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		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 4

Anonymous20231211		99.29 2	99.41 2	98.91 22	99.94 2	97.08 37	99.47 3	99.51 5	99.56 2	99.83 3	99.80 2	99.13 3	99.90 13	97.55 50	99.93 21	99.75 13

pmmvs6		99.07 4	99.24 4	98.56 45	99.81 3	96.38 57	98.87 10	99.30 9	99.01 15	99.63 9	99.66 4	99.27 2	99.68 104	97.75 42	99.89 33	99.62 32

OurMVSNet-221017-0		98.61 19	98.61 26	98.63 41	99.77 4	96.35 58	99.17 6	99.05 38	98.05 42	99.61 11	99.52 5	93.72 165	99.88 19	98.72 20	99.88 34	99.65 25

Gipuma		98.07 42	98.31 38	97.36 127	99.76 5	96.28 61	98.51 22	99.10 25	98.76 20	96.79 186	99.34 20	96.61 66	98.82 292	96.38 84	99.50 113	96.98 291
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		98.51 24	98.45 32	98.67 38	99.72 6	96.71 46	98.76 11	98.89 78	98.49 25	99.38 18	99.14 42	95.44 107	99.84 28	96.47 82	99.80 47	99.47 65

LTVRE_ROB	96.88 1	99.18 3	99.34 3	98.72 35	99.71 7	96.99 40	99.69 2	99.57 3	99.02 14	99.62 10	99.36 16	98.53 8	99.52 164	98.58 24	99.95 13	99.66 24
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
mvs_tets		98.90 5	98.94 8	98.75 30	99.69 8	96.48 55	98.54 21	99.22 10	96.23 111	99.71 5	99.48 7	98.77 7	99.93 2	98.89 10	99.95 13	99.84 6

PS-MVSNAJss		98.53 23	98.63 22	98.21 70	99.68 9	94.82 106	98.10 45	99.21 11	96.91 88	99.75 4	99.45 9	95.82 91	99.92 4	98.80 13	99.96 11	99.89 1

jajsoiax		98.77 11	98.79 15	98.74 32	99.66 10	96.48 55	98.45 26	99.12 22	95.83 126	99.67 7	99.37 14	98.25 11	99.92 4	98.77 14	99.94 19	99.82 7

pcd1.5k->3k		41.47 333	44.19 334	33.29 346	99.65 11	0.00 364	0.00 355	99.07 34	0.00 359	0.00 360	0.00 361	99.04 4	0.00 362	0.00 359	99.96 11	99.87 2

v7n		98.73 13	98.99 7	97.95 83	99.64 12	94.20 127	98.67 13	99.14 20	99.08 9	99.42 16	99.23 29	96.53 69	99.91 12	99.27 4	99.93 21	99.73 16

test_djsdf		98.73 13	98.74 18	98.69 37	99.63 13	96.30 60	98.67 13	99.02 51	96.50 100	99.32 21	99.44 10	97.43 30	99.92 4	98.73 17	99.95 13	99.86 3

anonymousdsp		98.72 16	98.63 22	98.99 10	99.62 14	97.29 34	98.65 16	99.19 14	95.62 132	99.35 20	99.37 14	97.38 32	99.90 13	98.59 23	99.91 27	99.77 9

v748		98.58 20	98.89 10	97.67 100	99.61 15	93.53 150	98.59 17	98.90 76	98.97 17	99.43 15	99.15 41	96.53 69	99.85 24	98.88 11	99.91 27	99.64 28

v52		98.85 8	99.01 5	98.37 56	99.61 15	95.53 84	99.01 7	99.04 45	98.48 26	99.31 22	99.41 11	96.82 56	99.87 21	99.44 2	99.95 13	99.70 19

V4		98.85 8	99.01 5	98.37 56	99.61 15	95.53 84	99.01 7	99.04 45	98.48 26	99.31 22	99.41 11	96.81 57	99.87 21	99.44 2	99.95 13	99.70 19

PEN-MVS		98.75 12	98.85 13	98.44 50	99.58 18	95.67 77	98.45 26	99.15 19	99.33 4	99.30 24	99.00 49	97.27 37	99.92 4	97.64 45	99.92 24	99.75 13

Baseline_NR-MVSNet		97.72 74	97.79 60	97.50 113	99.56 19	93.29 155	95.44 189	98.86 84	98.20 38	98.37 77	99.24 27	94.69 127	99.55 157	95.98 98	99.79 48	99.65 25

SixPastTwentyTwo		97.49 91	97.57 81	97.26 133	99.56 19	92.33 168	98.28 32	96.97 260	98.30 34	99.45 14	99.35 18	88.43 257	99.89 17	98.01 32	99.76 51	99.54 46

PS-CasMVS		98.73 13	98.85 13	98.39 55	99.55 21	95.47 86	98.49 23	99.13 21	99.22 7	99.22 29	98.96 53	97.35 33	99.92 4	97.79 40	99.93 21	99.79 8

DTE-MVSNet		98.79 10	98.86 11	98.59 43	99.55 21	96.12 64	98.48 25	99.10 25	99.36 3	99.29 25	99.06 48	97.27 37	99.93 2	97.71 44	99.91 27	99.70 19

HPM-MVS_fast		98.32 31	98.13 45	98.88 23	99.54 23	97.48 27	98.35 29	99.03 50	95.88 123	97.88 133	98.22 110	98.15 13	99.74 60	96.50 81	99.62 80	99.42 87

TDRefinement		98.90 5	98.86 11	99.02 8	99.54 23	98.06 6	99.34 5	99.44 7	98.85 19	99.00 41	99.20 31	97.42 31	99.59 145	97.21 63	99.76 51	99.40 92

Anonymous20240521		98.58 20	98.65 21	98.36 59	99.52 25	95.60 79	98.96 9	98.95 72	98.36 30	99.25 27	99.17 39	95.28 113	99.80 37	98.46 25	99.88 34	99.68 23

pm-mvs1		98.47 25	98.67 19	97.86 87	99.52 25	94.58 114	98.28 32	99.00 62	97.57 64	99.27 26	99.22 30	98.32 10	99.50 176	97.09 69	99.75 55	99.50 51

TransMVSNet (Re)		98.38 29	98.67 19	97.51 110	99.51 27	93.39 154	98.20 40	98.87 82	98.23 36	99.48 12	99.27 25	98.47 9	99.55 157	96.52 79	99.53 106	99.60 35

WR-MVS_H		98.65 18	98.62 24	98.75 30	99.51 27	96.61 51	98.55 20	99.17 15	99.05 12	99.17 32	98.79 61	95.47 105	99.89 17	97.95 33	99.91 27	99.75 13

PMVS	89.60 17	96.71 144	96.97 121	95.95 212	99.51 27	97.81 13	97.42 89	97.49 240	97.93 46	95.95 222	98.58 76	96.88 52	96.91 346	89.59 257	99.36 156	93.12 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss		97.69 77	97.36 91	98.70 36	99.50 30	96.84 43	95.38 198	98.99 65	92.45 238	98.11 102	98.31 98	97.25 39	99.77 48	96.60 76	99.62 80	99.48 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		98.16 37	98.37 34	97.56 105	99.49 31	93.10 158	98.35 29	99.21 11	98.43 28	98.89 45	98.83 60	94.30 144	99.81 33	97.87 36	99.91 27	99.77 9

VPNet		97.26 106	97.49 87	96.59 166	99.47 32	90.58 202	96.27 140	98.53 146	97.77 50	98.46 72	98.41 89	94.59 133	99.68 104	94.61 151	99.29 172	99.52 49

CP-MVSNet		98.42 27	98.46 30	98.30 65	99.46 33	95.22 94	98.27 34	98.84 88	99.05 12	99.01 39	98.65 74	95.37 108	99.90 13	97.57 49	99.91 27	99.77 9

XXY-MVS		97.54 88	97.70 66	97.07 141	99.46 33	92.21 172	97.22 96	99.00 62	94.93 165	98.58 63	98.92 57	97.31 35	99.41 208	94.44 155	99.43 141	99.59 36

zzz-MVS		98.01 47	97.66 70	99.06 5	99.44 35	97.90 8	95.66 178	98.73 114	97.69 58	97.90 129	97.96 139	95.81 95	99.82 31	96.13 89	99.61 85	99.45 72

MTAPA		98.14 38	97.84 58	99.06 5	99.44 35	97.90 8	97.25 93	98.73 114	97.69 58	97.90 129	97.96 139	95.81 95	99.82 31	96.13 89	99.61 85	99.45 72

wuykxyi23d		98.68 17	98.53 27	99.13 3	99.44 35	97.97 7	96.85 118	99.02 51	95.81 127	99.88 2	99.38 13	98.14 14	99.69 98	98.32 29	99.95 13	99.73 16

SteuartSystems-ACMMP		98.02 45	97.76 63	98.79 28	99.43 38	97.21 36	97.15 97	98.90 76	96.58 99	98.08 108	97.87 150	97.02 47	99.76 49	95.25 125	99.59 90	99.40 92
Skip Steuart: Steuart Systems R&D Blog.
ACMH	93.61 9	98.44 26	98.76 16	97.51 110	99.43 38	93.54 149	98.23 35	99.05 38	97.40 80	99.37 19	99.08 47	98.79 6	99.47 183	97.74 43	99.71 64	99.50 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 41	97.83 59	98.92 19	99.42 40	97.46 28	98.57 18	99.05 38	95.43 141	97.41 157	97.50 181	97.98 17	99.79 39	95.58 115	99.57 96	99.50 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
K. test v3		96.44 156	96.28 156	96.95 147	99.41 41	91.53 188	97.65 72	90.31 335	98.89 18	98.93 44	99.36 16	84.57 282	99.92 4	97.81 38	99.56 98	99.39 95

VDDNet		96.98 117	96.84 128	97.41 124	99.40 42	93.26 156	97.94 54	95.31 288	99.26 6	98.39 76	99.18 35	87.85 264	99.62 129	95.13 135	99.09 192	99.35 106

ACMH+	93.58 10	98.23 36	98.31 38	97.98 82	99.39 43	95.22 94	97.55 82	99.20 13	98.21 37	99.25 27	98.51 83	98.21 12	99.40 211	94.79 146	99.72 60	99.32 108

TSAR-MVS + MP.		97.42 94	97.23 103	98.00 81	99.38 44	95.00 100	97.63 74	98.20 190	93.00 224	98.16 97	98.06 131	95.89 86	99.72 71	95.67 106	99.10 191	99.28 119

FIs		97.93 55	98.07 48	97.48 117	99.38 44	92.95 160	98.03 51	99.11 23	98.04 43	98.62 58	98.66 72	93.75 164	99.78 40	97.23 62	99.84 41	99.73 16

lessismore_v0				97.05 142	99.36 46	92.12 176		84.07 355		98.77 52	98.98 51	85.36 276	99.74 60	97.34 60	99.37 153	99.30 112

ACMMP_Plus		97.89 60	97.63 75	98.67 38	99.35 47	96.84 43	96.36 136	98.79 103	95.07 161	97.88 133	98.35 94	97.24 40	99.72 71	96.05 92	99.58 93	99.45 72

Vis-MVSNet		98.27 33	98.34 36	98.07 75	99.33 48	95.21 96	98.04 49	99.46 6	97.32 83	97.82 142	99.11 44	96.75 59	99.86 23	97.84 37	99.36 156	99.15 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high		98.31 32	98.94 8	96.41 178	99.33 48	89.64 215	97.92 56	99.56 4	99.27 5	99.66 8	99.50 6	97.67 25	99.83 30	97.55 50	99.98 3	99.77 9

MP-MVS		97.64 80	97.18 107	99.00 9	99.32 50	97.77 14	97.49 85	98.73 114	96.27 108	95.59 235	97.75 160	96.30 79	99.78 40	93.70 182	99.48 123	99.45 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PVSNet_Blended_VisFu		95.95 170	95.80 172	96.42 177	99.28 51	90.62 201	95.31 204	99.08 30	88.40 280	96.97 180	98.17 117	92.11 207	99.78 40	93.64 183	99.21 179	98.86 185

tfpnnormal		97.72 74	97.97 52	96.94 148	99.26 52	92.23 171	97.83 61	98.45 153	98.25 35	99.13 33	98.66 72	96.65 63	99.69 98	93.92 176	99.62 80	98.91 174

HSP-MVS		97.37 98	96.85 127	98.92 19	99.26 52	97.70 15	97.66 71	98.23 186	95.65 130	98.51 67	96.46 243	92.15 205	99.81 33	95.14 134	98.58 238	99.26 123

testgi		96.07 166	96.50 150	94.80 250	99.26 52	87.69 269	95.96 162	98.58 144	95.08 160	98.02 115	96.25 254	97.92 18	97.60 342	88.68 272	98.74 224	99.11 146

IS-MVSNet		96.93 122	96.68 137	97.70 96	99.25 55	94.00 133	98.57 18	96.74 268	98.36 30	98.14 100	97.98 138	88.23 258	99.71 81	93.10 193	99.72 60	99.38 97

ACMMP		98.05 43	97.75 64	98.93 18	99.23 56	97.60 19	98.09 46	98.96 70	95.75 129	97.91 128	98.06 131	96.89 50	99.76 49	95.32 123	99.57 96	99.43 85
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
nrg030		98.54 22	98.62 24	98.32 62	99.22 57	95.66 78	97.90 57	99.08 30	98.31 33	99.02 38	98.74 66	97.68 24	99.61 135	97.77 41	99.85 40	99.70 19

region2R		97.92 56	97.59 79	98.92 19	99.22 57	97.55 23	97.60 78	98.84 88	96.00 118	97.22 162	97.62 170	96.87 53	99.76 49	95.48 116	99.43 141	99.46 67

mPP-MVS		97.91 58	97.53 83	99.04 7	99.22 57	97.87 11	97.74 68	98.78 106	96.04 116	97.10 169	97.73 163	96.53 69	99.78 40	95.16 132	99.50 113	99.46 67

COLMAP_ROB	94.48 6	98.25 35	98.11 46	98.64 40	99.21 60	97.35 32	97.96 53	99.16 16	98.34 32	98.78 49	98.52 82	97.32 34	99.45 192	94.08 169	99.67 74	99.13 138
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		97.95 51	97.62 77	98.94 15	99.20 61	97.56 22	97.59 79	98.83 96	96.05 114	97.46 155	97.63 169	96.77 58	99.76 49	95.61 112	99.46 127	99.49 59

PGM-MVS		97.88 61	97.52 84	98.96 13	99.20 61	97.62 18	97.09 106	99.06 36	95.45 139	97.55 146	97.94 143	97.11 42	99.78 40	94.77 148	99.46 127	99.48 62

test_0402		97.84 64	97.97 52	97.47 118	99.19 63	94.07 130	96.71 124	98.73 114	98.66 22	98.56 64	98.41 89	96.84 55	99.69 98	94.82 143	99.81 44	98.64 202

EPP-MVSNet		96.84 131	96.58 141	97.65 101	99.18 64	93.78 141	98.68 12	96.34 271	97.91 47	97.30 160	98.06 131	88.46 256	99.85 24	93.85 178	99.40 151	99.32 108

abl_6		98.42 27	98.19 42	99.09 4	99.16 65	98.10 5	97.73 70	99.11 23	97.76 51	98.62 58	98.27 105	97.88 21	99.80 37	95.67 106	99.50 113	99.38 97

XVG-ACMP-BASELINE		97.58 85	97.28 96	98.49 47	99.16 65	96.90 42	96.39 131	98.98 67	95.05 162	98.06 111	98.02 134	95.86 87	99.56 153	94.37 160	99.64 78	99.00 159

CHOSEN 1792x2688		94.10 237	93.41 242	96.18 195	99.16 65	90.04 208	92.15 311	98.68 126	79.90 338	96.22 214	97.83 151	87.92 263	99.42 197	89.18 263	99.65 77	99.08 151

HFP-MVS		97.94 53	97.64 73	98.83 25	99.15 68	97.50 25	97.59 79	98.84 88	96.05 114	97.49 150	97.54 176	97.07 45	99.70 89	95.61 112	99.46 127	99.30 112

#test#		97.62 81	97.22 104	98.83 25	99.15 68	97.50 25	96.81 120	98.84 88	94.25 189	97.49 150	97.54 176	97.07 45	99.70 89	94.37 160	99.46 127	99.30 112

XVS		97.96 49	97.63 75	98.94 15	99.15 68	97.66 16	97.77 63	98.83 96	97.42 72	96.32 207	97.64 168	96.49 72	99.72 71	95.66 108	99.37 153	99.45 72

X-MVStestdata		92.86 259	90.83 293	98.94 15	99.15 68	97.66 16	97.77 63	98.83 96	97.42 72	96.32 207	36.50 356	96.49 72	99.72 71	95.66 108	99.37 153	99.45 72

LPG-MVS_test		97.94 53	97.67 69	98.74 32	99.15 68	97.02 38	97.09 106	99.02 51	95.15 154	98.34 80	98.23 107	97.91 19	99.70 89	94.41 157	99.73 57	99.50 51

LGP-MVS_train				98.74 32	99.15 68	97.02 38		99.02 51	95.15 154	98.34 80	98.23 107	97.91 19	99.70 89	94.41 157	99.73 57	99.50 51

RPSCF		97.87 62	97.51 85	98.95 14	99.15 68	98.43 3	97.56 81	99.06 36	96.19 112	98.48 70	98.70 69	94.72 125	99.24 245	94.37 160	99.33 166	99.17 131

ACMM	93.33 11	98.05 43	97.79 60	98.85 24	99.15 68	97.55 23	96.68 125	98.83 96	95.21 149	98.36 78	98.13 122	98.13 16	99.62 129	96.04 93	99.54 104	99.39 95
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		97.95 51	98.08 47	97.56 105	99.14 76	93.67 143	98.23 35	98.66 131	97.41 79	99.00 41	99.19 32	95.47 105	99.73 65	95.83 102	99.76 51	99.30 112

Vis-MVSNet (Re-imp)		95.11 203	94.85 201	95.87 217	99.12 77	89.17 231	97.54 84	94.92 290	96.50 100	96.58 191	97.27 196	83.64 283	99.48 181	88.42 275	99.67 74	98.97 163

OPM-MVS		97.54 88	97.25 97	98.41 52	99.11 78	96.61 51	95.24 210	98.46 152	94.58 177	98.10 105	98.07 128	97.09 44	99.39 217	95.16 132	99.44 132	99.21 126

UA-Net		98.88 7	98.76 16	99.22 2	99.11 78	97.89 10	99.47 3	99.32 8	99.08 9	97.87 138	99.67 3	96.47 74	99.92 4	97.88 35	99.98 3	99.85 4

AllTest		97.20 110	96.92 125	98.06 76	99.08 80	96.16 62	97.14 99	99.16 16	94.35 186	97.78 143	98.07 128	95.84 88	99.12 254	91.41 216	99.42 144	98.91 174

TestCases				98.06 76	99.08 80	96.16 62		99.16 16	94.35 186	97.78 143	98.07 128	95.84 88	99.12 254	91.41 216	99.42 144	98.91 174

TranMVSNet+NR-MVSNet		98.33 30	98.30 40	98.43 51	99.07 82	95.87 70	96.73 123	99.05 38	98.67 21	98.84 46	98.45 87	97.58 27	99.88 19	96.45 83	99.86 39	99.54 46

VPA-MVSNet		98.27 33	98.46 30	97.70 96	99.06 83	93.80 139	97.76 65	99.00 62	98.40 29	99.07 36	98.98 51	96.89 50	99.75 55	97.19 66	99.79 48	99.55 45

114514_t		93.96 241	93.22 246	96.19 194	99.06 83	90.97 195	95.99 156	98.94 73	73.88 352	93.43 300	96.93 214	92.38 203	99.37 225	89.09 264	99.28 173	98.25 238

EG-PatchMatch MVS		97.69 77	97.79 60	97.40 125	99.06 83	93.52 151	95.96 162	98.97 69	94.55 178	98.82 47	98.76 64	97.31 35	99.29 239	97.20 65	99.44 132	99.38 97

ACMP	92.54 13	97.47 92	97.10 114	98.55 46	99.04 86	96.70 48	96.24 144	98.89 78	93.71 208	97.97 120	97.75 160	97.44 29	99.63 123	93.22 190	99.70 67	99.32 108
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part2					99.03 87	96.07 65				98.08 108

ESAPD		97.22 109	96.82 130	98.40 54	99.03 87	96.07 65	95.64 182	98.84 88	94.84 166	98.08 108	97.60 172	96.69 61	99.76 49	91.22 222	99.44 132	99.37 102

v13		98.02 45	98.52 28	96.51 171	99.02 89	90.14 206	98.07 47	99.09 29	98.10 41	99.13 33	99.35 18	94.84 123	99.74 60	99.12 5	99.98 3	99.65 25

XVG-OURS-SEG-HR		97.38 97	97.07 117	98.30 65	99.01 90	97.41 31	94.66 236	99.02 51	95.20 150	98.15 99	97.52 179	98.83 5	98.43 320	94.87 141	96.41 314	99.07 153

XVG-OURS		97.12 111	96.74 135	98.26 67	98.99 91	97.45 29	93.82 272	99.05 38	95.19 151	98.32 83	97.70 166	95.22 115	98.41 321	94.27 165	98.13 255	98.93 171

CP-MVS		97.92 56	97.56 82	98.99 10	98.99 91	97.82 12	97.93 55	98.96 70	96.11 113	96.89 184	97.45 184	96.85 54	99.78 40	95.19 128	99.63 79	99.38 97

v12		97.97 48	98.47 29	96.46 175	98.98 93	90.01 210	97.97 52	99.08 30	98.00 44	99.11 35	99.34 20	94.70 126	99.73 65	99.07 6	99.98 3	99.64 28

CSCG		97.40 96	97.30 93	97.69 98	98.95 94	94.83 105	97.28 92	98.99 65	96.35 107	98.13 101	95.95 267	95.99 84	99.66 116	94.36 163	99.73 57	98.59 207

v11		97.82 68	98.36 35	96.17 196	98.93 95	89.16 232	97.79 62	99.08 30	97.64 61	99.19 30	99.32 22	94.28 145	99.72 71	99.07 6	99.97 8	99.63 30

V9		97.90 59	98.40 33	96.40 179	98.93 95	89.86 212	97.86 59	99.07 34	97.88 48	99.05 37	99.30 23	94.53 137	99.72 71	99.01 8	99.98 3	99.63 30

HyFIR lowres test		93.72 245	92.65 256	96.91 151	98.93 95	91.81 185	91.23 325	98.52 147	82.69 326	96.46 198	96.52 241	80.38 292	99.90 13	90.36 248	98.79 219	99.03 157

testing_2		97.43 93	97.71 65	96.60 164	98.91 98	90.85 196	96.01 155	98.54 145	94.78 170	98.78 49	98.96 53	96.35 78	99.54 159	97.25 61	99.82 43	99.40 92

PM-MVS		97.36 101	97.10 114	98.14 73	98.91 98	96.77 45	96.20 146	98.63 138	93.82 205	98.54 65	98.33 96	93.98 155	99.05 264	95.99 97	99.45 131	98.61 206

CPTT-MVS		96.69 145	96.08 162	98.49 47	98.89 100	96.64 50	97.25 93	98.77 107	92.89 231	96.01 221	97.13 201	92.23 204	99.67 111	92.24 202	99.34 161	99.17 131

V14		97.83 65	98.33 37	96.35 180	98.88 101	89.72 213	97.75 66	99.05 38	97.74 52	99.01 39	99.27 25	94.35 142	99.71 81	98.95 9	99.97 8	99.62 32

SMA-MVS		97.55 86	97.19 106	98.61 42	98.83 102	96.71 46	96.74 122	98.81 102	91.81 250	98.78 49	98.36 93	96.63 65	99.68 104	95.17 130	99.59 90	99.45 72

v15		97.77 71	98.26 41	96.30 185	98.81 103	89.59 220	97.62 75	99.04 45	97.59 63	98.97 43	99.24 27	94.19 149	99.70 89	98.88 11	99.97 8	99.61 34

UniMVSNet (Re)		97.83 65	97.65 71	98.35 61	98.80 104	95.86 71	95.92 166	99.04 45	97.51 69	98.22 92	97.81 155	94.68 129	99.78 40	97.14 68	99.75 55	99.41 89

APD-MVS_3200maxsize		98.13 40	97.90 55	98.79 28	98.79 105	97.31 33	97.55 82	98.92 74	97.72 56	98.25 90	98.13 122	97.10 43	99.75 55	95.44 118	99.24 177	99.32 108

DeepC-MVS	95.41 4	97.82 68	97.70 66	98.16 71	98.78 106	95.72 74	96.23 145	99.02 51	93.92 198	98.62 58	98.99 50	97.69 23	99.62 129	96.18 88	99.87 37	99.15 135
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v17		97.70 76	98.17 43	96.28 188	98.77 107	89.59 220	97.62 75	99.01 60	97.54 66	98.72 55	99.18 35	94.06 153	99.68 104	98.74 16	99.92 24	99.58 37

v16		97.69 77	98.16 44	96.29 187	98.75 108	89.60 218	97.62 75	99.01 60	97.53 68	98.69 57	99.18 35	94.05 154	99.68 104	98.73 17	99.88 34	99.58 37

MCST-MVS		96.24 160	95.80 172	97.56 105	98.75 108	94.13 129	94.66 236	98.17 195	90.17 266	96.21 215	96.10 261	95.14 116	99.43 196	94.13 168	98.85 218	99.13 138

DU-MVS		97.79 70	97.60 78	98.36 59	98.73 110	95.78 72	95.65 180	98.87 82	97.57 64	98.31 85	97.83 151	94.69 127	99.85 24	97.02 71	99.71 64	99.46 67

NR-MVSNet		97.96 49	97.86 57	98.26 67	98.73 110	95.54 82	98.14 43	98.73 114	97.79 49	99.42 16	97.83 151	94.40 141	99.78 40	95.91 101	99.76 51	99.46 67

Anonymous20231206		95.27 199	95.06 193	95.88 216	98.72 112	89.37 228	95.70 174	97.85 216	88.00 287	96.98 175	97.62 170	91.95 213	99.34 229	89.21 262	99.53 106	98.94 167

APDe-MVS		98.14 38	98.03 51	98.47 49	98.72 112	96.04 67	98.07 47	99.10 25	95.96 120	98.59 62	98.69 70	96.94 48	99.81 33	96.64 75	99.58 93	99.57 41

UniMVSNet_NR-MVSNet		97.83 65	97.65 71	98.37 56	98.72 112	95.78 72	95.66 178	99.02 51	98.11 40	98.31 85	97.69 167	94.65 131	99.85 24	97.02 71	99.71 64	99.48 62

v8		97.60 83	98.06 49	96.23 189	98.71 115	89.44 224	97.43 88	98.82 100	97.29 84	98.74 53	99.10 45	93.86 157	99.68 104	98.61 22	99.94 19	99.56 42

v6		96.97 118	97.24 99	96.15 197	98.71 115	89.44 224	95.97 158	98.33 172	95.25 146	97.89 131	98.15 118	93.86 157	99.61 135	97.51 53	99.50 113	99.42 87

v1neww		96.97 118	97.24 99	96.15 197	98.70 117	89.44 224	95.97 158	98.33 172	95.25 146	97.88 133	98.15 118	93.83 160	99.61 135	97.50 54	99.50 113	99.41 89

v7new		96.97 118	97.24 99	96.15 197	98.70 117	89.44 224	95.97 158	98.33 172	95.25 146	97.88 133	98.15 118	93.83 160	99.61 135	97.50 54	99.50 113	99.41 89

HQP_MVS		96.66 147	96.33 155	97.68 99	98.70 117	94.29 122	96.50 128	98.75 111	96.36 105	96.16 217	96.77 225	91.91 217	99.46 188	92.59 198	99.20 180	99.28 119

plane_prior7					98.70 117	94.67 112

VDD-MVS		97.37 98	97.25 97	97.74 94	98.69 121	94.50 117	97.04 108	95.61 286	98.59 23	98.51 67	98.72 67	92.54 197	99.58 147	96.02 95	99.49 120	99.12 143

v18		97.60 83	98.06 49	96.23 189	98.68 122	89.46 223	97.48 86	98.98 67	97.33 82	98.60 61	99.13 43	93.86 157	99.67 111	98.62 21	99.87 37	99.56 42

HPM-MVS++		96.99 114	96.38 152	98.81 27	98.64 123	97.59 20	95.97 158	98.20 190	95.51 137	95.06 243	96.53 239	94.10 152	99.70 89	94.29 164	99.15 184	99.13 138

ab-mvs		96.59 149	96.59 140	96.60 164	98.64 123	92.21 172	98.35 29	97.67 228	94.45 179	96.99 174	98.79 61	94.96 121	99.49 178	90.39 247	99.07 195	98.08 248

F-COLMAP		95.30 197	94.38 222	98.05 79	98.64 123	96.04 67	95.61 186	98.66 131	89.00 274	93.22 305	96.40 249	92.90 185	99.35 228	87.45 294	97.53 290	98.77 194

ITE_SJBPF				97.85 88	98.64 123	96.66 49		98.51 149	95.63 131	97.22 162	97.30 195	95.52 102	98.55 315	90.97 227	98.90 209	98.34 229

v148		96.58 150	96.97 121	95.42 230	98.63 127	87.57 270	95.09 216	97.90 213	95.91 122	98.24 91	97.96 139	93.42 171	99.39 217	96.04 93	99.52 110	99.29 118

UnsupCasMVSNet_bld		94.72 217	94.26 224	96.08 202	98.62 128	90.54 205	93.38 288	98.05 209	90.30 264	97.02 172	96.80 223	89.54 245	99.16 253	88.44 274	96.18 317	98.56 209

DP-MVS		97.87 62	97.89 56	97.81 90	98.62 128	94.82 106	97.13 100	98.79 103	98.98 16	98.74 53	98.49 84	95.80 97	99.49 178	95.04 139	99.44 132	99.11 146

v10		97.55 86	97.97 52	96.31 184	98.60 130	89.64 215	97.44 87	99.02 51	96.60 97	98.72 55	99.16 40	93.48 169	99.72 71	98.76 15	99.92 24	99.58 37

Test_1112_low_res		93.53 251	92.86 251	95.54 227	98.60 130	88.86 241	92.75 300	98.69 124	82.66 327	92.65 314	96.92 215	84.75 280	99.56 153	90.94 228	97.76 268	98.19 243

v7		96.93 122	97.17 108	96.23 189	98.59 132	89.64 215	95.96 162	98.66 131	94.41 182	97.87 138	98.38 92	93.47 170	99.64 120	97.93 34	99.24 177	99.43 85

V42		97.04 112	97.16 109	96.68 162	98.59 132	91.05 193	96.33 138	98.36 167	94.60 174	97.99 116	98.30 101	93.32 175	99.62 129	97.40 59	99.53 106	99.38 97

1112_ss		94.12 236	93.42 241	96.23 189	98.59 132	90.85 196	94.24 249	98.85 85	85.49 309	92.97 307	94.94 287	86.01 273	99.64 120	91.78 210	97.92 264	98.20 242

v2v482		96.78 138	97.06 118	95.95 212	98.57 135	88.77 245	95.36 199	98.26 184	95.18 152	97.85 140	98.23 107	92.58 194	99.63 123	97.80 39	99.69 68	99.45 72

WR-MVS		96.90 126	96.81 131	97.16 135	98.56 136	92.20 174	94.33 244	98.12 201	97.34 81	98.20 94	97.33 194	92.81 186	99.75 55	94.79 146	99.81 44	99.54 46

v1141		96.86 128	97.14 111	96.04 204	98.55 137	89.06 235	95.44 189	98.33 172	95.14 156	97.93 126	98.19 112	93.36 173	99.62 129	97.61 46	99.69 68	99.44 81

v1		96.86 128	97.14 111	96.04 204	98.55 137	89.06 235	95.44 189	98.33 172	95.14 156	97.94 123	98.18 116	93.39 172	99.61 135	97.61 46	99.69 68	99.44 81

APD-MVS		97.00 113	96.53 147	98.41 52	98.55 137	96.31 59	96.32 139	98.77 107	92.96 230	97.44 156	97.58 175	95.84 88	99.74 60	91.96 204	99.35 159	99.19 128
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmatch-RL test		94.66 219	94.49 216	95.19 236	98.54 140	88.91 239	92.57 304	98.74 113	91.46 253	98.32 83	97.75 160	77.31 306	98.81 294	96.06 91	99.61 85	97.85 263

divwei89l23v2f112		96.86 128	97.14 111	96.04 204	98.54 140	89.06 235	95.44 189	98.33 172	95.14 156	97.93 126	98.19 112	93.36 173	99.61 135	97.61 46	99.68 72	99.44 81

1111		88.78 313	89.39 306	86.96 338	98.53 142	62.84 357	91.49 320	97.48 242	94.45 179	96.56 193	96.45 244	43.83 363	98.87 288	86.33 301	99.40 151	99.18 130

.test1245		73.49 331	79.27 332	56.15 344	98.53 142	62.84 357	91.49 320	97.48 242	94.45 179	96.56 193	96.45 244	43.83 363	98.87 288	86.33 301	8.32 358	6.75 358

IterMVS-LS		96.92 124	97.29 94	95.79 219	98.51 144	88.13 255	95.10 214	98.66 131	96.99 85	98.46 72	98.68 71	92.55 195	99.74 60	96.91 73	99.79 48	99.50 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		95.55 181	95.13 189	96.80 154	98.51 144	93.99 134	94.60 238	98.69 124	90.20 265	95.78 229	96.21 257	92.73 189	98.98 274	90.58 241	98.86 216	97.42 278

test20.03		96.58 150	96.61 139	96.48 174	98.49 146	91.72 186	95.68 177	97.69 227	96.81 93	98.27 89	97.92 146	94.18 150	98.71 302	90.78 234	99.66 76	99.00 159

plane_prior1					98.49 146

MDA-MVSNet-bldmvs		95.69 175	95.67 175	95.74 220	98.48 148	88.76 246	92.84 297	97.25 248	96.00 118	97.59 145	97.95 142	91.38 225	99.46 188	93.16 192	96.35 315	98.99 162

UnsupCasMVSNet_eth		95.91 171	95.73 174	96.44 176	98.48 148	91.52 189	95.31 204	98.45 153	95.76 128	97.48 153	97.54 176	89.53 247	98.69 304	94.43 156	94.61 331	99.13 138

view600		92.56 263	92.11 263	93.91 276	98.45 150	84.76 305	97.10 102	90.23 336	97.42 72	96.98 175	94.48 296	73.62 322	99.60 141	82.49 326	98.28 247	97.36 279

view800		92.56 263	92.11 263	93.91 276	98.45 150	84.76 305	97.10 102	90.23 336	97.42 72	96.98 175	94.48 296	73.62 322	99.60 141	82.49 326	98.28 247	97.36 279

conf0.05thres1000		92.56 263	92.11 263	93.91 276	98.45 150	84.76 305	97.10 102	90.23 336	97.42 72	96.98 175	94.48 296	73.62 322	99.60 141	82.49 326	98.28 247	97.36 279

tfpn		92.56 263	92.11 263	93.91 276	98.45 150	84.76 305	97.10 102	90.23 336	97.42 72	96.98 175	94.48 296	73.62 322	99.60 141	82.49 326	98.28 247	97.36 279

v1144		96.84 131	97.08 116	96.13 201	98.42 154	89.28 230	95.41 196	98.67 129	94.21 191	97.97 120	98.31 98	93.06 180	99.65 117	98.06 31	99.62 80	99.45 72

plane_prior6					98.38 155	94.37 121						91.91 217

FPMVS		89.92 307	88.63 314	93.82 281	98.37 156	96.94 41	91.58 319	93.34 306	88.00 287	90.32 335	97.10 204	70.87 337	91.13 355	71.91 351	96.16 318	93.39 344

PAPM_NR		94.61 222	94.17 229	95.96 210	98.36 157	91.23 191	95.93 165	97.95 211	92.98 225	93.42 301	94.43 301	90.53 233	98.38 325	87.60 291	96.29 316	98.27 236

MVS_111021_HR		96.73 141	96.54 146	97.27 131	98.35 158	93.66 146	93.42 286	98.36 167	94.74 171	96.58 191	96.76 227	96.54 68	98.99 272	94.87 141	99.27 175	99.15 135

TAMVS		95.49 184	94.94 196	97.16 135	98.31 159	93.41 153	95.07 219	96.82 265	91.09 256	97.51 148	97.82 154	89.96 242	99.42 197	88.42 275	99.44 132	98.64 202

OMC-MVS		96.48 154	96.00 165	97.91 85	98.30 160	96.01 69	94.86 230	98.60 141	91.88 248	97.18 164	97.21 198	96.11 82	99.04 265	90.49 245	99.34 161	98.69 200

新几何1				97.25 134	98.29 161	94.70 111		97.73 224	77.98 345	94.83 250	96.67 232	92.08 209	99.45 192	88.17 279	98.65 232	97.61 272

jason		94.39 227	94.04 232	95.41 232	98.29 161	87.85 266	92.74 302	96.75 267	85.38 314	95.29 239	96.15 258	88.21 259	99.65 117	94.24 166	99.34 161	98.74 196
jason: jason.
no-one		94.84 212	94.76 205	95.09 240	98.29 161	87.49 272	91.82 317	97.49 240	88.21 283	97.84 141	98.75 65	91.51 222	99.27 241	88.96 267	99.99 2	98.52 212

v1192		96.83 134	97.06 118	96.15 197	98.28 164	89.29 229	95.36 199	98.77 107	93.73 207	98.11 102	98.34 95	93.02 184	99.67 111	98.35 27	99.58 93	99.50 51

CDPH-MVS		95.45 190	94.65 208	97.84 89	98.28 164	94.96 102	93.73 275	98.33 172	85.03 316	95.44 236	96.60 235	95.31 111	99.44 195	90.01 252	99.13 187	99.11 146

conf0.01		91.90 279	90.98 284	94.67 254	98.27 166	88.03 257	96.98 111	88.58 344	93.90 199	94.64 254	91.45 332	69.62 341	99.52 164	87.62 285	97.74 269	96.46 310

conf0.002		91.90 279	90.98 284	94.67 254	98.27 166	88.03 257	96.98 111	88.58 344	93.90 199	94.64 254	91.45 332	69.62 341	99.52 164	87.62 285	97.74 269	96.46 310

thresconf0.02		91.72 286	90.98 284	93.97 272	98.27 166	88.03 257	96.98 111	88.58 344	93.90 199	94.64 254	91.45 332	69.62 341	99.52 164	87.62 285	97.74 269	94.35 336

tfpn_n400		91.72 286	90.98 284	93.97 272	98.27 166	88.03 257	96.98 111	88.58 344	93.90 199	94.64 254	91.45 332	69.62 341	99.52 164	87.62 285	97.74 269	94.35 336

tfpnconf		91.72 286	90.98 284	93.97 272	98.27 166	88.03 257	96.98 111	88.58 344	93.90 199	94.64 254	91.45 332	69.62 341	99.52 164	87.62 285	97.74 269	94.35 336

tfpnview11		91.72 286	90.98 284	93.97 272	98.27 166	88.03 257	96.98 111	88.58 344	93.90 199	94.64 254	91.45 332	69.62 341	99.52 164	87.62 285	97.74 269	94.35 336

MVS_111021_LR		96.82 135	96.55 144	97.62 102	98.27 166	95.34 89	93.81 273	98.33 172	94.59 176	96.56 193	96.63 234	96.61 66	98.73 300	94.80 145	99.34 161	98.78 193

CLD-MVS		95.47 187	95.07 191	96.69 161	98.27 166	92.53 165	91.36 323	98.67 129	91.22 255	95.78 229	94.12 305	95.65 100	98.98 274	90.81 232	99.72 60	98.57 208
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
1121		94.26 228	93.26 244	97.27 131	98.26 174	94.73 108	95.86 167	97.71 226	77.96 346	94.53 262	96.71 229	91.93 215	99.40 211	87.71 281	98.64 233	97.69 269

pmmvs-eth3d		96.49 153	96.18 158	97.42 123	98.25 175	94.29 122	94.77 235	98.07 207	89.81 269	97.97 120	98.33 96	93.11 179	99.08 261	95.46 117	99.84 41	98.89 178

v144192		96.69 145	96.90 126	96.03 207	98.25 175	88.92 238	95.49 187	98.77 107	93.05 223	98.09 106	98.29 102	92.51 199	99.70 89	98.11 30	99.56 98	99.47 65

ambc				96.56 170	98.23 177	91.68 187	97.88 58	98.13 200		98.42 75	98.56 79	94.22 148	99.04 265	94.05 172	99.35 159	98.95 165

testmv		95.51 182	95.33 184	96.05 203	98.23 177	89.51 222	93.50 284	98.63 138	94.25 189	98.22 92	97.73 163	92.51 199	99.47 183	85.22 311	99.72 60	99.17 131

tfpn111		91.92 278	91.39 273	93.49 288	98.21 179	84.50 310	96.39 131	90.39 331	96.87 89	96.33 203	93.08 313	73.44 328	99.51 174	79.87 334	97.94 263	96.46 310

conf200view11		91.81 283	91.26 278	93.46 289	98.21 179	84.50 310	96.39 131	90.39 331	96.87 89	96.33 203	93.08 313	73.44 328	99.42 197	78.85 339	97.74 269	96.46 310

thres100view900		91.76 285	91.26 278	93.26 293	98.21 179	84.50 310	96.39 131	90.39 331	96.87 89	96.33 203	93.08 313	73.44 328	99.42 197	78.85 339	97.74 269	95.85 320

v1921920		96.72 142	96.96 123	95.99 208	98.21 179	88.79 244	95.42 194	98.79 103	93.22 215	98.19 95	98.26 106	92.68 190	99.70 89	98.34 28	99.55 102	99.49 59

thres600view7		92.03 276	91.43 272	93.82 281	98.19 183	84.61 309	96.27 140	90.39 331	96.81 93	96.37 202	93.11 311	73.44 328	99.49 178	80.32 333	97.95 260	97.36 279

PatchMatch-RL		94.61 222	93.81 236	97.02 146	98.19 183	95.72 74	93.66 277	97.23 249	88.17 284	94.94 247	95.62 275	91.43 224	98.57 312	87.36 295	97.68 282	96.76 301

LF4IMVS		96.07 166	95.63 177	97.36 127	98.19 183	95.55 81	95.44 189	98.82 100	92.29 240	95.70 233	96.55 237	92.63 193	98.69 304	91.75 213	99.33 166	97.85 263

v1240		96.74 139	97.02 120	95.91 215	98.18 186	88.52 247	95.39 197	98.88 80	93.15 221	98.46 72	98.40 91	92.80 187	99.71 81	98.45 26	99.49 120	99.49 59

TAPA-MVS	93.32 12	94.93 210	94.23 225	97.04 143	98.18 186	94.51 115	95.22 211	98.73 114	81.22 333	96.25 213	95.95 267	93.80 163	98.98 274	89.89 253	98.87 214	97.62 271
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.17 188	93.24 157	92.74 302	97.61 238	75.17 350	94.65 253	96.69 231	90.96 230			98.66 231	97.66 270

MIMVSNet		93.42 252	92.86 251	95.10 239	98.17 188	88.19 252	98.13 44	93.69 299	92.07 241	95.04 245	98.21 111	80.95 290	99.03 268	81.42 331	98.06 257	98.07 250

原ACMM1				96.58 167	98.16 190	92.12 176		98.15 198	85.90 306	93.49 296	96.43 246	92.47 201	99.38 222	87.66 284	98.62 234	98.23 239

testdata				95.70 223	98.16 190	90.58 202		97.72 225	80.38 336	95.62 234	97.02 208	92.06 211	98.98 274	89.06 266	98.52 239	97.54 275

MVP-Stereo		95.69 175	95.28 185	96.92 149	98.15 192	93.03 159	95.64 182	98.20 190	90.39 263	96.63 190	97.73 163	91.63 220	99.10 259	91.84 209	97.31 299	98.63 204
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		97.37 98	97.70 66	96.35 180	98.14 193	95.13 97	96.54 126	98.92 74	95.94 121	99.19 30	98.08 127	97.74 22	95.06 352	95.24 126	99.54 104	98.87 184

EU-MVSNet		94.25 229	94.47 217	93.60 285	98.14 193	82.60 321	97.24 95	92.72 314	85.08 315	98.48 70	98.94 55	82.59 286	98.76 298	97.47 57	99.53 106	99.44 81

NP-MVS					98.14 193	93.72 142					95.08 283

LCM-MVSNet-Re		97.33 102	97.33 92	97.32 129	98.13 196	93.79 140	96.99 110	99.65 2	96.74 95	99.47 13	98.93 56	96.91 49	99.84 28	90.11 250	99.06 197	98.32 230

3Dnovator+	96.13 3	97.73 73	97.59 79	98.15 72	98.11 197	95.60 79	98.04 49	98.70 123	98.13 39	96.93 182	98.45 87	95.30 112	99.62 129	95.64 110	98.96 203	99.24 124

Test4		95.39 192	95.24 186	95.82 218	98.07 198	89.60 218	94.40 242	98.49 150	91.39 254	97.40 158	96.32 252	87.32 268	99.41 208	95.09 138	98.71 229	98.44 218

VNet		96.84 131	96.83 129	96.88 152	98.06 199	92.02 179	96.35 137	97.57 239	97.70 57	97.88 133	97.80 156	92.40 202	99.54 159	94.73 150	98.96 203	99.08 151

DI_MVS_plusplus_test		95.46 188	95.43 182	95.55 226	98.05 200	88.84 242	94.18 254	95.75 282	91.92 247	97.32 159	96.94 212	91.44 223	99.39 217	94.81 144	98.48 242	98.43 219

LFMVS		95.32 196	94.88 200	96.62 163	98.03 201	91.47 190	97.65 72	90.72 330	99.11 8	97.89 131	98.31 98	79.20 295	99.48 181	93.91 177	99.12 190	98.93 171

tfpn200view9		91.55 291	91.00 282	93.21 295	98.02 202	84.35 314	95.70 174	90.79 328	96.26 109	95.90 226	92.13 328	73.62 322	99.42 197	78.85 339	97.74 269	95.85 320

thres400		91.68 290	91.00 282	93.71 283	98.02 202	84.35 314	95.70 174	90.79 328	96.26 109	95.90 226	92.13 328	73.62 322	99.42 197	78.85 339	97.74 269	97.36 279

xiu_mvs_v1_base_debu		95.62 178	95.96 167	94.60 258	98.01 204	88.42 248	93.99 264	98.21 187	92.98 225	95.91 223	94.53 293	96.39 75	99.72 71	95.43 120	98.19 252	95.64 324

xiu_mvs_v1_base		95.62 178	95.96 167	94.60 258	98.01 204	88.42 248	93.99 264	98.21 187	92.98 225	95.91 223	94.53 293	96.39 75	99.72 71	95.43 120	98.19 252	95.64 324

xiu_mvs_v1_base_debi		95.62 178	95.96 167	94.60 258	98.01 204	88.42 248	93.99 264	98.21 187	92.98 225	95.91 223	94.53 293	96.39 75	99.72 71	95.43 120	98.19 252	95.64 324

CNVR-MVS		96.92 124	96.55 144	98.03 80	98.00 207	95.54 82	94.87 229	98.17 195	94.60 174	96.38 201	97.05 206	95.67 99	99.36 226	95.12 136	99.08 193	99.19 128

PLC	91.02 16	94.05 240	92.90 250	97.51 110	98.00 207	95.12 98	94.25 248	98.25 185	86.17 302	91.48 326	95.25 281	91.01 228	99.19 249	85.02 313	96.69 310	98.22 240
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_normal		95.51 182	95.46 181	95.68 224	97.97 209	89.12 234	93.73 275	95.86 280	91.98 244	97.17 165	96.94 212	91.55 221	99.42 197	95.21 127	98.73 227	98.51 213

tfpn1000		91.88 282	91.20 280	93.89 280	97.96 210	87.13 280	97.13 100	88.16 351	94.41 182	94.87 249	92.77 320	68.34 348	99.47 183	89.24 261	97.95 260	95.06 330

GBi-Net		96.99 114	96.80 132	97.56 105	97.96 210	93.67 143	98.23 35	98.66 131	95.59 134	97.99 116	99.19 32	89.51 248	99.73 65	94.60 152	99.44 132	99.30 112

test1		96.99 114	96.80 132	97.56 105	97.96 210	93.67 143	98.23 35	98.66 131	95.59 134	97.99 116	99.19 32	89.51 248	99.73 65	94.60 152	99.44 132	99.30 112

FMVSNet2		96.72 142	96.67 138	96.87 153	97.96 210	91.88 182	97.15 97	98.06 208	95.59 134	98.50 69	98.62 75	89.51 248	99.65 117	94.99 140	99.60 88	99.07 153

BH-untuned		94.69 218	94.75 206	94.52 263	97.95 214	87.53 271	94.07 261	97.01 258	93.99 196	97.10 169	95.65 273	92.65 192	98.95 279	87.60 291	96.74 309	97.09 287

QAPM		95.88 173	95.57 178	96.80 154	97.90 215	91.84 184	98.18 42	98.73 114	88.41 279	96.42 199	98.13 122	94.73 124	99.75 55	88.72 270	98.94 207	98.81 189

TinyColmap		96.00 169	96.34 154	94.96 244	97.90 215	87.91 264	94.13 259	98.49 150	94.41 182	98.16 97	97.76 157	96.29 80	98.68 307	90.52 242	99.42 144	98.30 233

HQP-NCC					97.85 217		94.26 245		93.18 217	92.86 309

ACMP_Plane					97.85 217		94.26 245		93.18 217	92.86 309

N_pmnet		95.18 201	94.23 225	98.06 76	97.85 217	96.55 53	92.49 306	91.63 322	89.34 271	98.09 106	97.41 186	90.33 236	99.06 263	91.58 215	99.31 168	98.56 209

HQP-MVS		95.17 202	94.58 213	96.92 149	97.85 217	92.47 166	94.26 245	98.43 157	93.18 217	92.86 309	95.08 283	90.33 236	99.23 247	90.51 243	98.74 224	99.05 156

TEST9					97.84 221	95.23 91	93.62 279	98.39 163	86.81 297	93.78 283	95.99 262	94.68 129	99.52 164

train_agg		95.46 188	94.66 207	97.88 86	97.84 221	95.23 91	93.62 279	98.39 163	87.04 295	93.78 283	95.99 262	94.58 134	99.52 164	91.76 211	98.90 209	98.89 178

MSLP-MVS++		96.42 158	96.71 136	95.57 225	97.82 223	90.56 204	95.71 173	98.84 88	94.72 172	96.71 188	97.39 190	94.91 122	98.10 336	95.28 124	99.02 199	98.05 253

test_8					97.81 224	95.07 99	93.54 282	98.38 165	87.04 295	93.71 287	95.96 266	94.58 134	99.52 164

NCCC		96.52 152	95.99 166	98.10 74	97.81 224	95.68 76	95.00 225	98.20 190	95.39 142	95.40 238	96.36 250	93.81 162	99.45 192	93.55 185	98.42 244	99.17 131

WTY-MVS		93.55 250	93.00 249	95.19 236	97.81 224	87.86 265	93.89 269	96.00 275	89.02 273	94.07 275	95.44 279	86.27 271	99.33 232	87.69 283	96.82 306	98.39 222

CNLPA		95.04 206	94.47 217	96.75 157	97.81 224	95.25 90	94.12 260	97.89 214	94.41 182	94.57 260	95.69 271	90.30 239	98.35 328	86.72 300	98.76 222	96.64 305

agg_prior3		95.30 197	94.46 220	97.80 91	97.80 228	95.00 100	93.63 278	98.34 171	86.33 301	93.40 303	95.84 269	94.15 151	99.50 176	91.76 211	98.90 209	98.89 178

agg_prior1		95.39 192	94.60 211	97.75 93	97.80 228	94.96 102	93.39 287	98.36 167	87.20 293	93.49 296	95.97 265	94.65 131	99.53 161	91.69 214	98.86 216	98.77 194

agg_prior					97.80 228	94.96 102		98.36 167		93.49 296			99.53 161

旧先验1					97.80 228	93.87 136		97.75 222			97.04 207	93.57 168			98.68 230	98.72 198

PCF-MVS	89.43 18	92.12 275	90.64 296	96.57 169	97.80 228	93.48 152	89.88 339	98.45 153	74.46 351	96.04 220	95.68 272	90.71 232	99.31 234	73.73 347	99.01 201	96.91 295
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior3		95.91 171	95.39 183	97.46 119	97.79 233	94.26 125	93.33 290	98.42 160	94.21 191	94.02 277	96.25 254	93.64 166	99.34 229	91.90 205	98.96 203	98.79 191

test_prior				97.46 119	97.79 233	94.26 125		98.42 160					99.34 229			98.79 191

PVSNet_BlendedMVS		95.02 208	94.93 198	95.27 234	97.79 233	87.40 275	94.14 258	98.68 126	88.94 275	94.51 263	98.01 135	93.04 181	99.30 236	89.77 255	99.49 120	99.11 146

PVSNet_Blended		93.96 241	93.65 238	94.91 245	97.79 233	87.40 275	91.43 322	98.68 126	84.50 320	94.51 263	94.48 296	93.04 181	99.30 236	89.77 255	98.61 235	98.02 258

USDC		94.56 224	94.57 215	94.55 262	97.78 237	86.43 288	92.75 300	98.65 137	85.96 304	96.91 183	97.93 145	90.82 231	98.74 299	90.71 237	99.59 90	98.47 215

alignmvs		96.01 168	95.52 179	97.50 113	97.77 238	94.71 110	96.07 151	96.84 263	97.48 70	96.78 187	94.28 304	85.50 275	99.40 211	96.22 87	98.73 227	98.40 220

TSAR-MVS + GP.		96.47 155	96.12 159	97.49 116	97.74 239	95.23 91	94.15 257	96.90 262	93.26 214	98.04 113	96.70 230	94.41 140	98.89 284	94.77 148	99.14 185	98.37 223

3Dnovator	96.53 2	97.61 82	97.64 73	97.50 113	97.74 239	93.65 147	98.49 23	98.88 80	96.86 92	97.11 168	98.55 80	95.82 91	99.73 65	95.94 99	99.42 144	99.13 138

tfpn_ndepth		90.98 297	90.24 302	93.20 297	97.72 241	87.18 279	96.52 127	88.20 350	92.63 234	93.69 289	90.70 345	68.22 349	99.42 197	86.98 297	97.47 294	93.00 346

sss		94.22 230	93.72 237	95.74 220	97.71 242	89.95 211	93.84 271	96.98 259	88.38 282	93.75 285	95.74 270	87.94 260	98.89 284	91.02 225	98.10 256	98.37 223

DeepC-MVS_fast	94.34 7	96.74 139	96.51 149	97.44 122	97.69 243	94.15 128	96.02 154	98.43 157	93.17 220	97.30 160	97.38 192	95.48 104	99.28 240	93.74 181	99.34 161	98.88 182
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
semantic-postprocess				94.85 249	97.68 244	85.53 292		97.63 236	96.99 85	98.36 78	98.54 81	87.44 266	99.75 55	97.07 70	99.08 193	99.27 122

MVSFormer		96.14 165	96.36 153	95.49 229	97.68 244	87.81 267	98.67 13	99.02 51	96.50 100	94.48 265	96.15 258	86.90 269	99.92 4	98.73 17	99.13 187	98.74 196

lupinMVS		93.77 243	93.28 243	95.24 235	97.68 244	87.81 267	92.12 312	96.05 274	84.52 319	94.48 265	95.06 285	86.90 269	99.63 123	93.62 184	99.13 187	98.27 236

Fast-Effi-MVS+		95.49 184	95.07 191	96.75 157	97.67 247	92.82 161	94.22 251	98.60 141	91.61 251	93.42 301	92.90 318	96.73 60	99.70 89	92.60 197	97.89 267	97.74 268

canonicalmvs		97.23 108	97.21 105	97.30 130	97.65 248	94.39 119	97.84 60	99.05 38	97.42 72	96.68 189	93.85 307	97.63 26	99.33 232	96.29 86	98.47 243	98.18 245

CDS-MVSNet		94.88 211	94.12 230	97.14 137	97.64 249	93.57 148	93.96 267	97.06 257	90.05 267	96.30 210	96.55 237	86.10 272	99.47 183	90.10 251	99.31 168	98.40 220
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs5		94.63 221	94.34 223	95.50 228	97.63 250	88.34 251	94.02 262	97.13 254	87.15 294	95.22 241	97.15 200	87.50 265	99.27 241	93.99 174	99.26 176	98.88 182

test12				97.46 119	97.61 251	94.07 130		97.78 221		93.57 294		93.31 176	99.42 197		98.78 220	98.89 178

PMMVS2		93.66 247	94.07 231	92.45 310	97.57 252	80.67 329	86.46 346	96.00 275	93.99 196	97.10 169	97.38 192	89.90 243	97.82 339	88.76 269	99.47 125	98.86 185

BH-RMVSNet		94.56 224	94.44 221	94.91 245	97.57 252	87.44 274	93.78 274	96.26 272	93.69 209	96.41 200	96.50 242	92.10 208	99.00 271	85.96 303	97.71 279	98.31 231

PVSNet	86.72 19	91.10 294	90.97 290	91.49 317	97.56 254	78.04 338	87.17 344	94.60 293	84.65 318	92.34 318	92.20 327	87.37 267	98.47 318	85.17 312	97.69 281	97.96 260

DELS-MVS		96.17 164	96.23 157	95.99 208	97.55 255	90.04 208	92.38 309	98.52 147	94.13 194	96.55 196	97.06 205	94.99 120	99.58 147	95.62 111	99.28 173	98.37 223
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
IterMVS		95.42 191	95.83 171	94.20 270	97.52 256	83.78 318	92.41 308	97.47 245	95.49 138	98.06 111	98.49 84	87.94 260	99.58 147	96.02 95	99.02 199	99.23 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet		95.67 177	96.58 141	92.94 303	97.48 257	80.21 330	92.96 296	98.19 194	94.83 168	98.82 47	98.79 61	93.31 176	99.51 174	95.83 102	99.04 198	99.12 143

MDA-MVSNet_test_wron		94.73 215	94.83 204	94.42 264	97.48 257	85.15 298	90.28 334	95.87 279	92.52 235	97.48 153	97.76 157	91.92 216	99.17 252	93.32 186	96.80 308	98.94 167

PHI-MVS		96.96 121	96.53 147	98.25 69	97.48 257	96.50 54	96.76 121	98.85 85	93.52 211	96.19 216	96.85 218	95.94 85	99.42 197	93.79 180	99.43 141	98.83 188

DeepPCF-MVS	94.58 5	96.90 126	96.43 151	98.31 64	97.48 257	97.23 35	92.56 305	98.60 141	92.84 232	98.54 65	97.40 187	96.64 64	98.78 296	94.40 159	99.41 150	98.93 171

thres200		91.00 296	90.42 300	92.77 305	97.47 261	83.98 317	94.01 263	91.18 326	95.12 159	95.44 236	91.21 340	73.93 318	99.31 234	77.76 343	97.63 287	95.01 331

YYNet1		94.73 215	94.84 202	94.41 265	97.47 261	85.09 300	90.29 333	95.85 281	92.52 235	97.53 147	97.76 157	91.97 212	99.18 250	93.31 187	96.86 305	98.95 165

Effi-MVS+		96.19 163	96.01 164	96.71 159	97.43 263	92.19 175	96.12 150	99.10 25	95.45 139	93.33 304	94.71 291	97.23 41	99.56 153	93.21 191	97.54 289	98.37 223

pmmvs4		94.82 214	94.19 228	96.70 160	97.42 264	92.75 163	92.09 314	96.76 266	86.80 298	95.73 232	97.22 197	89.28 251	98.89 284	93.28 188	99.14 185	98.46 217

MSDG		95.33 195	95.13 189	95.94 214	97.40 265	91.85 183	91.02 326	98.37 166	95.30 144	96.31 209	95.99 262	94.51 138	98.38 325	89.59 257	97.65 285	97.60 273

EI-MVSNet-Vis-set		97.32 103	97.39 90	97.11 138	97.36 266	92.08 178	95.34 201	97.65 232	97.74 52	98.29 88	98.11 125	95.05 117	99.68 104	97.50 54	99.50 113	99.56 42

PS-MVSNAJ		94.10 237	94.47 217	93.00 301	97.35 267	84.88 302	91.86 316	97.84 217	91.96 245	94.17 270	92.50 325	95.82 91	99.71 81	91.27 219	97.48 292	94.40 335

Regformer-3		97.25 107	97.29 94	97.11 138	97.35 267	92.32 169	95.26 208	97.62 237	97.67 60	98.17 96	97.89 148	95.05 117	99.56 153	97.16 67	99.42 144	99.46 67

Regformer-4		97.53 90	97.47 88	97.71 95	97.35 267	93.91 135	95.26 208	98.14 199	97.97 45	98.34 80	97.89 148	95.49 103	99.71 81	97.41 58	99.42 144	99.51 50

EI-MVSNet-UG-set		97.32 103	97.40 89	97.09 140	97.34 270	92.01 180	95.33 202	97.65 232	97.74 52	98.30 87	98.14 121	95.04 119	99.69 98	97.55 50	99.52 110	99.58 37

AdaColmap		95.11 203	94.62 210	96.58 167	97.33 271	94.45 118	94.92 227	98.08 205	93.15 221	93.98 280	95.53 278	94.34 143	99.10 259	85.69 306	98.61 235	96.20 317

xiu_mvs_v2_base		94.22 230	94.63 209	92.99 302	97.32 272	84.84 303	92.12 312	97.84 217	91.96 245	94.17 270	93.43 308	96.07 83	99.71 81	91.27 219	97.48 292	94.42 334

OpenMVS_ROB	91.80 14	93.64 248	93.05 247	95.42 230	97.31 273	91.21 192	95.08 218	96.68 270	81.56 330	96.88 185	96.41 247	90.44 235	99.25 244	85.39 310	97.67 283	95.80 322

EI-MVSNet		96.63 148	96.93 124	95.74 220	97.26 274	88.13 255	95.29 206	97.65 232	96.99 85	97.94 123	98.19 112	92.55 195	99.58 147	96.91 73	99.56 98	99.50 51

CVMVSNet		92.33 271	92.79 253	90.95 323	97.26 274	75.84 345	95.29 206	92.33 317	81.86 328	96.27 211	98.19 112	81.44 288	98.46 319	94.23 167	98.29 246	98.55 211

Regformer-1		97.27 105	97.16 109	97.61 103	97.21 276	93.86 137	94.85 231	98.04 210	97.62 62	98.03 114	97.50 181	95.34 109	99.63 123	96.52 79	99.31 168	99.35 106

Regformer-2		97.41 95	97.24 99	97.93 84	97.21 276	94.72 109	94.85 231	98.27 182	97.74 52	98.11 102	97.50 181	95.58 101	99.69 98	96.57 78	99.31 168	99.37 102

Fast-Effi-MVS+-dtu		96.44 156	96.12 159	97.39 126	97.18 278	94.39 119	95.46 188	98.73 114	96.03 117	94.72 251	94.92 289	96.28 81	99.69 98	93.81 179	97.98 259	98.09 247

OpenMVS	94.22 8	95.48 186	95.20 187	96.32 183	97.16 279	91.96 181	97.74 68	98.84 88	87.26 291	94.36 267	98.01 135	93.95 156	99.67 111	90.70 238	98.75 223	97.35 285

BH-w/o		92.14 274	91.94 267	92.73 306	97.13 280	85.30 295	92.46 307	95.64 285	89.33 272	94.21 269	92.74 322	89.60 244	98.24 331	81.68 330	94.66 330	94.66 333

MG-MVS		94.08 239	94.00 234	94.32 267	97.09 281	85.89 289	93.19 294	95.96 277	92.52 235	94.93 248	97.51 180	89.54 245	98.77 297	87.52 293	97.71 279	98.31 231

MVS_0304		96.22 161	95.94 170	97.04 143	97.07 282	92.54 164	94.19 253	99.04 45	95.17 153	93.74 286	96.92 215	91.77 219	99.73 65	95.76 104	99.81 44	98.85 187

MVS-HIRNet		88.40 317	90.20 303	82.99 341	97.01 283	60.04 359	93.11 295	85.61 353	84.45 321	88.72 343	99.09 46	84.72 281	98.23 332	82.52 325	96.59 312	90.69 352

GA-MVS		92.83 260	92.15 262	94.87 248	96.97 284	87.27 278	90.03 335	96.12 273	91.83 249	94.05 276	94.57 292	76.01 313	98.97 278	92.46 200	97.34 298	98.36 228

test1235678		92.95 258	92.40 258	94.61 257	96.95 285	86.87 283	90.75 328	97.75 222	91.00 258	96.33 203	95.38 280	85.21 277	98.92 280	79.00 337	99.20 180	98.03 256

MVS_Test		96.27 159	96.79 134	94.73 253	96.94 286	86.63 286	96.18 147	98.33 172	94.94 163	96.07 219	98.28 103	95.25 114	99.26 243	97.21 63	97.90 266	98.30 233

MAR-MVS		94.21 233	93.03 248	97.76 92	96.94 286	97.44 30	96.97 117	97.15 253	87.89 289	92.00 321	92.73 323	92.14 206	99.12 254	83.92 319	97.51 291	96.73 302
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
Effi-MVS+-dtu		96.81 136	96.09 161	98.99 10	96.90 288	98.69 2	96.42 130	98.09 203	95.86 124	95.15 242	95.54 277	94.26 146	99.81 33	94.06 170	98.51 241	98.47 215

mvs-test1		96.20 162	95.50 180	98.32 62	96.90 288	98.16 4	95.07 219	98.09 203	95.86 124	93.63 290	94.32 303	94.26 146	99.71 81	94.06 170	97.27 301	97.07 288

MS-PatchMatch		94.83 213	94.91 199	94.57 261	96.81 290	87.10 281	94.23 250	97.34 246	88.74 277	97.14 166	97.11 203	91.94 214	98.23 332	92.99 195	97.92 264	98.37 223

UGNet		96.81 136	96.56 143	97.58 104	96.64 291	93.84 138	97.75 66	97.12 255	96.47 103	93.62 291	98.88 59	93.22 178	99.53 161	95.61 112	99.69 68	99.36 105
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
API-MVS		95.09 205	95.01 194	95.31 233	96.61 292	94.02 132	96.83 119	97.18 252	95.60 133	95.79 228	94.33 302	94.54 136	98.37 327	85.70 305	98.52 239	93.52 342

PAPM		87.64 324	85.84 327	93.04 299	96.54 293	84.99 301	88.42 343	95.57 287	79.52 339	83.82 352	93.05 317	80.57 291	98.41 321	62.29 355	92.79 337	95.71 323

diffmvs		95.00 209	95.00 195	95.01 243	96.53 294	87.96 263	95.73 171	98.32 181	90.67 261	91.89 323	97.43 185	92.07 210	98.90 281	95.44 118	96.88 304	98.16 246

FMVSNet3		95.26 200	94.94 196	96.22 193	96.53 294	90.06 207	95.99 156	97.66 230	94.11 195	97.99 116	97.91 147	80.22 293	99.63 123	94.60 152	99.44 132	98.96 164

HY-MVS	91.43 15	92.58 262	91.81 270	94.90 247	96.49 296	88.87 240	97.31 90	94.62 292	85.92 305	90.50 334	96.84 219	85.05 278	99.40 211	83.77 322	95.78 322	96.43 314

TR-MVS		92.54 267	92.20 261	93.57 286	96.49 296	86.66 285	93.51 283	94.73 291	89.96 268	94.95 246	93.87 306	90.24 241	98.61 310	81.18 332	94.88 328	95.45 328

CANet		95.86 174	95.65 176	96.49 173	96.41 298	90.82 198	94.36 243	98.41 162	94.94 163	92.62 316	96.73 228	92.68 190	99.71 81	95.12 136	99.60 88	98.94 167

mvs_anonymous		95.36 194	96.07 163	93.21 295	96.29 299	81.56 324	94.60 238	97.66 230	93.30 213	96.95 181	98.91 58	93.03 183	99.38 222	96.60 76	97.30 300	98.69 200

Patchmatch-test1		93.38 254	93.59 239	92.73 306	96.24 300	81.40 325	93.24 292	94.00 297	91.58 252	94.57 260	96.67 232	87.94 260	99.03 268	90.42 246	97.66 284	97.77 267

LS3D		97.77 71	97.50 86	98.57 44	96.24 300	97.58 21	98.45 26	98.85 85	98.58 24	97.51 148	97.94 143	95.74 98	99.63 123	95.19 128	98.97 202	98.51 213

new_pmnet		92.34 270	91.69 271	94.32 267	96.23 302	89.16 232	92.27 310	92.88 311	84.39 322	95.29 239	96.35 251	85.66 274	96.74 349	84.53 316	97.56 288	97.05 289

MVE	73.61 22	86.48 326	85.92 326	88.18 335	96.23 302	85.28 296	81.78 354	75.79 357	86.01 303	82.53 354	91.88 330	92.74 188	87.47 357	71.42 352	94.86 329	91.78 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DSMNet-mixed		92.19 273	91.83 269	93.25 294	96.18 304	83.68 319	96.27 140	93.68 301	76.97 349	92.54 317	99.18 35	89.20 253	98.55 315	83.88 320	98.60 237	97.51 276

our_test_3		94.20 235	94.58 213	93.07 298	96.16 305	81.20 326	90.42 332	96.84 263	90.72 260	97.14 166	97.13 201	90.47 234	99.11 257	94.04 173	98.25 251	98.91 174

ppachtmachnet_test		94.49 226	94.84 202	93.46 289	96.16 305	82.10 323	90.59 330	97.48 242	90.53 262	97.01 173	97.59 174	91.01 228	99.36 226	93.97 175	99.18 183	98.94 167

Patchmatch-test		93.60 249	93.25 245	94.63 256	96.14 307	87.47 273	96.04 153	94.50 294	93.57 210	96.47 197	96.97 210	76.50 309	98.61 310	90.67 239	98.41 245	97.81 266

testus		90.90 299	90.51 298	92.06 314	96.07 308	79.45 332	88.99 340	98.44 156	85.46 311	94.15 272	90.77 342	89.12 254	98.01 338	73.66 348	97.95 260	98.71 199

PNet_i23d		83.82 329	83.39 329	85.10 340	96.07 308	65.16 355	81.87 353	94.37 295	90.87 259	93.92 281	92.89 319	52.80 361	96.44 351	77.52 345	70.22 355	93.70 341

wuyk23d		93.25 256	95.20 187	87.40 337	96.07 308	95.38 87	97.04 108	94.97 289	95.33 143	99.70 6	98.11 125	98.14 14	91.94 354	77.76 343	99.68 72	74.89 354

CANet_DTU		94.65 220	94.21 227	95.96 210	95.90 311	89.68 214	93.92 268	97.83 219	93.19 216	90.12 337	95.64 274	88.52 255	99.57 152	93.27 189	99.47 125	98.62 205

test12356		87.98 321	88.41 316	86.69 339	95.84 312	63.49 356	87.15 345	97.32 247	87.21 292	91.78 325	93.36 309	70.66 339	98.39 323	74.70 346	97.64 286	98.19 243

MVSTER		94.21 233	93.93 235	95.05 242	95.83 313	86.46 287	95.18 212	97.65 232	92.41 239	97.94 123	98.00 137	72.39 332	99.58 147	96.36 85	99.56 98	99.12 143

FMVSNet5		93.39 253	92.35 259	96.50 172	95.83 313	90.81 200	97.31 90	98.27 182	92.74 233	96.27 211	98.28 103	62.23 353	99.67 111	90.86 230	99.36 156	99.03 157

PVSNet_0	81.89 21	84.49 328	83.21 331	88.34 334	95.76 315	74.97 348	83.49 350	92.70 315	78.47 344	87.94 346	86.90 353	83.38 284	96.63 350	73.44 349	66.86 356	93.40 343

PAPR		92.22 272	91.27 277	95.07 241	95.73 316	88.81 243	91.97 315	97.87 215	85.80 307	90.91 328	92.73 323	91.16 226	98.33 329	79.48 335	95.76 323	98.08 248

CHOSEN 280x420		89.98 305	89.19 311	92.37 311	95.60 317	81.13 327	86.22 347	97.09 256	81.44 332	87.44 348	93.15 310	73.99 317	99.47 183	88.69 271	99.07 195	96.52 309

ADS-MVSNet2		91.47 292	90.51 298	94.36 266	95.51 318	85.63 290	95.05 222	95.70 283	83.46 324	92.69 312	96.84 219	79.15 296	99.41 208	85.66 307	90.52 341	98.04 254

ADS-MVSNet		90.95 298	90.26 301	93.04 299	95.51 318	82.37 322	95.05 222	93.41 305	83.46 324	92.69 312	96.84 219	79.15 296	98.70 303	85.66 307	90.52 341	98.04 254

CR-MVSNet		93.29 255	92.79 253	94.78 251	95.44 320	88.15 253	96.18 147	97.20 250	84.94 317	94.10 273	98.57 77	77.67 301	99.39 217	95.17 130	95.81 319	96.81 299

RPMNet		94.22 230	94.03 233	94.78 251	95.44 320	88.15 253	96.18 147	93.73 298	97.43 71	94.10 273	98.49 84	79.40 294	99.39 217	95.69 105	95.81 319	96.81 299

1314		92.38 269	92.30 260	92.64 308	95.42 322	85.15 298	95.86 167	96.97 260	85.40 313	90.62 330	93.06 316	91.12 227	97.80 340	86.74 299	95.49 327	94.97 332

tpm		91.08 295	90.85 292	91.75 316	95.33 323	78.09 336	95.03 224	91.27 325	88.75 276	93.53 295	97.40 187	71.24 335	99.30 236	91.25 221	93.87 333	97.87 262

IB-MVS	85.98 20	88.63 314	86.95 323	93.68 284	95.12 324	84.82 304	90.85 327	90.17 340	87.55 290	88.48 344	91.34 339	58.01 355	99.59 145	87.24 296	93.80 334	96.63 307
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
PatchT		93.75 244	93.57 240	94.29 269	95.05 325	87.32 277	96.05 152	92.98 309	97.54 66	94.25 268	98.72 67	75.79 314	99.24 245	95.92 100	95.81 319	96.32 315

tpm2		88.47 315	87.69 318	90.79 324	94.98 326	77.34 341	95.09 216	91.83 320	77.51 348	89.40 340	96.41 247	67.83 350	98.73 300	83.58 324	92.60 339	96.29 316

Patchmtry		95.03 207	94.59 212	96.33 182	94.83 327	90.82 198	96.38 135	97.20 250	96.59 98	97.49 150	98.57 77	77.67 301	99.38 222	92.95 196	99.62 80	98.80 190

MVS		90.02 303	89.20 310	92.47 309	94.71 328	86.90 282	95.86 167	96.74 268	64.72 354	90.62 330	92.77 320	92.54 197	98.39 323	79.30 336	95.56 326	92.12 347

CostFormer		89.75 308	89.25 307	91.26 320	94.69 329	78.00 339	95.32 203	91.98 319	81.50 331	90.55 332	96.96 211	71.06 336	98.89 284	88.59 273	92.63 338	96.87 296

PatchmatchNet		91.98 277	91.87 268	92.30 312	94.60 330	79.71 331	95.12 213	93.59 304	89.52 270	93.61 292	97.02 208	77.94 299	99.18 250	90.84 231	94.57 332	98.01 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		88.46 316	87.54 319	91.22 321	94.56 331	78.08 337	95.63 185	93.17 307	79.08 342	85.85 350	96.80 223	65.86 352	98.85 291	84.10 318	92.85 336	96.72 303

LP		93.12 257	92.78 255	94.14 271	94.50 332	85.48 293	95.73 171	95.68 284	92.97 229	95.05 244	97.17 199	81.93 287	99.40 211	93.06 194	88.96 346	97.55 274

tpm cat1		88.01 320	87.33 320	90.05 329	94.48 333	76.28 344	94.47 241	94.35 296	73.84 353	89.26 341	95.61 276	73.64 321	98.30 330	84.13 317	86.20 350	95.57 327

MDTV_nov1_ep13			91.28 276		94.31 334	73.51 349	94.80 233	93.16 308	86.75 299	93.45 299	97.40 187	76.37 310	98.55 315	88.85 268	96.43 313

cascas		91.89 281	91.35 275	93.51 287	94.27 335	85.60 291	88.86 342	98.61 140	79.32 340	92.16 320	91.44 338	89.22 252	98.12 335	90.80 233	97.47 294	96.82 298

test-LLR		89.97 306	89.90 304	90.16 327	94.24 336	74.98 346	89.89 336	89.06 341	92.02 242	89.97 338	90.77 342	73.92 319	98.57 312	91.88 207	97.36 296	96.92 293

test-mter		87.92 322	87.17 321	90.16 327	94.24 336	74.98 346	89.89 336	89.06 341	86.44 300	89.97 338	90.77 342	54.96 359	98.57 312	91.88 207	97.36 296	96.92 293

pmmvs3		90.00 304	88.90 313	93.32 291	94.20 338	85.34 294	91.25 324	92.56 316	78.59 343	93.82 282	95.17 282	67.36 351	98.69 304	89.08 265	98.03 258	95.92 318

tpmrst		90.31 301	90.61 297	89.41 330	94.06 339	72.37 352	95.06 221	93.69 299	88.01 286	92.32 319	96.86 217	77.45 303	98.82 292	91.04 224	87.01 349	97.04 290

test0.0.03 1		90.11 302	89.21 309	92.83 304	93.89 340	86.87 283	91.74 318	88.74 343	92.02 242	94.71 252	91.14 341	73.92 319	94.48 353	83.75 323	92.94 335	97.16 286

JIA-IIPM		91.79 284	90.69 295	95.11 238	93.80 341	90.98 194	94.16 256	91.78 321	96.38 104	90.30 336	99.30 23	72.02 334	98.90 281	88.28 277	90.17 343	95.45 328

TESTMET0.1,1		87.20 325	86.57 325	89.07 331	93.62 342	72.84 351	89.89 336	87.01 352	85.46 311	89.12 342	90.20 347	56.00 358	97.72 341	90.91 229	96.92 302	96.64 305

PatchFormer-LS_test		89.62 309	89.12 312	91.11 322	93.62 342	78.42 335	94.57 240	93.62 303	88.39 281	90.54 333	88.40 350	72.33 333	99.03 268	92.41 201	88.20 347	95.89 319

CMPMVS	73.10 23	92.74 261	91.39 273	96.77 156	93.57 344	94.67 112	94.21 252	97.67 228	80.36 337	93.61 292	96.60 235	82.85 285	97.35 343	84.86 314	98.78 220	98.29 235
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DWT-MVSNet_test		87.92 322	86.77 324	91.39 318	93.18 345	78.62 334	95.10 214	91.42 323	85.58 308	88.00 345	88.73 349	60.60 354	98.90 281	90.60 240	87.70 348	96.65 304

E-PMN		89.52 310	89.78 305	88.73 332	93.14 346	77.61 340	83.26 351	92.02 318	94.82 169	93.71 287	93.11 311	75.31 315	96.81 347	85.81 304	96.81 307	91.77 349

PMMVS		92.39 268	91.08 281	96.30 185	93.12 347	92.81 162	90.58 331	95.96 277	79.17 341	91.85 324	92.27 326	90.29 240	98.66 309	89.85 254	96.68 311	97.43 277

EMVS		89.06 312	89.22 308	88.61 333	93.00 348	77.34 341	82.91 352	90.92 327	94.64 173	92.63 315	91.81 331	76.30 311	97.02 345	83.83 321	96.90 303	91.48 350

dp		88.08 319	88.05 317	88.16 336	92.85 349	68.81 354	94.17 255	92.88 311	85.47 310	91.38 327	96.14 260	68.87 347	98.81 294	86.88 298	83.80 353	96.87 296

gg-mvs-nofinetune		88.28 318	86.96 322	92.23 313	92.84 350	84.44 313	98.19 41	74.60 358	99.08 9	87.01 349	99.47 8	56.93 356	98.23 332	78.91 338	95.61 325	94.01 340

tpmvs		90.79 300	90.87 291	90.57 326	92.75 351	76.30 343	95.79 170	93.64 302	91.04 257	91.91 322	96.26 253	77.19 307	98.86 290	89.38 260	89.85 344	96.56 308

EPMVS		89.26 311	88.55 315	91.39 318	92.36 352	79.11 333	95.65 180	79.86 356	88.60 278	93.12 306	96.53 239	70.73 338	98.10 336	90.75 235	89.32 345	96.98 291

gm-plane-assit					91.79 353	71.40 353			81.67 329		90.11 348		98.99 272	84.86 314

test2356		85.45 327	83.26 330	92.01 315	91.12 354	80.76 328	85.16 348	92.90 310	83.90 323	90.63 329	87.71 352	53.10 360	97.24 344	69.20 353	95.65 324	98.03 256

GG-mvs-BLEND				90.60 325	91.00 355	84.21 316	98.23 35	72.63 361		82.76 353	84.11 354	56.14 357	96.79 348	72.20 350	92.09 340	90.78 351

DeepMVS_CX				77.17 343	90.94 356	85.28 296		74.08 360	52.51 355	80.87 356	88.03 351	75.25 316	70.63 358	59.23 356	84.94 351	75.62 353

EPNet_dtu		91.39 293	90.75 294	93.31 292	90.48 357	82.61 320	94.80 233	92.88 311	93.39 212	81.74 355	94.90 290	81.36 289	99.11 257	88.28 277	98.87 214	98.21 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testpf		82.70 330	84.35 328	77.74 342	88.97 358	73.23 350	93.85 270	84.33 354	88.10 285	85.06 351	90.42 346	52.62 362	91.05 356	91.00 226	84.82 352	68.93 355

EPNet		93.72 245	92.62 257	97.03 145	87.61 359	92.25 170	96.27 140	91.28 324	96.74 95	87.65 347	97.39 190	85.00 279	99.64 120	92.14 203	99.48 123	99.20 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		57.23 332	62.50 333	41.44 345	34.77 360	49.21 361	83.93 349	60.22 362	15.31 356	71.11 357	79.37 355	70.09 340	44.86 359	64.76 354	82.93 354	30.25 356

test123		12.59 335	15.49 336	3.87 347	6.07 361	2.55 362	90.75 328	2.59 364	2.52 357	5.20 359	13.02 358	4.96 365	1.85 361	5.20 357	9.09 357	7.23 357

testmvs		12.33 336	15.23 337	3.64 348	5.77 362	2.23 363	88.99 340	3.62 363	2.30 358	5.29 358	13.09 357	4.52 366	1.95 360	5.16 358	8.32 358	6.75 358

cdsmvs_eth3d_5k		24.22 334	32.30 335	0.00 349	0.00 363	0.00 364	0.00 355	98.10 202	0.00 359	0.00 360	95.06 285	97.54 28	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		7.98 337	10.65 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	95.82 91	0.00 362	0.00 359	0.00 360	0.00 360

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

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

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

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

ab-mvs-re		7.91 338	10.55 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	94.94 287	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

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

GSMVS																98.06 251

test_part3							95.64 182		94.84 166		97.60 172		99.76 49	91.22 222

test_part1								98.84 88				96.69 61			99.44 132	99.37 102

sam_mvs1												77.80 300				98.06 251

sam_mvs												77.38 304

MTGPA								98.73 114

test_post1							94.98 226				10.37 360	76.21 312	99.04 265	89.47 259

test_post											10.87 359	76.83 308	99.07 262

patchmatchnet-post											96.84 219	77.36 305	99.42 197

MTMP								74.60 358

test9_res														91.29 218	98.89 213	99.00 159

agg_prior2														90.34 249	98.90 209	99.10 150

test_prior4						95.38 87	93.61 281

test_prior2							93.33 290		94.21 191	94.02 277	96.25 254	93.64 166		91.90 205	98.96 203

旧先验2							93.35 289		77.95 347	95.77 231			98.67 308	90.74 236

新几何2							93.43 285

无先验							93.20 293	97.91 212	80.78 334				99.40 211	87.71 281		97.94 261

原ACMM2							92.82 298

testdata2													99.46 188	87.84 280

segment_acmp												95.34 109

testdata1							92.77 299		93.78 206

plane_prior5								98.75 111					99.46 188	92.59 198	99.20 180	99.28 119

plane_prior4											96.77 225

plane_prior3						94.51 115			95.29 145	96.16 217

plane_prior2							96.50 128		96.36 105

plane_prior						94.29 122	95.42 194		94.31 188						98.93 208

n2								0.00 365

nn								0.00 365

door-mid								98.17 195

test11								98.08 205

door								97.81 220

HQP5-MVS						92.47 166

BP-MVS														90.51 243

HQP4-MVS										92.87 308			99.23 247			99.06 155

HQP3-MVS								98.43 157							98.74 224

HQP2-MVS												90.33 236

MDTV_nov1_ep13_2view						57.28 360	94.89 228		80.59 335	94.02 277		78.66 298		85.50 309		97.82 265

ACMMP++_ref															99.52 110

ACMMP++															99.55 102

Test By Simon												94.51 138

High-resolution multi-view benchmark