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.

DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 63	95.39 11	99.29 1	98.28 47	94.78 59	98.93 18	98.87 29	96.04 2	99.86 9	97.45 44	99.58 23	99.59 28

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 36	98.27 50	95.13 38	99.19 11	98.89 26	95.54 5	99.85 18	97.52 40	99.66 10	99.56 36

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 43	97.85 131	94.92 48	98.73 28	98.87 29	95.08 8	99.84 23	97.52 40	99.67 6	99.48 52
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
DPE-MVS		97.86 4	97.65 9	98.47 5	99.17 34	95.78 7	97.21 186	98.35 38	95.16 36	98.71 30	98.80 36	95.05 10	99.89 3	96.70 63	99.73 1	99.73 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 5	97.73 8	98.08 18	99.15 35	94.82 28	98.81 8	98.30 43	94.76 62	98.30 38	98.90 23	93.77 17	99.68 69	97.93 27	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 6	97.44 21	98.37 7	98.90 55	95.86 6	97.27 177	98.08 88	95.81 18	97.87 52	98.31 75	94.26 13	99.68 69	97.02 52	99.49 39	99.57 32

fmvsm_l_conf0.5_n		97.65 7	97.75 7	97.34 57	98.21 100	92.75 88	97.83 92	98.73 10	95.04 43	99.30 5	98.84 34	93.34 22	99.78 43	99.32 6	99.13 92	99.50 48

fmvsm_l_conf0.5_n_3		97.64 8	97.60 11	97.79 30	98.14 107	93.94 52	97.93 78	98.65 20	96.70 6	99.38 3	99.07 10	89.92 88	99.81 30	99.16 12	99.43 49	99.61 26

fmvsm_l_conf0.5_n_a		97.63 9	97.76 6	97.26 64	98.25 94	92.59 96	97.81 97	98.68 15	94.93 46	99.24 8	98.87 29	93.52 20	99.79 40	99.32 6	99.21 77	99.40 62

SteuartSystems-ACMMP		97.62 10	97.53 15	97.87 24	98.39 83	94.25 40	98.43 23	98.27 50	95.34 30	98.11 41	98.56 45	94.53 12	99.71 61	96.57 67	99.62 17	99.65 19
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_9		97.59 11	97.79 5	96.97 82	98.28 89	91.49 139	97.61 131	98.71 12	97.10 4	99.70 1	98.93 20	90.95 73	99.77 46	99.35 5	99.53 29	99.65 19

MSP-MVS		97.59 11	97.54 14	97.73 38	99.40 11	93.77 57	98.53 15	98.29 45	95.55 25	98.56 33	97.81 123	93.90 15	99.65 73	96.62 64	99.21 77	99.77 2
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
lecture		97.58 13	97.63 10	97.43 54	99.37 16	92.93 82	98.86 7	98.85 5	95.27 32	98.65 31	98.90 23	91.97 49	99.80 35	97.63 36	99.21 77	99.57 32

test_fmvsm_n_1920		97.55 14	97.89 3	96.53 100	98.41 80	91.73 125	98.01 61	99.02 1	96.37 11	99.30 5	98.92 21	92.39 41	99.79 40	99.16 12	99.46 42	98.08 211

reproduce-ours		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 113	98.20 64	95.80 19	97.88 49	98.98 16	92.91 27	99.81 30	97.68 31	99.43 49	99.67 14

our_new_method		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 113	98.20 64	95.80 19	97.88 49	98.98 16	92.91 27	99.81 30	97.68 31	99.43 49	99.67 14

reproduce_model		97.51 17	97.51 17	97.50 50	98.99 48	93.01 78	97.79 100	98.21 62	95.73 22	97.99 45	99.03 13	92.63 36	99.82 28	97.80 29	99.42 52	99.67 14

test_fmvsmconf_n		97.49 18	97.56 13	97.29 60	97.44 159	92.37 103	97.91 80	98.88 4	95.83 17	98.92 21	99.05 12	91.45 58	99.80 35	99.12 14	99.46 42	99.69 13

TSAR-MVS + MP.		97.42 19	97.33 24	97.69 42	99.25 29	94.24 41	98.07 56	97.85 131	93.72 99	98.57 32	98.35 66	93.69 18	99.40 127	97.06 51	99.46 42	99.44 57
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		97.41 20	97.53 15	97.06 78	98.57 74	94.46 34	97.92 79	98.14 78	94.82 55	99.01 15	98.55 47	94.18 14	97.41 374	96.94 53	99.64 14	99.32 70
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
SF-MVS		97.39 21	97.13 26	98.17 15	99.02 44	95.28 19	98.23 40	98.27 50	92.37 159	98.27 39	98.65 43	93.33 23	99.72 59	96.49 69	99.52 31	99.51 45

SMA-MVS		97.35 22	97.03 35	98.30 8	99.06 40	95.42 10	97.94 76	98.18 71	90.57 240	98.85 25	98.94 19	93.33 23	99.83 26	96.72 61	99.68 4	99.63 22
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
HPM-MVS++		97.34 23	96.97 38	98.47 5	99.08 38	96.16 4	97.55 142	97.97 115	95.59 23	96.61 91	97.89 110	92.57 38	99.84 23	95.95 93	99.51 34	99.40 62

fmvsm_s_conf0.5_n_9		97.33 24	97.57 12	96.62 96	98.43 78	90.32 194	97.80 98	98.53 26	97.24 3	99.62 2	99.14 1	88.65 105	99.80 35	99.54 1	99.15 89	99.74 8

fmvsm_s_conf0.5_n_8		97.32 25	97.48 20	96.85 83	98.28 89	91.07 163	97.76 102	98.62 22	97.53 2	99.20 10	99.12 4	88.24 113	99.81 30	99.41 3	99.17 85	99.67 14

NCCC		97.30 26	97.03 35	98.11 17	98.77 58	95.06 25	97.34 170	98.04 103	95.96 13	97.09 73	97.88 113	93.18 25	99.71 61	95.84 98	99.17 85	99.56 36

MM		97.29 27	96.98 37	98.23 11	98.01 117	95.03 26	98.07 56	95.76 330	97.78 1	97.52 56	98.80 36	88.09 115	99.86 9	99.44 2	99.37 63	99.80 1

ACMMP_NAP		97.20 28	96.86 44	98.23 11	99.09 36	95.16 22	97.60 132	98.19 69	92.82 148	97.93 48	98.74 40	91.60 56	99.86 9	96.26 74	99.52 31	99.67 14

XVS		97.18 29	96.96 40	97.81 28	99.38 14	94.03 50	98.59 13	98.20 64	94.85 51	96.59 93	98.29 78	91.70 53	99.80 35	95.66 102	99.40 57	99.62 23

MCST-MVS		97.18 29	96.84 46	98.20 14	99.30 26	95.35 15	97.12 193	98.07 93	93.54 108	96.08 119	97.69 133	93.86 16	99.71 61	96.50 68	99.39 59	99.55 39

fmvsm_s_conf0.5_n_3		97.15 31	97.36 23	96.52 102	97.98 120	91.19 155	97.84 89	98.65 20	97.08 5	99.25 7	99.10 5	87.88 121	99.79 40	99.32 6	99.18 84	98.59 155

HFP-MVS		97.14 32	96.92 42	97.83 26	99.42 7	94.12 46	98.52 16	98.32 41	93.21 121	97.18 67	98.29 78	92.08 46	99.83 26	95.63 107	99.59 19	99.54 41

test_fmvsmconf0.1_n		97.09 33	97.06 30	97.19 69	95.67 290	92.21 110	97.95 75	98.27 50	95.78 21	98.40 37	99.00 14	89.99 86	99.78 43	99.06 16	99.41 55	99.59 28

fmvsm_s_conf0.5_n_6		97.08 34	97.17 25	96.81 84	97.28 164	91.73 125	97.75 104	98.50 27	94.86 50	99.22 9	98.78 38	89.75 91	99.76 48	99.10 15	99.29 68	98.94 113

MTAPA		97.08 34	96.78 54	97.97 23	99.37 16	94.42 36	97.24 179	98.08 88	95.07 42	96.11 117	98.59 44	90.88 76	99.90 2	96.18 86	99.50 36	99.58 31

region2R		97.07 36	96.84 46	97.77 34	99.46 2	93.79 55	98.52 16	98.24 58	93.19 124	97.14 70	98.34 69	91.59 57	99.87 7	95.46 113	99.59 19	99.64 21

ACMMPR		97.07 36	96.84 46	97.79 30	99.44 6	93.88 53	98.52 16	98.31 42	93.21 121	97.15 69	98.33 72	91.35 62	99.86 9	95.63 107	99.59 19	99.62 23

CP-MVS		97.02 38	96.81 51	97.64 45	99.33 23	93.54 60	98.80 9	98.28 47	92.99 134	96.45 105	98.30 77	91.90 50	99.85 18	95.61 109	99.68 4	99.54 41

SR-MVS		97.01 39	96.86 44	97.47 52	99.09 36	93.27 71	97.98 66	98.07 93	93.75 98	97.45 58	98.48 55	91.43 60	99.59 89	96.22 77	99.27 70	99.54 41

fmvsm_s_conf0.5_n_5		97.00 40	96.97 38	97.09 75	97.58 155	92.56 97	97.68 117	98.47 31	94.02 89	98.90 23	98.89 26	88.94 99	99.78 43	99.18 10	99.03 101	98.93 117

ZNCC-MVS		96.96 41	96.67 59	97.85 25	99.37 16	94.12 46	98.49 20	98.18 71	92.64 154	96.39 107	98.18 85	91.61 55	99.88 4	95.59 112	99.55 26	99.57 32

APD-MVS		96.95 42	96.60 61	98.01 20	99.03 43	94.93 27	97.72 111	98.10 86	91.50 190	98.01 44	98.32 74	92.33 42	99.58 92	94.85 127	99.51 34	99.53 44
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 43	97.06 30	96.59 97	98.72 60	91.86 123	97.67 118	98.49 28	94.66 67	97.24 66	98.41 61	92.31 44	98.94 189	96.61 65	99.46 42	98.96 109

DeepC-MVS_fast	93.89 2	96.93 44	96.64 60	97.78 32	98.64 69	94.30 37	97.41 160	98.04 103	94.81 57	96.59 93	98.37 64	91.24 65	99.64 81	95.16 118	99.52 31	99.42 61
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		96.89 45	97.04 34	96.45 113	98.29 88	91.66 132	99.03 4	97.85 131	95.84 16	96.90 77	97.97 103	91.24 65	98.75 215	96.92 54	99.33 65	98.94 113

SR-MVS-dyc-post		96.88 46	96.80 52	97.11 74	99.02 44	92.34 104	97.98 66	98.03 105	93.52 111	97.43 61	98.51 50	91.40 61	99.56 100	96.05 88	99.26 72	99.43 59

CS-MVS		96.86 47	97.06 30	96.26 129	98.16 106	91.16 160	99.09 3	97.87 126	95.30 31	97.06 74	98.03 95	91.72 51	98.71 225	97.10 50	99.17 85	98.90 122

mPP-MVS		96.86 47	96.60 61	97.64 45	99.40 11	93.44 62	98.50 19	98.09 87	93.27 120	95.95 125	98.33 72	91.04 70	99.88 4	95.20 116	99.57 25	99.60 27

fmvsm_s_conf0.5_n		96.85 49	97.13 26	96.04 142	98.07 114	90.28 195	97.97 72	98.76 9	94.93 46	98.84 26	99.06 11	88.80 102	99.65 73	99.06 16	98.63 117	98.18 197

GST-MVS		96.85 49	96.52 65	97.82 27	99.36 20	94.14 45	98.29 30	98.13 79	92.72 151	96.70 85	98.06 92	91.35 62	99.86 9	94.83 129	99.28 69	99.47 54

balanced_conf03		96.84 51	96.89 43	96.68 88	97.63 147	92.22 109	98.17 49	97.82 137	94.44 77	98.23 40	97.36 162	90.97 72	99.22 145	97.74 30	99.66 10	98.61 152

patch_mono-2		96.83 52	97.44 21	95.01 209	99.05 41	85.39 347	96.98 206	98.77 8	94.70 64	97.99 45	98.66 41	93.61 19	99.91 1	97.67 35	99.50 36	99.72 12

APD-MVS_3200maxsize		96.81 53	96.71 58	97.12 72	99.01 47	92.31 106	97.98 66	98.06 96	93.11 130	97.44 59	98.55 47	90.93 74	99.55 102	96.06 87	99.25 74	99.51 45

PGM-MVS		96.81 53	96.53 64	97.65 43	99.35 22	93.53 61	97.65 122	98.98 2	92.22 163	97.14 70	98.44 58	91.17 68	99.85 18	94.35 147	99.46 42	99.57 32

MP-MVS		96.77 55	96.45 72	97.72 39	99.39 13	93.80 54	98.41 24	98.06 96	93.37 116	95.54 143	98.34 69	90.59 80	99.88 4	94.83 129	99.54 28	99.49 50
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 55	96.46 71	97.71 41	98.40 81	94.07 48	98.21 43	98.45 33	89.86 257	97.11 72	98.01 98	92.52 39	99.69 67	96.03 91	99.53 29	99.36 68

fmvsm_s_conf0.5_n_4		96.75 57	97.07 29	95.79 164	97.76 136	89.57 220	97.66 121	98.66 18	95.36 28	99.03 14	98.90 23	88.39 110	99.73 55	99.17 11	98.66 115	98.08 211

fmvsm_s_conf0.5_n_a		96.75 57	96.93 41	96.20 134	97.64 145	90.72 177	98.00 62	98.73 10	94.55 71	98.91 22	99.08 7	88.22 114	99.63 82	98.91 19	98.37 130	98.25 192

MVS_0304		96.74 59	96.31 76	98.02 19	96.87 194	94.65 30	97.58 133	94.39 396	96.47 10	97.16 68	98.39 62	87.53 131	99.87 7	98.97 18	99.41 55	99.55 39

test_fmvsmvis_n_1920		96.70 60	96.84 46	96.31 123	96.62 218	91.73 125	97.98 66	98.30 43	96.19 12	96.10 118	98.95 18	89.42 92	99.76 48	98.90 20	99.08 96	97.43 251

MP-MVS-pluss		96.70 60	96.27 78	97.98 22	99.23 32	94.71 29	96.96 208	98.06 96	90.67 230	95.55 141	98.78 38	91.07 69	99.86 9	96.58 66	99.55 26	99.38 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 62	96.49 66	97.27 63	98.31 87	93.39 63	96.79 226	96.72 279	94.17 85	97.44 59	97.66 137	92.76 31	99.33 133	96.86 57	97.76 156	99.08 93

HPM-MVS		96.69 62	96.45 72	97.40 55	99.36 20	93.11 76	98.87 6	98.06 96	91.17 209	96.40 106	97.99 101	90.99 71	99.58 92	95.61 109	99.61 18	99.49 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 64	96.58 63	96.99 80	98.46 75	92.31 106	96.20 286	98.90 3	94.30 84	95.86 128	97.74 128	92.33 42	99.38 130	96.04 90	99.42 52	99.28 73

fmvsm_s_conf0.5_n_2		96.62 65	96.82 50	96.02 144	97.98 120	90.43 187	97.50 146	98.59 23	96.59 8	99.31 4	99.08 7	84.47 188	99.75 52	99.37 4	98.45 127	97.88 224

DELS-MVS		96.61 66	96.38 75	97.30 59	97.79 134	93.19 74	95.96 300	98.18 71	95.23 33	95.87 127	97.65 138	91.45 58	99.70 66	95.87 94	99.44 48	99.00 104
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
DeepPCF-MVS	93.97 1	96.61 66	97.09 28	95.15 200	98.09 110	86.63 314	96.00 298	98.15 76	95.43 26	97.95 47	98.56 45	93.40 21	99.36 131	96.77 58	99.48 40	99.45 55

fmvsm_s_conf0.1_n		96.58 68	96.77 55	96.01 147	96.67 216	90.25 196	97.91 80	98.38 34	94.48 75	98.84 26	99.14 1	88.06 116	99.62 83	98.82 21	98.60 119	98.15 201

MVSMamba_PlusPlus		96.51 69	96.48 67	96.59 97	98.07 114	91.97 120	98.14 50	97.79 139	90.43 244	97.34 64	97.52 153	91.29 64	99.19 148	98.12 26	99.64 14	98.60 153

EI-MVSNet-Vis-set		96.51 69	96.47 68	96.63 93	98.24 95	91.20 154	96.89 215	97.73 146	94.74 63	96.49 100	98.49 52	90.88 76	99.58 92	96.44 70	98.32 132	99.13 85

HPM-MVS_fast		96.51 69	96.27 78	97.22 66	99.32 24	92.74 89	98.74 10	98.06 96	90.57 240	96.77 82	98.35 66	90.21 83	99.53 106	94.80 132	99.63 16	99.38 66

fmvsm_s_conf0.5_n_7		96.45 72	96.80 52	95.37 192	97.29 163	88.38 264	97.23 183	98.47 31	95.14 37	98.43 36	99.09 6	87.58 128	99.72 59	98.80 23	99.21 77	98.02 215

EC-MVSNet		96.42 73	96.47 68	96.26 129	97.01 185	91.52 138	98.89 5	97.75 143	94.42 78	96.64 90	97.68 134	89.32 93	98.60 240	97.45 44	99.11 95	98.67 150

fmvsm_s_conf0.1_n_a		96.40 74	96.47 68	96.16 136	95.48 299	90.69 178	97.91 80	98.33 40	94.07 87	98.93 18	99.14 1	87.44 135	99.61 84	98.63 24	98.32 132	98.18 197

CANet		96.39 75	96.02 83	97.50 50	97.62 148	93.38 64	97.02 199	97.96 116	95.42 27	94.86 157	97.81 123	87.38 137	99.82 28	96.88 55	99.20 82	99.29 71

dcpmvs_2		96.37 76	97.05 33	94.31 254	98.96 51	84.11 368	97.56 137	97.51 178	93.92 93	97.43 61	98.52 49	92.75 32	99.32 135	97.32 49	99.50 36	99.51 45

NormalMVS		96.36 77	96.11 81	97.12 72	99.37 16	92.90 83	97.99 63	97.63 160	95.92 14	96.57 96	97.93 105	85.34 170	99.50 114	94.99 123	99.21 77	98.97 106

EI-MVSNet-UG-set		96.34 78	96.30 77	96.47 110	98.20 101	90.93 168	96.86 218	97.72 148	94.67 66	96.16 116	98.46 56	90.43 81	99.58 92	96.23 76	97.96 149	98.90 122

fmvsm_s_conf0.1_n_2		96.33 79	96.44 74	96.00 148	97.30 162	90.37 193	97.53 143	97.92 121	96.52 9	99.14 13	99.08 7	83.21 210	99.74 53	99.22 9	98.06 144	97.88 224

train_agg		96.30 80	95.83 88	97.72 39	98.70 61	94.19 42	96.41 263	98.02 108	88.58 303	96.03 120	97.56 150	92.73 34	99.59 89	95.04 120	99.37 63	99.39 64

ACMMP		96.27 81	95.93 84	97.28 62	99.24 30	92.62 94	98.25 36	98.81 6	92.99 134	94.56 167	98.39 62	88.96 98	99.85 18	94.57 142	97.63 157	99.36 68
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
MVS_111021_LR		96.24 82	96.19 80	96.39 118	98.23 99	91.35 147	96.24 284	98.79 7	93.99 91	95.80 130	97.65 138	89.92 88	99.24 143	95.87 94	99.20 82	98.58 156

test_fmvsmconf0.01_n		96.15 83	95.85 87	97.03 79	92.66 413	91.83 124	97.97 72	97.84 135	95.57 24	97.53 55	99.00 14	84.20 194	99.76 48	98.82 21	99.08 96	99.48 52

DeepC-MVS	93.07 3	96.06 84	95.66 89	97.29 60	97.96 122	93.17 75	97.30 175	98.06 96	93.92 93	93.38 205	98.66 41	86.83 143	99.73 55	95.60 111	99.22 76	98.96 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		96.05 85	95.91 85	96.46 112	99.24 30	90.47 184	98.30 29	98.57 25	89.01 285	93.97 186	97.57 148	92.62 37	99.76 48	94.66 136	99.27 70	99.15 83

sasdasda		96.02 86	95.45 96	97.75 36	97.59 151	95.15 23	98.28 31	97.60 165	94.52 73	96.27 111	96.12 242	87.65 125	99.18 151	96.20 82	94.82 239	98.91 119

ETV-MVS		96.02 86	95.89 86	96.40 116	97.16 170	92.44 101	97.47 155	97.77 142	94.55 71	96.48 101	94.51 324	91.23 67	98.92 192	95.65 105	98.19 138	97.82 232

canonicalmvs		96.02 86	95.45 96	97.75 36	97.59 151	95.15 23	98.28 31	97.60 165	94.52 73	96.27 111	96.12 242	87.65 125	99.18 151	96.20 82	94.82 239	98.91 119

CDPH-MVS		95.97 89	95.38 101	97.77 34	98.93 52	94.44 35	96.35 271	97.88 124	86.98 349	96.65 89	97.89 110	91.99 48	99.47 119	92.26 186	99.46 42	99.39 64

UA-Net		95.95 90	95.53 92	97.20 68	97.67 141	92.98 80	97.65 122	98.13 79	94.81 57	96.61 91	98.35 66	88.87 100	99.51 111	90.36 238	97.35 168	99.11 89

SymmetryMVS		95.94 91	95.54 91	97.15 70	97.85 130	92.90 83	97.99 63	96.91 266	95.92 14	96.57 96	97.93 105	85.34 170	99.50 114	94.99 123	96.39 204	99.05 97

MGCFI-Net		95.94 91	95.40 100	97.56 49	97.59 151	94.62 31	98.21 43	97.57 170	94.41 79	96.17 115	96.16 240	87.54 130	99.17 153	96.19 84	94.73 244	98.91 119

BP-MVS1		95.89 93	95.49 93	97.08 77	96.67 216	93.20 73	98.08 54	96.32 304	94.56 70	96.32 108	97.84 119	84.07 197	99.15 157	96.75 59	98.78 110	98.90 122

VNet		95.89 93	95.45 96	97.21 67	98.07 114	92.94 81	97.50 146	98.15 76	93.87 95	97.52 56	97.61 144	85.29 172	99.53 106	95.81 99	95.27 230	99.16 81

alignmvs		95.87 95	95.23 106	97.78 32	97.56 157	95.19 21	97.86 85	97.17 231	94.39 81	96.47 102	96.40 227	85.89 159	99.20 147	96.21 81	95.11 235	98.95 112

casdiffmvs_mvg		95.81 96	95.57 90	96.51 106	96.87 194	91.49 139	97.50 146	97.56 174	93.99 91	95.13 152	97.92 108	87.89 120	98.78 208	95.97 92	97.33 169	99.26 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		95.69 97	94.92 115	98.01 20	98.08 113	95.71 9	95.27 341	97.62 164	90.43 244	95.55 141	97.07 182	91.72 51	99.50 114	89.62 254	98.94 105	98.82 135

DP-MVS Recon		95.68 98	95.12 111	97.37 56	99.19 33	94.19 42	97.03 197	98.08 88	88.35 312	95.09 153	97.65 138	89.97 87	99.48 118	92.08 197	98.59 120	98.44 174

casdiffmvs		95.64 99	95.49 93	96.08 138	96.76 214	90.45 185	97.29 176	97.44 198	94.00 90	95.46 146	97.98 102	87.52 133	98.73 219	95.64 106	97.33 169	99.08 93
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GDP-MVS		95.62 100	95.13 109	97.09 75	96.79 205	93.26 72	97.89 83	97.83 136	93.58 103	96.80 79	97.82 121	83.06 217	99.16 155	94.40 145	97.95 150	98.87 129

MG-MVS		95.61 101	95.38 101	96.31 123	98.42 79	90.53 182	96.04 295	97.48 183	93.47 113	95.67 138	98.10 88	89.17 95	99.25 142	91.27 215	98.77 111	99.13 85

baseline		95.58 102	95.42 99	96.08 138	96.78 209	90.41 188	97.16 190	97.45 194	93.69 102	95.65 139	97.85 117	87.29 138	98.68 228	95.66 102	97.25 175	99.13 85

CPTT-MVS		95.57 103	95.19 107	96.70 87	99.27 28	91.48 141	98.33 27	98.11 84	87.79 330	95.17 151	98.03 95	87.09 141	99.61 84	93.51 164	99.42 52	99.02 98

EIA-MVS		95.53 104	95.47 95	95.71 172	97.06 178	89.63 216	97.82 94	97.87 126	93.57 104	93.92 187	95.04 296	90.61 79	98.95 187	94.62 138	98.68 114	98.54 159

3Dnovator+	91.43 4	95.40 105	94.48 135	98.16 16	96.90 192	95.34 16	98.48 21	97.87 126	94.65 68	88.53 335	98.02 97	83.69 201	99.71 61	93.18 172	98.96 104	99.44 57

PS-MVSNAJ		95.37 106	95.33 103	95.49 186	97.35 161	90.66 180	95.31 338	97.48 183	93.85 96	96.51 99	95.70 267	88.65 105	99.65 73	94.80 132	98.27 135	96.17 290

MVSFormer		95.37 106	95.16 108	95.99 149	96.34 250	91.21 152	98.22 41	97.57 170	91.42 194	96.22 113	97.32 163	86.20 155	97.92 324	94.07 150	99.05 98	98.85 131

diffmvs_AUTHOR		95.33 108	95.27 105	95.50 185	96.37 248	89.08 246	96.08 293	97.38 208	93.09 132	96.53 98	97.74 128	86.45 149	98.68 228	96.32 72	97.48 160	98.75 141

xiu_mvs_v2_base		95.32 109	95.29 104	95.40 191	97.22 166	90.50 183	95.44 331	97.44 198	93.70 101	96.46 103	96.18 237	88.59 109	99.53 106	94.79 135	97.81 153	96.17 290

PVSNet_Blended_VisFu		95.27 110	94.91 116	96.38 119	98.20 101	90.86 171	97.27 177	98.25 56	90.21 248	94.18 179	97.27 169	87.48 134	99.73 55	93.53 163	97.77 155	98.55 158

KinetiMVS		95.26 111	94.75 122	96.79 85	96.99 187	92.05 116	97.82 94	97.78 140	94.77 61	96.46 103	97.70 131	80.62 271	99.34 132	92.37 185	98.28 134	98.97 106

diffmvs		95.25 112	95.13 109	95.63 175	96.43 243	89.34 233	95.99 299	97.35 213	92.83 147	96.31 109	97.37 161	86.44 150	98.67 231	96.26 74	97.19 178	98.87 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas		95.24 113	95.02 113	95.91 152	96.87 194	89.98 205	96.82 223	97.49 181	92.26 161	95.47 145	97.82 121	86.47 148	98.69 226	94.80 132	97.20 177	99.06 96

Vis-MVSNet		95.23 114	94.81 117	96.51 106	97.18 169	91.58 136	98.26 35	98.12 81	94.38 82	94.90 156	98.15 87	82.28 238	98.92 192	91.45 212	98.58 121	99.01 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 115	95.04 112	95.76 165	97.49 158	89.56 221	98.67 11	97.00 256	90.69 228	94.24 175	97.62 143	89.79 90	98.81 205	93.39 169	96.49 201	98.92 118

EPNet		95.20 116	94.56 128	97.14 71	92.80 410	92.68 93	97.85 88	94.87 380	96.64 7	92.46 222	97.80 125	86.23 152	99.65 73	93.72 160	98.62 118	99.10 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 117	94.44 137	97.44 53	96.56 226	93.36 66	98.65 12	98.36 35	94.12 86	89.25 318	98.06 92	82.20 240	99.77 46	93.41 168	99.32 66	99.18 80

guyue		95.17 118	94.96 114	95.82 161	96.97 189	89.65 215	97.56 137	95.58 342	94.82 55	95.72 133	97.42 159	82.90 222	98.84 201	96.71 62	96.93 184	98.96 109

OMC-MVS		95.09 119	94.70 123	96.25 132	98.46 75	91.28 148	96.43 259	97.57 170	92.04 172	94.77 162	97.96 104	87.01 142	99.09 168	91.31 214	96.77 188	98.36 181

viewmacassd2359aftdt		95.07 120	94.80 118	95.87 155	96.53 231	89.84 211	96.90 214	97.48 183	92.44 156	95.36 147	97.89 110	85.23 173	98.68 228	94.40 145	97.00 183	99.09 91

xiu_mvs_v1_base_debu		95.01 121	94.76 119	95.75 167	96.58 222	91.71 128	96.25 281	97.35 213	92.99 134	96.70 85	96.63 214	82.67 228	99.44 123	96.22 77	97.46 161	96.11 296

xiu_mvs_v1_base		95.01 121	94.76 119	95.75 167	96.58 222	91.71 128	96.25 281	97.35 213	92.99 134	96.70 85	96.63 214	82.67 228	99.44 123	96.22 77	97.46 161	96.11 296

xiu_mvs_v1_base_debi		95.01 121	94.76 119	95.75 167	96.58 222	91.71 128	96.25 281	97.35 213	92.99 134	96.70 85	96.63 214	82.67 228	99.44 123	96.22 77	97.46 161	96.11 296

PAPM_NR		95.01 121	94.59 126	96.26 129	98.89 56	90.68 179	97.24 179	97.73 146	91.80 177	92.93 219	96.62 217	89.13 96	99.14 160	89.21 267	97.78 154	98.97 106

lupinMVS		94.99 125	94.56 128	96.29 127	96.34 250	91.21 152	95.83 308	96.27 308	88.93 291	96.22 113	96.88 196	86.20 155	98.85 199	95.27 115	99.05 98	98.82 135

Effi-MVS+		94.93 126	94.45 136	96.36 121	96.61 219	91.47 142	96.41 263	97.41 203	91.02 217	94.50 169	95.92 251	87.53 131	98.78 208	93.89 156	96.81 187	98.84 134

IS-MVSNet		94.90 127	94.52 132	96.05 141	97.67 141	90.56 181	98.44 22	96.22 311	93.21 121	93.99 184	97.74 128	85.55 168	98.45 254	89.98 243	97.86 151	99.14 84

LuminaMVS		94.89 128	94.35 139	96.53 100	95.48 299	92.80 87	96.88 217	96.18 315	92.85 146	95.92 126	96.87 198	81.44 255	98.83 202	96.43 71	97.10 181	97.94 220

MVS_Test		94.89 128	94.62 125	95.68 173	96.83 200	89.55 222	96.70 237	97.17 231	91.17 209	95.60 140	96.11 246	87.87 122	98.76 212	93.01 180	97.17 179	98.72 145

viewdifsd2359ckpt13		94.87 130	94.52 132	95.90 153	96.88 193	90.19 198	96.92 211	97.36 211	91.26 202	94.65 164	97.46 154	85.79 163	98.64 235	93.64 162	96.76 189	98.88 128

PVSNet_Blended		94.87 130	94.56 128	95.81 162	98.27 91	89.46 228	95.47 330	98.36 35	88.84 294	94.36 172	96.09 247	88.02 117	99.58 92	93.44 166	98.18 139	98.40 177

jason		94.84 132	94.39 138	96.18 135	95.52 297	90.93 168	96.09 292	96.52 294	89.28 276	96.01 123	97.32 163	84.70 184	98.77 211	95.15 119	98.91 107	98.85 131
jason: jason.
API-MVS		94.84 132	94.49 134	95.90 153	97.90 128	92.00 119	97.80 98	97.48 183	89.19 279	94.81 160	96.71 203	88.84 101	99.17 153	88.91 274	98.76 112	96.53 279

AstraMVS		94.82 134	94.64 124	95.34 194	96.36 249	88.09 276	97.58 133	94.56 389	94.98 44	95.70 136	97.92 108	81.93 248	98.93 190	96.87 56	95.88 211	98.99 105

test_yl		94.78 135	94.23 142	96.43 114	97.74 137	91.22 150	96.85 219	97.10 237	91.23 206	95.71 134	96.93 191	84.30 191	99.31 137	93.10 173	95.12 233	98.75 141

DCV-MVSNet		94.78 135	94.23 142	96.43 114	97.74 137	91.22 150	96.85 219	97.10 237	91.23 206	95.71 134	96.93 191	84.30 191	99.31 137	93.10 173	95.12 233	98.75 141

SSM_0404		94.73 137	94.31 141	95.98 150	97.05 180	90.90 170	97.01 202	97.29 218	91.24 203	94.17 180	97.60 145	85.03 177	98.76 212	92.14 191	97.30 172	98.29 190

WTY-MVS		94.71 138	94.02 147	96.79 85	97.71 139	92.05 116	96.59 252	97.35 213	90.61 236	94.64 165	96.93 191	86.41 151	99.39 128	91.20 217	94.71 245	98.94 113

mamv4		94.66 139	96.10 82	90.37 394	98.01 117	73.41 445	96.82 223	97.78 140	89.95 255	94.52 168	97.43 158	92.91 27	99.09 168	98.28 25	99.16 88	98.60 153

mvsmamba		94.57 140	94.14 144	95.87 155	97.03 183	89.93 209	97.84 89	95.85 326	91.34 197	94.79 161	96.80 199	80.67 269	98.81 205	94.85 127	98.12 142	98.85 131

SSM_0407		94.54 141	94.12 146	95.80 163	96.79 205	90.38 190	96.79 226	97.29 218	91.24 203	93.68 191	97.60 145	85.03 177	98.67 231	92.14 191	96.51 197	98.35 183

RRT-MVS		94.51 142	94.35 139	94.98 212	96.40 244	86.55 317	97.56 137	97.41 203	93.19 124	94.93 155	97.04 184	79.12 299	99.30 139	96.19 84	97.32 171	99.09 91

sss		94.51 142	93.80 151	96.64 89	97.07 175	91.97 120	96.32 276	98.06 96	88.94 290	94.50 169	96.78 200	84.60 185	99.27 141	91.90 198	96.02 207	98.68 149

test_cas_vis1_n_1920		94.48 144	94.55 131	94.28 256	96.78 209	86.45 319	97.63 128	97.64 158	93.32 119	97.68 54	98.36 65	73.75 362	99.08 171	96.73 60	99.05 98	97.31 258

CANet_DTU		94.37 145	93.65 157	96.55 99	96.46 241	92.13 114	96.21 285	96.67 286	94.38 82	93.53 199	97.03 189	79.34 295	99.71 61	90.76 227	98.45 127	97.82 232

AdaColmap		94.34 146	93.68 156	96.31 123	98.59 71	91.68 131	96.59 252	97.81 138	89.87 256	92.15 233	97.06 183	83.62 204	99.54 104	89.34 261	98.07 143	97.70 237

viewmambaseed2359dif		94.28 147	94.14 144	94.71 230	96.21 254	86.97 304	95.93 302	97.11 236	89.00 286	95.00 154	97.70 131	86.02 158	98.59 244	93.71 161	96.59 196	98.57 157

CNLPA		94.28 147	93.53 162	96.52 102	98.38 84	92.55 98	96.59 252	96.88 270	90.13 252	91.91 241	97.24 171	85.21 174	99.09 168	87.64 300	97.83 152	97.92 221

MAR-MVS		94.22 149	93.46 167	96.51 106	98.00 119	92.19 113	97.67 118	97.47 187	88.13 320	93.00 214	95.84 255	84.86 183	99.51 111	87.99 287	98.17 140	97.83 231
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
PAPR		94.18 150	93.42 172	96.48 109	97.64 145	91.42 145	95.55 325	97.71 152	88.99 287	92.34 229	95.82 257	89.19 94	99.11 163	86.14 326	97.38 166	98.90 122

SDMVSNet		94.17 151	93.61 158	95.86 158	98.09 110	91.37 146	97.35 169	98.20 64	93.18 126	91.79 245	97.28 167	79.13 298	98.93 190	94.61 139	92.84 277	97.28 259

test_vis1_n_1920		94.17 151	94.58 127	92.91 325	97.42 160	82.02 395	97.83 92	97.85 131	94.68 65	98.10 42	98.49 52	70.15 386	99.32 135	97.91 28	98.82 108	97.40 253

h-mvs33		94.15 153	93.52 164	96.04 142	97.81 133	90.22 197	97.62 130	97.58 169	95.19 34	96.74 83	97.45 155	83.67 202	99.61 84	95.85 96	79.73 417	98.29 190

CHOSEN 1792x2688		94.15 153	93.51 165	96.06 140	98.27 91	89.38 231	95.18 348	98.48 30	85.60 372	93.76 190	97.11 180	83.15 213	99.61 84	91.33 213	98.72 113	99.19 79

Vis-MVSNet (Re-imp)		94.15 153	93.88 150	94.95 216	97.61 149	87.92 280	98.10 52	95.80 329	92.22 163	93.02 213	97.45 155	84.53 187	97.91 327	88.24 283	97.97 148	99.02 98

CDS-MVSNet		94.14 156	93.54 161	95.93 151	96.18 262	91.46 143	96.33 275	97.04 251	88.97 289	93.56 196	96.51 221	87.55 129	97.89 328	89.80 248	95.95 209	98.44 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 157	93.43 170	96.13 137	98.58 73	91.15 161	96.69 239	97.39 205	87.29 344	91.37 255	96.71 203	88.39 110	99.52 110	87.33 307	97.13 180	97.73 235
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 158	93.70 155	95.27 196	95.70 288	92.03 118	98.10 52	98.68 15	93.36 118	90.39 276	96.70 205	87.63 127	97.94 321	92.25 188	90.50 318	95.84 304

PVSNet_BlendedMVS		94.06 159	93.92 149	94.47 243	98.27 91	89.46 228	96.73 233	98.36 35	90.17 249	94.36 172	95.24 290	88.02 117	99.58 92	93.44 166	90.72 314	94.36 389

nrg030		94.05 160	93.31 174	96.27 128	95.22 322	94.59 32	98.34 26	97.46 189	92.93 141	91.21 265	96.64 210	87.23 140	98.22 274	94.99 123	85.80 365	95.98 300

UGNet		94.04 161	93.28 175	96.31 123	96.85 197	91.19 155	97.88 84	97.68 153	94.40 80	93.00 214	96.18 237	73.39 364	99.61 84	91.72 204	98.46 126	98.13 202
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
TAMVS		94.01 162	93.46 167	95.64 174	96.16 264	90.45 185	96.71 236	96.89 269	89.27 277	93.46 203	96.92 194	87.29 138	97.94 321	88.70 279	95.74 215	98.53 160

Elysia		94.00 163	93.12 180	96.64 89	96.08 274	92.72 91	97.50 146	97.63 160	91.15 211	94.82 158	97.12 178	74.98 349	99.06 177	90.78 225	98.02 145	98.12 204

StellarMVS		94.00 163	93.12 180	96.64 89	96.08 274	92.72 91	97.50 146	97.63 160	91.15 211	94.82 158	97.12 178	74.98 349	99.06 177	90.78 225	98.02 145	98.12 204

IMVS_0403		93.98 165	93.79 152	94.55 239	96.19 258	86.16 328	96.35 271	97.24 225	91.54 185	93.59 195	97.04 184	85.86 160	98.73 219	90.68 230	95.59 221	98.76 137

114514_t		93.95 166	93.06 183	96.63 93	99.07 39	91.61 133	97.46 157	97.96 116	77.99 436	93.00 214	97.57 148	86.14 157	99.33 133	89.22 266	99.15 89	98.94 113

IMVS_0407		93.94 167	93.75 153	94.49 242	96.19 258	86.16 328	96.35 271	97.24 225	91.54 185	93.50 200	97.04 184	85.64 166	98.54 247	90.68 230	95.59 221	98.76 137

FC-MVSNet-test		93.94 167	93.57 159	95.04 207	95.48 299	91.45 144	98.12 51	98.71 12	93.37 116	90.23 279	96.70 205	87.66 124	97.85 330	91.49 210	90.39 319	95.83 305

mvsany_test1		93.93 169	93.98 148	93.78 288	94.94 339	86.80 307	94.62 360	92.55 429	88.77 300	96.85 78	98.49 52	88.98 97	98.08 292	95.03 121	95.62 220	96.46 284

GeoE		93.89 170	93.28 175	95.72 171	96.96 190	89.75 214	98.24 39	96.92 265	89.47 270	92.12 235	97.21 173	84.42 189	98.39 262	87.71 294	96.50 200	99.01 101

HY-MVS	89.66 9	93.87 171	92.95 188	96.63 93	97.10 174	92.49 100	95.64 322	96.64 287	89.05 284	93.00 214	95.79 261	85.77 164	99.45 122	89.16 270	94.35 247	97.96 218

XVG-OURS-SEG-HR		93.86 172	93.55 160	94.81 222	97.06 178	88.53 260	95.28 339	97.45 194	91.68 182	94.08 183	97.68 134	82.41 236	98.90 195	93.84 158	92.47 283	96.98 267

VDD-MVS		93.82 173	93.08 182	96.02 144	97.88 129	89.96 208	97.72 111	95.85 326	92.43 157	95.86 128	98.44 58	68.42 403	99.39 128	96.31 73	94.85 237	98.71 147

mvs_anonymous		93.82 173	93.74 154	94.06 266	96.44 242	85.41 345	95.81 309	97.05 249	89.85 259	90.09 289	96.36 229	87.44 135	97.75 344	93.97 152	96.69 193	99.02 98

HQP_MVS		93.78 175	93.43 170	94.82 220	96.21 254	89.99 203	97.74 106	97.51 178	94.85 51	91.34 256	96.64 210	81.32 257	98.60 240	93.02 178	92.23 286	95.86 301

PS-MVSNAJss		93.74 176	93.51 165	94.44 245	93.91 377	89.28 238	97.75 104	97.56 174	92.50 155	89.94 292	96.54 220	88.65 105	98.18 279	93.83 159	90.90 312	95.86 301

XVG-OURS		93.72 177	93.35 173	94.80 225	97.07 175	88.61 255	94.79 357	97.46 189	91.97 175	93.99 184	97.86 116	81.74 251	98.88 196	92.64 184	92.67 282	96.92 271

mamba_0408		93.70 178	92.99 184	95.83 160	96.79 205	90.38 190	88.69 447	97.07 243	90.96 219	93.68 191	97.31 165	84.97 180	98.76 212	90.95 221	96.51 197	98.35 183

HyFIR lowres test		93.66 179	92.92 189	95.87 155	98.24 95	89.88 210	94.58 362	98.49 28	85.06 382	93.78 189	95.78 262	82.86 223	98.67 231	91.77 203	95.71 217	99.07 95

LFMVS		93.60 180	92.63 203	96.52 102	98.13 109	91.27 149	97.94 76	93.39 417	90.57 240	96.29 110	98.31 75	69.00 396	99.16 155	94.18 149	95.87 212	99.12 88

icg_test_0407_2		93.58 181	93.46 167	93.94 278	96.19 258	86.16 328	93.73 397	97.24 225	91.54 185	93.50 200	97.04 184	85.64 166	96.91 394	90.68 230	95.59 221	98.76 137

F-COLMAP		93.58 181	92.98 187	95.37 192	98.40 81	88.98 248	97.18 188	97.29 218	87.75 333	90.49 274	97.10 181	85.21 174	99.50 114	86.70 317	96.72 192	97.63 239

ab-mvs		93.57 183	92.55 207	96.64 89	97.28 164	91.96 122	95.40 332	97.45 194	89.81 261	93.22 211	96.28 233	79.62 292	99.46 120	90.74 228	93.11 274	98.50 164

LS3D		93.57 183	92.61 205	96.47 110	97.59 151	91.61 133	97.67 118	97.72 148	85.17 380	90.29 278	98.34 69	84.60 185	99.73 55	83.85 362	98.27 135	98.06 213

FA-MVS(test-final)		93.52 185	92.92 189	95.31 195	96.77 211	88.54 259	94.82 356	96.21 313	89.61 265	94.20 177	95.25 289	83.24 209	99.14 160	90.01 242	96.16 206	98.25 192

SSM_04072		93.51 186	92.99 184	95.05 205	96.79 205	90.38 190	88.69 447	97.07 243	90.96 219	93.68 191	97.31 165	84.97 180	96.42 405	90.95 221	96.51 197	98.35 183

viewdifsd2359ckpt11		93.46 187	93.22 178	94.17 259	96.11 271	85.42 343	96.43 259	97.07 243	92.91 142	94.20 177	98.00 99	80.82 267	98.73 219	94.42 143	89.04 332	98.34 187

viewmsd2359difaftdt		93.46 187	93.23 177	94.17 259	96.12 269	85.42 343	96.43 259	97.08 240	92.91 142	94.21 176	98.00 99	80.82 267	98.74 217	94.41 144	89.05 330	98.34 187

Fast-Effi-MVS+		93.46 187	92.75 197	95.59 178	96.77 211	90.03 200	96.81 225	97.13 233	88.19 315	91.30 259	94.27 342	86.21 154	98.63 237	87.66 299	96.46 203	98.12 204

hse-mvs2		93.45 190	92.99 184	94.81 222	97.02 184	88.59 256	96.69 239	96.47 297	95.19 34	96.74 83	96.16 240	83.67 202	98.48 253	95.85 96	79.13 421	97.35 256

QAPM		93.45 190	92.27 217	96.98 81	96.77 211	92.62 94	98.39 25	98.12 81	84.50 390	88.27 343	97.77 126	82.39 237	99.81 30	85.40 339	98.81 109	98.51 163

UniMVSNet_NR-MVSNet		93.37 192	92.67 201	95.47 189	95.34 311	92.83 85	97.17 189	98.58 24	92.98 139	90.13 284	95.80 258	88.37 112	97.85 330	91.71 205	83.93 394	95.73 315

1112_ss		93.37 192	92.42 214	96.21 133	97.05 180	90.99 164	96.31 277	96.72 279	86.87 352	89.83 296	96.69 207	86.51 147	99.14 160	88.12 284	93.67 268	98.50 164

UniMVSNet (Re)		93.31 194	92.55 207	95.61 177	95.39 305	93.34 67	97.39 165	98.71 12	93.14 129	90.10 288	94.83 307	87.71 123	98.03 303	91.67 208	83.99 393	95.46 324

OPM-MVS		93.28 195	92.76 195	94.82 220	94.63 355	90.77 175	96.65 243	97.18 229	93.72 99	91.68 249	97.26 170	79.33 296	98.63 237	92.13 194	92.28 285	95.07 352
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 196	92.48 212	95.51 183	95.70 288	92.39 102	97.86 85	98.66 18	92.30 160	92.09 237	95.37 282	80.49 274	98.40 257	93.95 153	85.86 364	95.75 313

test_fmvs1		93.21 197	93.53 162	92.25 348	96.55 228	81.20 402	97.40 164	96.96 258	90.68 229	96.80 79	98.04 94	69.25 394	98.40 257	97.58 39	98.50 122	97.16 264

MVSTER		93.20 198	92.81 194	94.37 248	96.56 226	89.59 219	97.06 196	97.12 234	91.24 203	91.30 259	95.96 249	82.02 244	98.05 299	93.48 165	90.55 316	95.47 323

test1111		93.19 199	92.82 193	94.30 255	97.58 155	84.56 362	98.21 43	89.02 448	93.53 109	94.58 166	98.21 82	72.69 365	99.05 180	93.06 176	98.48 125	99.28 73

ECVR-MVS		93.19 199	92.73 199	94.57 238	97.66 143	85.41 345	98.21 43	88.23 450	93.43 114	94.70 163	98.21 82	72.57 366	99.07 175	93.05 177	98.49 123	99.25 76

HQP-MVS		93.19 199	92.74 198	94.54 240	95.86 280	89.33 234	96.65 243	97.39 205	93.55 105	90.14 280	95.87 253	80.95 261	98.50 250	92.13 194	92.10 291	95.78 309

CHOSEN 280x420		93.12 202	92.72 200	94.34 251	96.71 215	87.27 294	90.29 437	97.72 148	86.61 356	91.34 256	95.29 284	84.29 193	98.41 256	93.25 170	98.94 105	97.35 256

sd_testset		93.10 203	92.45 213	95.05 205	98.09 110	89.21 240	96.89 215	97.64 158	93.18 126	91.79 245	97.28 167	75.35 346	98.65 234	88.99 272	92.84 277	97.28 259

Effi-MVS+-dtu		93.08 204	93.21 179	92.68 336	96.02 277	83.25 378	97.14 192	96.72 279	93.85 96	91.20 266	93.44 380	83.08 215	98.30 269	91.69 207	95.73 216	96.50 281

test_djsdf		93.07 205	92.76 195	94.00 270	93.49 392	88.70 254	98.22 41	97.57 170	91.42 194	90.08 290	95.55 275	82.85 224	97.92 324	94.07 150	91.58 298	95.40 331

VDDNet		93.05 206	92.07 221	96.02 144	96.84 198	90.39 189	98.08 54	95.85 326	86.22 364	95.79 131	98.46 56	67.59 406	99.19 148	94.92 126	94.85 237	98.47 169

thisisatest0530		93.03 207	92.21 219	95.49 186	97.07 175	89.11 245	97.49 154	92.19 431	90.16 250	94.09 182	96.41 226	76.43 337	99.05 180	90.38 237	95.68 218	98.31 189

EI-MVSNet		93.03 207	92.88 191	93.48 304	95.77 286	86.98 303	96.44 257	97.12 234	90.66 232	91.30 259	97.64 141	86.56 145	98.05 299	89.91 245	90.55 316	95.41 328

CLD-MVS		92.98 209	92.53 209	94.32 252	96.12 269	89.20 241	95.28 339	97.47 187	92.66 152	89.90 293	95.62 271	80.58 272	98.40 257	92.73 183	92.40 284	95.38 333
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 210	92.33 216	94.87 219	97.11 173	87.16 300	97.97 72	92.09 432	90.63 234	93.88 188	97.01 190	76.50 334	99.06 177	90.29 240	95.45 227	98.38 179

ACMM	89.79 8	92.96 210	92.50 211	94.35 249	96.30 252	88.71 253	97.58 133	97.36 211	91.40 196	90.53 273	96.65 209	79.77 288	98.75 215	91.24 216	91.64 296	95.59 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 212	92.56 206	94.10 264	96.16 264	88.26 268	97.65 122	97.46 189	91.29 198	90.12 286	97.16 175	79.05 301	98.73 219	92.25 188	91.89 294	95.31 338

BH-untuned		92.94 212	92.62 204	93.92 282	97.22 166	86.16 328	96.40 267	96.25 310	90.06 253	89.79 297	96.17 239	83.19 211	98.35 265	87.19 310	97.27 174	97.24 261

DU-MVS		92.90 214	92.04 223	95.49 186	94.95 337	92.83 85	97.16 190	98.24 58	93.02 133	90.13 284	95.71 265	83.47 205	97.85 330	91.71 205	83.93 394	95.78 309

PatchMatch-RL		92.90 214	92.02 225	95.56 179	98.19 103	90.80 173	95.27 341	97.18 229	87.96 322	91.86 244	95.68 268	80.44 275	98.99 185	84.01 357	97.54 159	96.89 272

VortexMVS		92.88 216	92.64 202	93.58 299	96.58 222	87.53 290	96.93 210	97.28 221	92.78 150	89.75 298	94.99 297	82.73 227	97.76 342	94.60 140	88.16 341	95.46 324

PMMVS		92.86 217	92.34 215	94.42 247	94.92 340	86.73 310	94.53 364	96.38 302	84.78 387	94.27 174	95.12 295	83.13 214	98.40 257	91.47 211	96.49 201	98.12 204

OpenMVS	89.19 12	92.86 217	91.68 238	96.40 116	95.34 311	92.73 90	98.27 33	98.12 81	84.86 385	85.78 387	97.75 127	78.89 308	99.74 53	87.50 304	98.65 116	96.73 276

Test_1112_low_res		92.84 219	91.84 232	95.85 159	97.04 182	89.97 207	95.53 327	96.64 287	85.38 375	89.65 303	95.18 291	85.86 160	99.10 165	87.70 295	93.58 273	98.49 166

baseline1		92.82 220	91.90 230	95.55 181	97.20 168	90.77 175	97.19 187	94.58 388	92.20 165	92.36 226	96.34 230	84.16 195	98.21 275	89.20 268	83.90 397	97.68 238

1314		92.81 221	92.03 224	95.14 201	95.33 314	89.52 225	96.04 295	97.44 198	87.72 334	86.25 384	95.33 283	83.84 199	98.79 207	89.26 264	97.05 182	97.11 265

DP-MVS		92.76 222	91.51 246	96.52 102	98.77 58	90.99 164	97.38 167	96.08 318	82.38 412	89.29 315	97.87 114	83.77 200	99.69 67	81.37 385	96.69 193	98.89 126

test_fmvs1_n		92.73 223	92.88 191	92.29 345	96.08 274	81.05 403	97.98 66	97.08 240	90.72 227	96.79 81	98.18 85	63.07 429	98.45 254	97.62 38	98.42 129	97.36 254

BH-RMVSNet		92.72 224	91.97 227	94.97 214	97.16 170	87.99 278	96.15 290	95.60 340	90.62 235	91.87 243	97.15 177	78.41 314	98.57 245	83.16 364	97.60 158	98.36 181

ACMP	89.59 10	92.62 225	92.14 220	94.05 267	96.40 244	88.20 271	97.36 168	97.25 224	91.52 189	88.30 341	96.64 210	78.46 313	98.72 224	91.86 201	91.48 300	95.23 345
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 226	92.52 210	92.44 338	96.82 202	81.89 396	96.92 211	93.71 414	92.41 158	84.30 400	94.60 319	85.08 176	97.03 388	91.51 209	97.36 167	98.40 177

TranMVSNet+NR-MVSNet		92.50 226	91.63 239	95.14 201	94.76 348	92.07 115	97.53 143	98.11 84	92.90 145	89.56 306	96.12 242	83.16 212	97.60 357	89.30 262	83.20 403	95.75 313

thres600view7		92.49 228	91.60 240	95.18 199	97.91 127	89.47 226	97.65 122	94.66 385	92.18 169	93.33 206	94.91 302	78.06 321	99.10 165	81.61 378	94.06 262	96.98 267

IMVS_0404		92.44 229	91.92 229	94.00 270	96.19 258	86.16 328	93.84 394	97.24 225	91.54 185	88.17 347	97.04 184	76.96 331	97.09 385	90.68 230	95.59 221	98.76 137

thres100view900		92.43 230	91.58 241	94.98 212	97.92 126	89.37 232	97.71 113	94.66 385	92.20 165	93.31 207	94.90 303	78.06 321	99.08 171	81.40 382	94.08 258	96.48 282

jajsoiax		92.42 231	91.89 231	94.03 269	93.33 400	88.50 261	97.73 108	97.53 176	92.00 174	88.85 327	96.50 222	75.62 344	98.11 286	93.88 157	91.56 299	95.48 321

thres400		92.42 231	91.52 244	95.12 203	97.85 130	89.29 236	97.41 160	94.88 377	92.19 167	93.27 209	94.46 329	78.17 317	99.08 171	81.40 382	94.08 258	96.98 267

tfpn200view9		92.38 233	91.52 244	94.95 216	97.85 130	89.29 236	97.41 160	94.88 377	92.19 167	93.27 209	94.46 329	78.17 317	99.08 171	81.40 382	94.08 258	96.48 282

test_vis1_n		92.37 234	92.26 218	92.72 333	94.75 349	82.64 385	98.02 60	96.80 276	91.18 208	97.77 53	97.93 105	58.02 439	98.29 270	97.63 36	98.21 137	97.23 262

WR-MVS		92.34 235	91.53 243	94.77 227	95.13 330	90.83 172	96.40 267	97.98 114	91.88 176	89.29 315	95.54 276	82.50 233	97.80 337	89.79 249	85.27 373	95.69 316

NR-MVSNet		92.34 235	91.27 254	95.53 182	94.95 337	93.05 77	97.39 165	98.07 93	92.65 153	84.46 398	95.71 265	85.00 179	97.77 341	89.71 250	83.52 400	95.78 309

mvs_tets		92.31 237	91.76 234	93.94 278	93.41 397	88.29 266	97.63 128	97.53 176	92.04 172	88.76 330	96.45 224	74.62 354	98.09 291	93.91 155	91.48 300	95.45 326

TAPA-MVS	90.10 7	92.30 238	91.22 257	95.56 179	98.33 86	89.60 218	96.79 226	97.65 156	81.83 416	91.52 251	97.23 172	87.94 119	98.91 194	71.31 439	98.37 130	98.17 200
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 239	91.30 252	95.25 197	96.60 220	88.90 250	94.36 373	92.32 430	87.92 323	93.43 204	94.57 320	77.28 328	99.00 184	89.42 259	95.86 213	97.86 228

Fast-Effi-MVS+-dtu		92.29 239	91.99 226	93.21 315	95.27 318	85.52 341	97.03 197	96.63 290	92.09 170	89.11 321	95.14 293	80.33 278	98.08 292	87.54 303	94.74 243	96.03 299

IterMVS-LS		92.29 239	91.94 228	93.34 309	96.25 253	86.97 304	96.57 255	97.05 249	90.67 230	89.50 309	94.80 309	86.59 144	97.64 352	89.91 245	86.11 363	95.40 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 242	91.74 237	93.73 289	97.77 135	83.69 375	92.88 417	96.72 279	87.91 324	93.00 214	94.86 305	78.51 312	99.05 180	86.53 318	97.45 165	98.47 169

VPNet		92.23 243	91.31 251	94.99 210	95.56 295	90.96 166	97.22 185	97.86 130	92.96 140	90.96 267	96.62 217	75.06 347	98.20 276	91.90 198	83.65 399	95.80 307

thres200		92.23 243	91.39 247	94.75 229	97.61 149	89.03 247	96.60 251	95.09 366	92.08 171	93.28 208	94.00 357	78.39 315	99.04 183	81.26 388	94.18 254	96.19 289

anonymousdsp		92.16 245	91.55 242	93.97 274	92.58 415	89.55 222	97.51 145	97.42 202	89.42 273	88.40 337	94.84 306	80.66 270	97.88 329	91.87 200	91.28 304	94.48 384

XXY-MVS		92.16 245	91.23 256	94.95 216	94.75 349	90.94 167	97.47 155	97.43 201	89.14 280	88.90 323	96.43 225	79.71 289	98.24 272	89.56 255	87.68 346	95.67 317

BH-w/o		92.14 247	91.75 235	93.31 310	96.99 187	85.73 338	95.67 317	95.69 335	88.73 301	89.26 317	94.82 308	82.97 220	98.07 296	85.26 342	96.32 205	96.13 295

testing3-2		92.10 248	92.05 222	92.27 346	97.71 139	79.56 422	97.42 159	94.41 395	93.53 109	93.22 211	95.49 278	69.16 395	99.11 163	93.25 170	94.22 252	98.13 202

Anonymous202405211		92.07 249	90.83 273	95.76 165	98.19 103	88.75 252	97.58 133	95.00 369	86.00 367	93.64 194	97.45 155	66.24 418	99.53 106	90.68 230	92.71 280	99.01 101

FE-MVS		92.05 250	91.05 262	95.08 204	96.83 200	87.93 279	93.91 391	95.70 333	86.30 361	94.15 181	94.97 298	76.59 333	99.21 146	84.10 355	96.86 185	98.09 210

WR-MVS_H		92.00 251	91.35 248	93.95 276	95.09 332	89.47 226	98.04 59	98.68 15	91.46 192	88.34 339	94.68 314	85.86 160	97.56 359	85.77 334	84.24 391	94.82 369

Anonymous20240529		91.98 252	90.73 279	95.73 170	98.14 107	89.40 230	97.99 63	97.72 148	79.63 430	93.54 198	97.41 160	69.94 388	99.56 100	91.04 220	91.11 307	98.22 194

MonoMVSNet		91.92 253	91.77 233	92.37 340	92.94 406	83.11 381	97.09 195	95.55 344	92.91 142	90.85 269	94.55 321	81.27 259	96.52 403	93.01 180	87.76 345	97.47 250

PatchmatchNet		91.91 254	91.35 248	93.59 298	95.38 306	84.11 368	93.15 412	95.39 349	89.54 267	92.10 236	93.68 370	82.82 225	98.13 282	84.81 346	95.32 229	98.52 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 255	91.02 263	94.53 241	96.54 229	86.55 317	95.86 306	95.64 339	91.77 179	91.89 242	93.47 379	69.94 388	98.86 197	90.23 241	93.86 265	98.18 197

CP-MVSNet		91.89 256	91.24 255	93.82 285	95.05 333	88.57 257	97.82 94	98.19 69	91.70 181	88.21 345	95.76 263	81.96 245	97.52 365	87.86 289	84.65 382	95.37 334

SCA		91.84 257	91.18 259	93.83 284	95.59 293	84.95 358	94.72 358	95.58 342	90.82 222	92.25 231	93.69 368	75.80 341	98.10 287	86.20 324	95.98 208	98.45 171

FMVSNet3		91.78 258	90.69 282	95.03 208	96.53 231	92.27 108	97.02 199	96.93 261	89.79 262	89.35 312	94.65 317	77.01 329	97.47 368	86.12 327	88.82 333	95.35 335

AUN-MVS		91.76 259	90.75 277	94.81 222	97.00 186	88.57 257	96.65 243	96.49 296	89.63 264	92.15 233	96.12 242	78.66 310	98.50 250	90.83 223	79.18 420	97.36 254

X-MVStestdata		91.71 260	89.67 326	97.81 28	99.38 14	94.03 50	98.59 13	98.20 64	94.85 51	96.59 93	32.69 465	91.70 53	99.80 35	95.66 102	99.40 57	99.62 23

MVS		91.71 260	90.44 289	95.51 183	95.20 324	91.59 135	96.04 295	97.45 194	73.44 446	87.36 363	95.60 272	85.42 169	99.10 165	85.97 331	97.46 161	95.83 305

EPNet_dtu		91.71 260	91.28 253	92.99 322	93.76 382	83.71 374	96.69 239	95.28 356	93.15 128	87.02 372	95.95 250	83.37 208	97.38 376	79.46 401	96.84 186	97.88 224
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 263	90.75 277	94.47 243	96.53 231	86.56 316	95.76 313	94.51 392	91.10 215	91.24 264	93.59 374	68.59 400	98.86 197	91.10 218	94.29 250	98.00 217

baseline2		91.63 264	90.86 269	93.94 278	94.33 366	86.32 321	95.92 303	91.64 436	89.37 274	86.94 375	94.69 313	81.62 253	98.69 226	88.64 280	94.57 246	96.81 274

testing99		91.62 265	90.72 280	94.32 252	96.48 238	86.11 333	95.81 309	94.76 382	91.55 184	91.75 247	93.44 380	68.55 401	98.82 203	90.43 235	93.69 267	98.04 214

test2506		91.60 266	90.78 274	94.04 268	97.66 143	83.81 371	98.27 33	75.53 466	93.43 114	95.23 149	98.21 82	67.21 409	99.07 175	93.01 180	98.49 123	99.25 76

miper_ehance_all_eth		91.59 267	91.13 260	92.97 323	95.55 296	86.57 315	94.47 367	96.88 270	87.77 331	88.88 325	94.01 356	86.22 153	97.54 361	89.49 256	86.93 354	94.79 374

v2v482		91.59 267	90.85 271	93.80 286	93.87 379	88.17 273	96.94 209	96.88 270	89.54 267	89.53 307	94.90 303	81.70 252	98.02 304	89.25 265	85.04 379	95.20 346

V42		91.58 269	90.87 268	93.73 289	94.05 374	88.50 261	97.32 173	96.97 257	88.80 299	89.71 299	94.33 337	82.54 232	98.05 299	89.01 271	85.07 377	94.64 382

PCF-MVS	89.48 11	91.56 270	89.95 314	96.36 121	96.60 220	92.52 99	92.51 422	97.26 222	79.41 431	88.90 323	96.56 219	84.04 198	99.55 102	77.01 415	97.30 172	97.01 266
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 271	90.76 275	93.94 278	96.52 234	85.06 354	95.22 344	94.54 390	90.47 243	91.98 239	92.71 391	72.02 369	98.74 217	88.10 285	95.26 231	98.01 216

PS-CasMVS		91.55 271	90.84 272	93.69 293	94.96 336	88.28 267	97.84 89	98.24 58	91.46 192	88.04 350	95.80 258	79.67 290	97.48 367	87.02 314	84.54 388	95.31 338

miper_enhance_ethall		91.54 273	91.01 264	93.15 317	95.35 310	87.07 302	93.97 386	96.90 267	86.79 353	89.17 319	93.43 383	86.55 146	97.64 352	89.97 244	86.93 354	94.74 378

myMVS_eth3d28		91.52 274	90.97 265	93.17 316	96.91 191	83.24 379	95.61 323	94.96 373	92.24 162	91.98 239	93.28 384	69.31 393	98.40 257	88.71 278	95.68 218	97.88 224

PAPM		91.52 274	90.30 295	95.20 198	95.30 317	89.83 212	93.38 408	96.85 273	86.26 363	88.59 333	95.80 258	84.88 182	98.15 281	75.67 420	95.93 210	97.63 239

ET-MVSNet_ETH3D		91.49 276	90.11 305	95.63 175	96.40 244	91.57 137	95.34 335	93.48 416	90.60 238	75.58 441	95.49 278	80.08 282	96.79 399	94.25 148	89.76 324	98.52 161

TR-MVS		91.48 277	90.59 285	94.16 262	96.40 244	87.33 291	95.67 317	95.34 355	87.68 335	91.46 253	95.52 277	76.77 332	98.35 265	82.85 369	93.61 271	96.79 275

tpmrst		91.44 278	91.32 250	91.79 363	95.15 328	79.20 428	93.42 407	95.37 351	88.55 306	93.49 202	93.67 371	82.49 234	98.27 271	90.41 236	89.34 328	97.90 222

test-LLR		91.42 279	91.19 258	92.12 351	94.59 356	80.66 406	94.29 378	92.98 422	91.11 213	90.76 271	92.37 399	79.02 303	98.07 296	88.81 275	96.74 190	97.63 239

MSDG		91.42 279	90.24 299	94.96 215	97.15 172	88.91 249	93.69 400	96.32 304	85.72 371	86.93 376	96.47 223	80.24 279	98.98 186	80.57 392	95.05 236	96.98 267

c3_l		91.38 281	90.89 267	92.88 327	95.58 294	86.30 322	94.68 359	96.84 274	88.17 316	88.83 329	94.23 345	85.65 165	97.47 368	89.36 260	84.63 383	94.89 364

GA-MVS		91.38 281	90.31 294	94.59 233	94.65 354	87.62 288	94.34 374	96.19 314	90.73 226	90.35 277	93.83 361	71.84 371	97.96 315	87.22 309	93.61 271	98.21 195

v1144		91.37 283	90.60 284	93.68 294	93.89 378	88.23 270	96.84 221	97.03 253	88.37 311	89.69 301	94.39 331	82.04 243	97.98 308	87.80 291	85.37 370	94.84 366

GBi-Net		91.35 284	90.27 297	94.59 233	96.51 235	91.18 157	97.50 146	96.93 261	88.82 296	89.35 312	94.51 324	73.87 358	97.29 380	86.12 327	88.82 333	95.31 338

test1		91.35 284	90.27 297	94.59 233	96.51 235	91.18 157	97.50 146	96.93 261	88.82 296	89.35 312	94.51 324	73.87 358	97.29 380	86.12 327	88.82 333	95.31 338

UniMVSNet_ETH3D		91.34 286	90.22 302	94.68 231	94.86 344	87.86 283	97.23 183	97.46 189	87.99 321	89.90 293	96.92 194	66.35 416	98.23 273	90.30 239	90.99 310	97.96 218

FMVSNet2		91.31 287	90.08 306	94.99 210	96.51 235	92.21 110	97.41 160	96.95 259	88.82 296	88.62 332	94.75 311	73.87 358	97.42 373	85.20 343	88.55 338	95.35 335

reproduce_monomvs		91.30 288	91.10 261	91.92 355	96.82 202	82.48 389	97.01 202	97.49 181	94.64 69	88.35 338	95.27 287	70.53 381	98.10 287	95.20 116	84.60 385	95.19 349

D2MVS		91.30 288	90.95 266	92.35 341	94.71 352	85.52 341	96.18 288	98.21 62	88.89 292	86.60 379	93.82 363	79.92 286	97.95 319	89.29 263	90.95 311	93.56 404

v8		91.29 290	90.53 288	93.57 301	94.15 370	88.12 275	97.34 170	97.06 248	88.99 287	88.32 340	94.26 344	83.08 215	98.01 305	87.62 301	83.92 396	94.57 383

CVMVSNet		91.23 291	91.75 235	89.67 403	95.77 286	74.69 440	96.44 257	94.88 377	85.81 369	92.18 232	97.64 141	79.07 300	95.58 421	88.06 286	95.86 213	98.74 144

cl22		91.21 292	90.56 287	93.14 318	96.09 273	86.80 307	94.41 371	96.58 293	87.80 329	88.58 334	93.99 358	80.85 266	97.62 355	89.87 247	86.93 354	94.99 355

PEN-MVS		91.20 293	90.44 289	93.48 304	94.49 360	87.91 282	97.76 102	98.18 71	91.29 198	87.78 354	95.74 264	80.35 277	97.33 378	85.46 338	82.96 404	95.19 349

Baseline_NR-MVSNet		91.20 293	90.62 283	92.95 324	93.83 380	88.03 277	97.01 202	95.12 365	88.42 310	89.70 300	95.13 294	83.47 205	97.44 371	89.66 253	83.24 402	93.37 408

cascas		91.20 293	90.08 306	94.58 237	94.97 335	89.16 244	93.65 402	97.59 168	79.90 429	89.40 310	92.92 389	75.36 345	98.36 264	92.14 191	94.75 242	96.23 286

CostFormer		91.18 296	90.70 281	92.62 337	94.84 345	81.76 397	94.09 384	94.43 393	84.15 393	92.72 221	93.77 365	79.43 294	98.20 276	90.70 229	92.18 289	97.90 222

tt0805		91.09 297	90.07 309	94.16 262	95.61 292	88.31 265	97.56 137	96.51 295	89.56 266	89.17 319	95.64 270	67.08 413	98.38 263	91.07 219	88.44 339	95.80 307

v1192		91.07 298	90.23 300	93.58 299	93.70 383	87.82 285	96.73 233	97.07 243	87.77 331	89.58 304	94.32 339	80.90 265	97.97 311	86.52 319	85.48 368	94.95 356

v144192		91.06 299	90.28 296	93.39 307	93.66 386	87.23 297	96.83 222	97.07 243	87.43 340	89.69 301	94.28 341	81.48 254	98.00 306	87.18 311	84.92 381	94.93 360

v10		91.04 300	90.23 300	93.49 303	94.12 371	88.16 274	97.32 173	97.08 240	88.26 314	88.29 342	94.22 347	82.17 241	97.97 311	86.45 321	84.12 392	94.33 390

eth_miper_zixun_eth		91.02 301	90.59 285	92.34 343	95.33 314	84.35 364	94.10 383	96.90 267	88.56 305	88.84 328	94.33 337	84.08 196	97.60 357	88.77 277	84.37 390	95.06 353

v148		90.99 302	90.38 291	92.81 330	93.83 380	85.80 335	96.78 230	96.68 284	89.45 272	88.75 331	93.93 360	82.96 221	97.82 334	87.83 290	83.25 401	94.80 372

LTVRE_ROB	88.41 13	90.99 302	89.92 316	94.19 258	96.18 262	89.55 222	96.31 277	97.09 239	87.88 325	85.67 388	95.91 252	78.79 309	98.57 245	81.50 379	89.98 321	94.44 387
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
DIV-MVS_self_test		90.97 304	90.33 292	92.88 327	95.36 309	86.19 327	94.46 369	96.63 290	87.82 327	88.18 346	94.23 345	82.99 218	97.53 363	87.72 292	85.57 367	94.93 360

cl____		90.96 305	90.32 293	92.89 326	95.37 308	86.21 325	94.46 369	96.64 287	87.82 327	88.15 348	94.18 348	82.98 219	97.54 361	87.70 295	85.59 366	94.92 362

pmmvs4		90.93 306	89.85 318	94.17 259	93.34 399	90.79 174	94.60 361	96.02 319	84.62 388	87.45 359	95.15 292	81.88 249	97.45 370	87.70 295	87.87 344	94.27 394

XVG-ACMP-BASELINE		90.93 306	90.21 303	93.09 319	94.31 368	85.89 334	95.33 336	97.26 222	91.06 216	89.38 311	95.44 281	68.61 399	98.60 240	89.46 257	91.05 308	94.79 374

v1921920		90.85 308	90.03 311	93.29 311	93.55 388	86.96 306	96.74 232	97.04 251	87.36 342	89.52 308	94.34 336	80.23 280	97.97 311	86.27 322	85.21 374	94.94 358

CR-MVSNet		90.82 309	89.77 322	93.95 276	94.45 362	87.19 298	90.23 438	95.68 337	86.89 351	92.40 223	92.36 402	80.91 263	97.05 387	81.09 389	93.95 263	97.60 244

v7n		90.76 310	89.86 317	93.45 306	93.54 389	87.60 289	97.70 116	97.37 209	88.85 293	87.65 356	94.08 354	81.08 260	98.10 287	84.68 348	83.79 398	94.66 381

RPSCF		90.75 311	90.86 269	90.42 393	96.84 198	76.29 438	95.61 323	96.34 303	83.89 396	91.38 254	97.87 114	76.45 335	98.78 208	87.16 312	92.23 286	96.20 288

MVP-Stereo		90.74 312	90.08 306	92.71 334	93.19 402	88.20 271	95.86 306	96.27 308	86.07 366	84.86 396	94.76 310	77.84 324	97.75 344	83.88 361	98.01 147	92.17 429
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 313	89.65 328	93.96 275	94.29 369	89.63 216	97.79 100	96.82 275	89.07 282	86.12 386	95.48 280	78.61 311	97.78 339	86.97 315	81.67 409	94.46 385

v1240		90.70 314	89.85 318	93.23 313	93.51 391	86.80 307	96.61 249	97.02 255	87.16 347	89.58 304	94.31 340	79.55 293	97.98 308	85.52 337	85.44 369	94.90 363

EPMVS		90.70 314	89.81 320	93.37 308	94.73 351	84.21 366	93.67 401	88.02 451	89.50 269	92.38 225	93.49 377	77.82 325	97.78 339	86.03 330	92.68 281	98.11 209

WBMVS		90.69 316	89.99 313	92.81 330	96.48 238	85.00 355	95.21 346	96.30 306	89.46 271	89.04 322	94.05 355	72.45 368	97.82 334	89.46 257	87.41 351	95.61 318

Anonymous20231211		90.63 317	89.42 333	94.27 257	98.24 95	89.19 243	98.05 58	97.89 122	79.95 428	88.25 344	94.96 299	72.56 367	98.13 282	89.70 251	85.14 375	95.49 320

DTE-MVSNet		90.56 318	89.75 324	93.01 321	93.95 375	87.25 295	97.64 126	97.65 156	90.74 225	87.12 367	95.68 268	79.97 285	97.00 391	83.33 363	81.66 410	94.78 376

ACMH	87.59 16	90.53 319	89.42 333	93.87 283	96.21 254	87.92 280	97.24 179	96.94 260	88.45 309	83.91 408	96.27 234	71.92 370	98.62 239	84.43 351	89.43 327	95.05 354
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 320	89.14 341	94.67 232	96.81 204	87.85 284	95.91 304	93.97 408	89.71 263	92.34 229	92.48 397	65.41 424	97.96 315	81.37 385	94.27 251	98.21 195

OurMVSNet-221017-0		90.51 321	90.19 304	91.44 372	93.41 397	81.25 400	96.98 206	96.28 307	91.68 182	86.55 381	96.30 231	74.20 357	97.98 308	88.96 273	87.40 352	95.09 351

miper_lstm_enhance		90.50 322	90.06 310	91.83 360	95.33 314	83.74 372	93.86 392	96.70 283	87.56 338	87.79 353	93.81 364	83.45 207	96.92 393	87.39 305	84.62 384	94.82 369

COLMAP_ROB	87.81 15	90.40 323	89.28 336	93.79 287	97.95 123	87.13 301	96.92 211	95.89 325	82.83 409	86.88 378	97.18 174	73.77 361	99.29 140	78.44 406	93.62 270	94.95 356
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 324	88.96 343	94.35 249	96.54 229	87.29 292	95.50 328	93.84 412	90.97 218	91.75 247	92.96 388	62.18 434	98.00 306	82.86 367	94.08 258	97.76 234

IterMVS-SCA-FT		90.31 324	89.81 320	91.82 361	95.52 297	84.20 367	94.30 377	96.15 316	90.61 236	87.39 362	94.27 342	75.80 341	96.44 404	87.34 306	86.88 358	94.82 369

MS-PatchMatch		90.27 326	89.77 322	91.78 364	94.33 366	84.72 361	95.55 325	96.73 278	86.17 365	86.36 383	95.28 286	71.28 375	97.80 337	84.09 356	98.14 141	92.81 414

tpm		90.25 327	89.74 325	91.76 366	93.92 376	79.73 421	93.98 385	93.54 415	88.28 313	91.99 238	93.25 385	77.51 327	97.44 371	87.30 308	87.94 343	98.12 204

AllTest		90.23 328	88.98 342	93.98 272	97.94 124	86.64 311	96.51 256	95.54 345	85.38 375	85.49 390	96.77 201	70.28 383	99.15 157	80.02 396	92.87 275	96.15 293

dmvs_re		90.21 329	89.50 331	92.35 341	95.47 303	85.15 351	95.70 316	94.37 398	90.94 221	88.42 336	93.57 375	74.63 353	95.67 418	82.80 370	89.57 326	96.22 287

ACMH+	87.92 14	90.20 330	89.18 339	93.25 312	96.48 238	86.45 319	96.99 205	96.68 284	88.83 295	84.79 397	96.22 236	70.16 385	98.53 248	84.42 352	88.04 342	94.77 377

test-mter		90.19 331	89.54 330	92.12 351	94.59 356	80.66 406	94.29 378	92.98 422	87.68 335	90.76 271	92.37 399	67.67 405	98.07 296	88.81 275	96.74 190	97.63 239

IterMVS		90.15 332	89.67 326	91.61 368	95.48 299	83.72 373	94.33 375	96.12 317	89.99 254	87.31 365	94.15 350	75.78 343	96.27 408	86.97 315	86.89 357	94.83 367
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 333	89.42 333	91.97 354	94.41 364	80.62 408	94.29 378	91.97 434	87.28 345	90.44 275	92.47 398	68.79 397	97.67 349	88.50 282	96.60 195	97.61 243

SD_0403		90.01 334	90.02 312	89.96 400	95.65 291	76.76 435	95.76 313	96.46 298	90.58 239	86.59 380	96.29 232	82.12 242	94.78 429	73.00 434	93.76 266	98.35 183

tpm2		89.96 335	89.21 338	92.23 349	94.91 342	81.25 400	93.78 395	94.42 394	80.62 426	91.56 250	93.44 380	76.44 336	97.94 321	85.60 336	92.08 293	97.49 248

UWE-MVS		89.91 336	89.48 332	91.21 376	95.88 279	78.23 433	94.91 355	90.26 444	89.11 281	92.35 228	94.52 323	68.76 398	97.96 315	83.95 359	95.59 221	97.42 252

IB-MVS	87.33 17	89.91 336	88.28 353	94.79 226	95.26 321	87.70 287	95.12 350	93.95 409	89.35 275	87.03 371	92.49 396	70.74 380	99.19 148	89.18 269	81.37 411	97.49 248
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
ADS-MVSNet		89.89 338	88.68 348	93.53 302	95.86 280	84.89 359	90.93 433	95.07 367	83.23 407	91.28 262	91.81 412	79.01 305	97.85 330	79.52 398	91.39 302	97.84 229

WB-MVSnew		89.88 339	89.56 329	90.82 385	94.57 359	83.06 382	95.65 321	92.85 424	87.86 326	90.83 270	94.10 351	79.66 291	96.88 395	76.34 416	94.19 253	92.54 420

FMVSNet1		89.88 339	88.31 352	94.59 233	95.41 304	91.18 157	97.50 146	96.93 261	86.62 355	87.41 361	94.51 324	65.94 421	97.29 380	83.04 366	87.43 349	95.31 338

pmmvs5		89.86 341	88.87 346	92.82 329	92.86 408	86.23 324	96.26 280	95.39 349	84.24 392	87.12 367	94.51 324	74.27 356	97.36 377	87.61 302	87.57 347	94.86 365

tpmvs		89.83 342	89.15 340	91.89 358	94.92 340	80.30 413	93.11 413	95.46 348	86.28 362	88.08 349	92.65 392	80.44 275	98.52 249	81.47 381	89.92 322	96.84 273

test_fmvs2		89.77 343	89.93 315	89.31 410	93.68 385	76.37 437	97.64 126	95.90 323	89.84 260	91.49 252	96.26 235	58.77 437	97.10 384	94.65 137	91.13 306	94.46 385

SSC-MVS3.2		89.74 344	89.26 337	91.19 379	95.16 325	80.29 414	94.53 364	97.03 253	91.79 178	88.86 326	94.10 351	69.94 388	97.82 334	85.29 340	86.66 359	95.45 326

mmtdpeth		89.70 345	88.96 343	91.90 357	95.84 285	84.42 363	97.46 157	95.53 347	90.27 247	94.46 171	90.50 421	69.74 392	98.95 187	97.39 48	69.48 447	92.34 423

tfpnnormal		89.70 345	88.40 351	93.60 297	95.15 328	90.10 199	97.56 137	98.16 75	87.28 345	86.16 385	94.63 318	77.57 326	98.05 299	74.48 424	84.59 386	92.65 417

ADS-MVSNet2		89.45 347	88.59 349	92.03 353	95.86 280	82.26 393	90.93 433	94.32 401	83.23 407	91.28 262	91.81 412	79.01 305	95.99 410	79.52 398	91.39 302	97.84 229

Patchmatch-test		89.42 348	87.99 355	93.70 292	95.27 318	85.11 352	88.98 445	94.37 398	81.11 420	87.10 370	93.69 368	82.28 238	97.50 366	74.37 426	94.76 241	98.48 168

test0.0.03 1		89.37 349	88.70 347	91.41 373	92.47 417	85.63 339	95.22 344	92.70 427	91.11 213	86.91 377	93.65 372	79.02 303	93.19 446	78.00 408	89.18 329	95.41 328

SixPastTwentyTwo		89.15 350	88.54 350	90.98 381	93.49 392	80.28 415	96.70 237	94.70 384	90.78 223	84.15 403	95.57 273	71.78 372	97.71 347	84.63 349	85.07 377	94.94 358

RPMNet		88.98 351	87.05 365	94.77 227	94.45 362	87.19 298	90.23 438	98.03 105	77.87 438	92.40 223	87.55 445	80.17 281	99.51 111	68.84 445	93.95 263	97.60 244

TransMVSNet (Re)		88.94 352	87.56 358	93.08 320	94.35 365	88.45 263	97.73 108	95.23 360	87.47 339	84.26 401	95.29 284	79.86 287	97.33 378	79.44 402	74.44 438	93.45 407

USDC		88.94 352	87.83 357	92.27 346	94.66 353	84.96 357	93.86 392	95.90 323	87.34 343	83.40 410	95.56 274	67.43 407	98.19 278	82.64 374	89.67 325	93.66 403

dp		88.90 354	88.26 354	90.81 386	94.58 358	76.62 436	92.85 418	94.93 374	85.12 381	90.07 291	93.07 386	75.81 340	98.12 285	80.53 393	87.42 350	97.71 236

PatchT		88.87 355	87.42 359	93.22 314	94.08 373	85.10 353	89.51 443	94.64 387	81.92 415	92.36 226	88.15 441	80.05 283	97.01 390	72.43 435	93.65 269	97.54 247

our_test_3		88.78 356	87.98 356	91.20 378	92.45 418	82.53 387	93.61 404	95.69 335	85.77 370	84.88 395	93.71 366	79.99 284	96.78 400	79.47 400	86.24 360	94.28 393

EU-MVSNet		88.72 357	88.90 345	88.20 414	93.15 403	74.21 442	96.63 248	94.22 403	85.18 379	87.32 364	95.97 248	76.16 338	94.98 427	85.27 341	86.17 361	95.41 328

Patchmtry		88.64 358	87.25 361	92.78 332	94.09 372	86.64 311	89.82 442	95.68 337	80.81 424	87.63 357	92.36 402	80.91 263	97.03 388	78.86 404	85.12 376	94.67 380

MIMVSNet		88.50 359	86.76 369	93.72 291	94.84 345	87.77 286	91.39 428	94.05 405	86.41 359	87.99 351	92.59 395	63.27 428	95.82 415	77.44 409	92.84 277	97.57 246

tpm cat1		88.36 360	87.21 363	91.81 362	95.13 330	80.55 409	92.58 421	95.70 333	74.97 442	87.45 359	91.96 410	78.01 323	98.17 280	80.39 394	88.74 336	96.72 277

ppachtmachnet_test		88.35 361	87.29 360	91.53 369	92.45 418	83.57 376	93.75 396	95.97 320	84.28 391	85.32 393	94.18 348	79.00 307	96.93 392	75.71 419	84.99 380	94.10 395

JIA-IIPM		88.26 362	87.04 366	91.91 356	93.52 390	81.42 399	89.38 444	94.38 397	80.84 423	90.93 268	80.74 453	79.22 297	97.92 324	82.76 371	91.62 297	96.38 285

testgi		87.97 363	87.21 363	90.24 396	92.86 408	80.76 404	96.67 242	94.97 371	91.74 180	85.52 389	95.83 256	62.66 432	94.47 432	76.25 417	88.36 340	95.48 321

LF4IMVS		87.94 364	87.25 361	89.98 399	92.38 420	80.05 419	94.38 372	95.25 359	87.59 337	84.34 399	94.74 312	64.31 426	97.66 351	84.83 345	87.45 348	92.23 426

gg-mvs-nofinetune		87.82 365	85.61 378	94.44 245	94.46 361	89.27 239	91.21 432	84.61 460	80.88 422	89.89 295	74.98 456	71.50 373	97.53 363	85.75 335	97.21 176	96.51 280

pmmvs6		87.81 366	86.19 374	92.69 335	91.32 425	86.30 322	97.34 170	96.41 301	80.59 427	84.05 407	94.37 333	67.37 408	97.67 349	84.75 347	79.51 419	94.09 397

testing3		87.67 367	86.88 368	90.05 398	96.14 267	80.71 405	97.10 194	92.85 424	90.15 251	87.54 358	94.55 321	55.70 444	94.10 435	73.77 430	94.10 257	95.35 335

K. test v3		87.64 368	86.75 370	90.32 395	93.02 405	79.48 426	96.61 249	92.08 433	90.66 232	80.25 429	94.09 353	67.21 409	96.65 402	85.96 332	80.83 413	94.83 367

Patchmatch-RL test		87.38 369	86.24 373	90.81 386	88.74 443	78.40 432	88.12 452	93.17 419	87.11 348	82.17 419	89.29 432	81.95 246	95.60 420	88.64 280	77.02 427	98.41 176

FMVSNet5		87.29 370	85.79 377	91.78 364	94.80 347	87.28 293	95.49 329	95.28 356	84.09 394	83.85 409	91.82 411	62.95 430	94.17 434	78.48 405	85.34 372	93.91 401

myMVS_eth3d		87.18 371	86.38 372	89.58 404	95.16 325	79.53 423	95.00 352	93.93 410	88.55 306	86.96 373	91.99 408	56.23 443	94.00 436	75.47 422	94.11 255	95.20 346

Syy-MVS		87.13 372	87.02 367	87.47 418	95.16 325	73.21 446	95.00 352	93.93 410	88.55 306	86.96 373	91.99 408	75.90 339	94.00 436	61.59 452	94.11 255	95.20 346

Anonymous20231206		87.09 373	86.14 375	89.93 401	91.22 426	80.35 411	96.11 291	95.35 352	83.57 403	84.16 402	93.02 387	73.54 363	95.61 419	72.16 436	86.14 362	93.84 402

EG-PatchMatch MVS		87.02 374	85.44 379	91.76 366	92.67 412	85.00 355	96.08 293	96.45 299	83.41 406	79.52 431	93.49 377	57.10 441	97.72 346	79.34 403	90.87 313	92.56 419

TinyColmap		86.82 375	85.35 382	91.21 376	94.91 342	82.99 383	93.94 388	94.02 407	83.58 402	81.56 421	94.68 314	62.34 433	98.13 282	75.78 418	87.35 353	92.52 421

UWE-MVS-28		86.81 376	86.41 371	88.02 416	92.87 407	74.60 441	95.38 334	86.70 456	88.17 316	87.28 366	94.67 316	70.83 379	93.30 444	67.45 446	94.31 249	96.17 290

mvs5depth		86.53 377	85.08 384	90.87 383	88.74 443	82.52 388	91.91 426	94.23 402	86.35 360	87.11 369	93.70 367	66.52 414	97.76 342	81.37 385	75.80 432	92.31 425

TDRefinement		86.53 377	84.76 389	91.85 359	82.23 459	84.25 365	96.38 269	95.35 352	84.97 384	84.09 405	94.94 300	65.76 422	98.34 268	84.60 350	74.52 437	92.97 411

sc_t1		86.48 379	84.10 395	93.63 295	93.45 395	85.76 337	96.79 226	94.71 383	73.06 447	86.45 382	94.35 334	55.13 445	97.95 319	84.38 353	78.55 424	97.18 263

test_0402		86.46 380	84.79 388	91.45 371	95.02 334	85.55 340	96.29 279	94.89 376	80.90 421	82.21 418	93.97 359	68.21 404	97.29 380	62.98 450	88.68 337	91.51 434

Anonymous20240521		86.42 381	85.44 379	89.34 409	90.33 430	79.79 420	96.73 233	95.92 321	83.71 401	83.25 412	91.36 417	63.92 427	96.01 409	78.39 407	85.36 371	92.22 427

DSMNet-mixed		86.34 382	86.12 376	87.00 422	89.88 434	70.43 448	94.93 354	90.08 445	77.97 437	85.42 392	92.78 390	74.44 355	93.96 438	74.43 425	95.14 232	96.62 278

CL-MVSNet_self_test		86.31 383	85.15 383	89.80 402	88.83 441	81.74 398	93.93 389	96.22 311	86.67 354	85.03 394	90.80 420	78.09 320	94.50 430	74.92 423	71.86 443	93.15 410

pmmvs-eth3d		86.22 384	84.45 391	91.53 369	88.34 445	87.25 295	94.47 367	95.01 368	83.47 404	79.51 432	89.61 430	69.75 391	95.71 416	83.13 365	76.73 430	91.64 431

test_vis1_rt		86.16 385	85.06 385	89.46 406	93.47 394	80.46 410	96.41 263	86.61 457	85.22 378	79.15 433	88.64 436	52.41 449	97.06 386	93.08 175	90.57 315	90.87 440

test20.03		86.14 386	85.40 381	88.35 412	90.12 431	80.06 418	95.90 305	95.20 361	88.59 302	81.29 422	93.62 373	71.43 374	92.65 447	71.26 440	81.17 412	92.34 423

UnsupCasMVSNet_eth		85.99 387	84.45 391	90.62 390	89.97 433	82.40 392	93.62 403	97.37 209	89.86 257	78.59 436	92.37 399	65.25 425	95.35 425	82.27 376	70.75 444	94.10 395

KD-MVS_self_test		85.95 388	84.95 386	88.96 411	89.55 437	79.11 429	95.13 349	96.42 300	85.91 368	84.07 406	90.48 422	70.03 387	94.82 428	80.04 395	72.94 441	92.94 412

ttmdpeth		85.91 389	84.76 389	89.36 408	89.14 438	80.25 416	95.66 320	93.16 421	83.77 399	83.39 411	95.26 288	66.24 418	95.26 426	80.65 391	75.57 433	92.57 418

YYNet1		85.87 390	84.23 393	90.78 389	92.38 420	82.46 391	93.17 410	95.14 364	82.12 414	67.69 449	92.36 402	78.16 319	95.50 423	77.31 411	79.73 417	94.39 388

MDA-MVSNet_test_wron		85.87 390	84.23 393	90.80 388	92.38 420	82.57 386	93.17 410	95.15 363	82.15 413	67.65 451	92.33 405	78.20 316	95.51 422	77.33 410	79.74 416	94.31 392

CMPMVS	62.92 21	85.62 392	84.92 387	87.74 417	89.14 438	73.12 447	94.17 381	96.80 276	73.98 443	73.65 445	94.93 301	66.36 415	97.61 356	83.95 359	91.28 304	92.48 422
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 393	83.64 396	90.92 382	95.27 318	79.49 425	90.55 436	95.60 340	83.76 400	83.00 415	89.95 427	71.09 376	97.97 311	82.75 372	60.79 458	95.31 338

tt0320		85.39 394	83.12 397	92.19 350	93.44 396	85.79 336	96.19 287	94.87 380	71.19 449	82.92 416	91.76 414	58.43 438	96.81 398	81.03 390	78.26 425	93.98 399

MDA-MVSNet-bldmvs		85.00 395	82.95 400	91.17 380	93.13 404	83.33 377	94.56 363	95.00 369	84.57 389	65.13 455	92.65 392	70.45 382	95.85 413	73.57 431	77.49 426	94.33 390

MIMVSNet1		84.93 396	83.05 398	90.56 391	89.56 436	84.84 360	95.40 332	95.35 352	83.91 395	80.38 427	92.21 407	57.23 440	93.34 443	70.69 442	82.75 407	93.50 405

tt0320-xc		84.83 397	82.33 405	92.31 344	93.66 386	86.20 326	96.17 289	94.06 404	71.26 448	82.04 420	92.22 406	55.07 446	96.72 401	81.49 380	75.04 436	94.02 398

KD-MVS_2432*1600		84.81 398	82.64 401	91.31 374	91.07 427	85.34 349	91.22 430	95.75 331	85.56 373	83.09 413	90.21 425	67.21 409	95.89 411	77.18 413	62.48 456	92.69 415

miper_refine_blended		84.81 398	82.64 401	91.31 374	91.07 427	85.34 349	91.22 430	95.75 331	85.56 373	83.09 413	90.21 425	67.21 409	95.89 411	77.18 413	62.48 456	92.69 415

OpenMVS_ROB	81.14 20	84.42 400	82.28 406	90.83 384	90.06 432	84.05 370	95.73 315	94.04 406	73.89 445	80.17 430	91.53 416	59.15 436	97.64 352	66.92 448	89.05 330	90.80 441

FE-MVSNET		83.85 401	81.97 407	89.51 405	87.19 449	83.19 380	95.21 346	93.17 419	83.45 405	78.90 434	89.05 434	65.46 423	93.84 440	69.71 444	75.56 434	91.51 434

mvsany_test3		83.59 402	82.44 404	87.03 421	83.80 454	73.82 443	93.70 398	90.92 442	86.42 358	82.51 417	90.26 424	46.76 454	95.71 416	90.82 224	76.76 429	91.57 433

PM-MVS		83.48 403	81.86 409	88.31 413	87.83 447	77.59 434	93.43 406	91.75 435	86.91 350	80.63 425	89.91 428	44.42 455	95.84 414	85.17 344	76.73 430	91.50 436

test_fmvs3		83.21 404	83.02 399	83.78 427	86.77 451	68.34 453	96.76 231	94.91 375	86.49 357	84.14 404	89.48 431	36.04 459	91.73 449	91.86 201	80.77 414	91.26 439

new-patchmatchnet		83.18 405	81.87 408	87.11 420	86.88 450	75.99 439	93.70 398	95.18 362	85.02 383	77.30 439	88.40 438	65.99 420	93.88 439	74.19 428	70.18 445	91.47 437

new_pmnet		82.89 406	81.12 411	88.18 415	89.63 435	80.18 417	91.77 427	92.57 428	76.79 440	75.56 442	88.23 440	61.22 435	94.48 431	71.43 438	82.92 405	89.87 444

MVS-HIRNet		82.47 407	81.21 410	86.26 424	95.38 306	69.21 451	88.96 446	89.49 446	66.28 453	80.79 424	74.08 458	68.48 402	97.39 375	71.93 437	95.47 226	92.18 428

MVStest1		82.38 408	80.04 412	89.37 407	87.63 448	82.83 384	95.03 351	93.37 418	73.90 444	73.50 446	94.35 334	62.89 431	93.25 445	73.80 429	65.92 453	92.04 430

UnsupCasMVSNet_bld		82.13 409	79.46 414	90.14 397	88.00 446	82.47 390	90.89 435	96.62 292	78.94 433	75.61 440	84.40 451	56.63 442	96.31 407	77.30 412	66.77 452	91.63 432

dmvs_testset		81.38 410	82.60 403	77.73 433	91.74 424	51.49 468	93.03 415	84.21 461	89.07 282	78.28 437	91.25 418	76.97 330	88.53 456	56.57 456	82.24 408	93.16 409

test_f		80.57 411	79.62 413	83.41 428	83.38 457	67.80 455	93.57 405	93.72 413	80.80 425	77.91 438	87.63 444	33.40 460	92.08 448	87.14 313	79.04 422	90.34 443

pmmvs3		79.97 412	77.50 417	87.39 419	82.80 458	79.38 427	92.70 420	90.75 443	70.69 450	78.66 435	87.47 446	51.34 450	93.40 442	73.39 432	69.65 446	89.38 445

APD_test1		79.31 413	77.70 416	84.14 426	89.11 440	69.07 452	92.36 425	91.50 437	69.07 451	73.87 444	92.63 394	39.93 457	94.32 433	70.54 443	80.25 415	89.02 446

N_pmnet		78.73 414	78.71 415	78.79 432	92.80 410	46.50 471	94.14 382	43.71 473	78.61 434	80.83 423	91.66 415	74.94 351	96.36 406	67.24 447	84.45 389	93.50 405

WB-MVS		76.77 415	76.63 418	77.18 434	85.32 452	56.82 466	94.53 364	89.39 447	82.66 411	71.35 447	89.18 433	75.03 348	88.88 454	35.42 463	66.79 451	85.84 448

SSC-MVS		76.05 416	75.83 419	76.72 438	84.77 453	56.22 467	94.32 376	88.96 449	81.82 417	70.52 448	88.91 435	74.79 352	88.71 455	33.69 464	64.71 454	85.23 449

test_vis3_rt		72.73 417	70.55 420	79.27 431	80.02 460	68.13 454	93.92 390	74.30 468	76.90 439	58.99 459	73.58 459	20.29 468	95.37 424	84.16 354	72.80 442	74.31 456

LCM-MVSNet		72.55 418	69.39 422	82.03 429	70.81 469	65.42 458	90.12 440	94.36 400	55.02 459	65.88 453	81.72 452	24.16 467	89.96 450	74.32 427	68.10 450	90.71 442

FPMVS		71.27 419	69.85 421	75.50 439	74.64 464	59.03 464	91.30 429	91.50 437	58.80 456	57.92 460	88.28 439	29.98 463	85.53 459	53.43 457	82.84 406	81.95 452

PMMVS2		70.19 420	66.92 424	80.01 430	76.35 463	65.67 457	86.22 453	87.58 453	64.83 455	62.38 456	80.29 455	26.78 465	88.49 457	63.79 449	54.07 460	85.88 447

dongtai		69.99 421	69.33 423	71.98 442	88.78 442	61.64 462	89.86 441	59.93 472	75.67 441	74.96 443	85.45 448	50.19 451	81.66 461	43.86 460	55.27 459	72.63 457

testf1		69.31 422	66.76 425	76.94 436	78.61 461	61.93 460	88.27 450	86.11 458	55.62 457	59.69 457	85.31 449	20.19 469	89.32 451	57.62 453	69.44 448	79.58 453

APD_test2		69.31 422	66.76 425	76.94 436	78.61 461	61.93 460	88.27 450	86.11 458	55.62 457	59.69 457	85.31 449	20.19 469	89.32 451	57.62 453	69.44 448	79.58 453

EGC-MVSNET		68.77 424	63.01 430	86.07 425	92.49 416	82.24 394	93.96 387	90.96 441	0.71 470	2.62 471	90.89 419	53.66 447	93.46 441	57.25 455	84.55 387	82.51 451

Gipuma		67.86 425	65.41 427	75.18 440	92.66 413	73.45 444	66.50 461	94.52 391	53.33 460	57.80 461	66.07 461	30.81 461	89.20 453	48.15 459	78.88 423	62.90 461
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 426	64.89 428	69.79 443	72.62 467	35.23 475	65.19 462	92.83 426	20.35 465	65.20 454	88.08 442	43.14 456	82.70 460	73.12 433	63.46 455	91.45 438

kuosan		65.27 427	64.66 429	67.11 445	83.80 454	61.32 463	88.53 449	60.77 471	68.22 452	67.67 450	80.52 454	49.12 452	70.76 467	29.67 466	53.64 461	69.26 459

ANet_high		63.94 428	59.58 431	77.02 435	61.24 471	66.06 456	85.66 455	87.93 452	78.53 435	42.94 463	71.04 460	25.42 466	80.71 462	52.60 458	30.83 464	84.28 450

PMVS	53.92 22	58.58 429	55.40 432	68.12 444	51.00 472	48.64 469	78.86 458	87.10 455	46.77 461	35.84 467	74.28 457	8.76 471	86.34 458	42.07 461	73.91 439	69.38 458
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 430	52.56 434	55.43 447	74.43 465	47.13 470	83.63 457	76.30 465	42.23 462	42.59 464	62.22 463	28.57 464	74.40 464	31.53 465	31.51 463	44.78 462

MVE	50.73 23	53.25 431	48.81 436	66.58 446	65.34 470	57.50 465	72.49 460	70.94 469	40.15 464	39.28 466	63.51 462	6.89 473	73.48 466	38.29 462	42.38 462	68.76 460
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 432	51.31 435	54.39 448	72.62 467	45.39 472	83.84 456	75.51 467	41.13 463	40.77 465	59.65 464	30.08 462	73.60 465	28.31 467	29.90 465	44.18 463

tmp_tt		51.94 433	53.82 433	46.29 449	33.73 473	45.30 473	78.32 459	67.24 470	18.02 466	50.93 462	87.05 447	52.99 448	53.11 468	70.76 441	25.29 466	40.46 464

wuyk23d		25.11 434	24.57 438	26.74 450	73.98 466	39.89 474	57.88 463	9.80 474	12.27 467	10.39 468	6.97 470	7.03 472	36.44 469	25.43 468	17.39 467	3.89 467

cdsmvs_eth3d_5k		23.24 435	30.99 437	0.00 453	0.00 476	0.00 478	0.00 464	97.63 160	0.00 471	0.00 472	96.88 196	84.38 190	0.00 472	0.00 471	0.00 470	0.00 468

testmvs		13.36 436	16.33 439	4.48 452	5.04 474	2.26 477	93.18 409	3.28 475	2.70 468	8.24 469	21.66 466	2.29 475	2.19 470	7.58 469	2.96 468	9.00 466

test123		13.04 437	15.66 440	5.18 451	4.51 475	3.45 476	92.50 423	1.81 476	2.50 469	7.58 470	20.15 467	3.67 474	2.18 471	7.13 470	1.07 469	9.90 465

ab-mvs-re		8.06 438	10.74 441	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	96.69 207	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

pcd_1.5k_mvsjas		7.39 439	9.85 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	88.65 105	0.00 472	0.00 471	0.00 470	0.00 468

mmdepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

monomultidepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

test_blank		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uanet_test		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

DCPMVS		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

sosnet-low-res		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

sosnet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uncertanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

Regformer		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

WAC-MVS						79.53 423								75.56 421

FOURS1					99.55 1	93.34 67	99.29 1	98.35 38	94.98 44	98.49 34

MSC_two_6792asdad				98.86 1	98.67 63	96.94 1		97.93 119					99.86 9	97.68 31	99.67 6	99.77 2

PC_three_1452									90.77 224	98.89 24	98.28 80	96.24 1	98.35 265	95.76 100	99.58 23	99.59 28

No_MVS				98.86 1	98.67 63	96.94 1		97.93 119					99.86 9	97.68 31	99.67 6	99.77 2

test_one_0601					99.32 24	95.20 20		98.25 56	95.13 38	98.48 35	98.87 29	95.16 7

eth-test2					0.00 476

eth-test					0.00 476

ZD-MVS					99.05 41	94.59 32		98.08 88	89.22 278	97.03 75	98.10 88	92.52 39	99.65 73	94.58 141	99.31 67

RE-MVS-def			96.72 57		99.02 44	92.34 104	97.98 66	98.03 105	93.52 111	97.43 61	98.51 50	90.71 78		96.05 88	99.26 72	99.43 59

IU-MVS					99.42 7	95.39 11		97.94 118	90.40 246	98.94 17				97.41 47	99.66 10	99.74 8

OPU-MVS				98.55 3	98.82 57	96.86 3	98.25 36				98.26 81	96.04 2	99.24 143	95.36 114	99.59 19	99.56 36

test_241102_TWO								98.27 50	95.13 38	98.93 18	98.89 26	94.99 11	99.85 18	97.52 40	99.65 13	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 50	95.09 41	99.19 11	98.81 35	95.54 5	99.65 73

9.14			96.75 56		98.93 52		97.73 108	98.23 61	91.28 201	97.88 49	98.44 58	93.00 26	99.65 73	95.76 100	99.47 41

save fliter					98.91 54	94.28 38	97.02 199	98.02 108	95.35 29

test_0728_THIRD									94.78 59	98.73 28	98.87 29	95.87 4	99.84 23	97.45 44	99.72 2	99.77 2

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 43	98.28 47					99.86 9	97.52 40	99.67 6	99.75 6

test0726					99.45 3	95.36 13	98.31 28	98.29 45	94.92 48	98.99 16	98.92 21	95.08 8

GSMVS																98.45 171

test_part2					99.28 27	95.74 8				98.10 42

sam_mvs1												82.76 226				98.45 171

sam_mvs												81.94 247

ambc				86.56 423	83.60 456	70.00 450	85.69 454	94.97 371		80.60 426	88.45 437	37.42 458	96.84 397	82.69 373	75.44 435	92.86 413

MTGPA								98.08 88

test_post1							92.81 419				16.58 469	80.53 273	97.68 348	86.20 324

test_post											17.58 468	81.76 250	98.08 292

patchmatchnet-post											90.45 423	82.65 231	98.10 287

GG-mvs-BLEND				93.62 296	93.69 384	89.20 241	92.39 424	83.33 462		87.98 352	89.84 429	71.00 377	96.87 396	82.08 377	95.40 228	94.80 372

MTMP							97.86 85	82.03 463

gm-plane-assit					93.22 401	78.89 431			84.82 386		93.52 376		98.64 235	87.72 292

test9_res														94.81 131	99.38 60	99.45 55

TEST9					98.70 61	94.19 42	96.41 263	98.02 108	88.17 316	96.03 120	97.56 150	92.74 33	99.59 89

test_8					98.67 63	94.06 49	96.37 270	98.01 111	88.58 303	95.98 124	97.55 152	92.73 34	99.58 92

agg_prior2														93.94 154	99.38 60	99.50 48

agg_prior					98.67 63	93.79 55		98.00 112		95.68 137			99.57 99

TestCases				93.98 272	97.94 124	86.64 311		95.54 345	85.38 375	85.49 390	96.77 201	70.28 383	99.15 157	80.02 396	92.87 275	96.15 293

test_prior4						93.66 58	96.42 262

test_prior2							96.35 271		92.80 149	96.03 120	97.59 147	92.01 47		95.01 122	99.38 60

test_prior				97.23 65	98.67 63	92.99 79		98.00 112					99.41 126			99.29 71

旧先验2							95.94 301		81.66 418	97.34 64			98.82 203	92.26 186

新几何2							95.79 311

新几何1				97.32 58	98.60 70	93.59 59		97.75 143	81.58 419	95.75 132	97.85 117	90.04 85	99.67 71	86.50 320	99.13 92	98.69 148

旧先验1					98.38 84	93.38 64		97.75 143			98.09 90	92.30 45			99.01 102	99.16 81

无先验							95.79 311	97.87 126	83.87 398				99.65 73	87.68 298		98.89 126

原ACMM2							95.67 317

原ACMM1				96.38 119	98.59 71	91.09 162		97.89 122	87.41 341	95.22 150	97.68 134	90.25 82	99.54 104	87.95 288	99.12 94	98.49 166

test222					98.24 95	92.21 110	95.33 336	97.60 165	79.22 432	95.25 148	97.84 119	88.80 102			99.15 89	98.72 145

testdata2													99.67 71	85.96 332

segment_acmp												92.89 30

testdata				95.46 190	98.18 105	88.90 250		97.66 154	82.73 410	97.03 75	98.07 91	90.06 84	98.85 199	89.67 252	98.98 103	98.64 151

testdata1							95.26 343		93.10 131

test12				97.65 43	98.46 75	94.26 39		97.66 154		95.52 144		90.89 75	99.46 120		99.25 74	99.22 78

plane_prior7					96.21 254	89.98 205

plane_prior6					96.10 272	90.00 201						81.32 257

plane_prior5								97.51 178					98.60 240	93.02 178	92.23 286	95.86 301

plane_prior4											96.64 210

plane_prior3						90.00 201			94.46 76	91.34 256

plane_prior2							97.74 106		94.85 51

plane_prior1					96.14 267

plane_prior						89.99 203	97.24 179		94.06 88						92.16 290

n2								0.00 477

nn								0.00 477

door-mid								91.06 440

lessismore_v0				90.45 392	91.96 423	79.09 430		87.19 454		80.32 428	94.39 331	66.31 417	97.55 360	84.00 358	76.84 428	94.70 379

LGP-MVS_train				94.10 264	96.16 264	88.26 268		97.46 189	91.29 198	90.12 286	97.16 175	79.05 301	98.73 219	92.25 188	91.89 294	95.31 338

test11								97.88 124

door								91.13 439

HQP5-MVS						89.33 234

HQP-NCC					95.86 280		96.65 243		93.55 105	90.14 280

ACMP_Plane					95.86 280		96.65 243		93.55 105	90.14 280

BP-MVS														92.13 194

HQP4-MVS										90.14 280			98.50 250			95.78 309

HQP3-MVS								97.39 205							92.10 291

HQP2-MVS												80.95 261

NP-MVS					95.99 278	89.81 213					95.87 253

MDTV_nov1_ep13_2view						70.35 449	93.10 414		83.88 397	93.55 197		82.47 235		86.25 323		98.38 179

MDTV_nov1_ep13			90.76 275		95.22 322	80.33 412	93.03 415	95.28 356	88.14 319	92.84 220	93.83 361	81.34 256	98.08 292	82.86 367	94.34 248

ACMMP++_ref															90.30 320

ACMMP++															91.02 309

Test By Simon												88.73 104

ITE_SJBPF				92.43 339	95.34 311	85.37 348		95.92 321	91.47 191	87.75 355	96.39 228	71.00 377	97.96 315	82.36 375	89.86 323	93.97 400

DeepMVS_CX				74.68 441	90.84 429	64.34 459		81.61 464	65.34 454	67.47 452	88.01 443	48.60 453	80.13 463	62.33 451	73.68 440	79.58 453

High-resolution multi-view benchmark