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.

fmvsm_l_conf0.5_n_a		99.09 1	99.08 1	99.11 61	99.43 63	97.48 89	98.88 127	99.30 14	98.47 18	99.85 11	99.43 43	96.71 19	99.96 4	99.86 1	99.80 25	99.89 6

SED-MVS		99.09 1	98.91 4	99.63 5	99.71 24	99.24 6	99.02 84	98.87 85	97.65 39	99.73 22	99.48 33	97.53 9	99.94 14	98.43 67	99.81 16	99.70 67

DVP-MVS++		99.08 3	98.89 5	99.64 4	99.17 111	99.23 8	99.69 1	98.88 78	97.32 63	99.53 37	99.47 35	97.81 3	99.94 14	98.47 63	99.72 68	99.74 50

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 57	99.41 66	97.54 87	98.89 120	99.31 13	98.49 17	99.86 8	99.42 44	96.45 27	99.96 4	99.86 1	99.74 59	99.90 5

DVP-MVS		99.03 5	98.83 10	99.63 5	99.72 17	99.25 3	98.97 95	98.58 177	97.62 41	99.45 39	99.46 40	97.42 11	99.94 14	98.47 63	99.81 16	99.69 70
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
MED-MVS		99.02 6	98.85 8	99.52 13	99.77 2	98.86 22	99.32 22	99.24 20	97.00 89	99.30 50	99.35 60	97.61 6	99.92 43	98.30 75	99.80 25	99.79 28

TestfortrainingZip a		99.02 6	98.79 12	99.70 2	99.77 2	99.30 2	99.32 22	99.24 20	96.41 121	99.30 50	99.35 60	97.61 6	99.92 43	98.35 72	99.80 25	99.88 10

APDe-MVS		99.02 6	98.84 9	99.55 10	99.57 39	98.96 17	99.39 11	98.93 65	97.38 60	99.41 42	99.54 20	96.66 20	99.84 88	98.86 39	99.85 6	99.87 11
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lecture		98.95 9	98.78 14	99.45 19	99.75 6	98.63 30	99.43 10	99.38 8	97.60 44	99.58 33	99.47 35	95.36 64	99.93 34	98.87 38	99.57 100	99.78 33

reproduce_model		98.94 10	98.81 11	99.34 31	99.52 45	98.26 54	98.94 105	98.84 96	98.06 25	99.35 46	99.61 5	96.39 30	99.94 14	98.77 42	99.82 14	99.83 18

reproduce-ours		98.93 11	98.78 14	99.38 23	99.49 52	98.38 40	98.86 138	98.83 98	98.06 25	99.29 53	99.58 16	96.40 28	99.94 14	98.68 45	99.81 16	99.81 24

our_new_method		98.93 11	98.78 14	99.38 23	99.49 52	98.38 40	98.86 138	98.83 98	98.06 25	99.29 53	99.58 16	96.40 28	99.94 14	98.68 45	99.81 16	99.81 24

test_fmvsmconf_n		98.92 13	98.87 6	99.04 67	98.88 147	97.25 111	98.82 151	99.34 11	98.75 11	99.80 14	99.61 5	95.16 77	99.95 9	99.70 17	99.80 25	99.93 1

DPE-MVS		98.92 13	98.67 20	99.65 3	99.58 37	99.20 10	98.42 264	98.91 72	97.58 45	99.54 36	99.46 40	97.10 14	99.94 14	97.64 119	99.84 11	99.83 18
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_9		98.90 15	98.79 12	99.24 45	99.34 71	97.83 78	98.70 192	99.26 16	98.85 6	99.92 1	99.51 26	93.91 106	99.95 9	99.86 1	99.79 35	99.92 2

fmvsm_l_conf0.5_n_3		98.90 15	98.74 18	99.37 27	99.36 68	98.25 55	98.89 120	99.24 20	98.77 10	99.89 3	99.59 13	93.39 112	99.96 4	99.78 10	99.76 48	99.89 6

SteuartSystems-ACMMP		98.90 15	98.75 17	99.36 29	99.22 106	98.43 38	99.10 68	98.87 85	97.38 60	99.35 46	99.40 47	97.78 5	99.87 79	97.77 107	99.85 6	99.78 33
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 18	99.01 3	98.45 123	99.42 64	96.43 155	98.96 101	99.36 10	98.63 13	99.86 8	99.51 26	95.91 46	99.97 1	99.72 14	99.75 55	98.94 230

ME-MVS		98.83 19	98.60 24	99.52 13	99.58 37	98.86 22	98.69 195	98.93 65	97.00 89	99.17 62	99.35 60	96.62 23	99.90 64	98.30 75	99.80 25	99.79 28

TSAR-MVS + MP.		98.78 20	98.62 22	99.24 45	99.69 29	98.28 53	99.14 59	98.66 154	96.84 96	99.56 34	99.31 71	96.34 31	99.70 142	98.32 74	99.73 63	99.73 55
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		98.78 20	98.56 28	99.45 19	99.32 77	98.87 20	98.47 251	98.81 107	97.72 34	98.76 95	99.16 107	97.05 15	99.78 124	98.06 89	99.66 79	99.69 70

MSP-MVS		98.74 22	98.55 29	99.29 38	99.75 6	98.23 56	99.26 32	98.88 78	97.52 48	99.41 42	98.78 186	96.00 42	99.79 121	97.79 106	99.59 96	99.85 15
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
fmvsm_s_conf0.5_n_8		98.73 23	98.62 22	99.05 66	99.35 70	97.27 105	98.80 160	99.23 28	98.93 3	99.79 15	99.59 13	92.34 129	99.95 9	99.82 6	99.71 70	99.92 2

XVS		98.70 24	98.49 36	99.34 31	99.70 27	98.35 49	99.29 27	98.88 78	97.40 57	98.46 118	99.20 92	95.90 48	99.89 68	97.85 102	99.74 59	99.78 33

fmvsm_s_conf0.5_n_10		98.66 25	98.54 31	99.02 68	99.36 68	97.21 114	98.86 138	99.23 28	98.90 5	99.83 12	99.59 13	91.57 160	99.94 14	99.79 9	99.74 59	99.89 6

fmvsm_s_conf0.5_n_6		98.65 26	98.55 29	98.95 77	98.50 187	97.30 101	98.79 168	99.16 40	98.14 23	99.86 8	99.41 46	93.71 109	99.91 56	99.71 15	99.64 87	99.65 83

MCST-MVS		98.65 26	98.37 45	99.48 17	99.60 36	98.87 20	98.41 265	98.68 146	97.04 86	98.52 116	98.80 180	96.78 18	99.83 90	97.93 96	99.61 92	99.74 50

SD-MVS		98.64 28	98.68 19	98.53 112	99.33 74	98.36 48	98.90 116	98.85 95	97.28 67	99.72 25	99.39 48	96.63 22	97.60 429	98.17 84	99.85 6	99.64 86
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
fmvsm_s_conf0.5_n_9		98.63 29	98.66 21	98.54 109	99.40 67	95.83 199	98.79 168	99.17 38	98.94 2	99.92 1	99.61 5	92.49 124	99.93 34	99.86 1	99.76 48	99.86 12

HFP-MVS		98.63 29	98.40 42	99.32 37	99.72 17	98.29 52	99.23 37	98.96 60	96.10 138	98.94 77	99.17 104	96.06 39	99.92 43	97.62 120	99.78 40	99.75 48

ACMMP_NAP		98.61 31	98.30 60	99.55 10	99.62 35	98.95 18	98.82 151	98.81 107	95.80 152	99.16 66	99.47 35	95.37 63	99.92 43	97.89 100	99.75 55	99.79 28

region2R		98.61 31	98.38 44	99.29 38	99.74 12	98.16 62	99.23 37	98.93 65	96.15 134	98.94 77	99.17 104	95.91 46	99.94 14	97.55 132	99.79 35	99.78 33

NCCC		98.61 31	98.35 48	99.38 23	99.28 92	98.61 31	98.45 253	98.76 125	97.82 33	98.45 121	98.93 158	96.65 21	99.83 90	97.38 153	99.41 129	99.71 63

SF-MVS		98.59 34	98.32 59	99.41 22	99.54 41	98.71 26	99.04 78	98.81 107	95.12 205	99.32 49	99.39 48	96.22 33	99.84 88	97.72 110	99.73 63	99.67 79

ACMMPR		98.59 34	98.36 46	99.29 38	99.74 12	98.15 63	99.23 37	98.95 61	96.10 138	98.93 81	99.19 99	95.70 52	99.94 14	97.62 120	99.79 35	99.78 33

fmvsm_s_conf0.5_n_11		98.58 36	98.57 26	98.62 99	99.42 64	97.16 117	98.97 95	98.86 91	98.91 4	99.87 4	99.66 3	91.82 152	99.95 9	99.82 6	99.82 14	98.75 251

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 70	97.73 300	97.15 118	98.84 147	98.97 57	98.75 11	99.43 41	99.54 20	93.29 114	99.93 34	99.64 20	99.79 35	99.89 6

SMA-MVS		98.58 36	98.25 63	99.56 9	99.51 46	99.04 16	98.95 102	98.80 114	93.67 299	99.37 45	99.52 23	96.52 25	99.89 68	98.06 89	99.81 16	99.76 47
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
MTAPA		98.58 36	98.29 61	99.46 18	99.76 5	98.64 29	98.90 116	98.74 129	97.27 71	98.02 147	99.39 48	94.81 87	99.96 4	97.91 98	99.79 35	99.77 40

HPM-MVS++		98.58 36	98.25 63	99.55 10	99.50 48	99.08 12	98.72 187	98.66 154	97.51 49	98.15 132	98.83 177	95.70 52	99.92 43	97.53 134	99.67 76	99.66 82

SR-MVS		98.57 41	98.35 48	99.24 45	99.53 42	98.18 60	99.09 69	98.82 101	96.58 112	99.10 68	99.32 69	95.39 61	99.82 97	97.70 115	99.63 89	99.72 59

CP-MVS		98.57 41	98.36 46	99.19 50	99.66 31	97.86 74	99.34 17	98.87 85	95.96 144	98.60 112	99.13 114	96.05 40	99.94 14	97.77 107	99.86 2	99.77 40

MSLP-MVS++		98.56 43	98.57 26	98.55 107	99.26 95	96.80 133	98.71 188	99.05 50	97.28 67	98.84 87	99.28 76	96.47 26	99.40 206	98.52 61	99.70 72	99.47 115

DeepC-MVS_fast	96.70 1	98.55 44	98.34 54	99.18 52	99.25 96	98.04 68	98.50 246	98.78 121	97.72 34	98.92 83	99.28 76	95.27 70	99.82 97	97.55 132	99.77 42	99.69 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		98.54 45	98.35 48	99.13 58	99.49 52	97.86 74	99.11 65	98.80 114	96.49 116	99.17 62	99.35 60	95.34 66	99.82 97	97.72 110	99.65 82	99.71 63

fmvsm_s_conf0.5_n_5		98.53 46	98.35 48	99.08 63	99.07 126	97.46 93	98.68 198	99.20 34	97.50 50	99.87 4	99.50 29	91.96 149	99.96 4	99.76 11	99.65 82	99.82 22

fmvsm_s_conf0.5_n_3		98.53 46	98.45 39	98.79 85	99.23 104	97.32 98	98.80 160	99.26 16	98.82 7	99.87 4	99.60 10	90.95 193	99.93 34	99.76 11	99.73 63	99.12 200

APD-MVS_3200maxsize		98.53 46	98.33 58	99.15 56	99.50 48	97.92 73	99.15 56	98.81 107	96.24 130	99.20 59	99.37 54	95.30 68	99.80 109	97.73 109	99.67 76	99.72 59

MM		98.51 49	98.24 65	99.33 35	99.12 120	98.14 65	98.93 111	97.02 416	98.96 1	99.17 62	99.47 35	91.97 148	99.94 14	99.85 5	99.69 73	99.91 4

mPP-MVS		98.51 49	98.26 62	99.25 44	99.75 6	98.04 68	99.28 29	98.81 107	96.24 130	98.35 128	99.23 86	95.46 58	99.94 14	97.42 148	99.81 16	99.77 40

ZNCC-MVS		98.49 51	98.20 71	99.35 30	99.73 16	98.39 39	99.19 49	98.86 91	95.77 154	98.31 131	99.10 122	95.46 58	99.93 34	97.57 131	99.81 16	99.74 50

SPE-MVS-test		98.49 51	98.50 34	98.46 122	99.20 109	97.05 123	99.64 4	98.50 199	97.45 56	98.88 84	99.14 111	95.25 72	99.15 255	98.83 40	99.56 108	99.20 184

PGM-MVS		98.49 51	98.23 67	99.27 43	99.72 17	98.08 67	98.99 91	99.49 5	95.43 181	99.03 69	99.32 69	95.56 55	99.94 14	96.80 186	99.77 42	99.78 33

EI-MVSNet-Vis-set		98.47 54	98.39 43	98.69 93	99.46 58	96.49 152	98.30 278	98.69 143	97.21 74	98.84 87	99.36 58	95.41 60	99.78 124	98.62 49	99.65 82	99.80 27

MVS_111021_HR		98.47 54	98.34 54	98.88 82	99.22 106	97.32 98	97.91 336	99.58 3	97.20 75	98.33 129	99.00 146	95.99 43	99.64 156	98.05 91	99.76 48	99.69 70

balanced_conf03		98.45 56	98.35 48	98.74 89	98.65 176	97.55 85	99.19 49	98.60 165	96.72 106	99.35 46	98.77 189	95.06 82	99.55 179	98.95 35	99.87 1	99.12 200

test_fmvsmvis_n_1920		98.44 57	98.51 32	98.23 144	98.33 218	96.15 169	98.97 95	99.15 42	98.55 16	98.45 121	99.55 18	94.26 100	99.97 1	99.65 18	99.66 79	98.57 276

CS-MVS		98.44 57	98.49 36	98.31 136	99.08 125	96.73 137	99.67 3	98.47 206	97.17 78	98.94 77	99.10 122	95.73 51	99.13 260	98.71 44	99.49 119	99.09 208

GST-MVS		98.43 59	98.12 75	99.34 31	99.72 17	98.38 40	99.09 69	98.82 101	95.71 158	98.73 98	99.06 137	95.27 70	99.93 34	97.07 163	99.63 89	99.72 59

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 160	99.30 83	95.25 236	98.85 143	99.39 7	97.94 29	99.74 21	99.62 4	92.59 123	99.91 56	99.65 18	99.52 114	99.25 177

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 101	99.45 61	96.32 162	98.28 281	98.68 146	97.17 78	98.74 96	99.37 54	95.25 72	99.79 121	98.57 52	99.54 111	99.73 55

DELS-MVS		98.40 62	98.20 71	98.99 70	99.00 134	97.66 80	97.75 357	98.89 75	97.71 36	98.33 129	98.97 148	94.97 84	99.88 77	98.42 69	99.76 48	99.42 130
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
fmvsm_s_conf0.5_n_a		98.38 63	98.42 41	98.27 138	99.09 124	95.41 222	98.86 138	99.37 9	97.69 38	99.78 17	99.61 5	92.38 127	99.91 56	99.58 23	99.43 127	99.49 111

TSAR-MVS + GP.		98.38 63	98.24 65	98.81 84	99.22 106	97.25 111	98.11 311	98.29 270	97.19 76	98.99 75	99.02 140	96.22 33	99.67 149	98.52 61	98.56 183	99.51 104

HPM-MVS_fast		98.38 63	98.13 74	99.12 60	99.75 6	97.86 74	99.44 9	98.82 101	94.46 253	98.94 77	99.20 92	95.16 77	99.74 134	97.58 127	99.85 6	99.77 40

patch_mono-2		98.36 66	98.87 6	96.82 274	99.53 42	90.68 393	98.64 209	99.29 15	97.88 30	99.19 61	99.52 23	96.80 17	99.97 1	99.11 31	99.86 2	99.82 22

HPM-MVS		98.36 66	98.10 78	99.13 58	99.74 12	97.82 79	99.53 6	98.80 114	94.63 240	98.61 111	98.97 148	95.13 79	99.77 129	97.65 118	99.83 13	99.79 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_4		98.35 68	98.50 34	97.90 188	99.16 115	95.08 245	98.75 173	99.24 20	98.39 19	99.81 13	99.52 23	92.35 128	99.90 64	99.74 13	99.51 116	98.71 257

APD-MVS		98.35 68	98.00 84	99.42 21	99.51 46	98.72 25	98.80 160	98.82 101	94.52 248	99.23 58	99.25 85	95.54 57	99.80 109	96.52 195	99.77 42	99.74 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 70	98.23 67	98.67 95	99.27 93	96.90 129	97.95 329	99.58 3	97.14 81	98.44 123	99.01 144	95.03 83	99.62 163	97.91 98	99.75 55	99.50 106

PHI-MVS		98.34 70	98.06 79	99.18 52	99.15 118	98.12 66	99.04 78	99.09 45	93.32 316	98.83 90	99.10 122	96.54 24	99.83 90	97.70 115	99.76 48	99.59 94

MP-MVS		98.33 72	98.01 83	99.28 41	99.75 6	98.18 60	99.22 41	98.79 119	96.13 135	97.92 161	99.23 86	94.54 90	99.94 14	96.74 189	99.78 40	99.73 55
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus		98.31 73	98.19 73	98.67 95	98.96 141	97.36 96	99.24 35	98.57 179	94.81 228	98.99 75	98.90 164	95.22 75	99.59 166	99.15 30	99.84 11	99.07 216

MP-MVS-pluss		98.31 73	97.92 86	99.49 16	99.72 17	98.88 19	98.43 261	98.78 121	94.10 264	97.69 185	99.42 44	95.25 72	99.92 43	98.09 88	99.80 25	99.67 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_2		98.30 75	98.21 69	98.57 104	99.25 96	97.11 120	98.66 205	99.20 34	98.82 7	99.79 15	99.60 10	89.38 238	99.92 43	99.80 8	99.38 134	98.69 259

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 190	98.86 151	94.99 251	98.58 222	99.00 53	98.29 20	99.73 22	99.60 10	91.70 155	99.92 43	99.63 21	99.73 63	98.76 250

MGCNet		98.23 76	97.91 87	99.21 49	98.06 263	97.96 72	98.58 222	95.51 455	98.58 14	98.87 85	99.26 80	92.99 118	99.95 9	99.62 22	99.67 76	99.73 55

ACMMP		98.23 76	97.95 85	99.09 62	99.74 12	97.62 83	99.03 81	99.41 6	95.98 143	97.60 197	99.36 58	94.45 95	99.93 34	97.14 160	98.85 167	99.70 67
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
EC-MVSNet		98.21 79	98.11 76	98.49 119	98.34 215	97.26 110	99.61 5	98.43 226	96.78 99	98.87 85	98.84 173	93.72 108	99.01 284	98.91 37	99.50 117	99.19 188

fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 165	98.54 185	95.24 237	98.87 130	99.24 20	97.50 50	99.70 26	99.67 1	91.33 172	99.89 68	99.47 25	99.54 111	99.21 183

fmvsm_s_conf0.1_n_2		98.14 81	98.02 82	98.53 112	98.88 147	97.07 122	98.69 195	98.82 101	98.78 9	99.77 18	99.61 5	88.83 258	99.91 56	99.71 15	99.07 150	98.61 269

fmvsm_s_conf0.1_n_a		98.08 82	98.04 81	98.21 145	97.66 306	95.39 227	98.89 120	99.17 38	97.24 72	99.76 20	99.67 1	91.13 184	99.88 77	99.39 26	99.41 129	99.35 144

dcpmvs_2		98.08 82	98.59 25	96.56 303	99.57 39	90.34 405	99.15 56	98.38 244	96.82 98	99.29 53	99.49 32	95.78 50	99.57 169	98.94 36	99.86 2	99.77 40

NormalMVS		98.07 84	97.90 88	98.59 103	99.75 6	96.60 143	98.94 105	98.60 165	97.86 31	98.71 101	99.08 132	91.22 179	99.80 109	97.40 150	99.57 100	99.37 139

CANet		98.05 85	97.76 91	98.90 81	98.73 161	97.27 105	98.35 268	98.78 121	97.37 62	97.72 182	98.96 153	91.53 165	99.92 43	98.79 41	99.65 82	99.51 104

train_agg		97.97 86	97.52 104	99.33 35	99.31 79	98.50 34	97.92 334	98.73 132	92.98 332	97.74 179	98.68 202	96.20 35	99.80 109	96.59 190	99.57 100	99.68 75

ETV-MVS		97.96 87	97.81 89	98.40 131	98.42 198	97.27 105	98.73 183	98.55 184	96.84 96	98.38 125	97.44 324	95.39 61	99.35 211	97.62 120	98.89 161	98.58 275

UA-Net		97.96 87	97.62 95	98.98 72	98.86 151	97.47 91	98.89 120	99.08 46	96.67 109	98.72 100	99.54 20	93.15 116	99.81 102	94.87 253	98.83 168	99.65 83

CDPH-MVS		97.94 89	97.49 106	99.28 41	99.47 56	98.44 36	97.91 336	98.67 151	92.57 348	98.77 94	98.85 172	95.93 45	99.72 136	95.56 231	99.69 73	99.68 75

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 329	99.00 134	89.54 421	97.43 382	98.87 85	98.16 22	99.26 57	99.38 53	96.12 38	99.64 156	98.30 75	99.77 42	99.72 59

DeepC-MVS	95.98 3	97.88 91	97.58 97	98.77 87	99.25 96	96.93 127	98.83 149	98.75 127	96.96 92	96.89 229	99.50 29	90.46 204	99.87 79	97.84 104	99.76 48	99.52 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n		97.86 92	97.54 103	98.83 83	95.48 430	96.83 132	98.95 102	98.60 165	98.58 14	98.93 81	99.55 18	88.57 263	99.91 56	99.54 24	99.61 92	99.77 40

DP-MVS Recon		97.86 92	97.46 109	99.06 65	99.53 42	98.35 49	98.33 270	98.89 75	92.62 345	98.05 142	98.94 156	95.34 66	99.65 153	96.04 211	99.42 128	99.19 188

CSCG		97.85 94	97.74 92	98.20 147	99.67 30	95.16 240	99.22 41	99.32 12	93.04 330	97.02 222	98.92 162	95.36 64	99.91 56	97.43 146	99.64 87	99.52 101

SymmetryMVS		97.84 95	97.58 97	98.62 99	99.01 132	96.60 143	98.94 105	98.44 215	97.86 31	98.71 101	99.08 132	91.22 179	99.80 109	97.40 150	97.53 250	99.47 115

BP-MVS1		97.82 96	97.51 105	98.76 88	98.25 233	97.39 95	99.15 56	97.68 347	96.69 107	98.47 117	99.10 122	90.29 212	99.51 186	98.60 50	99.35 137	99.37 139

MG-MVS		97.81 97	97.60 96	98.44 125	99.12 120	95.97 180	97.75 357	98.78 121	96.89 95	98.46 118	99.22 88	93.90 107	99.68 148	94.81 257	99.52 114	99.67 79

VNet		97.79 98	97.40 114	98.96 75	98.88 147	97.55 85	98.63 212	98.93 65	96.74 103	99.02 70	98.84 173	90.33 211	99.83 90	98.53 55	96.66 273	99.50 106

EIA-MVS		97.75 99	97.58 97	98.27 138	98.38 205	96.44 154	99.01 86	98.60 165	95.88 148	97.26 208	97.53 318	94.97 84	99.33 214	97.38 153	99.20 146	99.05 217

PS-MVSNAJ		97.73 100	97.77 90	97.62 220	98.68 171	95.58 212	97.34 391	98.51 194	97.29 65	98.66 108	97.88 282	94.51 91	99.90 64	97.87 101	99.17 148	97.39 319

casdiffmvs_mvg		97.72 101	97.48 108	98.44 125	98.42 198	96.59 147	98.92 113	98.44 215	96.20 132	97.76 176	99.20 92	91.66 158	99.23 242	98.27 82	98.41 203	99.49 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CPTT-MVS		97.72 101	97.32 120	98.92 78	99.64 33	97.10 121	99.12 63	98.81 107	92.34 356	98.09 137	99.08 132	93.01 117	99.92 43	96.06 210	99.77 42	99.75 48

PVSNet_Blended_VisFu		97.70 103	97.46 109	98.44 125	99.27 93	95.91 188	98.63 212	99.16 40	94.48 252	97.67 186	98.88 168	92.80 120	99.91 56	97.11 161	99.12 149	99.50 106

mvsany_test1		97.69 104	97.70 93	97.66 216	98.24 234	94.18 294	97.53 373	97.53 368	95.52 176	99.66 28	99.51 26	94.30 98	99.56 172	98.38 70	98.62 178	99.23 179

sasdasda		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 239	96.76 101	97.67 186	97.40 328	92.26 133	99.49 190	98.28 79	96.28 291	99.08 212

canonicalmvs		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 239	96.76 101	97.67 186	97.40 328	92.26 133	99.49 190	98.28 79	96.28 291	99.08 212

xiu_mvs_v2_base		97.66 107	97.70 93	97.56 224	98.61 180	95.46 220	97.44 379	98.46 207	97.15 80	98.65 109	98.15 257	94.33 97	99.80 109	97.84 104	98.66 177	97.41 317

GDP-MVS		97.64 108	97.28 122	98.71 92	98.30 223	97.33 97	99.05 74	98.52 191	96.34 127	98.80 91	99.05 138	89.74 225	99.51 186	96.86 182	98.86 165	99.28 167

baseline		97.64 108	97.44 111	98.25 142	98.35 210	96.20 166	99.00 88	98.32 257	96.33 129	98.03 145	99.17 104	91.35 171	99.16 251	98.10 87	98.29 212	99.39 135

casdiffmvs		97.63 110	97.41 113	98.28 137	98.33 218	96.14 170	98.82 151	98.32 257	96.38 125	97.95 156	99.21 90	91.23 178	99.23 242	98.12 86	98.37 205	99.48 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCFI-Net		97.62 111	97.19 132	98.92 78	98.66 173	98.20 58	99.32 22	98.38 244	96.69 107	97.58 199	97.42 327	92.10 142	99.50 189	98.28 79	96.25 294	99.08 212

xiu_mvs_v1_base_debu		97.60 112	97.56 100	97.72 205	98.35 210	95.98 175	97.86 346	98.51 194	97.13 82	99.01 72	98.40 229	91.56 161	99.80 109	98.53 55	98.68 173	97.37 321

xiu_mvs_v1_base		97.60 112	97.56 100	97.72 205	98.35 210	95.98 175	97.86 346	98.51 194	97.13 82	99.01 72	98.40 229	91.56 161	99.80 109	98.53 55	98.68 173	97.37 321

xiu_mvs_v1_base_debi		97.60 112	97.56 100	97.72 205	98.35 210	95.98 175	97.86 346	98.51 194	97.13 82	99.01 72	98.40 229	91.56 161	99.80 109	98.53 55	98.68 173	97.37 321

diffmvs_AUTHOR		97.59 115	97.44 111	98.01 177	98.26 232	95.47 219	98.12 307	98.36 250	96.38 125	98.84 87	99.10 122	91.13 184	99.26 227	98.24 83	98.56 183	99.30 158

diffmvs		97.58 116	97.40 114	98.13 160	98.32 221	95.81 202	98.06 317	98.37 246	96.20 132	98.74 96	98.89 167	91.31 174	99.25 231	98.16 85	98.52 187	99.34 146
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
guyue		97.57 117	97.37 116	98.20 147	98.50 187	95.86 196	98.89 120	97.03 413	97.29 65	98.73 98	98.90 164	89.41 237	99.32 215	98.68 45	98.86 165	99.42 130

MVSFormer		97.57 117	97.49 106	97.84 192	98.07 260	95.76 206	99.47 7	98.40 234	94.98 217	98.79 92	98.83 177	92.34 129	98.41 358	96.91 170	99.59 96	99.34 146

alignmvs		97.56 119	97.07 144	99.01 69	98.66 173	98.37 47	98.83 149	98.06 322	96.74 103	98.00 151	97.65 305	90.80 195	99.48 195	98.37 71	96.56 277	99.19 188

E3new		97.55 120	97.35 118	98.16 151	98.48 192	95.85 197	98.55 235	98.41 231	95.42 183	98.06 140	99.12 117	92.23 136	99.24 238	97.43 146	98.45 193	99.39 135

DPM-MVS		97.55 120	96.99 151	99.23 48	99.04 128	98.55 32	97.17 408	98.35 251	94.85 227	97.93 160	98.58 212	95.07 81	99.71 141	92.60 337	99.34 138	99.43 127

OMC-MVS		97.55 120	97.34 119	98.20 147	99.33 74	95.92 187	98.28 281	98.59 172	95.52 176	97.97 154	99.10 122	93.28 115	99.49 190	95.09 248	98.88 162	99.19 188

viewcassd2359sk11		97.53 123	97.32 120	98.16 151	98.45 195	95.83 199	98.57 231	98.42 230	95.52 176	98.07 138	99.12 117	91.81 153	99.25 231	97.46 144	98.48 192	99.41 133

LuminaMVS		97.49 124	97.18 133	98.42 129	97.50 321	97.15 118	98.45 253	97.68 347	96.56 115	98.68 103	98.78 186	89.84 222	99.32 215	98.60 50	98.57 182	98.79 242

E2		97.48 125	97.25 124	98.16 151	98.40 202	95.79 203	98.58 222	98.44 215	95.58 165	98.00 151	99.14 111	91.21 183	99.24 238	97.50 139	98.43 197	99.45 122

E3		97.48 125	97.25 124	98.16 151	98.38 205	95.79 203	98.58 222	98.44 215	95.58 165	98.00 151	99.14 111	91.25 177	99.24 238	97.50 139	98.44 194	99.45 122

KinetiMVS		97.48 125	97.05 146	98.78 86	98.37 208	97.30 101	98.99 91	98.70 141	97.18 77	99.02 70	99.01 144	87.50 293	99.67 149	95.33 238	99.33 140	99.37 139

viewmanbaseed2359cas		97.47 128	97.25 124	98.14 155	98.41 200	95.84 198	98.57 231	98.43 226	95.55 172	97.97 154	99.12 117	91.26 176	99.15 255	97.42 148	98.53 186	99.43 127

PAPM_NR		97.46 129	97.11 141	98.50 117	99.50 48	96.41 157	98.63 212	98.60 165	95.18 198	97.06 220	98.06 263	94.26 100	99.57 169	93.80 299	98.87 164	99.52 101

EPP-MVSNet		97.46 129	97.28 122	97.99 179	98.64 177	95.38 228	99.33 21	98.31 261	93.61 304	97.19 212	99.07 136	94.05 103	99.23 242	96.89 174	98.43 197	99.37 139

3Dnovator	94.51 5	97.46 129	96.93 155	99.07 64	97.78 294	97.64 81	99.35 16	99.06 48	97.02 87	93.75 347	99.16 107	89.25 242	99.92 43	97.22 159	99.75 55	99.64 86

CNLPA		97.45 132	97.03 148	98.73 90	99.05 127	97.44 94	98.07 316	98.53 188	95.32 191	96.80 234	98.53 217	93.32 113	99.72 136	94.31 280	99.31 141	99.02 221

lupinMVS		97.44 133	97.22 131	98.12 163	98.07 260	95.76 206	97.68 362	97.76 344	94.50 251	98.79 92	98.61 207	92.34 129	99.30 220	97.58 127	99.59 96	99.31 154

3Dnovator+	94.38 6	97.43 134	96.78 166	99.38 23	97.83 291	98.52 33	99.37 13	98.71 137	97.09 85	92.99 377	99.13 114	89.36 239	99.89 68	96.97 166	99.57 100	99.71 63

Vis-MVSNet		97.42 135	97.11 141	98.34 134	98.66 173	96.23 165	99.22 41	99.00 53	96.63 111	98.04 144	99.21 90	88.05 280	99.35 211	96.01 213	99.21 145	99.45 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 136	97.25 124	97.91 187	98.70 166	96.80 133	98.82 151	98.69 143	94.53 246	98.11 135	98.28 244	94.50 94	99.57 169	94.12 288	99.49 119	97.37 321

sss		97.39 137	96.98 153	98.61 101	98.60 181	96.61 142	98.22 287	98.93 65	93.97 274	98.01 150	98.48 222	91.98 146	99.85 84	96.45 197	98.15 221	99.39 135

test_cas_vis1_n_1920		97.38 138	97.36 117	97.45 228	98.95 142	93.25 336	99.00 88	98.53 188	97.70 37	99.77 18	99.35 60	84.71 349	99.85 84	98.57 52	99.66 79	99.26 175

PVSNet_Blended		97.38 138	97.12 140	98.14 155	99.25 96	95.35 231	97.28 396	99.26 16	93.13 326	97.94 158	98.21 252	92.74 121	99.81 102	96.88 176	99.40 132	99.27 168

E5new		97.37 140	97.16 135	97.98 180	98.30 223	95.41 222	98.87 130	98.45 211	95.56 167	97.84 167	99.19 99	90.39 207	99.25 231	97.61 123	98.22 216	99.29 161

E6new		97.37 140	97.16 135	97.98 180	98.28 229	95.40 225	98.87 130	98.45 211	95.55 172	97.84 167	99.20 92	90.44 205	99.25 231	97.61 123	98.22 216	99.29 161

E6		97.37 140	97.16 135	97.98 180	98.28 229	95.40 225	98.87 130	98.45 211	95.55 172	97.84 167	99.20 92	90.44 205	99.25 231	97.61 123	98.22 216	99.29 161

E5		97.37 140	97.16 135	97.98 180	98.30 223	95.41 222	98.87 130	98.45 211	95.56 167	97.84 167	99.19 99	90.39 207	99.25 231	97.61 123	98.22 216	99.29 161

E4		97.37 140	97.13 139	98.12 163	98.27 231	95.70 208	98.59 218	98.44 215	95.56 167	97.80 173	99.18 102	90.57 202	99.26 227	97.45 145	98.28 214	99.40 134

WTY-MVS		97.37 140	96.92 156	98.72 91	98.86 151	96.89 131	98.31 275	98.71 137	95.26 194	97.67 186	98.56 216	92.21 138	99.78 124	95.89 215	96.85 267	99.48 113

AstraMVS		97.34 146	97.24 128	97.65 217	98.13 254	94.15 295	98.94 105	96.25 445	97.47 54	98.60 112	99.28 76	89.67 227	99.41 205	98.73 43	98.07 225	99.38 138

viewmacassd2359aftdt		97.32 147	97.07 144	98.08 168	98.30 223	95.69 209	98.62 215	98.44 215	95.56 167	97.86 166	99.22 88	89.91 220	99.14 258	97.29 156	98.43 197	99.42 130

jason		97.32 147	97.08 143	98.06 172	97.45 327	95.59 211	97.87 344	97.91 333	94.79 230	98.55 115	98.83 177	91.12 186	99.23 242	97.58 127	99.60 94	99.34 146
jason: jason.
MVS_Test		97.28 149	97.00 149	98.13 160	98.33 218	95.97 180	98.74 177	98.07 317	94.27 259	98.44 123	98.07 262	92.48 125	99.26 227	96.43 198	98.19 220	99.16 194

EPNet		97.28 149	96.87 158	98.51 114	94.98 439	96.14 170	98.90 116	97.02 416	98.28 21	95.99 269	99.11 120	91.36 170	99.89 68	96.98 165	99.19 147	99.50 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_0404		97.26 151	97.00 149	98.03 174	98.46 193	95.99 174	98.62 215	98.44 215	94.77 231	97.24 209	98.93 158	91.22 179	99.28 224	96.54 192	98.74 172	98.84 238

mvsmamba		97.25 152	96.99 151	98.02 176	98.34 215	95.54 216	99.18 53	97.47 374	95.04 211	98.15 132	98.57 215	89.46 234	99.31 219	97.68 117	99.01 155	99.22 181

viewdifsd2359ckpt13		97.24 153	96.97 154	98.06 172	98.43 196	95.77 205	98.59 218	98.34 254	94.81 228	97.60 197	98.94 156	90.78 199	99.09 270	96.93 169	98.33 208	99.32 153

test_yl		97.22 154	96.78 166	98.54 109	98.73 161	96.60 143	98.45 253	98.31 261	94.70 234	98.02 147	98.42 227	90.80 195	99.70 142	96.81 183	96.79 269	99.34 146

DCV-MVSNet		97.22 154	96.78 166	98.54 109	98.73 161	96.60 143	98.45 253	98.31 261	94.70 234	98.02 147	98.42 227	90.80 195	99.70 142	96.81 183	96.79 269	99.34 146

IS-MVSNet		97.22 154	96.88 157	98.25 142	98.85 154	96.36 160	99.19 49	97.97 327	95.39 185	97.23 210	98.99 147	91.11 187	98.93 296	94.60 268	98.59 180	99.47 115

viewdifsd2359ckpt07		97.20 157	97.05 146	97.65 217	98.40 202	94.33 287	98.39 266	98.43 226	95.67 160	97.66 190	99.08 132	90.04 217	99.32 215	97.47 143	98.29 212	99.31 154

PLC	95.07 4	97.20 157	96.78 166	98.44 125	99.29 88	96.31 164	98.14 304	98.76 125	92.41 354	96.39 257	98.31 242	94.92 86	99.78 124	94.06 291	98.77 171	99.23 179
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 159	97.18 133	97.20 241	98.81 157	93.27 333	95.78 455	99.15 42	95.25 195	96.79 235	98.11 260	92.29 132	99.07 273	98.56 54	99.85 6	99.25 177

SSM_0407		97.17 160	96.87 158	98.08 168	98.19 242	95.90 189	98.52 238	98.44 215	94.77 231	96.75 236	98.93 158	91.22 179	99.22 246	96.54 192	98.43 197	99.10 205

LS3D		97.16 161	96.66 175	98.68 94	98.53 186	97.19 115	98.93 111	98.90 73	92.83 339	95.99 269	99.37 54	92.12 141	99.87 79	93.67 303	99.57 100	98.97 226

AdaColmap		97.15 162	96.70 171	98.48 120	99.16 115	96.69 139	98.01 323	98.89 75	94.44 254	96.83 230	98.68 202	90.69 200	99.76 130	94.36 276	99.29 142	98.98 225

viewdifsd2359ckpt09		97.13 163	96.79 164	98.14 155	98.43 196	95.90 189	98.52 238	98.37 246	94.32 257	97.33 204	98.86 171	90.23 215	99.16 251	96.81 183	98.25 215	99.36 143

mamv4		97.13 163	98.11 76	94.17 419	98.97 140	83.70 464	98.66 205	98.71 137	94.63 240	97.83 171	98.90 164	96.25 32	99.55 179	99.27 28	99.76 48	99.27 168

Effi-MVS+		97.12 165	96.69 172	98.39 132	98.19 242	96.72 138	97.37 387	98.43 226	93.71 292	97.65 191	98.02 266	92.20 139	99.25 231	96.87 179	97.79 234	99.19 188

CHOSEN 1792x2688		97.12 165	96.80 162	98.08 168	99.30 83	94.56 276	98.05 318	99.71 1	93.57 306	97.09 216	98.91 163	88.17 274	99.89 68	96.87 179	99.56 108	99.81 24

F-COLMAP		97.09 167	96.80 162	97.97 184	99.45 61	94.95 255	98.55 235	98.62 164	93.02 331	96.17 264	98.58 212	94.01 104	99.81 102	93.95 293	98.90 160	99.14 198

RRT-MVS		97.03 168	96.78 166	97.77 201	97.90 287	94.34 285	99.12 63	98.35 251	95.87 149	98.06 140	98.70 200	86.45 312	99.63 159	98.04 92	98.54 185	99.35 144

TAMVS		97.02 169	96.79 164	97.70 208	98.06 263	95.31 234	98.52 238	98.31 261	93.95 275	97.05 221	98.61 207	93.49 111	98.52 340	95.33 238	97.81 233	99.29 161

viewmambaseed2359dif		97.01 170	96.84 160	97.51 226	98.19 242	94.21 293	98.16 300	98.23 282	93.61 304	97.78 174	99.13 114	90.79 198	99.18 250	97.24 157	98.40 204	99.15 195

CDS-MVSNet		96.99 171	96.69 172	97.90 188	98.05 265	95.98 175	98.20 290	98.33 256	93.67 299	96.95 223	98.49 221	93.54 110	98.42 351	95.24 245	97.74 237	99.31 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 172	96.55 180	98.21 145	98.17 251	96.07 173	97.98 327	98.21 284	97.24 72	97.13 214	98.93 158	86.88 304	99.91 56	95.00 251	99.37 136	98.66 265

114514_t		96.93 173	96.27 193	98.92 78	99.50 48	97.63 82	98.85 143	98.90 73	84.80 459	97.77 175	99.11 120	92.84 119	99.66 152	94.85 254	99.77 42	99.47 115

MAR-MVS		96.91 174	96.40 187	98.45 123	98.69 169	96.90 129	98.66 205	98.68 146	92.40 355	97.07 219	97.96 273	91.54 164	99.75 132	93.68 301	98.92 159	98.69 259
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
HyFIR lowres test		96.90 175	96.49 184	98.14 155	99.33 74	95.56 213	97.38 385	99.65 2	92.34 356	97.61 194	98.20 253	89.29 241	99.10 269	96.97 166	97.60 242	99.77 40

Vis-MVSNet (Re-imp)		96.87 176	96.55 180	97.83 193	98.73 161	95.46 220	99.20 47	98.30 268	94.96 219	96.60 245	98.87 169	90.05 216	98.59 335	93.67 303	98.60 179	99.46 120

SDMVSNet		96.85 177	96.42 185	98.14 155	99.30 83	96.38 158	99.21 44	99.23 28	95.92 145	95.96 271	98.76 194	85.88 324	99.44 202	97.93 96	95.59 306	98.60 270

PAPR		96.84 178	96.24 195	98.65 97	98.72 165	96.92 128	97.36 389	98.57 179	93.33 315	96.67 240	97.57 314	94.30 98	99.56 172	91.05 380	98.59 180	99.47 115

HY-MVS	93.96 8	96.82 179	96.23 196	98.57 104	98.46 193	97.00 124	98.14 304	98.21 284	93.95 275	96.72 239	97.99 270	91.58 159	99.76 130	94.51 272	96.54 278	98.95 229

mamba_0408		96.81 180	96.38 188	98.09 167	98.19 242	95.90 189	95.69 456	98.32 257	94.51 249	96.75 236	98.73 196	90.99 191	99.27 226	95.83 218	98.43 197	99.10 205

UGNet		96.78 181	96.30 192	98.19 150	98.24 234	95.89 194	98.88 127	98.93 65	97.39 59	96.81 233	97.84 286	82.60 378	99.90 64	96.53 194	99.49 119	98.79 242
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
IMVS_0407		96.74 182	96.64 176	97.05 256	97.99 274	92.82 349	98.45 253	98.27 271	95.16 199	97.30 205	98.79 182	91.53 165	99.06 274	94.74 259	97.54 246	99.27 168

IMVS_0403		96.74 182	96.61 177	97.12 250	97.99 274	92.82 349	98.47 251	98.27 271	95.16 199	97.13 214	98.79 182	91.44 168	99.26 227	94.74 259	97.54 246	99.27 168

PVSNet_BlendedMVS		96.73 184	96.60 178	97.12 250	99.25 96	95.35 231	98.26 284	99.26 16	94.28 258	97.94 158	97.46 321	92.74 121	99.81 102	96.88 176	93.32 344	96.20 420

SSM_04072		96.71 185	96.38 188	97.68 211	98.19 242	95.90 189	95.69 456	98.32 257	94.51 249	96.75 236	98.73 196	90.99 191	98.02 403	95.83 218	98.43 197	99.10 205

test_vis1_n_1920		96.71 185	96.84 160	96.31 328	99.11 122	89.74 414	99.05 74	98.58 177	98.08 24	99.87 4	99.37 54	78.48 414	99.93 34	99.29 27	99.69 73	99.27 168

mvs_anonymous		96.70 187	96.53 182	97.18 244	98.19 242	93.78 305	98.31 275	98.19 288	94.01 271	94.47 303	98.27 247	92.08 144	98.46 346	97.39 152	97.91 229	99.31 154

Elysia		96.64 188	96.02 205	98.51 114	98.04 267	97.30 101	98.74 177	98.60 165	95.04 211	97.91 162	98.84 173	83.59 373	99.48 195	94.20 284	99.25 143	98.75 251

StellarMVS		96.64 188	96.02 205	98.51 114	98.04 267	97.30 101	98.74 177	98.60 165	95.04 211	97.91 162	98.84 173	83.59 373	99.48 195	94.20 284	99.25 143	98.75 251

1112_ss		96.63 190	96.00 207	98.50 117	98.56 182	96.37 159	98.18 298	98.10 310	92.92 335	94.84 291	98.43 225	92.14 140	99.58 168	94.35 277	96.51 279	99.56 100

PMMVS		96.60 191	96.33 191	97.41 232	97.90 287	93.93 301	97.35 390	98.41 231	92.84 338	97.76 176	97.45 323	91.10 188	99.20 247	96.26 203	97.91 229	99.11 203

DP-MVS		96.59 192	95.93 210	98.57 104	99.34 71	96.19 168	98.70 192	98.39 239	89.45 426	94.52 301	99.35 60	91.85 150	99.85 84	92.89 327	98.88 162	99.68 75

PatchMatch-RL		96.59 192	96.03 204	98.27 138	99.31 79	96.51 151	97.91 336	99.06 48	93.72 291	96.92 227	98.06 263	88.50 268	99.65 153	91.77 362	99.00 157	98.66 265

GeoE		96.58 194	96.07 201	98.10 166	98.35 210	95.89 194	99.34 17	98.12 304	93.12 327	96.09 265	98.87 169	89.71 226	98.97 286	92.95 323	98.08 224	99.43 127

icg_test_0407_2		96.56 195	96.50 183	96.73 280	97.99 274	92.82 349	97.18 405	98.27 271	95.16 199	97.30 205	98.79 182	91.53 165	98.10 390	94.74 259	97.54 246	99.27 168

XVG-OURS		96.55 196	96.41 186	96.99 259	98.75 160	93.76 306	97.50 376	98.52 191	95.67 160	96.83 230	99.30 74	88.95 256	99.53 182	95.88 216	96.26 293	97.69 310

FIs		96.51 197	96.12 200	97.67 213	97.13 351	97.54 87	99.36 14	99.22 33	95.89 147	94.03 332	98.35 235	91.98 146	98.44 349	96.40 199	92.76 352	97.01 329

XVG-OURS-SEG-HR		96.51 197	96.34 190	97.02 258	98.77 159	93.76 306	97.79 355	98.50 199	95.45 180	96.94 224	99.09 130	87.87 285	99.55 179	96.76 188	95.83 305	97.74 307

PS-MVSNAJss		96.43 199	96.26 194	96.92 269	95.84 419	95.08 245	99.16 55	98.50 199	95.87 149	93.84 342	98.34 239	94.51 91	98.61 331	96.88 176	93.45 339	97.06 327

test_fmvs1		96.42 200	96.67 174	95.66 363	98.82 156	88.53 441	98.80 160	98.20 286	96.39 124	99.64 30	99.20 92	80.35 402	99.67 149	99.04 33	99.57 100	98.78 246

FC-MVSNet-test		96.42 200	96.05 202	97.53 225	96.95 360	97.27 105	99.36 14	99.23 28	95.83 151	93.93 335	98.37 233	92.00 145	98.32 370	96.02 212	92.72 353	97.00 330

ab-mvs		96.42 200	95.71 221	98.55 107	98.63 178	96.75 136	97.88 343	98.74 129	93.84 281	96.54 250	98.18 255	85.34 335	99.75 132	95.93 214	96.35 283	99.15 195

FA-MVS(test-final)		96.41 203	95.94 209	97.82 195	98.21 238	95.20 239	97.80 353	97.58 358	93.21 321	97.36 203	97.70 298	89.47 232	99.56 172	94.12 288	97.99 226	98.71 257

PVSNet	91.96 18	96.35 204	96.15 197	96.96 264	99.17 111	92.05 366	96.08 448	98.68 146	93.69 295	97.75 178	97.80 292	88.86 257	99.69 147	94.26 282	99.01 155	99.15 195

Test_1112_low_res		96.34 205	95.66 226	98.36 133	98.56 182	95.94 183	97.71 360	98.07 317	92.10 365	94.79 295	97.29 336	91.75 154	99.56 172	94.17 286	96.50 280	99.58 98

viewdifsd2359ckpt11		96.30 206	96.13 198	96.81 275	98.10 257	92.10 362	98.49 249	98.40 234	96.02 140	97.61 194	99.31 71	86.37 314	99.29 222	97.52 135	93.36 343	99.04 218

viewmsd2359difaftdt		96.30 206	96.13 198	96.81 275	98.10 257	92.10 362	98.49 249	98.40 234	96.02 140	97.61 194	99.31 71	86.37 314	99.30 220	97.52 135	93.37 342	99.04 218

Effi-MVS+-dtu		96.29 208	96.56 179	95.51 368	97.89 289	90.22 406	98.80 160	98.10 310	96.57 114	96.45 255	96.66 393	90.81 194	98.91 299	95.72 225	97.99 226	97.40 318

QAPM		96.29 208	95.40 232	98.96 75	97.85 290	97.60 84	99.23 37	98.93 65	89.76 420	93.11 374	99.02 140	89.11 247	99.93 34	91.99 356	99.62 91	99.34 146

Fast-Effi-MVS+		96.28 210	95.70 223	98.03 174	98.29 227	95.97 180	98.58 222	98.25 280	91.74 373	95.29 284	97.23 341	91.03 190	99.15 255	92.90 325	97.96 228	98.97 226

nrg030		96.28 210	95.72 218	97.96 186	96.90 365	98.15 63	99.39 11	98.31 261	95.47 179	94.42 309	98.35 235	92.09 143	98.69 323	97.50 139	89.05 404	97.04 328

1314		96.25 212	95.73 217	97.79 197	97.13 351	95.55 215	98.19 293	98.59 172	93.47 310	92.03 408	97.82 290	91.33 172	99.49 190	94.62 267	98.44 194	98.32 290

sd_testset		96.17 213	95.76 216	97.42 231	99.30 83	94.34 285	98.82 151	99.08 46	95.92 145	95.96 271	98.76 194	82.83 377	99.32 215	95.56 231	95.59 306	98.60 270

h-mvs33		96.17 213	95.62 227	97.81 196	99.03 129	94.45 278	98.64 209	98.75 127	97.48 52	98.67 104	98.72 199	89.76 223	99.86 83	97.95 94	81.59 454	99.11 203

HQP_MVS		96.14 215	95.90 211	96.85 272	97.42 329	94.60 274	98.80 160	98.56 182	97.28 67	95.34 280	98.28 244	87.09 299	99.03 279	96.07 207	94.27 314	96.92 337

tttt0517		96.07 216	95.51 230	97.78 198	98.41 200	94.84 259	99.28 29	94.33 468	94.26 260	97.64 192	98.64 206	84.05 364	99.47 199	95.34 237	97.60 242	99.03 220

MVSTER		96.06 217	95.72 218	97.08 254	98.23 236	95.93 186	98.73 183	98.27 271	94.86 225	95.07 286	98.09 261	88.21 273	98.54 338	96.59 190	93.46 337	96.79 356

thisisatest0530		96.01 218	95.36 237	97.97 184	98.38 205	95.52 217	98.88 127	94.19 470	94.04 266	97.64 192	98.31 242	83.82 371	99.46 200	95.29 242	97.70 239	98.93 231

test_djsdf		96.00 219	95.69 224	96.93 266	95.72 421	95.49 218	99.47 7	98.40 234	94.98 217	94.58 299	97.86 283	89.16 245	98.41 358	96.91 170	94.12 322	96.88 346

EI-MVSNet		95.96 220	95.83 213	96.36 324	97.93 285	93.70 312	98.12 307	98.27 271	93.70 294	95.07 286	99.02 140	92.23 136	98.54 338	94.68 263	93.46 337	96.84 352

VortexMVS		95.95 221	95.79 214	96.42 320	98.29 227	93.96 300	98.68 198	98.31 261	96.02 140	94.29 317	97.57 314	89.47 232	98.37 365	97.51 138	91.93 361	96.94 335

ECVR-MVS		95.95 221	95.71 221	96.65 288	99.02 130	90.86 388	99.03 81	91.80 481	96.96 92	98.10 136	99.26 80	81.31 388	99.51 186	96.90 173	99.04 152	99.59 94

BH-untuned		95.95 221	95.72 218	96.65 288	98.55 184	92.26 358	98.23 286	97.79 343	93.73 289	94.62 298	98.01 268	88.97 255	99.00 285	93.04 320	98.51 188	98.68 261

test1111		95.94 224	95.78 215	96.41 321	98.99 137	90.12 407	99.04 78	92.45 480	96.99 91	98.03 145	99.27 79	81.40 387	99.48 195	96.87 179	99.04 152	99.63 88

MSDG		95.93 225	95.30 244	97.83 193	98.90 145	95.36 229	96.83 435	98.37 246	91.32 389	94.43 308	98.73 196	90.27 213	99.60 165	90.05 394	98.82 169	98.52 278

BH-RMVSNet		95.92 226	95.32 242	97.69 209	98.32 221	94.64 268	98.19 293	97.45 379	94.56 244	96.03 267	98.61 207	85.02 340	99.12 263	90.68 385	99.06 151	99.30 158

test_fmvs1_n		95.90 227	95.99 208	95.63 364	98.67 172	88.32 445	99.26 32	98.22 283	96.40 123	99.67 27	99.26 80	73.91 454	99.70 142	99.02 34	99.50 117	98.87 235

Fast-Effi-MVS+-dtu		95.87 228	95.85 212	95.91 349	97.74 299	91.74 372	98.69 195	98.15 300	95.56 167	94.92 289	97.68 303	88.98 254	98.79 317	93.19 315	97.78 235	97.20 325

LFMVS		95.86 229	94.98 259	98.47 121	98.87 150	96.32 162	98.84 147	96.02 446	93.40 313	98.62 110	99.20 92	74.99 446	99.63 159	97.72 110	97.20 255	99.46 120

baseline1		95.84 230	95.12 252	98.01 177	98.49 191	95.98 175	98.73 183	97.03 413	95.37 188	96.22 260	98.19 254	89.96 219	99.16 251	94.60 268	87.48 420	98.90 234

OpenMVS	93.04 13	95.83 231	95.00 257	98.32 135	97.18 348	97.32 98	99.21 44	98.97 57	89.96 416	91.14 417	99.05 138	86.64 307	99.92 43	93.38 309	99.47 122	97.73 308

IMVS_0404		95.82 232	95.52 228	96.73 280	97.99 274	92.82 349	97.23 398	98.27 271	95.16 199	94.31 315	98.79 182	85.63 328	98.10 390	94.74 259	97.54 246	99.27 168

VDD-MVS		95.82 232	95.23 246	97.61 221	98.84 155	93.98 299	98.68 198	97.40 383	95.02 215	97.95 156	99.34 68	74.37 452	99.78 124	98.64 48	96.80 268	99.08 212

UniMVSNet (Re)		95.78 234	95.19 248	97.58 222	96.99 358	97.47 91	98.79 168	99.18 37	95.60 163	93.92 336	97.04 363	91.68 156	98.48 342	95.80 222	87.66 419	96.79 356

VPA-MVSNet		95.75 235	95.11 253	97.69 209	97.24 340	97.27 105	98.94 105	99.23 28	95.13 204	95.51 278	97.32 334	85.73 326	98.91 299	97.33 155	89.55 395	96.89 345

HQP-MVS		95.72 236	95.40 232	96.69 286	97.20 344	94.25 291	98.05 318	98.46 207	96.43 118	94.45 304	97.73 295	86.75 305	98.96 290	95.30 240	94.18 318	96.86 351

hse-mvs2		95.71 237	95.30 244	96.93 266	98.50 187	93.53 317	98.36 267	98.10 310	97.48 52	98.67 104	97.99 270	89.76 223	99.02 282	97.95 94	80.91 460	98.22 293

UniMVSNet_NR-MVSNet		95.71 237	95.15 249	97.40 234	96.84 368	96.97 125	98.74 177	99.24 20	95.16 199	93.88 338	97.72 297	91.68 156	98.31 372	95.81 220	87.25 425	96.92 337

PatchmatchNet		95.71 237	95.52 228	96.29 330	97.58 312	90.72 392	96.84 434	97.52 369	94.06 265	97.08 217	96.96 373	89.24 243	98.90 302	92.03 355	98.37 205	99.26 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 240	95.33 241	96.76 279	96.16 404	94.63 269	98.43 261	98.39 239	96.64 110	95.02 288	98.78 186	85.15 339	99.05 275	95.21 247	94.20 317	96.60 381
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 240	95.38 236	96.61 296	97.61 309	93.84 304	98.91 115	98.44 215	95.25 195	94.28 318	98.47 223	86.04 323	99.12 263	95.50 234	93.95 327	96.87 349
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 242	95.69 224	95.44 372	97.54 317	88.54 440	96.97 419	97.56 361	93.50 308	97.52 201	96.93 377	89.49 230	99.16 251	95.25 244	96.42 282	98.64 267

FE-MVS		95.62 243	94.90 263	97.78 198	98.37 208	94.92 256	97.17 408	97.38 385	90.95 400	97.73 181	97.70 298	85.32 337	99.63 159	91.18 372	98.33 208	98.79 242

LPG-MVS_test		95.62 243	95.34 238	96.47 314	97.46 324	93.54 315	98.99 91	98.54 186	94.67 238	94.36 312	98.77 189	85.39 332	99.11 265	95.71 226	94.15 320	96.76 359

CLD-MVS		95.62 243	95.34 238	96.46 317	97.52 320	93.75 308	97.27 397	98.46 207	95.53 175	94.42 309	98.00 269	86.21 318	98.97 286	96.25 205	94.37 312	96.66 374
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 246	94.89 264	97.76 202	98.15 253	95.15 242	96.77 436	94.41 466	92.95 334	97.18 213	97.43 325	84.78 346	99.45 201	94.63 265	97.73 238	98.68 261

MonoMVSNet		95.51 247	95.45 231	95.68 361	95.54 426	90.87 387	98.92 113	97.37 386	95.79 153	95.53 277	97.38 330	89.58 229	97.68 425	96.40 199	92.59 354	98.49 280

thres600view7		95.49 248	94.77 267	97.67 213	98.98 138	95.02 247	98.85 143	96.90 423	95.38 186	96.63 242	96.90 379	84.29 356	99.59 166	88.65 418	96.33 284	98.40 284

test_vis1_n		95.47 249	95.13 250	96.49 311	97.77 295	90.41 402	99.27 31	98.11 307	96.58 112	99.66 28	99.18 102	67.00 468	99.62 163	99.21 29	99.40 132	99.44 125

SCA		95.46 250	95.13 250	96.46 317	97.67 304	91.29 380	97.33 392	97.60 357	94.68 237	96.92 227	97.10 348	83.97 366	98.89 303	92.59 339	98.32 211	99.20 184

IterMVS-LS		95.46 250	95.21 247	96.22 332	98.12 255	93.72 311	98.32 274	98.13 303	93.71 292	94.26 319	97.31 335	92.24 135	98.10 390	94.63 265	90.12 386	96.84 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 252	95.34 238	95.77 359	98.69 169	88.75 436	98.87 130	97.21 400	96.13 135	97.22 211	97.68 303	77.95 422	99.65 153	97.58 127	96.77 271	98.91 233

jajsoiax		95.45 252	95.03 256	96.73 280	95.42 434	94.63 269	99.14 59	98.52 191	95.74 155	93.22 367	98.36 234	83.87 369	98.65 328	96.95 168	94.04 323	96.91 342

CVMVSNet		95.43 254	96.04 203	93.57 426	97.93 285	83.62 465	98.12 307	98.59 172	95.68 159	96.56 246	99.02 140	87.51 291	97.51 434	93.56 307	97.44 251	99.60 92

anonymousdsp		95.42 255	94.91 262	96.94 265	95.10 438	95.90 189	99.14 59	98.41 231	93.75 286	93.16 370	97.46 321	87.50 293	98.41 358	95.63 230	94.03 324	96.50 404

DU-MVS		95.42 255	94.76 268	97.40 234	96.53 385	96.97 125	98.66 205	98.99 56	95.43 181	93.88 338	97.69 300	88.57 263	98.31 372	95.81 220	87.25 425	96.92 337

mvs_tets		95.41 257	95.00 257	96.65 288	95.58 425	94.42 280	99.00 88	98.55 184	95.73 157	93.21 368	98.38 232	83.45 375	98.63 329	97.09 162	94.00 325	96.91 342

thres100view900		95.38 258	94.70 272	97.41 232	98.98 138	94.92 256	98.87 130	96.90 423	95.38 186	96.61 244	96.88 380	84.29 356	99.56 172	88.11 421	96.29 288	97.76 305

thres400		95.38 258	94.62 276	97.65 217	98.94 143	94.98 252	98.68 198	96.93 421	95.33 189	96.55 248	96.53 399	84.23 360	99.56 172	88.11 421	96.29 288	98.40 284

BH-w/o		95.38 258	95.08 254	96.26 331	98.34 215	91.79 369	97.70 361	97.43 381	92.87 337	94.24 321	97.22 342	88.66 261	98.84 309	91.55 368	97.70 239	98.16 296

VDDNet		95.36 261	94.53 281	97.86 191	98.10 257	95.13 243	98.85 143	97.75 345	90.46 407	98.36 126	99.39 48	73.27 456	99.64 156	97.98 93	96.58 276	98.81 241

TAPA-MVS	93.98 7	95.35 262	94.56 280	97.74 204	99.13 119	94.83 261	98.33 270	98.64 159	86.62 447	96.29 259	98.61 207	94.00 105	99.29 222	80.00 465	99.41 129	99.09 208
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 263	94.98 259	96.43 319	97.67 304	93.48 319	98.73 183	98.44 215	94.94 223	92.53 391	98.53 217	84.50 355	99.14 258	95.48 235	94.00 325	96.66 374
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 264	94.87 265	96.71 283	99.29 88	93.24 337	98.58 222	98.11 307	89.92 417	93.57 352	99.10 122	86.37 314	99.79 121	90.78 383	98.10 223	97.09 326
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 265	94.72 271	97.13 248	98.05 265	93.26 334	97.87 344	97.20 401	94.96 219	96.18 263	95.66 433	80.97 394	99.35 211	94.47 274	97.08 258	98.78 246

tfpn200view9		95.32 265	94.62 276	97.43 230	98.94 143	94.98 252	98.68 198	96.93 421	95.33 189	96.55 248	96.53 399	84.23 360	99.56 172	88.11 421	96.29 288	97.76 305

Anonymous202405211		95.28 267	94.49 283	97.67 213	99.00 134	93.75 308	98.70 192	97.04 412	90.66 403	96.49 252	98.80 180	78.13 418	99.83 90	96.21 206	95.36 310	99.44 125

thres200		95.25 268	94.57 279	97.28 238	98.81 157	94.92 256	98.20 290	97.11 405	95.24 197	96.54 250	96.22 411	84.58 353	99.53 182	87.93 426	96.50 280	97.39 319

AllTest		95.24 269	94.65 275	96.99 259	99.25 96	93.21 338	98.59 218	98.18 291	91.36 385	93.52 354	98.77 189	84.67 350	99.72 136	89.70 401	97.87 231	98.02 300

LCM-MVSNet-Re		95.22 270	95.32 242	94.91 389	98.18 248	87.85 451	98.75 173	95.66 453	95.11 206	88.96 437	96.85 383	90.26 214	97.65 426	95.65 229	98.44 194	99.22 181

EPNet_dtu		95.21 271	94.95 261	95.99 342	96.17 402	90.45 400	98.16 300	97.27 395	96.77 100	93.14 373	98.33 240	90.34 210	98.42 351	85.57 440	98.81 170	99.09 208
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 272	94.45 289	97.46 227	96.75 375	96.56 149	98.86 138	98.65 158	93.30 318	93.27 366	98.27 247	84.85 344	98.87 306	94.82 256	91.26 372	96.96 332

D2MVS		95.18 273	95.08 254	95.48 369	97.10 353	92.07 365	98.30 278	99.13 44	94.02 268	92.90 378	96.73 389	89.48 231	98.73 321	94.48 273	93.60 336	95.65 434

WR-MVS		95.15 274	94.46 286	97.22 240	96.67 380	96.45 153	98.21 288	98.81 107	94.15 262	93.16 370	97.69 300	87.51 291	98.30 374	95.29 242	88.62 410	96.90 344

TranMVSNet+NR-MVSNet		95.14 275	94.48 284	97.11 252	96.45 391	96.36 160	99.03 81	99.03 51	95.04 211	93.58 351	97.93 276	88.27 272	98.03 402	94.13 287	86.90 430	96.95 334

myMVS_eth3d28		95.12 276	94.62 276	96.64 292	98.17 251	92.17 359	98.02 322	97.32 389	95.41 184	96.22 260	96.05 417	78.01 420	99.13 260	95.22 246	97.16 256	98.60 270

baseline2		95.11 277	94.52 282	96.87 271	96.65 381	93.56 314	98.27 283	94.10 472	93.45 311	92.02 409	97.43 325	87.45 296	99.19 248	93.88 296	97.41 253	97.87 303

miper_enhance_ethall		95.10 278	94.75 269	96.12 336	97.53 319	93.73 310	96.61 442	98.08 315	92.20 364	93.89 337	96.65 395	92.44 126	98.30 374	94.21 283	91.16 373	96.34 413

Anonymous20240529		95.10 278	94.22 300	97.75 203	99.01 132	94.26 290	98.87 130	98.83 98	85.79 455	96.64 241	98.97 148	78.73 411	99.85 84	96.27 202	94.89 311	99.12 200

test-LLR		95.10 278	94.87 265	95.80 356	96.77 372	89.70 416	96.91 424	95.21 458	95.11 206	94.83 293	95.72 430	87.71 287	98.97 286	93.06 318	98.50 189	98.72 254

WR-MVS_H		95.05 281	94.46 286	96.81 275	96.86 367	95.82 201	99.24 35	99.24 20	93.87 280	92.53 391	96.84 384	90.37 209	98.24 380	93.24 313	87.93 416	96.38 412

miper_ehance_all_eth		95.01 282	94.69 273	95.97 346	97.70 302	93.31 330	97.02 417	98.07 317	92.23 361	93.51 356	96.96 373	91.85 150	98.15 385	93.68 301	91.16 373	96.44 410

testing11		95.00 283	94.28 296	97.16 246	97.96 282	93.36 327	98.09 314	97.06 411	94.94 223	95.33 283	96.15 413	76.89 435	99.40 206	95.77 224	96.30 287	98.72 254

ADS-MVSNet		95.00 283	94.45 289	96.63 293	98.00 272	91.91 368	96.04 449	97.74 346	90.15 413	96.47 253	96.64 396	87.89 283	98.96 290	90.08 392	97.06 259	99.02 221

VPNet		94.99 285	94.19 302	97.40 234	97.16 349	96.57 148	98.71 188	98.97 57	95.67 160	94.84 291	98.24 251	80.36 401	98.67 327	96.46 196	87.32 424	96.96 332

EPMVS		94.99 285	94.48 284	96.52 309	97.22 342	91.75 371	97.23 398	91.66 482	94.11 263	97.28 207	96.81 386	85.70 327	98.84 309	93.04 320	97.28 254	98.97 226

testing91		94.98 287	94.25 299	97.20 241	97.94 283	93.41 322	98.00 325	97.58 358	94.99 216	95.45 279	96.04 418	77.20 430	99.42 204	94.97 252	96.02 301	98.78 246

NR-MVSNet		94.98 287	94.16 305	97.44 229	96.53 385	97.22 113	98.74 177	98.95 61	94.96 219	89.25 436	97.69 300	89.32 240	98.18 383	94.59 270	87.40 422	96.92 337

FMVSNet3		94.97 289	94.26 298	97.11 252	98.18 248	96.62 140	98.56 234	98.26 279	93.67 299	94.09 328	97.10 348	84.25 358	98.01 404	92.08 351	92.14 358	96.70 368

FE-MVSNET3		94.96 290	94.28 296	96.98 262	95.93 415	96.11 172	97.08 414	98.39 239	93.62 303	93.86 340	96.40 404	88.28 271	98.21 381	92.61 335	92.36 357	96.63 376

CostFormer		94.95 291	94.73 270	95.60 366	97.28 338	89.06 429	97.53 373	96.89 425	89.66 422	96.82 232	96.72 390	86.05 321	98.95 295	95.53 233	96.13 299	98.79 242

PAPM		94.95 291	94.00 318	97.78 198	97.04 355	95.65 210	96.03 451	98.25 280	91.23 394	94.19 324	97.80 292	91.27 175	98.86 308	82.61 457	97.61 241	98.84 238

CP-MVSNet		94.94 293	94.30 295	96.83 273	96.72 377	95.56 213	99.11 65	98.95 61	93.89 278	92.42 397	97.90 279	87.19 298	98.12 389	94.32 279	88.21 413	96.82 355

TR-MVS		94.94 293	94.20 301	97.17 245	97.75 296	94.14 296	97.59 370	97.02 416	92.28 360	95.75 275	97.64 308	83.88 368	98.96 290	89.77 398	96.15 298	98.40 284

RPSCF		94.87 295	95.40 232	93.26 432	98.89 146	82.06 471	98.33 270	98.06 322	90.30 412	96.56 246	99.26 80	87.09 299	99.49 190	93.82 298	96.32 285	98.24 291

testing99		94.83 296	94.08 310	97.07 255	97.94 283	93.13 340	98.10 313	97.17 403	94.86 225	95.34 280	96.00 422	76.31 438	99.40 206	95.08 249	95.90 302	98.68 261

GA-MVS		94.81 297	94.03 314	97.14 247	97.15 350	93.86 303	96.76 437	97.58 358	94.00 272	94.76 297	97.04 363	80.91 395	98.48 342	91.79 361	96.25 294	99.09 208

c3_l		94.79 298	94.43 291	95.89 351	97.75 296	93.12 342	97.16 410	98.03 324	92.23 361	93.46 360	97.05 362	91.39 169	98.01 404	93.58 306	89.21 402	96.53 395

V42		94.78 299	94.14 307	96.70 285	96.33 396	95.22 238	98.97 95	98.09 314	92.32 358	94.31 315	97.06 359	88.39 269	98.55 337	92.90 325	88.87 408	96.34 413

reproduce_monomvs		94.77 300	94.67 274	95.08 384	98.40 202	89.48 422	98.80 160	98.64 159	97.57 46	93.21 368	97.65 305	80.57 400	98.83 312	97.72 110	89.47 398	96.93 336

CR-MVSNet		94.76 301	94.15 306	96.59 299	97.00 356	93.43 320	94.96 464	97.56 361	92.46 349	96.93 225	96.24 407	88.15 275	97.88 417	87.38 429	96.65 274	98.46 282

v2v482		94.69 302	94.03 314	96.65 288	96.17 402	94.79 264	98.67 203	98.08 315	92.72 341	94.00 333	97.16 345	87.69 290	98.45 347	92.91 324	88.87 408	96.72 364

pmmvs4		94.69 302	93.99 320	96.81 275	95.74 420	95.94 183	97.40 383	97.67 350	90.42 409	93.37 363	97.59 312	89.08 248	98.20 382	92.97 322	91.67 366	96.30 416

cl22		94.68 304	94.19 302	96.13 335	98.11 256	93.60 313	96.94 421	98.31 261	92.43 353	93.32 365	96.87 382	86.51 308	98.28 378	94.10 290	91.16 373	96.51 402

eth_miper_zixun_eth		94.68 304	94.41 292	95.47 370	97.64 307	91.71 373	96.73 439	98.07 317	92.71 342	93.64 348	97.21 343	90.54 203	98.17 384	93.38 309	89.76 390	96.54 393

PCF-MVS	93.45 11	94.68 304	93.43 356	98.42 129	98.62 179	96.77 135	95.48 461	98.20 286	84.63 460	93.34 364	98.32 241	88.55 266	99.81 102	84.80 449	98.96 158	98.68 261
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 307	93.54 351	98.08 168	96.88 366	96.56 149	98.19 293	98.50 199	78.05 473	92.69 385	98.02 266	91.07 189	99.63 159	90.09 391	98.36 207	98.04 299

PS-CasMVS		94.67 307	93.99 320	96.71 283	96.68 379	95.26 235	99.13 62	99.03 51	93.68 297	92.33 400	97.95 274	85.35 334	98.10 390	93.59 305	88.16 415	96.79 356

cascas		94.63 309	93.86 330	96.93 266	96.91 364	94.27 289	96.00 452	98.51 194	85.55 456	94.54 300	96.23 409	84.20 362	98.87 306	95.80 222	96.98 264	97.66 311

tpmvs		94.60 310	94.36 294	95.33 376	97.46 324	88.60 439	96.88 431	97.68 347	91.29 391	93.80 344	96.42 403	88.58 262	99.24 238	91.06 378	96.04 300	98.17 295

LTVRE_ROB	92.95 15	94.60 310	93.90 326	96.68 287	97.41 332	94.42 280	98.52 238	98.59 172	91.69 376	91.21 416	98.35 235	84.87 343	99.04 278	91.06 378	93.44 340	96.60 381
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
v1144		94.59 312	93.92 323	96.60 298	96.21 398	94.78 265	98.59 218	98.14 302	91.86 372	94.21 323	97.02 366	87.97 281	98.41 358	91.72 363	89.57 393	96.61 379

ADS-MVSNet2		94.58 313	94.40 293	95.11 382	98.00 272	88.74 437	96.04 449	97.30 391	90.15 413	96.47 253	96.64 396	87.89 283	97.56 432	90.08 392	97.06 259	99.02 221

WBMVS		94.56 314	94.04 312	96.10 337	98.03 269	93.08 344	97.82 352	98.18 291	94.02 268	93.77 346	96.82 385	81.28 389	98.34 367	95.47 236	91.00 376	96.88 346

ACMH	92.88 16	94.55 315	93.95 322	96.34 326	97.63 308	93.26 334	98.81 159	98.49 204	93.43 312	89.74 430	98.53 217	81.91 382	99.08 272	93.69 300	93.30 345	96.70 368
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 316	93.85 331	96.63 293	97.98 280	93.06 345	98.77 172	97.84 336	93.67 299	93.80 344	98.04 265	76.88 436	98.96 290	94.79 258	92.86 350	97.86 304

XVG-ACMP-BASELINE		94.54 316	94.14 307	95.75 360	96.55 384	91.65 374	98.11 311	98.44 215	94.96 219	94.22 322	97.90 279	79.18 410	99.11 265	94.05 292	93.85 329	96.48 407

AUN-MVS		94.53 318	93.73 341	96.92 269	98.50 187	93.52 318	98.34 269	98.10 310	93.83 283	95.94 273	97.98 272	85.59 330	99.03 279	94.35 277	80.94 459	98.22 293

DIV-MVS_self_test		94.52 319	94.03 314	95.99 342	97.57 316	93.38 325	97.05 415	97.94 330	91.74 373	92.81 380	97.10 348	89.12 246	98.07 398	92.60 337	90.30 383	96.53 395

cl____		94.51 320	94.01 317	96.02 339	97.58 312	93.40 324	97.05 415	97.96 329	91.73 375	92.76 382	97.08 354	89.06 249	98.13 387	92.61 335	90.29 384	96.52 398

ETVMVS		94.50 321	93.44 355	97.68 211	98.18 248	95.35 231	98.19 293	97.11 405	93.73 289	96.40 256	95.39 436	74.53 449	98.84 309	91.10 374	96.31 286	98.84 238

GBi-Net		94.49 322	93.80 334	96.56 303	98.21 238	95.00 248	98.82 151	98.18 291	92.46 349	94.09 328	97.07 355	81.16 390	97.95 409	92.08 351	92.14 358	96.72 364

test1		94.49 322	93.80 334	96.56 303	98.21 238	95.00 248	98.82 151	98.18 291	92.46 349	94.09 328	97.07 355	81.16 390	97.95 409	92.08 351	92.14 358	96.72 364

dmvs_re		94.48 324	94.18 304	95.37 374	97.68 303	90.11 408	98.54 237	97.08 407	94.56 244	94.42 309	97.24 340	84.25 358	97.76 423	91.02 381	92.83 351	98.24 291

v8		94.47 325	93.77 337	96.57 302	96.36 394	94.83 261	99.05 74	98.19 288	91.92 369	93.16 370	96.97 371	88.82 260	98.48 342	91.69 364	87.79 417	96.39 411

FMVSNet2		94.47 325	93.61 347	97.04 257	98.21 238	96.43 155	98.79 168	98.27 271	92.46 349	93.50 357	97.09 352	81.16 390	98.00 406	91.09 375	91.93 361	96.70 368

test2506		94.44 327	93.91 325	96.04 338	99.02 130	88.99 432	99.06 72	79.47 494	96.96 92	98.36 126	99.26 80	77.21 429	99.52 185	96.78 187	99.04 152	99.59 94

Patchmatch-test		94.42 328	93.68 345	96.63 293	97.60 310	91.76 370	94.83 468	97.49 373	89.45 426	94.14 326	97.10 348	88.99 251	98.83 312	85.37 443	98.13 222	99.29 161

PEN-MVS		94.42 328	93.73 341	96.49 311	96.28 397	94.84 259	99.17 54	99.00 53	93.51 307	92.23 402	97.83 289	86.10 320	97.90 413	92.55 342	86.92 429	96.74 361

v144192		94.39 330	93.70 343	96.48 313	96.06 408	94.35 284	98.58 222	98.16 299	91.45 382	94.33 314	97.02 366	87.50 293	98.45 347	91.08 377	89.11 403	96.63 376

Baseline_NR-MVSNet		94.35 331	93.81 333	95.96 347	96.20 399	94.05 298	98.61 217	96.67 435	91.44 383	93.85 341	97.60 311	88.57 263	98.14 386	94.39 275	86.93 428	95.68 433

miper_lstm_enhance		94.33 332	94.07 311	95.11 382	97.75 296	90.97 384	97.22 400	98.03 324	91.67 377	92.76 382	96.97 371	90.03 218	97.78 422	92.51 344	89.64 392	96.56 390

v1192		94.32 333	93.58 348	96.53 308	96.10 406	94.45 278	98.50 246	98.17 297	91.54 380	94.19 324	97.06 359	86.95 303	98.43 350	90.14 390	89.57 393	96.70 368

UWE-MVS		94.30 334	93.89 328	95.53 367	97.83 291	88.95 433	97.52 375	93.25 474	94.44 254	96.63 242	97.07 355	78.70 412	99.28 224	91.99 356	97.56 245	98.36 287

ACMH+	92.99 14	94.30 334	93.77 337	95.88 352	97.81 293	92.04 367	98.71 188	98.37 246	93.99 273	90.60 423	98.47 223	80.86 397	99.05 275	92.75 332	92.40 356	96.55 392

v148		94.29 336	93.76 339	95.91 349	96.10 406	92.93 347	98.58 222	97.97 327	92.59 347	93.47 359	96.95 375	88.53 267	98.32 370	92.56 341	87.06 427	96.49 405

v10		94.29 336	93.55 350	96.51 310	96.39 393	94.80 263	98.99 91	98.19 288	91.35 387	93.02 376	96.99 369	88.09 277	98.41 358	90.50 387	88.41 412	96.33 415

SD_0403		94.28 338	94.46 286	93.73 423	98.02 270	85.32 460	98.31 275	98.40 234	94.75 233	93.59 349	98.16 256	89.01 250	96.54 453	82.32 458	97.58 244	99.34 146

MVP-Stereo		94.28 338	93.92 323	95.35 375	94.95 440	92.60 354	97.97 328	97.65 351	91.61 378	90.68 422	97.09 352	86.32 317	98.42 351	89.70 401	99.34 138	95.02 447
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 340	93.33 358	96.97 263	97.19 347	93.38 325	98.74 177	98.57 179	91.21 396	93.81 343	98.58 212	72.85 457	98.77 319	95.05 250	93.93 328	98.77 249

OurMVSNet-221017-0		94.21 341	94.00 318	94.85 394	95.60 424	89.22 427	98.89 120	97.43 381	95.29 192	92.18 405	98.52 220	82.86 376	98.59 335	93.46 308	91.76 364	96.74 361

v1921920		94.20 342	93.47 354	96.40 323	95.98 412	94.08 297	98.52 238	98.15 300	91.33 388	94.25 320	97.20 344	86.41 313	98.42 351	90.04 395	89.39 400	96.69 373

WB-MVSnew		94.19 343	94.04 312	94.66 402	96.82 370	92.14 360	97.86 346	95.96 449	93.50 308	95.64 276	96.77 388	88.06 279	97.99 407	84.87 446	96.86 265	93.85 466

v7n		94.19 343	93.43 356	96.47 314	95.90 416	94.38 283	99.26 32	98.34 254	91.99 367	92.76 382	97.13 347	88.31 270	98.52 340	89.48 406	87.70 418	96.52 398

tpm2		94.19 343	93.76 339	95.46 371	97.23 341	89.04 430	97.31 394	96.85 429	87.08 442	96.21 262	96.79 387	83.75 372	98.74 320	92.43 347	96.23 296	98.59 273

TESTMET0.1,1		94.18 346	93.69 344	95.63 364	96.92 362	89.12 428	96.91 424	94.78 463	93.17 323	94.88 290	96.45 402	78.52 413	98.92 297	93.09 317	98.50 189	98.85 236

dp		94.15 347	93.90 326	94.90 390	97.31 337	86.82 456	96.97 419	97.19 402	91.22 395	96.02 268	96.61 398	85.51 331	99.02 282	90.00 396	94.30 313	98.85 236

ET-MVSNet_ETH3D		94.13 348	92.98 366	97.58 222	98.22 237	96.20 166	97.31 394	95.37 457	94.53 246	79.56 475	97.63 310	86.51 308	97.53 433	96.91 170	90.74 378	99.02 221

tpm		94.13 348	93.80 334	95.12 381	96.50 387	87.91 450	97.44 379	95.89 452	92.62 345	96.37 258	96.30 406	84.13 363	98.30 374	93.24 313	91.66 367	99.14 198

testing222		94.12 350	93.03 365	97.37 237	98.02 270	94.66 266	97.94 332	96.65 437	94.63 240	95.78 274	95.76 425	71.49 458	98.92 297	91.17 373	95.88 303	98.52 278

IterMVS-SCA-FT		94.11 351	93.87 329	94.85 394	97.98 280	90.56 399	97.18 405	98.11 307	93.75 286	92.58 388	97.48 320	83.97 366	97.41 436	92.48 346	91.30 370	96.58 386

Anonymous20231211		94.10 352	93.26 361	96.61 296	99.11 122	94.28 288	99.01 86	98.88 78	86.43 449	92.81 380	97.57 314	81.66 386	98.68 326	94.83 255	89.02 406	96.88 346

IterMVS		94.09 353	93.85 331	94.80 398	97.99 274	90.35 404	97.18 405	98.12 304	93.68 297	92.46 395	97.34 331	84.05 364	97.41 436	92.51 344	91.33 369	96.62 378
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 354	93.51 352	95.80 356	96.77 372	89.70 416	96.91 424	95.21 458	92.89 336	94.83 293	95.72 430	77.69 424	98.97 286	93.06 318	98.50 189	98.72 254

test0.0.03 1		94.08 354	93.51 352	95.80 356	95.53 428	92.89 348	97.38 385	95.97 448	95.11 206	92.51 393	96.66 393	87.71 287	96.94 443	87.03 431	93.67 332	97.57 315

v1240		94.06 356	93.29 360	96.34 326	96.03 410	93.90 302	98.44 259	98.17 297	91.18 397	94.13 327	97.01 368	86.05 321	98.42 351	89.13 412	89.50 397	96.70 368

X-MVStestdata		94.06 356	92.30 382	99.34 31	99.70 27	98.35 49	99.29 27	98.88 78	97.40 57	98.46 118	43.50 489	95.90 48	99.89 68	97.85 102	99.74 59	99.78 33

DTE-MVSNet		93.98 358	93.26 361	96.14 334	96.06 408	94.39 282	99.20 47	98.86 91	93.06 329	91.78 410	97.81 291	85.87 325	97.58 431	90.53 386	86.17 434	96.46 409

pm-mvs1		93.94 359	93.06 364	96.59 299	96.49 388	95.16 240	98.95 102	98.03 324	92.32 358	91.08 418	97.84 286	84.54 354	98.41 358	92.16 349	86.13 437	96.19 421

MS-PatchMatch		93.84 360	93.63 346	94.46 412	96.18 401	89.45 423	97.76 356	98.27 271	92.23 361	92.13 406	97.49 319	79.50 407	98.69 323	89.75 399	99.38 134	95.25 439

tfpnnormal		93.66 361	92.70 372	96.55 307	96.94 361	95.94 183	98.97 95	99.19 36	91.04 398	91.38 415	97.34 331	84.94 342	98.61 331	85.45 442	89.02 406	95.11 443

EU-MVSNet		93.66 361	94.14 307	92.25 443	95.96 414	83.38 467	98.52 238	98.12 304	94.69 236	92.61 387	98.13 259	87.36 297	96.39 458	91.82 360	90.00 388	96.98 331

our_test_3		93.65 363	93.30 359	94.69 400	95.45 432	89.68 418	96.91 424	97.65 351	91.97 368	91.66 413	96.88 380	89.67 227	97.93 412	88.02 424	91.49 368	96.48 407

pmmvs5		93.65 363	92.97 367	95.68 361	95.49 429	92.37 355	98.20 290	97.28 394	89.66 422	92.58 388	97.26 337	82.14 381	98.09 394	93.18 316	90.95 377	96.58 386

SSC-MVS3.2		93.59 365	93.13 363	94.97 387	96.81 371	89.71 415	97.95 329	98.49 204	94.59 243	93.50 357	96.91 378	77.74 423	98.37 365	91.69 364	90.47 381	96.83 354

test_fmvs2		93.43 366	93.58 348	92.95 437	96.97 359	83.91 463	99.19 49	97.24 397	95.74 155	95.20 285	98.27 247	69.65 460	98.72 322	96.26 203	93.73 331	96.24 418

tpm cat1		93.36 367	92.80 369	95.07 385	97.58 312	87.97 449	96.76 437	97.86 335	82.17 467	93.53 353	96.04 418	86.13 319	99.13 260	89.24 410	95.87 304	98.10 298

JIA-IIPM		93.35 368	92.49 378	95.92 348	96.48 389	90.65 394	95.01 463	96.96 419	85.93 453	96.08 266	87.33 479	87.70 289	98.78 318	91.35 370	95.58 308	98.34 288

SixPastTwentyTwo		93.34 369	92.86 368	94.75 399	95.67 422	89.41 425	98.75 173	96.67 435	93.89 278	90.15 428	98.25 250	80.87 396	98.27 379	90.90 382	90.64 379	96.57 388

USDC		93.33 370	92.71 371	95.21 378	96.83 369	90.83 390	96.91 424	97.50 371	93.84 281	90.72 421	98.14 258	77.69 424	98.82 314	89.51 405	93.21 347	95.97 427

IB-MVS	91.98 17	93.27 371	91.97 386	97.19 243	97.47 323	93.41 322	97.09 413	95.99 447	93.32 316	92.47 394	95.73 428	78.06 419	99.53 182	94.59 270	82.98 448	98.62 268
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
MIMVSNet		93.26 372	92.21 383	96.41 321	97.73 300	93.13 340	95.65 458	97.03 413	91.27 393	94.04 331	96.06 416	75.33 443	97.19 439	86.56 433	96.23 296	98.92 232

ppachtmachnet_test		93.22 373	92.63 373	94.97 387	95.45 432	90.84 389	96.88 431	97.88 334	90.60 404	92.08 407	97.26 337	88.08 278	97.86 418	85.12 445	90.33 382	96.22 419

Patchmtry		93.22 373	92.35 381	95.84 355	96.77 372	93.09 343	94.66 471	97.56 361	87.37 441	92.90 378	96.24 407	88.15 275	97.90 413	87.37 430	90.10 387	96.53 395

testing3		93.19 375	92.48 379	95.30 377	98.07 260	92.27 356	98.64 209	97.17 403	93.94 277	93.98 334	97.04 363	67.97 465	96.01 462	88.40 419	97.14 257	97.63 312

FMVSNet1		93.19 375	92.07 384	96.56 303	97.54 317	95.00 248	98.82 151	98.18 291	90.38 410	92.27 401	97.07 355	73.68 455	97.95 409	89.36 408	91.30 370	96.72 364

LF4IMVS		93.14 377	92.79 370	94.20 417	95.88 417	88.67 438	97.66 364	97.07 409	93.81 284	91.71 411	97.65 305	77.96 421	98.81 315	91.47 369	91.92 363	95.12 442

mmtdpeth		93.12 378	92.61 374	94.63 404	97.60 310	89.68 418	99.21 44	97.32 389	94.02 268	97.72 182	94.42 447	77.01 434	99.44 202	99.05 32	77.18 471	94.78 452

testgi		93.06 379	92.45 380	94.88 392	96.43 392	89.90 410	98.75 173	97.54 367	95.60 163	91.63 414	97.91 278	74.46 451	97.02 441	86.10 436	93.67 332	97.72 309

PatchT		93.06 379	91.97 386	96.35 325	96.69 378	92.67 353	94.48 474	97.08 407	86.62 447	97.08 217	92.23 470	87.94 282	97.90 413	78.89 469	96.69 272	98.49 280

RPMNet		92.81 381	91.34 392	97.24 239	97.00 356	93.43 320	94.96 464	98.80 114	82.27 466	96.93 225	92.12 471	86.98 302	99.82 97	76.32 475	96.65 274	98.46 282

UWE-MVS-28		92.79 382	92.51 377	93.62 425	96.46 390	86.28 457	97.93 333	92.71 479	94.17 261	94.78 296	97.16 345	81.05 393	96.43 456	81.45 461	96.86 265	98.14 297

myMVS_eth3d		92.73 383	92.01 385	94.89 391	97.39 333	90.94 385	97.91 336	97.46 375	93.16 324	93.42 361	95.37 437	68.09 464	96.12 460	88.34 420	96.99 261	97.60 313

TransMVSNet (Re)		92.67 384	91.51 391	96.15 333	96.58 383	94.65 267	98.90 116	96.73 431	90.86 401	89.46 435	97.86 283	85.62 329	98.09 394	86.45 434	81.12 457	95.71 432

ttmdpeth		92.61 385	91.96 388	94.55 406	94.10 450	90.60 398	98.52 238	97.29 392	92.67 343	90.18 426	97.92 277	79.75 406	97.79 420	91.09 375	86.15 436	95.26 438

Syy-MVS		92.55 386	92.61 374	92.38 440	97.39 333	83.41 466	97.91 336	97.46 375	93.16 324	93.42 361	95.37 437	84.75 347	96.12 460	77.00 474	96.99 261	97.60 313

K. test v3		92.55 386	91.91 389	94.48 410	95.64 423	89.24 426	99.07 71	94.88 462	94.04 266	86.78 452	97.59 312	77.64 427	97.64 427	92.08 351	89.43 399	96.57 388

DSMNet-mixed		92.52 388	92.58 376	92.33 441	94.15 449	82.65 469	98.30 278	94.26 469	89.08 431	92.65 386	95.73 428	85.01 341	95.76 464	86.24 435	97.76 236	98.59 273

TinyColmap		92.31 389	91.53 390	94.65 403	96.92 362	89.75 413	96.92 422	96.68 434	90.45 408	89.62 432	97.85 285	76.06 441	98.81 315	86.74 432	92.51 355	95.41 436

gg-mvs-nofinetune		92.21 390	90.58 398	97.13 248	96.75 375	95.09 244	95.85 453	89.40 487	85.43 457	94.50 302	81.98 482	80.80 398	98.40 364	92.16 349	98.33 208	97.88 302

FMVSNet5		91.81 391	90.92 394	94.49 409	97.21 343	92.09 364	98.00 325	97.55 366	89.31 429	90.86 420	95.61 434	74.48 450	95.32 468	85.57 440	89.70 391	96.07 425

pmmvs6		91.77 392	90.63 397	95.17 380	94.69 446	91.24 381	98.67 203	97.92 332	86.14 451	89.62 432	97.56 317	75.79 442	98.34 367	90.75 384	84.56 441	95.94 428

Anonymous20231206		91.66 393	91.10 393	93.33 430	94.02 454	87.35 453	98.58 222	97.26 396	90.48 406	90.16 427	96.31 405	83.83 370	96.53 454	79.36 467	89.90 389	96.12 423

Patchmatch-RL test		91.49 394	90.85 395	93.41 428	91.37 468	84.40 461	92.81 478	95.93 451	91.87 371	87.25 448	94.87 443	88.99 251	96.53 454	92.54 343	82.00 451	99.30 158

blended_shiyan8		91.42 395	89.89 406	96.01 340	91.50 466	93.30 331	97.48 377	97.83 337	86.93 443	92.57 390	92.37 468	82.46 379	98.13 387	92.86 330	74.99 474	96.61 379

blended_shiyan6		91.37 396	89.84 407	95.98 345	91.49 467	93.28 332	97.48 377	97.83 337	86.93 443	92.43 396	92.36 469	82.44 380	98.06 399	92.74 334	74.82 477	96.59 383

test_0402		91.32 397	90.27 401	94.48 410	96.60 382	91.12 382	98.50 246	97.22 398	86.10 452	88.30 444	96.98 370	77.65 426	97.99 407	78.13 471	92.94 349	94.34 454

test_vis1_rt		91.29 398	90.65 396	93.19 434	97.45 327	86.25 458	98.57 231	90.90 485	93.30 318	86.94 451	93.59 456	62.07 476	99.11 265	97.48 142	95.58 308	94.22 457

PVSNet_0	88.72 19	91.28 399	90.03 404	95.00 386	97.99 274	87.29 454	94.84 467	98.50 199	92.06 366	89.86 429	95.19 439	79.81 405	99.39 209	92.27 348	69.79 482	98.33 289

mvs5depth		91.23 400	90.17 402	94.41 414	92.09 462	89.79 412	95.26 462	96.50 439	90.73 402	91.69 412	97.06 359	76.12 440	98.62 330	88.02 424	84.11 444	94.82 449

Anonymous20240521		91.18 401	90.44 399	93.42 427	93.70 455	88.47 442	98.94 105	97.56 361	88.46 435	89.56 434	95.08 442	77.15 432	96.97 442	83.92 452	89.55 395	94.82 449

FE-blended-shiyan7		91.17 402	89.60 410	95.88 352	91.33 469	92.99 346	96.89 429	97.82 340	86.89 446	92.36 398	91.75 473	81.83 383	98.06 399	92.75 332	74.82 477	96.59 383

EG-PatchMatch MVS		91.13 403	90.12 403	94.17 419	94.73 445	89.00 431	98.13 306	97.81 342	89.22 430	85.32 462	96.46 401	67.71 466	98.42 351	87.89 428	93.82 330	95.08 444

TDRefinement		91.06 404	89.68 408	95.21 378	85.35 487	91.49 377	98.51 245	97.07 409	91.47 381	88.83 441	97.84 286	77.31 428	99.09 270	92.79 331	77.98 469	95.04 446

sc_t1		91.01 405	89.39 411	95.85 354	95.99 411	90.39 403	98.43 261	97.64 353	78.79 471	92.20 404	97.94 275	66.00 470	98.60 334	91.59 367	85.94 438	98.57 276

UnsupCasMVSNet_eth		90.99 406	89.92 405	94.19 418	94.08 451	89.83 411	97.13 412	98.67 151	93.69 295	85.83 458	96.19 412	75.15 445	96.74 447	89.14 411	79.41 464	96.00 426

test20.03		90.89 407	90.38 400	92.43 439	93.48 456	88.14 448	98.33 270	97.56 361	93.40 313	87.96 445	96.71 391	80.69 399	94.13 474	79.15 468	86.17 434	95.01 448

usedtu_blend_shiyan5		90.87 408	89.15 415	96.01 340	91.33 469	93.35 328	98.12 307	97.36 387	81.93 468	92.36 398	91.75 473	81.83 383	98.09 394	92.88 328	74.82 477	96.59 383

blend_shiyan4		90.76 409	89.01 418	95.99 342	91.69 465	93.35 328	97.44 379	97.83 337	86.93 443	92.23 402	91.98 472	75.19 444	98.09 394	92.88 328	74.96 475	96.52 398

MDA-MVSNet_test_wron		90.71 410	89.38 413	94.68 401	94.83 442	90.78 391	97.19 404	97.46 375	87.60 439	72.41 482	95.72 430	86.51 308	96.71 450	85.92 438	86.80 431	96.56 390

YYNet1		90.70 411	89.39 411	94.62 405	94.79 444	90.65 394	97.20 402	97.46 375	87.54 440	72.54 481	95.74 426	86.51 308	96.66 451	86.00 437	86.76 432	96.54 393

KD-MVS_self_test		90.38 412	89.38 413	93.40 429	92.85 459	88.94 434	97.95 329	97.94 330	90.35 411	90.25 425	93.96 453	79.82 404	95.94 463	84.62 451	76.69 472	95.33 437

pmmvs-eth3d		90.36 413	89.05 417	94.32 416	91.10 472	92.12 361	97.63 369	96.95 420	88.86 433	84.91 463	93.13 461	78.32 415	96.74 447	88.70 416	81.81 453	94.09 460

FE-MVSNET2		90.29 414	88.94 420	94.36 415	90.48 474	92.27 356	98.45 253	97.82 340	91.59 379	84.90 464	93.10 462	73.92 453	96.42 457	87.92 427	82.26 449	94.39 453

tt0320		90.26 415	88.73 422	94.86 393	96.12 405	90.62 396	98.17 299	97.63 354	77.46 474	89.68 431	96.04 418	69.19 462	97.79 420	88.98 413	85.29 440	96.16 422

CL-MVSNet_self_test		90.11 416	89.14 416	93.02 435	91.86 464	88.23 447	96.51 445	98.07 317	90.49 405	90.49 424	94.41 448	84.75 347	95.34 467	80.79 463	74.95 476	95.50 435

new_pmnet		90.06 417	89.00 419	93.22 433	94.18 448	88.32 445	96.42 447	96.89 425	86.19 450	85.67 459	93.62 455	77.18 431	97.10 440	81.61 460	89.29 401	94.23 456

MDA-MVSNet-bldmvs		89.97 418	88.35 424	94.83 397	95.21 436	91.34 378	97.64 366	97.51 370	88.36 437	71.17 483	96.13 414	79.22 409	96.63 452	83.65 453	86.27 433	96.52 398

tt0320-xc		89.79 419	88.11 426	94.84 396	96.19 400	90.61 397	98.16 300	97.22 398	77.35 475	88.75 442	96.70 392	65.94 471	97.63 428	89.31 409	83.39 446	96.28 417

CMPMVS	66.06 21	89.70 420	89.67 409	89.78 448	93.19 457	76.56 474	97.00 418	98.35 251	80.97 469	81.57 470	97.75 294	74.75 448	98.61 331	89.85 397	93.63 334	94.17 458
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 421	88.28 425	93.82 422	92.81 460	91.08 383	98.01 323	97.45 379	87.95 438	87.90 446	95.87 424	67.63 467	94.56 473	78.73 470	88.18 414	95.83 430

KD-MVS_2432*1600		89.61 422	87.96 430	94.54 407	94.06 452	91.59 375	95.59 459	97.63 354	89.87 418	88.95 438	94.38 450	78.28 416	96.82 445	84.83 447	68.05 483	95.21 440

miper_refine_blended		89.61 422	87.96 430	94.54 407	94.06 452	91.59 375	95.59 459	97.63 354	89.87 418	88.95 438	94.38 450	78.28 416	96.82 445	84.83 447	68.05 483	95.21 440

MVStest1		89.53 424	87.99 429	94.14 421	94.39 447	90.42 401	98.25 285	96.84 430	82.81 463	81.18 472	97.33 333	77.09 433	96.94 443	85.27 444	78.79 465	95.06 445

MVS-HIRNet		89.46 425	88.40 423	92.64 438	97.58 312	82.15 470	94.16 477	93.05 478	75.73 478	90.90 419	82.52 481	79.42 408	98.33 369	83.53 454	98.68 173	97.43 316

OpenMVS_ROB	86.42 20	89.00 426	87.43 434	93.69 424	93.08 458	89.42 424	97.91 336	96.89 425	78.58 472	85.86 457	94.69 444	69.48 461	98.29 377	77.13 473	93.29 346	93.36 468

mvsany_test3		88.80 427	88.04 427	91.09 447	89.78 477	81.57 472	97.83 351	95.49 456	93.81 284	87.53 447	93.95 454	56.14 479	97.43 435	94.68 263	83.13 447	94.26 455

FE-MVSNET		88.56 428	87.09 435	92.99 436	89.93 476	89.99 409	98.15 303	95.59 454	88.42 436	84.87 465	92.90 463	74.82 447	94.99 471	77.88 472	81.21 456	93.99 463

new-patchmatchnet		88.50 429	87.45 433	91.67 445	90.31 475	85.89 459	97.16 410	97.33 388	89.47 425	83.63 467	92.77 465	76.38 437	95.06 470	82.70 456	77.29 470	94.06 462

APD_test1		88.22 430	88.01 428	88.86 450	95.98 412	74.66 482	97.21 401	96.44 441	83.96 462	86.66 454	97.90 279	60.95 477	97.84 419	82.73 455	90.23 385	94.09 460

PM-MVS		87.77 431	86.55 437	91.40 446	91.03 473	83.36 468	96.92 422	95.18 460	91.28 392	86.48 456	93.42 457	53.27 480	96.74 447	89.43 407	81.97 452	94.11 459

dmvs_testset		87.64 432	88.93 421	83.79 459	95.25 435	63.36 491	97.20 402	91.17 483	93.07 328	85.64 460	95.98 423	85.30 338	91.52 481	69.42 480	87.33 423	96.49 405

test_fmvs3		87.17 433	87.06 436	87.50 452	91.21 471	75.66 477	99.05 74	96.61 438	92.79 340	88.85 440	92.78 464	43.72 483	93.49 475	93.95 293	84.56 441	93.34 469

UnsupCasMVSNet_bld		87.17 433	85.12 440	93.31 431	91.94 463	88.77 435	94.92 466	98.30 268	84.30 461	82.30 468	90.04 476	63.96 474	97.25 438	85.85 439	74.47 481	93.93 465

N_pmnet		87.12 435	87.77 432	85.17 456	95.46 431	61.92 492	97.37 387	70.66 497	85.83 454	88.73 443	96.04 418	85.33 336	97.76 423	80.02 464	90.48 380	95.84 429

pmmvs3		86.67 436	84.86 441	92.11 444	88.16 481	87.19 455	96.63 441	94.75 464	79.88 470	87.22 449	92.75 466	66.56 469	95.20 469	81.24 462	76.56 473	93.96 464

test_f		86.07 437	85.39 438	88.10 451	89.28 479	75.57 478	97.73 359	96.33 443	89.41 428	85.35 461	91.56 475	43.31 485	95.53 465	91.32 371	84.23 443	93.21 470

WB-MVS		84.86 438	85.33 439	83.46 460	89.48 478	69.56 486	98.19 293	96.42 442	89.55 424	81.79 469	94.67 445	84.80 345	90.12 482	52.44 486	80.64 461	90.69 473

SSC-MVS		84.27 439	84.71 442	82.96 464	89.19 480	68.83 487	98.08 315	96.30 444	89.04 432	81.37 471	94.47 446	84.60 352	89.89 483	49.80 488	79.52 463	90.15 474

dongtai		82.47 440	81.88 443	84.22 458	95.19 437	76.03 475	94.59 473	74.14 496	82.63 464	87.19 450	96.09 415	64.10 473	87.85 486	58.91 484	84.11 444	88.78 478

test_vis3_rt		79.22 441	77.40 448	84.67 457	86.44 485	74.85 481	97.66 364	81.43 492	84.98 458	67.12 485	81.91 483	28.09 493	97.60 429	88.96 414	80.04 462	81.55 483

test_method		79.03 442	78.17 444	81.63 465	86.06 486	54.40 497	82.75 486	96.89 425	39.54 489	80.98 473	95.57 435	58.37 478	94.73 472	84.74 450	78.61 466	95.75 431

testf1		79.02 443	77.70 445	82.99 462	88.10 482	66.90 488	94.67 469	93.11 475	71.08 480	74.02 478	93.41 458	34.15 489	93.25 476	72.25 478	78.50 467	88.82 476

APD_test2		79.02 443	77.70 445	82.99 462	88.10 482	66.90 488	94.67 469	93.11 475	71.08 480	74.02 478	93.41 458	34.15 489	93.25 476	72.25 478	78.50 467	88.82 476

LCM-MVSNet		78.70 445	76.24 451	86.08 454	77.26 493	71.99 484	94.34 475	96.72 432	61.62 484	76.53 476	89.33 477	33.91 491	92.78 479	81.85 459	74.60 480	93.46 467

kuosan		78.45 446	77.69 447	80.72 466	92.73 461	75.32 479	94.63 472	74.51 495	75.96 476	80.87 474	93.19 460	63.23 475	79.99 490	42.56 490	81.56 455	86.85 482

Gipuma		78.40 447	76.75 450	83.38 461	95.54 426	80.43 473	79.42 487	97.40 383	64.67 483	73.46 480	80.82 484	45.65 482	93.14 478	66.32 482	87.43 421	76.56 486
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 448	75.44 452	85.46 455	82.54 488	74.95 480	94.23 476	93.08 477	72.80 479	74.68 477	87.38 478	36.36 488	91.56 480	73.95 476	63.94 485	89.87 475

FPMVS		77.62 449	77.14 449	79.05 468	79.25 491	60.97 493	95.79 454	95.94 450	65.96 482	67.93 484	94.40 449	37.73 487	88.88 485	68.83 481	88.46 411	87.29 479

EGC-MVSNET		75.22 450	69.54 453	92.28 442	94.81 443	89.58 420	97.64 366	96.50 439	1.82 494	5.57 495	95.74 426	68.21 463	96.26 459	73.80 477	91.71 365	90.99 472

ANet_high		69.08 451	65.37 455	80.22 467	65.99 495	71.96 485	90.91 482	90.09 486	82.62 465	49.93 490	78.39 485	29.36 492	81.75 487	62.49 483	38.52 489	86.95 481

tmp_tt		68.90 452	66.97 454	74.68 470	50.78 497	59.95 494	87.13 483	83.47 491	38.80 490	62.21 486	96.23 409	64.70 472	76.91 492	88.91 415	30.49 490	87.19 480

PMVS	61.03 23	65.95 453	63.57 457	73.09 471	57.90 496	51.22 498	85.05 485	93.93 473	54.45 485	44.32 491	83.57 480	13.22 494	89.15 484	58.68 485	81.00 458	78.91 485
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 454	64.25 456	67.02 472	82.28 489	59.36 495	91.83 481	85.63 489	52.69 486	60.22 487	77.28 486	41.06 486	80.12 489	46.15 489	41.14 487	61.57 488

EMVS		64.07 455	63.26 458	66.53 473	81.73 490	58.81 496	91.85 480	84.75 490	51.93 488	59.09 488	75.13 487	43.32 484	79.09 491	42.03 491	39.47 488	61.69 487

MVE	62.14 22	63.28 456	59.38 459	74.99 469	74.33 494	65.47 490	85.55 484	80.50 493	52.02 487	51.10 489	75.00 488	10.91 497	80.50 488	51.60 487	53.40 486	78.99 484
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 457	30.18 461	30.16 474	78.61 492	43.29 499	66.79 488	14.21 498	17.31 491	14.82 494	11.93 494	11.55 496	41.43 493	37.08 492	19.30 491	5.76 491

cdsmvs_eth3d_5k		23.98 458	31.98 460	0.00 477	0.00 500	0.00 502	0.00 489	98.59 172	0.00 495	0.00 496	98.61 207	90.60 201	0.00 496	0.00 495	0.00 494	0.00 492

testmvs		21.48 459	24.95 462	11.09 476	14.89 498	6.47 501	96.56 443	9.87 499	7.55 492	17.93 492	39.02 490	9.43 498	5.90 495	16.56 494	12.72 492	20.91 490

test123		20.95 460	23.72 463	12.64 475	13.54 499	8.19 500	96.55 444	6.13 500	7.48 493	16.74 493	37.98 491	12.97 495	6.05 494	16.69 493	5.43 493	23.68 489

ab-mvs-re		8.20 461	10.94 464	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	98.43 225	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

pcd_1.5k_mvsjas		7.88 462	10.50 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	94.51 91	0.00 496	0.00 495	0.00 494	0.00 492

mmdepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

monomultidepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

test_blank		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet_test		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

DCPMVS		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

sosnet-low-res		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

sosnet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uncertanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

Regformer		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

MED-MVS test				99.52 13	99.77 2	98.86 22	99.32 22	99.24 20	96.41 121	99.30 50	99.35 60		99.92 43	98.30 75	99.80 25	99.79 28

TestfortrainingZip							99.32 22

WAC-MVS						90.94 385								88.66 417

FOURS1					99.82 1	98.66 28	99.69 1	98.95 61	97.46 55	99.39 44

MSC_two_6792asdad				99.62 7	99.17 111	99.08 12		98.63 162					99.94 14	98.53 55	99.80 25	99.86 12

PC_three_1452									95.08 210	99.60 32	99.16 107	97.86 2	98.47 345	97.52 135	99.72 68	99.74 50

No_MVS				99.62 7	99.17 111	99.08 12		98.63 162					99.94 14	98.53 55	99.80 25	99.86 12

test_one_0601					99.66 31	99.25 3		98.86 91	97.55 47	99.20 59	99.47 35	97.57 8

eth-test2					0.00 500

eth-test					0.00 500

ZD-MVS					99.46 58	98.70 27		98.79 119	93.21 321	98.67 104	98.97 148	95.70 52	99.83 90	96.07 207	99.58 99

RE-MVS-def			98.34 54		99.49 52	97.86 74	99.11 65	98.80 114	96.49 116	99.17 62	99.35 60	95.29 69		97.72 110	99.65 82	99.71 63

IU-MVS					99.71 24	99.23 8		98.64 159	95.28 193	99.63 31				98.35 72	99.81 16	99.83 18

OPU-MVS				99.37 27	99.24 103	99.05 15	99.02 84				99.16 107	97.81 3	99.37 210	97.24 157	99.73 63	99.70 67

test_241102_TWO								98.87 85	97.65 39	99.53 37	99.48 33	97.34 13	99.94 14	98.43 67	99.80 25	99.83 18

test_241102_ONE					99.71 24	99.24 6		98.87 85	97.62 41	99.73 22	99.39 48	97.53 9	99.74 134

9.14			98.06 79		99.47 56		98.71 188	98.82 101	94.36 256	99.16 66	99.29 75	96.05 40	99.81 102	97.00 164	99.71 70

save fliter					99.46 58	98.38 40	98.21 288	98.71 137	97.95 28

test_0728_THIRD									97.32 63	99.45 39	99.46 40	97.88 1	99.94 14	98.47 63	99.86 2	99.85 15

test_0728_SECOND				99.71 1	99.72 17	99.35 1	98.97 95	98.88 78					99.94 14	98.47 63	99.81 16	99.84 17

test0726					99.72 17	99.25 3	99.06 72	98.88 78	97.62 41	99.56 34	99.50 29	97.42 11

GSMVS																99.20 184

test_part2					99.63 34	99.18 11				99.27 56

sam_mvs1												89.45 235				99.20 184

sam_mvs												88.99 251

ambc				89.49 449	86.66 484	75.78 476	92.66 479	96.72 432		86.55 455	92.50 467	46.01 481	97.90 413	90.32 388	82.09 450	94.80 451

MTGPA								98.74 129

test_post1							96.68 440				30.43 493	87.85 286	98.69 323	92.59 339

test_post											31.83 492	88.83 258	98.91 299

patchmatchnet-post											95.10 441	89.42 236	98.89 303

GG-mvs-BLEND				96.59 299	96.34 395	94.98 252	96.51 445	88.58 488		93.10 375	94.34 452	80.34 403	98.05 401	89.53 404	96.99 261	96.74 361

MTMP							98.89 120	94.14 471

gm-plane-assit					95.88 417	87.47 452			89.74 421		96.94 376		99.19 248	93.32 312

test9_res														96.39 201	99.57 100	99.69 70

TEST9					99.31 79	98.50 34	97.92 334	98.73 132	92.63 344	97.74 179	98.68 202	96.20 35	99.80 109

test_8					99.29 88	98.44 36	97.89 342	98.72 134	92.98 332	97.70 184	98.66 205	96.20 35	99.80 109

agg_prior2														95.87 217	99.57 100	99.68 75

agg_prior					99.30 83	98.38 40		98.72 134		97.57 200			99.81 102

TestCases				96.99 259	99.25 96	93.21 338		98.18 291	91.36 385	93.52 354	98.77 189	84.67 350	99.72 136	89.70 401	97.87 231	98.02 300

test_prior4						98.01 70	97.86 346

test_prior2							97.80 353		96.12 137	97.89 165	98.69 201	95.96 44		96.89 174	99.60 94

test_prior				99.19 50	99.31 79	98.22 57		98.84 96					99.70 142			99.65 83

旧先验2							97.57 372		91.30 390	98.67 104			99.80 109	95.70 228

新几何2							97.64 366

新几何1				99.16 55	99.34 71	98.01 70		98.69 143	90.06 415	98.13 134	98.95 155	94.60 89	99.89 68	91.97 358	99.47 122	99.59 94

旧先验1					99.29 88	97.48 89		98.70 141			99.09 130	95.56 55			99.47 122	99.61 90

无先验							97.58 371	98.72 134	91.38 384				99.87 79	93.36 311		99.60 92

原ACMM2							97.67 363

原ACMM1				98.65 97	99.32 77	96.62 140		98.67 151	93.27 320	97.81 172	98.97 148	95.18 76	99.83 90	93.84 297	99.46 125	99.50 106

test222					99.23 104	97.17 116	97.40 383	98.66 154	88.68 434	98.05 142	98.96 153	94.14 102			99.53 113	99.61 90

testdata2													99.89 68	91.65 366

segment_acmp												96.85 16

testdata				98.26 141	99.20 109	95.36 229		98.68 146	91.89 370	98.60 112	99.10 122	94.44 96	99.82 97	94.27 281	99.44 126	99.58 98

testdata1							97.32 393		96.34 127

test12				99.18 52	99.16 115	98.19 59		98.53 188		98.07 138		95.13 79	99.72 136		99.56 108	99.63 88

plane_prior7					97.42 329	94.63 269

plane_prior6					97.35 336	94.61 272						87.09 299

plane_prior5								98.56 182					99.03 279	96.07 207	94.27 314	96.92 337

plane_prior4											98.28 244

plane_prior3						94.61 272			97.02 87	95.34 280

plane_prior2							98.80 160		97.28 67

plane_prior1					97.37 335

plane_prior						94.60 274	98.44 259		96.74 103						94.22 316

n2								0.00 501

nn								0.00 501

door-mid								94.37 467

lessismore_v0				94.45 413	94.93 441	88.44 443		91.03 484		86.77 453	97.64 308	76.23 439	98.42 351	90.31 389	85.64 439	96.51 402

LGP-MVS_train				96.47 314	97.46 324	93.54 315		98.54 186	94.67 238	94.36 312	98.77 189	85.39 332	99.11 265	95.71 226	94.15 320	96.76 359

test11								98.66 154

door								94.64 465

HQP5-MVS						94.25 291

HQP-NCC					97.20 344		98.05 318		96.43 118	94.45 304

ACMP_Plane					97.20 344		98.05 318		96.43 118	94.45 304

BP-MVS														95.30 240

HQP4-MVS										94.45 304			98.96 290			96.87 349

HQP3-MVS								98.46 207							94.18 318

HQP2-MVS												86.75 305

NP-MVS					97.28 338	94.51 277					97.73 295

MDTV_nov1_ep13_2view						84.26 462	96.89 429		90.97 399	97.90 164		89.89 221		93.91 295		99.18 193

MDTV_nov1_ep13			95.40 232		97.48 322	88.34 444	96.85 433	97.29 392	93.74 288	97.48 202	97.26 337	89.18 244	99.05 275	91.92 359	97.43 252

ACMMP++_ref															92.97 348

ACMMP++															93.61 335

Test By Simon												94.64 88

ITE_SJBPF				95.44 372	97.42 329	91.32 379		97.50 371	95.09 209	93.59 349	98.35 235	81.70 385	98.88 305	89.71 400	93.39 341	96.12 423

DeepMVS_CX				86.78 453	97.09 354	72.30 483		95.17 461	75.92 477	84.34 466	95.19 439	70.58 459	95.35 466	79.98 466	89.04 405	92.68 471

High-resolution multi-view benchmark