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		97.41 6	97.53 2	97.06 55	98.57 49	94.46 14	97.92 42	98.14 38	94.82 21	99.01 1	98.55 9	94.18 3	97.41 263	96.94 5	99.64 3	99.32 41

TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 18	94.24 21	98.07 34	97.85 86	93.72 45	98.57 2	98.35 22	93.69 7	99.40 85	97.06 3	99.46 23	99.44 30

HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 38	98.53 9	97.85 86	95.55 5	98.56 3	97.81 59	93.90 4	99.65 39	96.62 13	99.21 48	99.48 26

APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 23	94.82 10	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 6	99.68 35	97.93 1	99.69 1	99.75 1

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 57	94.25 20	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 1	99.71 27	96.57 16	99.62 5	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		96.95 22	96.60 27	98.01 8	99.03 27	94.93 9	97.72 58	98.10 45	91.50 108	98.01 6	98.32 30	92.33 21	99.58 53	94.85 58	99.51 17	99.53 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepPCF-MVS	93.97 1	96.61 35	97.09 7	95.15 134	98.09 78	86.63 240	96.00 213	98.15 36	95.43 7	97.95 7	98.56 7	93.40 8	99.36 89	96.77 12	99.48 22	99.45 28

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 24	95.16 6	97.60 71	98.19 32	92.82 76	97.93 8	98.74 3	91.60 36	99.86 6	96.26 20	99.52 15	99.67 2

CNVR-MVS		97.68 2	97.44 5	98.37 2	98.90 30	95.86 2	97.27 98	98.08 48	95.81 3	97.87 9	98.31 31	94.26 2	99.68 35	97.02 4	99.49 21	99.57 11

VNet		95.89 54	95.45 51	97.21 51	98.07 79	92.94 58	97.50 78	98.15 36	93.87 41	97.52 10	97.61 76	85.29 108	99.53 68	95.81 36	95.27 141	99.16 51

Regformer-2		97.16 12	96.99 10	97.67 28	98.32 63	93.84 33	96.83 137	98.10 45	95.24 10	97.49 11	98.25 37	92.57 17	99.61 45	96.80 9	99.29 41	99.56 13

Regformer-1		97.10 14	96.96 12	97.54 36	98.32 63	93.48 44	96.83 137	97.99 74	95.20 12	97.46 12	98.25 37	92.48 20	99.58 53	96.79 11	99.29 41	99.55 15

APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 28	92.31 71	97.98 40	98.06 55	93.11 64	97.44 13	98.55 9	90.93 45	99.55 63	96.06 29	99.25 44	99.51 21

TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 65	93.39 46	96.79 144	96.72 194	94.17 36	97.44 13	97.66 69	92.76 11	99.33 90	96.86 8	97.76 93	99.08 60

旧先验2							95.94 215		81.66 289	97.34 15			98.82 132	92.26 95

MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 35	91.86 86	97.67 61	98.49 12	94.66 27	97.24 16	98.41 19	92.31 24	98.94 122	96.61 14	99.46 23	98.96 69

abl_6		96.40 40	96.21 40	96.98 58	98.89 31	92.20 76	97.89 43	98.03 64	93.34 56	97.22 17	98.42 16	87.93 77	99.72 26	95.10 50	99.07 59	99.02 62

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 25	98.52 10	98.32 19	93.21 58	97.18 18	98.29 34	92.08 26	99.83 13	95.63 39	99.59 7	99.54 17

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 25	98.15 29	98.32 19	92.57 81	97.18 18	98.29 34	92.08 26	99.83 13	95.12 49	99.59 7	99.54 17

ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 31	98.52 10	98.31 21	93.21 58	97.15 20	98.33 28	91.35 39	99.86 6	95.63 39	99.59 7	99.62 5

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 35	98.52 10	98.24 27	93.19 61	97.14 21	98.34 25	91.59 37	99.87 5	95.46 44	99.59 7	99.64 4

Regformer-4		96.97 21	96.87 15	97.25 47	98.34 60	92.66 64	96.96 124	98.01 67	95.12 13	97.14 21	98.42 16	91.82 32	99.61 45	96.90 6	99.13 54	99.50 22

PGM-MVS		96.81 27	96.53 30	97.65 29	99.35 13	93.53 43	97.65 64	98.98 1	92.22 86	97.14 21	98.44 14	91.17 41	99.85 9	94.35 66	99.46 23	99.57 11

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 55	94.07 27	98.21 28	98.45 15	89.86 148	97.11 24	98.01 46	92.52 19	99.69 33	96.03 31	99.53 14	99.36 39

NCCC		97.30 8	97.03 9	98.11 6	98.77 33	95.06 8	97.34 92	98.04 62	95.96 2	97.09 25	97.88 52	93.18 9	99.71 27	95.84 35	99.17 51	99.56 13

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 60	92.02 82	96.96 124	97.76 89	95.01 16	97.08 26	98.42 16	91.71 33	99.54 65	96.80 9	99.13 54	99.48 26

testdata				95.46 123	98.18 76	88.90 176		97.66 101	82.73 282	97.03 27	98.07 42	90.06 55	98.85 130	89.67 138	98.98 63	98.64 91

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 61	98.74 4	98.06 55	90.57 139	96.77 28	98.35 22	90.21 54	99.53 68	94.80 61	99.63 4	99.38 37

xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 133	91.71 87	96.25 197	97.35 140	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 184

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 133	91.71 87	96.25 197	97.35 140	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 184

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 133	91.71 87	96.25 197	97.35 140	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 184

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 29	94.44 15	96.35 187	97.88 81	86.98 231	96.65 32	97.89 50	91.99 30	99.47 76	92.26 95	99.46 23	99.39 34

UA-Net		95.95 53	95.53 50	97.20 52	97.67 98	92.98 57	97.65 64	98.13 39	94.81 22	96.61 33	98.35 22	88.87 64	99.51 72	90.36 131	97.35 104	99.11 58

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 25	96.16 1	97.55 75	97.97 76	95.59 4	96.61 33	97.89 50	92.57 17	99.84 12	95.95 32	99.51 17	99.40 33

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	98.29 34	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

X-MVStestdata		91.71 168	89.67 221	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	32.69 337	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 44	94.30 18	97.41 84	98.04 62	94.81 22	96.59 35	98.37 21	91.24 40	99.64 44	95.16 47	99.52 15	99.42 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 108	90.66 124	95.31 243	97.48 117	93.85 42	96.51 38	95.70 165	88.65 68	99.65 39	94.80 61	98.27 79	96.17 179

EI-MVSNet-Vis-set		96.51 37	96.47 32	96.63 64	98.24 69	91.20 105	96.89 133	97.73 92	94.74 25	96.49 39	98.49 11	90.88 47	99.58 53	96.44 18	98.32 78	99.13 55

alignmvs		95.87 55	95.23 58	97.78 19	97.56 106	95.19 5	97.86 45	97.17 150	94.39 32	96.47 40	96.40 131	85.89 102	99.20 96	96.21 25	95.11 143	98.95 71

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 110	90.50 127	95.44 238	97.44 129	93.70 47	96.46 41	96.18 138	88.59 71	99.53 68	94.79 63	97.81 90	96.17 179

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 42	98.80 3	98.28 23	92.99 67	96.45 42	98.30 33	91.90 31	99.85 9	95.61 41	99.68 2	99.54 17

HPM-MVS		96.69 32	96.45 34	97.40 39	99.36 12	93.11 53	98.87 1	98.06 55	91.17 119	96.40 43	97.99 48	90.99 44	99.58 53	95.61 41	99.61 6	99.49 24

LFMVS		93.60 108	92.63 119	96.52 69	98.13 77	91.27 102	97.94 41	93.39 310	90.57 139	96.29 44	98.31 31	69.00 291	99.16 101	94.18 67	95.87 133	99.12 57

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 104	95.15 7	98.28 22	97.60 106	94.52 29	96.27 45	96.12 141	87.65 81	99.18 99	96.20 26	94.82 147	98.91 75

MVSFormer		95.37 60	95.16 60	95.99 98	96.34 148	91.21 103	98.22 26	97.57 109	91.42 112	96.22 46	97.32 87	86.20 99	97.92 224	94.07 68	99.05 60	98.85 80

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 148	91.21 103	95.83 220	96.27 213	88.93 178	96.22 46	96.88 103	86.20 99	98.85 130	95.27 45	99.05 60	98.82 83

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 73	90.93 116	96.86 135	97.72 95	94.67 26	96.16 48	98.46 12	90.43 51	99.58 53	96.23 21	97.96 87	98.90 76

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 16	97.15 112	98.08 48	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 16	97.24 100	98.08 48	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 3	97.12 114	98.07 53	93.54 51	96.08 51	97.69 66	93.86 5	99.71 27	96.50 17	99.39 32	99.55 15

TEST9					98.70 36	94.19 22	96.41 179	98.02 65	88.17 204	96.03 52	97.56 81	92.74 12	99.59 50

train_agg		96.30 43	95.83 46	97.72 24	98.70 36	94.19 22	96.41 179	98.02 65	88.58 189	96.03 52	97.56 81	92.73 13	99.59 50	95.04 51	99.37 37	99.39 34

test_prior3		96.46 39	96.20 41	97.23 48	98.67 38	92.99 55	96.35 187	98.00 69	92.80 77	96.03 52	97.59 77	92.01 28	99.41 83	95.01 53	99.38 33	99.29 43

test_prior2							96.35 187		92.80 77	96.03 52	97.59 77	92.01 28		95.01 53	99.38 33

jason		94.84 78	94.39 79	96.18 92	95.52 177	90.93 116	96.09 206	96.52 206	89.28 160	96.01 56	97.32 87	84.70 116	98.77 137	95.15 48	98.91 66	98.85 80
jason: jason.
test_8					98.67 38	94.06 28	96.37 186	98.01 67	88.58 189	95.98 57	97.55 83	92.73 13	99.58 53

mPP-MVS		96.86 25	96.60 27	97.64 31	99.40 5	93.44 45	98.50 13	98.09 47	93.27 57	95.95 58	98.33 28	91.04 43	99.88 3	95.20 46	99.57 11	99.60 8

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 94	93.19 51	95.96 214	98.18 34	95.23 11	95.87 59	97.65 70	91.45 38	99.70 32	95.87 33	99.44 27	99.00 67
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
VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 91	89.96 139	97.72 58	95.85 235	92.43 83	95.86 60	98.44 14	68.42 295	99.39 86	96.31 19	94.85 145	98.71 88

MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 51	92.31 71	96.20 202	98.90 2	94.30 35	95.86 60	97.74 64	92.33 21	99.38 88	96.04 30	99.42 28	99.28 46

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 38	93.88 31	96.41 179	98.00 69	87.93 208	95.81 62	97.47 85	92.33 21	99.59 50	95.04 51	99.37 37	99.39 34

MVS_111021_LR		96.24 45	96.19 42	96.39 79	98.23 72	91.35 100	96.24 200	98.79 4	93.99 39	95.80 63	97.65 70	89.92 58	99.24 95	95.87 33	99.20 49	98.58 92

VDDNet		93.05 125	92.07 133	96.02 96	96.84 124	90.39 130	98.08 33	95.85 235	86.22 245	95.79 64	98.46 12	67.59 298	99.19 97	94.92 57	94.85 145	98.47 105

新几何1				97.32 42	98.60 45	93.59 41		97.75 90	81.58 291	95.75 65	97.85 56	90.04 56	99.67 37	86.50 202	99.13 54	98.69 89

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 38	93.79 35	96.28 195	98.00 69	88.76 186	95.68 66	97.55 83	92.70 15	99.57 61	95.01 53	99.32 39	99.32 41

agg_prior					98.67 38	93.79 35		98.00 69		95.68 66			99.57 61

1121		94.71 80	93.83 83	97.34 41	98.57 49	93.64 40	96.04 209	97.73 92	81.56 293	95.68 66	97.85 56	90.23 53	99.65 39	87.68 179	99.12 57	98.73 85

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 54	90.53 126	96.04 209	97.48 117	93.47 52	95.67 69	98.10 40	89.17 61	99.25 94	91.27 124	98.77 68	99.13 55

MVS_0304		96.05 49	95.45 51	97.85 13	97.75 96	94.50 13	96.87 134	97.95 79	95.46 6	95.60 70	98.01 46	80.96 189	99.83 13	97.23 2	99.25 44	99.23 47

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 126	89.55 150	96.70 157	97.17 150	91.17 119	95.60 70	96.11 143	87.87 78	98.76 138	93.01 92	97.17 108	98.72 86

MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 21	94.71 11	96.96 124	98.06 55	90.67 130	95.55 72	98.78 2	91.07 42	99.86 6	96.58 15	99.55 12	99.38 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 34	98.41 17	98.06 55	93.37 53	95.54 73	98.34 25	90.59 50	99.88 3	94.83 59	99.54 13	99.49 24

test12				97.65 29	98.46 51	94.26 19		97.66 101		95.52 74		90.89 46	99.46 77		99.25 44	99.22 48

test222					98.24 69	92.21 74	95.33 241	97.60 106	79.22 304	95.25 75	97.84 58	88.80 66			99.15 52	98.72 86

原ACMM1				96.38 80	98.59 46	91.09 111		97.89 80	87.41 220	95.22 76	97.68 67	90.25 52	99.54 65	87.95 172	99.12 57	98.49 102

CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 17	91.48 95	98.33 20	98.11 43	87.79 211	95.17 77	98.03 44	87.09 90	99.61 45	93.51 81	99.42 28	99.02 62

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 22	94.19 22	97.03 117	98.08 48	88.35 197	95.09 78	97.65 70	89.97 57	99.48 75	92.08 104	98.59 73	98.44 107

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 112	91.58 94	98.26 24	98.12 40	94.38 33	94.90 79	98.15 39	82.28 168	98.92 123	91.45 121	98.58 74	99.01 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CANet		96.39 41	96.02 43	97.50 37	97.62 101	93.38 47	97.02 119	97.96 77	95.42 8	94.86 80	97.81 59	87.38 87	99.82 16	96.88 7	99.20 49	99.29 43

API-MVS		94.84 78	94.49 75	95.90 100	97.90 90	92.00 83	97.80 50	97.48 117	89.19 163	94.81 81	96.71 108	88.84 65	99.17 100	88.91 157	98.76 69	96.53 171

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 51	91.28 101	96.43 177	97.57 109	92.04 97	94.77 82	97.96 49	87.01 91	99.09 114	91.31 123	96.77 116	98.36 114

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 97	92.05 80	96.59 170	97.35 140	90.61 136	94.64 83	96.93 101	86.41 96	99.39 86	91.20 126	94.71 151	98.94 72

ACMMP		96.27 44	95.93 44	97.28 45	99.24 19	92.62 65	98.25 25	98.81 3	92.99 67	94.56 84	98.39 20	88.96 63	99.85 9	94.57 65	97.63 94	99.36 39
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
Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 131	91.47 96	96.41 179	97.41 133	91.02 124	94.50 85	95.92 148	87.53 84	98.78 135	93.89 74	96.81 115	98.84 82

sss		94.51 82	93.80 84	96.64 62	97.07 116	91.97 84	96.32 191	98.06 55	88.94 177	94.50 85	96.78 105	84.60 117	99.27 93	91.90 107	96.02 129	98.68 90

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 160	98.27 66	89.46 156	96.73 149	98.36 16	90.17 143	94.36 87	95.24 187	88.02 74	99.58 53	93.44 84	90.72 206	94.36 270

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 66	89.46 156	95.47 237	98.36 16	88.84 180	94.36 87	96.09 144	88.02 74	99.58 53	93.44 84	98.18 81	98.40 110

PMMVS		92.86 133	92.34 130	94.42 163	94.92 210	86.73 236	94.53 258	96.38 209	84.78 263	94.27 89	95.12 192	83.13 134	98.40 162	91.47 120	96.49 124	98.12 120

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 107	89.56 149	98.67 5	97.00 173	90.69 129	94.24 90	97.62 75	89.79 59	98.81 133	93.39 87	96.49 124	98.92 74

PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 73	90.86 118	97.27 98	98.25 25	90.21 142	94.18 91	97.27 89	87.48 85	99.73 23	93.53 80	97.77 92	98.55 93

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 149	97.06 118	88.53 181	95.28 244	97.45 126	91.68 105	94.08 92	97.68 67	82.41 166	98.90 125	93.84 76	92.47 176	96.98 155

XVG-OURS		93.72 105	93.35 101	94.80 150	97.07 116	88.61 179	94.79 253	97.46 122	91.97 100	93.99 93	97.86 55	81.74 180	98.88 129	92.64 94	92.67 175	96.92 163

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 98	90.56 125	98.44 15	96.22 217	93.21 58	93.99 93	97.74 64	85.55 106	98.45 160	89.98 132	97.86 88	99.14 54

CSCG		96.05 49	95.91 45	96.46 77	99.24 19	90.47 128	98.30 21	98.57 11	89.01 173	93.97 95	97.57 79	92.62 16	99.76 21	94.66 64	99.27 43	99.15 53

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 69	89.88 140	94.58 256	98.49 12	85.06 258	93.78 96	95.78 159	82.86 153	98.67 143	91.77 111	95.71 137	99.07 61

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 66	89.38 162	95.18 249	98.48 14	85.60 251	93.76 97	97.11 97	83.15 132	99.61 45	91.33 122	98.72 70	99.19 49

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 155	91.46 97	96.33 190	97.04 169	88.97 176	93.56 98	96.51 126	87.55 83	97.89 228	89.80 135	95.95 131	98.44 107
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MDTV_nov1_ep13_2view						70.35 320	93.10 288		83.88 272	93.55 99		82.47 165		86.25 205		98.38 113

CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 144	92.13 78	96.21 201	96.67 201	94.38 33	93.53 100	97.03 100	79.34 218	99.71 27	90.76 127	98.45 76	97.82 134

tpmrst		91.44 186	91.32 161	91.79 264	95.15 199	79.20 304	93.42 280	95.37 252	88.55 191	93.49 101	93.67 247	82.49 163	98.27 171	90.41 130	89.34 220	97.90 128

TAMVS		94.01 96	93.46 96	95.64 111	96.16 157	90.45 129	96.71 154	96.89 187	89.27 161	93.46 102	96.92 102	87.29 88	97.94 220	88.70 163	95.74 135	98.53 95

DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 85	93.17 52	97.30 97	98.06 55	93.92 40	93.38 103	98.66 4	86.83 92	99.73 23	95.60 43	99.22 47	98.96 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thres600view7		92.49 146	91.60 152	95.18 129	97.91 89	89.47 154	97.65 64	94.66 282	92.18 90	93.33 104	94.91 195	78.06 243	99.10 111	81.61 265	94.06 157	96.98 155

thres200		92.23 157	91.39 158	94.75 153	97.61 102	89.03 173	96.60 169	95.09 267	92.08 96	93.28 105	94.00 237	78.39 234	99.04 119	81.26 275	94.18 154	96.19 178

tfpn200view9		92.38 149	91.52 155	94.95 144	97.85 92	89.29 166	97.41 84	94.88 277	92.19 88	93.27 106	94.46 214	78.17 236	99.08 116	81.40 272	94.08 155	96.48 174

thres400		92.42 147	91.52 155	95.12 137	97.85 92	89.29 166	97.41 84	94.88 277	92.19 88	93.27 106	94.46 214	78.17 236	99.08 116	81.40 272	94.08 155	96.98 155

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 109	91.96 85	95.40 239	97.45 126	89.81 152	93.22 108	96.28 135	79.62 215	99.46 77	90.74 128	93.11 170	98.50 100

view600		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 284	92.09 91	93.17 109	95.52 174	78.14 239	99.11 106	81.61 265	94.04 158	96.98 155

view800		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 284	92.09 91	93.17 109	95.52 174	78.14 239	99.11 106	81.61 265	94.04 158	96.98 155

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 284	92.09 91	93.17 109	95.52 174	78.14 239	99.11 106	81.61 265	94.04 158	96.98 155

tfpn		92.55 140	91.68 146	95.18 129	97.98 81	89.44 158	98.00 36	94.57 284	92.09 91	93.17 109	95.52 174	78.14 239	99.11 106	81.61 265	94.04 158	96.98 155

diffmvs		93.43 114	92.75 114	95.48 121	96.47 143	89.61 146	96.09 206	97.14 154	85.97 248	93.09 113	95.35 182	84.87 114	98.55 153	89.51 142	96.26 128	98.28 116

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 144	97.61 102	87.92 212	98.10 31	95.80 238	92.22 86	93.02 114	97.45 86	84.53 119	97.91 227	88.24 166	97.97 86	99.02 62

114514_t		93.95 97	93.06 106	96.63 64	99.07 26	91.61 91	97.46 83	97.96 77	77.99 309	93.00 115	97.57 79	86.14 101	99.33 90	89.22 148	99.15 52	98.94 72

UGNet		94.04 95	93.28 103	96.31 84	96.85 123	91.19 106	97.88 44	97.68 100	94.40 31	93.00 115	96.18 138	73.39 274	99.61 45	91.72 112	98.46 75	98.13 119
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
HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 115	92.49 69	95.64 229	96.64 202	89.05 172	93.00 115	95.79 158	85.77 105	99.45 79	89.16 151	94.35 152	97.96 125

PVSNet	86.66 18	92.24 156	91.74 145	93.73 197	97.77 95	83.69 271	92.88 290	96.72 194	87.91 209	93.00 115	94.86 197	78.51 231	99.05 118	86.53 200	97.45 101	98.47 105

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 80	92.19 77	97.67 61	97.47 120	88.13 206	93.00 115	95.84 152	84.86 115	99.51 72	87.99 171	98.17 82	97.83 133
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		95.01 69	94.59 70	96.26 89	98.89 31	90.68 123	97.24 100	97.73 92	91.80 102	92.93 120	96.62 122	89.13 62	99.14 104	89.21 149	97.78 91	98.97 68

MDTV_nov1_ep13			90.76 182		95.22 195	80.33 294	93.03 289	95.28 257	88.14 205	92.84 121	93.83 242	81.34 184	98.08 187	82.86 255	94.34 153

CostFormer		91.18 199	90.70 185	92.62 240	94.84 214	81.76 283	94.09 269	94.43 289	84.15 268	92.72 122	93.77 244	79.43 217	98.20 174	90.70 129	92.18 182	97.90 128

EPNet		95.20 66	94.56 71	97.14 53	92.80 291	92.68 63	97.85 47	94.87 280	96.64 1	92.46 123	97.80 61	86.23 97	99.65 39	93.72 78	98.62 72	99.10 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		90.82 208	89.77 217	93.95 182	94.45 228	87.19 227	90.23 312	95.68 241	86.89 236	92.40 124	92.36 275	80.91 193	97.05 274	81.09 276	93.95 162	97.60 144

RPMNet		88.52 252	86.72 264	93.95 182	94.45 228	87.19 227	90.23 312	94.99 272	77.87 311	92.40 124	87.55 316	80.17 208	97.05 274	68.84 313	93.95 162	97.60 144

EPMVS		90.70 215	89.81 216	93.37 217	94.73 219	84.21 264	93.67 276	88.02 329	89.50 156	92.38 126	93.49 254	77.82 246	97.78 237	86.03 211	92.68 174	98.11 123

PatchT		88.87 245	87.42 250	93.22 223	94.08 250	85.10 255	89.51 316	94.64 283	81.92 287	92.36 127	88.15 311	80.05 209	97.01 278	72.43 305	93.65 165	97.54 147

PAPR		94.18 87	93.42 100	96.48 74	97.64 100	91.42 99	95.55 232	97.71 98	88.99 174	92.34 128	95.82 154	89.19 60	99.11 106	86.14 207	97.38 102	98.90 76

mvs-test1		93.63 107	93.69 87	93.46 213	96.02 163	84.61 262	97.24 100	96.72 194	93.85 42	92.30 129	95.76 160	83.08 138	98.89 127	91.69 115	96.54 123	96.87 165

Patchmatch-test1		91.54 182	90.85 178	93.59 205	95.59 175	84.95 258	94.72 254	95.58 245	90.82 125	92.25 130	93.58 250	75.80 254	97.41 263	83.35 249	95.98 130	98.40 110

CVMVSNet		91.23 195	91.75 143	89.67 293	95.77 171	74.69 312	96.44 175	94.88 277	85.81 249	92.18 131	97.64 73	79.07 221	95.58 304	88.06 169	95.86 134	98.74 84

AdaColmap		94.34 84	93.68 88	96.31 84	98.59 46	91.68 90	96.59 170	97.81 88	89.87 147	92.15 132	97.06 99	83.62 126	99.54 65	89.34 144	98.07 84	97.70 138

PatchmatchNet		91.91 165	91.35 159	93.59 205	95.38 183	84.11 266	93.15 286	95.39 250	89.54 154	92.10 133	93.68 246	82.82 155	98.13 180	84.81 227	95.32 140	98.52 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 173	92.39 70	97.86 45	98.66 9	92.30 85	92.09 134	95.37 181	80.49 201	98.40 162	93.95 71	85.86 246	95.75 201

tpm		90.25 224	89.74 220	91.76 267	93.92 258	79.73 300	93.98 270	93.54 309	88.28 198	91.99 135	93.25 260	77.51 247	97.44 260	87.30 191	87.94 232	98.12 120

CNLPA		94.28 85	93.53 93	96.52 69	98.38 58	92.55 67	96.59 170	96.88 188	90.13 144	91.91 136	97.24 91	85.21 109	99.09 114	87.64 182	97.83 89	97.92 127

BH-RMVSNet		92.72 138	91.97 138	94.97 142	97.16 113	87.99 207	96.15 203	95.60 243	90.62 134	91.87 137	97.15 96	78.41 233	98.57 151	83.16 252	97.60 95	98.36 114

PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 75	90.80 120	95.27 246	97.18 148	87.96 207	91.86 138	95.68 166	80.44 202	98.99 120	84.01 243	97.54 96	96.89 164

OPM-MVS		93.28 118	92.76 112	94.82 147	94.63 222	90.77 122	96.65 162	97.18 148	93.72 45	91.68 139	97.26 90	79.33 219	98.63 145	92.13 101	92.28 178	95.07 235

tpm2		89.96 230	89.21 229	92.23 248	94.91 212	81.25 286	93.78 273	94.42 290	80.62 299	91.56 140	93.44 257	76.44 251	97.94 220	85.60 218	92.08 186	97.49 148

TAPA-MVS	90.10 7	92.30 153	91.22 167	95.56 114	98.33 62	89.60 147	96.79 144	97.65 103	81.83 288	91.52 141	97.23 92	87.94 76	98.91 124	71.31 309	98.37 77	98.17 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		91.48 184	90.59 189	94.16 171	96.40 146	87.33 221	95.67 226	95.34 256	87.68 215	91.46 142	95.52 174	76.77 249	98.35 166	82.85 256	93.61 167	96.79 167

RPSCF		90.75 211	90.86 177	90.42 287	96.84 124	76.29 310	95.61 231	96.34 210	83.89 271	91.38 143	97.87 53	76.45 250	98.78 135	87.16 195	92.23 179	96.20 177

PatchFormer-LS_test		91.68 174	91.18 169	93.19 225	95.24 194	83.63 272	95.53 234	95.44 249	89.82 151	91.37 144	92.58 269	80.85 197	98.52 155	89.65 140	90.16 213	97.42 150

PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 48	91.15 110	96.69 159	97.39 134	87.29 223	91.37 144	96.71 108	88.39 72	99.52 71	87.33 190	97.13 109	97.73 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		93.12 122	92.72 117	94.34 166	96.71 130	87.27 223	90.29 311	97.72 95	86.61 241	91.34 146	95.29 184	84.29 121	98.41 161	93.25 88	98.94 65	97.35 151

HQP_MVS		93.78 103	93.43 98	94.82 147	96.21 152	89.99 134	97.74 54	97.51 115	94.85 17	91.34 146	96.64 115	81.32 185	98.60 148	93.02 90	92.23 179	95.86 190

plane_prior3						90.00 132			94.46 30	91.34 146

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 128	90.03 131	96.81 141	97.13 156	88.19 202	91.30 149	94.27 230	86.21 98	98.63 145	87.66 181	96.46 126	98.12 120

EI-MVSNet		93.03 126	92.88 110	93.48 211	95.77 171	86.98 232	96.44 175	97.12 157	90.66 132	91.30 149	97.64 73	86.56 94	98.05 199	89.91 133	90.55 208	95.41 212

MVSTER		93.20 120	92.81 111	94.37 164	96.56 136	89.59 148	97.06 116	97.12 157	91.24 118	91.30 149	95.96 146	82.02 174	98.05 199	93.48 83	90.55 208	95.47 209

ADS-MVSNet2		89.45 238	88.59 237	92.03 257	95.86 166	82.26 280	90.93 307	94.32 294	83.23 279	91.28 152	91.81 282	79.01 226	95.99 296	79.52 282	91.39 196	97.84 131

ADS-MVSNet		89.89 232	88.68 236	93.53 209	95.86 166	84.89 259	90.93 307	95.07 269	83.23 279	91.28 152	91.81 282	79.01 226	97.85 230	79.52 282	91.39 196	97.84 131

nrg030		94.05 94	93.31 102	96.27 88	95.22 195	94.59 12	98.34 19	97.46 122	92.93 74	91.21 154	96.64 115	87.23 89	98.22 173	94.99 56	85.80 247	95.98 188

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 239	96.02 163	83.25 274	97.14 113	96.72 194	93.85 42	91.20 155	93.44 257	83.08 138	98.30 170	91.69 115	95.73 136	96.50 173

VPNet		92.23 157	91.31 162	94.99 140	95.56 176	90.96 114	97.22 105	97.86 85	92.96 73	90.96 156	96.62 122	75.06 260	98.20 174	91.90 107	83.65 279	95.80 196

JIA-IIPM		88.26 260	87.04 261	91.91 259	93.52 270	81.42 285	89.38 317	94.38 291	80.84 297	90.93 157	80.74 323	79.22 220	97.92 224	82.76 257	91.62 191	96.38 175

test-LLR		91.42 187	91.19 168	92.12 254	94.59 223	80.66 289	94.29 263	92.98 312	91.11 121	90.76 158	92.37 272	79.02 224	98.07 191	88.81 161	96.74 117	97.63 139

test-mter		90.19 227	89.54 224	92.12 254	94.59 223	80.66 289	94.29 263	92.98 312	87.68 215	90.76 158	92.37 272	67.67 297	98.07 191	88.81 161	96.74 117	97.63 139

ACMM	89.79 8	92.96 128	92.50 127	94.35 165	96.30 150	88.71 177	97.58 74	97.36 139	91.40 114	90.53 160	96.65 114	79.77 212	98.75 139	91.24 125	91.64 190	95.59 206
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 55	88.98 174	97.18 109	97.29 144	87.75 213	90.49 161	97.10 98	85.21 109	99.50 74	86.70 199	96.72 119	97.63 139

DWT-MVSNet_test		90.76 209	89.89 212	93.38 216	95.04 204	83.70 270	95.85 219	94.30 295	88.19 202	90.46 162	92.80 264	73.61 272	98.50 157	88.16 167	90.58 207	97.95 126

TESTMET0.1,1		90.06 229	89.42 226	91.97 258	94.41 230	80.62 291	94.29 263	91.97 319	87.28 224	90.44 163	92.47 271	68.79 292	97.67 245	88.50 165	96.60 122	97.61 143

FIs		94.09 92	93.70 86	95.27 127	95.70 173	92.03 81	98.10 31	98.68 7	93.36 55	90.39 164	96.70 110	87.63 82	97.94 220	92.25 97	90.50 210	95.84 193

GA-MVS		91.38 189	90.31 194	94.59 154	94.65 221	87.62 219	94.34 261	96.19 218	90.73 128	90.35 165	93.83 242	71.84 277	97.96 218	87.22 192	93.61 167	98.21 117

LS3D		93.57 110	92.61 121	96.47 75	97.59 104	91.61 91	97.67 61	97.72 95	85.17 256	90.29 166	98.34 25	84.60 117	99.73 23	83.85 247	98.27 79	98.06 124

FC-MVSNet-test		93.94 98	93.57 90	95.04 138	95.48 179	91.45 98	98.12 30	98.71 5	93.37 53	90.23 167	96.70 110	87.66 80	97.85 230	91.49 119	90.39 211	95.83 194

HQP-NCC					95.86 166		96.65 162		93.55 48	90.14 168

ACMP_Plane					95.86 166		96.65 162		93.55 48	90.14 168

HQP4-MVS										90.14 168			98.50 157			95.78 197

HQP-MVS		93.19 121	92.74 116	94.54 159	95.86 166	89.33 163	96.65 162	97.39 134	93.55 48	90.14 168	95.87 150	80.95 190	98.50 157	92.13 101	92.10 184	95.78 197

UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 185	92.83 59	97.17 110	98.58 10	92.98 72	90.13 172	95.80 155	88.37 73	97.85 230	91.71 113	83.93 273	95.73 203

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 208	92.83 59	97.16 111	98.24 27	93.02 66	90.13 172	95.71 163	83.47 127	97.85 230	91.71 113	83.93 273	95.78 197

LPG-MVS_test		92.94 129	92.56 122	94.10 172	96.16 157	88.26 187	97.65 64	97.46 122	91.29 115	90.12 174	97.16 94	79.05 222	98.73 140	92.25 97	91.89 187	95.31 222

LGP-MVS_train				94.10 172	96.16 157	88.26 187		97.46 122	91.29 115	90.12 174	97.16 94	79.05 222	98.73 140	92.25 97	91.89 187	95.31 222

UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 182	93.34 50	97.39 88	98.71 5	93.14 63	90.10 176	94.83 199	87.71 79	98.03 203	91.67 117	83.99 272	95.46 210

mvs_anonymous		93.82 101	93.74 85	94.06 174	96.44 145	85.41 252	95.81 221	97.05 166	89.85 150	90.09 177	96.36 133	87.44 86	97.75 240	93.97 70	96.69 120	99.02 62

test_djsdf		93.07 124	92.76 112	94.00 177	93.49 272	88.70 178	98.22 26	97.57 109	91.42 112	90.08 178	95.55 172	82.85 154	97.92 224	94.07 68	91.58 192	95.40 216

dp		88.90 244	88.26 242	90.81 280	94.58 225	76.62 309	92.85 291	94.93 275	85.12 257	90.07 179	93.07 261	75.81 253	98.12 182	80.53 278	87.42 238	97.71 137

PS-MVSNAJss		93.74 104	93.51 94	94.44 161	93.91 259	89.28 168	97.75 53	97.56 112	92.50 82	89.94 180	96.54 125	88.65 68	98.18 177	93.83 77	90.90 203	95.86 190

v6		91.69 173	91.00 172	93.75 194	94.14 241	88.12 200	97.20 106	96.98 174	89.19 163	89.90 181	94.42 218	83.04 142	98.07 191	89.07 152	85.10 256	95.07 235

CLD-MVS		92.98 127	92.53 125	94.32 167	96.12 161	89.20 170	95.28 244	97.47 120	92.66 79	89.90 181	95.62 168	80.58 199	98.40 162	92.73 93	92.40 177	95.38 218
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
gg-mvs-nofinetune		87.82 263	85.61 270	94.44 161	94.46 227	89.27 169	91.21 306	84.61 335	80.88 296	89.89 183	74.98 326	71.50 279	97.53 254	85.75 216	97.21 107	96.51 172

v1neww		91.70 171	91.01 170	93.75 194	94.19 236	88.14 198	97.20 106	96.98 174	89.18 165	89.87 184	94.44 216	83.10 136	98.06 196	89.06 153	85.09 257	95.06 238

v7new		91.70 171	91.01 170	93.75 194	94.19 236	88.14 198	97.20 106	96.98 174	89.18 165	89.87 184	94.44 216	83.10 136	98.06 196	89.06 153	85.09 257	95.06 238

1112_ss		93.37 115	92.42 129	96.21 91	97.05 119	90.99 112	96.31 192	96.72 194	86.87 237	89.83 186	96.69 112	86.51 95	99.14 104	88.12 168	93.67 164	98.50 100

v7		91.47 185	90.73 184	93.68 202	94.13 242	88.16 196	97.09 115	97.05 166	88.38 195	89.80 187	94.52 209	82.21 170	98.01 207	88.00 170	85.42 250	94.87 247

BH-untuned		92.94 129	92.62 120	93.92 186	97.22 110	86.16 244	96.40 183	96.25 215	90.06 145	89.79 188	96.17 140	83.19 130	98.35 166	87.19 193	97.27 106	97.24 152

v1		91.61 175	90.89 173	93.78 191	94.01 254	88.21 192	96.96 124	96.96 178	89.17 167	89.78 189	94.29 226	82.97 148	98.05 199	88.85 159	84.99 263	95.08 233

v1141		91.61 175	90.89 173	93.78 191	94.01 254	88.24 189	96.96 124	96.96 178	89.17 167	89.75 190	94.29 226	82.99 146	98.03 203	88.85 159	85.00 262	95.07 235

divwei89l23v2f112		91.61 175	90.89 173	93.78 191	94.01 254	88.22 191	96.96 124	96.96 178	89.17 167	89.75 190	94.28 228	83.02 144	98.03 203	88.86 158	84.98 264	95.08 233

V42		91.58 179	90.87 176	93.73 197	94.05 253	88.50 182	97.32 95	96.97 177	88.80 185	89.71 192	94.33 223	82.54 161	98.05 199	89.01 155	85.07 259	94.64 263

Baseline_NR-MVSNet		91.20 196	90.62 187	92.95 230	93.83 262	88.03 206	97.01 121	95.12 266	88.42 194	89.70 193	95.13 191	83.47 127	97.44 260	89.66 139	83.24 282	93.37 285

v144192		91.06 201	90.28 196	93.39 215	93.66 267	87.23 226	96.83 137	97.07 163	87.43 219	89.69 194	94.28 228	81.48 182	98.00 210	87.18 194	84.92 265	94.93 245

v1144		91.37 190	90.60 188	93.68 202	93.89 260	88.23 190	96.84 136	97.03 171	88.37 196	89.69 194	94.39 219	82.04 173	97.98 211	87.80 175	85.37 251	94.84 249

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 120	89.97 137	95.53 234	96.64 202	85.38 252	89.65 196	95.18 188	85.86 103	99.10 111	87.70 177	93.58 169	98.49 102

v1192		91.07 200	90.23 200	93.58 207	93.70 265	87.82 215	96.73 149	97.07 163	87.77 212	89.58 197	94.32 224	80.90 196	97.97 214	86.52 201	85.48 248	94.95 241

v1240		90.70 215	89.85 214	93.23 222	93.51 271	86.80 235	96.61 167	97.02 172	87.16 225	89.58 197	94.31 225	79.55 216	97.98 211	85.52 219	85.44 249	94.90 246

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 217	92.07 79	97.53 76	98.11 43	92.90 75	89.56 199	96.12 141	83.16 131	97.60 251	89.30 145	83.20 283	95.75 201

v2v482		91.59 178	90.85 178	93.80 189	93.87 261	88.17 195	96.94 130	96.88 188	89.54 154	89.53 200	94.90 196	81.70 181	98.02 206	89.25 147	85.04 261	95.20 230

v1921920		90.85 207	90.03 207	93.29 220	93.55 268	86.96 234	96.74 148	97.04 169	87.36 221	89.52 201	94.34 222	80.23 207	97.97 214	86.27 204	85.21 254	94.94 243

IterMVS-LS		92.29 154	91.94 139	93.34 218	96.25 151	86.97 233	96.57 173	97.05 166	90.67 130	89.50 202	94.80 201	86.59 93	97.64 248	89.91 133	86.11 245	95.40 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cascas		91.20 196	90.08 204	94.58 158	94.97 206	89.16 172	93.65 277	97.59 108	79.90 301	89.40 203	92.92 263	75.36 258	98.36 165	92.14 100	94.75 149	96.23 176

XVG-ACMP-BASELINE		90.93 205	90.21 202	93.09 226	94.31 233	85.89 245	95.33 241	97.26 145	91.06 123	89.38 204	95.44 180	68.61 293	98.60 148	89.46 143	91.05 201	94.79 257

GBi-Net		91.35 191	90.27 197	94.59 154	96.51 139	91.18 107	97.50 78	96.93 183	88.82 182	89.35 205	94.51 210	73.87 268	97.29 270	86.12 208	88.82 223	95.31 222

test1		91.35 191	90.27 197	94.59 154	96.51 139	91.18 107	97.50 78	96.93 183	88.82 182	89.35 205	94.51 210	73.87 268	97.29 270	86.12 208	88.82 223	95.31 222

FMVSNet3		91.78 167	90.69 186	95.03 139	96.53 138	92.27 73	97.02 119	96.93 183	89.79 153	89.35 205	94.65 207	77.01 248	97.47 258	86.12 208	88.82 223	95.35 220

WR-MVS		92.34 150	91.53 154	94.77 152	95.13 200	90.83 119	96.40 183	97.98 75	91.88 101	89.29 208	95.54 173	82.50 162	97.80 235	89.79 136	85.27 253	95.69 204

DP-MVS		92.76 137	91.51 157	96.52 69	98.77 33	90.99 112	97.38 90	96.08 222	82.38 284	89.29 208	97.87 53	83.77 124	99.69 33	81.37 274	96.69 120	98.89 78

BH-w/o		92.14 161	91.75 143	93.31 219	96.99 121	85.73 247	95.67 226	95.69 240	88.73 187	89.26 210	94.82 200	82.97 148	98.07 191	85.26 223	96.32 127	96.13 183

3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 136	93.36 49	98.65 6	98.36 16	94.12 37	89.25 211	98.06 43	82.20 171	99.77 20	93.41 86	99.32 39	99.18 50

Fast-Effi-MVS+-dtu		92.29 154	91.99 137	93.21 224	95.27 190	85.52 251	97.03 117	96.63 204	92.09 91	89.11 212	95.14 190	80.33 205	98.08 187	87.54 185	94.74 150	96.03 187

XXY-MVS		92.16 159	91.23 166	94.95 144	94.75 218	90.94 115	97.47 82	97.43 131	89.14 170	88.90 213	96.43 130	79.71 213	98.24 172	89.56 141	87.68 234	95.67 205

PCF-MVS	89.48 11	91.56 180	89.95 210	96.36 82	96.60 132	92.52 68	92.51 295	97.26 145	79.41 302	88.90 213	96.56 124	84.04 122	99.55 63	77.01 295	97.30 105	97.01 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
jajsoiax		92.42 147	91.89 140	94.03 176	93.33 278	88.50 182	97.73 56	97.53 113	92.00 99	88.85 215	96.50 127	75.62 257	98.11 183	93.88 75	91.56 193	95.48 207

mvs_tets		92.31 152	91.76 142	93.94 185	93.41 274	88.29 185	97.63 69	97.53 113	92.04 97	88.76 216	96.45 129	74.62 264	98.09 186	93.91 73	91.48 194	95.45 211

v148		90.99 203	90.38 193	92.81 234	93.83 262	85.80 246	96.78 146	96.68 199	89.45 157	88.75 217	93.93 240	82.96 150	97.82 234	87.83 174	83.25 281	94.80 255

FMVSNet2		91.31 193	90.08 204	94.99 140	96.51 139	92.21 74	97.41 84	96.95 181	88.82 182	88.62 218	94.75 203	73.87 268	97.42 262	85.20 224	88.55 229	95.35 220

tpmp4_e23		89.58 236	88.59 237	92.54 241	95.16 198	81.53 284	94.11 268	95.09 267	81.66 289	88.60 219	93.44 257	75.11 259	98.33 169	82.45 261	91.72 189	97.75 135

PAPM		91.52 183	90.30 195	95.20 128	95.30 189	89.83 141	93.38 281	96.85 190	86.26 244	88.59 220	95.80 155	84.88 113	98.15 179	75.67 298	95.93 132	97.63 139

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 122	95.34 4	98.48 14	97.87 83	94.65 28	88.53 221	98.02 45	83.69 125	99.71 27	93.18 89	98.96 64	99.44 30

anonymousdsp		92.16 159	91.55 153	93.97 180	92.58 295	89.55 150	97.51 77	97.42 132	89.42 158	88.40 222	94.84 198	80.66 198	97.88 229	91.87 109	91.28 198	94.48 266

WR-MVS_H		92.00 164	91.35 159	93.95 182	95.09 202	89.47 154	98.04 35	98.68 7	91.46 110	88.34 223	94.68 205	85.86 103	97.56 252	85.77 215	84.24 270	94.82 253

v8		91.29 194	90.53 190	93.57 208	94.15 240	88.12 200	97.34 92	97.06 165	88.99 174	88.32 224	94.26 232	83.08 138	98.01 207	87.62 183	83.92 275	94.57 264

ACMP	89.59 10	92.62 139	92.14 132	94.05 175	96.40 146	88.20 193	97.36 91	97.25 147	91.52 107	88.30 225	96.64 115	78.46 232	98.72 142	91.86 110	91.48 194	95.23 229
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		91.04 202	90.23 200	93.49 210	94.12 244	88.16 196	97.32 95	97.08 162	88.26 199	88.29 226	94.22 233	82.17 172	97.97 214	86.45 203	84.12 271	94.33 271

QAPM		93.45 113	92.27 131	96.98 58	96.77 128	92.62 65	98.39 18	98.12 40	84.50 266	88.27 227	97.77 62	82.39 167	99.81 17	85.40 221	98.81 67	98.51 98

CP-MVSNet		91.89 166	91.24 165	93.82 188	95.05 203	88.57 180	97.82 49	98.19 32	91.70 104	88.21 228	95.76 160	81.96 175	97.52 255	87.86 173	84.65 267	95.37 219

tpmvs		89.83 235	89.15 231	91.89 260	94.92 210	80.30 295	93.11 287	95.46 248	86.28 243	88.08 229	92.65 266	80.44 202	98.52 155	81.47 271	89.92 216	96.84 166

PS-CasMVS		91.55 181	90.84 180	93.69 201	94.96 207	88.28 186	97.84 48	98.24 27	91.46 110	88.04 230	95.80 155	79.67 214	97.48 257	87.02 196	84.54 268	95.31 222

MIMVSNet		88.50 254	86.76 262	93.72 199	94.84 214	87.77 216	91.39 302	94.05 300	86.41 242	87.99 231	92.59 268	63.27 309	95.82 300	77.44 291	92.84 173	97.57 146

GG-mvs-BLEND				93.62 204	93.69 266	89.20 170	92.39 298	83.33 336		87.98 232	89.84 291	71.00 283	96.87 280	82.08 264	95.40 139	94.80 255

PEN-MVS		91.20 196	90.44 191	93.48 211	94.49 226	87.91 214	97.76 52	98.18 34	91.29 115	87.78 233	95.74 162	80.35 204	97.33 268	85.46 220	82.96 284	95.19 231

ITE_SJBPF				92.43 243	95.34 185	85.37 253		95.92 227	91.47 109	87.75 234	96.39 132	71.00 283	97.96 218	82.36 262	89.86 217	93.97 277

v52		90.70 215	90.00 208	92.82 231	93.24 280	87.03 230	97.60 71	97.14 154	88.21 200	87.69 235	93.94 239	80.91 193	98.07 191	87.39 187	83.87 277	93.36 286

V4		90.71 214	90.00 208	92.82 231	93.21 283	87.03 230	97.59 73	97.16 153	88.21 200	87.69 235	93.92 241	80.93 192	98.06 196	87.39 187	83.90 276	93.39 284

v7n		90.76 209	89.86 213	93.45 214	93.54 269	87.60 220	97.70 60	97.37 137	88.85 179	87.65 237	94.08 236	81.08 187	98.10 184	84.68 230	83.79 278	94.66 262

Patchmtry		88.64 250	87.25 254	92.78 235	94.09 248	86.64 237	89.82 315	95.68 241	80.81 298	87.63 238	92.36 275	80.91 193	97.03 276	78.86 287	85.12 255	94.67 261

pmmvs4		90.93 205	89.85 214	94.17 170	93.34 276	90.79 121	94.60 255	96.02 223	84.62 264	87.45 239	95.15 189	81.88 178	97.45 259	87.70 177	87.87 233	94.27 274

tpm cat1		88.36 259	87.21 258	91.81 263	95.13 200	80.55 292	92.58 294	95.70 239	74.97 317	87.45 239	91.96 280	78.01 244	98.17 178	80.39 279	88.74 226	96.72 169

FMVSNet1		89.88 233	88.31 240	94.59 154	95.41 181	91.18 107	97.50 78	96.93 183	86.62 240	87.41 241	94.51 210	65.94 305	97.29 270	83.04 254	87.43 237	95.31 222

semantic-postprocess				91.82 262	95.52 177	84.20 265		96.15 220	90.61 136	87.39 242	94.27 230	75.63 256	96.44 283	87.34 189	86.88 242	94.82 253

MVS		91.71 168	90.44 191	95.51 117	95.20 197	91.59 93	96.04 209	97.45 126	73.44 321	87.36 243	95.60 169	85.42 107	99.10 111	85.97 212	97.46 97	95.83 194

EU-MVSNet		88.72 246	88.90 233	88.20 296	93.15 286	74.21 313	96.63 166	94.22 298	85.18 255	87.32 244	95.97 145	76.16 252	94.98 309	85.27 222	86.17 243	95.41 212

IterMVS		90.15 228	89.67 221	91.61 269	95.48 179	83.72 268	94.33 262	96.12 221	89.99 146	87.31 245	94.15 234	75.78 255	96.27 286	86.97 197	86.89 241	94.83 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v748		90.34 222	89.54 224	92.75 236	93.25 279	85.71 248	97.61 70	97.17 150	88.54 192	87.20 246	93.54 251	81.02 188	98.01 207	85.73 217	81.80 288	94.52 265

pmmvs5		89.86 234	88.87 234	92.82 231	92.86 289	86.23 243	96.26 196	95.39 250	84.24 267	87.12 247	94.51 210	74.27 266	97.36 267	87.61 184	87.57 235	94.86 248

DTE-MVSNet		90.56 218	89.75 219	93.01 228	93.95 257	87.25 224	97.64 68	97.65 103	90.74 127	87.12 247	95.68 166	79.97 210	97.00 279	83.33 251	81.66 291	94.78 258

Patchmatch-test		89.42 239	87.99 243	93.70 200	95.27 190	85.11 254	88.98 318	94.37 292	81.11 294	87.10 249	93.69 245	82.28 168	97.50 256	74.37 300	94.76 148	98.48 104

IB-MVS	87.33 17	89.91 231	88.28 241	94.79 151	95.26 193	87.70 218	95.12 250	93.95 303	89.35 159	87.03 250	92.49 270	70.74 285	99.19 97	89.18 150	81.37 292	97.49 148
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
EPNet_dtu		91.71 168	91.28 163	92.99 229	93.76 264	83.71 269	96.69 159	95.28 257	93.15 62	87.02 251	95.95 147	83.37 129	97.38 266	79.46 284	96.84 113	97.88 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		91.42 187	90.24 199	94.96 143	97.15 114	88.91 175	93.69 275	96.32 211	85.72 250	86.93 252	96.47 128	80.24 206	98.98 121	80.57 277	95.05 144	96.98 155

test0.0.03 1		89.37 240	88.70 235	91.41 273	92.47 296	85.63 249	95.22 248	92.70 315	91.11 121	86.91 253	93.65 248	79.02 224	93.19 317	78.00 290	89.18 221	95.41 212

COLMAP_ROB	87.81 15	90.40 221	89.28 228	93.79 190	97.95 86	87.13 229	96.92 131	95.89 234	82.83 281	86.88 254	97.18 93	73.77 271	99.29 92	78.44 289	93.62 166	94.95 241
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		90.51 220	90.19 203	91.44 272	93.41 274	81.25 286	96.98 123	96.28 212	91.68 105	86.55 255	96.30 134	74.20 267	97.98 211	88.96 156	87.40 239	95.09 232

MS-PatchMatch		90.27 223	89.77 217	91.78 265	94.33 232	84.72 261	95.55 232	96.73 193	86.17 246	86.36 256	95.28 186	71.28 281	97.80 235	84.09 240	98.14 83	92.81 291

1314		92.81 136	92.03 135	95.14 135	95.33 188	89.52 153	96.04 209	97.44 129	87.72 214	86.25 257	95.33 183	83.84 123	98.79 134	89.26 146	97.05 110	97.11 153

pm-mvs1		90.72 213	89.65 223	93.96 181	94.29 234	89.63 145	97.79 51	96.82 191	89.07 171	86.12 258	95.48 179	78.61 230	97.78 237	86.97 197	81.67 290	94.46 267

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 185	92.73 62	98.27 23	98.12 40	84.86 261	85.78 259	97.75 63	78.89 228	99.74 22	87.50 186	98.65 71	96.73 168

LTVRE_ROB	88.41 13	90.99 203	89.92 211	94.19 169	96.18 155	89.55 150	96.31 192	97.09 160	87.88 210	85.67 260	95.91 149	78.79 229	98.57 151	81.50 270	89.98 214	94.44 268
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
testgi		87.97 261	87.21 258	90.24 289	92.86 289	80.76 288	96.67 161	94.97 273	91.74 103	85.52 261	95.83 153	62.66 311	94.47 311	76.25 296	88.36 230	95.48 207

AllTest		90.23 225	88.98 232	93.98 178	97.94 87	86.64 237	96.51 174	95.54 246	85.38 252	85.49 262	96.77 106	70.28 287	99.15 102	80.02 280	92.87 171	96.15 181

TestCases				93.98 178	97.94 87	86.64 237		95.54 246	85.38 252	85.49 262	96.77 106	70.28 287	99.15 102	80.02 280	92.87 171	96.15 181

DSMNet-mixed		86.34 274	86.12 268	87.00 301	89.88 309	70.43 318	94.93 252	90.08 326	77.97 310	85.42 264	92.78 265	74.44 265	93.96 313	74.43 299	95.14 142	96.62 170

MVP-Stereo		90.74 212	90.08 204	92.71 237	93.19 285	88.20 193	95.86 218	96.27 213	86.07 247	84.86 265	94.76 202	77.84 245	97.75 240	83.88 246	98.01 85	92.17 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	87.92 14	90.20 226	89.18 230	93.25 221	96.48 142	86.45 241	96.99 122	96.68 199	88.83 181	84.79 266	96.22 137	70.16 289	98.53 154	84.42 236	88.04 231	94.77 259

NR-MVSNet		92.34 150	91.27 164	95.53 116	94.95 208	93.05 54	97.39 88	98.07 53	92.65 80	84.46 267	95.71 163	85.00 112	97.77 239	89.71 137	83.52 280	95.78 197

LF4IMVS		87.94 262	87.25 254	89.98 291	92.38 297	80.05 299	94.38 260	95.25 260	87.59 217	84.34 268	94.74 204	64.31 308	97.66 247	84.83 226	87.45 236	92.23 306

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 242	96.82 127	81.89 282	96.92 131	93.71 305	92.41 84	84.30 269	94.60 208	85.08 111	97.03 276	91.51 118	97.36 103	98.40 110

TransMVSNet (Re)		88.94 242	87.56 245	93.08 227	94.35 231	88.45 184	97.73 56	95.23 261	87.47 218	84.26 270	95.29 184	79.86 211	97.33 268	79.44 285	74.44 318	93.45 283

Anonymous20231206		87.09 269	86.14 267	89.93 292	91.22 303	80.35 293	96.11 205	95.35 253	83.57 276	84.16 271	93.02 262	73.54 273	95.61 302	72.16 306	86.14 244	93.84 279

SixPastTwentyTwo		89.15 241	88.54 239	90.98 276	93.49 272	80.28 296	96.70 157	94.70 281	90.78 126	84.15 272	95.57 170	71.78 278	97.71 243	84.63 231	85.07 259	94.94 243

TDRefinement		86.53 272	84.76 277	91.85 261	82.23 327	84.25 263	96.38 185	95.35 253	84.97 260	84.09 273	94.94 193	65.76 306	98.34 168	84.60 234	74.52 316	92.97 287

pmmvs6		87.81 264	86.19 266	92.69 238	91.32 302	86.30 242	97.34 92	96.41 208	80.59 300	84.05 274	94.37 221	67.37 300	97.67 245	84.75 228	79.51 298	94.09 276

ACMH	87.59 16	90.53 219	89.42 226	93.87 187	96.21 152	87.92 212	97.24 100	96.94 182	88.45 193	83.91 275	96.27 136	71.92 276	98.62 147	84.43 235	89.43 219	95.05 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet5		87.29 268	85.79 269	91.78 265	94.80 216	87.28 222	95.49 236	95.28 257	84.09 269	83.85 276	91.82 281	62.95 310	94.17 312	78.48 288	85.34 252	93.91 278

USDC		88.94 242	87.83 244	92.27 244	94.66 220	84.96 257	93.86 272	95.90 229	87.34 222	83.40 277	95.56 171	67.43 299	98.19 176	82.64 260	89.67 218	93.66 280

PVSNet_0	82.17 19	85.46 281	83.64 282	90.92 278	95.27 190	79.49 301	90.55 310	95.60 243	83.76 274	83.00 278	89.95 289	71.09 282	97.97 214	82.75 258	60.79 328	95.31 222

test_0402		86.46 273	84.79 276	91.45 271	95.02 205	85.55 250	96.29 194	94.89 276	80.90 295	82.21 279	93.97 238	68.21 296	97.29 270	62.98 319	88.68 228	91.51 312

Patchmatch-RL test		87.38 266	86.24 265	90.81 280	88.74 313	78.40 307	88.12 321	93.17 311	87.11 226	82.17 280	89.29 302	81.95 176	95.60 303	88.64 164	77.02 302	98.41 109

testus		82.63 290	82.15 286	84.07 306	87.31 318	67.67 324	93.18 282	94.29 296	82.47 283	82.14 281	90.69 287	53.01 325	91.94 321	66.30 316	89.96 215	92.62 292

LP		84.13 285	81.85 290	90.97 277	93.20 284	82.12 281	87.68 322	94.27 297	76.80 312	81.93 282	88.52 306	72.97 275	95.95 297	59.53 324	81.73 289	94.84 249

test2356		82.77 289	82.14 287	84.65 305	85.77 321	70.36 319	91.22 305	93.69 308	81.58 291	81.82 283	89.00 304	60.63 315	90.77 324	64.74 317	90.80 205	92.82 289

DI_MVS_plusplus_test		92.01 162	90.77 181	95.73 109	93.34 276	89.78 143	96.14 204	96.18 219	90.58 138	81.80 284	93.50 253	74.95 262	98.90 125	93.51 81	96.94 112	98.51 98

test_normal		92.01 162	90.75 183	95.80 104	93.24 280	89.97 137	95.93 216	96.24 216	90.62 134	81.63 285	93.45 256	74.98 261	98.89 127	93.61 79	97.04 111	98.55 93

TinyColmap		86.82 271	85.35 273	91.21 274	94.91 212	82.99 275	93.94 271	94.02 302	83.58 275	81.56 286	94.68 205	62.34 312	98.13 180	75.78 297	87.35 240	92.52 294

test20.03		86.14 276	85.40 272	88.35 294	90.12 306	80.06 298	95.90 217	95.20 262	88.59 188	81.29 287	93.62 249	71.43 280	92.65 318	71.26 310	81.17 293	92.34 305

N_pmnet		78.73 296	78.71 295	78.79 314	92.80 291	46.50 340	94.14 267	43.71 344	78.61 307	80.83 288	91.66 285	74.94 263	96.36 284	67.24 314	84.45 269	93.50 281

testpf		80.97 293	81.40 291	79.65 312	91.53 301	72.43 317	73.47 333	89.55 327	78.63 306	80.81 289	89.06 303	61.36 313	91.36 323	83.34 250	84.89 266	75.15 328

MVS-HIRNet		82.47 291	81.21 292	86.26 304	95.38 183	69.21 323	88.96 319	89.49 328	66.28 325	80.79 290	74.08 328	68.48 294	97.39 265	71.93 307	95.47 138	92.18 307

v18		88.71 247	87.52 246	92.27 244	94.16 239	88.11 202	96.82 140	95.96 224	87.03 227	80.76 291	89.81 292	83.15 132	96.22 287	84.69 229	75.31 309	92.49 295

v17		88.67 249	87.47 249	92.26 246	94.13 242	88.09 204	96.81 141	95.95 225	87.02 228	80.72 292	89.75 294	83.11 135	96.20 288	84.61 232	75.15 311	92.49 295

v16		88.69 248	87.50 247	92.26 246	94.19 236	88.11 202	96.81 141	95.95 225	87.01 229	80.71 293	89.80 293	83.08 138	96.20 288	84.61 232	75.34 308	92.48 297

PM-MVS		83.48 286	81.86 289	88.31 295	87.83 316	77.59 308	93.43 279	91.75 320	86.91 234	80.63 294	89.91 290	44.42 330	95.84 299	85.17 225	76.73 304	91.50 313

ambc				86.56 303	83.60 324	70.00 322	85.69 325	94.97 273		80.60 295	88.45 307	37.42 332	96.84 281	82.69 259	75.44 307	92.86 288

v15		88.53 251	87.31 251	92.20 249	94.09 248	88.05 205	96.72 152	95.90 229	87.01 229	80.53 296	89.60 298	83.02 144	96.13 290	84.29 237	74.64 312	92.41 301

v11		88.41 258	87.19 260	92.08 256	94.08 250	87.77 216	96.75 147	95.85 235	86.74 239	80.50 297	89.50 301	82.49 163	96.08 295	83.55 248	75.20 310	92.38 304

V14		88.52 252	87.30 252	92.17 251	94.12 244	87.99 207	96.72 152	95.91 228	86.98 231	80.50 297	89.63 295	83.03 143	96.12 292	84.23 238	74.60 314	92.40 302

V9		88.49 255	87.26 253	92.18 250	94.12 244	87.97 210	96.73 149	95.90 229	86.95 233	80.40 299	89.61 296	82.98 147	96.13 290	84.14 239	74.55 315	92.44 299

MIMVSNet1		84.93 283	83.05 283	90.56 285	89.56 311	84.84 260	95.40 239	95.35 253	83.91 270	80.38 300	92.21 279	57.23 318	93.34 316	70.69 312	82.75 287	93.50 281

v12		88.46 256	87.23 256	92.17 251	94.10 247	87.99 207	96.71 154	95.90 229	86.91 234	80.34 301	89.58 299	82.92 151	96.11 294	84.09 240	74.50 317	92.42 300

lessismore_v0				90.45 286	91.96 300	79.09 305		87.19 332		80.32 302	94.39 219	66.31 303	97.55 253	84.00 244	76.84 303	94.70 260

v13		88.45 257	87.22 257	92.16 253	94.08 250	87.95 211	96.71 154	95.90 229	86.86 238	80.27 303	89.55 300	82.92 151	96.12 292	84.02 242	74.63 313	92.40 302

K. test v3		87.64 265	86.75 263	90.32 288	93.02 288	79.48 302	96.61 167	92.08 318	90.66 132	80.25 304	94.09 235	67.21 301	96.65 282	85.96 213	80.83 295	94.83 251

OpenMVS_ROB	81.14 20	84.42 284	82.28 285	90.83 279	90.06 307	84.05 267	95.73 225	94.04 301	73.89 320	80.17 305	91.53 286	59.15 316	97.64 248	66.92 315	89.05 222	90.80 315

EG-PatchMatch MVS		87.02 270	85.44 271	91.76 267	92.67 293	85.00 256	96.08 208	96.45 207	83.41 278	79.52 306	93.49 254	57.10 319	97.72 242	79.34 286	90.87 204	92.56 293

pmmvs-eth3d		86.22 275	84.45 278	91.53 270	88.34 314	87.25 224	94.47 259	95.01 270	83.47 277	79.51 307	89.61 296	69.75 290	95.71 301	83.13 253	76.73 304	91.64 310

pmmvs3		79.97 294	77.50 298	87.39 299	82.80 325	79.38 303	92.70 293	90.75 324	70.69 323	78.66 308	87.47 317	51.34 327	93.40 315	73.39 304	69.65 323	89.38 318

UnsupCasMVSNet_eth		85.99 277	84.45 278	90.62 284	89.97 308	82.40 279	93.62 278	97.37 137	89.86 148	78.59 309	92.37 272	65.25 307	95.35 307	82.27 263	70.75 321	94.10 275

Test4		89.48 237	87.50 247	95.44 124	90.76 305	89.72 144	95.78 224	97.09 160	90.28 141	77.67 310	91.74 284	55.42 323	98.08 187	91.92 106	96.83 114	98.52 96

new-patchmatchnet		83.18 287	81.87 288	87.11 300	86.88 319	75.99 311	93.70 274	95.18 263	85.02 259	77.30 311	88.40 308	65.99 304	93.88 314	74.19 302	70.18 322	91.47 314

UnsupCasMVSNet_bld		82.13 292	79.46 294	90.14 290	88.00 315	82.47 277	90.89 309	96.62 205	78.94 305	75.61 312	84.40 321	56.63 320	96.31 285	77.30 294	66.77 327	91.63 311

new_pmnet		82.89 288	81.12 293	88.18 297	89.63 310	80.18 297	91.77 301	92.57 316	76.79 313	75.56 313	88.23 310	61.22 314	94.48 310	71.43 308	82.92 285	89.87 317

Anonymous20231211		78.22 298	75.30 299	86.99 302	86.14 320	74.16 314	95.62 230	93.88 304	66.43 324	74.44 314	87.86 313	41.39 331	95.11 308	62.49 320	69.46 324	91.71 309

CMPMVS	62.92 21	85.62 280	84.92 275	87.74 298	89.14 312	73.12 316	94.17 266	96.80 192	73.98 319	73.65 315	94.93 194	66.36 302	97.61 250	83.95 245	91.28 198	92.48 297
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		78.29 297	77.55 297	80.50 310	83.89 322	59.98 332	91.89 299	93.71 305	75.06 315	73.60 316	87.67 314	55.66 321	92.60 319	58.54 326	77.92 300	88.93 319

.test1245		65.38 306	69.22 304	53.86 326	83.89 322	59.98 332	91.89 299	93.71 305	75.06 315	73.60 316	87.67 314	55.66 321	92.60 319	58.54 326	2.96 339	9.00 337

test1235678		79.82 295	78.53 296	83.69 307	82.55 326	67.55 325	92.50 296	94.13 299	79.28 303	72.10 318	86.45 319	57.27 317	90.68 325	61.60 322	80.90 294	92.82 289

testing_2		87.33 267	85.03 274	94.22 168	87.77 317	89.32 165	94.97 251	97.11 159	89.22 162	71.64 319	88.73 305	55.16 324	97.94 220	91.95 105	88.73 227	95.41 212

YYNet1		85.87 278	84.23 280	90.78 283	92.38 297	82.46 278	93.17 284	95.14 265	82.12 286	67.69 320	92.36 275	78.16 238	95.50 306	77.31 293	79.73 297	94.39 269

MDA-MVSNet_test_wron		85.87 278	84.23 280	90.80 282	92.38 297	82.57 276	93.17 284	95.15 264	82.15 285	67.65 321	92.33 278	78.20 235	95.51 305	77.33 292	79.74 296	94.31 273

test12356		74.97 299	74.13 300	77.49 315	78.81 328	56.23 336	88.53 320	92.75 314	75.14 314	67.50 322	85.07 320	44.88 329	89.96 326	58.71 325	75.75 306	86.26 320

DeepMVS_CX				74.68 319	90.84 304	64.34 329		81.61 339	65.34 326	67.47 323	88.01 312	48.60 328	80.13 335	62.33 321	73.68 320	79.58 326

LCM-MVSNet		72.55 300	69.39 303	82.03 308	70.81 337	65.42 328	90.12 314	94.36 293	55.02 329	65.88 324	81.72 322	24.16 341	89.96 326	74.32 301	68.10 325	90.71 316

MDA-MVSNet-bldmvs		85.00 282	82.95 284	91.17 275	93.13 287	83.33 273	94.56 257	95.00 271	84.57 265	65.13 325	92.65 266	70.45 286	95.85 298	73.57 303	77.49 301	94.33 271

PMMVS2		70.19 303	66.92 305	80.01 311	76.35 329	65.67 327	86.22 324	87.58 331	64.83 327	62.38 326	80.29 325	26.78 339	88.49 330	63.79 318	54.07 329	85.88 322

testmv		72.22 301	70.02 301	78.82 313	73.06 335	61.75 330	91.24 304	92.31 317	74.45 318	61.06 327	80.51 324	34.21 333	88.63 329	55.31 329	68.07 326	86.06 321

FPMVS		71.27 302	69.85 302	75.50 317	74.64 330	59.03 334	91.30 303	91.50 321	58.80 328	57.92 328	88.28 309	29.98 337	85.53 332	53.43 330	82.84 286	81.95 324

Gipuma		67.86 305	65.41 306	75.18 318	92.66 294	73.45 315	66.50 335	94.52 288	53.33 330	57.80 329	66.07 332	30.81 334	89.20 328	48.15 333	78.88 299	62.90 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		68.12 304	63.78 307	81.13 309	74.01 332	70.22 321	87.61 323	90.71 325	72.63 322	53.13 330	71.89 329	30.29 335	91.45 322	61.53 323	32.21 333	81.72 325

tmp_tt		51.94 314	53.82 311	46.29 327	33.73 342	45.30 342	78.32 332	67.24 343	18.02 337	50.93 331	87.05 318	52.99 326	53.11 340	70.76 311	25.29 337	40.46 335

ANet_high		63.94 307	59.58 308	77.02 316	61.24 340	66.06 326	85.66 326	87.93 330	78.53 308	42.94 332	71.04 330	25.42 340	80.71 334	52.60 331	30.83 335	84.28 323

E-PMN		53.28 311	52.56 312	55.43 324	74.43 331	47.13 339	83.63 328	76.30 340	42.23 334	42.59 333	62.22 334	28.57 338	74.40 337	31.53 336	31.51 334	44.78 333

PNet_i23d		59.01 308	55.87 309	68.44 321	73.98 333	51.37 337	81.36 329	82.41 337	52.37 331	42.49 334	70.39 331	11.39 342	79.99 336	49.77 332	38.71 331	73.97 329

EMVS		52.08 313	51.31 313	54.39 325	72.62 336	45.39 341	83.84 327	75.51 341	41.13 335	40.77 335	59.65 335	30.08 336	73.60 338	28.31 337	29.90 336	44.18 334

MVE	50.73 23	53.25 312	48.81 315	66.58 323	65.34 338	57.50 335	72.49 334	70.94 342	40.15 336	39.28 336	63.51 333	6.89 346	73.48 339	38.29 335	42.38 330	68.76 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	53.92 22	58.58 309	55.40 310	68.12 322	51.00 341	48.64 338	78.86 331	87.10 333	46.77 333	35.84 337	74.28 327	8.76 343	86.34 331	42.07 334	73.91 319	69.38 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		56.92 310	51.11 314	74.38 320	62.30 339	61.47 331	80.09 330	84.87 334	49.62 332	30.80 338	57.20 336	7.03 344	82.94 333	55.69 328	32.36 332	78.72 327

wuyk23d		25.11 316	24.57 318	26.74 329	73.98 333	39.89 343	57.88 336	9.80 345	12.27 338	10.39 339	6.97 342	7.03 344	36.44 341	25.43 338	17.39 338	3.89 339

testmvs		13.36 318	16.33 319	4.48 331	5.04 343	2.26 345	93.18 282	3.28 346	2.70 339	8.24 340	21.66 338	2.29 348	2.19 342	7.58 339	2.96 339	9.00 337

test123		13.04 319	15.66 320	5.18 330	4.51 344	3.45 344	92.50 296	1.81 347	2.50 340	7.58 341	20.15 339	3.67 347	2.18 343	7.13 340	1.07 341	9.90 336

cdsmvs_eth3d_5k		23.24 317	30.99 317	0.00 332	0.00 345	0.00 346	0.00 337	97.63 105	0.00 341	0.00 342	96.88 103	84.38 120	0.00 344	0.00 341	0.00 342	0.00 340

pcd_1.5k_mvsjas		7.39 321	9.85 322	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	88.65 68	0.00 344	0.00 341	0.00 342	0.00 340

pcd1.5k->3k		38.37 315	40.51 316	31.96 328	94.29 234	0.00 346	0.00 337	97.69 99	0.00 341	0.00 342	0.00 343	81.45 183	0.00 344	0.00 341	91.11 200	95.89 189

sosnet-low-res		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sosnet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uncertanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

Regformer		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

ab-mvs-re		8.06 320	10.74 321	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	96.69 112	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

ESAPD								98.25 25

sam_mvs1												82.76 156

sam_mvs												81.94 177

MTGPA								98.08 48

test_post1							92.81 292				16.58 341	80.53 200	97.68 244	86.20 206

test_post											17.58 340	81.76 179	98.08 187

patchmatchnet-post											90.45 288	82.65 160	98.10 184

MTMP								82.03 338

gm-plane-assit					93.22 282	78.89 306			84.82 262		93.52 252		98.64 144	87.72 176

test9_res														94.81 60	99.38 33	99.45 28

agg_prior2														93.94 72	99.38 33	99.50 22

test_prior4						93.66 39	96.42 178

test_prior				97.23 48	98.67 38	92.99 55		98.00 69					99.41 83			99.29 43

新几何2							95.79 222

旧先验1					98.38 58	93.38 47		97.75 90			98.09 41	92.30 25			99.01 62	99.16 51

无先验							95.79 222	97.87 83	83.87 273				99.65 39	87.68 179		98.89 78

原ACMM2							95.67 226

testdata2													99.67 37	85.96 213

segment_acmp												92.89 10

testdata1							95.26 247		93.10 65

plane_prior7					96.21 152	89.98 136

plane_prior6					96.10 162	90.00 132						81.32 185

plane_prior5								97.51 115					98.60 148	93.02 90	92.23 179	95.86 190

plane_prior4											96.64 115

plane_prior2							97.74 54		94.85 17

plane_prior1					96.14 160

plane_prior						89.99 134	97.24 100		94.06 38						92.16 183

n2								0.00 348

nn								0.00 348

door-mid								91.06 323

test11								97.88 81

door								91.13 322

HQP5-MVS						89.33 163

BP-MVS														92.13 101

HQP3-MVS								97.39 134							92.10 184

HQP2-MVS												80.95 190

NP-MVS					95.99 165	89.81 142					95.87 150

ACMMP++_ref															90.30 212

ACMMP++															91.02 202

Test By Simon												88.73 67

High-resolution multi-view benchmark