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.

DeepPCF-MVS	89.82 1	94.61 11	96.17 3	89.91 168	97.09 74	70.21 289	98.99 8	96.69 62	95.57 1	95.08 17	99.23 1	86.40 13	99.87 8	97.84 1	98.66 22	99.65 1

test_part3							98.15 25		84.95 101		98.83 2		99.80 13	97.78 2

ESAPD		95.32 5	95.52 6	94.70 19	98.90 7	85.14 43	98.15 25	96.77 52	84.95 101	96.07 7	98.83 2	89.33 6	99.80 13	97.78 2	98.95 12	99.18 10

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 24	94.66 3	96.79 3	98.78 4	86.42 12	99.95 2	97.59 4	99.18 3	99.00 14

MSLP-MVS++		94.28 15	94.39 16	93.97 32	98.30 40	84.06 61	98.64 13	96.93 44	90.71 22	93.08 39	98.70 5	79.98 59	99.21 65	94.12 30	99.07 6	98.63 29

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 42	99.16 3	96.96 41	94.11 6	95.59 11	98.64 6	85.07 15	99.91 3	95.61 18	99.10 5	99.00 14

DeepC-MVS_fast	89.06 2	94.48 13	94.30 19	95.02 14	98.86 10	85.68 33	98.06 29	96.64 68	93.64 8	91.74 52	98.54 7	80.17 57	99.90 4	92.28 50	98.75 18	99.49 3
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 24	86.04 27	97.81 39	96.93 44	92.45 11	95.69 10	98.50 8	85.38 14	99.85 10	94.75 23	99.18 3	98.65 28

PHI-MVS		93.59 30	93.63 25	93.48 55	98.05 47	81.76 106	98.64 13	97.13 23	82.60 154	94.09 31	98.49 9	80.35 52	99.85 10	94.74 24	98.62 23	98.83 19

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 31	95.17 2	92.11 47	98.46 10	87.33 8	99.97 1	97.21 6	99.31 1	99.63 2

MP-MVS-pluss		92.58 49	92.35 46	93.29 60	97.30 70	82.53 89	96.44 135	96.04 120	84.68 108	89.12 83	98.37 11	77.48 87	99.74 20	93.31 39	98.38 33	97.59 92
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		94.13 19	94.44 15	93.20 63	95.41 106	81.35 116	99.02 7	96.59 74	89.50 33	94.18 30	98.36 12	83.68 30	99.45 50	94.77 22	98.45 28	98.81 20
Skip Steuart: Steuart Systems R&D Blog.
HSP-MVS		95.55 4	96.51 2	92.66 84	98.31 39	80.10 145	97.42 67	96.46 86	92.20 13	97.11 2	98.29 13	93.46 1	99.10 79	96.01 13	99.30 2	98.77 22

APDe-MVS		94.56 12	94.75 10	93.96 33	98.84 11	83.40 74	98.04 30	96.41 91	85.79 80	95.00 19	98.28 14	84.32 25	99.18 72	97.35 5	98.77 17	99.28 5

CDPH-MVS		93.12 35	92.91 37	93.74 39	98.65 20	83.88 62	97.67 50	96.26 105	83.00 147	93.22 38	98.24 15	81.31 46	99.21 65	89.12 78	98.74 19	98.14 52

APD-MVS		93.61 29	93.59 26	93.69 42	98.76 12	83.26 75	97.21 74	96.09 116	82.41 156	94.65 24	98.21 16	81.96 39	98.81 95	94.65 25	98.36 35	99.01 13
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MPTG		92.74 41	92.71 39	92.86 75	97.90 49	80.85 125	96.47 130	96.33 101	87.92 52	90.20 70	98.18 17	76.71 99	99.76 15	92.57 47	98.09 40	97.96 68

MTAPA		92.45 51	92.31 47	92.86 75	97.90 49	80.85 125	92.88 246	96.33 101	87.92 52	90.20 70	98.18 17	76.71 99	99.76 15	92.57 47	98.09 40	97.96 68

PS-MVSNAJ		94.17 18	93.52 28	96.10 4	95.65 100	92.35 1	98.21 23	95.79 132	92.42 12	96.24 5	98.18 17	71.04 160	99.17 73	96.77 9	97.39 58	96.79 125

MAR-MVS		90.63 74	90.22 69	91.86 115	98.47 33	78.20 210	97.18 78	96.61 72	83.87 131	88.18 94	98.18 17	68.71 173	99.75 18	83.66 124	97.15 62	97.63 89
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
SD-MVS		94.84 9	95.02 9	94.29 25	97.87 53	84.61 51	97.76 45	96.19 111	89.59 32	96.66 4	98.17 21	84.33 22	99.60 36	96.09 12	98.50 26	98.66 27

xiu_mvs_v2_base		93.92 25	93.26 30	95.91 6	95.07 116	92.02 2	98.19 24	95.68 136	92.06 14	96.01 9	98.14 22	70.83 163	98.96 87	96.74 10	96.57 72	96.76 128

agg_prior1		94.10 20	94.31 18	93.48 55	98.59 26	83.13 77	97.77 42	96.56 76	84.38 117	94.19 28	98.13 23	84.66 19	99.16 74	95.74 17	98.74 19	98.15 51

Regformer-1		94.00 24	94.04 22	93.87 35	98.41 34	84.29 58	97.43 65	97.04 35	89.50 33	92.75 43	98.13 23	82.60 36	99.26 60	93.55 33	96.99 64	98.06 57

Regformer-2		93.92 25	94.01 23	93.67 43	98.41 34	83.75 66	97.43 65	97.00 37	89.43 35	92.69 44	98.13 23	82.48 37	99.22 63	93.51 34	96.99 64	98.04 58

PAPR		92.74 41	92.17 50	94.45 21	98.89 9	84.87 49	97.20 76	96.20 109	87.73 57	88.40 90	98.12 26	78.71 73	99.76 15	87.99 90	96.28 74	98.74 23

test_8					98.63 23	83.64 70	97.81 39	96.63 71	84.50 113	95.10 16	98.11 27	84.33 22	99.23 61

TEST9					98.64 21	83.71 67	97.82 37	96.65 65	84.29 121	95.16 14	98.09 28	84.39 21	99.36 55

train_agg		94.28 15	94.45 14	93.74 39	98.64 21	83.71 67	97.82 37	96.65 65	84.50 113	95.16 14	98.09 28	84.33 22	99.36 55	95.91 15	98.96 10	98.16 49

CP-MVS		92.54 50	92.60 44	92.34 95	98.50 31	79.90 148	98.40 17	96.40 93	84.75 106	90.48 68	98.09 28	77.40 88	99.21 65	91.15 58	98.23 39	97.92 71

旧先验1					97.39 65	79.58 163		96.54 79			98.08 31	84.00 26			97.42 57	97.62 90

agg_prior3		94.10 20	94.29 20	93.53 52	98.62 24	83.03 81	97.80 41	96.64 68	84.28 122	95.01 18	98.03 32	83.40 31	99.41 52	95.91 15	98.96 10	98.16 49

MVS_0304		93.82 28	93.11 34	95.95 5	96.79 76	89.15 7	98.56 15	95.30 157	93.61 9	94.82 22	98.02 33	66.60 195	99.88 7	96.94 8	97.39 58	98.81 20

test_prior3		94.03 23	94.34 17	93.09 68	98.68 15	81.91 100	98.37 18	96.40 93	86.08 75	94.57 25	98.02 33	83.14 33	99.06 81	95.05 20	98.79 15	98.29 43

test_prior2							98.37 18		86.08 75	94.57 25	98.02 33	83.14 33		95.05 20	98.79 15

ACMMP_Plus		93.46 31	93.23 31	94.17 28	97.16 72	84.28 59	96.82 110	96.65 65	86.24 72	94.27 27	97.99 36	77.94 82	99.83 12	93.39 35	98.57 24	98.39 37

testdata				90.13 158	95.92 90	74.17 256		96.49 85	73.49 281	94.82 22	97.99 36	78.80 72	97.93 117	83.53 127	97.52 52	98.29 43

region2R		92.72 44	92.70 41	92.79 78	98.68 15	80.53 134	97.53 58	96.51 81	85.22 92	91.94 48	97.98 38	77.26 89	99.67 31	90.83 62	98.37 34	98.18 48

CSCG		92.02 54	91.65 56	93.12 66	98.53 28	80.59 130	97.47 61	97.18 22	77.06 241	84.64 122	97.98 38	83.98 27	99.52 43	90.72 63	97.33 60	99.23 7

HFP-MVS		92.89 39	92.86 38	92.98 71	98.71 13	81.12 119	97.58 54	96.70 60	85.20 94	91.75 50	97.97 40	78.47 75	99.71 23	90.95 59	98.41 30	98.12 54

#test#		92.99 37	92.99 35	92.98 71	98.71 13	81.12 119	97.77 42	96.70 60	85.75 81	91.75 50	97.97 40	78.47 75	99.71 23	91.36 55	98.41 30	98.12 54

ACMMPR		92.69 45	92.67 42	92.75 80	98.66 18	80.57 131	97.58 54	96.69 62	85.20 94	91.57 53	97.92 42	77.01 93	99.67 31	90.95 59	98.41 30	98.00 65

Regformer-3		93.19 33	93.19 32	93.19 64	98.10 45	83.01 82	97.08 94	96.98 39	88.98 37	91.35 59	97.89 43	80.80 49	99.23 61	92.30 49	95.20 86	97.32 105

Regformer-4		93.06 36	93.12 33	92.89 74	98.10 45	82.20 95	97.08 94	96.92 46	88.87 39	91.23 61	97.89 43	80.57 51	99.19 70	92.21 51	95.20 86	97.29 110

APD-MVS_3200maxsize		91.23 68	91.35 59	90.89 139	97.89 51	76.35 240	96.30 149	95.52 144	79.82 205	91.03 64	97.88 45	74.70 134	98.54 100	92.11 53	96.89 68	97.77 80

XVS		92.69 45	92.71 39	92.63 87	98.52 29	80.29 138	97.37 69	96.44 88	87.04 67	91.38 55	97.83 46	77.24 91	99.59 37	90.46 65	98.07 42	98.02 60

CANet		94.89 8	94.64 11	95.63 8	97.55 60	88.12 11	99.06 5	96.39 96	94.07 7	95.34 13	97.80 47	76.83 96	99.87 8	97.08 7	97.64 51	98.89 17

PGM-MVS		91.93 55	91.80 53	92.32 97	98.27 41	79.74 152	95.28 184	97.27 19	83.83 132	90.89 65	97.78 48	76.12 107	99.56 41	88.82 80	97.93 48	97.66 86

API-MVS		90.18 83	88.97 89	93.80 37	98.66 18	82.95 84	97.50 60	95.63 139	75.16 261	86.31 107	97.69 49	72.49 146	99.90 4	81.26 141	96.07 77	98.56 32

cdsmvs_eth3d_5k		21.43 328	28.57 329	0.00 344	0.00 358	0.00 359	0.00 350	95.93 126	0.00 354	0.00 355	97.66 50	63.57 218	0.00 357	0.00 354	0.00 355	0.00 355

MP-MVS		92.61 48	92.67 42	92.42 92	98.13 44	79.73 153	97.33 71	96.20 109	85.63 83	90.53 66	97.66 50	78.14 80	99.70 26	92.12 52	98.30 37	97.85 75

mPP-MVS		91.88 56	91.82 52	92.07 104	98.38 36	78.63 194	97.29 72	96.09 116	85.12 96	88.45 89	97.66 50	75.53 113	99.68 29	89.83 71	98.02 45	97.88 72

lupinMVS		93.87 27	93.58 27	94.75 18	93.00 164	88.08 12	99.15 4	95.50 145	91.03 19	94.90 20	97.66 50	78.84 70	97.56 136	94.64 26	97.46 53	98.62 30

PAPM_NR		91.46 64	90.82 65	93.37 59	98.50 31	81.81 105	95.03 198	96.13 113	84.65 109	86.10 110	97.65 54	79.24 66	99.75 18	83.20 130	96.88 69	98.56 32

DP-MVS Recon		91.72 58	90.85 64	94.34 23	99.50 1	85.00 45	98.51 16	95.96 123	80.57 186	88.08 95	97.63 55	76.84 95	99.89 6	85.67 104	94.88 90	98.13 53

新几何1				93.12 66	97.44 63	81.60 112		96.71 59	74.54 273	91.22 62	97.57 56	79.13 68	99.51 46	77.40 173	98.46 27	98.26 46

xiu_mvs_v1_base_debu		90.54 76	89.54 83	93.55 49	92.31 174	87.58 18	96.99 98	94.87 171	87.23 62	93.27 35	97.56 57	57.43 263	98.32 107	92.72 44	93.46 104	94.74 172

xiu_mvs_v1_base		90.54 76	89.54 83	93.55 49	92.31 174	87.58 18	96.99 98	94.87 171	87.23 62	93.27 35	97.56 57	57.43 263	98.32 107	92.72 44	93.46 104	94.74 172

xiu_mvs_v1_base_debi		90.54 76	89.54 83	93.55 49	92.31 174	87.58 18	96.99 98	94.87 171	87.23 62	93.27 35	97.56 57	57.43 263	98.32 107	92.72 44	93.46 104	94.74 172

EI-MVSNet-Vis-set		91.84 57	91.77 54	92.04 106	97.60 57	81.17 118	96.61 124	96.87 48	88.20 48	89.19 82	97.55 60	78.69 74	99.14 76	90.29 67	90.94 126	95.80 149

1121		90.66 73	89.82 79	93.16 65	97.39 65	81.71 110	93.33 233	96.66 64	74.45 274	91.38 55	97.55 60	79.27 64	99.52 43	79.95 149	98.43 29	98.26 46

alignmvs		92.97 38	92.26 48	95.12 13	95.54 102	87.77 15	98.67 11	96.38 97	88.04 50	93.01 40	97.45 62	79.20 67	98.60 97	93.25 40	88.76 137	98.99 16

test222					96.15 83	78.41 203	95.87 169	96.46 86	71.97 290	89.66 75	97.45 62	76.33 104			98.24 38	98.30 42

TSAR-MVS + GP.		94.35 14	94.50 12	93.89 34	97.38 68	83.04 80	98.10 28	95.29 158	91.57 15	93.81 32	97.45 62	86.64 9	99.43 51	96.28 11	94.01 96	99.20 8

CPTT-MVS		89.72 89	89.87 78	89.29 178	98.33 38	73.30 260	97.70 48	95.35 155	75.68 253	87.40 98	97.44 65	70.43 165	98.25 110	89.56 75	96.90 67	96.33 141

原ACMM1				91.22 130	97.77 54	78.10 212		96.61 72	81.05 174	91.28 60	97.42 66	77.92 83	98.98 86	79.85 151	98.51 25	96.59 132

EI-MVSNet-UG-set		91.35 66	91.22 60	91.73 118	97.39 65	80.68 128	96.47 130	96.83 50	87.92 52	88.30 93	97.36 67	77.84 84	99.13 77	89.43 77	89.45 132	95.37 159

canonicalmvs		92.27 52	91.22 60	95.41 11	95.80 97	88.31 9	97.09 92	94.64 188	88.49 43	92.99 41	97.31 68	72.68 145	98.57 99	93.38 37	88.58 140	99.36 4

MVS		90.60 75	88.64 93	96.50 1	94.25 139	90.53 4	93.33 233	97.21 21	77.59 232	78.88 189	97.31 68	71.52 155	99.69 27	89.60 73	98.03 44	99.27 6

1112_ss		88.60 111	87.47 114	92.00 107	93.21 159	80.97 122	96.47 130	92.46 265	83.64 137	80.86 165	97.30 70	80.24 55	97.62 134	77.60 170	85.49 166	97.40 102

ab-mvs-re		8.11 332	10.81 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	97.30 70	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ACMMP		90.39 80	89.97 73	91.64 120	97.58 59	78.21 209	96.78 112	96.72 58	84.73 107	84.72 120	97.23 72	71.22 157	99.63 34	88.37 87	92.41 112	97.08 116
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
WTY-MVS		92.65 47	91.68 55	95.56 9	96.00 88	88.90 8	98.23 22	97.65 15	88.57 40	89.82 72	97.22 73	79.29 63	99.06 81	89.57 74	88.73 138	98.73 25

HPM-MVS		91.62 61	91.53 58	91.89 110	97.88 52	79.22 171	96.99 98	95.73 134	82.07 160	89.50 80	97.19 74	75.59 112	98.93 92	90.91 61	97.94 46	97.54 93

MVS_111021_HR		93.41 32	93.39 29	93.47 58	97.34 69	82.83 85	97.56 56	98.27 11	89.16 36	89.71 73	97.14 75	79.77 61	99.56 41	93.65 32	97.94 46	98.02 60

MVSFormer		91.36 65	90.57 67	93.73 41	93.00 164	88.08 12	94.80 203	94.48 193	80.74 182	94.90 20	97.13 76	78.84 70	95.10 255	83.77 119	97.46 53	98.02 60

jason		92.73 43	92.23 49	94.21 27	90.50 212	87.30 21	98.65 12	95.09 162	90.61 23	92.76 42	97.13 76	75.28 128	97.30 149	93.32 38	96.75 71	98.02 60
jason: jason.
DELS-MVS		94.98 7	94.49 13	96.44 2	96.42 79	90.59 3	99.21 2	97.02 36	94.40 5	91.46 54	97.08 78	83.32 32	99.69 27	92.83 43	98.70 21	99.04 12
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_LR		91.60 62	91.64 57	91.47 123	95.74 98	78.79 191	96.15 154	96.77 52	88.49 43	88.64 88	97.07 79	72.33 148	99.19 70	93.13 41	96.48 73	96.43 136

abl_6		89.80 87	89.71 82	90.07 159	96.53 78	75.52 246	94.48 206	95.04 165	81.12 173	89.22 81	97.00 80	68.83 172	98.96 87	89.86 70	95.27 85	95.73 151

MG-MVS		94.25 17	93.72 24	95.85 7	99.38 3	89.35 6	97.98 32	98.09 13	89.99 29	92.34 46	96.97 81	81.30 47	98.99 85	88.54 82	98.88 14	99.20 8

HPM-MVS_fast		90.38 81	90.17 71	91.03 134	97.61 56	77.35 229	97.15 83	95.48 146	79.51 210	88.79 86	96.90 82	71.64 154	98.81 95	87.01 99	97.44 55	96.94 119

PAPM		92.87 40	92.40 45	94.30 24	92.25 181	87.85 14	96.40 141	96.38 97	91.07 18	88.72 87	96.90 82	82.11 38	97.37 146	90.05 69	97.70 50	97.67 85

EPNet		94.06 22	94.15 21	93.76 38	97.27 71	84.35 56	98.29 20	97.64 16	94.57 4	95.36 12	96.88 84	79.96 60	99.12 78	91.30 56	96.11 76	97.82 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		88.80 105	88.16 98	90.72 141	95.30 109	77.92 218	94.81 202	94.51 192	86.80 69	84.97 115	96.85 85	67.53 177	98.60 97	85.08 108	87.62 146	95.63 154

TAPA-MVS	81.61 12	85.02 174	83.67 174	89.06 180	96.79 76	73.27 262	95.92 164	94.79 178	74.81 268	80.47 169	96.83 86	71.07 159	98.19 113	49.82 322	92.57 109	95.71 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CANet_DTU		90.98 70	90.04 72	93.83 36	94.76 122	86.23 26	96.32 145	93.12 258	93.11 10	93.71 33	96.82 87	63.08 223	99.48 48	84.29 114	95.12 89	95.77 150

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 44	97.66 55	84.10 60	95.85 171	96.42 90	91.26 17	97.49 1	96.80 88	86.50 11	98.49 103	95.54 19	99.03 7	98.33 39

DeepC-MVS	86.58 3	91.53 63	91.06 63	92.94 73	94.52 133	81.89 102	95.95 162	95.98 122	90.76 21	83.76 132	96.76 89	73.24 142	99.71 23	91.67 54	96.96 66	97.22 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA		86.96 139	85.37 144	91.72 119	97.59 58	79.34 166	97.21 74	91.05 282	74.22 275	78.90 188	96.75 90	67.21 181	98.95 89	74.68 199	90.77 127	96.88 123

AdaColmap		88.81 104	87.61 110	92.39 94	99.33 4	79.95 146	96.70 119	95.58 140	77.51 233	83.05 140	96.69 91	61.90 234	99.72 22	84.29 114	93.47 103	97.50 97

LFMVS		89.27 95	87.64 107	94.16 30	97.16 72	85.52 36	97.18 78	94.66 185	79.17 217	89.63 76	96.57 92	55.35 280	98.22 111	89.52 76	89.54 131	98.74 23

PMMVS		89.46 92	89.92 76	88.06 201	94.64 123	69.57 295	96.22 152	94.95 168	87.27 61	91.37 58	96.54 93	65.88 200	97.39 145	88.54 82	93.89 98	97.23 113

1314		88.94 100	87.20 118	94.17 28	93.21 159	85.73 31	93.33 233	96.64 68	82.89 148	75.98 224	96.36 94	66.83 191	99.39 53	83.52 128	96.02 79	97.39 103

PLC	83.97 7	88.00 123	87.38 116	89.83 171	98.02 48	76.46 238	97.16 82	94.43 197	79.26 216	81.98 157	96.28 95	69.36 170	99.27 58	77.71 169	92.25 116	93.77 186
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_Blended		93.13 34	92.98 36	93.57 48	97.47 61	83.86 63	99.32 1	96.73 56	91.02 20	89.53 78	96.21 96	76.42 101	99.57 39	94.29 28	95.81 83	97.29 110

sss		90.87 71	89.96 74	93.60 47	94.15 141	83.84 65	97.14 84	98.13 12	85.93 78	89.68 74	96.09 97	71.67 152	99.30 57	87.69 92	89.16 133	97.66 86

3Dnovator+	82.88 8	89.63 90	87.85 102	94.99 15	94.49 136	86.76 23	97.84 36	95.74 133	86.10 74	75.47 231	96.02 98	65.00 212	99.51 46	82.91 134	97.07 63	98.72 26

3Dnovator	82.32 10	89.33 94	87.64 107	94.42 22	93.73 152	85.70 32	97.73 47	96.75 55	86.73 70	76.21 222	95.93 99	62.17 227	99.68 29	81.67 139	97.81 49	97.88 72

VDD-MVS		88.28 118	87.02 124	92.06 105	95.09 114	80.18 144	97.55 57	94.45 196	83.09 144	89.10 84	95.92 100	47.97 303	98.49 103	93.08 42	86.91 150	97.52 96

VNet		92.11 53	91.22 60	94.79 17	96.91 75	86.98 22	97.91 33	97.96 14	86.38 71	93.65 34	95.74 101	70.16 168	98.95 89	93.39 35	88.87 136	98.43 35

OpenMVS	79.58 14	86.09 154	83.62 176	93.50 53	90.95 206	86.71 24	97.44 62	95.83 130	75.35 257	72.64 250	95.72 102	57.42 266	99.64 33	71.41 219	95.85 82	94.13 179

Effi-MVS+		90.70 72	89.90 77	93.09 68	93.61 153	83.48 72	95.20 187	92.79 262	83.22 142	91.82 49	95.70 103	71.82 151	97.48 143	91.25 57	93.67 101	98.32 40

114514_t		88.79 106	87.57 111	92.45 91	98.21 42	81.74 107	96.99 98	95.45 150	75.16 261	82.48 143	95.69 104	68.59 174	98.50 102	80.33 144	95.18 88	97.10 115

Vis-MVSNet (Re-imp)		88.88 103	88.87 92	88.91 184	93.89 147	74.43 254	96.93 106	94.19 205	84.39 116	83.22 138	95.67 105	78.24 78	94.70 264	78.88 158	94.40 93	97.61 91

QAPM		86.88 141	84.51 157	93.98 31	94.04 145	85.89 29	97.19 77	96.05 119	73.62 279	75.12 234	95.62 106	62.02 230	99.74 20	70.88 225	96.06 78	96.30 143

IS-MVSNet		88.67 108	88.16 98	90.20 153	93.61 153	76.86 235	96.77 114	93.07 259	84.02 127	83.62 133	95.60 107	74.69 135	96.24 192	78.43 160	93.66 102	97.49 98

BH-RMVSNet		86.84 142	85.28 145	91.49 122	95.35 108	80.26 141	96.95 104	92.21 267	82.86 149	81.77 159	95.46 108	59.34 246	97.64 133	69.79 233	93.81 100	96.57 133

CLD-MVS		87.97 124	87.48 113	89.44 176	92.16 184	80.54 133	98.14 27	94.92 169	91.41 16	79.43 185	95.40 109	62.34 226	97.27 152	90.60 64	82.90 198	90.50 209
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DWT-MVSNet_test		90.52 79	89.80 80	92.70 83	95.73 99	82.20 95	93.69 224	96.55 78	88.34 45	87.04 104	95.34 110	86.53 10	97.55 138	76.32 185	88.66 139	98.34 38

conf0.01		85.70 166	84.35 162	89.77 173	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	93.89 183

conf0.002		85.70 166	84.35 162	89.77 173	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	93.89 183

thresconf0.02		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

tfpn_n400		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

tfpnconf		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

tfpnview11		85.80 159	84.35 162	90.17 154	94.53 127	79.70 154	95.17 188	97.11 25	75.97 244	79.44 179	95.31 111	81.90 40	95.73 224	56.78 298	82.91 192	95.09 163

XVG-OURS-SEG-HR		85.74 165	85.16 147	87.49 220	90.22 216	71.45 279	91.29 272	94.09 216	81.37 170	83.90 131	95.22 117	60.30 238	97.53 142	85.58 105	84.42 179	93.50 189

PatchFormer-LS_test		90.14 84	89.30 87	92.65 86	95.43 104	82.46 90	93.46 229	96.35 99	88.56 41	84.82 117	95.22 117	84.63 20	97.55 138	78.40 161	86.81 151	97.94 70

LS3D		82.22 220	79.94 228	89.06 180	97.43 64	74.06 258	93.20 241	92.05 269	61.90 318	73.33 243	95.21 119	59.35 245	99.21 65	54.54 309	92.48 111	93.90 182

VDDNet		86.44 150	84.51 157	92.22 100	91.56 197	81.83 104	97.10 91	94.64 188	69.50 300	87.84 96	95.19 120	48.01 302	97.92 123	89.82 72	86.92 149	96.89 122

F-COLMAP		84.50 183	83.44 181	87.67 213	95.22 111	72.22 267	95.95 162	93.78 232	75.74 251	76.30 220	95.18 121	59.50 243	98.45 105	72.67 209	86.59 154	92.35 199

TR-MVS		86.30 152	84.93 154	90.42 146	94.63 124	77.58 224	96.57 126	93.82 227	80.30 193	82.42 145	95.16 122	58.74 250	97.55 138	74.88 197	87.82 145	96.13 144

gm-plane-assit					92.27 178	79.64 161			84.47 115		95.15 123		97.93 117	85.81 103

mvs-test1		86.83 143	87.17 119	85.81 239	91.96 191	65.24 307	97.90 34	93.34 252	85.57 84	84.51 124	95.14 124	61.99 231	97.19 156	83.55 125	90.55 128	95.00 167

Vis-MVSNet		88.67 108	87.82 103	91.24 129	92.68 167	78.82 188	96.95 104	93.85 226	87.55 58	87.07 103	95.13 125	63.43 221	97.21 154	77.58 171	96.15 75	97.70 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PVSNet	82.34 9	89.02 98	87.79 104	92.71 82	95.49 103	81.50 113	97.70 48	97.29 18	87.76 56	85.47 112	95.12 126	56.90 268	98.90 93	80.33 144	94.02 95	97.71 83

XVG-OURS		85.18 173	84.38 161	87.59 216	90.42 214	71.73 276	91.06 275	94.07 217	82.00 162	83.29 137	95.08 127	56.42 274	97.55 138	83.70 123	83.42 185	93.49 190

tfpn_ndepth		87.25 137	86.00 134	91.01 136	95.86 95	81.46 114	96.53 127	97.09 32	77.35 236	81.36 160	95.07 128	84.74 18	95.86 213	60.88 284	85.14 174	95.72 152

EPNet_dtu		87.65 129	87.89 101	86.93 228	94.57 125	71.37 280	96.72 115	96.50 83	88.56 41	87.12 102	95.02 129	75.91 110	94.01 277	66.62 254	90.00 130	95.42 158
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPP-MVSNet		89.76 88	89.72 81	89.87 169	93.78 148	76.02 243	97.22 73	96.51 81	79.35 212	85.11 114	95.01 130	84.82 16	97.10 161	87.46 95	88.21 143	96.50 134

tfpn1000		86.43 151	85.10 149	90.41 147	95.56 101	80.51 135	95.90 167	97.09 32	75.91 250	80.02 176	94.82 131	84.78 17	95.47 241	57.36 293	84.46 177	95.26 162

PCF-MVS	84.09 5	86.77 146	85.00 151	92.08 103	92.06 188	83.07 79	92.14 262	94.47 195	79.63 209	76.90 212	94.78 132	71.15 158	99.20 69	72.87 207	91.05 125	93.98 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet		85.80 159	85.20 146	87.59 216	91.55 198	77.41 227	95.13 194	95.36 153	80.43 190	80.33 172	94.71 133	73.72 140	95.97 204	76.96 180	78.64 220	89.39 228

CVMVSNet		84.83 178	85.57 141	82.63 288	91.55 198	60.38 320	95.13 194	95.03 166	80.60 185	82.10 156	94.71 133	66.40 197	90.19 318	74.30 201	90.32 129	97.31 107

NP-MVS					92.04 189	78.22 207					94.56 135

HQP-MVS		87.91 127	87.55 112	88.98 183	92.08 185	78.48 199	97.63 51	94.80 176	90.52 24	82.30 146	94.56 135	65.40 208	97.32 147	87.67 93	83.01 189	91.13 202

BH-w/o		88.24 120	87.47 114	90.54 145	95.03 117	78.54 197	97.41 68	93.82 227	84.08 125	78.23 194	94.51 137	69.34 171	97.21 154	80.21 147	94.58 91	95.87 148

CHOSEN 280x420		91.71 59	91.85 51	91.29 126	94.94 118	82.69 87	87.89 296	96.17 112	85.94 77	87.27 101	94.31 138	90.27 4	95.65 230	94.04 31	95.86 81	95.53 156

GG-mvs-BLEND				93.49 54	94.94 118	86.26 25	81.62 321	97.00 37		88.32 92	94.30 139	91.23 2	96.21 193	88.49 84	97.43 56	98.00 65

CDS-MVSNet		89.50 91	88.96 90	91.14 132	91.94 194	80.93 123	97.09 92	95.81 131	84.26 123	84.72 120	94.20 140	80.31 53	95.64 231	83.37 129	88.96 135	96.85 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		87.50 130	87.09 123	88.74 188	91.86 195	77.96 215	97.18 78	94.69 181	89.89 30	81.33 161	94.15 141	64.77 213	97.30 149	87.08 96	82.82 199	90.96 204

plane_prior4											94.15 141

OPM-MVS		85.84 157	85.10 149	88.06 201	88.34 242	77.83 221	95.72 174	94.20 203	87.89 55	80.45 170	94.05 143	58.57 251	97.26 153	83.88 117	82.76 201	89.09 234

thres200		88.92 101	87.65 106	92.73 81	96.30 80	85.62 34	97.85 35	98.86 1	84.38 117	84.82 117	93.99 144	75.12 130	98.01 115	70.86 226	86.67 152	94.56 175

PVSNet_Blended_VisFu		91.24 67	90.77 66	92.66 84	95.09 114	82.40 91	97.77 42	95.87 129	88.26 47	86.39 106	93.94 145	76.77 97	99.27 58	88.80 81	94.00 97	96.31 142

UA-Net		88.92 101	88.48 96	90.24 151	94.06 144	77.18 233	93.04 243	94.66 185	87.39 60	91.09 63	93.89 146	74.92 132	98.18 114	75.83 189	91.43 123	95.35 160

tfpn200view9		88.48 112	87.15 120	92.47 90	96.21 81	85.30 39	97.44 62	98.85 2	83.37 140	83.99 127	93.82 147	75.36 125	97.93 117	69.04 236	86.24 157	94.17 176

thres400		88.42 115	87.15 120	92.23 99	96.21 81	85.30 39	97.44 62	98.85 2	83.37 140	83.99 127	93.82 147	75.36 125	97.93 117	69.04 236	86.24 157	93.45 191

BH-untuned		86.95 140	85.94 139	89.99 163	94.52 133	77.46 226	96.78 112	93.37 251	81.80 167	76.62 215	93.81 149	66.64 194	97.02 164	76.06 187	93.88 99	95.48 157

conf200view11		88.27 119	86.95 125	92.24 98	96.10 85	84.90 47	97.14 84	98.85 2	82.69 152	83.41 134	93.66 150	75.43 121	97.93 117	69.04 236	86.24 157	93.89 183

thres100view900		88.30 117	86.95 125	92.33 96	96.10 85	84.90 47	97.14 84	98.85 2	82.69 152	83.41 134	93.66 150	75.43 121	97.93 117	69.04 236	86.24 157	94.17 176

thres600view7		88.06 121	86.70 129	92.15 102	96.10 85	85.17 41	97.14 84	98.85 2	82.70 151	83.41 134	93.66 150	75.43 121	97.82 126	67.13 251	85.88 162	93.45 191

view600		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 153	75.44 117	97.66 129	64.02 269	85.36 168	93.45 191

view800		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 153	75.44 117	97.66 129	64.02 269	85.36 168	93.45 191

conf0.05thres1000		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 153	75.44 117	97.66 129	64.02 269	85.36 168	93.45 191

tfpn		87.45 132	85.98 135	91.88 111	95.90 91	84.52 52	96.68 120	98.85 2	81.85 163	82.30 146	93.39 153	75.44 117	97.66 129	64.02 269	85.36 168	93.45 191

TAMVS		88.48 112	87.79 104	90.56 144	91.09 204	79.18 172	96.45 133	95.88 128	83.64 137	83.12 139	93.33 157	75.94 109	95.74 223	82.40 135	88.27 142	96.75 129

test0.0.03 1		82.79 210	82.48 192	83.74 277	86.81 254	72.22 267	96.52 128	95.03 166	83.76 134	73.00 246	93.20 158	72.30 149	88.88 321	64.15 268	77.52 227	90.12 217

LPG-MVS_test		84.20 186	83.49 180	86.33 232	90.88 207	73.06 263	95.28 184	94.13 210	82.20 158	76.31 218	93.20 158	54.83 285	96.95 166	83.72 121	80.83 205	88.98 238

LGP-MVS_train				86.33 232	90.88 207	73.06 263		94.13 210	82.20 158	76.31 218	93.20 158	54.83 285	96.95 166	83.72 121	80.83 205	88.98 238

CHOSEN 1792x2688		91.07 69	90.21 70	93.64 44	95.18 112	83.53 71	96.26 151	96.13 113	88.92 38	84.90 116	93.10 161	72.86 144	99.62 35	88.86 79	95.67 84	97.79 79

Fast-Effi-MVS+		87.93 126	86.94 127	90.92 138	94.04 145	79.16 173	98.26 21	93.72 235	81.29 171	83.94 130	92.90 162	69.83 169	96.68 179	76.70 181	91.74 122	96.93 120

RPSCF		77.73 260	76.63 251	81.06 297	88.66 240	55.76 329	87.77 297	87.88 313	64.82 311	74.14 237	92.79 163	49.22 299	96.81 174	67.47 250	76.88 228	90.62 207

DP-MVS		81.47 227	78.28 237	91.04 133	98.14 43	78.48 199	95.09 197	86.97 318	61.14 323	71.12 257	92.78 164	59.59 241	99.38 54	53.11 313	86.61 153	95.27 161

FIs		86.73 147	86.10 133	88.61 190	90.05 220	80.21 142	96.14 155	96.95 42	85.56 87	78.37 193	92.30 165	76.73 98	95.28 249	79.51 152	79.27 215	90.35 211

ACMP	81.66 11	84.00 187	83.22 183	86.33 232	91.53 200	72.95 265	95.91 166	93.79 231	83.70 136	73.79 238	92.22 166	54.31 288	96.89 170	83.98 116	79.74 210	89.16 233
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPNet		84.69 180	82.92 185	90.01 162	89.01 234	83.45 73	96.71 117	95.46 147	85.71 82	79.65 178	92.18 167	56.66 271	96.01 203	83.05 133	67.84 284	90.56 208

nrg030		86.79 145	85.43 142	90.87 140	88.76 236	85.34 38	97.06 96	94.33 199	84.31 119	80.45 170	91.98 168	72.36 147	96.36 186	88.48 85	71.13 250	90.93 206

HY-MVS	84.06 6	91.63 60	90.37 68	95.39 12	96.12 84	88.25 10	90.22 278	97.58 17	88.33 46	90.50 67	91.96 169	79.26 65	99.06 81	90.29 67	89.07 134	98.88 18

ACMM	80.70 13	83.72 191	82.85 187	86.31 235	91.19 203	72.12 270	95.88 168	94.29 200	80.44 188	77.02 210	91.96 169	55.24 281	97.14 160	79.30 154	80.38 207	89.67 225
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-test		85.96 155	85.39 143	87.66 214	89.38 232	78.02 213	95.65 177	96.87 48	85.12 96	77.34 205	91.94 171	76.28 105	94.74 263	77.09 177	78.82 218	90.21 214

MSDG		80.62 236	77.77 241	89.14 179	93.43 157	77.24 230	91.89 266	90.18 296	69.86 299	68.02 272	91.94 171	52.21 290	98.84 94	59.32 289	83.12 187	91.35 201

TESTMET0.1,1		89.83 86	89.34 86	91.31 124	92.54 172	80.19 143	97.11 88	96.57 75	86.15 73	86.85 105	91.83 173	79.32 62	96.95 166	81.30 140	92.35 113	96.77 127

PatchMatch-RL		85.00 175	83.66 175	89.02 182	95.86 95	74.55 253	92.49 254	93.60 240	79.30 215	79.29 187	91.47 174	58.53 252	98.45 105	70.22 229	92.17 117	94.07 180

Fast-Effi-MVS+-dtu		83.33 202	82.60 191	85.50 243	89.55 228	69.38 296	96.09 159	91.38 276	82.30 157	75.96 225	91.41 175	56.71 269	95.58 236	75.13 196	84.90 176	91.54 200

test-LLR		88.48 112	87.98 100	89.98 164	92.26 179	77.23 231	97.11 88	95.96 123	83.76 134	86.30 108	91.38 176	72.30 149	96.78 176	80.82 142	91.92 119	95.94 146

test-mter		88.95 99	88.60 94	89.98 164	92.26 179	77.23 231	97.11 88	95.96 123	85.32 90	86.30 108	91.38 176	76.37 103	96.78 176	80.82 142	91.92 119	95.94 146

ITE_SJBPF				82.38 290	87.00 253	65.59 306		89.55 300	79.99 202	69.37 269	91.30 178	41.60 320	95.33 247	62.86 279	74.63 238	86.24 288

HyFIR lowres test		89.36 93	88.60 94	91.63 121	94.91 120	80.76 127	95.60 178	95.53 142	82.56 155	84.03 126	91.24 179	78.03 81	96.81 174	87.07 98	88.41 141	97.32 105

Test_1112_low_res		88.03 122	86.73 128	91.94 109	93.15 161	80.88 124	96.44 135	92.41 266	83.59 139	80.74 167	91.16 180	80.18 56	97.59 135	77.48 172	85.40 167	97.36 104

testgi		74.88 281	73.40 279	79.32 304	80.13 316	61.75 317	93.21 240	86.64 321	79.49 211	66.56 279	91.06 181	35.51 329	88.67 322	56.79 297	71.25 249	87.56 273

MVS_Test		90.29 82	89.18 88	93.62 46	95.23 110	84.93 46	94.41 209	94.66 185	84.31 119	90.37 69	91.02 182	75.13 129	97.82 126	83.11 132	94.42 92	98.12 54

cascas		86.50 148	84.48 159	92.55 89	92.64 171	85.95 28	97.04 97	95.07 164	75.32 258	80.50 168	91.02 182	54.33 287	97.98 116	86.79 100	87.62 146	93.71 187

UniMVSNet_NR-MVSNet		85.49 169	84.59 156	88.21 200	89.44 231	79.36 164	96.71 117	96.41 91	85.22 92	78.11 195	90.98 184	76.97 94	95.14 253	79.14 155	68.30 279	90.12 217

DU-MVS		84.57 182	83.33 182	88.28 198	88.76 236	79.36 164	96.43 139	95.41 152	85.42 88	78.11 195	90.82 185	67.61 175	95.14 253	79.14 155	68.30 279	90.33 212

NR-MVSNet		83.35 201	81.52 207	88.84 185	88.76 236	81.31 117	94.45 208	95.16 161	84.65 109	67.81 273	90.82 185	70.36 166	94.87 260	74.75 198	66.89 291	90.33 212

TranMVSNet+NR-MVSNet		83.24 203	81.71 204	87.83 209	87.71 248	78.81 190	96.13 157	94.82 175	84.52 112	76.18 223	90.78 187	64.07 216	94.60 266	74.60 200	66.59 295	90.09 219

XXY-MVS		83.84 189	82.00 195	89.35 177	87.13 252	81.38 115	95.72 174	94.26 201	80.15 198	75.92 226	90.63 188	61.96 233	96.52 181	78.98 157	73.28 244	90.14 215

MVSTER		89.25 96	88.92 91	90.24 151	95.98 89	84.66 50	96.79 111	95.36 153	87.19 65	80.33 172	90.61 189	90.02 5	95.97 204	85.38 107	78.64 220	90.09 219

UGNet		87.73 128	86.55 130	91.27 127	95.16 113	79.11 175	96.35 143	96.23 107	88.14 49	87.83 97	90.48 190	50.65 292	99.09 80	80.13 148	94.03 94	95.60 155
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
IB-MVS	85.34 4	88.67 108	87.14 122	93.26 61	93.12 163	84.32 57	98.76 10	97.27 19	87.19 65	79.36 186	90.45 191	83.92 28	98.53 101	84.41 113	69.79 267	96.93 120
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
mvs_anonymous		88.68 107	87.62 109	91.86 115	94.80 121	81.69 111	93.53 228	94.92 169	82.03 161	78.87 190	90.43 192	75.77 111	95.34 246	85.04 109	93.16 107	98.55 34

WR-MVS		84.32 184	82.96 184	88.41 193	89.38 232	80.32 137	96.59 125	96.25 106	83.97 128	76.63 214	90.36 193	67.53 177	94.86 261	75.82 190	70.09 262	90.06 221

COLMAP_ROB	73.24 19	75.74 277	73.00 282	83.94 273	92.38 173	69.08 297	91.85 267	86.93 319	61.48 321	65.32 285	90.27 194	42.27 317	96.93 169	50.91 319	75.63 232	85.80 293
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		75.92 276	73.06 281	84.47 264	92.18 182	67.29 301	91.07 274	84.43 329	67.63 303	63.48 290	90.18 195	38.20 324	97.16 157	57.04 294	73.37 241	88.97 240

TestCases				84.47 264	92.18 182	67.29 301		84.43 329	67.63 303	63.48 290	90.18 195	38.20 324	97.16 157	57.04 294	73.37 241	88.97 240

ab-mvs		87.08 138	84.94 153	93.48 55	93.34 158	83.67 69	88.82 288	95.70 135	81.18 172	84.55 123	90.14 197	62.72 224	98.94 91	85.49 106	82.54 202	97.85 75

PS-MVSNAJss		84.91 176	84.30 168	86.74 229	85.89 285	74.40 255	94.95 199	94.16 209	83.93 129	76.45 217	90.11 198	71.04 160	95.77 218	83.16 131	79.02 217	90.06 221

diffmvs		87.96 125	86.47 131	92.42 92	94.26 138	82.70 86	92.79 250	94.03 218	77.94 227	88.99 85	89.98 199	70.72 164	97.56 136	77.75 163	91.80 121	96.98 117

jajsoiax		82.12 221	81.15 212	85.03 247	84.19 300	70.70 285	94.22 217	93.95 221	83.07 145	73.48 240	89.75 200	49.66 297	95.37 245	82.24 137	79.76 208	89.02 237

MS-PatchMatch		83.05 204	81.82 200	86.72 231	89.64 226	79.10 176	94.88 201	94.59 191	79.70 207	70.67 260	89.65 201	50.43 294	96.82 173	70.82 228	95.99 80	84.25 303

PVSNet_BlendedMVS		90.05 85	89.96 74	90.33 149	97.47 61	83.86 63	98.02 31	96.73 56	87.98 51	89.53 78	89.61 202	76.42 101	99.57 39	94.29 28	79.59 211	87.57 272

mvs_tets		81.74 224	80.71 217	84.84 251	84.22 299	70.29 288	93.91 220	93.78 232	82.77 150	73.37 241	89.46 203	47.36 306	95.31 248	81.99 138	79.55 214	88.92 242

pmmvs4		82.54 213	80.79 214	87.79 210	86.11 280	80.49 136	93.55 227	93.18 255	77.29 237	73.35 242	89.40 204	65.26 211	95.05 258	75.32 194	73.61 240	87.83 266

GA-MVS		85.79 164	84.04 171	91.02 135	89.47 230	80.27 140	96.90 107	94.84 174	85.57 84	80.88 164	89.08 205	56.56 273	96.47 183	77.72 168	85.35 172	96.34 139

CMPMVS	54.94 21	75.71 278	74.56 272	79.17 305	79.69 317	55.98 327	89.59 281	93.30 254	60.28 325	53.85 326	89.07 206	47.68 305	96.33 187	76.55 182	81.02 204	85.22 295
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
VPA-MVSNet		85.32 171	83.83 172	89.77 173	90.25 215	82.63 88	96.36 142	97.07 34	83.03 146	81.21 163	89.02 207	61.58 235	96.31 188	85.02 110	70.95 252	90.36 210

UniMVSNet (Re)		85.31 172	84.23 169	88.55 191	89.75 223	80.55 132	96.72 115	96.89 47	85.42 88	78.40 192	88.93 208	75.38 124	95.52 238	78.58 159	68.02 282	89.57 226

CP-MVSNet		81.01 233	80.08 224	83.79 275	87.91 247	70.51 286	94.29 216	95.65 137	80.83 178	72.54 251	88.84 209	63.71 217	92.32 291	68.58 245	68.36 278	88.55 250

EU-MVSNet		76.92 272	76.95 246	76.83 309	84.10 301	54.73 330	91.77 268	92.71 263	72.74 286	69.57 267	88.69 210	58.03 259	87.43 326	64.91 265	70.00 266	88.33 258

pmmvs5		81.34 229	79.54 230	86.73 230	85.02 293	76.91 234	96.22 152	91.65 274	77.65 231	73.55 239	88.61 211	55.70 278	94.43 269	74.12 203	73.35 243	88.86 243

PEN-MVS		79.47 242	78.26 238	83.08 284	86.36 270	68.58 298	93.85 221	94.77 179	79.76 206	71.37 254	88.55 212	59.79 239	92.46 289	64.50 266	65.40 296	88.19 260

ACMH+	76.62 16	77.47 262	74.94 265	85.05 246	91.07 205	71.58 278	93.26 238	90.01 297	71.80 292	64.76 287	88.55 212	41.62 319	96.48 182	62.35 280	71.00 251	87.09 280

PVSNet_0	77.72 15	81.70 225	78.95 235	89.94 167	90.77 209	76.72 237	95.96 161	96.95 42	85.01 100	70.24 265	88.53 214	52.32 289	98.20 112	86.68 101	44.08 339	94.89 168

PS-CasMVS		80.27 237	79.18 232	83.52 281	87.56 250	69.88 291	94.08 218	95.29 158	80.27 195	72.08 252	88.51 215	59.22 248	92.23 293	67.49 249	68.15 281	88.45 254

DTE-MVSNet		78.37 252	77.06 245	82.32 292	85.22 292	67.17 303	93.40 230	93.66 237	78.71 224	70.53 261	88.29 216	59.06 249	92.23 293	61.38 282	63.28 303	87.56 273

v2v482		83.46 194	81.86 199	88.25 199	86.19 277	79.65 160	96.34 144	94.02 219	81.56 169	77.32 206	88.23 217	65.62 203	96.03 199	77.77 162	69.72 269	89.09 234

USDC		78.65 251	76.25 253	85.85 238	87.58 249	74.60 252	89.58 282	90.58 295	84.05 126	63.13 294	88.23 217	40.69 322	96.86 172	66.57 256	75.81 231	86.09 291

XVG-ACMP-BASELINE		79.38 243	77.90 240	83.81 274	84.98 294	67.14 304	89.03 287	93.18 255	80.26 196	72.87 248	88.15 219	38.55 323	96.26 190	76.05 188	78.05 224	88.02 263

FMVSNet3		84.71 179	82.71 190	90.70 142	94.55 126	87.71 16	95.92 164	94.67 184	81.73 168	75.82 227	88.08 220	66.99 189	94.47 268	71.23 221	75.38 233	89.91 223

MVP-Stereo		82.65 212	81.67 205	85.59 242	86.10 281	78.29 205	93.33 233	92.82 261	77.75 230	69.17 271	87.98 221	59.28 247	95.76 219	71.77 215	96.88 69	82.73 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		77.18 268	76.06 254	80.55 299	83.78 304	60.00 321	90.35 277	91.05 282	77.01 242	66.62 278	87.92 222	47.73 304	94.03 276	71.63 217	68.44 277	87.62 270

test_djsdf		83.00 207	82.45 193	84.64 259	84.07 302	69.78 292	94.80 203	94.48 193	80.74 182	75.41 232	87.70 223	61.32 236	95.10 255	83.77 119	79.76 208	89.04 236

ACMH	75.40 17	77.99 255	74.96 264	87.10 226	90.67 210	76.41 239	93.19 242	91.64 275	72.47 289	63.44 292	87.61 224	43.34 313	97.16 157	58.34 291	73.94 239	87.72 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		80.05 238	78.02 239	86.15 237	85.42 289	75.81 244	95.11 196	92.69 264	77.13 238	70.36 262	87.43 225	58.44 253	95.27 250	71.36 220	64.25 299	87.36 277

FMVSNet2		82.79 210	80.44 219	89.83 171	92.66 168	85.43 37	95.42 183	94.35 198	79.06 219	74.46 235	87.28 226	56.38 275	94.31 271	69.72 234	74.68 237	89.76 224

LTVRE_ROB	73.68 18	77.99 255	75.74 257	84.74 254	90.45 213	72.02 271	86.41 309	91.12 279	72.57 288	66.63 277	87.27 227	54.95 284	96.98 165	56.29 304	75.98 229	85.21 296
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
IterMVS-LS		83.93 188	82.80 188	87.31 223	91.46 201	77.39 228	95.66 176	93.43 245	80.44 188	75.51 230	87.26 228	73.72 140	95.16 252	76.99 178	70.72 254	89.39 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1neww		83.45 195	81.95 196	87.95 206	86.66 256	79.04 179	96.32 145	94.17 206	80.76 179	77.56 198	87.25 229	67.02 187	96.08 195	77.73 165	70.07 263	88.74 248

v7new		83.45 195	81.95 196	87.95 206	86.66 256	79.04 179	96.32 145	94.17 206	80.76 179	77.56 198	87.25 229	67.02 187	96.08 195	77.73 165	70.07 263	88.74 248

v6		83.45 195	81.94 198	87.95 206	86.62 260	79.03 182	96.32 145	94.17 206	80.76 179	77.57 197	87.23 231	67.03 186	96.09 194	77.73 165	70.06 265	88.75 246

Effi-MVS+-dtu		84.61 181	84.90 155	83.72 278	91.96 191	63.14 314	94.95 199	93.34 252	85.57 84	79.79 177	87.12 232	61.99 231	95.61 234	83.55 125	85.83 163	92.41 198

v7		82.99 208	81.41 208	87.73 212	86.41 267	78.86 187	96.10 158	93.98 220	79.88 204	77.49 200	87.11 233	65.44 206	95.97 204	75.69 192	70.59 256	88.36 256

CostFormer		89.08 97	88.39 97	91.15 131	93.13 162	79.15 174	88.61 291	96.11 115	83.14 143	89.58 77	86.93 234	83.83 29	96.87 171	88.22 88	85.92 161	97.42 101

v1		83.37 198	81.82 200	88.01 203	86.58 264	79.24 168	96.45 133	94.13 210	80.88 175	77.48 202	86.88 235	67.15 182	96.04 198	77.15 174	69.67 271	88.76 244

v1141		83.36 199	81.81 202	88.01 203	86.61 262	79.26 167	96.44 135	94.12 213	80.88 175	77.48 202	86.87 236	67.08 184	96.03 199	77.14 175	69.69 270	88.75 246

divwei89l23v2f112		83.36 199	81.81 202	88.01 203	86.60 263	79.23 170	96.44 135	94.12 213	80.88 175	77.49 200	86.87 236	67.08 184	96.03 199	77.14 175	69.67 271	88.76 244

DI_MVS_plusplus_test		85.92 156	83.61 177	92.86 75	86.43 266	83.20 76	95.57 179	95.46 147	85.10 99	65.99 281	86.84 238	56.70 270	97.89 124	88.10 89	92.33 114	97.48 99

WR-MVS_H		81.02 232	80.09 223	83.79 275	88.08 245	71.26 283	94.46 207	96.54 79	80.08 199	72.81 249	86.82 239	70.36 166	92.65 288	64.18 267	67.50 287	87.46 276

v1144		82.90 209	81.27 211	87.78 211	86.29 273	79.07 178	96.14 155	93.93 222	80.05 200	77.38 204	86.80 240	65.50 204	95.93 210	75.21 195	70.13 259	88.33 258

V42		83.04 205	81.53 206	87.57 218	86.27 275	79.09 177	95.87 169	94.11 215	80.35 192	77.22 208	86.79 241	65.32 210	96.02 202	77.74 164	70.14 258	87.61 271

LF4IMVS		72.36 293	70.82 288	76.95 308	79.18 318	56.33 326	86.12 310	86.11 323	69.30 301	63.06 296	86.66 242	33.03 333	92.25 292	65.33 263	68.64 276	82.28 325

test_normal		85.83 158	83.51 179	92.78 79	86.33 271	83.01 82	95.56 181	95.46 147	85.11 98	65.73 283	86.63 243	56.62 272	97.86 125	87.87 91	92.29 115	97.47 100

LCM-MVSNet-Re		83.75 190	83.54 178	84.39 268	93.54 155	64.14 310	92.51 253	84.03 332	83.90 130	66.14 280	86.59 244	67.36 179	92.68 287	84.89 111	92.87 108	96.35 138

v1192		82.31 218	80.55 218	87.60 215	85.94 283	78.47 202	95.85 171	93.80 230	79.33 213	76.97 211	86.51 245	63.33 222	95.87 212	73.11 206	70.13 259	88.46 253

v144192		82.43 214	80.73 216	87.54 219	85.81 286	78.22 207	95.98 160	93.78 232	79.09 218	77.11 209	86.49 246	64.66 215	95.91 211	74.20 202	69.42 273	88.49 251

TransMVSNet (Re)		76.94 271	74.38 274	84.62 261	85.92 284	75.25 249	95.28 184	89.18 304	73.88 278	67.22 274	86.46 247	59.64 240	94.10 275	59.24 290	52.57 329	84.50 302

v1921920		82.02 222	80.23 222	87.41 221	85.62 287	77.92 218	95.79 173	93.69 236	78.86 222	76.67 213	86.44 248	62.50 225	95.83 215	72.69 208	69.77 268	88.47 252

v1240		81.70 225	79.83 229	87.30 224	85.50 288	77.70 223	95.48 182	93.44 243	78.46 226	76.53 216	86.44 248	60.85 237	95.84 214	71.59 218	70.17 257	88.35 257

tpm2		87.35 136	86.26 132	90.62 143	92.93 166	78.67 192	88.06 295	95.99 121	79.33 213	87.40 98	86.43 250	80.28 54	96.40 184	80.23 146	85.73 165	96.79 125

Baseline_NR-MVSNet		81.22 231	80.07 225	84.68 257	85.32 291	75.12 250	96.48 129	88.80 307	76.24 243	77.28 207	86.40 251	67.61 175	94.39 270	75.73 191	66.73 294	84.54 301

anonymousdsp		80.98 234	79.97 227	84.01 270	81.73 309	70.44 287	92.49 254	93.58 242	77.10 240	72.98 247	86.31 252	57.58 262	94.90 259	79.32 153	78.63 222	86.69 284

SixPastTwentyTwo		76.04 275	74.32 275	81.22 296	84.54 296	61.43 319	91.16 273	89.30 303	77.89 228	64.04 289	86.31 252	48.23 300	94.29 272	63.54 276	63.84 301	87.93 265

tpmrst		88.36 116	87.38 116	91.31 124	94.36 137	79.92 147	87.32 300	95.26 160	85.32 90	88.34 91	86.13 254	80.60 50	96.70 178	83.78 118	85.34 173	97.30 108

v148		82.41 217	80.89 213	86.99 227	86.18 278	76.81 236	96.27 150	93.82 227	80.49 187	75.28 233	86.11 255	67.32 180	95.75 220	75.48 193	67.03 290	88.42 255

tpmp4_e23		86.46 149	84.95 152	90.98 137	93.74 151	78.60 196	88.13 294	95.90 127	79.65 208	85.42 113	85.67 256	80.08 58	97.06 162	71.71 216	84.26 180	97.28 112

GBi-Net		82.42 215	80.43 220	88.39 194	92.66 168	81.95 97	94.30 213	93.38 248	79.06 219	75.82 227	85.66 257	56.38 275	93.84 279	71.23 221	75.38 233	89.38 230

test1		82.42 215	80.43 220	88.39 194	92.66 168	81.95 97	94.30 213	93.38 248	79.06 219	75.82 227	85.66 257	56.38 275	93.84 279	71.23 221	75.38 233	89.38 230

FMVSNet1		79.50 241	76.54 252	88.39 194	88.47 241	81.95 97	94.30 213	93.38 248	73.14 282	72.04 253	85.66 257	43.86 310	93.84 279	65.48 262	72.53 247	89.38 230

TDRefinement		69.20 301	65.78 303	79.48 303	66.04 340	62.21 316	88.21 293	86.12 322	62.92 314	61.03 311	85.61 260	33.23 332	94.16 274	55.82 307	53.02 327	82.08 326

v8		81.88 223	80.06 226	87.32 222	86.63 259	79.04 179	94.41 209	93.65 238	78.77 223	73.19 245	85.57 261	66.87 190	95.81 216	73.84 205	67.61 286	87.11 279

EPMVS		87.47 131	85.90 140	92.18 101	95.41 106	82.26 94	87.00 304	96.28 104	85.88 79	84.23 125	85.57 261	75.07 131	96.26 190	71.14 224	92.50 110	98.03 59

tfpnnormal		78.14 254	75.42 260	86.31 235	88.33 243	79.24 168	94.41 209	96.22 108	73.51 280	69.81 266	85.52 263	55.43 279	95.75 220	47.65 326	67.86 283	83.95 307

v10		81.43 228	79.53 231	87.11 225	86.38 268	78.87 186	94.31 212	93.43 245	77.88 229	73.24 244	85.26 264	65.44 206	95.75 220	72.14 212	67.71 285	86.72 283

tpm		85.55 168	84.47 160	88.80 187	90.19 217	75.39 248	88.79 289	94.69 181	84.83 105	83.96 129	85.21 265	78.22 79	94.68 265	76.32 185	78.02 225	96.34 139

Patchmatch-test1		84.89 177	82.76 189	91.27 127	92.30 177	81.86 103	92.88 246	95.56 141	84.85 104	82.52 142	85.19 266	58.04 257	94.21 273	65.93 260	87.58 148	97.74 81

semantic-postprocess				84.73 255	89.63 227	74.66 251		91.81 273	80.05 200	71.06 258	85.18 267	57.98 260	91.40 308	72.48 211	70.70 255	88.12 262

dp		84.30 185	82.31 194	90.28 150	94.24 140	77.97 214	86.57 307	95.53 142	79.94 203	80.75 166	85.16 268	71.49 156	96.39 185	63.73 274	83.36 186	96.48 135

IterMVS		80.67 235	79.16 233	85.20 245	89.79 222	76.08 242	92.97 245	91.86 271	80.28 194	71.20 256	85.14 269	57.93 261	91.34 309	72.52 210	70.74 253	88.18 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		78.25 253	74.72 268	88.83 186	91.20 202	74.10 257	73.91 337	88.70 310	59.89 327	66.82 276	85.12 270	78.38 77	94.54 267	48.84 324	79.58 212	97.86 74

PatchmatchNet		86.83 143	85.12 148	91.95 108	94.12 142	82.27 93	86.55 308	95.64 138	84.59 111	82.98 141	84.99 271	77.26 89	95.96 208	68.61 244	91.34 124	97.64 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		72.41 292	68.99 297	82.68 287	88.11 244	69.59 294	88.41 292	85.20 326	65.55 309	57.91 320	84.82 272	30.80 337	95.94 209	51.38 315	68.70 275	82.49 324

v7n		79.32 244	77.34 243	85.28 244	84.05 303	72.89 266	93.38 231	93.87 225	75.02 264	70.68 259	84.37 273	59.58 242	95.62 233	67.60 248	67.50 287	87.32 278

test20.03		72.36 293	71.15 287	75.98 313	77.79 321	59.16 324	92.40 257	89.35 302	74.09 276	61.50 309	84.32 274	48.09 301	85.54 334	50.63 320	62.15 305	83.24 317

MDTV_nov1_ep13			83.69 173		94.09 143	81.01 121	86.78 306	96.09 116	83.81 133	84.75 119	84.32 274	74.44 136	96.54 180	63.88 273	85.07 175

pmmvs6		74.65 282	71.67 285	83.60 280	79.13 319	69.94 290	93.31 237	90.88 286	61.05 324	65.83 282	84.15 276	43.43 312	94.83 262	66.62 254	60.63 307	86.02 292

test_0402		72.68 291	69.54 295	82.09 293	88.67 239	71.81 274	92.72 251	86.77 320	61.52 320	62.21 303	83.91 277	43.22 314	93.76 282	34.60 339	72.23 248	80.72 329

Test4		82.30 219	79.15 234	91.78 117	81.84 308	81.74 107	94.04 219	94.20 203	84.86 103	59.75 314	83.88 278	37.14 326	96.28 189	84.60 112	92.00 118	97.30 108

EG-PatchMatch MVS		74.92 280	72.02 284	83.62 279	83.76 305	73.28 261	93.62 225	92.04 270	68.57 302	58.88 316	83.80 279	31.87 335	95.57 237	56.97 296	78.67 219	82.00 327

v748		78.69 250	76.79 250	84.39 268	83.40 306	70.78 284	93.25 239	93.62 239	74.96 265	69.40 268	83.74 280	59.40 244	95.39 243	68.74 242	64.59 298	86.99 282

V4		78.70 248	76.95 246	83.95 271	81.66 311	71.34 281	91.94 264	93.44 243	74.69 271	70.35 264	83.73 281	58.07 256	95.50 239	71.84 213	66.86 292	85.02 297

v52		78.70 248	76.95 246	83.95 271	81.71 310	71.34 281	91.93 265	93.43 245	74.69 271	70.36 262	83.71 282	58.04 257	95.50 239	71.84 213	66.82 293	85.00 298

Anonymous20231206		75.29 279	73.64 278	80.22 300	80.75 312	63.38 313	93.36 232	90.71 291	73.09 283	67.12 275	83.70 283	50.33 295	90.85 313	53.63 312	70.10 261	86.44 285

tpmvs		83.04 205	80.77 215	89.84 170	95.43 104	77.96 215	85.59 313	95.32 156	75.31 259	76.27 221	83.70 283	73.89 138	97.41 144	59.53 286	81.93 203	94.14 178

testus		70.06 298	69.09 296	72.98 318	74.54 331	51.28 336	93.78 222	87.34 315	71.49 294	62.69 299	83.46 285	24.44 340	84.77 335	51.22 318	72.85 245	82.90 318

lessismore_v0				79.98 301	80.59 314	58.34 325		80.87 340		58.49 317	83.46 285	43.10 315	93.89 278	63.11 278	48.68 332	87.72 267

tpm cat1		83.63 192	81.38 209	90.39 148	93.53 156	78.19 211	85.56 314	95.09 162	70.78 296	78.51 191	83.28 287	74.80 133	97.03 163	66.77 253	84.05 181	95.95 145

OpenMVS_ROB	68.52 20	73.02 290	69.57 294	83.37 282	80.54 315	71.82 273	93.60 226	88.22 311	62.37 316	61.98 306	83.15 288	35.31 330	95.47 241	45.08 329	75.88 230	82.82 319

test2356		74.41 283	74.53 273	74.07 316	76.13 328	54.45 331	94.74 205	92.08 268	71.96 291	65.51 284	83.05 289	56.96 267	83.71 336	52.74 314	77.58 226	84.06 305

K. test v3		73.62 284	71.59 286	79.69 302	82.98 307	59.85 322	90.85 276	88.83 306	77.13 238	58.90 315	82.11 290	43.62 311	91.72 306	65.83 261	54.10 326	87.50 275

MDA-MVSNet-bldmvs		71.45 295	67.94 298	81.98 294	85.33 290	68.50 299	92.35 258	88.76 308	70.40 297	42.99 335	81.96 291	46.57 307	91.31 310	48.75 325	54.39 325	86.11 290

MIMVSNet		79.18 246	75.99 255	88.72 189	87.37 251	80.66 129	79.96 322	91.82 272	77.38 235	74.33 236	81.87 292	41.78 318	90.74 314	66.36 259	83.10 188	94.76 171

UnsupCasMVSNet_eth		73.25 288	70.57 290	81.30 295	77.53 322	66.33 305	87.24 301	93.89 224	80.38 191	57.90 321	81.59 293	42.91 316	90.56 315	65.18 264	48.51 333	87.01 281

DSMNet-mixed		73.13 289	72.45 283	75.19 314	77.51 323	46.82 338	85.09 315	82.01 338	67.61 307	69.27 270	81.33 294	50.89 291	86.28 329	54.54 309	83.80 182	92.46 197

YYNet1		73.53 287	70.43 292	82.85 286	84.52 297	71.73 276	91.69 270	91.37 277	67.63 303	46.79 333	81.21 295	55.04 283	90.43 316	55.93 305	59.70 310	86.38 286

MDA-MVSNet_test_wron		73.54 286	70.43 292	82.86 285	84.55 295	71.85 272	91.74 269	91.32 278	67.63 303	46.73 334	81.09 296	55.11 282	90.42 317	55.91 306	59.76 309	86.31 287

tmp_tt		41.54 320	41.93 320	40.38 337	20.10 355	26.84 352	61.93 343	59.09 352	14.81 350	28.51 342	80.58 297	35.53 328	48.33 353	63.70 275	13.11 349	45.96 347

FMVSNet5		76.46 274	74.16 276	83.35 283	90.05 220	76.17 241	89.58 282	89.85 298	71.39 295	65.29 286	80.42 298	50.61 293	87.70 325	61.05 283	69.24 274	86.18 289

CR-MVSNet		83.53 193	81.36 210	90.06 160	90.16 218	79.75 150	79.02 327	91.12 279	84.24 124	82.27 154	80.35 299	75.45 115	93.67 283	63.37 277	86.25 155	96.75 129

Patchmtry		77.36 264	74.59 271	85.67 241	89.75 223	75.75 245	77.85 330	91.12 279	60.28 325	71.23 255	80.35 299	75.45 115	93.56 285	57.94 292	67.34 289	87.68 269

ADS-MVSNet2		79.57 240	77.53 242	85.71 240	93.78 148	72.13 269	79.48 323	86.11 323	73.09 283	80.14 174	79.99 301	62.15 228	90.14 319	59.49 287	83.52 183	94.85 169

ADS-MVSNet		81.26 230	78.36 236	89.96 166	93.78 148	79.78 149	79.48 323	93.60 240	73.09 283	80.14 174	79.99 301	62.15 228	95.24 251	59.49 287	83.52 183	94.85 169

MIMVSNet1		69.44 299	66.65 301	77.84 306	76.48 325	62.84 315	87.42 299	88.97 305	66.96 308	57.75 322	79.72 303	32.77 334	85.83 331	46.32 327	63.42 302	84.85 300

testpf		70.88 297	70.47 291	72.08 319	88.92 235	59.57 323	48.62 347	93.15 257	63.05 313	63.07 295	79.51 304	58.33 254	86.63 328	66.93 252	72.69 246	70.05 339

N_pmnet		61.30 309	60.20 310	64.60 326	84.32 298	17.00 356	91.67 271	10.98 356	61.77 319	58.45 318	78.55 305	49.89 296	91.83 302	42.27 331	63.94 300	84.97 299

PM-MVS		69.32 300	66.93 300	76.49 310	73.60 332	55.84 328	85.91 311	79.32 344	74.72 270	61.09 310	78.18 306	21.76 341	91.10 312	70.86 226	56.90 313	82.51 322

pmmvs-eth3d		73.59 285	70.66 289	82.38 290	76.40 326	73.38 259	89.39 286	89.43 301	72.69 287	60.34 313	77.79 307	46.43 308	91.26 311	66.42 258	57.06 312	82.51 322

v18		77.96 257	75.61 258	84.98 248	86.66 256	79.01 183	93.02 244	90.94 284	75.69 252	63.19 293	77.62 308	67.11 183	92.07 296	70.05 230	56.24 315	83.87 308

v16		77.84 258	75.47 259	84.93 250	86.62 260	78.93 185	92.84 248	90.89 285	75.50 255	63.03 297	77.54 309	66.82 192	92.04 297	69.82 231	56.22 316	83.82 310

v17		77.79 259	75.41 261	84.94 249	86.53 265	78.94 184	92.83 249	90.88 286	75.51 254	62.97 298	77.50 310	66.69 193	92.03 298	69.80 232	56.01 317	83.83 309

v15		77.52 261	75.09 262	84.82 252	86.37 269	78.82 188	92.58 252	90.78 288	75.47 256	62.53 300	77.17 311	66.58 196	91.92 299	69.18 235	55.16 319	83.73 311

V14		77.43 263	74.99 263	84.75 253	86.32 272	78.67 192	92.44 256	90.77 289	75.28 260	62.42 301	77.13 312	66.40 197	91.88 300	69.01 240	55.14 320	83.70 312

patchmatchnet-post											77.09 313	77.78 85	95.39 243

V9		77.32 265	74.87 266	84.69 256	86.26 276	78.52 198	92.33 259	90.72 290	75.11 263	62.21 303	77.08 314	66.19 199	91.87 301	68.84 241	55.06 322	83.69 313

v12		77.20 267	74.73 267	84.63 260	86.15 279	78.41 203	92.17 261	90.71 291	74.92 266	62.05 305	77.00 315	65.83 201	91.83 302	68.69 243	55.01 323	83.64 314

v13		77.11 269	74.63 270	84.55 262	86.08 282	78.27 206	92.06 263	90.68 293	74.73 269	61.86 308	76.93 316	65.73 202	91.81 305	68.55 246	55.07 321	83.59 315

v11		77.21 266	74.72 268	84.68 257	86.29 273	78.62 195	92.30 260	90.63 294	74.86 267	62.32 302	76.73 317	65.49 205	91.83 302	68.17 247	55.72 318	83.59 315

DeepMVS_CX				64.06 327	78.53 320	43.26 343		68.11 349	69.94 298	38.55 337	76.14 318	18.53 344	79.34 340	43.72 330	41.62 341	69.57 340

testing_2		76.96 270	73.18 280	88.30 197	75.87 329	79.64 161	89.92 280	94.21 202	80.16 197	51.23 328	75.94 319	33.94 331	95.81 216	82.28 136	75.12 236	89.46 227

ambc				76.02 312	68.11 337	51.43 334	64.97 342	89.59 299		60.49 312	74.49 320	17.17 345	92.46 289	61.50 281	52.85 328	84.17 304

pmmvs3		65.75 306	62.18 309	76.45 311	67.12 338	64.54 308	88.68 290	85.05 327	54.77 336	57.54 323	73.79 321	29.40 339	86.21 330	55.49 308	47.77 335	78.62 331

new-patchmatchnet		68.85 302	65.93 302	77.61 307	73.57 333	63.94 312	90.11 279	88.73 309	71.62 293	55.08 324	73.60 322	40.84 321	87.22 327	51.35 317	48.49 334	81.67 328

1111		65.60 307	64.33 305	69.41 321	68.26 335	45.11 341	87.06 302	87.32 316	54.99 333	51.20 329	73.45 323	63.57 218	85.70 332	36.53 336	56.59 314	77.42 333

.test1245		54.61 313	58.07 311	44.24 336	68.26 335	45.11 341	87.06 302	87.32 316	54.99 333	51.20 329	73.45 323	63.57 218	85.70 332	36.53 336	0.21 352	1.91 352

Patchmatch-RL test		76.65 273	74.01 277	84.55 262	77.37 324	64.23 309	78.49 329	82.84 337	78.48 225	64.63 288	73.40 325	76.05 108	91.70 307	76.99 178	57.84 311	97.72 82

PatchT		79.75 239	76.85 249	88.42 192	89.55 228	75.49 247	77.37 331	94.61 190	63.07 312	82.46 144	73.32 326	75.52 114	93.41 286	51.36 316	84.43 178	96.36 137

RPMNet		79.32 244	75.75 256	90.06 160	90.16 218	79.75 150	79.02 327	93.92 223	58.43 329	82.27 154	72.55 327	73.03 143	93.67 283	46.10 328	86.25 155	96.75 129

FPMVS		55.09 312	52.93 314	61.57 329	55.98 342	40.51 346	83.11 319	83.41 336	37.61 341	34.95 340	71.95 328	14.40 348	76.95 342	29.81 343	65.16 297	67.25 341

new_pmnet		66.18 305	63.18 307	75.18 315	76.27 327	61.74 318	83.79 317	84.66 328	56.64 331	51.57 327	71.85 329	31.29 336	87.93 324	49.98 321	62.55 304	75.86 334

Anonymous20231211		61.03 310	56.76 312	73.82 317	71.24 334	53.47 332	87.60 298	81.65 339	44.19 339	51.08 331	71.82 330	20.79 342	88.46 323	35.45 338	47.07 336	79.52 330

LP		68.54 304	63.92 306	82.39 289	87.93 246	71.79 275	72.37 340	86.01 325	55.89 332	58.33 319	71.46 331	49.58 298	90.10 320	32.25 341	61.48 306	85.27 294

test1235678		64.50 308	62.19 308	71.42 320	66.82 339	48.00 337	89.44 284	87.90 312	62.82 315	49.12 332	71.31 332	30.14 338	82.19 338	41.88 332	60.32 308	84.06 305

test12356		58.24 311	56.06 313	64.77 324	60.65 341	39.42 347	82.78 320	84.34 331	57.47 330	42.65 336	69.10 333	19.21 343	81.18 339	38.97 335	49.40 330	73.69 335

UnsupCasMVSNet_bld		68.60 303	64.50 304	80.92 298	74.63 330	67.80 300	83.97 316	92.94 260	65.12 310	54.63 325	68.23 334	35.97 327	92.17 295	60.13 285	44.83 337	82.78 320

testmv		54.58 314	51.53 315	63.74 328	53.58 346	40.82 345	83.26 318	83.92 333	54.07 337	36.35 339	61.26 335	14.80 347	77.07 341	33.00 340	43.53 340	73.33 336

PMMVS2		50.90 317	46.31 317	64.67 325	55.53 343	46.67 339	77.30 332	71.02 346	40.89 340	34.16 341	59.32 336	9.83 353	76.14 345	40.09 333	28.63 342	71.21 337

JIA-IIPM		79.00 247	77.20 244	84.40 267	89.74 225	64.06 311	75.30 333	95.44 151	62.15 317	81.90 158	59.08 337	78.92 69	95.59 235	66.51 257	85.78 164	93.54 188

LCM-MVSNet		52.52 315	48.24 316	65.35 323	47.63 350	41.45 344	72.55 339	83.62 335	31.75 342	37.66 338	57.92 338	9.19 354	76.76 343	49.26 323	44.60 338	77.84 332

gg-mvs-nofinetune		85.48 170	82.90 186	93.24 62	94.51 135	85.82 30	79.22 325	96.97 40	61.19 322	87.33 100	53.01 339	90.58 3	96.07 197	86.07 102	97.23 61	97.81 78

MVS-HIRNet		71.36 296	67.00 299	84.46 266	90.58 211	69.74 293	79.15 326	87.74 314	46.09 338	61.96 307	50.50 340	45.14 309	95.64 231	53.74 311	88.11 144	88.00 264

PMVS	34.80 23	39.19 322	35.53 323	50.18 334	29.72 354	30.30 351	59.60 345	66.20 350	26.06 345	17.91 348	49.53 341	3.12 356	74.09 346	18.19 348	49.40 330	46.14 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		51.12 316	45.81 318	67.03 322	53.16 348	52.22 333	75.21 334	80.40 341	54.89 335	28.26 343	48.50 342	15.54 346	82.81 337	39.29 334	17.06 345	66.07 342

ANet_high		46.22 318	41.28 321	61.04 330	39.91 353	46.25 340	70.59 341	76.18 345	58.87 328	23.09 345	48.00 343	12.58 350	66.54 348	28.65 344	13.62 348	70.35 338

PNet_i23d		41.20 321	38.13 322	50.41 333	55.23 344	36.24 350	73.80 338	65.45 351	29.75 343	21.36 346	47.05 344	3.43 355	63.23 349	28.17 345	18.92 344	51.76 344

MVE	35.65 22	33.85 325	29.49 328	46.92 335	41.86 351	36.28 349	50.45 346	56.52 353	18.75 349	18.28 347	37.84 345	2.41 357	58.41 350	18.71 347	20.62 343	46.06 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.11 319	42.05 319	54.30 332	80.69 313	51.30 335	35.80 348	83.81 334	28.13 344	27.94 344	34.53 346	11.41 352	76.70 344	21.45 346	54.65 324	34.90 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_post											33.80 347	76.17 106	95.97 204

E-PMN		32.70 326	32.39 325	33.65 338	53.35 347	25.70 353	74.07 336	53.33 354	21.08 347	17.17 349	33.63 348	11.85 351	54.84 351	12.98 349	14.04 347	20.42 349

EMVS		31.70 327	31.45 327	32.48 339	50.72 349	23.95 354	74.78 335	52.30 355	20.36 348	16.08 350	31.48 349	12.80 349	53.60 352	11.39 350	13.10 350	19.88 350

wuykxyi23d		37.75 323	31.85 326	55.46 331	40.00 352	38.01 348	59.81 344	69.47 347	25.46 346	12.42 351	30.55 350	2.06 358	67.08 347	31.81 342	15.03 346	61.29 343

test_post1							85.88 312				30.24 351	73.77 139	95.07 257	73.89 204

X-MVStestdata		86.26 153	84.14 170	92.63 87	98.52 29	80.29 138	97.37 69	96.44 88	87.04 67	91.38 55	20.73 352	77.24 91	99.59 37	90.46 65	98.07 42	98.02 60

testmvs		9.92 330	12.94 331	0.84 343	0.65 356	0.29 358	93.78 222	0.39 358	0.42 352	2.85 353	15.84 353	0.17 361	0.30 356	2.18 352	0.21 352	1.91 352

test123		9.07 331	11.73 332	1.11 342	0.50 357	0.77 357	89.44 284	0.20 359	0.34 353	2.15 354	10.72 354	0.34 360	0.32 355	1.79 353	0.08 354	2.23 351

wuyk23d		14.10 329	13.89 330	14.72 341	55.23 344	22.91 355	33.83 349	3.56 357	4.94 351	4.11 352	2.28 355	2.06 358	19.66 354	10.23 351	8.74 351	1.59 354

pcd_1.5k_mvsjas		5.92 333	7.89 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	71.04 160	0.00 357	0.00 354	0.00 355	0.00 355

pcd1.5k->3k		34.11 324	35.46 324	30.05 340	86.70 255	0.00 359	0.00 350	94.74 180	0.00 354	0.00 355	0.00 356	58.13 255	0.00 357	0.00 354	79.56 213	90.14 215

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

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

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

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

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

GSMVS																97.54 93

test_part2					98.90 7	85.14 43				96.07 7

test_part1								96.77 52				89.33 6			98.95 12	99.18 10

sam_mvs1												77.59 86				97.54 93

sam_mvs												75.35 127

MTGPA								96.33 101

MTMP								68.16 348

test9_res														96.00 14	99.03 7	98.31 41

agg_prior2														94.30 27	99.00 9	98.57 31

agg_prior					98.59 26	83.13 77		96.56 76		94.19 28			99.16 74

test_prior4						82.34 92	97.75 46

test_prior				93.09 68	98.68 15	81.91 100		96.40 93					99.06 81			98.29 43

旧先验2							96.97 103		74.06 277	96.10 6			97.76 128	88.38 86

新几何2							96.42 140

无先验							96.87 108	96.78 51	77.39 234				99.52 43	79.95 149		98.43 35

原ACMM2							96.84 109

testdata2													99.48 48	76.45 184

segment_acmp												82.69 35

testdata1							95.57 179		87.44 59

test12				94.25 26	98.34 37	85.55 35		96.35 99		92.36 45		80.84 48	99.22 63		98.31 36	97.98 67

plane_prior7					91.86 195	77.55 225

plane_prior6					91.98 190	77.92 218						64.77 213

plane_prior5								94.69 181					97.30 149	87.08 96	82.82 199	90.96 204

plane_prior3						77.75 222			90.17 28	81.33 161

plane_prior2							97.18 78		89.89 30

plane_prior1					91.95 193

plane_prior						77.96 215	97.52 59		90.36 27						82.96 191

n2								0.00 360

nn								0.00 360

door-mid								79.75 343

test11								96.50 83

door								80.13 342

HQP5-MVS						78.48 199

HQP-NCC					92.08 185		97.63 51		90.52 24	82.30 146

ACMP_Plane					92.08 185		97.63 51		90.52 24	82.30 146

BP-MVS														87.67 93

HQP4-MVS										82.30 146			97.32 147			91.13 202

HQP3-MVS								94.80 176							83.01 189

HQP2-MVS												65.40 208

MDTV_nov1_ep13_2view						81.74 107	86.80 305		80.65 184	85.65 111		74.26 137		76.52 183		96.98 117

ACMMP++_ref															78.45 223

ACMMP++															79.05 216

Test By Simon												71.65 153

High-resolution multi-view benchmark