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 56	99.43 59	97.48 84	98.88 122	99.30 14	98.47 15	99.85 9	99.43 40	96.71 17	99.96 4	99.86 1	99.80 24	99.89 5

SED-MVS		99.09 1	98.91 4	99.63 4	99.71 21	99.24 5	99.02 80	98.87 79	97.65 36	99.73 19	99.48 30	97.53 7	99.94 12	98.43 64	99.81 15	99.70 61

DVP-MVS++		99.08 3	98.89 5	99.64 3	99.17 104	99.23 7	99.69 1	98.88 72	97.32 60	99.53 34	99.47 32	97.81 3	99.94 12	98.47 60	99.72 61	99.74 44

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 52	99.41 61	97.54 82	98.89 115	99.31 13	98.49 14	99.86 6	99.42 41	96.45 24	99.96 4	99.86 1	99.74 53	99.90 4

DVP-MVS		99.03 5	98.83 9	99.63 4	99.72 14	99.25 2	98.97 91	98.58 170	97.62 38	99.45 36	99.46 37	97.42 9	99.94 12	98.47 60	99.81 15	99.69 64
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
APDe-MVS		99.02 6	98.84 8	99.55 9	99.57 35	98.96 16	99.39 11	98.93 60	97.38 57	99.41 39	99.54 18	96.66 18	99.84 81	98.86 36	99.85 6	99.87 8
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lecture		98.95 7	98.78 11	99.45 15	99.75 3	98.63 26	99.43 10	99.38 8	97.60 41	99.58 30	99.47 32	95.36 61	99.93 31	98.87 35	99.57 93	99.78 27

reproduce_model		98.94 8	98.81 10	99.34 27	99.52 41	98.26 50	98.94 100	98.84 89	98.06 22	99.35 43	99.61 4	96.39 27	99.94 12	98.77 39	99.82 14	99.83 15

reproduce-ours		98.93 9	98.78 11	99.38 19	99.49 48	98.38 36	98.86 129	98.83 91	98.06 22	99.29 47	99.58 14	96.40 25	99.94 12	98.68 42	99.81 15	99.81 21

our_new_method		98.93 9	98.78 11	99.38 19	99.49 48	98.38 36	98.86 129	98.83 91	98.06 22	99.29 47	99.58 14	96.40 25	99.94 12	98.68 42	99.81 15	99.81 21

test_fmvsmconf_n		98.92 11	98.87 6	99.04 62	98.88 140	97.25 106	98.82 141	99.34 11	98.75 8	99.80 11	99.61 4	95.16 74	99.95 9	99.70 14	99.80 24	99.93 1

DPE-MVS		98.92 11	98.67 17	99.65 2	99.58 34	99.20 9	98.42 241	98.91 66	97.58 42	99.54 33	99.46 37	97.10 12	99.94 12	97.64 111	99.84 11	99.83 15
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_3		98.90 13	98.74 15	99.37 23	99.36 63	98.25 51	98.89 115	99.24 19	98.77 7	99.89 2	99.59 12	93.39 108	99.96 4	99.78 7	99.76 42	99.89 5

SteuartSystems-ACMMP		98.90 13	98.75 14	99.36 25	99.22 99	98.43 34	99.10 64	98.87 79	97.38 57	99.35 43	99.40 44	97.78 5	99.87 72	97.77 99	99.85 6	99.78 27
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 15	99.01 3	98.45 116	99.42 60	96.43 148	98.96 96	99.36 10	98.63 10	99.86 6	99.51 24	95.91 43	99.97 1	99.72 11	99.75 49	98.94 208

TSAR-MVS + MP.		98.78 16	98.62 19	99.24 41	99.69 26	98.28 49	99.14 55	98.66 147	96.84 91	99.56 31	99.31 64	96.34 28	99.70 135	98.32 70	99.73 56	99.73 49
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 16	98.56 23	99.45 15	99.32 70	98.87 19	98.47 229	98.81 100	97.72 31	98.76 87	99.16 92	97.05 13	99.78 117	98.06 81	99.66 72	99.69 64

MSP-MVS		98.74 18	98.55 24	99.29 34	99.75 3	98.23 52	99.26 28	98.88 72	97.52 45	99.41 39	98.78 163	96.00 39	99.79 114	97.79 98	99.59 89	99.85 12
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 19	98.62 19	99.05 61	99.35 64	97.27 100	98.80 150	99.23 24	98.93 3	99.79 12	99.59 12	92.34 125	99.95 9	99.82 5	99.71 63	99.92 2

XVS		98.70 20	98.49 30	99.34 27	99.70 24	98.35 45	99.29 23	98.88 72	97.40 54	98.46 110	99.20 82	95.90 45	99.89 61	97.85 94	99.74 53	99.78 27

fmvsm_s_conf0.5_n_6		98.65 21	98.55 24	98.95 71	98.50 180	97.30 96	98.79 158	99.16 35	98.14 20	99.86 6	99.41 43	93.71 105	99.91 50	99.71 12	99.64 80	99.65 77

MCST-MVS		98.65 21	98.37 39	99.48 13	99.60 33	98.87 19	98.41 242	98.68 139	97.04 83	98.52 108	98.80 157	96.78 16	99.83 83	97.93 88	99.61 85	99.74 44

SD-MVS		98.64 23	98.68 16	98.53 105	99.33 67	98.36 44	98.90 111	98.85 88	97.28 64	99.72 22	99.39 45	96.63 20	97.60 400	98.17 76	99.85 6	99.64 80
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 24	98.66 18	98.54 102	99.40 62	95.83 189	98.79 158	99.17 33	98.94 2	99.92 1	99.61 4	92.49 120	99.93 31	99.86 1	99.76 42	99.86 9

HFP-MVS		98.63 24	98.40 36	99.32 33	99.72 14	98.29 48	99.23 33	98.96 55	96.10 130	98.94 70	99.17 89	96.06 36	99.92 40	97.62 112	99.78 34	99.75 42

ACMMP_NAP		98.61 26	98.30 54	99.55 9	99.62 32	98.95 17	98.82 141	98.81 100	95.80 142	99.16 59	99.47 32	95.37 60	99.92 40	97.89 92	99.75 49	99.79 25

region2R		98.61 26	98.38 38	99.29 34	99.74 9	98.16 58	99.23 33	98.93 60	96.15 126	98.94 70	99.17 89	95.91 43	99.94 12	97.55 120	99.79 30	99.78 27

NCCC		98.61 26	98.35 42	99.38 19	99.28 85	98.61 27	98.45 231	98.76 118	97.82 30	98.45 113	98.93 136	96.65 19	99.83 83	97.38 133	99.41 122	99.71 57

SF-MVS		98.59 29	98.32 53	99.41 18	99.54 37	98.71 22	99.04 74	98.81 100	95.12 184	99.32 46	99.39 45	96.22 30	99.84 81	97.72 102	99.73 56	99.67 73

ACMMPR		98.59 29	98.36 40	99.29 34	99.74 9	98.15 59	99.23 33	98.95 56	96.10 130	98.93 74	99.19 87	95.70 49	99.94 12	97.62 112	99.79 30	99.78 27

test_fmvsmconf0.1_n		98.58 31	98.44 34	98.99 64	97.73 277	97.15 111	98.84 137	98.97 52	98.75 8	99.43 38	99.54 18	93.29 110	99.93 31	99.64 17	99.79 30	99.89 5

SMA-MVS		98.58 31	98.25 57	99.56 8	99.51 42	99.04 15	98.95 97	98.80 107	93.67 276	99.37 42	99.52 21	96.52 22	99.89 61	98.06 81	99.81 15	99.76 41
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 31	98.29 55	99.46 14	99.76 2	98.64 25	98.90 111	98.74 122	97.27 68	98.02 137	99.39 45	94.81 84	99.96 4	97.91 90	99.79 30	99.77 34

HPM-MVS++		98.58 31	98.25 57	99.55 9	99.50 44	99.08 11	98.72 177	98.66 147	97.51 46	98.15 124	98.83 154	95.70 49	99.92 40	97.53 122	99.67 69	99.66 76

SR-MVS		98.57 35	98.35 42	99.24 41	99.53 38	98.18 56	99.09 65	98.82 94	96.58 107	99.10 61	99.32 62	95.39 58	99.82 90	97.70 107	99.63 82	99.72 53

CP-MVS		98.57 35	98.36 40	99.19 45	99.66 28	97.86 70	99.34 17	98.87 79	95.96 134	98.60 104	99.13 97	96.05 37	99.94 12	97.77 99	99.86 2	99.77 34

MSLP-MVS++		98.56 37	98.57 22	98.55 100	99.26 88	96.80 126	98.71 178	99.05 45	97.28 64	98.84 80	99.28 67	96.47 23	99.40 199	98.52 58	99.70 65	99.47 109

DeepC-MVS_fast	96.70 1	98.55 38	98.34 48	99.18 47	99.25 89	98.04 64	98.50 226	98.78 114	97.72 31	98.92 76	99.28 67	95.27 67	99.82 90	97.55 120	99.77 36	99.69 64
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 39	98.35 42	99.13 53	99.49 48	97.86 70	99.11 61	98.80 107	96.49 111	99.17 56	99.35 57	95.34 63	99.82 90	97.72 102	99.65 75	99.71 57

fmvsm_s_conf0.5_n_5		98.53 40	98.35 42	99.08 58	99.07 119	97.46 88	98.68 186	99.20 29	97.50 47	99.87 3	99.50 26	91.96 144	99.96 4	99.76 8	99.65 75	99.82 19

fmvsm_s_conf0.5_n_3		98.53 40	98.45 33	98.79 79	99.23 97	97.32 93	98.80 150	99.26 16	98.82 4	99.87 3	99.60 9	90.95 182	99.93 31	99.76 8	99.73 56	99.12 180

APD-MVS_3200maxsize		98.53 40	98.33 52	99.15 51	99.50 44	97.92 69	99.15 52	98.81 100	96.24 122	99.20 53	99.37 51	95.30 65	99.80 102	97.73 101	99.67 69	99.72 53

MM		98.51 43	98.24 59	99.33 31	99.12 113	98.14 61	98.93 106	97.02 386	98.96 1	99.17 56	99.47 32	91.97 143	99.94 12	99.85 4	99.69 66	99.91 3

mPP-MVS		98.51 43	98.26 56	99.25 40	99.75 3	98.04 64	99.28 25	98.81 100	96.24 122	98.35 120	99.23 77	95.46 55	99.94 12	97.42 128	99.81 15	99.77 34

ZNCC-MVS		98.49 45	98.20 65	99.35 26	99.73 13	98.39 35	99.19 45	98.86 85	95.77 144	98.31 123	99.10 103	95.46 55	99.93 31	97.57 119	99.81 15	99.74 44

SPE-MVS-test		98.49 45	98.50 28	98.46 115	99.20 102	97.05 116	99.64 4	98.50 192	97.45 53	98.88 77	99.14 96	95.25 69	99.15 234	98.83 37	99.56 101	99.20 164

PGM-MVS		98.49 45	98.23 61	99.27 39	99.72 14	98.08 63	98.99 87	99.49 5	95.43 161	99.03 62	99.32 62	95.56 52	99.94 12	96.80 163	99.77 36	99.78 27

EI-MVSNet-Vis-set		98.47 48	98.39 37	98.69 87	99.46 54	96.49 145	98.30 254	98.69 136	97.21 71	98.84 80	99.36 55	95.41 57	99.78 117	98.62 46	99.65 75	99.80 24

MVS_111021_HR		98.47 48	98.34 48	98.88 76	99.22 99	97.32 93	97.91 309	99.58 3	97.20 72	98.33 121	99.00 125	95.99 40	99.64 149	98.05 83	99.76 42	99.69 64

balanced_conf03		98.45 50	98.35 42	98.74 83	98.65 169	97.55 80	99.19 45	98.60 158	96.72 101	99.35 43	98.77 166	95.06 79	99.55 172	98.95 32	99.87 1	99.12 180

test_fmvsmvis_n_1920		98.44 51	98.51 26	98.23 137	98.33 204	96.15 162	98.97 91	99.15 37	98.55 13	98.45 113	99.55 16	94.26 97	99.97 1	99.65 15	99.66 72	98.57 253

CS-MVS		98.44 51	98.49 30	98.31 129	99.08 118	96.73 130	99.67 3	98.47 199	97.17 75	98.94 70	99.10 103	95.73 48	99.13 238	98.71 41	99.49 112	99.09 188

GST-MVS		98.43 53	98.12 69	99.34 27	99.72 14	98.38 36	99.09 65	98.82 94	95.71 148	98.73 90	99.06 116	95.27 67	99.93 31	97.07 142	99.63 82	99.72 53

fmvsm_s_conf0.5_n		98.42 54	98.51 26	98.13 148	99.30 76	95.25 215	98.85 133	99.39 7	97.94 26	99.74 18	99.62 3	92.59 119	99.91 50	99.65 15	99.52 107	99.25 157

EI-MVSNet-UG-set		98.41 55	98.34 48	98.61 94	99.45 57	96.32 155	98.28 257	98.68 139	97.17 75	98.74 88	99.37 51	95.25 69	99.79 114	98.57 49	99.54 104	99.73 49

DELS-MVS		98.40 56	98.20 65	98.99 64	99.00 127	97.66 75	97.75 330	98.89 69	97.71 33	98.33 121	98.97 127	94.97 81	99.88 70	98.42 66	99.76 42	99.42 122
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 57	98.42 35	98.27 131	99.09 117	95.41 205	98.86 129	99.37 9	97.69 35	99.78 14	99.61 4	92.38 123	99.91 50	99.58 20	99.43 120	99.49 105

TSAR-MVS + GP.		98.38 57	98.24 59	98.81 78	99.22 99	97.25 106	98.11 284	98.29 246	97.19 73	98.99 68	99.02 119	96.22 30	99.67 142	98.52 58	98.56 176	99.51 98

HPM-MVS_fast		98.38 57	98.13 68	99.12 55	99.75 3	97.86 70	99.44 9	98.82 94	94.46 231	98.94 70	99.20 82	95.16 74	99.74 127	97.58 115	99.85 6	99.77 34

patch_mono-2		98.36 60	98.87 6	96.82 252	99.53 38	90.68 363	98.64 197	99.29 15	97.88 27	99.19 55	99.52 21	96.80 15	99.97 1	99.11 28	99.86 2	99.82 19

HPM-MVS		98.36 60	98.10 72	99.13 53	99.74 9	97.82 74	99.53 6	98.80 107	94.63 218	98.61 103	98.97 127	95.13 76	99.77 122	97.65 110	99.83 13	99.79 25
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 62	98.50 28	97.90 168	99.16 108	95.08 224	98.75 163	99.24 19	98.39 16	99.81 10	99.52 21	92.35 124	99.90 58	99.74 10	99.51 109	98.71 234

APD-MVS		98.35 62	98.00 78	99.42 17	99.51 42	98.72 21	98.80 150	98.82 94	94.52 226	99.23 52	99.25 76	95.54 54	99.80 102	96.52 172	99.77 36	99.74 44
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 64	98.23 61	98.67 89	99.27 86	96.90 122	97.95 302	99.58 3	97.14 78	98.44 115	99.01 123	95.03 80	99.62 156	97.91 90	99.75 49	99.50 100

PHI-MVS		98.34 64	98.06 73	99.18 47	99.15 111	98.12 62	99.04 74	99.09 40	93.32 292	98.83 82	99.10 103	96.54 21	99.83 83	97.70 107	99.76 42	99.59 88

MP-MVS		98.33 66	98.01 77	99.28 37	99.75 3	98.18 56	99.22 37	98.79 112	96.13 127	97.92 149	99.23 77	94.54 87	99.94 12	96.74 166	99.78 34	99.73 49
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus		98.31 67	98.19 67	98.67 89	98.96 134	97.36 91	99.24 31	98.57 172	94.81 207	98.99 68	98.90 142	95.22 72	99.59 159	99.15 27	99.84 11	99.07 196

MP-MVS-pluss		98.31 67	97.92 80	99.49 12	99.72 14	98.88 18	98.43 238	98.78 114	94.10 241	97.69 167	99.42 41	95.25 69	99.92 40	98.09 80	99.80 24	99.67 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_2		98.30 69	98.21 63	98.57 97	99.25 89	97.11 113	98.66 193	99.20 29	98.82 4	99.79 12	99.60 9	89.38 218	99.92 40	99.80 6	99.38 127	98.69 236

fmvsm_s_conf0.5_n_7		98.23 70	98.35 42	97.89 170	98.86 144	94.99 230	98.58 207	99.00 48	98.29 17	99.73 19	99.60 9	91.70 148	99.92 40	99.63 18	99.73 56	98.76 228

MVS_0304		98.23 70	97.91 81	99.21 44	98.06 240	97.96 68	98.58 207	95.51 424	98.58 11	98.87 78	99.26 71	92.99 114	99.95 9	99.62 19	99.67 69	99.73 49

ACMMP		98.23 70	97.95 79	99.09 57	99.74 9	97.62 78	99.03 77	99.41 6	95.98 133	97.60 176	99.36 55	94.45 92	99.93 31	97.14 139	98.85 160	99.70 61
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 73	98.11 70	98.49 112	98.34 201	97.26 105	99.61 5	98.43 211	96.78 94	98.87 78	98.84 150	93.72 104	99.01 261	98.91 34	99.50 110	99.19 168

fmvsm_s_conf0.1_n		98.18 74	98.21 63	98.11 152	98.54 178	95.24 216	98.87 125	99.24 19	97.50 47	99.70 23	99.67 1	91.33 164	99.89 61	99.47 22	99.54 104	99.21 163

fmvsm_s_conf0.1_n_2		98.14 75	98.02 76	98.53 105	98.88 140	97.07 115	98.69 184	98.82 94	98.78 6	99.77 15	99.61 4	88.83 238	99.91 50	99.71 12	99.07 143	98.61 246

fmvsm_s_conf0.1_n_a		98.08 76	98.04 75	98.21 138	97.66 283	95.39 206	98.89 115	99.17 33	97.24 69	99.76 17	99.67 1	91.13 174	99.88 70	99.39 23	99.41 122	99.35 131

dcpmvs_2		98.08 76	98.59 21	96.56 279	99.57 35	90.34 375	99.15 52	98.38 223	96.82 93	99.29 47	99.49 29	95.78 47	99.57 162	98.94 33	99.86 2	99.77 34

NormalMVS		98.07 78	97.90 82	98.59 96	99.75 3	96.60 136	98.94 100	98.60 158	97.86 28	98.71 93	99.08 112	91.22 170	99.80 102	97.40 130	99.57 93	99.37 127

CANet		98.05 79	97.76 85	98.90 75	98.73 154	97.27 100	98.35 244	98.78 114	97.37 59	97.72 164	98.96 132	91.53 157	99.92 40	98.79 38	99.65 75	99.51 98

train_agg		97.97 80	97.52 98	99.33 31	99.31 72	98.50 30	97.92 307	98.73 125	92.98 308	97.74 161	98.68 179	96.20 32	99.80 102	96.59 167	99.57 93	99.68 69

ETV-MVS		97.96 81	97.81 83	98.40 124	98.42 187	97.27 100	98.73 173	98.55 177	96.84 91	98.38 117	97.44 301	95.39 58	99.35 204	97.62 112	98.89 154	98.58 252

UA-Net		97.96 81	97.62 89	98.98 66	98.86 144	97.47 86	98.89 115	99.08 41	96.67 104	98.72 92	99.54 18	93.15 112	99.81 95	94.87 230	98.83 161	99.65 77

CDPH-MVS		97.94 83	97.49 100	99.28 37	99.47 52	98.44 32	97.91 309	98.67 144	92.57 324	98.77 86	98.85 149	95.93 42	99.72 129	95.56 208	99.69 66	99.68 69

DeepPCF-MVS	96.37 2	97.93 84	98.48 32	96.30 305	99.00 127	89.54 390	97.43 352	98.87 79	98.16 19	99.26 51	99.38 50	96.12 35	99.64 149	98.30 71	99.77 36	99.72 53

DeepC-MVS	95.98 3	97.88 85	97.58 91	98.77 81	99.25 89	96.93 120	98.83 139	98.75 120	96.96 87	96.89 206	99.50 26	90.46 191	99.87 72	97.84 96	99.76 42	99.52 95
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 86	97.54 97	98.83 77	95.48 406	96.83 125	98.95 97	98.60 158	98.58 11	98.93 74	99.55 16	88.57 243	99.91 50	99.54 21	99.61 85	99.77 34

DP-MVS Recon		97.86 86	97.46 103	99.06 60	99.53 38	98.35 45	98.33 246	98.89 69	92.62 321	98.05 132	98.94 135	95.34 63	99.65 146	96.04 188	99.42 121	99.19 168

CSCG		97.85 88	97.74 86	98.20 140	99.67 27	95.16 219	99.22 37	99.32 12	93.04 306	97.02 199	98.92 140	95.36 61	99.91 50	97.43 127	99.64 80	99.52 95

SymmetryMVS		97.84 89	97.58 91	98.62 93	99.01 125	96.60 136	98.94 100	98.44 204	97.86 28	98.71 93	99.08 112	91.22 170	99.80 102	97.40 130	97.53 229	99.47 109

BP-MVS1		97.82 90	97.51 99	98.76 82	98.25 212	97.39 90	99.15 52	97.68 318	96.69 102	98.47 109	99.10 103	90.29 195	99.51 179	98.60 47	99.35 130	99.37 127

MG-MVS		97.81 91	97.60 90	98.44 118	99.12 113	95.97 172	97.75 330	98.78 114	96.89 90	98.46 110	99.22 79	93.90 103	99.68 141	94.81 234	99.52 107	99.67 73

VNet		97.79 92	97.40 107	98.96 69	98.88 140	97.55 80	98.63 200	98.93 60	96.74 98	99.02 63	98.84 150	90.33 194	99.83 83	98.53 52	96.66 252	99.50 100

EIA-MVS		97.75 93	97.58 91	98.27 131	98.38 192	96.44 147	99.01 82	98.60 158	95.88 138	97.26 185	97.53 295	94.97 81	99.33 207	97.38 133	99.20 139	99.05 197

PS-MVSNAJ		97.73 94	97.77 84	97.62 199	98.68 164	95.58 196	97.34 361	98.51 187	97.29 62	98.66 100	97.88 259	94.51 88	99.90 58	97.87 93	99.17 141	97.39 296

casdiffmvs_mvg		97.72 95	97.48 102	98.44 118	98.42 187	96.59 140	98.92 108	98.44 204	96.20 124	97.76 158	99.20 82	91.66 151	99.23 222	98.27 75	98.41 190	99.49 105
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 95	97.32 112	98.92 72	99.64 30	97.10 114	99.12 59	98.81 100	92.34 332	98.09 129	99.08 112	93.01 113	99.92 40	96.06 187	99.77 36	99.75 42

PVSNet_Blended_VisFu		97.70 97	97.46 103	98.44 118	99.27 86	95.91 180	98.63 200	99.16 35	94.48 230	97.67 168	98.88 146	92.80 116	99.91 50	97.11 140	99.12 142	99.50 100

mvsany_test1		97.69 98	97.70 87	97.66 196	98.24 213	94.18 272	97.53 346	97.53 339	95.52 157	99.66 25	99.51 24	94.30 95	99.56 165	98.38 67	98.62 171	99.23 159

sasdasda		97.67 99	97.23 118	98.98 66	98.70 159	98.38 36	99.34 17	98.39 219	96.76 96	97.67 168	97.40 305	92.26 129	99.49 183	98.28 72	96.28 270	99.08 192

canonicalmvs		97.67 99	97.23 118	98.98 66	98.70 159	98.38 36	99.34 17	98.39 219	96.76 96	97.67 168	97.40 305	92.26 129	99.49 183	98.28 72	96.28 270	99.08 192

xiu_mvs_v2_base		97.66 101	97.70 87	97.56 203	98.61 173	95.46 203	97.44 350	98.46 200	97.15 77	98.65 101	98.15 234	94.33 94	99.80 102	97.84 96	98.66 170	97.41 294

GDP-MVS		97.64 102	97.28 113	98.71 86	98.30 209	97.33 92	99.05 70	98.52 184	96.34 119	98.80 83	99.05 117	89.74 205	99.51 179	96.86 160	98.86 158	99.28 147

baseline		97.64 102	97.44 105	98.25 135	98.35 196	96.20 159	99.00 84	98.32 233	96.33 121	98.03 135	99.17 89	91.35 163	99.16 231	98.10 79	98.29 198	99.39 124

casdiffmvs		97.63 104	97.41 106	98.28 130	98.33 204	96.14 163	98.82 141	98.32 233	96.38 118	97.95 144	99.21 80	91.23 169	99.23 222	98.12 78	98.37 192	99.48 107
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		97.62 105	97.19 121	98.92 72	98.66 166	98.20 54	99.32 22	98.38 223	96.69 102	97.58 177	97.42 304	92.10 137	99.50 182	98.28 72	96.25 273	99.08 192

xiu_mvs_v1_base_debu		97.60 106	97.56 94	97.72 185	98.35 196	95.98 167	97.86 319	98.51 187	97.13 79	99.01 65	98.40 206	91.56 153	99.80 102	98.53 52	98.68 166	97.37 298

xiu_mvs_v1_base		97.60 106	97.56 94	97.72 185	98.35 196	95.98 167	97.86 319	98.51 187	97.13 79	99.01 65	98.40 206	91.56 153	99.80 102	98.53 52	98.68 166	97.37 298

xiu_mvs_v1_base_debi		97.60 106	97.56 94	97.72 185	98.35 196	95.98 167	97.86 319	98.51 187	97.13 79	99.01 65	98.40 206	91.56 153	99.80 102	98.53 52	98.68 166	97.37 298

diffmvs		97.58 109	97.40 107	98.13 148	98.32 207	95.81 191	98.06 290	98.37 225	96.20 124	98.74 88	98.89 145	91.31 166	99.25 219	98.16 77	98.52 179	99.34 133
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		97.57 110	97.37 109	98.20 140	98.50 180	95.86 187	98.89 115	97.03 383	97.29 62	98.73 90	98.90 142	89.41 217	99.32 208	98.68 42	98.86 158	99.42 122

MVSFormer		97.57 110	97.49 100	97.84 172	98.07 237	95.76 192	99.47 7	98.40 216	94.98 196	98.79 84	98.83 154	92.34 125	98.41 335	96.91 148	99.59 89	99.34 133

alignmvs		97.56 112	97.07 128	99.01 63	98.66 166	98.37 43	98.83 139	98.06 298	96.74 98	98.00 141	97.65 282	90.80 184	99.48 188	98.37 68	96.56 256	99.19 168

DPM-MVS		97.55 113	96.99 133	99.23 43	99.04 121	98.55 28	97.17 378	98.35 228	94.85 206	97.93 148	98.58 189	95.07 78	99.71 134	92.60 308	99.34 131	99.43 119

OMC-MVS		97.55 113	97.34 111	98.20 140	99.33 67	95.92 179	98.28 257	98.59 165	95.52 157	97.97 142	99.10 103	93.28 111	99.49 183	95.09 225	98.88 155	99.19 168

LuminaMVS		97.49 115	97.18 122	98.42 122	97.50 298	97.15 111	98.45 231	97.68 318	96.56 110	98.68 95	98.78 163	89.84 202	99.32 208	98.60 47	98.57 175	98.79 220

KinetiMVS		97.48 116	97.05 129	98.78 80	98.37 194	97.30 96	98.99 87	98.70 134	97.18 74	99.02 63	99.01 123	87.50 272	99.67 142	95.33 215	99.33 133	99.37 127

viewmanbaseed2359cas		97.47 117	97.25 115	98.14 144	98.41 189	95.84 188	98.57 214	98.43 211	95.55 155	97.97 142	99.12 100	91.26 168	99.15 234	97.42 128	98.53 178	99.43 119

PAPM_NR		97.46 118	97.11 125	98.50 110	99.50 44	96.41 150	98.63 200	98.60 158	95.18 177	97.06 197	98.06 240	94.26 97	99.57 162	93.80 276	98.87 157	99.52 95

EPP-MVSNet		97.46 118	97.28 113	97.99 163	98.64 170	95.38 207	99.33 21	98.31 237	93.61 280	97.19 189	99.07 115	94.05 100	99.23 222	96.89 152	98.43 186	99.37 127

3Dnovator	94.51 5	97.46 118	96.93 136	99.07 59	97.78 271	97.64 76	99.35 16	99.06 43	97.02 84	93.75 323	99.16 92	89.25 222	99.92 40	97.22 138	99.75 49	99.64 80

CNLPA		97.45 121	97.03 130	98.73 84	99.05 120	97.44 89	98.07 289	98.53 181	95.32 170	96.80 211	98.53 194	93.32 109	99.72 129	94.31 257	99.31 134	99.02 199

lupinMVS		97.44 122	97.22 120	98.12 151	98.07 237	95.76 192	97.68 335	97.76 315	94.50 229	98.79 84	98.61 184	92.34 125	99.30 212	97.58 115	99.59 89	99.31 140

3Dnovator+	94.38 6	97.43 123	96.78 146	99.38 19	97.83 268	98.52 29	99.37 13	98.71 130	97.09 82	92.99 353	99.13 97	89.36 219	99.89 61	96.97 145	99.57 93	99.71 57

Vis-MVSNet		97.42 124	97.11 125	98.34 127	98.66 166	96.23 158	99.22 37	99.00 48	96.63 106	98.04 134	99.21 80	88.05 259	99.35 204	96.01 190	99.21 138	99.45 116
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 125	97.25 115	97.91 167	98.70 159	96.80 126	98.82 141	98.69 136	94.53 224	98.11 127	98.28 221	94.50 91	99.57 162	94.12 265	99.49 112	97.37 298

sss		97.39 126	96.98 135	98.61 94	98.60 174	96.61 135	98.22 263	98.93 60	93.97 251	98.01 140	98.48 199	91.98 141	99.85 77	96.45 174	98.15 200	99.39 124

test_cas_vis1_n_1920		97.38 127	97.36 110	97.45 207	98.95 135	93.25 310	99.00 84	98.53 181	97.70 34	99.77 15	99.35 57	84.71 326	99.85 77	98.57 49	99.66 72	99.26 155

PVSNet_Blended		97.38 127	97.12 124	98.14 144	99.25 89	95.35 210	97.28 366	99.26 16	93.13 302	97.94 146	98.21 229	92.74 117	99.81 95	96.88 154	99.40 125	99.27 148

WTY-MVS		97.37 129	96.92 137	98.72 85	98.86 144	96.89 124	98.31 251	98.71 130	95.26 173	97.67 168	98.56 193	92.21 133	99.78 117	95.89 192	96.85 246	99.48 107

AstraMVS		97.34 130	97.24 117	97.65 197	98.13 233	94.15 273	98.94 100	96.25 415	97.47 51	98.60 104	99.28 67	89.67 207	99.41 198	98.73 40	98.07 204	99.38 126

jason		97.32 131	97.08 127	98.06 158	97.45 304	95.59 195	97.87 317	97.91 309	94.79 208	98.55 107	98.83 154	91.12 175	99.23 222	97.58 115	99.60 87	99.34 133
jason: jason.
MVS_Test		97.28 132	97.00 131	98.13 148	98.33 204	95.97 172	98.74 167	98.07 293	94.27 236	98.44 115	98.07 239	92.48 121	99.26 217	96.43 175	98.19 199	99.16 174

EPNet		97.28 132	96.87 139	98.51 107	94.98 415	96.14 163	98.90 111	97.02 386	98.28 18	95.99 246	99.11 101	91.36 162	99.89 61	96.98 144	99.19 140	99.50 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mamba_0404		97.26 134	97.00 131	98.03 159	98.46 185	95.99 166	98.62 203	98.44 204	94.77 209	97.24 186	98.93 136	91.22 170	99.28 214	96.54 169	98.74 165	98.84 216

mvsmamba		97.25 135	96.99 133	98.02 161	98.34 201	95.54 200	99.18 49	97.47 345	95.04 190	98.15 124	98.57 192	89.46 214	99.31 211	97.68 109	99.01 148	99.22 161

test_yl		97.22 136	96.78 146	98.54 102	98.73 154	96.60 136	98.45 231	98.31 237	94.70 212	98.02 137	98.42 204	90.80 184	99.70 135	96.81 161	96.79 248	99.34 133

DCV-MVSNet		97.22 136	96.78 146	98.54 102	98.73 154	96.60 136	98.45 231	98.31 237	94.70 212	98.02 137	98.42 204	90.80 184	99.70 135	96.81 161	96.79 248	99.34 133

IS-MVSNet		97.22 136	96.88 138	98.25 135	98.85 147	96.36 153	99.19 45	97.97 303	95.39 164	97.23 187	98.99 126	91.11 176	98.93 273	94.60 245	98.59 173	99.47 109

PLC	95.07 4	97.20 139	96.78 146	98.44 118	99.29 81	96.31 157	98.14 279	98.76 118	92.41 330	96.39 234	98.31 219	94.92 83	99.78 117	94.06 268	98.77 164	99.23 159
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 140	97.18 122	97.20 220	98.81 150	93.27 307	95.78 423	99.15 37	95.25 174	96.79 212	98.11 237	92.29 128	99.07 250	98.56 51	99.85 6	99.25 157

mamba_test_0407		97.17 141	96.87 139	98.08 155	98.19 221	95.90 181	98.52 219	98.44 204	94.77 209	96.75 213	98.93 136	91.22 170	99.22 226	96.54 169	98.43 186	99.10 185

LS3D		97.16 142	96.66 155	98.68 88	98.53 179	97.19 109	98.93 106	98.90 67	92.83 315	95.99 246	99.37 51	92.12 136	99.87 72	93.67 280	99.57 93	98.97 204

AdaColmap		97.15 143	96.70 151	98.48 113	99.16 108	96.69 132	98.01 296	98.89 69	94.44 232	96.83 207	98.68 179	90.69 188	99.76 123	94.36 253	99.29 135	98.98 203

mamv4		97.13 144	98.11 70	94.17 389	98.97 133	83.70 433	98.66 193	98.71 130	94.63 218	97.83 154	98.90 142	96.25 29	99.55 172	99.27 25	99.76 42	99.27 148

Effi-MVS+		97.12 145	96.69 152	98.39 125	98.19 221	96.72 131	97.37 357	98.43 211	93.71 269	97.65 172	98.02 243	92.20 134	99.25 219	96.87 157	97.79 213	99.19 168

CHOSEN 1792x2688		97.12 145	96.80 143	98.08 155	99.30 76	94.56 255	98.05 291	99.71 1	93.57 282	97.09 193	98.91 141	88.17 253	99.89 61	96.87 157	99.56 101	99.81 21

F-COLMAP		97.09 147	96.80 143	97.97 164	99.45 57	94.95 234	98.55 217	98.62 157	93.02 307	96.17 241	98.58 189	94.01 101	99.81 95	93.95 270	98.90 153	99.14 178

RRT-MVS		97.03 148	96.78 146	97.77 181	97.90 264	94.34 264	99.12 59	98.35 228	95.87 139	98.06 131	98.70 177	86.45 291	99.63 152	98.04 84	98.54 177	99.35 131

TAMVS		97.02 149	96.79 145	97.70 188	98.06 240	95.31 213	98.52 219	98.31 237	93.95 252	97.05 198	98.61 184	93.49 107	98.52 317	95.33 215	97.81 212	99.29 145

viewmambaseed2359dif		97.01 150	96.84 141	97.51 205	98.19 221	94.21 271	98.16 276	98.23 258	93.61 280	97.78 156	99.13 97	90.79 187	99.18 230	97.24 136	98.40 191	99.15 175

CDS-MVSNet		96.99 151	96.69 152	97.90 168	98.05 242	95.98 167	98.20 266	98.33 232	93.67 276	96.95 200	98.49 198	93.54 106	98.42 328	95.24 222	97.74 216	99.31 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 152	96.55 160	98.21 138	98.17 230	96.07 165	97.98 300	98.21 260	97.24 69	97.13 191	98.93 136	86.88 283	99.91 50	95.00 228	99.37 129	98.66 242

114514_t		96.93 153	96.27 173	98.92 72	99.50 44	97.63 77	98.85 133	98.90 67	84.80 429	97.77 157	99.11 101	92.84 115	99.66 145	94.85 231	99.77 36	99.47 109

MAR-MVS		96.91 154	96.40 167	98.45 116	98.69 162	96.90 122	98.66 193	98.68 139	92.40 331	97.07 196	97.96 250	91.54 156	99.75 125	93.68 278	98.92 152	98.69 236
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		96.90 155	96.49 164	98.14 144	99.33 67	95.56 197	97.38 355	99.65 2	92.34 332	97.61 175	98.20 230	89.29 221	99.10 247	96.97 145	97.60 221	99.77 34

Vis-MVSNet (Re-imp)		96.87 156	96.55 160	97.83 173	98.73 154	95.46 203	99.20 43	98.30 244	94.96 198	96.60 222	98.87 147	90.05 198	98.59 312	93.67 280	98.60 172	99.46 114

SDMVSNet		96.85 157	96.42 165	98.14 144	99.30 76	96.38 151	99.21 40	99.23 24	95.92 135	95.96 248	98.76 171	85.88 301	99.44 195	97.93 88	95.59 285	98.60 247

PAPR		96.84 158	96.24 175	98.65 91	98.72 158	96.92 121	97.36 359	98.57 172	93.33 291	96.67 217	97.57 291	94.30 95	99.56 165	91.05 351	98.59 173	99.47 109

HY-MVS	93.96 8	96.82 159	96.23 176	98.57 97	98.46 185	97.00 117	98.14 279	98.21 260	93.95 252	96.72 216	97.99 247	91.58 152	99.76 123	94.51 249	96.54 257	98.95 207

mamba_0408		96.81 160	96.38 168	98.09 154	98.19 221	95.90 181	95.69 424	98.32 233	94.51 227	96.75 213	98.73 173	90.99 180	99.27 216	95.83 195	98.43 186	99.10 185

UGNet		96.78 161	96.30 172	98.19 143	98.24 213	95.89 185	98.88 122	98.93 60	97.39 56	96.81 210	97.84 263	82.60 355	99.90 58	96.53 171	99.49 112	98.79 220
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
icg_test_0407		96.74 162	96.64 156	97.05 235	97.99 251	92.82 322	98.45 231	98.27 247	95.16 178	97.30 182	98.79 159	91.53 157	99.06 251	94.74 236	97.54 225	99.27 148

icg_test_0403		96.74 162	96.61 157	97.12 229	97.99 251	92.82 322	98.47 229	98.27 247	95.16 178	97.13 191	98.79 159	91.44 160	99.26 217	94.74 236	97.54 225	99.27 148

PVSNet_BlendedMVS		96.73 164	96.60 158	97.12 229	99.25 89	95.35 210	98.26 260	99.26 16	94.28 235	97.94 146	97.46 298	92.74 117	99.81 95	96.88 154	93.32 321	96.20 391

mamba_test_0407_2		96.71 165	96.38 168	97.68 191	98.19 221	95.90 181	95.69 424	98.32 233	94.51 227	96.75 213	98.73 173	90.99 180	98.02 374	95.83 195	98.43 186	99.10 185

test_vis1_n_1920		96.71 165	96.84 141	96.31 304	99.11 115	89.74 383	99.05 70	98.58 170	98.08 21	99.87 3	99.37 51	78.48 387	99.93 31	99.29 24	99.69 66	99.27 148

mvs_anonymous		96.70 167	96.53 162	97.18 223	98.19 221	93.78 283	98.31 251	98.19 264	94.01 248	94.47 280	98.27 224	92.08 139	98.46 323	97.39 132	97.91 208	99.31 140

Elysia		96.64 168	96.02 183	98.51 107	98.04 244	97.30 96	98.74 167	98.60 158	95.04 190	97.91 150	98.84 150	83.59 350	99.48 188	94.20 261	99.25 136	98.75 229

StellarMVS		96.64 168	96.02 183	98.51 107	98.04 244	97.30 96	98.74 167	98.60 158	95.04 190	97.91 150	98.84 150	83.59 350	99.48 188	94.20 261	99.25 136	98.75 229

1112_ss		96.63 170	96.00 185	98.50 110	98.56 175	96.37 152	98.18 274	98.10 286	92.92 311	94.84 268	98.43 202	92.14 135	99.58 161	94.35 254	96.51 258	99.56 94

PMMVS		96.60 171	96.33 171	97.41 211	97.90 264	93.93 279	97.35 360	98.41 214	92.84 314	97.76 158	97.45 300	91.10 177	99.20 227	96.26 180	97.91 208	99.11 183

DP-MVS		96.59 172	95.93 188	98.57 97	99.34 65	96.19 161	98.70 182	98.39 219	89.45 401	94.52 278	99.35 57	91.85 145	99.85 77	92.89 304	98.88 155	99.68 69

PatchMatch-RL		96.59 172	96.03 182	98.27 131	99.31 72	96.51 144	97.91 309	99.06 43	93.72 268	96.92 204	98.06 240	88.50 248	99.65 146	91.77 333	99.00 150	98.66 242

GeoE		96.58 174	96.07 179	98.10 153	98.35 196	95.89 185	99.34 17	98.12 280	93.12 303	96.09 242	98.87 147	89.71 206	98.97 263	92.95 300	98.08 203	99.43 119

icg_test_0407_2		96.56 175	96.50 163	96.73 256	97.99 251	92.82 322	97.18 375	98.27 247	95.16 178	97.30 182	98.79 159	91.53 157	98.10 365	94.74 236	97.54 225	99.27 148

XVG-OURS		96.55 176	96.41 166	96.99 238	98.75 153	93.76 284	97.50 349	98.52 184	95.67 150	96.83 207	99.30 65	88.95 236	99.53 175	95.88 193	96.26 272	97.69 287

FIs		96.51 177	96.12 178	97.67 193	97.13 328	97.54 82	99.36 14	99.22 28	95.89 137	94.03 309	98.35 212	91.98 141	98.44 326	96.40 176	92.76 329	97.01 306

XVG-OURS-SEG-HR		96.51 177	96.34 170	97.02 237	98.77 152	93.76 284	97.79 328	98.50 192	95.45 160	96.94 201	99.09 110	87.87 264	99.55 172	96.76 165	95.83 284	97.74 284

PS-MVSNAJss		96.43 179	96.26 174	96.92 247	95.84 395	95.08 224	99.16 51	98.50 192	95.87 139	93.84 318	98.34 216	94.51 88	98.61 308	96.88 154	93.45 318	97.06 304

test_fmvs1		96.42 180	96.67 154	95.66 334	98.82 149	88.53 410	98.80 150	98.20 262	96.39 117	99.64 27	99.20 82	80.35 375	99.67 142	99.04 30	99.57 93	98.78 224

FC-MVSNet-test		96.42 180	96.05 180	97.53 204	96.95 337	97.27 100	99.36 14	99.23 24	95.83 141	93.93 312	98.37 210	92.00 140	98.32 347	96.02 189	92.72 330	97.00 307

ab-mvs		96.42 180	95.71 199	98.55 100	98.63 171	96.75 129	97.88 316	98.74 122	93.84 258	96.54 227	98.18 232	85.34 312	99.75 125	95.93 191	96.35 262	99.15 175

FA-MVS(test-final)		96.41 183	95.94 187	97.82 175	98.21 217	95.20 218	97.80 326	97.58 329	93.21 297	97.36 181	97.70 275	89.47 212	99.56 165	94.12 265	97.99 205	98.71 234

PVSNet	91.96 18	96.35 184	96.15 177	96.96 242	99.17 104	92.05 336	96.08 416	98.68 139	93.69 272	97.75 160	97.80 269	88.86 237	99.69 140	94.26 259	99.01 148	99.15 175

Test_1112_low_res		96.34 185	95.66 204	98.36 126	98.56 175	95.94 175	97.71 333	98.07 293	92.10 341	94.79 272	97.29 313	91.75 147	99.56 165	94.17 263	96.50 259	99.58 92

Effi-MVS+-dtu		96.29 186	96.56 159	95.51 339	97.89 266	90.22 376	98.80 150	98.10 286	96.57 109	96.45 232	96.66 370	90.81 183	98.91 276	95.72 202	97.99 205	97.40 295

QAPM		96.29 186	95.40 210	98.96 69	97.85 267	97.60 79	99.23 33	98.93 60	89.76 395	93.11 350	99.02 119	89.11 227	99.93 31	91.99 327	99.62 84	99.34 133

Fast-Effi-MVS+		96.28 188	95.70 201	98.03 159	98.29 210	95.97 172	98.58 207	98.25 256	91.74 349	95.29 261	97.23 318	91.03 179	99.15 234	92.90 302	97.96 207	98.97 204

nrg030		96.28 188	95.72 196	97.96 166	96.90 342	98.15 59	99.39 11	98.31 237	95.47 159	94.42 286	98.35 212	92.09 138	98.69 300	97.50 125	89.05 380	97.04 305

1314		96.25 190	95.73 195	97.79 177	97.13 328	95.55 199	98.19 269	98.59 165	93.47 286	92.03 379	97.82 267	91.33 164	99.49 183	94.62 244	98.44 184	98.32 267

sd_testset		96.17 191	95.76 194	97.42 210	99.30 76	94.34 264	98.82 141	99.08 41	95.92 135	95.96 248	98.76 171	82.83 354	99.32 208	95.56 208	95.59 285	98.60 247

h-mvs33		96.17 191	95.62 205	97.81 176	99.03 122	94.45 257	98.64 197	98.75 120	97.48 49	98.67 96	98.72 176	89.76 203	99.86 76	97.95 86	81.59 429	99.11 183

HQP_MVS		96.14 193	95.90 189	96.85 250	97.42 306	94.60 253	98.80 150	98.56 175	97.28 64	95.34 257	98.28 221	87.09 278	99.03 256	96.07 184	94.27 293	96.92 314

tttt0517		96.07 194	95.51 208	97.78 178	98.41 189	94.84 238	99.28 25	94.33 437	94.26 237	97.64 173	98.64 183	84.05 341	99.47 192	95.34 214	97.60 221	99.03 198

MVSTER		96.06 195	95.72 196	97.08 233	98.23 215	95.93 178	98.73 173	98.27 247	94.86 204	95.07 263	98.09 238	88.21 252	98.54 315	96.59 167	93.46 316	96.79 333

thisisatest0530		96.01 196	95.36 215	97.97 164	98.38 192	95.52 201	98.88 122	94.19 439	94.04 243	97.64 173	98.31 219	83.82 348	99.46 193	95.29 219	97.70 218	98.93 209

test_djsdf		96.00 197	95.69 202	96.93 244	95.72 397	95.49 202	99.47 7	98.40 216	94.98 196	94.58 276	97.86 260	89.16 225	98.41 335	96.91 148	94.12 301	96.88 323

EI-MVSNet		95.96 198	95.83 191	96.36 300	97.93 262	93.70 290	98.12 282	98.27 247	93.70 271	95.07 263	99.02 119	92.23 132	98.54 315	94.68 240	93.46 316	96.84 329

VortexMVS		95.95 199	95.79 192	96.42 296	98.29 210	93.96 278	98.68 186	98.31 237	96.02 132	94.29 294	97.57 291	89.47 212	98.37 342	97.51 124	91.93 337	96.94 312

ECVR-MVS		95.95 199	95.71 199	96.65 264	99.02 123	90.86 358	99.03 77	91.80 450	96.96 87	98.10 128	99.26 71	81.31 361	99.51 179	96.90 151	99.04 145	99.59 88

BH-untuned		95.95 199	95.72 196	96.65 264	98.55 177	92.26 330	98.23 262	97.79 314	93.73 266	94.62 275	98.01 245	88.97 235	99.00 262	93.04 297	98.51 180	98.68 238

test1111		95.94 202	95.78 193	96.41 297	98.99 130	90.12 377	99.04 74	92.45 449	96.99 86	98.03 135	99.27 70	81.40 360	99.48 188	96.87 157	99.04 145	99.63 82

MSDG		95.93 203	95.30 222	97.83 173	98.90 138	95.36 208	96.83 403	98.37 225	91.32 364	94.43 285	98.73 173	90.27 196	99.60 158	90.05 365	98.82 162	98.52 255

BH-RMVSNet		95.92 204	95.32 220	97.69 189	98.32 207	94.64 247	98.19 269	97.45 350	94.56 222	96.03 244	98.61 184	85.02 317	99.12 241	90.68 356	99.06 144	99.30 143

test_fmvs1_n		95.90 205	95.99 186	95.63 335	98.67 165	88.32 414	99.26 28	98.22 259	96.40 116	99.67 24	99.26 71	73.91 424	99.70 135	99.02 31	99.50 110	98.87 213

Fast-Effi-MVS+-dtu		95.87 206	95.85 190	95.91 321	97.74 276	91.74 342	98.69 184	98.15 276	95.56 154	94.92 266	97.68 280	88.98 234	98.79 294	93.19 292	97.78 214	97.20 302

LFMVS		95.86 207	94.98 237	98.47 114	98.87 143	96.32 155	98.84 137	96.02 416	93.40 289	98.62 102	99.20 82	74.99 418	99.63 152	97.72 102	97.20 234	99.46 114

baseline1		95.84 208	95.12 230	98.01 162	98.49 184	95.98 167	98.73 173	97.03 383	95.37 167	96.22 237	98.19 231	89.96 200	99.16 231	94.60 245	87.48 396	98.90 212

OpenMVS	93.04 13	95.83 209	95.00 235	98.32 128	97.18 325	97.32 93	99.21 40	98.97 52	89.96 391	91.14 388	99.05 117	86.64 286	99.92 40	93.38 286	99.47 115	97.73 285

ICG_test_0404		95.82 210	95.52 206	96.73 256	97.99 251	92.82 322	97.23 368	98.27 247	95.16 178	94.31 292	98.79 159	85.63 305	98.10 365	94.74 236	97.54 225	99.27 148

VDD-MVS		95.82 210	95.23 224	97.61 200	98.84 148	93.98 277	98.68 186	97.40 354	95.02 194	97.95 144	99.34 61	74.37 423	99.78 117	98.64 45	96.80 247	99.08 192

UniMVSNet (Re)		95.78 212	95.19 226	97.58 201	96.99 335	97.47 86	98.79 158	99.18 32	95.60 152	93.92 313	97.04 340	91.68 149	98.48 319	95.80 199	87.66 395	96.79 333

VPA-MVSNet		95.75 213	95.11 231	97.69 189	97.24 317	97.27 100	98.94 100	99.23 24	95.13 183	95.51 255	97.32 311	85.73 303	98.91 276	97.33 135	89.55 371	96.89 322

HQP-MVS		95.72 214	95.40 210	96.69 262	97.20 321	94.25 269	98.05 291	98.46 200	96.43 113	94.45 281	97.73 272	86.75 284	98.96 267	95.30 217	94.18 297	96.86 328

hse-mvs2		95.71 215	95.30 222	96.93 244	98.50 180	93.53 295	98.36 243	98.10 286	97.48 49	98.67 96	97.99 247	89.76 203	99.02 259	97.95 86	80.91 434	98.22 270

UniMVSNet_NR-MVSNet		95.71 215	95.15 227	97.40 213	96.84 345	96.97 118	98.74 167	99.24 19	95.16 178	93.88 315	97.72 274	91.68 149	98.31 349	95.81 197	87.25 401	96.92 314

PatchmatchNet		95.71 215	95.52 206	96.29 306	97.58 289	90.72 362	96.84 402	97.52 340	94.06 242	97.08 194	96.96 350	89.24 223	98.90 279	92.03 326	98.37 192	99.26 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 218	95.33 219	96.76 255	96.16 381	94.63 248	98.43 238	98.39 219	96.64 105	95.02 265	98.78 163	85.15 316	99.05 252	95.21 224	94.20 296	96.60 356
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 218	95.38 214	96.61 272	97.61 286	93.84 282	98.91 110	98.44 204	95.25 174	94.28 295	98.47 200	86.04 300	99.12 241	95.50 211	93.95 306	96.87 326
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 220	95.69 202	95.44 343	97.54 294	88.54 409	96.97 388	97.56 332	93.50 284	97.52 179	96.93 354	89.49 210	99.16 231	95.25 221	96.42 261	98.64 244

FE-MVS		95.62 221	94.90 241	97.78 178	98.37 194	94.92 235	97.17 378	97.38 356	90.95 375	97.73 163	97.70 275	85.32 314	99.63 152	91.18 343	98.33 195	98.79 220

LPG-MVS_test		95.62 221	95.34 216	96.47 290	97.46 301	93.54 293	98.99 87	98.54 179	94.67 216	94.36 289	98.77 166	85.39 309	99.11 243	95.71 203	94.15 299	96.76 336

CLD-MVS		95.62 221	95.34 216	96.46 293	97.52 297	93.75 286	97.27 367	98.46 200	95.53 156	94.42 286	98.00 246	86.21 295	98.97 263	96.25 182	94.37 291	96.66 351
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 224	94.89 242	97.76 182	98.15 232	95.15 221	96.77 404	94.41 435	92.95 310	97.18 190	97.43 302	84.78 323	99.45 194	94.63 242	97.73 217	98.68 238

MonoMVSNet		95.51 225	95.45 209	95.68 332	95.54 402	90.87 357	98.92 108	97.37 357	95.79 143	95.53 254	97.38 307	89.58 209	97.68 396	96.40 176	92.59 331	98.49 257

thres600view7		95.49 226	94.77 245	97.67 193	98.98 131	95.02 226	98.85 133	96.90 393	95.38 165	96.63 219	96.90 356	84.29 333	99.59 159	88.65 389	96.33 263	98.40 261

test_vis1_n		95.47 227	95.13 228	96.49 287	97.77 272	90.41 372	99.27 27	98.11 283	96.58 107	99.66 25	99.18 88	67.00 438	99.62 156	99.21 26	99.40 125	99.44 117

SCA		95.46 228	95.13 228	96.46 293	97.67 281	91.29 350	97.33 362	97.60 328	94.68 215	96.92 204	97.10 325	83.97 343	98.89 280	92.59 310	98.32 197	99.20 164

IterMVS-LS		95.46 228	95.21 225	96.22 308	98.12 234	93.72 289	98.32 250	98.13 279	93.71 269	94.26 296	97.31 312	92.24 131	98.10 365	94.63 242	90.12 362	96.84 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 230	95.34 216	95.77 330	98.69 162	88.75 405	98.87 125	97.21 370	96.13 127	97.22 188	97.68 280	77.95 395	99.65 146	97.58 115	96.77 250	98.91 211

jajsoiax		95.45 230	95.03 234	96.73 256	95.42 410	94.63 248	99.14 55	98.52 184	95.74 145	93.22 343	98.36 211	83.87 346	98.65 305	96.95 147	94.04 302	96.91 319

CVMVSNet		95.43 232	96.04 181	93.57 396	97.93 262	83.62 434	98.12 282	98.59 165	95.68 149	96.56 223	99.02 119	87.51 270	97.51 405	93.56 284	97.44 230	99.60 86

anonymousdsp		95.42 233	94.91 240	96.94 243	95.10 414	95.90 181	99.14 55	98.41 214	93.75 263	93.16 346	97.46 298	87.50 272	98.41 335	95.63 207	94.03 303	96.50 375

DU-MVS		95.42 233	94.76 246	97.40 213	96.53 362	96.97 118	98.66 193	98.99 51	95.43 161	93.88 315	97.69 277	88.57 243	98.31 349	95.81 197	87.25 401	96.92 314

mvs_tets		95.41 235	95.00 235	96.65 264	95.58 401	94.42 259	99.00 84	98.55 177	95.73 147	93.21 344	98.38 209	83.45 352	98.63 306	97.09 141	94.00 304	96.91 319

thres100view900		95.38 236	94.70 250	97.41 211	98.98 131	94.92 235	98.87 125	96.90 393	95.38 165	96.61 221	96.88 357	84.29 333	99.56 165	88.11 392	96.29 267	97.76 282

thres400		95.38 236	94.62 254	97.65 197	98.94 136	94.98 231	98.68 186	96.93 391	95.33 168	96.55 225	96.53 376	84.23 337	99.56 165	88.11 392	96.29 267	98.40 261

BH-w/o		95.38 236	95.08 232	96.26 307	98.34 201	91.79 339	97.70 334	97.43 352	92.87 313	94.24 298	97.22 319	88.66 241	98.84 286	91.55 339	97.70 218	98.16 273

VDDNet		95.36 239	94.53 259	97.86 171	98.10 236	95.13 222	98.85 133	97.75 316	90.46 382	98.36 118	99.39 45	73.27 426	99.64 149	97.98 85	96.58 255	98.81 219

TAPA-MVS	93.98 7	95.35 240	94.56 258	97.74 184	99.13 112	94.83 240	98.33 246	98.64 152	86.62 417	96.29 236	98.61 184	94.00 102	99.29 213	80.00 435	99.41 122	99.09 188
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 241	94.98 237	96.43 295	97.67 281	93.48 297	98.73 173	98.44 204	94.94 202	92.53 366	98.53 194	84.50 332	99.14 237	95.48 212	94.00 304	96.66 351
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 242	94.87 243	96.71 259	99.29 81	93.24 311	98.58 207	98.11 283	89.92 392	93.57 328	99.10 103	86.37 293	99.79 114	90.78 354	98.10 202	97.09 303
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 243	94.72 249	97.13 227	98.05 242	93.26 308	97.87 317	97.20 371	94.96 198	96.18 240	95.66 409	80.97 367	99.35 204	94.47 251	97.08 237	98.78 224

tfpn200view9		95.32 243	94.62 254	97.43 209	98.94 136	94.98 231	98.68 186	96.93 391	95.33 168	96.55 225	96.53 376	84.23 337	99.56 165	88.11 392	96.29 267	97.76 282

Anonymous202405211		95.28 245	94.49 261	97.67 193	99.00 127	93.75 286	98.70 182	97.04 382	90.66 378	96.49 229	98.80 157	78.13 391	99.83 83	96.21 183	95.36 289	99.44 117

thres200		95.25 246	94.57 257	97.28 217	98.81 150	94.92 235	98.20 266	97.11 375	95.24 176	96.54 227	96.22 387	84.58 330	99.53 175	87.93 397	96.50 259	97.39 296

AllTest		95.24 247	94.65 253	96.99 238	99.25 89	93.21 312	98.59 205	98.18 267	91.36 360	93.52 330	98.77 166	84.67 327	99.72 129	89.70 372	97.87 210	98.02 277

LCM-MVSNet-Re		95.22 248	95.32 220	94.91 360	98.18 227	87.85 420	98.75 163	95.66 423	95.11 185	88.96 408	96.85 360	90.26 197	97.65 397	95.65 206	98.44 184	99.22 161

EPNet_dtu		95.21 249	94.95 239	95.99 316	96.17 379	90.45 370	98.16 276	97.27 365	96.77 95	93.14 349	98.33 217	90.34 193	98.42 328	85.57 410	98.81 163	99.09 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 250	94.45 267	97.46 206	96.75 352	96.56 142	98.86 129	98.65 151	93.30 294	93.27 342	98.27 224	84.85 321	98.87 283	94.82 233	91.26 348	96.96 309

D2MVS		95.18 251	95.08 232	95.48 340	97.10 330	92.07 335	98.30 254	99.13 39	94.02 245	92.90 354	96.73 366	89.48 211	98.73 298	94.48 250	93.60 315	95.65 405

WR-MVS		95.15 252	94.46 264	97.22 219	96.67 357	96.45 146	98.21 264	98.81 100	94.15 239	93.16 346	97.69 277	87.51 270	98.30 351	95.29 219	88.62 386	96.90 321

TranMVSNet+NR-MVSNet		95.14 253	94.48 262	97.11 231	96.45 368	96.36 153	99.03 77	99.03 46	95.04 190	93.58 327	97.93 253	88.27 251	98.03 373	94.13 264	86.90 406	96.95 311

myMVS_eth3d28		95.12 254	94.62 254	96.64 268	98.17 230	92.17 331	98.02 295	97.32 359	95.41 163	96.22 237	96.05 393	78.01 393	99.13 238	95.22 223	97.16 235	98.60 247

baseline2		95.11 255	94.52 260	96.87 249	96.65 358	93.56 292	98.27 259	94.10 441	93.45 287	92.02 380	97.43 302	87.45 275	99.19 228	93.88 273	97.41 232	97.87 280

miper_enhance_ethall		95.10 256	94.75 247	96.12 312	97.53 296	93.73 288	96.61 410	98.08 291	92.20 340	93.89 314	96.65 372	92.44 122	98.30 351	94.21 260	91.16 349	96.34 384

Anonymous20240529		95.10 256	94.22 277	97.75 183	99.01 125	94.26 268	98.87 125	98.83 91	85.79 425	96.64 218	98.97 127	78.73 384	99.85 77	96.27 179	94.89 290	99.12 180

test-LLR		95.10 256	94.87 243	95.80 327	96.77 349	89.70 385	96.91 393	95.21 427	95.11 185	94.83 270	95.72 406	87.71 266	98.97 263	93.06 295	98.50 181	98.72 231

WR-MVS_H		95.05 259	94.46 264	96.81 253	96.86 344	95.82 190	99.24 31	99.24 19	93.87 257	92.53 366	96.84 361	90.37 192	98.24 357	93.24 290	87.93 392	96.38 383

miper_ehance_all_eth		95.01 260	94.69 251	95.97 318	97.70 279	93.31 306	97.02 386	98.07 293	92.23 337	93.51 332	96.96 350	91.85 145	98.15 361	93.68 278	91.16 349	96.44 381

testing11		95.00 261	94.28 274	97.16 225	97.96 259	93.36 305	98.09 287	97.06 381	94.94 202	95.33 260	96.15 389	76.89 408	99.40 199	95.77 201	96.30 266	98.72 231

ADS-MVSNet		95.00 261	94.45 267	96.63 269	98.00 249	91.91 338	96.04 417	97.74 317	90.15 388	96.47 230	96.64 373	87.89 262	98.96 267	90.08 363	97.06 238	99.02 199

VPNet		94.99 263	94.19 279	97.40 213	97.16 326	96.57 141	98.71 178	98.97 52	95.67 150	94.84 268	98.24 228	80.36 374	98.67 304	96.46 173	87.32 400	96.96 309

EPMVS		94.99 263	94.48 262	96.52 285	97.22 319	91.75 341	97.23 368	91.66 451	94.11 240	97.28 184	96.81 363	85.70 304	98.84 286	93.04 297	97.28 233	98.97 204

testing91		94.98 265	94.25 276	97.20 220	97.94 260	93.41 300	98.00 298	97.58 329	94.99 195	95.45 256	96.04 394	77.20 403	99.42 197	94.97 229	96.02 280	98.78 224

NR-MVSNet		94.98 265	94.16 282	97.44 208	96.53 362	97.22 108	98.74 167	98.95 56	94.96 198	89.25 407	97.69 277	89.32 220	98.18 359	94.59 247	87.40 398	96.92 314

FMVSNet3		94.97 267	94.26 275	97.11 231	98.18 227	96.62 133	98.56 216	98.26 255	93.67 276	94.09 305	97.10 325	84.25 335	98.01 375	92.08 322	92.14 334	96.70 345

CostFormer		94.95 268	94.73 248	95.60 337	97.28 315	89.06 398	97.53 346	96.89 395	89.66 397	96.82 209	96.72 367	86.05 298	98.95 272	95.53 210	96.13 278	98.79 220

PAPM		94.95 268	94.00 295	97.78 178	97.04 332	95.65 194	96.03 419	98.25 256	91.23 369	94.19 301	97.80 269	91.27 167	98.86 285	82.61 427	97.61 220	98.84 216

CP-MVSNet		94.94 270	94.30 273	96.83 251	96.72 354	95.56 197	99.11 61	98.95 56	93.89 255	92.42 371	97.90 256	87.19 277	98.12 364	94.32 256	88.21 389	96.82 332

TR-MVS		94.94 270	94.20 278	97.17 224	97.75 273	94.14 274	97.59 343	97.02 386	92.28 336	95.75 252	97.64 285	83.88 345	98.96 267	89.77 369	96.15 277	98.40 261

RPSCF		94.87 272	95.40 210	93.26 402	98.89 139	82.06 440	98.33 246	98.06 298	90.30 387	96.56 223	99.26 71	87.09 278	99.49 183	93.82 275	96.32 264	98.24 268

testing99		94.83 273	94.08 287	97.07 234	97.94 260	93.13 314	98.10 286	97.17 373	94.86 204	95.34 257	96.00 398	76.31 411	99.40 199	95.08 226	95.90 281	98.68 238

GA-MVS		94.81 274	94.03 291	97.14 226	97.15 327	93.86 281	96.76 405	97.58 329	94.00 249	94.76 274	97.04 340	80.91 368	98.48 319	91.79 332	96.25 273	99.09 188

c3_l		94.79 275	94.43 269	95.89 323	97.75 273	93.12 316	97.16 380	98.03 300	92.23 337	93.46 336	97.05 339	91.39 161	98.01 375	93.58 283	89.21 378	96.53 367

V42		94.78 276	94.14 284	96.70 261	96.33 373	95.22 217	98.97 91	98.09 290	92.32 334	94.31 292	97.06 336	88.39 249	98.55 314	92.90 302	88.87 384	96.34 384

reproduce_monomvs		94.77 277	94.67 252	95.08 355	98.40 191	89.48 391	98.80 150	98.64 152	97.57 43	93.21 344	97.65 282	80.57 373	98.83 289	97.72 102	89.47 374	96.93 313

CR-MVSNet		94.76 278	94.15 283	96.59 275	97.00 333	93.43 298	94.96 432	97.56 332	92.46 325	96.93 202	96.24 383	88.15 254	97.88 388	87.38 399	96.65 253	98.46 259

v2v482		94.69 279	94.03 291	96.65 264	96.17 379	94.79 243	98.67 191	98.08 291	92.72 317	94.00 310	97.16 322	87.69 269	98.45 324	92.91 301	88.87 384	96.72 341

pmmvs4		94.69 279	93.99 297	96.81 253	95.74 396	95.94 175	97.40 353	97.67 321	90.42 384	93.37 339	97.59 289	89.08 228	98.20 358	92.97 299	91.67 342	96.30 387

cl22		94.68 281	94.19 279	96.13 311	98.11 235	93.60 291	96.94 390	98.31 237	92.43 329	93.32 341	96.87 359	86.51 287	98.28 355	94.10 267	91.16 349	96.51 373

eth_miper_zixun_eth		94.68 281	94.41 270	95.47 341	97.64 284	91.71 343	96.73 407	98.07 293	92.71 318	93.64 324	97.21 320	90.54 190	98.17 360	93.38 286	89.76 366	96.54 365

PCF-MVS	93.45 11	94.68 281	93.43 333	98.42 122	98.62 172	96.77 128	95.48 429	98.20 262	84.63 430	93.34 340	98.32 218	88.55 246	99.81 95	84.80 419	98.96 151	98.68 238
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 284	93.54 328	98.08 155	96.88 343	96.56 142	98.19 269	98.50 192	78.05 442	92.69 361	98.02 243	91.07 178	99.63 152	90.09 362	98.36 194	98.04 276

PS-CasMVS		94.67 284	93.99 297	96.71 259	96.68 356	95.26 214	99.13 58	99.03 46	93.68 274	92.33 372	97.95 251	85.35 311	98.10 365	93.59 282	88.16 391	96.79 333

cascas		94.63 286	93.86 307	96.93 244	96.91 341	94.27 267	96.00 420	98.51 187	85.55 426	94.54 277	96.23 385	84.20 339	98.87 283	95.80 199	96.98 243	97.66 288

tpmvs		94.60 287	94.36 272	95.33 347	97.46 301	88.60 408	96.88 399	97.68 318	91.29 366	93.80 320	96.42 380	88.58 242	99.24 221	91.06 349	96.04 279	98.17 272

LTVRE_ROB	92.95 15	94.60 287	93.90 303	96.68 263	97.41 309	94.42 259	98.52 219	98.59 165	91.69 352	91.21 387	98.35 212	84.87 320	99.04 255	91.06 349	93.44 319	96.60 356
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		94.59 289	93.92 300	96.60 274	96.21 375	94.78 244	98.59 205	98.14 278	91.86 348	94.21 300	97.02 343	87.97 260	98.41 335	91.72 334	89.57 369	96.61 355

ADS-MVSNet2		94.58 290	94.40 271	95.11 353	98.00 249	88.74 406	96.04 417	97.30 361	90.15 388	96.47 230	96.64 373	87.89 262	97.56 403	90.08 363	97.06 238	99.02 199

WBMVS		94.56 291	94.04 289	96.10 313	98.03 246	93.08 318	97.82 325	98.18 267	94.02 245	93.77 322	96.82 362	81.28 362	98.34 344	95.47 213	91.00 352	96.88 323

ACMH	92.88 16	94.55 292	93.95 299	96.34 302	97.63 285	93.26 308	98.81 149	98.49 197	93.43 288	89.74 401	98.53 194	81.91 357	99.08 249	93.69 277	93.30 322	96.70 345
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 293	93.85 308	96.63 269	97.98 257	93.06 319	98.77 162	97.84 312	93.67 276	93.80 320	98.04 242	76.88 409	98.96 267	94.79 235	92.86 327	97.86 281

XVG-ACMP-BASELINE		94.54 293	94.14 284	95.75 331	96.55 361	91.65 344	98.11 284	98.44 204	94.96 198	94.22 299	97.90 256	79.18 383	99.11 243	94.05 269	93.85 308	96.48 378

AUN-MVS		94.53 295	93.73 318	96.92 247	98.50 180	93.52 296	98.34 245	98.10 286	93.83 260	95.94 250	97.98 249	85.59 307	99.03 256	94.35 254	80.94 433	98.22 270

DIV-MVS_self_test		94.52 296	94.03 291	95.99 316	97.57 293	93.38 303	97.05 384	97.94 306	91.74 349	92.81 356	97.10 325	89.12 226	98.07 371	92.60 308	90.30 359	96.53 367

cl____		94.51 297	94.01 294	96.02 315	97.58 289	93.40 302	97.05 384	97.96 305	91.73 351	92.76 358	97.08 331	89.06 229	98.13 363	92.61 307	90.29 360	96.52 370

ETVMVS		94.50 298	93.44 332	97.68 191	98.18 227	95.35 210	98.19 269	97.11 375	93.73 266	96.40 233	95.39 412	74.53 420	98.84 286	91.10 345	96.31 265	98.84 216

GBi-Net		94.49 299	93.80 311	96.56 279	98.21 217	95.00 227	98.82 141	98.18 267	92.46 325	94.09 305	97.07 332	81.16 363	97.95 380	92.08 322	92.14 334	96.72 341

test1		94.49 299	93.80 311	96.56 279	98.21 217	95.00 227	98.82 141	98.18 267	92.46 325	94.09 305	97.07 332	81.16 363	97.95 380	92.08 322	92.14 334	96.72 341

dmvs_re		94.48 301	94.18 281	95.37 345	97.68 280	90.11 378	98.54 218	97.08 377	94.56 222	94.42 286	97.24 317	84.25 335	97.76 394	91.02 352	92.83 328	98.24 268

v8		94.47 302	93.77 314	96.57 278	96.36 371	94.83 240	99.05 70	98.19 264	91.92 345	93.16 346	96.97 348	88.82 240	98.48 319	91.69 335	87.79 393	96.39 382

FMVSNet2		94.47 302	93.61 324	97.04 236	98.21 217	96.43 148	98.79 158	98.27 247	92.46 325	93.50 333	97.09 329	81.16 363	98.00 377	91.09 346	91.93 337	96.70 345

test2506		94.44 304	93.91 302	96.04 314	99.02 123	88.99 401	99.06 68	79.47 463	96.96 87	98.36 118	99.26 71	77.21 402	99.52 178	96.78 164	99.04 145	99.59 88

Patchmatch-test		94.42 305	93.68 322	96.63 269	97.60 287	91.76 340	94.83 436	97.49 344	89.45 401	94.14 303	97.10 325	88.99 231	98.83 289	85.37 413	98.13 201	99.29 145

PEN-MVS		94.42 305	93.73 318	96.49 287	96.28 374	94.84 238	99.17 50	99.00 48	93.51 283	92.23 374	97.83 266	86.10 297	97.90 384	92.55 313	86.92 405	96.74 338

v144192		94.39 307	93.70 320	96.48 289	96.06 385	94.35 263	98.58 207	98.16 275	91.45 357	94.33 291	97.02 343	87.50 272	98.45 324	91.08 348	89.11 379	96.63 353

Baseline_NR-MVSNet		94.35 308	93.81 310	95.96 319	96.20 376	94.05 276	98.61 204	96.67 405	91.44 358	93.85 317	97.60 288	88.57 243	98.14 362	94.39 252	86.93 404	95.68 404

miper_lstm_enhance		94.33 309	94.07 288	95.11 353	97.75 273	90.97 354	97.22 370	98.03 300	91.67 353	92.76 358	96.97 348	90.03 199	97.78 393	92.51 315	89.64 368	96.56 362

v1192		94.32 310	93.58 325	96.53 284	96.10 383	94.45 257	98.50 226	98.17 273	91.54 355	94.19 301	97.06 336	86.95 282	98.43 327	90.14 361	89.57 369	96.70 345

UWE-MVS		94.30 311	93.89 305	95.53 338	97.83 268	88.95 402	97.52 348	93.25 443	94.44 232	96.63 219	97.07 332	78.70 385	99.28 214	91.99 327	97.56 224	98.36 264

ACMH+	92.99 14	94.30 311	93.77 314	95.88 324	97.81 270	92.04 337	98.71 178	98.37 225	93.99 250	90.60 394	98.47 200	80.86 370	99.05 252	92.75 306	92.40 333	96.55 364

v148		94.29 313	93.76 316	95.91 321	96.10 383	92.93 320	98.58 207	97.97 303	92.59 323	93.47 335	96.95 352	88.53 247	98.32 347	92.56 312	87.06 403	96.49 376

v10		94.29 313	93.55 327	96.51 286	96.39 370	94.80 242	98.99 87	98.19 264	91.35 362	93.02 352	96.99 346	88.09 256	98.41 335	90.50 358	88.41 388	96.33 386

SD_0403		94.28 315	94.46 264	93.73 393	98.02 247	85.32 429	98.31 251	98.40 216	94.75 211	93.59 325	98.16 233	89.01 230	96.54 424	82.32 428	97.58 223	99.34 133

MVP-Stereo		94.28 315	93.92 300	95.35 346	94.95 416	92.60 327	97.97 301	97.65 322	91.61 354	90.68 393	97.09 329	86.32 294	98.42 328	89.70 372	99.34 131	95.02 418
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 317	93.33 335	96.97 241	97.19 324	93.38 303	98.74 167	98.57 172	91.21 371	93.81 319	98.58 189	72.85 427	98.77 296	95.05 227	93.93 307	98.77 227

OurMVSNet-221017-0		94.21 318	94.00 295	94.85 365	95.60 400	89.22 396	98.89 115	97.43 352	95.29 171	92.18 376	98.52 197	82.86 353	98.59 312	93.46 285	91.76 340	96.74 338

v1921920		94.20 319	93.47 331	96.40 299	95.98 389	94.08 275	98.52 219	98.15 276	91.33 363	94.25 297	97.20 321	86.41 292	98.42 328	90.04 366	89.39 376	96.69 350

WB-MVSnew		94.19 320	94.04 289	94.66 373	96.82 347	92.14 332	97.86 319	95.96 419	93.50 284	95.64 253	96.77 365	88.06 258	97.99 378	84.87 416	96.86 244	93.85 435

v7n		94.19 320	93.43 333	96.47 290	95.90 392	94.38 262	99.26 28	98.34 231	91.99 343	92.76 358	97.13 324	88.31 250	98.52 317	89.48 377	87.70 394	96.52 370

tpm2		94.19 320	93.76 316	95.46 342	97.23 318	89.04 399	97.31 364	96.85 399	87.08 416	96.21 239	96.79 364	83.75 349	98.74 297	92.43 318	96.23 275	98.59 250

TESTMET0.1,1		94.18 323	93.69 321	95.63 335	96.92 339	89.12 397	96.91 393	94.78 432	93.17 299	94.88 267	96.45 379	78.52 386	98.92 274	93.09 294	98.50 181	98.85 214

dp		94.15 324	93.90 303	94.90 361	97.31 314	86.82 425	96.97 388	97.19 372	91.22 370	96.02 245	96.61 375	85.51 308	99.02 259	90.00 367	94.30 292	98.85 214

ET-MVSNet_ETH3D		94.13 325	92.98 343	97.58 201	98.22 216	96.20 159	97.31 364	95.37 426	94.53 224	79.56 444	97.63 287	86.51 287	97.53 404	96.91 148	90.74 354	99.02 199

tpm		94.13 325	93.80 311	95.12 352	96.50 364	87.91 419	97.44 350	95.89 422	92.62 321	96.37 235	96.30 382	84.13 340	98.30 351	93.24 290	91.66 343	99.14 178

testing222		94.12 327	93.03 342	97.37 216	98.02 247	94.66 245	97.94 305	96.65 407	94.63 218	95.78 251	95.76 401	71.49 428	98.92 274	91.17 344	95.88 282	98.52 255

IterMVS-SCA-FT		94.11 328	93.87 306	94.85 365	97.98 257	90.56 369	97.18 375	98.11 283	93.75 263	92.58 364	97.48 297	83.97 343	97.41 407	92.48 317	91.30 346	96.58 358

Anonymous20231211		94.10 329	93.26 338	96.61 272	99.11 115	94.28 266	99.01 82	98.88 72	86.43 419	92.81 356	97.57 291	81.66 359	98.68 303	94.83 232	89.02 382	96.88 323

IterMVS		94.09 330	93.85 308	94.80 369	97.99 251	90.35 374	97.18 375	98.12 280	93.68 274	92.46 370	97.34 308	84.05 341	97.41 407	92.51 315	91.33 345	96.62 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 331	93.51 329	95.80 327	96.77 349	89.70 385	96.91 393	95.21 427	92.89 312	94.83 270	95.72 406	77.69 397	98.97 263	93.06 295	98.50 181	98.72 231

test0.0.03 1		94.08 331	93.51 329	95.80 327	95.53 404	92.89 321	97.38 355	95.97 418	95.11 185	92.51 368	96.66 370	87.71 266	96.94 414	87.03 401	93.67 311	97.57 292

v1240		94.06 333	93.29 337	96.34 302	96.03 387	93.90 280	98.44 236	98.17 273	91.18 372	94.13 304	97.01 345	86.05 298	98.42 328	89.13 383	89.50 373	96.70 345

X-MVStestdata		94.06 333	92.30 359	99.34 27	99.70 24	98.35 45	99.29 23	98.88 72	97.40 54	98.46 110	43.50 458	95.90 45	99.89 61	97.85 94	99.74 53	99.78 27

DTE-MVSNet		93.98 335	93.26 338	96.14 310	96.06 385	94.39 261	99.20 43	98.86 85	93.06 305	91.78 381	97.81 268	85.87 302	97.58 402	90.53 357	86.17 410	96.46 380

pm-mvs1		93.94 336	93.06 341	96.59 275	96.49 365	95.16 219	98.95 97	98.03 300	92.32 334	91.08 389	97.84 263	84.54 331	98.41 335	92.16 320	86.13 413	96.19 392

MS-PatchMatch		93.84 337	93.63 323	94.46 383	96.18 378	89.45 392	97.76 329	98.27 247	92.23 337	92.13 377	97.49 296	79.50 380	98.69 300	89.75 370	99.38 127	95.25 410

tfpnnormal		93.66 338	92.70 349	96.55 283	96.94 338	95.94 175	98.97 91	99.19 31	91.04 373	91.38 386	97.34 308	84.94 319	98.61 308	85.45 412	89.02 382	95.11 414

EU-MVSNet		93.66 338	94.14 284	92.25 412	95.96 391	83.38 436	98.52 219	98.12 280	94.69 214	92.61 363	98.13 236	87.36 276	96.39 428	91.82 331	90.00 364	96.98 308

our_test_3		93.65 340	93.30 336	94.69 371	95.45 408	89.68 387	96.91 393	97.65 322	91.97 344	91.66 384	96.88 357	89.67 207	97.93 383	88.02 395	91.49 344	96.48 378

pmmvs5		93.65 340	92.97 344	95.68 332	95.49 405	92.37 328	98.20 266	97.28 364	89.66 397	92.58 364	97.26 314	82.14 356	98.09 369	93.18 293	90.95 353	96.58 358

SSC-MVS3.2		93.59 342	93.13 340	94.97 358	96.81 348	89.71 384	97.95 302	98.49 197	94.59 221	93.50 333	96.91 355	77.74 396	98.37 342	91.69 335	90.47 357	96.83 331

test_fmvs2		93.43 343	93.58 325	92.95 406	96.97 336	83.91 432	99.19 45	97.24 367	95.74 145	95.20 262	98.27 224	69.65 430	98.72 299	96.26 180	93.73 310	96.24 389

tpm cat1		93.36 344	92.80 346	95.07 356	97.58 289	87.97 418	96.76 405	97.86 311	82.17 437	93.53 329	96.04 394	86.13 296	99.13 238	89.24 381	95.87 283	98.10 275

JIA-IIPM		93.35 345	92.49 355	95.92 320	96.48 366	90.65 364	95.01 431	96.96 389	85.93 423	96.08 243	87.33 448	87.70 268	98.78 295	91.35 341	95.58 287	98.34 265

SixPastTwentyTwo		93.34 346	92.86 345	94.75 370	95.67 398	89.41 394	98.75 163	96.67 405	93.89 255	90.15 399	98.25 227	80.87 369	98.27 356	90.90 353	90.64 355	96.57 360

USDC		93.33 347	92.71 348	95.21 349	96.83 346	90.83 360	96.91 393	97.50 342	93.84 258	90.72 392	98.14 235	77.69 397	98.82 291	89.51 376	93.21 324	95.97 398

IB-MVS	91.98 17	93.27 348	91.97 363	97.19 222	97.47 300	93.41 300	97.09 383	95.99 417	93.32 292	92.47 369	95.73 404	78.06 392	99.53 175	94.59 247	82.98 424	98.62 245
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		93.26 349	92.21 360	96.41 297	97.73 277	93.13 314	95.65 426	97.03 383	91.27 368	94.04 308	96.06 392	75.33 416	97.19 410	86.56 403	96.23 275	98.92 210

ppachtmachnet_test		93.22 350	92.63 350	94.97 358	95.45 408	90.84 359	96.88 399	97.88 310	90.60 379	92.08 378	97.26 314	88.08 257	97.86 389	85.12 415	90.33 358	96.22 390

Patchmtry		93.22 350	92.35 358	95.84 326	96.77 349	93.09 317	94.66 439	97.56 332	87.37 415	92.90 354	96.24 383	88.15 254	97.90 384	87.37 400	90.10 363	96.53 367

testing3		93.19 352	92.48 356	95.30 348	98.07 237	92.27 329	98.64 197	97.17 373	93.94 254	93.98 311	97.04 340	67.97 435	96.01 432	88.40 390	97.14 236	97.63 289

FMVSNet1		93.19 352	92.07 361	96.56 279	97.54 294	95.00 227	98.82 141	98.18 267	90.38 385	92.27 373	97.07 332	73.68 425	97.95 380	89.36 379	91.30 346	96.72 341

LF4IMVS		93.14 354	92.79 347	94.20 387	95.88 393	88.67 407	97.66 337	97.07 379	93.81 261	91.71 382	97.65 282	77.96 394	98.81 292	91.47 340	91.92 339	95.12 413

mmtdpeth		93.12 355	92.61 351	94.63 375	97.60 287	89.68 387	99.21 40	97.32 359	94.02 245	97.72 164	94.42 423	77.01 407	99.44 195	99.05 29	77.18 445	94.78 423

testgi		93.06 356	92.45 357	94.88 363	96.43 369	89.90 379	98.75 163	97.54 338	95.60 152	91.63 385	97.91 255	74.46 422	97.02 412	86.10 406	93.67 311	97.72 286

PatchT		93.06 356	91.97 363	96.35 301	96.69 355	92.67 326	94.48 442	97.08 377	86.62 417	97.08 194	92.23 442	87.94 261	97.90 384	78.89 439	96.69 251	98.49 257

RPMNet		92.81 358	91.34 369	97.24 218	97.00 333	93.43 298	94.96 432	98.80 107	82.27 436	96.93 202	92.12 443	86.98 281	99.82 90	76.32 444	96.65 253	98.46 259

UWE-MVS-28		92.79 359	92.51 354	93.62 395	96.46 367	86.28 426	97.93 306	92.71 448	94.17 238	94.78 273	97.16 322	81.05 366	96.43 427	81.45 431	96.86 244	98.14 274

myMVS_eth3d		92.73 360	92.01 362	94.89 362	97.39 310	90.94 355	97.91 309	97.46 346	93.16 300	93.42 337	95.37 413	68.09 434	96.12 430	88.34 391	96.99 240	97.60 290

TransMVSNet (Re)		92.67 361	91.51 368	96.15 309	96.58 360	94.65 246	98.90 111	96.73 401	90.86 376	89.46 406	97.86 260	85.62 306	98.09 369	86.45 404	81.12 431	95.71 403

ttmdpeth		92.61 362	91.96 365	94.55 377	94.10 426	90.60 368	98.52 219	97.29 362	92.67 319	90.18 397	97.92 254	79.75 379	97.79 391	91.09 346	86.15 412	95.26 409

Syy-MVS		92.55 363	92.61 351	92.38 409	97.39 310	83.41 435	97.91 309	97.46 346	93.16 300	93.42 337	95.37 413	84.75 324	96.12 430	77.00 443	96.99 240	97.60 290

K. test v3		92.55 363	91.91 366	94.48 381	95.64 399	89.24 395	99.07 67	94.88 431	94.04 243	86.78 423	97.59 289	77.64 400	97.64 398	92.08 322	89.43 375	96.57 360

DSMNet-mixed		92.52 365	92.58 353	92.33 410	94.15 425	82.65 438	98.30 254	94.26 438	89.08 406	92.65 362	95.73 404	85.01 318	95.76 434	86.24 405	97.76 215	98.59 250

TinyColmap		92.31 366	91.53 367	94.65 374	96.92 339	89.75 382	96.92 391	96.68 404	90.45 383	89.62 403	97.85 262	76.06 414	98.81 292	86.74 402	92.51 332	95.41 407

gg-mvs-nofinetune		92.21 367	90.58 375	97.13 227	96.75 352	95.09 223	95.85 421	89.40 456	85.43 427	94.50 279	81.98 451	80.80 371	98.40 341	92.16 320	98.33 195	97.88 279

FMVSNet5		91.81 368	90.92 371	94.49 380	97.21 320	92.09 334	98.00 298	97.55 337	89.31 404	90.86 391	95.61 410	74.48 421	95.32 438	85.57 410	89.70 367	96.07 396

pmmvs6		91.77 369	90.63 374	95.17 351	94.69 422	91.24 351	98.67 191	97.92 308	86.14 421	89.62 403	97.56 294	75.79 415	98.34 344	90.75 355	84.56 417	95.94 399

Anonymous20231206		91.66 370	91.10 370	93.33 400	94.02 430	87.35 422	98.58 207	97.26 366	90.48 381	90.16 398	96.31 381	83.83 347	96.53 425	79.36 437	89.90 365	96.12 394

Patchmatch-RL test		91.49 371	90.85 372	93.41 398	91.37 441	84.40 430	92.81 446	95.93 421	91.87 347	87.25 419	94.87 419	88.99 231	96.53 425	92.54 314	82.00 426	99.30 143

test_0402		91.32 372	90.27 378	94.48 381	96.60 359	91.12 352	98.50 226	97.22 368	86.10 422	88.30 415	96.98 347	77.65 399	97.99 378	78.13 441	92.94 326	94.34 424

test_vis1_rt		91.29 373	90.65 373	93.19 404	97.45 304	86.25 427	98.57 214	90.90 454	93.30 294	86.94 422	93.59 432	62.07 446	99.11 243	97.48 126	95.58 287	94.22 427

PVSNet_0	88.72 19	91.28 374	90.03 381	95.00 357	97.99 251	87.29 423	94.84 435	98.50 192	92.06 342	89.86 400	95.19 415	79.81 378	99.39 202	92.27 319	69.79 451	98.33 266

mvs5depth		91.23 375	90.17 379	94.41 385	92.09 438	89.79 381	95.26 430	96.50 409	90.73 377	91.69 383	97.06 336	76.12 413	98.62 307	88.02 395	84.11 420	94.82 420

Anonymous20240521		91.18 376	90.44 376	93.42 397	93.70 431	88.47 411	98.94 100	97.56 332	88.46 410	89.56 405	95.08 418	77.15 405	96.97 413	83.92 422	89.55 371	94.82 420

EG-PatchMatch MVS		91.13 377	90.12 380	94.17 389	94.73 421	89.00 400	98.13 281	97.81 313	89.22 405	85.32 433	96.46 378	67.71 436	98.42 328	87.89 398	93.82 309	95.08 415

TDRefinement		91.06 378	89.68 383	95.21 349	85.35 456	91.49 347	98.51 225	97.07 379	91.47 356	88.83 412	97.84 263	77.31 401	99.09 248	92.79 305	77.98 443	95.04 417

sc_t1		91.01 379	89.39 385	95.85 325	95.99 388	90.39 373	98.43 238	97.64 324	78.79 440	92.20 375	97.94 252	66.00 440	98.60 311	91.59 338	85.94 414	98.57 253

UnsupCasMVSNet_eth		90.99 380	89.92 382	94.19 388	94.08 427	89.83 380	97.13 382	98.67 144	93.69 272	85.83 429	96.19 388	75.15 417	96.74 418	89.14 382	79.41 438	96.00 397

test20.03		90.89 381	90.38 377	92.43 408	93.48 432	88.14 417	98.33 246	97.56 332	93.40 289	87.96 416	96.71 368	80.69 372	94.13 443	79.15 438	86.17 410	95.01 419

MDA-MVSNet_test_wron		90.71 382	89.38 387	94.68 372	94.83 418	90.78 361	97.19 374	97.46 346	87.60 413	72.41 451	95.72 406	86.51 287	96.71 421	85.92 408	86.80 407	96.56 362

YYNet1		90.70 383	89.39 385	94.62 376	94.79 420	90.65 364	97.20 372	97.46 346	87.54 414	72.54 450	95.74 402	86.51 287	96.66 422	86.00 407	86.76 408	96.54 365

KD-MVS_self_test		90.38 384	89.38 387	93.40 399	92.85 435	88.94 403	97.95 302	97.94 306	90.35 386	90.25 396	93.96 429	79.82 377	95.94 433	84.62 421	76.69 446	95.33 408

pmmvs-eth3d		90.36 385	89.05 390	94.32 386	91.10 443	92.12 333	97.63 342	96.95 390	88.86 408	84.91 434	93.13 437	78.32 388	96.74 418	88.70 387	81.81 428	94.09 430

tt0320		90.26 386	88.73 393	94.86 364	96.12 382	90.62 366	98.17 275	97.63 325	77.46 443	89.68 402	96.04 394	69.19 432	97.79 391	88.98 384	85.29 416	96.16 393

CL-MVSNet_self_test		90.11 387	89.14 389	93.02 405	91.86 440	88.23 416	96.51 413	98.07 293	90.49 380	90.49 395	94.41 424	84.75 324	95.34 437	80.79 433	74.95 448	95.50 406

new_pmnet		90.06 388	89.00 391	93.22 403	94.18 424	88.32 414	96.42 415	96.89 395	86.19 420	85.67 430	93.62 431	77.18 404	97.10 411	81.61 430	89.29 377	94.23 426

MDA-MVSNet-bldmvs		89.97 389	88.35 395	94.83 368	95.21 412	91.34 348	97.64 339	97.51 341	88.36 411	71.17 452	96.13 390	79.22 382	96.63 423	83.65 423	86.27 409	96.52 370

tt0320-xc		89.79 390	88.11 397	94.84 367	96.19 377	90.61 367	98.16 276	97.22 368	77.35 444	88.75 413	96.70 369	65.94 441	97.63 399	89.31 380	83.39 422	96.28 388

CMPMVS	66.06 21	89.70 391	89.67 384	89.78 417	93.19 433	76.56 443	97.00 387	98.35 228	80.97 438	81.57 439	97.75 271	74.75 419	98.61 308	89.85 368	93.63 313	94.17 428
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 392	88.28 396	93.82 392	92.81 436	91.08 353	98.01 296	97.45 350	87.95 412	87.90 417	95.87 400	67.63 437	94.56 442	78.73 440	88.18 390	95.83 401

KD-MVS_2432*1600		89.61 393	87.96 401	94.54 378	94.06 428	91.59 345	95.59 427	97.63 325	89.87 393	88.95 409	94.38 426	78.28 389	96.82 416	84.83 417	68.05 452	95.21 411

miper_refine_blended		89.61 393	87.96 401	94.54 378	94.06 428	91.59 345	95.59 427	97.63 325	89.87 393	88.95 409	94.38 426	78.28 389	96.82 416	84.83 417	68.05 452	95.21 411

MVStest1		89.53 395	87.99 400	94.14 391	94.39 423	90.42 371	98.25 261	96.84 400	82.81 433	81.18 441	97.33 310	77.09 406	96.94 414	85.27 414	78.79 439	95.06 416

MVS-HIRNet		89.46 396	88.40 394	92.64 407	97.58 289	82.15 439	94.16 445	93.05 447	75.73 447	90.90 390	82.52 450	79.42 381	98.33 346	83.53 424	98.68 166	97.43 293

OpenMVS_ROB	86.42 20	89.00 397	87.43 405	93.69 394	93.08 434	89.42 393	97.91 309	96.89 395	78.58 441	85.86 428	94.69 420	69.48 431	98.29 354	77.13 442	93.29 323	93.36 437

mvsany_test3		88.80 398	88.04 398	91.09 416	89.78 446	81.57 441	97.83 324	95.49 425	93.81 261	87.53 418	93.95 430	56.14 449	97.43 406	94.68 240	83.13 423	94.26 425

new-patchmatchnet		88.50 399	87.45 404	91.67 414	90.31 445	85.89 428	97.16 380	97.33 358	89.47 400	83.63 436	92.77 439	76.38 410	95.06 440	82.70 426	77.29 444	94.06 432

APD_test1		88.22 400	88.01 399	88.86 419	95.98 389	74.66 451	97.21 371	96.44 411	83.96 432	86.66 425	97.90 256	60.95 447	97.84 390	82.73 425	90.23 361	94.09 430

PM-MVS		87.77 401	86.55 407	91.40 415	91.03 444	83.36 437	96.92 391	95.18 429	91.28 367	86.48 427	93.42 433	53.27 450	96.74 418	89.43 378	81.97 427	94.11 429

dmvs_testset		87.64 402	88.93 392	83.79 428	95.25 411	63.36 460	97.20 372	91.17 452	93.07 304	85.64 431	95.98 399	85.30 315	91.52 450	69.42 449	87.33 399	96.49 376

test_fmvs3		87.17 403	87.06 406	87.50 421	91.21 442	75.66 446	99.05 70	96.61 408	92.79 316	88.85 411	92.78 438	43.72 453	93.49 444	93.95 270	84.56 417	93.34 438

UnsupCasMVSNet_bld		87.17 403	85.12 410	93.31 401	91.94 439	88.77 404	94.92 434	98.30 244	84.30 431	82.30 437	90.04 445	63.96 444	97.25 409	85.85 409	74.47 450	93.93 434

N_pmnet		87.12 405	87.77 403	85.17 425	95.46 407	61.92 461	97.37 357	70.66 466	85.83 424	88.73 414	96.04 394	85.33 313	97.76 394	80.02 434	90.48 356	95.84 400

pmmvs3		86.67 406	84.86 411	92.11 413	88.16 450	87.19 424	96.63 409	94.75 433	79.88 439	87.22 420	92.75 440	66.56 439	95.20 439	81.24 432	76.56 447	93.96 433

test_f		86.07 407	85.39 408	88.10 420	89.28 448	75.57 447	97.73 332	96.33 413	89.41 403	85.35 432	91.56 444	43.31 455	95.53 435	91.32 342	84.23 419	93.21 439

WB-MVS		84.86 408	85.33 409	83.46 429	89.48 447	69.56 455	98.19 269	96.42 412	89.55 399	81.79 438	94.67 421	84.80 322	90.12 451	52.44 455	80.64 435	90.69 442

SSC-MVS		84.27 409	84.71 412	82.96 433	89.19 449	68.83 456	98.08 288	96.30 414	89.04 407	81.37 440	94.47 422	84.60 329	89.89 452	49.80 457	79.52 437	90.15 443

dongtai		82.47 410	81.88 413	84.22 427	95.19 413	76.03 444	94.59 441	74.14 465	82.63 434	87.19 421	96.09 391	64.10 443	87.85 455	58.91 453	84.11 420	88.78 447

test_vis3_rt		79.22 411	77.40 418	84.67 426	86.44 454	74.85 450	97.66 337	81.43 461	84.98 428	67.12 454	81.91 452	28.09 463	97.60 400	88.96 385	80.04 436	81.55 452

test_method		79.03 412	78.17 414	81.63 434	86.06 455	54.40 466	82.75 454	96.89 395	39.54 458	80.98 442	95.57 411	58.37 448	94.73 441	84.74 420	78.61 440	95.75 402

testf1		79.02 413	77.70 415	82.99 431	88.10 451	66.90 457	94.67 437	93.11 444	71.08 449	74.02 447	93.41 434	34.15 459	93.25 445	72.25 447	78.50 441	88.82 445

APD_test2		79.02 413	77.70 415	82.99 431	88.10 451	66.90 457	94.67 437	93.11 444	71.08 449	74.02 447	93.41 434	34.15 459	93.25 445	72.25 447	78.50 441	88.82 445

LCM-MVSNet		78.70 415	76.24 421	86.08 423	77.26 462	71.99 453	94.34 443	96.72 402	61.62 453	76.53 445	89.33 446	33.91 461	92.78 448	81.85 429	74.60 449	93.46 436

kuosan		78.45 416	77.69 417	80.72 435	92.73 437	75.32 448	94.63 440	74.51 464	75.96 445	80.87 443	93.19 436	63.23 445	79.99 459	42.56 459	81.56 430	86.85 451

Gipuma		78.40 417	76.75 420	83.38 430	95.54 402	80.43 442	79.42 455	97.40 354	64.67 452	73.46 449	80.82 453	45.65 452	93.14 447	66.32 451	87.43 397	76.56 455
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 418	75.44 422	85.46 424	82.54 457	74.95 449	94.23 444	93.08 446	72.80 448	74.68 446	87.38 447	36.36 458	91.56 449	73.95 445	63.94 454	89.87 444

FPMVS		77.62 419	77.14 419	79.05 437	79.25 460	60.97 462	95.79 422	95.94 420	65.96 451	67.93 453	94.40 425	37.73 457	88.88 454	68.83 450	88.46 387	87.29 448

EGC-MVSNET		75.22 420	69.54 423	92.28 411	94.81 419	89.58 389	97.64 339	96.50 409	1.82 463	5.57 464	95.74 402	68.21 433	96.26 429	73.80 446	91.71 341	90.99 441

ANet_high		69.08 421	65.37 425	80.22 436	65.99 464	71.96 454	90.91 450	90.09 455	82.62 435	49.93 459	78.39 454	29.36 462	81.75 456	62.49 452	38.52 458	86.95 450

tmp_tt		68.90 422	66.97 424	74.68 439	50.78 466	59.95 463	87.13 451	83.47 460	38.80 459	62.21 455	96.23 385	64.70 442	76.91 461	88.91 386	30.49 459	87.19 449

PMVS	61.03 23	65.95 423	63.57 427	73.09 440	57.90 465	51.22 467	85.05 453	93.93 442	54.45 454	44.32 460	83.57 449	13.22 464	89.15 453	58.68 454	81.00 432	78.91 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 424	64.25 426	67.02 441	82.28 458	59.36 464	91.83 449	85.63 458	52.69 455	60.22 456	77.28 455	41.06 456	80.12 458	46.15 458	41.14 456	61.57 457

EMVS		64.07 425	63.26 428	66.53 442	81.73 459	58.81 465	91.85 448	84.75 459	51.93 457	59.09 457	75.13 456	43.32 454	79.09 460	42.03 460	39.47 457	61.69 456

MVE	62.14 22	63.28 426	59.38 429	74.99 438	74.33 463	65.47 459	85.55 452	80.50 462	52.02 456	51.10 458	75.00 457	10.91 467	80.50 457	51.60 456	53.40 455	78.99 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 427	30.18 431	30.16 443	78.61 461	43.29 468	66.79 456	14.21 467	17.31 460	14.82 463	11.93 463	11.55 466	41.43 462	37.08 461	19.30 460	5.76 460

cdsmvs_eth3d_5k		23.98 428	31.98 430	0.00 446	0.00 469	0.00 471	0.00 457	98.59 165	0.00 464	0.00 465	98.61 184	90.60 189	0.00 465	0.00 464	0.00 463	0.00 461

testmvs		21.48 429	24.95 432	11.09 445	14.89 467	6.47 470	96.56 411	9.87 468	7.55 461	17.93 461	39.02 459	9.43 468	5.90 464	16.56 463	12.72 461	20.91 459

test123		20.95 430	23.72 433	12.64 444	13.54 468	8.19 469	96.55 412	6.13 469	7.48 462	16.74 462	37.98 460	12.97 465	6.05 463	16.69 462	5.43 462	23.68 458

ab-mvs-re		8.20 431	10.94 434	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	98.43 202	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

pcd_1.5k_mvsjas		7.88 432	10.50 435	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	94.51 88	0.00 465	0.00 464	0.00 463	0.00 461

mmdepth		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

monomultidepth		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

test_blank		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uanet_test		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

DCPMVS		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

sosnet-low-res		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

sosnet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uncertanet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

Regformer		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uanet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

WAC-MVS						90.94 355								88.66 388

FOURS1					99.82 1	98.66 24	99.69 1	98.95 56	97.46 52	99.39 41

MSC_two_6792asdad				99.62 6	99.17 104	99.08 11		98.63 155					99.94 12	98.53 52	99.80 24	99.86 9

PC_three_1452									95.08 189	99.60 29	99.16 92	97.86 2	98.47 322	97.52 123	99.72 61	99.74 44

No_MVS				99.62 6	99.17 104	99.08 11		98.63 155					99.94 12	98.53 52	99.80 24	99.86 9

test_one_0601					99.66 28	99.25 2		98.86 85	97.55 44	99.20 53	99.47 32	97.57 6

eth-test2					0.00 469

eth-test					0.00 469

ZD-MVS					99.46 54	98.70 23		98.79 112	93.21 297	98.67 96	98.97 127	95.70 49	99.83 83	96.07 184	99.58 92

RE-MVS-def			98.34 48		99.49 48	97.86 70	99.11 61	98.80 107	96.49 111	99.17 56	99.35 57	95.29 66		97.72 102	99.65 75	99.71 57

IU-MVS					99.71 21	99.23 7		98.64 152	95.28 172	99.63 28				98.35 69	99.81 15	99.83 15

OPU-MVS				99.37 23	99.24 96	99.05 14	99.02 80				99.16 92	97.81 3	99.37 203	97.24 136	99.73 56	99.70 61

test_241102_TWO								98.87 79	97.65 36	99.53 34	99.48 30	97.34 11	99.94 12	98.43 64	99.80 24	99.83 15

test_241102_ONE					99.71 21	99.24 5		98.87 79	97.62 38	99.73 19	99.39 45	97.53 7	99.74 127

9.14			98.06 73		99.47 52		98.71 178	98.82 94	94.36 234	99.16 59	99.29 66	96.05 37	99.81 95	97.00 143	99.71 63

save fliter					99.46 54	98.38 36	98.21 264	98.71 130	97.95 25

test_0728_THIRD									97.32 60	99.45 36	99.46 37	97.88 1	99.94 12	98.47 60	99.86 2	99.85 12

test_0728_SECOND				99.71 1	99.72 14	99.35 1	98.97 91	98.88 72					99.94 12	98.47 60	99.81 15	99.84 14

test0726					99.72 14	99.25 2	99.06 68	98.88 72	97.62 38	99.56 31	99.50 26	97.42 9

GSMVS																99.20 164

test_part2					99.63 31	99.18 10				99.27 50

sam_mvs1												89.45 215				99.20 164

sam_mvs												88.99 231

ambc				89.49 418	86.66 453	75.78 445	92.66 447	96.72 402		86.55 426	92.50 441	46.01 451	97.90 384	90.32 359	82.09 425	94.80 422

MTGPA								98.74 122

test_post1							96.68 408				30.43 462	87.85 265	98.69 300	92.59 310

test_post											31.83 461	88.83 238	98.91 276

patchmatchnet-post											95.10 417	89.42 216	98.89 280

GG-mvs-BLEND				96.59 275	96.34 372	94.98 231	96.51 413	88.58 457		93.10 351	94.34 428	80.34 376	98.05 372	89.53 375	96.99 240	96.74 338

MTMP							98.89 115	94.14 440

gm-plane-assit					95.88 393	87.47 421			89.74 396		96.94 353		99.19 228	93.32 289

test9_res														96.39 178	99.57 93	99.69 64

TEST9					99.31 72	98.50 30	97.92 307	98.73 125	92.63 320	97.74 161	98.68 179	96.20 32	99.80 102

test_8					99.29 81	98.44 32	97.89 315	98.72 127	92.98 308	97.70 166	98.66 182	96.20 32	99.80 102

agg_prior2														95.87 194	99.57 93	99.68 69

agg_prior					99.30 76	98.38 36		98.72 127		97.57 178			99.81 95

TestCases				96.99 238	99.25 89	93.21 312		98.18 267	91.36 360	93.52 330	98.77 166	84.67 327	99.72 129	89.70 372	97.87 210	98.02 277

test_prior4						98.01 66	97.86 319

test_prior2							97.80 326		96.12 129	97.89 153	98.69 178	95.96 41		96.89 152	99.60 87

test_prior				99.19 45	99.31 72	98.22 53		98.84 89					99.70 135			99.65 77

旧先验2							97.57 345		91.30 365	98.67 96			99.80 102	95.70 205

新几何2							97.64 339

新几何1				99.16 50	99.34 65	98.01 66		98.69 136	90.06 390	98.13 126	98.95 134	94.60 86	99.89 61	91.97 329	99.47 115	99.59 88

旧先验1					99.29 81	97.48 84		98.70 134			99.09 110	95.56 52			99.47 115	99.61 84

无先验							97.58 344	98.72 127	91.38 359				99.87 72	93.36 288		99.60 86

原ACMM2							97.67 336

原ACMM1				98.65 91	99.32 70	96.62 133		98.67 144	93.27 296	97.81 155	98.97 127	95.18 73	99.83 83	93.84 274	99.46 118	99.50 100

test222					99.23 97	97.17 110	97.40 353	98.66 147	88.68 409	98.05 132	98.96 132	94.14 99			99.53 106	99.61 84

testdata2													99.89 61	91.65 337

segment_acmp												96.85 14

testdata				98.26 134	99.20 102	95.36 208		98.68 139	91.89 346	98.60 104	99.10 103	94.44 93	99.82 90	94.27 258	99.44 119	99.58 92

testdata1							97.32 363		96.34 119

test12				99.18 47	99.16 108	98.19 55		98.53 181		98.07 130		95.13 76	99.72 129		99.56 101	99.63 82

plane_prior7					97.42 306	94.63 248

plane_prior6					97.35 313	94.61 251						87.09 278

plane_prior5								98.56 175					99.03 256	96.07 184	94.27 293	96.92 314

plane_prior4											98.28 221

plane_prior3						94.61 251			97.02 84	95.34 257

plane_prior2							98.80 150		97.28 64

plane_prior1					97.37 312

plane_prior						94.60 253	98.44 236		96.74 98						94.22 295

n2								0.00 470

nn								0.00 470

door-mid								94.37 436

lessismore_v0				94.45 384	94.93 417	88.44 412		91.03 453		86.77 424	97.64 285	76.23 412	98.42 328	90.31 360	85.64 415	96.51 373

LGP-MVS_train				96.47 290	97.46 301	93.54 293		98.54 179	94.67 216	94.36 289	98.77 166	85.39 309	99.11 243	95.71 203	94.15 299	96.76 336

test11								98.66 147

door								94.64 434

HQP5-MVS						94.25 269

HQP-NCC					97.20 321		98.05 291		96.43 113	94.45 281

ACMP_Plane					97.20 321		98.05 291		96.43 113	94.45 281

BP-MVS														95.30 217

HQP4-MVS										94.45 281			98.96 267			96.87 326

HQP3-MVS								98.46 200							94.18 297

HQP2-MVS												86.75 284

NP-MVS					97.28 315	94.51 256					97.73 272

MDTV_nov1_ep13_2view						84.26 431	96.89 398		90.97 374	97.90 152		89.89 201		93.91 272		99.18 173

MDTV_nov1_ep13			95.40 210		97.48 299	88.34 413	96.85 401	97.29 362	93.74 265	97.48 180	97.26 314	89.18 224	99.05 252	91.92 330	97.43 231

ACMMP++_ref															92.97 325

ACMMP++															93.61 314

Test By Simon												94.64 85

ITE_SJBPF				95.44 343	97.42 306	91.32 349		97.50 342	95.09 188	93.59 325	98.35 212	81.70 358	98.88 282	89.71 371	93.39 320	96.12 394

DeepMVS_CX				86.78 422	97.09 331	72.30 452		95.17 430	75.92 446	84.34 435	95.19 415	70.58 429	95.35 436	79.98 436	89.04 381	92.68 440

High-resolution multi-view benchmark