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 37	99.27 3	99.54 1

wuykxyi23d		88.46 62	88.80 61	87.44 81	90.96 122	93.03 1	85.85 117	81.96 246	74.58 150	98.58 2	97.29 4	87.73 31	87.31 246	82.84 69	99.41 1	81.99 309

DTE-MVSNet		89.98 38	91.91 11	84.21 143	96.51 7	57.84 260	88.93 66	92.84 65	91.92 2	96.16 3	96.23 20	86.95 37	95.99 6	79.05 115	98.57 16	98.80 6

PS-CasMVS		90.06 35	91.92 9	84.47 134	96.56 6	58.83 257	89.04 63	92.74 68	91.40 4	96.12 4	96.06 25	87.23 35	95.57 23	79.42 113	98.74 7	99.00 2

LCM-MVSNet-Re		83.48 160	85.06 116	78.75 231	85.94 243	55.75 277	80.05 237	94.27 13	76.47 125	96.09 5	94.54 66	83.31 66	89.75 212	59.95 258	94.89 141	90.75 195

PEN-MVS		90.03 36	91.88 12	84.48 133	96.57 5	58.88 255	88.95 64	93.19 49	91.62 3	96.01 6	96.16 22	87.02 36	95.60 22	78.69 118	98.72 10	98.97 3

V4		89.97 40	90.60 37	88.07 70	88.69 159	72.01 128	91.35 30	92.64 71	82.22 52	95.98 7	96.31 16	84.80 54	93.98 75	88.59 4	94.83 145	98.23 7

v52		89.97 40	90.60 37	88.07 70	88.69 159	72.01 128	91.35 30	92.64 71	82.22 52	95.97 8	96.31 16	84.82 52	93.98 75	88.59 4	94.83 145	98.23 7

CP-MVSNet		89.27 53	90.91 34	84.37 138	96.34 8	58.61 259	88.66 72	92.06 85	90.78 5	95.67 9	95.17 47	81.80 90	95.54 26	79.00 116	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 12	95.63 10	96.99 8	84.81 53	93.16 123	91.10 1	97.53 59	96.58 40
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 24	85.71 110	96.32 9	62.39 217	89.54 54	93.31 42	90.21 9	95.57 11	95.66 32	81.42 94	95.90 11	80.94 88	98.80 4	98.84 5

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 62	80.37 59	91.91 27	93.11 51	81.10 65	95.32 12	97.24 5	72.94 195	94.85 50	85.07 37	97.78 47	97.26 22

anonymousdsp		89.73 46	88.88 58	92.27 7	89.82 144	86.67 12	90.51 37	90.20 154	69.87 214	95.06 13	96.14 23	84.28 57	93.07 131	87.68 14	96.34 91	97.09 29

wuyk23d		75.13 245	79.30 206	62.63 329	75.56 335	75.18 105	80.89 230	73.10 299	75.06 146	94.76 14	95.32 41	87.73 31	52.85 359	34.16 353	97.11 67	59.85 354

ACMH	76.49 14	89.34 52	91.14 27	83.96 150	92.50 78	70.36 144	89.55 52	93.84 30	81.89 58	94.70 15	95.44 40	90.69 9	88.31 239	83.33 60	98.30 27	93.20 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 77	87.45 77	86.45 91	92.52 77	69.19 154	87.84 84	88.05 188	81.66 60	94.64 16	96.53 14	65.94 234	94.75 52	83.02 66	96.83 75	95.41 68

mvs_tets		89.78 45	89.27 53	91.30 24	93.51 52	84.79 36	89.89 45	90.63 133	70.00 213	94.55 17	96.67 11	87.94 29	93.59 93	84.27 50	95.97 107	95.52 66

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 10	94.43 18	95.27 43	91.86 3	95.09 43	87.54 19	98.02 38	93.71 113

jajsoiax		89.41 50	88.81 60	91.19 28	93.38 56	84.72 37	89.70 47	90.29 149	69.27 217	94.39 19	96.38 15	86.02 48	93.52 103	83.96 53	95.92 110	95.34 69

test_0402		88.65 59	89.58 51	85.88 107	92.55 76	72.22 126	84.01 144	89.44 170	88.63 13	94.38 20	95.77 29	86.38 45	93.59 93	79.84 106	95.21 130	91.82 169

v7n		90.13 33	90.96 32	87.65 77	91.95 94	71.06 140	89.99 42	93.05 54	86.53 21	94.29 21	96.27 19	82.69 71	94.08 71	86.25 30	97.63 54	97.82 10

test_djsdf		89.62 47	89.01 55	91.45 21	92.36 82	82.98 49	91.98 25	90.08 155	71.54 200	94.28 22	96.54 13	81.57 92	94.27 61	86.26 28	96.49 86	97.09 29

v748		88.91 58	89.82 46	86.19 101	90.06 140	68.53 156	88.81 69	91.48 104	84.36 31	94.19 23	95.98 26	82.52 74	92.67 142	84.30 49	96.67 79	97.37 19

PS-MVSNAJss		88.31 64	87.90 69	89.56 52	93.31 58	77.96 80	87.94 80	91.97 89	70.73 206	94.19 23	96.67 11	76.94 135	94.57 57	83.07 64	96.28 93	96.15 43

ESAPD		90.53 30	91.08 28	88.88 59	93.38 56	78.65 76	89.15 62	94.05 23	84.68 29	93.90 25	94.11 84	88.13 26	96.30 3	84.51 46	97.81 46	91.70 173

APDe-MVS		91.22 20	91.92 9	89.14 57	92.97 67	78.04 79	92.84 12	94.14 21	83.33 38	93.90 25	95.73 30	88.77 17	96.41 1	87.60 17	97.98 42	92.98 131

ACMH+	77.89 11	90.73 25	91.50 18	88.44 64	93.00 66	76.26 100	89.65 50	95.55 3	87.72 17	93.89 27	94.94 53	91.62 4	93.44 109	78.35 120	98.76 5	95.61 65

ACMM	79.39 9	90.65 26	90.99 31	89.63 50	95.03 31	83.53 44	89.62 51	93.35 39	79.20 85	93.83 28	93.60 100	90.81 8	92.96 132	85.02 39	98.45 20	92.41 151
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v13		87.31 75	88.10 66	84.94 118	88.84 156	63.75 190	87.85 83	91.47 107	79.12 86	93.72 29	95.82 28	75.20 153	93.58 96	84.76 43	96.16 98	97.48 16

LPG-MVS_test		91.47 16	91.68 14	90.82 34	94.75 36	81.69 51	90.00 40	94.27 13	82.35 50	93.67 30	94.82 57	91.18 5	95.52 27	85.36 35	98.73 8	95.23 74

LGP-MVS_train				90.82 34	94.75 36	81.69 51		94.27 13	82.35 50	93.67 30	94.82 57	91.18 5	95.52 27	85.36 35	98.73 8	95.23 74

v12		87.15 78	87.91 68	84.84 121	88.69 159	63.52 193	87.58 86	91.46 108	78.74 94	93.57 32	95.66 32	74.94 157	93.57 97	84.50 47	96.08 103	97.43 17

V9		86.96 79	87.70 73	84.74 125	88.52 164	63.27 199	87.31 91	91.45 110	78.28 99	93.43 33	95.45 39	74.59 165	93.57 97	84.23 51	96.01 106	97.38 18

v11		86.96 79	87.78 72	84.51 131	88.50 165	62.60 213	87.21 92	91.63 99	78.08 103	93.40 34	95.56 37	75.07 154	93.57 97	84.46 48	96.08 103	97.36 20

Anonymous20240521		87.68 73	88.61 63	84.87 120	91.76 99	64.76 181	89.28 60	91.66 98	83.02 43	93.29 35	96.10 24	77.37 128	92.89 137	77.27 134	97.75 50	96.97 34

V14		86.75 85	87.46 76	84.62 129	88.35 169	63.00 204	87.02 97	91.42 113	77.78 105	93.27 36	95.23 46	74.22 168	93.56 100	83.95 54	95.93 109	97.31 21

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 65	85.17 31	92.47 21	95.05 8	87.65 18	93.21 37	94.39 74	90.09 13	95.08 44	86.67 25	97.60 57	94.18 96

v15		86.56 88	87.25 80	84.51 131	88.15 176	62.72 209	86.72 107	91.40 115	77.38 110	93.11 38	95.00 51	73.93 173	93.55 101	83.67 58	95.86 113	97.26 22

Anonymous20231211		88.40 63	89.62 50	84.73 126	90.46 132	65.27 177	88.86 67	93.02 58	87.15 19	93.05 39	97.10 6	82.28 78	92.02 154	76.70 139	97.99 40	96.88 35

MP-MVS-pluss		90.81 24	91.08 28	89.99 46	95.97 12	79.88 63	88.13 78	94.51 11	75.79 135	92.94 40	94.96 52	88.36 19	95.01 46	90.70 2	98.40 21	95.09 77
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 56	89.88 44	86.22 97	91.63 101	77.07 90	89.82 46	93.77 31	78.90 90	92.88 41	92.29 130	86.11 47	90.22 202	86.24 31	97.24 64	91.36 181

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 89	92.87 42	93.74 98	90.60 11	95.21 41	82.87 67	98.76 5	94.87 79
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 7	92.86 43	96.29 18	88.16 25	94.17 68	86.07 33	98.48 19	97.22 25

SMA-MVS		90.31 32	90.48 40	89.83 47	95.31 27	79.52 69	90.98 34	93.24 48	75.37 144	92.84 44	95.28 42	85.58 50	96.09 5	87.92 11	97.76 48	93.88 105

test_part2					93.86 47	77.77 81				92.84 44

v1.0		38.20 339	50.94 338	0.00 355	93.86 47	0.00 370	0.00 361	93.93 26	84.39 30	92.84 44	93.43 102	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

v10		86.54 89	87.10 82	84.84 121	88.16 175	63.28 198	86.64 109	92.20 82	75.42 143	92.81 47	94.50 67	74.05 171	94.06 72	83.88 55	96.28 93	97.17 27

v17		86.32 93	86.95 87	84.44 135	88.00 179	62.62 212	86.74 105	91.48 104	77.17 118	92.74 48	94.56 63	73.74 177	93.53 102	83.27 61	94.87 144	97.18 26

v8		86.22 98	86.83 91	84.36 139	87.82 187	62.35 218	86.42 112	91.33 118	76.78 123	92.73 49	94.48 68	73.41 185	93.72 84	83.10 63	95.41 123	97.01 32

nrg030		87.85 70	88.49 64	85.91 105	90.07 139	69.73 146	87.86 81	94.20 17	74.04 155	92.70 50	94.66 61	85.88 49	91.50 166	79.72 107	97.32 62	96.50 41

v16		86.24 96	86.85 90	84.43 136	87.96 181	62.59 214	86.73 106	91.48 104	77.17 118	92.67 51	94.55 64	73.63 178	93.52 103	83.26 62	94.16 159	97.17 27

SteuartSystems-ACMMP		91.16 22	91.36 21	90.55 37	93.91 46	80.97 58	91.49 29	93.48 37	82.82 46	92.60 52	93.97 88	88.19 23	96.29 4	87.61 16	98.20 32	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 44	89.97 43	89.27 55	94.76 35	79.86 64	86.76 103	92.78 67	78.78 92	92.51 53	93.64 99	88.13 26	93.84 82	84.83 42	97.55 58	94.10 99

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 25	92.51 53	95.13 48	90.65 10	95.34 34	88.06 10	98.15 33	95.95 52

K. test v3		85.14 113	84.73 123	86.37 92	91.13 119	69.63 148	85.45 122	76.68 274	84.06 35	92.44 55	96.99 8	62.03 246	94.65 54	80.58 95	93.24 186	94.83 82

v18		85.99 103	86.55 94	84.30 141	87.73 193	62.29 222	86.40 113	91.49 103	76.64 124	92.40 56	94.20 80	73.28 189	93.52 103	82.87 67	93.99 163	97.09 29

ACMMP		91.91 9	91.87 13	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 49	92.39 57	94.14 82	89.15 16	95.62 21	87.35 20	98.24 29	94.56 83
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 8	92.11 58	95.03 50	89.75 14	94.93 48	79.95 105	98.27 28	95.04 78
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 34	84.50 41	89.49 56	93.98 25	79.68 78	92.09 59	93.89 95	83.80 60	93.10 126	82.67 71	98.04 35	93.64 115

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 116	94.02 43	64.13 186	84.38 138	91.29 119	84.88 28	92.06 60	93.84 96	86.45 43	93.73 83	73.22 163	98.66 12	97.69 11

zzz-MVS		91.27 19	91.26 26	91.29 25	96.59 3	86.29 14	88.94 65	91.81 94	84.07 33	92.00 61	94.40 72	86.63 39	95.28 37	88.59 4	98.31 25	92.30 155

MTAPA		91.52 13	91.60 15	91.29 25	96.59 3	86.29 14	92.02 24	91.81 94	84.07 33	92.00 61	94.40 72	86.63 39	95.28 37	88.59 4	98.31 25	92.30 155

ACMMP_Plus		90.65 26	91.07 30	89.42 53	95.93 14	79.54 68	89.95 43	93.68 32	77.65 106	91.97 63	94.89 54	88.38 18	95.45 30	89.27 3	97.87 45	93.27 123

FC-MVSNet-test		85.93 105	87.05 84	82.58 182	92.25 86	56.44 272	85.75 118	93.09 52	77.33 115	91.94 64	94.65 62	74.78 161	93.41 112	75.11 148	98.58 15	97.88 9

lessismore_v0				85.95 104	91.10 120	70.99 141		70.91 318		91.79 65	94.42 70	61.76 247	92.93 134	79.52 112	93.03 190	93.93 104

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 45	91.77 66	93.94 94	90.55 12	95.73 19	88.50 8	98.23 30	95.33 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 40	93.90 29	80.32 72	91.74 67	94.41 71	88.17 24	95.98 7	86.37 26	97.99 40	93.96 103

testing_2		84.36 134	84.64 132	83.50 168	86.74 221	63.97 189	84.56 134	90.31 144	66.22 241	91.62 68	94.55 64	75.88 147	91.95 155	77.02 137	94.89 141	94.56 83

mPP-MVS		91.69 10	91.47 19	92.37 5	96.04 11	88.48 8	92.72 14	92.60 73	83.09 41	91.54 69	94.25 78	87.67 33	95.51 29	87.21 24	98.11 34	93.12 128

HFP-MVS		91.30 18	91.39 20	91.02 29	95.43 25	84.66 39	92.58 18	93.29 45	81.99 55	91.47 70	93.96 90	88.35 20	95.56 24	87.74 12	97.74 51	92.85 133

#test#		90.49 31	90.31 42	91.02 29	95.43 25	84.66 39	90.65 36	93.29 45	77.00 121	91.47 70	93.96 90	88.35 20	95.56 24	84.88 40	97.74 51	92.85 133

ACMMPR		91.49 14	91.35 23	91.92 14	95.74 18	85.88 24	92.58 18	93.25 47	81.99 55	91.40 72	94.17 81	87.51 34	95.87 12	87.74 12	97.76 48	93.99 101

ANet_high		83.17 165	85.68 108	75.65 274	81.24 291	45.26 342	79.94 240	92.91 61	83.83 36	91.33 73	96.88 10	80.25 104	85.92 272	68.89 198	95.89 111	95.76 54

APD-MVS		89.54 49	89.63 49	89.26 56	92.57 75	81.34 56	90.19 39	93.08 53	80.87 67	91.13 74	93.19 104	86.22 46	95.97 8	82.23 75	97.18 66	90.45 204
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		91.67 11	91.58 16	91.96 12	95.29 28	87.62 9	93.38 7	93.36 38	83.16 40	91.06 75	94.00 87	88.26 22	95.71 20	87.28 23	98.39 22	92.55 148

FIs		85.35 112	86.27 98	82.60 181	91.86 97	57.31 264	85.10 126	93.05 54	75.83 134	91.02 76	93.97 88	73.57 182	92.91 136	73.97 155	98.02 38	97.58 14

UniMVSNet_NR-MVSNet		86.84 83	87.06 83	86.17 102	92.86 71	67.02 166	82.55 192	91.56 100	83.08 42	90.92 77	91.82 142	78.25 119	93.99 73	74.16 152	98.35 23	97.49 15

DU-MVS		86.80 84	86.99 85	86.21 99	93.24 60	67.02 166	83.16 176	92.21 81	81.73 59	90.92 77	91.97 136	77.20 129	93.99 73	74.16 152	98.35 23	97.61 12

V42		83.47 161	83.37 158	83.75 155	83.16 279	63.33 197	81.31 221	90.23 153	69.51 216	90.91 79	90.81 176	74.16 169	92.29 149	80.06 103	90.22 247	95.62 61

region2R		91.44 17	91.30 25	91.87 16	95.75 17	85.90 23	92.63 17	93.30 43	81.91 57	90.88 80	94.21 79	87.75 30	95.87 12	87.60 17	97.71 53	93.83 106

WR-MVS		83.56 158	84.40 139	81.06 206	93.43 55	54.88 283	78.67 266	85.02 230	81.24 63	90.74 81	91.56 149	72.85 196	91.08 177	68.00 205	98.04 35	97.23 24

v1240		84.30 137	84.51 136	83.65 160	87.65 196	61.26 235	82.85 183	91.54 101	67.94 230	90.68 82	90.65 182	71.71 212	93.64 87	82.84 69	94.78 147	96.07 46

MIMVSNet1		83.63 156	84.59 133	80.74 209	94.06 42	62.77 208	82.72 188	84.53 234	77.57 108	90.34 83	95.92 27	76.88 141	85.83 274	61.88 244	97.42 60	93.62 116

LS3D		90.60 28	90.34 41	91.38 23	89.03 153	84.23 42	93.58 4	94.68 10	90.65 6	90.33 84	93.95 93	84.50 56	95.37 33	80.87 89	95.50 122	94.53 87

PMVS	80.48 6	90.08 34	90.66 36	88.34 66	96.71 2	92.97 2	90.31 38	89.57 168	88.51 14	90.11 85	95.12 49	90.98 7	88.92 226	77.55 128	97.07 68	83.13 296
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v7		84.81 121	85.00 118	84.23 142	88.15 176	63.27 199	83.79 154	91.39 116	71.10 204	90.07 86	91.28 153	74.04 172	93.63 88	81.48 82	93.67 173	95.79 53

v1921920		84.23 140	84.37 143	83.79 153	87.64 197	61.71 226	82.91 182	91.20 122	67.94 230	90.06 87	90.34 187	72.04 209	93.59 93	82.32 74	94.91 140	96.07 46

ITE_SJBPF				90.11 45	90.72 127	84.97 33		90.30 146	81.56 61	90.02 88	91.20 158	82.40 76	90.81 186	73.58 159	94.66 152	94.56 83

XVS		91.54 12	91.36 21	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 26	89.99 89	94.03 86	86.57 41	95.80 15	87.35 20	97.62 55	94.20 94

X-MVStestdata		85.04 116	82.70 165	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 26	89.99 89	16.05 362	86.57 41	95.80 15	87.35 20	97.62 55	94.20 94

v1192		84.57 126	84.69 127	84.21 143	87.75 192	62.88 206	83.02 178	91.43 111	69.08 221	89.98 91	90.89 173	72.70 200	93.62 92	82.41 72	94.97 139	96.13 44

Anonymous20240529		86.20 99	87.13 81	83.42 169	90.19 136	64.55 184	84.55 135	90.71 130	85.85 24	89.94 92	95.24 45	82.13 80	90.40 197	69.19 195	96.40 89	95.31 71

v6		84.43 131	84.66 129	83.75 155	87.81 188	62.34 219	83.59 158	90.26 152	72.33 183	89.94 92	91.19 159	73.30 188	93.29 115	80.26 100	93.26 183	95.62 61

v1neww		84.43 131	84.66 129	83.75 155	87.81 188	62.34 219	83.59 158	90.27 150	72.33 183	89.93 94	91.22 155	73.28 189	93.29 115	80.25 101	93.25 184	95.62 61

v7new		84.43 131	84.66 129	83.75 155	87.81 188	62.34 219	83.59 158	90.27 150	72.33 183	89.93 94	91.22 155	73.28 189	93.29 115	80.25 101	93.25 184	95.62 61

Regformer-2		86.74 86	86.08 102	88.73 61	84.18 268	79.20 70	83.52 162	89.33 171	83.33 38	89.92 96	85.07 267	83.23 67	93.16 123	83.39 59	92.72 197	93.83 106

pmmvs6		86.52 90	88.06 67	81.90 191	92.22 88	62.28 223	84.66 132	89.15 173	83.54 37	89.85 97	97.32 3	88.08 28	86.80 259	70.43 185	97.30 63	96.62 38

Regformer-4		86.41 91	85.71 107	88.52 63	84.27 264	77.57 84	84.07 142	88.00 190	82.82 46	89.84 98	85.48 257	82.06 82	92.77 139	83.83 57	91.04 227	95.22 76

v144192		84.24 139	84.41 138	83.71 159	87.59 198	61.57 231	82.95 181	91.03 126	67.82 233	89.80 99	90.49 185	73.28 189	93.51 106	81.88 79	94.89 141	96.04 48

v1144		84.54 129	84.72 125	84.00 148	87.67 195	62.55 215	82.97 180	90.93 128	70.32 210	89.80 99	90.99 168	73.50 183	93.48 107	81.69 81	94.65 153	95.97 50

DeepC-MVS	82.31 4	89.15 55	89.08 54	89.37 54	93.64 51	79.07 71	88.54 74	94.20 17	73.53 160	89.71 101	94.82 57	85.09 51	95.77 17	84.17 52	98.03 37	93.26 124
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 67	86.93 88	91.22 27	90.08 138	89.30 6	89.68 49	91.11 124	79.26 84	89.68 102	94.81 60	82.44 75	87.74 243	76.54 141	88.74 262	96.61 39

FMVSNet1		84.55 127	85.45 112	81.85 194	90.27 135	61.05 238	86.83 100	88.27 185	78.57 96	89.66 103	95.64 34	75.43 150	90.68 190	69.09 196	95.33 125	93.82 108

v1141		84.16 142	84.38 140	83.52 165	87.32 204	61.70 228	82.79 185	89.74 162	71.90 197	89.64 104	91.12 162	72.68 201	93.10 126	80.39 99	93.80 168	95.75 55

divwei89l23v2f112		84.16 142	84.38 140	83.52 165	87.32 204	61.70 228	82.79 185	89.74 162	71.90 197	89.64 104	91.12 162	72.68 201	93.10 126	80.40 97	93.81 167	95.75 55

v1		84.16 142	84.38 140	83.52 165	87.33 203	61.71 226	82.79 185	89.73 164	71.89 199	89.64 104	91.11 164	72.72 198	93.10 126	80.40 97	93.79 169	95.75 55

IterMVS-LS		84.73 123	84.98 119	83.96 150	87.35 202	63.66 191	83.25 173	89.88 161	76.06 128	89.62 107	92.37 129	73.40 187	92.52 145	78.16 123	94.77 149	95.69 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 61	89.01 55	87.36 82	91.30 112	77.50 85	87.55 87	92.97 60	87.95 16	89.62 107	92.87 115	84.56 55	93.89 79	77.65 127	96.62 80	90.70 196

UniMVSNet (Re)		86.87 81	86.98 86	86.55 89	93.11 64	68.48 157	83.80 153	92.87 62	80.37 70	89.61 109	91.81 143	77.72 123	94.18 66	75.00 149	98.53 17	96.99 33

IS-MVSNet		86.66 87	86.82 92	86.17 102	92.05 92	66.87 168	91.21 33	88.64 178	86.30 23	89.60 110	92.59 121	69.22 220	94.91 49	73.89 156	97.89 44	96.72 36

v2v482		84.09 145	84.24 145	83.62 161	87.13 212	61.40 232	82.71 189	89.71 165	72.19 187	89.55 111	91.41 152	70.70 217	93.20 120	81.02 86	93.76 170	96.25 42

Baseline_NR-MVSNet		84.00 149	85.90 104	78.29 239	91.47 110	53.44 292	82.29 199	87.00 208	79.06 88	89.55 111	95.72 31	77.20 129	86.14 270	72.30 172	98.51 18	95.28 72

CSCG		86.26 95	86.47 95	85.60 112	90.87 124	74.26 110	87.98 79	91.85 92	80.35 71	89.54 113	88.01 224	79.09 111	92.13 151	75.51 145	95.06 136	90.41 205

ambc				82.98 175	90.55 131	64.86 180	88.20 76	89.15 173		89.40 114	93.96 90	71.67 213	91.38 173	78.83 117	96.55 82	92.71 139

DeepPCF-MVS	81.24 5	87.28 76	86.21 100	90.49 38	91.48 109	84.90 34	83.41 167	92.38 78	70.25 211	89.35 115	90.68 180	82.85 70	94.57 57	79.55 109	95.95 108	92.00 163

Regformer-1		86.00 101	85.50 111	87.49 79	84.18 268	76.90 92	83.52 162	87.94 192	82.18 54	89.19 116	85.07 267	82.28 78	91.89 158	82.40 73	92.72 197	93.69 114

HSP-MVS		88.63 60	87.84 70	91.02 29	95.76 16	86.14 19	92.75 13	91.01 127	78.43 97	89.16 117	92.25 132	72.03 210	96.36 2	88.21 9	90.93 234	90.55 202

XVG-OURS		89.18 54	88.83 59	90.23 43	94.28 41	86.11 20	85.91 115	93.60 35	80.16 74	89.13 118	93.44 101	83.82 59	90.98 179	83.86 56	95.30 129	93.60 117

Test4		81.31 187	81.13 190	81.88 193	84.89 253	63.05 203	82.37 196	90.50 136	62.75 267	89.00 119	88.29 221	67.55 227	91.68 163	73.55 160	91.24 224	90.89 190

MDA-MVSNet-bldmvs		77.47 219	76.90 220	79.16 227	79.03 312	64.59 182	66.58 333	75.67 278	73.15 172	88.86 120	88.99 208	66.94 229	81.23 303	64.71 226	88.22 269	91.64 175

EG-PatchMatch MVS		84.08 146	84.11 146	83.98 149	92.22 88	72.61 120	82.20 205	87.02 207	72.63 178	88.86 120	91.02 167	78.52 115	91.11 176	73.41 162	91.09 225	88.21 231

3Dnovator+	83.92 2	89.97 40	89.66 48	90.92 32	91.27 114	81.66 54	91.25 32	94.13 22	88.89 11	88.83 122	94.26 77	77.55 126	95.86 14	84.88 40	95.87 112	95.24 73

EI-MVSNet-UG-set		85.04 116	84.44 137	86.85 85	83.87 273	72.52 121	83.82 151	85.15 227	80.27 73	88.75 123	85.45 260	79.95 107	91.90 157	81.92 78	90.80 238	96.13 44

EI-MVSNet-Vis-set		85.12 114	84.53 135	86.88 84	84.01 270	72.76 118	83.91 149	85.18 226	80.44 69	88.75 123	85.49 256	80.08 105	91.92 156	82.02 76	90.85 237	95.97 50

OMC-MVS		88.19 65	87.52 75	90.19 44	91.94 96	81.68 53	87.49 89	93.17 50	76.02 130	88.64 125	91.22 155	84.24 58	93.37 113	77.97 126	97.03 69	95.52 66

Regformer-3		85.06 115	84.67 128	86.22 97	84.27 264	73.43 114	84.07 142	85.26 224	80.77 68	88.62 126	85.48 257	80.56 102	90.39 198	81.99 77	91.04 227	94.85 81

UA-Net		91.49 14	91.53 17	91.39 22	94.98 32	82.95 50	93.52 5	92.79 66	88.22 15	88.53 127	97.64 2	83.45 64	94.55 59	86.02 34	98.60 14	96.67 37

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 57	82.59 48	88.52 128	94.37 75	86.74 38	95.41 32	86.32 27	98.21 31	93.19 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		85.50 110	86.14 101	83.58 162	87.97 180	67.13 165	87.55 87	94.32 12	73.44 163	88.47 129	87.54 233	86.45 43	91.06 178	75.76 144	93.76 170	92.54 149

NR-MVSNet		86.00 101	86.22 99	85.34 114	93.24 60	64.56 183	82.21 203	90.46 137	80.99 66	88.42 130	91.97 136	77.56 125	93.85 80	72.46 171	98.65 13	97.61 12

alignmvs		83.94 151	83.98 149	83.80 152	87.80 191	67.88 162	84.54 136	91.42 113	73.27 170	88.41 131	87.96 225	72.33 205	90.83 185	76.02 143	94.11 160	92.69 140

TransMVSNet (Re)		84.02 148	85.74 106	78.85 229	91.00 121	55.20 282	82.29 199	87.26 198	79.65 79	88.38 132	95.52 38	83.00 68	86.88 251	67.97 206	96.60 81	94.45 90

PM-MVS		80.20 204	79.00 207	83.78 154	88.17 174	86.66 13	81.31 221	66.81 342	69.64 215	88.33 133	90.19 191	64.58 237	83.63 294	71.99 176	90.03 248	81.06 328

v148		82.31 175	82.48 172	81.81 197	85.59 245	59.66 250	81.47 219	86.02 216	72.85 175	88.05 134	90.65 182	70.73 216	90.91 183	75.15 147	91.79 209	94.87 79

AllTest		87.97 68	87.40 79	89.68 48	91.59 102	83.40 45	89.50 55	95.44 5	79.47 80	88.00 135	93.03 108	82.66 72	91.47 167	70.81 178	96.14 100	94.16 97

TestCases				89.68 48	91.59 102	83.40 45		95.44 5	79.47 80	88.00 135	93.03 108	82.66 72	91.47 167	70.81 178	96.14 100	94.16 97

pm-mvs1		83.69 154	84.95 120	79.91 217	90.04 142	59.66 250	82.43 194	87.44 195	75.52 140	87.85 137	95.26 44	81.25 96	85.65 276	68.74 200	96.04 105	94.42 91

PCF-MVS	74.62 15	82.15 178	80.92 193	85.84 108	89.43 146	72.30 124	80.53 233	91.82 93	57.36 297	87.81 138	89.92 196	77.67 124	93.63 88	58.69 270	95.08 135	91.58 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 187	81.61 183	80.41 212	86.38 224	58.75 258	83.93 148	86.58 211	72.43 179	87.65 139	92.98 110	63.78 241	90.22 202	66.86 211	93.92 165	92.27 157

VPA-MVSNet		83.47 161	84.73 123	79.69 221	90.29 134	57.52 263	81.30 223	88.69 177	76.29 126	87.58 140	94.44 69	80.60 101	87.20 247	66.60 215	96.82 76	94.34 93

DI_MVS_plusplus_test		81.27 189	81.26 186	81.29 202	84.98 251	61.65 230	81.98 208	87.25 199	63.56 259	87.56 141	89.60 200	73.62 179	91.83 160	72.20 173	90.59 245	90.38 206

CPTT-MVS		89.39 51	88.98 57	90.63 36	95.09 30	86.95 10	92.09 23	92.30 79	79.74 77	87.50 142	92.38 126	81.42 94	93.28 118	83.07 64	97.24 64	91.67 174

VDDNet		84.35 136	85.39 113	81.25 203	95.13 29	59.32 253	85.42 123	81.11 252	86.41 22	87.41 143	96.21 21	73.61 181	90.61 193	66.33 216	96.85 73	93.81 111

test_normal		81.23 191	81.16 189	81.43 200	84.77 256	61.99 225	81.46 220	86.95 209	63.16 264	87.22 144	89.63 199	73.62 179	91.65 164	72.92 168	90.70 240	90.65 199

VDD-MVS		84.23 140	84.58 134	83.20 172	91.17 118	65.16 179	83.25 173	84.97 232	79.79 76	87.18 145	94.27 76	74.77 162	90.89 184	69.24 192	96.54 83	93.55 121

MSLP-MVS++		85.00 118	86.03 103	81.90 191	91.84 98	71.56 138	86.75 104	93.02 58	75.95 132	87.12 146	89.39 203	77.98 120	89.40 217	77.46 129	94.78 147	84.75 272

YYNet1		70.06 292	70.44 285	68.90 310	73.76 347	53.42 293	58.99 348	67.20 338	58.42 292	87.10 147	85.39 262	59.82 258	67.32 338	59.79 259	83.50 310	85.96 254

MDA-MVSNet_test_wron		70.05 293	70.44 285	68.88 311	73.84 346	53.47 291	58.93 349	67.28 337	58.43 291	87.09 148	85.40 261	59.80 259	67.25 339	59.66 261	83.54 309	85.92 256

CNVR-MVS		87.81 71	87.68 74	88.21 67	92.87 69	77.30 89	85.25 124	91.23 121	77.31 116	87.07 149	91.47 151	82.94 69	94.71 53	84.67 44	96.27 95	92.62 147

EPP-MVSNet		85.47 111	85.04 117	86.77 86	91.52 108	69.37 149	91.63 28	87.98 191	81.51 62	87.05 150	91.83 141	66.18 233	95.29 35	70.75 180	96.89 72	95.64 60

TinyColmap		81.25 190	82.34 175	77.99 244	85.33 249	60.68 243	82.32 198	88.33 183	71.26 202	86.97 151	92.22 134	77.10 132	86.98 250	62.37 239	95.17 132	86.31 252

Patchmatch-RL test		74.48 253	73.68 252	76.89 260	84.83 254	66.54 170	72.29 312	69.16 326	57.70 295	86.76 152	86.33 248	45.79 313	82.59 298	69.63 189	90.65 243	81.54 318

XVG-OURS-SEG-HR		89.59 48	89.37 52	90.28 42	94.47 39	85.95 21	86.84 99	93.91 28	80.07 75	86.75 153	93.26 103	93.64 2	90.93 181	84.60 45	90.75 239	93.97 102

HPM-MVS++		88.93 57	88.45 65	90.38 40	94.92 33	85.85 25	89.70 47	91.27 120	78.20 100	86.69 154	92.28 131	80.36 103	95.06 45	86.17 32	96.49 86	90.22 208

TSAR-MVS + MP.		88.14 66	87.82 71	89.09 58	95.72 19	76.74 94	92.49 20	91.19 123	67.85 232	86.63 155	94.84 56	79.58 109	95.96 9	87.62 15	94.50 155	94.56 83

EI-MVSNet		82.61 171	82.42 173	83.20 172	83.25 277	63.66 191	83.50 165	85.07 228	76.06 128	86.55 156	85.10 265	73.41 185	90.25 199	78.15 124	90.67 241	95.68 59

HQP_MVS		87.75 72	87.43 78	88.70 62	93.45 53	76.42 98	89.45 57	93.61 33	79.44 82	86.55 156	92.95 112	74.84 159	95.22 39	80.78 91	95.83 114	94.46 88

plane_prior3						76.85 93			77.79 104	86.55 156

BH-untuned		80.96 193	80.99 191	80.84 208	88.55 163	68.23 158	80.33 235	88.46 179	72.79 176	86.55 156	86.76 240	74.72 163	91.77 162	61.79 245	88.99 258	82.52 302

MVSTER		77.09 223	75.70 235	81.25 203	75.27 341	61.08 237	77.49 278	85.07 228	60.78 283	86.55 156	88.68 212	43.14 334	90.25 199	73.69 158	90.67 241	92.42 150

旧先验2							81.73 214		56.88 301	86.54 161			84.90 282	72.81 169

semantic-postprocess				84.34 140	83.93 271	69.66 147		81.09 253	72.43 179	86.47 162	90.19 191	57.56 272	93.15 125	77.45 130	86.39 286	90.22 208

CDPH-MVS		86.17 100	85.54 110	88.05 73	92.25 86	75.45 103	83.85 150	92.01 87	65.91 244	86.19 163	91.75 145	83.77 61	94.98 47	77.43 131	96.71 78	93.73 112

MVS_111021_LR		84.28 138	83.76 152	85.83 109	89.23 150	83.07 48	80.99 229	83.56 236	72.71 177	86.07 164	89.07 207	81.75 91	86.19 269	77.11 135	93.36 179	88.24 230

GBi-Net		82.02 180	82.07 177	81.85 194	86.38 224	61.05 238	86.83 100	88.27 185	72.43 179	86.00 165	95.64 34	63.78 241	90.68 190	65.95 218	93.34 180	93.82 108

test1		82.02 180	82.07 177	81.85 194	86.38 224	61.05 238	86.83 100	88.27 185	72.43 179	86.00 165	95.64 34	63.78 241	90.68 190	65.95 218	93.34 180	93.82 108

FMVSNet3		78.80 210	78.55 210	79.57 223	82.89 281	56.89 269	81.76 213	85.77 218	69.04 222	86.00 165	90.44 186	51.75 292	90.09 208	65.95 218	93.34 180	91.72 172

tfpnnormal		81.79 183	82.95 163	78.31 238	88.93 155	55.40 278	80.83 232	82.85 240	76.81 122	85.90 168	94.14 82	74.58 166	86.51 264	66.82 214	95.68 120	93.01 130

TAPA-MVS	77.73 12	85.71 109	84.83 122	88.37 65	88.78 158	79.72 65	87.15 95	93.50 36	69.17 219	85.80 169	89.56 201	80.76 99	92.13 151	73.21 167	95.51 121	93.25 125
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 150	82.69 166	87.72 75	89.27 149	81.45 55	83.72 156	81.58 251	74.73 148	85.66 170	86.06 252	72.56 204	92.69 141	75.44 146	95.21 130	89.01 227

EU-MVSNet		75.12 246	74.43 247	77.18 256	83.11 280	59.48 252	85.71 120	82.43 243	39.76 357	85.64 171	88.76 210	44.71 330	87.88 242	73.86 157	85.88 290	84.16 280

LF4IMVS		82.75 170	81.93 180	85.19 115	82.08 284	80.15 61	85.53 121	88.76 176	68.01 227	85.58 172	87.75 229	71.80 211	86.85 252	74.02 154	93.87 166	88.58 229

Patchmtry		76.56 233	77.46 216	73.83 286	79.37 308	46.60 339	82.41 195	76.90 271	73.81 158	85.56 173	92.38 126	48.07 301	83.98 291	63.36 235	95.31 128	90.92 189

MVS_111021_HR		84.63 124	84.34 144	85.49 113	90.18 137	75.86 102	79.23 260	87.13 203	73.35 164	85.56 173	89.34 204	83.60 63	90.50 195	76.64 140	94.05 162	90.09 213

testdata				79.54 224	92.87 69	72.34 123		80.14 258	59.91 287	85.47 175	91.75 145	67.96 226	85.24 278	68.57 203	92.18 208	81.06 328

CLD-MVS		83.18 164	82.64 167	84.79 123	89.05 152	67.82 163	77.93 272	92.52 74	68.33 225	85.07 176	81.54 315	82.06 82	92.96 132	69.35 191	97.91 43	93.57 118
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 97	85.65 109	87.96 74	91.30 112	76.92 91	87.19 93	91.99 88	70.56 207	84.96 177	90.69 179	80.01 106	95.14 42	78.37 119	95.78 116	91.82 169
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	84.80 122	84.71 126	85.06 117	86.36 227	74.71 107	88.77 70	90.00 158	75.65 139	84.96 177	93.17 105	74.06 170	91.19 174	78.28 122	91.09 225	89.29 221

QAPM		82.59 172	82.59 169	82.58 182	86.44 222	66.69 169	89.94 44	90.36 141	67.97 229	84.94 179	92.58 123	72.71 199	92.18 150	70.63 183	87.73 274	88.85 228

VPNet		80.25 202	81.68 181	75.94 272	92.46 79	47.98 337	76.70 282	81.67 250	73.45 162	84.87 180	92.82 116	74.66 164	86.51 264	61.66 247	96.85 73	93.33 122

NCCC		87.36 74	86.87 89	88.83 60	92.32 85	78.84 74	86.58 110	91.09 125	78.77 93	84.85 181	90.89 173	80.85 98	95.29 35	81.14 85	95.32 126	92.34 154

PHI-MVS		86.38 92	85.81 105	88.08 69	88.44 168	77.34 87	89.35 59	93.05 54	73.15 172	84.76 182	87.70 230	78.87 113	94.18 66	80.67 94	96.29 92	92.73 138

pmmvs-eth3d		78.42 213	77.04 219	82.57 184	87.44 200	74.41 109	80.86 231	79.67 260	55.68 304	84.69 183	90.31 190	60.91 250	85.42 277	62.20 241	91.59 212	87.88 238

view600		76.79 226	76.54 223	77.56 249	87.91 183	50.77 318	81.92 209	71.35 314	77.38 110	84.62 184	88.40 216	45.18 324	89.26 219	58.58 271	93.49 175	92.66 141

view800		76.79 226	76.54 223	77.56 249	87.91 183	50.77 318	81.92 209	71.35 314	77.38 110	84.62 184	88.40 216	45.18 324	89.26 219	58.58 271	93.49 175	92.66 141

conf0.05thres1000		76.79 226	76.54 223	77.56 249	87.91 183	50.77 318	81.92 209	71.35 314	77.38 110	84.62 184	88.40 216	45.18 324	89.26 219	58.58 271	93.49 175	92.66 141

tfpn		76.79 226	76.54 223	77.56 249	87.91 183	50.77 318	81.92 209	71.35 314	77.38 110	84.62 184	88.40 216	45.18 324	89.26 219	58.58 271	93.49 175	92.66 141

test_prior3		86.31 94	86.31 97	86.32 93	90.59 129	71.99 130	83.37 168	92.85 63	75.43 141	84.58 188	91.57 147	81.92 88	94.17 68	79.54 110	96.97 70	92.80 136

test_prior2							83.37 168		75.43 141	84.58 188	91.57 147	81.92 88		79.54 110	96.97 70

TEST9					92.34 83	79.70 66	83.94 146	90.32 142	65.41 252	84.49 190	90.97 169	82.03 84	93.63 88

train_agg		85.98 104	85.28 114	88.07 70	92.34 83	79.70 66	83.94 146	90.32 142	65.79 245	84.49 190	90.97 169	81.93 86	93.63 88	81.21 83	96.54 83	90.88 191

Gipuma		84.44 130	86.33 96	78.78 230	84.20 267	73.57 113	89.55 52	90.44 138	84.24 32	84.38 192	94.89 54	76.35 145	80.40 306	76.14 142	96.80 77	82.36 304
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_8					92.09 90	78.87 73	83.82 151	90.31 144	65.79 245	84.36 193	90.96 171	81.93 86	93.44 109

agg_prior1		85.72 108	85.20 115	87.28 83	91.58 105	77.69 82	83.69 157	90.30 146	66.29 240	84.32 194	91.07 166	82.13 80	93.18 121	81.02 86	96.36 90	90.98 186

agg_prior					91.58 105	77.69 82		90.30 146		84.32 194			93.18 121

Anonymous202405211		80.51 199	81.19 188	78.49 236	88.48 166	57.26 265	76.63 283	82.49 242	81.21 64	84.30 196	92.24 133	67.99 225	86.24 268	62.22 240	95.13 133	91.98 166

LFMVS		80.15 205	80.56 195	78.89 228	89.19 151	55.93 274	85.22 125	73.78 290	82.96 44	84.28 197	92.72 120	57.38 273	90.07 209	63.80 232	95.75 117	90.68 197

Vis-MVSNet		86.86 82	86.58 93	87.72 75	92.09 90	77.43 86	87.35 90	92.09 84	78.87 91	84.27 198	94.05 85	78.35 118	93.65 86	80.54 96	91.58 213	92.08 162
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MCST-MVS		84.36 134	83.93 151	85.63 111	91.59 102	71.58 137	83.52 162	92.13 83	61.82 274	83.96 199	89.75 198	79.93 108	93.46 108	78.33 121	94.34 158	91.87 168

新几何1				82.95 176	93.96 44	78.56 77		80.24 256	55.45 305	83.93 200	91.08 165	71.19 215	88.33 238	65.84 221	93.07 188	81.95 311

BH-RMVSNet		80.53 198	80.22 200	81.49 199	87.19 211	66.21 172	77.79 274	86.23 213	74.21 154	83.69 201	88.50 214	73.25 193	90.75 187	63.18 237	87.90 271	87.52 240

1121		80.86 194	79.81 205	84.02 147	93.93 45	78.70 75	81.64 216	80.18 257	55.43 306	83.67 202	91.15 160	71.29 214	91.41 171	67.95 207	93.06 189	81.96 310

agg_prior3		85.76 107	84.95 120	88.16 68	92.43 80	79.92 62	83.98 145	90.03 157	65.11 254	83.66 203	90.64 184	81.00 97	93.67 85	81.21 83	96.54 83	90.88 191

USDC		76.63 231	76.73 222	76.34 267	83.46 275	57.20 266	80.02 238	88.04 189	52.14 324	83.65 204	91.25 154	63.24 244	86.65 263	54.66 299	94.11 160	85.17 263

Effi-MVS+-dtu		85.82 106	83.38 157	93.14 3	87.13 212	91.15 3	87.70 85	88.42 180	74.57 151	83.56 205	85.65 254	78.49 116	94.21 65	72.04 174	92.88 193	94.05 100

CNLPA		83.55 159	83.10 162	84.90 119	89.34 148	83.87 43	84.54 136	88.77 175	79.09 87	83.54 206	88.66 213	74.87 158	81.73 302	66.84 213	92.29 202	89.11 222

OpenMVS_ROB	70.19 17	77.77 218	77.46 216	78.71 232	84.39 262	61.15 236	81.18 225	82.52 241	62.45 271	83.34 207	87.37 235	66.20 232	88.66 235	64.69 227	85.02 300	86.32 251

tfpn111		76.03 238	75.53 236	77.53 253	87.27 206	51.88 304	81.07 226	73.26 295	75.68 136	83.25 208	86.37 245	45.54 314	89.38 218	55.07 296	92.26 204	91.34 182

conf200view11		75.62 242	75.05 240	77.34 255	87.27 206	51.88 304	81.07 226	73.26 295	75.68 136	83.25 208	86.37 245	45.54 314	88.80 227	51.98 309	90.99 229	91.34 182

thres100view900		75.45 243	75.05 240	76.66 264	87.27 206	51.88 304	81.07 226	73.26 295	75.68 136	83.25 208	86.37 245	45.54 314	88.80 227	51.98 309	90.99 229	89.31 219

IterMVS		76.91 225	76.34 229	78.64 233	80.91 295	64.03 187	76.30 288	79.03 261	64.88 256	83.11 211	89.16 205	59.90 257	84.46 286	68.61 202	85.15 299	87.42 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 239	75.35 239	77.85 247	87.01 217	51.84 308	80.45 234	73.26 295	75.20 145	83.10 212	86.31 250	45.54 314	89.05 223	55.03 297	92.24 205	92.66 141

mvs_anonymous		78.13 214	78.76 208	76.23 269	79.24 309	50.31 324	78.69 265	84.82 233	61.60 278	83.09 213	92.82 116	73.89 175	87.01 248	68.33 204	86.41 285	91.37 180

casdiffmvs1		83.63 156	83.44 155	84.20 145	88.08 178	66.53 171	88.62 73	92.02 86	58.66 290	82.94 214	93.84 96	78.76 114	93.10 126	76.73 138	91.29 222	92.96 132

MVS_Test		82.47 174	83.22 159	80.22 214	82.62 283	57.75 262	82.54 193	91.96 90	71.16 203	82.89 215	92.52 125	77.41 127	90.50 195	80.04 104	87.84 273	92.40 152

test12				86.57 88	90.74 126	72.63 119		90.69 131		82.76 216		79.20 110	94.80 51		95.32 126	92.27 157

原ACMM1				84.60 130	92.81 73	74.01 111		91.50 102	62.59 268	82.73 217	90.67 181	76.53 142	94.25 63	69.24 192	95.69 119	85.55 259

0601test		78.71 212	78.51 211	79.32 226	84.32 263	58.84 256	78.38 267	85.33 223	75.99 131	82.49 218	86.57 241	58.01 267	90.02 210	62.74 238	92.73 196	89.10 223

diffmvs1		82.95 168	83.63 154	80.90 207	80.05 303	61.05 238	82.98 179	89.93 160	74.72 149	82.37 219	92.93 114	76.47 143	88.80 227	81.73 80	91.54 214	92.85 133

test222					93.31 58	76.54 95	79.38 254	77.79 266	52.59 319	82.36 220	90.84 175	66.83 230			91.69 211	81.25 323

VNet		79.31 208	80.27 199	76.44 265	87.92 182	53.95 287	75.58 294	84.35 235	74.39 153	82.23 221	90.72 178	72.84 197	84.39 287	60.38 257	93.98 164	90.97 187

Vis-MVSNet (Re-imp)		77.82 216	77.79 215	77.92 245	88.82 157	51.29 312	83.28 171	71.97 306	74.04 155	82.23 221	89.78 197	57.38 273	89.41 216	57.22 282	95.41 123	93.05 129

API-MVS		82.28 176	82.61 168	81.30 201	86.29 230	69.79 145	88.71 71	87.67 194	78.42 98	82.15 223	84.15 280	77.98 120	91.59 165	65.39 224	92.75 195	82.51 303

DP-MVS Recon		84.05 147	83.22 159	86.52 90	91.73 100	75.27 104	83.23 175	92.40 76	72.04 188	82.04 224	88.33 220	77.91 122	93.95 78	66.17 217	95.12 134	90.34 207

MSDG		80.06 206	79.99 204	80.25 213	83.91 272	68.04 160	77.51 277	89.19 172	77.65 106	81.94 225	83.45 286	76.37 144	86.31 267	63.31 236	86.59 283	86.41 250

Fast-Effi-MVS+		81.04 192	80.57 194	82.46 186	87.50 199	63.22 201	78.37 268	89.63 166	68.01 227	81.87 226	82.08 310	82.31 77	92.65 143	67.10 209	88.30 268	91.51 179

testgi		72.36 274	74.61 243	65.59 322	80.56 301	42.82 350	68.29 325	73.35 294	66.87 237	81.84 227	89.93 195	72.08 208	66.92 341	46.05 334	92.54 199	87.01 246

tfpn200view9		74.86 250	74.23 248	76.74 263	86.24 231	52.12 301	79.24 257	73.87 288	73.34 165	81.82 228	84.60 275	46.02 308	88.80 227	51.98 309	90.99 229	89.31 219

thres400		75.14 244	74.23 248	77.86 246	86.24 231	52.12 301	79.24 257	73.87 288	73.34 165	81.82 228	84.60 275	46.02 308	88.80 227	51.98 309	90.99 229	92.66 141

OpenMVS	76.72 13	81.98 182	82.00 179	81.93 190	84.42 261	68.22 159	88.50 75	89.48 169	66.92 236	81.80 230	91.86 138	72.59 203	90.16 204	71.19 177	91.25 223	87.40 242

AdaColmap		83.66 155	83.69 153	83.57 163	90.05 141	72.26 125	86.29 114	90.00 158	78.19 101	81.65 231	87.16 236	83.40 65	94.24 64	61.69 246	94.76 150	84.21 279

DELS-MVS		81.44 186	81.25 187	82.03 189	84.27 264	62.87 207	76.47 287	92.49 75	70.97 205	81.64 232	83.83 281	75.03 155	92.70 140	74.29 150	92.22 207	90.51 203
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		83.10 166	82.54 170	84.77 124	92.90 68	69.10 155	86.65 108	90.62 134	54.66 309	81.46 233	90.81 176	76.98 134	94.38 60	72.62 170	96.18 97	90.82 194

TR-MVS		76.77 230	75.79 232	79.72 220	86.10 242	65.79 175	77.14 279	83.02 238	65.20 253	81.40 234	82.10 309	66.30 231	90.73 189	55.57 291	85.27 296	82.65 298

TAMVS		78.08 215	76.36 228	83.23 171	90.62 128	72.87 117	79.08 261	80.01 259	61.72 276	81.35 235	86.92 239	63.96 240	88.78 232	50.61 314	93.01 191	88.04 234

Effi-MVS+		83.90 152	84.01 148	83.57 163	87.22 210	65.61 176	86.55 111	92.40 76	78.64 95	81.34 236	84.18 278	83.65 62	92.93 134	74.22 151	87.87 272	92.17 161

casdiffmvs		82.99 167	82.51 171	84.42 137	86.34 228	67.92 161	87.86 81	92.28 80	60.95 281	81.12 237	93.08 106	76.07 146	93.43 111	79.41 114	85.45 293	91.93 167

new-patchmatchnet		70.10 291	73.37 262	60.29 337	81.23 292	16.95 365	59.54 344	74.62 284	62.93 265	80.97 238	87.93 227	62.83 245	71.90 327	55.24 294	95.01 138	92.00 163

PVSNet_Blended_VisFu		81.55 185	80.49 197	84.70 128	91.58 105	73.24 116	84.21 139	91.67 97	62.86 266	80.94 239	87.16 236	67.27 228	92.87 138	69.82 188	88.94 259	87.99 235

BH-w/o		76.57 232	76.07 231	78.10 242	86.88 219	65.92 174	77.63 275	86.33 212	65.69 247	80.89 240	79.95 324	68.97 223	90.74 188	53.01 305	85.25 297	77.62 333

PAPM_NR		83.23 163	83.19 161	83.33 170	90.90 123	65.98 173	88.19 77	90.78 129	78.13 102	80.87 241	87.92 228	73.49 184	92.42 146	70.07 186	88.40 263	91.60 176

ab-mvs		79.67 207	80.56 195	76.99 257	88.48 166	56.93 267	84.70 131	86.06 215	68.95 223	80.78 242	93.08 106	75.30 152	84.62 285	56.78 284	90.90 235	89.43 218

conf0.01		74.17 256	73.53 255	76.08 270	86.13 236	50.06 327	79.45 248	68.54 327	72.01 189	80.76 243	82.50 301	41.39 338	86.83 253	59.66 261	91.36 215	91.34 182

conf0.002		74.17 256	73.53 255	76.08 270	86.13 236	50.06 327	79.45 248	68.54 327	72.01 189	80.76 243	82.50 301	41.39 338	86.83 253	59.66 261	91.36 215	91.34 182

thresconf0.02		73.65 261	73.53 255	73.98 281	86.13 236	50.06 327	79.45 248	68.54 327	72.01 189	80.76 243	82.50 301	41.39 338	86.83 253	59.66 261	91.36 215	85.06 265

tfpn_n400		73.65 261	73.53 255	73.98 281	86.13 236	50.06 327	79.45 248	68.54 327	72.01 189	80.76 243	82.50 301	41.39 338	86.83 253	59.66 261	91.36 215	85.06 265

tfpnconf		73.65 261	73.53 255	73.98 281	86.13 236	50.06 327	79.45 248	68.54 327	72.01 189	80.76 243	82.50 301	41.39 338	86.83 253	59.66 261	91.36 215	85.06 265

tfpnview11		73.65 261	73.53 255	73.98 281	86.13 236	50.06 327	79.45 248	68.54 327	72.01 189	80.76 243	82.50 301	41.39 338	86.83 253	59.66 261	91.36 215	85.06 265

tfpn1000		73.63 265	73.58 253	73.79 287	85.46 248	50.31 324	79.99 239	68.18 333	72.33 183	80.66 249	83.05 289	39.80 349	86.74 262	60.96 253	91.78 210	84.32 277

mvs-test1		84.55 127	82.12 176	91.84 17	87.13 212	89.54 5	85.05 127	88.42 180	74.57 151	80.60 250	82.98 291	78.49 116	93.98 75	72.04 174	89.77 251	92.00 163

XXY-MVS		74.44 255	76.19 230	69.21 309	84.61 257	52.43 300	71.70 314	77.18 269	60.73 284	80.60 250	90.96 171	75.44 149	69.35 333	56.13 287	88.33 264	85.86 257

HQP4-MVS										80.56 252			94.61 55			93.56 119

HQP-NCC					91.19 115		84.77 128		73.30 167	80.55 253

ACMP_Plane					91.19 115		84.77 128		73.30 167	80.55 253

HQP-MVS		84.61 125	84.06 147	86.27 95	91.19 115	70.66 142	84.77 128	92.68 69	73.30 167	80.55 253	90.17 193	72.10 206	94.61 55	77.30 132	94.47 156	93.56 119

HyFIR lowres test		75.12 246	72.66 268	82.50 185	91.44 111	65.19 178	72.47 311	87.31 197	46.79 345	80.29 256	84.30 277	52.70 290	92.10 153	51.88 313	86.73 282	90.22 208

test20.03		73.75 260	74.59 245	71.22 303	81.11 293	51.12 314	70.15 320	72.10 305	70.42 208	80.28 257	91.50 150	64.21 238	74.72 323	46.96 332	94.58 154	87.82 239

F-COLMAP		84.97 119	83.42 156	89.63 50	92.39 81	83.40 45	88.83 68	91.92 91	73.19 171	80.18 258	89.15 206	77.04 133	93.28 118	65.82 222	92.28 203	92.21 160

GA-MVS		75.83 240	74.61 243	79.48 225	81.87 286	59.25 254	73.42 309	82.88 239	68.68 224	79.75 259	81.80 312	50.62 294	89.46 214	66.85 212	85.64 292	89.72 214

xiu_mvs_v1_base_debu		80.84 195	80.14 201	82.93 177	88.31 170	71.73 133	79.53 244	87.17 200	65.43 249	79.59 260	82.73 298	76.94 135	90.14 205	73.22 163	88.33 264	86.90 247

xiu_mvs_v1_base		80.84 195	80.14 201	82.93 177	88.31 170	71.73 133	79.53 244	87.17 200	65.43 249	79.59 260	82.73 298	76.94 135	90.14 205	73.22 163	88.33 264	86.90 247

xiu_mvs_v1_base_debi		80.84 195	80.14 201	82.93 177	88.31 170	71.73 133	79.53 244	87.17 200	65.43 249	79.59 260	82.73 298	76.94 135	90.14 205	73.22 163	88.33 264	86.90 247

UnsupCasMVSNet_eth		71.63 281	72.30 273	69.62 307	76.47 329	52.70 298	70.03 321	80.97 254	59.18 288	79.36 263	88.21 222	60.50 251	69.12 334	58.33 277	77.62 336	87.04 245

ppachtmachnet_test		74.73 252	74.00 250	76.90 259	80.71 299	56.89 269	71.53 315	78.42 263	58.24 293	79.32 264	82.92 295	57.91 269	84.26 288	65.60 223	91.36 215	89.56 215

MG-MVS		80.32 201	80.94 192	78.47 237	88.18 173	52.62 299	82.29 199	85.01 231	72.01 189	79.24 265	92.54 124	69.36 219	93.36 114	70.65 182	89.19 257	89.45 216

Fast-Effi-MVS+-dtu		82.54 173	81.41 185	85.90 106	85.60 244	76.53 97	83.07 177	89.62 167	73.02 174	79.11 266	83.51 284	80.74 100	90.24 201	68.76 199	89.29 254	90.94 188

CDS-MVSNet		77.32 221	75.40 237	83.06 174	89.00 154	72.48 122	77.90 273	82.17 245	60.81 282	78.94 267	83.49 285	59.30 261	88.76 233	54.64 300	92.37 201	87.93 237
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres200		72.34 275	71.55 280	74.70 279	83.48 274	51.60 309	75.02 298	73.71 291	70.14 212	78.56 268	80.57 319	46.20 306	88.20 240	46.99 331	89.29 254	84.32 277

tfpn_ndepth		72.54 272	72.30 273	73.24 290	84.81 255	51.42 310	79.24 257	70.49 320	69.26 218	78.48 269	79.80 325	40.16 348	86.77 260	58.08 280	90.43 246	81.53 319

diffmvs		81.78 184	82.36 174	80.02 216	79.06 311	59.93 248	83.30 170	88.41 182	73.47 161	78.38 270	92.05 135	75.85 148	88.38 237	80.73 93	89.98 250	91.76 171

testmv		70.47 288	70.70 283	69.77 306	86.22 233	53.89 288	67.32 330	71.91 307	63.32 261	78.16 271	89.47 202	56.12 279	73.10 324	36.43 350	87.33 277	82.33 305

our_test_3		71.85 278	71.59 278	72.62 297	80.71 299	53.78 289	69.72 322	71.71 312	58.80 289	78.03 272	80.51 320	56.61 276	78.84 311	62.20 241	86.04 289	85.23 262

jason		77.42 220	75.75 234	82.43 187	87.10 215	69.27 150	77.99 271	81.94 248	51.47 329	77.84 273	85.07 267	60.32 253	89.00 224	70.74 181	89.27 256	89.03 225
jason: jason.
MAR-MVS		80.24 203	78.74 209	84.73 126	86.87 220	78.18 78	85.75 118	87.81 193	65.67 248	77.84 273	78.50 330	73.79 176	90.53 194	61.59 249	90.87 236	85.49 261
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		72.29 276	72.00 275	73.14 292	88.63 162	85.00 32	74.65 301	67.39 336	71.94 196	77.80 275	87.66 231	50.48 295	75.83 319	49.95 316	79.51 328	58.58 356

pmmvs4		74.92 249	72.98 266	80.73 210	84.95 252	71.71 136	76.23 289	77.59 267	52.83 318	77.73 276	86.38 244	56.35 277	84.97 281	57.72 281	87.05 280	85.51 260

UnsupCasMVSNet_bld		69.21 299	69.68 291	67.82 316	79.42 306	51.15 313	67.82 329	75.79 276	54.15 311	77.47 277	85.36 264	59.26 262	70.64 329	48.46 324	79.35 330	81.66 316

MVS_0304		84.88 120	83.96 150	87.64 78	87.43 201	74.83 106	84.18 140	93.30 43	77.48 109	77.39 278	88.46 215	74.53 167	95.74 18	78.09 125	94.75 151	92.36 153

no-one		71.52 282	70.43 287	74.81 277	78.45 317	63.41 194	57.73 350	77.03 270	51.46 330	77.17 279	90.33 188	54.96 286	80.35 307	47.41 328	99.29 2	80.68 330

Anonymous20231206		71.38 283	71.88 276	69.88 304	86.31 229	54.37 284	70.39 319	74.62 284	52.57 320	76.73 280	88.76 210	59.94 256	72.06 326	44.35 337	93.23 187	83.23 294

CMPMVS	59.41 20	75.12 246	73.57 254	79.77 218	75.84 333	67.22 164	81.21 224	82.18 244	50.78 334	76.50 281	87.66 231	55.20 284	82.99 296	62.17 243	90.64 244	89.09 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 277	71.69 277	73.32 288	81.57 289	53.02 295	76.77 281	78.37 264	63.31 262	76.37 282	91.85 139	36.68 352	78.98 310	47.87 327	92.45 200	87.95 236

CVMVSNet		72.62 271	71.41 281	76.28 268	83.25 277	60.34 245	83.50 165	79.02 262	37.77 358	76.33 283	85.10 265	49.60 297	87.41 245	70.54 184	77.54 337	81.08 326

PLC	73.85 16	82.09 179	80.31 198	87.45 80	90.86 125	80.29 60	85.88 116	90.65 132	68.17 226	76.32 284	86.33 248	73.12 194	92.61 144	61.40 250	90.02 249	89.44 217
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 177	81.57 184	84.19 146	85.54 246	69.26 151	91.98 25	90.08 155	71.54 200	76.23 285	85.07 267	58.69 264	94.27 61	86.26 28	88.77 260	89.03 225

lupinMVS		76.37 235	74.46 246	82.09 188	85.54 246	69.26 151	76.79 280	80.77 255	50.68 336	76.23 285	82.82 296	58.69 264	88.94 225	69.85 187	88.77 260	88.07 232

PatchMatch-RL		74.48 253	73.22 263	78.27 240	87.70 194	85.26 30	75.92 290	70.09 321	64.34 258	76.09 287	81.25 317	65.87 235	78.07 312	53.86 302	83.82 308	71.48 344

MS-PatchMatch		70.93 285	70.22 288	73.06 293	81.85 287	62.50 216	73.82 307	77.90 265	52.44 321	75.92 288	81.27 316	55.67 281	81.75 301	55.37 293	77.70 335	74.94 338

CHOSEN 1792x2688		72.45 273	70.56 284	78.13 241	90.02 143	63.08 202	68.72 324	83.16 237	42.99 354	75.92 288	85.46 259	57.22 275	85.18 280	49.87 318	81.67 320	86.14 253

CR-MVSNet		74.00 258	73.04 265	76.85 261	79.58 304	62.64 210	82.58 190	76.90 271	50.50 337	75.72 290	92.38 126	48.07 301	84.07 289	68.72 201	82.91 314	83.85 283

RPMNet		76.06 237	75.79 232	76.85 261	79.58 304	62.64 210	82.58 190	71.75 310	74.80 147	75.72 290	92.59 121	48.69 299	84.07 289	73.48 161	82.91 314	83.85 283

PVSNet_BlendedMVS		78.80 210	77.84 214	81.65 198	84.43 259	63.41 194	79.49 247	90.44 138	61.70 277	75.43 292	87.07 238	69.11 221	91.44 169	60.68 255	92.24 205	90.11 212

PVSNet_Blended		76.49 234	75.40 237	79.76 219	84.43 259	63.41 194	75.14 297	90.44 138	57.36 297	75.43 292	78.30 331	69.11 221	91.44 169	60.68 255	87.70 275	84.42 275

PAPR		78.84 209	78.10 213	81.07 205	85.17 250	60.22 246	82.21 203	90.57 135	62.51 269	75.32 294	84.61 274	74.99 156	92.30 148	59.48 268	88.04 270	90.68 197

N_pmnet		70.20 289	68.80 296	74.38 280	80.91 295	84.81 35	59.12 347	76.45 275	55.06 307	75.31 295	82.36 307	55.74 280	54.82 358	47.02 330	87.24 279	83.52 287

cascas		76.29 236	74.81 242	80.72 211	84.47 258	62.94 205	73.89 306	87.34 196	55.94 303	75.16 296	76.53 338	63.97 239	91.16 175	65.00 225	90.97 233	88.06 233

Patchmatch-test1		72.75 270	72.61 269	73.19 291	81.62 288	55.86 275	78.89 263	71.37 313	61.73 275	74.93 297	82.15 308	60.46 252	81.80 300	59.68 260	82.63 318	81.92 312

1111		61.71 320	63.77 316	55.55 343	78.05 318	25.74 362	60.62 341	67.52 334	66.09 242	74.68 298	86.50 242	16.00 367	59.22 356	38.79 345	85.65 291	81.70 314

.test1245		48.02 337	54.41 336	28.84 350	78.05 318	25.74 362	60.62 341	67.52 334	66.09 242	74.68 298	86.50 242	16.00 367	59.22 356	38.79 345	1.47 362	1.55 363

xiu_mvs_v2_base		77.19 222	76.75 221	78.52 235	87.01 217	61.30 234	75.55 295	87.12 205	61.24 280	74.45 300	78.79 329	77.20 129	90.93 181	64.62 229	84.80 304	83.32 292

CANet		83.79 153	82.85 164	86.63 87	86.17 235	72.21 127	83.76 155	91.43 111	77.24 117	74.39 301	87.45 234	75.36 151	95.42 31	77.03 136	92.83 194	92.25 159

PS-MVSNAJ		77.04 224	76.53 227	78.56 234	87.09 216	61.40 232	75.26 296	87.13 203	61.25 279	74.38 302	77.22 335	76.94 135	90.94 180	64.63 228	84.83 303	83.35 291

MVP-Stereo		75.81 241	73.51 261	82.71 180	89.35 147	73.62 112	80.06 236	85.20 225	60.30 285	73.96 303	87.94 226	57.89 270	89.45 215	52.02 308	74.87 341	85.06 265
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 169	81.64 182	86.21 99	86.20 234	76.24 101	86.86 98	85.68 219	77.07 120	73.76 304	92.82 116	69.64 218	91.82 161	69.04 197	93.69 172	90.56 201
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		74.82 251	73.74 251	78.04 243	89.57 145	60.04 247	76.49 286	87.09 206	54.31 310	73.66 305	79.80 325	60.25 254	86.76 261	58.37 275	84.15 307	87.32 243

Test_1112_low_res		73.90 259	73.08 264	76.35 266	90.35 133	55.95 273	73.40 310	86.17 214	50.70 335	73.14 306	85.94 253	58.31 266	85.90 273	56.51 285	83.22 311	87.20 244

1314		73.22 266	72.56 271	75.20 275	80.41 302	57.84 260	81.64 216	85.36 222	51.68 327	73.10 307	76.65 337	61.45 248	85.19 279	63.54 233	79.21 332	82.59 299

Patchmatch-test		65.91 311	67.38 302	61.48 334	75.51 337	43.21 349	68.84 323	63.79 346	62.48 270	72.80 308	83.42 287	44.89 329	59.52 355	48.27 326	86.45 284	81.70 314

PatchmatchNet		69.71 295	68.83 295	72.33 299	77.66 322	53.60 290	79.29 255	69.99 322	57.66 296	72.53 309	82.93 294	46.45 305	80.08 309	60.91 254	72.09 346	83.31 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test1235678		65.57 313	65.73 310	65.06 325	82.84 282	50.90 316	62.90 338	69.26 324	57.17 300	72.36 310	83.04 290	46.02 308	70.10 330	32.79 355	85.24 298	74.19 340

tpm		67.95 302	68.08 301	67.55 317	78.74 315	43.53 348	75.60 293	67.10 341	54.92 308	72.23 311	88.10 223	42.87 335	75.97 318	52.21 307	80.95 326	83.15 295

pmmvs5		70.73 286	70.07 289	72.72 295	77.03 326	52.73 297	74.14 303	75.65 279	50.36 338	72.17 312	85.37 263	55.42 283	80.67 305	52.86 306	87.59 276	84.77 271

PatchT		70.52 287	72.76 267	63.79 328	79.38 307	33.53 358	77.63 275	65.37 344	73.61 159	71.77 313	92.79 119	44.38 331	75.65 320	64.53 230	85.37 295	82.18 307

MVS		73.21 267	72.59 270	75.06 276	80.97 294	60.81 242	81.64 216	85.92 217	46.03 348	71.68 314	77.54 332	68.47 224	89.77 211	55.70 290	85.39 294	74.60 339

testus		62.33 318	63.03 318	60.20 338	78.78 314	40.74 351	59.14 345	69.80 323	49.26 341	71.41 315	74.72 344	52.33 291	63.52 350	29.84 357	82.01 319	76.36 335

MIMVSNet		71.09 284	71.59 278	69.57 308	87.23 209	50.07 326	78.91 262	71.83 308	60.20 286	71.26 316	91.76 144	55.08 285	76.09 317	41.06 342	87.02 281	82.54 301

WTY-MVS		67.91 303	68.35 298	66.58 320	80.82 297	48.12 336	65.96 334	72.60 301	53.67 314	71.20 317	81.68 314	58.97 263	69.06 335	48.57 323	81.67 320	82.55 300

test0.0.03 1		64.66 315	64.36 314	65.57 323	75.03 343	46.89 338	64.69 336	61.58 352	62.43 272	71.18 318	77.54 332	43.41 332	68.47 336	40.75 343	82.65 316	81.35 320

CostFormer		69.98 294	68.68 297	73.87 285	77.14 324	50.72 322	79.26 256	74.51 286	51.94 326	70.97 319	84.75 272	45.16 328	87.49 244	55.16 295	79.23 331	83.40 290

LP		69.42 297	68.30 299	72.77 294	71.48 358	56.84 271	73.66 308	74.84 282	63.52 260	70.95 320	83.35 288	49.55 298	77.15 315	57.13 283	70.21 349	84.33 276

tpmvs		70.16 290	69.56 292	71.96 301	74.71 345	48.13 335	79.63 242	75.45 280	65.02 255	70.26 321	81.88 311	45.34 321	85.68 275	58.34 276	75.39 340	82.08 308

sss		66.92 306	67.26 304	65.90 321	77.23 323	51.10 315	64.79 335	71.72 311	52.12 325	70.13 322	80.18 322	57.96 268	65.36 348	50.21 315	81.01 325	81.25 323

tpm2		68.45 301	66.83 305	73.30 289	78.93 313	48.50 334	79.76 241	71.76 309	47.50 344	69.92 323	83.60 283	42.07 337	88.40 236	48.44 325	79.51 328	83.01 297

HY-MVS	64.64 18	73.03 268	72.47 272	74.71 278	83.36 276	54.19 285	82.14 206	81.96 246	56.76 302	69.57 324	86.21 251	60.03 255	84.83 284	49.58 320	82.65 316	85.11 264

PatchFormer-LS_test		67.91 303	66.49 309	72.17 300	75.29 340	51.85 307	75.68 291	73.62 293	57.23 299	68.64 325	68.13 354	42.19 336	82.76 297	64.06 231	73.51 343	81.89 313

tpm cat1		66.76 307	65.21 312	71.42 302	77.09 325	50.62 323	78.01 270	73.68 292	44.89 350	68.64 325	79.00 328	45.51 318	82.42 299	49.91 317	70.15 350	81.23 325

IB-MVS	62.13 19	71.64 280	68.97 294	79.66 222	80.80 298	62.26 224	73.94 305	76.90 271	63.27 263	68.63 327	76.79 336	33.83 355	91.84 159	59.28 269	87.26 278	84.88 270
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		80.37 200	78.41 212	86.23 96	76.75 327	73.28 115	87.18 94	77.45 268	76.24 127	68.14 328	88.93 209	65.41 236	93.85 80	69.47 190	96.12 102	91.55 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 309	65.63 311	68.75 312	81.96 285	49.88 333	62.19 340	72.51 303	51.03 332	68.04 329	75.34 342	50.84 293	74.77 321	45.82 335	82.96 312	81.60 317

tpmrst		66.28 310	66.69 307	65.05 326	72.82 354	39.33 353	78.20 269	70.69 319	53.16 317	67.88 330	80.36 321	48.18 300	74.75 322	58.13 278	70.79 348	81.08 326

CANet_DTU		77.81 217	77.05 218	80.09 215	81.37 290	59.90 249	83.26 172	88.29 184	69.16 220	67.83 331	83.72 282	60.93 249	89.47 213	69.22 194	89.70 252	90.88 191

EPMVS		62.47 316	62.63 320	62.01 330	70.63 359	38.74 354	74.76 299	52.86 361	53.91 313	67.71 332	80.01 323	39.40 350	66.60 343	55.54 292	68.81 355	80.68 330

MDTV_nov1_ep13			68.29 300		78.03 320	43.87 347	74.12 304	72.22 304	52.17 322	67.02 333	85.54 255	45.36 320	80.85 304	55.73 288	84.42 306

tpmp4_e23		69.43 296	67.33 303	75.72 273	78.53 316	52.75 296	82.13 207	74.91 281	49.23 342	66.37 334	84.17 279	41.28 344	88.67 234	49.73 319	79.63 327	85.75 258

pmmvs3		62.47 316	60.02 329	69.80 305	71.58 357	64.00 188	70.52 318	58.44 356	39.77 356	66.05 335	75.84 339	27.10 364	72.28 325	46.15 333	84.77 305	73.11 342

ADS-MVSNet2		65.87 312	63.64 317	72.55 298	73.16 351	56.92 268	67.10 331	74.81 283	49.74 339	66.04 336	82.97 292	46.71 303	77.26 313	42.29 339	69.96 351	83.46 288

ADS-MVSNet		61.90 319	62.19 321	61.03 336	73.16 351	36.42 356	67.10 331	61.75 350	49.74 339	66.04 336	82.97 292	46.71 303	63.21 352	42.29 339	69.96 351	83.46 288

DWT-MVSNet_test		66.43 308	64.37 313	72.63 296	74.86 344	50.86 317	76.52 285	72.74 300	54.06 312	65.50 338	68.30 353	32.13 357	84.84 283	61.63 248	73.59 342	82.19 306

DSMNet-mixed		60.98 326	61.61 323	59.09 340	72.88 353	45.05 344	74.70 300	46.61 365	26.20 360	65.34 339	90.32 189	55.46 282	63.12 353	41.72 341	81.30 324	69.09 348

JIA-IIPM		69.41 298	66.64 308	77.70 248	73.19 350	71.24 139	75.67 292	65.56 343	70.42 208	65.18 340	92.97 111	33.64 356	83.06 295	53.52 304	69.61 353	78.79 332

test-LLR		67.21 305	66.74 306	68.63 313	76.45 330	55.21 280	67.89 326	67.14 339	62.43 272	65.08 341	72.39 346	43.41 332	69.37 331	61.00 251	84.89 301	81.31 321

test-mter		65.00 314	63.79 315	68.63 313	76.45 330	55.21 280	67.89 326	67.14 339	50.98 333	65.08 341	72.39 346	28.27 362	69.37 331	61.00 251	84.89 301	81.31 321

PMMVS2		55.64 333	59.27 330	44.74 348	64.30 364	12.32 366	40.60 358	49.79 364	53.19 316	65.06 343	84.81 271	53.60 289	49.76 360	32.68 356	89.41 253	72.15 343

gg-mvs-nofinetune		68.96 300	69.11 293	68.52 315	76.12 332	45.32 341	83.59 158	55.88 359	86.68 20	64.62 344	97.01 7	30.36 359	83.97 292	44.78 336	82.94 313	76.26 336

PAPM		71.77 279	70.06 290	76.92 258	86.39 223	53.97 286	76.62 284	86.62 210	53.44 315	63.97 345	84.73 273	57.79 271	92.34 147	39.65 344	81.33 323	84.45 274

new_pmnet		55.69 332	57.66 331	49.76 345	75.47 338	30.59 359	59.56 343	51.45 363	43.62 353	62.49 346	75.48 340	40.96 346	49.15 361	37.39 349	72.52 344	69.55 347

test2356		56.69 330	55.15 334	61.32 335	73.20 349	44.11 346	54.95 352	62.52 347	48.75 343	62.45 347	68.42 351	21.10 366	65.67 347	26.86 359	78.08 334	74.19 340

MDTV_nov1_ep13_2view						27.60 361	70.76 317		46.47 347	61.27 348		45.20 322		49.18 321		83.75 285

dp		60.70 327	60.29 328	61.92 332	72.04 356	38.67 355	70.83 316	64.08 345	51.28 331	60.75 349	77.28 334	36.59 353	71.58 328	47.41 328	62.34 357	75.52 337

TESTMET0.1,1		61.29 323	60.32 327	64.19 327	72.06 355	51.30 311	67.89 326	62.09 348	45.27 349	60.65 350	69.01 349	27.93 363	64.74 349	56.31 286	81.65 322	76.53 334

test12356		54.91 334	57.14 332	48.22 347	75.83 334	17.47 364	52.31 356	69.20 325	51.66 328	60.11 351	75.40 341	29.77 361	62.62 354	27.64 358	72.37 345	64.59 350

PMMVS		61.65 321	60.38 326	65.47 324	65.40 363	69.26 151	63.97 337	61.73 351	36.80 359	60.11 351	68.43 350	59.42 260	66.35 344	48.97 322	78.57 333	60.81 353

testpf		58.55 329	61.58 324	49.48 346	66.03 361	40.05 352	74.40 302	58.07 357	64.72 257	59.36 353	72.67 345	22.76 365	66.92 341	67.07 210	69.15 354	41.46 359

PVSNet_0	51.08 22	56.10 331	54.97 335	59.48 339	75.12 342	53.28 294	55.16 351	61.89 349	44.30 351	59.16 354	62.48 358	54.22 287	65.91 346	35.40 352	47.01 358	59.25 355

MVS-HIRNet		61.16 324	62.92 319	55.87 341	79.09 310	35.34 357	71.83 313	57.98 358	46.56 346	59.05 355	91.14 161	49.95 296	76.43 316	38.74 347	71.92 347	55.84 357

E-PMN		61.59 322	61.62 322	61.49 333	66.81 360	55.40 278	53.77 354	60.34 353	66.80 238	58.90 356	65.50 356	40.48 347	66.12 345	55.72 289	86.25 287	62.95 352

GG-mvs-BLEND				67.16 318	73.36 348	46.54 340	84.15 141	55.04 360		58.64 357	61.95 359	29.93 360	83.87 293	38.71 348	76.92 338	71.07 345

EPNet_dtu		72.87 269	71.33 282	77.49 254	77.72 321	60.55 244	82.35 197	75.79 276	66.49 239	58.39 358	81.06 318	53.68 288	85.98 271	53.55 303	92.97 192	85.95 255
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PNet_i23d		52.13 335	51.24 337	54.79 344	75.56 335	45.26 342	54.54 353	52.55 362	66.95 235	57.19 359	65.82 355	13.15 369	63.40 351	36.39 351	39.04 360	55.71 358

EMVS		61.10 325	60.81 325	61.99 331	65.96 362	55.86 275	53.10 355	58.97 355	67.06 234	56.89 360	63.33 357	40.98 345	67.03 340	54.79 298	86.18 288	63.08 351

CHOSEN 280x420		59.08 328	56.52 333	66.76 319	76.51 328	64.39 185	49.62 357	59.00 354	43.86 352	55.66 361	68.41 352	35.55 354	68.21 337	43.25 338	76.78 339	67.69 349

MVE	40.22 23	51.82 336	50.47 339	55.87 341	62.66 365	51.91 303	31.61 360	39.28 366	40.65 355	50.76 362	74.98 343	56.24 278	44.67 362	33.94 354	64.11 356	71.04 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 351	32.95 366	29.49 360		21.63 369	12.07 361	37.95 363	45.07 360	30.84 358	19.21 363	17.94 361	33.06 361	23.69 360

tmp_tt		20.25 341	24.50 342	7.49 352	4.47 367	8.70 367	34.17 359	25.16 368	1.00 362	32.43 364	18.49 361	39.37 351	9.21 364	21.64 360	43.75 359	4.57 361

testmvs		5.91 345	7.65 346	0.72 354	1.20 368	0.37 369	59.14 345	0.67 371	0.49 364	1.11 365	2.76 365	0.94 371	0.24 366	1.02 363	1.47 362	1.55 363

test123		6.27 344	8.08 345	0.84 353	1.11 369	0.57 368	62.90 338	0.82 370	0.54 363	1.07 366	2.75 366	1.26 370	0.30 365	1.04 362	1.26 364	1.66 362

cdsmvs_eth3d_5k		20.81 340	27.75 341	0.00 355	0.00 370	0.00 370	0.00 361	85.44 221	0.00 365	0.00 367	82.82 296	81.46 93	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		6.41 343	8.55 344	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	76.94 135	0.00 367	0.00 364	0.00 365	0.00 365

pcd1.5k->3k		38.83 338	41.11 340	32.01 349	93.13 63	0.00 370	0.00 361	91.38 117	0.00 365	0.00 367	0.00 367	89.24 15	0.00 367	0.00 364	96.24 96	96.02 49

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

ab-mvs-re		6.65 342	8.87 343	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	79.80 325	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																83.88 281

test_part1				0.00 355		0.00 370	0.00 361	93.93 26				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sam_mvs1												46.11 307				83.88 281

sam_mvs												45.92 312

MTGPA								91.81 94

test_post1							78.85 264				3.13 363	45.19 323	80.13 308	58.11 279

test_post											3.10 364	45.43 319	77.22 314

patchmatchnet-post											81.71 313	45.93 311	87.01 248

MTMP							90.66 35	33.14 367

gm-plane-assit					75.42 339	44.97 345			52.17 322		72.36 348		87.90 241	54.10 301

test9_res														80.83 90	96.45 88	90.57 200

agg_prior2														79.68 108	96.16 98	90.22 208

test_prior4						78.97 72	84.59 133

test_prior				86.32 93	90.59 129	71.99 130		92.85 63					94.17 68			92.80 136

新几何2							81.72 215

旧先验1					91.97 93	71.77 132		81.78 249			91.84 140	73.92 174			93.65 174	83.61 286

无先验							82.81 184	85.62 220	58.09 294				91.41 171	67.95 207		84.48 273

原ACMM2							82.26 202

testdata2													86.43 266	63.52 234

segment_acmp												81.94 85

testdata1							79.62 243		73.95 157

plane_prior7					93.45 53	77.31 88

plane_prior6					92.61 74	76.54 95						74.84 159

plane_prior5								93.61 33					95.22 39	80.78 91	95.83 114	94.46 88

plane_prior4											92.95 112

plane_prior2							89.45 57		79.44 82

plane_prior1					92.83 72

plane_prior						76.42 98	87.15 95		75.94 133						95.03 137

n2								0.00 372

nn								0.00 372

door-mid								74.45 287

test11								91.46 108

door								72.57 302

HQP5-MVS						70.66 142

BP-MVS														77.30 132

HQP3-MVS								92.68 69							94.47 156

HQP2-MVS												72.10 206

NP-MVS					91.95 94	74.55 108					90.17 193

ACMMP++_ref															95.74 118

ACMMP++															97.35 61

Test By Simon												79.09 111

High-resolution multi-view benchmark