High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

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

no-one		87.84 247	87.21 249	89.74 248	93.58 278	78.64 250	81.28 338	92.69 261	74.36 304	92.05 220	97.14 88	81.86 240	96.07 294	72.03 315	99.90 2	94.52 275

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

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

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

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

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

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

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

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

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

LTVRE_ROB	93.87 1	97.93 2	98.16 3	97.26 23	98.81 23	93.86 27	99.07 2	98.98 3	97.01 11	98.92 5	98.78 14	95.22 32	98.61 158	96.85 4	99.77 12	99.31 38
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
v7n		96.82 11	97.31 10	95.33 79	98.54 39	86.81 125	96.83 19	98.07 35	96.59 17	98.46 19	98.43 32	92.91 75	99.52 17	96.25 8	99.76 13	99.65 9

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

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

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

OurMVSNet-221017-0		96.80 14	96.75 20	96.96 32	99.03 10	91.85 52	97.98 6	98.01 43	94.15 44	98.93 4	99.07 5	88.07 169	99.57 13	95.86 11	99.69 15	99.46 25

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

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

DeepC-MVS	91.39 4	95.43 61	95.33 77	95.71 67	97.67 96	90.17 68	93.86 125	98.02 42	87.35 198	96.22 90	97.99 47	94.48 51	99.05 82	92.73 78	99.68 18	97.93 144
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v12		95.29 70	96.02 50	93.10 151	97.14 116	80.63 198	95.39 68	97.55 86	93.19 61	97.98 30	98.44 30	89.40 145	99.16 66	95.38 16	99.67 21	99.52 20

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ACMH	88.36 12	96.59 26	97.43 5	94.07 122	98.56 35	85.33 150	96.33 39	98.30 16	94.66 35	98.72 9	98.30 36	97.51 5	98.00 211	94.87 21	99.59 34	98.86 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		95.35 66	95.21 83	95.76 64	97.69 94	88.59 95	92.26 177	97.84 61	94.91 31	96.80 64	95.78 166	90.42 129	99.41 32	91.60 106	99.58 39	99.29 39

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

ACMP	88.15 13	95.71 54	95.43 73	96.54 42	98.17 65	91.73 55	94.24 110	98.08 32	89.46 149	96.61 73	96.47 121	95.85 17	99.12 74	90.45 118	99.56 41	98.77 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		94.68 98	95.27 81	92.90 163	96.57 147	80.15 205	94.65 94	97.57 82	90.68 128	97.43 45	98.00 46	88.18 161	99.15 68	94.84 24	99.55 42	99.41 28

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

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

pcd1.5k->3k		41.03 331	43.65 333	33.18 344	98.74 26	0.00 363	0.00 354	97.57 82	0.00 358	0.00 359	0.00 360	97.01 6	0.00 361	0.00 358	99.52 45	99.53 17

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

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

MP-MVS-pluss		96.08 47	95.92 53	96.57 41	99.06 9	91.21 60	93.25 142	98.32 13	87.89 190	96.86 62	97.38 76	95.55 21	99.39 41	95.47 13	99.47 48	99.11 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_tets		96.83 10	96.71 21	97.17 25	98.83 21	92.51 43	96.58 27	97.61 78	87.57 196	98.80 8	98.90 9	96.50 11	99.59 12	96.15 9	99.47 48	99.40 31

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

CLD-MVS		91.82 178	91.41 182	93.04 152	96.37 160	83.65 167	86.82 308	97.29 113	84.65 235	92.27 215	89.67 310	92.20 87	97.85 228	83.95 219	99.47 48	97.62 168
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
jajsoiax		96.59 26	96.42 29	97.12 27	98.76 25	92.49 44	96.44 35	97.42 96	86.96 206	98.71 10	98.72 17	95.36 26	99.56 16	95.92 10	99.45 52	99.32 37

test_djsdf		96.62 23	96.49 28	97.01 30	98.55 38	91.77 54	97.15 13	97.37 100	88.98 157	98.26 23	98.86 10	93.35 67	99.60 8	96.41 6	99.45 52	99.66 7

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

COLMAP_ROB	91.06 5	96.75 17	96.62 24	97.13 26	98.38 50	94.31 12	96.79 21	98.32 13	96.69 15	96.86 62	97.56 65	95.48 22	98.77 138	90.11 132	99.44 54	98.31 120
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
zzz-MVS		96.47 31	96.14 40	97.47 11	98.95 15	94.05 18	93.69 129	97.62 75	94.46 40	96.29 84	96.94 95	93.56 58	99.37 45	94.29 35	99.42 56	98.99 70

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

pm-mvs1		95.43 61	95.94 51	93.93 128	98.38 50	85.08 152	95.46 67	97.12 125	91.84 99	97.28 48	98.46 28	95.30 29	97.71 239	90.17 130	99.42 56	98.99 70

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

wuyk23d		87.83 248	90.79 197	78.96 334	90.46 319	88.63 93	92.72 155	90.67 282	91.65 110	98.68 11	97.64 62	96.06 15	77.53 355	59.84 345	99.41 60	70.73 352

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

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

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

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

HPM-MVS		96.81 13	96.62 24	97.36 20	98.89 18	93.53 34	97.51 9	98.44 8	92.35 81	95.95 103	96.41 126	96.71 9	99.42 28	93.99 42	99.36 65	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SMA-MVS		95.85 52	95.63 65	96.51 43	98.27 58	91.30 58	95.09 77	97.88 56	86.59 211	97.63 38	97.51 70	94.82 43	99.29 54	93.55 52	99.34 66	98.93 79

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

SteuartSystems-ACMMP		96.40 37	96.30 33	96.71 38	98.63 28	91.96 50	95.70 59	98.01 43	93.34 60	96.64 71	96.57 117	94.99 40	99.36 47	93.48 55	99.34 66	98.82 91
Skip Steuart: Steuart Systems R&D Blog.
ACMMP		96.61 24	96.34 32	97.43 15	98.61 31	93.88 25	96.95 17	98.18 25	92.26 84	96.33 80	96.84 104	95.10 35	99.40 36	93.47 56	99.33 69	99.02 67
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
ACMM	88.83 9	96.30 42	96.07 46	96.97 31	98.39 49	92.95 41	94.74 90	98.03 40	90.82 125	97.15 52	96.85 102	96.25 14	99.00 92	93.10 69	99.33 69	98.95 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v16		94.79 94	95.44 72	92.83 166	96.73 135	80.03 212	94.85 86	97.41 97	92.23 86	97.41 47	98.04 42	88.40 159	99.06 80	94.56 27	99.30 71	99.41 28

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

MP-MVS		96.14 45	95.68 63	97.51 10	98.81 23	94.06 16	96.10 47	97.78 67	92.73 69	93.48 180	96.72 111	94.23 52	99.42 28	91.99 96	99.29 73	99.05 63
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_0402		95.73 53	96.22 37	94.26 118	98.19 64	85.77 145	93.24 143	97.24 117	96.88 14	97.69 36	97.77 58	94.12 54	99.13 72	91.54 110	99.29 73	97.88 150

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

ACMMP++															99.25 76

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

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

TransMVSNet (Re)		95.27 73	96.04 48	92.97 157	98.37 52	81.92 183	95.07 79	96.76 151	93.97 47	97.77 34	98.57 21	95.72 18	97.90 214	88.89 157	99.23 79	99.08 59

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

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

SD-MVS		95.19 74	95.73 62	93.55 138	96.62 144	88.88 90	94.67 92	98.05 37	91.26 116	97.25 51	96.40 127	95.42 23	94.36 320	92.72 79	99.19 82	97.40 179

Vis-MVSNet (Re-imp)		90.42 202	90.16 205	91.20 222	97.66 97	77.32 262	94.33 108	87.66 300	91.20 118	92.99 196	95.13 189	75.40 278	98.28 193	77.86 277	99.19 82	97.99 139

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

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

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

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

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

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

region2R		96.41 36	96.09 44	97.38 19	98.62 29	93.81 31	96.32 40	97.96 49	92.26 84	95.28 129	96.57 117	95.02 38	99.41 32	93.63 49	99.11 90	98.94 78

test_part1								98.14 28				94.69 45			99.10 91	98.17 128

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

Gipuma		95.31 69	95.80 59	93.81 133	97.99 79	90.91 64	96.42 36	97.95 51	96.69 15	91.78 223	98.85 12	91.77 96	95.49 303	91.72 102	99.08 93	95.02 264
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OPM-MVS		95.61 57	95.45 70	96.08 50	98.49 46	91.00 62	92.65 158	97.33 109	90.05 140	96.77 66	96.85 102	95.04 36	98.56 166	92.77 75	99.06 94	98.70 102

VPNet		93.08 148	93.76 126	91.03 224	98.60 32	75.83 279	91.51 210	95.62 198	91.84 99	95.74 115	97.10 91	89.31 146	98.32 191	85.07 209	99.06 94	98.93 79

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

X-MVStestdata		90.70 197	88.45 225	97.44 13	98.56 35	93.99 22	96.50 31	97.95 51	94.58 36	94.38 158	26.89 355	94.56 48	99.39 41	93.57 50	99.05 96	98.93 79

test20.03		90.80 195	90.85 195	90.63 231	95.63 220	79.24 234	89.81 263	92.87 255	89.90 144	94.39 157	96.40 127	85.77 211	95.27 311	73.86 304	99.05 96	97.39 180

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

IterMVS-LS		93.78 122	94.28 109	92.27 190	96.27 174	79.21 239	91.87 194	96.78 149	91.77 106	96.57 75	97.07 92	87.15 187	98.74 142	91.99 96	99.03 100	98.86 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_0304		92.99 152	92.54 159	94.35 116	94.67 255	86.06 140	91.16 218	97.92 55	90.01 141	88.33 289	94.41 214	87.02 190	99.22 62	90.36 123	99.00 101	97.76 158

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

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

Patchmtry		90.11 212	89.92 208	90.66 230	90.35 321	77.00 267	92.96 149	92.81 256	90.25 138	94.74 150	96.93 97	67.11 296	97.52 245	85.17 203	98.98 102	97.46 175

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

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

EPNet		89.80 215	88.25 228	94.45 112	83.91 355	86.18 137	93.87 124	87.07 305	91.16 120	80.64 342	94.72 207	78.83 256	98.89 106	85.17 203	98.89 107	98.28 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPP-MVSNet		93.91 120	93.68 132	94.59 104	98.08 71	85.55 148	97.44 10	94.03 235	94.22 43	94.94 144	96.19 149	82.07 236	99.57 13	87.28 181	98.89 107	98.65 103

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

v1144		93.50 130	93.81 122	92.57 179	96.28 173	79.61 228	91.86 198	96.96 133	86.95 207	95.91 109	96.32 137	87.65 175	98.96 98	93.51 53	98.88 109	99.13 50

APD-MVS_3200maxsize		96.82 11	96.65 22	97.32 22	97.95 80	93.82 29	96.31 41	98.25 19	95.51 30	96.99 60	97.05 94	95.63 20	99.39 41	93.31 63	98.88 109	98.75 97

APD-MVS		95.00 81	94.69 95	95.93 56	97.38 106	90.88 65	94.59 96	97.81 63	89.22 155	95.46 124	96.17 151	93.42 63	99.34 49	89.30 145	98.87 112	97.56 172
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OMC-MVS		94.22 114	93.69 131	95.81 61	97.25 109	91.27 59	92.27 176	97.40 98	87.10 204	94.56 154	95.42 180	93.74 56	98.11 208	86.62 189	98.85 113	98.06 135

v144192		93.20 147	93.54 137	92.16 194	96.05 189	78.26 252	91.95 186	97.14 122	84.98 231	95.96 102	96.11 152	87.08 189	99.04 85	93.79 44	98.84 114	99.17 46

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

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

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

VDDNet		94.03 118	94.27 111	93.31 146	98.87 19	82.36 179	95.51 66	91.78 275	97.19 10	96.32 81	98.60 20	84.24 221	98.75 139	87.09 182	98.83 117	98.81 92

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

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

CPTT-MVS		94.74 95	94.12 114	96.60 40	98.15 66	93.01 39	95.84 56	97.66 73	89.21 156	93.28 187	95.46 177	88.89 151	98.98 93	89.80 138	98.82 120	97.80 157

ACMMP++_ref															98.82 120

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

USDC		89.02 224	89.08 214	88.84 272	95.07 240	74.50 293	88.97 282	96.39 170	73.21 312	93.27 188	96.28 139	82.16 235	96.39 287	77.55 281	98.80 124	95.62 249

PMVS	87.21 14	94.97 82	95.33 77	93.91 129	98.97 14	97.16 2	95.54 65	95.85 193	96.47 18	93.40 183	97.46 72	95.31 28	95.47 304	86.18 197	98.78 125	89.11 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TinyColmap		92.00 177	92.76 153	89.71 249	95.62 221	77.02 266	90.72 230	96.17 184	87.70 194	95.26 130	96.29 138	92.54 83	96.45 284	81.77 236	98.77 126	95.66 247

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

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

Anonymous20231206		88.77 231	88.29 227	90.20 244	96.31 171	78.81 246	89.56 268	93.49 246	74.26 306	92.38 209	95.58 172	82.21 234	95.43 306	72.07 314	98.75 129	96.34 224

UGNet		93.08 148	92.50 161	94.79 95	93.87 273	87.99 108	95.07 79	94.26 232	90.64 129	87.33 301	97.67 61	86.89 197	98.49 176	88.10 170	98.71 130	97.91 147
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LFMVS		91.33 190	91.16 190	91.82 202	96.27 174	79.36 232	95.01 82	85.61 317	96.04 27	94.82 147	97.06 93	72.03 284	98.46 182	84.96 210	98.70 131	97.65 166

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

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

DeepC-MVS_fast	89.96 7	93.73 123	93.44 139	94.60 103	96.14 184	87.90 109	93.36 135	97.14 122	85.53 223	93.90 172	95.45 178	91.30 107	98.59 162	89.51 142	98.62 134	97.31 185
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testmv		88.46 235	88.11 234	89.48 253	96.00 194	76.14 273	86.20 314	93.75 240	84.48 236	93.57 178	95.52 176	80.91 248	95.09 312	63.97 341	98.61 135	97.22 188

114514_t		90.51 199	89.80 209	92.63 176	98.00 76	82.24 180	93.40 134	97.29 113	65.84 342	89.40 272	94.80 204	86.99 192	98.75 139	83.88 220	98.61 135	96.89 201

CDPH-MVS		92.67 162	91.83 170	95.18 85	96.94 123	88.46 102	90.70 231	97.07 126	77.38 292	92.34 213	95.08 191	92.67 81	98.88 110	85.74 199	98.57 137	98.20 127

test_prior3		93.29 139	92.85 150	94.61 99	95.95 203	87.23 117	90.21 245	97.36 106	89.33 152	90.77 243	94.81 201	90.41 130	98.68 152	88.21 166	98.55 138	97.93 144

test_prior2							90.21 245		89.33 152	90.77 243	94.81 201	90.41 130		88.21 166	98.55 138

LCM-MVSNet-Re		94.20 115	94.58 99	93.04 152	95.91 206	83.13 173	93.79 126	99.19 2	92.00 93	98.84 6	98.04 42	93.64 57	99.02 89	81.28 241	98.54 140	96.96 197

Patchmatch-RL test		88.81 230	88.52 224	89.69 252	95.33 235	79.94 217	86.22 313	92.71 260	78.46 286	95.80 113	94.18 224	66.25 304	95.33 309	89.22 151	98.53 141	93.78 294

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

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

plane_prior5								97.81 63					98.95 100	89.26 149	98.51 143	98.60 109

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

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

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

train_agg		92.71 161	91.83 170	95.35 77	96.45 157	89.46 74	90.60 234	96.92 138	79.37 277	90.49 250	94.39 217	91.20 112	98.88 110	88.66 162	98.43 148	97.72 160

agg_prior3		92.56 167	91.62 175	95.35 77	96.39 159	89.45 76	90.61 233	96.82 146	78.82 285	90.03 258	94.14 226	90.72 124	98.88 110	88.66 162	98.43 148	97.72 160

test9_res														88.16 169	98.40 150	97.83 154

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

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

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

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

FMVSNet3		90.78 196	90.32 204	92.16 194	93.03 286	79.92 218	92.54 160	94.95 214	86.17 214	95.10 137	96.01 155	69.97 290	98.75 139	86.74 185	98.38 152	97.82 156

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

agg_prior1		92.60 164	91.76 173	95.10 87	96.20 179	88.89 88	90.37 240	96.88 143	79.67 274	90.21 253	94.41 214	91.30 107	98.78 134	88.46 165	98.37 157	97.64 167

agg_prior2														87.06 183	98.36 158	97.98 140

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

pmmvs-eth3d		91.54 182	90.73 199	93.99 123	95.76 212	87.86 111	90.83 227	93.98 237	78.23 288	94.02 170	96.22 147	82.62 233	96.83 272	86.57 190	98.33 159	97.29 186

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

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

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

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

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

CANet		92.38 170	91.99 168	93.52 142	93.82 275	83.46 168	91.14 219	97.00 129	89.81 145	86.47 306	94.04 229	87.90 173	99.21 63	89.50 143	98.27 166	97.90 148

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

MVSTER		89.32 219	88.75 222	91.03 224	90.10 323	76.62 269	90.85 226	94.67 225	82.27 256	95.24 132	95.79 164	61.09 331	98.49 176	90.49 117	98.26 167	97.97 143

MSLP-MVS++		93.25 144	93.88 121	91.37 216	96.34 169	82.81 176	93.11 144	97.74 68	89.37 150	94.08 168	95.29 185	90.40 132	96.35 290	90.35 124	98.25 169	94.96 265

LF4IMVS		92.72 160	92.02 167	94.84 93	95.65 218	91.99 49	92.92 150	96.60 157	85.08 229	92.44 207	93.62 239	86.80 198	96.35 290	86.81 184	98.25 169	96.18 231

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

PM-MVS		93.33 138	92.67 156	95.33 79	96.58 146	94.06 16	92.26 177	92.18 267	85.92 218	96.22 90	96.61 115	85.64 215	95.99 296	90.35 124	98.23 171	95.93 239

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

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

TAMVS		90.16 211	89.05 215	93.49 143	96.49 151	86.37 133	90.34 242	92.55 263	80.84 266	92.99 196	94.57 212	81.94 239	98.20 202	73.51 305	98.21 174	95.90 240

K. test v3		93.37 137	93.27 144	93.66 134	98.05 72	82.62 177	94.35 107	86.62 307	96.05 26	97.51 41	98.85 12	76.59 276	99.65 3	93.21 66	98.20 176	98.73 100

DELS-MVS		92.05 176	92.16 164	91.72 205	94.44 262	80.13 208	87.62 295	97.25 116	87.34 199	92.22 216	93.18 251	89.54 144	98.73 143	89.67 141	98.20 176	96.30 226
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
TAPA-MVS	88.58 10	92.49 168	91.75 174	94.73 96	96.50 150	89.69 73	92.91 151	97.68 72	78.02 289	92.79 200	94.10 227	90.85 118	97.96 213	84.76 213	98.16 178	96.54 209
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		96.11 46	95.83 58	96.95 33	94.75 249	94.20 14	97.34 11	97.98 45	97.31 9	95.32 127	96.77 105	93.08 71	99.20 64	91.79 101	98.16 178	97.44 176

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

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

DP-MVS Recon		92.31 171	91.88 169	93.60 136	97.18 112	86.87 124	91.10 221	97.37 100	84.92 232	92.08 218	94.08 228	88.59 154	98.20 202	83.50 222	98.14 180	95.73 244

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

PCF-MVS	84.52 17	89.12 223	87.71 242	93.34 144	96.06 188	85.84 143	86.58 312	97.31 110	68.46 334	93.61 177	93.89 234	87.51 178	98.52 174	67.85 332	98.11 184	95.66 247
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	92.54 3	94.80 92	94.90 90	94.47 110	95.47 226	87.06 121	96.63 24	97.28 115	91.82 103	94.34 161	97.41 74	90.60 127	98.65 156	92.47 86	98.11 184	97.70 162

PMMVS2		81.31 310	83.44 293	74.92 338	90.52 317	46.49 355	69.19 351	85.23 325	84.30 237	87.95 294	94.71 208	76.95 273	84.36 353	64.07 340	98.09 186	93.89 291

lessismore_v0				93.87 131	98.05 72	83.77 166		80.32 350		97.13 53	97.91 52	77.49 266	99.11 75	92.62 81	98.08 187	98.74 98

new-patchmatchnet		88.97 226	90.79 197	83.50 323	94.28 266	55.83 353	85.34 318	93.56 244	86.18 213	95.47 122	95.73 167	83.10 226	96.51 281	85.40 202	98.06 188	98.16 130

plane_prior						88.12 106	93.01 145		88.98 157						98.06 188

PVSNet_BlendedMVS		90.35 206	89.96 207	91.54 212	94.81 246	78.80 247	90.14 249	96.93 136	79.43 275	88.68 286	95.06 193	86.27 207	98.15 206	80.27 251	98.04 190	97.68 164

FMVSNet5		87.82 249	86.56 263	91.62 208	92.31 295	79.81 220	93.49 132	94.81 219	83.26 242	91.36 228	96.93 97	52.77 350	97.49 247	76.07 292	98.03 191	97.55 173

原ACMM1				92.87 164	96.91 126	84.22 160		97.01 128	76.84 296	89.64 269	94.46 213	88.00 170	98.70 150	81.53 239	98.01 192	95.70 246

v148		92.87 156	93.29 141	91.62 208	96.25 177	77.72 258	91.28 216	95.05 212	89.69 146	95.93 106	96.04 154	87.34 183	98.38 187	90.05 135	97.99 193	98.78 95

ITE_SJBPF				95.95 53	97.34 108	93.36 37		96.55 161	91.93 94	94.82 147	95.39 183	91.99 92	97.08 263	85.53 201	97.96 194	97.41 177

test12				94.43 113	95.95 203	86.75 126		96.24 179		89.76 267		89.79 141	98.79 131		97.95 195	97.75 159

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

CDS-MVSNet		89.55 216	88.22 231	93.53 141	95.37 231	86.49 128	89.26 276	93.59 243	79.76 272	91.15 239	92.31 270	77.12 270	98.38 187	77.51 282	97.92 197	95.71 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
旧先验1					96.20 179	84.17 161		94.82 217			95.57 173	89.57 143			97.89 198	96.32 225

alignmvs		93.26 142	92.85 150	94.50 108	95.70 214	87.45 114	93.45 133	95.76 195	91.58 111	95.25 131	92.42 268	81.96 238	98.72 144	91.61 105	97.87 199	97.33 184

testgi		90.38 204	91.34 185	87.50 294	97.49 104	71.54 316	89.43 270	95.16 211	88.38 179	94.54 155	94.68 209	92.88 77	93.09 330	71.60 319	97.85 200	97.88 150

新几何1				93.17 150	97.16 113	87.29 116		94.43 227	67.95 335	91.29 229	94.94 198	86.97 193	98.23 199	81.06 246	97.75 201	93.98 289

HQP3-MVS								97.31 110							97.73 202

HQP-MVS		92.09 175	91.49 180	93.88 130	96.36 165	84.89 153	91.37 212	97.31 110	87.16 201	88.81 279	93.40 247	84.76 218	98.60 160	86.55 191	97.73 202	98.14 132

1121		90.26 209	89.23 211	93.34 144	97.15 115	87.40 115	91.94 188	94.39 228	67.88 336	91.02 241	94.91 199	86.91 196	98.59 162	81.17 244	97.71 204	94.02 288

CANet_DTU		89.85 214	89.17 213	91.87 201	92.20 299	80.02 214	90.79 228	95.87 192	86.02 216	82.53 331	91.77 279	80.01 252	98.57 165	85.66 200	97.70 205	97.01 195

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

Vis-MVSNet		95.50 60	95.48 68	95.56 73	98.11 68	89.40 79	95.35 69	98.22 23	92.36 79	94.11 166	98.07 41	92.02 90	99.44 24	93.38 61	97.67 207	97.85 153
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
AdaColmap		91.63 180	91.36 184	92.47 185	95.56 223	86.36 134	92.24 179	96.27 177	88.88 161	89.90 263	92.69 259	91.65 98	98.32 191	77.38 284	97.64 208	92.72 314

EPNet_dtu		85.63 285	84.37 287	89.40 261	86.30 348	74.33 295	91.64 207	88.26 293	84.84 234	72.96 353	89.85 303	71.27 286	97.69 240	76.60 289	97.62 209	96.18 231
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-OURS		94.72 96	94.12 114	96.50 45	98.00 76	94.23 13	91.48 211	98.17 26	90.72 126	95.30 128	96.47 121	87.94 172	96.98 266	91.41 112	97.61 210	98.30 121

canonicalmvs		94.59 101	94.69 95	94.30 117	95.60 222	87.03 122	95.59 62	98.24 22	91.56 112	95.21 134	92.04 276	94.95 41	98.66 154	91.45 111	97.57 211	97.20 189

XXY-MVS		92.58 165	93.16 146	90.84 229	97.75 86	79.84 219	91.87 194	96.22 182	85.94 217	95.53 121	97.68 60	92.69 80	94.48 316	83.21 225	97.51 212	98.21 126

view600		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 330	93.02 64	94.18 162	92.68 260	63.33 321	98.56 166	75.87 295	97.50 213	96.51 211

view800		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 330	93.02 64	94.18 162	92.68 260	63.33 321	98.56 166	75.87 295	97.50 213	96.51 211

conf0.05thres1000		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 330	93.02 64	94.18 162	92.68 260	63.33 321	98.56 166	75.87 295	97.50 213	96.51 211

tfpn		88.32 238	87.94 237	89.46 255	96.49 151	73.31 301	93.95 118	84.46 330	93.02 64	94.18 162	92.68 260	63.33 321	98.56 166	75.87 295	97.50 213	96.51 211

Effi-MVS+-dtu		93.90 121	92.60 158	97.77 4	94.74 250	96.67 4	94.00 115	95.41 208	89.94 142	91.93 222	92.13 274	90.12 134	98.97 97	87.68 174	97.48 217	97.67 165

OpenMVS	89.45 8	92.27 173	92.13 166	92.68 174	94.53 261	84.10 162	95.70 59	97.03 127	82.44 255	91.14 240	96.42 125	88.47 156	98.38 187	85.95 198	97.47 218	95.55 254

ab-mvs		92.40 169	92.62 157	91.74 204	97.02 120	81.65 186	95.84 56	95.50 206	86.95 207	92.95 198	97.56 65	90.70 125	97.50 246	79.63 260	97.43 219	96.06 235

1111		80.36 318	81.32 306	77.48 335	94.61 258	44.56 356	81.59 336	90.66 283	86.78 209	90.60 248	93.52 244	30.37 361	90.67 339	66.36 336	97.42 220	97.20 189

test1235678		84.54 290	83.85 292	86.59 301	93.81 276	73.41 300	82.38 333	91.79 274	79.43 275	89.50 270	91.61 283	70.59 287	92.94 332	58.14 347	97.40 221	93.44 303

test222					96.95 122	85.27 151	88.83 285	93.61 242	65.09 344	90.74 245	94.85 200	84.62 220			97.36 222	93.91 290

API-MVS		91.52 183	91.61 176	91.26 220	94.16 267	86.26 136	94.66 93	94.82 217	91.17 119	92.13 217	91.08 289	90.03 140	97.06 264	79.09 265	97.35 223	90.45 335

testdata				91.03 224	96.87 128	82.01 181		94.28 231	71.55 319	92.46 206	95.42 180	85.65 214	97.38 255	82.64 230	97.27 224	93.70 297

N_pmnet		88.90 228	87.25 248	93.83 132	94.40 264	93.81 31	84.73 321	87.09 304	79.36 279	93.26 189	92.43 267	79.29 255	91.68 336	77.50 283	97.22 225	96.00 236

ppachtmachnet_test		88.61 233	88.64 223	88.50 282	91.76 304	70.99 319	84.59 324	92.98 253	79.30 281	92.38 209	93.53 243	79.57 254	97.45 248	86.50 193	97.17 226	97.07 192

CNLPA		91.72 179	91.20 188	93.26 147	96.17 182	91.02 61	91.14 219	95.55 204	90.16 139	90.87 242	93.56 242	86.31 206	94.40 319	79.92 259	97.12 227	94.37 279

Test4		91.41 189	91.25 187	91.89 200	95.35 232	80.32 201	90.97 223	96.92 138	81.96 258	95.11 136	93.81 236	81.34 243	98.48 179	88.71 161	97.08 228	96.87 203

jason		89.17 222	88.32 226	91.70 206	95.73 213	80.07 209	88.10 292	93.22 250	71.98 318	90.09 255	92.79 255	78.53 260	98.56 166	87.43 178	97.06 229	96.46 220
jason: jason.
RPSCF		95.58 58	94.89 91	97.62 8	97.58 99	96.30 5	95.97 51	97.53 87	92.42 77	93.41 181	97.78 56	91.21 111	97.77 234	91.06 115	97.06 229	98.80 93

QAPM		92.88 155	92.77 152	93.22 148	95.82 208	83.31 169	96.45 33	97.35 108	83.91 239	93.75 174	96.77 105	89.25 147	98.88 110	84.56 215	97.02 231	97.49 174

thres600view7		87.66 252	87.10 254	89.36 262	96.05 189	73.17 305	92.72 155	85.31 320	91.89 96	93.29 186	90.97 290	63.42 317	98.39 185	73.23 307	96.99 232	96.51 211

tfpn111		87.60 254	87.12 252	89.04 268	96.14 184	73.09 307	93.00 146	85.31 320	92.13 90	93.26 189	90.96 291	63.42 317	98.48 179	72.87 310	96.98 233	95.56 250

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

tfpn1000		86.83 274	86.23 270	88.64 277	95.53 224	75.25 287	93.57 130	82.28 344	89.27 154	91.46 226	89.24 313	57.22 344	97.86 225	80.63 249	96.88 235	92.81 311

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

pmmvs5		87.87 246	87.14 251	90.07 245	93.26 283	76.97 268	88.89 284	92.18 267	73.71 310	88.36 288	93.89 234	76.86 274	96.73 275	80.32 250	96.81 236	96.51 211

PVSNet_Blended_VisFu		91.63 180	91.20 188	92.94 160	97.73 90	83.95 164	92.14 181	97.46 94	78.85 284	92.35 211	94.98 197	84.16 222	99.08 77	86.36 195	96.77 238	95.79 242

MVSFormer		92.18 174	92.23 163	92.04 198	94.74 250	80.06 210	97.15 13	97.37 100	88.98 157	88.83 277	92.79 255	77.02 271	99.60 8	96.41 6	96.75 239	96.46 220

lupinMVS		88.34 237	87.31 246	91.45 214	94.74 250	80.06 210	87.23 301	92.27 266	71.10 322	88.83 277	91.15 287	77.02 271	98.53 173	86.67 188	96.75 239	95.76 243

conf0.01		86.95 271	86.04 271	89.70 250	95.99 195	75.66 280	93.28 136	82.70 337	88.81 162	91.26 230	88.01 322	58.77 336	97.89 216	78.93 266	96.60 241	95.56 250

conf0.002		86.95 271	86.04 271	89.70 250	95.99 195	75.66 280	93.28 136	82.70 337	88.81 162	91.26 230	88.01 322	58.77 336	97.89 216	78.93 266	96.60 241	95.56 250

thresconf0.02		86.69 276	86.04 271	88.64 277	95.99 195	75.66 280	93.28 136	82.70 337	88.81 162	91.26 230	88.01 322	58.77 336	97.89 216	78.93 266	96.60 241	92.36 318

tfpn_n400		86.69 276	86.04 271	88.64 277	95.99 195	75.66 280	93.28 136	82.70 337	88.81 162	91.26 230	88.01 322	58.77 336	97.89 216	78.93 266	96.60 241	92.36 318

tfpnconf		86.69 276	86.04 271	88.64 277	95.99 195	75.66 280	93.28 136	82.70 337	88.81 162	91.26 230	88.01 322	58.77 336	97.89 216	78.93 266	96.60 241	92.36 318

tfpnview11		86.69 276	86.04 271	88.64 277	95.99 195	75.66 280	93.28 136	82.70 337	88.81 162	91.26 230	88.01 322	58.77 336	97.89 216	78.93 266	96.60 241	92.36 318

conf200view11		87.41 258	86.89 256	88.97 269	96.14 184	73.09 307	93.00 146	85.31 320	92.13 90	93.26 189	90.96 291	63.42 317	98.28 193	71.27 322	96.54 247	95.56 250

thres100view900		87.35 260	86.89 256	88.72 274	96.14 184	73.09 307	93.00 146	85.31 320	92.13 90	93.26 189	90.96 291	63.42 317	98.28 193	71.27 322	96.54 247	94.79 268

tfpn200view9		87.05 269	86.52 265	88.67 275	95.77 210	72.94 310	91.89 191	86.00 312	90.84 123	92.61 203	89.80 305	63.93 314	98.28 193	71.27 322	96.54 247	94.79 268

thres400		87.20 265	86.52 265	89.24 266	95.77 210	72.94 310	91.89 191	86.00 312	90.84 123	92.61 203	89.80 305	63.93 314	98.28 193	71.27 322	96.54 247	96.51 211

CMPMVS	68.83 22	87.28 261	85.67 280	92.09 196	88.77 336	85.42 149	90.31 243	94.38 229	70.02 329	88.00 293	93.30 249	73.78 280	94.03 324	75.96 294	96.54 247	96.83 205
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DI_MVS_plusplus_test		91.42 188	91.41 182	91.46 213	95.34 233	79.06 241	90.58 236	93.74 241	82.59 252	94.69 152	94.76 205	86.54 204	98.44 184	87.93 172	96.49 252	96.87 203

pmmvs4		88.95 227	87.70 243	92.70 173	94.30 265	85.60 147	87.22 302	92.16 269	74.62 302	89.75 268	94.19 223	77.97 264	96.41 286	82.71 229	96.36 253	96.09 233

Fast-Effi-MVS+-dtu		92.77 159	92.16 164	94.58 106	94.66 256	88.25 104	92.05 183	96.65 155	89.62 148	90.08 256	91.23 286	92.56 82	98.60 160	86.30 196	96.27 254	96.90 200

tfpn_ndepth		85.85 283	85.15 284	87.98 288	95.19 238	75.36 286	92.79 154	83.18 336	86.97 205	89.92 261	86.43 334	57.44 343	97.85 228	78.18 275	96.22 255	90.72 333

MAR-MVS		90.32 208	88.87 221	94.66 98	94.82 245	91.85 52	94.22 111	94.75 220	80.91 263	87.52 300	88.07 321	86.63 202	97.87 224	76.67 288	96.21 256	94.25 281
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PVSNet_Blended		88.74 232	88.16 233	90.46 234	94.81 246	78.80 247	86.64 310	96.93 136	74.67 301	88.68 286	89.18 314	86.27 207	98.15 206	80.27 251	96.00 257	94.44 278

F-COLMAP		92.28 172	91.06 191	95.95 53	97.52 102	91.90 51	93.53 131	97.18 120	83.98 238	88.70 285	94.04 229	88.41 158	98.55 172	80.17 254	95.99 258	97.39 180

xiu_mvs_v1_base_debu		91.47 185	91.52 177	91.33 217	95.69 215	81.56 187	89.92 257	96.05 186	83.22 243	91.26 230	90.74 295	91.55 100	98.82 124	89.29 146	95.91 259	93.62 299

xiu_mvs_v1_base		91.47 185	91.52 177	91.33 217	95.69 215	81.56 187	89.92 257	96.05 186	83.22 243	91.26 230	90.74 295	91.55 100	98.82 124	89.29 146	95.91 259	93.62 299

xiu_mvs_v1_base_debi		91.47 185	91.52 177	91.33 217	95.69 215	81.56 187	89.92 257	96.05 186	83.22 243	91.26 230	90.74 295	91.55 100	98.82 124	89.29 146	95.91 259	93.62 299

thres200		85.85 283	85.18 283	87.88 291	94.44 262	72.52 312	89.08 280	86.21 309	88.57 173	91.44 227	88.40 318	64.22 312	98.00 211	68.35 331	95.88 262	93.12 307

Patchmatch-test		86.10 282	86.01 277	86.38 304	90.63 315	74.22 296	89.57 267	86.69 306	85.73 222	89.81 266	92.83 254	65.24 309	91.04 338	77.82 280	95.78 263	93.88 292

mvs-test1		93.07 150	91.80 172	96.89 35	94.74 250	95.83 7	92.17 180	95.41 208	89.94 142	89.85 264	90.59 301	90.12 134	98.88 110	87.68 174	95.66 264	95.97 237

cascas		87.02 270	86.28 269	89.25 265	91.56 307	76.45 270	84.33 326	96.78 149	71.01 323	86.89 305	85.91 336	81.35 242	96.94 267	83.09 226	95.60 265	94.35 280

XVG-OURS-SEG-HR		95.38 65	95.00 89	96.51 43	98.10 70	94.07 15	92.46 168	98.13 31	90.69 127	93.75 174	96.25 141	98.03 3	97.02 265	92.08 93	95.55 266	98.45 115

DSMNet-mixed		82.21 304	81.56 303	84.16 320	89.57 328	70.00 323	90.65 232	77.66 353	54.99 353	83.30 327	97.57 64	77.89 265	90.50 342	66.86 335	95.54 267	91.97 324

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

testus		82.09 306	81.78 301	83.03 325	92.35 294	64.37 344	79.44 341	93.27 249	73.08 313	87.06 303	85.21 339	76.80 275	89.27 346	53.30 350	95.48 269	95.46 256

MIMVSNet		87.13 268	86.54 264	88.89 271	96.05 189	76.11 274	94.39 105	88.51 291	81.37 262	88.27 291	96.75 108	72.38 282	95.52 302	65.71 339	95.47 270	95.03 263

Fast-Effi-MVS+		91.28 191	90.86 194	92.53 183	95.45 227	82.53 178	89.25 278	96.52 162	85.00 230	89.91 262	88.55 317	92.94 74	98.84 122	84.72 214	95.44 271	96.22 229

BH-RMVSNet		90.47 200	90.44 202	90.56 232	95.21 236	78.65 249	89.15 279	93.94 238	88.21 184	92.74 201	94.22 222	86.38 205	97.88 222	78.67 273	95.39 272	95.14 261

CHOSEN 1792x2688		87.19 266	85.92 279	91.00 227	97.13 117	79.41 231	84.51 325	95.60 199	64.14 345	90.07 257	94.81 201	78.26 262	97.14 261	73.34 306	95.38 273	96.46 220

Effi-MVS+		92.79 157	92.74 154	92.94 160	95.10 239	83.30 170	94.00 115	97.53 87	91.36 115	89.35 273	90.65 300	94.01 55	98.66 154	87.40 179	95.30 274	96.88 202

MG-MVS		89.54 217	89.80 209	88.76 273	94.88 242	72.47 313	89.60 266	92.44 265	85.82 220	89.48 271	95.98 156	82.85 229	97.74 238	81.87 235	95.27 275	96.08 234

HyFIR lowres test		87.19 266	85.51 281	92.24 191	97.12 118	80.51 199	85.03 319	96.06 185	66.11 341	91.66 224	92.98 253	70.12 289	99.14 70	75.29 300	95.23 276	97.07 192

BH-untuned		90.68 198	90.90 192	90.05 246	95.98 201	79.57 229	90.04 253	94.94 215	87.91 188	94.07 169	93.00 252	87.76 174	97.78 233	79.19 264	95.17 277	92.80 312

pmmvs3		80.83 314	78.96 321	86.45 303	87.23 344	77.48 260	84.87 320	82.31 343	63.83 346	85.03 314	89.50 312	49.66 352	93.10 329	73.12 309	95.10 278	88.78 341

mvs_anonymous		90.37 205	91.30 186	87.58 293	92.17 300	68.00 327	89.84 262	94.73 221	83.82 241	93.22 193	97.40 75	87.54 177	97.40 252	87.94 171	95.05 279	97.34 183

semantic-postprocess				91.94 199	93.89 272	79.22 238		93.51 245	91.53 113	95.37 126	96.62 114	77.17 269	98.90 104	91.89 100	94.95 280	97.70 162

test-LLR		83.58 295	83.17 295	84.79 316	89.68 326	66.86 333	83.08 330	84.52 328	83.07 247	82.85 329	84.78 340	62.86 326	93.49 327	82.85 227	94.86 281	94.03 286

test-mter		81.21 312	80.01 318	84.79 316	89.68 326	66.86 333	83.08 330	84.52 328	73.85 309	82.85 329	84.78 340	43.66 358	93.49 327	82.85 227	94.86 281	94.03 286

PatchMatch-RL		89.18 221	88.02 236	92.64 175	95.90 207	92.87 42	88.67 288	91.06 279	80.34 267	90.03 258	91.67 281	83.34 224	94.42 318	76.35 291	94.84 283	90.64 334

OpenMVS_ROB	85.12 16	89.52 218	89.05 215	90.92 228	94.58 260	81.21 192	91.10 221	93.41 247	77.03 295	93.41 181	93.99 233	83.23 225	97.80 231	79.93 258	94.80 284	93.74 296

our_test_3		87.55 255	87.59 244	87.44 295	91.76 304	70.48 320	83.83 329	90.55 285	79.79 271	92.06 219	92.17 273	78.63 259	95.63 300	84.77 212	94.73 285	96.22 229

CHOSEN 280x420		80.04 320	77.97 324	86.23 306	90.13 322	74.53 292	72.87 348	89.59 287	66.38 340	76.29 349	85.32 338	56.96 345	95.36 307	69.49 330	94.72 286	88.79 340

IterMVS		90.18 210	90.16 205	90.21 243	93.15 284	75.98 276	87.56 298	92.97 254	86.43 212	94.09 167	96.40 127	78.32 261	97.43 249	87.87 173	94.69 287	97.23 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EMVS		80.35 319	80.28 316	80.54 331	84.73 354	69.07 325	72.54 349	80.73 348	87.80 192	81.66 338	81.73 347	62.89 325	89.84 344	75.79 299	94.65 288	82.71 348

PLC	85.34 15	90.40 203	88.92 219	94.85 92	96.53 149	90.02 69	91.58 208	96.48 164	80.16 269	86.14 308	92.18 272	85.73 212	98.25 198	76.87 287	94.61 289	96.30 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSDG		90.82 194	90.67 200	91.26 220	94.16 267	83.08 174	86.63 311	96.19 183	90.60 131	91.94 221	91.89 277	89.16 148	95.75 299	80.96 248	94.51 290	94.95 266

xiu_mvs_v2_base		89.00 225	89.19 212	88.46 284	94.86 244	74.63 290	86.97 305	95.60 199	80.88 264	87.83 295	88.62 316	91.04 116	98.81 129	82.51 232	94.38 291	91.93 325

PS-MVSNAJ		88.86 229	88.99 218	88.48 283	94.88 242	74.71 288	86.69 309	95.60 199	80.88 264	87.83 295	87.37 329	90.77 119	98.82 124	82.52 231	94.37 292	91.93 325

EU-MVSNet		87.39 259	86.71 261	89.44 259	93.40 280	76.11 274	94.93 85	90.00 286	57.17 351	95.71 116	97.37 77	64.77 311	97.68 241	92.67 80	94.37 292	94.52 275

E-PMN		80.72 316	80.86 311	80.29 332	85.11 352	68.77 326	72.96 347	81.97 345	87.76 193	83.25 328	83.01 346	62.22 329	89.17 347	77.15 286	94.31 294	82.93 347

GA-MVS		87.70 250	86.82 258	90.31 237	93.27 282	77.22 264	84.72 323	92.79 258	85.11 228	89.82 265	90.07 302	66.80 299	97.76 236	84.56 215	94.27 295	95.96 238

sss		87.23 263	86.82 258	88.46 284	93.96 270	77.94 253	86.84 307	92.78 259	77.59 290	87.61 299	91.83 278	78.75 257	91.92 335	77.84 278	94.20 296	95.52 255

MDA-MVSNet-bldmvs		91.04 192	90.88 193	91.55 211	94.68 254	80.16 204	85.49 317	92.14 270	90.41 136	94.93 145	95.79 164	85.10 216	96.93 268	85.15 205	94.19 297	97.57 170

diffmvs		90.45 201	90.49 201	90.34 236	92.25 296	77.09 265	91.80 205	95.96 189	82.68 250	85.83 310	95.07 192	87.01 191	97.09 262	89.68 140	94.10 298	96.83 205

PAPM_NR		91.03 193	90.81 196	91.68 207	96.73 135	81.10 193	93.72 128	96.35 175	88.19 185	88.77 283	92.12 275	85.09 217	97.25 257	82.40 233	93.90 299	96.68 208

YYNet1		88.17 242	88.24 229	87.93 289	92.21 298	73.62 298	80.75 339	88.77 289	82.51 254	94.99 143	95.11 190	82.70 231	93.70 325	83.33 223	93.83 300	96.48 219

MDA-MVSNet_test_wron		88.16 243	88.23 230	87.93 289	92.22 297	73.71 297	80.71 340	88.84 288	82.52 253	94.88 146	95.14 188	82.70 231	93.61 326	83.28 224	93.80 301	96.46 220

1112_ss		88.42 236	87.41 245	91.45 214	96.69 137	80.99 194	89.72 264	96.72 153	73.37 311	87.00 304	90.69 298	77.38 268	98.20 202	81.38 240	93.72 302	95.15 260

PVSNet	76.22 20	82.89 299	82.37 298	84.48 318	93.96 270	64.38 343	78.60 343	88.61 290	71.50 320	84.43 320	86.36 335	74.27 279	94.60 315	69.87 329	93.69 303	94.46 277

TESTMET0.1,1		79.09 322	78.04 323	82.25 328	87.52 341	64.03 345	83.08 330	80.62 349	70.28 328	80.16 344	83.22 345	44.13 357	90.56 341	79.95 256	93.36 304	92.15 323

PAPR		87.65 253	86.77 260	90.27 239	92.85 287	77.38 261	88.56 289	96.23 180	76.82 297	84.98 315	89.75 309	86.08 209	97.16 260	72.33 313	93.35 305	96.26 228

Patchmatch-test1		87.28 261	87.30 247	87.22 297	92.01 303	71.98 315	89.43 270	88.11 297	82.26 257	88.71 284	92.20 271	78.65 258	95.81 298	80.99 247	93.30 306	93.87 293

Test_1112_low_res		87.50 257	86.58 262	90.25 240	96.80 132	77.75 257	87.53 299	96.25 178	69.73 330	86.47 306	93.61 240	75.67 277	97.88 222	79.95 256	93.20 307	95.11 262

MDTV_nov1_ep13			83.88 291		89.42 330	61.52 347	88.74 286	87.41 302	73.99 308	84.96 316	94.01 232	65.25 308	95.53 301	78.02 276	93.16 308

WTY-MVS		86.93 273	86.50 267	88.24 286	94.96 241	74.64 289	87.19 303	92.07 272	78.29 287	88.32 290	91.59 284	78.06 263	94.27 321	74.88 302	93.15 309	95.80 241

PMMVS		83.00 298	81.11 307	88.66 276	83.81 356	86.44 131	82.24 335	85.65 315	61.75 349	82.07 334	85.64 337	79.75 253	91.59 337	75.99 293	93.09 310	87.94 342

UnsupCasMVSNet_bld		88.50 234	88.03 235	89.90 247	95.52 225	78.88 244	87.39 300	94.02 236	79.32 280	93.06 194	94.02 231	80.72 250	94.27 321	75.16 301	93.08 311	96.54 209

MVS		84.98 289	84.30 288	87.01 298	91.03 309	77.69 259	91.94 188	94.16 233	59.36 350	84.23 321	87.50 328	85.66 213	96.80 273	71.79 316	93.05 312	86.54 343

PatchT		87.51 256	88.17 232	85.55 308	90.64 314	66.91 331	92.02 184	86.09 310	92.20 88	89.05 276	97.16 87	64.15 313	96.37 289	89.21 152	92.98 313	93.37 305

MS-PatchMatch		88.05 244	87.75 241	88.95 270	93.28 281	77.93 254	87.88 294	92.49 264	75.42 300	92.57 205	93.59 241	80.44 251	94.24 323	81.28 241	92.75 314	94.69 272

CR-MVSNet		87.89 245	87.12 252	90.22 241	91.01 310	78.93 242	92.52 162	92.81 256	73.08 313	89.10 274	96.93 97	67.11 296	97.64 242	88.80 158	92.70 315	94.08 283

RPMNet		89.30 220	89.00 217	90.22 241	91.01 310	78.93 242	92.52 162	87.85 299	91.91 95	89.10 274	96.89 100	68.84 291	97.64 242	90.17 130	92.70 315	94.08 283

BH-w/o		87.21 264	87.02 255	87.79 292	94.77 248	77.27 263	87.90 293	93.21 252	81.74 260	89.99 260	88.39 319	83.47 223	96.93 268	71.29 321	92.43 317	89.15 337

test2356		75.58 325	73.13 327	82.95 326	86.10 349	66.42 335	75.07 344	84.87 327	70.91 324	80.85 341	80.66 348	38.02 360	88.98 349	49.32 353	92.35 318	93.44 303

test12356		76.35 324	77.41 325	73.19 340	90.70 313	38.86 359	74.56 345	91.14 278	74.55 303	80.54 343	88.18 320	52.36 351	90.49 343	52.38 352	92.26 319	90.21 336

IB-MVS	77.21 19	83.11 296	81.05 308	89.29 263	91.15 308	75.85 277	85.66 316	86.00 312	79.70 273	82.02 336	86.61 331	48.26 354	98.39 185	77.84 278	92.22 320	93.63 298
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
gg-mvs-nofinetune		82.10 305	81.02 309	85.34 311	87.46 343	71.04 317	94.74 90	67.56 356	96.44 19	79.43 345	98.99 6	45.24 355	96.15 292	67.18 334	92.17 321	88.85 339

HY-MVS	82.50 18	86.81 275	85.93 278	89.47 254	93.63 277	77.93 254	94.02 114	91.58 276	75.68 298	83.64 324	93.64 238	77.40 267	97.42 250	71.70 318	92.07 322	93.05 308

TR-MVS		87.70 250	87.17 250	89.27 264	94.11 269	79.26 233	88.69 287	91.86 273	81.94 259	90.69 246	89.79 307	82.82 230	97.42 250	72.65 312	91.98 323	91.14 330

new_pmnet		81.22 311	81.01 310	81.86 329	90.92 312	70.15 322	84.03 327	80.25 351	70.83 325	85.97 309	89.78 308	67.93 295	84.65 352	67.44 333	91.90 324	90.78 332

FPMVS		84.50 291	83.28 294	88.16 287	96.32 170	94.49 11	85.76 315	85.47 318	83.09 246	85.20 313	94.26 220	63.79 316	86.58 351	63.72 342	91.88 325	83.40 346

UnsupCasMVSNet_eth		90.33 207	90.34 203	90.28 238	94.64 257	80.24 202	89.69 265	95.88 191	85.77 221	93.94 171	95.69 168	81.99 237	92.98 331	84.21 217	91.30 326	97.62 168

MVP-Stereo		90.07 213	88.92 219	93.54 140	96.31 171	86.49 128	90.93 225	95.59 202	79.80 270	91.48 225	95.59 169	80.79 249	97.39 253	78.57 274	91.19 327	96.76 207
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		86.46 280	86.33 268	86.87 300	91.65 306	74.54 291	91.94 188	94.10 234	74.28 305	84.78 317	87.33 330	83.03 227	95.00 313	78.72 272	91.16 328	91.06 331

tpm		84.38 292	84.08 289	85.30 313	90.47 318	63.43 346	89.34 273	85.63 316	77.24 294	87.62 298	95.03 196	61.00 332	97.30 256	79.26 263	91.09 329	95.16 259

CVMVSNet		85.16 287	84.72 285	86.48 302	92.12 301	70.19 321	92.32 174	88.17 296	56.15 352	90.64 247	95.85 160	67.97 294	96.69 276	88.78 159	90.52 330	92.56 315

test0.0.03 1		82.48 302	81.47 305	85.48 309	89.70 325	73.57 299	84.73 321	81.64 346	83.07 247	88.13 292	86.61 331	62.86 326	89.10 348	66.24 338	90.29 331	93.77 295

PAPM		81.91 307	80.11 317	87.31 296	93.87 273	72.32 314	84.02 328	93.22 250	69.47 331	76.13 350	89.84 304	72.15 283	97.23 258	53.27 351	89.02 332	92.37 317

MVS-HIRNet		78.83 323	80.60 312	73.51 339	93.07 285	47.37 354	87.10 304	78.00 352	68.94 332	77.53 348	97.26 82	71.45 285	94.62 314	63.28 343	88.74 333	78.55 351

tpmp4_e23		81.87 308	80.41 313	86.27 305	89.29 331	67.84 328	91.58 208	87.61 301	67.42 337	78.60 346	92.71 258	56.42 347	96.87 270	71.44 320	88.63 334	94.10 282

tpm2		81.46 309	80.35 315	84.80 315	89.90 324	65.14 339	90.44 239	85.36 319	65.82 343	82.05 335	92.44 266	57.94 342	96.69 276	70.71 326	88.49 335	92.56 315

CostFormer		83.09 297	82.21 299	85.73 307	89.27 332	67.01 330	90.35 241	86.47 308	70.42 327	83.52 326	93.23 250	61.18 330	96.85 271	77.21 285	88.26 336	93.34 306

GG-mvs-BLEND				83.24 324	85.06 353	71.03 318	94.99 84	65.55 357		74.09 352	75.51 352	44.57 356	94.46 317	59.57 346	87.54 337	84.24 345

PatchmatchNet		85.22 286	84.64 286	86.98 299	89.51 329	69.83 324	90.52 237	87.34 303	78.87 283	87.22 302	92.74 257	66.91 298	96.53 279	81.77 236	86.88 338	94.58 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmvs		84.22 293	83.97 290	84.94 314	87.09 345	65.18 338	91.21 217	88.35 292	82.87 249	85.21 312	90.96 291	65.24 309	96.75 274	79.60 262	85.25 339	92.90 310

ADS-MVSNet2		84.01 294	82.20 300	89.41 260	89.04 333	76.37 271	87.57 296	90.98 281	72.71 316	84.46 318	92.45 264	68.08 292	96.48 282	70.58 327	83.97 340	95.38 257

ADS-MVSNet		82.25 303	81.55 304	84.34 319	89.04 333	65.30 337	87.57 296	85.13 326	72.71 316	84.46 318	92.45 264	68.08 292	92.33 334	70.58 327	83.97 340	95.38 257

PatchFormer-LS_test		82.62 301	81.71 302	85.32 312	87.92 337	67.31 329	89.03 281	88.20 295	77.58 291	83.79 323	80.50 350	60.96 333	96.42 285	83.86 221	83.59 342	92.23 322

JIA-IIPM		85.08 288	83.04 296	91.19 223	87.56 340	86.14 138	89.40 272	84.44 334	88.98 157	82.20 333	97.95 48	56.82 346	96.15 292	76.55 290	83.45 343	91.30 329

MVE	59.87 23	73.86 328	72.65 329	77.47 336	87.00 347	74.35 294	61.37 353	60.93 358	67.27 338	69.69 354	86.49 333	81.24 247	72.33 356	56.45 349	83.45 343	85.74 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DWT-MVSNet_test		80.74 315	79.18 320	85.43 310	87.51 342	66.87 332	89.87 261	86.01 311	74.20 307	80.86 340	80.62 349	48.84 353	96.68 278	81.54 238	83.14 345	92.75 313

EPMVS		81.17 313	80.37 314	83.58 322	85.58 351	65.08 340	90.31 243	71.34 355	77.31 293	85.80 311	91.30 285	59.38 334	92.70 333	79.99 255	82.34 346	92.96 309

LP		86.29 281	85.35 282	89.10 267	87.80 338	76.21 272	89.92 257	90.99 280	84.86 233	87.66 297	92.32 269	70.40 288	96.48 282	81.94 234	82.24 347	94.63 273

tpmrst		82.85 300	82.93 297	82.64 327	87.65 339	58.99 350	90.14 249	87.90 298	75.54 299	83.93 322	91.63 282	66.79 301	95.36 307	81.21 243	81.54 348	93.57 302

tpm cat1		80.61 317	79.46 319	84.07 321	88.78 335	65.06 341	89.26 276	88.23 294	62.27 348	81.90 337	89.66 311	62.70 328	95.29 310	71.72 317	80.60 349	91.86 327

testpf		74.01 327	76.37 326	66.95 341	80.56 357	60.00 348	88.43 291	75.07 354	81.54 261	75.75 351	83.73 342	38.93 359	83.09 354	84.01 218	79.32 350	57.75 353

dp		79.28 321	78.62 322	81.24 330	85.97 350	56.45 352	86.91 306	85.26 324	72.97 315	81.45 339	89.17 315	56.01 349	95.45 305	73.19 308	76.68 351	91.82 328

DeepMVS_CX				53.83 342	70.38 358	64.56 342		48.52 360	33.01 354	65.50 355	74.21 353	56.19 348	46.64 357	38.45 355	70.07 352	50.30 354

tmp_tt		37.97 332	44.33 332	18.88 345	11.80 359	21.54 360	63.51 352	45.66 361	4.23 355	51.34 356	50.48 354	59.08 335	22.11 358	44.50 354	68.35 353	13.00 355

PVSNet_0	70.34 21	74.58 326	72.96 328	79.47 333	90.63 315	66.24 336	73.26 346	83.40 335	63.67 347	78.02 347	78.35 351	72.53 281	89.59 345	56.68 348	60.05 354	82.57 349

PNet_i23d		72.03 329	70.91 330	75.38 337	90.46 319	57.84 351	71.73 350	81.53 347	83.86 240	82.21 332	83.49 344	29.97 363	87.80 350	60.78 344	54.12 355	80.51 350

test123		9.49 334	12.01 335	1.91 346	2.87 360	1.30 361	82.38 333	1.34 363	1.36 356	2.84 357	6.56 357	2.45 364	0.97 359	2.73 356	5.56 356	3.47 356

.test1245		64.72 330	70.88 331	46.22 343	94.61 258	44.56 356	81.59 336	90.66 283	86.78 209	90.60 248	93.52 244	30.37 361	90.67 339	66.36 336	3.45 357	3.44 357

testmvs		9.02 335	11.42 336	1.81 347	2.77 361	1.13 362	79.44 341	1.90 362	1.18 357	2.65 358	6.80 356	1.95 365	0.87 360	2.62 357	3.45 357	3.44 357

cdsmvs_eth3d_5k		23.35 333	31.13 334	0.00 348	0.00 362	0.00 363	0.00 354	95.58 203	0.00 358	0.00 359	91.15 287	93.43 62	0.00 361	0.00 358	0.00 359	0.00 359

pcd_1.5k_mvsjas		7.56 336	10.09 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	90.77 119	0.00 361	0.00 358	0.00 359	0.00 359

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

ab-mvs-re		7.56 336	10.08 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	90.69 298	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																94.75 270

test_part3							93.92 122		91.83 101		96.39 131		99.44 24	89.00 154

test_part2					98.21 62	89.41 77				96.72 67

sam_mvs1												66.64 302				94.75 270

sam_mvs												66.41 303

MTGPA								97.62 75

test_post1							90.21 245				5.85 359	65.36 307	96.00 295	79.61 261

test_post											6.07 358	65.74 306	95.84 297

patchmatchnet-post											91.71 280	66.22 305	97.59 244

MTMP								54.62 359

gm-plane-assit					87.08 346	59.33 349			71.22 321		83.58 343		97.20 259	73.95 303

TEST9					96.45 157	89.46 74	90.60 234	96.92 138	79.09 282	90.49 250	94.39 217	91.31 106	98.88 110

test_8					96.37 160	89.14 83	90.51 238	96.89 142	79.37 277	90.42 252	94.36 219	91.20 112	98.82 124

agg_prior					96.20 179	88.89 88		96.88 143		90.21 253			98.78 134

test_prior4						89.91 71	90.74 229

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

旧先验2							90.00 255		68.65 333	92.71 202			96.52 280	85.15 205

新几何2							90.02 254

无先验							89.94 256	95.75 196	70.81 326				98.59 162	81.17 244		94.81 267

原ACMM2							89.34 273

testdata2													98.03 210	80.24 253

segment_acmp												92.14 88

testdata1							88.96 283		88.44 178

plane_prior7					97.71 91	88.68 92

plane_prior6					97.21 111	88.23 105						86.93 194

plane_prior4											95.59 169

plane_prior3						88.43 103			90.35 137	93.31 184

plane_prior2							94.56 100		91.74 108

plane_prior1					97.38 106

n2								0.00 364

nn								0.00 364

door-mid								92.13 271

test11								96.65 155

door								91.26 277

HQP5-MVS						84.89 153

HQP-NCC					96.36 165		91.37 212		87.16 201	88.81 279

ACMP_Plane					96.36 165		91.37 212		87.16 201	88.81 279

BP-MVS														86.55 191

HQP4-MVS										88.81 279			98.61 158			98.15 131

HQP2-MVS												84.76 218

NP-MVS					96.82 130	87.10 120					93.40 247

MDTV_nov1_ep13_2view						42.48 358	88.45 290		67.22 339	83.56 325		66.80 299		72.86 311		94.06 285

Test By Simon												90.61 126

High-resolution multi-view benchmark