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 35	99.56 48	99.02 10	99.88 3	99.85 26	99.18 6	99.96 19	99.22 32	99.92 12	99.90 1

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 44	99.41 74	99.58 101	99.49 105	99.02 10	99.88 3	99.80 65	99.00 18	99.94 42	99.45 15	99.92 12	99.84 12

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 81	99.39 182	98.91 29	99.78 23	99.85 26	99.36 2	99.94 42	98.84 68	99.88 35	99.82 32

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 64	99.78 36	99.14 101	99.60 92	99.45 152	99.01 13	99.90 1	99.83 37	98.98 19	99.93 57	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 64	99.78 36	99.15 100	99.61 90	99.45 152	99.01 13	99.89 2	99.82 44	99.01 12	99.92 65	99.56 5	99.95 6	99.85 8

Regformer-3		99.57 6	99.53 5	99.68 52	99.76 44	99.29 85	99.58 101	99.44 160	99.01 13	99.87 6	99.80 65	98.97 20	99.91 75	99.44 16	99.92 12	99.83 23

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 82	99.52 61	99.49 144	99.49 105	98.94 26	99.83 12	99.76 89	99.01 12	99.94 42	99.15 40	99.87 39	99.80 42

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 14	99.59 94	99.51 85	98.62 49	99.79 19	99.83 37	99.28 3	99.97 11	98.48 111	99.90 25	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
Regformer-1		99.53 9	99.47 8	99.72 49	99.71 82	99.44 71	99.49 144	99.46 140	98.95 24	99.83 12	99.76 89	99.01 12	99.93 57	99.17 37	99.87 39	99.80 42

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 8	99.69 47	99.68 19	98.98 19	99.37 110	99.74 99	98.81 36	99.94 42	98.79 74	99.86 49	99.84 12

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 67	99.47 131	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 59	99.90 25	99.83 23

HPM-MVS_fast		99.51 12	99.40 14	99.85 19	99.91 1	99.79 19	99.76 28	99.56 48	97.72 136	99.76 29	99.75 94	99.13 7	99.92 65	99.07 46	99.92 12	99.85 8

zzz-MVS		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 198	99.47 131	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 59	99.90 25	99.83 23

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 67	99.67 22	98.15 80	99.68 38	99.69 117	99.06 9	99.96 19	98.69 85	99.87 39	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 67	99.67 22	98.15 80	99.67 44	99.69 117	98.95 26	99.96 19	98.69 85	99.87 39	99.84 12

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 37	99.63 109	99.59 49	99.36 198	99.46 140	99.07 9	99.79 19	99.82 44	98.85 33	99.92 65	98.68 87	99.87 39	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 15	99.65 77	99.66 25	98.13 82	99.66 49	99.68 122	98.96 21	99.96 19	98.62 93	99.87 39	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 19	99.76 44	99.83 8	99.63 81	99.54 62	98.36 65	99.79 19	99.82 44	98.86 32	99.95 33	98.62 93	99.81 69	99.78 50

DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 76	99.40 76	99.05 272	99.66 25	99.14 6	99.57 68	99.80 65	98.46 62	99.94 42	99.57 4	99.84 58	99.60 106
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
SMA-MVS		99.47 20	99.34 24	99.86 13	99.73 72	99.85 6	99.56 114	99.50 99	97.61 145	99.84 8	99.82 44	99.28 3	99.91 75	98.79 74	99.91 17	99.81 36

ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 153	99.48 115	98.05 98	99.76 29	99.86 22	98.82 35	99.93 57	98.82 73	99.91 17	99.84 12

MSLP-MVS++		99.46 22	99.47 8	99.44 100	99.60 119	99.16 97	99.41 177	99.71 13	98.98 19	99.45 92	99.78 79	99.19 5	99.54 212	99.28 27	99.84 58	99.63 102

PGM-MVS		99.45 23	99.31 32	99.86 13	99.87 15	99.78 23	99.58 101	99.65 30	97.84 122	99.71 32	99.80 65	99.12 8	99.97 11	98.33 124	99.87 39	99.83 23

CP-MVS		99.45 23	99.32 27	99.85 19	99.83 28	99.75 24	99.69 47	99.52 76	98.07 93	99.53 79	99.63 144	98.93 28	99.97 11	98.74 78	99.91 17	99.83 23

ACMMP		99.45 23	99.32 27	99.82 26	99.89 8	99.67 35	99.62 84	99.69 18	98.12 84	99.63 54	99.84 35	98.73 49	99.96 19	98.55 106	99.83 64	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 26	99.31 32	99.83 24	99.85 23	99.75 24	99.66 67	99.59 38	98.13 82	99.82 15	99.81 54	98.60 57	99.96 19	98.46 114	99.88 35	99.79 46

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 47	99.48 115	98.12 84	99.50 84	99.75 94	98.78 39	99.97 11	98.57 100	99.89 33	99.83 23

#test#		99.43 28	99.29 37	99.86 13	99.87 15	99.80 15	99.55 120	99.67 22	97.83 123	99.68 38	99.69 117	99.06 9	99.96 19	98.39 117	99.87 39	99.84 12

MCST-MVS		99.43 28	99.30 34	99.82 26	99.79 35	99.74 27	99.29 216	99.40 179	98.79 40	99.52 81	99.62 149	98.91 29	99.90 88	98.64 90	99.75 80	99.82 32

UA-Net		99.42 30	99.29 37	99.80 31	99.62 113	99.55 54	99.50 136	99.70 15	98.79 40	99.77 24	99.96 1	97.45 94	99.96 19	98.92 57	99.90 25	99.89 2

HPM-MVS		99.42 30	99.28 39	99.83 24	99.90 3	99.72 28	99.81 15	99.54 62	97.59 146	99.68 38	99.63 144	98.91 29	99.94 42	98.58 98	99.91 17	99.84 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.42 30	99.30 34	99.78 35	99.62 113	99.71 29	99.26 230	99.52 76	98.82 35	99.39 106	99.71 108	98.96 21	99.85 114	98.59 97	99.80 71	99.77 52

HSP-MVS		99.41 33	99.26 44	99.85 19	99.89 8	99.80 15	99.67 58	99.37 196	98.70 45	99.77 24	99.49 193	98.21 76	99.95 33	98.46 114	99.77 77	99.81 36

SD-MVS		99.41 33	99.52 6	99.05 149	99.74 67	99.68 33	99.46 156	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 335	98.72 82	99.93 11	99.77 52

MVS_111021_LR		99.41 33	99.33 26	99.65 59	99.77 41	99.51 63	98.94 302	99.85 6	98.82 35	99.65 52	99.74 99	98.51 59	99.80 144	98.83 70	99.89 33	99.64 98

MVS_111021_HR		99.41 33	99.32 27	99.66 55	99.72 76	99.47 67	98.95 300	99.85 6	98.82 35	99.54 78	99.73 103	98.51 59	99.74 163	98.91 58	99.88 35	99.77 52

HPM-MVS++		99.39 37	99.23 46	99.87 6	99.75 56	99.84 7	99.43 166	99.51 85	98.68 47	99.27 137	99.53 180	98.64 55	99.96 19	98.44 116	99.80 71	99.79 46

MP-MVS-pluss		99.37 38	99.20 47	99.88 4	99.90 3	99.87 2	99.30 212	99.52 76	97.18 183	99.60 61	99.79 73	98.79 38	99.95 33	98.83 70	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		99.36 39	99.36 19	99.36 107	99.67 93	98.61 187	99.07 266	99.33 217	99.00 17	99.82 15	99.81 54	99.06 9	99.84 120	99.09 44	99.42 109	99.65 92

PVSNet_Blended_VisFu		99.36 39	99.28 39	99.61 68	99.86 20	99.07 108	99.47 153	99.93 2	97.66 143	99.71 32	99.86 22	97.73 89	99.96 19	99.47 13	99.82 68	99.79 46

NCCC		99.34 41	99.19 48	99.79 34	99.61 117	99.65 40	99.30 212	99.48 115	98.86 31	99.21 159	99.63 144	98.72 50	99.90 88	98.25 128	99.63 103	99.80 42

MP-MVS		99.33 42	99.15 51	99.87 6	99.88 11	99.82 13	99.66 67	99.46 140	98.09 89	99.48 88	99.74 99	98.29 73	99.96 19	97.93 152	99.87 39	99.82 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PS-MVSNAJ		99.32 43	99.32 27	99.30 116	99.57 124	98.94 136	98.97 294	99.46 140	98.92 28	99.71 32	99.24 262	99.01 12	99.98 5	99.35 18	99.66 98	98.97 185

CSCG		99.32 43	99.32 27	99.32 112	99.85 23	98.29 205	99.71 43	99.66 25	98.11 86	99.41 101	99.80 65	98.37 70	99.96 19	98.99 52	99.96 5	99.72 72

ESAPD		99.31 45	99.13 53	99.87 6	99.81 32	99.83 8	99.37 192	99.48 115	97.97 108	99.77 24	99.78 79	98.96 21	99.95 33	97.15 216	99.84 58	99.83 23

PHI-MVS		99.30 46	99.17 50	99.70 51	99.56 128	99.52 61	99.58 101	99.80 8	97.12 189	99.62 57	99.73 103	98.58 58	99.90 88	98.61 95	99.91 17	99.68 85

DeepC-MVS	98.35 2	99.30 46	99.19 48	99.64 64	99.82 29	99.23 92	99.62 84	99.55 55	98.94 26	99.63 54	99.95 2	95.82 143	99.94 42	99.37 17	99.97 3	99.73 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v1_base_debu		99.29 48	99.27 41	99.34 108	99.63 109	98.97 128	99.12 255	99.51 85	98.86 31	99.84 8	99.47 203	98.18 77	99.99 1	99.50 8	99.31 116	99.08 171

xiu_mvs_v1_base		99.29 48	99.27 41	99.34 108	99.63 109	98.97 128	99.12 255	99.51 85	98.86 31	99.84 8	99.47 203	98.18 77	99.99 1	99.50 8	99.31 116	99.08 171

xiu_mvs_v1_base_debi		99.29 48	99.27 41	99.34 108	99.63 109	98.97 128	99.12 255	99.51 85	98.86 31	99.84 8	99.47 203	98.18 77	99.99 1	99.50 8	99.31 116	99.08 171

APD-MVS		99.27 51	99.08 58	99.84 23	99.75 56	99.79 19	99.50 136	99.50 99	97.16 185	99.77 24	99.82 44	98.78 39	99.94 42	97.56 187	99.86 49	99.80 42
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	99.18 204	99.75 24	99.56 114	99.57 44	98.45 59	99.49 87	99.85 26	97.77 88	99.94 42	98.33 124	99.84 58	99.52 122

xiu_mvs_v2_base		99.26 53	99.25 45	99.29 119	99.53 130	98.91 141	99.02 281	99.45 152	98.80 39	99.71 32	99.26 259	98.94 27	99.98 5	99.34 22	99.23 120	98.98 184

CANet		99.25 54	99.14 52	99.59 70	99.41 155	99.16 97	99.35 202	99.57 44	98.82 35	99.51 83	99.61 153	96.46 123	99.95 33	99.59 2	99.98 2	99.65 92

3Dnovator	97.25 9	99.24 55	99.05 60	99.81 29	99.12 217	99.66 37	99.84 9	99.74 10	99.09 8	98.92 209	99.90 7	95.94 138	99.98 5	98.95 55	99.92 12	99.79 46

test_prior3		99.21 56	99.05 60	99.68 52	99.67 93	99.48 65	98.96 296	99.56 48	98.34 66	99.01 194	99.52 185	98.68 52	99.83 127	97.96 149	99.74 82	99.74 61

CHOSEN 1792x2688		99.19 57	99.10 57	99.45 97	99.89 8	98.52 194	99.39 185	99.94 1	98.73 44	99.11 176	99.89 10	95.50 150	99.94 42	99.50 8	99.97 3	99.89 2

F-COLMAP		99.19 57	99.04 63	99.64 64	99.78 36	99.27 88	99.42 173	99.54 62	97.29 174	99.41 101	99.59 158	98.42 67	99.93 57	98.19 130	99.69 93	99.73 66

3Dnovator+	97.12 13	99.18 59	98.97 73	99.82 26	99.17 209	99.68 33	99.81 15	99.51 85	99.20 4	98.72 231	99.89 10	95.68 147	99.97 11	98.86 66	99.86 49	99.81 36

MVSFormer		99.17 60	99.12 55	99.29 119	99.51 133	98.94 136	99.88 1	99.46 140	97.55 151	99.80 17	99.65 133	97.39 95	99.28 255	99.03 48	99.85 53	99.65 92

sss		99.17 60	99.05 60	99.53 81	99.62 113	98.97 128	99.36 198	99.62 31	97.83 123	99.67 44	99.65 133	97.37 98	99.95 33	99.19 34	99.19 123	99.68 85

DP-MVS		99.16 62	98.95 78	99.78 35	99.77 41	99.53 58	99.41 177	99.50 99	97.03 204	99.04 191	99.88 14	97.39 95	99.92 65	98.66 88	99.90 25	99.87 4

CNLPA		99.14 63	98.99 70	99.59 70	99.58 122	99.41 74	99.16 248	99.44 160	98.45 59	99.19 165	99.49 193	98.08 80	99.89 96	97.73 171	99.75 80	99.48 133

CDPH-MVS		99.13 64	98.91 82	99.80 31	99.75 56	99.71 29	99.15 251	99.41 172	96.60 231	99.60 61	99.55 170	98.83 34	99.90 88	97.48 195	99.83 64	99.78 50

jason		99.13 64	99.03 65	99.45 97	99.46 146	98.87 144	99.12 255	99.26 244	98.03 101	99.79 19	99.65 133	97.02 106	99.85 114	99.02 50	99.90 25	99.65 92
jason: jason.
lupinMVS		99.13 64	99.01 69	99.46 96	99.51 133	98.94 136	99.05 272	99.16 255	97.86 117	99.80 17	99.56 167	97.39 95	99.86 108	98.94 56	99.85 53	99.58 112

EPP-MVSNet		99.13 64	98.99 70	99.53 81	99.65 105	99.06 109	99.81 15	99.33 217	97.43 162	99.60 61	99.88 14	97.14 102	99.84 120	99.13 41	98.94 142	99.69 81

MG-MVS		99.13 64	99.02 68	99.45 97	99.57 124	98.63 182	99.07 266	99.34 209	98.99 18	99.61 59	99.82 44	97.98 83	99.87 105	97.00 225	99.80 71	99.85 8

CHOSEN 280x420		99.12 69	99.13 53	99.08 145	99.66 103	97.89 222	98.43 332	99.71 13	98.88 30	99.62 57	99.76 89	96.63 119	99.70 187	99.46 14	99.99 1	99.66 89

DP-MVS Recon		99.12 69	98.95 78	99.65 59	99.74 67	99.70 31	99.27 222	99.57 44	96.40 249	99.42 99	99.68 122	98.75 47	99.80 144	97.98 148	99.72 86	99.44 143

Vis-MVSNet		99.12 69	98.97 73	99.56 76	99.78 36	99.10 104	99.68 56	99.66 25	98.49 56	99.86 7	99.87 19	94.77 190	99.84 120	99.19 34	99.41 110	99.74 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 69	99.08 58	99.24 130	99.46 146	98.55 189	99.51 131	99.46 140	98.09 89	99.45 92	99.82 44	98.34 71	99.51 213	98.70 83	98.93 143	99.67 88

VNet		99.11 73	98.90 83	99.73 47	99.52 131	99.56 52	99.41 177	99.39 182	99.01 13	99.74 31	99.78 79	95.56 148	99.92 65	99.52 7	98.18 186	99.72 72

CPTT-MVS		99.11 73	98.90 83	99.74 45	99.80 34	99.46 68	99.59 94	99.49 105	97.03 204	99.63 54	99.69 117	97.27 100	99.96 19	97.82 160	99.84 58	99.81 36

HyFIR lowres test		99.11 73	98.92 80	99.65 59	99.90 3	99.37 77	99.02 281	99.91 3	97.67 142	99.59 64	99.75 94	95.90 140	99.73 171	99.53 6	99.02 135	99.86 5

MVS_Test		99.10 76	98.97 73	99.48 90	99.49 140	99.14 101	99.67 58	99.34 209	97.31 172	99.58 65	99.76 89	97.65 91	99.82 136	98.87 63	99.07 132	99.46 140

1121		99.09 77	98.87 87	99.75 40	99.74 67	99.60 47	99.27 222	99.48 115	96.82 218	99.25 145	99.65 133	98.38 68	99.93 57	97.53 190	99.67 97	99.73 66

casdiffmvs		99.09 77	98.97 73	99.47 93	99.47 144	99.10 104	99.74 40	99.38 188	97.86 117	99.32 122	99.79 73	97.08 105	99.77 157	99.24 30	98.82 152	99.54 116

CDS-MVSNet		99.09 77	99.03 65	99.25 127	99.42 152	98.73 172	99.45 157	99.46 140	98.11 86	99.46 91	99.77 86	98.01 82	99.37 232	98.70 83	98.92 145	99.66 89
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		99.08 80	98.97 73	99.42 104	99.76 44	98.79 167	98.78 314	99.91 3	96.74 220	99.67 44	99.49 193	97.53 92	99.88 103	98.98 53	99.85 53	99.60 106

OMC-MVS		99.08 80	99.04 63	99.20 134	99.67 93	98.22 208	99.28 219	99.52 76	98.07 93	99.66 49	99.81 54	97.79 87	99.78 155	97.79 163	99.81 69	99.60 106

MVS_0304		99.06 82	98.86 90	99.66 55	99.51 133	99.36 78	99.22 239	99.51 85	98.95 24	99.58 65	99.65 133	93.74 232	99.98 5	99.66 1	99.95 6	99.64 98

WTY-MVS		99.06 82	98.88 86	99.61 68	99.62 113	99.16 97	99.37 192	99.56 48	98.04 99	99.53 79	99.62 149	96.84 111	99.94 42	98.85 67	98.49 169	99.72 72

IS-MVSNet		99.05 84	98.87 87	99.57 74	99.73 72	99.32 81	99.75 35	99.20 251	98.02 102	99.56 69	99.86 22	96.54 121	99.67 192	98.09 138	99.13 126	99.73 66

PAPM_NR		99.04 85	98.84 93	99.66 55	99.74 67	99.44 71	99.39 185	99.38 188	97.70 139	99.28 133	99.28 256	98.34 71	99.85 114	96.96 229	99.45 107	99.69 81

API-MVS		99.04 85	99.03 65	99.06 147	99.40 160	99.31 84	99.55 120	99.56 48	98.54 53	99.33 121	99.39 225	98.76 44	99.78 155	96.98 227	99.78 75	98.07 312

mvs_anonymous		99.03 87	98.99 70	99.16 137	99.38 163	98.52 194	99.51 131	99.38 188	97.79 128	99.38 108	99.81 54	97.30 99	99.45 217	99.35 18	98.99 137	99.51 127

train_agg		99.02 88	98.77 100	99.77 37	99.67 93	99.65 40	99.05 272	99.41 172	96.28 255	98.95 205	99.49 193	98.76 44	99.91 75	97.63 180	99.72 86	99.75 56

canonicalmvs		99.02 88	98.86 90	99.51 87	99.42 152	99.32 81	99.80 19	99.48 115	98.63 48	99.31 124	98.81 297	97.09 103	99.75 162	99.27 29	97.90 208	99.47 137

PLC	97.94 4	99.02 88	98.85 92	99.53 81	99.66 103	99.01 121	99.24 234	99.52 76	96.85 215	99.27 137	99.48 199	98.25 75	99.91 75	97.76 167	99.62 104	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 91	98.76 102	99.76 39	99.67 93	99.62 43	98.99 287	99.40 179	96.26 258	98.87 215	99.49 193	98.77 42	99.91 75	97.69 177	99.72 86	99.75 56

AdaColmap		99.01 91	98.80 97	99.66 55	99.56 128	99.54 55	99.18 246	99.70 15	98.18 79	99.35 117	99.63 144	96.32 128	99.90 88	97.48 195	99.77 77	99.55 114

1112_ss		98.98 93	98.77 100	99.59 70	99.68 92	99.02 119	99.25 232	99.48 115	97.23 180	99.13 172	99.58 161	96.93 110	99.90 88	98.87 63	98.78 156	99.84 12

MSDG		98.98 93	98.80 97	99.53 81	99.76 44	99.19 94	98.75 317	99.55 55	97.25 177	99.47 89	99.77 86	97.82 86	99.87 105	96.93 232	99.90 25	99.54 116

CANet_DTU		98.97 95	98.87 87	99.25 127	99.33 172	98.42 203	99.08 265	99.30 226	99.16 5	99.43 96	99.75 94	95.27 156	99.97 11	98.56 103	99.95 6	99.36 151

agg_prior3		98.97 95	98.71 106	99.75 40	99.67 93	99.60 47	99.04 277	99.41 172	95.93 279	98.87 215	99.48 199	98.61 56	99.91 75	97.63 180	99.72 86	99.75 56

114514_t		98.93 97	98.67 110	99.72 49	99.85 23	99.53 58	99.62 84	99.59 38	92.65 325	99.71 32	99.78 79	98.06 81	99.90 88	98.84 68	99.91 17	99.74 61

PS-MVSNAJss		98.92 98	98.92 80	98.90 178	98.78 284	98.53 191	99.78 22	99.54 62	98.07 93	99.00 201	99.76 89	99.01 12	99.37 232	99.13 41	97.23 240	98.81 203

Test_1112_low_res		98.89 99	98.66 113	99.57 74	99.69 89	98.95 133	99.03 278	99.47 131	96.98 206	99.15 171	99.23 263	96.77 115	99.89 96	98.83 70	98.78 156	99.86 5

AllTest		98.87 100	98.72 104	99.31 113	99.86 20	98.48 199	99.56 114	99.61 32	97.85 120	99.36 114	99.85 26	95.95 136	99.85 114	96.66 251	99.83 64	99.59 110

UGNet		98.87 100	98.69 108	99.40 105	99.22 196	98.72 174	99.44 161	99.68 19	99.24 3	99.18 168	99.42 214	92.74 247	99.96 19	99.34 22	99.94 10	99.53 121
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)		98.87 100	98.72 104	99.31 113	99.71 82	98.88 143	99.80 19	99.44 160	97.91 115	99.36 114	99.78 79	95.49 151	99.43 226	97.91 153	99.11 127	99.62 104

mvs-test1		98.86 103	98.84 93	98.89 180	99.33 172	97.77 233	99.44 161	99.30 226	98.47 57	99.10 179	99.43 212	96.78 113	99.95 33	98.73 80	99.02 135	98.96 191

EPNet		98.86 103	98.71 106	99.30 116	97.20 332	98.18 209	99.62 84	98.91 285	99.28 2	98.63 249	99.81 54	95.96 135	99.99 1	99.24 30	99.72 86	99.73 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 103	98.80 97	99.03 150	99.76 44	98.79 167	99.28 219	99.91 3	97.42 164	99.67 44	99.37 230	97.53 92	99.88 103	98.98 53	97.29 239	98.42 300

ab-mvs		98.86 103	98.63 115	99.54 77	99.64 106	99.19 94	99.44 161	99.54 62	97.77 130	99.30 125	99.81 54	94.20 213	99.93 57	99.17 37	98.82 152	99.49 131

MAR-MVS		98.86 103	98.63 115	99.54 77	99.37 165	99.66 37	99.45 157	99.54 62	96.61 229	99.01 194	99.40 221	97.09 103	99.86 108	97.68 179	99.53 106	99.10 166
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	98.86 103	98.75 103	99.17 136	99.88 11	98.53 191	99.34 205	99.59 38	97.55 151	98.70 238	99.89 10	95.83 142	99.90 88	98.10 137	99.90 25	99.08 171
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	98.85 109	98.64 114	99.47 93	99.42 152	99.08 107	99.62 84	99.36 197	97.39 167	99.28 133	99.68 122	96.44 125	99.92 65	98.37 120	98.22 182	99.40 148

PVSNet	96.02 17	98.85 109	98.84 93	98.89 180	99.73 72	97.28 240	98.32 336	99.60 35	97.86 117	99.50 84	99.57 165	96.75 116	99.86 108	98.56 103	99.70 92	99.54 116

PatchMatch-RL		98.84 111	98.62 118	99.52 85	99.71 82	99.28 86	99.06 270	99.77 9	97.74 134	99.50 84	99.53 180	95.41 152	99.84 120	97.17 215	99.64 101	99.44 143

Effi-MVS+		98.81 112	98.59 123	99.48 90	99.46 146	99.12 103	98.08 342	99.50 99	97.50 156	99.38 108	99.41 217	96.37 127	99.81 140	99.11 43	98.54 166	99.51 127

alignmvs		98.81 112	98.56 125	99.58 73	99.43 151	99.42 73	99.51 131	98.96 278	98.61 50	99.35 117	98.92 288	94.78 186	99.77 157	99.35 18	98.11 202	99.54 116

DeepPCF-MVS	98.18 3	98.81 112	99.37 17	97.12 306	99.60 119	91.75 334	98.61 325	99.44 160	99.35 1	99.83 12	99.85 26	98.70 51	99.81 140	99.02 50	99.91 17	99.81 36

PMMVS		98.80 115	98.62 118	99.34 108	99.27 189	98.70 175	98.76 316	99.31 224	97.34 169	99.21 159	99.07 275	97.20 101	99.82 136	98.56 103	98.87 149	99.52 122

Effi-MVS+-dtu		98.78 116	98.89 85	98.47 234	99.33 172	96.91 265	99.57 107	99.30 226	98.47 57	99.41 101	98.99 282	96.78 113	99.74 163	98.73 80	99.38 111	98.74 215

FIs		98.78 116	98.63 115	99.23 132	99.18 204	99.54 55	99.83 12	99.59 38	98.28 70	98.79 225	99.81 54	96.75 116	99.37 232	99.08 45	96.38 255	98.78 206

Fast-Effi-MVS+-dtu		98.77 118	98.83 96	98.60 220	99.41 155	96.99 259	99.52 127	99.49 105	98.11 86	99.24 150	99.34 244	96.96 109	99.79 147	97.95 151	99.45 107	99.02 180

FC-MVSNet-test		98.75 119	98.62 118	99.15 139	99.08 225	99.45 70	99.86 8	99.60 35	98.23 75	98.70 238	99.82 44	96.80 112	99.22 270	99.07 46	96.38 255	98.79 205

XVG-OURS		98.73 120	98.68 109	98.88 187	99.70 87	97.73 235	98.92 303	99.55 55	98.52 55	99.45 92	99.84 35	95.27 156	99.91 75	98.08 142	98.84 151	99.00 181

diffmvs		98.72 121	98.49 127	99.43 103	99.48 143	99.19 94	99.62 84	99.42 169	95.58 285	99.37 110	99.67 126	96.14 133	99.74 163	98.14 135	98.96 140	99.37 150

Fast-Effi-MVS+		98.70 122	98.43 129	99.51 87	99.51 133	99.28 86	99.52 127	99.47 131	96.11 272	99.01 194	99.34 244	96.20 132	99.84 120	97.88 155	98.82 152	99.39 149

XVG-OURS-SEG-HR		98.69 123	98.62 118	98.89 180	99.71 82	97.74 234	99.12 255	99.54 62	98.44 62	99.42 99	99.71 108	94.20 213	99.92 65	98.54 108	98.90 147	99.00 181

1314		98.68 124	98.54 126	99.11 144	98.89 267	98.65 180	99.27 222	99.49 105	96.89 213	97.99 284	99.56 167	97.72 90	99.83 127	97.74 170	99.27 119	98.84 201

EI-MVSNet		98.67 125	98.67 110	98.68 215	99.35 168	97.97 218	99.50 136	99.38 188	96.93 210	99.20 162	99.83 37	97.87 84	99.36 236	98.38 119	97.56 220	98.71 219

test_djsdf		98.67 125	98.57 124	98.98 156	98.70 295	98.91 141	99.88 1	99.46 140	97.55 151	99.22 157	99.88 14	95.73 146	99.28 255	99.03 48	97.62 215	98.75 212

QAPM		98.67 125	98.30 138	99.80 31	99.20 199	99.67 35	99.77 25	99.72 11	94.74 294	98.73 230	99.90 7	95.78 144	99.98 5	96.96 229	99.88 35	99.76 55

nrg030		98.64 128	98.42 130	99.28 121	99.05 231	99.69 32	99.81 15	99.46 140	98.04 99	99.01 194	99.82 44	96.69 118	99.38 228	99.34 22	94.59 296	98.78 206

PAPR		98.63 129	98.34 134	99.51 87	99.40 160	99.03 118	98.80 312	99.36 197	96.33 251	99.00 201	99.12 273	98.46 62	99.84 120	95.23 281	99.37 115	99.66 89

CVMVSNet		98.57 130	98.67 110	98.30 249	99.35 168	95.59 294	99.50 136	99.55 55	98.60 51	99.39 106	99.83 37	94.48 204	99.45 217	98.75 77	98.56 165	99.85 8

MVSTER		98.49 131	98.32 136	99.00 154	99.35 168	99.02 119	99.54 123	99.38 188	97.41 165	99.20 162	99.73 103	93.86 227	99.36 236	98.87 63	97.56 220	98.62 270

OpenMVS	96.50 16	98.47 132	98.12 146	99.52 85	99.04 232	99.53 58	99.82 13	99.72 11	94.56 300	98.08 278	99.88 14	94.73 193	99.98 5	97.47 197	99.76 79	99.06 176

IterMVS-LS		98.46 133	98.42 130	98.58 222	99.59 121	98.00 216	99.37 192	99.43 168	96.94 209	99.07 185	99.59 158	97.87 84	99.03 291	98.32 126	95.62 270	98.71 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 134	98.28 139	98.94 162	98.50 311	98.96 132	99.77 25	99.50 99	97.07 200	98.87 215	99.77 86	94.76 191	99.28 255	98.66 88	97.60 216	98.57 291

jajsoiax		98.43 135	98.28 139	98.88 187	98.60 306	98.43 201	99.82 13	99.53 72	98.19 76	98.63 249	99.80 65	93.22 237	99.44 222	99.22 32	97.50 225	98.77 209

BH-untuned		98.42 136	98.36 132	98.59 221	99.49 140	96.70 271	99.27 222	99.13 259	97.24 179	98.80 224	99.38 226	95.75 145	99.74 163	97.07 222	99.16 124	99.33 154

BH-RMVSNet		98.41 137	98.08 150	99.40 105	99.41 155	98.83 151	99.30 212	98.77 299	97.70 139	98.94 207	99.65 133	92.91 243	99.74 163	96.52 255	99.55 105	99.64 98

mvs_tets		98.40 138	98.23 141	98.91 174	98.67 299	98.51 196	99.66 67	99.53 72	98.19 76	98.65 247	99.81 54	92.75 245	99.44 222	99.31 25	97.48 229	98.77 209

XXY-MVS		98.38 139	98.09 149	99.24 130	99.26 191	99.32 81	99.56 114	99.55 55	97.45 161	98.71 232	99.83 37	93.23 236	99.63 203	98.88 59	96.32 257	98.76 211

ACMM	97.58 5	98.37 140	98.34 134	98.48 232	99.41 155	97.10 248	99.56 114	99.45 152	98.53 54	99.04 191	99.85 26	93.00 239	99.71 181	98.74 78	97.45 230	98.64 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn1000		98.33 141	98.02 155	99.25 127	99.78 36	98.73 172	99.70 44	97.55 347	97.48 157	99.69 37	99.53 180	92.37 269	99.85 114	97.82 160	98.26 181	99.16 162

tpmrst		98.33 141	98.48 128	97.90 282	99.16 211	94.78 312	99.31 210	99.11 260	97.27 175	99.45 92	99.59 158	95.33 153	99.84 120	98.48 111	98.61 159	99.09 170

PatchmatchNet		98.31 143	98.36 132	98.19 265	99.16 211	95.32 302	99.27 222	98.92 282	97.37 168	99.37 110	99.58 161	94.90 178	99.70 187	97.43 201	99.21 121	99.54 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		98.30 144	97.98 160	99.26 126	99.57 124	98.16 210	99.41 177	98.55 322	96.03 277	99.19 165	99.74 99	91.87 278	99.92 65	99.16 39	98.29 176	99.70 80

Anonymous20240521		98.30 144	98.00 157	99.18 135	98.98 242	99.46 68	99.78 22	99.49 105	96.91 212	98.00 283	99.25 260	96.51 122	99.38 228	98.15 134	94.95 285	98.71 219

VPA-MVSNet		98.29 146	97.95 163	99.30 116	99.16 211	99.54 55	99.50 136	99.58 43	98.27 71	99.35 117	99.37 230	92.53 262	99.65 196	99.35 18	94.46 297	98.72 217

UniMVSNet (Re)		98.29 146	98.00 157	99.13 143	99.00 237	99.36 78	99.49 144	99.51 85	97.95 110	98.97 204	99.13 270	96.30 129	99.38 228	98.36 122	93.34 313	98.66 256

HQP_MVS		98.27 148	98.22 142	98.44 239	99.29 184	96.97 261	99.39 185	99.47 131	98.97 22	99.11 176	99.61 153	92.71 249	99.69 190	97.78 164	97.63 213	98.67 245

thresconf0.02		98.24 149	97.89 170	99.27 122	99.76 44	99.04 111	99.67 58	97.71 339	97.10 193	99.55 72	99.54 173	92.70 251	99.79 147	96.90 235	98.12 196	98.97 185

tfpn_n400		98.24 149	97.89 170	99.27 122	99.76 44	99.04 111	99.67 58	97.71 339	97.10 193	99.55 72	99.54 173	92.70 251	99.79 147	96.90 235	98.12 196	98.97 185

tfpnconf		98.24 149	97.89 170	99.27 122	99.76 44	99.04 111	99.67 58	97.71 339	97.10 193	99.55 72	99.54 173	92.70 251	99.79 147	96.90 235	98.12 196	98.97 185

tfpnview11		98.24 149	97.89 170	99.27 122	99.76 44	99.04 111	99.67 58	97.71 339	97.10 193	99.55 72	99.54 173	92.70 251	99.79 147	96.90 235	98.12 196	98.97 185

UniMVSNet_NR-MVSNet		98.22 153	97.97 161	98.96 159	98.92 261	98.98 125	99.48 149	99.53 72	97.76 131	98.71 232	99.46 207	96.43 126	99.22 270	98.57 100	92.87 319	98.69 229

LPG-MVS_test		98.22 153	98.13 145	98.49 230	99.33 172	97.05 254	99.58 101	99.55 55	97.46 158	99.24 150	99.83 37	92.58 260	99.72 175	98.09 138	97.51 223	98.68 234

RPSCF		98.22 153	98.62 118	96.99 307	99.82 29	91.58 335	99.72 41	99.44 160	96.61 229	99.66 49	99.89 10	95.92 139	99.82 136	97.46 198	99.10 129	99.57 113

conf0.01		98.21 156	97.89 170	99.15 139	99.76 44	99.04 111	99.67 58	97.71 339	97.10 193	99.55 72	99.54 173	92.70 251	99.79 147	96.90 235	98.12 196	98.61 279

conf0.002		98.21 156	97.89 170	99.15 139	99.76 44	99.04 111	99.67 58	97.71 339	97.10 193	99.55 72	99.54 173	92.70 251	99.79 147	96.90 235	98.12 196	98.61 279

ADS-MVSNet		98.20 158	98.08 150	98.56 225	99.33 172	96.48 278	99.23 235	99.15 256	96.24 260	99.10 179	99.67 126	94.11 218	99.71 181	96.81 242	99.05 133	99.48 133

OPM-MVS		98.19 159	98.10 147	98.45 236	98.88 268	97.07 252	99.28 219	99.38 188	98.57 52	99.22 157	99.81 54	92.12 272	99.66 194	98.08 142	97.54 222	98.61 279

tfpn_ndepth		98.17 160	97.84 178	99.15 139	99.75 56	98.76 171	99.61 90	97.39 349	96.92 211	99.61 59	99.38 226	92.19 271	99.86 108	97.57 185	98.13 194	98.82 202

CR-MVSNet		98.17 160	97.93 165	98.87 191	99.18 204	98.49 197	99.22 239	99.33 217	96.96 207	99.56 69	99.38 226	94.33 209	99.00 295	94.83 288	98.58 162	99.14 163

Patchmatch-test1		98.16 162	98.14 144	98.22 262	99.30 181	95.55 295	99.07 266	98.97 276	97.57 149	99.43 96	99.60 156	92.72 248	99.60 206	97.38 203	99.20 122	99.50 130

CLD-MVS		98.16 162	98.10 147	98.33 246	99.29 184	96.82 268	98.75 317	99.44 160	97.83 123	99.13 172	99.55 170	92.92 241	99.67 192	98.32 126	97.69 212	98.48 296
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		98.13 164	97.90 166	98.81 202	98.61 305	98.87 144	98.99 287	99.21 250	96.44 244	99.06 189	99.58 161	95.90 140	99.11 283	97.18 214	96.11 260	98.46 299

WR-MVS_H		98.13 164	97.87 177	98.90 178	99.02 235	98.84 148	99.70 44	99.59 38	97.27 175	98.40 261	99.19 266	95.53 149	99.23 267	98.34 123	93.78 310	98.61 279

v1neww		98.12 166	97.84 178	98.93 165	98.97 246	98.81 160	99.66 67	99.35 201	96.49 236	99.29 129	99.37 230	95.02 168	99.32 246	97.73 171	94.73 288	98.67 245

v7new		98.12 166	97.84 178	98.93 165	98.97 246	98.81 160	99.66 67	99.35 201	96.49 236	99.29 129	99.37 230	95.02 168	99.32 246	97.73 171	94.73 288	98.67 245

v6		98.12 166	97.84 178	98.94 162	98.94 254	98.83 151	99.66 67	99.34 209	96.49 236	99.30 125	99.37 230	94.95 172	99.34 242	97.77 166	94.74 287	98.67 245

ACMH	97.28 8	98.10 169	97.99 159	98.44 239	99.41 155	96.96 263	99.60 92	99.56 48	98.09 89	98.15 275	99.91 5	90.87 296	99.70 187	98.88 59	97.45 230	98.67 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		98.09 170	97.78 185	99.01 152	98.97 246	99.24 91	99.67 58	99.46 140	97.25 177	98.48 258	99.64 140	93.79 228	99.06 287	98.63 91	94.10 304	98.74 215

DU-MVS		98.08 171	97.79 183	98.96 159	98.87 271	98.98 125	99.41 177	99.45 152	97.87 116	98.71 232	99.50 190	94.82 183	99.22 270	98.57 100	92.87 319	98.68 234

divwei89l23v2f112		98.06 172	97.78 185	98.91 174	98.90 264	98.77 170	99.57 107	99.35 201	96.45 243	99.24 150	99.37 230	94.92 176	99.27 258	97.50 193	94.71 292	98.68 234

v2v482		98.06 172	97.77 189	98.92 170	98.90 264	98.82 158	99.57 107	99.36 197	96.65 226	99.19 165	99.35 241	94.20 213	99.25 264	97.72 175	94.97 283	98.69 229

V42		98.06 172	97.79 183	98.86 195	98.98 242	98.84 148	99.69 47	99.34 209	96.53 235	99.30 125	99.37 230	94.67 196	99.32 246	97.57 185	94.66 293	98.42 300

test-LLR		98.06 172	97.90 166	98.55 227	98.79 280	97.10 248	98.67 321	97.75 336	97.34 169	98.61 252	98.85 293	94.45 205	99.45 217	97.25 208	99.38 111	99.10 166

WR-MVS		98.06 172	97.73 196	99.06 147	98.86 274	99.25 90	99.19 245	99.35 201	97.30 173	98.66 241	99.43 212	93.94 223	99.21 274	98.58 98	94.28 300	98.71 219

ACMP	97.20 11	98.06 172	97.94 164	98.45 236	99.37 165	97.01 257	99.44 161	99.49 105	97.54 154	98.45 259	99.79 73	91.95 273	99.72 175	97.91 153	97.49 228	98.62 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1141		98.05 178	97.76 192	98.91 174	98.91 263	98.78 169	99.57 107	99.35 201	96.41 248	99.23 155	99.36 237	94.93 175	99.27 258	97.38 203	94.72 290	98.68 234

v7		98.05 178	97.78 185	98.87 191	98.99 238	98.67 177	99.64 79	99.34 209	96.31 254	99.29 129	99.51 188	94.78 186	99.27 258	97.03 223	95.15 279	98.66 256

v1		98.05 178	97.76 192	98.93 165	98.92 261	98.80 165	99.57 107	99.35 201	96.39 250	99.28 133	99.36 237	94.86 181	99.32 246	97.38 203	94.72 290	98.68 234

EPNet_dtu		98.03 181	97.96 162	98.23 260	98.27 316	95.54 297	99.23 235	98.75 300	99.02 10	97.82 289	99.71 108	96.11 134	99.48 214	93.04 319	99.65 100	99.69 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 181	97.76 192	98.84 199	99.39 162	98.98 125	99.40 184	99.38 188	96.67 225	99.07 185	99.28 256	92.93 240	98.98 297	97.10 219	96.65 248	98.56 292

ADS-MVSNet2		98.02 183	98.07 152	97.87 283	99.33 172	95.19 306	99.23 235	99.08 263	96.24 260	99.10 179	99.67 126	94.11 218	98.93 306	96.81 242	99.05 133	99.48 133

HQP-MVS		98.02 183	97.90 166	98.37 244	99.19 201	96.83 266	98.98 291	99.39 182	98.24 72	98.66 241	99.40 221	92.47 264	99.64 198	97.19 212	97.58 218	98.64 261

LTVRE_ROB	97.16 12	98.02 183	97.90 166	98.40 242	99.23 194	96.80 269	99.70 44	99.60 35	97.12 189	98.18 274	99.70 111	91.73 284	99.72 175	98.39 117	97.45 230	98.68 234
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		98.01 186	98.05 153	97.87 283	99.15 214	94.76 313	99.42 173	98.93 280	97.12 189	98.84 221	98.59 309	93.74 232	99.80 144	98.55 106	98.17 192	99.06 176

BH-w/o		98.00 187	97.89 170	98.32 247	99.35 168	96.20 287	99.01 285	98.90 287	96.42 246	98.38 262	99.00 281	95.26 158	99.72 175	96.06 263	98.61 159	99.03 178

v1144		97.98 188	97.69 199	98.85 198	98.87 271	98.66 179	99.54 123	99.35 201	96.27 257	99.23 155	99.35 241	94.67 196	99.23 267	96.73 246	95.16 278	98.68 234

EU-MVSNet		97.98 188	98.03 154	97.81 289	98.72 292	96.65 274	99.66 67	99.66 25	98.09 89	98.35 265	99.82 44	95.25 159	98.01 327	97.41 202	95.30 275	98.78 206

tpmvs		97.98 188	98.02 155	97.84 286	99.04 232	94.73 314	99.31 210	99.20 251	96.10 276	98.76 228	99.42 214	94.94 173	99.81 140	96.97 228	98.45 170	98.97 185

view600		97.97 191	97.66 201	98.89 180	99.75 56	97.81 228	99.69 47	98.80 295	98.02 102	99.25 145	98.88 289	91.95 273	99.89 96	94.36 297	98.29 176	98.96 191

view800		97.97 191	97.66 201	98.89 180	99.75 56	97.81 228	99.69 47	98.80 295	98.02 102	99.25 145	98.88 289	91.95 273	99.89 96	94.36 297	98.29 176	98.96 191

conf0.05thres1000		97.97 191	97.66 201	98.89 180	99.75 56	97.81 228	99.69 47	98.80 295	98.02 102	99.25 145	98.88 289	91.95 273	99.89 96	94.36 297	98.29 176	98.96 191

tfpn		97.97 191	97.66 201	98.89 180	99.75 56	97.81 228	99.69 47	98.80 295	98.02 102	99.25 145	98.88 289	91.95 273	99.89 96	94.36 297	98.29 176	98.96 191

NR-MVSNet		97.97 191	97.61 209	99.02 151	98.87 271	99.26 89	99.47 153	99.42 169	97.63 144	97.08 300	99.50 190	95.07 166	99.13 280	97.86 157	93.59 311	98.68 234

v8		97.95 196	97.63 208	98.93 165	98.95 251	98.81 160	99.80 19	99.41 172	96.03 277	99.10 179	99.42 214	94.92 176	99.30 252	96.94 231	94.08 305	98.66 256

Patchmatch-test		97.93 197	97.65 206	98.77 208	99.18 204	97.07 252	99.03 278	99.14 258	96.16 267	98.74 229	99.57 165	94.56 200	99.72 175	93.36 314	99.11 127	99.52 122

PS-CasMVS		97.93 197	97.59 211	98.95 161	98.99 238	99.06 109	99.68 56	99.52 76	97.13 187	98.31 267	99.68 122	92.44 268	99.05 288	98.51 109	94.08 305	98.75 212

TranMVSNet+NR-MVSNet		97.93 197	97.66 201	98.76 210	98.78 284	98.62 184	99.65 77	99.49 105	97.76 131	98.49 257	99.60 156	94.23 212	98.97 304	98.00 147	92.90 317	98.70 224

v144192		97.92 200	97.60 210	98.87 191	98.83 277	98.65 180	99.55 120	99.34 209	96.20 263	99.32 122	99.40 221	94.36 208	99.26 263	96.37 260	95.03 282	98.70 224

ACMH+	97.24 10	97.92 200	97.78 185	98.32 247	99.46 146	96.68 273	99.56 114	99.54 62	98.41 63	97.79 291	99.87 19	90.18 303	99.66 194	98.05 146	97.18 243	98.62 270

LFMVS		97.90 202	97.35 244	99.54 77	99.52 131	99.01 121	99.39 185	98.24 328	97.10 193	99.65 52	99.79 73	84.79 339	99.91 75	99.28 27	98.38 173	99.69 81

OurMVSNet-221017-0		97.88 203	97.77 189	98.19 265	98.71 294	96.53 276	99.88 1	99.00 273	97.79 128	98.78 226	99.94 3	91.68 285	99.35 239	97.21 210	96.99 246	98.69 229

v7n		97.87 204	97.52 214	98.92 170	98.76 288	98.58 188	99.84 9	99.46 140	96.20 263	98.91 210	99.70 111	94.89 179	99.44 222	96.03 264	93.89 309	98.75 212

thres600view7		97.86 205	97.51 216	98.92 170	99.72 76	97.95 221	99.59 94	98.74 303	97.94 111	99.27 137	98.62 304	91.75 280	99.86 108	93.73 310	98.19 185	98.96 191

v10		97.85 206	97.52 214	98.86 195	98.99 238	98.67 177	99.75 35	99.41 172	95.70 283	98.98 203	99.41 217	94.75 192	99.23 267	96.01 265	94.63 295	98.67 245

GA-MVS		97.85 206	97.47 223	99.00 154	99.38 163	97.99 217	98.57 327	99.15 256	97.04 203	98.90 212	99.30 253	89.83 305	99.38 228	96.70 248	98.33 174	99.62 104

tfpnnormal		97.84 208	97.47 223	98.98 156	99.20 199	99.22 93	99.64 79	99.61 32	96.32 252	98.27 270	99.70 111	93.35 235	99.44 222	95.69 271	95.40 273	98.27 307

VPNet		97.84 208	97.44 232	99.01 152	99.21 197	98.94 136	99.48 149	99.57 44	98.38 64	99.28 133	99.73 103	88.89 313	99.39 227	99.19 34	93.27 314	98.71 219

LCM-MVSNet-Re		97.83 210	98.15 143	96.87 311	99.30 181	92.25 333	99.59 94	98.26 327	97.43 162	96.20 309	99.13 270	96.27 130	98.73 311	98.17 132	98.99 137	99.64 98

XVG-ACMP-BASELINE		97.83 210	97.71 198	98.20 264	99.11 219	96.33 283	99.41 177	99.52 76	98.06 97	99.05 190	99.50 190	89.64 307	99.73 171	97.73 171	97.38 236	98.53 293

IterMVS		97.83 210	97.77 189	98.02 273	99.58 122	96.27 285	99.02 281	99.48 115	97.22 181	98.71 232	99.70 111	92.75 245	99.13 280	97.46 198	96.00 263	98.67 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS		97.82 213	97.65 206	98.35 245	98.88 268	95.98 289	99.49 144	94.71 355	97.57 149	99.26 141	99.48 199	92.46 267	99.71 181	97.87 156	99.08 131	99.35 152

tfpn111		97.81 214	97.49 220	98.78 207	99.72 76	97.86 224	99.59 94	98.74 303	97.93 112	99.26 141	98.62 304	91.75 280	99.86 108	93.57 311	98.18 186	98.61 279

MVP-Stereo		97.81 214	97.75 195	97.99 276	97.53 325	96.60 275	98.96 296	98.85 291	97.22 181	97.23 297	99.36 237	95.28 155	99.46 216	95.51 275	99.78 75	97.92 322
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 214	97.44 232	98.91 174	98.88 268	98.68 176	99.51 131	99.34 209	96.18 265	99.20 162	99.34 244	94.03 221	99.36 236	95.32 280	95.18 277	98.69 229

v1921920		97.80 217	97.45 226	98.84 199	98.80 278	98.53 191	99.52 127	99.34 209	96.15 269	99.24 150	99.47 203	93.98 222	99.29 254	95.40 278	95.13 280	98.69 229

V4		97.80 217	97.51 216	98.67 217	98.79 280	98.63 182	99.87 4	99.44 160	95.87 280	99.01 194	99.46 207	94.52 203	99.33 243	96.64 254	93.97 307	98.05 313

v148		97.79 219	97.55 212	98.50 229	98.74 289	97.72 236	99.54 123	99.33 217	96.26 258	98.90 212	99.51 188	94.68 195	99.14 277	97.83 159	93.15 316	98.63 268

v52		97.79 219	97.50 218	98.66 218	98.80 278	98.62 184	99.87 4	99.44 160	95.87 280	99.01 194	99.46 207	94.44 207	99.33 243	96.65 253	93.96 308	98.05 313

conf200view11		97.78 221	97.45 226	98.77 208	99.72 76	97.86 224	99.59 94	98.74 303	97.93 112	99.26 141	98.62 304	91.75 280	99.83 127	93.22 315	98.18 186	98.61 279

thres400		97.77 222	97.38 240	98.92 170	99.69 89	97.96 219	99.50 136	98.73 312	97.83 123	99.17 169	98.45 313	91.67 286	99.83 127	93.22 315	98.18 186	98.96 191

thres100view900		97.76 223	97.45 226	98.69 214	99.72 76	97.86 224	99.59 94	98.74 303	97.93 112	99.26 141	98.62 304	91.75 280	99.83 127	93.22 315	98.18 186	98.37 304

PEN-MVS		97.76 223	97.44 232	98.72 212	98.77 287	98.54 190	99.78 22	99.51 85	97.06 202	98.29 269	99.64 140	92.63 259	98.89 307	98.09 138	93.16 315	98.72 217

Baseline_NR-MVSNet		97.76 223	97.45 226	98.68 215	99.09 224	98.29 205	99.41 177	98.85 291	95.65 284	98.63 249	99.67 126	94.82 183	99.10 285	98.07 144	92.89 318	98.64 261

TR-MVS		97.76 223	97.41 237	98.82 201	99.06 228	97.87 223	98.87 308	98.56 321	96.63 228	98.68 240	99.22 264	92.49 263	99.65 196	95.40 278	97.79 210	98.95 198

Patchmtry		97.75 227	97.40 238	98.81 202	99.10 222	98.87 144	99.11 261	99.33 217	94.83 292	98.81 223	99.38 226	94.33 209	99.02 292	96.10 262	95.57 271	98.53 293

dp		97.75 227	97.80 182	97.59 297	99.10 222	93.71 324	99.32 207	98.88 289	96.48 242	99.08 184	99.55 170	92.67 258	99.82 136	96.52 255	98.58 162	99.24 159

TAPA-MVS	97.07 15	97.74 229	97.34 247	98.94 162	99.70 87	97.53 237	99.25 232	99.51 85	91.90 329	99.30 125	99.63 144	98.78 39	99.64 198	88.09 336	99.87 39	99.65 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 230	97.35 244	98.88 187	99.47 144	97.12 247	99.34 205	98.85 291	98.19 76	99.67 44	99.85 26	82.98 343	99.92 65	99.49 12	98.32 175	99.60 106

MIMVSNet		97.73 230	97.45 226	98.57 223	99.45 150	97.50 238	99.02 281	98.98 275	96.11 272	99.41 101	99.14 269	90.28 299	98.74 310	95.74 269	98.93 143	99.47 137

tfpn200view9		97.72 232	97.38 240	98.72 212	99.69 89	97.96 219	99.50 136	98.73 312	97.83 123	99.17 169	98.45 313	91.67 286	99.83 127	93.22 315	98.18 186	98.37 304

CostFormer		97.72 232	97.73 196	97.71 294	99.15 214	94.02 320	99.54 123	99.02 272	94.67 295	99.04 191	99.35 241	92.35 270	99.77 157	98.50 110	97.94 207	99.34 153

FMVSNet2		97.72 232	97.36 242	98.80 204	99.51 133	98.84 148	99.45 157	99.42 169	96.49 236	98.86 220	99.29 255	90.26 300	98.98 297	96.44 257	96.56 251	98.58 290

test0.0.03 1		97.71 235	97.42 236	98.56 225	98.41 314	97.82 227	98.78 314	98.63 317	97.34 169	98.05 282	98.98 285	94.45 205	98.98 297	95.04 284	97.15 244	98.89 199

v1240		97.69 236	97.32 250	98.79 205	98.85 275	98.43 201	99.48 149	99.36 197	96.11 272	99.27 137	99.36 237	93.76 230	99.24 266	94.46 294	95.23 276	98.70 224

cascas		97.69 236	97.43 235	98.48 232	98.60 306	97.30 239	98.18 341	99.39 182	92.96 322	98.41 260	98.78 300	93.77 229	99.27 258	98.16 133	98.61 159	98.86 200

pm-mvs1		97.68 238	97.28 254	98.88 187	99.06 228	98.62 184	99.50 136	99.45 152	96.32 252	97.87 287	99.79 73	92.47 264	99.35 239	97.54 189	93.54 312	98.67 245

GBi-Net		97.68 238	97.48 221	98.29 250	99.51 133	97.26 242	99.43 166	99.48 115	96.49 236	99.07 185	99.32 250	90.26 300	98.98 297	97.10 219	96.65 248	98.62 270

test1		97.68 238	97.48 221	98.29 250	99.51 133	97.26 242	99.43 166	99.48 115	96.49 236	99.07 185	99.32 250	90.26 300	98.98 297	97.10 219	96.65 248	98.62 270

tpm		97.67 241	97.55 212	98.03 271	99.02 235	95.01 309	99.43 166	98.54 323	96.44 244	99.12 174	99.34 244	91.83 279	99.60 206	97.75 169	96.46 253	99.48 133

PCF-MVS	97.08 14	97.66 242	97.06 262	99.47 93	99.61 117	99.09 106	98.04 343	99.25 246	91.24 332	98.51 255	99.70 111	94.55 201	99.91 75	92.76 322	99.85 53	99.42 146
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
our_test_3		97.65 243	97.68 200	97.55 298	98.62 303	94.97 310	98.84 310	99.30 226	96.83 217	98.19 273	99.34 244	97.01 107	99.02 292	95.00 285	96.01 262	98.64 261

testgi		97.65 243	97.50 218	98.13 268	99.36 167	96.45 279	99.42 173	99.48 115	97.76 131	97.87 287	99.45 210	91.09 293	98.81 309	94.53 292	98.52 167	99.13 165

thres200		97.61 245	97.28 254	98.62 219	99.64 106	98.03 215	99.26 230	98.74 303	97.68 141	99.09 183	98.32 315	91.66 288	99.81 140	92.88 321	98.22 182	98.03 316

PAPM		97.59 246	97.09 261	99.07 146	99.06 228	98.26 207	98.30 337	99.10 261	94.88 291	98.08 278	99.34 244	96.27 130	99.64 198	89.87 330	98.92 145	99.31 155

VDDNet		97.55 247	97.02 263	99.16 137	99.49 140	98.12 214	99.38 190	99.30 226	95.35 287	99.68 38	99.90 7	82.62 345	99.93 57	99.31 25	98.13 194	99.42 146

TESTMET0.1,1		97.55 247	97.27 256	98.40 242	98.93 259	96.53 276	98.67 321	97.61 346	96.96 207	98.64 248	99.28 256	88.63 319	99.45 217	97.30 207	99.38 111	99.21 160

DWT-MVSNet_test		97.53 249	97.40 238	97.93 279	99.03 234	94.86 311	99.57 107	98.63 317	96.59 233	98.36 264	98.79 298	89.32 309	99.74 163	98.14 135	98.16 193	99.20 161

pmmvs5		97.52 250	97.30 252	98.16 267	98.57 308	96.73 270	99.27 222	98.90 287	96.14 270	98.37 263	99.53 180	91.54 290	99.14 277	97.51 192	95.87 265	98.63 268

v748		97.52 250	97.23 257	98.41 241	98.69 296	97.23 245	99.87 4	99.45 152	95.72 282	98.51 255	99.53 180	94.13 217	99.30 252	96.78 244	92.39 323	98.70 224

LF4IMVS		97.52 250	97.46 225	97.70 295	98.98 242	95.55 295	99.29 216	98.82 294	98.07 93	98.66 241	99.64 140	89.97 304	99.61 205	97.01 224	96.68 247	97.94 320

DTE-MVSNet		97.51 253	97.19 259	98.46 235	98.63 302	98.13 213	99.84 9	99.48 115	96.68 224	97.97 285	99.67 126	92.92 241	98.56 313	96.88 241	92.60 322	98.70 224

SixPastTwentyTwo		97.50 254	97.33 249	98.03 271	98.65 300	96.23 286	99.77 25	98.68 315	97.14 186	97.90 286	99.93 4	90.45 298	99.18 276	97.00 225	96.43 254	98.67 245

JIA-IIPM		97.50 254	97.02 263	98.93 165	98.73 290	97.80 232	99.30 212	98.97 276	91.73 330	98.91 210	94.86 346	95.10 165	99.71 181	97.58 183	97.98 206	99.28 157

ppachtmachnet_test		97.49 256	97.45 226	97.61 296	98.62 303	95.24 303	98.80 312	99.46 140	96.11 272	98.22 271	99.62 149	96.45 124	98.97 304	93.77 309	95.97 264	98.61 279

test-mter		97.49 256	97.13 260	98.55 227	98.79 280	97.10 248	98.67 321	97.75 336	96.65 226	98.61 252	98.85 293	88.23 324	99.45 217	97.25 208	99.38 111	99.10 166

DI_MVS_plusplus_test		97.45 258	96.79 267	99.44 100	97.76 323	99.04 111	99.21 242	98.61 319	97.74 134	94.01 325	98.83 295	87.38 330	99.83 127	98.63 91	98.90 147	99.44 143

test_normal		97.44 259	96.77 269	99.44 100	97.75 324	99.00 123	99.10 263	98.64 316	97.71 137	93.93 328	98.82 296	87.39 329	99.83 127	98.61 95	98.97 139	99.49 131

tpm2		97.44 259	97.34 247	97.74 293	99.15 214	94.36 317	99.45 157	98.94 279	93.45 320	98.90 212	99.44 211	91.35 291	99.59 208	97.31 206	98.07 203	99.29 156

tpm cat1		97.39 261	97.36 242	97.50 301	99.17 209	93.73 322	99.43 166	99.31 224	91.27 331	98.71 232	99.08 274	94.31 211	99.77 157	96.41 259	98.50 168	99.00 181

tpmp4_e23		97.34 262	97.29 253	97.52 299	99.25 193	93.73 322	99.58 101	99.19 254	94.00 311	98.20 272	99.41 217	90.74 297	99.74 163	97.13 218	98.07 203	99.07 175

USDC		97.34 262	97.20 258	97.75 292	99.07 226	95.20 305	98.51 330	99.04 270	97.99 107	98.31 267	99.86 22	89.02 311	99.55 211	95.67 273	97.36 237	98.49 295

MVS		97.28 264	96.55 271	99.48 90	98.78 284	98.95 133	99.27 222	99.39 182	83.53 345	98.08 278	99.54 173	96.97 108	99.87 105	94.23 305	99.16 124	99.63 102

DSMNet-mixed		97.25 265	97.35 244	96.95 309	97.84 321	93.61 326	99.57 107	96.63 351	96.13 271	98.87 215	98.61 308	94.59 199	97.70 335	95.08 283	98.86 150	99.55 114

MS-PatchMatch		97.24 266	97.32 250	96.99 307	98.45 313	93.51 327	98.82 311	99.32 223	97.41 165	98.13 276	99.30 253	88.99 312	99.56 209	95.68 272	99.80 71	97.90 323

TransMVSNet (Re)		97.15 267	96.58 270	98.86 195	99.12 217	98.85 147	99.49 144	98.91 285	95.48 286	97.16 299	99.80 65	93.38 234	99.11 283	94.16 307	91.73 324	98.62 270

TinyColmap		97.12 268	96.89 265	97.83 287	99.07 226	95.52 298	98.57 327	98.74 303	97.58 148	97.81 290	99.79 73	88.16 325	99.56 209	95.10 282	97.21 241	98.39 303

K. test v3		97.10 269	96.79 267	98.01 274	98.72 292	96.33 283	99.87 4	97.05 350	97.59 146	96.16 310	99.80 65	88.71 315	99.04 289	96.69 249	96.55 252	98.65 259

LP		97.04 270	96.80 266	97.77 291	98.90 264	95.23 304	98.97 294	99.06 268	94.02 310	98.09 277	99.41 217	93.88 225	98.82 308	90.46 328	98.42 172	99.26 158

PatchT		97.03 271	96.44 272	98.79 205	98.99 238	98.34 204	99.16 248	99.07 266	92.13 326	99.52 81	97.31 339	94.54 202	98.98 297	88.54 334	98.73 158	99.03 178

FMVSNet1		96.84 272	96.36 273	98.29 250	99.32 179	97.26 242	99.43 166	99.48 115	95.11 289	98.55 254	99.32 250	83.95 342	98.98 297	95.81 268	96.26 258	98.62 270

test_0402		96.64 273	96.24 274	97.85 285	98.85 275	96.43 280	99.44 161	99.26 244	93.52 317	96.98 303	99.52 185	88.52 320	99.20 275	92.58 324	97.50 225	97.93 321

RPMNet		96.61 274	95.85 282	98.87 191	99.18 204	98.49 197	99.22 239	99.08 263	88.72 341	99.56 69	97.38 337	94.08 220	99.00 295	86.87 341	98.58 162	99.14 163

X-MVStestdata		96.55 275	95.45 296	99.87 6	99.85 23	99.83 8	99.69 47	99.68 19	98.98 19	99.37 110	64.01 361	98.81 36	99.94 42	98.79 74	99.86 49	99.84 12

pmmvs6		96.53 276	96.09 277	97.82 288	98.69 296	95.47 299	99.37 192	99.47 131	93.46 319	97.41 294	99.78 79	87.06 331	99.33 243	96.92 233	92.70 321	98.65 259

UnsupCasMVSNet_eth		96.44 277	96.12 276	97.40 303	98.65 300	95.65 292	99.36 198	99.51 85	97.13 187	96.04 313	98.99 282	88.40 322	98.17 316	96.71 247	90.27 327	98.40 302

FMVSNet5		96.43 278	96.19 275	97.15 304	99.11 219	95.89 291	99.32 207	99.52 76	94.47 304	98.34 266	99.07 275	87.54 328	97.07 338	92.61 323	95.72 268	98.47 297

v18		96.42 279	95.80 286	98.26 253	98.95 251	98.82 158	99.76 28	99.28 238	94.58 297	94.12 320	97.70 324	95.22 161	98.16 317	94.83 288	87.80 334	97.79 331

v17		96.42 279	95.81 284	98.25 257	98.94 254	98.80 165	99.76 28	99.28 238	94.57 298	94.18 319	97.71 323	95.23 160	98.16 317	94.86 286	87.73 336	97.80 326

v16		96.39 281	95.76 287	98.26 253	98.96 249	98.81 160	99.76 28	99.28 238	94.57 298	94.10 321	97.70 324	95.04 167	98.16 317	94.70 290	87.77 335	97.80 326

new_pmnet		96.38 282	96.03 278	97.41 302	98.13 319	95.16 308	99.05 272	99.20 251	93.94 312	97.39 295	98.79 298	91.61 289	99.04 289	90.43 329	95.77 267	98.05 313

v15		96.28 283	95.62 289	98.25 257	98.94 254	98.83 151	99.76 28	99.29 231	94.52 302	94.02 324	97.61 331	95.02 168	98.13 321	94.53 292	86.92 339	97.80 326

V14		96.26 284	95.60 290	98.26 253	98.94 254	98.83 151	99.76 28	99.29 231	94.49 303	93.96 326	97.66 327	94.99 171	98.13 321	94.41 295	86.90 340	97.80 326

V9		96.25 285	95.58 291	98.26 253	98.94 254	98.83 151	99.75 35	99.29 231	94.45 305	93.96 326	97.62 330	94.94 173	98.14 320	94.40 296	86.87 341	97.81 324

v13		96.24 286	95.58 291	98.25 257	98.98 242	98.83 151	99.75 35	99.29 231	94.35 307	93.89 329	97.60 332	95.17 163	98.11 323	94.27 304	86.86 342	97.81 324

v12		96.24 286	95.58 291	98.23 260	98.96 249	98.81 160	99.76 28	99.29 231	94.42 306	93.85 330	97.60 332	95.12 164	98.09 324	94.32 301	86.85 343	97.80 326

v11		96.23 288	95.57 294	98.21 263	98.93 259	98.83 151	99.72 41	99.29 231	94.29 308	94.05 323	97.64 329	94.88 180	98.04 325	92.89 320	88.43 332	97.77 332

Anonymous20231206		96.22 289	96.03 278	96.79 313	97.31 330	94.14 319	99.63 81	99.08 263	96.17 266	97.04 301	99.06 277	93.94 223	97.76 334	86.96 340	95.06 281	98.47 297

IB-MVS	95.67 18	96.22 289	95.44 297	98.57 223	99.21 197	96.70 271	98.65 324	97.74 338	96.71 222	97.27 296	98.54 311	86.03 333	99.92 65	98.47 113	86.30 344	99.10 166
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		96.17 291	95.32 298	98.73 211	98.79 280	98.14 212	99.38 190	94.09 356	91.07 334	98.07 281	91.04 352	89.62 308	99.35 239	96.75 245	99.09 130	98.68 234

test20.03		96.12 292	95.96 281	96.63 314	97.44 326	95.45 300	99.51 131	99.38 188	96.55 234	96.16 310	99.25 260	93.76 230	96.17 343	87.35 339	94.22 302	98.27 307

PVSNet_0	94.43 19	96.09 293	95.47 295	97.94 278	99.31 180	94.34 318	97.81 344	99.70 15	97.12 189	97.46 293	98.75 301	89.71 306	99.79 147	97.69 177	81.69 348	99.68 85

EG-PatchMatch MVS		95.97 294	95.69 288	96.81 312	97.78 322	92.79 330	99.16 248	98.93 280	96.16 267	94.08 322	99.22 264	82.72 344	99.47 215	95.67 273	97.50 225	98.17 310

Patchmatch-RL test		95.84 295	95.81 284	95.95 318	95.61 335	90.57 336	98.24 338	98.39 324	95.10 290	95.20 315	98.67 303	94.78 186	97.77 333	96.28 261	90.02 328	99.51 127

MVS-HIRNet		95.75 296	95.16 300	97.51 300	99.30 181	93.69 325	98.88 307	95.78 352	85.09 344	98.78 226	92.65 348	91.29 292	99.37 232	94.85 287	99.85 53	99.46 140

testpf		95.66 297	96.02 280	94.58 321	98.35 315	92.32 332	97.25 349	97.91 335	92.83 323	97.03 302	98.99 282	88.69 316	98.61 312	95.72 270	97.40 234	92.80 348

MIMVSNet1		95.51 298	95.04 301	96.92 310	97.38 327	95.60 293	99.52 127	99.50 99	93.65 315	96.97 304	99.17 267	85.28 337	96.56 342	88.36 335	95.55 272	98.60 286

MDA-MVSNet_test_wron		95.45 299	94.60 304	98.01 274	98.16 318	97.21 246	99.11 261	99.24 247	93.49 318	80.73 350	98.98 285	93.02 238	98.18 315	94.22 306	94.45 298	98.64 261

TDRefinement		95.42 300	94.57 305	97.97 277	89.83 351	96.11 288	99.48 149	98.75 300	96.74 220	96.68 305	99.88 14	88.65 318	99.71 181	98.37 120	82.74 347	98.09 311

YYNet1		95.36 301	94.51 306	97.92 280	97.89 320	97.10 248	99.10 263	99.23 248	93.26 321	80.77 349	99.04 279	92.81 244	98.02 326	94.30 302	94.18 303	98.64 261

pmmvs-eth3d		95.34 302	94.73 303	97.15 304	95.53 337	95.94 290	99.35 202	99.10 261	95.13 288	93.55 331	97.54 335	88.15 326	97.91 329	94.58 291	89.69 330	97.61 335

Test4		95.05 303	93.67 311	99.22 133	96.07 334	98.94 136	99.20 244	99.27 243	97.71 137	89.96 343	97.59 334	66.18 351	99.25 264	98.06 145	98.96 140	99.47 137

MDA-MVSNet-bldmvs		94.96 304	93.98 309	97.92 280	98.24 317	97.27 241	99.15 251	99.33 217	93.80 314	80.09 351	99.03 280	88.31 323	97.86 331	93.49 313	94.36 299	98.62 270

N_pmnet		94.95 305	95.83 283	92.31 329	98.47 312	79.33 352	99.12 255	92.81 361	93.87 313	97.68 292	99.13 270	93.87 226	99.01 294	91.38 326	96.19 259	98.59 287

testus		94.61 306	95.30 299	92.54 328	96.44 333	84.18 344	98.36 333	99.03 271	94.18 309	96.49 306	98.57 310	88.74 314	95.09 347	87.41 338	98.45 170	98.36 306

new-patchmatchnet		94.48 307	94.08 308	95.67 319	95.08 339	92.41 331	99.18 246	99.28 238	94.55 301	93.49 332	97.37 338	87.86 327	97.01 339	91.57 325	88.36 333	97.61 335

testing_2		94.44 308	92.93 314	98.98 156	94.16 342	99.00 123	99.42 173	99.28 238	96.60 231	84.86 345	96.84 340	70.91 348	99.27 258	98.23 129	96.08 261	98.68 234

OpenMVS_ROB	92.34 20	94.38 309	93.70 310	96.41 317	97.38 327	93.17 328	99.06 270	98.75 300	86.58 342	94.84 318	98.26 317	81.53 346	99.32 246	89.01 333	97.87 209	96.76 339

CMPMVS	69.68 23	94.13 310	94.90 302	91.84 330	97.24 331	80.01 351	98.52 329	99.48 115	89.01 339	91.99 337	99.67 126	85.67 335	99.13 280	95.44 276	97.03 245	96.39 341
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 311	93.25 313	96.60 315	94.76 340	94.49 315	98.92 303	98.18 331	89.66 336	96.48 307	98.06 318	86.28 332	97.33 337	89.68 331	87.20 338	97.97 319

test2356		94.07 312	94.46 307	92.89 326	95.18 338	86.13 342	97.60 347	99.06 268	93.61 316	96.15 312	98.28 316	85.60 336	93.95 349	86.68 342	98.00 205	98.59 287

UnsupCasMVSNet_bld		93.53 313	92.51 315	96.58 316	97.38 327	93.82 321	98.24 338	99.48 115	91.10 333	93.10 333	96.66 341	74.89 347	98.37 314	94.03 308	87.71 337	97.56 337

PM-MVS		92.96 314	92.23 316	95.14 320	95.61 335	89.98 338	99.37 192	98.21 329	94.80 293	95.04 317	97.69 326	65.06 352	97.90 330	94.30 302	89.98 329	97.54 338

test1235678		92.91 315	93.30 312	91.71 332	93.14 345	83.01 346	98.75 317	98.58 320	92.80 324	92.45 335	97.91 320	88.51 321	93.54 350	82.26 346	95.35 274	98.59 287

1111		92.30 316	92.21 317	92.55 327	93.30 343	86.27 340	99.15 251	98.74 303	91.94 327	90.85 340	97.82 321	84.18 340	95.21 345	79.65 348	94.27 301	96.19 342

test12356		91.74 317	92.19 318	90.37 335	91.22 347	82.41 347	98.61 325	98.28 326	90.66 335	91.82 338	97.92 319	84.90 338	92.61 351	81.64 347	94.66 293	96.09 343

Gipuma		90.99 318	90.15 319	93.51 323	98.73 290	90.12 337	93.98 353	99.45 152	79.32 348	92.28 336	94.91 345	69.61 349	97.98 328	87.42 337	95.67 269	92.45 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		90.69 319	89.39 320	94.58 321	94.25 341	88.18 339	99.29 216	99.07 266	82.45 347	92.95 334	97.65 328	63.96 354	97.79 332	89.27 332	85.63 345	97.77 332

testmv		87.91 320	87.80 321	88.24 336	87.68 354	77.50 354	99.07 266	97.66 345	89.27 337	86.47 344	96.22 343	68.35 350	92.49 353	76.63 352	88.82 331	94.72 346

PMMVS2		86.87 321	85.37 324	91.35 334	90.21 350	83.80 345	98.89 306	97.45 348	83.13 346	91.67 339	95.03 344	48.49 358	94.70 348	85.86 343	77.62 349	95.54 344

LCM-MVSNet		86.80 322	85.22 325	91.53 333	87.81 353	80.96 350	98.23 340	98.99 274	71.05 351	90.13 342	96.51 342	48.45 359	96.88 340	90.51 327	85.30 346	96.76 339

FPMVS		84.93 323	85.65 323	82.75 343	86.77 355	63.39 361	98.35 335	98.92 282	74.11 350	83.39 347	98.98 285	50.85 357	92.40 354	84.54 344	94.97 283	92.46 349

.test1245		83.42 324	86.17 322	75.15 346	93.30 343	86.27 340	99.15 251	98.74 303	91.94 327	90.85 340	97.82 321	84.18 340	95.21 345	79.65 348	39.90 358	43.98 359

no-one		83.04 325	80.12 327	91.79 331	89.44 352	85.65 343	99.32 207	98.32 325	89.06 338	79.79 353	89.16 354	44.86 360	96.67 341	84.33 345	46.78 356	93.05 347

tmp_tt		82.80 326	81.52 326	86.66 337	66.61 362	68.44 360	92.79 355	97.92 333	68.96 353	80.04 352	99.85 26	85.77 334	96.15 344	97.86 157	43.89 357	95.39 345

E-PMN		80.61 327	79.88 328	82.81 342	90.75 349	76.38 356	97.69 345	95.76 353	66.44 355	83.52 346	92.25 349	62.54 355	87.16 358	68.53 356	61.40 352	84.89 357

EMVS		80.02 328	79.22 329	82.43 344	91.19 348	76.40 355	97.55 348	92.49 363	66.36 356	83.01 348	91.27 350	64.63 353	85.79 359	65.82 357	60.65 353	85.08 356

PNet_i23d		79.43 329	77.68 330	84.67 339	86.18 356	71.69 359	96.50 351	93.68 357	75.17 349	71.33 354	91.18 351	32.18 363	90.62 355	78.57 351	74.34 350	91.71 352

ANet_high		77.30 330	74.86 332	84.62 340	75.88 360	77.61 353	97.63 346	93.15 360	88.81 340	64.27 356	89.29 353	36.51 361	83.93 360	75.89 353	52.31 355	92.33 351

MVE	76.82 21	76.91 331	74.31 333	84.70 338	85.38 358	76.05 357	96.88 350	93.17 359	67.39 354	71.28 355	89.01 355	21.66 368	87.69 357	71.74 355	72.29 351	90.35 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 332	74.97 331	79.01 345	70.98 361	55.18 362	93.37 354	98.21 329	65.08 357	61.78 358	93.83 347	21.74 367	92.53 352	78.59 350	91.12 326	89.34 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		74.42 333	71.19 334	84.14 341	76.16 359	74.29 358	96.00 352	92.57 362	69.57 352	63.84 357	87.49 356	21.98 365	88.86 356	75.56 354	57.50 354	89.26 355

pcd1.5k->3k		40.85 334	43.49 336	32.93 348	98.95 251	0.00 366	0.00 357	99.53 72	0.00 361	0.00 362	0.27 363	95.32 154	0.00 364	0.00 361	97.30 238	98.80 204

wuyk23d		40.18 335	41.29 338	36.84 347	86.18 356	49.12 363	79.73 356	22.81 365	27.64 358	25.46 361	28.45 362	21.98 365	48.89 361	55.80 358	23.56 361	12.51 361

testmvs		39.17 336	43.78 335	25.37 350	36.04 364	16.84 365	98.36 333	26.56 364	20.06 359	38.51 360	67.32 357	29.64 364	15.30 363	37.59 359	39.90 358	43.98 359

test123		39.01 337	42.50 337	28.53 349	39.17 363	20.91 364	98.75 317	19.17 366	19.83 360	38.57 359	66.67 358	33.16 362	15.42 362	37.50 360	29.66 360	49.26 358

cdsmvs_eth3d_5k		24.64 338	32.85 339	0.00 351	0.00 365	0.00 366	0.00 357	99.51 85	0.00 361	0.00 362	99.56 167	96.58 120	0.00 364	0.00 361	0.00 362	0.00 362

ab-mvs-re		8.30 339	11.06 340	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	99.58 161	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

pcd_1.5k_mvsjas		8.27 340	11.03 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.27 363	99.01 12	0.00 364	0.00 361	0.00 362	0.00 362

sosnet-low-res		0.02 341	0.03 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.27 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

sosnet		0.02 341	0.03 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.27 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uncertanet		0.02 341	0.03 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.27 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

Regformer		0.02 341	0.03 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.27 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uanet		0.02 341	0.03 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.27 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

GSMVS																99.52 122

test_part3							99.37 192		97.97 108		99.78 79		99.95 33	97.15 216

test_part2					99.81 32	99.83 8				99.77 24

test_part1								99.48 115				98.96 21			99.84 58	99.83 23

sam_mvs1												94.86 181				99.52 122

sam_mvs												94.72 194

semantic-postprocess				98.06 270	99.57 124	96.36 282		99.49 105	97.18 183	98.71 232	99.72 107	92.70 251	99.14 277	97.44 200	95.86 266	98.67 245

ambc				93.06 325	92.68 346	82.36 348	98.47 331	98.73 312		95.09 316	97.41 336	55.55 356	99.10 285	96.42 258	91.32 325	97.71 334

MTGPA								99.47 131

test_post1							99.23 235				65.14 360	94.18 216	99.71 181	97.58 183

test_post											65.99 359	94.65 198	99.73 171

patchmatchnet-post											98.70 302	94.79 185	99.74 163

GG-mvs-BLEND				98.45 236	98.55 309	98.16 210	99.43 166	93.68 357		97.23 297	98.46 312	89.30 310	99.22 270	95.43 277	98.22 182	97.98 318

MTMP								98.88 289

gm-plane-assit					98.54 310	92.96 329			94.65 296		99.15 268		99.64 198	97.56 187

test9_res														97.49 194	99.72 86	99.75 56

TEST9					99.67 93	99.65 40	99.05 272	99.41 172	96.22 262	98.95 205	99.49 193	98.77 42	99.91 75

test_8					99.67 93	99.61 45	99.03 278	99.41 172	96.28 255	98.93 208	99.48 199	98.76 44	99.91 75

agg_prior2														97.21 210	99.73 85	99.75 56

agg_prior					99.67 93	99.62 43		99.40 179		98.87 215			99.91 75

TestCases				99.31 113	99.86 20	98.48 199		99.61 32	97.85 120	99.36 114	99.85 26	95.95 136	99.85 114	96.66 251	99.83 64	99.59 110

test_prior4						99.56 52	98.99 287

test_prior2							98.96 296		98.34 66	99.01 194	99.52 185	98.68 52		97.96 149	99.74 82

test_prior				99.68 52	99.67 93	99.48 65		99.56 48					99.83 127			99.74 61

旧先验2							98.96 296		96.70 223	99.47 89			99.94 42	98.19 130

新几何2							99.01 285

新几何1				99.75 40	99.75 56	99.59 49		99.54 62	96.76 219	99.29 129	99.64 140	98.43 64	99.94 42	96.92 233	99.66 98	99.72 72

旧先验1					99.74 67	99.59 49		99.54 62			99.69 117	98.47 61			99.68 96	99.73 66

无先验							98.99 287	99.51 85	96.89 213				99.93 57	97.53 190		99.72 72

原ACMM2							98.95 300

原ACMM1				99.65 59	99.73 72	99.33 80		99.47 131	97.46 158	99.12 174	99.66 132	98.67 54	99.91 75	97.70 176	99.69 93	99.71 79

test222					99.75 56	99.49 64	98.91 305	99.49 105	96.42 246	99.34 120	99.65 133	98.28 74			99.69 93	99.72 72

testdata2													99.95 33	96.67 250

segment_acmp												98.96 21

testdata				99.54 77	99.75 56	98.95 133		99.51 85	97.07 200	99.43 96	99.70 111	98.87 31	99.94 42	97.76 167	99.64 101	99.72 72

testdata1							98.85 309		98.32 69

test12				99.75 40	99.64 106	99.61 45		99.29 231		99.21 159		98.38 68	99.89 96		99.74 82	99.74 61

plane_prior7					99.29 184	97.03 256

plane_prior6					99.27 189	96.98 260						92.71 249

plane_prior5								99.47 131					99.69 190	97.78 164	97.63 213	98.67 245

plane_prior4											99.61 153

plane_prior3						97.00 258			98.69 46	99.11 176

plane_prior2							99.39 185		98.97 22

plane_prior1					99.26 191

plane_prior						96.97 261	99.21 242		98.45 59						97.60 216

n2								0.00 367

nn								0.00 367

door-mid								98.05 332

lessismore_v0				97.79 290	98.69 296	95.44 301		94.75 354		95.71 314	99.87 19	88.69 316	99.32 246	95.89 266	94.93 286	98.62 270

LGP-MVS_train				98.49 230	99.33 172	97.05 254		99.55 55	97.46 158	99.24 150	99.83 37	92.58 260	99.72 175	98.09 138	97.51 223	98.68 234

test11								99.35 201

door								97.92 333

HQP5-MVS						96.83 266

HQP-NCC					99.19 201		98.98 291		98.24 72	98.66 241

ACMP_Plane					99.19 201		98.98 291		98.24 72	98.66 241

BP-MVS														97.19 212

HQP4-MVS										98.66 241			99.64 198			98.64 261

HQP3-MVS								99.39 182							97.58 218

HQP2-MVS												92.47 264

NP-MVS					99.23 194	96.92 264					99.40 221

MDTV_nov1_ep13_2view						95.18 307	99.35 202		96.84 216	99.58 65		95.19 162		97.82 160		99.46 140

MDTV_nov1_ep13			98.32 136		99.11 219	94.44 316	99.27 222	98.74 303	97.51 155	99.40 105	99.62 149	94.78 186	99.76 161	97.59 182	98.81 155

ACMMP++_ref															97.19 242

ACMMP++															97.43 233

Test By Simon												98.75 47

ITE_SJBPF				98.08 269	99.29 184	96.37 281		98.92 282	98.34 66	98.83 222	99.75 94	91.09 293	99.62 204	95.82 267	97.40 234	98.25 309

DeepMVS_CX				93.34 324	99.29 184	82.27 349		99.22 249	85.15 343	96.33 308	99.05 278	90.97 295	99.73 171	93.57 311	97.77 211	98.01 317

High-resolution multi-view benchmark