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.

CNVR-MVS		98.46 1	98.38 1	98.72 3	99.80 4	96.19 9	99.80 7	97.99 50	97.05 3	99.41 1	99.59 2	92.89 11	100.00 1	98.99 6	99.90 4	99.96 4

TSAR-MVS + MP.		97.44 10	97.46 9	97.39 39	99.12 52	93.49 55	98.52 134	97.50 112	94.46 17	98.99 2	98.64 75	91.58 16	99.08 114	98.49 17	99.83 12	99.60 58

PS-MVSNAJ		96.87 24	96.40 29	98.29 11	97.35 106	97.29 1	99.03 75	97.11 146	95.83 9	98.97 3	99.14 28	82.48 144	99.60 76	98.60 11	99.08 59	98.00 153

旧先验2							98.67 116		85.75 191	98.96 4			98.97 117	93.84 85

xiu_mvs_v2_base		96.66 28	96.17 37	98.11 17	97.11 114	96.96 2	99.01 78	97.04 154	95.51 13	98.86 5	99.11 34	82.19 150	99.36 99	98.59 13	98.14 85	98.00 153

NCCC		98.12 3	98.11 3	98.13 15	99.76 6	94.46 38	99.81 5	97.88 56	96.54 4	98.84 6	99.46 5	92.55 13	99.98 8	98.25 22	99.93 1	99.94 6

SD-MVS		97.51 8	97.40 11	97.81 23	99.01 58	93.79 49	99.33 49	97.38 127	93.73 29	98.83 7	99.02 41	90.87 30	99.88 34	98.69 10	99.74 20	99.77 34

HSP-MVS		97.73 5	98.15 2	96.44 90	99.54 27	90.14 126	99.41 38	97.47 115	95.46 14	98.60 8	99.19 18	95.71 4	99.49 86	98.15 23	99.85 9	99.69 46

APD-MVS		96.95 21	96.72 23	97.63 27	99.51 34	93.58 51	99.16 58	97.44 120	90.08 98	98.59 9	99.07 35	89.06 47	99.42 94	97.92 25	99.66 28	99.88 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testdata				95.26 130	98.20 80	87.28 186		97.60 94	85.21 199	98.48 10	99.15 26	88.15 62	98.72 127	90.29 122	99.45 46	99.78 29

TEST9					99.57 23	93.17 58	99.38 40	97.66 82	89.57 105	98.39 11	99.18 20	90.88 29	99.66 65

train_agg		97.20 13	97.08 14	97.57 31	99.57 23	93.17 58	99.38 40	97.66 82	90.18 93	98.39 11	99.18 20	90.94 27	99.66 65	98.58 14	99.85 9	99.88 15

test_8					99.55 26	93.07 62	99.37 43	97.64 87	90.18 93	98.36 13	99.19 18	90.94 27	99.64 71

agg_prior3		97.09 17	96.97 16	97.45 34	99.56 25	92.79 69	99.36 44	97.67 81	89.59 103	98.36 13	99.16 24	90.57 34	99.68 62	98.58 14	99.85 9	99.88 15

HPM-MVS++		97.72 6	97.59 7	98.14 14	99.53 33	94.76 29	99.19 53	97.75 72	95.66 11	98.21 15	99.29 9	91.10 19	99.99 4	97.68 28	99.87 5	99.68 47

test_part2					99.54 27	95.42 14				98.13 16

ESAPD		97.97 4	97.82 6	98.43 10	99.54 27	95.42 14	99.43 33	97.69 78	92.81 44	98.13 16	99.48 3	93.96 6	99.97 13	99.52 1	99.83 12	99.90 9

SteuartSystems-ACMMP		97.25 11	97.34 12	97.01 50	97.38 105	91.46 89	99.75 8	97.66 82	94.14 21	98.13 16	99.26 10	92.16 14	99.66 65	97.91 26	99.64 30	99.90 9
Skip Steuart: Steuart Systems R&D Blog.
test_prior3		97.07 18	97.09 13	97.01 50	99.58 19	91.77 80	99.57 19	97.57 100	91.43 70	98.12 19	98.97 47	90.43 36	99.49 86	98.33 19	99.81 15	99.79 25

test_prior2							99.57 19		91.43 70	98.12 19	98.97 47	90.43 36		98.33 19	99.81 15

PHI-MVS		96.65 29	96.46 28	97.21 44	99.34 40	91.77 80	99.70 10	98.05 46	86.48 184	98.05 21	99.20 17	89.33 45	99.96 17	98.38 18	99.62 34	99.90 9

MVSFormer		94.71 73	94.08 73	96.61 83	95.05 180	94.87 22	97.77 197	96.17 199	86.84 178	98.04 22	98.52 81	85.52 102	95.99 259	89.83 125	98.97 64	98.96 98

lupinMVS		96.32 40	95.94 42	97.44 35	95.05 180	94.87 22	99.86 2	96.50 178	93.82 27	98.04 22	98.77 64	85.52 102	98.09 145	96.98 37	98.97 64	99.37 70

APDe-MVS		97.53 7	97.47 8	97.70 25	99.58 19	93.63 50	99.56 21	97.52 107	93.59 32	98.01 24	99.12 31	90.80 32	99.55 78	99.26 4	99.79 17	99.93 7

ACMMP_Plus		96.59 30	96.18 35	97.81 23	98.82 68	93.55 52	98.88 95	97.59 95	90.66 80	97.98 25	99.14 28	86.59 89	100.00 1	96.47 44	99.46 44	99.89 14

agg_prior1		97.12 15	97.03 15	97.38 40	99.54 27	92.66 70	99.35 46	97.64 87	90.38 88	97.98 25	99.17 22	90.84 31	99.61 74	98.57 16	99.78 19	99.87 19

agg_prior					99.54 27	92.66 70		97.64 87		97.98 25			99.61 74

CDPH-MVS		96.56 31	96.18 35	97.70 25	99.59 18	93.92 47	99.13 68	97.44 120	89.02 119	97.90 28	99.22 15	88.90 50	99.49 86	94.63 77	99.79 17	99.68 47

EPNet		96.82 25	96.68 25	97.25 43	98.65 72	93.10 61	99.48 26	98.76 17	96.54 4	97.84 29	98.22 93	87.49 71	99.66 65	95.35 64	97.78 91	99.00 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		97.50 9	97.45 10	97.63 27	99.65 13	93.21 57	99.70 10	98.13 44	94.61 16	97.78 30	99.46 5	89.85 40	99.81 52	97.97 24	99.91 3	99.88 15

test12				97.83 22	99.33 43	94.45 39		97.55 103		97.56 31		88.60 53	99.50 85		99.71 26	99.55 60

MVS_0304		96.12 45	95.26 55	98.69 4	98.44 77	96.54 7	99.70 10	96.89 163	95.76 10	97.53 32	99.12 31	72.42 229	99.93 24	98.75 8	98.69 76	99.61 57

xiu_mvs_v1_base_debu		94.73 70	93.98 75	96.99 53	95.19 170	95.24 17	98.62 122	96.50 178	92.99 37	97.52 33	98.83 61	72.37 230	99.15 108	97.03 33	96.74 103	96.58 190

xiu_mvs_v1_base		94.73 70	93.98 75	96.99 53	95.19 170	95.24 17	98.62 122	96.50 178	92.99 37	97.52 33	98.83 61	72.37 230	99.15 108	97.03 33	96.74 103	96.58 190

xiu_mvs_v1_base_debi		94.73 70	93.98 75	96.99 53	95.19 170	95.24 17	98.62 122	96.50 178	92.99 37	97.52 33	98.83 61	72.37 230	99.15 108	97.03 33	96.74 103	96.58 190

DeepPCF-MVS	93.56 1	96.55 32	97.84 5	92.68 191	98.71 71	78.11 300	99.70 10	97.71 77	98.18 1	97.36 36	99.76 1	90.37 38	99.94 22	99.27 3	99.54 41	99.99 1

CANet		97.00 19	96.49 27	98.55 6	98.86 67	96.10 10	99.83 4	97.52 107	95.90 8	97.21 37	98.90 57	82.66 141	99.93 24	98.71 9	98.80 73	99.63 54

CANet_DTU		94.31 82	93.35 87	97.20 45	97.03 117	94.71 31	98.62 122	95.54 241	95.61 12	97.21 37	98.47 87	71.88 235	99.84 45	88.38 143	97.46 97	97.04 177

VNet		95.08 64	94.26 68	97.55 32	98.07 84	93.88 48	98.68 114	98.73 20	90.33 90	97.16 39	97.43 114	79.19 166	99.53 80	96.91 39	91.85 158	99.24 82

region2R		96.30 41	96.17 37	96.70 75	99.70 7	90.31 123	99.46 30	97.66 82	90.55 84	97.07 40	99.07 35	86.85 86	99.97 13	95.43 62	99.74 20	99.81 22

原ACMM1				96.18 98	99.03 57	90.08 129		97.63 91	88.98 120	97.00 41	98.97 47	88.14 63	99.71 61	88.23 144	99.62 34	98.76 118

Regformer-1		96.97 20	96.80 21	97.47 33	99.46 37	93.11 60	98.89 93	97.94 52	92.89 41	96.90 42	99.02 41	89.78 41	99.53 80	97.06 32	99.26 56	99.75 35

HFP-MVS		96.42 37	96.26 34	96.90 61	99.69 8	90.96 110	99.47 27	97.81 65	90.54 85	96.88 43	99.05 38	87.57 68	99.96 17	95.65 57	99.72 22	99.78 29

#test#		96.48 34	96.34 32	96.90 61	99.69 8	90.96 110	99.53 24	97.81 65	90.94 78	96.88 43	99.05 38	87.57 68	99.96 17	95.87 56	99.72 22	99.78 29

Regformer-2		96.94 23	96.78 22	97.42 36	99.46 37	92.97 65	98.89 93	97.93 53	92.86 43	96.88 43	99.02 41	89.74 42	99.53 80	97.03 33	99.26 56	99.75 35

XVS		96.47 35	96.37 30	96.77 68	99.62 15	90.66 119	99.43 33	97.58 97	92.41 54	96.86 46	98.96 50	87.37 74	99.87 37	95.65 57	99.43 47	99.78 29

X-MVStestdata		90.69 162	88.66 175	96.77 68	99.62 15	90.66 119	99.43 33	97.58 97	92.41 54	96.86 46	29.59 354	87.37 74	99.87 37	95.65 57	99.43 47	99.78 29

1121		95.19 63	94.45 65	97.42 36	98.88 65	92.58 74	96.22 249	97.75 72	85.50 195	96.86 46	99.01 45	88.59 55	99.90 30	87.64 150	99.60 37	99.79 25

TSAR-MVS + GP.		96.95 21	96.91 17	97.07 47	98.88 65	91.62 85	99.58 18	96.54 177	95.09 15	96.84 49	98.63 76	91.16 17	99.77 57	99.04 5	96.42 108	99.81 22

ACMMPR		96.28 42	96.14 40	96.73 72	99.68 10	90.47 121	99.47 27	97.80 67	90.54 85	96.83 50	99.03 40	86.51 92	99.95 20	95.65 57	99.72 22	99.75 35

PMMVS		93.62 97	93.90 82	92.79 187	96.79 130	81.40 273	98.85 96	96.81 164	91.25 75	96.82 51	98.15 97	77.02 181	98.13 144	93.15 98	96.30 112	98.83 110

PGM-MVS		95.85 51	95.65 50	96.45 89	99.50 35	89.77 138	98.22 171	98.90 16	89.19 113	96.74 52	98.95 52	85.91 100	99.92 26	93.94 82	99.46 44	99.66 50

jason		95.40 61	94.86 61	97.03 49	92.91 225	94.23 44	99.70 10	96.30 189	93.56 33	96.73 53	98.52 81	81.46 155	97.91 153	96.08 53	98.47 82	98.96 98
jason: jason.
新几何1				97.40 38	98.92 63	92.51 76		97.77 71	85.52 193	96.69 54	99.06 37	88.08 64	99.89 33	84.88 173	99.62 34	99.79 25

APD-MVS_3200maxsize		95.64 57	95.65 50	95.62 116	99.24 48	87.80 170	98.42 148	97.22 137	88.93 124	96.64 55	98.98 46	85.49 105	99.36 99	96.68 40	99.27 55	99.70 44

MG-MVS		97.24 12	96.83 20	98.47 9	99.79 5	95.71 12	99.07 71	99.06 14	94.45 18	96.42 56	98.70 72	88.81 51	99.74 60	95.35 64	99.86 8	99.97 3

alignmvs		95.77 55	95.00 60	98.06 18	97.35 106	95.68 13	99.71 9	97.50 112	91.50 68	96.16 57	98.61 77	86.28 96	99.00 116	96.19 50	91.74 160	99.51 63

Regformer-3		96.50 33	96.36 31	96.91 60	99.34 40	91.72 83	98.71 107	97.90 55	92.48 49	96.00 58	98.95 52	88.60 53	99.52 83	96.44 45	98.83 70	99.49 65

CP-MVS		96.22 43	96.15 39	96.42 91	99.67 11	89.62 141	99.70 10	97.61 93	90.07 99	96.00 58	99.16 24	87.43 72	99.92 26	96.03 54	99.72 22	99.70 44

Regformer-4		96.45 36	96.33 33	96.81 67	99.34 40	91.44 90	98.71 107	97.88 56	92.43 50	95.97 60	98.95 52	88.42 57	99.51 84	96.40 46	98.83 70	99.49 65

MCST-MVS		98.18 2	97.95 4	98.86 1	99.85 3	96.60 5	99.70 10	97.98 51	97.18 2	95.96 61	99.33 8	92.62 12	100.00 1	98.99 6	99.93 1	99.98 2

DeepC-MVS_fast	93.52 2	97.16 14	96.84 19	98.13 15	99.61 17	94.45 39	98.85 96	97.64 87	96.51 6	95.88 62	99.39 7	87.35 78	99.99 4	96.61 41	99.69 27	99.96 4
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test222					98.32 78	91.21 99	98.08 184	97.58 97	83.74 228	95.87 63	99.02 41	86.74 87			99.64 30	99.81 22

canonicalmvs		95.02 65	93.96 78	98.20 12	97.53 99	95.92 11	98.71 107	96.19 198	91.78 64	95.86 64	98.49 85	79.53 163	99.03 115	96.12 51	91.42 166	99.66 50

abl_6		94.63 76	94.48 64	95.09 134	98.61 74	86.96 191	98.06 186	96.97 160	89.31 109	95.86 64	98.56 79	79.82 161	99.64 71	94.53 79	98.65 79	98.66 123

Effi-MVS+		93.87 88	93.15 91	96.02 104	95.79 154	90.76 115	96.70 231	95.78 223	86.98 175	95.71 66	97.17 130	79.58 162	98.01 151	94.57 78	96.09 116	99.31 75

HPM-MVS_fast		94.89 66	94.62 63	95.70 115	99.11 53	88.44 161	99.14 65	97.11 146	85.82 190	95.69 67	98.47 87	83.46 125	99.32 103	93.16 97	99.63 33	99.35 71

HY-MVS	88.56 7	95.29 62	94.23 69	98.48 8	97.72 90	96.41 8	94.03 287	98.74 18	92.42 53	95.65 68	94.76 180	86.52 91	99.49 86	95.29 66	92.97 142	99.53 61

CHOSEN 280x420		96.80 26	96.85 18	96.66 78	97.85 87	94.42 41	94.76 279	98.36 25	92.50 48	95.62 69	97.52 110	97.92 1	97.38 193	98.31 21	98.80 73	98.20 148

MP-MVS		96.00 47	95.82 45	96.54 86	99.47 36	90.13 128	99.36 44	97.41 124	90.64 83	95.49 70	98.95 52	85.51 104	99.98 8	96.00 55	99.59 39	99.52 62

HPM-MVS		95.41 60	95.22 56	95.99 105	99.29 44	89.14 147	99.17 57	97.09 150	87.28 171	95.40 71	98.48 86	84.93 111	99.38 97	95.64 61	99.65 29	99.47 68

UA-Net		93.30 106	92.62 101	95.34 127	96.27 142	88.53 160	95.88 263	96.97 160	90.90 79	95.37 72	97.07 135	82.38 147	99.10 113	83.91 186	94.86 131	98.38 138

sss		94.85 68	93.94 80	97.58 29	96.43 138	94.09 46	98.93 84	99.16 13	89.50 107	95.27 73	97.85 99	81.50 154	99.65 69	92.79 103	94.02 136	98.99 95

WTY-MVS		95.97 48	95.11 58	98.54 7	97.62 93	96.65 4	99.44 31	98.74 18	92.25 57	95.21 74	98.46 89	86.56 90	99.46 93	95.00 70	92.69 146	99.50 64

DELS-MVS		97.12 15	96.60 26	98.68 5	98.03 85	96.57 6	99.84 3	97.84 60	96.36 7	95.20 75	98.24 92	88.17 61	99.83 47	96.11 52	99.60 37	99.64 52
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		96.69 27	96.69 24	96.72 74	98.58 75	91.00 109	99.14 65	99.45 1	93.86 26	95.15 76	98.73 68	88.48 56	99.76 58	97.23 31	99.56 40	99.40 69

MVS_Test		93.67 95	92.67 100	96.69 76	96.72 132	92.66 70	97.22 214	96.03 204	87.69 161	95.12 77	94.03 187	81.55 153	98.28 140	89.17 138	96.46 106	99.14 87

MVS_111021_LR		95.78 54	95.94 42	95.28 129	98.19 82	87.69 171	98.80 101	99.26 12	93.39 34	95.04 78	98.69 73	84.09 119	99.76 58	96.96 38	99.06 60	98.38 138

CostFormer		92.89 114	92.48 104	94.12 161	94.99 182	85.89 226	92.89 297	97.00 159	86.98 175	95.00 79	90.78 243	90.05 39	97.51 183	92.92 101	91.73 161	98.96 98

mPP-MVS		95.90 50	95.75 48	96.38 93	99.58 19	89.41 146	99.26 51	97.41 124	90.66 80	94.82 80	98.95 52	86.15 98	99.98 8	95.24 67	99.64 30	99.74 38

EI-MVSNet-Vis-set		95.76 56	95.63 52	96.17 100	99.14 51	90.33 122	98.49 140	97.82 62	91.92 61	94.75 81	98.88 59	87.06 82	99.48 91	95.40 63	97.17 101	98.70 121

LFMVS		92.23 130	90.84 144	96.42 91	98.24 79	91.08 107	98.24 169	96.22 196	83.39 240	94.74 82	98.31 91	61.12 298	98.85 118	94.45 80	92.82 143	99.32 74

tpmrst		92.78 115	92.16 111	94.65 147	96.27 142	87.45 178	91.83 306	97.10 149	89.10 118	94.68 83	90.69 248	88.22 60	97.73 171	89.78 127	91.80 159	98.77 117

DP-MVS Recon		95.85 51	95.15 57	97.95 19	99.87 2	94.38 42	99.60 17	97.48 114	86.58 182	94.42 84	99.13 30	87.36 77	99.98 8	93.64 89	98.33 84	99.48 67

MPTG		96.21 44	95.96 41	96.96 58	99.29 44	91.19 100	98.69 111	97.45 117	92.58 46	94.39 85	99.24 13	86.43 94	99.99 4	96.22 48	99.40 50	99.71 42

MTAPA		96.09 46	95.80 47	96.96 58	99.29 44	91.19 100	97.23 213	97.45 117	92.58 46	94.39 85	99.24 13	86.43 94	99.99 4	96.22 48	99.40 50	99.71 42

CPTT-MVS		94.60 77	94.43 66	95.09 134	99.66 12	86.85 194	99.44 31	97.47 115	83.22 242	94.34 87	98.96 50	82.50 142	99.55 78	94.81 73	99.50 42	98.88 106

diffmvs		92.07 136	90.77 148	95.97 107	96.41 139	91.32 98	96.46 238	95.98 205	81.73 264	94.33 88	93.36 205	78.72 171	98.20 141	84.28 178	95.66 124	98.41 134

PVSNet_BlendedMVS		93.36 103	93.20 90	93.84 170	98.77 69	91.61 86	99.47 27	98.04 47	91.44 69	94.21 89	92.63 219	83.50 123	99.87 37	97.41 29	83.37 225	90.05 283

PVSNet_Blended		95.94 49	95.66 49	96.75 70	98.77 69	91.61 86	99.88 1	98.04 47	93.64 31	94.21 89	97.76 103	83.50 123	99.87 37	97.41 29	97.75 92	98.79 113

EI-MVSNet-UG-set		95.43 58	95.29 54	95.86 111	99.07 56	89.87 135	98.43 147	97.80 67	91.78 64	94.11 91	98.77 64	86.25 97	99.48 91	94.95 72	96.45 107	98.22 146

MAR-MVS		94.43 79	94.09 72	95.45 125	99.10 54	87.47 177	98.39 154	97.79 69	88.37 140	94.02 92	99.17 22	78.64 173	99.91 28	92.48 104	98.85 69	98.96 98
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		96.35 38	95.94 42	97.58 29	94.10 196	95.25 16	98.93 84	98.17 40	94.26 19	93.94 93	98.72 70	89.68 43	97.88 156	96.36 47	99.29 54	99.62 56

GG-mvs-BLEND				96.98 56	96.53 135	94.81 28	87.20 321	97.74 74		93.91 94	96.40 158	96.56 2	96.94 207	95.08 68	98.95 67	99.20 85

API-MVS		94.78 69	94.18 70	96.59 84	99.21 49	90.06 132	98.80 101	97.78 70	83.59 232	93.85 95	99.21 16	83.79 121	99.97 13	92.37 105	99.00 63	99.74 38

tpm2		91.77 142	91.09 132	93.82 171	94.83 186	85.56 235	92.51 300	97.16 142	84.00 219	93.83 96	90.66 253	87.54 70	97.17 198	87.73 149	91.55 164	98.72 119

PAPR		96.35 38	95.82 45	97.94 20	99.63 14	94.19 45	99.42 37	97.55 103	92.43 50	93.82 97	99.12 31	87.30 79	99.91 28	94.02 81	99.06 60	99.74 38

PVSNet	87.13 12	93.69 92	92.83 97	96.28 96	97.99 86	90.22 125	99.38 40	98.93 15	91.42 72	93.66 98	97.68 106	71.29 241	99.64 71	87.94 147	97.20 100	98.98 96

VDD-MVS		91.24 152	90.18 155	94.45 152	97.08 115	85.84 230	98.40 153	96.10 202	86.99 173	93.36 99	98.16 96	54.27 316	99.20 105	96.59 42	90.63 175	98.31 144

VDDNet		90.08 171	88.54 181	94.69 146	94.41 192	87.68 172	98.21 174	96.40 183	76.21 302	93.33 100	97.75 104	54.93 314	98.77 121	94.71 76	90.96 169	97.61 165

MP-MVS-pluss		95.80 53	95.30 53	97.29 42	98.95 62	92.66 70	98.59 128	97.14 143	88.95 122	93.12 101	99.25 11	85.62 101	99.94 22	96.56 43	99.48 43	99.28 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MDTV_nov1_ep13_2view						91.17 102	91.38 309		87.45 165	93.08 102		86.67 88		87.02 155		98.95 102

DWT-MVSNet_test		94.36 80	93.95 79	95.62 116	96.99 118	89.47 144	96.62 234	97.38 127	90.96 77	93.07 103	97.27 122	93.73 8	98.09 145	85.86 167	93.65 138	99.29 77

EPNet_dtu		92.28 128	92.15 112	92.70 190	97.29 108	84.84 242	98.64 120	97.82 62	92.91 40	93.02 104	97.02 137	85.48 107	95.70 269	72.25 295	94.89 130	97.55 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gg-mvs-nofinetune		90.00 172	87.71 188	96.89 65	96.15 147	94.69 32	85.15 327	97.74 74	68.32 326	92.97 105	60.16 340	96.10 3	96.84 209	93.89 83	98.87 68	99.14 87

114514_t		94.06 85	93.05 93	97.06 48	99.08 55	92.26 78	98.97 82	97.01 158	82.58 253	92.57 106	98.22 93	80.68 159	99.30 104	89.34 134	99.02 62	99.63 54

OMC-MVS		93.90 87	93.62 85	94.73 145	98.63 73	87.00 190	98.04 187	96.56 175	92.19 58	92.46 107	98.73 68	79.49 164	99.14 111	92.16 108	94.34 134	98.03 152

PAPM_NR		95.43 58	95.05 59	96.57 85	99.42 39	90.14 126	98.58 129	97.51 109	90.65 82	92.44 108	98.90 57	87.77 67	99.90 30	90.88 117	99.32 53	99.68 47

UGNet		91.91 141	90.85 143	95.10 133	97.06 116	88.69 156	98.01 188	98.24 29	92.41 54	92.39 109	93.61 200	60.52 299	99.68 62	88.14 145	97.25 99	96.92 182
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
MDTV_nov1_ep13			90.47 154		96.14 148	88.55 158	91.34 310	97.51 109	89.58 104	92.24 110	90.50 262	86.99 85	97.61 177	77.64 241	92.34 149

PatchFormer-LS_test		94.08 84	93.60 86	95.53 123	96.92 119	89.57 142	96.51 237	97.34 131	91.29 74	92.22 111	97.18 128	91.66 15	98.02 150	87.05 154	92.21 153	99.00 93

Vis-MVSNet		92.64 123	91.85 118	95.03 139	95.12 176	88.23 162	98.48 141	96.81 164	91.61 66	92.16 112	97.22 126	71.58 239	98.00 152	85.85 168	97.81 88	98.88 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TESTMET0.1,1		93.82 89	93.26 89	95.49 124	95.21 169	90.25 124	99.15 62	97.54 106	89.18 115	91.79 113	94.87 178	89.13 46	97.63 175	86.21 161	96.29 113	98.60 124

EPMVS		92.59 125	91.59 125	95.59 118	97.22 110	90.03 133	91.78 307	98.04 47	90.42 87	91.66 114	90.65 254	86.49 93	97.46 184	81.78 207	96.31 111	99.28 79

test-LLR		93.11 112	92.68 99	94.40 153	94.94 184	87.27 187	99.15 62	97.25 133	90.21 91	91.57 115	94.04 185	84.89 112	97.58 178	85.94 164	96.13 114	98.36 141

test-mter		93.27 108	92.89 96	94.40 153	94.94 184	87.27 187	99.15 62	97.25 133	88.95 122	91.57 115	94.04 185	88.03 65	97.58 178	85.94 164	96.13 114	98.36 141

JIA-IIPM		85.97 233	84.85 231	89.33 256	93.23 222	73.68 312	85.05 328	97.13 145	69.62 322	91.56 117	68.03 338	88.03 65	96.96 205	77.89 240	93.12 140	97.34 169

tpmp4_e23		91.05 154	90.07 156	93.97 167	95.77 156	85.30 237	92.64 298	97.09 150	84.42 214	91.53 118	90.31 265	87.38 73	97.82 160	80.86 215	90.62 176	98.79 113

PVSNet_Blended_VisFu		94.67 74	94.11 71	96.34 95	97.14 113	91.10 105	99.32 50	97.43 122	92.10 60	91.53 118	96.38 161	83.29 129	99.68 62	93.42 94	96.37 109	98.25 145

CHOSEN 1792x2688		94.35 81	93.82 83	95.95 108	97.40 104	88.74 155	98.41 150	98.27 27	92.18 59	91.43 120	96.40 158	78.88 167	99.81 52	93.59 90	97.81 88	99.30 76

ACMMP		94.67 74	94.30 67	95.79 112	99.25 47	88.13 164	98.41 150	98.67 22	90.38 88	91.43 120	98.72 70	82.22 149	99.95 20	93.83 86	95.76 122	99.29 77
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
EPP-MVSNet		93.75 91	93.67 84	94.01 165	95.86 153	85.70 232	98.67 116	97.66 82	84.46 212	91.36 122	97.18 128	91.16 17	97.79 162	92.93 100	93.75 137	98.53 129

PLC	91.07 3	94.23 83	94.01 74	94.87 141	99.17 50	87.49 176	99.25 52	96.55 176	88.43 138	91.26 123	98.21 95	85.92 99	99.86 42	89.77 128	97.57 93	97.24 171
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HyFIR lowres test		93.68 94	93.29 88	94.87 141	97.57 98	88.04 166	98.18 176	98.47 23	87.57 163	91.24 124	95.05 176	85.49 105	97.46 184	93.22 96	92.82 143	99.10 89

thres200		93.69 92	92.59 102	96.97 57	97.76 88	94.74 30	99.35 46	99.36 2	89.23 112	91.21 125	96.97 140	83.42 126	98.77 121	85.08 171	90.96 169	97.39 168

mvs-test1		91.57 144	92.20 110	89.70 248	95.15 174	74.34 309	99.51 25	95.40 252	91.92 61	91.02 126	97.25 123	74.27 206	98.08 148	89.45 130	95.83 121	96.67 183

CDS-MVSNet		93.47 98	93.04 94	94.76 143	94.75 188	89.45 145	98.82 99	97.03 156	87.91 153	90.97 127	96.48 156	89.06 47	96.36 236	89.50 129	92.81 145	98.49 131
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpn200view9		93.43 100	92.27 108	96.90 61	97.68 91	94.84 24	99.18 55	99.36 2	88.45 135	90.79 128	96.90 142	83.31 127	98.75 123	84.11 182	90.69 171	97.12 172

thres400		93.39 102	92.27 108	96.73 72	97.68 91	94.84 24	99.18 55	99.36 2	88.45 135	90.79 128	96.90 142	83.31 127	98.75 123	84.11 182	90.69 171	96.61 184

CR-MVSNet		88.83 189	87.38 192	93.16 180	93.47 215	86.24 213	84.97 329	94.20 282	88.92 125	90.76 130	86.88 302	84.43 116	94.82 289	70.64 300	92.17 155	98.41 134

RPMNet		84.62 248	81.78 261	93.16 180	93.47 215	86.24 213	84.97 329	96.28 193	64.85 332	90.76 130	78.80 331	80.95 158	94.82 289	53.76 330	92.17 155	98.41 134

PatchmatchNet		92.05 140	91.04 133	95.06 137	96.17 146	89.04 149	91.26 311	97.26 132	89.56 106	90.64 132	90.56 260	88.35 59	97.11 200	79.53 224	96.07 118	99.03 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchT		85.44 242	83.19 246	92.22 196	93.13 224	83.00 258	83.80 335	96.37 184	70.62 316	90.55 133	79.63 329	84.81 114	94.87 287	58.18 327	91.59 163	98.79 113

tpm		89.67 176	88.95 169	91.82 203	92.54 228	81.43 272	92.95 296	95.92 214	87.81 155	90.50 134	89.44 280	84.99 110	95.65 270	83.67 189	82.71 231	98.38 138

conf200view11		93.32 105	92.15 112	96.84 66	97.62 93	94.84 24	99.06 73	99.36 2	87.96 150	90.47 135	96.78 144	83.29 129	98.75 123	84.11 182	90.69 171	96.94 179

thres100view900		93.34 104	92.15 112	96.90 61	97.62 93	94.84 24	99.06 73	99.36 2	87.96 150	90.47 135	96.78 144	83.29 129	98.75 123	84.11 182	90.69 171	97.12 172

thres600view7		93.18 110	92.00 116	96.75 70	97.62 93	94.92 21	99.07 71	99.36 2	87.96 150	90.47 135	96.78 144	83.29 129	98.71 128	82.93 194	90.47 178	96.61 184

AdaColmap		93.82 89	93.06 92	96.10 103	99.88 1	89.07 148	98.33 156	97.55 103	86.81 180	90.39 138	98.65 74	75.09 189	99.98 8	93.32 95	97.53 95	99.26 81

XVG-OURS-SEG-HR		90.95 156	90.66 151	91.83 202	95.18 173	81.14 279	95.92 260	95.92 214	88.40 139	90.33 139	97.85 99	70.66 244	99.38 97	92.83 102	88.83 194	94.98 198

IS-MVSNet		93.00 113	92.51 103	94.49 150	96.14 148	87.36 184	98.31 159	95.70 229	88.58 131	90.17 140	97.50 111	83.02 137	97.22 196	87.06 153	96.07 118	98.90 105

CSCG		94.87 67	94.71 62	95.36 126	99.54 27	86.49 204	99.34 48	98.15 42	82.71 251	90.15 141	99.25 11	89.48 44	99.86 42	94.97 71	98.82 72	99.72 41

Patchmatch-test1		90.10 169	88.61 176	94.57 149	94.95 183	88.83 151	96.26 245	97.21 138	90.06 100	90.03 142	90.68 250	66.61 273	95.83 266	77.31 242	94.36 133	99.05 91

XVG-OURS		90.83 158	90.49 153	91.86 201	95.23 168	81.25 277	95.79 268	95.92 214	88.96 121	90.02 143	98.03 98	71.60 238	99.35 101	91.06 114	87.78 198	94.98 198

ADS-MVSNet2		87.62 204	86.88 199	89.86 244	96.21 144	79.14 289	87.15 322	92.99 297	83.01 246	89.91 144	87.27 298	78.87 168	92.80 307	74.20 276	92.27 151	97.64 161

ADS-MVSNet		88.99 184	87.30 193	94.07 162	96.21 144	87.56 175	87.15 322	96.78 166	83.01 246	89.91 144	87.27 298	78.87 168	97.01 204	74.20 276	92.27 151	97.64 161

tfpn_ndepth		93.28 107	92.32 105	96.16 101	97.74 89	92.86 68	99.01 78	98.19 38	85.50 195	89.84 146	97.12 132	93.57 9	97.58 178	79.39 227	90.50 177	98.04 151

ab-mvs		91.05 154	89.17 165	96.69 76	95.96 151	91.72 83	92.62 299	97.23 136	85.61 192	89.74 147	93.89 193	68.55 257	99.42 94	91.09 113	87.84 197	98.92 104

TAMVS		92.62 124	92.09 115	94.20 159	94.10 196	87.68 172	98.41 150	96.97 160	87.53 164	89.74 147	96.04 165	84.77 115	96.49 225	88.97 140	92.31 150	98.42 133

Vis-MVSNet (Re-imp)		93.26 109	93.00 95	94.06 163	96.14 148	86.71 200	98.68 114	96.70 167	88.30 142	89.71 149	97.64 107	85.43 108	96.39 234	88.06 146	96.32 110	99.08 90

view600		92.78 115	91.50 127	96.63 79	97.51 100	94.66 33	98.91 87	99.36 2	87.31 167	89.64 150	96.59 150	83.26 133	98.63 131	80.76 216	90.15 180	96.61 184

view800		92.78 115	91.50 127	96.63 79	97.51 100	94.66 33	98.91 87	99.36 2	87.31 167	89.64 150	96.59 150	83.26 133	98.63 131	80.76 216	90.15 180	96.61 184

conf0.05thres1000		92.78 115	91.50 127	96.63 79	97.51 100	94.66 33	98.91 87	99.36 2	87.31 167	89.64 150	96.59 150	83.26 133	98.63 131	80.76 216	90.15 180	96.61 184

tfpn		92.78 115	91.50 127	96.63 79	97.51 100	94.66 33	98.91 87	99.36 2	87.31 167	89.64 150	96.59 150	83.26 133	98.63 131	80.76 216	90.15 180	96.61 184

CNLPA		93.64 96	92.74 98	96.36 94	98.96 61	90.01 134	99.19 53	95.89 220	86.22 187	89.40 154	98.85 60	80.66 160	99.84 45	88.57 142	96.92 102	99.24 82

Fast-Effi-MVS+		91.72 143	90.79 147	94.49 150	95.89 152	87.40 181	99.54 23	95.70 229	85.01 205	89.28 155	95.68 168	77.75 177	97.57 182	83.22 190	95.06 128	98.51 130

PatchMatch-RL		91.47 146	90.54 152	94.26 157	98.20 80	86.36 210	96.94 221	97.14 143	87.75 157	88.98 156	95.75 167	71.80 237	99.40 96	80.92 213	97.39 98	97.02 178

dp		90.16 168	88.83 172	94.14 160	96.38 140	86.42 206	91.57 308	97.06 153	84.76 209	88.81 157	90.19 273	84.29 118	97.43 186	75.05 268	91.35 168	98.56 128

tfpn1000		92.67 122	91.64 124	95.78 113	97.61 97	92.34 77	98.69 111	98.18 39	84.15 217	88.80 158	96.99 139	93.56 10	97.21 197	76.56 252	90.19 179	97.77 160

DeepC-MVS	91.02 4	94.56 78	93.92 81	96.46 88	97.16 112	90.76 115	98.39 154	97.11 146	93.92 22	88.66 159	98.33 90	78.14 175	99.85 44	95.02 69	98.57 80	98.78 116
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CVMVSNet		90.30 164	90.91 142	88.46 271	94.32 193	73.58 313	97.61 202	97.59 95	90.16 96	88.43 160	97.10 133	76.83 182	92.86 303	82.64 196	93.54 139	98.93 103

TR-MVS		90.77 159	89.44 161	94.76 143	96.31 141	88.02 167	97.92 190	95.96 209	85.52 193	88.22 161	97.23 125	66.80 271	98.09 145	84.58 176	92.38 148	98.17 149

conf0.01		92.06 138	90.99 134	95.24 131	96.84 122	91.39 91	98.31 159	98.20 31	83.57 233	88.08 162	97.34 116	91.05 20	97.40 187	75.80 258	89.74 187	96.94 179

conf0.002		92.06 138	90.99 134	95.24 131	96.84 122	91.39 91	98.31 159	98.20 31	83.57 233	88.08 162	97.34 116	91.05 20	97.40 187	75.80 258	89.74 187	96.94 179

thresconf0.02		92.14 131	90.99 134	95.58 119	96.84 122	91.39 91	98.31 159	98.20 31	83.57 233	88.08 162	97.34 116	91.05 20	97.40 187	75.80 258	89.74 187	97.94 155

tfpn_n400		92.14 131	90.99 134	95.58 119	96.84 122	91.39 91	98.31 159	98.20 31	83.57 233	88.08 162	97.34 116	91.05 20	97.40 187	75.80 258	89.74 187	97.94 155

tfpnconf		92.14 131	90.99 134	95.58 119	96.84 122	91.39 91	98.31 159	98.20 31	83.57 233	88.08 162	97.34 116	91.05 20	97.40 187	75.80 258	89.74 187	97.94 155

tfpnview11		92.14 131	90.99 134	95.58 119	96.84 122	91.39 91	98.31 159	98.20 31	83.57 233	88.08 162	97.34 116	91.05 20	97.40 187	75.80 258	89.74 187	97.94 155

F-COLMAP		92.07 136	91.75 122	93.02 183	98.16 83	82.89 262	98.79 104	95.97 207	86.54 183	87.92 168	97.80 101	78.69 172	99.65 69	85.97 163	95.93 120	96.53 193

BH-RMVSNet		91.25 151	89.99 157	95.03 139	96.75 131	88.55 158	98.65 118	94.95 265	87.74 158	87.74 169	97.80 101	68.27 259	98.14 143	80.53 221	97.49 96	98.41 134

Effi-MVS+-dtu		89.97 173	90.68 150	87.81 284	95.15 174	71.98 318	97.87 194	95.40 252	91.92 61	87.57 170	91.44 232	74.27 206	96.84 209	89.45 130	93.10 141	94.60 200

HQP-NCC					93.95 200		99.16 58		93.92 22	87.57 170

ACMP_Plane					93.95 200		99.16 58		93.92 22	87.57 170

HQP4-MVS										87.57 170			97.77 164			92.72 207

HQP-MVS		91.50 145	91.23 131	92.29 195	93.95 200	86.39 208	99.16 58	96.37 184	93.92 22	87.57 170	96.67 148	73.34 219	97.77 164	93.82 87	86.29 202	92.72 207

TAPA-MVS	87.50 9	90.35 163	89.05 167	94.25 158	98.48 76	85.17 240	98.42 148	96.58 174	82.44 257	87.24 175	98.53 80	82.77 140	98.84 119	59.09 325	97.88 87	98.72 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HQP_MVS		91.26 149	90.95 141	92.16 197	93.84 207	86.07 221	99.02 76	96.30 189	93.38 35	86.99 176	96.52 154	72.92 224	97.75 169	93.46 92	86.17 205	92.67 209

plane_prior3						85.91 225			93.65 30	86.99 176

GA-MVS		90.10 169	88.69 174	94.33 155	92.44 229	87.97 168	99.08 70	96.26 194	89.65 102	86.92 178	93.11 212	68.09 260	96.96 205	82.54 197	90.15 180	98.05 150

1112_ss		92.71 120	91.55 126	96.20 97	95.56 161	91.12 103	98.48 141	94.69 271	88.29 143	86.89 179	98.50 83	87.02 83	98.66 129	84.75 174	89.77 186	98.81 111

Test_1112_low_res		92.27 129	90.97 140	96.18 98	95.53 162	91.10 105	98.47 143	94.66 272	88.28 144	86.83 180	93.50 204	87.00 84	98.65 130	84.69 175	89.74 187	98.80 112

cascas		90.93 157	89.33 164	95.76 114	95.69 158	93.03 64	98.99 81	96.59 171	80.49 274	86.79 181	94.45 183	65.23 281	98.60 135	93.52 91	92.18 154	95.66 197

OPM-MVS		89.76 175	89.15 166	91.57 212	90.53 254	85.58 234	98.11 181	95.93 213	92.88 42	86.05 182	96.47 157	67.06 270	97.87 157	89.29 137	86.08 207	91.26 247

VPA-MVSNet		89.10 183	87.66 189	93.45 175	92.56 227	91.02 108	97.97 189	98.32 26	86.92 177	86.03 183	92.01 224	68.84 256	97.10 202	90.92 116	75.34 260	92.23 219

tpm cat1		88.89 186	87.27 194	93.76 172	95.79 154	85.32 236	90.76 315	97.09 150	76.14 303	85.72 184	88.59 288	82.92 138	98.04 149	76.96 246	91.43 165	97.90 159

IB-MVS	89.43 6	92.12 135	90.83 146	95.98 106	95.40 166	90.78 114	99.81 5	98.06 45	91.23 76	85.63 185	93.66 199	90.63 33	98.78 120	91.22 112	71.85 296	98.36 141
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
EI-MVSNet		89.87 174	89.38 163	91.36 217	94.32 193	85.87 227	97.61 202	96.59 171	85.10 201	85.51 186	97.10 133	81.30 157	96.56 217	83.85 188	83.03 228	91.64 234

MVSTER		92.71 120	92.32 105	93.86 169	97.29 108	92.95 66	99.01 78	96.59 171	90.09 97	85.51 186	94.00 189	94.61 5	96.56 217	90.77 120	83.03 228	92.08 226

RPSCF		85.33 243	85.55 220	84.67 304	94.63 190	62.28 330	93.73 290	93.76 286	74.38 309	85.23 188	97.06 136	64.09 284	98.31 138	80.98 211	86.08 207	93.41 206

BH-w/o		92.32 127	91.79 120	93.91 168	96.85 121	86.18 216	99.11 69	95.74 225	88.13 147	84.81 189	97.00 138	77.26 180	97.91 153	89.16 139	98.03 86	97.64 161

CLD-MVS		91.06 153	90.71 149	92.10 198	94.05 199	86.10 219	99.55 22	96.29 192	94.16 20	84.70 190	97.17 130	69.62 249	97.82 160	94.74 75	86.08 207	92.39 212
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmvs		89.16 182	87.76 186	93.35 176	97.19 111	84.75 244	90.58 317	97.36 129	81.99 260	84.56 191	89.31 283	83.98 120	98.17 142	74.85 271	90.00 185	97.12 172

nrg030		90.23 165	88.87 170	94.32 156	91.53 243	93.54 53	98.79 104	95.89 220	88.12 148	84.55 192	94.61 182	78.80 170	96.88 208	92.35 106	75.21 261	92.53 211

VPNet		88.30 198	86.57 200	93.49 174	91.95 236	91.35 97	98.18 176	97.20 139	88.61 130	84.52 193	94.89 177	62.21 293	96.76 213	89.34 134	72.26 292	92.36 213

MVS		93.92 86	92.28 107	98.83 2	95.69 158	96.82 3	96.22 249	98.17 40	84.89 207	84.34 194	98.61 77	79.32 165	99.83 47	93.88 84	99.43 47	99.86 20

mvs_anonymous		92.50 126	91.65 123	95.06 137	96.60 134	89.64 140	97.06 219	96.44 182	86.64 181	84.14 195	93.93 191	82.49 143	96.17 253	91.47 111	96.08 117	99.35 71

Fast-Effi-MVS+-dtu		88.84 188	88.59 179	89.58 251	93.44 218	78.18 298	98.65 118	94.62 273	88.46 134	84.12 196	95.37 174	68.91 254	96.52 223	82.06 201	91.70 162	94.06 201

LS3D		90.19 167	88.72 173	94.59 148	98.97 59	86.33 211	96.90 223	96.60 170	74.96 306	84.06 197	98.74 67	75.78 186	99.83 47	74.93 269	97.57 93	97.62 164

ACMM	86.95 13	88.77 192	88.22 185	90.43 233	93.61 212	81.34 275	98.50 138	95.92 214	87.88 154	83.85 198	95.20 175	67.20 268	97.89 155	86.90 158	84.90 214	92.06 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-untuned		91.46 147	90.84 144	93.33 177	96.51 137	84.83 243	98.84 98	95.50 244	86.44 186	83.50 199	96.70 147	75.49 188	97.77 164	86.78 160	97.81 88	97.40 167

FIs		90.70 161	89.87 158	93.18 179	92.29 230	91.12 103	98.17 179	98.25 28	89.11 117	83.44 200	94.82 179	82.26 148	96.17 253	87.76 148	82.76 230	92.25 217

UniMVSNet (Re)		89.50 179	88.32 183	93.03 182	92.21 232	90.96 110	98.90 92	98.39 24	89.13 116	83.22 201	92.03 222	81.69 152	96.34 242	86.79 159	72.53 287	91.81 231

UniMVSNet_NR-MVSNet		89.60 177	88.55 180	92.75 189	92.17 233	90.07 130	98.74 106	98.15 42	88.37 140	83.21 202	93.98 190	82.86 139	95.93 263	86.95 156	72.47 288	92.25 217

DU-MVS		88.83 189	87.51 190	92.79 187	91.46 244	90.07 130	98.71 107	97.62 92	88.87 126	83.21 202	93.68 197	74.63 194	95.93 263	86.95 156	72.47 288	92.36 213

LPG-MVS_test		88.86 187	88.47 182	90.06 240	93.35 220	80.95 281	98.22 171	95.94 211	87.73 159	83.17 204	96.11 163	66.28 275	97.77 164	90.19 123	85.19 211	91.46 241

LGP-MVS_train				90.06 240	93.35 220	80.95 281		95.94 211	87.73 159	83.17 204	96.11 163	66.28 275	97.77 164	90.19 123	85.19 211	91.46 241

v6		87.27 210	85.86 212	91.50 213	89.97 263	86.84 196	98.45 144	95.67 231	83.85 224	83.11 206	90.97 239	74.46 201	96.58 215	81.97 203	74.34 269	91.09 251

v1neww		87.29 208	85.88 210	91.50 213	90.07 256	86.87 192	98.45 144	95.66 234	83.84 225	83.07 207	90.99 237	74.58 198	96.56 217	81.96 204	74.33 270	91.07 254

v7new		87.29 208	85.88 210	91.50 213	90.07 256	86.87 192	98.45 144	95.66 234	83.84 225	83.07 207	90.99 237	74.58 198	96.56 217	81.96 204	74.33 270	91.07 254

FC-MVSNet-test		90.22 166	89.40 162	92.67 192	91.78 240	89.86 136	97.89 191	98.22 30	88.81 127	82.96 209	94.66 181	81.90 151	95.96 261	85.89 166	82.52 233	92.20 222

v7		86.91 217	85.45 222	91.29 218	90.06 258	86.73 198	98.26 167	95.49 245	83.08 245	82.95 210	90.96 240	73.37 217	96.42 231	79.90 223	74.97 262	90.71 269

v1		87.23 212	85.76 214	91.66 210	89.88 268	87.37 183	98.54 132	95.64 236	83.91 221	82.88 211	90.70 246	74.64 192	96.53 221	81.54 209	74.08 276	91.08 252

divwei89l23v2f112		87.23 212	85.75 216	91.66 210	89.88 268	87.40 181	98.53 133	95.62 237	83.91 221	82.84 212	90.67 251	74.75 190	96.49 225	81.55 208	74.05 278	91.08 252

v1141		87.23 212	85.75 216	91.67 209	89.88 268	87.43 180	98.52 134	95.62 237	83.91 221	82.83 213	90.69 248	74.70 191	96.49 225	81.53 210	74.08 276	91.07 254

PCF-MVS	89.78 5	91.26 149	89.63 159	96.16 101	95.44 164	91.58 88	95.29 275	96.10 202	85.07 203	82.75 214	97.45 113	78.28 174	99.78 56	80.60 220	95.65 125	97.12 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
V42		87.00 216	85.68 219	90.98 223	89.91 264	86.08 220	98.32 158	95.61 239	83.67 231	82.72 215	90.67 251	74.00 212	96.53 221	81.94 206	74.28 273	90.32 277

v1144		86.83 219	85.31 224	91.40 216	89.75 273	87.21 189	98.31 159	95.45 249	83.22 242	82.70 216	90.78 243	73.36 218	96.36 236	79.49 225	74.69 266	90.63 272

v144192		86.40 227	84.89 230	90.91 224	89.48 284	85.59 233	98.21 174	95.43 251	82.45 256	82.62 217	90.58 259	72.79 227	96.36 236	78.45 235	74.04 279	90.79 264

3Dnovator	87.35 11	93.17 111	91.77 121	97.37 41	95.41 165	93.07 62	98.82 99	97.85 59	91.53 67	82.56 218	97.58 109	71.97 234	99.82 50	91.01 115	99.23 58	99.22 84

v2v482		87.27 210	85.76 214	91.78 208	89.59 279	87.58 174	98.56 130	95.54 241	84.53 211	82.51 219	91.78 228	73.11 223	96.47 228	82.07 200	74.14 275	91.30 246

Baseline_NR-MVSNet		85.83 236	84.82 232	88.87 264	88.73 292	83.34 255	98.63 121	91.66 320	80.41 275	82.44 220	91.35 233	74.63 194	95.42 276	84.13 181	71.39 299	87.84 301

v1192		86.32 229	84.71 234	91.17 219	89.53 282	86.40 207	98.13 180	95.44 250	82.52 255	82.42 221	90.62 256	71.58 239	96.33 243	77.23 243	74.88 263	90.79 264

test_djsdf		88.26 200	87.73 187	89.84 245	88.05 300	82.21 267	97.77 197	96.17 199	86.84 178	82.41 222	91.95 227	72.07 233	95.99 259	89.83 125	84.50 217	91.32 245

1314		93.44 99	91.98 117	97.84 21	95.24 167	94.38 42	96.22 249	97.92 54	90.18 93	82.28 223	97.71 105	77.63 178	99.80 54	91.94 110	98.67 78	99.34 73

v1921920		86.02 232	84.44 238	90.77 226	89.32 286	85.20 238	98.10 182	95.35 256	82.19 258	82.25 224	90.71 245	70.73 242	96.30 249	76.85 249	74.49 267	90.80 263

v1240		85.77 239	84.11 241	90.73 227	89.26 287	85.15 241	97.88 193	95.23 263	81.89 263	82.16 225	90.55 261	69.60 250	96.31 246	75.59 266	74.87 264	90.72 268

XVG-ACMP-BASELINE		85.86 235	84.95 229	88.57 268	89.90 266	77.12 303	94.30 283	95.60 240	87.40 166	82.12 226	92.99 215	53.42 319	97.66 173	85.02 172	83.83 221	90.92 260

GBi-Net		86.67 222	84.96 227	91.80 204	95.11 177	88.81 152	96.77 226	95.25 259	82.94 248	82.12 226	90.25 267	62.89 290	94.97 284	79.04 229	80.24 239	91.62 236

test1		86.67 222	84.96 227	91.80 204	95.11 177	88.81 152	96.77 226	95.25 259	82.94 248	82.12 226	90.25 267	62.89 290	94.97 284	79.04 229	80.24 239	91.62 236

FMVSNet3		88.81 191	87.08 198	93.99 166	96.52 136	94.59 37	98.08 184	96.20 197	85.85 189	82.12 226	91.60 231	74.05 211	95.40 277	79.04 229	80.24 239	91.99 229

IterMVS-LS		88.34 197	87.44 191	91.04 221	94.10 196	85.85 229	98.10 182	95.48 246	85.12 200	82.03 230	91.21 234	81.35 156	95.63 271	83.86 187	75.73 258	91.63 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		84.48 252	81.83 260	92.42 194	91.73 241	87.36 184	85.52 325	94.42 277	81.40 267	81.91 231	87.58 294	51.92 321	92.81 306	73.84 281	88.15 196	97.08 176

PS-MVSNAJss		89.54 178	89.05 167	91.00 222	88.77 291	84.36 247	97.39 205	95.97 207	88.47 132	81.88 232	93.80 195	82.48 144	96.50 224	89.34 134	83.34 226	92.15 223

WR-MVS		88.54 196	87.22 196	92.52 193	91.93 238	89.50 143	98.56 130	97.84 60	86.99 173	81.87 233	93.81 194	74.25 208	95.92 265	85.29 169	74.43 268	92.12 224

TranMVSNet+NR-MVSNet		87.75 201	86.31 204	92.07 199	90.81 251	88.56 157	98.33 156	97.18 140	87.76 156	81.87 233	93.90 192	72.45 228	95.43 275	83.13 192	71.30 300	92.23 219

DP-MVS		88.75 193	86.56 201	95.34 127	98.92 63	87.45 178	97.64 201	93.52 291	70.55 317	81.49 235	97.25 123	74.43 203	99.88 34	71.14 299	94.09 135	98.67 122

3Dnovator+	87.72 8	93.43 100	91.84 119	98.17 13	95.73 157	95.08 20	98.92 86	97.04 154	91.42 72	81.48 236	97.60 108	74.60 196	99.79 55	90.84 118	98.97 64	99.64 52

QAPM		91.41 148	89.49 160	97.17 46	95.66 160	93.42 56	98.60 126	97.51 109	80.92 272	81.39 237	97.41 115	72.89 226	99.87 37	82.33 198	98.68 77	98.21 147

XXY-MVS		87.75 201	86.02 207	92.95 185	90.46 255	89.70 139	97.71 199	95.90 218	84.02 218	80.95 238	94.05 184	67.51 266	97.10 202	85.16 170	78.41 248	92.04 228

v148		86.38 228	85.06 226	90.37 235	89.47 285	84.10 249	98.52 134	95.48 246	83.80 227	80.93 239	90.22 270	74.60 196	96.31 246	80.92 213	71.55 298	90.69 270

FMVSNet2		86.90 218	84.79 233	93.24 178	95.11 177	92.54 75	97.67 200	95.86 222	82.94 248	80.55 240	91.17 235	62.89 290	95.29 279	77.23 243	79.71 245	91.90 230

pmmvs4		87.58 205	86.17 206	91.80 204	89.58 280	88.92 150	97.25 211	95.28 258	82.54 254	80.49 241	93.17 211	75.62 187	96.05 258	82.75 195	78.90 246	90.42 275

ACMP	87.39 10	88.71 194	88.24 184	90.12 239	93.91 205	81.06 280	98.50 138	95.67 231	89.43 108	80.37 242	95.55 169	65.67 278	97.83 159	90.55 121	84.51 216	91.47 240
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		85.87 234	84.40 240	90.30 236	88.53 295	84.23 248	98.60 126	93.71 288	81.53 266	80.29 243	92.02 223	64.51 283	95.52 273	82.04 202	78.34 249	91.15 249

test0.0.03 1		88.96 185	88.61 176	90.03 242	91.09 248	84.43 246	98.97 82	97.02 157	90.21 91	80.29 243	96.31 162	84.89 112	91.93 323	72.98 291	85.70 210	93.73 202

jajsoiax		87.35 206	86.51 202	89.87 243	87.75 305	81.74 270	97.03 220	95.98 205	88.47 132	80.15 245	93.80 195	61.47 295	96.36 236	89.44 132	84.47 218	91.50 239

mvs_tets		87.09 215	86.22 205	89.71 247	87.87 301	81.39 274	96.73 230	95.90 218	88.19 146	79.99 246	93.61 200	59.96 301	96.31 246	89.40 133	84.34 219	91.43 243

ITE_SJBPF				87.93 282	92.26 231	76.44 304		93.47 292	87.67 162	79.95 247	95.49 172	56.50 308	97.38 193	75.24 267	82.33 234	89.98 285

v8		86.11 231	84.45 237	91.10 220	89.99 262	86.85 194	97.24 212	95.36 254	81.99 260	79.89 248	89.86 276	74.53 200	96.39 234	78.83 233	72.32 290	90.05 283

v10		85.73 240	84.01 243	90.87 225	90.03 259	86.73 198	97.20 215	95.22 264	81.25 268	79.85 249	89.75 277	73.30 222	96.28 250	76.87 247	72.64 286	89.61 290

WR-MVS_H		86.53 226	85.49 221	89.66 250	91.04 249	83.31 256	97.53 204	98.20 31	84.95 206	79.64 250	90.90 242	78.01 176	95.33 278	76.29 254	72.81 284	90.35 276

anonymousdsp		86.69 221	85.75 216	89.53 252	86.46 314	82.94 259	96.39 240	95.71 228	83.97 220	79.63 251	90.70 246	68.85 255	95.94 262	86.01 162	84.02 220	89.72 289

Patchmtry		83.61 263	81.64 263	89.50 253	93.36 219	82.84 264	84.10 332	94.20 282	69.47 323	79.57 252	86.88 302	84.43 116	94.78 291	68.48 304	74.30 272	90.88 261

CP-MVSNet		86.54 225	85.45 222	89.79 246	91.02 250	82.78 265	97.38 207	97.56 102	85.37 197	79.53 253	93.03 213	71.86 236	95.25 280	79.92 222	73.43 282	91.34 244

Patchmatch-test		86.25 230	84.06 242	92.82 186	94.42 191	82.88 263	82.88 337	94.23 281	71.58 313	79.39 254	90.62 256	89.00 49	96.42 231	63.03 315	91.37 167	99.16 86

DSMNet-mixed		81.60 274	81.43 265	82.10 310	84.36 319	60.79 331	93.63 292	86.74 341	79.00 282	79.32 255	87.15 300	63.87 286	89.78 328	66.89 308	91.92 157	95.73 196

MSDG		88.29 199	86.37 203	94.04 164	96.90 120	86.15 218	96.52 236	94.36 279	77.89 299	79.22 256	96.95 141	69.72 248	99.59 77	73.20 288	92.58 147	96.37 194

PS-CasMVS		85.81 237	84.58 236	89.49 254	90.77 252	82.11 268	97.20 215	97.36 129	84.83 208	79.12 257	92.84 216	67.42 267	95.16 282	78.39 236	73.25 283	91.21 248

IterMVS		85.81 237	84.67 235	89.22 257	93.51 214	83.67 253	96.32 243	94.80 267	85.09 202	78.69 258	90.17 274	66.57 274	93.17 299	79.48 226	77.42 254	90.81 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		85.21 244	83.93 244	89.07 261	89.89 267	81.31 276	97.09 218	97.24 135	84.45 213	78.66 259	92.68 218	68.44 258	94.87 287	75.98 256	70.92 301	91.04 257

semantic-postprocess				89.00 262	93.46 217	82.90 261		94.70 270	85.02 204	78.62 260	90.35 263	66.63 272	93.33 298	79.38 228	77.36 255	90.76 266

OpenMVS	85.28 14	90.75 160	88.84 171	96.48 87	93.58 213	93.51 54	98.80 101	97.41 124	82.59 252	78.62 260	97.49 112	68.00 262	99.82 50	84.52 177	98.55 81	96.11 195

PVSNet_0	83.28 16	87.31 207	85.16 225	93.74 173	94.78 187	84.59 245	98.91 87	98.69 21	89.81 101	78.59 262	93.23 209	61.95 294	99.34 102	94.75 74	55.72 335	97.30 170

v52		84.19 256	82.92 249	88.01 280	87.64 307	79.92 285	96.23 247	95.32 257	79.87 278	78.51 263	89.05 284	69.50 252	96.32 244	77.95 239	72.24 293	87.79 304

V4		84.20 255	82.92 249	88.02 279	87.59 308	79.91 286	96.21 252	95.36 254	79.88 277	78.51 263	89.00 285	69.52 251	96.32 244	77.96 238	72.29 291	87.83 303

EU-MVSNet		84.19 256	84.42 239	83.52 307	88.64 294	67.37 327	96.04 257	95.76 224	85.29 198	78.44 265	93.18 210	70.67 243	91.48 326	75.79 264	75.98 256	91.70 233

v7n		84.42 253	82.75 254	89.43 255	88.15 298	81.86 269	96.75 229	95.67 231	80.53 273	78.38 266	89.43 281	69.89 246	96.35 241	73.83 282	72.13 294	90.07 282

FMVSNet1		83.94 260	81.32 267	91.80 204	91.94 237	88.81 152	96.77 226	95.25 259	77.98 294	78.25 267	90.25 267	50.37 325	94.97 284	73.27 287	77.81 252	91.62 236

MS-PatchMatch		86.75 220	85.92 209	89.22 257	91.97 235	82.47 266	96.91 222	96.14 201	83.74 228	77.73 268	93.53 203	58.19 303	97.37 195	76.75 250	98.35 83	87.84 301

v748		83.84 261	82.31 259	88.41 273	87.65 306	79.10 290	96.66 232	95.51 243	80.09 276	77.65 269	88.53 289	69.81 247	96.23 251	75.67 265	69.25 303	89.91 286

DTE-MVSNet		84.14 258	82.80 252	88.14 278	88.95 289	79.87 287	96.81 225	96.24 195	83.50 239	77.60 270	92.52 220	67.89 264	94.24 294	72.64 294	69.05 305	90.32 277

COLMAP_ROB	82.69 18	84.54 251	82.82 251	89.70 248	96.72 132	78.85 292	95.89 261	92.83 306	71.55 314	77.54 271	95.89 166	59.40 302	99.14 111	67.26 306	88.26 195	91.11 250
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testpf		80.59 284	80.13 271	81.97 312	94.25 195	71.65 319	60.37 347	95.46 248	70.99 315	76.97 272	87.74 292	73.58 216	91.67 324	76.86 248	84.97 213	82.60 335

OurMVSNet-221017-0		84.13 259	83.59 245	85.77 298	87.81 302	70.24 322	94.89 278	93.65 290	86.08 188	76.53 273	93.28 208	61.41 296	96.14 255	80.95 212	77.69 253	90.93 259

tfpnnormal		83.65 262	81.35 266	90.56 230	91.37 246	88.06 165	97.29 209	97.87 58	78.51 288	76.20 274	90.91 241	64.78 282	96.47 228	61.71 318	73.50 280	87.13 311

pm-mvs1		84.68 247	82.78 253	90.40 234	89.58 280	85.18 239	97.31 208	94.73 269	81.93 262	76.05 275	92.01 224	65.48 280	96.11 256	78.75 234	69.14 304	89.91 286

AllTest		84.97 245	83.12 247	90.52 231	96.82 128	78.84 293	95.89 261	92.17 313	77.96 296	75.94 276	95.50 170	55.48 311	99.18 106	71.15 297	87.14 199	93.55 204

TestCases				90.52 231	96.82 128	78.84 293		92.17 313	77.96 296	75.94 276	95.50 170	55.48 311	99.18 106	71.15 297	87.14 199	93.55 204

testgi		82.29 265	81.00 269	86.17 295	87.24 310	74.84 308	97.39 205	91.62 321	88.63 129	75.85 278	95.42 173	46.07 330	91.55 325	66.87 309	79.94 242	92.12 224

MVP-Stereo		86.61 224	85.83 213	88.93 263	88.70 293	83.85 252	96.07 256	94.41 278	82.15 259	75.64 279	91.96 226	67.65 265	96.45 230	77.20 245	98.72 75	86.51 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LF4IMVS		81.94 269	81.17 268	84.25 305	87.23 311	68.87 326	93.35 294	91.93 318	83.35 241	75.40 280	93.00 214	49.25 327	96.65 214	78.88 232	78.11 250	87.22 310

LTVRE_ROB	81.71 19	84.59 250	82.72 255	90.18 237	92.89 226	83.18 257	93.15 295	94.74 268	78.99 283	75.14 281	92.69 217	65.64 279	97.63 175	69.46 301	81.82 236	89.74 288
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
test2356		80.96 281	81.77 262	78.52 318	81.02 326	62.33 329	98.22 171	94.49 274	79.38 281	74.56 282	90.34 264	70.65 245	85.10 337	60.83 319	86.42 201	88.14 298

Anonymous20231206		80.76 283	79.42 279	84.79 303	84.78 317	72.98 314	96.53 235	92.97 298	79.56 280	74.33 283	88.83 286	61.27 297	92.15 320	60.59 321	75.92 257	89.24 294

FMVSNet5		82.29 265	80.54 270	87.52 286	93.79 210	84.01 250	93.73 290	92.47 310	76.92 301	74.27 284	86.15 306	63.69 287	89.24 329	69.07 302	74.79 265	89.29 293

MVS-HIRNet		79.01 291	75.13 298	90.66 228	93.82 209	81.69 271	85.16 326	93.75 287	54.54 338	74.17 285	59.15 342	57.46 305	96.58 215	63.74 314	94.38 132	93.72 203

ACMH+	83.78 15	84.21 254	82.56 258	89.15 259	93.73 211	79.16 288	96.43 239	94.28 280	81.09 269	74.00 286	94.03 187	54.58 315	97.67 172	76.10 255	78.81 247	90.63 272

NR-MVSNet		87.74 203	86.00 208	92.96 184	91.46 244	90.68 118	96.65 233	97.42 123	88.02 149	73.42 287	93.68 197	77.31 179	95.83 266	84.26 179	71.82 297	92.36 213

USDC		84.74 246	82.93 248	90.16 238	91.73 241	83.54 254	95.00 277	93.30 293	88.77 128	73.19 288	93.30 207	53.62 318	97.65 174	75.88 257	81.54 237	89.30 292

LCM-MVSNet-Re		88.59 195	88.61 176	88.51 270	95.53 162	72.68 316	96.85 224	88.43 339	88.45 135	73.14 289	90.63 255	75.82 185	94.38 293	92.95 99	95.71 123	98.48 132

TDRefinement		78.01 296	75.31 297	86.10 296	70.06 340	73.84 311	93.59 293	91.58 322	74.51 308	73.08 290	91.04 236	49.63 326	97.12 199	74.88 270	59.47 329	87.33 307

TransMVSNet (Re)		81.97 268	79.61 277	89.08 260	89.70 275	84.01 250	97.26 210	91.85 319	78.84 284	73.07 291	91.62 230	67.17 269	95.21 281	67.50 305	59.46 330	88.02 300

SixPastTwentyTwo		82.63 264	81.58 264	85.79 297	88.12 299	71.01 321	95.17 276	92.54 309	84.33 215	72.93 292	92.08 221	60.41 300	95.61 272	74.47 273	74.15 274	90.75 267

testus		77.11 300	76.95 293	77.58 319	80.02 329	58.93 335	97.78 195	90.48 329	79.68 279	72.84 293	90.61 258	37.72 340	86.57 336	60.28 323	83.18 227	87.23 309

pmmvs6		79.90 288	77.31 289	87.67 285	84.17 320	78.13 299	95.86 265	93.68 289	67.94 327	72.67 294	89.62 279	50.98 324	95.75 268	74.80 272	66.04 311	89.14 295

ACMH	83.09 17	84.60 249	82.61 256	90.57 229	93.18 223	82.94 259	96.27 244	94.92 266	81.01 270	72.61 295	93.61 200	56.54 307	97.79 162	74.31 274	81.07 238	90.99 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LP		77.80 299	74.39 301	88.01 280	91.93 238	79.02 291	80.88 339	92.90 303	65.43 330	72.00 296	81.29 321	65.78 277	92.73 312	43.76 340	75.58 259	92.27 216

Patchmatch-RL test		81.90 270	80.13 271	87.23 289	80.71 327	70.12 324	84.07 333	88.19 340	83.16 244	70.57 297	82.18 310	87.18 80	92.59 315	82.28 199	62.78 316	98.98 96

DI_MVS_plusplus_test		89.41 180	87.24 195	95.92 110	89.06 288	90.75 117	98.18 176	96.63 168	89.29 111	70.54 298	90.31 265	63.50 288	98.40 136	92.25 107	95.44 126	98.60 124

test_0402		78.81 293	76.33 295	86.26 294	91.18 247	78.44 297	95.88 263	91.34 324	68.55 324	70.51 299	89.91 275	52.65 320	94.99 283	47.14 335	79.78 244	85.34 328

test_normal		89.37 181	87.18 197	95.93 109	88.94 290	90.83 113	98.24 169	96.62 169	89.31 109	70.38 300	90.20 272	63.50 288	98.37 137	92.06 109	95.41 127	98.59 127

TinyColmap		80.42 286	77.94 286	87.85 283	92.09 234	78.58 295	93.74 289	89.94 333	74.99 305	69.77 301	91.78 228	46.09 329	97.58 178	65.17 313	77.89 251	87.38 306

test20.03		78.51 295	77.48 288	81.62 313	83.07 323	71.03 320	96.11 255	92.83 306	81.66 265	69.31 302	89.68 278	57.53 304	87.29 333	58.65 326	68.47 306	86.53 313

N_pmnet		70.19 309	69.87 308	71.12 324	88.24 297	30.63 355	95.85 266	28.70 356	70.18 320	68.73 303	86.55 304	64.04 285	93.81 295	53.12 331	73.46 281	88.94 296

v18		82.00 267	79.76 275	88.72 265	90.03 259	86.81 197	96.17 254	93.12 294	78.70 285	68.39 304	82.10 311	74.64 192	93.00 300	74.21 275	60.45 323	86.35 315

OpenMVS_ROB	73.86 20	77.99 297	75.06 299	86.77 292	83.81 322	77.94 301	96.38 241	91.53 323	67.54 328	68.38 305	87.13 301	43.94 331	96.08 257	55.03 329	81.83 235	86.29 318

v17		81.87 272	79.61 277	88.64 267	89.91 264	86.64 202	96.01 258	93.08 295	78.54 286	68.27 306	81.96 313	74.44 202	92.95 302	74.03 278	60.22 325	86.34 316

v16		81.90 270	79.65 276	88.65 266	90.02 261	86.66 201	96.01 258	93.07 296	78.53 287	68.27 306	82.05 312	74.39 204	92.96 301	74.02 279	60.48 322	86.33 317

ambc				79.60 316	72.76 338	56.61 339	76.20 341	92.01 317		68.25 308	80.23 327	23.34 345	94.73 292	73.78 283	60.81 321	87.48 305

PM-MVS		74.88 303	72.85 304	80.98 315	78.98 331	64.75 328	90.81 314	85.77 343	80.95 271	68.23 309	82.81 308	29.08 343	92.84 304	76.54 253	62.46 318	85.36 327

v15		81.62 273	79.32 280	88.52 269	89.80 271	86.56 203	95.83 267	92.96 299	78.50 289	67.88 310	81.68 315	74.22 209	92.82 305	73.46 285	59.55 326	86.18 320

V14		81.55 275	79.26 281	88.42 272	89.80 271	86.33 211	95.72 270	92.96 299	78.35 290	67.82 311	81.70 314	74.13 210	92.78 309	73.32 286	59.50 328	86.16 322

v11		81.38 277	79.03 284	88.41 273	89.68 276	86.43 205	95.74 269	92.82 308	78.03 293	67.74 312	81.45 319	73.33 221	92.69 313	72.23 296	60.27 324	86.11 324

pmmvs3		72.86 306	69.76 309	82.17 309	73.86 335	74.19 310	94.20 284	89.01 336	64.23 333	67.72 313	80.91 323	41.48 334	88.65 331	62.40 316	54.02 337	83.68 332

lessismore_v0				85.08 300	85.59 315	69.28 325		90.56 328		67.68 314	90.21 271	54.21 317	95.46 274	73.88 280	62.64 317	90.50 274

V9		81.46 276	79.15 282	88.39 275	89.75 273	86.17 217	95.62 271	92.92 301	78.22 291	67.65 315	81.64 316	73.95 213	92.80 307	73.15 289	59.43 331	86.21 319

v12		81.37 278	79.05 283	88.33 276	89.68 276	86.05 223	95.48 273	92.92 301	78.08 292	67.55 316	81.58 317	73.75 214	92.75 310	73.05 290	59.37 332	86.18 320

K. test v3		81.04 280	79.77 274	84.83 302	87.41 309	70.23 323	95.60 272	93.93 285	83.70 230	67.51 317	89.35 282	55.76 309	93.58 297	76.67 251	68.03 308	90.67 271

MIMVSNet1		75.92 302	73.30 303	83.81 306	81.29 325	75.57 306	92.26 303	92.05 316	73.09 312	67.48 318	86.18 305	40.87 336	87.64 332	55.78 328	70.68 302	88.21 297

v13		81.30 279	78.99 285	88.25 277	89.61 278	85.87 227	95.39 274	92.90 303	77.93 298	67.45 319	81.52 318	73.66 215	92.75 310	72.91 292	59.53 327	86.14 323

pmmvs-eth3d		78.71 294	76.16 296	86.38 293	80.25 328	81.19 278	94.17 285	92.13 315	77.97 295	66.90 320	82.31 309	55.76 309	92.56 316	73.63 284	62.31 319	85.38 326

EG-PatchMatch MVS		79.92 287	77.59 287	86.90 291	87.06 312	77.90 302	96.20 253	94.06 284	74.61 307	66.53 321	88.76 287	40.40 338	96.20 252	67.02 307	83.66 224	86.61 312

UnsupCasMVSNet_eth		78.90 292	76.67 294	85.58 299	82.81 324	74.94 307	91.98 305	96.31 188	84.64 210	65.84 322	87.71 293	51.33 322	92.23 319	72.89 293	56.50 334	89.56 291

Test4		85.71 241	82.59 257	95.07 136	84.45 318	89.84 137	97.20 215	95.73 226	89.19 113	64.59 323	87.58 294	40.59 337	96.77 212	88.95 141	95.01 129	98.60 124

new-patchmatchnet		74.80 304	72.40 305	81.99 311	78.36 333	72.20 317	94.44 280	92.36 311	77.06 300	63.47 324	79.98 328	51.04 323	88.85 330	60.53 322	54.35 336	84.92 329

new_pmnet		76.02 301	73.71 302	82.95 308	83.88 321	72.85 315	91.26 311	92.26 312	70.44 318	62.60 325	81.37 320	47.64 328	92.32 318	61.85 317	72.10 295	83.68 332

UnsupCasMVSNet_bld		73.85 305	70.14 307	84.99 301	79.44 330	75.73 305	88.53 320	95.24 262	70.12 321	61.94 326	74.81 334	41.41 335	93.62 296	68.65 303	51.13 341	85.62 325

CMPMVS	58.40 21	80.48 285	80.11 273	81.59 314	85.10 316	59.56 333	94.14 286	95.95 210	68.54 325	60.71 327	93.31 206	55.35 313	97.87 157	83.06 193	84.85 215	87.33 307
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		72.28 307	71.36 306	75.02 322	73.04 336	57.38 337	92.30 301	90.22 331	62.27 334	59.46 328	80.36 325	76.23 183	87.07 334	44.29 338	64.08 315	80.59 336

.test1245		61.50 313	64.44 312	52.65 337	73.04 336	57.38 337	92.30 301	90.22 331	62.27 334	59.46 328	80.36 325	76.23 183	87.07 334	44.29 338	1.80 352	13.50 352

Anonymous20231211		67.10 310	63.29 313	78.54 317	75.68 334	60.00 332	92.05 304	88.86 337	49.84 339	59.35 330	78.48 332	26.15 344	90.76 327	45.96 337	53.24 338	84.88 330

DeepMVS_CX				76.08 320	90.74 253	51.65 343		90.84 326	86.47 185	57.89 331	87.98 290	35.88 341	92.60 314	65.77 312	65.06 313	83.97 331

test1235678		71.07 308	69.53 310	75.71 321	71.87 339	55.27 341	94.32 281	90.76 327	70.23 319	57.61 332	79.06 330	43.13 332	83.72 339	50.48 332	68.30 307	88.14 298

testing_2		80.92 282	77.24 290	91.98 200	78.88 332	87.83 169	93.96 288	95.72 227	84.27 216	56.20 333	80.42 324	38.64 339	96.40 233	87.20 152	79.85 243	91.72 232

test12356		66.36 311	65.12 311	70.08 327	66.92 341	50.46 344	89.96 318	88.58 338	66.00 329	53.38 334	78.13 333	32.89 342	82.87 340	48.36 334	61.87 320	76.92 337

YYNet1		79.64 290	77.04 292	87.43 288	87.80 303	79.98 284	96.23 247	94.44 275	73.83 311	51.83 335	87.53 296	67.96 263	92.07 322	66.00 311	67.75 310	90.23 279

MDA-MVSNet_test_wron		79.65 289	77.05 291	87.45 287	87.79 304	80.13 283	96.25 246	94.44 275	73.87 310	51.80 336	87.47 297	68.04 261	92.12 321	66.02 310	67.79 309	90.09 280

LCM-MVSNet		60.07 315	56.37 316	71.18 323	54.81 350	48.67 345	82.17 338	89.48 335	37.95 342	49.13 337	69.12 335	13.75 354	81.76 341	59.28 324	51.63 340	83.10 334

MDA-MVSNet-bldmvs		77.82 298	74.75 300	87.03 290	88.33 296	78.52 296	96.34 242	92.85 305	75.57 304	48.87 338	87.89 291	57.32 306	92.49 317	60.79 320	64.80 314	90.08 281

PMMVS2		58.97 316	55.07 317	70.69 326	62.72 342	55.37 340	85.97 324	80.52 347	49.48 340	45.94 339	68.31 337	15.73 352	80.78 343	49.79 333	37.12 342	75.91 339

testmv		60.41 314	57.98 315	67.69 328	58.16 349	47.14 346	89.09 319	86.74 341	61.52 337	44.30 340	68.44 336	20.98 346	79.92 345	40.94 342	51.67 339	76.01 338

FPMVS		61.57 312	60.32 314	65.34 329	60.14 346	42.44 349	91.02 313	89.72 334	44.15 341	42.63 341	80.93 322	19.02 347	80.59 344	42.50 341	72.76 285	73.00 340

Gipuma		54.77 318	52.22 319	62.40 331	86.50 313	59.37 334	50.20 348	90.35 330	36.52 344	41.20 342	49.49 346	18.33 349	81.29 342	32.10 346	65.34 312	46.54 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 319	52.86 318	56.05 334	32.75 355	41.97 351	73.42 343	76.12 350	21.91 350	39.68 343	96.39 160	42.59 333	65.10 350	78.00 237	14.92 350	61.08 345

no-one		56.69 317	51.89 320	71.08 325	59.35 348	58.65 336	83.78 336	84.81 346	61.73 336	36.46 344	56.52 344	18.15 350	84.78 338	47.03 336	19.19 346	69.81 342

E-PMN		41.02 325	40.93 324	41.29 338	61.97 343	33.83 352	84.00 334	65.17 354	27.17 347	27.56 345	46.72 348	17.63 351	60.41 352	19.32 349	18.82 347	29.61 349

PNet_i23d		48.05 321	44.98 322	57.28 333	60.15 344	42.39 350	80.85 340	73.14 352	36.78 343	27.46 346	56.66 343	6.38 355	68.34 348	36.65 344	26.72 344	61.10 344

ANet_high		50.71 320	46.17 321	64.33 330	44.27 353	52.30 342	76.13 342	78.73 348	64.95 331	27.37 347	55.23 345	14.61 353	67.74 349	36.01 345	18.23 348	72.95 341

EMVS		39.96 326	39.88 325	40.18 339	59.57 347	32.12 354	84.79 331	64.57 355	26.27 348	26.14 348	44.18 351	18.73 348	59.29 353	17.03 350	17.67 349	29.12 350

MVE	44.00 22	41.70 324	37.64 327	53.90 336	49.46 351	43.37 348	65.09 346	66.66 353	26.19 349	25.77 349	48.53 347	3.58 359	63.35 351	26.15 348	27.28 343	54.97 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	41.42 23	45.67 322	42.50 323	55.17 335	34.28 354	32.37 353	66.24 345	78.71 349	30.72 346	22.04 350	59.59 341	4.59 356	77.85 346	27.49 347	58.84 333	55.29 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		18.81 329	23.05 330	6.10 343	4.48 356	2.29 358	97.78 195	3.00 358	3.27 352	18.60 351	62.71 339	1.53 361	2.49 356	14.26 352	1.80 352	13.50 352

wuykxyi23d		43.53 323	37.95 326	60.27 332	45.36 352	44.79 347	68.27 344	74.26 351	33.48 345	18.21 352	40.16 353	3.64 357	71.01 347	38.85 343	19.31 345	65.02 343

test123		16.58 331	19.47 331	7.91 342	3.59 357	5.37 357	94.32 281	1.39 359	2.49 353	13.98 353	44.60 350	2.91 360	2.65 355	11.35 353	0.57 354	15.70 351

wuyk23d		16.71 330	16.73 332	16.65 341	60.15 344	25.22 356	41.24 349	5.17 357	6.56 351	5.48 354	3.61 355	3.64 357	22.72 354	15.20 351	9.52 351	1.99 354

cdsmvs_eth3d_5k		22.52 328	30.03 329	0.00 344	0.00 358	0.00 359	0.00 350	97.17 141	0.00 354	0.00 355	98.77 64	74.35 205	0.00 357	0.00 354	0.00 355	0.00 355

pcd_1.5k_mvsjas		6.87 333	9.16 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	82.48 144	0.00 357	0.00 354	0.00 355	0.00 355

pcd1.5k->3k		35.91 327	37.64 327	30.74 340	89.49 283	0.00 359	0.00 350	96.36 187	0.00 354	0.00 355	0.00 356	69.17 253	0.00 357	0.00 354	83.71 223	92.21 221

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		8.21 332	10.94 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	98.50 83	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																98.84 108

test_part3							99.43 33		92.81 44		99.48 3		99.97 13	99.52 1

test_part1								97.69 78				93.96 6			99.83 12	99.90 9

sam_mvs1												88.39 58				98.84 108

sam_mvs												87.08 81

MTGPA								97.45 117

test_post1							90.74 316				41.37 352	85.38 109	96.36 236	83.16 191

test_post											46.00 349	87.37 74	97.11 200

patchmatchnet-post											84.86 307	88.73 52	96.81 211

MTMP								91.09 325

gm-plane-assit					94.69 189	88.14 163			88.22 145		97.20 127		98.29 139	90.79 119

test9_res														98.60 11	99.87 5	99.90 9

agg_prior2														97.84 27	99.87 5	99.91 8

test_prior4						92.00 79	99.41 38

test_prior				97.01 50	99.58 19	91.77 80		97.57 100					99.49 86			99.79 25

新几何2							98.26 167

旧先验1					98.97 59	92.90 67		97.74 74			99.15 26	91.05 20			99.33 52	99.60 58

无先验							98.52 134	97.82 62	87.20 172				99.90 30	87.64 150		99.85 21

原ACMM2							98.69 111

testdata2													99.88 34	84.16 180

segment_acmp												90.56 35

testdata1							97.89 191		92.43 50

plane_prior7					93.84 207	85.73 231

plane_prior6					93.92 204	86.02 224						72.92 224

plane_prior5								96.30 189					97.75 169	93.46 92	86.17 205	92.67 209

plane_prior4											96.52 154

plane_prior2							99.02 76		93.38 35

plane_prior1					93.90 206

plane_prior						86.07 221	99.14 65		93.81 28						86.26 204

n2								0.00 360

nn								0.00 360

door-mid								84.90 345

test11								97.68 80

door								85.30 344

HQP5-MVS						86.39 208

BP-MVS														93.82 87

HQP3-MVS								96.37 184							86.29 202

HQP2-MVS												73.34 219

NP-MVS					93.94 203	86.22 215					96.67 148

ACMMP++_ref															82.64 232

ACMMP++															83.83 221

Test By Simon												83.62 122

High-resolution multi-view benchmark