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.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

Anonymous20231211		97.78 3	98.31 2	96.16 47	99.55 2	89.37 81	98.40 5	98.89 4	98.75 2	99.48 3	99.62 2	98.70 2	99.40 36	91.60 108	99.84 5	99.71 3

PEN-MVS		96.69 20	97.39 8	94.61 100	99.16 3	84.50 158	96.54 30	98.05 37	98.06 4	98.64 13	98.25 39	95.01 39	99.65 3	92.95 75	99.83 8	99.68 5

MIMVSNet1		95.52 60	95.45 72	95.72 66	99.14 4	89.02 85	96.23 47	96.87 147	93.73 54	97.87 33	98.49 26	90.73 125	99.05 83	86.43 197	99.60 33	99.10 56

PS-CasMVS		96.69 20	97.43 5	94.49 111	99.13 5	84.09 165	96.61 26	97.97 48	97.91 5	98.64 13	98.13 41	95.24 31	99.65 3	93.39 61	99.84 5	99.72 2

DTE-MVSNet		96.74 18	97.43 5	94.67 98	99.13 5	84.68 157	96.51 31	97.94 54	98.14 3	98.67 12	98.32 36	95.04 36	99.69 2	93.27 65	99.82 10	99.62 11

pmmvs6		96.80 14	97.36 9	95.15 87	99.12 7	87.82 113	96.68 24	97.86 57	96.10 25	98.14 26	99.28 3	97.94 4	98.21 203	91.38 115	99.69 16	99.42 27

HPM-MVS_fast		97.01 7	96.89 17	97.39 18	99.12 7	93.92 24	97.16 13	98.17 26	93.11 65	96.48 78	97.36 81	96.92 7	99.34 49	94.31 33	99.38 65	98.92 83

MP-MVS-pluss		96.08 48	95.92 54	96.57 41	99.06 9	91.21 59	93.25 146	98.32 13	87.89 193	96.86 64	97.38 78	95.55 21	99.39 41	95.47 13	99.47 49	99.11 53
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OurMVSNet-221017-0		96.80 14	96.75 20	96.96 32	99.03 10	91.85 52	97.98 6	98.01 43	94.15 46	98.93 4	99.07 5	88.07 172	99.57 13	95.86 11	99.69 16	99.46 25

WR-MVS_H		96.60 25	97.05 15	95.24 83	99.02 11	86.44 132	96.78 23	98.08 32	97.42 7	98.48 18	97.86 57	91.76 98	99.63 6	94.23 37	99.84 5	99.66 7

TDRefinement		97.68 4	97.60 4	97.93 2	99.02 11	95.95 6	98.61 3	98.81 5	97.41 8	97.28 50	98.46 29	94.62 46	98.84 123	94.64 26	99.53 44	98.99 71

CP-MVSNet		96.19 45	96.80 19	94.38 117	98.99 13	83.82 167	96.31 42	97.53 88	97.60 6	98.34 22	97.52 70	91.98 94	99.63 6	93.08 73	99.81 11	99.70 4

PMVS	87.21 14	94.97 84	95.33 79	93.91 131	98.97 14	97.16 2	95.54 66	95.85 196	96.47 19	93.40 186	97.46 73	95.31 28	95.47 308	86.18 200	98.78 127	89.11 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
zzz-MVS		96.47 31	96.14 41	97.47 11	98.95 15	94.05 18	93.69 133	97.62 76	94.46 42	96.29 86	96.94 98	93.56 58	99.37 45	94.29 35	99.42 57	98.99 71

MTAPA		96.65 22	96.38 32	97.47 11	98.95 15	94.05 18	95.88 56	97.62 76	94.46 42	96.29 86	96.94 98	93.56 58	99.37 45	94.29 35	99.42 57	98.99 71

ACMMP_Plus		96.21 44	96.12 43	96.49 45	98.90 17	91.42 57	94.57 102	98.03 40	90.42 138	96.37 81	97.35 82	95.68 19	99.25 61	94.44 31	99.34 67	98.80 93

HPM-MVS		96.81 13	96.62 25	97.36 20	98.89 18	93.53 34	97.51 9	98.44 8	92.35 84	95.95 105	96.41 130	96.71 9	99.42 28	93.99 43	99.36 66	99.13 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDDNet		94.03 120	94.27 113	93.31 148	98.87 19	82.36 183	95.51 67	91.78 278	97.19 10	96.32 83	98.60 20	84.24 224	98.75 142	87.09 185	98.83 119	98.81 92

TSAR-MVS + MP.		94.96 85	94.75 95	95.57 72	98.86 20	88.69 91	96.37 39	96.81 149	85.23 227	94.75 152	97.12 92	91.85 96	99.40 36	93.45 57	98.33 162	98.62 109

mvs_tets		96.83 10	96.71 21	97.17 25	98.83 21	92.51 43	96.58 28	97.61 79	87.57 199	98.80 8	98.90 9	96.50 11	99.59 12	96.15 9	99.47 49	99.40 31

PS-MVSNAJss		96.01 50	96.04 49	95.89 58	98.82 22	88.51 99	95.57 64	97.88 56	88.72 172	98.81 7	98.86 10	90.77 121	99.60 8	95.43 14	99.53 44	99.57 15

MP-MVS		96.14 46	95.68 65	97.51 10	98.81 23	94.06 16	96.10 48	97.78 67	92.73 72	93.48 183	96.72 114	94.23 52	99.42 28	91.99 98	99.29 73	99.05 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LTVRE_ROB	93.87 1	97.93 2	98.16 3	97.26 23	98.81 23	93.86 27	99.07 2	98.98 3	97.01 11	98.92 5	98.78 14	95.22 32	98.61 161	96.85 4	99.77 12	99.31 39
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
jajsoiax		96.59 26	96.42 30	97.12 27	98.76 25	92.49 44	96.44 36	97.42 97	86.96 209	98.71 10	98.72 17	95.36 26	99.56 16	95.92 10	99.45 53	99.32 38

pcd1.5k->3k		41.03 335	43.65 337	33.18 348	98.74 26	0.00 367	0.00 359	97.57 83	0.00 362	0.00 363	0.00 364	97.01 6	0.00 365	0.00 362	99.52 46	99.53 17

HSP-MVS		95.18 77	94.49 104	97.23 24	98.67 27	94.05 18	96.41 38	97.00 131	91.26 119	95.12 138	95.15 191	86.60 206	99.50 18	93.43 59	96.81 239	98.13 135

SteuartSystems-ACMMP		96.40 37	96.30 34	96.71 38	98.63 28	91.96 50	95.70 60	98.01 43	93.34 63	96.64 73	96.57 121	94.99 40	99.36 47	93.48 55	99.34 67	98.82 91
Skip Steuart: Steuart Systems R&D Blog.
region2R		96.41 36	96.09 45	97.38 19	98.62 29	93.81 31	96.32 41	97.96 49	92.26 87	95.28 132	96.57 121	95.02 38	99.41 32	93.63 50	99.11 91	98.94 79

mPP-MVS		96.46 32	96.05 48	97.69 5	98.62 29	94.65 9	96.45 34	97.74 69	92.59 78	95.47 125	96.68 116	94.50 49	99.42 28	93.10 71	99.26 76	98.99 71

ACMMP		96.61 24	96.34 33	97.43 15	98.61 31	93.88 25	96.95 18	98.18 25	92.26 87	96.33 82	96.84 107	95.10 35	99.40 36	93.47 56	99.33 69	99.02 68
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
VPNet		93.08 150	93.76 128	91.03 228	98.60 32	75.83 283	91.51 215	95.62 201	91.84 102	95.74 117	97.10 93	89.31 148	98.32 194	85.07 213	99.06 95	98.93 80

ACMMPR		96.46 32	96.14 41	97.41 17	98.60 32	93.82 29	96.30 44	97.96 49	92.35 84	95.57 123	96.61 119	94.93 42	99.41 32	93.78 46	99.15 87	99.00 69

PGM-MVS		96.32 41	95.94 52	97.43 15	98.59 34	93.84 28	95.33 71	98.30 16	91.40 117	95.76 116	96.87 104	95.26 30	99.45 23	92.77 77	99.21 82	99.00 69

XVS		96.49 29	96.18 39	97.44 13	98.56 35	93.99 22	96.50 32	97.95 51	94.58 38	94.38 161	96.49 123	94.56 47	99.39 41	93.57 51	99.05 97	98.93 80

X-MVStestdata		90.70 201	88.45 229	97.44 13	98.56 35	93.99 22	96.50 32	97.95 51	94.58 38	94.38 161	26.89 359	94.56 47	99.39 41	93.57 51	99.05 97	98.93 80

ACMH	88.36 12	96.59 26	97.43 5	94.07 124	98.56 35	85.33 152	96.33 40	98.30 16	94.66 37	98.72 9	98.30 37	97.51 5	98.00 214	94.87 21	99.59 35	98.86 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		96.62 23	96.49 29	97.01 30	98.55 38	91.77 54	97.15 14	97.37 101	88.98 160	98.26 23	98.86 10	93.35 67	99.60 8	96.41 6	99.45 53	99.66 7

v7n		96.82 11	97.31 10	95.33 80	98.54 39	86.81 126	96.83 20	98.07 35	96.59 18	98.46 19	98.43 33	92.91 76	99.52 17	96.25 8	99.76 13	99.65 9

abl_6		97.31 6	97.12 14	97.86 3	98.54 39	95.32 8	96.61 26	98.35 12	95.81 30	97.55 40	97.44 74	96.51 10	99.40 36	94.06 42	99.23 80	98.85 89

ACMH+	88.43 11	96.48 30	96.82 18	95.47 76	98.54 39	89.06 84	95.65 62	98.61 7	96.10 25	98.16 25	97.52 70	96.90 8	98.62 160	90.30 128	99.60 33	98.72 103

SixPastTwentyTwo		94.91 87	95.21 85	93.98 126	98.52 42	83.19 175	95.93 53	94.84 219	94.86 35	98.49 17	98.74 16	81.45 245	99.60 8	94.69 25	99.39 64	99.15 49

HFP-MVS		96.39 38	96.17 40	97.04 28	98.51 43	93.37 35	96.30 44	97.98 45	92.35 84	95.63 120	96.47 125	95.37 24	99.27 58	93.78 46	99.14 88	98.48 114

#test#		95.89 51	95.51 68	97.04 28	98.51 43	93.37 35	95.14 77	97.98 45	89.34 154	95.63 120	96.47 125	95.37 24	99.27 58	91.99 98	99.14 88	98.48 114

Baseline_NR-MVSNet		94.47 108	95.09 90	92.60 182	98.50 45	80.82 200	92.08 187	96.68 157	93.82 53	96.29 86	98.56 22	90.10 139	97.75 240	90.10 136	99.66 24	99.24 43

Anonymous20240521		96.37 40	96.66 22	95.50 74	98.49 46	87.84 112	97.47 10	97.77 68	94.75 36	98.22 24	98.49 26	90.93 119	99.28 55	94.12 41	99.74 15	99.38 32

OPM-MVS		95.61 58	95.45 72	96.08 50	98.49 46	91.00 62	92.65 162	97.33 110	90.05 143	96.77 68	96.85 105	95.04 36	98.56 169	92.77 77	99.06 95	98.70 104

FC-MVSNet-test		95.32 69	95.88 56	93.62 137	98.49 46	81.77 188	95.90 55	98.32 13	93.93 51	97.53 41	97.56 67	88.48 157	99.40 36	92.91 76	99.83 8	99.68 5

XVG-ACMP-BASELINE		95.68 56	95.34 77	96.69 39	98.40 49	93.04 38	94.54 106	98.05 37	90.45 137	96.31 84	96.76 110	92.91 76	98.72 147	91.19 116	99.42 57	98.32 120

ACMM	88.83 9	96.30 43	96.07 47	96.97 31	98.39 50	92.95 41	94.74 93	98.03 40	90.82 128	97.15 54	96.85 105	96.25 14	99.00 93	93.10 71	99.33 69	98.95 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		95.43 63	95.94 52	93.93 130	98.38 51	85.08 154	95.46 68	97.12 126	91.84 102	97.28 50	98.46 29	95.30 29	97.71 242	90.17 132	99.42 57	98.99 71

COLMAP_ROB	91.06 5	96.75 17	96.62 25	97.13 26	98.38 51	94.31 12	96.79 22	98.32 13	96.69 15	96.86 64	97.56 67	95.48 22	98.77 140	90.11 134	99.44 55	98.31 122
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TransMVSNet (Re)		95.27 75	96.04 49	92.97 160	98.37 53	81.92 187	95.07 81	96.76 154	93.97 50	97.77 35	98.57 21	95.72 18	97.90 217	88.89 159	99.23 80	99.08 60

LPG-MVS_test		96.38 39	96.23 37	96.84 36	98.36 54	92.13 47	95.33 71	98.25 19	91.78 107	97.07 56	97.22 86	96.38 12	99.28 55	92.07 96	99.59 35	99.11 53

LGP-MVS_train				96.84 36	98.36 54	92.13 47		98.25 19	91.78 107	97.07 56	97.22 86	96.38 12	99.28 55	92.07 96	99.59 35	99.11 53

v748		96.51 28	97.05 15	94.89 92	98.35 56	85.82 146	96.58 28	97.47 94	96.25 22	98.46 19	98.35 34	93.27 68	99.33 52	95.13 19	99.59 35	99.52 20

CP-MVS		96.44 35	96.08 46	97.54 9	98.29 57	94.62 10	96.80 21	98.08 32	92.67 75	95.08 143	96.39 135	94.77 43	99.42 28	93.17 69	99.44 55	98.58 113

FIs		94.90 88	95.35 76	93.55 140	98.28 58	81.76 189	95.33 71	98.14 28	93.05 66	97.07 56	97.18 88	87.65 178	99.29 54	91.72 104	99.69 16	99.61 12

TranMVSNet+NR-MVSNet		96.07 49	96.26 36	95.50 74	98.26 59	87.69 114	93.75 131	97.86 57	95.96 29	97.48 43	97.14 90	95.33 27	99.44 24	90.79 118	99.76 13	99.38 32

IS-MVSNet		94.49 107	94.35 108	94.92 91	98.25 60	86.46 131	97.13 16	94.31 233	96.24 23	96.28 89	96.36 140	82.88 231	99.35 48	88.19 171	99.52 46	98.96 77

UA-Net		97.35 5	97.24 13	97.69 5	98.22 61	93.87 26	98.42 4	98.19 24	96.95 12	95.46 127	99.23 4	93.45 60	99.57 13	95.34 17	99.89 4	99.63 10

test_part2					98.21 62	89.41 77				96.72 69

ESAPD		95.42 65	95.34 77	95.68 69	98.21 62	89.41 77	93.92 126	98.14 28	91.83 104	96.72 69	96.39 135	94.69 44	99.44 24	89.00 156	99.10 92	98.17 130

test_0402		95.73 53	96.22 38	94.26 120	98.19 64	85.77 147	93.24 147	97.24 118	96.88 14	97.69 37	97.77 60	94.12 54	99.13 73	91.54 112	99.29 73	97.88 152

SMA-MVS		95.73 53	95.51 68	96.41 46	98.17 65	91.19 60	95.09 79	97.79 66	86.48 214	97.42 48	97.42 75	94.47 51	99.26 60	93.42 60	99.29 73	98.79 95

ACMP	88.15 13	95.71 55	95.43 75	96.54 42	98.17 65	91.73 55	94.24 114	98.08 32	89.46 152	96.61 75	96.47 125	95.85 17	99.12 75	90.45 120	99.56 42	98.77 98
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CPTT-MVS		94.74 97	94.12 116	96.60 40	98.15 67	93.01 39	95.84 57	97.66 74	89.21 159	93.28 190	95.46 181	88.89 153	98.98 94	89.80 140	98.82 122	97.80 159

v52		96.93 8	97.29 11	95.86 59	98.12 68	88.48 100	97.69 7	97.74 69	94.90 34	98.55 15	98.72 17	93.39 64	99.49 21	96.92 2	99.62 30	99.61 12

V4		96.93 8	97.29 11	95.86 59	98.11 69	88.47 101	97.69 7	97.74 69	94.91 32	98.55 15	98.72 17	93.37 65	99.49 21	96.92 2	99.62 30	99.61 12

Vis-MVSNet		95.50 61	95.48 70	95.56 73	98.11 69	89.40 79	95.35 70	98.22 23	92.36 82	94.11 169	98.07 42	92.02 91	99.44 24	93.38 62	97.67 210	97.85 155
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
XVG-OURS-SEG-HR		95.38 67	95.00 91	96.51 43	98.10 71	94.07 15	92.46 172	98.13 31	90.69 130	93.75 177	96.25 145	98.03 3	97.02 269	92.08 95	95.55 269	98.45 117

EPP-MVSNet		93.91 122	93.68 134	94.59 105	98.08 72	85.55 150	97.44 11	94.03 238	94.22 45	94.94 147	96.19 153	82.07 239	99.57 13	87.28 184	98.89 109	98.65 105

K. test v3		93.37 139	93.27 146	93.66 136	98.05 73	82.62 181	94.35 111	86.62 311	96.05 27	97.51 42	98.85 12	76.59 280	99.65 3	93.21 68	98.20 179	98.73 102

lessismore_v0				93.87 133	98.05 73	83.77 168		80.32 354		97.13 55	97.91 54	77.49 270	99.11 76	92.62 83	98.08 190	98.74 100

AllTest		94.88 90	94.51 103	96.00 51	98.02 75	92.17 45	95.26 74	98.43 9	90.48 135	95.04 144	96.74 112	92.54 84	97.86 228	85.11 211	98.98 104	97.98 142

TestCases				96.00 51	98.02 75	92.17 45		98.43 9	90.48 135	95.04 144	96.74 112	92.54 84	97.86 228	85.11 211	98.98 104	97.98 142

anonymousdsp		96.74 18	96.42 30	97.68 7	98.00 77	94.03 21	96.97 17	97.61 79	87.68 198	98.45 21	98.77 15	94.20 53	99.50 18	96.70 5	99.40 62	99.53 17

XVG-OURS		94.72 98	94.12 116	96.50 44	98.00 77	94.23 13	91.48 216	98.17 26	90.72 129	95.30 131	96.47 125	87.94 175	96.98 270	91.41 114	97.61 213	98.30 123

114514_t		90.51 203	89.80 213	92.63 180	98.00 77	82.24 184	93.40 138	97.29 114	65.84 346	89.40 276	94.80 208	86.99 195	98.75 142	83.88 224	98.61 137	96.89 205

Gipuma		95.31 71	95.80 61	93.81 135	97.99 80	90.91 64	96.42 37	97.95 51	96.69 15	91.78 226	98.85 12	91.77 97	95.49 307	91.72 104	99.08 94	95.02 268
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
APD-MVS_3200maxsize		96.82 11	96.65 23	97.32 22	97.95 81	93.82 29	96.31 42	98.25 19	95.51 31	96.99 62	97.05 96	95.63 20	99.39 41	93.31 64	98.88 111	98.75 99

HPM-MVS++		95.02 82	94.39 105	96.91 34	97.88 82	93.58 33	94.09 117	96.99 133	91.05 124	92.40 211	95.22 190	91.03 118	99.25 61	92.11 93	98.69 134	97.90 150

EG-PatchMatch MVS		94.54 106	94.67 99	94.14 122	97.87 83	86.50 128	92.00 190	96.74 155	88.16 189	96.93 63	97.61 65	93.04 74	97.90 217	91.60 108	98.12 186	98.03 139

nrg030		96.32 41	96.55 28	95.62 70	97.83 84	88.55 97	95.77 59	98.29 18	92.68 73	98.03 28	97.91 54	95.13 33	98.95 101	93.85 44	99.49 48	99.36 36

UniMVSNet (Re)		95.32 69	95.15 87	95.80 62	97.79 85	88.91 87	92.91 155	98.07 35	93.46 60	96.31 84	95.97 161	90.14 135	99.34 49	92.11 93	99.64 27	99.16 48

VPA-MVSNet		95.14 79	95.67 66	93.58 139	97.76 86	83.15 176	94.58 101	97.58 82	93.39 62	97.05 60	98.04 44	93.25 69	98.51 178	89.75 141	99.59 35	99.08 60

DU-MVS		95.28 73	95.12 89	95.75 65	97.75 87	88.59 95	92.58 163	97.81 62	93.99 48	96.80 66	95.90 162	90.10 139	99.41 32	91.60 108	99.58 40	99.26 41

NR-MVSNet		95.28 73	95.28 82	95.26 82	97.75 87	87.21 120	95.08 80	97.37 101	93.92 52	97.65 38	95.90 162	90.10 139	99.33 52	90.11 134	99.66 24	99.26 41

XXY-MVS		92.58 167	93.16 148	90.84 233	97.75 87	79.84 223	91.87 199	96.22 185	85.94 220	95.53 124	97.68 62	92.69 81	94.48 320	83.21 229	97.51 215	98.21 128

wuykxyi23d		96.76 16	96.57 27	97.34 21	97.75 87	96.73 3	94.37 110	96.48 167	91.00 125	99.72 2	98.99 6	96.06 15	98.21 203	94.86 22	99.90 2	97.09 194

PVSNet_Blended_VisFu		91.63 184	91.20 192	92.94 163	97.73 91	83.95 166	92.14 186	97.46 95	78.85 288	92.35 214	94.98 201	84.16 225	99.08 78	86.36 198	96.77 241	95.79 246

tfpnnormal		94.27 114	94.87 94	92.48 188	97.71 92	80.88 199	94.55 105	95.41 211	93.70 55	96.67 72	97.72 61	91.40 104	98.18 208	87.45 180	99.18 85	98.36 118

HQP_MVS		94.26 115	93.93 119	95.23 84	97.71 92	88.12 106	94.56 103	97.81 62	91.74 111	93.31 187	95.59 173	86.93 197	98.95 101	89.26 151	98.51 146	98.60 111

plane_prior7					97.71 92	88.68 92

UniMVSNet_NR-MVSNet		95.35 68	95.21 85	95.76 64	97.69 95	88.59 95	92.26 182	97.84 60	94.91 32	96.80 66	95.78 170	90.42 131	99.41 32	91.60 108	99.58 40	99.29 40

APDe-MVS		96.46 32	96.64 24	95.93 56	97.68 96	89.38 80	96.90 19	98.41 11	92.52 79	97.43 46	97.92 52	95.11 34	99.50 18	94.45 30	99.30 71	98.92 83

DeepC-MVS	91.39 4	95.43 63	95.33 79	95.71 67	97.67 97	90.17 68	93.86 129	98.02 42	87.35 201	96.22 92	97.99 49	94.48 50	99.05 83	92.73 80	99.68 19	97.93 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Vis-MVSNet (Re-imp)		90.42 206	90.16 209	91.20 226	97.66 98	77.32 266	94.33 112	87.66 304	91.20 121	92.99 199	95.13 193	75.40 282	98.28 196	77.86 281	99.19 83	97.99 141

FMVSNet1		94.84 92	95.13 88	93.97 127	97.60 99	84.29 159	95.99 49	96.56 161	92.38 81	97.03 61	98.53 23	90.12 136	98.98 94	88.78 161	99.16 86	98.65 105

RPSCF		95.58 59	94.89 93	97.62 8	97.58 100	96.30 5	95.97 52	97.53 88	92.42 80	93.41 184	97.78 58	91.21 112	97.77 237	91.06 117	97.06 232	98.80 93

WR-MVS		93.49 133	93.72 131	92.80 172	97.57 101	80.03 216	90.14 254	95.68 200	93.70 55	96.62 74	95.39 187	87.21 189	99.04 86	87.50 179	99.64 27	99.33 37

CSCG		94.69 99	94.75 95	94.52 108	97.55 102	87.87 110	95.01 84	97.57 83	92.68 73	96.20 94	93.44 250	91.92 95	98.78 136	89.11 155	99.24 78	96.92 202

MCST-MVS		92.91 156	92.51 162	94.10 123	97.52 103	85.72 148	91.36 220	97.13 125	80.33 272	92.91 202	94.24 225	91.23 111	98.72 147	89.99 138	97.93 199	97.86 154

F-COLMAP		92.28 176	91.06 195	95.95 53	97.52 103	91.90 51	93.53 135	97.18 121	83.98 241	88.70 289	94.04 233	88.41 160	98.55 175	80.17 258	95.99 261	97.39 183

VDD-MVS		94.37 109	94.37 107	94.40 116	97.49 105	86.07 140	93.97 121	93.28 251	94.49 41	96.24 90	97.78 58	87.99 174	98.79 132	88.92 158	99.14 88	98.34 119

testgi		90.38 208	91.34 189	87.50 298	97.49 105	71.54 320	89.43 275	95.16 214	88.38 182	94.54 158	94.68 213	92.88 78	93.09 334	71.60 323	97.85 203	97.88 152

plane_prior1					97.38 107

APD-MVS		95.00 83	94.69 97	95.93 56	97.38 107	90.88 65	94.59 99	97.81 62	89.22 158	95.46 127	96.17 155	93.42 63	99.34 49	89.30 147	98.87 114	97.56 175
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ITE_SJBPF				95.95 53	97.34 109	93.36 37		96.55 164	91.93 97	94.82 150	95.39 187	91.99 93	97.08 267	85.53 204	97.96 197	97.41 180

Anonymous20240529		95.50 61	95.83 59	94.50 109	97.33 110	85.93 143	95.19 76	96.77 153	96.64 17	97.61 39	98.05 43	93.23 70	98.79 132	88.60 166	99.04 100	98.78 96

OMC-MVS		94.22 116	93.69 133	95.81 61	97.25 111	91.27 58	92.27 181	97.40 99	87.10 207	94.56 157	95.42 184	93.74 56	98.11 211	86.62 192	98.85 115	98.06 137

v13		95.39 66	96.12 43	93.18 151	97.22 112	80.81 201	95.55 65	97.57 83	93.42 61	98.02 30	98.49 26	89.62 144	99.18 66	95.54 12	99.68 19	99.54 16

plane_prior6					97.21 113	88.23 105						86.93 197

DP-MVS Recon		92.31 175	91.88 173	93.60 138	97.18 114	86.87 125	91.10 226	97.37 101	84.92 235	92.08 221	94.08 232	88.59 156	98.20 205	83.50 226	98.14 183	95.73 248

新几何1				93.17 152	97.16 115	87.29 117		94.43 230	67.95 339	91.29 232	94.94 202	86.97 196	98.23 202	81.06 250	97.75 204	93.98 293

DP-MVS		95.62 57	95.84 58	94.97 90	97.16 115	88.62 94	94.54 106	97.64 75	96.94 13	96.58 76	97.32 83	93.07 73	98.72 147	90.45 120	98.84 116	97.57 173

1121		90.26 213	89.23 215	93.34 146	97.15 117	87.40 116	91.94 193	94.39 231	67.88 340	91.02 244	94.91 203	86.91 199	98.59 165	81.17 248	97.71 207	94.02 292

v12		95.29 72	96.02 51	93.10 153	97.14 118	80.63 202	95.39 69	97.55 87	93.19 64	97.98 31	98.44 31	89.40 147	99.16 67	95.38 16	99.67 22	99.52 20

CHOSEN 1792x2688		87.19 270	85.92 283	91.00 231	97.13 119	79.41 235	84.51 330	95.60 202	64.14 349	90.07 260	94.81 205	78.26 266	97.14 265	73.34 310	95.38 276	96.46 224

HyFIR lowres test		87.19 270	85.51 285	92.24 195	97.12 120	80.51 203	85.03 324	96.06 188	66.11 345	91.66 227	92.98 257	70.12 293	99.14 71	75.29 304	95.23 279	97.07 195

V9		95.17 78	95.89 55	93.02 156	97.04 121	80.42 204	95.22 75	97.53 88	92.92 71	97.90 32	98.35 34	89.15 151	99.14 71	95.21 18	99.65 26	99.50 22

ab-mvs		92.40 173	92.62 159	91.74 208	97.02 122	81.65 190	95.84 57	95.50 209	86.95 210	92.95 201	97.56 67	90.70 127	97.50 249	79.63 264	97.43 222	96.06 239

v11		95.10 80	95.88 56	92.76 173	96.98 123	79.64 230	95.12 78	97.60 81	92.64 76	98.03 28	98.44 31	89.06 152	99.15 69	95.42 15	99.67 22	99.50 22

test222					96.95 124	85.27 153	88.83 290	93.61 245	65.09 348	90.74 248	94.85 204	84.62 223			97.36 225	93.91 294

V14		95.05 81	95.75 63	92.94 163	96.94 125	80.21 207	95.03 83	97.50 92	92.62 77	97.84 34	98.28 38	88.87 154	99.13 73	95.03 20	99.64 27	99.48 24

CDPH-MVS		92.67 164	91.83 174	95.18 86	96.94 125	88.46 102	90.70 236	97.07 127	77.38 296	92.34 216	95.08 195	92.67 82	98.88 111	85.74 202	98.57 139	98.20 129

CNVR-MVS		94.58 104	94.29 110	95.46 77	96.94 125	89.35 82	91.81 208	96.80 150	89.66 150	93.90 175	95.44 183	92.80 80	98.72 147	92.74 79	98.52 144	98.32 120

原ACMM1				92.87 167	96.91 128	84.22 162		97.01 130	76.84 300	89.64 273	94.46 217	88.00 173	98.70 153	81.53 243	98.01 195	95.70 250

ambc				92.98 159	96.88 129	83.01 179	95.92 54	96.38 174		96.41 79	97.48 72	88.26 162	97.80 234	89.96 139	98.93 108	98.12 136

testdata				91.03 228	96.87 130	82.01 185		94.28 234	71.55 323	92.46 209	95.42 184	85.65 217	97.38 259	82.64 234	97.27 227	93.70 301

v15		94.93 86	95.62 67	92.86 168	96.83 131	80.01 220	94.84 90	97.48 93	92.36 82	97.76 36	98.20 40	88.61 155	99.11 76	94.86 22	99.62 30	99.46 25

NP-MVS					96.82 132	87.10 121					93.40 251

3Dnovator+	92.74 2	95.86 52	95.77 62	96.13 49	96.81 133	90.79 67	96.30 44	97.82 61	96.13 24	94.74 153	97.23 85	91.33 106	99.16 67	93.25 66	98.30 167	98.46 116

Test_1112_low_res		87.50 261	86.58 266	90.25 244	96.80 134	77.75 261	87.53 304	96.25 181	69.73 334	86.47 310	93.61 244	75.67 281	97.88 225	79.95 260	93.20 311	95.11 266

testing_2		94.03 120	94.38 106	93.00 158	96.79 135	81.41 194	92.87 157	96.96 135	85.88 222	97.06 59	97.92 52	91.18 116	98.71 152	91.72 104	99.04 100	98.87 85

v17		94.80 94	95.46 71	92.83 169	96.76 136	80.02 218	94.85 88	97.40 99	92.23 89	97.45 45	98.04 44	88.46 159	99.06 81	94.56 27	99.40 62	99.41 28

v16		94.79 96	95.44 74	92.83 169	96.73 137	80.03 216	94.85 88	97.41 98	92.23 89	97.41 49	98.04 44	88.40 161	99.06 81	94.56 27	99.30 71	99.41 28

PAPM_NR		91.03 197	90.81 200	91.68 211	96.73 137	81.10 197	93.72 132	96.35 178	88.19 188	88.77 287	92.12 279	85.09 220	97.25 261	82.40 237	93.90 303	96.68 212

1112_ss		88.42 240	87.41 249	91.45 218	96.69 139	80.99 198	89.72 269	96.72 156	73.37 315	87.00 308	90.69 302	77.38 272	98.20 205	81.38 244	93.72 306	95.15 264

v8		94.65 101	95.29 81	92.74 174	96.65 140	79.77 226	94.59 99	97.17 122	91.86 101	97.47 44	97.93 51	88.16 166	99.08 78	94.32 32	99.47 49	99.38 32

v6		93.59 129	93.93 119	92.56 184	96.65 140	79.77 226	92.50 169	96.40 171	88.55 177	95.94 107	96.23 148	88.13 167	98.87 117	92.46 89	98.50 148	99.06 63

MVS_111021_HR		93.63 128	93.42 142	94.26 120	96.65 140	86.96 124	89.30 280	96.23 183	88.36 183	93.57 181	94.60 214	93.45 60	97.77 237	90.23 130	98.38 155	98.03 139

ANet_high		94.83 93	96.28 35	90.47 237	96.65 140	73.16 310	94.33 112	98.74 6	96.39 21	98.09 27	98.93 8	93.37 65	98.70 153	90.38 123	99.68 19	99.53 17

v1neww		93.58 130	93.92 121	92.56 184	96.64 144	79.77 226	92.50 169	96.41 169	88.55 177	95.93 108	96.24 146	88.08 169	98.87 117	92.45 90	98.50 148	99.05 64

v7new		93.58 130	93.92 121	92.56 184	96.64 144	79.77 226	92.50 169	96.41 169	88.55 177	95.93 108	96.24 146	88.08 169	98.87 117	92.45 90	98.50 148	99.05 64

SD-MVS		95.19 76	95.73 64	93.55 140	96.62 146	88.88 90	94.67 95	98.05 37	91.26 119	97.25 53	96.40 131	95.42 23	94.36 324	92.72 81	99.19 83	97.40 182

v18		94.63 102	95.26 84	92.74 174	96.60 147	79.81 224	94.64 98	97.37 101	91.87 100	97.26 52	97.91 54	88.13 167	99.04 86	94.30 34	99.24 78	99.38 32

PM-MVS		93.33 140	92.67 158	95.33 80	96.58 148	94.06 16	92.26 182	92.18 270	85.92 221	96.22 92	96.61 119	85.64 218	95.99 300	90.35 126	98.23 174	95.93 243

v10		94.68 100	95.27 83	92.90 166	96.57 149	80.15 209	94.65 97	97.57 83	90.68 131	97.43 46	98.00 48	88.18 164	99.15 69	94.84 24	99.55 43	99.41 28

Anonymous202405211		92.58 167	92.50 163	92.83 169	96.55 150	83.22 174	92.43 174	91.64 279	94.10 47	95.59 122	96.64 117	81.88 243	97.50 249	85.12 210	98.52 144	97.77 160

v7		93.66 126	93.97 118	92.73 176	96.55 150	80.15 209	92.54 164	96.99 133	87.36 200	95.99 102	96.48 124	88.18 164	98.94 104	93.35 63	98.31 164	99.09 57

PLC	85.34 15	90.40 207	88.92 223	94.85 93	96.53 152	90.02 69	91.58 213	96.48 167	80.16 273	86.14 312	92.18 276	85.73 215	98.25 201	76.87 291	94.61 292	96.30 230
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	88.58 10	92.49 172	91.75 178	94.73 97	96.50 153	89.69 73	92.91 155	97.68 73	78.02 293	92.79 203	94.10 231	90.85 120	97.96 216	84.76 217	98.16 181	96.54 213
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
view600		88.32 242	87.94 241	89.46 259	96.49 154	73.31 305	93.95 122	84.46 334	93.02 67	94.18 165	92.68 264	63.33 325	98.56 169	75.87 299	97.50 216	96.51 215

view800		88.32 242	87.94 241	89.46 259	96.49 154	73.31 305	93.95 122	84.46 334	93.02 67	94.18 165	92.68 264	63.33 325	98.56 169	75.87 299	97.50 216	96.51 215

conf0.05thres1000		88.32 242	87.94 241	89.46 259	96.49 154	73.31 305	93.95 122	84.46 334	93.02 67	94.18 165	92.68 264	63.33 325	98.56 169	75.87 299	97.50 216	96.51 215

tfpn		88.32 242	87.94 241	89.46 259	96.49 154	73.31 305	93.95 122	84.46 334	93.02 67	94.18 165	92.68 264	63.33 325	98.56 169	75.87 299	97.50 216	96.51 215

NCCC		94.08 119	93.54 139	95.70 68	96.49 154	89.90 72	92.39 176	96.91 143	90.64 132	92.33 217	94.60 214	90.58 130	98.96 99	90.21 131	97.70 208	98.23 126

TAMVS		90.16 215	89.05 219	93.49 145	96.49 154	86.37 134	90.34 247	92.55 266	80.84 270	92.99 199	94.57 216	81.94 242	98.20 205	73.51 309	98.21 177	95.90 244

TEST9					96.45 160	89.46 74	90.60 239	96.92 140	79.09 286	90.49 253	94.39 221	91.31 107	98.88 111

train_agg		92.71 163	91.83 174	95.35 78	96.45 160	89.46 74	90.60 239	96.92 140	79.37 281	90.49 253	94.39 221	91.20 113	98.88 111	88.66 164	98.43 151	97.72 163

agg_prior3		92.56 170	91.62 179	95.35 78	96.39 162	89.45 76	90.61 238	96.82 148	78.82 289	90.03 261	94.14 230	90.72 126	98.88 111	88.66 164	98.43 151	97.72 163

test_8					96.37 163	89.14 83	90.51 243	96.89 144	79.37 281	90.42 255	94.36 223	91.20 113	98.82 125

v1141		93.42 137	93.76 128	92.40 192	96.37 163	79.24 238	91.84 204	96.38 174	88.33 184	95.86 113	96.23 148	87.41 184	98.89 107	92.61 84	98.82 122	99.08 60

divwei89l23v2f112		93.42 137	93.76 128	92.41 190	96.37 163	79.24 238	91.84 204	96.38 174	88.33 184	95.86 113	96.23 148	87.41 184	98.89 107	92.61 84	98.83 119	99.09 57

v1		93.43 135	93.77 127	92.41 190	96.37 163	79.24 238	91.84 204	96.38 174	88.33 184	95.87 112	96.22 151	87.45 182	98.89 107	92.61 84	98.83 119	99.09 57

CLD-MVS		91.82 182	91.41 186	93.04 154	96.37 163	83.65 169	86.82 313	97.29 114	84.65 238	92.27 218	89.67 314	92.20 88	97.85 231	83.95 223	99.47 49	97.62 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					96.36 168		91.37 217		87.16 204	88.81 283

ACMP_Plane					96.36 168		91.37 217		87.16 204	88.81 283

HQP-MVS		92.09 179	91.49 184	93.88 132	96.36 168	84.89 155	91.37 217	97.31 111	87.16 204	88.81 283	93.40 251	84.76 221	98.60 163	86.55 194	97.73 205	98.14 134

v2v482		93.29 141	93.63 135	92.29 193	96.35 171	78.82 249	91.77 211	96.28 179	88.45 180	95.70 119	96.26 144	86.02 213	98.90 105	93.02 74	98.81 125	99.14 50

MSLP-MVS++		93.25 146	93.88 123	91.37 220	96.34 172	82.81 180	93.11 148	97.74 69	89.37 153	94.08 171	95.29 189	90.40 134	96.35 294	90.35 126	98.25 172	94.96 269

FPMVS		84.50 295	83.28 298	88.16 291	96.32 173	94.49 11	85.76 320	85.47 322	83.09 249	85.20 317	94.26 224	63.79 320	86.58 355	63.72 346	91.88 329	83.40 350

Anonymous20231206		88.77 235	88.29 231	90.20 248	96.31 174	78.81 250	89.56 273	93.49 249	74.26 310	92.38 212	95.58 176	82.21 237	95.43 310	72.07 318	98.75 131	96.34 228

MVP-Stereo		90.07 217	88.92 223	93.54 142	96.31 174	86.49 129	90.93 230	95.59 205	79.80 274	91.48 228	95.59 173	80.79 253	97.39 257	78.57 278	91.19 331	96.76 211
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		93.50 132	93.81 124	92.57 183	96.28 176	79.61 232	91.86 203	96.96 135	86.95 210	95.91 111	96.32 141	87.65 178	98.96 99	93.51 53	98.88 111	99.13 51

LFMVS		91.33 194	91.16 194	91.82 206	96.27 177	79.36 236	95.01 84	85.61 321	96.04 28	94.82 150	97.06 95	72.03 288	98.46 185	84.96 214	98.70 133	97.65 169

VNet		92.67 164	92.96 149	91.79 207	96.27 177	80.15 209	91.95 191	94.98 216	92.19 92	94.52 159	96.07 157	87.43 183	97.39 257	84.83 215	98.38 155	97.83 156

IterMVS-LS		93.78 124	94.28 111	92.27 194	96.27 177	79.21 243	91.87 199	96.78 151	91.77 109	96.57 77	97.07 94	87.15 190	98.74 145	91.99 98	99.03 102	98.86 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		92.87 158	93.29 143	91.62 212	96.25 180	77.72 262	91.28 221	95.05 215	89.69 149	95.93 108	96.04 158	87.34 186	98.38 190	90.05 137	97.99 196	98.78 96

MVS_111021_LR		93.66 126	93.28 145	94.80 95	96.25 180	90.95 63	90.21 250	95.43 210	87.91 191	93.74 179	94.40 220	92.88 78	96.38 292	90.39 122	98.28 168	97.07 195

agg_prior1		92.60 166	91.76 177	95.10 88	96.20 182	88.89 88	90.37 245	96.88 145	79.67 278	90.21 256	94.41 218	91.30 108	98.78 136	88.46 168	98.37 160	97.64 170

agg_prior					96.20 182	88.89 88		96.88 145		90.21 256			98.78 136

旧先验1					96.20 182	84.17 163		94.82 220			95.57 177	89.57 145			97.89 201	96.32 229

CNLPA		91.72 183	91.20 192	93.26 149	96.17 185	91.02 61	91.14 224	95.55 207	90.16 142	90.87 245	93.56 246	86.31 209	94.40 323	79.92 263	97.12 230	94.37 283

v1192		93.49 133	93.78 126	92.62 181	96.16 186	79.62 231	91.83 207	97.22 120	86.07 218	96.10 100	96.38 138	87.22 188	99.02 90	94.14 40	98.88 111	99.22 44

tfpn111		87.60 258	87.12 256	89.04 272	96.14 187	73.09 311	93.00 150	85.31 324	92.13 93	93.26 192	90.96 295	63.42 321	98.48 182	72.87 314	96.98 236	95.56 254

conf200view11		87.41 262	86.89 260	88.97 273	96.14 187	73.09 311	93.00 150	85.31 324	92.13 93	93.26 192	90.96 295	63.42 321	98.28 196	71.27 326	96.54 250	95.56 254

thres100view900		87.35 264	86.89 260	88.72 278	96.14 187	73.09 311	93.00 150	85.31 324	92.13 93	93.26 192	90.96 295	63.42 321	98.28 196	71.27 326	96.54 250	94.79 272

DeepC-MVS_fast	89.96 7	93.73 125	93.44 141	94.60 104	96.14 187	87.90 109	93.36 139	97.14 123	85.53 226	93.90 175	95.45 182	91.30 108	98.59 165	89.51 144	98.62 136	97.31 188
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PCF-MVS	84.52 17	89.12 227	87.71 246	93.34 146	96.06 191	85.84 145	86.58 317	97.31 111	68.46 338	93.61 180	93.89 238	87.51 181	98.52 177	67.85 336	98.11 187	95.66 251
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v144192		93.20 149	93.54 139	92.16 198	96.05 192	78.26 256	91.95 191	97.14 123	84.98 234	95.96 104	96.11 156	87.08 192	99.04 86	93.79 45	98.84 116	99.17 47

thres600view7		87.66 256	87.10 258	89.36 266	96.05 192	73.17 309	92.72 159	85.31 324	91.89 99	93.29 189	90.97 294	63.42 321	98.39 188	73.23 311	96.99 235	96.51 215

MIMVSNet		87.13 272	86.54 268	88.89 275	96.05 192	76.11 278	94.39 109	88.51 295	81.37 266	88.27 295	96.75 111	72.38 286	95.52 306	65.71 343	95.47 273	95.03 267

v1921920		93.26 144	93.61 136	92.19 196	96.04 195	78.31 255	91.88 198	97.24 118	85.17 228	96.19 96	96.19 153	86.76 202	99.05 83	94.18 39	98.84 116	99.22 44

v1240		93.29 141	93.71 132	92.06 201	96.01 196	77.89 260	91.81 208	97.37 101	85.12 230	96.69 71	96.40 131	86.67 203	99.07 80	94.51 29	98.76 129	99.22 44

testmv		88.46 239	88.11 238	89.48 257	96.00 197	76.14 277	86.20 319	93.75 243	84.48 239	93.57 181	95.52 180	80.91 252	95.09 316	63.97 345	98.61 137	97.22 191

conf0.01		86.95 275	86.04 275	89.70 254	95.99 198	75.66 284	93.28 140	82.70 341	88.81 165	91.26 233	88.01 326	58.77 340	97.89 219	78.93 270	96.60 244	95.56 254

conf0.002		86.95 275	86.04 275	89.70 254	95.99 198	75.66 284	93.28 140	82.70 341	88.81 165	91.26 233	88.01 326	58.77 340	97.89 219	78.93 270	96.60 244	95.56 254

thresconf0.02		86.69 280	86.04 275	88.64 281	95.99 198	75.66 284	93.28 140	82.70 341	88.81 165	91.26 233	88.01 326	58.77 340	97.89 219	78.93 270	96.60 244	92.36 322

tfpn_n400		86.69 280	86.04 275	88.64 281	95.99 198	75.66 284	93.28 140	82.70 341	88.81 165	91.26 233	88.01 326	58.77 340	97.89 219	78.93 270	96.60 244	92.36 322

tfpnconf		86.69 280	86.04 275	88.64 281	95.99 198	75.66 284	93.28 140	82.70 341	88.81 165	91.26 233	88.01 326	58.77 340	97.89 219	78.93 270	96.60 244	92.36 322

tfpnview11		86.69 280	86.04 275	88.64 281	95.99 198	75.66 284	93.28 140	82.70 341	88.81 165	91.26 233	88.01 326	58.77 340	97.89 219	78.93 270	96.60 244	92.36 322

BH-untuned		90.68 202	90.90 196	90.05 250	95.98 204	79.57 233	90.04 258	94.94 218	87.91 191	94.07 172	93.00 256	87.76 177	97.78 236	79.19 268	95.17 280	92.80 316

DeepPCF-MVS	90.46 6	94.20 117	93.56 138	96.14 48	95.96 205	92.96 40	89.48 274	97.46 95	85.14 229	96.23 91	95.42 184	93.19 71	98.08 212	90.37 124	98.76 129	97.38 185

test_prior3		93.29 141	92.85 152	94.61 100	95.95 206	87.23 118	90.21 250	97.36 107	89.33 155	90.77 246	94.81 205	90.41 132	98.68 155	88.21 169	98.55 140	97.93 146

test_prior				94.61 100	95.95 206	87.23 118		97.36 107					98.68 155			97.93 146

test12				94.43 115	95.95 206	86.75 127		96.24 182		89.76 271		89.79 143	98.79 132		97.95 198	97.75 162

LCM-MVSNet-Re		94.20 117	94.58 101	93.04 154	95.91 209	83.13 177	93.79 130	99.19 2	92.00 96	98.84 6	98.04 44	93.64 57	99.02 90	81.28 245	98.54 142	96.96 200

PatchMatch-RL		89.18 225	88.02 240	92.64 179	95.90 210	92.87 42	88.67 293	91.06 283	80.34 271	90.03 261	91.67 285	83.34 227	94.42 322	76.35 295	94.84 286	90.64 338

TSAR-MVS + GP.		93.07 152	92.41 165	95.06 89	95.82 211	90.87 66	90.97 228	92.61 265	88.04 190	94.61 156	93.79 241	88.08 169	97.81 233	89.41 146	98.39 154	96.50 222

QAPM		92.88 157	92.77 154	93.22 150	95.82 211	83.31 172	96.45 34	97.35 109	83.91 242	93.75 177	96.77 108	89.25 149	98.88 111	84.56 219	97.02 234	97.49 177

tfpn200view9		87.05 273	86.52 269	88.67 279	95.77 213	72.94 314	91.89 196	86.00 316	90.84 126	92.61 206	89.80 309	63.93 318	98.28 196	71.27 326	96.54 250	94.79 272

thres400		87.20 269	86.52 269	89.24 270	95.77 213	72.94 314	91.89 196	86.00 316	90.84 126	92.61 206	89.80 309	63.93 318	98.28 196	71.27 326	96.54 250	96.51 215

pmmvs-eth3d		91.54 186	90.73 203	93.99 125	95.76 215	87.86 111	90.83 232	93.98 240	78.23 292	94.02 173	96.22 151	82.62 236	96.83 276	86.57 193	98.33 162	97.29 189

jason		89.17 226	88.32 230	91.70 210	95.73 216	80.07 213	88.10 297	93.22 253	71.98 322	90.09 258	92.79 259	78.53 264	98.56 169	87.43 181	97.06 232	96.46 224
jason: jason.
alignmvs		93.26 144	92.85 152	94.50 109	95.70 217	87.45 115	93.45 137	95.76 198	91.58 114	95.25 134	92.42 272	81.96 241	98.72 147	91.61 107	97.87 202	97.33 187

xiu_mvs_v1_base_debu		91.47 189	91.52 181	91.33 221	95.69 218	81.56 191	89.92 262	96.05 189	83.22 246	91.26 233	90.74 299	91.55 101	98.82 125	89.29 148	95.91 262	93.62 303

xiu_mvs_v1_base		91.47 189	91.52 181	91.33 221	95.69 218	81.56 191	89.92 262	96.05 189	83.22 246	91.26 233	90.74 299	91.55 101	98.82 125	89.29 148	95.91 262	93.62 303

xiu_mvs_v1_base_debi		91.47 189	91.52 181	91.33 221	95.69 218	81.56 191	89.92 262	96.05 189	83.22 246	91.26 233	90.74 299	91.55 101	98.82 125	89.29 148	95.91 262	93.62 303

PHI-MVS		94.34 112	93.80 125	95.95 53	95.65 221	91.67 56	94.82 91	97.86 57	87.86 194	93.04 198	94.16 229	91.58 100	98.78 136	90.27 129	98.96 107	97.41 180

LF4IMVS		92.72 162	92.02 171	94.84 94	95.65 221	91.99 49	92.92 154	96.60 160	85.08 232	92.44 210	93.62 243	86.80 201	96.35 294	86.81 187	98.25 172	96.18 235

test20.03		90.80 199	90.85 199	90.63 235	95.63 223	79.24 238	89.81 268	92.87 258	89.90 147	94.39 160	96.40 131	85.77 214	95.27 315	73.86 308	99.05 97	97.39 183

TinyColmap		92.00 181	92.76 155	89.71 253	95.62 224	77.02 270	90.72 235	96.17 187	87.70 197	95.26 133	96.29 142	92.54 84	96.45 288	81.77 240	98.77 128	95.66 251

canonicalmvs		94.59 103	94.69 97	94.30 119	95.60 225	87.03 123	95.59 63	98.24 22	91.56 115	95.21 137	92.04 280	94.95 41	98.66 157	91.45 113	97.57 214	97.20 192

AdaColmap		91.63 184	91.36 188	92.47 189	95.56 226	86.36 135	92.24 184	96.27 180	88.88 164	89.90 267	92.69 263	91.65 99	98.32 194	77.38 288	97.64 211	92.72 318

tfpn1000		86.83 278	86.23 274	88.64 281	95.53 227	75.25 291	93.57 134	82.28 348	89.27 157	91.46 229	89.24 317	57.22 348	97.86 228	80.63 253	96.88 238	92.81 315

UnsupCasMVSNet_bld		88.50 238	88.03 239	89.90 251	95.52 228	78.88 248	87.39 305	94.02 239	79.32 284	93.06 197	94.02 235	80.72 254	94.27 325	75.16 305	93.08 315	96.54 213

3Dnovator	92.54 3	94.80 94	94.90 92	94.47 112	95.47 229	87.06 122	96.63 25	97.28 116	91.82 106	94.34 164	97.41 76	90.60 129	98.65 159	92.47 88	98.11 187	97.70 165

Fast-Effi-MVS+		91.28 195	90.86 198	92.53 187	95.45 230	82.53 182	89.25 283	96.52 165	85.00 233	89.91 266	88.55 321	92.94 75	98.84 123	84.72 218	95.44 274	96.22 233

GBi-Net		93.21 147	92.96 149	93.97 127	95.40 231	84.29 159	95.99 49	96.56 161	88.63 173	95.10 140	98.53 23	81.31 248	98.98 94	86.74 188	98.38 155	98.65 105

test1		93.21 147	92.96 149	93.97 127	95.40 231	84.29 159	95.99 49	96.56 161	88.63 173	95.10 140	98.53 23	81.31 248	98.98 94	86.74 188	98.38 155	98.65 105

FMVSNet2		92.78 160	92.73 157	92.95 162	95.40 231	81.98 186	94.18 116	95.53 208	88.63 173	96.05 101	97.37 79	81.31 248	98.81 130	87.38 183	98.67 135	98.06 137

CDS-MVSNet		89.55 220	88.22 235	93.53 143	95.37 234	86.49 129	89.26 281	93.59 246	79.76 276	91.15 242	92.31 274	77.12 274	98.38 190	77.51 286	97.92 200	95.71 249
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Test4		91.41 193	91.25 191	91.89 204	95.35 235	80.32 205	90.97 228	96.92 140	81.96 262	95.11 139	93.81 240	81.34 247	98.48 182	88.71 163	97.08 231	96.87 207

V42		93.43 135	93.58 137	92.97 160	95.34 236	81.22 195	92.67 161	96.49 166	87.25 203	96.20 94	96.37 139	87.32 187	98.85 122	92.39 92	98.21 177	98.85 89

DI_MVS_plusplus_test		91.42 192	91.41 186	91.46 217	95.34 236	79.06 245	90.58 241	93.74 244	82.59 256	94.69 155	94.76 209	86.54 207	98.44 187	87.93 175	96.49 255	96.87 207

Patchmatch-RL test		88.81 234	88.52 228	89.69 256	95.33 238	79.94 221	86.22 318	92.71 263	78.46 290	95.80 115	94.18 228	66.25 308	95.33 313	89.22 153	98.53 143	93.78 298

test_normal		91.49 188	91.44 185	91.62 212	95.21 239	79.44 234	90.08 257	93.84 242	82.60 255	94.37 163	94.74 210	86.66 204	98.46 185	88.58 167	96.92 237	96.95 201

BH-RMVSNet		90.47 204	90.44 206	90.56 236	95.21 239	78.65 253	89.15 284	93.94 241	88.21 187	92.74 204	94.22 226	86.38 208	97.88 225	78.67 277	95.39 275	95.14 265

tfpn_ndepth		85.85 287	85.15 288	87.98 292	95.19 241	75.36 290	92.79 158	83.18 340	86.97 208	89.92 265	86.43 338	57.44 347	97.85 231	78.18 279	96.22 258	90.72 337

Effi-MVS+		92.79 159	92.74 156	92.94 163	95.10 242	83.30 173	94.00 119	97.53 88	91.36 118	89.35 277	90.65 304	94.01 55	98.66 157	87.40 182	95.30 277	96.88 206

USDC		89.02 228	89.08 218	88.84 276	95.07 243	74.50 297	88.97 287	96.39 173	73.21 316	93.27 191	96.28 143	82.16 238	96.39 291	77.55 285	98.80 126	95.62 253

WTY-MVS		86.93 277	86.50 271	88.24 290	94.96 244	74.64 293	87.19 308	92.07 275	78.29 291	88.32 294	91.59 288	78.06 267	94.27 325	74.88 306	93.15 313	95.80 245

PS-MVSNAJ		88.86 233	88.99 222	88.48 287	94.88 245	74.71 292	86.69 314	95.60 202	80.88 268	87.83 299	87.37 333	90.77 121	98.82 125	82.52 235	94.37 295	91.93 329

MG-MVS		89.54 221	89.80 213	88.76 277	94.88 245	72.47 317	89.60 271	92.44 268	85.82 223	89.48 275	95.98 160	82.85 232	97.74 241	81.87 239	95.27 278	96.08 238

xiu_mvs_v2_base		89.00 229	89.19 216	88.46 288	94.86 247	74.63 294	86.97 310	95.60 202	80.88 268	87.83 299	88.62 320	91.04 117	98.81 130	82.51 236	94.38 294	91.93 329

MAR-MVS		90.32 212	88.87 225	94.66 99	94.82 248	91.85 52	94.22 115	94.75 223	80.91 267	87.52 304	88.07 325	86.63 205	97.87 227	76.67 292	96.21 259	94.25 285
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
PVSNet_BlendedMVS		90.35 210	89.96 211	91.54 216	94.81 249	78.80 251	90.14 254	96.93 138	79.43 279	88.68 290	95.06 197	86.27 210	98.15 209	80.27 255	98.04 193	97.68 167

PVSNet_Blended		88.74 236	88.16 237	90.46 238	94.81 249	78.80 251	86.64 315	96.93 138	74.67 305	88.68 290	89.18 318	86.27 210	98.15 209	80.27 255	96.00 260	94.44 282

BH-w/o		87.21 268	87.02 259	87.79 296	94.77 251	77.27 267	87.90 298	93.21 255	81.74 264	89.99 263	88.39 323	83.47 226	96.93 272	71.29 325	92.43 321	89.15 341

LS3D		96.11 47	95.83 59	96.95 33	94.75 252	94.20 14	97.34 12	97.98 45	97.31 9	95.32 130	96.77 108	93.08 72	99.20 65	91.79 103	98.16 181	97.44 179

Effi-MVS+-dtu		93.90 123	92.60 160	97.77 4	94.74 253	96.67 4	94.00 119	95.41 211	89.94 145	91.93 225	92.13 278	90.12 136	98.97 98	87.68 177	97.48 220	97.67 168

mvs-test1		93.07 152	91.80 176	96.89 35	94.74 253	95.83 7	92.17 185	95.41 211	89.94 145	89.85 268	90.59 305	90.12 136	98.88 111	87.68 177	95.66 267	95.97 241

MVSFormer		92.18 178	92.23 167	92.04 202	94.74 253	80.06 214	97.15 14	97.37 101	88.98 160	88.83 281	92.79 259	77.02 275	99.60 8	96.41 6	96.75 242	96.46 224

lupinMVS		88.34 241	87.31 250	91.45 218	94.74 253	80.06 214	87.23 306	92.27 269	71.10 326	88.83 281	91.15 291	77.02 275	98.53 176	86.67 191	96.75 242	95.76 247

casdiffmvs		92.55 171	92.40 166	93.01 157	94.72 257	83.36 171	94.54 106	97.04 128	83.00 252	89.97 264	96.95 97	88.23 163	98.76 141	93.22 67	93.95 302	96.92 202

MDA-MVSNet-bldmvs		91.04 196	90.88 197	91.55 215	94.68 258	80.16 208	85.49 322	92.14 273	90.41 139	94.93 148	95.79 168	85.10 219	96.93 272	85.15 208	94.19 300	97.57 173

MVS_0304		92.99 154	92.54 161	94.35 118	94.67 259	86.06 141	91.16 223	97.92 55	90.01 144	88.33 293	94.41 218	87.02 193	99.22 63	90.36 125	99.00 103	97.76 161

Fast-Effi-MVS+-dtu		92.77 161	92.16 168	94.58 107	94.66 260	88.25 104	92.05 188	96.65 158	89.62 151	90.08 259	91.23 290	92.56 83	98.60 163	86.30 199	96.27 257	96.90 204

UnsupCasMVSNet_eth		90.33 211	90.34 207	90.28 242	94.64 261	80.24 206	89.69 270	95.88 194	85.77 224	93.94 174	95.69 172	81.99 240	92.98 335	84.21 221	91.30 330	97.62 171

1111		80.36 322	81.32 310	77.48 339	94.61 262	44.56 360	81.59 341	90.66 287	86.78 212	90.60 251	93.52 248	30.37 365	90.67 343	66.36 340	97.42 223	97.20 192

.test1245		64.72 334	70.88 335	46.22 347	94.61 262	44.56 360	81.59 341	90.66 287	86.78 212	90.60 251	93.52 248	30.37 365	90.67 343	66.36 340	3.45 361	3.44 361

OpenMVS_ROB	85.12 16	89.52 222	89.05 219	90.92 232	94.58 264	81.21 196	91.10 226	93.41 250	77.03 299	93.41 184	93.99 237	83.23 228	97.80 234	79.93 262	94.80 287	93.74 300

OpenMVS	89.45 8	92.27 177	92.13 170	92.68 178	94.53 265	84.10 164	95.70 60	97.03 129	82.44 259	91.14 243	96.42 129	88.47 158	98.38 190	85.95 201	97.47 221	95.55 258

thres200		85.85 287	85.18 287	87.88 295	94.44 266	72.52 316	89.08 285	86.21 313	88.57 176	91.44 230	88.40 322	64.22 316	98.00 214	68.35 335	95.88 265	93.12 311

DELS-MVS		92.05 180	92.16 168	91.72 209	94.44 266	80.13 212	87.62 300	97.25 117	87.34 202	92.22 219	93.18 255	89.54 146	98.73 146	89.67 143	98.20 179	96.30 230
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
N_pmnet		88.90 232	87.25 252	93.83 134	94.40 268	93.81 31	84.73 326	87.09 308	79.36 283	93.26 192	92.43 271	79.29 259	91.68 340	77.50 287	97.22 228	96.00 240

pmmvs4		88.95 231	87.70 247	92.70 177	94.30 269	85.60 149	87.22 307	92.16 272	74.62 306	89.75 272	94.19 227	77.97 268	96.41 290	82.71 233	96.36 256	96.09 237

new-patchmatchnet		88.97 230	90.79 201	83.50 327	94.28 270	55.83 357	85.34 323	93.56 247	86.18 216	95.47 125	95.73 171	83.10 229	96.51 285	85.40 205	98.06 191	98.16 132

API-MVS		91.52 187	91.61 180	91.26 224	94.16 271	86.26 137	94.66 96	94.82 220	91.17 122	92.13 220	91.08 293	90.03 142	97.06 268	79.09 269	97.35 226	90.45 339

MSDG		90.82 198	90.67 204	91.26 224	94.16 271	83.08 178	86.63 316	96.19 186	90.60 134	91.94 224	91.89 281	89.16 150	95.75 303	80.96 252	94.51 293	94.95 270

TR-MVS		87.70 254	87.17 254	89.27 268	94.11 273	79.26 237	88.69 292	91.86 276	81.94 263	90.69 249	89.79 311	82.82 233	97.42 254	72.65 316	91.98 327	91.14 334

sss		87.23 267	86.82 262	88.46 288	93.96 274	77.94 257	86.84 312	92.78 262	77.59 294	87.61 303	91.83 282	78.75 261	91.92 339	77.84 282	94.20 299	95.52 259

PVSNet	76.22 20	82.89 303	82.37 302	84.48 322	93.96 274	64.38 347	78.60 348	88.61 294	71.50 324	84.43 324	86.36 339	74.27 283	94.60 319	69.87 333	93.69 307	94.46 281

semantic-postprocess				91.94 203	93.89 276	79.22 242		93.51 248	91.53 116	95.37 129	96.62 118	77.17 273	98.90 105	91.89 102	94.95 283	97.70 165

UGNet		93.08 150	92.50 163	94.79 96	93.87 277	87.99 108	95.07 81	94.26 235	90.64 132	87.33 305	97.67 63	86.89 200	98.49 179	88.10 173	98.71 132	97.91 149
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
PAPM		81.91 311	80.11 321	87.31 300	93.87 277	72.32 318	84.02 333	93.22 253	69.47 335	76.13 354	89.84 308	72.15 287	97.23 262	53.27 355	89.02 336	92.37 321

CANet		92.38 174	91.99 172	93.52 144	93.82 279	83.46 170	91.14 224	97.00 131	89.81 148	86.47 310	94.04 233	87.90 176	99.21 64	89.50 145	98.27 169	97.90 150

test1235678		84.54 294	83.85 296	86.59 305	93.81 280	73.41 304	82.38 338	91.79 277	79.43 279	89.50 274	91.61 287	70.59 291	92.94 336	58.14 351	97.40 224	93.44 307

HY-MVS	82.50 18	86.81 279	85.93 282	89.47 258	93.63 281	77.93 258	94.02 118	91.58 280	75.68 302	83.64 328	93.64 242	77.40 271	97.42 254	71.70 322	92.07 326	93.05 312

no-one		87.84 251	87.21 253	89.74 252	93.58 282	78.64 254	81.28 343	92.69 264	74.36 308	92.05 223	97.14 90	81.86 244	96.07 298	72.03 319	99.90 2	94.52 279

MVS_Test		92.57 169	93.29 143	90.40 239	93.53 283	75.85 281	92.52 166	96.96 135	88.73 171	92.35 214	96.70 115	90.77 121	98.37 193	92.53 87	95.49 271	96.99 199

EU-MVSNet		87.39 263	86.71 265	89.44 263	93.40 284	76.11 278	94.93 87	90.00 290	57.17 355	95.71 118	97.37 79	64.77 315	97.68 244	92.67 82	94.37 295	94.52 279

MS-PatchMatch		88.05 248	87.75 245	88.95 274	93.28 285	77.93 258	87.88 299	92.49 267	75.42 304	92.57 208	93.59 245	80.44 255	94.24 327	81.28 245	92.75 318	94.69 276

GA-MVS		87.70 254	86.82 262	90.31 241	93.27 286	77.22 268	84.72 328	92.79 261	85.11 231	89.82 269	90.07 306	66.80 303	97.76 239	84.56 219	94.27 298	95.96 242

pmmvs5		87.87 250	87.14 255	90.07 249	93.26 287	76.97 272	88.89 289	92.18 270	73.71 314	88.36 292	93.89 238	76.86 278	96.73 279	80.32 254	96.81 239	96.51 215

IterMVS		90.18 214	90.16 209	90.21 247	93.15 288	75.98 280	87.56 303	92.97 257	86.43 215	94.09 170	96.40 131	78.32 265	97.43 253	87.87 176	94.69 290	97.23 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		78.83 327	80.60 316	73.51 343	93.07 289	47.37 358	87.10 309	78.00 356	68.94 336	77.53 352	97.26 84	71.45 289	94.62 318	63.28 347	88.74 337	78.55 355

FMVSNet3		90.78 200	90.32 208	92.16 198	93.03 290	79.92 222	92.54 164	94.95 217	86.17 217	95.10 140	96.01 159	69.97 294	98.75 142	86.74 188	98.38 155	97.82 158

PAPR		87.65 257	86.77 264	90.27 243	92.85 291	77.38 265	88.56 294	96.23 183	76.82 301	84.98 319	89.75 313	86.08 212	97.16 264	72.33 317	93.35 309	96.26 232

Regformer-1		94.55 105	94.33 109	95.19 85	92.83 292	88.54 98	91.87 199	95.84 197	93.99 48	95.95 105	95.04 198	92.00 92	98.79 132	93.14 70	98.31 164	98.23 126

Regformer-2		94.86 91	94.55 102	95.77 63	92.83 292	89.98 70	91.87 199	96.40 171	94.38 44	96.19 96	95.04 198	92.47 87	99.04 86	93.49 54	98.31 164	98.28 124

Regformer-3		94.28 113	94.23 115	94.46 113	92.78 294	86.28 136	92.39 176	94.70 225	93.69 58	95.97 103	95.56 178	91.34 105	98.48 182	93.45 57	98.14 183	98.62 109

Regformer-4		94.90 88	94.67 99	95.59 71	92.78 294	89.02 85	92.39 176	95.91 193	94.50 40	96.41 79	95.56 178	92.10 90	99.01 92	94.23 37	98.14 183	98.74 100

EI-MVSNet-Vis-set		94.36 110	94.28 111	94.61 100	92.55 296	85.98 142	92.44 173	94.69 226	93.70 55	96.12 99	95.81 167	91.24 110	98.86 120	93.76 49	98.22 176	98.98 76

EI-MVSNet-UG-set		94.35 111	94.27 113	94.59 105	92.46 297	85.87 144	92.42 175	94.69 226	93.67 59	96.13 98	95.84 166	91.20 113	98.86 120	93.78 46	98.23 174	99.03 67

testus		82.09 310	81.78 305	83.03 329	92.35 298	64.37 348	79.44 346	93.27 252	73.08 317	87.06 307	85.21 343	76.80 279	89.27 350	53.30 354	95.48 272	95.46 260

FMVSNet5		87.82 253	86.56 267	91.62 212	92.31 299	79.81 224	93.49 136	94.81 222	83.26 245	91.36 231	96.93 100	52.77 354	97.49 251	76.07 296	98.03 194	97.55 176

diffmvs		90.45 205	90.49 205	90.34 240	92.25 300	77.09 269	91.80 210	95.96 192	82.68 254	85.83 314	95.07 196	87.01 194	97.09 266	89.68 142	94.10 301	96.83 209

MDA-MVSNet_test_wron		88.16 247	88.23 234	87.93 293	92.22 301	73.71 301	80.71 345	88.84 292	82.52 257	94.88 149	95.14 192	82.70 234	93.61 330	83.28 228	93.80 305	96.46 224

YYNet1		88.17 246	88.24 233	87.93 293	92.21 302	73.62 302	80.75 344	88.77 293	82.51 258	94.99 146	95.11 194	82.70 234	93.70 329	83.33 227	93.83 304	96.48 223

CANet_DTU		89.85 218	89.17 217	91.87 205	92.20 303	80.02 218	90.79 233	95.87 195	86.02 219	82.53 335	91.77 283	80.01 256	98.57 168	85.66 203	97.70 208	97.01 198

mvs_anonymous		90.37 209	91.30 190	87.58 297	92.17 304	68.00 331	89.84 267	94.73 224	83.82 244	93.22 196	97.40 77	87.54 180	97.40 256	87.94 174	95.05 282	97.34 186

EI-MVSNet		92.99 154	93.26 147	92.19 196	92.12 305	79.21 243	92.32 179	94.67 228	91.77 109	95.24 135	95.85 164	87.14 191	98.49 179	91.99 98	98.26 170	98.86 86

CVMVSNet		85.16 291	84.72 289	86.48 306	92.12 305	70.19 325	92.32 179	88.17 300	56.15 356	90.64 250	95.85 164	67.97 298	96.69 280	88.78 161	90.52 334	92.56 319

Patchmatch-test1		87.28 265	87.30 251	87.22 301	92.01 307	71.98 319	89.43 275	88.11 301	82.26 261	88.71 288	92.20 275	78.65 262	95.81 302	80.99 251	93.30 310	93.87 297

our_test_3		87.55 259	87.59 248	87.44 299	91.76 308	70.48 324	83.83 334	90.55 289	79.79 275	92.06 222	92.17 277	78.63 263	95.63 304	84.77 216	94.73 288	96.22 233

ppachtmachnet_test		88.61 237	88.64 227	88.50 286	91.76 308	70.99 323	84.59 329	92.98 256	79.30 285	92.38 212	93.53 247	79.57 258	97.45 252	86.50 196	97.17 229	97.07 195

1314		86.46 284	86.33 272	86.87 304	91.65 310	74.54 295	91.94 193	94.10 237	74.28 309	84.78 321	87.33 334	83.03 230	95.00 317	78.72 276	91.16 332	91.06 335

cascas		87.02 274	86.28 273	89.25 269	91.56 311	76.45 274	84.33 331	96.78 151	71.01 327	86.89 309	85.91 340	81.35 246	96.94 271	83.09 230	95.60 268	94.35 284

IB-MVS	77.21 19	83.11 300	81.05 312	89.29 267	91.15 312	75.85 281	85.66 321	86.00 316	79.70 277	82.02 340	86.61 335	48.26 358	98.39 188	77.84 282	92.22 324	93.63 302
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		84.98 293	84.30 292	87.01 302	91.03 313	77.69 263	91.94 193	94.16 236	59.36 354	84.23 325	87.50 332	85.66 216	96.80 277	71.79 320	93.05 316	86.54 347

CR-MVSNet		87.89 249	87.12 256	90.22 245	91.01 314	78.93 246	92.52 166	92.81 259	73.08 317	89.10 278	96.93 100	67.11 300	97.64 245	88.80 160	92.70 319	94.08 287

RPMNet		89.30 224	89.00 221	90.22 245	91.01 314	78.93 246	92.52 166	87.85 303	91.91 98	89.10 278	96.89 103	68.84 295	97.64 245	90.17 132	92.70 319	94.08 287

new_pmnet		81.22 315	81.01 314	81.86 333	90.92 316	70.15 326	84.03 332	80.25 355	70.83 329	85.97 313	89.78 312	67.93 299	84.65 356	67.44 337	91.90 328	90.78 336

test12356		76.35 328	77.41 329	73.19 344	90.70 317	38.86 363	74.56 350	91.14 282	74.55 307	80.54 347	88.18 324	52.36 355	90.49 347	52.38 356	92.26 323	90.21 340

PatchT		87.51 260	88.17 236	85.55 312	90.64 318	66.91 335	92.02 189	86.09 314	92.20 91	89.05 280	97.16 89	64.15 317	96.37 293	89.21 154	92.98 317	93.37 309

Patchmatch-test		86.10 286	86.01 281	86.38 308	90.63 319	74.22 300	89.57 272	86.69 310	85.73 225	89.81 270	92.83 258	65.24 313	91.04 342	77.82 284	95.78 266	93.88 296

PVSNet_0	70.34 21	74.58 330	72.96 332	79.47 337	90.63 319	66.24 340	73.26 351	83.40 339	63.67 351	78.02 351	78.35 355	72.53 285	89.59 349	56.68 352	60.05 358	82.57 353

PMMVS2		81.31 314	83.44 297	74.92 342	90.52 321	46.49 359	69.19 356	85.23 329	84.30 240	87.95 298	94.71 212	76.95 277	84.36 357	64.07 344	98.09 189	93.89 295

tpm		84.38 296	84.08 293	85.30 317	90.47 322	63.43 350	89.34 278	85.63 320	77.24 298	87.62 302	95.03 200	61.00 336	97.30 260	79.26 267	91.09 333	95.16 263

PNet_i23d		72.03 333	70.91 334	75.38 341	90.46 323	57.84 355	71.73 355	81.53 351	83.86 243	82.21 336	83.49 348	29.97 367	87.80 354	60.78 348	54.12 359	80.51 354

wuyk23d		87.83 252	90.79 201	78.96 338	90.46 323	88.63 93	92.72 159	90.67 286	91.65 113	98.68 11	97.64 64	96.06 15	77.53 359	59.84 349	99.41 61	70.73 356

Patchmtry		90.11 216	89.92 212	90.66 234	90.35 325	77.00 271	92.96 153	92.81 259	90.25 141	94.74 153	96.93 100	67.11 300	97.52 248	85.17 206	98.98 104	97.46 178

CHOSEN 280x420		80.04 324	77.97 328	86.23 310	90.13 326	74.53 296	72.87 353	89.59 291	66.38 344	76.29 353	85.32 342	56.96 349	95.36 311	69.49 334	94.72 289	88.79 344

MVSTER		89.32 223	88.75 226	91.03 228	90.10 327	76.62 273	90.85 231	94.67 228	82.27 260	95.24 135	95.79 168	61.09 335	98.49 179	90.49 119	98.26 170	97.97 145

tpm2		81.46 313	80.35 319	84.80 319	89.90 328	65.14 343	90.44 244	85.36 323	65.82 347	82.05 339	92.44 270	57.94 346	96.69 280	70.71 330	88.49 339	92.56 319

test0.0.03 1		82.48 306	81.47 309	85.48 313	89.70 329	73.57 303	84.73 326	81.64 350	83.07 250	88.13 296	86.61 335	62.86 330	89.10 352	66.24 342	90.29 335	93.77 299

test-LLR		83.58 299	83.17 299	84.79 320	89.68 330	66.86 337	83.08 335	84.52 332	83.07 250	82.85 333	84.78 344	62.86 330	93.49 331	82.85 231	94.86 284	94.03 290

test-mter		81.21 316	80.01 322	84.79 320	89.68 330	66.86 337	83.08 335	84.52 332	73.85 313	82.85 333	84.78 344	43.66 362	93.49 331	82.85 231	94.86 284	94.03 290

DSMNet-mixed		82.21 308	81.56 307	84.16 324	89.57 332	70.00 327	90.65 237	77.66 357	54.99 357	83.30 331	97.57 66	77.89 269	90.50 346	66.86 339	95.54 270	91.97 328

PatchmatchNet		85.22 290	84.64 290	86.98 303	89.51 333	69.83 328	90.52 242	87.34 307	78.87 287	87.22 306	92.74 261	66.91 302	96.53 283	81.77 240	86.88 342	94.58 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.88 295		89.42 334	61.52 351	88.74 291	87.41 306	73.99 312	84.96 320	94.01 236	65.25 312	95.53 305	78.02 280	93.16 312

tpmp4_e23		81.87 312	80.41 317	86.27 309	89.29 335	67.84 332	91.58 213	87.61 305	67.42 341	78.60 350	92.71 262	56.42 351	96.87 274	71.44 324	88.63 338	94.10 286

CostFormer		83.09 301	82.21 303	85.73 311	89.27 336	67.01 334	90.35 246	86.47 312	70.42 331	83.52 330	93.23 254	61.18 334	96.85 275	77.21 289	88.26 340	93.34 310

ADS-MVSNet2		84.01 298	82.20 304	89.41 264	89.04 337	76.37 275	87.57 301	90.98 285	72.71 320	84.46 322	92.45 268	68.08 296	96.48 286	70.58 331	83.97 344	95.38 261

ADS-MVSNet		82.25 307	81.55 308	84.34 323	89.04 337	65.30 341	87.57 301	85.13 330	72.71 320	84.46 322	92.45 268	68.08 296	92.33 338	70.58 331	83.97 344	95.38 261

tpm cat1		80.61 321	79.46 323	84.07 325	88.78 339	65.06 345	89.26 281	88.23 298	62.27 352	81.90 341	89.66 315	62.70 332	95.29 314	71.72 321	80.60 353	91.86 331

CMPMVS	68.83 22	87.28 265	85.67 284	92.09 200	88.77 340	85.42 151	90.31 248	94.38 232	70.02 333	88.00 297	93.30 253	73.78 284	94.03 328	75.96 298	96.54 250	96.83 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PatchFormer-LS_test		82.62 305	81.71 306	85.32 316	87.92 341	67.31 333	89.03 286	88.20 299	77.58 295	83.79 327	80.50 354	60.96 337	96.42 289	83.86 225	83.59 346	92.23 326

LP		86.29 285	85.35 286	89.10 271	87.80 342	76.21 276	89.92 262	90.99 284	84.86 236	87.66 301	92.32 273	70.40 292	96.48 286	81.94 238	82.24 351	94.63 277

tpmrst		82.85 304	82.93 301	82.64 331	87.65 343	58.99 354	90.14 254	87.90 302	75.54 303	83.93 326	91.63 286	66.79 305	95.36 311	81.21 247	81.54 352	93.57 306

JIA-IIPM		85.08 292	83.04 300	91.19 227	87.56 344	86.14 139	89.40 277	84.44 338	88.98 160	82.20 337	97.95 50	56.82 350	96.15 296	76.55 294	83.45 347	91.30 333

TESTMET0.1,1		79.09 326	78.04 327	82.25 332	87.52 345	64.03 349	83.08 335	80.62 353	70.28 332	80.16 348	83.22 349	44.13 361	90.56 345	79.95 260	93.36 308	92.15 327

DWT-MVSNet_test		80.74 319	79.18 324	85.43 314	87.51 346	66.87 336	89.87 266	86.01 315	74.20 311	80.86 344	80.62 353	48.84 357	96.68 282	81.54 242	83.14 349	92.75 317

gg-mvs-nofinetune		82.10 309	81.02 313	85.34 315	87.46 347	71.04 321	94.74 93	67.56 360	96.44 20	79.43 349	98.99 6	45.24 359	96.15 296	67.18 338	92.17 325	88.85 343

pmmvs3		80.83 318	78.96 325	86.45 307	87.23 348	77.48 264	84.87 325	82.31 347	63.83 350	85.03 318	89.50 316	49.66 356	93.10 333	73.12 313	95.10 281	88.78 345

tpmvs		84.22 297	83.97 294	84.94 318	87.09 349	65.18 342	91.21 222	88.35 296	82.87 253	85.21 316	90.96 295	65.24 313	96.75 278	79.60 266	85.25 343	92.90 314

gm-plane-assit					87.08 350	59.33 353			71.22 325		83.58 347		97.20 263	73.95 307

MVE	59.87 23	73.86 332	72.65 333	77.47 340	87.00 351	74.35 298	61.37 358	60.93 362	67.27 342	69.69 358	86.49 337	81.24 251	72.33 360	56.45 353	83.45 347	85.74 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EPNet_dtu		85.63 289	84.37 291	89.40 265	86.30 352	74.33 299	91.64 212	88.26 297	84.84 237	72.96 357	89.85 307	71.27 290	97.69 243	76.60 293	97.62 212	96.18 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2356		75.58 329	73.13 331	82.95 330	86.10 353	66.42 339	75.07 349	84.87 331	70.91 328	80.85 345	80.66 352	38.02 364	88.98 353	49.32 357	92.35 322	93.44 307

dp		79.28 325	78.62 326	81.24 334	85.97 354	56.45 356	86.91 311	85.26 328	72.97 319	81.45 343	89.17 319	56.01 353	95.45 309	73.19 312	76.68 355	91.82 332

EPMVS		81.17 317	80.37 318	83.58 326	85.58 355	65.08 344	90.31 248	71.34 359	77.31 297	85.80 315	91.30 289	59.38 338	92.70 337	79.99 259	82.34 350	92.96 313

E-PMN		80.72 320	80.86 315	80.29 336	85.11 356	68.77 330	72.96 352	81.97 349	87.76 196	83.25 332	83.01 350	62.22 333	89.17 351	77.15 290	94.31 297	82.93 351

GG-mvs-BLEND				83.24 328	85.06 357	71.03 322	94.99 86	65.55 361		74.09 356	75.51 356	44.57 360	94.46 321	59.57 350	87.54 341	84.24 349

EMVS		80.35 323	80.28 320	80.54 335	84.73 358	69.07 329	72.54 354	80.73 352	87.80 195	81.66 342	81.73 351	62.89 329	89.84 348	75.79 303	94.65 291	82.71 352

EPNet		89.80 219	88.25 232	94.45 114	83.91 359	86.18 138	93.87 128	87.07 309	91.16 123	80.64 346	94.72 211	78.83 260	98.89 107	85.17 206	98.89 109	98.28 124
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS		83.00 302	81.11 311	88.66 280	83.81 360	86.44 132	82.24 340	85.65 319	61.75 353	82.07 338	85.64 341	79.75 257	91.59 341	75.99 297	93.09 314	87.94 346

testpf		74.01 331	76.37 330	66.95 345	80.56 361	60.00 352	88.43 296	75.07 358	81.54 265	75.75 355	83.73 346	38.93 363	83.09 358	84.01 222	79.32 354	57.75 357

DeepMVS_CX				53.83 346	70.38 362	64.56 346		48.52 364	33.01 358	65.50 359	74.21 357	56.19 352	46.64 361	38.45 359	70.07 356	50.30 358

tmp_tt		37.97 336	44.33 336	18.88 349	11.80 363	21.54 364	63.51 357	45.66 365	4.23 359	51.34 360	50.48 358	59.08 339	22.11 362	44.50 358	68.35 357	13.00 359

test123		9.49 338	12.01 339	1.91 350	2.87 364	1.30 365	82.38 338	1.34 367	1.36 360	2.84 361	6.56 361	2.45 368	0.97 363	2.73 360	5.56 360	3.47 360

testmvs		9.02 339	11.42 340	1.81 351	2.77 365	1.13 366	79.44 346	1.90 366	1.18 361	2.65 362	6.80 360	1.95 369	0.87 364	2.62 361	3.45 361	3.44 361

cdsmvs_eth3d_5k		23.35 337	31.13 338	0.00 352	0.00 366	0.00 367	0.00 359	95.58 206	0.00 362	0.00 363	91.15 291	93.43 62	0.00 365	0.00 362	0.00 363	0.00 363

pcd_1.5k_mvsjas		7.56 340	10.09 341	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	90.77 121	0.00 365	0.00 362	0.00 363	0.00 363

sosnet-low-res		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sosnet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uncertanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

Regformer		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

ab-mvs-re		7.56 340	10.08 342	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	90.69 302	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

GSMVS																94.75 274

test_part3							93.92 126		91.83 104		96.39 135		99.44 24	89.00 156

test_part1								98.14 28				94.69 44			99.10 92	98.17 130

sam_mvs1												66.64 306				94.75 274

sam_mvs												66.41 307

MTGPA								97.62 76

test_post1							90.21 250				5.85 363	65.36 311	96.00 299	79.61 265

test_post											6.07 362	65.74 310	95.84 301

patchmatchnet-post											91.71 284	66.22 309	97.59 247

MTMP							94.82 91	54.62 363

test9_res														88.16 172	98.40 153	97.83 156

agg_prior2														87.06 186	98.36 161	97.98 142

test_prior4						89.91 71	90.74 234

test_prior2							90.21 250		89.33 155	90.77 246	94.81 205	90.41 132		88.21 169	98.55 140

旧先验2							90.00 260		68.65 337	92.71 205			96.52 284	85.15 208

新几何2							90.02 259

无先验							89.94 261	95.75 199	70.81 330				98.59 165	81.17 248		94.81 271

原ACMM2							89.34 278

testdata2													98.03 213	80.24 257

segment_acmp												92.14 89

testdata1							88.96 288		88.44 181

plane_prior5								97.81 62					98.95 101	89.26 151	98.51 146	98.60 111

plane_prior4											95.59 173

plane_prior3						88.43 103			90.35 140	93.31 187

plane_prior2							94.56 103		91.74 111

plane_prior						88.12 106	93.01 149		88.98 160						98.06 191

n2								0.00 368

nn								0.00 368

door-mid								92.13 274

test11								96.65 158

door								91.26 281

HQP5-MVS						84.89 155

BP-MVS														86.55 194

HQP4-MVS										88.81 283			98.61 161			98.15 133

HQP3-MVS								97.31 111							97.73 205

HQP2-MVS												84.76 221

MDTV_nov1_ep13_2view						42.48 362	88.45 295		67.22 343	83.56 329		66.80 303		72.86 315		94.06 289

ACMMP++_ref															98.82 122

ACMMP++															99.25 77

Test By Simon												90.61 128

High-resolution multi-view benchmark