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 52	97.05 3	99.41 1	99.59 2	92.89 11	100.00 1	98.99 6	99.90 4	99.96 4

SMA-MVS		97.25 11	97.00 16	98.00 19	99.31 44	94.20 46	99.16 59	97.65 89	89.55 107	99.22 2	99.53 3	90.34 39	99.99 4	98.43 18	99.83 12	99.81 22

TSAR-MVS + MP.		97.44 10	97.46 9	97.39 40	99.12 53	93.49 57	98.52 138	97.50 115	94.46 17	98.99 3	98.64 76	91.58 16	99.08 118	98.49 17	99.83 12	99.60 59

PS-MVSNAJ		96.87 25	96.40 30	98.29 11	97.35 112	97.29 1	99.03 79	97.11 150	95.83 9	98.97 4	99.14 29	82.48 148	99.60 77	98.60 11	99.08 60	98.00 157

旧先验2							98.67 120		85.75 196	98.96 5			98.97 121	93.84 87

xiu_mvs_v2_base		96.66 29	96.17 38	98.11 17	97.11 120	96.96 2	99.01 82	97.04 158	95.51 13	98.86 6	99.11 35	82.19 154	99.36 103	98.59 13	98.14 86	98.00 157

NCCC		98.12 3	98.11 3	98.13 15	99.76 6	94.46 39	99.81 5	97.88 58	96.54 4	98.84 7	99.46 6	92.55 13	99.98 9	98.25 23	99.93 1	99.94 6

SD-MVS		97.51 8	97.40 11	97.81 24	99.01 59	93.79 51	99.33 49	97.38 130	93.73 29	98.83 8	99.02 42	90.87 30	99.88 35	98.69 10	99.74 21	99.77 35

HSP-MVS		97.73 5	98.15 2	96.44 93	99.54 27	90.14 129	99.41 38	97.47 118	95.46 14	98.60 9	99.19 19	95.71 4	99.49 89	98.15 24	99.85 9	99.69 47

APD-MVS		96.95 22	96.72 24	97.63 28	99.51 34	93.58 53	99.16 59	97.44 123	90.08 98	98.59 10	99.07 36	89.06 48	99.42 98	97.92 26	99.66 29	99.88 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testdata				95.26 133	98.20 82	87.28 190		97.60 97	85.21 204	98.48 11	99.15 27	88.15 63	98.72 131	90.29 125	99.45 47	99.78 30

TEST9					99.57 23	93.17 60	99.38 40	97.66 84	89.57 105	98.39 12	99.18 21	90.88 29	99.66 66

train_agg		97.20 14	97.08 14	97.57 32	99.57 23	93.17 60	99.38 40	97.66 84	90.18 93	98.39 12	99.18 21	90.94 27	99.66 66	98.58 14	99.85 9	99.88 15

test_8					99.55 26	93.07 64	99.37 43	97.64 90	90.18 93	98.36 14	99.19 19	90.94 27	99.64 72

agg_prior3		97.09 18	96.97 17	97.45 35	99.56 25	92.79 72	99.36 44	97.67 83	89.59 103	98.36 14	99.16 25	90.57 34	99.68 63	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 54	97.75 74	95.66 11	98.21 16	99.29 10	91.10 19	99.99 4	97.68 29	99.87 5	99.68 48

test_part2					99.54 27	95.42 14				98.13 17

ESAPD		97.97 4	97.82 6	98.43 10	99.54 27	95.42 14	99.43 33	97.69 80	92.81 44	98.13 17	99.48 4	93.96 6	99.97 14	99.52 1	99.83 12	99.90 9

SteuartSystems-ACMMP		97.25 11	97.34 12	97.01 52	97.38 111	91.46 92	99.75 8	97.66 84	94.14 21	98.13 17	99.26 11	92.16 14	99.66 66	97.91 27	99.64 31	99.90 9
Skip Steuart: Steuart Systems R&D Blog.
test_prior3		97.07 19	97.09 13	97.01 52	99.58 19	91.77 83	99.57 19	97.57 103	91.43 70	98.12 20	98.97 48	90.43 36	99.49 89	98.33 20	99.81 16	99.79 26

test_prior2							99.57 19		91.43 70	98.12 20	98.97 48	90.43 36		98.33 20	99.81 16

PHI-MVS		96.65 30	96.46 29	97.21 46	99.34 40	91.77 83	99.70 10	98.05 48	86.48 189	98.05 22	99.20 18	89.33 46	99.96 18	98.38 19	99.62 35	99.90 9

MVSFormer		94.71 75	94.08 75	96.61 86	95.05 187	94.87 22	97.77 203	96.17 206	86.84 182	98.04 23	98.52 82	85.52 104	95.99 267	89.83 128	98.97 65	98.96 103

lupinMVS		96.32 41	95.94 43	97.44 36	95.05 187	94.87 22	99.86 2	96.50 183	93.82 27	98.04 23	98.77 65	85.52 104	98.09 151	96.98 38	98.97 65	99.37 72

APDe-MVS		97.53 7	97.47 8	97.70 26	99.58 19	93.63 52	99.56 21	97.52 110	93.59 32	98.01 25	99.12 32	90.80 32	99.55 79	99.26 4	99.79 18	99.93 7

ACMMP_Plus		96.59 31	96.18 36	97.81 24	98.82 69	93.55 54	98.88 99	97.59 98	90.66 80	97.98 26	99.14 29	86.59 91	100.00 1	96.47 45	99.46 45	99.89 14

agg_prior1		97.12 16	97.03 15	97.38 41	99.54 27	92.66 73	99.35 46	97.64 90	90.38 88	97.98 26	99.17 23	90.84 31	99.61 75	98.57 16	99.78 20	99.87 19

agg_prior					99.54 27	92.66 73		97.64 90		97.98 26			99.61 75

CDPH-MVS		96.56 32	96.18 36	97.70 26	99.59 18	93.92 49	99.13 70	97.44 123	89.02 120	97.90 29	99.22 16	88.90 51	99.49 89	94.63 79	99.79 18	99.68 48

EPNet		96.82 26	96.68 26	97.25 45	98.65 73	93.10 63	99.48 26	98.76 18	96.54 4	97.84 30	98.22 94	87.49 73	99.66 66	95.35 66	97.78 92	99.00 98
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 28	99.65 13	93.21 59	99.70 10	98.13 46	94.61 16	97.78 31	99.46 6	89.85 41	99.81 53	97.97 25	99.91 3	99.88 15

test12				97.83 23	99.33 43	94.45 40		97.55 106		97.56 32		88.60 54	99.50 88		99.71 27	99.55 61

MVS_0304		96.12 46	95.26 56	98.69 4	98.44 79	96.54 7	99.70 10	96.89 167	95.76 10	97.53 33	99.12 32	72.42 233	99.93 25	98.75 8	98.69 77	99.61 58

xiu_mvs_v1_base_debu		94.73 72	93.98 77	96.99 55	95.19 177	95.24 17	98.62 126	96.50 183	92.99 37	97.52 34	98.83 62	72.37 234	99.15 112	97.03 34	96.74 104	96.58 196

xiu_mvs_v1_base		94.73 72	93.98 77	96.99 55	95.19 177	95.24 17	98.62 126	96.50 183	92.99 37	97.52 34	98.83 62	72.37 234	99.15 112	97.03 34	96.74 104	96.58 196

xiu_mvs_v1_base_debi		94.73 72	93.98 77	96.99 55	95.19 177	95.24 17	98.62 126	96.50 183	92.99 37	97.52 34	98.83 62	72.37 234	99.15 112	97.03 34	96.74 104	96.58 196

DeepPCF-MVS	93.56 1	96.55 33	97.84 5	92.68 197	98.71 72	78.11 310	99.70 10	97.71 79	98.18 1	97.36 37	99.76 1	90.37 38	99.94 23	99.27 3	99.54 42	99.99 1

CANet		97.00 20	96.49 28	98.55 6	98.86 68	96.10 10	99.83 4	97.52 110	95.90 8	97.21 38	98.90 58	82.66 145	99.93 25	98.71 9	98.80 74	99.63 55

CANet_DTU		94.31 84	93.35 90	97.20 47	97.03 123	94.71 32	98.62 126	95.54 249	95.61 12	97.21 38	98.47 88	71.88 239	99.84 46	88.38 147	97.46 98	97.04 182

VNet		95.08 66	94.26 70	97.55 33	98.07 87	93.88 50	98.68 118	98.73 21	90.33 90	97.16 40	97.43 116	79.19 171	99.53 82	96.91 40	91.85 161	99.24 84

region2R		96.30 42	96.17 38	96.70 78	99.70 7	90.31 126	99.46 30	97.66 84	90.55 84	97.07 41	99.07 36	86.85 88	99.97 14	95.43 64	99.74 21	99.81 22

原ACMM1				96.18 102	99.03 58	90.08 132		97.63 94	88.98 121	97.00 42	98.97 48	88.14 64	99.71 62	88.23 148	99.62 35	98.76 123

Regformer-1		96.97 21	96.80 22	97.47 34	99.46 37	93.11 62	98.89 97	97.94 54	92.89 41	96.90 43	99.02 42	89.78 42	99.53 82	97.06 33	99.26 57	99.75 36

HFP-MVS		96.42 38	96.26 35	96.90 63	99.69 8	90.96 113	99.47 27	97.81 67	90.54 85	96.88 44	99.05 39	87.57 70	99.96 18	95.65 58	99.72 23	99.78 30

#test#		96.48 35	96.34 33	96.90 63	99.69 8	90.96 113	99.53 24	97.81 67	90.94 78	96.88 44	99.05 39	87.57 70	99.96 18	95.87 57	99.72 23	99.78 30

Regformer-2		96.94 24	96.78 23	97.42 37	99.46 37	92.97 68	98.89 97	97.93 55	92.86 43	96.88 44	99.02 42	89.74 43	99.53 82	97.03 34	99.26 57	99.75 36

XVS		96.47 36	96.37 31	96.77 71	99.62 15	90.66 122	99.43 33	97.58 100	92.41 54	96.86 47	98.96 51	87.37 76	99.87 38	95.65 58	99.43 48	99.78 30

X-MVStestdata		90.69 166	88.66 179	96.77 71	99.62 15	90.66 122	99.43 33	97.58 100	92.41 54	96.86 47	29.59 363	87.37 76	99.87 38	95.65 58	99.43 48	99.78 30

1121		95.19 65	94.45 67	97.42 37	98.88 66	92.58 77	96.22 260	97.75 74	85.50 200	96.86 47	99.01 46	88.59 56	99.90 31	87.64 154	99.60 38	99.79 26

TSAR-MVS + GP.		96.95 22	96.91 18	97.07 49	98.88 66	91.62 88	99.58 18	96.54 182	95.09 15	96.84 50	98.63 77	91.16 17	99.77 58	99.04 5	96.42 109	99.81 22

ACMMPR		96.28 43	96.14 41	96.73 75	99.68 10	90.47 124	99.47 27	97.80 69	90.54 85	96.83 51	99.03 41	86.51 94	99.95 21	95.65 58	99.72 23	99.75 36

PMMVS		93.62 100	93.90 84	92.79 193	96.79 136	81.40 283	98.85 100	96.81 168	91.25 75	96.82 52	98.15 98	77.02 185	98.13 148	93.15 101	96.30 113	98.83 115

PGM-MVS		95.85 52	95.65 51	96.45 92	99.50 35	89.77 141	98.22 176	98.90 17	89.19 114	96.74 53	98.95 53	85.91 102	99.92 27	93.94 84	99.46 45	99.66 51

jason		95.40 62	94.86 62	97.03 51	92.91 232	94.23 45	99.70 10	96.30 194	93.56 33	96.73 54	98.52 82	81.46 159	97.91 159	96.08 54	98.47 83	98.96 103
jason: jason.
新几何1				97.40 39	98.92 64	92.51 79		97.77 73	85.52 198	96.69 55	99.06 38	88.08 65	99.89 34	84.88 179	99.62 35	99.79 26

APD-MVS_3200maxsize		95.64 58	95.65 51	95.62 119	99.24 49	87.80 174	98.42 153	97.22 140	88.93 125	96.64 56	98.98 47	85.49 107	99.36 103	96.68 41	99.27 56	99.70 45

MG-MVS		97.24 13	96.83 21	98.47 9	99.79 5	95.71 12	99.07 73	99.06 15	94.45 18	96.42 57	98.70 73	88.81 52	99.74 61	95.35 66	99.86 8	99.97 3

alignmvs		95.77 56	95.00 61	98.06 18	97.35 112	95.68 13	99.71 9	97.50 115	91.50 68	96.16 58	98.61 78	86.28 98	99.00 120	96.19 51	91.74 163	99.51 64

Regformer-3		96.50 34	96.36 32	96.91 62	99.34 40	91.72 86	98.71 111	97.90 57	92.48 49	96.00 59	98.95 53	88.60 54	99.52 85	96.44 46	98.83 71	99.49 66

CP-MVS		96.22 44	96.15 40	96.42 94	99.67 11	89.62 144	99.70 10	97.61 96	90.07 99	96.00 59	99.16 25	87.43 74	99.92 27	96.03 55	99.72 23	99.70 45

Regformer-4		96.45 37	96.33 34	96.81 70	99.34 40	91.44 94	98.71 111	97.88 58	92.43 50	95.97 61	98.95 53	88.42 58	99.51 86	96.40 47	98.83 71	99.49 66

MCST-MVS		98.18 2	97.95 4	98.86 1	99.85 3	96.60 5	99.70 10	97.98 53	97.18 2	95.96 62	99.33 9	92.62 12	100.00 1	98.99 6	99.93 1	99.98 2

DeepC-MVS_fast	93.52 2	97.16 15	96.84 20	98.13 15	99.61 17	94.45 40	98.85 100	97.64 90	96.51 6	95.88 63	99.39 8	87.35 80	99.99 4	96.61 42	99.69 28	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 80	91.21 102	98.08 190	97.58 100	83.74 233	95.87 64	99.02 42	86.74 89			99.64 31	99.81 22

canonicalmvs		95.02 67	93.96 80	98.20 12	97.53 105	95.92 11	98.71 111	96.19 204	91.78 64	95.86 65	98.49 86	79.53 168	99.03 119	96.12 52	91.42 169	99.66 51

abl_6		94.63 78	94.48 66	95.09 138	98.61 75	86.96 195	98.06 192	96.97 164	89.31 110	95.86 65	98.56 80	79.82 166	99.64 72	94.53 81	98.65 80	98.66 128

Effi-MVS+		93.87 91	93.15 94	96.02 108	95.79 161	90.76 118	96.70 242	95.78 230	86.98 179	95.71 67	97.17 132	79.58 167	98.01 157	94.57 80	96.09 117	99.31 77

casdiffmvs		94.10 86	93.40 89	96.20 100	96.31 146	91.46 92	97.65 207	96.22 201	88.49 134	95.69 68	94.11 189	83.01 141	98.10 150	93.33 97	95.82 123	99.04 95

HPM-MVS_fast		94.89 68	94.62 64	95.70 118	99.11 54	88.44 165	99.14 67	97.11 150	85.82 195	95.69 68	98.47 88	83.46 127	99.32 107	93.16 100	99.63 34	99.35 73

HY-MVS	88.56 7	95.29 63	94.23 71	98.48 8	97.72 93	96.41 8	94.03 298	98.74 19	92.42 53	95.65 70	94.76 185	86.52 93	99.49 89	95.29 68	92.97 144	99.53 62

CHOSEN 280x420		96.80 27	96.85 19	96.66 81	97.85 90	94.42 42	94.76 290	98.36 27	92.50 48	95.62 71	97.52 112	97.92 1	97.38 199	98.31 22	98.80 74	98.20 152

MP-MVS		96.00 48	95.82 46	96.54 89	99.47 36	90.13 131	99.36 44	97.41 127	90.64 83	95.49 72	98.95 53	85.51 106	99.98 9	96.00 56	99.59 40	99.52 63
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS		95.41 61	95.22 57	95.99 109	99.29 45	89.14 150	99.17 58	97.09 154	87.28 175	95.40 73	98.48 87	84.93 113	99.38 101	95.64 62	99.65 30	99.47 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
UA-Net		93.30 109	92.62 104	95.34 130	96.27 148	88.53 164	95.88 274	96.97 164	90.90 79	95.37 74	97.07 137	82.38 151	99.10 117	83.91 191	94.86 133	98.38 142

sss		94.85 70	93.94 82	97.58 30	96.43 144	94.09 48	98.93 88	99.16 14	89.50 108	95.27 75	97.85 100	81.50 158	99.65 70	92.79 106	94.02 138	98.99 100

WTY-MVS		95.97 49	95.11 59	98.54 7	97.62 97	96.65 4	99.44 31	98.74 19	92.25 57	95.21 76	98.46 90	86.56 92	99.46 96	95.00 72	92.69 148	99.50 65

DELS-MVS		97.12 16	96.60 27	98.68 5	98.03 88	96.57 6	99.84 3	97.84 62	96.36 7	95.20 77	98.24 93	88.17 62	99.83 48	96.11 53	99.60 38	99.64 53
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 28	96.69 25	96.72 77	98.58 76	91.00 112	99.14 67	99.45 1	93.86 26	95.15 78	98.73 69	88.48 57	99.76 59	97.23 32	99.56 41	99.40 71

MVS_Test		93.67 98	92.67 103	96.69 79	96.72 138	92.66 73	97.22 222	96.03 212	87.69 165	95.12 79	94.03 193	81.55 157	98.28 145	89.17 142	96.46 107	99.14 89

MVS_111021_LR		95.78 55	95.94 43	95.28 132	98.19 84	87.69 175	98.80 105	99.26 13	93.39 34	95.04 80	98.69 74	84.09 121	99.76 59	96.96 39	99.06 61	98.38 142

CostFormer		92.89 119	92.48 107	94.12 166	94.99 189	85.89 230	92.89 308	97.00 163	86.98 179	95.00 81	90.78 252	90.05 40	97.51 189	92.92 104	91.73 164	98.96 103

mPP-MVS		95.90 51	95.75 49	96.38 96	99.58 19	89.41 149	99.26 51	97.41 127	90.66 80	94.82 82	98.95 53	86.15 100	99.98 9	95.24 69	99.64 31	99.74 39

EI-MVSNet-Vis-set		95.76 57	95.63 53	96.17 104	99.14 52	90.33 125	98.49 144	97.82 64	91.92 61	94.75 83	98.88 60	87.06 84	99.48 94	95.40 65	97.17 102	98.70 126

LFMVS		92.23 135	90.84 149	96.42 94	98.24 81	91.08 110	98.24 174	96.22 201	83.39 245	94.74 84	98.31 92	61.12 305	98.85 122	94.45 82	92.82 145	99.32 76

tpmrst		92.78 120	92.16 115	94.65 151	96.27 148	87.45 182	91.83 316	97.10 153	89.10 119	94.68 85	90.69 257	88.22 61	97.73 177	89.78 130	91.80 162	98.77 122

0601test		95.27 64	94.60 65	97.28 44	98.53 77	92.98 67	99.05 78	98.70 22	86.76 185	94.65 86	97.74 106	87.78 68	99.44 97	95.57 63	92.61 149	99.44 70

DP-MVS Recon		95.85 52	95.15 58	97.95 20	99.87 2	94.38 43	99.60 17	97.48 117	86.58 187	94.42 87	99.13 31	87.36 79	99.98 9	93.64 91	98.33 85	99.48 68

zzz-MVS		96.21 45	95.96 42	96.96 60	99.29 45	91.19 103	98.69 115	97.45 120	92.58 46	94.39 88	99.24 14	86.43 96	99.99 4	96.22 49	99.40 51	99.71 43

MTAPA		96.09 47	95.80 48	96.96 60	99.29 45	91.19 103	97.23 221	97.45 120	92.58 46	94.39 88	99.24 14	86.43 96	99.99 4	96.22 49	99.40 51	99.71 43

CPTT-MVS		94.60 79	94.43 68	95.09 138	99.66 12	86.85 198	99.44 31	97.47 118	83.22 247	94.34 90	98.96 51	82.50 146	99.55 79	94.81 75	99.50 43	98.88 111

PVSNet_BlendedMVS		93.36 106	93.20 93	93.84 176	98.77 70	91.61 89	99.47 27	98.04 49	91.44 69	94.21 91	92.63 225	83.50 125	99.87 38	97.41 30	83.37 231	90.05 291

PVSNet_Blended		95.94 50	95.66 50	96.75 73	98.77 70	91.61 89	99.88 1	98.04 49	93.64 31	94.21 91	97.76 104	83.50 125	99.87 38	97.41 30	97.75 93	98.79 118

diffmvs		93.00 117	92.26 113	95.25 134	96.12 157	88.59 160	96.60 246	96.19 204	88.88 127	94.19 93	93.73 203	80.40 165	98.12 149	89.18 141	95.02 130	99.02 97

EI-MVSNet-UG-set		95.43 59	95.29 55	95.86 114	99.07 57	89.87 138	98.43 152	97.80 69	91.78 64	94.11 94	98.77 65	86.25 99	99.48 94	94.95 74	96.45 108	98.22 150

MAR-MVS		94.43 81	94.09 74	95.45 128	99.10 55	87.47 181	98.39 159	97.79 71	88.37 143	94.02 95	99.17 23	78.64 177	99.91 29	92.48 107	98.85 70	98.96 103
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 39	95.94 43	97.58 30	94.10 203	95.25 16	98.93 88	98.17 42	94.26 19	93.94 96	98.72 71	89.68 44	97.88 162	96.36 48	99.29 55	99.62 57

GG-mvs-BLEND				96.98 58	96.53 141	94.81 29	87.20 331	97.74 76		93.91 97	96.40 162	96.56 2	96.94 213	95.08 70	98.95 68	99.20 87

API-MVS		94.78 71	94.18 72	96.59 87	99.21 50	90.06 135	98.80 105	97.78 72	83.59 237	93.85 98	99.21 17	83.79 123	99.97 14	92.37 108	99.00 64	99.74 39

tpm2		91.77 146	91.09 137	93.82 177	94.83 193	85.56 239	92.51 311	97.16 145	84.00 224	93.83 99	90.66 262	87.54 72	97.17 204	87.73 153	91.55 167	98.72 124

PAPR		96.35 39	95.82 46	97.94 21	99.63 14	94.19 47	99.42 37	97.55 106	92.43 50	93.82 100	99.12 32	87.30 81	99.91 29	94.02 83	99.06 61	99.74 39

PVSNet	87.13 12	93.69 95	92.83 100	96.28 99	97.99 89	90.22 128	99.38 40	98.93 16	91.42 72	93.66 101	97.68 108	71.29 247	99.64 72	87.94 151	97.20 101	98.98 101

VDD-MVS		91.24 156	90.18 159	94.45 156	97.08 121	85.84 234	98.40 158	96.10 210	86.99 177	93.36 102	98.16 97	54.27 325	99.20 109	96.59 43	90.63 178	98.31 148

VDDNet		90.08 175	88.54 185	94.69 150	94.41 199	87.68 176	98.21 179	96.40 188	76.21 310	93.33 103	97.75 105	54.93 323	98.77 125	94.71 78	90.96 172	97.61 169

MP-MVS-pluss		95.80 54	95.30 54	97.29 43	98.95 63	92.66 73	98.59 132	97.14 146	88.95 123	93.12 104	99.25 12	85.62 103	99.94 23	96.56 44	99.48 44	99.28 81
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MDTV_nov1_ep13_2view						91.17 105	91.38 319		87.45 169	93.08 105		86.67 90		87.02 160		98.95 107

DWT-MVSNet_test		94.36 82	93.95 81	95.62 119	96.99 124	89.47 147	96.62 245	97.38 130	90.96 77	93.07 106	97.27 124	93.73 8	98.09 151	85.86 173	93.65 140	99.29 79

EPNet_dtu		92.28 133	92.15 116	92.70 196	97.29 114	84.84 248	98.64 124	97.82 64	92.91 40	93.02 107	97.02 139	85.48 109	95.70 277	72.25 303	94.89 132	97.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gg-mvs-nofinetune		90.00 176	87.71 192	96.89 67	96.15 153	94.69 33	85.15 337	97.74 76	68.32 334	92.97 108	60.16 349	96.10 3	96.84 215	93.89 85	98.87 69	99.14 89

114514_t		94.06 88	93.05 96	97.06 50	99.08 56	92.26 81	98.97 86	97.01 162	82.58 258	92.57 109	98.22 94	80.68 163	99.30 108	89.34 137	99.02 63	99.63 55

OMC-MVS		93.90 90	93.62 87	94.73 149	98.63 74	87.00 194	98.04 193	96.56 180	92.19 58	92.46 110	98.73 69	79.49 169	99.14 115	92.16 111	94.34 136	98.03 156

PAPM_NR		95.43 59	95.05 60	96.57 88	99.42 39	90.14 129	98.58 133	97.51 112	90.65 82	92.44 111	98.90 58	87.77 69	99.90 31	90.88 120	99.32 54	99.68 48

UGNet		91.91 145	90.85 148	95.10 137	97.06 122	88.69 159	98.01 194	98.24 31	92.41 54	92.39 112	93.61 207	60.52 306	99.68 63	88.14 149	97.25 100	96.92 188
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			90.47 158		96.14 154	88.55 162	91.34 320	97.51 112	89.58 104	92.24 113	90.50 272	86.99 87	97.61 183	77.64 248	92.34 152

PatchFormer-LS_test		94.08 87	93.60 88	95.53 126	96.92 125	89.57 145	96.51 249	97.34 134	91.29 74	92.22 114	97.18 130	91.66 15	98.02 156	87.05 159	92.21 156	99.00 98

Vis-MVSNet		92.64 128	91.85 123	95.03 143	95.12 183	88.23 166	98.48 145	96.81 168	91.61 66	92.16 115	97.22 128	71.58 244	98.00 158	85.85 174	97.81 89	98.88 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TESTMET0.1,1		93.82 92	93.26 92	95.49 127	95.21 176	90.25 127	99.15 64	97.54 109	89.18 116	91.79 116	94.87 183	89.13 47	97.63 181	86.21 167	96.29 114	98.60 129

EPMVS		92.59 130	91.59 130	95.59 121	97.22 116	90.03 136	91.78 317	98.04 49	90.42 87	91.66 117	90.65 263	86.49 95	97.46 190	81.78 214	96.31 112	99.28 81

test-LLR		93.11 116	92.68 102	94.40 157	94.94 191	87.27 191	99.15 64	97.25 136	90.21 91	91.57 118	94.04 191	84.89 114	97.58 184	85.94 170	96.13 115	98.36 145

test-mter		93.27 111	92.89 99	94.40 157	94.94 191	87.27 191	99.15 64	97.25 136	88.95 123	91.57 118	94.04 191	88.03 66	97.58 184	85.94 170	96.13 115	98.36 145

JIA-IIPM		85.97 239	84.85 237	89.33 266	93.23 229	73.68 322	85.05 338	97.13 148	69.62 330	91.56 120	68.03 347	88.03 66	96.96 211	77.89 247	93.12 142	97.34 173

tpmp4_e23		91.05 158	90.07 160	93.97 172	95.77 163	85.30 241	92.64 309	97.09 154	84.42 219	91.53 121	90.31 275	87.38 75	97.82 166	80.86 222	90.62 179	98.79 118

PVSNet_Blended_VisFu		94.67 76	94.11 73	96.34 98	97.14 119	91.10 108	99.32 50	97.43 125	92.10 60	91.53 121	96.38 165	83.29 131	99.68 63	93.42 96	96.37 110	98.25 149

CHOSEN 1792x2688		94.35 83	93.82 85	95.95 111	97.40 110	88.74 158	98.41 155	98.27 29	92.18 59	91.43 123	96.40 162	78.88 172	99.81 53	93.59 92	97.81 89	99.30 78

ACMMP		94.67 76	94.30 69	95.79 115	99.25 48	88.13 168	98.41 155	98.67 24	90.38 88	91.43 123	98.72 71	82.22 153	99.95 21	93.83 88	95.76 124	99.29 79
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 94	93.67 86	94.01 170	95.86 160	85.70 236	98.67 120	97.66 84	84.46 217	91.36 125	97.18 130	91.16 17	97.79 168	92.93 103	93.75 139	98.53 134

PLC	91.07 3	94.23 85	94.01 76	94.87 145	99.17 51	87.49 180	99.25 52	96.55 181	88.43 141	91.26 126	98.21 96	85.92 101	99.86 43	89.77 131	97.57 94	97.24 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HyFIR lowres test		93.68 97	93.29 91	94.87 145	97.57 104	88.04 170	98.18 181	98.47 25	87.57 167	91.24 127	95.05 181	85.49 107	97.46 190	93.22 99	92.82 145	99.10 91

thres200		93.69 95	92.59 105	96.97 59	97.76 91	94.74 31	99.35 46	99.36 2	89.23 113	91.21 128	96.97 142	83.42 128	98.77 125	85.08 177	90.96 172	97.39 172

mvs-test1		91.57 148	92.20 114	89.70 257	95.15 181	74.34 319	99.51 25	95.40 260	91.92 61	91.02 129	97.25 125	74.27 210	98.08 154	89.45 133	95.83 122	96.67 189

CDS-MVSNet		93.47 101	93.04 97	94.76 147	94.75 195	89.45 148	98.82 103	97.03 160	87.91 157	90.97 130	96.48 160	89.06 48	96.36 243	89.50 132	92.81 147	98.49 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpn200view9		93.43 103	92.27 111	96.90 63	97.68 95	94.84 24	99.18 56	99.36 2	88.45 138	90.79 131	96.90 144	83.31 129	98.75 127	84.11 187	90.69 174	97.12 177

thres400		93.39 105	92.27 111	96.73 75	97.68 95	94.84 24	99.18 56	99.36 2	88.45 138	90.79 131	96.90 144	83.31 129	98.75 127	84.11 187	90.69 174	96.61 190

CR-MVSNet		88.83 194	87.38 196	93.16 186	93.47 222	86.24 217	84.97 339	94.20 292	88.92 126	90.76 133	86.88 312	84.43 118	94.82 297	70.64 308	92.17 158	98.41 139

RPMNet		84.62 256	81.78 270	93.16 186	93.47 222	86.24 217	84.97 339	96.28 198	64.85 342	90.76 133	78.80 341	80.95 162	94.82 297	53.76 340	92.17 158	98.41 139

PatchmatchNet		92.05 144	91.04 138	95.06 141	96.17 152	89.04 152	91.26 321	97.26 135	89.56 106	90.64 135	90.56 269	88.35 60	97.11 206	79.53 231	96.07 119	99.03 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchT		85.44 249	83.19 253	92.22 202	93.13 231	83.00 268	83.80 345	96.37 189	70.62 324	90.55 136	79.63 339	84.81 116	94.87 295	58.18 337	91.59 166	98.79 118

tpm		89.67 180	88.95 173	91.82 209	92.54 235	81.43 282	92.95 307	95.92 221	87.81 159	90.50 137	89.44 290	84.99 112	95.65 278	83.67 194	82.71 237	98.38 142

tfpn111		93.20 113	92.00 120	96.83 69	97.62 97	94.84 24	99.06 75	99.36 2	87.96 153	90.47 138	96.78 147	83.29 131	98.71 132	82.93 199	90.47 181	96.94 184

conf200view11		93.32 108	92.15 116	96.84 68	97.62 97	94.84 24	99.06 75	99.36 2	87.96 153	90.47 138	96.78 147	83.29 131	98.75 127	84.11 187	90.69 174	96.94 184

thres100view900		93.34 107	92.15 116	96.90 63	97.62 97	94.84 24	99.06 75	99.36 2	87.96 153	90.47 138	96.78 147	83.29 131	98.75 127	84.11 187	90.69 174	97.12 177

thres600view7		93.18 114	92.00 120	96.75 73	97.62 97	94.92 21	99.07 73	99.36 2	87.96 153	90.47 138	96.78 147	83.29 131	98.71 132	82.93 199	90.47 181	96.61 190

AdaColmap		93.82 92	93.06 95	96.10 107	99.88 1	89.07 151	98.33 161	97.55 106	86.81 184	90.39 142	98.65 75	75.09 193	99.98 9	93.32 98	97.53 96	99.26 83

XVG-OURS-SEG-HR		90.95 160	90.66 155	91.83 208	95.18 180	81.14 289	95.92 271	95.92 221	88.40 142	90.33 143	97.85 100	70.66 250	99.38 101	92.83 105	88.83 198	94.98 204

IS-MVSNet		93.00 117	92.51 106	94.49 154	96.14 154	87.36 188	98.31 164	95.70 236	88.58 133	90.17 144	97.50 113	83.02 140	97.22 202	87.06 158	96.07 119	98.90 110

CSCG		94.87 69	94.71 63	95.36 129	99.54 27	86.49 208	99.34 48	98.15 44	82.71 256	90.15 145	99.25 12	89.48 45	99.86 43	94.97 73	98.82 73	99.72 42

Patchmatch-test1		90.10 173	88.61 180	94.57 153	94.95 190	88.83 154	96.26 256	97.21 141	90.06 100	90.03 146	90.68 259	66.61 280	95.83 274	77.31 249	94.36 135	99.05 94

XVG-OURS		90.83 162	90.49 157	91.86 207	95.23 175	81.25 287	95.79 279	95.92 221	88.96 122	90.02 147	98.03 99	71.60 243	99.35 105	91.06 117	87.78 202	94.98 204

ADS-MVSNet2		87.62 210	86.88 204	89.86 252	96.21 150	79.14 299	87.15 332	92.99 307	83.01 251	89.91 148	87.27 308	78.87 173	92.80 317	74.20 284	92.27 154	97.64 165

ADS-MVSNet		88.99 188	87.30 197	94.07 167	96.21 150	87.56 179	87.15 332	96.78 170	83.01 251	89.91 148	87.27 308	78.87 173	97.01 210	74.20 284	92.27 154	97.64 165

tfpn_ndepth		93.28 110	92.32 108	96.16 105	97.74 92	92.86 71	99.01 82	98.19 40	85.50 200	89.84 150	97.12 134	93.57 9	97.58 184	79.39 234	90.50 180	98.04 155

ab-mvs		91.05 158	89.17 169	96.69 79	95.96 158	91.72 86	92.62 310	97.23 139	85.61 197	89.74 151	93.89 199	68.55 264	99.42 98	91.09 116	87.84 201	98.92 109

TAMVS		92.62 129	92.09 119	94.20 164	94.10 203	87.68 176	98.41 155	96.97 164	87.53 168	89.74 151	96.04 169	84.77 117	96.49 232	88.97 144	92.31 153	98.42 138

Vis-MVSNet (Re-imp)		93.26 112	93.00 98	94.06 168	96.14 154	86.71 204	98.68 118	96.70 171	88.30 145	89.71 153	97.64 109	85.43 110	96.39 241	88.06 150	96.32 111	99.08 93

view600		92.78 120	91.50 132	96.63 82	97.51 106	94.66 34	98.91 91	99.36 2	87.31 171	89.64 154	96.59 154	83.26 136	98.63 136	80.76 223	90.15 184	96.61 190

view800		92.78 120	91.50 132	96.63 82	97.51 106	94.66 34	98.91 91	99.36 2	87.31 171	89.64 154	96.59 154	83.26 136	98.63 136	80.76 223	90.15 184	96.61 190

conf0.05thres1000		92.78 120	91.50 132	96.63 82	97.51 106	94.66 34	98.91 91	99.36 2	87.31 171	89.64 154	96.59 154	83.26 136	98.63 136	80.76 223	90.15 184	96.61 190

tfpn		92.78 120	91.50 132	96.63 82	97.51 106	94.66 34	98.91 91	99.36 2	87.31 171	89.64 154	96.59 154	83.26 136	98.63 136	80.76 223	90.15 184	96.61 190

CNLPA		93.64 99	92.74 101	96.36 97	98.96 62	90.01 137	99.19 54	95.89 227	86.22 192	89.40 158	98.85 61	80.66 164	99.84 46	88.57 146	96.92 103	99.24 84

Anonymous202405211		88.84 192	87.03 203	94.27 161	98.14 86	84.18 256	98.44 151	95.58 248	76.79 309	89.34 159	96.88 146	53.42 328	99.54 81	87.53 156	87.12 205	99.09 92

Fast-Effi-MVS+		91.72 147	90.79 152	94.49 154	95.89 159	87.40 185	99.54 23	95.70 236	85.01 210	89.28 160	95.68 173	77.75 181	97.57 188	83.22 195	95.06 129	98.51 135

PatchMatch-RL		91.47 150	90.54 156	94.26 162	98.20 82	86.36 214	96.94 232	97.14 146	87.75 161	88.98 161	95.75 172	71.80 241	99.40 100	80.92 220	97.39 99	97.02 183

dp		90.16 172	88.83 176	94.14 165	96.38 145	86.42 210	91.57 318	97.06 157	84.76 214	88.81 162	90.19 283	84.29 120	97.43 192	75.05 276	91.35 171	98.56 133

tfpn1000		92.67 127	91.64 129	95.78 116	97.61 102	92.34 80	98.69 115	98.18 41	84.15 222	88.80 163	96.99 141	93.56 10	97.21 203	76.56 259	90.19 183	97.77 164

DeepC-MVS	91.02 4	94.56 80	93.92 83	96.46 91	97.16 118	90.76 118	98.39 159	97.11 150	93.92 22	88.66 164	98.33 91	78.14 179	99.85 45	95.02 71	98.57 81	98.78 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous20240529		87.66 209	85.58 225	93.92 173	97.59 103	85.01 247	98.13 185	97.13 148	66.69 338	88.47 165	96.01 170	55.09 322	99.51 86	87.00 161	84.12 225	97.23 176

CVMVSNet		90.30 168	90.91 147	88.46 281	94.32 200	73.58 323	97.61 209	97.59 98	90.16 96	88.43 166	97.10 135	76.83 186	92.86 313	82.64 202	93.54 141	98.93 108

TR-MVS		90.77 163	89.44 165	94.76 147	96.31 146	88.02 171	97.92 196	95.96 216	85.52 198	88.22 167	97.23 127	66.80 278	98.09 151	84.58 182	92.38 151	98.17 153

conf0.01		92.06 142	90.99 139	95.24 135	96.84 128	91.39 95	98.31 164	98.20 33	83.57 238	88.08 168	97.34 118	91.05 20	97.40 193	75.80 265	89.74 191	96.94 184

conf0.002		92.06 142	90.99 139	95.24 135	96.84 128	91.39 95	98.31 164	98.20 33	83.57 238	88.08 168	97.34 118	91.05 20	97.40 193	75.80 265	89.74 191	96.94 184

thresconf0.02		92.14 136	90.99 139	95.58 122	96.84 128	91.39 95	98.31 164	98.20 33	83.57 238	88.08 168	97.34 118	91.05 20	97.40 193	75.80 265	89.74 191	97.94 159

tfpn_n400		92.14 136	90.99 139	95.58 122	96.84 128	91.39 95	98.31 164	98.20 33	83.57 238	88.08 168	97.34 118	91.05 20	97.40 193	75.80 265	89.74 191	97.94 159

tfpnconf		92.14 136	90.99 139	95.58 122	96.84 128	91.39 95	98.31 164	98.20 33	83.57 238	88.08 168	97.34 118	91.05 20	97.40 193	75.80 265	89.74 191	97.94 159

tfpnview11		92.14 136	90.99 139	95.58 122	96.84 128	91.39 95	98.31 164	98.20 33	83.57 238	88.08 168	97.34 118	91.05 20	97.40 193	75.80 265	89.74 191	97.94 159

F-COLMAP		92.07 141	91.75 127	93.02 189	98.16 85	82.89 272	98.79 108	95.97 214	86.54 188	87.92 174	97.80 102	78.69 176	99.65 70	85.97 169	95.93 121	96.53 199

BH-RMVSNet		91.25 155	89.99 161	95.03 143	96.75 137	88.55 162	98.65 122	94.95 273	87.74 162	87.74 175	97.80 102	68.27 266	98.14 147	80.53 228	97.49 97	98.41 139

Effi-MVS+-dtu		89.97 177	90.68 154	87.81 294	95.15 181	71.98 328	97.87 200	95.40 260	91.92 61	87.57 176	91.44 238	74.27 210	96.84 215	89.45 133	93.10 143	94.60 206

HQP-NCC					93.95 207		99.16 59		93.92 22	87.57 176

ACMP_Plane					93.95 207		99.16 59		93.92 22	87.57 176

HQP4-MVS										87.57 176			97.77 170			92.72 213

HQP-MVS		91.50 149	91.23 136	92.29 201	93.95 207	86.39 212	99.16 59	96.37 189	93.92 22	87.57 176	96.67 152	73.34 223	97.77 170	93.82 89	86.29 207	92.72 213

TAPA-MVS	87.50 9	90.35 167	89.05 171	94.25 163	98.48 78	85.17 244	98.42 153	96.58 179	82.44 262	87.24 181	98.53 81	82.77 144	98.84 123	59.09 335	97.88 88	98.72 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HQP_MVS		91.26 153	90.95 146	92.16 203	93.84 214	86.07 225	99.02 80	96.30 194	93.38 35	86.99 182	96.52 158	72.92 228	97.75 175	93.46 94	86.17 210	92.67 215

plane_prior3						85.91 229			93.65 30	86.99 182

GA-MVS		90.10 173	88.69 178	94.33 159	92.44 236	87.97 172	99.08 72	96.26 199	89.65 102	86.92 184	93.11 218	68.09 267	96.96 211	82.54 204	90.15 184	98.05 154

1112_ss		92.71 125	91.55 131	96.20 100	95.56 168	91.12 106	98.48 145	94.69 279	88.29 146	86.89 185	98.50 84	87.02 85	98.66 134	84.75 180	89.77 190	98.81 116

Test_1112_low_res		92.27 134	90.97 145	96.18 102	95.53 169	91.10 108	98.47 147	94.66 280	88.28 147	86.83 186	93.50 211	87.00 86	98.65 135	84.69 181	89.74 191	98.80 117

cascas		90.93 161	89.33 168	95.76 117	95.69 165	93.03 66	98.99 85	96.59 176	80.49 280	86.79 187	94.45 188	65.23 288	98.60 140	93.52 93	92.18 157	95.66 203

OPM-MVS		89.76 179	89.15 170	91.57 218	90.53 262	85.58 238	98.11 187	95.93 220	92.88 42	86.05 188	96.47 161	67.06 277	97.87 163	89.29 140	86.08 212	91.26 254

VPA-MVSNet		89.10 187	87.66 193	93.45 181	92.56 234	91.02 111	97.97 195	98.32 28	86.92 181	86.03 189	92.01 230	68.84 263	97.10 208	90.92 119	75.34 267	92.23 225

tpm cat1		88.89 190	87.27 198	93.76 178	95.79 161	85.32 240	90.76 325	97.09 154	76.14 311	85.72 190	88.59 298	82.92 142	98.04 155	76.96 253	91.43 168	97.90 163

IB-MVS	89.43 6	92.12 140	90.83 151	95.98 110	95.40 173	90.78 117	99.81 5	98.06 47	91.23 76	85.63 191	93.66 206	90.63 33	98.78 124	91.22 115	71.85 306	98.36 145
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		89.87 178	89.38 167	91.36 223	94.32 200	85.87 231	97.61 209	96.59 176	85.10 206	85.51 192	97.10 135	81.30 161	96.56 224	83.85 193	83.03 234	91.64 240

MVSTER		92.71 125	92.32 108	93.86 175	97.29 114	92.95 69	99.01 82	96.59 176	90.09 97	85.51 192	94.00 195	94.61 5	96.56 224	90.77 123	83.03 234	92.08 232

RPSCF		85.33 250	85.55 226	84.67 314	94.63 197	62.28 340	93.73 301	93.76 296	74.38 317	85.23 194	97.06 138	64.09 291	98.31 143	80.98 218	86.08 212	93.41 212

BH-w/o		92.32 132	91.79 125	93.91 174	96.85 127	86.18 220	99.11 71	95.74 232	88.13 150	84.81 195	97.00 140	77.26 184	97.91 159	89.16 143	98.03 87	97.64 165

CLD-MVS		91.06 157	90.71 153	92.10 204	94.05 206	86.10 223	99.55 22	96.29 197	94.16 20	84.70 196	97.17 132	69.62 256	97.82 166	94.74 77	86.08 212	92.39 218
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmvs		89.16 186	87.76 190	93.35 182	97.19 117	84.75 250	90.58 327	97.36 132	81.99 266	84.56 197	89.31 293	83.98 122	98.17 146	74.85 279	90.00 189	97.12 177

nrg030		90.23 169	88.87 174	94.32 160	91.53 250	93.54 55	98.79 108	95.89 227	88.12 151	84.55 198	94.61 187	78.80 175	96.88 214	92.35 109	75.21 268	92.53 217

VPNet		88.30 203	86.57 205	93.49 180	91.95 243	91.35 101	98.18 181	97.20 142	88.61 132	84.52 199	94.89 182	62.21 300	96.76 219	89.34 137	72.26 302	92.36 219

MVS		93.92 89	92.28 110	98.83 2	95.69 165	96.82 3	96.22 260	98.17 42	84.89 212	84.34 200	98.61 78	79.32 170	99.83 48	93.88 86	99.43 48	99.86 20

mvs_anonymous		92.50 131	91.65 128	95.06 141	96.60 140	89.64 143	97.06 229	96.44 187	86.64 186	84.14 201	93.93 197	82.49 147	96.17 261	91.47 114	96.08 118	99.35 73

Fast-Effi-MVS+-dtu		88.84 192	88.59 183	89.58 260	93.44 225	78.18 308	98.65 122	94.62 281	88.46 137	84.12 202	95.37 179	68.91 261	96.52 230	82.06 208	91.70 165	94.06 207

LS3D		90.19 171	88.72 177	94.59 152	98.97 60	86.33 215	96.90 234	96.60 175	74.96 314	84.06 203	98.74 68	75.78 190	99.83 48	74.93 277	97.57 94	97.62 168

ACMM	86.95 13	88.77 197	88.22 189	90.43 240	93.61 219	81.34 285	98.50 142	95.92 221	87.88 158	83.85 204	95.20 180	67.20 275	97.89 161	86.90 164	84.90 219	92.06 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-untuned		91.46 151	90.84 149	93.33 183	96.51 143	84.83 249	98.84 102	95.50 252	86.44 191	83.50 205	96.70 151	75.49 192	97.77 170	86.78 166	97.81 89	97.40 171

FIs		90.70 165	89.87 162	93.18 185	92.29 237	91.12 106	98.17 184	98.25 30	89.11 118	83.44 206	94.82 184	82.26 152	96.17 261	87.76 152	82.76 236	92.25 223

UniMVSNet (Re)		89.50 183	88.32 187	93.03 188	92.21 239	90.96 113	98.90 96	98.39 26	89.13 117	83.22 207	92.03 228	81.69 156	96.34 250	86.79 165	72.53 297	91.81 237

UniMVSNet_NR-MVSNet		89.60 181	88.55 184	92.75 195	92.17 240	90.07 133	98.74 110	98.15 44	88.37 143	83.21 208	93.98 196	82.86 143	95.93 271	86.95 162	72.47 298	92.25 223

DU-MVS		88.83 194	87.51 194	92.79 193	91.46 251	90.07 133	98.71 111	97.62 95	88.87 128	83.21 208	93.68 204	74.63 198	95.93 271	86.95 162	72.47 298	92.36 219

LPG-MVS_test		88.86 191	88.47 186	90.06 247	93.35 227	80.95 291	98.22 176	95.94 218	87.73 163	83.17 210	96.11 167	66.28 282	97.77 170	90.19 126	85.19 216	91.46 248

LGP-MVS_train				90.06 247	93.35 227	80.95 291		95.94 218	87.73 163	83.17 210	96.11 167	66.28 282	97.77 170	90.19 126	85.19 216	91.46 248

v6		87.27 216	85.86 217	91.50 219	89.97 271	86.84 200	98.45 148	95.67 238	83.85 229	83.11 212	90.97 246	74.46 205	96.58 222	81.97 210	74.34 278	91.09 258

v1neww		87.29 214	85.88 215	91.50 219	90.07 264	86.87 196	98.45 148	95.66 241	83.84 230	83.07 213	90.99 244	74.58 202	96.56 224	81.96 211	74.33 279	91.07 261

v7new		87.29 214	85.88 215	91.50 219	90.07 264	86.87 196	98.45 148	95.66 241	83.84 230	83.07 213	90.99 244	74.58 202	96.56 224	81.96 211	74.33 279	91.07 261

FC-MVSNet-test		90.22 170	89.40 166	92.67 198	91.78 247	89.86 139	97.89 197	98.22 32	88.81 129	82.96 215	94.66 186	81.90 155	95.96 269	85.89 172	82.52 239	92.20 228

v7		86.91 223	85.45 228	91.29 224	90.06 266	86.73 202	98.26 172	95.49 253	83.08 250	82.95 216	90.96 247	73.37 221	96.42 238	79.90 230	74.97 269	90.71 276

v1		87.23 218	85.76 219	91.66 216	89.88 276	87.37 187	98.54 136	95.64 243	83.91 226	82.88 217	90.70 255	74.64 196	96.53 228	81.54 216	74.08 286	91.08 259

divwei89l23v2f112		87.23 218	85.75 221	91.66 216	89.88 276	87.40 185	98.53 137	95.62 244	83.91 226	82.84 218	90.67 260	74.75 194	96.49 232	81.55 215	74.05 288	91.08 259

v1141		87.23 218	85.75 221	91.67 215	89.88 276	87.43 184	98.52 138	95.62 244	83.91 226	82.83 219	90.69 257	74.70 195	96.49 232	81.53 217	74.08 286	91.07 261

PCF-MVS	89.78 5	91.26 153	89.63 163	96.16 105	95.44 171	91.58 91	95.29 286	96.10 210	85.07 208	82.75 220	97.45 115	78.28 178	99.78 57	80.60 227	95.65 126	97.12 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
V42		87.00 222	85.68 224	90.98 229	89.91 272	86.08 224	98.32 163	95.61 246	83.67 236	82.72 221	90.67 260	74.00 216	96.53 228	81.94 213	74.28 282	90.32 284

v1144		86.83 225	85.31 230	91.40 222	89.75 281	87.21 193	98.31 164	95.45 257	83.22 247	82.70 222	90.78 252	73.36 222	96.36 243	79.49 232	74.69 273	90.63 279

v144192		86.40 233	84.89 236	90.91 230	89.48 292	85.59 237	98.21 179	95.43 259	82.45 261	82.62 223	90.58 268	72.79 231	96.36 243	78.45 242	74.04 289	90.79 271

3Dnovator	87.35 11	93.17 115	91.77 126	97.37 42	95.41 172	93.07 64	98.82 103	97.85 61	91.53 67	82.56 224	97.58 111	71.97 238	99.82 51	91.01 118	99.23 59	99.22 86

v2v482		87.27 216	85.76 219	91.78 214	89.59 287	87.58 178	98.56 134	95.54 249	84.53 216	82.51 225	91.78 234	73.11 227	96.47 235	82.07 207	74.14 285	91.30 253

Baseline_NR-MVSNet		85.83 242	84.82 238	88.87 274	88.73 302	83.34 265	98.63 125	91.66 330	80.41 281	82.44 226	91.35 239	74.63 198	95.42 284	84.13 186	71.39 309	87.84 311

v1192		86.32 235	84.71 240	91.17 225	89.53 290	86.40 211	98.13 185	95.44 258	82.52 260	82.42 227	90.62 265	71.58 244	96.33 251	77.23 250	74.88 270	90.79 271

test_djsdf		88.26 205	87.73 191	89.84 253	88.05 310	82.21 277	97.77 203	96.17 206	86.84 182	82.41 228	91.95 233	72.07 237	95.99 267	89.83 128	84.50 222	91.32 252

1314		93.44 102	91.98 122	97.84 22	95.24 174	94.38 43	96.22 260	97.92 56	90.18 93	82.28 229	97.71 107	77.63 182	99.80 55	91.94 113	98.67 79	99.34 75

v1921920		86.02 238	84.44 244	90.77 233	89.32 294	85.20 242	98.10 188	95.35 264	82.19 263	82.25 230	90.71 254	70.73 248	96.30 257	76.85 256	74.49 275	90.80 270

v1240		85.77 245	84.11 247	90.73 234	89.26 295	85.15 245	97.88 199	95.23 271	81.89 269	82.16 231	90.55 270	69.60 257	96.31 254	75.59 273	74.87 271	90.72 275

XVG-ACMP-BASELINE		85.86 241	84.95 235	88.57 278	89.90 274	77.12 313	94.30 294	95.60 247	87.40 170	82.12 232	92.99 221	53.42 328	97.66 179	85.02 178	83.83 227	90.92 267

GBi-Net		86.67 228	84.96 233	91.80 210	95.11 184	88.81 155	96.77 237	95.25 267	82.94 253	82.12 232	90.25 277	62.89 297	94.97 292	79.04 236	80.24 245	91.62 242

test1		86.67 228	84.96 233	91.80 210	95.11 184	88.81 155	96.77 237	95.25 267	82.94 253	82.12 232	90.25 277	62.89 297	94.97 292	79.04 236	80.24 245	91.62 242

FMVSNet3		88.81 196	87.08 202	93.99 171	96.52 142	94.59 38	98.08 190	96.20 203	85.85 194	82.12 232	91.60 237	74.05 215	95.40 285	79.04 236	80.24 245	91.99 235

IterMVS-LS		88.34 202	87.44 195	91.04 227	94.10 203	85.85 233	98.10 188	95.48 254	85.12 205	82.03 236	91.21 240	81.35 160	95.63 279	83.86 192	75.73 265	91.63 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		84.48 260	81.83 269	92.42 200	91.73 248	87.36 188	85.52 335	94.42 287	81.40 272	81.91 237	87.58 304	51.92 331	92.81 316	73.84 289	88.15 200	97.08 181

PS-MVSNAJss		89.54 182	89.05 171	91.00 228	88.77 301	84.36 254	97.39 212	95.97 214	88.47 135	81.88 238	93.80 201	82.48 148	96.50 231	89.34 137	83.34 232	92.15 229

WR-MVS		88.54 201	87.22 200	92.52 199	91.93 245	89.50 146	98.56 134	97.84 62	86.99 177	81.87 239	93.81 200	74.25 212	95.92 273	85.29 175	74.43 276	92.12 230

TranMVSNet+NR-MVSNet		87.75 206	86.31 209	92.07 205	90.81 258	88.56 161	98.33 161	97.18 143	87.76 160	81.87 239	93.90 198	72.45 232	95.43 283	83.13 197	71.30 310	92.23 225

DP-MVS		88.75 198	86.56 206	95.34 130	98.92 64	87.45 182	97.64 208	93.52 301	70.55 325	81.49 241	97.25 125	74.43 207	99.88 35	71.14 307	94.09 137	98.67 127

3Dnovator+	87.72 8	93.43 103	91.84 124	98.17 13	95.73 164	95.08 20	98.92 90	97.04 158	91.42 72	81.48 242	97.60 110	74.60 200	99.79 56	90.84 121	98.97 65	99.64 53

QAPM		91.41 152	89.49 164	97.17 48	95.66 167	93.42 58	98.60 130	97.51 112	80.92 278	81.39 243	97.41 117	72.89 230	99.87 38	82.33 205	98.68 78	98.21 151

XXY-MVS		87.75 206	86.02 212	92.95 191	90.46 263	89.70 142	97.71 205	95.90 225	84.02 223	80.95 244	94.05 190	67.51 273	97.10 208	85.16 176	78.41 254	92.04 234

v148		86.38 234	85.06 232	90.37 242	89.47 293	84.10 257	98.52 138	95.48 254	83.80 232	80.93 245	90.22 280	74.60 200	96.31 254	80.92 220	71.55 308	90.69 277

FMVSNet2		86.90 224	84.79 239	93.24 184	95.11 184	92.54 78	97.67 206	95.86 229	82.94 253	80.55 246	91.17 241	62.89 297	95.29 287	77.23 250	79.71 251	91.90 236

pmmvs4		87.58 211	86.17 211	91.80 210	89.58 288	88.92 153	97.25 219	95.28 266	82.54 259	80.49 247	93.17 217	75.62 191	96.05 266	82.75 201	78.90 252	90.42 282

ACMP	87.39 10	88.71 199	88.24 188	90.12 246	93.91 212	81.06 290	98.50 142	95.67 238	89.43 109	80.37 248	95.55 174	65.67 285	97.83 165	90.55 124	84.51 221	91.47 247
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		85.87 240	84.40 246	90.30 243	88.53 305	84.23 255	98.60 130	93.71 298	81.53 271	80.29 249	92.02 229	64.51 290	95.52 281	82.04 209	78.34 255	91.15 256

test0.0.03 1		88.96 189	88.61 180	90.03 250	91.09 255	84.43 253	98.97 86	97.02 161	90.21 91	80.29 249	96.31 166	84.89 114	91.93 333	72.98 299	85.70 215	93.73 208

jajsoiax		87.35 212	86.51 207	89.87 251	87.75 315	81.74 280	97.03 230	95.98 213	88.47 135	80.15 251	93.80 201	61.47 302	96.36 243	89.44 135	84.47 223	91.50 246

mvs_tets		87.09 221	86.22 210	89.71 256	87.87 311	81.39 284	96.73 241	95.90 225	88.19 149	79.99 252	93.61 207	59.96 308	96.31 254	89.40 136	84.34 224	91.43 250

ITE_SJBPF				87.93 292	92.26 238	76.44 314		93.47 302	87.67 166	79.95 253	95.49 177	56.50 316	97.38 199	75.24 275	82.33 240	89.98 294

v8		86.11 237	84.45 243	91.10 226	89.99 270	86.85 198	97.24 220	95.36 262	81.99 266	79.89 254	89.86 286	74.53 204	96.39 241	78.83 240	72.32 300	90.05 291

v10		85.73 246	84.01 249	90.87 232	90.03 267	86.73 202	97.20 223	95.22 272	81.25 274	79.85 255	89.75 287	73.30 226	96.28 258	76.87 254	72.64 296	89.61 300

WR-MVS_H		86.53 232	85.49 227	89.66 259	91.04 256	83.31 266	97.53 211	98.20 33	84.95 211	79.64 256	90.90 249	78.01 180	95.33 286	76.29 261	72.81 294	90.35 283

anonymousdsp		86.69 227	85.75 221	89.53 261	86.46 324	82.94 269	96.39 251	95.71 235	83.97 225	79.63 257	90.70 255	68.85 262	95.94 270	86.01 168	84.02 226	89.72 298

Patchmtry		83.61 273	81.64 272	89.50 262	93.36 226	82.84 274	84.10 342	94.20 292	69.47 331	79.57 258	86.88 312	84.43 118	94.78 299	68.48 313	74.30 281	90.88 268

CP-MVSNet		86.54 231	85.45 228	89.79 255	91.02 257	82.78 275	97.38 214	97.56 105	85.37 202	79.53 259	93.03 219	71.86 240	95.25 288	79.92 229	73.43 292	91.34 251

Patchmatch-test		86.25 236	84.06 248	92.82 192	94.42 198	82.88 273	82.88 347	94.23 291	71.58 321	79.39 260	90.62 265	89.00 50	96.42 238	63.03 325	91.37 170	99.16 88

DSMNet-mixed		81.60 284	81.43 275	82.10 320	84.36 329	60.79 341	93.63 303	86.74 350	79.00 288	79.32 261	87.15 310	63.87 293	89.78 337	66.89 317	91.92 160	95.73 202

MSDG		88.29 204	86.37 208	94.04 169	96.90 126	86.15 222	96.52 248	94.36 289	77.89 306	79.22 262	96.95 143	69.72 255	99.59 78	73.20 296	92.58 150	96.37 200

Anonymous20231211		84.72 254	82.65 264	90.91 230	97.71 94	84.55 252	97.28 217	96.67 172	66.88 337	79.18 263	90.87 250	58.47 310	96.60 221	82.61 203	74.20 283	91.59 245

PS-CasMVS		85.81 243	84.58 242	89.49 264	90.77 259	82.11 278	97.20 223	97.36 132	84.83 213	79.12 264	92.84 222	67.42 274	95.16 290	78.39 243	73.25 293	91.21 255

IterMVS		85.81 243	84.67 241	89.22 267	93.51 221	83.67 263	96.32 254	94.80 275	85.09 207	78.69 265	90.17 284	66.57 281	93.17 309	79.48 233	77.42 260	90.81 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		85.21 251	83.93 250	89.07 271	89.89 275	81.31 286	97.09 228	97.24 138	84.45 218	78.66 266	92.68 224	68.44 265	94.87 295	75.98 263	70.92 311	91.04 264

semantic-postprocess				89.00 272	93.46 224	82.90 271		94.70 278	85.02 209	78.62 267	90.35 273	66.63 279	93.33 308	79.38 235	77.36 261	90.76 273

OpenMVS	85.28 14	90.75 164	88.84 175	96.48 90	93.58 220	93.51 56	98.80 105	97.41 127	82.59 257	78.62 267	97.49 114	68.00 269	99.82 51	84.52 183	98.55 82	96.11 201

PVSNet_0	83.28 16	87.31 213	85.16 231	93.74 179	94.78 194	84.59 251	98.91 91	98.69 23	89.81 101	78.59 269	93.23 215	61.95 301	99.34 106	94.75 76	55.72 345	97.30 174

v52		84.19 265	82.92 256	88.01 290	87.64 317	79.92 295	96.23 258	95.32 265	79.87 284	78.51 270	89.05 294	69.50 259	96.32 252	77.95 246	72.24 303	87.79 314

V4		84.20 264	82.92 256	88.02 289	87.59 318	79.91 296	96.21 263	95.36 262	79.88 283	78.51 270	89.00 295	69.52 258	96.32 252	77.96 245	72.29 301	87.83 313

EU-MVSNet		84.19 265	84.42 245	83.52 317	88.64 304	67.37 337	96.04 268	95.76 231	85.29 203	78.44 272	93.18 216	70.67 249	91.48 336	75.79 271	75.98 263	91.70 239

v7n		84.42 262	82.75 262	89.43 265	88.15 308	81.86 279	96.75 240	95.67 238	80.53 279	78.38 273	89.43 291	69.89 252	96.35 249	73.83 290	72.13 304	90.07 289

FMVSNet1		83.94 269	81.32 277	91.80 210	91.94 244	88.81 155	96.77 237	95.25 267	77.98 301	78.25 274	90.25 277	50.37 335	94.97 292	73.27 295	77.81 258	91.62 242

MS-PatchMatch		86.75 226	85.92 214	89.22 267	91.97 242	82.47 276	96.91 233	96.14 209	83.74 233	77.73 275	93.53 210	58.19 311	97.37 201	76.75 257	98.35 84	87.84 311

v748		83.84 270	82.31 268	88.41 283	87.65 316	79.10 300	96.66 243	95.51 251	80.09 282	77.65 276	88.53 299	69.81 253	96.23 259	75.67 272	69.25 313	89.91 295

DTE-MVSNet		84.14 267	82.80 259	88.14 288	88.95 299	79.87 297	96.81 236	96.24 200	83.50 244	77.60 277	92.52 226	67.89 271	94.24 304	72.64 302	69.05 315	90.32 284

COLMAP_ROB	82.69 18	84.54 259	82.82 258	89.70 257	96.72 138	78.85 302	95.89 272	92.83 316	71.55 322	77.54 278	95.89 171	59.40 309	99.14 115	67.26 315	88.26 199	91.11 257
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testpf		80.59 294	80.13 281	81.97 322	94.25 202	71.65 329	60.37 357	95.46 256	70.99 323	76.97 279	87.74 302	73.58 220	91.67 334	76.86 255	84.97 218	82.60 344

Anonymous20240521		85.45 248	83.91 251	90.05 249	90.73 261	83.74 262	97.13 226	96.15 208	82.08 265	76.93 280	90.84 251	71.53 246	96.36 243	75.26 274	74.57 274	90.04 293

OurMVSNet-221017-0		84.13 268	83.59 252	85.77 308	87.81 312	70.24 332	94.89 289	93.65 300	86.08 193	76.53 281	93.28 214	61.41 303	96.14 263	80.95 219	77.69 259	90.93 266

tfpnnormal		83.65 271	81.35 276	90.56 237	91.37 253	88.06 169	97.29 216	97.87 60	78.51 295	76.20 282	90.91 248	64.78 289	96.47 235	61.71 328	73.50 290	87.13 321

ppachtmachnet_test		83.63 272	81.57 274	89.80 254	89.01 297	85.09 246	97.13 226	94.50 283	78.84 290	76.14 283	91.00 243	69.78 254	94.61 301	63.40 324	74.36 277	89.71 299

pm-mvs1		84.68 255	82.78 261	90.40 241	89.58 288	85.18 243	97.31 215	94.73 277	81.93 268	76.05 284	92.01 230	65.48 287	96.11 264	78.75 241	69.14 314	89.91 295

AllTest		84.97 252	83.12 254	90.52 238	96.82 134	78.84 303	95.89 272	92.17 323	77.96 303	75.94 285	95.50 175	55.48 319	99.18 110	71.15 305	87.14 203	93.55 210

TestCases				90.52 238	96.82 134	78.84 303		92.17 323	77.96 303	75.94 285	95.50 175	55.48 319	99.18 110	71.15 305	87.14 203	93.55 210

testgi		82.29 275	81.00 279	86.17 305	87.24 320	74.84 318	97.39 212	91.62 331	88.63 131	75.85 287	95.42 178	46.07 340	91.55 335	66.87 318	79.94 248	92.12 230

MVP-Stereo		86.61 230	85.83 218	88.93 273	88.70 303	83.85 261	96.07 267	94.41 288	82.15 264	75.64 288	91.96 232	67.65 272	96.45 237	77.20 252	98.72 76	86.51 324
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LF4IMVS		81.94 279	81.17 278	84.25 315	87.23 321	68.87 336	93.35 305	91.93 328	83.35 246	75.40 289	93.00 220	49.25 337	96.65 220	78.88 239	78.11 256	87.22 320

our_test_3		84.47 261	82.80 259	89.50 262	89.01 297	83.90 260	97.03 230	94.56 282	81.33 273	75.36 290	90.52 271	71.69 242	94.54 302	68.81 311	76.84 262	90.07 289

LTVRE_ROB	81.71 19	84.59 258	82.72 263	90.18 244	92.89 233	83.18 267	93.15 306	94.74 276	78.99 289	75.14 291	92.69 223	65.64 286	97.63 181	69.46 309	81.82 242	89.74 297
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		80.96 291	81.77 271	78.52 327	81.02 336	62.33 339	98.22 176	94.49 284	79.38 287	74.56 292	90.34 274	70.65 251	85.10 346	60.83 329	86.42 206	88.14 308

Anonymous20231206		80.76 293	79.42 289	84.79 313	84.78 327	72.98 324	96.53 247	92.97 308	79.56 286	74.33 293	88.83 296	61.27 304	92.15 330	60.59 331	75.92 264	89.24 304

FMVSNet5		82.29 275	80.54 280	87.52 296	93.79 217	84.01 258	93.73 301	92.47 320	76.92 308	74.27 294	86.15 316	63.69 294	89.24 338	69.07 310	74.79 272	89.29 303

MVS-HIRNet		79.01 301	75.13 308	90.66 235	93.82 216	81.69 281	85.16 336	93.75 297	54.54 348	74.17 295	59.15 351	57.46 313	96.58 222	63.74 323	94.38 134	93.72 209

ACMH+	83.78 15	84.21 263	82.56 267	89.15 269	93.73 218	79.16 298	96.43 250	94.28 290	81.09 275	74.00 296	94.03 193	54.58 324	97.67 178	76.10 262	78.81 253	90.63 279

NR-MVSNet		87.74 208	86.00 213	92.96 190	91.46 251	90.68 121	96.65 244	97.42 126	88.02 152	73.42 297	93.68 204	77.31 183	95.83 274	84.26 184	71.82 307	92.36 219

USDC		84.74 253	82.93 255	90.16 245	91.73 248	83.54 264	95.00 288	93.30 303	88.77 130	73.19 298	93.30 213	53.62 327	97.65 180	75.88 264	81.54 243	89.30 302

LCM-MVSNet-Re		88.59 200	88.61 180	88.51 280	95.53 169	72.68 326	96.85 235	88.43 348	88.45 138	73.14 299	90.63 264	75.82 189	94.38 303	92.95 102	95.71 125	98.48 137

TDRefinement		78.01 306	75.31 307	86.10 306	70.06 349	73.84 321	93.59 304	91.58 332	74.51 316	73.08 300	91.04 242	49.63 336	97.12 205	74.88 278	59.47 339	87.33 317

TransMVSNet (Re)		81.97 278	79.61 287	89.08 270	89.70 283	84.01 258	97.26 218	91.85 329	78.84 290	73.07 301	91.62 236	67.17 276	95.21 289	67.50 314	59.46 340	88.02 310

SixPastTwentyTwo		82.63 274	81.58 273	85.79 307	88.12 309	71.01 331	95.17 287	92.54 319	84.33 220	72.93 302	92.08 227	60.41 307	95.61 280	74.47 281	74.15 284	90.75 274

testus		77.11 310	76.95 303	77.58 328	80.02 339	58.93 344	97.78 201	90.48 339	79.68 285	72.84 303	90.61 267	37.72 350	86.57 345	60.28 333	83.18 233	87.23 319

pmmvs6		79.90 298	77.31 299	87.67 295	84.17 330	78.13 309	95.86 276	93.68 299	67.94 335	72.67 304	89.62 289	50.98 334	95.75 276	74.80 280	66.04 321	89.14 305

ACMH	83.09 17	84.60 257	82.61 265	90.57 236	93.18 230	82.94 269	96.27 255	94.92 274	81.01 276	72.61 305	93.61 207	56.54 315	97.79 168	74.31 282	81.07 244	90.99 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LP		77.80 309	74.39 311	88.01 290	91.93 245	79.02 301	80.88 349	92.90 313	65.43 340	72.00 306	81.29 331	65.78 284	92.73 322	43.76 349	75.58 266	92.27 222

Patchmatch-RL test		81.90 280	80.13 281	87.23 299	80.71 337	70.12 334	84.07 343	88.19 349	83.16 249	70.57 307	82.18 320	87.18 82	92.59 325	82.28 206	62.78 326	98.98 101

DI_MVS_plusplus_test		89.41 184	87.24 199	95.92 113	89.06 296	90.75 120	98.18 181	96.63 173	89.29 112	70.54 308	90.31 275	63.50 295	98.40 141	92.25 110	95.44 127	98.60 129

test_0402		78.81 303	76.33 305	86.26 304	91.18 254	78.44 307	95.88 274	91.34 334	68.55 332	70.51 309	89.91 285	52.65 330	94.99 291	47.14 345	79.78 250	85.34 338

test_normal		89.37 185	87.18 201	95.93 112	88.94 300	90.83 116	98.24 174	96.62 174	89.31 110	70.38 310	90.20 282	63.50 295	98.37 142	92.06 112	95.41 128	98.59 132

TinyColmap		80.42 296	77.94 296	87.85 293	92.09 241	78.58 305	93.74 300	89.94 343	74.99 313	69.77 311	91.78 234	46.09 339	97.58 184	65.17 322	77.89 257	87.38 316

test20.03		78.51 305	77.48 298	81.62 323	83.07 333	71.03 330	96.11 266	92.83 316	81.66 270	69.31 312	89.68 288	57.53 312	87.29 342	58.65 336	68.47 316	86.53 323

N_pmnet		70.19 319	69.87 318	71.12 333	88.24 307	30.63 364	95.85 277	28.70 365	70.18 328	68.73 313	86.55 314	64.04 292	93.81 305	53.12 341	73.46 291	88.94 306

v18		82.00 277	79.76 285	88.72 275	90.03 267	86.81 201	96.17 265	93.12 304	78.70 292	68.39 314	82.10 321	74.64 196	93.00 310	74.21 283	60.45 333	86.35 325

OpenMVS_ROB	73.86 20	77.99 307	75.06 309	86.77 302	83.81 332	77.94 311	96.38 252	91.53 333	67.54 336	68.38 315	87.13 311	43.94 341	96.08 265	55.03 339	81.83 241	86.29 328

v17		81.87 282	79.61 287	88.64 277	89.91 272	86.64 206	96.01 269	93.08 305	78.54 293	68.27 316	81.96 323	74.44 206	92.95 312	74.03 286	60.22 335	86.34 326

v16		81.90 280	79.65 286	88.65 276	90.02 269	86.66 205	96.01 269	93.07 306	78.53 294	68.27 316	82.05 322	74.39 208	92.96 311	74.02 287	60.48 332	86.33 327

ambc				79.60 326	72.76 347	56.61 348	76.20 351	92.01 327		68.25 318	80.23 337	23.34 354	94.73 300	73.78 291	60.81 331	87.48 315

PM-MVS		74.88 313	72.85 314	80.98 325	78.98 341	64.75 338	90.81 324	85.77 352	80.95 277	68.23 319	82.81 318	29.08 353	92.84 314	76.54 260	62.46 328	85.36 337

v15		81.62 283	79.32 290	88.52 279	89.80 279	86.56 207	95.83 278	92.96 309	78.50 296	67.88 320	81.68 325	74.22 213	92.82 315	73.46 293	59.55 336	86.18 330

V14		81.55 285	79.26 291	88.42 282	89.80 279	86.33 215	95.72 281	92.96 309	78.35 297	67.82 321	81.70 324	74.13 214	92.78 319	73.32 294	59.50 338	86.16 332

v11		81.38 287	79.03 294	88.41 283	89.68 284	86.43 209	95.74 280	92.82 318	78.03 300	67.74 322	81.45 329	73.33 225	92.69 323	72.23 304	60.27 334	86.11 334

pmmvs3		72.86 316	69.76 319	82.17 319	73.86 344	74.19 320	94.20 295	89.01 346	64.23 343	67.72 323	80.91 333	41.48 344	88.65 340	62.40 326	54.02 347	83.68 341

lessismore_v0				85.08 310	85.59 325	69.28 335		90.56 338		67.68 324	90.21 281	54.21 326	95.46 282	73.88 288	62.64 327	90.50 281

V9		81.46 286	79.15 292	88.39 285	89.75 281	86.17 221	95.62 282	92.92 311	78.22 298	67.65 325	81.64 326	73.95 217	92.80 317	73.15 297	59.43 341	86.21 329

v12		81.37 288	79.05 293	88.33 286	89.68 284	86.05 227	95.48 284	92.92 311	78.08 299	67.55 326	81.58 327	73.75 218	92.75 320	73.05 298	59.37 342	86.18 330

K. test v3		81.04 290	79.77 284	84.83 312	87.41 319	70.23 333	95.60 283	93.93 295	83.70 235	67.51 327	89.35 292	55.76 317	93.58 307	76.67 258	68.03 318	90.67 278

MIMVSNet1		75.92 312	73.30 313	83.81 316	81.29 335	75.57 316	92.26 314	92.05 326	73.09 320	67.48 328	86.18 315	40.87 346	87.64 341	55.78 338	70.68 312	88.21 307

v13		81.30 289	78.99 295	88.25 287	89.61 286	85.87 231	95.39 285	92.90 313	77.93 305	67.45 329	81.52 328	73.66 219	92.75 320	72.91 300	59.53 337	86.14 333

pmmvs-eth3d		78.71 304	76.16 306	86.38 303	80.25 338	81.19 288	94.17 296	92.13 325	77.97 302	66.90 330	82.31 319	55.76 317	92.56 326	73.63 292	62.31 329	85.38 336

EG-PatchMatch MVS		79.92 297	77.59 297	86.90 301	87.06 322	77.90 312	96.20 264	94.06 294	74.61 315	66.53 331	88.76 297	40.40 348	96.20 260	67.02 316	83.66 230	86.61 322

UnsupCasMVSNet_eth		78.90 302	76.67 304	85.58 309	82.81 334	74.94 317	91.98 315	96.31 193	84.64 215	65.84 332	87.71 303	51.33 332	92.23 329	72.89 301	56.50 344	89.56 301

Test4		85.71 247	82.59 266	95.07 140	84.45 328	89.84 140	97.20 223	95.73 233	89.19 114	64.59 333	87.58 304	40.59 347	96.77 218	88.95 145	95.01 131	98.60 129

new-patchmatchnet		74.80 314	72.40 315	81.99 321	78.36 343	72.20 327	94.44 291	92.36 321	77.06 307	63.47 334	79.98 338	51.04 333	88.85 339	60.53 332	54.35 346	84.92 339

new_pmnet		76.02 311	73.71 312	82.95 318	83.88 331	72.85 325	91.26 321	92.26 322	70.44 326	62.60 335	81.37 330	47.64 338	92.32 328	61.85 327	72.10 305	83.68 341

UnsupCasMVSNet_bld		73.85 315	70.14 317	84.99 311	79.44 340	75.73 315	88.53 330	95.24 270	70.12 329	61.94 336	74.81 343	41.41 345	93.62 306	68.65 312	51.13 350	85.62 335

CMPMVS	58.40 21	80.48 295	80.11 283	81.59 324	85.10 326	59.56 342	94.14 297	95.95 217	68.54 333	60.71 337	93.31 212	55.35 321	97.87 163	83.06 198	84.85 220	87.33 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		72.28 317	71.36 316	75.02 331	73.04 345	57.38 346	92.30 312	90.22 341	62.27 344	59.46 338	80.36 335	76.23 187	87.07 343	44.29 347	64.08 325	80.59 345

.test1245		61.50 322	64.44 322	52.65 346	73.04 345	57.38 346	92.30 312	90.22 341	62.27 344	59.46 338	80.36 335	76.23 187	87.07 343	44.29 347	1.80 361	13.50 361

DeepMVS_CX				76.08 329	90.74 260	51.65 352		90.84 336	86.47 190	57.89 340	87.98 300	35.88 351	92.60 324	65.77 321	65.06 323	83.97 340

test1235678		71.07 318	69.53 320	75.71 330	71.87 348	55.27 350	94.32 292	90.76 337	70.23 327	57.61 341	79.06 340	43.13 342	83.72 348	50.48 342	68.30 317	88.14 308

testing_2		80.92 292	77.24 300	91.98 206	78.88 342	87.83 173	93.96 299	95.72 234	84.27 221	56.20 342	80.42 334	38.64 349	96.40 240	87.20 157	79.85 249	91.72 238

test12356		66.36 320	65.12 321	70.08 336	66.92 350	50.46 353	89.96 328	88.58 347	66.00 339	53.38 343	78.13 342	32.89 352	82.87 349	48.36 344	61.87 330	76.92 346

YYNet1		79.64 300	77.04 302	87.43 298	87.80 313	79.98 294	96.23 258	94.44 285	73.83 319	51.83 344	87.53 306	67.96 270	92.07 332	66.00 320	67.75 320	90.23 286

MDA-MVSNet_test_wron		79.65 299	77.05 301	87.45 297	87.79 314	80.13 293	96.25 257	94.44 285	73.87 318	51.80 345	87.47 307	68.04 268	92.12 331	66.02 319	67.79 319	90.09 287

LCM-MVSNet		60.07 324	56.37 325	71.18 332	54.81 359	48.67 354	82.17 348	89.48 345	37.95 351	49.13 346	69.12 344	13.75 363	81.76 350	59.28 334	51.63 349	83.10 343

MDA-MVSNet-bldmvs		77.82 308	74.75 310	87.03 300	88.33 306	78.52 306	96.34 253	92.85 315	75.57 312	48.87 347	87.89 301	57.32 314	92.49 327	60.79 330	64.80 324	90.08 288

PMMVS2		58.97 325	55.07 326	70.69 335	62.72 351	55.37 349	85.97 334	80.52 356	49.48 349	45.94 348	68.31 346	15.73 361	80.78 352	49.79 343	37.12 351	75.91 348

testmv		60.41 323	57.98 324	67.69 337	58.16 358	47.14 355	89.09 329	86.74 350	61.52 347	44.30 349	68.44 345	20.98 355	79.92 354	40.94 351	51.67 348	76.01 347

FPMVS		61.57 321	60.32 323	65.34 338	60.14 355	42.44 358	91.02 323	89.72 344	44.15 350	42.63 350	80.93 332	19.02 356	80.59 353	42.50 350	72.76 295	73.00 349

Gipuma		54.77 327	52.22 328	62.40 340	86.50 323	59.37 343	50.20 358	90.35 340	36.52 353	41.20 351	49.49 355	18.33 358	81.29 351	32.10 355	65.34 322	46.54 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 328	52.86 327	56.05 343	32.75 364	41.97 360	73.42 353	76.12 359	21.91 359	39.68 352	96.39 164	42.59 343	65.10 359	78.00 244	14.92 359	61.08 354

no-one		56.69 326	51.89 329	71.08 334	59.35 357	58.65 345	83.78 346	84.81 355	61.73 346	36.46 353	56.52 353	18.15 359	84.78 347	47.03 346	19.19 355	69.81 351

E-PMN		41.02 334	40.93 333	41.29 347	61.97 352	33.83 361	84.00 344	65.17 363	27.17 356	27.56 354	46.72 357	17.63 360	60.41 361	19.32 358	18.82 356	29.61 358

PNet_i23d		48.05 330	44.98 331	57.28 342	60.15 353	42.39 359	80.85 350	73.14 361	36.78 352	27.46 355	56.66 352	6.38 364	68.34 357	36.65 353	26.72 353	61.10 353

ANet_high		50.71 329	46.17 330	64.33 339	44.27 362	52.30 351	76.13 352	78.73 357	64.95 341	27.37 356	55.23 354	14.61 362	67.74 358	36.01 354	18.23 357	72.95 350

EMVS		39.96 335	39.88 334	40.18 348	59.57 356	32.12 363	84.79 341	64.57 364	26.27 357	26.14 357	44.18 360	18.73 357	59.29 362	17.03 359	17.67 358	29.12 359

MVE	44.00 22	41.70 333	37.64 336	53.90 345	49.46 360	43.37 357	65.09 356	66.66 362	26.19 358	25.77 358	48.53 356	3.58 368	63.35 360	26.15 357	27.28 352	54.97 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	41.42 23	45.67 331	42.50 332	55.17 344	34.28 363	32.37 362	66.24 355	78.71 358	30.72 355	22.04 359	59.59 350	4.59 365	77.85 355	27.49 356	58.84 343	55.29 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		18.81 338	23.05 339	6.10 352	4.48 365	2.29 367	97.78 201	3.00 367	3.27 361	18.60 360	62.71 348	1.53 370	2.49 365	14.26 361	1.80 361	13.50 361

wuykxyi23d		43.53 332	37.95 335	60.27 341	45.36 361	44.79 356	68.27 354	74.26 360	33.48 354	18.21 361	40.16 362	3.64 366	71.01 356	38.85 352	19.31 354	65.02 352

test123		16.58 340	19.47 340	7.91 351	3.59 366	5.37 366	94.32 292	1.39 368	2.49 362	13.98 362	44.60 359	2.91 369	2.65 364	11.35 362	0.57 363	15.70 360

wuyk23d		16.71 339	16.73 341	16.65 350	60.15 353	25.22 365	41.24 359	5.17 366	6.56 360	5.48 363	3.61 364	3.64 366	22.72 363	15.20 360	9.52 360	1.99 363

cdsmvs_eth3d_5k		22.52 337	30.03 338	0.00 353	0.00 367	0.00 368	0.00 360	97.17 144	0.00 363	0.00 364	98.77 65	74.35 209	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		6.87 342	9.16 343	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	82.48 148	0.00 366	0.00 363	0.00 364	0.00 364

pcd1.5k->3k		35.91 336	37.64 336	30.74 349	89.49 291	0.00 368	0.00 360	96.36 192	0.00 363	0.00 364	0.00 365	69.17 260	0.00 366	0.00 363	83.71 229	92.21 227

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

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

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

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

ab-mvs-re		8.21 341	10.94 342	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	98.50 84	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

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

GSMVS																98.84 113

test_part3							99.43 33		92.81 44		99.48 4		99.97 14	99.52 1

test_part1								97.69 80				93.96 6			99.83 12	99.90 9

sam_mvs1												88.39 59				98.84 113

sam_mvs												87.08 83

MTGPA								97.45 120

test_post1							90.74 326				41.37 361	85.38 111	96.36 243	83.16 196

test_post											46.00 358	87.37 76	97.11 206

patchmatchnet-post											84.86 317	88.73 53	96.81 217

MTMP							99.21 53	91.09 335

gm-plane-assit					94.69 196	88.14 167			88.22 148		97.20 129		98.29 144	90.79 122

test9_res														98.60 11	99.87 5	99.90 9

agg_prior2														97.84 28	99.87 5	99.91 8

test_prior4						92.00 82	99.41 38

test_prior				97.01 52	99.58 19	91.77 83		97.57 103					99.49 89			99.79 26

新几何2							98.26 172

旧先验1					98.97 60	92.90 70		97.74 76			99.15 27	91.05 20			99.33 53	99.60 59

无先验							98.52 138	97.82 64	87.20 176				99.90 31	87.64 154		99.85 21

原ACMM2							98.69 115

testdata2													99.88 35	84.16 185

segment_acmp												90.56 35

testdata1							97.89 197		92.43 50

plane_prior7					93.84 214	85.73 235

plane_prior6					93.92 211	86.02 228						72.92 228

plane_prior5								96.30 194					97.75 175	93.46 94	86.17 210	92.67 215

plane_prior4											96.52 158

plane_prior2							99.02 80		93.38 35

plane_prior1					93.90 213

plane_prior						86.07 225	99.14 67		93.81 28						86.26 209

n2								0.00 369

nn								0.00 369

door-mid								84.90 354

test11								97.68 82

door								85.30 353

HQP5-MVS						86.39 212

BP-MVS														93.82 89

HQP3-MVS								96.37 189							86.29 207

HQP2-MVS												73.34 223

NP-MVS					93.94 210	86.22 219					96.67 152

ACMMP++_ref															82.64 238

ACMMP++															83.83 227

Test By Simon												83.62 124

High-resolution multi-view benchmark