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.

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

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 45	99.41 74	99.58 103	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

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 82	99.39 187	98.91 29	99.78 23	99.85 27	99.36 2	99.94 41	98.84 72	99.88 35	99.82 31

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 64	99.78 36	99.14 102	99.60 94	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

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 64	99.78 36	99.15 101	99.61 91	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-3		99.57 6	99.53 5	99.68 52	99.76 45	99.29 86	99.58 103	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

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 83	99.52 61	99.49 148	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

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 7	99.82 29	99.81 14	99.59 96	99.51 86	98.62 50	99.79 19	99.83 40	99.28 3	99.97 11	98.48 117	99.90 24	99.84 13
Skip Steuart: Steuart Systems R&D Blog.
Regformer-1		99.53 9	99.47 8	99.72 49	99.71 83	99.44 71	99.49 148	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

XVS		99.53 9	99.42 11	99.87 7	99.85 23	99.83 8	99.69 48	99.68 19	98.98 19	99.37 114	99.74 101	98.81 34	99.94 41	98.79 79	99.86 50	99.84 13

MTAPA		99.52 11	99.39 15	99.89 3	99.90 3	99.86 4	99.66 68	99.47 133	98.79 40	99.68 39	99.81 57	98.43 63	99.97 11	98.88 62	99.90 24	99.83 24

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 30	99.75 96	99.13 7	99.92 65	99.07 48	99.92 12	99.85 9

zzz-MVS		99.49 13	99.36 19	99.89 3	99.90 3	99.86 4	99.36 202	99.47 133	98.79 40	99.68 39	99.81 57	98.43 63	99.97 11	98.88 62	99.90 24	99.83 24

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 68	99.67 22	98.15 82	99.68 39	99.69 121	99.06 9	99.96 19	98.69 90	99.87 39	99.84 13

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 68	99.67 22	98.15 82	99.67 45	99.69 121	98.95 24	99.96 19	98.69 90	99.87 39	99.84 13

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 37	99.63 112	99.59 49	99.36 202	99.46 143	99.07 9	99.79 19	99.82 47	98.85 31	99.92 65	98.68 92	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
region2R		99.48 17	99.35 22	99.87 7	99.88 11	99.80 15	99.65 78	99.66 25	98.13 84	99.66 50	99.68 126	98.96 21	99.96 19	98.62 98	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 82	99.54 63	98.36 66	99.79 19	99.82 47	98.86 30	99.95 34	98.62 98	99.81 68	99.78 49

DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 77	99.40 76	99.05 276	99.66 25	99.14 6	99.57 70	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
ACMMP_Plus		99.47 20	99.34 24	99.88 5	99.87 15	99.86 4	99.47 158	99.48 117	98.05 100	99.76 30	99.86 23	98.82 33	99.93 56	98.82 78	99.91 17	99.84 13

ESAPD		99.46 21	99.32 26	99.91 2	99.78 36	99.88 2	99.36 202	99.51 86	98.73 44	99.88 3	99.84 36	98.72 48	99.96 19	98.16 139	99.87 39	99.88 4

MSLP-MVS++		99.46 21	99.47 8	99.44 103	99.60 122	99.16 98	99.41 182	99.71 13	98.98 19	99.45 96	99.78 82	99.19 4	99.54 216	99.28 28	99.84 59	99.63 102

PGM-MVS		99.45 23	99.31 32	99.86 13	99.87 15	99.78 23	99.58 103	99.65 30	97.84 124	99.71 33	99.80 68	99.12 8	99.97 11	98.33 130	99.87 39	99.83 24

CP-MVS		99.45 23	99.32 26	99.85 18	99.83 28	99.75 24	99.69 48	99.52 77	98.07 95	99.53 82	99.63 148	98.93 26	99.97 11	98.74 83	99.91 17	99.83 24

ACMMP		99.45 23	99.32 26	99.82 26	99.89 8	99.67 35	99.62 85	99.69 18	98.12 86	99.63 55	99.84 36	98.73 47	99.96 19	98.55 112	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
SMA-MVS		99.44 26	99.30 34	99.85 18	99.73 73	99.83 8	99.56 116	99.47 133	97.45 163	99.78 23	99.82 47	99.18 5	99.91 75	98.79 79	99.89 32	99.81 35

abl_6		99.44 26	99.31 32	99.83 24	99.85 23	99.75 24	99.66 68	99.59 38	98.13 84	99.82 15	99.81 57	98.60 56	99.96 19	98.46 120	99.88 35	99.79 45

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 48	99.48 117	98.12 86	99.50 88	99.75 96	98.78 37	99.97 11	98.57 106	99.89 32	99.83 24

#test#		99.43 29	99.29 38	99.86 13	99.87 15	99.80 15	99.55 122	99.67 22	97.83 125	99.68 39	99.69 121	99.06 9	99.96 19	98.39 123	99.87 39	99.84 13

MCST-MVS		99.43 29	99.30 34	99.82 26	99.79 35	99.74 27	99.29 221	99.40 184	98.79 40	99.52 84	99.62 153	98.91 27	99.90 88	98.64 95	99.75 79	99.82 31

UA-Net		99.42 31	99.29 38	99.80 31	99.62 116	99.55 54	99.50 139	99.70 15	98.79 40	99.77 25	99.96 1	97.45 95	99.96 19	98.92 60	99.90 24	99.89 2

HPM-MVS		99.42 31	99.28 40	99.83 24	99.90 3	99.72 28	99.81 15	99.54 63	97.59 148	99.68 39	99.63 148	98.91 27	99.94 41	98.58 104	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
CNVR-MVS		99.42 31	99.30 34	99.78 35	99.62 116	99.71 29	99.26 234	99.52 77	98.82 35	99.39 110	99.71 112	98.96 21	99.85 117	98.59 103	99.80 70	99.77 51

HSP-MVS		99.41 34	99.26 45	99.85 18	99.89 8	99.80 15	99.67 59	99.37 201	98.70 46	99.77 25	99.49 198	98.21 75	99.95 34	98.46 120	99.77 76	99.81 35

SD-MVS		99.41 34	99.52 6	99.05 154	99.74 68	99.68 33	99.46 161	99.52 77	99.11 7	99.88 3	99.91 5	99.43 1	97.70 339	98.72 87	99.93 11	99.77 51

MVS_111021_LR		99.41 34	99.33 25	99.65 59	99.77 42	99.51 63	98.94 306	99.85 6	98.82 35	99.65 53	99.74 101	98.51 58	99.80 148	98.83 75	99.89 32	99.64 98

MVS_111021_HR		99.41 34	99.32 26	99.66 55	99.72 77	99.47 67	98.95 304	99.85 6	98.82 35	99.54 80	99.73 106	98.51 58	99.74 168	98.91 61	99.88 35	99.77 51

HPM-MVS++		99.39 38	99.23 47	99.87 7	99.75 57	99.84 7	99.43 171	99.51 86	98.68 48	99.27 141	99.53 184	98.64 54	99.96 19	98.44 122	99.80 70	99.79 45

MP-MVS-pluss		99.37 39	99.20 48	99.88 5	99.90 3	99.87 3	99.30 217	99.52 77	97.18 187	99.60 63	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
TSAR-MVS + GP.		99.36 40	99.36 19	99.36 109	99.67 95	98.61 190	99.07 270	99.33 222	99.00 17	99.82 15	99.81 57	99.06 9	99.84 123	99.09 46	99.42 109	99.65 92

PVSNet_Blended_VisFu		99.36 40	99.28 40	99.61 68	99.86 20	99.07 110	99.47 158	99.93 2	97.66 146	99.71 33	99.86 23	97.73 88	99.96 19	99.47 13	99.82 67	99.79 45

NCCC		99.34 42	99.19 49	99.79 34	99.61 120	99.65 40	99.30 217	99.48 117	98.86 31	99.21 164	99.63 148	98.72 48	99.90 88	98.25 134	99.63 102	99.80 41

MP-MVS		99.33 43	99.15 52	99.87 7	99.88 11	99.82 13	99.66 68	99.46 143	98.09 91	99.48 92	99.74 101	98.29 72	99.96 19	97.93 159	99.87 39	99.82 31
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PS-MVSNAJ		99.32 44	99.32 26	99.30 120	99.57 127	98.94 138	98.97 298	99.46 143	98.92 28	99.71 33	99.24 268	99.01 12	99.98 5	99.35 19	99.66 97	98.97 189

CSCG		99.32 44	99.32 26	99.32 116	99.85 23	98.29 211	99.71 44	99.66 25	98.11 88	99.41 105	99.80 68	98.37 69	99.96 19	98.99 54	99.96 5	99.72 71

PHI-MVS		99.30 46	99.17 51	99.70 51	99.56 131	99.52 61	99.58 103	99.80 8	97.12 193	99.62 58	99.73 106	98.58 57	99.90 88	98.61 100	99.91 17	99.68 84

DeepC-MVS	98.35 2	99.30 46	99.19 49	99.64 64	99.82 29	99.23 93	99.62 85	99.55 56	98.94 26	99.63 55	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
xiu_mvs_v1_base_debu		99.29 48	99.27 42	99.34 111	99.63 112	98.97 130	99.12 259	99.51 86	98.86 31	99.84 9	99.47 208	98.18 76	99.99 1	99.50 8	99.31 116	99.08 175

xiu_mvs_v1_base		99.29 48	99.27 42	99.34 111	99.63 112	98.97 130	99.12 259	99.51 86	98.86 31	99.84 9	99.47 208	98.18 76	99.99 1	99.50 8	99.31 116	99.08 175

xiu_mvs_v1_base_debi		99.29 48	99.27 42	99.34 111	99.63 112	98.97 130	99.12 259	99.51 86	98.86 31	99.84 9	99.47 208	98.18 76	99.99 1	99.50 8	99.31 116	99.08 175

APD-MVS		99.27 51	99.08 59	99.84 23	99.75 57	99.79 19	99.50 139	99.50 101	97.16 189	99.77 25	99.82 47	98.78 37	99.94 41	97.56 194	99.86 50	99.80 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 51	99.12 55	99.74 45	99.18 211	99.75 24	99.56 116	99.57 44	98.45 60	99.49 91	99.85 27	97.77 87	99.94 41	98.33 130	99.84 59	99.52 124

xiu_mvs_v2_base		99.26 53	99.25 46	99.29 123	99.53 133	98.91 143	99.02 285	99.45 155	98.80 39	99.71 33	99.26 265	98.94 25	99.98 5	99.34 23	99.23 120	98.98 188

CANet		99.25 54	99.14 53	99.59 70	99.41 162	99.16 98	99.35 207	99.57 44	98.82 35	99.51 87	99.61 157	96.46 125	99.95 34	99.59 2	99.98 2	99.65 92

3Dnovator	97.25 9	99.24 55	99.05 61	99.81 29	99.12 224	99.66 37	99.84 9	99.74 10	99.09 8	98.92 215	99.90 7	95.94 139	99.98 5	98.95 57	99.92 12	99.79 45

casdiffmvs1		99.23 56	99.11 57	99.58 73	99.53 133	99.36 78	99.76 28	99.43 172	97.99 109	99.52 84	99.84 36	97.50 94	99.77 161	99.42 17	98.97 140	99.61 106

test_prior3		99.21 57	99.05 61	99.68 52	99.67 95	99.48 65	98.96 300	99.56 49	98.34 67	99.01 200	99.52 189	98.68 51	99.83 131	97.96 156	99.74 81	99.74 60

CHOSEN 1792x2688		99.19 58	99.10 58	99.45 100	99.89 8	98.52 198	99.39 191	99.94 1	98.73 44	99.11 182	99.89 10	95.50 151	99.94 41	99.50 8	99.97 3	99.89 2

F-COLMAP		99.19 58	99.04 64	99.64 64	99.78 36	99.27 89	99.42 178	99.54 63	97.29 178	99.41 105	99.59 162	98.42 66	99.93 56	98.19 136	99.69 92	99.73 65

3Dnovator+	97.12 13	99.18 60	98.97 75	99.82 26	99.17 216	99.68 33	99.81 15	99.51 86	99.20 4	98.72 238	99.89 10	95.68 148	99.97 11	98.86 69	99.86 50	99.81 35

MVSFormer		99.17 61	99.12 55	99.29 123	99.51 137	98.94 138	99.88 1	99.46 143	97.55 153	99.80 17	99.65 137	97.39 96	99.28 260	99.03 50	99.85 54	99.65 92

sss		99.17 61	99.05 61	99.53 83	99.62 116	98.97 130	99.36 202	99.62 31	97.83 125	99.67 45	99.65 137	97.37 99	99.95 34	99.19 36	99.19 123	99.68 84

DP-MVS		99.16 63	98.95 80	99.78 35	99.77 42	99.53 58	99.41 182	99.50 101	97.03 208	99.04 197	99.88 15	97.39 96	99.92 65	98.66 93	99.90 24	99.87 5

CNLPA		99.14 64	98.99 72	99.59 70	99.58 125	99.41 74	99.16 252	99.44 164	98.45 60	99.19 170	99.49 198	98.08 79	99.89 96	97.73 178	99.75 79	99.48 135

CDPH-MVS		99.13 65	98.91 84	99.80 31	99.75 57	99.71 29	99.15 255	99.41 177	96.60 236	99.60 63	99.55 174	98.83 32	99.90 88	97.48 202	99.83 63	99.78 49

jason		99.13 65	99.03 66	99.45 100	99.46 152	98.87 146	99.12 259	99.26 249	98.03 103	99.79 19	99.65 137	97.02 108	99.85 117	99.02 52	99.90 24	99.65 92
jason: jason.
lupinMVS		99.13 65	99.01 70	99.46 99	99.51 137	98.94 138	99.05 276	99.16 260	97.86 119	99.80 17	99.56 171	97.39 96	99.86 110	98.94 59	99.85 54	99.58 114

EPP-MVSNet		99.13 65	98.99 72	99.53 83	99.65 108	99.06 111	99.81 15	99.33 222	97.43 165	99.60 63	99.88 15	97.14 104	99.84 123	99.13 43	98.94 143	99.69 80

MG-MVS		99.13 65	99.02 69	99.45 100	99.57 127	98.63 185	99.07 270	99.34 214	98.99 18	99.61 60	99.82 47	97.98 82	99.87 106	97.00 230	99.80 70	99.85 9

CHOSEN 280x420		99.12 70	99.13 54	99.08 150	99.66 105	97.89 228	98.43 337	99.71 13	98.88 30	99.62 58	99.76 91	96.63 121	99.70 191	99.46 14	99.99 1	99.66 88

diffmvs1		99.12 70	99.00 71	99.48 92	99.51 137	99.10 105	99.61 91	99.49 106	97.67 144	99.36 117	99.74 101	97.67 90	99.88 103	98.95 57	98.99 137	99.47 139

DP-MVS Recon		99.12 70	98.95 80	99.65 59	99.74 68	99.70 31	99.27 226	99.57 44	96.40 254	99.42 103	99.68 126	98.75 45	99.80 148	97.98 155	99.72 85	99.44 147

Vis-MVSNet		99.12 70	98.97 75	99.56 77	99.78 36	99.10 105	99.68 57	99.66 25	98.49 57	99.86 8	99.87 20	94.77 192	99.84 123	99.19 36	99.41 110	99.74 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 70	99.08 59	99.24 134	99.46 152	98.55 192	99.51 134	99.46 143	98.09 91	99.45 96	99.82 47	98.34 70	99.51 217	98.70 88	98.93 144	99.67 87

VNet		99.11 75	98.90 85	99.73 47	99.52 135	99.56 52	99.41 182	99.39 187	99.01 13	99.74 32	99.78 82	95.56 149	99.92 65	99.52 7	98.18 190	99.72 71

CPTT-MVS		99.11 75	98.90 85	99.74 45	99.80 34	99.46 68	99.59 96	99.49 106	97.03 208	99.63 55	99.69 121	97.27 101	99.96 19	97.82 167	99.84 59	99.81 35

HyFIR lowres test		99.11 75	98.92 82	99.65 59	99.90 3	99.37 77	99.02 285	99.91 3	97.67 144	99.59 66	99.75 96	95.90 141	99.73 175	99.53 6	99.02 135	99.86 6

MVS_Test		99.10 78	98.97 75	99.48 92	99.49 146	99.14 102	99.67 59	99.34 214	97.31 176	99.58 67	99.76 91	97.65 91	99.82 140	98.87 66	99.07 132	99.46 143

1121		99.09 79	98.87 89	99.75 40	99.74 68	99.60 47	99.27 226	99.48 117	96.82 223	99.25 149	99.65 137	98.38 67	99.93 56	97.53 197	99.67 96	99.73 65

casdiffmvs		99.09 79	98.97 75	99.47 96	99.47 150	99.10 105	99.74 41	99.38 193	97.86 119	99.32 126	99.79 76	97.08 107	99.77 161	99.24 32	98.82 154	99.54 118

CDS-MVSNet		99.09 79	99.03 66	99.25 131	99.42 159	98.73 175	99.45 162	99.46 143	98.11 88	99.46 95	99.77 88	98.01 81	99.37 237	98.70 88	98.92 146	99.66 88
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		99.08 82	98.97 75	99.42 106	99.76 45	98.79 169	98.78 318	99.91 3	96.74 225	99.67 45	99.49 198	97.53 92	99.88 103	98.98 55	99.85 54	99.60 107

OMC-MVS		99.08 82	99.04 64	99.20 138	99.67 95	98.22 214	99.28 223	99.52 77	98.07 95	99.66 50	99.81 57	97.79 86	99.78 159	97.79 170	99.81 68	99.60 107

MVS_0304		99.06 84	98.86 93	99.66 55	99.51 137	99.36 78	99.22 243	99.51 86	98.95 24	99.58 67	99.65 137	93.74 234	99.98 5	99.66 1	99.95 6	99.64 98

WTY-MVS		99.06 84	98.88 88	99.61 68	99.62 116	99.16 98	99.37 198	99.56 49	98.04 101	99.53 82	99.62 153	96.84 113	99.94 41	98.85 71	98.49 171	99.72 71

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 71	99.86 23	96.54 123	99.67 196	98.09 144	99.13 126	99.73 65

PAPM_NR		99.04 87	98.84 96	99.66 55	99.74 68	99.44 71	99.39 191	99.38 193	97.70 141	99.28 137	99.28 262	98.34 70	99.85 117	96.96 234	99.45 107	99.69 80

API-MVS		99.04 87	99.03 66	99.06 152	99.40 167	99.31 85	99.55 122	99.56 49	98.54 54	99.33 125	99.39 231	98.76 42	99.78 159	96.98 232	99.78 74	98.07 317

mvs_anonymous		99.03 89	98.99 72	99.16 142	99.38 170	98.52 198	99.51 134	99.38 193	97.79 130	99.38 112	99.81 57	97.30 100	99.45 221	99.35 19	98.99 137	99.51 129

train_agg		99.02 90	98.77 103	99.77 37	99.67 95	99.65 40	99.05 276	99.41 177	96.28 260	98.95 211	99.49 198	98.76 42	99.91 75	97.63 187	99.72 85	99.75 55

canonicalmvs		99.02 90	98.86 93	99.51 89	99.42 159	99.32 82	99.80 19	99.48 117	98.63 49	99.31 128	98.81 303	97.09 105	99.75 167	99.27 30	97.90 212	99.47 139

PLC	97.94 4	99.02 90	98.85 95	99.53 83	99.66 105	99.01 123	99.24 238	99.52 77	96.85 220	99.27 141	99.48 204	98.25 74	99.91 75	97.76 174	99.62 103	99.65 92
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
agg_prior1		99.01 93	98.76 105	99.76 39	99.67 95	99.62 43	98.99 291	99.40 184	96.26 263	98.87 221	99.49 198	98.77 40	99.91 75	97.69 184	99.72 85	99.75 55

AdaColmap		99.01 93	98.80 100	99.66 55	99.56 131	99.54 55	99.18 250	99.70 15	98.18 81	99.35 121	99.63 148	96.32 130	99.90 88	97.48 202	99.77 76	99.55 116

diffmvs		98.99 95	98.87 89	99.35 110	99.45 156	98.74 174	99.62 85	99.45 155	97.43 165	99.13 177	99.72 110	97.23 102	99.87 106	98.86 69	98.90 148	99.45 146

1112_ss		98.98 96	98.77 103	99.59 70	99.68 94	99.02 121	99.25 236	99.48 117	97.23 184	99.13 177	99.58 165	96.93 112	99.90 88	98.87 66	98.78 158	99.84 13

MSDG		98.98 96	98.80 100	99.53 83	99.76 45	99.19 95	98.75 321	99.55 56	97.25 181	99.47 93	99.77 88	97.82 85	99.87 106	96.93 237	99.90 24	99.54 118

CANet_DTU		98.97 98	98.87 89	99.25 131	99.33 179	98.42 209	99.08 269	99.30 231	99.16 5	99.43 100	99.75 96	95.27 158	99.97 11	98.56 109	99.95 6	99.36 154

agg_prior3		98.97 98	98.71 109	99.75 40	99.67 95	99.60 47	99.04 281	99.41 177	95.93 284	98.87 221	99.48 204	98.61 55	99.91 75	97.63 187	99.72 85	99.75 55

114514_t		98.93 100	98.67 113	99.72 49	99.85 23	99.53 58	99.62 85	99.59 38	92.65 331	99.71 33	99.78 82	98.06 80	99.90 88	98.84 72	99.91 17	99.74 60

PS-MVSNAJss		98.92 101	98.92 82	98.90 183	98.78 291	98.53 194	99.78 22	99.54 63	98.07 95	99.00 207	99.76 91	99.01 12	99.37 237	99.13 43	97.23 244	98.81 207

Test_1112_low_res		98.89 102	98.66 116	99.57 75	99.69 91	98.95 135	99.03 282	99.47 133	96.98 210	99.15 176	99.23 269	96.77 117	99.89 96	98.83 75	98.78 158	99.86 6

AllTest		98.87 103	98.72 107	99.31 117	99.86 20	98.48 204	99.56 116	99.61 32	97.85 122	99.36 117	99.85 27	95.95 137	99.85 117	96.66 256	99.83 63	99.59 111

UGNet		98.87 103	98.69 111	99.40 107	99.22 203	98.72 177	99.44 166	99.68 19	99.24 3	99.18 173	99.42 220	92.74 250	99.96 19	99.34 23	99.94 10	99.53 123
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Vis-MVSNet (Re-imp)		98.87 103	98.72 107	99.31 117	99.71 83	98.88 145	99.80 19	99.44 164	97.91 117	99.36 117	99.78 82	95.49 152	99.43 230	97.91 160	99.11 127	99.62 104

0601test		98.86 106	98.63 118	99.54 78	99.49 146	99.18 97	99.50 139	99.07 271	98.22 77	99.61 60	99.51 192	95.37 154	99.84 123	98.60 102	98.33 176	99.59 111

mvs-test1		98.86 106	98.84 96	98.89 186	99.33 179	97.77 239	99.44 166	99.30 231	98.47 58	99.10 185	99.43 217	96.78 115	99.95 34	98.73 85	99.02 135	98.96 195

EPNet		98.86 106	98.71 109	99.30 120	97.20 339	98.18 215	99.62 85	98.91 290	99.28 2	98.63 256	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
PVSNet_BlendedMVS		98.86 106	98.80 100	99.03 155	99.76 45	98.79 169	99.28 223	99.91 3	97.42 168	99.67 45	99.37 236	97.53 92	99.88 103	98.98 55	97.29 243	98.42 305

ab-mvs		98.86 106	98.63 118	99.54 78	99.64 109	99.19 95	99.44 166	99.54 63	97.77 132	99.30 129	99.81 57	94.20 215	99.93 56	99.17 39	98.82 154	99.49 133

MAR-MVS		98.86 106	98.63 118	99.54 78	99.37 172	99.66 37	99.45 162	99.54 63	96.61 234	99.01 200	99.40 227	97.09 105	99.86 110	97.68 186	99.53 105	99.10 170
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
COLMAP_ROB	97.56 6	98.86 106	98.75 106	99.17 141	99.88 11	98.53 194	99.34 210	99.59 38	97.55 153	98.70 245	99.89 10	95.83 143	99.90 88	98.10 143	99.90 24	99.08 175
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HY-MVS	97.30 7	98.85 113	98.64 117	99.47 96	99.42 159	99.08 109	99.62 85	99.36 202	97.39 171	99.28 137	99.68 126	96.44 127	99.92 65	98.37 126	98.22 186	99.40 152

PVSNet	96.02 17	98.85 113	98.84 96	98.89 186	99.73 73	97.28 246	98.32 341	99.60 35	97.86 119	99.50 88	99.57 169	96.75 118	99.86 110	98.56 109	99.70 91	99.54 118

PatchMatch-RL		98.84 115	98.62 122	99.52 87	99.71 83	99.28 87	99.06 274	99.77 9	97.74 136	99.50 88	99.53 184	95.41 153	99.84 123	97.17 222	99.64 100	99.44 147

Effi-MVS+		98.81 116	98.59 127	99.48 92	99.46 152	99.12 104	98.08 347	99.50 101	97.50 158	99.38 112	99.41 223	96.37 129	99.81 144	99.11 45	98.54 168	99.51 129

alignmvs		98.81 116	98.56 129	99.58 73	99.43 158	99.42 73	99.51 134	98.96 283	98.61 51	99.35 121	98.92 294	94.78 188	99.77 161	99.35 19	98.11 206	99.54 118

DeepPCF-MVS	98.18 3	98.81 116	99.37 17	97.12 312	99.60 122	91.75 340	98.61 329	99.44 164	99.35 1	99.83 12	99.85 27	98.70 50	99.81 144	99.02 52	99.91 17	99.81 35

PMMVS		98.80 119	98.62 122	99.34 111	99.27 196	98.70 178	98.76 320	99.31 229	97.34 173	99.21 164	99.07 281	97.20 103	99.82 140	98.56 109	98.87 151	99.52 124

Effi-MVS+-dtu		98.78 120	98.89 87	98.47 240	99.33 179	96.91 271	99.57 109	99.30 231	98.47 58	99.41 105	98.99 288	96.78 115	99.74 168	98.73 85	99.38 111	98.74 220

FIs		98.78 120	98.63 118	99.23 136	99.18 211	99.54 55	99.83 12	99.59 38	98.28 71	98.79 232	99.81 57	96.75 118	99.37 237	99.08 47	96.38 259	98.78 210

Fast-Effi-MVS+-dtu		98.77 122	98.83 99	98.60 226	99.41 162	96.99 265	99.52 130	99.49 106	98.11 88	99.24 154	99.34 250	96.96 111	99.79 151	97.95 158	99.45 107	99.02 184

FC-MVSNet-test		98.75 123	98.62 122	99.15 144	99.08 232	99.45 70	99.86 8	99.60 35	98.23 76	98.70 245	99.82 47	96.80 114	99.22 275	99.07 48	96.38 259	98.79 209

XVG-OURS		98.73 124	98.68 112	98.88 193	99.70 89	97.73 241	98.92 307	99.55 56	98.52 56	99.45 96	99.84 36	95.27 158	99.91 75	98.08 148	98.84 153	99.00 185

Fast-Effi-MVS+		98.70 125	98.43 132	99.51 89	99.51 137	99.28 87	99.52 130	99.47 133	96.11 277	99.01 200	99.34 250	96.20 134	99.84 123	97.88 162	98.82 154	99.39 153

XVG-OURS-SEG-HR		98.69 126	98.62 122	98.89 186	99.71 83	97.74 240	99.12 259	99.54 63	98.44 63	99.42 103	99.71 112	94.20 215	99.92 65	98.54 114	98.90 148	99.00 185

1314		98.68 127	98.54 130	99.11 149	98.89 274	98.65 183	99.27 226	99.49 106	96.89 218	97.99 291	99.56 171	97.72 89	99.83 131	97.74 177	99.27 119	98.84 205

EI-MVSNet		98.67 128	98.67 113	98.68 221	99.35 175	97.97 224	99.50 139	99.38 193	96.93 215	99.20 167	99.83 40	97.87 83	99.36 241	98.38 125	97.56 224	98.71 224

test_djsdf		98.67 128	98.57 128	98.98 161	98.70 302	98.91 143	99.88 1	99.46 143	97.55 153	99.22 161	99.88 15	95.73 147	99.28 260	99.03 50	97.62 219	98.75 217

QAPM		98.67 128	98.30 141	99.80 31	99.20 206	99.67 35	99.77 25	99.72 11	94.74 298	98.73 237	99.90 7	95.78 145	99.98 5	96.96 234	99.88 35	99.76 54

nrg030		98.64 131	98.42 133	99.28 125	99.05 238	99.69 32	99.81 15	99.46 143	98.04 101	99.01 200	99.82 47	96.69 120	99.38 233	99.34 23	94.59 300	98.78 210

PAPR		98.63 132	98.34 137	99.51 89	99.40 167	99.03 120	98.80 316	99.36 202	96.33 256	99.00 207	99.12 279	98.46 61	99.84 123	95.23 286	99.37 115	99.66 88

CVMVSNet		98.57 133	98.67 113	98.30 255	99.35 175	95.59 300	99.50 139	99.55 56	98.60 52	99.39 110	99.83 40	94.48 206	99.45 221	98.75 82	98.56 167	99.85 9

MVSTER		98.49 134	98.32 139	99.00 159	99.35 175	99.02 121	99.54 125	99.38 193	97.41 169	99.20 167	99.73 106	93.86 229	99.36 241	98.87 66	97.56 224	98.62 275

OpenMVS	96.50 16	98.47 135	98.12 149	99.52 87	99.04 239	99.53 58	99.82 13	99.72 11	94.56 304	98.08 285	99.88 15	94.73 195	99.98 5	97.47 204	99.76 78	99.06 180

IterMVS-LS		98.46 136	98.42 133	98.58 228	99.59 124	98.00 222	99.37 198	99.43 172	96.94 214	99.07 191	99.59 162	97.87 83	99.03 296	98.32 132	95.62 274	98.71 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 137	98.28 142	98.94 167	98.50 318	98.96 134	99.77 25	99.50 101	97.07 204	98.87 221	99.77 88	94.76 193	99.28 260	98.66 93	97.60 220	98.57 296

jajsoiax		98.43 138	98.28 142	98.88 193	98.60 313	98.43 207	99.82 13	99.53 73	98.19 78	98.63 256	99.80 68	93.22 240	99.44 226	99.22 34	97.50 229	98.77 213

BH-untuned		98.42 139	98.36 135	98.59 227	99.49 146	96.70 277	99.27 226	99.13 264	97.24 183	98.80 231	99.38 232	95.75 146	99.74 168	97.07 227	99.16 124	99.33 157

BH-RMVSNet		98.41 140	98.08 153	99.40 107	99.41 162	98.83 153	99.30 217	98.77 304	97.70 141	98.94 213	99.65 137	92.91 246	99.74 168	96.52 260	99.55 104	99.64 98

mvs_tets		98.40 141	98.23 144	98.91 179	98.67 306	98.51 200	99.66 68	99.53 73	98.19 78	98.65 254	99.81 57	92.75 248	99.44 226	99.31 26	97.48 233	98.77 213

XXY-MVS		98.38 142	98.09 152	99.24 134	99.26 198	99.32 82	99.56 116	99.55 56	97.45 163	98.71 239	99.83 40	93.23 238	99.63 207	98.88 62	96.32 261	98.76 215

ACMM	97.58 5	98.37 143	98.34 137	98.48 238	99.41 162	97.10 254	99.56 116	99.45 155	98.53 55	99.04 197	99.85 27	93.00 242	99.71 185	98.74 83	97.45 234	98.64 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn1000		98.33 144	98.02 158	99.25 131	99.78 36	98.73 175	99.70 45	97.55 352	97.48 159	99.69 38	99.53 184	92.37 272	99.85 117	97.82 167	98.26 185	99.16 166

tpmrst		98.33 144	98.48 131	97.90 288	99.16 218	94.78 318	99.31 215	99.11 265	97.27 179	99.45 96	99.59 162	95.33 155	99.84 123	98.48 117	98.61 161	99.09 174

PatchmatchNet		98.31 146	98.36 135	98.19 271	99.16 218	95.32 308	99.27 226	98.92 287	97.37 172	99.37 114	99.58 165	94.90 180	99.70 191	97.43 208	99.21 121	99.54 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		98.30 147	97.98 163	99.26 130	99.57 127	98.16 216	99.41 182	98.55 327	96.03 282	99.19 170	99.74 101	91.87 281	99.92 65	99.16 41	98.29 180	99.70 79

Anonymous20240521		98.30 147	98.00 160	99.18 140	98.98 249	99.46 68	99.78 22	99.49 106	96.91 217	98.00 290	99.25 266	96.51 124	99.38 233	98.15 141	94.95 289	98.71 224

VPA-MVSNet		98.29 149	97.95 166	99.30 120	99.16 218	99.54 55	99.50 139	99.58 43	98.27 72	99.35 121	99.37 236	92.53 265	99.65 200	99.35 19	94.46 301	98.72 222

UniMVSNet (Re)		98.29 149	98.00 160	99.13 148	99.00 244	99.36 78	99.49 148	99.51 86	97.95 112	98.97 210	99.13 276	96.30 131	99.38 233	98.36 128	93.34 318	98.66 261

HQP_MVS		98.27 151	98.22 145	98.44 245	99.29 191	96.97 267	99.39 191	99.47 133	98.97 22	99.11 182	99.61 157	92.71 252	99.69 194	97.78 171	97.63 217	98.67 250

thresconf0.02		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 151	96.90 240	98.12 200	98.97 189

tfpn_n400		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 151	96.90 240	98.12 200	98.97 189

tfpnconf		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 151	96.90 240	98.12 200	98.97 189

tfpnview11		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 151	96.90 240	98.12 200	98.97 189

UniMVSNet_NR-MVSNet		98.22 156	97.97 164	98.96 164	98.92 268	98.98 127	99.48 153	99.53 73	97.76 133	98.71 239	99.46 212	96.43 128	99.22 275	98.57 106	92.87 324	98.69 234

LPG-MVS_test		98.22 156	98.13 148	98.49 236	99.33 179	97.05 260	99.58 103	99.55 56	97.46 160	99.24 154	99.83 40	92.58 263	99.72 179	98.09 144	97.51 227	98.68 239

RPSCF		98.22 156	98.62 122	96.99 313	99.82 29	91.58 341	99.72 42	99.44 164	96.61 234	99.66 50	99.89 10	95.92 140	99.82 140	97.46 205	99.10 129	99.57 115

conf0.01		98.21 159	97.89 173	99.15 144	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 151	96.90 240	98.12 200	98.61 284

conf0.002		98.21 159	97.89 173	99.15 144	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 151	96.90 240	98.12 200	98.61 284

ADS-MVSNet		98.20 161	98.08 153	98.56 231	99.33 179	96.48 284	99.23 239	99.15 261	96.24 265	99.10 185	99.67 131	94.11 220	99.71 185	96.81 247	99.05 133	99.48 135

OPM-MVS		98.19 162	98.10 150	98.45 242	98.88 275	97.07 258	99.28 223	99.38 193	98.57 53	99.22 161	99.81 57	92.12 275	99.66 198	98.08 148	97.54 226	98.61 284

tfpn_ndepth		98.17 163	97.84 181	99.15 144	99.75 57	98.76 173	99.61 91	97.39 354	96.92 216	99.61 60	99.38 232	92.19 274	99.86 110	97.57 192	98.13 198	98.82 206

CR-MVSNet		98.17 163	97.93 168	98.87 197	99.18 211	98.49 202	99.22 243	99.33 222	96.96 211	99.56 71	99.38 232	94.33 211	99.00 300	94.83 293	98.58 164	99.14 167

Patchmatch-test1		98.16 165	98.14 147	98.22 268	99.30 188	95.55 301	99.07 270	98.97 281	97.57 151	99.43 100	99.60 160	92.72 251	99.60 210	97.38 210	99.20 122	99.50 132

CLD-MVS		98.16 165	98.10 150	98.33 252	99.29 191	96.82 274	98.75 321	99.44 164	97.83 125	99.13 177	99.55 174	92.92 244	99.67 196	98.32 132	97.69 216	98.48 301
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		98.14 167	97.79 186	99.19 139	99.50 145	98.50 201	98.61 329	96.82 356	96.95 213	99.54 80	99.43 217	91.66 291	99.86 110	98.08 148	99.51 106	99.22 163

pmmvs4		98.13 168	97.90 169	98.81 208	98.61 312	98.87 146	98.99 291	99.21 255	96.44 249	99.06 195	99.58 165	95.90 141	99.11 288	97.18 221	96.11 264	98.46 304

WR-MVS_H		98.13 168	97.87 180	98.90 183	99.02 242	98.84 150	99.70 45	99.59 38	97.27 179	98.40 268	99.19 272	95.53 150	99.23 272	98.34 129	93.78 315	98.61 284

v1neww		98.12 170	97.84 181	98.93 170	98.97 253	98.81 162	99.66 68	99.35 206	96.49 241	99.29 133	99.37 236	95.02 170	99.32 251	97.73 178	94.73 292	98.67 250

v7new		98.12 170	97.84 181	98.93 170	98.97 253	98.81 162	99.66 68	99.35 206	96.49 241	99.29 133	99.37 236	95.02 170	99.32 251	97.73 178	94.73 292	98.67 250

v6		98.12 170	97.84 181	98.94 167	98.94 261	98.83 153	99.66 68	99.34 214	96.49 241	99.30 129	99.37 236	94.95 174	99.34 247	97.77 173	94.74 291	98.67 250

ACMH	97.28 8	98.10 173	97.99 162	98.44 245	99.41 162	96.96 269	99.60 94	99.56 49	98.09 91	98.15 282	99.91 5	90.87 300	99.70 191	98.88 62	97.45 234	98.67 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		98.09 174	97.68 204	99.34 111	99.66 105	98.44 206	99.40 189	99.43 172	93.67 320	99.22 161	99.89 10	90.23 307	99.93 56	99.26 31	98.33 176	99.66 88

CP-MVSNet		98.09 174	97.78 189	99.01 157	98.97 253	99.24 92	99.67 59	99.46 143	97.25 181	98.48 265	99.64 144	93.79 230	99.06 292	98.63 96	94.10 309	98.74 220

DU-MVS		98.08 176	97.79 186	98.96 164	98.87 278	98.98 127	99.41 182	99.45 155	97.87 118	98.71 239	99.50 195	94.82 185	99.22 275	98.57 106	92.87 324	98.68 239

divwei89l23v2f112		98.06 177	97.78 189	98.91 179	98.90 271	98.77 172	99.57 109	99.35 206	96.45 248	99.24 154	99.37 236	94.92 178	99.27 263	97.50 200	94.71 296	98.68 239

v2v482		98.06 177	97.77 193	98.92 175	98.90 271	98.82 160	99.57 109	99.36 202	96.65 231	99.19 170	99.35 247	94.20 215	99.25 269	97.72 182	94.97 287	98.69 234

V42		98.06 177	97.79 186	98.86 201	98.98 249	98.84 150	99.69 48	99.34 214	96.53 240	99.30 129	99.37 236	94.67 198	99.32 251	97.57 192	94.66 297	98.42 305

test-LLR		98.06 177	97.90 169	98.55 233	98.79 287	97.10 254	98.67 325	97.75 341	97.34 173	98.61 259	98.85 299	94.45 207	99.45 221	97.25 215	99.38 111	99.10 170

WR-MVS		98.06 177	97.73 200	99.06 152	98.86 281	99.25 91	99.19 249	99.35 206	97.30 177	98.66 248	99.43 217	93.94 225	99.21 279	98.58 104	94.28 305	98.71 224

ACMP	97.20 11	98.06 177	97.94 167	98.45 242	99.37 172	97.01 263	99.44 166	99.49 106	97.54 156	98.45 266	99.79 76	91.95 276	99.72 179	97.91 160	97.49 232	98.62 275
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1141		98.05 183	97.76 196	98.91 179	98.91 270	98.78 171	99.57 109	99.35 206	96.41 253	99.23 159	99.36 243	94.93 177	99.27 263	97.38 210	94.72 294	98.68 239

v7		98.05 183	97.78 189	98.87 197	98.99 245	98.67 180	99.64 80	99.34 214	96.31 259	99.29 133	99.51 192	94.78 188	99.27 263	97.03 228	95.15 283	98.66 261

v1		98.05 183	97.76 196	98.93 170	98.92 268	98.80 167	99.57 109	99.35 206	96.39 255	99.28 137	99.36 243	94.86 183	99.32 251	97.38 210	94.72 294	98.68 239

EPNet_dtu		98.03 186	97.96 165	98.23 266	98.27 323	95.54 303	99.23 239	98.75 305	99.02 10	97.82 296	99.71 112	96.11 135	99.48 218	93.04 324	99.65 99	99.69 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 186	97.76 196	98.84 205	99.39 169	98.98 127	99.40 189	99.38 193	96.67 230	99.07 191	99.28 262	92.93 243	98.98 302	97.10 224	96.65 252	98.56 297

ADS-MVSNet2		98.02 188	98.07 155	97.87 289	99.33 179	95.19 312	99.23 239	99.08 268	96.24 265	99.10 185	99.67 131	94.11 220	98.93 311	96.81 247	99.05 133	99.48 135

HQP-MVS		98.02 188	97.90 169	98.37 250	99.19 208	96.83 272	98.98 295	99.39 187	98.24 73	98.66 248	99.40 227	92.47 267	99.64 202	97.19 219	97.58 222	98.64 266

LTVRE_ROB	97.16 12	98.02 188	97.90 169	98.40 248	99.23 201	96.80 275	99.70 45	99.60 35	97.12 193	98.18 281	99.70 115	91.73 287	99.72 179	98.39 123	97.45 234	98.68 239
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
PatchFormer-LS_test		98.01 191	98.05 156	97.87 289	99.15 221	94.76 319	99.42 178	98.93 285	97.12 193	98.84 227	98.59 315	93.74 234	99.80 148	98.55 112	98.17 196	99.06 180

BH-w/o		98.00 192	97.89 173	98.32 253	99.35 175	96.20 293	99.01 289	98.90 292	96.42 251	98.38 269	99.00 287	95.26 160	99.72 179	96.06 268	98.61 161	99.03 182

v1144		97.98 193	97.69 203	98.85 204	98.87 278	98.66 182	99.54 125	99.35 206	96.27 262	99.23 159	99.35 247	94.67 198	99.23 272	96.73 251	95.16 282	98.68 239

EU-MVSNet		97.98 193	98.03 157	97.81 295	98.72 299	96.65 280	99.66 68	99.66 25	98.09 91	98.35 272	99.82 47	95.25 161	98.01 332	97.41 209	95.30 279	98.78 210

tpmvs		97.98 193	98.02 158	97.84 292	99.04 239	94.73 320	99.31 215	99.20 256	96.10 281	98.76 235	99.42 220	94.94 175	99.81 144	96.97 233	98.45 172	98.97 189

view600		97.97 196	97.66 206	98.89 186	99.75 57	97.81 234	99.69 48	98.80 300	98.02 104	99.25 149	98.88 295	91.95 276	99.89 96	94.36 302	98.29 180	98.96 195

view800		97.97 196	97.66 206	98.89 186	99.75 57	97.81 234	99.69 48	98.80 300	98.02 104	99.25 149	98.88 295	91.95 276	99.89 96	94.36 302	98.29 180	98.96 195

conf0.05thres1000		97.97 196	97.66 206	98.89 186	99.75 57	97.81 234	99.69 48	98.80 300	98.02 104	99.25 149	98.88 295	91.95 276	99.89 96	94.36 302	98.29 180	98.96 195

tfpn		97.97 196	97.66 206	98.89 186	99.75 57	97.81 234	99.69 48	98.80 300	98.02 104	99.25 149	98.88 295	91.95 276	99.89 96	94.36 302	98.29 180	98.96 195

NR-MVSNet		97.97 196	97.61 214	99.02 156	98.87 278	99.26 90	99.47 158	99.42 175	97.63 147	97.08 307	99.50 195	95.07 168	99.13 285	97.86 164	93.59 316	98.68 239

v8		97.95 201	97.63 213	98.93 170	98.95 258	98.81 162	99.80 19	99.41 177	96.03 282	99.10 185	99.42 220	94.92 178	99.30 257	96.94 236	94.08 310	98.66 261

Patchmatch-test		97.93 202	97.65 211	98.77 214	99.18 211	97.07 258	99.03 282	99.14 263	96.16 272	98.74 236	99.57 169	94.56 202	99.72 179	93.36 319	99.11 127	99.52 124

PS-CasMVS		97.93 202	97.59 216	98.95 166	98.99 245	99.06 111	99.68 57	99.52 77	97.13 191	98.31 274	99.68 126	92.44 271	99.05 293	98.51 115	94.08 310	98.75 217

TranMVSNet+NR-MVSNet		97.93 202	97.66 206	98.76 216	98.78 291	98.62 187	99.65 78	99.49 106	97.76 133	98.49 264	99.60 160	94.23 214	98.97 309	98.00 154	92.90 322	98.70 229

v144192		97.92 205	97.60 215	98.87 197	98.83 284	98.65 183	99.55 122	99.34 214	96.20 268	99.32 126	99.40 227	94.36 210	99.26 268	96.37 265	95.03 286	98.70 229

ACMH+	97.24 10	97.92 205	97.78 189	98.32 253	99.46 152	96.68 279	99.56 116	99.54 63	98.41 64	97.79 298	99.87 20	90.18 308	99.66 198	98.05 153	97.18 247	98.62 275

LFMVS		97.90 207	97.35 250	99.54 78	99.52 135	99.01 123	99.39 191	98.24 333	97.10 197	99.65 53	99.79 76	84.79 344	99.91 75	99.28 28	98.38 175	99.69 80

Anonymous20231211		97.88 208	97.54 219	98.90 183	99.71 83	98.53 194	99.48 153	99.57 44	94.16 314	98.81 229	99.68 126	93.23 238	99.42 231	98.84 72	94.42 303	98.76 215

OurMVSNet-221017-0		97.88 208	97.77 193	98.19 271	98.71 301	96.53 282	99.88 1	99.00 278	97.79 130	98.78 233	99.94 3	91.68 288	99.35 244	97.21 217	96.99 250	98.69 234

v7n		97.87 210	97.52 220	98.92 175	98.76 295	98.58 191	99.84 9	99.46 143	96.20 268	98.91 216	99.70 115	94.89 181	99.44 226	96.03 269	93.89 314	98.75 217

thres600view7		97.86 211	97.51 222	98.92 175	99.72 77	97.95 227	99.59 96	98.74 308	97.94 113	99.27 141	98.62 310	91.75 283	99.86 110	93.73 315	98.19 189	98.96 195

v10		97.85 212	97.52 220	98.86 201	98.99 245	98.67 180	99.75 36	99.41 177	95.70 288	98.98 209	99.41 223	94.75 194	99.23 272	96.01 270	94.63 299	98.67 250

GA-MVS		97.85 212	97.47 229	99.00 159	99.38 170	97.99 223	98.57 332	99.15 261	97.04 207	98.90 218	99.30 259	89.83 310	99.38 233	96.70 253	98.33 176	99.62 104

tfpnnormal		97.84 214	97.47 229	98.98 161	99.20 206	99.22 94	99.64 80	99.61 32	96.32 257	98.27 277	99.70 115	93.35 237	99.44 226	95.69 276	95.40 277	98.27 312

VPNet		97.84 214	97.44 238	99.01 157	99.21 204	98.94 138	99.48 153	99.57 44	98.38 65	99.28 137	99.73 106	88.89 318	99.39 232	99.19 36	93.27 319	98.71 224

LCM-MVSNet-Re		97.83 216	98.15 146	96.87 317	99.30 188	92.25 339	99.59 96	98.26 332	97.43 165	96.20 316	99.13 276	96.27 132	98.73 316	98.17 138	98.99 137	99.64 98

XVG-ACMP-BASELINE		97.83 216	97.71 202	98.20 270	99.11 226	96.33 289	99.41 182	99.52 77	98.06 99	99.05 196	99.50 195	89.64 312	99.73 175	97.73 178	97.38 240	98.53 298

IterMVS		97.83 216	97.77 193	98.02 279	99.58 125	96.27 291	99.02 285	99.48 117	97.22 185	98.71 239	99.70 115	92.75 248	99.13 285	97.46 205	96.00 267	98.67 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS		97.82 219	97.65 211	98.35 251	98.88 275	95.98 295	99.49 148	94.71 361	97.57 151	99.26 145	99.48 204	92.46 270	99.71 185	97.87 163	99.08 131	99.35 155

tfpn111		97.81 220	97.49 226	98.78 213	99.72 77	97.86 230	99.59 96	98.74 308	97.93 114	99.26 145	98.62 310	91.75 283	99.86 110	93.57 316	98.18 190	98.61 284

MVP-Stereo		97.81 220	97.75 199	97.99 282	97.53 332	96.60 281	98.96 300	98.85 296	97.22 185	97.23 304	99.36 243	95.28 157	99.46 220	95.51 280	99.78 74	97.92 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 220	97.44 238	98.91 179	98.88 275	98.68 179	99.51 134	99.34 214	96.18 270	99.20 167	99.34 250	94.03 223	99.36 241	95.32 285	95.18 281	98.69 234

v1921920		97.80 223	97.45 232	98.84 205	98.80 285	98.53 194	99.52 130	99.34 214	96.15 274	99.24 154	99.47 208	93.98 224	99.29 259	95.40 283	95.13 284	98.69 234

V4		97.80 223	97.51 222	98.67 223	98.79 287	98.63 185	99.87 4	99.44 164	95.87 285	99.01 200	99.46 212	94.52 205	99.33 248	96.64 259	93.97 312	98.05 318

v148		97.79 225	97.55 217	98.50 235	98.74 296	97.72 242	99.54 125	99.33 222	96.26 263	98.90 218	99.51 192	94.68 197	99.14 282	97.83 166	93.15 321	98.63 273

v52		97.79 225	97.50 224	98.66 224	98.80 285	98.62 187	99.87 4	99.44 164	95.87 285	99.01 200	99.46 212	94.44 209	99.33 248	96.65 258	93.96 313	98.05 318

conf200view11		97.78 227	97.45 232	98.77 214	99.72 77	97.86 230	99.59 96	98.74 308	97.93 114	99.26 145	98.62 310	91.75 283	99.83 131	93.22 320	98.18 190	98.61 284

thres400		97.77 228	97.38 246	98.92 175	99.69 91	97.96 225	99.50 139	98.73 317	97.83 125	99.17 174	98.45 319	91.67 289	99.83 131	93.22 320	98.18 190	98.96 195

thres100view900		97.76 229	97.45 232	98.69 220	99.72 77	97.86 230	99.59 96	98.74 308	97.93 114	99.26 145	98.62 310	91.75 283	99.83 131	93.22 320	98.18 190	98.37 309

PEN-MVS		97.76 229	97.44 238	98.72 218	98.77 294	98.54 193	99.78 22	99.51 86	97.06 206	98.29 276	99.64 144	92.63 262	98.89 312	98.09 144	93.16 320	98.72 222

Baseline_NR-MVSNet		97.76 229	97.45 232	98.68 221	99.09 231	98.29 211	99.41 182	98.85 296	95.65 289	98.63 256	99.67 131	94.82 185	99.10 290	98.07 151	92.89 323	98.64 266

TR-MVS		97.76 229	97.41 243	98.82 207	99.06 235	97.87 229	98.87 312	98.56 326	96.63 233	98.68 247	99.22 270	92.49 266	99.65 200	95.40 283	97.79 214	98.95 202

Patchmtry		97.75 233	97.40 244	98.81 208	99.10 229	98.87 146	99.11 265	99.33 222	94.83 296	98.81 229	99.38 232	94.33 211	99.02 297	96.10 267	95.57 275	98.53 298

dp		97.75 233	97.80 185	97.59 303	99.10 229	93.71 330	99.32 212	98.88 294	96.48 247	99.08 190	99.55 174	92.67 261	99.82 140	96.52 260	98.58 164	99.24 162

TAPA-MVS	97.07 15	97.74 235	97.34 253	98.94 167	99.70 89	97.53 243	99.25 236	99.51 86	91.90 335	99.30 129	99.63 148	98.78 37	99.64 202	88.09 340	99.87 39	99.65 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 236	97.35 250	98.88 193	99.47 150	97.12 253	99.34 210	98.85 296	98.19 78	99.67 45	99.85 27	82.98 348	99.92 65	99.49 12	98.32 179	99.60 107

MIMVSNet		97.73 236	97.45 232	98.57 229	99.45 156	97.50 244	99.02 285	98.98 280	96.11 277	99.41 105	99.14 275	90.28 303	98.74 315	95.74 274	98.93 144	99.47 139

tfpn200view9		97.72 238	97.38 246	98.72 218	99.69 91	97.96 225	99.50 139	98.73 317	97.83 125	99.17 174	98.45 319	91.67 289	99.83 131	93.22 320	98.18 190	98.37 309

CostFormer		97.72 238	97.73 200	97.71 300	99.15 221	94.02 326	99.54 125	99.02 277	94.67 299	99.04 197	99.35 247	92.35 273	99.77 161	98.50 116	97.94 211	99.34 156

FMVSNet2		97.72 238	97.36 248	98.80 210	99.51 137	98.84 150	99.45 162	99.42 175	96.49 241	98.86 226	99.29 261	90.26 304	98.98 302	96.44 262	96.56 255	98.58 295

test0.0.03 1		97.71 241	97.42 242	98.56 231	98.41 321	97.82 233	98.78 318	98.63 322	97.34 173	98.05 289	98.98 291	94.45 207	98.98 302	95.04 289	97.15 248	98.89 203

v1240		97.69 242	97.32 256	98.79 211	98.85 282	98.43 207	99.48 153	99.36 202	96.11 277	99.27 141	99.36 243	93.76 232	99.24 271	94.46 299	95.23 280	98.70 229

cascas		97.69 242	97.43 241	98.48 238	98.60 313	97.30 245	98.18 346	99.39 187	92.96 328	98.41 267	98.78 306	93.77 231	99.27 263	98.16 139	98.61 161	98.86 204

pm-mvs1		97.68 244	97.28 260	98.88 193	99.06 235	98.62 187	99.50 139	99.45 155	96.32 257	97.87 294	99.79 76	92.47 267	99.35 244	97.54 196	93.54 317	98.67 250

GBi-Net		97.68 244	97.48 227	98.29 256	99.51 137	97.26 248	99.43 171	99.48 117	96.49 241	99.07 191	99.32 256	90.26 304	98.98 302	97.10 224	96.65 252	98.62 275

test1		97.68 244	97.48 227	98.29 256	99.51 137	97.26 248	99.43 171	99.48 117	96.49 241	99.07 191	99.32 256	90.26 304	98.98 302	97.10 224	96.65 252	98.62 275

tpm		97.67 247	97.55 217	98.03 277	99.02 242	95.01 315	99.43 171	98.54 328	96.44 249	99.12 180	99.34 250	91.83 282	99.60 210	97.75 176	96.46 257	99.48 135

PCF-MVS	97.08 14	97.66 248	97.06 268	99.47 96	99.61 120	99.09 108	98.04 348	99.25 251	91.24 338	98.51 262	99.70 115	94.55 203	99.91 75	92.76 327	99.85 54	99.42 150
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
our_test_3		97.65 249	97.68 204	97.55 304	98.62 310	94.97 316	98.84 314	99.30 231	96.83 222	98.19 280	99.34 250	97.01 109	99.02 297	95.00 290	96.01 266	98.64 266

testgi		97.65 249	97.50 224	98.13 274	99.36 174	96.45 285	99.42 178	99.48 117	97.76 133	97.87 294	99.45 215	91.09 297	98.81 314	94.53 297	98.52 169	99.13 169

thres200		97.61 251	97.28 260	98.62 225	99.64 109	98.03 221	99.26 234	98.74 308	97.68 143	99.09 189	98.32 321	91.66 291	99.81 144	92.88 326	98.22 186	98.03 321

PAPM		97.59 252	97.09 267	99.07 151	99.06 235	98.26 213	98.30 342	99.10 266	94.88 295	98.08 285	99.34 250	96.27 132	99.64 202	89.87 335	98.92 146	99.31 158

VDDNet		97.55 253	97.02 269	99.16 142	99.49 146	98.12 220	99.38 196	99.30 231	95.35 291	99.68 39	99.90 7	82.62 350	99.93 56	99.31 26	98.13 198	99.42 150

TESTMET0.1,1		97.55 253	97.27 262	98.40 248	98.93 266	96.53 282	98.67 325	97.61 351	96.96 211	98.64 255	99.28 262	88.63 324	99.45 221	97.30 214	99.38 111	99.21 164

DWT-MVSNet_test		97.53 255	97.40 244	97.93 285	99.03 241	94.86 317	99.57 109	98.63 322	96.59 238	98.36 271	98.79 304	89.32 314	99.74 168	98.14 142	98.16 197	99.20 165

pmmvs5		97.52 256	97.30 258	98.16 273	98.57 315	96.73 276	99.27 226	98.90 292	96.14 275	98.37 270	99.53 184	91.54 294	99.14 282	97.51 199	95.87 269	98.63 273

v748		97.52 256	97.23 263	98.41 247	98.69 303	97.23 251	99.87 4	99.45 155	95.72 287	98.51 262	99.53 184	94.13 219	99.30 257	96.78 249	92.39 328	98.70 229

LF4IMVS		97.52 256	97.46 231	97.70 301	98.98 249	95.55 301	99.29 221	98.82 299	98.07 95	98.66 248	99.64 144	89.97 309	99.61 209	97.01 229	96.68 251	97.94 325

DTE-MVSNet		97.51 259	97.19 265	98.46 241	98.63 309	98.13 219	99.84 9	99.48 117	96.68 229	97.97 292	99.67 131	92.92 244	98.56 318	96.88 246	92.60 327	98.70 229

SixPastTwentyTwo		97.50 260	97.33 255	98.03 277	98.65 307	96.23 292	99.77 25	98.68 320	97.14 190	97.90 293	99.93 4	90.45 302	99.18 281	97.00 230	96.43 258	98.67 250

JIA-IIPM		97.50 260	97.02 269	98.93 170	98.73 297	97.80 238	99.30 217	98.97 281	91.73 336	98.91 216	94.86 351	95.10 167	99.71 185	97.58 190	97.98 210	99.28 160

ppachtmachnet_test		97.49 262	97.45 232	97.61 302	98.62 310	95.24 309	98.80 316	99.46 143	96.11 277	98.22 278	99.62 153	96.45 126	98.97 309	93.77 314	95.97 268	98.61 284

test-mter		97.49 262	97.13 266	98.55 233	98.79 287	97.10 254	98.67 325	97.75 341	96.65 231	98.61 259	98.85 299	88.23 329	99.45 221	97.25 215	99.38 111	99.10 170

DI_MVS_plusplus_test		97.45 264	96.79 273	99.44 103	97.76 330	99.04 113	99.21 246	98.61 324	97.74 136	94.01 332	98.83 301	87.38 335	99.83 131	98.63 96	98.90 148	99.44 147

test_normal		97.44 265	96.77 275	99.44 103	97.75 331	99.00 125	99.10 267	98.64 321	97.71 139	93.93 335	98.82 302	87.39 334	99.83 131	98.61 100	98.97 140	99.49 133

tpm2		97.44 265	97.34 253	97.74 299	99.15 221	94.36 323	99.45 162	98.94 284	93.45 326	98.90 218	99.44 216	91.35 295	99.59 212	97.31 213	98.07 207	99.29 159

tpm cat1		97.39 267	97.36 248	97.50 307	99.17 216	93.73 328	99.43 171	99.31 229	91.27 337	98.71 239	99.08 280	94.31 213	99.77 161	96.41 264	98.50 170	99.00 185

tpmp4_e23		97.34 268	97.29 259	97.52 305	99.25 200	93.73 328	99.58 103	99.19 259	94.00 316	98.20 279	99.41 223	90.74 301	99.74 168	97.13 223	98.07 207	99.07 179

USDC		97.34 268	97.20 264	97.75 298	99.07 233	95.20 311	98.51 335	99.04 275	97.99 109	98.31 274	99.86 23	89.02 316	99.55 215	95.67 278	97.36 241	98.49 300

MVS		97.28 270	96.55 277	99.48 92	98.78 291	98.95 135	99.27 226	99.39 187	83.53 351	98.08 285	99.54 177	96.97 110	99.87 106	94.23 310	99.16 124	99.63 102

DSMNet-mixed		97.25 271	97.35 250	96.95 315	97.84 328	93.61 332	99.57 109	96.63 357	96.13 276	98.87 221	98.61 314	94.59 201	97.70 339	95.08 288	98.86 152	99.55 116

MS-PatchMatch		97.24 272	97.32 256	96.99 313	98.45 320	93.51 333	98.82 315	99.32 228	97.41 169	98.13 283	99.30 259	88.99 317	99.56 213	95.68 277	99.80 70	97.90 328

TransMVSNet (Re)		97.15 273	96.58 276	98.86 201	99.12 224	98.85 149	99.49 148	98.91 290	95.48 290	97.16 306	99.80 68	93.38 236	99.11 288	94.16 312	91.73 329	98.62 275

TinyColmap		97.12 274	96.89 271	97.83 293	99.07 233	95.52 304	98.57 332	98.74 308	97.58 150	97.81 297	99.79 76	88.16 330	99.56 213	95.10 287	97.21 245	98.39 308

K. test v3		97.10 275	96.79 273	98.01 280	98.72 299	96.33 289	99.87 4	97.05 355	97.59 148	96.16 317	99.80 68	88.71 320	99.04 294	96.69 254	96.55 256	98.65 264

LP		97.04 276	96.80 272	97.77 297	98.90 271	95.23 310	98.97 298	99.06 273	94.02 315	98.09 284	99.41 223	93.88 227	98.82 313	90.46 333	98.42 174	99.26 161

PatchT		97.03 277	96.44 278	98.79 211	98.99 245	98.34 210	99.16 252	99.07 271	92.13 332	99.52 84	97.31 344	94.54 204	98.98 302	88.54 338	98.73 160	99.03 182

FMVSNet1		96.84 278	96.36 279	98.29 256	99.32 186	97.26 248	99.43 171	99.48 117	95.11 293	98.55 261	99.32 256	83.95 347	98.98 302	95.81 273	96.26 262	98.62 275

test_0402		96.64 279	96.24 280	97.85 291	98.85 282	96.43 286	99.44 166	99.26 249	93.52 323	96.98 310	99.52 189	88.52 325	99.20 280	92.58 329	97.50 229	97.93 326

RPMNet		96.61 280	95.85 288	98.87 197	99.18 211	98.49 202	99.22 243	99.08 268	88.72 347	99.56 71	97.38 342	94.08 222	99.00 300	86.87 345	98.58 164	99.14 167

X-MVStestdata		96.55 281	95.45 302	99.87 7	99.85 23	99.83 8	99.69 48	99.68 19	98.98 19	99.37 114	64.01 366	98.81 34	99.94 41	98.79 79	99.86 50	99.84 13

pmmvs6		96.53 282	96.09 283	97.82 294	98.69 303	95.47 305	99.37 198	99.47 133	93.46 325	97.41 301	99.78 82	87.06 336	99.33 248	96.92 238	92.70 326	98.65 264

UnsupCasMVSNet_eth		96.44 283	96.12 282	97.40 309	98.65 307	95.65 298	99.36 202	99.51 86	97.13 191	96.04 320	98.99 288	88.40 327	98.17 321	96.71 252	90.27 332	98.40 307

FMVSNet5		96.43 284	96.19 281	97.15 310	99.11 226	95.89 297	99.32 212	99.52 77	94.47 308	98.34 273	99.07 281	87.54 333	97.07 342	92.61 328	95.72 272	98.47 302

v18		96.42 285	95.80 292	98.26 259	98.95 258	98.82 160	99.76 28	99.28 243	94.58 301	94.12 327	97.70 330	95.22 163	98.16 322	94.83 293	87.80 339	97.79 336

v17		96.42 285	95.81 290	98.25 263	98.94 261	98.80 167	99.76 28	99.28 243	94.57 302	94.18 326	97.71 329	95.23 162	98.16 322	94.86 291	87.73 341	97.80 331

v16		96.39 287	95.76 293	98.26 259	98.96 256	98.81 162	99.76 28	99.28 243	94.57 302	94.10 328	97.70 330	95.04 169	98.16 322	94.70 295	87.77 340	97.80 331

new_pmnet		96.38 288	96.03 284	97.41 308	98.13 326	95.16 314	99.05 276	99.20 256	93.94 317	97.39 302	98.79 304	91.61 293	99.04 294	90.43 334	95.77 271	98.05 318

v15		96.28 289	95.62 295	98.25 263	98.94 261	98.83 153	99.76 28	99.29 236	94.52 306	94.02 331	97.61 336	95.02 170	98.13 326	94.53 297	86.92 344	97.80 331

V14		96.26 290	95.60 296	98.26 259	98.94 261	98.83 153	99.76 28	99.29 236	94.49 307	93.96 333	97.66 333	94.99 173	98.13 326	94.41 300	86.90 345	97.80 331

V9		96.25 291	95.58 297	98.26 259	98.94 261	98.83 153	99.75 36	99.29 236	94.45 309	93.96 333	97.62 335	94.94 175	98.14 325	94.40 301	86.87 346	97.81 329

v13		96.24 292	95.58 297	98.25 263	98.98 249	98.83 153	99.75 36	99.29 236	94.35 311	93.89 336	97.60 337	95.17 165	98.11 328	94.27 309	86.86 347	97.81 329

v12		96.24 292	95.58 297	98.23 266	98.96 256	98.81 162	99.76 28	99.29 236	94.42 310	93.85 337	97.60 337	95.12 166	98.09 329	94.32 306	86.85 348	97.80 331

v11		96.23 294	95.57 300	98.21 269	98.93 266	98.83 153	99.72 42	99.29 236	94.29 312	94.05 330	97.64 334	94.88 182	98.04 330	92.89 325	88.43 337	97.77 337

Anonymous20231206		96.22 295	96.03 284	96.79 319	97.31 337	94.14 325	99.63 82	99.08 268	96.17 271	97.04 308	99.06 283	93.94 225	97.76 338	86.96 344	95.06 285	98.47 302

IB-MVS	95.67 18	96.22 295	95.44 303	98.57 229	99.21 204	96.70 277	98.65 328	97.74 343	96.71 227	97.27 303	98.54 317	86.03 338	99.92 65	98.47 119	86.30 349	99.10 170
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		96.17 297	95.32 304	98.73 217	98.79 287	98.14 218	99.38 196	94.09 362	91.07 340	98.07 288	91.04 357	89.62 313	99.35 244	96.75 250	99.09 130	98.68 239

test20.03		96.12 298	95.96 287	96.63 320	97.44 333	95.45 306	99.51 134	99.38 193	96.55 239	96.16 317	99.25 266	93.76 232	96.17 347	87.35 343	94.22 307	98.27 312

PVSNet_0	94.43 19	96.09 299	95.47 301	97.94 284	99.31 187	94.34 324	97.81 349	99.70 15	97.12 193	97.46 300	98.75 307	89.71 311	99.79 151	97.69 184	81.69 352	99.68 84

EG-PatchMatch MVS		95.97 300	95.69 294	96.81 318	97.78 329	92.79 336	99.16 252	98.93 285	96.16 272	94.08 329	99.22 270	82.72 349	99.47 219	95.67 278	97.50 229	98.17 315

Patchmatch-RL test		95.84 301	95.81 290	95.95 324	95.61 342	90.57 342	98.24 343	98.39 329	95.10 294	95.20 322	98.67 309	94.78 188	97.77 337	96.28 266	90.02 333	99.51 129

MVS-HIRNet		95.75 302	95.16 306	97.51 306	99.30 188	93.69 331	98.88 311	95.78 358	85.09 350	98.78 233	92.65 353	91.29 296	99.37 237	94.85 292	99.85 54	99.46 143

testpf		95.66 303	96.02 286	94.58 327	98.35 322	92.32 338	97.25 354	97.91 340	92.83 329	97.03 309	98.99 288	88.69 321	98.61 317	95.72 275	97.40 238	92.80 352

MIMVSNet1		95.51 304	95.04 307	96.92 316	97.38 334	95.60 299	99.52 130	99.50 101	93.65 321	96.97 311	99.17 273	85.28 342	96.56 346	88.36 339	95.55 276	98.60 291

MDA-MVSNet_test_wron		95.45 305	94.60 310	98.01 280	98.16 325	97.21 252	99.11 265	99.24 252	93.49 324	80.73 356	98.98 291	93.02 241	98.18 320	94.22 311	94.45 302	98.64 266

TDRefinement		95.42 306	94.57 311	97.97 283	89.83 357	96.11 294	99.48 153	98.75 305	96.74 225	96.68 312	99.88 15	88.65 323	99.71 185	98.37 126	82.74 351	98.09 316

YYNet1		95.36 307	94.51 312	97.92 286	97.89 327	97.10 254	99.10 267	99.23 253	93.26 327	80.77 355	99.04 285	92.81 247	98.02 331	94.30 307	94.18 308	98.64 266

pmmvs-eth3d		95.34 308	94.73 309	97.15 310	95.53 344	95.94 296	99.35 207	99.10 266	95.13 292	93.55 338	97.54 340	88.15 331	97.91 334	94.58 296	89.69 335	97.61 339

Test4		95.05 309	93.67 317	99.22 137	96.07 341	98.94 138	99.20 248	99.27 248	97.71 139	89.96 349	97.59 339	66.18 356	99.25 269	98.06 152	98.96 142	99.47 139

MDA-MVSNet-bldmvs		94.96 310	93.98 315	97.92 286	98.24 324	97.27 247	99.15 255	99.33 222	93.80 319	80.09 357	99.03 286	88.31 328	97.86 336	93.49 318	94.36 304	98.62 275

N_pmnet		94.95 311	95.83 289	92.31 334	98.47 319	79.33 357	99.12 259	92.81 367	93.87 318	97.68 299	99.13 276	93.87 228	99.01 299	91.38 331	96.19 263	98.59 292

testus		94.61 312	95.30 305	92.54 333	96.44 340	84.18 349	98.36 338	99.03 276	94.18 313	96.49 313	98.57 316	88.74 319	95.09 351	87.41 342	98.45 172	98.36 311

new-patchmatchnet		94.48 313	94.08 314	95.67 325	95.08 346	92.41 337	99.18 250	99.28 243	94.55 305	93.49 339	97.37 343	87.86 332	97.01 343	91.57 330	88.36 338	97.61 339

testing_2		94.44 314	92.93 320	98.98 161	94.16 348	99.00 125	99.42 178	99.28 243	96.60 236	84.86 351	96.84 345	70.91 353	99.27 263	98.23 135	96.08 265	98.68 239

OpenMVS_ROB	92.34 20	94.38 315	93.70 316	96.41 323	97.38 334	93.17 334	99.06 274	98.75 305	86.58 348	94.84 325	98.26 323	81.53 351	99.32 251	89.01 337	97.87 213	96.76 343

CMPMVS	69.68 23	94.13 316	94.90 308	91.84 335	97.24 338	80.01 356	98.52 334	99.48 117	89.01 345	91.99 343	99.67 131	85.67 340	99.13 285	95.44 281	97.03 249	96.39 345
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 317	93.25 319	96.60 321	94.76 347	94.49 321	98.92 307	98.18 336	89.66 342	96.48 314	98.06 324	86.28 337	97.33 341	89.68 336	87.20 343	97.97 324

test2356		94.07 318	94.46 313	92.89 331	95.18 345	86.13 347	97.60 352	99.06 273	93.61 322	96.15 319	98.28 322	85.60 341	93.95 353	86.68 346	98.00 209	98.59 292

UnsupCasMVSNet_bld		93.53 319	92.51 321	96.58 322	97.38 334	93.82 327	98.24 343	99.48 117	91.10 339	93.10 340	96.66 346	74.89 352	98.37 319	94.03 313	87.71 342	97.56 341

PM-MVS		92.96 320	92.23 322	95.14 326	95.61 342	89.98 344	99.37 198	98.21 334	94.80 297	95.04 324	97.69 332	65.06 357	97.90 335	94.30 307	89.98 334	97.54 342

test1235678		92.91 321	93.30 318	91.71 337	93.14 351	83.01 351	98.75 321	98.58 325	92.80 330	92.45 341	97.91 326	88.51 326	93.54 354	82.26 350	95.35 278	98.59 292

1111		92.30 322	92.21 323	92.55 332	93.30 349	86.27 345	99.15 255	98.74 308	91.94 333	90.85 346	97.82 327	84.18 345	95.21 349	79.65 352	94.27 306	96.19 346

test12356		91.74 323	92.19 324	90.37 340	91.22 353	82.41 352	98.61 329	98.28 331	90.66 341	91.82 344	97.92 325	84.90 343	92.61 355	81.64 351	94.66 297	96.09 347

Gipuma		90.99 324	90.15 325	93.51 328	98.73 297	90.12 343	93.98 358	99.45 155	79.32 353	92.28 342	94.91 350	69.61 354	97.98 333	87.42 341	95.67 273	92.45 354
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		87.91 325	87.80 326	88.24 341	87.68 360	77.50 359	99.07 270	97.66 350	89.27 343	86.47 350	96.22 348	68.35 355	92.49 357	76.63 356	88.82 336	94.72 350

PMMVS2		86.87 326	85.37 329	91.35 339	90.21 356	83.80 350	98.89 310	97.45 353	83.13 352	91.67 345	95.03 349	48.49 362	94.70 352	85.86 347	77.62 353	95.54 348

LCM-MVSNet		86.80 327	85.22 330	91.53 338	87.81 359	80.96 355	98.23 345	98.99 279	71.05 356	90.13 348	96.51 347	48.45 363	96.88 344	90.51 332	85.30 350	96.76 343

FPMVS		84.93 328	85.65 328	82.75 348	86.77 361	63.39 366	98.35 340	98.92 287	74.11 355	83.39 353	98.98 291	50.85 361	92.40 358	84.54 348	94.97 287	92.46 353

.test1245		83.42 329	86.17 327	75.15 351	93.30 349	86.27 345	99.15 255	98.74 308	91.94 333	90.85 346	97.82 327	84.18 345	95.21 349	79.65 352	39.90 362	43.98 363

no-one		83.04 330	80.12 332	91.79 336	89.44 358	85.65 348	99.32 212	98.32 330	89.06 344	79.79 359	89.16 359	44.86 364	96.67 345	84.33 349	46.78 360	93.05 351

tmp_tt		82.80 331	81.52 331	86.66 342	66.61 368	68.44 365	92.79 360	97.92 338	68.96 358	80.04 358	99.85 27	85.77 339	96.15 348	97.86 164	43.89 361	95.39 349

E-PMN		80.61 332	79.88 333	82.81 347	90.75 355	76.38 361	97.69 350	95.76 359	66.44 360	83.52 352	92.25 354	62.54 359	87.16 362	68.53 360	61.40 356	84.89 361

EMVS		80.02 333	79.22 334	82.43 349	91.19 354	76.40 360	97.55 353	92.49 369	66.36 361	83.01 354	91.27 355	64.63 358	85.79 363	65.82 361	60.65 357	85.08 360

PNet_i23d		79.43 334	77.68 335	84.67 344	86.18 362	71.69 364	96.50 356	93.68 363	75.17 354	71.33 360	91.18 356	32.18 367	90.62 359	78.57 355	74.34 354	91.71 356

ANet_high		77.30 335	74.86 337	84.62 345	75.88 366	77.61 358	97.63 351	93.15 366	88.81 346	64.27 362	89.29 358	36.51 365	83.93 364	75.89 357	52.31 359	92.33 355

MVE	76.82 21	76.91 336	74.31 338	84.70 343	85.38 364	76.05 362	96.88 355	93.17 365	67.39 359	71.28 361	89.01 360	21.66 372	87.69 361	71.74 359	72.29 355	90.35 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 337	74.97 336	79.01 350	70.98 367	55.18 367	93.37 359	98.21 334	65.08 362	61.78 364	93.83 352	21.74 371	92.53 356	78.59 354	91.12 331	89.34 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		74.42 338	71.19 339	84.14 346	76.16 365	74.29 363	96.00 357	92.57 368	69.57 357	63.84 363	87.49 361	21.98 369	88.86 360	75.56 358	57.50 358	89.26 359

v1.0		41.40 339	55.20 340	0.00 356	99.81 32	0.00 371	0.00 362	99.48 117	97.97 111	99.77 25	99.78 82	0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

pcd1.5k->3k		40.85 340	43.49 342	32.93 353	98.95 258	0.00 371	0.00 362	99.53 73	0.00 366	0.00 368	0.27 368	95.32 156	0.00 368	0.00 365	97.30 242	98.80 208

wuyk23d		40.18 341	41.29 344	36.84 352	86.18 362	49.12 368	79.73 361	22.81 371	27.64 363	25.46 367	28.45 367	21.98 369	48.89 365	55.80 362	23.56 365	12.51 365

testmvs		39.17 342	43.78 341	25.37 355	36.04 370	16.84 370	98.36 338	26.56 370	20.06 364	38.51 366	67.32 362	29.64 368	15.30 367	37.59 363	39.90 362	43.98 363

test123		39.01 343	42.50 343	28.53 354	39.17 369	20.91 369	98.75 321	19.17 372	19.83 365	38.57 365	66.67 363	33.16 366	15.42 366	37.50 364	29.66 364	49.26 362

cdsmvs_eth3d_5k		24.64 344	32.85 345	0.00 356	0.00 371	0.00 371	0.00 362	99.51 86	0.00 366	0.00 368	99.56 171	96.58 122	0.00 368	0.00 365	0.00 366	0.00 366

ab-mvs-re		8.30 345	11.06 346	0.00 356	0.00 371	0.00 371	0.00 362	0.00 373	0.00 366	0.00 368	99.58 165	0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

pcd_1.5k_mvsjas		8.27 346	11.03 347	0.00 356	0.00 371	0.00 371	0.00 362	0.00 373	0.00 366	0.00 368	0.27 368	99.01 12	0.00 368	0.00 365	0.00 366	0.00 366

sosnet-low-res		0.02 347	0.03 348	0.00 356	0.00 371	0.00 371	0.00 362	0.00 373	0.00 366	0.00 368	0.27 368	0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

sosnet		0.02 347	0.03 348	0.00 356	0.00 371	0.00 371	0.00 362	0.00 373	0.00 366	0.00 368	0.27 368	0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

uncertanet		0.02 347	0.03 348	0.00 356	0.00 371	0.00 371	0.00 362	0.00 373	0.00 366	0.00 368	0.27 368	0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

Regformer		0.02 347	0.03 348	0.00 356	0.00 371	0.00 371	0.00 362	0.00 373	0.00 366	0.00 368	0.27 368	0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

uanet		0.02 347	0.03 348	0.00 356	0.00 371	0.00 371	0.00 362	0.00 373	0.00 366	0.00 368	0.27 368	0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

GSMVS																99.52 124

test_part2					99.81 32	99.83 8				99.77 25

test_part1				0.00 356		0.00 371	0.00 362	99.48 117				0.00 373	0.00 368	0.00 365	0.00 366	0.00 366

sam_mvs1												94.86 183				99.52 124

sam_mvs												94.72 196

semantic-postprocess				98.06 276	99.57 127	96.36 288		99.49 106	97.18 187	98.71 239	99.72 110	92.70 254	99.14 282	97.44 207	95.86 270	98.67 250

ambc				93.06 330	92.68 352	82.36 353	98.47 336	98.73 317		95.09 323	97.41 341	55.55 360	99.10 290	96.42 263	91.32 330	97.71 338

MTGPA								99.47 133

test_post1							99.23 239				65.14 365	94.18 218	99.71 185	97.58 190

test_post											65.99 364	94.65 200	99.73 175

patchmatchnet-post											98.70 308	94.79 187	99.74 168

GG-mvs-BLEND				98.45 242	98.55 316	98.16 216	99.43 171	93.68 363		97.23 304	98.46 318	89.30 315	99.22 275	95.43 282	98.22 186	97.98 323

MTMP							99.54 125	98.88 294

gm-plane-assit					98.54 317	92.96 335			94.65 300		99.15 274		99.64 202	97.56 194

test9_res														97.49 201	99.72 85	99.75 55

TEST9					99.67 95	99.65 40	99.05 276	99.41 177	96.22 267	98.95 211	99.49 198	98.77 40	99.91 75

test_8					99.67 95	99.61 45	99.03 282	99.41 177	96.28 260	98.93 214	99.48 204	98.76 42	99.91 75

agg_prior2														97.21 217	99.73 84	99.75 55

agg_prior					99.67 95	99.62 43		99.40 184		98.87 221			99.91 75

TestCases				99.31 117	99.86 20	98.48 204		99.61 32	97.85 122	99.36 117	99.85 27	95.95 137	99.85 117	96.66 256	99.83 63	99.59 111

test_prior4						99.56 52	98.99 291

test_prior2							98.96 300		98.34 67	99.01 200	99.52 189	98.68 51		97.96 156	99.74 81

test_prior				99.68 52	99.67 95	99.48 65		99.56 49					99.83 131			99.74 60

旧先验2							98.96 300		96.70 228	99.47 93			99.94 41	98.19 136

新几何2							99.01 289

新几何1				99.75 40	99.75 57	99.59 49		99.54 63	96.76 224	99.29 133	99.64 144	98.43 63	99.94 41	96.92 238	99.66 97	99.72 71

旧先验1					99.74 68	99.59 49		99.54 63			99.69 121	98.47 60			99.68 95	99.73 65

无先验							98.99 291	99.51 86	96.89 218				99.93 56	97.53 197		99.72 71

原ACMM2							98.95 304

原ACMM1				99.65 59	99.73 73	99.33 81		99.47 133	97.46 160	99.12 180	99.66 136	98.67 53	99.91 75	97.70 183	99.69 92	99.71 78

test222					99.75 57	99.49 64	98.91 309	99.49 106	96.42 251	99.34 124	99.65 137	98.28 73			99.69 92	99.72 71

testdata2													99.95 34	96.67 255

segment_acmp												98.96 21

testdata				99.54 78	99.75 57	98.95 135		99.51 86	97.07 204	99.43 100	99.70 115	98.87 29	99.94 41	97.76 174	99.64 100	99.72 71

testdata1							98.85 313		98.32 70

test12				99.75 40	99.64 109	99.61 45		99.29 236		99.21 164		98.38 67	99.89 96		99.74 81	99.74 60

plane_prior7					99.29 191	97.03 262

plane_prior6					99.27 196	96.98 266						92.71 252

plane_prior5								99.47 133					99.69 194	97.78 171	97.63 217	98.67 250

plane_prior4											99.61 157

plane_prior3						97.00 264			98.69 47	99.11 182

plane_prior2							99.39 191		98.97 22

plane_prior1					99.26 198

plane_prior						96.97 267	99.21 246		98.45 60						97.60 220

n2								0.00 373

nn								0.00 373

door-mid								98.05 337

lessismore_v0				97.79 296	98.69 303	95.44 307		94.75 360		95.71 321	99.87 20	88.69 321	99.32 251	95.89 271	94.93 290	98.62 275

LGP-MVS_train				98.49 236	99.33 179	97.05 260		99.55 56	97.46 160	99.24 154	99.83 40	92.58 263	99.72 179	98.09 144	97.51 227	98.68 239

test11								99.35 206

door								97.92 338

HQP5-MVS						96.83 272

HQP-NCC					99.19 208		98.98 295		98.24 73	98.66 248

ACMP_Plane					99.19 208		98.98 295		98.24 73	98.66 248

BP-MVS														97.19 219

HQP4-MVS										98.66 248			99.64 202			98.64 266

HQP3-MVS								99.39 187							97.58 222

HQP2-MVS												92.47 267

NP-MVS					99.23 201	96.92 270					99.40 227

MDTV_nov1_ep13_2view						95.18 313	99.35 207		96.84 221	99.58 67		95.19 164		97.82 167		99.46 143

MDTV_nov1_ep13			98.32 139		99.11 226	94.44 322	99.27 226	98.74 308	97.51 157	99.40 109	99.62 153	94.78 188	99.76 166	97.59 189	98.81 157

ACMMP++_ref															97.19 246

ACMMP++															97.43 237

Test By Simon												98.75 45

ITE_SJBPF				98.08 275	99.29 191	96.37 287		98.92 287	98.34 67	98.83 228	99.75 96	91.09 297	99.62 208	95.82 272	97.40 238	98.25 314

DeepMVS_CX				93.34 329	99.29 191	82.27 354		99.22 254	85.15 349	96.33 315	99.05 284	90.97 299	99.73 175	93.57 316	97.77 215	98.01 322

High-resolution multi-view benchmark