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		99.02 1	98.84 1	99.55 3	99.57 27	98.96 5	99.39 5	98.93 36	97.38 18	99.41 4	99.54 1	96.66 6	99.84 46	98.86 2	99.85 2	99.87 1

SteuartSystems-ACMMP		98.90 3	98.75 2	99.36 14	99.22 76	98.43 19	99.10 53	98.87 51	97.38 18	99.35 7	99.40 8	97.78 1	99.87 38	97.77 41	99.85 2	99.78 8
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		98.64 11	98.68 3	98.53 78	99.33 47	98.36 25	98.90 79	98.85 55	97.28 22	99.72 1	99.39 9	96.63 8	97.60 310	98.17 23	99.85 2	99.64 56

ESAPD		98.92 2	98.67 4	99.65 1	99.58 26	99.20 1	98.42 182	98.91 42	97.58 7	99.54 3	99.46 6	97.10 2	99.94 3	97.64 48	99.84 7	99.83 2

TSAR-MVS + MP.		98.78 4	98.62 5	99.24 28	99.69 18	98.28 31	99.14 45	98.66 112	96.84 44	99.56 2	99.31 23	96.34 12	99.70 96	98.32 20	99.73 36	99.73 29

MSLP-MVS++		98.56 23	98.57 6	98.55 74	99.26 68	96.80 87	98.71 133	99.05 23	97.28 22	98.84 31	99.28 28	96.47 11	99.40 142	98.52 14	99.70 39	99.47 80

CNVR-MVS		98.78 4	98.56 7	99.45 10	99.32 50	98.87 8	98.47 174	98.81 65	97.72 4	98.76 37	99.16 45	97.05 3	99.78 79	98.06 26	99.66 44	99.69 38

Regformer-4		98.64 11	98.53 8	98.99 50	99.43 40	97.37 67	98.40 184	98.79 74	97.46 13	99.09 17	99.31 23	95.86 33	99.80 62	98.64 4	99.76 25	99.79 5

HSP-MVS		98.70 6	98.52 9	99.24 28	99.75 3	98.23 32	99.26 18	98.58 125	97.52 8	99.41 4	98.78 91	96.00 25	99.79 74	97.79 40	99.59 55	99.69 38

Regformer-2		98.69 8	98.52 9	99.19 31	99.35 42	98.01 45	98.37 186	98.81 65	97.48 12	99.21 13	99.21 35	96.13 18	99.80 62	98.40 18	99.73 36	99.75 22

Regformer-1		98.66 9	98.51 11	99.12 43	99.35 42	97.81 54	98.37 186	98.76 80	97.49 11	99.20 14	99.21 35	96.08 21	99.79 74	98.42 16	99.73 36	99.75 22

Regformer-3		98.59 17	98.50 12	98.86 60	99.43 40	97.05 78	98.40 184	98.68 102	97.43 14	99.06 18	99.31 23	95.80 34	99.77 84	98.62 6	99.76 25	99.78 8

XVS		98.70 6	98.49 13	99.34 15	99.70 16	98.35 26	99.29 14	98.88 48	97.40 15	98.46 50	99.20 38	95.90 31	99.89 29	97.85 36	99.74 34	99.78 8

DeepPCF-MVS	96.37 2	97.93 51	98.48 14	96.30 244	99.00 93	89.54 308	97.43 275	98.87 51	98.16 2	99.26 10	99.38 13	96.12 19	99.64 107	98.30 21	99.77 19	99.72 32

HFP-MVS		98.63 13	98.40 15	99.32 19	99.72 11	98.29 29	99.23 23	98.96 31	96.10 67	98.94 25	99.17 42	96.06 22	99.92 15	97.62 49	99.78 16	99.75 22

EI-MVSNet-Vis-set		98.47 30	98.39 16	98.69 65	99.46 37	96.49 104	98.30 197	98.69 99	97.21 29	98.84 31	99.36 18	95.41 41	99.78 79	98.62 6	99.65 45	99.80 4

region2R		98.61 14	98.38 17	99.29 21	99.74 7	98.16 38	99.23 23	98.93 36	96.15 62	98.94 25	99.17 42	95.91 30	99.94 3	97.55 54	99.79 12	99.78 8

MCST-MVS		98.65 10	98.37 18	99.48 7	99.60 25	98.87 8	98.41 183	98.68 102	97.04 39	98.52 49	98.80 89	96.78 5	99.83 47	97.93 30	99.61 51	99.74 27

ACMMPR		98.59 17	98.36 19	99.29 21	99.74 7	98.15 39	99.23 23	98.95 33	96.10 67	98.93 29	99.19 41	95.70 35	99.94 3	97.62 49	99.79 12	99.78 8

CP-MVS		98.57 22	98.36 19	99.19 31	99.66 20	97.86 50	99.34 11	98.87 51	95.96 70	98.60 46	99.13 47	96.05 24	99.94 3	97.77 41	99.86 1	99.77 15

NCCC		98.61 14	98.35 21	99.38 12	99.28 65	98.61 13	98.45 175	98.76 80	97.82 3	98.45 54	98.93 78	96.65 7	99.83 47	97.38 61	99.41 79	99.71 35

EI-MVSNet-UG-set		98.41 33	98.34 22	98.61 70	99.45 38	96.32 111	98.28 199	98.68 102	97.17 32	98.74 38	99.37 14	95.25 48	99.79 74	98.57 8	99.54 67	99.73 29

MVS_111021_HR		98.47 30	98.34 22	98.88 59	99.22 76	97.32 68	97.91 239	99.58 3	97.20 30	98.33 59	99.00 67	95.99 26	99.64 107	98.05 28	99.76 25	99.69 38

DeepC-MVS_fast	96.70 1	98.55 24	98.34 22	99.18 35	99.25 69	98.04 43	98.50 171	98.78 76	97.72 4	98.92 30	99.28 28	95.27 46	99.82 53	97.55 54	99.77 19	99.69 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS_3200maxsize		98.53 27	98.33 25	99.15 40	99.50 32	97.92 49	99.15 44	98.81 65	96.24 60	99.20 14	99.37 14	95.30 45	99.80 62	97.73 43	99.67 41	99.72 32

ACMMP_Plus		98.61 14	98.30 26	99.55 3	99.62 24	98.95 6	98.82 102	98.81 65	95.80 74	99.16 16	99.47 5	95.37 42	99.92 15	97.89 34	99.75 31	99.79 5

MTAPA		98.58 19	98.29 27	99.46 8	99.76 1	98.64 11	98.90 79	98.74 84	97.27 26	98.02 70	99.39 9	94.81 57	99.96 1	97.91 31	99.79 12	99.77 15

#test#		98.54 26	98.27 28	99.32 19	99.72 11	98.29 29	98.98 71	98.96 31	95.65 81	98.94 25	99.17 42	96.06 22	99.92 15	97.21 64	99.78 16	99.75 22

mPP-MVS		98.51 28	98.26 29	99.25 27	99.75 3	98.04 43	99.28 16	98.81 65	96.24 60	98.35 58	99.23 32	95.46 40	99.94 3	97.42 59	99.81 9	99.77 15

SMA-MVS		98.58 19	98.25 30	99.56 2	99.51 30	99.04 4	98.95 74	98.80 72	93.67 182	99.37 6	99.52 3	96.52 10	99.89 29	98.06 26	99.81 9	99.76 21

zzz-MVS		98.55 24	98.25 30	99.46 8	99.76 1	98.64 11	98.55 163	98.74 84	97.27 26	98.02 70	99.39 9	94.81 57	99.96 1	97.91 31	99.79 12	99.77 15

HPM-MVS++		98.58 19	98.25 30	99.55 3	99.50 32	99.08 3	98.72 132	98.66 112	97.51 9	98.15 61	98.83 86	95.70 35	99.92 15	97.53 56	99.67 41	99.66 51

TSAR-MVS + GP.		98.38 35	98.24 33	98.81 61	99.22 76	97.25 73	98.11 220	98.29 176	97.19 31	98.99 24	99.02 62	96.22 13	99.67 103	98.52 14	98.56 113	99.51 72

PGM-MVS		98.49 29	98.23 34	99.27 26	99.72 11	98.08 42	98.99 68	99.49 5	95.43 90	99.03 19	99.32 22	95.56 37	99.94 3	96.80 86	99.77 19	99.78 8

MVS_111021_LR		98.34 39	98.23 34	98.67 67	99.27 66	96.90 84	97.95 234	99.58 3	97.14 34	98.44 55	99.01 66	95.03 54	99.62 112	97.91 31	99.75 31	99.50 74

DELS-MVS		98.40 34	98.20 36	98.99 50	99.00 93	97.66 56	97.75 256	98.89 45	97.71 6	98.33 59	98.97 69	94.97 55	99.88 37	98.42 16	99.76 25	99.42 89
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
HPM-MVS_fast		98.38 35	98.13 37	99.12 43	99.75 3	97.86 50	99.44 4	98.82 61	94.46 144	98.94 25	99.20 38	95.16 51	99.74 90	97.58 51	99.85 2	99.77 15

GST-MVS		98.43 32	98.12 38	99.34 15	99.72 11	98.38 20	99.09 54	98.82 61	95.71 77	98.73 39	99.06 59	95.27 46	99.93 10	97.07 68	99.63 49	99.72 32

HPM-MVS		98.36 37	98.10 39	99.13 41	99.74 7	97.82 53	99.53 1	98.80 72	94.63 137	98.61 45	98.97 69	95.13 52	99.77 84	97.65 47	99.83 8	99.79 5
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PHI-MVS		98.34 39	98.06 40	99.18 35	99.15 83	98.12 41	99.04 63	99.09 19	93.32 200	98.83 33	99.10 51	96.54 9	99.83 47	97.70 45	99.76 25	99.59 64

abl_6		98.30 43	98.03 41	99.13 41	99.56 28	97.76 55	99.13 49	98.82 61	96.14 63	99.26 10	99.37 14	93.33 79	99.93 10	96.96 72	99.67 41	99.69 38

MP-MVS		98.33 41	98.01 42	99.28 23	99.75 3	98.18 37	99.22 29	98.79 74	96.13 64	97.92 81	99.23 32	94.54 62	99.94 3	96.74 88	99.78 16	99.73 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		98.35 38	98.00 43	99.42 11	99.51 30	98.72 10	98.80 111	98.82 61	94.52 140	99.23 12	99.25 31	95.54 39	99.80 62	96.52 96	99.77 19	99.74 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP		98.23 44	97.95 44	99.09 45	99.74 7	97.62 59	99.03 64	99.41 6	95.98 69	97.60 102	99.36 18	94.45 67	99.93 10	97.14 65	98.85 100	99.70 37
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
MP-MVS-pluss		98.31 42	97.92 45	99.49 6	99.72 11	98.88 7	98.43 180	98.78 76	94.10 151	97.69 93	99.42 7	95.25 48	99.92 15	98.09 25	99.80 11	99.67 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_prior3		98.22 45	97.90 46	99.19 31	99.31 52	98.22 34	97.80 252	98.84 56	96.12 65	97.89 83	98.69 98	95.96 27	99.70 96	96.89 76	99.60 52	99.65 53

PS-MVSNAJ		97.73 58	97.77 47	97.62 143	98.68 131	95.58 150	97.34 284	98.51 137	97.29 21	98.66 42	97.88 175	94.51 63	99.90 27	97.87 35	99.17 89	97.39 208

CANet		98.05 46	97.76 48	98.90 58	98.73 123	97.27 70	98.35 188	98.78 76	97.37 20	97.72 91	98.96 74	91.53 117	99.92 15	98.79 3	99.65 45	99.51 72

CSCG		97.85 55	97.74 49	98.20 99	99.67 19	95.16 168	99.22 29	99.32 7	93.04 208	97.02 121	98.92 80	95.36 43	99.91 24	97.43 58	99.64 47	99.52 69

xiu_mvs_v2_base		97.66 64	97.70 50	97.56 151	98.61 137	95.46 157	97.44 273	98.46 147	97.15 33	98.65 43	98.15 152	94.33 69	99.80 62	97.84 38	98.66 109	97.41 206

UA-Net		97.96 48	97.62 51	98.98 52	98.86 115	97.47 64	98.89 83	99.08 20	96.67 50	98.72 40	99.54 1	93.15 82	99.81 55	94.87 147	98.83 101	99.65 53

MG-MVS		97.81 56	97.60 52	98.44 85	99.12 85	95.97 123	97.75 256	98.78 76	96.89 43	98.46 50	99.22 34	93.90 76	99.68 102	94.81 151	99.52 69	99.67 49

DeepC-MVS	95.98 3	97.88 52	97.58 53	98.77 62	99.25 69	96.93 82	98.83 100	98.75 83	96.96 42	96.89 129	99.50 4	90.46 133	99.87 38	97.84 38	99.76 25	99.52 69
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		97.60 65	97.56 54	97.72 131	98.35 147	95.98 119	97.86 247	98.51 137	97.13 35	99.01 21	98.40 126	91.56 113	99.80 62	98.53 10	98.68 105	97.37 210

xiu_mvs_v1_base		97.60 65	97.56 54	97.72 131	98.35 147	95.98 119	97.86 247	98.51 137	97.13 35	99.01 21	98.40 126	91.56 113	99.80 62	98.53 10	98.68 105	97.37 210

xiu_mvs_v1_base_debi		97.60 65	97.56 54	97.72 131	98.35 147	95.98 119	97.86 247	98.51 137	97.13 35	99.01 21	98.40 126	91.56 113	99.80 62	98.53 10	98.68 105	97.37 210

train_agg		97.97 47	97.52 57	99.33 18	99.31 52	98.50 15	97.92 236	98.73 89	92.98 211	97.74 89	98.68 100	96.20 14	99.80 62	96.59 92	99.57 58	99.68 44

agg_prior1		97.95 49	97.51 58	99.28 23	99.30 57	98.38 20	97.81 251	98.72 91	93.16 205	97.57 104	98.66 103	96.14 17	99.81 55	96.63 91	99.56 64	99.66 51

CDPH-MVS		97.94 50	97.49 59	99.28 23	99.47 36	98.44 17	97.91 239	98.67 109	92.57 225	98.77 36	98.85 84	95.93 29	99.72 91	95.56 129	99.69 40	99.68 44

casdiffmvs1		97.72 59	97.49 59	98.41 89	98.52 143	96.71 92	99.14 45	98.32 168	95.15 112	98.46 50	98.31 139	93.10 83	99.21 164	98.14 24	98.27 127	99.31 97

MVSFormer		97.57 68	97.49 59	97.84 121	98.07 170	95.76 145	99.47 2	98.40 157	94.98 120	98.79 34	98.83 86	92.34 91	98.41 265	96.91 74	99.59 55	99.34 92

PVSNet_Blended_VisFu		97.70 61	97.46 62	98.44 85	99.27 66	95.91 139	98.63 149	99.16 17	94.48 143	97.67 94	98.88 82	92.80 86	99.91 24	97.11 66	99.12 90	99.50 74

DP-MVS Recon		97.86 54	97.46 62	99.06 48	99.53 29	98.35 26	98.33 190	98.89 45	92.62 222	98.05 66	98.94 77	95.34 44	99.65 105	96.04 110	99.42 78	99.19 113

agg_prior3		97.87 53	97.42 64	99.23 30	99.29 60	98.23 32	97.92 236	98.72 91	92.38 238	97.59 103	98.64 105	96.09 20	99.79 74	96.59 92	99.57 58	99.68 44

VNet		97.79 57	97.40 65	98.96 54	98.88 113	97.55 61	98.63 149	98.93 36	96.74 47	99.02 20	98.84 85	90.33 136	99.83 47	98.53 10	96.66 167	99.50 74

OMC-MVS		97.55 70	97.34 66	98.20 99	99.33 47	95.92 137	98.28 199	98.59 120	95.52 86	97.97 77	99.10 51	93.28 81	99.49 133	95.09 145	98.88 97	99.19 113

CPTT-MVS		97.72 59	97.32 67	98.92 56	99.64 21	97.10 77	99.12 51	98.81 65	92.34 239	98.09 64	99.08 57	93.01 84	99.92 15	96.06 109	99.77 19	99.75 22

EPP-MVSNet		97.46 71	97.28 68	97.99 113	98.64 134	95.38 159	99.33 13	98.31 169	93.61 185	97.19 111	99.07 58	94.05 73	99.23 156	96.89 76	98.43 120	99.37 91

MVS_0304		97.70 61	97.25 69	99.07 46	98.90 104	97.83 52	98.20 205	98.74 84	97.51 9	98.03 69	99.06 59	86.12 233	99.93 10	99.02 1	99.64 47	99.44 87

API-MVS		97.41 79	97.25 69	97.91 117	98.70 128	96.80 87	98.82 102	98.69 99	94.53 139	98.11 63	98.28 142	94.50 66	99.57 121	94.12 170	99.49 70	97.37 210

canonicalmvs		97.67 63	97.23 71	98.98 52	98.70 128	98.38 20	99.34 11	98.39 159	96.76 46	97.67 94	97.40 212	92.26 95	99.49 133	98.28 22	96.28 189	99.08 131

lupinMVS		97.44 75	97.22 72	98.12 106	98.07 170	95.76 145	97.68 261	97.76 238	94.50 141	98.79 34	98.61 107	92.34 91	99.30 148	97.58 51	99.59 55	99.31 97

CHOSEN 280x420		97.18 92	97.18 73	97.20 170	98.81 119	93.27 258	95.78 334	99.15 18	95.25 106	96.79 136	98.11 155	92.29 94	99.07 182	98.56 9	99.85 2	99.25 107

casdiffmvs		97.42 77	97.12 74	98.31 94	98.35 147	96.55 102	99.05 60	98.20 189	94.97 122	97.55 106	98.11 155	92.33 93	99.18 167	97.70 45	97.85 142	99.18 117

PVSNet_Blended		97.38 81	97.12 74	98.14 103	99.25 69	95.35 162	97.28 288	99.26 8	93.13 206	97.94 79	98.21 149	92.74 87	99.81 55	96.88 79	99.40 81	99.27 105

Vis-MVSNet		97.42 77	97.11 76	98.34 92	98.66 132	96.23 114	99.22 29	99.00 26	96.63 52	98.04 68	99.21 35	88.05 195	99.35 147	96.01 112	99.21 87	99.45 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPM_NR		97.46 71	97.11 76	98.50 80	99.50 32	96.41 107	98.63 149	98.60 119	95.18 109	97.06 118	98.06 159	94.26 71	99.57 121	93.80 177	98.87 99	99.52 69

jason		97.32 85	97.08 78	98.06 111	97.45 209	95.59 149	97.87 246	97.91 233	94.79 128	98.55 48	98.83 86	91.12 121	99.23 156	97.58 51	99.60 52	99.34 92
jason: jason.
alignmvs		97.56 69	97.07 79	99.01 49	98.66 132	98.37 24	98.83 100	98.06 225	96.74 47	98.00 76	97.65 195	90.80 128	99.48 137	98.37 19	96.56 171	99.19 113

diffmvs1		97.35 84	97.07 79	98.20 99	98.25 155	96.13 117	98.61 152	98.34 165	95.47 87	97.66 97	98.01 163	92.54 89	99.30 148	96.44 99	98.29 126	99.17 119

CNLPA		97.45 74	97.03 81	98.73 63	99.05 87	97.44 66	98.07 224	98.53 133	95.32 103	96.80 135	98.53 114	93.32 80	99.72 91	94.31 165	99.31 85	99.02 135

MVS_Test		97.28 86	97.00 82	98.13 105	98.33 152	95.97 123	98.74 127	98.07 223	94.27 147	98.44 55	98.07 158	92.48 90	99.26 153	96.43 100	98.19 130	99.16 120

sss		97.39 80	96.98 83	98.61 70	98.60 138	96.61 96	98.22 203	98.93 36	93.97 160	98.01 74	98.48 119	91.98 105	99.85 43	96.45 98	98.15 131	99.39 90

3Dnovator	94.51 5	97.46 71	96.93 84	99.07 46	97.78 187	97.64 57	99.35 10	99.06 21	97.02 40	93.75 242	99.16 45	89.25 148	99.92 15	97.22 63	99.75 31	99.64 56

WTY-MVS		97.37 82	96.92 85	98.72 64	98.86 115	96.89 86	98.31 195	98.71 96	95.26 105	97.67 94	98.56 113	92.21 98	99.78 79	95.89 115	96.85 162	99.48 79

IS-MVSNet		97.22 88	96.88 86	98.25 97	98.85 117	96.36 109	99.19 35	97.97 230	95.39 92	97.23 110	98.99 68	91.11 122	98.93 200	94.60 156	98.59 111	99.47 80

EPNet		97.28 86	96.87 87	98.51 79	94.98 327	96.14 116	98.90 79	97.02 295	98.28 1	95.99 175	99.11 49	91.36 118	99.89 29	96.98 69	99.19 88	99.50 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.12 95	96.80 88	98.08 109	99.30 57	94.56 222	98.05 225	99.71 1	93.57 186	97.09 114	98.91 81	88.17 189	99.89 29	96.87 82	99.56 64	99.81 3

F-COLMAP		97.09 97	96.80 88	97.97 114	99.45 38	94.95 181	98.55 163	98.62 118	93.02 209	96.17 170	98.58 112	94.01 74	99.81 55	93.95 173	98.90 96	99.14 123

TAMVS		97.02 99	96.79 90	97.70 136	98.06 172	95.31 164	98.52 166	98.31 169	93.95 161	97.05 119	98.61 107	93.49 78	98.52 241	95.33 136	97.81 143	99.29 103

0601test		97.22 88	96.78 91	98.54 76	98.73 123	96.60 97	98.45 175	98.31 169	94.70 129	98.02 70	98.42 124	90.80 128	99.70 96	96.81 84	96.79 164	99.34 92

Anonymous20240521		97.22 88	96.78 91	98.54 76	98.73 123	96.60 97	98.45 175	98.31 169	94.70 129	98.02 70	98.42 124	90.80 128	99.70 96	96.81 84	96.79 164	99.34 92

PLC	95.07 4	97.20 91	96.78 91	98.44 85	99.29 60	96.31 113	98.14 215	98.76 80	92.41 236	96.39 166	98.31 139	94.92 56	99.78 79	94.06 171	98.77 104	99.23 109
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator+	94.38 6	97.43 76	96.78 91	99.38 12	97.83 185	98.52 14	99.37 7	98.71 96	97.09 38	92.99 263	99.13 47	89.36 145	99.89 29	96.97 70	99.57 58	99.71 35

1121		97.37 82	96.77 95	99.16 38	99.34 44	97.99 48	98.19 209	98.68 102	90.14 290	98.01 74	98.97 69	94.80 59	99.87 38	93.36 186	99.46 75	99.61 59

diffmvs		97.03 98	96.75 96	97.88 119	98.14 167	95.25 166	98.54 165	98.13 205	95.17 110	97.03 120	97.94 169	91.83 108	99.30 148	96.01 112	97.94 137	99.11 126

AdaColmap		97.15 94	96.70 97	98.48 82	99.16 81	96.69 93	98.01 229	98.89 45	94.44 145	96.83 131	98.68 100	90.69 131	99.76 86	94.36 162	99.29 86	98.98 139

Effi-MVS+		97.12 95	96.69 98	98.39 90	98.19 161	96.72 91	97.37 280	98.43 154	93.71 175	97.65 98	98.02 161	92.20 99	99.25 154	96.87 82	97.79 144	99.19 113

CDS-MVSNet		96.99 100	96.69 98	97.90 118	98.05 173	95.98 119	98.20 205	98.33 167	93.67 182	96.95 122	98.49 118	93.54 77	98.42 258	95.24 143	97.74 147	99.31 97
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs-test1		96.60 112	96.68 100	96.37 238	97.89 182	91.81 277	98.56 161	98.10 218	96.57 53	96.52 151	97.94 169	90.81 126	99.45 140	95.72 122	98.01 134	97.86 194

LS3D		97.16 93	96.66 101	98.68 66	98.53 142	97.19 75	98.93 77	98.90 43	92.83 219	95.99 175	99.37 14	92.12 101	99.87 38	93.67 180	99.57 58	98.97 140

PVSNet_BlendedMVS		96.73 109	96.60 102	97.12 176	99.25 69	95.35 162	98.26 201	99.26 8	94.28 146	97.94 79	97.46 207	92.74 87	99.81 55	96.88 79	93.32 243	96.20 303

Effi-MVS+-dtu		96.29 124	96.56 103	95.51 269	97.89 182	90.22 301	98.80 111	98.10 218	96.57 53	96.45 165	96.66 280	90.81 126	98.91 202	95.72 122	97.99 135	97.40 207

CANet_DTU		96.96 101	96.55 104	98.21 98	98.17 165	96.07 118	97.98 232	98.21 186	97.24 28	97.13 113	98.93 78	86.88 222	99.91 24	95.00 146	99.37 83	98.66 160

Vis-MVSNet (Re-imp)		96.87 105	96.55 104	97.83 122	98.73 123	95.46 157	99.20 33	98.30 174	94.96 123	96.60 144	98.87 83	90.05 139	98.59 230	93.67 180	98.60 110	99.46 84

mvs_anonymous		96.70 110	96.53 106	97.18 172	98.19 161	93.78 245	98.31 195	98.19 191	94.01 156	94.47 199	98.27 145	92.08 103	98.46 250	97.39 60	97.91 138	99.31 97

HyFIR lowres test		96.90 104	96.49 107	98.14 103	99.33 47	95.56 152	97.38 278	99.65 2	92.34 239	97.61 101	98.20 150	89.29 147	99.10 179	96.97 70	97.60 152	99.77 15

XVG-OURS		96.55 116	96.41 108	96.99 182	98.75 122	93.76 246	97.50 272	98.52 135	95.67 79	96.83 131	99.30 27	88.95 159	99.53 130	95.88 116	96.26 190	97.69 201

MAR-MVS		96.91 103	96.40 109	98.45 84	98.69 130	96.90 84	98.66 147	98.68 102	92.40 237	97.07 117	97.96 167	91.54 116	99.75 88	93.68 179	98.92 95	98.69 156
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
XVG-OURS-SEG-HR		96.51 117	96.34 110	97.02 181	98.77 121	93.76 246	97.79 254	98.50 142	95.45 89	96.94 124	99.09 55	87.87 201	99.55 129	96.76 87	95.83 207	97.74 197

PMMVS		96.60 112	96.33 111	97.41 162	97.90 181	93.93 241	97.35 283	98.41 155	92.84 218	97.76 87	97.45 209	91.10 123	99.20 165	96.26 104	97.91 138	99.11 126

UGNet		96.78 108	96.30 112	98.19 102	98.24 156	95.89 141	98.88 86	98.93 36	97.39 17	96.81 134	97.84 179	82.60 294	99.90 27	96.53 95	99.49 70	98.79 151
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
114514_t		96.93 102	96.27 113	98.92 56	99.50 32	97.63 58	98.85 96	98.90 43	84.80 338	97.77 86	99.11 49	92.84 85	99.66 104	94.85 148	99.77 19	99.47 80

PS-MVSNAJss		96.43 119	96.26 114	96.92 190	95.84 307	95.08 173	99.16 43	98.50 142	95.87 72	93.84 240	98.34 136	94.51 63	98.61 227	96.88 79	93.45 240	97.06 220

PAPR		96.84 106	96.24 115	98.65 68	98.72 127	96.92 83	97.36 282	98.57 126	93.33 199	96.67 138	97.57 202	94.30 70	99.56 123	91.05 248	98.59 111	99.47 80

HY-MVS	93.96 8	96.82 107	96.23 116	98.57 72	98.46 144	97.00 79	98.14 215	98.21 186	93.95 161	96.72 137	97.99 166	91.58 112	99.76 86	94.51 160	96.54 172	98.95 144

PVSNet	91.96 18	96.35 122	96.15 117	96.96 185	99.17 80	92.05 274	96.08 326	98.68 102	93.69 178	97.75 88	97.80 185	88.86 162	99.69 101	94.26 167	99.01 92	99.15 121

FIs		96.51 117	96.12 118	97.67 139	97.13 230	97.54 62	99.36 8	99.22 14	95.89 71	94.03 233	98.35 132	91.98 105	98.44 255	96.40 101	92.76 250	97.01 223

FC-MVSNet-test		96.42 120	96.05 119	97.53 152	96.95 237	97.27 70	99.36 8	99.23 12	95.83 73	93.93 235	98.37 130	92.00 104	98.32 274	96.02 111	92.72 251	97.00 224

CVMVSNet		95.43 169	96.04 120	93.57 317	97.93 179	83.62 340	98.12 218	98.59 120	95.68 78	96.56 145	99.02 62	87.51 211	97.51 313	93.56 183	97.44 154	99.60 62

PatchMatch-RL		96.59 114	96.03 121	98.27 95	99.31 52	96.51 103	97.91 239	99.06 21	93.72 174	96.92 127	98.06 159	88.50 184	99.65 105	91.77 233	99.00 93	98.66 160

1112_ss		96.63 111	96.00 122	98.50 80	98.56 139	96.37 108	98.18 213	98.10 218	92.92 214	94.84 188	98.43 122	92.14 100	99.58 120	94.35 163	96.51 173	99.56 68

DP-MVS		96.59 114	95.93 123	98.57 72	99.34 44	96.19 115	98.70 136	98.39 159	89.45 308	94.52 197	99.35 20	91.85 107	99.85 43	92.89 205	98.88 97	99.68 44

HQP_MVS		96.14 129	95.90 124	96.85 191	97.42 210	94.60 220	98.80 111	98.56 127	97.28 22	95.34 179	98.28 142	87.09 217	99.03 188	96.07 107	94.27 217	96.92 229

Fast-Effi-MVS+-dtu		95.87 138	95.85 125	95.91 257	97.74 189	91.74 281	98.69 138	98.15 202	95.56 84	94.92 186	97.68 194	88.98 157	98.79 217	93.19 191	97.78 145	97.20 218

EI-MVSNet		95.96 134	95.83 126	96.36 239	97.93 179	93.70 251	98.12 218	98.27 177	93.70 177	95.07 183	99.02 62	92.23 97	98.54 234	94.68 152	93.46 238	96.84 244

1314		96.25 128	95.73 127	97.79 125	97.13 230	95.55 154	98.19 209	98.59 120	93.47 189	92.03 287	97.82 183	91.33 119	99.49 133	94.62 155	98.44 118	98.32 181

nrg030		96.28 126	95.72 128	97.96 116	96.90 242	98.15 39	99.39 5	98.31 169	95.47 87	94.42 208	98.35 132	92.09 102	98.69 220	97.50 57	89.05 287	97.04 222

BH-untuned		95.95 135	95.72 128	96.65 208	98.55 141	92.26 271	98.23 202	97.79 237	93.73 173	94.62 194	98.01 163	88.97 158	99.00 191	93.04 196	98.51 114	98.68 157

MVSTER		96.06 131	95.72 128	97.08 179	98.23 157	95.93 130	98.73 130	98.27 177	94.86 127	95.07 183	98.09 157	88.21 188	98.54 234	96.59 92	93.46 238	96.79 248

ab-mvs		96.42 120	95.71 131	98.55 74	98.63 135	96.75 90	97.88 245	98.74 84	93.84 166	96.54 149	98.18 151	85.34 254	99.75 88	95.93 114	96.35 181	99.15 121

Fast-Effi-MVS+		96.28 126	95.70 132	98.03 112	98.29 154	95.97 123	98.58 156	98.25 182	91.74 254	95.29 182	97.23 225	91.03 125	99.15 169	92.90 203	97.96 136	98.97 140

test_djsdf		96.00 133	95.69 133	96.93 188	95.72 311	95.49 156	99.47 2	98.40 157	94.98 120	94.58 195	97.86 176	89.16 151	98.41 265	96.91 74	94.12 225	96.88 239

tpmrst		95.63 150	95.69 133	95.44 275	97.54 201	88.54 324	96.97 298	97.56 247	93.50 188	97.52 107	96.93 264	89.49 142	99.16 168	95.25 142	96.42 176	98.64 162

Test_1112_low_res		96.34 123	95.66 135	98.36 91	98.56 139	95.94 127	97.71 258	98.07 223	92.10 245	94.79 192	97.29 221	91.75 109	99.56 123	94.17 168	96.50 174	99.58 66

PatchmatchNet		95.71 146	95.52 136	96.29 245	97.58 198	90.72 294	96.84 311	97.52 253	94.06 153	97.08 115	96.96 257	89.24 149	98.90 205	92.03 225	98.37 121	99.26 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tttt0517		96.07 130	95.51 137	97.78 126	98.41 145	94.84 197	99.28 16	94.33 352	94.26 148	97.64 99	98.64 105	84.05 282	99.47 138	95.34 135	97.60 152	99.03 134

MDTV_nov1_ep13			95.40 138		97.48 204	88.34 326	96.85 310	97.29 282	93.74 172	97.48 108	97.26 222	89.18 150	99.05 183	91.92 229	97.43 155

HQP-MVS		95.72 144	95.40 138	96.69 200	97.20 224	94.25 235	98.05 225	98.46 147	96.43 55	94.45 200	97.73 188	86.75 223	98.96 195	95.30 137	94.18 221	96.86 243

QAPM		96.29 124	95.40 138	98.96 54	97.85 184	97.60 60	99.23 23	98.93 36	89.76 300	93.11 260	99.02 62	89.11 152	99.93 10	91.99 226	99.62 50	99.34 92

RPSCF		94.87 204	95.40 138	93.26 321	98.89 112	82.06 346	98.33 190	98.06 225	90.30 287	96.56 145	99.26 30	87.09 217	99.49 133	93.82 176	96.32 183	98.24 182

ACMM	93.85 9	95.69 148	95.38 142	96.61 214	97.61 195	93.84 244	98.91 78	98.44 151	95.25 106	94.28 218	98.47 120	86.04 243	99.12 172	95.50 131	93.95 230	96.87 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thisisatest0530		96.01 132	95.36 143	97.97 114	98.38 146	95.52 155	98.88 86	94.19 355	94.04 154	97.64 99	98.31 139	83.82 289	99.46 139	95.29 139	97.70 149	98.93 145

LPG-MVS_test		95.62 151	95.34 144	96.47 231	97.46 206	93.54 252	98.99 68	98.54 130	94.67 133	94.36 210	98.77 93	85.39 251	99.11 176	95.71 124	94.15 223	96.76 251

CLD-MVS		95.62 151	95.34 144	96.46 234	97.52 203	93.75 248	97.27 289	98.46 147	95.53 85	94.42 208	98.00 165	86.21 231	98.97 192	96.25 105	94.37 215	96.66 269
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		95.69 148	95.33 146	96.76 195	96.16 294	94.63 215	98.43 180	98.39 159	96.64 51	95.02 185	98.78 91	85.15 256	99.05 183	95.21 144	94.20 220	96.60 278

LCM-MVSNet-Re		95.22 187	95.32 147	94.91 293	98.18 163	87.85 331	98.75 123	95.66 337	95.11 114	88.96 313	96.85 273	90.26 138	97.65 308	95.65 127	98.44 118	99.22 110

BH-RMVSNet		95.92 137	95.32 147	97.69 137	98.32 153	94.64 214	98.19 209	97.45 268	94.56 138	96.03 173	98.61 107	85.02 257	99.12 172	90.68 252	99.06 91	99.30 101

MSDG		95.93 136	95.30 149	97.83 122	98.90 104	95.36 160	96.83 312	98.37 162	91.32 269	94.43 207	98.73 97	90.27 137	99.60 113	90.05 269	98.82 102	98.52 166

PatchFormer-LS_test		95.47 166	95.27 150	96.08 253	97.59 197	90.66 295	98.10 222	97.34 277	93.98 159	96.08 171	96.15 300	87.65 209	99.12 172	95.27 141	95.24 212	98.44 171

VDD-MVS		95.82 141	95.23 151	97.61 148	98.84 118	93.98 240	98.68 142	97.40 273	95.02 119	97.95 78	99.34 21	74.37 340	99.78 79	98.64 4	96.80 163	99.08 131

IterMVS-LS		95.46 167	95.21 152	96.22 247	98.12 168	93.72 250	98.32 194	98.13 205	93.71 175	94.26 219	97.31 220	92.24 96	98.10 287	94.63 153	90.12 273	96.84 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		95.78 142	95.19 153	97.58 149	96.99 236	97.47 64	98.79 116	99.18 16	95.60 82	93.92 236	97.04 249	91.68 110	98.48 245	95.80 120	87.66 310	96.79 248

UniMVSNet_NR-MVSNet		95.71 146	95.15 154	97.40 164	96.84 245	96.97 80	98.74 127	99.24 10	95.16 111	93.88 237	97.72 190	91.68 110	98.31 276	95.81 118	87.25 315	96.92 229

tfpn1000		95.72 144	95.11 155	97.58 149	99.00 93	95.73 147	99.24 21	95.49 339	94.08 152	96.87 130	97.45 209	85.81 245	99.30 148	91.78 232	96.22 194	97.71 200

VPA-MVSNet		95.75 143	95.11 155	97.69 137	97.24 220	97.27 70	98.94 76	99.23 12	95.13 113	95.51 178	97.32 219	85.73 246	98.91 202	97.33 62	89.55 281	96.89 237

BH-w/o		95.38 175	95.08 157	96.26 246	98.34 151	91.79 278	97.70 259	97.43 270	92.87 217	94.24 221	97.22 226	88.66 177	98.84 211	91.55 237	97.70 149	98.16 184

jajsoiax		95.45 168	95.03 158	96.73 196	95.42 322	94.63 215	99.14 45	98.52 135	95.74 75	93.22 254	98.36 131	83.87 287	98.65 225	96.95 73	94.04 226	96.91 234

mvs_tets		95.41 173	95.00 159	96.65 208	95.58 315	94.42 225	99.00 67	98.55 129	95.73 76	93.21 255	98.38 129	83.45 291	98.63 226	97.09 67	94.00 228	96.91 234

OpenMVS	93.04 13	95.83 140	95.00 159	98.32 93	97.18 227	97.32 68	99.21 32	98.97 29	89.96 293	91.14 294	99.05 61	86.64 225	99.92 15	93.38 185	99.47 72	97.73 198

LFMVS		95.86 139	94.98 161	98.47 83	98.87 114	96.32 111	98.84 99	96.02 325	93.40 197	98.62 44	99.20 38	74.99 335	99.63 110	97.72 44	97.20 157	99.46 84

ACMP	93.49 10	95.34 180	94.98 161	96.43 235	97.67 191	93.48 254	98.73 130	98.44 151	94.94 126	92.53 274	98.53 114	84.50 271	99.14 170	95.48 132	94.00 228	96.66 269
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Patchmatch-test1		95.32 182	94.97 163	96.35 240	97.67 191	91.29 286	97.33 285	97.60 245	94.68 132	96.92 127	96.95 258	83.97 284	98.50 244	91.33 243	98.32 124	99.25 107

EPNet_dtu		95.21 188	94.95 164	95.99 254	96.17 291	90.45 299	98.16 214	97.27 284	96.77 45	93.14 259	98.33 137	90.34 135	98.42 258	85.57 323	98.81 103	99.09 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
view600		95.60 154	94.93 165	97.62 143	99.05 87	94.85 188	99.09 54	97.01 297	95.36 97	96.52 151	97.37 213	84.55 266	99.59 114	89.07 288	96.39 177	98.40 172

view800		95.60 154	94.93 165	97.62 143	99.05 87	94.85 188	99.09 54	97.01 297	95.36 97	96.52 151	97.37 213	84.55 266	99.59 114	89.07 288	96.39 177	98.40 172

conf0.05thres1000		95.60 154	94.93 165	97.62 143	99.05 87	94.85 188	99.09 54	97.01 297	95.36 97	96.52 151	97.37 213	84.55 266	99.59 114	89.07 288	96.39 177	98.40 172

tfpn		95.60 154	94.93 165	97.62 143	99.05 87	94.85 188	99.09 54	97.01 297	95.36 97	96.52 151	97.37 213	84.55 266	99.59 114	89.07 288	96.39 177	98.40 172

anonymousdsp		95.42 171	94.91 169	96.94 187	95.10 326	95.90 140	99.14 45	98.41 155	93.75 170	93.16 256	97.46 207	87.50 213	98.41 265	95.63 128	94.03 227	96.50 291

tfpn_ndepth		95.53 160	94.90 170	97.39 167	98.96 101	95.88 142	99.05 60	95.27 340	93.80 169	96.95 122	96.93 264	85.53 249	99.40 142	91.54 238	96.10 197	96.89 237

thisisatest0515		95.61 153	94.89 171	97.76 128	98.15 166	95.15 170	96.77 313	94.41 350	92.95 213	97.18 112	97.43 211	84.78 262	99.45 140	94.63 153	97.73 148	98.68 157

test-LLR		95.10 192	94.87 172	95.80 262	96.77 247	89.70 305	96.91 302	95.21 341	95.11 114	94.83 190	95.72 311	87.71 205	98.97 192	93.06 194	98.50 115	98.72 153

COLMAP_ROB	93.27 12	95.33 181	94.87 172	96.71 197	99.29 60	93.24 260	98.58 156	98.11 213	89.92 296	93.57 245	99.10 51	86.37 229	99.79 74	90.78 250	98.10 133	97.09 219
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
conf0.01		95.56 158	94.84 174	97.72 131	98.90 104	95.93 130	99.17 36	95.70 331	93.42 191	96.50 156	97.16 228	86.12 233	99.22 158	90.51 256	96.06 198	98.02 187

conf0.002		95.56 158	94.84 174	97.72 131	98.90 104	95.93 130	99.17 36	95.70 331	93.42 191	96.50 156	97.16 228	86.12 233	99.22 158	90.51 256	96.06 198	98.02 187

thresconf0.02		95.50 161	94.84 174	97.51 153	98.90 104	95.93 130	99.17 36	95.70 331	93.42 191	96.50 156	97.16 228	86.12 233	99.22 158	90.51 256	96.06 198	97.37 210

tfpn_n400		95.50 161	94.84 174	97.51 153	98.90 104	95.93 130	99.17 36	95.70 331	93.42 191	96.50 156	97.16 228	86.12 233	99.22 158	90.51 256	96.06 198	97.37 210

tfpnconf		95.50 161	94.84 174	97.51 153	98.90 104	95.93 130	99.17 36	95.70 331	93.42 191	96.50 156	97.16 228	86.12 233	99.22 158	90.51 256	96.06 198	97.37 210

tfpnview11		95.50 161	94.84 174	97.51 153	98.90 104	95.93 130	99.17 36	95.70 331	93.42 191	96.50 156	97.16 228	86.12 233	99.22 158	90.51 256	96.06 198	97.37 210

thres600view7		95.49 165	94.77 180	97.67 139	98.98 97	95.02 174	98.85 96	96.90 305	95.38 93	96.63 140	96.90 266	84.29 273	99.59 114	88.65 297	96.33 182	98.40 172

DU-MVS		95.42 171	94.76 181	97.40 164	96.53 259	96.97 80	98.66 147	98.99 28	95.43 90	93.88 237	97.69 191	88.57 179	98.31 276	95.81 118	87.25 315	96.92 229

tfpn111		95.43 169	94.74 182	97.51 153	98.98 97	94.92 182	98.87 88	96.90 305	95.38 93	96.61 141	96.88 269	84.29 273	99.59 114	88.43 298	96.32 183	98.02 187

CostFormer		94.95 200	94.73 183	95.60 268	97.28 218	89.06 315	97.53 270	96.89 309	89.66 304	96.82 133	96.72 278	86.05 241	98.95 199	95.53 130	96.13 196	98.79 151

conf200view11		95.40 174	94.70 184	97.50 158	98.98 97	94.92 182	98.87 88	96.90 305	95.38 93	96.61 141	96.88 269	84.29 273	99.56 123	88.11 304	96.29 185	98.02 187

thres100view900		95.38 175	94.70 184	97.41 162	98.98 97	94.92 182	98.87 88	96.90 305	95.38 93	96.61 141	96.88 269	84.29 273	99.56 123	88.11 304	96.29 185	97.76 195

AllTest		95.24 186	94.65 186	96.99 182	99.25 69	93.21 261	98.59 154	98.18 194	91.36 265	93.52 247	98.77 93	84.67 263	99.72 91	89.70 277	97.87 140	98.02 187

tfpn200view9		95.32 182	94.62 187	97.43 161	98.94 102	94.98 178	98.68 142	96.93 303	95.33 101	96.55 147	96.53 285	84.23 278	99.56 123	88.11 304	96.29 185	97.76 195

thres400		95.38 175	94.62 187	97.65 142	98.94 102	94.98 178	98.68 142	96.93 303	95.33 101	96.55 147	96.53 285	84.23 278	99.56 123	88.11 304	96.29 185	98.40 172

thres200		95.25 185	94.57 189	97.28 168	98.81 119	94.92 182	98.20 205	97.11 289	95.24 108	96.54 149	96.22 298	84.58 265	99.53 130	87.93 309	96.50 174	97.39 208

TAPA-MVS	93.98 7	95.35 179	94.56 190	97.74 130	99.13 84	94.83 200	98.33 190	98.64 117	86.62 324	96.29 168	98.61 107	94.00 75	99.29 152	80.00 337	99.41 79	99.09 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDDNet		95.36 178	94.53 191	97.86 120	98.10 169	95.13 171	98.85 96	97.75 239	90.46 283	98.36 57	99.39 9	73.27 342	99.64 107	97.98 29	96.58 170	98.81 150

Anonymous202405211		95.28 184	94.49 192	97.67 139	99.00 93	93.75 248	98.70 136	97.04 293	90.66 280	96.49 162	98.80 89	78.13 320	99.83 47	96.21 106	95.36 211	99.44 87

TranMVSNet+NR-MVSNet		95.14 191	94.48 193	97.11 177	96.45 264	96.36 109	99.03 64	99.03 24	95.04 118	93.58 244	97.93 171	88.27 187	98.03 292	94.13 169	86.90 320	96.95 228

EPMVS		94.99 196	94.48 193	96.52 227	97.22 222	91.75 280	97.23 290	91.66 361	94.11 150	97.28 109	96.81 275	85.70 247	98.84 211	93.04 196	97.28 156	98.97 140

WR-MVS_H		95.05 194	94.46 195	96.81 193	96.86 244	95.82 144	99.24 21	99.24 10	93.87 165	92.53 274	96.84 274	90.37 134	98.24 282	93.24 189	87.93 305	96.38 297

WR-MVS		95.15 190	94.46 195	97.22 169	96.67 255	96.45 105	98.21 204	98.81 65	94.15 149	93.16 256	97.69 191	87.51 211	98.30 278	95.29 139	88.62 299	96.90 236

ADS-MVSNet		95.00 195	94.45 197	96.63 211	98.00 174	91.91 276	96.04 327	97.74 240	90.15 288	96.47 163	96.64 282	87.89 199	98.96 195	90.08 267	97.06 158	99.02 135

XXY-MVS		95.20 189	94.45 197	97.46 159	96.75 250	96.56 100	98.86 95	98.65 116	93.30 202	93.27 253	98.27 145	84.85 261	98.87 208	94.82 150	91.26 268	96.96 226

ADS-MVSNet2		94.58 227	94.40 199	95.11 289	98.00 174	88.74 319	96.04 327	97.30 281	90.15 288	96.47 163	96.64 282	87.89 199	97.56 312	90.08 267	97.06 158	99.02 135

tpmvs		94.60 224	94.36 200	95.33 284	97.46 206	88.60 322	96.88 308	97.68 241	91.29 271	93.80 241	96.42 291	88.58 178	99.24 155	91.06 246	96.04 204	98.17 183

DWT-MVSNet_test		94.82 208	94.36 200	96.20 248	97.35 215	90.79 292	98.34 189	96.57 320	92.91 215	95.33 181	96.44 290	82.00 296	99.12 172	94.52 159	95.78 208	98.70 155

CP-MVSNet		94.94 202	94.30 202	96.83 192	96.72 252	95.56 152	99.11 52	98.95 33	93.89 163	92.42 279	97.90 173	87.19 216	98.12 286	94.32 164	88.21 302	96.82 247

FMVSNet3		94.97 199	94.26 203	97.11 177	98.18 163	96.62 94	98.56 161	98.26 181	93.67 182	94.09 229	97.10 237	84.25 277	98.01 293	92.08 221	92.14 254	96.70 260

Anonymous20240529		95.10 192	94.22 204	97.75 129	99.01 92	94.26 234	98.87 88	98.83 60	85.79 333	96.64 139	98.97 69	78.73 317	99.85 43	96.27 103	94.89 214	99.12 125

v1neww		94.83 205	94.22 204	96.68 203	96.39 267	94.85 188	98.87 88	98.11 213	92.45 231	94.45 200	97.06 244	88.82 167	98.54 234	92.93 200	88.91 292	96.65 271

v7new		94.83 205	94.22 204	96.68 203	96.39 267	94.85 188	98.87 88	98.11 213	92.45 231	94.45 200	97.06 244	88.82 167	98.54 234	92.93 200	88.91 292	96.65 271

v6		94.83 205	94.21 207	96.69 200	96.36 271	94.85 188	98.87 88	98.11 213	92.46 226	94.44 206	97.05 248	88.76 173	98.57 232	92.95 199	88.92 291	96.65 271

TR-MVS		94.94 202	94.20 208	97.17 173	97.75 188	94.14 237	97.59 267	97.02 295	92.28 243	95.75 177	97.64 197	83.88 286	98.96 195	89.77 273	96.15 195	98.40 172

VPNet		94.99 196	94.19 209	97.40 164	97.16 228	96.57 99	98.71 133	98.97 29	95.67 79	94.84 188	98.24 148	80.36 310	98.67 224	96.46 97	87.32 313	96.96 226

NR-MVSNet		94.98 198	94.16 210	97.44 160	96.53 259	97.22 74	98.74 127	98.95 33	94.96 123	89.25 311	97.69 191	89.32 146	98.18 284	94.59 157	87.40 312	96.92 229

CR-MVSNet		94.76 211	94.15 211	96.59 217	97.00 234	93.43 255	94.96 340	97.56 247	92.46 226	96.93 125	96.24 294	88.15 190	97.88 304	87.38 311	96.65 168	98.46 169

V42		94.78 210	94.14 212	96.70 199	96.33 278	95.22 167	98.97 72	98.09 221	92.32 241	94.31 214	97.06 244	88.39 185	98.55 233	92.90 203	88.87 294	96.34 299

EU-MVSNet		93.66 263	94.14 212	92.25 326	95.96 301	83.38 341	98.52 166	98.12 208	94.69 131	92.61 271	98.13 154	87.36 215	96.39 339	91.82 230	90.00 275	96.98 225

XVG-ACMP-BASELINE		94.54 229	94.14 212	95.75 265	96.55 258	91.65 282	98.11 220	98.44 151	94.96 123	94.22 222	97.90 173	79.18 316	99.11 176	94.05 172	93.85 231	96.48 293

divwei89l23v2f112		94.76 211	94.12 215	96.67 206	96.28 284	94.85 188	98.69 138	98.12 208	92.44 233	94.29 217	96.94 260	88.85 164	98.48 245	92.67 208	88.79 298	96.67 266

v1141		94.75 213	94.11 216	96.67 206	96.27 286	94.86 187	98.69 138	98.12 208	92.43 234	94.31 214	96.94 260	88.78 172	98.48 245	92.63 210	88.85 296	96.67 266

v1		94.75 213	94.11 216	96.69 200	96.27 286	94.87 186	98.69 138	98.12 208	92.43 234	94.32 213	96.94 260	88.71 176	98.54 234	92.66 209	88.84 297	96.67 266

v7		94.69 217	94.04 218	96.62 213	96.41 266	94.79 208	98.78 118	98.13 205	91.89 250	94.30 216	97.16 228	88.13 192	98.45 252	91.96 228	89.65 278	96.61 276

v2v482		94.69 217	94.03 219	96.65 208	96.17 291	94.79 208	98.67 145	98.08 222	92.72 220	94.00 234	97.16 228	87.69 208	98.45 252	92.91 202	88.87 294	96.72 256

GA-MVS		94.81 209	94.03 219	97.14 174	97.15 229	93.86 243	96.76 314	97.58 246	94.00 157	94.76 193	97.04 249	80.91 303	98.48 245	91.79 231	96.25 191	99.09 128

OurMVSNet-221017-0		94.21 243	94.00 221	94.85 296	95.60 314	89.22 313	98.89 83	97.43 270	95.29 104	92.18 284	98.52 117	82.86 293	98.59 230	93.46 184	91.76 261	96.74 253

PAPM		94.95 200	94.00 221	97.78 126	97.04 233	95.65 148	96.03 329	98.25 182	91.23 274	94.19 224	97.80 185	91.27 120	98.86 210	82.61 332	97.61 151	98.84 149

pmmvs4		94.69 217	93.99 223	96.81 193	95.74 309	95.94 127	97.40 276	97.67 242	90.42 285	93.37 251	97.59 200	89.08 153	98.20 283	92.97 198	91.67 262	96.30 301

PS-CasMVS		94.67 221	93.99 223	96.71 197	96.68 254	95.26 165	99.13 49	99.03 24	93.68 180	92.33 280	97.95 168	85.35 253	98.10 287	93.59 182	88.16 304	96.79 248

ACMH	92.88 16	94.55 228	93.95 225	96.34 242	97.63 193	93.26 259	98.81 108	98.49 146	93.43 190	89.74 306	98.53 114	81.91 297	99.08 181	93.69 178	93.30 244	96.70 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		94.28 242	93.92 226	95.35 283	94.95 328	92.60 269	97.97 233	97.65 243	91.61 256	90.68 300	97.09 239	86.32 230	98.42 258	89.70 277	99.34 84	95.02 326
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1144		94.59 226	93.92 226	96.60 216	96.21 288	94.78 210	98.59 154	98.14 204	91.86 253	94.21 223	97.02 251	87.97 196	98.41 265	91.72 234	89.57 279	96.61 276

dp		94.15 250	93.90 228	94.90 294	97.31 217	86.82 336	96.97 298	97.19 288	91.22 275	96.02 174	96.61 284	85.51 250	99.02 190	90.00 271	94.30 216	98.85 147

LTVRE_ROB	92.95 15	94.60 224	93.90 228	96.68 203	97.41 213	94.42 225	98.52 166	98.59 120	91.69 255	91.21 293	98.35 132	84.87 260	99.04 187	91.06 246	93.44 241	96.60 278
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
cascas		94.63 223	93.86 230	96.93 188	96.91 241	94.27 233	96.00 330	98.51 137	85.55 334	94.54 196	96.23 296	84.20 280	98.87 208	95.80 120	96.98 161	97.66 202

IterMVS		94.09 253	93.85 231	94.80 299	97.99 176	90.35 300	97.18 293	98.12 208	93.68 180	92.46 278	97.34 217	84.05 282	97.41 315	92.51 215	91.33 265	96.62 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		94.35 237	93.81 232	95.96 255	96.20 289	94.05 239	98.61 152	96.67 317	91.44 261	93.85 239	97.60 199	88.57 179	98.14 285	94.39 161	86.93 318	95.68 316

tpm		94.13 251	93.80 233	95.12 288	96.50 261	87.91 330	97.44 273	95.89 330	92.62 222	96.37 167	96.30 293	84.13 281	98.30 278	93.24 189	91.66 263	99.14 123

GBi-Net		94.49 230	93.80 233	96.56 222	98.21 158	95.00 175	98.82 102	98.18 194	92.46 226	94.09 229	97.07 241	81.16 300	97.95 296	92.08 221	92.14 254	96.72 256

test1		94.49 230	93.80 233	96.56 222	98.21 158	95.00 175	98.82 102	98.18 194	92.46 226	94.09 229	97.07 241	81.16 300	97.95 296	92.08 221	92.14 254	96.72 256

v8		94.47 232	93.77 236	96.57 221	96.36 271	94.83 200	99.05 60	98.19 191	91.92 249	93.16 256	96.97 256	88.82 167	98.48 245	91.69 235	87.79 308	96.39 296

ACMH+	92.99 14	94.30 239	93.77 236	95.88 259	97.81 186	92.04 275	98.71 133	98.37 162	93.99 158	90.60 301	98.47 120	80.86 305	99.05 183	92.75 207	92.40 253	96.55 285

v148		94.29 240	93.76 238	95.91 257	96.10 295	92.93 265	98.58 156	97.97 230	92.59 224	93.47 250	96.95 258	88.53 182	98.32 274	92.56 212	87.06 317	96.49 292

tpm2		94.19 245	93.76 238	95.46 273	97.23 221	89.04 316	97.31 287	96.85 312	87.08 323	96.21 169	96.79 276	83.75 290	98.74 219	92.43 217	96.23 192	98.59 164

PEN-MVS		94.42 234	93.73 240	96.49 229	96.28 284	94.84 197	99.17 36	99.00 26	93.51 187	92.23 282	97.83 182	86.10 240	97.90 300	92.55 213	86.92 319	96.74 253

v144192		94.39 236	93.70 241	96.48 230	96.06 297	94.35 229	98.58 156	98.16 201	91.45 260	94.33 212	97.02 251	87.50 213	98.45 252	91.08 245	89.11 286	96.63 274

TESTMET0.1,1		94.18 247	93.69 242	95.63 267	96.92 239	89.12 314	96.91 302	94.78 346	93.17 204	94.88 187	96.45 289	78.52 318	98.92 201	93.09 193	98.50 115	98.85 147

Patchmatch-test		94.42 234	93.68 243	96.63 211	97.60 196	91.76 279	94.83 344	97.49 265	89.45 308	94.14 227	97.10 237	88.99 154	98.83 213	85.37 326	98.13 132	99.29 103

MS-PatchMatch		93.84 261	93.63 244	94.46 309	96.18 290	89.45 309	97.76 255	98.27 177	92.23 244	92.13 285	97.49 205	79.50 313	98.69 220	89.75 275	99.38 82	95.25 321

DI_MVS_plusplus_test		94.74 215	93.62 245	98.09 108	95.34 323	95.92 137	98.09 223	97.34 277	94.66 135	85.89 325	95.91 305	80.49 309	99.38 145	96.66 90	98.22 128	98.97 140

FMVSNet2		94.47 232	93.61 246	97.04 180	98.21 158	96.43 106	98.79 116	98.27 177	92.46 226	93.50 249	97.09 239	81.16 300	98.00 294	91.09 244	91.93 258	96.70 260

test_normal		94.72 216	93.59 247	98.11 107	95.30 324	95.95 126	97.91 239	97.39 275	94.64 136	85.70 328	95.88 306	80.52 308	99.36 146	96.69 89	98.30 125	99.01 138

v1192		94.32 238	93.58 248	96.53 226	96.10 295	94.45 224	98.50 171	98.17 199	91.54 258	94.19 224	97.06 244	86.95 221	98.43 257	90.14 265	89.57 279	96.70 260

v10		94.29 240	93.55 249	96.51 228	96.39 267	94.80 205	98.99 68	98.19 191	91.35 267	93.02 262	96.99 254	88.09 193	98.41 265	90.50 262	88.41 301	96.33 300

MVS		94.67 221	93.54 250	98.08 109	96.88 243	96.56 100	98.19 209	98.50 142	78.05 352	92.69 269	98.02 161	91.07 124	99.63 110	90.09 266	98.36 122	98.04 186

v52		94.18 247	93.52 251	96.13 251	95.95 302	94.29 231	99.23 23	98.21 186	91.42 262	92.84 265	96.89 267	87.85 202	98.53 240	91.51 239	87.81 306	95.57 319

V4		94.18 247	93.52 251	96.13 251	95.89 304	94.31 230	99.23 23	98.22 185	91.42 262	92.82 266	96.89 267	87.93 198	98.52 241	91.51 239	87.81 306	95.58 318

test-mter		94.08 254	93.51 253	95.80 262	96.77 247	89.70 305	96.91 302	95.21 341	92.89 216	94.83 190	95.72 311	77.69 323	98.97 192	93.06 194	98.50 115	98.72 153

test0.0.03 1		94.08 254	93.51 253	95.80 262	95.53 317	92.89 266	97.38 278	95.97 327	95.11 114	92.51 276	96.66 280	87.71 205	96.94 321	87.03 314	93.67 233	97.57 203

v1921920		94.20 244	93.47 255	96.40 237	95.98 300	94.08 238	98.52 166	98.15 202	91.33 268	94.25 220	97.20 227	86.41 228	98.42 258	90.04 270	89.39 284	96.69 265

v7n		94.19 245	93.43 256	96.47 231	95.90 303	94.38 228	99.26 18	98.34 165	91.99 247	92.76 268	97.13 236	88.31 186	98.52 241	89.48 282	87.70 309	96.52 288

PCF-MVS	93.45 11	94.68 220	93.43 256	98.42 88	98.62 136	96.77 89	95.48 336	98.20 189	84.63 339	93.34 252	98.32 138	88.55 181	99.81 55	84.80 328	98.96 94	98.68 157
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tpmp4_e23		93.91 260	93.42 258	95.38 281	97.62 194	88.59 323	97.52 271	97.34 277	87.94 319	94.17 226	96.79 276	82.91 292	99.05 183	90.62 254	95.91 205	98.50 167

our_test_3		93.65 265	93.30 259	94.69 301	95.45 320	89.68 307	96.91 302	97.65 243	91.97 248	91.66 290	96.88 269	89.67 141	97.93 299	88.02 308	91.49 264	96.48 293

v1240		94.06 256	93.29 260	96.34 242	96.03 299	93.90 242	98.44 178	98.17 199	91.18 276	94.13 228	97.01 253	86.05 241	98.42 258	89.13 287	89.50 282	96.70 260

Anonymous20231211		94.10 252	93.26 261	96.61 214	99.11 86	94.28 232	99.01 66	98.88 48	86.43 326	92.81 267	97.57 202	81.66 299	98.68 223	94.83 149	89.02 289	96.88 239

DTE-MVSNet		93.98 258	93.26 261	96.14 250	96.06 297	94.39 227	99.20 33	98.86 54	93.06 207	91.78 288	97.81 184	85.87 244	97.58 311	90.53 255	86.17 324	96.46 295

v748		93.75 262	93.06 263	95.82 261	95.73 310	92.64 268	99.25 20	98.24 184	91.60 257	92.22 283	96.52 287	87.60 210	98.46 250	90.64 253	85.72 327	96.36 298

pm-mvs1		93.94 259	93.06 263	96.59 217	96.49 262	95.16 168	98.95 74	98.03 229	92.32 241	91.08 295	97.84 179	84.54 270	98.41 265	92.16 219	86.13 326	96.19 304

pmmvs5		93.65 265	92.97 265	95.68 266	95.49 318	92.37 270	98.20 205	97.28 283	89.66 304	92.58 272	97.26 222	82.14 295	98.09 289	93.18 192	90.95 269	96.58 280

SixPastTwentyTwo		93.34 269	92.86 266	94.75 300	95.67 312	89.41 311	98.75 123	96.67 317	93.89 163	90.15 304	98.25 147	80.87 304	98.27 281	90.90 249	90.64 270	96.57 282

tpm cat1		93.36 267	92.80 267	95.07 290	97.58 198	87.97 329	96.76 314	97.86 235	82.17 346	93.53 246	96.04 303	86.13 232	99.13 171	89.24 285	95.87 206	98.10 185

LF4IMVS		93.14 276	92.79 268	94.20 312	95.88 305	88.67 321	97.66 263	97.07 291	93.81 168	91.71 289	97.65 195	77.96 322	98.81 215	91.47 241	91.92 259	95.12 322

USDC		93.33 270	92.71 269	95.21 285	96.83 246	90.83 291	96.91 302	97.50 259	93.84 166	90.72 299	98.14 153	77.69 323	98.82 214	89.51 281	93.21 247	95.97 309

tfpnnormal		93.66 263	92.70 270	96.55 225	96.94 238	95.94 127	98.97 72	99.19 15	91.04 277	91.38 292	97.34 217	84.94 259	98.61 227	85.45 325	89.02 289	95.11 323

ppachtmachnet_test		93.22 273	92.63 271	94.97 292	95.45 320	90.84 290	96.88 308	97.88 234	90.60 281	92.08 286	97.26 222	88.08 194	97.86 306	85.12 327	90.33 272	96.22 302

DSMNet-mixed		92.52 281	92.58 272	92.33 325	94.15 335	82.65 344	98.30 197	94.26 353	89.08 313	92.65 270	95.73 309	85.01 258	95.76 341	86.24 318	97.76 146	98.59 164

JIA-IIPM		93.35 268	92.49 273	95.92 256	96.48 263	90.65 296	95.01 339	96.96 301	85.93 331	96.08 171	87.33 352	87.70 207	98.78 218	91.35 242	95.58 209	98.34 179

testgi		93.06 277	92.45 274	94.88 295	96.43 265	89.90 302	98.75 123	97.54 252	95.60 82	91.63 291	97.91 172	74.46 339	97.02 320	86.10 319	93.67 233	97.72 199

Patchmtry		93.22 273	92.35 275	95.84 260	96.77 247	93.09 264	94.66 346	97.56 247	87.37 322	92.90 264	96.24 294	88.15 190	97.90 300	87.37 312	90.10 274	96.53 287

X-MVStestdata		94.06 256	92.30 276	99.34 15	99.70 16	98.35 26	99.29 14	98.88 48	97.40 15	98.46 50	43.50 365	95.90 31	99.89 29	97.85 36	99.74 34	99.78 8

MIMVSNet		93.26 272	92.21 277	96.41 236	97.73 190	93.13 263	95.65 335	97.03 294	91.27 273	94.04 232	96.06 302	75.33 333	97.19 318	86.56 316	96.23 192	98.92 146

FMVSNet1		93.19 275	92.07 278	96.56 222	97.54 201	95.00 175	98.82 102	98.18 194	90.38 286	92.27 281	97.07 241	73.68 341	97.95 296	89.36 284	91.30 266	96.72 256

PatchT		93.06 277	91.97 279	96.35 240	96.69 253	92.67 267	94.48 347	97.08 290	86.62 324	97.08 115	92.23 347	87.94 197	97.90 300	78.89 341	96.69 166	98.49 168

IB-MVS	91.98 17	93.27 271	91.97 279	97.19 171	97.47 205	93.41 257	97.09 296	95.99 326	93.32 200	92.47 277	95.73 309	78.06 321	99.53 130	94.59 157	82.98 332	98.62 163
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
K. test v3		92.55 280	91.91 281	94.48 307	95.64 313	89.24 312	99.07 59	94.88 345	94.04 154	86.78 321	97.59 200	77.64 326	97.64 309	92.08 221	89.43 283	96.57 282

TinyColmap		92.31 283	91.53 282	94.65 303	96.92 239	89.75 304	96.92 300	96.68 316	90.45 284	89.62 307	97.85 178	76.06 331	98.81 215	86.74 315	92.51 252	95.41 320

TransMVSNet (Re)		92.67 279	91.51 283	96.15 249	96.58 257	94.65 213	98.90 79	96.73 313	90.86 279	89.46 309	97.86 176	85.62 248	98.09 289	86.45 317	81.12 337	95.71 315

RPMNet		92.52 281	91.17 284	96.59 217	97.00 234	93.43 255	94.96 340	97.26 285	82.27 345	96.93 125	92.12 348	86.98 220	97.88 304	76.32 346	96.65 168	98.46 169

Anonymous20231206		91.66 297	91.10 285	93.33 319	94.02 337	87.35 333	98.58 156	97.26 285	90.48 282	90.16 303	96.31 292	83.83 288	96.53 337	79.36 339	89.90 276	96.12 305

v18		92.10 286	90.97 286	95.50 270	96.34 274	94.85 188	98.82 102	97.52 253	89.99 292	85.31 332	93.26 330	88.90 161	96.92 322	88.82 293	79.77 341	94.73 329

v17		92.08 287	90.94 287	95.48 272	96.34 274	94.83 200	98.81 108	97.52 253	89.95 294	85.32 330	93.24 331	88.91 160	96.91 323	88.76 294	79.63 342	94.71 331

v16		92.08 287	90.94 287	95.49 271	96.38 270	94.84 197	98.81 108	97.51 256	89.94 295	85.25 333	93.28 329	88.86 162	96.91 323	88.70 295	79.78 340	94.72 330

FMVSNet5		91.81 295	90.92 289	94.49 306	97.21 223	92.09 273	98.00 231	97.55 251	89.31 311	90.86 298	95.61 314	74.48 338	95.32 343	85.57 323	89.70 277	96.07 307

Patchmatch-RL test		91.49 298	90.85 290	93.41 318	91.37 345	84.40 338	92.81 352	95.93 329	91.87 252	87.25 319	94.87 319	88.99 154	96.53 337	92.54 214	82.00 334	99.30 101

v15		91.94 289	90.77 291	95.43 277	96.31 282	94.83 200	98.77 119	97.50 259	89.92 296	85.13 334	93.08 334	88.76 173	96.86 325	88.40 299	79.10 344	94.61 335

V14		91.93 290	90.76 292	95.42 280	96.33 278	94.81 204	98.77 119	97.51 256	89.86 298	85.09 335	93.13 332	88.80 171	96.83 327	88.32 300	79.06 346	94.60 336

V9		91.91 291	90.73 293	95.45 274	96.32 281	94.80 205	98.77 119	97.50 259	89.81 299	85.03 337	93.08 334	88.76 173	96.86 325	88.24 301	79.03 347	94.69 332

v12		91.89 292	90.70 294	95.43 277	96.31 282	94.80 205	98.76 122	97.50 259	89.76 300	84.95 338	93.00 337	88.82 167	96.82 329	88.23 302	79.00 348	94.68 334

v13		91.88 293	90.69 295	95.43 277	96.33 278	94.78 210	98.75 123	97.50 259	89.68 303	84.93 339	92.98 338	88.84 165	96.83 327	88.14 303	79.09 345	94.69 332

v11		91.85 294	90.68 296	95.36 282	96.34 274	94.74 212	98.80 111	97.43 270	89.60 306	85.09 335	93.03 336	88.53 182	96.75 330	87.37 312	79.96 339	94.58 337

pmmvs6		91.77 296	90.63 297	95.17 287	94.69 333	91.24 287	98.67 145	97.92 232	86.14 328	89.62 307	97.56 204	75.79 332	98.34 272	90.75 251	84.56 331	95.94 310

gg-mvs-nofinetune		92.21 284	90.58 298	97.13 175	96.75 250	95.09 172	95.85 332	89.40 364	85.43 335	94.50 198	81.98 356	80.80 306	98.40 271	92.16 219	98.33 123	97.88 193

test20.03		90.89 305	90.38 299	92.43 324	93.48 338	88.14 328	98.33 190	97.56 247	93.40 197	87.96 317	96.71 279	80.69 307	94.13 347	79.15 340	86.17 324	95.01 327

Test4		92.21 284	90.34 300	97.82 124	92.83 341	95.87 143	97.94 235	98.05 228	94.50 141	82.12 344	94.48 322	59.54 357	98.54 234	95.39 134	98.22 128	99.06 133

test_0402		91.32 299	90.27 301	94.48 307	96.60 256	91.12 288	98.50 171	97.22 287	86.10 329	88.30 316	96.98 255	77.65 325	97.99 295	78.13 343	92.94 249	94.34 338

EG-PatchMatch MVS		91.13 301	90.12 302	94.17 314	94.73 332	89.00 317	98.13 217	97.81 236	89.22 312	85.32 330	96.46 288	67.71 350	98.42 258	87.89 310	93.82 232	95.08 324

PVSNet_0	88.72 19	91.28 300	90.03 303	95.00 291	97.99 176	87.29 334	94.84 343	98.50 142	92.06 246	89.86 305	95.19 315	79.81 312	99.39 144	92.27 218	69.79 355	98.33 180

LP		91.12 302	89.99 304	94.53 305	96.35 273	88.70 320	93.86 351	97.35 276	84.88 337	90.98 296	94.77 320	84.40 272	97.43 314	75.41 348	91.89 260	97.47 204

UnsupCasMVSNet_eth		90.99 304	89.92 305	94.19 313	94.08 336	89.83 303	97.13 295	98.67 109	93.69 178	85.83 327	96.19 299	75.15 334	96.74 331	89.14 286	79.41 343	96.00 308

TDRefinement		91.06 303	89.68 306	95.21 285	85.35 356	91.49 283	98.51 170	97.07 291	91.47 259	88.83 314	97.84 179	77.31 327	99.09 180	92.79 206	77.98 349	95.04 325

CMPMVS	66.06 21	89.70 312	89.67 307	89.78 330	93.19 339	76.56 350	97.00 297	98.35 164	80.97 348	81.57 345	97.75 187	74.75 337	98.61 227	89.85 272	93.63 235	94.17 340
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
YYNet1		90.70 307	89.39 308	94.62 304	94.79 331	90.65 296	97.20 291	97.46 266	87.54 321	72.54 354	95.74 308	86.51 226	96.66 335	86.00 320	86.76 322	96.54 286

MDA-MVSNet_test_wron		90.71 306	89.38 309	94.68 302	94.83 330	90.78 293	97.19 292	97.46 266	87.60 320	72.41 355	95.72 311	86.51 226	96.71 334	85.92 321	86.80 321	96.56 284

testpf		88.74 317	89.09 310	87.69 334	95.78 308	83.16 343	84.05 362	94.13 357	85.22 336	90.30 302	94.39 324	74.92 336	95.80 340	89.77 273	93.28 246	84.10 357

testus		88.91 316	89.08 311	88.40 333	91.39 344	76.05 351	96.56 320	96.48 321	89.38 310	89.39 310	95.17 317	70.94 345	93.56 350	77.04 345	95.41 210	95.61 317

pmmvs-eth3d		90.36 309	89.05 312	94.32 311	91.10 346	92.12 272	97.63 266	96.95 302	88.86 314	84.91 340	93.13 332	78.32 319	96.74 331	88.70 295	81.81 336	94.09 342

new_pmnet		90.06 310	89.00 313	93.22 322	94.18 334	88.32 327	96.42 325	96.89 309	86.19 327	85.67 329	93.62 327	77.18 328	97.10 319	81.61 334	89.29 285	94.23 339

test2356		88.68 318	88.61 314	88.87 332	89.90 350	78.23 348	95.11 338	96.66 319	88.66 316	89.06 312	94.33 326	73.14 343	92.56 354	75.56 347	95.11 213	95.81 313

testing_2		90.61 308	88.50 315	96.95 186	90.08 349	95.57 151	97.69 260	98.06 225	93.02 209	76.55 350	92.48 345	61.18 356	98.44 255	95.45 133	91.98 257	96.84 244

MVS-HIRNet		89.46 314	88.40 316	92.64 323	97.58 198	82.15 345	94.16 350	93.05 360	75.73 354	90.90 297	82.52 355	79.42 314	98.33 273	83.53 330	98.68 105	97.43 205

MDA-MVSNet-bldmvs		89.97 311	88.35 317	94.83 298	95.21 325	91.34 284	97.64 264	97.51 256	88.36 317	71.17 356	96.13 301	79.22 315	96.63 336	83.65 329	86.27 323	96.52 288

MIMVSNet1		89.67 313	88.28 318	93.82 315	92.81 342	91.08 289	98.01 229	97.45 268	87.95 318	87.90 318	95.87 307	67.63 351	94.56 346	78.73 342	88.18 303	95.83 312

N_pmnet		87.12 322	87.77 319	85.17 341	95.46 319	61.92 364	97.37 280	70.66 372	85.83 332	88.73 315	96.04 303	85.33 255	97.76 307	80.02 336	90.48 271	95.84 311

new-patchmatchnet		88.50 319	87.45 320	91.67 328	90.31 348	85.89 337	97.16 294	97.33 280	89.47 307	83.63 342	92.77 342	76.38 329	95.06 345	82.70 331	77.29 350	94.06 343

OpenMVS_ROB	86.42 20	89.00 315	87.43 321	93.69 316	93.08 340	89.42 310	97.91 239	96.89 309	78.58 351	85.86 326	94.69 321	69.48 347	98.29 280	77.13 344	93.29 245	93.36 347

PM-MVS		87.77 320	86.55 322	91.40 329	91.03 347	83.36 342	96.92 300	95.18 343	91.28 272	86.48 324	93.42 328	53.27 358	96.74 331	89.43 283	81.97 335	94.11 341

test1235678		86.26 324	85.81 323	87.62 335	86.97 354	75.00 355	96.55 322	96.32 324	86.08 330	81.32 346	92.98 338	73.10 344	92.05 355	71.64 351	87.32 313	95.81 313

UnsupCasMVSNet_bld		87.17 321	85.12 324	93.31 320	91.94 343	88.77 318	94.92 342	98.30 174	84.30 340	82.30 343	90.04 349	63.96 355	97.25 317	85.85 322	74.47 354	93.93 345

pmmvs3		86.67 323	84.86 325	92.11 327	88.16 351	87.19 335	96.63 317	94.75 347	79.88 350	87.22 320	92.75 343	66.56 352	95.20 344	81.24 335	76.56 352	93.96 344

1111		84.94 325	84.30 326	86.86 336	87.59 352	75.10 353	96.63 317	96.43 322	82.53 343	80.75 347	92.91 340	68.94 348	93.79 348	68.24 354	84.66 330	91.70 349

test12356		83.47 326	83.37 327	83.78 342	84.43 357	70.09 360	95.12 337	95.60 338	82.98 341	78.89 349	92.43 346	64.99 353	91.41 357	70.36 352	85.55 329	89.82 351

testmv		78.74 327	77.35 328	82.89 344	78.16 365	69.30 361	95.87 331	94.65 348	81.11 347	70.98 357	87.11 353	46.31 359	90.42 358	65.28 357	76.72 351	88.95 352

FPMVS		77.62 331	77.14 329	79.05 346	79.25 362	60.97 365	95.79 333	95.94 328	65.96 356	67.93 358	94.40 323	37.73 364	88.88 360	68.83 353	88.46 300	87.29 353

Gipuma		78.40 329	76.75 330	83.38 343	95.54 316	80.43 347	79.42 363	97.40 273	64.67 357	73.46 353	80.82 358	45.65 361	93.14 352	66.32 356	87.43 311	76.56 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		73.05 333	76.31 331	63.27 354	87.59 352	75.10 353	96.63 317	96.43 322	82.53 343	80.75 347	92.91 340	68.94 348	93.79 348	68.24 354	12.72 366	20.91 366

LCM-MVSNet		78.70 328	76.24 332	86.08 338	77.26 366	71.99 358	94.34 348	96.72 314	61.62 359	76.53 351	89.33 350	33.91 367	92.78 353	81.85 333	74.60 353	93.46 346

PMMVS2		77.95 330	75.44 333	85.46 339	82.54 358	74.95 356	94.23 349	93.08 359	72.80 355	74.68 352	87.38 351	36.36 365	91.56 356	73.95 349	63.94 356	89.87 350

no-one		74.41 332	70.76 334	85.35 340	79.88 361	76.83 349	94.68 345	94.22 354	80.33 349	63.81 359	79.73 359	35.45 366	93.36 351	71.78 350	36.99 363	85.86 356

tmp_tt		68.90 335	66.97 335	74.68 350	50.78 371	59.95 366	87.13 358	83.47 370	38.80 365	62.21 360	96.23 296	64.70 354	76.91 367	88.91 292	30.49 364	87.19 354

ANet_high		69.08 334	65.37 336	80.22 345	65.99 369	71.96 359	90.91 356	90.09 363	82.62 342	49.93 365	78.39 360	29.36 368	81.75 363	62.49 360	38.52 362	86.95 355

PNet_i23d		67.70 336	65.07 337	75.60 348	78.61 363	59.61 367	89.14 357	88.24 366	61.83 358	52.37 363	80.89 357	18.91 369	84.91 362	62.70 359	52.93 358	82.28 358

E-PMN		64.94 338	64.25 338	67.02 352	82.28 359	59.36 368	91.83 355	85.63 368	52.69 362	60.22 361	77.28 361	41.06 363	80.12 365	46.15 363	41.14 360	61.57 364

PMVS	61.03 23	65.95 337	63.57 339	73.09 351	57.90 370	51.22 370	85.05 361	93.93 358	54.45 361	44.32 366	83.57 354	13.22 370	89.15 359	58.68 361	81.00 338	78.91 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		64.07 339	63.26 340	66.53 353	81.73 360	58.81 369	91.85 354	84.75 369	51.93 364	59.09 362	75.13 362	43.32 362	79.09 366	42.03 364	39.47 361	61.69 363

MVE	62.14 22	63.28 341	59.38 341	74.99 349	74.33 367	65.47 363	85.55 360	80.50 371	52.02 363	51.10 364	75.00 363	10.91 374	80.50 364	51.60 362	53.40 357	78.99 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		63.73 340	58.86 342	78.35 347	67.62 368	67.90 362	86.56 359	87.81 367	58.26 360	42.49 367	70.28 364	11.55 372	85.05 361	63.66 358	41.50 359	82.11 359

v1.0		41.12 342	54.83 343	0.00 359	99.63 22	0.00 374	0.00 365	98.84 56	96.40 58	99.27 8	99.31 23	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

pcd1.5k->3k		39.42 343	41.78 344	32.35 355	96.17 291	0.00 374	0.00 365	98.54 130	0.00 369	0.00 371	0.00 371	87.78 204	0.00 371	0.00 368	93.56 237	97.06 220

cdsmvs_eth3d_5k		23.98 345	31.98 345	0.00 359	0.00 374	0.00 374	0.00 365	98.59 120	0.00 369	0.00 371	98.61 107	90.60 132	0.00 371	0.00 368	0.00 369	0.00 369

wuyk23d		30.17 344	30.18 346	30.16 356	78.61 363	43.29 371	66.79 364	14.21 373	17.31 366	14.82 370	11.93 370	11.55 372	41.43 368	37.08 365	19.30 365	5.76 368

testmvs		21.48 346	24.95 347	11.09 358	14.89 372	6.47 373	96.56 320	9.87 374	7.55 367	17.93 368	39.02 366	9.43 375	5.90 370	16.56 367	12.72 366	20.91 366

test123		20.95 347	23.72 348	12.64 357	13.54 373	8.19 372	96.55 322	6.13 375	7.48 368	16.74 369	37.98 367	12.97 371	6.05 369	16.69 366	5.43 368	23.68 365

ab-mvs-re		8.20 348	10.94 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	98.43 122	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		7.88 349	10.50 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	94.51 63	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																99.20 111

test_part2					99.63 22	99.18 2				99.27 8

test_part1				0.00 359		0.00 374	0.00 365	98.84 56				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												89.45 143				99.20 111

sam_mvs												88.99 154

semantic-postprocess				94.85 296	97.98 178	90.56 298		98.11 213	93.75 170	92.58 272	97.48 206	83.91 285	97.41 315	92.48 216	91.30 266	96.58 280

ambc				89.49 331	86.66 355	75.78 352	92.66 353	96.72 314		86.55 323	92.50 344	46.01 360	97.90 300	90.32 263	82.09 333	94.80 328

MTGPA								98.74 84

test_post1							96.68 316				30.43 369	87.85 202	98.69 220	92.59 211

test_post											31.83 368	88.83 166	98.91 202

patchmatchnet-post											95.10 318	89.42 144	98.89 206

GG-mvs-BLEND				96.59 217	96.34 274	94.98 178	96.51 324	88.58 365		93.10 261	94.34 325	80.34 311	98.05 291	89.53 280	96.99 160	96.74 253

MTMP							98.89 83	94.14 356

gm-plane-assit					95.88 305	87.47 332			89.74 302		96.94 260		99.19 166	93.32 188

test9_res														96.39 102	99.57 58	99.69 38

TEST9					99.31 52	98.50 15	97.92 236	98.73 89	92.63 221	97.74 89	98.68 100	96.20 14	99.80 62

test_8					99.29 60	98.44 17	97.89 244	98.72 91	92.98 211	97.70 92	98.66 103	96.20 14	99.80 62

agg_prior2														95.87 117	99.57 58	99.68 44

agg_prior					99.30 57	98.38 20		98.72 91		97.57 104			99.81 55

TestCases				96.99 182	99.25 69	93.21 261		98.18 194	91.36 265	93.52 247	98.77 93	84.67 263	99.72 91	89.70 277	97.87 140	98.02 187

test_prior4						98.01 45	97.86 247

test_prior2							97.80 252		96.12 65	97.89 83	98.69 98	95.96 27		96.89 76	99.60 52

test_prior				99.19 31	99.31 52	98.22 34		98.84 56					99.70 96			99.65 53

旧先验2							97.57 269		91.30 270	98.67 41			99.80 62	95.70 126

新几何2							97.64 264

新几何1				99.16 38	99.34 44	98.01 45		98.69 99	90.06 291	98.13 62	98.95 76	94.60 61	99.89 29	91.97 227	99.47 72	99.59 64

旧先验1					99.29 60	97.48 63		98.70 98			99.09 55	95.56 37			99.47 72	99.61 59

无先验							97.58 268	98.72 91	91.38 264				99.87 38	93.36 186		99.60 62

原ACMM2							97.67 262

原ACMM1				98.65 68	99.32 50	96.62 94		98.67 109	93.27 203	97.81 85	98.97 69	95.18 50	99.83 47	93.84 175	99.46 75	99.50 74

test222					99.23 75	97.17 76	97.40 276	98.66 112	88.68 315	98.05 66	98.96 74	94.14 72			99.53 68	99.61 59

testdata2													99.89 29	91.65 236

segment_acmp												96.85 4

testdata				98.26 96	99.20 79	95.36 160		98.68 102	91.89 250	98.60 46	99.10 51	94.44 68	99.82 53	94.27 166	99.44 77	99.58 66

testdata1							97.32 286		96.34 59

test12				99.18 35	99.16 81	98.19 36		98.53 133		98.07 65		95.13 52	99.72 91		99.56 64	99.63 58

plane_prior7					97.42 210	94.63 215

plane_prior6					97.35 215	94.61 218						87.09 217

plane_prior5								98.56 127					99.03 188	96.07 107	94.27 217	96.92 229

plane_prior4											98.28 142

plane_prior3						94.61 218			97.02 40	95.34 179

plane_prior2							98.80 111		97.28 22

plane_prior1					97.37 214

plane_prior						94.60 220	98.44 178		96.74 47						94.22 219

n2								0.00 376

nn								0.00 376

door-mid								94.37 351

lessismore_v0				94.45 310	94.93 329	88.44 325		91.03 362		86.77 322	97.64 197	76.23 330	98.42 258	90.31 264	85.64 328	96.51 290

LGP-MVS_train				96.47 231	97.46 206	93.54 252		98.54 130	94.67 133	94.36 210	98.77 93	85.39 251	99.11 176	95.71 124	94.15 223	96.76 251

test11								98.66 112

door								94.64 349

HQP5-MVS						94.25 235

HQP-NCC					97.20 224		98.05 225		96.43 55	94.45 200

ACMP_Plane					97.20 224		98.05 225		96.43 55	94.45 200

BP-MVS														95.30 137

HQP4-MVS										94.45 200			98.96 195			96.87 241

HQP3-MVS								98.46 147							94.18 221

HQP2-MVS												86.75 223

NP-MVS					97.28 218	94.51 223					97.73 188

MDTV_nov1_ep13_2view						84.26 339	96.89 307		90.97 278	97.90 82		89.89 140		93.91 174		99.18 117

ACMMP++_ref															92.97 248

ACMMP++															93.61 236

Test By Simon												94.64 60

ITE_SJBPF				95.44 275	97.42 210	91.32 285		97.50 259	95.09 117	93.59 243	98.35 132	81.70 298	98.88 207	89.71 276	93.39 242	96.12 305

DeepMVS_CX				86.78 337	97.09 232	72.30 357		95.17 344	75.92 353	84.34 341	95.19 315	70.58 346	95.35 342	79.98 338	89.04 288	92.68 348

High-resolution multi-view benchmark