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

pmmvs6		99.07 3	99.24 3	98.56 44	99.81 2	96.38 56	98.87 9	99.30 8	99.01 15	99.63 9	99.66 3	99.27 2	99.68 103	97.75 45	99.89 32	99.62 32

OurMVSNet-221017-0		98.61 18	98.61 26	98.63 40	99.77 3	96.35 57	99.17 5	99.05 38	98.05 43	99.61 11	99.52 4	93.72 168	99.88 18	98.72 20	99.88 33	99.65 25

Gipuma		98.07 42	98.31 38	97.36 129	99.76 4	96.28 60	98.51 22	99.10 25	98.76 21	96.79 189	99.34 20	96.61 63	98.82 297	96.38 88	99.50 114	96.98 296
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 37	99.72 5	96.71 45	98.76 11	98.89 79	98.49 26	99.38 18	99.14 42	95.44 107	99.84 27	96.47 86	99.80 47	99.47 66

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 34	99.71 6	96.99 39	99.69 2	99.57 3	99.02 14	99.62 10	99.36 16	98.53 7	99.52 165	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 4	98.94 7	98.75 29	99.69 7	96.48 54	98.54 21	99.22 9	96.23 115	99.71 4	99.48 6	98.77 6	99.93 2	98.89 10	99.95 13	99.84 6

PS-MVSNAJss		98.53 23	98.63 22	98.21 69	99.68 8	94.82 105	98.10 45	99.21 10	96.91 90	99.75 3	99.45 8	95.82 89	99.92 4	98.80 13	99.96 11	99.89 1

jajsoiax		98.77 10	98.79 14	98.74 31	99.66 9	96.48 54	98.45 26	99.12 22	95.83 132	99.67 6	99.37 14	98.25 10	99.92 4	98.77 14	99.94 19	99.82 7

pcd1.5k->3k		41.47 339	44.19 341	33.29 352	99.65 10	0.00 370	0.00 361	99.07 34	0.00 365	0.00 367	0.00 367	99.04 3	0.00 367	0.00 364	99.96 11	99.87 2

v7n		98.73 12	98.99 6	97.95 82	99.64 11	94.20 128	98.67 13	99.14 20	99.08 9	99.42 16	99.23 29	96.53 66	99.91 12	99.27 4	99.93 21	99.73 15

test_djsdf		98.73 12	98.74 18	98.69 36	99.63 12	96.30 59	98.67 13	99.02 51	96.50 103	99.32 21	99.44 9	97.43 29	99.92 4	98.73 17	99.95 13	99.86 3

anonymousdsp		98.72 15	98.63 22	98.99 10	99.62 13	97.29 34	98.65 16	99.19 13	95.62 138	99.35 20	99.37 14	97.38 31	99.90 13	98.59 23	99.91 26	99.77 9

v748		98.58 19	98.89 9	97.67 101	99.61 14	93.53 152	98.59 17	98.90 77	98.97 17	99.43 15	99.15 41	96.53 66	99.85 23	98.88 11	99.91 26	99.64 28

v52		98.85 7	99.01 4	98.37 55	99.61 14	95.53 83	99.01 6	99.04 45	98.48 27	99.31 22	99.41 11	96.82 56	99.87 20	99.44 2	99.95 13	99.70 19

V4		98.85 7	99.01 4	98.37 55	99.61 14	95.53 83	99.01 6	99.04 45	98.48 27	99.31 22	99.41 11	96.81 57	99.87 20	99.44 2	99.95 13	99.70 19

PEN-MVS		98.75 11	98.85 12	98.44 50	99.58 17	95.67 76	98.45 26	99.15 18	99.33 4	99.30 24	99.00 49	97.27 36	99.92 4	97.64 48	99.92 23	99.75 13

Baseline_NR-MVSNet		97.72 75	97.79 61	97.50 114	99.56 18	93.29 157	95.44 193	98.86 85	98.20 39	98.37 79	99.24 27	94.69 127	99.55 157	95.98 102	99.79 48	99.65 25

SixPastTwentyTwo		97.49 92	97.57 82	97.26 135	99.56 18	92.33 170	98.28 32	96.97 265	98.30 35	99.45 14	99.35 18	88.43 260	99.89 16	98.01 35	99.76 51	99.54 46

PS-CasMVS		98.73 12	98.85 12	98.39 54	99.55 20	95.47 85	98.49 23	99.13 21	99.22 7	99.22 30	98.96 53	97.35 32	99.92 4	97.79 43	99.93 21	99.79 8

DTE-MVSNet		98.79 9	98.86 10	98.59 42	99.55 20	96.12 64	98.48 25	99.10 25	99.36 3	99.29 25	99.06 48	97.27 36	99.93 2	97.71 47	99.91 26	99.70 19

HPM-MVS_fast		98.32 31	98.13 45	98.88 22	99.54 22	97.48 27	98.35 29	99.03 50	95.88 128	97.88 136	98.22 115	98.15 12	99.74 58	96.50 85	99.62 80	99.42 88

TDRefinement		98.90 4	98.86 10	99.02 8	99.54 22	98.06 6	99.34 4	99.44 6	98.85 19	99.00 42	99.20 31	97.42 30	99.59 145	97.21 66	99.76 51	99.40 93

Anonymous20240521		98.58 19	98.65 21	98.36 58	99.52 24	95.60 78	98.96 8	98.95 73	98.36 31	99.25 27	99.17 39	95.28 113	99.80 37	98.46 25	99.88 33	99.68 23

pm-mvs1		98.47 25	98.67 19	97.86 87	99.52 24	94.58 113	98.28 32	99.00 62	97.57 66	99.27 26	99.22 30	98.32 9	99.50 177	97.09 73	99.75 55	99.50 52

TransMVSNet (Re)		98.38 29	98.67 19	97.51 111	99.51 26	93.39 156	98.20 40	98.87 83	98.23 37	99.48 12	99.27 25	98.47 8	99.55 157	96.52 83	99.53 107	99.60 35

WR-MVS_H		98.65 17	98.62 24	98.75 29	99.51 26	96.61 50	98.55 20	99.17 14	99.05 12	99.17 33	98.79 62	95.47 105	99.89 16	97.95 36	99.91 26	99.75 13

PMVS	89.60 17	96.71 146	96.97 123	95.95 216	99.51 26	97.81 13	97.42 90	97.49 245	97.93 47	95.95 227	98.58 78	96.88 51	96.91 351	89.59 262	99.36 155	93.12 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss		97.69 78	97.36 92	98.70 35	99.50 29	96.84 42	95.38 202	98.99 65	92.45 244	98.11 105	98.31 102	97.25 38	99.77 48	96.60 80	99.62 80	99.48 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		98.16 37	98.37 34	97.56 106	99.49 30	93.10 160	98.35 29	99.21 10	98.43 29	98.89 46	98.83 61	94.30 145	99.81 32	97.87 39	99.91 26	99.77 9

VPNet		97.26 108	97.49 88	96.59 170	99.47 31	90.58 206	96.27 145	98.53 149	97.77 51	98.46 74	98.41 91	94.59 133	99.68 103	94.61 155	99.29 172	99.52 49

CP-MVSNet		98.42 27	98.46 30	98.30 64	99.46 32	95.22 93	98.27 34	98.84 89	99.05 12	99.01 40	98.65 75	95.37 108	99.90 13	97.57 53	99.91 26	99.77 9

XXY-MVS		97.54 89	97.70 67	97.07 143	99.46 32	92.21 174	97.22 97	99.00 62	94.93 171	98.58 64	98.92 57	97.31 34	99.41 211	94.44 159	99.43 140	99.59 36

zzz-MVS		98.01 47	97.66 71	99.06 5	99.44 34	97.90 8	95.66 184	98.73 114	97.69 60	97.90 132	97.96 146	95.81 93	99.82 30	96.13 93	99.61 85	99.45 73

MTAPA		98.14 38	97.84 59	99.06 5	99.44 34	97.90 8	97.25 94	98.73 114	97.69 60	97.90 132	97.96 146	95.81 93	99.82 30	96.13 93	99.61 85	99.45 73

wuykxyi23d		98.68 16	98.53 27	99.13 3	99.44 34	97.97 7	96.85 121	99.02 51	95.81 133	99.88 2	99.38 13	98.14 13	99.69 97	98.32 29	99.95 13	99.73 15

SteuartSystems-ACMMP		98.02 45	97.76 64	98.79 27	99.43 37	97.21 36	97.15 99	98.90 77	96.58 101	98.08 111	97.87 157	97.02 46	99.76 49	95.25 129	99.59 90	99.40 93
Skip Steuart: Steuart Systems R&D Blog.
ACMH	93.61 9	98.44 26	98.76 15	97.51 111	99.43 37	93.54 151	98.23 35	99.05 38	97.40 82	99.37 19	99.08 47	98.79 5	99.47 185	97.74 46	99.71 64	99.50 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 41	97.83 60	98.92 19	99.42 39	97.46 28	98.57 18	99.05 38	95.43 147	97.41 160	97.50 187	97.98 16	99.79 39	95.58 119	99.57 95	99.50 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
K. test v3		96.44 159	96.28 161	96.95 149	99.41 40	91.53 192	97.65 73	90.31 342	98.89 18	98.93 45	99.36 16	84.57 286	99.92 4	97.81 41	99.56 98	99.39 96

VDDNet		96.98 118	96.84 131	97.41 126	99.40 41	93.26 158	97.94 54	95.31 294	99.26 6	98.39 78	99.18 35	87.85 267	99.62 128	95.13 139	99.09 192	99.35 105

ACMH+	93.58 10	98.23 36	98.31 38	97.98 81	99.39 42	95.22 93	97.55 83	99.20 12	98.21 38	99.25 27	98.51 85	98.21 11	99.40 214	94.79 150	99.72 60	99.32 107

TSAR-MVS + MP.		97.42 96	97.23 105	98.00 80	99.38 43	95.00 99	97.63 75	98.20 194	93.00 230	98.16 100	98.06 137	95.89 84	99.72 69	95.67 110	99.10 191	99.28 118

FIs		97.93 56	98.07 48	97.48 118	99.38 43	92.95 162	98.03 51	99.11 23	98.04 44	98.62 59	98.66 73	93.75 167	99.78 40	97.23 65	99.84 41	99.73 15

lessismore_v0				97.05 144	99.36 45	92.12 178		84.07 362		98.77 53	98.98 51	85.36 280	99.74 58	97.34 63	99.37 152	99.30 111

ACMMP_Plus		97.89 61	97.63 76	98.67 37	99.35 46	96.84 42	96.36 141	98.79 103	95.07 167	97.88 136	98.35 97	97.24 39	99.72 69	96.05 96	99.58 92	99.45 73

Vis-MVSNet		98.27 33	98.34 36	98.07 74	99.33 47	95.21 95	98.04 49	99.46 5	97.32 85	97.82 145	99.11 44	96.75 59	99.86 22	97.84 40	99.36 155	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 7	96.41 182	99.33 47	89.64 219	97.92 56	99.56 4	99.27 5	99.66 8	99.50 5	97.67 24	99.83 29	97.55 54	99.98 3	99.77 9

MP-MVS		97.64 81	97.18 108	99.00 9	99.32 49	97.77 14	97.49 86	98.73 114	96.27 112	95.59 240	97.75 167	96.30 77	99.78 40	93.70 188	99.48 124	99.45 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PVSNet_Blended_VisFu		95.95 176	95.80 178	96.42 181	99.28 50	90.62 205	95.31 208	99.08 30	88.40 286	96.97 183	98.17 122	92.11 211	99.78 40	93.64 189	99.21 179	98.86 186

tfpnnormal		97.72 75	97.97 53	96.94 150	99.26 51	92.23 173	97.83 62	98.45 156	98.25 36	99.13 34	98.66 73	96.65 61	99.69 97	93.92 182	99.62 80	98.91 175

HSP-MVS		97.37 100	96.85 130	98.92 19	99.26 51	97.70 15	97.66 72	98.23 190	95.65 136	98.51 68	96.46 248	92.15 209	99.81 32	95.14 138	98.58 241	99.26 122

testgi		96.07 172	96.50 153	94.80 256	99.26 51	87.69 275	95.96 167	98.58 146	95.08 166	98.02 118	96.25 259	97.92 17	97.60 347	88.68 277	98.74 225	99.11 146

IS-MVSNet		96.93 123	96.68 140	97.70 97	99.25 54	94.00 134	98.57 18	96.74 273	98.36 31	98.14 103	97.98 145	88.23 261	99.71 80	93.10 199	99.72 60	99.38 98

ACMMP		98.05 43	97.75 65	98.93 18	99.23 55	97.60 19	98.09 46	98.96 71	95.75 135	97.91 131	98.06 137	96.89 49	99.76 49	95.32 127	99.57 95	99.43 86
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 61	99.22 56	95.66 77	97.90 57	99.08 30	98.31 34	99.02 39	98.74 67	97.68 23	99.61 134	97.77 44	99.85 40	99.70 19

region2R		97.92 57	97.59 80	98.92 19	99.22 56	97.55 23	97.60 79	98.84 89	96.00 123	97.22 165	97.62 177	96.87 52	99.76 49	95.48 121	99.43 140	99.46 68

mPP-MVS		97.91 59	97.53 84	99.04 7	99.22 56	97.87 11	97.74 69	98.78 106	96.04 121	97.10 172	97.73 170	96.53 66	99.78 40	95.16 136	99.50 114	99.46 68

COLMAP_ROB	94.48 6	98.25 35	98.11 46	98.64 39	99.21 59	97.35 32	97.96 53	99.16 15	98.34 33	98.78 50	98.52 84	97.32 33	99.45 194	94.08 173	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 52	97.62 78	98.94 15	99.20 60	97.56 22	97.59 80	98.83 97	96.05 119	97.46 158	97.63 176	96.77 58	99.76 49	95.61 116	99.46 128	99.49 60

PGM-MVS		97.88 62	97.52 85	98.96 13	99.20 60	97.62 18	97.09 108	99.06 36	95.45 145	97.55 149	97.94 150	97.11 41	99.78 40	94.77 152	99.46 128	99.48 63

test_0402		97.84 65	97.97 53	97.47 119	99.19 62	94.07 131	96.71 128	98.73 114	98.66 23	98.56 65	98.41 91	96.84 54	99.69 97	94.82 147	99.81 44	98.64 204

EPP-MVSNet		96.84 132	96.58 144	97.65 102	99.18 63	93.78 143	98.68 12	96.34 276	97.91 48	97.30 163	98.06 137	88.46 259	99.85 23	93.85 184	99.40 150	99.32 107

abl_6		98.42 27	98.19 42	99.09 4	99.16 64	98.10 5	97.73 71	99.11 23	97.76 52	98.62 59	98.27 109	97.88 20	99.80 37	95.67 110	99.50 114	99.38 98

XVG-ACMP-BASELINE		97.58 87	97.28 98	98.49 47	99.16 64	96.90 41	96.39 136	98.98 68	95.05 168	98.06 114	98.02 141	95.86 85	99.56 153	94.37 164	99.64 78	99.00 159

CHOSEN 1792x2688		94.10 243	93.41 248	96.18 199	99.16 64	90.04 212	92.15 317	98.68 127	79.90 344	96.22 218	97.83 158	87.92 266	99.42 200	89.18 268	99.65 77	99.08 151

HFP-MVS		97.94 54	97.64 74	98.83 24	99.15 67	97.50 25	97.59 80	98.84 89	96.05 119	97.49 153	97.54 182	97.07 44	99.70 88	95.61 116	99.46 128	99.30 111

#test#		97.62 83	97.22 106	98.83 24	99.15 67	97.50 25	96.81 123	98.84 89	94.25 194	97.49 153	97.54 182	97.07 44	99.70 88	94.37 164	99.46 128	99.30 111

XVS		97.96 49	97.63 76	98.94 15	99.15 67	97.66 16	97.77 64	98.83 97	97.42 74	96.32 211	97.64 175	96.49 70	99.72 69	95.66 112	99.37 152	99.45 73

X-MVStestdata		92.86 265	90.83 299	98.94 15	99.15 67	97.66 16	97.77 64	98.83 97	97.42 74	96.32 211	36.50 362	96.49 70	99.72 69	95.66 112	99.37 152	99.45 73

LPG-MVS_test		97.94 54	97.67 70	98.74 31	99.15 67	97.02 37	97.09 108	99.02 51	95.15 160	98.34 83	98.23 112	97.91 18	99.70 88	94.41 161	99.73 57	99.50 52

LGP-MVS_train				98.74 31	99.15 67	97.02 37		99.02 51	95.15 160	98.34 83	98.23 112	97.91 18	99.70 88	94.41 161	99.73 57	99.50 52

RPSCF		97.87 63	97.51 86	98.95 14	99.15 67	98.43 3	97.56 82	99.06 36	96.19 116	98.48 71	98.70 70	94.72 125	99.24 250	94.37 164	99.33 166	99.17 131

ACMM	93.33 11	98.05 43	97.79 61	98.85 23	99.15 67	97.55 23	96.68 129	98.83 97	95.21 155	98.36 81	98.13 127	98.13 15	99.62 128	96.04 97	99.54 104	99.39 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		97.95 52	98.08 47	97.56 106	99.14 75	93.67 145	98.23 35	98.66 133	97.41 81	99.00 42	99.19 32	95.47 105	99.73 63	95.83 106	99.76 51	99.30 111

Vis-MVSNet (Re-imp)		95.11 209	94.85 206	95.87 221	99.12 76	89.17 237	97.54 85	94.92 296	96.50 103	96.58 195	97.27 201	83.64 287	99.48 183	88.42 280	99.67 74	98.97 163

OPM-MVS		97.54 89	97.25 99	98.41 52	99.11 77	96.61 50	95.24 214	98.46 155	94.58 182	98.10 108	98.07 134	97.09 43	99.39 220	95.16 136	99.44 133	99.21 126

UA-Net		98.88 6	98.76 15	99.22 2	99.11 77	97.89 10	99.47 3	99.32 7	99.08 9	97.87 141	99.67 2	96.47 72	99.92 4	97.88 38	99.98 3	99.85 4

AllTest		97.20 111	96.92 127	98.06 75	99.08 79	96.16 62	97.14 101	99.16 15	94.35 191	97.78 146	98.07 134	95.84 86	99.12 260	91.41 222	99.42 143	98.91 175

TestCases				98.06 75	99.08 79	96.16 62		99.16 15	94.35 191	97.78 146	98.07 134	95.84 86	99.12 260	91.41 222	99.42 143	98.91 175

TranMVSNet+NR-MVSNet		98.33 30	98.30 40	98.43 51	99.07 81	95.87 69	96.73 127	99.05 38	98.67 22	98.84 47	98.45 89	97.58 26	99.88 18	96.45 87	99.86 38	99.54 46

VPA-MVSNet		98.27 33	98.46 30	97.70 97	99.06 82	93.80 141	97.76 66	99.00 62	98.40 30	99.07 37	98.98 51	96.89 49	99.75 53	97.19 69	99.79 48	99.55 45

114514_t		93.96 247	93.22 252	96.19 198	99.06 82	90.97 199	95.99 161	98.94 74	73.88 358	93.43 307	96.93 219	92.38 207	99.37 228	89.09 269	99.28 173	98.25 242

EG-PatchMatch MVS		97.69 78	97.79 61	97.40 127	99.06 82	93.52 153	95.96 167	98.97 70	94.55 183	98.82 48	98.76 65	97.31 34	99.29 242	97.20 68	99.44 133	99.38 98

ACMP	92.54 13	97.47 94	97.10 116	98.55 45	99.04 85	96.70 46	96.24 149	98.89 79	93.71 213	97.97 123	97.75 167	97.44 28	99.63 122	93.22 196	99.70 67	99.32 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part2					99.03 86	96.07 65				98.08 111

v1.0		40.70 340	54.26 340	0.00 355	99.03 86	0.00 370	0.00 361	98.84 89	94.84 172	98.08 111	97.60 179	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

v13		98.02 45	98.52 28	96.51 175	99.02 88	90.14 210	98.07 47	99.09 29	98.10 42	99.13 34	99.35 18	94.84 123	99.74 58	99.12 5	99.98 3	99.65 25

XVG-OURS-SEG-HR		97.38 99	97.07 119	98.30 64	99.01 89	97.41 31	94.66 242	99.02 51	95.20 156	98.15 102	97.52 185	98.83 4	98.43 325	94.87 145	96.41 319	99.07 153

XVG-OURS		97.12 112	96.74 138	98.26 66	98.99 90	97.45 29	93.82 278	99.05 38	95.19 157	98.32 86	97.70 173	95.22 115	98.41 326	94.27 169	98.13 259	98.93 171

CP-MVS		97.92 57	97.56 83	98.99 10	98.99 90	97.82 12	97.93 55	98.96 71	96.11 118	96.89 187	97.45 190	96.85 53	99.78 40	95.19 132	99.63 79	99.38 98

v12		97.97 48	98.47 29	96.46 179	98.98 92	90.01 214	97.97 52	99.08 30	98.00 45	99.11 36	99.34 20	94.70 126	99.73 63	99.07 6	99.98 3	99.64 28

CSCG		97.40 98	97.30 95	97.69 99	98.95 93	94.83 104	97.28 93	98.99 65	96.35 110	98.13 104	95.95 273	95.99 82	99.66 115	94.36 167	99.73 57	98.59 209

v11		97.82 69	98.36 35	96.17 200	98.93 94	89.16 238	97.79 63	99.08 30	97.64 63	99.19 31	99.32 22	94.28 146	99.72 69	99.07 6	99.97 8	99.63 30

V9		97.90 60	98.40 33	96.40 183	98.93 94	89.86 216	97.86 59	99.07 34	97.88 49	99.05 38	99.30 23	94.53 137	99.72 69	99.01 8	99.98 3	99.63 30

HyFIR lowres test		93.72 251	92.65 262	96.91 153	98.93 94	91.81 189	91.23 331	98.52 150	82.69 332	96.46 202	96.52 246	80.38 297	99.90 13	90.36 253	98.79 220	99.03 157

testing_2		97.43 95	97.71 66	96.60 167	98.91 97	90.85 200	96.01 160	98.54 148	94.78 175	98.78 50	98.96 53	96.35 76	99.54 159	97.25 64	99.82 43	99.40 93

PM-MVS		97.36 103	97.10 116	98.14 72	98.91 97	96.77 44	96.20 151	98.63 140	93.82 210	98.54 66	98.33 100	93.98 157	99.05 270	95.99 101	99.45 132	98.61 208

CPTT-MVS		96.69 147	96.08 167	98.49 47	98.89 99	96.64 49	97.25 94	98.77 107	92.89 237	96.01 226	97.13 206	92.23 208	99.67 110	92.24 208	99.34 161	99.17 131

V14		97.83 66	98.33 37	96.35 184	98.88 100	89.72 217	97.75 67	99.05 38	97.74 53	99.01 40	99.27 25	94.35 143	99.71 80	98.95 9	99.97 8	99.62 32

ESAPD		97.64 81	97.35 93	98.50 46	98.85 101	96.18 61	95.21 216	98.99 65	95.84 131	98.78 50	98.08 132	96.84 54	99.81 32	93.98 180	99.57 95	99.52 49

SMA-MVS		97.48 93	97.11 115	98.60 41	98.83 102	96.67 47	96.74 125	98.73 114	91.61 257	98.48 71	98.36 96	96.53 66	99.68 103	95.17 134	99.54 104	99.45 73

v15		97.77 72	98.26 41	96.30 189	98.81 103	89.59 224	97.62 76	99.04 45	97.59 65	98.97 44	99.24 27	94.19 151	99.70 88	98.88 11	99.97 8	99.61 34

UniMVSNet (Re)		97.83 66	97.65 72	98.35 60	98.80 104	95.86 70	95.92 172	99.04 45	97.51 71	98.22 95	97.81 162	94.68 129	99.78 40	97.14 71	99.75 55	99.41 90

Anonymous20231211		98.55 21	98.76 15	97.94 83	98.79 105	94.37 120	98.84 10	99.15 18	99.37 2	99.67 6	99.43 10	95.61 100	99.72 69	98.12 31	99.86 38	99.73 15

APD-MVS_3200maxsize		98.13 40	97.90 56	98.79 27	98.79 105	97.31 33	97.55 83	98.92 75	97.72 57	98.25 93	98.13 127	97.10 42	99.75 53	95.44 123	99.24 177	99.32 107

DeepC-MVS	95.41 4	97.82 69	97.70 67	98.16 70	98.78 107	95.72 73	96.23 150	99.02 51	93.92 203	98.62 59	98.99 50	97.69 22	99.62 128	96.18 92	99.87 36	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 77	98.17 43	96.28 192	98.77 108	89.59 224	97.62 76	99.01 60	97.54 68	98.72 56	99.18 35	94.06 155	99.68 103	98.74 16	99.92 23	99.58 37

v16		97.69 78	98.16 44	96.29 191	98.75 109	89.60 222	97.62 76	99.01 60	97.53 70	98.69 58	99.18 35	94.05 156	99.68 103	98.73 17	99.88 33	99.58 37

MCST-MVS		96.24 165	95.80 178	97.56 106	98.75 109	94.13 130	94.66 242	98.17 199	90.17 272	96.21 219	96.10 267	95.14 116	99.43 199	94.13 172	98.85 219	99.13 138

DU-MVS		97.79 71	97.60 79	98.36 58	98.73 111	95.78 71	95.65 186	98.87 83	97.57 66	98.31 88	97.83 158	94.69 127	99.85 23	97.02 75	99.71 64	99.46 68

NR-MVSNet		97.96 49	97.86 58	98.26 66	98.73 111	95.54 81	98.14 43	98.73 114	97.79 50	99.42 16	97.83 158	94.40 142	99.78 40	95.91 105	99.76 51	99.46 68

Anonymous20231206		95.27 205	95.06 199	95.88 220	98.72 113	89.37 233	95.70 180	97.85 221	88.00 293	96.98 178	97.62 177	91.95 216	99.34 232	89.21 267	99.53 107	98.94 167

APDe-MVS		98.14 38	98.03 52	98.47 49	98.72 113	96.04 66	98.07 47	99.10 25	95.96 125	98.59 63	98.69 71	96.94 47	99.81 32	96.64 79	99.58 92	99.57 41

UniMVSNet_NR-MVSNet		97.83 66	97.65 72	98.37 55	98.72 113	95.78 71	95.66 184	99.02 51	98.11 41	98.31 88	97.69 174	94.65 131	99.85 23	97.02 75	99.71 64	99.48 63

v8		97.60 85	98.06 49	96.23 193	98.71 116	89.44 229	97.43 89	98.82 101	97.29 86	98.74 54	99.10 45	93.86 159	99.68 103	98.61 22	99.94 19	99.56 42

v6		96.97 119	97.24 101	96.15 201	98.71 116	89.44 229	95.97 163	98.33 176	95.25 152	97.89 134	98.15 123	93.86 159	99.61 134	97.51 56	99.50 114	99.42 88

v1neww		96.97 119	97.24 101	96.15 201	98.70 118	89.44 229	95.97 163	98.33 176	95.25 152	97.88 136	98.15 123	93.83 162	99.61 134	97.50 57	99.50 114	99.41 90

v7new		96.97 119	97.24 101	96.15 201	98.70 118	89.44 229	95.97 163	98.33 176	95.25 152	97.88 136	98.15 123	93.83 162	99.61 134	97.50 57	99.50 114	99.41 90

HQP_MVS		96.66 149	96.33 160	97.68 100	98.70 118	94.29 122	96.50 133	98.75 111	96.36 108	96.16 221	96.77 230	91.91 220	99.46 190	92.59 204	99.20 180	99.28 118

plane_prior7					98.70 118	94.67 111

Anonymous20240529		97.96 49	98.04 51	97.71 95	98.69 122	94.28 125	97.86 59	98.31 185	98.79 20	99.23 29	98.86 60	95.76 96	99.61 134	95.49 120	99.36 155	99.23 124

VDD-MVS		97.37 100	97.25 99	97.74 94	98.69 122	94.50 116	97.04 111	95.61 291	98.59 24	98.51 68	98.72 68	92.54 201	99.58 147	96.02 99	99.49 121	99.12 143

v18		97.60 85	98.06 49	96.23 193	98.68 124	89.46 228	97.48 87	98.98 68	97.33 84	98.60 62	99.13 43	93.86 159	99.67 110	98.62 21	99.87 36	99.56 42

HPM-MVS++		96.99 115	96.38 156	98.81 26	98.64 125	97.59 20	95.97 163	98.20 194	95.51 143	95.06 248	96.53 244	94.10 154	99.70 88	94.29 168	99.15 184	99.13 138

ab-mvs		96.59 151	96.59 143	96.60 167	98.64 125	92.21 174	98.35 29	97.67 233	94.45 184	96.99 177	98.79 62	94.96 121	99.49 180	90.39 252	99.07 195	98.08 252

F-COLMAP		95.30 203	94.38 227	98.05 78	98.64 125	96.04 66	95.61 190	98.66 133	89.00 280	93.22 312	96.40 254	92.90 189	99.35 231	87.45 299	97.53 295	98.77 196

ITE_SJBPF				97.85 88	98.64 125	96.66 48		98.51 152	95.63 137	97.22 165	97.30 200	95.52 102	98.55 320	90.97 231	98.90 210	98.34 233

v148		96.58 152	96.97 123	95.42 236	98.63 129	87.57 276	95.09 221	97.90 218	95.91 127	98.24 94	97.96 146	93.42 174	99.39 220	96.04 97	99.52 111	99.29 117

UnsupCasMVSNet_bld		94.72 222	94.26 229	96.08 206	98.62 130	90.54 209	93.38 294	98.05 213	90.30 270	97.02 175	96.80 228	89.54 248	99.16 259	88.44 279	96.18 322	98.56 212

DP-MVS		97.87 63	97.89 57	97.81 90	98.62 130	94.82 105	97.13 102	98.79 103	98.98 16	98.74 54	98.49 86	95.80 95	99.49 180	95.04 143	99.44 133	99.11 146

v10		97.55 88	97.97 53	96.31 188	98.60 132	89.64 219	97.44 88	99.02 51	96.60 99	98.72 56	99.16 40	93.48 172	99.72 69	98.76 15	99.92 23	99.58 37

Test_1112_low_res		93.53 257	92.86 257	95.54 233	98.60 132	88.86 247	92.75 306	98.69 125	82.66 333	92.65 322	96.92 220	84.75 284	99.56 153	90.94 232	97.76 273	98.19 248

v7		96.93 123	97.17 109	96.23 193	98.59 134	89.64 219	95.96 167	98.66 133	94.41 187	97.87 141	98.38 94	93.47 173	99.64 119	97.93 37	99.24 177	99.43 86

V42		97.04 113	97.16 110	96.68 165	98.59 134	91.05 197	96.33 143	98.36 171	94.60 179	97.99 119	98.30 105	93.32 178	99.62 128	97.40 62	99.53 107	99.38 98

1112_ss		94.12 242	93.42 247	96.23 193	98.59 134	90.85 200	94.24 255	98.85 86	85.49 315	92.97 315	94.94 293	86.01 277	99.64 119	91.78 216	97.92 268	98.20 247

v2v482		96.78 140	97.06 120	95.95 216	98.57 137	88.77 251	95.36 203	98.26 188	95.18 158	97.85 143	98.23 112	92.58 198	99.63 122	97.80 42	99.69 68	99.45 73

WR-MVS		96.90 127	96.81 134	97.16 137	98.56 138	92.20 176	94.33 250	98.12 205	97.34 83	98.20 97	97.33 199	92.81 190	99.75 53	94.79 150	99.81 44	99.54 46

v1141		96.86 129	97.14 112	96.04 208	98.55 139	89.06 241	95.44 193	98.33 176	95.14 162	97.93 129	98.19 117	93.36 176	99.62 128	97.61 49	99.69 68	99.44 82

v1		96.86 129	97.14 112	96.04 208	98.55 139	89.06 241	95.44 193	98.33 176	95.14 162	97.94 126	98.18 121	93.39 175	99.61 134	97.61 49	99.69 68	99.44 82

APD-MVS		97.00 114	96.53 150	98.41 52	98.55 139	96.31 58	96.32 144	98.77 107	92.96 236	97.44 159	97.58 181	95.84 86	99.74 58	91.96 210	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		94.66 224	94.49 221	95.19 242	98.54 142	88.91 245	92.57 310	98.74 113	91.46 260	98.32 86	97.75 167	77.31 311	98.81 299	96.06 95	99.61 85	97.85 268

divwei89l23v2f112		96.86 129	97.14 112	96.04 208	98.54 142	89.06 241	95.44 193	98.33 176	95.14 162	97.93 129	98.19 117	93.36 176	99.61 134	97.61 49	99.68 72	99.44 82

1111		88.78 319	89.39 312	86.96 344	98.53 144	62.84 363	91.49 326	97.48 247	94.45 184	96.56 197	96.45 249	43.83 368	98.87 293	86.33 306	99.40 150	99.18 130

.test1245		73.49 337	79.27 338	56.15 350	98.53 144	62.84 363	91.49 326	97.48 247	94.45 184	96.56 197	96.45 249	43.83 368	98.87 293	86.33 306	8.32 363	6.75 363

IterMVS-LS		96.92 125	97.29 96	95.79 223	98.51 146	88.13 261	95.10 219	98.66 133	96.99 87	98.46 74	98.68 72	92.55 199	99.74 58	96.91 77	99.79 48	99.50 52
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		95.55 187	95.13 195	96.80 156	98.51 146	93.99 135	94.60 244	98.69 125	90.20 271	95.78 234	96.21 262	92.73 193	98.98 280	90.58 246	98.86 217	97.42 283

test20.03		96.58 152	96.61 142	96.48 178	98.49 148	91.72 190	95.68 183	97.69 232	96.81 95	98.27 92	97.92 153	94.18 152	98.71 307	90.78 238	99.66 76	99.00 159

plane_prior1					98.49 148

MDA-MVSNet-bldmvs		95.69 181	95.67 181	95.74 224	98.48 150	88.76 252	92.84 303	97.25 253	96.00 123	97.59 148	97.95 149	91.38 228	99.46 190	93.16 198	96.35 320	98.99 162

UnsupCasMVSNet_eth		95.91 177	95.73 180	96.44 180	98.48 150	91.52 193	95.31 208	98.45 156	95.76 134	97.48 156	97.54 182	89.53 250	98.69 309	94.43 160	94.61 336	99.13 138

view600		92.56 269	92.11 269	93.91 282	98.45 152	84.76 311	97.10 104	90.23 343	97.42 74	96.98 178	94.48 302	73.62 327	99.60 141	82.49 331	98.28 250	97.36 284

view800		92.56 269	92.11 269	93.91 282	98.45 152	84.76 311	97.10 104	90.23 343	97.42 74	96.98 178	94.48 302	73.62 327	99.60 141	82.49 331	98.28 250	97.36 284

conf0.05thres1000		92.56 269	92.11 269	93.91 282	98.45 152	84.76 311	97.10 104	90.23 343	97.42 74	96.98 178	94.48 302	73.62 327	99.60 141	82.49 331	98.28 250	97.36 284

tfpn		92.56 269	92.11 269	93.91 282	98.45 152	84.76 311	97.10 104	90.23 343	97.42 74	96.98 178	94.48 302	73.62 327	99.60 141	82.49 331	98.28 250	97.36 284

v1144		96.84 132	97.08 118	96.13 205	98.42 156	89.28 236	95.41 200	98.67 130	94.21 196	97.97 123	98.31 102	93.06 183	99.65 116	98.06 34	99.62 80	99.45 73

plane_prior6					98.38 157	94.37 120						91.91 220

FPMVS		89.92 313	88.63 320	93.82 287	98.37 158	96.94 40	91.58 325	93.34 313	88.00 293	90.32 342	97.10 209	70.87 342	91.13 360	71.91 356	96.16 323	93.39 349

PAPM_NR		94.61 227	94.17 234	95.96 214	98.36 159	91.23 195	95.93 171	97.95 215	92.98 231	93.42 308	94.43 307	90.53 236	98.38 330	87.60 296	96.29 321	98.27 240

MVS_111021_HR		96.73 143	96.54 149	97.27 133	98.35 160	93.66 148	93.42 292	98.36 171	94.74 176	96.58 195	96.76 232	96.54 65	98.99 278	94.87 145	99.27 175	99.15 135

TAMVS		95.49 190	94.94 201	97.16 137	98.31 161	93.41 155	95.07 224	96.82 270	91.09 263	97.51 151	97.82 161	89.96 245	99.42 200	88.42 280	99.44 133	98.64 204

OMC-MVS		96.48 157	96.00 170	97.91 85	98.30 162	96.01 68	94.86 235	98.60 143	91.88 255	97.18 167	97.21 203	96.11 80	99.04 271	90.49 250	99.34 161	98.69 202

新几何1				97.25 136	98.29 163	94.70 110		97.73 229	77.98 351	94.83 256	96.67 237	92.08 213	99.45 194	88.17 284	98.65 234	97.61 277

jason		94.39 233	94.04 237	95.41 238	98.29 163	87.85 271	92.74 308	96.75 272	85.38 320	95.29 244	96.15 263	88.21 262	99.65 116	94.24 170	99.34 161	98.74 198
jason: jason.
no-one		94.84 217	94.76 210	95.09 247	98.29 163	87.49 278	91.82 323	97.49 245	88.21 289	97.84 144	98.75 66	91.51 225	99.27 246	88.96 272	99.99 2	98.52 215

v1192		96.83 135	97.06 120	96.15 201	98.28 166	89.29 235	95.36 203	98.77 107	93.73 212	98.11 105	98.34 98	93.02 188	99.67 110	98.35 27	99.58 92	99.50 52

CDPH-MVS		95.45 196	94.65 213	97.84 89	98.28 166	94.96 101	93.73 281	98.33 176	85.03 322	95.44 241	96.60 240	95.31 111	99.44 198	90.01 257	99.13 187	99.11 146

conf0.01		91.90 285	90.98 290	94.67 260	98.27 168	88.03 263	96.98 114	88.58 351	93.90 204	94.64 260	91.45 338	69.62 346	99.52 165	87.62 290	97.74 274	96.46 315

conf0.002		91.90 285	90.98 290	94.67 260	98.27 168	88.03 263	96.98 114	88.58 351	93.90 204	94.64 260	91.45 338	69.62 346	99.52 165	87.62 290	97.74 274	96.46 315

thresconf0.02		91.72 292	90.98 290	93.97 278	98.27 168	88.03 263	96.98 114	88.58 351	93.90 204	94.64 260	91.45 338	69.62 346	99.52 165	87.62 290	97.74 274	94.35 341

tfpn_n400		91.72 292	90.98 290	93.97 278	98.27 168	88.03 263	96.98 114	88.58 351	93.90 204	94.64 260	91.45 338	69.62 346	99.52 165	87.62 290	97.74 274	94.35 341

tfpnconf		91.72 292	90.98 290	93.97 278	98.27 168	88.03 263	96.98 114	88.58 351	93.90 204	94.64 260	91.45 338	69.62 346	99.52 165	87.62 290	97.74 274	94.35 341

tfpnview11		91.72 292	90.98 290	93.97 278	98.27 168	88.03 263	96.98 114	88.58 351	93.90 204	94.64 260	91.45 338	69.62 346	99.52 165	87.62 290	97.74 274	94.35 341

MVS_111021_LR		96.82 136	96.55 147	97.62 103	98.27 168	95.34 88	93.81 279	98.33 176	94.59 181	96.56 197	96.63 239	96.61 63	98.73 305	94.80 149	99.34 161	98.78 195

CLD-MVS		95.47 193	95.07 197	96.69 164	98.27 168	92.53 167	91.36 329	98.67 130	91.22 262	95.78 234	94.12 311	95.65 99	98.98 280	90.81 236	99.72 60	98.57 211
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
1121		94.26 234	93.26 250	97.27 133	98.26 176	94.73 107	95.86 174	97.71 231	77.96 352	94.53 268	96.71 234	91.93 218	99.40 214	87.71 286	98.64 235	97.69 274

Anonymous202405211		96.34 163	95.98 172	97.43 124	98.25 177	93.85 139	96.74 125	94.41 301	97.72 57	98.37 79	98.03 140	87.15 272	99.53 161	94.06 174	99.07 195	98.92 174

pmmvs-eth3d		96.49 156	96.18 163	97.42 125	98.25 177	94.29 122	94.77 240	98.07 211	89.81 275	97.97 123	98.33 100	93.11 182	99.08 267	95.46 122	99.84 41	98.89 179

v144192		96.69 147	96.90 128	96.03 211	98.25 177	88.92 244	95.49 191	98.77 107	93.05 229	98.09 109	98.29 106	92.51 203	99.70 88	98.11 32	99.56 98	99.47 66

ambc				96.56 174	98.23 180	91.68 191	97.88 58	98.13 204		98.42 77	98.56 81	94.22 150	99.04 271	94.05 177	99.35 159	98.95 165

testmv		95.51 188	95.33 190	96.05 207	98.23 180	89.51 227	93.50 290	98.63 140	94.25 194	98.22 95	97.73 170	92.51 203	99.47 185	85.22 316	99.72 60	99.17 131

tfpn111		91.92 284	91.39 279	93.49 294	98.21 182	84.50 316	96.39 136	90.39 338	96.87 91	96.33 207	93.08 319	73.44 333	99.51 175	79.87 339	97.94 267	96.46 315

conf200view11		91.81 289	91.26 284	93.46 295	98.21 182	84.50 316	96.39 136	90.39 338	96.87 91	96.33 207	93.08 319	73.44 333	99.42 200	78.85 344	97.74 274	96.46 315

thres100view900		91.76 291	91.26 284	93.26 299	98.21 182	84.50 316	96.39 136	90.39 338	96.87 91	96.33 207	93.08 319	73.44 333	99.42 200	78.85 344	97.74 274	95.85 325

v1921920		96.72 144	96.96 125	95.99 212	98.21 182	88.79 250	95.42 198	98.79 103	93.22 221	98.19 98	98.26 110	92.68 194	99.70 88	98.34 28	99.55 102	99.49 60

thres600view7		92.03 282	91.43 278	93.82 287	98.19 186	84.61 315	96.27 145	90.39 338	96.81 95	96.37 206	93.11 317	73.44 333	99.49 180	80.32 338	97.95 264	97.36 284

PatchMatch-RL		94.61 227	93.81 242	97.02 148	98.19 186	95.72 73	93.66 283	97.23 254	88.17 290	94.94 252	95.62 281	91.43 227	98.57 317	87.36 300	97.68 287	96.76 306

LF4IMVS		96.07 172	95.63 183	97.36 129	98.19 186	95.55 80	95.44 193	98.82 101	92.29 247	95.70 238	96.55 242	92.63 197	98.69 309	91.75 219	99.33 166	97.85 268

v1240		96.74 141	97.02 122	95.91 219	98.18 189	88.52 253	95.39 201	98.88 81	93.15 227	98.46 74	98.40 93	92.80 191	99.71 80	98.45 26	99.49 121	99.49 60

TAPA-MVS	93.32 12	94.93 215	94.23 230	97.04 145	98.18 189	94.51 114	95.22 215	98.73 114	81.22 339	96.25 217	95.95 273	93.80 166	98.98 280	89.89 258	98.87 215	97.62 276
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.17 191	93.24 159	92.74 308	97.61 243	75.17 356	94.65 259	96.69 236	90.96 233			98.66 233	97.66 275

MIMVSNet		93.42 258	92.86 257	95.10 246	98.17 191	88.19 258	98.13 44	93.69 306	92.07 248	95.04 250	98.21 116	80.95 295	99.03 274	81.42 336	98.06 261	98.07 254

原ACMM1				96.58 171	98.16 193	92.12 178		98.15 202	85.90 312	93.49 303	96.43 251	92.47 205	99.38 225	87.66 289	98.62 236	98.23 244

testdata				95.70 228	98.16 193	90.58 206		97.72 230	80.38 342	95.62 239	97.02 213	92.06 214	98.98 280	89.06 271	98.52 242	97.54 280

MVP-Stereo		95.69 181	95.28 191	96.92 151	98.15 195	93.03 161	95.64 188	98.20 194	90.39 269	96.63 194	97.73 170	91.63 223	99.10 265	91.84 215	97.31 304	98.63 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		97.37 100	97.70 67	96.35 184	98.14 196	95.13 96	96.54 131	98.92 75	95.94 126	99.19 31	98.08 132	97.74 21	95.06 357	95.24 130	99.54 104	98.87 185

EU-MVSNet		94.25 235	94.47 222	93.60 291	98.14 196	82.60 327	97.24 96	92.72 321	85.08 321	98.48 71	98.94 55	82.59 290	98.76 303	97.47 60	99.53 107	99.44 82

NP-MVS					98.14 196	93.72 144					95.08 289

LCM-MVSNet-Re		97.33 104	97.33 94	97.32 131	98.13 199	93.79 142	96.99 113	99.65 2	96.74 97	99.47 13	98.93 56	96.91 48	99.84 27	90.11 255	99.06 198	98.32 234

3Dnovator+	96.13 3	97.73 74	97.59 80	98.15 71	98.11 200	95.60 78	98.04 49	98.70 124	98.13 40	96.93 185	98.45 89	95.30 112	99.62 128	95.64 114	98.96 204	99.24 123

Test4		95.39 198	95.24 192	95.82 222	98.07 201	89.60 222	94.40 248	98.49 153	91.39 261	97.40 161	96.32 257	87.32 271	99.41 211	95.09 142	98.71 231	98.44 221

VNet		96.84 132	96.83 133	96.88 154	98.06 202	92.02 181	96.35 142	97.57 244	97.70 59	97.88 136	97.80 163	92.40 206	99.54 159	94.73 154	98.96 204	99.08 151

DI_MVS_plusplus_test		95.46 194	95.43 188	95.55 232	98.05 203	88.84 248	94.18 260	95.75 287	91.92 254	97.32 162	96.94 217	91.44 226	99.39 220	94.81 148	98.48 245	98.43 222

LFMVS		95.32 202	94.88 205	96.62 166	98.03 204	91.47 194	97.65 73	90.72 337	99.11 8	97.89 134	98.31 102	79.20 300	99.48 183	93.91 183	99.12 190	98.93 171

tfpn200view9		91.55 297	91.00 288	93.21 301	98.02 205	84.35 320	95.70 180	90.79 335	96.26 113	95.90 231	92.13 334	73.62 327	99.42 200	78.85 344	97.74 274	95.85 325

thres400		91.68 296	91.00 288	93.71 289	98.02 205	84.35 320	95.70 180	90.79 335	96.26 113	95.90 231	92.13 334	73.62 327	99.42 200	78.85 344	97.74 274	97.36 284

xiu_mvs_v1_base_debu		95.62 184	95.96 173	94.60 264	98.01 207	88.42 254	93.99 270	98.21 191	92.98 231	95.91 228	94.53 299	96.39 73	99.72 69	95.43 124	98.19 256	95.64 329

xiu_mvs_v1_base		95.62 184	95.96 173	94.60 264	98.01 207	88.42 254	93.99 270	98.21 191	92.98 231	95.91 228	94.53 299	96.39 73	99.72 69	95.43 124	98.19 256	95.64 329

xiu_mvs_v1_base_debi		95.62 184	95.96 173	94.60 264	98.01 207	88.42 254	93.99 270	98.21 191	92.98 231	95.91 228	94.53 299	96.39 73	99.72 69	95.43 124	98.19 256	95.64 329

casdiffmvs1		96.82 136	96.84 131	96.77 158	98.01 207	92.02 181	97.20 98	98.67 130	92.30 246	96.09 223	98.64 76	93.81 164	99.50 177	98.22 30	98.62 236	98.79 192

CNVR-MVS		96.92 125	96.55 147	98.03 79	98.00 211	95.54 81	94.87 234	98.17 199	94.60 179	96.38 205	97.05 211	95.67 98	99.36 229	95.12 140	99.08 193	99.19 128

PLC	91.02 16	94.05 246	92.90 256	97.51 111	98.00 211	95.12 97	94.25 254	98.25 189	86.17 308	91.48 333	95.25 287	91.01 231	99.19 254	85.02 318	96.69 315	98.22 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_normal		95.51 188	95.46 187	95.68 229	97.97 213	89.12 240	93.73 281	95.86 285	91.98 251	97.17 168	96.94 217	91.55 224	99.42 200	95.21 131	98.73 228	98.51 216

tfpn1000		91.88 288	91.20 286	93.89 286	97.96 214	87.13 286	97.13 102	88.16 358	94.41 187	94.87 255	92.77 326	68.34 353	99.47 185	89.24 266	97.95 264	95.06 335

GBi-Net		96.99 115	96.80 135	97.56 106	97.96 214	93.67 145	98.23 35	98.66 133	95.59 140	97.99 119	99.19 32	89.51 251	99.73 63	94.60 156	99.44 133	99.30 111

test1		96.99 115	96.80 135	97.56 106	97.96 214	93.67 145	98.23 35	98.66 133	95.59 140	97.99 119	99.19 32	89.51 251	99.73 63	94.60 156	99.44 133	99.30 111

FMVSNet2		96.72 144	96.67 141	96.87 155	97.96 214	91.88 186	97.15 99	98.06 212	95.59 140	98.50 70	98.62 77	89.51 251	99.65 116	94.99 144	99.60 88	99.07 153

BH-untuned		94.69 223	94.75 211	94.52 269	97.95 218	87.53 277	94.07 267	97.01 263	93.99 201	97.10 172	95.65 279	92.65 196	98.95 285	87.60 296	96.74 314	97.09 292

QAPM		95.88 179	95.57 184	96.80 156	97.90 219	91.84 188	98.18 42	98.73 114	88.41 285	96.42 203	98.13 127	94.73 124	99.75 53	88.72 275	98.94 208	98.81 190

TinyColmap		96.00 175	96.34 159	94.96 250	97.90 219	87.91 269	94.13 265	98.49 153	94.41 187	98.16 100	97.76 164	96.29 78	98.68 312	90.52 247	99.42 143	98.30 237

HQP-NCC					97.85 221		94.26 251		93.18 223	92.86 317

ACMP_Plane					97.85 221		94.26 251		93.18 223	92.86 317

N_pmnet		95.18 207	94.23 230	98.06 75	97.85 221	96.55 52	92.49 312	91.63 329	89.34 277	98.09 109	97.41 191	90.33 239	99.06 269	91.58 221	99.31 168	98.56 212

HQP-MVS		95.17 208	94.58 218	96.92 151	97.85 221	92.47 168	94.26 251	98.43 160	93.18 223	92.86 317	95.08 289	90.33 239	99.23 252	90.51 248	98.74 225	99.05 156

TEST9					97.84 225	95.23 90	93.62 285	98.39 167	86.81 303	93.78 290	95.99 268	94.68 129	99.52 165

train_agg		95.46 194	94.66 212	97.88 86	97.84 225	95.23 90	93.62 285	98.39 167	87.04 301	93.78 290	95.99 268	94.58 134	99.52 165	91.76 217	98.90 210	98.89 179

MSLP-MVS++		96.42 162	96.71 139	95.57 231	97.82 227	90.56 208	95.71 179	98.84 89	94.72 177	96.71 192	97.39 195	94.91 122	98.10 341	95.28 128	99.02 200	98.05 258

test_8					97.81 228	95.07 98	93.54 288	98.38 169	87.04 301	93.71 294	95.96 272	94.58 134	99.52 165

NCCC		96.52 155	95.99 171	98.10 73	97.81 228	95.68 75	95.00 230	98.20 194	95.39 148	95.40 243	96.36 255	93.81 164	99.45 194	93.55 191	98.42 247	99.17 131

WTY-MVS		93.55 256	93.00 255	95.19 242	97.81 228	87.86 270	93.89 275	96.00 280	89.02 279	94.07 281	95.44 285	86.27 275	99.33 235	87.69 288	96.82 311	98.39 225

CNLPA		95.04 212	94.47 222	96.75 160	97.81 228	95.25 89	94.12 266	97.89 219	94.41 187	94.57 266	95.69 277	90.30 242	98.35 333	86.72 305	98.76 223	96.64 310

agg_prior3		95.30 203	94.46 225	97.80 91	97.80 232	95.00 99	93.63 284	98.34 175	86.33 307	93.40 310	95.84 275	94.15 153	99.50 177	91.76 217	98.90 210	98.89 179

agg_prior1		95.39 198	94.60 216	97.75 93	97.80 232	94.96 101	93.39 293	98.36 171	87.20 299	93.49 303	95.97 271	94.65 131	99.53 161	91.69 220	98.86 217	98.77 196

agg_prior					97.80 232	94.96 101		98.36 171		93.49 303			99.53 161

旧先验1					97.80 232	93.87 137		97.75 227			97.04 212	93.57 171			98.68 232	98.72 200

PCF-MVS	89.43 18	92.12 281	90.64 302	96.57 173	97.80 232	93.48 154	89.88 345	98.45 156	74.46 357	96.04 225	95.68 278	90.71 235	99.31 237	73.73 352	99.01 202	96.91 300
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior3		95.91 177	95.39 189	97.46 120	97.79 237	94.26 126	93.33 296	98.42 163	94.21 196	94.02 283	96.25 259	93.64 169	99.34 232	91.90 211	98.96 204	98.79 192

test_prior				97.46 120	97.79 237	94.26 126		98.42 163					99.34 232			98.79 192

PVSNet_BlendedMVS		95.02 214	94.93 203	95.27 240	97.79 237	87.40 281	94.14 264	98.68 127	88.94 281	94.51 269	98.01 142	93.04 184	99.30 239	89.77 260	99.49 121	99.11 146

PVSNet_Blended		93.96 247	93.65 244	94.91 251	97.79 237	87.40 281	91.43 328	98.68 127	84.50 326	94.51 269	94.48 302	93.04 184	99.30 239	89.77 260	98.61 238	98.02 263

USDC		94.56 229	94.57 220	94.55 268	97.78 241	86.43 294	92.75 306	98.65 139	85.96 310	96.91 186	97.93 152	90.82 234	98.74 304	90.71 242	99.59 90	98.47 218

alignmvs		96.01 174	95.52 185	97.50 114	97.77 242	94.71 109	96.07 156	96.84 268	97.48 72	96.78 191	94.28 310	85.50 279	99.40 214	96.22 91	98.73 228	98.40 223

TSAR-MVS + GP.		96.47 158	96.12 164	97.49 117	97.74 243	95.23 90	94.15 263	96.90 267	93.26 220	98.04 116	96.70 235	94.41 141	98.89 289	94.77 152	99.14 185	98.37 227

3Dnovator	96.53 2	97.61 84	97.64 74	97.50 114	97.74 243	93.65 149	98.49 23	98.88 81	96.86 94	97.11 171	98.55 82	95.82 89	99.73 63	95.94 103	99.42 143	99.13 138

tfpn_ndepth		90.98 303	90.24 308	93.20 303	97.72 245	87.18 285	96.52 132	88.20 357	92.63 240	93.69 296	90.70 351	68.22 354	99.42 200	86.98 302	97.47 299	93.00 351

sss		94.22 236	93.72 243	95.74 224	97.71 246	89.95 215	93.84 277	96.98 264	88.38 288	93.75 292	95.74 276	87.94 263	98.89 289	91.02 229	98.10 260	98.37 227

DeepC-MVS_fast	94.34 7	96.74 141	96.51 152	97.44 123	97.69 247	94.15 129	96.02 159	98.43 160	93.17 226	97.30 163	97.38 197	95.48 104	99.28 244	93.74 187	99.34 161	98.88 183
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
semantic-postprocess				94.85 255	97.68 248	85.53 298		97.63 241	96.99 87	98.36 81	98.54 83	87.44 269	99.75 53	97.07 74	99.08 193	99.27 121

MVSFormer		96.14 170	96.36 158	95.49 235	97.68 248	87.81 273	98.67 13	99.02 51	96.50 103	94.48 271	96.15 263	86.90 273	99.92 4	98.73 17	99.13 187	98.74 198

lupinMVS		93.77 249	93.28 249	95.24 241	97.68 248	87.81 273	92.12 318	96.05 279	84.52 325	94.48 271	95.06 291	86.90 273	99.63 122	93.62 190	99.13 187	98.27 240

Fast-Effi-MVS+		95.49 190	95.07 197	96.75 160	97.67 251	92.82 163	94.22 257	98.60 143	91.61 257	93.42 308	92.90 324	96.73 60	99.70 88	92.60 203	97.89 271	97.74 273

canonicalmvs		97.23 110	97.21 107	97.30 132	97.65 252	94.39 118	97.84 61	99.05 38	97.42 74	96.68 193	93.85 313	97.63 25	99.33 235	96.29 90	98.47 246	98.18 250

CDS-MVSNet		94.88 216	94.12 235	97.14 139	97.64 253	93.57 150	93.96 273	97.06 262	90.05 273	96.30 214	96.55 242	86.10 276	99.47 185	90.10 256	99.31 168	98.40 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs5		94.63 226	94.34 228	95.50 234	97.63 254	88.34 257	94.02 268	97.13 259	87.15 300	95.22 246	97.15 205	87.50 268	99.27 246	93.99 179	99.26 176	98.88 183

test12				97.46 120	97.61 255	94.07 131		97.78 226		93.57 301		93.31 179	99.42 200		98.78 221	98.89 179

PMMVS2		93.66 253	94.07 236	92.45 316	97.57 256	80.67 335	86.46 352	96.00 280	93.99 201	97.10 172	97.38 197	89.90 246	97.82 344	88.76 274	99.47 126	98.86 186

BH-RMVSNet		94.56 229	94.44 226	94.91 251	97.57 256	87.44 280	93.78 280	96.26 277	93.69 214	96.41 204	96.50 247	92.10 212	99.00 277	85.96 308	97.71 284	98.31 235

PVSNet	86.72 19	91.10 300	90.97 296	91.49 323	97.56 258	78.04 344	87.17 350	94.60 299	84.65 324	92.34 326	92.20 333	87.37 270	98.47 323	85.17 317	97.69 286	97.96 265

DELS-MVS		96.17 169	96.23 162	95.99 212	97.55 259	90.04 212	92.38 315	98.52 150	94.13 199	96.55 200	97.06 210	94.99 120	99.58 147	95.62 115	99.28 173	98.37 227
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		95.42 197	95.83 177	94.20 276	97.52 260	83.78 324	92.41 314	97.47 250	95.49 144	98.06 114	98.49 86	87.94 263	99.58 147	96.02 99	99.02 200	99.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
casdiffmvs		96.43 161	96.38 156	96.60 167	97.51 261	91.95 185	97.08 110	98.41 165	93.69 214	93.95 287	98.34 98	93.03 186	99.45 194	98.09 33	97.30 305	98.39 225

new-patchmatchnet		95.67 183	96.58 144	92.94 309	97.48 262	80.21 336	92.96 302	98.19 198	94.83 173	98.82 48	98.79 62	93.31 179	99.51 175	95.83 106	99.04 199	99.12 143

MDA-MVSNet_test_wron		94.73 220	94.83 209	94.42 270	97.48 262	85.15 304	90.28 340	95.87 284	92.52 241	97.48 156	97.76 164	91.92 219	99.17 258	93.32 192	96.80 313	98.94 167

PHI-MVS		96.96 122	96.53 150	98.25 68	97.48 262	96.50 53	96.76 124	98.85 86	93.52 217	96.19 220	96.85 223	95.94 83	99.42 200	93.79 186	99.43 140	98.83 189

DeepPCF-MVS	94.58 5	96.90 127	96.43 154	98.31 63	97.48 262	97.23 35	92.56 311	98.60 143	92.84 238	98.54 66	97.40 192	96.64 62	98.78 301	94.40 163	99.41 149	98.93 171

thres200		91.00 302	90.42 306	92.77 311	97.47 266	83.98 323	94.01 269	91.18 333	95.12 165	95.44 241	91.21 346	73.93 323	99.31 237	77.76 348	97.63 292	95.01 336

YYNet1		94.73 220	94.84 207	94.41 271	97.47 266	85.09 306	90.29 339	95.85 286	92.52 241	97.53 150	97.76 164	91.97 215	99.18 255	93.31 193	96.86 310	98.95 165

Effi-MVS+		96.19 168	96.01 169	96.71 162	97.43 268	92.19 177	96.12 155	99.10 25	95.45 145	93.33 311	94.71 297	97.23 40	99.56 153	93.21 197	97.54 294	98.37 227

pmmvs4		94.82 219	94.19 233	96.70 163	97.42 269	92.75 165	92.09 320	96.76 271	86.80 304	95.73 237	97.22 202	89.28 254	98.89 289	93.28 194	99.14 185	98.46 220

MSDG		95.33 201	95.13 195	95.94 218	97.40 270	91.85 187	91.02 332	98.37 170	95.30 150	96.31 213	95.99 268	94.51 139	98.38 330	89.59 262	97.65 290	97.60 278

EI-MVSNet-Vis-set		97.32 105	97.39 91	97.11 140	97.36 271	92.08 180	95.34 205	97.65 237	97.74 53	98.29 91	98.11 130	95.05 117	99.68 103	97.50 57	99.50 114	99.56 42

PS-MVSNAJ		94.10 243	94.47 222	93.00 307	97.35 272	84.88 308	91.86 322	97.84 222	91.96 252	94.17 276	92.50 331	95.82 89	99.71 80	91.27 225	97.48 297	94.40 340

Regformer-3		97.25 109	97.29 96	97.11 140	97.35 272	92.32 171	95.26 212	97.62 242	97.67 62	98.17 99	97.89 155	95.05 117	99.56 153	97.16 70	99.42 143	99.46 68

Regformer-4		97.53 91	97.47 89	97.71 95	97.35 272	93.91 136	95.26 212	98.14 203	97.97 46	98.34 83	97.89 155	95.49 103	99.71 80	97.41 61	99.42 143	99.51 51

EI-MVSNet-UG-set		97.32 105	97.40 90	97.09 142	97.34 275	92.01 183	95.33 206	97.65 237	97.74 53	98.30 90	98.14 126	95.04 119	99.69 97	97.55 54	99.52 111	99.58 37

AdaColmap		95.11 209	94.62 215	96.58 171	97.33 276	94.45 117	94.92 232	98.08 209	93.15 227	93.98 286	95.53 284	94.34 144	99.10 265	85.69 311	98.61 238	96.20 322

xiu_mvs_v2_base		94.22 236	94.63 214	92.99 308	97.32 277	84.84 309	92.12 318	97.84 222	91.96 252	94.17 276	93.43 314	96.07 81	99.71 80	91.27 225	97.48 297	94.42 339

OpenMVS_ROB	91.80 14	93.64 254	93.05 253	95.42 236	97.31 278	91.21 196	95.08 223	96.68 275	81.56 336	96.88 188	96.41 252	90.44 238	99.25 249	85.39 315	97.67 288	95.80 327

EI-MVSNet		96.63 150	96.93 126	95.74 224	97.26 279	88.13 261	95.29 210	97.65 237	96.99 87	97.94 126	98.19 117	92.55 199	99.58 147	96.91 77	99.56 98	99.50 52

CVMVSNet		92.33 277	92.79 259	90.95 329	97.26 279	75.84 351	95.29 210	92.33 324	81.86 334	96.27 215	98.19 117	81.44 292	98.46 324	94.23 171	98.29 249	98.55 214

Regformer-1		97.27 107	97.16 110	97.61 104	97.21 281	93.86 138	94.85 236	98.04 214	97.62 64	98.03 117	97.50 187	95.34 109	99.63 122	96.52 83	99.31 168	99.35 105

Regformer-2		97.41 97	97.24 101	97.93 84	97.21 281	94.72 108	94.85 236	98.27 186	97.74 53	98.11 105	97.50 187	95.58 101	99.69 97	96.57 82	99.31 168	99.37 103

Fast-Effi-MVS+-dtu		96.44 159	96.12 164	97.39 128	97.18 283	94.39 118	95.46 192	98.73 114	96.03 122	94.72 257	94.92 295	96.28 79	99.69 97	93.81 185	97.98 263	98.09 251

OpenMVS	94.22 8	95.48 192	95.20 193	96.32 187	97.16 284	91.96 184	97.74 69	98.84 89	87.26 297	94.36 273	98.01 142	93.95 158	99.67 110	90.70 243	98.75 224	97.35 290

BH-w/o		92.14 280	91.94 273	92.73 312	97.13 285	85.30 301	92.46 313	95.64 290	89.33 278	94.21 275	92.74 328	89.60 247	98.24 336	81.68 335	94.66 335	94.66 338

MG-MVS		94.08 245	94.00 239	94.32 273	97.09 286	85.89 295	93.19 300	95.96 282	92.52 241	94.93 253	97.51 186	89.54 248	98.77 302	87.52 298	97.71 284	98.31 235

MVS_0304		96.22 166	95.94 176	97.04 145	97.07 287	92.54 166	94.19 259	99.04 45	95.17 159	93.74 293	96.92 220	91.77 222	99.73 63	95.76 108	99.81 44	98.85 188

MVS-HIRNet		88.40 323	90.20 309	82.99 347	97.01 288	60.04 365	93.11 301	85.61 360	84.45 327	88.72 350	99.09 46	84.72 285	98.23 337	82.52 330	96.59 317	90.69 357

GA-MVS		92.83 266	92.15 268	94.87 254	96.97 289	87.27 284	90.03 341	96.12 278	91.83 256	94.05 282	94.57 298	76.01 318	98.97 284	92.46 206	97.34 303	98.36 232

0601test		94.40 232	94.00 239	95.59 230	96.95 290	89.52 226	94.75 241	95.55 293	96.18 117	96.79 189	96.14 265	81.09 294	99.18 255	90.75 239	97.77 272	98.07 254

test1235678		92.95 264	92.40 264	94.61 263	96.95 290	86.87 289	90.75 334	97.75 227	91.00 265	96.33 207	95.38 286	85.21 281	98.92 286	79.00 342	99.20 180	98.03 261

MVS_Test		96.27 164	96.79 137	94.73 259	96.94 292	86.63 292	96.18 152	98.33 176	94.94 169	96.07 224	98.28 107	95.25 114	99.26 248	97.21 66	97.90 270	98.30 237

MAR-MVS		94.21 239	93.03 254	97.76 92	96.94 292	97.44 30	96.97 120	97.15 258	87.89 295	92.00 329	92.73 329	92.14 210	99.12 260	83.92 324	97.51 296	96.73 307
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		96.81 138	96.09 166	98.99 10	96.90 294	98.69 2	96.42 135	98.09 207	95.86 129	95.15 247	95.54 283	94.26 148	99.81 32	94.06 174	98.51 244	98.47 218

mvs-test1		96.20 167	95.50 186	98.32 61	96.90 294	98.16 4	95.07 224	98.09 207	95.86 129	93.63 297	94.32 309	94.26 148	99.71 80	94.06 174	97.27 307	97.07 293

MS-PatchMatch		94.83 218	94.91 204	94.57 267	96.81 296	87.10 287	94.23 256	97.34 251	88.74 283	97.14 169	97.11 208	91.94 217	98.23 337	92.99 201	97.92 268	98.37 227

diffmvs1		96.57 154	96.86 129	95.72 227	96.74 297	89.30 234	95.90 173	98.58 146	96.33 111	94.93 253	98.37 95	94.52 138	99.29 242	97.60 52	98.73 228	98.58 210

UGNet		96.81 138	96.56 146	97.58 105	96.64 298	93.84 140	97.75 67	97.12 260	96.47 106	93.62 298	98.88 59	93.22 181	99.53 161	95.61 116	99.69 68	99.36 104
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		95.09 211	95.01 200	95.31 239	96.61 299	94.02 133	96.83 122	97.18 257	95.60 139	95.79 233	94.33 308	94.54 136	98.37 332	85.70 310	98.52 242	93.52 347

PAPM		87.64 330	85.84 333	93.04 305	96.54 300	84.99 307	88.42 349	95.57 292	79.52 345	83.82 359	93.05 323	80.57 296	98.41 326	62.29 360	92.79 342	95.71 328

FMVSNet3		95.26 206	94.94 201	96.22 197	96.53 301	90.06 211	95.99 161	97.66 235	94.11 200	97.99 119	97.91 154	80.22 298	99.63 122	94.60 156	99.44 133	98.96 164

diffmvs		96.10 171	96.43 154	95.12 244	96.52 302	87.85 271	95.95 170	97.91 216	96.52 102	93.02 314	98.25 111	94.28 146	99.28 244	97.11 72	98.26 254	98.24 243

HY-MVS	91.43 15	92.58 268	91.81 276	94.90 253	96.49 303	88.87 246	97.31 91	94.62 298	85.92 311	90.50 341	96.84 224	85.05 282	99.40 214	83.77 327	95.78 327	96.43 319

TR-MVS		92.54 273	92.20 267	93.57 292	96.49 303	86.66 291	93.51 289	94.73 297	89.96 274	94.95 251	93.87 312	90.24 244	98.61 315	81.18 337	94.88 333	95.45 333

CANet		95.86 180	95.65 182	96.49 177	96.41 305	90.82 202	94.36 249	98.41 165	94.94 169	92.62 324	96.73 233	92.68 194	99.71 80	95.12 140	99.60 88	98.94 167

mvs_anonymous		95.36 200	96.07 168	93.21 301	96.29 306	81.56 330	94.60 244	97.66 235	93.30 219	96.95 184	98.91 58	93.03 186	99.38 225	96.60 80	97.30 305	98.69 202

Patchmatch-test1		93.38 260	93.59 245	92.73 312	96.24 307	81.40 331	93.24 298	94.00 304	91.58 259	94.57 266	96.67 237	87.94 263	99.03 274	90.42 251	97.66 289	97.77 272

LS3D		97.77 72	97.50 87	98.57 43	96.24 307	97.58 21	98.45 26	98.85 86	98.58 25	97.51 151	97.94 150	95.74 97	99.63 122	95.19 132	98.97 203	98.51 216

new_pmnet		92.34 276	91.69 277	94.32 273	96.23 309	89.16 238	92.27 316	92.88 318	84.39 328	95.29 244	96.35 256	85.66 278	96.74 354	84.53 321	97.56 293	97.05 294

MVE	73.61 22	86.48 332	85.92 332	88.18 341	96.23 309	85.28 302	81.78 360	75.79 364	86.01 309	82.53 361	91.88 336	92.74 192	87.47 362	71.42 357	94.86 334	91.78 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DSMNet-mixed		92.19 279	91.83 275	93.25 300	96.18 311	83.68 325	96.27 145	93.68 308	76.97 355	92.54 325	99.18 35	89.20 256	98.55 320	83.88 325	98.60 240	97.51 281

our_test_3		94.20 241	94.58 218	93.07 304	96.16 312	81.20 332	90.42 338	96.84 268	90.72 267	97.14 169	97.13 206	90.47 237	99.11 263	94.04 178	98.25 255	98.91 175

ppachtmachnet_test		94.49 231	94.84 207	93.46 295	96.16 312	82.10 329	90.59 336	97.48 247	90.53 268	97.01 176	97.59 180	91.01 231	99.36 229	93.97 181	99.18 183	98.94 167

Patchmatch-test		93.60 255	93.25 251	94.63 262	96.14 314	87.47 279	96.04 158	94.50 300	93.57 216	96.47 201	96.97 215	76.50 314	98.61 315	90.67 244	98.41 248	97.81 271

testus		90.90 305	90.51 304	92.06 320	96.07 315	79.45 338	88.99 346	98.44 159	85.46 317	94.15 278	90.77 348	89.12 257	98.01 343	73.66 353	97.95 264	98.71 201

PNet_i23d		83.82 335	83.39 335	85.10 346	96.07 315	65.16 361	81.87 359	94.37 302	90.87 266	93.92 288	92.89 325	52.80 366	96.44 356	77.52 350	70.22 360	93.70 346

wuyk23d		93.25 262	95.20 193	87.40 343	96.07 315	95.38 86	97.04 111	94.97 295	95.33 149	99.70 5	98.11 130	98.14 13	91.94 359	77.76 348	99.68 72	74.89 359

CANet_DTU		94.65 225	94.21 232	95.96 214	95.90 318	89.68 218	93.92 274	97.83 224	93.19 222	90.12 344	95.64 280	88.52 258	99.57 152	93.27 195	99.47 126	98.62 207

test12356		87.98 327	88.41 322	86.69 345	95.84 319	63.49 362	87.15 351	97.32 252	87.21 298	91.78 332	93.36 315	70.66 344	98.39 328	74.70 351	97.64 291	98.19 248

MVSTER		94.21 239	93.93 241	95.05 249	95.83 320	86.46 293	95.18 217	97.65 237	92.41 245	97.94 126	98.00 144	72.39 337	99.58 147	96.36 89	99.56 98	99.12 143

FMVSNet5		93.39 259	92.35 265	96.50 176	95.83 320	90.81 204	97.31 91	98.27 186	92.74 239	96.27 215	98.28 107	62.23 358	99.67 110	90.86 234	99.36 155	99.03 157

PVSNet_0	81.89 21	84.49 334	83.21 337	88.34 340	95.76 322	74.97 354	83.49 356	92.70 322	78.47 350	87.94 353	86.90 359	83.38 288	96.63 355	73.44 354	66.86 361	93.40 348

PAPR		92.22 278	91.27 283	95.07 248	95.73 323	88.81 249	91.97 321	97.87 220	85.80 313	90.91 335	92.73 329	91.16 229	98.33 334	79.48 340	95.76 328	98.08 252

CHOSEN 280x420		89.98 311	89.19 317	92.37 317	95.60 324	81.13 333	86.22 353	97.09 261	81.44 338	87.44 355	93.15 316	73.99 322	99.47 185	88.69 276	99.07 195	96.52 314

ADS-MVSNet2		91.47 298	90.51 304	94.36 272	95.51 325	85.63 296	95.05 227	95.70 288	83.46 330	92.69 320	96.84 224	79.15 301	99.41 211	85.66 312	90.52 346	98.04 259

ADS-MVSNet		90.95 304	90.26 307	93.04 305	95.51 325	82.37 328	95.05 227	93.41 312	83.46 330	92.69 320	96.84 224	79.15 301	98.70 308	85.66 312	90.52 346	98.04 259

CR-MVSNet		93.29 261	92.79 259	94.78 257	95.44 327	88.15 259	96.18 152	97.20 255	84.94 323	94.10 279	98.57 79	77.67 306	99.39 220	95.17 134	95.81 324	96.81 304

RPMNet		94.22 236	94.03 238	94.78 257	95.44 327	88.15 259	96.18 152	93.73 305	97.43 73	94.10 279	98.49 86	79.40 299	99.39 220	95.69 109	95.81 324	96.81 304

1314		92.38 275	92.30 266	92.64 314	95.42 329	85.15 304	95.86 174	96.97 265	85.40 319	90.62 337	93.06 322	91.12 230	97.80 345	86.74 304	95.49 332	94.97 337

tpm		91.08 301	90.85 298	91.75 322	95.33 330	78.09 342	95.03 229	91.27 332	88.75 282	93.53 302	97.40 192	71.24 340	99.30 239	91.25 227	93.87 338	97.87 267

IB-MVS	85.98 20	88.63 320	86.95 329	93.68 290	95.12 331	84.82 310	90.85 333	90.17 347	87.55 296	88.48 351	91.34 345	58.01 360	99.59 145	87.24 301	93.80 339	96.63 312
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		93.75 250	93.57 246	94.29 275	95.05 332	87.32 283	96.05 157	92.98 316	97.54 68	94.25 274	98.72 68	75.79 319	99.24 250	95.92 104	95.81 324	96.32 320

tpm2		88.47 321	87.69 324	90.79 330	94.98 333	77.34 347	95.09 221	91.83 327	77.51 354	89.40 347	96.41 252	67.83 355	98.73 305	83.58 329	92.60 344	96.29 321

Patchmtry		95.03 213	94.59 217	96.33 186	94.83 334	90.82 202	96.38 140	97.20 255	96.59 100	97.49 153	98.57 79	77.67 306	99.38 225	92.95 202	99.62 80	98.80 191

MVS		90.02 309	89.20 316	92.47 315	94.71 335	86.90 288	95.86 174	96.74 273	64.72 360	90.62 337	92.77 326	92.54 201	98.39 328	79.30 341	95.56 331	92.12 352

CostFormer		89.75 314	89.25 313	91.26 326	94.69 336	78.00 345	95.32 207	91.98 326	81.50 337	90.55 339	96.96 216	71.06 341	98.89 289	88.59 278	92.63 343	96.87 301

PatchmatchNet		91.98 283	91.87 274	92.30 318	94.60 337	79.71 337	95.12 218	93.59 311	89.52 276	93.61 299	97.02 213	77.94 304	99.18 255	90.84 235	94.57 337	98.01 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		88.46 322	87.54 325	91.22 327	94.56 338	78.08 343	95.63 189	93.17 314	79.08 348	85.85 357	96.80 228	65.86 357	98.85 296	84.10 323	92.85 341	96.72 308

LP		93.12 263	92.78 261	94.14 277	94.50 339	85.48 299	95.73 178	95.68 289	92.97 235	95.05 249	97.17 204	81.93 291	99.40 214	93.06 200	88.96 351	97.55 279

tpm cat1		88.01 326	87.33 326	90.05 335	94.48 340	76.28 350	94.47 247	94.35 303	73.84 359	89.26 348	95.61 282	73.64 326	98.30 335	84.13 322	86.20 355	95.57 332

MDTV_nov1_ep13			91.28 282		94.31 341	73.51 355	94.80 238	93.16 315	86.75 305	93.45 306	97.40 192	76.37 315	98.55 320	88.85 273	96.43 318

cascas		91.89 287	91.35 281	93.51 293	94.27 342	85.60 297	88.86 348	98.61 142	79.32 346	92.16 328	91.44 344	89.22 255	98.12 340	90.80 237	97.47 299	96.82 303

test-LLR		89.97 312	89.90 310	90.16 333	94.24 343	74.98 352	89.89 342	89.06 348	92.02 249	89.97 345	90.77 348	73.92 324	98.57 317	91.88 213	97.36 301	96.92 298

test-mter		87.92 328	87.17 327	90.16 333	94.24 343	74.98 352	89.89 342	89.06 348	86.44 306	89.97 345	90.77 348	54.96 364	98.57 317	91.88 213	97.36 301	96.92 298

pmmvs3		90.00 310	88.90 319	93.32 297	94.20 345	85.34 300	91.25 330	92.56 323	78.59 349	93.82 289	95.17 288	67.36 356	98.69 309	89.08 270	98.03 262	95.92 323

tpmrst		90.31 307	90.61 303	89.41 336	94.06 346	72.37 358	95.06 226	93.69 306	88.01 292	92.32 327	96.86 222	77.45 308	98.82 297	91.04 228	87.01 354	97.04 295

test0.0.03 1		90.11 308	89.21 315	92.83 310	93.89 347	86.87 289	91.74 324	88.74 350	92.02 249	94.71 258	91.14 347	73.92 324	94.48 358	83.75 328	92.94 340	97.16 291

JIA-IIPM		91.79 290	90.69 301	95.11 245	93.80 348	90.98 198	94.16 262	91.78 328	96.38 107	90.30 343	99.30 23	72.02 339	98.90 287	88.28 282	90.17 348	95.45 333

TESTMET0.1,1		87.20 331	86.57 331	89.07 337	93.62 349	72.84 357	89.89 342	87.01 359	85.46 317	89.12 349	90.20 353	56.00 363	97.72 346	90.91 233	96.92 308	96.64 310

PatchFormer-LS_test		89.62 315	89.12 318	91.11 328	93.62 349	78.42 341	94.57 246	93.62 310	88.39 287	90.54 340	88.40 356	72.33 338	99.03 274	92.41 207	88.20 352	95.89 324

CMPMVS	73.10 23	92.74 267	91.39 279	96.77 158	93.57 351	94.67 111	94.21 258	97.67 233	80.36 343	93.61 299	96.60 240	82.85 289	97.35 348	84.86 319	98.78 221	98.29 239
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DWT-MVSNet_test		87.92 328	86.77 330	91.39 324	93.18 352	78.62 340	95.10 219	91.42 330	85.58 314	88.00 352	88.73 355	60.60 359	98.90 287	90.60 245	87.70 353	96.65 309

E-PMN		89.52 316	89.78 311	88.73 338	93.14 353	77.61 346	83.26 357	92.02 325	94.82 174	93.71 294	93.11 317	75.31 320	96.81 352	85.81 309	96.81 312	91.77 354

PMMVS		92.39 274	91.08 287	96.30 189	93.12 354	92.81 164	90.58 337	95.96 282	79.17 347	91.85 331	92.27 332	90.29 243	98.66 314	89.85 259	96.68 316	97.43 282

EMVS		89.06 318	89.22 314	88.61 339	93.00 355	77.34 347	82.91 358	90.92 334	94.64 178	92.63 323	91.81 337	76.30 316	97.02 350	83.83 326	96.90 309	91.48 355

dp		88.08 325	88.05 323	88.16 342	92.85 356	68.81 360	94.17 261	92.88 318	85.47 316	91.38 334	96.14 265	68.87 352	98.81 299	86.88 303	83.80 358	96.87 301

gg-mvs-nofinetune		88.28 324	86.96 328	92.23 319	92.84 357	84.44 319	98.19 41	74.60 365	99.08 9	87.01 356	99.47 7	56.93 361	98.23 337	78.91 343	95.61 330	94.01 345

tpmvs		90.79 306	90.87 297	90.57 332	92.75 358	76.30 349	95.79 177	93.64 309	91.04 264	91.91 330	96.26 258	77.19 312	98.86 295	89.38 265	89.85 349	96.56 313

EPMVS		89.26 317	88.55 321	91.39 324	92.36 359	79.11 339	95.65 186	79.86 363	88.60 284	93.12 313	96.53 244	70.73 343	98.10 341	90.75 239	89.32 350	96.98 296

gm-plane-assit					91.79 360	71.40 359			81.67 335		90.11 354		98.99 278	84.86 319

test2356		85.45 333	83.26 336	92.01 321	91.12 361	80.76 334	85.16 354	92.90 317	83.90 329	90.63 336	87.71 358	53.10 365	97.24 349	69.20 358	95.65 329	98.03 261

GG-mvs-BLEND				90.60 331	91.00 362	84.21 322	98.23 35	72.63 368		82.76 360	84.11 360	56.14 362	96.79 353	72.20 355	92.09 345	90.78 356

DeepMVS_CX				77.17 349	90.94 363	85.28 302		74.08 367	52.51 361	80.87 363	88.03 357	75.25 321	70.63 363	59.23 361	84.94 356	75.62 358

EPNet_dtu		91.39 299	90.75 300	93.31 298	90.48 364	82.61 326	94.80 238	92.88 318	93.39 218	81.74 362	94.90 296	81.36 293	99.11 263	88.28 282	98.87 215	98.21 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testpf		82.70 336	84.35 334	77.74 348	88.97 365	73.23 356	93.85 276	84.33 361	88.10 291	85.06 358	90.42 352	52.62 367	91.05 361	91.00 230	84.82 357	68.93 360

EPNet		93.72 251	92.62 263	97.03 147	87.61 366	92.25 172	96.27 145	91.28 331	96.74 97	87.65 354	97.39 195	85.00 283	99.64 119	92.14 209	99.48 124	99.20 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		57.23 338	62.50 339	41.44 351	34.77 367	49.21 367	83.93 355	60.22 369	15.31 362	71.11 364	79.37 361	70.09 345	44.86 364	64.76 359	82.93 359	30.25 361

test123		12.59 342	15.49 343	3.87 353	6.07 368	2.55 368	90.75 334	2.59 371	2.52 363	5.20 366	13.02 364	4.96 370	1.85 366	5.20 362	9.09 362	7.23 362

testmvs		12.33 343	15.23 344	3.64 354	5.77 369	2.23 369	88.99 346	3.62 370	2.30 364	5.29 365	13.09 363	4.52 371	1.95 365	5.16 363	8.32 363	6.75 363

cdsmvs_eth3d_5k		24.22 341	32.30 342	0.00 355	0.00 370	0.00 370	0.00 361	98.10 206	0.00 365	0.00 367	95.06 291	97.54 27	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		7.98 344	10.65 345	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	95.82 89	0.00 367	0.00 364	0.00 365	0.00 365

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

ab-mvs-re		7.91 345	10.55 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	94.94 293	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																98.06 256

test_part1				0.00 355		0.00 370	0.00 361	98.84 89				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sam_mvs1												77.80 305				98.06 256

sam_mvs												77.38 309

MTGPA								98.73 114

test_post1							94.98 231				10.37 366	76.21 317	99.04 271	89.47 264

test_post											10.87 365	76.83 313	99.07 268

patchmatchnet-post											96.84 224	77.36 310	99.42 200

MTMP							96.55 130	74.60 365

test9_res														91.29 224	98.89 214	99.00 159

agg_prior2														90.34 254	98.90 210	99.10 150

test_prior4						95.38 86	93.61 287

test_prior2							93.33 296		94.21 196	94.02 283	96.25 259	93.64 169		91.90 211	98.96 204

旧先验2							93.35 295		77.95 353	95.77 236			98.67 313	90.74 241

新几何2							93.43 291

无先验							93.20 299	97.91 216	80.78 340				99.40 214	87.71 286		97.94 266

原ACMM2							92.82 304

testdata2													99.46 190	87.84 285

segment_acmp												95.34 109

testdata1							92.77 305		93.78 211

plane_prior5								98.75 111					99.46 190	92.59 204	99.20 180	99.28 118

plane_prior4											96.77 230

plane_prior3						94.51 114			95.29 151	96.16 221

plane_prior2							96.50 133		96.36 108

plane_prior						94.29 122	95.42 198		94.31 193						98.93 209

n2								0.00 372

nn								0.00 372

door-mid								98.17 199

test11								98.08 209

door								97.81 225

HQP5-MVS						92.47 168

BP-MVS														90.51 248

HQP4-MVS										92.87 316			99.23 252			99.06 155

HQP3-MVS								98.43 160							98.74 225

HQP2-MVS												90.33 239

MDTV_nov1_ep13_2view						57.28 366	94.89 233		80.59 341	94.02 283		78.66 303		85.50 314		97.82 270

ACMMP++_ref															99.52 111

ACMMP++															99.55 102

Test By Simon												94.51 139

High-resolution multi-view benchmark