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 51	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 56	98.52 135	97.50 113	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	97.35 107	97.29 1	99.03 76	97.11 147	95.83 9	98.97 3	99.14 28	82.48 145	99.60 76	98.60 11	99.08 59	98.00 153

旧先验2							98.67 117		85.75 192	98.96 4			98.97 117	93.84 85

xiu_mvs_v2_base		96.66 28	96.17 37	98.11 17	97.11 115	96.96 2	99.01 79	97.04 155	95.51 13	98.86 5	99.11 34	82.19 151	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 39	99.81 5	97.88 57	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 50	99.33 49	97.38 128	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 91	99.54 27	90.14 127	99.41 38	97.47 116	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 52	99.16 58	97.44 121	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				95.26 131	98.20 80	87.28 187		97.60 95	85.21 200	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 59	99.38 40	97.66 83	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 59	99.38 40	97.66 83	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 63	99.37 43	97.64 88	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 70	99.36 44	97.67 82	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 30	99.19 53	97.75 73	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 79	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	97.38 106	91.46 90	99.75 8	97.66 83	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 81	99.57 19	97.57 101	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 81	99.70 10	98.05 47	86.48 185	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 84	95.05 181	94.87 22	97.77 198	96.17 200	86.84 179	98.04 22	98.52 81	85.52 102	95.99 260	89.83 125	98.97 64	98.96 98

lupinMVS		96.32 40	95.94 42	97.44 35	95.05 181	94.87 22	99.86 2	96.50 179	93.82 27	98.04 22	98.77 64	85.52 102	98.09 146	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 51	99.56 21	97.52 108	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 53	98.88 96	97.59 96	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 71	99.35 46	97.64 88	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 71		97.64 88		97.98 25			99.61 74

CDPH-MVS		96.56 31	96.18 35	97.70 25	99.59 18	93.92 48	99.13 68	97.44 121	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 62	99.48 26	98.76 18	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 58	99.70 10	98.13 45	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 40		97.55 104		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	96.89 164	95.76 10	97.53 32	99.12 31	72.42 230	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	95.19 171	95.24 17	98.62 123	96.50 179	92.99 37	97.52 33	98.83 61	72.37 231	99.15 108	97.03 33	96.74 103	96.58 191

xiu_mvs_v1_base		94.73 70	93.98 75	96.99 53	95.19 171	95.24 17	98.62 123	96.50 179	92.99 37	97.52 33	98.83 61	72.37 231	99.15 108	97.03 33	96.74 103	96.58 191

xiu_mvs_v1_base_debi		94.73 70	93.98 75	96.99 53	95.19 171	95.24 17	98.62 123	96.50 179	92.99 37	97.52 33	98.83 61	72.37 231	99.15 108	97.03 33	96.74 103	96.58 191

DeepPCF-MVS	93.56 1	96.55 32	97.84 5	92.68 192	98.71 71	78.11 301	99.70 10	97.71 78	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	97.52 108	95.90 8	97.21 37	98.90 57	82.66 142	99.93 24	98.71 9	98.80 73	99.63 54

CANet_DTU		94.31 82	93.35 87	97.20 45	97.03 118	94.71 32	98.62 123	95.54 242	95.61 12	97.21 37	98.47 87	71.88 236	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 49	98.68 115	98.73 21	90.33 90	97.16 39	97.43 114	79.19 167	99.53 80	96.91 39	91.85 158	99.24 82

region2R		96.30 41	96.17 37	96.70 76	99.70 7	90.31 124	99.46 30	97.66 83	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 99	99.03 57	90.08 130		97.63 92	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 61	98.89 94	97.94 53	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	90.96 111	99.47 27	97.81 66	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	90.96 111	99.53 24	97.81 66	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 66	98.89 94	97.93 54	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 69	99.62 15	90.66 120	99.43 33	97.58 98	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		90.69 163	88.66 176	96.77 69	99.62 15	90.66 120	99.43 33	97.58 98	92.41 54	96.86 46	29.59 355	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 75	96.22 250	97.75 73	85.50 196	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 86	99.58 18	96.54 178	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 73	99.68 10	90.47 122	99.47 27	97.80 68	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	92.79 188	96.79 131	81.40 274	98.85 97	96.81 165	91.25 75	96.82 51	98.15 97	77.02 182	98.13 145	93.15 98	96.30 112	98.83 110

PGM-MVS		95.85 51	95.65 50	96.45 90	99.50 35	89.77 139	98.22 172	98.90 17	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	92.91 226	94.23 45	99.70 10	96.30 190	93.56 33	96.73 53	98.52 81	81.46 156	97.91 154	96.08 53	98.47 82	98.96 98
jason: jason.
新几何1				97.40 38	98.92 63	92.51 77		97.77 72	85.52 194	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	95.62 117	99.24 48	87.80 171	98.42 149	97.22 138	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 15	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	97.35 107	95.68 13	99.71 9	97.50 113	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 84	98.71 108	97.90 56	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 92	99.67 11	89.62 142	99.70 10	97.61 94	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 68	99.34 40	91.44 91	98.71 108	97.88 57	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 52	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 40	98.85 97	97.64 88	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	91.21 100	98.08 185	97.58 98	83.74 229	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	97.53 100	95.92 11	98.71 108	96.19 199	91.78 64	95.86 64	98.49 85	79.53 164	99.03 115	96.12 51	91.42 166	99.66 50

abl_6		94.63 76	94.48 64	95.09 135	98.61 74	86.96 192	98.06 187	96.97 161	89.31 109	95.86 64	98.56 79	79.82 162	99.64 71	94.53 79	98.65 79	98.66 123

Effi-MVS+		93.87 88	93.15 91	96.02 105	95.79 155	90.76 116	96.70 232	95.78 224	86.98 176	95.71 66	97.17 130	79.58 163	98.01 152	94.57 78	96.09 116	99.31 75

HPM-MVS_fast		94.89 66	94.62 63	95.70 116	99.11 53	88.44 162	99.14 65	97.11 147	85.82 191	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	94.03 288	98.74 19	92.42 53	95.65 68	94.76 181	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 79	97.85 87	94.42 42	94.76 280	98.36 26	92.50 48	95.62 69	97.52 110	97.92 1	97.38 194	98.31 21	98.80 73	98.20 148

MP-MVS		96.00 47	95.82 45	96.54 87	99.47 36	90.13 129	99.36 44	97.41 125	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	95.99 106	99.29 44	89.14 148	99.17 57	97.09 151	87.28 172	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	95.34 128	96.27 143	88.53 161	95.88 264	96.97 161	90.90 79	95.37 72	97.07 135	82.38 148	99.10 113	83.91 186	94.86 131	98.38 138

sss		94.85 68	93.94 80	97.58 29	96.43 139	94.09 47	98.93 85	99.16 14	89.50 107	95.27 73	97.85 99	81.50 155	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 19	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 61	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 75	98.58 75	91.00 110	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 77	96.72 133	92.66 71	97.22 215	96.03 205	87.69 162	95.12 77	94.03 188	81.55 154	98.28 141	89.17 138	96.46 106	99.14 87

MVS_111021_LR		95.78 54	95.94 42	95.28 130	98.19 82	87.69 172	98.80 102	99.26 13	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		92.89 115	92.48 104	94.12 162	94.99 183	85.89 227	92.89 298	97.00 160	86.98 176	95.00 79	90.78 244	90.05 39	97.51 184	92.92 101	91.73 161	98.96 98

mPP-MVS		95.90 50	95.75 48	96.38 94	99.58 19	89.41 147	99.26 51	97.41 125	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	96.17 101	99.14 51	90.33 123	98.49 141	97.82 63	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		92.23 131	90.84 145	96.42 92	98.24 79	91.08 108	98.24 170	96.22 197	83.39 241	94.74 82	98.31 91	61.12 299	98.85 118	94.45 80	92.82 143	99.32 74

tpmrst		92.78 116	92.16 111	94.65 148	96.27 143	87.45 179	91.83 307	97.10 150	89.10 118	94.68 83	90.69 249	88.22 60	97.73 172	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 43	99.60 17	97.48 115	86.58 183	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	91.19 101	98.69 112	97.45 118	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	91.19 101	97.23 214	97.45 118	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	95.09 135	99.66 12	86.85 195	99.44 31	97.47 116	83.22 243	94.34 87	98.96 50	82.50 143	99.55 78	94.81 73	99.50 42	98.88 106

diffmvs		92.07 137	90.77 149	95.97 108	96.41 140	91.32 99	96.46 239	95.98 206	81.73 265	94.33 88	93.36 206	78.72 172	98.20 142	84.28 178	95.66 124	98.41 134

PVSNet_BlendedMVS		93.36 103	93.20 90	93.84 171	98.77 69	91.61 87	99.47 27	98.04 48	91.44 69	94.21 89	92.63 220	83.50 123	99.87 37	97.41 29	83.37 226	90.05 284

PVSNet_Blended		95.94 49	95.66 49	96.75 71	98.77 69	91.61 87	99.88 1	98.04 48	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	95.86 112	99.07 56	89.87 136	98.43 148	97.80 68	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	95.45 126	99.10 54	87.47 178	98.39 155	97.79 70	88.37 140	94.02 92	99.17 22	78.64 174	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	94.10 197	95.25 16	98.93 85	98.17 41	94.26 19	93.94 93	98.72 70	89.68 43	97.88 157	96.36 47	99.29 54	99.62 56

GG-mvs-BLEND				96.98 56	96.53 136	94.81 29	87.20 322	97.74 75		93.91 94	96.40 159	96.56 2	96.94 208	95.08 68	98.95 67	99.20 85

API-MVS		94.78 69	94.18 70	96.59 85	99.21 49	90.06 133	98.80 102	97.78 71	83.59 233	93.85 95	99.21 16	83.79 121	99.97 13	92.37 105	99.00 63	99.74 38

tpm2		91.77 143	91.09 133	93.82 172	94.83 187	85.56 236	92.51 301	97.16 143	84.00 220	93.83 96	90.66 254	87.54 70	97.17 199	87.73 149	91.55 164	98.72 119

PAPR		96.35 38	95.82 45	97.94 20	99.63 14	94.19 46	99.42 37	97.55 104	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 97	97.99 86	90.22 126	99.38 40	98.93 16	91.42 72	93.66 98	97.68 106	71.29 242	99.64 71	87.94 147	97.20 100	98.98 96

VDD-MVS		91.24 153	90.18 156	94.45 153	97.08 116	85.84 231	98.40 154	96.10 203	86.99 174	93.36 99	98.16 96	54.27 317	99.20 105	96.59 42	90.63 175	98.31 144

VDDNet		90.08 172	88.54 182	94.69 147	94.41 193	87.68 173	98.21 175	96.40 184	76.21 303	93.33 100	97.75 104	54.93 315	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 71	98.59 129	97.14 144	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						91.17 103	91.38 310		87.45 166	93.08 102		86.67 88		87.02 155		98.95 102

DWT-MVSNet_test		94.36 80	93.95 79	95.62 117	96.99 119	89.47 145	96.62 235	97.38 128	90.96 77	93.07 103	97.27 122	93.73 8	98.09 146	85.86 167	93.65 138	99.29 77

EPNet_dtu		92.28 129	92.15 112	92.70 191	97.29 109	84.84 243	98.64 121	97.82 63	92.91 40	93.02 104	97.02 137	85.48 107	95.70 270	72.25 296	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		90.00 173	87.71 189	96.89 65	96.15 148	94.69 33	85.15 328	97.74 75	68.32 327	92.97 105	60.16 341	96.10 3	96.84 210	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 79	98.97 83	97.01 159	82.58 254	92.57 106	98.22 93	80.68 160	99.30 104	89.34 134	99.02 62	99.63 54

OMC-MVS		93.90 87	93.62 85	94.73 146	98.63 73	87.00 191	98.04 188	96.56 176	92.19 58	92.46 107	98.73 68	79.49 165	99.14 111	92.16 108	94.34 134	98.03 152

PAPM_NR		95.43 58	95.05 59	96.57 86	99.42 39	90.14 127	98.58 130	97.51 110	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		91.91 142	90.85 144	95.10 134	97.06 117	88.69 157	98.01 189	98.24 30	92.41 54	92.39 109	93.61 201	60.52 300	99.68 62	88.14 145	97.25 99	96.92 183
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			90.47 155		96.14 149	88.55 159	91.34 311	97.51 110	89.58 104	92.24 110	90.50 263	86.99 85	97.61 178	77.64 242	92.34 149

PatchFormer-LS_test		94.08 84	93.60 86	95.53 124	96.92 120	89.57 143	96.51 238	97.34 132	91.29 74	92.22 111	97.18 128	91.66 15	98.02 151	87.05 154	92.21 153	99.00 93

Vis-MVSNet		92.64 124	91.85 119	95.03 140	95.12 177	88.23 163	98.48 142	96.81 165	91.61 66	92.16 112	97.22 126	71.58 240	98.00 153	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	95.49 125	95.21 170	90.25 125	99.15 62	97.54 107	89.18 115	91.79 113	94.87 179	89.13 46	97.63 176	86.21 161	96.29 113	98.60 124

EPMVS		92.59 126	91.59 126	95.59 119	97.22 111	90.03 134	91.78 308	98.04 48	90.42 87	91.66 114	90.65 255	86.49 93	97.46 185	81.78 208	96.31 111	99.28 79

test-LLR		93.11 113	92.68 99	94.40 154	94.94 185	87.27 188	99.15 62	97.25 134	90.21 91	91.57 115	94.04 186	84.89 112	97.58 179	85.94 164	96.13 114	98.36 141

test-mter		93.27 108	92.89 96	94.40 154	94.94 185	87.27 188	99.15 62	97.25 134	88.95 122	91.57 115	94.04 186	88.03 65	97.58 179	85.94 164	96.13 114	98.36 141

JIA-IIPM		85.97 234	84.85 232	89.33 257	93.23 223	73.68 313	85.05 329	97.13 146	69.62 323	91.56 117	68.03 339	88.03 65	96.96 206	77.89 241	93.12 140	97.34 169

tpmp4_e23		91.05 155	90.07 157	93.97 168	95.77 157	85.30 238	92.64 299	97.09 151	84.42 215	91.53 118	90.31 266	87.38 73	97.82 161	80.86 216	90.62 176	98.79 113

PVSNet_Blended_VisFu		94.67 74	94.11 71	96.34 96	97.14 114	91.10 106	99.32 50	97.43 123	92.10 60	91.53 118	96.38 162	83.29 129	99.68 62	93.42 94	96.37 109	98.25 145

CHOSEN 1792x2688		94.35 81	93.82 83	95.95 109	97.40 105	88.74 156	98.41 151	98.27 28	92.18 59	91.43 120	96.40 159	78.88 168	99.81 52	93.59 90	97.81 88	99.30 76

ACMMP		94.67 74	94.30 67	95.79 113	99.25 47	88.13 165	98.41 151	98.67 23	90.38 88	91.43 120	98.72 70	82.22 150	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	94.01 166	95.86 154	85.70 233	98.67 117	97.66 83	84.46 213	91.36 122	97.18 128	91.16 17	97.79 163	92.93 100	93.75 137	98.53 129

PLC	91.07 3	94.23 83	94.01 74	94.87 142	99.17 50	87.49 177	99.25 52	96.55 177	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	94.87 142	97.57 99	88.04 167	98.18 177	98.47 24	87.57 164	91.24 124	95.05 177	85.49 105	97.46 185	93.22 96	92.82 143	99.10 89

thres200		93.69 92	92.59 102	96.97 57	97.76 88	94.74 31	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		91.57 145	92.20 110	89.70 249	95.15 175	74.34 310	99.51 25	95.40 253	91.92 61	91.02 126	97.25 123	74.27 207	98.08 149	89.45 130	95.83 121	96.67 184

CDS-MVSNet		93.47 98	93.04 94	94.76 144	94.75 189	89.45 146	98.82 100	97.03 157	87.91 154	90.97 127	96.48 157	89.06 47	96.36 237	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 73	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	96.61 185

CR-MVSNet		88.83 190	87.38 193	93.16 181	93.47 216	86.24 214	84.97 330	94.20 283	88.92 125	90.76 130	86.88 303	84.43 116	94.82 290	70.64 301	92.17 155	98.41 134

RPMNet		84.62 249	81.78 262	93.16 181	93.47 216	86.24 214	84.97 330	96.28 194	64.85 333	90.76 130	78.80 332	80.95 159	94.82 290	53.76 331	92.17 155	98.41 134

PatchmatchNet		92.05 141	91.04 134	95.06 138	96.17 147	89.04 150	91.26 312	97.26 133	89.56 106	90.64 132	90.56 261	88.35 59	97.11 201	79.53 225	96.07 118	99.03 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchT		85.44 243	83.19 247	92.22 197	93.13 225	83.00 259	83.80 336	96.37 185	70.62 317	90.55 133	79.63 330	84.81 114	94.87 288	58.18 328	91.59 163	98.79 113

tpm		89.67 177	88.95 170	91.82 204	92.54 229	81.43 273	92.95 297	95.92 215	87.81 156	90.50 134	89.44 281	84.99 110	95.65 271	83.67 189	82.71 232	98.38 138

tfpn111		93.20 110	92.00 116	96.83 67	97.62 93	94.84 24	99.06 73	99.36 2	87.96 150	90.47 135	96.78 144	83.29 129	98.71 128	82.93 194	90.47 178	96.94 179

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		93.18 111	92.00 116	96.75 71	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	96.61 185

AdaColmap		93.82 89	93.06 92	96.10 104	99.88 1	89.07 149	98.33 157	97.55 104	86.81 181	90.39 139	98.65 74	75.09 190	99.98 8	93.32 95	97.53 95	99.26 81

XVG-OURS-SEG-HR		90.95 157	90.66 152	91.83 203	95.18 174	81.14 280	95.92 261	95.92 215	88.40 139	90.33 140	97.85 99	70.66 245	99.38 97	92.83 102	88.83 195	94.98 199

IS-MVSNet		93.00 114	92.51 103	94.49 151	96.14 149	87.36 185	98.31 160	95.70 230	88.58 131	90.17 141	97.50 111	83.02 138	97.22 197	87.06 153	96.07 118	98.90 105

CSCG		94.87 67	94.71 62	95.36 127	99.54 27	86.49 205	99.34 48	98.15 43	82.71 252	90.15 142	99.25 11	89.48 44	99.86 42	94.97 71	98.82 72	99.72 41

Patchmatch-test1		90.10 170	88.61 177	94.57 150	94.95 184	88.83 152	96.26 246	97.21 139	90.06 100	90.03 143	90.68 251	66.61 274	95.83 267	77.31 243	94.36 133	99.05 91

XVG-OURS		90.83 159	90.49 154	91.86 202	95.23 169	81.25 278	95.79 269	95.92 215	88.96 121	90.02 144	98.03 98	71.60 239	99.35 101	91.06 114	87.78 199	94.98 199

ADS-MVSNet2		87.62 205	86.88 200	89.86 245	96.21 145	79.14 290	87.15 323	92.99 298	83.01 247	89.91 145	87.27 299	78.87 169	92.80 308	74.20 277	92.27 151	97.64 161

ADS-MVSNet		88.99 185	87.30 194	94.07 163	96.21 145	87.56 176	87.15 323	96.78 167	83.01 247	89.91 145	87.27 299	78.87 169	97.01 205	74.20 277	92.27 151	97.64 161

tfpn_ndepth		93.28 107	92.32 105	96.16 102	97.74 89	92.86 69	99.01 79	98.19 39	85.50 196	89.84 147	97.12 132	93.57 9	97.58 179	79.39 228	90.50 177	98.04 151

ab-mvs		91.05 155	89.17 166	96.69 77	95.96 152	91.72 84	92.62 300	97.23 137	85.61 193	89.74 148	93.89 194	68.55 258	99.42 94	91.09 113	87.84 198	98.92 104

TAMVS		92.62 125	92.09 115	94.20 160	94.10 197	87.68 173	98.41 151	96.97 161	87.53 165	89.74 148	96.04 166	84.77 115	96.49 226	88.97 140	92.31 150	98.42 133

Vis-MVSNet (Re-imp)		93.26 109	93.00 95	94.06 164	96.14 149	86.71 201	98.68 115	96.70 168	88.30 142	89.71 150	97.64 107	85.43 108	96.39 235	88.06 146	96.32 110	99.08 90

view600		92.78 116	91.50 128	96.63 80	97.51 101	94.66 34	98.91 88	99.36 2	87.31 168	89.64 151	96.59 151	83.26 134	98.63 132	80.76 217	90.15 181	96.61 185

view800		92.78 116	91.50 128	96.63 80	97.51 101	94.66 34	98.91 88	99.36 2	87.31 168	89.64 151	96.59 151	83.26 134	98.63 132	80.76 217	90.15 181	96.61 185

conf0.05thres1000		92.78 116	91.50 128	96.63 80	97.51 101	94.66 34	98.91 88	99.36 2	87.31 168	89.64 151	96.59 151	83.26 134	98.63 132	80.76 217	90.15 181	96.61 185

tfpn		92.78 116	91.50 128	96.63 80	97.51 101	94.66 34	98.91 88	99.36 2	87.31 168	89.64 151	96.59 151	83.26 134	98.63 132	80.76 217	90.15 181	96.61 185

CNLPA		93.64 96	92.74 98	96.36 95	98.96 61	90.01 135	99.19 53	95.89 221	86.22 188	89.40 155	98.85 60	80.66 161	99.84 45	88.57 142	96.92 102	99.24 82

Fast-Effi-MVS+		91.72 144	90.79 148	94.49 151	95.89 153	87.40 182	99.54 23	95.70 230	85.01 206	89.28 156	95.68 169	77.75 178	97.57 183	83.22 190	95.06 128	98.51 130

PatchMatch-RL		91.47 147	90.54 153	94.26 158	98.20 80	86.36 211	96.94 222	97.14 144	87.75 158	88.98 157	95.75 168	71.80 238	99.40 96	80.92 214	97.39 98	97.02 178

dp		90.16 169	88.83 173	94.14 161	96.38 141	86.42 207	91.57 309	97.06 154	84.76 210	88.81 158	90.19 274	84.29 118	97.43 187	75.05 269	91.35 168	98.56 128

tfpn1000		92.67 123	91.64 125	95.78 114	97.61 98	92.34 78	98.69 112	98.18 40	84.15 218	88.80 159	96.99 139	93.56 10	97.21 198	76.56 253	90.19 180	97.77 160

DeepC-MVS	91.02 4	94.56 78	93.92 81	96.46 89	97.16 113	90.76 116	98.39 155	97.11 147	93.92 22	88.66 160	98.33 90	78.14 176	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		90.30 165	90.91 143	88.46 272	94.32 194	73.58 314	97.61 203	97.59 96	90.16 96	88.43 161	97.10 133	76.83 183	92.86 304	82.64 197	93.54 139	98.93 103

TR-MVS		90.77 160	89.44 162	94.76 144	96.31 142	88.02 168	97.92 191	95.96 210	85.52 194	88.22 162	97.23 125	66.80 272	98.09 146	84.58 176	92.38 148	98.17 149

conf0.01		92.06 139	90.99 135	95.24 132	96.84 123	91.39 92	98.31 160	98.20 32	83.57 234	88.08 163	97.34 116	91.05 20	97.40 188	75.80 259	89.74 188	96.94 179

conf0.002		92.06 139	90.99 135	95.24 132	96.84 123	91.39 92	98.31 160	98.20 32	83.57 234	88.08 163	97.34 116	91.05 20	97.40 188	75.80 259	89.74 188	96.94 179

thresconf0.02		92.14 132	90.99 135	95.58 120	96.84 123	91.39 92	98.31 160	98.20 32	83.57 234	88.08 163	97.34 116	91.05 20	97.40 188	75.80 259	89.74 188	97.94 155

tfpn_n400		92.14 132	90.99 135	95.58 120	96.84 123	91.39 92	98.31 160	98.20 32	83.57 234	88.08 163	97.34 116	91.05 20	97.40 188	75.80 259	89.74 188	97.94 155

tfpnconf		92.14 132	90.99 135	95.58 120	96.84 123	91.39 92	98.31 160	98.20 32	83.57 234	88.08 163	97.34 116	91.05 20	97.40 188	75.80 259	89.74 188	97.94 155

tfpnview11		92.14 132	90.99 135	95.58 120	96.84 123	91.39 92	98.31 160	98.20 32	83.57 234	88.08 163	97.34 116	91.05 20	97.40 188	75.80 259	89.74 188	97.94 155

F-COLMAP		92.07 137	91.75 123	93.02 184	98.16 83	82.89 263	98.79 105	95.97 208	86.54 184	87.92 169	97.80 101	78.69 173	99.65 69	85.97 163	95.93 120	96.53 194

BH-RMVSNet		91.25 152	89.99 158	95.03 140	96.75 132	88.55 159	98.65 119	94.95 266	87.74 159	87.74 170	97.80 101	68.27 260	98.14 144	80.53 222	97.49 96	98.41 134

Effi-MVS+-dtu		89.97 174	90.68 151	87.81 285	95.15 175	71.98 319	97.87 195	95.40 253	91.92 61	87.57 171	91.44 233	74.27 207	96.84 210	89.45 130	93.10 141	94.60 201

HQP-NCC					93.95 201		99.16 58		93.92 22	87.57 171

ACMP_Plane					93.95 201		99.16 58		93.92 22	87.57 171

HQP4-MVS										87.57 171			97.77 165			92.72 208

HQP-MVS		91.50 146	91.23 132	92.29 196	93.95 201	86.39 209	99.16 58	96.37 185	93.92 22	87.57 171	96.67 149	73.34 220	97.77 165	93.82 87	86.29 203	92.72 208

TAPA-MVS	87.50 9	90.35 164	89.05 168	94.25 159	98.48 76	85.17 241	98.42 149	96.58 175	82.44 258	87.24 176	98.53 80	82.77 141	98.84 119	59.09 326	97.88 87	98.72 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HQP_MVS		91.26 150	90.95 142	92.16 198	93.84 208	86.07 222	99.02 77	96.30 190	93.38 35	86.99 177	96.52 155	72.92 225	97.75 170	93.46 92	86.17 206	92.67 210

plane_prior3						85.91 226			93.65 30	86.99 177

GA-MVS		90.10 170	88.69 175	94.33 156	92.44 230	87.97 169	99.08 70	96.26 195	89.65 102	86.92 179	93.11 213	68.09 261	96.96 206	82.54 198	90.15 181	98.05 150

1112_ss		92.71 121	91.55 127	96.20 98	95.56 162	91.12 104	98.48 142	94.69 272	88.29 143	86.89 180	98.50 83	87.02 83	98.66 130	84.75 174	89.77 187	98.81 111

Test_1112_low_res		92.27 130	90.97 141	96.18 99	95.53 163	91.10 106	98.47 144	94.66 273	88.28 144	86.83 181	93.50 205	87.00 84	98.65 131	84.69 175	89.74 188	98.80 112

cascas		90.93 158	89.33 165	95.76 115	95.69 159	93.03 65	98.99 82	96.59 172	80.49 275	86.79 182	94.45 184	65.23 282	98.60 136	93.52 91	92.18 154	95.66 198

OPM-MVS		89.76 176	89.15 167	91.57 213	90.53 255	85.58 235	98.11 182	95.93 214	92.88 42	86.05 183	96.47 158	67.06 271	97.87 158	89.29 137	86.08 208	91.26 248

VPA-MVSNet		89.10 184	87.66 190	93.45 176	92.56 228	91.02 109	97.97 190	98.32 27	86.92 178	86.03 184	92.01 225	68.84 257	97.10 203	90.92 116	75.34 261	92.23 220

tpm cat1		88.89 187	87.27 195	93.76 173	95.79 155	85.32 237	90.76 316	97.09 151	76.14 304	85.72 185	88.59 289	82.92 139	98.04 150	76.96 247	91.43 165	97.90 159

IB-MVS	89.43 6	92.12 136	90.83 147	95.98 107	95.40 167	90.78 115	99.81 5	98.06 46	91.23 76	85.63 186	93.66 200	90.63 33	98.78 120	91.22 112	71.85 297	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		89.87 175	89.38 164	91.36 218	94.32 194	85.87 228	97.61 203	96.59 172	85.10 202	85.51 187	97.10 133	81.30 158	96.56 218	83.85 188	83.03 229	91.64 235

MVSTER		92.71 121	92.32 105	93.86 170	97.29 109	92.95 67	99.01 79	96.59 172	90.09 97	85.51 187	94.00 190	94.61 5	96.56 218	90.77 120	83.03 229	92.08 227

RPSCF		85.33 244	85.55 221	84.67 305	94.63 191	62.28 331	93.73 291	93.76 287	74.38 310	85.23 189	97.06 136	64.09 285	98.31 139	80.98 212	86.08 208	93.41 207

BH-w/o		92.32 128	91.79 121	93.91 169	96.85 122	86.18 217	99.11 69	95.74 226	88.13 147	84.81 190	97.00 138	77.26 181	97.91 154	89.16 139	98.03 86	97.64 161

CLD-MVS		91.06 154	90.71 150	92.10 199	94.05 200	86.10 220	99.55 22	96.29 193	94.16 20	84.70 191	97.17 130	69.62 250	97.82 161	94.74 75	86.08 208	92.39 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmvs		89.16 183	87.76 187	93.35 177	97.19 112	84.75 245	90.58 318	97.36 130	81.99 261	84.56 192	89.31 284	83.98 120	98.17 143	74.85 272	90.00 186	97.12 172

nrg030		90.23 166	88.87 171	94.32 157	91.53 244	93.54 54	98.79 105	95.89 221	88.12 148	84.55 193	94.61 183	78.80 171	96.88 209	92.35 106	75.21 262	92.53 212

VPNet		88.30 199	86.57 201	93.49 175	91.95 237	91.35 98	98.18 177	97.20 140	88.61 130	84.52 194	94.89 178	62.21 294	96.76 214	89.34 134	72.26 293	92.36 214

MVS		93.92 86	92.28 107	98.83 2	95.69 159	96.82 3	96.22 250	98.17 41	84.89 208	84.34 195	98.61 77	79.32 166	99.83 47	93.88 84	99.43 47	99.86 20

mvs_anonymous		92.50 127	91.65 124	95.06 138	96.60 135	89.64 141	97.06 220	96.44 183	86.64 182	84.14 196	93.93 192	82.49 144	96.17 254	91.47 111	96.08 117	99.35 71

Fast-Effi-MVS+-dtu		88.84 189	88.59 180	89.58 252	93.44 219	78.18 299	98.65 119	94.62 274	88.46 134	84.12 197	95.37 175	68.91 255	96.52 224	82.06 202	91.70 162	94.06 202

LS3D		90.19 168	88.72 174	94.59 149	98.97 59	86.33 212	96.90 224	96.60 171	74.96 307	84.06 198	98.74 67	75.78 187	99.83 47	74.93 270	97.57 93	97.62 164

ACMM	86.95 13	88.77 193	88.22 186	90.43 234	93.61 213	81.34 276	98.50 139	95.92 215	87.88 155	83.85 199	95.20 176	67.20 269	97.89 156	86.90 158	84.90 215	92.06 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-untuned		91.46 148	90.84 145	93.33 178	96.51 138	84.83 244	98.84 99	95.50 245	86.44 187	83.50 200	96.70 148	75.49 189	97.77 165	86.78 160	97.81 88	97.40 167

FIs		90.70 162	89.87 159	93.18 180	92.29 231	91.12 104	98.17 180	98.25 29	89.11 117	83.44 201	94.82 180	82.26 149	96.17 254	87.76 148	82.76 231	92.25 218

UniMVSNet (Re)		89.50 180	88.32 184	93.03 183	92.21 233	90.96 111	98.90 93	98.39 25	89.13 116	83.22 202	92.03 223	81.69 153	96.34 243	86.79 159	72.53 288	91.81 232

UniMVSNet_NR-MVSNet		89.60 178	88.55 181	92.75 190	92.17 234	90.07 131	98.74 107	98.15 43	88.37 140	83.21 203	93.98 191	82.86 140	95.93 264	86.95 156	72.47 289	92.25 218

DU-MVS		88.83 190	87.51 191	92.79 188	91.46 245	90.07 131	98.71 108	97.62 93	88.87 126	83.21 203	93.68 198	74.63 195	95.93 264	86.95 156	72.47 289	92.36 214

LPG-MVS_test		88.86 188	88.47 183	90.06 241	93.35 221	80.95 282	98.22 172	95.94 212	87.73 160	83.17 205	96.11 164	66.28 276	97.77 165	90.19 123	85.19 212	91.46 242

LGP-MVS_train				90.06 241	93.35 221	80.95 282		95.94 212	87.73 160	83.17 205	96.11 164	66.28 276	97.77 165	90.19 123	85.19 212	91.46 242

v6		87.27 211	85.86 213	91.50 214	89.97 264	86.84 197	98.45 145	95.67 232	83.85 225	83.11 207	90.97 240	74.46 202	96.58 216	81.97 204	74.34 270	91.09 252

v1neww		87.29 209	85.88 211	91.50 214	90.07 257	86.87 193	98.45 145	95.66 235	83.84 226	83.07 208	90.99 238	74.58 199	96.56 218	81.96 205	74.33 271	91.07 255

v7new		87.29 209	85.88 211	91.50 214	90.07 257	86.87 193	98.45 145	95.66 235	83.84 226	83.07 208	90.99 238	74.58 199	96.56 218	81.96 205	74.33 271	91.07 255

FC-MVSNet-test		90.22 167	89.40 163	92.67 193	91.78 241	89.86 137	97.89 192	98.22 31	88.81 127	82.96 210	94.66 182	81.90 152	95.96 262	85.89 166	82.52 234	92.20 223

v7		86.91 218	85.45 223	91.29 219	90.06 259	86.73 199	98.26 168	95.49 246	83.08 246	82.95 211	90.96 241	73.37 218	96.42 232	79.90 224	74.97 263	90.71 270

v1		87.23 213	85.76 215	91.66 211	89.88 269	87.37 184	98.54 133	95.64 237	83.91 222	82.88 212	90.70 247	74.64 193	96.53 222	81.54 210	74.08 277	91.08 253

divwei89l23v2f112		87.23 213	85.75 217	91.66 211	89.88 269	87.40 182	98.53 134	95.62 238	83.91 222	82.84 213	90.67 252	74.75 191	96.49 226	81.55 209	74.05 279	91.08 253

v1141		87.23 213	85.75 217	91.67 210	89.88 269	87.43 181	98.52 135	95.62 238	83.91 222	82.83 214	90.69 249	74.70 192	96.49 226	81.53 211	74.08 277	91.07 255

PCF-MVS	89.78 5	91.26 150	89.63 160	96.16 102	95.44 165	91.58 89	95.29 276	96.10 203	85.07 204	82.75 215	97.45 113	78.28 175	99.78 56	80.60 221	95.65 125	97.12 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
V42		87.00 217	85.68 220	90.98 224	89.91 265	86.08 221	98.32 159	95.61 240	83.67 232	82.72 216	90.67 252	74.00 213	96.53 222	81.94 207	74.28 274	90.32 278

v1144		86.83 220	85.31 225	91.40 217	89.75 274	87.21 190	98.31 160	95.45 250	83.22 243	82.70 217	90.78 244	73.36 219	96.36 237	79.49 226	74.69 267	90.63 273

v144192		86.40 228	84.89 231	90.91 225	89.48 285	85.59 234	98.21 175	95.43 252	82.45 257	82.62 218	90.58 260	72.79 228	96.36 237	78.45 236	74.04 280	90.79 265

3Dnovator	87.35 11	93.17 112	91.77 122	97.37 41	95.41 166	93.07 63	98.82 100	97.85 60	91.53 67	82.56 219	97.58 109	71.97 235	99.82 50	91.01 115	99.23 58	99.22 84

v2v482		87.27 211	85.76 215	91.78 209	89.59 280	87.58 175	98.56 131	95.54 242	84.53 212	82.51 220	91.78 229	73.11 224	96.47 229	82.07 201	74.14 276	91.30 247

Baseline_NR-MVSNet		85.83 237	84.82 233	88.87 265	88.73 293	83.34 256	98.63 122	91.66 321	80.41 276	82.44 221	91.35 234	74.63 195	95.42 277	84.13 181	71.39 300	87.84 302

v1192		86.32 230	84.71 235	91.17 220	89.53 283	86.40 208	98.13 181	95.44 251	82.52 256	82.42 222	90.62 257	71.58 240	96.33 244	77.23 244	74.88 264	90.79 265

test_djsdf		88.26 201	87.73 188	89.84 246	88.05 301	82.21 268	97.77 198	96.17 200	86.84 179	82.41 223	91.95 228	72.07 234	95.99 260	89.83 125	84.50 218	91.32 246

1314		93.44 99	91.98 118	97.84 21	95.24 168	94.38 43	96.22 250	97.92 55	90.18 93	82.28 224	97.71 105	77.63 179	99.80 54	91.94 110	98.67 78	99.34 73

v1921920		86.02 233	84.44 239	90.77 227	89.32 287	85.20 239	98.10 183	95.35 257	82.19 259	82.25 225	90.71 246	70.73 243	96.30 250	76.85 250	74.49 268	90.80 264

v1240		85.77 240	84.11 242	90.73 228	89.26 288	85.15 242	97.88 194	95.23 264	81.89 264	82.16 226	90.55 262	69.60 251	96.31 247	75.59 267	74.87 265	90.72 269

XVG-ACMP-BASELINE		85.86 236	84.95 230	88.57 269	89.90 267	77.12 304	94.30 284	95.60 241	87.40 167	82.12 227	92.99 216	53.42 320	97.66 174	85.02 172	83.83 222	90.92 261

GBi-Net		86.67 223	84.96 228	91.80 205	95.11 178	88.81 153	96.77 227	95.25 260	82.94 249	82.12 227	90.25 268	62.89 291	94.97 285	79.04 230	80.24 240	91.62 237

test1		86.67 223	84.96 228	91.80 205	95.11 178	88.81 153	96.77 227	95.25 260	82.94 249	82.12 227	90.25 268	62.89 291	94.97 285	79.04 230	80.24 240	91.62 237

FMVSNet3		88.81 192	87.08 199	93.99 167	96.52 137	94.59 38	98.08 185	96.20 198	85.85 190	82.12 227	91.60 232	74.05 212	95.40 278	79.04 230	80.24 240	91.99 230

IterMVS-LS		88.34 198	87.44 192	91.04 222	94.10 197	85.85 230	98.10 183	95.48 247	85.12 201	82.03 231	91.21 235	81.35 157	95.63 272	83.86 187	75.73 259	91.63 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		84.48 253	81.83 261	92.42 195	91.73 242	87.36 185	85.52 326	94.42 278	81.40 268	81.91 232	87.58 295	51.92 322	92.81 307	73.84 282	88.15 197	97.08 176

PS-MVSNAJss		89.54 179	89.05 168	91.00 223	88.77 292	84.36 248	97.39 206	95.97 208	88.47 132	81.88 233	93.80 196	82.48 145	96.50 225	89.34 134	83.34 227	92.15 224

WR-MVS		88.54 197	87.22 197	92.52 194	91.93 239	89.50 144	98.56 131	97.84 61	86.99 174	81.87 234	93.81 195	74.25 209	95.92 266	85.29 169	74.43 269	92.12 225

TranMVSNet+NR-MVSNet		87.75 202	86.31 205	92.07 200	90.81 252	88.56 158	98.33 157	97.18 141	87.76 157	81.87 234	93.90 193	72.45 229	95.43 276	83.13 192	71.30 301	92.23 220

DP-MVS		88.75 194	86.56 202	95.34 128	98.92 63	87.45 179	97.64 202	93.52 292	70.55 318	81.49 236	97.25 123	74.43 204	99.88 34	71.14 300	94.09 135	98.67 122

3Dnovator+	87.72 8	93.43 100	91.84 120	98.17 13	95.73 158	95.08 20	98.92 87	97.04 155	91.42 72	81.48 237	97.60 108	74.60 197	99.79 55	90.84 118	98.97 64	99.64 52

QAPM		91.41 149	89.49 161	97.17 46	95.66 161	93.42 57	98.60 127	97.51 110	80.92 273	81.39 238	97.41 115	72.89 227	99.87 37	82.33 199	98.68 77	98.21 147

XXY-MVS		87.75 202	86.02 208	92.95 186	90.46 256	89.70 140	97.71 200	95.90 219	84.02 219	80.95 239	94.05 185	67.51 267	97.10 203	85.16 170	78.41 249	92.04 229

v148		86.38 229	85.06 227	90.37 236	89.47 286	84.10 250	98.52 135	95.48 247	83.80 228	80.93 240	90.22 271	74.60 197	96.31 247	80.92 214	71.55 299	90.69 271

FMVSNet2		86.90 219	84.79 234	93.24 179	95.11 178	92.54 76	97.67 201	95.86 223	82.94 249	80.55 241	91.17 236	62.89 291	95.29 280	77.23 244	79.71 246	91.90 231

pmmvs4		87.58 206	86.17 207	91.80 205	89.58 281	88.92 151	97.25 212	95.28 259	82.54 255	80.49 242	93.17 212	75.62 188	96.05 259	82.75 196	78.90 247	90.42 276

ACMP	87.39 10	88.71 195	88.24 185	90.12 240	93.91 206	81.06 281	98.50 139	95.67 232	89.43 108	80.37 243	95.55 170	65.67 279	97.83 160	90.55 121	84.51 217	91.47 241
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		85.87 235	84.40 241	90.30 237	88.53 296	84.23 249	98.60 127	93.71 289	81.53 267	80.29 244	92.02 224	64.51 284	95.52 274	82.04 203	78.34 250	91.15 250

test0.0.03 1		88.96 186	88.61 177	90.03 243	91.09 249	84.43 247	98.97 83	97.02 158	90.21 91	80.29 244	96.31 163	84.89 112	91.93 324	72.98 292	85.70 211	93.73 203

jajsoiax		87.35 207	86.51 203	89.87 244	87.75 306	81.74 271	97.03 221	95.98 206	88.47 132	80.15 246	93.80 196	61.47 296	96.36 237	89.44 132	84.47 219	91.50 240

mvs_tets		87.09 216	86.22 206	89.71 248	87.87 302	81.39 275	96.73 231	95.90 219	88.19 146	79.99 247	93.61 201	59.96 302	96.31 247	89.40 133	84.34 220	91.43 244

ITE_SJBPF				87.93 283	92.26 232	76.44 305		93.47 293	87.67 163	79.95 248	95.49 173	56.50 309	97.38 194	75.24 268	82.33 235	89.98 286

v8		86.11 232	84.45 238	91.10 221	89.99 263	86.85 195	97.24 213	95.36 255	81.99 261	79.89 249	89.86 277	74.53 201	96.39 235	78.83 234	72.32 291	90.05 284

v10		85.73 241	84.01 244	90.87 226	90.03 260	86.73 199	97.20 216	95.22 265	81.25 269	79.85 250	89.75 278	73.30 223	96.28 251	76.87 248	72.64 287	89.61 291

WR-MVS_H		86.53 227	85.49 222	89.66 251	91.04 250	83.31 257	97.53 205	98.20 32	84.95 207	79.64 251	90.90 243	78.01 177	95.33 279	76.29 255	72.81 285	90.35 277

anonymousdsp		86.69 222	85.75 217	89.53 253	86.46 315	82.94 260	96.39 241	95.71 229	83.97 221	79.63 252	90.70 247	68.85 256	95.94 263	86.01 162	84.02 221	89.72 290

Patchmtry		83.61 264	81.64 264	89.50 254	93.36 220	82.84 265	84.10 333	94.20 283	69.47 324	79.57 253	86.88 303	84.43 116	94.78 292	68.48 305	74.30 273	90.88 262

CP-MVSNet		86.54 226	85.45 223	89.79 247	91.02 251	82.78 266	97.38 208	97.56 103	85.37 198	79.53 254	93.03 214	71.86 237	95.25 281	79.92 223	73.43 283	91.34 245

Patchmatch-test		86.25 231	84.06 243	92.82 187	94.42 192	82.88 264	82.88 338	94.23 282	71.58 314	79.39 255	90.62 257	89.00 49	96.42 232	63.03 316	91.37 167	99.16 86

DSMNet-mixed		81.60 275	81.43 266	82.10 311	84.36 320	60.79 332	93.63 293	86.74 342	79.00 283	79.32 256	87.15 301	63.87 287	89.78 329	66.89 309	91.92 157	95.73 197

MSDG		88.29 200	86.37 204	94.04 165	96.90 121	86.15 219	96.52 237	94.36 280	77.89 300	79.22 257	96.95 141	69.72 249	99.59 77	73.20 289	92.58 147	96.37 195

PS-CasMVS		85.81 238	84.58 237	89.49 255	90.77 253	82.11 269	97.20 216	97.36 130	84.83 209	79.12 258	92.84 217	67.42 268	95.16 283	78.39 237	73.25 284	91.21 249

IterMVS		85.81 238	84.67 236	89.22 258	93.51 215	83.67 254	96.32 244	94.80 268	85.09 203	78.69 259	90.17 275	66.57 275	93.17 300	79.48 227	77.42 255	90.81 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		85.21 245	83.93 245	89.07 262	89.89 268	81.31 277	97.09 219	97.24 136	84.45 214	78.66 260	92.68 219	68.44 259	94.87 288	75.98 257	70.92 302	91.04 258

semantic-postprocess				89.00 263	93.46 218	82.90 262		94.70 271	85.02 205	78.62 261	90.35 264	66.63 273	93.33 299	79.38 229	77.36 256	90.76 267

OpenMVS	85.28 14	90.75 161	88.84 172	96.48 88	93.58 214	93.51 55	98.80 102	97.41 125	82.59 253	78.62 261	97.49 112	68.00 263	99.82 50	84.52 177	98.55 81	96.11 196

PVSNet_0	83.28 16	87.31 208	85.16 226	93.74 174	94.78 188	84.59 246	98.91 88	98.69 22	89.81 101	78.59 263	93.23 210	61.95 295	99.34 102	94.75 74	55.72 336	97.30 170

v52		84.19 257	82.92 250	88.01 281	87.64 308	79.92 286	96.23 248	95.32 258	79.87 279	78.51 264	89.05 285	69.50 253	96.32 245	77.95 240	72.24 294	87.79 305

V4		84.20 256	82.92 250	88.02 280	87.59 309	79.91 287	96.21 253	95.36 255	79.88 278	78.51 264	89.00 286	69.52 252	96.32 245	77.96 239	72.29 292	87.83 304

EU-MVSNet		84.19 257	84.42 240	83.52 308	88.64 295	67.37 328	96.04 258	95.76 225	85.29 199	78.44 266	93.18 211	70.67 244	91.48 327	75.79 265	75.98 257	91.70 234

v7n		84.42 254	82.75 255	89.43 256	88.15 299	81.86 270	96.75 230	95.67 232	80.53 274	78.38 267	89.43 282	69.89 247	96.35 242	73.83 283	72.13 295	90.07 283

FMVSNet1		83.94 261	81.32 268	91.80 205	91.94 238	88.81 153	96.77 227	95.25 260	77.98 295	78.25 268	90.25 268	50.37 326	94.97 285	73.27 288	77.81 253	91.62 237

MS-PatchMatch		86.75 221	85.92 210	89.22 258	91.97 236	82.47 267	96.91 223	96.14 202	83.74 229	77.73 269	93.53 204	58.19 304	97.37 196	76.75 251	98.35 83	87.84 302

v748		83.84 262	82.31 260	88.41 274	87.65 307	79.10 291	96.66 233	95.51 244	80.09 277	77.65 270	88.53 290	69.81 248	96.23 252	75.67 266	69.25 304	89.91 287

DTE-MVSNet		84.14 259	82.80 253	88.14 279	88.95 290	79.87 288	96.81 226	96.24 196	83.50 240	77.60 271	92.52 221	67.89 265	94.24 295	72.64 295	69.05 306	90.32 278

COLMAP_ROB	82.69 18	84.54 252	82.82 252	89.70 249	96.72 133	78.85 293	95.89 262	92.83 307	71.55 315	77.54 272	95.89 167	59.40 303	99.14 111	67.26 307	88.26 196	91.11 251
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testpf		80.59 285	80.13 272	81.97 313	94.25 196	71.65 320	60.37 348	95.46 249	70.99 316	76.97 273	87.74 293	73.58 217	91.67 325	76.86 249	84.97 214	82.60 336

OurMVSNet-221017-0		84.13 260	83.59 246	85.77 299	87.81 303	70.24 323	94.89 279	93.65 291	86.08 189	76.53 274	93.28 209	61.41 297	96.14 256	80.95 213	77.69 254	90.93 260

tfpnnormal		83.65 263	81.35 267	90.56 231	91.37 247	88.06 166	97.29 210	97.87 59	78.51 289	76.20 275	90.91 242	64.78 283	96.47 229	61.71 319	73.50 281	87.13 312

pm-mvs1		84.68 248	82.78 254	90.40 235	89.58 281	85.18 240	97.31 209	94.73 270	81.93 263	76.05 276	92.01 225	65.48 281	96.11 257	78.75 235	69.14 305	89.91 287

AllTest		84.97 246	83.12 248	90.52 232	96.82 129	78.84 294	95.89 262	92.17 314	77.96 297	75.94 277	95.50 171	55.48 312	99.18 106	71.15 298	87.14 200	93.55 205

TestCases				90.52 232	96.82 129	78.84 294		92.17 314	77.96 297	75.94 277	95.50 171	55.48 312	99.18 106	71.15 298	87.14 200	93.55 205

testgi		82.29 266	81.00 270	86.17 296	87.24 311	74.84 309	97.39 206	91.62 322	88.63 129	75.85 279	95.42 174	46.07 331	91.55 326	66.87 310	79.94 243	92.12 225

MVP-Stereo		86.61 225	85.83 214	88.93 264	88.70 294	83.85 253	96.07 257	94.41 279	82.15 260	75.64 280	91.96 227	67.65 266	96.45 231	77.20 246	98.72 75	86.51 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LF4IMVS		81.94 270	81.17 269	84.25 306	87.23 312	68.87 327	93.35 295	91.93 319	83.35 242	75.40 281	93.00 215	49.25 328	96.65 215	78.88 233	78.11 251	87.22 311

LTVRE_ROB	81.71 19	84.59 251	82.72 256	90.18 238	92.89 227	83.18 258	93.15 296	94.74 269	78.99 284	75.14 282	92.69 218	65.64 280	97.63 176	69.46 302	81.82 237	89.74 289
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		80.96 282	81.77 263	78.52 319	81.02 327	62.33 330	98.22 172	94.49 275	79.38 282	74.56 283	90.34 265	70.65 246	85.10 338	60.83 320	86.42 202	88.14 299

Anonymous20231206		80.76 284	79.42 280	84.79 304	84.78 318	72.98 315	96.53 236	92.97 299	79.56 281	74.33 284	88.83 287	61.27 298	92.15 321	60.59 322	75.92 258	89.24 295

FMVSNet5		82.29 266	80.54 271	87.52 287	93.79 211	84.01 251	93.73 291	92.47 311	76.92 302	74.27 285	86.15 307	63.69 288	89.24 330	69.07 303	74.79 266	89.29 294

MVS-HIRNet		79.01 292	75.13 299	90.66 229	93.82 210	81.69 272	85.16 327	93.75 288	54.54 339	74.17 286	59.15 343	57.46 306	96.58 216	63.74 315	94.38 132	93.72 204

ACMH+	83.78 15	84.21 255	82.56 259	89.15 260	93.73 212	79.16 289	96.43 240	94.28 281	81.09 270	74.00 287	94.03 188	54.58 316	97.67 173	76.10 256	78.81 248	90.63 273

NR-MVSNet		87.74 204	86.00 209	92.96 185	91.46 245	90.68 119	96.65 234	97.42 124	88.02 149	73.42 288	93.68 198	77.31 180	95.83 267	84.26 179	71.82 298	92.36 214

USDC		84.74 247	82.93 249	90.16 239	91.73 242	83.54 255	95.00 278	93.30 294	88.77 128	73.19 289	93.30 208	53.62 319	97.65 175	75.88 258	81.54 238	89.30 293

LCM-MVSNet-Re		88.59 196	88.61 177	88.51 271	95.53 163	72.68 317	96.85 225	88.43 340	88.45 135	73.14 290	90.63 256	75.82 186	94.38 294	92.95 99	95.71 123	98.48 132

TDRefinement		78.01 297	75.31 298	86.10 297	70.06 341	73.84 312	93.59 294	91.58 323	74.51 309	73.08 291	91.04 237	49.63 327	97.12 200	74.88 271	59.47 330	87.33 308

TransMVSNet (Re)		81.97 269	79.61 278	89.08 261	89.70 276	84.01 251	97.26 211	91.85 320	78.84 285	73.07 292	91.62 231	67.17 270	95.21 282	67.50 306	59.46 331	88.02 301

SixPastTwentyTwo		82.63 265	81.58 265	85.79 298	88.12 300	71.01 322	95.17 277	92.54 310	84.33 216	72.93 293	92.08 222	60.41 301	95.61 273	74.47 274	74.15 275	90.75 268

testus		77.11 301	76.95 294	77.58 320	80.02 330	58.93 336	97.78 196	90.48 330	79.68 280	72.84 294	90.61 259	37.72 341	86.57 337	60.28 324	83.18 228	87.23 310

pmmvs6		79.90 289	77.31 290	87.67 286	84.17 321	78.13 300	95.86 266	93.68 290	67.94 328	72.67 295	89.62 280	50.98 325	95.75 269	74.80 273	66.04 312	89.14 296

ACMH	83.09 17	84.60 250	82.61 257	90.57 230	93.18 224	82.94 260	96.27 245	94.92 267	81.01 271	72.61 296	93.61 201	56.54 308	97.79 163	74.31 275	81.07 239	90.99 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LP		77.80 300	74.39 302	88.01 281	91.93 239	79.02 292	80.88 340	92.90 304	65.43 331	72.00 297	81.29 322	65.78 278	92.73 313	43.76 341	75.58 260	92.27 217

Patchmatch-RL test		81.90 271	80.13 272	87.23 290	80.71 328	70.12 325	84.07 334	88.19 341	83.16 245	70.57 298	82.18 311	87.18 80	92.59 316	82.28 200	62.78 317	98.98 96

DI_MVS_plusplus_test		89.41 181	87.24 196	95.92 111	89.06 289	90.75 118	98.18 177	96.63 169	89.29 111	70.54 299	90.31 266	63.50 289	98.40 137	92.25 107	95.44 126	98.60 124

test_0402		78.81 294	76.33 296	86.26 295	91.18 248	78.44 298	95.88 264	91.34 325	68.55 325	70.51 300	89.91 276	52.65 321	94.99 284	47.14 336	79.78 245	85.34 329

test_normal		89.37 182	87.18 198	95.93 110	88.94 291	90.83 114	98.24 170	96.62 170	89.31 109	70.38 301	90.20 273	63.50 289	98.37 138	92.06 109	95.41 127	98.59 127

TinyColmap		80.42 287	77.94 287	87.85 284	92.09 235	78.58 296	93.74 290	89.94 334	74.99 306	69.77 302	91.78 229	46.09 330	97.58 179	65.17 314	77.89 252	87.38 307

test20.03		78.51 296	77.48 289	81.62 314	83.07 324	71.03 321	96.11 256	92.83 307	81.66 266	69.31 303	89.68 279	57.53 305	87.29 334	58.65 327	68.47 307	86.53 314

N_pmnet		70.19 310	69.87 309	71.12 325	88.24 298	30.63 356	95.85 267	28.70 357	70.18 321	68.73 304	86.55 305	64.04 286	93.81 296	53.12 332	73.46 282	88.94 297

v18		82.00 268	79.76 276	88.72 266	90.03 260	86.81 198	96.17 255	93.12 295	78.70 286	68.39 305	82.10 312	74.64 193	93.00 301	74.21 276	60.45 324	86.35 316

OpenMVS_ROB	73.86 20	77.99 298	75.06 300	86.77 293	83.81 323	77.94 302	96.38 242	91.53 324	67.54 329	68.38 306	87.13 302	43.94 332	96.08 258	55.03 330	81.83 236	86.29 319

v17		81.87 273	79.61 278	88.64 268	89.91 265	86.64 203	96.01 259	93.08 296	78.54 287	68.27 307	81.96 314	74.44 203	92.95 303	74.03 279	60.22 326	86.34 317

v16		81.90 271	79.65 277	88.65 267	90.02 262	86.66 202	96.01 259	93.07 297	78.53 288	68.27 307	82.05 313	74.39 205	92.96 302	74.02 280	60.48 323	86.33 318

ambc				79.60 317	72.76 339	56.61 340	76.20 342	92.01 318		68.25 309	80.23 328	23.34 346	94.73 293	73.78 284	60.81 322	87.48 306

PM-MVS		74.88 304	72.85 305	80.98 316	78.98 332	64.75 329	90.81 315	85.77 344	80.95 272	68.23 310	82.81 309	29.08 344	92.84 305	76.54 254	62.46 319	85.36 328

v15		81.62 274	79.32 281	88.52 270	89.80 272	86.56 204	95.83 268	92.96 300	78.50 290	67.88 311	81.68 316	74.22 210	92.82 306	73.46 286	59.55 327	86.18 321

V14		81.55 276	79.26 282	88.42 273	89.80 272	86.33 212	95.72 271	92.96 300	78.35 291	67.82 312	81.70 315	74.13 211	92.78 310	73.32 287	59.50 329	86.16 323

v11		81.38 278	79.03 285	88.41 274	89.68 277	86.43 206	95.74 270	92.82 309	78.03 294	67.74 313	81.45 320	73.33 222	92.69 314	72.23 297	60.27 325	86.11 325

pmmvs3		72.86 307	69.76 310	82.17 310	73.86 336	74.19 311	94.20 285	89.01 337	64.23 334	67.72 314	80.91 324	41.48 335	88.65 332	62.40 317	54.02 338	83.68 333

lessismore_v0				85.08 301	85.59 316	69.28 326		90.56 329		67.68 315	90.21 272	54.21 318	95.46 275	73.88 281	62.64 318	90.50 275

V9		81.46 277	79.15 283	88.39 276	89.75 274	86.17 218	95.62 272	92.92 302	78.22 292	67.65 316	81.64 317	73.95 214	92.80 308	73.15 290	59.43 332	86.21 320

v12		81.37 279	79.05 284	88.33 277	89.68 277	86.05 224	95.48 274	92.92 302	78.08 293	67.55 317	81.58 318	73.75 215	92.75 311	73.05 291	59.37 333	86.18 321

K. test v3		81.04 281	79.77 275	84.83 303	87.41 310	70.23 324	95.60 273	93.93 286	83.70 231	67.51 318	89.35 283	55.76 310	93.58 298	76.67 252	68.03 309	90.67 272

MIMVSNet1		75.92 303	73.30 304	83.81 307	81.29 326	75.57 307	92.26 304	92.05 317	73.09 313	67.48 319	86.18 306	40.87 337	87.64 333	55.78 329	70.68 303	88.21 298

v13		81.30 280	78.99 286	88.25 278	89.61 279	85.87 228	95.39 275	92.90 304	77.93 299	67.45 320	81.52 319	73.66 216	92.75 311	72.91 293	59.53 328	86.14 324

pmmvs-eth3d		78.71 295	76.16 297	86.38 294	80.25 329	81.19 279	94.17 286	92.13 316	77.97 296	66.90 321	82.31 310	55.76 310	92.56 317	73.63 285	62.31 320	85.38 327

EG-PatchMatch MVS		79.92 288	77.59 288	86.90 292	87.06 313	77.90 303	96.20 254	94.06 285	74.61 308	66.53 322	88.76 288	40.40 339	96.20 253	67.02 308	83.66 225	86.61 313

UnsupCasMVSNet_eth		78.90 293	76.67 295	85.58 300	82.81 325	74.94 308	91.98 306	96.31 189	84.64 211	65.84 323	87.71 294	51.33 323	92.23 320	72.89 294	56.50 335	89.56 292

Test4		85.71 242	82.59 258	95.07 137	84.45 319	89.84 138	97.20 216	95.73 227	89.19 113	64.59 324	87.58 295	40.59 338	96.77 213	88.95 141	95.01 129	98.60 124

new-patchmatchnet		74.80 305	72.40 306	81.99 312	78.36 334	72.20 318	94.44 281	92.36 312	77.06 301	63.47 325	79.98 329	51.04 324	88.85 331	60.53 323	54.35 337	84.92 330

new_pmnet		76.02 302	73.71 303	82.95 309	83.88 322	72.85 316	91.26 312	92.26 313	70.44 319	62.60 326	81.37 321	47.64 329	92.32 319	61.85 318	72.10 296	83.68 333

UnsupCasMVSNet_bld		73.85 306	70.14 308	84.99 302	79.44 331	75.73 306	88.53 321	95.24 263	70.12 322	61.94 327	74.81 335	41.41 336	93.62 297	68.65 304	51.13 342	85.62 326

CMPMVS	58.40 21	80.48 286	80.11 274	81.59 315	85.10 317	59.56 334	94.14 287	95.95 211	68.54 326	60.71 328	93.31 207	55.35 314	97.87 158	83.06 193	84.85 216	87.33 308
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		72.28 308	71.36 307	75.02 323	73.04 337	57.38 338	92.30 302	90.22 332	62.27 335	59.46 329	80.36 326	76.23 184	87.07 335	44.29 339	64.08 316	80.59 337

.test1245		61.50 314	64.44 313	52.65 338	73.04 337	57.38 338	92.30 302	90.22 332	62.27 335	59.46 329	80.36 326	76.23 184	87.07 335	44.29 339	1.80 353	13.50 353

Anonymous20231211		67.10 311	63.29 314	78.54 318	75.68 335	60.00 333	92.05 305	88.86 338	49.84 340	59.35 331	78.48 333	26.15 345	90.76 328	45.96 338	53.24 339	84.88 331

DeepMVS_CX				76.08 321	90.74 254	51.65 344		90.84 327	86.47 186	57.89 332	87.98 291	35.88 342	92.60 315	65.77 313	65.06 314	83.97 332

test1235678		71.07 309	69.53 311	75.71 322	71.87 340	55.27 342	94.32 282	90.76 328	70.23 320	57.61 333	79.06 331	43.13 333	83.72 340	50.48 333	68.30 308	88.14 299

testing_2		80.92 283	77.24 291	91.98 201	78.88 333	87.83 170	93.96 289	95.72 228	84.27 217	56.20 334	80.42 325	38.64 340	96.40 234	87.20 152	79.85 244	91.72 233

test12356		66.36 312	65.12 312	70.08 328	66.92 342	50.46 345	89.96 319	88.58 339	66.00 330	53.38 335	78.13 334	32.89 343	82.87 341	48.36 335	61.87 321	76.92 338

YYNet1		79.64 291	77.04 293	87.43 289	87.80 304	79.98 285	96.23 248	94.44 276	73.83 312	51.83 336	87.53 297	67.96 264	92.07 323	66.00 312	67.75 311	90.23 280

MDA-MVSNet_test_wron		79.65 290	77.05 292	87.45 288	87.79 305	80.13 284	96.25 247	94.44 276	73.87 311	51.80 337	87.47 298	68.04 262	92.12 322	66.02 311	67.79 310	90.09 281

LCM-MVSNet		60.07 316	56.37 317	71.18 324	54.81 351	48.67 346	82.17 339	89.48 336	37.95 343	49.13 338	69.12 336	13.75 355	81.76 342	59.28 325	51.63 341	83.10 335

MDA-MVSNet-bldmvs		77.82 299	74.75 301	87.03 291	88.33 297	78.52 297	96.34 243	92.85 306	75.57 305	48.87 339	87.89 292	57.32 307	92.49 318	60.79 321	64.80 315	90.08 282

PMMVS2		58.97 317	55.07 318	70.69 327	62.72 343	55.37 341	85.97 325	80.52 348	49.48 341	45.94 340	68.31 338	15.73 353	80.78 344	49.79 334	37.12 343	75.91 340

testmv		60.41 315	57.98 316	67.69 329	58.16 350	47.14 347	89.09 320	86.74 342	61.52 338	44.30 341	68.44 337	20.98 347	79.92 346	40.94 343	51.67 340	76.01 339

FPMVS		61.57 313	60.32 315	65.34 330	60.14 347	42.44 350	91.02 314	89.72 335	44.15 342	42.63 342	80.93 323	19.02 348	80.59 345	42.50 342	72.76 286	73.00 341

Gipuma		54.77 319	52.22 320	62.40 332	86.50 314	59.37 335	50.20 349	90.35 331	36.52 345	41.20 343	49.49 347	18.33 350	81.29 343	32.10 347	65.34 313	46.54 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 320	52.86 319	56.05 335	32.75 356	41.97 352	73.42 344	76.12 351	21.91 351	39.68 344	96.39 161	42.59 334	65.10 351	78.00 238	14.92 351	61.08 346

no-one		56.69 318	51.89 321	71.08 326	59.35 349	58.65 337	83.78 337	84.81 347	61.73 337	36.46 345	56.52 345	18.15 351	84.78 339	47.03 337	19.19 347	69.81 343

E-PMN		41.02 326	40.93 325	41.29 339	61.97 344	33.83 353	84.00 335	65.17 355	27.17 348	27.56 346	46.72 349	17.63 352	60.41 353	19.32 350	18.82 348	29.61 350

PNet_i23d		48.05 322	44.98 323	57.28 334	60.15 345	42.39 351	80.85 341	73.14 353	36.78 344	27.46 347	56.66 344	6.38 356	68.34 349	36.65 345	26.72 345	61.10 345

ANet_high		50.71 321	46.17 322	64.33 331	44.27 354	52.30 343	76.13 343	78.73 349	64.95 332	27.37 348	55.23 346	14.61 354	67.74 350	36.01 346	18.23 349	72.95 342

EMVS		39.96 327	39.88 326	40.18 340	59.57 348	32.12 355	84.79 332	64.57 356	26.27 349	26.14 349	44.18 352	18.73 349	59.29 354	17.03 351	17.67 350	29.12 351

MVE	44.00 22	41.70 325	37.64 328	53.90 337	49.46 352	43.37 349	65.09 347	66.66 354	26.19 350	25.77 350	48.53 348	3.58 360	63.35 352	26.15 349	27.28 344	54.97 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	41.42 23	45.67 323	42.50 324	55.17 336	34.28 355	32.37 354	66.24 346	78.71 350	30.72 347	22.04 351	59.59 342	4.59 357	77.85 347	27.49 348	58.84 334	55.29 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		18.81 330	23.05 331	6.10 344	4.48 357	2.29 359	97.78 196	3.00 359	3.27 353	18.60 352	62.71 340	1.53 362	2.49 357	14.26 353	1.80 353	13.50 353

wuykxyi23d		43.53 324	37.95 327	60.27 333	45.36 353	44.79 348	68.27 345	74.26 352	33.48 346	18.21 353	40.16 354	3.64 358	71.01 348	38.85 344	19.31 346	65.02 344

test123		16.58 332	19.47 332	7.91 343	3.59 358	5.37 358	94.32 282	1.39 360	2.49 354	13.98 354	44.60 351	2.91 361	2.65 356	11.35 354	0.57 355	15.70 352

wuyk23d		16.71 331	16.73 333	16.65 342	60.15 345	25.22 357	41.24 350	5.17 358	6.56 352	5.48 355	3.61 356	3.64 358	22.72 355	15.20 352	9.52 352	1.99 355

cdsmvs_eth3d_5k		22.52 329	30.03 330	0.00 345	0.00 359	0.00 360	0.00 351	97.17 142	0.00 355	0.00 356	98.77 64	74.35 206	0.00 358	0.00 355	0.00 356	0.00 356

pcd_1.5k_mvsjas		6.87 334	9.16 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	82.48 145	0.00 358	0.00 355	0.00 356	0.00 356

pcd1.5k->3k		35.91 328	37.64 328	30.74 341	89.49 284	0.00 360	0.00 351	96.36 188	0.00 355	0.00 356	0.00 357	69.17 254	0.00 358	0.00 355	83.71 224	92.21 222

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		8.21 333	10.94 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	98.50 83	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

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 79				93.96 6			99.83 12	99.90 9

sam_mvs1												88.39 58				98.84 108

sam_mvs												87.08 81

MTGPA								97.45 118

test_post1							90.74 317				41.37 353	85.38 109	96.36 237	83.16 191

test_post											46.00 350	87.37 74	97.11 201

patchmatchnet-post											84.86 308	88.73 52	96.81 212

MTMP								91.09 326

gm-plane-assit					94.69 190	88.14 164			88.22 145		97.20 127		98.29 140	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 80	99.41 38

test_prior				97.01 50	99.58 19	91.77 81		97.57 101					99.49 86			99.79 25

新几何2							98.26 168

旧先验1					98.97 59	92.90 68		97.74 75			99.15 26	91.05 20			99.33 52	99.60 58

无先验							98.52 135	97.82 63	87.20 173				99.90 30	87.64 150		99.85 21

原ACMM2							98.69 112

testdata2													99.88 34	84.16 180

segment_acmp												90.56 35

testdata1							97.89 192		92.43 50

plane_prior7					93.84 208	85.73 232

plane_prior6					93.92 205	86.02 225						72.92 225

plane_prior5								96.30 190					97.75 170	93.46 92	86.17 206	92.67 210

plane_prior4											96.52 155

plane_prior2							99.02 77		93.38 35

plane_prior1					93.90 207

plane_prior						86.07 222	99.14 65		93.81 28						86.26 205

n2								0.00 361

nn								0.00 361

door-mid								84.90 346

test11								97.68 81

door								85.30 345

HQP5-MVS						86.39 209

BP-MVS														93.82 87

HQP3-MVS								96.37 185							86.29 203

HQP2-MVS												73.34 220

NP-MVS					93.94 204	86.22 216					96.67 149

ACMMP++_ref															82.64 233

ACMMP++															83.83 222

Test By Simon												83.62 122

High-resolution multi-view benchmark