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		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 1	100.00 1	99.85 7

Gipuma		99.03 34	99.16 29	98.64 165	99.94 2	98.51 92	99.32 15	99.75 7	99.58 21	98.60 172	99.62 21	98.22 54	99.51 293	97.70 100	99.73 104	97.89 300
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OurMVSNet-221017-0		99.37 21	99.31 22	99.53 34	99.91 3	98.98 57	99.63 6	99.58 26	99.44 28	99.78 10	99.76 6	96.39 172	99.92 32	99.44 13	99.92 33	99.68 30

pmmvs6		99.67 3	99.70 3	99.60 13	99.90 4	99.27 17	99.53 7	99.76 6	99.64 11	99.84 8	99.83 2	99.50 5	99.87 78	99.36 14	99.92 33	99.64 38

PS-MVSNAJss		99.46 12	99.49 10	99.35 66	99.90 4	98.15 115	99.20 32	99.65 17	99.48 23	99.92 3	99.71 12	98.07 64	99.96 8	99.53 9	100.00 1	99.93 1

ANet_high		99.57 7	99.67 5	99.28 76	99.89 6	98.09 119	99.14 40	99.93 1	99.82 3	99.93 2	99.81 3	99.17 12	99.94 22	99.31 16	100.00 1	99.82 9

anonymousdsp		99.51 10	99.47 12	99.62 6	99.88 7	99.08 56	99.34 13	99.69 12	98.93 74	99.65 22	99.72 11	98.93 18	99.95 14	99.11 25	100.00 1	99.82 9

v7n		99.53 8	99.57 8	99.41 57	99.88 7	98.54 90	99.45 9	99.61 21	99.66 10	99.68 19	99.66 17	98.44 38	99.95 14	99.73 2	99.96 14	99.75 22

mvs_tets		99.63 5	99.67 5	99.49 46	99.88 7	98.61 82	99.34 13	99.71 9	99.27 40	99.90 4	99.74 8	99.68 2	99.97 3	99.55 8	99.99 5	99.88 3

jajsoiax		99.58 6	99.61 7	99.48 48	99.87 10	98.61 82	99.28 27	99.66 16	99.09 60	99.89 6	99.68 14	99.53 4	99.97 3	99.50 10	99.99 5	99.87 4

test_djsdf		99.52 9	99.51 9	99.53 34	99.86 11	98.74 71	99.39 11	99.56 40	99.11 53	99.70 15	99.73 10	99.00 15	99.97 3	99.26 18	99.98 9	99.89 2

MIMVSNet1		99.38 20	99.32 21	99.55 24	99.86 11	99.19 31	99.41 10	99.59 24	99.59 19	99.71 14	99.57 27	97.12 130	99.90 45	99.21 21	99.87 50	99.54 81

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 22	99.85 13	99.11 52	99.90 1	99.78 4	99.63 13	99.78 10	99.67 16	99.48 6	99.81 149	99.30 17	99.97 11	99.77 16
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
UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 14	99.34 11	99.69 4	99.58 26	99.90 2	99.86 7	99.78 5	99.58 3	99.95 14	99.00 31	99.95 15	99.78 14

SixPastTwentyTwo		98.75 68	98.62 71	99.16 92	99.83 15	97.96 141	99.28 27	98.20 280	99.37 33	99.70 15	99.65 19	92.65 265	99.93 26	99.04 29	99.84 54	99.60 47

Baseline_NR-MVSNet		98.98 41	98.86 44	99.36 61	99.82 16	98.55 87	97.47 194	99.57 33	99.37 33	99.21 82	99.61 23	96.76 154	99.83 126	98.06 78	99.83 60	99.71 25

pm-mvs1		99.44 13	99.48 11	99.33 71	99.80 17	98.63 79	99.29 23	99.63 18	99.30 38	99.65 22	99.60 25	99.16 14	99.82 136	99.07 27	99.83 60	99.56 69

TransMVSNet (Re)		99.44 13	99.47 12	99.36 61	99.80 17	98.58 85	99.27 29	99.57 33	99.39 31	99.75 12	99.62 21	99.17 12	99.83 126	99.06 28	99.62 149	99.66 33

K. test v3		98.00 157	97.66 175	99.03 117	99.79 19	97.56 171	99.19 36	92.47 340	99.62 16	99.52 33	99.66 17	89.61 283	99.96 8	99.25 20	99.81 67	99.56 69

FC-MVSNet-test		99.27 25	99.25 25	99.34 69	99.77 20	98.37 100	99.30 22	99.57 33	99.61 18	99.40 51	99.50 34	97.12 130	99.85 94	99.02 30	99.94 19	99.80 12

XXY-MVS		99.14 31	99.15 31	99.10 101	99.76 21	97.74 162	98.85 62	99.62 19	98.48 94	99.37 55	99.49 37	98.75 23	99.86 84	98.20 72	99.80 75	99.71 25

TDRefinement		99.42 16	99.38 15	99.55 24	99.76 21	99.33 12	99.68 5	99.71 9	99.38 32	99.53 31	99.61 23	98.64 27	99.80 159	98.24 69	99.84 54	99.52 91

PEN-MVS		99.41 17	99.34 19	99.62 6	99.73 23	99.14 45	99.29 23	99.54 47	99.62 16	99.56 26	99.42 47	98.16 60	99.96 8	98.78 42	99.93 24	99.77 16

lessismore_v0				98.97 124	99.73 23	97.53 173		86.71 350		99.37 55	99.52 33	89.93 281	99.92 32	98.99 32	99.72 110	99.44 125

SteuartSystems-ACMMP		98.79 61	98.54 79	99.54 27	99.73 23	99.16 37	98.23 107	99.31 126	97.92 129	98.90 131	98.90 141	98.00 70	99.88 62	96.15 201	99.72 110	99.58 59
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended_VisFu		98.17 147	98.15 137	98.22 212	99.73 23	95.15 247	97.36 199	99.68 13	94.45 275	98.99 116	99.27 65	96.87 144	99.94 22	97.13 126	99.91 38	99.57 64

Vis-MVSNet		99.34 22	99.36 16	99.27 79	99.73 23	98.26 104	99.17 37	99.78 4	99.11 53	99.27 71	99.48 38	98.82 20	99.95 14	98.94 33	99.93 24	99.59 53
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH	96.65 7	99.25 27	99.24 26	99.26 81	99.72 28	98.38 99	99.07 45	99.55 43	98.30 101	99.65 22	99.45 44	99.22 9	99.76 195	98.44 61	99.77 88	99.64 38
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-CasMVS		99.40 18	99.33 20	99.62 6	99.71 29	99.10 53	99.29 23	99.53 49	99.53 22	99.46 41	99.41 49	98.23 51	99.95 14	98.89 37	99.95 15	99.81 11

DTE-MVSNet		99.43 15	99.35 17	99.66 4	99.71 29	99.30 13	99.31 18	99.51 53	99.64 11	99.56 26	99.46 40	98.23 51	99.97 3	98.78 42	99.93 24	99.72 24

WR-MVS_H		99.33 23	99.22 27	99.65 5	99.71 29	99.24 20	99.32 15	99.55 43	99.46 26	99.50 37	99.34 58	97.30 119	99.93 26	98.90 35	99.93 24	99.77 16

HPM-MVS_fast		99.01 35	98.82 47	99.57 18	99.71 29	99.35 8	99.00 50	99.50 55	97.33 179	98.94 128	98.86 153	98.75 23	99.82 136	97.53 106	99.71 114	99.56 69

ACMH+	96.62 9	99.08 33	99.00 38	99.33 71	99.71 29	98.83 65	98.60 74	99.58 26	99.11 53	99.53 31	99.18 78	98.81 21	99.67 238	96.71 164	99.77 88	99.50 99

PMVS	91.26 20	97.86 168	97.94 156	97.65 241	99.71 29	97.94 144	98.52 83	98.68 260	98.99 66	97.52 248	99.35 56	97.41 114	98.18 345	91.59 313	99.67 135	96.82 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FIs		99.14 31	99.09 33	99.29 74	99.70 35	98.28 103	99.13 41	99.52 52	99.48 23	99.24 78	99.41 49	96.79 151	99.82 136	98.69 49	99.88 47	99.76 20

VPNet		98.87 54	98.83 46	99.01 121	99.70 35	97.62 170	98.43 95	99.35 108	99.47 25	99.28 69	99.05 106	96.72 157	99.82 136	98.09 76	99.36 209	99.59 53

MP-MVS-pluss		98.57 98	98.23 126	99.60 13	99.69 37	99.35 8	97.16 218	99.38 95	94.87 266	98.97 121	98.99 122	98.01 69	99.88 62	97.29 116	99.70 118	99.58 59
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CHOSEN 1792x2688		97.49 196	97.14 209	98.54 184	99.68 38	96.09 224	96.50 254	99.62 19	91.58 312	98.84 143	98.97 128	92.36 267	99.88 62	96.76 157	99.95 15	99.67 32

tfpnnormal		98.90 52	98.90 42	98.91 132	99.67 39	97.82 154	99.00 50	99.44 79	99.45 27	99.51 36	99.24 70	98.20 57	99.86 84	95.92 209	99.69 124	99.04 225

zzz-MVS		98.79 61	98.52 81	99.61 9	99.67 39	99.36 6	97.33 201	99.20 161	98.83 80	98.89 133	98.90 141	96.98 139	99.92 32	97.16 122	99.70 118	99.56 69

MTAPA		98.88 53	98.64 69	99.61 9	99.67 39	99.36 6	98.43 95	99.20 161	98.83 80	98.89 133	98.90 141	96.98 139	99.92 32	97.16 122	99.70 118	99.56 69

CP-MVSNet		99.21 28	99.09 33	99.56 22	99.65 42	98.96 61	99.13 41	99.34 114	99.42 29	99.33 61	99.26 67	97.01 137	99.94 22	98.74 46	99.93 24	99.79 13

HPM-MVS		98.79 61	98.53 80	99.59 17	99.65 42	99.29 14	99.16 38	99.43 84	96.74 217	98.61 170	98.38 230	98.62 28	99.87 78	96.47 182	99.67 135	99.59 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPSCF		98.62 91	98.36 110	99.42 54	99.65 42	99.42 4	98.55 80	99.57 33	97.72 142	98.90 131	99.26 67	96.12 180	99.52 289	95.72 220	99.71 114	99.32 172

TSAR-MVS + MP.		98.63 89	98.49 88	99.06 112	99.64 45	97.90 146	98.51 87	98.94 219	96.96 208	99.24 78	98.89 149	97.83 80	99.81 149	96.88 147	99.49 195	99.48 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PM-MVS		98.82 58	98.72 57	99.12 97	99.64 45	98.54 90	97.98 140	99.68 13	97.62 148	99.34 60	99.18 78	97.54 102	99.77 188	97.79 92	99.74 101	99.04 225

EU-MVSNet		97.66 185	98.50 85	95.13 313	99.63 47	85.84 338	98.35 101	98.21 279	98.23 109	99.54 28	99.46 40	95.02 218	99.68 235	98.24 69	99.87 50	99.87 4

HyFIR lowres test		97.19 219	96.60 239	98.96 125	99.62 48	97.28 184	95.17 307	99.50 55	94.21 280	99.01 113	98.32 237	86.61 296	99.99 2	97.10 128	99.84 54	99.60 47

ACMMP_NAP		98.75 68	98.48 89	99.57 18	99.58 49	99.29 14	97.82 156	99.25 150	96.94 209	98.78 151	99.12 92	98.02 68	99.84 111	97.13 126	99.67 135	99.59 53

nrg030		99.40 18	99.35 17	99.54 27	99.58 49	99.13 48	98.98 53	99.48 65	99.68 8	99.46 41	99.26 67	98.62 28	99.73 211	99.17 24	99.92 33	99.76 20

VDDNet		98.21 142	97.95 154	99.01 121	99.58 49	97.74 162	99.01 48	97.29 302	99.67 9	98.97 121	99.50 34	90.45 278	99.80 159	97.88 89	99.20 234	99.48 109

COLMAP_ROB	96.50 10	98.99 37	98.85 45	99.41 57	99.58 49	99.10 53	98.74 65	99.56 40	99.09 60	99.33 61	99.19 76	98.40 40	99.72 219	95.98 207	99.76 97	99.42 132
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ZNCC-MVS		98.68 81	98.40 103	99.54 27	99.57 53	99.21 23	98.46 92	99.29 138	97.28 185	98.11 209	98.39 228	98.00 70	99.87 78	96.86 150	99.64 143	99.55 77

DVP-MVS		98.40 122	98.00 151	99.61 9	99.57 53	99.25 19	98.57 78	99.35 108	97.55 156	99.31 68	97.71 273	94.61 230	99.88 62	96.14 202	99.19 238	99.70 28

testing_2		98.93 47	98.99 40	98.76 155	99.57 53	97.03 198	97.85 153	99.13 186	98.46 95	99.44 44	99.44 45	98.22 54	99.74 206	98.85 38	99.94 19	99.51 94

testgi		98.32 129	98.39 106	98.13 216	99.57 53	95.54 234	97.78 158	99.49 63	97.37 176	99.19 84	97.65 276	98.96 17	99.49 295	96.50 181	98.99 266	99.34 164

MP-MVS		98.46 115	98.09 142	99.54 27	99.57 53	99.22 22	98.50 88	99.19 166	97.61 150	97.58 242	98.66 191	97.40 115	99.88 62	94.72 246	99.60 157	99.54 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LPG-MVS_test		98.71 73	98.46 93	99.47 51	99.57 53	98.97 58	98.23 107	99.48 65	96.60 222	99.10 96	99.06 99	98.71 25	99.83 126	95.58 229	99.78 84	99.62 42

LGP-MVS_train				99.47 51	99.57 53	98.97 58		99.48 65	96.60 222	99.10 96	99.06 99	98.71 25	99.83 126	95.58 229	99.78 84	99.62 42

IS-MVSNet		98.19 144	97.90 159	99.08 104	99.57 53	97.97 137	99.31 18	98.32 275	99.01 65	98.98 118	99.03 112	91.59 273	99.79 172	95.49 231	99.80 75	99.48 109

test_0402		98.76 67	98.71 59	98.93 129	99.56 61	98.14 117	98.45 94	99.34 114	99.28 39	98.95 124	98.91 138	98.34 46	99.79 172	95.63 226	99.91 38	98.86 253

EPP-MVSNet		98.30 131	98.04 148	99.07 107	99.56 61	97.83 151	99.29 23	98.07 284	99.03 63	98.59 173	99.13 91	92.16 269	99.90 45	96.87 148	99.68 129	99.49 103

ACMMP		98.75 68	98.50 85	99.52 39	99.56 61	99.16 37	98.87 59	99.37 99	97.16 199	98.82 148	99.01 119	97.71 88	99.87 78	96.29 193	99.69 124	99.54 81
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
FMVSNet1		99.17 29	99.17 28	99.17 89	99.55 64	98.24 106	99.20 32	99.44 79	99.21 42	99.43 46	99.55 29	97.82 83	99.86 84	98.42 63	99.89 46	99.41 134

Vis-MVSNet (Re-imp)		97.46 199	97.16 206	98.34 203	99.55 64	96.10 222	98.94 55	98.44 271	98.32 100	98.16 204	98.62 202	88.76 288	99.73 211	93.88 274	99.79 80	99.18 206

ACMM	96.08 12	98.91 50	98.73 55	99.48 48	99.55 64	99.14 45	98.07 125	99.37 99	97.62 148	99.04 109	98.96 131	98.84 19	99.79 172	97.43 110	99.65 141	99.49 103
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mPP-MVS		98.64 87	98.34 113	99.54 27	99.54 67	99.17 33	98.63 71	99.24 155	97.47 162	98.09 211	98.68 186	97.62 96	99.89 54	96.22 196	99.62 149	99.57 64

XVG-ACMP-BASELINE		98.56 99	98.34 113	99.22 86	99.54 67	98.59 84	97.71 167	99.46 73	97.25 188	98.98 118	98.99 122	97.54 102	99.84 111	95.88 210	99.74 101	99.23 194

region2R		98.69 78	98.40 103	99.54 27	99.53 69	99.17 33	98.52 83	99.31 126	97.46 167	98.44 187	98.51 214	97.83 80	99.88 62	96.46 183	99.58 165	99.58 59

PGM-MVS		98.66 84	98.37 109	99.55 24	99.53 69	99.18 32	98.23 107	99.49 63	97.01 207	98.69 160	98.88 150	98.00 70	99.89 54	95.87 213	99.59 159	99.58 59

Patchmatch-RL test		97.26 212	97.02 212	97.99 226	99.52 71	95.53 235	96.13 272	99.71 9	97.47 162	99.27 71	99.16 84	84.30 316	99.62 257	97.89 86	99.77 88	98.81 258

ACMMPR		98.70 76	98.42 101	99.54 27	99.52 71	99.14 45	98.52 83	99.31 126	97.47 162	98.56 177	98.54 211	97.75 87	99.88 62	96.57 173	99.59 159	99.58 59

GST-MVS		98.61 92	98.30 118	99.52 39	99.51 73	99.20 29	98.26 105	99.25 150	97.44 170	98.67 162	98.39 228	97.68 89	99.85 94	96.00 205	99.51 187	99.52 91

Anonymous20231206		98.21 142	98.21 127	98.20 213	99.51 73	95.43 240	98.13 117	99.32 121	96.16 236	98.93 129	98.82 165	96.00 185	99.83 126	97.32 115	99.73 104	99.36 158

ACMP	95.32 15	98.41 120	98.09 142	99.36 61	99.51 73	98.79 69	97.68 170	99.38 95	95.76 249	98.81 150	98.82 165	98.36 42	99.82 136	94.75 243	99.77 88	99.48 109
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSP-MVS		98.77 66	98.52 81	99.52 39	99.50 76	99.21 23	98.02 134	98.84 239	97.97 125	99.08 99	99.02 113	97.61 97	99.88 62	96.99 134	99.63 146	99.48 109

test_0728_SECOND				99.60 13	99.50 76	99.23 21	98.02 134	99.32 121					99.88 62	96.99 134	99.63 146	99.68 30

test0726					99.50 76	99.21 23	98.17 116	99.35 108	97.97 125	99.26 75	99.06 99	97.61 97

AllTest		98.44 117	98.20 128	99.16 92	99.50 76	98.55 87	98.25 106	99.58 26	96.80 214	98.88 137	99.06 99	97.65 92	99.57 274	94.45 253	99.61 155	99.37 152

TestCases				99.16 92	99.50 76	98.55 87		99.58 26	96.80 214	98.88 137	99.06 99	97.65 92	99.57 274	94.45 253	99.61 155	99.37 152

XVG-OURS		98.53 108	98.34 113	99.11 99	99.50 76	98.82 67	95.97 276	99.50 55	97.30 183	99.05 107	98.98 126	99.35 7	99.32 318	95.72 220	99.68 129	99.18 206

EG-PatchMatch MVS		98.99 37	99.01 37	98.94 128	99.50 76	97.47 175	98.04 131	99.59 24	98.15 118	99.40 51	99.36 55	98.58 31	99.76 195	98.78 42	99.68 129	99.59 53

SED-MVS		98.91 50	98.72 57	99.49 46	99.49 83	99.17 33	98.10 122	99.31 126	98.03 122	99.66 20	99.02 113	98.36 42	99.88 62	96.91 140	99.62 149	99.41 134

IU-MVS					99.49 83	99.15 42		98.87 232	92.97 295	99.41 48				96.76 157	99.62 149	99.66 33

test_241102_ONE					99.49 83	99.17 33		99.31 126	97.98 124	99.66 20	98.90 141	98.36 42	99.48 298

UA-Net		99.47 11	99.40 14	99.70 2	99.49 83	99.29 14	99.80 3	99.72 8	99.82 3	99.04 109	99.81 3	98.05 67	99.96 8	98.85 38	99.99 5	99.86 6

HFP-MVS		98.71 73	98.44 97	99.51 43	99.49 83	99.16 37	98.52 83	99.31 126	97.47 162	98.58 175	98.50 217	97.97 74	99.85 94	96.57 173	99.59 159	99.53 87

#test#		98.50 111	98.16 135	99.51 43	99.49 83	99.16 37	98.03 132	99.31 126	96.30 233	98.58 175	98.50 217	97.97 74	99.85 94	95.68 223	99.59 159	99.53 87

VPA-MVSNet		99.30 24	99.30 23	99.28 76	99.49 83	98.36 101	99.00 50	99.45 76	99.63 13	99.52 33	99.44 45	98.25 49	99.88 62	99.09 26	99.84 54	99.62 42

XVG-OURS-SEG-HR		98.49 112	98.28 120	99.14 95	99.49 83	98.83 65	96.54 250	99.48 65	97.32 181	99.11 93	98.61 205	99.33 8	99.30 321	96.23 195	98.38 291	99.28 184

114514_t		96.50 254	95.77 258	98.69 162	99.48 91	97.43 178	97.84 154	99.55 43	81.42 345	96.51 293	98.58 208	95.53 204	99.67 238	93.41 287	99.58 165	98.98 234

IterMVS-LS		98.55 103	98.70 62	98.09 217	99.48 91	94.73 255	97.22 211	99.39 93	98.97 69	99.38 53	99.31 62	96.00 185	99.93 26	98.58 52	99.97 11	99.60 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v8		99.01 35	99.16 29	98.57 176	99.47 93	96.31 219	98.90 57	99.47 71	99.03 63	99.52 33	99.57 27	96.93 141	99.81 149	99.60 4	99.98 9	99.60 47

XVS		98.72 72	98.45 95	99.53 34	99.46 94	99.21 23	98.65 69	99.34 114	98.62 86	97.54 246	98.63 200	97.50 108	99.83 126	96.79 153	99.53 181	99.56 69

X-MVStestdata		94.32 293	92.59 310	99.53 34	99.46 94	99.21 23	98.65 69	99.34 114	98.62 86	97.54 246	45.85 348	97.50 108	99.83 126	96.79 153	99.53 181	99.56 69

test20.03		98.78 64	98.77 53	98.78 152	99.46 94	97.20 190	97.78 158	99.24 155	99.04 62	99.41 48	98.90 141	97.65 92	99.76 195	97.70 100	99.79 80	99.39 143

abl_6		98.99 37	98.78 51	99.61 9	99.45 97	99.46 3	98.60 74	99.50 55	98.59 88	99.24 78	99.04 109	98.54 33	99.89 54	96.45 184	99.62 149	99.50 99

CSCG		98.68 81	98.50 85	99.20 87	99.45 97	98.63 79	98.56 79	99.57 33	97.87 133	98.85 141	98.04 256	97.66 91	99.84 111	96.72 162	99.81 67	99.13 214

v148		98.45 116	98.60 76	98.00 225	99.44 99	94.98 250	97.44 196	99.06 196	98.30 101	99.32 66	98.97 128	96.65 160	99.62 257	98.37 65	99.85 52	99.39 143

v10		98.97 42	99.11 32	98.55 181	99.44 99	96.21 221	98.90 57	99.55 43	98.73 82	99.48 38	99.60 25	96.63 161	99.83 126	99.70 3	99.99 5	99.61 46

V42		98.78 64	98.78 51	98.76 155	99.44 99	97.04 197	98.27 104	99.19 166	97.87 133	99.25 77	99.16 84	96.84 145	99.78 182	99.21 21	99.84 54	99.46 118

MDA-MVSNet-bldmvs		97.94 161	97.91 158	98.06 221	99.44 99	94.96 251	96.63 248	99.15 185	98.35 97	98.83 144	99.11 94	94.31 237	99.85 94	96.60 170	98.72 278	99.37 152

v2v482		98.56 99	98.62 71	98.37 201	99.42 103	95.81 231	97.58 182	99.16 179	97.90 131	99.28 69	99.01 119	95.98 189	99.79 172	99.33 15	99.90 42	99.51 94

OPM-MVS		98.56 99	98.32 117	99.25 83	99.41 104	98.73 74	97.13 220	99.18 170	97.10 202	98.75 156	98.92 137	98.18 58	99.65 251	96.68 166	99.56 174	99.37 152

PMMVS2		98.07 152	98.08 145	98.04 223	99.41 104	94.59 261	94.59 325	99.40 91	97.50 159	98.82 148	98.83 162	96.83 147	99.84 111	97.50 108	99.81 67	99.71 25

casdiffmvs		98.95 45	99.00 38	98.81 145	99.38 106	97.33 181	97.82 156	99.57 33	99.17 50	99.35 58	99.17 82	98.35 45	99.69 226	98.46 60	99.73 104	99.41 134

baseline		98.96 44	99.02 36	98.76 155	99.38 106	97.26 185	98.49 89	99.50 55	98.86 77	99.19 84	99.06 99	98.23 51	99.69 226	98.71 48	99.76 97	99.33 170

TranMVSNet+NR-MVSNet		99.17 29	99.07 35	99.46 53	99.37 108	98.87 63	98.39 98	99.42 87	99.42 29	99.36 57	99.06 99	98.38 41	99.95 14	98.34 66	99.90 42	99.57 64

tttt0517		95.64 273	94.98 283	97.64 243	99.36 109	93.81 283	98.72 67	90.47 346	98.08 120	98.67 162	98.34 234	73.88 346	99.92 32	97.77 94	99.51 187	99.20 199

test_part2					99.36 109	99.10 53				99.05 107

v1144		98.60 94	98.66 67	98.41 197	99.36 109	95.90 227	97.58 182	99.34 114	97.51 158	99.27 71	99.15 88	96.34 176	99.80 159	99.47 12	99.93 24	99.51 94

CP-MVS		98.70 76	98.42 101	99.52 39	99.36 109	99.12 50	98.72 67	99.36 103	97.54 157	98.30 197	98.40 226	97.86 79	99.89 54	96.53 179	99.72 110	99.56 69

Test_1112_low_res		96.99 235	96.55 242	98.31 206	99.35 113	95.47 238	95.84 287	99.53 49	91.51 314	96.80 283	98.48 222	91.36 274	99.83 126	96.58 171	99.53 181	99.62 42

DeepC-MVS	97.60 4	98.97 42	98.93 41	99.10 101	99.35 113	97.98 136	98.01 137	99.46 73	97.56 155	99.54 28	99.50 34	98.97 16	99.84 111	98.06 78	99.92 33	99.49 103
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1112_ss		97.29 211	96.86 221	98.58 173	99.34 115	96.32 218	96.75 242	99.58 26	93.14 294	96.89 278	97.48 287	92.11 270	99.86 84	96.91 140	99.54 177	99.57 64

SF-MVS		98.53 108	98.27 121	99.32 73	99.31 116	98.75 70	98.19 112	99.41 88	96.77 216	98.83 144	98.90 141	97.80 84	99.82 136	95.68 223	99.52 184	99.38 149

CPTT-MVS		97.84 174	97.36 196	99.27 79	99.31 116	98.46 95	98.29 102	99.27 144	94.90 265	97.83 225	98.37 231	94.90 220	99.84 111	93.85 276	99.54 177	99.51 94

UnsupCasMVSNet_eth		97.89 164	97.60 181	98.75 158	99.31 116	97.17 193	97.62 176	99.35 108	98.72 83	98.76 155	98.68 186	92.57 266	99.74 206	97.76 98	95.60 335	99.34 164

pmmvs-eth3d		98.47 114	98.34 113	98.86 139	99.30 119	97.76 159	97.16 218	99.28 140	95.54 252	99.42 47	99.19 76	97.27 122	99.63 255	97.89 86	99.97 11	99.20 199

Anonymous20231211		99.27 25	99.27 24	99.26 81	99.29 120	98.18 113	99.49 8	99.51 53	99.70 7	99.80 9	99.68 14	96.84 145	99.83 126	99.21 21	99.91 38	99.77 16

UnsupCasMVSNet_bld		97.30 209	96.92 217	98.45 194	99.28 121	96.78 209	96.20 270	99.27 144	95.42 257	98.28 199	98.30 238	93.16 254	99.71 220	94.99 238	97.37 316	98.87 252

DPE-MVS		98.59 97	98.26 122	99.57 18	99.27 122	99.15 42	97.01 223	99.39 93	97.67 144	99.44 44	98.99 122	97.53 104	99.89 54	95.40 233	99.68 129	99.66 33

IterMVS-SCA-FT		97.85 173	98.18 131	96.87 281	99.27 122	91.16 323	95.53 297	99.25 150	99.10 57	99.41 48	99.35 56	93.10 256	99.96 8	98.65 50	99.94 19	99.49 103

v1192		98.60 94	98.66 67	98.41 197	99.27 122	95.88 228	97.52 188	99.36 103	97.41 172	99.33 61	99.20 75	96.37 175	99.82 136	99.57 6	99.92 33	99.55 77

N_pmnet		97.63 188	97.17 205	98.99 123	99.27 122	97.86 149	95.98 275	93.41 337	95.25 259	99.47 40	98.90 141	95.63 201	99.85 94	96.91 140	99.73 104	99.27 186

FPMVS		93.44 308	92.23 312	97.08 271	99.25 126	97.86 149	95.61 294	97.16 304	92.90 297	93.76 337	98.65 193	75.94 344	95.66 347	79.30 346	97.49 313	97.73 311

new-patchmatchnet		98.35 127	98.74 54	97.18 267	99.24 127	92.23 308	96.42 259	99.48 65	98.30 101	99.69 17	99.53 32	97.44 113	99.82 136	98.84 40	99.77 88	99.49 103

MCST-MVS		98.00 157	97.63 178	99.10 101	99.24 127	98.17 114	96.89 234	98.73 257	95.66 250	97.92 218	97.70 274	97.17 129	99.66 246	96.18 200	99.23 230	99.47 116

UniMVSNet (Re)		98.87 54	98.71 59	99.35 66	99.24 127	98.73 74	97.73 166	99.38 95	98.93 74	99.12 91	98.73 177	96.77 152	99.86 84	98.63 51	99.80 75	99.46 118

jason		97.45 200	97.35 197	97.76 235	99.24 127	93.93 277	95.86 284	98.42 272	94.24 279	98.50 184	98.13 247	94.82 224	99.91 42	97.22 119	99.73 104	99.43 129
jason: jason.
IterMVS		97.73 180	98.11 141	96.57 288	99.24 127	90.28 324	95.52 299	99.21 159	98.86 77	99.33 61	99.33 60	93.11 255	99.94 22	98.49 58	99.94 19	99.48 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		98.55 103	98.62 71	98.32 204	99.22 132	95.58 233	97.51 190	99.45 76	97.16 199	99.45 43	99.24 70	96.12 180	99.85 94	99.60 4	99.88 47	99.55 77

ITE_SJBPF				98.87 137	99.22 132	98.48 94		99.35 108	97.50 159	98.28 199	98.60 206	97.64 95	99.35 314	93.86 275	99.27 224	98.79 262

v144192		98.54 106	98.57 78	98.45 194	99.21 134	95.98 225	97.63 175	99.36 103	97.15 201	99.32 66	99.18 78	95.84 196	99.84 111	99.50 10	99.91 38	99.54 81

APDe-MVS		98.99 37	98.79 50	99.60 13	99.21 134	99.15 42	98.87 59	99.48 65	97.57 153	99.35 58	99.24 70	97.83 80	99.89 54	97.88 89	99.70 118	99.75 22

DP-MVS		98.93 47	98.81 49	99.28 76	99.21 134	98.45 96	98.46 92	99.33 119	99.63 13	99.48 38	99.15 88	97.23 127	99.75 202	97.17 121	99.66 140	99.63 41

v1921920		98.54 106	98.60 76	98.38 200	99.20 137	95.76 232	97.56 184	99.36 103	97.23 194	99.38 53	99.17 82	96.02 183	99.84 111	99.57 6	99.90 42	99.54 81

thisisatest0530		95.27 280	94.45 289	97.74 237	99.19 138	94.37 263	97.86 151	90.20 347	97.17 198	98.22 201	97.65 276	73.53 347	99.90 45	96.90 145	99.35 211	98.95 239

Anonymous20240529		98.93 47	98.87 43	99.12 97	99.19 138	98.22 111	99.01 48	98.99 216	99.25 41	99.54 28	99.37 52	97.04 133	99.80 159	97.89 86	99.52 184	99.35 162

APD-MVS_3200maxsize		98.84 57	98.61 74	99.53 34	99.19 138	99.27 17	98.49 89	99.33 119	98.64 84	99.03 112	98.98 126	97.89 77	99.85 94	96.54 178	99.42 203	99.46 118

HQP_MVS		97.99 160	97.67 172	98.93 129	99.19 138	97.65 167	97.77 161	99.27 144	98.20 113	97.79 228	97.98 259	94.90 220	99.70 222	94.42 255	99.51 187	99.45 122

plane_prior7					99.19 138	97.87 148

ab-mvs		98.41 120	98.36 110	98.59 172	99.19 138	97.23 186	99.32 15	98.81 245	97.66 145	98.62 168	99.40 51	96.82 148	99.80 159	95.88 210	99.51 187	98.75 266

F-COLMAP		97.30 209	96.68 232	99.14 95	99.19 138	98.39 98	97.27 207	99.30 134	92.93 296	96.62 288	98.00 257	95.73 199	99.68 235	92.62 301	98.46 290	99.35 162

SR-MVS		98.71 73	98.43 99	99.57 18	99.18 145	99.35 8	98.36 100	99.29 138	98.29 104	98.88 137	98.85 156	97.53 104	99.87 78	96.14 202	99.31 217	99.48 109

UniMVSNet_NR-MVSNet		98.86 56	98.68 64	99.40 59	99.17 146	98.74 71	97.68 170	99.40 91	99.14 51	99.06 102	98.59 207	96.71 158	99.93 26	98.57 54	99.77 88	99.53 87

LF4IMVS		97.90 162	97.69 171	98.52 185	99.17 146	97.66 166	97.19 215	99.47 71	96.31 232	97.85 224	98.20 245	96.71 158	99.52 289	94.62 247	99.72 110	98.38 286

SMA-MVS		98.40 122	98.03 149	99.51 43	99.16 148	99.21 23	98.05 129	99.22 158	94.16 282	98.98 118	99.10 96	97.52 106	99.79 172	96.45 184	99.64 143	99.53 87

DU-MVS		98.82 58	98.63 70	99.39 60	99.16 148	98.74 71	97.54 186	99.25 150	98.84 79	99.06 102	98.76 174	96.76 154	99.93 26	98.57 54	99.77 88	99.50 99

NR-MVSNet		98.95 45	98.82 47	99.36 61	99.16 148	98.72 76	99.22 31	99.20 161	99.10 57	99.72 13	98.76 174	96.38 174	99.86 84	98.00 83	99.82 63	99.50 99

MVS_111021_LR		98.30 131	98.12 140	98.83 142	99.16 148	98.03 130	96.09 273	99.30 134	97.58 152	98.10 210	98.24 241	98.25 49	99.34 315	96.69 165	99.65 141	99.12 215

DSMNet-mixed		97.42 201	97.60 181	96.87 281	99.15 152	91.46 314	98.54 81	99.12 189	92.87 298	97.58 242	99.63 20	96.21 178	99.90 45	95.74 219	99.54 177	99.27 186

D2MVS		97.84 174	97.84 163	97.83 231	99.14 153	94.74 254	96.94 227	98.88 230	95.84 246	98.89 133	98.96 131	94.40 235	99.69 226	97.55 103	99.95 15	99.05 221

pmmvs5		97.64 186	97.49 187	98.08 220	99.14 153	95.12 249	96.70 245	99.05 200	93.77 287	98.62 168	98.83 162	93.23 252	99.75 202	98.33 68	99.76 97	99.36 158

VDD-MVS		98.56 99	98.39 106	99.07 107	99.13 155	98.07 125	98.59 76	97.01 306	99.59 19	99.11 93	99.27 65	94.82 224	99.79 172	98.34 66	99.63 146	99.34 164

xxxxxxxxxxxxxcwj		98.44 117	98.24 124	99.06 112	99.11 156	97.97 137	96.53 251	99.54 47	98.24 107	98.83 144	98.90 141	97.80 84	99.82 136	95.68 223	99.52 184	99.38 149

ETH3D-3000-0.1		98.03 153	97.62 179	99.29 74	99.11 156	98.80 68	97.47 194	99.32 121	95.54 252	98.43 190	98.62 202	96.61 162	99.77 188	93.95 271	99.49 195	99.30 179

save fliter					99.11 156	97.97 137	96.53 251	99.02 209	98.24 107

APD-MVS		98.10 149	97.67 172	99.42 54	99.11 156	98.93 62	97.76 163	99.28 140	94.97 263	98.72 159	98.77 172	97.04 133	99.85 94	93.79 277	99.54 177	99.49 103
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-UG-set		98.69 78	98.71 59	98.62 169	99.10 160	96.37 216	97.23 208	98.87 232	99.20 45	99.19 84	98.99 122	97.30 119	99.85 94	98.77 45	99.79 80	99.65 37

EI-MVSNet		98.40 122	98.51 83	98.04 223	99.10 160	94.73 255	97.20 212	98.87 232	98.97 69	99.06 102	99.02 113	96.00 185	99.80 159	98.58 52	99.82 63	99.60 47

CVMVSNet		96.25 261	97.21 204	93.38 329	99.10 160	80.56 351	97.20 212	98.19 282	96.94 209	99.00 115	99.02 113	89.50 285	99.80 159	96.36 189	99.59 159	99.78 14

EI-MVSNet-Vis-set		98.68 81	98.70 62	98.63 167	99.09 163	96.40 215	97.23 208	98.86 237	99.20 45	99.18 88	98.97 128	97.29 121	99.85 94	98.72 47	99.78 84	99.64 38

HPM-MVS++		98.10 149	97.64 177	99.48 48	99.09 163	99.13 48	97.52 188	98.75 254	97.46 167	96.90 277	97.83 267	96.01 184	99.84 111	95.82 217	99.35 211	99.46 118

DP-MVS Recon		97.33 207	96.92 217	98.57 176	99.09 163	97.99 132	96.79 238	99.35 108	93.18 293	97.71 232	98.07 255	95.00 219	99.31 319	93.97 269	99.13 248	98.42 285

MVS_111021_HR		98.25 139	98.08 145	98.75 158	99.09 163	97.46 176	95.97 276	99.27 144	97.60 151	97.99 217	98.25 240	98.15 62	99.38 312	96.87 148	99.57 169	99.42 132

9.14			97.78 165		99.07 167		97.53 187	99.32 121	95.53 254	98.54 181	98.70 183	97.58 99	99.76 195	94.32 260	99.46 199

PAPM_NR		96.82 241	96.32 249	98.30 207	99.07 167	96.69 211	97.48 192	98.76 251	95.81 248	96.61 289	96.47 313	94.12 243	99.17 329	90.82 322	97.78 310	99.06 220

TAMVS		98.24 140	98.05 147	98.80 147	99.07 167	97.18 192	97.88 148	98.81 245	96.66 221	99.17 89	99.21 73	94.81 226	99.77 188	96.96 138	99.88 47	99.44 125

CLD-MVS		97.49 196	97.16 206	98.48 191	99.07 167	97.03 198	94.71 318	99.21 159	94.46 273	98.06 213	97.16 300	97.57 100	99.48 298	94.46 252	99.78 84	98.95 239
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		94.19 296	93.67 299	95.75 304	99.06 171	91.35 317	98.03 132	94.24 333	98.33 99	97.40 257	94.98 334	79.84 332	99.62 257	83.05 339	98.08 304	96.29 333

thres600view7		94.45 291	93.83 296	96.29 293	99.06 171	91.53 313	97.99 138	94.24 333	98.34 98	97.44 255	95.01 332	79.84 332	99.67 238	84.33 337	98.23 294	97.66 315

plane_prior1					99.05 173

YYNet1		97.60 189	97.67 172	97.39 261	99.04 174	93.04 295	95.27 304	98.38 274	97.25 188	98.92 130	98.95 133	95.48 209	99.73 211	96.99 134	98.74 276	99.41 134

MDA-MVSNet_test_wron		97.60 189	97.66 175	97.41 260	99.04 174	93.09 291	95.27 304	98.42 272	97.26 187	98.88 137	98.95 133	95.43 210	99.73 211	97.02 131	98.72 278	99.41 134

MIMVSNet		96.62 250	96.25 253	97.71 238	99.04 174	94.66 258	99.16 38	96.92 310	97.23 194	97.87 222	99.10 96	86.11 302	99.65 251	91.65 311	99.21 233	98.82 256

testtj		97.79 179	97.25 201	99.42 54	99.03 177	98.85 64	97.78 158	99.18 170	95.83 247	98.12 208	98.50 217	95.50 207	99.86 84	92.23 306	99.07 254	99.54 81

PatchMatch-RL		97.24 215	96.78 226	98.61 171	99.03 177	97.83 151	96.36 262	99.06 196	93.49 292	97.36 260	97.78 269	95.75 198	99.49 295	93.44 286	98.77 275	98.52 278

Regformer-3		98.61 92	98.61 74	98.63 167	99.02 179	96.53 213	97.17 216	98.84 239	99.13 52	99.10 96	98.85 156	97.24 126	99.79 172	98.41 64	99.70 118	99.57 64

Regformer-4		98.73 71	98.68 64	98.89 135	99.02 179	97.22 188	97.17 216	99.06 196	99.21 42	99.17 89	98.85 156	97.45 112	99.86 84	98.48 59	99.70 118	99.60 47

CDPH-MVS		97.26 212	96.66 235	99.07 107	99.00 181	98.15 115	96.03 274	99.01 212	91.21 318	97.79 228	97.85 266	96.89 143	99.69 226	92.75 299	99.38 208	99.39 143

diffmvs		98.22 141	98.24 124	98.17 215	99.00 181	95.44 239	96.38 261	99.58 26	97.79 139	98.53 182	98.50 217	96.76 154	99.74 206	97.95 85	99.64 143	99.34 164

WR-MVS		98.40 122	98.19 130	99.03 117	99.00 181	97.65 167	96.85 235	98.94 219	98.57 92	98.89 133	98.50 217	95.60 202	99.85 94	97.54 105	99.85 52	99.59 53

plane_prior6					98.99 184	97.70 165						94.90 220

xiu_mvs_v1_base_debu		97.86 168	98.17 132	96.92 278	98.98 185	93.91 278	96.45 256	99.17 176	97.85 135	98.41 191	97.14 302	98.47 35	99.92 32	98.02 80	99.05 255	96.92 326

xiu_mvs_v1_base		97.86 168	98.17 132	96.92 278	98.98 185	93.91 278	96.45 256	99.17 176	97.85 135	98.41 191	97.14 302	98.47 35	99.92 32	98.02 80	99.05 255	96.92 326

xiu_mvs_v1_base_debi		97.86 168	98.17 132	96.92 278	98.98 185	93.91 278	96.45 256	99.17 176	97.85 135	98.41 191	97.14 302	98.47 35	99.92 32	98.02 80	99.05 255	96.92 326

MVP-Stereo		98.08 151	97.92 157	98.57 176	98.96 188	96.79 206	97.90 147	99.18 170	96.41 228	98.46 185	98.95 133	95.93 192	99.60 264	96.51 180	98.98 268	99.31 176
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 122	98.68 64	97.54 252	98.96 188	97.99 132	97.88 148	99.36 103	98.20 113	99.63 25	99.04 109	98.76 22	95.33 349	96.56 176	99.74 101	99.31 176
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
1121		96.73 244	96.00 254	98.91 132	98.95 190	97.76 159	98.07 125	98.73 257	87.65 336	96.54 290	98.13 247	94.52 232	99.73 211	92.38 304	99.02 262	99.24 193

新几何1				98.91 132	98.94 191	97.76 159		98.76 251	87.58 337	96.75 284	98.10 251	94.80 227	99.78 182	92.73 300	99.00 265	99.20 199

USDC		97.41 202	97.40 192	97.44 258	98.94 191	93.67 287	95.17 307	99.53 49	94.03 284	98.97 121	99.10 96	95.29 212	99.34 315	95.84 216	99.73 104	99.30 179

tfpn200view9		94.03 300	93.44 301	95.78 303	98.93 193	91.44 315	97.60 179	94.29 331	97.94 127	97.10 263	94.31 341	79.67 334	99.62 257	83.05 339	98.08 304	96.29 333

testdata				98.09 217	98.93 193	95.40 241		98.80 247	90.08 326	97.45 254	98.37 231	95.26 213	99.70 222	93.58 282	98.95 270	99.17 210

thres400		94.14 298	93.44 301	96.24 295	98.93 193	91.44 315	97.60 179	94.29 331	97.94 127	97.10 263	94.31 341	79.67 334	99.62 257	83.05 339	98.08 304	97.66 315

TAPA-MVS	96.21 11	96.63 249	95.95 256	98.65 164	98.93 193	98.09 119	96.93 229	99.28 140	83.58 343	98.13 207	97.78 269	96.13 179	99.40 308	93.52 283	99.29 222	98.45 282
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.92 197	96.93 203	95.54 296	98.78 249	85.72 340	96.86 280	98.11 250	94.43 233			99.10 253	99.23 194

PVSNet_BlendedMVS		97.55 192	97.53 184	97.60 245	98.92 197	93.77 285	96.64 247	99.43 84	94.49 271	97.62 238	99.18 78	96.82 148	99.67 238	94.73 244	99.93 24	99.36 158

PVSNet_Blended		96.88 237	96.68 232	97.47 256	98.92 197	93.77 285	94.71 318	99.43 84	90.98 320	97.62 238	97.36 295	96.82 148	99.67 238	94.73 244	99.56 174	98.98 234

MSDG		97.71 181	97.52 185	98.28 209	98.91 200	96.82 205	94.42 328	99.37 99	97.65 146	98.37 196	98.29 239	97.40 115	99.33 317	94.09 267	99.22 231	98.68 274

Anonymous202405211		97.90 162	97.50 186	99.08 104	98.90 201	98.25 105	98.53 82	96.16 319	98.87 76	99.11 93	98.86 153	90.40 279	99.78 182	97.36 113	99.31 217	99.19 204

原ACMM1				98.35 202	98.90 201	96.25 220		98.83 244	92.48 302	96.07 305	98.10 251	95.39 211	99.71 220	92.61 302	98.99 266	99.08 218

GBi-Net		98.65 85	98.47 91	99.17 89	98.90 201	98.24 106	99.20 32	99.44 79	98.59 88	98.95 124	99.55 29	94.14 240	99.86 84	97.77 94	99.69 124	99.41 134

test1		98.65 85	98.47 91	99.17 89	98.90 201	98.24 106	99.20 32	99.44 79	98.59 88	98.95 124	99.55 29	94.14 240	99.86 84	97.77 94	99.69 124	99.41 134

FMVSNet2		98.49 112	98.40 103	98.75 158	98.90 201	97.14 196	98.61 73	99.13 186	98.59 88	99.19 84	99.28 63	94.14 240	99.82 136	97.97 84	99.80 75	99.29 183

OMC-MVS		97.88 166	97.49 187	99.04 116	98.89 206	98.63 79	96.94 227	99.25 150	95.02 261	98.53 182	98.51 214	97.27 122	99.47 300	93.50 285	99.51 187	99.01 229

ETH3 D test6400		96.46 256	95.59 266	99.08 104	98.88 207	98.21 112	96.53 251	99.18 170	88.87 332	97.08 265	97.79 268	93.64 251	99.77 188	88.92 328	99.40 206	99.28 184

MVSFormer		98.26 137	98.43 99	97.77 234	98.88 207	93.89 281	99.39 11	99.56 40	99.11 53	98.16 204	98.13 247	93.81 246	99.97 3	99.26 18	99.57 169	99.43 129

lupinMVS		97.06 228	96.86 221	97.65 241	98.88 207	93.89 281	95.48 300	97.97 287	93.53 290	98.16 204	97.58 280	93.81 246	99.91 42	96.77 156	99.57 169	99.17 210

DELS-MVS		98.27 135	98.20 128	98.48 191	98.86 210	96.70 210	95.60 295	99.20 161	97.73 141	98.45 186	98.71 180	97.50 108	99.82 136	98.21 71	99.59 159	98.93 244
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
TinyColmap		97.89 164	97.98 152	97.60 245	98.86 210	94.35 264	96.21 269	99.44 79	97.45 169	99.06 102	98.88 150	97.99 73	99.28 324	94.38 259	99.58 165	99.18 206

Regformer-1		98.55 103	98.44 97	98.87 137	98.85 212	97.29 182	96.91 232	98.99 216	98.97 69	98.99 116	98.64 196	97.26 125	99.81 149	97.79 92	99.57 169	99.51 94

Regformer-2		98.60 94	98.46 93	99.02 120	98.85 212	97.71 164	96.91 232	99.09 193	98.98 68	99.01 113	98.64 196	97.37 117	99.84 111	97.75 99	99.57 169	99.52 91

LCM-MVSNet-Re		98.64 87	98.48 89	99.11 99	98.85 212	98.51 92	98.49 89	99.83 3	98.37 96	99.69 17	99.46 40	98.21 56	99.92 32	94.13 266	99.30 220	98.91 248

pmmvs4		97.58 191	97.28 200	98.51 188	98.84 215	96.93 203	95.40 303	98.52 268	93.60 289	98.61 170	98.65 193	95.10 217	99.60 264	96.97 137	99.79 80	98.99 233

NP-MVS					98.84 215	97.39 180					96.84 305

sss		97.21 217	96.93 216	98.06 221	98.83 217	95.22 245	96.75 242	98.48 270	94.49 271	97.27 261	97.90 263	92.77 263	99.80 159	96.57 173	99.32 215	99.16 213

PVSNet	93.40 17	95.67 272	95.70 261	95.57 308	98.83 217	88.57 327	92.50 342	97.72 292	92.69 300	96.49 296	96.44 314	93.72 249	99.43 306	93.61 280	99.28 223	98.71 268

MVE	83.40 22	92.50 313	91.92 315	94.25 320	98.83 217	91.64 312	92.71 341	83.52 352	95.92 244	86.46 350	95.46 328	95.20 214	95.40 348	80.51 344	98.64 284	95.73 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ambc				98.24 211	98.82 220	95.97 226	98.62 72	99.00 215		99.27 71	99.21 73	96.99 138	99.50 294	96.55 177	99.50 194	99.26 189

旧先验1					98.82 220	97.45 177		98.76 251			98.34 234	95.50 207			99.01 264	99.23 194

WTY-MVS		96.67 247	96.27 252	97.87 229	98.81 222	94.61 260	96.77 240	97.92 289	94.94 264	97.12 262	97.74 272	91.11 275	99.82 136	93.89 273	98.15 300	99.18 206

3Dnovator+	97.89 3	98.69 78	98.51 83	99.24 84	98.81 222	98.40 97	99.02 47	99.19 166	98.99 66	98.07 212	99.28 63	97.11 132	99.84 111	96.84 151	99.32 215	99.47 116

QAPM		97.31 208	96.81 225	98.82 143	98.80 224	97.49 174	99.06 46	99.19 166	90.22 324	97.69 234	99.16 84	96.91 142	99.90 45	90.89 321	99.41 204	99.07 219

VNet		98.42 119	98.30 118	98.79 149	98.79 225	97.29 182	98.23 107	98.66 261	99.31 37	98.85 141	98.80 167	94.80 227	99.78 182	98.13 74	99.13 248	99.31 176

CS-MVS		97.82 178	97.59 183	98.52 185	98.76 226	98.04 129	98.20 111	99.61 21	97.10 202	96.02 308	94.87 338	98.27 48	99.84 111	96.31 191	99.17 240	97.69 314

DPM-MVS		96.32 258	95.59 266	98.51 188	98.76 226	97.21 189	94.54 327	98.26 277	91.94 308	96.37 298	97.25 297	93.06 258	99.43 306	91.42 315	98.74 276	98.89 249

3Dnovator	98.27 2	98.81 60	98.73 55	99.05 114	98.76 226	97.81 156	99.25 30	99.30 134	98.57 92	98.55 179	99.33 60	97.95 76	99.90 45	97.16 122	99.67 135	99.44 125

PLC	94.65 16	96.51 252	95.73 260	98.85 140	98.75 229	97.91 145	96.42 259	99.06 196	90.94 321	95.59 312	97.38 293	94.41 234	99.59 268	90.93 319	98.04 307	99.05 221
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 239	96.75 228	97.08 271	98.74 230	93.33 289	96.71 244	98.26 277	96.72 218	98.44 187	97.37 294	95.20 214	99.47 300	91.89 308	97.43 315	98.44 283

CDS-MVSNet		97.69 182	97.35 197	98.69 162	98.73 231	97.02 200	96.92 231	98.75 254	95.89 245	98.59 173	98.67 188	92.08 271	99.74 206	96.72 162	99.81 67	99.32 172
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EIA-MVS		98.00 157	97.74 168	98.80 147	98.72 232	98.09 119	98.05 129	99.60 23	97.39 174	96.63 287	95.55 325	97.68 89	99.80 159	96.73 161	99.27 224	98.52 278

LFMVS		97.20 218	96.72 229	98.64 165	98.72 232	96.95 202	98.93 56	94.14 335	99.74 6	98.78 151	99.01 119	84.45 313	99.73 211	97.44 109	99.27 224	99.25 190

new_pmnet		96.99 235	96.76 227	97.67 239	98.72 232	94.89 252	95.95 280	98.20 280	92.62 301	98.55 179	98.54 211	94.88 223	99.52 289	93.96 270	99.44 202	98.59 277

Fast-Effi-MVS+		97.67 184	97.38 194	98.57 176	98.71 235	97.43 178	97.23 208	99.45 76	94.82 267	96.13 301	96.51 310	98.52 34	99.91 42	96.19 198	98.83 273	98.37 288

TEST9					98.71 235	98.08 123	95.96 278	99.03 205	91.40 315	95.85 309	97.53 282	96.52 165	99.76 195

train_agg		97.10 224	96.45 245	99.07 107	98.71 235	98.08 123	95.96 278	99.03 205	91.64 310	95.85 309	97.53 282	96.47 168	99.76 195	93.67 279	99.16 241	99.36 158

TSAR-MVS + GP.		98.18 145	97.98 152	98.77 154	98.71 235	97.88 147	96.32 264	98.66 261	96.33 230	99.23 81	98.51 214	97.48 111	99.40 308	97.16 122	99.46 199	99.02 228

our_test_3		97.39 203	97.73 170	96.34 292	98.70 239	89.78 326	94.61 324	98.97 218	96.50 224	99.04 109	98.85 156	95.98 189	99.84 111	97.26 118	99.67 135	99.41 134

ppachtmachnet_test		97.50 194	97.74 168	96.78 286	98.70 239	91.23 322	94.55 326	99.05 200	96.36 229	99.21 82	98.79 169	96.39 172	99.78 182	96.74 159	99.82 63	99.34 164

PCF-MVS	92.86 18	94.36 292	93.00 308	98.42 196	98.70 239	97.56 171	93.16 340	99.11 191	79.59 346	97.55 245	97.43 290	92.19 268	99.73 211	79.85 345	99.45 201	97.97 299
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ETV-MVS		98.03 153	97.86 162	98.56 180	98.69 242	98.07 125	97.51 190	99.50 55	98.10 119	97.50 250	95.51 326	98.41 39	99.88 62	96.27 194	99.24 229	97.71 313

test_prior3		97.48 198	97.00 213	98.95 126	98.69 242	97.95 142	95.74 290	99.03 205	96.48 225	96.11 302	97.63 278	95.92 193	99.59 268	94.16 261	99.20 234	99.30 179

test_prior				98.95 126	98.69 242	97.95 142		99.03 205					99.59 268			99.30 179

agg_prior1		97.06 228	96.40 246	99.03 117	98.68 245	97.99 132	95.76 288	99.01 212	91.73 309	95.59 312	97.50 285	96.49 167	99.77 188	93.71 278	99.14 245	99.34 164

agg_prior					98.68 245	97.99 132		99.01 212		95.59 312			99.77 188

test_8					98.67 247	98.01 131	95.91 283	99.02 209	91.64 310	95.79 311	97.50 285	96.47 168	99.76 195

HQP-NCC					98.67 247		96.29 265		96.05 239	95.55 316

ACMP_Plane					98.67 247		96.29 265		96.05 239	95.55 316

CNVR-MVS		98.17 147	97.87 161	99.07 107	98.67 247	98.24 106	97.01 223	98.93 221	97.25 188	97.62 238	98.34 234	97.27 122	99.57 274	96.42 186	99.33 214	99.39 143

HQP-MVS		97.00 234	96.49 244	98.55 181	98.67 247	96.79 206	96.29 265	99.04 203	96.05 239	95.55 316	96.84 305	93.84 244	99.54 283	92.82 296	99.26 227	99.32 172

thres200		93.72 305	93.14 306	95.46 310	98.66 252	91.29 319	96.61 249	94.63 329	97.39 174	96.83 281	93.71 344	79.88 331	99.56 277	82.40 342	98.13 301	95.54 342

wuyk23d		96.06 263	97.62 179	91.38 332	98.65 253	98.57 86	98.85 62	96.95 308	96.86 213	99.90 4	99.16 84	99.18 11	98.40 344	89.23 327	99.77 88	77.18 347

NCCC		97.86 168	97.47 191	99.05 114	98.61 254	98.07 125	96.98 225	98.90 227	97.63 147	97.04 268	97.93 262	95.99 188	99.66 246	95.31 234	98.82 274	99.43 129

DeepC-MVS_fast	96.85 6	98.30 131	98.15 137	98.75 158	98.61 254	97.23 186	97.76 163	99.09 193	97.31 182	98.75 156	98.66 191	97.56 101	99.64 253	96.10 204	99.55 176	99.39 143
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thisisatest0515		94.12 299	93.16 305	96.97 276	98.60 256	92.90 296	93.77 336	90.61 345	94.10 283	96.91 274	95.87 321	74.99 345	99.80 159	94.52 250	99.12 251	98.20 291

GA-MVS		95.86 268	95.32 275	97.49 255	98.60 256	94.15 269	93.83 335	97.93 288	95.49 255	96.68 285	97.42 291	83.21 321	99.30 321	96.22 196	98.55 289	99.01 229

OPU-MVS				98.82 143	98.59 258	98.30 102	98.10 122				98.52 213	98.18 58	98.75 342	94.62 247	99.48 197	99.41 134

MSLP-MVS++		98.02 155	98.14 139	97.64 243	98.58 259	95.19 246	97.48 192	99.23 157	97.47 162	97.90 220	98.62 202	97.04 133	98.81 341	97.55 103	99.41 204	98.94 243

test12				98.93 129	98.58 259	97.83 151		98.66 261		96.53 291		95.51 206	99.69 226		99.13 248	99.27 186

PS-MVSNAJ		97.08 226	97.39 193	96.16 299	98.56 261	92.46 303	95.24 306	98.85 238	97.25 188	97.49 251	95.99 318	98.07 64	99.90 45	96.37 187	98.67 283	96.12 338

CNLPA		97.17 221	96.71 230	98.55 181	98.56 261	98.05 128	96.33 263	98.93 221	96.91 211	97.06 267	97.39 292	94.38 236	99.45 304	91.66 310	99.18 239	98.14 294

xiu_mvs_v2_base		97.16 222	97.49 187	96.17 297	98.54 263	92.46 303	95.45 301	98.84 239	97.25 188	97.48 252	96.49 311	98.31 47	99.90 45	96.34 190	98.68 282	96.15 337

alignmvs		97.35 205	96.88 220	98.78 152	98.54 263	98.09 119	97.71 167	97.69 294	99.20 45	97.59 241	95.90 320	88.12 292	99.55 280	98.18 73	98.96 269	98.70 270

Effi-MVS+		98.02 155	97.82 164	98.62 169	98.53 265	97.19 191	97.33 201	99.68 13	97.30 183	96.68 285	97.46 289	98.56 32	99.80 159	96.63 169	98.20 296	98.86 253

ETH3D cwj APD-0.16		97.55 192	97.00 213	99.19 88	98.51 266	98.64 78	96.85 235	99.13 186	94.19 281	97.65 236	98.40 226	95.78 197	99.81 149	93.37 288	99.16 241	99.12 215

baseline1		95.96 266	95.44 270	97.52 254	98.51 266	93.99 275	98.39 98	96.09 321	98.21 110	98.40 195	97.76 271	86.88 294	99.63 255	95.42 232	89.27 347	98.95 239

MVS_Test		98.18 145	98.36 110	97.67 239	98.48 268	94.73 255	98.18 113	99.02 209	97.69 143	98.04 215	99.11 94	97.22 128	99.56 277	98.57 54	98.90 272	98.71 268

BH-RMVSNet		96.83 239	96.58 240	97.58 247	98.47 269	94.05 270	96.67 246	97.36 298	96.70 220	97.87 222	97.98 259	95.14 216	99.44 305	90.47 323	98.58 288	99.25 190

canonicalmvs		98.34 128	98.26 122	98.58 173	98.46 270	97.82 154	98.96 54	99.46 73	99.19 49	97.46 253	95.46 328	98.59 30	99.46 302	98.08 77	98.71 280	98.46 280

MVS-HIRNet		94.32 293	95.62 264	90.42 333	98.46 270	75.36 352	96.29 265	89.13 349	95.25 259	95.38 321	99.75 7	92.88 261	99.19 328	94.07 268	99.39 207	96.72 331

PHI-MVS		98.29 134	97.95 154	99.34 69	98.44 272	99.16 37	98.12 119	99.38 95	96.01 242	98.06 213	98.43 224	97.80 84	99.67 238	95.69 222	99.58 165	99.20 199

Fast-Effi-MVS+-dtu		98.27 135	98.09 142	98.81 145	98.43 273	98.11 118	97.61 178	99.50 55	98.64 84	97.39 258	97.52 284	98.12 63	99.95 14	96.90 145	98.71 280	98.38 286

OpenMVS_ROB	95.38 14	95.84 269	95.18 279	97.81 232	98.41 274	97.15 195	97.37 198	98.62 264	83.86 342	98.65 164	98.37 231	94.29 238	99.68 235	88.41 329	98.62 286	96.60 332

DeepPCF-MVS	96.93 5	98.32 129	98.01 150	99.23 85	98.39 275	98.97 58	95.03 311	99.18 170	96.88 212	99.33 61	98.78 170	98.16 60	99.28 324	96.74 159	99.62 149	99.44 125

Patchmatch-test		96.55 251	96.34 248	97.17 268	98.35 276	93.06 292	98.40 97	97.79 290	97.33 179	98.41 191	98.67 188	83.68 320	99.69 226	95.16 235	99.31 217	98.77 264

AdaColmap		97.14 223	96.71 230	98.46 193	98.34 277	97.80 157	96.95 226	98.93 221	95.58 251	96.92 272	97.66 275	95.87 195	99.53 285	90.97 318	99.14 245	98.04 297

OpenMVS	96.65 7	97.09 225	96.68 232	98.32 204	98.32 278	97.16 194	98.86 61	99.37 99	89.48 328	96.29 300	99.15 88	96.56 163	99.90 45	92.90 293	99.20 234	97.89 300

MG-MVS		96.77 243	96.61 238	97.26 265	98.31 279	93.06 292	95.93 281	98.12 283	96.45 227	97.92 218	98.73 177	93.77 248	99.39 310	91.19 317	99.04 258	99.33 170

test_yl		96.69 245	96.29 250	97.90 227	98.28 280	95.24 243	97.29 204	97.36 298	98.21 110	98.17 202	97.86 264	86.27 298	99.55 280	94.87 241	98.32 292	98.89 249

DCV-MVSNet		96.69 245	96.29 250	97.90 227	98.28 280	95.24 243	97.29 204	97.36 298	98.21 110	98.17 202	97.86 264	86.27 298	99.55 280	94.87 241	98.32 292	98.89 249

CHOSEN 280x420		95.51 277	95.47 268	95.65 307	98.25 282	88.27 330	93.25 339	98.88 230	93.53 290	94.65 327	97.15 301	86.17 300	99.93 26	97.41 111	99.93 24	98.73 267

SCA		96.41 257	96.66 235	95.67 305	98.24 283	88.35 329	95.85 286	96.88 312	96.11 237	97.67 235	98.67 188	93.10 256	99.85 94	94.16 261	99.22 231	98.81 258

DeepMVS_CX				93.44 328	98.24 283	94.21 267		94.34 330	64.28 348	91.34 344	94.87 338	89.45 286	92.77 350	77.54 347	93.14 344	93.35 345

MS-PatchMatch		97.68 183	97.75 167	97.45 257	98.23 285	93.78 284	97.29 204	98.84 239	96.10 238	98.64 165	98.65 193	96.04 182	99.36 313	96.84 151	99.14 245	99.20 199

BH-w/o		95.13 283	94.89 286	95.86 301	98.20 286	91.31 318	95.65 293	97.37 297	93.64 288	96.52 292	95.70 323	93.04 259	99.02 334	88.10 330	95.82 334	97.24 324

mvs_anonymous		97.83 176	98.16 135	96.87 281	98.18 287	91.89 310	97.31 203	98.90 227	97.37 176	98.83 144	99.46 40	96.28 177	99.79 172	98.90 35	98.16 299	98.95 239

miper_lstm_enhance		97.18 220	97.16 206	97.25 266	98.16 288	92.85 297	95.15 309	99.31 126	97.25 188	98.74 158	98.78 170	90.07 280	99.78 182	97.19 120	99.80 75	99.11 217

ET-MVSNet_ETH3D		94.30 295	93.21 304	97.58 247	98.14 289	94.47 262	94.78 317	93.24 339	94.72 268	89.56 346	95.87 321	78.57 340	99.81 149	96.91 140	97.11 323	98.46 280

ADS-MVSNet2		95.43 278	94.98 283	96.76 287	98.14 289	91.74 311	97.92 144	97.76 291	90.23 322	96.51 293	98.91 138	85.61 305	99.85 94	92.88 294	96.90 324	98.69 271

ADS-MVSNet		95.24 281	94.93 285	96.18 296	98.14 289	90.10 325	97.92 144	97.32 301	90.23 322	96.51 293	98.91 138	85.61 305	99.74 206	92.88 294	96.90 324	98.69 271

cl_fuxian		97.36 204	97.37 195	97.31 262	98.09 292	93.25 290	95.01 312	99.16 179	97.05 204	98.77 154	98.72 179	92.88 261	99.64 253	96.93 139	99.76 97	99.05 221

FMVSNet3		97.50 194	97.24 203	98.29 208	98.08 293	95.83 230	97.86 151	98.91 226	97.89 132	98.95 124	98.95 133	87.06 293	99.81 149	97.77 94	99.69 124	99.23 194

PAPM		91.88 318	90.34 320	96.51 289	98.06 294	92.56 301	92.44 343	97.17 303	86.35 338	90.38 345	96.01 317	86.61 296	99.21 327	70.65 348	95.43 336	97.75 310

Effi-MVS+-dtu		98.26 137	97.90 159	99.35 66	98.02 295	99.49 2	98.02 134	99.16 179	98.29 104	97.64 237	97.99 258	96.44 170	99.95 14	96.66 167	98.93 271	98.60 275

mvs-test1		97.83 176	97.48 190	98.89 135	98.02 295	99.20 29	97.20 212	99.16 179	98.29 104	96.46 297	97.17 299	96.44 170	99.92 32	96.66 167	97.90 309	97.54 320

eth_miper_zixun_eth		97.23 216	97.25 201	97.17 268	98.00 297	92.77 299	94.71 318	99.18 170	97.27 186	98.56 177	98.74 176	91.89 272	99.69 226	97.06 130	99.81 67	99.05 221

HY-MVS	95.94 13	95.90 267	95.35 274	97.55 251	97.95 298	94.79 253	98.81 64	96.94 309	92.28 305	95.17 323	98.57 209	89.90 282	99.75 202	91.20 316	97.33 320	98.10 295

UGNet		98.53 108	98.45 95	98.79 149	97.94 299	96.96 201	99.08 44	98.54 266	99.10 57	96.82 282	99.47 39	96.55 164	99.84 111	98.56 57	99.94 19	99.55 77
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
MAR-MVS		96.47 255	95.70 261	98.79 149	97.92 300	99.12 50	98.28 103	98.60 265	92.16 307	95.54 319	96.17 316	94.77 229	99.52 289	89.62 326	98.23 294	97.72 312
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
MVSTER		96.86 238	96.55 242	97.79 233	97.91 301	94.21 267	97.56 184	98.87 232	97.49 161	99.06 102	99.05 106	80.72 329	99.80 159	98.44 61	99.82 63	99.37 152

API-MVS		97.04 231	96.91 219	97.42 259	97.88 302	98.23 110	98.18 113	98.50 269	97.57 153	97.39 258	96.75 307	96.77 152	99.15 331	90.16 324	99.02 262	94.88 343

MVS_0304		97.64 186	97.35 197	98.52 185	97.87 303	96.69 211	98.59 76	98.05 286	97.44 170	93.74 338	98.85 156	93.69 250	99.88 62	98.11 75	99.81 67	98.98 234

miper_ehance_all_eth		97.06 228	97.03 211	97.16 270	97.83 304	93.06 292	94.66 321	99.09 193	95.99 243	98.69 160	98.45 223	92.73 264	99.61 263	96.79 153	99.03 259	98.82 256

cl-mvsnet_		97.02 232	96.83 224	97.58 247	97.82 305	94.04 271	94.66 321	99.16 179	97.04 205	98.63 166	98.71 180	88.68 290	99.69 226	97.00 132	99.81 67	99.00 232

cl-mvsnet1		97.02 232	96.84 223	97.58 247	97.82 305	94.03 272	94.66 321	99.16 179	97.04 205	98.63 166	98.71 180	88.69 289	99.69 226	97.00 132	99.81 67	99.01 229

CANet		97.87 167	97.76 166	98.19 214	97.75 307	95.51 236	96.76 241	99.05 200	97.74 140	96.93 271	98.21 244	95.59 203	99.89 54	97.86 91	99.93 24	99.19 204

PVSNet_0	89.98 21	91.15 319	90.30 321	93.70 325	97.72 308	84.34 346	90.24 345	97.42 296	90.20 325	93.79 336	93.09 345	90.90 276	98.89 340	86.57 334	72.76 348	97.87 302

CR-MVSNet		96.28 260	95.95 256	97.28 263	97.71 309	94.22 265	98.11 120	98.92 224	92.31 304	96.91 274	99.37 52	85.44 308	99.81 149	97.39 112	97.36 318	97.81 306

RPMNet		96.82 241	96.66 235	97.28 263	97.71 309	94.22 265	98.11 120	96.90 311	99.37 33	96.91 274	99.34 58	86.72 295	99.81 149	97.53 106	97.36 318	97.81 306

pmmvs3		95.03 285	94.40 290	96.93 277	97.70 311	92.53 302	95.08 310	97.71 293	88.57 333	97.71 232	98.08 254	79.39 336	99.82 136	96.19 198	99.11 252	98.43 284

baseline2		93.73 304	92.83 309	96.42 291	97.70 311	91.28 320	96.84 237	89.77 348	93.96 286	92.44 341	95.93 319	79.14 337	99.77 188	92.94 292	96.76 328	98.21 290

tpm		94.67 289	94.34 292	95.66 306	97.68 313	88.42 328	97.88 148	94.90 327	94.46 273	96.03 307	98.56 210	78.66 338	99.79 172	95.88 210	95.01 338	98.78 263

CANet_DTU		97.26 212	97.06 210	97.84 230	97.57 314	94.65 259	96.19 271	98.79 248	97.23 194	95.14 324	98.24 241	93.22 253	99.84 111	97.34 114	99.84 54	99.04 225

tpm2		93.09 311	92.58 311	94.62 317	97.56 315	86.53 336	97.66 172	95.79 324	86.15 339	94.07 334	98.23 243	75.95 343	99.53 285	90.91 320	96.86 327	97.81 306

TR-MVS		95.55 275	95.12 281	96.86 284	97.54 316	93.94 276	96.49 255	96.53 316	94.36 278	97.03 269	96.61 309	94.26 239	99.16 330	86.91 333	96.31 331	97.47 322

1314		95.74 271	95.60 265	96.17 297	97.53 317	92.75 300	98.07 125	98.31 276	91.22 317	94.25 330	96.68 308	95.53 204	99.03 333	91.64 312	97.18 321	96.74 330

CostFormer		93.97 301	93.78 297	94.51 318	97.53 317	85.83 339	97.98 140	95.96 322	89.29 330	94.99 326	98.63 200	78.63 339	99.62 257	94.54 249	96.50 329	98.09 296

FMVSNet5		96.01 264	95.20 278	98.41 197	97.53 317	96.10 222	98.74 65	99.50 55	97.22 197	98.03 216	99.04 109	69.80 349	99.88 62	97.27 117	99.71 114	99.25 190

PMMVS		96.51 252	95.98 255	98.09 217	97.53 317	95.84 229	94.92 314	98.84 239	91.58 312	96.05 306	95.58 324	95.68 200	99.66 246	95.59 228	98.09 303	98.76 265

PAPR		95.29 279	94.47 288	97.75 236	97.50 321	95.14 248	94.89 315	98.71 259	91.39 316	95.35 322	95.48 327	94.57 231	99.14 332	84.95 336	97.37 316	98.97 238

PatchT		96.65 248	96.35 247	97.54 252	97.40 322	95.32 242	97.98 140	96.64 315	99.33 36	96.89 278	99.42 47	84.32 315	99.81 149	97.69 102	97.49 313	97.48 321

tpm cat1		93.29 309	93.13 307	93.75 324	97.39 323	84.74 342	97.39 197	97.65 295	83.39 344	94.16 331	98.41 225	82.86 324	99.39 310	91.56 314	95.35 337	97.14 325

PatchmatchNet		95.58 274	95.67 263	95.30 312	97.34 324	87.32 333	97.65 174	96.65 314	95.30 258	97.07 266	98.69 184	84.77 310	99.75 202	94.97 239	98.64 284	98.83 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
RRT_test8_iter05		95.24 281	95.13 280	95.57 308	97.32 325	87.02 335	97.99 138	99.41 88	98.06 121	99.12 91	99.05 106	66.85 352	99.85 94	98.93 34	99.47 198	99.84 8

Patchmtry		97.35 205	96.97 215	98.50 190	97.31 326	96.47 214	98.18 113	98.92 224	98.95 73	98.78 151	99.37 52	85.44 308	99.85 94	95.96 208	99.83 60	99.17 210

LS3D		98.63 89	98.38 108	99.36 61	97.25 327	99.38 5	99.12 43	99.32 121	99.21 42	98.44 187	98.88 150	97.31 118	99.80 159	96.58 171	99.34 213	98.92 245

IB-MVS	91.63 19	92.24 316	90.90 319	96.27 294	97.22 328	91.24 321	94.36 330	93.33 338	92.37 303	92.24 342	94.58 340	66.20 354	99.89 54	93.16 291	94.63 340	97.66 315
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
tpmrst		95.07 284	95.46 269	93.91 323	97.11 329	84.36 345	97.62 176	96.96 307	94.98 262	96.35 299	98.80 167	85.46 307	99.59 268	95.60 227	96.23 332	97.79 309

MDTV_nov1_ep13			95.22 277		97.06 330	83.20 347	97.74 165	96.16 319	94.37 277	96.99 270	98.83 162	83.95 318	99.53 285	93.90 272	97.95 308

MVS		93.19 310	92.09 313	96.50 290	96.91 331	94.03 272	98.07 125	98.06 285	68.01 347	94.56 329	96.48 312	95.96 191	99.30 321	83.84 338	96.89 326	96.17 335

E-PMN		94.17 297	94.37 291	93.58 326	96.86 332	85.71 340	90.11 346	97.07 305	98.17 116	97.82 227	97.19 298	84.62 312	98.94 337	89.77 325	97.68 312	96.09 339

JIA-IIPM		95.52 276	95.03 282	97.00 273	96.85 333	94.03 272	96.93 229	95.82 323	99.20 45	94.63 328	99.71 12	83.09 322	99.60 264	94.42 255	94.64 339	97.36 323

EMVS		93.83 303	94.02 294	93.23 330	96.83 334	84.96 341	89.77 347	96.32 318	97.92 129	97.43 256	96.36 315	86.17 300	98.93 338	87.68 331	97.73 311	95.81 340

cl-mvsnet2		95.79 270	95.39 273	96.98 275	96.77 335	92.79 298	94.40 329	98.53 267	94.59 270	97.89 221	98.17 246	82.82 325	99.24 326	96.37 187	99.03 259	98.92 245

dp		93.47 307	93.59 300	93.13 331	96.64 336	81.62 350	97.66 172	96.42 317	92.80 299	96.11 302	98.64 196	78.55 341	99.59 268	93.31 289	92.18 346	98.16 293

test-LLR		93.90 302	93.85 295	94.04 321	96.53 337	84.62 343	94.05 332	92.39 341	96.17 234	94.12 332	95.07 330	82.30 326	99.67 238	95.87 213	98.18 297	97.82 304

test-mter		92.33 315	91.76 317	94.04 321	96.53 337	84.62 343	94.05 332	92.39 341	94.00 285	94.12 332	95.07 330	65.63 355	99.67 238	95.87 213	98.18 297	97.82 304

TESTMET0.1,1		92.19 317	91.77 316	93.46 327	96.48 339	82.80 348	94.05 332	91.52 344	94.45 275	94.00 335	94.88 336	66.65 353	99.56 277	95.78 218	98.11 302	98.02 298

DWT-MVSNet_test		92.75 312	92.05 314	94.85 315	96.48 339	87.21 334	97.83 155	94.99 326	92.22 306	92.72 340	94.11 343	70.75 348	99.46 302	95.01 237	94.33 342	97.87 302

miper_enhance_ethall		96.01 264	95.74 259	96.81 285	96.41 341	92.27 307	93.69 337	98.89 229	91.14 319	98.30 197	97.35 296	90.58 277	99.58 273	96.31 191	99.03 259	98.60 275

tpmvs		95.02 286	95.25 276	94.33 319	96.39 342	85.87 337	98.08 124	96.83 313	95.46 256	95.51 320	98.69 184	85.91 303	99.53 285	94.16 261	96.23 332	97.58 318

CMPMVS	75.91 23	96.29 259	95.44 270	98.84 141	96.25 343	98.69 77	97.02 222	99.12 189	88.90 331	97.83 225	98.86 153	89.51 284	98.90 339	91.92 307	99.51 187	98.92 245
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		94.51 290	93.69 298	96.99 274	96.05 344	93.61 288	94.97 313	93.49 336	96.17 234	97.57 244	94.88 336	82.30 326	99.01 336	93.60 281	94.17 343	98.37 288

EPMVS		93.72 305	93.27 303	95.09 314	96.04 345	87.76 331	98.13 117	85.01 351	94.69 269	96.92 272	98.64 196	78.47 342	99.31 319	95.04 236	96.46 330	98.20 291

cascas		94.79 288	94.33 293	96.15 300	96.02 346	92.36 306	92.34 344	99.26 149	85.34 341	95.08 325	94.96 335	92.96 260	98.53 343	94.41 258	98.59 287	97.56 319

RRT_MVS		97.07 227	96.57 241	98.58 173	95.89 347	96.33 217	97.36 199	98.77 250	97.85 135	99.08 99	99.12 92	82.30 326	99.96 8	98.82 41	99.90 42	99.45 122

gg-mvs-nofinetune		92.37 314	91.20 318	95.85 302	95.80 348	92.38 305	99.31 18	81.84 353	99.75 5	91.83 343	99.74 8	68.29 350	99.02 334	87.15 332	97.12 322	96.16 336

gm-plane-assit					94.83 349	81.97 349			88.07 335		94.99 333		99.60 264	91.76 309

GG-mvs-BLEND				94.76 316	94.54 350	92.13 309	99.31 18	80.47 354		88.73 348	91.01 347	67.59 351	98.16 346	82.30 343	94.53 341	93.98 344

EPNet_dtu		94.93 287	94.78 287	95.38 311	93.58 351	87.68 332	96.78 239	95.69 325	97.35 178	89.14 347	98.09 253	88.15 291	99.49 295	94.95 240	99.30 220	98.98 234
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		96.14 262	95.44 270	98.25 210	90.76 352	95.50 237	97.92 144	94.65 328	98.97 69	92.98 339	98.85 156	89.12 287	99.87 78	95.99 206	99.68 129	99.39 143
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		78.77 320	78.73 322	78.90 334	58.45 353	74.76 354	94.20 331	78.26 355	39.16 349	86.71 349	92.82 346	80.50 330	75.19 351	86.16 335	92.29 345	86.74 346

testmvs		17.12 322	20.53 324	6.87 336	12.05 354	4.20 356	93.62 338	6.73 356	4.62 351	10.41 351	24.33 349	8.28 357	3.56 353	9.69 350	15.07 349	12.86 349

test123		17.04 323	20.11 325	7.82 335	10.25 355	4.91 355	94.80 316	4.47 357	4.93 350	10.00 352	24.28 350	9.69 356	3.64 352	10.14 349	12.43 350	14.92 348

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

cdsmvs_eth3d_5k		24.66 321	32.88 323	0.00 337	0.00 356	0.00 357	0.00 348	99.10 192	0.00 352	0.00 353	97.58 280	99.21 10	0.00 354	0.00 351	0.00 351	0.00 350

pcd_1.5k_mvsjas		8.17 324	10.90 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	98.07 64	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

ab-mvs-re		8.12 325	10.83 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	97.48 287	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_241102_TWO								99.30 134	98.03 122	99.26 75	99.02 113	97.51 107	99.88 62	96.91 140	99.60 157	99.66 33

test_0728_THIRD									98.17 116	99.08 99	99.02 113	97.89 77	99.88 62	97.07 129	99.71 114	99.70 28

GSMVS																98.81 258

test_part1				0.00 337		0.00 357	0.00 348	99.28 140				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sam_mvs1												84.74 311				98.81 258

sam_mvs												84.29 317

MTGPA								99.20 161

test_post1							97.59 181				20.48 352	83.07 323	99.66 246	94.16 261

test_post											21.25 351	83.86 319	99.70 222

patchmatchnet-post											98.77 172	84.37 314	99.85 94

MTMP							97.93 143	91.91 343

test9_res														93.28 290	99.15 244	99.38 149

agg_prior2														92.50 303	99.16 241	99.37 152

test_prior4						97.97 137	95.86 284

test_prior2							95.74 290		96.48 225	96.11 302	97.63 278	95.92 193		94.16 261	99.20 234

旧先验2							95.76 288		88.56 334	97.52 248			99.66 246	94.48 251

新几何2							95.93 281

无先验							95.74 290	98.74 256	89.38 329				99.73 211	92.38 304		99.22 198

原ACMM2							95.53 297

testdata2													99.79 172	92.80 298

segment_acmp												97.02 136

testdata1							95.44 302		96.32 231

plane_prior5								99.27 144					99.70 222	94.42 255	99.51 187	99.45 122

plane_prior4											97.98 259

plane_prior3						97.78 158			97.41 172	97.79 228

plane_prior2							97.77 161		98.20 113

plane_prior						97.65 167	97.07 221		96.72 218						99.36 209

n2								0.00 358

nn								0.00 358

door-mid								99.57 33

test11								98.87 232

door								99.41 88

HQP5-MVS						96.79 206

BP-MVS														92.82 296

HQP4-MVS										95.56 315			99.54 283			99.32 172

HQP3-MVS								99.04 203							99.26 227

HQP2-MVS												93.84 244

MDTV_nov1_ep13_2view						74.92 353	97.69 169		90.06 327	97.75 231		85.78 304		93.52 283		98.69 271

ACMMP++_ref															99.77 88

ACMMP++															99.68 129

Test By Simon												96.52 165

High-resolution multi-view benchmark