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	91.60 107	99.84 5	99.71 3

PEN-MVS		96.69 20	97.39 8	94.61 100	99.16 3	84.50 157	96.54 30	98.05 37	98.06 4	98.64 13	98.25 39	95.01 39	99.65 3	92.95 74	99.83 8	99.68 5

MIMVSNet1		95.52 60	95.45 71	95.72 66	99.14 4	89.02 85	96.23 47	96.87 146	93.73 52	97.87 33	98.49 26	90.73 124	99.05 83	86.43 195	99.60 33	99.10 56

PS-CasMVS		96.69 20	97.43 5	94.49 110	99.13 5	84.09 164	96.61 26	97.97 48	97.91 5	98.64 13	98.13 41	95.24 31	99.65 3	93.39 61	99.84 5	99.72 2

DTE-MVSNet		96.74 18	97.43 5	94.67 98	99.13 5	84.68 156	96.51 31	97.94 54	98.14 3	98.67 12	98.32 36	95.04 36	99.69 2	93.27 65	99.82 10	99.62 11

pmmvs6		96.80 14	97.36 9	95.15 87	99.12 7	87.82 113	96.68 24	97.86 58	96.10 24	98.14 26	99.28 3	97.94 4	98.21 201	91.38 114	99.69 16	99.42 27

HPM-MVS_fast		97.01 7	96.89 17	97.39 18	99.12 7	93.92 24	97.16 13	98.17 26	93.11 63	96.48 77	97.36 80	96.92 7	99.34 49	94.31 33	99.38 65	98.92 84

MP-MVS-pluss		96.08 48	95.92 54	96.57 41	99.06 9	91.21 60	93.25 143	98.32 13	87.89 191	96.86 63	97.38 77	95.55 21	99.39 41	95.47 13	99.47 49	99.11 53
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 45	98.93 4	99.07 5	88.07 170	99.57 13	95.86 11	99.69 16	99.46 25

WR-MVS_H		96.60 25	97.05 15	95.24 83	99.02 11	86.44 132	96.78 23	98.08 32	97.42 7	98.48 18	97.86 56	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 49	98.46 29	94.62 47	98.84 123	94.64 26	99.53 44	98.99 71

CP-MVSNet		96.19 45	96.80 19	94.38 116	98.99 13	83.82 166	96.31 42	97.53 88	97.60 6	98.34 22	97.52 69	91.98 93	99.63 6	93.08 72	99.81 11	99.70 4

PMVS	87.21 14	94.97 83	95.33 78	93.91 130	98.97 14	97.16 2	95.54 66	95.85 194	96.47 18	93.40 184	97.46 73	95.31 28	95.47 305	86.18 198	98.78 126	89.11 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
zzz-MVS		96.47 31	96.14 41	97.47 11	98.95 15	94.05 18	93.69 130	97.62 76	94.46 41	96.29 85	96.94 96	93.56 58	99.37 45	94.29 35	99.42 57	98.99 71

MTAPA		96.65 22	96.38 32	97.47 11	98.95 15	94.05 18	95.88 56	97.62 76	94.46 41	96.29 85	96.94 96	93.56 58	99.37 45	94.29 35	99.42 57	98.99 71

ACMMP_Plus		96.21 44	96.12 43	96.49 46	98.90 17	91.42 57	94.57 100	98.03 40	90.42 136	96.37 80	97.35 81	95.68 19	99.25 61	94.44 31	99.34 67	98.80 94

HPM-MVS		96.81 13	96.62 25	97.36 20	98.89 18	93.53 34	97.51 9	98.44 8	92.35 82	95.95 104	96.41 127	96.71 9	99.42 28	93.99 43	99.36 66	99.13 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDDNet		94.03 119	94.27 112	93.31 147	98.87 19	82.36 180	95.51 67	91.78 276	97.19 10	96.32 82	98.60 20	84.24 222	98.75 140	87.09 183	98.83 118	98.81 93

TSAR-MVS + MP.		94.96 84	94.75 94	95.57 72	98.86 20	88.69 91	96.37 39	96.81 148	85.23 225	94.75 150	97.12 91	91.85 95	99.40 36	93.45 58	98.33 160	98.62 108

mvs_tets		96.83 10	96.71 21	97.17 25	98.83 21	92.51 43	96.58 28	97.61 79	87.57 197	98.80 8	98.90 9	96.50 11	99.59 12	96.15 9	99.47 49	99.40 31

PS-MVSNAJss		96.01 50	96.04 49	95.89 58	98.82 22	88.51 99	95.57 64	97.88 56	88.72 170	98.81 7	98.86 10	90.77 120	99.60 8	95.43 14	99.53 44	99.57 15

MP-MVS		96.14 46	95.68 64	97.51 10	98.81 23	94.06 16	96.10 48	97.78 67	92.73 70	93.48 181	96.72 112	94.23 52	99.42 28	91.99 97	99.29 74	99.05 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
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	98.61 159	96.85 4	99.77 12	99.31 39
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 30	97.12 27	98.76 25	92.49 44	96.44 36	97.42 97	86.96 207	98.71 10	98.72 17	95.36 26	99.56 16	95.92 10	99.45 53	99.32 38

pcd1.5k->3k		41.03 332	43.65 334	33.18 345	98.74 26	0.00 364	0.00 355	97.57 83	0.00 359	0.00 360	0.00 361	97.01 6	0.00 362	0.00 359	99.52 46	99.53 17

HSP-MVS		95.18 76	94.49 103	97.23 24	98.67 27	94.05 18	96.41 38	97.00 130	91.26 117	95.12 136	95.15 188	86.60 204	99.50 18	93.43 60	96.81 237	98.13 134

SteuartSystems-ACMMP		96.40 37	96.30 34	96.71 38	98.63 28	91.96 50	95.70 60	98.01 43	93.34 61	96.64 72	96.57 118	94.99 40	99.36 47	93.48 56	99.34 67	98.82 92
Skip Steuart: Steuart Systems R&D Blog.
region2R		96.41 36	96.09 45	97.38 19	98.62 29	93.81 31	96.32 41	97.96 49	92.26 85	95.28 130	96.57 118	95.02 38	99.41 32	93.63 50	99.11 91	98.94 79

mPP-MVS		96.46 32	96.05 48	97.69 5	98.62 29	94.65 9	96.45 34	97.74 69	92.59 76	95.47 123	96.68 114	94.50 50	99.42 28	93.10 70	99.26 76	98.99 71

ACMMP		96.61 24	96.34 33	97.43 15	98.61 31	93.88 25	96.95 18	98.18 25	92.26 85	96.33 81	96.84 105	95.10 35	99.40 36	93.47 57	99.33 70	99.02 68
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		93.08 149	93.76 127	91.03 225	98.60 32	75.83 280	91.51 211	95.62 199	91.84 100	95.74 116	97.10 92	89.31 147	98.32 192	85.07 210	99.06 95	98.93 80

ACMMPR		96.46 32	96.14 41	97.41 17	98.60 32	93.82 29	96.30 44	97.96 49	92.35 82	95.57 121	96.61 116	94.93 42	99.41 32	93.78 46	99.15 87	99.00 69

PGM-MVS		96.32 41	95.94 52	97.43 15	98.59 34	93.84 28	95.33 71	98.30 16	91.40 115	95.76 115	96.87 102	95.26 30	99.45 23	92.77 76	99.21 82	99.00 69

XVS		96.49 29	96.18 39	97.44 13	98.56 35	93.99 22	96.50 32	97.95 51	94.58 37	94.38 159	96.49 120	94.56 48	99.39 41	93.57 51	99.05 97	98.93 80

X-MVStestdata		90.70 198	88.45 226	97.44 13	98.56 35	93.99 22	96.50 32	97.95 51	94.58 37	94.38 159	26.89 356	94.56 48	99.39 41	93.57 51	99.05 97	98.93 80

ACMH	88.36 12	96.59 26	97.43 5	94.07 123	98.56 35	85.33 151	96.33 40	98.30 16	94.66 36	98.72 9	98.30 37	97.51 5	98.00 212	94.87 21	99.59 35	98.86 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		96.62 23	96.49 29	97.01 30	98.55 38	91.77 54	97.15 14	97.37 101	88.98 158	98.26 23	98.86 10	93.35 67	99.60 8	96.41 6	99.45 53	99.66 7

v7n		96.82 11	97.31 10	95.33 80	98.54 39	86.81 126	96.83 20	98.07 35	96.59 17	98.46 19	98.43 33	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 26	98.35 12	95.81 29	97.55 40	97.44 74	96.51 10	99.40 36	94.06 42	99.23 80	98.85 90

ACMH+	88.43 11	96.48 30	96.82 18	95.47 76	98.54 39	89.06 84	95.65 62	98.61 7	96.10 24	98.16 25	97.52 69	96.90 8	98.62 158	90.30 127	99.60 33	98.72 102

SixPastTwentyTwo		94.91 86	95.21 84	93.98 125	98.52 42	83.19 172	95.93 53	94.84 217	94.86 34	98.49 17	98.74 16	81.45 242	99.60 8	94.69 25	99.39 64	99.15 49

HFP-MVS		96.39 38	96.17 40	97.04 28	98.51 43	93.37 35	96.30 44	97.98 45	92.35 82	95.63 119	96.47 122	95.37 24	99.27 59	93.78 46	99.14 88	98.48 113

#test#		95.89 51	95.51 68	97.04 28	98.51 43	93.37 35	95.14 76	97.98 45	89.34 152	95.63 119	96.47 122	95.37 24	99.27 59	91.99 97	99.14 88	98.48 113

Baseline_NR-MVSNet		94.47 107	95.09 89	92.60 179	98.50 45	80.82 197	92.08 183	96.68 155	93.82 51	96.29 85	98.56 22	90.10 138	97.75 238	90.10 135	99.66 24	99.24 43

Anonymous20240521		96.37 40	96.66 22	95.50 74	98.49 46	87.84 112	97.47 10	97.77 68	94.75 35	98.22 24	98.49 26	90.93 118	99.28 56	94.12 41	99.74 15	99.38 32

OPM-MVS		95.61 58	95.45 71	96.08 50	98.49 46	91.00 62	92.65 159	97.33 110	90.05 141	96.77 67	96.85 103	95.04 36	98.56 167	92.77 76	99.06 95	98.70 103

FC-MVSNet-test		95.32 68	95.88 56	93.62 136	98.49 46	81.77 185	95.90 55	98.32 13	93.93 49	97.53 41	97.56 66	88.48 156	99.40 36	92.91 75	99.83 8	99.68 5

XVG-ACMP-BASELINE		95.68 56	95.34 76	96.69 39	98.40 49	93.04 38	94.54 104	98.05 37	90.45 135	96.31 83	96.76 108	92.91 75	98.72 145	91.19 115	99.42 57	98.32 119

ACMM	88.83 9	96.30 43	96.07 47	96.97 31	98.39 50	92.95 41	94.74 91	98.03 40	90.82 126	97.15 53	96.85 103	96.25 14	99.00 93	93.10 70	99.33 70	98.95 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		95.43 62	95.94 52	93.93 129	98.38 51	85.08 153	95.46 68	97.12 126	91.84 100	97.28 49	98.46 29	95.30 29	97.71 240	90.17 131	99.42 57	98.99 71

COLMAP_ROB	91.06 5	96.75 17	96.62 25	97.13 26	98.38 51	94.31 12	96.79 22	98.32 13	96.69 15	96.86 63	97.56 66	95.48 22	98.77 139	90.11 133	99.44 55	98.31 121
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 74	96.04 49	92.97 158	98.37 53	81.92 184	95.07 80	96.76 152	93.97 48	97.77 35	98.57 21	95.72 18	97.90 215	88.89 158	99.23 80	99.08 60

LPG-MVS_test		96.38 39	96.23 37	96.84 36	98.36 54	92.13 47	95.33 71	98.25 19	91.78 105	97.07 55	97.22 85	96.38 12	99.28 56	92.07 95	99.59 35	99.11 53

LGP-MVS_train				96.84 36	98.36 54	92.13 47		98.25 19	91.78 105	97.07 55	97.22 85	96.38 12	99.28 56	92.07 95	99.59 35	99.11 53

v748		96.51 28	97.05 15	94.89 92	98.35 56	85.82 145	96.58 28	97.47 94	96.25 21	98.46 19	98.35 34	93.27 68	99.33 52	95.13 19	99.59 35	99.52 20

CP-MVS		96.44 35	96.08 46	97.54 9	98.29 57	94.62 10	96.80 21	98.08 32	92.67 73	95.08 141	96.39 132	94.77 44	99.42 28	93.17 68	99.44 55	98.58 112

FIs		94.90 87	95.35 75	93.55 139	98.28 58	81.76 186	95.33 71	98.14 28	93.05 64	97.07 55	97.18 87	87.65 176	99.29 54	91.72 103	99.69 16	99.61 12

SMA-MVS		95.85 53	95.63 66	96.51 43	98.27 59	91.30 58	95.09 78	97.88 56	86.59 212	97.63 39	97.51 71	94.82 43	99.29 54	93.55 53	99.34 67	98.93 80

TranMVSNet+NR-MVSNet		96.07 49	96.26 36	95.50 74	98.26 60	87.69 114	93.75 128	97.86 58	95.96 28	97.48 43	97.14 89	95.33 27	99.44 24	90.79 117	99.76 13	99.38 32

IS-MVSNet		94.49 106	94.35 107	94.92 91	98.25 61	86.46 131	97.13 16	94.31 231	96.24 22	96.28 88	96.36 137	82.88 229	99.35 48	88.19 169	99.52 46	98.96 77

UA-Net		97.35 5	97.24 13	97.69 5	98.22 62	93.87 26	98.42 4	98.19 24	96.95 12	95.46 125	99.23 4	93.45 60	99.57 13	95.34 17	99.89 4	99.63 10

test_part2					98.21 63	89.41 77				96.72 68

ESAPD		95.42 64	95.34 76	95.68 69	98.21 63	89.41 77	93.92 123	98.14 28	91.83 102	96.72 68	96.39 132	94.69 45	99.44 24	89.00 155	99.10 92	98.17 129

test_0402		95.73 54	96.22 38	94.26 119	98.19 65	85.77 146	93.24 144	97.24 118	96.88 14	97.69 37	97.77 59	94.12 54	99.13 73	91.54 111	99.29 74	97.88 151

ACMP	88.15 13	95.71 55	95.43 74	96.54 42	98.17 66	91.73 55	94.24 111	98.08 32	89.46 150	96.61 74	96.47 122	95.85 17	99.12 75	90.45 119	99.56 42	98.77 97
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CPTT-MVS		94.74 96	94.12 115	96.60 40	98.15 67	93.01 39	95.84 57	97.66 74	89.21 157	93.28 188	95.46 178	88.89 152	98.98 94	89.80 139	98.82 121	97.80 158

v52		96.93 8	97.29 11	95.86 59	98.12 68	88.48 100	97.69 7	97.74 69	94.90 33	98.55 15	98.72 17	93.39 64	99.49 21	96.92 2	99.62 30	99.61 12

V4		96.93 8	97.29 11	95.86 59	98.11 69	88.47 101	97.69 7	97.74 69	94.91 31	98.55 15	98.72 17	93.37 65	99.49 21	96.92 2	99.62 30	99.61 12

Vis-MVSNet		95.50 61	95.48 69	95.56 73	98.11 69	89.40 79	95.35 70	98.22 23	92.36 80	94.11 167	98.07 42	92.02 90	99.44 24	93.38 62	97.67 208	97.85 154
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
XVG-OURS-SEG-HR		95.38 66	95.00 90	96.51 43	98.10 71	94.07 15	92.46 169	98.13 31	90.69 128	93.75 175	96.25 142	98.03 3	97.02 266	92.08 94	95.55 267	98.45 116

EPP-MVSNet		93.91 121	93.68 133	94.59 105	98.08 72	85.55 149	97.44 11	94.03 236	94.22 44	94.94 145	96.19 150	82.07 237	99.57 13	87.28 182	98.89 108	98.65 104

K. test v3		93.37 138	93.27 145	93.66 135	98.05 73	82.62 178	94.35 108	86.62 308	96.05 26	97.51 42	98.85 12	76.59 277	99.65 3	93.21 67	98.20 177	98.73 101

lessismore_v0				93.87 132	98.05 73	83.77 167		80.32 351		97.13 54	97.91 53	77.49 267	99.11 76	92.62 82	98.08 188	98.74 99

AllTest		94.88 89	94.51 102	96.00 51	98.02 75	92.17 45	95.26 74	98.43 9	90.48 133	95.04 142	96.74 110	92.54 83	97.86 226	85.11 208	98.98 103	97.98 141

TestCases				96.00 51	98.02 75	92.17 45		98.43 9	90.48 133	95.04 142	96.74 110	92.54 83	97.86 226	85.11 208	98.98 103	97.98 141

anonymousdsp		96.74 18	96.42 30	97.68 7	98.00 77	94.03 21	96.97 17	97.61 79	87.68 196	98.45 21	98.77 15	94.20 53	99.50 18	96.70 5	99.40 62	99.53 17

XVG-OURS		94.72 97	94.12 115	96.50 45	98.00 77	94.23 13	91.48 212	98.17 26	90.72 127	95.30 129	96.47 122	87.94 173	96.98 267	91.41 113	97.61 211	98.30 122

114514_t		90.51 200	89.80 210	92.63 177	98.00 77	82.24 181	93.40 135	97.29 114	65.84 343	89.40 273	94.80 205	86.99 193	98.75 140	83.88 221	98.61 136	96.89 202

Gipuma		95.31 70	95.80 60	93.81 134	97.99 80	90.91 64	96.42 37	97.95 51	96.69 15	91.78 224	98.85 12	91.77 96	95.49 304	91.72 103	99.08 94	95.02 265
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
APD-MVS_3200maxsize		96.82 11	96.65 23	97.32 22	97.95 81	93.82 29	96.31 42	98.25 19	95.51 30	96.99 61	97.05 95	95.63 20	99.39 41	93.31 64	98.88 110	98.75 98

HPM-MVS++		95.02 81	94.39 104	96.91 34	97.88 82	93.58 33	94.09 114	96.99 132	91.05 122	92.40 209	95.22 187	91.03 117	99.25 61	92.11 92	98.69 133	97.90 149

EG-PatchMatch MVS		94.54 105	94.67 98	94.14 121	97.87 83	86.50 128	92.00 186	96.74 153	88.16 187	96.93 62	97.61 64	93.04 73	97.90 215	91.60 107	98.12 184	98.03 138

nrg030		96.32 41	96.55 28	95.62 70	97.83 84	88.55 97	95.77 59	98.29 18	92.68 71	98.03 28	97.91 53	95.13 33	98.95 101	93.85 44	99.49 48	99.36 36

UniMVSNet (Re)		95.32 68	95.15 86	95.80 62	97.79 85	88.91 87	92.91 152	98.07 35	93.46 58	96.31 83	95.97 158	90.14 134	99.34 49	92.11 92	99.64 27	99.16 48

VPA-MVSNet		95.14 78	95.67 65	93.58 138	97.76 86	83.15 173	94.58 99	97.58 82	93.39 60	97.05 59	98.04 43	93.25 69	98.51 176	89.75 140	99.59 35	99.08 60

DU-MVS		95.28 72	95.12 88	95.75 65	97.75 87	88.59 95	92.58 160	97.81 63	93.99 46	96.80 65	95.90 159	90.10 138	99.41 32	91.60 107	99.58 40	99.26 41

NR-MVSNet		95.28 72	95.28 81	95.26 82	97.75 87	87.21 120	95.08 79	97.37 101	93.92 50	97.65 38	95.90 159	90.10 138	99.33 52	90.11 133	99.66 24	99.26 41

XXY-MVS		92.58 166	93.16 147	90.84 230	97.75 87	79.84 220	91.87 195	96.22 183	85.94 218	95.53 122	97.68 61	92.69 80	94.48 317	83.21 226	97.51 213	98.21 127

wuykxyi23d		96.76 16	96.57 27	97.34 21	97.75 87	96.73 3	94.37 107	96.48 165	91.00 123	99.72 2	98.99 6	96.06 15	98.21 201	94.86 22	99.90 2	97.09 192

PVSNet_Blended_VisFu		91.63 181	91.20 189	92.94 161	97.73 91	83.95 165	92.14 182	97.46 95	78.85 285	92.35 212	94.98 198	84.16 223	99.08 78	86.36 196	96.77 239	95.79 243

tfpnnormal		94.27 113	94.87 93	92.48 185	97.71 92	80.88 196	94.55 103	95.41 209	93.70 53	96.67 71	97.72 60	91.40 103	98.18 206	87.45 178	99.18 85	98.36 117

HQP_MVS		94.26 114	93.93 118	95.23 84	97.71 92	88.12 106	94.56 101	97.81 63	91.74 109	93.31 185	95.59 170	86.93 195	98.95 101	89.26 150	98.51 144	98.60 110

plane_prior7					97.71 92	88.68 92

UniMVSNet_NR-MVSNet		95.35 67	95.21 84	95.76 64	97.69 95	88.59 95	92.26 178	97.84 61	94.91 31	96.80 65	95.78 167	90.42 130	99.41 32	91.60 107	99.58 40	99.29 40

APDe-MVS		96.46 32	96.64 24	95.93 56	97.68 96	89.38 80	96.90 19	98.41 11	92.52 77	97.43 46	97.92 51	95.11 34	99.50 18	94.45 30	99.30 72	98.92 84

DeepC-MVS	91.39 4	95.43 62	95.33 78	95.71 67	97.67 97	90.17 68	93.86 126	98.02 42	87.35 199	96.22 91	97.99 48	94.48 51	99.05 83	92.73 79	99.68 19	97.93 145
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)		90.42 203	90.16 206	91.20 223	97.66 98	77.32 263	94.33 109	87.66 301	91.20 119	92.99 197	95.13 190	75.40 279	98.28 194	77.86 278	99.19 83	97.99 140

FMVSNet1		94.84 91	95.13 87	93.97 126	97.60 99	84.29 158	95.99 49	96.56 159	92.38 79	97.03 60	98.53 23	90.12 135	98.98 94	88.78 160	99.16 86	98.65 104

RPSCF		95.58 59	94.89 92	97.62 8	97.58 100	96.30 5	95.97 52	97.53 88	92.42 78	93.41 182	97.78 57	91.21 111	97.77 235	91.06 116	97.06 230	98.80 94

WR-MVS		93.49 132	93.72 130	92.80 169	97.57 101	80.03 213	90.14 250	95.68 198	93.70 53	96.62 73	95.39 184	87.21 187	99.04 86	87.50 177	99.64 27	99.33 37

CSCG		94.69 98	94.75 94	94.52 108	97.55 102	87.87 110	95.01 83	97.57 83	92.68 71	96.20 93	93.44 247	91.92 94	98.78 135	89.11 154	99.24 78	96.92 200

MCST-MVS		92.91 155	92.51 161	94.10 122	97.52 103	85.72 147	91.36 216	97.13 125	80.33 269	92.91 200	94.24 222	91.23 110	98.72 145	89.99 137	97.93 197	97.86 153

F-COLMAP		92.28 173	91.06 192	95.95 53	97.52 103	91.90 51	93.53 132	97.18 121	83.98 239	88.70 286	94.04 230	88.41 159	98.55 173	80.17 255	95.99 259	97.39 181

VDD-MVS		94.37 108	94.37 106	94.40 115	97.49 105	86.07 140	93.97 118	93.28 249	94.49 40	96.24 89	97.78 57	87.99 172	98.79 132	88.92 157	99.14 88	98.34 118

testgi		90.38 205	91.34 186	87.50 295	97.49 105	71.54 317	89.43 271	95.16 212	88.38 180	94.54 156	94.68 210	92.88 77	93.09 331	71.60 320	97.85 201	97.88 151

plane_prior1					97.38 107

APD-MVS		95.00 82	94.69 96	95.93 56	97.38 107	90.88 65	94.59 97	97.81 63	89.22 156	95.46 125	96.17 152	93.42 63	99.34 49	89.30 146	98.87 113	97.56 173
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ITE_SJBPF				95.95 53	97.34 109	93.36 37		96.55 162	91.93 95	94.82 148	95.39 184	91.99 92	97.08 264	85.53 202	97.96 195	97.41 178

OMC-MVS		94.22 115	93.69 132	95.81 61	97.25 110	91.27 59	92.27 177	97.40 99	87.10 205	94.56 155	95.42 181	93.74 56	98.11 209	86.62 190	98.85 114	98.06 136

v13		95.39 65	96.12 43	93.18 150	97.22 111	80.81 198	95.55 65	97.57 83	93.42 59	98.02 30	98.49 26	89.62 143	99.18 66	95.54 12	99.68 19	99.54 16

plane_prior6					97.21 112	88.23 105						86.93 195

DP-MVS Recon		92.31 172	91.88 170	93.60 137	97.18 113	86.87 125	91.10 222	97.37 101	84.92 233	92.08 219	94.08 229	88.59 155	98.20 203	83.50 223	98.14 181	95.73 245

新几何1				93.17 151	97.16 114	87.29 117		94.43 228	67.95 336	91.29 230	94.94 199	86.97 194	98.23 200	81.06 247	97.75 202	93.98 290

DP-MVS		95.62 57	95.84 58	94.97 90	97.16 114	88.62 94	94.54 104	97.64 75	96.94 13	96.58 75	97.32 82	93.07 72	98.72 145	90.45 119	98.84 115	97.57 171

1121		90.26 210	89.23 212	93.34 145	97.15 116	87.40 116	91.94 189	94.39 229	67.88 337	91.02 242	94.91 200	86.91 197	98.59 163	81.17 245	97.71 205	94.02 289

v12		95.29 71	96.02 51	93.10 152	97.14 117	80.63 199	95.39 69	97.55 87	93.19 62	97.98 31	98.44 31	89.40 146	99.16 67	95.38 16	99.67 22	99.52 20

CHOSEN 1792x2688		87.19 267	85.92 280	91.00 228	97.13 118	79.41 232	84.51 326	95.60 200	64.14 346	90.07 258	94.81 202	78.26 263	97.14 262	73.34 307	95.38 274	96.46 221

HyFIR lowres test		87.19 267	85.51 282	92.24 192	97.12 119	80.51 200	85.03 320	96.06 186	66.11 342	91.66 225	92.98 254	70.12 290	99.14 71	75.29 301	95.23 277	97.07 193

V9		95.17 77	95.89 55	93.02 155	97.04 120	80.42 201	95.22 75	97.53 88	92.92 69	97.90 32	98.35 34	89.15 150	99.14 71	95.21 18	99.65 26	99.50 22

ab-mvs		92.40 170	92.62 158	91.74 205	97.02 121	81.65 187	95.84 57	95.50 207	86.95 208	92.95 199	97.56 66	90.70 126	97.50 247	79.63 261	97.43 220	96.06 236

v11		95.10 79	95.88 56	92.76 170	96.98 122	79.64 227	95.12 77	97.60 81	92.64 74	98.03 28	98.44 31	89.06 151	99.15 69	95.42 15	99.67 22	99.50 22

test222					96.95 123	85.27 152	88.83 286	93.61 243	65.09 345	90.74 246	94.85 201	84.62 221			97.36 223	93.91 291

V14		95.05 80	95.75 62	92.94 161	96.94 124	80.21 204	95.03 82	97.50 92	92.62 75	97.84 34	98.28 38	88.87 153	99.13 73	95.03 20	99.64 27	99.48 24

CDPH-MVS		92.67 163	91.83 171	95.18 86	96.94 124	88.46 102	90.70 232	97.07 127	77.38 293	92.34 214	95.08 192	92.67 81	98.88 111	85.74 200	98.57 138	98.20 128

CNVR-MVS		94.58 103	94.29 109	95.46 77	96.94 124	89.35 82	91.81 204	96.80 149	89.66 148	93.90 173	95.44 180	92.80 79	98.72 145	92.74 78	98.52 143	98.32 119

原ACMM1				92.87 165	96.91 127	84.22 161		97.01 129	76.84 297	89.64 270	94.46 214	88.00 171	98.70 151	81.53 240	98.01 193	95.70 247

ambc				92.98 157	96.88 128	83.01 176	95.92 54	96.38 172		96.41 78	97.48 72	88.26 161	97.80 232	89.96 138	98.93 107	98.12 135

testdata				91.03 225	96.87 129	82.01 182		94.28 232	71.55 320	92.46 207	95.42 181	85.65 215	97.38 256	82.64 231	97.27 225	93.70 298

v15		94.93 85	95.62 67	92.86 166	96.83 130	80.01 217	94.84 89	97.48 93	92.36 80	97.76 36	98.20 40	88.61 154	99.11 76	94.86 22	99.62 30	99.46 25

NP-MVS					96.82 131	87.10 121					93.40 248

3Dnovator+	92.74 2	95.86 52	95.77 61	96.13 49	96.81 132	90.79 67	96.30 44	97.82 62	96.13 23	94.74 151	97.23 84	91.33 105	99.16 67	93.25 66	98.30 165	98.46 115

Test_1112_low_res		87.50 258	86.58 263	90.25 241	96.80 133	77.75 258	87.53 300	96.25 179	69.73 331	86.47 307	93.61 241	75.67 278	97.88 223	79.95 257	93.20 308	95.11 263

testing_2		94.03 119	94.38 105	93.00 156	96.79 134	81.41 191	92.87 154	96.96 134	85.88 220	97.06 58	97.92 51	91.18 115	98.71 150	91.72 103	99.04 100	98.87 86

v17		94.80 93	95.46 70	92.83 167	96.76 135	80.02 215	94.85 87	97.40 99	92.23 87	97.45 45	98.04 43	88.46 158	99.06 81	94.56 27	99.40 62	99.41 28

v16		94.79 95	95.44 73	92.83 167	96.73 136	80.03 213	94.85 87	97.41 98	92.23 87	97.41 48	98.04 43	88.40 160	99.06 81	94.56 27	99.30 72	99.41 28

PAPM_NR		91.03 194	90.81 197	91.68 208	96.73 136	81.10 194	93.72 129	96.35 176	88.19 186	88.77 284	92.12 276	85.09 218	97.25 258	82.40 234	93.90 300	96.68 209

1112_ss		88.42 237	87.41 246	91.45 215	96.69 138	80.99 195	89.72 265	96.72 154	73.37 312	87.00 305	90.69 299	77.38 269	98.20 203	81.38 241	93.72 303	95.15 261

v8		94.65 100	95.29 80	92.74 171	96.65 139	79.77 223	94.59 97	97.17 122	91.86 99	97.47 44	97.93 50	88.16 164	99.08 78	94.32 32	99.47 49	99.38 32

v6		93.59 128	93.93 118	92.56 181	96.65 139	79.77 223	92.50 166	96.40 169	88.55 175	95.94 106	96.23 145	88.13 165	98.87 117	92.46 88	98.50 146	99.06 63

MVS_111021_HR		93.63 127	93.42 141	94.26 119	96.65 139	86.96 124	89.30 276	96.23 181	88.36 181	93.57 179	94.60 211	93.45 60	97.77 235	90.23 129	98.38 153	98.03 138

ANet_high		94.83 92	96.28 35	90.47 234	96.65 139	73.16 307	94.33 109	98.74 6	96.39 20	98.09 27	98.93 8	93.37 65	98.70 151	90.38 122	99.68 19	99.53 17

v1neww		93.58 129	93.92 120	92.56 181	96.64 143	79.77 223	92.50 166	96.41 167	88.55 175	95.93 107	96.24 143	88.08 167	98.87 117	92.45 89	98.50 146	99.05 64

v7new		93.58 129	93.92 120	92.56 181	96.64 143	79.77 223	92.50 166	96.41 167	88.55 175	95.93 107	96.24 143	88.08 167	98.87 117	92.45 89	98.50 146	99.05 64

SD-MVS		95.19 75	95.73 63	93.55 139	96.62 145	88.88 90	94.67 93	98.05 37	91.26 117	97.25 52	96.40 128	95.42 23	94.36 321	92.72 80	99.19 83	97.40 180

v18		94.63 101	95.26 83	92.74 171	96.60 146	79.81 221	94.64 96	97.37 101	91.87 98	97.26 51	97.91 53	88.13 165	99.04 86	94.30 34	99.24 78	99.38 32

PM-MVS		93.33 139	92.67 157	95.33 80	96.58 147	94.06 16	92.26 178	92.18 268	85.92 219	96.22 91	96.61 116	85.64 216	95.99 297	90.35 125	98.23 172	95.93 240

v10		94.68 99	95.27 82	92.90 164	96.57 148	80.15 206	94.65 95	97.57 83	90.68 129	97.43 46	98.00 47	88.18 162	99.15 69	94.84 24	99.55 43	99.41 28

v7		93.66 125	93.97 117	92.73 173	96.55 149	80.15 206	92.54 161	96.99 132	87.36 198	95.99 101	96.48 121	88.18 162	98.94 104	93.35 63	98.31 162	99.09 57

PLC	85.34 15	90.40 204	88.92 220	94.85 93	96.53 150	90.02 69	91.58 209	96.48 165	80.16 270	86.14 309	92.18 273	85.73 213	98.25 199	76.87 288	94.61 290	96.30 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	88.58 10	92.49 169	91.75 175	94.73 97	96.50 151	89.69 73	92.91 152	97.68 73	78.02 290	92.79 201	94.10 228	90.85 119	97.96 214	84.76 214	98.16 179	96.54 210
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
view600		88.32 239	87.94 238	89.46 256	96.49 152	73.31 302	93.95 119	84.46 331	93.02 65	94.18 163	92.68 261	63.33 322	98.56 167	75.87 296	97.50 214	96.51 212

view800		88.32 239	87.94 238	89.46 256	96.49 152	73.31 302	93.95 119	84.46 331	93.02 65	94.18 163	92.68 261	63.33 322	98.56 167	75.87 296	97.50 214	96.51 212

conf0.05thres1000		88.32 239	87.94 238	89.46 256	96.49 152	73.31 302	93.95 119	84.46 331	93.02 65	94.18 163	92.68 261	63.33 322	98.56 167	75.87 296	97.50 214	96.51 212

tfpn		88.32 239	87.94 238	89.46 256	96.49 152	73.31 302	93.95 119	84.46 331	93.02 65	94.18 163	92.68 261	63.33 322	98.56 167	75.87 296	97.50 214	96.51 212

NCCC		94.08 118	93.54 138	95.70 68	96.49 152	89.90 72	92.39 172	96.91 142	90.64 130	92.33 215	94.60 211	90.58 129	98.96 99	90.21 130	97.70 206	98.23 125

TAMVS		90.16 212	89.05 216	93.49 144	96.49 152	86.37 134	90.34 243	92.55 264	80.84 267	92.99 197	94.57 213	81.94 240	98.20 203	73.51 306	98.21 175	95.90 241

TEST9					96.45 158	89.46 74	90.60 235	96.92 139	79.09 283	90.49 251	94.39 218	91.31 106	98.88 111

train_agg		92.71 162	91.83 171	95.35 78	96.45 158	89.46 74	90.60 235	96.92 139	79.37 278	90.49 251	94.39 218	91.20 112	98.88 111	88.66 163	98.43 149	97.72 161

agg_prior3		92.56 168	91.62 176	95.35 78	96.39 160	89.45 76	90.61 234	96.82 147	78.82 286	90.03 259	94.14 227	90.72 125	98.88 111	88.66 163	98.43 149	97.72 161

test_8					96.37 161	89.14 83	90.51 239	96.89 143	79.37 278	90.42 253	94.36 220	91.20 112	98.82 125

v1141		93.42 136	93.76 127	92.40 189	96.37 161	79.24 235	91.84 200	96.38 172	88.33 182	95.86 112	96.23 145	87.41 182	98.89 107	92.61 83	98.82 121	99.08 60

divwei89l23v2f112		93.42 136	93.76 127	92.41 187	96.37 161	79.24 235	91.84 200	96.38 172	88.33 182	95.86 112	96.23 145	87.41 182	98.89 107	92.61 83	98.83 118	99.09 57

v1		93.43 134	93.77 126	92.41 187	96.37 161	79.24 235	91.84 200	96.38 172	88.33 182	95.87 111	96.22 148	87.45 180	98.89 107	92.61 83	98.83 118	99.09 57

CLD-MVS		91.82 179	91.41 183	93.04 153	96.37 161	83.65 168	86.82 309	97.29 114	84.65 236	92.27 216	89.67 311	92.20 87	97.85 229	83.95 220	99.47 49	97.62 169
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					96.36 166		91.37 213		87.16 202	88.81 280

ACMP_Plane					96.36 166		91.37 213		87.16 202	88.81 280

HQP-MVS		92.09 176	91.49 181	93.88 131	96.36 166	84.89 154	91.37 213	97.31 111	87.16 202	88.81 280	93.40 248	84.76 219	98.60 161	86.55 192	97.73 203	98.14 133

v2v482		93.29 140	93.63 134	92.29 190	96.35 169	78.82 246	91.77 207	96.28 177	88.45 178	95.70 118	96.26 141	86.02 211	98.90 105	93.02 73	98.81 124	99.14 50

MSLP-MVS++		93.25 145	93.88 122	91.37 217	96.34 170	82.81 177	93.11 145	97.74 69	89.37 151	94.08 169	95.29 186	90.40 133	96.35 291	90.35 125	98.25 170	94.96 266

FPMVS		84.50 292	83.28 295	88.16 288	96.32 171	94.49 11	85.76 316	85.47 319	83.09 247	85.20 314	94.26 221	63.79 317	86.58 352	63.72 343	91.88 326	83.40 347

Anonymous20231206		88.77 232	88.29 228	90.20 245	96.31 172	78.81 247	89.56 269	93.49 247	74.26 307	92.38 210	95.58 173	82.21 235	95.43 307	72.07 315	98.75 130	96.34 225

MVP-Stereo		90.07 214	88.92 220	93.54 141	96.31 172	86.49 129	90.93 226	95.59 203	79.80 271	91.48 226	95.59 170	80.79 250	97.39 254	78.57 275	91.19 328	96.76 208
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		93.50 131	93.81 123	92.57 180	96.28 174	79.61 229	91.86 199	96.96 134	86.95 208	95.91 110	96.32 138	87.65 176	98.96 99	93.51 54	98.88 110	99.13 51

LFMVS		91.33 191	91.16 191	91.82 203	96.27 175	79.36 233	95.01 83	85.61 318	96.04 27	94.82 148	97.06 94	72.03 285	98.46 183	84.96 211	98.70 132	97.65 167

VNet		92.67 163	92.96 148	91.79 204	96.27 175	80.15 206	91.95 187	94.98 214	92.19 90	94.52 157	96.07 154	87.43 181	97.39 254	84.83 212	98.38 153	97.83 155

IterMVS-LS		93.78 123	94.28 110	92.27 191	96.27 175	79.21 240	91.87 195	96.78 150	91.77 107	96.57 76	97.07 93	87.15 188	98.74 143	91.99 97	99.03 101	98.86 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		92.87 157	93.29 142	91.62 209	96.25 178	77.72 259	91.28 217	95.05 213	89.69 147	95.93 107	96.04 155	87.34 184	98.38 188	90.05 136	97.99 194	98.78 96

MVS_111021_LR		93.66 125	93.28 144	94.80 95	96.25 178	90.95 63	90.21 246	95.43 208	87.91 189	93.74 177	94.40 217	92.88 77	96.38 289	90.39 121	98.28 166	97.07 193

agg_prior1		92.60 165	91.76 174	95.10 88	96.20 180	88.89 88	90.37 241	96.88 144	79.67 275	90.21 254	94.41 215	91.30 107	98.78 135	88.46 166	98.37 158	97.64 168

agg_prior					96.20 180	88.89 88		96.88 144		90.21 254			98.78 135

旧先验1					96.20 180	84.17 162		94.82 218			95.57 174	89.57 144			97.89 199	96.32 226

CNLPA		91.72 180	91.20 189	93.26 148	96.17 183	91.02 61	91.14 220	95.55 205	90.16 140	90.87 243	93.56 243	86.31 207	94.40 320	79.92 260	97.12 228	94.37 280

v1192		93.49 132	93.78 125	92.62 178	96.16 184	79.62 228	91.83 203	97.22 120	86.07 216	96.10 99	96.38 135	87.22 186	99.02 90	94.14 40	98.88 110	99.22 44

tfpn111		87.60 255	87.12 253	89.04 269	96.14 185	73.09 308	93.00 147	85.31 321	92.13 91	93.26 190	90.96 292	63.42 318	98.48 180	72.87 311	96.98 234	95.56 251

conf200view11		87.41 259	86.89 257	88.97 270	96.14 185	73.09 308	93.00 147	85.31 321	92.13 91	93.26 190	90.96 292	63.42 318	98.28 194	71.27 323	96.54 248	95.56 251

thres100view900		87.35 261	86.89 257	88.72 275	96.14 185	73.09 308	93.00 147	85.31 321	92.13 91	93.26 190	90.96 292	63.42 318	98.28 194	71.27 323	96.54 248	94.79 269

DeepC-MVS_fast	89.96 7	93.73 124	93.44 140	94.60 104	96.14 185	87.90 109	93.36 136	97.14 123	85.53 224	93.90 173	95.45 179	91.30 107	98.59 163	89.51 143	98.62 135	97.31 186
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	89.12 224	87.71 243	93.34 145	96.06 189	85.84 144	86.58 313	97.31 111	68.46 335	93.61 178	93.89 235	87.51 179	98.52 175	67.85 333	98.11 185	95.66 248
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v144192		93.20 148	93.54 138	92.16 195	96.05 190	78.26 253	91.95 187	97.14 123	84.98 232	95.96 103	96.11 153	87.08 190	99.04 86	93.79 45	98.84 115	99.17 47

thres600view7		87.66 253	87.10 255	89.36 263	96.05 190	73.17 306	92.72 156	85.31 321	91.89 97	93.29 187	90.97 291	63.42 318	98.39 186	73.23 308	96.99 233	96.51 212

MIMVSNet		87.13 269	86.54 265	88.89 272	96.05 190	76.11 275	94.39 106	88.51 292	81.37 263	88.27 292	96.75 109	72.38 283	95.52 303	65.71 340	95.47 271	95.03 264

v1921920		93.26 143	93.61 135	92.19 193	96.04 193	78.31 252	91.88 194	97.24 118	85.17 226	96.19 95	96.19 150	86.76 200	99.05 83	94.18 39	98.84 115	99.22 44

v1240		93.29 140	93.71 131	92.06 198	96.01 194	77.89 257	91.81 204	97.37 101	85.12 228	96.69 70	96.40 128	86.67 201	99.07 80	94.51 29	98.76 128	99.22 44

testmv		88.46 236	88.11 235	89.48 254	96.00 195	76.14 274	86.20 315	93.75 241	84.48 237	93.57 179	95.52 177	80.91 249	95.09 313	63.97 342	98.61 136	97.22 189

conf0.01		86.95 272	86.04 272	89.70 251	95.99 196	75.66 281	93.28 137	82.70 338	88.81 163	91.26 231	88.01 323	58.77 337	97.89 217	78.93 267	96.60 242	95.56 251

conf0.002		86.95 272	86.04 272	89.70 251	95.99 196	75.66 281	93.28 137	82.70 338	88.81 163	91.26 231	88.01 323	58.77 337	97.89 217	78.93 267	96.60 242	95.56 251

thresconf0.02		86.69 277	86.04 272	88.64 278	95.99 196	75.66 281	93.28 137	82.70 338	88.81 163	91.26 231	88.01 323	58.77 337	97.89 217	78.93 267	96.60 242	92.36 319

tfpn_n400		86.69 277	86.04 272	88.64 278	95.99 196	75.66 281	93.28 137	82.70 338	88.81 163	91.26 231	88.01 323	58.77 337	97.89 217	78.93 267	96.60 242	92.36 319

tfpnconf		86.69 277	86.04 272	88.64 278	95.99 196	75.66 281	93.28 137	82.70 338	88.81 163	91.26 231	88.01 323	58.77 337	97.89 217	78.93 267	96.60 242	92.36 319

tfpnview11		86.69 277	86.04 272	88.64 278	95.99 196	75.66 281	93.28 137	82.70 338	88.81 163	91.26 231	88.01 323	58.77 337	97.89 217	78.93 267	96.60 242	92.36 319

BH-untuned		90.68 199	90.90 193	90.05 247	95.98 202	79.57 230	90.04 254	94.94 216	87.91 189	94.07 170	93.00 253	87.76 175	97.78 234	79.19 265	95.17 278	92.80 313

DeepPCF-MVS	90.46 6	94.20 116	93.56 137	96.14 48	95.96 203	92.96 40	89.48 270	97.46 95	85.14 227	96.23 90	95.42 181	93.19 70	98.08 210	90.37 123	98.76 128	97.38 183

test_prior3		93.29 140	92.85 151	94.61 100	95.95 204	87.23 118	90.21 246	97.36 107	89.33 153	90.77 244	94.81 202	90.41 131	98.68 153	88.21 167	98.55 139	97.93 145

test_prior				94.61 100	95.95 204	87.23 118		97.36 107					98.68 153			97.93 145

test12				94.43 114	95.95 204	86.75 127		96.24 180		89.76 268		89.79 142	98.79 132		97.95 196	97.75 160

LCM-MVSNet-Re		94.20 116	94.58 100	93.04 153	95.91 207	83.13 174	93.79 127	99.19 2	92.00 94	98.84 6	98.04 43	93.64 57	99.02 90	81.28 242	98.54 141	96.96 198

PatchMatch-RL		89.18 222	88.02 237	92.64 176	95.90 208	92.87 42	88.67 289	91.06 280	80.34 268	90.03 259	91.67 282	83.34 225	94.42 319	76.35 292	94.84 284	90.64 335

TSAR-MVS + GP.		93.07 151	92.41 163	95.06 89	95.82 209	90.87 66	90.97 224	92.61 263	88.04 188	94.61 154	93.79 238	88.08 167	97.81 231	89.41 145	98.39 152	96.50 219

QAPM		92.88 156	92.77 153	93.22 149	95.82 209	83.31 170	96.45 34	97.35 109	83.91 240	93.75 175	96.77 106	89.25 148	98.88 111	84.56 216	97.02 232	97.49 175

tfpn200view9		87.05 270	86.52 266	88.67 276	95.77 211	72.94 311	91.89 192	86.00 313	90.84 124	92.61 204	89.80 306	63.93 315	98.28 194	71.27 323	96.54 248	94.79 269

thres400		87.20 266	86.52 266	89.24 267	95.77 211	72.94 311	91.89 192	86.00 313	90.84 124	92.61 204	89.80 306	63.93 315	98.28 194	71.27 323	96.54 248	96.51 212

pmmvs-eth3d		91.54 183	90.73 200	93.99 124	95.76 213	87.86 111	90.83 228	93.98 238	78.23 289	94.02 171	96.22 148	82.62 234	96.83 273	86.57 191	98.33 160	97.29 187

jason		89.17 223	88.32 227	91.70 207	95.73 214	80.07 210	88.10 293	93.22 251	71.98 319	90.09 256	92.79 256	78.53 261	98.56 167	87.43 179	97.06 230	96.46 221
jason: jason.
alignmvs		93.26 143	92.85 151	94.50 109	95.70 215	87.45 115	93.45 134	95.76 196	91.58 112	95.25 132	92.42 269	81.96 239	98.72 145	91.61 106	97.87 200	97.33 185

xiu_mvs_v1_base_debu		91.47 186	91.52 178	91.33 218	95.69 216	81.56 188	89.92 258	96.05 187	83.22 244	91.26 231	90.74 296	91.55 100	98.82 125	89.29 147	95.91 260	93.62 300

xiu_mvs_v1_base		91.47 186	91.52 178	91.33 218	95.69 216	81.56 188	89.92 258	96.05 187	83.22 244	91.26 231	90.74 296	91.55 100	98.82 125	89.29 147	95.91 260	93.62 300

xiu_mvs_v1_base_debi		91.47 186	91.52 178	91.33 218	95.69 216	81.56 188	89.92 258	96.05 187	83.22 244	91.26 231	90.74 296	91.55 100	98.82 125	89.29 147	95.91 260	93.62 300

PHI-MVS		94.34 111	93.80 124	95.95 53	95.65 219	91.67 56	94.82 90	97.86 58	87.86 192	93.04 196	94.16 226	91.58 99	98.78 135	90.27 128	98.96 106	97.41 178

LF4IMVS		92.72 161	92.02 168	94.84 94	95.65 219	91.99 49	92.92 151	96.60 158	85.08 230	92.44 208	93.62 240	86.80 199	96.35 291	86.81 185	98.25 170	96.18 232

test20.03		90.80 196	90.85 196	90.63 232	95.63 221	79.24 235	89.81 264	92.87 256	89.90 145	94.39 158	96.40 128	85.77 212	95.27 312	73.86 305	99.05 97	97.39 181

TinyColmap		92.00 178	92.76 154	89.71 250	95.62 222	77.02 267	90.72 231	96.17 185	87.70 195	95.26 131	96.29 139	92.54 83	96.45 285	81.77 237	98.77 127	95.66 248

canonicalmvs		94.59 102	94.69 96	94.30 118	95.60 223	87.03 123	95.59 63	98.24 22	91.56 113	95.21 135	92.04 277	94.95 41	98.66 155	91.45 112	97.57 212	97.20 190

AdaColmap		91.63 181	91.36 185	92.47 186	95.56 224	86.36 135	92.24 180	96.27 178	88.88 162	89.90 264	92.69 260	91.65 98	98.32 192	77.38 285	97.64 209	92.72 315

tfpn1000		86.83 275	86.23 271	88.64 278	95.53 225	75.25 288	93.57 131	82.28 345	89.27 155	91.46 227	89.24 314	57.22 345	97.86 226	80.63 250	96.88 236	92.81 312

UnsupCasMVSNet_bld		88.50 235	88.03 236	89.90 248	95.52 226	78.88 245	87.39 301	94.02 237	79.32 281	93.06 195	94.02 232	80.72 251	94.27 322	75.16 302	93.08 312	96.54 210

3Dnovator	92.54 3	94.80 93	94.90 91	94.47 111	95.47 227	87.06 122	96.63 25	97.28 116	91.82 104	94.34 162	97.41 75	90.60 128	98.65 157	92.47 87	98.11 185	97.70 163

Fast-Effi-MVS+		91.28 192	90.86 195	92.53 184	95.45 228	82.53 179	89.25 279	96.52 163	85.00 231	89.91 263	88.55 318	92.94 74	98.84 123	84.72 215	95.44 272	96.22 230

GBi-Net		93.21 146	92.96 148	93.97 126	95.40 229	84.29 158	95.99 49	96.56 159	88.63 171	95.10 138	98.53 23	81.31 245	98.98 94	86.74 186	98.38 153	98.65 104

test1		93.21 146	92.96 148	93.97 126	95.40 229	84.29 158	95.99 49	96.56 159	88.63 171	95.10 138	98.53 23	81.31 245	98.98 94	86.74 186	98.38 153	98.65 104

FMVSNet2		92.78 159	92.73 156	92.95 160	95.40 229	81.98 183	94.18 113	95.53 206	88.63 171	96.05 100	97.37 78	81.31 245	98.81 130	87.38 181	98.67 134	98.06 136

CDS-MVSNet		89.55 217	88.22 232	93.53 142	95.37 232	86.49 129	89.26 277	93.59 244	79.76 273	91.15 240	92.31 271	77.12 271	98.38 188	77.51 283	97.92 198	95.71 246
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Test4		91.41 190	91.25 188	91.89 201	95.35 233	80.32 202	90.97 224	96.92 139	81.96 259	95.11 137	93.81 237	81.34 244	98.48 180	88.71 162	97.08 229	96.87 204

V42		93.43 134	93.58 136	92.97 158	95.34 234	81.22 192	92.67 158	96.49 164	87.25 201	96.20 93	96.37 136	87.32 185	98.85 122	92.39 91	98.21 175	98.85 90

DI_MVS_plusplus_test		91.42 189	91.41 183	91.46 214	95.34 234	79.06 242	90.58 237	93.74 242	82.59 253	94.69 153	94.76 206	86.54 205	98.44 185	87.93 173	96.49 253	96.87 204

Patchmatch-RL test		88.81 231	88.52 225	89.69 253	95.33 236	79.94 218	86.22 314	92.71 261	78.46 287	95.80 114	94.18 225	66.25 305	95.33 310	89.22 152	98.53 142	93.78 295

test_normal		91.49 185	91.44 182	91.62 209	95.21 237	79.44 231	90.08 253	93.84 240	82.60 252	94.37 161	94.74 207	86.66 202	98.46 183	88.58 165	96.92 235	96.95 199

BH-RMVSNet		90.47 201	90.44 203	90.56 233	95.21 237	78.65 250	89.15 280	93.94 239	88.21 185	92.74 202	94.22 223	86.38 206	97.88 223	78.67 274	95.39 273	95.14 262

tfpn_ndepth		85.85 284	85.15 285	87.98 289	95.19 239	75.36 287	92.79 155	83.18 337	86.97 206	89.92 262	86.43 335	57.44 344	97.85 229	78.18 276	96.22 256	90.72 334

Effi-MVS+		92.79 158	92.74 155	92.94 161	95.10 240	83.30 171	94.00 116	97.53 88	91.36 116	89.35 274	90.65 301	94.01 55	98.66 155	87.40 180	95.30 275	96.88 203

USDC		89.02 225	89.08 215	88.84 273	95.07 241	74.50 294	88.97 283	96.39 171	73.21 313	93.27 189	96.28 140	82.16 236	96.39 288	77.55 282	98.80 125	95.62 250

WTY-MVS		86.93 274	86.50 268	88.24 287	94.96 242	74.64 290	87.19 304	92.07 273	78.29 288	88.32 291	91.59 285	78.06 264	94.27 322	74.88 303	93.15 310	95.80 242

PS-MVSNAJ		88.86 230	88.99 219	88.48 284	94.88 243	74.71 289	86.69 310	95.60 200	80.88 265	87.83 296	87.37 330	90.77 120	98.82 125	82.52 232	94.37 293	91.93 326

MG-MVS		89.54 218	89.80 210	88.76 274	94.88 243	72.47 314	89.60 267	92.44 266	85.82 221	89.48 272	95.98 157	82.85 230	97.74 239	81.87 236	95.27 276	96.08 235

xiu_mvs_v2_base		89.00 226	89.19 213	88.46 285	94.86 245	74.63 291	86.97 306	95.60 200	80.88 265	87.83 296	88.62 317	91.04 116	98.81 130	82.51 233	94.38 292	91.93 326

MAR-MVS		90.32 209	88.87 222	94.66 99	94.82 246	91.85 52	94.22 112	94.75 221	80.91 264	87.52 301	88.07 322	86.63 203	97.87 225	76.67 289	96.21 257	94.25 282
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		90.35 207	89.96 208	91.54 213	94.81 247	78.80 248	90.14 250	96.93 137	79.43 276	88.68 287	95.06 194	86.27 208	98.15 207	80.27 252	98.04 191	97.68 165

PVSNet_Blended		88.74 233	88.16 234	90.46 235	94.81 247	78.80 248	86.64 311	96.93 137	74.67 302	88.68 287	89.18 315	86.27 208	98.15 207	80.27 252	96.00 258	94.44 279

BH-w/o		87.21 265	87.02 256	87.79 293	94.77 249	77.27 264	87.90 294	93.21 253	81.74 261	89.99 261	88.39 320	83.47 224	96.93 269	71.29 322	92.43 318	89.15 338

LS3D		96.11 47	95.83 59	96.95 33	94.75 250	94.20 14	97.34 12	97.98 45	97.31 9	95.32 128	96.77 106	93.08 71	99.20 65	91.79 102	98.16 179	97.44 177

Effi-MVS+-dtu		93.90 122	92.60 159	97.77 4	94.74 251	96.67 4	94.00 116	95.41 209	89.94 143	91.93 223	92.13 275	90.12 135	98.97 98	87.68 175	97.48 218	97.67 166

mvs-test1		93.07 151	91.80 173	96.89 35	94.74 251	95.83 7	92.17 181	95.41 209	89.94 143	89.85 265	90.59 302	90.12 135	98.88 111	87.68 175	95.66 265	95.97 238

MVSFormer		92.18 175	92.23 164	92.04 199	94.74 251	80.06 211	97.15 14	97.37 101	88.98 158	88.83 278	92.79 256	77.02 272	99.60 8	96.41 6	96.75 240	96.46 221

lupinMVS		88.34 238	87.31 247	91.45 215	94.74 251	80.06 211	87.23 302	92.27 267	71.10 323	88.83 278	91.15 288	77.02 272	98.53 174	86.67 189	96.75 240	95.76 244

MDA-MVSNet-bldmvs		91.04 193	90.88 194	91.55 212	94.68 255	80.16 205	85.49 318	92.14 271	90.41 137	94.93 146	95.79 165	85.10 217	96.93 269	85.15 206	94.19 298	97.57 171

MVS_0304		92.99 153	92.54 160	94.35 117	94.67 256	86.06 141	91.16 219	97.92 55	90.01 142	88.33 290	94.41 215	87.02 191	99.22 63	90.36 124	99.00 102	97.76 159

Fast-Effi-MVS+-dtu		92.77 160	92.16 165	94.58 107	94.66 257	88.25 104	92.05 184	96.65 156	89.62 149	90.08 257	91.23 287	92.56 82	98.60 161	86.30 197	96.27 255	96.90 201

UnsupCasMVSNet_eth		90.33 208	90.34 204	90.28 239	94.64 258	80.24 203	89.69 266	95.88 192	85.77 222	93.94 172	95.69 169	81.99 238	92.98 332	84.21 218	91.30 327	97.62 169

1111		80.36 319	81.32 307	77.48 336	94.61 259	44.56 357	81.59 337	90.66 284	86.78 210	90.60 249	93.52 245	30.37 362	90.67 340	66.36 337	97.42 221	97.20 190

.test1245		64.72 331	70.88 332	46.22 344	94.61 259	44.56 357	81.59 337	90.66 284	86.78 210	90.60 249	93.52 245	30.37 362	90.67 340	66.36 337	3.45 358	3.44 358

OpenMVS_ROB	85.12 16	89.52 219	89.05 216	90.92 229	94.58 261	81.21 193	91.10 222	93.41 248	77.03 296	93.41 182	93.99 234	83.23 226	97.80 232	79.93 259	94.80 285	93.74 297

OpenMVS	89.45 8	92.27 174	92.13 167	92.68 175	94.53 262	84.10 163	95.70 60	97.03 128	82.44 256	91.14 241	96.42 126	88.47 157	98.38 188	85.95 199	97.47 219	95.55 255

thres200		85.85 284	85.18 284	87.88 292	94.44 263	72.52 313	89.08 281	86.21 310	88.57 174	91.44 228	88.40 319	64.22 313	98.00 212	68.35 332	95.88 263	93.12 308

DELS-MVS		92.05 177	92.16 165	91.72 206	94.44 263	80.13 209	87.62 296	97.25 117	87.34 200	92.22 217	93.18 252	89.54 145	98.73 144	89.67 142	98.20 177	96.30 227
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		88.90 229	87.25 249	93.83 133	94.40 265	93.81 31	84.73 322	87.09 305	79.36 280	93.26 190	92.43 268	79.29 256	91.68 337	77.50 284	97.22 226	96.00 237

pmmvs4		88.95 228	87.70 244	92.70 174	94.30 266	85.60 148	87.22 303	92.16 270	74.62 303	89.75 269	94.19 224	77.97 265	96.41 287	82.71 230	96.36 254	96.09 234

new-patchmatchnet		88.97 227	90.79 198	83.50 324	94.28 267	55.83 354	85.34 319	93.56 245	86.18 214	95.47 123	95.73 168	83.10 227	96.51 282	85.40 203	98.06 189	98.16 131

API-MVS		91.52 184	91.61 177	91.26 221	94.16 268	86.26 137	94.66 94	94.82 218	91.17 120	92.13 218	91.08 290	90.03 141	97.06 265	79.09 266	97.35 224	90.45 336

MSDG		90.82 195	90.67 201	91.26 221	94.16 268	83.08 175	86.63 312	96.19 184	90.60 132	91.94 222	91.89 278	89.16 149	95.75 300	80.96 249	94.51 291	94.95 267

TR-MVS		87.70 251	87.17 251	89.27 265	94.11 270	79.26 234	88.69 288	91.86 274	81.94 260	90.69 247	89.79 308	82.82 231	97.42 251	72.65 313	91.98 324	91.14 331

sss		87.23 264	86.82 259	88.46 285	93.96 271	77.94 254	86.84 308	92.78 260	77.59 291	87.61 300	91.83 279	78.75 258	91.92 336	77.84 279	94.20 297	95.52 256

PVSNet	76.22 20	82.89 300	82.37 299	84.48 319	93.96 271	64.38 344	78.60 344	88.61 291	71.50 321	84.43 321	86.36 336	74.27 280	94.60 316	69.87 330	93.69 304	94.46 278

semantic-postprocess				91.94 200	93.89 273	79.22 239		93.51 246	91.53 114	95.37 127	96.62 115	77.17 270	98.90 105	91.89 101	94.95 281	97.70 163

UGNet		93.08 149	92.50 162	94.79 96	93.87 274	87.99 108	95.07 80	94.26 233	90.64 130	87.33 302	97.67 62	86.89 198	98.49 177	88.10 171	98.71 131	97.91 148
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		81.91 308	80.11 318	87.31 297	93.87 274	72.32 315	84.02 329	93.22 251	69.47 332	76.13 351	89.84 305	72.15 284	97.23 259	53.27 352	89.02 333	92.37 318

CANet		92.38 171	91.99 169	93.52 143	93.82 276	83.46 169	91.14 220	97.00 130	89.81 146	86.47 307	94.04 230	87.90 174	99.21 64	89.50 144	98.27 167	97.90 149

test1235678		84.54 291	83.85 293	86.59 302	93.81 277	73.41 301	82.38 334	91.79 275	79.43 276	89.50 271	91.61 284	70.59 288	92.94 333	58.14 348	97.40 222	93.44 304

HY-MVS	82.50 18	86.81 276	85.93 279	89.47 255	93.63 278	77.93 255	94.02 115	91.58 277	75.68 299	83.64 325	93.64 239	77.40 268	97.42 251	71.70 319	92.07 323	93.05 309

no-one		87.84 248	87.21 250	89.74 249	93.58 279	78.64 251	81.28 339	92.69 262	74.36 305	92.05 221	97.14 89	81.86 241	96.07 295	72.03 316	99.90 2	94.52 276

MVS_Test		92.57 167	93.29 142	90.40 236	93.53 280	75.85 278	92.52 163	96.96 134	88.73 169	92.35 212	96.70 113	90.77 120	98.37 191	92.53 86	95.49 269	96.99 197

EU-MVSNet		87.39 260	86.71 262	89.44 260	93.40 281	76.11 275	94.93 86	90.00 287	57.17 352	95.71 117	97.37 78	64.77 312	97.68 242	92.67 81	94.37 293	94.52 276

MS-PatchMatch		88.05 245	87.75 242	88.95 271	93.28 282	77.93 255	87.88 295	92.49 265	75.42 301	92.57 206	93.59 242	80.44 252	94.24 324	81.28 242	92.75 315	94.69 273

GA-MVS		87.70 251	86.82 259	90.31 238	93.27 283	77.22 265	84.72 324	92.79 259	85.11 229	89.82 266	90.07 303	66.80 300	97.76 237	84.56 216	94.27 296	95.96 239

pmmvs5		87.87 247	87.14 252	90.07 246	93.26 284	76.97 269	88.89 285	92.18 268	73.71 311	88.36 289	93.89 235	76.86 275	96.73 276	80.32 251	96.81 237	96.51 212

IterMVS		90.18 211	90.16 206	90.21 244	93.15 285	75.98 277	87.56 299	92.97 255	86.43 213	94.09 168	96.40 128	78.32 262	97.43 250	87.87 174	94.69 288	97.23 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		78.83 324	80.60 313	73.51 340	93.07 286	47.37 355	87.10 305	78.00 353	68.94 333	77.53 349	97.26 83	71.45 286	94.62 315	63.28 344	88.74 334	78.55 352

FMVSNet3		90.78 197	90.32 205	92.16 195	93.03 287	79.92 219	92.54 161	94.95 215	86.17 215	95.10 138	96.01 156	69.97 291	98.75 140	86.74 186	98.38 153	97.82 157

PAPR		87.65 254	86.77 261	90.27 240	92.85 288	77.38 262	88.56 290	96.23 181	76.82 298	84.98 316	89.75 310	86.08 210	97.16 261	72.33 314	93.35 306	96.26 229

Regformer-1		94.55 104	94.33 108	95.19 85	92.83 289	88.54 98	91.87 195	95.84 195	93.99 46	95.95 104	95.04 195	92.00 91	98.79 132	93.14 69	98.31 162	98.23 125

Regformer-2		94.86 90	94.55 101	95.77 63	92.83 289	89.98 70	91.87 195	96.40 169	94.38 43	96.19 95	95.04 195	92.47 86	99.04 86	93.49 55	98.31 162	98.28 123

Regformer-3		94.28 112	94.23 114	94.46 112	92.78 291	86.28 136	92.39 172	94.70 223	93.69 56	95.97 102	95.56 175	91.34 104	98.48 180	93.45 58	98.14 181	98.62 108

Regformer-4		94.90 87	94.67 98	95.59 71	92.78 291	89.02 85	92.39 172	95.91 191	94.50 39	96.41 78	95.56 175	92.10 89	99.01 92	94.23 37	98.14 181	98.74 99

EI-MVSNet-Vis-set		94.36 109	94.28 110	94.61 100	92.55 293	85.98 142	92.44 170	94.69 224	93.70 53	96.12 98	95.81 164	91.24 109	98.86 120	93.76 49	98.22 174	98.98 76

EI-MVSNet-UG-set		94.35 110	94.27 112	94.59 105	92.46 294	85.87 143	92.42 171	94.69 224	93.67 57	96.13 97	95.84 163	91.20 112	98.86 120	93.78 46	98.23 172	99.03 67

testus		82.09 307	81.78 302	83.03 326	92.35 295	64.37 345	79.44 342	93.27 250	73.08 314	87.06 304	85.21 340	76.80 276	89.27 347	53.30 351	95.48 270	95.46 257

FMVSNet5		87.82 250	86.56 264	91.62 209	92.31 296	79.81 221	93.49 133	94.81 220	83.26 243	91.36 229	96.93 98	52.77 351	97.49 248	76.07 293	98.03 192	97.55 174

diffmvs		90.45 202	90.49 202	90.34 237	92.25 297	77.09 266	91.80 206	95.96 190	82.68 251	85.83 311	95.07 193	87.01 192	97.09 263	89.68 141	94.10 299	96.83 206

MDA-MVSNet_test_wron		88.16 244	88.23 231	87.93 290	92.22 298	73.71 298	80.71 341	88.84 289	82.52 254	94.88 147	95.14 189	82.70 232	93.61 327	83.28 225	93.80 302	96.46 221

YYNet1		88.17 243	88.24 230	87.93 290	92.21 299	73.62 299	80.75 340	88.77 290	82.51 255	94.99 144	95.11 191	82.70 232	93.70 326	83.33 224	93.83 301	96.48 220

CANet_DTU		89.85 215	89.17 214	91.87 202	92.20 300	80.02 215	90.79 229	95.87 193	86.02 217	82.53 332	91.77 280	80.01 253	98.57 166	85.66 201	97.70 206	97.01 196

mvs_anonymous		90.37 206	91.30 187	87.58 294	92.17 301	68.00 328	89.84 263	94.73 222	83.82 242	93.22 194	97.40 76	87.54 178	97.40 253	87.94 172	95.05 280	97.34 184

EI-MVSNet		92.99 153	93.26 146	92.19 193	92.12 302	79.21 240	92.32 175	94.67 226	91.77 107	95.24 133	95.85 161	87.14 189	98.49 177	91.99 97	98.26 168	98.86 87

CVMVSNet		85.16 288	84.72 286	86.48 303	92.12 302	70.19 322	92.32 175	88.17 297	56.15 353	90.64 248	95.85 161	67.97 295	96.69 277	88.78 160	90.52 331	92.56 316

Patchmatch-test1		87.28 262	87.30 248	87.22 298	92.01 304	71.98 316	89.43 271	88.11 298	82.26 258	88.71 285	92.20 272	78.65 259	95.81 299	80.99 248	93.30 307	93.87 294

our_test_3		87.55 256	87.59 245	87.44 296	91.76 305	70.48 321	83.83 330	90.55 286	79.79 272	92.06 220	92.17 274	78.63 260	95.63 301	84.77 213	94.73 286	96.22 230

ppachtmachnet_test		88.61 234	88.64 224	88.50 283	91.76 305	70.99 320	84.59 325	92.98 254	79.30 282	92.38 210	93.53 244	79.57 255	97.45 249	86.50 194	97.17 227	97.07 193

1314		86.46 281	86.33 269	86.87 301	91.65 307	74.54 292	91.94 189	94.10 235	74.28 306	84.78 318	87.33 331	83.03 228	95.00 314	78.72 273	91.16 329	91.06 332

cascas		87.02 271	86.28 270	89.25 266	91.56 308	76.45 271	84.33 327	96.78 150	71.01 324	86.89 306	85.91 337	81.35 243	96.94 268	83.09 227	95.60 266	94.35 281

IB-MVS	77.21 19	83.11 297	81.05 309	89.29 264	91.15 309	75.85 278	85.66 317	86.00 313	79.70 274	82.02 337	86.61 332	48.26 355	98.39 186	77.84 279	92.22 321	93.63 299
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		84.98 290	84.30 289	87.01 299	91.03 310	77.69 260	91.94 189	94.16 234	59.36 351	84.23 322	87.50 329	85.66 214	96.80 274	71.79 317	93.05 313	86.54 344

CR-MVSNet		87.89 246	87.12 253	90.22 242	91.01 311	78.93 243	92.52 163	92.81 257	73.08 314	89.10 275	96.93 98	67.11 297	97.64 243	88.80 159	92.70 316	94.08 284

RPMNet		89.30 221	89.00 218	90.22 242	91.01 311	78.93 243	92.52 163	87.85 300	91.91 96	89.10 275	96.89 101	68.84 292	97.64 243	90.17 131	92.70 316	94.08 284

new_pmnet		81.22 312	81.01 311	81.86 330	90.92 313	70.15 323	84.03 328	80.25 352	70.83 326	85.97 310	89.78 309	67.93 296	84.65 353	67.44 334	91.90 325	90.78 333

test12356		76.35 325	77.41 326	73.19 341	90.70 314	38.86 360	74.56 346	91.14 279	74.55 304	80.54 344	88.18 321	52.36 352	90.49 344	52.38 353	92.26 320	90.21 337

PatchT		87.51 257	88.17 233	85.55 309	90.64 315	66.91 332	92.02 185	86.09 311	92.20 89	89.05 277	97.16 88	64.15 314	96.37 290	89.21 153	92.98 314	93.37 306

Patchmatch-test		86.10 283	86.01 278	86.38 305	90.63 316	74.22 297	89.57 268	86.69 307	85.73 223	89.81 267	92.83 255	65.24 310	91.04 339	77.82 281	95.78 264	93.88 293

PVSNet_0	70.34 21	74.58 327	72.96 329	79.47 334	90.63 316	66.24 337	73.26 347	83.40 336	63.67 348	78.02 348	78.35 352	72.53 282	89.59 346	56.68 349	60.05 355	82.57 350

PMMVS2		81.31 311	83.44 294	74.92 339	90.52 318	46.49 356	69.19 352	85.23 326	84.30 238	87.95 295	94.71 209	76.95 274	84.36 354	64.07 341	98.09 187	93.89 292

tpm		84.38 293	84.08 290	85.30 314	90.47 319	63.43 347	89.34 274	85.63 317	77.24 295	87.62 299	95.03 197	61.00 333	97.30 257	79.26 264	91.09 330	95.16 260

PNet_i23d		72.03 330	70.91 331	75.38 338	90.46 320	57.84 352	71.73 351	81.53 348	83.86 241	82.21 333	83.49 345	29.97 364	87.80 351	60.78 345	54.12 356	80.51 351

wuyk23d		87.83 249	90.79 198	78.96 335	90.46 320	88.63 93	92.72 156	90.67 283	91.65 111	98.68 11	97.64 63	96.06 15	77.53 356	59.84 346	99.41 61	70.73 353

Patchmtry		90.11 213	89.92 209	90.66 231	90.35 322	77.00 268	92.96 150	92.81 257	90.25 139	94.74 151	96.93 98	67.11 297	97.52 246	85.17 204	98.98 103	97.46 176

CHOSEN 280x420		80.04 321	77.97 325	86.23 307	90.13 323	74.53 293	72.87 349	89.59 288	66.38 341	76.29 350	85.32 339	56.96 346	95.36 308	69.49 331	94.72 287	88.79 341

MVSTER		89.32 220	88.75 223	91.03 225	90.10 324	76.62 270	90.85 227	94.67 226	82.27 257	95.24 133	95.79 165	61.09 332	98.49 177	90.49 118	98.26 168	97.97 144

tpm2		81.46 310	80.35 316	84.80 316	89.90 325	65.14 340	90.44 240	85.36 320	65.82 344	82.05 336	92.44 267	57.94 343	96.69 277	70.71 327	88.49 336	92.56 316

test0.0.03 1		82.48 303	81.47 306	85.48 310	89.70 326	73.57 300	84.73 322	81.64 347	83.07 248	88.13 293	86.61 332	62.86 327	89.10 349	66.24 339	90.29 332	93.77 296

test-LLR		83.58 296	83.17 296	84.79 317	89.68 327	66.86 334	83.08 331	84.52 329	83.07 248	82.85 330	84.78 341	62.86 327	93.49 328	82.85 228	94.86 282	94.03 287

test-mter		81.21 313	80.01 319	84.79 317	89.68 327	66.86 334	83.08 331	84.52 329	73.85 310	82.85 330	84.78 341	43.66 359	93.49 328	82.85 228	94.86 282	94.03 287

DSMNet-mixed		82.21 305	81.56 304	84.16 321	89.57 329	70.00 324	90.65 233	77.66 354	54.99 354	83.30 328	97.57 65	77.89 266	90.50 343	66.86 336	95.54 268	91.97 325

PatchmatchNet		85.22 287	84.64 287	86.98 300	89.51 330	69.83 325	90.52 238	87.34 304	78.87 284	87.22 303	92.74 258	66.91 299	96.53 280	81.77 237	86.88 339	94.58 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.88 292		89.42 331	61.52 348	88.74 287	87.41 303	73.99 309	84.96 317	94.01 233	65.25 309	95.53 302	78.02 277	93.16 309

tpmp4_e23		81.87 309	80.41 314	86.27 306	89.29 332	67.84 329	91.58 209	87.61 302	67.42 338	78.60 347	92.71 259	56.42 348	96.87 271	71.44 321	88.63 335	94.10 283

CostFormer		83.09 298	82.21 300	85.73 308	89.27 333	67.01 331	90.35 242	86.47 309	70.42 328	83.52 327	93.23 251	61.18 331	96.85 272	77.21 286	88.26 337	93.34 307

ADS-MVSNet2		84.01 295	82.20 301	89.41 261	89.04 334	76.37 272	87.57 297	90.98 282	72.71 317	84.46 319	92.45 265	68.08 293	96.48 283	70.58 328	83.97 341	95.38 258

ADS-MVSNet		82.25 304	81.55 305	84.34 320	89.04 334	65.30 338	87.57 297	85.13 327	72.71 317	84.46 319	92.45 265	68.08 293	92.33 335	70.58 328	83.97 341	95.38 258

tpm cat1		80.61 318	79.46 320	84.07 322	88.78 336	65.06 342	89.26 277	88.23 295	62.27 349	81.90 338	89.66 312	62.70 329	95.29 311	71.72 318	80.60 350	91.86 328

CMPMVS	68.83 22	87.28 262	85.67 281	92.09 197	88.77 337	85.42 150	90.31 244	94.38 230	70.02 330	88.00 294	93.30 250	73.78 281	94.03 325	75.96 295	96.54 248	96.83 206
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PatchFormer-LS_test		82.62 302	81.71 303	85.32 313	87.92 338	67.31 330	89.03 282	88.20 296	77.58 292	83.79 324	80.50 351	60.96 334	96.42 286	83.86 222	83.59 343	92.23 323

LP		86.29 282	85.35 283	89.10 268	87.80 339	76.21 273	89.92 258	90.99 281	84.86 234	87.66 298	92.32 270	70.40 289	96.48 283	81.94 235	82.24 348	94.63 274

tpmrst		82.85 301	82.93 298	82.64 328	87.65 340	58.99 351	90.14 250	87.90 299	75.54 300	83.93 323	91.63 283	66.79 302	95.36 308	81.21 244	81.54 349	93.57 303

JIA-IIPM		85.08 289	83.04 297	91.19 224	87.56 341	86.14 139	89.40 273	84.44 335	88.98 158	82.20 334	97.95 49	56.82 347	96.15 293	76.55 291	83.45 344	91.30 330

TESTMET0.1,1		79.09 323	78.04 324	82.25 329	87.52 342	64.03 346	83.08 331	80.62 350	70.28 329	80.16 345	83.22 346	44.13 358	90.56 342	79.95 257	93.36 305	92.15 324

DWT-MVSNet_test		80.74 316	79.18 321	85.43 311	87.51 343	66.87 333	89.87 262	86.01 312	74.20 308	80.86 341	80.62 350	48.84 354	96.68 279	81.54 239	83.14 346	92.75 314

gg-mvs-nofinetune		82.10 306	81.02 310	85.34 312	87.46 344	71.04 318	94.74 91	67.56 357	96.44 19	79.43 346	98.99 6	45.24 356	96.15 293	67.18 335	92.17 322	88.85 340

pmmvs3		80.83 315	78.96 322	86.45 304	87.23 345	77.48 261	84.87 321	82.31 344	63.83 347	85.03 315	89.50 313	49.66 353	93.10 330	73.12 310	95.10 279	88.78 342

tpmvs		84.22 294	83.97 291	84.94 315	87.09 346	65.18 339	91.21 218	88.35 293	82.87 250	85.21 313	90.96 292	65.24 310	96.75 275	79.60 263	85.25 340	92.90 311

gm-plane-assit					87.08 347	59.33 350			71.22 322		83.58 344		97.20 260	73.95 304

MVE	59.87 23	73.86 329	72.65 330	77.47 337	87.00 348	74.35 295	61.37 354	60.93 359	67.27 339	69.69 355	86.49 334	81.24 248	72.33 357	56.45 350	83.45 344	85.74 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EPNet_dtu		85.63 286	84.37 288	89.40 262	86.30 349	74.33 296	91.64 208	88.26 294	84.84 235	72.96 354	89.85 304	71.27 287	97.69 241	76.60 290	97.62 210	96.18 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2356		75.58 326	73.13 328	82.95 327	86.10 350	66.42 336	75.07 345	84.87 328	70.91 325	80.85 342	80.66 349	38.02 361	88.98 350	49.32 354	92.35 319	93.44 304

dp		79.28 322	78.62 323	81.24 331	85.97 351	56.45 353	86.91 307	85.26 325	72.97 316	81.45 340	89.17 316	56.01 350	95.45 306	73.19 309	76.68 352	91.82 329

EPMVS		81.17 314	80.37 315	83.58 323	85.58 352	65.08 341	90.31 244	71.34 356	77.31 294	85.80 312	91.30 286	59.38 335	92.70 334	79.99 256	82.34 347	92.96 310

E-PMN		80.72 317	80.86 312	80.29 333	85.11 353	68.77 327	72.96 348	81.97 346	87.76 194	83.25 329	83.01 347	62.22 330	89.17 348	77.15 287	94.31 295	82.93 348

GG-mvs-BLEND				83.24 325	85.06 354	71.03 319	94.99 85	65.55 358		74.09 353	75.51 353	44.57 357	94.46 318	59.57 347	87.54 338	84.24 346

EMVS		80.35 320	80.28 317	80.54 332	84.73 355	69.07 326	72.54 350	80.73 349	87.80 193	81.66 339	81.73 348	62.89 326	89.84 345	75.79 300	94.65 289	82.71 349

EPNet		89.80 216	88.25 229	94.45 113	83.91 356	86.18 138	93.87 125	87.07 306	91.16 121	80.64 343	94.72 208	78.83 257	98.89 107	85.17 204	98.89 108	98.28 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS		83.00 299	81.11 308	88.66 277	83.81 357	86.44 132	82.24 336	85.65 316	61.75 350	82.07 335	85.64 338	79.75 254	91.59 338	75.99 294	93.09 311	87.94 343

testpf		74.01 328	76.37 327	66.95 342	80.56 358	60.00 349	88.43 292	75.07 355	81.54 262	75.75 352	83.73 343	38.93 360	83.09 355	84.01 219	79.32 351	57.75 354

DeepMVS_CX				53.83 343	70.38 359	64.56 343		48.52 361	33.01 355	65.50 356	74.21 354	56.19 349	46.64 358	38.45 356	70.07 353	50.30 355

tmp_tt		37.97 333	44.33 333	18.88 346	11.80 360	21.54 361	63.51 353	45.66 362	4.23 356	51.34 357	50.48 355	59.08 336	22.11 359	44.50 355	68.35 354	13.00 356

test123		9.49 335	12.01 336	1.91 347	2.87 361	1.30 362	82.38 334	1.34 364	1.36 357	2.84 358	6.56 358	2.45 365	0.97 360	2.73 357	5.56 357	3.47 357

testmvs		9.02 336	11.42 337	1.81 348	2.77 362	1.13 363	79.44 342	1.90 363	1.18 358	2.65 359	6.80 357	1.95 366	0.87 361	2.62 358	3.45 358	3.44 358

cdsmvs_eth3d_5k		23.35 334	31.13 335	0.00 349	0.00 363	0.00 364	0.00 355	95.58 204	0.00 359	0.00 360	91.15 288	93.43 62	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		7.56 337	10.09 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	90.77 120	0.00 362	0.00 359	0.00 360	0.00 360

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		7.56 337	10.08 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	90.69 299	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																94.75 271

test_part3							93.92 123		91.83 102		96.39 132		99.44 24	89.00 155

test_part1								98.14 28				94.69 45			99.10 92	98.17 129

sam_mvs1												66.64 303				94.75 271

sam_mvs												66.41 304

MTGPA								97.62 76

test_post1							90.21 246				5.85 360	65.36 308	96.00 296	79.61 262

test_post											6.07 359	65.74 307	95.84 298

patchmatchnet-post											91.71 281	66.22 306	97.59 245

MTMP								54.62 360

test9_res														88.16 170	98.40 151	97.83 155

agg_prior2														87.06 184	98.36 159	97.98 141

test_prior4						89.91 71	90.74 230

test_prior2							90.21 246		89.33 153	90.77 244	94.81 202	90.41 131		88.21 167	98.55 139

旧先验2							90.00 256		68.65 334	92.71 203			96.52 281	85.15 206

新几何2							90.02 255

无先验							89.94 257	95.75 197	70.81 327				98.59 163	81.17 245		94.81 268

原ACMM2							89.34 274

testdata2													98.03 211	80.24 254

segment_acmp												92.14 88

testdata1							88.96 284		88.44 179

plane_prior5								97.81 63					98.95 101	89.26 150	98.51 144	98.60 110

plane_prior4											95.59 170

plane_prior3						88.43 103			90.35 138	93.31 185

plane_prior2							94.56 101		91.74 109

plane_prior						88.12 106	93.01 146		88.98 158						98.06 189

n2								0.00 365

nn								0.00 365

door-mid								92.13 272

test11								96.65 156

door								91.26 278

HQP5-MVS						84.89 154

BP-MVS														86.55 192

HQP4-MVS										88.81 280			98.61 159			98.15 132

HQP3-MVS								97.31 111							97.73 203

HQP2-MVS												84.76 219

MDTV_nov1_ep13_2view						42.48 359	88.45 291		67.22 340	83.56 326		66.80 300		72.86 312		94.06 286

ACMMP++_ref															98.82 121

ACMMP++															99.25 77

Test By Simon												90.61 127

High-resolution multi-view benchmark