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.

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 65	99.78 37	99.14 103	99.60 94	99.45 155	99.01 13	99.90 1	99.83 40	98.98 19	99.93 57	99.59 2	99.95 6	99.86 6

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 43	99.89 1	99.75 35	99.56 50	99.02 10	99.88 3	99.85 27	99.18 5	99.96 19	99.22 34	99.92 12	99.90 1

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 65	99.78 37	99.15 102	99.61 91	99.45 155	99.01 13	99.89 2	99.82 47	99.01 12	99.92 66	99.56 5	99.95 6	99.85 9

Regformer-4		99.59 2	99.54 4	99.73 48	99.76 46	99.41 74	99.58 104	99.49 107	99.02 10	99.88 3	99.80 68	99.00 18	99.94 42	99.45 15	99.92 12	99.84 13

Regformer-3		99.57 6	99.53 5	99.68 53	99.76 46	99.29 86	99.58 104	99.44 164	99.01 13	99.87 7	99.80 68	98.97 20	99.91 76	99.44 16	99.92 12	99.83 24

SD-MVS		99.41 34	99.52 6	99.05 157	99.74 69	99.68 34	99.46 164	99.52 78	99.11 7	99.88 3	99.91 5	99.43 1	97.70 342	98.72 88	99.93 11	99.77 52

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 30	99.91 1	99.66 38	99.63 82	99.39 187	98.91 29	99.78 24	99.85 27	99.36 2	99.94 42	98.84 72	99.88 35	99.82 31

Regformer-1		99.53 9	99.47 8	99.72 50	99.71 84	99.44 71	99.49 151	99.46 143	98.95 24	99.83 12	99.76 91	99.01 12	99.93 57	99.17 39	99.87 39	99.80 42

Regformer-2		99.54 7	99.47 8	99.75 41	99.71 84	99.52 62	99.49 151	99.49 107	98.94 26	99.83 12	99.76 91	99.01 12	99.94 42	99.15 42	99.87 39	99.80 42

MSLP-MVS++		99.46 21	99.47 8	99.44 105	99.60 125	99.16 99	99.41 185	99.71 13	98.98 19	99.45 100	99.78 82	99.19 4	99.54 220	99.28 28	99.84 60	99.63 103

XVS		99.53 9	99.42 11	99.87 7	99.85 24	99.83 8	99.69 48	99.68 19	98.98 19	99.37 118	99.74 101	98.81 34	99.94 42	98.79 80	99.86 50	99.84 13

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 7	99.82 30	99.81 14	99.59 97	99.51 87	98.62 50	99.79 19	99.83 40	99.28 3	99.97 11	98.48 120	99.90 24	99.84 13
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		99.48 17	99.42 11	99.65 60	99.72 78	99.40 76	99.05 279	99.66 26	99.14 6	99.57 74	99.80 68	98.46 62	99.94 42	99.57 4	99.84 60	99.60 108
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
HPM-MVS_fast		99.51 12	99.40 14	99.85 18	99.91 1	99.79 19	99.76 27	99.56 50	97.72 140	99.76 31	99.75 96	99.13 7	99.92 66	99.07 48	99.92 12	99.85 9

MTAPA		99.52 11	99.39 15	99.89 3	99.90 3	99.86 4	99.66 68	99.47 133	98.79 40	99.68 41	99.81 57	98.43 64	99.97 11	98.88 62	99.90 24	99.83 24

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 38	99.63 114	99.59 50	99.36 205	99.46 143	99.07 9	99.79 19	99.82 47	98.85 31	99.92 66	98.68 93	99.87 39	99.82 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 68	99.67 22	98.15 83	99.68 41	99.69 122	99.06 9	99.96 19	98.69 91	99.87 39	99.84 13

DeepPCF-MVS	98.18 3	98.81 118	99.37 17	97.12 315	99.60 125	91.75 343	98.61 332	99.44 164	99.35 1	99.83 12	99.85 27	98.70 50	99.81 148	99.02 52	99.91 17	99.81 35

zzz-MVS		99.49 13	99.36 19	99.89 3	99.90 3	99.86 4	99.36 205	99.47 133	98.79 40	99.68 41	99.81 57	98.43 64	99.97 11	98.88 62	99.90 24	99.83 24

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 68	99.67 22	98.15 83	99.67 47	99.69 122	98.95 24	99.96 19	98.69 91	99.87 39	99.84 13

TSAR-MVS + GP.		99.36 41	99.36 19	99.36 111	99.67 96	98.61 191	99.07 273	99.33 222	99.00 17	99.82 15	99.81 57	99.06 9	99.84 126	99.09 46	99.42 112	99.65 93

region2R		99.48 17	99.35 22	99.87 7	99.88 11	99.80 15	99.65 78	99.66 26	98.13 85	99.66 52	99.68 127	98.96 21	99.96 19	98.62 99	99.87 39	99.84 13

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 18	99.76 46	99.83 8	99.63 82	99.54 64	98.36 66	99.79 19	99.82 47	98.86 30	99.95 34	98.62 99	99.81 69	99.78 50

ACMMP_Plus		99.47 20	99.34 24	99.88 5	99.87 15	99.86 4	99.47 161	99.48 117	98.05 101	99.76 31	99.86 23	98.82 33	99.93 57	98.82 79	99.91 17	99.84 13

MVS_111021_LR		99.41 34	99.33 25	99.65 60	99.77 43	99.51 64	98.94 309	99.85 6	98.82 35	99.65 55	99.74 101	98.51 59	99.80 152	98.83 75	99.89 32	99.64 99

ESAPD		99.46 21	99.32 26	99.91 2	99.78 37	99.88 2	99.36 205	99.51 87	98.73 44	99.88 3	99.84 36	98.72 48	99.96 19	98.16 143	99.87 39	99.88 4

PS-MVSNAJ		99.32 45	99.32 26	99.30 123	99.57 130	98.94 139	98.97 301	99.46 143	98.92 28	99.71 35	99.24 271	99.01 12	99.98 5	99.35 19	99.66 98	98.97 193

CP-MVS		99.45 23	99.32 26	99.85 18	99.83 29	99.75 25	99.69 48	99.52 78	98.07 96	99.53 86	99.63 151	98.93 26	99.97 11	98.74 84	99.91 17	99.83 24

MVS_111021_HR		99.41 34	99.32 26	99.66 56	99.72 78	99.47 68	98.95 307	99.85 6	98.82 35	99.54 84	99.73 107	98.51 59	99.74 172	98.91 61	99.88 35	99.77 52

CSCG		99.32 45	99.32 26	99.32 118	99.85 24	98.29 214	99.71 44	99.66 26	98.11 89	99.41 109	99.80 68	98.37 70	99.96 19	98.99 54	99.96 5	99.72 72

ACMMP		99.45 23	99.32 26	99.82 27	99.89 8	99.67 36	99.62 85	99.69 18	98.12 87	99.63 57	99.84 36	98.73 47	99.96 19	98.55 115	99.83 64	99.81 35
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
PGM-MVS		99.45 23	99.31 32	99.86 13	99.87 15	99.78 24	99.58 104	99.65 31	97.84 126	99.71 35	99.80 68	99.12 8	99.97 11	98.33 134	99.87 39	99.83 24

abl_6		99.44 26	99.31 32	99.83 25	99.85 24	99.75 25	99.66 68	99.59 39	98.13 85	99.82 15	99.81 57	98.60 56	99.96 19	98.46 123	99.88 35	99.79 46

SMA-MVS		99.44 26	99.30 34	99.85 18	99.73 74	99.83 8	99.56 117	99.47 133	97.45 167	99.78 24	99.82 47	99.18 5	99.91 76	98.79 80	99.89 32	99.81 35

MCST-MVS		99.43 29	99.30 34	99.82 27	99.79 36	99.74 28	99.29 224	99.40 184	98.79 40	99.52 88	99.62 156	98.91 27	99.90 89	98.64 96	99.75 80	99.82 31

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 48	99.48 117	98.12 87	99.50 92	99.75 96	98.78 37	99.97 11	98.57 109	99.89 32	99.83 24

CNVR-MVS		99.42 31	99.30 34	99.78 36	99.62 119	99.71 30	99.26 237	99.52 78	98.82 35	99.39 114	99.71 113	98.96 21	99.85 120	98.59 106	99.80 71	99.77 52

UA-Net		99.42 31	99.29 38	99.80 32	99.62 119	99.55 55	99.50 141	99.70 15	98.79 40	99.77 26	99.96 1	97.45 96	99.96 19	98.92 60	99.90 24	99.89 2

#test#		99.43 29	99.29 38	99.86 13	99.87 15	99.80 15	99.55 123	99.67 22	97.83 127	99.68 41	99.69 122	99.06 9	99.96 19	98.39 126	99.87 39	99.84 13

HPM-MVS		99.42 31	99.28 40	99.83 25	99.90 3	99.72 29	99.81 15	99.54 64	97.59 151	99.68 41	99.63 151	98.91 27	99.94 42	98.58 107	99.91 17	99.84 13
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PVSNet_Blended_VisFu		99.36 41	99.28 40	99.61 69	99.86 20	99.07 111	99.47 161	99.93 2	97.66 149	99.71 35	99.86 23	97.73 89	99.96 19	99.47 13	99.82 68	99.79 46

xiu_mvs_v1_base_debu		99.29 49	99.27 42	99.34 113	99.63 114	98.97 131	99.12 262	99.51 87	98.86 31	99.84 9	99.47 212	98.18 77	99.99 1	99.50 8	99.31 119	99.08 179

xiu_mvs_v1_base		99.29 49	99.27 42	99.34 113	99.63 114	98.97 131	99.12 262	99.51 87	98.86 31	99.84 9	99.47 212	98.18 77	99.99 1	99.50 8	99.31 119	99.08 179

xiu_mvs_v1_base_debi		99.29 49	99.27 42	99.34 113	99.63 114	98.97 131	99.12 262	99.51 87	98.86 31	99.84 9	99.47 212	98.18 77	99.99 1	99.50 8	99.31 119	99.08 179

HSP-MVS		99.41 34	99.26 45	99.85 18	99.89 8	99.80 15	99.67 59	99.37 201	98.70 46	99.77 26	99.49 202	98.21 76	99.95 34	98.46 123	99.77 77	99.81 35

xiu_mvs_v2_base		99.26 54	99.25 46	99.29 126	99.53 136	98.91 144	99.02 288	99.45 155	98.80 39	99.71 35	99.26 269	98.94 25	99.98 5	99.34 23	99.23 123	98.98 192

GST-MVS		99.40 38	99.24 47	99.85 18	99.86 20	99.79 19	99.60 94	99.67 22	97.97 112	99.63 57	99.68 127	98.52 58	99.95 34	98.38 128	99.86 50	99.81 35

HPM-MVS++		99.39 39	99.23 48	99.87 7	99.75 58	99.84 7	99.43 174	99.51 87	98.68 48	99.27 145	99.53 187	98.64 54	99.96 19	98.44 125	99.80 71	99.79 46

MP-MVS-pluss		99.37 40	99.20 49	99.88 5	99.90 3	99.87 3	99.30 220	99.52 78	97.18 191	99.60 67	99.79 76	98.79 36	99.95 34	98.83 75	99.91 17	99.83 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 43	99.19 50	99.79 35	99.61 123	99.65 41	99.30 220	99.48 117	98.86 31	99.21 168	99.63 151	98.72 48	99.90 89	98.25 138	99.63 103	99.80 42

DeepC-MVS	98.35 2	99.30 47	99.19 50	99.64 65	99.82 30	99.23 93	99.62 85	99.55 57	98.94 26	99.63 57	99.95 2	95.82 144	99.94 42	99.37 18	99.97 3	99.73 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		99.30 47	99.17 52	99.70 52	99.56 134	99.52 62	99.58 104	99.80 8	97.12 197	99.62 61	99.73 107	98.58 57	99.90 89	98.61 102	99.91 17	99.68 85

MP-MVS		99.33 44	99.15 53	99.87 7	99.88 11	99.82 13	99.66 68	99.46 143	98.09 92	99.48 96	99.74 101	98.29 73	99.96 19	97.93 162	99.87 39	99.82 31
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CANet		99.25 55	99.14 54	99.59 71	99.41 166	99.16 99	99.35 210	99.57 45	98.82 35	99.51 91	99.61 160	96.46 125	99.95 34	99.59 2	99.98 2	99.65 93

CHOSEN 280x420		99.12 71	99.13 55	99.08 153	99.66 106	97.89 231	98.43 340	99.71 13	98.88 30	99.62 61	99.76 91	96.63 122	99.70 195	99.46 14	99.99 1	99.66 89

MVSFormer		99.17 62	99.12 56	99.29 126	99.51 140	98.94 139	99.88 1	99.46 143	97.55 156	99.80 17	99.65 139	97.39 97	99.28 263	99.03 50	99.85 55	99.65 93

LS3D		99.27 52	99.12 56	99.74 46	99.18 215	99.75 25	99.56 117	99.57 45	98.45 60	99.49 95	99.85 27	97.77 88	99.94 42	98.33 134	99.84 60	99.52 127

casdiffmvs1		99.23 57	99.11 58	99.58 74	99.53 136	99.36 78	99.76 27	99.43 172	97.99 110	99.52 88	99.84 36	97.50 95	99.77 165	99.42 17	98.97 143	99.61 107

CHOSEN 1792x2688		99.19 59	99.10 59	99.45 102	99.89 8	98.52 200	99.39 194	99.94 1	98.73 44	99.11 186	99.89 10	95.50 151	99.94 42	99.50 8	99.97 3	99.89 2

APD-MVS		99.27 52	99.08 60	99.84 24	99.75 58	99.79 19	99.50 141	99.50 102	97.16 193	99.77 26	99.82 47	98.78 37	99.94 42	97.56 197	99.86 50	99.80 42
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.12 71	99.08 60	99.24 138	99.46 156	98.55 194	99.51 136	99.46 143	98.09 92	99.45 100	99.82 47	98.34 71	99.51 221	98.70 89	98.93 147	99.67 88

test_prior3		99.21 58	99.05 62	99.68 53	99.67 96	99.48 66	98.96 303	99.56 50	98.34 67	99.01 204	99.52 192	98.68 51	99.83 135	97.96 159	99.74 82	99.74 61

sss		99.17 62	99.05 62	99.53 85	99.62 119	98.97 131	99.36 205	99.62 32	97.83 127	99.67 47	99.65 139	97.37 100	99.95 34	99.19 36	99.19 126	99.68 85

3Dnovator	97.25 9	99.24 56	99.05 62	99.81 30	99.12 228	99.66 38	99.84 9	99.74 10	99.09 8	98.92 219	99.90 7	95.94 139	99.98 5	98.95 57	99.92 12	99.79 46

F-COLMAP		99.19 59	99.04 65	99.64 65	99.78 37	99.27 89	99.42 181	99.54 64	97.29 182	99.41 109	99.59 165	98.42 67	99.93 57	98.19 140	99.69 93	99.73 66

OMC-MVS		99.08 83	99.04 65	99.20 142	99.67 96	98.22 217	99.28 226	99.52 78	98.07 96	99.66 52	99.81 57	97.79 87	99.78 163	97.79 173	99.81 69	99.60 108

jason		99.13 66	99.03 67	99.45 102	99.46 156	98.87 147	99.12 262	99.26 249	98.03 104	99.79 19	99.65 139	97.02 109	99.85 120	99.02 52	99.90 24	99.65 93
jason: jason.
CDS-MVSNet		99.09 80	99.03 67	99.25 135	99.42 163	98.73 176	99.45 165	99.46 143	98.11 89	99.46 99	99.77 88	98.01 82	99.37 240	98.70 89	98.92 149	99.66 89
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
API-MVS		99.04 88	99.03 67	99.06 155	99.40 171	99.31 85	99.55 123	99.56 50	98.54 54	99.33 129	99.39 235	98.76 42	99.78 163	96.98 235	99.78 75	98.07 320

MG-MVS		99.13 66	99.02 70	99.45 102	99.57 130	98.63 186	99.07 273	99.34 214	98.99 18	99.61 63	99.82 47	97.98 83	99.87 109	97.00 233	99.80 71	99.85 9

lupinMVS		99.13 66	99.01 71	99.46 101	99.51 140	98.94 139	99.05 279	99.16 260	97.86 121	99.80 17	99.56 174	97.39 97	99.86 113	98.94 59	99.85 55	99.58 116

diffmvs1		99.12 71	99.00 72	99.48 94	99.51 140	99.10 106	99.61 91	99.49 107	97.67 147	99.36 121	99.74 101	97.67 91	99.88 106	98.95 57	98.99 140	99.47 143

mvs_anonymous		99.03 90	98.99 73	99.16 145	99.38 174	98.52 200	99.51 136	99.38 193	97.79 132	99.38 116	99.81 57	97.30 101	99.45 225	99.35 19	98.99 140	99.51 132

EPP-MVSNet		99.13 66	98.99 73	99.53 85	99.65 110	99.06 112	99.81 15	99.33 222	97.43 169	99.60 67	99.88 15	97.14 105	99.84 126	99.13 43	98.94 146	99.69 81

CNLPA		99.14 65	98.99 73	99.59 71	99.58 128	99.41 74	99.16 255	99.44 164	98.45 60	99.19 174	99.49 202	98.08 80	99.89 97	97.73 181	99.75 80	99.48 138

MVS_Test		99.10 79	98.97 76	99.48 94	99.49 149	99.14 103	99.67 59	99.34 214	97.31 180	99.58 71	99.76 91	97.65 92	99.82 144	98.87 66	99.07 135	99.46 147

casdiffmvs		99.09 80	98.97 76	99.47 98	99.47 154	99.10 106	99.74 40	99.38 193	97.86 121	99.32 130	99.79 76	97.08 108	99.77 165	99.24 32	98.82 157	99.54 121

PVSNet_Blended		99.08 83	98.97 76	99.42 108	99.76 46	98.79 170	98.78 321	99.91 3	96.74 228	99.67 47	99.49 202	97.53 93	99.88 106	98.98 55	99.85 55	99.60 108

Vis-MVSNet		99.12 71	98.97 76	99.56 78	99.78 37	99.10 106	99.68 57	99.66 26	98.49 57	99.86 8	99.87 20	94.77 193	99.84 126	99.19 36	99.41 113	99.74 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	97.12 13	99.18 61	98.97 76	99.82 27	99.17 220	99.68 34	99.81 15	99.51 87	99.20 4	98.72 242	99.89 10	95.68 148	99.97 11	98.86 69	99.86 50	99.81 35

DP-MVS Recon		99.12 71	98.95 81	99.65 60	99.74 69	99.70 32	99.27 229	99.57 45	96.40 257	99.42 107	99.68 127	98.75 45	99.80 152	97.98 158	99.72 86	99.44 151

DP-MVS		99.16 64	98.95 81	99.78 36	99.77 43	99.53 59	99.41 185	99.50 102	97.03 212	99.04 201	99.88 15	97.39 97	99.92 66	98.66 94	99.90 24	99.87 5

PS-MVSNAJss		98.92 102	98.92 83	98.90 186	98.78 294	98.53 196	99.78 22	99.54 64	98.07 96	99.00 211	99.76 91	99.01 12	99.37 240	99.13 43	97.23 248	98.81 211

HyFIR lowres test		99.11 76	98.92 83	99.65 60	99.90 3	99.37 77	99.02 288	99.91 3	97.67 147	99.59 70	99.75 96	95.90 141	99.73 179	99.53 6	99.02 138	99.86 6

CDPH-MVS		99.13 66	98.91 85	99.80 32	99.75 58	99.71 30	99.15 258	99.41 177	96.60 239	99.60 67	99.55 177	98.83 32	99.90 89	97.48 205	99.83 64	99.78 50

VNet		99.11 76	98.90 86	99.73 48	99.52 138	99.56 53	99.41 185	99.39 187	99.01 13	99.74 33	99.78 82	95.56 149	99.92 66	99.52 7	98.18 194	99.72 72

CPTT-MVS		99.11 76	98.90 86	99.74 46	99.80 35	99.46 69	99.59 97	99.49 107	97.03 212	99.63 57	99.69 122	97.27 102	99.96 19	97.82 170	99.84 60	99.81 35

Effi-MVS+-dtu		98.78 122	98.89 88	98.47 243	99.33 183	96.91 274	99.57 110	99.30 231	98.47 58	99.41 109	98.99 291	96.78 116	99.74 172	98.73 86	99.38 114	98.74 224

WTY-MVS		99.06 85	98.88 89	99.61 69	99.62 119	99.16 99	99.37 201	99.56 50	98.04 102	99.53 86	99.62 156	96.84 114	99.94 42	98.85 71	98.49 174	99.72 72

CANet_DTU		98.97 99	98.87 90	99.25 135	99.33 183	98.42 211	99.08 272	99.30 231	99.16 5	99.43 104	99.75 96	95.27 159	99.97 11	98.56 112	99.95 6	99.36 158

1121		99.09 80	98.87 90	99.75 41	99.74 69	99.60 48	99.27 229	99.48 117	96.82 226	99.25 153	99.65 139	98.38 68	99.93 57	97.53 200	99.67 97	99.73 66

diffmvs		98.99 96	98.87 90	99.35 112	99.45 160	98.74 175	99.62 85	99.45 155	97.43 169	99.13 181	99.72 111	97.23 103	99.87 109	98.86 69	98.90 151	99.45 150

IS-MVSNet		99.05 87	98.87 90	99.57 76	99.73 74	99.32 82	99.75 35	99.20 256	98.02 105	99.56 75	99.86 23	96.54 124	99.67 200	98.09 147	99.13 129	99.73 66

MVS_0304		99.06 85	98.86 94	99.66 56	99.51 140	99.36 78	99.22 246	99.51 87	98.95 24	99.58 71	99.65 139	93.74 235	99.98 5	99.66 1	99.95 6	99.64 99

canonicalmvs		99.02 91	98.86 94	99.51 91	99.42 163	99.32 82	99.80 19	99.48 117	98.63 49	99.31 132	98.81 306	97.09 106	99.75 171	99.27 30	97.90 216	99.47 143

PLC	97.94 4	99.02 91	98.85 96	99.53 85	99.66 106	99.01 124	99.24 241	99.52 78	96.85 223	99.27 145	99.48 208	98.25 75	99.91 76	97.76 177	99.62 104	99.65 93
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs-test1		98.86 107	98.84 97	98.89 189	99.33 183	97.77 242	99.44 169	99.30 231	98.47 58	99.10 189	99.43 221	96.78 116	99.95 34	98.73 86	99.02 138	98.96 199

PAPM_NR		99.04 88	98.84 97	99.66 56	99.74 69	99.44 71	99.39 194	99.38 193	97.70 143	99.28 141	99.28 266	98.34 71	99.85 120	96.96 237	99.45 110	99.69 81

PVSNet	96.02 17	98.85 115	98.84 97	98.89 189	99.73 74	97.28 249	98.32 344	99.60 36	97.86 121	99.50 92	99.57 172	96.75 119	99.86 113	98.56 112	99.70 92	99.54 121

Fast-Effi-MVS+-dtu		98.77 124	98.83 100	98.60 229	99.41 166	96.99 268	99.52 132	99.49 107	98.11 89	99.24 158	99.34 254	96.96 112	99.79 155	97.95 161	99.45 110	99.02 188

PVSNet_BlendedMVS		98.86 107	98.80 101	99.03 158	99.76 46	98.79 170	99.28 226	99.91 3	97.42 172	99.67 47	99.37 240	97.53 93	99.88 106	98.98 55	97.29 247	98.42 308

AdaColmap		99.01 94	98.80 101	99.66 56	99.56 134	99.54 56	99.18 253	99.70 15	98.18 82	99.35 125	99.63 151	96.32 130	99.90 89	97.48 205	99.77 77	99.55 119

MSDG		98.98 97	98.80 101	99.53 85	99.76 46	99.19 95	98.75 324	99.55 57	97.25 185	99.47 97	99.77 88	97.82 86	99.87 109	96.93 240	99.90 24	99.54 121

train_agg		99.02 91	98.77 104	99.77 38	99.67 96	99.65 41	99.05 279	99.41 177	96.28 263	98.95 215	99.49 202	98.76 42	99.91 76	97.63 190	99.72 86	99.75 56

1112_ss		98.98 97	98.77 104	99.59 71	99.68 95	99.02 122	99.25 239	99.48 117	97.23 188	99.13 181	99.58 168	96.93 113	99.90 89	98.87 66	98.78 161	99.84 13

agg_prior1		99.01 94	98.76 106	99.76 40	99.67 96	99.62 44	98.99 294	99.40 184	96.26 266	98.87 225	99.49 202	98.77 40	99.91 76	97.69 187	99.72 86	99.75 56

COLMAP_ROB	97.56 6	98.86 107	98.75 107	99.17 144	99.88 11	98.53 196	99.34 213	99.59 39	97.55 156	98.70 249	99.89 10	95.83 143	99.90 89	98.10 146	99.90 24	99.08 179
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.87 104	98.72 108	99.31 119	99.86 20	98.48 206	99.56 117	99.61 33	97.85 124	99.36 121	99.85 27	95.95 137	99.85 120	96.66 259	99.83 64	99.59 112

Vis-MVSNet (Re-imp)		98.87 104	98.72 108	99.31 119	99.71 84	98.88 146	99.80 19	99.44 164	97.91 119	99.36 121	99.78 82	95.49 152	99.43 234	97.91 163	99.11 130	99.62 105

agg_prior3		98.97 99	98.71 110	99.75 41	99.67 96	99.60 48	99.04 284	99.41 177	95.93 287	98.87 225	99.48 208	98.61 55	99.91 76	97.63 190	99.72 86	99.75 56

EPNet		98.86 107	98.71 110	99.30 123	97.20 342	98.18 218	99.62 85	98.91 291	99.28 2	98.63 260	99.81 57	95.96 136	99.99 1	99.24 32	99.72 86	99.73 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UGNet		98.87 104	98.69 112	99.40 109	99.22 207	98.72 178	99.44 169	99.68 19	99.24 3	99.18 177	99.42 224	92.74 251	99.96 19	99.34 23	99.94 10	99.53 126
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
XVG-OURS		98.73 126	98.68 113	98.88 196	99.70 90	97.73 244	98.92 310	99.55 57	98.52 56	99.45 100	99.84 36	95.27 159	99.91 76	98.08 151	98.84 156	99.00 189

EI-MVSNet		98.67 130	98.67 114	98.68 224	99.35 179	97.97 227	99.50 141	99.38 193	96.93 219	99.20 171	99.83 40	97.87 84	99.36 244	98.38 128	97.56 228	98.71 228

CVMVSNet		98.57 135	98.67 114	98.30 258	99.35 179	95.59 303	99.50 141	99.55 57	98.60 52	99.39 114	99.83 40	94.48 207	99.45 225	98.75 83	98.56 170	99.85 9

114514_t		98.93 101	98.67 114	99.72 50	99.85 24	99.53 59	99.62 85	99.59 39	92.65 334	99.71 35	99.78 82	98.06 81	99.90 89	98.84 72	99.91 17	99.74 61

Test_1112_low_res		98.89 103	98.66 117	99.57 76	99.69 92	98.95 136	99.03 285	99.47 133	96.98 214	99.15 180	99.23 272	96.77 118	99.89 97	98.83 75	98.78 161	99.86 6

HY-MVS	97.30 7	98.85 115	98.64 118	99.47 98	99.42 163	99.08 110	99.62 85	99.36 202	97.39 175	99.28 141	99.68 127	96.44 127	99.92 66	98.37 130	98.22 190	99.40 156

0601test		98.86 107	98.63 119	99.54 79	99.49 149	99.18 97	99.50 141	99.07 271	98.22 77	99.61 63	99.51 195	95.37 154	99.84 126	98.60 104	98.33 179	99.59 112

Anonymous20240521		98.86 107	98.63 119	99.54 79	99.49 149	99.18 97	99.50 141	99.07 271	98.22 77	99.61 63	99.51 195	95.37 154	99.84 126	98.60 104	98.33 179	99.59 112

FIs		98.78 122	98.63 119	99.23 140	99.18 215	99.54 56	99.83 12	99.59 39	98.28 71	98.79 236	99.81 57	96.75 119	99.37 240	99.08 47	96.38 263	98.78 214

ab-mvs		98.86 107	98.63 119	99.54 79	99.64 111	99.19 95	99.44 169	99.54 64	97.77 134	99.30 133	99.81 57	94.20 216	99.93 57	99.17 39	98.82 157	99.49 136

MAR-MVS		98.86 107	98.63 119	99.54 79	99.37 176	99.66 38	99.45 165	99.54 64	96.61 237	99.01 204	99.40 231	97.09 106	99.86 113	97.68 189	99.53 108	99.10 174
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
FC-MVSNet-test		98.75 125	98.62 124	99.15 147	99.08 236	99.45 70	99.86 8	99.60 36	98.23 76	98.70 249	99.82 47	96.80 115	99.22 278	99.07 48	96.38 263	98.79 213

XVG-OURS-SEG-HR		98.69 128	98.62 124	98.89 189	99.71 84	97.74 243	99.12 262	99.54 64	98.44 63	99.42 107	99.71 113	94.20 216	99.92 66	98.54 117	98.90 151	99.00 189

RPSCF		98.22 159	98.62 124	96.99 316	99.82 30	91.58 344	99.72 42	99.44 164	96.61 237	99.66 52	99.89 10	95.92 140	99.82 144	97.46 208	99.10 132	99.57 117

PatchMatch-RL		98.84 117	98.62 124	99.52 89	99.71 84	99.28 87	99.06 277	99.77 9	97.74 138	99.50 92	99.53 187	95.41 153	99.84 126	97.17 225	99.64 101	99.44 151

PMMVS		98.80 121	98.62 124	99.34 113	99.27 200	98.70 179	98.76 323	99.31 229	97.34 177	99.21 168	99.07 284	97.20 104	99.82 144	98.56 112	98.87 154	99.52 127

Effi-MVS+		98.81 118	98.59 129	99.48 94	99.46 156	99.12 105	98.08 350	99.50 102	97.50 162	99.38 116	99.41 227	96.37 129	99.81 148	99.11 45	98.54 171	99.51 132

test_djsdf		98.67 130	98.57 130	98.98 164	98.70 305	98.91 144	99.88 1	99.46 143	97.55 156	99.22 165	99.88 15	95.73 147	99.28 263	99.03 50	97.62 223	98.75 221

alignmvs		98.81 118	98.56 131	99.58 74	99.43 162	99.42 73	99.51 136	98.96 284	98.61 51	99.35 125	98.92 297	94.78 189	99.77 165	99.35 19	98.11 210	99.54 121

1314		98.68 129	98.54 132	99.11 152	98.89 277	98.65 184	99.27 229	99.49 107	96.89 221	97.99 294	99.56 174	97.72 90	99.83 135	97.74 180	99.27 122	98.84 209

tpmrst		98.33 148	98.48 133	97.90 291	99.16 222	94.78 321	99.31 218	99.11 265	97.27 183	99.45 100	99.59 165	95.33 156	99.84 126	98.48 120	98.61 164	99.09 178

Fast-Effi-MVS+		98.70 127	98.43 134	99.51 91	99.51 140	99.28 87	99.52 132	99.47 133	96.11 280	99.01 204	99.34 254	96.20 134	99.84 126	97.88 165	98.82 157	99.39 157

nrg030		98.64 133	98.42 135	99.28 128	99.05 242	99.69 33	99.81 15	99.46 143	98.04 102	99.01 204	99.82 47	96.69 121	99.38 237	99.34 23	94.59 303	98.78 214

IterMVS-LS		98.46 138	98.42 135	98.58 231	99.59 127	98.00 225	99.37 201	99.43 172	96.94 218	99.07 195	99.59 165	97.87 84	99.03 299	98.32 136	95.62 278	98.71 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		98.42 141	98.36 137	98.59 230	99.49 149	96.70 280	99.27 229	99.13 264	97.24 187	98.80 235	99.38 236	95.75 146	99.74 172	97.07 230	99.16 127	99.33 161

PatchmatchNet		98.31 150	98.36 137	98.19 274	99.16 222	95.32 311	99.27 229	98.92 288	97.37 176	99.37 118	99.58 168	94.90 181	99.70 195	97.43 211	99.21 124	99.54 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PAPR		98.63 134	98.34 139	99.51 91	99.40 171	99.03 121	98.80 319	99.36 202	96.33 259	99.00 211	99.12 282	98.46 62	99.84 126	95.23 289	99.37 118	99.66 89

ACMM	97.58 5	98.37 146	98.34 139	98.48 241	99.41 166	97.10 257	99.56 117	99.45 155	98.53 55	99.04 201	99.85 27	93.00 243	99.71 189	98.74 84	97.45 238	98.64 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		98.49 136	98.32 141	99.00 162	99.35 179	99.02 122	99.54 126	99.38 193	97.41 173	99.20 171	99.73 107	93.86 230	99.36 244	98.87 66	97.56 228	98.62 278

MDTV_nov1_ep13			98.32 141		99.11 230	94.44 325	99.27 229	98.74 309	97.51 161	99.40 113	99.62 156	94.78 189	99.76 170	97.59 192	98.81 160

QAPM		98.67 130	98.30 143	99.80 32	99.20 210	99.67 36	99.77 24	99.72 11	94.74 301	98.73 241	99.90 7	95.78 145	99.98 5	96.96 237	99.88 35	99.76 55

anonymousdsp		98.44 139	98.28 144	98.94 170	98.50 321	98.96 135	99.77 24	99.50 102	97.07 208	98.87 225	99.77 88	94.76 194	99.28 263	98.66 94	97.60 224	98.57 299

jajsoiax		98.43 140	98.28 144	98.88 196	98.60 316	98.43 209	99.82 13	99.53 74	98.19 79	98.63 260	99.80 68	93.22 241	99.44 230	99.22 34	97.50 233	98.77 217

mvs_tets		98.40 144	98.23 146	98.91 182	98.67 309	98.51 202	99.66 68	99.53 74	98.19 79	98.65 258	99.81 57	92.75 249	99.44 230	99.31 26	97.48 237	98.77 217

HQP_MVS		98.27 154	98.22 147	98.44 248	99.29 195	96.97 270	99.39 194	99.47 133	98.97 22	99.11 186	99.61 160	92.71 253	99.69 198	97.78 174	97.63 221	98.67 253

LCM-MVSNet-Re		97.83 219	98.15 148	96.87 320	99.30 192	92.25 342	99.59 97	98.26 333	97.43 169	96.20 319	99.13 279	96.27 132	98.73 319	98.17 142	98.99 140	99.64 99

tttt0517		98.42 141	98.14 149	99.28 128	99.66 106	98.38 212	99.74 40	96.85 357	97.68 145	99.79 19	99.74 101	91.39 296	99.89 97	98.83 75	99.56 105	99.57 117

Patchmatch-test1		98.16 168	98.14 149	98.22 271	99.30 192	95.55 304	99.07 273	98.97 282	97.57 154	99.43 104	99.60 163	92.72 252	99.60 214	97.38 213	99.20 125	99.50 135

LPG-MVS_test		98.22 159	98.13 151	98.49 239	99.33 183	97.05 263	99.58 104	99.55 57	97.46 164	99.24 158	99.83 40	92.58 264	99.72 183	98.09 147	97.51 231	98.68 242

OpenMVS	96.50 16	98.47 137	98.12 152	99.52 89	99.04 243	99.53 59	99.82 13	99.72 11	94.56 307	98.08 289	99.88 15	94.73 196	99.98 5	97.47 207	99.76 79	99.06 184

OPM-MVS		98.19 165	98.10 153	98.45 245	98.88 278	97.07 261	99.28 226	99.38 193	98.57 53	99.22 165	99.81 57	92.12 276	99.66 202	98.08 151	97.54 230	98.61 287

CLD-MVS		98.16 168	98.10 153	98.33 255	99.29 195	96.82 277	98.75 324	99.44 164	97.83 127	99.13 181	99.55 177	92.92 245	99.67 200	98.32 136	97.69 220	98.48 304
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		98.38 145	98.09 155	99.24 138	99.26 202	99.32 82	99.56 117	99.55 57	97.45 167	98.71 243	99.83 40	93.23 239	99.63 211	98.88 62	96.32 265	98.76 219

ADS-MVSNet		98.20 164	98.08 156	98.56 234	99.33 183	96.48 287	99.23 242	99.15 261	96.24 268	99.10 189	99.67 133	94.11 221	99.71 189	96.81 250	99.05 136	99.48 138

BH-RMVSNet		98.41 143	98.08 156	99.40 109	99.41 166	98.83 154	99.30 220	98.77 305	97.70 143	98.94 217	99.65 139	92.91 247	99.74 172	96.52 263	99.55 107	99.64 99

ADS-MVSNet2		98.02 191	98.07 158	97.87 292	99.33 183	95.19 315	99.23 242	99.08 268	96.24 268	99.10 189	99.67 133	94.11 221	98.93 314	96.81 250	99.05 136	99.48 138

PatchFormer-LS_test		98.01 194	98.05 159	97.87 292	99.15 225	94.76 322	99.42 181	98.93 286	97.12 197	98.84 231	98.59 318	93.74 235	99.80 152	98.55 115	98.17 200	99.06 184

thisisatest0530		98.35 147	98.03 160	99.31 119	99.63 114	98.56 193	99.54 126	96.75 359	97.53 160	99.73 34	99.65 139	91.25 299	99.89 97	98.62 99	99.56 105	99.48 138

EU-MVSNet		97.98 196	98.03 160	97.81 298	98.72 302	96.65 283	99.66 68	99.66 26	98.09 92	98.35 276	99.82 47	95.25 162	98.01 335	97.41 212	95.30 283	98.78 214

tfpn1000		98.33 148	98.02 162	99.25 135	99.78 37	98.73 176	99.70 45	97.55 353	97.48 163	99.69 40	99.53 187	92.37 273	99.85 120	97.82 170	98.26 189	99.16 170

tpmvs		97.98 196	98.02 162	97.84 295	99.04 243	94.73 323	99.31 218	99.20 256	96.10 284	98.76 239	99.42 224	94.94 176	99.81 148	96.97 236	98.45 175	98.97 193

UniMVSNet (Re)		98.29 152	98.00 164	99.13 151	99.00 248	99.36 78	99.49 151	99.51 87	97.95 114	98.97 214	99.13 279	96.30 131	99.38 237	98.36 132	93.34 321	98.66 264

ACMH	97.28 8	98.10 176	97.99 165	98.44 248	99.41 166	96.96 272	99.60 94	99.56 50	98.09 92	98.15 286	99.91 5	90.87 303	99.70 195	98.88 62	97.45 238	98.67 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous202405211		98.30 151	97.98 166	99.26 134	99.57 130	98.16 219	99.41 185	98.55 328	96.03 285	99.19 174	99.74 101	91.87 282	99.92 66	99.16 41	98.29 184	99.70 80

UniMVSNet_NR-MVSNet		98.22 159	97.97 167	98.96 167	98.92 271	98.98 128	99.48 156	99.53 74	97.76 135	98.71 243	99.46 216	96.43 128	99.22 278	98.57 109	92.87 327	98.69 237

EPNet_dtu		98.03 189	97.96 168	98.23 269	98.27 326	95.54 306	99.23 242	98.75 306	99.02 10	97.82 299	99.71 113	96.11 135	99.48 222	93.04 327	99.65 100	99.69 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPA-MVSNet		98.29 152	97.95 169	99.30 123	99.16 222	99.54 56	99.50 141	99.58 44	98.27 72	99.35 125	99.37 240	92.53 266	99.65 204	99.35 19	94.46 304	98.72 226

ACMP	97.20 11	98.06 180	97.94 170	98.45 245	99.37 176	97.01 266	99.44 169	99.49 107	97.54 159	98.45 270	99.79 76	91.95 277	99.72 183	97.91 163	97.49 236	98.62 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CR-MVSNet		98.17 166	97.93 171	98.87 200	99.18 215	98.49 204	99.22 246	99.33 222	96.96 215	99.56 75	99.38 236	94.33 212	99.00 303	94.83 296	98.58 167	99.14 171

pmmvs4		98.13 171	97.90 172	98.81 211	98.61 315	98.87 147	98.99 294	99.21 255	96.44 252	99.06 199	99.58 168	95.90 141	99.11 291	97.18 224	96.11 268	98.46 307

test-LLR		98.06 180	97.90 172	98.55 236	98.79 290	97.10 257	98.67 328	97.75 342	97.34 177	98.61 263	98.85 302	94.45 208	99.45 225	97.25 218	99.38 114	99.10 174

HQP-MVS		98.02 191	97.90 172	98.37 253	99.19 212	96.83 275	98.98 298	99.39 187	98.24 73	98.66 252	99.40 231	92.47 268	99.64 206	97.19 222	97.58 226	98.64 269

LTVRE_ROB	97.16 12	98.02 191	97.90 172	98.40 251	99.23 205	96.80 278	99.70 45	99.60 36	97.12 197	98.18 285	99.70 116	91.73 288	99.72 183	98.39 126	97.45 238	98.68 242
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
conf0.01		98.21 162	97.89 176	99.15 147	99.76 46	99.04 114	99.67 59	97.71 345	97.10 201	99.55 78	99.54 180	92.70 255	99.79 155	96.90 243	98.12 204	98.61 287

conf0.002		98.21 162	97.89 176	99.15 147	99.76 46	99.04 114	99.67 59	97.71 345	97.10 201	99.55 78	99.54 180	92.70 255	99.79 155	96.90 243	98.12 204	98.61 287

thresconf0.02		98.24 155	97.89 176	99.27 130	99.76 46	99.04 114	99.67 59	97.71 345	97.10 201	99.55 78	99.54 180	92.70 255	99.79 155	96.90 243	98.12 204	98.97 193

tfpn_n400		98.24 155	97.89 176	99.27 130	99.76 46	99.04 114	99.67 59	97.71 345	97.10 201	99.55 78	99.54 180	92.70 255	99.79 155	96.90 243	98.12 204	98.97 193

tfpnconf		98.24 155	97.89 176	99.27 130	99.76 46	99.04 114	99.67 59	97.71 345	97.10 201	99.55 78	99.54 180	92.70 255	99.79 155	96.90 243	98.12 204	98.97 193

tfpnview11		98.24 155	97.89 176	99.27 130	99.76 46	99.04 114	99.67 59	97.71 345	97.10 201	99.55 78	99.54 180	92.70 255	99.79 155	96.90 243	98.12 204	98.97 193

BH-w/o		98.00 195	97.89 176	98.32 256	99.35 179	96.20 296	99.01 292	98.90 293	96.42 254	98.38 273	99.00 290	95.26 161	99.72 183	96.06 271	98.61 164	99.03 186

WR-MVS_H		98.13 171	97.87 183	98.90 186	99.02 246	98.84 151	99.70 45	99.59 39	97.27 183	98.40 272	99.19 275	95.53 150	99.23 275	98.34 133	93.78 318	98.61 287

tfpn_ndepth		98.17 166	97.84 184	99.15 147	99.75 58	98.76 174	99.61 91	97.39 355	96.92 220	99.61 63	99.38 236	92.19 275	99.86 113	97.57 195	98.13 202	98.82 210

v1neww		98.12 173	97.84 184	98.93 173	98.97 256	98.81 163	99.66 68	99.35 206	96.49 244	99.29 137	99.37 240	95.02 171	99.32 254	97.73 181	94.73 295	98.67 253

v7new		98.12 173	97.84 184	98.93 173	98.97 256	98.81 163	99.66 68	99.35 206	96.49 244	99.29 137	99.37 240	95.02 171	99.32 254	97.73 181	94.73 295	98.67 253

v6		98.12 173	97.84 184	98.94 170	98.94 264	98.83 154	99.66 68	99.34 214	96.49 244	99.30 133	99.37 240	94.95 175	99.34 250	97.77 176	94.74 294	98.67 253

dp		97.75 236	97.80 188	97.59 306	99.10 233	93.71 333	99.32 215	98.88 295	96.48 250	99.08 194	99.55 177	92.67 262	99.82 144	96.52 263	98.58 167	99.24 166

thisisatest0515		98.14 170	97.79 189	99.19 143	99.50 148	98.50 203	98.61 332	96.82 358	96.95 217	99.54 84	99.43 221	91.66 292	99.86 113	98.08 151	99.51 109	99.22 167

V42		98.06 180	97.79 189	98.86 204	98.98 253	98.84 151	99.69 48	99.34 214	96.53 243	99.30 133	99.37 240	94.67 199	99.32 254	97.57 195	94.66 300	98.42 308

DU-MVS		98.08 179	97.79 189	98.96 167	98.87 281	98.98 128	99.41 185	99.45 155	97.87 120	98.71 243	99.50 199	94.82 186	99.22 278	98.57 109	92.87 327	98.68 242

divwei89l23v2f112		98.06 180	97.78 192	98.91 182	98.90 274	98.77 173	99.57 110	99.35 206	96.45 251	99.24 158	99.37 240	94.92 179	99.27 266	97.50 203	94.71 299	98.68 242

v7		98.05 186	97.78 192	98.87 200	98.99 249	98.67 181	99.64 80	99.34 214	96.31 262	99.29 137	99.51 195	94.78 189	99.27 266	97.03 231	95.15 287	98.66 264

CP-MVSNet		98.09 177	97.78 192	99.01 160	98.97 256	99.24 92	99.67 59	99.46 143	97.25 185	98.48 269	99.64 147	93.79 231	99.06 295	98.63 97	94.10 312	98.74 224

ACMH+	97.24 10	97.92 208	97.78 192	98.32 256	99.46 156	96.68 282	99.56 117	99.54 64	98.41 64	97.79 301	99.87 20	90.18 311	99.66 202	98.05 156	97.18 251	98.62 278

v2v482		98.06 180	97.77 196	98.92 178	98.90 274	98.82 161	99.57 110	99.36 202	96.65 234	99.19 174	99.35 251	94.20 216	99.25 272	97.72 185	94.97 291	98.69 237

OurMVSNet-221017-0		97.88 211	97.77 196	98.19 274	98.71 304	96.53 285	99.88 1	99.00 279	97.79 132	98.78 237	99.94 3	91.68 289	99.35 247	97.21 220	96.99 254	98.69 237

IterMVS		97.83 219	97.77 196	98.02 282	99.58 128	96.27 294	99.02 288	99.48 117	97.22 189	98.71 243	99.70 116	92.75 249	99.13 288	97.46 208	96.00 271	98.67 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1141		98.05 186	97.76 199	98.91 182	98.91 273	98.78 172	99.57 110	99.35 206	96.41 256	99.23 163	99.36 247	94.93 178	99.27 266	97.38 213	94.72 297	98.68 242

v1		98.05 186	97.76 199	98.93 173	98.92 271	98.80 168	99.57 110	99.35 206	96.39 258	99.28 141	99.36 247	94.86 184	99.32 254	97.38 213	94.72 297	98.68 242

FMVSNet3		98.03 189	97.76 199	98.84 208	99.39 173	98.98 128	99.40 192	99.38 193	96.67 233	99.07 195	99.28 266	92.93 244	98.98 305	97.10 227	96.65 256	98.56 300

MVP-Stereo		97.81 223	97.75 202	97.99 285	97.53 335	96.60 284	98.96 303	98.85 297	97.22 189	97.23 307	99.36 247	95.28 158	99.46 224	95.51 283	99.78 75	97.92 330
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WR-MVS		98.06 180	97.73 203	99.06 155	98.86 284	99.25 91	99.19 252	99.35 206	97.30 181	98.66 252	99.43 221	93.94 226	99.21 282	98.58 107	94.28 308	98.71 228

CostFormer		97.72 241	97.73 203	97.71 303	99.15 225	94.02 329	99.54 126	99.02 278	94.67 302	99.04 201	99.35 251	92.35 274	99.77 165	98.50 119	97.94 215	99.34 160

XVG-ACMP-BASELINE		97.83 219	97.71 205	98.20 273	99.11 230	96.33 292	99.41 185	99.52 78	98.06 100	99.05 200	99.50 199	89.64 315	99.73 179	97.73 181	97.38 244	98.53 301

v1144		97.98 196	97.69 206	98.85 207	98.87 281	98.66 183	99.54 126	99.35 206	96.27 265	99.23 163	99.35 251	94.67 199	99.23 275	96.73 254	95.16 286	98.68 242

Anonymous20240529		98.09 177	97.68 207	99.34 113	99.66 106	98.44 208	99.40 192	99.43 172	93.67 323	99.22 165	99.89 10	90.23 310	99.93 57	99.26 31	98.33 179	99.66 89

our_test_3		97.65 252	97.68 207	97.55 307	98.62 313	94.97 319	98.84 317	99.30 231	96.83 225	98.19 284	99.34 254	97.01 110	99.02 300	95.00 293	96.01 270	98.64 269

view600		97.97 199	97.66 209	98.89 189	99.75 58	97.81 237	99.69 48	98.80 301	98.02 105	99.25 153	98.88 298	91.95 277	99.89 97	94.36 305	98.29 184	98.96 199

view800		97.97 199	97.66 209	98.89 189	99.75 58	97.81 237	99.69 48	98.80 301	98.02 105	99.25 153	98.88 298	91.95 277	99.89 97	94.36 305	98.29 184	98.96 199

conf0.05thres1000		97.97 199	97.66 209	98.89 189	99.75 58	97.81 237	99.69 48	98.80 301	98.02 105	99.25 153	98.88 298	91.95 277	99.89 97	94.36 305	98.29 184	98.96 199

tfpn		97.97 199	97.66 209	98.89 189	99.75 58	97.81 237	99.69 48	98.80 301	98.02 105	99.25 153	98.88 298	91.95 277	99.89 97	94.36 305	98.29 184	98.96 199

TranMVSNet+NR-MVSNet		97.93 205	97.66 209	98.76 219	98.78 294	98.62 188	99.65 78	99.49 107	97.76 135	98.49 268	99.60 163	94.23 215	98.97 312	98.00 157	92.90 325	98.70 232

Patchmatch-test		97.93 205	97.65 214	98.77 217	99.18 215	97.07 261	99.03 285	99.14 263	96.16 275	98.74 240	99.57 172	94.56 203	99.72 183	93.36 322	99.11 130	99.52 127

EPMVS		97.82 222	97.65 214	98.35 254	98.88 278	95.98 298	99.49 151	94.71 364	97.57 154	99.26 149	99.48 208	92.46 271	99.71 189	97.87 166	99.08 134	99.35 159

v8		97.95 204	97.63 216	98.93 173	98.95 261	98.81 163	99.80 19	99.41 177	96.03 285	99.10 189	99.42 224	94.92 179	99.30 260	96.94 239	94.08 313	98.66 264

NR-MVSNet		97.97 199	97.61 217	99.02 159	98.87 281	99.26 90	99.47 161	99.42 175	97.63 150	97.08 310	99.50 199	95.07 169	99.13 288	97.86 167	93.59 319	98.68 242

v144192		97.92 208	97.60 218	98.87 200	98.83 287	98.65 184	99.55 123	99.34 214	96.20 271	99.32 130	99.40 231	94.36 211	99.26 271	96.37 268	95.03 290	98.70 232

PS-CasMVS		97.93 205	97.59 219	98.95 169	98.99 249	99.06 112	99.68 57	99.52 78	97.13 195	98.31 278	99.68 127	92.44 272	99.05 296	98.51 118	94.08 313	98.75 221

v148		97.79 228	97.55 220	98.50 238	98.74 299	97.72 245	99.54 126	99.33 222	96.26 266	98.90 222	99.51 195	94.68 198	99.14 285	97.83 169	93.15 324	98.63 276

tpm		97.67 250	97.55 220	98.03 280	99.02 246	95.01 318	99.43 174	98.54 329	96.44 252	99.12 184	99.34 254	91.83 283	99.60 214	97.75 179	96.46 261	99.48 138

Anonymous20231211		97.88 211	97.54 222	98.90 186	99.71 84	98.53 196	99.48 156	99.57 45	94.16 317	98.81 233	99.68 127	93.23 239	99.42 235	98.84 72	94.42 306	98.76 219

v7n		97.87 213	97.52 223	98.92 178	98.76 298	98.58 192	99.84 9	99.46 143	96.20 271	98.91 220	99.70 116	94.89 182	99.44 230	96.03 272	93.89 317	98.75 221

v10		97.85 215	97.52 223	98.86 204	98.99 249	98.67 181	99.75 35	99.41 177	95.70 291	98.98 213	99.41 227	94.75 195	99.23 275	96.01 273	94.63 302	98.67 253

V4		97.80 226	97.51 225	98.67 226	98.79 290	98.63 186	99.87 4	99.44 164	95.87 288	99.01 204	99.46 216	94.52 206	99.33 251	96.64 262	93.97 315	98.05 321

thres600view7		97.86 214	97.51 225	98.92 178	99.72 78	97.95 230	99.59 97	98.74 309	97.94 115	99.27 145	98.62 313	91.75 284	99.86 113	93.73 318	98.19 193	98.96 199

v52		97.79 228	97.50 227	98.66 227	98.80 288	98.62 188	99.87 4	99.44 164	95.87 288	99.01 204	99.46 216	94.44 210	99.33 251	96.65 261	93.96 316	98.05 321

testgi		97.65 252	97.50 227	98.13 277	99.36 178	96.45 288	99.42 181	99.48 117	97.76 135	97.87 297	99.45 219	91.09 300	98.81 317	94.53 300	98.52 172	99.13 173

tfpn111		97.81 223	97.49 229	98.78 216	99.72 78	97.86 233	99.59 97	98.74 309	97.93 116	99.26 149	98.62 313	91.75 284	99.86 113	93.57 319	98.18 194	98.61 287

GBi-Net		97.68 247	97.48 230	98.29 259	99.51 140	97.26 251	99.43 174	99.48 117	96.49 244	99.07 195	99.32 260	90.26 307	98.98 305	97.10 227	96.65 256	98.62 278

test1		97.68 247	97.48 230	98.29 259	99.51 140	97.26 251	99.43 174	99.48 117	96.49 244	99.07 195	99.32 260	90.26 307	98.98 305	97.10 227	96.65 256	98.62 278

tfpnnormal		97.84 217	97.47 232	98.98 164	99.20 210	99.22 94	99.64 80	99.61 33	96.32 260	98.27 281	99.70 116	93.35 238	99.44 230	95.69 279	95.40 281	98.27 315

GA-MVS		97.85 215	97.47 232	99.00 162	99.38 174	97.99 226	98.57 335	99.15 261	97.04 211	98.90 222	99.30 263	89.83 313	99.38 237	96.70 256	98.33 179	99.62 105

LF4IMVS		97.52 259	97.46 234	97.70 304	98.98 253	95.55 304	99.29 224	98.82 300	98.07 96	98.66 252	99.64 147	89.97 312	99.61 213	97.01 232	96.68 255	97.94 328

ppachtmachnet_test		97.49 265	97.45 235	97.61 305	98.62 313	95.24 312	98.80 319	99.46 143	96.11 280	98.22 282	99.62 156	96.45 126	98.97 312	93.77 317	95.97 272	98.61 287

conf200view11		97.78 230	97.45 235	98.77 217	99.72 78	97.86 233	99.59 97	98.74 309	97.93 116	99.26 149	98.62 313	91.75 284	99.83 135	93.22 323	98.18 194	98.61 287

thres100view900		97.76 232	97.45 235	98.69 223	99.72 78	97.86 233	99.59 97	98.74 309	97.93 116	99.26 149	98.62 313	91.75 284	99.83 135	93.22 323	98.18 194	98.37 312

v1921920		97.80 226	97.45 235	98.84 208	98.80 288	98.53 196	99.52 132	99.34 214	96.15 277	99.24 158	99.47 212	93.98 225	99.29 262	95.40 286	95.13 288	98.69 237

Baseline_NR-MVSNet		97.76 232	97.45 235	98.68 224	99.09 235	98.29 214	99.41 185	98.85 297	95.65 292	98.63 260	99.67 133	94.82 186	99.10 293	98.07 154	92.89 326	98.64 269

MIMVSNet		97.73 239	97.45 235	98.57 232	99.45 160	97.50 247	99.02 288	98.98 281	96.11 280	99.41 109	99.14 278	90.28 306	98.74 318	95.74 277	98.93 147	99.47 143

v1192		97.81 223	97.44 241	98.91 182	98.88 278	98.68 180	99.51 136	99.34 214	96.18 273	99.20 171	99.34 254	94.03 224	99.36 244	95.32 288	95.18 285	98.69 237

VPNet		97.84 217	97.44 241	99.01 160	99.21 208	98.94 139	99.48 156	99.57 45	98.38 65	99.28 141	99.73 107	88.89 321	99.39 236	99.19 36	93.27 322	98.71 228

PEN-MVS		97.76 232	97.44 241	98.72 221	98.77 297	98.54 195	99.78 22	99.51 87	97.06 210	98.29 280	99.64 147	92.63 263	98.89 315	98.09 147	93.16 323	98.72 226

cascas		97.69 245	97.43 244	98.48 241	98.60 316	97.30 248	98.18 349	99.39 187	92.96 331	98.41 271	98.78 309	93.77 232	99.27 266	98.16 143	98.61 164	98.86 208

test0.0.03 1		97.71 244	97.42 245	98.56 234	98.41 324	97.82 236	98.78 321	98.63 323	97.34 177	98.05 293	98.98 294	94.45 208	98.98 305	95.04 292	97.15 252	98.89 207

TR-MVS		97.76 232	97.41 246	98.82 210	99.06 239	97.87 232	98.87 315	98.56 327	96.63 236	98.68 251	99.22 273	92.49 267	99.65 204	95.40 286	97.79 218	98.95 206

DWT-MVSNet_test		97.53 258	97.40 247	97.93 288	99.03 245	94.86 320	99.57 110	98.63 323	96.59 241	98.36 275	98.79 307	89.32 317	99.74 172	98.14 145	98.16 201	99.20 169

Patchmtry		97.75 236	97.40 247	98.81 211	99.10 233	98.87 147	99.11 268	99.33 222	94.83 299	98.81 233	99.38 236	94.33 212	99.02 300	96.10 270	95.57 279	98.53 301

tfpn200view9		97.72 241	97.38 249	98.72 221	99.69 92	97.96 228	99.50 141	98.73 318	97.83 127	99.17 178	98.45 322	91.67 290	99.83 135	93.22 323	98.18 194	98.37 312

thres400		97.77 231	97.38 249	98.92 178	99.69 92	97.96 228	99.50 141	98.73 318	97.83 127	99.17 178	98.45 322	91.67 290	99.83 135	93.22 323	98.18 194	98.96 199

tpm cat1		97.39 270	97.36 251	97.50 310	99.17 220	93.73 331	99.43 174	99.31 229	91.27 340	98.71 243	99.08 283	94.31 214	99.77 165	96.41 267	98.50 173	99.00 189

FMVSNet2		97.72 241	97.36 251	98.80 213	99.51 140	98.84 151	99.45 165	99.42 175	96.49 244	98.86 230	99.29 265	90.26 307	98.98 305	96.44 265	96.56 259	98.58 298

LFMVS		97.90 210	97.35 253	99.54 79	99.52 138	99.01 124	99.39 194	98.24 334	97.10 201	99.65 55	99.79 76	84.79 347	99.91 76	99.28 28	98.38 178	99.69 81

VDD-MVS		97.73 239	97.35 253	98.88 196	99.47 154	97.12 256	99.34 213	98.85 297	98.19 79	99.67 47	99.85 27	82.98 351	99.92 66	99.49 12	98.32 183	99.60 108

DSMNet-mixed		97.25 274	97.35 253	96.95 318	97.84 331	93.61 335	99.57 110	96.63 360	96.13 279	98.87 225	98.61 317	94.59 202	97.70 342	95.08 291	98.86 155	99.55 119

tpm2		97.44 268	97.34 256	97.74 302	99.15 225	94.36 326	99.45 165	98.94 285	93.45 329	98.90 222	99.44 220	91.35 297	99.59 216	97.31 216	98.07 211	99.29 163

TAPA-MVS	97.07 15	97.74 238	97.34 256	98.94 170	99.70 90	97.53 246	99.25 239	99.51 87	91.90 338	99.30 133	99.63 151	98.78 37	99.64 206	88.09 343	99.87 39	99.65 93
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SixPastTwentyTwo		97.50 263	97.33 258	98.03 280	98.65 310	96.23 295	99.77 24	98.68 321	97.14 194	97.90 296	99.93 4	90.45 305	99.18 284	97.00 233	96.43 262	98.67 253

MS-PatchMatch		97.24 275	97.32 259	96.99 316	98.45 323	93.51 336	98.82 318	99.32 228	97.41 173	98.13 287	99.30 263	88.99 320	99.56 217	95.68 280	99.80 71	97.90 331

v1240		97.69 245	97.32 259	98.79 214	98.85 285	98.43 209	99.48 156	99.36 202	96.11 280	99.27 145	99.36 247	93.76 233	99.24 274	94.46 302	95.23 284	98.70 232

pmmvs5		97.52 259	97.30 261	98.16 276	98.57 318	96.73 279	99.27 229	98.90 293	96.14 278	98.37 274	99.53 187	91.54 295	99.14 285	97.51 202	95.87 273	98.63 276

tpmp4_e23		97.34 271	97.29 262	97.52 308	99.25 204	93.73 331	99.58 104	99.19 259	94.00 319	98.20 283	99.41 227	90.74 304	99.74 172	97.13 226	98.07 211	99.07 183

pm-mvs1		97.68 247	97.28 263	98.88 196	99.06 239	98.62 188	99.50 141	99.45 155	96.32 260	97.87 297	99.79 76	92.47 268	99.35 247	97.54 199	93.54 320	98.67 253

thres200		97.61 254	97.28 263	98.62 228	99.64 111	98.03 224	99.26 237	98.74 309	97.68 145	99.09 193	98.32 324	91.66 292	99.81 148	92.88 329	98.22 190	98.03 324

TESTMET0.1,1		97.55 256	97.27 265	98.40 251	98.93 269	96.53 285	98.67 328	97.61 352	96.96 215	98.64 259	99.28 266	88.63 327	99.45 225	97.30 217	99.38 114	99.21 168

v748		97.52 259	97.23 266	98.41 250	98.69 306	97.23 254	99.87 4	99.45 155	95.72 290	98.51 266	99.53 187	94.13 220	99.30 260	96.78 252	92.39 331	98.70 232

USDC		97.34 271	97.20 267	97.75 301	99.07 237	95.20 314	98.51 338	99.04 276	97.99 110	98.31 278	99.86 23	89.02 319	99.55 219	95.67 281	97.36 245	98.49 303

DTE-MVSNet		97.51 262	97.19 268	98.46 244	98.63 312	98.13 222	99.84 9	99.48 117	96.68 232	97.97 295	99.67 133	92.92 245	98.56 321	96.88 249	92.60 330	98.70 232

test-mter		97.49 265	97.13 269	98.55 236	98.79 290	97.10 257	98.67 328	97.75 342	96.65 234	98.61 263	98.85 302	88.23 332	99.45 225	97.25 218	99.38 114	99.10 174

PAPM		97.59 255	97.09 270	99.07 154	99.06 239	98.26 216	98.30 345	99.10 266	94.88 298	98.08 289	99.34 254	96.27 132	99.64 206	89.87 338	98.92 149	99.31 162

PCF-MVS	97.08 14	97.66 251	97.06 271	99.47 98	99.61 123	99.09 109	98.04 351	99.25 251	91.24 341	98.51 266	99.70 116	94.55 204	99.91 76	92.76 330	99.85 55	99.42 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDDNet		97.55 256	97.02 272	99.16 145	99.49 149	98.12 223	99.38 199	99.30 231	95.35 294	99.68 41	99.90 7	82.62 353	99.93 57	99.31 26	98.13 202	99.42 154

JIA-IIPM		97.50 263	97.02 272	98.93 173	98.73 300	97.80 241	99.30 220	98.97 282	91.73 339	98.91 220	94.86 354	95.10 168	99.71 189	97.58 193	97.98 214	99.28 164

TinyColmap		97.12 277	96.89 274	97.83 296	99.07 237	95.52 307	98.57 335	98.74 309	97.58 153	97.81 300	99.79 76	88.16 333	99.56 217	95.10 290	97.21 249	98.39 311

LP		97.04 279	96.80 275	97.77 300	98.90 274	95.23 313	98.97 301	99.06 274	94.02 318	98.09 288	99.41 227	93.88 228	98.82 316	90.46 336	98.42 177	99.26 165

DI_MVS_plusplus_test		97.45 267	96.79 276	99.44 105	97.76 333	99.04 114	99.21 249	98.61 325	97.74 138	94.01 335	98.83 304	87.38 338	99.83 135	98.63 97	98.90 151	99.44 151

K. test v3		97.10 278	96.79 276	98.01 283	98.72 302	96.33 292	99.87 4	97.05 356	97.59 151	96.16 320	99.80 68	88.71 323	99.04 297	96.69 257	96.55 260	98.65 267

test_normal		97.44 268	96.77 278	99.44 105	97.75 334	99.00 126	99.10 270	98.64 322	97.71 141	93.93 338	98.82 305	87.39 337	99.83 135	98.61 102	98.97 143	99.49 136

TransMVSNet (Re)		97.15 276	96.58 279	98.86 204	99.12 228	98.85 150	99.49 151	98.91 291	95.48 293	97.16 309	99.80 68	93.38 237	99.11 291	94.16 315	91.73 332	98.62 278

MVS		97.28 273	96.55 280	99.48 94	98.78 294	98.95 136	99.27 229	99.39 187	83.53 354	98.08 289	99.54 180	96.97 111	99.87 109	94.23 313	99.16 127	99.63 103

PatchT		97.03 280	96.44 281	98.79 214	98.99 249	98.34 213	99.16 255	99.07 271	92.13 335	99.52 88	97.31 347	94.54 205	98.98 305	88.54 341	98.73 163	99.03 186

FMVSNet1		96.84 281	96.36 282	98.29 259	99.32 190	97.26 251	99.43 174	99.48 117	95.11 296	98.55 265	99.32 260	83.95 350	98.98 305	95.81 276	96.26 266	98.62 278

test_0402		96.64 282	96.24 283	97.85 294	98.85 285	96.43 289	99.44 169	99.26 249	93.52 326	96.98 313	99.52 192	88.52 328	99.20 283	92.58 332	97.50 233	97.93 329

FMVSNet5		96.43 287	96.19 284	97.15 313	99.11 230	95.89 300	99.32 215	99.52 78	94.47 311	98.34 277	99.07 284	87.54 336	97.07 345	92.61 331	95.72 276	98.47 305

UnsupCasMVSNet_eth		96.44 286	96.12 285	97.40 312	98.65 310	95.65 301	99.36 205	99.51 87	97.13 195	96.04 323	98.99 291	88.40 330	98.17 324	96.71 255	90.27 335	98.40 310

pmmvs6		96.53 285	96.09 286	97.82 297	98.69 306	95.47 308	99.37 201	99.47 133	93.46 328	97.41 304	99.78 82	87.06 339	99.33 251	96.92 241	92.70 329	98.65 267

Anonymous20231206		96.22 298	96.03 287	96.79 322	97.31 340	94.14 328	99.63 82	99.08 268	96.17 274	97.04 311	99.06 286	93.94 226	97.76 341	86.96 347	95.06 289	98.47 305

new_pmnet		96.38 291	96.03 287	97.41 311	98.13 329	95.16 317	99.05 279	99.20 256	93.94 320	97.39 305	98.79 307	91.61 294	99.04 297	90.43 337	95.77 275	98.05 321

testpf		95.66 306	96.02 289	94.58 330	98.35 325	92.32 341	97.25 357	97.91 341	92.83 332	97.03 312	98.99 291	88.69 324	98.61 320	95.72 278	97.40 242	92.80 355

test20.03		96.12 301	95.96 290	96.63 323	97.44 336	95.45 309	99.51 136	99.38 193	96.55 242	96.16 320	99.25 270	93.76 233	96.17 350	87.35 346	94.22 310	98.27 315

RPMNet		96.61 283	95.85 291	98.87 200	99.18 215	98.49 204	99.22 246	99.08 268	88.72 350	99.56 75	97.38 345	94.08 223	99.00 303	86.87 348	98.58 167	99.14 171

N_pmnet		94.95 314	95.83 292	92.31 337	98.47 322	79.33 360	99.12 262	92.81 370	93.87 321	97.68 302	99.13 279	93.87 229	99.01 302	91.38 334	96.19 267	98.59 295

Patchmatch-RL test		95.84 304	95.81 293	95.95 327	95.61 345	90.57 345	98.24 346	98.39 330	95.10 297	95.20 325	98.67 312	94.78 189	97.77 340	96.28 269	90.02 336	99.51 132

v17		96.42 288	95.81 293	98.25 266	98.94 264	98.80 168	99.76 27	99.28 243	94.57 305	94.18 329	97.71 332	95.23 163	98.16 325	94.86 294	87.73 344	97.80 334

v18		96.42 288	95.80 295	98.26 262	98.95 261	98.82 161	99.76 27	99.28 243	94.58 304	94.12 330	97.70 333	95.22 164	98.16 325	94.83 296	87.80 342	97.79 339

v16		96.39 290	95.76 296	98.26 262	98.96 259	98.81 163	99.76 27	99.28 243	94.57 305	94.10 331	97.70 333	95.04 170	98.16 325	94.70 298	87.77 343	97.80 334

EG-PatchMatch MVS		95.97 303	95.69 297	96.81 321	97.78 332	92.79 339	99.16 255	98.93 286	96.16 275	94.08 332	99.22 273	82.72 352	99.47 223	95.67 281	97.50 233	98.17 318

v15		96.28 292	95.62 298	98.25 266	98.94 264	98.83 154	99.76 27	99.29 236	94.52 309	94.02 334	97.61 339	95.02 171	98.13 329	94.53 300	86.92 347	97.80 334

V14		96.26 293	95.60 299	98.26 262	98.94 264	98.83 154	99.76 27	99.29 236	94.49 310	93.96 336	97.66 336	94.99 174	98.13 329	94.41 303	86.90 348	97.80 334

v13		96.24 295	95.58 300	98.25 266	98.98 253	98.83 154	99.75 35	99.29 236	94.35 314	93.89 339	97.60 340	95.17 166	98.11 331	94.27 312	86.86 350	97.81 332

v12		96.24 295	95.58 300	98.23 269	98.96 259	98.81 163	99.76 27	99.29 236	94.42 313	93.85 340	97.60 340	95.12 167	98.09 332	94.32 309	86.85 351	97.80 334

V9		96.25 294	95.58 300	98.26 262	98.94 264	98.83 154	99.75 35	99.29 236	94.45 312	93.96 336	97.62 338	94.94 176	98.14 328	94.40 304	86.87 349	97.81 332

v11		96.23 297	95.57 303	98.21 272	98.93 269	98.83 154	99.72 42	99.29 236	94.29 315	94.05 333	97.64 337	94.88 183	98.04 333	92.89 328	88.43 340	97.77 340

PVSNet_0	94.43 19	96.09 302	95.47 304	97.94 287	99.31 191	94.34 327	97.81 352	99.70 15	97.12 197	97.46 303	98.75 310	89.71 314	99.79 155	97.69 187	81.69 355	99.68 85

X-MVStestdata		96.55 284	95.45 305	99.87 7	99.85 24	99.83 8	99.69 48	99.68 19	98.98 19	99.37 118	64.01 369	98.81 34	99.94 42	98.79 80	99.86 50	99.84 13

IB-MVS	95.67 18	96.22 298	95.44 306	98.57 232	99.21 208	96.70 280	98.65 331	97.74 344	96.71 230	97.27 306	98.54 320	86.03 341	99.92 66	98.47 122	86.30 352	99.10 174
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
gg-mvs-nofinetune		96.17 300	95.32 307	98.73 220	98.79 290	98.14 221	99.38 199	94.09 365	91.07 343	98.07 292	91.04 360	89.62 316	99.35 247	96.75 253	99.09 133	98.68 242

testus		94.61 315	95.30 308	92.54 336	96.44 343	84.18 352	98.36 341	99.03 277	94.18 316	96.49 316	98.57 319	88.74 322	95.09 354	87.41 345	98.45 175	98.36 314

MVS-HIRNet		95.75 305	95.16 309	97.51 309	99.30 192	93.69 334	98.88 314	95.78 361	85.09 353	98.78 237	92.65 356	91.29 298	99.37 240	94.85 295	99.85 55	99.46 147

MIMVSNet1		95.51 307	95.04 310	96.92 319	97.38 337	95.60 302	99.52 132	99.50 102	93.65 324	96.97 314	99.17 276	85.28 345	96.56 349	88.36 342	95.55 280	98.60 294

CMPMVS	69.68 23	94.13 319	94.90 311	91.84 338	97.24 341	80.01 359	98.52 337	99.48 117	89.01 348	91.99 346	99.67 133	85.67 343	99.13 288	95.44 284	97.03 253	96.39 348
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		95.34 311	94.73 312	97.15 313	95.53 347	95.94 299	99.35 210	99.10 266	95.13 295	93.55 341	97.54 343	88.15 334	97.91 337	94.58 299	89.69 338	97.61 342

MDA-MVSNet_test_wron		95.45 308	94.60 313	98.01 283	98.16 328	97.21 255	99.11 268	99.24 252	93.49 327	80.73 359	98.98 294	93.02 242	98.18 323	94.22 314	94.45 305	98.64 269

TDRefinement		95.42 309	94.57 314	97.97 286	89.83 360	96.11 297	99.48 156	98.75 306	96.74 228	96.68 315	99.88 15	88.65 326	99.71 189	98.37 130	82.74 354	98.09 319

YYNet1		95.36 310	94.51 315	97.92 289	97.89 330	97.10 257	99.10 270	99.23 253	93.26 330	80.77 358	99.04 288	92.81 248	98.02 334	94.30 310	94.18 311	98.64 269

test2356		94.07 321	94.46 316	92.89 334	95.18 348	86.13 350	97.60 355	99.06 274	93.61 325	96.15 322	98.28 325	85.60 344	93.95 356	86.68 349	98.00 213	98.59 295

new-patchmatchnet		94.48 316	94.08 317	95.67 328	95.08 349	92.41 340	99.18 253	99.28 243	94.55 308	93.49 342	97.37 346	87.86 335	97.01 346	91.57 333	88.36 341	97.61 342

MDA-MVSNet-bldmvs		94.96 313	93.98 318	97.92 289	98.24 327	97.27 250	99.15 258	99.33 222	93.80 322	80.09 360	99.03 289	88.31 331	97.86 339	93.49 321	94.36 307	98.62 278

OpenMVS_ROB	92.34 20	94.38 318	93.70 319	96.41 326	97.38 337	93.17 337	99.06 277	98.75 306	86.58 351	94.84 328	98.26 326	81.53 354	99.32 254	89.01 340	97.87 217	96.76 346

Test4		95.05 312	93.67 320	99.22 141	96.07 344	98.94 139	99.20 251	99.27 248	97.71 141	89.96 352	97.59 342	66.18 359	99.25 272	98.06 155	98.96 145	99.47 143

test1235678		92.91 324	93.30 321	91.71 340	93.14 354	83.01 354	98.75 324	98.58 326	92.80 333	92.45 344	97.91 329	88.51 329	93.54 357	82.26 353	95.35 282	98.59 295

pmmvs3		94.09 320	93.25 322	96.60 324	94.76 350	94.49 324	98.92 310	98.18 337	89.66 345	96.48 317	98.06 327	86.28 340	97.33 344	89.68 339	87.20 346	97.97 327

testing_2		94.44 317	92.93 323	98.98 164	94.16 351	99.00 126	99.42 181	99.28 243	96.60 239	84.86 354	96.84 348	70.91 356	99.27 266	98.23 139	96.08 269	98.68 242

UnsupCasMVSNet_bld		93.53 322	92.51 324	96.58 325	97.38 337	93.82 330	98.24 346	99.48 117	91.10 342	93.10 343	96.66 349	74.89 355	98.37 322	94.03 316	87.71 345	97.56 344

PM-MVS		92.96 323	92.23 325	95.14 329	95.61 345	89.98 347	99.37 201	98.21 335	94.80 300	95.04 327	97.69 335	65.06 360	97.90 338	94.30 310	89.98 337	97.54 345

1111		92.30 325	92.21 326	92.55 335	93.30 352	86.27 348	99.15 258	98.74 309	91.94 336	90.85 349	97.82 330	84.18 348	95.21 352	79.65 355	94.27 309	96.19 349

test12356		91.74 326	92.19 327	90.37 343	91.22 356	82.41 355	98.61 332	98.28 332	90.66 344	91.82 347	97.92 328	84.90 346	92.61 358	81.64 354	94.66 300	96.09 350

Gipuma		90.99 327	90.15 328	93.51 331	98.73 300	90.12 346	93.98 361	99.45 155	79.32 356	92.28 345	94.91 353	69.61 357	97.98 336	87.42 344	95.67 277	92.45 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		87.91 328	87.80 329	88.24 344	87.68 363	77.50 362	99.07 273	97.66 351	89.27 346	86.47 353	96.22 351	68.35 358	92.49 360	76.63 359	88.82 339	94.72 353

.test1245		83.42 332	86.17 330	75.15 354	93.30 352	86.27 348	99.15 258	98.74 309	91.94 336	90.85 349	97.82 330	84.18 348	95.21 352	79.65 355	39.90 365	43.98 366

FPMVS		84.93 331	85.65 331	82.75 351	86.77 364	63.39 369	98.35 343	98.92 288	74.11 358	83.39 356	98.98 294	50.85 364	92.40 361	84.54 351	94.97 291	92.46 356

PMMVS2		86.87 329	85.37 332	91.35 342	90.21 359	83.80 353	98.89 313	97.45 354	83.13 355	91.67 348	95.03 352	48.49 365	94.70 355	85.86 350	77.62 356	95.54 351

LCM-MVSNet		86.80 330	85.22 333	91.53 341	87.81 362	80.96 358	98.23 348	98.99 280	71.05 359	90.13 351	96.51 350	48.45 366	96.88 347	90.51 335	85.30 353	96.76 346

tmp_tt		82.80 334	81.52 334	86.66 345	66.61 371	68.44 368	92.79 363	97.92 339	68.96 361	80.04 361	99.85 27	85.77 342	96.15 351	97.86 167	43.89 364	95.39 352

no-one		83.04 333	80.12 335	91.79 339	89.44 361	85.65 351	99.32 215	98.32 331	89.06 347	79.79 362	89.16 362	44.86 367	96.67 348	84.33 352	46.78 363	93.05 354

E-PMN		80.61 335	79.88 336	82.81 350	90.75 358	76.38 364	97.69 353	95.76 362	66.44 363	83.52 355	92.25 357	62.54 362	87.16 365	68.53 363	61.40 359	84.89 364

EMVS		80.02 336	79.22 337	82.43 352	91.19 357	76.40 363	97.55 356	92.49 372	66.36 364	83.01 357	91.27 358	64.63 361	85.79 366	65.82 364	60.65 360	85.08 363

PNet_i23d		79.43 337	77.68 338	84.67 347	86.18 365	71.69 367	96.50 359	93.68 366	75.17 357	71.33 363	91.18 359	32.18 370	90.62 362	78.57 358	74.34 357	91.71 359

PMVS	70.75 22	75.98 340	74.97 339	79.01 353	70.98 370	55.18 370	93.37 362	98.21 335	65.08 365	61.78 367	93.83 355	21.74 374	92.53 359	78.59 357	91.12 334	89.34 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		77.30 338	74.86 340	84.62 348	75.88 369	77.61 361	97.63 354	93.15 369	88.81 349	64.27 365	89.29 361	36.51 368	83.93 367	75.89 360	52.31 362	92.33 358

MVE	76.82 21	76.91 339	74.31 341	84.70 346	85.38 367	76.05 365	96.88 358	93.17 368	67.39 362	71.28 364	89.01 363	21.66 375	87.69 364	71.74 362	72.29 358	90.35 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		74.42 341	71.19 342	84.14 349	76.16 368	74.29 366	96.00 360	92.57 371	69.57 360	63.84 366	87.49 364	21.98 372	88.86 363	75.56 361	57.50 361	89.26 362

v1.0		41.40 342	55.20 343	0.00 359	99.81 33	0.00 374	0.00 365	99.48 117	97.97 112	99.77 26	99.78 82	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

testmvs		39.17 345	43.78 344	25.37 358	36.04 373	16.84 373	98.36 341	26.56 373	20.06 367	38.51 369	67.32 365	29.64 371	15.30 370	37.59 366	39.90 365	43.98 366

pcd1.5k->3k		40.85 343	43.49 345	32.93 356	98.95 261	0.00 374	0.00 365	99.53 74	0.00 369	0.00 371	0.27 371	95.32 157	0.00 371	0.00 368	97.30 246	98.80 212

test123		39.01 346	42.50 346	28.53 357	39.17 372	20.91 372	98.75 324	19.17 375	19.83 368	38.57 368	66.67 366	33.16 369	15.42 369	37.50 367	29.66 367	49.26 365

wuyk23d		40.18 344	41.29 347	36.84 355	86.18 365	49.12 371	79.73 364	22.81 374	27.64 366	25.46 370	28.45 370	21.98 372	48.89 368	55.80 365	23.56 368	12.51 368

cdsmvs_eth3d_5k		24.64 347	32.85 348	0.00 359	0.00 374	0.00 374	0.00 365	99.51 87	0.00 369	0.00 371	99.56 174	96.58 123	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		8.30 348	11.06 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	99.58 168	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		8.27 349	11.03 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.27 371	99.01 12	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.02 350	0.03 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.27 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.02 350	0.03 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.27 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.02 350	0.03 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.27 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.02 350	0.03 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.27 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.02 350	0.03 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.27 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																99.52 127

test_part2					99.81 33	99.83 8				99.77 26

test_part1				0.00 359		0.00 374	0.00 365	99.48 117				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												94.86 184				99.52 127

sam_mvs												94.72 197

semantic-postprocess				98.06 279	99.57 130	96.36 291		99.49 107	97.18 191	98.71 243	99.72 111	92.70 255	99.14 285	97.44 210	95.86 274	98.67 253

ambc				93.06 333	92.68 355	82.36 356	98.47 339	98.73 318		95.09 326	97.41 344	55.55 363	99.10 293	96.42 266	91.32 333	97.71 341

MTGPA								99.47 133

test_post1							99.23 242				65.14 368	94.18 219	99.71 189	97.58 193

test_post											65.99 367	94.65 201	99.73 179

patchmatchnet-post											98.70 311	94.79 188	99.74 172

GG-mvs-BLEND				98.45 245	98.55 319	98.16 219	99.43 174	93.68 366		97.23 307	98.46 321	89.30 318	99.22 278	95.43 285	98.22 190	97.98 326

MTMP							99.54 126	98.88 295

gm-plane-assit					98.54 320	92.96 338			94.65 303		99.15 277		99.64 206	97.56 197

test9_res														97.49 204	99.72 86	99.75 56

TEST9					99.67 96	99.65 41	99.05 279	99.41 177	96.22 270	98.95 215	99.49 202	98.77 40	99.91 76

test_8					99.67 96	99.61 46	99.03 285	99.41 177	96.28 263	98.93 218	99.48 208	98.76 42	99.91 76

agg_prior2														97.21 220	99.73 85	99.75 56

agg_prior					99.67 96	99.62 44		99.40 184		98.87 225			99.91 76

TestCases				99.31 119	99.86 20	98.48 206		99.61 33	97.85 124	99.36 121	99.85 27	95.95 137	99.85 120	96.66 259	99.83 64	99.59 112

test_prior4						99.56 53	98.99 294

test_prior2							98.96 303		98.34 67	99.01 204	99.52 192	98.68 51		97.96 159	99.74 82

test_prior				99.68 53	99.67 96	99.48 66		99.56 50					99.83 135			99.74 61

旧先验2							98.96 303		96.70 231	99.47 97			99.94 42	98.19 140

新几何2							99.01 292

新几何1				99.75 41	99.75 58	99.59 50		99.54 64	96.76 227	99.29 137	99.64 147	98.43 64	99.94 42	96.92 241	99.66 98	99.72 72

旧先验1					99.74 69	99.59 50		99.54 64			99.69 122	98.47 61			99.68 96	99.73 66

无先验							98.99 294	99.51 87	96.89 221				99.93 57	97.53 200		99.72 72

原ACMM2							98.95 307

原ACMM1				99.65 60	99.73 74	99.33 81		99.47 133	97.46 164	99.12 184	99.66 138	98.67 53	99.91 76	97.70 186	99.69 93	99.71 79

test222					99.75 58	99.49 65	98.91 312	99.49 107	96.42 254	99.34 128	99.65 139	98.28 74			99.69 93	99.72 72

testdata2													99.95 34	96.67 258

segment_acmp												98.96 21

testdata				99.54 79	99.75 58	98.95 136		99.51 87	97.07 208	99.43 104	99.70 116	98.87 29	99.94 42	97.76 177	99.64 101	99.72 72

testdata1							98.85 316		98.32 70

test12				99.75 41	99.64 111	99.61 46		99.29 236		99.21 168		98.38 68	99.89 97		99.74 82	99.74 61

plane_prior7					99.29 195	97.03 265

plane_prior6					99.27 200	96.98 269						92.71 253

plane_prior5								99.47 133					99.69 198	97.78 174	97.63 221	98.67 253

plane_prior4											99.61 160

plane_prior3						97.00 267			98.69 47	99.11 186

plane_prior2							99.39 194		98.97 22

plane_prior1					99.26 202

plane_prior						96.97 270	99.21 249		98.45 60						97.60 224

n2								0.00 376

nn								0.00 376

door-mid								98.05 338

lessismore_v0				97.79 299	98.69 306	95.44 310		94.75 363		95.71 324	99.87 20	88.69 324	99.32 254	95.89 274	94.93 293	98.62 278

LGP-MVS_train				98.49 239	99.33 183	97.05 263		99.55 57	97.46 164	99.24 158	99.83 40	92.58 264	99.72 183	98.09 147	97.51 231	98.68 242

test11								99.35 206

door								97.92 339

HQP5-MVS						96.83 275

HQP-NCC					99.19 212		98.98 298		98.24 73	98.66 252

ACMP_Plane					99.19 212		98.98 298		98.24 73	98.66 252

BP-MVS														97.19 222

HQP4-MVS										98.66 252			99.64 206			98.64 269

HQP3-MVS								99.39 187							97.58 226

HQP2-MVS												92.47 268

NP-MVS					99.23 205	96.92 273					99.40 231

MDTV_nov1_ep13_2view						95.18 316	99.35 210		96.84 224	99.58 71		95.19 165		97.82 170		99.46 147

ACMMP++_ref															97.19 250

ACMMP++															97.43 241

Test By Simon												98.75 45

ITE_SJBPF				98.08 278	99.29 195	96.37 290		98.92 288	98.34 67	98.83 232	99.75 96	91.09 300	99.62 212	95.82 275	97.40 242	98.25 317

DeepMVS_CX				93.34 332	99.29 195	82.27 357		99.22 254	85.15 352	96.33 318	99.05 287	90.97 302	99.73 179	93.57 319	97.77 219	98.01 325

High-resolution multi-view benchmark