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 51	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 43	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 43	99.61 11	99.52 5	93.72 166	99.88 19	98.72 20	99.88 34	99.65 25

Gipuma		98.07 42	98.31 38	97.36 129	99.76 5	96.28 61	98.51 22	99.10 25	98.76 21	96.79 188	99.34 20	96.61 66	98.82 295	96.38 85	99.50 113	96.98 294
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 26	99.38 18	99.14 42	95.44 108	99.84 28	96.47 83	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	99.52 166	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	96.23 113	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 90	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	95.83 128	99.67 7	99.37 14	98.25 11	99.92 4	98.77 14	99.94 19	99.82 7

pcd1.5k->3k		41.47 336	44.19 337	33.29 349	99.65 11	0.00 367	0.00 358	99.07 34	0.00 362	0.00 363	0.00 364	99.04 4	0.00 365	0.00 362	99.96 11	99.87 2

v7n		98.73 13	98.99 7	97.95 83	99.64 12	94.20 128	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	96.50 102	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	95.62 134	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	97.67 101	99.61 15	93.53 152	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 27	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 27	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 46	99.92 24	99.75 13

Baseline_NR-MVSNet		97.72 75	97.79 61	97.50 114	99.56 19	93.29 157	95.44 192	98.86 84	98.20 39	98.37 78	99.24 27	94.69 128	99.55 158	95.98 99	99.79 48	99.65 25

SixPastTwentyTwo		97.49 92	97.57 82	97.26 135	99.56 19	92.33 170	98.28 32	96.97 262	98.30 35	99.45 14	99.35 18	88.43 259	99.89 17	98.01 33	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 30	98.96 53	97.35 33	99.92 4	97.79 41	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 45	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	95.88 125	97.88 135	98.22 112	98.15 13	99.74 60	96.50 82	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 42	99.20 31	97.42 31	99.59 146	97.21 64	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 31	99.25 27	99.17 39	95.28 114	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 66	99.27 26	99.22 30	98.32 10	99.50 178	97.09 70	99.75 55	99.50 51

TransMVSNet (Re)		98.38 29	98.67 19	97.51 111	99.51 27	93.39 156	98.20 40	98.87 82	98.23 37	99.48 12	99.27 25	98.47 9	99.55 158	96.52 80	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 33	98.79 62	95.47 106	99.89 17	97.95 34	99.91 27	99.75 13

PMVS	89.60 17	96.71 145	96.97 122	95.95 215	99.51 27	97.81 13	97.42 90	97.49 242	97.93 47	95.95 224	98.58 77	96.88 52	96.91 349	89.59 260	99.36 156	93.12 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss		97.69 78	97.36 92	98.70 36	99.50 30	96.84 43	95.38 201	98.99 65	92.45 241	98.11 104	98.31 100	97.25 39	99.77 48	96.60 77	99.62 80	99.48 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		98.16 37	98.37 34	97.56 106	99.49 31	93.10 160	98.35 29	99.21 11	98.43 29	98.89 46	98.83 61	94.30 145	99.81 33	97.87 37	99.91 27	99.77 9

VPNet		97.26 107	97.49 88	96.59 169	99.47 32	90.58 205	96.27 143	98.53 146	97.77 51	98.46 73	98.41 90	94.59 134	99.68 104	94.61 153	99.29 173	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 40	98.65 75	95.37 109	99.90 13	97.57 50	99.91 27	99.77 9

XXY-MVS		97.54 89	97.70 67	97.07 143	99.46 33	92.21 174	97.22 97	99.00 62	94.93 167	98.58 64	98.92 57	97.31 35	99.41 211	94.44 157	99.43 141	99.59 36

zzz-MVS		98.01 47	97.66 71	99.06 5	99.44 35	97.90 8	95.66 181	98.73 114	97.69 60	97.90 131	97.96 142	95.81 95	99.82 31	96.13 90	99.61 85	99.45 72

MTAPA		98.14 38	97.84 59	99.06 5	99.44 35	97.90 8	97.25 94	98.73 114	97.69 60	97.90 131	97.96 142	95.81 95	99.82 31	96.13 90	99.61 85	99.45 72

wuykxyi23d		98.68 17	98.53 27	99.13 3	99.44 35	97.97 7	96.85 120	99.02 51	95.81 129	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 64	98.79 28	99.43 38	97.21 36	97.15 98	98.90 76	96.58 101	98.08 110	97.87 153	97.02 47	99.76 49	95.25 127	99.59 90	99.40 92
Skip Steuart: Steuart Systems R&D Blog.
ACMH	93.61 9	98.44 26	98.76 16	97.51 111	99.43 38	93.54 151	98.23 35	99.05 38	97.40 82	99.37 19	99.08 47	98.79 6	99.47 185	97.74 44	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 60	98.92 19	99.42 40	97.46 28	98.57 18	99.05 38	95.43 143	97.41 159	97.50 184	97.98 17	99.79 39	95.58 116	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		96.44 157	96.28 158	96.95 149	99.41 41	91.53 191	97.65 73	90.31 338	98.89 18	98.93 45	99.36 16	84.57 285	99.92 4	97.81 39	99.56 98	99.39 95

VDDNet		96.98 118	96.84 129	97.41 126	99.40 42	93.26 158	97.94 54	95.31 290	99.26 6	98.39 77	99.18 35	87.85 266	99.62 129	95.13 137	99.09 193	99.35 106

ACMH+	93.58 10	98.23 36	98.31 38	97.98 82	99.39 43	95.22 94	97.55 83	99.20 13	98.21 38	99.25 27	98.51 84	98.21 12	99.40 214	94.79 148	99.72 60	99.32 108

TSAR-MVS + MP.		97.42 95	97.23 104	98.00 81	99.38 44	95.00 100	97.63 75	98.20 192	93.00 227	98.16 99	98.06 133	95.89 86	99.72 71	95.67 107	99.10 192	99.28 119

FIs		97.93 56	98.07 48	97.48 118	99.38 44	92.95 162	98.03 51	99.11 23	98.04 44	98.62 59	98.66 73	93.75 165	99.78 40	97.23 63	99.84 41	99.73 16

lessismore_v0				97.05 144	99.36 46	92.12 178		84.07 358		98.77 53	98.98 51	85.36 279	99.74 60	97.34 61	99.37 153	99.30 112

ACMMP_Plus		97.89 61	97.63 76	98.67 38	99.35 47	96.84 43	96.36 139	98.79 103	95.07 163	97.88 135	98.35 95	97.24 40	99.72 71	96.05 93	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 85	97.82 144	99.11 44	96.75 59	99.86 23	97.84 38	99.36 156	99.15 136
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	96.41 181	99.33 48	89.64 218	97.92 56	99.56 4	99.27 5	99.66 8	99.50 6	97.67 25	99.83 30	97.55 51	99.98 3	99.77 9

MP-MVS		97.64 81	97.18 108	99.00 9	99.32 50	97.77 14	97.49 86	98.73 114	96.27 110	95.59 237	97.75 163	96.30 79	99.78 40	93.70 185	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		95.95 173	95.80 175	96.42 180	99.28 51	90.62 204	95.31 207	99.08 30	88.40 283	96.97 182	98.17 119	92.11 209	99.78 40	93.64 186	99.21 180	98.86 187

tfpnnormal		97.72 75	97.97 53	96.94 150	99.26 52	92.23 173	97.83 62	98.45 153	98.25 36	99.13 34	98.66 73	96.65 63	99.69 98	93.92 179	99.62 80	98.91 176

HSP-MVS		97.37 99	96.85 128	98.92 19	99.26 52	97.70 15	97.66 72	98.23 188	95.65 132	98.51 68	96.46 246	92.15 207	99.81 33	95.14 136	98.58 240	99.26 123

testgi		96.07 169	96.50 151	94.80 253	99.26 52	87.69 272	95.96 165	98.58 144	95.08 162	98.02 117	96.25 257	97.92 18	97.60 345	88.68 275	98.74 226	99.11 147

IS-MVSNet		96.93 123	96.68 138	97.70 97	99.25 55	94.00 134	98.57 18	96.74 270	98.36 31	98.14 102	97.98 141	88.23 260	99.71 81	93.10 196	99.72 60	99.38 97

ACMMP		98.05 43	97.75 65	98.93 18	99.23 56	97.60 19	98.09 46	98.96 70	95.75 131	97.91 130	98.06 133	96.89 50	99.76 49	95.32 125	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 34	99.02 39	98.74 67	97.68 24	99.61 135	97.77 42	99.85 40	99.70 19

region2R		97.92 57	97.59 80	98.92 19	99.22 57	97.55 23	97.60 79	98.84 88	96.00 120	97.22 164	97.62 173	96.87 53	99.76 49	95.48 118	99.43 141	99.46 67

mPP-MVS		97.91 59	97.53 84	99.04 7	99.22 57	97.87 11	97.74 69	98.78 106	96.04 118	97.10 171	97.73 166	96.53 69	99.78 40	95.16 134	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 33	98.78 50	98.52 83	97.32 34	99.45 194	94.08 171	99.67 74	99.13 139
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 61	97.56 22	97.59 80	98.83 96	96.05 116	97.46 157	97.63 172	96.77 58	99.76 49	95.61 113	99.46 127	99.49 59

PGM-MVS		97.88 62	97.52 85	98.96 13	99.20 61	97.62 18	97.09 107	99.06 36	95.45 141	97.55 148	97.94 146	97.11 42	99.78 40	94.77 150	99.46 127	99.48 62

test_0402		97.84 65	97.97 53	97.47 119	99.19 63	94.07 131	96.71 127	98.73 114	98.66 23	98.56 65	98.41 90	96.84 55	99.69 98	94.82 145	99.81 44	98.64 204

EPP-MVSNet		96.84 132	96.58 142	97.65 102	99.18 64	93.78 143	98.68 12	96.34 273	97.91 48	97.30 162	98.06 133	88.46 258	99.85 24	93.85 181	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 71	99.11 23	97.76 52	98.62 59	98.27 107	97.88 21	99.80 37	95.67 107	99.50 113	99.38 97

XVG-ACMP-BASELINE		97.58 86	97.28 97	98.49 47	99.16 65	96.90 42	96.39 134	98.98 67	95.05 164	98.06 113	98.02 137	95.86 87	99.56 154	94.37 162	99.64 78	99.00 160

CHOSEN 1792x2688		94.10 240	93.41 245	96.18 198	99.16 65	90.04 211	92.15 314	98.68 126	79.90 341	96.22 216	97.83 154	87.92 265	99.42 200	89.18 266	99.65 77	99.08 152

HFP-MVS		97.94 54	97.64 74	98.83 25	99.15 68	97.50 25	97.59 80	98.84 88	96.05 116	97.49 152	97.54 179	97.07 45	99.70 89	95.61 113	99.46 127	99.30 112

#test#		97.62 82	97.22 105	98.83 25	99.15 68	97.50 25	96.81 122	98.84 88	94.25 191	97.49 152	97.54 179	97.07 45	99.70 89	94.37 162	99.46 127	99.30 112

XVS		97.96 49	97.63 76	98.94 15	99.15 68	97.66 16	97.77 64	98.83 96	97.42 74	96.32 209	97.64 171	96.49 72	99.72 71	95.66 109	99.37 153	99.45 72

X-MVStestdata		92.86 262	90.83 296	98.94 15	99.15 68	97.66 16	97.77 64	98.83 96	97.42 74	96.32 209	36.50 359	96.49 72	99.72 71	95.66 109	99.37 153	99.45 72

LPG-MVS_test		97.94 54	97.67 70	98.74 32	99.15 68	97.02 38	97.09 107	99.02 51	95.15 156	98.34 82	98.23 109	97.91 19	99.70 89	94.41 159	99.73 57	99.50 51

LGP-MVS_train				98.74 32	99.15 68	97.02 38		99.02 51	95.15 156	98.34 82	98.23 109	97.91 19	99.70 89	94.41 159	99.73 57	99.50 51

RPSCF		97.87 63	97.51 86	98.95 14	99.15 68	98.43 3	97.56 82	99.06 36	96.19 114	98.48 71	98.70 70	94.72 126	99.24 248	94.37 162	99.33 167	99.17 132

ACMM	93.33 11	98.05 43	97.79 61	98.85 24	99.15 68	97.55 23	96.68 128	98.83 96	95.21 151	98.36 80	98.13 124	98.13 16	99.62 129	96.04 94	99.54 104	99.39 95
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 76	93.67 145	98.23 35	98.66 131	97.41 81	99.00 42	99.19 32	95.47 106	99.73 65	95.83 103	99.76 51	99.30 112

Vis-MVSNet (Re-imp)		95.11 206	94.85 204	95.87 220	99.12 77	89.17 234	97.54 85	94.92 292	96.50 102	96.58 193	97.27 199	83.64 286	99.48 183	88.42 278	99.67 74	98.97 164

OPM-MVS		97.54 89	97.25 98	98.41 52	99.11 78	96.61 51	95.24 213	98.46 152	94.58 179	98.10 107	98.07 130	97.09 44	99.39 220	95.16 134	99.44 132	99.21 127

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	97.87 140	99.67 3	96.47 74	99.92 4	97.88 36	99.98 3	99.85 4

AllTest		97.20 111	96.92 126	98.06 76	99.08 80	96.16 62	97.14 100	99.16 16	94.35 188	97.78 145	98.07 130	95.84 88	99.12 257	91.41 219	99.42 144	98.91 176

TestCases				98.06 76	99.08 80	96.16 62		99.16 16	94.35 188	97.78 145	98.07 130	95.84 88	99.12 257	91.41 219	99.42 144	98.91 176

TranMVSNet+NR-MVSNet		98.33 30	98.30 40	98.43 51	99.07 82	95.87 70	96.73 126	99.05 38	98.67 22	98.84 47	98.45 88	97.58 27	99.88 19	96.45 84	99.86 39	99.54 46

VPA-MVSNet		98.27 33	98.46 30	97.70 97	99.06 83	93.80 141	97.76 66	99.00 62	98.40 30	99.07 37	98.98 51	96.89 50	99.75 55	97.19 67	99.79 48	99.55 45

114514_t		93.96 244	93.22 249	96.19 197	99.06 83	90.97 198	95.99 159	98.94 73	73.88 355	93.43 303	96.93 217	92.38 205	99.37 228	89.09 267	99.28 174	98.25 241

EG-PatchMatch MVS		97.69 78	97.79 61	97.40 127	99.06 83	93.52 153	95.96 165	98.97 69	94.55 180	98.82 48	98.76 65	97.31 35	99.29 242	97.20 66	99.44 132	99.38 97

ACMP	92.54 13	97.47 93	97.10 115	98.55 46	99.04 86	96.70 48	96.24 147	98.89 78	93.71 210	97.97 122	97.75 163	97.44 29	99.63 123	93.22 193	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				98.08 110

ESAPD		97.22 110	96.82 131	98.40 54	99.03 87	96.07 65	95.64 185	98.84 88	94.84 168	98.08 110	97.60 175	96.69 61	99.76 49	91.22 225	99.44 132	99.37 102

v13		98.02 45	98.52 28	96.51 174	99.02 89	90.14 209	98.07 47	99.09 29	98.10 42	99.13 34	99.35 18	94.84 124	99.74 60	99.12 5	99.98 3	99.65 25

XVG-OURS-SEG-HR		97.38 98	97.07 118	98.30 65	99.01 90	97.41 31	94.66 239	99.02 51	95.20 152	98.15 101	97.52 182	98.83 5	98.43 323	94.87 143	96.41 317	99.07 154

XVG-OURS		97.12 112	96.74 136	98.26 67	98.99 91	97.45 29	93.82 275	99.05 38	95.19 153	98.32 85	97.70 169	95.22 116	98.41 324	94.27 167	98.13 257	98.93 172

CP-MVS		97.92 57	97.56 83	98.99 10	98.99 91	97.82 12	97.93 55	98.96 70	96.11 115	96.89 186	97.45 187	96.85 54	99.78 40	95.19 130	99.63 79	99.38 97

v12		97.97 48	98.47 29	96.46 178	98.98 93	90.01 213	97.97 52	99.08 30	98.00 45	99.11 36	99.34 20	94.70 127	99.73 65	99.07 6	99.98 3	99.64 28

CSCG		97.40 97	97.30 94	97.69 99	98.95 94	94.83 105	97.28 93	98.99 65	96.35 109	98.13 103	95.95 270	95.99 84	99.66 116	94.36 165	99.73 57	98.59 209

v11		97.82 69	98.36 35	96.17 199	98.93 95	89.16 235	97.79 63	99.08 30	97.64 63	99.19 31	99.32 22	94.28 146	99.72 71	99.07 6	99.97 8	99.63 30

V9		97.90 60	98.40 33	96.40 182	98.93 95	89.86 215	97.86 59	99.07 34	97.88 49	99.05 38	99.30 23	94.53 138	99.72 71	99.01 8	99.98 3	99.63 30

HyFIR lowres test		93.72 248	92.65 259	96.91 153	98.93 95	91.81 188	91.23 328	98.52 147	82.69 329	96.46 200	96.52 244	80.38 295	99.90 13	90.36 251	98.79 221	99.03 158

testing_2		97.43 94	97.71 66	96.60 166	98.91 98	90.85 199	96.01 158	98.54 145	94.78 172	98.78 50	98.96 53	96.35 78	99.54 160	97.25 62	99.82 43	99.40 92

PM-MVS		97.36 102	97.10 115	98.14 73	98.91 98	96.77 45	96.20 149	98.63 138	93.82 207	98.54 66	98.33 98	93.98 156	99.05 267	95.99 98	99.45 131	98.61 208

CPTT-MVS		96.69 146	96.08 164	98.49 47	98.89 100	96.64 50	97.25 94	98.77 107	92.89 234	96.01 223	97.13 204	92.23 206	99.67 111	92.24 205	99.34 162	99.17 132

V14		97.83 66	98.33 37	96.35 183	98.88 101	89.72 216	97.75 67	99.05 38	97.74 53	99.01 40	99.27 25	94.35 143	99.71 81	98.95 9	99.97 8	99.62 32

SMA-MVS		97.55 87	97.19 107	98.61 42	98.83 102	96.71 46	96.74 124	98.81 102	91.81 253	98.78 50	98.36 94	96.63 65	99.68 104	95.17 132	99.59 90	99.45 72

v15		97.77 72	98.26 41	96.30 188	98.81 103	89.59 223	97.62 76	99.04 45	97.59 65	98.97 44	99.24 27	94.19 150	99.70 89	98.88 11	99.97 8	99.61 34

UniMVSNet (Re)		97.83 66	97.65 72	98.35 61	98.80 104	95.86 71	95.92 169	99.04 45	97.51 71	98.22 94	97.81 158	94.68 130	99.78 40	97.14 69	99.75 55	99.41 89

APD-MVS_3200maxsize		98.13 40	97.90 56	98.79 28	98.79 105	97.31 33	97.55 83	98.92 74	97.72 57	98.25 92	98.13 124	97.10 43	99.75 55	95.44 120	99.24 178	99.32 108

DeepC-MVS	95.41 4	97.82 69	97.70 67	98.16 71	98.78 106	95.72 74	96.23 148	99.02 51	93.92 200	98.62 59	98.99 50	97.69 23	99.62 129	96.18 89	99.87 37	99.15 136
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	96.28 191	98.77 107	89.59 223	97.62 76	99.01 60	97.54 68	98.72 56	99.18 35	94.06 154	99.68 104	98.74 16	99.92 24	99.58 37

v16		97.69 78	98.16 44	96.29 190	98.75 108	89.60 221	97.62 76	99.01 60	97.53 70	98.69 58	99.18 35	94.05 155	99.68 104	98.73 17	99.88 34	99.58 37

MCST-MVS		96.24 163	95.80 175	97.56 106	98.75 108	94.13 130	94.66 239	98.17 197	90.17 269	96.21 217	96.10 264	95.14 117	99.43 199	94.13 170	98.85 220	99.13 139

DU-MVS		97.79 71	97.60 79	98.36 59	98.73 110	95.78 72	95.65 183	98.87 82	97.57 66	98.31 87	97.83 154	94.69 128	99.85 24	97.02 72	99.71 64	99.46 67

NR-MVSNet		97.96 49	97.86 58	98.26 67	98.73 110	95.54 82	98.14 43	98.73 114	97.79 50	99.42 16	97.83 154	94.40 142	99.78 40	95.91 102	99.76 51	99.46 67

Anonymous20231206		95.27 202	95.06 196	95.88 219	98.72 112	89.37 231	95.70 177	97.85 218	88.00 290	96.98 177	97.62 173	91.95 215	99.34 232	89.21 265	99.53 106	98.94 168

APDe-MVS		98.14 38	98.03 52	98.47 49	98.72 112	96.04 67	98.07 47	99.10 25	95.96 122	98.59 63	98.69 71	96.94 48	99.81 33	96.64 76	99.58 93	99.57 41

UniMVSNet_NR-MVSNet		97.83 66	97.65 72	98.37 56	98.72 112	95.78 72	95.66 181	99.02 51	98.11 41	98.31 87	97.69 170	94.65 132	99.85 24	97.02 72	99.71 64	99.48 62

v8		97.60 84	98.06 49	96.23 192	98.71 115	89.44 227	97.43 89	98.82 100	97.29 86	98.74 54	99.10 45	93.86 158	99.68 104	98.61 22	99.94 19	99.56 42

v6		96.97 119	97.24 100	96.15 200	98.71 115	89.44 227	95.97 161	98.33 173	95.25 148	97.89 133	98.15 120	93.86 158	99.61 135	97.51 54	99.50 113	99.42 87

v1neww		96.97 119	97.24 100	96.15 200	98.70 117	89.44 227	95.97 161	98.33 173	95.25 148	97.88 135	98.15 120	93.83 161	99.61 135	97.50 55	99.50 113	99.41 89

v7new		96.97 119	97.24 100	96.15 200	98.70 117	89.44 227	95.97 161	98.33 173	95.25 148	97.88 135	98.15 120	93.83 161	99.61 135	97.50 55	99.50 113	99.41 89

HQP_MVS		96.66 148	96.33 157	97.68 100	98.70 117	94.29 122	96.50 131	98.75 111	96.36 107	96.16 219	96.77 228	91.91 219	99.46 190	92.59 201	99.20 181	99.28 119

plane_prior7					98.70 117	94.67 112

Anonymous20240529		97.96 49	98.04 51	97.71 95	98.69 121	94.28 125	97.86 59	98.31 183	98.79 20	99.23 29	98.86 60	95.76 98	99.61 135	95.49 117	99.36 156	99.23 125

VDD-MVS		97.37 99	97.25 98	97.74 94	98.69 121	94.50 117	97.04 110	95.61 288	98.59 24	98.51 68	98.72 68	92.54 199	99.58 148	96.02 96	99.49 120	99.12 144

v18		97.60 84	98.06 49	96.23 192	98.68 123	89.46 226	97.48 87	98.98 67	97.33 84	98.60 62	99.13 43	93.86 158	99.67 111	98.62 21	99.87 37	99.56 42

HPM-MVS++		96.99 115	96.38 153	98.81 27	98.64 124	97.59 20	95.97 161	98.20 192	95.51 139	95.06 245	96.53 242	94.10 153	99.70 89	94.29 166	99.15 185	99.13 139

ab-mvs		96.59 150	96.59 141	96.60 166	98.64 124	92.21 174	98.35 29	97.67 230	94.45 181	96.99 176	98.79 62	94.96 122	99.49 180	90.39 250	99.07 196	98.08 251

F-COLMAP		95.30 200	94.38 225	98.05 79	98.64 124	96.04 67	95.61 189	98.66 131	89.00 277	93.22 308	96.40 252	92.90 187	99.35 231	87.45 297	97.53 292	98.77 196

ITE_SJBPF				97.85 88	98.64 124	96.66 49		98.51 149	95.63 133	97.22 164	97.30 198	95.52 103	98.55 318	90.97 230	98.90 211	98.34 232

v148		96.58 151	96.97 122	95.42 233	98.63 128	87.57 273	95.09 219	97.90 215	95.91 124	98.24 93	97.96 142	93.42 172	99.39 220	96.04 94	99.52 110	99.29 118

UnsupCasMVSNet_bld		94.72 220	94.26 227	96.08 205	98.62 129	90.54 208	93.38 291	98.05 211	90.30 267	97.02 174	96.80 226	89.54 247	99.16 256	88.44 277	96.18 320	98.56 211

DP-MVS		97.87 63	97.89 57	97.81 90	98.62 129	94.82 106	97.13 101	98.79 103	98.98 16	98.74 54	98.49 85	95.80 97	99.49 180	95.04 141	99.44 132	99.11 147

v10		97.55 87	97.97 53	96.31 187	98.60 131	89.64 218	97.44 88	99.02 51	96.60 99	98.72 56	99.16 40	93.48 170	99.72 71	98.76 15	99.92 24	99.58 37

Test_1112_low_res		93.53 254	92.86 254	95.54 230	98.60 131	88.86 244	92.75 303	98.69 124	82.66 330	92.65 317	96.92 218	84.75 283	99.56 154	90.94 231	97.76 270	98.19 246

v7		96.93 123	97.17 109	96.23 192	98.59 133	89.64 218	95.96 165	98.66 131	94.41 184	97.87 140	98.38 93	93.47 171	99.64 120	97.93 35	99.24 178	99.43 85

V42		97.04 113	97.16 110	96.68 164	98.59 133	91.05 196	96.33 141	98.36 168	94.60 176	97.99 118	98.30 103	93.32 176	99.62 129	97.40 60	99.53 106	99.38 97

1112_ss		94.12 239	93.42 244	96.23 192	98.59 133	90.85 199	94.24 252	98.85 85	85.49 312	92.97 310	94.94 290	86.01 276	99.64 120	91.78 213	97.92 266	98.20 245

v2v482		96.78 139	97.06 119	95.95 215	98.57 136	88.77 248	95.36 202	98.26 186	95.18 154	97.85 142	98.23 109	92.58 196	99.63 123	97.80 40	99.69 68	99.45 72

WR-MVS		96.90 127	96.81 132	97.16 137	98.56 137	92.20 176	94.33 247	98.12 203	97.34 83	98.20 96	97.33 197	92.81 188	99.75 55	94.79 148	99.81 44	99.54 46

v1141		96.86 129	97.14 112	96.04 207	98.55 138	89.06 238	95.44 192	98.33 173	95.14 158	97.93 128	98.19 114	93.36 174	99.62 129	97.61 47	99.69 68	99.44 81

v1		96.86 129	97.14 112	96.04 207	98.55 138	89.06 238	95.44 192	98.33 173	95.14 158	97.94 125	98.18 118	93.39 173	99.61 135	97.61 47	99.69 68	99.44 81

APD-MVS		97.00 114	96.53 148	98.41 52	98.55 138	96.31 59	96.32 142	98.77 107	92.96 233	97.44 158	97.58 178	95.84 88	99.74 60	91.96 207	99.35 160	99.19 129
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmatch-RL test		94.66 222	94.49 219	95.19 239	98.54 141	88.91 242	92.57 307	98.74 113	91.46 256	98.32 85	97.75 163	77.31 309	98.81 297	96.06 92	99.61 85	97.85 266

divwei89l23v2f112		96.86 129	97.14 112	96.04 207	98.54 141	89.06 238	95.44 192	98.33 173	95.14 158	97.93 128	98.19 114	93.36 174	99.61 135	97.61 47	99.68 72	99.44 81

1111		88.78 316	89.39 309	86.96 341	98.53 143	62.84 360	91.49 323	97.48 244	94.45 181	96.56 195	96.45 247	43.83 366	98.87 291	86.33 304	99.40 151	99.18 131

.test1245		73.49 334	79.27 335	56.15 347	98.53 143	62.84 360	91.49 323	97.48 244	94.45 181	96.56 195	96.45 247	43.83 366	98.87 291	86.33 304	8.32 361	6.75 361

IterMVS-LS		96.92 125	97.29 95	95.79 222	98.51 145	88.13 258	95.10 217	98.66 131	96.99 87	98.46 73	98.68 72	92.55 197	99.74 60	96.91 74	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		95.55 184	95.13 192	96.80 156	98.51 145	93.99 135	94.60 241	98.69 124	90.20 268	95.78 231	96.21 260	92.73 191	98.98 277	90.58 244	98.86 218	97.42 281

test20.03		96.58 151	96.61 140	96.48 177	98.49 147	91.72 189	95.68 180	97.69 229	96.81 95	98.27 91	97.92 149	94.18 151	98.71 305	90.78 237	99.66 76	99.00 160

plane_prior1					98.49 147

MDA-MVSNet-bldmvs		95.69 178	95.67 178	95.74 223	98.48 149	88.76 249	92.84 300	97.25 250	96.00 120	97.59 147	97.95 145	91.38 227	99.46 190	93.16 195	96.35 318	98.99 163

UnsupCasMVSNet_eth		95.91 174	95.73 177	96.44 179	98.48 149	91.52 192	95.31 207	98.45 153	95.76 130	97.48 155	97.54 179	89.53 249	98.69 307	94.43 158	94.61 334	99.13 139

view600		92.56 266	92.11 266	93.91 279	98.45 151	84.76 308	97.10 103	90.23 339	97.42 74	96.98 177	94.48 299	73.62 325	99.60 142	82.49 329	98.28 249	97.36 282

view800		92.56 266	92.11 266	93.91 279	98.45 151	84.76 308	97.10 103	90.23 339	97.42 74	96.98 177	94.48 299	73.62 325	99.60 142	82.49 329	98.28 249	97.36 282

conf0.05thres1000		92.56 266	92.11 266	93.91 279	98.45 151	84.76 308	97.10 103	90.23 339	97.42 74	96.98 177	94.48 299	73.62 325	99.60 142	82.49 329	98.28 249	97.36 282

tfpn		92.56 266	92.11 266	93.91 279	98.45 151	84.76 308	97.10 103	90.23 339	97.42 74	96.98 177	94.48 299	73.62 325	99.60 142	82.49 329	98.28 249	97.36 282

v1144		96.84 132	97.08 117	96.13 204	98.42 155	89.28 233	95.41 199	98.67 129	94.21 193	97.97 122	98.31 100	93.06 181	99.65 117	98.06 32	99.62 80	99.45 72

plane_prior6					98.38 156	94.37 121						91.91 219

FPMVS		89.92 310	88.63 317	93.82 284	98.37 157	96.94 41	91.58 322	93.34 309	88.00 290	90.32 338	97.10 207	70.87 340	91.13 358	71.91 354	96.16 321	93.39 347

PAPM_NR		94.61 225	94.17 232	95.96 213	98.36 158	91.23 194	95.93 168	97.95 213	92.98 228	93.42 304	94.43 304	90.53 235	98.38 328	87.60 294	96.29 319	98.27 239

MVS_111021_HR		96.73 142	96.54 147	97.27 133	98.35 159	93.66 148	93.42 289	98.36 168	94.74 173	96.58 193	96.76 230	96.54 68	98.99 275	94.87 143	99.27 176	99.15 136

TAMVS		95.49 187	94.94 199	97.16 137	98.31 160	93.41 155	95.07 222	96.82 267	91.09 259	97.51 150	97.82 157	89.96 244	99.42 200	88.42 278	99.44 132	98.64 204

OMC-MVS		96.48 155	96.00 167	97.91 85	98.30 161	96.01 69	94.86 233	98.60 141	91.88 251	97.18 166	97.21 201	96.11 82	99.04 268	90.49 248	99.34 162	98.69 202

新几何1				97.25 136	98.29 162	94.70 111		97.73 226	77.98 348	94.83 252	96.67 235	92.08 211	99.45 194	88.17 282	98.65 234	97.61 275

jason		94.39 230	94.04 235	95.41 235	98.29 162	87.85 269	92.74 305	96.75 269	85.38 317	95.29 241	96.15 261	88.21 261	99.65 117	94.24 168	99.34 162	98.74 198
jason: jason.
no-one		94.84 215	94.76 208	95.09 243	98.29 162	87.49 275	91.82 320	97.49 242	88.21 286	97.84 143	98.75 66	91.51 224	99.27 244	88.96 270	99.99 2	98.52 214

v1192		96.83 135	97.06 119	96.15 200	98.28 165	89.29 232	95.36 202	98.77 107	93.73 209	98.11 104	98.34 96	93.02 186	99.67 111	98.35 27	99.58 93	99.50 51

CDPH-MVS		95.45 193	94.65 211	97.84 89	98.28 165	94.96 102	93.73 278	98.33 173	85.03 319	95.44 238	96.60 238	95.31 112	99.44 198	90.01 255	99.13 188	99.11 147

conf0.01		91.90 282	90.98 287	94.67 257	98.27 167	88.03 260	96.98 113	88.58 347	93.90 201	94.64 256	91.45 335	69.62 344	99.52 166	87.62 288	97.74 271	96.46 313

conf0.002		91.90 282	90.98 287	94.67 257	98.27 167	88.03 260	96.98 113	88.58 347	93.90 201	94.64 256	91.45 335	69.62 344	99.52 166	87.62 288	97.74 271	96.46 313

thresconf0.02		91.72 289	90.98 287	93.97 275	98.27 167	88.03 260	96.98 113	88.58 347	93.90 201	94.64 256	91.45 335	69.62 344	99.52 166	87.62 288	97.74 271	94.35 339

tfpn_n400		91.72 289	90.98 287	93.97 275	98.27 167	88.03 260	96.98 113	88.58 347	93.90 201	94.64 256	91.45 335	69.62 344	99.52 166	87.62 288	97.74 271	94.35 339

tfpnconf		91.72 289	90.98 287	93.97 275	98.27 167	88.03 260	96.98 113	88.58 347	93.90 201	94.64 256	91.45 335	69.62 344	99.52 166	87.62 288	97.74 271	94.35 339

tfpnview11		91.72 289	90.98 287	93.97 275	98.27 167	88.03 260	96.98 113	88.58 347	93.90 201	94.64 256	91.45 335	69.62 344	99.52 166	87.62 288	97.74 271	94.35 339

MVS_111021_LR		96.82 136	96.55 145	97.62 103	98.27 167	95.34 89	93.81 276	98.33 173	94.59 178	96.56 195	96.63 237	96.61 66	98.73 303	94.80 147	99.34 162	98.78 195

CLD-MVS		95.47 190	95.07 194	96.69 163	98.27 167	92.53 167	91.36 326	98.67 129	91.22 258	95.78 231	94.12 308	95.65 101	98.98 277	90.81 235	99.72 60	98.57 210
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
1121		94.26 231	93.26 247	97.27 133	98.26 175	94.73 108	95.86 170	97.71 228	77.96 349	94.53 264	96.71 232	91.93 217	99.40 214	87.71 284	98.64 235	97.69 272

Anonymous202405211		96.34 161	95.98 169	97.43 124	98.25 176	93.85 139	96.74 124	94.41 297	97.72 57	98.37 78	98.03 136	87.15 271	99.53 162	94.06 172	99.07 196	98.92 175

pmmvs-eth3d		96.49 154	96.18 160	97.42 125	98.25 176	94.29 122	94.77 238	98.07 209	89.81 272	97.97 122	98.33 98	93.11 180	99.08 264	95.46 119	99.84 41	98.89 180

v144192		96.69 146	96.90 127	96.03 210	98.25 176	88.92 241	95.49 190	98.77 107	93.05 226	98.09 108	98.29 104	92.51 201	99.70 89	98.11 30	99.56 98	99.47 65

ambc				96.56 173	98.23 179	91.68 190	97.88 58	98.13 202		98.42 76	98.56 80	94.22 149	99.04 268	94.05 175	99.35 160	98.95 166

testmv		95.51 185	95.33 187	96.05 206	98.23 179	89.51 225	93.50 287	98.63 138	94.25 191	98.22 94	97.73 166	92.51 201	99.47 185	85.22 314	99.72 60	99.17 132

tfpn111		91.92 281	91.39 276	93.49 291	98.21 181	84.50 313	96.39 134	90.39 334	96.87 91	96.33 205	93.08 316	73.44 331	99.51 176	79.87 337	97.94 265	96.46 313

conf200view11		91.81 286	91.26 281	93.46 292	98.21 181	84.50 313	96.39 134	90.39 334	96.87 91	96.33 205	93.08 316	73.44 331	99.42 200	78.85 342	97.74 271	96.46 313

thres100view900		91.76 288	91.26 281	93.26 296	98.21 181	84.50 313	96.39 134	90.39 334	96.87 91	96.33 205	93.08 316	73.44 331	99.42 200	78.85 342	97.74 271	95.85 323

v1921920		96.72 143	96.96 124	95.99 211	98.21 181	88.79 247	95.42 197	98.79 103	93.22 218	98.19 97	98.26 108	92.68 192	99.70 89	98.34 28	99.55 102	99.49 59

thres600view7		92.03 279	91.43 275	93.82 284	98.19 185	84.61 312	96.27 143	90.39 334	96.81 95	96.37 204	93.11 314	73.44 331	99.49 180	80.32 336	97.95 262	97.36 282

PatchMatch-RL		94.61 225	93.81 239	97.02 148	98.19 185	95.72 74	93.66 280	97.23 251	88.17 287	94.94 249	95.62 278	91.43 226	98.57 315	87.36 298	97.68 284	96.76 304

LF4IMVS		96.07 169	95.63 180	97.36 129	98.19 185	95.55 81	95.44 192	98.82 100	92.29 243	95.70 235	96.55 240	92.63 195	98.69 307	91.75 216	99.33 167	97.85 266

v1240		96.74 140	97.02 121	95.91 218	98.18 188	88.52 250	95.39 200	98.88 80	93.15 224	98.46 73	98.40 92	92.80 189	99.71 81	98.45 26	99.49 120	99.49 59

TAPA-MVS	93.32 12	94.93 213	94.23 228	97.04 145	98.18 188	94.51 115	95.22 214	98.73 114	81.22 336	96.25 215	95.95 270	93.80 164	98.98 277	89.89 256	98.87 216	97.62 274
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.17 190	93.24 159	92.74 305	97.61 240	75.17 353	94.65 255	96.69 234	90.96 232			98.66 233	97.66 273

MIMVSNet		93.42 255	92.86 254	95.10 242	98.17 190	88.19 255	98.13 44	93.69 302	92.07 244	95.04 247	98.21 113	80.95 293	99.03 271	81.42 334	98.06 259	98.07 253

原ACMM1				96.58 170	98.16 192	92.12 178		98.15 200	85.90 309	93.49 299	96.43 249	92.47 203	99.38 225	87.66 287	98.62 236	98.23 242

testdata				95.70 226	98.16 192	90.58 205		97.72 227	80.38 339	95.62 236	97.02 211	92.06 213	98.98 277	89.06 269	98.52 241	97.54 278

MVP-Stereo		95.69 178	95.28 188	96.92 151	98.15 194	93.03 161	95.64 185	98.20 192	90.39 266	96.63 192	97.73 166	91.63 222	99.10 262	91.84 212	97.31 301	98.63 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		97.37 99	97.70 67	96.35 183	98.14 195	95.13 97	96.54 129	98.92 74	95.94 123	99.19 31	98.08 129	97.74 22	95.06 355	95.24 128	99.54 104	98.87 186

EU-MVSNet		94.25 232	94.47 220	93.60 288	98.14 195	82.60 324	97.24 96	92.72 317	85.08 318	98.48 71	98.94 55	82.59 289	98.76 301	97.47 58	99.53 106	99.44 81

NP-MVS					98.14 195	93.72 144					95.08 286

LCM-MVSNet-Re		97.33 103	97.33 93	97.32 131	98.13 198	93.79 142	96.99 112	99.65 2	96.74 97	99.47 13	98.93 56	96.91 49	99.84 28	90.11 253	99.06 199	98.32 233

3Dnovator+	96.13 3	97.73 74	97.59 80	98.15 72	98.11 199	95.60 79	98.04 49	98.70 123	98.13 40	96.93 184	98.45 88	95.30 113	99.62 129	95.64 111	98.96 205	99.24 124

Test4		95.39 195	95.24 189	95.82 221	98.07 200	89.60 221	94.40 245	98.49 150	91.39 257	97.40 160	96.32 255	87.32 270	99.41 211	95.09 140	98.71 231	98.44 220

VNet		96.84 132	96.83 130	96.88 154	98.06 201	92.02 181	96.35 140	97.57 241	97.70 59	97.88 135	97.80 159	92.40 204	99.54 160	94.73 152	98.96 205	99.08 152

DI_MVS_plusplus_test		95.46 191	95.43 185	95.55 229	98.05 202	88.84 245	94.18 257	95.75 284	91.92 250	97.32 161	96.94 215	91.44 225	99.39 220	94.81 146	98.48 244	98.43 221

LFMVS		95.32 199	94.88 203	96.62 165	98.03 203	91.47 193	97.65 73	90.72 333	99.11 8	97.89 133	98.31 100	79.20 298	99.48 183	93.91 180	99.12 191	98.93 172

tfpn200view9		91.55 294	91.00 285	93.21 298	98.02 204	84.35 317	95.70 177	90.79 331	96.26 111	95.90 228	92.13 331	73.62 325	99.42 200	78.85 342	97.74 271	95.85 323

thres400		91.68 293	91.00 285	93.71 286	98.02 204	84.35 317	95.70 177	90.79 331	96.26 111	95.90 228	92.13 331	73.62 325	99.42 200	78.85 342	97.74 271	97.36 282

xiu_mvs_v1_base_debu		95.62 181	95.96 170	94.60 261	98.01 206	88.42 251	93.99 267	98.21 189	92.98 228	95.91 225	94.53 296	96.39 75	99.72 71	95.43 122	98.19 254	95.64 327

xiu_mvs_v1_base		95.62 181	95.96 170	94.60 261	98.01 206	88.42 251	93.99 267	98.21 189	92.98 228	95.91 225	94.53 296	96.39 75	99.72 71	95.43 122	98.19 254	95.64 327

xiu_mvs_v1_base_debi		95.62 181	95.96 170	94.60 261	98.01 206	88.42 251	93.99 267	98.21 189	92.98 228	95.91 225	94.53 296	96.39 75	99.72 71	95.43 122	98.19 254	95.64 327

CNVR-MVS		96.92 125	96.55 145	98.03 80	98.00 209	95.54 82	94.87 232	98.17 197	94.60 176	96.38 203	97.05 209	95.67 100	99.36 229	95.12 138	99.08 194	99.19 129

PLC	91.02 16	94.05 243	92.90 253	97.51 111	98.00 209	95.12 98	94.25 251	98.25 187	86.17 305	91.48 329	95.25 284	91.01 230	99.19 252	85.02 316	96.69 313	98.22 243
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_normal		95.51 185	95.46 184	95.68 227	97.97 211	89.12 237	93.73 278	95.86 282	91.98 247	97.17 167	96.94 215	91.55 223	99.42 200	95.21 129	98.73 229	98.51 215

tfpn1000		91.88 285	91.20 283	93.89 283	97.96 212	87.13 283	97.13 101	88.16 354	94.41 184	94.87 251	92.77 323	68.34 351	99.47 185	89.24 264	97.95 262	95.06 333

GBi-Net		96.99 115	96.80 133	97.56 106	97.96 212	93.67 145	98.23 35	98.66 131	95.59 136	97.99 118	99.19 32	89.51 250	99.73 65	94.60 154	99.44 132	99.30 112

test1		96.99 115	96.80 133	97.56 106	97.96 212	93.67 145	98.23 35	98.66 131	95.59 136	97.99 118	99.19 32	89.51 250	99.73 65	94.60 154	99.44 132	99.30 112

FMVSNet2		96.72 143	96.67 139	96.87 155	97.96 212	91.88 185	97.15 98	98.06 210	95.59 136	98.50 70	98.62 76	89.51 250	99.65 117	94.99 142	99.60 88	99.07 154

BH-untuned		94.69 221	94.75 209	94.52 266	97.95 216	87.53 274	94.07 264	97.01 260	93.99 198	97.10 171	95.65 276	92.65 194	98.95 282	87.60 294	96.74 312	97.09 290

QAPM		95.88 176	95.57 181	96.80 156	97.90 217	91.84 187	98.18 42	98.73 114	88.41 282	96.42 201	98.13 124	94.73 125	99.75 55	88.72 273	98.94 209	98.81 191

TinyColmap		96.00 172	96.34 156	94.96 247	97.90 217	87.91 267	94.13 262	98.49 150	94.41 184	98.16 99	97.76 160	96.29 80	98.68 310	90.52 245	99.42 144	98.30 236

HQP-NCC					97.85 219		94.26 248		93.18 220	92.86 312

ACMP_Plane					97.85 219		94.26 248		93.18 220	92.86 312

N_pmnet		95.18 204	94.23 228	98.06 76	97.85 219	96.55 53	92.49 309	91.63 325	89.34 274	98.09 108	97.41 189	90.33 238	99.06 266	91.58 218	99.31 169	98.56 211

HQP-MVS		95.17 205	94.58 216	96.92 151	97.85 219	92.47 168	94.26 248	98.43 157	93.18 220	92.86 312	95.08 286	90.33 238	99.23 250	90.51 246	98.74 226	99.05 157

TEST9					97.84 223	95.23 91	93.62 282	98.39 164	86.81 300	93.78 286	95.99 265	94.68 130	99.52 166

train_agg		95.46 191	94.66 210	97.88 86	97.84 223	95.23 91	93.62 282	98.39 164	87.04 298	93.78 286	95.99 265	94.58 135	99.52 166	91.76 214	98.90 211	98.89 180

MSLP-MVS++		96.42 160	96.71 137	95.57 228	97.82 225	90.56 207	95.71 176	98.84 88	94.72 174	96.71 190	97.39 193	94.91 123	98.10 339	95.28 126	99.02 201	98.05 256

test_8					97.81 226	95.07 99	93.54 285	98.38 166	87.04 298	93.71 290	95.96 269	94.58 135	99.52 166

NCCC		96.52 153	95.99 168	98.10 74	97.81 226	95.68 76	95.00 228	98.20 192	95.39 144	95.40 240	96.36 253	93.81 163	99.45 194	93.55 188	98.42 246	99.17 132

WTY-MVS		93.55 253	93.00 252	95.19 239	97.81 226	87.86 268	93.89 272	96.00 277	89.02 276	94.07 277	95.44 282	86.27 274	99.33 235	87.69 286	96.82 309	98.39 224

CNLPA		95.04 209	94.47 220	96.75 159	97.81 226	95.25 90	94.12 263	97.89 216	94.41 184	94.57 262	95.69 274	90.30 241	98.35 331	86.72 303	98.76 224	96.64 308

agg_prior3		95.30 200	94.46 223	97.80 91	97.80 230	95.00 100	93.63 281	98.34 172	86.33 304	93.40 306	95.84 272	94.15 152	99.50 178	91.76 214	98.90 211	98.89 180

agg_prior1		95.39 195	94.60 214	97.75 93	97.80 230	94.96 102	93.39 290	98.36 168	87.20 296	93.49 299	95.97 268	94.65 132	99.53 162	91.69 217	98.86 218	98.77 196

agg_prior					97.80 230	94.96 102		98.36 168		93.49 299			99.53 162

旧先验1					97.80 230	93.87 137		97.75 224			97.04 210	93.57 169			98.68 232	98.72 200

PCF-MVS	89.43 18	92.12 278	90.64 299	96.57 172	97.80 230	93.48 154	89.88 342	98.45 153	74.46 354	96.04 222	95.68 275	90.71 234	99.31 237	73.73 350	99.01 203	96.91 298
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior3		95.91 174	95.39 186	97.46 120	97.79 235	94.26 126	93.33 293	98.42 160	94.21 193	94.02 279	96.25 257	93.64 167	99.34 232	91.90 208	98.96 205	98.79 193

test_prior				97.46 120	97.79 235	94.26 126		98.42 160					99.34 232			98.79 193

PVSNet_BlendedMVS		95.02 211	94.93 201	95.27 237	97.79 235	87.40 278	94.14 261	98.68 126	88.94 278	94.51 265	98.01 138	93.04 182	99.30 239	89.77 258	99.49 120	99.11 147

PVSNet_Blended		93.96 244	93.65 241	94.91 248	97.79 235	87.40 278	91.43 325	98.68 126	84.50 323	94.51 265	94.48 299	93.04 182	99.30 239	89.77 258	98.61 237	98.02 261

USDC		94.56 227	94.57 218	94.55 265	97.78 239	86.43 291	92.75 303	98.65 137	85.96 307	96.91 185	97.93 148	90.82 233	98.74 302	90.71 240	99.59 90	98.47 217

alignmvs		96.01 171	95.52 182	97.50 114	97.77 240	94.71 110	96.07 154	96.84 265	97.48 72	96.78 189	94.28 307	85.50 278	99.40 214	96.22 88	98.73 229	98.40 222

TSAR-MVS + GP.		96.47 156	96.12 161	97.49 117	97.74 241	95.23 91	94.15 260	96.90 264	93.26 217	98.04 115	96.70 233	94.41 141	98.89 287	94.77 150	99.14 186	98.37 226

3Dnovator	96.53 2	97.61 83	97.64 74	97.50 114	97.74 241	93.65 149	98.49 23	98.88 80	96.86 94	97.11 170	98.55 81	95.82 91	99.73 65	95.94 100	99.42 144	99.13 139

tfpn_ndepth		90.98 300	90.24 305	93.20 300	97.72 243	87.18 282	96.52 130	88.20 353	92.63 237	93.69 292	90.70 348	68.22 352	99.42 200	86.98 300	97.47 296	93.00 349

sss		94.22 233	93.72 240	95.74 223	97.71 244	89.95 214	93.84 274	96.98 261	88.38 285	93.75 288	95.74 273	87.94 262	98.89 287	91.02 228	98.10 258	98.37 226

DeepC-MVS_fast	94.34 7	96.74 140	96.51 150	97.44 123	97.69 245	94.15 129	96.02 157	98.43 157	93.17 223	97.30 162	97.38 195	95.48 105	99.28 243	93.74 184	99.34 162	98.88 184
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
semantic-postprocess				94.85 252	97.68 246	85.53 295		97.63 238	96.99 87	98.36 80	98.54 82	87.44 268	99.75 55	97.07 71	99.08 194	99.27 122

MVSFormer		96.14 168	96.36 155	95.49 232	97.68 246	87.81 270	98.67 13	99.02 51	96.50 102	94.48 267	96.15 261	86.90 272	99.92 4	98.73 17	99.13 188	98.74 198

lupinMVS		93.77 246	93.28 246	95.24 238	97.68 246	87.81 270	92.12 315	96.05 276	84.52 322	94.48 267	95.06 288	86.90 272	99.63 123	93.62 187	99.13 188	98.27 239

Fast-Effi-MVS+		95.49 187	95.07 194	96.75 159	97.67 249	92.82 163	94.22 254	98.60 141	91.61 254	93.42 304	92.90 321	96.73 60	99.70 89	92.60 200	97.89 269	97.74 271

canonicalmvs		97.23 109	97.21 106	97.30 132	97.65 250	94.39 119	97.84 61	99.05 38	97.42 74	96.68 191	93.85 310	97.63 26	99.33 235	96.29 87	98.47 245	98.18 248

CDS-MVSNet		94.88 214	94.12 233	97.14 139	97.64 251	93.57 150	93.96 270	97.06 259	90.05 270	96.30 212	96.55 240	86.10 275	99.47 185	90.10 254	99.31 169	98.40 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs5		94.63 224	94.34 226	95.50 231	97.63 252	88.34 254	94.02 265	97.13 256	87.15 297	95.22 243	97.15 203	87.50 267	99.27 244	93.99 177	99.26 177	98.88 184

test12				97.46 120	97.61 253	94.07 131		97.78 223		93.57 297		93.31 177	99.42 200		98.78 222	98.89 180

PMMVS2		93.66 250	94.07 234	92.45 313	97.57 254	80.67 332	86.46 349	96.00 277	93.99 198	97.10 171	97.38 195	89.90 245	97.82 342	88.76 272	99.47 125	98.86 187

BH-RMVSNet		94.56 227	94.44 224	94.91 248	97.57 254	87.44 277	93.78 277	96.26 274	93.69 211	96.41 202	96.50 245	92.10 210	99.00 274	85.96 306	97.71 281	98.31 234

PVSNet	86.72 19	91.10 297	90.97 293	91.49 320	97.56 256	78.04 341	87.17 347	94.60 295	84.65 321	92.34 321	92.20 330	87.37 269	98.47 321	85.17 315	97.69 283	97.96 263

DELS-MVS		96.17 167	96.23 159	95.99 211	97.55 257	90.04 211	92.38 312	98.52 147	94.13 196	96.55 198	97.06 208	94.99 121	99.58 148	95.62 112	99.28 174	98.37 226
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		95.42 194	95.83 174	94.20 273	97.52 258	83.78 321	92.41 311	97.47 247	95.49 140	98.06 113	98.49 85	87.94 262	99.58 148	96.02 96	99.02 201	99.23 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
casdiffmvs		96.43 159	96.38 153	96.60 166	97.51 259	91.95 184	97.08 109	98.41 162	93.69 211	93.95 283	98.34 96	93.03 184	99.45 194	98.09 31	97.30 302	98.39 224

new-patchmatchnet		95.67 180	96.58 142	92.94 306	97.48 260	80.21 333	92.96 299	98.19 196	94.83 170	98.82 48	98.79 62	93.31 177	99.51 176	95.83 103	99.04 200	99.12 144

MDA-MVSNet_test_wron		94.73 218	94.83 207	94.42 267	97.48 260	85.15 301	90.28 337	95.87 281	92.52 238	97.48 155	97.76 160	91.92 218	99.17 255	93.32 189	96.80 311	98.94 168

PHI-MVS		96.96 122	96.53 148	98.25 69	97.48 260	96.50 54	96.76 123	98.85 85	93.52 214	96.19 218	96.85 221	95.94 85	99.42 200	93.79 183	99.43 141	98.83 190

DeepPCF-MVS	94.58 5	96.90 127	96.43 152	98.31 64	97.48 260	97.23 35	92.56 308	98.60 141	92.84 235	98.54 66	97.40 190	96.64 64	98.78 299	94.40 161	99.41 150	98.93 172

thres200		91.00 299	90.42 303	92.77 308	97.47 264	83.98 320	94.01 266	91.18 329	95.12 161	95.44 238	91.21 343	73.93 321	99.31 237	77.76 346	97.63 289	95.01 334

YYNet1		94.73 218	94.84 205	94.41 268	97.47 264	85.09 303	90.29 336	95.85 283	92.52 238	97.53 149	97.76 160	91.97 214	99.18 253	93.31 190	96.86 308	98.95 166

Effi-MVS+		96.19 166	96.01 166	96.71 161	97.43 266	92.19 177	96.12 153	99.10 25	95.45 141	93.33 307	94.71 294	97.23 41	99.56 154	93.21 194	97.54 291	98.37 226

pmmvs4		94.82 217	94.19 231	96.70 162	97.42 267	92.75 165	92.09 317	96.76 268	86.80 301	95.73 234	97.22 200	89.28 253	98.89 287	93.28 191	99.14 186	98.46 219

MSDG		95.33 198	95.13 192	95.94 217	97.40 268	91.85 186	91.02 329	98.37 167	95.30 146	96.31 211	95.99 265	94.51 139	98.38 328	89.59 260	97.65 287	97.60 276

EI-MVSNet-Vis-set		97.32 104	97.39 91	97.11 140	97.36 269	92.08 180	95.34 204	97.65 234	97.74 53	98.29 90	98.11 127	95.05 118	99.68 104	97.50 55	99.50 113	99.56 42

PS-MVSNAJ		94.10 240	94.47 220	93.00 304	97.35 270	84.88 305	91.86 319	97.84 219	91.96 248	94.17 272	92.50 328	95.82 91	99.71 81	91.27 222	97.48 294	94.40 338

Regformer-3		97.25 108	97.29 95	97.11 140	97.35 270	92.32 171	95.26 211	97.62 239	97.67 62	98.17 98	97.89 151	95.05 118	99.56 154	97.16 68	99.42 144	99.46 67

Regformer-4		97.53 91	97.47 89	97.71 95	97.35 270	93.91 136	95.26 211	98.14 201	97.97 46	98.34 82	97.89 151	95.49 104	99.71 81	97.41 59	99.42 144	99.51 50

EI-MVSNet-UG-set		97.32 104	97.40 90	97.09 142	97.34 273	92.01 182	95.33 205	97.65 234	97.74 53	98.30 89	98.14 123	95.04 120	99.69 98	97.55 51	99.52 110	99.58 37

AdaColmap		95.11 206	94.62 213	96.58 170	97.33 274	94.45 118	94.92 230	98.08 207	93.15 224	93.98 282	95.53 281	94.34 144	99.10 262	85.69 309	98.61 237	96.20 320

xiu_mvs_v2_base		94.22 233	94.63 212	92.99 305	97.32 275	84.84 306	92.12 315	97.84 219	91.96 248	94.17 272	93.43 311	96.07 83	99.71 81	91.27 222	97.48 294	94.42 337

OpenMVS_ROB	91.80 14	93.64 251	93.05 250	95.42 233	97.31 276	91.21 195	95.08 221	96.68 272	81.56 333	96.88 187	96.41 250	90.44 237	99.25 247	85.39 313	97.67 285	95.80 325

EI-MVSNet		96.63 149	96.93 125	95.74 223	97.26 277	88.13 258	95.29 209	97.65 234	96.99 87	97.94 125	98.19 114	92.55 197	99.58 148	96.91 74	99.56 98	99.50 51

CVMVSNet		92.33 274	92.79 256	90.95 326	97.26 277	75.84 348	95.29 209	92.33 320	81.86 331	96.27 213	98.19 114	81.44 291	98.46 322	94.23 169	98.29 248	98.55 213

Regformer-1		97.27 106	97.16 110	97.61 104	97.21 279	93.86 138	94.85 234	98.04 212	97.62 64	98.03 116	97.50 184	95.34 110	99.63 123	96.52 80	99.31 169	99.35 106

Regformer-2		97.41 96	97.24 100	97.93 84	97.21 279	94.72 109	94.85 234	98.27 184	97.74 53	98.11 104	97.50 184	95.58 102	99.69 98	96.57 79	99.31 169	99.37 102

Fast-Effi-MVS+-dtu		96.44 157	96.12 161	97.39 128	97.18 281	94.39 119	95.46 191	98.73 114	96.03 119	94.72 253	94.92 292	96.28 81	99.69 98	93.81 182	97.98 261	98.09 250

OpenMVS	94.22 8	95.48 189	95.20 190	96.32 186	97.16 282	91.96 183	97.74 69	98.84 88	87.26 294	94.36 269	98.01 138	93.95 157	99.67 111	90.70 241	98.75 225	97.35 288

BH-w/o		92.14 277	91.94 270	92.73 309	97.13 283	85.30 298	92.46 310	95.64 287	89.33 275	94.21 271	92.74 325	89.60 246	98.24 334	81.68 333	94.66 333	94.66 336

MG-MVS		94.08 242	94.00 237	94.32 270	97.09 284	85.89 292	93.19 297	95.96 279	92.52 238	94.93 250	97.51 183	89.54 247	98.77 300	87.52 296	97.71 281	98.31 234

MVS_0304		96.22 164	95.94 173	97.04 145	97.07 285	92.54 166	94.19 256	99.04 45	95.17 155	93.74 289	96.92 218	91.77 221	99.73 65	95.76 105	99.81 44	98.85 189

MVS-HIRNet		88.40 320	90.20 306	82.99 344	97.01 286	60.04 362	93.11 298	85.61 356	84.45 324	88.72 346	99.09 46	84.72 284	98.23 335	82.52 328	96.59 315	90.69 355

GA-MVS		92.83 263	92.15 265	94.87 251	96.97 287	87.27 281	90.03 338	96.12 275	91.83 252	94.05 278	94.57 295	76.01 316	98.97 281	92.46 203	97.34 300	98.36 231

test1235678		92.95 261	92.40 261	94.61 260	96.95 288	86.87 286	90.75 331	97.75 224	91.00 261	96.33 205	95.38 283	85.21 280	98.92 283	79.00 340	99.20 181	98.03 259

MVS_Test		96.27 162	96.79 135	94.73 256	96.94 289	86.63 289	96.18 150	98.33 173	94.94 165	96.07 221	98.28 105	95.25 115	99.26 246	97.21 64	97.90 268	98.30 236

MAR-MVS		94.21 236	93.03 251	97.76 92	96.94 289	97.44 30	96.97 119	97.15 255	87.89 292	92.00 324	92.73 326	92.14 208	99.12 257	83.92 322	97.51 293	96.73 305
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		96.81 137	96.09 163	98.99 10	96.90 291	98.69 2	96.42 133	98.09 205	95.86 126	95.15 244	95.54 280	94.26 147	99.81 33	94.06 172	98.51 243	98.47 217

mvs-test1		96.20 165	95.50 183	98.32 62	96.90 291	98.16 4	95.07 222	98.09 205	95.86 126	93.63 293	94.32 306	94.26 147	99.71 81	94.06 172	97.27 304	97.07 291

MS-PatchMatch		94.83 216	94.91 202	94.57 264	96.81 293	87.10 284	94.23 253	97.34 248	88.74 280	97.14 168	97.11 206	91.94 216	98.23 335	92.99 198	97.92 266	98.37 226

UGNet		96.81 137	96.56 144	97.58 105	96.64 294	93.84 140	97.75 67	97.12 257	96.47 105	93.62 294	98.88 59	93.22 179	99.53 162	95.61 113	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		95.09 208	95.01 197	95.31 236	96.61 295	94.02 133	96.83 121	97.18 254	95.60 135	95.79 230	94.33 305	94.54 137	98.37 330	85.70 308	98.52 241	93.52 345

PAPM		87.64 327	85.84 330	93.04 302	96.54 296	84.99 304	88.42 346	95.57 289	79.52 342	83.82 355	93.05 320	80.57 294	98.41 324	62.29 358	92.79 340	95.71 326

diffmvs		95.00 212	95.00 198	95.01 246	96.53 297	87.96 266	95.73 174	98.32 182	90.67 264	91.89 326	97.43 188	92.07 212	98.90 284	95.44 120	96.88 307	98.16 249

FMVSNet3		95.26 203	94.94 199	96.22 196	96.53 297	90.06 210	95.99 159	97.66 232	94.11 197	97.99 118	97.91 150	80.22 296	99.63 123	94.60 154	99.44 132	98.96 165

HY-MVS	91.43 15	92.58 265	91.81 273	94.90 250	96.49 299	88.87 243	97.31 91	94.62 294	85.92 308	90.50 337	96.84 222	85.05 281	99.40 214	83.77 325	95.78 325	96.43 317

TR-MVS		92.54 270	92.20 264	93.57 289	96.49 299	86.66 288	93.51 286	94.73 293	89.96 271	94.95 248	93.87 309	90.24 243	98.61 313	81.18 335	94.88 331	95.45 331

CANet		95.86 177	95.65 179	96.49 176	96.41 301	90.82 201	94.36 246	98.41 162	94.94 165	92.62 319	96.73 231	92.68 192	99.71 81	95.12 138	99.60 88	98.94 168

mvs_anonymous		95.36 197	96.07 165	93.21 298	96.29 302	81.56 327	94.60 241	97.66 232	93.30 216	96.95 183	98.91 58	93.03 184	99.38 225	96.60 77	97.30 302	98.69 202

Patchmatch-test1		93.38 257	93.59 242	92.73 309	96.24 303	81.40 328	93.24 295	94.00 300	91.58 255	94.57 262	96.67 235	87.94 262	99.03 271	90.42 249	97.66 286	97.77 270

LS3D		97.77 72	97.50 87	98.57 44	96.24 303	97.58 21	98.45 26	98.85 85	98.58 25	97.51 150	97.94 146	95.74 99	99.63 123	95.19 130	98.97 204	98.51 215

new_pmnet		92.34 273	91.69 274	94.32 270	96.23 305	89.16 235	92.27 313	92.88 314	84.39 325	95.29 241	96.35 254	85.66 277	96.74 352	84.53 319	97.56 290	97.05 292

MVE	73.61 22	86.48 329	85.92 329	88.18 338	96.23 305	85.28 299	81.78 357	75.79 360	86.01 306	82.53 357	91.88 333	92.74 190	87.47 360	71.42 355	94.86 332	91.78 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DSMNet-mixed		92.19 276	91.83 272	93.25 297	96.18 307	83.68 322	96.27 143	93.68 304	76.97 352	92.54 320	99.18 35	89.20 255	98.55 318	83.88 323	98.60 239	97.51 279

our_test_3		94.20 238	94.58 216	93.07 301	96.16 308	81.20 329	90.42 335	96.84 265	90.72 263	97.14 168	97.13 204	90.47 236	99.11 260	94.04 176	98.25 253	98.91 176

ppachtmachnet_test		94.49 229	94.84 205	93.46 292	96.16 308	82.10 326	90.59 333	97.48 244	90.53 265	97.01 175	97.59 177	91.01 230	99.36 229	93.97 178	99.18 184	98.94 168

Patchmatch-test		93.60 252	93.25 248	94.63 259	96.14 310	87.47 276	96.04 156	94.50 296	93.57 213	96.47 199	96.97 213	76.50 312	98.61 313	90.67 242	98.41 247	97.81 269

testus		90.90 302	90.51 301	92.06 317	96.07 311	79.45 335	88.99 343	98.44 156	85.46 314	94.15 274	90.77 345	89.12 256	98.01 341	73.66 351	97.95 262	98.71 201

PNet_i23d		83.82 332	83.39 332	85.10 343	96.07 311	65.16 358	81.87 356	94.37 298	90.87 262	93.92 284	92.89 322	52.80 364	96.44 354	77.52 348	70.22 358	93.70 344

wuyk23d		93.25 259	95.20 190	87.40 340	96.07 311	95.38 87	97.04 110	94.97 291	95.33 145	99.70 6	98.11 127	98.14 14	91.94 357	77.76 346	99.68 72	74.89 357

CANet_DTU		94.65 223	94.21 230	95.96 213	95.90 314	89.68 217	93.92 271	97.83 221	93.19 219	90.12 340	95.64 277	88.52 257	99.57 153	93.27 192	99.47 125	98.62 207

test12356		87.98 324	88.41 319	86.69 342	95.84 315	63.49 359	87.15 348	97.32 249	87.21 295	91.78 328	93.36 312	70.66 342	98.39 326	74.70 349	97.64 288	98.19 246

MVSTER		94.21 236	93.93 238	95.05 245	95.83 316	86.46 290	95.18 215	97.65 234	92.41 242	97.94 125	98.00 140	72.39 335	99.58 148	96.36 86	99.56 98	99.12 144

FMVSNet5		93.39 256	92.35 262	96.50 175	95.83 316	90.81 203	97.31 91	98.27 184	92.74 236	96.27 213	98.28 105	62.23 356	99.67 111	90.86 233	99.36 156	99.03 158

PVSNet_0	81.89 21	84.49 331	83.21 334	88.34 337	95.76 318	74.97 351	83.49 353	92.70 318	78.47 347	87.94 349	86.90 356	83.38 287	96.63 353	73.44 352	66.86 359	93.40 346

PAPR		92.22 275	91.27 280	95.07 244	95.73 319	88.81 246	91.97 318	97.87 217	85.80 310	90.91 331	92.73 326	91.16 228	98.33 332	79.48 338	95.76 326	98.08 251

CHOSEN 280x420		89.98 308	89.19 314	92.37 314	95.60 320	81.13 330	86.22 350	97.09 258	81.44 335	87.44 351	93.15 313	73.99 320	99.47 185	88.69 274	99.07 196	96.52 312

ADS-MVSNet2		91.47 295	90.51 301	94.36 269	95.51 321	85.63 293	95.05 225	95.70 285	83.46 327	92.69 315	96.84 222	79.15 299	99.41 211	85.66 310	90.52 344	98.04 257

ADS-MVSNet		90.95 301	90.26 304	93.04 302	95.51 321	82.37 325	95.05 225	93.41 308	83.46 327	92.69 315	96.84 222	79.15 299	98.70 306	85.66 310	90.52 344	98.04 257

CR-MVSNet		93.29 258	92.79 256	94.78 254	95.44 323	88.15 256	96.18 150	97.20 252	84.94 320	94.10 275	98.57 78	77.67 304	99.39 220	95.17 132	95.81 322	96.81 302

RPMNet		94.22 233	94.03 236	94.78 254	95.44 323	88.15 256	96.18 150	93.73 301	97.43 73	94.10 275	98.49 85	79.40 297	99.39 220	95.69 106	95.81 322	96.81 302

1314		92.38 272	92.30 263	92.64 311	95.42 325	85.15 301	95.86 170	96.97 262	85.40 316	90.62 333	93.06 319	91.12 229	97.80 343	86.74 302	95.49 330	94.97 335

tpm		91.08 298	90.85 295	91.75 319	95.33 326	78.09 339	95.03 227	91.27 328	88.75 279	93.53 298	97.40 190	71.24 338	99.30 239	91.25 224	93.87 336	97.87 265

IB-MVS	85.98 20	88.63 317	86.95 326	93.68 287	95.12 327	84.82 307	90.85 330	90.17 343	87.55 293	88.48 347	91.34 342	58.01 358	99.59 146	87.24 299	93.80 337	96.63 310
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		93.75 247	93.57 243	94.29 272	95.05 328	87.32 280	96.05 155	92.98 312	97.54 68	94.25 270	98.72 68	75.79 317	99.24 248	95.92 101	95.81 322	96.32 318

tpm2		88.47 318	87.69 321	90.79 327	94.98 329	77.34 344	95.09 219	91.83 323	77.51 351	89.40 343	96.41 250	67.83 353	98.73 303	83.58 327	92.60 342	96.29 319

Patchmtry		95.03 210	94.59 215	96.33 185	94.83 330	90.82 201	96.38 138	97.20 252	96.59 100	97.49 152	98.57 78	77.67 304	99.38 225	92.95 199	99.62 80	98.80 192

MVS		90.02 306	89.20 313	92.47 312	94.71 331	86.90 285	95.86 170	96.74 270	64.72 357	90.62 333	92.77 323	92.54 199	98.39 326	79.30 339	95.56 329	92.12 350

CostFormer		89.75 311	89.25 310	91.26 323	94.69 332	78.00 342	95.32 206	91.98 322	81.50 334	90.55 335	96.96 214	71.06 339	98.89 287	88.59 276	92.63 341	96.87 299

PatchmatchNet		91.98 280	91.87 271	92.30 315	94.60 333	79.71 334	95.12 216	93.59 307	89.52 273	93.61 295	97.02 211	77.94 302	99.18 253	90.84 234	94.57 335	98.01 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		88.46 319	87.54 322	91.22 324	94.56 334	78.08 340	95.63 188	93.17 310	79.08 345	85.85 353	96.80 226	65.86 355	98.85 294	84.10 321	92.85 339	96.72 306

LP		93.12 260	92.78 258	94.14 274	94.50 335	85.48 296	95.73 174	95.68 286	92.97 232	95.05 246	97.17 202	81.93 290	99.40 214	93.06 197	88.96 349	97.55 277

tpm cat1		88.01 323	87.33 323	90.05 332	94.48 336	76.28 347	94.47 244	94.35 299	73.84 356	89.26 344	95.61 279	73.64 324	98.30 333	84.13 320	86.20 353	95.57 330

MDTV_nov1_ep13			91.28 279		94.31 337	73.51 352	94.80 236	93.16 311	86.75 302	93.45 302	97.40 190	76.37 313	98.55 318	88.85 271	96.43 316

cascas		91.89 284	91.35 278	93.51 290	94.27 338	85.60 294	88.86 345	98.61 140	79.32 343	92.16 323	91.44 341	89.22 254	98.12 338	90.80 236	97.47 296	96.82 301

test-LLR		89.97 309	89.90 307	90.16 330	94.24 339	74.98 349	89.89 339	89.06 344	92.02 245	89.97 341	90.77 345	73.92 322	98.57 315	91.88 210	97.36 298	96.92 296

test-mter		87.92 325	87.17 324	90.16 330	94.24 339	74.98 349	89.89 339	89.06 344	86.44 303	89.97 341	90.77 345	54.96 362	98.57 315	91.88 210	97.36 298	96.92 296

pmmvs3		90.00 307	88.90 316	93.32 294	94.20 341	85.34 297	91.25 327	92.56 319	78.59 346	93.82 285	95.17 285	67.36 354	98.69 307	89.08 268	98.03 260	95.92 321

tpmrst		90.31 304	90.61 300	89.41 333	94.06 342	72.37 355	95.06 224	93.69 302	88.01 289	92.32 322	96.86 220	77.45 306	98.82 295	91.04 227	87.01 352	97.04 293

test0.0.03 1		90.11 305	89.21 312	92.83 307	93.89 343	86.87 286	91.74 321	88.74 346	92.02 245	94.71 254	91.14 344	73.92 322	94.48 356	83.75 326	92.94 338	97.16 289

JIA-IIPM		91.79 287	90.69 298	95.11 241	93.80 344	90.98 197	94.16 259	91.78 324	96.38 106	90.30 339	99.30 23	72.02 337	98.90 284	88.28 280	90.17 346	95.45 331

TESTMET0.1,1		87.20 328	86.57 328	89.07 334	93.62 345	72.84 354	89.89 339	87.01 355	85.46 314	89.12 345	90.20 350	56.00 361	97.72 344	90.91 232	96.92 305	96.64 308

PatchFormer-LS_test		89.62 312	89.12 315	91.11 325	93.62 345	78.42 338	94.57 243	93.62 306	88.39 284	90.54 336	88.40 353	72.33 336	99.03 271	92.41 204	88.20 350	95.89 322

CMPMVS	73.10 23	92.74 264	91.39 276	96.77 158	93.57 347	94.67 112	94.21 255	97.67 230	80.36 340	93.61 295	96.60 238	82.85 288	97.35 346	84.86 317	98.78 222	98.29 238
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DWT-MVSNet_test		87.92 325	86.77 327	91.39 321	93.18 348	78.62 337	95.10 217	91.42 326	85.58 311	88.00 348	88.73 352	60.60 357	98.90 284	90.60 243	87.70 351	96.65 307

E-PMN		89.52 313	89.78 308	88.73 335	93.14 349	77.61 343	83.26 354	92.02 321	94.82 171	93.71 290	93.11 314	75.31 318	96.81 350	85.81 307	96.81 310	91.77 352

PMMVS		92.39 271	91.08 284	96.30 188	93.12 350	92.81 164	90.58 334	95.96 279	79.17 344	91.85 327	92.27 329	90.29 242	98.66 312	89.85 257	96.68 314	97.43 280

EMVS		89.06 315	89.22 311	88.61 336	93.00 351	77.34 344	82.91 355	90.92 330	94.64 175	92.63 318	91.81 334	76.30 314	97.02 348	83.83 324	96.90 306	91.48 353

dp		88.08 322	88.05 320	88.16 339	92.85 352	68.81 357	94.17 258	92.88 314	85.47 313	91.38 330	96.14 263	68.87 350	98.81 297	86.88 301	83.80 356	96.87 299

gg-mvs-nofinetune		88.28 321	86.96 325	92.23 316	92.84 353	84.44 316	98.19 41	74.60 361	99.08 9	87.01 352	99.47 8	56.93 359	98.23 335	78.91 341	95.61 328	94.01 343

tpmvs		90.79 303	90.87 294	90.57 329	92.75 354	76.30 346	95.79 173	93.64 305	91.04 260	91.91 325	96.26 256	77.19 310	98.86 293	89.38 263	89.85 347	96.56 311

EPMVS		89.26 314	88.55 318	91.39 321	92.36 355	79.11 336	95.65 183	79.86 359	88.60 281	93.12 309	96.53 242	70.73 341	98.10 339	90.75 238	89.32 348	96.98 294

gm-plane-assit					91.79 356	71.40 356			81.67 332		90.11 351		98.99 275	84.86 317

test2356		85.45 330	83.26 333	92.01 318	91.12 357	80.76 331	85.16 351	92.90 313	83.90 326	90.63 332	87.71 355	53.10 363	97.24 347	69.20 356	95.65 327	98.03 259

GG-mvs-BLEND				90.60 328	91.00 358	84.21 319	98.23 35	72.63 364		82.76 356	84.11 357	56.14 360	96.79 351	72.20 353	92.09 343	90.78 354

DeepMVS_CX				77.17 346	90.94 359	85.28 299		74.08 363	52.51 358	80.87 359	88.03 354	75.25 319	70.63 361	59.23 359	84.94 354	75.62 356

EPNet_dtu		91.39 296	90.75 297	93.31 295	90.48 360	82.61 323	94.80 236	92.88 314	93.39 215	81.74 358	94.90 293	81.36 292	99.11 260	88.28 280	98.87 216	98.21 244
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testpf		82.70 333	84.35 331	77.74 345	88.97 361	73.23 353	93.85 273	84.33 357	88.10 288	85.06 354	90.42 349	52.62 365	91.05 359	91.00 229	84.82 355	68.93 358

EPNet		93.72 248	92.62 260	97.03 147	87.61 362	92.25 172	96.27 143	91.28 327	96.74 97	87.65 350	97.39 193	85.00 282	99.64 120	92.14 206	99.48 123	99.20 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		57.23 335	62.50 336	41.44 348	34.77 363	49.21 364	83.93 352	60.22 365	15.31 359	71.11 360	79.37 358	70.09 343	44.86 362	64.76 357	82.93 357	30.25 359

test123		12.59 338	15.49 339	3.87 350	6.07 364	2.55 365	90.75 331	2.59 367	2.52 360	5.20 362	13.02 361	4.96 368	1.85 364	5.20 360	9.09 360	7.23 360

testmvs		12.33 339	15.23 340	3.64 351	5.77 365	2.23 366	88.99 343	3.62 366	2.30 361	5.29 361	13.09 360	4.52 369	1.95 363	5.16 361	8.32 361	6.75 361

cdsmvs_eth3d_5k		24.22 337	32.30 338	0.00 352	0.00 366	0.00 367	0.00 358	98.10 204	0.00 362	0.00 363	95.06 288	97.54 28	0.00 365	0.00 362	0.00 363	0.00 363

pcd_1.5k_mvsjas		7.98 340	10.65 341	0.00 352	0.00 366	0.00 367	0.00 358	0.00 368	0.00 362	0.00 363	0.00 364	95.82 91	0.00 365	0.00 362	0.00 363	0.00 363

sosnet-low-res		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 358	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sosnet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 358	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uncertanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 358	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

Regformer		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 358	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

ab-mvs-re		7.91 341	10.55 342	0.00 352	0.00 366	0.00 367	0.00 358	0.00 368	0.00 362	0.00 363	94.94 290	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 358	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

GSMVS																98.06 254

test_part3							95.64 185		94.84 168		97.60 175		99.76 49	91.22 225

test_part1								98.84 88				96.69 61			99.44 132	99.37 102

sam_mvs1												77.80 303				98.06 254

sam_mvs												77.38 307

MTGPA								98.73 114

test_post1							94.98 229				10.37 363	76.21 315	99.04 268	89.47 262

test_post											10.87 362	76.83 311	99.07 265

patchmatchnet-post											96.84 222	77.36 308	99.42 200

MTMP								74.60 361

test9_res														91.29 221	98.89 215	99.00 160

agg_prior2														90.34 252	98.90 211	99.10 151

test_prior4						95.38 87	93.61 284

test_prior2							93.33 293		94.21 193	94.02 279	96.25 257	93.64 167		91.90 208	98.96 205

旧先验2							93.35 292		77.95 350	95.77 233			98.67 311	90.74 239

新几何2							93.43 288

无先验							93.20 296	97.91 214	80.78 337				99.40 214	87.71 284		97.94 264

原ACMM2							92.82 301

testdata2													99.46 190	87.84 283

segment_acmp												95.34 110

testdata1							92.77 302		93.78 208

plane_prior5								98.75 111					99.46 190	92.59 201	99.20 181	99.28 119

plane_prior4											96.77 228

plane_prior3						94.51 115			95.29 147	96.16 219

plane_prior2							96.50 131		96.36 107

plane_prior						94.29 122	95.42 197		94.31 190						98.93 210

n2								0.00 368

nn								0.00 368

door-mid								98.17 197

test11								98.08 207

door								97.81 222

HQP5-MVS						92.47 168

BP-MVS														90.51 246

HQP4-MVS										92.87 311			99.23 250			99.06 156

HQP3-MVS								98.43 157							98.74 226

HQP2-MVS												90.33 238

MDTV_nov1_ep13_2view						57.28 363	94.89 231		80.59 338	94.02 279		78.66 301		85.50 312		97.82 268

ACMMP++_ref															99.52 110

ACMMP++															99.55 102

Test By Simon												94.51 139

High-resolution multi-view benchmark