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	93.72 167	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	98.82 296	96.38 87	99.50 114	96.98 295
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 85	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	96.23 114	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	95.83 131	99.67 6	99.37 14	98.25 10	99.92 4	98.77 14	99.94 19	99.82 7

pcd1.5k->3k		41.47 338	44.19 340	33.29 351	99.65 10	0.00 369	0.00 360	99.07 34	0.00 364	0.00 366	0.00 366	99.04 3	0.00 366	0.00 363	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	95.62 137	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	95.44 192	98.86 85	98.20 39	98.37 79	99.24 27	94.69 127	99.55 157	95.98 101	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	96.97 264	98.30 35	99.45 14	99.35 18	88.43 259	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	95.88 127	97.88 136	98.22 114	98.15 12	99.74 58	96.50 84	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 65	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 72	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 82	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	97.49 244	97.93 47	95.95 227	98.58 78	96.88 51	96.91 350	89.59 261	99.36 155	93.12 349
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	95.38 201	98.99 65	92.45 243	98.11 105	98.31 101	97.25 38	99.77 48	96.60 79	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	94.30 144	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	98.53 148	97.77 51	98.46 74	98.41 91	94.59 133	99.68 103	94.61 154	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 52	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	94.93 170	98.58 64	98.92 57	97.31 34	99.41 211	94.44 158	99.43 140	99.59 36

zzz-MVS		98.01 47	97.66 71	99.06 5	99.44 34	97.90 8	95.66 183	98.73 114	97.69 60	97.90 132	97.96 145	95.81 93	99.82 30	96.13 92	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	97.96 145	95.81 93	99.82 30	96.13 92	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	95.81 132	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	97.87 156	97.02 46	99.76 49	95.25 128	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	95.43 146	97.41 160	97.50 186	97.98 16	99.79 39	95.58 118	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		96.44 158	96.28 160	96.95 149	99.41 40	91.53 192	97.65 73	90.31 341	98.89 18	98.93 45	99.36 16	84.57 285	99.92 4	97.81 41	99.56 98	99.39 96

VDDNet		96.98 118	96.84 130	97.41 126	99.40 41	93.26 158	97.94 54	95.31 293	99.26 6	98.39 78	99.18 35	87.85 266	99.62 128	95.13 138	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	94.79 149	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	98.20 193	93.00 229	98.16 100	98.06 136	95.89 84	99.72 69	95.67 109	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	93.75 166	99.78 40	97.23 64	99.84 41	99.73 15

lessismore_v0				97.05 144	99.36 45	92.12 178		84.07 361		98.77 53	98.98 51	85.36 279	99.74 58	97.34 62	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	95.07 166	97.88 136	98.35 96	97.24 39	99.72 69	96.05 95	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 53	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	96.27 111	95.59 240	97.75 166	96.30 77	99.78 40	93.70 187	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		95.95 175	95.80 177	96.42 181	99.28 50	90.62 205	95.31 207	99.08 30	88.40 285	96.97 183	98.17 121	92.11 210	99.78 40	93.64 188	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	98.45 155	98.25 36	99.13 34	98.66 73	96.65 61	99.69 97	93.92 181	99.62 80	98.91 175

HSP-MVS		97.37 100	96.85 129	98.92 19	99.26 51	97.70 15	97.66 72	98.23 189	95.65 135	98.51 68	96.46 247	92.15 208	99.81 32	95.14 137	98.58 240	99.26 122

testgi		96.07 171	96.50 152	94.80 255	99.26 51	87.69 274	95.96 167	98.58 146	95.08 165	98.02 118	96.25 258	97.92 17	97.60 346	88.68 276	98.74 225	99.11 146

IS-MVSNet		96.93 123	96.68 139	97.70 97	99.25 54	94.00 134	98.57 18	96.74 272	98.36 31	98.14 103	97.98 144	88.23 260	99.71 80	93.10 198	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	95.75 134	97.91 131	98.06 136	96.89 49	99.76 49	95.32 126	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	96.00 122	97.22 165	97.62 176	96.87 52	99.76 49	95.48 120	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	96.04 120	97.10 172	97.73 169	96.53 66	99.78 40	95.16 135	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	94.08 172	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	96.05 118	97.46 158	97.63 175	96.77 58	99.76 49	95.61 115	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	95.45 144	97.55 149	97.94 149	97.11 41	99.78 40	94.77 151	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	94.82 146	99.81 44	98.64 204

EPP-MVSNet		96.84 132	96.58 143	97.65 102	99.18 63	93.78 143	98.68 12	96.34 275	97.91 48	97.30 163	98.06 136	88.46 258	99.85 23	93.85 183	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	98.27 108	97.88 20	99.80 37	95.67 109	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	95.05 167	98.06 114	98.02 140	95.86 85	99.56 153	94.37 163	99.64 78	99.00 159

CHOSEN 1792x2688		94.10 242	93.41 247	96.18 199	99.16 64	90.04 212	92.15 316	98.68 127	79.90 343	96.22 218	97.83 157	87.92 265	99.42 200	89.18 267	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	96.05 118	97.49 153	97.54 181	97.07 44	99.70 88	95.61 115	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	94.25 193	97.49 153	97.54 181	97.07 44	99.70 88	94.37 163	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	97.64 174	96.49 70	99.72 69	95.66 111	99.37 152	99.45 73

X-MVStestdata		92.86 264	90.83 298	98.94 15	99.15 67	97.66 16	97.77 64	98.83 97	97.42 74	96.32 211	36.50 361	96.49 70	99.72 69	95.66 111	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	95.15 159	98.34 83	98.23 111	97.91 18	99.70 88	94.41 160	99.73 57	99.50 52

LGP-MVS_train				98.74 31	99.15 67	97.02 37		99.02 51	95.15 159	98.34 83	98.23 111	97.91 18	99.70 88	94.41 160	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	96.19 115	98.48 71	98.70 70	94.72 125	99.24 249	94.37 163	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	95.21 154	98.36 81	98.13 126	98.13 15	99.62 128	96.04 96	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	95.83 105	99.76 51	99.30 111

Vis-MVSNet (Re-imp)		95.11 208	94.85 205	95.87 221	99.12 76	89.17 236	97.54 85	94.92 295	96.50 103	96.58 195	97.27 200	83.64 286	99.48 183	88.42 279	99.67 74	98.97 163

OPM-MVS		97.54 89	97.25 99	98.41 52	99.11 77	96.61 50	95.24 213	98.46 154	94.58 181	98.10 108	98.07 133	97.09 43	99.39 220	95.16 135	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	94.35 190	97.78 146	98.07 133	95.84 86	99.12 259	91.41 221	99.42 143	98.91 175

TestCases				98.06 75	99.08 79	96.16 62		99.16 15	94.35 190	97.78 146	98.07 133	95.84 86	99.12 259	91.41 221	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 86	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 68	99.79 48	99.55 45

114514_t		93.96 246	93.22 251	96.19 198	99.06 82	90.97 199	95.99 161	98.94 74	73.88 357	93.43 306	96.93 218	92.38 206	99.37 228	89.09 268	99.28 173	98.25 241

EG-PatchMatch MVS		97.69 78	97.79 61	97.40 127	99.06 82	93.52 153	95.96 167	98.97 70	94.55 182	98.82 48	98.76 65	97.31 34	99.29 242	97.20 67	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	93.71 212	97.97 123	97.75 166	97.44 28	99.63 122	93.22 195	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		40.70 339	54.26 339	0.00 354	99.03 86	0.00 369	0.00 360	98.84 89	94.84 171	98.08 111	97.60 178	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

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	94.66 241	99.02 51	95.20 155	98.15 102	97.52 184	98.83 4	98.43 324	94.87 144	96.41 318	99.07 153

XVG-OURS		97.12 112	96.74 137	98.26 66	98.99 90	97.45 29	93.82 277	99.05 38	95.19 156	98.32 86	97.70 172	95.22 115	98.41 325	94.27 168	98.13 258	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	96.11 117	96.89 187	97.45 189	96.85 53	99.78 40	95.19 131	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	95.95 272	95.99 82	99.66 115	94.36 166	99.73 57	98.59 209

v11		97.82 69	98.36 35	96.17 200	98.93 94	89.16 237	97.79 63	99.08 30	97.64 63	99.19 31	99.32 22	94.28 145	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		93.72 250	92.65 261	96.91 153	98.93 94	91.81 189	91.23 330	98.52 149	82.69 331	96.46 202	96.52 245	80.38 296	99.90 13	90.36 252	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	98.54 147	94.78 174	98.78 50	98.96 53	96.35 76	99.54 159	97.25 63	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	93.82 209	98.54 66	98.33 99	93.98 156	99.05 269	95.99 100	99.45 132	98.61 208

CPTT-MVS		96.69 147	96.08 166	98.49 47	98.89 99	96.64 49	97.25 94	98.77 107	92.89 236	96.01 226	97.13 205	92.23 207	99.67 110	92.24 207	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	94.35 142	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	95.21 215	98.99 65	95.84 130	98.78 50	98.08 131	96.84 54	99.81 32	93.98 179	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	91.61 256	98.48 71	98.36 95	96.53 66	99.68 103	95.17 133	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	94.19 150	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	97.81 161	94.68 129	99.78 40	97.14 70	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	98.13 126	97.10 42	99.75 53	95.44 122	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	93.92 202	98.62 59	98.99 50	97.69 22	99.62 128	96.18 91	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	94.06 154	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	94.05 155	99.68 103	98.73 17	99.88 33	99.58 37

MCST-MVS		96.24 164	95.80 177	97.56 106	98.75 109	94.13 130	94.66 241	98.17 198	90.17 271	96.21 219	96.10 266	95.14 116	99.43 199	94.13 171	98.85 219	99.13 138

DU-MVS		97.79 71	97.60 79	98.36 58	98.73 111	95.78 71	95.65 185	98.87 83	97.57 66	98.31 88	97.83 157	94.69 127	99.85 23	97.02 74	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	97.83 157	94.40 141	99.78 40	95.91 104	99.76 51	99.46 68

Anonymous20231206		95.27 204	95.06 198	95.88 220	98.72 113	89.37 233	95.70 179	97.85 220	88.00 292	96.98 178	97.62 176	91.95 215	99.34 232	89.21 266	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	95.96 124	98.59 63	98.69 71	96.94 47	99.81 32	96.64 78	99.58 92	99.57 41

UniMVSNet_NR-MVSNet		97.83 66	97.65 72	98.37 55	98.72 113	95.78 71	95.66 183	99.02 51	98.11 41	98.31 88	97.69 173	94.65 131	99.85 23	97.02 74	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	93.86 158	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	98.33 175	95.25 151	97.89 134	98.15 122	93.86 158	99.61 134	97.51 55	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	98.33 175	95.25 151	97.88 136	98.15 122	93.83 161	99.61 134	97.50 56	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	98.33 175	95.25 151	97.88 136	98.15 122	93.83 161	99.61 134	97.50 56	99.50 114	99.41 90

HQP_MVS		96.66 149	96.33 159	97.68 100	98.70 118	94.29 122	96.50 133	98.75 111	96.36 108	96.16 221	96.77 229	91.91 219	99.46 190	92.59 203	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	98.31 184	98.79 20	99.23 29	98.86 60	95.76 96	99.61 134	95.49 119	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	95.61 290	98.59 24	98.51 68	98.72 68	92.54 200	99.58 147	96.02 98	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	93.86 158	99.67 110	98.62 21	99.87 36	99.56 42

HPM-MVS++		96.99 115	96.38 155	98.81 26	98.64 125	97.59 20	95.97 163	98.20 193	95.51 142	95.06 248	96.53 243	94.10 153	99.70 88	94.29 167	99.15 184	99.13 138

ab-mvs		96.59 151	96.59 142	96.60 167	98.64 125	92.21 174	98.35 29	97.67 232	94.45 183	96.99 177	98.79 62	94.96 121	99.49 180	90.39 251	99.07 195	98.08 251

F-COLMAP		95.30 202	94.38 226	98.05 78	98.64 125	96.04 66	95.61 189	98.66 133	89.00 279	93.22 311	96.40 253	92.90 188	99.35 231	87.45 298	97.53 294	98.77 196

ITE_SJBPF				97.85 88	98.64 125	96.66 48		98.51 151	95.63 136	97.22 165	97.30 199	95.52 102	98.55 319	90.97 230	98.90 210	98.34 232

v148		96.58 152	96.97 123	95.42 235	98.63 129	87.57 275	95.09 220	97.90 217	95.91 126	98.24 94	97.96 145	93.42 173	99.39 220	96.04 96	99.52 111	99.29 117

UnsupCasMVSNet_bld		94.72 221	94.26 228	96.08 206	98.62 130	90.54 209	93.38 293	98.05 212	90.30 269	97.02 175	96.80 227	89.54 247	99.16 258	88.44 278	96.18 321	98.56 211

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	95.04 142	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	93.48 171	99.72 69	98.76 15	99.92 23	99.58 37

Test_1112_low_res		93.53 256	92.86 256	95.54 232	98.60 132	88.86 246	92.75 305	98.69 125	82.66 332	92.65 321	96.92 219	84.75 283	99.56 153	90.94 231	97.76 272	98.19 247

v7		96.93 123	97.17 109	96.23 193	98.59 134	89.64 219	95.96 167	98.66 133	94.41 186	97.87 141	98.38 94	93.47 172	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	98.36 170	94.60 178	97.99 119	98.30 104	93.32 177	99.62 128	97.40 61	99.53 107	99.38 98

1112_ss		94.12 241	93.42 246	96.23 193	98.59 134	90.85 200	94.24 254	98.85 86	85.49 314	92.97 314	94.94 292	86.01 276	99.64 119	91.78 215	97.92 267	98.20 246

v2v482		96.78 140	97.06 120	95.95 216	98.57 137	88.77 250	95.36 202	98.26 187	95.18 157	97.85 143	98.23 111	92.58 197	99.63 122	97.80 42	99.69 68	99.45 73

WR-MVS		96.90 127	96.81 133	97.16 137	98.56 138	92.20 176	94.33 249	98.12 204	97.34 83	98.20 97	97.33 198	92.81 189	99.75 53	94.79 149	99.81 44	99.54 46

v1141		96.86 129	97.14 112	96.04 208	98.55 139	89.06 240	95.44 192	98.33 175	95.14 161	97.93 129	98.19 116	93.36 175	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	89.06 240	95.44 192	98.33 175	95.14 161	97.94 126	98.18 120	93.39 174	99.61 134	97.61 49	99.69 68	99.44 82

APD-MVS		97.00 114	96.53 149	98.41 52	98.55 139	96.31 58	96.32 144	98.77 107	92.96 235	97.44 159	97.58 180	95.84 86	99.74 58	91.96 209	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		94.66 223	94.49 220	95.19 241	98.54 142	88.91 244	92.57 309	98.74 113	91.46 259	98.32 86	97.75 166	77.31 310	98.81 298	96.06 94	99.61 85	97.85 267

divwei89l23v2f112		96.86 129	97.14 112	96.04 208	98.54 142	89.06 240	95.44 192	98.33 175	95.14 161	97.93 129	98.19 116	93.36 175	99.61 134	97.61 49	99.68 72	99.44 82

1111		88.78 318	89.39 311	86.96 343	98.53 144	62.84 362	91.49 325	97.48 246	94.45 183	96.56 197	96.45 248	43.83 367	98.87 292	86.33 305	99.40 150	99.18 130

.test1245		73.49 336	79.27 337	56.15 349	98.53 144	62.84 362	91.49 325	97.48 246	94.45 183	96.56 197	96.45 248	43.83 367	98.87 292	86.33 305	8.32 362	6.75 362

IterMVS-LS		96.92 125	97.29 96	95.79 223	98.51 146	88.13 260	95.10 218	98.66 133	96.99 87	98.46 74	98.68 72	92.55 198	99.74 58	96.91 76	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		95.55 186	95.13 194	96.80 156	98.51 146	93.99 135	94.60 243	98.69 125	90.20 270	95.78 234	96.21 261	92.73 192	98.98 279	90.58 245	98.86 217	97.42 282

test20.03		96.58 152	96.61 141	96.48 178	98.49 148	91.72 190	95.68 182	97.69 231	96.81 95	98.27 92	97.92 152	94.18 151	98.71 306	90.78 237	99.66 76	99.00 159

plane_prior1					98.49 148

MDA-MVSNet-bldmvs		95.69 180	95.67 180	95.74 224	98.48 150	88.76 251	92.84 302	97.25 252	96.00 122	97.59 148	97.95 148	91.38 227	99.46 190	93.16 197	96.35 319	98.99 162

UnsupCasMVSNet_eth		95.91 176	95.73 179	96.44 180	98.48 150	91.52 193	95.31 207	98.45 155	95.76 133	97.48 156	97.54 181	89.53 249	98.69 308	94.43 159	94.61 335	99.13 138

view600		92.56 268	92.11 268	93.91 281	98.45 152	84.76 310	97.10 104	90.23 342	97.42 74	96.98 178	94.48 301	73.62 326	99.60 141	82.49 330	98.28 249	97.36 283

view800		92.56 268	92.11 268	93.91 281	98.45 152	84.76 310	97.10 104	90.23 342	97.42 74	96.98 178	94.48 301	73.62 326	99.60 141	82.49 330	98.28 249	97.36 283

conf0.05thres1000		92.56 268	92.11 268	93.91 281	98.45 152	84.76 310	97.10 104	90.23 342	97.42 74	96.98 178	94.48 301	73.62 326	99.60 141	82.49 330	98.28 249	97.36 283

tfpn		92.56 268	92.11 268	93.91 281	98.45 152	84.76 310	97.10 104	90.23 342	97.42 74	96.98 178	94.48 301	73.62 326	99.60 141	82.49 330	98.28 249	97.36 283

v1144		96.84 132	97.08 118	96.13 205	98.42 156	89.28 235	95.41 199	98.67 130	94.21 195	97.97 123	98.31 101	93.06 182	99.65 116	98.06 34	99.62 80	99.45 73

plane_prior6					98.38 157	94.37 120						91.91 219

FPMVS		89.92 312	88.63 319	93.82 286	98.37 158	96.94 40	91.58 324	93.34 312	88.00 292	90.32 341	97.10 208	70.87 341	91.13 359	71.91 355	96.16 322	93.39 348

PAPM_NR		94.61 226	94.17 233	95.96 214	98.36 159	91.23 195	95.93 171	97.95 214	92.98 230	93.42 307	94.43 306	90.53 235	98.38 329	87.60 295	96.29 320	98.27 239

MVS_111021_HR		96.73 143	96.54 148	97.27 133	98.35 160	93.66 148	93.42 291	98.36 170	94.74 175	96.58 195	96.76 231	96.54 65	98.99 277	94.87 144	99.27 175	99.15 135

TAMVS		95.49 189	94.94 200	97.16 137	98.31 161	93.41 155	95.07 223	96.82 269	91.09 262	97.51 151	97.82 160	89.96 244	99.42 200	88.42 279	99.44 133	98.64 204

OMC-MVS		96.48 156	96.00 169	97.91 85	98.30 162	96.01 68	94.86 234	98.60 143	91.88 254	97.18 167	97.21 202	96.11 80	99.04 270	90.49 249	99.34 161	98.69 202

新几何1				97.25 136	98.29 163	94.70 110		97.73 228	77.98 350	94.83 255	96.67 236	92.08 212	99.45 194	88.17 283	98.65 233	97.61 276

jason		94.39 232	94.04 236	95.41 237	98.29 163	87.85 270	92.74 307	96.75 271	85.38 319	95.29 244	96.15 262	88.21 261	99.65 116	94.24 169	99.34 161	98.74 198
jason: jason.
no-one		94.84 216	94.76 209	95.09 246	98.29 163	87.49 277	91.82 322	97.49 244	88.21 288	97.84 144	98.75 66	91.51 224	99.27 245	88.96 271	99.99 2	98.52 214

v1192		96.83 135	97.06 120	96.15 201	98.28 166	89.29 234	95.36 202	98.77 107	93.73 211	98.11 105	98.34 97	93.02 187	99.67 110	98.35 27	99.58 92	99.50 52

CDPH-MVS		95.45 195	94.65 212	97.84 89	98.28 166	94.96 101	93.73 280	98.33 175	85.03 321	95.44 241	96.60 239	95.31 111	99.44 198	90.01 256	99.13 187	99.11 146

conf0.01		91.90 284	90.98 289	94.67 259	98.27 168	88.03 262	96.98 114	88.58 350	93.90 203	94.64 259	91.45 337	69.62 345	99.52 165	87.62 289	97.74 273	96.46 314

conf0.002		91.90 284	90.98 289	94.67 259	98.27 168	88.03 262	96.98 114	88.58 350	93.90 203	94.64 259	91.45 337	69.62 345	99.52 165	87.62 289	97.74 273	96.46 314

thresconf0.02		91.72 291	90.98 289	93.97 277	98.27 168	88.03 262	96.98 114	88.58 350	93.90 203	94.64 259	91.45 337	69.62 345	99.52 165	87.62 289	97.74 273	94.35 340

tfpn_n400		91.72 291	90.98 289	93.97 277	98.27 168	88.03 262	96.98 114	88.58 350	93.90 203	94.64 259	91.45 337	69.62 345	99.52 165	87.62 289	97.74 273	94.35 340

tfpnconf		91.72 291	90.98 289	93.97 277	98.27 168	88.03 262	96.98 114	88.58 350	93.90 203	94.64 259	91.45 337	69.62 345	99.52 165	87.62 289	97.74 273	94.35 340

tfpnview11		91.72 291	90.98 289	93.97 277	98.27 168	88.03 262	96.98 114	88.58 350	93.90 203	94.64 259	91.45 337	69.62 345	99.52 165	87.62 289	97.74 273	94.35 340

MVS_111021_LR		96.82 136	96.55 146	97.62 103	98.27 168	95.34 88	93.81 278	98.33 175	94.59 180	96.56 197	96.63 238	96.61 63	98.73 304	94.80 148	99.34 161	98.78 195

CLD-MVS		95.47 192	95.07 196	96.69 164	98.27 168	92.53 167	91.36 328	98.67 130	91.22 261	95.78 234	94.12 310	95.65 99	98.98 279	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		94.26 233	93.26 249	97.27 133	98.26 176	94.73 107	95.86 173	97.71 230	77.96 351	94.53 267	96.71 233	91.93 217	99.40 214	87.71 285	98.64 234	97.69 273

Anonymous202405211		96.34 162	95.98 171	97.43 124	98.25 177	93.85 139	96.74 125	94.41 300	97.72 57	98.37 79	98.03 139	87.15 271	99.53 161	94.06 173	99.07 195	98.92 174

pmmvs-eth3d		96.49 155	96.18 162	97.42 125	98.25 177	94.29 122	94.77 239	98.07 210	89.81 274	97.97 123	98.33 99	93.11 181	99.08 266	95.46 121	99.84 41	98.89 179

v144192		96.69 147	96.90 128	96.03 211	98.25 177	88.92 243	95.49 190	98.77 107	93.05 228	98.09 109	98.29 105	92.51 202	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	98.13 203		98.42 77	98.56 81	94.22 149	99.04 270	94.05 176	99.35 159	98.95 165

testmv		95.51 187	95.33 189	96.05 207	98.23 180	89.51 227	93.50 289	98.63 140	94.25 193	98.22 95	97.73 169	92.51 202	99.47 185	85.22 315	99.72 60	99.17 131

tfpn111		91.92 283	91.39 278	93.49 293	98.21 182	84.50 315	96.39 136	90.39 337	96.87 91	96.33 207	93.08 318	73.44 332	99.51 175	79.87 338	97.94 266	96.46 314

conf200view11		91.81 288	91.26 283	93.46 294	98.21 182	84.50 315	96.39 136	90.39 337	96.87 91	96.33 207	93.08 318	73.44 332	99.42 200	78.85 343	97.74 273	96.46 314

thres100view900		91.76 290	91.26 283	93.26 298	98.21 182	84.50 315	96.39 136	90.39 337	96.87 91	96.33 207	93.08 318	73.44 332	99.42 200	78.85 343	97.74 273	95.85 324

v1921920		96.72 144	96.96 125	95.99 212	98.21 182	88.79 249	95.42 197	98.79 103	93.22 220	98.19 98	98.26 109	92.68 193	99.70 88	98.34 28	99.55 102	99.49 60

thres600view7		92.03 281	91.43 277	93.82 286	98.19 186	84.61 314	96.27 145	90.39 337	96.81 95	96.37 206	93.11 316	73.44 332	99.49 180	80.32 337	97.95 263	97.36 283

PatchMatch-RL		94.61 226	93.81 241	97.02 148	98.19 186	95.72 73	93.66 282	97.23 253	88.17 289	94.94 252	95.62 280	91.43 226	98.57 316	87.36 299	97.68 286	96.76 305

LF4IMVS		96.07 171	95.63 182	97.36 129	98.19 186	95.55 80	95.44 192	98.82 101	92.29 246	95.70 238	96.55 241	92.63 196	98.69 308	91.75 218	99.33 166	97.85 267

v1240		96.74 141	97.02 122	95.91 219	98.18 189	88.52 252	95.39 200	98.88 81	93.15 226	98.46 74	98.40 93	92.80 190	99.71 80	98.45 26	99.49 121	99.49 60

TAPA-MVS	93.32 12	94.93 214	94.23 229	97.04 145	98.18 189	94.51 114	95.22 214	98.73 114	81.22 338	96.25 217	95.95 272	93.80 165	98.98 279	89.89 257	98.87 215	97.62 275
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.17 191	93.24 159	92.74 307	97.61 242	75.17 355	94.65 258	96.69 235	90.96 232			98.66 232	97.66 274

MIMVSNet		93.42 257	92.86 256	95.10 245	98.17 191	88.19 257	98.13 44	93.69 305	92.07 247	95.04 250	98.21 115	80.95 294	99.03 273	81.42 335	98.06 260	98.07 253

原ACMM1				96.58 171	98.16 193	92.12 178		98.15 201	85.90 311	93.49 302	96.43 250	92.47 204	99.38 225	87.66 288	98.62 235	98.23 243

testdata				95.70 227	98.16 193	90.58 206		97.72 229	80.38 341	95.62 239	97.02 212	92.06 213	98.98 279	89.06 270	98.52 241	97.54 279

MVP-Stereo		95.69 180	95.28 190	96.92 151	98.15 195	93.03 161	95.64 187	98.20 193	90.39 268	96.63 194	97.73 169	91.63 222	99.10 264	91.84 214	97.31 303	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	95.94 125	99.19 31	98.08 131	97.74 21	95.06 356	95.24 129	99.54 104	98.87 185

EU-MVSNet		94.25 234	94.47 221	93.60 290	98.14 196	82.60 326	97.24 96	92.72 320	85.08 320	98.48 71	98.94 55	82.59 289	98.76 302	97.47 59	99.53 107	99.44 82

NP-MVS					98.14 196	93.72 144					95.08 288

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	90.11 254	99.06 198	98.32 233

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	95.64 113	98.96 204	99.24 123

Test4		95.39 197	95.24 191	95.82 222	98.07 201	89.60 222	94.40 247	98.49 152	91.39 260	97.40 161	96.32 256	87.32 270	99.41 211	95.09 141	98.71 230	98.44 220

VNet		96.84 132	96.83 132	96.88 154	98.06 202	92.02 181	96.35 142	97.57 243	97.70 59	97.88 136	97.80 162	92.40 205	99.54 159	94.73 153	98.96 204	99.08 151

DI_MVS_plusplus_test		95.46 193	95.43 187	95.55 231	98.05 203	88.84 247	94.18 259	95.75 286	91.92 253	97.32 162	96.94 216	91.44 225	99.39 220	94.81 147	98.48 244	98.43 221

LFMVS		95.32 201	94.88 204	96.62 166	98.03 204	91.47 194	97.65 73	90.72 336	99.11 8	97.89 134	98.31 101	79.20 299	99.48 183	93.91 182	99.12 190	98.93 171

tfpn200view9		91.55 296	91.00 287	93.21 300	98.02 205	84.35 319	95.70 179	90.79 334	96.26 112	95.90 231	92.13 333	73.62 326	99.42 200	78.85 343	97.74 273	95.85 324

thres400		91.68 295	91.00 287	93.71 288	98.02 205	84.35 319	95.70 179	90.79 334	96.26 112	95.90 231	92.13 333	73.62 326	99.42 200	78.85 343	97.74 273	97.36 283

xiu_mvs_v1_base_debu		95.62 183	95.96 172	94.60 263	98.01 207	88.42 253	93.99 269	98.21 190	92.98 230	95.91 228	94.53 298	96.39 73	99.72 69	95.43 123	98.19 255	95.64 328

xiu_mvs_v1_base		95.62 183	95.96 172	94.60 263	98.01 207	88.42 253	93.99 269	98.21 190	92.98 230	95.91 228	94.53 298	96.39 73	99.72 69	95.43 123	98.19 255	95.64 328

xiu_mvs_v1_base_debi		95.62 183	95.96 172	94.60 263	98.01 207	88.42 253	93.99 269	98.21 190	92.98 230	95.91 228	94.53 298	96.39 73	99.72 69	95.43 123	98.19 255	95.64 328

casdiffmvs1		96.82 136	96.84 130	96.77 158	98.01 207	92.02 181	97.20 98	98.67 130	92.30 245	96.09 223	98.64 76	93.81 163	99.50 177	98.22 30	98.62 235	98.79 192

CNVR-MVS		96.92 125	96.55 146	98.03 79	98.00 211	95.54 81	94.87 233	98.17 198	94.60 178	96.38 205	97.05 210	95.67 98	99.36 229	95.12 139	99.08 193	99.19 128

PLC	91.02 16	94.05 245	92.90 255	97.51 111	98.00 211	95.12 97	94.25 253	98.25 188	86.17 307	91.48 332	95.25 286	91.01 230	99.19 253	85.02 317	96.69 314	98.22 244
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_normal		95.51 187	95.46 186	95.68 228	97.97 213	89.12 239	93.73 280	95.86 284	91.98 250	97.17 168	96.94 216	91.55 223	99.42 200	95.21 130	98.73 228	98.51 215

tfpn1000		91.88 287	91.20 285	93.89 285	97.96 214	87.13 285	97.13 102	88.16 357	94.41 186	94.87 254	92.77 325	68.34 352	99.47 185	89.24 265	97.95 263	95.06 334

GBi-Net		96.99 115	96.80 134	97.56 106	97.96 214	93.67 145	98.23 35	98.66 133	95.59 139	97.99 119	99.19 32	89.51 250	99.73 63	94.60 155	99.44 133	99.30 111

test1		96.99 115	96.80 134	97.56 106	97.96 214	93.67 145	98.23 35	98.66 133	95.59 139	97.99 119	99.19 32	89.51 250	99.73 63	94.60 155	99.44 133	99.30 111

FMVSNet2		96.72 144	96.67 140	96.87 155	97.96 214	91.88 186	97.15 99	98.06 211	95.59 139	98.50 70	98.62 77	89.51 250	99.65 116	94.99 143	99.60 88	99.07 153

BH-untuned		94.69 222	94.75 210	94.52 268	97.95 218	87.53 276	94.07 266	97.01 262	93.99 200	97.10 172	95.65 278	92.65 195	98.95 284	87.60 295	96.74 313	97.09 291

QAPM		95.88 178	95.57 183	96.80 156	97.90 219	91.84 188	98.18 42	98.73 114	88.41 284	96.42 203	98.13 126	94.73 124	99.75 53	88.72 274	98.94 208	98.81 190

TinyColmap		96.00 174	96.34 158	94.96 249	97.90 219	87.91 268	94.13 264	98.49 152	94.41 186	98.16 100	97.76 163	96.29 78	98.68 311	90.52 246	99.42 143	98.30 236

HQP-NCC					97.85 221		94.26 250		93.18 222	92.86 316

ACMP_Plane					97.85 221		94.26 250		93.18 222	92.86 316

N_pmnet		95.18 206	94.23 229	98.06 75	97.85 221	96.55 52	92.49 311	91.63 328	89.34 276	98.09 109	97.41 190	90.33 238	99.06 268	91.58 220	99.31 168	98.56 211

HQP-MVS		95.17 207	94.58 217	96.92 151	97.85 221	92.47 168	94.26 250	98.43 159	93.18 222	92.86 316	95.08 288	90.33 238	99.23 251	90.51 247	98.74 225	99.05 156

TEST9					97.84 225	95.23 90	93.62 284	98.39 166	86.81 302	93.78 289	95.99 267	94.68 129	99.52 165

train_agg		95.46 193	94.66 211	97.88 86	97.84 225	95.23 90	93.62 284	98.39 166	87.04 300	93.78 289	95.99 267	94.58 134	99.52 165	91.76 216	98.90 210	98.89 179

MSLP-MVS++		96.42 161	96.71 138	95.57 230	97.82 227	90.56 208	95.71 178	98.84 89	94.72 176	96.71 192	97.39 194	94.91 122	98.10 340	95.28 127	99.02 200	98.05 257

test_8					97.81 228	95.07 98	93.54 287	98.38 168	87.04 300	93.71 293	95.96 271	94.58 134	99.52 165

NCCC		96.52 154	95.99 170	98.10 73	97.81 228	95.68 75	95.00 229	98.20 193	95.39 147	95.40 243	96.36 254	93.81 163	99.45 194	93.55 190	98.42 246	99.17 131

WTY-MVS		93.55 255	93.00 254	95.19 241	97.81 228	87.86 269	93.89 274	96.00 279	89.02 278	94.07 280	95.44 284	86.27 274	99.33 235	87.69 287	96.82 310	98.39 224

CNLPA		95.04 211	94.47 221	96.75 160	97.81 228	95.25 89	94.12 265	97.89 218	94.41 186	94.57 265	95.69 276	90.30 241	98.35 332	86.72 304	98.76 223	96.64 309

agg_prior3		95.30 202	94.46 224	97.80 91	97.80 232	95.00 99	93.63 283	98.34 174	86.33 306	93.40 309	95.84 274	94.15 152	99.50 177	91.76 216	98.90 210	98.89 179

agg_prior1		95.39 197	94.60 215	97.75 93	97.80 232	94.96 101	93.39 292	98.36 170	87.20 298	93.49 302	95.97 270	94.65 131	99.53 161	91.69 219	98.86 217	98.77 196

agg_prior					97.80 232	94.96 101		98.36 170		93.49 302			99.53 161

旧先验1					97.80 232	93.87 137		97.75 226			97.04 211	93.57 170			98.68 231	98.72 200

PCF-MVS	89.43 18	92.12 280	90.64 301	96.57 173	97.80 232	93.48 154	89.88 344	98.45 155	74.46 356	96.04 225	95.68 277	90.71 234	99.31 237	73.73 351	99.01 202	96.91 299
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior3		95.91 176	95.39 188	97.46 120	97.79 237	94.26 126	93.33 295	98.42 162	94.21 195	94.02 282	96.25 258	93.64 168	99.34 232	91.90 210	98.96 204	98.79 192

test_prior				97.46 120	97.79 237	94.26 126		98.42 162					99.34 232			98.79 192

PVSNet_BlendedMVS		95.02 213	94.93 202	95.27 239	97.79 237	87.40 280	94.14 263	98.68 127	88.94 280	94.51 268	98.01 141	93.04 183	99.30 239	89.77 259	99.49 121	99.11 146

PVSNet_Blended		93.96 246	93.65 243	94.91 250	97.79 237	87.40 280	91.43 327	98.68 127	84.50 325	94.51 268	94.48 301	93.04 183	99.30 239	89.77 259	98.61 237	98.02 262

USDC		94.56 228	94.57 219	94.55 267	97.78 241	86.43 293	92.75 305	98.65 139	85.96 309	96.91 186	97.93 151	90.82 233	98.74 303	90.71 241	99.59 90	98.47 217

alignmvs		96.01 173	95.52 184	97.50 114	97.77 242	94.71 109	96.07 156	96.84 267	97.48 72	96.78 191	94.28 309	85.50 278	99.40 214	96.22 90	98.73 228	98.40 222

TSAR-MVS + GP.		96.47 157	96.12 163	97.49 117	97.74 243	95.23 90	94.15 262	96.90 266	93.26 219	98.04 116	96.70 234	94.41 140	98.89 288	94.77 151	99.14 185	98.37 226

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	95.94 102	99.42 143	99.13 138

tfpn_ndepth		90.98 302	90.24 307	93.20 302	97.72 245	87.18 284	96.52 132	88.20 356	92.63 239	93.69 295	90.70 350	68.22 353	99.42 200	86.98 301	97.47 298	93.00 350

sss		94.22 235	93.72 242	95.74 224	97.71 246	89.95 215	93.84 276	96.98 263	88.38 287	93.75 291	95.74 275	87.94 262	98.89 288	91.02 228	98.10 259	98.37 226

DeepC-MVS_fast	94.34 7	96.74 141	96.51 151	97.44 123	97.69 247	94.15 129	96.02 159	98.43 159	93.17 225	97.30 163	97.38 196	95.48 104	99.28 243	93.74 186	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				94.85 254	97.68 248	85.53 297		97.63 240	96.99 87	98.36 81	98.54 83	87.44 268	99.75 53	97.07 73	99.08 193	99.27 121

MVSFormer		96.14 169	96.36 157	95.49 234	97.68 248	87.81 272	98.67 13	99.02 51	96.50 103	94.48 270	96.15 262	86.90 272	99.92 4	98.73 17	99.13 187	98.74 198

lupinMVS		93.77 248	93.28 248	95.24 240	97.68 248	87.81 272	92.12 317	96.05 278	84.52 324	94.48 270	95.06 290	86.90 272	99.63 122	93.62 189	99.13 187	98.27 239

Fast-Effi-MVS+		95.49 189	95.07 196	96.75 160	97.67 251	92.82 163	94.22 256	98.60 143	91.61 256	93.42 307	92.90 323	96.73 60	99.70 88	92.60 202	97.89 270	97.74 272

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	93.85 312	97.63 25	99.33 235	96.29 89	98.47 245	98.18 249

CDS-MVSNet		94.88 215	94.12 234	97.14 139	97.64 253	93.57 150	93.96 272	97.06 261	90.05 272	96.30 214	96.55 241	86.10 275	99.47 185	90.10 255	99.31 168	98.40 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs5		94.63 225	94.34 227	95.50 233	97.63 254	88.34 256	94.02 267	97.13 258	87.15 299	95.22 246	97.15 204	87.50 267	99.27 245	93.99 178	99.26 176	98.88 183

test12				97.46 120	97.61 255	94.07 131		97.78 225		93.57 300		93.31 178	99.42 200		98.78 221	98.89 179

PMMVS2		93.66 252	94.07 235	92.45 315	97.57 256	80.67 334	86.46 351	96.00 279	93.99 200	97.10 172	97.38 196	89.90 245	97.82 343	88.76 273	99.47 126	98.86 186

BH-RMVSNet		94.56 228	94.44 225	94.91 250	97.57 256	87.44 279	93.78 279	96.26 276	93.69 213	96.41 204	96.50 246	92.10 211	99.00 276	85.96 307	97.71 283	98.31 234

PVSNet	86.72 19	91.10 299	90.97 295	91.49 322	97.56 258	78.04 343	87.17 349	94.60 298	84.65 323	92.34 325	92.20 332	87.37 269	98.47 322	85.17 316	97.69 285	97.96 264

DELS-MVS		96.17 168	96.23 161	95.99 212	97.55 259	90.04 212	92.38 314	98.52 149	94.13 198	96.55 200	97.06 209	94.99 120	99.58 147	95.62 114	99.28 173	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		95.42 196	95.83 176	94.20 275	97.52 260	83.78 323	92.41 313	97.47 249	95.49 143	98.06 114	98.49 86	87.94 262	99.58 147	96.02 98	99.02 200	99.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
casdiffmvs		96.43 160	96.38 155	96.60 167	97.51 261	91.95 185	97.08 110	98.41 164	93.69 213	93.95 286	98.34 97	93.03 185	99.45 194	98.09 33	97.30 304	98.39 224

new-patchmatchnet		95.67 182	96.58 143	92.94 308	97.48 262	80.21 335	92.96 301	98.19 197	94.83 172	98.82 48	98.79 62	93.31 178	99.51 175	95.83 105	99.04 199	99.12 143

MDA-MVSNet_test_wron		94.73 219	94.83 208	94.42 269	97.48 262	85.15 303	90.28 339	95.87 283	92.52 240	97.48 156	97.76 163	91.92 218	99.17 257	93.32 191	96.80 312	98.94 167

PHI-MVS		96.96 122	96.53 149	98.25 68	97.48 262	96.50 53	96.76 124	98.85 86	93.52 216	96.19 220	96.85 222	95.94 83	99.42 200	93.79 185	99.43 140	98.83 189

DeepPCF-MVS	94.58 5	96.90 127	96.43 153	98.31 63	97.48 262	97.23 35	92.56 310	98.60 143	92.84 237	98.54 66	97.40 191	96.64 62	98.78 300	94.40 162	99.41 149	98.93 171

thres200		91.00 301	90.42 305	92.77 310	97.47 266	83.98 322	94.01 268	91.18 332	95.12 164	95.44 241	91.21 345	73.93 322	99.31 237	77.76 347	97.63 291	95.01 335

YYNet1		94.73 219	94.84 206	94.41 270	97.47 266	85.09 305	90.29 338	95.85 285	92.52 240	97.53 150	97.76 163	91.97 214	99.18 254	93.31 192	96.86 309	98.95 165

Effi-MVS+		96.19 167	96.01 168	96.71 162	97.43 268	92.19 177	96.12 155	99.10 25	95.45 144	93.33 310	94.71 296	97.23 40	99.56 153	93.21 196	97.54 293	98.37 226

pmmvs4		94.82 218	94.19 232	96.70 163	97.42 269	92.75 165	92.09 319	96.76 270	86.80 303	95.73 237	97.22 201	89.28 253	98.89 288	93.28 193	99.14 185	98.46 219

MSDG		95.33 200	95.13 194	95.94 218	97.40 270	91.85 187	91.02 331	98.37 169	95.30 149	96.31 213	95.99 267	94.51 138	98.38 329	89.59 261	97.65 289	97.60 277

EI-MVSNet-Vis-set		97.32 105	97.39 91	97.11 140	97.36 271	92.08 180	95.34 204	97.65 236	97.74 53	98.29 91	98.11 129	95.05 117	99.68 103	97.50 56	99.50 114	99.56 42

PS-MVSNAJ		94.10 242	94.47 221	93.00 306	97.35 272	84.88 307	91.86 321	97.84 221	91.96 251	94.17 275	92.50 330	95.82 89	99.71 80	91.27 224	97.48 296	94.40 339

Regformer-3		97.25 109	97.29 96	97.11 140	97.35 272	92.32 171	95.26 211	97.62 241	97.67 62	98.17 99	97.89 154	95.05 117	99.56 153	97.16 69	99.42 143	99.46 68

Regformer-4		97.53 91	97.47 89	97.71 95	97.35 272	93.91 136	95.26 211	98.14 202	97.97 46	98.34 83	97.89 154	95.49 103	99.71 80	97.41 60	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	95.33 205	97.65 236	97.74 53	98.30 90	98.14 125	95.04 119	99.69 97	97.55 53	99.52 111	99.58 37

AdaColmap		95.11 208	94.62 214	96.58 171	97.33 276	94.45 117	94.92 231	98.08 208	93.15 226	93.98 285	95.53 283	94.34 143	99.10 264	85.69 310	98.61 237	96.20 321

xiu_mvs_v2_base		94.22 235	94.63 213	92.99 307	97.32 277	84.84 308	92.12 317	97.84 221	91.96 251	94.17 275	93.43 313	96.07 81	99.71 80	91.27 224	97.48 296	94.42 338

OpenMVS_ROB	91.80 14	93.64 253	93.05 252	95.42 235	97.31 278	91.21 196	95.08 222	96.68 274	81.56 335	96.88 188	96.41 251	90.44 237	99.25 248	85.39 314	97.67 287	95.80 326

EI-MVSNet		96.63 150	96.93 126	95.74 224	97.26 279	88.13 260	95.29 209	97.65 236	96.99 87	97.94 126	98.19 116	92.55 198	99.58 147	96.91 76	99.56 98	99.50 52

CVMVSNet		92.33 276	92.79 258	90.95 328	97.26 279	75.84 350	95.29 209	92.33 323	81.86 333	96.27 215	98.19 116	81.44 291	98.46 323	94.23 170	98.29 248	98.55 213

Regformer-1		97.27 107	97.16 110	97.61 104	97.21 281	93.86 138	94.85 235	98.04 213	97.62 64	98.03 117	97.50 186	95.34 109	99.63 122	96.52 82	99.31 168	99.35 105

Regformer-2		97.41 97	97.24 101	97.93 84	97.21 281	94.72 108	94.85 235	98.27 185	97.74 53	98.11 105	97.50 186	95.58 101	99.69 97	96.57 81	99.31 168	99.37 103

Fast-Effi-MVS+-dtu		96.44 158	96.12 163	97.39 128	97.18 283	94.39 118	95.46 191	98.73 114	96.03 121	94.72 256	94.92 294	96.28 79	99.69 97	93.81 184	97.98 262	98.09 250

OpenMVS	94.22 8	95.48 191	95.20 192	96.32 187	97.16 284	91.96 184	97.74 69	98.84 89	87.26 296	94.36 272	98.01 141	93.95 157	99.67 110	90.70 242	98.75 224	97.35 289

BH-w/o		92.14 279	91.94 272	92.73 311	97.13 285	85.30 300	92.46 312	95.64 289	89.33 277	94.21 274	92.74 327	89.60 246	98.24 335	81.68 334	94.66 334	94.66 337

MG-MVS		94.08 244	94.00 238	94.32 272	97.09 286	85.89 294	93.19 299	95.96 281	92.52 240	94.93 253	97.51 185	89.54 247	98.77 301	87.52 297	97.71 283	98.31 234

MVS_0304		96.22 165	95.94 175	97.04 145	97.07 287	92.54 166	94.19 258	99.04 45	95.17 158	93.74 292	96.92 219	91.77 221	99.73 63	95.76 107	99.81 44	98.85 188

MVS-HIRNet		88.40 322	90.20 308	82.99 346	97.01 288	60.04 364	93.11 300	85.61 359	84.45 326	88.72 349	99.09 46	84.72 284	98.23 336	82.52 329	96.59 316	90.69 356

GA-MVS		92.83 265	92.15 267	94.87 253	96.97 289	87.27 283	90.03 340	96.12 277	91.83 255	94.05 281	94.57 297	76.01 317	98.97 283	92.46 205	97.34 302	98.36 231

0601test		94.40 231	94.00 238	95.59 229	96.95 290	89.52 226	94.75 240	95.55 292	96.18 116	96.79 189	96.14 264	81.09 293	99.18 254	90.75 238	97.77 271	98.07 253

test1235678		92.95 263	92.40 263	94.61 262	96.95 290	86.87 288	90.75 333	97.75 226	91.00 264	96.33 207	95.38 285	85.21 280	98.92 285	79.00 341	99.20 180	98.03 260

MVS_Test		96.27 163	96.79 136	94.73 258	96.94 292	86.63 291	96.18 152	98.33 175	94.94 168	96.07 224	98.28 106	95.25 114	99.26 247	97.21 65	97.90 269	98.30 236

MAR-MVS		94.21 238	93.03 253	97.76 92	96.94 292	97.44 30	96.97 120	97.15 257	87.89 294	92.00 328	92.73 328	92.14 209	99.12 259	83.92 323	97.51 295	96.73 306
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	96.09 165	98.99 10	96.90 294	98.69 2	96.42 135	98.09 206	95.86 128	95.15 247	95.54 282	94.26 147	99.81 32	94.06 173	98.51 243	98.47 217

mvs-test1		96.20 166	95.50 185	98.32 61	96.90 294	98.16 4	95.07 223	98.09 206	95.86 128	93.63 296	94.32 308	94.26 147	99.71 80	94.06 173	97.27 306	97.07 292

MS-PatchMatch		94.83 217	94.91 203	94.57 266	96.81 296	87.10 286	94.23 255	97.34 250	88.74 282	97.14 169	97.11 207	91.94 216	98.23 336	92.99 200	97.92 267	98.37 226

UGNet		96.81 138	96.56 145	97.58 105	96.64 297	93.84 140	97.75 67	97.12 259	96.47 106	93.62 297	98.88 59	93.22 180	99.53 161	95.61 115	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		95.09 210	95.01 199	95.31 238	96.61 298	94.02 133	96.83 122	97.18 256	95.60 138	95.79 233	94.33 307	94.54 136	98.37 331	85.70 309	98.52 241	93.52 346

PAPM		87.64 329	85.84 332	93.04 304	96.54 299	84.99 306	88.42 348	95.57 291	79.52 344	83.82 358	93.05 322	80.57 295	98.41 325	62.29 359	92.79 341	95.71 327

FMVSNet3		95.26 205	94.94 200	96.22 197	96.53 300	90.06 211	95.99 161	97.66 234	94.11 199	97.99 119	97.91 153	80.22 297	99.63 122	94.60 155	99.44 133	98.96 164

diffmvs		96.10 170	96.43 153	95.12 243	96.52 301	87.85 270	95.95 170	97.91 215	96.52 102	93.02 313	98.25 110	94.28 145	99.28 243	97.11 71	98.26 253	98.24 242

HY-MVS	91.43 15	92.58 267	91.81 275	94.90 252	96.49 302	88.87 245	97.31 91	94.62 297	85.92 310	90.50 340	96.84 223	85.05 281	99.40 214	83.77 326	95.78 326	96.43 318

TR-MVS		92.54 272	92.20 266	93.57 291	96.49 302	86.66 290	93.51 288	94.73 296	89.96 273	94.95 251	93.87 311	90.24 243	98.61 314	81.18 336	94.88 332	95.45 332

CANet		95.86 179	95.65 181	96.49 177	96.41 304	90.82 202	94.36 248	98.41 164	94.94 168	92.62 323	96.73 232	92.68 193	99.71 80	95.12 139	99.60 88	98.94 167

mvs_anonymous		95.36 199	96.07 167	93.21 300	96.29 305	81.56 329	94.60 243	97.66 234	93.30 218	96.95 184	98.91 58	93.03 185	99.38 225	96.60 79	97.30 304	98.69 202

Patchmatch-test1		93.38 259	93.59 244	92.73 311	96.24 306	81.40 330	93.24 297	94.00 303	91.58 258	94.57 265	96.67 236	87.94 262	99.03 273	90.42 250	97.66 288	97.77 271

LS3D		97.77 72	97.50 87	98.57 43	96.24 306	97.58 21	98.45 26	98.85 86	98.58 25	97.51 151	97.94 149	95.74 97	99.63 122	95.19 131	98.97 203	98.51 215

new_pmnet		92.34 275	91.69 276	94.32 272	96.23 308	89.16 237	92.27 315	92.88 317	84.39 327	95.29 244	96.35 255	85.66 277	96.74 353	84.53 320	97.56 292	97.05 293

MVE	73.61 22	86.48 331	85.92 331	88.18 340	96.23 308	85.28 301	81.78 359	75.79 363	86.01 308	82.53 360	91.88 335	92.74 191	87.47 361	71.42 356	94.86 333	91.78 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DSMNet-mixed		92.19 278	91.83 274	93.25 299	96.18 310	83.68 324	96.27 145	93.68 307	76.97 354	92.54 324	99.18 35	89.20 255	98.55 319	83.88 324	98.60 239	97.51 280

our_test_3		94.20 240	94.58 217	93.07 303	96.16 311	81.20 331	90.42 337	96.84 267	90.72 266	97.14 169	97.13 205	90.47 236	99.11 262	94.04 177	98.25 254	98.91 175

ppachtmachnet_test		94.49 230	94.84 206	93.46 294	96.16 311	82.10 328	90.59 335	97.48 246	90.53 267	97.01 176	97.59 179	91.01 230	99.36 229	93.97 180	99.18 183	98.94 167

Patchmatch-test		93.60 254	93.25 250	94.63 261	96.14 313	87.47 278	96.04 158	94.50 299	93.57 215	96.47 201	96.97 214	76.50 313	98.61 314	90.67 243	98.41 247	97.81 270

testus		90.90 304	90.51 303	92.06 319	96.07 314	79.45 337	88.99 345	98.44 158	85.46 316	94.15 277	90.77 347	89.12 256	98.01 342	73.66 352	97.95 263	98.71 201

PNet_i23d		83.82 334	83.39 334	85.10 345	96.07 314	65.16 360	81.87 358	94.37 301	90.87 265	93.92 287	92.89 324	52.80 365	96.44 355	77.52 349	70.22 359	93.70 345

wuyk23d		93.25 261	95.20 192	87.40 342	96.07 314	95.38 86	97.04 111	94.97 294	95.33 148	99.70 5	98.11 129	98.14 13	91.94 358	77.76 347	99.68 72	74.89 358

CANet_DTU		94.65 224	94.21 231	95.96 214	95.90 317	89.68 218	93.92 273	97.83 223	93.19 221	90.12 343	95.64 279	88.52 257	99.57 152	93.27 194	99.47 126	98.62 207

test12356		87.98 326	88.41 321	86.69 344	95.84 318	63.49 361	87.15 350	97.32 251	87.21 297	91.78 331	93.36 314	70.66 343	98.39 327	74.70 350	97.64 290	98.19 247

MVSTER		94.21 238	93.93 240	95.05 248	95.83 319	86.46 292	95.18 216	97.65 236	92.41 244	97.94 126	98.00 143	72.39 336	99.58 147	96.36 88	99.56 98	99.12 143

FMVSNet5		93.39 258	92.35 264	96.50 176	95.83 319	90.81 204	97.31 91	98.27 185	92.74 238	96.27 215	98.28 106	62.23 357	99.67 110	90.86 233	99.36 155	99.03 157

PVSNet_0	81.89 21	84.49 333	83.21 336	88.34 339	95.76 321	74.97 353	83.49 355	92.70 321	78.47 349	87.94 352	86.90 358	83.38 287	96.63 354	73.44 353	66.86 360	93.40 347

PAPR		92.22 277	91.27 282	95.07 247	95.73 322	88.81 248	91.97 320	97.87 219	85.80 312	90.91 334	92.73 328	91.16 228	98.33 333	79.48 339	95.76 327	98.08 251

CHOSEN 280x420		89.98 310	89.19 316	92.37 316	95.60 323	81.13 332	86.22 352	97.09 260	81.44 337	87.44 354	93.15 315	73.99 321	99.47 185	88.69 275	99.07 195	96.52 313

ADS-MVSNet2		91.47 297	90.51 303	94.36 271	95.51 324	85.63 295	95.05 226	95.70 287	83.46 329	92.69 319	96.84 223	79.15 300	99.41 211	85.66 311	90.52 345	98.04 258

ADS-MVSNet		90.95 303	90.26 306	93.04 304	95.51 324	82.37 327	95.05 226	93.41 311	83.46 329	92.69 319	96.84 223	79.15 300	98.70 307	85.66 311	90.52 345	98.04 258

CR-MVSNet		93.29 260	92.79 258	94.78 256	95.44 326	88.15 258	96.18 152	97.20 254	84.94 322	94.10 278	98.57 79	77.67 305	99.39 220	95.17 133	95.81 323	96.81 303

RPMNet		94.22 235	94.03 237	94.78 256	95.44 326	88.15 258	96.18 152	93.73 304	97.43 73	94.10 278	98.49 86	79.40 298	99.39 220	95.69 108	95.81 323	96.81 303

1314		92.38 274	92.30 265	92.64 313	95.42 328	85.15 303	95.86 173	96.97 264	85.40 318	90.62 336	93.06 321	91.12 229	97.80 344	86.74 303	95.49 331	94.97 336

tpm		91.08 300	90.85 297	91.75 321	95.33 329	78.09 341	95.03 228	91.27 331	88.75 281	93.53 301	97.40 191	71.24 339	99.30 239	91.25 226	93.87 337	97.87 266

IB-MVS	85.98 20	88.63 319	86.95 328	93.68 289	95.12 330	84.82 309	90.85 332	90.17 346	87.55 295	88.48 350	91.34 344	58.01 359	99.59 145	87.24 300	93.80 338	96.63 311
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		93.75 249	93.57 245	94.29 274	95.05 331	87.32 282	96.05 157	92.98 315	97.54 68	94.25 273	98.72 68	75.79 318	99.24 249	95.92 103	95.81 323	96.32 319

tpm2		88.47 320	87.69 323	90.79 329	94.98 332	77.34 346	95.09 220	91.83 326	77.51 353	89.40 346	96.41 251	67.83 354	98.73 304	83.58 328	92.60 343	96.29 320

Patchmtry		95.03 212	94.59 216	96.33 186	94.83 333	90.82 202	96.38 140	97.20 254	96.59 100	97.49 153	98.57 79	77.67 305	99.38 225	92.95 201	99.62 80	98.80 191

MVS		90.02 308	89.20 315	92.47 314	94.71 334	86.90 287	95.86 173	96.74 272	64.72 359	90.62 336	92.77 325	92.54 200	98.39 327	79.30 340	95.56 330	92.12 351

CostFormer		89.75 313	89.25 312	91.26 325	94.69 335	78.00 344	95.32 206	91.98 325	81.50 336	90.55 338	96.96 215	71.06 340	98.89 288	88.59 277	92.63 342	96.87 300

PatchmatchNet		91.98 282	91.87 273	92.30 317	94.60 336	79.71 336	95.12 217	93.59 310	89.52 275	93.61 298	97.02 212	77.94 303	99.18 254	90.84 234	94.57 336	98.01 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		88.46 321	87.54 324	91.22 326	94.56 337	78.08 342	95.63 188	93.17 313	79.08 347	85.85 356	96.80 227	65.86 356	98.85 295	84.10 322	92.85 340	96.72 307

LP		93.12 262	92.78 260	94.14 276	94.50 338	85.48 298	95.73 177	95.68 288	92.97 234	95.05 249	97.17 203	81.93 290	99.40 214	93.06 199	88.96 350	97.55 278

tpm cat1		88.01 325	87.33 325	90.05 334	94.48 339	76.28 349	94.47 246	94.35 302	73.84 358	89.26 347	95.61 281	73.64 325	98.30 334	84.13 321	86.20 354	95.57 331

MDTV_nov1_ep13			91.28 281		94.31 340	73.51 354	94.80 237	93.16 314	86.75 304	93.45 305	97.40 191	76.37 314	98.55 319	88.85 272	96.43 317

cascas		91.89 286	91.35 280	93.51 292	94.27 341	85.60 296	88.86 347	98.61 142	79.32 345	92.16 327	91.44 343	89.22 254	98.12 339	90.80 236	97.47 298	96.82 302

test-LLR		89.97 311	89.90 309	90.16 332	94.24 342	74.98 351	89.89 341	89.06 347	92.02 248	89.97 344	90.77 347	73.92 323	98.57 316	91.88 212	97.36 300	96.92 297

test-mter		87.92 327	87.17 326	90.16 332	94.24 342	74.98 351	89.89 341	89.06 347	86.44 305	89.97 344	90.77 347	54.96 363	98.57 316	91.88 212	97.36 300	96.92 297

pmmvs3		90.00 309	88.90 318	93.32 296	94.20 344	85.34 299	91.25 329	92.56 322	78.59 348	93.82 288	95.17 287	67.36 355	98.69 308	89.08 269	98.03 261	95.92 322

tpmrst		90.31 306	90.61 302	89.41 335	94.06 345	72.37 357	95.06 225	93.69 305	88.01 291	92.32 326	96.86 221	77.45 307	98.82 296	91.04 227	87.01 353	97.04 294

test0.0.03 1		90.11 307	89.21 314	92.83 309	93.89 346	86.87 288	91.74 323	88.74 349	92.02 248	94.71 257	91.14 346	73.92 323	94.48 357	83.75 327	92.94 339	97.16 290

JIA-IIPM		91.79 289	90.69 300	95.11 244	93.80 347	90.98 198	94.16 261	91.78 327	96.38 107	90.30 342	99.30 23	72.02 338	98.90 286	88.28 281	90.17 347	95.45 332

TESTMET0.1,1		87.20 330	86.57 330	89.07 336	93.62 348	72.84 356	89.89 341	87.01 358	85.46 316	89.12 348	90.20 352	56.00 362	97.72 345	90.91 232	96.92 307	96.64 309

PatchFormer-LS_test		89.62 314	89.12 317	91.11 327	93.62 348	78.42 340	94.57 245	93.62 309	88.39 286	90.54 339	88.40 355	72.33 337	99.03 273	92.41 206	88.20 351	95.89 323

CMPMVS	73.10 23	92.74 266	91.39 278	96.77 158	93.57 350	94.67 111	94.21 257	97.67 232	80.36 342	93.61 298	96.60 239	82.85 288	97.35 347	84.86 318	98.78 221	98.29 238
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DWT-MVSNet_test		87.92 327	86.77 329	91.39 323	93.18 351	78.62 339	95.10 218	91.42 329	85.58 313	88.00 351	88.73 354	60.60 358	98.90 286	90.60 244	87.70 352	96.65 308

E-PMN		89.52 315	89.78 310	88.73 337	93.14 352	77.61 345	83.26 356	92.02 324	94.82 173	93.71 293	93.11 316	75.31 319	96.81 351	85.81 308	96.81 311	91.77 353

PMMVS		92.39 273	91.08 286	96.30 189	93.12 353	92.81 164	90.58 336	95.96 281	79.17 346	91.85 330	92.27 331	90.29 242	98.66 313	89.85 258	96.68 315	97.43 281

EMVS		89.06 317	89.22 313	88.61 338	93.00 354	77.34 346	82.91 357	90.92 333	94.64 177	92.63 322	91.81 336	76.30 315	97.02 349	83.83 325	96.90 308	91.48 354

dp		88.08 324	88.05 322	88.16 341	92.85 355	68.81 359	94.17 260	92.88 317	85.47 315	91.38 333	96.14 264	68.87 351	98.81 298	86.88 302	83.80 357	96.87 300

gg-mvs-nofinetune		88.28 323	86.96 327	92.23 318	92.84 356	84.44 318	98.19 41	74.60 364	99.08 9	87.01 355	99.47 7	56.93 360	98.23 336	78.91 342	95.61 329	94.01 344

tpmvs		90.79 305	90.87 296	90.57 331	92.75 357	76.30 348	95.79 176	93.64 308	91.04 263	91.91 329	96.26 257	77.19 311	98.86 294	89.38 264	89.85 348	96.56 312

EPMVS		89.26 316	88.55 320	91.39 323	92.36 358	79.11 338	95.65 185	79.86 362	88.60 283	93.12 312	96.53 243	70.73 342	98.10 340	90.75 238	89.32 349	96.98 295

gm-plane-assit					91.79 359	71.40 358			81.67 334		90.11 353		98.99 277	84.86 318

test2356		85.45 332	83.26 335	92.01 320	91.12 360	80.76 333	85.16 353	92.90 316	83.90 328	90.63 335	87.71 357	53.10 364	97.24 348	69.20 357	95.65 328	98.03 260

GG-mvs-BLEND				90.60 330	91.00 361	84.21 321	98.23 35	72.63 367		82.76 359	84.11 359	56.14 361	96.79 352	72.20 354	92.09 344	90.78 355

DeepMVS_CX				77.17 348	90.94 362	85.28 301		74.08 366	52.51 360	80.87 362	88.03 356	75.25 320	70.63 362	59.23 360	84.94 355	75.62 357

EPNet_dtu		91.39 298	90.75 299	93.31 297	90.48 363	82.61 325	94.80 237	92.88 317	93.39 217	81.74 361	94.90 295	81.36 292	99.11 262	88.28 281	98.87 215	98.21 245
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testpf		82.70 335	84.35 333	77.74 347	88.97 364	73.23 355	93.85 275	84.33 360	88.10 290	85.06 357	90.42 351	52.62 366	91.05 360	91.00 229	84.82 356	68.93 359

EPNet		93.72 250	92.62 262	97.03 147	87.61 365	92.25 172	96.27 145	91.28 330	96.74 97	87.65 353	97.39 194	85.00 282	99.64 119	92.14 208	99.48 124	99.20 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		57.23 337	62.50 338	41.44 350	34.77 366	49.21 366	83.93 354	60.22 368	15.31 361	71.11 363	79.37 360	70.09 344	44.86 363	64.76 358	82.93 358	30.25 360

test123		12.59 341	15.49 342	3.87 352	6.07 367	2.55 367	90.75 333	2.59 370	2.52 362	5.20 365	13.02 363	4.96 369	1.85 365	5.20 361	9.09 361	7.23 361

testmvs		12.33 342	15.23 343	3.64 353	5.77 368	2.23 368	88.99 345	3.62 369	2.30 363	5.29 364	13.09 362	4.52 370	1.95 364	5.16 362	8.32 362	6.75 362

cdsmvs_eth3d_5k		24.22 340	32.30 341	0.00 354	0.00 369	0.00 369	0.00 360	98.10 205	0.00 364	0.00 366	95.06 290	97.54 27	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		7.98 343	10.65 344	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	95.82 89	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		7.91 344	10.55 345	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	94.94 292	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																98.06 255

test_part1				0.00 354		0.00 369	0.00 360	98.84 89				0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sam_mvs1												77.80 304				98.06 255

sam_mvs												77.38 308

MTGPA								98.73 114

test_post1							94.98 230				10.37 365	76.21 316	99.04 270	89.47 263

test_post											10.87 364	76.83 312	99.07 267

patchmatchnet-post											96.84 223	77.36 309	99.42 200

MTMP							96.55 130	74.60 364

test9_res														91.29 223	98.89 214	99.00 159

agg_prior2														90.34 253	98.90 210	99.10 150

test_prior4						95.38 86	93.61 286

test_prior2							93.33 295		94.21 195	94.02 282	96.25 258	93.64 168		91.90 210	98.96 204

旧先验2							93.35 294		77.95 352	95.77 236			98.67 312	90.74 240

新几何2							93.43 290

无先验							93.20 298	97.91 215	80.78 339				99.40 214	87.71 285		97.94 265

原ACMM2							92.82 303

testdata2													99.46 190	87.84 284

segment_acmp												95.34 109

testdata1							92.77 304		93.78 210

plane_prior5								98.75 111					99.46 190	92.59 203	99.20 180	99.28 118

plane_prior4											96.77 229

plane_prior3						94.51 114			95.29 150	96.16 221

plane_prior2							96.50 133		96.36 108

plane_prior						94.29 122	95.42 197		94.31 192						98.93 209

n2								0.00 371

nn								0.00 371

door-mid								98.17 198

test11								98.08 208

door								97.81 224

HQP5-MVS						92.47 168

BP-MVS														90.51 247

HQP4-MVS										92.87 315			99.23 251			99.06 155

HQP3-MVS								98.43 159							98.74 225

HQP2-MVS												90.33 238

MDTV_nov1_ep13_2view						57.28 365	94.89 232		80.59 340	94.02 282		78.66 302		85.50 313		97.82 269

ACMMP++_ref															99.52 111

ACMMP++															99.55 102

Test By Simon												94.51 138

High-resolution multi-view benchmark