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

SMA-MVS		97.21 13	96.98 16	97.91 21	99.30 44	93.93 48	99.16 58	97.58 98	89.53 107	99.35 2	99.52 3	90.24 39	99.99 4	98.32 21	99.77 20	99.82 22

TSAR-MVS + MP.		97.44 10	97.46 9	97.39 40	99.12 53	93.49 57	98.52 136	97.50 114	94.46 17	98.99 3	98.64 76	91.58 16	99.08 115	98.49 17	99.83 12	99.60 59

PS-MVSNAJ		96.87 25	96.40 30	98.29 11	97.35 108	97.29 1	99.03 77	97.11 148	95.83 9	98.97 4	99.14 29	82.48 146	99.60 77	98.60 11	99.08 60	98.00 154

旧先验2							98.67 118		85.75 193	98.96 5			98.97 118	93.84 86

xiu_mvs_v2_base		96.66 29	96.17 38	98.11 17	97.11 116	96.96 2	99.01 80	97.04 156	95.51 13	98.86 6	99.11 35	82.19 152	99.36 100	98.59 13	98.14 86	98.00 154

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 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	97.38 129	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 92	99.54 27	90.14 128	99.41 38	97.47 117	95.46 14	98.60 9	99.19 19	95.71 4	99.49 87	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 58	97.44 122	90.08 98	98.59 10	99.07 36	89.06 48	99.42 95	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				95.26 132	98.20 81	87.28 188		97.60 95	85.21 201	98.48 11	99.15 27	88.15 63	98.72 128	90.29 123	99.45 47	99.78 30

TEST9					99.57 23	93.17 60	99.38 40	97.66 83	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 83	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 88	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 71	99.36 44	97.67 82	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 53	97.75 73	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 79	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 51	97.38 107	91.46 91	99.75 8	97.66 83	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 51	99.58 19	91.77 82	99.57 19	97.57 102	91.43 70	98.12 20	98.97 48	90.43 36	99.49 87	98.33 19	99.81 15	99.79 26

test_prior2							99.57 19		91.43 70	98.12 20	98.97 48	90.43 36		98.33 19	99.81 15

PHI-MVS		96.65 30	96.46 29	97.21 45	99.34 40	91.77 82	99.70 10	98.05 47	86.48 186	98.05 22	99.20 18	89.33 46	99.96 18	98.38 18	99.62 35	99.90 9

MVSFormer		94.71 74	94.08 74	96.61 85	95.05 182	94.87 22	97.77 199	96.17 201	86.84 180	98.04 23	98.52 82	85.52 103	95.99 262	89.83 126	98.97 65	98.96 99

lupinMVS		96.32 41	95.94 43	97.44 36	95.05 182	94.87 22	99.86 2	96.50 180	93.82 27	98.04 23	98.77 65	85.52 103	98.09 147	96.98 38	98.97 65	99.37 71

APDe-MVS		97.53 7	97.47 8	97.70 26	99.58 19	93.63 52	99.56 21	97.52 109	93.59 32	98.01 25	99.12 32	90.80 32	99.55 79	99.26 4	99.79 17	99.93 7

ACMMP_Plus		96.59 31	96.18 36	97.81 24	98.82 69	93.55 54	98.88 97	97.59 96	90.66 80	97.98 26	99.14 29	86.59 90	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 72	99.35 46	97.64 88	90.38 88	97.98 26	99.17 23	90.84 31	99.61 75	98.57 16	99.78 19	99.87 19

agg_prior					99.54 27	92.66 72		97.64 88		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 69	97.44 122	89.02 120	97.90 29	99.22 16	88.90 51	99.49 87	94.63 78	99.79 17	99.68 48

EPNet		96.82 26	96.68 26	97.25 44	98.65 73	93.10 63	99.48 26	98.76 18	96.54 4	97.84 30	98.22 94	87.49 72	99.66 66	95.35 65	97.78 92	99.00 94
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 45	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		97.55 105		97.56 32		88.60 54	99.50 86		99.71 27	99.55 61

MVS_0304		96.12 46	95.26 56	98.69 4	98.44 78	96.54 7	99.70 10	96.89 165	95.76 10	97.53 33	99.12 32	72.42 231	99.93 25	98.75 8	98.69 77	99.61 58

xiu_mvs_v1_base_debu		94.73 71	93.98 76	96.99 54	95.19 172	95.24 17	98.62 124	96.50 180	92.99 37	97.52 34	98.83 62	72.37 232	99.15 109	97.03 34	96.74 104	96.58 192

xiu_mvs_v1_base		94.73 71	93.98 76	96.99 54	95.19 172	95.24 17	98.62 124	96.50 180	92.99 37	97.52 34	98.83 62	72.37 232	99.15 109	97.03 34	96.74 104	96.58 192

xiu_mvs_v1_base_debi		94.73 71	93.98 76	96.99 54	95.19 172	95.24 17	98.62 124	96.50 180	92.99 37	97.52 34	98.83 62	72.37 232	99.15 109	97.03 34	96.74 104	96.58 192

DeepPCF-MVS	93.56 1	96.55 33	97.84 5	92.68 193	98.71 72	78.11 305	99.70 10	97.71 78	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	97.52 109	95.90 8	97.21 38	98.90 58	82.66 143	99.93 25	98.71 9	98.80 74	99.63 55

CANet_DTU		94.31 83	93.35 88	97.20 46	97.03 119	94.71 32	98.62 124	95.54 244	95.61 12	97.21 38	98.47 88	71.88 237	99.84 46	88.38 144	97.46 98	97.04 178

VNet		95.08 65	94.26 69	97.55 33	98.07 85	93.88 50	98.68 116	98.73 21	90.33 90	97.16 40	97.43 115	79.19 168	99.53 81	96.91 40	91.85 159	99.24 83

region2R		96.30 42	96.17 38	96.70 77	99.70 7	90.31 125	99.46 30	97.66 83	90.55 84	97.07 41	99.07 36	86.85 87	99.97 14	95.43 63	99.74 21	99.81 23

原ACMM1				96.18 100	99.03 58	90.08 131		97.63 92	88.98 121	97.00 42	98.97 48	88.14 64	99.71 62	88.23 145	99.62 35	98.76 119

Regformer-1		96.97 21	96.80 22	97.47 34	99.46 37	93.11 62	98.89 95	97.94 53	92.89 41	96.90 43	99.02 42	89.78 42	99.53 81	97.06 33	99.26 57	99.75 36

HFP-MVS		96.42 38	96.26 35	96.90 62	99.69 8	90.96 112	99.47 27	97.81 66	90.54 85	96.88 44	99.05 39	87.57 69	99.96 18	95.65 58	99.72 23	99.78 30

#test#		96.48 35	96.34 33	96.90 62	99.69 8	90.96 112	99.53 24	97.81 66	90.94 78	96.88 44	99.05 39	87.57 69	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 67	98.89 95	97.93 54	92.86 43	96.88 44	99.02 42	89.74 43	99.53 81	97.03 34	99.26 57	99.75 36

XVS		96.47 36	96.37 31	96.77 70	99.62 15	90.66 121	99.43 33	97.58 98	92.41 54	96.86 47	98.96 51	87.37 75	99.87 38	95.65 58	99.43 48	99.78 30

X-MVStestdata		90.69 164	88.66 177	96.77 70	99.62 15	90.66 121	99.43 33	97.58 98	92.41 54	96.86 47	29.59 359	87.37 75	99.87 38	95.65 58	99.43 48	99.78 30

1121		95.19 64	94.45 66	97.42 37	98.88 66	92.58 76	96.22 254	97.75 73	85.50 197	96.86 47	99.01 46	88.59 56	99.90 31	87.64 151	99.60 38	99.79 26

TSAR-MVS + GP.		96.95 22	96.91 18	97.07 48	98.88 66	91.62 87	99.58 18	96.54 179	95.09 15	96.84 50	98.63 77	91.16 17	99.77 58	99.04 5	96.42 109	99.81 23

ACMMPR		96.28 43	96.14 41	96.73 74	99.68 10	90.47 123	99.47 27	97.80 68	90.54 85	96.83 51	99.03 41	86.51 93	99.95 21	95.65 58	99.72 23	99.75 36

PMMVS		93.62 98	93.90 83	92.79 189	96.79 132	81.40 278	98.85 98	96.81 166	91.25 75	96.82 52	98.15 98	77.02 183	98.13 146	93.15 99	96.30 113	98.83 111

PGM-MVS		95.85 52	95.65 51	96.45 91	99.50 35	89.77 140	98.22 173	98.90 17	89.19 114	96.74 53	98.95 53	85.91 101	99.92 27	93.94 83	99.46 45	99.66 51

jason		95.40 62	94.86 62	97.03 50	92.91 227	94.23 45	99.70 10	96.30 191	93.56 33	96.73 54	98.52 82	81.46 157	97.91 155	96.08 54	98.47 83	98.96 99
jason: jason.
新几何1				97.40 39	98.92 64	92.51 78		97.77 72	85.52 195	96.69 55	99.06 38	88.08 65	99.89 34	84.88 174	99.62 35	99.79 26

APD-MVS_3200maxsize		95.64 58	95.65 51	95.62 118	99.24 49	87.80 172	98.42 150	97.22 139	88.93 125	96.64 56	98.98 47	85.49 106	99.36 100	96.68 41	99.27 56	99.70 45

MG-MVS		97.24 12	96.83 21	98.47 9	99.79 5	95.71 12	99.07 72	99.06 15	94.45 18	96.42 57	98.70 73	88.81 52	99.74 61	95.35 65	99.86 8	99.97 3

alignmvs		95.77 56	95.00 61	98.06 18	97.35 108	95.68 13	99.71 9	97.50 114	91.50 68	96.16 58	98.61 78	86.28 97	99.00 117	96.19 51	91.74 161	99.51 64

Regformer-3		96.50 34	96.36 32	96.91 61	99.34 40	91.72 85	98.71 109	97.90 56	92.48 49	96.00 59	98.95 53	88.60 54	99.52 84	96.44 46	98.83 71	99.49 66

CP-MVS		96.22 44	96.15 40	96.42 93	99.67 11	89.62 143	99.70 10	97.61 94	90.07 99	96.00 59	99.16 25	87.43 73	99.92 27	96.03 55	99.72 23	99.70 45

Regformer-4		96.45 37	96.33 34	96.81 69	99.34 40	91.44 92	98.71 109	97.88 57	92.43 50	95.97 61	98.95 53	88.42 58	99.51 85	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 52	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 98	97.64 88	96.51 6	95.88 63	99.39 8	87.35 79	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 79	91.21 101	98.08 186	97.58 98	83.74 230	95.87 64	99.02 42	86.74 88			99.64 31	99.81 23

canonicalmvs		95.02 66	93.96 79	98.20 12	97.53 101	95.92 11	98.71 109	96.19 200	91.78 64	95.86 65	98.49 86	79.53 165	99.03 116	96.12 52	91.42 167	99.66 51

abl_6		94.63 77	94.48 65	95.09 136	98.61 75	86.96 193	98.06 188	96.97 162	89.31 110	95.86 65	98.56 80	79.82 163	99.64 72	94.53 80	98.65 80	98.66 124

Effi-MVS+		93.87 89	93.15 92	96.02 106	95.79 156	90.76 117	96.70 236	95.78 226	86.98 177	95.71 67	97.17 131	79.58 164	98.01 153	94.57 79	96.09 117	99.31 76

HPM-MVS_fast		94.89 67	94.62 64	95.70 117	99.11 54	88.44 163	99.14 66	97.11 148	85.82 192	95.69 68	98.47 88	83.46 126	99.32 104	93.16 98	99.63 34	99.35 72

HY-MVS	88.56 7	95.29 63	94.23 70	98.48 8	97.72 91	96.41 8	94.03 292	98.74 19	92.42 53	95.65 69	94.76 182	86.52 92	99.49 87	95.29 67	92.97 143	99.53 62

CHOSEN 280x420		96.80 27	96.85 19	96.66 80	97.85 88	94.42 42	94.76 284	98.36 26	92.50 48	95.62 70	97.52 111	97.92 1	97.38 195	98.31 22	98.80 74	98.20 149

MP-MVS		96.00 48	95.82 46	96.54 88	99.47 36	90.13 130	99.36 44	97.41 126	90.64 83	95.49 71	98.95 53	85.51 105	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	95.99 107	99.29 45	89.14 149	99.17 57	97.09 152	87.28 173	95.40 72	98.48 87	84.93 112	99.38 98	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		93.30 107	92.62 102	95.34 129	96.27 144	88.53 162	95.88 268	96.97 162	90.90 79	95.37 73	97.07 136	82.38 149	99.10 114	83.91 187	94.86 132	98.38 139

sss		94.85 69	93.94 81	97.58 30	96.43 140	94.09 47	98.93 86	99.16 14	89.50 108	95.27 74	97.85 100	81.50 156	99.65 70	92.79 104	94.02 137	98.99 96

WTY-MVS		95.97 49	95.11 59	98.54 7	97.62 94	96.65 4	99.44 31	98.74 19	92.25 57	95.21 75	98.46 90	86.56 91	99.46 94	95.00 71	92.69 147	99.50 65

DELS-MVS		97.12 16	96.60 27	98.68 5	98.03 86	96.57 6	99.84 3	97.84 61	96.36 7	95.20 76	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 76	98.58 76	91.00 111	99.14 66	99.45 1	93.86 26	95.15 77	98.73 69	88.48 57	99.76 59	97.23 32	99.56 41	99.40 70

MVS_Test		93.67 96	92.67 101	96.69 78	96.72 134	92.66 72	97.22 216	96.03 207	87.69 163	95.12 78	94.03 189	81.55 155	98.28 142	89.17 139	96.46 107	99.14 88

MVS_111021_LR		95.78 55	95.94 43	95.28 131	98.19 83	87.69 173	98.80 103	99.26 13	93.39 34	95.04 79	98.69 74	84.09 120	99.76 59	96.96 39	99.06 61	98.38 139

CostFormer		92.89 116	92.48 105	94.12 163	94.99 184	85.89 228	92.89 302	97.00 161	86.98 177	95.00 80	90.78 247	90.05 40	97.51 185	92.92 102	91.73 162	98.96 99

mPP-MVS		95.90 51	95.75 49	96.38 95	99.58 19	89.41 148	99.26 51	97.41 126	90.66 80	94.82 81	98.95 53	86.15 99	99.98 9	95.24 68	99.64 31	99.74 39

EI-MVSNet-Vis-set		95.76 57	95.63 53	96.17 102	99.14 52	90.33 124	98.49 142	97.82 63	91.92 61	94.75 82	98.88 60	87.06 83	99.48 92	95.40 64	97.17 102	98.70 122

LFMVS		92.23 132	90.84 146	96.42 93	98.24 80	91.08 109	98.24 171	96.22 198	83.39 242	94.74 83	98.31 92	61.12 303	98.85 119	94.45 81	92.82 144	99.32 75

tpmrst		92.78 117	92.16 112	94.65 149	96.27 144	87.45 180	91.83 311	97.10 151	89.10 119	94.68 84	90.69 252	88.22 61	97.73 173	89.78 128	91.80 160	98.77 118

DP-MVS Recon		95.85 52	95.15 58	97.95 19	99.87 2	94.38 43	99.60 17	97.48 116	86.58 184	94.42 85	99.13 31	87.36 78	99.98 9	93.64 90	98.33 85	99.48 68

zzz-MVS		96.21 45	95.96 42	96.96 59	99.29 45	91.19 102	98.69 113	97.45 119	92.58 46	94.39 86	99.24 14	86.43 95	99.99 4	96.22 49	99.40 51	99.71 43

MTAPA		96.09 47	95.80 48	96.96 59	99.29 45	91.19 102	97.23 215	97.45 119	92.58 46	94.39 86	99.24 14	86.43 95	99.99 4	96.22 49	99.40 51	99.71 43

CPTT-MVS		94.60 78	94.43 67	95.09 136	99.66 12	86.85 196	99.44 31	97.47 117	83.22 244	94.34 88	98.96 51	82.50 144	99.55 79	94.81 74	99.50 43	98.88 107

diffmvs		92.07 138	90.77 150	95.97 109	96.41 141	91.32 100	96.46 243	95.98 208	81.73 267	94.33 89	93.36 207	78.72 173	98.20 143	84.28 179	95.66 125	98.41 135

PVSNet_BlendedMVS		93.36 104	93.20 91	93.84 172	98.77 70	91.61 88	99.47 27	98.04 48	91.44 69	94.21 90	92.63 221	83.50 124	99.87 38	97.41 30	83.37 227	90.05 286

PVSNet_Blended		95.94 50	95.66 50	96.75 72	98.77 70	91.61 88	99.88 1	98.04 48	93.64 31	94.21 90	97.76 104	83.50 124	99.87 38	97.41 30	97.75 93	98.79 114

EI-MVSNet-UG-set		95.43 59	95.29 55	95.86 113	99.07 57	89.87 137	98.43 149	97.80 68	91.78 64	94.11 92	98.77 65	86.25 98	99.48 92	94.95 73	96.45 108	98.22 147

MAR-MVS		94.43 80	94.09 73	95.45 127	99.10 55	87.47 179	98.39 156	97.79 70	88.37 141	94.02 93	99.17 23	78.64 175	99.91 29	92.48 105	98.85 70	98.96 99
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	94.10 198	95.25 16	98.93 86	98.17 41	94.26 19	93.94 94	98.72 71	89.68 44	97.88 158	96.36 48	99.29 55	99.62 57

GG-mvs-BLEND				96.98 57	96.53 137	94.81 29	87.20 326	97.74 75		93.91 95	96.40 160	96.56 2	96.94 209	95.08 69	98.95 68	99.20 86

API-MVS		94.78 70	94.18 71	96.59 86	99.21 50	90.06 134	98.80 103	97.78 71	83.59 234	93.85 96	99.21 17	83.79 122	99.97 14	92.37 106	99.00 64	99.74 39

tpm2		91.77 144	91.09 134	93.82 173	94.83 188	85.56 237	92.51 305	97.16 144	84.00 221	93.83 97	90.66 257	87.54 71	97.17 200	87.73 150	91.55 165	98.72 120

PAPR		96.35 39	95.82 46	97.94 20	99.63 14	94.19 46	99.42 37	97.55 105	92.43 50	93.82 98	99.12 32	87.30 80	99.91 29	94.02 82	99.06 61	99.74 39

PVSNet	87.13 12	93.69 93	92.83 98	96.28 98	97.99 87	90.22 127	99.38 40	98.93 16	91.42 72	93.66 99	97.68 107	71.29 245	99.64 72	87.94 148	97.20 101	98.98 97

VDD-MVS		91.24 154	90.18 157	94.45 154	97.08 117	85.84 232	98.40 155	96.10 205	86.99 175	93.36 100	98.16 97	54.27 321	99.20 106	96.59 43	90.63 176	98.31 145

VDDNet		90.08 173	88.54 183	94.69 148	94.41 194	87.68 174	98.21 176	96.40 185	76.21 307	93.33 101	97.75 105	54.93 319	98.77 122	94.71 77	90.96 170	97.61 166

MP-MVS-pluss		95.80 54	95.30 54	97.29 43	98.95 63	92.66 72	98.59 130	97.14 145	88.95 123	93.12 102	99.25 12	85.62 102	99.94 23	96.56 44	99.48 44	99.28 80
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MDTV_nov1_ep13_2view						91.17 104	91.38 314		87.45 167	93.08 103		86.67 89		87.02 156		98.95 103

DWT-MVSNet_test		94.36 81	93.95 80	95.62 118	96.99 120	89.47 146	96.62 239	97.38 129	90.96 77	93.07 104	97.27 123	93.73 8	98.09 147	85.86 168	93.65 139	99.29 78

EPNet_dtu		92.28 130	92.15 113	92.70 192	97.29 110	84.84 245	98.64 122	97.82 63	92.91 40	93.02 105	97.02 138	85.48 108	95.70 272	72.25 298	94.89 131	97.55 167
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gg-mvs-nofinetune		90.00 174	87.71 190	96.89 66	96.15 149	94.69 33	85.15 332	97.74 75	68.32 331	92.97 106	60.16 345	96.10 3	96.84 211	93.89 84	98.87 69	99.14 88

114514_t		94.06 86	93.05 94	97.06 49	99.08 56	92.26 80	98.97 84	97.01 160	82.58 255	92.57 107	98.22 94	80.68 161	99.30 105	89.34 135	99.02 63	99.63 55

OMC-MVS		93.90 88	93.62 86	94.73 147	98.63 74	87.00 192	98.04 189	96.56 177	92.19 58	92.46 108	98.73 69	79.49 166	99.14 112	92.16 109	94.34 135	98.03 153

PAPM_NR		95.43 59	95.05 60	96.57 87	99.42 39	90.14 128	98.58 131	97.51 111	90.65 82	92.44 109	98.90 58	87.77 68	99.90 31	90.88 118	99.32 54	99.68 48

UGNet		91.91 143	90.85 145	95.10 135	97.06 118	88.69 158	98.01 190	98.24 30	92.41 54	92.39 110	93.61 202	60.52 304	99.68 63	88.14 146	97.25 100	96.92 184
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			90.47 156		96.14 150	88.55 160	91.34 315	97.51 111	89.58 104	92.24 111	90.50 267	86.99 86	97.61 179	77.64 243	92.34 150

PatchFormer-LS_test		94.08 85	93.60 87	95.53 125	96.92 121	89.57 144	96.51 242	97.34 133	91.29 74	92.22 112	97.18 129	91.66 15	98.02 152	87.05 155	92.21 154	99.00 94

Vis-MVSNet		92.64 125	91.85 120	95.03 141	95.12 178	88.23 164	98.48 143	96.81 166	91.61 66	92.16 113	97.22 127	71.58 242	98.00 154	85.85 169	97.81 89	98.88 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TESTMET0.1,1		93.82 90	93.26 90	95.49 126	95.21 171	90.25 126	99.15 63	97.54 108	89.18 116	91.79 114	94.87 180	89.13 47	97.63 177	86.21 162	96.29 114	98.60 125

EPMVS		92.59 127	91.59 127	95.59 120	97.22 112	90.03 135	91.78 312	98.04 48	90.42 87	91.66 115	90.65 258	86.49 94	97.46 186	81.78 209	96.31 112	99.28 80

test-LLR		93.11 114	92.68 100	94.40 155	94.94 186	87.27 189	99.15 63	97.25 135	90.21 91	91.57 116	94.04 187	84.89 113	97.58 180	85.94 165	96.13 115	98.36 142

test-mter		93.27 109	92.89 97	94.40 155	94.94 186	87.27 189	99.15 63	97.25 135	88.95 123	91.57 116	94.04 187	88.03 66	97.58 180	85.94 165	96.13 115	98.36 142

JIA-IIPM		85.97 235	84.85 233	89.33 261	93.23 224	73.68 317	85.05 333	97.13 147	69.62 327	91.56 118	68.03 343	88.03 66	96.96 207	77.89 242	93.12 141	97.34 170

tpmp4_e23		91.05 156	90.07 158	93.97 169	95.77 158	85.30 239	92.64 303	97.09 152	84.42 216	91.53 119	90.31 270	87.38 74	97.82 162	80.86 217	90.62 177	98.79 114

PVSNet_Blended_VisFu		94.67 75	94.11 72	96.34 97	97.14 115	91.10 107	99.32 50	97.43 124	92.10 60	91.53 119	96.38 163	83.29 130	99.68 63	93.42 95	96.37 110	98.25 146

CHOSEN 1792x2688		94.35 82	93.82 84	95.95 110	97.40 106	88.74 157	98.41 152	98.27 28	92.18 59	91.43 121	96.40 160	78.88 169	99.81 53	93.59 91	97.81 89	99.30 77

ACMMP		94.67 75	94.30 68	95.79 114	99.25 48	88.13 166	98.41 152	98.67 23	90.38 88	91.43 121	98.72 71	82.22 151	99.95 21	93.83 87	95.76 123	99.29 78
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 92	93.67 85	94.01 167	95.86 155	85.70 234	98.67 118	97.66 83	84.46 214	91.36 123	97.18 129	91.16 17	97.79 164	92.93 101	93.75 138	98.53 130

PLC	91.07 3	94.23 84	94.01 75	94.87 143	99.17 51	87.49 178	99.25 52	96.55 178	88.43 139	91.26 124	98.21 96	85.92 100	99.86 43	89.77 129	97.57 94	97.24 172
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HyFIR lowres test		93.68 95	93.29 89	94.87 143	97.57 100	88.04 168	98.18 178	98.47 24	87.57 165	91.24 125	95.05 178	85.49 106	97.46 186	93.22 97	92.82 144	99.10 90

thres200		93.69 93	92.59 103	96.97 58	97.76 89	94.74 31	99.35 46	99.36 2	89.23 113	91.21 126	96.97 141	83.42 127	98.77 122	85.08 172	90.96 170	97.39 169

mvs-test1		91.57 146	92.20 111	89.70 252	95.15 176	74.34 314	99.51 25	95.40 255	91.92 61	91.02 127	97.25 124	74.27 208	98.08 150	89.45 131	95.83 122	96.67 185

CDS-MVSNet		93.47 99	93.04 95	94.76 145	94.75 190	89.45 147	98.82 101	97.03 158	87.91 155	90.97 128	96.48 158	89.06 48	96.36 238	89.50 130	92.81 146	98.49 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpn200view9		93.43 101	92.27 109	96.90 62	97.68 92	94.84 24	99.18 55	99.36 2	88.45 136	90.79 129	96.90 143	83.31 128	98.75 124	84.11 183	90.69 172	97.12 173

thres400		93.39 103	92.27 109	96.73 74	97.68 92	94.84 24	99.18 55	99.36 2	88.45 136	90.79 129	96.90 143	83.31 128	98.75 124	84.11 183	90.69 172	96.61 186

CR-MVSNet		88.83 191	87.38 194	93.16 182	93.47 217	86.24 215	84.97 334	94.20 287	88.92 126	90.76 131	86.88 307	84.43 117	94.82 292	70.64 303	92.17 156	98.41 135

RPMNet		84.62 251	81.78 265	93.16 182	93.47 217	86.24 215	84.97 334	96.28 195	64.85 337	90.76 131	78.80 336	80.95 160	94.82 292	53.76 335	92.17 156	98.41 135

PatchmatchNet		92.05 142	91.04 135	95.06 139	96.17 148	89.04 151	91.26 316	97.26 134	89.56 106	90.64 133	90.56 264	88.35 60	97.11 202	79.53 226	96.07 119	99.03 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchT		85.44 245	83.19 249	92.22 198	93.13 226	83.00 263	83.80 340	96.37 186	70.62 321	90.55 134	79.63 334	84.81 115	94.87 290	58.18 332	91.59 164	98.79 114

tpm		89.67 178	88.95 171	91.82 205	92.54 230	81.43 277	92.95 301	95.92 217	87.81 157	90.50 135	89.44 285	84.99 111	95.65 273	83.67 190	82.71 233	98.38 139

tfpn111		93.20 111	92.00 117	96.83 68	97.62 94	94.84 24	99.06 74	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.71 129	82.93 195	90.47 179	96.94 180

conf200view11		93.32 106	92.15 113	96.84 67	97.62 94	94.84 24	99.06 74	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.75 124	84.11 183	90.69 172	96.94 180

thres100view900		93.34 105	92.15 113	96.90 62	97.62 94	94.84 24	99.06 74	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.75 124	84.11 183	90.69 172	97.12 173

thres600view7		93.18 112	92.00 117	96.75 72	97.62 94	94.92 21	99.07 72	99.36 2	87.96 151	90.47 136	96.78 145	83.29 130	98.71 129	82.93 195	90.47 179	96.61 186

AdaColmap		93.82 90	93.06 93	96.10 105	99.88 1	89.07 150	98.33 158	97.55 105	86.81 182	90.39 140	98.65 75	75.09 191	99.98 9	93.32 96	97.53 96	99.26 82

XVG-OURS-SEG-HR		90.95 158	90.66 153	91.83 204	95.18 175	81.14 284	95.92 265	95.92 217	88.40 140	90.33 141	97.85 100	70.66 248	99.38 98	92.83 103	88.83 196	94.98 200

IS-MVSNet		93.00 115	92.51 104	94.49 152	96.14 150	87.36 186	98.31 161	95.70 232	88.58 132	90.17 142	97.50 112	83.02 139	97.22 198	87.06 154	96.07 119	98.90 106

CSCG		94.87 68	94.71 63	95.36 128	99.54 27	86.49 206	99.34 48	98.15 43	82.71 253	90.15 143	99.25 12	89.48 45	99.86 43	94.97 72	98.82 73	99.72 42

Patchmatch-test1		90.10 171	88.61 178	94.57 151	94.95 185	88.83 153	96.26 250	97.21 140	90.06 100	90.03 144	90.68 254	66.61 278	95.83 269	77.31 244	94.36 134	99.05 92

XVG-OURS		90.83 160	90.49 155	91.86 203	95.23 170	81.25 282	95.79 273	95.92 217	88.96 122	90.02 145	98.03 99	71.60 241	99.35 102	91.06 115	87.78 200	94.98 200

ADS-MVSNet2		87.62 206	86.88 201	89.86 247	96.21 146	79.14 294	87.15 327	92.99 302	83.01 248	89.91 146	87.27 303	78.87 170	92.80 312	74.20 279	92.27 152	97.64 162

ADS-MVSNet		88.99 186	87.30 195	94.07 164	96.21 146	87.56 177	87.15 327	96.78 168	83.01 248	89.91 146	87.27 303	78.87 170	97.01 206	74.20 279	92.27 152	97.64 162

tfpn_ndepth		93.28 108	92.32 106	96.16 103	97.74 90	92.86 70	99.01 80	98.19 39	85.50 197	89.84 148	97.12 133	93.57 9	97.58 180	79.39 229	90.50 178	98.04 152

ab-mvs		91.05 156	89.17 167	96.69 78	95.96 153	91.72 85	92.62 304	97.23 138	85.61 194	89.74 149	93.89 195	68.55 262	99.42 95	91.09 114	87.84 199	98.92 105

TAMVS		92.62 126	92.09 116	94.20 161	94.10 198	87.68 174	98.41 152	96.97 162	87.53 166	89.74 149	96.04 167	84.77 116	96.49 227	88.97 141	92.31 151	98.42 134

Vis-MVSNet (Re-imp)		93.26 110	93.00 96	94.06 165	96.14 150	86.71 202	98.68 116	96.70 169	88.30 143	89.71 151	97.64 108	85.43 109	96.39 236	88.06 147	96.32 111	99.08 91

view600		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

view800		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

conf0.05thres1000		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

tfpn		92.78 117	91.50 129	96.63 81	97.51 102	94.66 34	98.91 89	99.36 2	87.31 169	89.64 152	96.59 152	83.26 135	98.63 133	80.76 218	90.15 182	96.61 186

CNLPA		93.64 97	92.74 99	96.36 96	98.96 62	90.01 136	99.19 53	95.89 223	86.22 189	89.40 156	98.85 61	80.66 162	99.84 46	88.57 143	96.92 103	99.24 83

Fast-Effi-MVS+		91.72 145	90.79 149	94.49 152	95.89 154	87.40 183	99.54 23	95.70 232	85.01 207	89.28 157	95.68 170	77.75 179	97.57 184	83.22 191	95.06 129	98.51 131

PatchMatch-RL		91.47 148	90.54 154	94.26 159	98.20 81	86.36 212	96.94 226	97.14 145	87.75 159	88.98 158	95.75 169	71.80 239	99.40 97	80.92 215	97.39 99	97.02 179

dp		90.16 170	88.83 174	94.14 162	96.38 142	86.42 208	91.57 313	97.06 155	84.76 211	88.81 159	90.19 278	84.29 119	97.43 188	75.05 271	91.35 169	98.56 129

tfpn1000		92.67 124	91.64 126	95.78 115	97.61 99	92.34 79	98.69 113	98.18 40	84.15 219	88.80 160	96.99 140	93.56 10	97.21 199	76.56 254	90.19 181	97.77 161

DeepC-MVS	91.02 4	94.56 79	93.92 82	96.46 90	97.16 114	90.76 117	98.39 156	97.11 148	93.92 22	88.66 161	98.33 91	78.14 177	99.85 45	95.02 70	98.57 81	98.78 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CVMVSNet		90.30 166	90.91 144	88.46 276	94.32 195	73.58 318	97.61 204	97.59 96	90.16 96	88.43 162	97.10 134	76.83 184	92.86 308	82.64 198	93.54 140	98.93 104

TR-MVS		90.77 161	89.44 163	94.76 145	96.31 143	88.02 169	97.92 192	95.96 212	85.52 195	88.22 163	97.23 126	66.80 276	98.09 147	84.58 177	92.38 149	98.17 150

conf0.01		92.06 140	90.99 136	95.24 133	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	96.94 180

conf0.002		92.06 140	90.99 136	95.24 133	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	96.94 180

thresconf0.02		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

tfpn_n400		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

tfpnconf		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

tfpnview11		92.14 133	90.99 136	95.58 121	96.84 124	91.39 93	98.31 161	98.20 32	83.57 235	88.08 164	97.34 117	91.05 20	97.40 189	75.80 260	89.74 189	97.94 156

F-COLMAP		92.07 138	91.75 124	93.02 185	98.16 84	82.89 267	98.79 106	95.97 210	86.54 185	87.92 170	97.80 102	78.69 174	99.65 70	85.97 164	95.93 121	96.53 195

BH-RMVSNet		91.25 153	89.99 159	95.03 141	96.75 133	88.55 160	98.65 120	94.95 268	87.74 160	87.74 171	97.80 102	68.27 264	98.14 145	80.53 223	97.49 97	98.41 135

Effi-MVS+-dtu		89.97 175	90.68 152	87.81 289	95.15 176	71.98 323	97.87 196	95.40 255	91.92 61	87.57 172	91.44 234	74.27 208	96.84 211	89.45 131	93.10 142	94.60 202

HQP-NCC					93.95 202		99.16 58		93.92 22	87.57 172

ACMP_Plane					93.95 202		99.16 58		93.92 22	87.57 172

HQP4-MVS										87.57 172			97.77 166			92.72 209

HQP-MVS		91.50 147	91.23 133	92.29 197	93.95 202	86.39 210	99.16 58	96.37 186	93.92 22	87.57 172	96.67 150	73.34 221	97.77 166	93.82 88	86.29 204	92.72 209

TAPA-MVS	87.50 9	90.35 165	89.05 169	94.25 160	98.48 77	85.17 242	98.42 150	96.58 176	82.44 259	87.24 177	98.53 81	82.77 142	98.84 120	59.09 330	97.88 88	98.72 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HQP_MVS		91.26 151	90.95 143	92.16 199	93.84 209	86.07 223	99.02 78	96.30 191	93.38 35	86.99 178	96.52 156	72.92 226	97.75 171	93.46 93	86.17 207	92.67 211

plane_prior3						85.91 227			93.65 30	86.99 178

GA-MVS		90.10 171	88.69 176	94.33 157	92.44 231	87.97 170	99.08 71	96.26 196	89.65 102	86.92 180	93.11 214	68.09 265	96.96 207	82.54 199	90.15 182	98.05 151

1112_ss		92.71 122	91.55 128	96.20 99	95.56 163	91.12 105	98.48 143	94.69 274	88.29 144	86.89 181	98.50 84	87.02 84	98.66 131	84.75 175	89.77 188	98.81 112

Test_1112_low_res		92.27 131	90.97 142	96.18 100	95.53 164	91.10 107	98.47 145	94.66 275	88.28 145	86.83 182	93.50 206	87.00 85	98.65 132	84.69 176	89.74 189	98.80 113

cascas		90.93 159	89.33 166	95.76 116	95.69 160	93.03 66	98.99 83	96.59 173	80.49 278	86.79 183	94.45 185	65.23 286	98.60 137	93.52 92	92.18 155	95.66 199

OPM-MVS		89.76 177	89.15 168	91.57 214	90.53 257	85.58 236	98.11 183	95.93 216	92.88 42	86.05 184	96.47 159	67.06 275	97.87 159	89.29 138	86.08 209	91.26 249

VPA-MVSNet		89.10 185	87.66 191	93.45 177	92.56 229	91.02 110	97.97 191	98.32 27	86.92 179	86.03 185	92.01 226	68.84 261	97.10 204	90.92 117	75.34 263	92.23 221

tpm cat1		88.89 188	87.27 196	93.76 174	95.79 156	85.32 238	90.76 320	97.09 152	76.14 308	85.72 186	88.59 293	82.92 140	98.04 151	76.96 248	91.43 166	97.90 160

IB-MVS	89.43 6	92.12 137	90.83 148	95.98 108	95.40 168	90.78 116	99.81 5	98.06 46	91.23 76	85.63 187	93.66 201	90.63 33	98.78 121	91.22 113	71.85 301	98.36 142
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		89.87 176	89.38 165	91.36 219	94.32 195	85.87 229	97.61 204	96.59 173	85.10 203	85.51 188	97.10 134	81.30 159	96.56 219	83.85 189	83.03 230	91.64 236

MVSTER		92.71 122	92.32 106	93.86 171	97.29 110	92.95 68	99.01 80	96.59 173	90.09 97	85.51 188	94.00 191	94.61 5	96.56 219	90.77 121	83.03 230	92.08 228

RPSCF		85.33 246	85.55 222	84.67 309	94.63 192	62.28 335	93.73 295	93.76 291	74.38 314	85.23 190	97.06 137	64.09 289	98.31 140	80.98 213	86.08 209	93.41 208

BH-w/o		92.32 129	91.79 122	93.91 170	96.85 123	86.18 218	99.11 70	95.74 228	88.13 148	84.81 191	97.00 139	77.26 182	97.91 155	89.16 140	98.03 87	97.64 162

CLD-MVS		91.06 155	90.71 151	92.10 200	94.05 201	86.10 221	99.55 22	96.29 194	94.16 20	84.70 192	97.17 131	69.62 254	97.82 162	94.74 76	86.08 209	92.39 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmvs		89.16 184	87.76 188	93.35 178	97.19 113	84.75 247	90.58 322	97.36 131	81.99 263	84.56 193	89.31 288	83.98 121	98.17 144	74.85 274	90.00 187	97.12 173

nrg030		90.23 167	88.87 172	94.32 158	91.53 245	93.54 55	98.79 106	95.89 223	88.12 149	84.55 194	94.61 184	78.80 172	96.88 210	92.35 107	75.21 264	92.53 213

VPNet		88.30 200	86.57 202	93.49 176	91.95 238	91.35 99	98.18 178	97.20 141	88.61 131	84.52 195	94.89 179	62.21 298	96.76 215	89.34 135	72.26 297	92.36 215

MVS		93.92 87	92.28 108	98.83 2	95.69 160	96.82 3	96.22 254	98.17 41	84.89 209	84.34 196	98.61 78	79.32 167	99.83 48	93.88 85	99.43 48	99.86 20

mvs_anonymous		92.50 128	91.65 125	95.06 139	96.60 136	89.64 142	97.06 223	96.44 184	86.64 183	84.14 197	93.93 193	82.49 145	96.17 256	91.47 112	96.08 118	99.35 72

Fast-Effi-MVS+-dtu		88.84 190	88.59 181	89.58 255	93.44 220	78.18 303	98.65 120	94.62 276	88.46 135	84.12 198	95.37 176	68.91 259	96.52 225	82.06 203	91.70 163	94.06 203

LS3D		90.19 169	88.72 175	94.59 150	98.97 60	86.33 213	96.90 228	96.60 172	74.96 311	84.06 199	98.74 68	75.78 188	99.83 48	74.93 272	97.57 94	97.62 165

ACMM	86.95 13	88.77 194	88.22 187	90.43 235	93.61 214	81.34 280	98.50 140	95.92 217	87.88 156	83.85 200	95.20 177	67.20 273	97.89 157	86.90 159	84.90 216	92.06 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-untuned		91.46 149	90.84 146	93.33 179	96.51 139	84.83 246	98.84 100	95.50 247	86.44 188	83.50 201	96.70 149	75.49 190	97.77 166	86.78 161	97.81 89	97.40 168

FIs		90.70 163	89.87 160	93.18 181	92.29 232	91.12 105	98.17 181	98.25 29	89.11 118	83.44 202	94.82 181	82.26 150	96.17 256	87.76 149	82.76 232	92.25 219

UniMVSNet (Re)		89.50 181	88.32 185	93.03 184	92.21 234	90.96 112	98.90 94	98.39 25	89.13 117	83.22 203	92.03 224	81.69 154	96.34 245	86.79 160	72.53 292	91.81 233

UniMVSNet_NR-MVSNet		89.60 179	88.55 182	92.75 191	92.17 235	90.07 132	98.74 108	98.15 43	88.37 141	83.21 204	93.98 192	82.86 141	95.93 266	86.95 157	72.47 293	92.25 219

DU-MVS		88.83 191	87.51 192	92.79 189	91.46 246	90.07 132	98.71 109	97.62 93	88.87 127	83.21 204	93.68 199	74.63 196	95.93 266	86.95 157	72.47 293	92.36 215

LPG-MVS_test		88.86 189	88.47 184	90.06 242	93.35 222	80.95 286	98.22 173	95.94 214	87.73 161	83.17 206	96.11 165	66.28 280	97.77 166	90.19 124	85.19 213	91.46 243

LGP-MVS_train				90.06 242	93.35 222	80.95 286		95.94 214	87.73 161	83.17 206	96.11 165	66.28 280	97.77 166	90.19 124	85.19 213	91.46 243

v6		87.27 212	85.86 214	91.50 215	89.97 266	86.84 198	98.45 146	95.67 234	83.85 226	83.11 208	90.97 242	74.46 203	96.58 217	81.97 205	74.34 274	91.09 253

v1neww		87.29 210	85.88 212	91.50 215	90.07 259	86.87 194	98.45 146	95.66 237	83.84 227	83.07 209	90.99 240	74.58 200	96.56 219	81.96 206	74.33 275	91.07 256

v7new		87.29 210	85.88 212	91.50 215	90.07 259	86.87 194	98.45 146	95.66 237	83.84 227	83.07 209	90.99 240	74.58 200	96.56 219	81.96 206	74.33 275	91.07 256

FC-MVSNet-test		90.22 168	89.40 164	92.67 194	91.78 242	89.86 138	97.89 193	98.22 31	88.81 128	82.96 211	94.66 183	81.90 153	95.96 264	85.89 167	82.52 235	92.20 224

v7		86.91 219	85.45 224	91.29 220	90.06 261	86.73 200	98.26 169	95.49 248	83.08 247	82.95 212	90.96 243	73.37 219	96.42 233	79.90 225	74.97 265	90.71 271

v1		87.23 214	85.76 216	91.66 212	89.88 271	87.37 185	98.54 134	95.64 239	83.91 223	82.88 213	90.70 250	74.64 194	96.53 223	81.54 211	74.08 281	91.08 254

divwei89l23v2f112		87.23 214	85.75 218	91.66 212	89.88 271	87.40 183	98.53 135	95.62 240	83.91 223	82.84 214	90.67 255	74.75 192	96.49 227	81.55 210	74.05 283	91.08 254

v1141		87.23 214	85.75 218	91.67 211	89.88 271	87.43 182	98.52 136	95.62 240	83.91 223	82.83 215	90.69 252	74.70 193	96.49 227	81.53 212	74.08 281	91.07 256

PCF-MVS	89.78 5	91.26 151	89.63 161	96.16 103	95.44 166	91.58 90	95.29 280	96.10 205	85.07 205	82.75 216	97.45 114	78.28 176	99.78 57	80.60 222	95.65 126	97.12 173
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
V42		87.00 218	85.68 221	90.98 225	89.91 267	86.08 222	98.32 160	95.61 242	83.67 233	82.72 217	90.67 255	74.00 214	96.53 223	81.94 208	74.28 278	90.32 279

v1144		86.83 221	85.31 226	91.40 218	89.75 276	87.21 191	98.31 161	95.45 252	83.22 244	82.70 218	90.78 247	73.36 220	96.36 238	79.49 227	74.69 269	90.63 274

v144192		86.40 229	84.89 232	90.91 226	89.48 287	85.59 235	98.21 176	95.43 254	82.45 258	82.62 219	90.58 263	72.79 229	96.36 238	78.45 237	74.04 284	90.79 266

3Dnovator	87.35 11	93.17 113	91.77 123	97.37 42	95.41 167	93.07 64	98.82 101	97.85 60	91.53 67	82.56 220	97.58 110	71.97 236	99.82 51	91.01 116	99.23 59	99.22 85

v2v482		87.27 212	85.76 216	91.78 210	89.59 282	87.58 176	98.56 132	95.54 244	84.53 213	82.51 221	91.78 230	73.11 225	96.47 230	82.07 202	74.14 280	91.30 248

Baseline_NR-MVSNet		85.83 238	84.82 234	88.87 269	88.73 297	83.34 260	98.63 123	91.66 325	80.41 279	82.44 222	91.35 235	74.63 196	95.42 279	84.13 182	71.39 304	87.84 306

v1192		86.32 231	84.71 236	91.17 221	89.53 285	86.40 209	98.13 182	95.44 253	82.52 257	82.42 223	90.62 260	71.58 242	96.33 246	77.23 245	74.88 266	90.79 266

test_djsdf		88.26 202	87.73 189	89.84 248	88.05 305	82.21 272	97.77 199	96.17 201	86.84 180	82.41 224	91.95 229	72.07 235	95.99 262	89.83 126	84.50 219	91.32 247

1314		93.44 100	91.98 119	97.84 22	95.24 169	94.38 43	96.22 254	97.92 55	90.18 93	82.28 225	97.71 106	77.63 180	99.80 55	91.94 111	98.67 79	99.34 74

v1921920		86.02 234	84.44 240	90.77 228	89.32 289	85.20 240	98.10 184	95.35 259	82.19 260	82.25 226	90.71 249	70.73 246	96.30 252	76.85 251	74.49 271	90.80 265

v1240		85.77 241	84.11 243	90.73 229	89.26 290	85.15 243	97.88 195	95.23 266	81.89 266	82.16 227	90.55 265	69.60 255	96.31 249	75.59 268	74.87 267	90.72 270

XVG-ACMP-BASELINE		85.86 237	84.95 231	88.57 273	89.90 269	77.12 308	94.30 288	95.60 243	87.40 168	82.12 228	92.99 217	53.42 324	97.66 175	85.02 173	83.83 223	90.92 262

GBi-Net		86.67 224	84.96 229	91.80 206	95.11 179	88.81 154	96.77 231	95.25 262	82.94 250	82.12 228	90.25 272	62.89 295	94.97 287	79.04 231	80.24 241	91.62 238

test1		86.67 224	84.96 229	91.80 206	95.11 179	88.81 154	96.77 231	95.25 262	82.94 250	82.12 228	90.25 272	62.89 295	94.97 287	79.04 231	80.24 241	91.62 238

FMVSNet3		88.81 193	87.08 200	93.99 168	96.52 138	94.59 38	98.08 186	96.20 199	85.85 191	82.12 228	91.60 233	74.05 213	95.40 280	79.04 231	80.24 241	91.99 231

IterMVS-LS		88.34 199	87.44 193	91.04 223	94.10 198	85.85 231	98.10 184	95.48 249	85.12 202	82.03 232	91.21 236	81.35 158	95.63 274	83.86 188	75.73 261	91.63 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		84.48 255	81.83 264	92.42 196	91.73 243	87.36 186	85.52 330	94.42 282	81.40 270	81.91 233	87.58 299	51.92 326	92.81 311	73.84 284	88.15 198	97.08 177

PS-MVSNAJss		89.54 180	89.05 169	91.00 224	88.77 296	84.36 250	97.39 207	95.97 210	88.47 133	81.88 234	93.80 197	82.48 146	96.50 226	89.34 135	83.34 228	92.15 225

WR-MVS		88.54 198	87.22 198	92.52 195	91.93 240	89.50 145	98.56 132	97.84 61	86.99 175	81.87 235	93.81 196	74.25 210	95.92 268	85.29 170	74.43 272	92.12 226

TranMVSNet+NR-MVSNet		87.75 203	86.31 206	92.07 201	90.81 253	88.56 159	98.33 158	97.18 142	87.76 158	81.87 235	93.90 194	72.45 230	95.43 278	83.13 193	71.30 305	92.23 221

DP-MVS		88.75 195	86.56 203	95.34 129	98.92 64	87.45 180	97.64 203	93.52 296	70.55 322	81.49 237	97.25 124	74.43 205	99.88 35	71.14 302	94.09 136	98.67 123

3Dnovator+	87.72 8	93.43 101	91.84 121	98.17 13	95.73 159	95.08 20	98.92 88	97.04 156	91.42 72	81.48 238	97.60 109	74.60 198	99.79 56	90.84 119	98.97 65	99.64 53

QAPM		91.41 150	89.49 162	97.17 47	95.66 162	93.42 58	98.60 128	97.51 111	80.92 276	81.39 239	97.41 116	72.89 228	99.87 38	82.33 200	98.68 78	98.21 148

XXY-MVS		87.75 203	86.02 209	92.95 187	90.46 258	89.70 141	97.71 201	95.90 221	84.02 220	80.95 240	94.05 186	67.51 271	97.10 204	85.16 171	78.41 250	92.04 230

v148		86.38 230	85.06 228	90.37 237	89.47 288	84.10 252	98.52 136	95.48 249	83.80 229	80.93 241	90.22 275	74.60 198	96.31 249	80.92 215	71.55 303	90.69 272

FMVSNet2		86.90 220	84.79 235	93.24 180	95.11 179	92.54 77	97.67 202	95.86 225	82.94 250	80.55 242	91.17 237	62.89 295	95.29 282	77.23 245	79.71 247	91.90 232

pmmvs4		87.58 207	86.17 208	91.80 206	89.58 283	88.92 152	97.25 213	95.28 261	82.54 256	80.49 243	93.17 213	75.62 189	96.05 261	82.75 197	78.90 248	90.42 277

ACMP	87.39 10	88.71 196	88.24 186	90.12 241	93.91 207	81.06 285	98.50 140	95.67 234	89.43 109	80.37 244	95.55 171	65.67 283	97.83 161	90.55 122	84.51 218	91.47 242
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		85.87 236	84.40 242	90.30 238	88.53 300	84.23 251	98.60 128	93.71 293	81.53 269	80.29 245	92.02 225	64.51 288	95.52 276	82.04 204	78.34 251	91.15 251

test0.0.03 1		88.96 187	88.61 178	90.03 245	91.09 250	84.43 249	98.97 84	97.02 159	90.21 91	80.29 245	96.31 164	84.89 113	91.93 328	72.98 294	85.70 212	93.73 204

jajsoiax		87.35 208	86.51 204	89.87 246	87.75 310	81.74 275	97.03 224	95.98 208	88.47 133	80.15 247	93.80 197	61.47 300	96.36 238	89.44 133	84.47 220	91.50 241

mvs_tets		87.09 217	86.22 207	89.71 251	87.87 306	81.39 279	96.73 235	95.90 221	88.19 147	79.99 248	93.61 202	59.96 306	96.31 249	89.40 134	84.34 221	91.43 245

ITE_SJBPF				87.93 287	92.26 233	76.44 309		93.47 297	87.67 164	79.95 249	95.49 174	56.50 313	97.38 195	75.24 270	82.33 236	89.98 289

v8		86.11 233	84.45 239	91.10 222	89.99 265	86.85 196	97.24 214	95.36 257	81.99 263	79.89 250	89.86 281	74.53 202	96.39 236	78.83 235	72.32 295	90.05 286

v10		85.73 242	84.01 245	90.87 227	90.03 262	86.73 200	97.20 217	95.22 267	81.25 272	79.85 251	89.75 282	73.30 224	96.28 253	76.87 249	72.64 291	89.61 295

WR-MVS_H		86.53 228	85.49 223	89.66 254	91.04 251	83.31 261	97.53 206	98.20 32	84.95 208	79.64 252	90.90 245	78.01 178	95.33 281	76.29 256	72.81 289	90.35 278

anonymousdsp		86.69 223	85.75 218	89.53 256	86.46 319	82.94 264	96.39 245	95.71 231	83.97 222	79.63 253	90.70 250	68.85 260	95.94 265	86.01 163	84.02 222	89.72 293

Patchmtry		83.61 268	81.64 267	89.50 257	93.36 221	82.84 269	84.10 337	94.20 287	69.47 328	79.57 254	86.88 307	84.43 117	94.78 294	68.48 308	74.30 277	90.88 263

CP-MVSNet		86.54 227	85.45 224	89.79 250	91.02 252	82.78 270	97.38 209	97.56 104	85.37 199	79.53 255	93.03 215	71.86 238	95.25 283	79.92 224	73.43 287	91.34 246

Patchmatch-test		86.25 232	84.06 244	92.82 188	94.42 193	82.88 268	82.88 342	94.23 286	71.58 318	79.39 256	90.62 260	89.00 50	96.42 233	63.03 320	91.37 168	99.16 87

DSMNet-mixed		81.60 279	81.43 270	82.10 315	84.36 324	60.79 336	93.63 297	86.74 346	79.00 286	79.32 257	87.15 305	63.87 291	89.78 333	66.89 312	91.92 158	95.73 198

MSDG		88.29 201	86.37 205	94.04 166	96.90 122	86.15 220	96.52 241	94.36 284	77.89 304	79.22 258	96.95 142	69.72 253	99.59 78	73.20 291	92.58 148	96.37 196

PS-CasMVS		85.81 239	84.58 238	89.49 259	90.77 254	82.11 273	97.20 217	97.36 131	84.83 210	79.12 259	92.84 218	67.42 272	95.16 285	78.39 238	73.25 288	91.21 250

IterMVS		85.81 239	84.67 237	89.22 262	93.51 216	83.67 258	96.32 248	94.80 270	85.09 204	78.69 260	90.17 279	66.57 279	93.17 304	79.48 228	77.42 256	90.81 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		85.21 247	83.93 246	89.07 266	89.89 270	81.31 281	97.09 222	97.24 137	84.45 215	78.66 261	92.68 220	68.44 263	94.87 290	75.98 258	70.92 306	91.04 259

semantic-postprocess				89.00 267	93.46 219	82.90 266		94.70 273	85.02 206	78.62 262	90.35 268	66.63 277	93.33 303	79.38 230	77.36 257	90.76 268

OpenMVS	85.28 14	90.75 162	88.84 173	96.48 89	93.58 215	93.51 56	98.80 103	97.41 126	82.59 254	78.62 262	97.49 113	68.00 267	99.82 51	84.52 178	98.55 82	96.11 197

PVSNet_0	83.28 16	87.31 209	85.16 227	93.74 175	94.78 189	84.59 248	98.91 89	98.69 22	89.81 101	78.59 264	93.23 211	61.95 299	99.34 103	94.75 75	55.72 340	97.30 171

v52		84.19 260	82.92 252	88.01 285	87.64 312	79.92 290	96.23 252	95.32 260	79.87 282	78.51 265	89.05 289	69.50 257	96.32 247	77.95 241	72.24 298	87.79 309

V4		84.20 259	82.92 252	88.02 284	87.59 313	79.91 291	96.21 257	95.36 257	79.88 281	78.51 265	89.00 290	69.52 256	96.32 247	77.96 240	72.29 296	87.83 308

EU-MVSNet		84.19 260	84.42 241	83.52 312	88.64 299	67.37 332	96.04 262	95.76 227	85.29 200	78.44 267	93.18 212	70.67 247	91.48 331	75.79 266	75.98 259	91.70 235

v7n		84.42 257	82.75 258	89.43 260	88.15 303	81.86 274	96.75 234	95.67 234	80.53 277	78.38 268	89.43 286	69.89 250	96.35 244	73.83 285	72.13 299	90.07 284

FMVSNet1		83.94 264	81.32 272	91.80 206	91.94 239	88.81 154	96.77 231	95.25 262	77.98 299	78.25 269	90.25 272	50.37 330	94.97 287	73.27 290	77.81 254	91.62 238

MS-PatchMatch		86.75 222	85.92 211	89.22 262	91.97 237	82.47 271	96.91 227	96.14 204	83.74 230	77.73 270	93.53 205	58.19 308	97.37 197	76.75 252	98.35 84	87.84 306

v748		83.84 265	82.31 263	88.41 278	87.65 311	79.10 295	96.66 237	95.51 246	80.09 280	77.65 271	88.53 294	69.81 251	96.23 254	75.67 267	69.25 308	89.91 290

DTE-MVSNet		84.14 262	82.80 255	88.14 283	88.95 294	79.87 292	96.81 230	96.24 197	83.50 241	77.60 272	92.52 222	67.89 269	94.24 299	72.64 297	69.05 310	90.32 279

COLMAP_ROB	82.69 18	84.54 254	82.82 254	89.70 252	96.72 134	78.85 297	95.89 266	92.83 311	71.55 319	77.54 273	95.89 168	59.40 307	99.14 112	67.26 310	88.26 197	91.11 252
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testpf		80.59 289	80.13 276	81.97 317	94.25 197	71.65 324	60.37 352	95.46 251	70.99 320	76.97 274	87.74 297	73.58 218	91.67 329	76.86 250	84.97 215	82.60 340

Anonymous20240521		85.45 244	83.91 247	90.05 244	90.73 256	83.74 257	97.13 220	96.15 203	82.08 262	76.93 275	90.84 246	71.53 244	96.36 238	75.26 269	74.57 270	90.04 288

OurMVSNet-221017-0		84.13 263	83.59 248	85.77 303	87.81 307	70.24 327	94.89 283	93.65 295	86.08 190	76.53 276	93.28 210	61.41 301	96.14 258	80.95 214	77.69 255	90.93 261

tfpnnormal		83.65 266	81.35 271	90.56 232	91.37 248	88.06 167	97.29 211	97.87 59	78.51 293	76.20 277	90.91 244	64.78 287	96.47 230	61.71 323	73.50 285	87.13 316

ppachtmachnet_test		83.63 267	81.57 269	89.80 249	89.01 292	85.09 244	97.13 220	94.50 278	78.84 288	76.14 278	91.00 239	69.78 252	94.61 296	63.40 319	74.36 273	89.71 294

pm-mvs1		84.68 250	82.78 257	90.40 236	89.58 283	85.18 241	97.31 210	94.73 272	81.93 265	76.05 279	92.01 226	65.48 285	96.11 259	78.75 236	69.14 309	89.91 290

AllTest		84.97 248	83.12 250	90.52 233	96.82 130	78.84 298	95.89 266	92.17 318	77.96 301	75.94 280	95.50 172	55.48 316	99.18 107	71.15 300	87.14 201	93.55 206

TestCases				90.52 233	96.82 130	78.84 298		92.17 318	77.96 301	75.94 280	95.50 172	55.48 316	99.18 107	71.15 300	87.14 201	93.55 206

testgi		82.29 270	81.00 274	86.17 300	87.24 315	74.84 313	97.39 207	91.62 326	88.63 130	75.85 282	95.42 175	46.07 335	91.55 330	66.87 313	79.94 244	92.12 226

MVP-Stereo		86.61 226	85.83 215	88.93 268	88.70 298	83.85 256	96.07 261	94.41 283	82.15 261	75.64 283	91.96 228	67.65 270	96.45 232	77.20 247	98.72 76	86.51 319
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LF4IMVS		81.94 274	81.17 273	84.25 310	87.23 316	68.87 331	93.35 299	91.93 323	83.35 243	75.40 284	93.00 216	49.25 332	96.65 216	78.88 234	78.11 252	87.22 315

our_test_3		84.47 256	82.80 255	89.50 257	89.01 292	83.90 255	97.03 224	94.56 277	81.33 271	75.36 285	90.52 266	71.69 240	94.54 297	68.81 306	76.84 258	90.07 284

LTVRE_ROB	81.71 19	84.59 253	82.72 259	90.18 239	92.89 228	83.18 262	93.15 300	94.74 271	78.99 287	75.14 286	92.69 219	65.64 284	97.63 177	69.46 304	81.82 238	89.74 292
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		80.96 286	81.77 266	78.52 323	81.02 331	62.33 334	98.22 173	94.49 279	79.38 285	74.56 287	90.34 269	70.65 249	85.10 342	60.83 324	86.42 203	88.14 303

Anonymous20231206		80.76 288	79.42 284	84.79 308	84.78 322	72.98 319	96.53 240	92.97 303	79.56 284	74.33 288	88.83 291	61.27 302	92.15 325	60.59 326	75.92 260	89.24 299

FMVSNet5		82.29 270	80.54 275	87.52 291	93.79 212	84.01 253	93.73 295	92.47 315	76.92 306	74.27 289	86.15 311	63.69 292	89.24 334	69.07 305	74.79 268	89.29 298

MVS-HIRNet		79.01 296	75.13 303	90.66 230	93.82 211	81.69 276	85.16 331	93.75 292	54.54 343	74.17 290	59.15 347	57.46 310	96.58 217	63.74 318	94.38 133	93.72 205

ACMH+	83.78 15	84.21 258	82.56 262	89.15 264	93.73 213	79.16 293	96.43 244	94.28 285	81.09 273	74.00 291	94.03 189	54.58 320	97.67 174	76.10 257	78.81 249	90.63 274

NR-MVSNet		87.74 205	86.00 210	92.96 186	91.46 246	90.68 120	96.65 238	97.42 125	88.02 150	73.42 292	93.68 199	77.31 181	95.83 269	84.26 180	71.82 302	92.36 215

USDC		84.74 249	82.93 251	90.16 240	91.73 243	83.54 259	95.00 282	93.30 298	88.77 129	73.19 293	93.30 209	53.62 323	97.65 176	75.88 259	81.54 239	89.30 297

LCM-MVSNet-Re		88.59 197	88.61 178	88.51 275	95.53 164	72.68 321	96.85 229	88.43 344	88.45 136	73.14 294	90.63 259	75.82 187	94.38 298	92.95 100	95.71 124	98.48 133

TDRefinement		78.01 301	75.31 302	86.10 301	70.06 345	73.84 316	93.59 298	91.58 327	74.51 313	73.08 295	91.04 238	49.63 331	97.12 201	74.88 273	59.47 334	87.33 312

TransMVSNet (Re)		81.97 273	79.61 282	89.08 265	89.70 278	84.01 253	97.26 212	91.85 324	78.84 288	73.07 296	91.62 232	67.17 274	95.21 284	67.50 309	59.46 335	88.02 305

SixPastTwentyTwo		82.63 269	81.58 268	85.79 302	88.12 304	71.01 326	95.17 281	92.54 314	84.33 217	72.93 297	92.08 223	60.41 305	95.61 275	74.47 276	74.15 279	90.75 269

testus		77.11 305	76.95 298	77.58 324	80.02 334	58.93 340	97.78 197	90.48 334	79.68 283	72.84 298	90.61 262	37.72 345	86.57 341	60.28 328	83.18 229	87.23 314

pmmvs6		79.90 293	77.31 294	87.67 290	84.17 325	78.13 304	95.86 270	93.68 294	67.94 332	72.67 299	89.62 284	50.98 329	95.75 271	74.80 275	66.04 316	89.14 300

ACMH	83.09 17	84.60 252	82.61 260	90.57 231	93.18 225	82.94 264	96.27 249	94.92 269	81.01 274	72.61 300	93.61 202	56.54 312	97.79 164	74.31 277	81.07 240	90.99 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LP		77.80 304	74.39 306	88.01 285	91.93 240	79.02 296	80.88 344	92.90 308	65.43 335	72.00 301	81.29 326	65.78 282	92.73 317	43.76 345	75.58 262	92.27 218

Patchmatch-RL test		81.90 275	80.13 276	87.23 294	80.71 332	70.12 329	84.07 338	88.19 345	83.16 246	70.57 302	82.18 315	87.18 81	92.59 320	82.28 201	62.78 321	98.98 97

DI_MVS_plusplus_test		89.41 182	87.24 197	95.92 112	89.06 291	90.75 119	98.18 178	96.63 170	89.29 112	70.54 303	90.31 270	63.50 293	98.40 138	92.25 108	95.44 127	98.60 125

test_0402		78.81 298	76.33 300	86.26 299	91.18 249	78.44 302	95.88 268	91.34 329	68.55 329	70.51 304	89.91 280	52.65 325	94.99 286	47.14 340	79.78 246	85.34 333

test_normal		89.37 183	87.18 199	95.93 111	88.94 295	90.83 115	98.24 171	96.62 171	89.31 110	70.38 305	90.20 277	63.50 293	98.37 139	92.06 110	95.41 128	98.59 128

TinyColmap		80.42 291	77.94 291	87.85 288	92.09 236	78.58 300	93.74 294	89.94 338	74.99 310	69.77 306	91.78 230	46.09 334	97.58 180	65.17 317	77.89 253	87.38 311

test20.03		78.51 300	77.48 293	81.62 318	83.07 328	71.03 325	96.11 260	92.83 311	81.66 268	69.31 307	89.68 283	57.53 309	87.29 338	58.65 331	68.47 311	86.53 318

N_pmnet		70.19 314	69.87 313	71.12 329	88.24 302	30.63 360	95.85 271	28.70 361	70.18 325	68.73 308	86.55 309	64.04 290	93.81 300	53.12 336	73.46 286	88.94 301

v18		82.00 272	79.76 280	88.72 270	90.03 262	86.81 199	96.17 259	93.12 299	78.70 290	68.39 309	82.10 316	74.64 194	93.00 305	74.21 278	60.45 328	86.35 320

OpenMVS_ROB	73.86 20	77.99 302	75.06 304	86.77 297	83.81 327	77.94 306	96.38 246	91.53 328	67.54 333	68.38 310	87.13 306	43.94 336	96.08 260	55.03 334	81.83 237	86.29 323

v17		81.87 277	79.61 282	88.64 272	89.91 267	86.64 204	96.01 263	93.08 300	78.54 291	68.27 311	81.96 318	74.44 204	92.95 307	74.03 281	60.22 330	86.34 321

v16		81.90 275	79.65 281	88.65 271	90.02 264	86.66 203	96.01 263	93.07 301	78.53 292	68.27 311	82.05 317	74.39 206	92.96 306	74.02 282	60.48 327	86.33 322

ambc				79.60 321	72.76 343	56.61 344	76.20 346	92.01 322		68.25 313	80.23 332	23.34 350	94.73 295	73.78 286	60.81 326	87.48 310

PM-MVS		74.88 308	72.85 309	80.98 320	78.98 336	64.75 333	90.81 319	85.77 348	80.95 275	68.23 314	82.81 313	29.08 348	92.84 309	76.54 255	62.46 323	85.36 332

v15		81.62 278	79.32 285	88.52 274	89.80 274	86.56 205	95.83 272	92.96 304	78.50 294	67.88 315	81.68 320	74.22 211	92.82 310	73.46 288	59.55 331	86.18 325

V14		81.55 280	79.26 286	88.42 277	89.80 274	86.33 213	95.72 275	92.96 304	78.35 295	67.82 316	81.70 319	74.13 212	92.78 314	73.32 289	59.50 333	86.16 327

v11		81.38 282	79.03 289	88.41 278	89.68 279	86.43 207	95.74 274	92.82 313	78.03 298	67.74 317	81.45 324	73.33 223	92.69 318	72.23 299	60.27 329	86.11 329

pmmvs3		72.86 311	69.76 314	82.17 314	73.86 340	74.19 315	94.20 289	89.01 341	64.23 338	67.72 318	80.91 328	41.48 339	88.65 336	62.40 321	54.02 342	83.68 337

lessismore_v0				85.08 305	85.59 320	69.28 330		90.56 333		67.68 319	90.21 276	54.21 322	95.46 277	73.88 283	62.64 322	90.50 276

V9		81.46 281	79.15 287	88.39 280	89.75 276	86.17 219	95.62 276	92.92 306	78.22 296	67.65 320	81.64 321	73.95 215	92.80 312	73.15 292	59.43 336	86.21 324

v12		81.37 283	79.05 288	88.33 281	89.68 279	86.05 225	95.48 278	92.92 306	78.08 297	67.55 321	81.58 322	73.75 216	92.75 315	73.05 293	59.37 337	86.18 325

K. test v3		81.04 285	79.77 279	84.83 307	87.41 314	70.23 328	95.60 277	93.93 290	83.70 232	67.51 322	89.35 287	55.76 314	93.58 302	76.67 253	68.03 313	90.67 273

MIMVSNet1		75.92 307	73.30 308	83.81 311	81.29 330	75.57 311	92.26 308	92.05 321	73.09 317	67.48 323	86.18 310	40.87 341	87.64 337	55.78 333	70.68 307	88.21 302

v13		81.30 284	78.99 290	88.25 282	89.61 281	85.87 229	95.39 279	92.90 308	77.93 303	67.45 324	81.52 323	73.66 217	92.75 315	72.91 295	59.53 332	86.14 328

pmmvs-eth3d		78.71 299	76.16 301	86.38 298	80.25 333	81.19 283	94.17 290	92.13 320	77.97 300	66.90 325	82.31 314	55.76 314	92.56 321	73.63 287	62.31 324	85.38 331

EG-PatchMatch MVS		79.92 292	77.59 292	86.90 296	87.06 317	77.90 307	96.20 258	94.06 289	74.61 312	66.53 326	88.76 292	40.40 343	96.20 255	67.02 311	83.66 226	86.61 317

UnsupCasMVSNet_eth		78.90 297	76.67 299	85.58 304	82.81 329	74.94 312	91.98 310	96.31 190	84.64 212	65.84 327	87.71 298	51.33 327	92.23 324	72.89 296	56.50 339	89.56 296

Test4		85.71 243	82.59 261	95.07 138	84.45 323	89.84 139	97.20 217	95.73 229	89.19 114	64.59 328	87.58 299	40.59 342	96.77 214	88.95 142	95.01 130	98.60 125

new-patchmatchnet		74.80 309	72.40 310	81.99 316	78.36 338	72.20 322	94.44 285	92.36 316	77.06 305	63.47 329	79.98 333	51.04 328	88.85 335	60.53 327	54.35 341	84.92 334

new_pmnet		76.02 306	73.71 307	82.95 313	83.88 326	72.85 320	91.26 316	92.26 317	70.44 323	62.60 330	81.37 325	47.64 333	92.32 323	61.85 322	72.10 300	83.68 337

UnsupCasMVSNet_bld		73.85 310	70.14 312	84.99 306	79.44 335	75.73 310	88.53 325	95.24 265	70.12 326	61.94 331	74.81 339	41.41 340	93.62 301	68.65 307	51.13 346	85.62 330

CMPMVS	58.40 21	80.48 290	80.11 278	81.59 319	85.10 321	59.56 338	94.14 291	95.95 213	68.54 330	60.71 332	93.31 208	55.35 318	97.87 159	83.06 194	84.85 217	87.33 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		72.28 312	71.36 311	75.02 327	73.04 341	57.38 342	92.30 306	90.22 336	62.27 339	59.46 333	80.36 330	76.23 185	87.07 339	44.29 343	64.08 320	80.59 341

.test1245		61.50 318	64.44 317	52.65 342	73.04 341	57.38 342	92.30 306	90.22 336	62.27 339	59.46 333	80.36 330	76.23 185	87.07 339	44.29 343	1.80 357	13.50 357

Anonymous20231211		67.10 315	63.29 318	78.54 322	75.68 339	60.00 337	92.05 309	88.86 342	49.84 344	59.35 335	78.48 337	26.15 349	90.76 332	45.96 342	53.24 343	84.88 335

DeepMVS_CX				76.08 325	90.74 255	51.65 348		90.84 331	86.47 187	57.89 336	87.98 295	35.88 346	92.60 319	65.77 316	65.06 318	83.97 336

test1235678		71.07 313	69.53 315	75.71 326	71.87 344	55.27 346	94.32 286	90.76 332	70.23 324	57.61 337	79.06 335	43.13 337	83.72 344	50.48 337	68.30 312	88.14 303

testing_2		80.92 287	77.24 295	91.98 202	78.88 337	87.83 171	93.96 293	95.72 230	84.27 218	56.20 338	80.42 329	38.64 344	96.40 235	87.20 153	79.85 245	91.72 234

test12356		66.36 316	65.12 316	70.08 332	66.92 346	50.46 349	89.96 323	88.58 343	66.00 334	53.38 339	78.13 338	32.89 347	82.87 345	48.36 339	61.87 325	76.92 342

YYNet1		79.64 295	77.04 297	87.43 293	87.80 308	79.98 289	96.23 252	94.44 280	73.83 316	51.83 340	87.53 301	67.96 268	92.07 327	66.00 315	67.75 315	90.23 281

MDA-MVSNet_test_wron		79.65 294	77.05 296	87.45 292	87.79 309	80.13 288	96.25 251	94.44 280	73.87 315	51.80 341	87.47 302	68.04 266	92.12 326	66.02 314	67.79 314	90.09 282

LCM-MVSNet		60.07 320	56.37 321	71.18 328	54.81 355	48.67 350	82.17 343	89.48 340	37.95 347	49.13 342	69.12 340	13.75 359	81.76 346	59.28 329	51.63 345	83.10 339

MDA-MVSNet-bldmvs		77.82 303	74.75 305	87.03 295	88.33 301	78.52 301	96.34 247	92.85 310	75.57 309	48.87 343	87.89 296	57.32 311	92.49 322	60.79 325	64.80 319	90.08 283

PMMVS2		58.97 321	55.07 322	70.69 331	62.72 347	55.37 345	85.97 329	80.52 352	49.48 345	45.94 344	68.31 342	15.73 357	80.78 348	49.79 338	37.12 347	75.91 344

testmv		60.41 319	57.98 320	67.69 333	58.16 354	47.14 351	89.09 324	86.74 346	61.52 342	44.30 345	68.44 341	20.98 351	79.92 350	40.94 347	51.67 344	76.01 343

FPMVS		61.57 317	60.32 319	65.34 334	60.14 351	42.44 354	91.02 318	89.72 339	44.15 346	42.63 346	80.93 327	19.02 352	80.59 349	42.50 346	72.76 290	73.00 345

Gipuma		54.77 323	52.22 324	62.40 336	86.50 318	59.37 339	50.20 353	90.35 335	36.52 349	41.20 347	49.49 351	18.33 354	81.29 347	32.10 351	65.34 317	46.54 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 324	52.86 323	56.05 339	32.75 360	41.97 356	73.42 348	76.12 355	21.91 355	39.68 348	96.39 162	42.59 338	65.10 355	78.00 239	14.92 355	61.08 350

no-one		56.69 322	51.89 325	71.08 330	59.35 353	58.65 341	83.78 341	84.81 351	61.73 341	36.46 349	56.52 349	18.15 355	84.78 343	47.03 341	19.19 351	69.81 347

E-PMN		41.02 330	40.93 329	41.29 343	61.97 348	33.83 357	84.00 339	65.17 359	27.17 352	27.56 350	46.72 353	17.63 356	60.41 357	19.32 354	18.82 352	29.61 354

PNet_i23d		48.05 326	44.98 327	57.28 338	60.15 349	42.39 355	80.85 345	73.14 357	36.78 348	27.46 351	56.66 348	6.38 360	68.34 353	36.65 349	26.72 349	61.10 349

ANet_high		50.71 325	46.17 326	64.33 335	44.27 358	52.30 347	76.13 347	78.73 353	64.95 336	27.37 352	55.23 350	14.61 358	67.74 354	36.01 350	18.23 353	72.95 346

EMVS		39.96 331	39.88 330	40.18 344	59.57 352	32.12 359	84.79 336	64.57 360	26.27 353	26.14 353	44.18 356	18.73 353	59.29 358	17.03 355	17.67 354	29.12 355

MVE	44.00 22	41.70 329	37.64 332	53.90 341	49.46 356	43.37 353	65.09 351	66.66 358	26.19 354	25.77 354	48.53 352	3.58 364	63.35 356	26.15 353	27.28 348	54.97 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	41.42 23	45.67 327	42.50 328	55.17 340	34.28 359	32.37 358	66.24 350	78.71 354	30.72 351	22.04 355	59.59 346	4.59 361	77.85 351	27.49 352	58.84 338	55.29 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		18.81 334	23.05 335	6.10 348	4.48 361	2.29 363	97.78 197	3.00 363	3.27 357	18.60 356	62.71 344	1.53 366	2.49 361	14.26 357	1.80 357	13.50 357

wuykxyi23d		43.53 328	37.95 331	60.27 337	45.36 357	44.79 352	68.27 349	74.26 356	33.48 350	18.21 357	40.16 358	3.64 362	71.01 352	38.85 348	19.31 350	65.02 348

test123		16.58 336	19.47 336	7.91 347	3.59 362	5.37 362	94.32 286	1.39 364	2.49 358	13.98 358	44.60 355	2.91 365	2.65 360	11.35 358	0.57 359	15.70 356

wuyk23d		16.71 335	16.73 337	16.65 346	60.15 349	25.22 361	41.24 354	5.17 362	6.56 356	5.48 359	3.61 360	3.64 362	22.72 359	15.20 356	9.52 356	1.99 359

cdsmvs_eth3d_5k		22.52 333	30.03 334	0.00 349	0.00 363	0.00 364	0.00 355	97.17 143	0.00 359	0.00 360	98.77 65	74.35 207	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		6.87 338	9.16 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	82.48 146	0.00 362	0.00 359	0.00 360	0.00 360

pcd1.5k->3k		35.91 332	37.64 332	30.74 345	89.49 286	0.00 364	0.00 355	96.36 189	0.00 359	0.00 360	0.00 361	69.17 258	0.00 362	0.00 359	83.71 225	92.21 223

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.21 337	10.94 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	98.50 84	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																98.84 109

test_part3							99.43 33		92.81 44		99.48 4		99.97 14	99.52 1

test_part1								97.69 79				93.96 6			99.83 12	99.90 9

sam_mvs1												88.39 59				98.84 109

sam_mvs												87.08 82

MTGPA								97.45 119

test_post1							90.74 321				41.37 357	85.38 110	96.36 238	83.16 192

test_post											46.00 354	87.37 75	97.11 202

patchmatchnet-post											84.86 312	88.73 53	96.81 213

MTMP								91.09 330

gm-plane-assit					94.69 191	88.14 165			88.22 146		97.20 128		98.29 141	90.79 120

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 81	99.41 38

test_prior				97.01 51	99.58 19	91.77 82		97.57 102					99.49 87			99.79 26

新几何2							98.26 169

旧先验1					98.97 60	92.90 69		97.74 75			99.15 27	91.05 20			99.33 53	99.60 59

无先验							98.52 136	97.82 63	87.20 174				99.90 31	87.64 151		99.85 21

原ACMM2							98.69 113

testdata2													99.88 35	84.16 181

segment_acmp												90.56 35

testdata1							97.89 193		92.43 50

plane_prior7					93.84 209	85.73 233

plane_prior6					93.92 206	86.02 226						72.92 226

plane_prior5								96.30 191					97.75 171	93.46 93	86.17 207	92.67 211

plane_prior4											96.52 156

plane_prior2							99.02 78		93.38 35

plane_prior1					93.90 208

plane_prior						86.07 223	99.14 66		93.81 28						86.26 206

n2								0.00 365

nn								0.00 365

door-mid								84.90 350

test11								97.68 81

door								85.30 349

HQP5-MVS						86.39 210

BP-MVS														93.82 88

HQP3-MVS								96.37 186							86.29 204

HQP2-MVS												73.34 221

NP-MVS					93.94 205	86.22 217					96.67 150

ACMMP++_ref															82.64 234

ACMMP++															83.83 223

Test By Simon												83.62 123

High-resolution multi-view benchmark