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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

gg-mvs-nofinetune		98.40 187	98.26 180	98.57 210	99.83 100	98.86 187	98.77 214	99.97 1	99.57 33	99.99 1	99.99 1	93.81 203	93.50 234	98.91 167	98.20 184	99.33 183	98.52 197

v7n		99.89 2	99.86 4	99.93 1	99.97 1	99.83 2	99.93 1	99.96 14	99.77 5	99.89 18	99.99 1	99.86 69	99.84 5	99.89 9	99.81 10	99.97 1	99.88 9

new-patchmatchnet		98.49 183	97.60 192	99.53 113	99.90 34	99.55 63	99.77 36	99.48 202	99.67 12	99.86 33	99.98 3	99.98 4	99.50 84	96.90 224	91.52 225	98.67 210	95.62 225

v11		99.72 24	99.62 24	99.85 21	99.87 51	99.71 28	99.81 26	99.96 14	99.63 19	99.83 53	99.97 4	99.58 117	99.75 21	99.33 85	99.33 62	99.87 47	99.79 24

anonymousdsp		99.87 8	99.86 4	99.88 15	99.95 11	99.75 23	99.90 7	99.96 14	99.69 10	99.83 53	99.96 5	99.99 3	99.74 26	99.95 3	99.83 3	99.91 24	99.88 9

v748		99.89 2	99.87 1	99.92 4	99.96 8	99.80 11	99.91 5	99.95 28	99.77 5	99.92 8	99.96 5	99.93 38	99.81 9	99.92 7	99.82 6	99.96 3	99.90 2

v1240		99.58 54	99.38 75	99.82 30	99.89 39	99.49 84	99.82 24	99.83 100	99.63 19	99.86 33	99.96 5	98.92 168	99.75 21	99.15 130	98.96 114	99.76 91	99.56 88

v52		99.89 2	99.85 6	99.92 4	99.97 1	99.80 11	99.92 2	99.97 1	99.78 3	99.90 14	99.96 5	99.85 75	99.82 7	99.88 12	99.82 6	99.96 3	99.89 5

V4		99.89 2	99.85 6	99.92 4	99.97 1	99.80 11	99.92 2	99.97 1	99.78 3	99.90 14	99.96 5	99.84 77	99.82 7	99.88 12	99.82 6	99.96 3	99.89 5

v1921920		99.59 50	99.40 65	99.82 30	99.88 44	99.45 88	99.81 26	99.83 100	99.65 15	99.86 33	99.95 10	99.29 154	99.75 21	98.98 156	98.86 131	99.78 85	99.59 73

v1192		99.60 48	99.41 62	99.82 30	99.89 39	99.43 94	99.81 26	99.84 91	99.63 19	99.85 40	99.95 10	99.35 149	99.72 34	99.01 150	98.90 122	99.82 76	99.58 83

FC-MVSNet-test		99.84 9	99.80 9	99.89 12	99.96 8	99.83 2	99.84 17	99.95 28	99.37 57	99.77 75	99.95 10	99.96 13	99.85 3	99.93 4	99.83 3	99.95 9	99.72 45

v1144		99.61 42	99.43 58	99.82 30	99.88 44	99.41 98	99.76 39	99.86 66	99.64 17	99.84 44	99.95 10	99.49 133	99.74 26	99.00 152	98.93 119	99.84 61	99.58 83

v2v482		99.56 65	99.35 77	99.81 34	99.87 51	99.35 119	99.75 45	99.85 79	99.56 35	99.87 28	99.95 10	99.44 139	99.66 51	98.91 167	98.76 146	99.86 53	99.45 118

v7		99.61 42	99.46 54	99.79 45	99.83 100	99.37 112	99.75 45	99.84 91	99.56 35	99.76 78	99.94 15	99.60 114	99.73 30	99.11 136	99.01 104	99.85 57	99.63 66

v10		99.65 37	99.51 44	99.81 34	99.83 100	99.61 46	99.75 45	99.94 31	99.56 35	99.76 78	99.94 15	99.60 114	99.73 30	99.11 136	99.01 104	99.85 57	99.74 39

SixPastTwentyTwo		99.89 2	99.85 6	99.93 1	99.97 1	99.88 1	99.92 2	99.97 1	99.66 14	99.94 3	99.94 15	99.74 94	99.81 9	99.97 2	99.89 1	99.96 3	99.89 5

HyFIR lowres test		99.50 71	99.26 87	99.80 38	99.95 11	99.62 43	99.76 39	99.97 1	99.67 12	99.56 139	99.94 15	98.40 178	99.78 15	98.84 183	98.59 164	99.76 91	99.72 45

v144192		99.58 54	99.39 69	99.80 38	99.87 51	99.44 90	99.77 36	99.84 91	99.64 17	99.86 33	99.93 19	99.35 149	99.72 34	98.92 164	98.82 137	99.74 98	99.66 58

v15		99.67 35	99.54 40	99.83 29	99.86 68	99.67 39	99.76 39	99.95 28	99.59 28	99.83 53	99.93 19	99.55 121	99.71 38	99.23 108	99.05 97	99.87 47	99.75 36

v13		99.73 20	99.63 21	99.85 21	99.87 51	99.71 28	99.80 29	99.96 14	99.62 22	99.83 53	99.93 19	99.66 106	99.75 21	99.41 72	99.26 69	99.89 32	99.80 23

v12		99.72 24	99.61 26	99.85 21	99.86 68	99.70 33	99.79 31	99.96 14	99.61 23	99.83 53	99.93 19	99.61 110	99.74 26	99.38 74	99.22 71	99.89 32	99.79 24

V14		99.69 31	99.56 37	99.84 24	99.86 68	99.68 36	99.78 34	99.96 14	99.60 27	99.83 53	99.93 19	99.58 117	99.72 34	99.28 99	99.11 89	99.88 36	99.77 29

V9		99.71 28	99.59 32	99.84 24	99.86 68	99.69 35	99.78 34	99.96 14	99.61 23	99.84 44	99.93 19	99.61 110	99.73 30	99.34 84	99.17 77	99.88 36	99.78 27

CHOSEN 1792x2688		99.65 37	99.55 38	99.77 52	99.93 24	99.60 48	99.79 31	99.92 43	99.73 8	99.74 91	99.93 19	99.98 4	99.80 13	98.83 184	99.01 104	99.45 166	99.76 33

pmmvs6		99.88 7	99.87 1	99.89 12	99.97 1	99.76 18	99.89 8	99.96 14	99.82 2	99.90 14	99.92 26	99.95 22	99.68 39	99.93 4	99.88 2	99.95 9	99.86 12

pmmvs5		99.58 54	99.47 51	99.70 75	99.84 89	99.50 82	99.58 94	99.80 125	98.98 107	99.73 97	99.92 26	99.81 83	99.49 88	99.28 99	99.05 97	99.77 89	99.73 42

gm-plane-assit		96.82 215	94.84 222	99.13 179	99.95 11	99.78 16	99.69 70	99.92 43	99.19 78	99.84 44	99.92 26	72.93 240	96.44 219	98.21 209	97.01 213	98.92 204	96.87 221

v1141		99.58 54	99.39 69	99.80 38	99.87 51	99.39 101	99.74 53	99.85 79	99.58 30	99.84 44	99.92 26	99.49 133	99.68 39	98.98 156	98.83 134	99.84 61	99.52 104

pm-mvs1		99.77 13	99.69 15	99.86 18	99.94 21	99.68 36	99.84 17	99.93 35	99.59 28	99.87 28	99.92 26	99.21 157	99.65 54	99.88 12	99.77 13	99.93 18	99.78 27

divwei89l23v2f112		99.58 54	99.39 69	99.80 38	99.87 51	99.39 101	99.74 53	99.85 79	99.57 33	99.84 44	99.92 26	99.48 135	99.67 43	98.98 156	98.83 134	99.84 61	99.52 104

v1		99.58 54	99.39 69	99.80 38	99.87 51	99.39 101	99.74 53	99.85 79	99.58 30	99.84 44	99.92 26	99.51 128	99.67 43	98.98 156	98.82 137	99.84 61	99.52 104

test20.03		99.68 33	99.60 30	99.76 56	99.91 31	99.70 33	99.68 71	99.87 62	99.05 99	99.88 23	99.92 26	99.88 63	99.50 84	99.77 30	99.42 57	99.75 94	99.49 110

MIMVSNet1		99.79 11	99.75 12	99.84 24	99.89 39	99.83 2	99.84 17	99.89 56	99.31 63	99.93 4	99.92 26	99.97 9	99.68 39	99.89 9	99.64 27	99.82 76	99.66 58

EU-MVSNet		99.76 15	99.74 13	99.78 48	99.82 105	99.81 9	99.88 10	99.87 62	99.31 63	99.75 85	99.91 35	99.76 93	99.78 15	99.84 19	99.74 17	99.56 152	99.81 19

PMMVS2		99.23 123	99.22 95	99.24 164	99.80 110	99.14 161	99.50 113	99.82 108	99.12 90	98.41 228	99.91 35	99.98 4	98.51 172	99.48 58	98.76 146	99.38 177	98.14 208

Baseline_NR-MVSNet		99.62 40	99.48 47	99.78 48	99.85 81	99.76 18	99.59 90	99.82 108	98.84 125	99.88 23	99.91 35	99.04 162	99.61 62	99.46 63	99.78 12	99.94 16	99.60 72

IterMVS		99.08 141	98.90 145	99.29 156	99.87 51	99.53 68	99.52 106	99.77 140	98.94 111	99.75 85	99.91 35	97.52 190	98.72 167	98.86 177	98.14 188	98.09 215	99.43 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v8		99.61 42	99.45 55	99.79 45	99.80 110	99.59 52	99.73 57	99.93 35	99.48 44	99.77 75	99.90 39	99.48 135	99.67 43	99.11 136	98.89 123	99.84 61	99.73 42

TransMVSNet (Re)		99.72 24	99.59 32	99.88 15	99.95 11	99.76 18	99.88 10	99.94 31	99.58 30	99.92 8	99.90 39	98.55 174	99.65 54	99.89 9	99.76 14	99.95 9	99.70 50

no-one		99.73 20	99.70 14	99.76 56	99.77 129	99.58 54	99.76 39	99.90 55	99.08 92	99.86 33	99.90 39	99.98 4	99.66 51	99.98 1	99.73 18	99.59 146	99.67 55

v1neww		99.57 61	99.40 65	99.77 52	99.80 110	99.34 121	99.72 62	99.82 108	99.49 41	99.76 78	99.89 42	99.50 130	99.67 43	99.10 144	98.89 123	99.84 61	99.59 73

v7new		99.57 61	99.40 65	99.77 52	99.80 110	99.34 121	99.72 62	99.82 108	99.49 41	99.76 78	99.89 42	99.50 130	99.67 43	99.10 144	98.89 123	99.84 61	99.59 73

v6		99.57 61	99.40 65	99.77 52	99.80 110	99.34 121	99.72 62	99.82 108	99.49 41	99.76 78	99.89 42	99.52 126	99.67 43	99.10 144	98.89 123	99.84 61	99.59 73

V42		99.57 61	99.41 62	99.75 62	99.84 89	99.37 112	99.73 57	99.83 100	99.41 52	99.75 85	99.89 42	99.42 141	99.60 64	99.15 130	98.96 114	99.76 91	99.65 62

CVMVSNet		99.06 146	98.88 149	99.28 161	99.52 196	99.53 68	99.42 130	99.69 167	98.74 134	98.27 231	99.89 42	95.48 199	99.44 96	99.46 63	99.33 62	99.32 184	99.75 36

LTVRE_ROB	99.39 1	99.90 1	99.87 1	99.93 1	99.97 1	99.82 6	99.91 5	99.92 43	99.75 7	99.93 4	99.89 42	100.00 1	99.87 2	99.93 4	99.82 6	99.96 3	99.90 2
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
pmmvs-eth3d		99.61 42	99.48 47	99.75 62	99.87 51	99.30 132	99.75 45	99.89 56	99.23 70	99.85 40	99.88 48	99.97 9	99.49 88	99.46 63	99.01 104	99.68 112	99.52 104

v17		99.62 40	99.48 47	99.79 45	99.80 110	99.60 48	99.73 57	99.94 31	99.46 46	99.73 97	99.88 48	99.52 126	99.67 43	99.16 129	98.96 114	99.84 61	99.75 36

1111		96.83 214	95.02 221	98.95 194	99.90 34	99.57 56	99.62 83	99.97 1	98.58 155	98.06 233	99.87 50	69.04 243	96.43 220	99.36 79	99.14 82	99.73 102	99.54 94

.test1245		79.44 233	75.07 236	84.53 235	99.90 34	99.57 56	99.62 83	99.97 1	98.58 155	98.06 233	99.87 50	69.04 243	96.43 220	99.36 79	24.74 237	13.21 241	34.30 237

test12356		99.12 137	99.03 131	99.23 165	99.78 123	98.95 180	99.10 181	99.72 159	98.26 180	99.81 63	99.87 50	99.20 158	98.06 186	99.47 61	98.80 143	98.91 205	98.67 193

WR-MVS		99.79 11	99.68 16	99.91 8	99.95 11	99.83 2	99.87 12	99.96 14	99.39 56	99.93 4	99.87 50	99.29 154	99.77 17	99.83 20	99.72 20	99.97 1	99.82 16

TAMVS		99.05 147	99.02 134	99.08 187	99.69 160	99.22 152	99.33 148	99.32 218	99.16 85	98.97 202	99.87 50	97.36 191	97.76 194	99.21 114	99.00 109	99.44 168	99.33 147

DeepC-MVS	99.05 5	99.74 18	99.64 19	99.84 24	99.90 34	99.39 101	99.79 31	99.81 119	99.69 10	99.90 14	99.87 50	99.98 4	99.81 9	99.62 51	99.32 64	99.83 72	99.65 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MDTV_nov1_ep13_2view		98.73 173	98.31 179	99.22 170	99.75 138	99.24 149	99.75 45	99.93 35	99.31 63	99.84 44	99.86 56	99.81 83	99.31 112	97.40 221	94.77 219	96.73 224	97.81 213

v18		99.59 50	99.44 57	99.76 56	99.78 123	99.57 56	99.70 69	99.93 35	99.43 49	99.69 110	99.85 57	99.51 128	99.65 54	99.08 147	98.87 128	99.82 76	99.74 39

v16		99.61 42	99.47 51	99.78 48	99.79 118	99.60 48	99.72 62	99.94 31	99.45 48	99.70 108	99.85 57	99.54 124	99.67 43	99.15 130	98.96 114	99.83 72	99.76 33

PVSNet_Blended_VisFu		99.66 36	99.64 19	99.67 82	99.91 31	99.71 28	99.61 85	99.79 128	99.41 52	99.91 12	99.85 57	99.61 110	99.00 146	99.67 42	99.42 57	99.81 80	99.81 19

CDS-MVSNet		99.15 135	99.10 122	99.21 172	99.59 189	99.22 152	99.48 122	99.47 203	98.89 117	99.41 170	99.84 60	98.11 184	97.76 194	99.26 104	99.01 104	99.57 149	99.38 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		99.76 15	99.78 10	99.75 62	99.92 26	99.77 17	99.83 20	99.85 79	99.43 49	99.85 40	99.84 60	100.00 1	99.13 136	99.83 20	99.66 25	99.90 27	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FMVSNet1		99.50 71	99.57 36	99.42 133	99.67 168	99.65 41	99.60 89	99.91 48	99.40 54	99.39 172	99.83 62	99.27 156	98.14 182	99.68 39	99.50 45	99.81 80	99.68 52

TDRefinement		99.81 10	99.76 11	99.86 18	99.83 100	99.53 68	99.89 8	99.91 48	99.73 8	99.88 23	99.83 62	99.96 13	99.76 19	99.91 8	99.81 10	99.86 53	99.59 73

v148		99.58 54	99.43 58	99.76 56	99.87 51	99.40 100	99.76 39	99.85 79	99.48 44	99.83 53	99.82 64	99.83 80	99.51 80	99.20 117	98.82 137	99.75 94	99.45 118

testmv		99.39 95	99.19 103	99.62 97	99.84 89	99.38 106	99.37 141	99.86 66	98.47 163	99.79 68	99.82 64	99.39 145	99.63 59	99.30 90	98.70 153	99.21 193	99.28 153

test1235678		99.39 95	99.20 100	99.62 97	99.84 89	99.38 106	99.38 139	99.86 66	98.47 163	99.79 68	99.82 64	99.41 143	99.63 59	99.30 90	98.71 151	99.21 193	99.28 153

DELS-MVS		99.42 88	99.53 42	99.29 156	99.52 196	99.43 94	99.42 130	99.28 219	99.16 85	99.72 101	99.82 64	99.97 9	98.17 179	99.56 53	99.16 79	99.65 118	99.59 73
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
new_pmnet		98.91 162	98.89 146	98.94 195	99.51 201	98.27 220	99.15 175	98.66 228	99.17 81	99.48 157	99.79 68	99.80 88	98.49 174	99.23 108	98.20 184	98.34 213	97.74 216

N_pmnet		98.64 178	98.23 183	99.11 184	99.78 123	99.25 144	99.75 45	99.39 213	99.65 15	99.70 108	99.78 69	99.89 58	98.81 163	97.60 217	94.28 220	97.24 221	97.15 220

pmmvs3		98.85 166	98.60 163	99.13 179	99.66 169	98.72 197	99.37 141	99.06 224	98.44 169	99.76 78	99.74 70	99.55 121	99.15 132	99.04 148	96.00 218	97.80 217	98.72 192

MVS_Test		99.09 140	98.92 142	99.29 156	99.61 181	99.07 171	99.04 184	99.81 119	98.58 155	99.37 175	99.74 70	98.87 169	98.41 176	98.61 197	98.01 196	99.50 161	99.57 87

PS-CasMVS		99.73 20	99.59 32	99.90 11	99.95 11	99.80 11	99.85 16	99.97 1	98.95 109	99.86 33	99.73 72	99.36 146	99.81 9	99.83 20	99.67 24	99.95 9	99.83 15

IterMVS-LS		99.16 133	98.82 155	99.57 106	99.87 51	99.71 28	99.58 94	99.92 43	99.24 69	99.71 106	99.73 72	95.79 195	98.91 157	98.82 185	98.66 157	99.43 171	99.77 29
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CSCG		99.61 42	99.52 43	99.71 72	99.89 39	99.62 43	99.52 106	99.76 148	99.61 23	99.69 110	99.73 72	99.96 13	99.57 69	99.27 102	98.62 161	99.81 80	99.85 14

Anonymous20231206		99.48 75	99.31 81	99.69 79	99.79 118	99.57 56	99.63 79	99.79 128	98.88 118	99.91 12	99.72 75	99.93 38	99.59 65	99.24 105	98.63 160	99.43 171	99.18 162

pmmvs4		99.34 105	99.15 114	99.57 106	99.77 129	98.90 182	99.51 109	99.77 140	99.07 95	99.73 97	99.72 75	99.84 77	99.07 140	98.85 179	98.39 175	99.55 156	99.27 155

TranMVSNet+NR-MVSNet		99.59 50	99.42 61	99.80 38	99.87 51	99.55 63	99.64 76	99.86 66	99.05 99	99.88 23	99.72 75	99.33 152	99.64 57	99.47 61	99.14 82	99.91 24	99.67 55

WR-MVS_H		99.73 20	99.61 26	99.88 15	99.95 11	99.82 6	99.83 20	99.96 14	99.01 102	99.84 44	99.71 78	99.41 143	99.74 26	99.77 30	99.70 22	99.95 9	99.82 16

FC-MVSNet-train		99.70 29	99.67 17	99.74 68	99.94 21	99.71 28	99.82 24	99.91 48	99.14 88	99.53 143	99.70 79	99.88 63	99.33 107	99.88 12	99.61 32	99.94 16	99.77 29

PEN-MVS		99.77 13	99.65 18	99.91 8	99.95 11	99.80 11	99.86 13	99.97 1	99.08 92	99.89 18	99.69 80	99.68 103	99.84 5	99.81 24	99.64 27	99.95 9	99.81 19

GA-MVS		98.59 181	98.15 185	99.09 186	99.59 189	99.13 164	98.84 208	99.52 198	98.61 152	99.35 181	99.67 81	93.03 206	97.73 196	98.90 171	98.26 181	99.51 160	99.48 113

DTE-MVSNet		99.75 17	99.61 26	99.92 4	99.95 11	99.81 9	99.86 13	99.96 14	99.18 80	99.92 8	99.66 82	99.45 137	99.85 3	99.80 25	99.56 33	99.96 3	99.79 24

ACMH	99.11 4	99.72 24	99.63 21	99.84 24	99.87 51	99.59 52	99.83 20	99.88 60	99.46 46	99.87 28	99.66 82	99.95 22	99.76 19	99.73 35	99.47 50	99.84 61	99.52 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.68 33	99.51 44	99.89 12	99.95 11	99.76 18	99.83 20	99.96 14	98.83 127	99.84 44	99.65 84	99.09 161	99.80 13	99.78 28	99.62 31	99.95 9	99.82 16

UniMVSNet (Re)		99.50 71	99.29 83	99.75 62	99.86 68	99.47 86	99.51 109	99.82 108	98.90 116	99.89 18	99.64 85	99.00 163	99.55 71	99.32 87	99.08 92	99.90 27	99.59 73

FMVSNet2		99.07 145	99.19 103	98.93 197	99.02 233	99.53 68	99.31 151	99.84 91	98.86 120	98.88 207	99.64 85	98.44 177	96.92 212	99.35 81	99.00 109	99.61 137	99.53 99

COLMAP_ROB	99.18 2	99.70 29	99.60 30	99.81 34	99.84 89	99.37 112	99.76 39	99.84 91	99.54 39	99.82 60	99.64 85	99.95 22	99.75 21	99.79 27	99.56 33	99.83 72	99.37 143
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CHOSEN 280x420		98.99 154	98.91 144	99.07 188	99.77 129	99.26 141	99.55 99	99.92 43	98.62 149	98.67 216	99.62 88	97.20 192	98.44 175	99.50 56	99.18 75	98.08 216	98.99 186

MVS-HIRNet		98.45 185	98.25 181	98.69 207	99.12 229	97.81 234	98.55 224	99.85 79	98.58 155	99.67 118	99.61 89	99.86 69	97.46 200	97.95 215	96.37 217	97.49 219	97.56 217

EPP-MVSNet		99.34 105	99.10 122	99.62 97	99.94 21	99.74 25	99.66 73	99.80 125	99.07 95	98.93 204	99.61 89	96.13 194	99.49 88	99.67 42	99.63 29	99.92 22	99.86 12

DeepPCF-MVS	98.38 11	99.16 133	99.20 100	99.12 181	99.20 228	98.71 198	98.85 207	99.06 224	99.17 81	98.96 203	99.61 89	99.86 69	99.29 115	99.17 127	98.72 150	99.36 179	99.15 170

TSAR-MVS + MP.		99.56 65	99.54 40	99.58 102	99.69 160	99.14 161	99.73 57	99.45 205	99.50 40	99.35 181	99.60 92	99.93 38	99.50 84	99.56 53	99.37 61	99.77 89	99.64 65

casdiffmvs		99.28 119	99.16 113	99.42 133	99.64 174	99.30 132	99.21 166	99.84 91	98.85 124	99.54 142	99.60 92	99.71 99	98.65 170	98.70 194	98.33 179	99.62 135	99.67 55

PMVS	94.32 17	99.27 120	99.55 38	98.94 195	99.60 185	99.43 94	99.39 135	99.54 192	98.99 104	99.69 110	99.60 92	99.81 83	95.68 226	99.88 12	99.83 3	99.73 102	99.31 149
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
USDC		99.29 118	98.98 138	99.65 86	99.72 151	98.87 186	99.47 124	99.66 178	99.35 60	99.87 28	99.58 95	99.87 68	99.51 80	98.85 179	97.93 198	99.65 118	98.38 200

CLD-MVS		99.30 114	99.01 135	99.63 92	99.75 138	98.89 185	99.35 146	99.60 182	98.53 160	99.86 33	99.57 96	99.94 32	99.52 79	98.96 160	98.10 191	99.70 110	99.08 174
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
APDe-MVS		99.60 48	99.48 47	99.73 70	99.85 81	99.51 81	99.75 45	99.85 79	99.17 81	99.81 63	99.56 97	99.94 32	99.44 96	99.42 71	99.22 71	99.67 114	99.54 94

CANet_DTU		99.03 150	99.18 106	98.87 200	99.58 192	99.03 173	99.18 169	99.41 209	98.65 144	99.74 91	99.55 98	99.71 99	96.13 224	99.19 122	98.92 120	99.17 197	99.18 162

CANet		99.36 100	99.39 69	99.34 152	99.80 110	99.35 119	99.41 133	99.47 203	99.20 75	99.74 91	99.54 99	99.68 103	98.05 188	99.23 108	98.97 112	99.57 149	99.73 42

TSAR-MVS + ACMM		99.31 112	99.26 87	99.37 145	99.66 169	98.97 179	99.20 167	99.56 189	99.33 61	99.19 191	99.54 99	99.91 53	99.32 110	99.12 135	98.34 178	99.29 185	99.65 62

test-LLR		97.74 203	97.46 194	98.08 222	99.62 176	98.37 215	98.26 228	99.41 209	97.03 219	97.38 237	99.54 99	92.89 208	95.12 229	98.78 189	97.68 205	98.65 211	97.90 210

TESTMET0.1,1		97.62 209	97.46 194	97.81 228	99.07 232	98.37 215	98.26 228	98.35 232	97.03 219	97.38 237	99.54 99	92.89 208	95.12 229	98.78 189	97.68 205	98.65 211	97.90 210

EG-PatchMatch MVS		99.59 50	99.49 46	99.70 75	99.82 105	99.26 141	99.39 135	99.83 100	98.99 104	99.93 4	99.54 99	99.92 47	99.51 80	99.78 28	99.50 45	99.73 102	99.41 130

ACMH+	98.94 6	99.69 31	99.59 32	99.81 34	99.88 44	99.41 98	99.75 45	99.86 66	99.43 49	99.80 65	99.54 99	99.97 9	99.73 30	99.82 23	99.52 44	99.85 57	99.43 125

SMA-MVS		99.43 85	99.41 62	99.45 128	99.82 105	99.31 130	99.02 189	99.59 186	99.06 97	99.34 184	99.53 105	99.96 13	99.38 100	99.29 94	99.13 85	99.53 158	99.59 73

TSAR-MVS + GP.		99.33 107	99.17 110	99.51 119	99.71 154	99.00 176	98.84 208	99.71 162	98.23 181	99.74 91	99.53 105	99.90 55	99.35 102	99.38 74	98.85 132	99.72 106	99.31 149

test-mter		97.65 208	97.57 193	97.75 230	98.90 236	98.56 207	98.15 234	98.45 231	96.92 223	96.84 240	99.52 107	92.53 213	95.24 228	99.04 148	98.12 189	98.90 206	98.29 205

testgi		99.43 85	99.47 51	99.38 142	99.90 34	99.67 39	99.30 156	99.73 157	98.64 148	99.53 143	99.52 107	99.90 55	98.08 185	99.65 46	99.40 60	99.75 94	99.55 93

DU-MVS		99.48 75	99.26 87	99.75 62	99.85 81	99.38 106	99.50 113	99.81 119	98.86 120	99.89 18	99.51 109	98.98 164	99.59 65	99.46 63	98.97 112	99.87 47	99.63 66

NR-MVSNet		99.52 68	99.29 83	99.80 38	99.96 8	99.38 106	99.55 99	99.81 119	98.86 120	99.87 28	99.51 109	98.81 170	99.72 34	99.86 17	99.04 100	99.89 32	99.54 94

Fast-Effi-MVS+		99.39 95	99.18 106	99.63 92	99.86 68	99.28 138	99.45 127	99.91 48	98.47 163	99.61 127	99.50 111	99.57 119	99.17 124	99.24 105	98.66 157	99.78 85	99.59 73

TAPA-MVS	98.54 10	99.30 114	99.24 91	99.36 151	99.44 210	98.77 193	99.00 192	99.41 209	99.23 70	99.60 131	99.50 111	99.86 69	99.15 132	99.29 94	98.95 118	99.56 152	99.08 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PM-MVS		99.49 74	99.43 58	99.57 106	99.76 134	99.34 121	99.53 103	99.77 140	98.93 113	99.75 85	99.46 113	99.83 80	99.11 138	99.72 36	99.29 66	99.49 162	99.46 117

UniMVSNet_NR-MVSNet		99.41 90	99.12 119	99.76 56	99.86 68	99.48 85	99.50 113	99.81 119	98.84 125	99.89 18	99.45 114	98.32 181	99.59 65	99.22 111	98.89 123	99.90 27	99.63 66

diffmvs		98.99 154	98.88 149	99.11 184	99.62 176	99.12 167	98.70 218	99.86 66	98.72 138	99.43 165	99.44 115	99.14 160	97.87 192	98.31 204	97.73 203	99.18 196	99.72 45

tfpnnormal		99.74 18	99.63 21	99.86 18	99.93 24	99.75 23	99.80 29	99.89 56	99.31 63	99.88 23	99.43 116	99.66 106	99.77 17	99.80 25	99.71 21	99.92 22	99.76 33

MDA-MVSNet-bldmvs		99.11 138	99.11 121	99.12 181	99.91 31	99.38 106	99.77 36	98.72 227	99.31 63	99.85 40	99.43 116	98.26 182	99.48 92	99.85 18	98.47 169	96.99 222	99.08 174

GBi-Net		98.96 156	99.05 128	98.85 201	99.02 233	99.53 68	99.31 151	99.78 134	98.13 185	98.48 224	99.43 116	97.58 187	96.92 212	99.68 39	99.50 45	99.61 137	99.53 99

test1		98.96 156	99.05 128	98.85 201	99.02 233	99.53 68	99.31 151	99.78 134	98.13 185	98.48 224	99.43 116	97.58 187	96.92 212	99.68 39	99.50 45	99.61 137	99.53 99

FMVSNet3		98.63 180	98.75 158	98.49 212	98.10 240	99.44 90	99.02 189	99.78 134	98.13 185	98.48 224	99.43 116	97.58 187	96.16 223	98.85 179	98.39 175	99.40 175	99.41 130

QAPM		99.41 90	99.21 99	99.64 91	99.78 123	99.16 158	99.51 109	99.85 79	99.20 75	99.72 101	99.43 116	99.81 83	99.25 119	98.87 173	98.71 151	99.71 108	99.30 151

tmp_tt				88.14 234	96.68 241	91.91 242	93.70 242	61.38 238	99.61 23	90.51 242	99.40 122	99.71 99	90.32 237	99.22 111	99.44 56	96.25 227

RPSCF		99.48 75	99.45 55	99.52 117	99.73 150	99.33 125	99.13 178	99.77 140	99.33 61	99.47 160	99.39 123	99.92 47	99.36 101	99.63 48	99.13 85	99.63 129	99.41 130

UA-Net		99.64 39	99.62 24	99.66 84	99.97 1	99.82 6	99.14 177	99.96 14	98.95 109	99.52 149	99.38 124	99.86 69	99.55 71	99.72 36	99.66 25	99.80 83	99.94 1

TSAR-MVS + COLMAP		98.74 170	98.58 165	98.93 197	99.29 224	98.23 221	99.04 184	99.24 220	98.79 130	98.80 210	99.37 125	99.71 99	98.06 186	98.02 213	97.46 209	99.16 198	98.48 198

ambc			98.83 152		99.72 151	98.52 208	98.84 208		98.96 108	99.92 8	99.34 126	99.74 94	99.04 144	98.68 195	97.57 208	99.46 164	98.99 186

PVSNet_BlendedMVS		99.20 127	99.17 110	99.23 165	99.69 160	99.33 125	99.04 184	99.13 222	98.41 172	99.79 68	99.33 127	99.36 146	98.10 183	99.29 94	98.87 128	99.65 118	99.56 88

PVSNet_Blended		99.20 127	99.17 110	99.23 165	99.69 160	99.33 125	99.04 184	99.13 222	98.41 172	99.79 68	99.33 127	99.36 146	98.10 183	99.29 94	98.87 128	99.65 118	99.56 88

UGNet		99.40 93	99.61 26	99.16 177	99.88 44	99.64 42	99.61 85	99.77 140	99.31 63	99.63 124	99.33 127	99.93 38	96.46 218	99.63 48	99.53 43	99.63 129	99.89 5
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
PCF-MVS	97.86 15	98.95 158	98.53 169	99.44 131	99.70 158	98.80 190	98.96 195	99.69 167	98.65 144	99.59 133	99.33 127	99.94 32	99.12 137	98.01 214	97.11 210	99.59 146	97.83 212
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OPM-MVS		99.39 95	99.22 95	99.59 100	99.76 134	98.82 188	99.51 109	99.79 128	99.17 81	99.53 143	99.31 131	99.95 22	99.35 102	99.22 111	98.79 145	99.60 140	99.27 155

test0.0.03 1		98.41 186	98.41 177	98.40 216	99.62 176	99.16 158	98.87 205	99.41 209	97.15 215	96.60 241	99.31 131	97.00 193	96.55 217	98.91 167	98.51 168	99.37 178	98.82 189

MVSTER		97.55 210	96.75 204	98.48 213	99.46 207	99.54 66	98.24 230	99.77 140	97.56 206	99.41 170	99.31 131	84.86 233	94.66 231	98.86 177	97.75 201	99.34 182	99.38 139

tpmrst		96.18 225	94.47 224	98.18 219	99.52 196	97.89 232	98.96 195	99.79 128	98.07 190	99.16 192	99.30 134	92.69 212	96.69 215	90.76 234	88.85 233	94.96 233	93.69 233

MIMVSNet		99.00 152	99.03 131	98.97 193	99.32 222	99.32 129	99.39 135	99.91 48	98.41 172	98.76 211	99.24 135	99.17 159	97.13 205	99.30 90	98.80 143	99.29 185	99.01 183

MSDG		99.32 109	99.09 124	99.58 102	99.75 138	98.74 195	99.36 143	99.54 192	99.14 88	99.72 101	99.24 135	99.89 58	99.51 80	99.30 90	98.76 146	99.62 135	98.54 196

MVS_0304		99.36 100	99.35 77	99.37 145	99.85 81	99.36 115	99.39 135	99.56 189	99.36 59	99.75 85	99.23 137	99.90 55	97.97 191	99.00 152	98.83 134	99.69 111	99.77 29

v1.0		91.57 232	84.95 235	99.29 156	99.79 118	99.44 90	99.02 189	99.79 128	97.96 195	99.12 196	99.22 138	99.95 22	98.50 173	99.21 114	98.84 133	99.56 152	0.00 240

MDTV_nov1_ep13		97.41 212	96.26 215	98.76 205	99.47 206	98.43 213	99.26 162	99.82 108	98.06 191	99.23 188	99.22 138	92.86 210	98.05 188	95.33 227	93.66 222	96.73 224	96.26 222

tpm		96.56 221	94.68 223	98.74 206	99.12 229	97.90 231	98.79 213	99.93 35	96.79 227	99.69 110	99.19 140	81.48 235	97.56 198	95.46 226	93.97 221	97.37 220	97.99 209

DeepC-MVS_fast	98.69 9	99.32 109	99.13 117	99.53 113	99.63 175	98.78 191	99.53 103	99.33 217	99.08 92	99.77 75	99.18 141	99.89 58	99.29 115	99.00 152	98.70 153	99.65 118	99.30 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous20231211		99.47 78	99.39 69	99.57 106	99.89 39	99.60 48	99.50 113	99.69 167	98.91 115	99.62 125	99.17 142	99.35 149	98.86 162	99.63 48	99.46 52	99.84 61	99.62 70

PHI-MVS		99.33 107	99.19 103	99.49 124	99.69 160	99.25 144	99.27 160	99.59 186	98.44 169	99.78 74	99.15 143	99.92 47	98.95 156	99.39 73	99.04 100	99.64 127	99.18 162

TinyColmap		99.21 125	98.89 146	99.59 100	99.61 181	98.61 204	99.47 124	99.67 174	99.02 101	99.82 60	99.15 143	99.74 94	99.35 102	99.17 127	98.33 179	99.63 129	98.22 206

RPMNet		97.70 204	96.54 208	99.06 189	99.57 195	98.23 221	98.95 198	99.97 1	96.89 224	99.49 156	99.13 145	89.63 218	97.09 207	96.68 225	97.02 212	99.26 188	98.19 207

OMC-MVS		99.11 138	98.95 140	99.29 156	99.37 217	98.57 206	99.19 168	99.20 221	98.87 119	99.58 137	99.13 145	99.88 63	99.00 146	99.19 122	98.46 170	99.43 171	98.57 194

Effi-MVS+		99.20 127	98.93 141	99.50 121	99.79 118	99.26 141	98.82 211	99.96 14	98.37 175	99.60 131	99.12 147	98.36 179	99.05 143	98.93 162	98.82 137	99.78 85	99.68 52

LP		97.43 211	96.28 214	98.77 204	99.69 160	98.92 181	99.49 120	99.70 164	98.53 160	99.82 60	99.12 147	95.67 197	97.30 203	94.65 228	91.76 223	96.65 226	95.34 227

MVS_111021_LR		99.25 122	99.13 117	99.39 139	99.50 203	99.14 161	99.23 164	99.50 200	98.67 142	99.61 127	99.12 147	99.81 83	99.16 128	99.28 99	98.67 156	99.35 181	99.21 160

CR-MVSNet		97.91 194	96.80 203	99.22 170	99.60 185	98.23 221	98.91 201	99.97 1	96.89 224	99.43 165	99.10 150	89.24 219	98.15 180	98.04 211	97.78 199	99.26 188	98.30 203

DI_MVS_plusplus_trai		98.74 170	98.08 188	99.51 119	99.79 118	99.29 137	99.61 85	99.60 182	99.20 75	99.46 161	99.09 151	92.93 207	98.97 153	98.27 208	98.35 177	99.65 118	99.45 118

ADS-MVSNet		97.29 213	96.17 216	98.59 209	99.59 189	98.70 199	99.32 149	99.86 66	98.47 163	99.56 139	99.08 152	98.16 183	97.34 202	92.92 229	91.17 226	95.91 228	94.72 229

Vis-MVSNet (Re-imp)		99.40 93	99.28 85	99.55 111	99.92 26	99.68 36	99.31 151	99.87 62	98.69 140	99.16 192	99.08 152	98.64 173	99.20 123	99.65 46	99.46 52	99.83 72	99.72 45

EPNet_dtu		98.09 192	98.25 181	97.91 226	99.58 192	98.02 229	98.19 232	99.67 174	97.94 196	99.74 91	99.07 154	98.71 172	93.40 235	97.50 219	97.09 211	96.89 223	99.44 121
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		96.76 218	95.30 220	98.46 214	99.42 213	98.47 211	99.32 149	99.91 48	98.42 171	99.51 153	99.07 154	92.81 211	97.12 206	92.39 232	91.71 224	95.51 230	94.20 231

PatchT		98.11 190	97.12 199	99.26 163	99.65 172	98.34 217	99.57 96	99.97 1	97.48 209	99.43 165	99.04 156	90.84 216	98.15 180	98.04 211	97.78 199	98.82 207	98.30 203

ACMMP_Plus		99.47 78	99.33 79	99.63 92	99.85 81	99.28 138	99.56 97	99.83 100	98.75 133	99.48 157	99.03 157	99.95 22	99.47 95	99.48 58	99.19 74	99.57 149	99.59 73

MVS_111021_HR		99.30 114	99.14 115	99.48 125	99.58 192	99.25 144	99.27 160	99.61 180	98.74 134	99.66 120	99.02 158	99.84 77	99.33 107	99.20 117	98.76 146	99.44 168	99.18 162

HSP-MVS		99.27 120	99.07 126	99.50 121	99.76 134	99.54 66	99.73 57	99.72 159	98.94 111	99.23 188	98.96 159	99.96 13	98.91 157	98.72 193	97.71 204	99.63 129	99.66 58

tfpn_n400		99.08 141	98.56 166	99.70 75	99.85 81	99.56 61	99.63 79	99.86 66	99.21 73	99.37 175	98.95 160	94.24 200	99.55 71	99.20 117	99.29 66	99.93 18	99.44 121

tfpnconf		99.08 141	98.56 166	99.70 75	99.85 81	99.56 61	99.63 79	99.86 66	99.21 73	99.37 175	98.95 160	94.24 200	99.55 71	99.20 117	99.29 66	99.93 18	99.44 121

tfpnview11		99.04 149	98.49 174	99.68 80	99.84 89	99.58 54	99.56 97	99.86 66	98.86 120	99.37 175	98.95 160	94.24 200	99.54 75	98.87 173	99.54 41	99.91 24	99.39 138

zzz-MVS		99.51 69	99.36 76	99.68 80	99.88 44	99.38 106	99.53 103	99.84 91	99.11 91	99.59 133	98.93 163	99.95 22	99.58 68	99.44 69	99.21 73	99.65 118	99.52 104

LGP-MVS_train		99.46 82	99.18 106	99.78 48	99.87 51	99.25 144	99.71 68	99.87 62	98.02 192	99.79 68	98.90 164	99.96 13	99.66 51	99.49 57	99.17 77	99.79 84	99.49 110

HFP-MVS		99.46 82	99.30 82	99.65 86	99.82 105	99.25 144	99.50 113	99.82 108	99.23 70	99.58 137	98.86 165	99.94 32	99.56 70	99.14 133	99.12 88	99.63 129	99.56 88

FPMVS		98.48 184	98.83 152	98.07 224	99.09 231	97.98 230	99.07 183	98.04 236	98.99 104	99.22 190	98.85 166	99.43 140	93.79 233	99.66 44	99.11 89	99.24 190	97.76 214

SD-MVS		99.35 103	99.26 87	99.46 126	99.66 169	99.15 160	98.92 200	99.67 174	99.55 38	99.35 181	98.83 167	99.91 53	99.35 102	99.19 122	98.53 166	99.78 85	99.68 52

CostFormer		95.61 226	93.35 231	98.24 218	99.48 205	98.03 228	98.65 219	99.83 100	96.93 222	99.42 169	98.83 167	83.65 234	97.08 208	90.39 235	89.54 232	94.94 234	96.11 224

PatchmatchNet		96.81 216	95.41 218	98.43 215	99.43 212	98.30 218	99.23 164	99.93 35	98.19 182	99.64 122	98.81 169	93.50 204	97.43 201	92.89 231	90.78 228	94.94 234	95.41 226
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PMMVS		98.71 175	98.55 168	98.90 199	99.28 225	98.45 212	98.53 225	99.45 205	97.67 204	99.15 195	98.76 170	99.54 124	97.79 193	98.77 191	98.23 182	99.16 198	98.46 199

IS_MVSNet		99.15 135	99.12 119	99.19 175	99.92 26	99.73 27	99.55 99	99.86 66	98.45 168	96.91 239	98.74 171	98.33 180	99.02 145	99.54 55	99.47 50	99.88 36	99.61 71

ACMMPR		99.51 69	99.32 80	99.72 71	99.87 51	99.33 125	99.61 85	99.85 79	99.19 78	99.73 97	98.73 172	99.95 22	99.61 62	99.35 81	99.14 82	99.66 116	99.58 83

HPM-MVS++		99.23 123	98.98 138	99.53 113	99.75 138	99.02 175	99.44 128	99.77 140	98.65 144	99.52 149	98.72 173	99.92 47	99.33 107	98.77 191	98.40 174	99.40 175	99.36 144

tfpn1000		98.73 173	98.07 189	99.50 121	99.84 89	99.61 46	99.48 122	99.84 91	98.71 139	98.74 212	98.71 174	91.70 214	99.17 124	98.81 186	99.55 39	99.90 27	99.43 125

SteuartSystems-ACMMP		99.47 78	99.22 95	99.76 56	99.88 44	99.36 115	99.65 75	99.84 91	98.47 163	99.80 65	98.68 175	99.96 13	99.68 39	99.37 76	99.06 94	99.72 106	99.66 58
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		99.41 90	99.20 100	99.65 86	99.80 110	99.23 151	99.44 128	99.75 156	98.60 153	99.74 91	98.66 176	99.93 38	99.48 92	99.33 85	99.16 79	99.73 102	99.48 113

MVE	91.08 18	97.68 207	97.65 191	97.71 232	98.46 239	91.62 243	97.92 239	98.86 226	98.73 136	97.99 235	98.64 177	99.96 13	99.17 124	99.59 52	97.75 201	93.87 239	97.27 218
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Anonymous202405211			99.14 115		99.87 51	99.55 63	99.50 113	99.70 164	98.55 159		98.61 178	98.46 175	98.76 165	99.66 44	99.50 45	99.85 57	99.63 66

MS-PatchMatch		98.94 159	98.71 160	99.21 172	99.52 196	98.22 224	98.97 194	99.53 197	98.76 131	99.50 155	98.59 179	99.56 120	98.68 168	98.63 196	98.45 172	99.05 202	98.73 190

3Dnovator	99.16 3	99.42 88	99.22 95	99.65 86	99.78 123	99.13 164	99.50 113	99.85 79	99.40 54	99.80 65	98.59 179	99.79 90	99.30 114	99.20 117	99.06 94	99.71 108	99.35 146

MCST-MVS		99.17 130	98.82 155	99.57 106	99.75 138	98.70 199	99.25 163	99.69 167	98.62 149	99.59 133	98.54 181	99.79 90	99.53 76	98.48 201	98.15 187	99.64 127	99.43 125

Effi-MVS+-dtu		99.01 151	99.05 128	98.98 191	99.60 185	99.13 164	99.03 188	99.61 180	98.52 162	99.01 199	98.53 182	99.83 80	96.95 211	99.48 58	98.59 164	99.66 116	99.25 159

ACMM	98.37 12	99.47 78	99.23 92	99.74 68	99.86 68	99.19 156	99.68 71	99.86 66	99.16 85	99.71 106	98.52 183	99.95 22	99.62 61	99.35 81	99.02 102	99.74 98	99.42 129
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.39 95	99.28 85	99.52 117	99.77 129	99.39 101	99.55 99	99.82 108	98.93 113	99.64 122	98.52 183	99.67 105	98.58 171	99.74 34	99.63 29	99.75 94	99.06 177

Fast-Effi-MVS+-dtu		98.82 167	98.80 157	98.84 203	99.51 201	98.90 182	98.96 195	99.91 48	98.29 178	99.11 197	98.47 185	99.63 109	96.03 225	99.21 114	98.12 189	99.52 159	99.01 183

OpenMVS	98.82 8	99.17 130	98.85 151	99.53 113	99.75 138	99.06 172	99.36 143	99.82 108	98.28 179	99.76 78	98.47 185	99.61 110	98.91 157	98.80 187	98.70 153	99.60 140	99.04 182

MP-MVS		99.35 103	99.09 124	99.65 86	99.84 89	99.22 152	99.59 90	99.78 134	98.13 185	99.67 118	98.44 187	99.93 38	99.43 98	99.31 89	99.09 91	99.60 140	99.49 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		99.00 152	98.68 161	99.37 145	99.68 167	99.42 97	98.94 199	99.89 56	99.00 103	98.99 200	98.43 188	95.69 196	98.96 155	99.18 125	99.18 75	99.74 98	99.88 9

ACMP	98.32 13	99.44 84	99.18 106	99.75 62	99.83 100	99.18 157	99.64 76	99.83 100	98.81 129	99.79 68	98.42 189	99.96 13	99.64 57	99.46 63	98.98 111	99.74 98	99.44 121
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tfpn_ndepth		98.67 177	98.03 190	99.42 133	99.65 172	99.50 82	99.29 158	99.78 134	98.17 184	99.04 198	98.36 190	93.29 205	98.88 160	98.46 202	99.26 69	99.88 36	99.14 171

CMPMVS	76.62 19	98.64 178	98.60 163	98.68 208	99.33 220	97.07 236	98.11 237	98.50 230	97.69 203	99.26 187	98.35 191	99.66 106	97.62 197	99.43 70	99.02 102	99.24 190	99.01 183
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
APD-MVS		99.17 130	98.92 142	99.46 126	99.78 123	99.24 149	99.34 147	99.78 134	97.79 200	99.48 157	98.25 192	99.88 63	98.77 164	99.18 125	98.92 120	99.63 129	99.18 162
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
tfpn111		98.25 189	97.29 197	99.37 145	99.74 146	99.52 74	99.17 170	99.76 148	96.10 234	98.65 218	98.23 193	89.10 220	99.00 146	99.11 136	99.56 33	99.88 36	99.41 130

thresconf0.02		98.10 191	96.83 202	99.58 102	99.71 154	99.28 138	99.40 134	99.72 159	98.65 144	99.39 172	98.23 193	86.73 230	99.43 98	97.73 216	98.17 186	99.86 53	99.05 179

PGM-MVS		99.32 109	98.99 136	99.71 72	99.86 68	99.31 130	99.59 90	99.86 66	97.51 207	99.75 85	98.23 193	99.94 32	99.53 76	99.29 94	99.08 92	99.65 118	99.54 94

CDPH-MVS		99.05 147	98.63 162	99.54 112	99.75 138	98.78 191	99.59 90	99.68 172	97.79 200	99.37 175	98.20 196	99.86 69	99.14 134	98.58 198	98.01 196	99.68 112	99.16 168

CNVR-MVS		99.08 141	98.83 152	99.37 145	99.61 181	98.74 195	99.15 175	99.54 192	98.59 154	99.37 175	98.15 197	99.88 63	99.08 139	98.91 167	98.46 170	99.48 163	99.06 177

E-PMN		96.72 219	95.78 217	97.81 228	99.45 208	95.46 239	98.14 236	98.33 234	97.99 193	98.73 213	98.09 198	98.97 165	97.54 199	97.45 220	91.09 227	94.70 236	91.40 235

testus		98.74 170	98.33 178	99.23 165	99.71 154	99.03 173	98.17 233	99.60 182	97.18 214	99.52 149	98.07 199	98.45 176	99.21 122	98.30 205	98.06 194	99.14 200	99.21 160

tpm cat1		95.52 228	93.49 229	97.88 227	99.28 225	97.87 233	98.65 219	99.77 140	97.27 213	99.46 161	98.04 200	90.99 215	95.46 227	88.57 238	88.14 236	94.64 237	93.54 234

3Dnovator+	98.92 7	99.31 112	99.03 131	99.63 92	99.77 129	98.90 182	99.52 106	99.81 119	99.37 57	99.72 101	98.03 201	99.73 97	99.32 110	98.99 155	98.81 142	99.67 114	99.36 144

test2356		96.34 224	94.05 227	99.00 190	99.39 215	98.28 219	98.15 234	99.51 199	96.23 231	99.16 192	97.95 202	73.39 239	98.75 166	97.07 223	96.86 214	99.06 201	98.57 194

train_agg		98.89 163	98.48 175	99.38 142	99.69 160	98.76 194	99.31 151	99.60 182	97.71 202	98.98 201	97.89 203	99.89 58	99.29 115	98.32 203	97.59 207	99.42 174	99.16 168

EPNet		98.06 193	98.11 187	98.00 225	99.60 185	98.99 178	98.38 226	99.68 172	98.18 183	98.85 209	97.89 203	95.60 198	92.72 236	98.30 205	98.10 191	98.76 208	99.72 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20240521		99.43 85	99.23 92	99.67 82	99.92 26	99.76 18	99.64 76	99.93 35	99.06 97	99.68 117	97.77 205	98.97 165	98.97 153	99.72 36	99.54 41	99.88 36	99.81 19

dps		95.59 227	93.46 230	98.08 222	99.33 220	98.22 224	98.87 205	99.70 164	96.17 232	98.87 208	97.75 206	86.85 229	96.60 216	91.24 233	89.62 231	95.10 232	94.34 230

ACMMP		99.36 100	99.06 127	99.71 72	99.86 68	99.36 115	99.63 79	99.85 79	98.33 176	99.72 101	97.73 207	99.94 32	99.53 76	99.37 76	99.13 85	99.65 118	99.56 88
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
PatchMatch-RL		98.80 169	98.52 171	99.12 181	99.38 216	98.70 199	98.56 222	99.55 191	97.81 199	99.34 184	97.57 208	99.31 153	98.67 169	99.27 102	98.62 161	99.22 192	98.35 202

CNLPA		98.82 167	98.52 171	99.18 176	99.21 227	98.50 210	98.73 216	99.34 216	98.73 136	99.56 139	97.55 209	99.42 141	99.06 142	98.93 162	98.10 191	99.21 193	98.38 200

CPTT-MVS		99.21 125	98.89 146	99.58 102	99.72 151	99.12 167	99.30 156	99.76 148	98.62 149	99.66 120	97.51 210	99.89 58	99.48 92	99.01 150	98.64 159	99.58 148	99.40 137

EMVS		96.47 222	95.38 219	97.74 231	99.42 213	95.37 240	98.07 238	98.27 235	97.85 198	98.90 206	97.48 211	98.73 171	97.20 204	97.21 222	90.39 229	94.59 238	90.65 236

tpmp4_e23		95.42 229	92.99 232	98.27 217	99.32 222	97.77 235	98.74 215	99.79 128	97.11 217	99.61 127	97.47 212	80.64 236	96.36 222	92.92 229	88.79 234	95.80 229	96.19 223

AdaColmap		98.93 160	98.53 169	99.39 139	99.52 196	98.65 202	99.11 180	99.59 186	98.08 189	99.44 163	97.46 213	99.45 137	99.24 120	98.92 164	98.44 173	99.44 168	98.73 190

HQP-MVS		98.70 176	98.19 184	99.28 161	99.61 181	98.52 208	98.71 217	99.35 214	97.97 194	99.53 143	97.38 214	99.85 75	99.14 134	97.53 218	96.85 215	99.36 179	99.26 158

DWT-MVSNet_training		94.92 230	92.14 233	98.15 221	99.37 217	98.43 213	98.99 193	98.51 229	96.76 228	99.52 149	97.35 215	77.20 237	97.08 208	89.76 236	90.38 230	95.43 231	95.13 228

X-MVS		99.30 114	98.99 136	99.66 84	99.85 81	99.30 132	99.49 120	99.82 108	98.32 177	99.69 110	97.31 216	99.93 38	99.50 84	99.37 76	99.16 79	99.60 140	99.53 99

conf0.05thres1000		98.36 188	97.28 198	99.63 92	99.92 26	99.74 25	99.66 73	99.88 60	98.68 141	98.92 205	97.30 217	86.02 232	99.49 88	99.77 30	99.73 18	99.93 18	99.69 51

abl_6				99.21 172	99.49 204	98.62 203	98.90 203	99.44 207	97.08 218	99.61 127	97.19 218	99.73 97	98.35 177			99.45 166	98.84 188

MAR-MVS		98.54 182	98.15 185	98.98 191	99.37 217	98.09 227	98.56 222	99.65 179	96.11 233	99.27 186	97.16 219	99.50 130	98.03 190	98.87 173	98.23 182	99.01 203	99.13 172
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
Gipuma		99.55 67	99.23 92	99.91 8	99.87 51	99.52 74	99.86 13	99.93 35	99.87 1	99.96 2	96.72 220	99.55 121	99.97 1	99.77 30	99.46 52	99.87 47	99.74 39
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PLC	97.83 16	98.88 164	98.52 171	99.30 155	99.45 208	98.60 205	98.65 219	99.49 201	98.66 143	99.59 133	96.33 221	99.59 116	99.17 124	98.87 173	98.53 166	99.46 164	99.05 179
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSLP-MVS++		98.92 161	98.73 159	99.14 178	99.44 210	99.00 176	98.36 227	99.35 214	98.82 128	99.38 174	96.06 222	99.79 90	99.07 140	98.88 172	99.05 97	99.27 187	99.53 99

NCCC		98.88 164	98.42 176	99.42 133	99.62 176	98.81 189	99.10 181	99.54 192	98.76 131	99.53 143	95.97 223	99.80 88	99.16 128	98.49 200	98.06 194	99.55 156	99.05 179

DeepMVS_CX						96.39 238	97.15 240	88.89 237	97.94 196	99.51 153	95.71 224	97.88 185	98.19 178	98.92 164		97.73 218	97.75 215

FMVSNet5		97.69 205	96.98 200	98.53 211	98.53 238	99.36 115	98.90 203	99.54 192	96.38 230	98.44 227	95.38 225	90.08 217	97.05 210	99.46 63	99.06 94	98.73 209	99.12 173

GG-mvs-BLEND		70.44 234	96.91 201	39.57 236	3.32 244	96.51 237	91.01 243	4.05 241	97.03 219	33.20 243	94.67 226	97.75 186	7.59 241	98.28 207	96.85 215	98.24 214	97.26 219

testpf		93.65 231	91.79 234	95.82 233	98.71 237	93.25 241	96.38 241	99.67 174	95.38 240	97.83 236	94.48 227	87.69 226	89.61 238	88.96 237	88.79 234	92.71 240	93.97 232

tfpn		96.77 217	94.47 224	99.45 128	99.88 44	99.62 43	99.46 126	99.83 100	97.61 205	98.27 231	94.22 228	71.45 242	99.34 106	99.32 87	99.46 52	99.90 27	99.58 83

view800		97.89 195	96.56 205	99.45 128	99.86 68	99.57 56	99.42 130	99.80 125	97.50 208	98.40 229	93.78 229	86.63 231	99.31 112	99.24 105	99.68 23	99.89 32	99.54 94

view600		97.88 196	96.55 207	99.44 131	99.84 89	99.52 74	99.38 139	99.76 148	97.36 211	98.50 223	93.29 230	87.31 227	99.26 118	99.13 134	99.76 14	99.88 36	99.48 113

thres600view7		97.86 198	96.53 211	99.41 137	99.84 89	99.52 74	99.36 143	99.76 148	97.32 212	98.38 230	93.24 231	87.25 228	99.23 121	99.11 136	99.75 16	99.88 36	99.48 113

conf200view11		97.85 199	96.54 208	99.37 145	99.74 146	99.52 74	99.17 170	99.76 148	96.10 234	98.65 218	92.99 232	89.10 220	99.00 146	99.11 136	99.56 33	99.88 36	99.41 130

thres100view900		97.69 205	96.37 213	99.23 165	99.74 146	99.21 155	98.81 212	99.43 208	96.10 234	98.70 214	92.99 232	89.10 220	98.88 160	98.58 198	99.31 65	99.82 76	99.27 155

tfpn200view9		97.85 199	96.54 208	99.38 142	99.74 146	99.52 74	99.17 170	99.76 148	96.10 234	98.70 214	92.99 232	89.10 220	99.00 146	99.11 136	99.56 33	99.88 36	99.41 130

thres200		97.87 197	96.56 205	99.39 139	99.76 134	99.52 74	99.13 178	99.76 148	96.88 226	98.66 217	92.87 235	88.77 224	99.16 128	99.11 136	99.42 57	99.88 36	99.33 147

thres400		97.82 201	96.47 212	99.40 138	99.81 109	99.44 90	99.29 158	99.69 167	97.15 215	98.57 220	92.82 236	87.96 225	99.16 128	98.96 160	99.55 39	99.86 53	99.41 130

conf0.01		96.70 220	94.44 226	99.34 152	99.71 154	99.46 87	99.17 170	99.73 157	96.10 234	98.53 221	91.96 237	75.75 238	99.00 146	98.85 179	99.56 33	99.87 47	99.38 139

conf0.002		96.39 223	93.87 228	99.33 154	99.70 158	99.45 88	99.17 170	99.71 162	96.10 234	98.51 222	91.88 238	72.65 241	99.00 146	98.80 187	98.82 137	99.87 47	99.38 139

IB-MVS	98.10 14	97.76 202	97.40 196	98.18 219	99.62 176	99.11 169	98.24 230	98.35 232	96.56 229	99.44 163	91.28 239	98.96 167	93.84 232	98.09 210	98.62 161	99.56 152	99.18 162
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
testmvs		22.33 235	29.66 237	13.79 237	8.97 242	10.35 244	15.53 246	8.09 240	32.51 241	19.87 244	45.18 240	30.56 246	17.05 240	29.96 239	24.74 237	13.21 241	34.30 237

test123		21.52 236	28.47 238	13.42 238	7.29 243	10.12 245	15.70 245	8.31 239	31.54 242	19.34 245	36.33 241	37.40 245	17.14 239	27.45 240	23.17 239	12.73 243	33.30 239

sosnet-low-res		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 240

sosnet		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 240

our_test_3					99.75 138	99.11 169	99.74 53

MTAPA										99.62 125		99.95 22

MTMP										99.53 143		99.92 47

Patchmatch-RL test							65.75 244

XVS					99.86 68	99.30 132	99.72 62			99.69 110		99.93 38				99.60 140

X-MVStestdata					99.86 68	99.30 132	99.72 62			99.69 110		99.93 38				99.60 140

mPP-MVS					99.84 89							99.92 47

NP-MVS									97.37 210

Patchmtry						98.19 226	98.91 201	99.97 1		99.43 165

High-resolution multi-view benchmark