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 62	99.78 35	99.14 98	99.60 88	99.45 149	99.01 13	99.90 1	99.83 37	98.98 18	99.93 55	99.59 2	99.95 6	99.86 5

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 40	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 62	99.78 35	99.15 97	99.61 86	99.45 149	99.01 13	99.89 2	99.82 44	99.01 11	99.92 63	99.56 5	99.95 6	99.85 8

Regformer-4		99.59 2	99.54 4	99.73 45	99.76 43	99.41 71	99.58 96	99.49 104	99.02 10	99.88 3	99.80 64	99.00 17	99.94 40	99.45 15	99.92 12	99.84 12

Regformer-3		99.57 6	99.53 5	99.68 50	99.76 43	99.29 82	99.58 96	99.44 157	99.01 13	99.87 6	99.80 64	98.97 19	99.91 72	99.44 16	99.92 12	99.83 23

SD-MVS		99.41 32	99.52 6	99.05 142	99.74 64	99.68 31	99.46 150	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 324	98.72 79	99.93 11	99.77 50

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 27	99.91 1	99.66 35	99.63 77	99.39 179	98.91 29	99.78 22	99.85 26	99.36 2	99.94 40	98.84 66	99.88 34	99.82 31

Regformer-1		99.53 9	99.47 8	99.72 47	99.71 77	99.44 68	99.49 138	99.46 138	98.95 24	99.83 11	99.76 85	99.01 11	99.93 55	99.17 36	99.87 38	99.80 40

Regformer-2		99.54 7	99.47 8	99.75 38	99.71 77	99.52 59	99.49 138	99.49 104	98.94 26	99.83 11	99.76 85	99.01 11	99.94 40	99.15 38	99.87 38	99.80 40

MSLP-MVS++		99.46 21	99.47 8	99.44 97	99.60 114	99.16 94	99.41 171	99.71 13	98.98 19	99.45 88	99.78 77	99.19 4	99.54 204	99.28 27	99.84 57	99.63 99

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 106	99.74 95	98.81 34	99.94 40	98.79 72	99.86 48	99.84 12

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 12	99.59 90	99.51 85	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		99.48 17	99.42 11	99.65 57	99.72 72	99.40 73	99.05 263	99.66 25	99.14 6	99.57 66	99.80 64	98.46 60	99.94 40	99.57 4	99.84 57	99.60 103
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 17	99.91 1	99.79 17	99.76 27	99.56 48	97.72 132	99.76 27	99.75 90	99.13 6	99.92 63	99.07 44	99.92 12	99.85 8

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 63	99.47 128	98.79 40	99.68 36	99.81 53	98.43 62	99.97 11	98.88 57	99.90 24	99.83 23

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 35	99.63 104	99.59 47	99.36 189	99.46 138	99.07 9	99.79 18	99.82 44	98.85 31	99.92 63	98.68 84	99.87 38	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 12	99.87 15	99.80 13	99.66 63	99.67 22	98.15 80	99.68 36	99.69 112	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 296	99.60 114	91.75 324	98.61 314	99.44 157	99.35 1	99.83 11	99.85 26	98.70 49	99.81 135	99.02 48	99.91 17	99.81 35

MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 189	99.47 128	98.79 40	99.68 36	99.81 53	98.43 62	99.97 11	98.88 57	99.90 24	99.83 23

ACMMPR		99.49 13	99.36 19	99.86 12	99.87 15	99.79 17	99.66 63	99.67 22	98.15 80	99.67 42	99.69 112	98.95 24	99.96 19	98.69 82	99.87 38	99.84 12

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 104	99.67 88	98.61 181	99.07 257	99.33 213	99.00 17	99.82 14	99.81 53	99.06 8	99.84 115	99.09 42	99.42 107	99.65 89

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 13	99.65 73	99.66 25	98.13 82	99.66 47	99.68 117	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 17	99.76 43	99.83 7	99.63 77	99.54 62	98.36 65	99.79 18	99.82 44	98.86 30	99.95 33	98.62 90	99.81 67	99.78 48

ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 147	99.48 113	98.05 98	99.76 27	99.86 22	98.82 33	99.93 55	98.82 71	99.91 17	99.84 12

MVS_111021_LR		99.41 32	99.33 25	99.65 57	99.77 40	99.51 61	98.94 293	99.85 6	98.82 35	99.65 50	99.74 95	98.51 57	99.80 139	98.83 68	99.89 32	99.64 95

PS-MVSNAJ		99.32 42	99.32 26	99.30 113	99.57 119	98.94 130	98.97 285	99.46 138	98.92 28	99.71 30	99.24 252	99.01 11	99.98 5	99.35 18	99.66 96	98.97 179

CP-MVS		99.45 22	99.32 26	99.85 17	99.83 28	99.75 22	99.69 45	99.52 76	98.07 93	99.53 75	99.63 139	98.93 26	99.97 11	98.74 75	99.91 17	99.83 23

MVS_111021_HR		99.41 32	99.32 26	99.66 53	99.72 72	99.47 65	98.95 291	99.85 6	98.82 35	99.54 74	99.73 98	98.51 57	99.74 155	98.91 56	99.88 34	99.77 50

CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 199	99.71 41	99.66 25	98.11 86	99.41 97	99.80 64	98.37 68	99.96 19	98.99 50	99.96 5	99.72 70

ACMMP		99.45 22	99.32 26	99.82 24	99.89 8	99.67 33	99.62 80	99.69 18	98.12 84	99.63 52	99.84 35	98.73 47	99.96 19	98.55 103	99.83 62	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 22	99.31 31	99.86 12	99.87 15	99.78 21	99.58 96	99.65 30	97.84 118	99.71 30	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

abl_6		99.44 25	99.31 31	99.83 22	99.85 23	99.75 22	99.66 63	99.59 38	98.13 82	99.82 14	99.81 53	98.60 55	99.96 19	98.46 111	99.88 34	99.79 44

MCST-MVS		99.43 27	99.30 33	99.82 24	99.79 34	99.74 25	99.29 207	99.40 176	98.79 40	99.52 77	99.62 144	98.91 27	99.90 84	98.64 87	99.75 78	99.82 31

mPP-MVS		99.44 25	99.30 33	99.86 12	99.88 11	99.79 17	99.69 45	99.48 113	98.12 84	99.50 80	99.75 90	98.78 37	99.97 11	98.57 97	99.89 32	99.83 23

CNVR-MVS		99.42 29	99.30 33	99.78 33	99.62 108	99.71 27	99.26 221	99.52 76	98.82 35	99.39 102	99.71 103	98.96 20	99.85 109	98.59 94	99.80 69	99.77 50

UA-Net		99.42 29	99.29 36	99.80 29	99.62 108	99.55 52	99.50 130	99.70 15	98.79 40	99.77 23	99.96 1	97.45 92	99.96 19	98.92 55	99.90 24	99.89 2

#test#		99.43 27	99.29 36	99.86 12	99.87 15	99.80 13	99.55 114	99.67 22	97.83 119	99.68 36	99.69 112	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

HPM-MVS		99.42 29	99.28 38	99.83 22	99.90 3	99.72 26	99.81 15	99.54 62	97.59 141	99.68 36	99.63 139	98.91 27	99.94 40	98.58 95	99.91 17	99.84 12

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 66	99.86 20	99.07 104	99.47 147	99.93 2	97.66 139	99.71 30	99.86 22	97.73 87	99.96 19	99.47 13	99.82 66	99.79 44

xiu_mvs_v1_base_debu		99.29 46	99.27 40	99.34 105	99.63 104	98.97 122	99.12 246	99.51 85	98.86 31	99.84 8	99.47 195	98.18 75	99.99 1	99.50 8	99.31 114	99.08 165

xiu_mvs_v1_base		99.29 46	99.27 40	99.34 105	99.63 104	98.97 122	99.12 246	99.51 85	98.86 31	99.84 8	99.47 195	98.18 75	99.99 1	99.50 8	99.31 114	99.08 165

xiu_mvs_v1_base_debi		99.29 46	99.27 40	99.34 105	99.63 104	98.97 122	99.12 246	99.51 85	98.86 31	99.84 8	99.47 195	98.18 75	99.99 1	99.50 8	99.31 114	99.08 165

HSP-MVS		99.41 32	99.26 43	99.85 17	99.89 8	99.80 13	99.67 56	99.37 192	98.70 45	99.77 23	99.49 185	98.21 74	99.95 33	98.46 111	99.77 75	99.81 35

xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 124	98.91 135	99.02 272	99.45 149	98.80 39	99.71 30	99.26 250	98.94 25	99.98 5	99.34 22	99.23 118	98.98 178

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 53	99.84 6	99.43 160	99.51 85	98.68 47	99.27 132	99.53 172	98.64 53	99.96 19	98.44 113	99.80 69	99.79 44

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 203	99.52 76	97.18 178	99.60 59	99.79 72	98.79 36	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 40	99.19 47	99.79 32	99.61 112	99.65 38	99.30 203	99.48 113	98.86 31	99.21 153	99.63 139	98.72 48	99.90 84	98.25 125	99.63 101	99.80 40

DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 89	99.62 80	99.55 55	98.94 26	99.63 52	99.95 2	95.82 137	99.94 40	99.37 17	99.97 3	99.73 64
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 44	99.17 49	99.70 49	99.56 122	99.52 59	99.58 96	99.80 8	97.12 184	99.62 55	99.73 98	98.58 56	99.90 84	98.61 92	99.91 17	99.68 82

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 11	99.66 63	99.46 138	98.09 89	99.48 84	99.74 95	98.29 71	99.96 19	97.93 148	99.87 38	99.82 31

CANet		99.25 52	99.14 51	99.59 68	99.41 148	99.16 94	99.35 193	99.57 44	98.82 35	99.51 79	99.61 147	96.46 118	99.95 33	99.59 2	99.98 2	99.65 89

CHOSEN 280x420		99.12 67	99.13 52	99.08 138	99.66 98	97.89 215	98.43 321	99.71 13	98.88 30	99.62 55	99.76 85	96.63 115	99.70 179	99.46 14	99.99 1	99.66 86

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 127	98.94 130	99.88 1	99.46 138	97.55 146	99.80 16	99.65 128	97.39 93	99.28 246	99.03 46	99.85 52	99.65 89

LS3D		99.27 49	99.12 53	99.74 43	99.18 197	99.75 22	99.56 109	99.57 44	98.45 59	99.49 83	99.85 26	97.77 86	99.94 40	98.33 121	99.84 57	99.52 118

CHOSEN 1792x2688		99.19 55	99.10 55	99.45 94	99.89 8	98.52 188	99.39 178	99.94 1	98.73 44	99.11 169	99.89 10	95.50 144	99.94 40	99.50 8	99.97 3	99.89 2

APD-MVS		99.27 49	99.08 56	99.84 21	99.75 53	99.79 17	99.50 130	99.50 99	97.16 180	99.77 23	99.82 44	98.78 37	99.94 40	97.56 183	99.86 48	99.80 40
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.12 67	99.08 56	99.24 126	99.46 139	98.55 183	99.51 125	99.46 138	98.09 89	99.45 88	99.82 44	98.34 69	99.51 205	98.70 80	98.93 141	99.67 85

test_prior3		99.21 54	99.05 58	99.68 50	99.67 88	99.48 63	98.96 287	99.56 48	98.34 66	99.01 187	99.52 177	98.68 50	99.83 122	97.96 145	99.74 80	99.74 59

sss		99.17 58	99.05 58	99.53 79	99.62 108	98.97 122	99.36 189	99.62 31	97.83 119	99.67 42	99.65 128	97.37 96	99.95 33	99.19 33	99.19 121	99.68 82

3Dnovator	97.25 9	99.24 53	99.05 58	99.81 27	99.12 210	99.66 35	99.84 9	99.74 10	99.09 8	98.92 202	99.90 7	95.94 132	99.98 5	98.95 53	99.92 12	99.79 44

F-COLMAP		99.19 55	99.04 61	99.64 62	99.78 35	99.27 85	99.42 167	99.54 62	97.29 169	99.41 97	99.59 152	98.42 65	99.93 55	98.19 127	99.69 91	99.73 64

OMC-MVS		99.08 77	99.04 61	99.20 130	99.67 88	98.22 202	99.28 210	99.52 76	98.07 93	99.66 47	99.81 53	97.79 85	99.78 148	97.79 159	99.81 67	99.60 103

jason		99.13 62	99.03 63	99.45 94	99.46 139	98.87 138	99.12 246	99.26 239	98.03 101	99.79 18	99.65 128	97.02 103	99.85 109	99.02 48	99.90 24	99.65 89
jason: jason.
CDS-MVSNet		99.09 75	99.03 63	99.25 123	99.42 145	98.73 166	99.45 151	99.46 138	98.11 86	99.46 87	99.77 82	98.01 80	99.37 223	98.70 80	98.92 143	99.66 86
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
API-MVS		99.04 82	99.03 63	99.06 140	99.40 153	99.31 81	99.55 114	99.56 48	98.54 53	99.33 117	99.39 217	98.76 42	99.78 148	96.98 221	99.78 73	98.07 300

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 119	98.63 176	99.07 257	99.34 205	98.99 18	99.61 57	99.82 44	97.98 81	99.87 101	97.00 219	99.80 69	99.85 8

lupinMVS		99.13 62	99.01 67	99.46 93	99.51 127	98.94 130	99.05 263	99.16 250	97.86 114	99.80 16	99.56 161	97.39 93	99.86 104	98.94 54	99.85 52	99.58 109

mvs_anonymous		99.03 84	98.99 68	99.16 132	99.38 156	98.52 188	99.51 125	99.38 185	97.79 124	99.38 104	99.81 53	97.30 97	99.45 209	99.35 18	98.99 135	99.51 121

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 100	99.06 105	99.81 15	99.33 213	97.43 157	99.60 59	99.88 14	97.14 100	99.84 115	99.13 39	98.94 140	99.69 78

CNLPA		99.14 61	98.99 68	99.59 68	99.58 117	99.41 71	99.16 239	99.44 157	98.45 59	99.19 159	99.49 185	98.08 78	99.89 92	97.73 167	99.75 78	99.48 127

MVS_Test		99.10 74	98.97 71	99.48 88	99.49 134	99.14 98	99.67 56	99.34 205	97.31 167	99.58 63	99.76 85	97.65 89	99.82 131	98.87 61	99.07 130	99.46 134

PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 43	98.79 161	98.78 303	99.91 3	96.74 211	99.67 42	99.49 185	97.53 90	99.88 99	98.98 51	99.85 52	99.60 103

Vis-MVSNet		99.12 67	98.97 71	99.56 74	99.78 35	99.10 101	99.68 54	99.66 25	98.49 56	99.86 7	99.87 19	94.77 184	99.84 115	99.19 33	99.41 108	99.74 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 202	99.68 31	99.81 15	99.51 85	99.20 4	98.72 224	99.89 10	95.68 141	99.97 11	98.86 64	99.86 48	99.81 35

DP-MVS Recon		99.12 67	98.95 75	99.65 57	99.74 64	99.70 29	99.27 213	99.57 44	96.40 240	99.42 95	99.68 117	98.75 45	99.80 139	97.98 144	99.72 84	99.44 137

DP-MVS		99.16 60	98.95 75	99.78 33	99.77 40	99.53 56	99.41 171	99.50 99	97.03 197	99.04 184	99.88 14	97.39 93	99.92 63	98.66 85	99.90 24	99.87 4

PS-MVSNAJss		98.92 95	98.92 77	98.90 171	98.78 276	98.53 185	99.78 22	99.54 62	98.07 93	99.00 194	99.76 85	99.01 11	99.37 223	99.13 39	97.23 233	98.81 197

HyFIR lowres test		99.11 71	98.92 77	99.65 57	99.90 3	99.37 74	99.02 272	99.91 3	97.67 138	99.59 62	99.75 90	95.90 134	99.73 163	99.53 6	99.02 133	99.86 5

CDPH-MVS		99.13 62	98.91 79	99.80 29	99.75 53	99.71 27	99.15 242	99.41 169	96.60 222	99.60 59	99.55 164	98.83 32	99.90 84	97.48 191	99.83 62	99.78 48

VNet		99.11 71	98.90 80	99.73 45	99.52 125	99.56 50	99.41 171	99.39 179	99.01 13	99.74 29	99.78 77	95.56 142	99.92 63	99.52 7	98.18 182	99.72 70

CPTT-MVS		99.11 71	98.90 80	99.74 43	99.80 33	99.46 66	99.59 90	99.49 104	97.03 197	99.63 52	99.69 112	97.27 98	99.96 19	97.82 156	99.84 57	99.81 35

Effi-MVS+-dtu		98.78 113	98.89 82	98.47 226	99.33 165	96.91 257	99.57 102	99.30 222	98.47 57	99.41 97	98.99 272	96.78 109	99.74 155	98.73 77	99.38 109	98.74 209

WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 108	99.16 94	99.37 185	99.56 48	98.04 99	99.53 75	99.62 144	96.84 107	99.94 40	98.85 65	98.49 166	99.72 70

CANet_DTU		98.97 92	98.87 84	99.25 123	99.33 165	98.42 197	99.08 256	99.30 222	99.16 5	99.43 92	99.75 90	95.27 150	99.97 11	98.56 100	99.95 6	99.36 145

1121		99.09 75	98.87 84	99.75 38	99.74 64	99.60 45	99.27 213	99.48 113	96.82 209	99.25 139	99.65 128	98.38 66	99.93 55	97.53 186	99.67 95	99.73 64

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 69	99.32 78	99.75 34	99.20 246	98.02 102	99.56 67	99.86 22	96.54 117	99.67 184	98.09 134	99.13 124	99.73 64

MVS_0304		99.06 79	98.86 87	99.66 53	99.51 127	99.36 75	99.22 230	99.51 85	98.95 24	99.58 63	99.65 128	93.74 226	99.98 5	99.66 1	99.95 6	99.64 95

canonicalmvs		99.02 85	98.86 87	99.51 85	99.42 145	99.32 78	99.80 19	99.48 113	98.63 48	99.31 119	98.81 287	97.09 101	99.75 154	99.27 29	97.90 201	99.47 131

PLC	97.94 4	99.02 85	98.85 89	99.53 79	99.66 98	99.01 115	99.24 225	99.52 76	96.85 207	99.27 132	99.48 191	98.25 73	99.91 72	97.76 163	99.62 102	99.65 89
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs-test1		98.86 100	98.84 90	98.89 173	99.33 165	97.77 225	99.44 155	99.30 222	98.47 57	99.10 172	99.43 204	96.78 109	99.95 33	98.73 77	99.02 133	98.96 185

PAPM_NR		99.04 82	98.84 90	99.66 53	99.74 64	99.44 68	99.39 178	99.38 185	97.70 135	99.28 128	99.28 247	98.34 69	99.85 109	96.96 223	99.45 105	99.69 78

PVSNet	96.02 17	98.85 106	98.84 90	98.89 173	99.73 69	97.28 232	98.32 325	99.60 35	97.86 114	99.50 80	99.57 159	96.75 112	99.86 104	98.56 100	99.70 90	99.54 113

Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 212	99.41 148	96.99 251	99.52 121	99.49 104	98.11 86	99.24 144	99.34 236	96.96 105	99.79 142	97.95 147	99.45 105	99.02 174

PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 143	99.76 43	98.79 161	99.28 210	99.91 3	97.42 159	99.67 42	99.37 222	97.53 90	99.88 99	98.98 51	97.29 232	98.42 288

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 122	99.54 53	99.18 237	99.70 15	98.18 79	99.35 113	99.63 139	96.32 122	99.90 84	97.48 191	99.77 75	99.55 111

MSDG		98.98 90	98.80 94	99.53 79	99.76 43	99.19 91	98.75 306	99.55 55	97.25 172	99.47 85	99.77 82	97.82 84	99.87 101	96.93 226	99.90 24	99.54 113

train_agg		99.02 85	98.77 97	99.77 35	99.67 88	99.65 38	99.05 263	99.41 169	96.28 246	98.95 198	99.49 185	98.76 42	99.91 72	97.63 176	99.72 84	99.75 54

1112_ss		98.98 90	98.77 97	99.59 68	99.68 87	99.02 113	99.25 223	99.48 113	97.23 175	99.13 165	99.58 155	96.93 106	99.90 84	98.87 61	98.78 153	99.84 12

agg_prior1		99.01 88	98.76 99	99.76 37	99.67 88	99.62 41	98.99 278	99.40 176	96.26 249	98.87 208	99.49 185	98.77 40	99.91 72	97.69 173	99.72 84	99.75 54

COLMAP_ROB	97.56 6	98.86 100	98.75 100	99.17 131	99.88 11	98.53 185	99.34 196	99.59 38	97.55 146	98.70 231	99.89 10	95.83 136	99.90 84	98.10 133	99.90 24	99.08 165
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 193	99.56 109	99.61 32	97.85 116	99.36 110	99.85 26	95.95 130	99.85 109	96.66 243	99.83 62	99.59 107

Vis-MVSNet (Re-imp)		98.87 97	98.72 101	99.31 110	99.71 77	98.88 137	99.80 19	99.44 157	97.91 112	99.36 110	99.78 77	95.49 145	99.43 218	97.91 149	99.11 125	99.62 101

agg_prior3		98.97 92	98.71 103	99.75 38	99.67 88	99.60 45	99.04 268	99.41 169	95.93 268	98.87 208	99.48 191	98.61 54	99.91 72	97.63 176	99.72 84	99.75 54

EPNet		98.86 100	98.71 103	99.30 113	97.20 322	98.18 203	99.62 80	98.91 280	99.28 2	98.63 242	99.81 53	95.96 129	99.99 1	99.24 30	99.72 84	99.73 64
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UGNet		98.87 97	98.69 105	99.40 102	99.22 189	98.72 168	99.44 155	99.68 19	99.24 3	99.18 161	99.42 206	92.74 241	99.96 19	99.34 22	99.94 10	99.53 117
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		98.73 117	98.68 106	98.88 180	99.70 82	97.73 227	98.92 294	99.55 55	98.52 55	99.45 88	99.84 35	95.27 150	99.91 72	98.08 138	98.84 149	99.00 175

EI-MVSNet		98.67 122	98.67 107	98.68 207	99.35 161	97.97 211	99.50 130	99.38 185	96.93 203	99.20 156	99.83 37	97.87 82	99.36 227	98.38 116	97.56 213	98.71 213

CVMVSNet		98.57 127	98.67 107	98.30 241	99.35 161	95.59 286	99.50 130	99.55 55	98.60 51	99.39 102	99.83 37	94.48 198	99.45 209	98.75 74	98.56 162	99.85 8

114514_t		98.93 94	98.67 107	99.72 47	99.85 23	99.53 56	99.62 80	99.59 38	92.65 314	99.71 30	99.78 77	98.06 79	99.90 84	98.84 66	99.91 17	99.74 59

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 84	98.95 127	99.03 269	99.47 128	96.98 199	99.15 164	99.23 253	96.77 111	99.89 92	98.83 68	98.78 153	99.86 5

HY-MVS	97.30 7	98.85 106	98.64 111	99.47 91	99.42 145	99.08 103	99.62 80	99.36 193	97.39 162	99.28 128	99.68 117	96.44 119	99.92 63	98.37 117	98.22 178	99.40 142

FIs		98.78 113	98.63 112	99.23 128	99.18 197	99.54 53	99.83 12	99.59 38	98.28 70	98.79 218	99.81 53	96.75 112	99.37 223	99.08 43	96.38 248	98.78 200

ab-mvs		98.86 100	98.63 112	99.54 75	99.64 101	99.19 91	99.44 155	99.54 62	97.77 126	99.30 120	99.81 53	94.20 207	99.93 55	99.17 36	98.82 150	99.49 125

MAR-MVS		98.86 100	98.63 112	99.54 75	99.37 158	99.66 35	99.45 151	99.54 62	96.61 220	99.01 187	99.40 213	97.09 101	99.86 104	97.68 175	99.53 104	99.10 160
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		98.75 116	98.62 115	99.15 134	99.08 218	99.45 67	99.86 8	99.60 35	98.23 75	98.70 231	99.82 44	96.80 108	99.22 261	99.07 44	96.38 248	98.79 199

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 173	99.71 77	97.74 226	99.12 246	99.54 62	98.44 62	99.42 95	99.71 103	94.20 207	99.92 63	98.54 105	98.90 145	99.00 175

RPSCF		98.22 148	98.62 115	96.99 297	99.82 29	91.58 325	99.72 39	99.44 157	96.61 220	99.66 47	99.89 10	95.92 133	99.82 131	97.46 194	99.10 127	99.57 110

PatchMatch-RL		98.84 108	98.62 115	99.52 83	99.71 77	99.28 83	99.06 261	99.77 9	97.74 130	99.50 80	99.53 172	95.41 146	99.84 115	97.17 211	99.64 99	99.44 137

PMMVS		98.80 112	98.62 115	99.34 105	99.27 182	98.70 169	98.76 305	99.31 220	97.34 164	99.21 153	99.07 265	97.20 99	99.82 131	98.56 100	98.87 147	99.52 118

Effi-MVS+		98.81 109	98.59 120	99.48 88	99.46 139	99.12 100	98.08 331	99.50 99	97.50 151	99.38 104	99.41 209	96.37 121	99.81 135	99.11 41	98.54 163	99.51 121

test_djsdf		98.67 122	98.57 121	98.98 149	98.70 287	98.91 135	99.88 1	99.46 138	97.55 146	99.22 151	99.88 14	95.73 140	99.28 246	99.03 46	97.62 208	98.75 206

alignmvs		98.81 109	98.56 122	99.58 71	99.43 144	99.42 70	99.51 125	98.96 273	98.61 50	99.35 113	98.92 278	94.78 180	99.77 150	99.35 18	98.11 195	99.54 113

1314		98.68 121	98.54 123	99.11 137	98.89 259	98.65 174	99.27 213	99.49 104	96.89 205	97.99 274	99.56 161	97.72 88	99.83 122	97.74 166	99.27 117	98.84 195

diffmvs		98.72 118	98.49 124	99.43 100	99.48 137	99.19 91	99.62 80	99.42 166	95.58 274	99.37 106	99.67 121	96.14 127	99.74 155	98.14 131	98.96 138	99.37 144

tpmrst		98.33 138	98.48 125	97.90 274	99.16 204	94.78 302	99.31 201	99.11 255	97.27 170	99.45 88	99.59 152	95.33 147	99.84 115	98.48 108	98.61 156	99.09 164

Fast-Effi-MVS+		98.70 119	98.43 126	99.51 85	99.51 127	99.28 83	99.52 121	99.47 128	96.11 263	99.01 187	99.34 236	96.20 126	99.84 115	97.88 151	98.82 150	99.39 143

nrg030		98.64 125	98.42 127	99.28 118	99.05 224	99.69 30	99.81 15	99.46 138	98.04 99	99.01 187	99.82 44	96.69 114	99.38 220	99.34 22	94.59 286	98.78 200

IterMVS-LS		98.46 130	98.42 127	98.58 214	99.59 116	98.00 209	99.37 185	99.43 165	96.94 202	99.07 178	99.59 152	97.87 82	99.03 282	98.32 123	95.62 261	98.71 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		98.42 133	98.36 129	98.59 213	99.49 134	96.70 263	99.27 213	99.13 254	97.24 174	98.80 217	99.38 218	95.75 139	99.74 155	97.07 216	99.16 122	99.33 148

PatchmatchNet		98.31 140	98.36 129	98.19 257	99.16 204	95.32 294	99.27 213	98.92 277	97.37 163	99.37 106	99.58 155	94.90 172	99.70 179	97.43 197	99.21 119	99.54 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PAPR		98.63 126	98.34 131	99.51 85	99.40 153	99.03 112	98.80 302	99.36 193	96.33 242	99.00 194	99.12 263	98.46 60	99.84 115	95.23 273	99.37 113	99.66 86

ACMM	97.58 5	98.37 137	98.34 131	98.48 224	99.41 148	97.10 240	99.56 109	99.45 149	98.53 54	99.04 184	99.85 26	93.00 233	99.71 173	98.74 75	97.45 223	98.64 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		98.49 128	98.32 133	99.00 147	99.35 161	99.02 113	99.54 117	99.38 185	97.41 160	99.20 156	99.73 98	93.86 221	99.36 227	98.87 61	97.56 213	98.62 262

MDTV_nov1_ep13			98.32 133		99.11 212	94.44 306	99.27 213	98.74 298	97.51 150	99.40 101	99.62 144	94.78 180	99.76 153	97.59 178	98.81 152

QAPM		98.67 122	98.30 135	99.80 29	99.20 192	99.67 33	99.77 24	99.72 11	94.74 283	98.73 223	99.90 7	95.78 138	99.98 5	96.96 223	99.88 34	99.76 53

anonymousdsp		98.44 131	98.28 136	98.94 155	98.50 301	98.96 126	99.77 24	99.50 99	97.07 193	98.87 208	99.77 82	94.76 185	99.28 246	98.66 85	97.60 209	98.57 279

jajsoiax		98.43 132	98.28 136	98.88 180	98.60 296	98.43 195	99.82 13	99.53 72	98.19 76	98.63 242	99.80 64	93.22 231	99.44 214	99.22 31	97.50 218	98.77 203

mvs_tets		98.40 135	98.23 138	98.91 167	98.67 291	98.51 190	99.66 63	99.53 72	98.19 76	98.65 240	99.81 53	92.75 239	99.44 214	99.31 25	97.48 222	98.77 203

HQP_MVS		98.27 143	98.22 139	98.44 231	99.29 177	96.97 253	99.39 178	99.47 128	98.97 22	99.11 169	99.61 147	92.71 243	99.69 182	97.78 160	97.63 206	98.67 238

LCM-MVSNet-Re		97.83 203	98.15 140	96.87 301	99.30 174	92.25 323	99.59 90	98.26 320	97.43 157	96.20 299	99.13 260	96.27 124	98.73 300	98.17 129	98.99 135	99.64 95

Patchmatch-test1		98.16 155	98.14 141	98.22 254	99.30 174	95.55 287	99.07 257	98.97 271	97.57 144	99.43 92	99.60 150	92.72 242	99.60 198	97.38 199	99.20 120	99.50 124

LPG-MVS_test		98.22 148	98.13 142	98.49 222	99.33 165	97.05 246	99.58 96	99.55 55	97.46 153	99.24 144	99.83 37	92.58 252	99.72 167	98.09 134	97.51 216	98.68 227

OpenMVS	96.50 16	98.47 129	98.12 143	99.52 83	99.04 225	99.53 56	99.82 13	99.72 11	94.56 289	98.08 269	99.88 14	94.73 187	99.98 5	97.47 193	99.76 77	99.06 170

OPM-MVS		98.19 152	98.10 144	98.45 228	98.88 260	97.07 244	99.28 210	99.38 185	98.57 52	99.22 151	99.81 53	92.12 264	99.66 186	98.08 138	97.54 215	98.61 271

CLD-MVS		98.16 155	98.10 144	98.33 238	99.29 177	96.82 260	98.75 306	99.44 157	97.83 119	99.13 165	99.55 164	92.92 235	99.67 184	98.32 123	97.69 205	98.48 284
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		98.38 136	98.09 146	99.24 126	99.26 184	99.32 78	99.56 109	99.55 55	97.45 156	98.71 225	99.83 37	93.23 230	99.63 195	98.88 57	96.32 250	98.76 205

ADS-MVSNet		98.20 151	98.08 147	98.56 217	99.33 165	96.48 270	99.23 226	99.15 251	96.24 251	99.10 172	99.67 121	94.11 212	99.71 173	96.81 234	99.05 131	99.48 127

BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 148	98.83 145	99.30 203	98.77 294	97.70 135	98.94 200	99.65 128	92.91 237	99.74 155	96.52 247	99.55 103	99.64 95

ADS-MVSNet2		98.02 176	98.07 149	97.87 275	99.33 165	95.19 297	99.23 226	99.08 258	96.24 251	99.10 172	99.67 121	94.11 212	98.93 295	96.81 234	99.05 131	99.48 127

PatchFormer-LS_test		98.01 179	98.05 150	97.87 275	99.15 207	94.76 303	99.42 167	98.93 275	97.12 184	98.84 214	98.59 298	93.74 226	99.80 139	98.55 103	98.17 187	99.06 170

EU-MVSNet		97.98 181	98.03 151	97.81 281	98.72 284	96.65 266	99.66 63	99.66 25	98.09 89	98.35 258	99.82 44	95.25 153	98.01 316	97.41 198	95.30 266	98.78 200

tfpn1000		98.33 138	98.02 152	99.25 123	99.78 35	98.73 166	99.70 42	97.55 338	97.48 152	99.69 35	99.53 172	92.37 261	99.85 109	97.82 156	98.26 177	99.16 156

tpmvs		97.98 181	98.02 152	97.84 278	99.04 225	94.73 304	99.31 201	99.20 246	96.10 266	98.76 221	99.42 206	94.94 167	99.81 135	96.97 222	98.45 167	98.97 179

UniMVSNet (Re)		98.29 141	98.00 154	99.13 136	99.00 230	99.36 75	99.49 138	99.51 85	97.95 108	98.97 197	99.13 260	96.30 123	99.38 220	98.36 119	93.34 303	98.66 249

ACMH	97.28 8	98.10 162	97.99 155	98.44 231	99.41 148	96.96 255	99.60 88	99.56 48	98.09 89	98.15 266	99.91 5	90.87 286	99.70 179	98.88 57	97.45 223	98.67 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		98.22 148	97.97 156	98.96 152	98.92 253	98.98 119	99.48 143	99.53 72	97.76 127	98.71 225	99.46 199	96.43 120	99.22 261	98.57 97	92.87 309	98.69 222

EPNet_dtu		98.03 174	97.96 157	98.23 252	98.27 306	95.54 289	99.23 226	98.75 295	99.02 10	97.82 279	99.71 103	96.11 128	99.48 206	93.04 308	99.65 98	99.69 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPA-MVSNet		98.29 141	97.95 158	99.30 113	99.16 204	99.54 53	99.50 130	99.58 43	98.27 71	99.35 113	99.37 222	92.53 254	99.65 188	99.35 18	94.46 287	98.72 211

ACMP	97.20 11	98.06 165	97.94 159	98.45 228	99.37 158	97.01 249	99.44 155	99.49 104	97.54 149	98.45 252	99.79 72	91.95 265	99.72 167	97.91 149	97.49 221	98.62 262
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CR-MVSNet		98.17 153	97.93 160	98.87 184	99.18 197	98.49 191	99.22 230	99.33 213	96.96 200	99.56 67	99.38 218	94.33 203	99.00 285	94.83 279	98.58 159	99.14 157

pmmvs4		98.13 157	97.90 161	98.81 195	98.61 295	98.87 138	98.99 278	99.21 245	96.44 235	99.06 182	99.58 155	95.90 134	99.11 274	97.18 210	96.11 253	98.46 287

test-LLR		98.06 165	97.90 161	98.55 219	98.79 272	97.10 240	98.67 310	97.75 329	97.34 164	98.61 245	98.85 283	94.45 199	99.45 209	97.25 204	99.38 109	99.10 160

HQP-MVS		98.02 176	97.90 161	98.37 236	99.19 194	96.83 258	98.98 282	99.39 179	98.24 72	98.66 234	99.40 213	92.47 256	99.64 190	97.19 208	97.58 211	98.64 254

LTVRE_ROB	97.16 12	98.02 176	97.90 161	98.40 234	99.23 187	96.80 261	99.70 42	99.60 35	97.12 184	98.18 265	99.70 106	91.73 274	99.72 167	98.39 114	97.45 223	98.68 227
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
thresconf0.02		98.24 144	97.89 165	99.27 119	99.76 43	99.04 107	99.67 56	97.71 332	97.10 188	99.55 70	99.54 167	92.70 245	99.79 142	96.90 229	98.12 191	98.97 179

tfpn_n400		98.24 144	97.89 165	99.27 119	99.76 43	99.04 107	99.67 56	97.71 332	97.10 188	99.55 70	99.54 167	92.70 245	99.79 142	96.90 229	98.12 191	98.97 179

tfpnconf		98.24 144	97.89 165	99.27 119	99.76 43	99.04 107	99.67 56	97.71 332	97.10 188	99.55 70	99.54 167	92.70 245	99.79 142	96.90 229	98.12 191	98.97 179

tfpnview11		98.24 144	97.89 165	99.27 119	99.76 43	99.04 107	99.67 56	97.71 332	97.10 188	99.55 70	99.54 167	92.70 245	99.79 142	96.90 229	98.12 191	98.97 179

BH-w/o		98.00 180	97.89 165	98.32 239	99.35 161	96.20 279	99.01 276	98.90 282	96.42 237	98.38 255	99.00 271	95.26 152	99.72 167	96.06 255	98.61 156	99.03 172

WR-MVS_H		98.13 157	97.87 170	98.90 171	99.02 228	98.84 142	99.70 42	99.59 38	97.27 170	98.40 254	99.19 256	95.53 143	99.23 258	98.34 120	93.78 300	98.61 271

tfpn_ndepth		98.17 153	97.84 171	99.15 134	99.75 53	98.76 165	99.61 86	97.39 340	96.92 204	99.61 57	99.38 218	92.19 263	99.86 104	97.57 181	98.13 189	98.82 196

v1neww		98.12 159	97.84 171	98.93 158	98.97 238	98.81 154	99.66 63	99.35 197	96.49 227	99.29 124	99.37 222	95.02 162	99.32 237	97.73 167	94.73 278	98.67 238

v7new		98.12 159	97.84 171	98.93 158	98.97 238	98.81 154	99.66 63	99.35 197	96.49 227	99.29 124	99.37 222	95.02 162	99.32 237	97.73 167	94.73 278	98.67 238

v6		98.12 159	97.84 171	98.94 155	98.94 246	98.83 145	99.66 63	99.34 205	96.49 227	99.30 120	99.37 222	94.95 166	99.34 233	97.77 162	94.74 277	98.67 238

dp		97.75 219	97.80 175	97.59 288	99.10 215	93.71 314	99.32 198	98.88 284	96.48 233	99.08 177	99.55 164	92.67 250	99.82 131	96.52 247	98.58 159	99.24 153

V42		98.06 165	97.79 176	98.86 188	98.98 235	98.84 142	99.69 45	99.34 205	96.53 226	99.30 120	99.37 222	94.67 190	99.32 237	97.57 181	94.66 283	98.42 288

DU-MVS		98.08 164	97.79 176	98.96 152	98.87 263	98.98 119	99.41 171	99.45 149	97.87 113	98.71 225	99.50 182	94.82 177	99.22 261	98.57 97	92.87 309	98.68 227

divwei89l23v2f112		98.06 165	97.78 178	98.91 167	98.90 256	98.77 164	99.57 102	99.35 197	96.45 234	99.24 144	99.37 222	94.92 170	99.27 249	97.50 189	94.71 282	98.68 227

v7		98.05 171	97.78 178	98.87 184	98.99 231	98.67 171	99.64 75	99.34 205	96.31 245	99.29 124	99.51 180	94.78 180	99.27 249	97.03 217	95.15 270	98.66 249

CP-MVSNet		98.09 163	97.78 178	99.01 145	98.97 238	99.24 88	99.67 56	99.46 138	97.25 172	98.48 251	99.64 135	93.79 222	99.06 278	98.63 88	94.10 294	98.74 209

ACMH+	97.24 10	97.92 193	97.78 178	98.32 239	99.46 139	96.68 265	99.56 109	99.54 62	98.41 63	97.79 281	99.87 19	90.18 293	99.66 186	98.05 142	97.18 236	98.62 262

v2v482		98.06 165	97.77 182	98.92 163	98.90 256	98.82 152	99.57 102	99.36 193	96.65 217	99.19 159	99.35 233	94.20 207	99.25 255	97.72 171	94.97 274	98.69 222

OurMVSNet-221017-0		97.88 196	97.77 182	98.19 257	98.71 286	96.53 268	99.88 1	99.00 268	97.79 124	98.78 219	99.94 3	91.68 275	99.35 230	97.21 206	96.99 239	98.69 222

IterMVS		97.83 203	97.77 182	98.02 265	99.58 117	96.27 277	99.02 272	99.48 113	97.22 176	98.71 225	99.70 106	92.75 239	99.13 271	97.46 194	96.00 255	98.67 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1141		98.05 171	97.76 185	98.91 167	98.91 255	98.78 163	99.57 102	99.35 197	96.41 239	99.23 149	99.36 229	94.93 169	99.27 249	97.38 199	94.72 280	98.68 227

v1		98.05 171	97.76 185	98.93 158	98.92 253	98.80 159	99.57 102	99.35 197	96.39 241	99.28 128	99.36 229	94.86 175	99.32 237	97.38 199	94.72 280	98.68 227

FMVSNet3		98.03 174	97.76 185	98.84 192	99.39 155	98.98 119	99.40 177	99.38 185	96.67 216	99.07 178	99.28 247	92.93 234	98.98 287	97.10 213	96.65 241	98.56 280

MVP-Stereo		97.81 207	97.75 188	97.99 268	97.53 315	96.60 267	98.96 287	98.85 286	97.22 176	97.23 287	99.36 229	95.28 149	99.46 208	95.51 267	99.78 73	97.92 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WR-MVS		98.06 165	97.73 189	99.06 140	98.86 266	99.25 87	99.19 236	99.35 197	97.30 168	98.66 234	99.43 204	93.94 217	99.21 265	98.58 95	94.28 290	98.71 213

CostFormer		97.72 224	97.73 189	97.71 286	99.15 207	94.02 310	99.54 117	99.02 267	94.67 284	99.04 184	99.35 233	92.35 262	99.77 150	98.50 107	97.94 200	99.34 147

XVG-ACMP-BASELINE		97.83 203	97.71 191	98.20 256	99.11 212	96.33 275	99.41 171	99.52 76	98.06 97	99.05 183	99.50 182	89.64 297	99.73 163	97.73 167	97.38 229	98.53 281

v1144		97.98 181	97.69 192	98.85 191	98.87 263	98.66 173	99.54 117	99.35 197	96.27 248	99.23 149	99.35 233	94.67 190	99.23 258	96.73 238	95.16 269	98.68 227

view600		97.97 184	97.66 193	98.89 173	99.75 53	97.81 220	99.69 45	98.80 290	98.02 102	99.25 139	98.88 279	91.95 265	99.89 92	94.36 288	98.29 173	98.96 185

view800		97.97 184	97.66 193	98.89 173	99.75 53	97.81 220	99.69 45	98.80 290	98.02 102	99.25 139	98.88 279	91.95 265	99.89 92	94.36 288	98.29 173	98.96 185

conf0.05thres1000		97.97 184	97.66 193	98.89 173	99.75 53	97.81 220	99.69 45	98.80 290	98.02 102	99.25 139	98.88 279	91.95 265	99.89 92	94.36 288	98.29 173	98.96 185

tfpn		97.97 184	97.66 193	98.89 173	99.75 53	97.81 220	99.69 45	98.80 290	98.02 102	99.25 139	98.88 279	91.95 265	99.89 92	94.36 288	98.29 173	98.96 185

TranMVSNet+NR-MVSNet		97.93 190	97.66 193	98.76 202	98.78 276	98.62 178	99.65 73	99.49 104	97.76 127	98.49 250	99.60 150	94.23 206	98.97 294	98.00 143	92.90 307	98.70 217

Patchmatch-test		97.93 190	97.65 198	98.77 200	99.18 197	97.07 244	99.03 269	99.14 253	96.16 258	98.74 222	99.57 159	94.56 194	99.72 167	93.36 303	99.11 125	99.52 118

EPMVS		97.82 206	97.65 198	98.35 237	98.88 260	95.98 281	99.49 138	94.71 346	97.57 144	99.26 136	99.48 191	92.46 259	99.71 173	97.87 152	99.08 129	99.35 146

v8		97.95 189	97.63 200	98.93 158	98.95 243	98.81 154	99.80 19	99.41 169	96.03 267	99.10 172	99.42 206	94.92 170	99.30 243	96.94 225	94.08 295	98.66 249

NR-MVSNet		97.97 184	97.61 201	99.02 144	98.87 263	99.26 86	99.47 147	99.42 166	97.63 140	97.08 290	99.50 182	95.07 160	99.13 271	97.86 153	93.59 301	98.68 227

v144192		97.92 193	97.60 202	98.87 184	98.83 269	98.65 174	99.55 114	99.34 205	96.20 254	99.32 118	99.40 213	94.36 202	99.26 254	96.37 252	95.03 273	98.70 217

PS-CasMVS		97.93 190	97.59 203	98.95 154	98.99 231	99.06 105	99.68 54	99.52 76	97.13 182	98.31 260	99.68 117	92.44 260	99.05 279	98.51 106	94.08 295	98.75 206

v148		97.79 211	97.55 204	98.50 221	98.74 281	97.72 228	99.54 117	99.33 213	96.26 249	98.90 205	99.51 180	94.68 189	99.14 268	97.83 155	93.15 306	98.63 260

tpm		97.67 233	97.55 204	98.03 263	99.02 228	95.01 300	99.43 160	98.54 316	96.44 235	99.12 167	99.34 236	91.83 270	99.60 198	97.75 165	96.46 246	99.48 127

v7n		97.87 197	97.52 206	98.92 163	98.76 280	98.58 182	99.84 9	99.46 138	96.20 254	98.91 203	99.70 106	94.89 173	99.44 214	96.03 256	93.89 299	98.75 206

v10		97.85 199	97.52 206	98.86 188	98.99 231	98.67 171	99.75 34	99.41 169	95.70 272	98.98 196	99.41 209	94.75 186	99.23 258	96.01 257	94.63 285	98.67 238

V4		97.80 209	97.51 208	98.67 209	98.79 272	98.63 176	99.87 4	99.44 157	95.87 269	99.01 187	99.46 199	94.52 197	99.33 234	96.64 246	93.97 297	98.05 301

thres600view7		97.86 198	97.51 208	98.92 163	99.72 72	97.95 214	99.59 90	98.74 298	97.94 109	99.27 132	98.62 294	91.75 271	99.86 104	93.73 300	98.19 181	98.96 185

v52		97.79 211	97.50 210	98.66 210	98.80 270	98.62 178	99.87 4	99.44 157	95.87 269	99.01 187	99.46 199	94.44 201	99.33 234	96.65 245	93.96 298	98.05 301

testgi		97.65 235	97.50 210	98.13 260	99.36 160	96.45 271	99.42 167	99.48 113	97.76 127	97.87 277	99.45 202	91.09 283	98.81 298	94.53 283	98.52 164	99.13 159

GBi-Net		97.68 230	97.48 212	98.29 242	99.51 127	97.26 234	99.43 160	99.48 113	96.49 227	99.07 178	99.32 241	90.26 290	98.98 287	97.10 213	96.65 241	98.62 262

test1		97.68 230	97.48 212	98.29 242	99.51 127	97.26 234	99.43 160	99.48 113	96.49 227	99.07 178	99.32 241	90.26 290	98.98 287	97.10 213	96.65 241	98.62 262

tfpnnormal		97.84 201	97.47 214	98.98 149	99.20 192	99.22 90	99.64 75	99.61 32	96.32 243	98.27 263	99.70 106	93.35 229	99.44 214	95.69 263	95.40 264	98.27 295

GA-MVS		97.85 199	97.47 214	99.00 147	99.38 156	97.99 210	98.57 316	99.15 251	97.04 196	98.90 205	99.30 244	89.83 295	99.38 220	96.70 240	98.33 171	99.62 101

LF4IMVS		97.52 241	97.46 216	97.70 287	98.98 235	95.55 287	99.29 207	98.82 289	98.07 93	98.66 234	99.64 135	89.97 294	99.61 197	97.01 218	96.68 240	97.94 308

conf200view11		97.78 213	97.45 217	98.77 200	99.72 72	97.86 217	99.59 90	98.74 298	97.93 110	99.26 136	98.62 294	91.75 271	99.83 122	93.22 304	98.18 182	98.61 271

thres100view900		97.76 215	97.45 217	98.69 206	99.72 72	97.86 217	99.59 90	98.74 298	97.93 110	99.26 136	98.62 294	91.75 271	99.83 122	93.22 304	98.18 182	98.37 292

v1921920		97.80 209	97.45 217	98.84 192	98.80 270	98.53 185	99.52 121	99.34 205	96.15 260	99.24 144	99.47 195	93.98 216	99.29 245	95.40 270	95.13 271	98.69 222

Baseline_NR-MVSNet		97.76 215	97.45 217	98.68 207	99.09 217	98.29 199	99.41 171	98.85 286	95.65 273	98.63 242	99.67 121	94.82 177	99.10 276	98.07 140	92.89 308	98.64 254

MIMVSNet		97.73 222	97.45 217	98.57 215	99.45 143	97.50 230	99.02 272	98.98 270	96.11 263	99.41 97	99.14 259	90.28 289	98.74 299	95.74 261	98.93 141	99.47 131

v1192		97.81 207	97.44 222	98.91 167	98.88 260	98.68 170	99.51 125	99.34 205	96.18 256	99.20 156	99.34 236	94.03 215	99.36 227	95.32 272	95.18 268	98.69 222

VPNet		97.84 201	97.44 222	99.01 145	99.21 190	98.94 130	99.48 143	99.57 44	98.38 64	99.28 128	99.73 98	88.89 303	99.39 219	99.19 33	93.27 304	98.71 213

PEN-MVS		97.76 215	97.44 222	98.72 204	98.77 279	98.54 184	99.78 22	99.51 85	97.06 195	98.29 262	99.64 135	92.63 251	98.89 296	98.09 134	93.16 305	98.72 211

cascas		97.69 228	97.43 225	98.48 224	98.60 296	97.30 231	98.18 330	99.39 179	92.96 311	98.41 253	98.78 290	93.77 223	99.27 249	98.16 130	98.61 156	98.86 194

test0.0.03 1		97.71 227	97.42 226	98.56 217	98.41 304	97.82 219	98.78 303	98.63 311	97.34 164	98.05 273	98.98 275	94.45 199	98.98 287	95.04 276	97.15 237	98.89 193

TR-MVS		97.76 215	97.41 227	98.82 194	99.06 221	97.87 216	98.87 299	98.56 315	96.63 219	98.68 233	99.22 254	92.49 255	99.65 188	95.40 270	97.79 203	98.95 192

DWT-MVSNet_test		97.53 240	97.40 228	97.93 271	99.03 227	94.86 301	99.57 102	98.63 311	96.59 224	98.36 257	98.79 288	89.32 299	99.74 155	98.14 131	98.16 188	99.20 155

Patchmtry		97.75 219	97.40 228	98.81 195	99.10 215	98.87 138	99.11 252	99.33 213	94.83 281	98.81 216	99.38 218	94.33 203	99.02 283	96.10 254	95.57 262	98.53 281

tfpn200view9		97.72 224	97.38 230	98.72 204	99.69 84	97.96 212	99.50 130	98.73 306	97.83 119	99.17 162	98.45 302	91.67 276	99.83 122	93.22 304	98.18 182	98.37 292

thres400		97.77 214	97.38 230	98.92 163	99.69 84	97.96 212	99.50 130	98.73 306	97.83 119	99.17 162	98.45 302	91.67 276	99.83 122	93.22 304	98.18 182	98.96 185

tpm cat1		97.39 251	97.36 232	97.50 291	99.17 202	93.73 312	99.43 160	99.31 220	91.27 320	98.71 225	99.08 264	94.31 205	99.77 150	96.41 251	98.50 165	99.00 175

FMVSNet2		97.72 224	97.36 232	98.80 197	99.51 127	98.84 142	99.45 151	99.42 166	96.49 227	98.86 213	99.29 246	90.26 290	98.98 287	96.44 249	96.56 244	98.58 278

LFMVS		97.90 195	97.35 234	99.54 75	99.52 125	99.01 115	99.39 178	98.24 321	97.10 188	99.65 50	99.79 72	84.79 329	99.91 72	99.28 27	98.38 170	99.69 78

VDD-MVS		97.73 222	97.35 234	98.88 180	99.47 138	97.12 239	99.34 196	98.85 286	98.19 76	99.67 42	99.85 26	82.98 333	99.92 63	99.49 12	98.32 172	99.60 103

DSMNet-mixed		97.25 255	97.35 234	96.95 299	97.84 311	93.61 316	99.57 102	96.63 342	96.13 262	98.87 208	98.61 297	94.59 193	97.70 324	95.08 275	98.86 148	99.55 111

tpm2		97.44 249	97.34 237	97.74 285	99.15 207	94.36 307	99.45 151	98.94 274	93.45 309	98.90 205	99.44 203	91.35 281	99.59 200	97.31 202	98.07 196	99.29 150

TAPA-MVS	97.07 15	97.74 221	97.34 237	98.94 155	99.70 82	97.53 229	99.25 223	99.51 85	91.90 318	99.30 120	99.63 139	98.78 37	99.64 190	88.09 325	99.87 38	99.65 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SixPastTwentyTwo		97.50 245	97.33 239	98.03 263	98.65 292	96.23 278	99.77 24	98.68 309	97.14 181	97.90 276	99.93 4	90.45 288	99.18 267	97.00 219	96.43 247	98.67 238

MS-PatchMatch		97.24 256	97.32 240	96.99 297	98.45 303	93.51 317	98.82 301	99.32 219	97.41 160	98.13 267	99.30 244	88.99 302	99.56 201	95.68 264	99.80 69	97.90 311

v1240		97.69 228	97.32 240	98.79 198	98.85 267	98.43 195	99.48 143	99.36 193	96.11 263	99.27 132	99.36 229	93.76 224	99.24 257	94.46 285	95.23 267	98.70 217

pmmvs5		97.52 241	97.30 242	98.16 259	98.57 298	96.73 262	99.27 213	98.90 282	96.14 261	98.37 256	99.53 172	91.54 280	99.14 268	97.51 188	95.87 256	98.63 260

tpmp4_e23		97.34 252	97.29 243	97.52 289	99.25 186	93.73 312	99.58 96	99.19 249	94.00 300	98.20 264	99.41 209	90.74 287	99.74 155	97.13 212	98.07 196	99.07 169

pm-mvs1		97.68 230	97.28 244	98.88 180	99.06 221	98.62 178	99.50 130	99.45 149	96.32 243	97.87 277	99.79 72	92.47 256	99.35 230	97.54 185	93.54 302	98.67 238

thres200		97.61 236	97.28 244	98.62 211	99.64 101	98.03 208	99.26 221	98.74 298	97.68 137	99.09 176	98.32 304	91.66 278	99.81 135	92.88 310	98.22 178	98.03 304

TESTMET0.1,1		97.55 238	97.27 246	98.40 234	98.93 251	96.53 268	98.67 310	97.61 337	96.96 200	98.64 241	99.28 247	88.63 309	99.45 209	97.30 203	99.38 109	99.21 154

v748		97.52 241	97.23 247	98.41 233	98.69 288	97.23 237	99.87 4	99.45 149	95.72 271	98.51 248	99.53 172	94.13 211	99.30 243	96.78 236	92.39 313	98.70 217

USDC		97.34 252	97.20 248	97.75 284	99.07 219	95.20 296	98.51 319	99.04 265	97.99 107	98.31 260	99.86 22	89.02 301	99.55 203	95.67 265	97.36 230	98.49 283

DTE-MVSNet		97.51 244	97.19 249	98.46 227	98.63 294	98.13 206	99.84 9	99.48 113	96.68 215	97.97 275	99.67 121	92.92 235	98.56 302	96.88 233	92.60 312	98.70 217

test-mter		97.49 247	97.13 250	98.55 219	98.79 272	97.10 240	98.67 310	97.75 329	96.65 217	98.61 245	98.85 283	88.23 314	99.45 209	97.25 204	99.38 109	99.10 160

PAPM		97.59 237	97.09 251	99.07 139	99.06 221	98.26 201	98.30 326	99.10 256	94.88 280	98.08 269	99.34 236	96.27 124	99.64 190	89.87 319	98.92 143	99.31 149

PCF-MVS	97.08 14	97.66 234	97.06 252	99.47 91	99.61 112	99.09 102	98.04 332	99.25 241	91.24 321	98.51 248	99.70 106	94.55 195	99.91 72	92.76 311	99.85 52	99.42 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDDNet		97.55 238	97.02 253	99.16 132	99.49 134	98.12 207	99.38 183	99.30 222	95.35 276	99.68 36	99.90 7	82.62 335	99.93 55	99.31 25	98.13 189	99.42 140

JIA-IIPM		97.50 245	97.02 253	98.93 158	98.73 282	97.80 224	99.30 203	98.97 271	91.73 319	98.91 203	94.86 335	95.10 159	99.71 173	97.58 179	97.98 199	99.28 151

TinyColmap		97.12 258	96.89 255	97.83 279	99.07 219	95.52 290	98.57 316	98.74 298	97.58 143	97.81 280	99.79 72	88.16 315	99.56 201	95.10 274	97.21 234	98.39 291

LP		97.04 260	96.80 256	97.77 283	98.90 256	95.23 295	98.97 285	99.06 263	94.02 299	98.09 268	99.41 209	93.88 219	98.82 297	90.46 317	98.42 169	99.26 152

DI_MVS_plusplus_test		97.45 248	96.79 257	99.44 97	97.76 313	99.04 107	99.21 233	98.61 313	97.74 130	94.01 315	98.83 285	87.38 320	99.83 122	98.63 88	98.90 145	99.44 137

K. test v3		97.10 259	96.79 257	98.01 266	98.72 284	96.33 275	99.87 4	97.05 341	97.59 141	96.16 300	99.80 64	88.71 305	99.04 280	96.69 241	96.55 245	98.65 252

test_normal		97.44 249	96.77 259	99.44 97	97.75 314	99.00 117	99.10 254	98.64 310	97.71 133	93.93 318	98.82 286	87.39 319	99.83 122	98.61 92	98.97 137	99.49 125

TransMVSNet (Re)		97.15 257	96.58 260	98.86 188	99.12 210	98.85 141	99.49 138	98.91 280	95.48 275	97.16 289	99.80 64	93.38 228	99.11 274	94.16 298	91.73 314	98.62 262

MVS		97.28 254	96.55 261	99.48 88	98.78 276	98.95 127	99.27 213	99.39 179	83.53 334	98.08 269	99.54 167	96.97 104	99.87 101	94.23 296	99.16 122	99.63 99

PatchT		97.03 261	96.44 262	98.79 198	98.99 231	98.34 198	99.16 239	99.07 261	92.13 315	99.52 77	97.31 328	94.54 196	98.98 287	88.54 323	98.73 155	99.03 172

FMVSNet1		96.84 262	96.36 263	98.29 242	99.32 172	97.26 234	99.43 160	99.48 113	95.11 278	98.55 247	99.32 241	83.95 332	98.98 287	95.81 260	96.26 251	98.62 262

test_0402		96.64 263	96.24 264	97.85 277	98.85 267	96.43 272	99.44 155	99.26 239	93.52 306	96.98 293	99.52 177	88.52 310	99.20 266	92.58 313	97.50 218	97.93 309

FMVSNet5		96.43 268	96.19 265	97.15 294	99.11 212	95.89 283	99.32 198	99.52 76	94.47 293	98.34 259	99.07 265	87.54 318	97.07 327	92.61 312	95.72 259	98.47 285

UnsupCasMVSNet_eth		96.44 267	96.12 266	97.40 293	98.65 292	95.65 284	99.36 189	99.51 85	97.13 182	96.04 303	98.99 272	88.40 312	98.17 305	96.71 239	90.27 317	98.40 290

pmmvs6		96.53 266	96.09 267	97.82 280	98.69 288	95.47 291	99.37 185	99.47 128	93.46 308	97.41 284	99.78 77	87.06 321	99.33 234	96.92 227	92.70 311	98.65 252

Anonymous20231206		96.22 279	96.03 268	96.79 303	97.31 320	94.14 309	99.63 77	99.08 258	96.17 257	97.04 291	99.06 267	93.94 217	97.76 323	86.96 329	95.06 272	98.47 285

new_pmnet		96.38 272	96.03 268	97.41 292	98.13 309	95.16 299	99.05 263	99.20 246	93.94 301	97.39 285	98.79 288	91.61 279	99.04 280	90.43 318	95.77 258	98.05 301

testpf		95.66 287	96.02 270	94.58 311	98.35 305	92.32 322	97.25 338	97.91 328	92.83 312	97.03 292	98.99 272	88.69 306	98.61 301	95.72 262	97.40 227	92.80 336

test20.03		96.12 282	95.96 271	96.63 304	97.44 316	95.45 292	99.51 125	99.38 185	96.55 225	96.16 300	99.25 251	93.76 224	96.17 332	87.35 328	94.22 292	98.27 295

RPMNet		96.61 264	95.85 272	98.87 184	99.18 197	98.49 191	99.22 230	99.08 258	88.72 330	99.56 67	97.38 326	94.08 214	99.00 285	86.87 330	98.58 159	99.14 157

N_pmnet		94.95 295	95.83 273	92.31 319	98.47 302	79.33 342	99.12 246	92.81 352	93.87 302	97.68 282	99.13 260	93.87 220	99.01 284	91.38 315	96.19 252	98.59 275

Patchmatch-RL test		95.84 285	95.81 274	95.95 308	95.61 325	90.57 326	98.24 327	98.39 317	95.10 279	95.20 305	98.67 293	94.78 180	97.77 322	96.28 253	90.02 318	99.51 121

v17		96.42 269	95.81 274	98.25 249	98.94 246	98.80 159	99.76 27	99.28 233	94.57 287	94.18 309	97.71 312	95.23 154	98.16 306	94.86 277	87.73 326	97.80 314

v18		96.42 269	95.80 276	98.26 245	98.95 243	98.82 152	99.76 27	99.28 233	94.58 286	94.12 310	97.70 313	95.22 155	98.16 306	94.83 279	87.80 324	97.79 319

v16		96.39 271	95.76 277	98.26 245	98.96 241	98.81 154	99.76 27	99.28 233	94.57 287	94.10 311	97.70 313	95.04 161	98.16 306	94.70 281	87.77 325	97.80 314

EG-PatchMatch MVS		95.97 284	95.69 278	96.81 302	97.78 312	92.79 320	99.16 239	98.93 275	96.16 258	94.08 312	99.22 254	82.72 334	99.47 207	95.67 265	97.50 218	98.17 298

v15		96.28 273	95.62 279	98.25 249	98.94 246	98.83 145	99.76 27	99.29 226	94.52 291	94.02 314	97.61 320	95.02 162	98.13 310	94.53 283	86.92 329	97.80 314

V14		96.26 274	95.60 280	98.26 245	98.94 246	98.83 145	99.76 27	99.29 226	94.49 292	93.96 316	97.66 316	94.99 165	98.13 310	94.41 286	86.90 330	97.80 314

v13		96.24 276	95.58 281	98.25 249	98.98 235	98.83 145	99.75 34	99.29 226	94.35 296	93.89 319	97.60 321	95.17 157	98.11 312	94.27 295	86.86 332	97.81 312

v12		96.24 276	95.58 281	98.23 252	98.96 241	98.81 154	99.76 27	99.29 226	94.42 295	93.85 320	97.60 321	95.12 158	98.09 313	94.32 292	86.85 333	97.80 314

V9		96.25 275	95.58 281	98.26 245	98.94 246	98.83 145	99.75 34	99.29 226	94.45 294	93.96 316	97.62 319	94.94 167	98.14 309	94.40 287	86.87 331	97.81 312

v11		96.23 278	95.57 284	98.21 255	98.93 251	98.83 145	99.72 39	99.29 226	94.29 297	94.05 313	97.64 318	94.88 174	98.04 314	92.89 309	88.43 322	97.77 320

PVSNet_0	94.43 19	96.09 283	95.47 285	97.94 270	99.31 173	94.34 308	97.81 333	99.70 15	97.12 184	97.46 283	98.75 291	89.71 296	99.79 142	97.69 173	81.69 338	99.68 82

X-MVStestdata		96.55 265	95.45 286	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 106	64.01 350	98.81 34	99.94 40	98.79 72	99.86 48	99.84 12

IB-MVS	95.67 18	96.22 279	95.44 287	98.57 215	99.21 190	96.70 263	98.65 313	97.74 331	96.71 213	97.27 286	98.54 300	86.03 323	99.92 63	98.47 110	86.30 334	99.10 160
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		96.17 281	95.32 288	98.73 203	98.79 272	98.14 205	99.38 183	94.09 347	91.07 323	98.07 272	91.04 341	89.62 298	99.35 230	96.75 237	99.09 128	98.68 227

testus		94.61 296	95.30 289	92.54 318	96.44 323	84.18 334	98.36 322	99.03 266	94.18 298	96.49 296	98.57 299	88.74 304	95.09 336	87.41 327	98.45 167	98.36 294

MVS-HIRNet		95.75 286	95.16 290	97.51 290	99.30 174	93.69 315	98.88 298	95.78 343	85.09 333	98.78 219	92.65 337	91.29 282	99.37 223	94.85 278	99.85 52	99.46 134

MIMVSNet1		95.51 288	95.04 291	96.92 300	97.38 317	95.60 285	99.52 121	99.50 99	93.65 304	96.97 294	99.17 257	85.28 327	96.56 331	88.36 324	95.55 263	98.60 274

CMPMVS	69.68 23	94.13 300	94.90 292	91.84 320	97.24 321	80.01 341	98.52 318	99.48 113	89.01 328	91.99 327	99.67 121	85.67 325	99.13 271	95.44 268	97.03 238	96.39 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		95.34 292	94.73 293	97.15 294	95.53 327	95.94 282	99.35 193	99.10 256	95.13 277	93.55 321	97.54 324	88.15 316	97.91 318	94.58 282	89.69 320	97.61 323

MDA-MVSNet_test_wron		95.45 289	94.60 294	98.01 266	98.16 308	97.21 238	99.11 252	99.24 242	93.49 307	80.73 340	98.98 275	93.02 232	98.18 304	94.22 297	94.45 288	98.64 254

TDRefinement		95.42 290	94.57 295	97.97 269	89.83 341	96.11 280	99.48 143	98.75 295	96.74 211	96.68 295	99.88 14	88.65 308	99.71 173	98.37 117	82.74 337	98.09 299

YYNet1		95.36 291	94.51 296	97.92 272	97.89 310	97.10 240	99.10 254	99.23 243	93.26 310	80.77 339	99.04 269	92.81 238	98.02 315	94.30 293	94.18 293	98.64 254

test2356		94.07 302	94.46 297	92.89 316	95.18 328	86.13 332	97.60 336	99.06 263	93.61 305	96.15 302	98.28 305	85.60 326	93.95 338	86.68 331	98.00 198	98.59 275

new-patchmatchnet		94.48 297	94.08 298	95.67 309	95.08 329	92.41 321	99.18 237	99.28 233	94.55 290	93.49 322	97.37 327	87.86 317	97.01 328	91.57 314	88.36 323	97.61 323

MDA-MVSNet-bldmvs		94.96 294	93.98 299	97.92 272	98.24 307	97.27 233	99.15 242	99.33 213	93.80 303	80.09 341	99.03 270	88.31 313	97.86 320	93.49 302	94.36 289	98.62 262

OpenMVS_ROB	92.34 20	94.38 299	93.70 300	96.41 307	97.38 317	93.17 318	99.06 261	98.75 295	86.58 331	94.84 308	98.26 306	81.53 336	99.32 237	89.01 322	97.87 202	96.76 327

Test4		95.05 293	93.67 301	99.22 129	96.07 324	98.94 130	99.20 235	99.27 238	97.71 133	89.96 333	97.59 323	66.18 341	99.25 255	98.06 141	98.96 138	99.47 131

test1235678		92.91 305	93.30 302	91.71 322	93.14 335	83.01 336	98.75 306	98.58 314	92.80 313	92.45 325	97.91 309	88.51 311	93.54 339	82.26 335	95.35 265	98.59 275

pmmvs3		94.09 301	93.25 303	96.60 305	94.76 330	94.49 305	98.92 294	98.18 324	89.66 325	96.48 297	98.06 307	86.28 322	97.33 326	89.68 320	87.20 328	97.97 307

testing_2		94.44 298	92.93 304	98.98 149	94.16 332	99.00 117	99.42 167	99.28 233	96.60 222	84.86 335	96.84 329	70.91 338	99.27 249	98.23 126	96.08 254	98.68 227

UnsupCasMVSNet_bld		93.53 303	92.51 305	96.58 306	97.38 317	93.82 311	98.24 327	99.48 113	91.10 322	93.10 323	96.66 330	74.89 337	98.37 303	94.03 299	87.71 327	97.56 325

PM-MVS		92.96 304	92.23 306	95.14 310	95.61 325	89.98 328	99.37 185	98.21 322	94.80 282	95.04 307	97.69 315	65.06 342	97.90 319	94.30 293	89.98 319	97.54 326

1111		92.30 306	92.21 307	92.55 317	93.30 333	86.27 330	99.15 242	98.74 298	91.94 316	90.85 330	97.82 310	84.18 330	95.21 334	79.65 337	94.27 291	96.19 330

test12356		91.74 307	92.19 308	90.37 325	91.22 337	82.41 337	98.61 314	98.28 319	90.66 324	91.82 328	97.92 308	84.90 328	92.61 340	81.64 336	94.66 283	96.09 331

Gipuma		90.99 308	90.15 309	93.51 313	98.73 282	90.12 327	93.98 342	99.45 149	79.32 337	92.28 326	94.91 334	69.61 339	97.98 317	87.42 326	95.67 260	92.45 338
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		90.69 309	89.39 310	94.58 311	94.25 331	88.18 329	99.29 207	99.07 261	82.45 336	92.95 324	97.65 317	63.96 344	97.79 321	89.27 321	85.63 335	97.77 320

testmv		87.91 310	87.80 311	88.24 326	87.68 344	77.50 344	99.07 257	97.66 336	89.27 326	86.47 334	96.22 332	68.35 340	92.49 342	76.63 341	88.82 321	94.72 334

.test1245		83.42 314	86.17 312	75.15 336	93.30 333	86.27 330	99.15 242	98.74 298	91.94 316	90.85 330	97.82 310	84.18 330	95.21 334	79.65 337	39.90 348	43.98 347

FPMVS		84.93 313	85.65 313	82.75 333	86.77 345	63.39 351	98.35 324	98.92 277	74.11 339	83.39 337	98.98 275	50.85 347	92.40 343	84.54 333	94.97 274	92.46 337

PMMVS2		86.87 311	85.37 314	91.35 324	90.21 340	83.80 335	98.89 297	97.45 339	83.13 335	91.67 329	95.03 333	48.49 348	94.70 337	85.86 332	77.62 339	95.54 332

LCM-MVSNet		86.80 312	85.22 315	91.53 323	87.81 343	80.96 340	98.23 329	98.99 269	71.05 340	90.13 332	96.51 331	48.45 349	96.88 329	90.51 316	85.30 336	96.76 327

tmp_tt		82.80 316	81.52 316	86.66 327	66.61 352	68.44 350	92.79 344	97.92 326	68.96 342	80.04 342	99.85 26	85.77 324	96.15 333	97.86 153	43.89 347	95.39 333

no-one		83.04 315	80.12 317	91.79 321	89.44 342	85.65 333	99.32 198	98.32 318	89.06 327	79.79 343	89.16 343	44.86 350	96.67 330	84.33 334	46.78 346	93.05 335

E-PMN		80.61 317	79.88 318	82.81 332	90.75 339	76.38 346	97.69 334	95.76 344	66.44 344	83.52 336	92.25 338	62.54 345	87.16 347	68.53 345	61.40 342	84.89 345

EMVS		80.02 318	79.22 319	82.43 334	91.19 338	76.40 345	97.55 337	92.49 354	66.36 345	83.01 338	91.27 339	64.63 343	85.79 348	65.82 346	60.65 343	85.08 344

PNet_i23d		79.43 319	77.68 320	84.67 329	86.18 346	71.69 349	96.50 340	93.68 348	75.17 338	71.33 344	91.18 340	32.18 353	90.62 344	78.57 340	74.34 340	91.71 340

PMVS	70.75 22	75.98 322	74.97 321	79.01 335	70.98 351	55.18 352	93.37 343	98.21 322	65.08 346	61.78 348	93.83 336	21.74 357	92.53 341	78.59 339	91.12 316	89.34 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		77.30 320	74.86 322	84.62 330	75.88 350	77.61 343	97.63 335	93.15 351	88.81 329	64.27 346	89.29 342	36.51 351	83.93 349	75.89 342	52.31 345	92.33 339

MVE	76.82 21	76.91 321	74.31 323	84.70 328	85.38 348	76.05 347	96.88 339	93.17 350	67.39 343	71.28 345	89.01 344	21.66 358	87.69 346	71.74 344	72.29 341	90.35 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		74.42 323	71.19 324	84.14 331	76.16 349	74.29 348	96.00 341	92.57 353	69.57 341	63.84 347	87.49 345	21.98 355	88.86 345	75.56 343	57.50 344	89.26 343

testmvs		39.17 326	43.78 325	25.37 340	36.04 354	16.84 355	98.36 322	26.56 355	20.06 348	38.51 350	67.32 346	29.64 354	15.30 352	37.59 348	39.90 348	43.98 347

pcd1.5k->3k		40.85 324	43.49 326	32.93 338	98.95 243	0.00 356	0.00 346	99.53 72	0.00 350	0.00 352	0.27 352	95.32 148	0.00 353	0.00 350	97.30 231	98.80 198

test123		39.01 327	42.50 327	28.53 339	39.17 353	20.91 354	98.75 306	19.17 357	19.83 349	38.57 349	66.67 347	33.16 352	15.42 351	37.50 349	29.66 350	49.26 346

wuyk23d		40.18 325	41.29 328	36.84 337	86.18 346	49.12 353	79.73 345	22.81 356	27.64 347	25.46 351	28.45 351	21.98 355	48.89 350	55.80 347	23.56 351	12.51 349

cdsmvs_eth3d_5k		24.64 328	32.85 329	0.00 341	0.00 355	0.00 356	0.00 346	99.51 85	0.00 350	0.00 352	99.56 161	96.58 116	0.00 353	0.00 350	0.00 352	0.00 350

ab-mvs-re		8.30 329	11.06 330	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	99.58 155	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

pcd_1.5k_mvsjas		8.27 330	11.03 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.27 352	99.01 11	0.00 353	0.00 350	0.00 352	0.00 350

sosnet-low-res		0.02 331	0.03 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.27 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

sosnet		0.02 331	0.03 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.27 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uncertanet		0.02 331	0.03 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.27 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

Regformer		0.02 331	0.03 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.27 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uanet		0.02 331	0.03 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.27 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

ESAPD_part2					99.81 32	99.83 7				99.77 23

ESAPD_part1								99.48 113				98.96 20			99.84 57	99.83 23

ESAPD								99.47 128

sam_mvs1												94.86 175

sam_mvs												94.72 188

semantic-postprocess				98.06 262	99.57 119	96.36 274		99.49 104	97.18 178	98.71 225	99.72 102	92.70 245	99.14 268	97.44 196	95.86 257	98.67 238

ambc				93.06 315	92.68 336	82.36 338	98.47 320	98.73 306		95.09 306	97.41 325	55.55 346	99.10 276	96.42 250	91.32 315	97.71 322

MTGPA								99.47 128

test_post1							99.23 226				65.14 349	94.18 210	99.71 173	97.58 179

test_post											65.99 348	94.65 192	99.73 163

patchmatchnet-post											98.70 292	94.79 179	99.74 155

GG-mvs-BLEND				98.45 228	98.55 299	98.16 204	99.43 160	93.68 348		97.23 287	98.46 301	89.30 300	99.22 261	95.43 269	98.22 178	97.98 306

MTMP								98.88 284

gm-plane-assit					98.54 300	92.96 319			94.65 285		99.15 258		99.64 190	97.56 183

test9_res														97.49 190	99.72 84	99.75 54

TEST9					99.67 88	99.65 38	99.05 263	99.41 169	96.22 253	98.95 198	99.49 185	98.77 40	99.91 72

test_8					99.67 88	99.61 43	99.03 269	99.41 169	96.28 246	98.93 201	99.48 191	98.76 42	99.91 72

agg_prior2														97.21 206	99.73 83	99.75 54

agg_prior					99.67 88	99.62 41		99.40 176		98.87 208			99.91 72

TestCases				99.31 110	99.86 20	98.48 193		99.61 32	97.85 116	99.36 110	99.85 26	95.95 130	99.85 109	96.66 243	99.83 62	99.59 107

test_prior4						99.56 50	98.99 278

test_prior2							98.96 287		98.34 66	99.01 187	99.52 177	98.68 50		97.96 145	99.74 80

test_prior				99.68 50	99.67 88	99.48 63		99.56 48					99.83 122			99.74 59

旧先验2							98.96 287		96.70 214	99.47 85			99.94 40	98.19 127

新几何2							99.01 276

新几何1				99.75 38	99.75 53	99.59 47		99.54 62	96.76 210	99.29 124	99.64 135	98.43 62	99.94 40	96.92 227	99.66 96	99.72 70

旧先验1					99.74 64	99.59 47		99.54 62			99.69 112	98.47 59			99.68 94	99.73 64

无先验							98.99 278	99.51 85	96.89 205				99.93 55	97.53 186		99.72 70

原ACMM2							98.95 291

原ACMM1				99.65 57	99.73 69	99.33 77		99.47 128	97.46 153	99.12 167	99.66 127	98.67 52	99.91 72	97.70 172	99.69 91	99.71 77

test222					99.75 53	99.49 62	98.91 296	99.49 104	96.42 237	99.34 116	99.65 128	98.28 72			99.69 91	99.72 70

testdata2													99.95 33	96.67 242

segment_acmp												98.96 20

testdata				99.54 75	99.75 53	98.95 127		99.51 85	97.07 193	99.43 92	99.70 106	98.87 29	99.94 40	97.76 163	99.64 99	99.72 70

testdata1							98.85 300		98.32 69

test12				99.75 38	99.64 101	99.61 43		99.29 226		99.21 153		98.38 66	99.89 92		99.74 80	99.74 59

plane_prior7					99.29 177	97.03 248

plane_prior6					99.27 182	96.98 252						92.71 243

plane_prior5								99.47 128					99.69 182	97.78 160	97.63 206	98.67 238

plane_prior4											99.61 147

plane_prior3						97.00 250			98.69 46	99.11 169

plane_prior2							99.39 178		98.97 22

plane_prior1					99.26 184

plane_prior						96.97 253	99.21 233		98.45 59						97.60 209

n2								0.00 358

nn								0.00 358

door-mid								98.05 325

lessismore_v0				97.79 282	98.69 288	95.44 293		94.75 345		95.71 304	99.87 19	88.69 306	99.32 237	95.89 258	94.93 276	98.62 262

LGP-MVS_train				98.49 222	99.33 165	97.05 246		99.55 55	97.46 153	99.24 144	99.83 37	92.58 252	99.72 167	98.09 134	97.51 216	98.68 227

test11								99.35 197

door								97.92 326

HQP5-MVS						96.83 258

HQP-NCC					99.19 194		98.98 282		98.24 72	98.66 234

ACMP_Plane					99.19 194		98.98 282		98.24 72	98.66 234

BP-MVS														97.19 208

HQP4-MVS										98.66 234			99.64 190			98.64 254

HQP3-MVS								99.39 179							97.58 211

HQP2-MVS												92.47 256

NP-MVS					99.23 187	96.92 256					99.40 213

MDTV_nov1_ep13_2view						95.18 298	99.35 193		96.84 208	99.58 63		95.19 156		97.82 156		99.46 134

ACMMP++_ref															97.19 235

ACMMP++															97.43 226

Test By Simon												98.75 45

ITE_SJBPF				98.08 261	99.29 177	96.37 273		98.92 277	98.34 66	98.83 215	99.75 90	91.09 283	99.62 196	95.82 259	97.40 227	98.25 297

DeepMVS_CX				93.34 314	99.29 177	82.27 339		99.22 244	85.15 332	96.33 298	99.05 268	90.97 285	99.73 163	93.57 301	97.77 204	98.01 305

High-resolution multi-view benchmark