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.

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 9	98.69 57	98.20 3	99.93 1	99.98 2	96.82 22	100.00 1	99.75 28	100.00 1	99.99 24

NCCC		99.37 2	99.25 2	99.71 13	99.96 8	99.15 20	99.97 18	98.62 69	98.02 6	99.90 2	99.95 3	97.33 16	100.00 1	99.54 39	100.00 1	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 11	99.93 27	99.29 14	99.95 43	98.32 160	97.28 19	99.83 11	99.91 15	97.22 18	100.00 1	99.99 5	100.00 1	99.89 94
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 18	98.64 65	98.47 2	99.13 83	99.92 13	96.38 30	100.00 1	99.74 30	100.00 1	100.00 1

patch_mono-2		98.24 64	99.12 5	95.59 214	99.67 92	86.91 326	99.95 43	98.89 43	97.60 12	99.90 2	99.76 76	96.54 27	99.98 46	99.94 12	99.82 91	99.88 96

MSP-MVS		99.09 9	99.12 5	98.98 83	99.93 27	97.24 109	99.95 43	98.42 132	97.50 15	99.52 54	99.88 24	97.43 15	99.71 134	99.50 41	99.98 35	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SED-MVS		99.28 5	99.11 7	99.77 8	99.93 27	99.30 11	99.96 25	98.43 120	97.27 21	99.80 17	99.94 4	96.71 23	100.00 1	100.00 1	100.00 1	100.00 1

DPE-MVS		99.26 6	99.10 8	99.74 10	99.89 50	99.24 18	99.87 93	98.44 112	97.48 16	99.64 39	99.94 4	96.68 25	99.99 40	99.99 5	100.00 1	99.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 44	99.31 9	99.95 43	98.43 120	96.48 43	99.80 17	99.93 11	97.44 13	100.00 1	99.92 13	99.98 35	100.00 1

CHOSEN 280x420		99.01 13	99.03 10	98.95 86	99.38 111	98.87 31	98.46 291	99.42 21	97.03 28	99.02 87	99.09 145	99.35 1	98.21 226	99.73 33	99.78 94	99.77 110

MSLP-MVS++		99.13 8	99.01 11	99.49 34	99.94 14	98.46 63	99.98 9	98.86 47	97.10 26	99.80 17	99.94 4	95.92 37	100.00 1	99.51 40	100.00 1	100.00 1

DeepPCF-MVS	95.94 2	97.71 87	98.98 12	93.92 275	99.63 94	81.76 352	99.96 25	98.56 79	99.47 1	99.19 81	99.99 1	94.16 92	100.00 1	99.92 13	99.93 67	100.00 1

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 57	98.72 147	97.71 87	99.98 9	98.44 112	96.85 31	99.80 17	99.91 15	97.57 6	99.85 99	99.44 44	99.99 22	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		99.06 11	98.91 14	99.51 31	99.94 14	98.76 44	99.91 75	98.39 143	97.20 25	99.46 57	99.85 35	95.53 46	99.79 114	99.86 18	100.00 1	99.99 24

HPM-MVS++		99.07 10	98.88 15	99.63 15	99.90 47	99.02 23	99.95 43	98.56 79	97.56 14	99.44 59	99.85 35	95.38 49	100.00 1	99.31 49	99.99 22	99.87 98

test_prior3		98.99 14	98.84 16	99.43 38	99.94 14	98.49 61	99.95 43	98.65 62	95.78 64	99.73 30	99.76 76	96.00 33	99.80 111	99.78 26	100.00 1	99.99 24

xxxxxxxxxxxxxcwj		98.98 15	98.79 17	99.54 26	99.82 70	98.79 37	99.96 25	97.52 243	97.66 10	99.81 13	99.89 21	94.70 69	99.86 95	99.84 19	99.93 67	99.96 74

TSAR-MVS + MP.		98.93 16	98.77 18	99.41 42	99.74 82	98.67 48	99.77 132	98.38 147	96.73 37	99.88 4	99.74 87	94.89 66	99.59 145	99.80 24	99.98 35	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		98.92 17	98.70 19	99.56 24	99.70 90	98.73 45	99.94 61	98.34 157	96.38 48	99.81 13	99.76 76	94.59 71	99.98 46	99.84 19	99.96 52	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
testtj		98.89 19	98.69 20	99.52 29	99.94 14	98.56 57	99.90 79	98.55 85	95.14 84	99.72 33	99.84 48	95.46 47	100.00 1	99.65 38	99.99 22	99.99 24

agg_prior1		98.88 20	98.66 21	99.54 26	99.93 27	98.77 40	99.96 25	98.43 120	94.63 103	99.63 40	99.85 35	95.79 41	99.85 99	99.72 34	99.99 22	99.99 24

train_agg		98.88 20	98.65 22	99.59 21	99.92 36	98.92 27	99.96 25	98.43 120	94.35 115	99.71 34	99.86 31	95.94 35	99.85 99	99.69 37	99.98 35	99.99 24

MG-MVS		98.91 18	98.65 22	99.68 14	99.94 14	99.07 22	99.64 166	99.44 19	97.33 18	99.00 90	99.72 91	94.03 95	99.98 46	98.73 81	100.00 1	100.00 1

MVS_111021_HR		98.72 28	98.62 24	99.01 81	99.36 112	97.18 112	99.93 67	99.90 1	96.81 35	98.67 105	99.77 72	93.92 97	99.89 84	99.27 51	99.94 61	99.96 74

Regformer-1		98.79 25	98.60 25	99.36 48	99.85 60	98.34 66	99.87 93	98.52 92	96.05 57	99.41 62	99.79 64	94.93 64	99.76 123	99.07 55	99.90 76	99.99 24

Regformer-2		98.78 26	98.59 26	99.36 48	99.85 60	98.32 67	99.87 93	98.52 92	96.04 58	99.41 62	99.79 64	94.92 65	99.76 123	99.05 56	99.90 76	99.98 55

XVS		98.70 29	98.55 27	99.15 64	99.94 14	97.50 99	99.94 61	98.42 132	96.22 53	99.41 62	99.78 70	94.34 81	99.96 58	98.92 66	99.95 55	99.99 24

ETH3 D test6400		98.81 23	98.54 28	99.59 21	99.93 27	98.93 26	99.93 67	98.46 107	94.56 105	99.84 9	99.92 13	94.32 85	99.86 95	99.96 9	99.98 35	100.00 1

DeepC-MVS_fast	96.59 1	98.81 23	98.54 28	99.62 18	99.90 47	98.85 33	99.24 222	98.47 105	98.14 4	99.08 84	99.91 15	93.09 121	100.00 1	99.04 60	99.99 22	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		98.60 34	98.51 30	98.86 90	99.73 86	96.63 129	99.97 18	97.92 208	98.07 5	98.76 101	99.55 112	95.00 61	99.94 73	99.91 16	97.68 159	99.99 24

SMA-MVS		98.76 27	98.48 31	99.62 18	99.87 57	98.87 31	99.86 104	98.38 147	93.19 160	99.77 26	99.94 4	95.54 44	100.00 1	99.74 30	99.99 22	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPM-MVS		98.83 22	98.46 32	99.97 1	99.33 113	99.92 1	99.96 25	98.44 112	97.96 7	99.55 49	99.94 4	97.18 20	100.00 1	93.81 200	99.94 61	99.98 55

ETH3D-3000-0.1		98.68 30	98.42 33	99.47 37	99.83 68	98.57 55	99.90 79	98.37 150	93.81 142	99.81 13	99.90 19	94.34 81	99.86 95	99.84 19	99.98 35	99.97 67

PAPM		98.60 34	98.42 33	99.14 66	96.05 260	98.96 24	99.90 79	99.35 24	96.68 39	98.35 120	99.66 104	96.45 29	98.51 194	99.45 43	99.89 78	99.96 74

#test#		98.59 36	98.41 35	99.14 66	99.96 8	97.43 104	99.95 43	98.61 71	95.00 87	99.31 71	99.85 35	94.22 88	100.00 1	98.78 77	99.98 35	99.98 55

Regformer-3		98.58 37	98.41 35	99.10 72	99.84 65	97.57 93	99.66 159	98.52 92	95.79 63	99.01 88	99.77 72	94.40 75	99.75 126	98.82 73	99.83 85	99.98 55

SF-MVS		98.67 31	98.40 37	99.50 32	99.77 78	98.67 48	99.90 79	98.21 177	93.53 151	99.81 13	99.89 21	94.70 69	99.86 95	99.84 19	99.93 67	99.96 74

EPNet		98.49 44	98.40 37	98.77 93	99.62 95	96.80 126	99.90 79	99.51 16	97.60 12	99.20 78	99.36 129	93.71 104	99.91 79	97.99 113	98.71 135	99.61 136
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-4		98.56 38	98.39 39	99.08 74	99.84 65	97.52 96	99.66 159	98.52 92	95.76 66	99.01 88	99.77 72	94.33 84	99.75 126	98.80 76	99.83 85	99.98 55

9.14			98.38 40		99.87 57		99.91 75	98.33 158	93.22 159	99.78 25	99.89 21	94.57 72	99.85 99	99.84 19	99.97 48

MVS_111021_LR		98.42 49	98.38 40	98.53 115	99.39 110	95.79 161	99.87 93	99.86 2	96.70 38	98.78 98	99.79 64	92.03 148	99.90 80	99.17 52	99.86 83	99.88 96

HFP-MVS		98.56 38	98.37 42	99.14 66	99.96 8	97.43 104	99.95 43	98.61 71	94.77 96	99.31 71	99.85 35	94.22 88	100.00 1	98.70 82	99.98 35	99.98 55

region2R		98.54 40	98.37 42	99.05 76	99.96 8	97.18 112	99.96 25	98.55 85	94.87 94	99.45 58	99.85 35	94.07 94	100.00 1	98.67 84	100.00 1	99.98 55

CDPH-MVS		98.65 32	98.36 44	99.49 34	99.94 14	98.73 45	99.87 93	98.33 158	93.97 134	99.76 27	99.87 28	94.99 62	99.75 126	98.55 91	100.00 1	99.98 55

APD-MVS		98.62 33	98.35 45	99.41 42	99.90 47	98.51 60	99.87 93	98.36 152	94.08 127	99.74 29	99.73 89	94.08 93	99.74 130	99.42 45	99.99 22	99.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMPR		98.50 43	98.32 46	99.05 76	99.96 8	97.18 112	99.95 43	98.60 73	94.77 96	99.31 71	99.84 48	93.73 103	100.00 1	98.70 82	99.98 35	99.98 55

CP-MVS		98.45 47	98.32 46	98.87 89	99.96 8	96.62 130	99.97 18	98.39 143	94.43 110	98.90 94	99.87 28	94.30 86	100.00 1	99.04 60	99.99 22	99.99 24

SR-MVS		98.46 46	98.30 48	98.93 87	99.88 54	97.04 117	99.84 111	98.35 155	94.92 91	99.32 70	99.80 60	93.35 110	99.78 116	99.30 50	99.95 55	99.96 74

test1172		98.38 54	98.25 49	98.77 93	99.88 54	96.56 133	99.80 125	98.36 152	94.68 100	99.20 78	99.80 60	93.28 115	99.78 116	99.34 48	99.92 71	99.98 55

DELS-MVS		98.54 40	98.22 50	99.50 32	99.15 119	98.65 52	100.00 1	98.58 75	97.70 9	98.21 127	99.24 139	92.58 134	99.94 73	98.63 89	99.94 61	99.92 91
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
PHI-MVS		98.41 50	98.21 51	99.03 78	99.86 59	97.10 116	99.98 9	98.80 52	90.78 238	99.62 43	99.78 70	95.30 50	100.00 1	99.80 24	99.93 67	99.99 24

PS-MVSNAJ		98.44 48	98.20 52	99.16 62	98.80 144	98.92 27	99.54 181	98.17 183	97.34 17	99.85 7	99.85 35	91.20 158	99.89 84	99.41 46	99.67 101	98.69 212

mPP-MVS		98.39 53	98.20 52	98.97 84	99.97 3	96.92 122	99.95 43	98.38 147	95.04 86	98.61 109	99.80 60	93.39 109	100.00 1	98.64 88	100.00 1	99.98 55

SR-MVS-dyc-post		98.31 57	98.17 54	98.71 96	99.79 75	96.37 140	99.76 137	98.31 162	94.43 110	99.40 66	99.75 82	93.28 115	99.78 116	98.90 69	99.92 71	99.97 67

PAPR		98.52 42	98.16 55	99.58 23	99.97 3	98.77 40	99.95 43	98.43 120	95.35 79	98.03 130	99.75 82	94.03 95	99.98 46	98.11 106	99.83 85	99.99 24

ACMMP_NAP		98.49 44	98.14 56	99.54 26	99.66 93	98.62 54	99.85 107	98.37 150	94.68 100	99.53 51	99.83 51	92.87 126	100.00 1	98.66 87	99.84 84	99.99 24

RE-MVS-def			98.13 57		99.79 75	96.37 140	99.76 137	98.31 162	94.43 110	99.40 66	99.75 82	92.95 124		98.90 69	99.92 71	99.97 67

PGM-MVS		98.34 55	98.13 57	98.99 82	99.92 36	97.00 118	99.75 140	99.50 17	93.90 139	99.37 68	99.76 76	93.24 118	100.00 1	97.75 126	99.96 52	99.98 55

EI-MVSNet-Vis-set		98.27 60	98.11 59	98.75 95	99.83 68	96.59 132	99.40 199	98.51 99	95.29 81	98.51 112	99.76 76	93.60 107	99.71 134	98.53 92	99.52 112	99.95 82

dcpmvs_2		97.42 97	98.09 60	95.42 219	99.58 100	87.24 323	99.23 223	96.95 301	94.28 120	98.93 93	99.73 89	94.39 79	99.16 164	99.89 17	99.82 91	99.86 100

APD-MVS_3200maxsize		98.25 63	98.08 61	98.78 92	99.81 73	96.60 131	99.82 118	98.30 165	93.95 136	99.37 68	99.77 72	92.84 127	99.76 123	98.95 63	99.92 71	99.97 67

ETH3D cwj APD-0.16		98.40 52	98.07 62	99.40 44	99.59 96	98.41 64	99.86 104	98.24 173	92.18 199	99.73 30	99.87 28	93.47 108	99.85 99	99.74 30	99.95 55	99.93 85

ZNCC-MVS		98.31 57	98.03 63	99.17 60	99.88 54	97.59 92	99.94 61	98.44 112	94.31 118	98.50 113	99.82 55	93.06 122	99.99 40	98.30 100	99.99 22	99.93 85

DP-MVS Recon		98.41 50	98.02 64	99.56 24	99.97 3	98.70 47	99.92 71	98.44 112	92.06 204	98.40 118	99.84 48	95.68 42	100.00 1	98.19 101	99.71 99	99.97 67

zzz-MVS		98.33 56	98.00 65	99.30 50	99.85 60	97.93 82	99.80 125	98.28 167	95.76 66	97.18 148	99.88 24	92.74 130	100.00 1	98.67 84	99.88 80	99.99 24

EI-MVSNet-UG-set		98.14 67	97.99 66	98.60 105	99.80 74	96.27 142	99.36 208	98.50 103	95.21 83	98.30 122	99.75 82	93.29 114	99.73 133	98.37 97	99.30 121	99.81 104

GST-MVS		98.27 60	97.97 67	99.17 60	99.92 36	97.57 93	99.93 67	98.39 143	94.04 132	98.80 97	99.74 87	92.98 123	100.00 1	98.16 103	99.76 95	99.93 85

xiu_mvs_v2_base		98.23 65	97.97 67	99.02 80	98.69 148	98.66 50	99.52 183	98.08 194	97.05 27	99.86 5	99.86 31	90.65 169	99.71 134	99.39 47	98.63 136	98.69 212

MP-MVS		98.23 65	97.97 67	99.03 78	99.94 14	97.17 115	99.95 43	98.39 143	94.70 99	98.26 125	99.81 59	91.84 152	100.00 1	98.85 72	99.97 48	99.93 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		98.29 59	97.96 70	99.30 50	99.85 60	97.93 82	99.39 203	98.28 167	95.76 66	97.18 148	99.88 24	92.74 130	100.00 1	98.67 84	99.88 80	99.99 24

CS-MVS-test		97.88 76	97.94 71	97.70 152	99.28 115	95.20 183	99.98 9	97.15 279	95.53 75	99.62 43	99.79 64	92.08 147	98.38 210	98.75 80	99.28 122	99.52 156

PAPM_NR		98.12 68	97.93 72	98.70 97	99.94 14	96.13 151	99.82 118	98.43 120	94.56 105	97.52 141	99.70 95	94.40 75	99.98 46	97.00 141	99.98 35	99.99 24

CS-MVS		97.79 83	97.91 73	97.43 163	99.10 120	94.42 200	99.99 3	97.10 284	95.07 85	99.68 37	99.75 82	92.95 124	98.34 214	98.38 96	99.14 127	99.54 152

PLC	95.54 3	97.93 74	97.89 74	98.05 138	99.82 70	94.77 195	99.92 71	98.46 107	93.93 137	97.20 147	99.27 134	95.44 48	99.97 56	97.41 131	99.51 114	99.41 170
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		98.27 60	97.82 75	99.63 15	99.72 88	99.10 21	99.98 9	98.51 99	97.00 29	98.52 111	99.71 93	87.80 200	99.95 65	99.75 28	99.38 119	99.83 102

ETV-MVS		97.92 75	97.80 76	98.25 130	98.14 177	96.48 134	99.98 9	97.63 226	95.61 72	99.29 75	99.46 120	92.55 135	98.82 174	99.02 62	98.54 137	99.46 163

HPM-MVS		97.96 72	97.72 77	98.68 98	99.84 65	96.39 139	99.90 79	98.17 183	92.61 182	98.62 108	99.57 111	91.87 151	99.67 141	98.87 71	99.99 22	99.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
API-MVS		97.86 77	97.66 78	98.47 118	99.52 103	95.41 174	99.47 192	98.87 46	91.68 214	98.84 95	99.85 35	92.34 141	99.99 40	98.44 94	99.96 52	100.00 1

MP-MVS-pluss		98.07 70	97.64 79	99.38 47	99.74 82	98.41 64	99.74 143	98.18 182	93.35 155	96.45 167	99.85 35	92.64 133	99.97 56	98.91 68	99.89 78	99.77 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PVSNet_Blended		97.94 73	97.64 79	98.83 91	99.59 96	96.99 119	100.00 1	99.10 29	95.38 78	98.27 123	99.08 146	89.00 191	99.95 65	99.12 53	99.25 123	99.57 146

lupinMVS		97.85 78	97.60 81	98.62 103	97.28 226	97.70 89	99.99 3	97.55 237	95.50 77	99.43 60	99.67 102	90.92 165	98.71 184	98.40 95	99.62 104	99.45 165

WTY-MVS		98.10 69	97.60 81	99.60 20	98.92 134	99.28 16	99.89 87	99.52 14	95.58 73	98.24 126	99.39 126	93.33 111	99.74 130	97.98 115	95.58 202	99.78 109

1121		98.03 71	97.57 83	99.40 44	99.74 82	98.21 70	98.31 298	98.62 69	92.78 172	99.53 51	99.83 51	95.08 54	100.00 1	94.36 187	99.92 71	99.99 24

HPM-MVS_fast		97.80 82	97.50 84	98.68 98	99.79 75	96.42 136	99.88 90	98.16 186	91.75 213	98.94 92	99.54 114	91.82 153	99.65 143	97.62 128	99.99 22	99.99 24

EIA-MVS		97.53 91	97.46 85	97.76 149	98.04 181	94.84 191	99.98 9	97.61 231	94.41 113	97.90 134	99.59 109	92.40 139	98.87 172	98.04 110	99.13 128	99.59 139

ACMMP		97.74 86	97.44 86	98.66 100	99.92 36	96.13 151	99.18 227	99.45 18	94.84 95	96.41 170	99.71 93	91.40 155	99.99 40	97.99 113	98.03 155	99.87 98
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
CNLPA		97.76 85	97.38 87	98.92 88	99.53 102	96.84 124	99.87 93	98.14 189	93.78 144	96.55 165	99.69 98	92.28 142	99.98 46	97.13 137	99.44 117	99.93 85

test_yl		97.83 79	97.37 88	99.21 54	99.18 116	97.98 79	99.64 166	99.27 26	91.43 223	97.88 135	98.99 154	95.84 39	99.84 108	98.82 73	95.32 206	99.79 106

DCV-MVSNet		97.83 79	97.37 88	99.21 54	99.18 116	97.98 79	99.64 166	99.27 26	91.43 223	97.88 135	98.99 154	95.84 39	99.84 108	98.82 73	95.32 206	99.79 106

abl_6		97.67 88	97.34 90	98.66 100	99.68 91	96.11 154	99.68 156	98.14 189	93.80 143	99.27 76	99.70 95	88.65 196	99.98 46	97.46 130	99.72 98	99.89 94

alignmvs		97.81 81	97.33 91	99.25 52	98.77 146	98.66 50	99.99 3	98.44 112	94.40 114	98.41 116	99.47 118	93.65 105	99.42 156	98.57 90	94.26 215	99.67 122

CPTT-MVS		97.64 89	97.32 92	98.58 108	99.97 3	95.77 162	99.96 25	98.35 155	89.90 251	98.36 119	99.79 64	91.18 161	99.99 40	98.37 97	99.99 22	99.99 24

DROMVSNet		97.38 100	97.24 93	97.80 144	97.41 216	95.64 169	99.99 3	97.06 289	94.59 104	99.63 40	99.32 130	89.20 189	98.14 228	98.76 79	99.23 124	99.62 133

OMC-MVS		97.28 102	97.23 94	97.41 164	99.76 79	93.36 226	99.65 162	97.95 204	96.03 59	97.41 144	99.70 95	89.61 180	99.51 148	96.73 148	98.25 146	99.38 172

test2506		97.53 91	97.19 95	98.58 108	98.66 150	96.90 123	98.81 270	99.77 5	94.93 89	97.95 132	98.96 160	92.51 136	99.20 160	94.93 168	98.15 147	99.64 128

DWT-MVSNet_test		97.31 101	97.19 95	97.66 153	98.24 170	94.67 196	98.86 264	98.20 181	93.60 150	98.09 128	98.89 169	97.51 7	98.78 177	94.04 194	97.28 168	99.55 148

MAR-MVS		97.43 93	97.19 95	98.15 135	99.47 107	94.79 194	99.05 243	98.76 53	92.65 180	98.66 106	99.82 55	88.52 197	99.98 46	98.12 105	99.63 103	99.67 122
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
HY-MVS	92.50 7	97.79 83	97.17 98	99.63 15	98.98 127	99.32 8	97.49 319	99.52 14	95.69 70	98.32 121	97.41 221	93.32 112	99.77 120	98.08 109	95.75 199	99.81 104

xiu_mvs_v1_base_debu		97.43 93	97.06 99	98.55 110	97.74 200	98.14 71	99.31 213	97.86 214	96.43 45	99.62 43	99.69 98	85.56 221	99.68 138	99.05 56	98.31 143	97.83 221

xiu_mvs_v1_base		97.43 93	97.06 99	98.55 110	97.74 200	98.14 71	99.31 213	97.86 214	96.43 45	99.62 43	99.69 98	85.56 221	99.68 138	99.05 56	98.31 143	97.83 221

xiu_mvs_v1_base_debi		97.43 93	97.06 99	98.55 110	97.74 200	98.14 71	99.31 213	97.86 214	96.43 45	99.62 43	99.69 98	85.56 221	99.68 138	99.05 56	98.31 143	97.83 221

CSCG		97.10 108	97.04 102	97.27 172	99.89 50	91.92 256	99.90 79	99.07 32	88.67 271	95.26 190	99.82 55	93.17 120	99.98 46	98.15 104	99.47 115	99.90 93

sss		97.57 90	97.03 103	99.18 57	98.37 161	98.04 76	99.73 148	99.38 22	93.46 153	98.76 101	99.06 147	91.21 157	99.89 84	96.33 150	97.01 175	99.62 133

thisisatest0515		97.41 98	97.02 104	98.59 107	97.71 206	97.52 96	99.97 18	98.54 89	91.83 209	97.45 143	99.04 148	97.50 8	99.10 166	94.75 177	96.37 186	99.16 191

F-COLMAP		96.93 114	96.95 105	96.87 181	99.71 89	91.74 261	99.85 107	97.95 204	93.11 163	95.72 183	99.16 143	92.35 140	99.94 73	95.32 162	99.35 120	98.92 201

jason		97.24 104	96.86 106	98.38 126	95.73 272	97.32 108	99.97 18	97.40 258	95.34 80	98.60 110	99.54 114	87.70 201	98.56 191	97.94 116	99.47 115	99.25 186
jason: jason.
114514_t		97.41 98	96.83 107	99.14 66	99.51 105	97.83 84	99.89 87	98.27 170	88.48 275	99.06 85	99.66 104	90.30 173	99.64 144	96.32 151	99.97 48	99.96 74

PVSNet_Blended_VisFu		97.27 103	96.81 108	98.66 100	98.81 143	96.67 128	99.92 71	98.64 65	94.51 107	96.38 171	98.49 194	89.05 190	99.88 90	97.10 139	98.34 141	99.43 168

AdaColmap		97.23 105	96.80 109	98.51 116	99.99 1	95.60 170	99.09 232	98.84 49	93.32 156	96.74 159	99.72 91	86.04 217	100.00 1	98.01 111	99.43 118	99.94 84

PMMVS		96.76 121	96.76 110	96.76 184	98.28 166	92.10 251	99.91 75	97.98 201	94.12 125	99.53 51	99.39 126	86.93 210	98.73 182	96.95 144	97.73 157	99.45 165

thisisatest0530		97.10 108	96.72 111	98.22 131	97.60 209	96.70 127	99.92 71	98.54 89	91.11 230	97.07 151	98.97 158	97.47 11	99.03 167	93.73 205	96.09 189	98.92 201

PVSNet	91.05 13	97.13 107	96.69 112	98.45 120	99.52 103	95.81 160	99.95 43	99.65 11	94.73 98	99.04 86	99.21 141	84.48 231	99.95 65	94.92 169	98.74 134	99.58 145

diffmvs		97.00 111	96.64 113	98.09 136	97.64 207	96.17 150	99.81 120	97.19 273	94.67 102	98.95 91	99.28 131	86.43 214	98.76 180	98.37 97	97.42 165	99.33 179

MVSFormer		96.94 113	96.60 114	97.95 140	97.28 226	97.70 89	99.55 179	97.27 269	91.17 227	99.43 60	99.54 114	90.92 165	96.89 291	94.67 181	99.62 104	99.25 186

EPP-MVSNet		96.69 126	96.60 114	96.96 178	97.74 200	93.05 230	99.37 206	98.56 79	88.75 269	95.83 181	99.01 151	96.01 32	98.56 191	96.92 145	97.20 171	99.25 186

VNet		97.21 106	96.57 116	99.13 71	98.97 128	97.82 85	99.03 245	99.21 28	94.31 118	99.18 82	98.88 171	86.26 216	99.89 84	98.93 65	94.32 214	99.69 119

CHOSEN 1792x2688		96.81 118	96.53 117	97.64 154	98.91 136	93.07 228	99.65 162	99.80 3	95.64 71	95.39 187	98.86 175	84.35 233	99.90 80	96.98 142	99.16 126	99.95 82

tttt0517		96.85 116	96.49 118	97.92 142	97.48 215	95.89 159	99.85 107	98.54 89	90.72 239	96.63 161	98.93 168	97.47 11	99.02 168	93.03 218	95.76 198	98.85 205

baseline2		96.71 125	96.49 118	97.37 167	95.63 279	95.96 157	99.74 143	98.88 45	92.94 165	91.61 225	98.97 158	97.72 5	98.62 189	94.83 173	98.08 154	97.53 229

HyFIR lowres test		96.66 128	96.43 120	97.36 169	99.05 122	93.91 211	99.70 153	99.80 3	90.54 240	96.26 173	98.08 206	92.15 145	98.23 225	96.84 147	95.46 203	99.93 85

DeepC-MVS	94.51 4	96.92 115	96.40 121	98.45 120	99.16 118	95.90 158	99.66 159	98.06 195	96.37 51	94.37 199	99.49 117	83.29 240	99.90 80	97.63 127	99.61 107	99.55 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		97.09 110	96.32 122	99.39 46	98.93 132	98.95 25	99.72 151	97.35 261	94.45 108	97.88 135	99.42 122	86.71 211	99.52 147	98.48 93	93.97 219	99.72 116

TESTMET0.1,1		96.74 123	96.26 123	98.16 132	97.36 219	96.48 134	99.96 25	98.29 166	91.93 206	95.77 182	98.07 207	95.54 44	98.29 218	90.55 249	98.89 130	99.70 117

thres200		96.96 112	96.21 124	99.22 53	98.97 128	98.84 34	99.85 107	99.71 6	93.17 161	96.26 173	98.88 171	89.87 178	99.51 148	94.26 191	94.91 209	99.31 181

CANet_DTU		96.76 121	96.15 125	98.60 105	98.78 145	97.53 95	99.84 111	97.63 226	97.25 24	99.20 78	99.64 106	81.36 254	99.98 46	92.77 220	98.89 130	98.28 215

CDS-MVSNet		96.34 137	96.07 126	97.13 174	97.37 218	94.96 188	99.53 182	97.91 209	91.55 218	95.37 188	98.32 202	95.05 57	97.13 275	93.80 201	95.75 199	99.30 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test-LLR		96.47 132	96.04 127	97.78 146	97.02 234	95.44 172	99.96 25	98.21 177	94.07 128	95.55 184	96.38 255	93.90 99	98.27 222	90.42 252	98.83 132	99.64 128

EPMVS		96.53 131	96.01 128	98.09 136	98.43 160	96.12 153	96.36 335	99.43 20	93.53 151	97.64 139	95.04 304	94.41 74	98.38 210	91.13 236	98.11 150	99.75 112

tfpn200view9		96.79 119	95.99 129	99.19 56	98.94 130	98.82 35	99.78 129	99.71 6	92.86 166	96.02 176	98.87 173	89.33 184	99.50 150	93.84 197	94.57 210	99.27 184

thres400		96.78 120	95.99 129	99.16 62	98.94 130	98.82 35	99.78 129	99.71 6	92.86 166	96.02 176	98.87 173	89.33 184	99.50 150	93.84 197	94.57 210	99.16 191

baseline		96.43 134	95.98 131	97.76 149	97.34 220	95.17 184	99.51 185	97.17 276	93.92 138	96.90 154	99.28 131	85.37 224	98.64 188	97.50 129	96.86 179	99.46 163

tpmrst		96.27 143	95.98 131	97.13 174	97.96 184	93.15 227	96.34 336	98.17 183	92.07 202	98.71 104	95.12 302	93.91 98	98.73 182	94.91 171	96.62 180	99.50 160

Vis-MVSNet (Re-imp)		96.32 138	95.98 131	97.35 170	97.93 186	94.82 192	99.47 192	98.15 188	91.83 209	95.09 191	99.11 144	91.37 156	97.47 256	93.47 209	97.43 163	99.74 113

mvs-test1		95.53 159	95.97 134	94.20 263	97.77 197	85.44 334	99.95 43	97.06 289	94.92 91	96.58 163	98.72 181	85.81 218	98.98 169	94.80 174	98.11 150	98.18 216

casdiffmvs		96.42 135	95.97 134	97.77 148	97.30 224	94.98 187	99.84 111	97.09 286	93.75 146	96.58 163	99.26 137	85.07 227	98.78 177	97.77 124	97.04 174	99.54 152

UA-Net		96.54 130	95.96 136	98.27 129	98.23 171	95.71 166	98.00 312	98.45 109	93.72 147	98.41 116	99.27 134	88.71 195	99.66 142	91.19 235	97.69 158	99.44 167

1314		96.84 117	95.96 136	99.48 36	96.74 250	98.52 59	98.31 298	98.86 47	95.82 62	89.91 244	98.98 156	87.49 203	99.96 58	97.80 119	99.73 97	99.96 74

test-mter		96.39 136	95.93 138	97.78 146	97.02 234	95.44 172	99.96 25	98.21 177	91.81 211	95.55 184	96.38 255	95.17 51	98.27 222	90.42 252	98.83 132	99.64 128

thres100view900		96.74 123	95.92 139	99.18 57	98.90 137	98.77 40	99.74 143	99.71 6	92.59 184	95.84 179	98.86 175	89.25 186	99.50 150	93.84 197	94.57 210	99.27 184

IS-MVSNet		96.29 141	95.90 140	97.45 161	98.13 178	94.80 193	99.08 234	97.61 231	92.02 205	95.54 186	98.96 160	90.64 170	98.08 231	93.73 205	97.41 166	99.47 162

CostFormer		96.10 144	95.88 141	96.78 183	97.03 233	92.55 243	97.08 327	97.83 217	90.04 250	98.72 103	94.89 311	95.01 60	98.29 218	96.54 149	95.77 197	99.50 160

thres600view7		96.69 126	95.87 142	99.14 66	98.90 137	98.78 39	99.74 143	99.71 6	92.59 184	95.84 179	98.86 175	89.25 186	99.50 150	93.44 210	94.50 213	99.16 191

PVSNet_BlendedMVS		96.05 145	95.82 143	96.72 186	99.59 96	96.99 119	99.95 43	99.10 29	94.06 130	98.27 123	95.80 269	89.00 191	99.95 65	99.12 53	87.53 261	93.24 324

MVS_Test		96.46 133	95.74 144	98.61 104	98.18 174	97.23 110	99.31 213	97.15 279	91.07 231	98.84 95	97.05 234	88.17 199	98.97 170	94.39 186	97.50 162	99.61 136

Effi-MVS+		96.30 140	95.69 145	98.16 132	97.85 192	96.26 143	97.41 320	97.21 272	90.37 243	98.65 107	98.58 190	86.61 213	98.70 185	97.11 138	97.37 167	99.52 156

MDTV_nov1_ep13			95.69 145		97.90 187	94.15 204	95.98 342	98.44 112	93.12 162	97.98 131	95.74 271	95.10 53	98.58 190	90.02 258	96.92 177

TAMVS		95.85 149	95.58 147	96.65 189	97.07 230	93.50 220	99.17 228	97.82 218	91.39 226	95.02 192	98.01 208	92.20 143	97.30 264	93.75 204	95.83 196	99.14 194

MVS		96.60 129	95.56 148	99.72 12	96.85 243	99.22 19	98.31 298	98.94 37	91.57 217	90.90 232	99.61 108	86.66 212	99.96 58	97.36 132	99.88 80	99.99 24

PatchmatchNet		95.94 148	95.45 149	97.39 166	97.83 193	94.41 201	96.05 341	98.40 140	92.86 166	97.09 150	95.28 299	94.21 91	98.07 233	89.26 264	98.11 150	99.70 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		96.04 146	95.40 150	97.95 140	99.59 96	95.22 182	99.52 183	99.07 32	93.96 135	96.49 166	98.35 201	82.28 244	99.82 110	90.15 257	99.22 125	98.81 208

EPNet_dtu		95.71 154	95.39 151	96.66 188	98.92 134	93.41 223	99.57 175	98.90 42	96.19 55	97.52 141	98.56 192	92.65 132	97.36 259	77.89 339	98.33 142	99.20 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		95.71 154	95.38 152	96.68 187	98.49 158	92.28 247	99.84 111	97.50 246	92.12 201	92.06 223	98.79 179	84.69 229	98.67 187	95.29 163	99.66 102	99.09 197

3Dnovator	91.47 12	96.28 142	95.34 153	99.08 74	96.82 245	97.47 102	99.45 195	98.81 50	95.52 76	89.39 258	99.00 153	81.97 246	99.95 65	97.27 134	99.83 85	99.84 101

Effi-MVS+-dtu		94.53 185	95.30 154	92.22 305	97.77 197	82.54 345	99.59 172	97.06 289	94.92 91	95.29 189	95.37 292	85.81 218	97.89 243	94.80 174	97.07 173	96.23 236

3Dnovator+	91.53 11	96.31 139	95.24 155	99.52 29	96.88 242	98.64 53	99.72 151	98.24 173	95.27 82	88.42 281	98.98 156	82.76 242	99.94 73	97.10 139	99.83 85	99.96 74

MVSTER		95.53 159	95.22 156	96.45 194	98.56 152	97.72 86	99.91 75	97.67 224	92.38 194	91.39 227	97.14 228	97.24 17	97.30 264	94.80 174	87.85 256	94.34 259

1112_ss		96.01 147	95.20 157	98.42 123	97.80 195	96.41 137	99.65 162	96.66 323	92.71 175	92.88 218	99.40 124	92.16 144	99.30 157	91.92 227	93.66 220	99.55 148

tpm2		95.47 161	95.18 158	96.35 200	96.91 238	91.70 265	96.96 330	97.93 206	88.04 281	98.44 115	95.40 288	93.32 112	97.97 237	94.00 195	95.61 201	99.38 172

Vis-MVSNet		95.72 152	95.15 159	97.45 161	97.62 208	94.28 203	99.28 219	98.24 173	94.27 122	96.84 156	98.94 166	79.39 273	98.76 180	93.25 211	98.49 138	99.30 182
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LS3D		95.84 150	95.11 160	98.02 139	99.85 60	95.10 185	98.74 275	98.50 103	87.22 291	93.66 208	99.86 31	87.45 204	99.95 65	90.94 243	99.81 93	99.02 199

ECVR-MVS		95.66 156	95.05 161	97.51 159	98.66 150	93.71 215	98.85 267	98.45 109	94.93 89	96.86 155	98.96 160	75.22 305	99.20 160	95.34 161	98.15 147	99.64 128

mvs_anonymous		95.65 157	95.03 162	97.53 157	98.19 173	95.74 164	99.33 210	97.49 247	90.87 235	90.47 237	97.10 230	88.23 198	97.16 272	95.92 156	97.66 160	99.68 120

test1111		95.57 158	94.98 163	97.37 167	98.56 152	93.37 225	98.86 264	98.45 109	94.95 88	96.63 161	98.95 164	75.21 306	99.11 165	95.02 166	98.14 149	99.64 128

CVMVSNet		94.68 179	94.94 164	93.89 277	96.80 246	86.92 325	99.06 239	98.98 35	94.45 108	94.23 202	99.02 149	85.60 220	95.31 336	90.91 244	95.39 205	99.43 168

baseline1		95.78 151	94.86 165	98.54 113	98.47 159	98.07 74	99.06 239	97.99 199	92.68 178	94.13 203	98.62 187	93.28 115	98.69 186	93.79 202	85.76 270	98.84 206

BH-untuned		95.18 165	94.83 166	96.22 202	98.36 162	91.22 273	99.80 125	97.32 265	90.91 234	91.08 230	98.67 183	83.51 237	98.54 193	94.23 192	99.61 107	98.92 201

Test_1112_low_res		95.72 152	94.83 166	98.42 123	97.79 196	96.41 137	99.65 162	96.65 324	92.70 176	92.86 219	96.13 264	92.15 145	99.30 157	91.88 228	93.64 221	99.55 148

RRT_MVS		95.23 164	94.77 168	96.61 190	98.28 166	98.32 67	99.81 120	97.41 256	92.59 184	91.28 229	97.76 215	95.02 58	97.23 270	93.65 207	87.14 263	94.28 262

XVG-OURS		94.82 172	94.74 169	95.06 230	98.00 182	89.19 303	99.08 234	97.55 237	94.10 126	94.71 194	99.62 107	80.51 265	99.74 130	96.04 154	93.06 227	96.25 234

XVG-OURS-SEG-HR		94.79 173	94.70 170	95.08 229	98.05 180	89.19 303	99.08 234	97.54 239	93.66 148	94.87 193	99.58 110	78.78 278	99.79 114	97.31 133	93.40 223	96.25 234

UGNet		95.33 163	94.57 171	97.62 156	98.55 154	94.85 190	98.67 282	99.32 25	95.75 69	96.80 158	96.27 260	72.18 319	99.96 58	94.58 183	99.05 129	98.04 219
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
HQP-MVS		94.61 181	94.50 172	94.92 235	95.78 266	91.85 257	99.87 93	97.89 210	96.82 32	93.37 210	98.65 184	80.65 263	98.39 206	97.92 117	89.60 231	94.53 240

dp		95.05 168	94.43 173	96.91 179	97.99 183	92.73 237	96.29 337	97.98 201	89.70 254	95.93 178	94.67 317	93.83 102	98.45 199	86.91 294	96.53 182	99.54 152

h-mvs33		94.92 171	94.36 174	96.59 191	98.85 141	91.29 272	98.93 255	98.94 37	95.90 60	98.77 99	98.42 200	90.89 167	99.77 120	97.80 119	70.76 349	98.72 211

HQP_MVS		94.49 186	94.36 174	94.87 236	95.71 275	91.74 261	99.84 111	97.87 212	96.38 48	93.01 214	98.59 188	80.47 267	98.37 212	97.79 122	89.55 234	94.52 242

BH-RMVSNet		95.18 165	94.31 176	97.80 144	98.17 175	95.23 181	99.76 137	97.53 241	92.52 189	94.27 201	99.25 138	76.84 289	98.80 175	90.89 245	99.54 111	99.35 177

Fast-Effi-MVS+		95.02 169	94.19 177	97.52 158	97.88 188	94.55 197	99.97 18	97.08 287	88.85 268	94.47 198	97.96 212	84.59 230	98.41 202	89.84 260	97.10 172	99.59 139

QAPM		95.40 162	94.17 178	99.10 72	96.92 237	97.71 87	99.40 199	98.68 58	89.31 256	88.94 270	98.89 169	82.48 243	99.96 58	93.12 217	99.83 85	99.62 133

RRT_test8_iter05		94.58 182	94.11 179	95.98 207	97.88 188	96.11 154	99.89 87	97.45 249	91.66 215	88.28 282	96.71 246	96.53 28	97.40 257	94.73 179	83.85 290	94.45 250

PCF-MVS	94.20 5	95.18 165	94.10 180	98.43 122	98.55 154	95.99 156	97.91 314	97.31 266	90.35 244	89.48 257	99.22 140	85.19 226	99.89 84	90.40 254	98.47 139	99.41 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
hse-mvs2		94.38 188	94.08 181	95.31 223	98.27 168	90.02 294	99.29 218	98.56 79	95.90 60	98.77 99	98.00 209	90.89 167	98.26 224	97.80 119	69.20 355	97.64 226

ADS-MVSNet		94.79 173	94.02 182	97.11 176	97.87 190	93.79 212	94.24 347	98.16 186	90.07 248	96.43 168	94.48 322	90.29 174	98.19 227	87.44 282	97.23 169	99.36 175

miper_enhance_ethall		94.36 191	93.98 183	95.49 215	98.68 149	95.24 180	99.73 148	97.29 267	93.28 158	89.86 246	95.97 267	94.37 80	97.05 281	92.20 224	84.45 282	94.19 269

IB-MVS	92.85 6	94.99 170	93.94 184	98.16 132	97.72 204	95.69 168	99.99 3	98.81 50	94.28 120	92.70 220	96.90 238	95.08 54	99.17 163	96.07 153	73.88 347	99.60 138
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
CLD-MVS		94.06 195	93.90 185	94.55 249	96.02 261	90.69 279	99.98 9	97.72 221	96.62 42	91.05 231	98.85 178	77.21 285	98.47 195	98.11 106	89.51 236	94.48 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ADS-MVSNet2		93.80 200	93.88 186	93.55 287	97.87 190	85.94 330	94.24 347	96.84 312	90.07 248	96.43 168	94.48 322	90.29 174	95.37 334	87.44 282	97.23 169	99.36 175

Fast-Effi-MVS+-dtu		93.72 204	93.86 187	93.29 290	97.06 231	86.16 328	99.80 125	96.83 313	92.66 179	92.58 221	97.83 214	81.39 253	97.67 249	89.75 261	96.87 178	96.05 238

SCA		94.69 177	93.81 188	97.33 171	97.10 229	94.44 198	98.86 264	98.32 160	93.30 157	96.17 175	95.59 278	76.48 293	97.95 240	91.06 238	97.43 163	99.59 139

test0.0.03 1		93.86 196	93.61 189	94.64 244	95.02 288	92.18 250	99.93 67	98.58 75	94.07 128	87.96 286	98.50 193	93.90 99	94.96 340	81.33 325	93.17 225	96.78 231

cascas		94.64 180	93.61 189	97.74 151	97.82 194	96.26 143	99.96 25	97.78 220	85.76 310	94.00 204	97.54 218	76.95 288	99.21 159	97.23 135	95.43 204	97.76 225

TAPA-MVS	92.12 8	94.42 187	93.60 191	96.90 180	99.33 113	91.78 260	99.78 129	98.00 198	89.89 252	94.52 196	99.47 118	91.97 149	99.18 162	69.90 355	99.52 112	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	90.15 15	94.77 175	93.59 192	98.33 127	96.07 259	97.48 101	99.56 177	98.57 77	90.46 241	86.51 305	98.95 164	78.57 280	99.94 73	93.86 196	99.74 96	97.57 228

tpmvs		94.28 193	93.57 193	96.40 197	98.55 154	91.50 270	95.70 346	98.55 85	87.47 286	92.15 222	94.26 326	91.42 154	98.95 171	88.15 275	95.85 195	98.76 210

LFMVS		94.75 176	93.56 194	98.30 128	99.03 123	95.70 167	98.74 275	97.98 201	87.81 284	98.47 114	99.39 126	67.43 338	99.53 146	98.01 111	95.20 208	99.67 122

TR-MVS		94.54 183	93.56 194	97.49 160	97.96 184	94.34 202	98.71 278	97.51 245	90.30 246	94.51 197	98.69 182	75.56 300	98.77 179	92.82 219	95.99 191	99.35 177

GeoE		94.36 191	93.48 196	96.99 177	97.29 225	93.54 219	99.96 25	96.72 321	88.35 278	93.43 209	98.94 166	82.05 245	98.05 234	88.12 277	96.48 184	99.37 174

FIs		94.10 194	93.43 197	96.11 204	94.70 292	96.82 125	99.58 173	98.93 41	92.54 188	89.34 260	97.31 224	87.62 202	97.10 278	94.22 193	86.58 266	94.40 252

ab-mvs		94.69 177	93.42 198	98.51 116	98.07 179	96.26 143	96.49 334	98.68 58	90.31 245	94.54 195	97.00 236	76.30 295	99.71 134	95.98 155	93.38 224	99.56 147

DP-MVS		94.54 183	93.42 198	97.91 143	99.46 109	94.04 206	98.93 255	97.48 248	81.15 341	90.04 241	99.55 112	87.02 209	99.95 65	88.97 266	98.11 150	99.73 114

tpm		93.70 205	93.41 200	94.58 247	95.36 283	87.41 322	97.01 328	96.90 308	90.85 236	96.72 160	94.14 327	90.40 172	96.84 294	90.75 248	88.54 250	99.51 158

EI-MVSNet		93.73 203	93.40 201	94.74 240	96.80 246	92.69 238	99.06 239	97.67 224	88.96 264	91.39 227	99.02 149	88.75 194	97.30 264	91.07 237	87.85 256	94.22 266

MSDG		94.37 189	93.36 202	97.40 165	98.88 139	93.95 210	99.37 206	97.38 259	85.75 312	90.80 233	99.17 142	84.11 235	99.88 90	86.35 295	98.43 140	98.36 214

PS-MVSNAJss		93.64 206	93.31 203	94.61 245	92.11 333	92.19 249	99.12 230	97.38 259	92.51 190	88.45 276	96.99 237	91.20 158	97.29 267	94.36 187	87.71 258	94.36 255

ET-MVSNet_ETH3D		94.37 189	93.28 204	97.64 154	98.30 163	97.99 78	99.99 3	97.61 231	94.35 115	71.57 360	99.45 121	96.23 31	95.34 335	96.91 146	85.14 277	99.59 139

cl22		93.77 201	93.25 205	95.33 222	99.49 106	94.43 199	99.61 170	98.09 192	90.38 242	89.16 267	95.61 276	90.56 171	97.34 261	91.93 226	84.45 282	94.21 268

FC-MVSNet-test		93.81 199	93.15 206	95.80 212	94.30 298	96.20 148	99.42 198	98.89 43	92.33 196	89.03 269	97.27 226	87.39 205	96.83 295	93.20 212	86.48 267	94.36 255

VDD-MVS		93.77 201	92.94 207	96.27 201	98.55 154	90.22 290	98.77 274	97.79 219	90.85 236	96.82 157	99.42 122	61.18 356	99.77 120	98.95 63	94.13 216	98.82 207

GA-MVS		93.83 197	92.84 208	96.80 182	95.73 272	93.57 217	99.88 90	97.24 271	92.57 187	92.92 216	96.66 248	78.73 279	97.67 249	87.75 280	94.06 218	99.17 190

OPM-MVS		93.21 212	92.80 209	94.44 256	93.12 318	90.85 278	99.77 132	97.61 231	96.19 55	91.56 226	98.65 184	75.16 307	98.47 195	93.78 203	89.39 237	93.99 292
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
RPSCF		91.80 245	92.79 210	88.83 332	98.15 176	69.87 366	98.11 308	96.60 325	83.93 328	94.33 200	99.27 134	79.60 272	99.46 155	91.99 225	93.16 226	97.18 230

LPG-MVS_test		92.96 218	92.71 211	93.71 281	95.43 281	88.67 309	99.75 140	97.62 228	92.81 169	90.05 239	98.49 194	75.24 303	98.40 204	95.84 158	89.12 238	94.07 284

CR-MVSNet		93.45 210	92.62 212	95.94 208	96.29 255	92.66 239	92.01 358	96.23 332	92.62 181	96.94 152	93.31 335	91.04 162	96.03 326	79.23 332	95.96 192	99.13 195

AUN-MVS		93.28 211	92.60 213	95.34 221	98.29 164	90.09 293	99.31 213	98.56 79	91.80 212	96.35 172	98.00 209	89.38 183	98.28 220	92.46 221	69.22 354	97.64 226

miper_ehance_all_eth		93.16 213	92.60 213	94.82 239	97.57 210	93.56 218	99.50 187	97.07 288	88.75 269	88.85 271	95.52 282	90.97 164	96.74 298	90.77 247	84.45 282	94.17 270

LCM-MVSNet-Re		92.31 233	92.60 213	91.43 313	97.53 211	79.27 362	99.02 246	91.83 369	92.07 202	80.31 341	94.38 325	83.50 238	95.48 332	97.22 136	97.58 161	99.54 152

D2MVS		92.76 222	92.59 216	93.27 291	95.13 284	89.54 302	99.69 154	99.38 22	92.26 197	87.59 290	94.61 319	85.05 228	97.79 245	91.59 231	88.01 255	92.47 336

nrg030		93.51 207	92.53 217	96.45 194	94.36 296	97.20 111	99.81 120	97.16 278	91.60 216	89.86 246	97.46 219	86.37 215	97.68 248	95.88 157	80.31 316	94.46 245

tpm cat1		93.51 207	92.52 218	96.47 192	97.77 197	91.47 271	96.13 339	98.06 195	80.98 342	92.91 217	93.78 330	89.66 179	98.87 172	87.03 290	96.39 185	99.09 197

ACMM	91.95 10	92.88 220	92.52 218	93.98 274	95.75 271	89.08 306	99.77 132	97.52 243	93.00 164	89.95 243	97.99 211	76.17 297	98.46 198	93.63 208	88.87 242	94.39 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	92.05 9	92.74 223	92.42 220	93.73 279	95.91 265	88.72 308	99.81 120	97.53 241	94.13 124	87.00 299	98.23 203	74.07 313	98.47 195	96.22 152	88.86 243	93.99 292
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
bset_n11_16_dypcd		93.05 217	92.30 221	95.31 223	90.23 352	95.05 186	99.44 197	97.28 268	92.51 190	90.65 235	96.68 247	85.30 225	96.71 301	94.49 185	84.14 285	94.16 275

test_djsdf		92.83 221	92.29 222	94.47 254	91.90 336	92.46 244	99.55 179	97.27 269	91.17 227	89.96 242	96.07 266	81.10 256	96.89 291	94.67 181	88.91 240	94.05 286

UniMVSNet (Re)		93.07 216	92.13 223	95.88 209	94.84 289	96.24 147	99.88 90	98.98 35	92.49 192	89.25 262	95.40 288	87.09 208	97.14 274	93.13 216	78.16 328	94.26 263

UniMVSNet_NR-MVSNet		92.95 219	92.11 224	95.49 215	94.61 294	95.28 178	99.83 117	99.08 31	91.49 219	89.21 264	96.86 241	87.14 207	96.73 299	93.20 212	77.52 333	94.46 245

IterMVS-LS		92.69 225	92.11 224	94.43 258	96.80 246	92.74 235	99.45 195	96.89 309	88.98 262	89.65 253	95.38 291	88.77 193	96.34 314	90.98 242	82.04 298	94.22 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		93.83 197	92.06 226	99.15 64	99.94 14	97.50 99	99.94 61	98.42 132	96.22 53	99.41 62	41.37 377	94.34 81	99.96 58	98.92 66	99.95 55	99.99 24

Anonymous202405211		93.10 215	91.99 227	96.40 197	99.10 120	89.65 300	98.88 260	97.93 206	83.71 330	94.00 204	98.75 180	68.79 330	99.88 90	95.08 165	91.71 229	99.68 120

eth_miper_zixun_eth		92.41 231	91.93 228	93.84 278	97.28 226	90.68 280	98.83 268	96.97 300	88.57 274	89.19 266	95.73 273	89.24 188	96.69 302	89.97 259	81.55 301	94.15 277

VDDNet		93.12 214	91.91 229	96.76 184	96.67 253	92.65 241	98.69 280	98.21 177	82.81 335	97.75 138	99.28 131	61.57 354	99.48 154	98.09 108	94.09 217	98.15 217

c3_l		92.53 228	91.87 230	94.52 250	97.40 217	92.99 231	99.40 199	96.93 306	87.86 282	88.69 274	95.44 286	89.95 177	96.44 310	90.45 251	80.69 313	94.14 280

gg-mvs-nofinetune		93.51 207	91.86 231	98.47 118	97.72 204	97.96 81	92.62 355	98.51 99	74.70 358	97.33 145	69.59 369	98.91 3	97.79 245	97.77 124	99.56 110	99.67 122

AllTest		92.48 229	91.64 232	95.00 232	99.01 124	88.43 313	98.94 254	96.82 315	86.50 300	88.71 272	98.47 198	74.73 309	99.88 90	85.39 301	96.18 187	96.71 232

DIV-MVS_self_test		92.32 232	91.60 233	94.47 254	97.31 223	92.74 235	99.58 173	96.75 319	86.99 295	87.64 289	95.54 280	89.55 181	96.50 308	88.58 269	82.44 295	94.17 270

cl____		92.31 233	91.58 234	94.52 250	97.33 222	92.77 233	99.57 175	96.78 318	86.97 296	87.56 291	95.51 283	89.43 182	96.62 304	88.60 268	82.44 295	94.16 275

FMVSNet3		92.69 225	91.58 234	95.99 206	98.29 164	97.42 106	99.26 221	97.62 228	89.80 253	89.68 250	95.32 294	81.62 252	96.27 317	87.01 291	85.65 271	94.29 261

VPA-MVSNet		92.70 224	91.55 236	96.16 203	95.09 285	96.20 148	98.88 260	99.00 34	91.02 233	91.82 224	95.29 298	76.05 299	97.96 239	95.62 160	81.19 304	94.30 260

Patchmatch-test		92.65 227	91.50 237	96.10 205	96.85 243	90.49 285	91.50 360	97.19 273	82.76 336	90.23 238	95.59 278	95.02 58	98.00 236	77.41 341	96.98 176	99.82 103

COLMAP_ROB	90.47 14	92.18 236	91.49 238	94.25 262	99.00 126	88.04 319	98.42 296	96.70 322	82.30 338	88.43 279	99.01 151	76.97 287	99.85 99	86.11 298	96.50 183	94.86 239
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DU-MVS		92.46 230	91.45 239	95.49 215	94.05 301	95.28 178	99.81 120	98.74 54	92.25 198	89.21 264	96.64 250	81.66 250	96.73 299	93.20 212	77.52 333	94.46 245

miper_lstm_enhance		91.81 242	91.39 240	93.06 297	97.34 220	89.18 305	99.38 204	96.79 317	86.70 299	87.47 293	95.22 300	90.00 176	95.86 330	88.26 273	81.37 303	94.15 277

WR-MVS		92.31 233	91.25 241	95.48 218	94.45 295	95.29 177	99.60 171	98.68 58	90.10 247	88.07 285	96.89 239	80.68 262	96.80 297	93.14 215	79.67 320	94.36 255

jajsoiax		91.92 240	91.18 242	94.15 264	91.35 342	90.95 276	99.00 247	97.42 254	92.61 182	87.38 295	97.08 231	72.46 318	97.36 259	94.53 184	88.77 244	94.13 281

mvs_tets		91.81 242	91.08 243	94.00 272	91.63 340	90.58 283	98.67 282	97.43 252	92.43 193	87.37 296	97.05 234	71.76 320	97.32 263	94.75 177	88.68 246	94.11 282

pmmvs4		92.10 238	91.07 244	95.18 227	92.82 326	94.96 188	99.48 191	96.83 313	87.45 287	88.66 275	96.56 253	83.78 236	96.83 295	89.29 263	84.77 280	93.75 309

anonymousdsp		91.79 247	90.92 245	94.41 259	90.76 347	92.93 232	98.93 255	97.17 276	89.08 258	87.46 294	95.30 295	78.43 283	96.92 290	92.38 222	88.73 245	93.39 320

XVG-ACMP-BASELINE		91.22 255	90.75 246	92.63 302	93.73 307	85.61 331	98.52 290	97.44 251	92.77 173	89.90 245	96.85 242	66.64 340	98.39 206	92.29 223	88.61 247	93.89 300

test_part1		92.15 237	90.72 247	96.44 196	98.87 140	97.46 103	98.99 248	98.26 171	85.89 307	86.34 310	96.34 258	81.71 248	97.48 255	91.06 238	78.99 322	94.37 254

JIA-IIPM		91.76 248	90.70 248	94.94 234	96.11 258	87.51 321	93.16 354	98.13 191	75.79 355	97.58 140	77.68 366	92.84 127	97.97 237	88.47 272	96.54 181	99.33 179

Anonymous20240529		92.10 238	90.65 249	96.47 192	98.82 142	90.61 282	98.72 277	98.67 61	75.54 356	93.90 206	98.58 190	66.23 341	99.90 80	94.70 180	90.67 230	98.90 204

TranMVSNet+NR-MVSNet		91.68 249	90.61 250	94.87 236	93.69 308	93.98 209	99.69 154	98.65 62	91.03 232	88.44 277	96.83 245	80.05 270	96.18 320	90.26 256	76.89 341	94.45 250

VPNet		91.81 242	90.46 251	95.85 211	94.74 291	95.54 171	98.98 249	98.59 74	92.14 200	90.77 234	97.44 220	68.73 332	97.54 253	94.89 172	77.89 330	94.46 245

XXY-MVS		91.82 241	90.46 251	95.88 209	93.91 304	95.40 175	98.87 263	97.69 223	88.63 273	87.87 287	97.08 231	74.38 312	97.89 243	91.66 230	84.07 287	94.35 258

MVP-Stereo		90.93 258	90.45 253	92.37 304	91.25 344	88.76 307	98.05 311	96.17 334	87.27 290	84.04 323	95.30 295	78.46 282	97.27 269	83.78 312	99.70 100	91.09 347
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WR-MVS_H		91.30 251	90.35 254	94.15 264	94.17 300	92.62 242	99.17 228	98.94 37	88.87 267	86.48 307	94.46 324	84.36 232	96.61 305	88.19 274	78.51 326	93.21 325

EU-MVSNet		90.14 281	90.34 255	89.54 328	92.55 329	81.06 356	98.69 280	98.04 197	91.41 225	86.59 304	96.84 244	80.83 260	93.31 356	86.20 296	81.91 299	94.26 263

MS-PatchMatch		90.65 265	90.30 256	91.71 312	94.22 299	85.50 333	98.24 302	97.70 222	88.67 271	86.42 308	96.37 257	67.82 336	98.03 235	83.62 313	99.62 104	91.60 344

PVSNet_0	88.03 19	91.80 245	90.27 257	96.38 199	98.27 168	90.46 286	99.94 61	99.61 12	93.99 133	86.26 312	97.39 223	71.13 325	99.89 84	98.77 78	67.05 359	98.79 209

CP-MVSNet		91.23 254	90.22 258	94.26 261	93.96 303	92.39 246	99.09 232	98.57 77	88.95 265	86.42 308	96.57 252	79.19 275	96.37 312	90.29 255	78.95 323	94.02 287

NR-MVSNet		91.56 250	90.22 258	95.60 213	94.05 301	95.76 163	98.25 301	98.70 56	91.16 229	80.78 340	96.64 250	83.23 241	96.57 306	91.41 232	77.73 332	94.46 245

IterMVS		90.91 259	90.17 260	93.12 294	96.78 249	90.42 288	98.89 258	97.05 292	89.03 260	86.49 306	95.42 287	76.59 292	95.02 338	87.22 287	84.09 286	93.93 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		90.85 262	90.16 261	92.93 298	96.72 251	89.96 295	98.89 258	96.99 296	88.95 265	86.63 303	95.67 274	76.48 293	95.00 339	87.04 289	84.04 289	93.84 304

V42		91.28 253	90.12 262	94.74 240	93.42 313	93.46 221	99.68 156	97.02 293	87.36 288	89.85 248	95.05 303	81.31 255	97.34 261	87.34 285	80.07 318	93.40 319

v2v482		91.30 251	90.07 263	95.01 231	93.13 316	93.79 212	99.77 132	97.02 293	88.05 280	89.25 262	95.37 292	80.73 261	97.15 273	87.28 286	80.04 319	94.09 283

v1144		91.09 256	89.83 264	94.87 236	93.25 315	93.69 216	99.62 169	96.98 298	86.83 298	89.64 254	94.99 308	80.94 258	97.05 281	85.08 304	81.16 305	93.87 302

GBi-Net		90.88 260	89.82 265	94.08 267	97.53 211	91.97 252	98.43 293	96.95 301	87.05 292	89.68 250	94.72 313	71.34 322	96.11 321	87.01 291	85.65 271	94.17 270

test1		90.88 260	89.82 265	94.08 267	97.53 211	91.97 252	98.43 293	96.95 301	87.05 292	89.68 250	94.72 313	71.34 322	96.11 321	87.01 291	85.65 271	94.17 270

v148		90.70 264	89.63 267	93.92 275	92.97 322	90.97 275	99.75 140	96.89 309	87.51 285	88.27 283	95.01 305	81.67 249	97.04 283	87.40 284	77.17 338	93.75 309

ACMH	89.72 17	90.64 266	89.63 267	93.66 285	95.64 278	88.64 311	98.55 286	97.45 249	89.03 260	81.62 335	97.61 217	69.75 328	98.41 202	89.37 262	87.62 260	93.92 298
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet2		91.02 257	89.56 269	95.41 220	97.53 211	95.74 164	98.98 249	97.41 256	87.05 292	88.43 279	95.00 307	71.34 322	96.24 319	85.12 303	85.21 276	94.25 265

ACMH+	89.98 16	90.35 273	89.54 270	92.78 301	95.99 262	86.12 329	98.81 270	97.18 275	89.38 255	83.14 328	97.76 215	68.42 334	98.43 200	89.11 265	86.05 269	93.78 308

v144192		90.79 263	89.52 271	94.59 246	93.11 319	92.77 233	99.56 177	96.99 296	86.38 302	89.82 249	94.95 310	80.50 266	97.10 278	83.98 310	80.41 314	93.90 299

PS-CasMVS		90.63 267	89.51 272	93.99 273	93.83 305	91.70 265	98.98 249	98.52 92	88.48 275	86.15 313	96.53 254	75.46 301	96.31 315	88.83 267	78.86 325	93.95 295

Baseline_NR-MVSNet		90.33 274	89.51 272	92.81 300	92.84 324	89.95 296	99.77 132	93.94 364	84.69 325	89.04 268	95.66 275	81.66 250	96.52 307	90.99 241	76.98 339	91.97 342

our_test_3		90.39 271	89.48 274	93.12 294	92.40 330	89.57 301	99.33 210	96.35 331	87.84 283	85.30 318	94.99 308	84.14 234	96.09 324	80.38 329	84.56 281	93.71 314

OurMVSNet-221017-0		89.81 285	89.48 274	90.83 318	91.64 339	81.21 354	98.17 306	95.38 350	91.48 220	85.65 317	97.31 224	72.66 317	97.29 267	88.15 275	84.83 279	93.97 294

v1192		90.62 268	89.25 276	94.72 242	93.13 316	93.07 228	99.50 187	97.02 293	86.33 303	89.56 256	95.01 305	79.22 274	97.09 280	82.34 320	81.16 305	94.01 289

v8		90.54 269	89.17 277	94.66 243	93.43 312	93.40 224	99.20 225	96.94 305	85.76 310	87.56 291	94.51 320	81.96 247	97.19 271	84.94 305	78.25 327	93.38 321

v1921920		90.46 270	89.12 278	94.50 252	92.96 323	92.46 244	99.49 189	96.98 298	86.10 305	89.61 255	95.30 295	78.55 281	97.03 285	82.17 321	80.89 312	94.01 289

pmmvs5		90.17 280	89.09 279	93.40 288	92.10 334	89.77 299	99.74 143	95.58 346	85.88 309	87.24 298	95.74 271	73.41 316	96.48 309	88.54 270	83.56 291	93.95 295

PEN-MVS		90.19 279	89.06 280	93.57 286	93.06 320	90.90 277	99.06 239	98.47 105	88.11 279	85.91 315	96.30 259	76.67 290	95.94 329	87.07 288	76.91 340	93.89 300

LTVRE_ROB	88.28 18	90.29 276	89.05 281	94.02 270	95.08 286	90.15 292	97.19 324	97.43 252	84.91 323	83.99 324	97.06 233	74.00 314	98.28 220	84.08 308	87.71 258	93.62 315
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
USDC		90.00 283	88.96 282	93.10 296	94.81 290	88.16 317	98.71 278	95.54 347	93.66 148	83.75 326	97.20 227	65.58 343	98.31 217	83.96 311	87.49 262	92.85 331

LF4IMVS		89.25 294	88.85 283	90.45 322	92.81 327	81.19 355	98.12 307	94.79 357	91.44 222	86.29 311	97.11 229	65.30 346	98.11 230	88.53 271	85.25 275	92.07 339

v10		90.25 277	88.82 284	94.57 248	93.53 310	93.43 222	99.08 234	96.87 311	85.00 320	87.34 297	94.51 320	80.93 259	97.02 287	82.85 317	79.23 321	93.26 323

v1240		90.20 278	88.79 285	94.44 256	93.05 321	92.27 248	99.38 204	96.92 307	85.89 307	89.36 259	94.87 312	77.89 284	97.03 285	80.66 328	81.08 308	94.01 289

PatchT		90.38 272	88.75 286	95.25 226	95.99 262	90.16 291	91.22 362	97.54 239	76.80 351	97.26 146	86.01 361	91.88 150	96.07 325	66.16 362	95.91 194	99.51 158

MIMVSNet		90.30 275	88.67 287	95.17 228	96.45 254	91.64 267	92.39 356	97.15 279	85.99 306	90.50 236	93.19 337	66.95 339	94.86 342	82.01 322	93.43 222	99.01 200

UniMVSNet_ETH3D		90.06 282	88.58 288	94.49 253	94.67 293	88.09 318	97.81 316	97.57 236	83.91 329	88.44 277	97.41 221	57.44 360	97.62 251	91.41 232	88.59 249	97.77 224

Patchmtry		89.70 287	88.49 289	93.33 289	96.24 257	89.94 298	91.37 361	96.23 332	78.22 349	87.69 288	93.31 335	91.04 162	96.03 326	80.18 331	82.10 297	94.02 287

Anonymous20231211		89.86 284	88.44 290	94.13 266	98.93 132	90.68 280	98.54 288	98.26 171	76.28 352	86.73 301	95.54 280	70.60 326	97.56 252	90.82 246	80.27 317	94.15 277

ppachtmachnet_test		89.58 289	88.35 291	93.25 292	92.40 330	90.44 287	99.33 210	96.73 320	85.49 316	85.90 316	95.77 270	81.09 257	96.00 328	76.00 347	82.49 294	93.30 322

MVS_0304		89.28 293	88.31 292	92.21 306	97.05 232	86.53 327	97.76 317	99.57 13	85.58 315	93.86 207	92.71 339	51.04 367	96.30 316	84.49 307	92.72 228	93.79 307

v7n		89.65 288	88.29 293	93.72 280	92.22 332	90.56 284	99.07 238	97.10 284	85.42 318	86.73 301	94.72 313	80.06 269	97.13 275	81.14 326	78.12 329	93.49 317

DTE-MVSNet		89.40 290	88.24 294	92.88 299	92.66 328	89.95 296	99.10 231	98.22 176	87.29 289	85.12 320	96.22 261	76.27 296	95.30 337	83.56 314	75.74 344	93.41 318

DSMNet-mixed		88.28 299	88.24 294	88.42 336	89.64 355	75.38 364	98.06 310	89.86 372	85.59 314	88.20 284	92.14 346	76.15 298	91.95 360	78.46 337	96.05 190	97.92 220

testgi		89.01 295	88.04 296	91.90 310	93.49 311	84.89 337	99.73 148	95.66 344	93.89 141	85.14 319	98.17 204	59.68 357	94.66 344	77.73 340	88.88 241	96.16 237

SixPastTwentyTwo		88.73 296	88.01 297	90.88 316	91.85 337	82.24 347	98.22 304	95.18 355	88.97 263	82.26 331	96.89 239	71.75 321	96.67 303	84.00 309	82.98 292	93.72 313

pm-mvs1		89.36 291	87.81 298	94.01 271	93.40 314	91.93 255	98.62 285	96.48 329	86.25 304	83.86 325	96.14 263	73.68 315	97.04 283	86.16 297	75.73 345	93.04 328

tfpnnormal		89.29 292	87.61 299	94.34 260	94.35 297	94.13 205	98.95 253	98.94 37	83.94 327	84.47 322	95.51 283	74.84 308	97.39 258	77.05 344	80.41 314	91.48 346

FMVSNet5		88.32 298	87.47 300	90.88 316	96.90 241	88.39 315	97.28 322	95.68 343	82.60 337	84.67 321	92.40 344	79.83 271	91.16 362	76.39 346	81.51 302	93.09 326

RPMNet		89.76 286	87.28 301	97.19 173	96.29 255	92.66 239	92.01 358	98.31 162	70.19 363	96.94 152	85.87 362	87.25 206	99.78 116	62.69 365	95.96 192	99.13 195

K. test v3		88.05 300	87.24 302	90.47 321	91.82 338	82.23 348	98.96 252	97.42 254	89.05 259	76.93 351	95.60 277	68.49 333	95.42 333	85.87 300	81.01 310	93.75 309

FMVSNet1		88.50 297	86.64 303	94.08 267	95.62 280	91.97 252	98.43 293	96.95 301	83.00 333	86.08 314	94.72 313	59.09 358	96.11 321	81.82 324	84.07 287	94.17 270

TinyColmap		87.87 303	86.51 304	91.94 309	95.05 287	85.57 332	97.65 318	94.08 362	84.40 326	81.82 334	96.85 242	62.14 353	98.33 215	80.25 330	86.37 268	91.91 343

KD-MVS_2432*1600		88.00 301	86.10 305	93.70 283	96.91 238	94.04 206	97.17 325	97.12 282	84.93 321	81.96 332	92.41 342	92.48 137	94.51 345	79.23 332	52.68 367	92.56 333

miper_refine_blended		88.00 301	86.10 305	93.70 283	96.91 238	94.04 206	97.17 325	97.12 282	84.93 321	81.96 332	92.41 342	92.48 137	94.51 345	79.23 332	52.68 367	92.56 333

Patchmatch-RL test		86.90 305	85.98 307	89.67 327	84.45 365	75.59 363	89.71 363	92.43 367	86.89 297	77.83 349	90.94 350	94.22 88	93.63 353	87.75 280	69.61 351	99.79 106

Anonymous20231206		86.32 306	85.42 308	89.02 331	89.11 357	80.53 360	99.05 243	95.28 351	85.43 317	82.82 329	93.92 328	74.40 311	93.44 355	66.99 360	81.83 300	93.08 327

TransMVSNet (Re)		87.25 304	85.28 309	93.16 293	93.56 309	91.03 274	98.54 288	94.05 363	83.69 331	81.09 338	96.16 262	75.32 302	96.40 311	76.69 345	68.41 356	92.06 340

CMPMVS	61.59 21	84.75 316	85.14 310	83.57 344	90.32 350	62.54 370	96.98 329	97.59 235	74.33 359	69.95 362	96.66 248	64.17 348	98.32 216	87.88 279	88.41 252	89.84 357
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		84.72 317	83.99 311	86.91 339	88.19 360	80.62 359	98.88 260	95.94 338	88.36 277	78.87 345	94.62 318	68.75 331	89.11 366	66.52 361	75.82 343	91.00 348

UnsupCasMVSNet_eth		85.52 310	83.99 311	90.10 324	89.36 356	83.51 341	96.65 332	97.99 199	89.14 257	75.89 355	93.83 329	63.25 351	93.92 349	81.92 323	67.90 358	92.88 330

test_0402		85.58 309	83.94 313	90.50 320	93.81 306	85.04 336	98.55 286	95.20 354	76.01 353	79.72 344	95.13 301	64.15 349	96.26 318	66.04 363	86.88 265	90.21 355

pmmvs6		85.69 308	83.84 314	91.26 315	90.00 354	84.41 339	97.82 315	96.15 335	75.86 354	81.29 337	95.39 290	61.21 355	96.87 293	83.52 315	73.29 348	92.50 335

Anonymous20240521		85.15 314	83.81 315	89.16 330	88.32 358	82.69 343	98.80 272	95.74 341	79.72 345	81.53 336	90.99 349	65.38 345	94.16 347	72.69 351	81.11 307	90.63 352

EG-PatchMatch MVS		85.35 313	83.81 315	89.99 326	90.39 349	81.89 350	98.21 305	96.09 336	81.78 340	74.73 357	93.72 331	51.56 366	97.12 277	79.16 335	88.61 247	90.96 349

YYNet1		85.50 312	83.33 317	92.00 308	90.89 346	88.38 316	99.22 224	96.55 326	79.60 347	57.26 367	92.72 338	79.09 277	93.78 352	77.25 342	77.37 336	93.84 304

MDA-MVSNet_test_wron		85.51 311	83.32 318	92.10 307	90.96 345	88.58 312	99.20 225	96.52 327	79.70 346	57.12 368	92.69 340	79.11 276	93.86 351	77.10 343	77.46 335	93.86 303

MVS-HIRNet		86.22 307	83.19 319	95.31 223	96.71 252	90.29 289	92.12 357	97.33 264	62.85 364	86.82 300	70.37 368	69.37 329	97.49 254	75.12 348	97.99 156	98.15 217

CL-MVSNet_self_test		84.50 318	83.15 320	88.53 335	86.00 363	81.79 351	98.82 269	97.35 261	85.12 319	83.62 327	90.91 351	76.66 291	91.40 361	69.53 356	60.36 364	92.40 337

new_pmnet		84.49 319	82.92 321	89.21 329	90.03 353	82.60 344	96.89 331	95.62 345	80.59 343	75.77 356	89.17 353	65.04 347	94.79 343	72.12 352	81.02 309	90.23 354

TDRefinement		84.76 315	82.56 322	91.38 314	74.58 371	84.80 338	97.36 321	94.56 360	84.73 324	80.21 342	96.12 265	63.56 350	98.39 206	87.92 278	63.97 360	90.95 350

KD-MVS_self_test		83.59 323	82.06 323	88.20 337	86.93 361	80.70 358	97.21 323	96.38 330	82.87 334	82.49 330	88.97 354	67.63 337	92.32 358	73.75 350	62.30 363	91.58 345

pmmvs-eth3d		84.03 321	81.97 324	90.20 323	84.15 366	87.09 324	98.10 309	94.73 359	83.05 332	74.10 358	87.77 357	65.56 344	94.01 348	81.08 327	69.24 353	89.49 359

OpenMVS_ROB	79.82 20	83.77 322	81.68 325	90.03 325	88.30 359	82.82 342	98.46 291	95.22 353	73.92 360	76.00 354	91.29 348	55.00 362	96.94 289	68.40 358	88.51 251	90.34 353

MDA-MVSNet-bldmvs		84.09 320	81.52 326	91.81 311	91.32 343	88.00 320	98.67 282	95.92 339	80.22 344	55.60 369	93.32 334	68.29 335	93.60 354	73.76 349	76.61 342	93.82 306

N_pmnet		80.06 329	80.78 327	77.89 347	91.94 335	45.28 378	98.80 272	56.82 381	78.10 350	80.08 343	93.33 333	77.03 286	95.76 331	68.14 359	82.81 293	92.64 332

MIMVSNet1		82.58 324	80.51 328	88.78 333	86.68 362	84.20 340	96.65 332	95.41 349	78.75 348	78.59 347	92.44 341	51.88 365	89.76 365	65.26 364	78.95 323	92.38 338

test_method		80.79 326	79.70 329	84.08 343	92.83 325	67.06 368	99.51 185	95.42 348	54.34 366	81.07 339	93.53 332	44.48 369	92.22 359	78.90 336	77.23 337	92.94 329

new-patchmatchnet		81.19 325	79.34 330	86.76 340	82.86 368	80.36 361	97.92 313	95.27 352	82.09 339	72.02 359	86.87 359	62.81 352	90.74 364	71.10 353	63.08 361	89.19 361

PM-MVS		80.47 327	78.88 331	85.26 342	83.79 367	72.22 365	95.89 344	91.08 370	85.71 313	76.56 353	88.30 355	36.64 370	93.90 350	82.39 319	69.57 352	89.66 358

pmmvs3		80.27 328	77.77 332	87.76 338	80.32 369	82.43 346	98.23 303	91.97 368	72.74 361	78.75 346	87.97 356	57.30 361	90.99 363	70.31 354	62.37 362	89.87 356

UnsupCasMVSNet_bld		79.97 330	77.03 333	88.78 333	85.62 364	81.98 349	93.66 352	97.35 261	75.51 357	70.79 361	83.05 363	48.70 368	94.91 341	78.31 338	60.29 365	89.46 360

FPMVS		68.72 332	68.72 334	68.71 352	65.95 375	44.27 380	95.97 343	94.74 358	51.13 367	53.26 370	90.50 352	25.11 375	83.00 370	60.80 366	80.97 311	78.87 366

Gipuma		66.95 335	65.00 335	72.79 350	91.52 341	67.96 367	66.16 370	95.15 356	47.89 368	58.54 366	67.99 370	29.74 372	87.54 367	50.20 370	77.83 331	62.87 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		67.77 333	64.73 336	76.87 348	62.95 377	56.25 374	89.37 364	93.74 366	44.53 369	61.99 364	80.74 364	20.42 377	86.53 368	69.37 357	59.50 366	87.84 362

PMMVS2		67.15 334	64.15 337	76.14 349	70.56 374	62.07 371	93.89 350	87.52 376	58.09 365	60.02 365	78.32 365	22.38 376	84.54 369	59.56 367	47.03 369	81.80 365

EGC-MVSNET		69.38 331	63.76 338	86.26 341	90.32 350	81.66 353	96.24 338	93.85 365	0.99 378	3.22 379	92.33 345	52.44 364	92.92 357	59.53 368	84.90 278	84.21 364

tmp_tt		65.23 336	62.94 339	72.13 351	44.90 380	50.03 376	81.05 367	89.42 375	38.45 370	48.51 372	99.90 19	54.09 363	78.70 372	91.84 229	18.26 374	87.64 363

ANet_high		56.10 337	52.24 340	67.66 353	49.27 379	56.82 373	83.94 366	82.02 377	70.47 362	33.28 376	64.54 371	17.23 379	69.16 374	45.59 372	23.85 373	77.02 367

E-PMN		52.30 339	52.18 341	52.67 356	71.51 372	45.40 377	93.62 353	76.60 379	36.01 372	43.50 373	64.13 372	27.11 374	67.31 375	31.06 375	26.06 371	45.30 374

PMVS	49.05 23	53.75 338	51.34 342	60.97 355	40.80 381	34.68 381	74.82 369	89.62 374	37.55 371	28.67 377	72.12 367	7.09 381	81.63 371	43.17 373	68.21 357	66.59 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		51.44 341	51.22 343	52.11 357	70.71 373	44.97 379	94.04 349	75.66 380	35.34 374	42.40 374	61.56 375	28.93 373	65.87 376	27.64 376	24.73 372	45.49 373

MVE	53.74 22	51.54 340	47.86 344	62.60 354	59.56 378	50.93 375	79.41 368	77.69 378	35.69 373	36.27 375	61.76 374	5.79 383	69.63 373	37.97 374	36.61 370	67.24 368
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		40.60 342	44.45 345	29.05 359	19.49 383	14.11 384	99.68 156	18.47 382	20.74 375	64.59 363	98.48 197	10.95 380	17.09 379	56.66 369	11.01 375	55.94 372

test123		37.68 343	39.14 346	33.31 358	19.94 382	24.83 383	98.36 297	9.75 383	15.53 376	51.31 371	87.14 358	19.62 378	17.74 378	47.10 371	3.47 377	57.36 371

cdsmvs_eth3d_5k		23.43 344	31.24 347	0.00 361	0.00 384	0.00 385	0.00 372	98.09 192	0.00 379	0.00 380	99.67 102	83.37 239	0.00 380	0.00 378	0.00 378	0.00 376

wuyk23d		20.37 345	20.84 348	18.99 360	65.34 376	27.73 382	50.43 371	7.67 384	9.50 377	8.01 378	6.34 378	6.13 382	26.24 377	23.40 377	10.69 376	2.99 375

ab-mvs-re		8.28 346	11.04 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	99.40 124	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		7.60 347	10.13 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	91.20 158	0.00 380	0.00 378	0.00 378	0.00 376

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.02 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					99.92 36	97.66 91	99.95 43	98.36 152	95.58 73	99.52 54

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 136					100.00 1	99.96 9	100.00 1	100.00 1

PC_three_1452									96.96 30	99.80 17	99.79 64	97.49 9	100.00 1	99.99 5	99.98 35	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 136					100.00 1	99.96 9	100.00 1	100.00 1

test_one_0601					99.94 14	99.30 11		98.41 136	96.63 40	99.75 28	99.93 11	97.49 9

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					99.92 36	98.57 55		98.52 92	92.34 195	99.31 71	99.83 51	95.06 56	99.80 111	99.70 36	99.97 48

IU-MVS					99.93 27	99.31 9		98.41 136	97.71 8	99.84 9				100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 50	99.80 2	99.96 25				99.80 60	97.44 13	100.00 1	100.00 1	99.98 35	100.00 1

test_241102_TWO								98.43 120	97.27 21	99.80 17	99.94 4	97.18 20	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 27	99.30 11		98.43 120	97.26 23	99.80 17	99.88 24	96.71 23	100.00 1

save fliter					99.82 70	98.79 37	99.96 25	98.40 140	97.66 10

test_0728_THIRD									96.48 43	99.83 11	99.91 15	97.87 4	100.00 1	99.92 13	100.00 1	100.00 1

test_0728_SECOND				99.82 7	99.94 14	99.47 7	99.95 43	98.43 120					100.00 1	99.99 5	100.00 1	100.00 1

test0726					99.93 27	99.29 14	99.96 25	98.42 132	97.28 19	99.86 5	99.94 4	97.22 18

GSMVS																99.59 139

test_part2					99.89 50	99.25 17				99.49 56

sam_mvs1												94.72 68				99.59 139

sam_mvs												94.25 87

ambc				83.23 345	77.17 370	62.61 369	87.38 365	94.55 361		76.72 352	86.65 360	30.16 371	96.36 313	84.85 306	69.86 350	90.73 351

MTGPA								98.28 167

test_post1							95.78 345				59.23 376	93.20 119	97.74 247	91.06 238

test_post											63.35 373	94.43 73	98.13 229

patchmatchnet-post											91.70 347	95.12 52	97.95 240

GG-mvs-BLEND				98.54 113	98.21 172	98.01 77	93.87 351	98.52 92		97.92 133	97.92 213	99.02 2	97.94 242	98.17 102	99.58 109	99.67 122

MTMP							99.87 93	96.49 328

gm-plane-assit					96.97 236	93.76 214			91.47 221		98.96 160		98.79 176	94.92 169

test9_res														99.71 35	99.99 22	100.00 1

TEST9					99.92 36	98.92 27	99.96 25	98.43 120	93.90 139	99.71 34	99.86 31	95.88 38	99.85 99

test_8					99.92 36	98.88 30	99.96 25	98.43 120	94.35 115	99.69 36	99.85 35	95.94 35	99.85 99

agg_prior2														99.48 42	100.00 1	100.00 1

agg_prior					99.93 27	98.77 40		98.43 120		99.63 40			99.85 99

TestCases				95.00 232	99.01 124	88.43 313		96.82 315	86.50 300	88.71 272	98.47 198	74.73 309	99.88 90	85.39 301	96.18 187	96.71 232

test_prior4						98.05 75	99.94 61

test_prior2							99.95 43		95.78 64	99.73 30	99.76 76	96.00 33		99.78 26	100.00 1

test_prior				99.43 38	99.94 14	98.49 61		98.65 62					99.80 111			99.99 24

旧先验2							99.46 194		94.21 123	99.85 7			99.95 65	96.96 143

新几何2							99.40 199

新几何1				99.42 41	99.75 81	98.27 69		98.63 68	92.69 177	99.55 49	99.82 55	94.40 75	100.00 1	91.21 234	99.94 61	99.99 24

旧先验1					99.76 79	97.52 96		98.64 65			99.85 35	95.63 43			99.94 61	99.99 24

无先验							99.49 189	98.71 55	93.46 153				100.00 1	94.36 187		99.99 24

原ACMM2							99.90 79

原ACMM1				98.96 85	99.73 86	96.99 119		98.51 99	94.06 130	99.62 43	99.85 35	94.97 63	99.96 58	95.11 164	99.95 55	99.92 91

test222					99.55 101	97.41 107	99.34 209	98.55 85	91.86 208	99.27 76	99.83 51	93.84 101			99.95 55	99.99 24

testdata2													99.99 40	90.54 250

segment_acmp												96.68 25

testdata				98.42 123	99.47 107	95.33 176		98.56 79	93.78 144	99.79 24	99.85 35	93.64 106	99.94 73	94.97 167	99.94 61	100.00 1

testdata1							99.28 219		96.35 52

test12				99.43 38	99.74 82	98.56 57		98.40 140		99.65 38		94.76 67	99.75 126		99.98 35	99.99 24

plane_prior7					95.71 275	91.59 269

plane_prior6					95.76 270	91.72 264						80.47 267

plane_prior5								97.87 212					98.37 212	97.79 122	89.55 234	94.52 242

plane_prior4											98.59 188

plane_prior3						91.64 267			96.63 40	93.01 214

plane_prior2							99.84 111		96.38 48

plane_prior1					95.73 272

plane_prior						91.74 261	99.86 104		96.76 36						89.59 233

n2								0.00 385

nn								0.00 385

door-mid								89.69 373

lessismore_v0				90.53 319	90.58 348	80.90 357		95.80 340		77.01 350	95.84 268	66.15 342	96.95 288	83.03 316	75.05 346	93.74 312

LGP-MVS_train				93.71 281	95.43 281	88.67 309		97.62 228	92.81 169	90.05 239	98.49 194	75.24 303	98.40 204	95.84 158	89.12 238	94.07 284

test11								98.44 112

door								90.31 371

HQP5-MVS						91.85 257

HQP-NCC					95.78 266		99.87 93		96.82 32	93.37 210

ACMP_Plane					95.78 266		99.87 93		96.82 32	93.37 210

BP-MVS														97.92 117

HQP4-MVS										93.37 210			98.39 206			94.53 240

HQP3-MVS								97.89 210							89.60 231

HQP2-MVS												80.65 263

NP-MVS					95.77 269	91.79 259					98.65 184

MDTV_nov1_ep13_2view						96.26 143	96.11 340		91.89 207	98.06 129		94.40 75		94.30 190		99.67 122

ACMMP++_ref															87.04 264

ACMMP++															88.23 253

Test By Simon												92.82 129

ITE_SJBPF				92.38 303	95.69 277	85.14 335		95.71 342	92.81 169	89.33 261	98.11 205	70.23 327	98.42 201	85.91 299	88.16 254	93.59 316

DeepMVS_CX				82.92 346	95.98 264	58.66 372		96.01 337	92.72 174	78.34 348	95.51 283	58.29 359	98.08 231	82.57 318	85.29 274	92.03 341

High-resolution multi-view benchmark