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.

PS-MVSNAJss		98.53 22	98.63 21	98.21 69	99.68 8	94.82 105	98.10 44	99.21 10	96.91 89	99.75 3	99.45 8	95.82 90	99.92 4	98.80 13	99.96 11	99.89 1

pcd1.5k->3k		41.47 343	44.19 345	33.29 356	99.65 10	0.00 374	0.00 365	99.07 34	0.00 369	0.00 371	0.00 371	99.04 3	0.00 371	0.00 368	99.96 11	99.87 2

test_djsdf		98.73 12	98.74 18	98.69 37	99.63 12	96.30 60	98.67 12	99.02 51	96.50 102	99.32 21	99.44 9	97.43 29	99.92 4	98.73 17	99.95 13	99.86 3

UA-Net		98.88 6	98.76 15	99.22 2	99.11 77	97.89 10	99.47 3	99.32 7	99.08 9	97.87 140	99.67 2	96.47 73	99.92 4	97.88 37	99.98 3	99.85 4

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 4

mvs_tets		98.90 4	98.94 7	98.75 30	99.69 7	96.48 55	98.54 20	99.22 9	96.23 114	99.71 4	99.48 6	98.77 6	99.93 2	98.89 10	99.95 13	99.84 6

jajsoiax		98.77 10	98.79 14	98.74 32	99.66 9	96.48 55	98.45 25	99.12 22	95.83 133	99.67 6	99.37 14	98.25 10	99.92 4	98.77 14	99.94 19	99.82 7

PS-CasMVS		98.73 12	98.85 12	98.39 55	99.55 20	95.47 85	98.49 22	99.13 21	99.22 7	99.22 29	98.96 52	97.35 32	99.92 4	97.79 42	99.93 21	99.79 8

anonymousdsp		98.72 15	98.63 21	98.99 10	99.62 13	97.29 34	98.65 15	99.19 13	95.62 140	99.35 20	99.37 14	97.38 31	99.90 13	98.59 23	99.91 26	99.77 9

FC-MVSNet-test		98.16 36	98.37 33	97.56 106	99.49 29	93.10 160	98.35 28	99.21 10	98.43 29	98.89 45	98.83 60	94.30 145	99.81 32	97.87 38	99.91 26	99.77 9

CP-MVSNet		98.42 26	98.46 29	98.30 64	99.46 31	95.22 93	98.27 33	98.84 88	99.05 12	99.01 39	98.65 74	95.37 109	99.90 13	97.57 52	99.91 26	99.77 9

ANet_high		98.31 31	98.94 7	96.41 182	99.33 46	89.64 219	97.92 55	99.56 4	99.27 5	99.66 8	99.50 5	97.67 24	99.83 29	97.55 53	99.98 3	99.77 9

PEN-MVS		98.75 11	98.85 12	98.44 51	99.58 17	95.67 77	98.45 25	99.15 18	99.33 4	99.30 24	99.00 48	97.27 36	99.92 4	97.64 47	99.92 23	99.75 13

WR-MVS_H		98.65 17	98.62 23	98.75 30	99.51 25	96.61 51	98.55 19	99.17 14	99.05 12	99.17 32	98.79 61	95.47 106	99.89 16	97.95 35	99.91 26	99.75 13

Anonymous20231211		98.55 20	98.76 15	97.94 83	98.79 105	94.37 120	98.84 9	99.15 18	99.37 2	99.67 6	99.43 10	95.61 101	99.72 71	98.12 30	99.86 37	99.73 15

FIs		97.93 55	98.07 47	97.48 118	99.38 42	92.95 162	98.03 50	99.11 23	98.04 43	98.62 58	98.66 72	93.75 167	99.78 40	97.23 64	99.84 40	99.73 15

v7n		98.73 12	98.99 6	97.95 82	99.64 11	94.20 128	98.67 12	99.14 20	99.08 9	99.42 16	99.23 29	96.53 67	99.91 12	99.27 4	99.93 21	99.73 15

wuykxyi23d		98.68 16	98.53 26	99.13 3	99.44 33	97.97 7	96.85 122	99.02 51	95.81 134	99.88 2	99.38 13	98.14 13	99.69 99	98.32 28	99.95 13	99.73 15

nrg030		98.54 21	98.62 23	98.32 61	99.22 56	95.66 78	97.90 56	99.08 30	98.31 33	99.02 38	98.74 66	97.68 23	99.61 136	97.77 43	99.85 39	99.70 19

v52		98.85 7	99.01 4	98.37 56	99.61 14	95.53 83	99.01 6	99.04 45	98.48 27	99.31 22	99.41 11	96.82 57	99.87 20	99.44 2	99.95 13	99.70 19

V4		98.85 7	99.01 4	98.37 56	99.61 14	95.53 83	99.01 6	99.04 45	98.48 27	99.31 22	99.41 11	96.81 58	99.87 20	99.44 2	99.95 13	99.70 19

DTE-MVSNet		98.79 9	98.86 10	98.59 43	99.55 20	96.12 65	98.48 24	99.10 25	99.36 3	99.29 25	99.06 47	97.27 36	99.93 2	97.71 46	99.91 26	99.70 19

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 35	99.71 6	96.99 40	99.69 2	99.57 3	99.02 14	99.62 10	99.36 16	98.53 7	99.52 168	98.58 24	99.95 13	99.66 23
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
v13		98.02 44	98.52 27	96.51 175	99.02 88	90.14 210	98.07 46	99.09 29	98.10 41	99.13 33	99.35 18	94.84 123	99.74 60	99.12 5	99.98 3	99.65 24

Baseline_NR-MVSNet		97.72 75	97.79 60	97.50 114	99.56 18	93.29 157	95.44 195	98.86 84	98.20 38	98.37 78	99.24 27	94.69 127	99.55 160	95.98 102	99.79 47	99.65 24

OurMVSNet-221017-0		98.61 18	98.61 25	98.63 41	99.77 3	96.35 58	99.17 5	99.05 38	98.05 42	99.61 11	99.52 4	93.72 168	99.88 18	98.72 20	99.88 33	99.65 24

v748		98.58 19	98.89 9	97.67 101	99.61 14	93.53 152	98.59 16	98.90 76	98.97 17	99.43 15	99.15 40	96.53 67	99.85 23	98.88 11	99.91 26	99.64 27

v12		97.97 47	98.47 28	96.46 179	98.98 92	90.01 214	97.97 51	99.08 30	98.00 44	99.11 35	99.34 20	94.70 126	99.73 65	99.07 6	99.98 3	99.64 27

v11		97.82 68	98.36 34	96.17 200	98.93 94	89.16 239	97.79 62	99.08 30	97.64 62	99.19 30	99.32 22	94.28 146	99.72 71	99.07 6	99.97 8	99.63 29

V9		97.90 59	98.40 32	96.40 183	98.93 94	89.86 216	97.86 58	99.07 34	97.88 48	99.05 37	99.30 23	94.53 137	99.72 71	99.01 8	99.98 3	99.63 29

pmmvs6		99.07 3	99.24 3	98.56 45	99.81 2	96.38 57	98.87 8	99.30 8	99.01 15	99.63 9	99.66 3	99.27 2	99.68 105	97.75 44	99.89 32	99.62 31

V14		97.83 65	98.33 36	96.35 184	98.88 100	89.72 217	97.75 66	99.05 38	97.74 52	99.01 39	99.27 25	94.35 143	99.71 82	98.95 9	99.97 8	99.62 31

v15		97.77 72	98.26 40	96.30 189	98.81 103	89.59 224	97.62 75	99.04 45	97.59 64	98.97 43	99.24 27	94.19 151	99.70 90	98.88 11	99.97 8	99.61 33

TransMVSNet (Re)		98.38 28	98.67 19	97.51 111	99.51 25	93.39 156	98.20 39	98.87 82	98.23 36	99.48 12	99.27 25	98.47 8	99.55 160	96.52 82	99.53 106	99.60 34

XXY-MVS		97.54 89	97.70 66	97.07 143	99.46 31	92.21 174	97.22 97	99.00 62	94.93 173	98.58 63	98.92 56	97.31 34	99.41 214	94.44 161	99.43 139	99.59 35

EI-MVSNet-UG-set		97.32 105	97.40 90	97.09 142	97.34 277	92.01 183	95.33 208	97.65 237	97.74 52	98.30 89	98.14 125	95.04 119	99.69 99	97.55 53	99.52 110	99.58 36

v17		97.70 77	98.17 42	96.28 192	98.77 108	89.59 224	97.62 75	99.01 60	97.54 67	98.72 55	99.18 35	94.06 155	99.68 105	98.74 16	99.92 23	99.58 36

v16		97.69 78	98.16 43	96.29 191	98.75 109	89.60 222	97.62 75	99.01 60	97.53 69	98.69 57	99.18 35	94.05 156	99.68 105	98.73 17	99.88 33	99.58 36

v10		97.55 88	97.97 52	96.31 188	98.60 133	89.64 219	97.44 87	99.02 51	96.60 98	98.72 55	99.16 39	93.48 172	99.72 71	98.76 15	99.92 23	99.58 36

APDe-MVS		98.14 37	98.03 51	98.47 50	98.72 113	96.04 67	98.07 46	99.10 25	95.96 125	98.59 62	98.69 70	96.94 47	99.81 32	96.64 78	99.58 91	99.57 40

EI-MVSNet-Vis-set		97.32 105	97.39 91	97.11 140	97.36 273	92.08 180	95.34 207	97.65 237	97.74 52	98.29 90	98.11 129	95.05 117	99.68 105	97.50 56	99.50 113	99.56 41

v18		97.60 85	98.06 48	96.23 193	98.68 125	89.46 229	97.48 86	98.98 68	97.33 83	98.60 61	99.13 42	93.86 159	99.67 112	98.62 21	99.87 35	99.56 41

v8		97.60 85	98.06 48	96.23 193	98.71 116	89.44 230	97.43 89	98.82 100	97.29 85	98.74 53	99.10 44	93.86 159	99.68 105	98.61 22	99.94 19	99.56 41

VPA-MVSNet		98.27 32	98.46 29	97.70 97	99.06 82	93.80 141	97.76 65	99.00 62	98.40 30	99.07 36	98.98 50	96.89 50	99.75 55	97.19 68	99.79 47	99.55 44

WR-MVS		96.90 127	96.81 134	97.16 137	98.56 139	92.20 176	94.33 253	98.12 205	97.34 82	98.20 96	97.33 199	92.81 190	99.75 55	94.79 151	99.81 43	99.54 45

TranMVSNet+NR-MVSNet		98.33 29	98.30 39	98.43 52	99.07 81	95.87 70	96.73 128	99.05 38	98.67 22	98.84 46	98.45 88	97.58 26	99.88 18	96.45 86	99.86 37	99.54 45

SixPastTwentyTwo		97.49 92	97.57 81	97.26 135	99.56 18	92.33 170	98.28 31	96.97 265	98.30 34	99.45 14	99.35 18	88.43 260	99.89 16	98.01 34	99.76 50	99.54 45

ESAPD		97.64 81	97.35 93	98.50 47	98.85 101	96.18 62	95.21 218	98.99 65	95.84 132	98.78 49	98.08 131	96.84 55	99.81 32	93.98 182	99.57 94	99.52 48

VPNet		97.26 108	97.49 87	96.59 170	99.47 30	90.58 206	96.27 146	98.53 149	97.77 50	98.46 73	98.41 90	94.59 133	99.68 105	94.61 157	99.29 173	99.52 48

Regformer-4		97.53 91	97.47 89	97.71 95	97.35 274	93.91 136	95.26 214	98.14 203	97.97 45	98.34 82	97.89 154	95.49 104	99.71 82	97.41 60	99.42 142	99.51 50

v1192		96.83 135	97.06 120	96.15 201	98.28 168	89.29 236	95.36 205	98.77 107	93.73 215	98.11 104	98.34 97	93.02 188	99.67 112	98.35 26	99.58 91	99.50 51

pm-mvs1		98.47 24	98.67 19	97.86 87	99.52 24	94.58 113	98.28 31	99.00 62	97.57 65	99.27 26	99.22 30	98.32 9	99.50 180	97.09 72	99.75 54	99.50 51

EI-MVSNet		96.63 150	96.93 126	95.74 224	97.26 281	88.13 263	95.29 212	97.65 237	96.99 86	97.94 125	98.19 116	92.55 199	99.58 149	96.91 76	99.56 97	99.50 51

HPM-MVS		98.11 40	97.83 59	98.92 19	99.42 38	97.46 28	98.57 17	99.05 38	95.43 149	97.41 161	97.50 186	97.98 16	99.79 39	95.58 119	99.57 94	99.50 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
LPG-MVS_test		97.94 53	97.67 69	98.74 32	99.15 67	97.02 38	97.09 109	99.02 51	95.15 162	98.34 82	98.23 111	97.91 18	99.70 90	94.41 163	99.73 56	99.50 51

LGP-MVS_train				98.74 32	99.15 67	97.02 38		99.02 51	95.15 162	98.34 82	98.23 111	97.91 18	99.70 90	94.41 163	99.73 56	99.50 51

IterMVS-LS		96.92 125	97.29 96	95.79 223	98.51 147	88.13 263	95.10 221	98.66 133	96.99 86	98.46 73	98.68 71	92.55 199	99.74 60	96.91 76	99.79 47	99.50 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	93.61 9	98.44 25	98.76 15	97.51 111	99.43 36	93.54 151	98.23 34	99.05 38	97.40 81	99.37 19	99.08 46	98.79 5	99.47 188	97.74 45	99.71 63	99.50 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		96.72 144	96.96 125	95.99 212	98.21 184	88.79 251	95.42 200	98.79 102	93.22 224	98.19 97	98.26 109	92.68 194	99.70 90	98.34 27	99.55 101	99.49 59

v1240		96.74 141	97.02 122	95.91 219	98.18 191	88.52 254	95.39 203	98.88 80	93.15 230	98.46 73	98.40 92	92.80 191	99.71 82	98.45 25	99.49 120	99.49 59

ACMMPR		97.95 51	97.62 77	98.94 15	99.20 60	97.56 22	97.59 79	98.83 96	96.05 119	97.46 159	97.63 175	96.77 59	99.76 51	95.61 116	99.46 127	99.49 59

MP-MVS-pluss		97.69 78	97.36 92	98.70 36	99.50 28	96.84 43	95.38 204	98.99 65	92.45 247	98.11 104	98.31 101	97.25 38	99.77 48	96.60 79	99.62 79	99.48 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PGM-MVS		97.88 61	97.52 84	98.96 13	99.20 60	97.62 18	97.09 109	99.06 36	95.45 147	97.55 148	97.94 149	97.11 41	99.78 40	94.77 154	99.46 127	99.48 62

UniMVSNet_NR-MVSNet		97.83 65	97.65 71	98.37 56	98.72 113	95.78 72	95.66 186	99.02 51	98.11 40	98.31 87	97.69 173	94.65 131	99.85 23	97.02 74	99.71 63	99.48 62

v144192		96.69 147	96.90 128	96.03 211	98.25 179	88.92 245	95.49 193	98.77 107	93.05 232	98.09 108	98.29 105	92.51 203	99.70 90	98.11 31	99.56 97	99.47 65

MIMVSNet1		98.51 23	98.45 31	98.67 38	99.72 5	96.71 46	98.76 10	98.89 78	98.49 26	99.38 18	99.14 41	95.44 108	99.84 27	96.47 85	99.80 46	99.47 65

region2R		97.92 56	97.59 79	98.92 19	99.22 56	97.55 23	97.60 78	98.84 88	96.00 123	97.22 166	97.62 176	96.87 53	99.76 51	95.48 121	99.43 139	99.46 67

Regformer-3		97.25 109	97.29 96	97.11 140	97.35 274	92.32 171	95.26 214	97.62 242	97.67 61	98.17 98	97.89 154	95.05 117	99.56 156	97.16 69	99.42 142	99.46 67

DU-MVS		97.79 71	97.60 78	98.36 59	98.73 111	95.78 72	95.65 188	98.87 82	97.57 65	98.31 87	97.83 157	94.69 127	99.85 23	97.02 74	99.71 63	99.46 67

NR-MVSNet		97.96 48	97.86 57	98.26 66	98.73 111	95.54 81	98.14 42	98.73 114	97.79 49	99.42 16	97.83 157	94.40 142	99.78 40	95.91 105	99.76 50	99.46 67

mPP-MVS		97.91 58	97.53 83	99.04 7	99.22 56	97.87 11	97.74 68	98.78 106	96.04 121	97.10 173	97.73 169	96.53 67	99.78 40	95.16 136	99.50 113	99.46 67

SMA-MVS		97.48 93	97.11 115	98.60 42	98.83 102	96.67 48	96.74 126	98.73 114	91.61 260	98.48 70	98.36 95	96.53 67	99.68 105	95.17 134	99.54 103	99.45 72

ACMMP_Plus		97.89 60	97.63 75	98.67 38	99.35 45	96.84 43	96.36 142	98.79 102	95.07 169	97.88 135	98.35 96	97.24 39	99.72 71	96.05 96	99.58 91	99.45 72

zzz-MVS		98.01 46	97.66 70	99.06 5	99.44 33	97.90 8	95.66 186	98.73 114	97.69 59	97.90 131	97.96 145	95.81 94	99.82 30	96.13 93	99.61 84	99.45 72

MTAPA		98.14 37	97.84 58	99.06 5	99.44 33	97.90 8	97.25 94	98.73 114	97.69 59	97.90 131	97.96 145	95.81 94	99.82 30	96.13 93	99.61 84	99.45 72

v1144		96.84 132	97.08 118	96.13 205	98.42 157	89.28 237	95.41 202	98.67 130	94.21 198	97.97 122	98.31 101	93.06 183	99.65 118	98.06 33	99.62 79	99.45 72

XVS		97.96 48	97.63 75	98.94 15	99.15 67	97.66 16	97.77 63	98.83 96	97.42 73	96.32 214	97.64 174	96.49 71	99.72 71	95.66 112	99.37 151	99.45 72

X-MVStestdata		92.86 267	90.83 302	98.94 15	99.15 67	97.66 16	97.77 63	98.83 96	97.42 73	96.32 214	36.50 366	96.49 71	99.72 71	95.66 112	99.37 151	99.45 72

v2v482		96.78 140	97.06 120	95.95 216	98.57 138	88.77 252	95.36 205	98.26 188	95.18 160	97.85 142	98.23 111	92.58 198	99.63 124	97.80 41	99.69 67	99.45 72

MP-MVS		97.64 81	97.18 108	99.00 9	99.32 48	97.77 14	97.49 85	98.73 114	96.27 111	95.59 243	97.75 166	96.30 78	99.78 40	93.70 190	99.48 123	99.45 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v1141		96.86 129	97.14 112	96.04 208	98.55 140	89.06 242	95.44 195	98.33 176	95.14 164	97.93 128	98.19 116	93.36 176	99.62 130	97.61 48	99.69 67	99.44 81

divwei89l23v2f112		96.86 129	97.14 112	96.04 208	98.54 143	89.06 242	95.44 195	98.33 176	95.14 164	97.93 128	98.19 116	93.36 176	99.61 136	97.61 48	99.68 71	99.44 81

v1		96.86 129	97.14 112	96.04 208	98.55 140	89.06 242	95.44 195	98.33 176	95.14 164	97.94 125	98.18 120	93.39 175	99.61 136	97.61 48	99.69 67	99.44 81

EU-MVSNet		94.25 236	94.47 222	93.60 295	98.14 198	82.60 331	97.24 96	92.72 322	85.08 325	98.48 70	98.94 54	82.59 290	98.76 307	97.47 59	99.53 106	99.44 81

v7		96.93 123	97.17 109	96.23 193	98.59 135	89.64 219	95.96 169	98.66 133	94.41 189	97.87 140	98.38 93	93.47 173	99.64 121	97.93 36	99.24 178	99.43 85

ACMMP		98.05 42	97.75 64	98.93 18	99.23 54	97.60 19	98.09 45	98.96 71	95.75 137	97.91 130	98.06 136	96.89 50	99.76 51	95.32 127	99.57 94	99.43 85
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
GST-MVS		97.82 68	97.49 87	98.81 26	99.23 54	97.25 35	97.16 99	98.79 102	95.96 125	97.53 149	97.40 191	96.93 48	99.77 48	95.04 143	99.35 158	99.42 87

v6		96.97 119	97.24 101	96.15 201	98.71 116	89.44 230	95.97 165	98.33 176	95.25 154	97.89 133	98.15 122	93.86 159	99.61 136	97.51 55	99.50 113	99.42 87

HPM-MVS_fast		98.32 30	98.13 44	98.88 22	99.54 22	97.48 27	98.35 28	99.03 50	95.88 129	97.88 135	98.22 114	98.15 12	99.74 60	96.50 84	99.62 79	99.42 87

v1neww		96.97 119	97.24 101	96.15 201	98.70 119	89.44 230	95.97 165	98.33 176	95.25 154	97.88 135	98.15 122	93.83 162	99.61 136	97.50 56	99.50 113	99.41 90

v7new		96.97 119	97.24 101	96.15 201	98.70 119	89.44 230	95.97 165	98.33 176	95.25 154	97.88 135	98.15 122	93.83 162	99.61 136	97.50 56	99.50 113	99.41 90

UniMVSNet (Re)		97.83 65	97.65 71	98.35 60	98.80 104	95.86 71	95.92 174	99.04 45	97.51 70	98.22 94	97.81 161	94.68 129	99.78 40	97.14 70	99.75 54	99.41 90

testing_2		97.43 95	97.71 65	96.60 167	98.91 97	90.85 200	96.01 162	98.54 148	94.78 177	98.78 49	98.96 52	96.35 77	99.54 162	97.25 63	99.82 42	99.40 93

SteuartSystems-ACMMP		98.02 44	97.76 63	98.79 28	99.43 36	97.21 37	97.15 100	98.90 76	96.58 100	98.08 110	97.87 156	97.02 46	99.76 51	95.25 129	99.59 89	99.40 93
Skip Steuart: Steuart Systems R&D Blog.
TDRefinement		98.90 4	98.86 10	99.02 8	99.54 22	98.06 6	99.34 4	99.44 6	98.85 19	99.00 41	99.20 31	97.42 30	99.59 147	97.21 65	99.76 50	99.40 93

K. test v3		96.44 159	96.28 161	96.95 149	99.41 39	91.53 192	97.65 72	90.31 343	98.89 18	98.93 44	99.36 16	84.57 286	99.92 4	97.81 40	99.56 97	99.39 96

ACMM	93.33 11	98.05 42	97.79 60	98.85 23	99.15 67	97.55 23	96.68 130	98.83 96	95.21 157	98.36 80	98.13 126	98.13 15	99.62 130	96.04 97	99.54 103	99.39 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
V42		97.04 113	97.16 110	96.68 165	98.59 135	91.05 197	96.33 144	98.36 171	94.60 181	97.99 118	98.30 104	93.32 178	99.62 130	97.40 61	99.53 106	99.38 98

abl_6		98.42 26	98.19 41	99.09 4	99.16 64	98.10 5	97.73 70	99.11 23	97.76 51	98.62 58	98.27 108	97.88 20	99.80 38	95.67 110	99.50 113	99.38 98

CP-MVS		97.92 56	97.56 82	98.99 10	98.99 90	97.82 12	97.93 54	98.96 71	96.11 118	96.89 188	97.45 189	96.85 54	99.78 40	95.19 132	99.63 78	99.38 98

EG-PatchMatch MVS		97.69 78	97.79 60	97.40 127	99.06 82	93.52 153	95.96 169	98.97 70	94.55 185	98.82 47	98.76 64	97.31 34	99.29 245	97.20 67	99.44 132	99.38 98

IS-MVSNet		96.93 123	96.68 140	97.70 97	99.25 53	94.00 134	98.57 17	96.74 273	98.36 31	98.14 102	97.98 144	88.23 261	99.71 82	93.10 201	99.72 59	99.38 98

Regformer-2		97.41 97	97.24 101	97.93 84	97.21 283	94.72 108	94.85 238	98.27 186	97.74 52	98.11 104	97.50 186	95.58 102	99.69 99	96.57 81	99.31 169	99.37 103

UGNet		96.81 138	96.56 146	97.58 105	96.64 302	93.84 140	97.75 66	97.12 260	96.47 105	93.62 302	98.88 58	93.22 181	99.53 164	95.61 116	99.69 67	99.36 104
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
Regformer-1		97.27 107	97.16 110	97.61 104	97.21 283	93.86 138	94.85 238	98.04 214	97.62 63	98.03 116	97.50 186	95.34 110	99.63 124	96.52 82	99.31 169	99.35 105

VDDNet		96.98 118	96.84 131	97.41 126	99.40 40	93.26 158	97.94 53	95.31 295	99.26 6	98.39 77	99.18 35	87.85 267	99.62 130	95.13 139	99.09 194	99.35 105

APD-MVS_3200maxsize		98.13 39	97.90 55	98.79 28	98.79 105	97.31 33	97.55 82	98.92 74	97.72 56	98.25 92	98.13 126	97.10 42	99.75 55	95.44 123	99.24 178	99.32 107

EPP-MVSNet		96.84 132	96.58 144	97.65 102	99.18 63	93.78 143	98.68 11	96.34 276	97.91 47	97.30 164	98.06 136	88.46 259	99.85 23	93.85 186	99.40 149	99.32 107

ACMP	92.54 13	97.47 94	97.10 116	98.55 46	99.04 85	96.70 47	96.24 150	98.89 78	93.71 216	97.97 122	97.75 166	97.44 28	99.63 124	93.22 198	99.70 66	99.32 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	93.58 10	98.23 35	98.31 37	97.98 81	99.39 41	95.22 93	97.55 82	99.20 12	98.21 37	99.25 27	98.51 84	98.21 11	99.40 217	94.79 151	99.72 59	99.32 107

HFP-MVS		97.94 53	97.64 73	98.83 24	99.15 67	97.50 25	97.59 79	98.84 88	96.05 119	97.49 153	97.54 181	97.07 44	99.70 90	95.61 116	99.46 127	99.30 111

#test#		97.62 83	97.22 106	98.83 24	99.15 67	97.50 25	96.81 124	98.84 88	94.25 196	97.49 153	97.54 181	97.07 44	99.70 90	94.37 166	99.46 127	99.30 111

lessismore_v0				97.05 144	99.36 44	92.12 178		84.07 366		98.77 52	98.98 50	85.36 280	99.74 60	97.34 62	99.37 151	99.30 111

GBi-Net		96.99 115	96.80 135	97.56 106	97.96 216	93.67 145	98.23 34	98.66 133	95.59 142	97.99 118	99.19 32	89.51 251	99.73 65	94.60 158	99.44 132	99.30 111

test1		96.99 115	96.80 135	97.56 106	97.96 216	93.67 145	98.23 34	98.66 133	95.59 142	97.99 118	99.19 32	89.51 251	99.73 65	94.60 158	99.44 132	99.30 111

FMVSNet1		97.95 51	98.08 46	97.56 106	99.14 75	93.67 145	98.23 34	98.66 133	97.41 80	99.00 41	99.19 32	95.47 106	99.73 65	95.83 106	99.76 50	99.30 111

v148		96.58 152	96.97 123	95.42 239	98.63 130	87.57 279	95.09 223	97.90 218	95.91 128	98.24 93	97.96 145	93.42 174	99.39 223	96.04 97	99.52 110	99.29 117

TSAR-MVS + MP.		97.42 96	97.23 105	98.00 80	99.38 42	95.00 99	97.63 74	98.20 194	93.00 233	98.16 99	98.06 136	95.89 85	99.72 71	95.67 110	99.10 193	99.28 118

HQP_MVS		96.66 149	96.33 160	97.68 100	98.70 119	94.29 122	96.50 134	98.75 111	96.36 107	96.16 224	96.77 231	91.91 220	99.46 193	92.59 206	99.20 181	99.28 118

plane_prior5								98.75 111					99.46 193	92.59 206	99.20 181	99.28 118

semantic-postprocess				94.85 258	97.68 250	85.53 301		97.63 241	96.99 86	98.36 80	98.54 82	87.44 269	99.75 55	97.07 73	99.08 195	99.27 121

HSP-MVS		97.37 100	96.85 130	98.92 19	99.26 50	97.70 15	97.66 71	98.23 190	95.65 138	98.51 67	96.46 249	92.15 209	99.81 32	95.14 138	98.58 244	99.26 122

3Dnovator+	96.13 3	97.73 74	97.59 79	98.15 71	98.11 202	95.60 79	98.04 48	98.70 124	98.13 39	96.93 186	98.45 88	95.30 113	99.62 130	95.64 114	98.96 206	99.24 123

Anonymous20240529		97.96 48	98.04 50	97.71 95	98.69 123	94.28 125	97.86 58	98.31 185	98.79 20	99.23 28	98.86 59	95.76 97	99.61 136	95.49 120	99.36 154	99.23 124

IterMVS		95.42 197	95.83 177	94.20 279	97.52 262	83.78 328	92.41 317	97.47 250	95.49 146	98.06 113	98.49 85	87.94 263	99.58 149	96.02 99	99.02 202	99.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OPM-MVS		97.54 89	97.25 99	98.41 53	99.11 77	96.61 51	95.24 216	98.46 155	94.58 184	98.10 107	98.07 133	97.09 43	99.39 223	95.16 136	99.44 132	99.21 126

EPNet		93.72 252	92.62 264	97.03 147	87.61 370	92.25 172	96.27 146	91.28 332	96.74 96	87.65 358	97.39 195	85.00 283	99.64 121	92.14 211	99.48 123	99.20 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS		97.00 114	96.53 150	98.41 53	98.55 140	96.31 59	96.32 145	98.77 107	92.96 239	97.44 160	97.58 180	95.84 87	99.74 60	91.96 212	99.35 158	99.19 128
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		96.92 125	96.55 147	98.03 79	98.00 213	95.54 81	94.87 236	98.17 199	94.60 181	96.38 208	97.05 211	95.67 99	99.36 232	95.12 140	99.08 195	99.19 128

1111		88.78 323	89.39 315	86.96 348	98.53 145	62.84 367	91.49 329	97.48 247	94.45 186	96.56 199	96.45 250	43.83 372	98.87 297	86.33 310	99.40 149	99.18 130

testmv		95.51 188	95.33 190	96.05 207	98.23 182	89.51 228	93.50 293	98.63 140	94.25 196	98.22 94	97.73 169	92.51 203	99.47 188	85.22 320	99.72 59	99.17 131

NCCC		96.52 155	95.99 171	98.10 73	97.81 230	95.68 76	95.00 232	98.20 194	95.39 150	95.40 246	96.36 256	93.81 164	99.45 197	93.55 193	98.42 250	99.17 131

CPTT-MVS		96.69 147	96.08 167	98.49 48	98.89 99	96.64 50	97.25 94	98.77 107	92.89 240	96.01 229	97.13 206	92.23 208	99.67 112	92.24 210	99.34 161	99.17 131

RPSCF		97.87 62	97.51 85	98.95 14	99.15 67	98.43 3	97.56 81	99.06 36	96.19 115	98.48 70	98.70 69	94.72 125	99.24 253	94.37 166	99.33 166	99.17 131

Vis-MVSNet		98.27 32	98.34 35	98.07 74	99.33 46	95.21 95	98.04 48	99.46 5	97.32 84	97.82 144	99.11 43	96.75 60	99.86 22	97.84 39	99.36 154	99.15 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_111021_HR		96.73 143	96.54 149	97.27 133	98.35 162	93.66 148	93.42 295	98.36 171	94.74 178	96.58 197	96.76 233	96.54 66	98.99 282	94.87 146	99.27 176	99.15 135

DeepC-MVS	95.41 4	97.82 68	97.70 66	98.16 70	98.78 107	95.72 74	96.23 151	99.02 51	93.92 206	98.62 58	98.99 49	97.69 22	99.62 130	96.18 92	99.87 35	99.15 135
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		96.99 115	96.38 156	98.81 26	98.64 126	97.59 20	95.97 165	98.20 194	95.51 145	95.06 251	96.53 245	94.10 154	99.70 90	94.29 170	99.15 185	99.13 138

MCST-MVS		96.24 165	95.80 178	97.56 106	98.75 109	94.13 130	94.66 245	98.17 199	90.17 275	96.21 222	96.10 269	95.14 116	99.43 202	94.13 174	98.85 221	99.13 138

UnsupCasMVSNet_eth		95.91 177	95.73 180	96.44 180	98.48 151	91.52 193	95.31 210	98.45 156	95.76 136	97.48 156	97.54 181	89.53 250	98.69 313	94.43 162	94.61 340	99.13 138

3Dnovator	96.53 2	97.61 84	97.64 73	97.50 114	97.74 245	93.65 149	98.49 22	98.88 80	96.86 93	97.11 172	98.55 81	95.82 90	99.73 65	95.94 103	99.42 142	99.13 138

COLMAP_ROB	94.48 6	98.25 34	98.11 45	98.64 40	99.21 59	97.35 32	97.96 52	99.16 15	98.34 32	98.78 49	98.52 83	97.32 33	99.45 197	94.08 175	99.67 73	99.13 138
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		95.67 183	96.58 144	92.94 313	97.48 264	80.21 340	92.96 305	98.19 198	94.83 175	98.82 47	98.79 61	93.31 179	99.51 178	95.83 106	99.04 201	99.12 143

VDD-MVS		97.37 100	97.25 99	97.74 94	98.69 123	94.50 116	97.04 112	95.61 291	98.59 24	98.51 67	98.72 67	92.54 201	99.58 149	96.02 99	99.49 120	99.12 143

MVSTER		94.21 240	93.93 242	95.05 252	95.83 324	86.46 296	95.18 219	97.65 237	92.41 248	97.94 125	98.00 143	72.39 338	99.58 149	96.36 88	99.56 97	99.12 143

testgi		96.07 172	96.50 153	94.80 259	99.26 50	87.69 278	95.96 169	98.58 146	95.08 168	98.02 117	96.25 260	97.92 17	97.60 351	88.68 281	98.74 227	99.11 146

CDPH-MVS		95.45 196	94.65 213	97.84 89	98.28 168	94.96 101	93.73 284	98.33 176	85.03 326	95.44 244	96.60 241	95.31 112	99.44 201	90.01 261	99.13 188	99.11 146

PVSNet_BlendedMVS		95.02 214	94.93 203	95.27 243	97.79 239	87.40 284	94.14 267	98.68 127	88.94 284	94.51 272	98.01 141	93.04 184	99.30 242	89.77 264	99.49 120	99.11 146

DP-MVS		97.87 62	97.89 56	97.81 90	98.62 131	94.82 105	97.13 103	98.79 102	98.98 16	98.74 53	98.49 85	95.80 96	99.49 183	95.04 143	99.44 132	99.11 146

agg_prior2														90.34 258	98.90 212	99.10 150

VNet		96.84 132	96.83 133	96.88 154	98.06 204	92.02 181	96.35 143	97.57 244	97.70 58	97.88 135	97.80 162	92.40 206	99.54 162	94.73 156	98.96 206	99.08 151

CHOSEN 1792x2688		94.10 244	93.41 249	96.18 199	99.16 64	90.04 212	92.15 320	98.68 127	79.90 348	96.22 221	97.83 157	87.92 266	99.42 203	89.18 272	99.65 76	99.08 151

XVG-OURS-SEG-HR		97.38 99	97.07 119	98.30 64	99.01 89	97.41 31	94.66 245	99.02 51	95.20 158	98.15 101	97.52 184	98.83 4	98.43 329	94.87 146	96.41 323	99.07 153

FMVSNet2		96.72 144	96.67 141	96.87 155	97.96 216	91.88 186	97.15 100	98.06 212	95.59 142	98.50 69	98.62 76	89.51 251	99.65 118	94.99 145	99.60 87	99.07 153

HQP4-MVS										92.87 320			99.23 255			99.06 155

HQP-MVS		95.17 208	94.58 218	96.92 151	97.85 223	92.47 168	94.26 254	98.43 160	93.18 226	92.86 321	95.08 292	90.33 239	99.23 255	90.51 252	98.74 227	99.05 156

FMVSNet5		93.39 260	92.35 267	96.50 176	95.83 324	90.81 204	97.31 91	98.27 186	92.74 242	96.27 218	98.28 106	62.23 362	99.67 112	90.86 236	99.36 154	99.03 157

HyFIR lowres test		93.72 252	92.65 263	96.91 153	98.93 94	91.81 189	91.23 334	98.52 150	82.69 336	96.46 205	96.52 247	80.38 298	99.90 13	90.36 257	98.79 222	99.03 157

tttt0517		93.31 262	92.56 265	95.57 232	98.71 116	87.86 272	97.44 87	87.17 362	95.79 135	97.47 158	96.84 224	64.12 360	99.81 32	96.20 91	99.32 168	99.02 159

test9_res														91.29 226	98.89 216	99.00 160

test20.03		96.58 152	96.61 142	96.48 178	98.49 149	91.72 190	95.68 185	97.69 232	96.81 94	98.27 91	97.92 152	94.18 152	98.71 311	90.78 240	99.66 75	99.00 160

XVG-ACMP-BASELINE		97.58 87	97.28 98	98.49 48	99.16 64	96.90 42	96.39 137	98.98 68	95.05 170	98.06 113	98.02 140	95.86 86	99.56 156	94.37 166	99.64 77	99.00 160

MDA-MVSNet-bldmvs		95.69 181	95.67 181	95.74 224	98.48 151	88.76 253	92.84 306	97.25 253	96.00 123	97.59 147	97.95 148	91.38 228	99.46 193	93.16 200	96.35 324	98.99 163

Vis-MVSNet (Re-imp)		95.11 209	94.85 206	95.87 221	99.12 76	89.17 238	97.54 84	94.92 297	96.50 102	96.58 197	97.27 201	83.64 287	99.48 186	88.42 284	99.67 73	98.97 164

FMVSNet3		95.26 206	94.94 201	96.22 197	96.53 305	90.06 211	95.99 163	97.66 235	94.11 202	97.99 118	97.91 153	80.22 299	99.63 124	94.60 158	99.44 132	98.96 165

ambc				96.56 174	98.23 182	91.68 191	97.88 57	98.13 204		98.42 76	98.56 80	94.22 150	99.04 275	94.05 179	99.35 158	98.95 166

YYNet1		94.73 220	94.84 207	94.41 274	97.47 268	85.09 310	90.29 342	95.85 286	92.52 244	97.53 149	97.76 163	91.97 215	99.18 258	93.31 195	96.86 314	98.95 166

ppachtmachnet_test		94.49 231	94.84 207	93.46 299	96.16 316	82.10 333	90.59 339	97.48 247	90.53 271	97.01 177	97.59 179	91.01 231	99.36 232	93.97 183	99.18 184	98.94 168

CANet		95.86 180	95.65 182	96.49 177	96.41 309	90.82 202	94.36 252	98.41 165	94.94 171	92.62 328	96.73 234	92.68 194	99.71 82	95.12 140	99.60 87	98.94 168

Anonymous20231206		95.27 205	95.06 199	95.88 220	98.72 113	89.37 234	95.70 182	97.85 221	88.00 297	96.98 179	97.62 176	91.95 216	99.34 235	89.21 271	99.53 106	98.94 168

MDA-MVSNet_test_wron		94.73 220	94.83 209	94.42 273	97.48 264	85.15 308	90.28 343	95.87 284	92.52 244	97.48 156	97.76 163	91.92 219	99.17 262	93.32 194	96.80 317	98.94 168

LFMVS		95.32 202	94.88 205	96.62 166	98.03 206	91.47 194	97.65 72	90.72 338	99.11 8	97.89 133	98.31 101	79.20 301	99.48 186	93.91 185	99.12 191	98.93 172

XVG-OURS		97.12 112	96.74 138	98.26 66	98.99 90	97.45 29	93.82 281	99.05 38	95.19 159	98.32 85	97.70 172	95.22 115	98.41 330	94.27 171	98.13 262	98.93 172

DeepPCF-MVS	94.58 5	96.90 127	96.43 154	98.31 63	97.48 264	97.23 36	92.56 314	98.60 143	92.84 241	98.54 65	97.40 191	96.64 63	98.78 305	94.40 165	99.41 148	98.93 172

Anonymous202405211		96.34 163	95.98 172	97.43 124	98.25 179	93.85 139	96.74 126	94.41 302	97.72 56	98.37 78	98.03 139	87.15 272	99.53 164	94.06 176	99.07 197	98.92 175

our_test_3		94.20 242	94.58 218	93.07 308	96.16 316	81.20 336	90.42 341	96.84 268	90.72 270	97.14 170	97.13 206	90.47 237	99.11 267	94.04 180	98.25 258	98.91 176

tfpnnormal		97.72 75	97.97 52	96.94 150	99.26 50	92.23 173	97.83 61	98.45 156	98.25 35	99.13 33	98.66 72	96.65 62	99.69 99	93.92 184	99.62 79	98.91 176

AllTest		97.20 111	96.92 127	98.06 75	99.08 79	96.16 63	97.14 102	99.16 15	94.35 193	97.78 145	98.07 133	95.84 87	99.12 264	91.41 224	99.42 142	98.91 176

TestCases				98.06 75	99.08 79	96.16 63		99.16 15	94.35 193	97.78 145	98.07 133	95.84 87	99.12 264	91.41 224	99.42 142	98.91 176

pmmvs-eth3d		96.49 156	96.18 163	97.42 125	98.25 179	94.29 122	94.77 242	98.07 211	89.81 278	97.97 122	98.33 99	93.11 182	99.08 271	95.46 122	99.84 40	98.89 180

train_agg		95.46 194	94.66 212	97.88 86	97.84 227	95.23 90	93.62 288	98.39 167	87.04 305	93.78 293	95.99 270	94.58 134	99.52 168	91.76 219	98.90 212	98.89 180

agg_prior3		95.30 203	94.46 225	97.80 91	97.80 234	95.00 99	93.63 287	98.34 175	86.33 311	93.40 314	95.84 277	94.15 153	99.50 180	91.76 219	98.90 212	98.89 180

test12				97.46 120	97.61 257	94.07 131		97.78 226		93.57 305		93.31 179	99.42 203		98.78 223	98.89 180

pmmvs5		94.63 226	94.34 228	95.50 237	97.63 256	88.34 258	94.02 271	97.13 259	87.15 304	95.22 249	97.15 205	87.50 268	99.27 249	93.99 181	99.26 177	98.88 184

DeepC-MVS_fast	94.34 7	96.74 141	96.51 152	97.44 123	97.69 249	94.15 129	96.02 161	98.43 160	93.17 229	97.30 164	97.38 197	95.48 105	99.28 247	93.74 189	99.34 161	98.88 184
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		97.37 100	97.70 66	96.35 184	98.14 198	95.13 96	96.54 132	98.92 74	95.94 127	99.19 30	98.08 131	97.74 21	95.06 361	95.24 130	99.54 103	98.87 186

PMMVS2		93.66 254	94.07 236	92.45 320	97.57 258	80.67 339	86.46 356	96.00 280	93.99 204	97.10 173	97.38 197	89.90 246	97.82 348	88.76 278	99.47 125	98.86 187

PVSNet_Blended_VisFu		95.95 176	95.80 178	96.42 181	99.28 49	90.62 205	95.31 210	99.08 30	88.40 289	96.97 184	98.17 121	92.11 211	99.78 40	93.64 191	99.21 180	98.86 187

MVS_0304		96.22 166	95.94 176	97.04 145	97.07 289	92.54 166	94.19 262	99.04 45	95.17 161	93.74 296	96.92 220	91.77 222	99.73 65	95.76 108	99.81 43	98.85 189

PHI-MVS		96.96 122	96.53 150	98.25 68	97.48 264	96.50 54	96.76 125	98.85 85	93.52 220	96.19 223	96.85 223	95.94 84	99.42 203	93.79 188	99.43 139	98.83 190

QAPM		95.88 179	95.57 184	96.80 156	97.90 221	91.84 188	98.18 41	98.73 114	88.41 288	96.42 206	98.13 126	94.73 124	99.75 55	88.72 279	98.94 210	98.81 191

Patchmtry		95.03 213	94.59 217	96.33 186	94.83 338	90.82 202	96.38 141	97.20 255	96.59 99	97.49 153	98.57 78	77.67 307	99.38 228	92.95 204	99.62 79	98.80 192

test_prior3		95.91 177	95.39 189	97.46 120	97.79 239	94.26 126	93.33 299	98.42 163	94.21 198	94.02 286	96.25 260	93.64 169	99.34 235	91.90 213	98.96 206	98.79 193

test_prior				97.46 120	97.79 239	94.26 126		98.42 163					99.34 235			98.79 193

casdiffmvs1		96.82 136	96.84 131	96.77 158	98.01 209	92.02 181	97.20 98	98.67 130	92.30 249	96.09 226	98.64 75	93.81 164	99.50 180	98.22 29	98.62 239	98.79 193

MVS_111021_LR		96.82 136	96.55 147	97.62 103	98.27 170	95.34 88	93.81 282	98.33 176	94.59 183	96.56 199	96.63 240	96.61 64	98.73 309	94.80 150	99.34 161	98.78 196

agg_prior1		95.39 198	94.60 216	97.75 93	97.80 234	94.96 101	93.39 296	98.36 171	87.20 303	93.49 307	95.97 273	94.65 131	99.53 164	91.69 222	98.86 219	98.77 197

F-COLMAP		95.30 203	94.38 227	98.05 78	98.64 126	96.04 67	95.61 192	98.66 133	89.00 283	93.22 316	96.40 255	92.90 189	99.35 234	87.45 303	97.53 299	98.77 197

MVSFormer		96.14 170	96.36 158	95.49 238	97.68 250	87.81 276	98.67 12	99.02 51	96.50 102	94.48 274	96.15 264	86.90 273	99.92 4	98.73 17	99.13 188	98.74 199

jason		94.39 234	94.04 237	95.41 241	98.29 165	87.85 274	92.74 311	96.75 272	85.38 324	95.29 247	96.15 264	88.21 262	99.65 118	94.24 172	99.34 161	98.74 199
jason: jason.
旧先验1					97.80 234	93.87 137		97.75 227			97.04 212	93.57 171			98.68 234	98.72 201

testus		90.90 308	90.51 307	92.06 324	96.07 319	79.45 342	88.99 350	98.44 159	85.46 321	94.15 281	90.77 352	89.12 257	98.01 347	73.66 357	97.95 267	98.71 202

mvs_anonymous		95.36 200	96.07 168	93.21 305	96.29 310	81.56 334	94.60 247	97.66 235	93.30 222	96.95 185	98.91 57	93.03 186	99.38 228	96.60 79	97.30 309	98.69 203

OMC-MVS		96.48 157	96.00 170	97.91 85	98.30 164	96.01 69	94.86 237	98.60 143	91.88 258	97.18 168	97.21 203	96.11 81	99.04 275	90.49 254	99.34 161	98.69 203

thisisatest0530		92.71 270	91.76 279	95.56 234	98.42 157	88.23 259	96.03 160	87.35 361	94.04 203	96.56 199	95.47 287	64.03 361	99.77 48	94.78 153	99.11 192	98.68 205

TAMVS		95.49 190	94.94 201	97.16 137	98.31 163	93.41 155	95.07 226	96.82 270	91.09 266	97.51 151	97.82 160	89.96 245	99.42 203	88.42 284	99.44 132	98.64 206

test_0402		97.84 64	97.97 52	97.47 119	99.19 62	94.07 131	96.71 129	98.73 114	98.66 23	98.56 64	98.41 90	96.84 55	99.69 99	94.82 148	99.81 43	98.64 206

MVP-Stereo		95.69 181	95.28 191	96.92 151	98.15 197	93.03 161	95.64 190	98.20 194	90.39 272	96.63 196	97.73 169	91.63 223	99.10 269	91.84 217	97.31 308	98.63 208
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CANet_DTU		94.65 225	94.21 232	95.96 214	95.90 322	89.68 218	93.92 277	97.83 224	93.19 225	90.12 348	95.64 282	88.52 258	99.57 155	93.27 197	99.47 125	98.62 209

PM-MVS		97.36 103	97.10 116	98.14 72	98.91 97	96.77 45	96.20 152	98.63 140	93.82 213	98.54 65	98.33 99	93.98 157	99.05 274	95.99 101	99.45 131	98.61 210

CSCG		97.40 98	97.30 95	97.69 99	98.95 93	94.83 104	97.28 93	98.99 65	96.35 109	98.13 103	95.95 275	95.99 83	99.66 117	94.36 169	99.73 56	98.59 211

diffmvs1		96.57 154	96.86 129	95.72 227	96.74 301	89.30 235	95.90 175	98.58 146	96.33 110	94.93 256	98.37 94	94.52 138	99.29 245	97.60 51	98.73 230	98.58 212

CLD-MVS		95.47 193	95.07 197	96.69 164	98.27 170	92.53 167	91.36 332	98.67 130	91.22 265	95.78 237	94.12 314	95.65 100	98.98 284	90.81 238	99.72 59	98.57 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UnsupCasMVSNet_bld		94.72 222	94.26 229	96.08 206	98.62 131	90.54 209	93.38 297	98.05 213	90.30 273	97.02 176	96.80 229	89.54 248	99.16 263	88.44 283	96.18 326	98.56 214

N_pmnet		95.18 207	94.23 230	98.06 75	97.85 223	96.55 53	92.49 315	91.63 330	89.34 280	98.09 108	97.41 190	90.33 239	99.06 273	91.58 223	99.31 169	98.56 214

CVMVSNet		92.33 280	92.79 260	90.95 333	97.26 281	75.84 355	95.29 212	92.33 325	81.86 338	96.27 218	98.19 116	81.44 292	98.46 328	94.23 173	98.29 252	98.55 216

no-one		94.84 217	94.76 210	95.09 250	98.29 165	87.49 281	91.82 326	97.49 245	88.21 293	97.84 143	98.75 65	91.51 225	99.27 249	88.96 276	99.99 2	98.52 217

test_normal		95.51 188	95.46 187	95.68 229	97.97 215	89.12 241	93.73 284	95.86 285	91.98 254	97.17 169	96.94 217	91.55 224	99.42 203	95.21 131	98.73 230	98.51 218

LS3D		97.77 72	97.50 86	98.57 44	96.24 311	97.58 21	98.45 25	98.85 85	98.58 25	97.51 151	97.94 149	95.74 98	99.63 124	95.19 132	98.97 205	98.51 218

Effi-MVS+-dtu		96.81 138	96.09 166	98.99 10	96.90 298	98.69 2	96.42 136	98.09 207	95.86 130	95.15 250	95.54 285	94.26 148	99.81 32	94.06 176	98.51 247	98.47 220

USDC		94.56 229	94.57 220	94.55 271	97.78 243	86.43 297	92.75 309	98.65 139	85.96 314	96.91 187	97.93 151	90.82 234	98.74 308	90.71 245	99.59 89	98.47 220

pmmvs4		94.82 219	94.19 233	96.70 163	97.42 271	92.75 165	92.09 323	96.76 271	86.80 308	95.73 240	97.22 202	89.28 254	98.89 293	93.28 196	99.14 186	98.46 222

Test4		95.39 198	95.24 192	95.82 222	98.07 203	89.60 222	94.40 251	98.49 153	91.39 264	97.40 162	96.32 258	87.32 271	99.41 214	95.09 142	98.71 233	98.44 223

DI_MVS_plusplus_test		95.46 194	95.43 188	95.55 235	98.05 205	88.84 249	94.18 263	95.75 287	91.92 257	97.32 163	96.94 217	91.44 226	99.39 223	94.81 149	98.48 248	98.43 224

alignmvs		96.01 174	95.52 185	97.50 114	97.77 244	94.71 109	96.07 157	96.84 268	97.48 71	96.78 193	94.28 313	85.50 279	99.40 217	96.22 90	98.73 230	98.40 225

CDS-MVSNet		94.88 216	94.12 235	97.14 139	97.64 255	93.57 150	93.96 276	97.06 262	90.05 276	96.30 217	96.55 243	86.10 276	99.47 188	90.10 260	99.31 169	98.40 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
casdiffmvs		96.43 161	96.38 156	96.60 167	97.51 263	91.95 185	97.08 111	98.41 165	93.69 217	93.95 290	98.34 97	93.03 186	99.45 197	98.09 32	97.30 309	98.39 227

WTY-MVS		93.55 257	93.00 256	95.19 245	97.81 230	87.86 272	93.89 278	96.00 280	89.02 282	94.07 284	95.44 288	86.27 275	99.33 238	87.69 292	96.82 315	98.39 227

Effi-MVS+		96.19 168	96.01 169	96.71 162	97.43 270	92.19 177	96.12 156	99.10 25	95.45 147	93.33 315	94.71 300	97.23 40	99.56 156	93.21 199	97.54 298	98.37 229

MS-PatchMatch		94.83 218	94.91 204	94.57 270	96.81 300	87.10 290	94.23 259	97.34 251	88.74 286	97.14 170	97.11 208	91.94 217	98.23 341	92.99 203	97.92 271	98.37 229

TSAR-MVS + GP.		96.47 158	96.12 164	97.49 117	97.74 245	95.23 90	94.15 266	96.90 267	93.26 223	98.04 115	96.70 236	94.41 141	98.89 293	94.77 154	99.14 186	98.37 229

DELS-MVS		96.17 169	96.23 162	95.99 212	97.55 261	90.04 212	92.38 318	98.52 150	94.13 201	96.55 203	97.06 210	94.99 120	99.58 149	95.62 115	99.28 174	98.37 229
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
sss		94.22 237	93.72 244	95.74 224	97.71 248	89.95 215	93.84 280	96.98 264	88.38 291	93.75 295	95.74 278	87.94 263	98.89 293	91.02 231	98.10 263	98.37 229

GA-MVS		92.83 268	92.15 270	94.87 257	96.97 292	87.27 287	90.03 344	96.12 278	91.83 259	94.05 285	94.57 301	76.01 319	98.97 288	92.46 208	97.34 307	98.36 234

ITE_SJBPF				97.85 88	98.64 126	96.66 49		98.51 152	95.63 139	97.22 166	97.30 200	95.52 103	98.55 324	90.97 233	98.90 212	98.34 235

LCM-MVSNet-Re		97.33 104	97.33 94	97.32 131	98.13 201	93.79 142	96.99 114	99.65 2	96.74 96	99.47 13	98.93 55	96.91 49	99.84 27	90.11 259	99.06 200	98.32 236

BH-RMVSNet		94.56 229	94.44 226	94.91 254	97.57 258	87.44 283	93.78 283	96.26 277	93.69 217	96.41 207	96.50 248	92.10 212	99.00 281	85.96 312	97.71 288	98.31 237

MG-MVS		94.08 246	94.00 239	94.32 276	97.09 288	85.89 298	93.19 303	95.96 282	92.52 244	94.93 256	97.51 185	89.54 248	98.77 306	87.52 302	97.71 288	98.31 237

MVS_Test		96.27 164	96.79 137	94.73 262	96.94 296	86.63 295	96.18 153	98.33 176	94.94 171	96.07 227	98.28 106	95.25 114	99.26 251	97.21 65	97.90 273	98.30 239

TinyColmap		96.00 175	96.34 159	94.96 253	97.90 221	87.91 271	94.13 268	98.49 153	94.41 189	98.16 99	97.76 163	96.29 79	98.68 316	90.52 251	99.42 142	98.30 239

CMPMVS	73.10 23	92.74 269	91.39 282	96.77 158	93.57 355	94.67 111	94.21 261	97.67 233	80.36 347	93.61 303	96.60 241	82.85 289	97.35 352	84.86 323	98.78 223	98.29 241
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
lupinMVS		93.77 250	93.28 250	95.24 244	97.68 250	87.81 276	92.12 321	96.05 279	84.52 329	94.48 274	95.06 294	86.90 273	99.63 124	93.62 192	99.13 188	98.27 242

PAPM_NR		94.61 227	94.17 234	95.96 214	98.36 161	91.23 195	95.93 173	97.95 215	92.98 234	93.42 312	94.43 310	90.53 236	98.38 334	87.60 300	96.29 325	98.27 242

114514_t		93.96 248	93.22 253	96.19 198	99.06 82	90.97 199	95.99 163	98.94 73	73.88 362	93.43 311	96.93 219	92.38 207	99.37 231	89.09 273	99.28 174	98.25 244

diffmvs		96.10 171	96.43 154	95.12 247	96.52 306	87.85 274	95.95 172	97.91 216	96.52 101	93.02 318	98.25 110	94.28 146	99.28 247	97.11 71	98.26 257	98.24 245

原ACMM1				96.58 171	98.16 195	92.12 178		98.15 202	85.90 316	93.49 307	96.43 252	92.47 205	99.38 228	87.66 293	98.62 239	98.23 246

PLC	91.02 16	94.05 247	92.90 257	97.51 111	98.00 213	95.12 97	94.25 257	98.25 189	86.17 312	91.48 337	95.25 290	91.01 231	99.19 257	85.02 322	96.69 319	98.22 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPNet_dtu		91.39 302	90.75 303	93.31 302	90.48 368	82.61 330	94.80 240	92.88 319	93.39 221	81.74 366	94.90 299	81.36 293	99.11 267	88.28 286	98.87 217	98.21 248
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
1112_ss		94.12 243	93.42 248	96.23 193	98.59 135	90.85 200	94.24 258	98.85 85	85.49 319	92.97 319	94.94 296	86.01 277	99.64 121	91.78 218	97.92 271	98.20 249

test12356		87.98 331	88.41 326	86.69 349	95.84 323	63.49 366	87.15 355	97.32 252	87.21 302	91.78 336	93.36 318	70.66 345	98.39 332	74.70 355	97.64 295	98.19 250

Test_1112_low_res		93.53 258	92.86 258	95.54 236	98.60 133	88.86 248	92.75 309	98.69 125	82.66 337	92.65 326	96.92 220	84.75 284	99.56 156	90.94 234	97.76 277	98.19 250

canonicalmvs		97.23 110	97.21 107	97.30 132	97.65 254	94.39 118	97.84 60	99.05 38	97.42 73	96.68 195	93.85 316	97.63 25	99.33 238	96.29 89	98.47 249	98.18 252

Fast-Effi-MVS+-dtu		96.44 159	96.12 164	97.39 128	97.18 285	94.39 118	95.46 194	98.73 114	96.03 122	94.72 260	94.92 298	96.28 80	99.69 99	93.81 187	97.98 266	98.09 253

ab-mvs		96.59 151	96.59 143	96.60 167	98.64 126	92.21 174	98.35 28	97.67 233	94.45 186	96.99 178	98.79 61	94.96 121	99.49 183	90.39 256	99.07 197	98.08 254

PAPR		92.22 281	91.27 286	95.07 251	95.73 327	88.81 250	91.97 324	97.87 220	85.80 317	90.91 339	92.73 332	91.16 229	98.33 338	79.48 344	95.76 332	98.08 254

0601test		94.40 232	94.00 239	95.59 230	96.95 293	89.52 226	94.75 243	95.55 293	96.18 116	96.79 190	96.14 266	81.09 294	99.18 258	90.75 241	97.77 275	98.07 256

Anonymous20240521		94.40 232	94.00 239	95.59 230	96.95 293	89.52 226	94.75 243	95.55 293	96.18 116	96.79 190	96.14 266	81.09 294	99.18 258	90.75 241	97.77 275	98.07 256

MIMVSNet		93.42 259	92.86 258	95.10 249	98.17 193	88.19 260	98.13 43	93.69 307	92.07 251	95.04 253	98.21 115	80.95 296	99.03 278	81.42 340	98.06 264	98.07 256

GSMVS																98.06 259

sam_mvs1												77.80 306				98.06 259

MSLP-MVS++		96.42 162	96.71 139	95.57 232	97.82 229	90.56 208	95.71 181	98.84 88	94.72 179	96.71 194	97.39 195	94.91 122	98.10 345	95.28 128	99.02 202	98.05 261

ADS-MVSNet2		91.47 301	90.51 307	94.36 275	95.51 329	85.63 299	95.05 229	95.70 288	83.46 334	92.69 324	96.84 224	79.15 302	99.41 214	85.66 316	90.52 350	98.04 262

ADS-MVSNet		90.95 307	90.26 310	93.04 309	95.51 329	82.37 332	95.05 229	93.41 313	83.46 334	92.69 324	96.84 224	79.15 302	98.70 312	85.66 316	90.52 350	98.04 262

test2356		85.45 337	83.26 340	92.01 325	91.12 365	80.76 338	85.16 358	92.90 318	83.90 333	90.63 340	87.71 362	53.10 369	97.24 353	69.20 362	95.65 333	98.03 264

test1235678		92.95 266	92.40 266	94.61 266	96.95 293	86.87 292	90.75 337	97.75 227	91.00 268	96.33 210	95.38 289	85.21 281	98.92 290	79.00 346	99.20 181	98.03 264

PVSNet_Blended		93.96 248	93.65 245	94.91 254	97.79 239	87.40 284	91.43 331	98.68 127	84.50 330	94.51 272	94.48 305	93.04 184	99.30 242	89.77 264	98.61 241	98.02 266

PatchmatchNet		91.98 286	91.87 276	92.30 322	94.60 341	79.71 341	95.12 220	93.59 312	89.52 279	93.61 303	97.02 213	77.94 305	99.18 258	90.84 237	94.57 341	98.01 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PVSNet	86.72 19	91.10 303	90.97 299	91.49 327	97.56 260	78.04 348	87.17 354	94.60 300	84.65 328	92.34 330	92.20 336	87.37 270	98.47 327	85.17 321	97.69 290	97.96 268

无先验							93.20 302	97.91 216	80.78 344				99.40 217	87.71 290		97.94 269

tpm		91.08 304	90.85 301	91.75 326	95.33 334	78.09 346	95.03 231	91.27 333	88.75 285	93.53 306	97.40 191	71.24 341	99.30 242	91.25 229	93.87 342	97.87 270

Patchmatch-RL test		94.66 224	94.49 221	95.19 245	98.54 143	88.91 246	92.57 313	98.74 113	91.46 263	98.32 85	97.75 166	77.31 312	98.81 303	96.06 95	99.61 84	97.85 271

LF4IMVS		96.07 172	95.63 183	97.36 129	98.19 188	95.55 80	95.44 195	98.82 100	92.29 250	95.70 241	96.55 243	92.63 197	98.69 313	91.75 221	99.33 166	97.85 271

MDTV_nov1_ep13_2view						57.28 370	94.89 235		80.59 345	94.02 286		78.66 304		85.50 318		97.82 273

Patchmatch-test		93.60 256	93.25 252	94.63 265	96.14 318	87.47 282	96.04 159	94.50 301	93.57 219	96.47 204	96.97 215	76.50 315	98.61 319	90.67 247	98.41 251	97.81 274

Patchmatch-test1		93.38 261	93.59 246	92.73 316	96.24 311	81.40 335	93.24 301	94.00 305	91.58 262	94.57 269	96.67 238	87.94 263	99.03 278	90.42 255	97.66 293	97.77 275

Fast-Effi-MVS+		95.49 190	95.07 197	96.75 160	97.67 253	92.82 163	94.22 260	98.60 143	91.61 260	93.42 312	92.90 327	96.73 61	99.70 90	92.60 205	97.89 274	97.74 276

1121		94.26 235	93.26 251	97.27 133	98.26 178	94.73 107	95.86 176	97.71 231	77.96 356	94.53 271	96.71 235	91.93 218	99.40 217	87.71 290	98.64 238	97.69 277

test222					98.17 193	93.24 159	92.74 311	97.61 243	75.17 360	94.65 262	96.69 237	90.96 233			98.66 236	97.66 278

TAPA-MVS	93.32 12	94.93 215	94.23 230	97.04 145	98.18 191	94.51 114	95.22 217	98.73 114	81.22 343	96.25 220	95.95 275	93.80 166	98.98 284	89.89 262	98.87 217	97.62 279
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
新几何1				97.25 136	98.29 165	94.70 110		97.73 229	77.98 355	94.83 259	96.67 238	92.08 213	99.45 197	88.17 288	98.65 237	97.61 280

MSDG		95.33 201	95.13 195	95.94 218	97.40 272	91.85 187	91.02 335	98.37 170	95.30 152	96.31 216	95.99 270	94.51 139	98.38 334	89.59 266	97.65 294	97.60 281

LP		93.12 265	92.78 262	94.14 281	94.50 343	85.48 302	95.73 180	95.68 289	92.97 238	95.05 252	97.17 204	81.93 291	99.40 217	93.06 202	88.96 355	97.55 282

testdata				95.70 228	98.16 195	90.58 206		97.72 230	80.38 346	95.62 242	97.02 213	92.06 214	98.98 284	89.06 275	98.52 245	97.54 283

DSMNet-mixed		92.19 282	91.83 277	93.25 304	96.18 315	83.68 329	96.27 146	93.68 309	76.97 359	92.54 329	99.18 35	89.20 256	98.55 324	83.88 329	98.60 243	97.51 284

thisisatest0515		90.43 310	89.18 321	94.17 280	97.07 289	85.44 303	89.75 349	87.58 360	88.28 292	93.69 299	91.72 341	65.27 359	99.58 149	90.59 249	98.67 235	97.50 285

PMMVS		92.39 277	91.08 290	96.30 189	93.12 358	92.81 164	90.58 340	95.96 282	79.17 351	91.85 335	92.27 335	90.29 243	98.66 318	89.85 263	96.68 320	97.43 286

DP-MVS Recon		95.55 187	95.13 195	96.80 156	98.51 147	93.99 135	94.60 247	98.69 125	90.20 274	95.78 237	96.21 263	92.73 193	98.98 284	90.58 250	98.86 219	97.42 287

view600		92.56 272	92.11 271	93.91 286	98.45 153	84.76 315	97.10 105	90.23 344	97.42 73	96.98 179	94.48 305	73.62 328	99.60 143	82.49 335	98.28 253	97.36 288

view800		92.56 272	92.11 271	93.91 286	98.45 153	84.76 315	97.10 105	90.23 344	97.42 73	96.98 179	94.48 305	73.62 328	99.60 143	82.49 335	98.28 253	97.36 288

conf0.05thres1000		92.56 272	92.11 271	93.91 286	98.45 153	84.76 315	97.10 105	90.23 344	97.42 73	96.98 179	94.48 305	73.62 328	99.60 143	82.49 335	98.28 253	97.36 288

tfpn		92.56 272	92.11 271	93.91 286	98.45 153	84.76 315	97.10 105	90.23 344	97.42 73	96.98 179	94.48 305	73.62 328	99.60 143	82.49 335	98.28 253	97.36 288

thres600view7		92.03 285	91.43 281	93.82 291	98.19 188	84.61 319	96.27 146	90.39 339	96.81 94	96.37 209	93.11 320	73.44 334	99.49 183	80.32 342	97.95 267	97.36 288

thres400		91.68 299	91.00 291	93.71 293	98.02 207	84.35 324	95.70 182	90.79 336	96.26 112	95.90 234	92.13 337	73.62 328	99.42 203	78.85 348	97.74 278	97.36 288

OpenMVS	94.22 8	95.48 192	95.20 193	96.32 187	97.16 286	91.96 184	97.74 68	98.84 88	87.26 301	94.36 276	98.01 141	93.95 158	99.67 112	90.70 246	98.75 226	97.35 294

test0.0.03 1		90.11 312	89.21 318	92.83 314	93.89 351	86.87 292	91.74 327	88.74 351	92.02 252	94.71 261	91.14 351	73.92 325	94.48 362	83.75 332	92.94 344	97.16 295

BH-untuned		94.69 223	94.75 211	94.52 272	97.95 220	87.53 280	94.07 270	97.01 263	93.99 204	97.10 173	95.65 281	92.65 196	98.95 289	87.60 300	96.74 318	97.09 296

mvs-test1		96.20 167	95.50 186	98.32 61	96.90 298	98.16 4	95.07 226	98.09 207	95.86 130	93.63 301	94.32 312	94.26 148	99.71 82	94.06 176	97.27 311	97.07 297

new_pmnet		92.34 279	91.69 280	94.32 276	96.23 313	89.16 239	92.27 319	92.88 319	84.39 332	95.29 247	96.35 257	85.66 278	96.74 358	84.53 325	97.56 297	97.05 298

tpmrst		90.31 311	90.61 306	89.41 340	94.06 350	72.37 362	95.06 228	93.69 307	88.01 296	92.32 331	96.86 222	77.45 309	98.82 301	91.04 230	87.01 358	97.04 299

EPMVS		89.26 321	88.55 325	91.39 328	92.36 363	79.11 343	95.65 188	79.86 367	88.60 287	93.12 317	96.53 245	70.73 344	98.10 345	90.75 241	89.32 354	96.98 300

Gipuma		98.07 41	98.31 37	97.36 129	99.76 4	96.28 61	98.51 21	99.10 25	98.76 21	96.79 190	99.34 20	96.61 64	98.82 301	96.38 87	99.50 113	96.98 300
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test-LLR		89.97 316	89.90 313	90.16 337	94.24 347	74.98 356	89.89 345	89.06 349	92.02 252	89.97 349	90.77 352	73.92 325	98.57 321	91.88 215	97.36 305	96.92 302

test-mter		87.92 332	87.17 331	90.16 337	94.24 347	74.98 356	89.89 345	89.06 349	86.44 310	89.97 349	90.77 352	54.96 368	98.57 321	91.88 215	97.36 305	96.92 302

PCF-MVS	89.43 18	92.12 284	90.64 305	96.57 173	97.80 234	93.48 154	89.88 348	98.45 156	74.46 361	96.04 228	95.68 280	90.71 235	99.31 240	73.73 356	99.01 204	96.91 304
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		89.75 318	89.25 316	91.26 330	94.69 340	78.00 349	95.32 209	91.98 327	81.50 341	90.55 343	96.96 216	71.06 342	98.89 293	88.59 282	92.63 347	96.87 305

dp		88.08 329	88.05 327	88.16 346	92.85 360	68.81 364	94.17 264	92.88 319	85.47 320	91.38 338	96.14 266	68.87 353	98.81 303	86.88 307	83.80 362	96.87 305

cascas		91.89 290	91.35 284	93.51 297	94.27 346	85.60 300	88.86 352	98.61 142	79.32 350	92.16 332	91.44 348	89.22 255	98.12 344	90.80 239	97.47 303	96.82 307

CR-MVSNet		93.29 263	92.79 260	94.78 260	95.44 331	88.15 261	96.18 153	97.20 255	84.94 327	94.10 282	98.57 78	77.67 307	99.39 223	95.17 134	95.81 328	96.81 308

RPMNet		94.22 237	94.03 238	94.78 260	95.44 331	88.15 261	96.18 153	93.73 306	97.43 72	94.10 282	98.49 85	79.40 300	99.39 223	95.69 109	95.81 328	96.81 308

PatchMatch-RL		94.61 227	93.81 243	97.02 148	98.19 188	95.72 74	93.66 286	97.23 254	88.17 294	94.94 255	95.62 283	91.43 227	98.57 321	87.36 304	97.68 291	96.76 310

MAR-MVS		94.21 240	93.03 255	97.76 92	96.94 296	97.44 30	96.97 121	97.15 258	87.89 299	92.00 333	92.73 332	92.14 210	99.12 264	83.92 328	97.51 300	96.73 311
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
tpmp4_e23		88.46 326	87.54 329	91.22 331	94.56 342	78.08 347	95.63 191	93.17 315	79.08 352	85.85 361	96.80 229	65.86 358	98.85 300	84.10 327	92.85 345	96.72 312

DWT-MVSNet_test		87.92 332	86.77 334	91.39 328	93.18 356	78.62 344	95.10 221	91.42 331	85.58 318	88.00 356	88.73 359	60.60 363	98.90 291	90.60 248	87.70 357	96.65 313

TESTMET0.1,1		87.20 335	86.57 335	89.07 341	93.62 353	72.84 361	89.89 345	87.01 363	85.46 321	89.12 353	90.20 357	56.00 367	97.72 350	90.91 235	96.92 312	96.64 314

CNLPA		95.04 212	94.47 222	96.75 160	97.81 230	95.25 89	94.12 269	97.89 219	94.41 189	94.57 269	95.69 279	90.30 242	98.35 337	86.72 309	98.76 225	96.64 314

IB-MVS	85.98 20	88.63 324	86.95 333	93.68 294	95.12 335	84.82 314	90.85 336	90.17 348	87.55 300	88.48 355	91.34 349	58.01 364	99.59 147	87.24 305	93.80 343	96.63 316
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
tpmvs		90.79 309	90.87 300	90.57 336	92.75 362	76.30 353	95.79 179	93.64 310	91.04 267	91.91 334	96.26 259	77.19 313	98.86 299	89.38 269	89.85 353	96.56 317

CHOSEN 280x420		89.98 315	89.19 320	92.37 321	95.60 328	81.13 337	86.22 357	97.09 261	81.44 342	87.44 359	93.15 319	73.99 323	99.47 188	88.69 280	99.07 197	96.52 318

tfpn111		91.92 287	91.39 282	93.49 298	98.21 184	84.50 320	96.39 137	90.39 339	96.87 90	96.33 210	93.08 322	73.44 334	99.51 178	79.87 343	97.94 270	96.46 319

conf0.01		91.90 288	90.98 293	94.67 263	98.27 170	88.03 265	96.98 115	88.58 352	93.90 207	94.64 263	91.45 342	69.62 347	99.52 168	87.62 294	97.74 278	96.46 319

conf0.002		91.90 288	90.98 293	94.67 263	98.27 170	88.03 265	96.98 115	88.58 352	93.90 207	94.64 263	91.45 342	69.62 347	99.52 168	87.62 294	97.74 278	96.46 319

conf200view11		91.81 292	91.26 287	93.46 299	98.21 184	84.50 320	96.39 137	90.39 339	96.87 90	96.33 210	93.08 322	73.44 334	99.42 203	78.85 348	97.74 278	96.46 319

HY-MVS	91.43 15	92.58 271	91.81 278	94.90 256	96.49 307	88.87 247	97.31 91	94.62 299	85.92 315	90.50 345	96.84 224	85.05 282	99.40 217	83.77 331	95.78 331	96.43 323

PatchT		93.75 251	93.57 247	94.29 278	95.05 336	87.32 286	96.05 158	92.98 317	97.54 67	94.25 277	98.72 67	75.79 320	99.24 253	95.92 104	95.81 328	96.32 324

tpm2		88.47 325	87.69 328	90.79 334	94.98 337	77.34 351	95.09 223	91.83 328	77.51 358	89.40 351	96.41 253	67.83 356	98.73 309	83.58 333	92.60 348	96.29 325

AdaColmap		95.11 209	94.62 215	96.58 171	97.33 278	94.45 117	94.92 234	98.08 209	93.15 230	93.98 289	95.53 286	94.34 144	99.10 269	85.69 315	98.61 241	96.20 326

pmmvs3		90.00 314	88.90 323	93.32 301	94.20 349	85.34 304	91.25 333	92.56 324	78.59 353	93.82 292	95.17 291	67.36 357	98.69 313	89.08 274	98.03 265	95.92 327

PatchFormer-LS_test		89.62 319	89.12 322	91.11 332	93.62 353	78.42 345	94.57 249	93.62 311	88.39 290	90.54 344	88.40 360	72.33 339	99.03 278	92.41 209	88.20 356	95.89 328

thres100view900		91.76 294	91.26 287	93.26 303	98.21 184	84.50 320	96.39 137	90.39 339	96.87 90	96.33 210	93.08 322	73.44 334	99.42 203	78.85 348	97.74 278	95.85 329

tfpn200view9		91.55 300	91.00 291	93.21 305	98.02 207	84.35 324	95.70 182	90.79 336	96.26 112	95.90 234	92.13 337	73.62 328	99.42 203	78.85 348	97.74 278	95.85 329

OpenMVS_ROB	91.80 14	93.64 255	93.05 254	95.42 239	97.31 280	91.21 196	95.08 225	96.68 275	81.56 340	96.88 189	96.41 253	90.44 238	99.25 252	85.39 319	97.67 292	95.80 331

PAPM		87.64 334	85.84 337	93.04 309	96.54 304	84.99 311	88.42 353	95.57 292	79.52 349	83.82 363	93.05 326	80.57 297	98.41 330	62.29 364	92.79 346	95.71 332

xiu_mvs_v1_base_debu		95.62 184	95.96 173	94.60 267	98.01 209	88.42 255	93.99 273	98.21 191	92.98 234	95.91 231	94.53 302	96.39 74	99.72 71	95.43 124	98.19 259	95.64 333

xiu_mvs_v1_base		95.62 184	95.96 173	94.60 267	98.01 209	88.42 255	93.99 273	98.21 191	92.98 234	95.91 231	94.53 302	96.39 74	99.72 71	95.43 124	98.19 259	95.64 333

xiu_mvs_v1_base_debi		95.62 184	95.96 173	94.60 267	98.01 209	88.42 255	93.99 273	98.21 191	92.98 234	95.91 231	94.53 302	96.39 74	99.72 71	95.43 124	98.19 259	95.64 333

tpm cat1		88.01 330	87.33 330	90.05 339	94.48 344	76.28 354	94.47 250	94.35 304	73.84 363	89.26 352	95.61 284	73.64 327	98.30 339	84.13 326	86.20 359	95.57 336

JIA-IIPM		91.79 293	90.69 304	95.11 248	93.80 352	90.98 198	94.16 265	91.78 329	96.38 106	90.30 347	99.30 23	72.02 340	98.90 291	88.28 286	90.17 352	95.45 337

TR-MVS		92.54 276	92.20 269	93.57 296	96.49 307	86.66 294	93.51 292	94.73 298	89.96 277	94.95 254	93.87 315	90.24 244	98.61 319	81.18 341	94.88 337	95.45 337

tfpn1000		91.88 291	91.20 289	93.89 290	97.96 216	87.13 289	97.13 103	88.16 359	94.41 189	94.87 258	92.77 329	68.34 354	99.47 188	89.24 270	97.95 267	95.06 339

thres200		91.00 305	90.42 309	92.77 315	97.47 268	83.98 327	94.01 272	91.18 334	95.12 167	95.44 244	91.21 350	73.93 324	99.31 240	77.76 352	97.63 296	95.01 340

1314		92.38 278	92.30 268	92.64 318	95.42 333	85.15 308	95.86 176	96.97 265	85.40 323	90.62 341	93.06 325	91.12 230	97.80 349	86.74 308	95.49 336	94.97 341

BH-w/o		92.14 283	91.94 275	92.73 316	97.13 287	85.30 305	92.46 316	95.64 290	89.33 281	94.21 278	92.74 331	89.60 247	98.24 340	81.68 339	94.66 339	94.66 342

xiu_mvs_v2_base		94.22 237	94.63 214	92.99 312	97.32 279	84.84 313	92.12 321	97.84 222	91.96 255	94.17 279	93.43 317	96.07 82	99.71 82	91.27 227	97.48 301	94.42 343

PS-MVSNAJ		94.10 244	94.47 222	93.00 311	97.35 274	84.88 312	91.86 325	97.84 222	91.96 255	94.17 279	92.50 334	95.82 90	99.71 82	91.27 227	97.48 301	94.40 344

thresconf0.02		91.72 295	90.98 293	93.97 282	98.27 170	88.03 265	96.98 115	88.58 352	93.90 207	94.64 263	91.45 342	69.62 347	99.52 168	87.62 294	97.74 278	94.35 345

tfpn_n400		91.72 295	90.98 293	93.97 282	98.27 170	88.03 265	96.98 115	88.58 352	93.90 207	94.64 263	91.45 342	69.62 347	99.52 168	87.62 294	97.74 278	94.35 345

tfpnconf		91.72 295	90.98 293	93.97 282	98.27 170	88.03 265	96.98 115	88.58 352	93.90 207	94.64 263	91.45 342	69.62 347	99.52 168	87.62 294	97.74 278	94.35 345

tfpnview11		91.72 295	90.98 293	93.97 282	98.27 170	88.03 265	96.98 115	88.58 352	93.90 207	94.64 263	91.45 342	69.62 347	99.52 168	87.62 294	97.74 278	94.35 345

gg-mvs-nofinetune		88.28 328	86.96 332	92.23 323	92.84 361	84.44 323	98.19 40	74.60 369	99.08 9	87.01 360	99.47 7	56.93 365	98.23 341	78.91 347	95.61 334	94.01 349

PNet_i23d		83.82 339	83.39 339	85.10 350	96.07 319	65.16 365	81.87 363	94.37 303	90.87 269	93.92 291	92.89 328	52.80 370	96.44 360	77.52 354	70.22 364	93.70 350

API-MVS		95.09 211	95.01 200	95.31 242	96.61 303	94.02 133	96.83 123	97.18 257	95.60 141	95.79 236	94.33 311	94.54 136	98.37 336	85.70 314	98.52 245	93.52 351

PVSNet_0	81.89 21	84.49 338	83.21 341	88.34 344	95.76 326	74.97 358	83.49 360	92.70 323	78.47 354	87.94 357	86.90 363	83.38 288	96.63 359	73.44 358	66.86 365	93.40 352

FPMVS		89.92 317	88.63 324	93.82 291	98.37 160	96.94 41	91.58 328	93.34 314	88.00 297	90.32 346	97.10 209	70.87 343	91.13 364	71.91 360	96.16 327	93.39 353

PMVS	89.60 17	96.71 146	96.97 123	95.95 216	99.51 25	97.81 13	97.42 90	97.49 245	97.93 46	95.95 230	98.58 77	96.88 52	96.91 355	89.59 266	99.36 154	93.12 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tfpn_ndepth		90.98 306	90.24 311	93.20 307	97.72 247	87.18 288	96.52 133	88.20 358	92.63 243	93.69 299	90.70 355	68.22 355	99.42 203	86.98 306	97.47 303	93.00 355

MVS		90.02 313	89.20 319	92.47 319	94.71 339	86.90 291	95.86 176	96.74 273	64.72 364	90.62 341	92.77 329	92.54 201	98.39 332	79.30 345	95.56 335	92.12 356

MVE	73.61 22	86.48 336	85.92 336	88.18 345	96.23 313	85.28 306	81.78 364	75.79 368	86.01 313	82.53 365	91.88 339	92.74 192	87.47 366	71.42 361	94.86 338	91.78 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		89.52 320	89.78 314	88.73 342	93.14 357	77.61 350	83.26 361	92.02 326	94.82 176	93.71 297	93.11 320	75.31 321	96.81 356	85.81 313	96.81 316	91.77 358

EMVS		89.06 322	89.22 317	88.61 343	93.00 359	77.34 351	82.91 362	90.92 335	94.64 180	92.63 327	91.81 340	76.30 317	97.02 354	83.83 330	96.90 313	91.48 359

GG-mvs-BLEND				90.60 335	91.00 366	84.21 326	98.23 34	72.63 372		82.76 364	84.11 364	56.14 366	96.79 357	72.20 359	92.09 349	90.78 360

MVS-HIRNet		88.40 327	90.20 312	82.99 351	97.01 291	60.04 369	93.11 304	85.61 364	84.45 331	88.72 354	99.09 45	84.72 285	98.23 341	82.52 334	96.59 321	90.69 361

DeepMVS_CX				77.17 353	90.94 367	85.28 306		74.08 371	52.51 365	80.87 367	88.03 361	75.25 322	70.63 367	59.23 365	84.94 360	75.62 362

wuyk23d		93.25 264	95.20 193	87.40 347	96.07 319	95.38 86	97.04 112	94.97 296	95.33 151	99.70 5	98.11 129	98.14 13	91.94 363	77.76 352	99.68 71	74.89 363

testpf		82.70 340	84.35 338	77.74 352	88.97 369	73.23 360	93.85 279	84.33 365	88.10 295	85.06 362	90.42 356	52.62 371	91.05 365	91.00 232	84.82 361	68.93 364

tmp_tt		57.23 342	62.50 343	41.44 355	34.77 371	49.21 371	83.93 359	60.22 373	15.31 366	71.11 368	79.37 365	70.09 346	44.86 368	64.76 363	82.93 363	30.25 365

test123		12.59 346	15.49 347	3.87 357	6.07 372	2.55 372	90.75 337	2.59 375	2.52 367	5.20 370	13.02 368	4.96 374	1.85 370	5.20 366	9.09 366	7.23 366

.test1245		73.49 341	79.27 342	56.15 354	98.53 145	62.84 367	91.49 329	97.48 247	94.45 186	96.56 199	96.45 250	43.83 372	98.87 297	86.33 310	8.32 367	6.75 367

testmvs		12.33 347	15.23 348	3.64 358	5.77 373	2.23 373	88.99 350	3.62 374	2.30 368	5.29 369	13.09 367	4.52 375	1.95 369	5.16 367	8.32 367	6.75 367

test_part1				0.00 359		0.00 374	0.00 365	98.84 88				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

v1.0		40.70 344	54.26 344	0.00 359	99.03 86	0.00 374	0.00 365	98.84 88	94.84 174	98.08 110	97.60 178	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

cdsmvs_eth3d_5k		24.22 345	32.30 346	0.00 359	0.00 374	0.00 374	0.00 365	98.10 206	0.00 369	0.00 371	95.06 294	97.54 27	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		7.98 348	10.65 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	95.82 90	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		7.91 349	10.55 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	94.94 296	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

test_part2					99.03 86	96.07 66				98.08 110

sam_mvs												77.38 310

MTGPA								98.73 114

test_post1							94.98 233				10.37 370	76.21 318	99.04 275	89.47 268

test_post											10.87 369	76.83 314	99.07 272

patchmatchnet-post											96.84 224	77.36 311	99.42 203

MTMP							96.55 131	74.60 369

gm-plane-assit					91.79 364	71.40 363			81.67 339		90.11 358		98.99 282	84.86 323

TEST9					97.84 227	95.23 90	93.62 288	98.39 167	86.81 307	93.78 293	95.99 270	94.68 129	99.52 168

test_8					97.81 230	95.07 98	93.54 291	98.38 169	87.04 305	93.71 297	95.96 274	94.58 134	99.52 168

agg_prior					97.80 234	94.96 101		98.36 171		93.49 307			99.53 164

test_prior4						95.38 86	93.61 290

test_prior2							93.33 299		94.21 198	94.02 286	96.25 260	93.64 169		91.90 213	98.96 206

旧先验2							93.35 298		77.95 357	95.77 239			98.67 317	90.74 244

新几何2							93.43 294

原ACMM2							92.82 307

testdata2													99.46 193	87.84 289

segment_acmp												95.34 110

testdata1							92.77 308		93.78 214

plane_prior7					98.70 119	94.67 111

plane_prior6					98.38 159	94.37 120						91.91 220

plane_prior4											96.77 231

plane_prior3						94.51 114			95.29 153	96.16 224

plane_prior2							96.50 134		96.36 107

plane_prior1					98.49 149

plane_prior						94.29 122	95.42 200		94.31 195						98.93 211

n2								0.00 376

nn								0.00 376

door-mid								98.17 199

test11								98.08 209

door								97.81 225

HQP5-MVS						92.47 168

HQP-NCC					97.85 223		94.26 254		93.18 226	92.86 321

ACMP_Plane					97.85 223		94.26 254		93.18 226	92.86 321

BP-MVS														90.51 252

HQP3-MVS								98.43 160							98.74 227

HQP2-MVS												90.33 239

NP-MVS					98.14 198	93.72 144					95.08 292

MDTV_nov1_ep13			91.28 285		94.31 345	73.51 359	94.80 240	93.16 316	86.75 309	93.45 310	97.40 191	76.37 316	98.55 324	88.85 277	96.43 322

ACMMP++_ref															99.52 110

ACMMP++															99.55 101

Test By Simon												94.51 139

High-resolution multi-view benchmark