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		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 8	85.07 36	99.27 3	99.54 1

wuykxyi23d		88.46 61	88.80 59	87.44 79	90.96 119	93.03 1	85.85 111	81.96 237	74.58 141	98.58 2	97.29 5	87.73 31	87.31 237	82.84 67	99.41 1	81.99 290

DTE-MVSNet		89.98 37	91.91 11	84.21 139	96.51 7	57.84 252	88.93 63	92.84 63	91.92 2	96.16 3	96.23 20	86.95 38	95.99 4	79.05 110	98.57 16	98.80 6

PS-CasMVS		90.06 34	91.92 9	84.47 131	96.56 6	58.83 249	89.04 60	92.74 66	91.40 5	96.12 4	96.06 24	87.23 35	95.57 21	79.42 109	98.74 7	99.00 2

LCM-MVSNet-Re		83.48 155	85.06 112	78.75 223	85.94 228	55.75 267	80.05 228	94.27 13	76.47 120	96.09 5	94.54 63	83.31 66	89.75 205	59.95 247	94.89 137	90.75 185

PEN-MVS		90.03 35	91.88 12	84.48 130	96.57 5	58.88 248	88.95 61	93.19 47	91.62 4	96.01 6	96.16 22	87.02 37	95.60 20	78.69 113	98.72 10	98.97 3

V4		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.98 7	96.31 16	84.80 54	93.98 73	88.59 4	94.83 141	98.23 8

v52		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.97 8	96.31 16	84.82 52	93.98 73	88.59 4	94.83 141	98.23 8

CP-MVSNet		89.27 52	90.91 34	84.37 134	96.34 8	58.61 251	88.66 68	92.06 82	90.78 6	95.67 9	95.17 44	81.80 88	95.54 24	79.00 111	98.69 11	98.95 4

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 48	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 53	93.16 120	91.10 1	97.53 57	96.58 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
WR-MVS_H		89.91 42	91.31 25	85.71 108	96.32 9	62.39 211	89.54 53	93.31 41	90.21 10	95.57 11	95.66 31	81.42 92	95.90 9	80.94 85	98.80 4	98.84 5

Anonymous20231211		90.14 31	91.88 12	84.92 117	94.75 35	64.47 178	90.13 38	92.97 57	91.68 3	95.35 12	98.79 2	93.19 3	91.76 157	71.67 170	98.40 21	98.52 7

OurMVSNet-221017-0		90.01 36	89.74 46	90.83 33	93.16 60	80.37 59	91.91 27	93.11 49	81.10 60	95.32 13	97.24 6	72.94 189	94.85 48	85.07 36	97.78 46	97.26 23

anonymousdsp		89.73 45	88.88 56	92.27 7	89.82 139	86.67 12	90.51 35	90.20 148	69.87 198	95.06 14	96.14 23	84.28 57	93.07 127	87.68 13	96.34 88	97.09 30

wuyk23d		75.13 235	79.30 197	62.63 311	75.56 316	75.18 103	80.89 221	73.10 288	75.06 138	94.76 15	95.32 40	87.73 31	52.85 341	34.16 335	97.11 65	59.85 335

ACMH	76.49 14	89.34 51	91.14 28	83.96 145	92.50 76	70.36 142	89.55 51	93.84 28	81.89 54	94.70 16	95.44 39	90.69 10	88.31 229	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 74	87.45 74	86.45 89	92.52 75	69.19 152	87.84 78	88.05 180	81.66 56	94.64 17	96.53 14	65.94 227	94.75 50	83.02 64	96.83 73	95.41 67

mvs_tets		89.78 44	89.27 51	91.30 24	93.51 51	84.79 36	89.89 44	90.63 127	70.00 197	94.55 18	96.67 11	87.94 29	93.59 91	84.27 48	95.97 104	95.52 65

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 41	91.86 4	95.09 41	87.54 18	98.02 39	93.71 110

jajsoiax		89.41 49	88.81 58	91.19 28	93.38 55	84.72 37	89.70 46	90.29 143	69.27 201	94.39 20	96.38 15	86.02 49	93.52 101	83.96 51	95.92 107	95.34 68

test_0402		88.65 58	89.58 49	85.88 105	92.55 74	72.22 124	84.01 137	89.44 163	88.63 14	94.38 21	95.77 28	86.38 46	93.59 91	79.84 102	95.21 127	91.82 163

v7n		90.13 32	90.96 32	87.65 75	91.95 92	71.06 138	89.99 41	93.05 52	86.53 21	94.29 22	96.27 19	82.69 71	94.08 69	86.25 29	97.63 51	97.82 11

test_djsdf		89.62 46	89.01 53	91.45 21	92.36 80	82.98 49	91.98 25	90.08 149	71.54 184	94.28 23	96.54 13	81.57 90	94.27 59	86.26 27	96.49 84	97.09 30

v748		88.91 57	89.82 45	86.19 99	90.06 135	68.53 154	88.81 65	91.48 99	84.36 28	94.19 24	95.98 25	82.52 74	92.67 137	84.30 47	96.67 77	97.37 20

PS-MVSNAJss		88.31 62	87.90 66	89.56 51	93.31 56	77.96 78	87.94 75	91.97 85	70.73 190	94.19 24	96.67 11	76.94 131	94.57 55	83.07 62	96.28 90	96.15 42

APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 65	78.04 77	92.84 12	94.14 21	83.33 35	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 128

ACMH+	77.89 11	90.73 25	91.50 19	88.44 62	93.00 64	76.26 98	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 107	78.35 115	98.76 5	95.61 64

ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 50	93.35 38	79.20 80	93.83 28	93.60 95	90.81 9	92.96 128	85.02 38	98.45 20	92.41 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v13		87.31 72	88.10 63	84.94 116	88.84 151	63.75 184	87.85 77	91.47 102	79.12 81	93.72 29	95.82 27	75.20 146	93.58 94	84.76 42	96.16 95	97.48 17

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 35	81.69 51	90.00 39	94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

LGP-MVS_train				90.82 34	94.75 35	81.69 51		94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

v12		87.15 75	87.91 65	84.84 119	88.69 154	63.52 187	87.58 80	91.46 103	78.74 89	93.57 32	95.66 31	74.94 150	93.57 95	84.50 45	96.08 100	97.43 18

V9		86.96 76	87.70 70	84.74 123	88.52 159	63.27 193	87.31 85	91.45 105	78.28 94	93.43 33	95.45 38	74.59 158	93.57 95	84.23 49	96.01 103	97.38 19

v11		86.96 76	87.78 69	84.51 128	88.50 160	62.60 207	87.21 86	91.63 94	78.08 98	93.40 34	95.56 36	75.07 147	93.57 95	84.46 46	96.08 100	97.36 21

V14		86.75 82	87.46 73	84.62 126	88.35 163	63.00 198	87.02 91	91.42 108	77.78 100	93.27 35	95.23 43	74.22 161	93.56 98	83.95 52	95.93 106	97.31 22

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 63	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 42	86.67 24	97.60 55	94.18 94

v15		86.56 85	87.25 77	84.51 128	88.15 170	62.72 203	86.72 101	91.40 110	77.38 105	93.11 37	95.00 48	73.93 166	93.55 99	83.67 56	95.86 110	97.26 23

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 73	94.51 11	75.79 129	92.94 38	94.96 49	88.36 20	95.01 44	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 55	89.88 43	86.22 95	91.63 98	77.07 88	89.82 45	93.77 30	78.90 85	92.88 39	92.29 122	86.11 48	90.22 196	86.24 30	97.24 62	91.36 174

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 59	94.05 23	79.03 84	92.87 40	93.74 93	90.60 12	95.21 39	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 41	96.29 18	88.16 26	94.17 66	86.07 32	98.48 19	97.22 26

test_part2					93.86 47	77.77 79				92.84 42

v10		86.54 86	87.10 78	84.84 119	88.16 169	63.28 192	86.64 103	92.20 79	75.42 136	92.81 43	94.50 64	74.05 164	94.06 70	83.88 53	96.28 90	97.17 28

v17		86.32 90	86.95 83	84.44 132	88.00 172	62.62 206	86.74 99	91.48 99	77.17 113	92.74 44	94.56 60	73.74 170	93.53 100	83.27 59	94.87 140	97.18 27

v8		86.22 95	86.83 87	84.36 135	87.82 180	62.35 212	86.42 106	91.33 113	76.78 118	92.73 45	94.48 65	73.41 178	93.72 82	83.10 61	95.41 120	97.01 33

nrg030		87.85 68	88.49 61	85.91 103	90.07 134	69.73 144	87.86 76	94.20 17	74.04 146	92.70 46	94.66 58	85.88 50	91.50 161	79.72 103	97.32 60	96.50 40

v16		86.24 93	86.85 86	84.43 133	87.96 174	62.59 208	86.73 100	91.48 99	77.17 113	92.67 47	94.55 61	73.63 171	93.52 101	83.26 60	94.16 155	97.17 28

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 42	92.60 48	93.97 84	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 43	89.97 42	89.27 54	94.76 34	79.86 64	86.76 97	92.78 65	78.78 87	92.51 49	93.64 94	88.13 27	93.84 80	84.83 41	97.55 56	94.10 97

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 24	92.51 49	95.13 45	90.65 11	95.34 32	88.06 10	98.15 34	95.95 51

K. test v3		85.14 109	84.73 119	86.37 90	91.13 116	69.63 146	85.45 116	76.68 264	84.06 32	92.44 51	96.99 8	62.03 239	94.65 52	80.58 91	93.24 182	94.83 80

v18		85.99 99	86.55 90	84.30 137	87.73 186	62.29 216	86.40 107	91.49 98	76.64 119	92.40 52	94.20 77	73.28 182	93.52 101	82.87 65	93.99 159	97.09 30

ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 45	92.39 53	94.14 79	89.15 17	95.62 19	87.35 19	98.24 30	94.56 81
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 49	86.15 18	93.37 8	95.10 7	90.28 9	92.11 54	95.03 47	89.75 15	94.93 46	79.95 101	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		89.98 37	89.84 44	90.41 39	94.91 33	84.50 41	89.49 55	93.98 24	79.68 73	92.09 55	93.89 91	83.80 60	93.10 123	82.67 69	98.04 36	93.64 112

TranMVSNet+NR-MVSNet		87.86 67	88.76 60	85.18 114	94.02 43	64.13 180	84.38 131	91.29 114	84.88 27	92.06 56	93.84 92	86.45 44	93.73 81	73.22 156	98.66 12	97.69 12

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 62	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 31	77.65 101	91.97 59	94.89 51	88.38 19	95.45 28	89.27 3	97.87 45	93.27 120

FC-MVSNet-test		85.93 101	87.05 80	82.58 176	92.25 84	56.44 262	85.75 112	93.09 50	77.33 110	91.94 60	94.65 59	74.78 154	93.41 109	75.11 140	98.58 15	97.88 10

lessismore_v0				85.95 102	91.10 117	70.99 139		70.91 306		91.79 61	94.42 67	61.76 240	92.93 130	79.52 108	93.03 186	93.93 102

HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 41	91.77 62	93.94 90	90.55 13	95.73 17	88.50 8	98.23 31	95.33 69

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 27	80.32 67	91.74 63	94.41 68	88.17 25	95.98 5	86.37 25	97.99 41	93.96 101

testing_2		84.36 130	84.64 128	83.50 163	86.74 213	63.97 183	84.56 128	90.31 138	66.22 225	91.62 64	94.55 61	75.88 141	91.95 149	77.02 131	94.89 137	94.56 81

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 71	83.09 38	91.54 65	94.25 75	87.67 33	95.51 27	87.21 23	98.11 35	93.12 125

HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 51	91.47 66	93.96 86	88.35 21	95.56 22	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 41	91.02 29	95.43 25	84.66 39	90.65 34	93.29 44	77.00 116	91.47 66	93.96 86	88.35 21	95.56 22	84.88 39	97.74 48	92.85 129

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 51	91.40 68	94.17 78	87.51 34	95.87 10	87.74 11	97.76 47	93.99 99

ANet_high		83.17 160	85.68 104	75.65 261	81.24 275	45.26 324	79.94 231	92.91 59	83.83 33	91.33 69	96.88 10	80.25 102	85.92 256	68.89 191	95.89 108	95.76 53

APD-MVS		89.54 48	89.63 48	89.26 55	92.57 73	81.34 56	90.19 37	93.08 51	80.87 62	91.13 70	93.19 98	86.22 47	95.97 6	82.23 73	97.18 64	90.45 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 37	91.06 71	94.00 83	88.26 23	95.71 18	87.28 22	98.39 23	92.55 143

FIs		85.35 108	86.27 94	82.60 175	91.86 95	57.31 256	85.10 120	93.05 52	75.83 128	91.02 72	93.97 84	73.57 175	92.91 132	73.97 148	98.02 39	97.58 15

UniMVSNet_NR-MVSNet		86.84 80	87.06 79	86.17 100	92.86 69	67.02 163	82.55 183	91.56 95	83.08 39	90.92 73	91.82 132	78.25 116	93.99 71	74.16 145	98.35 24	97.49 16

DU-MVS		86.80 81	86.99 81	86.21 97	93.24 58	67.02 163	83.16 168	92.21 78	81.73 55	90.92 73	91.97 126	77.20 125	93.99 71	74.16 145	98.35 24	97.61 13

V42		83.47 156	83.37 152	83.75 150	83.16 263	63.33 191	81.31 213	90.23 147	69.51 200	90.91 75	90.81 166	74.16 162	92.29 144	80.06 99	90.22 232	95.62 60

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 53	90.88 76	94.21 76	87.75 30	95.87 10	87.60 16	97.71 50	93.83 103

WR-MVS		83.56 153	84.40 135	81.06 200	93.43 54	54.88 273	78.67 251	85.02 222	81.24 59	90.74 77	91.56 139	72.85 190	91.08 172	68.00 198	98.04 36	97.23 25

v1240		84.30 133	84.51 132	83.65 155	87.65 189	61.26 229	82.85 174	91.54 96	67.94 214	90.68 78	90.65 172	71.71 206	93.64 85	82.84 67	94.78 143	96.07 45

MIMVSNet1		83.63 152	84.59 129	80.74 202	94.06 42	62.77 202	82.72 179	84.53 226	77.57 103	90.34 79	95.92 26	76.88 137	85.83 258	61.88 233	97.42 58	93.62 113

LS3D		90.60 28	90.34 40	91.38 23	89.03 148	84.23 42	93.58 4	94.68 10	90.65 7	90.33 80	93.95 89	84.50 56	95.37 31	80.87 86	95.50 119	94.53 85

PMVS	80.48 6	90.08 33	90.66 36	88.34 64	96.71 2	92.97 2	90.31 36	89.57 161	88.51 15	90.11 81	95.12 46	90.98 8	88.92 218	77.55 123	97.07 66	83.13 277
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v7		84.81 117	85.00 114	84.23 138	88.15 170	63.27 193	83.79 147	91.39 111	71.10 188	90.07 82	91.28 143	74.04 165	93.63 86	81.48 79	93.67 169	95.79 52

v1921920		84.23 136	84.37 139	83.79 148	87.64 190	61.71 220	82.91 173	91.20 117	67.94 214	90.06 83	90.34 177	72.04 203	93.59 91	82.32 72	94.91 136	96.07 45

ITE_SJBPF				90.11 45	90.72 124	84.97 33		90.30 140	81.56 57	90.02 84	91.20 148	82.40 76	90.81 181	73.58 152	94.66 148	94.56 81

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	94.03 82	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 112	82.70 159	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	16.05 343	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

v1192		84.57 122	84.69 123	84.21 139	87.75 185	62.88 200	83.02 170	91.43 106	69.08 205	89.98 87	90.89 163	72.70 194	93.62 90	82.41 70	94.97 135	96.13 43

v6		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.26 146	72.33 173	89.94 88	91.19 149	73.30 181	93.29 112	80.26 96	93.26 179	95.62 60

v1neww		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

v7new		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

Regformer-2		86.74 83	86.08 98	88.73 59	84.18 252	79.20 69	83.52 155	89.33 164	83.33 35	89.92 91	85.07 256	83.23 67	93.16 120	83.39 57	92.72 192	93.83 103

pmmvs6		86.52 87	88.06 64	81.90 185	92.22 86	62.28 217	84.66 126	89.15 166	83.54 34	89.85 92	97.32 4	88.08 28	86.80 244	70.43 179	97.30 61	96.62 37

Regformer-4		86.41 88	85.71 103	88.52 61	84.27 248	77.57 82	84.07 135	88.00 182	82.82 42	89.84 93	85.48 246	82.06 80	92.77 134	83.83 55	91.04 212	95.22 74

v144192		84.24 135	84.41 134	83.71 154	87.59 191	61.57 225	82.95 172	91.03 121	67.82 217	89.80 94	90.49 175	73.28 182	93.51 104	81.88 77	94.89 137	96.04 47

v1144		84.54 125	84.72 121	84.00 143	87.67 188	62.55 209	82.97 171	90.93 123	70.32 194	89.80 94	90.99 158	73.50 176	93.48 105	81.69 78	94.65 149	95.97 49

DeepC-MVS	82.31 4	89.15 54	89.08 52	89.37 53	93.64 50	79.07 70	88.54 69	94.20 17	73.53 151	89.71 96	94.82 54	85.09 51	95.77 15	84.17 50	98.03 38	93.26 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 65	86.93 84	91.22 27	90.08 133	89.30 6	89.68 48	91.11 119	79.26 79	89.68 97	94.81 57	82.44 75	87.74 234	76.54 133	88.74 246	96.61 38

FMVSNet1		84.55 123	85.45 108	81.85 188	90.27 131	61.05 232	86.83 94	88.27 177	78.57 91	89.66 98	95.64 33	75.43 143	90.68 185	69.09 189	95.33 122	93.82 105

v1141		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 181	89.64 99	91.12 152	72.68 195	93.10 123	80.39 95	93.80 164	95.75 54

divwei89l23v2f112		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 181	89.64 99	91.12 152	72.68 195	93.10 123	80.40 93	93.81 163	95.75 54

v1		84.16 138	84.38 136	83.52 160	87.33 196	61.71 220	82.79 176	89.73 157	71.89 183	89.64 99	91.11 154	72.72 192	93.10 123	80.40 93	93.79 165	95.75 54

IterMVS-LS		84.73 119	84.98 115	83.96 145	87.35 195	63.66 185	83.25 165	89.88 154	76.06 123	89.62 102	92.37 121	73.40 180	92.52 140	78.16 118	94.77 145	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 60	89.01 53	87.36 80	91.30 109	77.50 83	87.55 81	92.97 57	87.95 17	89.62 102	92.87 107	84.56 55	93.89 77	77.65 122	96.62 78	90.70 186

UniMVSNet (Re)		86.87 78	86.98 82	86.55 87	93.11 62	68.48 155	83.80 146	92.87 60	80.37 65	89.61 104	91.81 133	77.72 120	94.18 64	75.00 142	98.53 17	96.99 34

IS-MVSNet		86.66 84	86.82 88	86.17 100	92.05 90	66.87 165	91.21 33	88.64 171	86.30 23	89.60 105	92.59 113	69.22 214	94.91 47	73.89 149	97.89 44	96.72 35

v2v482		84.09 141	84.24 141	83.62 156	87.13 204	61.40 226	82.71 180	89.71 158	72.19 177	89.55 106	91.41 142	70.70 211	93.20 117	81.02 83	93.76 166	96.25 41

Baseline_NR-MVSNet		84.00 145	85.90 100	78.29 230	91.47 107	53.44 281	82.29 190	87.00 201	79.06 83	89.55 106	95.72 30	77.20 125	86.14 254	72.30 165	98.51 18	95.28 70

CSCG		86.26 92	86.47 91	85.60 110	90.87 121	74.26 108	87.98 74	91.85 88	80.35 66	89.54 108	88.01 215	79.09 109	92.13 146	75.51 137	95.06 132	90.41 195

ambc				82.98 169	90.55 128	64.86 175	88.20 71	89.15 166		89.40 109	93.96 86	71.67 207	91.38 168	78.83 112	96.55 80	92.71 134

DeepPCF-MVS	81.24 5	87.28 73	86.21 96	90.49 38	91.48 106	84.90 34	83.41 160	92.38 76	70.25 195	89.35 110	90.68 170	82.85 70	94.57 55	79.55 105	95.95 105	92.00 159

Regformer-1		86.00 97	85.50 107	87.49 77	84.18 252	76.90 90	83.52 155	87.94 184	82.18 50	89.19 111	85.07 256	82.28 78	91.89 152	82.40 71	92.72 192	93.69 111

HSP-MVS		88.63 59	87.84 67	91.02 29	95.76 16	86.14 19	92.75 13	91.01 122	78.43 92	89.16 112	92.25 124	72.03 204	96.36 2	88.21 9	90.93 219	90.55 192

XVG-OURS		89.18 53	88.83 57	90.23 43	94.28 41	86.11 20	85.91 109	93.60 34	80.16 69	89.13 113	93.44 96	83.82 59	90.98 174	83.86 54	95.30 126	93.60 114

Test4		81.31 179	81.13 181	81.88 187	84.89 238	63.05 197	82.37 187	90.50 130	62.75 252	89.00 114	88.29 212	67.55 220	91.68 158	73.55 153	91.24 209	90.89 180

MDA-MVSNet-bldmvs		77.47 210	76.90 211	79.16 219	79.03 292	64.59 176	66.58 314	75.67 268	73.15 162	88.86 115	88.99 199	66.94 222	81.23 286	64.71 218	88.22 253	91.64 167

EG-PatchMatch MVS		84.08 142	84.11 142	83.98 144	92.22 86	72.61 118	82.20 196	87.02 200	72.63 168	88.86 115	91.02 157	78.52 112	91.11 171	73.41 155	91.09 210	88.21 219

3Dnovator+	83.92 2	89.97 39	89.66 47	90.92 32	91.27 111	81.66 54	91.25 32	94.13 22	88.89 12	88.83 117	94.26 74	77.55 123	95.86 12	84.88 39	95.87 109	95.24 71

EI-MVSNet-UG-set		85.04 112	84.44 133	86.85 83	83.87 257	72.52 119	83.82 144	85.15 219	80.27 68	88.75 118	85.45 249	79.95 105	91.90 151	81.92 76	90.80 223	96.13 43

EI-MVSNet-Vis-set		85.12 110	84.53 131	86.88 82	84.01 254	72.76 116	83.91 142	85.18 218	80.44 64	88.75 118	85.49 245	80.08 103	91.92 150	82.02 74	90.85 222	95.97 49

OMC-MVS		88.19 63	87.52 72	90.19 44	91.94 94	81.68 53	87.49 83	93.17 48	76.02 125	88.64 120	91.22 145	84.24 58	93.37 110	77.97 121	97.03 67	95.52 65

Regformer-3		85.06 111	84.67 124	86.22 95	84.27 248	73.43 112	84.07 135	85.26 216	80.77 63	88.62 121	85.48 246	80.56 100	90.39 192	81.99 75	91.04 212	94.85 79

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 64	88.22 16	88.53 122	97.64 3	83.45 64	94.55 57	86.02 33	98.60 14	96.67 36

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 55	82.59 44	88.52 123	94.37 72	86.74 39	95.41 30	86.32 26	98.21 32	93.19 124

canonicalmvs		85.50 106	86.14 97	83.58 157	87.97 173	67.13 162	87.55 81	94.32 12	73.44 153	88.47 124	87.54 224	86.45 44	91.06 173	75.76 136	93.76 166	92.54 144

NR-MVSNet		86.00 97	86.22 95	85.34 112	93.24 58	64.56 177	82.21 194	90.46 131	80.99 61	88.42 125	91.97 126	77.56 122	93.85 78	72.46 164	98.65 13	97.61 13

alignmvs		83.94 147	83.98 145	83.80 147	87.80 184	67.88 159	84.54 129	91.42 108	73.27 160	88.41 126	87.96 216	72.33 199	90.83 180	76.02 135	94.11 156	92.69 135

TransMVSNet (Re)		84.02 144	85.74 102	78.85 221	91.00 118	55.20 272	82.29 190	87.26 191	79.65 74	88.38 127	95.52 37	83.00 68	86.88 242	67.97 199	96.60 79	94.45 88

PM-MVS		80.20 195	79.00 199	83.78 149	88.17 168	86.66 13	81.31 213	66.81 324	69.64 199	88.33 128	90.19 181	64.58 230	83.63 277	71.99 169	90.03 233	81.06 309

v148		82.31 168	82.48 165	81.81 191	85.59 230	59.66 242	81.47 211	86.02 209	72.85 165	88.05 129	90.65 172	70.73 210	90.91 178	75.15 139	91.79 203	94.87 77

AllTest		87.97 66	87.40 76	89.68 47	91.59 99	83.40 45	89.50 54	95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

TestCases				89.68 47	91.59 99	83.40 45		95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

pm-mvs1		83.69 150	84.95 116	79.91 209	90.04 137	59.66 242	82.43 185	87.44 188	75.52 133	87.85 132	95.26 42	81.25 94	85.65 260	68.74 193	96.04 102	94.42 89

PCF-MVS	74.62 15	82.15 171	80.92 184	85.84 106	89.43 141	72.30 122	80.53 224	91.82 89	57.36 278	87.81 133	89.92 186	77.67 121	93.63 86	58.69 253	95.08 131	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 179	81.61 175	80.41 205	86.38 216	58.75 250	83.93 141	86.58 204	72.43 169	87.65 134	92.98 103	63.78 234	90.22 196	66.86 204	93.92 161	92.27 153

VPA-MVSNet		83.47 156	84.73 119	79.69 213	90.29 130	57.52 255	81.30 215	88.69 170	76.29 121	87.58 135	94.44 66	80.60 99	87.20 238	66.60 208	96.82 74	94.34 91

DI_MVS_plusplus_test		81.27 181	81.26 178	81.29 196	84.98 236	61.65 224	81.98 199	87.25 192	63.56 244	87.56 136	89.60 191	73.62 172	91.83 154	72.20 166	90.59 230	90.38 196

CPTT-MVS		89.39 50	88.98 55	90.63 36	95.09 29	86.95 10	92.09 23	92.30 77	79.74 72	87.50 137	92.38 118	81.42 92	93.28 115	83.07 62	97.24 62	91.67 166

VDDNet		84.35 132	85.39 109	81.25 197	95.13 28	59.32 245	85.42 117	81.11 243	86.41 22	87.41 138	96.21 21	73.61 174	90.61 188	66.33 209	96.85 71	93.81 108

test_normal		81.23 183	81.16 180	81.43 194	84.77 241	61.99 219	81.46 212	86.95 202	63.16 249	87.22 139	89.63 190	73.62 172	91.65 159	72.92 161	90.70 225	90.65 189

VDD-MVS		84.23 136	84.58 130	83.20 166	91.17 115	65.16 174	83.25 165	84.97 224	79.79 71	87.18 140	94.27 73	74.77 155	90.89 179	69.24 186	96.54 81	93.55 118

MSLP-MVS++		85.00 114	86.03 99	81.90 185	91.84 96	71.56 136	86.75 98	93.02 56	75.95 126	87.12 141	89.39 194	77.98 117	89.40 210	77.46 124	94.78 143	84.75 255

YYNet1		70.06 274	70.44 267	68.90 292	73.76 328	53.42 282	58.99 329	67.20 320	58.42 274	87.10 142	85.39 251	59.82 251	67.32 320	59.79 248	83.50 293	85.96 242

MDA-MVSNet_test_wron		70.05 275	70.44 267	68.88 293	73.84 327	53.47 280	58.93 330	67.28 319	58.43 273	87.09 143	85.40 250	59.80 252	67.25 321	59.66 250	83.54 292	85.92 244

CNVR-MVS		87.81 69	87.68 71	88.21 65	92.87 67	77.30 87	85.25 118	91.23 116	77.31 111	87.07 144	91.47 141	82.94 69	94.71 51	84.67 43	96.27 92	92.62 142

EPP-MVSNet		85.47 107	85.04 113	86.77 84	91.52 105	69.37 147	91.63 28	87.98 183	81.51 58	87.05 145	91.83 131	66.18 226	95.29 33	70.75 174	96.89 70	95.64 59

TinyColmap		81.25 182	82.34 167	77.99 235	85.33 234	60.68 236	82.32 189	88.33 175	71.26 186	86.97 146	92.22 125	77.10 128	86.98 241	62.37 230	95.17 129	86.31 240

Patchmatch-RL test		74.48 242	73.68 241	76.89 249	84.83 239	66.54 167	72.29 295	69.16 314	57.70 276	86.76 147	86.33 237	45.79 303	82.59 281	69.63 183	90.65 228	81.54 299

XVG-OURS-SEG-HR		89.59 47	89.37 50	90.28 42	94.47 39	85.95 21	86.84 93	93.91 26	80.07 70	86.75 148	93.26 97	93.64 2	90.93 176	84.60 44	90.75 224	93.97 100

HPM-MVS++		88.93 56	88.45 62	90.38 40	94.92 32	85.85 25	89.70 46	91.27 115	78.20 95	86.69 149	92.28 123	80.36 101	95.06 43	86.17 31	96.49 84	90.22 198

TSAR-MVS + MP.		88.14 64	87.82 68	89.09 57	95.72 19	76.74 92	92.49 20	91.19 118	67.85 216	86.63 150	94.84 53	79.58 107	95.96 7	87.62 14	94.50 151	94.56 81

EI-MVSNet		82.61 164	82.42 166	83.20 166	83.25 261	63.66 185	83.50 158	85.07 220	76.06 123	86.55 151	85.10 254	73.41 178	90.25 193	78.15 119	90.67 226	95.68 58

HQP_MVS		87.75 70	87.43 75	88.70 60	93.45 52	76.42 96	89.45 56	93.61 32	79.44 77	86.55 151	92.95 105	74.84 152	95.22 37	80.78 88	95.83 111	94.46 86

plane_prior3						76.85 91			77.79 99	86.55 151

BH-untuned		80.96 185	80.99 182	80.84 201	88.55 158	68.23 156	80.33 226	88.46 172	72.79 166	86.55 151	86.76 231	74.72 156	91.77 156	61.79 234	88.99 242	82.52 283

MVSTER		77.09 214	75.70 226	81.25 197	75.27 322	61.08 231	77.49 262	85.07 220	60.78 267	86.55 151	88.68 203	43.14 323	90.25 193	73.69 151	90.67 226	92.42 145

旧先验2							81.73 206		56.88 282	86.54 156			84.90 266	72.81 162

semantic-postprocess				84.34 136	83.93 255	69.66 145		81.09 244	72.43 169	86.47 157	90.19 181	57.56 263	93.15 122	77.45 125	86.39 271	90.22 198

CDPH-MVS		86.17 96	85.54 106	88.05 71	92.25 84	75.45 101	83.85 143	92.01 83	65.91 228	86.19 158	91.75 135	83.77 61	94.98 45	77.43 126	96.71 76	93.73 109

MVS_111021_LR		84.28 134	83.76 148	85.83 107	89.23 145	83.07 48	80.99 220	83.56 228	72.71 167	86.07 159	89.07 198	81.75 89	86.19 253	77.11 129	93.36 175	88.24 218

GBi-Net		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

test1		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

FMVSNet3		78.80 202	78.55 202	79.57 216	82.89 265	56.89 260	81.76 205	85.77 211	69.04 206	86.00 160	90.44 176	51.75 282	90.09 202	65.95 211	93.34 176	91.72 165

tfpnnormal		81.79 176	82.95 157	78.31 229	88.93 150	55.40 268	80.83 223	82.85 232	76.81 117	85.90 163	94.14 79	74.58 159	86.51 249	66.82 207	95.68 117	93.01 127

TAPA-MVS	77.73 12	85.71 105	84.83 118	88.37 63	88.78 153	79.72 65	87.15 89	93.50 35	69.17 203	85.80 164	89.56 192	80.76 97	92.13 146	73.21 160	95.51 118	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 146	82.69 160	87.72 73	89.27 144	81.45 55	83.72 149	81.58 242	74.73 140	85.66 165	86.06 241	72.56 198	92.69 136	75.44 138	95.21 127	89.01 215

EU-MVSNet		75.12 236	74.43 237	77.18 246	83.11 264	59.48 244	85.71 114	82.43 234	39.76 338	85.64 166	88.76 201	44.71 319	87.88 232	73.86 150	85.88 274	84.16 263

LF4IMVS		82.75 163	81.93 172	85.19 113	82.08 268	80.15 61	85.53 115	88.76 169	68.01 211	85.58 167	87.75 220	71.80 205	86.85 243	74.02 147	93.87 162	88.58 217

Patchmtry		76.56 224	77.46 207	73.83 269	79.37 289	46.60 321	82.41 186	76.90 261	73.81 149	85.56 168	92.38 118	48.07 291	83.98 274	63.36 227	95.31 125	90.92 179

MVS_111021_HR		84.63 120	84.34 140	85.49 111	90.18 132	75.86 100	79.23 245	87.13 196	73.35 154	85.56 168	89.34 195	83.60 63	90.50 190	76.64 132	94.05 158	90.09 203

testdata				79.54 217	92.87 67	72.34 121		80.14 249	59.91 271	85.47 170	91.75 135	67.96 219	85.24 262	68.57 196	92.18 202	81.06 309

CLD-MVS		83.18 159	82.64 161	84.79 121	89.05 147	67.82 160	77.93 256	92.52 72	68.33 209	85.07 171	81.54 297	82.06 80	92.96 128	69.35 185	97.91 43	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DeepC-MVS_fast	80.27 8	86.23 94	85.65 105	87.96 72	91.30 109	76.92 89	87.19 87	91.99 84	70.56 191	84.96 172	90.69 169	80.01 104	95.14 40	78.37 114	95.78 113	91.82 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	80.37 7	84.80 118	84.71 122	85.06 115	86.36 219	74.71 105	88.77 66	90.00 152	75.65 132	84.96 172	93.17 99	74.06 163	91.19 169	78.28 117	91.09 210	89.29 210

QAPM		82.59 165	82.59 163	82.58 176	86.44 214	66.69 166	89.94 43	90.36 135	67.97 213	84.94 174	92.58 115	72.71 193	92.18 145	70.63 177	87.73 258	88.85 216

VPNet		80.25 193	81.68 173	75.94 259	92.46 77	47.98 319	76.70 266	81.67 241	73.45 152	84.87 175	92.82 108	74.66 157	86.51 249	61.66 236	96.85 71	93.33 119

NCCC		87.36 71	86.87 85	88.83 58	92.32 83	78.84 73	86.58 104	91.09 120	78.77 88	84.85 176	90.89 163	80.85 96	95.29 33	81.14 82	95.32 123	92.34 150

PHI-MVS		86.38 89	85.81 101	88.08 67	88.44 162	77.34 85	89.35 58	93.05 52	73.15 162	84.76 177	87.70 221	78.87 111	94.18 64	80.67 90	96.29 89	92.73 133

pmmvs-eth3d		78.42 204	77.04 210	82.57 178	87.44 193	74.41 107	80.86 222	79.67 251	55.68 285	84.69 178	90.31 180	60.91 243	85.42 261	62.20 231	91.59 206	87.88 226

view600		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 254	93.49 171	92.66 136

view800		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 254	93.49 171	92.66 136

conf0.05thres1000		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 254	93.49 171	92.66 136

tfpn		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 254	93.49 171	92.66 136

test_prior3		86.31 91	86.31 93	86.32 91	90.59 126	71.99 128	83.37 161	92.85 61	75.43 134	84.58 183	91.57 137	81.92 86	94.17 66	79.54 106	96.97 68	92.80 131

test_prior2							83.37 161		75.43 134	84.58 183	91.57 137	81.92 86		79.54 106	96.97 68

TEST9					92.34 81	79.70 66	83.94 139	90.32 136	65.41 236	84.49 185	90.97 159	82.03 82	93.63 86

train_agg		85.98 100	85.28 110	88.07 68	92.34 81	79.70 66	83.94 139	90.32 136	65.79 229	84.49 185	90.97 159	81.93 84	93.63 86	81.21 80	96.54 81	90.88 181

Gipuma		84.44 126	86.33 92	78.78 222	84.20 251	73.57 111	89.55 51	90.44 132	84.24 29	84.38 187	94.89 51	76.35 140	80.40 289	76.14 134	96.80 75	82.36 285
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_8					92.09 88	78.87 72	83.82 144	90.31 138	65.79 229	84.36 188	90.96 161	81.93 84	93.44 107

agg_prior1		85.72 104	85.20 111	87.28 81	91.58 102	77.69 80	83.69 150	90.30 140	66.29 224	84.32 189	91.07 156	82.13 79	93.18 118	81.02 83	96.36 87	90.98 176

agg_prior					91.58 102	77.69 80		90.30 140		84.32 189			93.18 118

LFMVS		80.15 196	80.56 186	78.89 220	89.19 146	55.93 264	85.22 119	73.78 280	82.96 40	84.28 191	92.72 112	57.38 264	90.07 203	63.80 224	95.75 114	90.68 187

Vis-MVSNet		86.86 79	86.58 89	87.72 73	92.09 88	77.43 84	87.35 84	92.09 81	78.87 86	84.27 192	94.05 81	78.35 115	93.65 84	80.54 92	91.58 207	92.08 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MCST-MVS		84.36 130	83.93 147	85.63 109	91.59 99	71.58 135	83.52 155	92.13 80	61.82 259	83.96 193	89.75 189	79.93 106	93.46 106	78.33 116	94.34 154	91.87 162

新几何1				82.95 170	93.96 44	78.56 75		80.24 247	55.45 286	83.93 194	91.08 155	71.19 209	88.33 228	65.84 214	93.07 184	81.95 292

BH-RMVSNet		80.53 190	80.22 191	81.49 193	87.19 203	66.21 168	77.79 258	86.23 206	74.21 145	83.69 195	88.50 205	73.25 186	90.75 182	63.18 229	87.90 255	87.52 228

1121		80.86 186	79.81 196	84.02 142	93.93 45	78.70 74	81.64 208	80.18 248	55.43 287	83.67 196	91.15 150	71.29 208	91.41 166	67.95 200	93.06 185	81.96 291

agg_prior3		85.76 103	84.95 116	88.16 66	92.43 78	79.92 62	83.98 138	90.03 151	65.11 238	83.66 197	90.64 174	81.00 95	93.67 83	81.21 80	96.54 81	90.88 181

USDC		76.63 222	76.73 213	76.34 256	83.46 259	57.20 257	80.02 229	88.04 181	52.14 305	83.65 198	91.25 144	63.24 237	86.65 248	54.66 281	94.11 156	85.17 250

Effi-MVS+-dtu		85.82 102	83.38 151	93.14 3	87.13 204	91.15 3	87.70 79	88.42 173	74.57 142	83.56 199	85.65 243	78.49 113	94.21 63	72.04 167	92.88 189	94.05 98

CNLPA		83.55 154	83.10 156	84.90 118	89.34 143	83.87 43	84.54 129	88.77 168	79.09 82	83.54 200	88.66 204	74.87 151	81.73 285	66.84 206	92.29 197	89.11 211

OpenMVS_ROB	70.19 17	77.77 209	77.46 207	78.71 224	84.39 247	61.15 230	81.18 217	82.52 233	62.45 256	83.34 201	87.37 226	66.20 225	88.66 226	64.69 219	85.02 283	86.32 239

conf200view11		75.62 232	75.05 230	77.34 245	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 291	90.99 214	91.34 175

thres100view900		75.45 233	75.05 230	76.66 253	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 291	90.99 214	89.31 208

IterMVS		76.91 216	76.34 220	78.64 225	80.91 279	64.03 181	76.30 271	79.03 252	64.88 241	83.11 204	89.16 196	59.90 250	84.46 270	68.61 195	85.15 282	87.42 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 229	75.35 229	77.85 238	87.01 209	51.84 296	80.45 225	73.26 285	75.20 137	83.10 205	86.31 239	45.54 304	89.05 215	55.03 279	92.24 199	92.66 136

mvs_anonymous		78.13 205	78.76 200	76.23 258	79.24 290	50.31 312	78.69 250	84.82 225	61.60 263	83.09 206	92.82 108	73.89 168	87.01 239	68.33 197	86.41 270	91.37 173

MVS_Test		82.47 167	83.22 153	80.22 207	82.62 267	57.75 254	82.54 184	91.96 86	71.16 187	82.89 207	92.52 117	77.41 124	90.50 190	80.04 100	87.84 257	92.40 147

test12				86.57 86	90.74 123	72.63 117		90.69 125		82.76 208		79.20 108	94.80 49		95.32 123	92.27 153

原ACMM1				84.60 127	92.81 71	74.01 109		91.50 97	62.59 253	82.73 209	90.67 171	76.53 138	94.25 61	69.24 186	95.69 116	85.55 247

test222					93.31 56	76.54 93	79.38 239	77.79 256	52.59 300	82.36 210	90.84 165	66.83 223			91.69 205	81.25 304

VNet		79.31 199	80.27 190	76.44 254	87.92 175	53.95 277	75.58 277	84.35 227	74.39 144	82.23 211	90.72 168	72.84 191	84.39 271	60.38 246	93.98 160	90.97 177

Vis-MVSNet (Re-imp)		77.82 207	77.79 206	77.92 236	88.82 152	51.29 300	83.28 163	71.97 295	74.04 146	82.23 211	89.78 188	57.38 264	89.41 209	57.22 265	95.41 120	93.05 126

API-MVS		82.28 169	82.61 162	81.30 195	86.29 221	69.79 143	88.71 67	87.67 186	78.42 93	82.15 213	84.15 269	77.98 117	91.59 160	65.39 216	92.75 191	82.51 284

DP-MVS Recon		84.05 143	83.22 153	86.52 88	91.73 97	75.27 102	83.23 167	92.40 74	72.04 178	82.04 214	88.33 211	77.91 119	93.95 76	66.17 210	95.12 130	90.34 197

MSDG		80.06 197	79.99 195	80.25 206	83.91 256	68.04 158	77.51 261	89.19 165	77.65 101	81.94 215	83.45 275	76.37 139	86.31 252	63.31 228	86.59 268	86.41 238

Fast-Effi-MVS+		81.04 184	80.57 185	82.46 180	87.50 192	63.22 195	78.37 252	89.63 159	68.01 211	81.87 216	82.08 292	82.31 77	92.65 138	67.10 202	88.30 252	91.51 171

testgi		72.36 257	74.61 233	65.59 304	80.56 283	42.82 332	68.29 306	73.35 284	66.87 221	81.84 217	89.93 185	72.08 202	66.92 323	46.05 316	92.54 194	87.01 234

tfpn200view9		74.86 240	74.23 238	76.74 252	86.24 222	52.12 290	79.24 242	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 291	90.99 214	89.31 208

thres400		75.14 234	74.23 238	77.86 237	86.24 222	52.12 290	79.24 242	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 291	90.99 214	92.66 136

OpenMVS	76.72 13	81.98 175	82.00 171	81.93 184	84.42 246	68.22 157	88.50 70	89.48 162	66.92 220	81.80 220	91.86 128	72.59 197	90.16 198	71.19 171	91.25 208	87.40 230

AdaColmap		83.66 151	83.69 149	83.57 158	90.05 136	72.26 123	86.29 108	90.00 152	78.19 96	81.65 221	87.16 227	83.40 65	94.24 62	61.69 235	94.76 146	84.21 262

DELS-MVS		81.44 178	81.25 179	82.03 183	84.27 248	62.87 201	76.47 270	92.49 73	70.97 189	81.64 222	83.83 270	75.03 148	92.70 135	74.29 143	92.22 201	90.51 193
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
114514_t		83.10 161	82.54 164	84.77 122	92.90 66	69.10 153	86.65 102	90.62 128	54.66 290	81.46 223	90.81 166	76.98 130	94.38 58	72.62 163	96.18 94	90.82 184

TR-MVS		76.77 221	75.79 223	79.72 212	86.10 227	65.79 171	77.14 263	83.02 230	65.20 237	81.40 224	82.10 291	66.30 224	90.73 184	55.57 274	85.27 279	82.65 279

TAMVS		78.08 206	76.36 219	83.23 165	90.62 125	72.87 115	79.08 246	80.01 250	61.72 261	81.35 225	86.92 230	63.96 233	88.78 223	50.61 296	93.01 187	88.04 222

Effi-MVS+		83.90 148	84.01 144	83.57 158	87.22 202	65.61 172	86.55 105	92.40 74	78.64 90	81.34 226	84.18 267	83.65 62	92.93 130	74.22 144	87.87 256	92.17 157

new-patchmatchnet		70.10 273	73.37 245	60.29 319	81.23 276	16.95 347	59.54 325	74.62 274	62.93 250	80.97 227	87.93 218	62.83 238	71.90 309	55.24 277	95.01 134	92.00 159

PVSNet_Blended_VisFu		81.55 177	80.49 188	84.70 125	91.58 102	73.24 114	84.21 132	91.67 93	62.86 251	80.94 228	87.16 227	67.27 221	92.87 133	69.82 182	88.94 243	87.99 223

BH-w/o		76.57 223	76.07 222	78.10 233	86.88 211	65.92 170	77.63 259	86.33 205	65.69 231	80.89 229	79.95 305	68.97 217	90.74 183	53.01 287	85.25 280	77.62 314

PAPM_NR		83.23 158	83.19 155	83.33 164	90.90 120	65.98 169	88.19 72	90.78 124	78.13 97	80.87 230	87.92 219	73.49 177	92.42 141	70.07 180	88.40 247	91.60 168

ab-mvs		79.67 198	80.56 186	76.99 247	88.48 161	56.93 258	84.70 125	86.06 208	68.95 207	80.78 231	93.08 100	75.30 145	84.62 269	56.78 267	90.90 220	89.43 207

tfpn1000		73.63 248	73.58 242	73.79 270	85.46 233	50.31 312	79.99 230	68.18 315	72.33 173	80.66 232	83.05 278	39.80 332	86.74 247	60.96 242	91.78 204	84.32 260

mvs-test1		84.55 123	82.12 168	91.84 17	87.13 204	89.54 5	85.05 121	88.42 173	74.57 142	80.60 233	82.98 280	78.49 113	93.98 73	72.04 167	89.77 235	92.00 159

XXY-MVS		74.44 244	76.19 221	69.21 291	84.61 242	52.43 289	71.70 297	77.18 259	60.73 268	80.60 233	90.96 161	75.44 142	69.35 315	56.13 270	88.33 248	85.86 245

HQP4-MVS										80.56 235			94.61 53			93.56 116

HQP-NCC					91.19 112		84.77 122		73.30 157	80.55 236

ACMP_Plane					91.19 112		84.77 122		73.30 157	80.55 236

HQP-MVS		84.61 121	84.06 143	86.27 93	91.19 112	70.66 140	84.77 122	92.68 67	73.30 157	80.55 236	90.17 183	72.10 200	94.61 53	77.30 127	94.47 152	93.56 116

HyFIR lowres test		75.12 236	72.66 251	82.50 179	91.44 108	65.19 173	72.47 294	87.31 190	46.79 326	80.29 239	84.30 266	52.70 280	92.10 148	51.88 295	86.73 266	90.22 198

test20.03		73.75 247	74.59 235	71.22 285	81.11 277	51.12 302	70.15 302	72.10 294	70.42 192	80.28 240	91.50 140	64.21 231	74.72 305	46.96 314	94.58 150	87.82 227

F-COLMAP		84.97 115	83.42 150	89.63 49	92.39 79	83.40 45	88.83 64	91.92 87	73.19 161	80.18 241	89.15 197	77.04 129	93.28 115	65.82 215	92.28 198	92.21 156

GA-MVS		75.83 230	74.61 233	79.48 218	81.87 270	59.25 246	73.42 292	82.88 231	68.68 208	79.75 242	81.80 294	50.62 284	89.46 207	66.85 205	85.64 276	89.72 204

xiu_mvs_v1_base_debu		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 233	79.59 243	82.73 286	76.94 131	90.14 199	73.22 156	88.33 248	86.90 235

xiu_mvs_v1_base		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 233	79.59 243	82.73 286	76.94 131	90.14 199	73.22 156	88.33 248	86.90 235

xiu_mvs_v1_base_debi		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 233	79.59 243	82.73 286	76.94 131	90.14 199	73.22 156	88.33 248	86.90 235

UnsupCasMVSNet_eth		71.63 263	72.30 256	69.62 289	76.47 310	52.70 287	70.03 303	80.97 245	59.18 272	79.36 246	88.21 213	60.50 244	69.12 316	58.33 260	77.62 319	87.04 233

MG-MVS		80.32 192	80.94 183	78.47 228	88.18 167	52.62 288	82.29 190	85.01 223	72.01 179	79.24 247	92.54 116	69.36 213	93.36 111	70.65 176	89.19 241	89.45 205

Fast-Effi-MVS+-dtu		82.54 166	81.41 177	85.90 104	85.60 229	76.53 95	83.07 169	89.62 160	73.02 164	79.11 248	83.51 273	80.74 98	90.24 195	68.76 192	89.29 238	90.94 178

CDS-MVSNet		77.32 212	75.40 227	83.06 168	89.00 149	72.48 120	77.90 257	82.17 236	60.81 266	78.94 249	83.49 274	59.30 254	88.76 224	54.64 282	92.37 196	87.93 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres200		72.34 258	71.55 262	74.70 266	83.48 258	51.60 297	75.02 281	73.71 281	70.14 196	78.56 250	80.57 301	46.20 296	88.20 230	46.99 313	89.29 238	84.32 260

tfpn_ndepth		72.54 255	72.30 256	73.24 273	84.81 240	51.42 298	79.24 242	70.49 308	69.26 202	78.48 251	79.80 306	40.16 331	86.77 245	58.08 263	90.43 231	81.53 300

testmv		70.47 270	70.70 265	69.77 288	86.22 224	53.89 278	67.32 311	71.91 296	63.32 246	78.16 252	89.47 193	56.12 269	73.10 306	36.43 332	87.33 261	82.33 286

jason		77.42 211	75.75 225	82.43 181	87.10 207	69.27 148	77.99 255	81.94 239	51.47 310	77.84 253	85.07 256	60.32 246	89.00 216	70.74 175	89.27 240	89.03 213
jason: jason.
MAR-MVS		80.24 194	78.74 201	84.73 124	86.87 212	78.18 76	85.75 112	87.81 185	65.67 232	77.84 253	78.50 311	73.79 169	90.53 189	61.59 238	90.87 221	85.49 249
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
FPMVS		72.29 259	72.00 258	73.14 275	88.63 157	85.00 32	74.65 284	67.39 318	71.94 180	77.80 255	87.66 222	50.48 285	75.83 301	49.95 298	79.51 311	58.58 337

pmmvs4		74.92 239	72.98 249	80.73 203	84.95 237	71.71 134	76.23 272	77.59 257	52.83 299	77.73 256	86.38 234	56.35 267	84.97 265	57.72 264	87.05 264	85.51 248

UnsupCasMVSNet_bld		69.21 281	69.68 273	67.82 298	79.42 287	51.15 301	67.82 310	75.79 266	54.15 292	77.47 257	85.36 253	59.26 255	70.64 311	48.46 306	79.35 313	81.66 297

MVS_0304		84.88 116	83.96 146	87.64 76	87.43 194	74.83 104	84.18 133	93.30 42	77.48 104	77.39 258	88.46 206	74.53 160	95.74 16	78.09 120	94.75 147	92.36 149

no-one		71.52 264	70.43 269	74.81 264	78.45 298	63.41 188	57.73 331	77.03 260	51.46 311	77.17 259	90.33 178	54.96 276	80.35 290	47.41 310	99.29 2	80.68 311

Anonymous20231206		71.38 265	71.88 259	69.88 286	86.31 220	54.37 274	70.39 301	74.62 274	52.57 301	76.73 260	88.76 201	59.94 249	72.06 308	44.35 319	93.23 183	83.23 275

CMPMVS	59.41 20	75.12 236	73.57 243	79.77 210	75.84 314	67.22 161	81.21 216	82.18 235	50.78 315	76.50 261	87.66 222	55.20 274	82.99 279	62.17 232	90.64 229	89.09 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 260	71.69 260	73.32 271	81.57 273	53.02 284	76.77 265	78.37 254	63.31 247	76.37 262	91.85 129	36.68 335	78.98 293	47.87 309	92.45 195	87.95 224

CVMVSNet		72.62 254	71.41 263	76.28 257	83.25 261	60.34 238	83.50 158	79.02 253	37.77 339	76.33 263	85.10 254	49.60 287	87.41 236	70.54 178	77.54 320	81.08 307

PLC	73.85 16	82.09 172	80.31 189	87.45 78	90.86 122	80.29 60	85.88 110	90.65 126	68.17 210	76.32 264	86.33 237	73.12 187	92.61 139	61.40 239	90.02 234	89.44 206
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 170	81.57 176	84.19 141	85.54 231	69.26 149	91.98 25	90.08 149	71.54 184	76.23 265	85.07 256	58.69 257	94.27 59	86.26 27	88.77 244	89.03 213

lupinMVS		76.37 226	74.46 236	82.09 182	85.54 231	69.26 149	76.79 264	80.77 246	50.68 317	76.23 265	82.82 284	58.69 257	88.94 217	69.85 181	88.77 244	88.07 220

PatchMatch-RL		74.48 242	73.22 246	78.27 231	87.70 187	85.26 30	75.92 273	70.09 309	64.34 243	76.09 267	81.25 299	65.87 228	78.07 294	53.86 284	83.82 291	71.48 325

MS-PatchMatch		70.93 267	70.22 270	73.06 276	81.85 271	62.50 210	73.82 290	77.90 255	52.44 302	75.92 268	81.27 298	55.67 271	81.75 284	55.37 276	77.70 318	74.94 319

CHOSEN 1792x2688		72.45 256	70.56 266	78.13 232	90.02 138	63.08 196	68.72 305	83.16 229	42.99 335	75.92 268	85.46 248	57.22 266	85.18 264	49.87 300	81.67 303	86.14 241

CR-MVSNet		74.00 245	73.04 248	76.85 250	79.58 285	62.64 204	82.58 181	76.90 261	50.50 318	75.72 270	92.38 118	48.07 291	84.07 272	68.72 194	82.91 297	83.85 264

RPMNet		76.06 228	75.79 223	76.85 250	79.58 285	62.64 204	82.58 181	71.75 299	74.80 139	75.72 270	92.59 113	48.69 289	84.07 272	73.48 154	82.91 297	83.85 264

PVSNet_BlendedMVS		78.80 202	77.84 205	81.65 192	84.43 244	63.41 188	79.49 238	90.44 132	61.70 262	75.43 272	87.07 229	69.11 215	91.44 164	60.68 244	92.24 199	90.11 202

PVSNet_Blended		76.49 225	75.40 227	79.76 211	84.43 244	63.41 188	75.14 280	90.44 132	57.36 278	75.43 272	78.30 312	69.11 215	91.44 164	60.68 244	87.70 259	84.42 258

PAPR		78.84 201	78.10 204	81.07 199	85.17 235	60.22 239	82.21 194	90.57 129	62.51 254	75.32 274	84.61 263	74.99 149	92.30 143	59.48 251	88.04 254	90.68 187

N_pmnet		70.20 271	68.80 278	74.38 267	80.91 279	84.81 35	59.12 328	76.45 265	55.06 288	75.31 275	82.36 289	55.74 270	54.82 340	47.02 312	87.24 263	83.52 268

cascas		76.29 227	74.81 232	80.72 204	84.47 243	62.94 199	73.89 289	87.34 189	55.94 284	75.16 276	76.53 319	63.97 232	91.16 170	65.00 217	90.97 218	88.06 221

Patchmatch-test1		72.75 253	72.61 252	73.19 274	81.62 272	55.86 265	78.89 248	71.37 301	61.73 260	74.93 277	82.15 290	60.46 245	81.80 283	59.68 249	82.63 301	81.92 293

1111		61.71 302	63.77 298	55.55 325	78.05 299	25.74 344	60.62 322	67.52 316	66.09 226	74.68 278	86.50 232	16.00 350	59.22 338	38.79 327	85.65 275	81.70 295

.test1245		48.02 319	54.41 318	28.84 332	78.05 299	25.74 344	60.62 322	67.52 316	66.09 226	74.68 278	86.50 232	16.00 350	59.22 338	38.79 327	1.47 345	1.55 344

xiu_mvs_v2_base		77.19 213	76.75 212	78.52 227	87.01 209	61.30 228	75.55 278	87.12 198	61.24 265	74.45 280	78.79 310	77.20 125	90.93 176	64.62 221	84.80 287	83.32 273

CANet		83.79 149	82.85 158	86.63 85	86.17 226	72.21 125	83.76 148	91.43 106	77.24 112	74.39 281	87.45 225	75.36 144	95.42 29	77.03 130	92.83 190	92.25 155

PS-MVSNAJ		77.04 215	76.53 218	78.56 226	87.09 208	61.40 226	75.26 279	87.13 196	61.25 264	74.38 282	77.22 316	76.94 131	90.94 175	64.63 220	84.83 286	83.35 272

MVP-Stereo		75.81 231	73.51 244	82.71 174	89.35 142	73.62 110	80.06 227	85.20 217	60.30 269	73.96 283	87.94 217	57.89 261	89.45 208	52.02 290	74.87 324	85.06 252
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 162	81.64 174	86.21 97	86.20 225	76.24 99	86.86 92	85.68 212	77.07 115	73.76 284	92.82 108	69.64 212	91.82 155	69.04 190	93.69 168	90.56 191
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
1112_ss		74.82 241	73.74 240	78.04 234	89.57 140	60.04 240	76.49 269	87.09 199	54.31 291	73.66 285	79.80 306	60.25 247	86.76 246	58.37 258	84.15 290	87.32 231

diffmvs		79.20 200	79.04 198	79.69 213	78.64 296	58.90 247	81.79 204	87.61 187	65.07 239	73.65 286	89.80 187	73.10 188	87.79 233	75.02 141	86.63 267	92.38 148

Test_1112_low_res		73.90 246	73.08 247	76.35 255	90.35 129	55.95 263	73.40 293	86.17 207	50.70 316	73.14 287	85.94 242	58.31 259	85.90 257	56.51 268	83.22 294	87.20 232

1314		73.22 249	72.56 254	75.20 262	80.41 284	57.84 252	81.64 208	85.36 215	51.68 308	73.10 288	76.65 318	61.45 241	85.19 263	63.54 225	79.21 315	82.59 280

Patchmatch-test		65.91 293	67.38 284	61.48 316	75.51 318	43.21 331	68.84 304	63.79 328	62.48 255	72.80 289	83.42 276	44.89 318	59.52 337	48.27 308	86.45 269	81.70 295

PatchmatchNet		69.71 277	68.83 277	72.33 281	77.66 303	53.60 279	79.29 240	69.99 310	57.66 277	72.53 290	82.93 283	46.45 295	80.08 292	60.91 243	72.09 329	83.31 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test1235678		65.57 295	65.73 292	65.06 307	82.84 266	50.90 304	62.90 319	69.26 312	57.17 281	72.36 291	83.04 279	46.02 298	70.10 312	32.79 337	85.24 281	74.19 321

tpm		67.95 284	68.08 283	67.55 299	78.74 295	43.53 330	75.60 276	67.10 323	54.92 289	72.23 292	88.10 214	42.87 324	75.97 300	52.21 289	80.95 309	83.15 276

pmmvs5		70.73 268	70.07 271	72.72 278	77.03 307	52.73 286	74.14 286	75.65 269	50.36 319	72.17 293	85.37 252	55.42 273	80.67 288	52.86 288	87.59 260	84.77 254

PatchT		70.52 269	72.76 250	63.79 310	79.38 288	33.53 340	77.63 259	65.37 326	73.61 150	71.77 294	92.79 111	44.38 320	75.65 302	64.53 222	85.37 278	82.18 288

MVS		73.21 250	72.59 253	75.06 263	80.97 278	60.81 235	81.64 208	85.92 210	46.03 329	71.68 295	77.54 313	68.47 218	89.77 204	55.70 273	85.39 277	74.60 320

testus		62.33 300	63.03 300	60.20 320	78.78 294	40.74 333	59.14 326	69.80 311	49.26 322	71.41 296	74.72 325	52.33 281	63.52 332	29.84 339	82.01 302	76.36 316

MIMVSNet		71.09 266	71.59 261	69.57 290	87.23 201	50.07 314	78.91 247	71.83 297	60.20 270	71.26 297	91.76 134	55.08 275	76.09 299	41.06 324	87.02 265	82.54 282

WTY-MVS		67.91 285	68.35 280	66.58 302	80.82 281	48.12 318	65.96 315	72.60 290	53.67 295	71.20 298	81.68 296	58.97 256	69.06 317	48.57 305	81.67 303	82.55 281

test0.0.03 1		64.66 297	64.36 296	65.57 305	75.03 324	46.89 320	64.69 317	61.58 334	62.43 257	71.18 299	77.54 313	43.41 321	68.47 318	40.75 325	82.65 299	81.35 301

CostFormer		69.98 276	68.68 279	73.87 268	77.14 305	50.72 310	79.26 241	74.51 276	51.94 307	70.97 300	84.75 261	45.16 317	87.49 235	55.16 278	79.23 314	83.40 271

LP		69.42 279	68.30 281	72.77 277	71.48 339	56.84 261	73.66 291	74.84 272	63.52 245	70.95 301	83.35 277	49.55 288	77.15 297	57.13 266	70.21 332	84.33 259

tpmvs		70.16 272	69.56 274	71.96 283	74.71 326	48.13 317	79.63 233	75.45 270	65.02 240	70.26 302	81.88 293	45.34 310	85.68 259	58.34 259	75.39 323	82.08 289

sss		66.92 288	67.26 286	65.90 303	77.23 304	51.10 303	64.79 316	71.72 300	52.12 306	70.13 303	80.18 303	57.96 260	65.36 330	50.21 297	81.01 308	81.25 304

tpm2		68.45 283	66.83 287	73.30 272	78.93 293	48.50 316	79.76 232	71.76 298	47.50 325	69.92 304	83.60 272	42.07 326	88.40 227	48.44 307	79.51 311	83.01 278

HY-MVS	64.64 18	73.03 251	72.47 255	74.71 265	83.36 260	54.19 275	82.14 197	81.96 237	56.76 283	69.57 305	86.21 240	60.03 248	84.83 268	49.58 302	82.65 299	85.11 251

PatchFormer-LS_test		67.91 285	66.49 291	72.17 282	75.29 321	51.85 295	75.68 274	73.62 283	57.23 280	68.64 306	68.13 335	42.19 325	82.76 280	64.06 223	73.51 326	81.89 294

tpm cat1		66.76 289	65.21 294	71.42 284	77.09 306	50.62 311	78.01 254	73.68 282	44.89 331	68.64 306	79.00 309	45.51 307	82.42 282	49.91 299	70.15 333	81.23 306

IB-MVS	62.13 19	71.64 262	68.97 276	79.66 215	80.80 282	62.26 218	73.94 288	76.90 261	63.27 248	68.63 308	76.79 317	33.83 338	91.84 153	59.28 252	87.26 262	84.88 253
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
EPNet		80.37 191	78.41 203	86.23 94	76.75 308	73.28 113	87.18 88	77.45 258	76.24 122	68.14 309	88.93 200	65.41 229	93.85 78	69.47 184	96.12 99	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 291	65.63 293	68.75 294	81.96 269	49.88 315	62.19 321	72.51 292	51.03 313	68.04 310	75.34 323	50.84 283	74.77 303	45.82 317	82.96 295	81.60 298

tpmrst		66.28 292	66.69 289	65.05 308	72.82 335	39.33 335	78.20 253	70.69 307	53.16 298	67.88 311	80.36 302	48.18 290	74.75 304	58.13 261	70.79 331	81.08 307

CANet_DTU		77.81 208	77.05 209	80.09 208	81.37 274	59.90 241	83.26 164	88.29 176	69.16 204	67.83 312	83.72 271	60.93 242	89.47 206	69.22 188	89.70 236	90.88 181

EPMVS		62.47 298	62.63 302	62.01 312	70.63 340	38.74 336	74.76 282	52.86 343	53.91 294	67.71 313	80.01 304	39.40 333	66.60 325	55.54 275	68.81 338	80.68 311

MDTV_nov1_ep13			68.29 282		78.03 301	43.87 329	74.12 287	72.22 293	52.17 303	67.02 314	85.54 244	45.36 309	80.85 287	55.73 271	84.42 289

tpmp4_e23		69.43 278	67.33 285	75.72 260	78.53 297	52.75 285	82.13 198	74.91 271	49.23 323	66.37 315	84.17 268	41.28 327	88.67 225	49.73 301	79.63 310	85.75 246

pmmvs3		62.47 298	60.02 311	69.80 287	71.58 338	64.00 182	70.52 300	58.44 338	39.77 337	66.05 316	75.84 320	27.10 347	72.28 307	46.15 315	84.77 288	73.11 323

ADS-MVSNet2		65.87 294	63.64 299	72.55 280	73.16 332	56.92 259	67.10 312	74.81 273	49.74 320	66.04 317	82.97 281	46.71 293	77.26 295	42.29 321	69.96 334	83.46 269

ADS-MVSNet		61.90 301	62.19 303	61.03 318	73.16 332	36.42 338	67.10 312	61.75 332	49.74 320	66.04 317	82.97 281	46.71 293	63.21 334	42.29 321	69.96 334	83.46 269

DWT-MVSNet_test		66.43 290	64.37 295	72.63 279	74.86 325	50.86 305	76.52 268	72.74 289	54.06 293	65.50 319	68.30 334	32.13 340	84.84 267	61.63 237	73.59 325	82.19 287

DSMNet-mixed		60.98 308	61.61 305	59.09 322	72.88 334	45.05 326	74.70 283	46.61 347	26.20 341	65.34 320	90.32 179	55.46 272	63.12 335	41.72 323	81.30 307	69.09 329

JIA-IIPM		69.41 280	66.64 290	77.70 239	73.19 331	71.24 137	75.67 275	65.56 325	70.42 192	65.18 321	92.97 104	33.64 339	83.06 278	53.52 286	69.61 336	78.79 313

test-LLR		67.21 287	66.74 288	68.63 295	76.45 311	55.21 270	67.89 307	67.14 321	62.43 257	65.08 322	72.39 327	43.41 321	69.37 313	61.00 240	84.89 284	81.31 302

test-mter		65.00 296	63.79 297	68.63 295	76.45 311	55.21 270	67.89 307	67.14 321	50.98 314	65.08 322	72.39 327	28.27 345	69.37 313	61.00 240	84.89 284	81.31 302

PMMVS2		55.64 315	59.27 312	44.74 330	64.30 345	12.32 348	40.60 339	49.79 346	53.19 297	65.06 324	84.81 260	53.60 279	49.76 342	32.68 338	89.41 237	72.15 324

gg-mvs-nofinetune		68.96 282	69.11 275	68.52 297	76.12 313	45.32 323	83.59 151	55.88 341	86.68 20	64.62 325	97.01 7	30.36 342	83.97 275	44.78 318	82.94 296	76.26 317

PAPM		71.77 261	70.06 272	76.92 248	86.39 215	53.97 276	76.62 267	86.62 203	53.44 296	63.97 326	84.73 262	57.79 262	92.34 142	39.65 326	81.33 306	84.45 257

new_pmnet		55.69 314	57.66 313	49.76 327	75.47 319	30.59 341	59.56 324	51.45 345	43.62 334	62.49 327	75.48 321	40.96 329	49.15 343	37.39 331	72.52 327	69.55 328

test2356		56.69 312	55.15 316	61.32 317	73.20 330	44.11 328	54.95 333	62.52 329	48.75 324	62.45 328	68.42 332	21.10 349	65.67 329	26.86 341	78.08 317	74.19 321

MDTV_nov1_ep13_2view						27.60 343	70.76 299		46.47 328	61.27 329		45.20 311		49.18 303		83.75 266

dp		60.70 309	60.29 310	61.92 314	72.04 337	38.67 337	70.83 298	64.08 327	51.28 312	60.75 330	77.28 315	36.59 336	71.58 310	47.41 310	62.34 340	75.52 318

TESTMET0.1,1		61.29 305	60.32 309	64.19 309	72.06 336	51.30 299	67.89 307	62.09 330	45.27 330	60.65 331	69.01 330	27.93 346	64.74 331	56.31 269	81.65 305	76.53 315

test12356		54.91 316	57.14 314	48.22 329	75.83 315	17.47 346	52.31 337	69.20 313	51.66 309	60.11 332	75.40 322	29.77 344	62.62 336	27.64 340	72.37 328	64.59 331

PMMVS		61.65 303	60.38 308	65.47 306	65.40 344	69.26 149	63.97 318	61.73 333	36.80 340	60.11 332	68.43 331	59.42 253	66.35 326	48.97 304	78.57 316	60.81 334

testpf		58.55 311	61.58 306	49.48 328	66.03 342	40.05 334	74.40 285	58.07 339	64.72 242	59.36 334	72.67 326	22.76 348	66.92 323	67.07 203	69.15 337	41.46 340

PVSNet_0	51.08 22	56.10 313	54.97 317	59.48 321	75.12 323	53.28 283	55.16 332	61.89 331	44.30 332	59.16 335	62.48 339	54.22 277	65.91 328	35.40 334	47.01 341	59.25 336

MVS-HIRNet		61.16 306	62.92 301	55.87 323	79.09 291	35.34 339	71.83 296	57.98 340	46.56 327	59.05 336	91.14 151	49.95 286	76.43 298	38.74 329	71.92 330	55.84 338

E-PMN		61.59 304	61.62 304	61.49 315	66.81 341	55.40 268	53.77 335	60.34 335	66.80 222	58.90 337	65.50 337	40.48 330	66.12 327	55.72 272	86.25 272	62.95 333

GG-mvs-BLEND				67.16 300	73.36 329	46.54 322	84.15 134	55.04 342		58.64 338	61.95 340	29.93 343	83.87 276	38.71 330	76.92 321	71.07 326

EPNet_dtu		72.87 252	71.33 264	77.49 244	77.72 302	60.55 237	82.35 188	75.79 266	66.49 223	58.39 339	81.06 300	53.68 278	85.98 255	53.55 285	92.97 188	85.95 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PNet_i23d		52.13 317	51.24 319	54.79 326	75.56 316	45.26 324	54.54 334	52.55 344	66.95 219	57.19 340	65.82 336	13.15 352	63.40 333	36.39 333	39.04 343	55.71 339

EMVS		61.10 307	60.81 307	61.99 313	65.96 343	55.86 265	53.10 336	58.97 337	67.06 218	56.89 341	63.33 338	40.98 328	67.03 322	54.79 280	86.18 273	63.08 332

CHOSEN 280x420		59.08 310	56.52 315	66.76 301	76.51 309	64.39 179	49.62 338	59.00 336	43.86 333	55.66 342	68.41 333	35.55 337	68.21 319	43.25 320	76.78 322	67.69 330

MVE	40.22 23	51.82 318	50.47 320	55.87 323	62.66 346	51.91 292	31.61 341	39.28 348	40.65 336	50.76 343	74.98 324	56.24 268	44.67 344	33.94 336	64.11 339	71.04 327
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 333	32.95 347	29.49 342		21.63 351	12.07 342	37.95 344	45.07 341	30.84 341	19.21 345	17.94 343	33.06 344	23.69 341

tmp_tt		20.25 322	24.50 323	7.49 334	4.47 348	8.70 349	34.17 340	25.16 350	1.00 343	32.43 345	18.49 342	39.37 334	9.21 346	21.64 342	43.75 342	4.57 342

testmvs		5.91 326	7.65 327	0.72 336	1.20 349	0.37 351	59.14 326	0.67 353	0.49 345	1.11 346	2.76 346	0.94 354	0.24 348	1.02 345	1.47 345	1.55 344

test123		6.27 325	8.08 326	0.84 335	1.11 350	0.57 350	62.90 319	0.82 352	0.54 344	1.07 347	2.75 347	1.26 353	0.30 347	1.04 344	1.26 347	1.66 343

cdsmvs_eth3d_5k		20.81 321	27.75 322	0.00 337	0.00 351	0.00 352	0.00 342	85.44 214	0.00 346	0.00 348	82.82 284	81.46 91	0.00 349	0.00 346	0.00 348	0.00 346

pcd_1.5k_mvsjas		6.41 324	8.55 325	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	76.94 131	0.00 349	0.00 346	0.00 348	0.00 346

pcd1.5k->3k		38.83 320	41.11 321	32.01 331	93.13 61	0.00 352	0.00 342	91.38 112	0.00 346	0.00 348	0.00 348	89.24 16	0.00 349	0.00 346	96.24 93	96.02 48

sosnet-low-res		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

sosnet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uncertanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

Regformer		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

ab-mvs-re		6.65 323	8.87 324	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	79.80 306	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

test_part1								93.93 25				87.19 36			97.61 54	91.48 172

test11111								93.79 29

sam_mvs1												46.11 297

sam_mvs												45.92 302

MTGPA								91.81 90

test_post1							78.85 249				3.13 344	45.19 312	80.13 291	58.11 262

test_post											3.10 345	45.43 308	77.22 296

patchmatchnet-post											81.71 295	45.93 301	87.01 239

MTMP								33.14 349

gm-plane-assit					75.42 320	44.97 327			52.17 303		72.36 329		87.90 231	54.10 283

test9_res														80.83 87	96.45 86	90.57 190

agg_prior2														79.68 104	96.16 95	90.22 198

test_prior4						78.97 71	84.59 127

test_prior				86.32 91	90.59 126	71.99 128		92.85 61					94.17 66			92.80 131

新几何2							81.72 207

旧先验1					91.97 91	71.77 130		81.78 240			91.84 130	73.92 167			93.65 170	83.61 267

无先验							82.81 175	85.62 213	58.09 275				91.41 166	67.95 200		84.48 256

原ACMM2							82.26 193

testdata2													86.43 251	63.52 226

segment_acmp												81.94 83

testdata1							79.62 234		73.95 148

plane_prior7					93.45 52	77.31 86

plane_prior6					92.61 72	76.54 93						74.84 152

plane_prior5								93.61 32					95.22 37	80.78 88	95.83 111	94.46 86

plane_prior4											92.95 105

plane_prior2							89.45 56		79.44 77

plane_prior1					92.83 70

plane_prior						76.42 96	87.15 89		75.94 127						95.03 133

n2								0.00 354

nn								0.00 354

door-mid								74.45 277

test11								91.46 103

door								72.57 291

HQP5-MVS						70.66 140

BP-MVS														77.30 127

HQP3-MVS								92.68 67							94.47 152

HQP2-MVS												72.10 200

NP-MVS					91.95 92	74.55 106					90.17 183

ACMMP++_ref															95.74 115

ACMMP++															97.35 59

Test By Simon												79.09 109

High-resolution multi-view benchmark