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 23	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 70	98.02 6	99.90 2	99.95 3	97.33 17	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 44	98.32 161	97.28 20	99.83 11	99.91 15	97.22 19	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 66	98.47 2	99.13 84	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	95.59 215	99.67 92	86.91 329	99.95 44	98.89 43	97.60 12	99.90 2	99.76 76	96.54 28	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 44	98.42 133	97.50 16	99.52 55	99.88 24	97.43 16	99.71 135	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	98.43 121	97.27 22	99.80 17	99.94 4	96.71 24	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 94	98.44 113	97.48 17	99.64 40	99.94 4	96.68 26	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 44	98.43 121	96.48 44	99.80 17	99.93 11	97.44 14	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	98.46 295	99.42 21	97.03 29	99.02 88	99.09 145	99.35 1	98.21 228	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 27	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	93.92 278	99.63 94	81.76 356	99.96 25	98.56 80	99.47 1	99.19 82	99.99 1	94.16 91	100.00 1	99.92 13	99.93 67	100.00 1

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 57	98.72 148	97.71 86	99.98 9	98.44 113	96.85 32	99.80 17	99.91 15	97.57 8	99.85 100	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 76	98.39 144	97.20 26	99.46 58	99.85 35	95.53 46	99.79 115	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 44	98.56 80	97.56 15	99.44 60	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 44	98.65 63	95.78 65	99.73 30	99.76 76	96.00 33	99.80 112	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	97.52 245	97.66 10	99.81 13	99.89 21	94.70 68	99.86 96	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	99.77 134	98.38 148	96.73 38	99.88 4	99.74 87	94.89 65	99.59 146	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 62	98.34 158	96.38 49	99.81 13	99.76 76	94.59 70	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 81	98.55 86	95.14 85	99.72 34	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	98.43 121	94.63 105	99.63 41	99.85 35	95.79 41	99.85 100	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	98.43 121	94.35 117	99.71 35	99.86 31	95.94 35	99.85 100	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	99.64 170	99.44 19	97.33 19	99.00 91	99.72 91	94.03 94	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 68	99.90 1	96.81 36	98.67 106	99.77 72	93.92 96	99.89 85	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 94	98.52 93	96.05 58	99.41 63	99.79 64	94.93 63	99.76 124	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 94	98.52 93	96.04 59	99.41 63	99.79 64	94.92 64	99.76 124	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 62	98.42 133	96.22 54	99.41 63	99.78 70	94.34 80	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 68	98.46 108	94.56 107	99.84 9	99.92 13	94.32 84	99.86 96	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	99.24 225	98.47 106	98.14 4	99.08 85	99.91 15	93.09 120	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	96.63 130	99.97 18	97.92 209	98.07 5	98.76 102	99.55 112	95.00 60	99.94 73	99.91 16	97.68 161	99.99 24

SMA-MVS		98.76 27	98.48 31	99.62 18	99.87 57	98.87 31	99.86 106	98.38 148	93.19 164	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	98.44 113	97.96 7	99.55 50	99.94 4	97.18 21	100.00 1	93.81 203	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 81	98.37 151	93.81 146	99.81 13	99.90 19	94.34 80	99.86 96	99.84 19	99.98 35	99.97 67

PAPM		98.60 34	98.42 33	99.14 66	96.05 262	98.96 24	99.90 81	99.35 24	96.68 40	98.35 121	99.66 104	96.45 29	98.51 196	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 44	98.61 72	95.00 88	99.31 72	99.85 35	94.22 87	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 92	99.66 162	98.52 93	95.79 64	99.01 89	99.77 72	94.40 74	99.75 127	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 81	98.21 178	93.53 155	99.81 13	99.89 21	94.70 68	99.86 96	99.84 19	99.93 67	99.96 74

EPNet		98.49 44	98.40 37	98.77 93	99.62 95	96.80 127	99.90 81	99.51 16	97.60 12	99.20 79	99.36 129	93.71 103	99.91 80	97.99 114	98.71 136	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 95	99.66 162	98.52 93	95.76 67	99.01 89	99.77 72	94.33 83	99.75 127	98.80 76	99.83 85	99.98 55

9.14			98.38 40		99.87 57		99.91 76	98.33 159	93.22 163	99.78 25	99.89 21	94.57 71	99.85 100	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 94	99.86 2	96.70 39	98.78 99	99.79 64	92.03 148	99.90 81	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 44	98.61 72	94.77 97	99.31 72	99.85 35	94.22 87	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 86	94.87 95	99.45 59	99.85 35	94.07 93	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 94	98.33 159	93.97 138	99.76 27	99.87 28	94.99 61	99.75 127	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 94	98.36 153	94.08 130	99.74 29	99.73 89	94.08 92	99.74 131	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 44	98.60 74	94.77 97	99.31 72	99.84 48	93.73 102	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	96.62 131	99.97 18	98.39 144	94.43 112	98.90 95	99.87 28	94.30 85	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	99.84 113	98.35 156	94.92 92	99.32 71	99.80 60	93.35 109	99.78 117	99.30 50	99.95 55	99.96 74

test1172		98.38 54	98.25 49	98.77 93	99.88 54	96.56 134	99.80 127	98.36 153	94.68 102	99.20 79	99.80 60	93.28 114	99.78 117	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 76	97.70 9	98.21 128	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	90.78 243	99.62 44	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	98.80 145	98.92 27	99.54 185	98.17 183	97.34 18	99.85 7	99.85 35	91.20 158	99.89 85	99.41 46	99.67 101	98.69 213

mPP-MVS		98.39 53	98.20 52	98.97 84	99.97 3	96.92 123	99.95 44	98.38 148	95.04 87	98.61 110	99.80 60	93.39 108	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	96.37 141	99.76 139	98.31 163	94.43 112	99.40 67	99.75 82	93.28 114	99.78 117	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 44	98.43 121	95.35 80	98.03 130	99.75 82	94.03 94	99.98 46	98.11 107	99.83 85	99.99 24

ACMMP_NAP		98.49 44	98.14 56	99.54 26	99.66 93	98.62 54	99.85 109	98.37 151	94.68 102	99.53 52	99.83 51	92.87 125	100.00 1	98.66 87	99.84 84	99.99 24

RE-MVS-def			98.13 57		99.79 75	96.37 141	99.76 139	98.31 163	94.43 112	99.40 67	99.75 82	92.95 123		98.90 69	99.92 71	99.97 67

PGM-MVS		98.34 55	98.13 57	98.99 82	99.92 36	97.00 119	99.75 142	99.50 17	93.90 143	99.37 69	99.76 76	93.24 117	100.00 1	97.75 127	99.96 52	99.98 55

EI-MVSNet-Vis-set		98.27 60	98.11 59	98.75 95	99.83 68	96.59 133	99.40 202	98.51 100	95.29 82	98.51 113	99.76 76	93.60 106	99.71 135	98.53 92	99.52 112	99.95 82

dcpmvs_2		97.42 97	98.09 60	95.42 220	99.58 100	87.24 326	99.23 226	96.95 301	94.28 122	98.93 94	99.73 89	94.39 78	99.16 167	99.89 17	99.82 91	99.86 100

APD-MVS_3200maxsize		98.25 63	98.08 61	98.78 92	99.81 73	96.60 132	99.82 121	98.30 166	93.95 140	99.37 69	99.77 72	92.84 126	99.76 124	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	99.86 106	98.24 174	92.18 203	99.73 30	99.87 28	93.47 107	99.85 100	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 91	99.94 62	98.44 113	94.31 120	98.50 114	99.82 55	93.06 121	99.99 40	98.30 101	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 72	98.44 113	92.06 208	98.40 119	99.84 48	95.68 42	100.00 1	98.19 102	99.71 99	99.97 67

zzz-MVS		98.33 56	98.00 65	99.30 50	99.85 60	97.93 81	99.80 127	98.28 168	95.76 67	97.18 149	99.88 24	92.74 129	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	96.27 143	99.36 211	98.50 104	95.21 84	98.30 123	99.75 82	93.29 113	99.73 134	98.37 97	99.30 122	99.81 104

GST-MVS		98.27 60	97.97 67	99.17 60	99.92 36	97.57 92	99.93 68	98.39 144	94.04 136	98.80 98	99.74 87	92.98 122	100.00 1	98.16 104	99.76 95	99.93 85

xiu_mvs_v2_base		98.23 65	97.97 67	99.02 80	98.69 149	98.66 50	99.52 187	98.08 195	97.05 28	99.86 5	99.86 31	90.65 169	99.71 135	99.39 47	98.63 137	98.69 213

MP-MVS		98.23 65	97.97 67	99.03 78	99.94 14	97.17 115	99.95 44	98.39 144	94.70 101	98.26 126	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 81	99.39 206	98.28 168	95.76 67	97.18 149	99.88 24	92.74 129	100.00 1	98.67 84	99.88 80	99.99 24

CS-MVS-test		97.88 76	97.94 71	97.70 154	99.28 115	95.20 184	99.98 9	97.15 278	95.53 76	99.62 44	99.79 64	92.08 147	98.38 212	98.75 80	99.28 123	99.52 155

PAPM_NR		98.12 68	97.93 72	98.70 97	99.94 14	96.13 152	99.82 121	98.43 121	94.56 107	97.52 141	99.70 95	94.40 74	99.98 46	97.00 144	99.98 35	99.99 24

CS-MVS		97.79 83	97.91 73	97.43 164	99.10 120	94.42 200	99.99 3	97.10 283	95.07 86	99.68 38	99.75 82	92.95 123	98.34 216	98.38 96	99.14 128	99.54 151

PLC	95.54 3	97.93 74	97.89 74	98.05 139	99.82 70	94.77 195	99.92 72	98.46 108	93.93 141	97.20 148	99.27 134	95.44 48	99.97 56	97.41 132	99.51 114	99.41 169
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	98.51 100	97.00 30	98.52 112	99.71 93	87.80 201	99.95 65	99.75 28	99.38 119	99.83 102

ETV-MVS		97.92 75	97.80 76	98.25 130	98.14 179	96.48 135	99.98 9	97.63 228	95.61 73	99.29 76	99.46 120	92.55 135	98.82 177	99.02 62	98.54 138	99.46 162

HPM-MVS		97.96 72	97.72 77	98.68 98	99.84 65	96.39 140	99.90 81	98.17 183	92.61 187	98.62 109	99.57 111	91.87 151	99.67 142	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	95.41 175	99.47 196	98.87 46	91.68 218	98.84 96	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	99.74 145	98.18 182	93.35 159	96.45 168	99.85 35	92.64 132	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	96.99 120	100.00 1	99.10 29	95.38 79	98.27 124	99.08 146	89.00 192	99.95 65	99.12 53	99.25 124	99.57 146

lupinMVS		97.85 78	97.60 81	98.62 103	97.28 228	97.70 88	99.99 3	97.55 239	95.50 78	99.43 61	99.67 102	90.92 165	98.71 186	98.40 95	99.62 104	99.45 164

WTY-MVS		98.10 69	97.60 81	99.60 20	98.92 135	99.28 16	99.89 89	99.52 14	95.58 74	98.24 127	99.39 126	93.33 110	99.74 131	97.98 116	95.58 204	99.78 109

1121		98.03 71	97.57 83	99.40 44	99.74 82	98.21 69	98.31 302	98.62 70	92.78 177	99.53 52	99.83 51	95.08 54	100.00 1	94.36 190	99.92 71	99.99 24

HPM-MVS_fast		97.80 82	97.50 84	98.68 98	99.79 75	96.42 137	99.88 91	98.16 187	91.75 217	98.94 93	99.54 114	91.82 153	99.65 144	97.62 129	99.99 22	99.99 24

EIA-MVS		97.53 91	97.46 85	97.76 151	98.04 183	94.84 191	99.98 9	97.61 233	94.41 115	97.90 134	99.59 109	92.40 139	98.87 175	98.04 111	99.13 129	99.59 139

ACMMP		97.74 86	97.44 86	98.66 100	99.92 36	96.13 152	99.18 230	99.45 18	94.84 96	96.41 171	99.71 93	91.40 155	99.99 40	97.99 114	98.03 156	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	96.84 125	99.87 94	98.14 190	93.78 148	96.55 166	99.69 98	92.28 142	99.98 46	97.13 139	99.44 117	99.93 85

test_yl		97.83 79	97.37 88	99.21 54	99.18 116	97.98 78	99.64 170	99.27 26	91.43 226	97.88 135	98.99 154	95.84 39	99.84 109	98.82 73	95.32 208	99.79 106

DCV-MVSNet		97.83 79	97.37 88	99.21 54	99.18 116	97.98 78	99.64 170	99.27 26	91.43 226	97.88 135	98.99 154	95.84 39	99.84 109	98.82 73	95.32 208	99.79 106

abl_6		97.67 88	97.34 90	98.66 100	99.68 91	96.11 155	99.68 158	98.14 190	93.80 147	99.27 77	99.70 95	88.65 197	99.98 46	97.46 131	99.72 98	99.89 94

alignmvs		97.81 81	97.33 91	99.25 52	98.77 147	98.66 50	99.99 3	98.44 113	94.40 116	98.41 117	99.47 118	93.65 104	99.42 159	98.57 90	94.26 217	99.67 122

CPTT-MVS		97.64 89	97.32 92	98.58 108	99.97 3	95.77 162	99.96 25	98.35 156	89.90 256	98.36 120	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	97.80 145	97.41 218	95.64 169	99.99 3	97.06 288	94.59 106	99.63 41	99.32 130	89.20 190	98.14 230	98.76 79	99.23 125	99.62 133

OMC-MVS		97.28 101	97.23 94	97.41 165	99.76 79	93.36 228	99.65 166	97.95 205	96.03 60	97.41 145	99.70 95	89.61 181	99.51 149	96.73 151	98.25 147	99.38 171

test2506		97.53 91	97.19 95	98.58 108	98.66 151	96.90 124	98.81 274	99.77 5	94.93 90	97.95 132	98.96 160	92.51 136	99.20 163	94.93 173	98.15 148	99.64 128

MAR-MVS		97.43 93	97.19 95	98.15 135	99.47 107	94.79 194	99.05 247	98.76 53	92.65 185	98.66 107	99.82 55	88.52 198	99.98 46	98.12 106	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 97	99.63 15	98.98 128	99.32 8	97.49 323	99.52 14	95.69 71	98.32 122	97.41 225	93.32 111	99.77 121	98.08 110	95.75 201	99.81 104

xiu_mvs_v1_base_debu		97.43 93	97.06 98	98.55 110	97.74 201	98.14 70	99.31 216	97.86 215	96.43 46	99.62 44	99.69 98	85.56 223	99.68 139	99.05 56	98.31 144	97.83 223

xiu_mvs_v1_base		97.43 93	97.06 98	98.55 110	97.74 201	98.14 70	99.31 216	97.86 215	96.43 46	99.62 44	99.69 98	85.56 223	99.68 139	99.05 56	98.31 144	97.83 223

xiu_mvs_v1_base_debi		97.43 93	97.06 98	98.55 110	97.74 201	98.14 70	99.31 216	97.86 215	96.43 46	99.62 44	99.69 98	85.56 223	99.68 139	99.05 56	98.31 144	97.83 223

CSCG		97.10 107	97.04 101	97.27 173	99.89 50	91.92 258	99.90 81	99.07 32	88.67 277	95.26 192	99.82 55	93.17 119	99.98 46	98.15 105	99.47 115	99.90 93

sss		97.57 90	97.03 102	99.18 57	98.37 164	98.04 75	99.73 150	99.38 22	93.46 157	98.76 102	99.06 147	91.21 157	99.89 85	96.33 154	97.01 177	99.62 133

thisisatest0515		97.41 98	97.02 103	98.59 107	97.71 208	97.52 95	99.97 18	98.54 90	91.83 213	97.45 144	99.04 148	97.50 9	99.10 169	94.75 182	96.37 188	99.16 192

F-COLMAP		96.93 113	96.95 104	96.87 182	99.71 89	91.74 263	99.85 109	97.95 205	93.11 167	95.72 185	99.16 143	92.35 140	99.94 73	95.32 166	99.35 121	98.92 202

jason		97.24 103	96.86 105	98.38 126	95.73 274	97.32 108	99.97 18	97.40 258	95.34 81	98.60 111	99.54 114	87.70 202	98.56 193	97.94 117	99.47 115	99.25 187
jason: jason.
114514_t		97.41 98	96.83 106	99.14 66	99.51 105	97.83 83	99.89 89	98.27 171	88.48 281	99.06 86	99.66 104	90.30 174	99.64 145	96.32 155	99.97 48	99.96 74

PVSNet_Blended_VisFu		97.27 102	96.81 107	98.66 100	98.81 144	96.67 129	99.92 72	98.64 66	94.51 109	96.38 172	98.49 194	89.05 191	99.88 91	97.10 141	98.34 142	99.43 167

AdaColmap		97.23 104	96.80 108	98.51 116	99.99 1	95.60 171	99.09 236	98.84 49	93.32 160	96.74 160	99.72 91	86.04 219	100.00 1	98.01 112	99.43 118	99.94 84

PMMVS		96.76 120	96.76 109	96.76 185	98.28 169	92.10 253	99.91 76	97.98 202	94.12 128	99.53 52	99.39 126	86.93 211	98.73 184	96.95 147	97.73 159	99.45 164

thisisatest0530		97.10 107	96.72 110	98.22 131	97.60 211	96.70 128	99.92 72	98.54 90	91.11 235	97.07 152	98.97 158	97.47 12	99.03 170	93.73 208	96.09 191	98.92 202

PVSNet	91.05 13	97.13 106	96.69 111	98.45 120	99.52 103	95.81 160	99.95 44	99.65 11	94.73 99	99.04 87	99.21 141	84.48 232	99.95 65	94.92 174	98.74 135	99.58 145

diffmvs		97.00 110	96.64 112	98.09 137	97.64 209	96.17 151	99.81 123	97.19 272	94.67 104	98.95 92	99.28 131	86.43 215	98.76 182	98.37 97	97.42 167	99.33 179

MVSFormer		96.94 112	96.60 113	97.95 141	97.28 228	97.70 88	99.55 183	97.27 268	91.17 232	99.43 61	99.54 114	90.92 165	96.89 297	94.67 185	99.62 104	99.25 187

EPP-MVSNet		96.69 125	96.60 113	96.96 179	97.74 201	93.05 232	99.37 209	98.56 80	88.75 275	95.83 183	99.01 151	96.01 32	98.56 193	96.92 148	97.20 172	99.25 187

VNet		97.21 105	96.57 115	99.13 71	98.97 129	97.82 84	99.03 250	99.21 28	94.31 120	99.18 83	98.88 171	86.26 218	99.89 85	98.93 65	94.32 216	99.69 119

CHOSEN 1792x2688		96.81 117	96.53 116	97.64 155	98.91 137	93.07 230	99.65 166	99.80 3	95.64 72	95.39 189	98.86 175	84.35 235	99.90 81	96.98 145	99.16 127	99.95 82

tttt0517		96.85 115	96.49 117	97.92 143	97.48 217	95.89 159	99.85 109	98.54 90	90.72 244	96.63 162	98.93 169	97.47 12	99.02 171	93.03 220	95.76 200	98.85 206

baseline2		96.71 124	96.49 117	97.37 168	95.63 281	95.96 157	99.74 145	98.88 45	92.94 169	91.61 229	98.97 158	97.72 7	98.62 191	94.83 178	98.08 155	97.53 231

HyFIR lowres test		96.66 127	96.43 119	97.36 170	99.05 122	93.91 212	99.70 155	99.80 3	90.54 245	96.26 174	98.08 206	92.15 145	98.23 227	96.84 150	95.46 205	99.93 85

DeepC-MVS	94.51 4	96.92 114	96.40 120	98.45 120	99.16 118	95.90 158	99.66 162	98.06 196	96.37 52	94.37 201	99.49 117	83.29 242	99.90 81	97.63 128	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		97.09 109	96.32 121	99.39 46	98.93 133	98.95 25	99.72 153	97.35 261	94.45 110	97.88 135	99.42 122	86.71 212	99.52 148	98.48 93	93.97 221	99.72 116

TESTMET0.1,1		96.74 122	96.26 122	98.16 132	97.36 221	96.48 135	99.96 25	98.29 167	91.93 210	95.77 184	98.07 207	95.54 44	98.29 220	90.55 254	98.89 131	99.70 117

thres200		96.96 111	96.21 123	99.22 53	98.97 129	98.84 34	99.85 109	99.71 6	93.17 165	96.26 174	98.88 171	89.87 179	99.51 149	94.26 194	94.91 211	99.31 181

CANet_DTU		96.76 120	96.15 124	98.60 105	98.78 146	97.53 94	99.84 113	97.63 228	97.25 25	99.20 79	99.64 106	81.36 256	99.98 46	92.77 223	98.89 131	98.28 216

CDS-MVSNet		96.34 136	96.07 125	97.13 175	97.37 220	94.96 188	99.53 186	97.91 210	91.55 221	95.37 190	98.32 202	95.05 57	97.13 279	93.80 204	95.75 201	99.30 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test-LLR		96.47 131	96.04 126	97.78 148	97.02 236	95.44 173	99.96 25	98.21 178	94.07 131	95.55 186	96.38 258	93.90 98	98.27 224	90.42 257	98.83 133	99.64 128

EPMVS		96.53 130	96.01 127	98.09 137	98.43 161	96.12 154	96.36 341	99.43 20	93.53 155	97.64 139	95.04 309	94.41 73	98.38 212	91.13 240	98.11 151	99.75 112

tfpn200view9		96.79 118	95.99 128	99.19 56	98.94 131	98.82 35	99.78 131	99.71 6	92.86 170	96.02 178	98.87 173	89.33 185	99.50 151	93.84 200	94.57 212	99.27 185

thres400		96.78 119	95.99 128	99.16 62	98.94 131	98.82 35	99.78 131	99.71 6	92.86 170	96.02 178	98.87 173	89.33 185	99.50 151	93.84 200	94.57 212	99.16 192

baseline		96.43 133	95.98 130	97.76 151	97.34 222	95.17 185	99.51 189	97.17 275	93.92 142	96.90 155	99.28 131	85.37 226	98.64 190	97.50 130	96.86 181	99.46 162

tpmrst		96.27 142	95.98 130	97.13 175	97.96 186	93.15 229	96.34 342	98.17 183	92.07 206	98.71 105	95.12 307	93.91 97	98.73 184	94.91 176	96.62 182	99.50 159

Vis-MVSNet (Re-imp)		96.32 137	95.98 130	97.35 171	97.93 188	94.82 192	99.47 196	98.15 189	91.83 213	95.09 193	99.11 144	91.37 156	97.47 259	93.47 211	97.43 165	99.74 113

mvs-test1		95.53 162	95.97 133	94.20 266	97.77 198	85.44 337	99.95 44	97.06 288	94.92 92	96.58 164	98.72 181	85.81 220	98.98 172	94.80 179	98.11 151	98.18 217

casdiffmvs		96.42 134	95.97 133	97.77 150	97.30 226	94.98 187	99.84 113	97.09 285	93.75 150	96.58 164	99.26 137	85.07 228	98.78 180	97.77 125	97.04 176	99.54 151

UA-Net		96.54 129	95.96 135	98.27 129	98.23 172	95.71 166	98.00 316	98.45 110	93.72 151	98.41 117	99.27 134	88.71 196	99.66 143	91.19 239	97.69 160	99.44 166

1314		96.84 116	95.96 135	99.48 36	96.74 252	98.52 59	98.31 302	98.86 47	95.82 63	89.91 249	98.98 156	87.49 204	99.96 58	97.80 120	99.73 97	99.96 74

iter_conf05		96.07 144	95.95 137	96.44 196	98.43 161	97.52 95	99.91 76	96.85 312	94.16 126	92.49 225	97.98 212	98.20 4	97.34 263	97.26 136	88.29 256	94.45 253

iter_conf_final		96.01 147	95.93 138	96.28 202	98.38 163	97.03 118	99.87 94	97.03 292	94.05 135	92.61 223	97.98 212	98.01 5	97.34 263	97.02 143	88.39 255	94.47 247

test-mter		96.39 135	95.93 138	97.78 148	97.02 236	95.44 173	99.96 25	98.21 178	91.81 215	95.55 186	96.38 258	95.17 51	98.27 224	90.42 257	98.83 133	99.64 128

thres100view900		96.74 122	95.92 140	99.18 57	98.90 138	98.77 40	99.74 145	99.71 6	92.59 189	95.84 181	98.86 175	89.25 187	99.50 151	93.84 200	94.57 212	99.27 185

IS-MVSNet		96.29 140	95.90 141	97.45 162	98.13 180	94.80 193	99.08 238	97.61 233	92.02 209	95.54 188	98.96 160	90.64 170	98.08 233	93.73 208	97.41 168	99.47 161

CostFormer		96.10 143	95.88 142	96.78 184	97.03 235	92.55 245	97.08 332	97.83 218	90.04 255	98.72 104	94.89 316	95.01 59	98.29 220	96.54 153	95.77 199	99.50 159

thres600view7		96.69 125	95.87 143	99.14 66	98.90 138	98.78 39	99.74 145	99.71 6	92.59 189	95.84 181	98.86 175	89.25 187	99.50 151	93.44 212	94.50 215	99.16 192

PVSNet_BlendedMVS		96.05 145	95.82 144	96.72 187	99.59 96	96.99 120	99.95 44	99.10 29	94.06 133	98.27 124	95.80 274	89.00 192	99.95 65	99.12 53	87.53 268	93.24 328

MVS_Test		96.46 132	95.74 145	98.61 104	98.18 176	97.23 110	99.31 216	97.15 278	91.07 236	98.84 96	97.05 238	88.17 200	98.97 173	94.39 189	97.50 164	99.61 136

Effi-MVS+		96.30 139	95.69 146	98.16 132	97.85 193	96.26 144	97.41 324	97.21 271	90.37 248	98.65 108	98.58 190	86.61 214	98.70 187	97.11 140	97.37 169	99.52 155

MDTV_nov1_ep13			95.69 146		97.90 189	94.15 204	95.98 349	98.44 113	93.12 166	97.98 131	95.74 276	95.10 53	98.58 192	90.02 263	96.92 179

FMVS2_test		95.35 166	95.68 148	94.36 262	98.99 127	84.98 340	99.96 25	96.65 325	97.60 12	99.73 30	98.96 160	71.58 325	99.93 79	98.31 100	99.37 120	98.17 218

TAMVS		95.85 151	95.58 149	96.65 190	97.07 232	93.50 221	99.17 231	97.82 219	91.39 230	95.02 194	98.01 208	92.20 143	97.30 268	93.75 207	95.83 198	99.14 195

MVS		96.60 128	95.56 150	99.72 12	96.85 245	99.22 19	98.31 302	98.94 37	91.57 220	90.90 237	99.61 108	86.66 213	99.96 58	97.36 133	99.88 80	99.99 24

PatchmatchNet		95.94 149	95.45 151	97.39 167	97.83 194	94.41 201	96.05 348	98.40 141	92.86 170	97.09 151	95.28 304	94.21 90	98.07 235	89.26 270	98.11 151	99.70 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		96.04 146	95.40 152	97.95 141	99.59 96	95.22 183	99.52 187	99.07 32	93.96 139	96.49 167	98.35 201	82.28 246	99.82 111	90.15 262	99.22 126	98.81 209

EPNet_dtu		95.71 156	95.39 153	96.66 189	98.92 135	93.41 225	99.57 179	98.90 42	96.19 56	97.52 141	98.56 192	92.65 131	97.36 261	77.89 347	98.33 143	99.20 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		95.71 156	95.38 154	96.68 188	98.49 159	92.28 249	99.84 113	97.50 248	92.12 205	92.06 227	98.79 179	84.69 230	98.67 189	95.29 167	99.66 102	99.09 198

3Dnovator	91.47 12	96.28 141	95.34 155	99.08 74	96.82 247	97.47 102	99.45 199	98.81 50	95.52 77	89.39 263	99.00 153	81.97 248	99.95 65	97.27 135	99.83 85	99.84 101

Effi-MVS+-dtu		94.53 187	95.30 156	92.22 309	97.77 198	82.54 349	99.59 176	97.06 288	94.92 92	95.29 191	95.37 297	85.81 220	97.89 245	94.80 179	97.07 174	96.23 238

3Dnovator+	91.53 11	96.31 138	95.24 157	99.52 29	96.88 244	98.64 53	99.72 153	98.24 174	95.27 83	88.42 287	98.98 156	82.76 244	99.94 73	97.10 141	99.83 85	99.96 74

MVSTER		95.53 162	95.22 158	96.45 194	98.56 153	97.72 85	99.91 76	97.67 226	92.38 198	91.39 231	97.14 232	97.24 18	97.30 268	94.80 179	87.85 262	94.34 264

1112_ss		96.01 147	95.20 159	98.42 123	97.80 196	96.41 138	99.65 166	96.66 324	92.71 180	92.88 220	99.40 124	92.16 144	99.30 160	91.92 231	93.66 222	99.55 148

tpm2		95.47 164	95.18 160	96.35 201	96.91 240	91.70 267	96.96 335	97.93 207	88.04 288	98.44 116	95.40 293	93.32 111	97.97 239	94.00 197	95.61 203	99.38 171

Vis-MVSNet		95.72 154	95.15 161	97.45 162	97.62 210	94.28 203	99.28 222	98.24 174	94.27 124	96.84 157	98.94 167	79.39 275	98.76 182	93.25 213	98.49 139	99.30 183
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LS3D		95.84 152	95.11 162	98.02 140	99.85 60	95.10 186	98.74 279	98.50 104	87.22 298	93.66 210	99.86 31	87.45 205	99.95 65	90.94 247	99.81 93	99.02 200

FA-MVS(test-final)		95.86 150	95.09 163	98.15 135	97.74 201	95.62 170	96.31 343	98.17 183	91.42 228	96.26 174	96.13 268	90.56 171	99.47 157	92.18 228	97.07 174	99.35 176

ECVR-MVS		95.66 159	95.05 164	97.51 160	98.66 151	93.71 216	98.85 271	98.45 110	94.93 90	96.86 156	98.96 160	75.22 308	99.20 163	95.34 165	98.15 148	99.64 128

mvs_anonymous		95.65 160	95.03 165	97.53 158	98.19 175	95.74 164	99.33 213	97.49 249	90.87 240	90.47 241	97.10 234	88.23 199	97.16 276	95.92 160	97.66 162	99.68 120

FE-MVS		95.70 158	95.01 166	97.79 147	98.21 173	94.57 196	95.03 354	98.69 57	88.90 272	97.50 143	96.19 265	92.60 133	99.49 155	89.99 264	97.94 158	99.31 181

test1111		95.57 161	94.98 167	97.37 168	98.56 153	93.37 227	98.86 269	98.45 110	94.95 89	96.63 162	98.95 165	75.21 309	99.11 168	95.02 171	98.14 150	99.64 128

CVMVSNet		94.68 182	94.94 168	93.89 280	96.80 248	86.92 328	99.06 243	98.98 35	94.45 110	94.23 204	99.02 149	85.60 222	95.31 342	90.91 248	95.39 207	99.43 167

baseline1		95.78 153	94.86 169	98.54 113	98.47 160	98.07 73	99.06 243	97.99 200	92.68 183	94.13 205	98.62 187	93.28 114	98.69 188	93.79 205	85.76 277	98.84 207

BH-untuned		95.18 168	94.83 170	96.22 204	98.36 165	91.22 275	99.80 127	97.32 265	90.91 239	91.08 234	98.67 183	83.51 239	98.54 195	94.23 195	99.61 107	98.92 202

Test_1112_low_res		95.72 154	94.83 170	98.42 123	97.79 197	96.41 138	99.65 166	96.65 325	92.70 181	92.86 221	96.13 268	92.15 145	99.30 160	91.88 232	93.64 223	99.55 148

XVG-OURS		94.82 175	94.74 172	95.06 231	98.00 184	89.19 306	99.08 238	97.55 239	94.10 129	94.71 196	99.62 107	80.51 267	99.74 131	96.04 158	93.06 229	96.25 236

XVG-OURS-SEG-HR		94.79 176	94.70 173	95.08 230	98.05 182	89.19 306	99.08 238	97.54 241	93.66 152	94.87 195	99.58 110	78.78 280	99.79 115	97.31 134	93.40 225	96.25 236

UGNet		95.33 167	94.57 174	97.62 157	98.55 155	94.85 190	98.67 286	99.32 25	95.75 70	96.80 159	96.27 263	72.18 322	99.96 58	94.58 187	99.05 130	98.04 221
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		94.61 184	94.50 175	94.92 236	95.78 268	91.85 259	99.87 94	97.89 211	96.82 33	93.37 212	98.65 184	80.65 265	98.39 208	97.92 118	89.60 233	94.53 242

dp		95.05 171	94.43 176	96.91 180	97.99 185	92.73 239	96.29 344	97.98 202	89.70 259	95.93 180	94.67 322	93.83 101	98.45 201	86.91 301	96.53 184	99.54 151

h-mvs33		94.92 174	94.36 177	96.59 191	98.85 142	91.29 274	98.93 260	98.94 37	95.90 61	98.77 100	98.42 200	90.89 167	99.77 121	97.80 120	70.76 357	98.72 212

HQP_MVS		94.49 188	94.36 177	94.87 237	95.71 277	91.74 263	99.84 113	97.87 213	96.38 49	93.01 216	98.59 188	80.47 269	98.37 214	97.79 123	89.55 236	94.52 244

BH-RMVSNet		95.18 168	94.31 179	97.80 145	98.17 177	95.23 182	99.76 139	97.53 243	92.52 194	94.27 203	99.25 138	76.84 292	98.80 178	90.89 249	99.54 111	99.35 176

Fast-Effi-MVS+		95.02 172	94.19 180	97.52 159	97.88 190	94.55 197	99.97 18	97.08 286	88.85 274	94.47 200	97.96 214	84.59 231	98.41 204	89.84 266	97.10 173	99.59 139

QAPM		95.40 165	94.17 181	99.10 72	96.92 239	97.71 86	99.40 202	98.68 59	89.31 261	88.94 275	98.89 170	82.48 245	99.96 58	93.12 219	99.83 85	99.62 133

PCF-MVS	94.20 5	95.18 168	94.10 182	98.43 122	98.55 155	95.99 156	97.91 318	97.31 266	90.35 249	89.48 262	99.22 140	85.19 227	99.89 85	90.40 259	98.47 140	99.41 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
hse-mvs2		94.38 190	94.08 183	95.31 224	98.27 170	90.02 297	99.29 221	98.56 80	95.90 61	98.77 100	98.00 209	90.89 167	98.26 226	97.80 120	69.20 363	97.64 228

ADS-MVSNet		94.79 176	94.02 184	97.11 177	97.87 191	93.79 213	94.24 355	98.16 187	90.07 253	96.43 169	94.48 327	90.29 175	98.19 229	87.44 289	97.23 170	99.36 174

miper_enhance_ethall		94.36 193	93.98 185	95.49 216	98.68 150	95.24 181	99.73 150	97.29 267	93.28 162	89.86 251	95.97 272	94.37 79	97.05 285	92.20 227	84.45 289	94.19 273

IB-MVS	92.85 6	94.99 173	93.94 186	98.16 132	97.72 206	95.69 168	99.99 3	98.81 50	94.28 122	92.70 222	96.90 242	95.08 54	99.17 166	96.07 157	73.88 352	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		94.06 198	93.90 187	94.55 251	96.02 263	90.69 281	99.98 9	97.72 222	96.62 43	91.05 236	98.85 178	77.21 288	98.47 197	98.11 107	89.51 238	94.48 246
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ADS-MVSNet2		93.80 203	93.88 188	93.55 291	97.87 191	85.94 333	94.24 355	96.84 313	90.07 253	96.43 169	94.48 327	90.29 175	95.37 340	87.44 289	97.23 170	99.36 174

Fast-Effi-MVS+-dtu		93.72 207	93.86 189	93.29 294	97.06 233	86.16 331	99.80 127	96.83 314	92.66 184	92.58 224	97.83 216	81.39 255	97.67 252	89.75 267	96.87 180	96.05 240

SCA		94.69 180	93.81 190	97.33 172	97.10 231	94.44 198	98.86 269	98.32 161	93.30 161	96.17 177	95.59 283	76.48 296	97.95 242	91.06 242	97.43 165	99.59 139

mvsmamba		94.10 196	93.72 191	95.25 226	93.57 312	94.13 205	99.67 161	96.45 332	93.63 154	91.34 233	97.77 217	86.29 217	97.22 274	96.65 152	88.10 260	94.40 255

test0.0.03 1		93.86 199	93.61 192	94.64 246	95.02 290	92.18 252	99.93 68	98.58 76	94.07 131	87.96 291	98.50 193	93.90 98	94.96 346	81.33 333	93.17 227	96.78 233

cascas		94.64 183	93.61 192	97.74 153	97.82 195	96.26 144	99.96 25	97.78 221	85.76 317	94.00 206	97.54 221	76.95 291	99.21 162	97.23 137	95.43 206	97.76 227

TAPA-MVS	92.12 8	94.42 189	93.60 194	96.90 181	99.33 113	91.78 262	99.78 131	98.00 199	89.89 257	94.52 198	99.47 118	91.97 149	99.18 165	69.90 363	99.52 112	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	90.15 15	94.77 178	93.59 195	98.33 127	96.07 261	97.48 101	99.56 181	98.57 78	90.46 246	86.51 310	98.95 165	78.57 282	99.94 73	93.86 199	99.74 96	97.57 230

tpmvs		94.28 195	93.57 196	96.40 198	98.55 155	91.50 272	95.70 353	98.55 86	87.47 293	92.15 226	94.26 331	91.42 154	98.95 174	88.15 282	95.85 197	98.76 211

LFMVS		94.75 179	93.56 197	98.30 128	99.03 123	95.70 167	98.74 279	97.98 202	87.81 291	98.47 115	99.39 126	67.43 342	99.53 147	98.01 112	95.20 210	99.67 122

TR-MVS		94.54 185	93.56 197	97.49 161	97.96 186	94.34 202	98.71 282	97.51 247	90.30 251	94.51 199	98.69 182	75.56 303	98.77 181	92.82 222	95.99 193	99.35 176

GeoE		94.36 193	93.48 199	96.99 178	97.29 227	93.54 220	99.96 25	96.72 322	88.35 284	93.43 211	98.94 167	82.05 247	98.05 236	88.12 284	96.48 186	99.37 173

FIs		94.10 196	93.43 200	96.11 206	94.70 294	96.82 126	99.58 177	98.93 41	92.54 192	89.34 265	97.31 228	87.62 203	97.10 282	94.22 196	86.58 273	94.40 255

ab-mvs		94.69 180	93.42 201	98.51 116	98.07 181	96.26 144	96.49 340	98.68 59	90.31 250	94.54 197	97.00 240	76.30 298	99.71 135	95.98 159	93.38 226	99.56 147

DP-MVS		94.54 185	93.42 201	97.91 144	99.46 109	94.04 207	98.93 260	97.48 250	81.15 349	90.04 246	99.55 112	87.02 210	99.95 65	88.97 272	98.11 151	99.73 114

tpm		93.70 208	93.41 203	94.58 249	95.36 285	87.41 325	97.01 333	96.90 308	90.85 241	96.72 161	94.14 332	90.40 173	96.84 300	90.75 252	88.54 252	99.51 157

EI-MVSNet		93.73 206	93.40 204	94.74 242	96.80 248	92.69 240	99.06 243	97.67 226	88.96 269	91.39 231	99.02 149	88.75 195	97.30 268	91.07 241	87.85 262	94.22 270

MSDG		94.37 191	93.36 205	97.40 166	98.88 140	93.95 211	99.37 209	97.38 259	85.75 319	90.80 238	99.17 142	84.11 237	99.88 91	86.35 302	98.43 141	98.36 215

PS-MVSNAJss		93.64 209	93.31 206	94.61 247	92.11 339	92.19 251	99.12 233	97.38 259	92.51 195	88.45 282	96.99 241	91.20 158	97.29 271	94.36 190	87.71 265	94.36 260

ET-MVSNet_ETH3D		94.37 191	93.28 207	97.64 155	98.30 166	97.99 77	99.99 3	97.61 233	94.35 117	71.57 368	99.45 121	96.23 31	95.34 341	96.91 149	85.14 284	99.59 139

cl22		93.77 204	93.25 208	95.33 223	99.49 106	94.43 199	99.61 174	98.09 193	90.38 247	89.16 272	95.61 281	90.56 171	97.34 263	91.93 230	84.45 289	94.21 272

FC-MVSNet-test		93.81 202	93.15 209	95.80 213	94.30 301	96.20 149	99.42 201	98.89 43	92.33 200	89.03 274	97.27 230	87.39 206	96.83 301	93.20 214	86.48 274	94.36 260

VDD-MVS		93.77 204	92.94 210	96.27 203	98.55 155	90.22 292	98.77 278	97.79 220	90.85 241	96.82 158	99.42 122	61.18 360	99.77 121	98.95 63	94.13 218	98.82 208

RRT_MVS		93.14 217	92.92 211	93.78 282	93.31 319	90.04 296	99.66 162	97.69 224	92.53 193	88.91 276	97.76 218	84.36 233	96.93 295	95.10 169	86.99 271	94.37 258

GA-MVS		93.83 200	92.84 212	96.80 183	95.73 274	93.57 218	99.88 91	97.24 270	92.57 191	92.92 218	96.66 251	78.73 281	97.67 252	87.75 287	94.06 220	99.17 191

OPM-MVS		93.21 215	92.80 213	94.44 258	93.12 323	90.85 280	99.77 134	97.61 233	96.19 56	91.56 230	98.65 184	75.16 310	98.47 197	93.78 206	89.39 239	93.99 296
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
RPSCF		91.80 249	92.79 214	88.83 336	98.15 178	69.87 372	98.11 312	96.60 327	83.93 335	94.33 202	99.27 134	79.60 274	99.46 158	91.99 229	93.16 228	97.18 232

LPG-MVS_test		92.96 221	92.71 215	93.71 285	95.43 283	88.67 312	99.75 142	97.62 230	92.81 174	90.05 244	98.49 194	75.24 306	98.40 206	95.84 162	89.12 240	94.07 288

CR-MVSNet		93.45 213	92.62 216	95.94 209	96.29 257	92.66 241	92.01 366	96.23 335	92.62 186	96.94 153	93.31 340	91.04 162	96.03 331	79.23 340	95.96 194	99.13 196

AUN-MVS		93.28 214	92.60 217	95.34 222	98.29 167	90.09 295	99.31 216	98.56 80	91.80 216	96.35 173	98.00 209	89.38 184	98.28 222	92.46 224	69.22 362	97.64 228

miper_ehance_all_eth		93.16 216	92.60 217	94.82 241	97.57 212	93.56 219	99.50 191	97.07 287	88.75 275	88.85 277	95.52 287	90.97 164	96.74 304	90.77 251	84.45 289	94.17 274

LCM-MVSNet-Re		92.31 237	92.60 217	91.43 317	97.53 213	79.27 366	99.02 251	91.83 377	92.07 206	80.31 346	94.38 330	83.50 240	95.48 338	97.22 138	97.58 163	99.54 151

D2MVS		92.76 225	92.59 220	93.27 295	95.13 286	89.54 305	99.69 156	99.38 22	92.26 201	87.59 295	94.61 324	85.05 229	97.79 247	91.59 235	88.01 261	92.47 341

nrg030		93.51 210	92.53 221	96.45 194	94.36 299	97.20 111	99.81 123	97.16 277	91.60 219	89.86 251	97.46 223	86.37 216	97.68 251	95.88 161	80.31 321	94.46 248

tpm cat1		93.51 210	92.52 222	96.47 192	97.77 198	91.47 273	96.13 346	98.06 196	80.98 350	92.91 219	93.78 335	89.66 180	98.87 175	87.03 297	96.39 187	99.09 198

ACMM	91.95 10	92.88 223	92.52 222	93.98 277	95.75 273	89.08 309	99.77 134	97.52 245	93.00 168	89.95 248	97.99 211	76.17 300	98.46 200	93.63 210	88.87 244	94.39 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	92.05 9	92.74 226	92.42 224	93.73 283	95.91 267	88.72 311	99.81 123	97.53 243	94.13 127	87.00 304	98.23 203	74.07 316	98.47 197	96.22 156	88.86 245	93.99 296
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		92.83 224	92.29 225	94.47 256	91.90 342	92.46 246	99.55 183	97.27 268	91.17 232	89.96 247	96.07 271	81.10 258	96.89 297	94.67 185	88.91 242	94.05 290

UniMVSNet (Re)		93.07 220	92.13 226	95.88 210	94.84 291	96.24 148	99.88 91	98.98 35	92.49 196	89.25 267	95.40 293	87.09 209	97.14 278	93.13 218	78.16 333	94.26 267

UniMVSNet_NR-MVSNet		92.95 222	92.11 227	95.49 216	94.61 296	95.28 179	99.83 119	99.08 31	91.49 222	89.21 269	96.86 245	87.14 208	96.73 305	93.20 214	77.52 338	94.46 248

IterMVS-LS		92.69 229	92.11 227	94.43 260	96.80 248	92.74 237	99.45 199	96.89 309	88.98 267	89.65 258	95.38 296	88.77 194	96.34 319	90.98 246	82.04 303	94.22 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		93.83 200	92.06 229	99.15 64	99.94 14	97.50 99	99.94 62	98.42 133	96.22 54	99.41 63	41.37 387	94.34 80	99.96 58	98.92 66	99.95 55	99.99 24

bld_raw_dy_0_64		92.74 226	92.03 230	94.87 237	93.09 325	93.46 222	99.12 233	95.41 352	92.84 173	90.44 242	97.54 221	78.08 286	97.04 287	93.94 198	87.77 264	94.11 285

Anonymous202405211		93.10 219	91.99 231	96.40 198	99.10 120	89.65 303	98.88 265	97.93 207	83.71 337	94.00 206	98.75 180	68.79 334	99.88 91	95.08 170	91.71 231	99.68 120

eth_miper_zixun_eth		92.41 235	91.93 232	93.84 281	97.28 228	90.68 282	98.83 272	96.97 300	88.57 280	89.19 271	95.73 278	89.24 189	96.69 307	89.97 265	81.55 306	94.15 280

VDDNet		93.12 218	91.91 233	96.76 185	96.67 255	92.65 243	98.69 284	98.21 178	82.81 343	97.75 138	99.28 131	61.57 358	99.48 156	98.09 109	94.09 219	98.15 219

c3_l		92.53 232	91.87 234	94.52 252	97.40 219	92.99 233	99.40 202	96.93 306	87.86 289	88.69 280	95.44 291	89.95 178	96.44 315	90.45 256	80.69 318	94.14 283

gg-mvs-nofinetune		93.51 210	91.86 235	98.47 118	97.72 206	97.96 80	92.62 363	98.51 100	74.70 366	97.33 146	69.59 379	98.91 3	97.79 247	97.77 125	99.56 110	99.67 122

AllTest		92.48 233	91.64 236	95.00 233	99.01 124	88.43 316	98.94 259	96.82 316	86.50 307	88.71 278	98.47 198	74.73 312	99.88 91	85.39 308	96.18 189	96.71 234

DIV-MVS_self_test		92.32 236	91.60 237	94.47 256	97.31 225	92.74 237	99.58 177	96.75 320	86.99 302	87.64 294	95.54 285	89.55 182	96.50 313	88.58 276	82.44 300	94.17 274

cl____		92.31 237	91.58 238	94.52 252	97.33 224	92.77 235	99.57 179	96.78 319	86.97 303	87.56 296	95.51 288	89.43 183	96.62 309	88.60 275	82.44 300	94.16 279

FMVSNet3		92.69 229	91.58 238	95.99 208	98.29 167	97.42 106	99.26 224	97.62 230	89.80 258	89.68 255	95.32 299	81.62 254	96.27 322	87.01 298	85.65 278	94.29 266

VPA-MVSNet		92.70 228	91.55 240	96.16 205	95.09 287	96.20 149	98.88 265	99.00 34	91.02 238	91.82 228	95.29 303	76.05 302	97.96 241	95.62 164	81.19 309	94.30 265

Patchmatch-test		92.65 231	91.50 241	96.10 207	96.85 245	90.49 287	91.50 368	97.19 272	82.76 344	90.23 243	95.59 283	95.02 58	98.00 238	77.41 349	96.98 178	99.82 103

COLMAP_ROB	90.47 14	92.18 240	91.49 242	94.25 265	99.00 126	88.04 322	98.42 300	96.70 323	82.30 346	88.43 285	99.01 151	76.97 290	99.85 100	86.11 305	96.50 185	94.86 241
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DU-MVS		92.46 234	91.45 243	95.49 216	94.05 304	95.28 179	99.81 123	98.74 54	92.25 202	89.21 269	96.64 253	81.66 252	96.73 305	93.20 214	77.52 338	94.46 248

miper_lstm_enhance		91.81 246	91.39 244	93.06 301	97.34 222	89.18 308	99.38 207	96.79 318	86.70 306	87.47 298	95.22 305	90.00 177	95.86 335	88.26 280	81.37 308	94.15 280

WR-MVS		92.31 237	91.25 245	95.48 219	94.45 298	95.29 178	99.60 175	98.68 59	90.10 252	88.07 290	96.89 243	80.68 264	96.80 303	93.14 217	79.67 325	94.36 260

jajsoiax		91.92 244	91.18 246	94.15 267	91.35 348	90.95 278	99.00 252	97.42 255	92.61 187	87.38 300	97.08 235	72.46 321	97.36 261	94.53 188	88.77 246	94.13 284

mvs_tets		91.81 246	91.08 247	94.00 275	91.63 346	90.58 285	98.67 286	97.43 253	92.43 197	87.37 301	97.05 238	71.76 323	97.32 267	94.75 182	88.68 248	94.11 285

pmmvs4		92.10 242	91.07 248	95.18 228	92.82 332	94.96 188	99.48 195	96.83 314	87.45 294	88.66 281	96.56 256	83.78 238	96.83 301	89.29 269	84.77 287	93.75 313

anonymousdsp		91.79 251	90.92 249	94.41 261	90.76 353	92.93 234	98.93 260	97.17 275	89.08 263	87.46 299	95.30 300	78.43 285	96.92 296	92.38 225	88.73 247	93.39 324

XVG-ACMP-BASELINE		91.22 259	90.75 250	92.63 306	93.73 310	85.61 334	98.52 294	97.44 252	92.77 178	89.90 250	96.85 246	66.64 344	98.39 208	92.29 226	88.61 249	93.89 304

test_part1		92.15 241	90.72 251	96.44 196	98.87 141	97.46 103	98.99 253	98.26 172	85.89 314	86.34 315	96.34 261	81.71 250	97.48 258	91.06 242	78.99 327	94.37 258

JIA-IIPM		91.76 252	90.70 252	94.94 235	96.11 260	87.51 324	93.16 362	98.13 192	75.79 363	97.58 140	77.68 376	92.84 126	97.97 239	88.47 279	96.54 183	99.33 179

Anonymous20240529		92.10 242	90.65 253	96.47 192	98.82 143	90.61 284	98.72 281	98.67 62	75.54 364	93.90 208	98.58 190	66.23 345	99.90 81	94.70 184	90.67 232	98.90 205

TranMVSNet+NR-MVSNet		91.68 253	90.61 254	94.87 237	93.69 311	93.98 210	99.69 156	98.65 63	91.03 237	88.44 283	96.83 249	80.05 272	96.18 325	90.26 261	76.89 346	94.45 253

VPNet		91.81 246	90.46 255	95.85 212	94.74 293	95.54 172	98.98 254	98.59 75	92.14 204	90.77 239	97.44 224	68.73 336	97.54 256	94.89 177	77.89 335	94.46 248

XXY-MVS		91.82 245	90.46 255	95.88 210	93.91 307	95.40 176	98.87 268	97.69 224	88.63 279	87.87 292	97.08 235	74.38 315	97.89 245	91.66 234	84.07 293	94.35 263

MVP-Stereo		90.93 262	90.45 257	92.37 308	91.25 350	88.76 310	98.05 315	96.17 337	87.27 297	84.04 328	95.30 300	78.46 284	97.27 273	83.78 320	99.70 100	91.09 352
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WR-MVS_H		91.30 255	90.35 258	94.15 267	94.17 303	92.62 244	99.17 231	98.94 37	88.87 273	86.48 312	94.46 329	84.36 233	96.61 310	88.19 281	78.51 331	93.21 329

EU-MVSNet		90.14 285	90.34 259	89.54 332	92.55 335	81.06 360	98.69 284	98.04 198	91.41 229	86.59 309	96.84 248	80.83 262	93.31 362	86.20 303	81.91 304	94.26 267

MS-PatchMatch		90.65 269	90.30 260	91.71 316	94.22 302	85.50 336	98.24 306	97.70 223	88.67 277	86.42 313	96.37 260	67.82 340	98.03 237	83.62 321	99.62 104	91.60 349

PVSNet_0	88.03 19	91.80 249	90.27 261	96.38 200	98.27 170	90.46 288	99.94 62	99.61 12	93.99 137	86.26 317	97.39 227	71.13 329	99.89 85	98.77 78	67.05 367	98.79 210

CP-MVSNet		91.23 258	90.22 262	94.26 264	93.96 306	92.39 248	99.09 236	98.57 78	88.95 270	86.42 313	96.57 255	79.19 277	96.37 317	90.29 260	78.95 328	94.02 291

NR-MVSNet		91.56 254	90.22 262	95.60 214	94.05 304	95.76 163	98.25 305	98.70 56	91.16 234	80.78 345	96.64 253	83.23 243	96.57 311	91.41 236	77.73 337	94.46 248

IterMVS		90.91 263	90.17 264	93.12 298	96.78 251	90.42 290	98.89 263	97.05 291	89.03 265	86.49 311	95.42 292	76.59 295	95.02 344	87.22 294	84.09 292	93.93 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		90.85 266	90.16 265	92.93 302	96.72 253	89.96 298	98.89 263	96.99 296	88.95 270	86.63 308	95.67 279	76.48 296	95.00 345	87.04 296	84.04 295	93.84 308

V42		91.28 257	90.12 266	94.74 242	93.42 317	93.46 222	99.68 158	97.02 293	87.36 295	89.85 253	95.05 308	81.31 257	97.34 263	87.34 292	80.07 323	93.40 323

v2v482		91.30 255	90.07 267	95.01 232	93.13 321	93.79 213	99.77 134	97.02 293	88.05 287	89.25 267	95.37 297	80.73 263	97.15 277	87.28 293	80.04 324	94.09 287

v1144		91.09 260	89.83 268	94.87 237	93.25 320	93.69 217	99.62 173	96.98 298	86.83 305	89.64 259	94.99 313	80.94 260	97.05 285	85.08 311	81.16 310	93.87 306

GBi-Net		90.88 264	89.82 269	94.08 270	97.53 213	91.97 254	98.43 297	96.95 301	87.05 299	89.68 255	94.72 318	71.34 326	96.11 326	87.01 298	85.65 278	94.17 274

test1		90.88 264	89.82 269	94.08 270	97.53 213	91.97 254	98.43 297	96.95 301	87.05 299	89.68 255	94.72 318	71.34 326	96.11 326	87.01 298	85.65 278	94.17 274

FMVS2_test2		89.47 294	89.70 271	88.77 339	94.54 297	75.74 367	99.83 119	94.70 364	94.71 100	91.08 234	96.82 250	54.46 367	97.78 249	92.87 221	88.27 257	92.80 336

v148		90.70 268	89.63 272	93.92 278	92.97 328	90.97 277	99.75 142	96.89 309	87.51 292	88.27 288	95.01 310	81.67 251	97.04 287	87.40 291	77.17 343	93.75 313

ACMH	89.72 17	90.64 270	89.63 272	93.66 289	95.64 280	88.64 314	98.55 290	97.45 251	89.03 265	81.62 340	97.61 220	69.75 332	98.41 204	89.37 268	87.62 267	93.92 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet2		91.02 261	89.56 274	95.41 221	97.53 213	95.74 164	98.98 254	97.41 257	87.05 299	88.43 285	95.00 312	71.34 326	96.24 324	85.12 310	85.21 283	94.25 269

ACMH+	89.98 16	90.35 277	89.54 275	92.78 305	95.99 264	86.12 332	98.81 274	97.18 274	89.38 260	83.14 333	97.76 218	68.42 338	98.43 202	89.11 271	86.05 276	93.78 312

v144192		90.79 267	89.52 276	94.59 248	93.11 324	92.77 235	99.56 181	96.99 296	86.38 309	89.82 254	94.95 315	80.50 268	97.10 282	83.98 318	80.41 319	93.90 303

PS-CasMVS		90.63 271	89.51 277	93.99 276	93.83 308	91.70 267	98.98 254	98.52 93	88.48 281	86.15 318	96.53 257	75.46 304	96.31 320	88.83 273	78.86 330	93.95 299

Baseline_NR-MVSNet		90.33 278	89.51 277	92.81 304	92.84 330	89.95 299	99.77 134	93.94 369	84.69 332	89.04 273	95.66 280	81.66 252	96.52 312	90.99 245	76.98 344	91.97 347

our_test_3		90.39 275	89.48 279	93.12 298	92.40 336	89.57 304	99.33 213	96.35 334	87.84 290	85.30 323	94.99 313	84.14 236	96.09 329	80.38 337	84.56 288	93.71 318

OurMVSNet-221017-0		89.81 289	89.48 279	90.83 322	91.64 345	81.21 358	98.17 310	95.38 354	91.48 223	85.65 322	97.31 228	72.66 320	97.29 271	88.15 282	84.83 286	93.97 298

v1192		90.62 272	89.25 281	94.72 244	93.13 321	93.07 230	99.50 191	97.02 293	86.33 310	89.56 261	95.01 310	79.22 276	97.09 284	82.34 328	81.16 310	94.01 293

v8		90.54 273	89.17 282	94.66 245	93.43 316	93.40 226	99.20 228	96.94 305	85.76 317	87.56 296	94.51 325	81.96 249	97.19 275	84.94 312	78.25 332	93.38 325

v1921920		90.46 274	89.12 283	94.50 254	92.96 329	92.46 246	99.49 193	96.98 298	86.10 312	89.61 260	95.30 300	78.55 283	97.03 290	82.17 329	80.89 317	94.01 293

pmmvs5		90.17 284	89.09 284	93.40 292	92.10 340	89.77 302	99.74 145	95.58 349	85.88 316	87.24 303	95.74 276	73.41 319	96.48 314	88.54 277	83.56 296	93.95 299

PEN-MVS		90.19 283	89.06 285	93.57 290	93.06 326	90.90 279	99.06 243	98.47 106	88.11 286	85.91 320	96.30 262	76.67 293	95.94 334	87.07 295	76.91 345	93.89 304

LTVRE_ROB	88.28 18	90.29 280	89.05 286	94.02 273	95.08 288	90.15 294	97.19 328	97.43 253	84.91 330	83.99 329	97.06 237	74.00 317	98.28 222	84.08 316	87.71 265	93.62 319
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		90.00 287	88.96 287	93.10 300	94.81 292	88.16 320	98.71 282	95.54 350	93.66 152	83.75 331	97.20 231	65.58 347	98.31 219	83.96 319	87.49 269	92.85 335

LF4IMVS		89.25 299	88.85 288	90.45 326	92.81 333	81.19 359	98.12 311	94.79 361	91.44 225	86.29 316	97.11 233	65.30 350	98.11 232	88.53 278	85.25 282	92.07 344

v10		90.25 281	88.82 289	94.57 250	93.53 314	93.43 224	99.08 238	96.87 311	85.00 327	87.34 302	94.51 325	80.93 261	97.02 292	82.85 325	79.23 326	93.26 327

v1240		90.20 282	88.79 290	94.44 258	93.05 327	92.27 250	99.38 207	96.92 307	85.89 314	89.36 264	94.87 317	77.89 287	97.03 290	80.66 336	81.08 313	94.01 293

PatchT		90.38 276	88.75 291	95.25 226	95.99 264	90.16 293	91.22 370	97.54 241	76.80 359	97.26 147	86.01 370	91.88 150	96.07 330	66.16 370	95.91 196	99.51 157

MIMVSNet		90.30 279	88.67 292	95.17 229	96.45 256	91.64 269	92.39 364	97.15 278	85.99 313	90.50 240	93.19 342	66.95 343	94.86 348	82.01 330	93.43 224	99.01 201

UniMVSNet_ETH3D		90.06 286	88.58 293	94.49 255	94.67 295	88.09 321	97.81 320	97.57 238	83.91 336	88.44 283	97.41 225	57.44 364	97.62 254	91.41 236	88.59 251	97.77 226

Patchmtry		89.70 291	88.49 294	93.33 293	96.24 259	89.94 301	91.37 369	96.23 335	78.22 357	87.69 293	93.31 340	91.04 162	96.03 331	80.18 339	82.10 302	94.02 291

Anonymous20231211		89.86 288	88.44 295	94.13 269	98.93 133	90.68 282	98.54 292	98.26 172	76.28 360	86.73 306	95.54 285	70.60 330	97.56 255	90.82 250	80.27 322	94.15 280

ppachtmachnet_test		89.58 293	88.35 296	93.25 296	92.40 336	90.44 289	99.33 213	96.73 321	85.49 323	85.90 321	95.77 275	81.09 259	96.00 333	76.00 355	82.49 299	93.30 326

MVS_0304		89.28 298	88.31 297	92.21 310	97.05 234	86.53 330	97.76 321	99.57 13	85.58 322	93.86 209	92.71 344	51.04 372	96.30 321	84.49 314	92.72 230	93.79 311

v7n		89.65 292	88.29 298	93.72 284	92.22 338	90.56 286	99.07 242	97.10 283	85.42 325	86.73 306	94.72 318	80.06 271	97.13 279	81.14 334	78.12 334	93.49 321

DTE-MVSNet		89.40 295	88.24 299	92.88 303	92.66 334	89.95 299	99.10 235	98.22 177	87.29 296	85.12 325	96.22 264	76.27 299	95.30 343	83.56 322	75.74 349	93.41 322

DSMNet-mixed		88.28 304	88.24 299	88.42 341	89.64 360	75.38 369	98.06 314	89.86 380	85.59 321	88.20 289	92.14 351	76.15 301	91.95 367	78.46 345	96.05 192	97.92 222

testgi		89.01 300	88.04 301	91.90 314	93.49 315	84.89 341	99.73 150	95.66 347	93.89 145	85.14 324	98.17 204	59.68 361	94.66 350	77.73 348	88.88 243	96.16 239

SixPastTwentyTwo		88.73 301	88.01 302	90.88 320	91.85 343	82.24 351	98.22 308	95.18 359	88.97 268	82.26 336	96.89 243	71.75 324	96.67 308	84.00 317	82.98 297	93.72 317

pm-mvs1		89.36 296	87.81 303	94.01 274	93.40 318	91.93 257	98.62 289	96.48 331	86.25 311	83.86 330	96.14 267	73.68 318	97.04 287	86.16 304	75.73 350	93.04 332

tfpnnormal		89.29 297	87.61 304	94.34 263	94.35 300	94.13 205	98.95 258	98.94 37	83.94 334	84.47 327	95.51 288	74.84 311	97.39 260	77.05 352	80.41 319	91.48 351

FMVSNet5		88.32 303	87.47 305	90.88 320	96.90 243	88.39 318	97.28 326	95.68 346	82.60 345	84.67 326	92.40 349	79.83 273	91.16 369	76.39 354	81.51 307	93.09 330

RPMNet		89.76 290	87.28 306	97.19 174	96.29 257	92.66 241	92.01 366	98.31 163	70.19 371	96.94 153	85.87 371	87.25 207	99.78 117	62.69 373	95.96 194	99.13 196

K. test v3		88.05 305	87.24 307	90.47 325	91.82 344	82.23 352	98.96 257	97.42 255	89.05 264	76.93 358	95.60 282	68.49 337	95.42 339	85.87 307	81.01 315	93.75 313

FMVSNet1		88.50 302	86.64 308	94.08 270	95.62 282	91.97 254	98.43 297	96.95 301	83.00 341	86.08 319	94.72 318	59.09 362	96.11 326	81.82 332	84.07 293	94.17 274

TinyColmap		87.87 308	86.51 309	91.94 313	95.05 289	85.57 335	97.65 322	94.08 367	84.40 333	81.82 339	96.85 246	62.14 357	98.33 217	80.25 338	86.37 275	91.91 348

KD-MVS_2432*1600		88.00 306	86.10 310	93.70 287	96.91 240	94.04 207	97.17 329	97.12 281	84.93 328	81.96 337	92.41 347	92.48 137	94.51 351	79.23 340	52.68 377	92.56 338

miper_refine_blended		88.00 306	86.10 310	93.70 287	96.91 240	94.04 207	97.17 329	97.12 281	84.93 328	81.96 337	92.41 347	92.48 137	94.51 351	79.23 340	52.68 377	92.56 338

Patchmatch-RL test		86.90 310	85.98 312	89.67 331	84.45 370	75.59 368	89.71 373	92.43 374	86.89 304	77.83 356	90.94 355	94.22 87	93.63 359	87.75 287	69.61 359	99.79 106

Anonymous20231206		86.32 311	85.42 313	89.02 335	89.11 362	80.53 364	99.05 247	95.28 355	85.43 324	82.82 334	93.92 333	74.40 314	93.44 361	66.99 368	81.83 305	93.08 331

TransMVSNet (Re)		87.25 309	85.28 314	93.16 297	93.56 313	91.03 276	98.54 292	94.05 368	83.69 338	81.09 343	96.16 266	75.32 305	96.40 316	76.69 353	68.41 364	92.06 345

CMPMVS	61.59 21	84.75 321	85.14 315	83.57 350	90.32 356	62.54 377	96.98 334	97.59 237	74.33 367	69.95 370	96.66 251	64.17 352	98.32 218	87.88 286	88.41 254	89.84 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		84.72 322	83.99 316	86.91 344	88.19 365	80.62 363	98.88 265	95.94 341	88.36 283	78.87 351	94.62 323	68.75 335	89.11 373	66.52 369	75.82 348	91.00 353

UnsupCasMVSNet_eth		85.52 315	83.99 316	90.10 328	89.36 361	83.51 345	96.65 338	97.99 200	89.14 262	75.89 362	93.83 334	63.25 355	93.92 355	81.92 331	67.90 366	92.88 334

test_0402		85.58 314	83.94 318	90.50 324	93.81 309	85.04 339	98.55 290	95.20 358	76.01 361	79.72 350	95.13 306	64.15 353	96.26 323	66.04 371	86.88 272	90.21 360

pmmvs6		85.69 313	83.84 319	91.26 319	90.00 359	84.41 343	97.82 319	96.15 338	75.86 362	81.29 342	95.39 295	61.21 359	96.87 299	83.52 323	73.29 353	92.50 340

Anonymous20240521		85.15 319	83.81 320	89.16 334	88.32 363	82.69 347	98.80 276	95.74 344	79.72 353	81.53 341	90.99 354	65.38 349	94.16 353	72.69 359	81.11 312	90.63 357

EG-PatchMatch MVS		85.35 318	83.81 320	89.99 330	90.39 355	81.89 354	98.21 309	96.09 339	81.78 348	74.73 364	93.72 336	51.56 371	97.12 281	79.16 343	88.61 249	90.96 354

YYNet1		85.50 317	83.33 322	92.00 312	90.89 352	88.38 319	99.22 227	96.55 328	79.60 355	57.26 377	92.72 343	79.09 279	93.78 358	77.25 350	77.37 341	93.84 308

MDA-MVSNet_test_wron		85.51 316	83.32 323	92.10 311	90.96 351	88.58 315	99.20 228	96.52 329	79.70 354	57.12 378	92.69 345	79.11 278	93.86 357	77.10 351	77.46 340	93.86 307

MVS-HIRNet		86.22 312	83.19 324	95.31 224	96.71 254	90.29 291	92.12 365	97.33 264	62.85 372	86.82 305	70.37 378	69.37 333	97.49 257	75.12 356	97.99 157	98.15 219

CL-MVSNet_self_test		84.50 323	83.15 325	88.53 340	86.00 368	81.79 355	98.82 273	97.35 261	85.12 326	83.62 332	90.91 356	76.66 294	91.40 368	69.53 364	60.36 374	92.40 342

new_pmnet		84.49 324	82.92 326	89.21 333	90.03 358	82.60 348	96.89 337	95.62 348	80.59 351	75.77 363	89.17 358	65.04 351	94.79 349	72.12 360	81.02 314	90.23 359

TDRefinement		84.76 320	82.56 327	91.38 318	74.58 381	84.80 342	97.36 325	94.56 365	84.73 331	80.21 347	96.12 270	63.56 354	98.39 208	87.92 285	63.97 370	90.95 355

KD-MVS_self_test		83.59 328	82.06 328	88.20 342	86.93 366	80.70 362	97.21 327	96.38 333	82.87 342	82.49 335	88.97 359	67.63 341	92.32 365	73.75 358	62.30 373	91.58 350

pmmvs-eth3d		84.03 326	81.97 329	90.20 327	84.15 371	87.09 327	98.10 313	94.73 363	83.05 340	74.10 366	87.77 365	65.56 348	94.01 354	81.08 335	69.24 361	89.49 364

OpenMVS_ROB	79.82 20	83.77 327	81.68 330	90.03 329	88.30 364	82.82 346	98.46 295	95.22 357	73.92 368	76.00 361	91.29 353	55.00 366	96.94 294	68.40 366	88.51 253	90.34 358

MDA-MVSNet-bldmvs		84.09 325	81.52 331	91.81 315	91.32 349	88.00 323	98.67 286	95.92 342	80.22 352	55.60 379	93.32 339	68.29 339	93.60 360	73.76 357	76.61 347	93.82 310

mvsany_test		82.12 330	81.14 332	85.06 348	81.87 375	70.41 371	97.09 331	92.14 375	91.27 231	77.84 355	88.73 360	39.31 375	95.49 337	90.75 252	71.24 356	89.29 366

N_pmnet		80.06 335	80.78 333	77.89 355	91.94 341	45.28 388	98.80 276	56.82 391	78.10 358	80.08 348	93.33 338	77.03 289	95.76 336	68.14 367	82.81 298	92.64 337

MIMVSNet1		82.58 329	80.51 334	88.78 337	86.68 367	84.20 344	96.65 338	95.41 352	78.75 356	78.59 353	92.44 346	51.88 370	89.76 372	65.26 372	78.95 328	92.38 343

FMVS2		79.99 336	80.17 335	79.45 354	84.02 372	62.83 375	99.05 247	93.49 372	88.29 285	80.06 349	86.65 368	28.09 380	88.00 374	88.63 274	73.27 354	87.54 370

test_method		80.79 332	79.70 336	84.08 349	92.83 331	67.06 374	99.51 189	95.42 351	54.34 376	81.07 344	93.53 337	44.48 374	92.22 366	78.90 344	77.23 342	92.94 333

new-patchmatchnet		81.19 331	79.34 337	86.76 345	82.86 374	80.36 365	97.92 317	95.27 356	82.09 347	72.02 367	86.87 367	62.81 356	90.74 371	71.10 361	63.08 371	89.19 367

PM-MVS		80.47 333	78.88 338	85.26 347	83.79 373	72.22 370	95.89 351	91.08 378	85.71 320	76.56 360	88.30 361	36.64 376	93.90 356	82.39 327	69.57 360	89.66 363

pmmvs3		80.27 334	77.77 339	87.76 343	80.32 377	82.43 350	98.23 307	91.97 376	72.74 369	78.75 352	87.97 364	57.30 365	90.99 370	70.31 362	62.37 372	89.87 361

FMVS		78.40 338	77.59 340	80.81 353	80.82 376	62.48 378	96.96 335	93.08 373	83.44 339	74.57 365	84.57 372	27.95 381	92.63 364	84.15 315	72.79 355	87.32 371

UnsupCasMVSNet_bld		79.97 337	77.03 341	88.78 337	85.62 369	81.98 353	93.66 360	97.35 261	75.51 365	70.79 369	83.05 373	48.70 373	94.91 347	78.31 346	60.29 375	89.46 365

FPMVS		68.72 340	68.72 342	68.71 362	65.95 385	44.27 390	95.97 350	94.74 362	51.13 377	53.26 380	90.50 357	25.11 383	83.00 380	60.80 374	80.97 316	78.87 376

FMVS1		68.38 341	66.92 343	72.78 359	78.80 378	50.36 384	90.95 371	87.35 385	55.47 374	58.95 374	88.14 362	20.64 385	87.60 375	57.28 377	64.69 368	80.39 374

APD_test		68.38 341	66.92 343	72.78 359	78.80 378	50.36 384	90.95 371	87.35 385	55.47 374	58.95 374	88.14 362	20.64 385	87.60 375	57.28 377	64.69 368	80.39 374

Gipuma		66.95 345	65.00 345	72.79 358	91.52 347	67.96 373	66.16 380	95.15 360	47.89 378	58.54 376	67.99 380	29.74 378	87.54 377	50.20 380	77.83 336	62.87 380
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		67.77 343	64.73 346	76.87 356	62.95 387	56.25 382	89.37 374	93.74 371	44.53 379	61.99 372	80.74 374	20.42 387	86.53 378	69.37 365	59.50 376	87.84 368

PMMVS2		67.15 344	64.15 347	76.14 357	70.56 384	62.07 379	93.89 358	87.52 384	58.09 373	60.02 373	78.32 375	22.38 384	84.54 379	59.56 375	47.03 379	81.80 373

EGC-MVSNET		69.38 339	63.76 348	86.26 346	90.32 356	81.66 357	96.24 345	93.85 370	0.99 388	3.22 389	92.33 350	52.44 369	92.92 363	59.53 376	84.90 285	84.21 372

tmp_tt		65.23 346	62.94 349	72.13 361	44.90 390	50.03 386	81.05 377	89.42 383	38.45 380	48.51 382	99.90 19	54.09 368	78.70 382	91.84 233	18.26 384	87.64 369

ANet_high		56.10 347	52.24 350	67.66 363	49.27 389	56.82 381	83.94 376	82.02 387	70.47 370	33.28 386	64.54 381	17.23 389	69.16 384	45.59 382	23.85 383	77.02 377

E-PMN		52.30 349	52.18 351	52.67 366	71.51 382	45.40 387	93.62 361	76.60 389	36.01 382	43.50 383	64.13 382	27.11 382	67.31 385	31.06 385	26.06 381	45.30 384

PMVS	49.05 23	53.75 348	51.34 352	60.97 365	40.80 391	34.68 391	74.82 379	89.62 382	37.55 381	28.67 387	72.12 377	7.09 391	81.63 381	43.17 383	68.21 365	66.59 379
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		51.44 351	51.22 353	52.11 367	70.71 383	44.97 389	94.04 357	75.66 390	35.34 384	42.40 384	61.56 385	28.93 379	65.87 386	27.64 386	24.73 382	45.49 383

MVE	53.74 22	51.54 350	47.86 354	62.60 364	59.56 388	50.93 383	79.41 378	77.69 388	35.69 383	36.27 385	61.76 384	5.79 393	69.63 383	37.97 384	36.61 380	67.24 378
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		40.60 352	44.45 355	29.05 369	19.49 393	14.11 394	99.68 158	18.47 392	20.74 385	64.59 371	98.48 197	10.95 390	17.09 389	56.66 379	11.01 385	55.94 382

test123		37.68 353	39.14 356	33.31 368	19.94 392	24.83 393	98.36 301	9.75 393	15.53 386	51.31 381	87.14 366	19.62 388	17.74 388	47.10 381	3.47 387	57.36 381

cdsmvs_eth3d_5k		23.43 354	31.24 357	0.00 371	0.00 394	0.00 395	0.00 382	98.09 193	0.00 389	0.00 390	99.67 102	83.37 241	0.00 390	0.00 388	0.00 388	0.00 386

wuyk23d		20.37 355	20.84 358	18.99 370	65.34 386	27.73 392	50.43 381	7.67 394	9.50 387	8.01 388	6.34 388	6.13 392	26.24 387	23.40 387	10.69 386	2.99 385

ab-mvs-re		8.28 356	11.04 359	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	99.40 124	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

pcd_1.5k_mvsjas		7.60 357	10.13 360	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	91.20 158	0.00 390	0.00 388	0.00 388	0.00 386

test_blank		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.02 389	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

uanet_test		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

DCPMVS		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

sosnet-low-res		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

sosnet		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

uncertanet		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

Regformer		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

uanet		0.00 358	0.00 361	0.00 371	0.00 394	0.00 395	0.00 382	0.00 395	0.00 389	0.00 390	0.00 390	0.00 394	0.00 390	0.00 388	0.00 388	0.00 386

FOURS1					99.92 36	97.66 90	99.95 44	98.36 153	95.58 74	99.52 55

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 137					100.00 1	99.96 9	100.00 1	100.00 1

PC_three_1452									96.96 31	99.80 17	99.79 64	97.49 10	100.00 1	99.99 5	99.98 35	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 137					100.00 1	99.96 9	100.00 1	100.00 1

test_one_0601					99.94 14	99.30 11		98.41 137	96.63 41	99.75 28	99.93 11	97.49 10

eth-test2					0.00 394

eth-test					0.00 394

ZD-MVS					99.92 36	98.57 55		98.52 93	92.34 199	99.31 72	99.83 51	95.06 56	99.80 112	99.70 36	99.97 48

IU-MVS					99.93 27	99.31 9		98.41 137	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 14	100.00 1	100.00 1	99.98 35	100.00 1

test_241102_TWO								98.43 121	97.27 22	99.80 17	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 27	99.30 11		98.43 121	97.26 24	99.80 17	99.88 24	96.71 24	100.00 1

save fliter					99.82 70	98.79 37	99.96 25	98.40 141	97.66 10

test_0728_THIRD									96.48 44	99.83 11	99.91 15	97.87 6	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 44	98.43 121					100.00 1	99.99 5	100.00 1	100.00 1

test0726					99.93 27	99.29 14	99.96 25	98.42 133	97.28 20	99.86 5	99.94 4	97.22 19

GSMVS																99.59 139

test_part2					99.89 50	99.25 17				99.49 57

sam_mvs1												94.72 67				99.59 139

sam_mvs												94.25 86

ambc				83.23 351	77.17 380	62.61 376	87.38 375	94.55 366		76.72 359	86.65 368	30.16 377	96.36 318	84.85 313	69.86 358	90.73 356

MTGPA								98.28 168

test_post1							95.78 352				59.23 386	93.20 118	97.74 250	91.06 242

test_post											63.35 383	94.43 72	98.13 231

patchmatchnet-post											91.70 352	95.12 52	97.95 242

GG-mvs-BLEND				98.54 113	98.21 173	98.01 76	93.87 359	98.52 93		97.92 133	97.92 215	99.02 2	97.94 244	98.17 103	99.58 109	99.67 122

MTMP							99.87 94	96.49 330

gm-plane-assit					96.97 238	93.76 215			91.47 224		98.96 160		98.79 179	94.92 174

test9_res														99.71 35	99.99 22	100.00 1

TEST9					99.92 36	98.92 27	99.96 25	98.43 121	93.90 143	99.71 35	99.86 31	95.88 38	99.85 100

test_8					99.92 36	98.88 30	99.96 25	98.43 121	94.35 117	99.69 37	99.85 35	95.94 35	99.85 100

agg_prior2														99.48 42	100.00 1	100.00 1

agg_prior					99.93 27	98.77 40		98.43 121		99.63 41			99.85 100

TestCases				95.00 233	99.01 124	88.43 316		96.82 316	86.50 307	88.71 278	98.47 198	74.73 312	99.88 91	85.39 308	96.18 189	96.71 234

test_prior4						98.05 74	99.94 62

test_prior2							99.95 44		95.78 65	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 63					99.80 112			99.99 24

旧先验2							99.46 198		94.21 125	99.85 7			99.95 65	96.96 146

新几何2							99.40 202

新几何1				99.42 41	99.75 81	98.27 68		98.63 69	92.69 182	99.55 50	99.82 55	94.40 74	100.00 1	91.21 238	99.94 61	99.99 24

旧先验1					99.76 79	97.52 95		98.64 66			99.85 35	95.63 43			99.94 61	99.99 24

无先验							99.49 193	98.71 55	93.46 157				100.00 1	94.36 190		99.99 24

原ACMM2							99.90 81

原ACMM1				98.96 85	99.73 86	96.99 120		98.51 100	94.06 133	99.62 44	99.85 35	94.97 62	99.96 58	95.11 168	99.95 55	99.92 91

test222					99.55 101	97.41 107	99.34 212	98.55 86	91.86 212	99.27 77	99.83 51	93.84 100			99.95 55	99.99 24

testdata2													99.99 40	90.54 255

segment_acmp												96.68 26

testdata				98.42 123	99.47 107	95.33 177		98.56 80	93.78 148	99.79 24	99.85 35	93.64 105	99.94 73	94.97 172	99.94 61	100.00 1

testdata1							99.28 222		96.35 53

test12				99.43 38	99.74 82	98.56 57		98.40 141		99.65 39		94.76 66	99.75 127		99.98 35	99.99 24

plane_prior7					95.71 277	91.59 271

plane_prior6					95.76 272	91.72 266						80.47 269

plane_prior5								97.87 213					98.37 214	97.79 123	89.55 236	94.52 244

plane_prior4											98.59 188

plane_prior3						91.64 269			96.63 41	93.01 216

plane_prior2							99.84 113		96.38 49

plane_prior1					95.73 274

plane_prior						91.74 263	99.86 106		96.76 37						89.59 235

n2								0.00 395

nn								0.00 395

door-mid								89.69 381

lessismore_v0				90.53 323	90.58 354	80.90 361		95.80 343		77.01 357	95.84 273	66.15 346	96.95 293	83.03 324	75.05 351	93.74 316

LGP-MVS_train				93.71 285	95.43 283	88.67 312		97.62 230	92.81 174	90.05 244	98.49 194	75.24 306	98.40 206	95.84 162	89.12 240	94.07 288

test11								98.44 113

door								90.31 379

HQP5-MVS						91.85 259

HQP-NCC					95.78 268		99.87 94		96.82 33	93.37 212

ACMP_Plane					95.78 268		99.87 94		96.82 33	93.37 212

BP-MVS														97.92 118

HQP4-MVS										93.37 212			98.39 208			94.53 242

HQP3-MVS								97.89 211							89.60 233

HQP2-MVS												80.65 265

NP-MVS					95.77 271	91.79 261					98.65 184

MDTV_nov1_ep13_2view						96.26 144	96.11 347		91.89 211	98.06 129		94.40 74		94.30 193		99.67 122

ACMMP++_ref															87.04 270

ACMMP++															88.23 258

Test By Simon												92.82 128

ITE_SJBPF				92.38 307	95.69 279	85.14 338		95.71 345	92.81 174	89.33 266	98.11 205	70.23 331	98.42 203	85.91 306	88.16 259	93.59 320

DeepMVS_CX				82.92 352	95.98 266	58.66 380		96.01 340	92.72 179	78.34 354	95.51 288	58.29 363	98.08 233	82.57 326	85.29 281	92.03 346

High-resolution multi-view benchmark