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		98.40 179	98.26 172	98.57 196	99.83 89	98.86 172	98.77 197	99.97 1	99.57 25	99.99 1	99.99 1	93.81 197	93.50 217	98.91 155	98.20 172	99.33 171	98.52 183

v7n		99.89 2	99.86 4	99.93 1	99.97 2	99.83 4	99.93 1	99.96 12	99.77 4	99.89 17	99.99 1	99.86 79	99.84 5	99.89 11	99.81 12	99.97 1	99.88 7

new-patchmatchnet		98.49 175	97.60 184	99.53 99	99.90 40	99.55 56	99.77 33	99.48 182	99.67 11	99.86 35	99.98 3	99.98 5	99.50 68	96.90 208	91.52 212	98.67 194	95.62 212

anonymousdsp		99.87 6	99.86 4	99.88 12	99.95 11	99.75 28	99.90 4	99.96 12	99.69 8	99.83 52	99.96 4	99.99 3	99.74 21	99.95 2	99.83 8	99.91 24	99.88 7

v1240		99.58 48	99.38 66	99.82 27	99.89 43	99.49 75	99.82 24	99.83 83	99.63 18	99.86 35	99.96 4	98.92 164	99.75 18	99.15 127	98.96 110	99.76 73	99.56 84

v1921920		99.59 45	99.40 62	99.82 27	99.88 49	99.45 80	99.81 26	99.83 83	99.65 14	99.86 35	99.95 6	99.29 152	99.75 18	98.98 146	98.86 121	99.78 67	99.59 69

v1192		99.60 43	99.41 59	99.82 27	99.89 43	99.43 85	99.81 26	99.84 77	99.63 18	99.85 42	99.95 6	99.35 147	99.72 26	99.01 140	98.90 116	99.82 55	99.58 77

FC-MVSNet-test		99.84 7	99.80 7	99.89 9	99.96 7	99.83 4	99.84 16	99.95 23	99.37 48	99.77 69	99.95 6	99.96 14	99.85 3	99.93 3	99.83 8	99.95 8	99.72 40

v1144		99.61 38	99.43 55	99.82 27	99.88 49	99.41 90	99.76 36	99.86 60	99.64 16	99.84 46	99.95 6	99.49 135	99.74 21	99.00 142	98.93 113	99.84 47	99.58 77

v2v482		99.56 53	99.35 69	99.81 32	99.87 55	99.35 106	99.75 40	99.85 67	99.56 26	99.87 28	99.95 6	99.44 139	99.66 36	98.91 155	98.76 131	99.86 41	99.45 110

v10		99.65 33	99.51 42	99.81 32	99.83 89	99.61 45	99.75 40	99.94 24	99.56 26	99.76 72	99.94 11	99.60 126	99.73 24	99.11 131	99.01 101	99.85 44	99.74 35

SixPastTwentyTwo		99.89 2	99.85 6	99.93 1	99.97 2	99.88 3	99.92 2	99.97 1	99.66 13	99.94 4	99.94 11	99.74 108	99.81 7	99.97 1	99.89 1	99.96 3	99.89 5

HyFIR lowres test		99.50 60	99.26 82	99.80 37	99.95 11	99.62 43	99.76 36	99.97 1	99.67 11	99.56 130	99.94 11	98.40 173	99.78 12	98.84 171	98.59 151	99.76 73	99.72 40

v144192		99.58 48	99.39 63	99.80 37	99.87 55	99.44 82	99.77 33	99.84 77	99.64 16	99.86 35	99.93 14	99.35 147	99.72 26	98.92 152	98.82 125	99.74 81	99.66 54

CHOSEN 1792x2688		99.65 33	99.55 36	99.77 47	99.93 28	99.60 46	99.79 30	99.92 38	99.73 6	99.74 82	99.93 14	99.98 5	99.80 10	98.83 172	99.01 101	99.45 154	99.76 31

IterMVS-SCA-FT		99.15 130	98.96 133	99.38 126	99.87 55	99.54 59	99.53 89	99.79 109	98.94 103	99.82 55	99.92 16	97.65 183	98.82 141	98.95 149	98.26 168	98.45 197	99.47 108

pmmvs6		99.88 4	99.87 2	99.89 9	99.97 2	99.76 22	99.89 5	99.96 12	99.82 3	99.90 15	99.92 16	99.95 26	99.68 32	99.93 3	99.88 3	99.95 8	99.86 11

pmmvs5		99.58 48	99.47 50	99.70 65	99.84 83	99.50 74	99.58 80	99.80 106	98.98 98	99.73 88	99.92 16	99.81 94	99.49 73	99.28 101	99.05 95	99.77 71	99.73 37

gm-plane-assit		96.82 206	94.84 214	99.13 163	99.95 11	99.78 18	99.69 56	99.92 38	99.19 69	99.84 46	99.92 16	72.93 228	96.44 205	98.21 193	97.01 198	98.92 189	96.87 208

pm-mvs1		99.77 12	99.69 13	99.86 17	99.94 25	99.68 37	99.84 16	99.93 27	99.59 22	99.87 28	99.92 16	99.21 155	99.65 38	99.88 15	99.77 16	99.93 20	99.78 27

test20.03		99.68 30	99.60 28	99.76 48	99.91 36	99.70 36	99.68 57	99.87 56	99.05 90	99.88 22	99.92 16	99.88 71	99.50 68	99.77 34	99.42 54	99.75 76	99.49 102

MIMVSNet1		99.79 10	99.75 11	99.84 22	99.89 43	99.83 4	99.84 16	99.89 51	99.31 54	99.93 5	99.92 16	99.97 9	99.68 32	99.89 11	99.64 28	99.82 55	99.66 54

test_part1		99.88 4	99.89 1	99.88 12	99.96 7	99.90 2	99.83 19	99.97 1	99.84 2	99.93 5	99.91 23	99.83 89	99.63 44	99.89 11	99.88 3	99.96 3	99.95 1

EU-MVSNet		99.76 14	99.74 12	99.78 42	99.82 94	99.81 12	99.88 7	99.87 56	99.31 54	99.75 76	99.91 23	99.76 107	99.78 12	99.84 22	99.74 19	99.56 135	99.81 20

PMMVS2		99.23 116	99.22 91	99.24 148	99.80 100	99.14 144	99.50 100	99.82 90	99.12 81	98.41 216	99.91 23	99.98 5	98.51 157	99.48 63	98.76 131	99.38 165	98.14 194

Baseline_NR-MVSNet		99.62 37	99.48 47	99.78 42	99.85 77	99.76 22	99.59 76	99.82 90	98.84 117	99.88 22	99.91 23	99.04 158	99.61 47	99.46 67	99.78 14	99.94 16	99.60 67

IterMVS		99.08 136	98.90 140	99.29 141	99.87 55	99.53 62	99.52 93	99.77 121	98.94 103	99.75 76	99.91 23	97.52 187	98.72 149	98.86 164	98.14 175	98.09 200	99.43 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v8		99.61 38	99.45 53	99.79 41	99.80 100	99.59 49	99.73 48	99.93 27	99.48 35	99.77 69	99.90 28	99.48 136	99.67 35	99.11 131	98.89 117	99.84 47	99.73 37

casdiffmvs		99.61 38	99.55 36	99.68 68	99.89 43	99.53 62	99.64 65	99.68 148	99.51 32	99.62 116	99.90 28	99.96 14	99.37 84	99.28 101	99.25 63	99.88 33	99.44 113

TransMVSNet (Re)		99.72 24	99.59 30	99.88 12	99.95 11	99.76 22	99.88 7	99.94 24	99.58 24	99.92 10	99.90 28	98.55 170	99.65 38	99.89 11	99.76 17	99.95 8	99.70 48

test1111		99.21 119	98.67 157	99.84 22	99.96 7	99.82 8	99.72 52	99.94 24	99.54 30	99.78 67	99.89 31	91.89 210	99.69 31	99.93 3	99.89 1	99.95 8	99.75 33

V42		99.57 52	99.41 59	99.75 52	99.84 83	99.37 100	99.73 48	99.83 83	99.41 42	99.75 76	99.89 31	99.42 141	99.60 49	99.15 127	98.96 110	99.76 73	99.65 57

CVMVSNet		99.06 139	98.88 144	99.28 145	99.52 179	99.53 62	99.42 116	99.69 142	98.74 128	98.27 218	99.89 31	95.48 195	99.44 80	99.46 67	99.33 59	99.32 172	99.75 33

LTVRE_ROB	99.39 1	99.90 1	99.87 2	99.93 1	99.97 2	99.82 8	99.91 3	99.92 38	99.75 5	99.93 5	99.89 31	100.00 1	99.87 2	99.93 3	99.82 11	99.96 3	99.90 3
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 38	99.48 47	99.75 52	99.87 55	99.30 116	99.75 40	99.89 51	99.23 62	99.85 42	99.88 35	99.97 9	99.49 73	99.46 67	99.01 101	99.68 95	99.52 99

WR-MVS		99.79 10	99.68 14	99.91 5	99.95 11	99.83 4	99.87 9	99.96 12	99.39 46	99.93 5	99.87 36	99.29 152	99.77 14	99.83 23	99.72 20	99.97 1	99.82 16

TAMVS		99.05 140	99.02 126	99.08 170	99.69 144	99.22 134	99.33 132	99.32 200	99.16 76	98.97 192	99.87 36	97.36 188	97.76 181	99.21 114	99.00 105	99.44 156	99.33 134

DeepC-MVS	99.05 5	99.74 18	99.64 20	99.84 22	99.90 40	99.39 93	99.79 30	99.81 98	99.69 8	99.90 15	99.87 36	99.98 5	99.81 7	99.62 55	99.32 60	99.83 52	99.65 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ECVR-MVS		99.24 114	98.74 153	99.82 27	99.95 11	99.78 18	99.67 61	99.93 27	99.45 38	99.80 59	99.86 39	92.58 207	99.65 38	99.93 3	99.88 3	99.94 16	99.71 46

MDTV_nov1_ep13_2view		98.73 167	98.31 171	99.22 152	99.75 126	99.24 131	99.75 40	99.93 27	99.31 54	99.84 46	99.86 39	99.81 94	99.31 96	97.40 206	94.77 205	96.73 210	97.81 199

diffmvs		99.38 91	99.33 71	99.45 114	99.87 55	99.39 93	99.28 143	99.58 166	99.55 28	99.50 144	99.85 41	99.85 85	98.94 135	98.58 184	98.68 142	99.51 146	99.39 127

PVSNet_Blended_VisFu		99.66 32	99.64 20	99.67 70	99.91 36	99.71 33	99.61 71	99.79 109	99.41 42	99.91 13	99.85 41	99.61 124	99.00 127	99.67 46	99.42 54	99.81 58	99.81 20

baseline		99.24 114	99.30 76	99.17 160	99.78 109	99.14 144	99.10 163	99.69 142	98.97 99	99.49 146	99.84 43	99.88 71	97.99 177	98.85 166	98.73 137	98.98 188	99.72 40

CDS-MVSNet		99.15 130	99.10 115	99.21 155	99.59 172	99.22 134	99.48 108	99.47 183	98.89 110	99.41 161	99.84 43	98.11 179	97.76 181	99.26 106	99.01 101	99.57 132	99.38 128
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		99.76 14	99.78 9	99.75 52	99.92 31	99.77 21	99.83 19	99.85 67	99.43 40	99.85 42	99.84 43	100.00 1	99.13 117	99.83 23	99.66 25	99.90 28	99.90 3
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FMVSNet1		99.50 60	99.57 33	99.42 117	99.67 151	99.65 40	99.60 75	99.91 43	99.40 44	99.39 164	99.83 46	99.27 154	98.14 166	99.68 43	99.50 42	99.81 58	99.68 50

TDRefinement		99.81 8	99.76 10	99.86 17	99.83 89	99.53 62	99.89 5	99.91 43	99.73 6	99.88 22	99.83 46	99.96 14	99.76 16	99.91 9	99.81 12	99.86 41	99.59 69

pmnet_mix02		98.28 181	97.48 187	99.22 152	99.78 109	99.12 151	99.68 57	99.39 194	99.49 34	99.86 35	99.82 48	99.89 65	99.23 103	95.54 211	92.36 209	97.38 205	96.14 210

v148		99.58 48	99.43 55	99.76 48	99.87 55	99.40 92	99.76 36	99.85 67	99.48 35	99.83 52	99.82 48	99.83 89	99.51 64	99.20 117	98.82 125	99.75 76	99.45 110

DELS-MVS		99.42 80	99.53 40	99.29 141	99.52 179	99.43 85	99.42 116	99.28 201	99.16 76	99.72 91	99.82 48	99.97 9	98.17 163	99.56 58	99.16 71	99.65 101	99.59 69
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
DVP-MVS++		99.46 71	99.57 33	99.33 138	99.75 126	99.57 52	99.44 113	99.81 98	99.38 47	98.56 211	99.81 51	99.99 3	98.79 144	99.33 87	99.13 82	99.62 119	99.81 20

new_pmnet		98.91 155	98.89 141	98.94 179	99.51 185	98.27 203	99.15 156	98.66 212	99.17 72	99.48 148	99.79 52	99.80 100	98.49 158	99.23 109	98.20 172	98.34 198	97.74 202

N_pmnet		98.64 170	98.23 177	99.11 168	99.78 109	99.25 126	99.75 40	99.39 194	99.65 14	99.70 99	99.78 53	99.89 65	98.81 143	97.60 201	94.28 206	97.24 207	97.15 206

pmmvs3		98.85 159	98.60 159	99.13 163	99.66 152	98.72 182	99.37 126	99.06 207	98.44 155	99.76 72	99.74 54	99.55 131	99.15 113	99.04 136	96.00 203	97.80 202	98.72 180

MVS_Test		99.09 135	98.92 137	99.29 141	99.61 164	99.07 155	99.04 168	99.81 98	98.58 146	99.37 168	99.74 54	98.87 165	98.41 160	98.61 183	98.01 182	99.50 148	99.57 83

thisisatest0515		99.73 21	99.67 15	99.81 32	99.93 28	99.74 30	99.68 57	99.91 43	99.59 22	99.88 22	99.73 56	99.81 94	99.55 57	99.59 56	99.53 39	99.89 31	99.70 48

PS-CasMVS		99.73 21	99.59 30	99.90 8	99.95 11	99.80 16	99.85 13	99.97 1	98.95 101	99.86 35	99.73 56	99.36 144	99.81 7	99.83 23	99.67 24	99.95 8	99.83 15

IterMVS-LS		99.16 128	98.82 149	99.57 91	99.87 55	99.71 33	99.58 80	99.92 38	99.24 61	99.71 97	99.73 56	95.79 192	98.91 136	98.82 173	98.66 144	99.43 159	99.77 28
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CSCG		99.61 38	99.52 41	99.71 62	99.89 43	99.62 43	99.52 93	99.76 129	99.61 20	99.69 100	99.73 56	99.96 14	99.57 55	99.27 104	98.62 148	99.81 58	99.85 13

GeoE		99.63 36	99.51 42	99.78 42	99.91 36	99.57 52	99.78 32	99.97 1	99.23 62	99.72 91	99.72 60	99.80 100	99.50 68	99.45 74	99.10 88	99.79 65	99.71 46

Anonymous20231206		99.48 64	99.31 75	99.69 67	99.79 104	99.57 52	99.63 69	99.79 109	98.88 111	99.91 13	99.72 60	99.93 45	99.59 50	99.24 107	98.63 147	99.43 159	99.18 147

pmmvs4		99.34 97	99.15 107	99.57 91	99.77 117	98.90 165	99.51 96	99.77 121	99.07 85	99.73 88	99.72 60	99.84 87	99.07 121	98.85 166	98.39 162	99.55 138	99.27 141

TranMVSNet+NR-MVSNet		99.59 45	99.42 58	99.80 37	99.87 55	99.55 56	99.64 65	99.86 60	99.05 90	99.88 22	99.72 60	99.33 150	99.64 42	99.47 66	99.14 74	99.91 24	99.67 53

WR-MVS_H		99.73 21	99.61 25	99.88 12	99.95 11	99.82 8	99.83 19	99.96 12	99.01 93	99.84 46	99.71 64	99.41 143	99.74 21	99.77 34	99.70 23	99.95 8	99.82 16

DVP-MVS		99.53 56	99.51 42	99.55 96	99.82 94	99.58 51	99.54 88	99.78 114	99.28 60	99.21 181	99.70 65	99.97 9	99.32 93	99.32 89	99.14 74	99.64 110	99.58 77
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
FC-MVSNet-train		99.70 26	99.67 15	99.74 58	99.94 25	99.71 33	99.82 24	99.91 43	99.14 79	99.53 134	99.70 65	99.88 71	99.33 90	99.88 15	99.61 33	99.94 16	99.77 28

PEN-MVS		99.77 12	99.65 19	99.91 5	99.95 11	99.80 16	99.86 10	99.97 1	99.08 83	99.89 17	99.69 67	99.68 118	99.84 5	99.81 27	99.64 28	99.95 8	99.81 20

DPE-MVS		99.41 82	99.36 67	99.47 110	99.66 152	99.48 76	99.46 111	99.75 134	98.65 135	99.41 161	99.67 68	99.95 26	98.82 141	99.21 114	99.14 74	99.72 88	99.40 125
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
GA-MVS		98.59 173	98.15 179	99.09 169	99.59 172	99.13 148	98.84 191	99.52 179	98.61 143	99.35 171	99.67 68	93.03 199	97.73 183	98.90 159	98.26 168	99.51 146	99.48 105

SED-MVS		99.45 74	99.46 52	99.42 117	99.77 117	99.57 52	99.42 116	99.80 106	99.06 87	99.38 165	99.66 70	99.96 14	98.65 152	99.31 91	99.14 74	99.53 140	99.55 89

DTE-MVSNet		99.75 16	99.61 25	99.92 4	99.95 11	99.81 12	99.86 10	99.96 12	99.18 71	99.92 10	99.66 70	99.45 137	99.85 3	99.80 28	99.56 34	99.96 3	99.79 26

ACMH	99.11 4	99.72 24	99.63 22	99.84 22	99.87 55	99.59 49	99.83 19	99.88 55	99.46 37	99.87 28	99.66 70	99.95 26	99.76 16	99.73 39	99.47 47	99.84 47	99.52 99
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.68 30	99.51 42	99.89 9	99.95 11	99.76 22	99.83 19	99.96 12	98.83 119	99.84 46	99.65 73	99.09 157	99.80 10	99.78 32	99.62 32	99.95 8	99.82 16

UniMVSNet (Re)		99.50 60	99.29 78	99.75 52	99.86 69	99.47 78	99.51 96	99.82 90	98.90 109	99.89 17	99.64 74	99.00 159	99.55 57	99.32 89	99.08 90	99.90 28	99.59 69

FMVSNet2		99.07 138	99.19 98	98.93 181	99.02 216	99.53 62	99.31 135	99.84 77	98.86 114	98.88 197	99.64 74	98.44 172	96.92 198	99.35 83	99.00 105	99.61 121	99.53 94

COLMAP_ROB	99.18 2	99.70 26	99.60 28	99.81 32	99.84 83	99.37 100	99.76 36	99.84 77	99.54 30	99.82 55	99.64 74	99.95 26	99.75 18	99.79 30	99.56 34	99.83 52	99.37 130
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CS-MVS		99.74 18	99.66 17	99.83 26	99.92 31	99.81 12	99.81 26	99.85 67	98.81 122	99.87 28	99.62 77	99.95 26	99.62 45	99.79 30	99.78 14	99.92 21	99.84 14

CHOSEN 280x420		98.99 146	98.91 139	99.07 171	99.77 117	99.26 123	99.55 84	99.92 38	98.62 140	98.67 208	99.62 77	97.20 189	98.44 159	99.50 61	99.18 68	98.08 201	98.99 171

MVS-HIRNet		98.45 177	98.25 174	98.69 191	99.12 212	97.81 218	98.55 205	99.85 67	98.58 146	99.67 108	99.61 79	99.86 79	97.46 187	97.95 199	96.37 201	97.49 204	97.56 203

EPP-MVSNet		99.34 97	99.10 115	99.62 84	99.94 25	99.74 30	99.66 63	99.80 106	99.07 85	98.93 194	99.61 79	96.13 191	99.49 73	99.67 46	99.63 30	99.92 21	99.86 11

DeepPCF-MVS	98.38 11	99.16 128	99.20 96	99.12 165	99.20 211	98.71 183	98.85 190	99.06 207	99.17 72	98.96 193	99.61 79	99.86 79	99.29 98	99.17 125	98.72 138	99.36 167	99.15 155

TSAR-MVS + MP.		99.56 53	99.54 39	99.58 88	99.69 144	99.14 144	99.73 48	99.45 185	99.50 33	99.35 171	99.60 82	99.93 45	99.50 68	99.56 58	99.37 58	99.77 71	99.64 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PMVS	94.32 17	99.27 112	99.55 36	98.94 179	99.60 168	99.43 85	99.39 122	99.54 172	98.99 95	99.69 100	99.60 82	99.81 94	95.68 209	99.88 15	99.83 8	99.73 85	99.31 136
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UniMVSNet_ETH3D		99.81 8	99.79 8	99.85 20	99.98 1	99.76 22	99.73 48	99.96 12	99.68 10	99.87 28	99.59 84	99.91 59	99.58 53	99.90 10	99.85 7	99.96 3	99.81 20

USDC		99.29 111	98.98 130	99.65 74	99.72 139	98.87 171	99.47 109	99.66 154	99.35 51	99.87 28	99.58 85	99.87 78	99.51 64	98.85 166	97.93 184	99.65 101	98.38 186

CLD-MVS		99.30 107	99.01 127	99.63 80	99.75 126	98.89 168	99.35 130	99.60 160	98.53 149	99.86 35	99.57 86	99.94 38	99.52 63	98.96 147	98.10 178	99.70 93	99.08 160
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 43	99.48 47	99.73 60	99.85 77	99.51 73	99.75 40	99.85 67	99.17 72	99.81 58	99.56 87	99.94 38	99.44 80	99.42 77	99.22 64	99.67 97	99.54 91

CANet_DTU		99.03 142	99.18 100	98.87 184	99.58 175	99.03 157	99.18 153	99.41 190	98.65 135	99.74 82	99.55 88	99.71 113	96.13 207	99.19 120	98.92 114	99.17 182	99.18 147

CANet		99.36 92	99.39 63	99.34 137	99.80 100	99.35 106	99.41 120	99.47 183	99.20 66	99.74 82	99.54 89	99.68 118	98.05 171	99.23 109	98.97 108	99.57 132	99.73 37

TSAR-MVS + ACMM		99.31 105	99.26 82	99.37 132	99.66 152	98.97 163	99.20 151	99.56 169	99.33 52	99.19 182	99.54 89	99.91 59	99.32 93	99.12 130	98.34 165	99.29 173	99.65 57

test-LLR		97.74 193	97.46 188	98.08 206	99.62 160	98.37 199	98.26 211	99.41 190	97.03 209	97.38 220	99.54 89	92.89 201	95.12 212	98.78 175	97.68 189	98.65 195	97.90 196

TESTMET0.1,1		97.62 199	97.46 188	97.81 212	99.07 215	98.37 199	98.26 211	98.35 215	97.03 209	97.38 220	99.54 89	92.89 201	95.12 212	98.78 175	97.68 189	98.65 195	97.90 196

EG-PatchMatch MVS		99.59 45	99.49 46	99.70 65	99.82 94	99.26 123	99.39 122	99.83 83	98.99 95	99.93 5	99.54 89	99.92 53	99.51 64	99.78 32	99.50 42	99.73 85	99.41 120

ACMH+	98.94 6	99.69 29	99.59 30	99.81 32	99.88 49	99.41 90	99.75 40	99.86 60	99.43 40	99.80 59	99.54 89	99.97 9	99.73 24	99.82 26	99.52 41	99.85 44	99.43 116

SMA-MVS		99.43 77	99.41 59	99.45 114	99.82 94	99.31 114	99.02 173	99.59 163	99.06 87	99.34 174	99.53 95	99.96 14	99.38 83	99.29 96	99.13 82	99.53 140	99.59 69
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
TSAR-MVS + GP.		99.33 99	99.17 104	99.51 105	99.71 142	99.00 160	98.84 191	99.71 139	98.23 173	99.74 82	99.53 95	99.90 62	99.35 86	99.38 79	98.85 122	99.72 88	99.31 136

test-mter		97.65 198	97.57 186	97.75 214	98.90 219	98.56 192	98.15 216	98.45 214	96.92 213	96.84 223	99.52 97	92.53 209	95.24 211	99.04 136	98.12 176	98.90 190	98.29 191

testgi		99.43 77	99.47 50	99.38 126	99.90 40	99.67 39	99.30 140	99.73 137	98.64 139	99.53 134	99.52 97	99.90 62	98.08 169	99.65 50	99.40 57	99.75 76	99.55 89

DU-MVS		99.48 64	99.26 82	99.75 52	99.85 77	99.38 96	99.50 100	99.81 98	98.86 114	99.89 17	99.51 99	98.98 160	99.59 50	99.46 67	98.97 108	99.87 39	99.63 61

NR-MVSNet		99.52 57	99.29 78	99.80 37	99.96 7	99.38 96	99.55 84	99.81 98	98.86 114	99.87 28	99.51 99	98.81 166	99.72 26	99.86 18	99.04 97	99.89 31	99.54 91

CS-MVS-test		99.75 16	99.66 17	99.87 16	99.95 11	99.95 1	99.85 13	99.93 27	98.83 119	99.84 46	99.50 101	99.95 26	99.70 29	99.85 19	99.71 21	99.91 24	99.82 16

Fast-Effi-MVS+		99.39 88	99.18 100	99.63 80	99.86 69	99.28 121	99.45 112	99.91 43	98.47 151	99.61 119	99.50 101	99.57 128	99.17 106	99.24 107	98.66 144	99.78 67	99.59 69

TAPA-MVS	98.54 10	99.30 107	99.24 87	99.36 136	99.44 194	98.77 178	99.00 175	99.41 190	99.23 62	99.60 122	99.50 101	99.86 79	99.15 113	99.29 96	98.95 112	99.56 135	99.08 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DROMVSNet		99.70 26	99.57 33	99.85 20	99.95 11	99.81 12	99.85 13	99.93 27	98.39 165	99.76 72	99.48 104	99.94 38	99.70 29	99.85 19	99.66 25	99.91 24	99.87 10

PM-MVS		99.49 63	99.43 55	99.57 91	99.76 122	99.34 108	99.53 89	99.77 121	98.93 105	99.75 76	99.46 105	99.83 89	99.11 119	99.72 40	99.29 62	99.49 149	99.46 109

UniMVSNet_NR-MVSNet		99.41 82	99.12 112	99.76 48	99.86 69	99.48 76	99.50 100	99.81 98	98.84 117	99.89 17	99.45 106	98.32 176	99.59 50	99.22 111	98.89 117	99.90 28	99.63 61

tfpnnormal		99.74 18	99.63 22	99.86 17	99.93 28	99.75 28	99.80 29	99.89 51	99.31 54	99.88 22	99.43 107	99.66 121	99.77 14	99.80 28	99.71 21	99.92 21	99.76 31

MDA-MVSNet-bldmvs		99.11 133	99.11 114	99.12 165	99.91 36	99.38 96	99.77 33	98.72 211	99.31 54	99.85 42	99.43 107	98.26 177	99.48 76	99.85 19	98.47 156	96.99 208	99.08 160

GBi-Net		98.96 148	99.05 120	98.85 185	99.02 216	99.53 62	99.31 135	99.78 114	98.13 176	98.48 212	99.43 107	97.58 184	96.92 198	99.68 43	99.50 42	99.61 121	99.53 94

test1		98.96 148	99.05 120	98.85 185	99.02 216	99.53 62	99.31 135	99.78 114	98.13 176	98.48 212	99.43 107	97.58 184	96.92 198	99.68 43	99.50 42	99.61 121	99.53 94

FMVSNet3		98.63 172	98.75 152	98.49 198	98.10 222	99.44 82	99.02 173	99.78 114	98.13 176	98.48 212	99.43 107	97.58 184	96.16 206	98.85 166	98.39 162	99.40 163	99.41 120

QAPM		99.41 82	99.21 95	99.64 79	99.78 109	99.16 141	99.51 96	99.85 67	99.20 66	99.72 91	99.43 107	99.81 94	99.25 101	98.87 161	98.71 139	99.71 91	99.30 138

tmp_tt				88.14 217	96.68 224	91.91 224	93.70 224	61.38 222	99.61 20	90.51 226	99.40 113	99.71 113	90.32 220	99.22 111	99.44 52	96.25 212

RPSCF		99.48 64	99.45 53	99.52 103	99.73 137	99.33 109	99.13 159	99.77 121	99.33 52	99.47 151	99.39 114	99.92 53	99.36 85	99.63 52	99.13 82	99.63 113	99.41 120

UA-Net		99.64 35	99.62 24	99.66 72	99.97 2	99.82 8	99.14 158	99.96 12	98.95 101	99.52 140	99.38 115	99.86 79	99.55 57	99.72 40	99.66 25	99.80 62	99.94 2

TSAR-MVS + COLMAP		98.74 165	98.58 161	98.93 181	99.29 207	98.23 204	99.04 168	99.24 202	98.79 124	98.80 203	99.37 116	99.71 113	98.06 170	98.02 197	97.46 193	99.16 183	98.48 184

ambc			98.83 146		99.72 139	98.52 193	98.84 191		98.96 100	99.92 10	99.34 117	99.74 108	99.04 125	98.68 180	97.57 192	99.46 152	98.99 171

ETV-MVS		99.45 74	99.32 73	99.60 85	99.79 104	99.60 46	99.40 121	99.78 114	97.88 190	99.83 52	99.33 118	99.70 116	98.97 130	99.74 37	99.43 53	99.84 47	99.58 77

PVSNet_BlendedMVS		99.20 122	99.17 104	99.23 149	99.69 144	99.33 109	99.04 168	99.13 204	98.41 161	99.79 63	99.33 118	99.36 144	98.10 167	99.29 96	98.87 119	99.65 101	99.56 84

PVSNet_Blended		99.20 122	99.17 104	99.23 149	99.69 144	99.33 109	99.04 168	99.13 204	98.41 161	99.79 63	99.33 118	99.36 144	98.10 167	99.29 96	98.87 119	99.65 101	99.56 84

UGNet		99.40 86	99.61 25	99.16 161	99.88 49	99.64 41	99.61 71	99.77 121	99.31 54	99.63 115	99.33 118	99.93 45	96.46 204	99.63 52	99.53 39	99.63 113	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	98.95 151	98.53 163	99.44 116	99.70 143	98.80 175	98.96 177	99.69 142	98.65 135	99.59 124	99.33 118	99.94 38	99.12 118	98.01 198	97.11 195	99.59 130	97.83 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OPM-MVS		99.39 88	99.22 91	99.59 86	99.76 122	98.82 173	99.51 96	99.79 109	99.17 72	99.53 134	99.31 123	99.95 26	99.35 86	99.22 111	98.79 130	99.60 124	99.27 141
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test0.0.03 1		98.41 178	98.41 170	98.40 202	99.62 160	99.16 141	98.87 188	99.41 190	97.15 204	96.60 224	99.31 123	97.00 190	96.55 203	98.91 155	98.51 155	99.37 166	98.82 176

MVSTER		97.55 201	96.75 197	98.48 199	99.46 191	99.54 59	98.24 213	99.77 121	97.56 198	99.41 161	99.31 123	84.86 224	94.66 214	98.86 164	97.75 187	99.34 170	99.38 128

tpmrst		96.18 212	94.47 216	98.18 204	99.52 179	97.89 216	98.96 177	99.79 109	98.07 181	99.16 183	99.30 126	92.69 205	96.69 201	90.76 219	88.85 219	94.96 218	93.69 218

MIMVSNet		99.00 144	99.03 123	98.97 178	99.32 206	99.32 113	99.39 122	99.91 43	98.41 161	98.76 204	99.24 127	99.17 156	97.13 191	99.30 93	98.80 129	99.29 173	99.01 168

MSDG		99.32 101	99.09 117	99.58 88	99.75 126	98.74 180	99.36 127	99.54 172	99.14 79	99.72 91	99.24 127	99.89 65	99.51 64	99.30 93	98.76 131	99.62 119	98.54 182

MVS_0304		99.36 92	99.35 69	99.37 132	99.85 77	99.36 102	99.39 122	99.56 169	99.36 50	99.75 76	99.23 129	99.90 62	97.97 178	99.00 142	98.83 124	99.69 94	99.77 28

thisisatest0530		98.78 163	98.26 172	99.39 122	99.78 109	99.43 85	99.07 165	99.64 156	98.44 155	99.42 159	99.22 130	92.68 206	98.63 153	99.30 93	99.14 74	99.80 62	99.60 67

MDTV_nov1_ep13		97.41 203	96.26 205	98.76 189	99.47 190	98.43 198	99.26 147	99.82 90	98.06 182	99.23 179	99.22 130	92.86 203	98.05 171	95.33 213	93.66 208	96.73 210	96.26 209

tttt0517		98.77 164	98.25 174	99.38 126	99.79 104	99.46 79	99.07 165	99.64 156	98.40 164	99.38 165	99.21 132	92.54 208	98.63 153	99.34 86	99.14 74	99.80 62	99.62 64

test2506		97.57 200	95.67 209	99.78 42	99.95 11	99.78 18	99.67 61	99.93 27	99.45 38	99.55 133	99.20 133	71.73 229	99.65 38	99.93 3	99.88 3	99.94 16	99.72 40

test_method		91.96 216	95.51 210	87.82 218	70.84 225	82.79 226	92.13 225	87.74 221	98.88 111	95.40 225	99.20 133	98.04 180	85.65 221	97.71 200	94.95 204	95.13 216	97.00 207

tpm		96.56 210	94.68 215	98.74 190	99.12 212	97.90 215	98.79 196	99.93 27	96.79 217	99.69 100	99.19 135	81.48 227	97.56 185	95.46 212	93.97 207	97.37 206	97.99 195

DeepC-MVS_fast	98.69 9	99.32 101	99.13 110	99.53 99	99.63 159	98.78 176	99.53 89	99.33 199	99.08 83	99.77 69	99.18 136	99.89 65	99.29 98	99.00 142	98.70 140	99.65 101	99.30 138
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 67	99.39 63	99.57 91	99.89 43	99.60 46	99.50 100	99.69 142	98.91 108	99.62 116	99.17 137	99.35 147	98.86 140	99.63 52	99.46 49	99.84 47	99.62 64

PHI-MVS		99.33 99	99.19 98	99.49 108	99.69 144	99.25 126	99.27 144	99.59 163	98.44 155	99.78 67	99.15 138	99.92 53	98.95 134	99.39 78	99.04 97	99.64 110	99.18 147

TinyColmap		99.21 119	98.89 141	99.59 86	99.61 164	98.61 189	99.47 109	99.67 151	99.02 92	99.82 55	99.15 138	99.74 108	99.35 86	99.17 125	98.33 166	99.63 113	98.22 192

RPMNet		97.70 194	96.54 199	99.06 172	99.57 178	98.23 204	98.95 180	99.97 1	96.89 214	99.49 146	99.13 140	89.63 215	97.09 193	96.68 209	97.02 197	99.26 176	98.19 193

OMC-MVS		99.11 133	98.95 134	99.29 141	99.37 200	98.57 191	99.19 152	99.20 203	98.87 113	99.58 128	99.13 140	99.88 71	99.00 127	99.19 120	98.46 157	99.43 159	98.57 181

Effi-MVS+		99.20 122	98.93 136	99.50 107	99.79 104	99.26 123	98.82 194	99.96 12	98.37 166	99.60 122	99.12 142	98.36 174	99.05 124	98.93 150	98.82 125	99.78 67	99.68 50

MVS_111021_LR		99.25 113	99.13 110	99.39 122	99.50 187	99.14 144	99.23 149	99.50 180	98.67 133	99.61 119	99.12 142	99.81 94	99.16 109	99.28 101	98.67 143	99.35 169	99.21 146

CR-MVSNet		97.91 186	96.80 196	99.22 152	99.60 168	98.23 204	98.91 183	99.97 1	96.89 214	99.43 156	99.10 144	89.24 217	98.15 164	98.04 195	97.78 185	99.26 176	98.30 189

DI_MVS_plusplus_trai		98.74 165	98.08 182	99.51 105	99.79 104	99.29 120	99.61 71	99.60 160	99.20 66	99.46 152	99.09 145	92.93 200	98.97 130	98.27 192	98.35 164	99.65 101	99.45 110

ADS-MVSNet		97.29 204	96.17 206	98.59 195	99.59 172	98.70 184	99.32 133	99.86 60	98.47 151	99.56 130	99.08 146	98.16 178	97.34 189	92.92 215	91.17 213	95.91 213	94.72 215

Vis-MVSNet (Re-imp)		99.40 86	99.28 80	99.55 96	99.92 31	99.68 37	99.31 135	99.87 56	98.69 132	99.16 183	99.08 146	98.64 169	99.20 105	99.65 50	99.46 49	99.83 52	99.72 40

EPNet_dtu		98.09 184	98.25 174	97.91 210	99.58 175	98.02 212	98.19 215	99.67 151	97.94 188	99.74 82	99.07 148	98.71 168	93.40 218	97.50 203	97.09 196	96.89 209	99.44 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		96.76 208	95.30 213	98.46 200	99.42 197	98.47 196	99.32 133	99.91 43	98.42 158	99.51 142	99.07 148	92.81 204	97.12 192	92.39 217	91.71 211	95.51 215	94.20 217

PatchT		98.11 182	97.12 193	99.26 147	99.65 156	98.34 201	99.57 82	99.97 1	97.48 200	99.43 156	99.04 150	90.84 212	98.15 164	98.04 195	97.78 185	98.82 191	98.30 189

ACMMP_NAP		99.47 67	99.33 71	99.63 80	99.85 77	99.28 121	99.56 83	99.83 83	98.75 127	99.48 148	99.03 151	99.95 26	99.47 79	99.48 63	99.19 67	99.57 132	99.59 69

MVS_111021_HR		99.30 107	99.14 108	99.48 109	99.58 175	99.25 126	99.27 144	99.61 158	98.74 128	99.66 110	99.02 152	99.84 87	99.33 90	99.20 117	98.76 131	99.44 156	99.18 147

MSP-MVS		99.32 101	99.26 82	99.38 126	99.76 122	99.54 59	99.42 116	99.72 138	98.92 107	98.84 201	98.96 153	99.96 14	98.91 136	98.72 179	99.14 74	99.63 113	99.58 77
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
zzz-MVS		99.51 58	99.36 67	99.68 68	99.88 49	99.38 96	99.53 89	99.84 77	99.11 82	99.59 124	98.93 154	99.95 26	99.58 53	99.44 75	99.21 66	99.65 101	99.52 99

LGP-MVS_train		99.46 71	99.18 100	99.78 42	99.87 55	99.25 126	99.71 55	99.87 56	98.02 184	99.79 63	98.90 155	99.96 14	99.66 36	99.49 62	99.17 70	99.79 65	99.49 102

HFP-MVS		99.46 71	99.30 76	99.65 74	99.82 94	99.25 126	99.50 100	99.82 90	99.23 62	99.58 128	98.86 156	99.94 38	99.56 56	99.14 129	99.12 86	99.63 113	99.56 84

FPMVS		98.48 176	98.83 146	98.07 208	99.09 214	97.98 213	99.07 165	98.04 219	98.99 95	99.22 180	98.85 157	99.43 140	93.79 216	99.66 48	99.11 87	99.24 178	97.76 200

ET-MVSNet_ETH3D		97.44 202	96.29 204	98.78 188	97.93 223	98.95 164	98.91 183	99.09 206	98.00 185	99.24 178	98.83 158	84.62 225	98.02 176	97.43 205	97.38 194	99.48 150	98.84 173

SD-MVS		99.35 95	99.26 82	99.46 112	99.66 152	99.15 143	98.92 182	99.67 151	99.55 28	99.35 171	98.83 158	99.91 59	99.35 86	99.19 120	98.53 153	99.78 67	99.68 50
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CostFormer		95.61 213	93.35 219	98.24 203	99.48 189	98.03 211	98.65 200	99.83 83	96.93 212	99.42 159	98.83 158	83.65 226	97.08 194	90.39 220	89.54 218	94.94 219	96.11 211

PatchmatchNet		96.81 207	95.41 211	98.43 201	99.43 196	98.30 202	99.23 149	99.93 27	98.19 174	99.64 113	98.81 161	93.50 198	97.43 188	92.89 216	90.78 215	94.94 219	95.41 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PMMVS		98.71 168	98.55 162	98.90 183	99.28 208	98.45 197	98.53 206	99.45 185	97.67 197	99.15 185	98.76 162	99.54 133	97.79 180	98.77 177	98.23 170	99.16 183	98.46 185

baseline2		97.87 187	97.18 192	98.67 193	99.34 203	99.17 140	98.48 207	98.82 210	97.08 207	98.83 202	98.75 163	89.47 216	97.03 196	98.67 181	98.27 167	99.52 142	98.83 175

IS_MVSNet		99.15 130	99.12 112	99.19 158	99.92 31	99.73 32	99.55 84	99.86 60	98.45 154	96.91 222	98.74 164	98.33 175	99.02 126	99.54 60	99.47 47	99.88 33	99.61 66

ACMMPR		99.51 58	99.32 73	99.72 61	99.87 55	99.33 109	99.61 71	99.85 67	99.19 69	99.73 88	98.73 165	99.95 26	99.61 47	99.35 83	99.14 74	99.66 99	99.58 77

HPM-MVS++		99.23 116	98.98 130	99.53 99	99.75 126	99.02 158	99.44 113	99.77 121	98.65 135	99.52 140	98.72 166	99.92 53	99.33 90	98.77 177	98.40 161	99.40 163	99.36 131

SCA		97.25 205	96.05 207	98.64 194	99.36 202	99.02 158	99.27 144	99.96 12	98.25 171	99.69 100	98.71 167	94.66 196	97.95 179	93.95 214	92.35 210	95.64 214	95.40 214

SteuartSystems-ACMMP		99.47 67	99.22 91	99.76 48	99.88 49	99.36 102	99.65 64	99.84 77	98.47 151	99.80 59	98.68 168	99.96 14	99.68 32	99.37 80	99.06 92	99.72 88	99.66 54
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		99.41 82	99.20 96	99.65 74	99.80 100	99.23 133	99.44 113	99.75 134	98.60 144	99.74 82	98.66 169	99.93 45	99.48 76	99.33 87	99.16 71	99.73 85	99.48 105

MVE	91.08 18	97.68 197	97.65 183	97.71 216	98.46 221	91.62 225	97.92 222	98.86 209	98.73 130	97.99 219	98.64 170	99.96 14	99.17 106	99.59 56	97.75 187	93.87 224	97.27 204
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
xxxxxxxxxxxxxcwj		98.97 147	98.97 132	98.98 174	99.64 157	98.89 168	98.00 220	99.58 166	98.42 158	99.08 187	98.63 171	99.96 14	98.04 173	99.02 138	98.76 131	99.52 142	99.13 156

SF-MVS		98.96 148	98.95 134	98.98 174	99.64 157	98.89 168	98.00 220	99.58 166	98.42 158	99.08 187	98.63 171	99.83 89	98.04 173	99.02 138	98.76 131	99.52 142	99.13 156

baseline1		98.39 180	97.59 185	99.31 139	99.78 109	99.45 80	99.13 159	99.53 177	98.06 182	98.87 198	98.63 171	90.04 214	98.76 146	98.85 166	98.84 123	99.81 58	99.28 140

Anonymous202405211			99.14 108		99.87 55	99.55 56	99.50 100	99.70 140	98.55 148		98.61 174	98.46 171	98.76 146	99.66 48	99.50 42	99.85 44	99.63 61

MS-PatchMatch		98.94 152	98.71 155	99.21 155	99.52 179	98.22 207	98.97 176	99.53 177	98.76 125	99.50 144	98.59 175	99.56 130	98.68 150	98.63 182	98.45 159	99.05 185	98.73 178

3Dnovator	99.16 3	99.42 80	99.22 91	99.65 74	99.78 109	99.13 148	99.50 100	99.85 67	99.40 44	99.80 59	98.59 175	99.79 103	99.30 97	99.20 117	99.06 92	99.71 91	99.35 133

MCST-MVS		99.17 125	98.82 149	99.57 91	99.75 126	98.70 184	99.25 148	99.69 142	98.62 140	99.59 124	98.54 177	99.79 103	99.53 60	98.48 188	98.15 174	99.64 110	99.43 116

Effi-MVS+-dtu		99.01 143	99.05 120	98.98 174	99.60 168	99.13 148	99.03 172	99.61 158	98.52 150	99.01 189	98.53 178	99.83 89	96.95 197	99.48 63	98.59 151	99.66 99	99.25 145

ACMM	98.37 12	99.47 67	99.23 88	99.74 58	99.86 69	99.19 138	99.68 57	99.86 60	99.16 76	99.71 97	98.52 179	99.95 26	99.62 45	99.35 83	99.02 99	99.74 81	99.42 119
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.39 88	99.28 80	99.52 103	99.77 117	99.39 93	99.55 84	99.82 90	98.93 105	99.64 113	98.52 179	99.67 120	98.58 156	99.74 37	99.63 30	99.75 76	99.06 163

Fast-Effi-MVS+-dtu		98.82 160	98.80 151	98.84 187	99.51 185	98.90 165	98.96 177	99.91 43	98.29 169	99.11 186	98.47 181	99.63 123	96.03 208	99.21 114	98.12 176	99.52 142	99.01 168

OpenMVS	98.82 8	99.17 125	98.85 145	99.53 99	99.75 126	99.06 156	99.36 127	99.82 90	98.28 170	99.76 72	98.47 181	99.61 124	98.91 136	98.80 174	98.70 140	99.60 124	99.04 167

EIA-MVS		99.23 116	99.03 123	99.47 110	99.83 89	99.64 41	99.16 155	99.81 98	97.11 206	99.65 112	98.44 183	99.78 106	98.61 155	99.46 67	99.22 64	99.75 76	99.59 69

MP-MVS		99.35 95	99.09 117	99.65 74	99.84 83	99.22 134	99.59 76	99.78 114	98.13 176	99.67 108	98.44 183	99.93 45	99.43 82	99.31 91	99.09 89	99.60 124	99.49 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		99.00 144	98.68 156	99.37 132	99.68 150	99.42 89	98.94 181	99.89 51	99.00 94	98.99 190	98.43 185	95.69 193	98.96 133	99.18 123	99.18 68	99.74 81	99.88 7

ACMP	98.32 13	99.44 76	99.18 100	99.75 52	99.83 89	99.18 139	99.64 65	99.83 83	98.81 122	99.79 63	98.42 186	99.96 14	99.64 42	99.46 67	98.98 107	99.74 81	99.44 113
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CMPMVS	76.62 19	98.64 170	98.60 159	98.68 192	99.33 204	97.07 219	98.11 218	98.50 213	97.69 196	99.26 177	98.35 187	99.66 121	97.62 184	99.43 76	99.02 99	99.24 178	99.01 168
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
APD-MVS		99.17 125	98.92 137	99.46 112	99.78 109	99.24 131	99.34 131	99.78 114	97.79 193	99.48 148	98.25 188	99.88 71	98.77 145	99.18 123	98.92 114	99.63 113	99.18 147
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		99.32 101	98.99 128	99.71 62	99.86 69	99.31 114	99.59 76	99.86 60	97.51 199	99.75 76	98.23 189	99.94 38	99.53 60	99.29 96	99.08 90	99.65 101	99.54 91

CDPH-MVS		99.05 140	98.63 158	99.54 98	99.75 126	98.78 176	99.59 76	99.68 148	97.79 193	99.37 168	98.20 190	99.86 79	99.14 115	98.58 184	98.01 182	99.68 95	99.16 153

CNVR-MVS		99.08 136	98.83 146	99.37 132	99.61 164	98.74 180	99.15 156	99.54 172	98.59 145	99.37 168	98.15 191	99.88 71	99.08 120	98.91 155	98.46 157	99.48 150	99.06 163

E-PMN		96.72 209	95.78 208	97.81 212	99.45 192	95.46 222	98.14 217	98.33 217	97.99 186	98.73 205	98.09 192	98.97 161	97.54 186	97.45 204	91.09 214	94.70 221	91.40 220

tpm cat1		95.52 215	93.49 217	97.88 211	99.28 208	97.87 217	98.65 200	99.77 121	97.27 203	99.46 152	98.04 193	90.99 211	95.46 210	88.57 221	88.14 220	94.64 222	93.54 219

3Dnovator+	98.92 7	99.31 105	99.03 123	99.63 80	99.77 117	98.90 165	99.52 93	99.81 98	99.37 48	99.72 91	98.03 194	99.73 111	99.32 93	98.99 145	98.81 128	99.67 97	99.36 131

train_agg		98.89 156	98.48 168	99.38 126	99.69 144	98.76 179	99.31 135	99.60 160	97.71 195	98.98 191	97.89 195	99.89 65	99.29 98	98.32 189	97.59 191	99.42 162	99.16 153

EPNet		98.06 185	98.11 181	98.00 209	99.60 168	98.99 162	98.38 209	99.68 148	98.18 175	98.85 200	97.89 195	95.60 194	92.72 219	98.30 190	98.10 178	98.76 192	99.72 40
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DCV-MVSNet		99.43 77	99.23 88	99.67 70	99.92 31	99.76 22	99.64 65	99.93 27	99.06 87	99.68 107	97.77 197	98.97 161	98.97 130	99.72 40	99.54 38	99.88 33	99.81 20

dps		95.59 214	93.46 218	98.08 206	99.33 204	98.22 207	98.87 188	99.70 140	96.17 220	98.87 198	97.75 198	86.85 223	96.60 202	91.24 218	89.62 217	95.10 217	94.34 216

ACMMP		99.36 92	99.06 119	99.71 62	99.86 69	99.36 102	99.63 69	99.85 67	98.33 167	99.72 91	97.73 199	99.94 38	99.53 60	99.37 80	99.13 82	99.65 101	99.56 84
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		98.80 162	98.52 165	99.12 165	99.38 199	98.70 184	98.56 203	99.55 171	97.81 192	99.34 174	97.57 200	99.31 151	98.67 151	99.27 104	98.62 148	99.22 180	98.35 188

CNLPA		98.82 160	98.52 165	99.18 159	99.21 210	98.50 195	98.73 198	99.34 198	98.73 130	99.56 130	97.55 201	99.42 141	99.06 123	98.93 150	98.10 178	99.21 181	98.38 186

CPTT-MVS		99.21 119	98.89 141	99.58 88	99.72 139	99.12 151	99.30 140	99.76 129	98.62 140	99.66 110	97.51 202	99.89 65	99.48 76	99.01 140	98.64 146	99.58 131	99.40 125

EMVS		96.47 211	95.38 212	97.74 215	99.42 197	95.37 223	98.07 219	98.27 218	97.85 191	98.90 196	97.48 203	98.73 167	97.20 190	97.21 207	90.39 216	94.59 223	90.65 221

AdaColmap		98.93 153	98.53 163	99.39 122	99.52 179	98.65 187	99.11 162	99.59 163	98.08 180	99.44 154	97.46 204	99.45 137	99.24 102	98.92 152	98.44 160	99.44 156	98.73 178

HQP-MVS		98.70 169	98.19 178	99.28 145	99.61 164	98.52 193	98.71 199	99.35 196	97.97 187	99.53 134	97.38 205	99.85 85	99.14 115	97.53 202	96.85 199	99.36 167	99.26 144

X-MVS		99.30 107	98.99 128	99.66 72	99.85 77	99.30 116	99.49 107	99.82 90	98.32 168	99.69 100	97.31 206	99.93 45	99.50 68	99.37 80	99.16 71	99.60 124	99.53 94

abl_6				99.21 155	99.49 188	98.62 188	98.90 186	99.44 188	97.08 207	99.61 119	97.19 207	99.73 111	98.35 161			99.45 154	98.84 173

MAR-MVS		98.54 174	98.15 179	98.98 174	99.37 200	98.09 210	98.56 203	99.65 155	96.11 221	99.27 176	97.16 208	99.50 134	98.03 175	98.87 161	98.23 170	99.01 186	99.13 156
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 55	99.23 88	99.91 5	99.87 55	99.52 69	99.86 10	99.93 27	99.87 1	99.96 2	96.72 209	99.55 131	99.97 1	99.77 34	99.46 49	99.87 39	99.74 35
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PLC	97.83 16	98.88 157	98.52 165	99.30 140	99.45 192	98.60 190	98.65 200	99.49 181	98.66 134	99.59 124	96.33 210	99.59 127	99.17 106	98.87 161	98.53 153	99.46 152	99.05 165
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSLP-MVS++		98.92 154	98.73 154	99.14 162	99.44 194	99.00 160	98.36 210	99.35 196	98.82 121	99.38 165	96.06 211	99.79 103	99.07 121	98.88 160	99.05 95	99.27 175	99.53 94

NCCC		98.88 157	98.42 169	99.42 117	99.62 160	98.81 174	99.10 163	99.54 172	98.76 125	99.53 134	95.97 212	99.80 100	99.16 109	98.49 187	98.06 181	99.55 138	99.05 165

DeepMVS_CX						96.39 221	97.15 223	88.89 220	97.94 188	99.51 142	95.71 213	97.88 181	98.19 162	98.92 152		97.73 203	97.75 201

FMVSNet5		97.69 195	96.98 194	98.53 197	98.53 220	99.36 102	98.90 186	99.54 172	96.38 219	98.44 215	95.38 214	90.08 213	97.05 195	99.46 67	99.06 92	98.73 193	99.12 159

GG-mvs-BLEND		70.44 217	96.91 195	39.57 219	3.32 228	96.51 220	91.01 226	4.05 225	97.03 209	33.20 227	94.67 215	97.75 182	7.59 224	98.28 191	96.85 199	98.24 199	97.26 205

DPM-MVS		98.10 183	97.32 191	99.01 173	99.52 179	97.92 214	98.47 208	99.45 185	98.25 171	98.91 195	93.99 216	99.69 117	98.73 148	96.29 210	96.32 202	99.00 187	98.77 177

thres600view7		97.86 189	96.53 201	99.41 120	99.84 83	99.52 69	99.36 127	99.76 129	97.32 202	98.38 217	93.24 217	87.25 222	99.23 103	99.11 131	99.75 18	99.88 33	99.48 105

thres100view900		97.69 195	96.37 203	99.23 149	99.74 135	99.21 137	98.81 195	99.43 189	96.10 222	98.70 206	92.99 218	89.10 218	98.88 139	98.58 184	99.31 61	99.82 55	99.27 141

tfpn200view9		97.85 190	96.54 199	99.38 126	99.74 135	99.52 69	99.17 154	99.76 129	96.10 222	98.70 206	92.99 218	89.10 218	99.00 127	99.11 131	99.56 34	99.88 33	99.41 120

thres200		97.87 187	96.56 198	99.39 122	99.76 122	99.52 69	99.13 159	99.76 129	96.88 216	98.66 209	92.87 220	88.77 220	99.16 109	99.11 131	99.42 54	99.88 33	99.33 134

thres400		97.82 191	96.47 202	99.40 121	99.81 99	99.44 82	99.29 142	99.69 142	97.15 204	98.57 210	92.82 221	87.96 221	99.16 109	98.96 147	99.55 37	99.86 41	99.41 120

IB-MVS	98.10 14	97.76 192	97.40 190	98.18 204	99.62 160	99.11 153	98.24 213	98.35 215	96.56 218	99.44 154	91.28 222	98.96 163	93.84 215	98.09 194	98.62 148	99.56 135	99.18 147
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		22.33 218	29.66 220	13.79 220	8.97 226	10.35 227	15.53 229	8.09 224	32.51 224	19.87 228	45.18 223	30.56 231	17.05 223	29.96 222	24.74 221	13.21 225	34.30 222

test123		21.52 219	28.47 221	13.42 221	7.29 227	10.12 228	15.70 228	8.31 223	31.54 225	19.34 229	36.33 224	37.40 230	17.14 222	27.45 223	23.17 222	12.73 226	33.30 223

uanet_test		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

RE-MVS-def										99.96 2

9.14												99.57 128

SR-MVS					99.73 137			99.74 136				99.88 71

our_test_3					99.75 126	99.11 153	99.74 47

MTAPA										99.62 116		99.95 26

MTMP										99.53 134		99.92 53

Patchmatch-RL test							65.75 227

XVS					99.86 69	99.30 116	99.72 52			99.69 100		99.93 45				99.60 124

X-MVStestdata					99.86 69	99.30 116	99.72 52			99.69 100		99.93 45				99.60 124

mPP-MVS					99.84 83							99.92 53

NP-MVS									97.37 201

Patchmtry						98.19 209	98.91 183	99.97 1		99.43 156

High-resolution multi-view benchmark