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 5	99.24 5	98.56 50	99.81 2	96.38 62	98.87 8	99.30 9	99.01 17	99.63 10	99.66 4	99.27 2	99.68 115	97.75 30	99.89 21	99.62 25

UniMVSNet_ETH3D		99.12 4	99.28 4	98.65 44	99.77 3	96.34 64	99.18 6	99.20 14	99.67 3	99.73 4	99.65 5	99.15 3	99.86 21	97.22 44	99.92 13	99.77 9

OurMVSNet-221017-0		98.61 17	98.61 24	98.63 46	99.77 3	96.35 63	99.17 7	99.05 41	98.05 40	99.61 12	99.52 6	93.72 174	99.88 19	98.72 10	99.88 22	99.65 23

test_part1		99.41 2	99.62 2	98.80 31	99.76 5	96.58 57	99.49 3	99.65 2	99.89 2	99.94 2	99.77 2	99.03 4	99.92 4	99.05 3	99.99 2	99.90 1

Gipuma		98.07 41	98.31 30	97.36 142	99.76 5	96.28 67	98.51 20	99.10 29	98.76 22	96.79 189	99.34 18	96.61 74	98.82 291	96.38 69	99.50 106	96.98 300
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		98.51 21	98.45 27	98.67 42	99.72 7	96.71 50	98.76 10	98.89 75	98.49 27	99.38 18	99.14 31	95.44 125	99.84 26	96.47 67	99.80 33	99.47 59

LTVRE_ROB	96.88 1	99.18 3	99.34 3	98.72 39	99.71 8	96.99 44	99.69 2	99.57 4	99.02 16	99.62 11	99.36 15	98.53 8	99.52 169	98.58 13	99.95 6	99.66 22
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 6	98.94 7	98.75 34	99.69 9	96.48 60	98.54 19	99.22 11	96.23 103	99.71 5	99.48 8	98.77 7	99.93 2	98.89 4	99.95 6	99.84 6

PS-MVSNAJss		98.53 20	98.63 20	98.21 75	99.68 10	94.82 121	98.10 43	99.21 12	96.91 80	99.75 3	99.45 10	95.82 106	99.92 4	98.80 5	99.96 5	99.89 2

jajsoiax		98.77 10	98.79 13	98.74 36	99.66 11	96.48 60	98.45 24	99.12 26	95.83 128	99.67 7	99.37 13	98.25 11	99.92 4	98.77 6	99.94 9	99.82 7

v7n		98.73 12	98.99 6	97.95 92	99.64 12	94.20 146	98.67 12	99.14 24	99.08 11	99.42 16	99.23 22	96.53 79	99.91 13	99.27 2	99.93 11	99.73 16

test_djsdf		98.73 12	98.74 17	98.69 41	99.63 13	96.30 66	98.67 12	99.02 50	96.50 92	99.32 21	99.44 11	97.43 31	99.92 4	98.73 8	99.95 6	99.86 3

anonymousdsp		98.72 15	98.63 20	98.99 13	99.62 14	97.29 37	98.65 15	99.19 16	95.62 135	99.35 20	99.37 13	97.38 33	99.90 14	98.59 12	99.91 16	99.77 9

PEN-MVS		98.75 11	98.85 11	98.44 56	99.58 15	95.67 87	98.45 24	99.15 22	99.33 6	99.30 22	99.00 37	97.27 38	99.92 4	97.64 33	99.92 13	99.75 14

Baseline_NR-MVSNet		97.72 73	97.79 53	97.50 125	99.56 16	93.29 176	95.44 182	98.86 84	98.20 37	98.37 73	99.24 21	94.69 144	99.55 160	95.98 86	99.79 34	99.65 23

SixPastTwentyTwo		97.49 89	97.57 79	97.26 148	99.56 16	92.33 193	98.28 30	96.97 269	98.30 33	99.45 15	99.35 17	88.43 259	99.89 17	98.01 20	99.76 38	99.54 36

PS-CasMVS		98.73 12	98.85 11	98.39 60	99.55 18	95.47 97	98.49 21	99.13 25	99.22 9	99.22 27	98.96 41	97.35 34	99.92 4	97.79 28	99.93 11	99.79 8

DTE-MVSNet		98.79 9	98.86 9	98.59 48	99.55 18	96.12 71	98.48 23	99.10 29	99.36 5	99.29 23	99.06 36	97.27 38	99.93 2	97.71 32	99.91 16	99.70 19

HPM-MVS_fast		98.32 28	98.13 34	98.88 24	99.54 20	97.48 30	98.35 27	99.03 48	95.88 123	97.88 131	98.22 103	98.15 13	99.74 67	96.50 66	99.62 63	99.42 78

TDRefinement		98.90 6	98.86 9	99.02 9	99.54 20	98.06 7	99.34 5	99.44 7	98.85 20	99.00 36	99.20 24	97.42 32	99.59 147	97.21 45	99.76 38	99.40 81

pm-mvs1		98.47 22	98.67 18	97.86 98	99.52 22	94.58 131	98.28 30	99.00 58	97.57 60	99.27 24	99.22 23	98.32 10	99.50 174	97.09 51	99.75 42	99.50 43

TransMVSNet (Re)		98.38 26	98.67 18	97.51 122	99.51 23	93.39 175	98.20 38	98.87 82	98.23 35	99.48 13	99.27 20	98.47 9	99.55 160	96.52 64	99.53 94	99.60 26

WR-MVS_H		98.65 16	98.62 22	98.75 34	99.51 23	96.61 55	98.55 18	99.17 17	99.05 14	99.17 29	98.79 50	95.47 123	99.89 17	97.95 21	99.91 16	99.75 14

PMVS	89.60 17	96.71 138	96.97 116	95.95 216	99.51 23	97.81 16	97.42 83	97.49 251	97.93 43	95.95 229	98.58 64	96.88 62	96.91 345	89.59 275	99.36 148	93.12 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss		97.69 75	97.36 91	98.70 40	99.50 26	96.84 47	95.38 189	98.99 61	92.45 238	98.11 104	98.31 85	97.25 41	99.77 49	96.60 60	99.62 63	99.48 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		98.16 34	98.37 28	97.56 117	99.49 27	93.10 182	98.35 27	99.21 12	98.43 28	98.89 39	98.83 49	94.30 159	99.81 32	97.87 24	99.91 16	99.77 9

VPNet		97.26 106	97.49 85	96.59 184	99.47 28	90.58 229	96.27 135	98.53 157	97.77 46	98.46 66	98.41 77	94.59 150	99.68 115	94.61 154	99.29 171	99.52 40

CP-MVSNet		98.42 24	98.46 25	98.30 68	99.46 29	95.22 110	98.27 32	98.84 92	99.05 14	99.01 35	98.65 62	95.37 126	99.90 14	97.57 34	99.91 16	99.77 9

XXY-MVS		97.54 85	97.70 61	97.07 156	99.46 29	92.21 197	97.22 92	99.00 58	94.93 165	98.58 56	98.92 45	97.31 36	99.41 203	94.44 161	99.43 131	99.59 27

zzz-MVS		98.01 45	97.66 65	99.06 4	99.44 31	97.90 11	95.66 173	98.73 122	97.69 56	97.90 128	97.96 132	95.81 110	99.82 30	96.13 75	99.61 69	99.45 66

MTAPA		98.14 35	97.84 49	99.06 4	99.44 31	97.90 11	97.25 89	98.73 122	97.69 56	97.90 128	97.96 132	95.81 110	99.82 30	96.13 75	99.61 69	99.45 66

SteuartSystems-ACMMP		98.02 44	97.76 57	98.79 32	99.43 33	97.21 41	97.15 94	98.90 74	96.58 90	98.08 110	97.87 146	97.02 53	99.76 53	95.25 122	99.59 74	99.40 81
Skip Steuart: Steuart Systems R&D Blog.
ACMH	93.61 9	98.44 23	98.76 14	97.51 122	99.43 33	93.54 171	98.23 33	99.05 41	97.40 71	99.37 19	99.08 35	98.79 6	99.47 181	97.74 31	99.71 50	99.50 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 39	97.83 51	98.92 22	99.42 35	97.46 31	98.57 16	99.05 41	95.43 144	97.41 157	97.50 179	97.98 16	99.79 39	95.58 103	99.57 79	99.50 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
K. test v3		96.44 151	96.28 152	96.95 161	99.41 36	91.53 213	97.65 66	90.31 341	98.89 19	98.93 38	99.36 15	84.57 288	99.92 4	97.81 26	99.56 82	99.39 84

VDDNet		96.98 117	96.84 123	97.41 138	99.40 37	93.26 177	97.94 50	95.31 299	99.26 8	98.39 72	99.18 28	87.85 268	99.62 139	95.13 134	99.09 197	99.35 93

ACMH+	93.58 10	98.23 33	98.31 30	97.98 91	99.39 38	95.22 110	97.55 73	99.20 14	98.21 36	99.25 25	98.51 71	98.21 12	99.40 205	94.79 148	99.72 47	99.32 96

TSAR-MVS + MP.		97.42 95	97.23 101	98.00 90	99.38 39	95.00 116	97.63 68	98.20 196	93.00 226	98.16 98	98.06 122	95.89 101	99.72 77	95.67 94	99.10 196	99.28 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
FIs		97.93 55	98.07 37	97.48 129	99.38 39	92.95 185	98.03 48	99.11 27	98.04 41	98.62 51	98.66 60	93.75 173	99.78 41	97.23 43	99.84 27	99.73 16

lessismore_v0				97.05 157	99.36 41	92.12 201		84.07 353		98.77 46	98.98 39	85.36 282	99.74 67	97.34 41	99.37 145	99.30 102

ACMMP_NAP		97.89 60	97.63 72	98.67 42	99.35 42	96.84 47	96.36 131	98.79 109	95.07 158	97.88 131	98.35 81	97.24 42	99.72 77	96.05 79	99.58 76	99.45 66

Vis-MVSNet		98.27 30	98.34 29	98.07 83	99.33 43	95.21 112	98.04 46	99.46 6	97.32 73	97.82 139	99.11 32	96.75 68	99.86 21	97.84 25	99.36 148	99.15 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high		98.31 29	98.94 7	96.41 197	99.33 43	89.64 238	97.92 52	99.56 5	99.27 7	99.66 9	99.50 7	97.67 26	99.83 29	97.55 35	99.98 3	99.77 9

ZNCC-MVS		97.92 56	97.62 74	98.83 26	99.32 45	97.24 39	97.45 79	98.84 92	95.76 130	96.93 184	97.43 184	97.26 40	99.79 39	96.06 77	99.53 94	99.45 66

MP-MVS		97.64 77	97.18 104	99.00 12	99.32 45	97.77 17	97.49 78	98.73 122	96.27 100	95.59 243	97.75 158	96.30 93	99.78 41	93.70 195	99.48 114	99.45 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PVSNet_Blended_VisFu		95.95 169	95.80 172	96.42 195	99.28 47	90.62 228	95.31 195	99.08 35	88.40 282	96.97 182	98.17 107	92.11 211	99.78 41	93.64 196	99.21 178	98.86 188

tfpnnormal		97.72 73	97.97 41	96.94 162	99.26 48	92.23 196	97.83 57	98.45 164	98.25 34	99.13 30	98.66 60	96.65 71	99.69 109	93.92 187	99.62 63	98.91 178

MSP-MVS		97.45 92	96.92 120	99.03 8	99.26 48	97.70 18	97.66 65	98.89 75	95.65 133	98.51 60	96.46 251	92.15 209	99.81 32	95.14 132	98.58 250	99.58 28

testgi		96.07 163	96.50 145	94.80 261	99.26 48	87.69 276	95.96 157	98.58 153	95.08 157	98.02 117	96.25 260	97.92 17	97.60 342	88.68 289	98.74 236	99.11 145

IS-MVSNet		96.93 119	96.68 132	97.70 108	99.25 51	94.00 152	98.57 16	96.74 277	98.36 30	98.14 102	97.98 131	88.23 261	99.71 92	93.10 208	99.72 47	99.38 86

DVP-MVS		97.78 70	97.65 67	98.16 76	99.24 52	95.51 94	96.74 113	98.23 192	95.92 120	98.40 70	98.28 92	97.06 50	99.71 92	95.48 107	99.52 99	99.26 114

test0726					99.24 52	95.51 94	96.89 106	98.89 75	95.92 120	98.64 50	98.31 85	97.06 50

test_0728_SECOND				98.25 72	99.23 54	95.49 96	96.74 113	98.89 75					99.75 60	95.48 107	99.52 99	99.53 39

GST-MVS		97.82 67	97.49 85	98.81 29	99.23 54	97.25 38	97.16 93	98.79 109	95.96 117	97.53 144	97.40 186	96.93 57	99.77 49	95.04 138	99.35 153	99.42 78

ACMMP		98.05 42	97.75 59	98.93 21	99.23 54	97.60 22	98.09 44	98.96 68	95.75 132	97.91 127	98.06 122	96.89 60	99.76 53	95.32 118	99.57 79	99.43 77
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
SED-MVS		97.94 52	97.90 44	98.07 83	99.22 57	95.35 102	96.79 110	98.83 99	96.11 107	99.08 31	98.24 98	97.87 21	99.72 77	95.44 111	99.51 104	99.14 134

IU-MVS					99.22 57	95.40 98		98.14 206	85.77 306	98.36 75				95.23 124	99.51 104	99.49 51

test_241102_ONE					99.22 57	95.35 102		98.83 99	96.04 112	99.08 31	98.13 109	97.87 21	99.33 226

nrg030		98.54 19	98.62 22	98.32 65	99.22 57	95.66 88	97.90 53	99.08 35	98.31 32	99.02 34	98.74 54	97.68 25	99.61 145	97.77 29	99.85 26	99.70 19

region2R		97.92 56	97.59 77	98.92 22	99.22 57	97.55 26	97.60 69	98.84 92	96.00 115	97.22 161	97.62 170	96.87 63	99.76 53	95.48 107	99.43 131	99.46 61

mPP-MVS		97.91 59	97.53 81	99.04 7	99.22 57	97.87 14	97.74 62	98.78 113	96.04 112	97.10 169	97.73 161	96.53 79	99.78 41	95.16 129	99.50 106	99.46 61

COLMAP_ROB	94.48 6	98.25 32	98.11 35	98.64 45	99.21 63	97.35 35	97.96 49	99.16 18	98.34 31	98.78 43	98.52 70	97.32 35	99.45 188	94.08 178	99.67 57	99.13 137
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		97.95 50	97.62 74	98.94 18	99.20 64	97.56 25	97.59 70	98.83 99	96.05 110	97.46 154	97.63 169	96.77 67	99.76 53	95.61 100	99.46 119	99.49 51

PGM-MVS		97.88 61	97.52 82	98.96 16	99.20 64	97.62 21	97.09 98	99.06 39	95.45 142	97.55 143	97.94 137	97.11 44	99.78 41	94.77 151	99.46 119	99.48 56

test_0402		97.84 64	97.97 41	97.47 130	99.19 66	94.07 149	96.71 118	98.73 122	98.66 24	98.56 57	98.41 77	96.84 65	99.69 109	94.82 146	99.81 30	98.64 210

EPP-MVSNet		96.84 125	96.58 136	97.65 112	99.18 67	93.78 162	98.68 11	96.34 281	97.91 44	97.30 159	98.06 122	88.46 258	99.85 23	93.85 189	99.40 141	99.32 96

abl_6		98.42 24	98.19 33	99.09 3	99.16 68	98.10 5	97.73 64	99.11 27	97.76 49	98.62 51	98.27 96	97.88 20	99.80 38	95.67 94	99.50 106	99.38 86

XVG-ACMP-BASELINE		97.58 83	97.28 97	98.49 53	99.16 68	96.90 46	96.39 128	98.98 64	95.05 159	98.06 112	98.02 126	95.86 102	99.56 156	94.37 166	99.64 61	99.00 161

CHOSEN 1792x2688		94.10 243	93.41 250	96.18 207	99.16 68	90.04 234	92.15 306	98.68 137	79.90 338	96.22 219	97.83 149	87.92 267	99.42 194	89.18 281	99.65 60	99.08 150

HFP-MVS		97.94 52	97.64 70	98.83 26	99.15 71	97.50 28	97.59 70	98.84 92	96.05 110	97.49 148	97.54 174	97.07 48	99.70 101	95.61 100	99.46 119	99.30 102

#test#		97.62 79	97.22 102	98.83 26	99.15 71	97.50 28	96.81 109	98.84 92	94.25 187	97.49 148	97.54 174	97.07 48	99.70 101	94.37 166	99.46 119	99.30 102

XVS		97.96 47	97.63 72	98.94 18	99.15 71	97.66 19	97.77 58	98.83 99	97.42 67	96.32 212	97.64 168	96.49 82	99.72 77	95.66 96	99.37 145	99.45 66

X-MVStestdata		92.86 269	90.83 294	98.94 18	99.15 71	97.66 19	97.77 58	98.83 99	97.42 67	96.32 212	36.50 354	96.49 82	99.72 77	95.66 96	99.37 145	99.45 66

LPG-MVS_test		97.94 52	97.67 64	98.74 36	99.15 71	97.02 42	97.09 98	99.02 50	95.15 154	98.34 78	98.23 100	97.91 18	99.70 101	94.41 163	99.73 44	99.50 43

LGP-MVS_train				98.74 36	99.15 71	97.02 42		99.02 50	95.15 154	98.34 78	98.23 100	97.91 18	99.70 101	94.41 163	99.73 44	99.50 43

RPSCF		97.87 62	97.51 83	98.95 17	99.15 71	98.43 3	97.56 72	99.06 39	96.19 104	98.48 63	98.70 57	94.72 143	99.24 245	94.37 166	99.33 163	99.17 127

ACMM	93.33 11	98.05 42	97.79 53	98.85 25	99.15 71	97.55 26	96.68 119	98.83 99	95.21 150	98.36 75	98.13 109	98.13 15	99.62 139	96.04 80	99.54 91	99.39 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		97.95 50	98.08 36	97.56 117	99.14 79	93.67 165	98.23 33	98.66 142	97.41 70	99.00 36	99.19 25	95.47 123	99.73 73	95.83 90	99.76 38	99.30 102

Vis-MVSNet (Re-imp)		95.11 202	94.85 203	95.87 221	99.12 80	89.17 246	97.54 77	94.92 301	96.50 92	96.58 199	97.27 201	83.64 291	99.48 178	88.42 292	99.67 57	98.97 165

OPM-MVS		97.54 85	97.25 98	98.41 58	99.11 81	96.61 55	95.24 202	98.46 163	94.58 177	98.10 107	98.07 117	97.09 47	99.39 210	95.16 129	99.44 124	99.21 122

UA-Net		98.88 8	98.76 14	99.22 2	99.11 81	97.89 13	99.47 4	99.32 8	99.08 11	97.87 134	99.67 3	96.47 84	99.92 4	97.88 23	99.98 3	99.85 4

AllTest		97.20 110	96.92 120	98.06 85	99.08 83	96.16 69	97.14 96	99.16 18	94.35 183	97.78 140	98.07 117	95.84 103	99.12 259	91.41 231	99.42 134	98.91 178

TestCases				98.06 85	99.08 83	96.16 69		99.16 18	94.35 183	97.78 140	98.07 117	95.84 103	99.12 259	91.41 231	99.42 134	98.91 178

TranMVSNet+NR-MVSNet		98.33 27	98.30 32	98.43 57	99.07 85	95.87 78	96.73 117	99.05 41	98.67 23	98.84 40	98.45 75	97.58 28	99.88 19	96.45 68	99.86 24	99.54 36

VPA-MVSNet		98.27 30	98.46 25	97.70 108	99.06 86	93.80 160	97.76 60	99.00 58	98.40 29	99.07 33	98.98 39	96.89 60	99.75 60	97.19 48	99.79 34	99.55 35

114514_t		93.96 247	93.22 254	96.19 206	99.06 86	90.97 221	95.99 154	98.94 71	73.88 351	93.43 301	96.93 222	92.38 207	99.37 216	89.09 282	99.28 172	98.25 245

EG-PatchMatch MVS		97.69 75	97.79 53	97.40 139	99.06 86	93.52 172	95.96 157	98.97 67	94.55 178	98.82 41	98.76 53	97.31 36	99.29 237	97.20 47	99.44 124	99.38 86

ACMP	92.54 13	97.47 91	97.10 108	98.55 51	99.04 89	96.70 51	96.24 139	98.89 75	93.71 203	97.97 122	97.75 158	97.44 30	99.63 131	93.22 205	99.70 53	99.32 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part2					99.03 90	96.07 72				98.08 110

XVG-OURS-SEG-HR		97.38 98	97.07 111	98.30 68	99.01 91	97.41 34	94.66 232	99.02 50	95.20 151	98.15 100	97.52 177	98.83 5	98.43 322	94.87 144	96.41 316	99.07 152

XVG-OURS		97.12 111	96.74 129	98.26 70	98.99 92	97.45 32	93.82 267	99.05 41	95.19 152	98.32 82	97.70 164	95.22 132	98.41 323	94.27 171	98.13 265	98.93 173

CP-MVS		97.92 56	97.56 80	98.99 13	98.99 92	97.82 15	97.93 51	98.96 68	96.11 107	96.89 187	97.45 183	96.85 64	99.78 41	95.19 125	99.63 62	99.38 86

CSCG		97.40 97	97.30 94	97.69 110	98.95 94	94.83 120	97.28 88	98.99 61	96.35 99	98.13 103	95.95 276	95.99 99	99.66 125	94.36 169	99.73 44	98.59 216

SF-MVS		97.60 81	97.39 89	98.22 74	98.93 95	95.69 84	97.05 100	99.10 29	95.32 147	97.83 137	97.88 144	96.44 86	99.72 77	94.59 158	99.39 142	99.25 117

HyFIR lowres test		93.72 252	92.65 267	96.91 165	98.93 95	91.81 210	91.23 323	98.52 158	82.69 326	96.46 206	96.52 249	80.38 302	99.90 14	90.36 265	98.79 231	99.03 158

testing_2		97.43 94	97.71 60	96.60 182	98.91 97	90.85 222	96.01 153	98.54 156	94.78 168	98.78 43	98.96 41	96.35 92	99.54 163	97.25 42	99.82 29	99.40 81

PM-MVS		97.36 101	97.10 108	98.14 80	98.91 97	96.77 49	96.20 141	98.63 148	93.82 200	98.54 58	98.33 83	93.98 167	99.05 269	95.99 85	99.45 123	98.61 215

CPTT-MVS		96.69 139	96.08 161	98.49 53	98.89 99	96.64 54	97.25 89	98.77 114	92.89 232	96.01 228	97.13 207	92.23 208	99.67 120	92.24 217	99.34 156	99.17 127

DPE-MVS		97.64 77	97.35 92	98.50 52	98.85 100	96.18 68	95.21 204	98.99 61	95.84 127	98.78 43	98.08 115	96.84 65	99.81 32	93.98 185	99.57 79	99.52 40

SMA-MVS		97.48 90	97.11 107	98.60 47	98.83 101	96.67 52	96.74 113	98.73 122	91.61 249	98.48 63	98.36 80	96.53 79	99.68 115	95.17 127	99.54 91	99.45 66
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SR-MVS-dyc-post		98.14 35	97.84 49	99.02 9	98.81 102	98.05 8	97.55 73	98.86 84	97.77 46	98.20 93	98.07 117	96.60 76	99.76 53	95.49 104	99.20 179	99.26 114

RE-MVS-def			97.88 47		98.81 102	98.05 8	97.55 73	98.86 84	97.77 46	98.20 93	98.07 117	96.94 55		95.49 104	99.20 179	99.26 114

UniMVSNet (Re)		97.83 65	97.65 67	98.35 64	98.80 104	95.86 79	95.92 161	99.04 47	97.51 64	98.22 92	97.81 153	94.68 146	99.78 41	97.14 50	99.75 42	99.41 80

Anonymous20231211		98.55 18	98.76 14	97.94 93	98.79 105	94.37 138	98.84 9	99.15 22	99.37 4	99.67 7	99.43 12	95.61 118	99.72 77	98.12 17	99.86 24	99.73 16

APD-MVS_3200maxsize		98.13 38	97.90 44	98.79 32	98.79 105	97.31 36	97.55 73	98.92 72	97.72 53	98.25 89	98.13 109	97.10 45	99.75 60	95.44 111	99.24 177	99.32 96

test1172		98.08 40	97.76 57	99.05 6	98.78 107	98.07 6	97.41 84	98.85 88	97.57 60	98.15 100	97.96 132	96.60 76	99.76 53	95.30 119	99.18 183	99.33 95

DeepC-MVS	95.41 4	97.82 67	97.70 61	98.16 76	98.78 107	95.72 82	96.23 140	99.02 50	93.92 199	98.62 51	98.99 38	97.69 24	99.62 139	96.18 74	99.87 23	99.15 131
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		98.00 46	97.66 65	99.01 11	98.77 109	97.93 10	97.38 85	98.83 99	97.32 73	98.06 112	97.85 147	96.65 71	99.77 49	95.00 141	99.11 194	99.32 96

MCST-MVS		96.24 156	95.80 172	97.56 117	98.75 110	94.13 148	94.66 232	98.17 202	90.17 265	96.21 220	96.10 269	95.14 133	99.43 193	94.13 177	98.85 226	99.13 137

DU-MVS		97.79 69	97.60 76	98.36 62	98.73 111	95.78 80	95.65 175	98.87 82	97.57 60	98.31 84	97.83 149	94.69 144	99.85 23	97.02 54	99.71 50	99.46 61

NR-MVSNet		97.96 47	97.86 48	98.26 70	98.73 111	95.54 92	98.14 41	98.73 122	97.79 45	99.42 16	97.83 149	94.40 157	99.78 41	95.91 89	99.76 38	99.46 61

Anonymous20231206		95.27 196	95.06 194	95.88 220	98.72 113	89.37 243	95.70 169	97.85 227	88.00 287	96.98 181	97.62 170	91.95 216	99.34 223	89.21 280	99.53 94	98.94 169

APDe-MVS		98.14 35	98.03 40	98.47 55	98.72 113	96.04 73	98.07 45	99.10 29	95.96 117	98.59 55	98.69 58	96.94 55	99.81 32	96.64 59	99.58 76	99.57 32

UniMVSNet_NR-MVSNet		97.83 65	97.65 67	98.37 61	98.72 113	95.78 80	95.66 173	99.02 50	98.11 39	98.31 84	97.69 166	94.65 148	99.85 23	97.02 54	99.71 50	99.48 56

tttt0517		93.31 263	92.56 270	95.57 229	98.71 116	87.86 270	97.44 80	87.17 349	95.79 129	97.47 153	96.84 227	64.12 352	99.81 32	96.20 73	99.32 165	99.02 160

v8		97.60 81	98.06 38	96.23 203	98.71 116	89.44 242	97.43 82	98.82 107	97.29 75	98.74 47	99.10 33	93.86 169	99.68 115	98.61 11	99.94 9	99.56 33

HQP_MVS		96.66 142	96.33 151	97.68 111	98.70 118	94.29 140	96.50 123	98.75 118	96.36 97	96.16 222	96.77 233	91.91 220	99.46 184	92.59 214	99.20 179	99.28 109

plane_prior7					98.70 118	94.67 129

Anonymous20240529		97.96 47	98.04 39	97.71 106	98.69 120	94.28 143	97.86 55	98.31 186	98.79 21	99.23 26	98.86 48	95.76 113	99.61 145	95.49 104	99.36 148	99.23 120

VDD-MVS		97.37 99	97.25 98	97.74 104	98.69 120	94.50 134	97.04 101	95.61 294	98.59 25	98.51 60	98.72 55	92.54 202	99.58 149	96.02 82	99.49 110	99.12 142

HPM-MVS++		96.99 114	96.38 148	98.81 29	98.64 122	97.59 23	95.97 156	98.20 196	95.51 140	95.06 252	96.53 247	94.10 164	99.70 101	94.29 170	99.15 185	99.13 137

ab-mvs		96.59 144	96.59 135	96.60 182	98.64 122	92.21 197	98.35 27	97.67 239	94.45 179	96.99 179	98.79 50	94.96 139	99.49 175	90.39 264	99.07 200	98.08 254

F-COLMAP		95.30 195	94.38 227	98.05 88	98.64 122	96.04 73	95.61 178	98.66 142	89.00 275	93.22 305	96.40 255	92.90 190	99.35 221	87.45 306	97.53 292	98.77 199

ITE_SJBPF				97.85 99	98.64 122	96.66 53		98.51 160	95.63 134	97.22 161	97.30 200	95.52 120	98.55 316	90.97 241	98.90 218	98.34 235

v148		96.58 145	96.97 116	95.42 237	98.63 126	87.57 277	95.09 209	97.90 224	95.91 122	98.24 91	97.96 132	93.42 179	99.39 210	96.04 80	99.52 99	99.29 108

ETH3D-3000-0.1		96.89 124	96.46 146	98.16 76	98.62 127	95.69 84	95.96 157	98.98 64	93.36 211	97.04 175	97.31 199	94.93 140	99.63 131	92.60 212	99.34 156	99.17 127

UnsupCasMVSNet_bld		94.72 220	94.26 229	96.08 210	98.62 127	90.54 232	93.38 282	98.05 219	90.30 263	97.02 177	96.80 232	89.54 247	99.16 256	88.44 291	96.18 319	98.56 218

DP-MVS		97.87 62	97.89 46	97.81 101	98.62 127	94.82 121	97.13 97	98.79 109	98.98 18	98.74 47	98.49 72	95.80 112	99.49 175	95.04 138	99.44 124	99.11 145

v10		97.55 84	97.97 41	96.31 201	98.60 130	89.64 238	97.44 80	99.02 50	96.60 88	98.72 49	99.16 30	93.48 178	99.72 77	98.76 7	99.92 13	99.58 28

Test_1112_low_res		93.53 259	92.86 259	95.54 232	98.60 130	88.86 252	92.75 294	98.69 135	82.66 327	92.65 315	96.92 224	84.75 286	99.56 156	90.94 242	97.76 278	98.19 250

V42		97.04 112	97.16 105	96.68 180	98.59 132	91.05 218	96.33 133	98.36 178	94.60 174	97.99 118	98.30 89	93.32 180	99.62 139	97.40 40	99.53 94	99.38 86

1112_ss		94.12 242	93.42 249	96.23 203	98.59 132	90.85 222	94.24 246	98.85 88	85.49 308	92.97 308	94.94 297	86.01 278	99.64 129	91.78 225	97.92 272	98.20 249

v2v482		96.78 132	97.06 112	95.95 216	98.57 134	88.77 255	95.36 190	98.26 189	95.18 153	97.85 136	98.23 100	92.58 199	99.63 131	97.80 27	99.69 54	99.45 66

WR-MVS		96.90 122	96.81 125	97.16 150	98.56 135	92.20 199	94.33 241	98.12 209	97.34 72	98.20 93	97.33 197	92.81 191	99.75 60	94.79 148	99.81 30	99.54 36

APD-MVS		97.00 113	96.53 142	98.41 58	98.55 136	96.31 65	96.32 134	98.77 114	92.96 231	97.44 156	97.58 173	95.84 103	99.74 67	91.96 219	99.35 153	99.19 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmatch-RL test		94.66 224	94.49 222	95.19 244	98.54 137	88.91 250	92.57 298	98.74 120	91.46 252	98.32 82	97.75 158	77.31 317	98.81 293	96.06 77	99.61 69	97.85 273

9.14			96.69 131		98.53 138		96.02 151	98.98 64	93.23 216	97.18 163	97.46 182	96.47 84	99.62 139	92.99 209	99.32 165

baseline		97.44 93	97.78 56	96.43 194	98.52 139	90.75 227	96.84 107	99.03 48	96.51 91	97.86 135	98.02 126	96.67 70	99.36 218	97.09 51	99.47 116	99.19 124

casdiffmvs		97.50 88	97.81 52	96.56 188	98.51 140	91.04 219	95.83 165	99.09 34	97.23 76	98.33 81	98.30 89	97.03 52	99.37 216	96.58 62	99.38 144	99.28 109

IterMVS-LS		96.92 120	97.29 95	95.79 222	98.51 140	88.13 266	95.10 207	98.66 142	96.99 77	98.46 66	98.68 59	92.55 200	99.74 67	96.91 57	99.79 34	99.50 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		95.55 182	95.13 190	96.80 171	98.51 140	93.99 153	94.60 234	98.69 135	90.20 264	95.78 237	96.21 263	92.73 194	98.98 278	90.58 258	98.86 224	97.42 290

test20.03		96.58 145	96.61 134	96.48 192	98.49 143	91.72 211	95.68 172	97.69 238	96.81 83	98.27 88	97.92 140	94.18 163	98.71 302	90.78 248	99.66 59	99.00 161

plane_prior1					98.49 143

xxxxxxxxxxxxxcwj		97.24 108	97.03 114	97.89 96	98.48 145	94.71 125	94.53 237	99.07 38	95.02 161	97.83 137	97.88 144	96.44 86	99.72 77	94.59 158	99.39 142	99.25 117

save fliter					98.48 145	94.71 125	94.53 237	98.41 172	95.02 161

MDA-MVSNet-bldmvs		95.69 176	95.67 176	95.74 223	98.48 145	88.76 256	92.84 291	97.25 257	96.00 115	97.59 142	97.95 136	91.38 225	99.46 184	93.16 207	96.35 317	98.99 164

UnsupCasMVSNet_eth		95.91 170	95.73 175	96.44 193	98.48 145	91.52 214	95.31 195	98.45 164	95.76 130	97.48 151	97.54 174	89.53 249	98.69 304	94.43 162	94.61 332	99.13 137

testtj		96.69 139	96.13 157	98.36 62	98.46 149	96.02 75	96.44 125	98.70 132	94.26 186	96.79 189	97.13 207	94.07 165	99.75 60	90.53 259	98.80 230	99.31 101

ZD-MVS					98.43 150	95.94 77		98.56 154	90.72 259	96.66 196	97.07 213	95.02 137	99.74 67	91.08 238	98.93 215

thisisatest0530		92.71 272	91.76 280	95.56 231	98.42 151	88.23 262	96.03 150	87.35 348	94.04 195	96.56 201	95.47 289	64.03 353	99.77 49	94.78 150	99.11 194	98.68 209

v1144		96.84 125	97.08 110	96.13 209	98.42 151	89.28 245	95.41 186	98.67 140	94.21 188	97.97 122	98.31 85	93.06 185	99.65 126	98.06 19	99.62 63	99.45 66

plane_prior6					98.38 153	94.37 138						91.91 220

FPMVS		89.92 306	88.63 313	93.82 284	98.37 154	96.94 45	91.58 314	93.34 315	88.00 287	90.32 333	97.10 211	70.87 344	91.13 353	71.91 350	96.16 320	93.39 342

PAPM_NR		94.61 227	94.17 234	95.96 214	98.36 155	91.23 216	95.93 160	97.95 221	92.98 227	93.42 302	94.43 309	90.53 233	98.38 326	87.60 302	96.29 318	98.27 243

MVS_111021_HR		96.73 135	96.54 141	97.27 146	98.35 156	93.66 168	93.42 279	98.36 178	94.74 169	96.58 199	96.76 235	96.54 78	98.99 276	94.87 144	99.27 174	99.15 131

TAMVS		95.49 184	94.94 197	97.16 150	98.31 157	93.41 174	95.07 212	96.82 274	91.09 256	97.51 146	97.82 152	89.96 243	99.42 194	88.42 292	99.44 124	98.64 210

OMC-MVS		96.48 149	96.00 164	97.91 95	98.30 158	96.01 76	94.86 224	98.60 150	91.88 246	97.18 163	97.21 205	96.11 96	99.04 270	90.49 263	99.34 156	98.69 207

新几何1				97.25 149	98.29 159	94.70 128		97.73 235	77.98 344	94.83 257	96.67 240	92.08 213	99.45 188	88.17 296	98.65 244	97.61 284

jason		94.39 234	94.04 238	95.41 239	98.29 159	87.85 272	92.74 296	96.75 276	85.38 313	95.29 248	96.15 264	88.21 262	99.65 126	94.24 172	99.34 156	98.74 201
jason: jason.
v1192		96.83 128	97.06 112	96.15 208	98.28 161	89.29 244	95.36 190	98.77 114	93.73 202	98.11 104	98.34 82	93.02 189	99.67 120	98.35 15	99.58 76	99.50 43

CDPH-MVS		95.45 189	94.65 211	97.84 100	98.28 161	94.96 117	93.73 271	98.33 183	85.03 316	95.44 245	96.60 243	95.31 129	99.44 191	90.01 269	99.13 190	99.11 145

MVS_111021_LR		96.82 129	96.55 139	97.62 114	98.27 163	95.34 104	93.81 269	98.33 183	94.59 176	96.56 201	96.63 242	96.61 74	98.73 300	94.80 147	99.34 156	98.78 196

CLD-MVS		95.47 187	95.07 192	96.69 178	98.27 163	92.53 190	91.36 317	98.67 140	91.22 255	95.78 237	94.12 313	95.65 117	98.98 278	90.81 246	99.72 47	98.57 217
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
1121		94.26 235	93.26 252	97.27 146	98.26 165	94.73 123	95.86 162	97.71 237	77.96 345	94.53 264	96.71 237	91.93 218	99.40 205	87.71 298	98.64 245	97.69 281

Anonymous202405211		96.34 154	95.98 166	97.43 136	98.25 166	93.85 158	96.74 113	94.41 306	97.72 53	98.37 73	98.03 125	87.15 272	99.53 165	94.06 179	99.07 200	98.92 177

pmmvs-eth3d		96.49 148	96.18 156	97.42 137	98.25 166	94.29 140	94.77 229	98.07 217	89.81 268	97.97 122	98.33 83	93.11 184	99.08 266	95.46 110	99.84 27	98.89 182

v144192		96.69 139	96.90 122	96.03 211	98.25 166	88.92 249	95.49 180	98.77 114	93.05 225	98.09 108	98.29 91	92.51 204	99.70 101	98.11 18	99.56 82	99.47 59

ambc				96.56 188	98.23 169	91.68 212	97.88 54	98.13 208		98.42 69	98.56 67	94.22 162	99.04 270	94.05 182	99.35 153	98.95 167

thres100view900		91.76 288	91.26 287	93.26 294	98.21 170	84.50 318	96.39 128	90.39 339	96.87 81	96.33 211	93.08 320	73.44 336	99.42 194	78.85 342	97.74 279	95.85 324

v1921920		96.72 136	96.96 118	95.99 212	98.21 170	88.79 254	95.42 184	98.79 109	93.22 217	98.19 96	98.26 97	92.68 195	99.70 101	98.34 16	99.55 88	99.49 51

thres600view7		92.03 284	91.43 282	93.82 284	98.19 172	84.61 317	96.27 135	90.39 339	96.81 83	96.37 210	93.11 318	73.44 336	99.49 175	80.32 338	97.95 271	97.36 291

PatchMatch-RL		94.61 227	93.81 244	97.02 160	98.19 172	95.72 82	93.66 272	97.23 258	88.17 285	94.94 255	95.62 285	91.43 224	98.57 313	87.36 307	97.68 285	96.76 311

LF4IMVS		96.07 163	95.63 178	97.36 142	98.19 172	95.55 91	95.44 182	98.82 107	92.29 240	95.70 241	96.55 245	92.63 198	98.69 304	91.75 227	99.33 163	97.85 273

v1240		96.74 133	97.02 115	95.91 219	98.18 175	88.52 257	95.39 188	98.88 80	93.15 223	98.46 66	98.40 79	92.80 192	99.71 92	98.45 14	99.49 110	99.49 51

TAPA-MVS	93.32 12	94.93 208	94.23 230	97.04 158	98.18 175	94.51 132	95.22 203	98.73 122	81.22 333	96.25 218	95.95 276	93.80 172	98.98 278	89.89 271	98.87 222	97.62 283
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.17 177	93.24 178	92.74 296	97.61 249	75.17 349	94.65 261	96.69 239	90.96 230			98.66 243	97.66 282

MIMVSNet		93.42 260	92.86 259	95.10 247	98.17 177	88.19 263	98.13 42	93.69 309	92.07 241	95.04 253	98.21 104	80.95 300	99.03 273	81.42 336	98.06 268	98.07 256

原ACMM1				96.58 185	98.16 179	92.12 201		98.15 205	85.90 304	93.49 297	96.43 252	92.47 205	99.38 213	87.66 301	98.62 246	98.23 246

testdata				95.70 226	98.16 179	90.58 229		97.72 236	80.38 336	95.62 242	97.02 217	92.06 214	98.98 278	89.06 284	98.52 251	97.54 286

MVP-Stereo		95.69 176	95.28 186	96.92 163	98.15 181	93.03 183	95.64 177	98.20 196	90.39 262	96.63 198	97.73 161	91.63 223	99.10 264	91.84 224	97.31 300	98.63 212
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		97.37 99	97.70 61	96.35 198	98.14 182	95.13 113	96.54 122	98.92 72	95.94 119	99.19 28	98.08 115	97.74 23	95.06 350	95.24 123	99.54 91	98.87 187
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
EU-MVSNet		94.25 236	94.47 223	93.60 288	98.14 182	82.60 328	97.24 91	92.72 322	85.08 314	98.48 63	98.94 43	82.59 294	98.76 298	97.47 38	99.53 94	99.44 76

NP-MVS					98.14 182	93.72 164					95.08 293

LCM-MVSNet-Re		97.33 102	97.33 93	97.32 144	98.13 185	93.79 161	96.99 104	99.65 2	96.74 85	99.47 14	98.93 44	96.91 59	99.84 26	90.11 267	99.06 203	98.32 236

ETH3 D test6400		94.77 215	93.87 243	97.47 130	98.12 186	93.73 163	94.56 236	98.70 132	85.45 311	94.70 260	95.93 278	91.77 222	99.63 131	86.45 312	99.14 186	99.05 156

3Dnovator+	96.13 3	97.73 72	97.59 77	98.15 79	98.11 187	95.60 90	98.04 46	98.70 132	98.13 38	96.93 184	98.45 75	95.30 130	99.62 139	95.64 98	98.96 209	99.24 119

VNet		96.84 125	96.83 124	96.88 166	98.06 188	92.02 204	96.35 132	97.57 250	97.70 55	97.88 131	97.80 154	92.40 206	99.54 163	94.73 153	98.96 209	99.08 150

LFMVS		95.32 194	94.88 202	96.62 181	98.03 189	91.47 215	97.65 66	90.72 338	99.11 10	97.89 130	98.31 85	79.20 305	99.48 178	93.91 188	99.12 193	98.93 173

tfpn200view9		91.55 290	91.00 289	93.21 297	98.02 190	84.35 320	95.70 169	90.79 336	96.26 101	95.90 233	92.13 333	73.62 334	99.42 194	78.85 342	97.74 279	95.85 324

thres400		91.68 289	91.00 289	93.71 286	98.02 190	84.35 320	95.70 169	90.79 336	96.26 101	95.90 233	92.13 333	73.62 334	99.42 194	78.85 342	97.74 279	97.36 291

OPU-MVS				97.64 113	98.01 192	95.27 105	96.79 110				97.35 195	96.97 54	98.51 319	91.21 237	99.25 176	99.14 134

xiu_mvs_v1_base_debu		95.62 179	95.96 167	94.60 268	98.01 192	88.42 258	93.99 260	98.21 193	92.98 227	95.91 230	94.53 305	96.39 88	99.72 77	95.43 114	98.19 262	95.64 328

xiu_mvs_v1_base		95.62 179	95.96 167	94.60 268	98.01 192	88.42 258	93.99 260	98.21 193	92.98 227	95.91 230	94.53 305	96.39 88	99.72 77	95.43 114	98.19 262	95.64 328

xiu_mvs_v1_base_debi		95.62 179	95.96 167	94.60 268	98.01 192	88.42 258	93.99 260	98.21 193	92.98 227	95.91 230	94.53 305	96.39 88	99.72 77	95.43 114	98.19 262	95.64 328

CNVR-MVS		96.92 120	96.55 139	98.03 89	98.00 196	95.54 92	94.87 223	98.17 202	94.60 174	96.38 209	97.05 215	95.67 116	99.36 218	95.12 135	99.08 198	99.19 124

PLC	91.02 16	94.05 246	92.90 258	97.51 122	98.00 196	95.12 114	94.25 245	98.25 190	86.17 300	91.48 326	95.25 291	91.01 228	99.19 250	85.02 323	96.69 311	98.22 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		96.99 114	96.80 126	97.56 117	97.96 198	93.67 165	98.23 33	98.66 142	95.59 137	97.99 118	99.19 25	89.51 250	99.73 73	94.60 155	99.44 124	99.30 102

test1		96.99 114	96.80 126	97.56 117	97.96 198	93.67 165	98.23 33	98.66 142	95.59 137	97.99 118	99.19 25	89.51 250	99.73 73	94.60 155	99.44 124	99.30 102

FMVSNet2		96.72 136	96.67 133	96.87 167	97.96 198	91.88 207	97.15 94	98.06 218	95.59 137	98.50 62	98.62 63	89.51 250	99.65 126	94.99 142	99.60 72	99.07 152

BH-untuned		94.69 221	94.75 208	94.52 273	97.95 201	87.53 278	94.07 257	97.01 267	93.99 196	97.10 169	95.65 283	92.65 197	98.95 283	87.60 302	96.74 310	97.09 296

ETH3D cwj APD-0.16		96.23 157	95.61 179	98.09 82	97.91 202	95.65 89	94.94 220	98.74 120	91.31 253	96.02 227	97.08 212	94.05 166	99.69 109	91.51 230	98.94 213	98.93 173

DPM-MVS		93.68 254	92.77 265	96.42 195	97.91 202	92.54 189	91.17 324	97.47 254	84.99 317	93.08 307	94.74 301	89.90 244	99.00 274	87.54 304	98.09 267	97.72 279

QAPM		95.88 172	95.57 181	96.80 171	97.90 204	91.84 209	98.18 40	98.73 122	88.41 281	96.42 207	98.13 109	94.73 142	99.75 60	88.72 287	98.94 213	98.81 192

TinyColmap		96.00 168	96.34 150	94.96 252	97.90 204	87.91 269	94.13 255	98.49 161	94.41 180	98.16 98	97.76 155	96.29 94	98.68 307	90.52 260	99.42 134	98.30 239

CS-MVS		95.86 173	95.59 180	96.69 178	97.85 206	93.14 180	96.42 126	99.25 10	94.17 191	93.56 295	90.76 346	96.05 98	99.72 77	93.28 202	98.91 217	97.21 294

HQP-NCC					97.85 206		94.26 242		93.18 219	92.86 310

ACMP_Plane					97.85 206		94.26 242		93.18 219	92.86 310

N_pmnet		95.18 199	94.23 230	98.06 85	97.85 206	96.55 58	92.49 300	91.63 330	89.34 271	98.09 108	97.41 185	90.33 236	99.06 268	91.58 229	99.31 167	98.56 218

HQP-MVS		95.17 201	94.58 219	96.92 163	97.85 206	92.47 191	94.26 242	98.43 167	93.18 219	92.86 310	95.08 293	90.33 236	99.23 247	90.51 261	98.74 236	99.05 156

TEST9					97.84 211	95.23 107	93.62 273	98.39 174	86.81 296	93.78 283	95.99 271	94.68 146	99.52 169

train_agg		95.46 188	94.66 210	97.88 97	97.84 211	95.23 107	93.62 273	98.39 174	87.04 294	93.78 283	95.99 271	94.58 151	99.52 169	91.76 226	98.90 218	98.89 182

MSLP-MVS++		96.42 153	96.71 130	95.57 229	97.82 213	90.56 231	95.71 168	98.84 92	94.72 170	96.71 194	97.39 190	94.91 141	98.10 337	95.28 120	99.02 205	98.05 263

test_8					97.81 214	95.07 115	93.54 276	98.38 176	87.04 294	93.71 287	95.96 275	94.58 151	99.52 169

NCCC		96.52 147	95.99 165	98.10 81	97.81 214	95.68 86	95.00 218	98.20 196	95.39 145	95.40 247	96.36 256	93.81 171	99.45 188	93.55 198	98.42 255	99.17 127

WTY-MVS		93.55 258	93.00 257	95.19 244	97.81 214	87.86 270	93.89 265	96.00 286	89.02 274	94.07 276	95.44 290	86.27 276	99.33 226	87.69 300	96.82 307	98.39 228

CNLPA		95.04 205	94.47 223	96.75 174	97.81 214	95.25 106	94.12 256	97.89 225	94.41 180	94.57 262	95.69 281	90.30 239	98.35 329	86.72 311	98.76 234	96.64 314

AUN-MVS		93.95 249	92.69 266	97.74 104	97.80 218	95.38 99	95.57 179	95.46 298	91.26 254	92.64 316	96.10 269	74.67 328	99.55 160	93.72 194	96.97 303	98.30 239

EIA-MVS		96.04 165	95.77 174	96.85 168	97.80 218	92.98 184	96.12 145	99.16 18	94.65 172	93.77 285	91.69 338	95.68 115	99.67 120	94.18 174	98.85 226	97.91 271

agg_prior1		95.39 191	94.60 216	97.75 103	97.80 218	94.96 117	93.39 281	98.36 178	87.20 292	93.49 297	95.97 274	94.65 148	99.53 165	91.69 228	98.86 224	98.77 199

agg_prior					97.80 218	94.96 117		98.36 178		93.49 297			99.53 165

旧先验1					97.80 218	93.87 156		97.75 234			97.04 216	93.57 177			98.68 240	98.72 204

PCF-MVS	89.43 18	92.12 283	90.64 297	96.57 187	97.80 218	93.48 173	89.88 340	98.45 164	74.46 350	96.04 226	95.68 282	90.71 232	99.31 230	73.73 347	99.01 207	96.91 304
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior3		95.91 170	95.39 184	97.46 132	97.79 224	94.26 144	93.33 284	98.42 170	94.21 188	94.02 278	96.25 260	93.64 175	99.34 223	91.90 220	98.96 209	98.79 194

test_prior				97.46 132	97.79 224	94.26 144		98.42 170					99.34 223			98.79 194

PVSNet_BlendedMVS		95.02 207	94.93 199	95.27 241	97.79 224	87.40 281	94.14 254	98.68 137	88.94 276	94.51 265	98.01 128	93.04 186	99.30 233	89.77 273	99.49 110	99.11 145

PVSNet_Blended		93.96 247	93.65 246	94.91 253	97.79 224	87.40 281	91.43 316	98.68 137	84.50 321	94.51 265	94.48 308	93.04 186	99.30 233	89.77 273	98.61 247	98.02 266

USDC		94.56 229	94.57 221	94.55 272	97.78 228	86.43 295	92.75 294	98.65 147	85.96 302	96.91 186	97.93 139	90.82 231	98.74 299	90.71 253	99.59 74	98.47 223

alignmvs		96.01 167	95.52 182	97.50 125	97.77 229	94.71 125	96.07 147	96.84 272	97.48 65	96.78 193	94.28 312	85.50 281	99.40 205	96.22 72	98.73 239	98.40 226

ETV-MVS		96.13 162	95.90 170	96.82 170	97.76 230	93.89 155	95.40 187	98.95 70	95.87 124	95.58 244	91.00 343	96.36 91	99.72 77	93.36 199	98.83 228	96.85 307

D2MVS		95.18 199	95.17 189	95.21 243	97.76 230	87.76 275	94.15 252	97.94 222	89.77 269	96.99 179	97.68 167	87.45 270	99.14 257	95.03 140	99.81 30	98.74 201

TSAR-MVS + GP.		96.47 150	96.12 158	97.49 128	97.74 232	95.23 107	94.15 252	96.90 271	93.26 215	98.04 115	96.70 238	94.41 156	98.89 286	94.77 151	99.14 186	98.37 229

3Dnovator	96.53 2	97.61 80	97.64 70	97.50 125	97.74 232	93.65 169	98.49 21	98.88 80	96.86 82	97.11 168	98.55 68	95.82 106	99.73 73	95.94 87	99.42 134	99.13 137

sss		94.22 237	93.72 245	95.74 223	97.71 234	89.95 236	93.84 266	96.98 268	88.38 283	93.75 286	95.74 280	87.94 263	98.89 286	91.02 240	98.10 266	98.37 229

DeepC-MVS_fast	94.34 7	96.74 133	96.51 144	97.44 135	97.69 235	94.15 147	96.02 151	98.43 167	93.17 222	97.30 159	97.38 192	95.48 122	99.28 239	93.74 192	99.34 156	98.88 185
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IterMVS-SCA-FT		95.86 173	96.19 155	94.85 258	97.68 236	85.53 303	92.42 302	97.63 247	96.99 77	98.36 75	98.54 69	87.94 263	99.75 60	97.07 53	99.08 198	99.27 113

MVSFormer		96.14 161	96.36 149	95.49 234	97.68 236	87.81 273	98.67 12	99.02 50	96.50 92	94.48 267	96.15 264	86.90 273	99.92 4	98.73 8	99.13 190	98.74 201

lupinMVS		93.77 250	93.28 251	95.24 242	97.68 236	87.81 273	92.12 307	96.05 285	84.52 320	94.48 267	95.06 295	86.90 273	99.63 131	93.62 197	99.13 190	98.27 243

Fast-Effi-MVS+		95.49 184	95.07 192	96.75 174	97.67 239	92.82 186	94.22 248	98.60 150	91.61 249	93.42 302	92.90 323	96.73 69	99.70 101	92.60 212	97.89 275	97.74 278

canonicalmvs		97.23 109	97.21 103	97.30 145	97.65 240	94.39 136	97.84 56	99.05 41	97.42 67	96.68 195	93.85 315	97.63 27	99.33 226	96.29 71	98.47 254	98.18 251

CDS-MVSNet		94.88 211	94.12 235	97.14 152	97.64 241	93.57 170	93.96 263	97.06 266	90.05 266	96.30 215	96.55 245	86.10 277	99.47 181	90.10 268	99.31 167	98.40 226
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs5		94.63 226	94.34 228	95.50 233	97.63 242	88.34 261	94.02 258	97.13 263	87.15 293	95.22 250	97.15 206	87.50 269	99.27 241	93.99 184	99.26 175	98.88 185

test12				97.46 132	97.61 243	94.07 149		97.78 233		93.57 294		93.31 181	99.42 194		98.78 232	98.89 182

PMMVS2		93.66 255	94.07 236	92.45 311	97.57 244	80.67 335	86.46 346	96.00 286	93.99 196	97.10 169	97.38 192	89.90 244	97.82 339	88.76 286	99.47 116	98.86 188

BH-RMVSNet		94.56 229	94.44 226	94.91 253	97.57 244	87.44 280	93.78 270	96.26 282	93.69 204	96.41 208	96.50 250	92.10 212	99.00 274	85.96 314	97.71 282	98.31 237

PVSNet	86.72 19	91.10 294	90.97 291	91.49 317	97.56 246	78.04 341	87.17 345	94.60 304	84.65 319	92.34 320	92.20 332	87.37 271	98.47 320	85.17 322	97.69 284	97.96 268

DELS-MVS		96.17 160	96.23 153	95.99 212	97.55 247	90.04 234	92.38 304	98.52 158	94.13 192	96.55 203	97.06 214	94.99 138	99.58 149	95.62 99	99.28 172	98.37 229
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		95.42 190	95.83 171	94.20 281	97.52 248	83.78 324	92.41 303	97.47 254	95.49 141	98.06 112	98.49 72	87.94 263	99.58 149	96.02 82	99.02 205	99.23 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet		95.67 178	96.58 136	92.94 305	97.48 249	80.21 336	92.96 290	98.19 201	94.83 166	98.82 41	98.79 50	93.31 181	99.51 173	95.83 90	99.04 204	99.12 142

MDA-MVSNet_test_wron		94.73 216	94.83 206	94.42 275	97.48 249	85.15 310	90.28 334	95.87 290	92.52 235	97.48 151	97.76 155	91.92 219	99.17 255	93.32 200	96.80 309	98.94 169

PHI-MVS		96.96 118	96.53 142	98.25 72	97.48 249	96.50 59	96.76 112	98.85 88	93.52 206	96.19 221	96.85 226	95.94 100	99.42 194	93.79 191	99.43 131	98.83 190

DeepPCF-MVS	94.58 5	96.90 122	96.43 147	98.31 67	97.48 249	97.23 40	92.56 299	98.60 150	92.84 233	98.54 58	97.40 186	96.64 73	98.78 295	94.40 165	99.41 140	98.93 173

thres200		91.00 296	90.42 300	92.77 307	97.47 253	83.98 323	94.01 259	91.18 334	95.12 156	95.44 245	91.21 341	73.93 330	99.31 230	77.76 345	97.63 289	95.01 334

YYNet1		94.73 216	94.84 204	94.41 276	97.47 253	85.09 312	90.29 333	95.85 291	92.52 235	97.53 144	97.76 155	91.97 215	99.18 251	93.31 201	96.86 306	98.95 167

Effi-MVS+		96.19 159	96.01 163	96.71 176	97.43 255	92.19 200	96.12 145	99.10 29	95.45 142	93.33 304	94.71 302	97.23 43	99.56 156	93.21 206	97.54 291	98.37 229

pmmvs4		94.82 213	94.19 233	96.70 177	97.42 256	92.75 188	92.09 309	96.76 275	86.80 297	95.73 240	97.22 204	89.28 253	98.89 286	93.28 202	99.14 186	98.46 225

MSDG		95.33 193	95.13 190	95.94 218	97.40 257	91.85 208	91.02 327	98.37 177	95.30 148	96.31 214	95.99 271	94.51 154	98.38 326	89.59 275	97.65 288	97.60 285

EI-MVSNet-Vis-set		97.32 103	97.39 89	97.11 153	97.36 258	92.08 203	95.34 192	97.65 243	97.74 50	98.29 87	98.11 113	95.05 134	99.68 115	97.50 37	99.50 106	99.56 33

PS-MVSNAJ		94.10 243	94.47 223	93.00 302	97.35 259	84.88 314	91.86 311	97.84 229	91.96 244	94.17 272	92.50 330	95.82 106	99.71 92	91.27 234	97.48 294	94.40 338

Regformer-3		97.25 107	97.29 95	97.11 153	97.35 259	92.32 194	95.26 199	97.62 248	97.67 58	98.17 97	97.89 142	95.05 134	99.56 156	97.16 49	99.42 134	99.46 61

Regformer-4		97.53 87	97.47 87	97.71 106	97.35 259	93.91 154	95.26 199	98.14 206	97.97 42	98.34 78	97.89 142	95.49 121	99.71 92	97.41 39	99.42 134	99.51 42

diffmvs		96.04 165	96.23 153	95.46 236	97.35 259	88.03 268	93.42 279	99.08 35	94.09 194	96.66 196	96.93 222	93.85 170	99.29 237	96.01 84	98.67 241	99.06 154

EI-MVSNet-UG-set		97.32 103	97.40 88	97.09 155	97.34 263	92.01 205	95.33 193	97.65 243	97.74 50	98.30 86	98.14 108	95.04 136	99.69 109	97.55 35	99.52 99	99.58 28

baseline1		93.14 267	92.64 268	94.62 267	97.34 263	87.20 285	96.67 120	93.02 317	94.71 171	96.51 204	95.83 279	81.64 295	98.60 312	90.00 270	88.06 347	98.07 256

AdaColmap		95.11 202	94.62 215	96.58 185	97.33 265	94.45 135	94.92 221	98.08 213	93.15 223	93.98 281	95.53 288	94.34 158	99.10 264	85.69 317	98.61 247	96.20 322

xiu_mvs_v2_base		94.22 237	94.63 214	92.99 303	97.32 266	84.84 315	92.12 307	97.84 229	91.96 244	94.17 272	93.43 316	96.07 97	99.71 92	91.27 234	97.48 294	94.42 337

OpenMVS_ROB	91.80 14	93.64 256	93.05 255	95.42 237	97.31 267	91.21 217	95.08 211	96.68 279	81.56 330	96.88 188	96.41 253	90.44 235	99.25 244	85.39 321	97.67 286	95.80 326

EI-MVSNet		96.63 143	96.93 119	95.74 223	97.26 268	88.13 266	95.29 197	97.65 243	96.99 77	97.94 125	98.19 105	92.55 200	99.58 149	96.91 57	99.56 82	99.50 43

CVMVSNet		92.33 279	92.79 262	90.95 321	97.26 268	75.84 349	95.29 197	92.33 325	81.86 328	96.27 216	98.19 105	81.44 296	98.46 321	94.23 173	98.29 260	98.55 220

Regformer-1		97.27 105	97.16 105	97.61 115	97.21 270	93.86 157	94.85 225	98.04 220	97.62 59	98.03 116	97.50 179	95.34 127	99.63 131	96.52 64	99.31 167	99.35 93

Regformer-2		97.41 96	97.24 100	97.93 94	97.21 270	94.72 124	94.85 225	98.27 187	97.74 50	98.11 104	97.50 179	95.58 119	99.69 109	96.57 63	99.31 167	99.37 91

Fast-Effi-MVS+-dtu		96.44 151	96.12 158	97.39 140	97.18 272	94.39 136	95.46 181	98.73 122	96.03 114	94.72 258	94.92 299	96.28 95	99.69 109	93.81 190	97.98 270	98.09 253

OpenMVS	94.22 8	95.48 186	95.20 187	96.32 200	97.16 273	91.96 206	97.74 62	98.84 92	87.26 291	94.36 269	98.01 128	93.95 168	99.67 120	90.70 254	98.75 235	97.35 293

BH-w/o		92.14 282	91.94 276	92.73 308	97.13 274	85.30 306	92.46 301	95.64 293	89.33 272	94.21 271	92.74 326	89.60 246	98.24 332	81.68 335	94.66 331	94.66 336

MG-MVS		94.08 245	94.00 239	94.32 278	97.09 275	85.89 300	93.19 288	95.96 288	92.52 235	94.93 256	97.51 178	89.54 247	98.77 296	87.52 305	97.71 282	98.31 237

thisisatest0515		90.43 299	89.18 311	94.17 283	97.07 276	85.44 304	89.75 341	87.58 347	88.28 284	93.69 289	91.72 337	65.27 351	99.58 149	90.59 257	98.67 241	97.50 288

MVS-HIRNet		88.40 314	90.20 302	82.99 336	97.01 277	60.04 358	93.11 289	85.61 352	84.45 322	88.72 342	99.09 34	84.72 287	98.23 333	82.52 334	96.59 314	90.69 349

GA-MVS		92.83 270	92.15 275	94.87 257	96.97 278	87.27 284	90.03 335	96.12 284	91.83 247	94.05 277	94.57 303	76.01 324	98.97 282	92.46 216	97.34 299	98.36 234

test_yl		94.40 232	94.00 239	95.59 227	96.95 279	89.52 240	94.75 230	95.55 296	96.18 105	96.79 189	96.14 266	81.09 298	99.18 251	90.75 249	97.77 276	98.07 256

DCV-MVSNet		94.40 232	94.00 239	95.59 227	96.95 279	89.52 240	94.75 230	95.55 296	96.18 105	96.79 189	96.14 266	81.09 298	99.18 251	90.75 249	97.77 276	98.07 256

MVS_Test		96.27 155	96.79 128	94.73 264	96.94 281	86.63 292	96.18 142	98.33 183	94.94 163	96.07 225	98.28 92	95.25 131	99.26 242	97.21 45	97.90 274	98.30 239

MAR-MVS		94.21 239	93.03 256	97.76 102	96.94 281	97.44 33	96.97 105	97.15 262	87.89 289	92.00 323	92.73 327	92.14 210	99.12 259	83.92 328	97.51 293	96.73 312
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
Effi-MVS+-dtu		96.81 130	96.09 160	98.99 13	96.90 283	98.69 2	96.42 126	98.09 211	95.86 125	95.15 251	95.54 287	94.26 160	99.81 32	94.06 179	98.51 253	98.47 223

mvs-test1		96.20 158	95.50 183	98.32 65	96.90 283	98.16 4	95.07 212	98.09 211	95.86 125	93.63 290	94.32 311	94.26 160	99.71 92	94.06 179	97.27 302	97.07 297

MS-PatchMatch		94.83 212	94.91 201	94.57 271	96.81 285	87.10 286	94.23 247	97.34 256	88.74 279	97.14 165	97.11 210	91.94 217	98.23 333	92.99 209	97.92 272	98.37 229

UGNet		96.81 130	96.56 138	97.58 116	96.64 286	93.84 159	97.75 61	97.12 264	96.47 95	93.62 291	98.88 47	93.22 183	99.53 165	95.61 100	99.69 54	99.36 92
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		95.09 204	95.01 196	95.31 240	96.61 287	94.02 151	96.83 108	97.18 261	95.60 136	95.79 235	94.33 310	94.54 153	98.37 328	85.70 316	98.52 251	93.52 340

PAPM		87.64 320	85.84 325	93.04 300	96.54 288	84.99 313	88.42 344	95.57 295	79.52 339	83.82 351	93.05 322	80.57 301	98.41 323	62.29 353	92.79 338	95.71 327

FMVSNet3		95.26 197	94.94 197	96.22 205	96.53 289	90.06 233	95.99 154	97.66 241	94.11 193	97.99 118	97.91 141	80.22 303	99.63 131	94.60 155	99.44 124	98.96 166

HY-MVS	91.43 15	92.58 273	91.81 279	94.90 255	96.49 290	88.87 251	97.31 86	94.62 303	85.92 303	90.50 332	96.84 227	85.05 283	99.40 205	83.77 331	95.78 324	96.43 319

TR-MVS		92.54 274	92.20 274	93.57 289	96.49 290	86.66 291	93.51 277	94.73 302	89.96 267	94.95 254	93.87 314	90.24 241	98.61 310	81.18 337	94.88 329	95.45 332

ET-MVSNet_ETH3D		91.12 293	89.67 305	95.47 235	96.41 292	89.15 248	91.54 315	90.23 342	89.07 273	86.78 350	92.84 324	69.39 347	99.44 191	94.16 175	96.61 313	97.82 275

CANet		95.86 173	95.65 177	96.49 191	96.41 292	90.82 224	94.36 240	98.41 172	94.94 163	92.62 318	96.73 236	92.68 195	99.71 92	95.12 135	99.60 72	98.94 169

mvs_anonymous		95.36 192	96.07 162	93.21 297	96.29 294	81.56 331	94.60 234	97.66 241	93.30 214	96.95 183	98.91 46	93.03 188	99.38 213	96.60 60	97.30 301	98.69 207

MVS_0304		95.50 183	95.05 195	96.84 169	96.28 295	93.12 181	97.00 103	96.16 283	95.03 160	89.22 340	97.70 164	90.16 242	99.48 178	94.51 160	99.34 156	97.93 270

SCA		93.38 262	93.52 248	92.96 304	96.24 296	81.40 332	93.24 286	94.00 308	91.58 251	94.57 262	96.97 219	87.94 263	99.42 194	89.47 277	97.66 287	98.06 260

LS3D		97.77 71	97.50 84	98.57 49	96.24 296	97.58 24	98.45 24	98.85 88	98.58 26	97.51 146	97.94 137	95.74 114	99.63 131	95.19 125	98.97 208	98.51 221

new_pmnet		92.34 278	91.69 281	94.32 278	96.23 298	89.16 247	92.27 305	92.88 319	84.39 323	95.29 248	96.35 257	85.66 280	96.74 348	84.53 326	97.56 290	97.05 298

MVE	73.61 22	86.48 322	85.92 324	88.18 333	96.23 298	85.28 308	81.78 352	75.79 355	86.01 301	82.53 353	91.88 335	92.74 193	87.47 354	71.42 351	94.86 330	91.78 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cl_fuxian		95.20 198	95.32 185	94.83 260	96.19 300	86.43 295	91.83 312	98.35 182	93.47 208	97.36 158	97.26 202	88.69 256	99.28 239	95.41 117	99.36 148	98.78 196

DSMNet-mixed		92.19 281	91.83 278	93.25 295	96.18 301	83.68 325	96.27 135	93.68 311	76.97 348	92.54 319	99.18 28	89.20 255	98.55 316	83.88 329	98.60 249	97.51 287

miper_lstm_enhance		94.81 214	94.80 207	94.85 258	96.16 302	86.45 294	91.14 325	98.20 196	93.49 207	97.03 176	97.37 194	84.97 285	99.26 242	95.28 120	99.56 82	98.83 190

our_test_3		94.20 241	94.58 219	93.07 299	96.16 302	81.20 333	90.42 332	96.84 272	90.72 259	97.14 165	97.13 207	90.47 234	99.11 262	94.04 183	98.25 261	98.91 178

ppachtmachnet_test		94.49 231	94.84 204	93.46 291	96.16 302	82.10 330	90.59 330	97.48 253	90.53 261	97.01 178	97.59 172	91.01 228	99.36 218	93.97 186	99.18 183	98.94 169

Patchmatch-test		93.60 257	93.25 253	94.63 266	96.14 305	87.47 279	96.04 149	94.50 305	93.57 205	96.47 205	96.97 219	76.50 320	98.61 310	90.67 255	98.41 256	97.81 277

wuyk23d		93.25 265	95.20 187	87.40 335	96.07 306	95.38 99	97.04 101	94.97 300	95.33 146	99.70 6	98.11 113	98.14 14	91.94 352	77.76 345	99.68 56	74.89 351

eth_miper_zixun_eth		94.89 210	94.93 199	94.75 263	95.99 307	86.12 298	91.35 318	98.49 161	93.40 209	97.12 167	97.25 203	86.87 275	99.35 221	95.08 137	98.82 229	98.78 196

CANet_DTU		94.65 225	94.21 232	95.96 214	95.90 308	89.68 237	93.92 264	97.83 231	93.19 218	90.12 335	95.64 284	88.52 257	99.57 155	93.27 204	99.47 116	98.62 213

cl-mvsnet1		94.73 216	94.64 212	95.01 250	95.86 309	87.00 287	91.33 319	98.08 213	93.34 212	97.10 169	97.34 196	84.02 289	99.31 230	95.15 131	99.55 88	98.72 204

cl-mvsnet_		94.73 216	94.64 212	95.01 250	95.85 310	87.00 287	91.33 319	98.08 213	93.34 212	97.10 169	97.33 197	84.01 290	99.30 233	95.14 132	99.56 82	98.71 206

MVSTER		94.21 239	93.93 242	95.05 249	95.83 311	86.46 293	95.18 205	97.65 243	92.41 239	97.94 125	98.00 130	72.39 339	99.58 149	96.36 70	99.56 82	99.12 142

FMVSNet5		93.39 261	92.35 272	96.50 190	95.83 311	90.81 226	97.31 86	98.27 187	92.74 234	96.27 216	98.28 92	62.23 354	99.67 120	90.86 244	99.36 148	99.03 158

miper_ehance_all_eth		94.69 221	94.70 209	94.64 265	95.77 313	86.22 297	91.32 321	98.24 191	91.67 248	97.05 174	96.65 241	88.39 260	99.22 249	94.88 143	98.34 257	98.49 222

PVSNet_0	81.89 21	84.49 323	83.21 326	88.34 332	95.76 314	74.97 352	83.49 349	92.70 323	78.47 343	87.94 345	86.90 351	83.38 292	96.63 349	73.44 348	66.86 354	93.40 341

PAPR		92.22 280	91.27 286	95.07 248	95.73 315	88.81 253	91.97 310	97.87 226	85.80 305	90.91 328	92.73 327	91.16 226	98.33 330	79.48 339	95.76 325	98.08 254

baseline2		89.65 308	88.44 315	93.25 295	95.62 316	82.71 326	93.82 267	85.94 351	88.89 277	87.35 348	92.54 329	71.23 342	99.33 226	86.01 313	94.60 333	97.72 279

CHOSEN 280x420		89.98 304	89.19 310	92.37 312	95.60 317	81.13 334	86.22 347	97.09 265	81.44 332	87.44 347	93.15 317	73.99 329	99.47 181	88.69 288	99.07 200	96.52 318

ADS-MVSNet2		91.47 291	90.51 299	94.36 277	95.51 318	85.63 301	95.05 215	95.70 292	83.46 324	92.69 313	96.84 227	79.15 306	99.41 203	85.66 318	90.52 342	98.04 264

ADS-MVSNet		90.95 297	90.26 301	93.04 300	95.51 318	82.37 329	95.05 215	93.41 314	83.46 324	92.69 313	96.84 227	79.15 306	98.70 303	85.66 318	90.52 342	98.04 264

CR-MVSNet		93.29 264	92.79 262	94.78 262	95.44 320	88.15 264	96.18 142	97.20 259	84.94 318	94.10 274	98.57 65	77.67 312	99.39 210	95.17 127	95.81 321	96.81 309

RPMNet		94.68 223	94.60 216	94.90 255	95.44 320	88.15 264	96.18 142	98.86 84	97.43 66	94.10 274	98.49 72	79.40 304	99.76 53	95.69 93	95.81 321	96.81 309

1314		92.38 277	92.30 273	92.64 309	95.42 322	85.15 310	95.86 162	96.97 269	85.40 312	90.62 329	93.06 321	91.12 227	97.80 340	86.74 310	95.49 328	94.97 335

RRT_test8_iter05		92.46 275	92.52 271	92.29 314	95.33 323	77.43 344	95.73 167	98.55 155	94.41 180	97.46 154	97.72 163	57.44 357	99.74 67	96.92 56	99.14 186	99.69 21

tpm		91.08 295	90.85 293	91.75 316	95.33 323	78.09 340	95.03 217	91.27 333	88.75 278	93.53 296	97.40 186	71.24 341	99.30 233	91.25 236	93.87 335	97.87 272

IB-MVS	85.98 20	88.63 312	86.95 321	93.68 287	95.12 325	84.82 316	90.85 328	90.17 343	87.55 290	88.48 343	91.34 340	58.01 356	99.59 147	87.24 308	93.80 336	96.63 316
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		93.75 251	93.57 247	94.29 280	95.05 326	87.32 283	96.05 148	92.98 318	97.54 63	94.25 270	98.72 55	75.79 325	99.24 245	95.92 88	95.81 321	96.32 320

tpm2		88.47 313	87.69 317	90.79 322	94.98 327	77.34 345	95.09 209	91.83 328	77.51 347	89.40 338	96.41 253	67.83 349	98.73 300	83.58 333	92.60 340	96.29 321

Patchmtry		95.03 206	94.59 218	96.33 199	94.83 328	90.82 224	96.38 130	97.20 259	96.59 89	97.49 148	98.57 65	77.67 312	99.38 213	92.95 211	99.62 63	98.80 193

MVS		90.02 302	89.20 309	92.47 310	94.71 329	86.90 289	95.86 162	96.74 277	64.72 353	90.62 329	92.77 325	92.54 202	98.39 325	79.30 340	95.56 327	92.12 344

CostFormer		89.75 307	89.25 306	91.26 320	94.69 330	78.00 342	95.32 194	91.98 327	81.50 331	90.55 331	96.96 221	71.06 343	98.89 286	88.59 290	92.63 339	96.87 305

PatchmatchNet		91.98 285	91.87 277	92.30 313	94.60 331	79.71 337	95.12 206	93.59 313	89.52 270	93.61 292	97.02 217	77.94 310	99.18 251	90.84 245	94.57 334	98.01 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		88.01 317	87.33 318	90.05 327	94.48 332	76.28 348	94.47 239	94.35 307	73.84 352	89.26 339	95.61 286	73.64 333	98.30 331	84.13 327	86.20 350	95.57 331

MDTV_nov1_ep13			91.28 285		94.31 333	73.51 353	94.80 227	93.16 316	86.75 298	93.45 300	97.40 186	76.37 321	98.55 316	88.85 285	96.43 315

cl-mvsnet2		93.25 265	92.84 261	94.46 274	94.30 334	86.00 299	91.09 326	96.64 280	90.74 258	95.79 235	96.31 258	78.24 309	98.77 296	94.15 176	98.34 257	98.62 213

cascas		91.89 286	91.35 284	93.51 290	94.27 335	85.60 302	88.86 343	98.61 149	79.32 340	92.16 322	91.44 339	89.22 254	98.12 336	90.80 247	97.47 296	96.82 308

test-LLR		89.97 305	89.90 303	90.16 325	94.24 336	74.98 350	89.89 337	89.06 344	92.02 242	89.97 336	90.77 344	73.92 331	98.57 313	91.88 222	97.36 297	96.92 302

test-mter		87.92 318	87.17 319	90.16 325	94.24 336	74.98 350	89.89 337	89.06 344	86.44 299	89.97 336	90.77 344	54.96 361	98.57 313	91.88 222	97.36 297	96.92 302

pmmvs3		90.00 303	88.90 312	93.32 292	94.20 338	85.34 305	91.25 322	92.56 324	78.59 342	93.82 282	95.17 292	67.36 350	98.69 304	89.08 283	98.03 269	95.92 323

tpmrst		90.31 300	90.61 298	89.41 328	94.06 339	72.37 355	95.06 214	93.69 309	88.01 286	92.32 321	96.86 225	77.45 314	98.82 291	91.04 239	87.01 349	97.04 299

test0.0.03 1		90.11 301	89.21 308	92.83 306	93.89 340	86.87 290	91.74 313	88.74 346	92.02 242	94.71 259	91.14 342	73.92 331	94.48 351	83.75 332	92.94 337	97.16 295

JIA-IIPM		91.79 287	90.69 296	95.11 246	93.80 341	90.98 220	94.16 251	91.78 329	96.38 96	90.30 334	99.30 19	72.02 340	98.90 284	88.28 294	90.17 344	95.45 332

miper_enhance_ethall		93.14 267	92.78 264	94.20 281	93.65 342	85.29 307	89.97 336	97.85 227	85.05 315	96.15 224	94.56 304	85.74 279	99.14 257	93.74 192	98.34 257	98.17 252

TESTMET0.1,1		87.20 321	86.57 323	89.07 329	93.62 343	72.84 354	89.89 337	87.01 350	85.46 310	89.12 341	90.20 347	56.00 360	97.72 341	90.91 243	96.92 304	96.64 314

CMPMVS	73.10 23	92.74 271	91.39 283	96.77 173	93.57 344	94.67 129	94.21 249	97.67 239	80.36 337	93.61 292	96.60 243	82.85 293	97.35 343	84.86 324	98.78 232	98.29 242
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
RRT_MVS		94.90 209	94.07 236	97.39 140	93.18 345	93.21 179	95.26 199	97.49 251	93.94 198	98.25 89	97.85 147	72.96 338	99.84 26	97.90 22	99.78 37	99.14 134

DWT-MVSNet_test		87.92 318	86.77 322	91.39 318	93.18 345	78.62 339	95.10 207	91.42 331	85.58 307	88.00 344	88.73 349	60.60 355	98.90 284	90.60 256	87.70 348	96.65 313

E-PMN		89.52 309	89.78 304	88.73 330	93.14 347	77.61 343	83.26 350	92.02 326	94.82 167	93.71 287	93.11 318	75.31 326	96.81 346	85.81 315	96.81 308	91.77 346

PMMVS		92.39 276	91.08 288	96.30 202	93.12 348	92.81 187	90.58 331	95.96 288	79.17 341	91.85 325	92.27 331	90.29 240	98.66 309	89.85 272	96.68 312	97.43 289

EMVS		89.06 311	89.22 307	88.61 331	93.00 349	77.34 345	82.91 351	90.92 335	94.64 173	92.63 317	91.81 336	76.30 322	97.02 344	83.83 330	96.90 305	91.48 347

dp		88.08 316	88.05 316	88.16 334	92.85 350	68.81 357	94.17 250	92.88 319	85.47 309	91.38 327	96.14 266	68.87 348	98.81 293	86.88 309	83.80 352	96.87 305

gg-mvs-nofinetune		88.28 315	86.96 320	92.23 315	92.84 351	84.44 319	98.19 39	74.60 356	99.08 11	87.01 349	99.47 9	56.93 358	98.23 333	78.91 341	95.61 326	94.01 339

tpmvs		90.79 298	90.87 292	90.57 324	92.75 352	76.30 347	95.79 166	93.64 312	91.04 257	91.91 324	96.26 259	77.19 318	98.86 290	89.38 279	89.85 345	96.56 317

EPMVS		89.26 310	88.55 314	91.39 318	92.36 353	79.11 338	95.65 175	79.86 354	88.60 280	93.12 306	96.53 247	70.73 345	98.10 337	90.75 249	89.32 346	96.98 300

gm-plane-assit					91.79 354	71.40 356			81.67 329		90.11 348		98.99 276	84.86 324

GG-mvs-BLEND				90.60 323	91.00 355	84.21 322	98.23 33	72.63 359		82.76 352	84.11 352	56.14 359	96.79 347	72.20 349	92.09 341	90.78 348

DeepMVS_CX				77.17 337	90.94 356	85.28 308		74.08 358	52.51 354	80.87 355	88.03 350	75.25 327	70.63 355	59.23 354	84.94 351	75.62 350

EPNet_dtu		91.39 292	90.75 295	93.31 293	90.48 357	82.61 327	94.80 227	92.88 319	93.39 210	81.74 354	94.90 300	81.36 297	99.11 262	88.28 294	98.87 222	98.21 248
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		93.72 252	92.62 269	97.03 159	87.61 358	92.25 195	96.27 135	91.28 332	96.74 85	87.65 346	97.39 190	85.00 284	99.64 129	92.14 218	99.48 114	99.20 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		57.23 324	62.50 327	41.44 338	34.77 359	49.21 360	83.93 348	60.22 360	15.31 355	71.11 356	79.37 353	70.09 346	44.86 356	64.76 352	82.93 353	30.25 352

test123		12.59 326	15.49 329	3.87 339	6.07 360	2.55 361	90.75 329	2.59 362	2.52 356	5.20 358	13.02 356	4.96 362	1.85 358	5.20 355	9.09 355	7.23 353

testmvs		12.33 327	15.23 330	3.64 340	5.77 361	2.23 362	88.99 342	3.62 361	2.30 357	5.29 357	13.09 355	4.52 363	1.95 357	5.16 356	8.32 356	6.75 354

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

cdsmvs_eth3d_5k		24.22 325	32.30 328	0.00 341	0.00 362	0.00 363	0.00 353	98.10 210	0.00 358	0.00 359	95.06 295	97.54 29	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		7.98 328	10.65 331	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	95.82 106	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ab-mvs-re		7.91 329	10.55 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	94.94 297	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

test_241102_TWO								98.83 99	96.11 107	98.62 51	98.24 98	96.92 58	99.72 77	95.44 111	99.49 110	99.49 51

test_0728_THIRD									96.62 87	98.40 70	98.28 92	97.10 45	99.71 92	95.70 92	99.62 63	99.58 28

GSMVS																98.06 260

sam_mvs1												77.80 311				98.06 260

sam_mvs												77.38 315

MTGPA								98.73 122

test_post1							94.98 219				10.37 358	76.21 323	99.04 270	89.47 277

test_post											10.87 357	76.83 319	99.07 267

patchmatchnet-post											96.84 227	77.36 316	99.42 194

MTMP							96.55 121	74.60 356

test9_res														91.29 233	98.89 221	99.00 161

agg_prior2														90.34 266	98.90 218	99.10 149

test_prior4						95.38 99	93.61 275

test_prior2							93.33 284		94.21 188	94.02 278	96.25 260	93.64 175		91.90 220	98.96 209

旧先验2							93.35 283		77.95 346	95.77 239			98.67 308	90.74 252

新几何2							93.43 278

无先验							93.20 287	97.91 223	80.78 334				99.40 205	87.71 298		97.94 269

原ACMM2							92.82 292

testdata2													99.46 184	87.84 297

segment_acmp												95.34 127

testdata1							92.77 293		93.78 201

plane_prior5								98.75 118					99.46 184	92.59 214	99.20 179	99.28 109

plane_prior4											96.77 233

plane_prior3						94.51 132			95.29 149	96.16 222

plane_prior2							96.50 123		96.36 97

plane_prior						94.29 140	95.42 184		94.31 185						98.93 215

n2								0.00 363

nn								0.00 363

door-mid								98.17 202

test11								98.08 213

door								97.81 232

HQP5-MVS						92.47 191

BP-MVS														90.51 261

HQP4-MVS										92.87 309			99.23 247			99.06 154

HQP3-MVS								98.43 167							98.74 236

HQP2-MVS												90.33 236

MDTV_nov1_ep13_2view						57.28 359	94.89 222		80.59 335	94.02 278		78.66 308		85.50 320		97.82 275

ACMMP++_ref															99.52 99

ACMMP++															99.55 88

Test By Simon												94.51 154

High-resolution multi-view benchmark