconf		96.53 107	95.88 112	98.48 103	98.59 118	97.38 87	99.87 71	98.91 42	91.32 187	95.22 156	98.83 137	96.57 15	98.66 158	89.55 214	94.09 188	99.40 136

tfpnview11		96.53 107	95.88 112	98.48 103	98.59 118	97.38 87	99.87 71	98.91 42	91.32 187	95.22 156	98.83 137	96.57 15	98.66 158	89.55 214	94.09 188	99.40 136

gg-mvs-nofinetune		93.51 182	91.86 197	98.47 107	97.72 170	97.96 65	92.62 330	98.51 97	74.70 328	97.33 108	69.59 344	98.91 3	97.79 213	97.77 89	99.56 83	99.67 96

API-MVS		97.86 59	97.66 56	98.47 107	99.52 75	95.41 151	99.47 170	98.87 52	91.68 177	98.84 64	99.85 20	92.34 108	99.99 27	98.44 66	99.96 36	100.00 1

PVSNet	91.05 13	97.13 83	96.69 85	98.45 109	99.52 75	95.81 137	99.95 31	99.65 16	94.73 75	99.04 57	99.21 107	84.48 191	99.95 50	94.92 129	98.74 104	99.58 113

DeepC-MVS	94.51 4	96.92 90	96.40 92	98.45 109	99.16 85	95.90 136	99.66 143	98.06 164	96.37 37	94.37 170	99.49 89	83.29 199	99.90 65	97.63 91	99.61 80	99.55 116
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PCF-MVS	94.20 5	95.18 146	94.10 158	98.43 111	98.55 125	95.99 135	97.91 287	97.31 232	90.35 207	89.48 227	99.22 106	85.19 188	99.89 68	90.40 205	98.47 108	99.41 133
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testdata				98.42 112	99.47 78	95.33 153		98.56 84	93.78 109	99.79 10	99.85 20	93.64 87	99.94 58	94.97 128	99.94 43	100.00 1

Test_1112_low_res		95.72 135	94.83 145	98.42 112	97.79 162	96.41 118	99.65 146	96.65 285	92.70 138	92.86 185	96.13 225	92.15 112	99.30 133	91.88 184	93.64 198	99.55 116

1112_ss		96.01 131	95.20 139	98.42 112	97.80 161	96.41 118	99.65 146	96.66 284	92.71 137	92.88 184	99.40 94	92.16 111	99.30 133	91.92 183	93.66 197	99.55 116

conf0.01		96.52 112	95.88 112	98.41 115	98.59 118	97.38 87	99.87 71	98.91 42	91.32 187	95.22 156	98.83 137	96.57 15	98.66 158	89.55 214	94.09 188	99.20 159

conf0.002		96.52 112	95.88 112	98.41 115	98.59 118	97.38 87	99.87 71	98.91 42	91.32 187	95.22 156	98.83 137	96.57 15	98.66 158	89.55 214	94.09 188	99.20 159

jason		97.24 79	96.86 78	98.38 117	95.73 222	97.32 93	99.97 12	97.40 224	95.34 63	98.60 77	99.54 86	87.70 162	98.56 165	97.94 83	99.47 88	99.25 155
jason: jason.
OpenMVS	90.15 15	94.77 156	93.59 167	98.33 118	96.07 209	97.48 81	99.56 158	98.57 82	90.46 205	86.51 266	98.95 123	78.57 258	99.94 58	93.86 152	99.74 69	97.57 199

LFMVS		94.75 157	93.56 169	98.30 119	99.03 89	95.70 145	98.74 239	97.98 170	87.81 244	98.47 80	99.39 96	67.43 313	99.53 116	98.01 78	95.20 169	99.67 96

UA-Net		96.54 106	95.96 106	98.27 120	98.23 138	95.71 144	98.00 285	98.45 106	93.72 111	98.41 82	99.27 101	88.71 156	99.66 112	91.19 190	97.69 123	99.44 130

diffmvs		95.25 145	94.26 155	98.23 121	98.13 144	96.59 114	99.12 201	97.18 238	85.78 274	97.64 102	96.70 209	85.92 179	98.87 144	90.40 205	97.45 128	99.24 158

Effi-MVS+		96.30 124	95.69 123	98.16 122	97.85 157	96.26 122	97.41 292	97.21 236	90.37 206	98.65 74	98.58 159	86.61 174	98.70 155	97.11 101	97.37 132	99.52 122

TESTMET0.1,1		96.74 98	96.26 95	98.16 122	97.36 179	96.48 116	99.96 19	98.29 139	91.93 171	95.77 143	98.07 174	95.54 34	98.29 191	90.55 201	98.89 100	99.70 92

IB-MVS	92.85 6	94.99 152	93.94 160	98.16 122	97.72 170	95.69 146	99.99 3	98.81 56	94.28 88	92.70 186	96.90 201	95.08 43	99.17 137	96.07 115	73.88 316	99.60 108
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
MAR-MVS		97.43 71	97.19 69	98.15 125	99.47 78	94.79 166	99.05 215	98.76 59	92.65 143	98.66 73	99.82 39	88.52 158	99.98 31	98.12 73	99.63 76	99.67 96
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
test_normal		92.44 201	90.54 213	98.12 126	91.85 303	96.18 129	99.68 138	97.73 190	92.66 141	75.76 314	93.74 294	70.49 302	99.04 140	95.71 123	97.27 134	99.13 173

DI_MVS_plusplus_test		92.48 198	90.60 212	98.11 127	91.88 302	96.13 130	99.64 150	97.73 190	92.69 139	76.02 310	93.79 292	70.49 302	99.07 138	95.88 119	97.26 135	99.14 171

EPMVS		96.53 107	96.01 100	98.09 128	98.43 130	96.12 133	96.36 308	99.43 24	93.53 116	97.64 102	95.04 260	94.41 58	98.38 185	91.13 191	98.11 116	99.75 86

PLC	95.54 3	97.93 58	97.89 53	98.05 129	99.82 49	94.77 167	99.92 52	98.46 105	93.93 104	97.20 110	99.27 101	95.44 37	99.97 40	97.41 94	99.51 87	99.41 133
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		95.84 134	95.11 142	98.02 130	99.85 40	95.10 159	98.74 239	98.50 101	87.22 257	93.66 175	99.86 16	87.45 165	99.95 50	90.94 196	99.81 67	99.02 178

MVSFormer		96.94 88	96.60 87	97.95 131	97.28 180	97.70 73	99.55 160	97.27 233	91.17 193	99.43 37	99.54 86	90.92 130	96.89 264	94.67 137	99.62 77	99.25 155

PatchMatch-RL		96.04 130	95.40 132	97.95 131	99.59 69	95.22 158	99.52 164	99.07 33	93.96 102	96.49 124	98.35 168	82.28 203	99.82 86	90.15 209	99.22 97	98.81 183

DP-MVS		94.54 162	93.42 173	97.91 133	99.46 80	94.04 177	98.93 226	97.48 216	81.15 311	90.04 207	99.55 84	87.02 170	99.95 50	88.97 224	98.11 116	99.73 89

BH-RMVSNet		95.18 146	94.31 154	97.80 134	98.17 142	95.23 157	99.76 115	97.53 209	92.52 153	94.27 172	99.25 104	76.84 268	98.80 147	90.89 198	99.54 84	99.35 145

test-LLR		96.47 114	96.04 99	97.78 135	97.02 186	95.44 149	99.96 19	98.21 147	94.07 95	95.55 145	96.38 217	93.90 80	98.27 194	90.42 203	98.83 102	99.64 102

test-mter		96.39 120	95.93 108	97.78 135	97.02 186	95.44 149	99.96 19	98.21 147	91.81 175	95.55 145	96.38 217	95.17 40	98.27 194	90.42 203	98.83 102	99.64 102

cascas		94.64 160	93.61 164	97.74 137	97.82 160	96.26 122	99.96 19	97.78 189	85.76 275	94.00 174	97.54 182	76.95 267	99.21 135	97.23 98	95.43 167	97.76 198

PatchFormer-LS_test		97.01 86	96.79 82	97.69 138	98.26 136	94.80 164	98.66 249	98.13 160	93.70 112	97.86 100	98.80 143	95.54 34	98.67 156	94.12 149	96.00 154	99.60 108

DWT-MVSNet_test		97.31 76	97.19 69	97.66 139	98.24 137	94.67 168	98.86 234	98.20 150	93.60 115	98.09 94	98.89 125	97.51 5	98.78 149	94.04 150	97.28 133	99.55 116

CHOSEN 1792x2688		96.81 93	96.53 90	97.64 140	98.91 102	93.07 206	99.65 146	99.80 3	95.64 57	95.39 148	98.86 131	84.35 193	99.90 65	96.98 105	99.16 98	99.95 62

UGNet		95.33 144	94.57 150	97.62 141	98.55 125	94.85 162	98.67 246	99.32 28	95.75 55	96.80 119	96.27 221	72.18 295	99.96 42	94.58 139	99.05 99	98.04 193
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
mvs_anonymous		95.65 139	95.03 143	97.53 142	98.19 140	95.74 141	99.33 185	97.49 215	90.87 200	90.47 200	97.10 193	88.23 159	97.16 242	95.92 118	97.66 125	99.68 95

Fast-Effi-MVS+		95.02 151	94.19 156	97.52 143	97.88 154	94.55 169	99.97 12	97.08 245	88.85 231	94.47 169	97.96 177	84.59 190	98.41 177	89.84 211	97.10 140	99.59 110

TR-MVS		94.54 162	93.56 169	97.49 144	97.96 150	94.34 172	98.71 241	97.51 213	90.30 209	94.51 168	98.69 150	75.56 278	98.77 150	92.82 173	95.99 155	99.35 145

Test4		88.80 266	85.91 275	97.48 145	87.33 325	95.72 143	99.29 190	97.04 255	92.82 130	70.35 328	91.46 308	44.37 343	97.43 221	93.37 165	97.17 139	99.29 152

Vis-MVSNet		95.72 135	95.15 141	97.45 146	97.62 172	94.28 173	99.28 191	98.24 144	94.27 89	96.84 117	98.94 124	79.39 247	98.76 151	93.25 166	98.49 107	99.30 150
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		96.29 125	95.90 111	97.45 146	98.13 144	94.80 164	99.08 206	97.61 202	92.02 170	95.54 147	98.96 121	90.64 133	98.08 201	93.73 160	97.41 131	99.47 127

OMC-MVS		97.28 77	97.23 68	97.41 148	99.76 53	93.36 200	99.65 146	97.95 173	96.03 47	97.41 107	99.70 68	89.61 140	99.51 118	96.73 110	98.25 115	99.38 140

MSDG		94.37 168	93.36 177	97.40 149	98.88 107	93.95 179	99.37 181	97.38 226	85.75 278	90.80 197	99.17 108	84.11 194	99.88 74	86.35 256	98.43 109	98.36 188

PatchmatchNet		95.94 132	95.45 131	97.39 150	97.83 159	94.41 171	96.05 314	98.40 124	92.86 127	97.09 114	95.28 251	94.21 71	98.07 203	89.26 222	98.11 116	99.70 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
HyFIR lowres test		96.66 104	96.43 91	97.36 151	99.05 88	93.91 180	99.70 132	99.80 3	90.54 204	96.26 131	98.08 173	92.15 112	98.23 196	96.84 109	95.46 166	99.93 65

Vis-MVSNet (Re-imp)		96.32 122	95.98 103	97.35 152	97.93 152	94.82 163	99.47 170	98.15 157	91.83 174	95.09 162	99.11 110	91.37 122	97.47 220	93.47 162	97.43 129	99.74 87

CSCG		97.10 84	97.04 75	97.27 153	99.89 36	91.92 234	99.90 59	99.07 33	88.67 233	95.26 151	99.82 39	93.17 96	99.98 31	98.15 72	99.47 88	99.90 70

Patchmatch-test1		94.39 167	93.46 171	97.17 154	97.10 182	94.44 170	98.86 234	98.32 136	93.30 121	96.17 133	95.38 242	76.48 272	97.34 226	88.12 232	97.43 129	99.74 87

tpmrst		96.27 127	95.98 103	97.13 155	97.96 150	93.15 205	96.34 309	98.17 152	92.07 167	98.71 71	95.12 254	93.91 79	98.73 152	94.91 131	96.62 146	99.50 125

CDS-MVSNet		96.34 121	96.07 98	97.13 155	97.37 178	94.96 160	99.53 163	97.91 177	91.55 180	95.37 149	98.32 169	95.05 45	97.13 248	93.80 157	95.75 162	99.30 150
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ADS-MVSNet		94.79 154	94.02 159	97.11 157	97.87 155	93.79 182	94.24 321	98.16 155	90.07 211	96.43 127	94.48 281	90.29 136	98.19 198	87.44 238	97.23 136	99.36 143

EPP-MVSNet		96.69 101	96.60 87	96.96 158	97.74 166	93.05 208	99.37 181	98.56 84	88.75 232	95.83 142	99.01 116	96.01 24	98.56 165	96.92 108	97.20 138	99.25 155

dp		95.05 150	94.43 152	96.91 159	97.99 149	92.73 215	96.29 310	97.98 170	89.70 217	95.93 136	94.67 277	93.83 83	98.45 174	86.91 252	96.53 148	99.54 120

TAPA-MVS	92.12 8	94.42 166	93.60 166	96.90 160	99.33 84	91.78 238	99.78 104	98.00 168	89.89 215	94.52 167	99.47 90	91.97 115	99.18 136	69.90 322	99.52 85	99.73 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
F-COLMAP		96.93 89	96.95 77	96.87 161	99.71 63	91.74 240	99.85 87	97.95 173	93.11 124	95.72 144	99.16 109	92.35 107	99.94 58	95.32 125	99.35 93	98.92 180

GA-MVS		93.83 173	92.84 181	96.80 162	95.73 222	93.57 188	99.88 66	97.24 235	92.57 151	92.92 182	96.66 210	78.73 257	97.67 216	87.75 235	94.06 195	99.17 164

CostFormer		96.10 128	95.88 112	96.78 163	97.03 185	92.55 221	97.08 299	97.83 186	90.04 213	98.72 70	94.89 269	95.01 47	98.29 191	96.54 111	95.77 161	99.50 125

VDDNet		93.12 187	91.91 196	96.76 164	96.67 203	92.65 219	98.69 243	98.21 147	82.81 296	97.75 101	99.28 100	61.57 327	99.48 126	98.09 76	94.09 188	98.15 191

PMMVS		96.76 96	96.76 84	96.76 164	98.28 134	92.10 229	99.91 56	97.98 170	94.12 92	99.53 28	99.39 96	86.93 171	98.73 152	96.95 107	97.73 122	99.45 128

PVSNet_BlendedMVS		96.05 129	95.82 121	96.72 166	99.59 69	96.99 103	99.95 31	99.10 30	94.06 98	98.27 89	95.80 229	89.00 152	99.95 50	99.12 32	87.53 237	93.24 289

BH-w/o		95.71 137	95.38 134	96.68 167	98.49 129	92.28 225	99.84 90	97.50 214	92.12 165	92.06 189	98.79 148	84.69 189	98.67 156	95.29 126	99.66 75	99.09 176

EPNet_dtu		95.71 137	95.39 133	96.66 168	98.92 100	93.41 196	99.57 156	98.90 50	96.19 41	97.52 105	98.56 160	92.65 104	97.36 224	77.89 308	98.33 111	99.20 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAMVS		95.85 133	95.58 129	96.65 169	97.07 183	93.50 189	99.17 199	97.82 187	91.39 186	95.02 163	98.01 175	92.20 110	97.30 232	93.75 159	95.83 160	99.14 171

tpmp4_e23		95.15 149	94.69 149	96.55 170	97.84 158	91.77 239	97.10 298	97.91 177	88.33 239	97.19 111	95.06 258	93.92 77	98.51 168	89.64 213	95.19 170	99.37 142

tpm cat1		93.51 182	92.52 188	96.47 171	97.77 163	91.47 250	96.13 312	98.06 164	80.98 312	92.91 183	93.78 293	89.66 139	98.87 144	87.03 248	96.39 150	99.09 176

nrg030		93.51 182	92.53 187	96.45 172	94.36 244	97.20 96	99.81 97	97.16 241	91.60 178	89.86 213	97.46 183	86.37 176	97.68 215	95.88 119	80.31 277	94.46 216

MVSTER		95.53 140	95.22 138	96.45 172	98.56 124	97.72 70	99.91 56	97.67 195	92.38 157	91.39 192	97.14 191	97.24 10	97.30 232	94.80 133	87.85 232	94.34 229

tpmvs		94.28 169	93.57 168	96.40 174	98.55 125	91.50 249	95.70 319	98.55 88	87.47 252	92.15 188	94.26 285	91.42 120	98.95 143	88.15 230	95.85 159	98.76 185

PVSNet_0	88.03 19	91.80 210	90.27 222	96.38 175	98.27 135	90.46 261	99.94 45	99.61 17	93.99 100	86.26 272	97.39 186	71.13 301	99.89 68	98.77 52	67.05 326	98.79 184

tpm2		95.47 142	95.18 140	96.35 176	96.91 190	91.70 244	96.96 302	97.93 175	88.04 243	98.44 81	95.40 239	93.32 91	97.97 206	94.00 151	95.61 164	99.38 140

VDD-MVS		93.77 177	92.94 180	96.27 177	98.55 125	90.22 264	98.77 238	97.79 188	90.85 201	96.82 118	99.42 92	61.18 329	99.77 91	98.95 41	94.13 187	98.82 182

BH-untuned		95.18 146	94.83 145	96.22 178	98.36 132	91.22 251	99.80 100	97.32 231	90.91 199	91.08 194	98.67 151	83.51 196	98.54 167	94.23 147	99.61 80	98.92 180

VPA-MVSNet		92.70 194	91.55 200	96.16 179	95.09 233	96.20 127	98.88 229	99.00 35	91.02 198	91.82 190	95.29 250	76.05 277	97.96 208	95.62 124	81.19 267	94.30 231

FIs		94.10 170	93.43 172	96.11 180	94.70 241	96.82 108	99.58 155	98.93 41	92.54 152	89.34 230	97.31 187	87.62 163	97.10 251	94.22 148	86.58 241	94.40 222

Patchmatch-test		92.65 197	91.50 201	96.10 181	96.85 193	90.49 260	91.50 335	97.19 237	82.76 297	90.23 201	95.59 235	95.02 46	98.00 205	77.41 311	96.98 143	99.82 78

FMVSNet3		92.69 195	91.58 199	95.99 182	98.29 133	97.42 85	99.26 193	97.62 199	89.80 216	89.68 219	95.32 246	81.62 220	96.27 283	87.01 249	85.65 245	94.29 232

CR-MVSNet		93.45 185	92.62 185	95.94 183	96.29 205	92.66 217	92.01 333	96.23 292	92.62 144	96.94 115	93.31 299	91.04 128	96.03 291	79.23 301	95.96 156	99.13 173

RPMNet		89.39 259	87.20 271	95.94 183	96.29 205	92.66 217	92.01 333	97.63 197	70.19 336	96.94 115	85.87 336	87.25 167	96.03 291	62.69 333	95.96 156	99.13 173

UniMVSNet (Re)		93.07 188	92.13 192	95.88 185	94.84 238	96.24 126	99.88 66	98.98 36	92.49 155	89.25 232	95.40 239	87.09 169	97.14 246	93.13 171	78.16 295	94.26 233

XXY-MVS		91.82 207	90.46 214	95.88 185	93.91 252	95.40 152	98.87 232	97.69 194	88.63 235	87.87 251	97.08 194	74.38 288	97.89 211	91.66 187	84.07 255	94.35 228

VPNet		91.81 208	90.46 214	95.85 187	94.74 240	95.54 148	98.98 220	98.59 79	92.14 164	90.77 198	97.44 184	68.73 308	97.54 218	94.89 132	77.89 297	94.46 216

FC-MVSNet-test		93.81 175	93.15 179	95.80 188	94.30 246	96.20 127	99.42 175	98.89 51	92.33 158	89.03 237	97.27 189	87.39 166	96.83 268	93.20 167	86.48 242	94.36 225

NR-MVSNet		91.56 215	90.22 224	95.60 189	94.05 249	95.76 140	98.25 274	98.70 62	91.16 195	80.78 296	96.64 212	83.23 200	96.57 275	91.41 188	77.73 299	94.46 216

UniMVSNet_NR-MVSNet		92.95 190	92.11 193	95.49 190	94.61 242	95.28 155	99.83 94	99.08 32	91.49 181	89.21 234	96.86 204	87.14 168	96.73 271	93.20 167	77.52 301	94.46 216

DU-MVS		92.46 200	91.45 203	95.49 190	94.05 249	95.28 155	99.81 97	98.74 60	92.25 159	89.21 234	96.64 212	81.66 218	96.73 271	93.20 167	77.52 301	94.46 216

WR-MVS		92.31 202	91.25 204	95.48 192	94.45 243	95.29 154	99.60 153	98.68 64	90.10 210	88.07 249	96.89 202	80.68 235	96.80 270	93.14 170	79.67 286	94.36 225

FMVSNet2		91.02 229	89.56 238	95.41 193	97.53 174	95.74 141	98.98 220	97.41 223	87.05 258	88.43 244	95.00 263	71.34 298	96.24 285	85.12 266	85.21 250	94.25 235

MVS-HIRNet		86.22 282	83.19 297	95.31 194	96.71 202	90.29 263	92.12 332	97.33 230	62.85 339	86.82 262	70.37 343	69.37 306	97.49 219	75.12 317	97.99 121	98.15 191

PatchT		90.38 242	88.75 254	95.25 195	95.99 212	90.16 265	91.22 337	97.54 207	76.80 322	97.26 109	86.01 335	91.88 116	96.07 290	66.16 330	95.91 158	99.51 123

testing_2		85.10 294	81.72 301	95.22 196	82.25 334	94.16 174	97.54 290	97.01 259	88.15 240	62.23 336	86.43 333	44.43 342	97.18 241	92.28 181	85.20 251	94.31 230

pmmvs4		92.10 205	91.07 207	95.18 197	92.82 289	94.96 160	99.48 169	96.83 279	87.45 253	88.66 241	96.56 215	83.78 195	96.83 268	89.29 221	84.77 253	93.75 275

MIMVSNet		90.30 245	88.67 256	95.17 198	96.45 204	91.64 246	92.39 331	97.15 242	85.99 271	90.50 199	93.19 301	66.95 314	94.86 307	82.01 287	93.43 199	99.01 179

XVG-OURS-SEG-HR		94.79 154	94.70 148	95.08 199	98.05 147	89.19 274	99.08 206	97.54 207	93.66 113	94.87 164	99.58 82	78.78 256	99.79 89	97.31 96	93.40 200	96.25 205

XVG-OURS		94.82 153	94.74 147	95.06 200	98.00 148	89.19 274	99.08 206	97.55 205	94.10 93	94.71 165	99.62 80	80.51 238	99.74 100	96.04 116	93.06 205	96.25 205

v1		91.36 220	90.14 228	95.04 201	93.35 270	93.80 181	99.77 109	97.05 250	87.53 249	89.77 216	94.91 267	81.99 208	97.33 228	86.90 254	79.98 284	94.00 252

v2v482		91.30 222	90.07 231	95.01 202	93.13 280	93.79 182	99.77 109	97.02 256	88.05 242	89.25 232	95.37 244	80.73 234	97.15 244	87.28 244	80.04 282	94.09 243

AllTest		92.48 198	91.64 198	95.00 203	99.01 90	88.43 283	98.94 225	96.82 281	86.50 265	88.71 239	98.47 166	74.73 285	99.88 74	85.39 264	96.18 151	96.71 202

TestCases				95.00 203	99.01 90	88.43 283		96.82 281	86.50 265	88.71 239	98.47 166	74.73 285	99.88 74	85.39 264	96.18 151	96.71 202

v1141		91.36 220	90.14 228	95.00 203	93.33 272	93.79 182	99.78 104	97.05 250	87.52 250	89.75 217	94.89 269	82.13 205	97.21 238	86.84 255	80.00 283	94.00 252

divwei89l23v2f112		91.37 219	90.15 227	95.00 203	93.35 270	93.78 185	99.78 104	97.05 250	87.54 248	89.73 218	94.89 269	82.24 204	97.21 238	86.91 252	79.90 285	94.00 252

JIA-IIPM		91.76 213	90.70 210	94.94 207	96.11 208	87.51 290	93.16 328	98.13 160	75.79 325	97.58 104	77.68 340	92.84 99	97.97 206	88.47 228	96.54 147	99.33 147

v6		91.44 216	90.27 222	94.93 208	93.44 264	93.44 191	99.73 125	97.05 250	87.57 245	90.05 204	95.10 257	81.87 214	97.39 222	87.45 237	80.17 278	93.98 259

HQP-MVS		94.61 161	94.50 151	94.92 209	95.78 216	91.85 235	99.87 71	97.89 179	96.82 21	93.37 176	98.65 153	80.65 236	98.39 181	97.92 84	89.60 207	94.53 211

v1neww		91.44 216	90.28 220	94.91 210	93.50 260	93.43 192	99.73 125	97.06 246	87.55 246	90.08 202	95.11 255	81.98 209	97.32 229	87.41 240	80.15 279	93.99 255

v7new		91.44 216	90.28 220	94.91 210	93.50 260	93.43 192	99.73 125	97.06 246	87.55 246	90.08 202	95.11 255	81.98 209	97.32 229	87.41 240	80.15 279	93.99 255

v1144		91.09 228	89.83 233	94.87 212	93.25 277	93.69 187	99.62 152	96.98 262	86.83 262	89.64 223	94.99 264	80.94 230	97.05 254	85.08 267	81.16 268	93.87 269

HQP_MVS		94.49 165	94.36 153	94.87 212	95.71 225	91.74 240	99.84 90	97.87 181	96.38 34	93.01 180	98.59 157	80.47 240	98.37 186	97.79 87	89.55 210	94.52 213

TranMVSNet+NR-MVSNet		91.68 214	90.61 211	94.87 212	93.69 256	93.98 178	99.69 133	98.65 67	91.03 197	88.44 243	96.83 208	80.05 244	96.18 286	90.26 208	76.89 308	94.45 221

v7		91.20 227	89.99 232	94.82 215	93.57 257	93.41 196	99.57 156	96.98 262	86.83 262	89.88 212	95.22 252	81.01 229	97.14 246	85.53 262	81.31 266	93.90 265

V42		91.28 224	90.12 230	94.74 216	93.42 266	93.46 190	99.68 138	97.02 256	87.36 254	89.85 214	95.05 259	81.31 226	97.34 226	87.34 243	80.07 281	93.40 284

EI-MVSNet		93.73 178	93.40 176	94.74 216	96.80 196	92.69 216	99.06 212	97.67 195	88.96 227	91.39 192	99.02 114	88.75 155	97.30 232	91.07 192	87.85 232	94.22 236

v1192		90.62 239	89.25 244	94.72 218	93.13 280	93.07 206	99.50 166	97.02 256	86.33 268	89.56 226	95.01 261	79.22 250	97.09 253	82.34 285	81.16 268	94.01 249

v8		90.54 240	89.17 245	94.66 219	93.43 265	93.40 199	99.20 196	96.94 270	85.76 275	87.56 253	94.51 279	81.96 212	97.19 240	84.94 268	78.25 294	93.38 286

test0.0.03 1		93.86 172	93.61 164	94.64 220	95.02 237	92.18 228	99.93 50	98.58 80	94.07 95	87.96 250	98.50 161	93.90 80	94.96 305	81.33 290	93.17 203	96.78 201

PS-MVSNAJss		93.64 181	93.31 178	94.61 221	92.11 297	92.19 227	99.12 201	97.38 226	92.51 154	88.45 242	96.99 200	91.20 124	97.29 235	94.36 142	87.71 234	94.36 225

v144192		90.79 234	89.52 240	94.59 222	93.11 283	92.77 213	99.56 158	96.99 260	86.38 267	89.82 215	94.95 266	80.50 239	97.10 251	83.98 274	80.41 275	93.90 265

tpm		93.70 180	93.41 175	94.58 223	95.36 232	87.41 292	97.01 300	96.90 273	90.85 201	96.72 121	94.14 288	90.40 134	96.84 267	90.75 199	88.54 225	99.51 123

v10		90.25 247	88.82 252	94.57 224	93.53 259	93.43 192	99.08 206	96.87 277	85.00 283	87.34 257	94.51 279	80.93 231	97.02 260	82.85 282	79.23 287	93.26 288

CLD-MVS		94.06 171	93.90 161	94.55 225	96.02 211	90.69 257	99.98 6	97.72 192	96.62 30	91.05 195	98.85 136	77.21 264	98.47 170	98.11 74	89.51 212	94.48 215
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1921920		90.46 241	89.12 246	94.50 226	92.96 287	92.46 222	99.49 167	96.98 262	86.10 270	89.61 225	95.30 247	78.55 259	97.03 258	82.17 286	80.89 274	94.01 249

test_djsdf		92.83 192	92.29 191	94.47 227	91.90 301	92.46 222	99.55 160	97.27 233	91.17 193	89.96 208	96.07 227	81.10 228	96.89 264	94.67 137	88.91 216	94.05 246

OPM-MVS		93.21 186	92.80 182	94.44 228	93.12 282	90.85 256	99.77 109	97.61 202	96.19 41	91.56 191	98.65 153	75.16 283	98.47 170	93.78 158	89.39 213	93.99 255

v1240		90.20 248	88.79 253	94.44 228	93.05 285	92.27 226	99.38 180	96.92 271	85.89 272	89.36 229	94.87 272	77.89 263	97.03 258	80.66 293	81.08 270	94.01 249

IterMVS-LS		92.69 195	92.11 193	94.43 230	96.80 196	92.74 214	99.45 173	96.89 274	88.98 225	89.65 222	95.38 242	88.77 154	96.34 281	90.98 195	82.04 262	94.22 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		91.79 212	90.92 208	94.41 231	90.76 314	92.93 211	98.93 226	97.17 240	89.08 221	87.46 254	95.30 247	78.43 261	96.92 263	92.38 175	88.73 221	93.39 285

tfpnnormal		89.29 261	87.61 267	94.34 232	94.35 245	94.13 176	98.95 224	98.94 38	83.94 291	84.47 282	95.51 236	74.84 284	97.39 222	77.05 314	80.41 275	91.48 311

CP-MVSNet		91.23 225	90.22 224	94.26 233	93.96 251	92.39 224	99.09 204	98.57 82	88.95 228	86.42 269	96.57 214	79.19 251	96.37 279	90.29 207	78.95 288	94.02 247

COLMAP_ROB	90.47 14	92.18 204	91.49 202	94.25 234	99.00 92	88.04 288	98.42 265	96.70 283	82.30 301	88.43 244	99.01 116	76.97 266	99.85 78	86.11 259	96.50 149	94.86 210
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs-test1		95.53 140	95.97 105	94.20 235	97.77 163	85.44 302	99.95 31	97.06 246	94.92 69	96.58 122	98.72 149	85.81 180	98.98 141	94.80 133	98.11 116	98.18 190

jajsoiax		91.92 206	91.18 205	94.15 236	91.35 309	90.95 254	99.00 219	97.42 221	92.61 145	87.38 255	97.08 194	72.46 294	97.36 224	94.53 140	88.77 220	94.13 241

WR-MVS_H		91.30 222	90.35 217	94.15 236	94.17 248	92.62 220	99.17 199	98.94 38	88.87 230	86.48 268	94.46 283	84.36 192	96.61 274	88.19 229	78.51 291	93.21 290

GBi-Net		90.88 232	89.82 234	94.08 238	97.53 174	91.97 230	98.43 262	96.95 267	87.05 258	89.68 219	94.72 273	71.34 298	96.11 287	87.01 249	85.65 245	94.17 238

test1		90.88 232	89.82 234	94.08 238	97.53 174	91.97 230	98.43 262	96.95 267	87.05 258	89.68 219	94.72 273	71.34 298	96.11 287	87.01 249	85.65 245	94.17 238

FMVSNet1		88.50 268	86.64 272	94.08 238	95.62 229	91.97 230	98.43 262	96.95 267	83.00 295	86.08 274	94.72 273	59.09 332	96.11 287	81.82 289	84.07 255	94.17 238

LTVRE_ROB	88.28 18	90.29 246	89.05 249	94.02 241	95.08 234	90.15 266	97.19 297	97.43 219	84.91 284	83.99 284	97.06 196	74.00 290	98.28 193	84.08 272	87.71 234	93.62 280
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
pm-mvs1		89.36 260	87.81 265	94.01 242	93.40 268	91.93 233	98.62 250	96.48 291	86.25 269	83.86 285	96.14 224	73.68 291	97.04 255	86.16 258	75.73 312	93.04 293

mvs_tets		91.81 208	91.08 206	94.00 243	91.63 307	90.58 258	98.67 246	97.43 219	92.43 156	87.37 256	97.05 197	71.76 296	97.32 229	94.75 136	88.68 222	94.11 242

PS-CasMVS		90.63 238	89.51 241	93.99 244	93.83 253	91.70 244	98.98 220	98.52 91	88.48 236	86.15 273	96.53 216	75.46 279	96.31 282	88.83 225	78.86 290	93.95 261

ACMM	91.95 10	92.88 191	92.52 188	93.98 245	95.75 221	89.08 276	99.77 109	97.52 211	93.00 125	89.95 209	97.99 176	76.17 275	98.46 173	93.63 161	88.87 218	94.39 223
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v148		90.70 235	89.63 236	93.92 246	92.97 286	90.97 253	99.75 116	96.89 274	87.51 251	88.27 247	95.01 261	81.67 217	97.04 255	87.40 242	77.17 305	93.75 275

DeepPCF-MVS	95.94 2	97.71 66	98.98 8	93.92 246	99.63 67	81.76 319	99.96 19	98.56 84	99.47 1	99.19 52	99.99 1	94.16 72	100.00 1	99.92 3	99.93 48	100.00 1

CVMVSNet		94.68 159	94.94 144	93.89 248	96.80 196	86.92 294	99.06 212	98.98 36	94.45 81	94.23 173	99.02 114	85.60 182	95.31 300	90.91 197	95.39 168	99.43 131

ACMP	92.05 9	92.74 193	92.42 190	93.73 249	95.91 215	88.72 278	99.81 97	97.53 209	94.13 91	87.00 259	98.23 170	74.07 289	98.47 170	96.22 114	88.86 219	93.99 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v7n		89.65 255	88.29 260	93.72 250	92.22 295	90.56 259	99.07 210	97.10 244	85.42 282	86.73 263	94.72 273	80.06 243	97.13 248	81.14 291	78.12 296	93.49 282

LPG-MVS_test		92.96 189	92.71 184	93.71 251	95.43 230	88.67 279	99.75 116	97.62 199	92.81 131	90.05 204	98.49 162	75.24 281	98.40 179	95.84 121	89.12 214	94.07 244

LGP-MVS_train				93.71 251	95.43 230	88.67 279		97.62 199	92.81 131	90.05 204	98.49 162	75.24 281	98.40 179	95.84 121	89.12 214	94.07 244

ACMH	89.72 17	90.64 237	89.63 236	93.66 253	95.64 228	88.64 281	98.55 253	97.45 217	89.03 223	81.62 293	97.61 181	69.75 305	98.41 177	89.37 220	87.62 236	93.92 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		90.19 249	89.06 248	93.57 254	93.06 284	90.90 255	99.06 212	98.47 103	88.11 241	85.91 275	96.30 220	76.67 269	95.94 294	87.07 246	76.91 307	93.89 267

ADS-MVSNet2		93.80 176	93.88 162	93.55 255	97.87 155	85.94 297	94.24 321	96.84 278	90.07 211	96.43 127	94.48 281	90.29 136	95.37 299	87.44 238	97.23 136	99.36 143

pmmvs5		90.17 250	89.09 247	93.40 256	92.10 298	89.77 273	99.74 119	95.58 305	85.88 273	87.24 258	95.74 230	73.41 292	96.48 277	88.54 226	83.56 258	93.95 261

Patchmtry		89.70 254	88.49 257	93.33 257	96.24 207	89.94 272	91.37 336	96.23 292	78.22 319	87.69 252	93.31 299	91.04 128	96.03 291	80.18 295	82.10 261	94.02 247

Fast-Effi-MVS+-dtu		93.72 179	93.86 163	93.29 258	97.06 184	86.16 295	99.80 100	96.83 279	92.66 141	92.58 187	97.83 179	81.39 224	97.67 216	89.75 212	96.87 145	96.05 209

TransMVSNet (Re)		87.25 273	85.28 277	93.16 259	93.56 258	91.03 252	98.54 255	94.05 331	83.69 293	81.09 295	96.16 223	75.32 280	96.40 278	76.69 315	68.41 323	92.06 303

IterMVS		90.91 231	90.17 226	93.12 260	96.78 199	90.42 262	98.89 228	97.05 250	89.03 223	86.49 267	95.42 238	76.59 270	95.02 303	87.22 245	84.09 254	93.93 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
USDC		90.00 252	88.96 250	93.10 261	94.81 239	88.16 287	98.71 241	95.54 307	93.66 113	83.75 286	97.20 190	65.58 317	98.31 190	83.96 275	87.49 238	92.85 297

v52		89.55 256	88.41 258	92.98 262	92.32 294	90.01 268	98.88 229	96.89 274	84.51 288	86.89 260	94.22 286	79.23 249	97.16 242	84.46 270	78.27 293	91.76 307

V4		89.55 256	88.41 258	92.98 262	92.21 296	90.03 267	98.87 232	96.91 272	84.51 288	86.84 261	94.21 287	79.37 248	97.15 244	84.45 271	78.28 292	91.76 307

semantic-postprocess				92.93 264	96.72 201	89.96 269		96.99 260	88.95 228	86.63 264	95.67 232	76.50 271	95.00 304	87.04 247	84.04 257	93.84 271

DTE-MVSNet		89.40 258	88.24 261	92.88 265	92.66 292	89.95 270	99.10 203	98.22 146	87.29 255	85.12 279	96.22 222	76.27 274	95.30 301	83.56 278	75.74 311	93.41 283

Baseline_NR-MVSNet		90.33 244	89.51 241	92.81 266	92.84 288	89.95 270	99.77 109	93.94 332	84.69 287	89.04 236	95.66 233	81.66 218	96.52 276	90.99 194	76.98 306	91.97 305

ACMH+	89.98 16	90.35 243	89.54 239	92.78 267	95.99 212	86.12 296	98.81 236	97.18 238	89.38 218	83.14 288	97.76 180	68.42 310	98.43 175	89.11 223	86.05 244	93.78 274

v18		86.59 276	84.57 280	92.65 268	93.41 267	93.43 192	98.69 243	95.55 306	82.44 299	74.71 316	87.68 322	82.11 206	94.21 310	80.14 296	66.37 329	90.32 318

XVG-ACMP-BASELINE		91.22 226	90.75 209	92.63 269	93.73 255	85.61 299	98.52 257	97.44 218	92.77 135	89.90 211	96.85 205	66.64 315	98.39 181	92.29 176	88.61 223	93.89 267

v748		88.94 265	87.72 266	92.61 270	91.91 300	87.50 291	99.07 210	96.97 265	84.76 285	85.79 276	93.63 296	79.19 251	97.04 255	83.16 280	75.03 315	93.28 287

v16		86.52 277	84.49 281	92.60 271	93.45 263	93.31 201	98.60 252	95.52 309	82.30 301	74.59 318	87.70 321	81.95 213	94.18 311	79.93 298	66.38 328	90.30 319

v17		86.51 278	84.49 281	92.57 272	93.38 269	93.29 202	98.61 251	95.54 307	82.32 300	74.69 317	87.63 323	82.03 207	94.17 312	80.02 297	66.17 330	90.26 320

LP		86.76 275	84.85 279	92.50 273	95.08 234	85.89 298	89.97 338	96.97 265	75.28 327	84.97 280	90.68 311	80.78 233	95.13 302	61.64 335	88.31 228	96.46 204

v15		86.26 281	84.19 284	92.47 274	93.29 274	93.28 203	98.53 256	95.47 310	82.24 303	74.34 319	87.34 325	81.71 216	94.07 313	79.39 299	65.42 331	90.06 326

V9		86.16 284	84.07 286	92.43 275	93.27 276	93.04 209	98.40 266	95.45 312	81.98 306	74.18 322	87.31 326	81.58 222	94.06 314	79.12 303	65.33 334	90.20 323

v11		86.09 286	83.98 290	92.42 276	93.29 274	93.41 196	98.52 257	95.30 317	81.73 309	74.27 320	87.20 327	81.24 227	93.85 322	77.68 310	66.61 327	90.00 328

V14		86.22 282	84.15 285	92.41 277	93.30 273	93.16 204	98.47 259	95.47 310	82.10 304	74.27 320	87.41 324	81.73 215	94.02 315	79.26 300	65.37 333	90.04 327

ITE_SJBPF				92.38 278	95.69 227	85.14 303		95.71 301	92.81 131	89.33 231	98.11 172	70.23 304	98.42 176	85.91 260	88.16 230	93.59 281

MVP-Stereo		90.93 230	90.45 216	92.37 279	91.25 311	88.76 277	98.05 284	96.17 294	87.27 256	84.04 283	95.30 247	78.46 260	97.27 237	83.78 276	99.70 73	91.09 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v12		86.10 285	84.01 287	92.37 279	93.23 279	92.96 210	98.33 269	95.45 312	81.87 307	74.05 324	87.15 328	81.60 221	93.98 318	79.09 304	65.28 335	90.18 324

v13		86.06 287	83.97 291	92.34 281	93.25 277	92.85 212	98.26 273	95.44 314	81.70 310	74.02 325	87.11 330	81.58 222	94.00 317	78.94 305	65.41 332	90.18 324

Effi-MVS+-dtu		94.53 164	95.30 136	92.22 282	97.77 163	82.54 313	99.59 154	97.06 246	94.92 69	95.29 150	95.37 244	85.81 180	97.89 211	94.80 133	97.07 141	96.23 207

MDA-MVSNet_test_wron		85.51 291	83.32 296	92.10 283	90.96 312	88.58 282	99.20 196	96.52 289	79.70 315	57.12 341	92.69 303	79.11 253	93.86 321	77.10 313	77.46 303	93.86 270

YYNet1		85.50 292	83.33 295	92.00 284	90.89 313	88.38 286	99.22 195	96.55 288	79.60 317	57.26 340	92.72 302	79.09 254	93.78 323	77.25 312	77.37 304	93.84 271

TinyColmap		87.87 272	86.51 273	91.94 285	95.05 236	85.57 300	97.65 289	94.08 330	84.40 290	81.82 292	96.85 205	62.14 326	98.33 188	80.25 294	86.37 243	91.91 306

testgi		89.01 264	88.04 263	91.90 286	93.49 262	84.89 305	99.73 125	95.66 303	93.89 107	85.14 278	98.17 171	59.68 331	94.66 309	77.73 309	88.88 217	96.16 208

MDA-MVSNet-bldmvs		84.09 299	81.52 303	91.81 287	91.32 310	88.00 289	98.67 246	95.92 299	80.22 314	55.60 342	93.32 298	68.29 311	93.60 325	73.76 318	76.61 309	93.82 273

MS-PatchMatch		90.65 236	90.30 219	91.71 288	94.22 247	85.50 301	98.24 275	97.70 193	88.67 233	86.42 269	96.37 219	67.82 312	98.03 204	83.62 277	99.62 77	91.60 309

LCM-MVSNet-Re		92.31 202	92.60 186	91.43 289	97.53 174	79.27 327	99.02 218	91.83 341	92.07 167	80.31 297	94.38 284	83.50 197	95.48 297	97.22 99	97.58 126	99.54 120

TDRefinement		84.76 295	82.56 299	91.38 290	74.58 341	84.80 306	97.36 293	94.56 327	84.73 286	80.21 298	96.12 226	63.56 323	98.39 181	87.92 233	63.97 336	90.95 315

pmmvs6		85.69 288	83.84 293	91.26 291	90.00 319	84.41 307	97.82 288	96.15 295	75.86 324	81.29 294	95.39 241	61.21 328	96.87 266	83.52 279	73.29 318	92.50 299

SixPastTwentyTwo		88.73 267	88.01 264	90.88 292	91.85 303	82.24 315	98.22 277	95.18 322	88.97 226	82.26 291	96.89 202	71.75 297	96.67 273	84.00 273	82.98 259	93.72 279

FMVSNet5		88.32 269	87.47 269	90.88 292	96.90 191	88.39 285	97.28 296	95.68 302	82.60 298	84.67 281	92.40 305	79.83 245	91.16 330	76.39 316	81.51 265	93.09 291

OurMVSNet-221017-0		89.81 253	89.48 243	90.83 294	91.64 306	81.21 320	98.17 279	95.38 316	91.48 182	85.65 277	97.31 187	72.66 293	97.29 235	88.15 230	84.83 252	93.97 260

lessismore_v0				90.53 295	90.58 315	80.90 323		95.80 300		77.01 306	95.84 228	66.15 316	96.95 261	83.03 281	75.05 314	93.74 278

test_0402		85.58 289	83.94 292	90.50 296	93.81 254	85.04 304	98.55 253	95.20 321	76.01 323	79.72 300	95.13 253	64.15 322	96.26 284	66.04 331	86.88 240	90.21 322

K. test v3		88.05 271	87.24 270	90.47 297	91.82 305	82.23 316	98.96 223	97.42 221	89.05 222	76.93 307	95.60 234	68.49 309	95.42 298	85.87 261	81.01 272	93.75 275

LF4IMVS		89.25 262	88.85 251	90.45 298	92.81 290	81.19 321	98.12 280	94.79 324	91.44 184	86.29 271	97.11 192	65.30 319	98.11 200	88.53 227	85.25 249	92.07 302

testpf		89.10 263	88.73 255	90.24 299	97.59 173	83.48 310	74.22 348	97.39 225	79.66 316	89.64 223	93.92 289	86.38 175	95.76 295	85.42 263	94.31 184	91.49 310

pmmvs-eth3d		84.03 300	81.97 300	90.20 300	84.15 331	87.09 293	98.10 282	94.73 326	83.05 294	74.10 323	87.77 320	65.56 318	94.01 316	81.08 292	69.24 322	89.49 333

UnsupCasMVSNet_eth		85.52 290	83.99 288	90.10 301	89.36 321	83.51 309	96.65 304	97.99 169	89.14 220	75.89 312	93.83 291	63.25 324	93.92 319	81.92 288	67.90 325	92.88 296

OpenMVS_ROB	79.82 20	83.77 302	81.68 302	90.03 302	88.30 323	82.82 311	98.46 260	95.22 320	73.92 331	76.00 311	91.29 309	55.00 336	96.94 262	68.40 325	88.51 226	90.34 317

EG-PatchMatch MVS		85.35 293	83.81 294	89.99 303	90.39 316	81.89 318	98.21 278	96.09 296	81.78 308	74.73 315	93.72 295	51.56 340	97.12 250	79.16 302	88.61 223	90.96 314

Patchmatch-RL test		86.90 274	85.98 274	89.67 304	84.45 330	75.59 328	89.71 339	92.43 338	86.89 261	77.83 305	90.94 310	94.22 68	93.63 324	87.75 235	69.61 320	99.79 82

EU-MVSNet		90.14 251	90.34 218	89.54 305	92.55 293	81.06 322	98.69 243	98.04 166	91.41 185	86.59 265	96.84 207	80.83 232	93.31 327	86.20 257	81.91 263	94.26 233

new_pmnet		84.49 298	82.92 298	89.21 306	90.03 318	82.60 312	96.89 303	95.62 304	80.59 313	75.77 313	89.17 313	65.04 320	94.79 308	72.12 319	81.02 271	90.23 321

Anonymous20231206		86.32 280	85.42 276	89.02 307	89.11 322	80.53 325	99.05 215	95.28 318	85.43 281	82.82 289	93.92 289	74.40 287	93.44 326	66.99 328	81.83 264	93.08 292

RPSCF		91.80 210	92.79 183	88.83 308	98.15 143	69.87 331	98.11 281	96.60 287	83.93 292	94.33 171	99.27 101	79.60 246	99.46 127	91.99 182	93.16 204	97.18 200

UnsupCasMVSNet_bld		79.97 308	77.03 311	88.78 309	85.62 329	81.98 317	93.66 326	97.35 228	75.51 326	70.79 327	83.05 337	48.70 341	94.91 306	78.31 307	60.29 341	89.46 334

MIMVSNet1		82.58 303	80.51 305	88.78 309	86.68 326	84.20 308	96.65 304	95.41 315	78.75 318	78.59 303	92.44 304	51.88 339	89.76 333	65.26 332	78.95 288	92.38 300

DSMNet-mixed		88.28 270	88.24 261	88.42 311	89.64 320	75.38 329	98.06 283	89.86 346	85.59 280	88.20 248	92.14 306	76.15 276	91.95 329	78.46 306	96.05 153	97.92 194

pmmvs3		80.27 306	77.77 310	87.76 312	80.32 336	82.43 314	98.23 276	91.97 340	72.74 332	78.75 302	87.97 317	57.30 334	90.99 331	70.31 321	62.37 338	89.87 329

test20.03		84.72 297	83.99 288	86.91 313	88.19 324	80.62 324	98.88 229	95.94 298	88.36 238	78.87 301	94.62 278	68.75 307	89.11 334	66.52 329	75.82 310	91.00 313

new-patchmatchnet		81.19 304	79.34 306	86.76 314	82.86 333	80.36 326	97.92 286	95.27 319	82.09 305	72.02 326	86.87 331	62.81 325	90.74 332	71.10 320	63.08 337	89.19 335

PM-MVS		80.47 305	78.88 307	85.26 315	83.79 332	72.22 330	95.89 317	91.08 342	85.71 279	76.56 309	88.30 314	36.64 344	93.90 320	82.39 284	69.57 321	89.66 331

CMPMVS	61.59 21	84.75 296	85.14 278	83.57 316	90.32 317	62.54 340	96.98 301	97.59 204	74.33 329	69.95 329	96.66 210	64.17 321	98.32 189	87.88 234	88.41 227	89.84 330
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ambc				83.23 317	77.17 340	62.61 339	87.38 342	94.55 328		76.72 308	86.65 332	30.16 347	96.36 280	84.85 269	69.86 319	90.73 316

Anonymous20231211		74.17 312	71.17 314	83.17 318	80.58 335	67.02 336	96.27 311	94.45 329	57.31 341	69.60 330	86.25 334	33.67 345	92.96 328	61.86 334	60.50 340	89.54 332

test2356		86.43 279	87.59 268	82.95 319	85.90 327	69.43 332	99.79 103	96.63 286	85.76 275	83.44 287	94.99 264	80.45 242	86.52 341	68.12 327	93.21 202	92.90 294

DeepMVS_CX				82.92 320	95.98 214	58.66 344		96.01 297	92.72 136	78.34 304	95.51 236	58.29 333	98.08 201	82.57 283	85.29 248	92.03 304

testus		83.91 301	84.49 281	82.17 321	85.68 328	66.11 337	99.68 138	93.53 336	86.55 264	82.60 290	94.91 267	56.70 335	88.19 337	68.46 324	92.31 206	92.21 301

1111		79.11 309	78.74 308	80.23 322	78.33 337	67.13 334	97.31 294	93.65 334	71.34 333	68.35 332	87.87 318	85.42 186	88.46 335	52.93 342	73.46 317	85.11 338

test1235678		78.45 310	77.88 309	80.16 323	77.83 339	62.18 341	98.36 267	93.45 337	77.46 321	69.08 331	88.23 315	60.33 330	85.41 342	58.46 338	77.68 300	92.90 294

N_pmnet		80.06 307	80.78 304	77.89 324	91.94 299	45.28 353	98.80 237	56.82 357	78.10 320	80.08 299	93.33 297	77.03 265	95.76 295	68.14 326	82.81 260	92.64 298

LCM-MVSNet		67.77 315	64.73 318	76.87 325	62.95 351	56.25 346	89.37 340	93.74 333	44.53 345	61.99 337	80.74 338	20.42 354	86.53 340	69.37 323	59.50 342	87.84 336

PMMVS2		67.15 317	64.15 319	76.14 326	70.56 345	62.07 342	93.89 324	87.52 350	58.09 340	60.02 338	78.32 339	22.38 352	84.54 343	59.56 337	47.03 343	81.80 340

no-one		63.48 320	59.26 321	76.14 326	66.71 346	65.06 338	92.75 329	89.92 345	68.96 337	46.96 347	66.55 347	21.74 353	87.68 338	57.07 340	22.69 350	75.68 344

test12356		75.26 311	75.12 312	75.67 328	74.02 342	60.60 343	96.43 307	92.15 339	74.17 330	66.35 334	88.11 316	52.29 338	84.36 344	57.41 339	75.12 313	82.05 339

Gipuma		66.95 318	65.00 317	72.79 329	91.52 308	67.96 333	66.16 349	95.15 323	47.89 343	58.54 339	67.99 346	29.74 348	87.54 339	50.20 344	77.83 298	62.87 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		67.54 316	65.93 316	72.37 330	64.46 350	54.05 347	95.09 320	90.07 344	68.90 338	55.16 343	77.63 341	30.39 346	82.61 346	49.42 345	62.26 339	80.45 341

tmp_tt		65.23 319	62.94 320	72.13 331	44.90 355	50.03 351	81.05 344	89.42 349	38.45 347	48.51 346	99.90 10	54.09 337	78.70 348	91.84 186	18.26 351	87.64 337

.test1245		71.48 313	71.80 313	70.51 332	78.33 337	67.13 334	97.31 294	93.65 334	71.34 333	68.35 332	87.87 318	85.42 186	88.46 335	52.93 342	11.01 352	55.94 351

FPMVS		68.72 314	68.72 315	68.71 333	65.95 347	44.27 355	95.97 316	94.74 325	51.13 342	53.26 344	90.50 312	25.11 351	83.00 345	60.80 336	80.97 273	78.87 342

ANet_high		56.10 322	52.24 323	67.66 334	49.27 354	56.82 345	83.94 343	82.02 351	70.47 335	33.28 352	64.54 348	17.23 356	69.16 352	45.59 349	23.85 349	77.02 343

wuykxyi23d		50.36 327	45.43 328	65.16 335	51.13 353	51.75 348	77.46 346	78.42 353	41.45 346	26.98 354	54.30 354	6.13 360	74.03 350	46.82 348	26.19 346	69.71 346

PNet_i23d		56.44 321	53.54 322	65.14 336	65.34 348	50.33 350	89.06 341	79.57 352	45.77 344	35.75 351	68.95 345	10.75 358	74.40 349	48.48 346	38.20 344	70.70 345

MVE	53.74 22	51.54 325	47.86 327	62.60 337	59.56 352	50.93 349	79.41 345	77.69 354	35.69 350	36.27 350	61.76 351	5.79 362	69.63 351	37.97 351	36.61 345	67.24 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	49.05 23	53.75 323	51.34 325	60.97 338	40.80 356	34.68 356	74.82 347	89.62 348	37.55 348	28.67 353	72.12 342	7.09 359	81.63 347	43.17 350	68.21 324	66.59 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		52.30 324	52.18 324	52.67 339	71.51 343	45.40 352	93.62 327	76.60 355	36.01 349	43.50 348	64.13 349	27.11 350	67.31 353	31.06 352	26.06 347	45.30 354

EMVS		51.44 326	51.22 326	52.11 340	70.71 344	44.97 354	94.04 323	75.66 356	35.34 351	42.40 349	61.56 352	28.93 349	65.87 354	27.64 353	24.73 348	45.49 353

test123		37.68 329	39.14 331	33.31 341	19.94 357	24.83 358	98.36 267	9.75 359	15.53 353	51.31 345	87.14 329	19.62 355	17.74 356	47.10 347	3.47 355	57.36 350

pcd1.5k->3k		37.58 330	39.62 330	31.46 342	92.73 291	0.00 360	0.00 351	97.52 211	0.00 355	0.00 356	0.00 357	78.40 262	0.00 358	0.00 355	87.90 231	94.37 224

testmvs		40.60 328	44.45 329	29.05 343	19.49 358	14.11 359	99.68 138	18.47 358	20.74 352	64.59 335	98.48 165	10.95 357	17.09 357	56.66 341	11.01 352	55.94 351

wuyk23d		20.37 332	20.84 333	18.99 344	65.34 348	27.73 357	50.43 350	7.67 360	9.50 354	8.01 355	6.34 356	6.13 360	26.24 355	23.40 354	10.69 354	2.99 355

cdsmvs_eth3d_5k		23.43 331	31.24 332	0.00 345	0.00 359	0.00 360	0.00 351	98.09 162	0.00 355	0.00 356	99.67 75	83.37 198	0.00 358	0.00 355	0.00 356	0.00 356

pcd_1.5k_mvsjas		7.60 334	10.13 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	91.20 124	0.00 358	0.00 355	0.00 356	0.00 356

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		8.28 333	11.04 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	99.40 94	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																99.59 110

test_part3							99.88 66		96.14 43		99.91 6		100.00 1	99.99 1

test_part2					99.89 36	99.25 6				99.49 32

test_part1								98.41 122				97.20 11			99.99 13	99.99 11

sam_mvs1												94.72 55				99.59 110

sam_mvs												94.25 67

MTGPA								98.28 140

test_post1							95.78 318				59.23 353	93.20 95	97.74 214	91.06 193

test_post											63.35 350	94.43 57	98.13 199

patchmatchnet-post											91.70 307	95.12 41	97.95 209

MTMP								96.49 290

gm-plane-assit					96.97 188	93.76 186			91.47 183		98.96 121		98.79 148	94.92 129

test9_res														99.71 17	99.99 13	100.00 1

TEST9					99.92 27	98.92 15	99.96 19	98.43 112	93.90 105	99.71 15	99.86 16	95.88 30	99.85 78

test_8					99.92 27	98.88 18	99.96 19	98.43 112	94.35 85	99.69 17	99.85 20	95.94 27	99.85 78

agg_prior2														99.48 23	100.00 1	100.00 1

agg_prior					99.93 24	98.77 25		98.43 112		99.63 20			99.85 78

test_prior4						98.05 62	99.94 45

test_prior2							99.95 31		95.78 50	99.73 13	99.76 55	96.00 25		99.78 9	100.00 1

旧先验2							99.46 172		94.21 90	99.85 5			99.95 50	96.96 106

新几何2							99.40 176

旧先验1					99.76 53	97.52 77		98.64 70			99.85 20	95.63 33			99.94 43	99.99 11

无先验							99.49 167	98.71 61	93.46 117				100.00 1	94.36 142		99.99 11

原ACMM2							99.90 59

test222					99.55 73	97.41 86	99.34 184	98.55 88	91.86 173	99.27 48	99.83 36	93.84 82			99.95 39	99.99 11

testdata2													99.99 27	90.54 202

segment_acmp												96.68 14

testdata1							99.28 191		96.35 38

plane_prior7					95.71 225	91.59 248

plane_prior6					95.76 220	91.72 243						80.47 240

plane_prior5								97.87 181					98.37 186	97.79 87	89.55 210	94.52 213

plane_prior4											98.59 157

plane_prior3						91.64 246			96.63 29	93.01 180

plane_prior2							99.84 90		96.38 34

plane_prior1					95.73 222

plane_prior						91.74 240	99.86 86		96.76 25						89.59 209

n2								0.00 361

nn								0.00 361

door-mid								89.69 347

test11								98.44 107

door								90.31 343

HQP5-MVS						91.85 235

HQP-NCC					95.78 216		99.87 71		96.82 21	93.37 176

ACMP_Plane					95.78 216		99.87 71		96.82 21	93.37 176

BP-MVS														97.92 84

HQP4-MVS										93.37 176			98.39 181			94.53 211

HQP3-MVS								97.89 179							89.60 207

HQP2-MVS												80.65 236

NP-MVS					95.77 219	91.79 237					98.65 153

MDTV_nov1_ep13_2view						96.26 122	96.11 313		91.89 172	98.06 95		94.40 59		94.30 145		99.67 96

MDTV_nov1_ep13			95.69 123		97.90 153	94.15 175	95.98 315	98.44 107	93.12 123	97.98 97	95.74 230	95.10 42	98.58 164	90.02 210	96.92 144

ACMMP++_ref															87.04 239

ACMMP++															88.23 229

Test By Simon												92.82 101

High-resolution multi-view benchmark