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 34	99.86 20	99.61 74	99.56 130	99.63 41	99.48 3	99.98 8	99.83 76	98.75 58	99.99 4	99.97 1	99.96 13	99.94 13

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 34	99.84 32	99.63 71	99.56 130	99.63 41	99.47 4	99.98 8	99.82 85	98.75 58	99.99 4	99.97 1	99.97 7	99.94 13

test_fmvsmconf_n		99.70 3	99.64 4	99.87 16	99.80 53	99.66 60	99.48 189	99.64 38	99.45 8	99.92 20	99.92 17	98.62 73	99.99 4	99.96 8	99.99 1	99.96 7

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 59	99.84 32	99.44 103	99.58 117	99.69 18	99.43 11	99.98 8	99.91 23	98.62 73	100.00 1	99.97 1	99.95 18	99.90 19

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 65	99.89 4	99.75 42	99.56 74	99.02 46	99.88 28	99.85 61	99.18 10	99.96 34	99.22 77	99.92 30	99.90 19
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 62	99.38 228	99.37 109	99.58 117	99.62 43	99.41 13	99.87 33	99.92 17	98.81 47	100.00 1	99.97 1	99.93 27	99.94 13

reproduce_model		99.63 7	99.54 11	99.90 5	99.78 58	99.88 8	99.56 130	99.55 82	99.15 25	99.90 23	99.90 30	99.00 22	99.97 22	99.11 87	99.91 37	99.86 35

fmvsm_l_conf0.5_n_3		99.61 8	99.51 16	99.92 1	99.84 32	99.82 25	99.54 148	99.66 28	99.46 7	99.98 8	99.89 35	97.27 129	99.99 4	99.97 1	99.95 18	99.95 9

reproduce-ours		99.61 8	99.52 12	99.90 5	99.76 69	99.88 8	99.52 158	99.54 91	99.13 28	99.89 25	99.89 35	98.96 25	99.96 34	99.04 95	99.90 46	99.85 39

our_new_method		99.61 8	99.52 12	99.90 5	99.76 69	99.88 8	99.52 158	99.54 91	99.13 28	99.89 25	99.89 35	98.96 25	99.96 34	99.04 95	99.90 46	99.85 39

SED-MVS		99.61 8	99.52 12	99.88 10	99.84 32	99.90 2	99.60 102	99.48 165	99.08 41	99.91 21	99.81 99	99.20 7	99.96 34	98.91 113	99.85 78	99.79 80

DVP-MVS++		99.59 12	99.50 17	99.88 10	99.51 180	99.88 8	99.87 8	99.51 123	98.99 53	99.88 28	99.81 99	99.27 5	99.96 34	98.85 126	99.80 106	99.81 67

TSAR-MVS + MP.		99.58 13	99.50 17	99.81 50	99.91 1	99.66 60	99.63 90	99.39 234	98.91 66	99.78 58	99.85 61	99.36 2	99.94 76	98.84 129	99.88 60	99.82 60
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 13	99.57 8	99.64 87	99.78 58	99.14 143	99.60 102	99.45 206	99.01 48	99.90 23	99.83 76	98.98 24	99.93 94	99.59 33	99.95 18	99.86 35

EI-MVSNet-Vis-set		99.58 13	99.56 10	99.64 87	99.78 58	99.15 142	99.61 101	99.45 206	99.01 48	99.89 25	99.82 85	99.01 18	99.92 106	99.56 37	99.95 18	99.85 39

DVP-MVS		99.57 16	99.47 21	99.88 10	99.85 26	99.89 4	99.57 124	99.37 250	99.10 35	99.81 47	99.80 112	98.94 32	99.96 34	98.93 110	99.86 71	99.81 67
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 17	99.47 21	99.85 34	99.83 40	99.64 70	99.52 158	99.65 35	99.10 35	99.98 8	99.92 17	97.35 125	99.96 34	99.94 12	99.92 30	99.95 9

test_fmvsmconf0.1_n		99.55 18	99.45 25	99.86 27	99.44 210	99.65 64	99.50 174	99.61 50	99.45 8	99.87 33	99.92 17	97.31 126	99.97 22	99.95 10	99.99 1	99.97 4

SteuartSystems-ACMMP		99.54 19	99.42 26	99.87 16	99.82 43	99.81 29	99.59 109	99.51 123	98.62 93	99.79 53	99.83 76	99.28 4	99.97 22	98.48 180	99.90 46	99.84 45
Skip Steuart: Steuart Systems R&D Blog.
XVS		99.53 20	99.42 26	99.87 16	99.85 26	99.83 19	99.69 60	99.68 20	98.98 56	99.37 174	99.74 151	98.81 47	99.94 76	98.79 137	99.86 71	99.84 45

MTAPA		99.52 21	99.39 33	99.89 8	99.90 4	99.86 16	99.66 75	99.47 186	98.79 78	99.68 87	99.81 99	98.43 86	99.97 22	98.88 116	99.90 46	99.83 55

fmvsm_s_conf0.5_n		99.51 22	99.40 31	99.85 34	99.84 32	99.65 64	99.51 167	99.67 23	99.13 28	99.98 8	99.92 17	96.60 153	99.96 34	99.95 10	99.96 13	99.95 9

HPM-MVS_fast		99.51 22	99.40 31	99.85 34	99.91 1	99.79 34	99.76 37	99.56 74	97.72 203	99.76 68	99.75 146	99.13 12	99.92 106	99.07 93	99.92 30	99.85 39

mvsany_test1		99.50 24	99.46 24	99.62 94	99.61 149	99.09 148	98.94 356	99.48 165	99.10 35	99.96 18	99.91 23	98.85 42	99.96 34	99.72 23	99.58 149	99.82 60

CS-MVS		99.50 24	99.48 19	99.54 108	99.76 69	99.42 105	99.90 1	99.55 82	98.56 98	99.78 58	99.70 166	98.65 71	99.79 203	99.65 29	99.78 115	99.41 213

SPE-MVS-test		99.49 26	99.48 19	99.54 108	99.78 58	99.30 121	99.89 2	99.58 65	98.56 98	99.73 74	99.69 176	98.55 78	99.82 188	99.69 25	99.85 78	99.48 192

HFP-MVS		99.49 26	99.37 37	99.86 27	99.87 15	99.80 31	99.66 75	99.67 23	98.15 146	99.68 87	99.69 176	99.06 16	99.96 34	98.69 149	99.87 63	99.84 45

ACMMPR		99.49 26	99.36 39	99.86 27	99.87 15	99.79 34	99.66 75	99.67 23	98.15 146	99.67 91	99.69 176	98.95 30	99.96 34	98.69 149	99.87 63	99.84 45

DeepC-MVS_fast	98.69 1	99.49 26	99.39 33	99.77 62	99.63 139	99.59 77	99.36 246	99.46 195	99.07 43	99.79 53	99.82 85	98.85 42	99.92 106	98.68 151	99.87 63	99.82 60
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 30	99.35 41	99.87 16	99.88 11	99.80 31	99.65 81	99.66 28	98.13 151	99.66 96	99.68 183	98.96 25	99.96 34	98.62 158	99.87 63	99.84 45

APD-MVS_3200maxsize		99.48 30	99.35 41	99.85 34	99.76 69	99.83 19	99.63 90	99.54 91	98.36 119	99.79 53	99.82 85	98.86 41	99.95 65	98.62 158	99.81 102	99.78 86

DELS-MVS		99.48 30	99.42 26	99.65 81	99.72 98	99.40 108	99.05 328	99.66 28	99.14 27	99.57 128	99.80 112	98.46 84	99.94 76	99.57 36	99.84 86	99.60 156
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 33	99.33 45	99.87 16	99.87 15	99.81 29	99.64 84	99.67 23	98.08 161	99.55 133	99.64 202	98.91 37	99.96 34	98.72 144	99.90 46	99.82 60

ACMMP_NAP		99.47 33	99.34 43	99.88 10	99.87 15	99.86 16	99.47 196	99.48 165	98.05 168	99.76 68	99.86 56	98.82 46	99.93 94	98.82 136	99.91 37	99.84 45

MVSMamba_PlusPlus		99.46 35	99.41 30	99.64 87	99.68 116	99.50 95	99.75 42	99.50 143	98.27 129	99.87 33	99.92 17	98.09 104	99.94 76	99.65 29	99.95 18	99.47 198

balanced_conf03		99.46 35	99.39 33	99.67 76	99.55 168	99.58 82	99.74 46	99.51 123	98.42 112	99.87 33	99.84 71	98.05 107	99.91 118	99.58 35	99.94 25	99.52 179

DPE-MVS		99.46 35	99.32 47	99.91 3	99.78 58	99.88 8	99.36 246	99.51 123	98.73 85	99.88 28	99.84 71	98.72 64	99.96 34	98.16 210	99.87 63	99.88 28
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++		99.46 35	99.47 21	99.44 140	99.60 154	99.16 138	99.41 222	99.71 13	98.98 56	99.45 149	99.78 131	99.19 9	99.54 275	99.28 71	99.84 86	99.63 149

SR-MVS-dyc-post		99.45 39	99.31 53	99.85 34	99.76 69	99.82 25	99.63 90	99.52 109	98.38 115	99.76 68	99.82 85	98.53 79	99.95 65	98.61 161	99.81 102	99.77 88

PGM-MVS		99.45 39	99.31 53	99.86 27	99.87 15	99.78 40	99.58 117	99.65 35	97.84 189	99.71 81	99.80 112	99.12 13	99.97 22	98.33 196	99.87 63	99.83 55

CP-MVS		99.45 39	99.32 47	99.85 34	99.83 40	99.75 44	99.69 60	99.52 109	98.07 162	99.53 136	99.63 208	98.93 36	99.97 22	98.74 141	99.91 37	99.83 55

ACMMP		99.45 39	99.32 47	99.82 47	99.89 8	99.67 58	99.62 95	99.69 18	98.12 152	99.63 111	99.84 71	98.73 63	99.96 34	98.55 176	99.83 95	99.81 67
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SMA-MVS		99.44 43	99.30 55	99.85 34	99.73 94	99.83 19	99.56 130	99.47 186	97.45 236	99.78 58	99.82 85	99.18 10	99.91 118	98.79 137	99.89 57	99.81 67
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 43	99.30 55	99.86 27	99.88 11	99.79 34	99.69 60	99.48 165	98.12 152	99.50 141	99.75 146	98.78 51	99.97 22	98.57 170	99.89 57	99.83 55

EC-MVSNet		99.44 43	99.39 33	99.58 101	99.56 164	99.49 96	99.88 4	99.58 65	98.38 115	99.73 74	99.69 176	98.20 99	99.70 241	99.64 31	99.82 99	99.54 172

SR-MVS		99.43 46	99.29 59	99.86 27	99.75 79	99.83 19	99.59 109	99.62 43	98.21 139	99.73 74	99.79 124	98.68 67	99.96 34	98.44 186	99.77 118	99.79 80

MCST-MVS		99.43 46	99.30 55	99.82 47	99.79 56	99.74 47	99.29 267	99.40 231	98.79 78	99.52 138	99.62 213	98.91 37	99.90 130	98.64 155	99.75 123	99.82 60

MSP-MVS		99.42 48	99.27 64	99.88 10	99.89 8	99.80 31	99.67 69	99.50 143	98.70 87	99.77 62	99.49 259	98.21 98	99.95 65	98.46 184	99.77 118	99.88 28
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 48	99.29 59	99.80 53	99.62 145	99.55 85	99.50 174	99.70 15	98.79 78	99.77 62	99.96 1	97.45 120	99.96 34	98.92 112	99.90 46	99.89 22

HPM-MVS		99.42 48	99.28 61	99.83 46	99.90 4	99.72 48	99.81 20	99.54 91	97.59 217	99.68 87	99.63 208	98.91 37	99.94 76	98.58 167	99.91 37	99.84 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.42 48	99.30 55	99.78 59	99.62 145	99.71 50	99.26 286	99.52 109	98.82 73	99.39 170	99.71 162	98.96 25	99.85 161	98.59 166	99.80 106	99.77 88

SD-MVS		99.41 52	99.52 12	99.05 195	99.74 87	99.68 55	99.46 199	99.52 109	99.11 34	99.88 28	99.91 23	99.43 1	97.70 404	98.72 144	99.93 27	99.77 88
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 52	99.33 45	99.65 81	99.77 65	99.51 94	98.94 356	99.85 6	98.82 73	99.65 103	99.74 151	98.51 81	99.80 200	98.83 132	99.89 57	99.64 144

MVS_111021_HR		99.41 52	99.32 47	99.66 77	99.72 98	99.47 100	98.95 354	99.85 6	98.82 73	99.54 134	99.73 157	98.51 81	99.74 219	98.91 113	99.88 60	99.77 88

MM		99.40 55	99.28 61	99.74 68	99.67 118	99.31 119	99.52 158	98.87 358	99.55 1	99.74 72	99.80 112	96.47 159	99.98 14	99.97 1	99.97 7	99.94 13

GST-MVS		99.40 55	99.24 69	99.85 34	99.86 20	99.79 34	99.60 102	99.67 23	97.97 174	99.63 111	99.68 183	98.52 80	99.95 65	98.38 189	99.86 71	99.81 67

HPM-MVS++		99.39 57	99.23 71	99.87 16	99.75 79	99.84 18	99.43 212	99.51 123	98.68 90	99.27 198	99.53 246	98.64 72	99.96 34	98.44 186	99.80 106	99.79 80

SF-MVS		99.38 58	99.24 69	99.79 56	99.79 56	99.68 55	99.57 124	99.54 91	97.82 194	99.71 81	99.80 112	98.95 30	99.93 94	98.19 206	99.84 86	99.74 98

fmvsm_s_conf0.5_n_3		99.37 59	99.20 74	99.87 16	99.75 79	99.70 52	99.48 189	99.66 28	99.45 8	99.99 2	99.93 10	94.64 239	99.97 22	99.94 12	99.97 7	99.95 9

fmvsm_s_conf0.1_n_2		99.37 59	99.22 72	99.81 50	99.77 65	99.75 44	99.46 199	99.60 56	99.47 4	99.98 8	99.94 6	94.98 212	99.95 65	99.97 1	99.79 113	99.73 103

MP-MVS-pluss		99.37 59	99.20 74	99.88 10	99.90 4	99.87 15	99.30 262	99.52 109	97.18 262	99.60 121	99.79 124	98.79 50	99.95 65	98.83 132	99.91 37	99.83 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		99.36 62	99.36 39	99.36 149	99.67 118	98.61 210	99.07 323	99.33 270	99.00 51	99.82 46	99.81 99	99.06 16	99.84 168	99.09 91	99.42 160	99.65 137

PVSNet_Blended_VisFu		99.36 62	99.28 61	99.61 95	99.86 20	99.07 153	99.47 196	99.93 2	97.66 212	99.71 81	99.86 56	97.73 115	99.96 34	99.47 52	99.82 99	99.79 80

NCCC		99.34 64	99.19 76	99.79 56	99.61 149	99.65 64	99.30 262	99.48 165	98.86 68	99.21 212	99.63 208	98.72 64	99.90 130	98.25 202	99.63 144	99.80 76

mamv4		99.33 65	99.42 26	99.07 191	99.67 118	97.73 266	99.42 219	99.60 56	98.15 146	99.94 19	99.91 23	98.42 88	99.94 76	99.72 23	99.96 13	99.54 172

MP-MVS		99.33 65	99.15 79	99.87 16	99.88 11	99.82 25	99.66 75	99.46 195	98.09 157	99.48 145	99.74 151	98.29 95	99.96 34	97.93 228	99.87 63	99.82 60
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 67	99.13 81	99.89 8	99.80 53	99.77 41	99.44 207	99.58 65	99.47 4	99.99 2	99.93 10	94.04 263	99.96 34	99.96 8	99.93 27	99.93 18

PS-MVSNAJ		99.32 67	99.32 47	99.30 163	99.57 160	98.94 175	98.97 350	99.46 195	98.92 65	99.71 81	99.24 327	99.01 18	99.98 14	99.35 59	99.66 139	98.97 262

CSCG		99.32 67	99.32 47	99.32 157	99.85 26	98.29 235	99.71 55	99.66 28	98.11 154	99.41 163	99.80 112	98.37 92	99.96 34	98.99 101	99.96 13	99.72 110

PHI-MVS		99.30 70	99.17 78	99.70 74	99.56 164	99.52 93	99.58 117	99.80 8	97.12 268	99.62 115	99.73 157	98.58 75	99.90 130	98.61 161	99.91 37	99.68 127

DeepC-MVS	98.35 2	99.30 70	99.19 76	99.64 87	99.82 43	99.23 131	99.62 95	99.55 82	98.94 62	99.63 111	99.95 3	95.82 185	99.94 76	99.37 58	99.97 7	99.73 103
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 72	99.10 85	99.86 27	99.70 108	99.65 64	99.53 157	99.62 43	98.74 84	99.99 2	99.95 3	94.53 246	99.94 76	99.89 16	99.96 13	99.97 4

xiu_mvs_v1_base_debu		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 313	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 247

xiu_mvs_v1_base		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 313	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 247

xiu_mvs_v1_base_debi		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 313	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 247

APD-MVS		99.27 76	99.08 89	99.84 45	99.75 79	99.79 34	99.50 174	99.50 143	97.16 264	99.77 62	99.82 85	98.78 51	99.94 76	97.56 267	99.86 71	99.80 76
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 76	99.12 83	99.74 68	99.18 281	99.75 44	99.56 130	99.57 69	98.45 108	99.49 144	99.85 61	97.77 114	99.94 76	98.33 196	99.84 86	99.52 179

fmvsm_s_conf0.1_n_a		99.26 78	99.06 91	99.85 34	99.52 177	99.62 72	99.54 148	99.62 43	98.69 88	99.99 2	99.96 1	94.47 248	99.94 76	99.88 17	99.92 30	99.98 2

patch_mono-2		99.26 78	99.62 5	98.16 310	99.81 47	94.59 378	99.52 158	99.64 38	99.33 17	99.73 74	99.90 30	99.00 22	99.99 4	99.69 25	99.98 4	99.89 22

ETV-MVS		99.26 78	99.21 73	99.40 143	99.46 203	99.30 121	99.56 130	99.52 109	98.52 102	99.44 154	99.27 323	98.41 90	99.86 155	99.10 90	99.59 148	99.04 254

xiu_mvs_v2_base		99.26 78	99.25 68	99.29 166	99.53 172	98.91 179	99.02 336	99.45 206	98.80 77	99.71 81	99.26 325	98.94 32	99.98 14	99.34 64	99.23 175	98.98 261

CANet		99.25 82	99.14 80	99.59 98	99.41 218	99.16 138	99.35 251	99.57 69	98.82 73	99.51 140	99.61 217	96.46 160	99.95 65	99.59 33	99.98 4	99.65 137

3Dnovator	97.25 9	99.24 83	99.05 92	99.81 50	99.12 297	99.66 60	99.84 12	99.74 10	99.09 40	98.92 265	99.90 30	95.94 179	99.98 14	98.95 106	99.92 30	99.79 80

dcpmvs_2		99.23 84	99.58 7	98.16 310	99.83 40	94.68 376	99.76 37	99.52 109	99.07 43	99.98 8	99.88 43	98.56 77	99.93 94	99.67 27	99.98 4	99.87 33

test_fmvsmconf0.01_n		99.22 85	99.03 96	99.79 56	98.42 383	99.48 98	99.55 144	99.51 123	99.39 14	99.78 58	99.93 10	94.80 223	99.95 65	99.93 14	99.95 18	99.94 13

CHOSEN 1792x2688		99.19 86	99.10 85	99.45 136	99.89 8	98.52 220	99.39 234	99.94 1	98.73 85	99.11 231	99.89 35	95.50 195	99.94 76	99.50 45	99.97 7	99.89 22

F-COLMAP		99.19 86	99.04 94	99.64 87	99.78 58	99.27 126	99.42 219	99.54 91	97.29 253	99.41 163	99.59 222	98.42 88	99.93 94	98.19 206	99.69 134	99.73 103

EIA-MVS		99.18 88	99.09 88	99.45 136	99.49 193	99.18 135	99.67 69	99.53 104	97.66 212	99.40 168	99.44 275	98.10 103	99.81 193	98.94 107	99.62 145	99.35 222

3Dnovator+	97.12 13	99.18 88	98.97 110	99.82 47	99.17 289	99.68 55	99.81 20	99.51 123	99.20 22	98.72 292	99.89 35	95.68 190	99.97 22	98.86 124	99.86 71	99.81 67

MVSFormer		99.17 90	99.12 83	99.29 166	99.51 180	98.94 175	99.88 4	99.46 195	97.55 223	99.80 51	99.65 196	97.39 121	99.28 315	99.03 97	99.85 78	99.65 137

sss		99.17 90	99.05 92	99.53 116	99.62 145	98.97 165	99.36 246	99.62 43	97.83 190	99.67 91	99.65 196	97.37 124	99.95 65	99.19 79	99.19 178	99.68 127

test_cas_vis1_n_1920		99.16 92	99.01 104	99.61 95	99.81 47	98.86 185	99.65 81	99.64 38	99.39 14	99.97 17	99.94 6	93.20 285	99.98 14	99.55 38	99.91 37	99.99 1

DP-MVS		99.16 92	98.95 116	99.78 59	99.77 65	99.53 90	99.41 222	99.50 143	97.03 280	99.04 247	99.88 43	97.39 121	99.92 106	98.66 153	99.90 46	99.87 33

MVS_0304		99.15 94	98.96 114	99.73 71	98.92 332	99.37 109	99.37 241	96.92 409	99.51 2	99.66 96	99.78 131	96.69 150	99.97 22	99.84 19	99.97 7	99.84 45

casdiffmvs_mvg		99.15 94	99.02 100	99.55 107	99.66 128	99.09 148	99.64 84	99.56 74	98.26 131	99.45 149	99.87 52	96.03 174	99.81 193	99.54 39	99.15 182	99.73 103
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 94	99.02 100	99.53 116	99.66 128	99.14 143	99.72 52	99.48 165	98.35 120	99.42 159	99.84 71	96.07 172	99.79 203	99.51 44	99.14 183	99.67 130

diffmvs		99.14 97	99.02 100	99.51 124	99.61 149	98.96 169	99.28 272	99.49 153	98.46 107	99.72 79	99.71 162	96.50 158	99.88 147	99.31 67	99.11 185	99.67 130
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 97	98.99 106	99.59 98	99.58 158	99.41 107	99.16 304	99.44 214	98.45 108	99.19 218	99.49 259	98.08 105	99.89 142	97.73 250	99.75 123	99.48 192

CDPH-MVS		99.13 99	98.91 121	99.80 53	99.75 79	99.71 50	99.15 307	99.41 225	96.60 312	99.60 121	99.55 237	98.83 45	99.90 130	97.48 274	99.83 95	99.78 86

casdiffmvs		99.13 99	98.98 109	99.56 105	99.65 134	99.16 138	99.56 130	99.50 143	98.33 123	99.41 163	99.86 56	95.92 180	99.83 181	99.45 54	99.16 179	99.70 121
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 99	99.03 96	99.45 136	99.46 203	98.87 182	99.12 313	99.26 298	98.03 171	99.79 53	99.65 196	97.02 139	99.85 161	99.02 99	99.90 46	99.65 137
jason: jason.
lupinMVS		99.13 99	99.01 104	99.46 135	99.51 180	98.94 175	99.05 328	99.16 315	97.86 184	99.80 51	99.56 234	97.39 121	99.86 155	98.94 107	99.85 78	99.58 164

EPP-MVSNet		99.13 99	98.99 106	99.53 116	99.65 134	99.06 154	99.81 20	99.33 270	97.43 240	99.60 121	99.88 43	97.14 132	99.84 168	99.13 85	98.94 199	99.69 123

MG-MVS		99.13 99	99.02 100	99.45 136	99.57 160	98.63 207	99.07 323	99.34 263	98.99 53	99.61 118	99.82 85	97.98 109	99.87 152	97.00 304	99.80 106	99.85 39

BP-MVS1		99.12 105	98.94 118	99.65 81	99.51 180	99.30 121	99.67 69	98.92 346	98.48 105	99.84 39	99.69 176	94.96 213	99.92 106	99.62 32	99.79 113	99.71 119

CHOSEN 280x420		99.12 105	99.13 81	99.08 190	99.66 128	97.89 259	98.43 397	99.71 13	98.88 67	99.62 115	99.76 143	96.63 152	99.70 241	99.46 53	99.99 1	99.66 133

DP-MVS Recon		99.12 105	98.95 116	99.65 81	99.74 87	99.70 52	99.27 277	99.57 69	96.40 328	99.42 159	99.68 183	98.75 58	99.80 200	97.98 225	99.72 129	99.44 208

Vis-MVSNet		99.12 105	98.97 110	99.56 105	99.78 58	99.10 147	99.68 66	99.66 28	98.49 104	99.86 37	99.87 52	94.77 228	99.84 168	99.19 79	99.41 161	99.74 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 105	99.08 89	99.24 175	99.46 203	98.55 214	99.51 167	99.46 195	98.09 157	99.45 149	99.82 85	98.34 93	99.51 276	98.70 146	98.93 200	99.67 130

SDMVSNet		99.11 110	98.90 122	99.75 65	99.81 47	99.59 77	99.81 20	99.65 35	98.78 81	99.64 108	99.88 43	94.56 242	99.93 94	99.67 27	98.26 241	99.72 110

VNet		99.11 110	98.90 122	99.73 71	99.52 177	99.56 83	99.41 222	99.39 234	99.01 48	99.74 72	99.78 131	95.56 193	99.92 106	99.52 43	98.18 248	99.72 110

CPTT-MVS		99.11 110	98.90 122	99.74 68	99.80 53	99.46 101	99.59 109	99.49 153	97.03 280	99.63 111	99.69 176	97.27 129	99.96 34	97.82 239	99.84 86	99.81 67

HyFIR lowres test		99.11 110	98.92 119	99.65 81	99.90 4	99.37 109	99.02 336	99.91 3	97.67 211	99.59 124	99.75 146	95.90 182	99.73 225	99.53 41	99.02 196	99.86 35

MVS_Test		99.10 114	98.97 110	99.48 130	99.49 193	99.14 143	99.67 69	99.34 263	97.31 251	99.58 125	99.76 143	97.65 117	99.82 188	98.87 119	99.07 191	99.46 203

CDS-MVSNet		99.09 115	99.03 96	99.25 173	99.42 213	98.73 198	99.45 201	99.46 195	98.11 154	99.46 148	99.77 139	98.01 108	99.37 298	98.70 146	98.92 202	99.66 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GDP-MVS		99.08 116	98.89 125	99.64 87	99.53 172	99.34 113	99.64 84	99.48 165	98.32 124	99.77 62	99.66 194	95.14 209	99.93 94	98.97 105	99.50 155	99.64 144

PVSNet_Blended		99.08 116	98.97 110	99.42 141	99.76 69	98.79 194	98.78 372	99.91 3	96.74 297	99.67 91	99.49 259	97.53 118	99.88 147	98.98 102	99.85 78	99.60 156

OMC-MVS		99.08 116	99.04 94	99.20 179	99.67 118	98.22 239	99.28 272	99.52 109	98.07 162	99.66 96	99.81 99	97.79 113	99.78 208	97.79 241	99.81 102	99.60 156

mvsmamba		99.06 119	98.96 114	99.36 149	99.47 201	98.64 206	99.70 56	99.05 330	97.61 216	99.65 103	99.83 76	96.54 156	99.92 106	99.19 79	99.62 145	99.51 186

WTY-MVS		99.06 119	98.88 127	99.61 95	99.62 145	99.16 138	99.37 241	99.56 74	98.04 169	99.53 136	99.62 213	96.84 144	99.94 76	98.85 126	98.49 229	99.72 110

IS-MVSNet		99.05 121	98.87 128	99.57 103	99.73 94	99.32 115	99.75 42	99.20 310	98.02 172	99.56 129	99.86 56	96.54 156	99.67 249	98.09 213	99.13 184	99.73 103

PAPM_NR		99.04 122	98.84 134	99.66 77	99.74 87	99.44 103	99.39 234	99.38 242	97.70 207	99.28 193	99.28 320	98.34 93	99.85 161	96.96 308	99.45 158	99.69 123

API-MVS		99.04 122	99.03 96	99.06 193	99.40 223	99.31 119	99.55 144	99.56 74	98.54 100	99.33 184	99.39 291	98.76 55	99.78 208	96.98 306	99.78 115	98.07 381

mvs_anonymous		99.03 124	98.99 106	99.16 183	99.38 228	98.52 220	99.51 167	99.38 242	97.79 195	99.38 172	99.81 99	97.30 127	99.45 281	99.35 59	98.99 197	99.51 186

sasdasda		99.02 125	98.86 130	99.51 124	99.42 213	99.32 115	99.80 25	99.48 165	98.63 91	99.31 186	98.81 369	97.09 134	99.75 217	99.27 73	97.90 259	99.47 198

train_agg		99.02 125	98.77 141	99.77 62	99.67 118	99.65 64	99.05 328	99.41 225	96.28 332	98.95 261	99.49 259	98.76 55	99.91 118	97.63 258	99.72 129	99.75 94

canonicalmvs		99.02 125	98.86 130	99.51 124	99.42 213	99.32 115	99.80 25	99.48 165	98.63 91	99.31 186	98.81 369	97.09 134	99.75 217	99.27 73	97.90 259	99.47 198

PLC	97.94 4	99.02 125	98.85 132	99.53 116	99.66 128	99.01 160	99.24 290	99.52 109	96.85 292	99.27 198	99.48 265	98.25 97	99.91 118	97.76 246	99.62 145	99.65 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MGCFI-Net		99.01 129	98.85 132	99.50 129	99.42 213	99.26 127	99.82 16	99.48 165	98.60 95	99.28 193	98.81 369	97.04 138	99.76 214	99.29 70	97.87 262	99.47 198

AdaColmap		99.01 129	98.80 137	99.66 77	99.56 164	99.54 87	99.18 302	99.70 15	98.18 144	99.35 180	99.63 208	96.32 165	99.90 130	97.48 274	99.77 118	99.55 170

1112_ss		98.98 131	98.77 141	99.59 98	99.68 116	99.02 158	99.25 288	99.48 165	97.23 259	99.13 227	99.58 226	96.93 143	99.90 130	98.87 119	98.78 213	99.84 45

MSDG		98.98 131	98.80 137	99.53 116	99.76 69	99.19 133	98.75 375	99.55 82	97.25 256	99.47 146	99.77 139	97.82 112	99.87 152	96.93 311	99.90 46	99.54 172

CANet_DTU		98.97 133	98.87 128	99.25 173	99.33 240	98.42 232	99.08 322	99.30 288	99.16 24	99.43 156	99.75 146	95.27 203	99.97 22	98.56 173	99.95 18	99.36 221

DPM-MVS		98.95 134	98.71 147	99.66 77	99.63 139	99.55 85	98.64 386	99.10 321	97.93 177	99.42 159	99.55 237	98.67 69	99.80 200	95.80 343	99.68 137	99.61 153

114514_t		98.93 135	98.67 151	99.72 73	99.85 26	99.53 90	99.62 95	99.59 61	92.65 397	99.71 81	99.78 131	98.06 106	99.90 130	98.84 129	99.91 37	99.74 98

PS-MVSNAJss		98.92 136	98.92 119	98.90 220	98.78 350	98.53 216	99.78 32	99.54 91	98.07 162	99.00 254	99.76 143	99.01 18	99.37 298	99.13 85	97.23 301	98.81 271

RRT-MVS		98.91 137	98.75 143	99.39 147	99.46 203	98.61 210	99.76 37	99.50 143	98.06 166	99.81 47	99.88 43	93.91 270	99.94 76	99.11 87	99.27 173	99.61 153

Test_1112_low_res		98.89 138	98.66 154	99.57 103	99.69 112	98.95 172	99.03 333	99.47 186	96.98 282	99.15 225	99.23 328	96.77 147	99.89 142	98.83 132	98.78 213	99.86 35

test_fmvs1		98.88 139	98.79 140	99.16 183	99.69 112	97.61 275	99.55 144	99.49 153	99.32 18	99.98 8	99.91 23	91.41 333	99.96 34	99.82 20	99.92 30	99.90 19

AllTest		98.87 140	98.72 145	99.31 158	99.86 20	98.48 226	99.56 130	99.61 50	97.85 187	99.36 177	99.85 61	95.95 177	99.85 161	96.66 324	99.83 95	99.59 160

UGNet		98.87 140	98.69 149	99.40 143	99.22 272	98.72 199	99.44 207	99.68 20	99.24 21	99.18 222	99.42 279	92.74 295	99.96 34	99.34 64	99.94 25	99.53 178
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)		98.87 140	98.72 145	99.31 158	99.71 103	98.88 181	99.80 25	99.44 214	97.91 179	99.36 177	99.78 131	95.49 196	99.43 290	97.91 229	99.11 185	99.62 151

test_yl		98.86 143	98.63 156	99.54 108	99.49 193	99.18 135	99.50 174	99.07 327	98.22 137	99.61 118	99.51 253	95.37 199	99.84 168	98.60 164	98.33 235	99.59 160

DCV-MVSNet		98.86 143	98.63 156	99.54 108	99.49 193	99.18 135	99.50 174	99.07 327	98.22 137	99.61 118	99.51 253	95.37 199	99.84 168	98.60 164	98.33 235	99.59 160

EPNet		98.86 143	98.71 147	99.30 163	97.20 403	98.18 240	99.62 95	98.91 351	99.28 20	98.63 311	99.81 99	95.96 176	99.99 4	99.24 76	99.72 129	99.73 103
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 143	98.80 137	99.03 197	99.76 69	98.79 194	99.28 272	99.91 3	97.42 242	99.67 91	99.37 296	97.53 118	99.88 147	98.98 102	97.29 299	98.42 359

ab-mvs		98.86 143	98.63 156	99.54 108	99.64 136	99.19 133	99.44 207	99.54 91	97.77 198	99.30 189	99.81 99	94.20 256	99.93 94	99.17 83	98.82 210	99.49 191

MAR-MVS		98.86 143	98.63 156	99.54 108	99.37 231	99.66 60	99.45 201	99.54 91	96.61 309	99.01 250	99.40 287	97.09 134	99.86 155	97.68 257	99.53 153	99.10 242
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	98.86 143	98.75 143	99.17 182	99.88 11	98.53 216	99.34 254	99.59 61	97.55 223	98.70 299	99.89 35	95.83 184	99.90 130	98.10 212	99.90 46	99.08 247
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		98.85 150	98.62 161	99.53 116	99.61 149	99.08 151	99.80 25	99.51 123	97.10 272	99.31 186	99.78 131	95.23 207	99.77 210	98.21 204	99.03 194	99.75 94

HY-MVS	97.30 7	98.85 150	98.64 155	99.47 133	99.42 213	99.08 151	99.62 95	99.36 251	97.39 245	99.28 193	99.68 183	96.44 162	99.92 106	98.37 191	98.22 243	99.40 215

PVSNet	96.02 17	98.85 150	98.84 134	98.89 223	99.73 94	97.28 284	98.32 403	99.60 56	97.86 184	99.50 141	99.57 231	96.75 148	99.86 155	98.56 173	99.70 133	99.54 172

PatchMatch-RL		98.84 153	98.62 161	99.52 122	99.71 103	99.28 124	99.06 326	99.77 9	97.74 202	99.50 141	99.53 246	95.41 197	99.84 168	97.17 298	99.64 142	99.44 208

Effi-MVS+		98.81 154	98.59 167	99.48 130	99.46 203	99.12 146	98.08 410	99.50 143	97.50 231	99.38 172	99.41 283	96.37 164	99.81 193	99.11 87	98.54 226	99.51 186

alignmvs		98.81 154	98.56 170	99.58 101	99.43 211	99.42 105	99.51 167	98.96 341	98.61 94	99.35 180	98.92 364	94.78 225	99.77 210	99.35 59	98.11 253	99.54 172

DeepPCF-MVS	98.18 3	98.81 154	99.37 37	97.12 362	99.60 154	91.75 402	98.61 387	99.44 214	99.35 16	99.83 45	99.85 61	98.70 66	99.81 193	99.02 99	99.91 37	99.81 67

PMMVS		98.80 157	98.62 161	99.34 151	99.27 258	98.70 200	98.76 374	99.31 284	97.34 248	99.21 212	99.07 344	97.20 131	99.82 188	98.56 173	98.87 205	99.52 179

Effi-MVS+-dtu		98.78 158	98.89 125	98.47 279	99.33 240	96.91 313	99.57 124	99.30 288	98.47 106	99.41 163	98.99 354	96.78 146	99.74 219	98.73 143	99.38 162	98.74 284

FIs		98.78 158	98.63 156	99.23 177	99.18 281	99.54 87	99.83 15	99.59 61	98.28 127	98.79 286	99.81 99	96.75 148	99.37 298	99.08 92	96.38 317	98.78 273

Fast-Effi-MVS+-dtu		98.77 160	98.83 136	98.60 258	99.41 218	96.99 307	99.52 158	99.49 153	98.11 154	99.24 204	99.34 306	96.96 142	99.79 203	97.95 227	99.45 158	99.02 257

sd_testset		98.75 161	98.57 168	99.29 166	99.81 47	98.26 237	99.56 130	99.62 43	98.78 81	99.64 108	99.88 43	92.02 317	99.88 147	99.54 39	98.26 241	99.72 110

FA-MVS(test-final)		98.75 161	98.53 172	99.41 142	99.55 168	99.05 156	99.80 25	99.01 335	96.59 314	99.58 125	99.59 222	95.39 198	99.90 130	97.78 242	99.49 156	99.28 230

FC-MVSNet-test		98.75 161	98.62 161	99.15 187	99.08 308	99.45 102	99.86 11	99.60 56	98.23 136	98.70 299	99.82 85	96.80 145	99.22 327	99.07 93	96.38 317	98.79 272

XVG-OURS		98.73 164	98.68 150	98.88 225	99.70 108	97.73 266	98.92 358	99.55 82	98.52 102	99.45 149	99.84 71	95.27 203	99.91 118	98.08 217	98.84 208	99.00 258

Fast-Effi-MVS+		98.70 165	98.43 176	99.51 124	99.51 180	99.28 124	99.52 158	99.47 186	96.11 348	99.01 250	99.34 306	96.20 169	99.84 168	97.88 231	98.82 210	99.39 216

XVG-OURS-SEG-HR		98.69 166	98.62 161	98.89 223	99.71 103	97.74 265	99.12 313	99.54 91	98.44 111	99.42 159	99.71 162	94.20 256	99.92 106	98.54 177	98.90 204	99.00 258

1314		98.68 167	98.54 171	99.11 189	98.89 335	98.65 204	99.27 277	99.49 153	96.89 290	97.99 350	99.56 234	97.72 116	99.83 181	97.74 249	99.27 173	98.84 270

EI-MVSNet		98.67 168	98.67 151	98.68 254	99.35 235	97.97 252	99.50 174	99.38 242	96.93 289	99.20 215	99.83 76	97.87 110	99.36 302	98.38 189	97.56 278	98.71 288

test_djsdf		98.67 168	98.57 168	98.98 203	98.70 364	98.91 179	99.88 4	99.46 195	97.55 223	99.22 209	99.88 43	95.73 188	99.28 315	99.03 97	97.62 273	98.75 281

QAPM		98.67 168	98.30 186	99.80 53	99.20 275	99.67 58	99.77 34	99.72 11	94.74 375	98.73 291	99.90 30	95.78 186	99.98 14	96.96 308	99.88 60	99.76 93

nrg030		98.64 171	98.42 177	99.28 170	99.05 314	99.69 54	99.81 20	99.46 195	98.04 169	99.01 250	99.82 85	96.69 150	99.38 295	99.34 64	94.59 361	98.78 273

test_vis1_n_1920		98.63 172	98.40 179	99.31 158	99.86 20	97.94 258	99.67 69	99.62 43	99.43 11	99.99 2	99.91 23	87.29 382	100.00 1	99.92 15	99.92 30	99.98 2

PAPR		98.63 172	98.34 182	99.51 124	99.40 223	99.03 157	98.80 370	99.36 251	96.33 329	99.00 254	99.12 342	98.46 84	99.84 168	95.23 358	99.37 169	99.66 133

CVMVSNet		98.57 174	98.67 151	98.30 299	99.35 235	95.59 354	99.50 174	99.55 82	98.60 95	99.39 170	99.83 76	94.48 247	99.45 281	98.75 140	98.56 224	99.85 39

MVSTER		98.49 175	98.32 184	99.00 201	99.35 235	99.02 158	99.54 148	99.38 242	97.41 243	99.20 215	99.73 157	93.86 272	99.36 302	98.87 119	97.56 278	98.62 330

FE-MVS		98.48 176	98.17 191	99.40 143	99.54 171	98.96 169	99.68 66	98.81 365	95.54 359	99.62 115	99.70 166	93.82 273	99.93 94	97.35 285	99.46 157	99.32 227

OpenMVS	96.50 16	98.47 177	98.12 198	99.52 122	99.04 315	99.53 90	99.82 16	99.72 11	94.56 378	98.08 345	99.88 43	94.73 231	99.98 14	97.47 276	99.76 121	99.06 253

IterMVS-LS		98.46 178	98.42 177	98.58 262	99.59 156	98.00 250	99.37 241	99.43 220	96.94 288	99.07 239	99.59 222	97.87 110	99.03 355	98.32 198	95.62 339	98.71 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 179	98.28 187	98.94 210	98.50 380	98.96 169	99.77 34	99.50 143	97.07 274	98.87 274	99.77 139	94.76 229	99.28 315	98.66 153	97.60 274	98.57 345

jajsoiax		98.43 180	98.28 187	98.88 225	98.60 374	98.43 230	99.82 16	99.53 104	98.19 141	98.63 311	99.80 112	93.22 284	99.44 286	99.22 77	97.50 285	98.77 277

tttt0517		98.42 181	98.14 195	99.28 170	99.66 128	98.38 233	99.74 46	96.85 410	97.68 209	99.79 53	99.74 151	91.39 334	99.89 142	98.83 132	99.56 150	99.57 167

BH-untuned		98.42 181	98.36 180	98.59 259	99.49 193	96.70 321	99.27 277	99.13 319	97.24 258	98.80 284	99.38 293	95.75 187	99.74 219	97.07 302	99.16 179	99.33 226

test_fmvs1_n		98.41 183	98.14 195	99.21 178	99.82 43	97.71 271	99.74 46	99.49 153	99.32 18	99.99 2	99.95 3	85.32 393	99.97 22	99.82 20	99.84 86	99.96 7

D2MVS		98.41 183	98.50 173	98.15 313	99.26 260	96.62 327	99.40 230	99.61 50	97.71 204	98.98 256	99.36 299	96.04 173	99.67 249	98.70 146	97.41 295	98.15 377

BH-RMVSNet		98.41 183	98.08 204	99.40 143	99.41 218	98.83 190	99.30 262	98.77 369	97.70 207	98.94 263	99.65 196	92.91 291	99.74 219	96.52 328	99.55 152	99.64 144

mvs_tets		98.40 186	98.23 189	98.91 218	98.67 367	98.51 222	99.66 75	99.53 104	98.19 141	98.65 308	99.81 99	92.75 293	99.44 286	99.31 67	97.48 289	98.77 277

MonoMVSNet		98.38 187	98.47 175	98.12 315	98.59 376	96.19 344	99.72 52	98.79 368	97.89 181	99.44 154	99.52 249	96.13 170	98.90 376	98.64 155	97.54 280	99.28 230

XXY-MVS		98.38 187	98.09 203	99.24 175	99.26 260	99.32 115	99.56 130	99.55 82	97.45 236	98.71 293	99.83 76	93.23 282	99.63 266	98.88 116	96.32 319	98.76 279

ACMM	97.58 5	98.37 189	98.34 182	98.48 274	99.41 218	97.10 294	99.56 130	99.45 206	98.53 101	99.04 247	99.85 61	93.00 287	99.71 235	98.74 141	97.45 290	98.64 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thisisatest0530		98.35 190	98.03 210	99.31 158	99.63 139	98.56 213	99.54 148	96.75 412	97.53 227	99.73 74	99.65 196	91.25 337	99.89 142	98.62 158	99.56 150	99.48 192

tpmrst		98.33 191	98.48 174	97.90 331	99.16 291	94.78 374	99.31 260	99.11 320	97.27 254	99.45 149	99.59 222	95.33 201	99.84 168	98.48 180	98.61 218	99.09 246

baseline1		98.31 192	97.95 219	99.38 148	99.50 191	98.74 197	99.59 109	98.93 343	98.41 113	99.14 226	99.60 220	94.59 240	99.79 203	98.48 180	93.29 380	99.61 153

PatchmatchNet		98.31 192	98.36 180	98.19 308	99.16 291	95.32 364	99.27 277	98.92 346	97.37 246	99.37 174	99.58 226	94.90 218	99.70 241	97.43 280	99.21 176	99.54 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		98.30 194	97.98 215	99.26 172	99.57 160	98.16 241	99.41 222	98.55 387	96.03 353	99.19 218	99.74 151	91.87 320	99.92 106	99.16 84	98.29 240	99.70 121

VPA-MVSNet		98.29 195	97.95 219	99.30 163	99.16 291	99.54 87	99.50 174	99.58 65	98.27 129	99.35 180	99.37 296	92.53 305	99.65 257	99.35 59	94.46 362	98.72 286

UniMVSNet (Re)		98.29 195	98.00 213	99.13 188	99.00 319	99.36 112	99.49 185	99.51 123	97.95 175	98.97 258	99.13 339	96.30 166	99.38 295	98.36 193	93.34 379	98.66 317

HQP_MVS		98.27 197	98.22 190	98.44 285	99.29 253	96.97 309	99.39 234	99.47 186	98.97 59	99.11 231	99.61 217	92.71 298	99.69 246	97.78 242	97.63 271	98.67 309

UniMVSNet_NR-MVSNet		98.22 198	97.97 216	98.96 206	98.92 332	98.98 162	99.48 189	99.53 104	97.76 199	98.71 293	99.46 272	96.43 163	99.22 327	98.57 170	92.87 386	98.69 297

LPG-MVS_test		98.22 198	98.13 197	98.49 272	99.33 240	97.05 300	99.58 117	99.55 82	97.46 233	99.24 204	99.83 76	92.58 303	99.72 229	98.09 213	97.51 283	98.68 302

RPSCF		98.22 198	98.62 161	96.99 364	99.82 43	91.58 403	99.72 52	99.44 214	96.61 309	99.66 96	99.89 35	95.92 180	99.82 188	97.46 277	99.10 188	99.57 167

ADS-MVSNet		98.20 201	98.08 204	98.56 266	99.33 240	96.48 332	99.23 292	99.15 316	96.24 336	99.10 234	99.67 189	94.11 260	99.71 235	96.81 316	99.05 192	99.48 192

OPM-MVS		98.19 202	98.10 200	98.45 282	98.88 336	97.07 298	99.28 272	99.38 242	98.57 97	99.22 209	99.81 99	92.12 315	99.66 252	98.08 217	97.54 280	98.61 339
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SCA		98.19 202	98.16 192	98.27 305	99.30 249	95.55 355	99.07 323	98.97 339	97.57 220	99.43 156	99.57 231	92.72 296	99.74 219	97.58 262	99.20 177	99.52 179

miper_ehance_all_eth		98.18 204	98.10 200	98.41 288	99.23 268	97.72 268	98.72 378	99.31 284	96.60 312	98.88 271	99.29 318	97.29 128	99.13 341	97.60 260	95.99 328	98.38 364

CR-MVSNet		98.17 205	97.93 222	98.87 229	99.18 281	98.49 224	99.22 296	99.33 270	96.96 284	99.56 129	99.38 293	94.33 252	99.00 360	94.83 365	98.58 221	99.14 239

miper_enhance_ethall		98.16 206	98.08 204	98.41 288	98.96 328	97.72 268	98.45 396	99.32 280	96.95 286	98.97 258	99.17 334	97.06 137	99.22 327	97.86 234	95.99 328	98.29 368

CLD-MVS		98.16 206	98.10 200	98.33 295	99.29 253	96.82 318	98.75 375	99.44 214	97.83 190	99.13 227	99.55 237	92.92 289	99.67 249	98.32 198	97.69 269	98.48 351
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		98.14 208	97.79 234	99.19 180	99.50 191	98.50 223	98.61 387	96.82 411	96.95 286	99.54 134	99.43 277	91.66 329	99.86 155	98.08 217	99.51 154	99.22 236

pmmvs4		98.13 209	97.90 224	98.81 241	98.61 373	98.87 182	98.99 344	99.21 309	96.44 324	99.06 244	99.58 226	95.90 182	99.11 346	97.18 297	96.11 324	98.46 356

WR-MVS_H		98.13 209	97.87 229	98.90 220	99.02 317	98.84 187	99.70 56	99.59 61	97.27 254	98.40 327	99.19 333	95.53 194	99.23 323	98.34 195	93.78 376	98.61 339

c3_l		98.12 211	98.04 209	98.38 292	99.30 249	97.69 272	98.81 369	99.33 270	96.67 302	98.83 280	99.34 306	97.11 133	98.99 361	97.58 262	95.34 346	98.48 351

ACMH	97.28 8	98.10 212	97.99 214	98.44 285	99.41 218	96.96 311	99.60 102	99.56 74	98.09 157	98.15 343	99.91 23	90.87 341	99.70 241	98.88 116	97.45 290	98.67 309
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		98.09 213	97.68 250	99.34 151	99.66 128	98.44 229	99.40 230	99.43 220	93.67 385	99.22 209	99.89 35	90.23 349	99.93 94	99.26 75	98.33 235	99.66 133

CP-MVSNet		98.09 213	97.78 237	99.01 199	98.97 327	99.24 130	99.67 69	99.46 195	97.25 256	98.48 324	99.64 202	93.79 274	99.06 351	98.63 157	94.10 370	98.74 284

dmvs_re		98.08 215	98.16 192	97.85 334	99.55 168	94.67 377	99.70 56	98.92 346	98.15 146	99.06 244	99.35 302	93.67 278	99.25 320	97.77 245	97.25 300	99.64 144

DU-MVS		98.08 215	97.79 234	98.96 206	98.87 339	98.98 162	99.41 222	99.45 206	97.87 183	98.71 293	99.50 256	94.82 221	99.22 327	98.57 170	92.87 386	98.68 302

v2v482		98.06 217	97.77 239	98.92 214	98.90 334	98.82 191	99.57 124	99.36 251	96.65 304	99.19 218	99.35 302	94.20 256	99.25 320	97.72 252	94.97 354	98.69 297

V42		98.06 217	97.79 234	98.86 232	98.98 325	98.84 187	99.69 60	99.34 263	96.53 316	99.30 189	99.37 296	94.67 236	99.32 310	97.57 266	94.66 359	98.42 359

test-LLR		98.06 217	97.90 224	98.55 268	98.79 347	97.10 294	98.67 381	97.75 402	97.34 248	98.61 314	98.85 366	94.45 249	99.45 281	97.25 289	99.38 162	99.10 242

WR-MVS		98.06 217	97.73 246	99.06 193	98.86 342	99.25 129	99.19 300	99.35 258	97.30 252	98.66 302	99.43 277	93.94 267	99.21 332	98.58 167	94.28 366	98.71 288

ACMP	97.20 11	98.06 217	97.94 221	98.45 282	99.37 231	97.01 305	99.44 207	99.49 153	97.54 226	98.45 325	99.79 124	91.95 319	99.72 229	97.91 229	97.49 288	98.62 330
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth		98.05 222	97.96 217	98.33 295	99.26 260	97.38 281	98.56 392	99.31 284	96.65 304	98.88 271	99.52 249	96.58 154	99.12 345	97.39 282	95.53 343	98.47 353

test1111		98.04 223	98.11 199	97.83 337	99.74 87	93.82 386	99.58 117	95.40 419	99.12 33	99.65 103	99.93 10	90.73 342	99.84 168	99.43 55	99.38 162	99.82 60

ECVR-MVS		98.04 223	98.05 208	98.00 323	99.74 87	94.37 381	99.59 109	94.98 420	99.13 28	99.66 96	99.93 10	90.67 343	99.84 168	99.40 56	99.38 162	99.80 76

EPNet_dtu		98.03 225	97.96 217	98.23 306	98.27 385	95.54 357	99.23 292	98.75 370	99.02 46	97.82 357	99.71 162	96.11 171	99.48 277	93.04 385	99.65 141	99.69 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 225	97.76 243	98.84 236	99.39 226	98.98 162	99.40 230	99.38 242	96.67 302	99.07 239	99.28 320	92.93 288	98.98 362	97.10 299	96.65 310	98.56 346

ADS-MVSNet2		98.02 227	98.07 207	97.87 333	99.33 240	95.19 367	99.23 292	99.08 324	96.24 336	99.10 234	99.67 189	94.11 260	98.93 373	96.81 316	99.05 192	99.48 192

HQP-MVS		98.02 227	97.90 224	98.37 293	99.19 278	96.83 316	98.98 347	99.39 234	98.24 133	98.66 302	99.40 287	92.47 307	99.64 260	97.19 295	97.58 276	98.64 321

LTVRE_ROB	97.16 12	98.02 227	97.90 224	98.40 290	99.23 268	96.80 319	99.70 56	99.60 56	97.12 268	98.18 342	99.70 166	91.73 325	99.72 229	98.39 188	97.45 290	98.68 302
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____		98.01 230	97.84 232	98.55 268	99.25 264	97.97 252	98.71 379	99.34 263	96.47 323	98.59 317	99.54 242	95.65 191	99.21 332	97.21 291	95.77 334	98.46 356

DIV-MVS_self_test		98.01 230	97.85 231	98.48 274	99.24 266	97.95 256	98.71 379	99.35 258	96.50 317	98.60 316	99.54 242	95.72 189	99.03 355	97.21 291	95.77 334	98.46 356

miper_lstm_enhance		98.00 232	97.91 223	98.28 304	99.34 239	97.43 279	98.88 362	99.36 251	96.48 321	98.80 284	99.55 237	95.98 175	98.91 374	97.27 288	95.50 344	98.51 349

BH-w/o		98.00 232	97.89 228	98.32 297	99.35 235	96.20 343	99.01 341	98.90 353	96.42 326	98.38 328	99.00 353	95.26 205	99.72 229	96.06 336	98.61 218	99.03 255

v1144		97.98 234	97.69 249	98.85 235	98.87 339	98.66 203	99.54 148	99.35 258	96.27 334	99.23 208	99.35 302	94.67 236	99.23 323	96.73 319	95.16 350	98.68 302

EU-MVSNet		97.98 234	98.03 210	97.81 340	98.72 361	96.65 326	99.66 75	99.66 28	98.09 157	98.35 330	99.82 85	95.25 206	98.01 397	97.41 281	95.30 347	98.78 273

tpmvs		97.98 234	98.02 212	97.84 336	99.04 315	94.73 375	99.31 260	99.20 310	96.10 352	98.76 289	99.42 279	94.94 214	99.81 193	96.97 307	98.45 230	98.97 262

tt0805		97.97 237	97.77 239	98.57 263	99.59 156	96.61 328	99.45 201	99.08 324	98.21 139	98.88 271	99.80 112	88.66 367	99.70 241	98.58 167	97.72 268	99.39 216

NR-MVSNet		97.97 237	97.61 259	99.02 198	98.87 339	99.26 127	99.47 196	99.42 222	97.63 214	97.08 375	99.50 256	95.07 211	99.13 341	97.86 234	93.59 377	98.68 302

v8		97.95 239	97.63 257	98.93 212	98.95 329	98.81 193	99.80 25	99.41 225	96.03 353	99.10 234	99.42 279	94.92 217	99.30 313	96.94 310	94.08 371	98.66 317

Patchmatch-test		97.93 240	97.65 253	98.77 246	99.18 281	97.07 298	99.03 333	99.14 318	96.16 343	98.74 290	99.57 231	94.56 242	99.72 229	93.36 381	99.11 185	99.52 179

PS-CasMVS		97.93 240	97.59 261	98.95 208	98.99 322	99.06 154	99.68 66	99.52 109	97.13 266	98.31 332	99.68 183	92.44 311	99.05 352	98.51 178	94.08 371	98.75 281

TranMVSNet+NR-MVSNet		97.93 240	97.66 252	98.76 247	98.78 350	98.62 208	99.65 81	99.49 153	97.76 199	98.49 323	99.60 220	94.23 255	98.97 369	98.00 224	92.90 384	98.70 293

test_vis1_n		97.92 243	97.44 282	99.34 151	99.53 172	98.08 246	99.74 46	99.49 153	99.15 25	100.00 1	99.94 6	79.51 412	99.98 14	99.88 17	99.76 121	99.97 4

v144192		97.92 243	97.60 260	98.87 229	98.83 345	98.65 204	99.55 144	99.34 263	96.20 339	99.32 185	99.40 287	94.36 251	99.26 319	96.37 333	95.03 353	98.70 293

ACMH+	97.24 10	97.92 243	97.78 237	98.32 297	99.46 203	96.68 325	99.56 130	99.54 91	98.41 113	97.79 359	99.87 52	90.18 350	99.66 252	98.05 221	97.18 304	98.62 330

LFMVS		97.90 246	97.35 294	99.54 108	99.52 177	99.01 160	99.39 234	98.24 394	97.10 272	99.65 103	99.79 124	84.79 396	99.91 118	99.28 71	98.38 232	99.69 123

reproduce_monomvs		97.89 247	97.87 229	97.96 327	99.51 180	95.45 360	99.60 102	99.25 300	99.17 23	98.85 279	99.49 259	89.29 359	99.64 260	99.35 59	96.31 320	98.78 273

Anonymous20231211		97.88 248	97.54 265	98.90 220	99.71 103	98.53 216	99.48 189	99.57 69	94.16 381	98.81 282	99.68 183	93.23 282	99.42 291	98.84 129	94.42 364	98.76 279

OurMVSNet-221017-0		97.88 248	97.77 239	98.19 308	98.71 363	96.53 330	99.88 4	99.00 336	97.79 195	98.78 287	99.94 6	91.68 326	99.35 305	97.21 291	96.99 308	98.69 297

v7n		97.87 250	97.52 266	98.92 214	98.76 357	98.58 212	99.84 12	99.46 195	96.20 339	98.91 266	99.70 166	94.89 219	99.44 286	96.03 337	93.89 374	98.75 281

baseline2		97.87 250	97.55 262	98.82 238	99.18 281	98.02 249	99.41 222	96.58 416	96.97 283	96.51 382	99.17 334	93.43 279	99.57 271	97.71 253	99.03 194	98.86 268

thres600view7		97.86 252	97.51 268	98.92 214	99.72 98	97.95 256	99.59 109	98.74 373	97.94 176	99.27 198	98.62 377	91.75 323	99.86 155	93.73 377	98.19 247	98.96 264

UBG		97.85 253	97.48 271	98.95 208	99.25 264	97.64 273	99.24 290	98.74 373	97.90 180	98.64 309	98.20 393	88.65 368	99.81 193	98.27 201	98.40 231	99.42 210

cl22		97.85 253	97.64 256	98.48 274	99.09 305	97.87 260	98.60 389	99.33 270	97.11 271	98.87 274	99.22 329	92.38 312	99.17 336	98.21 204	95.99 328	98.42 359

v10		97.85 253	97.52 266	98.86 232	98.99 322	98.67 202	99.75 42	99.41 225	95.70 357	98.98 256	99.41 283	94.75 230	99.23 323	96.01 339	94.63 360	98.67 309

GA-MVS		97.85 253	97.47 274	99.00 201	99.38 228	97.99 251	98.57 390	99.15 316	97.04 279	98.90 268	99.30 316	89.83 353	99.38 295	96.70 321	98.33 235	99.62 151

tfpnnormal		97.84 257	97.47 274	98.98 203	99.20 275	99.22 132	99.64 84	99.61 50	96.32 330	98.27 336	99.70 166	93.35 281	99.44 286	95.69 346	95.40 345	98.27 369

VPNet		97.84 257	97.44 282	99.01 199	99.21 273	98.94 175	99.48 189	99.57 69	98.38 115	99.28 193	99.73 157	88.89 362	99.39 293	99.19 79	93.27 381	98.71 288

LCM-MVSNet-Re		97.83 259	98.15 194	96.87 370	99.30 249	92.25 400	99.59 109	98.26 392	97.43 240	96.20 386	99.13 339	96.27 167	98.73 383	98.17 209	98.99 197	99.64 144

XVG-ACMP-BASELINE		97.83 259	97.71 248	98.20 307	99.11 299	96.33 337	99.41 222	99.52 109	98.06 166	99.05 246	99.50 256	89.64 356	99.73 225	97.73 250	97.38 297	98.53 347

IterMVS		97.83 259	97.77 239	98.02 320	99.58 158	96.27 340	99.02 336	99.48 165	97.22 260	98.71 293	99.70 166	92.75 293	99.13 341	97.46 277	96.00 327	98.67 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		97.82 262	97.75 244	98.06 317	99.57 160	96.36 336	99.02 336	99.49 153	97.18 262	98.71 293	99.72 161	92.72 296	99.14 338	97.44 279	95.86 333	98.67 309

EPMVS		97.82 262	97.65 253	98.35 294	98.88 336	95.98 347	99.49 185	94.71 422	97.57 220	99.26 202	99.48 265	92.46 310	99.71 235	97.87 233	99.08 190	99.35 222

MVP-Stereo		97.81 264	97.75 244	97.99 324	97.53 396	96.60 329	98.96 351	98.85 360	97.22 260	97.23 370	99.36 299	95.28 202	99.46 280	95.51 350	99.78 115	97.92 394
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 264	97.44 282	98.91 218	98.88 336	98.68 201	99.51 167	99.34 263	96.18 341	99.20 215	99.34 306	94.03 264	99.36 302	95.32 356	95.18 349	98.69 297

ttmdpeth		97.80 266	97.63 257	98.29 300	98.77 355	97.38 281	99.64 84	99.36 251	98.78 81	96.30 385	99.58 226	92.34 314	99.39 293	98.36 193	95.58 340	98.10 379

v1921920		97.80 266	97.45 277	98.84 236	98.80 346	98.53 216	99.52 158	99.34 263	96.15 345	99.24 204	99.47 268	93.98 266	99.29 314	95.40 354	95.13 351	98.69 297

v148		97.79 268	97.55 262	98.50 271	98.74 358	97.72 268	99.54 148	99.33 270	96.26 335	98.90 268	99.51 253	94.68 235	99.14 338	97.83 238	93.15 383	98.63 328

thres400		97.77 269	97.38 290	98.92 214	99.69 112	97.96 254	99.50 174	98.73 379	97.83 190	99.17 223	98.45 383	91.67 327	99.83 181	93.22 382	98.18 248	98.96 264

thres100view900		97.76 270	97.45 277	98.69 253	99.72 98	97.86 262	99.59 109	98.74 373	97.93 177	99.26 202	98.62 377	91.75 323	99.83 181	93.22 382	98.18 248	98.37 365

PEN-MVS		97.76 270	97.44 282	98.72 249	98.77 355	98.54 215	99.78 32	99.51 123	97.06 276	98.29 335	99.64 202	92.63 302	98.89 377	98.09 213	93.16 382	98.72 286

Baseline_NR-MVSNet		97.76 270	97.45 277	98.68 254	99.09 305	98.29 235	99.41 222	98.85 360	95.65 358	98.63 311	99.67 189	94.82 221	99.10 348	98.07 220	92.89 385	98.64 321

TR-MVS		97.76 270	97.41 288	98.82 238	99.06 311	97.87 260	98.87 364	98.56 386	96.63 308	98.68 301	99.22 329	92.49 306	99.65 257	95.40 354	97.79 266	98.95 266

Patchmtry		97.75 274	97.40 289	98.81 241	99.10 302	98.87 182	99.11 319	99.33 270	94.83 373	98.81 282	99.38 293	94.33 252	99.02 357	96.10 335	95.57 341	98.53 347

dp		97.75 274	97.80 233	97.59 350	99.10 302	93.71 389	99.32 257	98.88 356	96.48 321	99.08 238	99.55 237	92.67 301	99.82 188	96.52 328	98.58 221	99.24 235

WBMVS		97.74 276	97.50 269	98.46 280	99.24 266	97.43 279	99.21 298	99.42 222	97.45 236	98.96 260	99.41 283	88.83 363	99.23 323	98.94 107	96.02 325	98.71 288

TAPA-MVS	97.07 15	97.74 276	97.34 297	98.94 210	99.70 108	97.53 276	99.25 288	99.51 123	91.90 399	99.30 189	99.63 208	98.78 51	99.64 260	88.09 408	99.87 63	99.65 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 278	97.35 294	98.88 225	99.47 201	97.12 293	99.34 254	98.85 360	98.19 141	99.67 91	99.85 61	82.98 403	99.92 106	99.49 49	98.32 239	99.60 156

MIMVSNet		97.73 278	97.45 277	98.57 263	99.45 209	97.50 277	99.02 336	98.98 338	96.11 348	99.41 163	99.14 338	90.28 345	98.74 382	95.74 344	98.93 200	99.47 198

tfpn200view9		97.72 280	97.38 290	98.72 249	99.69 112	97.96 254	99.50 174	98.73 379	97.83 190	99.17 223	98.45 383	91.67 327	99.83 181	93.22 382	98.18 248	98.37 365

CostFormer		97.72 280	97.73 246	97.71 344	99.15 295	94.02 385	99.54 148	99.02 334	94.67 376	99.04 247	99.35 302	92.35 313	99.77 210	98.50 179	97.94 258	99.34 225

FMVSNet2		97.72 280	97.36 292	98.80 243	99.51 180	98.84 187	99.45 201	99.42 222	96.49 318	98.86 278	99.29 318	90.26 346	98.98 362	96.44 330	96.56 313	98.58 344

test0.0.03 1		97.71 283	97.42 287	98.56 266	98.41 384	97.82 263	98.78 372	98.63 384	97.34 248	98.05 349	98.98 356	94.45 249	98.98 362	95.04 361	97.15 305	98.89 267

h-mvs33		97.70 284	97.28 305	98.97 205	99.70 108	97.27 285	99.36 246	99.45 206	98.94 62	99.66 96	99.64 202	94.93 215	99.99 4	99.48 50	84.36 411	99.65 137

v1240		97.69 285	97.32 300	98.79 244	98.85 343	98.43 230	99.48 189	99.36 251	96.11 348	99.27 198	99.36 299	93.76 276	99.24 322	94.46 368	95.23 348	98.70 293

cascas		97.69 285	97.43 286	98.48 274	98.60 374	97.30 283	98.18 408	99.39 234	92.96 393	98.41 326	98.78 373	93.77 275	99.27 318	98.16 210	98.61 218	98.86 268

pm-mvs1		97.68 287	97.28 305	98.88 225	99.06 311	98.62 208	99.50 174	99.45 206	96.32 330	97.87 355	99.79 124	92.47 307	99.35 305	97.54 269	93.54 378	98.67 309

GBi-Net		97.68 287	97.48 271	98.29 300	99.51 180	97.26 287	99.43 212	99.48 165	96.49 318	99.07 239	99.32 313	90.26 346	98.98 362	97.10 299	96.65 310	98.62 330

test1		97.68 287	97.48 271	98.29 300	99.51 180	97.26 287	99.43 212	99.48 165	96.49 318	99.07 239	99.32 313	90.26 346	98.98 362	97.10 299	96.65 310	98.62 330

tpm		97.67 290	97.55 262	98.03 318	99.02 317	95.01 370	99.43 212	98.54 388	96.44 324	99.12 229	99.34 306	91.83 322	99.60 269	97.75 248	96.46 315	99.48 192

PCF-MVS	97.08 14	97.66 291	97.06 316	99.47 133	99.61 149	99.09 148	98.04 411	99.25 300	91.24 402	98.51 321	99.70 166	94.55 244	99.91 118	92.76 390	99.85 78	99.42 210
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew		97.65 292	97.65 253	97.63 347	98.78 350	97.62 274	99.13 310	98.33 391	97.36 247	99.07 239	98.94 360	95.64 192	99.15 337	92.95 386	98.68 217	96.12 413

our_test_3		97.65 292	97.68 250	97.55 351	98.62 371	94.97 371	98.84 366	99.30 288	96.83 295	98.19 341	99.34 306	97.01 140	99.02 357	95.00 362	96.01 326	98.64 321

testgi		97.65 292	97.50 269	98.13 314	99.36 234	96.45 333	99.42 219	99.48 165	97.76 199	97.87 355	99.45 274	91.09 338	98.81 379	94.53 367	98.52 227	99.13 241

thres200		97.61 295	97.28 305	98.62 257	99.64 136	98.03 248	99.26 286	98.74 373	97.68 209	99.09 237	98.32 389	91.66 329	99.81 193	92.88 387	98.22 243	98.03 384

PAPM		97.59 296	97.09 315	99.07 191	99.06 311	98.26 237	98.30 404	99.10 321	94.88 371	98.08 345	99.34 306	96.27 167	99.64 260	89.87 401	98.92 202	99.31 228

UWE-MVS		97.58 297	97.29 304	98.48 274	99.09 305	96.25 341	99.01 341	96.61 415	97.86 184	99.19 218	99.01 352	88.72 364	99.90 130	97.38 283	98.69 216	99.28 230

VDDNet		97.55 298	97.02 317	99.16 183	99.49 193	98.12 245	99.38 239	99.30 288	95.35 361	99.68 87	99.90 30	82.62 405	99.93 94	99.31 67	98.13 252	99.42 210

TESTMET0.1,1		97.55 298	97.27 308	98.40 290	98.93 330	96.53 330	98.67 381	97.61 405	96.96 284	98.64 309	99.28 320	88.63 370	99.45 281	97.30 287	99.38 162	99.21 237

pmmvs5		97.52 300	97.30 302	98.16 310	98.57 377	96.73 320	99.27 277	98.90 353	96.14 346	98.37 329	99.53 246	91.54 332	99.14 338	97.51 271	95.87 332	98.63 328

LF4IMVS		97.52 300	97.46 276	97.70 345	98.98 325	95.55 355	99.29 267	98.82 363	98.07 162	98.66 302	99.64 202	89.97 351	99.61 268	97.01 303	96.68 309	97.94 392

DTE-MVSNet		97.51 302	97.19 310	98.46 280	98.63 370	98.13 244	99.84 12	99.48 165	96.68 301	97.97 352	99.67 189	92.92 289	98.56 386	96.88 315	92.60 390	98.70 293

testing11		97.50 303	97.10 314	98.71 251	99.20 275	96.91 313	99.29 267	98.82 363	97.89 181	98.21 340	98.40 385	85.63 390	99.83 181	98.45 185	98.04 255	99.37 220

ETVMVS		97.50 303	96.90 321	99.29 166	99.23 268	98.78 196	99.32 257	98.90 353	97.52 229	98.56 318	98.09 399	84.72 397	99.69 246	97.86 234	97.88 261	99.39 216

hse-mvs2		97.50 303	97.14 311	98.59 259	99.49 193	97.05 300	99.28 272	99.22 306	98.94 62	99.66 96	99.42 279	94.93 215	99.65 257	99.48 50	83.80 413	99.08 247

SixPastTwentyTwo		97.50 303	97.33 299	98.03 318	98.65 368	96.23 342	99.77 34	98.68 382	97.14 265	97.90 353	99.93 10	90.45 344	99.18 335	97.00 304	96.43 316	98.67 309

JIA-IIPM		97.50 303	97.02 317	98.93 212	98.73 359	97.80 264	99.30 262	98.97 339	91.73 400	98.91 266	94.86 415	95.10 210	99.71 235	97.58 262	97.98 256	99.28 230

ppachtmachnet_test		97.49 308	97.45 277	97.61 349	98.62 371	95.24 365	98.80 370	99.46 195	96.11 348	98.22 339	99.62 213	96.45 161	98.97 369	93.77 376	95.97 331	98.61 339

test-mter		97.49 308	97.13 313	98.55 268	98.79 347	97.10 294	98.67 381	97.75 402	96.65 304	98.61 314	98.85 366	88.23 374	99.45 281	97.25 289	99.38 162	99.10 242

testing91		97.44 310	97.02 317	98.71 251	99.18 281	96.89 315	99.19 300	99.04 331	97.78 197	98.31 332	98.29 390	85.41 392	99.85 161	98.01 223	97.95 257	99.39 216

tpm2		97.44 310	97.34 297	97.74 343	99.15 295	94.36 382	99.45 201	98.94 342	93.45 390	98.90 268	99.44 275	91.35 335	99.59 270	97.31 286	98.07 254	99.29 229

tpm cat1		97.39 312	97.36 292	97.50 353	99.17 289	93.73 388	99.43 212	99.31 284	91.27 401	98.71 293	99.08 343	94.31 254	99.77 210	96.41 332	98.50 228	99.00 258

testing99		97.36 313	96.94 320	98.63 256	99.18 281	96.70 321	99.30 262	98.93 343	97.71 204	98.23 337	98.26 391	84.92 395	99.84 168	98.04 222	97.85 264	99.35 222

USDC		97.34 314	97.20 309	97.75 342	99.07 309	95.20 366	98.51 394	99.04 331	97.99 173	98.31 332	99.86 56	89.02 360	99.55 274	95.67 348	97.36 298	98.49 350

UniMVSNet_ETH3D		97.32 315	96.81 323	98.87 229	99.40 223	97.46 278	99.51 167	99.53 104	95.86 356	98.54 320	99.77 139	82.44 406	99.66 252	98.68 151	97.52 282	99.50 190

testing3		97.28 316	96.76 325	98.82 238	99.37 231	98.07 247	99.45 201	99.36 251	97.56 222	97.89 354	98.95 359	83.70 401	98.82 378	96.03 337	98.56 224	99.58 164

MVS		97.28 316	96.55 329	99.48 130	98.78 350	98.95 172	99.27 277	99.39 234	83.53 415	98.08 345	99.54 242	96.97 141	99.87 152	94.23 372	99.16 179	99.63 149

test_fmvs2		97.25 318	97.30 302	97.09 363	99.43 211	93.31 394	99.73 50	98.87 358	98.83 72	99.28 193	99.80 112	84.45 398	99.66 252	97.88 231	97.45 290	98.30 367

DSMNet-mixed		97.25 318	97.35 294	96.95 367	97.84 391	93.61 392	99.57 124	96.63 414	96.13 347	98.87 274	98.61 379	94.59 240	97.70 404	95.08 360	98.86 206	99.55 170

MS-PatchMatch		97.24 320	97.32 300	96.99 364	98.45 382	93.51 393	98.82 368	99.32 280	97.41 243	98.13 344	99.30 316	88.99 361	99.56 272	95.68 347	99.80 106	97.90 395

testing222		97.16 321	96.50 330	99.16 183	99.16 291	98.47 228	99.27 277	98.66 383	97.71 204	98.23 337	98.15 394	82.28 408	99.84 168	97.36 284	97.66 270	99.18 238

TransMVSNet (Re)		97.15 322	96.58 328	98.86 232	99.12 297	98.85 186	99.49 185	98.91 351	95.48 360	97.16 373	99.80 112	93.38 280	99.11 346	94.16 374	91.73 392	98.62 330

TinyColmap		97.12 323	96.89 322	97.83 337	99.07 309	95.52 358	98.57 390	98.74 373	97.58 219	97.81 358	99.79 124	88.16 375	99.56 272	95.10 359	97.21 302	98.39 363

K. test v3		97.10 324	96.79 324	98.01 321	98.72 361	96.33 337	99.87 8	97.05 408	97.59 217	96.16 387	99.80 112	88.71 365	99.04 353	96.69 322	96.55 314	98.65 319

Syy-MVS		97.09 325	97.14 311	96.95 367	99.00 319	92.73 398	99.29 267	99.39 234	97.06 276	97.41 364	98.15 394	93.92 269	98.68 384	91.71 394	98.34 233	99.45 206

PatchT		97.03 326	96.44 332	98.79 244	98.99 322	98.34 234	99.16 304	99.07 327	92.13 398	99.52 138	97.31 408	94.54 245	98.98 362	88.54 406	98.73 215	99.03 255

mmtdpeth		96.95 327	96.71 326	97.67 346	99.33 240	94.90 373	99.89 2	99.28 294	98.15 146	99.72 79	98.57 380	86.56 385	99.90 130	99.82 20	89.02 404	98.20 374

myMVS_eth3d		96.89 328	96.37 333	98.43 287	99.00 319	97.16 291	99.29 267	99.39 234	97.06 276	97.41 364	98.15 394	83.46 402	98.68 384	95.27 357	98.34 233	99.45 206

AUN-MVS		96.88 329	96.31 335	98.59 259	99.48 200	97.04 303	99.27 277	99.22 306	97.44 239	98.51 321	99.41 283	91.97 318	99.66 252	97.71 253	83.83 412	99.07 252

FMVSNet1		96.84 330	96.36 334	98.29 300	99.32 247	97.26 287	99.43 212	99.48 165	95.11 365	98.55 319	99.32 313	83.95 400	98.98 362	95.81 342	96.26 321	98.62 330

test2506		96.81 331	96.65 327	97.29 358	99.74 87	92.21 401	99.60 102	85.06 432	99.13 28	99.77 62	99.93 10	87.82 380	99.85 161	99.38 57	99.38 162	99.80 76

RPMNet		96.72 332	95.90 345	99.19 180	99.18 281	98.49 224	99.22 296	99.52 109	88.72 411	99.56 129	97.38 405	94.08 262	99.95 65	86.87 413	98.58 221	99.14 239

mvs5depth		96.66 333	96.22 337	97.97 325	97.00 407	96.28 339	98.66 384	99.03 333	96.61 309	96.93 379	99.79 124	87.20 383	99.47 278	96.65 326	94.13 369	98.16 376

test_0402		96.64 334	96.24 336	97.85 334	98.85 343	96.43 334	99.44 207	99.26 298	93.52 387	96.98 377	99.52 249	88.52 371	99.20 334	92.58 392	97.50 285	97.93 393

X-MVStestdata		96.55 335	95.45 354	99.87 16	99.85 26	99.83 19	99.69 60	99.68 20	98.98 56	99.37 174	64.01 428	98.81 47	99.94 76	98.79 137	99.86 71	99.84 45

pmmvs6		96.53 336	96.09 341	97.82 339	98.69 365	95.47 359	99.37 241	99.47 186	93.46 389	97.41 364	99.78 131	87.06 384	99.33 308	96.92 313	92.70 388	98.65 319

ET-MVSNet_ETH3D		96.49 337	95.64 351	99.05 195	99.53 172	98.82 191	98.84 366	97.51 406	97.63 214	84.77 415	99.21 332	92.09 316	98.91 374	98.98 102	92.21 391	99.41 213

UnsupCasMVSNet_eth		96.44 338	96.12 339	97.40 355	98.65 368	95.65 352	99.36 246	99.51 123	97.13 266	96.04 389	98.99 354	88.40 372	98.17 393	96.71 320	90.27 400	98.40 362

FMVSNet5		96.43 339	96.19 338	97.15 359	99.11 299	95.89 349	99.32 257	99.52 109	94.47 380	98.34 331	99.07 344	87.54 381	97.07 409	92.61 391	95.72 337	98.47 353

new_pmnet		96.38 340	96.03 342	97.41 354	98.13 388	95.16 369	99.05 328	99.20 310	93.94 382	97.39 367	98.79 372	91.61 331	99.04 353	90.43 399	95.77 334	98.05 383

Anonymous20231206		96.22 341	96.03 342	96.79 372	97.31 401	94.14 384	99.63 90	99.08 324	96.17 342	97.04 376	99.06 346	93.94 267	97.76 403	86.96 412	95.06 352	98.47 353

IB-MVS	95.67 18	96.22 341	95.44 355	98.57 263	99.21 273	96.70 321	98.65 385	97.74 404	96.71 299	97.27 369	98.54 381	86.03 387	99.92 106	98.47 183	86.30 409	99.10 242
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		96.20 343	95.89 346	97.13 361	97.72 395	94.96 372	99.79 31	99.29 292	93.01 392	97.20 372	99.03 349	89.69 355	98.36 390	91.16 397	96.13 323	98.07 381

gg-mvs-nofinetune		96.17 344	95.32 356	98.73 248	98.79 347	98.14 243	99.38 239	94.09 423	91.07 404	98.07 348	91.04 421	89.62 357	99.35 305	96.75 318	99.09 189	98.68 302

test20.03		96.12 345	95.96 344	96.63 373	97.44 397	95.45 360	99.51 167	99.38 242	96.55 315	96.16 387	99.25 326	93.76 276	96.17 414	87.35 411	94.22 367	98.27 369

PVSNet_0	94.43 19	96.09 346	95.47 353	97.94 328	99.31 248	94.34 383	97.81 412	99.70 15	97.12 268	97.46 363	98.75 374	89.71 354	99.79 203	97.69 256	81.69 415	99.68 127

MVStest1		96.08 347	95.48 352	97.89 332	98.93 330	96.70 321	99.56 130	99.35 258	92.69 396	91.81 410	99.46 272	89.90 352	98.96 371	95.00 362	92.61 389	98.00 388

EG-PatchMatch MVS		95.97 348	95.69 349	96.81 371	97.78 392	92.79 397	99.16 304	98.93 343	96.16 343	94.08 400	99.22 329	82.72 404	99.47 278	95.67 348	97.50 285	98.17 375

APD_test1		95.87 349	96.49 331	94.00 384	99.53 172	84.01 413	99.54 148	99.32 280	95.91 355	97.99 350	99.85 61	85.49 391	99.88 147	91.96 393	98.84 208	98.12 378

Patchmatch-RL test		95.84 350	95.81 348	95.95 379	95.61 412	90.57 405	98.24 405	98.39 390	95.10 367	95.20 394	98.67 376	94.78 225	97.77 402	96.28 334	90.02 401	99.51 186

test_vis1_rt		95.81 351	95.65 350	96.32 377	99.67 118	91.35 404	99.49 185	96.74 413	98.25 132	95.24 392	98.10 398	74.96 413	99.90 130	99.53 41	98.85 207	97.70 398

MVS-HIRNet		95.75 352	95.16 357	97.51 352	99.30 249	93.69 390	98.88 362	95.78 417	85.09 414	98.78 287	92.65 417	91.29 336	99.37 298	94.85 364	99.85 78	99.46 203

MIMVSNet1		95.51 353	95.04 358	96.92 369	97.38 398	95.60 353	99.52 158	99.50 143	93.65 386	96.97 378	99.17 334	85.28 394	96.56 413	88.36 407	95.55 342	98.60 342

MDA-MVSNet_test_wron		95.45 354	94.60 361	98.01 321	98.16 387	97.21 290	99.11 319	99.24 303	93.49 388	80.73 421	98.98 356	93.02 286	98.18 392	94.22 373	94.45 363	98.64 321

TDRefinement		95.42 355	94.57 362	97.97 325	89.83 425	96.11 346	99.48 189	98.75 370	96.74 297	96.68 381	99.88 43	88.65 368	99.71 235	98.37 191	82.74 414	98.09 380

YYNet1		95.36 356	94.51 363	97.92 329	97.89 390	97.10 294	99.10 321	99.23 304	93.26 391	80.77 420	99.04 348	92.81 292	98.02 396	94.30 369	94.18 368	98.64 321

pmmvs-eth3d		95.34 357	94.73 360	97.15 359	95.53 414	95.94 348	99.35 251	99.10 321	95.13 363	93.55 402	97.54 403	88.15 376	97.91 399	94.58 366	89.69 403	97.61 399

dmvs_testset		95.02 358	96.12 339	91.72 393	99.10 302	80.43 421	99.58 117	97.87 401	97.47 232	95.22 393	98.82 368	93.99 265	95.18 418	88.09 408	94.91 357	99.56 169

KD-MVS_self_test		95.00 359	94.34 364	96.96 366	97.07 406	95.39 363	99.56 130	99.44 214	95.11 365	97.13 374	97.32 407	91.86 321	97.27 408	90.35 400	81.23 416	98.23 373

MDA-MVSNet-bldmvs		94.96 360	93.98 367	97.92 329	98.24 386	97.27 285	99.15 307	99.33 270	93.80 384	80.09 422	99.03 349	88.31 373	97.86 401	93.49 380	94.36 365	98.62 330

N_pmnet		94.95 361	95.83 347	92.31 391	98.47 381	79.33 423	99.12 313	92.81 429	93.87 383	97.68 360	99.13 339	93.87 271	99.01 359	91.38 396	96.19 322	98.59 343

KD-MVS_2432*1600		94.62 362	93.72 370	97.31 356	97.19 404	95.82 350	98.34 400	99.20 310	95.00 369	97.57 361	98.35 387	87.95 377	98.10 394	92.87 388	77.00 419	98.01 385

miper_refine_blended		94.62 362	93.72 370	97.31 356	97.19 404	95.82 350	98.34 400	99.20 310	95.00 369	97.57 361	98.35 387	87.95 377	98.10 394	92.87 388	77.00 419	98.01 385

CL-MVSNet_self_test		94.49 364	93.97 368	96.08 378	96.16 409	93.67 391	98.33 402	99.38 242	95.13 363	97.33 368	98.15 394	92.69 300	96.57 412	88.67 405	79.87 417	97.99 389

new-patchmatchnet		94.48 365	94.08 366	95.67 380	95.08 417	92.41 399	99.18 302	99.28 294	94.55 379	93.49 403	97.37 406	87.86 379	97.01 410	91.57 395	88.36 405	97.61 399

OpenMVS_ROB	92.34 20	94.38 366	93.70 372	96.41 376	97.38 398	93.17 395	99.06 326	98.75 370	86.58 412	94.84 398	98.26 391	81.53 409	99.32 310	89.01 404	97.87 262	96.76 406

CMPMVS	69.68 23	94.13 367	94.90 359	91.84 392	97.24 402	80.01 422	98.52 393	99.48 165	89.01 409	91.99 409	99.67 189	85.67 389	99.13 341	95.44 352	97.03 307	96.39 410
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 368	93.25 374	96.60 374	94.76 419	94.49 379	98.92 358	98.18 397	89.66 405	96.48 383	98.06 400	86.28 386	97.33 407	89.68 402	87.20 408	97.97 391

mvsany_test3		93.77 369	93.45 373	94.74 382	95.78 411	88.01 408	99.64 84	98.25 393	98.28 127	94.31 399	97.97 401	68.89 416	98.51 388	97.50 272	90.37 399	97.71 396

UnsupCasMVSNet_bld		93.53 370	92.51 376	96.58 375	97.38 398	93.82 386	98.24 405	99.48 165	91.10 403	93.10 404	96.66 410	74.89 414	98.37 389	94.03 375	87.71 407	97.56 401

dongtai		93.26 371	92.93 375	94.25 383	99.39 226	85.68 411	97.68 414	93.27 425	92.87 394	96.85 380	99.39 291	82.33 407	97.48 406	76.78 419	97.80 265	99.58 164

WB-MVS		93.10 372	94.10 365	90.12 398	95.51 416	81.88 418	99.73 50	99.27 297	95.05 368	93.09 405	98.91 365	94.70 234	91.89 422	76.62 420	94.02 373	96.58 408

PM-MVS		92.96 373	92.23 377	95.14 381	95.61 412	89.98 407	99.37 241	98.21 395	94.80 374	95.04 397	97.69 402	65.06 417	97.90 400	94.30 369	89.98 402	97.54 402

SSC-MVS		92.73 374	93.73 369	89.72 399	95.02 418	81.38 419	99.76 37	99.23 304	94.87 372	92.80 406	98.93 361	94.71 233	91.37 423	74.49 422	93.80 375	96.42 409

test_fmvs3		92.10 375	91.77 378	93.08 389	96.19 408	86.25 409	99.82 16	98.62 385	96.65 304	95.19 395	96.90 409	55.05 424	95.93 416	96.63 327	90.92 398	97.06 405

test_f		91.90 376	91.26 380	93.84 385	95.52 415	85.92 410	99.69 60	98.53 389	95.31 362	93.87 401	96.37 412	55.33 423	98.27 391	95.70 345	90.98 397	97.32 404

test_method		91.10 377	91.36 379	90.31 397	95.85 410	73.72 430	94.89 418	99.25 300	68.39 421	95.82 390	99.02 351	80.50 411	98.95 372	93.64 378	94.89 358	98.25 371

Gipuma		90.99 378	90.15 383	93.51 386	98.73 359	90.12 406	93.98 419	99.45 206	79.32 417	92.28 407	94.91 414	69.61 415	97.98 398	87.42 410	95.67 338	92.45 417
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		90.92 379	90.11 384	93.34 387	98.78 350	85.59 412	98.15 409	93.16 427	89.37 408	92.07 408	98.38 386	81.48 410	95.19 417	62.54 426	97.04 306	99.25 234

testf1		90.42 380	90.68 381	89.65 400	97.78 392	73.97 428	99.13 310	98.81 365	89.62 406	91.80 411	98.93 361	62.23 420	98.80 380	86.61 414	91.17 394	96.19 411

APD_test2		90.42 380	90.68 381	89.65 400	97.78 392	73.97 428	99.13 310	98.81 365	89.62 406	91.80 411	98.93 361	62.23 420	98.80 380	86.61 414	91.17 394	96.19 411

test_vis3_rt		87.04 382	85.81 385	90.73 396	93.99 420	81.96 417	99.76 37	90.23 431	92.81 395	81.35 419	91.56 419	40.06 428	99.07 350	94.27 371	88.23 406	91.15 419

PMMVS2		86.87 383	85.37 387	91.35 395	90.21 424	83.80 414	98.89 361	97.45 407	83.13 416	91.67 413	95.03 413	48.49 426	94.70 419	85.86 416	77.62 418	95.54 414

LCM-MVSNet		86.80 384	85.22 388	91.53 394	87.81 426	80.96 420	98.23 407	98.99 337	71.05 419	90.13 414	96.51 411	48.45 427	96.88 411	90.51 398	85.30 410	96.76 406

FPMVS		84.93 385	85.65 386	82.75 406	86.77 427	63.39 432	98.35 399	98.92 346	74.11 418	83.39 417	98.98 356	50.85 425	92.40 421	84.54 417	94.97 354	92.46 416

EGC-MVSNET		82.80 386	77.86 392	97.62 348	97.91 389	96.12 345	99.33 256	99.28 294	8.40 429	25.05 430	99.27 323	84.11 399	99.33 308	89.20 403	98.22 243	97.42 403

tmp_tt		82.80 386	81.52 389	86.66 402	66.61 432	68.44 431	92.79 421	97.92 399	68.96 420	80.04 423	99.85 61	85.77 388	96.15 415	97.86 234	43.89 425	95.39 415

E-PMN		80.61 388	79.88 390	82.81 405	90.75 423	76.38 426	97.69 413	95.76 418	66.44 423	83.52 416	92.25 418	62.54 419	87.16 425	68.53 424	61.40 422	84.89 423

EMVS		80.02 389	79.22 391	82.43 407	91.19 422	76.40 425	97.55 416	92.49 430	66.36 424	83.01 418	91.27 420	64.63 418	85.79 426	65.82 425	60.65 423	85.08 422

ANet_high		77.30 390	74.86 394	84.62 404	75.88 430	77.61 424	97.63 415	93.15 428	88.81 410	64.27 425	89.29 422	36.51 429	83.93 427	75.89 421	52.31 424	92.33 418

MVE	76.82 21	76.91 391	74.31 395	84.70 403	85.38 429	76.05 427	96.88 417	93.17 426	67.39 422	71.28 424	89.01 423	21.66 434	87.69 424	71.74 423	72.29 421	90.35 420
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 392	74.97 393	79.01 408	70.98 431	55.18 433	93.37 420	98.21 395	65.08 425	61.78 426	93.83 416	21.74 433	92.53 420	78.59 418	91.12 396	89.34 421
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 393	41.29 398	36.84 409	86.18 428	49.12 434	79.73 422	22.81 434	27.64 426	25.46 429	28.45 429	21.98 432	48.89 428	55.80 427	23.56 428	12.51 426

testmvs		39.17 394	43.78 396	25.37 411	36.04 434	16.84 436	98.36 398	26.56 433	20.06 427	38.51 428	67.32 424	29.64 431	15.30 430	37.59 428	39.90 426	43.98 425

test123		39.01 395	42.50 397	28.53 410	39.17 433	20.91 435	98.75 375	19.17 435	19.83 428	38.57 427	66.67 425	33.16 430	15.42 429	37.50 429	29.66 427	49.26 424

cdsmvs_eth3d_5k		24.64 396	32.85 399	0.00 412	0.00 435	0.00 437	0.00 423	99.51 123	0.00 430	0.00 431	99.56 234	96.58 154	0.00 431	0.00 430	0.00 429	0.00 427

ab-mvs-re		8.30 397	11.06 400	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	99.58 226	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		8.27 398	11.03 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	99.01 18	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.13 399	0.17 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	1.57 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

mmdepth		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet_test		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.02 400	0.03 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.27 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						97.16 291								95.47 351

FOURS1					99.91 1	99.93 1	99.87 8	99.56 74	99.10 35	99.81 47

MSC_two_6792asdad				99.87 16	99.51 180	99.76 42		99.33 270					99.96 34	98.87 119	99.84 86	99.89 22

PC_three_1452									98.18 144	99.84 39	99.70 166	99.31 3	98.52 387	98.30 200	99.80 106	99.81 67

No_MVS				99.87 16	99.51 180	99.76 42		99.33 270					99.96 34	98.87 119	99.84 86	99.89 22

test_one_0601					99.81 47	99.88 8		99.49 153	98.97 59	99.65 103	99.81 99	99.09 14

eth-test2					0.00 435

eth-test					0.00 435

ZD-MVS					99.71 103	99.79 34		99.61 50	96.84 293	99.56 129	99.54 242	98.58 75	99.96 34	96.93 311	99.75 123

RE-MVS-def			99.34 43		99.76 69	99.82 25	99.63 90	99.52 109	98.38 115	99.76 68	99.82 85	98.75 58		98.61 161	99.81 102	99.77 88

IU-MVS					99.84 32	99.88 8		99.32 280	98.30 126	99.84 39				98.86 124	99.85 78	99.89 22

OPU-MVS				99.64 87	99.56 164	99.72 48	99.60 102				99.70 166	99.27 5	99.42 291	98.24 203	99.80 106	99.79 80

test_241102_TWO								99.48 165	99.08 41	99.88 28	99.81 99	98.94 32	99.96 34	98.91 113	99.84 86	99.88 28

test_241102_ONE					99.84 32	99.90 2		99.48 165	99.07 43	99.91 21	99.74 151	99.20 7	99.76 214

9.14			99.10 85		99.72 98		99.40 230	99.51 123	97.53 227	99.64 108	99.78 131	98.84 44	99.91 118	97.63 258	99.82 99

save fliter					99.76 69	99.59 77	99.14 309	99.40 231	99.00 51

test_0728_THIRD									98.99 53	99.81 47	99.80 112	99.09 14	99.96 34	98.85 126	99.90 46	99.88 28

test_0728_SECOND				99.91 3	99.84 32	99.89 4	99.57 124	99.51 123					99.96 34	98.93 110	99.86 71	99.88 28

test0726					99.85 26	99.89 4	99.62 95	99.50 143	99.10 35	99.86 37	99.82 85	98.94 32

GSMVS																99.52 179

test_part2					99.81 47	99.83 19				99.77 62

sam_mvs1												94.86 220				99.52 179

sam_mvs												94.72 232

ambc				93.06 390	92.68 421	82.36 415	98.47 395	98.73 379		95.09 396	97.41 404	55.55 422	99.10 348	96.42 331	91.32 393	97.71 396

MTGPA								99.47 186

test_post1							99.23 292				65.14 427	94.18 259	99.71 235	97.58 262

test_post											65.99 426	94.65 238	99.73 225

patchmatchnet-post											98.70 375	94.79 224	99.74 219

GG-mvs-BLEND				98.45 282	98.55 378	98.16 241	99.43 212	93.68 424		97.23 370	98.46 382	89.30 358	99.22 327	95.43 353	98.22 243	97.98 390

MTMP							99.54 148	98.88 356

gm-plane-assit					98.54 379	92.96 396			94.65 377		99.15 337		99.64 260	97.56 267

test9_res														97.49 273	99.72 129	99.75 94

TEST9					99.67 118	99.65 64	99.05 328	99.41 225	96.22 338	98.95 261	99.49 259	98.77 54	99.91 118

test_8					99.67 118	99.61 74	99.03 333	99.41 225	96.28 332	98.93 264	99.48 265	98.76 55	99.91 118

agg_prior2														97.21 291	99.73 128	99.75 94

agg_prior					99.67 118	99.62 72		99.40 231		98.87 274			99.91 118

TestCases				99.31 158	99.86 20	98.48 226		99.61 50	97.85 187	99.36 177	99.85 61	95.95 177	99.85 161	96.66 324	99.83 95	99.59 160

test_prior4						99.56 83	98.99 344

test_prior2							98.96 351		98.34 121	99.01 250	99.52 249	98.68 67		97.96 226	99.74 126

test_prior				99.68 75	99.67 118	99.48 98		99.56 74					99.83 181			99.74 98

旧先验2							98.96 351		96.70 300	99.47 146			99.94 76	98.19 206

新几何2							99.01 341

新几何1				99.75 65	99.75 79	99.59 77		99.54 91	96.76 296	99.29 192	99.64 202	98.43 86	99.94 76	96.92 313	99.66 139	99.72 110

旧先验1					99.74 87	99.59 77		99.54 91			99.69 176	98.47 83			99.68 137	99.73 103

无先验							98.99 344	99.51 123	96.89 290				99.93 94	97.53 270		99.72 110

原ACMM2							98.95 354

原ACMM1				99.65 81	99.73 94	99.33 114		99.47 186	97.46 233	99.12 229	99.66 194	98.67 69	99.91 118	97.70 255	99.69 134	99.71 119

test222					99.75 79	99.49 96	98.91 360	99.49 153	96.42 326	99.34 183	99.65 196	98.28 96			99.69 134	99.72 110

testdata2													99.95 65	96.67 323

segment_acmp												98.96 25

testdata				99.54 108	99.75 79	98.95 172		99.51 123	97.07 274	99.43 156	99.70 166	98.87 40	99.94 76	97.76 246	99.64 142	99.72 110

testdata1							98.85 365		98.32 124

test12				99.75 65	99.64 136	99.61 74		99.29 292		99.21 212		98.38 91	99.89 142		99.74 126	99.74 98

plane_prior7					99.29 253	97.03 304

plane_prior6					99.27 258	96.98 308						92.71 298

plane_prior5								99.47 186					99.69 246	97.78 242	97.63 271	98.67 309

plane_prior4											99.61 217

plane_prior3						97.00 306			98.69 88	99.11 231

plane_prior2							99.39 234		98.97 59

plane_prior1					99.26 260

plane_prior						96.97 309	99.21 298		98.45 108						97.60 274

n2								0.00 436

nn								0.00 436

door-mid								98.05 398

lessismore_v0				97.79 341	98.69 365	95.44 362		94.75 421		95.71 391	99.87 52	88.69 366	99.32 310	95.89 340	94.93 356	98.62 330

LGP-MVS_train				98.49 272	99.33 240	97.05 300		99.55 82	97.46 233	99.24 204	99.83 76	92.58 303	99.72 229	98.09 213	97.51 283	98.68 302

test11								99.35 258

door								97.92 399

HQP5-MVS						96.83 316

HQP-NCC					99.19 278		98.98 347		98.24 133	98.66 302

ACMP_Plane					99.19 278		98.98 347		98.24 133	98.66 302

BP-MVS														97.19 295

HQP4-MVS										98.66 302			99.64 260			98.64 321

HQP3-MVS								99.39 234							97.58 276

HQP2-MVS												92.47 307

NP-MVS					99.23 268	96.92 312					99.40 287

MDTV_nov1_ep13_2view						95.18 368	99.35 251		96.84 293	99.58 125		95.19 208		97.82 239		99.46 203

MDTV_nov1_ep13			98.32 184		99.11 299	94.44 380	99.27 277	98.74 373	97.51 230	99.40 168	99.62 213	94.78 225	99.76 214	97.59 261	98.81 212

ACMMP++_ref															97.19 303

ACMMP++															97.43 294

Test By Simon												98.75 58

ITE_SJBPF				98.08 316	99.29 253	96.37 335		98.92 346	98.34 121	98.83 280	99.75 146	91.09 338	99.62 267	95.82 341	97.40 296	98.25 371

DeepMVS_CX				93.34 387	99.29 253	82.27 416		99.22 306	85.15 413	96.33 384	99.05 347	90.97 340	99.73 225	93.57 379	97.77 267	98.01 385

High-resolution multi-view benchmark