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		99.71 1	99.67 1	99.85 33	99.86 20	99.61 73	99.56 130	99.63 40	99.48 3	99.98 8	99.83 75	98.75 58	99.99 4	99.97 1	99.96 13	99.94 12

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 33	99.84 32	99.63 70	99.56 130	99.63 40	99.47 4	99.98 8	99.82 84	98.75 58	99.99 4	99.97 1	99.97 7	99.94 12

test_fmvsmconf_n		99.70 3	99.64 4	99.87 15	99.80 52	99.66 59	99.48 188	99.64 37	99.45 7	99.92 19	99.92 17	98.62 73	99.99 4	99.96 7	99.99 1	99.96 7

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 58	99.84 32	99.44 102	99.58 117	99.69 18	99.43 10	99.98 8	99.91 23	98.62 73	100.00 1	99.97 1	99.95 18	99.90 18

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 64	99.89 4	99.75 42	99.56 73	99.02 45	99.88 27	99.85 60	99.18 10	99.96 33	99.22 76	99.92 29	99.90 18
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 61	99.38 227	99.37 108	99.58 117	99.62 42	99.41 12	99.87 32	99.92 17	98.81 47	100.00 1	99.97 1	99.93 26	99.94 12

reproduce_model		99.63 7	99.54 11	99.90 4	99.78 57	99.88 8	99.56 130	99.55 81	99.15 24	99.90 22	99.90 30	99.00 22	99.97 21	99.11 86	99.91 36	99.86 34

reproduce-ours		99.61 8	99.52 12	99.90 4	99.76 68	99.88 8	99.52 157	99.54 90	99.13 27	99.89 24	99.89 35	98.96 25	99.96 33	99.04 94	99.90 45	99.85 38

our_new_method		99.61 8	99.52 12	99.90 4	99.76 68	99.88 8	99.52 157	99.54 90	99.13 27	99.89 24	99.89 35	98.96 25	99.96 33	99.04 94	99.90 45	99.85 38

SED-MVS		99.61 8	99.52 12	99.88 9	99.84 32	99.90 2	99.60 102	99.48 164	99.08 40	99.91 20	99.81 98	99.20 7	99.96 33	98.91 112	99.85 77	99.79 79

DVP-MVS++		99.59 11	99.50 16	99.88 9	99.51 179	99.88 8	99.87 8	99.51 122	98.99 52	99.88 27	99.81 98	99.27 5	99.96 33	98.85 125	99.80 105	99.81 66

TSAR-MVS + MP.		99.58 12	99.50 16	99.81 49	99.91 1	99.66 59	99.63 90	99.39 233	98.91 65	99.78 57	99.85 60	99.36 2	99.94 75	98.84 128	99.88 59	99.82 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet-UG-set		99.58 12	99.57 8	99.64 86	99.78 57	99.14 142	99.60 102	99.45 205	99.01 47	99.90 22	99.83 75	98.98 24	99.93 93	99.59 32	99.95 18	99.86 34

EI-MVSNet-Vis-set		99.58 12	99.56 10	99.64 86	99.78 57	99.15 141	99.61 101	99.45 205	99.01 47	99.89 24	99.82 84	99.01 18	99.92 105	99.56 36	99.95 18	99.85 38

DVP-MVS		99.57 15	99.47 20	99.88 9	99.85 26	99.89 4	99.57 124	99.37 249	99.10 34	99.81 46	99.80 111	98.94 32	99.96 33	98.93 109	99.86 70	99.81 66
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
fmvsm_s_conf0.5_n_a		99.56 16	99.47 20	99.85 33	99.83 39	99.64 69	99.52 157	99.65 34	99.10 34	99.98 8	99.92 17	97.35 125	99.96 33	99.94 11	99.92 29	99.95 9

test_fmvsmconf0.1_n		99.55 17	99.45 24	99.86 26	99.44 209	99.65 63	99.50 173	99.61 49	99.45 7	99.87 32	99.92 17	97.31 126	99.97 21	99.95 9	99.99 1	99.97 4

SteuartSystems-ACMMP		99.54 18	99.42 25	99.87 15	99.82 42	99.81 28	99.59 109	99.51 122	98.62 92	99.79 52	99.83 75	99.28 4	99.97 21	98.48 179	99.90 45	99.84 44
Skip Steuart: Steuart Systems R&D Blog.
XVS		99.53 19	99.42 25	99.87 15	99.85 26	99.83 19	99.69 60	99.68 20	98.98 55	99.37 173	99.74 150	98.81 47	99.94 75	98.79 136	99.86 70	99.84 44

MTAPA		99.52 20	99.39 32	99.89 7	99.90 4	99.86 16	99.66 75	99.47 185	98.79 77	99.68 86	99.81 98	98.43 86	99.97 21	98.88 115	99.90 45	99.83 54

fmvsm_s_conf0.5_n		99.51 21	99.40 30	99.85 33	99.84 32	99.65 63	99.51 166	99.67 23	99.13 27	99.98 8	99.92 17	96.60 152	99.96 33	99.95 9	99.96 13	99.95 9

HPM-MVS_fast		99.51 21	99.40 30	99.85 33	99.91 1	99.79 33	99.76 37	99.56 73	97.72 202	99.76 67	99.75 145	99.13 12	99.92 105	99.07 92	99.92 29	99.85 38

mvsany_test1		99.50 23	99.46 23	99.62 93	99.61 148	99.09 147	98.94 355	99.48 164	99.10 34	99.96 17	99.91 23	98.85 42	99.96 33	99.72 22	99.58 148	99.82 59

CS-MVS		99.50 23	99.48 18	99.54 107	99.76 68	99.42 104	99.90 1	99.55 81	98.56 97	99.78 57	99.70 165	98.65 71	99.79 202	99.65 28	99.78 114	99.41 212

SPE-MVS-test		99.49 25	99.48 18	99.54 107	99.78 57	99.30 120	99.89 2	99.58 64	98.56 97	99.73 73	99.69 175	98.55 78	99.82 187	99.69 24	99.85 77	99.48 191

HFP-MVS		99.49 25	99.37 36	99.86 26	99.87 15	99.80 30	99.66 75	99.67 23	98.15 145	99.68 86	99.69 175	99.06 16	99.96 33	98.69 148	99.87 62	99.84 44

ACMMPR		99.49 25	99.36 38	99.86 26	99.87 15	99.79 33	99.66 75	99.67 23	98.15 145	99.67 90	99.69 175	98.95 30	99.96 33	98.69 148	99.87 62	99.84 44

DeepC-MVS_fast	98.69 1	99.49 25	99.39 32	99.77 61	99.63 138	99.59 76	99.36 245	99.46 194	99.07 42	99.79 52	99.82 84	98.85 42	99.92 105	98.68 150	99.87 62	99.82 59
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R		99.48 29	99.35 40	99.87 15	99.88 11	99.80 30	99.65 81	99.66 28	98.13 150	99.66 95	99.68 182	98.96 25	99.96 33	98.62 157	99.87 62	99.84 44

APD-MVS_3200maxsize		99.48 29	99.35 40	99.85 33	99.76 68	99.83 19	99.63 90	99.54 90	98.36 118	99.79 52	99.82 84	98.86 41	99.95 64	98.62 157	99.81 101	99.78 85

DELS-MVS		99.48 29	99.42 25	99.65 80	99.72 97	99.40 107	99.05 327	99.66 28	99.14 26	99.57 127	99.80 111	98.46 84	99.94 75	99.57 35	99.84 85	99.60 155
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
ZNCC-MVS		99.47 32	99.33 44	99.87 15	99.87 15	99.81 28	99.64 84	99.67 23	98.08 160	99.55 132	99.64 201	98.91 37	99.96 33	98.72 143	99.90 45	99.82 59

ACMMP_NAP		99.47 32	99.34 42	99.88 9	99.87 15	99.86 16	99.47 195	99.48 164	98.05 167	99.76 67	99.86 55	98.82 46	99.93 93	98.82 135	99.91 36	99.84 44

MVSMamba_PlusPlus		99.46 34	99.41 29	99.64 86	99.68 115	99.50 94	99.75 42	99.50 142	98.27 128	99.87 32	99.92 17	98.09 104	99.94 75	99.65 28	99.95 18	99.47 197

balanced_conf03		99.46 34	99.39 32	99.67 75	99.55 167	99.58 81	99.74 46	99.51 122	98.42 111	99.87 32	99.84 70	98.05 107	99.91 117	99.58 34	99.94 24	99.52 178

DPE-MVS		99.46 34	99.32 46	99.91 2	99.78 57	99.88 8	99.36 245	99.51 122	98.73 84	99.88 27	99.84 70	98.72 64	99.96 33	98.16 209	99.87 62	99.88 27
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++		99.46 34	99.47 20	99.44 139	99.60 153	99.16 137	99.41 221	99.71 13	98.98 55	99.45 148	99.78 130	99.19 9	99.54 274	99.28 70	99.84 85	99.63 148

SR-MVS-dyc-post		99.45 38	99.31 52	99.85 33	99.76 68	99.82 25	99.63 90	99.52 108	98.38 114	99.76 67	99.82 84	98.53 79	99.95 64	98.61 160	99.81 101	99.77 87

PGM-MVS		99.45 38	99.31 52	99.86 26	99.87 15	99.78 39	99.58 117	99.65 34	97.84 188	99.71 80	99.80 111	99.12 13	99.97 21	98.33 195	99.87 62	99.83 54

CP-MVS		99.45 38	99.32 46	99.85 33	99.83 39	99.75 43	99.69 60	99.52 108	98.07 161	99.53 135	99.63 207	98.93 36	99.97 21	98.74 140	99.91 36	99.83 54

ACMMP		99.45 38	99.32 46	99.82 46	99.89 8	99.67 57	99.62 95	99.69 18	98.12 151	99.63 110	99.84 70	98.73 63	99.96 33	98.55 175	99.83 94	99.81 66
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
SMA-MVS		99.44 42	99.30 54	99.85 33	99.73 93	99.83 19	99.56 130	99.47 185	97.45 235	99.78 57	99.82 84	99.18 10	99.91 117	98.79 136	99.89 56	99.81 66
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
mPP-MVS		99.44 42	99.30 54	99.86 26	99.88 11	99.79 33	99.69 60	99.48 164	98.12 151	99.50 140	99.75 145	98.78 51	99.97 21	98.57 169	99.89 56	99.83 54

EC-MVSNet		99.44 42	99.39 32	99.58 100	99.56 163	99.49 95	99.88 4	99.58 64	98.38 114	99.73 73	99.69 175	98.20 99	99.70 240	99.64 30	99.82 98	99.54 171

SR-MVS		99.43 45	99.29 58	99.86 26	99.75 78	99.83 19	99.59 109	99.62 42	98.21 138	99.73 73	99.79 123	98.68 67	99.96 33	98.44 185	99.77 117	99.79 79

MCST-MVS		99.43 45	99.30 54	99.82 46	99.79 55	99.74 46	99.29 266	99.40 230	98.79 77	99.52 137	99.62 212	98.91 37	99.90 129	98.64 154	99.75 122	99.82 59

MSP-MVS		99.42 47	99.27 63	99.88 9	99.89 8	99.80 30	99.67 69	99.50 142	98.70 86	99.77 61	99.49 258	98.21 98	99.95 64	98.46 183	99.77 117	99.88 27
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
UA-Net		99.42 47	99.29 58	99.80 52	99.62 144	99.55 84	99.50 173	99.70 15	98.79 77	99.77 61	99.96 1	97.45 120	99.96 33	98.92 111	99.90 45	99.89 21

HPM-MVS		99.42 47	99.28 60	99.83 45	99.90 4	99.72 47	99.81 20	99.54 90	97.59 216	99.68 86	99.63 207	98.91 37	99.94 75	98.58 166	99.91 36	99.84 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.42 47	99.30 54	99.78 58	99.62 144	99.71 49	99.26 285	99.52 108	98.82 72	99.39 169	99.71 161	98.96 25	99.85 160	98.59 165	99.80 105	99.77 87

SD-MVS		99.41 51	99.52 12	99.05 194	99.74 86	99.68 54	99.46 198	99.52 108	99.11 33	99.88 27	99.91 23	99.43 1	97.70 403	98.72 143	99.93 26	99.77 87
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
MVS_111021_LR		99.41 51	99.33 44	99.65 80	99.77 64	99.51 93	98.94 355	99.85 6	98.82 72	99.65 102	99.74 150	98.51 81	99.80 199	98.83 131	99.89 56	99.64 143

MVS_111021_HR		99.41 51	99.32 46	99.66 76	99.72 97	99.47 99	98.95 353	99.85 6	98.82 72	99.54 133	99.73 156	98.51 81	99.74 218	98.91 112	99.88 59	99.77 87

MM		99.40 54	99.28 60	99.74 67	99.67 117	99.31 118	99.52 157	98.87 357	99.55 1	99.74 71	99.80 111	96.47 158	99.98 13	99.97 1	99.97 7	99.94 12

GST-MVS		99.40 54	99.24 68	99.85 33	99.86 20	99.79 33	99.60 102	99.67 23	97.97 173	99.63 110	99.68 182	98.52 80	99.95 64	98.38 188	99.86 70	99.81 66

HPM-MVS++		99.39 56	99.23 70	99.87 15	99.75 78	99.84 18	99.43 211	99.51 122	98.68 89	99.27 197	99.53 245	98.64 72	99.96 33	98.44 185	99.80 105	99.79 79

SF-MVS		99.38 57	99.24 68	99.79 55	99.79 55	99.68 54	99.57 124	99.54 90	97.82 193	99.71 80	99.80 111	98.95 30	99.93 93	98.19 205	99.84 85	99.74 97

fmvsm_s_conf0.5_n_3		99.37 58	99.20 73	99.87 15	99.75 78	99.70 51	99.48 188	99.66 28	99.45 7	99.99 2	99.93 10	94.64 238	99.97 21	99.94 11	99.97 7	99.95 9

fmvsm_s_conf0.1_n_2		99.37 58	99.22 71	99.81 49	99.77 64	99.75 43	99.46 198	99.60 55	99.47 4	99.98 8	99.94 6	94.98 211	99.95 64	99.97 1	99.79 112	99.73 102

MP-MVS-pluss		99.37 58	99.20 73	99.88 9	99.90 4	99.87 15	99.30 261	99.52 108	97.18 261	99.60 120	99.79 123	98.79 50	99.95 64	98.83 131	99.91 36	99.83 54
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		99.36 61	99.36 38	99.36 148	99.67 117	98.61 209	99.07 322	99.33 269	99.00 50	99.82 45	99.81 98	99.06 16	99.84 167	99.09 90	99.42 159	99.65 136

PVSNet_Blended_VisFu		99.36 61	99.28 60	99.61 94	99.86 20	99.07 152	99.47 195	99.93 2	97.66 211	99.71 80	99.86 55	97.73 115	99.96 33	99.47 51	99.82 98	99.79 79

NCCC		99.34 63	99.19 75	99.79 55	99.61 148	99.65 63	99.30 261	99.48 164	98.86 67	99.21 211	99.63 207	98.72 64	99.90 129	98.25 201	99.63 143	99.80 75

mamv4		99.33 64	99.42 25	99.07 190	99.67 117	97.73 265	99.42 218	99.60 55	98.15 145	99.94 18	99.91 23	98.42 88	99.94 75	99.72 22	99.96 13	99.54 171

MP-MVS		99.33 64	99.15 78	99.87 15	99.88 11	99.82 25	99.66 75	99.46 194	98.09 156	99.48 144	99.74 150	98.29 95	99.96 33	97.93 227	99.87 62	99.82 59
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_2		99.32 66	99.13 80	99.89 7	99.80 52	99.77 40	99.44 206	99.58 64	99.47 4	99.99 2	99.93 10	94.04 262	99.96 33	99.96 7	99.93 26	99.93 17

PS-MVSNAJ		99.32 66	99.32 46	99.30 162	99.57 159	98.94 174	98.97 349	99.46 194	98.92 64	99.71 80	99.24 326	99.01 18	99.98 13	99.35 58	99.66 138	98.97 261

CSCG		99.32 66	99.32 46	99.32 156	99.85 26	98.29 234	99.71 55	99.66 28	98.11 153	99.41 162	99.80 111	98.37 92	99.96 33	98.99 100	99.96 13	99.72 109

PHI-MVS		99.30 69	99.17 77	99.70 73	99.56 163	99.52 92	99.58 117	99.80 8	97.12 267	99.62 114	99.73 156	98.58 75	99.90 129	98.61 160	99.91 36	99.68 126

DeepC-MVS	98.35 2	99.30 69	99.19 75	99.64 86	99.82 42	99.23 130	99.62 95	99.55 81	98.94 61	99.63 110	99.95 3	95.82 184	99.94 75	99.37 57	99.97 7	99.73 102
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n		99.29 71	99.10 84	99.86 26	99.70 107	99.65 63	99.53 156	99.62 42	98.74 83	99.99 2	99.95 3	94.53 245	99.94 75	99.89 15	99.96 13	99.97 4

xiu_mvs_v1_base_debu		99.29 71	99.27 63	99.34 150	99.63 138	98.97 164	99.12 312	99.51 122	98.86 67	99.84 38	99.47 267	98.18 100	99.99 4	99.50 44	99.31 169	99.08 246

xiu_mvs_v1_base		99.29 71	99.27 63	99.34 150	99.63 138	98.97 164	99.12 312	99.51 122	98.86 67	99.84 38	99.47 267	98.18 100	99.99 4	99.50 44	99.31 169	99.08 246

xiu_mvs_v1_base_debi		99.29 71	99.27 63	99.34 150	99.63 138	98.97 164	99.12 312	99.51 122	98.86 67	99.84 38	99.47 267	98.18 100	99.99 4	99.50 44	99.31 169	99.08 246

APD-MVS		99.27 75	99.08 88	99.84 44	99.75 78	99.79 33	99.50 173	99.50 142	97.16 263	99.77 61	99.82 84	98.78 51	99.94 75	97.56 266	99.86 70	99.80 75
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 75	99.12 82	99.74 67	99.18 280	99.75 43	99.56 130	99.57 68	98.45 107	99.49 143	99.85 60	97.77 114	99.94 75	98.33 195	99.84 85	99.52 178

fmvsm_s_conf0.1_n_a		99.26 77	99.06 90	99.85 33	99.52 176	99.62 71	99.54 148	99.62 42	98.69 87	99.99 2	99.96 1	94.47 247	99.94 75	99.88 16	99.92 29	99.98 2

patch_mono-2		99.26 77	99.62 5	98.16 309	99.81 46	94.59 377	99.52 157	99.64 37	99.33 16	99.73 73	99.90 30	99.00 22	99.99 4	99.69 24	99.98 4	99.89 21

ETV-MVS		99.26 77	99.21 72	99.40 142	99.46 202	99.30 120	99.56 130	99.52 108	98.52 101	99.44 153	99.27 322	98.41 90	99.86 154	99.10 89	99.59 147	99.04 253

xiu_mvs_v2_base		99.26 77	99.25 67	99.29 165	99.53 171	98.91 178	99.02 335	99.45 205	98.80 76	99.71 80	99.26 324	98.94 32	99.98 13	99.34 63	99.23 174	98.98 260

CANet		99.25 81	99.14 79	99.59 97	99.41 217	99.16 137	99.35 250	99.57 68	98.82 72	99.51 139	99.61 216	96.46 159	99.95 64	99.59 32	99.98 4	99.65 136

3Dnovator	97.25 9	99.24 82	99.05 91	99.81 49	99.12 296	99.66 59	99.84 12	99.74 10	99.09 39	98.92 264	99.90 30	95.94 178	99.98 13	98.95 105	99.92 29	99.79 79

dcpmvs_2		99.23 83	99.58 7	98.16 309	99.83 39	94.68 375	99.76 37	99.52 108	99.07 42	99.98 8	99.88 42	98.56 77	99.93 93	99.67 26	99.98 4	99.87 32

test_fmvsmconf0.01_n		99.22 84	99.03 95	99.79 55	98.42 382	99.48 97	99.55 144	99.51 122	99.39 13	99.78 57	99.93 10	94.80 222	99.95 64	99.93 13	99.95 18	99.94 12

CHOSEN 1792x2688		99.19 85	99.10 84	99.45 135	99.89 8	98.52 219	99.39 233	99.94 1	98.73 84	99.11 230	99.89 35	95.50 194	99.94 75	99.50 44	99.97 7	99.89 21

F-COLMAP		99.19 85	99.04 93	99.64 86	99.78 57	99.27 125	99.42 218	99.54 90	97.29 252	99.41 162	99.59 221	98.42 88	99.93 93	98.19 205	99.69 133	99.73 102

EIA-MVS		99.18 87	99.09 87	99.45 135	99.49 192	99.18 134	99.67 69	99.53 103	97.66 211	99.40 167	99.44 274	98.10 103	99.81 192	98.94 106	99.62 144	99.35 221

3Dnovator+	97.12 13	99.18 87	98.97 109	99.82 46	99.17 288	99.68 54	99.81 20	99.51 122	99.20 21	98.72 291	99.89 35	95.68 189	99.97 21	98.86 123	99.86 70	99.81 66

MVSFormer		99.17 89	99.12 82	99.29 165	99.51 179	98.94 174	99.88 4	99.46 194	97.55 222	99.80 50	99.65 195	97.39 121	99.28 314	99.03 96	99.85 77	99.65 136

sss		99.17 89	99.05 91	99.53 115	99.62 144	98.97 164	99.36 245	99.62 42	97.83 189	99.67 90	99.65 195	97.37 124	99.95 64	99.19 78	99.19 177	99.68 126

test_cas_vis1_n_1920		99.16 91	99.01 103	99.61 94	99.81 46	98.86 184	99.65 81	99.64 37	99.39 13	99.97 16	99.94 6	93.20 284	99.98 13	99.55 37	99.91 36	99.99 1

DP-MVS		99.16 91	98.95 115	99.78 58	99.77 64	99.53 89	99.41 221	99.50 142	97.03 279	99.04 246	99.88 42	97.39 121	99.92 105	98.66 152	99.90 45	99.87 32

MVS_0304		99.15 93	98.96 113	99.73 70	98.92 331	99.37 108	99.37 240	96.92 408	99.51 2	99.66 95	99.78 130	96.69 149	99.97 21	99.84 18	99.97 7	99.84 44

casdiffmvs_mvg		99.15 93	99.02 99	99.55 106	99.66 127	99.09 147	99.64 84	99.56 73	98.26 130	99.45 148	99.87 51	96.03 173	99.81 192	99.54 38	99.15 181	99.73 102
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		99.15 93	99.02 99	99.53 115	99.66 127	99.14 142	99.72 52	99.48 164	98.35 119	99.42 158	99.84 70	96.07 171	99.79 202	99.51 43	99.14 182	99.67 129

diffmvs		99.14 96	99.02 99	99.51 123	99.61 148	98.96 168	99.28 271	99.49 152	98.46 106	99.72 78	99.71 161	96.50 157	99.88 146	99.31 66	99.11 184	99.67 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA		99.14 96	98.99 105	99.59 97	99.58 157	99.41 106	99.16 303	99.44 213	98.45 107	99.19 217	99.49 258	98.08 105	99.89 141	97.73 249	99.75 122	99.48 191

CDPH-MVS		99.13 98	98.91 120	99.80 52	99.75 78	99.71 49	99.15 306	99.41 224	96.60 311	99.60 120	99.55 236	98.83 45	99.90 129	97.48 273	99.83 94	99.78 85

casdiffmvs		99.13 98	98.98 108	99.56 104	99.65 133	99.16 137	99.56 130	99.50 142	98.33 122	99.41 162	99.86 55	95.92 179	99.83 180	99.45 53	99.16 178	99.70 120
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
jason		99.13 98	99.03 95	99.45 135	99.46 202	98.87 181	99.12 312	99.26 297	98.03 170	99.79 52	99.65 195	97.02 138	99.85 160	99.02 98	99.90 45	99.65 136
jason: jason.
lupinMVS		99.13 98	99.01 103	99.46 134	99.51 179	98.94 174	99.05 327	99.16 314	97.86 183	99.80 50	99.56 233	97.39 121	99.86 154	98.94 106	99.85 77	99.58 163

EPP-MVSNet		99.13 98	98.99 105	99.53 115	99.65 133	99.06 153	99.81 20	99.33 269	97.43 239	99.60 120	99.88 42	97.14 131	99.84 167	99.13 84	98.94 198	99.69 122

MG-MVS		99.13 98	99.02 99	99.45 135	99.57 159	98.63 206	99.07 322	99.34 262	98.99 52	99.61 117	99.82 84	97.98 109	99.87 151	97.00 303	99.80 105	99.85 38

BP-MVS1		99.12 104	98.94 117	99.65 80	99.51 179	99.30 120	99.67 69	98.92 345	98.48 104	99.84 38	99.69 175	94.96 212	99.92 105	99.62 31	99.79 112	99.71 118

CHOSEN 280x420		99.12 104	99.13 80	99.08 189	99.66 127	97.89 258	98.43 396	99.71 13	98.88 66	99.62 114	99.76 142	96.63 151	99.70 240	99.46 52	99.99 1	99.66 132

DP-MVS Recon		99.12 104	98.95 115	99.65 80	99.74 86	99.70 51	99.27 276	99.57 68	96.40 327	99.42 158	99.68 182	98.75 58	99.80 199	97.98 224	99.72 128	99.44 207

Vis-MVSNet		99.12 104	98.97 109	99.56 104	99.78 57	99.10 146	99.68 66	99.66 28	98.49 103	99.86 36	99.87 51	94.77 227	99.84 167	99.19 78	99.41 160	99.74 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 104	99.08 88	99.24 174	99.46 202	98.55 213	99.51 166	99.46 194	98.09 156	99.45 148	99.82 84	98.34 93	99.51 275	98.70 145	98.93 199	99.67 129

SDMVSNet		99.11 109	98.90 121	99.75 64	99.81 46	99.59 76	99.81 20	99.65 34	98.78 80	99.64 107	99.88 42	94.56 241	99.93 93	99.67 26	98.26 240	99.72 109

VNet		99.11 109	98.90 121	99.73 70	99.52 176	99.56 82	99.41 221	99.39 233	99.01 47	99.74 71	99.78 130	95.56 192	99.92 105	99.52 42	98.18 247	99.72 109

CPTT-MVS		99.11 109	98.90 121	99.74 67	99.80 52	99.46 100	99.59 109	99.49 152	97.03 279	99.63 110	99.69 175	97.27 129	99.96 33	97.82 238	99.84 85	99.81 66

HyFIR lowres test		99.11 109	98.92 118	99.65 80	99.90 4	99.37 108	99.02 335	99.91 3	97.67 210	99.59 123	99.75 145	95.90 181	99.73 224	99.53 40	99.02 195	99.86 34

MVS_Test		99.10 113	98.97 109	99.48 129	99.49 192	99.14 142	99.67 69	99.34 262	97.31 250	99.58 124	99.76 142	97.65 117	99.82 187	98.87 118	99.07 190	99.46 202

CDS-MVSNet		99.09 114	99.03 95	99.25 172	99.42 212	98.73 197	99.45 200	99.46 194	98.11 153	99.46 147	99.77 138	98.01 108	99.37 297	98.70 145	98.92 201	99.66 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GDP-MVS		99.08 115	98.89 124	99.64 86	99.53 171	99.34 112	99.64 84	99.48 164	98.32 123	99.77 61	99.66 193	95.14 208	99.93 93	98.97 104	99.50 154	99.64 143

PVSNet_Blended		99.08 115	98.97 109	99.42 140	99.76 68	98.79 193	98.78 371	99.91 3	96.74 296	99.67 90	99.49 258	97.53 118	99.88 146	98.98 101	99.85 77	99.60 155

OMC-MVS		99.08 115	99.04 93	99.20 178	99.67 117	98.22 238	99.28 271	99.52 108	98.07 161	99.66 95	99.81 98	97.79 113	99.78 207	97.79 240	99.81 101	99.60 155

mvsmamba		99.06 118	98.96 113	99.36 148	99.47 200	98.64 205	99.70 56	99.05 329	97.61 215	99.65 102	99.83 75	96.54 155	99.92 105	99.19 78	99.62 144	99.51 185

WTY-MVS		99.06 118	98.88 126	99.61 94	99.62 144	99.16 137	99.37 240	99.56 73	98.04 168	99.53 135	99.62 212	96.84 143	99.94 75	98.85 125	98.49 228	99.72 109

IS-MVSNet		99.05 120	98.87 127	99.57 102	99.73 93	99.32 114	99.75 42	99.20 309	98.02 171	99.56 128	99.86 55	96.54 155	99.67 248	98.09 212	99.13 183	99.73 102

PAPM_NR		99.04 121	98.84 133	99.66 76	99.74 86	99.44 102	99.39 233	99.38 241	97.70 206	99.28 192	99.28 319	98.34 93	99.85 160	96.96 307	99.45 157	99.69 122

API-MVS		99.04 121	99.03 95	99.06 192	99.40 222	99.31 118	99.55 144	99.56 73	98.54 99	99.33 183	99.39 290	98.76 55	99.78 207	96.98 305	99.78 114	98.07 380

mvs_anonymous		99.03 123	98.99 105	99.16 182	99.38 227	98.52 219	99.51 166	99.38 241	97.79 194	99.38 171	99.81 98	97.30 127	99.45 280	99.35 58	98.99 196	99.51 185

sasdasda		99.02 124	98.86 129	99.51 123	99.42 212	99.32 114	99.80 25	99.48 164	98.63 90	99.31 185	98.81 368	97.09 133	99.75 216	99.27 72	97.90 258	99.47 197

train_agg		99.02 124	98.77 140	99.77 61	99.67 117	99.65 63	99.05 327	99.41 224	96.28 331	98.95 260	99.49 258	98.76 55	99.91 117	97.63 257	99.72 128	99.75 93

canonicalmvs		99.02 124	98.86 129	99.51 123	99.42 212	99.32 114	99.80 25	99.48 164	98.63 90	99.31 185	98.81 368	97.09 133	99.75 216	99.27 72	97.90 258	99.47 197

PLC	97.94 4	99.02 124	98.85 131	99.53 115	99.66 127	99.01 159	99.24 289	99.52 108	96.85 291	99.27 197	99.48 264	98.25 97	99.91 117	97.76 245	99.62 144	99.65 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MGCFI-Net		99.01 128	98.85 131	99.50 128	99.42 212	99.26 126	99.82 16	99.48 164	98.60 94	99.28 192	98.81 368	97.04 137	99.76 213	99.29 69	97.87 261	99.47 197

AdaColmap		99.01 128	98.80 136	99.66 76	99.56 163	99.54 86	99.18 301	99.70 15	98.18 143	99.35 179	99.63 207	96.32 164	99.90 129	97.48 273	99.77 117	99.55 169

1112_ss		98.98 130	98.77 140	99.59 97	99.68 115	99.02 157	99.25 287	99.48 164	97.23 258	99.13 226	99.58 225	96.93 142	99.90 129	98.87 118	98.78 212	99.84 44

MSDG		98.98 130	98.80 136	99.53 115	99.76 68	99.19 132	98.75 374	99.55 81	97.25 255	99.47 145	99.77 138	97.82 112	99.87 151	96.93 310	99.90 45	99.54 171

CANet_DTU		98.97 132	98.87 127	99.25 172	99.33 239	98.42 231	99.08 321	99.30 287	99.16 23	99.43 155	99.75 145	95.27 202	99.97 21	98.56 172	99.95 18	99.36 220

DPM-MVS		98.95 133	98.71 146	99.66 76	99.63 138	99.55 84	98.64 385	99.10 320	97.93 176	99.42 158	99.55 236	98.67 69	99.80 199	95.80 342	99.68 136	99.61 152

114514_t		98.93 134	98.67 150	99.72 72	99.85 26	99.53 89	99.62 95	99.59 60	92.65 396	99.71 80	99.78 130	98.06 106	99.90 129	98.84 128	99.91 36	99.74 97

PS-MVSNAJss		98.92 135	98.92 118	98.90 219	98.78 349	98.53 215	99.78 32	99.54 90	98.07 161	99.00 253	99.76 142	99.01 18	99.37 297	99.13 84	97.23 300	98.81 270

RRT-MVS		98.91 136	98.75 142	99.39 146	99.46 202	98.61 209	99.76 37	99.50 142	98.06 165	99.81 46	99.88 42	93.91 269	99.94 75	99.11 86	99.27 172	99.61 152

Test_1112_low_res		98.89 137	98.66 153	99.57 102	99.69 111	98.95 171	99.03 332	99.47 185	96.98 281	99.15 224	99.23 327	96.77 146	99.89 141	98.83 131	98.78 212	99.86 34

test_fmvs1		98.88 138	98.79 139	99.16 182	99.69 111	97.61 274	99.55 144	99.49 152	99.32 17	99.98 8	99.91 23	91.41 332	99.96 33	99.82 19	99.92 29	99.90 18

AllTest		98.87 139	98.72 144	99.31 157	99.86 20	98.48 225	99.56 130	99.61 49	97.85 186	99.36 176	99.85 60	95.95 176	99.85 160	96.66 323	99.83 94	99.59 159

UGNet		98.87 139	98.69 148	99.40 142	99.22 271	98.72 198	99.44 206	99.68 20	99.24 20	99.18 221	99.42 278	92.74 294	99.96 33	99.34 63	99.94 24	99.53 177
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
Vis-MVSNet (Re-imp)		98.87 139	98.72 144	99.31 157	99.71 102	98.88 180	99.80 25	99.44 213	97.91 178	99.36 176	99.78 130	95.49 195	99.43 289	97.91 228	99.11 184	99.62 150

test_yl		98.86 142	98.63 155	99.54 107	99.49 192	99.18 134	99.50 173	99.07 326	98.22 136	99.61 117	99.51 252	95.37 198	99.84 167	98.60 163	98.33 234	99.59 159

DCV-MVSNet		98.86 142	98.63 155	99.54 107	99.49 192	99.18 134	99.50 173	99.07 326	98.22 136	99.61 117	99.51 252	95.37 198	99.84 167	98.60 163	98.33 234	99.59 159

EPNet		98.86 142	98.71 146	99.30 162	97.20 402	98.18 239	99.62 95	98.91 350	99.28 19	98.63 310	99.81 98	95.96 175	99.99 4	99.24 75	99.72 128	99.73 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 142	98.80 136	99.03 196	99.76 68	98.79 193	99.28 271	99.91 3	97.42 241	99.67 90	99.37 295	97.53 118	99.88 146	98.98 101	97.29 298	98.42 358

ab-mvs		98.86 142	98.63 155	99.54 107	99.64 135	99.19 132	99.44 206	99.54 90	97.77 197	99.30 188	99.81 98	94.20 255	99.93 93	99.17 82	98.82 209	99.49 190

MAR-MVS		98.86 142	98.63 155	99.54 107	99.37 230	99.66 59	99.45 200	99.54 90	96.61 308	99.01 249	99.40 286	97.09 133	99.86 154	97.68 256	99.53 152	99.10 241
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
COLMAP_ROB	97.56 6	98.86 142	98.75 142	99.17 181	99.88 11	98.53 215	99.34 253	99.59 60	97.55 222	98.70 298	99.89 35	95.83 183	99.90 129	98.10 211	99.90 45	99.08 246
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		98.85 149	98.62 160	99.53 115	99.61 148	99.08 150	99.80 25	99.51 122	97.10 271	99.31 185	99.78 130	95.23 206	99.77 209	98.21 203	99.03 193	99.75 93

HY-MVS	97.30 7	98.85 149	98.64 154	99.47 132	99.42 212	99.08 150	99.62 95	99.36 250	97.39 244	99.28 192	99.68 182	96.44 161	99.92 105	98.37 190	98.22 242	99.40 214

PVSNet	96.02 17	98.85 149	98.84 133	98.89 222	99.73 93	97.28 283	98.32 402	99.60 55	97.86 183	99.50 140	99.57 230	96.75 147	99.86 154	98.56 172	99.70 132	99.54 171

PatchMatch-RL		98.84 152	98.62 160	99.52 121	99.71 102	99.28 123	99.06 325	99.77 9	97.74 201	99.50 140	99.53 245	95.41 196	99.84 167	97.17 297	99.64 141	99.44 207

Effi-MVS+		98.81 153	98.59 166	99.48 129	99.46 202	99.12 145	98.08 409	99.50 142	97.50 230	99.38 171	99.41 282	96.37 163	99.81 192	99.11 86	98.54 225	99.51 185

alignmvs		98.81 153	98.56 169	99.58 100	99.43 210	99.42 104	99.51 166	98.96 340	98.61 93	99.35 179	98.92 363	94.78 224	99.77 209	99.35 58	98.11 252	99.54 171

DeepPCF-MVS	98.18 3	98.81 153	99.37 36	97.12 361	99.60 153	91.75 401	98.61 386	99.44 213	99.35 15	99.83 44	99.85 60	98.70 66	99.81 192	99.02 98	99.91 36	99.81 66

PMMVS		98.80 156	98.62 160	99.34 150	99.27 257	98.70 199	98.76 373	99.31 283	97.34 247	99.21 211	99.07 343	97.20 130	99.82 187	98.56 172	98.87 204	99.52 178

Effi-MVS+-dtu		98.78 157	98.89 124	98.47 278	99.33 239	96.91 312	99.57 124	99.30 287	98.47 105	99.41 162	98.99 353	96.78 145	99.74 218	98.73 142	99.38 161	98.74 283

FIs		98.78 157	98.63 155	99.23 176	99.18 280	99.54 86	99.83 15	99.59 60	98.28 126	98.79 285	99.81 98	96.75 147	99.37 297	99.08 91	96.38 316	98.78 272

Fast-Effi-MVS+-dtu		98.77 159	98.83 135	98.60 257	99.41 217	96.99 306	99.52 157	99.49 152	98.11 153	99.24 203	99.34 305	96.96 141	99.79 202	97.95 226	99.45 157	99.02 256

sd_testset		98.75 160	98.57 167	99.29 165	99.81 46	98.26 236	99.56 130	99.62 42	98.78 80	99.64 107	99.88 42	92.02 316	99.88 146	99.54 38	98.26 240	99.72 109

FA-MVS(test-final)		98.75 160	98.53 171	99.41 141	99.55 167	99.05 155	99.80 25	99.01 334	96.59 313	99.58 124	99.59 221	95.39 197	99.90 129	97.78 241	99.49 155	99.28 229

FC-MVSNet-test		98.75 160	98.62 160	99.15 186	99.08 307	99.45 101	99.86 11	99.60 55	98.23 135	98.70 298	99.82 84	96.80 144	99.22 326	99.07 92	96.38 316	98.79 271

XVG-OURS		98.73 163	98.68 149	98.88 224	99.70 107	97.73 265	98.92 357	99.55 81	98.52 101	99.45 148	99.84 70	95.27 202	99.91 117	98.08 216	98.84 207	99.00 257

Fast-Effi-MVS+		98.70 164	98.43 175	99.51 123	99.51 179	99.28 123	99.52 157	99.47 185	96.11 347	99.01 249	99.34 305	96.20 168	99.84 167	97.88 230	98.82 209	99.39 215

XVG-OURS-SEG-HR		98.69 165	98.62 160	98.89 222	99.71 102	97.74 264	99.12 312	99.54 90	98.44 110	99.42 158	99.71 161	94.20 255	99.92 105	98.54 176	98.90 203	99.00 257

1314		98.68 166	98.54 170	99.11 188	98.89 334	98.65 203	99.27 276	99.49 152	96.89 289	97.99 349	99.56 233	97.72 116	99.83 180	97.74 248	99.27 172	98.84 269

EI-MVSNet		98.67 167	98.67 150	98.68 253	99.35 234	97.97 251	99.50 173	99.38 241	96.93 288	99.20 214	99.83 75	97.87 110	99.36 301	98.38 188	97.56 277	98.71 287

test_djsdf		98.67 167	98.57 167	98.98 202	98.70 363	98.91 178	99.88 4	99.46 194	97.55 222	99.22 208	99.88 42	95.73 187	99.28 314	99.03 96	97.62 272	98.75 280

QAPM		98.67 167	98.30 185	99.80 52	99.20 274	99.67 57	99.77 34	99.72 11	94.74 374	98.73 290	99.90 30	95.78 185	99.98 13	96.96 307	99.88 59	99.76 92

nrg030		98.64 170	98.42 176	99.28 169	99.05 313	99.69 53	99.81 20	99.46 194	98.04 168	99.01 249	99.82 84	96.69 149	99.38 294	99.34 63	94.59 360	98.78 272

test_vis1_n_1920		98.63 171	98.40 178	99.31 157	99.86 20	97.94 257	99.67 69	99.62 42	99.43 10	99.99 2	99.91 23	87.29 381	100.00 1	99.92 14	99.92 29	99.98 2

PAPR		98.63 171	98.34 181	99.51 123	99.40 222	99.03 156	98.80 369	99.36 250	96.33 328	99.00 253	99.12 341	98.46 84	99.84 167	95.23 357	99.37 168	99.66 132

CVMVSNet		98.57 173	98.67 150	98.30 298	99.35 234	95.59 353	99.50 173	99.55 81	98.60 94	99.39 169	99.83 75	94.48 246	99.45 280	98.75 139	98.56 223	99.85 38

MVSTER		98.49 174	98.32 183	99.00 200	99.35 234	99.02 157	99.54 148	99.38 241	97.41 242	99.20 214	99.73 156	93.86 271	99.36 301	98.87 118	97.56 277	98.62 329

FE-MVS		98.48 175	98.17 190	99.40 142	99.54 170	98.96 168	99.68 66	98.81 364	95.54 358	99.62 114	99.70 165	93.82 272	99.93 93	97.35 284	99.46 156	99.32 226

OpenMVS	96.50 16	98.47 176	98.12 197	99.52 121	99.04 314	99.53 89	99.82 16	99.72 11	94.56 377	98.08 344	99.88 42	94.73 230	99.98 13	97.47 275	99.76 120	99.06 252

IterMVS-LS		98.46 177	98.42 176	98.58 261	99.59 155	98.00 249	99.37 240	99.43 219	96.94 287	99.07 238	99.59 221	97.87 110	99.03 354	98.32 197	95.62 338	98.71 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 178	98.28 186	98.94 209	98.50 379	98.96 168	99.77 34	99.50 142	97.07 273	98.87 273	99.77 138	94.76 228	99.28 314	98.66 152	97.60 273	98.57 344

jajsoiax		98.43 179	98.28 186	98.88 224	98.60 373	98.43 229	99.82 16	99.53 103	98.19 140	98.63 310	99.80 111	93.22 283	99.44 285	99.22 76	97.50 284	98.77 276

tttt0517		98.42 180	98.14 194	99.28 169	99.66 127	98.38 232	99.74 46	96.85 409	97.68 208	99.79 52	99.74 150	91.39 333	99.89 141	98.83 131	99.56 149	99.57 166

BH-untuned		98.42 180	98.36 179	98.59 258	99.49 192	96.70 320	99.27 276	99.13 318	97.24 257	98.80 283	99.38 292	95.75 186	99.74 218	97.07 301	99.16 178	99.33 225

test_fmvs1_n		98.41 182	98.14 194	99.21 177	99.82 42	97.71 270	99.74 46	99.49 152	99.32 17	99.99 2	99.95 3	85.32 392	99.97 21	99.82 19	99.84 85	99.96 7

D2MVS		98.41 182	98.50 172	98.15 312	99.26 259	96.62 326	99.40 229	99.61 49	97.71 203	98.98 255	99.36 298	96.04 172	99.67 248	98.70 145	97.41 294	98.15 376

BH-RMVSNet		98.41 182	98.08 203	99.40 142	99.41 217	98.83 189	99.30 261	98.77 368	97.70 206	98.94 262	99.65 195	92.91 290	99.74 218	96.52 327	99.55 151	99.64 143

mvs_tets		98.40 185	98.23 188	98.91 217	98.67 366	98.51 221	99.66 75	99.53 103	98.19 140	98.65 307	99.81 98	92.75 292	99.44 285	99.31 66	97.48 288	98.77 276

MonoMVSNet		98.38 186	98.47 174	98.12 314	98.59 375	96.19 343	99.72 52	98.79 367	97.89 180	99.44 153	99.52 248	96.13 169	98.90 375	98.64 154	97.54 279	99.28 229

XXY-MVS		98.38 186	98.09 202	99.24 174	99.26 259	99.32 114	99.56 130	99.55 81	97.45 235	98.71 292	99.83 75	93.23 281	99.63 265	98.88 115	96.32 318	98.76 278

ACMM	97.58 5	98.37 188	98.34 181	98.48 273	99.41 217	97.10 293	99.56 130	99.45 205	98.53 100	99.04 246	99.85 60	93.00 286	99.71 234	98.74 140	97.45 289	98.64 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thisisatest0530		98.35 189	98.03 209	99.31 157	99.63 138	98.56 212	99.54 148	96.75 411	97.53 226	99.73 73	99.65 195	91.25 336	99.89 141	98.62 157	99.56 149	99.48 191

tpmrst		98.33 190	98.48 173	97.90 330	99.16 290	94.78 373	99.31 259	99.11 319	97.27 253	99.45 148	99.59 221	95.33 200	99.84 167	98.48 179	98.61 217	99.09 245

baseline1		98.31 191	97.95 218	99.38 147	99.50 190	98.74 196	99.59 109	98.93 342	98.41 112	99.14 225	99.60 219	94.59 239	99.79 202	98.48 179	93.29 379	99.61 152

PatchmatchNet		98.31 191	98.36 179	98.19 307	99.16 290	95.32 363	99.27 276	98.92 345	97.37 245	99.37 173	99.58 225	94.90 217	99.70 240	97.43 279	99.21 175	99.54 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		98.30 193	97.98 214	99.26 171	99.57 159	98.16 240	99.41 221	98.55 386	96.03 352	99.19 217	99.74 150	91.87 319	99.92 105	99.16 83	98.29 239	99.70 120

VPA-MVSNet		98.29 194	97.95 218	99.30 162	99.16 290	99.54 86	99.50 173	99.58 64	98.27 128	99.35 179	99.37 295	92.53 304	99.65 256	99.35 58	94.46 361	98.72 285

UniMVSNet (Re)		98.29 194	98.00 212	99.13 187	99.00 318	99.36 111	99.49 184	99.51 122	97.95 174	98.97 257	99.13 338	96.30 165	99.38 294	98.36 192	93.34 378	98.66 316

HQP_MVS		98.27 196	98.22 189	98.44 284	99.29 252	96.97 308	99.39 233	99.47 185	98.97 58	99.11 230	99.61 216	92.71 297	99.69 245	97.78 241	97.63 270	98.67 308

UniMVSNet_NR-MVSNet		98.22 197	97.97 215	98.96 205	98.92 331	98.98 161	99.48 188	99.53 103	97.76 198	98.71 292	99.46 271	96.43 162	99.22 326	98.57 169	92.87 385	98.69 296

LPG-MVS_test		98.22 197	98.13 196	98.49 271	99.33 239	97.05 299	99.58 117	99.55 81	97.46 232	99.24 203	99.83 75	92.58 302	99.72 228	98.09 212	97.51 282	98.68 301

RPSCF		98.22 197	98.62 160	96.99 363	99.82 42	91.58 402	99.72 52	99.44 213	96.61 308	99.66 95	99.89 35	95.92 179	99.82 187	97.46 276	99.10 187	99.57 166

ADS-MVSNet		98.20 200	98.08 203	98.56 265	99.33 239	96.48 331	99.23 291	99.15 315	96.24 335	99.10 233	99.67 188	94.11 259	99.71 234	96.81 315	99.05 191	99.48 191

OPM-MVS		98.19 201	98.10 199	98.45 281	98.88 335	97.07 297	99.28 271	99.38 241	98.57 96	99.22 208	99.81 98	92.12 314	99.66 251	98.08 216	97.54 279	98.61 338
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SCA		98.19 201	98.16 191	98.27 304	99.30 248	95.55 354	99.07 322	98.97 338	97.57 219	99.43 155	99.57 230	92.72 295	99.74 218	97.58 261	99.20 176	99.52 178

miper_ehance_all_eth		98.18 203	98.10 199	98.41 287	99.23 267	97.72 267	98.72 377	99.31 283	96.60 311	98.88 270	99.29 317	97.29 128	99.13 340	97.60 259	95.99 327	98.38 363

CR-MVSNet		98.17 204	97.93 221	98.87 228	99.18 280	98.49 223	99.22 295	99.33 269	96.96 283	99.56 128	99.38 292	94.33 251	99.00 359	94.83 364	98.58 220	99.14 238

miper_enhance_ethall		98.16 205	98.08 203	98.41 287	98.96 327	97.72 267	98.45 395	99.32 279	96.95 285	98.97 257	99.17 333	97.06 136	99.22 326	97.86 233	95.99 327	98.29 367

CLD-MVS		98.16 205	98.10 199	98.33 294	99.29 252	96.82 317	98.75 374	99.44 213	97.83 189	99.13 226	99.55 236	92.92 288	99.67 248	98.32 197	97.69 268	98.48 350
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		98.14 207	97.79 233	99.19 179	99.50 190	98.50 222	98.61 386	96.82 410	96.95 285	99.54 133	99.43 276	91.66 328	99.86 154	98.08 216	99.51 153	99.22 235

pmmvs4		98.13 208	97.90 223	98.81 240	98.61 372	98.87 181	98.99 343	99.21 308	96.44 323	99.06 243	99.58 225	95.90 181	99.11 345	97.18 296	96.11 323	98.46 355

WR-MVS_H		98.13 208	97.87 228	98.90 219	99.02 316	98.84 186	99.70 56	99.59 60	97.27 253	98.40 326	99.19 332	95.53 193	99.23 322	98.34 194	93.78 375	98.61 338

c3_l		98.12 210	98.04 208	98.38 291	99.30 248	97.69 271	98.81 368	99.33 269	96.67 301	98.83 279	99.34 305	97.11 132	98.99 360	97.58 261	95.34 345	98.48 350

ACMH	97.28 8	98.10 211	97.99 213	98.44 284	99.41 217	96.96 310	99.60 102	99.56 73	98.09 156	98.15 342	99.91 23	90.87 340	99.70 240	98.88 115	97.45 289	98.67 308
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		98.09 212	97.68 249	99.34 150	99.66 127	98.44 228	99.40 229	99.43 219	93.67 384	99.22 208	99.89 35	90.23 348	99.93 93	99.26 74	98.33 234	99.66 132

CP-MVSNet		98.09 212	97.78 236	99.01 198	98.97 326	99.24 129	99.67 69	99.46 194	97.25 255	98.48 323	99.64 201	93.79 273	99.06 350	98.63 156	94.10 369	98.74 283

dmvs_re		98.08 214	98.16 191	97.85 333	99.55 167	94.67 376	99.70 56	98.92 345	98.15 145	99.06 243	99.35 301	93.67 277	99.25 319	97.77 244	97.25 299	99.64 143

DU-MVS		98.08 214	97.79 233	98.96 205	98.87 338	98.98 161	99.41 221	99.45 205	97.87 182	98.71 292	99.50 255	94.82 220	99.22 326	98.57 169	92.87 385	98.68 301

v2v482		98.06 216	97.77 238	98.92 213	98.90 333	98.82 190	99.57 124	99.36 250	96.65 303	99.19 217	99.35 301	94.20 255	99.25 319	97.72 251	94.97 353	98.69 296

V42		98.06 216	97.79 233	98.86 231	98.98 324	98.84 186	99.69 60	99.34 262	96.53 315	99.30 188	99.37 295	94.67 235	99.32 309	97.57 265	94.66 358	98.42 358

test-LLR		98.06 216	97.90 223	98.55 267	98.79 346	97.10 293	98.67 380	97.75 401	97.34 247	98.61 313	98.85 365	94.45 248	99.45 280	97.25 288	99.38 161	99.10 241

WR-MVS		98.06 216	97.73 245	99.06 192	98.86 341	99.25 128	99.19 299	99.35 257	97.30 251	98.66 301	99.43 276	93.94 266	99.21 331	98.58 166	94.28 365	98.71 287

ACMP	97.20 11	98.06 216	97.94 220	98.45 281	99.37 230	97.01 304	99.44 206	99.49 152	97.54 225	98.45 324	99.79 123	91.95 318	99.72 228	97.91 228	97.49 287	98.62 329
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth		98.05 221	97.96 216	98.33 294	99.26 259	97.38 280	98.56 391	99.31 283	96.65 303	98.88 270	99.52 248	96.58 153	99.12 344	97.39 281	95.53 342	98.47 352

test1111		98.04 222	98.11 198	97.83 336	99.74 86	93.82 385	99.58 117	95.40 418	99.12 32	99.65 102	99.93 10	90.73 341	99.84 167	99.43 54	99.38 161	99.82 59

ECVR-MVS		98.04 222	98.05 207	98.00 322	99.74 86	94.37 380	99.59 109	94.98 419	99.13 27	99.66 95	99.93 10	90.67 342	99.84 167	99.40 55	99.38 161	99.80 75

EPNet_dtu		98.03 224	97.96 216	98.23 305	98.27 384	95.54 356	99.23 291	98.75 369	99.02 45	97.82 356	99.71 161	96.11 170	99.48 276	93.04 384	99.65 140	99.69 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 224	97.76 242	98.84 235	99.39 225	98.98 161	99.40 229	99.38 241	96.67 301	99.07 238	99.28 319	92.93 287	98.98 361	97.10 298	96.65 309	98.56 345

ADS-MVSNet2		98.02 226	98.07 206	97.87 332	99.33 239	95.19 366	99.23 291	99.08 323	96.24 335	99.10 233	99.67 188	94.11 259	98.93 372	96.81 315	99.05 191	99.48 191

HQP-MVS		98.02 226	97.90 223	98.37 292	99.19 277	96.83 315	98.98 346	99.39 233	98.24 132	98.66 301	99.40 286	92.47 306	99.64 259	97.19 294	97.58 275	98.64 320

LTVRE_ROB	97.16 12	98.02 226	97.90 223	98.40 289	99.23 267	96.80 318	99.70 56	99.60 55	97.12 267	98.18 341	99.70 165	91.73 324	99.72 228	98.39 187	97.45 289	98.68 301
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
cl____		98.01 229	97.84 231	98.55 267	99.25 263	97.97 251	98.71 378	99.34 262	96.47 322	98.59 316	99.54 241	95.65 190	99.21 331	97.21 290	95.77 333	98.46 355

DIV-MVS_self_test		98.01 229	97.85 230	98.48 273	99.24 265	97.95 255	98.71 378	99.35 257	96.50 316	98.60 315	99.54 241	95.72 188	99.03 354	97.21 290	95.77 333	98.46 355

miper_lstm_enhance		98.00 231	97.91 222	98.28 303	99.34 238	97.43 278	98.88 361	99.36 250	96.48 320	98.80 283	99.55 236	95.98 174	98.91 373	97.27 287	95.50 343	98.51 348

BH-w/o		98.00 231	97.89 227	98.32 296	99.35 234	96.20 342	99.01 340	98.90 352	96.42 325	98.38 327	99.00 352	95.26 204	99.72 228	96.06 335	98.61 217	99.03 254

v1144		97.98 233	97.69 248	98.85 234	98.87 338	98.66 202	99.54 148	99.35 257	96.27 333	99.23 207	99.35 301	94.67 235	99.23 322	96.73 318	95.16 349	98.68 301

EU-MVSNet		97.98 233	98.03 209	97.81 339	98.72 360	96.65 325	99.66 75	99.66 28	98.09 156	98.35 329	99.82 84	95.25 205	98.01 396	97.41 280	95.30 346	98.78 272

tpmvs		97.98 233	98.02 211	97.84 335	99.04 314	94.73 374	99.31 259	99.20 309	96.10 351	98.76 288	99.42 278	94.94 213	99.81 192	96.97 306	98.45 229	98.97 261

tt0805		97.97 236	97.77 238	98.57 262	99.59 155	96.61 327	99.45 200	99.08 323	98.21 138	98.88 270	99.80 111	88.66 366	99.70 240	98.58 166	97.72 267	99.39 215

NR-MVSNet		97.97 236	97.61 258	99.02 197	98.87 338	99.26 126	99.47 195	99.42 221	97.63 213	97.08 374	99.50 255	95.07 210	99.13 340	97.86 233	93.59 376	98.68 301

v8		97.95 238	97.63 256	98.93 211	98.95 328	98.81 192	99.80 25	99.41 224	96.03 352	99.10 233	99.42 278	94.92 216	99.30 312	96.94 309	94.08 370	98.66 316

Patchmatch-test		97.93 239	97.65 252	98.77 245	99.18 280	97.07 297	99.03 332	99.14 317	96.16 342	98.74 289	99.57 230	94.56 241	99.72 228	93.36 380	99.11 184	99.52 178

PS-CasMVS		97.93 239	97.59 260	98.95 207	98.99 321	99.06 153	99.68 66	99.52 108	97.13 265	98.31 331	99.68 182	92.44 310	99.05 351	98.51 177	94.08 370	98.75 280

TranMVSNet+NR-MVSNet		97.93 239	97.66 251	98.76 246	98.78 349	98.62 207	99.65 81	99.49 152	97.76 198	98.49 322	99.60 219	94.23 254	98.97 368	98.00 223	92.90 383	98.70 292

test_vis1_n		97.92 242	97.44 281	99.34 150	99.53 171	98.08 245	99.74 46	99.49 152	99.15 24	100.00 1	99.94 6	79.51 411	99.98 13	99.88 16	99.76 120	99.97 4

v144192		97.92 242	97.60 259	98.87 228	98.83 344	98.65 203	99.55 144	99.34 262	96.20 338	99.32 184	99.40 286	94.36 250	99.26 318	96.37 332	95.03 352	98.70 292

ACMH+	97.24 10	97.92 242	97.78 236	98.32 296	99.46 202	96.68 324	99.56 130	99.54 90	98.41 112	97.79 358	99.87 51	90.18 349	99.66 251	98.05 220	97.18 303	98.62 329

LFMVS		97.90 245	97.35 293	99.54 107	99.52 176	99.01 159	99.39 233	98.24 393	97.10 271	99.65 102	99.79 123	84.79 395	99.91 117	99.28 70	98.38 231	99.69 122

reproduce_monomvs		97.89 246	97.87 228	97.96 326	99.51 179	95.45 359	99.60 102	99.25 299	99.17 22	98.85 278	99.49 258	89.29 358	99.64 259	99.35 58	96.31 319	98.78 272

Anonymous20231211		97.88 247	97.54 264	98.90 219	99.71 102	98.53 215	99.48 188	99.57 68	94.16 380	98.81 281	99.68 182	93.23 281	99.42 290	98.84 128	94.42 363	98.76 278

OurMVSNet-221017-0		97.88 247	97.77 238	98.19 307	98.71 362	96.53 329	99.88 4	99.00 335	97.79 194	98.78 286	99.94 6	91.68 325	99.35 304	97.21 290	96.99 307	98.69 296

v7n		97.87 249	97.52 265	98.92 213	98.76 356	98.58 211	99.84 12	99.46 194	96.20 338	98.91 265	99.70 165	94.89 218	99.44 285	96.03 336	93.89 373	98.75 280

baseline2		97.87 249	97.55 261	98.82 237	99.18 280	98.02 248	99.41 221	96.58 415	96.97 282	96.51 381	99.17 333	93.43 278	99.57 270	97.71 252	99.03 193	98.86 267

thres600view7		97.86 251	97.51 267	98.92 213	99.72 97	97.95 255	99.59 109	98.74 372	97.94 175	99.27 197	98.62 376	91.75 322	99.86 154	93.73 376	98.19 246	98.96 263

UBG		97.85 252	97.48 270	98.95 207	99.25 263	97.64 272	99.24 289	98.74 372	97.90 179	98.64 308	98.20 392	88.65 367	99.81 192	98.27 200	98.40 230	99.42 209

cl22		97.85 252	97.64 255	98.48 273	99.09 304	97.87 259	98.60 388	99.33 269	97.11 270	98.87 273	99.22 328	92.38 311	99.17 335	98.21 203	95.99 327	98.42 358

v10		97.85 252	97.52 265	98.86 231	98.99 321	98.67 201	99.75 42	99.41 224	95.70 356	98.98 255	99.41 282	94.75 229	99.23 322	96.01 338	94.63 359	98.67 308

GA-MVS		97.85 252	97.47 273	99.00 200	99.38 227	97.99 250	98.57 389	99.15 315	97.04 278	98.90 267	99.30 315	89.83 352	99.38 294	96.70 320	98.33 234	99.62 150

tfpnnormal		97.84 256	97.47 273	98.98 202	99.20 274	99.22 131	99.64 84	99.61 49	96.32 329	98.27 335	99.70 165	93.35 280	99.44 285	95.69 345	95.40 344	98.27 368

VPNet		97.84 256	97.44 281	99.01 198	99.21 272	98.94 174	99.48 188	99.57 68	98.38 114	99.28 192	99.73 156	88.89 361	99.39 292	99.19 78	93.27 380	98.71 287

LCM-MVSNet-Re		97.83 258	98.15 193	96.87 369	99.30 248	92.25 399	99.59 109	98.26 391	97.43 239	96.20 385	99.13 338	96.27 166	98.73 382	98.17 208	98.99 196	99.64 143

XVG-ACMP-BASELINE		97.83 258	97.71 247	98.20 306	99.11 298	96.33 336	99.41 221	99.52 108	98.06 165	99.05 245	99.50 255	89.64 355	99.73 224	97.73 249	97.38 296	98.53 346

IterMVS		97.83 258	97.77 238	98.02 319	99.58 157	96.27 339	99.02 335	99.48 164	97.22 259	98.71 292	99.70 165	92.75 292	99.13 340	97.46 276	96.00 326	98.67 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		97.82 261	97.75 243	98.06 316	99.57 159	96.36 335	99.02 335	99.49 152	97.18 261	98.71 292	99.72 160	92.72 295	99.14 337	97.44 278	95.86 332	98.67 308

EPMVS		97.82 261	97.65 252	98.35 293	98.88 335	95.98 346	99.49 184	94.71 421	97.57 219	99.26 201	99.48 264	92.46 309	99.71 234	97.87 232	99.08 189	99.35 221

MVP-Stereo		97.81 263	97.75 243	97.99 323	97.53 395	96.60 328	98.96 350	98.85 359	97.22 259	97.23 369	99.36 298	95.28 201	99.46 279	95.51 349	99.78 114	97.92 393
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 263	97.44 281	98.91 217	98.88 335	98.68 200	99.51 166	99.34 262	96.18 340	99.20 214	99.34 305	94.03 263	99.36 301	95.32 355	95.18 348	98.69 296

ttmdpeth		97.80 265	97.63 256	98.29 299	98.77 354	97.38 280	99.64 84	99.36 250	98.78 80	96.30 384	99.58 225	92.34 313	99.39 292	98.36 192	95.58 339	98.10 378

v1921920		97.80 265	97.45 276	98.84 235	98.80 345	98.53 215	99.52 157	99.34 262	96.15 344	99.24 203	99.47 267	93.98 265	99.29 313	95.40 353	95.13 350	98.69 296

v148		97.79 267	97.55 261	98.50 270	98.74 357	97.72 267	99.54 148	99.33 269	96.26 334	98.90 267	99.51 252	94.68 234	99.14 337	97.83 237	93.15 382	98.63 327

thres400		97.77 268	97.38 289	98.92 213	99.69 111	97.96 253	99.50 173	98.73 378	97.83 189	99.17 222	98.45 382	91.67 326	99.83 180	93.22 381	98.18 247	98.96 263

thres100view900		97.76 269	97.45 276	98.69 252	99.72 97	97.86 261	99.59 109	98.74 372	97.93 176	99.26 201	98.62 376	91.75 322	99.83 180	93.22 381	98.18 247	98.37 364

PEN-MVS		97.76 269	97.44 281	98.72 248	98.77 354	98.54 214	99.78 32	99.51 122	97.06 275	98.29 334	99.64 201	92.63 301	98.89 376	98.09 212	93.16 381	98.72 285

Baseline_NR-MVSNet		97.76 269	97.45 276	98.68 253	99.09 304	98.29 234	99.41 221	98.85 359	95.65 357	98.63 310	99.67 188	94.82 220	99.10 347	98.07 219	92.89 384	98.64 320

TR-MVS		97.76 269	97.41 287	98.82 237	99.06 310	97.87 259	98.87 363	98.56 385	96.63 307	98.68 300	99.22 328	92.49 305	99.65 256	95.40 353	97.79 265	98.95 265

Patchmtry		97.75 273	97.40 288	98.81 240	99.10 301	98.87 181	99.11 318	99.33 269	94.83 372	98.81 281	99.38 292	94.33 251	99.02 356	96.10 334	95.57 340	98.53 346

dp		97.75 273	97.80 232	97.59 349	99.10 301	93.71 388	99.32 256	98.88 355	96.48 320	99.08 237	99.55 236	92.67 300	99.82 187	96.52 327	98.58 220	99.24 234

WBMVS		97.74 275	97.50 268	98.46 279	99.24 265	97.43 278	99.21 297	99.42 221	97.45 235	98.96 259	99.41 282	88.83 362	99.23 322	98.94 106	96.02 324	98.71 287

TAPA-MVS	97.07 15	97.74 275	97.34 296	98.94 209	99.70 107	97.53 275	99.25 287	99.51 122	91.90 398	99.30 188	99.63 207	98.78 51	99.64 259	88.09 407	99.87 62	99.65 136
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 277	97.35 293	98.88 224	99.47 200	97.12 292	99.34 253	98.85 359	98.19 140	99.67 90	99.85 60	82.98 402	99.92 105	99.49 48	98.32 238	99.60 155

MIMVSNet		97.73 277	97.45 276	98.57 262	99.45 208	97.50 276	99.02 335	98.98 337	96.11 347	99.41 162	99.14 337	90.28 344	98.74 381	95.74 343	98.93 199	99.47 197

tfpn200view9		97.72 279	97.38 289	98.72 248	99.69 111	97.96 253	99.50 173	98.73 378	97.83 189	99.17 222	98.45 382	91.67 326	99.83 180	93.22 381	98.18 247	98.37 364

CostFormer		97.72 279	97.73 245	97.71 343	99.15 294	94.02 384	99.54 148	99.02 333	94.67 375	99.04 246	99.35 301	92.35 312	99.77 209	98.50 178	97.94 257	99.34 224

FMVSNet2		97.72 279	97.36 291	98.80 242	99.51 179	98.84 186	99.45 200	99.42 221	96.49 317	98.86 277	99.29 317	90.26 345	98.98 361	96.44 329	96.56 312	98.58 343

test0.0.03 1		97.71 282	97.42 286	98.56 265	98.41 383	97.82 262	98.78 371	98.63 383	97.34 247	98.05 348	98.98 355	94.45 248	98.98 361	95.04 360	97.15 304	98.89 266

h-mvs33		97.70 283	97.28 304	98.97 204	99.70 107	97.27 284	99.36 245	99.45 205	98.94 61	99.66 95	99.64 201	94.93 214	99.99 4	99.48 49	84.36 410	99.65 136

v1240		97.69 284	97.32 299	98.79 243	98.85 342	98.43 229	99.48 188	99.36 250	96.11 347	99.27 197	99.36 298	93.76 275	99.24 321	94.46 367	95.23 347	98.70 292

cascas		97.69 284	97.43 285	98.48 273	98.60 373	97.30 282	98.18 407	99.39 233	92.96 392	98.41 325	98.78 372	93.77 274	99.27 317	98.16 209	98.61 217	98.86 267

pm-mvs1		97.68 286	97.28 304	98.88 224	99.06 310	98.62 207	99.50 173	99.45 205	96.32 329	97.87 354	99.79 123	92.47 306	99.35 304	97.54 268	93.54 377	98.67 308

GBi-Net		97.68 286	97.48 270	98.29 299	99.51 179	97.26 286	99.43 211	99.48 164	96.49 317	99.07 238	99.32 312	90.26 345	98.98 361	97.10 298	96.65 309	98.62 329

test1		97.68 286	97.48 270	98.29 299	99.51 179	97.26 286	99.43 211	99.48 164	96.49 317	99.07 238	99.32 312	90.26 345	98.98 361	97.10 298	96.65 309	98.62 329

tpm		97.67 289	97.55 261	98.03 317	99.02 316	95.01 369	99.43 211	98.54 387	96.44 323	99.12 228	99.34 305	91.83 321	99.60 268	97.75 247	96.46 314	99.48 191

PCF-MVS	97.08 14	97.66 290	97.06 315	99.47 132	99.61 148	99.09 147	98.04 410	99.25 299	91.24 401	98.51 320	99.70 165	94.55 243	99.91 117	92.76 389	99.85 77	99.42 209
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew		97.65 291	97.65 252	97.63 346	98.78 349	97.62 273	99.13 309	98.33 390	97.36 246	99.07 238	98.94 359	95.64 191	99.15 336	92.95 385	98.68 216	96.12 412

our_test_3		97.65 291	97.68 249	97.55 350	98.62 370	94.97 370	98.84 365	99.30 287	96.83 294	98.19 340	99.34 305	97.01 139	99.02 356	95.00 361	96.01 325	98.64 320

testgi		97.65 291	97.50 268	98.13 313	99.36 233	96.45 332	99.42 218	99.48 164	97.76 198	97.87 354	99.45 273	91.09 337	98.81 378	94.53 366	98.52 226	99.13 240

thres200		97.61 294	97.28 304	98.62 256	99.64 135	98.03 247	99.26 285	98.74 372	97.68 208	99.09 236	98.32 388	91.66 328	99.81 192	92.88 386	98.22 242	98.03 383

PAPM		97.59 295	97.09 314	99.07 190	99.06 310	98.26 236	98.30 403	99.10 320	94.88 370	98.08 344	99.34 305	96.27 166	99.64 259	89.87 400	98.92 201	99.31 227

UWE-MVS		97.58 296	97.29 303	98.48 273	99.09 304	96.25 340	99.01 340	96.61 414	97.86 183	99.19 217	99.01 351	88.72 363	99.90 129	97.38 282	98.69 215	99.28 229

VDDNet		97.55 297	97.02 316	99.16 182	99.49 192	98.12 244	99.38 238	99.30 287	95.35 360	99.68 86	99.90 30	82.62 404	99.93 93	99.31 66	98.13 251	99.42 209

TESTMET0.1,1		97.55 297	97.27 307	98.40 289	98.93 329	96.53 329	98.67 380	97.61 404	96.96 283	98.64 308	99.28 319	88.63 369	99.45 280	97.30 286	99.38 161	99.21 236

pmmvs5		97.52 299	97.30 301	98.16 309	98.57 376	96.73 319	99.27 276	98.90 352	96.14 345	98.37 328	99.53 245	91.54 331	99.14 337	97.51 270	95.87 331	98.63 327

LF4IMVS		97.52 299	97.46 275	97.70 344	98.98 324	95.55 354	99.29 266	98.82 362	98.07 161	98.66 301	99.64 201	89.97 350	99.61 267	97.01 302	96.68 308	97.94 391

DTE-MVSNet		97.51 301	97.19 309	98.46 279	98.63 369	98.13 243	99.84 12	99.48 164	96.68 300	97.97 351	99.67 188	92.92 288	98.56 385	96.88 314	92.60 389	98.70 292

testing11		97.50 302	97.10 313	98.71 250	99.20 274	96.91 312	99.29 266	98.82 362	97.89 180	98.21 339	98.40 384	85.63 389	99.83 180	98.45 184	98.04 254	99.37 219

ETVMVS		97.50 302	96.90 320	99.29 165	99.23 267	98.78 195	99.32 256	98.90 352	97.52 228	98.56 317	98.09 398	84.72 396	99.69 245	97.86 233	97.88 260	99.39 215

hse-mvs2		97.50 302	97.14 310	98.59 258	99.49 192	97.05 299	99.28 271	99.22 305	98.94 61	99.66 95	99.42 278	94.93 214	99.65 256	99.48 49	83.80 412	99.08 246

SixPastTwentyTwo		97.50 302	97.33 298	98.03 317	98.65 367	96.23 341	99.77 34	98.68 381	97.14 264	97.90 352	99.93 10	90.45 343	99.18 334	97.00 303	96.43 315	98.67 308

JIA-IIPM		97.50 302	97.02 316	98.93 211	98.73 358	97.80 263	99.30 261	98.97 338	91.73 399	98.91 265	94.86 414	95.10 209	99.71 234	97.58 261	97.98 255	99.28 229

ppachtmachnet_test		97.49 307	97.45 276	97.61 348	98.62 370	95.24 364	98.80 369	99.46 194	96.11 347	98.22 338	99.62 212	96.45 160	98.97 368	93.77 375	95.97 330	98.61 338

test-mter		97.49 307	97.13 312	98.55 267	98.79 346	97.10 293	98.67 380	97.75 401	96.65 303	98.61 313	98.85 365	88.23 373	99.45 280	97.25 288	99.38 161	99.10 241

testing91		97.44 309	97.02 316	98.71 250	99.18 280	96.89 314	99.19 299	99.04 330	97.78 196	98.31 331	98.29 389	85.41 391	99.85 160	98.01 222	97.95 256	99.39 215

tpm2		97.44 309	97.34 296	97.74 342	99.15 294	94.36 381	99.45 200	98.94 341	93.45 389	98.90 267	99.44 274	91.35 334	99.59 269	97.31 285	98.07 253	99.29 228

tpm cat1		97.39 311	97.36 291	97.50 352	99.17 288	93.73 387	99.43 211	99.31 283	91.27 400	98.71 292	99.08 342	94.31 253	99.77 209	96.41 331	98.50 227	99.00 257

testing99		97.36 312	96.94 319	98.63 255	99.18 280	96.70 320	99.30 261	98.93 342	97.71 203	98.23 336	98.26 390	84.92 394	99.84 167	98.04 221	97.85 263	99.35 221

USDC		97.34 313	97.20 308	97.75 341	99.07 308	95.20 365	98.51 393	99.04 330	97.99 172	98.31 331	99.86 55	89.02 359	99.55 273	95.67 347	97.36 297	98.49 349

UniMVSNet_ETH3D		97.32 314	96.81 322	98.87 228	99.40 222	97.46 277	99.51 166	99.53 103	95.86 355	98.54 319	99.77 138	82.44 405	99.66 251	98.68 150	97.52 281	99.50 189

testing3		97.28 315	96.76 324	98.82 237	99.37 230	98.07 246	99.45 200	99.36 250	97.56 221	97.89 353	98.95 358	83.70 400	98.82 377	96.03 336	98.56 223	99.58 163

MVS		97.28 315	96.55 328	99.48 129	98.78 349	98.95 171	99.27 276	99.39 233	83.53 414	98.08 344	99.54 241	96.97 140	99.87 151	94.23 371	99.16 178	99.63 148

test_fmvs2		97.25 317	97.30 301	97.09 362	99.43 210	93.31 393	99.73 50	98.87 357	98.83 71	99.28 192	99.80 111	84.45 397	99.66 251	97.88 230	97.45 289	98.30 366

DSMNet-mixed		97.25 317	97.35 293	96.95 366	97.84 390	93.61 391	99.57 124	96.63 413	96.13 346	98.87 273	98.61 378	94.59 239	97.70 403	95.08 359	98.86 205	99.55 169

MS-PatchMatch		97.24 319	97.32 299	96.99 363	98.45 381	93.51 392	98.82 367	99.32 279	97.41 242	98.13 343	99.30 315	88.99 360	99.56 271	95.68 346	99.80 105	97.90 394

testing222		97.16 320	96.50 329	99.16 182	99.16 290	98.47 227	99.27 276	98.66 382	97.71 203	98.23 336	98.15 393	82.28 407	99.84 167	97.36 283	97.66 269	99.18 237

TransMVSNet (Re)		97.15 321	96.58 327	98.86 231	99.12 296	98.85 185	99.49 184	98.91 350	95.48 359	97.16 372	99.80 111	93.38 279	99.11 345	94.16 373	91.73 391	98.62 329

TinyColmap		97.12 322	96.89 321	97.83 336	99.07 308	95.52 357	98.57 389	98.74 372	97.58 218	97.81 357	99.79 123	88.16 374	99.56 271	95.10 358	97.21 301	98.39 362

K. test v3		97.10 323	96.79 323	98.01 320	98.72 360	96.33 336	99.87 8	97.05 407	97.59 216	96.16 386	99.80 111	88.71 364	99.04 352	96.69 321	96.55 313	98.65 318

Syy-MVS		97.09 324	97.14 310	96.95 366	99.00 318	92.73 397	99.29 266	99.39 233	97.06 275	97.41 363	98.15 393	93.92 268	98.68 383	91.71 393	98.34 232	99.45 205

PatchT		97.03 325	96.44 331	98.79 243	98.99 321	98.34 233	99.16 303	99.07 326	92.13 397	99.52 137	97.31 407	94.54 244	98.98 361	88.54 405	98.73 214	99.03 254

mmtdpeth		96.95 326	96.71 325	97.67 345	99.33 239	94.90 372	99.89 2	99.28 293	98.15 145	99.72 78	98.57 379	86.56 384	99.90 129	99.82 19	89.02 403	98.20 373

myMVS_eth3d		96.89 327	96.37 332	98.43 286	99.00 318	97.16 290	99.29 266	99.39 233	97.06 275	97.41 363	98.15 393	83.46 401	98.68 383	95.27 356	98.34 232	99.45 205

AUN-MVS		96.88 328	96.31 334	98.59 258	99.48 199	97.04 302	99.27 276	99.22 305	97.44 238	98.51 320	99.41 282	91.97 317	99.66 251	97.71 252	83.83 411	99.07 251

FMVSNet1		96.84 329	96.36 333	98.29 299	99.32 246	97.26 286	99.43 211	99.48 164	95.11 364	98.55 318	99.32 312	83.95 399	98.98 361	95.81 341	96.26 320	98.62 329

test2506		96.81 330	96.65 326	97.29 357	99.74 86	92.21 400	99.60 102	85.06 431	99.13 27	99.77 61	99.93 10	87.82 379	99.85 160	99.38 56	99.38 161	99.80 75

RPMNet		96.72 331	95.90 344	99.19 179	99.18 280	98.49 223	99.22 295	99.52 108	88.72 410	99.56 128	97.38 404	94.08 261	99.95 64	86.87 412	98.58 220	99.14 238

mvs5depth		96.66 332	96.22 336	97.97 324	97.00 406	96.28 338	98.66 383	99.03 332	96.61 308	96.93 378	99.79 123	87.20 382	99.47 277	96.65 325	94.13 368	98.16 375

test_0402		96.64 333	96.24 335	97.85 333	98.85 342	96.43 333	99.44 206	99.26 297	93.52 386	96.98 376	99.52 248	88.52 370	99.20 333	92.58 391	97.50 284	97.93 392

X-MVStestdata		96.55 334	95.45 353	99.87 15	99.85 26	99.83 19	99.69 60	99.68 20	98.98 55	99.37 173	64.01 427	98.81 47	99.94 75	98.79 136	99.86 70	99.84 44

pmmvs6		96.53 335	96.09 340	97.82 338	98.69 364	95.47 358	99.37 240	99.47 185	93.46 388	97.41 363	99.78 130	87.06 383	99.33 307	96.92 312	92.70 387	98.65 318

ET-MVSNet_ETH3D		96.49 336	95.64 350	99.05 194	99.53 171	98.82 190	98.84 365	97.51 405	97.63 213	84.77 414	99.21 331	92.09 315	98.91 373	98.98 101	92.21 390	99.41 212

UnsupCasMVSNet_eth		96.44 337	96.12 338	97.40 354	98.65 367	95.65 351	99.36 245	99.51 122	97.13 265	96.04 388	98.99 353	88.40 371	98.17 392	96.71 319	90.27 399	98.40 361

FMVSNet5		96.43 338	96.19 337	97.15 358	99.11 298	95.89 348	99.32 256	99.52 108	94.47 379	98.34 330	99.07 343	87.54 380	97.07 408	92.61 390	95.72 336	98.47 352

new_pmnet		96.38 339	96.03 341	97.41 353	98.13 387	95.16 368	99.05 327	99.20 309	93.94 381	97.39 366	98.79 371	91.61 330	99.04 352	90.43 398	95.77 333	98.05 382

Anonymous20231206		96.22 340	96.03 341	96.79 371	97.31 400	94.14 383	99.63 90	99.08 323	96.17 341	97.04 375	99.06 345	93.94 266	97.76 402	86.96 411	95.06 351	98.47 352

IB-MVS	95.67 18	96.22 340	95.44 354	98.57 262	99.21 272	96.70 320	98.65 384	97.74 403	96.71 298	97.27 368	98.54 380	86.03 386	99.92 105	98.47 182	86.30 408	99.10 241
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
Anonymous20240521		96.20 342	95.89 345	97.13 360	97.72 394	94.96 371	99.79 31	99.29 291	93.01 391	97.20 371	99.03 348	89.69 354	98.36 389	91.16 396	96.13 322	98.07 380

gg-mvs-nofinetune		96.17 343	95.32 355	98.73 247	98.79 346	98.14 242	99.38 238	94.09 422	91.07 403	98.07 347	91.04 420	89.62 356	99.35 304	96.75 317	99.09 188	98.68 301

test20.03		96.12 344	95.96 343	96.63 372	97.44 396	95.45 359	99.51 166	99.38 241	96.55 314	96.16 386	99.25 325	93.76 275	96.17 413	87.35 410	94.22 366	98.27 368

PVSNet_0	94.43 19	96.09 345	95.47 352	97.94 327	99.31 247	94.34 382	97.81 411	99.70 15	97.12 267	97.46 362	98.75 373	89.71 353	99.79 202	97.69 255	81.69 414	99.68 126

MVStest1		96.08 346	95.48 351	97.89 331	98.93 329	96.70 320	99.56 130	99.35 257	92.69 395	91.81 409	99.46 271	89.90 351	98.96 370	95.00 361	92.61 388	98.00 387

EG-PatchMatch MVS		95.97 347	95.69 348	96.81 370	97.78 391	92.79 396	99.16 303	98.93 342	96.16 342	94.08 399	99.22 328	82.72 403	99.47 277	95.67 347	97.50 284	98.17 374

APD_test1		95.87 348	96.49 330	94.00 383	99.53 171	84.01 412	99.54 148	99.32 279	95.91 354	97.99 349	99.85 60	85.49 390	99.88 146	91.96 392	98.84 207	98.12 377

Patchmatch-RL test		95.84 349	95.81 347	95.95 378	95.61 411	90.57 404	98.24 404	98.39 389	95.10 366	95.20 393	98.67 375	94.78 224	97.77 401	96.28 333	90.02 400	99.51 185

test_vis1_rt		95.81 350	95.65 349	96.32 376	99.67 117	91.35 403	99.49 184	96.74 412	98.25 131	95.24 391	98.10 397	74.96 412	99.90 129	99.53 40	98.85 206	97.70 397

MVS-HIRNet		95.75 351	95.16 356	97.51 351	99.30 248	93.69 389	98.88 361	95.78 416	85.09 413	98.78 286	92.65 416	91.29 335	99.37 297	94.85 363	99.85 77	99.46 202

MIMVSNet1		95.51 352	95.04 357	96.92 368	97.38 397	95.60 352	99.52 157	99.50 142	93.65 385	96.97 377	99.17 333	85.28 393	96.56 412	88.36 406	95.55 341	98.60 341

MDA-MVSNet_test_wron		95.45 353	94.60 360	98.01 320	98.16 386	97.21 289	99.11 318	99.24 302	93.49 387	80.73 420	98.98 355	93.02 285	98.18 391	94.22 372	94.45 362	98.64 320

TDRefinement		95.42 354	94.57 361	97.97 324	89.83 424	96.11 345	99.48 188	98.75 369	96.74 296	96.68 380	99.88 42	88.65 367	99.71 234	98.37 190	82.74 413	98.09 379

YYNet1		95.36 355	94.51 362	97.92 328	97.89 389	97.10 293	99.10 320	99.23 303	93.26 390	80.77 419	99.04 347	92.81 291	98.02 395	94.30 368	94.18 367	98.64 320

pmmvs-eth3d		95.34 356	94.73 359	97.15 358	95.53 413	95.94 347	99.35 250	99.10 320	95.13 362	93.55 401	97.54 402	88.15 375	97.91 398	94.58 365	89.69 402	97.61 398

dmvs_testset		95.02 357	96.12 338	91.72 392	99.10 301	80.43 420	99.58 117	97.87 400	97.47 231	95.22 392	98.82 367	93.99 264	95.18 417	88.09 407	94.91 356	99.56 168

KD-MVS_self_test		95.00 358	94.34 363	96.96 365	97.07 405	95.39 362	99.56 130	99.44 213	95.11 364	97.13 373	97.32 406	91.86 320	97.27 407	90.35 399	81.23 415	98.23 372

MDA-MVSNet-bldmvs		94.96 359	93.98 366	97.92 328	98.24 385	97.27 284	99.15 306	99.33 269	93.80 383	80.09 421	99.03 348	88.31 372	97.86 400	93.49 379	94.36 364	98.62 329

N_pmnet		94.95 360	95.83 346	92.31 390	98.47 380	79.33 422	99.12 312	92.81 428	93.87 382	97.68 359	99.13 338	93.87 270	99.01 358	91.38 395	96.19 321	98.59 342

KD-MVS_2432*1600		94.62 361	93.72 369	97.31 355	97.19 403	95.82 349	98.34 399	99.20 309	95.00 368	97.57 360	98.35 386	87.95 376	98.10 393	92.87 387	77.00 418	98.01 384

miper_refine_blended		94.62 361	93.72 369	97.31 355	97.19 403	95.82 349	98.34 399	99.20 309	95.00 368	97.57 360	98.35 386	87.95 376	98.10 393	92.87 387	77.00 418	98.01 384

CL-MVSNet_self_test		94.49 363	93.97 367	96.08 377	96.16 408	93.67 390	98.33 401	99.38 241	95.13 362	97.33 367	98.15 393	92.69 299	96.57 411	88.67 404	79.87 416	97.99 388

new-patchmatchnet		94.48 364	94.08 365	95.67 379	95.08 416	92.41 398	99.18 301	99.28 293	94.55 378	93.49 402	97.37 405	87.86 378	97.01 409	91.57 394	88.36 404	97.61 398

OpenMVS_ROB	92.34 20	94.38 365	93.70 371	96.41 375	97.38 397	93.17 394	99.06 325	98.75 369	86.58 411	94.84 397	98.26 390	81.53 408	99.32 309	89.01 403	97.87 261	96.76 405

CMPMVS	69.68 23	94.13 366	94.90 358	91.84 391	97.24 401	80.01 421	98.52 392	99.48 164	89.01 408	91.99 408	99.67 188	85.67 388	99.13 340	95.44 351	97.03 306	96.39 409
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 367	93.25 373	96.60 373	94.76 418	94.49 378	98.92 357	98.18 396	89.66 404	96.48 382	98.06 399	86.28 385	97.33 406	89.68 401	87.20 407	97.97 390

mvsany_test3		93.77 368	93.45 372	94.74 381	95.78 410	88.01 407	99.64 84	98.25 392	98.28 126	94.31 398	97.97 400	68.89 415	98.51 387	97.50 271	90.37 398	97.71 395

UnsupCasMVSNet_bld		93.53 369	92.51 375	96.58 374	97.38 397	93.82 385	98.24 404	99.48 164	91.10 402	93.10 403	96.66 409	74.89 413	98.37 388	94.03 374	87.71 406	97.56 400

dongtai		93.26 370	92.93 374	94.25 382	99.39 225	85.68 410	97.68 413	93.27 424	92.87 393	96.85 379	99.39 290	82.33 406	97.48 405	76.78 418	97.80 264	99.58 163

WB-MVS		93.10 371	94.10 364	90.12 397	95.51 415	81.88 417	99.73 50	99.27 296	95.05 367	93.09 404	98.91 364	94.70 233	91.89 421	76.62 419	94.02 372	96.58 407

PM-MVS		92.96 372	92.23 376	95.14 380	95.61 411	89.98 406	99.37 240	98.21 394	94.80 373	95.04 396	97.69 401	65.06 416	97.90 399	94.30 368	89.98 401	97.54 401

SSC-MVS		92.73 373	93.73 368	89.72 398	95.02 417	81.38 418	99.76 37	99.23 303	94.87 371	92.80 405	98.93 360	94.71 232	91.37 422	74.49 421	93.80 374	96.42 408

test_fmvs3		92.10 374	91.77 377	93.08 388	96.19 407	86.25 408	99.82 16	98.62 384	96.65 303	95.19 394	96.90 408	55.05 423	95.93 415	96.63 326	90.92 397	97.06 404

test_f		91.90 375	91.26 379	93.84 384	95.52 414	85.92 409	99.69 60	98.53 388	95.31 361	93.87 400	96.37 411	55.33 422	98.27 390	95.70 344	90.98 396	97.32 403

test_method		91.10 376	91.36 378	90.31 396	95.85 409	73.72 429	94.89 417	99.25 299	68.39 420	95.82 389	99.02 350	80.50 410	98.95 371	93.64 377	94.89 357	98.25 370

Gipuma		90.99 377	90.15 382	93.51 385	98.73 358	90.12 405	93.98 418	99.45 205	79.32 416	92.28 406	94.91 413	69.61 414	97.98 397	87.42 409	95.67 337	92.45 416
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		90.92 378	90.11 383	93.34 386	98.78 349	85.59 411	98.15 408	93.16 426	89.37 407	92.07 407	98.38 385	81.48 409	95.19 416	62.54 425	97.04 305	99.25 233

testf1		90.42 379	90.68 380	89.65 399	97.78 391	73.97 427	99.13 309	98.81 364	89.62 405	91.80 410	98.93 360	62.23 419	98.80 379	86.61 413	91.17 393	96.19 410

APD_test2		90.42 379	90.68 380	89.65 399	97.78 391	73.97 427	99.13 309	98.81 364	89.62 405	91.80 410	98.93 360	62.23 419	98.80 379	86.61 413	91.17 393	96.19 410

test_vis3_rt		87.04 381	85.81 384	90.73 395	93.99 419	81.96 416	99.76 37	90.23 430	92.81 394	81.35 418	91.56 418	40.06 427	99.07 349	94.27 370	88.23 405	91.15 418

PMMVS2		86.87 382	85.37 386	91.35 394	90.21 423	83.80 413	98.89 360	97.45 406	83.13 415	91.67 412	95.03 412	48.49 425	94.70 418	85.86 415	77.62 417	95.54 413

LCM-MVSNet		86.80 383	85.22 387	91.53 393	87.81 425	80.96 419	98.23 406	98.99 336	71.05 418	90.13 413	96.51 410	48.45 426	96.88 410	90.51 397	85.30 409	96.76 405

FPMVS		84.93 384	85.65 385	82.75 405	86.77 426	63.39 431	98.35 398	98.92 345	74.11 417	83.39 416	98.98 355	50.85 424	92.40 420	84.54 416	94.97 353	92.46 415

EGC-MVSNET		82.80 385	77.86 391	97.62 347	97.91 388	96.12 344	99.33 255	99.28 293	8.40 428	25.05 429	99.27 322	84.11 398	99.33 307	89.20 402	98.22 242	97.42 402

tmp_tt		82.80 385	81.52 388	86.66 401	66.61 431	68.44 430	92.79 420	97.92 398	68.96 419	80.04 422	99.85 60	85.77 387	96.15 414	97.86 233	43.89 424	95.39 414

E-PMN		80.61 387	79.88 389	82.81 404	90.75 422	76.38 425	97.69 412	95.76 417	66.44 422	83.52 415	92.25 417	62.54 418	87.16 424	68.53 423	61.40 421	84.89 422

EMVS		80.02 388	79.22 390	82.43 406	91.19 421	76.40 424	97.55 415	92.49 429	66.36 423	83.01 417	91.27 419	64.63 417	85.79 425	65.82 424	60.65 422	85.08 421

ANet_high		77.30 389	74.86 393	84.62 403	75.88 429	77.61 423	97.63 414	93.15 427	88.81 409	64.27 424	89.29 421	36.51 428	83.93 426	75.89 420	52.31 423	92.33 417

MVE	76.82 21	76.91 390	74.31 394	84.70 402	85.38 428	76.05 426	96.88 416	93.17 425	67.39 421	71.28 423	89.01 422	21.66 433	87.69 423	71.74 422	72.29 420	90.35 419
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 391	74.97 392	79.01 407	70.98 430	55.18 432	93.37 419	98.21 394	65.08 424	61.78 425	93.83 415	21.74 432	92.53 419	78.59 417	91.12 395	89.34 420
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 392	41.29 397	36.84 408	86.18 427	49.12 433	79.73 421	22.81 433	27.64 425	25.46 428	28.45 428	21.98 431	48.89 427	55.80 426	23.56 427	12.51 425

testmvs		39.17 393	43.78 395	25.37 410	36.04 433	16.84 435	98.36 397	26.56 432	20.06 426	38.51 427	67.32 423	29.64 430	15.30 429	37.59 427	39.90 425	43.98 424

test123		39.01 394	42.50 396	28.53 409	39.17 432	20.91 434	98.75 374	19.17 434	19.83 427	38.57 426	66.67 424	33.16 429	15.42 428	37.50 428	29.66 426	49.26 423

cdsmvs_eth3d_5k		24.64 395	32.85 398	0.00 411	0.00 434	0.00 436	0.00 422	99.51 122	0.00 429	0.00 430	99.56 233	96.58 153	0.00 430	0.00 429	0.00 428	0.00 426

ab-mvs-re		8.30 396	11.06 399	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	99.58 225	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

pcd_1.5k_mvsjas		8.27 397	11.03 400	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	99.01 18	0.00 430	0.00 429	0.00 428	0.00 426

test_blank		0.13 398	0.17 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	1.57 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

mmdepth		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

monomultidepth		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uanet_test		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

DCPMVS		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

sosnet-low-res		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

sosnet		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uncertanet		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

Regformer		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uanet		0.02 399	0.03 402	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.27 430	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

WAC-MVS						97.16 290								95.47 350

FOURS1					99.91 1	99.93 1	99.87 8	99.56 73	99.10 34	99.81 46

MSC_two_6792asdad				99.87 15	99.51 179	99.76 41		99.33 269					99.96 33	98.87 118	99.84 85	99.89 21

PC_three_1452									98.18 143	99.84 38	99.70 165	99.31 3	98.52 386	98.30 199	99.80 105	99.81 66

No_MVS				99.87 15	99.51 179	99.76 41		99.33 269					99.96 33	98.87 118	99.84 85	99.89 21

test_one_0601					99.81 46	99.88 8		99.49 152	98.97 58	99.65 102	99.81 98	99.09 14

eth-test2					0.00 434

eth-test					0.00 434

ZD-MVS					99.71 102	99.79 33		99.61 49	96.84 292	99.56 128	99.54 241	98.58 75	99.96 33	96.93 310	99.75 122

RE-MVS-def			99.34 42		99.76 68	99.82 25	99.63 90	99.52 108	98.38 114	99.76 67	99.82 84	98.75 58		98.61 160	99.81 101	99.77 87

IU-MVS					99.84 32	99.88 8		99.32 279	98.30 125	99.84 38				98.86 123	99.85 77	99.89 21

OPU-MVS				99.64 86	99.56 163	99.72 47	99.60 102				99.70 165	99.27 5	99.42 290	98.24 202	99.80 105	99.79 79

test_241102_TWO								99.48 164	99.08 40	99.88 27	99.81 98	98.94 32	99.96 33	98.91 112	99.84 85	99.88 27

test_241102_ONE					99.84 32	99.90 2		99.48 164	99.07 42	99.91 20	99.74 150	99.20 7	99.76 213

9.14			99.10 84		99.72 97		99.40 229	99.51 122	97.53 226	99.64 107	99.78 130	98.84 44	99.91 117	97.63 257	99.82 98

save fliter					99.76 68	99.59 76	99.14 308	99.40 230	99.00 50

test_0728_THIRD									98.99 52	99.81 46	99.80 111	99.09 14	99.96 33	98.85 125	99.90 45	99.88 27

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 124	99.51 122					99.96 33	98.93 109	99.86 70	99.88 27

test0726					99.85 26	99.89 4	99.62 95	99.50 142	99.10 34	99.86 36	99.82 84	98.94 32

GSMVS																99.52 178

test_part2					99.81 46	99.83 19				99.77 61

sam_mvs1												94.86 219				99.52 178

sam_mvs												94.72 231

ambc				93.06 389	92.68 420	82.36 414	98.47 394	98.73 378		95.09 395	97.41 403	55.55 421	99.10 347	96.42 330	91.32 392	97.71 395

MTGPA								99.47 185

test_post1							99.23 291				65.14 426	94.18 258	99.71 234	97.58 261

test_post											65.99 425	94.65 237	99.73 224

patchmatchnet-post											98.70 374	94.79 223	99.74 218

GG-mvs-BLEND				98.45 281	98.55 377	98.16 240	99.43 211	93.68 423		97.23 369	98.46 381	89.30 357	99.22 326	95.43 352	98.22 242	97.98 389

MTMP							99.54 148	98.88 355

gm-plane-assit					98.54 378	92.96 395			94.65 376		99.15 336		99.64 259	97.56 266

test9_res														97.49 272	99.72 128	99.75 93

TEST9					99.67 117	99.65 63	99.05 327	99.41 224	96.22 337	98.95 260	99.49 258	98.77 54	99.91 117

test_8					99.67 117	99.61 73	99.03 332	99.41 224	96.28 331	98.93 263	99.48 264	98.76 55	99.91 117

agg_prior2														97.21 290	99.73 127	99.75 93

agg_prior					99.67 117	99.62 71		99.40 230		98.87 273			99.91 117

TestCases				99.31 157	99.86 20	98.48 225		99.61 49	97.85 186	99.36 176	99.85 60	95.95 176	99.85 160	96.66 323	99.83 94	99.59 159

test_prior4						99.56 82	98.99 343

test_prior2							98.96 350		98.34 120	99.01 249	99.52 248	98.68 67		97.96 225	99.74 125

test_prior				99.68 74	99.67 117	99.48 97		99.56 73					99.83 180			99.74 97

旧先验2							98.96 350		96.70 299	99.47 145			99.94 75	98.19 205

新几何2							99.01 340

新几何1				99.75 64	99.75 78	99.59 76		99.54 90	96.76 295	99.29 191	99.64 201	98.43 86	99.94 75	96.92 312	99.66 138	99.72 109

旧先验1					99.74 86	99.59 76		99.54 90			99.69 175	98.47 83			99.68 136	99.73 102

无先验							98.99 343	99.51 122	96.89 289				99.93 93	97.53 269		99.72 109

原ACMM2							98.95 353

原ACMM1				99.65 80	99.73 93	99.33 113		99.47 185	97.46 232	99.12 228	99.66 193	98.67 69	99.91 117	97.70 254	99.69 133	99.71 118

test222					99.75 78	99.49 95	98.91 359	99.49 152	96.42 325	99.34 182	99.65 195	98.28 96			99.69 133	99.72 109

testdata2													99.95 64	96.67 322

segment_acmp												98.96 25

testdata				99.54 107	99.75 78	98.95 171		99.51 122	97.07 273	99.43 155	99.70 165	98.87 40	99.94 75	97.76 245	99.64 141	99.72 109

testdata1							98.85 364		98.32 123

test12				99.75 64	99.64 135	99.61 73		99.29 291		99.21 211		98.38 91	99.89 141		99.74 125	99.74 97

plane_prior7					99.29 252	97.03 303

plane_prior6					99.27 257	96.98 307						92.71 297

plane_prior5								99.47 185					99.69 245	97.78 241	97.63 270	98.67 308

plane_prior4											99.61 216

plane_prior3						97.00 305			98.69 87	99.11 230

plane_prior2							99.39 233		98.97 58

plane_prior1					99.26 259

plane_prior						96.97 308	99.21 297		98.45 107						97.60 273

n2								0.00 435

nn								0.00 435

door-mid								98.05 397

lessismore_v0				97.79 340	98.69 364	95.44 361		94.75 420		95.71 390	99.87 51	88.69 365	99.32 309	95.89 339	94.93 355	98.62 329

LGP-MVS_train				98.49 271	99.33 239	97.05 299		99.55 81	97.46 232	99.24 203	99.83 75	92.58 302	99.72 228	98.09 212	97.51 282	98.68 301

test11								99.35 257

door								97.92 398

HQP5-MVS						96.83 315

HQP-NCC					99.19 277		98.98 346		98.24 132	98.66 301

ACMP_Plane					99.19 277		98.98 346		98.24 132	98.66 301

BP-MVS														97.19 294

HQP4-MVS										98.66 301			99.64 259			98.64 320

HQP3-MVS								99.39 233							97.58 275

HQP2-MVS												92.47 306

NP-MVS					99.23 267	96.92 311					99.40 286

MDTV_nov1_ep13_2view						95.18 367	99.35 250		96.84 292	99.58 124		95.19 207		97.82 238		99.46 202

MDTV_nov1_ep13			98.32 183		99.11 298	94.44 379	99.27 276	98.74 372	97.51 229	99.40 167	99.62 212	94.78 224	99.76 213	97.59 260	98.81 211

ACMMP++_ref															97.19 302

ACMMP++															97.43 293

Test By Simon												98.75 58

ITE_SJBPF				98.08 315	99.29 252	96.37 334		98.92 345	98.34 120	98.83 279	99.75 145	91.09 337	99.62 266	95.82 340	97.40 295	98.25 370

DeepMVS_CX				93.34 386	99.29 252	82.27 415		99.22 305	85.15 412	96.33 383	99.05 346	90.97 339	99.73 224	93.57 378	97.77 266	98.01 384

High-resolution multi-view benchmark