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 64	99.78 36	99.14 102	99.60 95	99.45 155	99.01 13	99.90 1	99.83 40	98.98 19	99.93 56	99.59 2	99.95 6	99.86 6

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 42	99.89 1	99.75 36	99.56 49	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 64	99.78 36	99.15 101	99.61 92	99.45 155	99.01 13	99.89 2	99.82 47	99.01 12	99.92 65	99.56 5	99.95 6	99.85 9

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 45	99.41 74	99.58 104	99.49 106	99.02 10	99.88 3	99.80 68	99.00 18	99.94 41	99.45 15	99.92 12	99.84 13

Regformer-3		99.57 6	99.53 5	99.68 52	99.76 45	99.29 86	99.58 104	99.44 164	99.01 13	99.87 7	99.80 68	98.97 20	99.91 75	99.44 16	99.92 12	99.83 24

SD-MVS		99.41 34	99.52 6	99.05 155	99.74 68	99.68 33	99.46 162	99.52 77	99.11 7	99.88 3	99.91 5	99.43 1	97.70 340	98.72 88	99.93 11	99.77 51

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 83	99.39 187	98.91 29	99.78 24	99.85 27	99.36 2	99.94 41	98.84 72	99.88 35	99.82 31

Regformer-1		99.53 9	99.47 8	99.72 49	99.71 83	99.44 71	99.49 149	99.46 143	98.95 24	99.83 12	99.76 91	99.01 12	99.93 56	99.17 39	99.87 39	99.80 41

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 83	99.52 61	99.49 149	99.49 106	98.94 26	99.83 12	99.76 91	99.01 12	99.94 41	99.15 42	99.87 39	99.80 41

MSLP-MVS++		99.46 21	99.47 8	99.44 103	99.60 123	99.16 98	99.41 183	99.71 13	98.98 19	99.45 97	99.78 82	99.19 4	99.54 217	99.28 28	99.84 59	99.63 102

XVS		99.53 9	99.42 11	99.87 7	99.85 23	99.83 8	99.69 49	99.68 19	98.98 19	99.37 115	99.74 101	98.81 34	99.94 41	98.79 80	99.86 50	99.84 13

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 7	99.82 29	99.81 14	99.59 97	99.51 86	98.62 50	99.79 19	99.83 40	99.28 3	99.97 11	98.48 118	99.90 24	99.84 13
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 77	99.40 76	99.05 277	99.66 25	99.14 6	99.57 71	99.80 68	98.46 61	99.94 41	99.57 4	99.84 59	99.60 107
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 28	99.56 49	97.72 138	99.76 31	99.75 96	99.13 7	99.92 65	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 69	99.47 133	98.79 40	99.68 40	99.81 57	98.43 63	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 37	99.63 113	99.59 49	99.36 203	99.46 143	99.07 9	99.79 19	99.82 47	98.85 31	99.92 65	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 69	99.67 22	98.15 82	99.68 40	99.69 122	99.06 9	99.96 19	98.69 91	99.87 39	99.84 13

DeepPCF-MVS	98.18 3	98.81 116	99.37 17	97.12 313	99.60 123	91.75 341	98.61 330	99.44 164	99.35 1	99.83 12	99.85 27	98.70 50	99.81 145	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	99.36 203	99.47 133	98.79 40	99.68 40	99.81 57	98.43 63	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 69	99.67 22	98.15 82	99.67 46	99.69 122	98.95 24	99.96 19	98.69 91	99.87 39	99.84 13

TSAR-MVS + GP.		99.36 40	99.36 19	99.36 109	99.67 95	98.61 190	99.07 271	99.33 222	99.00 17	99.82 15	99.81 57	99.06 9	99.84 124	99.09 46	99.42 110	99.65 92

region2R		99.48 17	99.35 22	99.87 7	99.88 11	99.80 15	99.65 79	99.66 25	98.13 84	99.66 51	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 45	99.83 8	99.63 83	99.54 63	98.36 66	99.79 19	99.82 47	98.86 30	99.95 34	98.62 99	99.81 68	99.78 49

ACMMP_Plus		99.47 20	99.34 24	99.88 5	99.87 15	99.86 4	99.47 159	99.48 117	98.05 100	99.76 31	99.86 23	98.82 33	99.93 56	98.82 79	99.91 17	99.84 13

MVS_111021_LR		99.41 34	99.33 25	99.65 59	99.77 42	99.51 63	98.94 307	99.85 6	98.82 35	99.65 54	99.74 101	98.51 58	99.80 149	98.83 75	99.89 32	99.64 98

ESAPD		99.46 21	99.32 26	99.91 2	99.78 36	99.88 2	99.36 203	99.51 86	98.73 44	99.88 3	99.84 36	98.72 48	99.96 19	98.16 140	99.87 39	99.88 4

PS-MVSNAJ		99.32 44	99.32 26	99.30 120	99.57 128	98.94 138	98.97 299	99.46 143	98.92 28	99.71 34	99.24 269	99.01 12	99.98 5	99.35 19	99.66 97	98.97 190

CP-MVS		99.45 23	99.32 26	99.85 18	99.83 28	99.75 24	99.69 49	99.52 77	98.07 95	99.53 83	99.63 149	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 55	99.72 77	99.47 67	98.95 305	99.85 6	98.82 35	99.54 81	99.73 107	98.51 58	99.74 169	98.91 61	99.88 35	99.77 51

CSCG		99.32 44	99.32 26	99.32 116	99.85 23	98.29 212	99.71 45	99.66 25	98.11 88	99.41 106	99.80 68	98.37 69	99.96 19	98.99 54	99.96 5	99.72 71

ACMMP		99.45 23	99.32 26	99.82 26	99.89 8	99.67 35	99.62 86	99.69 18	98.12 86	99.63 56	99.84 36	98.73 47	99.96 19	98.55 113	99.83 63	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 23	99.58 104	99.65 30	97.84 124	99.71 34	99.80 68	99.12 8	99.97 11	98.33 131	99.87 39	99.83 24

abl_6		99.44 26	99.31 32	99.83 24	99.85 23	99.75 24	99.66 69	99.59 38	98.13 84	99.82 15	99.81 57	98.60 56	99.96 19	98.46 121	99.88 35	99.79 45

SMA-MVS		99.44 26	99.30 34	99.85 18	99.73 73	99.83 8	99.56 117	99.47 133	97.45 164	99.78 24	99.82 47	99.18 5	99.91 75	98.79 80	99.89 32	99.81 35

MCST-MVS		99.43 29	99.30 34	99.82 26	99.79 35	99.74 27	99.29 222	99.40 184	98.79 40	99.52 85	99.62 154	98.91 27	99.90 88	98.64 96	99.75 79	99.82 31

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 49	99.48 117	98.12 86	99.50 89	99.75 96	98.78 37	99.97 11	98.57 107	99.89 32	99.83 24

CNVR-MVS		99.42 31	99.30 34	99.78 35	99.62 117	99.71 29	99.26 235	99.52 77	98.82 35	99.39 111	99.71 113	98.96 21	99.85 118	98.59 104	99.80 70	99.77 51

UA-Net		99.42 31	99.29 38	99.80 31	99.62 117	99.55 54	99.50 140	99.70 15	98.79 40	99.77 26	99.96 1	97.45 95	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	97.83 125	99.68 40	99.69 122	99.06 9	99.96 19	98.39 124	99.87 39	99.84 13

HPM-MVS		99.42 31	99.28 40	99.83 24	99.90 3	99.72 28	99.81 15	99.54 63	97.59 149	99.68 40	99.63 149	98.91 27	99.94 41	98.58 105	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 40	99.28 40	99.61 68	99.86 20	99.07 110	99.47 159	99.93 2	97.66 147	99.71 34	99.86 23	97.73 88	99.96 19	99.47 13	99.82 67	99.79 45

xiu_mvs_v1_base_debu		99.29 48	99.27 42	99.34 111	99.63 113	98.97 130	99.12 260	99.51 86	98.86 31	99.84 9	99.47 209	98.18 76	99.99 1	99.50 8	99.31 117	99.08 176

xiu_mvs_v1_base		99.29 48	99.27 42	99.34 111	99.63 113	98.97 130	99.12 260	99.51 86	98.86 31	99.84 9	99.47 209	98.18 76	99.99 1	99.50 8	99.31 117	99.08 176

xiu_mvs_v1_base_debi		99.29 48	99.27 42	99.34 111	99.63 113	98.97 130	99.12 260	99.51 86	98.86 31	99.84 9	99.47 209	98.18 76	99.99 1	99.50 8	99.31 117	99.08 176

HSP-MVS		99.41 34	99.26 45	99.85 18	99.89 8	99.80 15	99.67 60	99.37 201	98.70 46	99.77 26	99.49 199	98.21 75	99.95 34	98.46 121	99.77 76	99.81 35

xiu_mvs_v2_base		99.26 53	99.25 46	99.29 123	99.53 134	98.91 143	99.02 286	99.45 155	98.80 39	99.71 34	99.26 266	98.94 25	99.98 5	99.34 23	99.23 121	98.98 189

HPM-MVS++		99.39 38	99.23 47	99.87 7	99.75 57	99.84 7	99.43 172	99.51 86	98.68 48	99.27 142	99.53 185	98.64 54	99.96 19	98.44 123	99.80 70	99.79 45

MP-MVS-pluss		99.37 39	99.20 48	99.88 5	99.90 3	99.87 3	99.30 218	99.52 77	97.18 188	99.60 64	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 42	99.19 49	99.79 34	99.61 121	99.65 40	99.30 218	99.48 117	98.86 31	99.21 165	99.63 149	98.72 48	99.90 88	98.25 135	99.63 102	99.80 41

DeepC-MVS	98.35 2	99.30 46	99.19 49	99.64 64	99.82 29	99.23 93	99.62 86	99.55 56	98.94 26	99.63 56	99.95 2	95.82 144	99.94 41	99.37 18	99.97 3	99.73 65
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 46	99.17 51	99.70 51	99.56 132	99.52 61	99.58 104	99.80 8	97.12 194	99.62 59	99.73 107	98.58 57	99.90 88	98.61 101	99.91 17	99.68 84

MP-MVS		99.33 43	99.15 52	99.87 7	99.88 11	99.82 13	99.66 69	99.46 143	98.09 91	99.48 93	99.74 101	98.29 72	99.96 19	97.93 160	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 54	99.14 53	99.59 70	99.41 163	99.16 98	99.35 208	99.57 44	98.82 35	99.51 88	99.61 158	96.46 125	99.95 34	99.59 2	99.98 2	99.65 92

CHOSEN 280x420		99.12 70	99.13 54	99.08 151	99.66 105	97.89 229	98.43 338	99.71 13	98.88 30	99.62 59	99.76 91	96.63 121	99.70 192	99.46 14	99.99 1	99.66 88

MVSFormer		99.17 61	99.12 55	99.29 123	99.51 138	98.94 138	99.88 1	99.46 143	97.55 154	99.80 17	99.65 138	97.39 96	99.28 261	99.03 50	99.85 54	99.65 92

LS3D		99.27 51	99.12 55	99.74 45	99.18 212	99.75 24	99.56 117	99.57 44	98.45 60	99.49 92	99.85 27	97.77 87	99.94 41	98.33 131	99.84 59	99.52 125

casdiffmvs1		99.23 56	99.11 57	99.58 73	99.53 134	99.36 78	99.76 28	99.43 172	97.99 109	99.52 85	99.84 36	97.50 94	99.77 162	99.42 17	98.97 141	99.61 106

CHOSEN 1792x2688		99.19 58	99.10 58	99.45 100	99.89 8	98.52 198	99.39 192	99.94 1	98.73 44	99.11 183	99.89 10	95.50 151	99.94 41	99.50 8	99.97 3	99.89 2

APD-MVS		99.27 51	99.08 59	99.84 23	99.75 57	99.79 19	99.50 140	99.50 101	97.16 190	99.77 26	99.82 47	98.78 37	99.94 41	97.56 195	99.86 50	99.80 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.12 70	99.08 59	99.24 135	99.46 153	98.55 192	99.51 135	99.46 143	98.09 91	99.45 97	99.82 47	98.34 70	99.51 218	98.70 89	98.93 145	99.67 87

test_prior3		99.21 57	99.05 61	99.68 52	99.67 95	99.48 65	98.96 301	99.56 49	98.34 67	99.01 201	99.52 190	98.68 51	99.83 132	97.96 157	99.74 81	99.74 60

sss		99.17 61	99.05 61	99.53 83	99.62 117	98.97 130	99.36 203	99.62 31	97.83 125	99.67 46	99.65 138	97.37 99	99.95 34	99.19 36	99.19 124	99.68 84

3Dnovator	97.25 9	99.24 55	99.05 61	99.81 29	99.12 225	99.66 37	99.84 9	99.74 10	99.09 8	98.92 216	99.90 7	95.94 139	99.98 5	98.95 57	99.92 12	99.79 45

F-COLMAP		99.19 58	99.04 64	99.64 64	99.78 36	99.27 89	99.42 179	99.54 63	97.29 179	99.41 106	99.59 163	98.42 66	99.93 56	98.19 137	99.69 92	99.73 65

OMC-MVS		99.08 82	99.04 64	99.20 139	99.67 95	98.22 215	99.28 224	99.52 77	98.07 95	99.66 51	99.81 57	97.79 86	99.78 160	97.79 171	99.81 68	99.60 107

jason		99.13 65	99.03 66	99.45 100	99.46 153	98.87 146	99.12 260	99.26 249	98.03 103	99.79 19	99.65 138	97.02 108	99.85 118	99.02 52	99.90 24	99.65 92
jason: jason.
CDS-MVSNet		99.09 79	99.03 66	99.25 132	99.42 160	98.73 175	99.45 163	99.46 143	98.11 88	99.46 96	99.77 88	98.01 81	99.37 238	98.70 89	98.92 147	99.66 88
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
API-MVS		99.04 87	99.03 66	99.06 153	99.40 168	99.31 85	99.55 123	99.56 49	98.54 54	99.33 126	99.39 232	98.76 42	99.78 160	96.98 233	99.78 74	98.07 318

MG-MVS		99.13 65	99.02 69	99.45 100	99.57 128	98.63 185	99.07 271	99.34 214	98.99 18	99.61 61	99.82 47	97.98 82	99.87 107	97.00 231	99.80 70	99.85 9

lupinMVS		99.13 65	99.01 70	99.46 99	99.51 138	98.94 138	99.05 277	99.16 260	97.86 119	99.80 17	99.56 172	97.39 96	99.86 111	98.94 59	99.85 54	99.58 114

diffmvs1		99.12 70	99.00 71	99.48 92	99.51 138	99.10 105	99.61 92	99.49 106	97.67 145	99.36 118	99.74 101	97.67 90	99.88 104	98.95 57	98.99 138	99.47 140

mvs_anonymous		99.03 89	98.99 72	99.16 143	99.38 171	98.52 198	99.51 135	99.38 193	97.79 130	99.38 113	99.81 57	97.30 100	99.45 222	99.35 19	98.99 138	99.51 130

EPP-MVSNet		99.13 65	98.99 72	99.53 83	99.65 109	99.06 111	99.81 15	99.33 222	97.43 166	99.60 64	99.88 15	97.14 104	99.84 124	99.13 43	98.94 144	99.69 80

CNLPA		99.14 64	98.99 72	99.59 70	99.58 126	99.41 74	99.16 253	99.44 164	98.45 60	99.19 171	99.49 199	98.08 79	99.89 96	97.73 179	99.75 79	99.48 136

MVS_Test		99.10 78	98.97 75	99.48 92	99.49 147	99.14 102	99.67 60	99.34 214	97.31 177	99.58 68	99.76 91	97.65 91	99.82 141	98.87 66	99.07 133	99.46 144

casdiffmvs		99.09 79	98.97 75	99.47 96	99.47 151	99.10 105	99.74 41	99.38 193	97.86 119	99.32 127	99.79 76	97.08 107	99.77 162	99.24 32	98.82 155	99.54 119

PVSNet_Blended		99.08 82	98.97 75	99.42 106	99.76 45	98.79 169	98.78 319	99.91 3	96.74 226	99.67 46	99.49 199	97.53 92	99.88 104	98.98 55	99.85 54	99.60 107

Vis-MVSNet		99.12 70	98.97 75	99.56 77	99.78 36	99.10 105	99.68 58	99.66 25	98.49 57	99.86 8	99.87 20	94.77 192	99.84 124	99.19 36	99.41 111	99.74 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	97.12 13	99.18 60	98.97 75	99.82 26	99.17 217	99.68 33	99.81 15	99.51 86	99.20 4	98.72 239	99.89 10	95.68 148	99.97 11	98.86 69	99.86 50	99.81 35

DP-MVS Recon		99.12 70	98.95 80	99.65 59	99.74 68	99.70 31	99.27 227	99.57 44	96.40 255	99.42 104	99.68 127	98.75 45	99.80 149	97.98 156	99.72 85	99.44 148

DP-MVS		99.16 63	98.95 80	99.78 35	99.77 42	99.53 58	99.41 183	99.50 101	97.03 209	99.04 198	99.88 15	97.39 96	99.92 65	98.66 94	99.90 24	99.87 5

PS-MVSNAJss		98.92 101	98.92 82	98.90 184	98.78 292	98.53 194	99.78 22	99.54 63	98.07 95	99.00 208	99.76 91	99.01 12	99.37 238	99.13 43	97.23 245	98.81 208

HyFIR lowres test		99.11 75	98.92 82	99.65 59	99.90 3	99.37 77	99.02 286	99.91 3	97.67 145	99.59 67	99.75 96	95.90 141	99.73 176	99.53 6	99.02 136	99.86 6

CDPH-MVS		99.13 65	98.91 84	99.80 31	99.75 57	99.71 29	99.15 256	99.41 177	96.60 237	99.60 64	99.55 175	98.83 32	99.90 88	97.48 203	99.83 63	99.78 49

VNet		99.11 75	98.90 85	99.73 47	99.52 136	99.56 52	99.41 183	99.39 187	99.01 13	99.74 33	99.78 82	95.56 149	99.92 65	99.52 7	98.18 191	99.72 71

CPTT-MVS		99.11 75	98.90 85	99.74 45	99.80 34	99.46 68	99.59 97	99.49 106	97.03 209	99.63 56	99.69 122	97.27 101	99.96 19	97.82 168	99.84 59	99.81 35

Effi-MVS+-dtu		98.78 120	98.89 87	98.47 241	99.33 180	96.91 272	99.57 110	99.30 231	98.47 58	99.41 106	98.99 289	96.78 115	99.74 169	98.73 86	99.38 112	98.74 221

WTY-MVS		99.06 84	98.88 88	99.61 68	99.62 117	99.16 98	99.37 199	99.56 49	98.04 101	99.53 83	99.62 154	96.84 113	99.94 41	98.85 71	98.49 172	99.72 71

CANet_DTU		98.97 98	98.87 89	99.25 132	99.33 180	98.42 209	99.08 270	99.30 231	99.16 5	99.43 101	99.75 96	95.27 158	99.97 11	98.56 110	99.95 6	99.36 155

1121		99.09 79	98.87 89	99.75 40	99.74 68	99.60 47	99.27 227	99.48 117	96.82 224	99.25 150	99.65 138	98.38 67	99.93 56	97.53 198	99.67 96	99.73 65

diffmvs		98.99 95	98.87 89	99.35 110	99.45 157	98.74 174	99.62 86	99.45 155	97.43 166	99.13 178	99.72 111	97.23 102	99.87 107	98.86 69	98.90 149	99.45 147

IS-MVSNet		99.05 86	98.87 89	99.57 75	99.73 73	99.32 82	99.75 36	99.20 256	98.02 104	99.56 72	99.86 23	96.54 123	99.67 197	98.09 145	99.13 127	99.73 65

MVS_0304		99.06 84	98.86 93	99.66 55	99.51 138	99.36 78	99.22 244	99.51 86	98.95 24	99.58 68	99.65 138	93.74 234	99.98 5	99.66 1	99.95 6	99.64 98

canonicalmvs		99.02 90	98.86 93	99.51 89	99.42 160	99.32 82	99.80 19	99.48 117	98.63 49	99.31 129	98.81 304	97.09 105	99.75 168	99.27 30	97.90 213	99.47 140

PLC	97.94 4	99.02 90	98.85 95	99.53 83	99.66 105	99.01 123	99.24 239	99.52 77	96.85 221	99.27 142	99.48 205	98.25 74	99.91 75	97.76 175	99.62 103	99.65 92
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs-test1		98.86 106	98.84 96	98.89 187	99.33 180	97.77 240	99.44 167	99.30 231	98.47 58	99.10 186	99.43 218	96.78 115	99.95 34	98.73 86	99.02 136	98.96 196

PAPM_NR		99.04 87	98.84 96	99.66 55	99.74 68	99.44 71	99.39 192	99.38 193	97.70 141	99.28 138	99.28 263	98.34 70	99.85 118	96.96 235	99.45 108	99.69 80

PVSNet	96.02 17	98.85 113	98.84 96	98.89 187	99.73 73	97.28 247	98.32 342	99.60 35	97.86 119	99.50 89	99.57 170	96.75 118	99.86 111	98.56 110	99.70 91	99.54 119

Fast-Effi-MVS+-dtu		98.77 122	98.83 99	98.60 227	99.41 163	96.99 266	99.52 131	99.49 106	98.11 88	99.24 155	99.34 251	96.96 111	99.79 152	97.95 159	99.45 108	99.02 185

PVSNet_BlendedMVS		98.86 106	98.80 100	99.03 156	99.76 45	98.79 169	99.28 224	99.91 3	97.42 169	99.67 46	99.37 237	97.53 92	99.88 104	98.98 55	97.29 244	98.42 306

AdaColmap		99.01 93	98.80 100	99.66 55	99.56 132	99.54 55	99.18 251	99.70 15	98.18 81	99.35 122	99.63 149	96.32 130	99.90 88	97.48 203	99.77 76	99.55 117

MSDG		98.98 96	98.80 100	99.53 83	99.76 45	99.19 95	98.75 322	99.55 56	97.25 182	99.47 94	99.77 88	97.82 85	99.87 107	96.93 238	99.90 24	99.54 119

train_agg		99.02 90	98.77 103	99.77 37	99.67 95	99.65 40	99.05 277	99.41 177	96.28 261	98.95 212	99.49 199	98.76 42	99.91 75	97.63 188	99.72 85	99.75 55

1112_ss		98.98 96	98.77 103	99.59 70	99.68 94	99.02 121	99.25 237	99.48 117	97.23 185	99.13 178	99.58 166	96.93 112	99.90 88	98.87 66	98.78 159	99.84 13

agg_prior1		99.01 93	98.76 105	99.76 39	99.67 95	99.62 43	98.99 292	99.40 184	96.26 264	98.87 222	99.49 199	98.77 40	99.91 75	97.69 185	99.72 85	99.75 55

COLMAP_ROB	97.56 6	98.86 106	98.75 106	99.17 142	99.88 11	98.53 194	99.34 211	99.59 38	97.55 154	98.70 246	99.89 10	95.83 143	99.90 88	98.10 144	99.90 24	99.08 176
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.87 103	98.72 107	99.31 117	99.86 20	98.48 204	99.56 117	99.61 32	97.85 122	99.36 118	99.85 27	95.95 137	99.85 118	96.66 257	99.83 63	99.59 111

Vis-MVSNet (Re-imp)		98.87 103	98.72 107	99.31 117	99.71 83	98.88 145	99.80 19	99.44 164	97.91 117	99.36 118	99.78 82	95.49 152	99.43 231	97.91 161	99.11 128	99.62 104

agg_prior3		98.97 98	98.71 109	99.75 40	99.67 95	99.60 47	99.04 282	99.41 177	95.93 285	98.87 222	99.48 205	98.61 55	99.91 75	97.63 188	99.72 85	99.75 55

EPNet		98.86 106	98.71 109	99.30 120	97.20 340	98.18 216	99.62 86	98.91 290	99.28 2	98.63 257	99.81 57	95.96 136	99.99 1	99.24 32	99.72 85	99.73 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UGNet		98.87 103	98.69 111	99.40 107	99.22 204	98.72 177	99.44 167	99.68 19	99.24 3	99.18 174	99.42 221	92.74 250	99.96 19	99.34 23	99.94 10	99.53 124
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		98.73 124	98.68 112	98.88 194	99.70 89	97.73 242	98.92 308	99.55 56	98.52 56	99.45 97	99.84 36	95.27 158	99.91 75	98.08 149	98.84 154	99.00 186

EI-MVSNet		98.67 128	98.67 113	98.68 222	99.35 176	97.97 225	99.50 140	99.38 193	96.93 216	99.20 168	99.83 40	97.87 83	99.36 242	98.38 126	97.56 225	98.71 225

CVMVSNet		98.57 133	98.67 113	98.30 256	99.35 176	95.59 301	99.50 140	99.55 56	98.60 52	99.39 111	99.83 40	94.48 206	99.45 222	98.75 83	98.56 168	99.85 9

114514_t		98.93 100	98.67 113	99.72 49	99.85 23	99.53 58	99.62 86	99.59 38	92.65 332	99.71 34	99.78 82	98.06 80	99.90 88	98.84 72	99.91 17	99.74 60

Test_1112_low_res		98.89 102	98.66 116	99.57 75	99.69 91	98.95 135	99.03 283	99.47 133	96.98 211	99.15 177	99.23 270	96.77 117	99.89 96	98.83 75	98.78 159	99.86 6

HY-MVS	97.30 7	98.85 113	98.64 117	99.47 96	99.42 160	99.08 109	99.62 86	99.36 202	97.39 172	99.28 138	99.68 127	96.44 127	99.92 65	98.37 127	98.22 187	99.40 153

0601test		98.86 106	98.63 118	99.54 78	99.49 147	99.18 97	99.50 140	99.07 271	98.22 77	99.61 61	99.51 193	95.37 154	99.84 124	98.60 103	98.33 177	99.59 111

FIs		98.78 120	98.63 118	99.23 137	99.18 212	99.54 55	99.83 12	99.59 38	98.28 71	98.79 233	99.81 57	96.75 118	99.37 238	99.08 47	96.38 260	98.78 211

ab-mvs		98.86 106	98.63 118	99.54 78	99.64 110	99.19 95	99.44 167	99.54 63	97.77 132	99.30 130	99.81 57	94.20 215	99.93 56	99.17 39	98.82 155	99.49 134

MAR-MVS		98.86 106	98.63 118	99.54 78	99.37 173	99.66 37	99.45 163	99.54 63	96.61 235	99.01 201	99.40 228	97.09 105	99.86 111	97.68 187	99.53 106	99.10 171
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		98.75 123	98.62 122	99.15 145	99.08 233	99.45 70	99.86 8	99.60 35	98.23 76	98.70 246	99.82 47	96.80 114	99.22 276	99.07 48	96.38 260	98.79 210

XVG-OURS-SEG-HR		98.69 126	98.62 122	98.89 187	99.71 83	97.74 241	99.12 260	99.54 63	98.44 63	99.42 104	99.71 113	94.20 215	99.92 65	98.54 115	98.90 149	99.00 186

RPSCF		98.22 157	98.62 122	96.99 314	99.82 29	91.58 342	99.72 43	99.44 164	96.61 235	99.66 51	99.89 10	95.92 140	99.82 141	97.46 206	99.10 130	99.57 115

PatchMatch-RL		98.84 115	98.62 122	99.52 87	99.71 83	99.28 87	99.06 275	99.77 9	97.74 136	99.50 89	99.53 185	95.41 153	99.84 124	97.17 223	99.64 100	99.44 148

PMMVS		98.80 119	98.62 122	99.34 111	99.27 197	98.70 178	98.76 321	99.31 229	97.34 174	99.21 165	99.07 282	97.20 103	99.82 141	98.56 110	98.87 152	99.52 125

Effi-MVS+		98.81 116	98.59 127	99.48 92	99.46 153	99.12 104	98.08 348	99.50 101	97.50 159	99.38 113	99.41 224	96.37 129	99.81 145	99.11 45	98.54 169	99.51 130

test_djsdf		98.67 128	98.57 128	98.98 162	98.70 303	98.91 143	99.88 1	99.46 143	97.55 154	99.22 162	99.88 15	95.73 147	99.28 261	99.03 50	97.62 220	98.75 218

alignmvs		98.81 116	98.56 129	99.58 73	99.43 159	99.42 73	99.51 135	98.96 283	98.61 51	99.35 122	98.92 295	94.78 188	99.77 162	99.35 19	98.11 207	99.54 119

1314		98.68 127	98.54 130	99.11 150	98.89 275	98.65 183	99.27 227	99.49 106	96.89 219	97.99 292	99.56 172	97.72 89	99.83 132	97.74 178	99.27 120	98.84 206

tpmrst		98.33 145	98.48 131	97.90 289	99.16 219	94.78 319	99.31 216	99.11 265	97.27 180	99.45 97	99.59 163	95.33 155	99.84 124	98.48 118	98.61 162	99.09 175

Fast-Effi-MVS+		98.70 125	98.43 132	99.51 89	99.51 138	99.28 87	99.52 131	99.47 133	96.11 278	99.01 201	99.34 251	96.20 134	99.84 124	97.88 163	98.82 155	99.39 154

nrg030		98.64 131	98.42 133	99.28 125	99.05 239	99.69 32	99.81 15	99.46 143	98.04 101	99.01 201	99.82 47	96.69 120	99.38 234	99.34 23	94.59 301	98.78 211

IterMVS-LS		98.46 136	98.42 133	98.58 229	99.59 125	98.00 223	99.37 199	99.43 172	96.94 215	99.07 192	99.59 163	97.87 83	99.03 297	98.32 133	95.62 275	98.71 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		98.42 139	98.36 135	98.59 228	99.49 147	96.70 278	99.27 227	99.13 264	97.24 184	98.80 232	99.38 233	95.75 146	99.74 169	97.07 228	99.16 125	99.33 158

PatchmatchNet		98.31 147	98.36 135	98.19 272	99.16 219	95.32 309	99.27 227	98.92 287	97.37 173	99.37 115	99.58 166	94.90 180	99.70 192	97.43 209	99.21 122	99.54 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PAPR		98.63 132	98.34 137	99.51 89	99.40 168	99.03 120	98.80 317	99.36 202	96.33 257	99.00 208	99.12 280	98.46 61	99.84 124	95.23 287	99.37 116	99.66 88

ACMM	97.58 5	98.37 144	98.34 137	98.48 239	99.41 163	97.10 255	99.56 117	99.45 155	98.53 55	99.04 198	99.85 27	93.00 242	99.71 186	98.74 84	97.45 235	98.64 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		98.49 134	98.32 139	99.00 160	99.35 176	99.02 121	99.54 126	99.38 193	97.41 170	99.20 168	99.73 107	93.86 229	99.36 242	98.87 66	97.56 225	98.62 276

MDTV_nov1_ep13			98.32 139		99.11 227	94.44 323	99.27 227	98.74 308	97.51 158	99.40 110	99.62 154	94.78 188	99.76 167	97.59 190	98.81 158

QAPM		98.67 128	98.30 141	99.80 31	99.20 207	99.67 35	99.77 25	99.72 11	94.74 299	98.73 238	99.90 7	95.78 145	99.98 5	96.96 235	99.88 35	99.76 54

anonymousdsp		98.44 137	98.28 142	98.94 168	98.50 319	98.96 134	99.77 25	99.50 101	97.07 205	98.87 222	99.77 88	94.76 193	99.28 261	98.66 94	97.60 221	98.57 297

jajsoiax		98.43 138	98.28 142	98.88 194	98.60 314	98.43 207	99.82 13	99.53 73	98.19 78	98.63 257	99.80 68	93.22 240	99.44 227	99.22 34	97.50 230	98.77 214

mvs_tets		98.40 142	98.23 144	98.91 180	98.67 307	98.51 200	99.66 69	99.53 73	98.19 78	98.65 255	99.81 57	92.75 248	99.44 227	99.31 26	97.48 234	98.77 214

HQP_MVS		98.27 152	98.22 145	98.44 246	99.29 192	96.97 268	99.39 192	99.47 133	98.97 22	99.11 183	99.61 158	92.71 252	99.69 195	97.78 172	97.63 218	98.67 251

LCM-MVSNet-Re		97.83 217	98.15 146	96.87 318	99.30 189	92.25 340	99.59 97	98.26 332	97.43 166	96.20 317	99.13 277	96.27 132	98.73 317	98.17 139	98.99 138	99.64 98

tttt0517		98.42 139	98.14 147	99.28 125	99.66 105	98.38 210	99.74 41	96.85 356	97.68 143	99.79 19	99.74 101	91.39 295	99.89 96	98.83 75	99.56 104	99.57 115

Patchmatch-test1		98.16 166	98.14 147	98.22 269	99.30 189	95.55 302	99.07 271	98.97 281	97.57 152	99.43 101	99.60 161	92.72 251	99.60 211	97.38 211	99.20 123	99.50 133

LPG-MVS_test		98.22 157	98.13 149	98.49 237	99.33 180	97.05 261	99.58 104	99.55 56	97.46 161	99.24 155	99.83 40	92.58 263	99.72 180	98.09 145	97.51 228	98.68 240

OpenMVS	96.50 16	98.47 135	98.12 150	99.52 87	99.04 240	99.53 58	99.82 13	99.72 11	94.56 305	98.08 286	99.88 15	94.73 195	99.98 5	97.47 205	99.76 78	99.06 181

OPM-MVS		98.19 163	98.10 151	98.45 243	98.88 276	97.07 259	99.28 224	99.38 193	98.57 53	99.22 162	99.81 57	92.12 275	99.66 199	98.08 149	97.54 227	98.61 285

CLD-MVS		98.16 166	98.10 151	98.33 253	99.29 192	96.82 275	98.75 322	99.44 164	97.83 125	99.13 178	99.55 175	92.92 244	99.67 197	98.32 133	97.69 217	98.48 302
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		98.38 143	98.09 153	99.24 135	99.26 199	99.32 82	99.56 117	99.55 56	97.45 164	98.71 240	99.83 40	93.23 238	99.63 208	98.88 62	96.32 262	98.76 216

ADS-MVSNet		98.20 162	98.08 154	98.56 232	99.33 180	96.48 285	99.23 240	99.15 261	96.24 266	99.10 186	99.67 132	94.11 220	99.71 186	96.81 248	99.05 134	99.48 136

BH-RMVSNet		98.41 141	98.08 154	99.40 107	99.41 163	98.83 153	99.30 218	98.77 304	97.70 141	98.94 214	99.65 138	92.91 246	99.74 169	96.52 261	99.55 105	99.64 98

ADS-MVSNet2		98.02 189	98.07 156	97.87 290	99.33 180	95.19 313	99.23 240	99.08 268	96.24 266	99.10 186	99.67 132	94.11 220	98.93 312	96.81 248	99.05 134	99.48 136

PatchFormer-LS_test		98.01 192	98.05 157	97.87 290	99.15 222	94.76 320	99.42 179	98.93 285	97.12 194	98.84 228	98.59 316	93.74 234	99.80 149	98.55 113	98.17 197	99.06 181

EU-MVSNet		97.98 194	98.03 158	97.81 296	98.72 300	96.65 281	99.66 69	99.66 25	98.09 91	98.35 273	99.82 47	95.25 161	98.01 333	97.41 210	95.30 280	98.78 211

tfpn1000		98.33 145	98.02 159	99.25 132	99.78 36	98.73 175	99.70 46	97.55 352	97.48 160	99.69 39	99.53 185	92.37 272	99.85 118	97.82 168	98.26 186	99.16 167

tpmvs		97.98 194	98.02 159	97.84 293	99.04 240	94.73 321	99.31 216	99.20 256	96.10 282	98.76 236	99.42 221	94.94 175	99.81 145	96.97 234	98.45 173	98.97 190

Anonymous20240521		98.30 148	98.00 161	99.18 141	98.98 250	99.46 68	99.78 22	99.49 106	96.91 218	98.00 291	99.25 267	96.51 124	99.38 234	98.15 142	94.95 290	98.71 225

UniMVSNet (Re)		98.29 150	98.00 161	99.13 149	99.00 245	99.36 78	99.49 149	99.51 86	97.95 112	98.97 211	99.13 277	96.30 131	99.38 234	98.36 129	93.34 319	98.66 262

ACMH	97.28 8	98.10 174	97.99 163	98.44 246	99.41 163	96.96 270	99.60 95	99.56 49	98.09 91	98.15 283	99.91 5	90.87 301	99.70 192	98.88 62	97.45 235	98.67 251
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous202405211		98.30 148	97.98 164	99.26 131	99.57 128	98.16 217	99.41 183	98.55 327	96.03 283	99.19 171	99.74 101	91.87 281	99.92 65	99.16 41	98.29 181	99.70 79

UniMVSNet_NR-MVSNet		98.22 157	97.97 165	98.96 165	98.92 269	98.98 127	99.48 154	99.53 73	97.76 133	98.71 240	99.46 213	96.43 128	99.22 276	98.57 107	92.87 325	98.69 235

EPNet_dtu		98.03 187	97.96 166	98.23 267	98.27 324	95.54 304	99.23 240	98.75 305	99.02 10	97.82 297	99.71 113	96.11 135	99.48 219	93.04 325	99.65 99	99.69 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPA-MVSNet		98.29 150	97.95 167	99.30 120	99.16 219	99.54 55	99.50 140	99.58 43	98.27 72	99.35 122	99.37 237	92.53 265	99.65 201	99.35 19	94.46 302	98.72 223

ACMP	97.20 11	98.06 178	97.94 168	98.45 243	99.37 173	97.01 264	99.44 167	99.49 106	97.54 157	98.45 267	99.79 76	91.95 276	99.72 180	97.91 161	97.49 233	98.62 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CR-MVSNet		98.17 164	97.93 169	98.87 198	99.18 212	98.49 202	99.22 244	99.33 222	96.96 212	99.56 72	99.38 233	94.33 211	99.00 301	94.83 294	98.58 165	99.14 168

pmmvs4		98.13 169	97.90 170	98.81 209	98.61 313	98.87 146	98.99 292	99.21 255	96.44 250	99.06 196	99.58 166	95.90 141	99.11 289	97.18 222	96.11 265	98.46 305

test-LLR		98.06 178	97.90 170	98.55 234	98.79 288	97.10 255	98.67 326	97.75 341	97.34 174	98.61 260	98.85 300	94.45 207	99.45 222	97.25 216	99.38 112	99.10 171

HQP-MVS		98.02 189	97.90 170	98.37 251	99.19 209	96.83 273	98.98 296	99.39 187	98.24 73	98.66 249	99.40 228	92.47 267	99.64 203	97.19 220	97.58 223	98.64 267

LTVRE_ROB	97.16 12	98.02 189	97.90 170	98.40 249	99.23 202	96.80 276	99.70 46	99.60 35	97.12 194	98.18 282	99.70 116	91.73 287	99.72 180	98.39 124	97.45 235	98.68 240
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		98.21 160	97.89 174	99.15 145	99.76 45	99.04 113	99.67 60	97.71 344	97.10 198	99.55 75	99.54 178	92.70 254	99.79 152	96.90 241	98.12 201	98.61 285

conf0.002		98.21 160	97.89 174	99.15 145	99.76 45	99.04 113	99.67 60	97.71 344	97.10 198	99.55 75	99.54 178	92.70 254	99.79 152	96.90 241	98.12 201	98.61 285

thresconf0.02		98.24 153	97.89 174	99.27 127	99.76 45	99.04 113	99.67 60	97.71 344	97.10 198	99.55 75	99.54 178	92.70 254	99.79 152	96.90 241	98.12 201	98.97 190

tfpn_n400		98.24 153	97.89 174	99.27 127	99.76 45	99.04 113	99.67 60	97.71 344	97.10 198	99.55 75	99.54 178	92.70 254	99.79 152	96.90 241	98.12 201	98.97 190

tfpnconf		98.24 153	97.89 174	99.27 127	99.76 45	99.04 113	99.67 60	97.71 344	97.10 198	99.55 75	99.54 178	92.70 254	99.79 152	96.90 241	98.12 201	98.97 190

tfpnview11		98.24 153	97.89 174	99.27 127	99.76 45	99.04 113	99.67 60	97.71 344	97.10 198	99.55 75	99.54 178	92.70 254	99.79 152	96.90 241	98.12 201	98.97 190

BH-w/o		98.00 193	97.89 174	98.32 254	99.35 176	96.20 294	99.01 290	98.90 292	96.42 252	98.38 270	99.00 288	95.26 160	99.72 180	96.06 269	98.61 162	99.03 183

WR-MVS_H		98.13 169	97.87 181	98.90 184	99.02 243	98.84 150	99.70 46	99.59 38	97.27 180	98.40 269	99.19 273	95.53 150	99.23 273	98.34 130	93.78 316	98.61 285

tfpn_ndepth		98.17 164	97.84 182	99.15 145	99.75 57	98.76 173	99.61 92	97.39 354	96.92 217	99.61 61	99.38 233	92.19 274	99.86 111	97.57 193	98.13 199	98.82 207

v1neww		98.12 171	97.84 182	98.93 171	98.97 254	98.81 162	99.66 69	99.35 206	96.49 242	99.29 134	99.37 237	95.02 170	99.32 252	97.73 179	94.73 293	98.67 251

v7new		98.12 171	97.84 182	98.93 171	98.97 254	98.81 162	99.66 69	99.35 206	96.49 242	99.29 134	99.37 237	95.02 170	99.32 252	97.73 179	94.73 293	98.67 251

v6		98.12 171	97.84 182	98.94 168	98.94 262	98.83 153	99.66 69	99.34 214	96.49 242	99.30 130	99.37 237	94.95 174	99.34 248	97.77 174	94.74 292	98.67 251

dp		97.75 234	97.80 186	97.59 304	99.10 230	93.71 331	99.32 213	98.88 294	96.48 248	99.08 191	99.55 175	92.67 261	99.82 141	96.52 261	98.58 165	99.24 163

thisisatest0515		98.14 168	97.79 187	99.19 140	99.50 146	98.50 201	98.61 330	96.82 357	96.95 214	99.54 81	99.43 218	91.66 291	99.86 111	98.08 149	99.51 107	99.22 164

V42		98.06 178	97.79 187	98.86 202	98.98 250	98.84 150	99.69 49	99.34 214	96.53 241	99.30 130	99.37 237	94.67 198	99.32 252	97.57 193	94.66 298	98.42 306

DU-MVS		98.08 177	97.79 187	98.96 165	98.87 279	98.98 127	99.41 183	99.45 155	97.87 118	98.71 240	99.50 196	94.82 185	99.22 276	98.57 107	92.87 325	98.68 240

divwei89l23v2f112		98.06 178	97.78 190	98.91 180	98.90 272	98.77 172	99.57 110	99.35 206	96.45 249	99.24 155	99.37 237	94.92 178	99.27 264	97.50 201	94.71 297	98.68 240

v7		98.05 184	97.78 190	98.87 198	98.99 246	98.67 180	99.64 81	99.34 214	96.31 260	99.29 134	99.51 193	94.78 188	99.27 264	97.03 229	95.15 284	98.66 262

CP-MVSNet		98.09 175	97.78 190	99.01 158	98.97 254	99.24 92	99.67 60	99.46 143	97.25 182	98.48 266	99.64 145	93.79 230	99.06 293	98.63 97	94.10 310	98.74 221

ACMH+	97.24 10	97.92 206	97.78 190	98.32 254	99.46 153	96.68 280	99.56 117	99.54 63	98.41 64	97.79 299	99.87 20	90.18 309	99.66 199	98.05 154	97.18 248	98.62 276

v2v482		98.06 178	97.77 194	98.92 176	98.90 272	98.82 160	99.57 110	99.36 202	96.65 232	99.19 171	99.35 248	94.20 215	99.25 270	97.72 183	94.97 288	98.69 235

OurMVSNet-221017-0		97.88 209	97.77 194	98.19 272	98.71 302	96.53 283	99.88 1	99.00 278	97.79 130	98.78 234	99.94 3	91.68 288	99.35 245	97.21 218	96.99 251	98.69 235

IterMVS		97.83 217	97.77 194	98.02 280	99.58 126	96.27 292	99.02 286	99.48 117	97.22 186	98.71 240	99.70 116	92.75 248	99.13 286	97.46 206	96.00 268	98.67 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1141		98.05 184	97.76 197	98.91 180	98.91 271	98.78 171	99.57 110	99.35 206	96.41 254	99.23 160	99.36 244	94.93 177	99.27 264	97.38 211	94.72 295	98.68 240

v1		98.05 184	97.76 197	98.93 171	98.92 269	98.80 167	99.57 110	99.35 206	96.39 256	99.28 138	99.36 244	94.86 183	99.32 252	97.38 211	94.72 295	98.68 240

FMVSNet3		98.03 187	97.76 197	98.84 206	99.39 170	98.98 127	99.40 190	99.38 193	96.67 231	99.07 192	99.28 263	92.93 243	98.98 303	97.10 225	96.65 253	98.56 298

MVP-Stereo		97.81 221	97.75 200	97.99 283	97.53 333	96.60 282	98.96 301	98.85 296	97.22 186	97.23 305	99.36 244	95.28 157	99.46 221	95.51 281	99.78 74	97.92 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WR-MVS		98.06 178	97.73 201	99.06 153	98.86 282	99.25 91	99.19 250	99.35 206	97.30 178	98.66 249	99.43 218	93.94 225	99.21 280	98.58 105	94.28 306	98.71 225

CostFormer		97.72 239	97.73 201	97.71 301	99.15 222	94.02 327	99.54 126	99.02 277	94.67 300	99.04 198	99.35 248	92.35 273	99.77 162	98.50 117	97.94 212	99.34 157

XVG-ACMP-BASELINE		97.83 217	97.71 203	98.20 271	99.11 227	96.33 290	99.41 183	99.52 77	98.06 99	99.05 197	99.50 196	89.64 313	99.73 176	97.73 179	97.38 241	98.53 299

v1144		97.98 194	97.69 204	98.85 205	98.87 279	98.66 182	99.54 126	99.35 206	96.27 263	99.23 160	99.35 248	94.67 198	99.23 273	96.73 252	95.16 283	98.68 240

Anonymous20240529		98.09 175	97.68 205	99.34 111	99.66 105	98.44 206	99.40 190	99.43 172	93.67 321	99.22 162	99.89 10	90.23 308	99.93 56	99.26 31	98.33 177	99.66 88

our_test_3		97.65 250	97.68 205	97.55 305	98.62 311	94.97 317	98.84 315	99.30 231	96.83 223	98.19 281	99.34 251	97.01 109	99.02 298	95.00 291	96.01 267	98.64 267

view600		97.97 197	97.66 207	98.89 187	99.75 57	97.81 235	99.69 49	98.80 300	98.02 104	99.25 150	98.88 296	91.95 276	99.89 96	94.36 303	98.29 181	98.96 196

view800		97.97 197	97.66 207	98.89 187	99.75 57	97.81 235	99.69 49	98.80 300	98.02 104	99.25 150	98.88 296	91.95 276	99.89 96	94.36 303	98.29 181	98.96 196

conf0.05thres1000		97.97 197	97.66 207	98.89 187	99.75 57	97.81 235	99.69 49	98.80 300	98.02 104	99.25 150	98.88 296	91.95 276	99.89 96	94.36 303	98.29 181	98.96 196

tfpn		97.97 197	97.66 207	98.89 187	99.75 57	97.81 235	99.69 49	98.80 300	98.02 104	99.25 150	98.88 296	91.95 276	99.89 96	94.36 303	98.29 181	98.96 196

TranMVSNet+NR-MVSNet		97.93 203	97.66 207	98.76 217	98.78 292	98.62 187	99.65 79	99.49 106	97.76 133	98.49 265	99.60 161	94.23 214	98.97 310	98.00 155	92.90 323	98.70 230

Patchmatch-test		97.93 203	97.65 212	98.77 215	99.18 212	97.07 259	99.03 283	99.14 263	96.16 273	98.74 237	99.57 170	94.56 202	99.72 180	93.36 320	99.11 128	99.52 125

EPMVS		97.82 220	97.65 212	98.35 252	98.88 276	95.98 296	99.49 149	94.71 362	97.57 152	99.26 146	99.48 205	92.46 270	99.71 186	97.87 164	99.08 132	99.35 156

v8		97.95 202	97.63 214	98.93 171	98.95 259	98.81 162	99.80 19	99.41 177	96.03 283	99.10 186	99.42 221	94.92 178	99.30 258	96.94 237	94.08 311	98.66 262

NR-MVSNet		97.97 197	97.61 215	99.02 157	98.87 279	99.26 90	99.47 159	99.42 175	97.63 148	97.08 308	99.50 196	95.07 168	99.13 286	97.86 165	93.59 317	98.68 240

v144192		97.92 206	97.60 216	98.87 198	98.83 285	98.65 183	99.55 123	99.34 214	96.20 269	99.32 127	99.40 228	94.36 210	99.26 269	96.37 266	95.03 287	98.70 230

PS-CasMVS		97.93 203	97.59 217	98.95 167	98.99 246	99.06 111	99.68 58	99.52 77	97.13 192	98.31 275	99.68 127	92.44 271	99.05 294	98.51 116	94.08 311	98.75 218

v148		97.79 226	97.55 218	98.50 236	98.74 297	97.72 243	99.54 126	99.33 222	96.26 264	98.90 219	99.51 193	94.68 197	99.14 283	97.83 167	93.15 322	98.63 274

tpm		97.67 248	97.55 218	98.03 278	99.02 243	95.01 316	99.43 172	98.54 328	96.44 250	99.12 181	99.34 251	91.83 282	99.60 211	97.75 177	96.46 258	99.48 136

Anonymous20231211		97.88 209	97.54 220	98.90 184	99.71 83	98.53 194	99.48 154	99.57 44	94.16 315	98.81 230	99.68 127	93.23 238	99.42 232	98.84 72	94.42 304	98.76 216

v7n		97.87 211	97.52 221	98.92 176	98.76 296	98.58 191	99.84 9	99.46 143	96.20 269	98.91 217	99.70 116	94.89 181	99.44 227	96.03 270	93.89 315	98.75 218

v10		97.85 213	97.52 221	98.86 202	98.99 246	98.67 180	99.75 36	99.41 177	95.70 289	98.98 210	99.41 224	94.75 194	99.23 273	96.01 271	94.63 300	98.67 251

V4		97.80 224	97.51 223	98.67 224	98.79 288	98.63 185	99.87 4	99.44 164	95.87 286	99.01 201	99.46 213	94.52 205	99.33 249	96.64 260	93.97 313	98.05 319

thres600view7		97.86 212	97.51 223	98.92 176	99.72 77	97.95 228	99.59 97	98.74 308	97.94 113	99.27 142	98.62 311	91.75 283	99.86 111	93.73 316	98.19 190	98.96 196

v52		97.79 226	97.50 225	98.66 225	98.80 286	98.62 187	99.87 4	99.44 164	95.87 286	99.01 201	99.46 213	94.44 209	99.33 249	96.65 259	93.96 314	98.05 319

testgi		97.65 250	97.50 225	98.13 275	99.36 175	96.45 286	99.42 179	99.48 117	97.76 133	97.87 295	99.45 216	91.09 298	98.81 315	94.53 298	98.52 170	99.13 170

tfpn111		97.81 221	97.49 227	98.78 214	99.72 77	97.86 231	99.59 97	98.74 308	97.93 114	99.26 146	98.62 311	91.75 283	99.86 111	93.57 317	98.18 191	98.61 285

GBi-Net		97.68 245	97.48 228	98.29 257	99.51 138	97.26 249	99.43 172	99.48 117	96.49 242	99.07 192	99.32 257	90.26 305	98.98 303	97.10 225	96.65 253	98.62 276

test1		97.68 245	97.48 228	98.29 257	99.51 138	97.26 249	99.43 172	99.48 117	96.49 242	99.07 192	99.32 257	90.26 305	98.98 303	97.10 225	96.65 253	98.62 276

tfpnnormal		97.84 215	97.47 230	98.98 162	99.20 207	99.22 94	99.64 81	99.61 32	96.32 258	98.27 278	99.70 116	93.35 237	99.44 227	95.69 277	95.40 278	98.27 313

GA-MVS		97.85 213	97.47 230	99.00 160	99.38 171	97.99 224	98.57 333	99.15 261	97.04 208	98.90 219	99.30 260	89.83 311	99.38 234	96.70 254	98.33 177	99.62 104

LF4IMVS		97.52 257	97.46 232	97.70 302	98.98 250	95.55 302	99.29 222	98.82 299	98.07 95	98.66 249	99.64 145	89.97 310	99.61 210	97.01 230	96.68 252	97.94 326

ppachtmachnet_test		97.49 263	97.45 233	97.61 303	98.62 311	95.24 310	98.80 317	99.46 143	96.11 278	98.22 279	99.62 154	96.45 126	98.97 310	93.77 315	95.97 269	98.61 285

conf200view11		97.78 228	97.45 233	98.77 215	99.72 77	97.86 231	99.59 97	98.74 308	97.93 114	99.26 146	98.62 311	91.75 283	99.83 132	93.22 321	98.18 191	98.61 285

thres100view900		97.76 230	97.45 233	98.69 221	99.72 77	97.86 231	99.59 97	98.74 308	97.93 114	99.26 146	98.62 311	91.75 283	99.83 132	93.22 321	98.18 191	98.37 310

v1921920		97.80 224	97.45 233	98.84 206	98.80 286	98.53 194	99.52 131	99.34 214	96.15 275	99.24 155	99.47 209	93.98 224	99.29 260	95.40 284	95.13 285	98.69 235

Baseline_NR-MVSNet		97.76 230	97.45 233	98.68 222	99.09 232	98.29 212	99.41 183	98.85 296	95.65 290	98.63 257	99.67 132	94.82 185	99.10 291	98.07 152	92.89 324	98.64 267

MIMVSNet		97.73 237	97.45 233	98.57 230	99.45 157	97.50 245	99.02 286	98.98 280	96.11 278	99.41 106	99.14 276	90.28 304	98.74 316	95.74 275	98.93 145	99.47 140

v1192		97.81 221	97.44 239	98.91 180	98.88 276	98.68 179	99.51 135	99.34 214	96.18 271	99.20 168	99.34 251	94.03 223	99.36 242	95.32 286	95.18 282	98.69 235

VPNet		97.84 215	97.44 239	99.01 158	99.21 205	98.94 138	99.48 154	99.57 44	98.38 65	99.28 138	99.73 107	88.89 319	99.39 233	99.19 36	93.27 320	98.71 225

PEN-MVS		97.76 230	97.44 239	98.72 219	98.77 295	98.54 193	99.78 22	99.51 86	97.06 207	98.29 277	99.64 145	92.63 262	98.89 313	98.09 145	93.16 321	98.72 223

cascas		97.69 243	97.43 242	98.48 239	98.60 314	97.30 246	98.18 347	99.39 187	92.96 329	98.41 268	98.78 307	93.77 231	99.27 264	98.16 140	98.61 162	98.86 205

test0.0.03 1		97.71 242	97.42 243	98.56 232	98.41 322	97.82 234	98.78 319	98.63 322	97.34 174	98.05 290	98.98 292	94.45 207	98.98 303	95.04 290	97.15 249	98.89 204

TR-MVS		97.76 230	97.41 244	98.82 208	99.06 236	97.87 230	98.87 313	98.56 326	96.63 234	98.68 248	99.22 271	92.49 266	99.65 201	95.40 284	97.79 215	98.95 203

DWT-MVSNet_test		97.53 256	97.40 245	97.93 286	99.03 242	94.86 318	99.57 110	98.63 322	96.59 239	98.36 272	98.79 305	89.32 315	99.74 169	98.14 143	98.16 198	99.20 166

Patchmtry		97.75 234	97.40 245	98.81 209	99.10 230	98.87 146	99.11 266	99.33 222	94.83 297	98.81 230	99.38 233	94.33 211	99.02 298	96.10 268	95.57 276	98.53 299

tfpn200view9		97.72 239	97.38 247	98.72 219	99.69 91	97.96 226	99.50 140	98.73 317	97.83 125	99.17 175	98.45 320	91.67 289	99.83 132	93.22 321	98.18 191	98.37 310

thres400		97.77 229	97.38 247	98.92 176	99.69 91	97.96 226	99.50 140	98.73 317	97.83 125	99.17 175	98.45 320	91.67 289	99.83 132	93.22 321	98.18 191	98.96 196

tpm cat1		97.39 268	97.36 249	97.50 308	99.17 217	93.73 329	99.43 172	99.31 229	91.27 338	98.71 240	99.08 281	94.31 213	99.77 162	96.41 265	98.50 171	99.00 186

FMVSNet2		97.72 239	97.36 249	98.80 211	99.51 138	98.84 150	99.45 163	99.42 175	96.49 242	98.86 227	99.29 262	90.26 305	98.98 303	96.44 263	96.56 256	98.58 296

LFMVS		97.90 208	97.35 251	99.54 78	99.52 136	99.01 123	99.39 192	98.24 333	97.10 198	99.65 54	99.79 76	84.79 345	99.91 75	99.28 28	98.38 176	99.69 80

VDD-MVS		97.73 237	97.35 251	98.88 194	99.47 151	97.12 254	99.34 211	98.85 296	98.19 78	99.67 46	99.85 27	82.98 349	99.92 65	99.49 12	98.32 180	99.60 107

DSMNet-mixed		97.25 272	97.35 251	96.95 316	97.84 329	93.61 333	99.57 110	96.63 358	96.13 277	98.87 222	98.61 315	94.59 201	97.70 340	95.08 289	98.86 153	99.55 117

tpm2		97.44 266	97.34 254	97.74 300	99.15 222	94.36 324	99.45 163	98.94 284	93.45 327	98.90 219	99.44 217	91.35 296	99.59 213	97.31 214	98.07 208	99.29 160

TAPA-MVS	97.07 15	97.74 236	97.34 254	98.94 168	99.70 89	97.53 244	99.25 237	99.51 86	91.90 336	99.30 130	99.63 149	98.78 37	99.64 203	88.09 341	99.87 39	99.65 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SixPastTwentyTwo		97.50 261	97.33 256	98.03 278	98.65 308	96.23 293	99.77 25	98.68 320	97.14 191	97.90 294	99.93 4	90.45 303	99.18 282	97.00 231	96.43 259	98.67 251

MS-PatchMatch		97.24 273	97.32 257	96.99 314	98.45 321	93.51 334	98.82 316	99.32 228	97.41 170	98.13 284	99.30 260	88.99 318	99.56 214	95.68 278	99.80 70	97.90 329

v1240		97.69 243	97.32 257	98.79 212	98.85 283	98.43 207	99.48 154	99.36 202	96.11 278	99.27 142	99.36 244	93.76 232	99.24 272	94.46 300	95.23 281	98.70 230

pmmvs5		97.52 257	97.30 259	98.16 274	98.57 316	96.73 277	99.27 227	98.90 292	96.14 276	98.37 271	99.53 185	91.54 294	99.14 283	97.51 200	95.87 270	98.63 274

tpmp4_e23		97.34 269	97.29 260	97.52 306	99.25 201	93.73 329	99.58 104	99.19 259	94.00 317	98.20 280	99.41 224	90.74 302	99.74 169	97.13 224	98.07 208	99.07 180

pm-mvs1		97.68 245	97.28 261	98.88 194	99.06 236	98.62 187	99.50 140	99.45 155	96.32 258	97.87 295	99.79 76	92.47 267	99.35 245	97.54 197	93.54 318	98.67 251

thres200		97.61 252	97.28 261	98.62 226	99.64 110	98.03 222	99.26 235	98.74 308	97.68 143	99.09 190	98.32 322	91.66 291	99.81 145	92.88 327	98.22 187	98.03 322

TESTMET0.1,1		97.55 254	97.27 263	98.40 249	98.93 267	96.53 283	98.67 326	97.61 351	96.96 212	98.64 256	99.28 263	88.63 325	99.45 222	97.30 215	99.38 112	99.21 165

v748		97.52 257	97.23 264	98.41 248	98.69 304	97.23 252	99.87 4	99.45 155	95.72 288	98.51 263	99.53 185	94.13 219	99.30 258	96.78 250	92.39 329	98.70 230

USDC		97.34 269	97.20 265	97.75 299	99.07 234	95.20 312	98.51 336	99.04 275	97.99 109	98.31 275	99.86 23	89.02 317	99.55 216	95.67 279	97.36 242	98.49 301

DTE-MVSNet		97.51 260	97.19 266	98.46 242	98.63 310	98.13 220	99.84 9	99.48 117	96.68 230	97.97 293	99.67 132	92.92 244	98.56 319	96.88 247	92.60 328	98.70 230

test-mter		97.49 263	97.13 267	98.55 234	98.79 288	97.10 255	98.67 326	97.75 341	96.65 232	98.61 260	98.85 300	88.23 330	99.45 222	97.25 216	99.38 112	99.10 171

PAPM		97.59 253	97.09 268	99.07 152	99.06 236	98.26 214	98.30 343	99.10 266	94.88 296	98.08 286	99.34 251	96.27 132	99.64 203	89.87 336	98.92 147	99.31 159

PCF-MVS	97.08 14	97.66 249	97.06 269	99.47 96	99.61 121	99.09 108	98.04 349	99.25 251	91.24 339	98.51 263	99.70 116	94.55 203	99.91 75	92.76 328	99.85 54	99.42 151
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDDNet		97.55 254	97.02 270	99.16 143	99.49 147	98.12 221	99.38 197	99.30 231	95.35 292	99.68 40	99.90 7	82.62 351	99.93 56	99.31 26	98.13 199	99.42 151

JIA-IIPM		97.50 261	97.02 270	98.93 171	98.73 298	97.80 239	99.30 218	98.97 281	91.73 337	98.91 217	94.86 352	95.10 167	99.71 186	97.58 191	97.98 211	99.28 161

TinyColmap		97.12 275	96.89 272	97.83 294	99.07 234	95.52 305	98.57 333	98.74 308	97.58 151	97.81 298	99.79 76	88.16 331	99.56 214	95.10 288	97.21 246	98.39 309

LP		97.04 277	96.80 273	97.77 298	98.90 272	95.23 311	98.97 299	99.06 273	94.02 316	98.09 285	99.41 224	93.88 227	98.82 314	90.46 334	98.42 175	99.26 162

DI_MVS_plusplus_test		97.45 265	96.79 274	99.44 103	97.76 331	99.04 113	99.21 247	98.61 324	97.74 136	94.01 333	98.83 302	87.38 336	99.83 132	98.63 97	98.90 149	99.44 148

K. test v3		97.10 276	96.79 274	98.01 281	98.72 300	96.33 290	99.87 4	97.05 355	97.59 149	96.16 318	99.80 68	88.71 321	99.04 295	96.69 255	96.55 257	98.65 265

test_normal		97.44 266	96.77 276	99.44 103	97.75 332	99.00 125	99.10 268	98.64 321	97.71 139	93.93 336	98.82 303	87.39 335	99.83 132	98.61 101	98.97 141	99.49 134

TransMVSNet (Re)		97.15 274	96.58 277	98.86 202	99.12 225	98.85 149	99.49 149	98.91 290	95.48 291	97.16 307	99.80 68	93.38 236	99.11 289	94.16 313	91.73 330	98.62 276

MVS		97.28 271	96.55 278	99.48 92	98.78 292	98.95 135	99.27 227	99.39 187	83.53 352	98.08 286	99.54 178	96.97 110	99.87 107	94.23 311	99.16 125	99.63 102

PatchT		97.03 278	96.44 279	98.79 212	98.99 246	98.34 211	99.16 253	99.07 271	92.13 333	99.52 85	97.31 345	94.54 204	98.98 303	88.54 339	98.73 161	99.03 183

FMVSNet1		96.84 279	96.36 280	98.29 257	99.32 187	97.26 249	99.43 172	99.48 117	95.11 294	98.55 262	99.32 257	83.95 348	98.98 303	95.81 274	96.26 263	98.62 276

test_0402		96.64 280	96.24 281	97.85 292	98.85 283	96.43 287	99.44 167	99.26 249	93.52 324	96.98 311	99.52 190	88.52 326	99.20 281	92.58 330	97.50 230	97.93 327

FMVSNet5		96.43 285	96.19 282	97.15 311	99.11 227	95.89 298	99.32 213	99.52 77	94.47 309	98.34 274	99.07 282	87.54 334	97.07 343	92.61 329	95.72 273	98.47 303

UnsupCasMVSNet_eth		96.44 284	96.12 283	97.40 310	98.65 308	95.65 299	99.36 203	99.51 86	97.13 192	96.04 321	98.99 289	88.40 328	98.17 322	96.71 253	90.27 333	98.40 308

pmmvs6		96.53 283	96.09 284	97.82 295	98.69 304	95.47 306	99.37 199	99.47 133	93.46 326	97.41 302	99.78 82	87.06 337	99.33 249	96.92 239	92.70 327	98.65 265

Anonymous20231206		96.22 296	96.03 285	96.79 320	97.31 338	94.14 326	99.63 83	99.08 268	96.17 272	97.04 309	99.06 284	93.94 225	97.76 339	86.96 345	95.06 286	98.47 303

new_pmnet		96.38 289	96.03 285	97.41 309	98.13 327	95.16 315	99.05 277	99.20 256	93.94 318	97.39 303	98.79 305	91.61 293	99.04 295	90.43 335	95.77 272	98.05 319

testpf		95.66 304	96.02 287	94.58 328	98.35 323	92.32 339	97.25 355	97.91 340	92.83 330	97.03 310	98.99 289	88.69 322	98.61 318	95.72 276	97.40 239	92.80 353

test20.03		96.12 299	95.96 288	96.63 321	97.44 334	95.45 307	99.51 135	99.38 193	96.55 240	96.16 318	99.25 267	93.76 232	96.17 348	87.35 344	94.22 308	98.27 313

RPMNet		96.61 281	95.85 289	98.87 198	99.18 212	98.49 202	99.22 244	99.08 268	88.72 348	99.56 72	97.38 343	94.08 222	99.00 301	86.87 346	98.58 165	99.14 168

N_pmnet		94.95 312	95.83 290	92.31 335	98.47 320	79.33 358	99.12 260	92.81 368	93.87 319	97.68 300	99.13 277	93.87 228	99.01 300	91.38 332	96.19 264	98.59 293

Patchmatch-RL test		95.84 302	95.81 291	95.95 325	95.61 343	90.57 343	98.24 344	98.39 329	95.10 295	95.20 323	98.67 310	94.78 188	97.77 338	96.28 267	90.02 334	99.51 130

v17		96.42 286	95.81 291	98.25 264	98.94 262	98.80 167	99.76 28	99.28 243	94.57 303	94.18 327	97.71 330	95.23 162	98.16 323	94.86 292	87.73 342	97.80 332

v18		96.42 286	95.80 293	98.26 260	98.95 259	98.82 160	99.76 28	99.28 243	94.58 302	94.12 328	97.70 331	95.22 163	98.16 323	94.83 294	87.80 340	97.79 337

v16		96.39 288	95.76 294	98.26 260	98.96 257	98.81 162	99.76 28	99.28 243	94.57 303	94.10 329	97.70 331	95.04 169	98.16 323	94.70 296	87.77 341	97.80 332

EG-PatchMatch MVS		95.97 301	95.69 295	96.81 319	97.78 330	92.79 337	99.16 253	98.93 285	96.16 273	94.08 330	99.22 271	82.72 350	99.47 220	95.67 279	97.50 230	98.17 316

v15		96.28 290	95.62 296	98.25 264	98.94 262	98.83 153	99.76 28	99.29 236	94.52 307	94.02 332	97.61 337	95.02 170	98.13 327	94.53 298	86.92 345	97.80 332

V14		96.26 291	95.60 297	98.26 260	98.94 262	98.83 153	99.76 28	99.29 236	94.49 308	93.96 334	97.66 334	94.99 173	98.13 327	94.41 301	86.90 346	97.80 332

v13		96.24 293	95.58 298	98.25 264	98.98 250	98.83 153	99.75 36	99.29 236	94.35 312	93.89 337	97.60 338	95.17 165	98.11 329	94.27 310	86.86 348	97.81 330

v12		96.24 293	95.58 298	98.23 267	98.96 257	98.81 162	99.76 28	99.29 236	94.42 311	93.85 338	97.60 338	95.12 166	98.09 330	94.32 307	86.85 349	97.80 332

V9		96.25 292	95.58 298	98.26 260	98.94 262	98.83 153	99.75 36	99.29 236	94.45 310	93.96 334	97.62 336	94.94 175	98.14 326	94.40 302	86.87 347	97.81 330

v11		96.23 295	95.57 301	98.21 270	98.93 267	98.83 153	99.72 43	99.29 236	94.29 313	94.05 331	97.64 335	94.88 182	98.04 331	92.89 326	88.43 338	97.77 338

PVSNet_0	94.43 19	96.09 300	95.47 302	97.94 285	99.31 188	94.34 325	97.81 350	99.70 15	97.12 194	97.46 301	98.75 308	89.71 312	99.79 152	97.69 185	81.69 353	99.68 84

X-MVStestdata		96.55 282	95.45 303	99.87 7	99.85 23	99.83 8	99.69 49	99.68 19	98.98 19	99.37 115	64.01 367	98.81 34	99.94 41	98.79 80	99.86 50	99.84 13

IB-MVS	95.67 18	96.22 296	95.44 304	98.57 230	99.21 205	96.70 278	98.65 329	97.74 343	96.71 228	97.27 304	98.54 318	86.03 339	99.92 65	98.47 120	86.30 350	99.10 171
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		96.17 298	95.32 305	98.73 218	98.79 288	98.14 219	99.38 197	94.09 363	91.07 341	98.07 289	91.04 358	89.62 314	99.35 245	96.75 251	99.09 131	98.68 240

testus		94.61 313	95.30 306	92.54 334	96.44 341	84.18 350	98.36 339	99.03 276	94.18 314	96.49 314	98.57 317	88.74 320	95.09 352	87.41 343	98.45 173	98.36 312

MVS-HIRNet		95.75 303	95.16 307	97.51 307	99.30 189	93.69 332	98.88 312	95.78 359	85.09 351	98.78 234	92.65 354	91.29 297	99.37 238	94.85 293	99.85 54	99.46 144

MIMVSNet1		95.51 305	95.04 308	96.92 317	97.38 335	95.60 300	99.52 131	99.50 101	93.65 322	96.97 312	99.17 274	85.28 343	96.56 347	88.36 340	95.55 277	98.60 292

CMPMVS	69.68 23	94.13 317	94.90 309	91.84 336	97.24 339	80.01 357	98.52 335	99.48 117	89.01 346	91.99 344	99.67 132	85.67 341	99.13 286	95.44 282	97.03 250	96.39 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		95.34 309	94.73 310	97.15 311	95.53 345	95.94 297	99.35 208	99.10 266	95.13 293	93.55 339	97.54 341	88.15 332	97.91 335	94.58 297	89.69 336	97.61 340

MDA-MVSNet_test_wron		95.45 306	94.60 311	98.01 281	98.16 326	97.21 253	99.11 266	99.24 252	93.49 325	80.73 357	98.98 292	93.02 241	98.18 321	94.22 312	94.45 303	98.64 267

TDRefinement		95.42 307	94.57 312	97.97 284	89.83 358	96.11 295	99.48 154	98.75 305	96.74 226	96.68 313	99.88 15	88.65 324	99.71 186	98.37 127	82.74 352	98.09 317

YYNet1		95.36 308	94.51 313	97.92 287	97.89 328	97.10 255	99.10 268	99.23 253	93.26 328	80.77 356	99.04 286	92.81 247	98.02 332	94.30 308	94.18 309	98.64 267

test2356		94.07 319	94.46 314	92.89 332	95.18 346	86.13 348	97.60 353	99.06 273	93.61 323	96.15 320	98.28 323	85.60 342	93.95 354	86.68 347	98.00 210	98.59 293

new-patchmatchnet		94.48 314	94.08 315	95.67 326	95.08 347	92.41 338	99.18 251	99.28 243	94.55 306	93.49 340	97.37 344	87.86 333	97.01 344	91.57 331	88.36 339	97.61 340

MDA-MVSNet-bldmvs		94.96 311	93.98 316	97.92 287	98.24 325	97.27 248	99.15 256	99.33 222	93.80 320	80.09 358	99.03 287	88.31 329	97.86 337	93.49 319	94.36 305	98.62 276

OpenMVS_ROB	92.34 20	94.38 316	93.70 317	96.41 324	97.38 335	93.17 335	99.06 275	98.75 305	86.58 349	94.84 326	98.26 324	81.53 352	99.32 252	89.01 338	97.87 214	96.76 344

Test4		95.05 310	93.67 318	99.22 138	96.07 342	98.94 138	99.20 249	99.27 248	97.71 139	89.96 350	97.59 340	66.18 357	99.25 270	98.06 153	98.96 143	99.47 140

test1235678		92.91 322	93.30 319	91.71 338	93.14 352	83.01 352	98.75 322	98.58 325	92.80 331	92.45 342	97.91 327	88.51 327	93.54 355	82.26 351	95.35 279	98.59 293

pmmvs3		94.09 318	93.25 320	96.60 322	94.76 348	94.49 322	98.92 308	98.18 336	89.66 343	96.48 315	98.06 325	86.28 338	97.33 342	89.68 337	87.20 344	97.97 325

testing_2		94.44 315	92.93 321	98.98 162	94.16 349	99.00 125	99.42 179	99.28 243	96.60 237	84.86 352	96.84 346	70.91 354	99.27 264	98.23 136	96.08 266	98.68 240

UnsupCasMVSNet_bld		93.53 320	92.51 322	96.58 323	97.38 335	93.82 328	98.24 344	99.48 117	91.10 340	93.10 341	96.66 347	74.89 353	98.37 320	94.03 314	87.71 343	97.56 342

PM-MVS		92.96 321	92.23 323	95.14 327	95.61 343	89.98 345	99.37 199	98.21 334	94.80 298	95.04 325	97.69 333	65.06 358	97.90 336	94.30 308	89.98 335	97.54 343

1111		92.30 323	92.21 324	92.55 333	93.30 350	86.27 346	99.15 256	98.74 308	91.94 334	90.85 347	97.82 328	84.18 346	95.21 350	79.65 353	94.27 307	96.19 347

test12356		91.74 324	92.19 325	90.37 341	91.22 354	82.41 353	98.61 330	98.28 331	90.66 342	91.82 345	97.92 326	84.90 344	92.61 356	81.64 352	94.66 298	96.09 348

Gipuma		90.99 325	90.15 326	93.51 329	98.73 298	90.12 344	93.98 359	99.45 155	79.32 354	92.28 343	94.91 351	69.61 355	97.98 334	87.42 342	95.67 274	92.45 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		87.91 326	87.80 327	88.24 342	87.68 361	77.50 360	99.07 271	97.66 350	89.27 344	86.47 351	96.22 349	68.35 356	92.49 358	76.63 357	88.82 337	94.72 351

.test1245		83.42 330	86.17 328	75.15 352	93.30 350	86.27 346	99.15 256	98.74 308	91.94 334	90.85 347	97.82 328	84.18 346	95.21 350	79.65 353	39.90 363	43.98 364

FPMVS		84.93 329	85.65 329	82.75 349	86.77 362	63.39 367	98.35 341	98.92 287	74.11 356	83.39 354	98.98 292	50.85 362	92.40 359	84.54 349	94.97 288	92.46 354

PMMVS2		86.87 327	85.37 330	91.35 340	90.21 357	83.80 351	98.89 311	97.45 353	83.13 353	91.67 346	95.03 350	48.49 363	94.70 353	85.86 348	77.62 354	95.54 349

LCM-MVSNet		86.80 328	85.22 331	91.53 339	87.81 360	80.96 356	98.23 346	98.99 279	71.05 357	90.13 349	96.51 348	48.45 364	96.88 345	90.51 333	85.30 351	96.76 344

tmp_tt		82.80 332	81.52 332	86.66 343	66.61 369	68.44 366	92.79 361	97.92 338	68.96 359	80.04 359	99.85 27	85.77 340	96.15 349	97.86 165	43.89 362	95.39 350

no-one		83.04 331	80.12 333	91.79 337	89.44 359	85.65 349	99.32 213	98.32 330	89.06 345	79.79 360	89.16 360	44.86 365	96.67 346	84.33 350	46.78 361	93.05 352

E-PMN		80.61 333	79.88 334	82.81 348	90.75 356	76.38 362	97.69 351	95.76 360	66.44 361	83.52 353	92.25 355	62.54 360	87.16 363	68.53 361	61.40 357	84.89 362

EMVS		80.02 334	79.22 335	82.43 350	91.19 355	76.40 361	97.55 354	92.49 370	66.36 362	83.01 355	91.27 356	64.63 359	85.79 364	65.82 362	60.65 358	85.08 361

PNet_i23d		79.43 335	77.68 336	84.67 345	86.18 363	71.69 365	96.50 357	93.68 364	75.17 355	71.33 361	91.18 357	32.18 368	90.62 360	78.57 356	74.34 355	91.71 357

PMVS	70.75 22	75.98 338	74.97 337	79.01 351	70.98 368	55.18 368	93.37 360	98.21 334	65.08 363	61.78 365	93.83 353	21.74 372	92.53 357	78.59 355	91.12 332	89.34 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		77.30 336	74.86 338	84.62 346	75.88 367	77.61 359	97.63 352	93.15 367	88.81 347	64.27 363	89.29 359	36.51 366	83.93 365	75.89 358	52.31 360	92.33 356

MVE	76.82 21	76.91 337	74.31 339	84.70 344	85.38 365	76.05 363	96.88 356	93.17 366	67.39 360	71.28 362	89.01 361	21.66 373	87.69 362	71.74 360	72.29 356	90.35 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		74.42 339	71.19 340	84.14 347	76.16 366	74.29 364	96.00 358	92.57 369	69.57 358	63.84 364	87.49 362	21.98 370	88.86 361	75.56 359	57.50 359	89.26 360

v1.0		41.40 340	55.20 341	0.00 357	99.81 32	0.00 372	0.00 363	99.48 117	97.97 111	99.77 26	99.78 82	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

testmvs		39.17 343	43.78 342	25.37 356	36.04 371	16.84 371	98.36 339	26.56 371	20.06 365	38.51 367	67.32 363	29.64 369	15.30 368	37.59 364	39.90 363	43.98 364

pcd1.5k->3k		40.85 341	43.49 343	32.93 354	98.95 259	0.00 372	0.00 363	99.53 73	0.00 367	0.00 369	0.27 369	95.32 156	0.00 369	0.00 366	97.30 243	98.80 209

test123		39.01 344	42.50 344	28.53 355	39.17 370	20.91 370	98.75 322	19.17 373	19.83 366	38.57 366	66.67 364	33.16 367	15.42 367	37.50 365	29.66 365	49.26 363

wuyk23d		40.18 342	41.29 345	36.84 353	86.18 363	49.12 369	79.73 362	22.81 372	27.64 364	25.46 368	28.45 368	21.98 370	48.89 366	55.80 363	23.56 366	12.51 366

cdsmvs_eth3d_5k		24.64 345	32.85 346	0.00 357	0.00 372	0.00 372	0.00 363	99.51 86	0.00 367	0.00 369	99.56 172	96.58 122	0.00 369	0.00 366	0.00 367	0.00 367

ab-mvs-re		8.30 346	11.06 347	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	99.58 166	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

pcd_1.5k_mvsjas		8.27 347	11.03 348	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.27 369	99.01 12	0.00 369	0.00 366	0.00 367	0.00 367

sosnet-low-res		0.02 348	0.03 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.27 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sosnet		0.02 348	0.03 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.27 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uncertanet		0.02 348	0.03 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.27 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

Regformer		0.02 348	0.03 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.27 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uanet		0.02 348	0.03 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.27 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

GSMVS																99.52 125

test_part2					99.81 32	99.83 8				99.77 26

test_part1				0.00 357		0.00 372	0.00 363	99.48 117				0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sam_mvs1												94.86 183				99.52 125

sam_mvs												94.72 196

semantic-postprocess				98.06 277	99.57 128	96.36 289		99.49 106	97.18 188	98.71 240	99.72 111	92.70 254	99.14 283	97.44 208	95.86 271	98.67 251

ambc				93.06 331	92.68 353	82.36 354	98.47 337	98.73 317		95.09 324	97.41 342	55.55 361	99.10 291	96.42 264	91.32 331	97.71 339

MTGPA								99.47 133

test_post1							99.23 240				65.14 366	94.18 218	99.71 186	97.58 191

test_post											65.99 365	94.65 200	99.73 176

patchmatchnet-post											98.70 309	94.79 187	99.74 169

GG-mvs-BLEND				98.45 243	98.55 317	98.16 217	99.43 172	93.68 364		97.23 305	98.46 319	89.30 316	99.22 276	95.43 283	98.22 187	97.98 324

MTMP							99.54 126	98.88 294

gm-plane-assit					98.54 318	92.96 336			94.65 301		99.15 275		99.64 203	97.56 195

test9_res														97.49 202	99.72 85	99.75 55

TEST9					99.67 95	99.65 40	99.05 277	99.41 177	96.22 268	98.95 212	99.49 199	98.77 40	99.91 75

test_8					99.67 95	99.61 45	99.03 283	99.41 177	96.28 261	98.93 215	99.48 205	98.76 42	99.91 75

agg_prior2														97.21 218	99.73 84	99.75 55

agg_prior					99.67 95	99.62 43		99.40 184		98.87 222			99.91 75

TestCases				99.31 117	99.86 20	98.48 204		99.61 32	97.85 122	99.36 118	99.85 27	95.95 137	99.85 118	96.66 257	99.83 63	99.59 111

test_prior4						99.56 52	98.99 292

test_prior2							98.96 301		98.34 67	99.01 201	99.52 190	98.68 51		97.96 157	99.74 81

test_prior				99.68 52	99.67 95	99.48 65		99.56 49					99.83 132			99.74 60

旧先验2							98.96 301		96.70 229	99.47 94			99.94 41	98.19 137

新几何2							99.01 290

新几何1				99.75 40	99.75 57	99.59 49		99.54 63	96.76 225	99.29 134	99.64 145	98.43 63	99.94 41	96.92 239	99.66 97	99.72 71

旧先验1					99.74 68	99.59 49		99.54 63			99.69 122	98.47 60			99.68 95	99.73 65

无先验							98.99 292	99.51 86	96.89 219				99.93 56	97.53 198		99.72 71

原ACMM2							98.95 305

原ACMM1				99.65 59	99.73 73	99.33 81		99.47 133	97.46 161	99.12 181	99.66 137	98.67 53	99.91 75	97.70 184	99.69 92	99.71 78

test222					99.75 57	99.49 64	98.91 310	99.49 106	96.42 252	99.34 125	99.65 138	98.28 73			99.69 92	99.72 71

testdata2													99.95 34	96.67 256

segment_acmp												98.96 21

testdata				99.54 78	99.75 57	98.95 135		99.51 86	97.07 205	99.43 101	99.70 116	98.87 29	99.94 41	97.76 175	99.64 100	99.72 71

testdata1							98.85 314		98.32 70

test12				99.75 40	99.64 110	99.61 45		99.29 236		99.21 165		98.38 67	99.89 96		99.74 81	99.74 60

plane_prior7					99.29 192	97.03 263

plane_prior6					99.27 197	96.98 267						92.71 252

plane_prior5								99.47 133					99.69 195	97.78 172	97.63 218	98.67 251

plane_prior4											99.61 158

plane_prior3						97.00 265			98.69 47	99.11 183

plane_prior2							99.39 192		98.97 22

plane_prior1					99.26 199

plane_prior						96.97 268	99.21 247		98.45 60						97.60 221

n2								0.00 374

nn								0.00 374

door-mid								98.05 337

lessismore_v0				97.79 297	98.69 304	95.44 308		94.75 361		95.71 322	99.87 20	88.69 322	99.32 252	95.89 272	94.93 291	98.62 276

LGP-MVS_train				98.49 237	99.33 180	97.05 261		99.55 56	97.46 161	99.24 155	99.83 40	92.58 263	99.72 180	98.09 145	97.51 228	98.68 240

test11								99.35 206

door								97.92 338

HQP5-MVS						96.83 273

HQP-NCC					99.19 209		98.98 296		98.24 73	98.66 249

ACMP_Plane					99.19 209		98.98 296		98.24 73	98.66 249

BP-MVS														97.19 220

HQP4-MVS										98.66 249			99.64 203			98.64 267

HQP3-MVS								99.39 187							97.58 223

HQP2-MVS												92.47 267

NP-MVS					99.23 202	96.92 271					99.40 228

MDTV_nov1_ep13_2view						95.18 314	99.35 208		96.84 222	99.58 68		95.19 164		97.82 168		99.46 144

ACMMP++_ref															97.19 247

ACMMP++															97.43 238

Test By Simon												98.75 45

ITE_SJBPF				98.08 276	99.29 192	96.37 288		98.92 287	98.34 67	98.83 229	99.75 96	91.09 298	99.62 209	95.82 273	97.40 239	98.25 315

DeepMVS_CX				93.34 330	99.29 192	82.27 355		99.22 254	85.15 350	96.33 316	99.05 285	90.97 300	99.73 176	93.57 317	97.77 216	98.01 323

High-resolution multi-view benchmark