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		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

Anonymous20231211		97.78 3	98.31 2	96.16 47	99.55 2	89.37 81	98.40 5	98.89 4	98.75 2	99.48 3	99.62 2	98.70 2	99.40 36	91.60 106	99.84 5	99.71 3

PEN-MVS		96.69 20	97.39 8	94.61 99	99.16 3	84.50 156	96.54 29	98.05 37	98.06 4	98.64 13	98.25 38	95.01 39	99.65 3	92.95 73	99.83 8	99.68 5

MIMVSNet1		95.52 59	95.45 70	95.72 66	99.14 4	89.02 85	96.23 46	96.87 145	93.73 51	97.87 32	98.49 26	90.73 123	99.05 82	86.43 194	99.60 32	99.10 55

PS-CasMVS		96.69 20	97.43 5	94.49 109	99.13 5	84.09 163	96.61 25	97.97 48	97.91 5	98.64 13	98.13 40	95.24 31	99.65 3	93.39 60	99.84 5	99.72 2

DTE-MVSNet		96.74 18	97.43 5	94.67 97	99.13 5	84.68 155	96.51 30	97.94 54	98.14 3	98.67 12	98.32 35	95.04 36	99.69 2	93.27 64	99.82 10	99.62 11

pmmvs6		96.80 14	97.36 9	95.15 86	99.12 7	87.82 112	96.68 23	97.86 58	96.10 24	98.14 25	99.28 3	97.94 4	98.21 200	91.38 113	99.69 15	99.42 27

HPM-MVS_fast		97.01 7	96.89 17	97.39 18	99.12 7	93.92 24	97.16 12	98.17 26	93.11 62	96.48 76	97.36 79	96.92 7	99.34 49	94.31 33	99.38 64	98.92 83

MP-MVS-pluss		96.08 47	95.92 53	96.57 41	99.06 9	91.21 60	93.25 142	98.32 13	87.89 190	96.86 62	97.38 76	95.55 21	99.39 41	95.47 13	99.47 48	99.11 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OurMVSNet-221017-0		96.80 14	96.75 20	96.96 32	99.03 10	91.85 52	97.98 6	98.01 43	94.15 44	98.93 4	99.07 5	88.07 169	99.57 13	95.86 11	99.69 15	99.46 25

WR-MVS_H		96.60 25	97.05 15	95.24 82	99.02 11	86.44 131	96.78 22	98.08 32	97.42 7	98.48 18	97.86 55	91.76 97	99.63 6	94.23 37	99.84 5	99.66 7

TDRefinement		97.68 4	97.60 4	97.93 2	99.02 11	95.95 6	98.61 3	98.81 5	97.41 8	97.28 48	98.46 28	94.62 47	98.84 122	94.64 26	99.53 43	98.99 70

CP-MVSNet		96.19 44	96.80 19	94.38 115	98.99 13	83.82 165	96.31 41	97.53 87	97.60 6	98.34 22	97.52 68	91.98 93	99.63 6	93.08 71	99.81 11	99.70 4

PMVS	87.21 14	94.97 82	95.33 77	93.91 129	98.97 14	97.16 2	95.54 65	95.85 193	96.47 18	93.40 183	97.46 72	95.31 28	95.47 303	86.18 197	98.78 125	89.11 337
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
zzz-MVS		96.47 31	96.14 40	97.47 11	98.95 15	94.05 18	93.69 129	97.62 75	94.46 40	96.29 84	96.94 95	93.56 58	99.37 45	94.29 35	99.42 56	98.99 70

MTAPA		96.65 22	96.38 31	97.47 11	98.95 15	94.05 18	95.88 55	97.62 75	94.46 40	96.29 84	96.94 95	93.56 58	99.37 45	94.29 35	99.42 56	98.99 70

ACMMP_Plus		96.21 43	96.12 42	96.49 46	98.90 17	91.42 57	94.57 99	98.03 40	90.42 135	96.37 79	97.35 80	95.68 19	99.25 60	94.44 31	99.34 66	98.80 93

HPM-MVS		96.81 13	96.62 24	97.36 20	98.89 18	93.53 34	97.51 9	98.44 8	92.35 81	95.95 103	96.41 126	96.71 9	99.42 28	93.99 42	99.36 65	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDDNet		94.03 118	94.27 111	93.31 146	98.87 19	82.36 179	95.51 66	91.78 275	97.19 10	96.32 81	98.60 20	84.24 221	98.75 139	87.09 182	98.83 117	98.81 92

TSAR-MVS + MP.		94.96 83	94.75 93	95.57 72	98.86 20	88.69 91	96.37 38	96.81 147	85.23 224	94.75 149	97.12 90	91.85 95	99.40 36	93.45 57	98.33 159	98.62 107

mvs_tets		96.83 10	96.71 21	97.17 25	98.83 21	92.51 43	96.58 27	97.61 78	87.57 196	98.80 8	98.90 9	96.50 11	99.59 12	96.15 9	99.47 48	99.40 31

PS-MVSNAJss		96.01 49	96.04 48	95.89 58	98.82 22	88.51 99	95.57 63	97.88 56	88.72 169	98.81 7	98.86 10	90.77 119	99.60 8	95.43 14	99.53 43	99.57 15

MP-MVS		96.14 45	95.68 63	97.51 10	98.81 23	94.06 16	96.10 47	97.78 67	92.73 69	93.48 180	96.72 111	94.23 52	99.42 28	91.99 96	99.29 73	99.05 63

LTVRE_ROB	93.87 1	97.93 2	98.16 3	97.26 23	98.81 23	93.86 27	99.07 2	98.98 3	97.01 11	98.92 5	98.78 14	95.22 32	98.61 158	96.85 4	99.77 12	99.31 38
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
jajsoiax		96.59 26	96.42 29	97.12 27	98.76 25	92.49 44	96.44 35	97.42 96	86.96 206	98.71 10	98.72 17	95.36 26	99.56 16	95.92 10	99.45 52	99.32 37

pcd1.5k->3k		41.03 330	43.65 332	33.18 343	98.74 26	0.00 362	0.00 353	97.57 82	0.00 357	0.00 358	0.00 359	97.01 6	0.00 360	0.00 357	99.52 45	99.53 17

HSP-MVS		95.18 75	94.49 102	97.23 24	98.67 27	94.05 18	96.41 37	97.00 129	91.26 116	95.12 135	95.15 187	86.60 203	99.50 18	93.43 59	96.81 236	98.13 133

SteuartSystems-ACMMP		96.40 37	96.30 33	96.71 38	98.63 28	91.96 50	95.70 59	98.01 43	93.34 60	96.64 71	96.57 117	94.99 40	99.36 47	93.48 55	99.34 66	98.82 91
Skip Steuart: Steuart Systems R&D Blog.
region2R		96.41 36	96.09 44	97.38 19	98.62 29	93.81 31	96.32 40	97.96 49	92.26 84	95.28 129	96.57 117	95.02 38	99.41 32	93.63 49	99.11 90	98.94 78

mPP-MVS		96.46 32	96.05 47	97.69 5	98.62 29	94.65 9	96.45 33	97.74 68	92.59 75	95.47 122	96.68 113	94.50 50	99.42 28	93.10 69	99.26 75	98.99 70

ACMMP		96.61 24	96.34 32	97.43 15	98.61 31	93.88 25	96.95 17	98.18 25	92.26 84	96.33 80	96.84 104	95.10 35	99.40 36	93.47 56	99.33 69	99.02 67
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
VPNet		93.08 148	93.76 126	91.03 224	98.60 32	75.83 279	91.51 210	95.62 198	91.84 99	95.74 115	97.10 91	89.31 146	98.32 191	85.07 209	99.06 94	98.93 79

ACMMPR		96.46 32	96.14 40	97.41 17	98.60 32	93.82 29	96.30 43	97.96 49	92.35 81	95.57 120	96.61 115	94.93 42	99.41 32	93.78 45	99.15 86	99.00 68

PGM-MVS		96.32 40	95.94 51	97.43 15	98.59 34	93.84 28	95.33 70	98.30 16	91.40 114	95.76 114	96.87 101	95.26 30	99.45 23	92.77 75	99.21 81	99.00 68

XVS		96.49 29	96.18 38	97.44 13	98.56 35	93.99 22	96.50 31	97.95 51	94.58 36	94.38 158	96.49 119	94.56 48	99.39 41	93.57 50	99.05 96	98.93 79

X-MVStestdata		90.70 197	88.45 225	97.44 13	98.56 35	93.99 22	96.50 31	97.95 51	94.58 36	94.38 158	26.89 354	94.56 48	99.39 41	93.57 50	99.05 96	98.93 79

ACMH	88.36 12	96.59 26	97.43 5	94.07 122	98.56 35	85.33 150	96.33 39	98.30 16	94.66 35	98.72 9	98.30 36	97.51 5	98.00 211	94.87 21	99.59 34	98.86 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		96.62 23	96.49 28	97.01 30	98.55 38	91.77 54	97.15 13	97.37 100	88.98 157	98.26 23	98.86 10	93.35 67	99.60 8	96.41 6	99.45 52	99.66 7

v7n		96.82 11	97.31 10	95.33 79	98.54 39	86.81 125	96.83 19	98.07 35	96.59 17	98.46 19	98.43 32	92.91 75	99.52 17	96.25 8	99.76 13	99.65 9

abl_6		97.31 6	97.12 14	97.86 3	98.54 39	95.32 8	96.61 25	98.35 12	95.81 29	97.55 39	97.44 73	96.51 10	99.40 36	94.06 41	99.23 79	98.85 89

ACMH+	88.43 11	96.48 30	96.82 18	95.47 75	98.54 39	89.06 84	95.65 61	98.61 7	96.10 24	98.16 24	97.52 68	96.90 8	98.62 157	90.30 126	99.60 32	98.72 101

SixPastTwentyTwo		94.91 85	95.21 83	93.98 124	98.52 42	83.19 171	95.93 52	94.84 216	94.86 34	98.49 17	98.74 16	81.45 241	99.60 8	94.69 25	99.39 63	99.15 48

HFP-MVS		96.39 38	96.17 39	97.04 28	98.51 43	93.37 35	96.30 43	97.98 45	92.35 81	95.63 118	96.47 121	95.37 24	99.27 58	93.78 45	99.14 87	98.48 112

#test#		95.89 50	95.51 67	97.04 28	98.51 43	93.37 35	95.14 75	97.98 45	89.34 151	95.63 118	96.47 121	95.37 24	99.27 58	91.99 96	99.14 87	98.48 112

Baseline_NR-MVSNet		94.47 106	95.09 88	92.60 178	98.50 45	80.82 196	92.08 182	96.68 154	93.82 50	96.29 84	98.56 22	90.10 137	97.75 237	90.10 134	99.66 23	99.24 42

OPM-MVS		95.61 57	95.45 70	96.08 50	98.49 46	91.00 62	92.65 158	97.33 109	90.05 140	96.77 66	96.85 102	95.04 36	98.56 166	92.77 75	99.06 94	98.70 102

FC-MVSNet-test		95.32 67	95.88 55	93.62 135	98.49 46	81.77 184	95.90 54	98.32 13	93.93 48	97.53 40	97.56 65	88.48 155	99.40 36	92.91 74	99.83 8	99.68 5

XVG-ACMP-BASELINE		95.68 55	95.34 75	96.69 39	98.40 48	93.04 38	94.54 103	98.05 37	90.45 134	96.31 82	96.76 107	92.91 75	98.72 144	91.19 114	99.42 56	98.32 118

ACMM	88.83 9	96.30 42	96.07 46	96.97 31	98.39 49	92.95 41	94.74 90	98.03 40	90.82 125	97.15 52	96.85 102	96.25 14	99.00 92	93.10 69	99.33 69	98.95 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		95.43 61	95.94 51	93.93 128	98.38 50	85.08 152	95.46 67	97.12 125	91.84 99	97.28 48	98.46 28	95.30 29	97.71 239	90.17 130	99.42 56	98.99 70

COLMAP_ROB	91.06 5	96.75 17	96.62 24	97.13 26	98.38 50	94.31 12	96.79 21	98.32 13	96.69 15	96.86 62	97.56 65	95.48 22	98.77 138	90.11 132	99.44 54	98.31 120
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TransMVSNet (Re)		95.27 73	96.04 48	92.97 157	98.37 52	81.92 183	95.07 79	96.76 151	93.97 47	97.77 34	98.57 21	95.72 18	97.90 214	88.89 157	99.23 79	99.08 59

LPG-MVS_test		96.38 39	96.23 36	96.84 36	98.36 53	92.13 47	95.33 70	98.25 19	91.78 104	97.07 54	97.22 84	96.38 12	99.28 56	92.07 94	99.59 34	99.11 52

LGP-MVS_train				96.84 36	98.36 53	92.13 47		98.25 19	91.78 104	97.07 54	97.22 84	96.38 12	99.28 56	92.07 94	99.59 34	99.11 52

v748		96.51 28	97.05 15	94.89 91	98.35 55	85.82 144	96.58 27	97.47 93	96.25 21	98.46 19	98.35 33	93.27 68	99.33 52	95.13 19	99.59 34	99.52 20

CP-MVS		96.44 35	96.08 45	97.54 9	98.29 56	94.62 10	96.80 20	98.08 32	92.67 72	95.08 140	96.39 131	94.77 44	99.42 28	93.17 67	99.44 54	98.58 111

FIs		94.90 86	95.35 74	93.55 138	98.28 57	81.76 185	95.33 70	98.14 28	93.05 63	97.07 54	97.18 86	87.65 175	99.29 54	91.72 102	99.69 15	99.61 12

SMA-MVS		95.85 52	95.63 65	96.51 43	98.27 58	91.30 58	95.09 77	97.88 56	86.59 211	97.63 38	97.51 70	94.82 43	99.29 54	93.55 52	99.34 66	98.93 79

TranMVSNet+NR-MVSNet		96.07 48	96.26 35	95.50 74	98.26 59	87.69 113	93.75 127	97.86 58	95.96 28	97.48 42	97.14 88	95.33 27	99.44 24	90.79 116	99.76 13	99.38 32

IS-MVSNet		94.49 105	94.35 106	94.92 90	98.25 60	86.46 130	97.13 15	94.31 230	96.24 22	96.28 87	96.36 136	82.88 228	99.35 48	88.19 168	99.52 45	98.96 76

UA-Net		97.35 5	97.24 13	97.69 5	98.22 61	93.87 26	98.42 4	98.19 24	96.95 12	95.46 124	99.23 4	93.45 60	99.57 13	95.34 17	99.89 4	99.63 10

test_part2					98.21 62	89.41 77				96.72 67

ESAPD		95.42 63	95.34 75	95.68 69	98.21 62	89.41 77	93.92 122	98.14 28	91.83 101	96.72 67	96.39 131	94.69 45	99.44 24	89.00 154	99.10 91	98.17 128

test_0402		95.73 53	96.22 37	94.26 118	98.19 64	85.77 145	93.24 143	97.24 117	96.88 14	97.69 36	97.77 58	94.12 54	99.13 72	91.54 110	99.29 73	97.88 150

ACMP	88.15 13	95.71 54	95.43 73	96.54 42	98.17 65	91.73 55	94.24 110	98.08 32	89.46 149	96.61 73	96.47 121	95.85 17	99.12 74	90.45 118	99.56 41	98.77 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CPTT-MVS		94.74 95	94.12 114	96.60 40	98.15 66	93.01 39	95.84 56	97.66 73	89.21 156	93.28 187	95.46 177	88.89 151	98.98 93	89.80 138	98.82 120	97.80 157

v52		96.93 8	97.29 11	95.86 59	98.12 67	88.48 100	97.69 7	97.74 68	94.90 33	98.55 15	98.72 17	93.39 64	99.49 21	96.92 2	99.62 29	99.61 12

V4		96.93 8	97.29 11	95.86 59	98.11 68	88.47 101	97.69 7	97.74 68	94.91 31	98.55 15	98.72 17	93.37 65	99.49 21	96.92 2	99.62 29	99.61 12

Vis-MVSNet		95.50 60	95.48 68	95.56 73	98.11 68	89.40 79	95.35 69	98.22 23	92.36 79	94.11 166	98.07 41	92.02 90	99.44 24	93.38 61	97.67 207	97.85 153
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
XVG-OURS-SEG-HR		95.38 65	95.00 89	96.51 43	98.10 70	94.07 15	92.46 168	98.13 31	90.69 127	93.75 174	96.25 141	98.03 3	97.02 265	92.08 93	95.55 266	98.45 115

EPP-MVSNet		93.91 120	93.68 132	94.59 104	98.08 71	85.55 148	97.44 10	94.03 235	94.22 43	94.94 144	96.19 149	82.07 236	99.57 13	87.28 181	98.89 107	98.65 103

K. test v3		93.37 137	93.27 144	93.66 134	98.05 72	82.62 177	94.35 107	86.62 306	96.05 26	97.51 41	98.85 12	76.59 275	99.65 3	93.21 66	98.20 176	98.73 100

lessismore_v0				93.87 131	98.05 72	83.77 166		80.32 349		97.13 53	97.91 52	77.49 265	99.11 75	92.62 81	98.08 187	98.74 98

AllTest		94.88 88	94.51 101	96.00 51	98.02 74	92.17 45	95.26 73	98.43 9	90.48 132	95.04 141	96.74 109	92.54 83	97.86 225	85.11 207	98.98 102	97.98 140

TestCases				96.00 51	98.02 74	92.17 45		98.43 9	90.48 132	95.04 141	96.74 109	92.54 83	97.86 225	85.11 207	98.98 102	97.98 140

anonymousdsp		96.74 18	96.42 29	97.68 7	98.00 76	94.03 21	96.97 16	97.61 78	87.68 195	98.45 21	98.77 15	94.20 53	99.50 18	96.70 5	99.40 61	99.53 17

XVG-OURS		94.72 96	94.12 114	96.50 45	98.00 76	94.23 13	91.48 211	98.17 26	90.72 126	95.30 128	96.47 121	87.94 172	96.98 266	91.41 112	97.61 210	98.30 121

114514_t		90.51 199	89.80 209	92.63 176	98.00 76	82.24 180	93.40 134	97.29 113	65.84 341	89.40 271	94.80 204	86.99 192	98.75 139	83.88 219	98.61 135	96.89 201

Gipuma		95.31 69	95.80 59	93.81 133	97.99 79	90.91 64	96.42 36	97.95 51	96.69 15	91.78 222	98.85 12	91.77 96	95.49 302	91.72 102	99.08 93	95.02 263
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
APD-MVS_3200maxsize		96.82 11	96.65 22	97.32 22	97.95 80	93.82 29	96.31 41	98.25 19	95.51 30	96.99 60	97.05 94	95.63 20	99.39 41	93.31 63	98.88 109	98.75 97

HPM-MVS++		95.02 80	94.39 103	96.91 34	97.88 81	93.58 33	94.09 113	96.99 131	91.05 121	92.40 208	95.22 186	91.03 117	99.25 60	92.11 91	98.69 132	97.90 148

EG-PatchMatch MVS		94.54 104	94.67 97	94.14 120	97.87 82	86.50 127	92.00 185	96.74 152	88.16 186	96.93 61	97.61 63	93.04 73	97.90 214	91.60 106	98.12 183	98.03 137

nrg030		96.32 40	96.55 27	95.62 70	97.83 83	88.55 97	95.77 58	98.29 18	92.68 70	98.03 27	97.91 52	95.13 33	98.95 100	93.85 43	99.49 47	99.36 35

UniMVSNet (Re)		95.32 67	95.15 85	95.80 62	97.79 84	88.91 87	92.91 151	98.07 35	93.46 57	96.31 82	95.97 157	90.14 133	99.34 49	92.11 91	99.64 26	99.16 47

VPA-MVSNet		95.14 77	95.67 64	93.58 137	97.76 85	83.15 172	94.58 98	97.58 81	93.39 59	97.05 58	98.04 42	93.25 69	98.51 175	89.75 139	99.59 34	99.08 59

DU-MVS		95.28 71	95.12 87	95.75 65	97.75 86	88.59 95	92.58 159	97.81 63	93.99 45	96.80 64	95.90 158	90.10 137	99.41 32	91.60 106	99.58 39	99.26 40

NR-MVSNet		95.28 71	95.28 80	95.26 81	97.75 86	87.21 119	95.08 78	97.37 100	93.92 49	97.65 37	95.90 158	90.10 137	99.33 52	90.11 132	99.66 23	99.26 40

XXY-MVS		92.58 165	93.16 146	90.84 229	97.75 86	79.84 219	91.87 194	96.22 182	85.94 217	95.53 121	97.68 60	92.69 80	94.48 315	83.21 224	97.51 212	98.21 126

wuykxyi23d		96.76 16	96.57 26	97.34 21	97.75 86	96.73 3	94.37 106	96.48 164	91.00 122	99.72 2	98.99 6	96.06 15	98.21 200	94.86 22	99.90 2	97.09 191

PVSNet_Blended_VisFu		91.63 180	91.20 188	92.94 160	97.73 90	83.95 164	92.14 181	97.46 94	78.85 283	92.35 211	94.98 197	84.16 222	99.08 77	86.36 195	96.77 238	95.79 241

tfpnnormal		94.27 112	94.87 92	92.48 184	97.71 91	80.88 195	94.55 102	95.41 208	93.70 52	96.67 70	97.72 59	91.40 103	98.18 205	87.45 177	99.18 84	98.36 116

HQP_MVS		94.26 113	93.93 117	95.23 83	97.71 91	88.12 106	94.56 100	97.81 63	91.74 108	93.31 184	95.59 169	86.93 194	98.95 100	89.26 149	98.51 143	98.60 109

plane_prior7					97.71 91	88.68 92

UniMVSNet_NR-MVSNet		95.35 66	95.21 83	95.76 64	97.69 94	88.59 95	92.26 177	97.84 61	94.91 31	96.80 64	95.78 166	90.42 129	99.41 32	91.60 106	99.58 39	99.29 39

APDe-MVS		96.46 32	96.64 23	95.93 56	97.68 95	89.38 80	96.90 18	98.41 11	92.52 76	97.43 45	97.92 50	95.11 34	99.50 18	94.45 30	99.30 71	98.92 83

DeepC-MVS	91.39 4	95.43 61	95.33 77	95.71 67	97.67 96	90.17 68	93.86 125	98.02 42	87.35 198	96.22 90	97.99 47	94.48 51	99.05 82	92.73 78	99.68 18	97.93 144
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Vis-MVSNet (Re-imp)		90.42 202	90.16 205	91.20 222	97.66 97	77.32 262	94.33 108	87.66 299	91.20 118	92.99 196	95.13 189	75.40 277	98.28 193	77.86 276	99.19 82	97.99 139

FMVSNet1		94.84 90	95.13 86	93.97 125	97.60 98	84.29 157	95.99 48	96.56 158	92.38 78	97.03 59	98.53 23	90.12 134	98.98 93	88.78 159	99.16 85	98.65 103

RPSCF		95.58 58	94.89 91	97.62 8	97.58 99	96.30 5	95.97 51	97.53 87	92.42 77	93.41 181	97.78 56	91.21 111	97.77 234	91.06 115	97.06 229	98.80 93

WR-MVS		93.49 131	93.72 129	92.80 168	97.57 100	80.03 212	90.14 249	95.68 197	93.70 52	96.62 72	95.39 183	87.21 186	99.04 85	87.50 176	99.64 26	99.33 36

CSCG		94.69 97	94.75 93	94.52 107	97.55 101	87.87 110	95.01 82	97.57 82	92.68 70	96.20 92	93.44 246	91.92 94	98.78 134	89.11 153	99.24 77	96.92 199

MCST-MVS		92.91 154	92.51 160	94.10 121	97.52 102	85.72 146	91.36 215	97.13 124	80.33 268	92.91 199	94.24 221	91.23 110	98.72 144	89.99 136	97.93 196	97.86 152

F-COLMAP		92.28 172	91.06 191	95.95 53	97.52 102	91.90 51	93.53 131	97.18 120	83.98 238	88.70 284	94.04 229	88.41 158	98.55 172	80.17 253	95.99 258	97.39 180

VDD-MVS		94.37 107	94.37 105	94.40 114	97.49 104	86.07 139	93.97 117	93.28 248	94.49 39	96.24 88	97.78 56	87.99 171	98.79 131	88.92 156	99.14 87	98.34 117

testgi		90.38 204	91.34 185	87.50 294	97.49 104	71.54 316	89.43 270	95.16 211	88.38 179	94.54 155	94.68 209	92.88 77	93.09 329	71.60 318	97.85 200	97.88 150

plane_prior1					97.38 106

APD-MVS		95.00 81	94.69 95	95.93 56	97.38 106	90.88 65	94.59 96	97.81 63	89.22 155	95.46 124	96.17 151	93.42 63	99.34 49	89.30 145	98.87 112	97.56 172
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ITE_SJBPF				95.95 53	97.34 108	93.36 37		96.55 161	91.93 94	94.82 147	95.39 183	91.99 92	97.08 263	85.53 201	97.96 194	97.41 177

OMC-MVS		94.22 114	93.69 131	95.81 61	97.25 109	91.27 59	92.27 176	97.40 98	87.10 204	94.56 154	95.42 180	93.74 56	98.11 208	86.62 189	98.85 113	98.06 135

v13		95.39 64	96.12 42	93.18 149	97.22 110	80.81 197	95.55 64	97.57 82	93.42 58	98.02 29	98.49 26	89.62 142	99.18 65	95.54 12	99.68 18	99.54 16

plane_prior6					97.21 111	88.23 105						86.93 194

DP-MVS Recon		92.31 171	91.88 169	93.60 136	97.18 112	86.87 124	91.10 221	97.37 100	84.92 232	92.08 218	94.08 228	88.59 154	98.20 202	83.50 221	98.14 180	95.73 243

新几何1				93.17 150	97.16 113	87.29 116		94.43 227	67.95 334	91.29 228	94.94 198	86.97 193	98.23 199	81.06 245	97.75 201	93.98 288

DP-MVS		95.62 56	95.84 57	94.97 89	97.16 113	88.62 94	94.54 103	97.64 74	96.94 13	96.58 74	97.32 81	93.07 72	98.72 144	90.45 118	98.84 114	97.57 170

1121		90.26 209	89.23 211	93.34 144	97.15 115	87.40 115	91.94 188	94.39 228	67.88 335	91.02 240	94.91 199	86.91 196	98.59 162	81.17 243	97.71 204	94.02 287

v12		95.29 70	96.02 50	93.10 151	97.14 116	80.63 198	95.39 68	97.55 86	93.19 61	97.98 30	98.44 30	89.40 145	99.16 66	95.38 16	99.67 21	99.52 20

CHOSEN 1792x2688		87.19 265	85.92 278	91.00 227	97.13 117	79.41 231	84.51 325	95.60 199	64.14 344	90.07 256	94.81 201	78.26 261	97.14 261	73.34 305	95.38 273	96.46 220

HyFIR lowres test		87.19 265	85.51 280	92.24 191	97.12 118	80.51 199	85.03 319	96.06 185	66.11 340	91.66 223	92.98 253	70.12 288	99.14 70	75.29 299	95.23 276	97.07 192

V9		95.17 76	95.89 54	93.02 154	97.04 119	80.42 200	95.22 74	97.53 87	92.92 68	97.90 31	98.35 33	89.15 149	99.14 70	95.21 18	99.65 25	99.50 22

ab-mvs		92.40 169	92.62 157	91.74 204	97.02 120	81.65 186	95.84 56	95.50 206	86.95 207	92.95 198	97.56 65	90.70 125	97.50 246	79.63 259	97.43 219	96.06 234

v11		95.10 78	95.88 55	92.76 169	96.98 121	79.64 226	95.12 76	97.60 80	92.64 73	98.03 27	98.44 30	89.06 150	99.15 68	95.42 15	99.67 21	99.50 22

test222					96.95 122	85.27 151	88.83 285	93.61 242	65.09 343	90.74 244	94.85 200	84.62 220			97.36 222	93.91 289

V14		95.05 79	95.75 61	92.94 160	96.94 123	80.21 203	95.03 81	97.50 91	92.62 74	97.84 33	98.28 37	88.87 152	99.13 72	95.03 20	99.64 26	99.48 24

CDPH-MVS		92.67 162	91.83 170	95.18 85	96.94 123	88.46 102	90.70 231	97.07 126	77.38 291	92.34 213	95.08 191	92.67 81	98.88 110	85.74 199	98.57 137	98.20 127

CNVR-MVS		94.58 102	94.29 108	95.46 76	96.94 123	89.35 82	91.81 203	96.80 148	89.66 147	93.90 172	95.44 179	92.80 79	98.72 144	92.74 77	98.52 142	98.32 118

原ACMM1				92.87 164	96.91 126	84.22 160		97.01 128	76.84 295	89.64 268	94.46 213	88.00 170	98.70 150	81.53 238	98.01 192	95.70 245

ambc				92.98 156	96.88 127	83.01 175	95.92 53	96.38 171		96.41 77	97.48 71	88.26 160	97.80 231	89.96 137	98.93 106	98.12 134

testdata				91.03 224	96.87 128	82.01 181		94.28 231	71.55 318	92.46 206	95.42 180	85.65 214	97.38 255	82.64 229	97.27 224	93.70 296

v15		94.93 84	95.62 66	92.86 165	96.83 129	80.01 216	94.84 88	97.48 92	92.36 79	97.76 35	98.20 39	88.61 153	99.11 75	94.86 22	99.62 29	99.46 25

NP-MVS					96.82 130	87.10 120					93.40 247

3Dnovator+	92.74 2	95.86 51	95.77 60	96.13 49	96.81 131	90.79 67	96.30 43	97.82 62	96.13 23	94.74 150	97.23 83	91.33 105	99.16 66	93.25 65	98.30 164	98.46 114

Test_1112_low_res		87.50 256	86.58 261	90.25 240	96.80 132	77.75 257	87.53 299	96.25 178	69.73 329	86.47 305	93.61 240	75.67 276	97.88 222	79.95 255	93.20 306	95.11 261

testing_2		94.03 118	94.38 104	93.00 155	96.79 133	81.41 190	92.87 153	96.96 133	85.88 219	97.06 57	97.92 50	91.18 115	98.71 149	91.72 102	99.04 99	98.87 85

v17		94.80 92	95.46 69	92.83 166	96.76 134	80.02 214	94.85 86	97.40 98	92.23 86	97.45 44	98.04 42	88.46 157	99.06 80	94.56 27	99.40 61	99.41 28

v16		94.79 94	95.44 72	92.83 166	96.73 135	80.03 212	94.85 86	97.41 97	92.23 86	97.41 47	98.04 42	88.40 159	99.06 80	94.56 27	99.30 71	99.41 28

PAPM_NR		91.03 193	90.81 196	91.68 207	96.73 135	81.10 193	93.72 128	96.35 175	88.19 185	88.77 282	92.12 274	85.09 217	97.25 257	82.40 232	93.90 298	96.68 208

1112_ss		88.42 236	87.41 244	91.45 214	96.69 137	80.99 194	89.72 264	96.72 153	73.37 310	87.00 303	90.69 297	77.38 267	98.20 202	81.38 239	93.72 301	95.15 259

v8		94.65 99	95.29 79	92.74 170	96.65 138	79.77 222	94.59 96	97.17 121	91.86 98	97.47 43	97.93 49	88.16 163	99.08 77	94.32 32	99.47 48	99.38 32

v6		93.59 127	93.93 117	92.56 180	96.65 138	79.77 222	92.50 165	96.40 168	88.55 174	95.94 105	96.23 144	88.13 164	98.87 116	92.46 87	98.50 145	99.06 62

MVS_111021_HR		93.63 126	93.42 140	94.26 118	96.65 138	86.96 123	89.30 275	96.23 180	88.36 180	93.57 178	94.60 210	93.45 60	97.77 234	90.23 128	98.38 152	98.03 137

ANet_high		94.83 91	96.28 34	90.47 233	96.65 138	73.16 306	94.33 108	98.74 6	96.39 20	98.09 26	98.93 8	93.37 65	98.70 150	90.38 121	99.68 18	99.53 17

v1neww		93.58 128	93.92 119	92.56 180	96.64 142	79.77 222	92.50 165	96.41 166	88.55 174	95.93 106	96.24 142	88.08 166	98.87 116	92.45 88	98.50 145	99.05 63

v7new		93.58 128	93.92 119	92.56 180	96.64 142	79.77 222	92.50 165	96.41 166	88.55 174	95.93 106	96.24 142	88.08 166	98.87 116	92.45 88	98.50 145	99.05 63

SD-MVS		95.19 74	95.73 62	93.55 138	96.62 144	88.88 90	94.67 92	98.05 37	91.26 116	97.25 51	96.40 127	95.42 23	94.36 319	92.72 79	99.19 82	97.40 179

v18		94.63 100	95.26 82	92.74 170	96.60 145	79.81 220	94.64 95	97.37 100	91.87 97	97.26 50	97.91 52	88.13 164	99.04 85	94.30 34	99.24 77	99.38 32

PM-MVS		93.33 138	92.67 156	95.33 79	96.58 146	94.06 16	92.26 177	92.18 267	85.92 218	96.22 90	96.61 115	85.64 215	95.99 296	90.35 124	98.23 171	95.93 238

v10		94.68 98	95.27 81	92.90 163	96.57 147	80.15 205	94.65 94	97.57 82	90.68 128	97.43 45	98.00 46	88.18 161	99.15 68	94.84 24	99.55 42	99.41 28

v7		93.66 124	93.97 116	92.73 172	96.55 148	80.15 205	92.54 160	96.99 131	87.36 197	95.99 100	96.48 120	88.18 161	98.94 103	93.35 62	98.31 161	99.09 56

PLC	85.34 15	90.40 203	88.92 219	94.85 92	96.53 149	90.02 69	91.58 208	96.48 164	80.16 269	86.14 307	92.18 272	85.73 212	98.25 198	76.87 286	94.61 288	96.30 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	88.58 10	92.49 168	91.75 174	94.73 96	96.50 150	89.69 73	92.91 151	97.68 72	78.02 288	92.79 200	94.10 227	90.85 118	97.96 213	84.76 212	98.16 178	96.54 209
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
view600		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 329	93.02 64	94.18 162	92.68 260	63.33 320	98.56 166	75.87 294	97.50 213	96.51 211

view800		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 329	93.02 64	94.18 162	92.68 260	63.33 320	98.56 166	75.87 294	97.50 213	96.51 211

conf0.05thres1000		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 329	93.02 64	94.18 162	92.68 260	63.33 320	98.56 166	75.87 294	97.50 213	96.51 211

tfpn		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 329	93.02 64	94.18 162	92.68 260	63.33 320	98.56 166	75.87 294	97.50 213	96.51 211

NCCC		94.08 117	93.54 137	95.70 68	96.49 151	89.90 72	92.39 171	96.91 141	90.64 129	92.33 214	94.60 210	90.58 128	98.96 98	90.21 129	97.70 205	98.23 124

TAMVS		90.16 211	89.05 215	93.49 143	96.49 151	86.37 133	90.34 242	92.55 263	80.84 266	92.99 196	94.57 212	81.94 239	98.20 202	73.51 304	98.21 174	95.90 239

TEST9					96.45 157	89.46 74	90.60 234	96.92 138	79.09 281	90.49 249	94.39 217	91.31 106	98.88 110

train_agg		92.71 161	91.83 170	95.35 77	96.45 157	89.46 74	90.60 234	96.92 138	79.37 276	90.49 249	94.39 217	91.20 112	98.88 110	88.66 162	98.43 148	97.72 160

agg_prior3		92.56 167	91.62 175	95.35 77	96.39 159	89.45 76	90.61 233	96.82 146	78.82 284	90.03 257	94.14 226	90.72 124	98.88 110	88.66 162	98.43 148	97.72 160

test_8					96.37 160	89.14 83	90.51 238	96.89 142	79.37 276	90.42 251	94.36 219	91.20 112	98.82 124

v1141		93.42 135	93.76 126	92.40 188	96.37 160	79.24 234	91.84 199	96.38 171	88.33 181	95.86 111	96.23 144	87.41 181	98.89 106	92.61 82	98.82 120	99.08 59

divwei89l23v2f112		93.42 135	93.76 126	92.41 186	96.37 160	79.24 234	91.84 199	96.38 171	88.33 181	95.86 111	96.23 144	87.41 181	98.89 106	92.61 82	98.83 117	99.09 56

v1		93.43 133	93.77 125	92.41 186	96.37 160	79.24 234	91.84 199	96.38 171	88.33 181	95.87 110	96.22 147	87.45 179	98.89 106	92.61 82	98.83 117	99.09 56

CLD-MVS		91.82 178	91.41 182	93.04 152	96.37 160	83.65 167	86.82 308	97.29 113	84.65 235	92.27 215	89.67 309	92.20 87	97.85 228	83.95 218	99.47 48	97.62 168
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					96.36 165		91.37 212		87.16 201	88.81 278

ACMP_Plane					96.36 165		91.37 212		87.16 201	88.81 278

HQP-MVS		92.09 175	91.49 180	93.88 130	96.36 165	84.89 153	91.37 212	97.31 110	87.16 201	88.81 278	93.40 247	84.76 218	98.60 160	86.55 191	97.73 202	98.14 132

v2v482		93.29 139	93.63 133	92.29 189	96.35 168	78.82 245	91.77 206	96.28 176	88.45 177	95.70 117	96.26 140	86.02 210	98.90 104	93.02 72	98.81 123	99.14 49

MSLP-MVS++		93.25 144	93.88 121	91.37 216	96.34 169	82.81 176	93.11 144	97.74 68	89.37 150	94.08 168	95.29 185	90.40 132	96.35 290	90.35 124	98.25 169	94.96 264

FPMVS		84.50 290	83.28 293	88.16 287	96.32 170	94.49 11	85.76 315	85.47 317	83.09 246	85.20 312	94.26 220	63.79 315	86.58 350	63.72 341	91.88 324	83.40 345

Anonymous20231206		88.77 231	88.29 227	90.20 244	96.31 171	78.81 246	89.56 268	93.49 246	74.26 305	92.38 209	95.58 172	82.21 234	95.43 305	72.07 313	98.75 129	96.34 224

MVP-Stereo		90.07 213	88.92 219	93.54 140	96.31 171	86.49 128	90.93 225	95.59 202	79.80 270	91.48 224	95.59 169	80.79 249	97.39 253	78.57 273	91.19 326	96.76 207
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		93.50 130	93.81 122	92.57 179	96.28 173	79.61 228	91.86 198	96.96 133	86.95 207	95.91 109	96.32 137	87.65 175	98.96 98	93.51 53	98.88 109	99.13 50

LFMVS		91.33 190	91.16 190	91.82 202	96.27 174	79.36 232	95.01 82	85.61 316	96.04 27	94.82 147	97.06 93	72.03 283	98.46 182	84.96 210	98.70 131	97.65 166

VNet		92.67 162	92.96 147	91.79 203	96.27 174	80.15 205	91.95 186	94.98 213	92.19 89	94.52 156	96.07 153	87.43 180	97.39 253	84.83 211	98.38 152	97.83 154

IterMVS-LS		93.78 122	94.28 109	92.27 190	96.27 174	79.21 239	91.87 194	96.78 149	91.77 106	96.57 75	97.07 92	87.15 187	98.74 142	91.99 96	99.03 100	98.86 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		92.87 156	93.29 141	91.62 208	96.25 177	77.72 258	91.28 216	95.05 212	89.69 146	95.93 106	96.04 154	87.34 183	98.38 187	90.05 135	97.99 193	98.78 95

MVS_111021_LR		93.66 124	93.28 143	94.80 94	96.25 177	90.95 63	90.21 245	95.43 207	87.91 188	93.74 176	94.40 216	92.88 77	96.38 288	90.39 120	98.28 165	97.07 192

agg_prior1		92.60 164	91.76 173	95.10 87	96.20 179	88.89 88	90.37 240	96.88 143	79.67 273	90.21 252	94.41 214	91.30 107	98.78 134	88.46 165	98.37 157	97.64 167

agg_prior					96.20 179	88.89 88		96.88 143		90.21 252			98.78 134

旧先验1					96.20 179	84.17 161		94.82 217			95.57 173	89.57 143			97.89 198	96.32 225

CNLPA		91.72 179	91.20 188	93.26 147	96.17 182	91.02 61	91.14 219	95.55 204	90.16 139	90.87 241	93.56 242	86.31 206	94.40 318	79.92 258	97.12 227	94.37 278

v1192		93.49 131	93.78 124	92.62 177	96.16 183	79.62 227	91.83 202	97.22 119	86.07 215	96.10 98	96.38 134	87.22 185	99.02 89	94.14 40	98.88 109	99.22 43

tfpn111		87.60 254	87.12 251	89.04 268	96.14 184	73.09 307	93.00 146	85.31 319	92.13 90	93.26 189	90.96 290	63.42 316	98.48 179	72.87 309	96.98 233	95.56 249

conf200view11		87.41 257	86.89 255	88.97 269	96.14 184	73.09 307	93.00 146	85.31 319	92.13 90	93.26 189	90.96 290	63.42 316	98.28 193	71.27 321	96.54 247	95.56 249

thres100view900		87.35 259	86.89 255	88.72 274	96.14 184	73.09 307	93.00 146	85.31 319	92.13 90	93.26 189	90.96 290	63.42 316	98.28 193	71.27 321	96.54 247	94.79 267

DeepC-MVS_fast	89.96 7	93.73 123	93.44 139	94.60 103	96.14 184	87.90 109	93.36 135	97.14 122	85.53 223	93.90 172	95.45 178	91.30 107	98.59 162	89.51 142	98.62 134	97.31 185
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PCF-MVS	84.52 17	89.12 223	87.71 242	93.34 144	96.06 188	85.84 143	86.58 312	97.31 110	68.46 333	93.61 177	93.89 234	87.51 178	98.52 174	67.85 331	98.11 184	95.66 246
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v144192		93.20 147	93.54 137	92.16 194	96.05 189	78.26 252	91.95 186	97.14 122	84.98 231	95.96 102	96.11 152	87.08 189	99.04 85	93.79 44	98.84 114	99.17 46

thres600view7		87.66 252	87.10 253	89.36 262	96.05 189	73.17 305	92.72 155	85.31 319	91.89 96	93.29 186	90.97 289	63.42 316	98.39 185	73.23 306	96.99 232	96.51 211

MIMVSNet		87.13 267	86.54 263	88.89 271	96.05 189	76.11 274	94.39 105	88.51 290	81.37 262	88.27 290	96.75 108	72.38 281	95.52 301	65.71 338	95.47 270	95.03 262

v1921920		93.26 142	93.61 134	92.19 192	96.04 192	78.31 251	91.88 193	97.24 117	85.17 225	96.19 94	96.19 149	86.76 199	99.05 82	94.18 39	98.84 114	99.22 43

v1240		93.29 139	93.71 130	92.06 197	96.01 193	77.89 256	91.81 203	97.37 100	85.12 227	96.69 69	96.40 127	86.67 200	99.07 79	94.51 29	98.76 127	99.22 43

testmv		88.46 235	88.11 234	89.48 253	96.00 194	76.14 273	86.20 314	93.75 240	84.48 236	93.57 178	95.52 176	80.91 248	95.09 311	63.97 340	98.61 135	97.22 188

conf0.01		86.95 270	86.04 270	89.70 250	95.99 195	75.66 280	93.28 136	82.70 336	88.81 162	91.26 229	88.01 321	58.77 335	97.89 216	78.93 265	96.60 241	95.56 249

conf0.002		86.95 270	86.04 270	89.70 250	95.99 195	75.66 280	93.28 136	82.70 336	88.81 162	91.26 229	88.01 321	58.77 335	97.89 216	78.93 265	96.60 241	95.56 249

thresconf0.02		86.69 275	86.04 270	88.64 277	95.99 195	75.66 280	93.28 136	82.70 336	88.81 162	91.26 229	88.01 321	58.77 335	97.89 216	78.93 265	96.60 241	92.36 317

tfpn_n400		86.69 275	86.04 270	88.64 277	95.99 195	75.66 280	93.28 136	82.70 336	88.81 162	91.26 229	88.01 321	58.77 335	97.89 216	78.93 265	96.60 241	92.36 317

tfpnconf		86.69 275	86.04 270	88.64 277	95.99 195	75.66 280	93.28 136	82.70 336	88.81 162	91.26 229	88.01 321	58.77 335	97.89 216	78.93 265	96.60 241	92.36 317

tfpnview11		86.69 275	86.04 270	88.64 277	95.99 195	75.66 280	93.28 136	82.70 336	88.81 162	91.26 229	88.01 321	58.77 335	97.89 216	78.93 265	96.60 241	92.36 317

BH-untuned		90.68 198	90.90 192	90.05 246	95.98 201	79.57 229	90.04 253	94.94 215	87.91 188	94.07 169	93.00 252	87.76 174	97.78 233	79.19 263	95.17 277	92.80 311

DeepPCF-MVS	90.46 6	94.20 115	93.56 136	96.14 48	95.96 202	92.96 40	89.48 269	97.46 94	85.14 226	96.23 89	95.42 180	93.19 70	98.08 209	90.37 122	98.76 127	97.38 182

test_prior3		93.29 139	92.85 150	94.61 99	95.95 203	87.23 117	90.21 245	97.36 106	89.33 152	90.77 242	94.81 201	90.41 130	98.68 152	88.21 166	98.55 138	97.93 144

test_prior				94.61 99	95.95 203	87.23 117		97.36 106					98.68 152			97.93 144

test12				94.43 113	95.95 203	86.75 126		96.24 179		89.76 266		89.79 141	98.79 131		97.95 195	97.75 159

LCM-MVSNet-Re		94.20 115	94.58 99	93.04 152	95.91 206	83.13 173	93.79 126	99.19 2	92.00 93	98.84 6	98.04 42	93.64 57	99.02 89	81.28 240	98.54 140	96.96 197

PatchMatch-RL		89.18 221	88.02 236	92.64 175	95.90 207	92.87 42	88.67 288	91.06 279	80.34 267	90.03 257	91.67 280	83.34 224	94.42 317	76.35 290	94.84 283	90.64 333

TSAR-MVS + GP.		93.07 150	92.41 162	95.06 88	95.82 208	90.87 66	90.97 223	92.61 262	88.04 187	94.61 153	93.79 237	88.08 166	97.81 230	89.41 144	98.39 151	96.50 218

QAPM		92.88 155	92.77 152	93.22 148	95.82 208	83.31 169	96.45 33	97.35 108	83.91 239	93.75 174	96.77 105	89.25 147	98.88 110	84.56 214	97.02 231	97.49 174

tfpn200view9		87.05 268	86.52 264	88.67 275	95.77 210	72.94 310	91.89 191	86.00 311	90.84 123	92.61 203	89.80 304	63.93 313	98.28 193	71.27 321	96.54 247	94.79 267

thres400		87.20 264	86.52 264	89.24 266	95.77 210	72.94 310	91.89 191	86.00 311	90.84 123	92.61 203	89.80 304	63.93 313	98.28 193	71.27 321	96.54 247	96.51 211

pmmvs-eth3d		91.54 182	90.73 199	93.99 123	95.76 212	87.86 111	90.83 227	93.98 237	78.23 287	94.02 170	96.22 147	82.62 233	96.83 272	86.57 190	98.33 159	97.29 186

jason		89.17 222	88.32 226	91.70 206	95.73 213	80.07 209	88.10 292	93.22 250	71.98 317	90.09 254	92.79 255	78.53 259	98.56 166	87.43 178	97.06 229	96.46 220
jason: jason.
alignmvs		93.26 142	92.85 150	94.50 108	95.70 214	87.45 114	93.45 133	95.76 195	91.58 111	95.25 131	92.42 268	81.96 238	98.72 144	91.61 105	97.87 199	97.33 184

xiu_mvs_v1_base_debu		91.47 185	91.52 177	91.33 217	95.69 215	81.56 187	89.92 257	96.05 186	83.22 243	91.26 229	90.74 294	91.55 100	98.82 124	89.29 146	95.91 259	93.62 298

xiu_mvs_v1_base		91.47 185	91.52 177	91.33 217	95.69 215	81.56 187	89.92 257	96.05 186	83.22 243	91.26 229	90.74 294	91.55 100	98.82 124	89.29 146	95.91 259	93.62 298

xiu_mvs_v1_base_debi		91.47 185	91.52 177	91.33 217	95.69 215	81.56 187	89.92 257	96.05 186	83.22 243	91.26 229	90.74 294	91.55 100	98.82 124	89.29 146	95.91 259	93.62 298

PHI-MVS		94.34 110	93.80 123	95.95 53	95.65 218	91.67 56	94.82 89	97.86 58	87.86 191	93.04 195	94.16 225	91.58 99	98.78 134	90.27 127	98.96 105	97.41 177

LF4IMVS		92.72 160	92.02 167	94.84 93	95.65 218	91.99 49	92.92 150	96.60 157	85.08 229	92.44 207	93.62 239	86.80 198	96.35 290	86.81 184	98.25 169	96.18 230

test20.03		90.80 195	90.85 195	90.63 231	95.63 220	79.24 234	89.81 263	92.87 255	89.90 144	94.39 157	96.40 127	85.77 211	95.27 310	73.86 303	99.05 96	97.39 180

TinyColmap		92.00 177	92.76 153	89.71 249	95.62 221	77.02 266	90.72 230	96.17 184	87.70 194	95.26 130	96.29 138	92.54 83	96.45 284	81.77 235	98.77 126	95.66 246

canonicalmvs		94.59 101	94.69 95	94.30 117	95.60 222	87.03 122	95.59 62	98.24 22	91.56 112	95.21 134	92.04 275	94.95 41	98.66 154	91.45 111	97.57 211	97.20 189

AdaColmap		91.63 180	91.36 184	92.47 185	95.56 223	86.36 134	92.24 179	96.27 177	88.88 161	89.90 262	92.69 259	91.65 98	98.32 191	77.38 283	97.64 208	92.72 313

tfpn1000		86.83 273	86.23 269	88.64 277	95.53 224	75.25 287	93.57 130	82.28 343	89.27 154	91.46 225	89.24 312	57.22 343	97.86 225	80.63 248	96.88 235	92.81 310

UnsupCasMVSNet_bld		88.50 234	88.03 235	89.90 247	95.52 225	78.88 244	87.39 300	94.02 236	79.32 279	93.06 194	94.02 231	80.72 250	94.27 320	75.16 300	93.08 310	96.54 209

3Dnovator	92.54 3	94.80 92	94.90 90	94.47 110	95.47 226	87.06 121	96.63 24	97.28 115	91.82 103	94.34 161	97.41 74	90.60 127	98.65 156	92.47 86	98.11 184	97.70 162

Fast-Effi-MVS+		91.28 191	90.86 194	92.53 183	95.45 227	82.53 178	89.25 278	96.52 162	85.00 230	89.91 261	88.55 316	92.94 74	98.84 122	84.72 213	95.44 271	96.22 229

GBi-Net		93.21 145	92.96 147	93.97 125	95.40 228	84.29 157	95.99 48	96.56 158	88.63 170	95.10 137	98.53 23	81.31 244	98.98 93	86.74 185	98.38 152	98.65 103

test1		93.21 145	92.96 147	93.97 125	95.40 228	84.29 157	95.99 48	96.56 158	88.63 170	95.10 137	98.53 23	81.31 244	98.98 93	86.74 185	98.38 152	98.65 103

FMVSNet2		92.78 158	92.73 155	92.95 159	95.40 228	81.98 182	94.18 112	95.53 205	88.63 170	96.05 99	97.37 77	81.31 244	98.81 129	87.38 180	98.67 133	98.06 135

CDS-MVSNet		89.55 216	88.22 231	93.53 141	95.37 231	86.49 128	89.26 276	93.59 243	79.76 271	91.15 238	92.31 270	77.12 269	98.38 187	77.51 281	97.92 197	95.71 244
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Test4		91.41 189	91.25 187	91.89 200	95.35 232	80.32 201	90.97 223	96.92 138	81.96 258	95.11 136	93.81 236	81.34 243	98.48 179	88.71 161	97.08 228	96.87 203

V42		93.43 133	93.58 135	92.97 157	95.34 233	81.22 191	92.67 157	96.49 163	87.25 200	96.20 92	96.37 135	87.32 184	98.85 121	92.39 90	98.21 174	98.85 89

DI_MVS_plusplus_test		91.42 188	91.41 182	91.46 213	95.34 233	79.06 241	90.58 236	93.74 241	82.59 252	94.69 152	94.76 205	86.54 204	98.44 184	87.93 172	96.49 252	96.87 203

Patchmatch-RL test		88.81 230	88.52 224	89.69 252	95.33 235	79.94 217	86.22 313	92.71 260	78.46 285	95.80 113	94.18 224	66.25 303	95.33 308	89.22 151	98.53 141	93.78 293

test_normal		91.49 184	91.44 181	91.62 208	95.21 236	79.44 230	90.08 252	93.84 239	82.60 251	94.37 160	94.74 206	86.66 201	98.46 182	88.58 164	96.92 234	96.95 198

BH-RMVSNet		90.47 200	90.44 202	90.56 232	95.21 236	78.65 249	89.15 279	93.94 238	88.21 184	92.74 201	94.22 222	86.38 205	97.88 222	78.67 272	95.39 272	95.14 260

tfpn_ndepth		85.85 282	85.15 283	87.98 288	95.19 238	75.36 286	92.79 154	83.18 335	86.97 205	89.92 260	86.43 333	57.44 342	97.85 228	78.18 274	96.22 255	90.72 332

Effi-MVS+		92.79 157	92.74 154	92.94 160	95.10 239	83.30 170	94.00 115	97.53 87	91.36 115	89.35 272	90.65 299	94.01 55	98.66 154	87.40 179	95.30 274	96.88 202

USDC		89.02 224	89.08 214	88.84 272	95.07 240	74.50 293	88.97 282	96.39 170	73.21 311	93.27 188	96.28 139	82.16 235	96.39 287	77.55 280	98.80 124	95.62 248

WTY-MVS		86.93 272	86.50 266	88.24 286	94.96 241	74.64 289	87.19 303	92.07 272	78.29 286	88.32 289	91.59 283	78.06 262	94.27 320	74.88 301	93.15 308	95.80 240

PS-MVSNAJ		88.86 229	88.99 218	88.48 283	94.88 242	74.71 288	86.69 309	95.60 199	80.88 264	87.83 294	87.37 328	90.77 119	98.82 124	82.52 230	94.37 291	91.93 324

MG-MVS		89.54 217	89.80 209	88.76 273	94.88 242	72.47 313	89.60 266	92.44 265	85.82 220	89.48 270	95.98 156	82.85 229	97.74 238	81.87 234	95.27 275	96.08 233

xiu_mvs_v2_base		89.00 225	89.19 212	88.46 284	94.86 244	74.63 290	86.97 305	95.60 199	80.88 264	87.83 294	88.62 315	91.04 116	98.81 129	82.51 231	94.38 290	91.93 324

MAR-MVS		90.32 208	88.87 221	94.66 98	94.82 245	91.85 52	94.22 111	94.75 220	80.91 263	87.52 299	88.07 320	86.63 202	97.87 224	76.67 287	96.21 256	94.25 280
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
PVSNet_BlendedMVS		90.35 206	89.96 207	91.54 212	94.81 246	78.80 247	90.14 249	96.93 136	79.43 274	88.68 285	95.06 193	86.27 207	98.15 206	80.27 250	98.04 190	97.68 164

PVSNet_Blended		88.74 232	88.16 233	90.46 234	94.81 246	78.80 247	86.64 310	96.93 136	74.67 300	88.68 285	89.18 313	86.27 207	98.15 206	80.27 250	96.00 257	94.44 277

BH-w/o		87.21 263	87.02 254	87.79 292	94.77 248	77.27 263	87.90 293	93.21 252	81.74 260	89.99 259	88.39 318	83.47 223	96.93 268	71.29 320	92.43 316	89.15 336

LS3D		96.11 46	95.83 58	96.95 33	94.75 249	94.20 14	97.34 11	97.98 45	97.31 9	95.32 127	96.77 105	93.08 71	99.20 64	91.79 101	98.16 178	97.44 176

Effi-MVS+-dtu		93.90 121	92.60 158	97.77 4	94.74 250	96.67 4	94.00 115	95.41 208	89.94 142	91.93 221	92.13 273	90.12 134	98.97 97	87.68 174	97.48 217	97.67 165

mvs-test1		93.07 150	91.80 172	96.89 35	94.74 250	95.83 7	92.17 180	95.41 208	89.94 142	89.85 263	90.59 300	90.12 134	98.88 110	87.68 174	95.66 264	95.97 236

MVSFormer		92.18 174	92.23 163	92.04 198	94.74 250	80.06 210	97.15 13	97.37 100	88.98 157	88.83 276	92.79 255	77.02 270	99.60 8	96.41 6	96.75 239	96.46 220

lupinMVS		88.34 237	87.31 245	91.45 214	94.74 250	80.06 210	87.23 301	92.27 266	71.10 321	88.83 276	91.15 286	77.02 270	98.53 173	86.67 188	96.75 239	95.76 242

MDA-MVSNet-bldmvs		91.04 192	90.88 193	91.55 211	94.68 254	80.16 204	85.49 317	92.14 270	90.41 136	94.93 145	95.79 164	85.10 216	96.93 268	85.15 205	94.19 296	97.57 170

MVS_0304		92.99 152	92.54 159	94.35 116	94.67 255	86.06 140	91.16 218	97.92 55	90.01 141	88.33 288	94.41 214	87.02 190	99.22 62	90.36 123	99.00 101	97.76 158

Fast-Effi-MVS+-dtu		92.77 159	92.16 164	94.58 106	94.66 256	88.25 104	92.05 183	96.65 155	89.62 148	90.08 255	91.23 285	92.56 82	98.60 160	86.30 196	96.27 254	96.90 200

UnsupCasMVSNet_eth		90.33 207	90.34 203	90.28 238	94.64 257	80.24 202	89.69 265	95.88 191	85.77 221	93.94 171	95.69 168	81.99 237	92.98 330	84.21 216	91.30 325	97.62 168

1111		80.36 317	81.32 305	77.48 334	94.61 258	44.56 355	81.59 335	90.66 283	86.78 209	90.60 247	93.52 244	30.37 360	90.67 338	66.36 335	97.42 220	97.20 189

.test1245		64.72 329	70.88 330	46.22 342	94.61 258	44.56 355	81.59 335	90.66 283	86.78 209	90.60 247	93.52 244	30.37 360	90.67 338	66.36 335	3.45 356	3.44 356

OpenMVS_ROB	85.12 16	89.52 218	89.05 215	90.92 228	94.58 260	81.21 192	91.10 221	93.41 247	77.03 294	93.41 181	93.99 233	83.23 225	97.80 231	79.93 257	94.80 284	93.74 295

OpenMVS	89.45 8	92.27 173	92.13 166	92.68 174	94.53 261	84.10 162	95.70 59	97.03 127	82.44 255	91.14 239	96.42 125	88.47 156	98.38 187	85.95 198	97.47 218	95.55 253

thres200		85.85 282	85.18 282	87.88 291	94.44 262	72.52 312	89.08 280	86.21 308	88.57 173	91.44 226	88.40 317	64.22 311	98.00 211	68.35 330	95.88 262	93.12 306

DELS-MVS		92.05 176	92.16 164	91.72 205	94.44 262	80.13 208	87.62 295	97.25 116	87.34 199	92.22 216	93.18 251	89.54 144	98.73 143	89.67 141	98.20 176	96.30 226
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
N_pmnet		88.90 228	87.25 247	93.83 132	94.40 264	93.81 31	84.73 321	87.09 303	79.36 278	93.26 189	92.43 267	79.29 255	91.68 335	77.50 282	97.22 225	96.00 235

pmmvs4		88.95 227	87.70 243	92.70 173	94.30 265	85.60 147	87.22 302	92.16 269	74.62 301	89.75 267	94.19 223	77.97 263	96.41 286	82.71 228	96.36 253	96.09 232

new-patchmatchnet		88.97 226	90.79 197	83.50 322	94.28 266	55.83 352	85.34 318	93.56 244	86.18 213	95.47 122	95.73 167	83.10 226	96.51 281	85.40 202	98.06 188	98.16 130

API-MVS		91.52 183	91.61 176	91.26 220	94.16 267	86.26 136	94.66 93	94.82 217	91.17 119	92.13 217	91.08 288	90.03 140	97.06 264	79.09 264	97.35 223	90.45 334

MSDG		90.82 194	90.67 200	91.26 220	94.16 267	83.08 174	86.63 311	96.19 183	90.60 131	91.94 220	91.89 276	89.16 148	95.75 299	80.96 247	94.51 289	94.95 265

TR-MVS		87.70 250	87.17 249	89.27 264	94.11 269	79.26 233	88.69 287	91.86 273	81.94 259	90.69 245	89.79 306	82.82 230	97.42 250	72.65 311	91.98 322	91.14 329

sss		87.23 262	86.82 257	88.46 284	93.96 270	77.94 253	86.84 307	92.78 259	77.59 289	87.61 298	91.83 277	78.75 257	91.92 334	77.84 277	94.20 295	95.52 254

PVSNet	76.22 20	82.89 298	82.37 297	84.48 317	93.96 270	64.38 342	78.60 342	88.61 289	71.50 319	84.43 319	86.36 334	74.27 278	94.60 314	69.87 328	93.69 302	94.46 276

semantic-postprocess				91.94 199	93.89 272	79.22 238		93.51 245	91.53 113	95.37 126	96.62 114	77.17 268	98.90 104	91.89 100	94.95 280	97.70 162

UGNet		93.08 148	92.50 161	94.79 95	93.87 273	87.99 108	95.07 79	94.26 232	90.64 129	87.33 300	97.67 61	86.89 197	98.49 176	88.10 170	98.71 130	97.91 147
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
PAPM		81.91 306	80.11 316	87.31 295	93.87 273	72.32 314	84.02 328	93.22 250	69.47 330	76.13 349	89.84 303	72.15 282	97.23 258	53.27 350	89.02 331	92.37 316

CANet		92.38 170	91.99 168	93.52 142	93.82 275	83.46 168	91.14 219	97.00 129	89.81 145	86.47 305	94.04 229	87.90 173	99.21 63	89.50 143	98.27 166	97.90 148

test1235678		84.54 289	83.85 291	86.59 300	93.81 276	73.41 300	82.38 332	91.79 274	79.43 274	89.50 269	91.61 282	70.59 286	92.94 331	58.14 346	97.40 221	93.44 302

HY-MVS	82.50 18	86.81 274	85.93 277	89.47 254	93.63 277	77.93 254	94.02 114	91.58 276	75.68 297	83.64 323	93.64 238	77.40 266	97.42 250	71.70 317	92.07 321	93.05 307

no-one		87.84 247	87.21 248	89.74 248	93.58 278	78.64 250	81.28 337	92.69 261	74.36 303	92.05 219	97.14 88	81.86 240	96.07 294	72.03 314	99.90 2	94.52 274

MVS_Test		92.57 166	93.29 141	90.40 235	93.53 279	75.85 277	92.52 162	96.96 133	88.73 168	92.35 211	96.70 112	90.77 119	98.37 190	92.53 85	95.49 268	96.99 196

EU-MVSNet		87.39 258	86.71 260	89.44 259	93.40 280	76.11 274	94.93 85	90.00 285	57.17 350	95.71 116	97.37 77	64.77 310	97.68 241	92.67 80	94.37 291	94.52 274

MS-PatchMatch		88.05 244	87.75 241	88.95 270	93.28 281	77.93 254	87.88 294	92.49 264	75.42 299	92.57 205	93.59 241	80.44 251	94.24 322	81.28 240	92.75 313	94.69 271

GA-MVS		87.70 250	86.82 257	90.31 237	93.27 282	77.22 264	84.72 323	92.79 258	85.11 228	89.82 264	90.07 301	66.80 298	97.76 236	84.56 214	94.27 294	95.96 237

pmmvs5		87.87 246	87.14 250	90.07 245	93.26 283	76.97 268	88.89 284	92.18 267	73.71 309	88.36 287	93.89 234	76.86 273	96.73 275	80.32 249	96.81 236	96.51 211

IterMVS		90.18 210	90.16 205	90.21 243	93.15 284	75.98 276	87.56 298	92.97 254	86.43 212	94.09 167	96.40 127	78.32 260	97.43 249	87.87 173	94.69 286	97.23 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		78.83 322	80.60 311	73.51 338	93.07 285	47.37 353	87.10 304	78.00 351	68.94 331	77.53 347	97.26 82	71.45 284	94.62 313	63.28 342	88.74 332	78.55 350

FMVSNet3		90.78 196	90.32 204	92.16 194	93.03 286	79.92 218	92.54 160	94.95 214	86.17 214	95.10 137	96.01 155	69.97 289	98.75 139	86.74 185	98.38 152	97.82 156

PAPR		87.65 253	86.77 259	90.27 239	92.85 287	77.38 261	88.56 289	96.23 180	76.82 296	84.98 314	89.75 308	86.08 209	97.16 260	72.33 312	93.35 304	96.26 228

Regformer-1		94.55 103	94.33 107	95.19 84	92.83 288	88.54 98	91.87 194	95.84 194	93.99 45	95.95 103	95.04 194	92.00 91	98.79 131	93.14 68	98.31 161	98.23 124

Regformer-2		94.86 89	94.55 100	95.77 63	92.83 288	89.98 70	91.87 194	96.40 168	94.38 42	96.19 94	95.04 194	92.47 86	99.04 85	93.49 54	98.31 161	98.28 122

Regformer-3		94.28 111	94.23 113	94.46 111	92.78 290	86.28 135	92.39 171	94.70 222	93.69 55	95.97 101	95.56 174	91.34 104	98.48 179	93.45 57	98.14 180	98.62 107

Regformer-4		94.90 86	94.67 97	95.59 71	92.78 290	89.02 85	92.39 171	95.91 190	94.50 38	96.41 77	95.56 174	92.10 89	99.01 91	94.23 37	98.14 180	98.74 98

EI-MVSNet-Vis-set		94.36 108	94.28 109	94.61 99	92.55 292	85.98 141	92.44 169	94.69 223	93.70 52	96.12 97	95.81 163	91.24 109	98.86 119	93.76 48	98.22 173	98.98 75

EI-MVSNet-UG-set		94.35 109	94.27 111	94.59 104	92.46 293	85.87 142	92.42 170	94.69 223	93.67 56	96.13 96	95.84 162	91.20 112	98.86 119	93.78 45	98.23 171	99.03 66

testus		82.09 305	81.78 300	83.03 324	92.35 294	64.37 343	79.44 340	93.27 249	73.08 312	87.06 302	85.21 338	76.80 274	89.27 345	53.30 349	95.48 269	95.46 255

FMVSNet5		87.82 249	86.56 262	91.62 208	92.31 295	79.81 220	93.49 132	94.81 219	83.26 242	91.36 227	96.93 97	52.77 349	97.49 247	76.07 291	98.03 191	97.55 173

diffmvs		90.45 201	90.49 201	90.34 236	92.25 296	77.09 265	91.80 205	95.96 189	82.68 250	85.83 309	95.07 192	87.01 191	97.09 262	89.68 140	94.10 297	96.83 205

MDA-MVSNet_test_wron		88.16 243	88.23 230	87.93 289	92.22 297	73.71 297	80.71 339	88.84 287	82.52 253	94.88 146	95.14 188	82.70 231	93.61 325	83.28 223	93.80 300	96.46 220

YYNet1		88.17 242	88.24 229	87.93 289	92.21 298	73.62 298	80.75 338	88.77 288	82.51 254	94.99 143	95.11 190	82.70 231	93.70 324	83.33 222	93.83 299	96.48 219

CANet_DTU		89.85 214	89.17 213	91.87 201	92.20 299	80.02 214	90.79 228	95.87 192	86.02 216	82.53 330	91.77 278	80.01 252	98.57 165	85.66 200	97.70 205	97.01 195

mvs_anonymous		90.37 205	91.30 186	87.58 293	92.17 300	68.00 326	89.84 262	94.73 221	83.82 241	93.22 193	97.40 75	87.54 177	97.40 252	87.94 171	95.05 279	97.34 183

EI-MVSNet		92.99 152	93.26 145	92.19 192	92.12 301	79.21 239	92.32 174	94.67 225	91.77 106	95.24 132	95.85 160	87.14 188	98.49 176	91.99 96	98.26 167	98.86 86

CVMVSNet		85.16 286	84.72 284	86.48 301	92.12 301	70.19 320	92.32 174	88.17 295	56.15 351	90.64 246	95.85 160	67.97 293	96.69 276	88.78 159	90.52 329	92.56 314

Patchmatch-test1		87.28 260	87.30 246	87.22 296	92.01 303	71.98 315	89.43 270	88.11 296	82.26 257	88.71 283	92.20 271	78.65 258	95.81 298	80.99 246	93.30 305	93.87 292

ppachtmachnet_test		88.61 233	88.64 223	88.50 282	91.76 304	70.99 319	84.59 324	92.98 253	79.30 280	92.38 209	93.53 243	79.57 254	97.45 248	86.50 193	97.17 226	97.07 192

1314		86.46 279	86.33 267	86.87 299	91.65 305	74.54 291	91.94 188	94.10 234	74.28 304	84.78 316	87.33 329	83.03 227	95.00 312	78.72 271	91.16 327	91.06 330

cascas		87.02 269	86.28 268	89.25 265	91.56 306	76.45 270	84.33 326	96.78 149	71.01 322	86.89 304	85.91 335	81.35 242	96.94 267	83.09 225	95.60 265	94.35 279

IB-MVS	77.21 19	83.11 295	81.05 307	89.29 263	91.15 307	75.85 277	85.66 316	86.00 311	79.70 272	82.02 335	86.61 330	48.26 353	98.39 185	77.84 277	92.22 319	93.63 297
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
MVS		84.98 288	84.30 287	87.01 297	91.03 308	77.69 259	91.94 188	94.16 233	59.36 349	84.23 320	87.50 327	85.66 213	96.80 273	71.79 315	93.05 311	86.54 342

CR-MVSNet		87.89 245	87.12 251	90.22 241	91.01 309	78.93 242	92.52 162	92.81 256	73.08 312	89.10 273	96.93 97	67.11 295	97.64 242	88.80 158	92.70 314	94.08 282

RPMNet		89.30 220	89.00 217	90.22 241	91.01 309	78.93 242	92.52 162	87.85 298	91.91 95	89.10 273	96.89 100	68.84 290	97.64 242	90.17 130	92.70 314	94.08 282

new_pmnet		81.22 310	81.01 309	81.86 328	90.92 311	70.15 321	84.03 327	80.25 350	70.83 324	85.97 308	89.78 307	67.93 294	84.65 351	67.44 332	91.90 323	90.78 331

test12356		76.35 323	77.41 324	73.19 339	90.70 312	38.86 358	74.56 344	91.14 278	74.55 302	80.54 342	88.18 319	52.36 350	90.49 342	52.38 351	92.26 318	90.21 335

PatchT		87.51 255	88.17 232	85.55 307	90.64 313	66.91 330	92.02 184	86.09 309	92.20 88	89.05 275	97.16 87	64.15 312	96.37 289	89.21 152	92.98 312	93.37 304

Patchmatch-test		86.10 281	86.01 276	86.38 303	90.63 314	74.22 296	89.57 267	86.69 305	85.73 222	89.81 265	92.83 254	65.24 308	91.04 337	77.82 279	95.78 263	93.88 291

PVSNet_0	70.34 21	74.58 325	72.96 327	79.47 332	90.63 314	66.24 335	73.26 345	83.40 334	63.67 346	78.02 346	78.35 350	72.53 280	89.59 344	56.68 347	60.05 353	82.57 348

PMMVS2		81.31 309	83.44 292	74.92 337	90.52 316	46.49 354	69.19 350	85.23 324	84.30 237	87.95 293	94.71 208	76.95 272	84.36 352	64.07 339	98.09 186	93.89 290

tpm		84.38 291	84.08 288	85.30 312	90.47 317	63.43 345	89.34 273	85.63 315	77.24 293	87.62 297	95.03 196	61.00 331	97.30 256	79.26 262	91.09 328	95.16 258

PNet_i23d		72.03 328	70.91 329	75.38 336	90.46 318	57.84 350	71.73 349	81.53 346	83.86 240	82.21 331	83.49 343	29.97 362	87.80 349	60.78 343	54.12 354	80.51 349

wuyk23d		87.83 248	90.79 197	78.96 333	90.46 318	88.63 93	92.72 155	90.67 282	91.65 110	98.68 11	97.64 62	96.06 15	77.53 354	59.84 344	99.41 60	70.73 351

Patchmtry		90.11 212	89.92 208	90.66 230	90.35 320	77.00 267	92.96 149	92.81 256	90.25 138	94.74 150	96.93 97	67.11 295	97.52 245	85.17 203	98.98 102	97.46 175

CHOSEN 280x420		80.04 319	77.97 323	86.23 305	90.13 321	74.53 292	72.87 347	89.59 286	66.38 339	76.29 348	85.32 337	56.96 344	95.36 306	69.49 329	94.72 285	88.79 339

MVSTER		89.32 219	88.75 222	91.03 224	90.10 322	76.62 269	90.85 226	94.67 225	82.27 256	95.24 132	95.79 164	61.09 330	98.49 176	90.49 117	98.26 167	97.97 143

tpm2		81.46 308	80.35 314	84.80 314	89.90 323	65.14 338	90.44 239	85.36 318	65.82 342	82.05 334	92.44 266	57.94 341	96.69 276	70.71 325	88.49 334	92.56 314

test0.0.03 1		82.48 301	81.47 304	85.48 308	89.70 324	73.57 299	84.73 321	81.64 345	83.07 247	88.13 291	86.61 330	62.86 325	89.10 347	66.24 337	90.29 330	93.77 294

test-LLR		83.58 294	83.17 294	84.79 315	89.68 325	66.86 332	83.08 329	84.52 327	83.07 247	82.85 328	84.78 339	62.86 325	93.49 326	82.85 226	94.86 281	94.03 285

test-mter		81.21 311	80.01 317	84.79 315	89.68 325	66.86 332	83.08 329	84.52 327	73.85 308	82.85 328	84.78 339	43.66 357	93.49 326	82.85 226	94.86 281	94.03 285

DSMNet-mixed		82.21 303	81.56 302	84.16 319	89.57 327	70.00 322	90.65 232	77.66 352	54.99 352	83.30 326	97.57 64	77.89 264	90.50 341	66.86 334	95.54 267	91.97 323

PatchmatchNet		85.22 285	84.64 285	86.98 298	89.51 328	69.83 323	90.52 237	87.34 302	78.87 282	87.22 301	92.74 257	66.91 297	96.53 279	81.77 235	86.88 337	94.58 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.88 290		89.42 329	61.52 346	88.74 286	87.41 301	73.99 307	84.96 315	94.01 232	65.25 307	95.53 300	78.02 275	93.16 307

tpmp4_e23		81.87 307	80.41 312	86.27 304	89.29 330	67.84 327	91.58 208	87.61 300	67.42 336	78.60 345	92.71 258	56.42 346	96.87 270	71.44 319	88.63 333	94.10 281

CostFormer		83.09 296	82.21 298	85.73 306	89.27 331	67.01 329	90.35 241	86.47 307	70.42 326	83.52 325	93.23 250	61.18 329	96.85 271	77.21 284	88.26 335	93.34 305

ADS-MVSNet2		84.01 293	82.20 299	89.41 260	89.04 332	76.37 271	87.57 296	90.98 281	72.71 315	84.46 317	92.45 264	68.08 291	96.48 282	70.58 326	83.97 339	95.38 256

ADS-MVSNet		82.25 302	81.55 303	84.34 318	89.04 332	65.30 336	87.57 296	85.13 325	72.71 315	84.46 317	92.45 264	68.08 291	92.33 333	70.58 326	83.97 339	95.38 256

tpm cat1		80.61 316	79.46 318	84.07 320	88.78 334	65.06 340	89.26 276	88.23 293	62.27 347	81.90 336	89.66 310	62.70 327	95.29 309	71.72 316	80.60 348	91.86 326

CMPMVS	68.83 22	87.28 260	85.67 279	92.09 196	88.77 335	85.42 149	90.31 243	94.38 229	70.02 328	88.00 292	93.30 249	73.78 279	94.03 323	75.96 293	96.54 247	96.83 205
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PatchFormer-LS_test		82.62 300	81.71 301	85.32 311	87.92 336	67.31 328	89.03 281	88.20 294	77.58 290	83.79 322	80.50 349	60.96 332	96.42 285	83.86 220	83.59 341	92.23 321

LP		86.29 280	85.35 281	89.10 267	87.80 337	76.21 272	89.92 257	90.99 280	84.86 233	87.66 296	92.32 269	70.40 287	96.48 282	81.94 233	82.24 346	94.63 272

tpmrst		82.85 299	82.93 296	82.64 326	87.65 338	58.99 349	90.14 249	87.90 297	75.54 298	83.93 321	91.63 281	66.79 300	95.36 306	81.21 242	81.54 347	93.57 301

JIA-IIPM		85.08 287	83.04 295	91.19 223	87.56 339	86.14 138	89.40 272	84.44 333	88.98 157	82.20 332	97.95 48	56.82 345	96.15 292	76.55 289	83.45 342	91.30 328

TESTMET0.1,1		79.09 321	78.04 322	82.25 327	87.52 340	64.03 344	83.08 329	80.62 348	70.28 327	80.16 343	83.22 344	44.13 356	90.56 340	79.95 255	93.36 303	92.15 322

DWT-MVSNet_test		80.74 314	79.18 319	85.43 309	87.51 341	66.87 331	89.87 261	86.01 310	74.20 306	80.86 339	80.62 348	48.84 352	96.68 278	81.54 237	83.14 344	92.75 312

gg-mvs-nofinetune		82.10 304	81.02 308	85.34 310	87.46 342	71.04 317	94.74 90	67.56 355	96.44 19	79.43 344	98.99 6	45.24 354	96.15 292	67.18 333	92.17 320	88.85 338

pmmvs3		80.83 313	78.96 320	86.45 302	87.23 343	77.48 260	84.87 320	82.31 342	63.83 345	85.03 313	89.50 311	49.66 351	93.10 328	73.12 308	95.10 278	88.78 340

tpmvs		84.22 292	83.97 289	84.94 313	87.09 344	65.18 337	91.21 217	88.35 291	82.87 249	85.21 311	90.96 290	65.24 308	96.75 274	79.60 261	85.25 338	92.90 309

gm-plane-assit					87.08 345	59.33 348			71.22 320		83.58 342		97.20 259	73.95 302

MVE	59.87 23	73.86 327	72.65 328	77.47 335	87.00 346	74.35 294	61.37 352	60.93 357	67.27 337	69.69 353	86.49 332	81.24 247	72.33 355	56.45 348	83.45 342	85.74 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EPNet_dtu		85.63 284	84.37 286	89.40 261	86.30 347	74.33 295	91.64 207	88.26 292	84.84 234	72.96 352	89.85 302	71.27 285	97.69 240	76.60 288	97.62 209	96.18 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2356		75.58 324	73.13 326	82.95 325	86.10 348	66.42 334	75.07 343	84.87 326	70.91 323	80.85 340	80.66 347	38.02 359	88.98 348	49.32 352	92.35 317	93.44 302

dp		79.28 320	78.62 321	81.24 329	85.97 349	56.45 351	86.91 306	85.26 323	72.97 314	81.45 338	89.17 314	56.01 348	95.45 304	73.19 307	76.68 350	91.82 327

EPMVS		81.17 312	80.37 313	83.58 321	85.58 350	65.08 339	90.31 243	71.34 354	77.31 292	85.80 310	91.30 284	59.38 333	92.70 332	79.99 254	82.34 345	92.96 308

E-PMN		80.72 315	80.86 310	80.29 331	85.11 351	68.77 325	72.96 346	81.97 344	87.76 193	83.25 327	83.01 345	62.22 328	89.17 346	77.15 285	94.31 293	82.93 346

GG-mvs-BLEND				83.24 323	85.06 352	71.03 318	94.99 84	65.55 356		74.09 351	75.51 351	44.57 355	94.46 316	59.57 345	87.54 336	84.24 344

EMVS		80.35 318	80.28 315	80.54 330	84.73 353	69.07 324	72.54 348	80.73 347	87.80 192	81.66 337	81.73 346	62.89 324	89.84 343	75.79 298	94.65 287	82.71 347

EPNet		89.80 215	88.25 228	94.45 112	83.91 354	86.18 137	93.87 124	87.07 304	91.16 120	80.64 341	94.72 207	78.83 256	98.89 106	85.17 203	98.89 107	98.28 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS		83.00 297	81.11 306	88.66 276	83.81 355	86.44 131	82.24 334	85.65 314	61.75 348	82.07 333	85.64 336	79.75 253	91.59 336	75.99 292	93.09 309	87.94 341

testpf		74.01 326	76.37 325	66.95 340	80.56 356	60.00 347	88.43 291	75.07 353	81.54 261	75.75 350	83.73 341	38.93 358	83.09 353	84.01 217	79.32 349	57.75 352

DeepMVS_CX				53.83 341	70.38 357	64.56 341		48.52 359	33.01 353	65.50 354	74.21 352	56.19 347	46.64 356	38.45 354	70.07 351	50.30 353

tmp_tt		37.97 331	44.33 331	18.88 344	11.80 358	21.54 359	63.51 351	45.66 360	4.23 354	51.34 355	50.48 353	59.08 334	22.11 357	44.50 353	68.35 352	13.00 354

test123		9.49 333	12.01 334	1.91 345	2.87 359	1.30 360	82.38 332	1.34 362	1.36 355	2.84 356	6.56 356	2.45 363	0.97 358	2.73 355	5.56 355	3.47 355

testmvs		9.02 334	11.42 335	1.81 346	2.77 360	1.13 361	79.44 340	1.90 361	1.18 356	2.65 357	6.80 355	1.95 364	0.87 359	2.62 356	3.45 356	3.44 356

cdsmvs_eth3d_5k		23.35 332	31.13 333	0.00 347	0.00 361	0.00 362	0.00 353	95.58 203	0.00 357	0.00 358	91.15 286	93.43 62	0.00 360	0.00 357	0.00 358	0.00 358

pcd_1.5k_mvsjas		7.56 335	10.09 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	90.77 119	0.00 360	0.00 357	0.00 358	0.00 358

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

ab-mvs-re		7.56 335	10.08 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	90.69 297	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																94.75 269

test_part3							93.92 122		91.83 101		96.39 131		99.44 24	89.00 154

test_part1								98.14 28				94.69 45			99.10 91	98.17 128

sam_mvs1												66.64 301				94.75 269

sam_mvs												66.41 302

MTGPA								97.62 75

test_post1							90.21 245				5.85 358	65.36 306	96.00 295	79.61 260

test_post											6.07 357	65.74 305	95.84 297

patchmatchnet-post											91.71 279	66.22 304	97.59 244

MTMP								54.62 358

test9_res														88.16 169	98.40 150	97.83 154

agg_prior2														87.06 183	98.36 158	97.98 140

test_prior4						89.91 71	90.74 229

test_prior2							90.21 245		89.33 152	90.77 242	94.81 201	90.41 130		88.21 166	98.55 138

旧先验2							90.00 255		68.65 332	92.71 202			96.52 280	85.15 205

新几何2							90.02 254

无先验							89.94 256	95.75 196	70.81 325				98.59 162	81.17 243		94.81 266

原ACMM2							89.34 273

testdata2													98.03 210	80.24 252

segment_acmp												92.14 88

testdata1							88.96 283		88.44 178

plane_prior5								97.81 63					98.95 100	89.26 149	98.51 143	98.60 109

plane_prior4											95.59 169

plane_prior3						88.43 103			90.35 137	93.31 184

plane_prior2							94.56 100		91.74 108

plane_prior						88.12 106	93.01 145		88.98 157						98.06 188

n2								0.00 363

nn								0.00 363

door-mid								92.13 271

test11								96.65 155

door								91.26 277

HQP5-MVS						84.89 153

BP-MVS														86.55 191

HQP4-MVS										88.81 278			98.61 158			98.15 131

HQP3-MVS								97.31 110							97.73 202

HQP2-MVS												84.76 218

MDTV_nov1_ep13_2view						42.48 357	88.45 290		67.22 338	83.56 324		66.80 298		72.86 310		94.06 284

ACMMP++_ref															98.82 120

ACMMP++															99.25 76

Test By Simon												90.61 126

High-resolution multi-view benchmark