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.

SD-MVS		98.64 11	98.68 3	98.53 76	99.33 45	98.36 24	98.90 77	98.85 54	97.28 21	99.72 1	99.39 8	96.63 9	97.60 305	98.17 23	99.85 2	99.64 56

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 27	99.69 17	98.28 30	99.14 45	98.66 110	96.84 43	99.56 2	99.31 22	96.34 13	99.70 96	98.32 20	99.73 37	99.73 30

HSP-MVS		98.70 5	98.52 8	99.24 27	99.75 3	98.23 31	99.26 18	98.58 123	97.52 7	99.41 3	98.78 90	96.00 26	99.79 74	97.79 39	99.59 55	99.69 38

APDe-MVS		99.02 1	98.84 1	99.55 3	99.57 25	98.96 5	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 7	99.84 46	98.86 2	99.85 2	99.87 1

SMA-MVS		98.57 21	98.24 32	99.56 2	99.48 33	99.04 4	98.95 72	98.80 70	93.67 176	99.37 5	99.50 3	96.52 11	99.89 29	98.06 25	99.81 8	99.75 22

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 14	99.22 74	98.43 19	99.10 52	98.87 50	97.38 17	99.35 6	99.40 7	97.78 1	99.87 38	97.77 40	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
test_part2					99.63 21	99.18 1				99.27 7

ESAPD		98.70 5	98.39 15	99.62 1	99.63 21	99.18 1	98.55 159	98.84 55	96.40 57	99.27 7	99.31 22	97.38 2	99.93 9	96.37 98	99.78 15	99.76 20

abl_6		98.30 42	98.03 40	99.13 40	99.56 26	97.76 54	99.13 48	98.82 60	96.14 63	99.26 9	99.37 13	93.33 79	99.93 9	96.96 69	99.67 42	99.69 38

DeepPCF-MVS	96.37 2	97.93 50	98.48 13	96.30 238	99.00 91	89.54 302	97.43 271	98.87 50	98.16 2	99.26 9	99.38 12	96.12 20	99.64 106	98.30 21	99.77 20	99.72 33

APD-MVS		98.35 37	98.00 42	99.42 11	99.51 28	98.72 10	98.80 108	98.82 60	94.52 136	99.23 11	99.25 31	95.54 40	99.80 62	96.52 92	99.77 20	99.74 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-2		98.69 8	98.52 8	99.19 30	99.35 40	98.01 44	98.37 182	98.81 63	97.48 11	99.21 12	99.21 35	96.13 19	99.80 62	98.40 18	99.73 37	99.75 22

Regformer-1		98.66 9	98.51 10	99.12 42	99.35 40	97.81 53	98.37 182	98.76 78	97.49 10	99.20 13	99.21 35	96.08 22	99.79 74	98.42 16	99.73 37	99.75 22

APD-MVS_3200maxsize		98.53 27	98.33 25	99.15 39	99.50 29	97.92 48	99.15 44	98.81 63	96.24 60	99.20 13	99.37 13	95.30 46	99.80 62	97.73 42	99.67 42	99.72 33

ACMMP_Plus		98.61 14	98.30 26	99.55 3	99.62 23	98.95 6	98.82 99	98.81 63	95.80 74	99.16 15	99.47 5	95.37 43	99.92 15	97.89 33	99.75 32	99.79 4

Regformer-4		98.64 11	98.53 7	98.99 49	99.43 38	97.37 66	98.40 180	98.79 72	97.46 12	99.09 16	99.31 22	95.86 34	99.80 62	98.64 4	99.76 26	99.79 4

Regformer-3		98.59 17	98.50 11	98.86 59	99.43 38	97.05 77	98.40 180	98.68 100	97.43 13	99.06 17	99.31 22	95.80 35	99.77 84	98.62 6	99.76 26	99.78 7

PGM-MVS		98.49 29	98.23 34	99.27 25	99.72 11	98.08 41	98.99 66	99.49 5	95.43 88	99.03 18	99.32 21	95.56 38	99.94 3	96.80 82	99.77 20	99.78 7

VNet		97.79 56	97.40 63	98.96 53	98.88 111	97.55 60	98.63 146	98.93 36	96.74 46	99.02 19	98.84 84	90.33 133	99.83 47	98.53 10	96.66 161	99.50 74

xiu_mvs_v1_base_debu		97.60 63	97.56 53	97.72 124	98.35 141	95.98 116	97.86 243	98.51 135	97.13 34	99.01 20	98.40 123	91.56 111	99.80 62	98.53 10	98.68 105	97.37 204

xiu_mvs_v1_base		97.60 63	97.56 53	97.72 124	98.35 141	95.98 116	97.86 243	98.51 135	97.13 34	99.01 20	98.40 123	91.56 111	99.80 62	98.53 10	98.68 105	97.37 204

xiu_mvs_v1_base_debi		97.60 63	97.56 53	97.72 124	98.35 141	95.98 116	97.86 243	98.51 135	97.13 34	99.01 20	98.40 123	91.56 111	99.80 62	98.53 10	98.68 105	97.37 204

TSAR-MVS + GP.		98.38 34	98.24 32	98.81 60	99.22 74	97.25 72	98.11 216	98.29 172	97.19 30	98.99 23	99.02 61	96.22 14	99.67 102	98.52 14	98.56 113	99.51 72

HFP-MVS		98.63 13	98.40 14	99.32 18	99.72 11	98.29 28	99.23 23	98.96 31	96.10 67	98.94 24	99.17 42	96.06 23	99.92 15	97.62 47	99.78 15	99.75 22

region2R		98.61 14	98.38 17	99.29 20	99.74 7	98.16 37	99.23 23	98.93 36	96.15 62	98.94 24	99.17 42	95.91 31	99.94 3	97.55 52	99.79 11	99.78 7

#test#		98.54 26	98.27 28	99.32 18	99.72 11	98.29 28	98.98 69	98.96 31	95.65 80	98.94 24	99.17 42	96.06 23	99.92 15	97.21 62	99.78 15	99.75 22

HPM-MVS_fast		98.38 34	98.13 37	99.12 42	99.75 3	97.86 49	99.44 4	98.82 60	94.46 140	98.94 24	99.20 38	95.16 51	99.74 90	97.58 49	99.85 2	99.77 14

ACMMPR		98.59 17	98.36 19	99.29 20	99.74 7	98.15 38	99.23 23	98.95 33	96.10 67	98.93 28	99.19 41	95.70 36	99.94 3	97.62 47	99.79 11	99.78 7

DeepC-MVS_fast	96.70 1	98.55 24	98.34 22	99.18 34	99.25 67	98.04 42	98.50 169	98.78 74	97.72 4	98.92 29	99.28 28	95.27 47	99.82 53	97.55 52	99.77 20	99.69 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		98.47 30	98.39 15	98.69 64	99.46 35	96.49 101	98.30 193	98.69 97	97.21 28	98.84 30	99.36 17	95.41 42	99.78 79	98.62 6	99.65 46	99.80 3

MSLP-MVS++		98.56 23	98.57 5	98.55 73	99.26 66	96.80 86	98.71 130	99.05 23	97.28 21	98.84 30	99.28 28	96.47 12	99.40 138	98.52 14	99.70 40	99.47 80

PHI-MVS		98.34 38	98.06 39	99.18 34	99.15 81	98.12 40	99.04 61	99.09 19	93.32 194	98.83 32	99.10 51	96.54 10	99.83 47	97.70 44	99.76 26	99.59 64

MVSFormer		97.57 66	97.49 58	97.84 116	98.07 162	95.76 142	99.47 2	98.40 156	94.98 117	98.79 33	98.83 85	92.34 89	98.41 260	96.91 71	99.59 55	99.34 92

lupinMVS		97.44 73	97.22 70	98.12 102	98.07 162	95.76 142	97.68 257	97.76 234	94.50 137	98.79 33	98.61 105	92.34 89	99.30 144	97.58 49	99.59 55	99.31 96

CDPH-MVS		97.94 49	97.49 58	99.28 22	99.47 34	98.44 17	97.91 235	98.67 107	92.57 219	98.77 35	98.85 83	95.93 30	99.72 91	95.56 126	99.69 41	99.68 44

CNVR-MVS		98.78 3	98.56 6	99.45 10	99.32 48	98.87 8	98.47 172	98.81 63	97.72 4	98.76 36	99.16 45	97.05 4	99.78 79	98.06 25	99.66 45	99.69 38

EI-MVSNet-UG-set		98.41 32	98.34 22	98.61 69	99.45 36	96.32 109	98.28 195	98.68 100	97.17 31	98.74 37	99.37 13	95.25 48	99.79 74	98.57 8	99.54 67	99.73 30

UA-Net		97.96 47	97.62 50	98.98 51	98.86 113	97.47 63	98.89 81	99.08 20	96.67 49	98.72 38	99.54 1	93.15 82	99.81 55	94.87 142	98.83 101	99.65 53

旧先验2							97.57 265		91.30 264	98.67 39			99.80 62	95.70 123

PS-MVSNAJ		97.73 57	97.77 46	97.62 136	98.68 128	95.58 147	97.34 280	98.51 135	97.29 20	98.66 40	97.88 169	94.51 63	99.90 27	97.87 34	99.17 89	97.39 202

xiu_mvs_v2_base		97.66 62	97.70 49	97.56 144	98.61 134	95.46 153	97.44 269	98.46 145	97.15 32	98.65 41	98.15 147	94.33 69	99.80 62	97.84 37	98.66 109	97.41 200

LFMVS		95.86 133	94.98 155	98.47 81	98.87 112	96.32 109	98.84 96	96.02 321	93.40 191	98.62 42	99.20 38	74.99 330	99.63 109	97.72 43	97.20 152	99.46 84

HPM-MVS		98.36 36	98.10 38	99.13 40	99.74 7	97.82 52	99.53 1	98.80 70	94.63 133	98.61 43	98.97 68	95.13 52	99.77 84	97.65 46	99.83 7	99.79 4
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				98.26 93	99.20 77	95.36 156		98.68 100	91.89 244	98.60 44	99.10 51	94.44 68	99.82 53	94.27 160	99.44 77	99.58 66

CP-MVS		98.57 21	98.36 19	99.19 30	99.66 19	97.86 49	99.34 11	98.87 50	95.96 70	98.60 44	99.13 47	96.05 25	99.94 3	97.77 40	99.86 1	99.77 14

jason		97.32 82	97.08 76	98.06 107	97.45 201	95.59 146	97.87 242	97.91 229	94.79 125	98.55 46	98.83 85	91.12 119	99.23 151	97.58 49	99.60 52	99.34 92
jason: jason.
MCST-MVS		98.65 10	98.37 18	99.48 7	99.60 24	98.87 8	98.41 179	98.68 100	97.04 38	98.52 47	98.80 88	96.78 6	99.83 47	97.93 29	99.61 51	99.74 28

XVS		98.70 5	98.49 12	99.34 15	99.70 15	98.35 25	99.29 15	98.88 47	97.40 14	98.46 48	99.20 38	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

X-MVStestdata		94.06 250	92.30 270	99.34 15	99.70 15	98.35 25	99.29 15	98.88 47	97.40 14	98.46 48	43.50 359	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

MG-MVS		97.81 55	97.60 51	98.44 83	99.12 83	95.97 120	97.75 252	98.78 74	96.89 42	98.46 48	99.22 34	93.90 76	99.68 101	94.81 146	99.52 69	99.67 49

NCCC		98.61 14	98.35 21	99.38 12	99.28 63	98.61 13	98.45 173	98.76 78	97.82 3	98.45 51	98.93 77	96.65 8	99.83 47	97.38 59	99.41 79	99.71 35

MVS_Test		97.28 83	97.00 79	98.13 101	98.33 146	95.97 120	98.74 124	98.07 219	94.27 143	98.44 52	98.07 153	92.48 88	99.26 148	96.43 95	98.19 128	99.16 117

MVS_111021_LR		98.34 38	98.23 34	98.67 66	99.27 64	96.90 83	97.95 230	99.58 3	97.14 33	98.44 52	99.01 65	95.03 54	99.62 111	97.91 30	99.75 32	99.50 74

VDDNet		95.36 171	94.53 184	97.86 115	98.10 161	95.13 166	98.85 93	97.75 235	90.46 277	98.36 54	99.39 8	73.27 337	99.64 106	97.98 28	96.58 164	98.81 145

mPP-MVS		98.51 28	98.26 29	99.25 26	99.75 3	98.04 42	99.28 17	98.81 63	96.24 60	98.35 55	99.23 32	95.46 41	99.94 3	97.42 57	99.81 8	99.77 14

DELS-MVS		98.40 33	98.20 36	98.99 49	99.00 91	97.66 55	97.75 252	98.89 44	97.71 6	98.33 56	98.97 68	94.97 55	99.88 37	98.42 16	99.76 26	99.42 89
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
MVS_111021_HR		98.47 30	98.34 22	98.88 58	99.22 74	97.32 67	97.91 235	99.58 3	97.20 29	98.33 56	99.00 66	95.99 27	99.64 106	98.05 27	99.76 26	99.69 38

HPM-MVS++		98.58 19	98.25 30	99.55 3	99.50 29	99.08 3	98.72 129	98.66 110	97.51 8	98.15 58	98.83 85	95.70 36	99.92 15	97.53 54	99.67 42	99.66 51

新几何1				99.16 37	99.34 42	98.01 44		98.69 97	90.06 285	98.13 59	98.95 75	94.60 61	99.89 29	91.97 222	99.47 72	99.59 64

API-MVS		97.41 77	97.25 67	97.91 112	98.70 125	96.80 86	98.82 99	98.69 97	94.53 135	98.11 60	98.28 137	94.50 66	99.57 120	94.12 164	99.49 70	97.37 204

CPTT-MVS		97.72 58	97.32 65	98.92 55	99.64 20	97.10 76	99.12 50	98.81 63	92.34 233	98.09 61	99.08 57	93.01 83	99.92 15	96.06 106	99.77 20	99.75 22

test12				99.18 34	99.16 79	98.19 35		98.53 131		98.07 62		95.13 52	99.72 91		99.56 64	99.63 58

test222					99.23 73	97.17 75	97.40 272	98.66 110	88.68 309	98.05 63	98.96 73	94.14 72			99.53 68	99.61 59

DP-MVS Recon		97.86 53	97.46 60	99.06 47	99.53 27	98.35 25	98.33 186	98.89 44	92.62 216	98.05 63	98.94 76	95.34 45	99.65 104	96.04 107	99.42 78	99.19 111

Vis-MVSNet		97.42 75	97.11 74	98.34 89	98.66 129	96.23 112	99.22 29	99.00 26	96.63 51	98.04 65	99.21 35	88.05 193	99.35 143	96.01 109	99.21 87	99.45 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		97.70 59	97.25 67	99.07 45	98.90 102	97.83 51	98.20 201	98.74 82	97.51 8	98.03 66	99.06 59	86.12 231	99.93 9	99.02 1	99.64 48	99.44 87

0601test		97.22 85	96.78 88	98.54 75	98.73 121	96.60 95	98.45 173	98.31 166	94.70 126	98.02 67	98.42 122	90.80 126	99.70 96	96.81 81	96.79 159	99.34 92

zzz-MVS		98.55 24	98.25 30	99.46 8	99.76 1	98.64 11	98.55 159	98.74 82	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

MTAPA		98.58 19	98.29 27	99.46 8	99.76 1	98.64 11	98.90 77	98.74 82	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

1121		97.37 80	96.77 91	99.16 37	99.34 42	97.99 47	98.19 205	98.68 100	90.14 284	98.01 70	98.97 68	94.80 59	99.87 38	93.36 180	99.46 75	99.61 59

sss		97.39 78	96.98 80	98.61 69	98.60 135	96.61 94	98.22 199	98.93 36	93.97 154	98.01 70	98.48 117	91.98 103	99.85 43	96.45 94	98.15 129	99.39 90

alignmvs		97.56 67	97.07 77	99.01 48	98.66 129	98.37 23	98.83 97	98.06 221	96.74 46	98.00 72	97.65 189	90.80 126	99.48 136	98.37 19	96.56 165	99.19 111

OMC-MVS		97.55 68	97.34 64	98.20 96	99.33 45	95.92 134	98.28 195	98.59 118	95.52 85	97.97 73	99.10 51	93.28 81	99.49 132	95.09 140	98.88 97	99.19 111

VDD-MVS		95.82 135	95.23 145	97.61 141	98.84 116	93.98 234	98.68 139	97.40 269	95.02 116	97.95 74	99.34 20	74.37 335	99.78 79	98.64 4	96.80 158	99.08 128

PVSNet_BlendedMVS		96.73 105	96.60 98	97.12 169	99.25 67	95.35 158	98.26 197	99.26 8	94.28 142	97.94 75	97.46 201	92.74 86	99.81 55	96.88 76	93.32 237	96.20 298

PVSNet_Blended		97.38 79	97.12 72	98.14 99	99.25 67	95.35 158	97.28 284	99.26 8	93.13 200	97.94 75	98.21 144	92.74 86	99.81 55	96.88 76	99.40 81	99.27 103

MP-MVS		98.33 40	98.01 41	99.28 22	99.75 3	98.18 36	99.22 29	98.79 72	96.13 64	97.92 77	99.23 32	94.54 62	99.94 3	96.74 84	99.78 15	99.73 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDTV_nov1_ep13_2view						84.26 333	96.89 303		90.97 272	97.90 78		89.89 137		93.91 168		99.18 115

test_prior3		98.22 44	97.90 45	99.19 30	99.31 50	98.22 33	97.80 248	98.84 55	96.12 65	97.89 79	98.69 97	95.96 28	99.70 96	96.89 73	99.60 52	99.65 53

test_prior2							97.80 248		96.12 65	97.89 79	98.69 97	95.96 28		96.89 73	99.60 52

原ACMM1				98.65 67	99.32 48	96.62 92		98.67 107	93.27 197	97.81 81	98.97 68	95.18 50	99.83 47	93.84 169	99.46 75	99.50 74

114514_t		96.93 98	96.27 109	98.92 55	99.50 29	97.63 57	98.85 93	98.90 42	84.80 332	97.77 82	99.11 49	92.84 84	99.66 103	94.85 143	99.77 20	99.47 80

PMMVS		96.60 108	96.33 107	97.41 155	97.90 173	93.93 235	97.35 279	98.41 154	92.84 212	97.76 83	97.45 203	91.10 121	99.20 159	96.26 101	97.91 136	99.11 123

PVSNet	91.96 18	96.35 118	96.15 113	96.96 179	99.17 78	92.05 268	96.08 321	98.68 100	93.69 172	97.75 84	97.80 179	88.86 160	99.69 100	94.26 161	99.01 92	99.15 118

TEST9					99.31 50	98.50 15	97.92 232	98.73 87	92.63 215	97.74 85	98.68 99	96.20 15	99.80 62

train_agg		97.97 46	97.52 56	99.33 17	99.31 50	98.50 15	97.92 232	98.73 87	92.98 205	97.74 85	98.68 99	96.20 15	99.80 62	96.59 88	99.57 58	99.68 44

CANet		98.05 45	97.76 47	98.90 57	98.73 121	97.27 69	98.35 184	98.78 74	97.37 19	97.72 87	98.96 73	91.53 115	99.92 15	98.79 3	99.65 46	99.51 72

test_8					99.29 58	98.44 17	97.89 240	98.72 89	92.98 205	97.70 88	98.66 102	96.20 15	99.80 62

MP-MVS-pluss		98.31 41	97.92 44	99.49 6	99.72 11	98.88 7	98.43 177	98.78 74	94.10 146	97.69 89	99.42 6	95.25 48	99.92 15	98.09 24	99.80 10	99.67 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		97.67 61	97.23 69	98.98 51	98.70 125	98.38 20	99.34 11	98.39 158	96.76 45	97.67 90	97.40 205	92.26 93	99.49 132	98.28 22	96.28 183	99.08 128

PVSNet_Blended_VisFu		97.70 59	97.46 60	98.44 83	99.27 64	95.91 136	98.63 146	99.16 17	94.48 139	97.67 90	98.88 81	92.80 85	99.91 24	97.11 64	99.12 90	99.50 74

WTY-MVS		97.37 80	96.92 82	98.72 63	98.86 113	96.89 85	98.31 191	98.71 94	95.26 103	97.67 90	98.56 111	92.21 96	99.78 79	95.89 112	96.85 157	99.48 79

Effi-MVS+		97.12 91	96.69 94	98.39 87	98.19 154	96.72 90	97.37 276	98.43 153	93.71 169	97.65 93	98.02 156	92.20 97	99.25 149	96.87 79	97.79 142	99.19 111

HyFIR lowres test		96.90 100	96.49 103	98.14 99	99.33 45	95.56 149	97.38 274	99.65 2	92.34 233	97.61 94	98.20 145	89.29 145	99.10 173	96.97 67	97.60 148	99.77 14

ACMMP		98.23 43	97.95 43	99.09 44	99.74 7	97.62 58	99.03 62	99.41 6	95.98 69	97.60 95	99.36 17	94.45 67	99.93 9	97.14 63	98.85 100	99.70 37
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
agg_prior3		97.87 52	97.42 62	99.23 29	99.29 58	98.23 31	97.92 232	98.72 89	92.38 232	97.59 96	98.64 104	96.09 21	99.79 74	96.59 88	99.57 58	99.68 44

agg_prior1		97.95 48	97.51 57	99.28 22	99.30 55	98.38 20	97.81 247	98.72 89	93.16 199	97.57 97	98.66 102	96.14 18	99.81 55	96.63 87	99.56 64	99.66 51

agg_prior					99.30 55	98.38 20		98.72 89		97.57 97			99.81 55

casdiffmvs		97.42 75	97.12 72	98.31 91	98.35 141	96.55 99	99.05 58	98.20 185	94.97 119	97.55 99	98.11 150	92.33 91	99.18 161	97.70 44	97.85 140	99.18 115

tpmrst		95.63 144	95.69 129	95.44 269	97.54 193	88.54 318	96.97 294	97.56 243	93.50 182	97.52 100	96.93 258	89.49 140	99.16 162	95.25 137	96.42 170	98.64 156

MDTV_nov1_ep13			95.40 133		97.48 196	88.34 320	96.85 306	97.29 278	93.74 166	97.48 101	97.26 215	89.18 148	99.05 177	91.92 224	97.43 150

EPMVS		94.99 189	94.48 186	96.52 221	97.22 214	91.75 274	97.23 286	91.66 354	94.11 145	97.28 102	96.81 269	85.70 245	98.84 205	93.04 190	97.28 151	98.97 136

IS-MVSNet		97.22 85	96.88 83	98.25 94	98.85 115	96.36 107	99.19 35	97.97 226	95.39 90	97.23 103	98.99 67	91.11 120	98.93 194	94.60 150	98.59 111	99.47 80

EPP-MVSNet		97.46 69	97.28 66	97.99 109	98.64 131	95.38 155	99.33 13	98.31 166	93.61 179	97.19 104	99.07 58	94.05 73	99.23 151	96.89 73	98.43 120	99.37 91

CANet_DTU		96.96 97	96.55 100	98.21 95	98.17 158	96.07 115	97.98 228	98.21 182	97.24 27	97.13 105	98.93 77	86.88 220	99.91 24	95.00 141	99.37 83	98.66 154

CHOSEN 1792x2688		97.12 91	96.80 85	98.08 105	99.30 55	94.56 216	98.05 221	99.71 1	93.57 180	97.09 106	98.91 80	88.17 187	99.89 29	96.87 79	99.56 64	99.81 2

PatchT		93.06 271	91.97 273	96.35 234	96.69 245	92.67 261	94.48 342	97.08 286	86.62 318	97.08 107	92.23 341	87.94 195	97.90 295	78.89 336	96.69 160	98.49 162

PatchmatchNet		95.71 140	95.52 132	96.29 239	97.58 190	90.72 288	96.84 307	97.52 249	94.06 148	97.08 107	96.96 251	89.24 147	98.90 199	92.03 219	98.37 121	99.26 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MAR-MVS		96.91 99	96.40 105	98.45 82	98.69 127	96.90 83	98.66 144	98.68 100	92.40 231	97.07 109	97.96 161	91.54 114	99.75 88	93.68 173	98.92 95	98.69 151
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
PAPM_NR		97.46 69	97.11 74	98.50 78	99.50 29	96.41 105	98.63 146	98.60 117	95.18 107	97.06 110	98.06 154	94.26 71	99.57 120	93.80 171	98.87 99	99.52 69

TAMVS		97.02 95	96.79 87	97.70 129	98.06 164	95.31 160	98.52 164	98.31 166	93.95 155	97.05 111	98.61 105	93.49 78	98.52 236	95.33 132	97.81 141	99.29 101

diffmvs		97.03 94	96.75 92	97.88 114	98.14 159	95.25 162	98.54 163	98.13 201	95.17 108	97.03 112	97.94 163	91.83 106	99.30 144	96.01 109	97.94 135	99.11 123

CSCG		97.85 54	97.74 48	98.20 96	99.67 18	95.16 164	99.22 29	99.32 7	93.04 202	97.02 113	98.92 79	95.36 44	99.91 24	97.43 56	99.64 48	99.52 69

tfpn_ndepth		95.53 153	94.90 164	97.39 160	98.96 99	95.88 139	99.05 58	95.27 336	93.80 163	96.95 114	96.93 258	85.53 247	99.40 138	91.54 233	96.10 191	96.89 231

CDS-MVSNet		96.99 96	96.69 94	97.90 113	98.05 165	95.98 116	98.20 201	98.33 165	93.67 176	96.95 114	98.49 116	93.54 77	98.42 253	95.24 138	97.74 145	99.31 96
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XVG-OURS-SEG-HR		96.51 113	96.34 106	97.02 175	98.77 119	93.76 240	97.79 250	98.50 140	95.45 87	96.94 116	99.09 55	87.87 199	99.55 128	96.76 83	95.83 201	97.74 191

CR-MVSNet		94.76 205	94.15 204	96.59 211	97.00 226	93.43 249	94.96 335	97.56 243	92.46 220	96.93 117	96.24 288	88.15 188	97.88 299	87.38 306	96.65 162	98.46 163

RPMNet		92.52 275	91.17 278	96.59 211	97.00 226	93.43 249	94.96 335	97.26 281	82.27 339	96.93 117	92.12 342	86.98 218	97.88 299	76.32 341	96.65 162	98.46 163

Patchmatch-test1		95.32 175	94.97 157	96.35 234	97.67 183	91.29 280	97.33 281	97.60 241	94.68 128	96.92 119	96.95 252	83.97 280	98.50 239	91.33 238	98.32 124	99.25 105

PatchMatch-RL		96.59 110	96.03 117	98.27 92	99.31 50	96.51 100	97.91 235	99.06 21	93.72 168	96.92 119	98.06 154	88.50 182	99.65 104	91.77 228	99.00 93	98.66 154

DeepC-MVS	95.98 3	97.88 51	97.58 52	98.77 61	99.25 67	96.93 81	98.83 97	98.75 81	96.96 41	96.89 121	99.50 3	90.46 130	99.87 38	97.84 37	99.76 26	99.52 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tfpn1000		95.72 138	95.11 149	97.58 142	99.00 91	95.73 144	99.24 21	95.49 335	94.08 147	96.87 122	97.45 203	85.81 243	99.30 144	91.78 227	96.22 188	97.71 194

XVG-OURS		96.55 112	96.41 104	96.99 176	98.75 120	93.76 240	97.50 268	98.52 133	95.67 78	96.83 123	99.30 27	88.95 157	99.53 129	95.88 113	96.26 184	97.69 195

AdaColmap		97.15 90	96.70 93	98.48 80	99.16 79	96.69 91	98.01 225	98.89 44	94.44 141	96.83 123	98.68 99	90.69 128	99.76 86	94.36 156	99.29 86	98.98 135

CostFormer		94.95 193	94.73 176	95.60 262	97.28 210	89.06 309	97.53 266	96.89 305	89.66 298	96.82 125	96.72 272	86.05 239	98.95 193	95.53 127	96.13 190	98.79 146

UGNet		96.78 104	96.30 108	98.19 98	98.24 149	95.89 138	98.88 84	98.93 36	97.39 16	96.81 126	97.84 173	82.60 289	99.90 27	96.53 91	99.49 70	98.79 146
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
CNLPA		97.45 72	97.03 78	98.73 62	99.05 85	97.44 65	98.07 220	98.53 131	95.32 101	96.80 127	98.53 112	93.32 80	99.72 91	94.31 159	99.31 85	99.02 131

CHOSEN 280x420		97.18 88	97.18 71	97.20 163	98.81 117	93.27 252	95.78 329	99.15 18	95.25 104	96.79 128	98.11 150	92.29 92	99.07 176	98.56 9	99.85 2	99.25 105

HY-MVS	93.96 8	96.82 103	96.23 112	98.57 71	98.46 140	97.00 78	98.14 211	98.21 182	93.95 155	96.72 129	97.99 160	91.58 110	99.76 86	94.51 154	96.54 166	98.95 140

PAPR		96.84 102	96.24 111	98.65 67	98.72 124	96.92 82	97.36 278	98.57 124	93.33 193	96.67 130	97.57 196	94.30 70	99.56 122	91.05 243	98.59 111	99.47 80

Anonymous20240529		95.10 185	94.22 197	97.75 122	99.01 90	94.26 228	98.87 85	98.83 59	85.79 327	96.64 131	98.97 68	78.73 312	99.85 43	96.27 100	94.89 208	99.12 122

thres600view7		95.49 158	94.77 173	97.67 132	98.98 95	95.02 169	98.85 93	96.90 301	95.38 91	96.63 132	96.90 260	84.29 270	99.59 113	88.65 292	96.33 176	98.40 166

tfpn111		95.43 162	94.74 175	97.51 146	98.98 95	94.92 177	98.87 85	96.90 301	95.38 91	96.61 133	96.88 263	84.29 270	99.59 113	88.43 293	96.32 177	98.02 181

conf200view11		95.40 167	94.70 177	97.50 151	98.98 95	94.92 177	98.87 85	96.90 301	95.38 91	96.61 133	96.88 263	84.29 270	99.56 122	88.11 299	96.29 179	98.02 181

thres100view900		95.38 168	94.70 177	97.41 155	98.98 95	94.92 177	98.87 85	96.90 301	95.38 91	96.61 133	96.88 263	84.29 270	99.56 122	88.11 299	96.29 179	97.76 189

Vis-MVSNet (Re-imp)		96.87 101	96.55 100	97.83 117	98.73 121	95.46 153	99.20 33	98.30 170	94.96 120	96.60 136	98.87 82	90.05 136	98.59 224	93.67 174	98.60 110	99.46 84

CVMVSNet		95.43 162	96.04 116	93.57 311	97.93 171	83.62 334	98.12 214	98.59 118	95.68 77	96.56 137	99.02 61	87.51 209	97.51 308	93.56 177	97.44 149	99.60 62

RPSCF		94.87 197	95.40 133	93.26 315	98.89 110	82.06 340	98.33 186	98.06 221	90.30 281	96.56 137	99.26 30	87.09 215	99.49 132	93.82 170	96.32 177	98.24 176

tfpn200view9		95.32 175	94.62 180	97.43 154	98.94 100	94.98 173	98.68 139	96.93 299	95.33 99	96.55 139	96.53 279	84.23 275	99.56 122	88.11 299	96.29 179	97.76 189

thres400		95.38 168	94.62 180	97.65 135	98.94 100	94.98 173	98.68 139	96.93 299	95.33 99	96.55 139	96.53 279	84.23 275	99.56 122	88.11 299	96.29 179	98.40 166

thres200		95.25 178	94.57 182	97.28 161	98.81 117	94.92 177	98.20 201	97.11 285	95.24 106	96.54 141	96.22 292	84.58 262	99.53 129	87.93 304	96.50 168	97.39 202

ab-mvs		96.42 116	95.71 127	98.55 73	98.63 132	96.75 89	97.88 241	98.74 82	93.84 160	96.54 141	98.18 146	85.34 252	99.75 88	95.93 111	96.35 175	99.15 118

view600		95.60 147	94.93 159	97.62 136	99.05 85	94.85 183	99.09 53	97.01 293	95.36 95	96.52 143	97.37 206	84.55 263	99.59 113	89.07 283	96.39 171	98.40 166

view800		95.60 147	94.93 159	97.62 136	99.05 85	94.85 183	99.09 53	97.01 293	95.36 95	96.52 143	97.37 206	84.55 263	99.59 113	89.07 283	96.39 171	98.40 166

conf0.05thres1000		95.60 147	94.93 159	97.62 136	99.05 85	94.85 183	99.09 53	97.01 293	95.36 95	96.52 143	97.37 206	84.55 263	99.59 113	89.07 283	96.39 171	98.40 166

tfpn		95.60 147	94.93 159	97.62 136	99.05 85	94.85 183	99.09 53	97.01 293	95.36 95	96.52 143	97.37 206	84.55 263	99.59 113	89.07 283	96.39 171	98.40 166

mvs-test1		96.60 108	96.68 96	96.37 232	97.89 174	91.81 271	98.56 157	98.10 214	96.57 52	96.52 143	97.94 163	90.81 124	99.45 137	95.72 119	98.01 132	97.86 188

conf0.01		95.56 151	94.84 167	97.72 124	98.90 102	95.93 127	99.17 36	95.70 327	93.42 185	96.50 148	97.16 221	86.12 231	99.22 153	90.51 251	96.06 192	98.02 181

conf0.002		95.56 151	94.84 167	97.72 124	98.90 102	95.93 127	99.17 36	95.70 327	93.42 185	96.50 148	97.16 221	86.12 231	99.22 153	90.51 251	96.06 192	98.02 181

thresconf0.02		95.50 154	94.84 167	97.51 146	98.90 102	95.93 127	99.17 36	95.70 327	93.42 185	96.50 148	97.16 221	86.12 231	99.22 153	90.51 251	96.06 192	97.37 204

tfpn_n400		95.50 154	94.84 167	97.51 146	98.90 102	95.93 127	99.17 36	95.70 327	93.42 185	96.50 148	97.16 221	86.12 231	99.22 153	90.51 251	96.06 192	97.37 204

tfpnconf		95.50 154	94.84 167	97.51 146	98.90 102	95.93 127	99.17 36	95.70 327	93.42 185	96.50 148	97.16 221	86.12 231	99.22 153	90.51 251	96.06 192	97.37 204

tfpnview11		95.50 154	94.84 167	97.51 146	98.90 102	95.93 127	99.17 36	95.70 327	93.42 185	96.50 148	97.16 221	86.12 231	99.22 153	90.51 251	96.06 192	97.37 204

Anonymous202405211		95.28 177	94.49 185	97.67 132	99.00 91	93.75 242	98.70 133	97.04 289	90.66 274	96.49 154	98.80 88	78.13 315	99.83 47	96.21 103	95.36 205	99.44 87

ADS-MVSNet2		94.58 221	94.40 192	95.11 283	98.00 166	88.74 313	96.04 322	97.30 277	90.15 282	96.47 155	96.64 276	87.89 197	97.56 307	90.08 262	97.06 153	99.02 131

ADS-MVSNet		95.00 188	94.45 190	96.63 205	98.00 166	91.91 270	96.04 322	97.74 236	90.15 282	96.47 155	96.64 276	87.89 197	98.96 189	90.08 262	97.06 153	99.02 131

Effi-MVS+-dtu		96.29 120	96.56 99	95.51 263	97.89 174	90.22 295	98.80 108	98.10 214	96.57 52	96.45 157	96.66 274	90.81 124	98.91 196	95.72 119	97.99 133	97.40 201

PLC	95.07 4	97.20 87	96.78 88	98.44 83	99.29 58	96.31 111	98.14 211	98.76 78	92.41 230	96.39 158	98.31 136	94.92 56	99.78 79	94.06 165	98.77 104	99.23 107
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		94.13 245	93.80 227	95.12 282	96.50 254	87.91 324	97.44 269	95.89 326	92.62 216	96.37 159	96.30 287	84.13 278	98.30 273	93.24 183	91.66 257	99.14 120

TAPA-MVS	93.98 7	95.35 172	94.56 183	97.74 123	99.13 82	94.83 194	98.33 186	98.64 115	86.62 318	96.29 160	98.61 105	94.00 75	99.29 147	80.00 332	99.41 79	99.09 125
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tpm2		94.19 239	93.76 232	95.46 267	97.23 213	89.04 310	97.31 283	96.85 308	87.08 317	96.21 161	96.79 270	83.75 285	98.74 213	92.43 211	96.23 186	98.59 158

F-COLMAP		97.09 93	96.80 85	97.97 110	99.45 36	94.95 176	98.55 159	98.62 116	93.02 203	96.17 162	98.58 110	94.01 74	99.81 55	93.95 167	98.90 96	99.14 120

PatchFormer-LS_test		95.47 159	95.27 144	96.08 247	97.59 189	90.66 289	98.10 218	97.34 273	93.98 153	96.08 163	96.15 294	87.65 207	99.12 166	95.27 136	95.24 206	98.44 165

JIA-IIPM		93.35 262	92.49 267	95.92 250	96.48 256	90.65 290	95.01 334	96.96 297	85.93 325	96.08 163	87.33 346	87.70 205	98.78 212	91.35 237	95.58 203	98.34 173

BH-RMVSNet		95.92 131	95.32 141	97.69 130	98.32 147	94.64 208	98.19 205	97.45 264	94.56 134	96.03 165	98.61 105	85.02 255	99.12 166	90.68 247	99.06 91	99.30 99

dp		94.15 244	93.90 222	94.90 288	97.31 209	86.82 330	96.97 294	97.19 284	91.22 269	96.02 166	96.61 278	85.51 248	99.02 184	90.00 266	94.30 210	98.85 142

EPNet		97.28 83	96.87 84	98.51 77	94.98 320	96.14 114	98.90 77	97.02 291	98.28 1	95.99 167	99.11 49	91.36 116	99.89 29	96.98 66	99.19 88	99.50 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		97.16 89	96.66 97	98.68 65	98.53 139	97.19 74	98.93 75	98.90 42	92.83 213	95.99 167	99.37 13	92.12 99	99.87 38	93.67 174	99.57 58	98.97 136

TR-MVS		94.94 195	94.20 201	97.17 166	97.75 180	94.14 231	97.59 263	97.02 291	92.28 237	95.75 169	97.64 191	83.88 282	98.96 189	89.77 268	96.15 189	98.40 166

VPA-MVSNet		95.75 137	95.11 149	97.69 130	97.24 212	97.27 69	98.94 74	99.23 12	95.13 110	95.51 170	97.32 212	85.73 244	98.91 196	97.33 60	89.55 275	96.89 231

HQP_MVS		96.14 125	95.90 120	96.85 185	97.42 202	94.60 214	98.80 108	98.56 125	97.28 21	95.34 171	98.28 137	87.09 215	99.03 182	96.07 104	94.27 211	96.92 223

plane_prior3						94.61 212			97.02 39	95.34 171

DWT-MVSNet_test		94.82 201	94.36 193	96.20 242	97.35 207	90.79 286	98.34 185	96.57 316	92.91 208	95.33 173	96.44 284	82.00 291	99.12 166	94.52 153	95.78 202	98.70 150

Fast-Effi-MVS+		96.28 122	95.70 128	98.03 108	98.29 148	95.97 120	98.58 152	98.25 178	91.74 248	95.29 174	97.23 218	91.03 123	99.15 163	92.90 197	97.96 134	98.97 136

EI-MVSNet		95.96 128	95.83 122	96.36 233	97.93 171	93.70 245	98.12 214	98.27 173	93.70 171	95.07 175	99.02 61	92.23 95	98.54 229	94.68 147	93.46 232	96.84 238

MVSTER		96.06 126	95.72 124	97.08 173	98.23 150	95.93 127	98.73 127	98.27 173	94.86 124	95.07 175	98.09 152	88.21 186	98.54 229	96.59 88	93.46 232	96.79 242

OPM-MVS		95.69 142	95.33 140	96.76 189	96.16 287	94.63 209	98.43 177	98.39 158	96.64 50	95.02 177	98.78 90	85.15 254	99.05 177	95.21 139	94.20 214	96.60 272

Fast-Effi-MVS+-dtu		95.87 132	95.85 121	95.91 251	97.74 181	91.74 275	98.69 135	98.15 198	95.56 83	94.92 178	97.68 188	88.98 155	98.79 211	93.19 185	97.78 143	97.20 212

TESTMET0.1,1		94.18 241	93.69 236	95.63 261	96.92 231	89.12 308	96.91 298	94.78 342	93.17 198	94.88 179	96.45 283	78.52 313	98.92 195	93.09 187	98.50 115	98.85 142

VPNet		94.99 189	94.19 202	97.40 157	97.16 220	96.57 96	98.71 130	98.97 29	95.67 78	94.84 180	98.24 143	80.36 305	98.67 218	96.46 93	87.32 308	96.96 220

1112_ss		96.63 107	96.00 118	98.50 78	98.56 136	96.37 106	98.18 209	98.10 214	92.92 207	94.84 180	98.43 120	92.14 98	99.58 119	94.35 157	96.51 167	99.56 68

test-LLR		95.10 185	94.87 165	95.80 256	96.77 239	89.70 299	96.91 298	95.21 337	95.11 111	94.83 182	95.72 305	87.71 203	98.97 186	93.06 188	98.50 115	98.72 148

test-mter		94.08 248	93.51 247	95.80 256	96.77 239	89.70 299	96.91 298	95.21 337	92.89 209	94.83 182	95.72 305	77.69 318	98.97 186	93.06 188	98.50 115	98.72 148

Test_1112_low_res		96.34 119	95.66 131	98.36 88	98.56 136	95.94 124	97.71 254	98.07 219	92.10 239	94.79 184	97.29 214	91.75 107	99.56 122	94.17 162	96.50 168	99.58 66

GA-MVS		94.81 202	94.03 212	97.14 167	97.15 221	93.86 237	96.76 309	97.58 242	94.00 151	94.76 185	97.04 242	80.91 298	98.48 240	91.79 226	96.25 185	99.09 125

BH-untuned		95.95 129	95.72 124	96.65 202	98.55 138	92.26 265	98.23 198	97.79 233	93.73 167	94.62 186	98.01 158	88.97 156	99.00 185	93.04 190	98.51 114	98.68 152

test_djsdf		96.00 127	95.69 129	96.93 182	95.72 304	95.49 152	99.47 2	98.40 156	94.98 117	94.58 187	97.86 170	89.16 149	98.41 260	96.91 71	94.12 219	96.88 233

cascas		94.63 217	93.86 224	96.93 182	96.91 233	94.27 227	96.00 325	98.51 135	85.55 328	94.54 188	96.23 290	84.20 277	98.87 202	95.80 117	96.98 156	97.66 196

DP-MVS		96.59 110	95.93 119	98.57 71	99.34 42	96.19 113	98.70 133	98.39 158	89.45 302	94.52 189	99.35 19	91.85 105	99.85 43	92.89 199	98.88 97	99.68 44

gg-mvs-nofinetune		92.21 278	90.58 292	97.13 168	96.75 242	95.09 167	95.85 327	89.40 357	85.43 329	94.50 190	81.98 350	80.80 301	98.40 266	92.16 213	98.33 123	97.88 187

mvs_anonymous		96.70 106	96.53 102	97.18 165	98.19 154	93.78 239	98.31 191	98.19 187	94.01 150	94.47 191	98.27 140	92.08 101	98.46 245	97.39 58	97.91 136	99.31 96

v1neww		94.83 198	94.22 197	96.68 197	96.39 260	94.85 183	98.87 85	98.11 209	92.45 225	94.45 192	97.06 237	88.82 165	98.54 229	92.93 194	88.91 287	96.65 265

v7new		94.83 198	94.22 197	96.68 197	96.39 260	94.85 183	98.87 85	98.11 209	92.45 225	94.45 192	97.06 237	88.82 165	98.54 229	92.93 194	88.91 287	96.65 265

HQP-NCC					97.20 216		98.05 221		96.43 54	94.45 192

ACMP_Plane					97.20 216		98.05 221		96.43 54	94.45 192

HQP4-MVS										94.45 192			98.96 189			96.87 235

HQP-MVS		95.72 138	95.40 133	96.69 194	97.20 216	94.25 229	98.05 221	98.46 145	96.43 54	94.45 192	97.73 182	86.75 221	98.96 189	95.30 133	94.18 215	96.86 237

v6		94.83 198	94.21 200	96.69 194	96.36 264	94.85 183	98.87 85	98.11 209	92.46 220	94.44 198	97.05 241	88.76 171	98.57 227	92.95 193	88.92 286	96.65 265

MSDG		95.93 130	95.30 143	97.83 117	98.90 102	95.36 156	96.83 308	98.37 161	91.32 263	94.43 199	98.73 96	90.27 134	99.60 112	90.05 264	98.82 102	98.52 160

nrg030		96.28 122	95.72 124	97.96 111	96.90 234	98.15 38	99.39 5	98.31 166	95.47 86	94.42 200	98.35 129	92.09 100	98.69 214	97.50 55	89.05 282	97.04 216

CLD-MVS		95.62 145	95.34 138	96.46 228	97.52 195	93.75 242	97.27 285	98.46 145	95.53 84	94.42 200	98.00 159	86.21 229	98.97 186	96.25 102	94.37 209	96.66 263
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		95.62 145	95.34 138	96.47 225	97.46 198	93.54 246	98.99 66	98.54 128	94.67 129	94.36 202	98.77 92	85.39 249	99.11 170	95.71 121	94.15 217	96.76 245

LGP-MVS_train				96.47 225	97.46 198	93.54 246		98.54 128	94.67 129	94.36 202	98.77 92	85.39 249	99.11 170	95.71 121	94.15 217	96.76 245

v144192		94.39 230	93.70 235	96.48 224	96.06 290	94.35 223	98.58 152	98.16 197	91.45 254	94.33 204	97.02 244	87.50 211	98.45 247	91.08 240	89.11 281	96.63 268

v1		94.75 207	94.11 209	96.69 194	96.27 279	94.87 181	98.69 135	98.12 204	92.43 228	94.32 205	96.94 254	88.71 174	98.54 229	92.66 203	88.84 292	96.67 260

v1141		94.75 207	94.11 209	96.67 200	96.27 279	94.86 182	98.69 135	98.12 204	92.43 228	94.31 206	96.94 254	88.78 170	98.48 240	92.63 204	88.85 291	96.67 260

V42		94.78 204	94.14 205	96.70 193	96.33 271	95.22 163	98.97 70	98.09 217	92.32 235	94.31 206	97.06 237	88.39 183	98.55 228	92.90 197	88.87 289	96.34 293

v7		94.69 211	94.04 211	96.62 207	96.41 259	94.79 202	98.78 115	98.13 201	91.89 244	94.30 208	97.16 221	88.13 190	98.45 247	91.96 223	89.65 272	96.61 270

divwei89l23v2f112		94.76 205	94.12 208	96.67 200	96.28 277	94.85 183	98.69 135	98.12 204	92.44 227	94.29 209	96.94 254	88.85 162	98.48 240	92.67 202	88.79 293	96.67 260

ACMM	93.85 9	95.69 142	95.38 137	96.61 208	97.61 187	93.84 238	98.91 76	98.44 149	95.25 104	94.28 210	98.47 118	86.04 241	99.12 166	95.50 128	93.95 224	96.87 235
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		95.46 160	95.21 146	96.22 241	98.12 160	93.72 244	98.32 190	98.13 201	93.71 169	94.26 211	97.31 213	92.24 94	98.10 282	94.63 148	90.12 267	96.84 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		94.20 238	93.47 249	96.40 231	95.98 293	94.08 232	98.52 164	98.15 198	91.33 262	94.25 212	97.20 220	86.41 226	98.42 253	90.04 265	89.39 278	96.69 259

BH-w/o		95.38 168	95.08 151	96.26 240	98.34 145	91.79 272	97.70 255	97.43 266	92.87 210	94.24 213	97.22 219	88.66 175	98.84 205	91.55 232	97.70 146	98.16 178

XVG-ACMP-BASELINE		94.54 223	94.14 205	95.75 259	96.55 251	91.65 276	98.11 216	98.44 149	94.96 120	94.22 214	97.90 167	79.18 311	99.11 170	94.05 166	93.85 225	96.48 287

v1144		94.59 220	93.92 220	96.60 210	96.21 281	94.78 204	98.59 150	98.14 200	91.86 247	94.21 215	97.02 244	87.97 194	98.41 260	91.72 229	89.57 273	96.61 270

v1192		94.32 232	93.58 242	96.53 220	96.10 288	94.45 218	98.50 169	98.17 195	91.54 252	94.19 216	97.06 237	86.95 219	98.43 252	90.14 260	89.57 273	96.70 254

PAPM		94.95 193	94.00 215	97.78 121	97.04 225	95.65 145	96.03 324	98.25 178	91.23 268	94.19 216	97.80 179	91.27 118	98.86 204	82.61 327	97.61 147	98.84 144

tpmp4_e23		93.91 254	93.42 252	95.38 275	97.62 186	88.59 317	97.52 267	97.34 273	87.94 313	94.17 218	96.79 270	82.91 287	99.05 177	90.62 249	95.91 199	98.50 161

Patchmatch-test		94.42 228	93.68 237	96.63 205	97.60 188	91.76 273	94.83 339	97.49 261	89.45 302	94.14 219	97.10 230	88.99 152	98.83 207	85.37 321	98.13 130	99.29 101

v1240		94.06 250	93.29 254	96.34 236	96.03 292	93.90 236	98.44 175	98.17 195	91.18 270	94.13 220	97.01 246	86.05 239	98.42 253	89.13 282	89.50 276	96.70 254

GBi-Net		94.49 224	93.80 227	96.56 216	98.21 151	95.00 170	98.82 99	98.18 190	92.46 220	94.09 221	97.07 234	81.16 295	97.95 291	92.08 215	92.14 248	96.72 250

test1		94.49 224	93.80 227	96.56 216	98.21 151	95.00 170	98.82 99	98.18 190	92.46 220	94.09 221	97.07 234	81.16 295	97.95 291	92.08 215	92.14 248	96.72 250

FMVSNet3		94.97 192	94.26 196	97.11 170	98.18 156	96.62 92	98.56 157	98.26 177	93.67 176	94.09 221	97.10 230	84.25 274	98.01 288	92.08 215	92.14 248	96.70 254

MIMVSNet		93.26 266	92.21 271	96.41 230	97.73 182	93.13 257	95.65 330	97.03 290	91.27 267	94.04 224	96.06 296	75.33 328	97.19 313	86.56 311	96.23 186	98.92 141

FIs		96.51 113	96.12 114	97.67 132	97.13 222	97.54 61	99.36 8	99.22 14	95.89 71	94.03 225	98.35 129	91.98 103	98.44 250	96.40 96	92.76 244	97.01 217

v2v482		94.69 211	94.03 212	96.65 202	96.17 284	94.79 202	98.67 142	98.08 218	92.72 214	94.00 226	97.16 221	87.69 206	98.45 247	92.91 196	88.87 289	96.72 250

FC-MVSNet-test		96.42 116	96.05 115	97.53 145	96.95 229	97.27 69	99.36 8	99.23 12	95.83 73	93.93 227	98.37 127	92.00 102	98.32 269	96.02 108	92.72 245	97.00 218

UniMVSNet (Re)		95.78 136	95.19 147	97.58 142	96.99 228	97.47 63	98.79 113	99.18 16	95.60 81	93.92 228	97.04 242	91.68 108	98.48 240	95.80 117	87.66 305	96.79 242

UniMVSNet_NR-MVSNet		95.71 140	95.15 148	97.40 157	96.84 237	96.97 79	98.74 124	99.24 10	95.16 109	93.88 229	97.72 184	91.68 108	98.31 271	95.81 115	87.25 310	96.92 223

DU-MVS		95.42 164	94.76 174	97.40 157	96.53 252	96.97 79	98.66 144	98.99 28	95.43 88	93.88 229	97.69 185	88.57 177	98.31 271	95.81 115	87.25 310	96.92 223

Baseline_NR-MVSNet		94.35 231	93.81 226	95.96 249	96.20 282	94.05 233	98.61 149	96.67 313	91.44 255	93.85 231	97.60 193	88.57 177	98.14 280	94.39 155	86.93 313	95.68 311

PS-MVSNAJss		96.43 115	96.26 110	96.92 184	95.84 300	95.08 168	99.16 43	98.50 140	95.87 72	93.84 232	98.34 133	94.51 63	98.61 221	96.88 76	93.45 234	97.06 214

tpmvs		94.60 218	94.36 193	95.33 278	97.46 198	88.60 316	96.88 304	97.68 237	91.29 265	93.80 233	96.42 285	88.58 176	99.24 150	91.06 241	96.04 198	98.17 177

3Dnovator	94.51 5	97.46 69	96.93 81	99.07 45	97.78 179	97.64 56	99.35 10	99.06 21	97.02 39	93.75 234	99.16 45	89.25 146	99.92 15	97.22 61	99.75 32	99.64 56

ITE_SJBPF				95.44 269	97.42 202	91.32 279		97.50 255	95.09 114	93.59 235	98.35 129	81.70 293	98.88 201	89.71 271	93.39 236	96.12 300

TranMVSNet+NR-MVSNet		95.14 184	94.48 186	97.11 170	96.45 257	96.36 107	99.03 62	99.03 24	95.04 115	93.58 236	97.93 165	88.27 185	98.03 287	94.13 163	86.90 315	96.95 222

COLMAP_ROB	93.27 12	95.33 174	94.87 165	96.71 191	99.29 58	93.24 254	98.58 152	98.11 209	89.92 290	93.57 237	99.10 51	86.37 227	99.79 74	90.78 245	98.10 131	97.09 213
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.36 261	92.80 261	95.07 284	97.58 190	87.97 323	96.76 309	97.86 231	82.17 340	93.53 238	96.04 297	86.13 230	99.13 165	89.24 280	95.87 200	98.10 179

AllTest		95.24 179	94.65 179	96.99 176	99.25 67	93.21 255	98.59 150	98.18 190	91.36 259	93.52 239	98.77 92	84.67 260	99.72 91	89.70 272	97.87 138	98.02 181

TestCases				96.99 176	99.25 67	93.21 255		98.18 190	91.36 259	93.52 239	98.77 92	84.67 260	99.72 91	89.70 272	97.87 138	98.02 181

FMVSNet2		94.47 226	93.61 240	97.04 174	98.21 151	96.43 104	98.79 113	98.27 173	92.46 220	93.50 241	97.09 232	81.16 295	98.00 289	91.09 239	91.93 252	96.70 254

v148		94.29 234	93.76 232	95.91 251	96.10 288	92.93 259	98.58 152	97.97 226	92.59 218	93.47 242	96.95 252	88.53 180	98.32 269	92.56 206	87.06 312	96.49 286

pmmvs4		94.69 211	93.99 217	96.81 187	95.74 302	95.94 124	97.40 272	97.67 238	90.42 279	93.37 243	97.59 194	89.08 151	98.20 278	92.97 192	91.67 256	96.30 295

PCF-MVS	93.45 11	94.68 214	93.43 250	98.42 86	98.62 133	96.77 88	95.48 331	98.20 185	84.63 333	93.34 244	98.32 135	88.55 179	99.81 55	84.80 323	98.96 94	98.68 152
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XXY-MVS		95.20 182	94.45 190	97.46 152	96.75 242	96.56 97	98.86 92	98.65 114	93.30 196	93.27 245	98.27 140	84.85 259	98.87 202	94.82 145	91.26 262	96.96 220

jajsoiax		95.45 161	95.03 152	96.73 190	95.42 315	94.63 209	99.14 45	98.52 133	95.74 75	93.22 246	98.36 128	83.87 283	98.65 219	96.95 70	94.04 220	96.91 228

mvs_tets		95.41 166	95.00 153	96.65 202	95.58 308	94.42 219	99.00 65	98.55 127	95.73 76	93.21 247	98.38 126	83.45 286	98.63 220	97.09 65	94.00 222	96.91 228

anonymousdsp		95.42 164	94.91 163	96.94 181	95.10 319	95.90 137	99.14 45	98.41 154	93.75 164	93.16 248	97.46 201	87.50 211	98.41 260	95.63 125	94.03 221	96.50 285

v8		94.47 226	93.77 230	96.57 215	96.36 264	94.83 194	99.05 58	98.19 187	91.92 243	93.16 248	96.97 250	88.82 165	98.48 240	91.69 230	87.79 303	96.39 290

WR-MVS		95.15 183	94.46 188	97.22 162	96.67 247	96.45 103	98.21 200	98.81 63	94.15 144	93.16 248	97.69 185	87.51 209	98.30 273	95.29 135	88.62 294	96.90 230

EPNet_dtu		95.21 181	94.95 158	95.99 248	96.17 284	90.45 293	98.16 210	97.27 280	96.77 44	93.14 251	98.33 134	90.34 132	98.42 253	85.57 318	98.81 103	99.09 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		96.29 120	95.40 133	98.96 53	97.85 176	97.60 59	99.23 23	98.93 36	89.76 294	93.11 252	99.02 61	89.11 150	99.93 9	91.99 221	99.62 50	99.34 92

GG-mvs-BLEND				96.59 211	96.34 267	94.98 173	96.51 319	88.58 358		93.10 253	94.34 319	80.34 306	98.05 286	89.53 275	96.99 155	96.74 247

v10		94.29 234	93.55 243	96.51 222	96.39 260	94.80 199	98.99 66	98.19 187	91.35 261	93.02 254	96.99 248	88.09 191	98.41 260	90.50 257	88.41 296	96.33 294

3Dnovator+	94.38 6	97.43 74	96.78 88	99.38 12	97.83 177	98.52 14	99.37 7	98.71 94	97.09 37	92.99 255	99.13 47	89.36 143	99.89 29	96.97 67	99.57 58	99.71 35

Patchmtry		93.22 267	92.35 269	95.84 254	96.77 239	93.09 258	94.66 341	97.56 243	87.37 316	92.90 256	96.24 288	88.15 188	97.90 295	87.37 307	90.10 268	96.53 281

v52		94.18 241	93.52 245	96.13 245	95.95 295	94.29 225	99.23 23	98.21 182	91.42 256	92.84 257	96.89 261	87.85 200	98.53 235	91.51 234	87.81 301	95.57 314

V4		94.18 241	93.52 245	96.13 245	95.89 297	94.31 224	99.23 23	98.22 181	91.42 256	92.82 258	96.89 261	87.93 196	98.52 236	91.51 234	87.81 301	95.58 313

Anonymous20231211		94.10 246	93.26 255	96.61 208	99.11 84	94.28 226	99.01 64	98.88 47	86.43 320	92.81 259	97.57 196	81.66 294	98.68 217	94.83 144	89.02 284	96.88 233

v7n		94.19 239	93.43 250	96.47 225	95.90 296	94.38 222	99.26 18	98.34 164	91.99 241	92.76 260	97.13 229	88.31 184	98.52 236	89.48 277	87.70 304	96.52 282

MVS		94.67 215	93.54 244	98.08 105	96.88 235	96.56 97	98.19 205	98.50 140	78.05 346	92.69 261	98.02 156	91.07 122	99.63 109	90.09 261	98.36 122	98.04 180

DSMNet-mixed		92.52 275	92.58 266	92.33 319	94.15 328	82.65 338	98.30 193	94.26 347	89.08 307	92.65 262	95.73 303	85.01 256	95.76 336	86.24 313	97.76 144	98.59 158

EU-MVSNet		93.66 257	94.14 205	92.25 320	95.96 294	83.38 335	98.52 164	98.12 204	94.69 127	92.61 263	98.13 149	87.36 213	96.39 334	91.82 225	90.00 269	96.98 219

semantic-postprocess				94.85 290	97.98 170	90.56 292		98.11 209	93.75 164	92.58 264	97.48 200	83.91 281	97.41 310	92.48 210	91.30 260	96.58 274

pmmvs5		93.65 259	92.97 259	95.68 260	95.49 311	92.37 264	98.20 201	97.28 279	89.66 298	92.58 264	97.26 215	82.14 290	98.09 284	93.18 186	90.95 263	96.58 274

WR-MVS_H		95.05 187	94.46 188	96.81 187	96.86 236	95.82 141	99.24 21	99.24 10	93.87 159	92.53 266	96.84 268	90.37 131	98.24 277	93.24 183	87.93 300	96.38 291

ACMP	93.49 10	95.34 173	94.98 155	96.43 229	97.67 183	93.48 248	98.73 127	98.44 149	94.94 123	92.53 266	98.53 112	84.50 268	99.14 164	95.48 129	94.00 222	96.66 263
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0.0.03 1		94.08 248	93.51 247	95.80 256	95.53 310	92.89 260	97.38 274	95.97 323	95.11 111	92.51 268	96.66 274	87.71 203	96.94 316	87.03 309	93.67 227	97.57 197

IB-MVS	91.98 17	93.27 265	91.97 273	97.19 164	97.47 197	93.41 251	97.09 292	95.99 322	93.32 194	92.47 269	95.73 303	78.06 316	99.53 129	94.59 151	82.98 327	98.62 157
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
IterMVS		94.09 247	93.85 225	94.80 293	97.99 168	90.35 294	97.18 289	98.12 204	93.68 174	92.46 270	97.34 210	84.05 279	97.41 310	92.51 209	91.33 259	96.62 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 195	94.30 195	96.83 186	96.72 244	95.56 149	99.11 51	98.95 33	93.89 157	92.42 271	97.90 167	87.19 214	98.12 281	94.32 158	88.21 297	96.82 241

PS-CasMVS		94.67 215	93.99 217	96.71 191	96.68 246	95.26 161	99.13 48	99.03 24	93.68 174	92.33 272	97.95 162	85.35 251	98.10 282	93.59 176	88.16 299	96.79 242

FMVSNet1		93.19 269	92.07 272	96.56 216	97.54 193	95.00 170	98.82 99	98.18 190	90.38 280	92.27 273	97.07 234	73.68 336	97.95 291	89.36 279	91.30 260	96.72 250

PEN-MVS		94.42 228	93.73 234	96.49 223	96.28 277	94.84 192	99.17 36	99.00 26	93.51 181	92.23 274	97.83 176	86.10 238	97.90 295	92.55 207	86.92 314	96.74 247

v748		93.75 256	93.06 257	95.82 255	95.73 303	92.64 262	99.25 20	98.24 180	91.60 251	92.22 275	96.52 281	87.60 208	98.46 245	90.64 248	85.72 322	96.36 292

OurMVSNet-221017-0		94.21 237	94.00 215	94.85 290	95.60 307	89.22 307	98.89 81	97.43 266	95.29 102	92.18 276	98.52 115	82.86 288	98.59 224	93.46 178	91.76 255	96.74 247

MS-PatchMatch		93.84 255	93.63 238	94.46 303	96.18 283	89.45 303	97.76 251	98.27 173	92.23 238	92.13 277	97.49 199	79.50 308	98.69 214	89.75 270	99.38 82	95.25 316

ppachtmachnet_test		93.22 267	92.63 265	94.97 286	95.45 313	90.84 284	96.88 304	97.88 230	90.60 275	92.08 278	97.26 215	88.08 192	97.86 301	85.12 322	90.33 266	96.22 297

1314		96.25 124	95.73 123	97.79 120	97.13 222	95.55 151	98.19 205	98.59 118	93.47 183	92.03 279	97.82 177	91.33 117	99.49 132	94.62 149	98.44 118	98.32 175

DTE-MVSNet		93.98 252	93.26 255	96.14 244	96.06 290	94.39 221	99.20 33	98.86 53	93.06 201	91.78 280	97.81 178	85.87 242	97.58 306	90.53 250	86.17 319	96.46 289

LF4IMVS		93.14 270	92.79 262	94.20 306	95.88 298	88.67 315	97.66 259	97.07 287	93.81 162	91.71 281	97.65 189	77.96 317	98.81 209	91.47 236	91.92 253	95.12 317

our_test_3		93.65 259	93.30 253	94.69 295	95.45 313	89.68 301	96.91 298	97.65 239	91.97 242	91.66 282	96.88 263	89.67 138	97.93 294	88.02 303	91.49 258	96.48 287

testgi		93.06 271	92.45 268	94.88 289	96.43 258	89.90 296	98.75 120	97.54 248	95.60 81	91.63 283	97.91 166	74.46 334	97.02 315	86.10 314	93.67 227	97.72 193

tfpnnormal		93.66 257	92.70 264	96.55 219	96.94 230	95.94 124	98.97 70	99.19 15	91.04 271	91.38 284	97.34 210	84.94 257	98.61 221	85.45 320	89.02 284	95.11 318

Anonymous20240521		94.80 203	94.03 212	97.11 170	96.56 250	96.46 102	99.30 14	98.44 149	92.86 211	91.21 285	97.01 246	89.59 139	98.58 226	92.03 219	89.23 280	96.30 295

LTVRE_ROB	92.95 15	94.60 218	93.90 222	96.68 197	97.41 205	94.42 219	98.52 164	98.59 118	91.69 249	91.21 285	98.35 129	84.87 258	99.04 181	91.06 241	93.44 235	96.60 272
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
OpenMVS	93.04 13	95.83 134	95.00 153	98.32 90	97.18 219	97.32 67	99.21 32	98.97 29	89.96 287	91.14 287	99.05 60	86.64 223	99.92 15	93.38 179	99.47 72	97.73 192

pm-mvs1		93.94 253	93.06 257	96.59 211	96.49 255	95.16 164	98.95 72	98.03 225	92.32 235	91.08 288	97.84 173	84.54 267	98.41 260	92.16 213	86.13 321	96.19 299

LP		91.12 296	89.99 298	94.53 299	96.35 266	88.70 314	93.86 346	97.35 272	84.88 331	90.98 289	94.77 314	84.40 269	97.43 309	75.41 343	91.89 254	97.47 198

MVS-HIRNet		89.46 308	88.40 310	92.64 317	97.58 190	82.15 339	94.16 345	93.05 353	75.73 348	90.90 290	82.52 349	79.42 309	98.33 268	83.53 325	98.68 105	97.43 199

FMVSNet5		91.81 289	90.92 283	94.49 300	97.21 215	92.09 267	98.00 227	97.55 247	89.31 305	90.86 291	95.61 308	74.48 333	95.32 338	85.57 318	89.70 271	96.07 302

USDC		93.33 264	92.71 263	95.21 279	96.83 238	90.83 285	96.91 298	97.50 255	93.84 160	90.72 292	98.14 148	77.69 318	98.82 208	89.51 276	93.21 241	95.97 304

MVP-Stereo		94.28 236	93.92 220	95.35 277	94.95 321	92.60 263	97.97 229	97.65 239	91.61 250	90.68 293	97.09 232	86.32 228	98.42 253	89.70 272	99.34 84	95.02 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	92.99 14	94.30 233	93.77 230	95.88 253	97.81 178	92.04 269	98.71 130	98.37 161	93.99 152	90.60 294	98.47 118	80.86 300	99.05 177	92.75 201	92.40 247	96.55 279

testpf		88.74 311	89.09 304	87.69 328	95.78 301	83.16 337	84.05 357	94.13 350	85.22 330	90.30 295	94.39 318	74.92 331	95.80 335	89.77 268	93.28 240	84.10 352

Anonymous20231206		91.66 291	91.10 279	93.33 313	94.02 330	87.35 327	98.58 152	97.26 281	90.48 276	90.16 296	96.31 286	83.83 284	96.53 332	79.36 334	89.90 270	96.12 300

SixPastTwentyTwo		93.34 263	92.86 260	94.75 294	95.67 305	89.41 305	98.75 120	96.67 313	93.89 157	90.15 297	98.25 142	80.87 299	98.27 276	90.90 244	90.64 264	96.57 276

PVSNet_0	88.72 19	91.28 294	90.03 297	95.00 285	97.99 168	87.29 328	94.84 338	98.50 140	92.06 240	89.86 298	95.19 309	79.81 307	99.39 140	92.27 212	69.79 350	98.33 174

ACMH	92.88 16	94.55 222	93.95 219	96.34 236	97.63 185	93.26 253	98.81 105	98.49 144	93.43 184	89.74 299	98.53 112	81.91 292	99.08 175	93.69 172	93.30 238	96.70 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		91.77 290	90.63 291	95.17 281	94.69 326	91.24 281	98.67 142	97.92 228	86.14 322	89.62 300	97.56 198	75.79 327	98.34 267	90.75 246	84.56 326	95.94 305

TinyColmap		92.31 277	91.53 276	94.65 297	96.92 231	89.75 298	96.92 296	96.68 312	90.45 278	89.62 300	97.85 172	76.06 326	98.81 209	86.74 310	92.51 246	95.41 315

TransMVSNet (Re)		92.67 273	91.51 277	96.15 243	96.58 249	94.65 207	98.90 77	96.73 309	90.86 273	89.46 302	97.86 170	85.62 246	98.09 284	86.45 312	81.12 332	95.71 310

testus		88.91 310	89.08 305	88.40 327	91.39 337	76.05 345	96.56 315	96.48 317	89.38 304	89.39 303	95.17 311	70.94 340	93.56 345	77.04 340	95.41 204	95.61 312

NR-MVSNet		94.98 191	94.16 203	97.44 153	96.53 252	97.22 73	98.74 124	98.95 33	94.96 120	89.25 304	97.69 185	89.32 144	98.18 279	94.59 151	87.40 307	96.92 223

test2356		88.68 312	88.61 308	88.87 326	89.90 343	78.23 342	95.11 333	96.66 315	88.66 310	89.06 305	94.33 320	73.14 338	92.56 349	75.56 342	95.11 207	95.81 308

LCM-MVSNet-Re		95.22 180	95.32 141	94.91 287	98.18 156	87.85 325	98.75 120	95.66 333	95.11 111	88.96 306	96.85 267	90.26 135	97.65 303	95.65 124	98.44 118	99.22 108

TDRefinement		91.06 297	89.68 300	95.21 279	85.35 349	91.49 277	98.51 168	97.07 287	91.47 253	88.83 307	97.84 173	77.31 322	99.09 174	92.79 200	77.98 344	95.04 320

N_pmnet		87.12 316	87.77 313	85.17 335	95.46 312	61.92 358	97.37 276	70.66 365	85.83 326	88.73 308	96.04 297	85.33 253	97.76 302	80.02 331	90.48 265	95.84 306

test_0402		91.32 293	90.27 295	94.48 301	96.60 248	91.12 282	98.50 169	97.22 283	86.10 323	88.30 309	96.98 249	77.65 320	97.99 290	78.13 338	92.94 243	94.34 333

test20.03		90.89 299	90.38 293	92.43 318	93.48 331	88.14 322	98.33 186	97.56 243	93.40 191	87.96 310	96.71 273	80.69 302	94.13 342	79.15 335	86.17 319	95.01 322

MIMVSNet1		89.67 307	88.28 312	93.82 309	92.81 335	91.08 283	98.01 225	97.45 264	87.95 312	87.90 311	95.87 301	67.63 346	94.56 341	78.73 337	88.18 298	95.83 307

Patchmatch-RL test		91.49 292	90.85 284	93.41 312	91.37 338	84.40 332	92.81 347	95.93 325	91.87 246	87.25 312	94.87 313	88.99 152	96.53 332	92.54 208	82.00 329	99.30 99

pmmvs3		86.67 317	84.86 319	92.11 321	88.16 344	87.19 329	96.63 312	94.75 343	79.88 344	87.22 313	92.75 337	66.56 347	95.20 339	81.24 330	76.56 347	93.96 339

K. test v3		92.55 274	91.91 275	94.48 301	95.64 306	89.24 306	99.07 57	94.88 341	94.04 149	86.78 314	97.59 194	77.64 321	97.64 304	92.08 215	89.43 277	96.57 276

lessismore_v0				94.45 304	94.93 322	88.44 319		91.03 355		86.77 315	97.64 191	76.23 325	98.42 253	90.31 259	85.64 323	96.51 284

ambc				89.49 325	86.66 348	75.78 346	92.66 348	96.72 310		86.55 316	92.50 338	46.01 355	97.90 295	90.32 258	82.09 328	94.80 323

PM-MVS		87.77 314	86.55 316	91.40 323	91.03 340	83.36 336	96.92 296	95.18 339	91.28 266	86.48 317	93.42 322	53.27 353	96.74 326	89.43 278	81.97 330	94.11 336

DI_MVS_plusplus_test		94.74 209	93.62 239	98.09 104	95.34 316	95.92 134	98.09 219	97.34 273	94.66 131	85.89 318	95.91 299	80.49 304	99.38 141	96.66 86	98.22 126	98.97 136

OpenMVS_ROB	86.42 20	89.00 309	87.43 315	93.69 310	93.08 333	89.42 304	97.91 235	96.89 305	78.58 345	85.86 319	94.69 315	69.48 342	98.29 275	77.13 339	93.29 239	93.36 342

UnsupCasMVSNet_eth		90.99 298	89.92 299	94.19 307	94.08 329	89.83 297	97.13 291	98.67 107	93.69 172	85.83 320	96.19 293	75.15 329	96.74 326	89.14 281	79.41 338	96.00 303

test_normal		94.72 210	93.59 241	98.11 103	95.30 317	95.95 123	97.91 235	97.39 271	94.64 132	85.70 321	95.88 300	80.52 303	99.36 142	96.69 85	98.30 125	99.01 134

new_pmnet		90.06 304	89.00 307	93.22 316	94.18 327	88.32 321	96.42 320	96.89 305	86.19 321	85.67 322	93.62 321	77.18 323	97.10 314	81.61 329	89.29 279	94.23 334

v17		92.08 281	90.94 281	95.48 266	96.34 267	94.83 194	98.81 105	97.52 249	89.95 288	85.32 323	93.24 325	88.91 158	96.91 318	88.76 289	79.63 337	94.71 326

EG-PatchMatch MVS		91.13 295	90.12 296	94.17 308	94.73 325	89.00 311	98.13 213	97.81 232	89.22 306	85.32 323	96.46 282	67.71 345	98.42 253	87.89 305	93.82 226	95.08 319

v18		92.10 280	90.97 280	95.50 264	96.34 267	94.85 183	98.82 99	97.52 249	89.99 286	85.31 325	93.26 324	88.90 159	96.92 317	88.82 288	79.77 336	94.73 324

v16		92.08 281	90.94 281	95.49 265	96.38 263	94.84 192	98.81 105	97.51 252	89.94 289	85.25 326	93.28 323	88.86 160	96.91 318	88.70 290	79.78 335	94.72 325

v15		91.94 283	90.77 285	95.43 271	96.31 275	94.83 194	98.77 116	97.50 255	89.92 290	85.13 327	93.08 328	88.76 171	96.86 320	88.40 294	79.10 339	94.61 330

v11		91.85 288	90.68 290	95.36 276	96.34 267	94.74 206	98.80 108	97.43 266	89.60 300	85.09 328	93.03 330	88.53 180	96.75 325	87.37 307	79.96 334	94.58 332

V14		91.93 284	90.76 286	95.42 274	96.33 271	94.81 198	98.77 116	97.51 252	89.86 292	85.09 328	93.13 326	88.80 169	96.83 322	88.32 295	79.06 341	94.60 331

V9		91.91 285	90.73 287	95.45 268	96.32 274	94.80 199	98.77 116	97.50 255	89.81 293	85.03 330	93.08 328	88.76 171	96.86 320	88.24 296	79.03 342	94.69 327

v12		91.89 286	90.70 288	95.43 271	96.31 275	94.80 199	98.76 119	97.50 255	89.76 294	84.95 331	93.00 331	88.82 165	96.82 324	88.23 297	79.00 343	94.68 329

v13		91.88 287	90.69 289	95.43 271	96.33 271	94.78 204	98.75 120	97.50 255	89.68 297	84.93 332	92.98 332	88.84 163	96.83 322	88.14 298	79.09 340	94.69 327

pmmvs-eth3d		90.36 303	89.05 306	94.32 305	91.10 339	92.12 266	97.63 262	96.95 298	88.86 308	84.91 333	93.13 326	78.32 314	96.74 326	88.70 290	81.81 331	94.09 337

DeepMVS_CX				86.78 331	97.09 224	72.30 351		95.17 340	75.92 347	84.34 334	95.19 309	70.58 341	95.35 337	79.98 333	89.04 283	92.68 343

new-patchmatchnet		88.50 313	87.45 314	91.67 322	90.31 341	85.89 331	97.16 290	97.33 276	89.47 301	83.63 335	92.77 336	76.38 324	95.06 340	82.70 326	77.29 345	94.06 338

UnsupCasMVSNet_bld		87.17 315	85.12 318	93.31 314	91.94 336	88.77 312	94.92 337	98.30 170	84.30 334	82.30 336	90.04 343	63.96 350	97.25 312	85.85 317	74.47 349	93.93 340

Test4		92.21 278	90.34 294	97.82 119	92.83 334	95.87 140	97.94 231	98.05 224	94.50 137	82.12 337	94.48 316	59.54 352	98.54 229	95.39 131	98.22 126	99.06 130

CMPMVS	66.06 21	89.70 306	89.67 301	89.78 324	93.19 332	76.56 344	97.00 293	98.35 163	80.97 342	81.57 338	97.75 181	74.75 332	98.61 221	89.85 267	93.63 229	94.17 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test1235678		86.26 318	85.81 317	87.62 329	86.97 347	75.00 349	96.55 317	96.32 320	86.08 324	81.32 339	92.98 332	73.10 339	92.05 350	71.64 346	87.32 308	95.81 308

1111		84.94 319	84.30 320	86.86 330	87.59 345	75.10 347	96.63 312	96.43 318	82.53 337	80.75 340	92.91 334	68.94 343	93.79 343	68.24 349	84.66 325	91.70 344

.test1245		73.05 327	76.31 325	63.27 348	87.59 345	75.10 347	96.63 312	96.43 318	82.53 337	80.75 340	92.91 334	68.94 343	93.79 343	68.24 349	12.72 361	20.91 361

test12356		83.47 320	83.37 321	83.78 336	84.43 350	70.09 354	95.12 332	95.60 334	82.98 335	78.89 342	92.43 340	64.99 348	91.41 352	70.36 347	85.55 324	89.82 346

testing_2		90.61 302	88.50 309	96.95 180	90.08 342	95.57 148	97.69 256	98.06 221	93.02 203	76.55 343	92.48 339	61.18 351	98.44 250	95.45 130	91.98 251	96.84 238

LCM-MVSNet		78.70 322	76.24 326	86.08 332	77.26 359	71.99 352	94.34 343	96.72 310	61.62 353	76.53 344	89.33 344	33.91 362	92.78 348	81.85 328	74.60 348	93.46 341

PMMVS2		77.95 324	75.44 327	85.46 333	82.54 351	74.95 350	94.23 344	93.08 352	72.80 349	74.68 345	87.38 345	36.36 360	91.56 351	73.95 344	63.94 351	89.87 345

Gipuma		78.40 323	76.75 324	83.38 337	95.54 309	80.43 341	79.42 358	97.40 269	64.67 351	73.46 346	80.82 352	45.65 356	93.14 347	66.32 351	87.43 306	76.56 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		90.70 301	89.39 302	94.62 298	94.79 324	90.65 290	97.20 287	97.46 262	87.54 315	72.54 347	95.74 302	86.51 224	96.66 330	86.00 315	86.76 317	96.54 280

MDA-MVSNet_test_wron		90.71 300	89.38 303	94.68 296	94.83 323	90.78 287	97.19 288	97.46 262	87.60 314	72.41 348	95.72 305	86.51 224	96.71 329	85.92 316	86.80 316	96.56 278

MDA-MVSNet-bldmvs		89.97 305	88.35 311	94.83 292	95.21 318	91.34 278	97.64 260	97.51 252	88.36 311	71.17 349	96.13 295	79.22 310	96.63 331	83.65 324	86.27 318	96.52 282

testmv		78.74 321	77.35 322	82.89 338	78.16 358	69.30 355	95.87 326	94.65 344	81.11 341	70.98 350	87.11 347	46.31 354	90.42 353	65.28 352	76.72 346	88.95 347

FPMVS		77.62 325	77.14 323	79.05 340	79.25 355	60.97 359	95.79 328	95.94 324	65.96 350	67.93 351	94.40 317	37.73 359	88.88 355	68.83 348	88.46 295	87.29 348

no-one		74.41 326	70.76 328	85.35 334	79.88 354	76.83 343	94.68 340	94.22 348	80.33 343	63.81 352	79.73 353	35.45 361	93.36 346	71.78 345	36.99 358	85.86 351

tmp_tt		68.90 329	66.97 329	74.68 344	50.78 364	59.95 360	87.13 353	83.47 363	38.80 359	62.21 353	96.23 290	64.70 349	76.91 362	88.91 287	30.49 359	87.19 349

E-PMN		64.94 332	64.25 332	67.02 346	82.28 352	59.36 362	91.83 350	85.63 361	52.69 356	60.22 354	77.28 355	41.06 358	80.12 360	46.15 358	41.14 355	61.57 359

EMVS		64.07 333	63.26 334	66.53 347	81.73 353	58.81 363	91.85 349	84.75 362	51.93 358	59.09 355	75.13 356	43.32 357	79.09 361	42.03 359	39.47 356	61.69 358

PNet_i23d		67.70 330	65.07 331	75.60 342	78.61 356	59.61 361	89.14 352	88.24 359	61.83 352	52.37 356	80.89 351	18.91 364	84.91 357	62.70 354	52.93 353	82.28 353

MVE	62.14 22	63.28 335	59.38 335	74.99 343	74.33 360	65.47 357	85.55 355	80.50 364	52.02 357	51.10 357	75.00 357	10.91 369	80.50 359	51.60 357	53.40 352	78.99 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		69.08 328	65.37 330	80.22 339	65.99 362	71.96 353	90.91 351	90.09 356	82.62 336	49.93 358	78.39 354	29.36 363	81.75 358	62.49 355	38.52 357	86.95 350

PMVS	61.03 23	65.95 331	63.57 333	73.09 345	57.90 363	51.22 364	85.05 356	93.93 351	54.45 355	44.32 359	83.57 348	13.22 365	89.15 354	58.68 356	81.00 333	78.91 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		63.73 334	58.86 336	78.35 341	67.62 361	67.90 356	86.56 354	87.81 360	58.26 354	42.49 360	70.28 358	11.55 367	85.05 356	63.66 353	41.50 354	82.11 354

testmvs		21.48 339	24.95 340	11.09 352	14.89 365	6.47 367	96.56 315	9.87 367	7.55 361	17.93 361	39.02 360	9.43 370	5.90 365	16.56 362	12.72 361	20.91 361

test123		20.95 340	23.72 341	12.64 351	13.54 366	8.19 366	96.55 317	6.13 368	7.48 362	16.74 362	37.98 361	12.97 366	6.05 364	16.69 361	5.43 363	23.68 360

wuyk23d		30.17 337	30.18 339	30.16 350	78.61 356	43.29 365	66.79 359	14.21 366	17.31 360	14.82 363	11.93 364	11.55 367	41.43 363	37.08 360	19.30 360	5.76 363

cdsmvs_eth3d_5k		23.98 338	31.98 338	0.00 353	0.00 367	0.00 368	0.00 360	98.59 118	0.00 363	0.00 364	98.61 105	90.60 129	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		7.88 342	10.50 343	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	94.51 63	0.00 366	0.00 363	0.00 364	0.00 364

pcd1.5k->3k		39.42 336	41.78 337	32.35 349	96.17 284	0.00 368	0.00 360	98.54 128	0.00 363	0.00 364	0.00 365	87.78 202	0.00 366	0.00 363	93.56 231	97.06 214

sosnet-low-res		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		8.20 341	10.94 342	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	98.43 120	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																99.20 109

test_part3							98.55 159		96.40 57		99.31 22		99.93 9	96.37 98

test_part1								98.84 55				97.38 2			99.78 15	99.76 20

sam_mvs1												89.45 141				99.20 109

sam_mvs												88.99 152

MTGPA								98.74 82

test_post1							96.68 311				30.43 363	87.85 200	98.69 214	92.59 205

test_post											31.83 362	88.83 164	98.91 196

patchmatchnet-post											95.10 312	89.42 142	98.89 200

MTMP							98.89 81	94.14 349

gm-plane-assit					95.88 298	87.47 326			89.74 296		96.94 254		99.19 160	93.32 182

test9_res														96.39 97	99.57 58	99.69 38

agg_prior2														95.87 114	99.57 58	99.68 44

test_prior4						98.01 44	97.86 243

test_prior				99.19 30	99.31 50	98.22 33		98.84 55					99.70 96			99.65 53

新几何2							97.64 260

旧先验1					99.29 58	97.48 62		98.70 96			99.09 55	95.56 38			99.47 72	99.61 59

无先验							97.58 264	98.72 89	91.38 258				99.87 38	93.36 180		99.60 62

原ACMM2							97.67 258

testdata2													99.89 29	91.65 231

segment_acmp												96.85 5

testdata1							97.32 282		96.34 59

plane_prior7					97.42 202	94.63 209

plane_prior6					97.35 207	94.61 212						87.09 215

plane_prior5								98.56 125					99.03 182	96.07 104	94.27 211	96.92 223

plane_prior4											98.28 137

plane_prior2							98.80 108		97.28 21

plane_prior1					97.37 206

plane_prior						94.60 214	98.44 175		96.74 46						94.22 213

n2								0.00 369

nn								0.00 369

door-mid								94.37 346

test11								98.66 110

door								94.64 345

HQP5-MVS						94.25 229

BP-MVS														95.30 133

HQP3-MVS								98.46 145							94.18 215

HQP2-MVS												86.75 221

NP-MVS					97.28 210	94.51 217					97.73 182

ACMMP++_ref															92.97 242

ACMMP++															93.61 230

Test By Simon												94.64 60

High-resolution multi-view benchmark