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 41	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

Regformer-4		99.59 2	99.54 4	99.73 46	99.76 44	99.41 72	99.58 97	99.49 104	99.02 10	99.88 3	99.80 64	99.00 17	99.94 42	99.45 15	99.92 12	99.84 12

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 28	99.91 1	99.66 36	99.63 78	99.39 179	98.91 29	99.78 22	99.85 26	99.36 2	99.94 42	98.84 66	99.88 34	99.82 32

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 63	99.78 36	99.14 99	99.60 89	99.45 149	99.01 13	99.90 1	99.83 37	98.98 18	99.93 57	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 63	99.78 36	99.15 98	99.61 87	99.45 149	99.01 13	99.89 2	99.82 44	99.01 11	99.92 65	99.56 5	99.95 6	99.85 8

Regformer-3		99.57 6	99.53 5	99.68 51	99.76 44	99.29 83	99.58 97	99.44 157	99.01 13	99.87 6	99.80 64	98.97 19	99.91 74	99.44 16	99.92 12	99.83 23

Regformer-2		99.54 7	99.47 8	99.75 39	99.71 79	99.52 60	99.49 139	99.49 104	98.94 26	99.83 11	99.76 87	99.01 11	99.94 42	99.15 38	99.87 38	99.80 41

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 13	99.59 91	99.51 85	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
Regformer-1		99.53 9	99.47 8	99.72 48	99.71 79	99.44 69	99.49 139	99.46 138	98.95 24	99.83 11	99.76 87	99.01 11	99.93 57	99.17 36	99.87 38	99.80 41

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 108	99.74 97	98.81 35	99.94 42	98.79 72	99.86 48	99.84 12

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 64	99.47 129	98.79 40	99.68 37	99.81 53	98.43 63	99.97 11	98.88 57	99.90 24	99.83 23

HPM-MVS_fast		99.51 12	99.40 14	99.85 18	99.91 1	99.79 18	99.76 27	99.56 48	97.72 134	99.76 28	99.75 92	99.13 6	99.92 65	99.07 44	99.92 12	99.85 8

MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 192	99.47 129	98.79 40	99.68 37	99.81 53	98.43 63	99.97 11	98.88 57	99.90 24	99.83 23

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 14	99.66 64	99.67 22	98.15 80	99.68 37	99.69 114	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 18	99.66 64	99.67 22	98.15 80	99.67 43	99.69 114	98.95 25	99.96 19	98.69 82	99.87 38	99.84 12

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 36	99.63 106	99.59 48	99.36 192	99.46 138	99.07 9	99.79 18	99.82 44	98.85 32	99.92 65	98.68 84	99.87 38	99.82 32
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 6	99.88 11	99.80 14	99.65 74	99.66 25	98.13 82	99.66 48	99.68 119	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 18	99.76 44	99.83 7	99.63 78	99.54 62	98.36 65	99.79 18	99.82 44	98.86 31	99.95 33	98.62 90	99.81 68	99.78 49

DELS-MVS		99.48 17	99.42 11	99.65 58	99.72 74	99.40 74	99.05 266	99.66 25	99.14 6	99.57 67	99.80 64	98.46 61	99.94 42	99.57 4	99.84 57	99.60 104
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 4	99.87 15	99.86 3	99.47 148	99.48 113	98.05 98	99.76 28	99.86 22	98.82 34	99.93 57	98.82 71	99.91 17	99.84 12

MSLP-MVS++		99.46 21	99.47 8	99.44 98	99.60 116	99.16 95	99.41 172	99.71 13	98.98 19	99.45 90	99.78 77	99.19 4	99.54 207	99.28 27	99.84 57	99.63 100

PGM-MVS		99.45 22	99.31 31	99.86 13	99.87 15	99.78 22	99.58 97	99.65 30	97.84 120	99.71 31	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

CP-MVS		99.45 22	99.32 26	99.85 18	99.83 28	99.75 23	99.69 45	99.52 76	98.07 93	99.53 77	99.63 141	98.93 27	99.97 11	98.74 75	99.91 17	99.83 23

ACMMP		99.45 22	99.32 26	99.82 25	99.89 8	99.67 34	99.62 81	99.69 18	98.12 84	99.63 53	99.84 35	98.73 48	99.96 19	98.55 103	99.83 63	99.81 36
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
abl_6		99.44 25	99.31 31	99.83 23	99.85 23	99.75 23	99.66 64	99.59 38	98.13 82	99.82 14	99.81 53	98.60 56	99.96 19	98.46 111	99.88 34	99.79 45

mPP-MVS		99.44 25	99.30 33	99.86 13	99.88 11	99.79 18	99.69 45	99.48 113	98.12 84	99.50 82	99.75 92	98.78 38	99.97 11	98.57 97	99.89 32	99.83 23

#test#		99.43 27	99.29 36	99.86 13	99.87 15	99.80 14	99.55 115	99.67 22	97.83 121	99.68 37	99.69 114	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

MCST-MVS		99.43 27	99.30 33	99.82 25	99.79 35	99.74 26	99.29 210	99.40 176	98.79 40	99.52 79	99.62 146	98.91 28	99.90 86	98.64 87	99.75 79	99.82 32

UA-Net		99.42 29	99.29 36	99.80 30	99.62 110	99.55 53	99.50 131	99.70 15	98.79 40	99.77 23	99.96 1	97.45 93	99.96 19	98.92 55	99.90 24	99.89 2

HPM-MVS		99.42 29	99.28 38	99.83 23	99.90 3	99.72 27	99.81 15	99.54 62	97.59 143	99.68 37	99.63 141	98.91 28	99.94 42	98.58 95	99.91 17	99.84 12

CNVR-MVS		99.42 29	99.30 33	99.78 34	99.62 110	99.71 28	99.26 224	99.52 76	98.82 35	99.39 104	99.71 105	98.96 20	99.85 111	98.59 94	99.80 70	99.77 51

HSP-MVS		99.41 32	99.26 43	99.85 18	99.89 8	99.80 14	99.67 56	99.37 192	98.70 45	99.77 23	99.49 188	98.21 75	99.95 33	98.46 111	99.77 76	99.81 36

SD-MVS		99.41 32	99.52 6	99.05 144	99.74 66	99.68 32	99.46 151	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 327	98.72 79	99.93 11	99.77 51

MVS_111021_LR		99.41 32	99.33 25	99.65 58	99.77 41	99.51 62	98.94 296	99.85 6	98.82 35	99.65 51	99.74 97	98.51 58	99.80 141	98.83 68	99.89 32	99.64 96

MVS_111021_HR		99.41 32	99.32 26	99.66 54	99.72 74	99.47 66	98.95 294	99.85 6	98.82 35	99.54 76	99.73 100	98.51 58	99.74 158	98.91 56	99.88 34	99.77 51

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 55	99.84 6	99.43 161	99.51 85	98.68 47	99.27 134	99.53 175	98.64 54	99.96 19	98.44 113	99.80 70	99.79 45

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 206	99.52 76	97.18 180	99.60 60	99.79 72	98.79 37	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		99.36 38	99.36 19	99.36 105	99.67 90	98.61 183	99.07 260	99.33 213	99.00 17	99.82 14	99.81 53	99.06 8	99.84 117	99.09 42	99.42 108	99.65 90

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 67	99.86 20	99.07 105	99.47 148	99.93 2	97.66 141	99.71 31	99.86 22	97.73 88	99.96 19	99.47 13	99.82 67	99.79 45

NCCC		99.34 40	99.19 47	99.79 33	99.61 114	99.65 39	99.30 206	99.48 113	98.86 31	99.21 155	99.63 141	98.72 49	99.90 86	98.25 125	99.63 102	99.80 41

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 12	99.66 64	99.46 138	98.09 89	99.48 86	99.74 97	98.29 72	99.96 19	97.93 148	99.87 38	99.82 32

PS-MVSNAJ		99.32 42	99.32 26	99.30 114	99.57 121	98.94 132	98.97 288	99.46 138	98.92 28	99.71 31	99.24 255	99.01 11	99.98 5	99.35 18	99.66 97	98.97 182

CSCG		99.32 42	99.32 26	99.32 110	99.85 23	98.29 201	99.71 41	99.66 25	98.11 86	99.41 99	99.80 64	98.37 69	99.96 19	98.99 50	99.96 5	99.72 71

ESAPD		99.31 44	99.13 52	99.87 6	99.81 32	99.83 7	99.37 186	99.48 113	97.97 108	99.77 23	99.78 77	98.96 20	99.95 33	97.15 212	99.84 57	99.83 23

PHI-MVS		99.30 45	99.17 49	99.70 50	99.56 124	99.52 60	99.58 97	99.80 8	97.12 186	99.62 56	99.73 100	98.58 57	99.90 86	98.61 92	99.91 17	99.68 83

DeepC-MVS	98.35 2	99.30 45	99.19 47	99.64 63	99.82 29	99.23 90	99.62 81	99.55 55	98.94 26	99.63 53	99.95 2	95.82 138	99.94 42	99.37 17	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 47	99.27 40	99.34 106	99.63 106	98.97 124	99.12 249	99.51 85	98.86 31	99.84 8	99.47 198	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

xiu_mvs_v1_base		99.29 47	99.27 40	99.34 106	99.63 106	98.97 124	99.12 249	99.51 85	98.86 31	99.84 8	99.47 198	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

xiu_mvs_v1_base_debi		99.29 47	99.27 40	99.34 106	99.63 106	98.97 124	99.12 249	99.51 85	98.86 31	99.84 8	99.47 198	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

APD-MVS		99.27 50	99.08 57	99.84 22	99.75 55	99.79 18	99.50 131	99.50 99	97.16 182	99.77 23	99.82 44	98.78 38	99.94 42	97.56 183	99.86 48	99.80 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 50	99.12 54	99.74 44	99.18 199	99.75 23	99.56 110	99.57 44	98.45 59	99.49 85	99.85 26	97.77 87	99.94 42	98.33 121	99.84 57	99.52 119

xiu_mvs_v2_base		99.26 52	99.25 44	99.29 117	99.53 126	98.91 137	99.02 275	99.45 149	98.80 39	99.71 31	99.26 253	98.94 26	99.98 5	99.34 22	99.23 119	98.98 181

CANet		99.25 53	99.14 51	99.59 69	99.41 150	99.16 95	99.35 196	99.57 44	98.82 35	99.51 81	99.61 149	96.46 119	99.95 33	99.59 2	99.98 2	99.65 90

3Dnovator	97.25 9	99.24 54	99.05 59	99.81 28	99.12 212	99.66 36	99.84 9	99.74 10	99.09 8	98.92 204	99.90 7	95.94 133	99.98 5	98.95 53	99.92 12	99.79 45

test_prior3		99.21 55	99.05 59	99.68 51	99.67 90	99.48 64	98.96 290	99.56 48	98.34 66	99.01 189	99.52 180	98.68 51	99.83 124	97.96 145	99.74 81	99.74 60

CHOSEN 1792x2688		99.19 56	99.10 56	99.45 95	99.89 8	98.52 190	99.39 179	99.94 1	98.73 44	99.11 171	99.89 10	95.50 145	99.94 42	99.50 8	99.97 3	99.89 2

F-COLMAP		99.19 56	99.04 62	99.64 63	99.78 36	99.27 86	99.42 168	99.54 62	97.29 171	99.41 99	99.59 154	98.42 66	99.93 57	98.19 127	99.69 92	99.73 65

3Dnovator+	97.12 13	99.18 58	98.97 72	99.82 25	99.17 204	99.68 32	99.81 15	99.51 85	99.20 4	98.72 226	99.89 10	95.68 142	99.97 11	98.86 64	99.86 48	99.81 36

MVSFormer		99.17 59	99.12 54	99.29 117	99.51 129	98.94 132	99.88 1	99.46 138	97.55 148	99.80 16	99.65 130	97.39 94	99.28 249	99.03 46	99.85 52	99.65 90

sss		99.17 59	99.05 59	99.53 80	99.62 110	98.97 124	99.36 192	99.62 31	97.83 121	99.67 43	99.65 130	97.37 97	99.95 33	99.19 33	99.19 122	99.68 83

DP-MVS		99.16 61	98.95 76	99.78 34	99.77 41	99.53 57	99.41 172	99.50 99	97.03 200	99.04 186	99.88 14	97.39 94	99.92 65	98.66 85	99.90 24	99.87 4

CNLPA		99.14 62	98.99 69	99.59 69	99.58 119	99.41 72	99.16 242	99.44 157	98.45 59	99.19 161	99.49 188	98.08 79	99.89 94	97.73 167	99.75 79	99.48 130

CDPH-MVS		99.13 63	98.91 80	99.80 30	99.75 55	99.71 28	99.15 245	99.41 169	96.60 225	99.60 60	99.55 166	98.83 33	99.90 86	97.48 191	99.83 63	99.78 49

jason		99.13 63	99.03 64	99.45 95	99.46 141	98.87 140	99.12 249	99.26 239	98.03 101	99.79 18	99.65 130	97.02 104	99.85 111	99.02 48	99.90 24	99.65 90
jason: jason.
lupinMVS		99.13 63	99.01 68	99.46 94	99.51 129	98.94 132	99.05 266	99.16 250	97.86 116	99.80 16	99.56 163	97.39 94	99.86 106	98.94 54	99.85 52	99.58 110

EPP-MVSNet		99.13 63	98.99 69	99.53 80	99.65 102	99.06 106	99.81 15	99.33 213	97.43 159	99.60 60	99.88 14	97.14 101	99.84 117	99.13 39	98.94 141	99.69 79

MG-MVS		99.13 63	99.02 67	99.45 95	99.57 121	98.63 178	99.07 260	99.34 205	98.99 18	99.61 58	99.82 44	97.98 82	99.87 103	97.00 221	99.80 70	99.85 8

CHOSEN 280x420		99.12 68	99.13 52	99.08 140	99.66 100	97.89 217	98.43 324	99.71 13	98.88 30	99.62 56	99.76 87	96.63 116	99.70 182	99.46 14	99.99 1	99.66 87

DP-MVS Recon		99.12 68	98.95 76	99.65 58	99.74 66	99.70 30	99.27 216	99.57 44	96.40 243	99.42 97	99.68 119	98.75 46	99.80 141	97.98 144	99.72 85	99.44 140

Vis-MVSNet		99.12 68	98.97 72	99.56 75	99.78 36	99.10 102	99.68 54	99.66 25	98.49 56	99.86 7	99.87 19	94.77 185	99.84 117	99.19 33	99.41 109	99.74 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 68	99.08 57	99.24 127	99.46 141	98.55 185	99.51 126	99.46 138	98.09 89	99.45 90	99.82 44	98.34 70	99.51 208	98.70 80	98.93 142	99.67 86

VNet		99.11 72	98.90 81	99.73 46	99.52 127	99.56 51	99.41 172	99.39 179	99.01 13	99.74 30	99.78 77	95.56 143	99.92 65	99.52 7	98.18 183	99.72 71

CPTT-MVS		99.11 72	98.90 81	99.74 44	99.80 34	99.46 67	99.59 91	99.49 104	97.03 200	99.63 53	99.69 114	97.27 99	99.96 19	97.82 156	99.84 57	99.81 36

HyFIR lowres test		99.11 72	98.92 78	99.65 58	99.90 3	99.37 75	99.02 275	99.91 3	97.67 140	99.59 63	99.75 92	95.90 135	99.73 166	99.53 6	99.02 134	99.86 5

MVS_Test		99.10 75	98.97 72	99.48 89	99.49 136	99.14 99	99.67 56	99.34 205	97.31 169	99.58 64	99.76 87	97.65 90	99.82 133	98.87 61	99.07 131	99.46 137

1121		99.09 76	98.87 85	99.75 39	99.74 66	99.60 46	99.27 216	99.48 113	96.82 212	99.25 141	99.65 130	98.38 67	99.93 57	97.53 186	99.67 96	99.73 65

CDS-MVSNet		99.09 76	99.03 64	99.25 124	99.42 147	98.73 168	99.45 152	99.46 138	98.11 86	99.46 89	99.77 84	98.01 81	99.37 226	98.70 80	98.92 144	99.66 87
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		99.08 78	98.97 72	99.42 102	99.76 44	98.79 163	98.78 306	99.91 3	96.74 214	99.67 43	99.49 188	97.53 91	99.88 101	98.98 51	99.85 52	99.60 104

OMC-MVS		99.08 78	99.04 62	99.20 131	99.67 90	98.22 204	99.28 213	99.52 76	98.07 93	99.66 48	99.81 53	97.79 86	99.78 151	97.79 159	99.81 68	99.60 104

MVS_0304		99.06 80	98.86 88	99.66 54	99.51 129	99.36 76	99.22 233	99.51 85	98.95 24	99.58 64	99.65 130	93.74 227	99.98 5	99.66 1	99.95 6	99.64 96

WTY-MVS		99.06 80	98.88 84	99.61 67	99.62 110	99.16 95	99.37 186	99.56 48	98.04 99	99.53 77	99.62 146	96.84 108	99.94 42	98.85 65	98.49 167	99.72 71

IS-MVSNet		99.05 82	98.87 85	99.57 73	99.73 71	99.32 79	99.75 34	99.20 246	98.02 102	99.56 68	99.86 22	96.54 118	99.67 187	98.09 134	99.13 125	99.73 65

PAPM_NR		99.04 83	98.84 91	99.66 54	99.74 66	99.44 69	99.39 179	99.38 185	97.70 137	99.28 130	99.28 250	98.34 70	99.85 111	96.96 225	99.45 106	99.69 79

API-MVS		99.04 83	99.03 64	99.06 142	99.40 155	99.31 82	99.55 115	99.56 48	98.54 53	99.33 119	99.39 220	98.76 43	99.78 151	96.98 223	99.78 74	98.07 304

mvs_anonymous		99.03 85	98.99 69	99.16 133	99.38 158	98.52 190	99.51 126	99.38 185	97.79 126	99.38 106	99.81 53	97.30 98	99.45 212	99.35 18	98.99 136	99.51 124

train_agg		99.02 86	98.77 98	99.77 36	99.67 90	99.65 39	99.05 266	99.41 169	96.28 249	98.95 200	99.49 188	98.76 43	99.91 74	97.63 176	99.72 85	99.75 55

canonicalmvs		99.02 86	98.86 88	99.51 86	99.42 147	99.32 79	99.80 19	99.48 113	98.63 48	99.31 121	98.81 290	97.09 102	99.75 157	99.27 29	97.90 203	99.47 134

PLC	97.94 4	99.02 86	98.85 90	99.53 80	99.66 100	99.01 117	99.24 228	99.52 76	96.85 210	99.27 134	99.48 194	98.25 74	99.91 74	97.76 163	99.62 103	99.65 90
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
agg_prior1		99.01 89	98.76 100	99.76 38	99.67 90	99.62 42	98.99 281	99.40 176	96.26 252	98.87 210	99.49 188	98.77 41	99.91 74	97.69 173	99.72 85	99.75 55

AdaColmap		99.01 89	98.80 95	99.66 54	99.56 124	99.54 54	99.18 240	99.70 15	98.18 79	99.35 115	99.63 141	96.32 123	99.90 86	97.48 191	99.77 76	99.55 112

1112_ss		98.98 91	98.77 98	99.59 69	99.68 89	99.02 115	99.25 226	99.48 113	97.23 177	99.13 167	99.58 157	96.93 107	99.90 86	98.87 61	98.78 154	99.84 12

MSDG		98.98 91	98.80 95	99.53 80	99.76 44	99.19 92	98.75 309	99.55 55	97.25 174	99.47 87	99.77 84	97.82 85	99.87 103	96.93 228	99.90 24	99.54 114

CANet_DTU		98.97 93	98.87 85	99.25 124	99.33 167	98.42 199	99.08 259	99.30 222	99.16 5	99.43 94	99.75 92	95.27 151	99.97 11	98.56 100	99.95 6	99.36 148

agg_prior3		98.97 93	98.71 104	99.75 39	99.67 90	99.60 46	99.04 271	99.41 169	95.93 271	98.87 210	99.48 194	98.61 55	99.91 74	97.63 176	99.72 85	99.75 55

114514_t		98.93 95	98.67 108	99.72 48	99.85 23	99.53 57	99.62 81	99.59 38	92.65 317	99.71 31	99.78 77	98.06 80	99.90 86	98.84 66	99.91 17	99.74 60

PS-MVSNAJss		98.92 96	98.92 78	98.90 173	98.78 278	98.53 187	99.78 22	99.54 62	98.07 93	99.00 196	99.76 87	99.01 11	99.37 226	99.13 39	97.23 235	98.81 200

Test_1112_low_res		98.89 97	98.66 111	99.57 73	99.69 86	98.95 129	99.03 272	99.47 129	96.98 202	99.15 166	99.23 256	96.77 112	99.89 94	98.83 68	98.78 154	99.86 5

AllTest		98.87 98	98.72 102	99.31 111	99.86 20	98.48 195	99.56 110	99.61 32	97.85 118	99.36 112	99.85 26	95.95 131	99.85 111	96.66 246	99.83 63	99.59 108

UGNet		98.87 98	98.69 106	99.40 103	99.22 191	98.72 170	99.44 156	99.68 19	99.24 3	99.18 163	99.42 209	92.74 242	99.96 19	99.34 22	99.94 10	99.53 118
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 98	98.72 102	99.31 111	99.71 79	98.88 139	99.80 19	99.44 157	97.91 114	99.36 112	99.78 77	95.49 146	99.43 221	97.91 149	99.11 126	99.62 102

mvs-test1		98.86 101	98.84 91	98.89 175	99.33 167	97.77 227	99.44 156	99.30 222	98.47 57	99.10 174	99.43 207	96.78 110	99.95 33	98.73 77	99.02 134	98.96 188

EPNet		98.86 101	98.71 104	99.30 114	97.20 324	98.18 205	99.62 81	98.91 280	99.28 2	98.63 244	99.81 53	95.96 130	99.99 1	99.24 30	99.72 85	99.73 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 101	98.80 95	99.03 145	99.76 44	98.79 163	99.28 213	99.91 3	97.42 161	99.67 43	99.37 225	97.53 91	99.88 101	98.98 51	97.29 234	98.42 292

ab-mvs		98.86 101	98.63 113	99.54 76	99.64 103	99.19 92	99.44 156	99.54 62	97.77 128	99.30 122	99.81 53	94.20 208	99.93 57	99.17 36	98.82 151	99.49 128

MAR-MVS		98.86 101	98.63 113	99.54 76	99.37 160	99.66 36	99.45 152	99.54 62	96.61 223	99.01 189	99.40 216	97.09 102	99.86 106	97.68 175	99.53 105	99.10 163
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	98.86 101	98.75 101	99.17 132	99.88 11	98.53 187	99.34 199	99.59 38	97.55 148	98.70 233	99.89 10	95.83 137	99.90 86	98.10 133	99.90 24	99.08 168
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	98.85 107	98.64 112	99.47 92	99.42 147	99.08 104	99.62 81	99.36 193	97.39 164	99.28 130	99.68 119	96.44 120	99.92 65	98.37 117	98.22 179	99.40 145

PVSNet	96.02 17	98.85 107	98.84 91	98.89 175	99.73 71	97.28 234	98.32 328	99.60 35	97.86 116	99.50 82	99.57 161	96.75 113	99.86 106	98.56 100	99.70 91	99.54 114

PatchMatch-RL		98.84 109	98.62 116	99.52 84	99.71 79	99.28 84	99.06 264	99.77 9	97.74 132	99.50 82	99.53 175	95.41 147	99.84 117	97.17 211	99.64 100	99.44 140

Effi-MVS+		98.81 110	98.59 121	99.48 89	99.46 141	99.12 101	98.08 334	99.50 99	97.50 153	99.38 106	99.41 212	96.37 122	99.81 137	99.11 41	98.54 164	99.51 124

alignmvs		98.81 110	98.56 123	99.58 72	99.43 146	99.42 71	99.51 126	98.96 273	98.61 50	99.35 115	98.92 281	94.78 181	99.77 153	99.35 18	98.11 197	99.54 114

DeepPCF-MVS	98.18 3	98.81 110	99.37 17	97.12 298	99.60 116	91.75 326	98.61 317	99.44 157	99.35 1	99.83 11	99.85 26	98.70 50	99.81 137	99.02 48	99.91 17	99.81 36

PMMVS		98.80 113	98.62 116	99.34 106	99.27 184	98.70 171	98.76 308	99.31 220	97.34 166	99.21 155	99.07 268	97.20 100	99.82 133	98.56 100	98.87 148	99.52 119

Effi-MVS+-dtu		98.78 114	98.89 83	98.47 228	99.33 167	96.91 259	99.57 103	99.30 222	98.47 57	99.41 99	98.99 275	96.78 110	99.74 158	98.73 77	99.38 110	98.74 212

FIs		98.78 114	98.63 113	99.23 129	99.18 199	99.54 54	99.83 12	99.59 38	98.28 70	98.79 220	99.81 53	96.75 113	99.37 226	99.08 43	96.38 250	98.78 203

Fast-Effi-MVS+-dtu		98.77 116	98.83 94	98.60 214	99.41 150	96.99 253	99.52 122	99.49 104	98.11 86	99.24 146	99.34 239	96.96 106	99.79 144	97.95 147	99.45 106	99.02 177

FC-MVSNet-test		98.75 117	98.62 116	99.15 135	99.08 220	99.45 68	99.86 8	99.60 35	98.23 75	98.70 233	99.82 44	96.80 109	99.22 264	99.07 44	96.38 250	98.79 202

XVG-OURS		98.73 118	98.68 107	98.88 182	99.70 84	97.73 229	98.92 297	99.55 55	98.52 55	99.45 90	99.84 35	95.27 151	99.91 74	98.08 138	98.84 150	99.00 178

diffmvs		98.72 119	98.49 125	99.43 101	99.48 139	99.19 92	99.62 81	99.42 166	95.58 277	99.37 108	99.67 123	96.14 128	99.74 158	98.14 131	98.96 139	99.37 147

Fast-Effi-MVS+		98.70 120	98.43 127	99.51 86	99.51 129	99.28 84	99.52 122	99.47 129	96.11 266	99.01 189	99.34 239	96.20 127	99.84 117	97.88 151	98.82 151	99.39 146

XVG-OURS-SEG-HR		98.69 121	98.62 116	98.89 175	99.71 79	97.74 228	99.12 249	99.54 62	98.44 62	99.42 97	99.71 105	94.20 208	99.92 65	98.54 105	98.90 146	99.00 178

1314		98.68 122	98.54 124	99.11 139	98.89 261	98.65 176	99.27 216	99.49 104	96.89 208	97.99 276	99.56 163	97.72 89	99.83 124	97.74 166	99.27 118	98.84 198

EI-MVSNet		98.67 123	98.67 108	98.68 209	99.35 163	97.97 213	99.50 131	99.38 185	96.93 206	99.20 158	99.83 37	97.87 83	99.36 230	98.38 116	97.56 215	98.71 216

test_djsdf		98.67 123	98.57 122	98.98 151	98.70 289	98.91 137	99.88 1	99.46 138	97.55 148	99.22 153	99.88 14	95.73 141	99.28 249	99.03 46	97.62 210	98.75 209

QAPM		98.67 123	98.30 136	99.80 30	99.20 194	99.67 34	99.77 24	99.72 11	94.74 286	98.73 225	99.90 7	95.78 139	99.98 5	96.96 225	99.88 34	99.76 54

nrg030		98.64 126	98.42 128	99.28 119	99.05 226	99.69 31	99.81 15	99.46 138	98.04 99	99.01 189	99.82 44	96.69 115	99.38 223	99.34 22	94.59 288	98.78 203

PAPR		98.63 127	98.34 132	99.51 86	99.40 155	99.03 114	98.80 305	99.36 193	96.33 245	99.00 196	99.12 266	98.46 61	99.84 117	95.23 276	99.37 114	99.66 87

CVMVSNet		98.57 128	98.67 108	98.30 243	99.35 163	95.59 288	99.50 131	99.55 55	98.60 51	99.39 104	99.83 37	94.48 199	99.45 212	98.75 74	98.56 163	99.85 8

MVSTER		98.49 129	98.32 134	99.00 149	99.35 163	99.02 115	99.54 118	99.38 185	97.41 162	99.20 158	99.73 100	93.86 222	99.36 230	98.87 61	97.56 215	98.62 265

OpenMVS	96.50 16	98.47 130	98.12 144	99.52 84	99.04 227	99.53 57	99.82 13	99.72 11	94.56 292	98.08 271	99.88 14	94.73 188	99.98 5	97.47 193	99.76 78	99.06 173

IterMVS-LS		98.46 131	98.42 128	98.58 216	99.59 118	98.00 211	99.37 186	99.43 165	96.94 205	99.07 180	99.59 154	97.87 83	99.03 285	98.32 123	95.62 263	98.71 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 132	98.28 137	98.94 157	98.50 303	98.96 128	99.77 24	99.50 99	97.07 196	98.87 210	99.77 84	94.76 186	99.28 249	98.66 85	97.60 211	98.57 283

jajsoiax		98.43 133	98.28 137	98.88 182	98.60 298	98.43 197	99.82 13	99.53 72	98.19 76	98.63 244	99.80 64	93.22 232	99.44 217	99.22 31	97.50 220	98.77 206

BH-untuned		98.42 134	98.36 130	98.59 215	99.49 136	96.70 265	99.27 216	99.13 254	97.24 176	98.80 219	99.38 221	95.75 140	99.74 158	97.07 218	99.16 123	99.33 151

BH-RMVSNet		98.41 135	98.08 148	99.40 103	99.41 150	98.83 147	99.30 206	98.77 294	97.70 137	98.94 202	99.65 130	92.91 238	99.74 158	96.52 250	99.55 104	99.64 96

mvs_tets		98.40 136	98.23 139	98.91 169	98.67 293	98.51 192	99.66 64	99.53 72	98.19 76	98.65 242	99.81 53	92.75 240	99.44 217	99.31 25	97.48 224	98.77 206

XXY-MVS		98.38 137	98.09 147	99.24 127	99.26 186	99.32 79	99.56 110	99.55 55	97.45 158	98.71 227	99.83 37	93.23 231	99.63 198	98.88 57	96.32 252	98.76 208

ACMM	97.58 5	98.37 138	98.34 132	98.48 226	99.41 150	97.10 242	99.56 110	99.45 149	98.53 54	99.04 186	99.85 26	93.00 234	99.71 176	98.74 75	97.45 225	98.64 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn1000		98.33 139	98.02 153	99.25 124	99.78 36	98.73 168	99.70 42	97.55 339	97.48 154	99.69 36	99.53 175	92.37 263	99.85 111	97.82 156	98.26 178	99.16 159

tpmrst		98.33 139	98.48 126	97.90 276	99.16 206	94.78 304	99.31 204	99.11 255	97.27 172	99.45 90	99.59 154	95.33 148	99.84 117	98.48 108	98.61 157	99.09 167

PatchmatchNet		98.31 141	98.36 130	98.19 259	99.16 206	95.32 296	99.27 216	98.92 277	97.37 165	99.37 108	99.58 157	94.90 173	99.70 182	97.43 197	99.21 120	99.54 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		98.29 142	97.95 159	99.30 114	99.16 206	99.54 54	99.50 131	99.58 43	98.27 71	99.35 115	99.37 225	92.53 256	99.65 191	99.35 18	94.46 289	98.72 214

UniMVSNet (Re)		98.29 142	98.00 155	99.13 138	99.00 232	99.36 76	99.49 139	99.51 85	97.95 110	98.97 199	99.13 263	96.30 124	99.38 223	98.36 119	93.34 305	98.66 252

HQP_MVS		98.27 144	98.22 140	98.44 233	99.29 179	96.97 255	99.39 179	99.47 129	98.97 22	99.11 171	99.61 149	92.71 244	99.69 185	97.78 160	97.63 208	98.67 241

thresconf0.02		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

tfpn_n400		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

tfpnconf		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

tfpnview11		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

UniMVSNet_NR-MVSNet		98.22 149	97.97 157	98.96 154	98.92 255	98.98 121	99.48 144	99.53 72	97.76 129	98.71 227	99.46 202	96.43 121	99.22 264	98.57 97	92.87 311	98.69 225

LPG-MVS_test		98.22 149	98.13 143	98.49 224	99.33 167	97.05 248	99.58 97	99.55 55	97.46 155	99.24 146	99.83 37	92.58 254	99.72 170	98.09 134	97.51 218	98.68 230

RPSCF		98.22 149	98.62 116	96.99 299	99.82 29	91.58 327	99.72 39	99.44 157	96.61 223	99.66 48	99.89 10	95.92 134	99.82 133	97.46 194	99.10 128	99.57 111

conf0.002		98.21 152	97.89 166	99.15 135	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.61 274

ADS-MVSNet		98.20 153	98.08 148	98.56 219	99.33 167	96.48 272	99.23 229	99.15 251	96.24 254	99.10 174	99.67 123	94.11 213	99.71 176	96.81 237	99.05 132	99.48 130

OPM-MVS		98.19 154	98.10 145	98.45 230	98.88 262	97.07 246	99.28 213	99.38 185	98.57 52	99.22 153	99.81 53	92.12 266	99.66 189	98.08 138	97.54 217	98.61 274

tfpn_ndepth		98.17 155	97.84 173	99.15 135	99.75 55	98.76 167	99.61 87	97.39 341	96.92 207	99.61 58	99.38 221	92.19 265	99.86 106	97.57 181	98.13 190	98.82 199

CR-MVSNet		98.17 155	97.93 161	98.87 186	99.18 199	98.49 193	99.22 233	99.33 213	96.96 203	99.56 68	99.38 221	94.33 204	99.00 288	94.83 282	98.58 160	99.14 160

Patchmatch-test1		98.16 157	98.14 142	98.22 256	99.30 176	95.55 289	99.07 260	98.97 271	97.57 146	99.43 94	99.60 152	92.72 243	99.60 201	97.38 199	99.20 121	99.50 127

CLD-MVS		98.16 157	98.10 145	98.33 240	99.29 179	96.82 262	98.75 309	99.44 157	97.83 121	99.13 167	99.55 166	92.92 236	99.67 187	98.32 123	97.69 207	98.48 288
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		98.13 159	97.90 162	98.81 197	98.61 297	98.87 140	98.99 281	99.21 245	96.44 238	99.06 184	99.58 157	95.90 135	99.11 277	97.18 210	96.11 255	98.46 291

WR-MVS_H		98.13 159	97.87 172	98.90 173	99.02 230	98.84 144	99.70 42	99.59 38	97.27 172	98.40 256	99.19 259	95.53 144	99.23 261	98.34 120	93.78 302	98.61 274

v1neww		98.12 161	97.84 173	98.93 160	98.97 240	98.81 156	99.66 64	99.35 197	96.49 230	99.29 126	99.37 225	95.02 163	99.32 240	97.73 167	94.73 280	98.67 241

v7new		98.12 161	97.84 173	98.93 160	98.97 240	98.81 156	99.66 64	99.35 197	96.49 230	99.29 126	99.37 225	95.02 163	99.32 240	97.73 167	94.73 280	98.67 241

v6		98.12 161	97.84 173	98.94 157	98.94 248	98.83 147	99.66 64	99.34 205	96.49 230	99.30 122	99.37 225	94.95 167	99.34 236	97.77 162	94.74 279	98.67 241

ACMH	97.28 8	98.10 164	97.99 156	98.44 233	99.41 150	96.96 257	99.60 89	99.56 48	98.09 89	98.15 268	99.91 5	90.87 288	99.70 182	98.88 57	97.45 225	98.67 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		98.09 165	97.78 180	99.01 147	98.97 240	99.24 89	99.67 56	99.46 138	97.25 174	98.48 253	99.64 137	93.79 223	99.06 281	98.63 88	94.10 296	98.74 212

DU-MVS		98.08 166	97.79 178	98.96 154	98.87 265	98.98 121	99.41 172	99.45 149	97.87 115	98.71 227	99.50 185	94.82 178	99.22 264	98.57 97	92.87 311	98.68 230

divwei89l23v2f112		98.06 167	97.78 180	98.91 169	98.90 258	98.77 166	99.57 103	99.35 197	96.45 237	99.24 146	99.37 225	94.92 171	99.27 252	97.50 189	94.71 284	98.68 230

v2v482		98.06 167	97.77 184	98.92 165	98.90 258	98.82 154	99.57 103	99.36 193	96.65 220	99.19 161	99.35 236	94.20 208	99.25 258	97.72 171	94.97 276	98.69 225

V42		98.06 167	97.79 178	98.86 190	98.98 237	98.84 144	99.69 45	99.34 205	96.53 229	99.30 122	99.37 225	94.67 191	99.32 240	97.57 181	94.66 285	98.42 292

test-LLR		98.06 167	97.90 162	98.55 221	98.79 274	97.10 242	98.67 313	97.75 329	97.34 166	98.61 247	98.85 286	94.45 200	99.45 212	97.25 204	99.38 110	99.10 163

WR-MVS		98.06 167	97.73 191	99.06 142	98.86 268	99.25 88	99.19 239	99.35 197	97.30 170	98.66 236	99.43 207	93.94 218	99.21 268	98.58 95	94.28 292	98.71 216

ACMP	97.20 11	98.06 167	97.94 160	98.45 230	99.37 160	97.01 251	99.44 156	99.49 104	97.54 151	98.45 254	99.79 72	91.95 267	99.72 170	97.91 149	97.49 223	98.62 265
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1141		98.05 173	97.76 187	98.91 169	98.91 257	98.78 165	99.57 103	99.35 197	96.41 242	99.23 151	99.36 232	94.93 170	99.27 252	97.38 199	94.72 282	98.68 230

v7		98.05 173	97.78 180	98.87 186	98.99 233	98.67 173	99.64 76	99.34 205	96.31 248	99.29 126	99.51 183	94.78 181	99.27 252	97.03 219	95.15 272	98.66 252

v1		98.05 173	97.76 187	98.93 160	98.92 255	98.80 161	99.57 103	99.35 197	96.39 244	99.28 130	99.36 232	94.86 176	99.32 240	97.38 199	94.72 282	98.68 230

EPNet_dtu		98.03 176	97.96 158	98.23 254	98.27 308	95.54 291	99.23 229	98.75 295	99.02 10	97.82 281	99.71 105	96.11 129	99.48 209	93.04 311	99.65 99	99.69 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 176	97.76 187	98.84 194	99.39 157	98.98 121	99.40 178	99.38 185	96.67 219	99.07 180	99.28 250	92.93 235	98.98 290	97.10 215	96.65 243	98.56 284

ADS-MVSNet2		98.02 178	98.07 150	97.87 277	99.33 167	95.19 299	99.23 229	99.08 258	96.24 254	99.10 174	99.67 123	94.11 213	98.93 298	96.81 237	99.05 132	99.48 130

HQP-MVS		98.02 178	97.90 162	98.37 238	99.19 196	96.83 260	98.98 285	99.39 179	98.24 72	98.66 236	99.40 216	92.47 258	99.64 193	97.19 208	97.58 213	98.64 257

LTVRE_ROB	97.16 12	98.02 178	97.90 162	98.40 236	99.23 189	96.80 263	99.70 42	99.60 35	97.12 186	98.18 267	99.70 108	91.73 276	99.72 170	98.39 114	97.45 225	98.68 230
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		98.01 181	98.05 151	97.87 277	99.15 209	94.76 305	99.42 168	98.93 275	97.12 186	98.84 216	98.59 301	93.74 227	99.80 141	98.55 103	98.17 188	99.06 173

BH-w/o		98.00 182	97.89 166	98.32 241	99.35 163	96.20 281	99.01 279	98.90 282	96.42 240	98.38 257	99.00 274	95.26 153	99.72 170	96.06 258	98.61 157	99.03 175

v1144		97.98 183	97.69 194	98.85 193	98.87 265	98.66 175	99.54 118	99.35 197	96.27 251	99.23 151	99.35 236	94.67 191	99.23 261	96.73 241	95.16 271	98.68 230

EU-MVSNet		97.98 183	98.03 152	97.81 283	98.72 286	96.65 268	99.66 64	99.66 25	98.09 89	98.35 260	99.82 44	95.25 154	98.01 319	97.41 198	95.30 268	98.78 203

tpmvs		97.98 183	98.02 153	97.84 280	99.04 227	94.73 306	99.31 204	99.20 246	96.10 269	98.76 223	99.42 209	94.94 168	99.81 137	96.97 224	98.45 168	98.97 182

view600		97.97 186	97.66 195	98.89 175	99.75 55	97.81 222	99.69 45	98.80 290	98.02 102	99.25 141	98.88 282	91.95 267	99.89 94	94.36 291	98.29 174	98.96 188

view800		97.97 186	97.66 195	98.89 175	99.75 55	97.81 222	99.69 45	98.80 290	98.02 102	99.25 141	98.88 282	91.95 267	99.89 94	94.36 291	98.29 174	98.96 188

conf0.05thres1000		97.97 186	97.66 195	98.89 175	99.75 55	97.81 222	99.69 45	98.80 290	98.02 102	99.25 141	98.88 282	91.95 267	99.89 94	94.36 291	98.29 174	98.96 188

tfpn		97.97 186	97.66 195	98.89 175	99.75 55	97.81 222	99.69 45	98.80 290	98.02 102	99.25 141	98.88 282	91.95 267	99.89 94	94.36 291	98.29 174	98.96 188

NR-MVSNet		97.97 186	97.61 203	99.02 146	98.87 265	99.26 87	99.47 148	99.42 166	97.63 142	97.08 292	99.50 185	95.07 161	99.13 274	97.86 153	93.59 303	98.68 230

v8		97.95 191	97.63 202	98.93 160	98.95 245	98.81 156	99.80 19	99.41 169	96.03 270	99.10 174	99.42 209	94.92 171	99.30 246	96.94 227	94.08 297	98.66 252

Patchmatch-test		97.93 192	97.65 200	98.77 202	99.18 199	97.07 246	99.03 272	99.14 253	96.16 261	98.74 224	99.57 161	94.56 195	99.72 170	93.36 306	99.11 126	99.52 119

PS-CasMVS		97.93 192	97.59 205	98.95 156	98.99 233	99.06 106	99.68 54	99.52 76	97.13 184	98.31 262	99.68 119	92.44 262	99.05 282	98.51 106	94.08 297	98.75 209

TranMVSNet+NR-MVSNet		97.93 192	97.66 195	98.76 204	98.78 278	98.62 180	99.65 74	99.49 104	97.76 129	98.49 252	99.60 152	94.23 207	98.97 297	98.00 143	92.90 309	98.70 220

v144192		97.92 195	97.60 204	98.87 186	98.83 271	98.65 176	99.55 115	99.34 205	96.20 257	99.32 120	99.40 216	94.36 203	99.26 257	96.37 255	95.03 275	98.70 220

ACMH+	97.24 10	97.92 195	97.78 180	98.32 241	99.46 141	96.68 267	99.56 110	99.54 62	98.41 63	97.79 283	99.87 19	90.18 295	99.66 189	98.05 142	97.18 238	98.62 265

LFMVS		97.90 197	97.35 236	99.54 76	99.52 127	99.01 117	99.39 179	98.24 321	97.10 190	99.65 51	99.79 72	84.79 331	99.91 74	99.28 27	98.38 171	99.69 79

OurMVSNet-221017-0		97.88 198	97.77 184	98.19 259	98.71 288	96.53 270	99.88 1	99.00 268	97.79 126	98.78 221	99.94 3	91.68 277	99.35 233	97.21 206	96.99 241	98.69 225

v7n		97.87 199	97.52 208	98.92 165	98.76 282	98.58 184	99.84 9	99.46 138	96.20 257	98.91 205	99.70 108	94.89 174	99.44 217	96.03 259	93.89 301	98.75 209

thres600view7		97.86 200	97.51 210	98.92 165	99.72 74	97.95 216	99.59 91	98.74 298	97.94 111	99.27 134	98.62 297	91.75 273	99.86 106	93.73 303	98.19 182	98.96 188

v10		97.85 201	97.52 208	98.86 190	98.99 233	98.67 173	99.75 34	99.41 169	95.70 275	98.98 198	99.41 212	94.75 187	99.23 261	96.01 260	94.63 287	98.67 241

GA-MVS		97.85 201	97.47 216	99.00 149	99.38 158	97.99 212	98.57 319	99.15 251	97.04 199	98.90 207	99.30 247	89.83 297	99.38 223	96.70 243	98.33 172	99.62 102

tfpnnormal		97.84 203	97.47 216	98.98 151	99.20 194	99.22 91	99.64 76	99.61 32	96.32 246	98.27 265	99.70 108	93.35 230	99.44 217	95.69 266	95.40 266	98.27 299

VPNet		97.84 203	97.44 224	99.01 147	99.21 192	98.94 132	99.48 144	99.57 44	98.38 64	99.28 130	99.73 100	88.89 305	99.39 222	99.19 33	93.27 306	98.71 216

LCM-MVSNet-Re		97.83 205	98.15 141	96.87 303	99.30 176	92.25 325	99.59 91	98.26 320	97.43 159	96.20 301	99.13 263	96.27 125	98.73 303	98.17 129	98.99 136	99.64 96

XVG-ACMP-BASELINE		97.83 205	97.71 193	98.20 258	99.11 214	96.33 277	99.41 172	99.52 76	98.06 97	99.05 185	99.50 185	89.64 299	99.73 166	97.73 167	97.38 231	98.53 285

IterMVS		97.83 205	97.77 184	98.02 267	99.58 119	96.27 279	99.02 275	99.48 113	97.22 178	98.71 227	99.70 108	92.75 240	99.13 274	97.46 194	96.00 257	98.67 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS		97.82 208	97.65 200	98.35 239	98.88 262	95.98 283	99.49 139	94.71 347	97.57 146	99.26 138	99.48 194	92.46 261	99.71 176	97.87 152	99.08 130	99.35 149

MVP-Stereo		97.81 209	97.75 190	97.99 270	97.53 317	96.60 269	98.96 290	98.85 286	97.22 178	97.23 289	99.36 232	95.28 150	99.46 211	95.51 270	99.78 74	97.92 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 209	97.44 224	98.91 169	98.88 262	98.68 172	99.51 126	99.34 205	96.18 259	99.20 158	99.34 239	94.03 216	99.36 230	95.32 275	95.18 270	98.69 225

v1921920		97.80 211	97.45 219	98.84 194	98.80 272	98.53 187	99.52 122	99.34 205	96.15 263	99.24 146	99.47 198	93.98 217	99.29 248	95.40 273	95.13 273	98.69 225

V4		97.80 211	97.51 210	98.67 211	98.79 274	98.63 178	99.87 4	99.44 157	95.87 272	99.01 189	99.46 202	94.52 198	99.33 237	96.64 249	93.97 299	98.05 305

v148		97.79 213	97.55 206	98.50 223	98.74 283	97.72 230	99.54 118	99.33 213	96.26 252	98.90 207	99.51 183	94.68 190	99.14 271	97.83 155	93.15 308	98.63 263

v52		97.79 213	97.50 212	98.66 212	98.80 272	98.62 180	99.87 4	99.44 157	95.87 272	99.01 189	99.46 202	94.44 202	99.33 237	96.65 248	93.96 300	98.05 305

conf200view11		97.78 215	97.45 219	98.77 202	99.72 74	97.86 219	99.59 91	98.74 298	97.93 112	99.26 138	98.62 297	91.75 273	99.83 124	93.22 307	98.18 183	98.61 274

thres400		97.77 216	97.38 232	98.92 165	99.69 86	97.96 214	99.50 131	98.73 306	97.83 121	99.17 164	98.45 305	91.67 278	99.83 124	93.22 307	98.18 183	98.96 188

thres100view900		97.76 217	97.45 219	98.69 208	99.72 74	97.86 219	99.59 91	98.74 298	97.93 112	99.26 138	98.62 297	91.75 273	99.83 124	93.22 307	98.18 183	98.37 296

PEN-MVS		97.76 217	97.44 224	98.72 206	98.77 281	98.54 186	99.78 22	99.51 85	97.06 198	98.29 264	99.64 137	92.63 253	98.89 299	98.09 134	93.16 307	98.72 214

Baseline_NR-MVSNet		97.76 217	97.45 219	98.68 209	99.09 219	98.29 201	99.41 172	98.85 286	95.65 276	98.63 244	99.67 123	94.82 178	99.10 279	98.07 140	92.89 310	98.64 257

TR-MVS		97.76 217	97.41 229	98.82 196	99.06 223	97.87 218	98.87 302	98.56 315	96.63 222	98.68 235	99.22 257	92.49 257	99.65 191	95.40 273	97.79 205	98.95 195

Patchmtry		97.75 221	97.40 230	98.81 197	99.10 217	98.87 140	99.11 255	99.33 213	94.83 284	98.81 218	99.38 221	94.33 204	99.02 286	96.10 257	95.57 264	98.53 285

dp		97.75 221	97.80 177	97.59 290	99.10 217	93.71 316	99.32 201	98.88 284	96.48 236	99.08 179	99.55 166	92.67 252	99.82 133	96.52 250	98.58 160	99.24 156

TAPA-MVS	97.07 15	97.74 223	97.34 239	98.94 157	99.70 84	97.53 231	99.25 226	99.51 85	91.90 321	99.30 122	99.63 141	98.78 38	99.64 193	88.09 328	99.87 38	99.65 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 224	97.35 236	98.88 182	99.47 140	97.12 241	99.34 199	98.85 286	98.19 76	99.67 43	99.85 26	82.98 335	99.92 65	99.49 12	98.32 173	99.60 104

MIMVSNet		97.73 224	97.45 219	98.57 217	99.45 145	97.50 232	99.02 275	98.98 270	96.11 266	99.41 99	99.14 262	90.28 291	98.74 302	95.74 264	98.93 142	99.47 134

tfpn200view9		97.72 226	97.38 232	98.72 206	99.69 86	97.96 214	99.50 131	98.73 306	97.83 121	99.17 164	98.45 305	91.67 278	99.83 124	93.22 307	98.18 183	98.37 296

CostFormer		97.72 226	97.73 191	97.71 288	99.15 209	94.02 312	99.54 118	99.02 267	94.67 287	99.04 186	99.35 236	92.35 264	99.77 153	98.50 107	97.94 202	99.34 150

FMVSNet2		97.72 226	97.36 234	98.80 199	99.51 129	98.84 144	99.45 152	99.42 166	96.49 230	98.86 215	99.29 249	90.26 292	98.98 290	96.44 252	96.56 246	98.58 282

test0.0.03 1		97.71 229	97.42 228	98.56 219	98.41 306	97.82 221	98.78 306	98.63 311	97.34 166	98.05 275	98.98 278	94.45 200	98.98 290	95.04 279	97.15 239	98.89 196

v1240		97.69 230	97.32 242	98.79 200	98.85 269	98.43 197	99.48 144	99.36 193	96.11 266	99.27 134	99.36 232	93.76 225	99.24 260	94.46 288	95.23 269	98.70 220

cascas		97.69 230	97.43 227	98.48 226	98.60 298	97.30 233	98.18 333	99.39 179	92.96 314	98.41 255	98.78 293	93.77 224	99.27 252	98.16 130	98.61 157	98.86 197

pm-mvs1		97.68 232	97.28 246	98.88 182	99.06 223	98.62 180	99.50 131	99.45 149	96.32 246	97.87 279	99.79 72	92.47 258	99.35 233	97.54 185	93.54 304	98.67 241

GBi-Net		97.68 232	97.48 214	98.29 244	99.51 129	97.26 236	99.43 161	99.48 113	96.49 230	99.07 180	99.32 244	90.26 292	98.98 290	97.10 215	96.65 243	98.62 265

test1		97.68 232	97.48 214	98.29 244	99.51 129	97.26 236	99.43 161	99.48 113	96.49 230	99.07 180	99.32 244	90.26 292	98.98 290	97.10 215	96.65 243	98.62 265

tpm		97.67 235	97.55 206	98.03 265	99.02 230	95.01 302	99.43 161	98.54 316	96.44 238	99.12 169	99.34 239	91.83 272	99.60 201	97.75 165	96.46 248	99.48 130

PCF-MVS	97.08 14	97.66 236	97.06 254	99.47 92	99.61 114	99.09 103	98.04 335	99.25 241	91.24 324	98.51 250	99.70 108	94.55 196	99.91 74	92.76 314	99.85 52	99.42 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testgi		97.65 237	97.50 212	98.13 262	99.36 162	96.45 273	99.42 168	99.48 113	97.76 129	97.87 279	99.45 205	91.09 285	98.81 301	94.53 286	98.52 165	99.13 162

thres200		97.61 238	97.28 246	98.62 213	99.64 103	98.03 210	99.26 224	98.74 298	97.68 139	99.09 178	98.32 307	91.66 280	99.81 137	92.88 313	98.22 179	98.03 308

PAPM		97.59 239	97.09 253	99.07 141	99.06 223	98.26 203	98.30 329	99.10 256	94.88 283	98.08 271	99.34 239	96.27 125	99.64 193	89.87 322	98.92 144	99.31 152

VDDNet		97.55 240	97.02 255	99.16 133	99.49 136	98.12 209	99.38 184	99.30 222	95.35 279	99.68 37	99.90 7	82.62 337	99.93 57	99.31 25	98.13 190	99.42 143

TESTMET0.1,1		97.55 240	97.27 248	98.40 236	98.93 253	96.53 270	98.67 313	97.61 338	96.96 203	98.64 243	99.28 250	88.63 311	99.45 212	97.30 203	99.38 110	99.21 157

DWT-MVSNet_test		97.53 242	97.40 230	97.93 273	99.03 229	94.86 303	99.57 103	98.63 311	96.59 227	98.36 259	98.79 291	89.32 301	99.74 158	98.14 131	98.16 189	99.20 158

pmmvs5		97.52 243	97.30 244	98.16 261	98.57 300	96.73 264	99.27 216	98.90 282	96.14 264	98.37 258	99.53 175	91.54 282	99.14 271	97.51 188	95.87 258	98.63 263

v748		97.52 243	97.23 249	98.41 235	98.69 290	97.23 239	99.87 4	99.45 149	95.72 274	98.51 250	99.53 175	94.13 212	99.30 246	96.78 239	92.39 315	98.70 220

LF4IMVS		97.52 243	97.46 218	97.70 289	98.98 237	95.55 289	99.29 210	98.82 289	98.07 93	98.66 236	99.64 137	89.97 296	99.61 200	97.01 220	96.68 242	97.94 312

DTE-MVSNet		97.51 246	97.19 251	98.46 229	98.63 296	98.13 208	99.84 9	99.48 113	96.68 218	97.97 277	99.67 123	92.92 236	98.56 305	96.88 236	92.60 314	98.70 220

SixPastTwentyTwo		97.50 247	97.33 241	98.03 265	98.65 294	96.23 280	99.77 24	98.68 309	97.14 183	97.90 278	99.93 4	90.45 290	99.18 270	97.00 221	96.43 249	98.67 241

JIA-IIPM		97.50 247	97.02 255	98.93 160	98.73 284	97.80 226	99.30 206	98.97 271	91.73 322	98.91 205	94.86 338	95.10 160	99.71 176	97.58 179	97.98 201	99.28 154

test-mter		97.49 249	97.13 252	98.55 221	98.79 274	97.10 242	98.67 313	97.75 329	96.65 220	98.61 247	98.85 286	88.23 316	99.45 212	97.25 204	99.38 110	99.10 163

DI_MVS_plusplus_test		97.45 250	96.79 259	99.44 98	97.76 315	99.04 108	99.21 236	98.61 313	97.74 132	94.01 317	98.83 288	87.38 322	99.83 124	98.63 88	98.90 146	99.44 140

test_normal		97.44 251	96.77 261	99.44 98	97.75 316	99.00 119	99.10 257	98.64 310	97.71 135	93.93 320	98.82 289	87.39 321	99.83 124	98.61 92	98.97 138	99.49 128

tpm2		97.44 251	97.34 239	97.74 287	99.15 209	94.36 309	99.45 152	98.94 274	93.45 312	98.90 207	99.44 206	91.35 283	99.59 203	97.31 202	98.07 198	99.29 153

tpm cat1		97.39 253	97.36 234	97.50 293	99.17 204	93.73 314	99.43 161	99.31 220	91.27 323	98.71 227	99.08 267	94.31 206	99.77 153	96.41 254	98.50 166	99.00 178

tpmp4_e23		97.34 254	97.29 245	97.52 291	99.25 188	93.73 314	99.58 97	99.19 249	94.00 303	98.20 266	99.41 212	90.74 289	99.74 158	97.13 214	98.07 198	99.07 172

USDC		97.34 254	97.20 250	97.75 286	99.07 221	95.20 298	98.51 322	99.04 265	97.99 107	98.31 262	99.86 22	89.02 303	99.55 206	95.67 268	97.36 232	98.49 287

MVS		97.28 256	96.55 263	99.48 89	98.78 278	98.95 129	99.27 216	99.39 179	83.53 337	98.08 271	99.54 169	96.97 105	99.87 103	94.23 299	99.16 123	99.63 100

DSMNet-mixed		97.25 257	97.35 236	96.95 301	97.84 313	93.61 318	99.57 103	96.63 343	96.13 265	98.87 210	98.61 300	94.59 194	97.70 327	95.08 278	98.86 149	99.55 112

MS-PatchMatch		97.24 258	97.32 242	96.99 299	98.45 305	93.51 319	98.82 304	99.32 219	97.41 162	98.13 269	99.30 247	88.99 304	99.56 204	95.68 267	99.80 70	97.90 315

TransMVSNet (Re)		97.15 259	96.58 262	98.86 190	99.12 212	98.85 143	99.49 139	98.91 280	95.48 278	97.16 291	99.80 64	93.38 229	99.11 277	94.16 301	91.73 316	98.62 265

TinyColmap		97.12 260	96.89 257	97.83 281	99.07 221	95.52 292	98.57 319	98.74 298	97.58 145	97.81 282	99.79 72	88.16 317	99.56 204	95.10 277	97.21 236	98.39 295

K. test v3		97.10 261	96.79 259	98.01 268	98.72 286	96.33 277	99.87 4	97.05 342	97.59 143	96.16 302	99.80 64	88.71 307	99.04 283	96.69 244	96.55 247	98.65 255

LP		97.04 262	96.80 258	97.77 285	98.90 258	95.23 297	98.97 288	99.06 263	94.02 302	98.09 270	99.41 212	93.88 220	98.82 300	90.46 320	98.42 170	99.26 155

PatchT		97.03 263	96.44 264	98.79 200	98.99 233	98.34 200	99.16 242	99.07 261	92.13 318	99.52 79	97.31 331	94.54 197	98.98 290	88.54 326	98.73 156	99.03 175

FMVSNet1		96.84 264	96.36 265	98.29 244	99.32 174	97.26 236	99.43 161	99.48 113	95.11 281	98.55 249	99.32 244	83.95 334	98.98 290	95.81 263	96.26 253	98.62 265

test_0402		96.64 265	96.24 266	97.85 279	98.85 269	96.43 274	99.44 156	99.26 239	93.52 309	96.98 295	99.52 180	88.52 312	99.20 269	92.58 316	97.50 220	97.93 313

RPMNet		96.61 266	95.85 274	98.87 186	99.18 199	98.49 193	99.22 233	99.08 258	88.72 333	99.56 68	97.38 329	94.08 215	99.00 288	86.87 333	98.58 160	99.14 160

X-MVStestdata		96.55 267	95.45 288	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 108	64.01 353	98.81 35	99.94 42	98.79 72	99.86 48	99.84 12

pmmvs6		96.53 268	96.09 269	97.82 282	98.69 290	95.47 293	99.37 186	99.47 129	93.46 311	97.41 286	99.78 77	87.06 323	99.33 237	96.92 229	92.70 313	98.65 255

UnsupCasMVSNet_eth		96.44 269	96.12 268	97.40 295	98.65 294	95.65 286	99.36 192	99.51 85	97.13 184	96.04 305	98.99 275	88.40 314	98.17 308	96.71 242	90.27 319	98.40 294

FMVSNet5		96.43 270	96.19 267	97.15 296	99.11 214	95.89 285	99.32 201	99.52 76	94.47 296	98.34 261	99.07 268	87.54 320	97.07 330	92.61 315	95.72 261	98.47 289

v18		96.42 271	95.80 278	98.26 247	98.95 245	98.82 154	99.76 27	99.28 233	94.58 289	94.12 312	97.70 316	95.22 156	98.16 309	94.83 282	87.80 326	97.79 323

v17		96.42 271	95.81 276	98.25 251	98.94 248	98.80 161	99.76 27	99.28 233	94.57 290	94.18 311	97.71 315	95.23 155	98.16 309	94.86 280	87.73 328	97.80 318

v16		96.39 273	95.76 279	98.26 247	98.96 243	98.81 156	99.76 27	99.28 233	94.57 290	94.10 313	97.70 316	95.04 162	98.16 309	94.70 284	87.77 327	97.80 318

new_pmnet		96.38 274	96.03 270	97.41 294	98.13 311	95.16 301	99.05 266	99.20 246	93.94 304	97.39 287	98.79 291	91.61 281	99.04 283	90.43 321	95.77 260	98.05 305

v15		96.28 275	95.62 281	98.25 251	98.94 248	98.83 147	99.76 27	99.29 226	94.52 294	94.02 316	97.61 323	95.02 163	98.13 313	94.53 286	86.92 331	97.80 318

V14		96.26 276	95.60 282	98.26 247	98.94 248	98.83 147	99.76 27	99.29 226	94.49 295	93.96 318	97.66 319	94.99 166	98.13 313	94.41 289	86.90 332	97.80 318

V9		96.25 277	95.58 283	98.26 247	98.94 248	98.83 147	99.75 34	99.29 226	94.45 297	93.96 318	97.62 322	94.94 168	98.14 312	94.40 290	86.87 333	97.81 316

v13		96.24 278	95.58 283	98.25 251	98.98 237	98.83 147	99.75 34	99.29 226	94.35 299	93.89 321	97.60 324	95.17 158	98.11 315	94.27 298	86.86 334	97.81 316

v12		96.24 278	95.58 283	98.23 254	98.96 243	98.81 156	99.76 27	99.29 226	94.42 298	93.85 322	97.60 324	95.12 159	98.09 316	94.32 295	86.85 335	97.80 318

v11		96.23 280	95.57 286	98.21 257	98.93 253	98.83 147	99.72 39	99.29 226	94.29 300	94.05 315	97.64 321	94.88 175	98.04 317	92.89 312	88.43 324	97.77 324

Anonymous20231206		96.22 281	96.03 270	96.79 305	97.31 322	94.14 311	99.63 78	99.08 258	96.17 260	97.04 293	99.06 270	93.94 218	97.76 326	86.96 332	95.06 274	98.47 289

IB-MVS	95.67 18	96.22 281	95.44 289	98.57 217	99.21 192	96.70 265	98.65 316	97.74 331	96.71 216	97.27 288	98.54 303	86.03 325	99.92 65	98.47 110	86.30 336	99.10 163
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		96.17 283	95.32 290	98.73 205	98.79 274	98.14 207	99.38 184	94.09 348	91.07 326	98.07 274	91.04 344	89.62 300	99.35 233	96.75 240	99.09 129	98.68 230

test20.03		96.12 284	95.96 273	96.63 306	97.44 318	95.45 294	99.51 126	99.38 185	96.55 228	96.16 302	99.25 254	93.76 225	96.17 335	87.35 331	94.22 294	98.27 299

PVSNet_0	94.43 19	96.09 285	95.47 287	97.94 272	99.31 175	94.34 310	97.81 336	99.70 15	97.12 186	97.46 285	98.75 294	89.71 298	99.79 144	97.69 173	81.69 340	99.68 83

EG-PatchMatch MVS		95.97 286	95.69 280	96.81 304	97.78 314	92.79 322	99.16 242	98.93 275	96.16 261	94.08 314	99.22 257	82.72 336	99.47 210	95.67 268	97.50 220	98.17 302

Patchmatch-RL test		95.84 287	95.81 276	95.95 310	95.61 327	90.57 328	98.24 330	98.39 317	95.10 282	95.20 307	98.67 296	94.78 181	97.77 325	96.28 256	90.02 320	99.51 124

MVS-HIRNet		95.75 288	95.16 292	97.51 292	99.30 176	93.69 317	98.88 301	95.78 344	85.09 336	98.78 221	92.65 340	91.29 284	99.37 226	94.85 281	99.85 52	99.46 137

testpf		95.66 289	96.02 272	94.58 313	98.35 307	92.32 324	97.25 341	97.91 328	92.83 315	97.03 294	98.99 275	88.69 308	98.61 304	95.72 265	97.40 229	92.80 340

MIMVSNet1		95.51 290	95.04 293	96.92 302	97.38 319	95.60 287	99.52 122	99.50 99	93.65 307	96.97 296	99.17 260	85.28 329	96.56 334	88.36 327	95.55 265	98.60 278

MDA-MVSNet_test_wron		95.45 291	94.60 296	98.01 268	98.16 310	97.21 240	99.11 255	99.24 242	93.49 310	80.73 342	98.98 278	93.02 233	98.18 307	94.22 300	94.45 290	98.64 257

TDRefinement		95.42 292	94.57 297	97.97 271	89.83 343	96.11 282	99.48 144	98.75 295	96.74 214	96.68 297	99.88 14	88.65 310	99.71 176	98.37 117	82.74 339	98.09 303

YYNet1		95.36 293	94.51 298	97.92 274	97.89 312	97.10 242	99.10 257	99.23 243	93.26 313	80.77 341	99.04 272	92.81 239	98.02 318	94.30 296	94.18 295	98.64 257

pmmvs-eth3d		95.34 294	94.73 295	97.15 296	95.53 329	95.94 284	99.35 196	99.10 256	95.13 280	93.55 323	97.54 327	88.15 318	97.91 321	94.58 285	89.69 322	97.61 327

Test4		95.05 295	93.67 303	99.22 130	96.07 326	98.94 132	99.20 238	99.27 238	97.71 135	89.96 335	97.59 326	66.18 343	99.25 258	98.06 141	98.96 139	99.47 134

MDA-MVSNet-bldmvs		94.96 296	93.98 301	97.92 274	98.24 309	97.27 235	99.15 245	99.33 213	93.80 306	80.09 343	99.03 273	88.31 315	97.86 323	93.49 305	94.36 291	98.62 265

N_pmnet		94.95 297	95.83 275	92.31 321	98.47 304	79.33 344	99.12 249	92.81 353	93.87 305	97.68 284	99.13 263	93.87 221	99.01 287	91.38 318	96.19 254	98.59 279

testus		94.61 298	95.30 291	92.54 320	96.44 325	84.18 336	98.36 325	99.03 266	94.18 301	96.49 298	98.57 302	88.74 306	95.09 339	87.41 330	98.45 168	98.36 298

new-patchmatchnet		94.48 299	94.08 300	95.67 311	95.08 331	92.41 323	99.18 240	99.28 233	94.55 293	93.49 324	97.37 330	87.86 319	97.01 331	91.57 317	88.36 325	97.61 327

testing_2		94.44 300	92.93 306	98.98 151	94.16 334	99.00 119	99.42 168	99.28 233	96.60 225	84.86 337	96.84 332	70.91 340	99.27 252	98.23 126	96.08 256	98.68 230

OpenMVS_ROB	92.34 20	94.38 301	93.70 302	96.41 309	97.38 319	93.17 320	99.06 264	98.75 295	86.58 334	94.84 310	98.26 309	81.53 338	99.32 240	89.01 325	97.87 204	96.76 331

CMPMVS	69.68 23	94.13 302	94.90 294	91.84 322	97.24 323	80.01 343	98.52 321	99.48 113	89.01 331	91.99 329	99.67 123	85.67 327	99.13 274	95.44 271	97.03 240	96.39 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 303	93.25 305	96.60 307	94.76 332	94.49 307	98.92 297	98.18 324	89.66 328	96.48 299	98.06 310	86.28 324	97.33 329	89.68 323	87.20 330	97.97 311

test2356		94.07 304	94.46 299	92.89 318	95.18 330	86.13 334	97.60 339	99.06 263	93.61 308	96.15 304	98.28 308	85.60 328	93.95 341	86.68 334	98.00 200	98.59 279

UnsupCasMVSNet_bld		93.53 305	92.51 307	96.58 308	97.38 319	93.82 313	98.24 330	99.48 113	91.10 325	93.10 325	96.66 333	74.89 339	98.37 306	94.03 302	87.71 329	97.56 329

PM-MVS		92.96 306	92.23 308	95.14 312	95.61 327	89.98 330	99.37 186	98.21 322	94.80 285	95.04 309	97.69 318	65.06 344	97.90 322	94.30 296	89.98 321	97.54 330

test1235678		92.91 307	93.30 304	91.71 324	93.14 337	83.01 338	98.75 309	98.58 314	92.80 316	92.45 327	97.91 312	88.51 313	93.54 342	82.26 338	95.35 267	98.59 279

1111		92.30 308	92.21 309	92.55 319	93.30 335	86.27 332	99.15 245	98.74 298	91.94 319	90.85 332	97.82 313	84.18 332	95.21 337	79.65 340	94.27 293	96.19 334

test12356		91.74 309	92.19 310	90.37 327	91.22 339	82.41 339	98.61 317	98.28 319	90.66 327	91.82 330	97.92 311	84.90 330	92.61 343	81.64 339	94.66 285	96.09 335

Gipuma		90.99 310	90.15 311	93.51 315	98.73 284	90.12 329	93.98 345	99.45 149	79.32 340	92.28 328	94.91 337	69.61 341	97.98 320	87.42 329	95.67 262	92.45 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		90.69 311	89.39 312	94.58 313	94.25 333	88.18 331	99.29 210	99.07 261	82.45 339	92.95 326	97.65 320	63.96 346	97.79 324	89.27 324	85.63 337	97.77 324

testmv		87.91 312	87.80 313	88.24 328	87.68 346	77.50 346	99.07 260	97.66 337	89.27 329	86.47 336	96.22 335	68.35 342	92.49 345	76.63 344	88.82 323	94.72 338

PMMVS2		86.87 313	85.37 316	91.35 326	90.21 342	83.80 337	98.89 300	97.45 340	83.13 338	91.67 331	95.03 336	48.49 350	94.70 340	85.86 335	77.62 341	95.54 336

LCM-MVSNet		86.80 314	85.22 317	91.53 325	87.81 345	80.96 342	98.23 332	98.99 269	71.05 343	90.13 334	96.51 334	48.45 351	96.88 332	90.51 319	85.30 338	96.76 331

FPMVS		84.93 315	85.65 315	82.75 335	86.77 347	63.39 353	98.35 327	98.92 277	74.11 342	83.39 339	98.98 278	50.85 349	92.40 346	84.54 336	94.97 276	92.46 341

.test1245		83.42 316	86.17 314	75.15 338	93.30 335	86.27 332	99.15 245	98.74 298	91.94 319	90.85 332	97.82 313	84.18 332	95.21 337	79.65 340	39.90 350	43.98 351

no-one		83.04 317	80.12 319	91.79 323	89.44 344	85.65 335	99.32 201	98.32 318	89.06 330	79.79 345	89.16 346	44.86 352	96.67 333	84.33 337	46.78 348	93.05 339

tmp_tt		82.80 318	81.52 318	86.66 329	66.61 354	68.44 352	92.79 347	97.92 326	68.96 345	80.04 344	99.85 26	85.77 326	96.15 336	97.86 153	43.89 349	95.39 337

E-PMN		80.61 319	79.88 320	82.81 334	90.75 341	76.38 348	97.69 337	95.76 345	66.44 347	83.52 338	92.25 341	62.54 347	87.16 350	68.53 348	61.40 344	84.89 349

EMVS		80.02 320	79.22 321	82.43 336	91.19 340	76.40 347	97.55 340	92.49 355	66.36 348	83.01 340	91.27 342	64.63 345	85.79 351	65.82 349	60.65 345	85.08 348

PNet_i23d		79.43 321	77.68 322	84.67 331	86.18 348	71.69 351	96.50 343	93.68 349	75.17 341	71.33 346	91.18 343	32.18 355	90.62 347	78.57 343	74.34 342	91.71 344

ANet_high		77.30 322	74.86 324	84.62 332	75.88 352	77.61 345	97.63 338	93.15 352	88.81 332	64.27 348	89.29 345	36.51 353	83.93 352	75.89 345	52.31 347	92.33 343

MVE	76.82 21	76.91 323	74.31 325	84.70 330	85.38 350	76.05 349	96.88 342	93.17 351	67.39 346	71.28 347	89.01 347	21.66 360	87.69 349	71.74 347	72.29 343	90.35 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 324	74.97 323	79.01 337	70.98 353	55.18 354	93.37 346	98.21 322	65.08 349	61.78 350	93.83 339	21.74 359	92.53 344	78.59 342	91.12 318	89.34 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		74.42 325	71.19 326	84.14 333	76.16 351	74.29 350	96.00 344	92.57 354	69.57 344	63.84 349	87.49 348	21.98 357	88.86 348	75.56 346	57.50 346	89.26 347

pcd1.5k->3k		40.85 326	43.49 328	32.93 340	98.95 245	0.00 358	0.00 349	99.53 72	0.00 353	0.00 354	0.27 355	95.32 149	0.00 356	0.00 353	97.30 233	98.80 201

wuyk23d		40.18 327	41.29 330	36.84 339	86.18 348	49.12 355	79.73 348	22.81 357	27.64 350	25.46 353	28.45 354	21.98 357	48.89 353	55.80 350	23.56 353	12.51 353

testmvs		39.17 328	43.78 327	25.37 342	36.04 356	16.84 357	98.36 325	26.56 356	20.06 351	38.51 352	67.32 349	29.64 356	15.30 355	37.59 351	39.90 350	43.98 351

test123		39.01 329	42.50 329	28.53 341	39.17 355	20.91 356	98.75 309	19.17 358	19.83 352	38.57 351	66.67 350	33.16 354	15.42 354	37.50 352	29.66 352	49.26 350

cdsmvs_eth3d_5k		24.64 330	32.85 331	0.00 343	0.00 357	0.00 358	0.00 349	99.51 85	0.00 353	0.00 354	99.56 163	96.58 117	0.00 356	0.00 353	0.00 354	0.00 354

ab-mvs-re		8.30 331	11.06 332	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	99.58 157	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

pcd_1.5k_mvsjas		8.27 332	11.03 333	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.27 355	99.01 11	0.00 356	0.00 353	0.00 354	0.00 354

sosnet-low-res		0.02 333	0.03 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.27 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

sosnet		0.02 333	0.03 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.27 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uncertanet		0.02 333	0.03 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.27 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

Regformer		0.02 333	0.03 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.27 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uanet		0.02 333	0.03 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.27 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

GSMVS																99.52 119

test_part3							99.37 186		97.97 108		99.78 77		99.95 33	97.15 212

test_part2					99.81 32	99.83 7				99.77 23

test_part1								99.48 113				98.96 20			99.84 57	99.83 23

sam_mvs1												94.86 176				99.52 119

sam_mvs												94.72 189

semantic-postprocess				98.06 264	99.57 121	96.36 276		99.49 104	97.18 180	98.71 227	99.72 104	92.70 246	99.14 271	97.44 196	95.86 259	98.67 241

ambc				93.06 317	92.68 338	82.36 340	98.47 323	98.73 306		95.09 308	97.41 328	55.55 348	99.10 279	96.42 253	91.32 317	97.71 326

MTGPA								99.47 129

test_post1							99.23 229				65.14 352	94.18 211	99.71 176	97.58 179

test_post											65.99 351	94.65 193	99.73 166

patchmatchnet-post											98.70 295	94.79 180	99.74 158

GG-mvs-BLEND				98.45 230	98.55 301	98.16 206	99.43 161	93.68 349		97.23 289	98.46 304	89.30 302	99.22 264	95.43 272	98.22 179	97.98 310

MTMP								98.88 284

gm-plane-assit					98.54 302	92.96 321			94.65 288		99.15 261		99.64 193	97.56 183

test9_res														97.49 190	99.72 85	99.75 55

TEST9					99.67 90	99.65 39	99.05 266	99.41 169	96.22 256	98.95 200	99.49 188	98.77 41	99.91 74

test_8					99.67 90	99.61 44	99.03 272	99.41 169	96.28 249	98.93 203	99.48 194	98.76 43	99.91 74

agg_prior2														97.21 206	99.73 84	99.75 55

agg_prior					99.67 90	99.62 42		99.40 176		98.87 210			99.91 74

TestCases				99.31 111	99.86 20	98.48 195		99.61 32	97.85 118	99.36 112	99.85 26	95.95 131	99.85 111	96.66 246	99.83 63	99.59 108

test_prior4						99.56 51	98.99 281

test_prior2							98.96 290		98.34 66	99.01 189	99.52 180	98.68 51		97.96 145	99.74 81

test_prior				99.68 51	99.67 90	99.48 64		99.56 48					99.83 124			99.74 60

旧先验2							98.96 290		96.70 217	99.47 87			99.94 42	98.19 127

新几何2							99.01 279

新几何1				99.75 39	99.75 55	99.59 48		99.54 62	96.76 213	99.29 126	99.64 137	98.43 63	99.94 42	96.92 229	99.66 97	99.72 71

旧先验1					99.74 66	99.59 48		99.54 62			99.69 114	98.47 60			99.68 95	99.73 65

无先验							98.99 281	99.51 85	96.89 208				99.93 57	97.53 186		99.72 71

原ACMM2							98.95 294

原ACMM1				99.65 58	99.73 71	99.33 78		99.47 129	97.46 155	99.12 169	99.66 129	98.67 53	99.91 74	97.70 172	99.69 92	99.71 78

test222					99.75 55	99.49 63	98.91 299	99.49 104	96.42 240	99.34 118	99.65 130	98.28 73			99.69 92	99.72 71

testdata2													99.95 33	96.67 245

segment_acmp												98.96 20

testdata				99.54 76	99.75 55	98.95 129		99.51 85	97.07 196	99.43 94	99.70 108	98.87 30	99.94 42	97.76 163	99.64 100	99.72 71

testdata1							98.85 303		98.32 69

test12				99.75 39	99.64 103	99.61 44		99.29 226		99.21 155		98.38 67	99.89 94		99.74 81	99.74 60

plane_prior7					99.29 179	97.03 250

plane_prior6					99.27 184	96.98 254						92.71 244

plane_prior5								99.47 129					99.69 185	97.78 160	97.63 208	98.67 241

plane_prior4											99.61 149

plane_prior3						97.00 252			98.69 46	99.11 171

plane_prior2							99.39 179		98.97 22

plane_prior1					99.26 186

plane_prior						96.97 255	99.21 236		98.45 59						97.60 211

n2								0.00 359

nn								0.00 359

door-mid								98.05 325

lessismore_v0				97.79 284	98.69 290	95.44 295		94.75 346		95.71 306	99.87 19	88.69 308	99.32 240	95.89 261	94.93 278	98.62 265

LGP-MVS_train				98.49 224	99.33 167	97.05 248		99.55 55	97.46 155	99.24 146	99.83 37	92.58 254	99.72 170	98.09 134	97.51 218	98.68 230

test11								99.35 197

door								97.92 326

HQP5-MVS						96.83 260

HQP-NCC					99.19 196		98.98 285		98.24 72	98.66 236

ACMP_Plane					99.19 196		98.98 285		98.24 72	98.66 236

BP-MVS														97.19 208

HQP4-MVS										98.66 236			99.64 193			98.64 257

HQP3-MVS								99.39 179							97.58 213

HQP2-MVS												92.47 258

NP-MVS					99.23 189	96.92 258					99.40 216

MDTV_nov1_ep13_2view						95.18 300	99.35 196		96.84 211	99.58 64		95.19 157		97.82 156		99.46 137

MDTV_nov1_ep13			98.32 134		99.11 214	94.44 308	99.27 216	98.74 298	97.51 152	99.40 103	99.62 146	94.78 181	99.76 156	97.59 178	98.81 153

ACMMP++_ref															97.19 237

ACMMP++															97.43 228

Test By Simon												98.75 46

ITE_SJBPF				98.08 263	99.29 179	96.37 275		98.92 277	98.34 66	98.83 217	99.75 92	91.09 285	99.62 199	95.82 262	97.40 229	98.25 301

DeepMVS_CX				93.34 316	99.29 179	82.27 341		99.22 244	85.15 335	96.33 300	99.05 271	90.97 287	99.73 166	93.57 304	97.77 206	98.01 309

High-resolution multi-view benchmark