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 10	85.07 36	99.27 3	99.54 1

wuykxyi23d		88.46 62	88.80 60	87.44 80	90.96 120	93.03 1	85.85 113	81.96 237	74.58 143	98.58 2	97.29 5	87.73 31	87.31 239	82.84 67	99.41 1	81.99 299

DTE-MVSNet		89.98 38	91.91 11	84.21 140	96.51 7	57.84 253	88.93 65	92.84 63	91.92 2	96.16 3	96.23 20	86.95 39	95.99 6	79.05 112	98.57 16	98.80 6

PS-CasMVS		90.06 34	91.92 9	84.47 132	96.56 6	58.83 250	89.04 62	92.74 66	91.40 5	96.12 4	96.06 24	87.23 35	95.57 23	79.42 111	98.74 7	99.00 2

LCM-MVSNet-Re		83.48 156	85.06 113	78.75 224	85.94 235	55.75 268	80.05 230	94.27 13	76.47 122	96.09 5	94.54 63	83.31 67	89.75 207	59.95 249	94.89 138	90.75 188

PEN-MVS		90.03 36	91.88 12	84.48 131	96.57 5	58.88 249	88.95 63	93.19 47	91.62 4	96.01 6	96.16 22	87.02 38	95.60 22	78.69 115	98.72 10	98.97 3

V4		89.97 40	90.60 37	88.07 69	88.69 155	72.01 127	91.35 30	92.64 69	82.22 50	95.98 7	96.31 16	84.80 55	93.98 75	88.59 4	94.83 142	98.23 8

v52		89.97 40	90.60 37	88.07 69	88.69 155	72.01 127	91.35 30	92.64 69	82.22 50	95.97 8	96.31 16	84.82 53	93.98 75	88.59 4	94.83 142	98.23 8

CP-MVSNet		89.27 53	90.91 34	84.37 135	96.34 8	58.61 252	88.66 70	92.06 82	90.78 6	95.67 9	95.17 44	81.80 89	95.54 26	79.00 113	98.69 11	98.95 4

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 49	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 54	93.16 122	91.10 1	97.53 58	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 43	91.31 25	85.71 109	96.32 9	62.39 212	89.54 55	93.31 41	90.21 10	95.57 11	95.66 31	81.42 93	95.90 11	80.94 87	98.80 4	98.84 5

Anonymous20231211		90.14 31	91.88 12	84.92 118	94.75 35	64.47 179	90.13 38	92.97 57	91.68 3	95.35 12	98.79 2	93.19 3	91.76 159	71.67 172	98.40 21	98.52 7

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 61	80.37 59	91.91 27	93.11 49	81.10 62	95.32 13	97.24 6	72.94 190	94.85 50	85.07 36	97.78 46	97.26 23

anonymousdsp		89.73 46	88.88 57	92.27 7	89.82 140	86.67 12	90.51 35	90.20 148	69.87 206	95.06 14	96.14 23	84.28 58	93.07 129	87.68 13	96.34 89	97.09 30

wuyk23d		75.13 236	79.30 198	62.63 318	75.56 323	75.18 104	80.89 223	73.10 288	75.06 140	94.76 15	95.32 40	87.73 31	52.85 349	34.16 343	97.11 66	59.85 344

ACMH	76.49 14	89.34 52	91.14 28	83.96 146	92.50 77	70.36 143	89.55 53	93.84 29	81.89 56	94.70 16	95.44 39	90.69 10	88.31 231	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 75	87.45 75	86.45 90	92.52 76	69.19 153	87.84 80	88.05 180	81.66 58	94.64 17	96.53 14	65.94 228	94.75 52	83.02 64	96.83 74	95.41 67

mvs_tets		89.78 45	89.27 52	91.30 24	93.51 52	84.79 36	89.89 44	90.63 127	70.00 205	94.55 18	96.67 11	87.94 29	93.59 93	84.27 48	95.97 105	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 43	87.54 18	98.02 39	93.71 110

jajsoiax		89.41 50	88.81 59	91.19 28	93.38 56	84.72 37	89.70 46	90.29 143	69.27 209	94.39 20	96.38 15	86.02 50	93.52 103	83.96 51	95.92 108	95.34 68

test_0402		88.65 59	89.58 50	85.88 106	92.55 75	72.22 125	84.01 139	89.44 163	88.63 14	94.38 21	95.77 28	86.38 47	93.59 93	79.84 104	95.21 128	91.82 163

v7n		90.13 32	90.96 32	87.65 76	91.95 93	71.06 139	89.99 41	93.05 52	86.53 21	94.29 22	96.27 19	82.69 72	94.08 71	86.25 29	97.63 51	97.82 11

test_djsdf		89.62 47	89.01 54	91.45 21	92.36 81	82.98 49	91.98 25	90.08 149	71.54 192	94.28 23	96.54 13	81.57 91	94.27 61	86.26 27	96.49 85	97.09 30

v748		88.91 58	89.82 46	86.19 100	90.06 136	68.53 155	88.81 67	91.48 99	84.36 30	94.19 24	95.98 25	82.52 75	92.67 139	84.30 47	96.67 78	97.37 20

PS-MVSNAJss		88.31 63	87.90 67	89.56 51	93.31 57	77.96 78	87.94 77	91.97 85	70.73 198	94.19 24	96.67 11	76.94 132	94.57 57	83.07 62	96.28 91	96.15 42

APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 66	78.04 77	92.84 12	94.14 21	83.33 37	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 63	93.00 65	76.26 99	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 109	78.35 117	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 52	93.35 38	79.20 82	93.83 28	93.60 95	90.81 9	92.96 130	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 73	88.10 64	84.94 117	88.84 152	63.75 185	87.85 79	91.47 102	79.12 83	93.72 29	95.82 27	75.20 147	93.58 96	84.76 42	96.16 96	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 48	93.67 30	94.82 54	91.18 6	95.52 27	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 48	93.67 30	94.82 54	91.18 6	95.52 27	85.36 34	98.73 8	95.23 72

v12		87.15 76	87.91 66	84.84 120	88.69 155	63.52 188	87.58 82	91.46 103	78.74 91	93.57 32	95.66 31	74.94 151	93.57 97	84.50 45	96.08 101	97.43 18

V9		86.96 77	87.70 71	84.74 124	88.52 160	63.27 194	87.31 87	91.45 105	78.28 96	93.43 33	95.45 38	74.59 159	93.57 97	84.23 49	96.01 104	97.38 19

v11		86.96 77	87.78 70	84.51 129	88.50 161	62.60 208	87.21 88	91.63 94	78.08 100	93.40 34	95.56 36	75.07 148	93.57 97	84.46 46	96.08 101	97.36 21

V14		86.75 83	87.46 74	84.62 127	88.35 164	63.00 199	87.02 93	91.42 108	77.78 102	93.27 35	95.23 43	74.22 162	93.56 100	83.95 52	95.93 107	97.31 22

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 64	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 44	86.67 24	97.60 56	94.18 94

v15		86.56 86	87.25 78	84.51 129	88.15 171	62.72 204	86.72 103	91.40 110	77.38 107	93.11 37	95.00 48	73.93 167	93.55 101	83.67 56	95.86 111	97.26 23

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 75	94.51 11	75.79 131	92.94 38	94.96 49	88.36 20	95.01 46	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 56	89.88 44	86.22 96	91.63 99	77.07 89	89.82 45	93.77 30	78.90 87	92.88 39	92.29 124	86.11 49	90.22 198	86.24 30	97.24 63	91.36 175

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 61	94.05 23	79.03 86	92.87 40	93.74 93	90.60 12	95.21 41	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 68	86.07 32	98.48 19	97.22 26

test_part2					93.86 47	77.77 79				92.84 42

ESAPD		90.05 35	90.56 40	88.50 62	93.86 47	77.77 79	89.63 50	93.93 25	84.39 28	92.84 42	93.43 97	87.19 36	96.26 4	82.18 74	97.61 54	91.48 172

v10		86.54 87	87.10 79	84.84 120	88.16 170	63.28 193	86.64 105	92.20 79	75.42 138	92.81 44	94.50 64	74.05 165	94.06 72	83.88 53	96.28 91	97.17 28

v17		86.32 91	86.95 84	84.44 133	88.00 173	62.62 207	86.74 101	91.48 99	77.17 115	92.74 45	94.56 60	73.74 171	93.53 102	83.27 59	94.87 141	97.18 27

v8		86.22 96	86.83 88	84.36 136	87.82 181	62.35 213	86.42 108	91.33 113	76.78 120	92.73 46	94.48 65	73.41 179	93.72 84	83.10 61	95.41 121	97.01 33

nrg030		87.85 69	88.49 62	85.91 104	90.07 135	69.73 145	87.86 78	94.20 17	74.04 148	92.70 47	94.66 58	85.88 51	91.50 163	79.72 105	97.32 61	96.50 40

v16		86.24 94	86.85 87	84.43 134	87.96 175	62.59 209	86.73 102	91.48 99	77.17 115	92.67 48	94.55 61	73.63 172	93.52 103	83.26 60	94.16 156	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 44	92.60 49	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 44	89.97 43	89.27 54	94.76 34	79.86 64	86.76 99	92.78 65	78.78 89	92.51 50	93.64 94	88.13 27	93.84 82	84.83 41	97.55 57	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 50	95.13 45	90.65 11	95.34 34	88.06 10	98.15 34	95.95 51

K. test v3		85.14 110	84.73 120	86.37 91	91.13 117	69.63 147	85.45 118	76.68 264	84.06 34	92.44 52	96.99 8	62.03 240	94.65 54	80.58 93	93.24 183	94.83 80

v18		85.99 100	86.55 91	84.30 138	87.73 187	62.29 217	86.40 109	91.49 98	76.64 121	92.40 53	94.20 77	73.28 183	93.52 103	82.87 65	93.99 160	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 47	92.39 54	94.14 79	89.15 17	95.62 21	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 50	86.15 18	93.37 8	95.10 7	90.28 9	92.11 55	95.03 47	89.75 15	94.93 48	79.95 103	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 38	89.84 45	90.41 39	94.91 33	84.50 41	89.49 57	93.98 24	79.68 75	92.09 56	93.89 91	83.80 61	93.10 125	82.67 69	98.04 36	93.64 112

TranMVSNet+NR-MVSNet		87.86 68	88.76 61	85.18 115	94.02 43	64.13 181	84.38 133	91.29 114	84.88 27	92.06 57	93.84 92	86.45 45	93.73 83	73.22 158	98.66 12	97.69 12

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 64	91.81 90	84.07 32	92.00 58	94.40 69	86.63 41	95.28 37	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 32	92.00 58	94.40 69	86.63 41	95.28 37	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	77.65 103	91.97 60	94.89 51	88.38 19	95.45 30	89.27 3	97.87 45	93.27 120

FC-MVSNet-test		85.93 102	87.05 81	82.58 177	92.25 85	56.44 263	85.75 114	93.09 50	77.33 112	91.94 61	94.65 59	74.78 155	93.41 111	75.11 142	98.58 15	97.88 10

lessismore_v0				85.95 103	91.10 118	70.99 140		70.91 306		91.79 62	94.42 67	61.76 241	92.93 132	79.52 110	93.03 187	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 43	91.77 63	93.94 90	90.55 13	95.73 19	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 28	80.32 69	91.74 64	94.41 68	88.17 25	95.98 7	86.37 25	97.99 41	93.96 101

testing_2		84.36 131	84.64 129	83.50 164	86.74 214	63.97 184	84.56 130	90.31 138	66.22 233	91.62 65	94.55 61	75.88 142	91.95 151	77.02 133	94.89 138	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 40	91.54 66	94.25 75	87.67 33	95.51 29	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 53	91.47 67	93.96 86	88.35 21	95.56 24	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 42	91.02 29	95.43 25	84.66 39	90.65 34	93.29 44	77.00 118	91.47 67	93.96 86	88.35 21	95.56 24	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 53	91.40 69	94.17 78	87.51 34	95.87 12	87.74 11	97.76 47	93.99 99

ANet_high		83.17 161	85.68 105	75.65 264	81.24 282	45.26 331	79.94 233	92.91 59	83.83 35	91.33 70	96.88 10	80.25 103	85.92 264	68.89 193	95.89 109	95.76 53

APD-MVS		89.54 49	89.63 49	89.26 55	92.57 74	81.34 56	90.19 37	93.08 51	80.87 64	91.13 71	93.19 100	86.22 48	95.97 8	82.23 73	97.18 65	90.45 197
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 39	91.06 72	94.00 83	88.26 23	95.71 20	87.28 22	98.39 23	92.55 143

FIs		85.35 109	86.27 95	82.60 176	91.86 96	57.31 257	85.10 122	93.05 52	75.83 130	91.02 73	93.97 84	73.57 176	92.91 134	73.97 150	98.02 39	97.58 15

UniMVSNet_NR-MVSNet		86.84 81	87.06 80	86.17 101	92.86 70	67.02 164	82.55 185	91.56 95	83.08 41	90.92 74	91.82 134	78.25 117	93.99 73	74.16 147	98.35 24	97.49 16

DU-MVS		86.80 82	86.99 82	86.21 98	93.24 59	67.02 164	83.16 170	92.21 78	81.73 57	90.92 74	91.97 128	77.20 126	93.99 73	74.16 147	98.35 24	97.61 13

V42		83.47 157	83.37 153	83.75 151	83.16 270	63.33 192	81.31 215	90.23 147	69.51 208	90.91 76	90.81 168	74.16 163	92.29 146	80.06 101	90.22 239	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 55	90.88 77	94.21 76	87.75 30	95.87 12	87.60 16	97.71 50	93.83 103

WR-MVS		83.56 154	84.40 136	81.06 201	93.43 55	54.88 274	78.67 259	85.02 222	81.24 61	90.74 78	91.56 141	72.85 191	91.08 174	68.00 200	98.04 36	97.23 25

v1240		84.30 134	84.51 133	83.65 156	87.65 190	61.26 230	82.85 176	91.54 96	67.94 222	90.68 79	90.65 174	71.71 207	93.64 87	82.84 67	94.78 144	96.07 45

MIMVSNet1		83.63 153	84.59 130	80.74 203	94.06 42	62.77 203	82.72 181	84.53 226	77.57 105	90.34 80	95.92 26	76.88 138	85.83 266	61.88 235	97.42 59	93.62 113

LS3D		90.60 28	90.34 41	91.38 23	89.03 149	84.23 42	93.58 4	94.68 10	90.65 7	90.33 81	93.95 89	84.50 57	95.37 33	80.87 88	95.50 120	94.53 85

PMVS	80.48 6	90.08 33	90.66 36	88.34 65	96.71 2	92.97 2	90.31 36	89.57 161	88.51 15	90.11 82	95.12 46	90.98 8	88.92 220	77.55 125	97.07 67	83.13 286
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v7		84.81 118	85.00 115	84.23 139	88.15 171	63.27 194	83.79 149	91.39 111	71.10 196	90.07 83	91.28 145	74.04 166	93.63 88	81.48 81	93.67 170	95.79 52

v1921920		84.23 137	84.37 140	83.79 149	87.64 191	61.71 221	82.91 175	91.20 117	67.94 222	90.06 84	90.34 179	72.04 204	93.59 93	82.32 72	94.91 137	96.07 45

ITE_SJBPF				90.11 45	90.72 125	84.97 33		90.30 140	81.56 59	90.02 85	91.20 150	82.40 77	90.81 183	73.58 154	94.66 149	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 86	94.03 82	86.57 43	95.80 15	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 113	82.70 160	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 86	16.05 351	86.57 43	95.80 15	87.35 19	97.62 52	94.20 92

v1192		84.57 123	84.69 124	84.21 140	87.75 186	62.88 201	83.02 172	91.43 106	69.08 213	89.98 88	90.89 165	72.70 195	93.62 92	82.41 70	94.97 136	96.13 43

v6		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.26 146	72.33 175	89.94 89	91.19 151	73.30 182	93.29 114	80.26 98	93.26 180	95.62 60

v1neww		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.27 144	72.33 175	89.93 90	91.22 147	73.28 183	93.29 114	80.25 99	93.25 181	95.62 60

v7new		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.27 144	72.33 175	89.93 90	91.22 147	73.28 183	93.29 114	80.25 99	93.25 181	95.62 60

Regformer-2		86.74 84	86.08 99	88.73 59	84.18 259	79.20 69	83.52 157	89.33 164	83.33 37	89.92 92	85.07 258	83.23 68	93.16 122	83.39 57	92.72 193	93.83 103

pmmvs6		86.52 88	88.06 65	81.90 186	92.22 87	62.28 218	84.66 128	89.15 166	83.54 36	89.85 93	97.32 4	88.08 28	86.80 252	70.43 181	97.30 62	96.62 37

Regformer-4		86.41 89	85.71 104	88.52 61	84.27 255	77.57 83	84.07 137	88.00 182	82.82 44	89.84 94	85.48 248	82.06 81	92.77 136	83.83 55	91.04 219	95.22 74

v144192		84.24 136	84.41 135	83.71 155	87.59 192	61.57 226	82.95 174	91.03 121	67.82 225	89.80 95	90.49 177	73.28 183	93.51 106	81.88 79	94.89 138	96.04 47

v1144		84.54 126	84.72 122	84.00 144	87.67 189	62.55 210	82.97 173	90.93 123	70.32 202	89.80 95	90.99 160	73.50 177	93.48 107	81.69 80	94.65 150	95.97 49

DeepC-MVS	82.31 4	89.15 55	89.08 53	89.37 53	93.64 51	79.07 70	88.54 71	94.20 17	73.53 153	89.71 97	94.82 54	85.09 52	95.77 17	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 66	86.93 85	91.22 27	90.08 134	89.30 6	89.68 48	91.11 119	79.26 81	89.68 98	94.81 57	82.44 76	87.74 236	76.54 135	88.74 253	96.61 38

FMVSNet1		84.55 124	85.45 109	81.85 189	90.27 132	61.05 233	86.83 96	88.27 177	78.57 93	89.66 99	95.64 33	75.43 144	90.68 187	69.09 191	95.33 123	93.82 105

v1141		84.16 139	84.38 137	83.52 161	87.32 198	61.70 223	82.79 178	89.74 155	71.90 189	89.64 100	91.12 154	72.68 196	93.10 125	80.39 97	93.80 165	95.75 54

divwei89l23v2f112		84.16 139	84.38 137	83.52 161	87.32 198	61.70 223	82.79 178	89.74 155	71.90 189	89.64 100	91.12 154	72.68 196	93.10 125	80.40 95	93.81 164	95.75 54

v1		84.16 139	84.38 137	83.52 161	87.33 197	61.71 221	82.79 178	89.73 157	71.89 191	89.64 100	91.11 156	72.72 193	93.10 125	80.40 95	93.79 166	95.75 54

IterMVS-LS		84.73 120	84.98 116	83.96 146	87.35 196	63.66 186	83.25 167	89.88 154	76.06 125	89.62 103	92.37 123	73.40 181	92.52 142	78.16 120	94.77 146	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 61	89.01 54	87.36 81	91.30 110	77.50 84	87.55 83	92.97 57	87.95 17	89.62 103	92.87 109	84.56 56	93.89 79	77.65 124	96.62 79	90.70 189

UniMVSNet (Re)		86.87 79	86.98 83	86.55 88	93.11 63	68.48 156	83.80 148	92.87 60	80.37 67	89.61 105	91.81 135	77.72 121	94.18 66	75.00 144	98.53 17	96.99 34

IS-MVSNet		86.66 85	86.82 89	86.17 101	92.05 91	66.87 166	91.21 33	88.64 171	86.30 23	89.60 106	92.59 115	69.22 215	94.91 49	73.89 151	97.89 44	96.72 35

v2v482		84.09 142	84.24 142	83.62 157	87.13 205	61.40 227	82.71 182	89.71 158	72.19 179	89.55 107	91.41 144	70.70 212	93.20 119	81.02 85	93.76 167	96.25 41

Baseline_NR-MVSNet		84.00 146	85.90 101	78.29 231	91.47 108	53.44 282	82.29 192	87.00 201	79.06 85	89.55 107	95.72 30	77.20 126	86.14 262	72.30 167	98.51 18	95.28 70

CSCG		86.26 93	86.47 92	85.60 111	90.87 122	74.26 109	87.98 76	91.85 88	80.35 68	89.54 109	88.01 217	79.09 110	92.13 148	75.51 139	95.06 133	90.41 198

ambc				82.98 170	90.55 129	64.86 176	88.20 73	89.15 166		89.40 110	93.96 86	71.67 208	91.38 170	78.83 114	96.55 81	92.71 134

DeepPCF-MVS	81.24 5	87.28 74	86.21 97	90.49 38	91.48 107	84.90 34	83.41 162	92.38 76	70.25 203	89.35 111	90.68 172	82.85 71	94.57 57	79.55 107	95.95 106	92.00 159

Regformer-1		86.00 98	85.50 108	87.49 78	84.18 259	76.90 91	83.52 157	87.94 184	82.18 52	89.19 112	85.07 258	82.28 79	91.89 154	82.40 71	92.72 193	93.69 111

HSP-MVS		88.63 60	87.84 68	91.02 29	95.76 16	86.14 19	92.75 13	91.01 122	78.43 94	89.16 113	92.25 126	72.03 205	96.36 2	88.21 9	90.93 226	90.55 195

XVG-OURS		89.18 54	88.83 58	90.23 43	94.28 41	86.11 20	85.91 111	93.60 34	80.16 71	89.13 114	93.44 96	83.82 60	90.98 176	83.86 54	95.30 127	93.60 114

Test4		81.31 180	81.13 182	81.88 188	84.89 245	63.05 198	82.37 189	90.50 130	62.75 260	89.00 115	88.29 214	67.55 221	91.68 160	73.55 155	91.24 216	90.89 183

MDA-MVSNet-bldmvs		77.47 211	76.90 212	79.16 220	79.03 299	64.59 177	66.58 322	75.67 268	73.15 164	88.86 116	88.99 201	66.94 223	81.23 294	64.71 220	88.22 260	91.64 167

EG-PatchMatch MVS		84.08 143	84.11 143	83.98 145	92.22 87	72.61 119	82.20 198	87.02 200	72.63 170	88.86 116	91.02 159	78.52 113	91.11 173	73.41 157	91.09 217	88.21 222

3Dnovator+	83.92 2	89.97 40	89.66 48	90.92 32	91.27 112	81.66 54	91.25 32	94.13 22	88.89 12	88.83 118	94.26 74	77.55 124	95.86 14	84.88 39	95.87 110	95.24 71

EI-MVSNet-UG-set		85.04 113	84.44 134	86.85 84	83.87 264	72.52 120	83.82 146	85.15 219	80.27 70	88.75 119	85.45 251	79.95 106	91.90 153	81.92 78	90.80 230	96.13 43

EI-MVSNet-Vis-set		85.12 111	84.53 132	86.88 83	84.01 261	72.76 117	83.91 144	85.18 218	80.44 66	88.75 119	85.49 247	80.08 104	91.92 152	82.02 76	90.85 229	95.97 49

OMC-MVS		88.19 64	87.52 73	90.19 44	91.94 95	81.68 53	87.49 85	93.17 48	76.02 127	88.64 121	91.22 147	84.24 59	93.37 112	77.97 123	97.03 68	95.52 65

Regformer-3		85.06 112	84.67 125	86.22 96	84.27 255	73.43 113	84.07 137	85.26 216	80.77 65	88.62 122	85.48 248	80.56 101	90.39 194	81.99 77	91.04 219	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	88.53 123	97.64 3	83.45 65	94.55 59	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 46	88.52 124	94.37 72	86.74 40	95.41 32	86.32 26	98.21 32	93.19 124

canonicalmvs		85.50 107	86.14 98	83.58 158	87.97 174	67.13 163	87.55 83	94.32 12	73.44 155	88.47 125	87.54 226	86.45 45	91.06 175	75.76 138	93.76 167	92.54 144

NR-MVSNet		86.00 98	86.22 96	85.34 113	93.24 59	64.56 178	82.21 196	90.46 131	80.99 63	88.42 126	91.97 128	77.56 123	93.85 80	72.46 166	98.65 13	97.61 13

alignmvs		83.94 148	83.98 146	83.80 148	87.80 185	67.88 160	84.54 131	91.42 108	73.27 162	88.41 127	87.96 218	72.33 200	90.83 182	76.02 137	94.11 157	92.69 135

TransMVSNet (Re)		84.02 145	85.74 103	78.85 222	91.00 119	55.20 273	82.29 192	87.26 191	79.65 76	88.38 128	95.52 37	83.00 69	86.88 244	67.97 201	96.60 80	94.45 88

PM-MVS		80.20 196	79.00 200	83.78 150	88.17 169	86.66 13	81.31 215	66.81 330	69.64 207	88.33 129	90.19 183	64.58 231	83.63 285	71.99 171	90.03 240	81.06 318

v148		82.31 169	82.48 166	81.81 192	85.59 237	59.66 243	81.47 213	86.02 209	72.85 167	88.05 130	90.65 174	70.73 211	90.91 180	75.15 141	91.79 204	94.87 77

AllTest		87.97 67	87.40 77	89.68 47	91.59 100	83.40 45	89.50 56	95.44 5	79.47 77	88.00 131	93.03 103	82.66 73	91.47 164	70.81 174	96.14 98	94.16 95

TestCases				89.68 47	91.59 100	83.40 45		95.44 5	79.47 77	88.00 131	93.03 103	82.66 73	91.47 164	70.81 174	96.14 98	94.16 95

pm-mvs1		83.69 151	84.95 117	79.91 210	90.04 138	59.66 243	82.43 187	87.44 188	75.52 135	87.85 133	95.26 42	81.25 95	85.65 268	68.74 195	96.04 103	94.42 89

PCF-MVS	74.62 15	82.15 172	80.92 185	85.84 107	89.43 142	72.30 123	80.53 226	91.82 89	57.36 286	87.81 134	89.92 188	77.67 122	93.63 88	58.69 261	95.08 132	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 180	81.61 176	80.41 206	86.38 217	58.75 251	83.93 143	86.58 204	72.43 171	87.65 135	92.98 105	63.78 235	90.22 198	66.86 206	93.92 162	92.27 153

VPA-MVSNet		83.47 157	84.73 120	79.69 214	90.29 131	57.52 256	81.30 217	88.69 170	76.29 123	87.58 136	94.44 66	80.60 100	87.20 240	66.60 210	96.82 75	94.34 91

DI_MVS_plusplus_test		81.27 182	81.26 179	81.29 197	84.98 243	61.65 225	81.98 201	87.25 192	63.56 252	87.56 137	89.60 193	73.62 173	91.83 156	72.20 168	90.59 237	90.38 199

CPTT-MVS		89.39 51	88.98 56	90.63 36	95.09 29	86.95 10	92.09 23	92.30 77	79.74 74	87.50 138	92.38 120	81.42 93	93.28 117	83.07 62	97.24 63	91.67 166

VDDNet		84.35 133	85.39 110	81.25 198	95.13 28	59.32 246	85.42 119	81.11 243	86.41 22	87.41 139	96.21 21	73.61 175	90.61 190	66.33 211	96.85 72	93.81 108

test_normal		81.23 184	81.16 181	81.43 195	84.77 248	61.99 220	81.46 214	86.95 202	63.16 257	87.22 140	89.63 192	73.62 173	91.65 161	72.92 163	90.70 232	90.65 192

VDD-MVS		84.23 137	84.58 131	83.20 167	91.17 116	65.16 175	83.25 167	84.97 224	79.79 73	87.18 141	94.27 73	74.77 156	90.89 181	69.24 188	96.54 82	93.55 118

MSLP-MVS++		85.00 115	86.03 100	81.90 186	91.84 97	71.56 137	86.75 100	93.02 56	75.95 128	87.12 142	89.39 196	77.98 118	89.40 212	77.46 126	94.78 144	84.75 262

YYNet1		70.06 281	70.44 274	68.90 299	73.76 335	53.42 283	58.99 337	67.20 326	58.42 282	87.10 143	85.39 253	59.82 252	67.32 328	59.79 250	83.50 300	85.96 245

MDA-MVSNet_test_wron		70.05 282	70.44 274	68.88 300	73.84 334	53.47 281	58.93 338	67.28 325	58.43 281	87.09 144	85.40 252	59.80 253	67.25 329	59.66 252	83.54 299	85.92 247

CNVR-MVS		87.81 70	87.68 72	88.21 66	92.87 68	77.30 88	85.25 120	91.23 116	77.31 113	87.07 145	91.47 143	82.94 70	94.71 53	84.67 43	96.27 93	92.62 142

EPP-MVSNet		85.47 108	85.04 114	86.77 85	91.52 106	69.37 148	91.63 28	87.98 183	81.51 60	87.05 146	91.83 133	66.18 227	95.29 35	70.75 176	96.89 71	95.64 59

TinyColmap		81.25 183	82.34 168	77.99 236	85.33 241	60.68 237	82.32 191	88.33 175	71.26 194	86.97 147	92.22 127	77.10 129	86.98 243	62.37 232	95.17 130	86.31 243

Patchmatch-RL test		74.48 243	73.68 242	76.89 250	84.83 246	66.54 168	72.29 303	69.16 314	57.70 284	86.76 148	86.33 239	45.79 304	82.59 289	69.63 185	90.65 235	81.54 308

XVG-OURS-SEG-HR		89.59 48	89.37 51	90.28 42	94.47 39	85.95 21	86.84 95	93.91 27	80.07 72	86.75 149	93.26 99	93.64 2	90.93 178	84.60 44	90.75 231	93.97 100

HPM-MVS++		88.93 57	88.45 63	90.38 40	94.92 32	85.85 25	89.70 46	91.27 115	78.20 97	86.69 150	92.28 125	80.36 102	95.06 45	86.17 31	96.49 85	90.22 201

TSAR-MVS + MP.		88.14 65	87.82 69	89.09 57	95.72 19	76.74 93	92.49 20	91.19 118	67.85 224	86.63 151	94.84 53	79.58 108	95.96 9	87.62 14	94.50 152	94.56 81

EI-MVSNet		82.61 165	82.42 167	83.20 167	83.25 268	63.66 186	83.50 160	85.07 220	76.06 125	86.55 152	85.10 256	73.41 179	90.25 195	78.15 121	90.67 233	95.68 58

HQP_MVS		87.75 71	87.43 76	88.70 60	93.45 53	76.42 97	89.45 58	93.61 32	79.44 79	86.55 152	92.95 107	74.84 153	95.22 39	80.78 90	95.83 112	94.46 86

plane_prior3						76.85 92			77.79 101	86.55 152

BH-untuned		80.96 186	80.99 183	80.84 202	88.55 159	68.23 157	80.33 228	88.46 172	72.79 168	86.55 152	86.76 233	74.72 157	91.77 158	61.79 236	88.99 249	82.52 292

MVSTER		77.09 215	75.70 227	81.25 198	75.27 329	61.08 232	77.49 270	85.07 220	60.78 275	86.55 152	88.68 205	43.14 324	90.25 195	73.69 153	90.67 233	92.42 145

旧先验2							81.73 208		56.88 290	86.54 157			84.90 274	72.81 164

semantic-postprocess				84.34 137	83.93 262	69.66 146		81.09 244	72.43 171	86.47 158	90.19 183	57.56 264	93.15 124	77.45 127	86.39 278	90.22 201

CDPH-MVS		86.17 97	85.54 107	88.05 72	92.25 85	75.45 102	83.85 145	92.01 83	65.91 236	86.19 159	91.75 137	83.77 62	94.98 47	77.43 128	96.71 77	93.73 109

MVS_111021_LR		84.28 135	83.76 149	85.83 108	89.23 146	83.07 48	80.99 222	83.56 228	72.71 169	86.07 160	89.07 200	81.75 90	86.19 261	77.11 131	93.36 176	88.24 221

GBi-Net		82.02 174	82.07 170	81.85 189	86.38 217	61.05 233	86.83 96	88.27 177	72.43 171	86.00 161	95.64 33	63.78 235	90.68 187	65.95 213	93.34 177	93.82 105

test1		82.02 174	82.07 170	81.85 189	86.38 217	61.05 233	86.83 96	88.27 177	72.43 171	86.00 161	95.64 33	63.78 235	90.68 187	65.95 213	93.34 177	93.82 105

FMVSNet3		78.80 203	78.55 203	79.57 217	82.89 272	56.89 261	81.76 207	85.77 211	69.04 214	86.00 161	90.44 178	51.75 283	90.09 204	65.95 213	93.34 177	91.72 165

tfpnnormal		81.79 177	82.95 158	78.31 230	88.93 151	55.40 269	80.83 225	82.85 232	76.81 119	85.90 164	94.14 79	74.58 160	86.51 257	66.82 209	95.68 118	93.01 127

TAPA-MVS	77.73 12	85.71 106	84.83 119	88.37 64	88.78 154	79.72 65	87.15 91	93.50 35	69.17 211	85.80 165	89.56 194	80.76 98	92.13 148	73.21 162	95.51 119	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 147	82.69 161	87.72 74	89.27 145	81.45 55	83.72 151	81.58 242	74.73 142	85.66 166	86.06 243	72.56 199	92.69 138	75.44 140	95.21 128	89.01 218

EU-MVSNet		75.12 237	74.43 238	77.18 247	83.11 271	59.48 245	85.71 116	82.43 234	39.76 346	85.64 167	88.76 203	44.71 320	87.88 234	73.86 152	85.88 281	84.16 270

LF4IMVS		82.75 164	81.93 173	85.19 114	82.08 275	80.15 61	85.53 117	88.76 169	68.01 219	85.58 168	87.75 222	71.80 206	86.85 245	74.02 149	93.87 163	88.58 220

Patchmtry		76.56 225	77.46 208	73.83 276	79.37 296	46.60 328	82.41 188	76.90 261	73.81 151	85.56 169	92.38 120	48.07 292	83.98 282	63.36 229	95.31 126	90.92 182

MVS_111021_HR		84.63 121	84.34 141	85.49 112	90.18 133	75.86 101	79.23 253	87.13 196	73.35 156	85.56 169	89.34 197	83.60 64	90.50 192	76.64 134	94.05 159	90.09 206

testdata				79.54 218	92.87 68	72.34 122		80.14 249	59.91 279	85.47 171	91.75 137	67.96 220	85.24 270	68.57 198	92.18 203	81.06 318

CLD-MVS		83.18 160	82.64 162	84.79 122	89.05 148	67.82 161	77.93 264	92.52 72	68.33 217	85.07 172	81.54 305	82.06 81	92.96 130	69.35 187	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 95	85.65 106	87.96 73	91.30 110	76.92 90	87.19 89	91.99 84	70.56 199	84.96 173	90.69 171	80.01 105	95.14 42	78.37 116	95.78 114	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	84.80 119	84.71 123	85.06 116	86.36 220	74.71 106	88.77 68	90.00 152	75.65 134	84.96 173	93.17 101	74.06 164	91.19 171	78.28 119	91.09 217	89.29 213

QAPM		82.59 166	82.59 164	82.58 177	86.44 215	66.69 167	89.94 43	90.36 135	67.97 221	84.94 175	92.58 117	72.71 194	92.18 147	70.63 179	87.73 265	88.85 219

VPNet		80.25 194	81.68 174	75.94 262	92.46 78	47.98 326	76.70 274	81.67 241	73.45 154	84.87 176	92.82 110	74.66 158	86.51 257	61.66 238	96.85 72	93.33 119

NCCC		87.36 72	86.87 86	88.83 58	92.32 84	78.84 73	86.58 106	91.09 120	78.77 90	84.85 177	90.89 165	80.85 97	95.29 35	81.14 84	95.32 124	92.34 150

PHI-MVS		86.38 90	85.81 102	88.08 68	88.44 163	77.34 86	89.35 60	93.05 52	73.15 164	84.76 178	87.70 223	78.87 112	94.18 66	80.67 92	96.29 90	92.73 133

pmmvs-eth3d		78.42 205	77.04 211	82.57 179	87.44 194	74.41 108	80.86 224	79.67 251	55.68 293	84.69 179	90.31 182	60.91 244	85.42 269	62.20 233	91.59 207	87.88 229

view600		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 262	93.49 172	92.66 136

view800		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 262	93.49 172	92.66 136

conf0.05thres1000		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 262	93.49 172	92.66 136

tfpn		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 262	93.49 172	92.66 136

test_prior3		86.31 92	86.31 94	86.32 92	90.59 127	71.99 129	83.37 163	92.85 61	75.43 136	84.58 184	91.57 139	81.92 87	94.17 68	79.54 108	96.97 69	92.80 131

test_prior2							83.37 163		75.43 136	84.58 184	91.57 139	81.92 87		79.54 108	96.97 69

TEST9					92.34 82	79.70 66	83.94 141	90.32 136	65.41 244	84.49 186	90.97 161	82.03 83	93.63 88

train_agg		85.98 101	85.28 111	88.07 69	92.34 82	79.70 66	83.94 141	90.32 136	65.79 237	84.49 186	90.97 161	81.93 85	93.63 88	81.21 82	96.54 82	90.88 184

Gipuma		84.44 127	86.33 93	78.78 223	84.20 258	73.57 112	89.55 53	90.44 132	84.24 31	84.38 188	94.89 51	76.35 141	80.40 297	76.14 136	96.80 76	82.36 294
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_8					92.09 89	78.87 72	83.82 146	90.31 138	65.79 237	84.36 189	90.96 163	81.93 85	93.44 109

agg_prior1		85.72 105	85.20 112	87.28 82	91.58 103	77.69 81	83.69 152	90.30 140	66.29 232	84.32 190	91.07 158	82.13 80	93.18 120	81.02 85	96.36 88	90.98 179

agg_prior					91.58 103	77.69 81		90.30 140		84.32 190			93.18 120

LFMVS		80.15 197	80.56 187	78.89 221	89.19 147	55.93 265	85.22 121	73.78 280	82.96 42	84.28 192	92.72 114	57.38 265	90.07 205	63.80 226	95.75 115	90.68 190

Vis-MVSNet		86.86 80	86.58 90	87.72 74	92.09 89	77.43 85	87.35 86	92.09 81	78.87 88	84.27 193	94.05 81	78.35 116	93.65 86	80.54 94	91.58 208	92.08 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MCST-MVS		84.36 131	83.93 148	85.63 110	91.59 100	71.58 136	83.52 157	92.13 80	61.82 267	83.96 194	89.75 191	79.93 107	93.46 108	78.33 118	94.34 155	91.87 162

新几何1				82.95 171	93.96 44	78.56 75		80.24 247	55.45 294	83.93 195	91.08 157	71.19 210	88.33 230	65.84 216	93.07 185	81.95 301

BH-RMVSNet		80.53 191	80.22 192	81.49 194	87.19 204	66.21 169	77.79 266	86.23 206	74.21 147	83.69 196	88.50 207	73.25 187	90.75 184	63.18 231	87.90 262	87.52 231

1121		80.86 187	79.81 197	84.02 143	93.93 45	78.70 74	81.64 210	80.18 248	55.43 295	83.67 197	91.15 152	71.29 209	91.41 168	67.95 202	93.06 186	81.96 300

agg_prior3		85.76 104	84.95 117	88.16 67	92.43 79	79.92 62	83.98 140	90.03 151	65.11 246	83.66 198	90.64 176	81.00 96	93.67 85	81.21 82	96.54 82	90.88 184

USDC		76.63 223	76.73 214	76.34 257	83.46 266	57.20 258	80.02 231	88.04 181	52.14 313	83.65 199	91.25 146	63.24 238	86.65 256	54.66 289	94.11 157	85.17 253

Effi-MVS+-dtu		85.82 103	83.38 152	93.14 3	87.13 205	91.15 3	87.70 81	88.42 173	74.57 144	83.56 200	85.65 245	78.49 114	94.21 65	72.04 169	92.88 190	94.05 98

CNLPA		83.55 155	83.10 157	84.90 119	89.34 144	83.87 43	84.54 131	88.77 168	79.09 84	83.54 201	88.66 206	74.87 152	81.73 293	66.84 208	92.29 198	89.11 214

OpenMVS_ROB	70.19 17	77.77 210	77.46 208	78.71 225	84.39 254	61.15 231	81.18 219	82.52 233	62.45 264	83.34 202	87.37 228	66.20 226	88.66 228	64.69 221	85.02 290	86.32 242

conf200view11		75.62 233	75.05 231	77.34 246	87.27 200	51.88 294	81.07 220	73.26 285	75.68 132	83.25 203	86.37 237	45.54 305	88.80 221	51.98 299	90.99 221	91.34 176

thres100view900		75.45 234	75.05 231	76.66 254	87.27 200	51.88 294	81.07 220	73.26 285	75.68 132	83.25 203	86.37 237	45.54 305	88.80 221	51.98 299	90.99 221	89.31 211

IterMVS		76.91 217	76.34 221	78.64 226	80.91 286	64.03 182	76.30 279	79.03 252	64.88 249	83.11 205	89.16 198	59.90 251	84.46 278	68.61 197	85.15 289	87.42 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 230	75.35 230	77.85 239	87.01 210	51.84 297	80.45 227	73.26 285	75.20 139	83.10 206	86.31 241	45.54 305	89.05 217	55.03 287	92.24 200	92.66 136

mvs_anonymous		78.13 206	78.76 201	76.23 259	79.24 297	50.31 313	78.69 258	84.82 225	61.60 271	83.09 207	92.82 110	73.89 169	87.01 241	68.33 199	86.41 277	91.37 174

MVS_Test		82.47 168	83.22 154	80.22 208	82.62 274	57.75 255	82.54 186	91.96 86	71.16 195	82.89 208	92.52 119	77.41 125	90.50 192	80.04 102	87.84 264	92.40 147

test12				86.57 87	90.74 124	72.63 118		90.69 125		82.76 209		79.20 109	94.80 51		95.32 124	92.27 153

原ACMM1				84.60 128	92.81 72	74.01 110		91.50 97	62.59 261	82.73 210	90.67 173	76.53 139	94.25 63	69.24 188	95.69 117	85.55 250

test222					93.31 57	76.54 94	79.38 247	77.79 256	52.59 308	82.36 211	90.84 167	66.83 224			91.69 206	81.25 313

VNet		79.31 200	80.27 191	76.44 255	87.92 176	53.95 278	75.58 285	84.35 227	74.39 146	82.23 212	90.72 170	72.84 192	84.39 279	60.38 248	93.98 161	90.97 180

Vis-MVSNet (Re-imp)		77.82 208	77.79 207	77.92 237	88.82 153	51.29 301	83.28 165	71.97 295	74.04 148	82.23 212	89.78 190	57.38 265	89.41 211	57.22 273	95.41 121	93.05 126

API-MVS		82.28 170	82.61 163	81.30 196	86.29 222	69.79 144	88.71 69	87.67 186	78.42 95	82.15 214	84.15 271	77.98 118	91.59 162	65.39 218	92.75 192	82.51 293

DP-MVS Recon		84.05 144	83.22 154	86.52 89	91.73 98	75.27 103	83.23 169	92.40 74	72.04 180	82.04 215	88.33 213	77.91 120	93.95 78	66.17 212	95.12 131	90.34 200

MSDG		80.06 198	79.99 196	80.25 207	83.91 263	68.04 159	77.51 269	89.19 165	77.65 103	81.94 216	83.45 277	76.37 140	86.31 260	63.31 230	86.59 275	86.41 241

Fast-Effi-MVS+		81.04 185	80.57 186	82.46 181	87.50 193	63.22 196	78.37 260	89.63 159	68.01 219	81.87 217	82.08 300	82.31 78	92.65 140	67.10 204	88.30 259	91.51 171

testgi		72.36 264	74.61 234	65.59 311	80.56 290	42.82 339	68.29 314	73.35 284	66.87 229	81.84 218	89.93 187	72.08 203	66.92 331	46.05 324	92.54 195	87.01 237

tfpn200view9		74.86 241	74.23 239	76.74 253	86.24 223	52.12 291	79.24 250	73.87 278	73.34 157	81.82 219	84.60 266	46.02 299	88.80 221	51.98 299	90.99 221	89.31 211

thres400		75.14 235	74.23 239	77.86 238	86.24 223	52.12 291	79.24 250	73.87 278	73.34 157	81.82 219	84.60 266	46.02 299	88.80 221	51.98 299	90.99 221	92.66 136

OpenMVS	76.72 13	81.98 176	82.00 172	81.93 185	84.42 253	68.22 158	88.50 72	89.48 162	66.92 228	81.80 221	91.86 130	72.59 198	90.16 200	71.19 173	91.25 215	87.40 233

AdaColmap		83.66 152	83.69 150	83.57 159	90.05 137	72.26 124	86.29 110	90.00 152	78.19 98	81.65 222	87.16 229	83.40 66	94.24 64	61.69 237	94.76 147	84.21 269

DELS-MVS		81.44 179	81.25 180	82.03 184	84.27 255	62.87 202	76.47 278	92.49 73	70.97 197	81.64 223	83.83 272	75.03 149	92.70 137	74.29 145	92.22 202	90.51 196
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		83.10 162	82.54 165	84.77 123	92.90 67	69.10 154	86.65 104	90.62 128	54.66 298	81.46 224	90.81 168	76.98 131	94.38 60	72.62 165	96.18 95	90.82 187

TR-MVS		76.77 222	75.79 224	79.72 213	86.10 234	65.79 172	77.14 271	83.02 230	65.20 245	81.40 225	82.10 299	66.30 225	90.73 186	55.57 282	85.27 286	82.65 288

TAMVS		78.08 207	76.36 220	83.23 166	90.62 126	72.87 116	79.08 254	80.01 250	61.72 269	81.35 226	86.92 232	63.96 234	88.78 225	50.61 304	93.01 188	88.04 225

Effi-MVS+		83.90 149	84.01 145	83.57 159	87.22 203	65.61 173	86.55 107	92.40 74	78.64 92	81.34 227	84.18 269	83.65 63	92.93 132	74.22 146	87.87 263	92.17 157

new-patchmatchnet		70.10 280	73.37 252	60.29 326	81.23 283	16.95 354	59.54 333	74.62 274	62.93 258	80.97 228	87.93 220	62.83 239	71.90 317	55.24 285	95.01 135	92.00 159

PVSNet_Blended_VisFu		81.55 178	80.49 189	84.70 126	91.58 103	73.24 115	84.21 134	91.67 93	62.86 259	80.94 229	87.16 229	67.27 222	92.87 135	69.82 184	88.94 250	87.99 226

BH-w/o		76.57 224	76.07 223	78.10 234	86.88 212	65.92 171	77.63 267	86.33 205	65.69 239	80.89 230	79.95 313	68.97 218	90.74 185	53.01 295	85.25 287	77.62 323

PAPM_NR		83.23 159	83.19 156	83.33 165	90.90 121	65.98 170	88.19 74	90.78 124	78.13 99	80.87 231	87.92 221	73.49 178	92.42 143	70.07 182	88.40 254	91.60 168

ab-mvs		79.67 199	80.56 187	76.99 248	88.48 162	56.93 259	84.70 127	86.06 208	68.95 215	80.78 232	93.08 102	75.30 146	84.62 277	56.78 275	90.90 227	89.43 210

conf0.01		74.17 246	73.53 245	76.08 260	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	91.34 176

conf0.002		74.17 246	73.53 245	76.08 260	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	91.34 176

thresconf0.02		73.65 251	73.53 245	73.98 271	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 255

tfpn_n400		73.65 251	73.53 245	73.98 271	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 255

tfpnconf		73.65 251	73.53 245	73.98 271	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 255

tfpnview11		73.65 251	73.53 245	73.98 271	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 255

tfpn1000		73.63 255	73.58 243	73.79 277	85.46 240	50.31 313	79.99 232	68.18 321	72.33 175	80.66 239	83.05 280	39.80 339	86.74 255	60.96 244	91.78 205	84.32 267

mvs-test1		84.55 124	82.12 169	91.84 17	87.13 205	89.54 5	85.05 123	88.42 173	74.57 144	80.60 240	82.98 282	78.49 114	93.98 75	72.04 169	89.77 242	92.00 159

XXY-MVS		74.44 245	76.19 222	69.21 298	84.61 249	52.43 290	71.70 305	77.18 259	60.73 276	80.60 240	90.96 163	75.44 143	69.35 323	56.13 278	88.33 255	85.86 248

HQP4-MVS										80.56 242			94.61 55			93.56 116

HQP-NCC					91.19 113		84.77 124		73.30 159	80.55 243

ACMP_Plane					91.19 113		84.77 124		73.30 159	80.55 243

HQP-MVS		84.61 122	84.06 144	86.27 94	91.19 113	70.66 141	84.77 124	92.68 67	73.30 159	80.55 243	90.17 185	72.10 201	94.61 55	77.30 129	94.47 153	93.56 116

HyFIR lowres test		75.12 237	72.66 258	82.50 180	91.44 109	65.19 174	72.47 302	87.31 190	46.79 334	80.29 246	84.30 268	52.70 281	92.10 150	51.88 303	86.73 273	90.22 201

test20.03		73.75 250	74.59 236	71.22 292	81.11 284	51.12 303	70.15 310	72.10 294	70.42 200	80.28 247	91.50 142	64.21 232	74.72 313	46.96 322	94.58 151	87.82 230

F-COLMAP		84.97 116	83.42 151	89.63 49	92.39 80	83.40 45	88.83 66	91.92 87	73.19 163	80.18 248	89.15 199	77.04 130	93.28 117	65.82 217	92.28 199	92.21 156

GA-MVS		75.83 231	74.61 234	79.48 219	81.87 277	59.25 247	73.42 300	82.88 231	68.68 216	79.75 249	81.80 302	50.62 285	89.46 209	66.85 207	85.64 283	89.72 207

xiu_mvs_v1_base_debu		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 237	87.17 193	65.43 241	79.59 250	82.73 288	76.94 132	90.14 201	73.22 158	88.33 255	86.90 238

xiu_mvs_v1_base		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 237	87.17 193	65.43 241	79.59 250	82.73 288	76.94 132	90.14 201	73.22 158	88.33 255	86.90 238

xiu_mvs_v1_base_debi		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 237	87.17 193	65.43 241	79.59 250	82.73 288	76.94 132	90.14 201	73.22 158	88.33 255	86.90 238

UnsupCasMVSNet_eth		71.63 270	72.30 263	69.62 296	76.47 317	52.70 288	70.03 311	80.97 245	59.18 280	79.36 253	88.21 215	60.50 245	69.12 324	58.33 268	77.62 326	87.04 236

MG-MVS		80.32 193	80.94 184	78.47 229	88.18 168	52.62 289	82.29 192	85.01 223	72.01 181	79.24 254	92.54 118	69.36 214	93.36 113	70.65 178	89.19 248	89.45 208

Fast-Effi-MVS+-dtu		82.54 167	81.41 178	85.90 105	85.60 236	76.53 96	83.07 171	89.62 160	73.02 166	79.11 255	83.51 275	80.74 99	90.24 197	68.76 194	89.29 245	90.94 181

CDS-MVSNet		77.32 213	75.40 228	83.06 169	89.00 150	72.48 121	77.90 265	82.17 236	60.81 274	78.94 256	83.49 276	59.30 255	88.76 226	54.64 290	92.37 197	87.93 228
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres200		72.34 265	71.55 269	74.70 269	83.48 265	51.60 298	75.02 289	73.71 281	70.14 204	78.56 257	80.57 309	46.20 297	88.20 232	46.99 321	89.29 245	84.32 267

tfpn_ndepth		72.54 262	72.30 263	73.24 280	84.81 247	51.42 299	79.24 250	70.49 308	69.26 210	78.48 258	79.80 314	40.16 338	86.77 253	58.08 271	90.43 238	81.53 309

testmv		70.47 277	70.70 272	69.77 295	86.22 225	53.89 279	67.32 319	71.91 296	63.32 254	78.16 259	89.47 195	56.12 270	73.10 314	36.43 340	87.33 268	82.33 295

jason		77.42 212	75.75 226	82.43 182	87.10 208	69.27 149	77.99 263	81.94 239	51.47 318	77.84 260	85.07 258	60.32 247	89.00 218	70.74 177	89.27 247	89.03 216
jason: jason.
MAR-MVS		80.24 195	78.74 202	84.73 125	86.87 213	78.18 76	85.75 114	87.81 185	65.67 240	77.84 260	78.50 319	73.79 170	90.53 191	61.59 240	90.87 228	85.49 252
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		72.29 266	72.00 265	73.14 282	88.63 158	85.00 32	74.65 292	67.39 324	71.94 188	77.80 262	87.66 224	50.48 286	75.83 309	49.95 306	79.51 318	58.58 346

pmmvs4		74.92 240	72.98 256	80.73 204	84.95 244	71.71 135	76.23 280	77.59 257	52.83 307	77.73 263	86.38 236	56.35 268	84.97 273	57.72 272	87.05 271	85.51 251

UnsupCasMVSNet_bld		69.21 288	69.68 280	67.82 305	79.42 294	51.15 302	67.82 318	75.79 266	54.15 300	77.47 264	85.36 255	59.26 256	70.64 319	48.46 314	79.35 320	81.66 306

MVS_0304		84.88 117	83.96 147	87.64 77	87.43 195	74.83 105	84.18 135	93.30 42	77.48 106	77.39 265	88.46 208	74.53 161	95.74 18	78.09 122	94.75 148	92.36 149

no-one		71.52 271	70.43 276	74.81 267	78.45 305	63.41 189	57.73 339	77.03 260	51.46 319	77.17 266	90.33 180	54.96 277	80.35 298	47.41 318	99.29 2	80.68 320

Anonymous20231206		71.38 272	71.88 266	69.88 293	86.31 221	54.37 275	70.39 309	74.62 274	52.57 309	76.73 267	88.76 203	59.94 250	72.06 316	44.35 327	93.23 184	83.23 284

CMPMVS	59.41 20	75.12 237	73.57 244	79.77 211	75.84 321	67.22 162	81.21 218	82.18 235	50.78 323	76.50 268	87.66 224	55.20 275	82.99 287	62.17 234	90.64 236	89.09 215
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 267	71.69 267	73.32 278	81.57 280	53.02 285	76.77 273	78.37 254	63.31 255	76.37 269	91.85 131	36.68 342	78.98 301	47.87 317	92.45 196	87.95 227

CVMVSNet		72.62 261	71.41 270	76.28 258	83.25 268	60.34 239	83.50 160	79.02 253	37.77 347	76.33 270	85.10 256	49.60 288	87.41 238	70.54 180	77.54 327	81.08 316

PLC	73.85 16	82.09 173	80.31 190	87.45 79	90.86 123	80.29 60	85.88 112	90.65 126	68.17 218	76.32 271	86.33 239	73.12 188	92.61 141	61.40 241	90.02 241	89.44 209
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 171	81.57 177	84.19 142	85.54 238	69.26 150	91.98 25	90.08 149	71.54 192	76.23 272	85.07 258	58.69 258	94.27 61	86.26 27	88.77 251	89.03 216

lupinMVS		76.37 227	74.46 237	82.09 183	85.54 238	69.26 150	76.79 272	80.77 246	50.68 325	76.23 272	82.82 286	58.69 258	88.94 219	69.85 183	88.77 251	88.07 223

PatchMatch-RL		74.48 243	73.22 253	78.27 232	87.70 188	85.26 30	75.92 281	70.09 309	64.34 251	76.09 274	81.25 307	65.87 229	78.07 302	53.86 292	83.82 298	71.48 334

MS-PatchMatch		70.93 274	70.22 277	73.06 283	81.85 278	62.50 211	73.82 298	77.90 255	52.44 310	75.92 275	81.27 306	55.67 272	81.75 292	55.37 284	77.70 325	74.94 328

CHOSEN 1792x2688		72.45 263	70.56 273	78.13 233	90.02 139	63.08 197	68.72 313	83.16 229	42.99 343	75.92 275	85.46 250	57.22 267	85.18 272	49.87 308	81.67 310	86.14 244

CR-MVSNet		74.00 248	73.04 255	76.85 251	79.58 292	62.64 205	82.58 183	76.90 261	50.50 326	75.72 277	92.38 120	48.07 292	84.07 280	68.72 196	82.91 304	83.85 273

RPMNet		76.06 229	75.79 224	76.85 251	79.58 292	62.64 205	82.58 183	71.75 299	74.80 141	75.72 277	92.59 115	48.69 290	84.07 280	73.48 156	82.91 304	83.85 273

PVSNet_BlendedMVS		78.80 203	77.84 206	81.65 193	84.43 251	63.41 189	79.49 240	90.44 132	61.70 270	75.43 279	87.07 231	69.11 216	91.44 166	60.68 246	92.24 200	90.11 205

PVSNet_Blended		76.49 226	75.40 228	79.76 212	84.43 251	63.41 189	75.14 288	90.44 132	57.36 286	75.43 279	78.30 320	69.11 216	91.44 166	60.68 246	87.70 266	84.42 265

PAPR		78.84 202	78.10 205	81.07 200	85.17 242	60.22 240	82.21 196	90.57 129	62.51 262	75.32 281	84.61 265	74.99 150	92.30 145	59.48 259	88.04 261	90.68 190

N_pmnet		70.20 278	68.80 285	74.38 270	80.91 286	84.81 35	59.12 336	76.45 265	55.06 296	75.31 282	82.36 297	55.74 271	54.82 348	47.02 320	87.24 270	83.52 277

cascas		76.29 228	74.81 233	80.72 205	84.47 250	62.94 200	73.89 297	87.34 189	55.94 292	75.16 283	76.53 327	63.97 233	91.16 172	65.00 219	90.97 225	88.06 224

Patchmatch-test1		72.75 260	72.61 259	73.19 281	81.62 279	55.86 266	78.89 256	71.37 301	61.73 268	74.93 284	82.15 298	60.46 246	81.80 291	59.68 251	82.63 308	81.92 302

1111		61.71 309	63.77 305	55.55 332	78.05 306	25.74 351	60.62 330	67.52 322	66.09 234	74.68 285	86.50 234	16.00 357	59.22 346	38.79 335	85.65 282	81.70 304

.test1245		48.02 326	54.41 325	28.84 339	78.05 306	25.74 351	60.62 330	67.52 322	66.09 234	74.68 285	86.50 234	16.00 357	59.22 346	38.79 335	1.47 352	1.55 353

xiu_mvs_v2_base		77.19 214	76.75 213	78.52 228	87.01 210	61.30 229	75.55 286	87.12 198	61.24 273	74.45 287	78.79 318	77.20 126	90.93 178	64.62 223	84.80 294	83.32 282

CANet		83.79 150	82.85 159	86.63 86	86.17 227	72.21 126	83.76 150	91.43 106	77.24 114	74.39 288	87.45 227	75.36 145	95.42 31	77.03 132	92.83 191	92.25 155

PS-MVSNAJ		77.04 216	76.53 219	78.56 227	87.09 209	61.40 227	75.26 287	87.13 196	61.25 272	74.38 289	77.22 324	76.94 132	90.94 177	64.63 222	84.83 293	83.35 281

MVP-Stereo		75.81 232	73.51 251	82.71 175	89.35 143	73.62 111	80.06 229	85.20 217	60.30 277	73.96 290	87.94 219	57.89 262	89.45 210	52.02 298	74.87 331	85.06 255
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 163	81.64 175	86.21 98	86.20 226	76.24 100	86.86 94	85.68 212	77.07 117	73.76 291	92.82 110	69.64 213	91.82 157	69.04 192	93.69 169	90.56 194
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		74.82 242	73.74 241	78.04 235	89.57 141	60.04 241	76.49 277	87.09 199	54.31 299	73.66 292	79.80 314	60.25 248	86.76 254	58.37 266	84.15 297	87.32 234

diffmvs		79.20 201	79.04 199	79.69 214	78.64 303	58.90 248	81.79 206	87.61 187	65.07 247	73.65 293	89.80 189	73.10 189	87.79 235	75.02 143	86.63 274	92.38 148

Test_1112_low_res		73.90 249	73.08 254	76.35 256	90.35 130	55.95 264	73.40 301	86.17 207	50.70 324	73.14 294	85.94 244	58.31 260	85.90 265	56.51 276	83.22 301	87.20 235

1314		73.22 256	72.56 261	75.20 265	80.41 291	57.84 253	81.64 210	85.36 215	51.68 316	73.10 295	76.65 326	61.45 242	85.19 271	63.54 227	79.21 322	82.59 289

Patchmatch-test		65.91 300	67.38 291	61.48 323	75.51 325	43.21 338	68.84 312	63.79 334	62.48 263	72.80 296	83.42 278	44.89 319	59.52 345	48.27 316	86.45 276	81.70 304

PatchmatchNet		69.71 284	68.83 284	72.33 288	77.66 310	53.60 280	79.29 248	69.99 310	57.66 285	72.53 297	82.93 285	46.45 296	80.08 300	60.91 245	72.09 336	83.31 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test1235678		65.57 302	65.73 299	65.06 314	82.84 273	50.90 305	62.90 327	69.26 312	57.17 289	72.36 298	83.04 281	46.02 299	70.10 320	32.79 345	85.24 288	74.19 330

tpm		67.95 291	68.08 290	67.55 306	78.74 302	43.53 337	75.60 284	67.10 329	54.92 297	72.23 299	88.10 216	42.87 325	75.97 308	52.21 297	80.95 316	83.15 285

pmmvs5		70.73 275	70.07 278	72.72 285	77.03 314	52.73 287	74.14 294	75.65 269	50.36 327	72.17 300	85.37 254	55.42 274	80.67 296	52.86 296	87.59 267	84.77 261

PatchT		70.52 276	72.76 257	63.79 317	79.38 295	33.53 347	77.63 267	65.37 332	73.61 152	71.77 301	92.79 113	44.38 321	75.65 310	64.53 224	85.37 285	82.18 297

MVS		73.21 257	72.59 260	75.06 266	80.97 285	60.81 236	81.64 210	85.92 210	46.03 337	71.68 302	77.54 321	68.47 219	89.77 206	55.70 281	85.39 284	74.60 329

testus		62.33 307	63.03 307	60.20 327	78.78 301	40.74 340	59.14 334	69.80 311	49.26 330	71.41 303	74.72 333	52.33 282	63.52 340	29.84 347	82.01 309	76.36 325

MIMVSNet		71.09 273	71.59 268	69.57 297	87.23 202	50.07 315	78.91 255	71.83 297	60.20 278	71.26 304	91.76 136	55.08 276	76.09 307	41.06 332	87.02 272	82.54 291

WTY-MVS		67.91 292	68.35 287	66.58 309	80.82 288	48.12 325	65.96 323	72.60 290	53.67 303	71.20 305	81.68 304	58.97 257	69.06 325	48.57 313	81.67 310	82.55 290

test0.0.03 1		64.66 304	64.36 303	65.57 312	75.03 331	46.89 327	64.69 325	61.58 340	62.43 265	71.18 306	77.54 321	43.41 322	68.47 326	40.75 333	82.65 306	81.35 310

CostFormer		69.98 283	68.68 286	73.87 275	77.14 312	50.72 311	79.26 249	74.51 276	51.94 315	70.97 307	84.75 263	45.16 318	87.49 237	55.16 286	79.23 321	83.40 280

LP		69.42 286	68.30 288	72.77 284	71.48 346	56.84 262	73.66 299	74.84 272	63.52 253	70.95 308	83.35 279	49.55 289	77.15 305	57.13 274	70.21 339	84.33 266

tpmvs		70.16 279	69.56 281	71.96 290	74.71 333	48.13 324	79.63 235	75.45 270	65.02 248	70.26 309	81.88 301	45.34 311	85.68 267	58.34 267	75.39 330	82.08 298

sss		66.92 295	67.26 293	65.90 310	77.23 311	51.10 304	64.79 324	71.72 300	52.12 314	70.13 310	80.18 311	57.96 261	65.36 338	50.21 305	81.01 315	81.25 313

tpm2		68.45 290	66.83 294	73.30 279	78.93 300	48.50 323	79.76 234	71.76 298	47.50 333	69.92 311	83.60 274	42.07 327	88.40 229	48.44 315	79.51 318	83.01 287

HY-MVS	64.64 18	73.03 258	72.47 262	74.71 268	83.36 267	54.19 276	82.14 199	81.96 237	56.76 291	69.57 312	86.21 242	60.03 249	84.83 276	49.58 310	82.65 306	85.11 254

PatchFormer-LS_test		67.91 292	66.49 298	72.17 289	75.29 328	51.85 296	75.68 282	73.62 283	57.23 288	68.64 313	68.13 343	42.19 326	82.76 288	64.06 225	73.51 333	81.89 303

tpm cat1		66.76 296	65.21 301	71.42 291	77.09 313	50.62 312	78.01 262	73.68 282	44.89 339	68.64 313	79.00 317	45.51 308	82.42 290	49.91 307	70.15 340	81.23 315

IB-MVS	62.13 19	71.64 269	68.97 283	79.66 216	80.80 289	62.26 219	73.94 296	76.90 261	63.27 256	68.63 315	76.79 325	33.83 345	91.84 155	59.28 260	87.26 269	84.88 260
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		80.37 192	78.41 204	86.23 95	76.75 315	73.28 114	87.18 90	77.45 258	76.24 124	68.14 316	88.93 202	65.41 230	93.85 80	69.47 186	96.12 100	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 298	65.63 300	68.75 301	81.96 276	49.88 322	62.19 329	72.51 292	51.03 321	68.04 317	75.34 331	50.84 284	74.77 311	45.82 325	82.96 302	81.60 307

tpmrst		66.28 299	66.69 296	65.05 315	72.82 342	39.33 342	78.20 261	70.69 307	53.16 306	67.88 318	80.36 310	48.18 291	74.75 312	58.13 269	70.79 338	81.08 316

CANet_DTU		77.81 209	77.05 210	80.09 209	81.37 281	59.90 242	83.26 166	88.29 176	69.16 212	67.83 319	83.72 273	60.93 243	89.47 208	69.22 190	89.70 243	90.88 184

EPMVS		62.47 305	62.63 309	62.01 319	70.63 347	38.74 343	74.76 290	52.86 349	53.91 302	67.71 320	80.01 312	39.40 340	66.60 333	55.54 283	68.81 345	80.68 320

MDTV_nov1_ep13			68.29 289		78.03 308	43.87 336	74.12 295	72.22 293	52.17 311	67.02 321	85.54 246	45.36 310	80.85 295	55.73 279	84.42 296

tpmp4_e23		69.43 285	67.33 292	75.72 263	78.53 304	52.75 286	82.13 200	74.91 271	49.23 331	66.37 322	84.17 270	41.28 334	88.67 227	49.73 309	79.63 317	85.75 249

pmmvs3		62.47 305	60.02 318	69.80 294	71.58 345	64.00 183	70.52 308	58.44 344	39.77 345	66.05 323	75.84 328	27.10 354	72.28 315	46.15 323	84.77 295	73.11 332

ADS-MVSNet2		65.87 301	63.64 306	72.55 287	73.16 339	56.92 260	67.10 320	74.81 273	49.74 328	66.04 324	82.97 283	46.71 294	77.26 303	42.29 329	69.96 341	83.46 278

ADS-MVSNet		61.90 308	62.19 310	61.03 325	73.16 339	36.42 345	67.10 320	61.75 338	49.74 328	66.04 324	82.97 283	46.71 294	63.21 342	42.29 329	69.96 341	83.46 278

DWT-MVSNet_test		66.43 297	64.37 302	72.63 286	74.86 332	50.86 306	76.52 276	72.74 289	54.06 301	65.50 326	68.30 342	32.13 347	84.84 275	61.63 239	73.59 332	82.19 296

DSMNet-mixed		60.98 315	61.61 312	59.09 329	72.88 341	45.05 333	74.70 291	46.61 353	26.20 349	65.34 327	90.32 181	55.46 273	63.12 343	41.72 331	81.30 314	69.09 338

JIA-IIPM		69.41 287	66.64 297	77.70 240	73.19 338	71.24 138	75.67 283	65.56 331	70.42 200	65.18 328	92.97 106	33.64 346	83.06 286	53.52 294	69.61 343	78.79 322

test-LLR		67.21 294	66.74 295	68.63 302	76.45 318	55.21 271	67.89 315	67.14 327	62.43 265	65.08 329	72.39 335	43.41 322	69.37 321	61.00 242	84.89 291	81.31 311

test-mter		65.00 303	63.79 304	68.63 302	76.45 318	55.21 271	67.89 315	67.14 327	50.98 322	65.08 329	72.39 335	28.27 352	69.37 321	61.00 242	84.89 291	81.31 311

PMMVS2		55.64 322	59.27 319	44.74 337	64.30 352	12.32 355	40.60 347	49.79 352	53.19 305	65.06 331	84.81 262	53.60 280	49.76 350	32.68 346	89.41 244	72.15 333

gg-mvs-nofinetune		68.96 289	69.11 282	68.52 304	76.12 320	45.32 330	83.59 153	55.88 347	86.68 20	64.62 332	97.01 7	30.36 349	83.97 283	44.78 326	82.94 303	76.26 326

PAPM		71.77 268	70.06 279	76.92 249	86.39 216	53.97 277	76.62 275	86.62 203	53.44 304	63.97 333	84.73 264	57.79 263	92.34 144	39.65 334	81.33 313	84.45 264

new_pmnet		55.69 321	57.66 320	49.76 334	75.47 326	30.59 348	59.56 332	51.45 351	43.62 342	62.49 334	75.48 329	40.96 336	49.15 351	37.39 339	72.52 334	69.55 337

test2356		56.69 319	55.15 323	61.32 324	73.20 337	44.11 335	54.95 341	62.52 335	48.75 332	62.45 335	68.42 340	21.10 356	65.67 337	26.86 349	78.08 324	74.19 330

MDTV_nov1_ep13_2view						27.60 350	70.76 307		46.47 336	61.27 336		45.20 312		49.18 311		83.75 275

dp		60.70 316	60.29 317	61.92 321	72.04 344	38.67 344	70.83 306	64.08 333	51.28 320	60.75 337	77.28 323	36.59 343	71.58 318	47.41 318	62.34 347	75.52 327

TESTMET0.1,1		61.29 312	60.32 316	64.19 316	72.06 343	51.30 300	67.89 315	62.09 336	45.27 338	60.65 338	69.01 338	27.93 353	64.74 339	56.31 277	81.65 312	76.53 324

test12356		54.91 323	57.14 321	48.22 336	75.83 322	17.47 353	52.31 345	69.20 313	51.66 317	60.11 339	75.40 330	29.77 351	62.62 344	27.64 348	72.37 335	64.59 340

PMMVS		61.65 310	60.38 315	65.47 313	65.40 351	69.26 150	63.97 326	61.73 339	36.80 348	60.11 339	68.43 339	59.42 254	66.35 334	48.97 312	78.57 323	60.81 343

testpf		58.55 318	61.58 313	49.48 335	66.03 349	40.05 341	74.40 293	58.07 345	64.72 250	59.36 341	72.67 334	22.76 355	66.92 331	67.07 205	69.15 344	41.46 349

PVSNet_0	51.08 22	56.10 320	54.97 324	59.48 328	75.12 330	53.28 284	55.16 340	61.89 337	44.30 340	59.16 342	62.48 347	54.22 278	65.91 336	35.40 342	47.01 348	59.25 345

MVS-HIRNet		61.16 313	62.92 308	55.87 330	79.09 298	35.34 346	71.83 304	57.98 346	46.56 335	59.05 343	91.14 153	49.95 287	76.43 306	38.74 337	71.92 337	55.84 347

E-PMN		61.59 311	61.62 311	61.49 322	66.81 348	55.40 269	53.77 343	60.34 341	66.80 230	58.90 344	65.50 345	40.48 337	66.12 335	55.72 280	86.25 279	62.95 342

GG-mvs-BLEND				67.16 307	73.36 336	46.54 329	84.15 136	55.04 348		58.64 345	61.95 348	29.93 350	83.87 284	38.71 338	76.92 328	71.07 335

EPNet_dtu		72.87 259	71.33 271	77.49 245	77.72 309	60.55 238	82.35 190	75.79 266	66.49 231	58.39 346	81.06 308	53.68 279	85.98 263	53.55 293	92.97 189	85.95 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PNet_i23d		52.13 324	51.24 326	54.79 333	75.56 323	45.26 331	54.54 342	52.55 350	66.95 227	57.19 347	65.82 344	13.15 359	63.40 341	36.39 341	39.04 350	55.71 348

EMVS		61.10 314	60.81 314	61.99 320	65.96 350	55.86 266	53.10 344	58.97 343	67.06 226	56.89 348	63.33 346	40.98 335	67.03 330	54.79 288	86.18 280	63.08 341

CHOSEN 280x420		59.08 317	56.52 322	66.76 308	76.51 316	64.39 180	49.62 346	59.00 342	43.86 341	55.66 349	68.41 341	35.55 344	68.21 327	43.25 328	76.78 329	67.69 339

MVE	40.22 23	51.82 325	50.47 327	55.87 330	62.66 353	51.91 293	31.61 349	39.28 354	40.65 344	50.76 350	74.98 332	56.24 269	44.67 352	33.94 344	64.11 346	71.04 336
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 340	32.95 354	29.49 349		21.63 357	12.07 350	37.95 351	45.07 349	30.84 348	19.21 353	17.94 351	33.06 351	23.69 350

tmp_tt		20.25 329	24.50 330	7.49 341	4.47 355	8.70 356	34.17 348	25.16 356	1.00 351	32.43 352	18.49 350	39.37 341	9.21 354	21.64 350	43.75 349	4.57 351

testmvs		5.91 333	7.65 334	0.72 343	1.20 356	0.37 358	59.14 334	0.67 359	0.49 353	1.11 353	2.76 354	0.94 361	0.24 356	1.02 353	1.47 352	1.55 353

test123		6.27 332	8.08 333	0.84 342	1.11 357	0.57 357	62.90 327	0.82 358	0.54 352	1.07 354	2.75 355	1.26 360	0.30 355	1.04 352	1.26 354	1.66 352

cdsmvs_eth3d_5k		20.81 328	27.75 329	0.00 344	0.00 358	0.00 359	0.00 350	85.44 214	0.00 354	0.00 355	82.82 286	81.46 92	0.00 357	0.00 354	0.00 355	0.00 355

pcd_1.5k_mvsjas		6.41 331	8.55 332	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	76.94 132	0.00 357	0.00 354	0.00 355	0.00 355

pcd1.5k->3k		38.83 327	41.11 328	32.01 338	93.13 62	0.00 359	0.00 350	91.38 112	0.00 354	0.00 355	0.00 356	89.24 16	0.00 357	0.00 354	96.24 94	96.02 48

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		6.65 330	8.87 331	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	79.80 314	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																83.88 271

test_part3							89.63 50		84.39 28		93.43 97		96.26 4	82.18 74

test_part1								93.93 25				87.19 36			97.61 54	91.48 172

sam_mvs1												46.11 298				83.88 271

sam_mvs												45.92 303

MTGPA								91.81 90

test_post1							78.85 257				3.13 352	45.19 313	80.13 299	58.11 270

test_post											3.10 353	45.43 309	77.22 304

patchmatchnet-post											81.71 303	45.93 302	87.01 241

MTMP								33.14 355

gm-plane-assit					75.42 327	44.97 334			52.17 311		72.36 337		87.90 233	54.10 291

test9_res														80.83 89	96.45 87	90.57 193

agg_prior2														79.68 106	96.16 96	90.22 201

test_prior4						78.97 71	84.59 129

test_prior				86.32 92	90.59 127	71.99 129		92.85 61					94.17 68			92.80 131

新几何2							81.72 209

旧先验1					91.97 92	71.77 131		81.78 240			91.84 132	73.92 168			93.65 171	83.61 276

无先验							82.81 177	85.62 213	58.09 283				91.41 168	67.95 202		84.48 263

原ACMM2							82.26 195

testdata2													86.43 259	63.52 228

segment_acmp												81.94 84

testdata1							79.62 236		73.95 150

plane_prior7					93.45 53	77.31 87

plane_prior6					92.61 73	76.54 94						74.84 153

plane_prior5								93.61 32					95.22 39	80.78 90	95.83 112	94.46 86

plane_prior4											92.95 107

plane_prior2							89.45 58		79.44 79

plane_prior1					92.83 71

plane_prior						76.42 97	87.15 91		75.94 129						95.03 134

n2								0.00 360

nn								0.00 360

door-mid								74.45 277

test11								91.46 103

door								72.57 291

HQP5-MVS						70.66 141

BP-MVS														77.30 129

HQP3-MVS								92.68 67							94.47 153

HQP2-MVS												72.10 201

NP-MVS					91.95 93	74.55 107					90.17 185

ACMMP++_ref															95.74 116

ACMMP++															97.35 60

Test By Simon												79.09 110

High-resolution multi-view benchmark