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.

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 64	98.20 3	99.93 1	99.98 2	96.82 13	100.00 1	99.75 11	100.00 1	99.99 12

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 76	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 21	100.00 1	100.00 1

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 72	98.47 2	99.13 55	99.92 5	96.38 22	100.00 1	99.74 13	100.00 1	100.00 1

ESAPD		99.18 4	98.99 7	99.75 3	99.89 36	99.25 6	99.88 66	98.41 124	96.14 43	99.49 33	99.91 7	97.20 11	100.00 1	99.99 1	99.99 13	99.99 12

MSLP-MVS++		99.13 5	99.01 6	99.49 23	99.94 14	98.46 52	99.98 6	98.86 54	97.10 15	99.80 8	99.94 4	95.92 29	100.00 1	99.51 22	100.00 1	100.00 1

HSP-MVS		99.07 6	99.11 4	98.95 76	99.93 24	97.24 96	99.95 31	98.32 139	97.50 10	99.52 32	99.88 12	97.43 6	99.71 108	99.50 23	99.98 26	99.89 72

HPM-MVS++		99.07 6	98.88 11	99.63 8	99.90 33	99.02 12	99.95 31	98.56 86	97.56 9	99.44 37	99.85 21	95.38 40	100.00 1	99.31 30	99.99 13	99.87 75

APDe-MVS		99.06 8	98.91 10	99.51 21	99.94 14	98.76 32	99.91 56	98.39 128	97.20 14	99.46 35	99.85 21	95.53 38	99.79 93	99.86 5	100.00 1	99.99 12

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 44	98.72 121	97.71 73	99.98 6	98.44 109	96.85 20	99.80 8	99.91 7	97.57 4	99.85 81	99.44 26	99.99 13	99.99 12
Skip Steuart: Steuart Systems R&D Blog.
CHOSEN 280x420		99.01 10	99.03 5	98.95 76	99.38 83	98.87 19	98.46 270	99.42 25	97.03 17	99.02 59	99.09 112	99.35 1	98.21 202	99.73 15	99.78 69	99.77 86

test_prior3		98.99 11	98.84 12	99.43 27	99.94 14	98.49 50	99.95 31	98.65 69	95.78 50	99.73 14	99.76 56	96.00 25	99.80 91	99.78 9	100.00 1	99.99 12

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 31	99.74 57	98.67 36	99.77 111	98.38 131	96.73 26	99.88 3	99.74 63	94.89 55	99.59 119	99.80 7	99.98 26	99.97 54

SD-MVS		98.92 13	98.70 14	99.56 16	99.70 65	98.73 33	99.94 45	98.34 137	96.38 34	99.81 7	99.76 56	94.59 58	99.98 32	99.84 6	99.96 37	99.97 54

MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 152	99.44 23	97.33 12	99.00 62	99.72 65	94.03 77	99.98 32	98.73 55	100.00 1	100.00 1

train_agg		98.88 15	98.65 16	99.59 14	99.92 27	98.92 15	99.96 19	98.43 114	94.35 85	99.71 16	99.86 17	95.94 27	99.85 81	99.69 18	99.98 26	99.99 12

agg_prior1		98.88 15	98.66 15	99.54 18	99.93 24	98.77 26	99.96 19	98.43 114	94.63 78	99.63 21	99.85 21	95.79 32	99.85 81	99.72 16	99.99 13	99.99 12

agg_prior3		98.84 17	98.62 18	99.47 26	99.92 27	98.56 46	99.96 19	98.43 114	94.07 95	99.67 19	99.85 21	96.05 23	99.85 81	99.69 18	99.98 26	99.99 12

DeepC-MVS_fast	96.59 1	98.81 18	98.54 23	99.62 11	99.90 33	98.85 21	99.24 200	98.47 105	98.14 4	99.08 56	99.91 7	93.09 99	100.00 1	99.04 40	99.99 13	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-1		98.79 19	98.60 20	99.36 36	99.85 41	98.34 54	99.87 71	98.52 93	96.05 45	99.41 40	99.79 45	94.93 53	99.76 97	99.07 35	99.90 53	99.99 12

Regformer-2		98.78 20	98.59 21	99.36 36	99.85 41	98.32 55	99.87 71	98.52 93	96.04 46	99.41 40	99.79 45	94.92 54	99.76 97	99.05 36	99.90 53	99.98 44

SMA-MVS		98.76 21	98.47 25	99.62 11	99.88 39	98.87 19	99.86 87	98.38 131	93.19 122	99.77 11	99.92 5	95.54 35	100.00 1	99.68 20	99.99 13	100.00 1

MVS_111021_HR		98.72 22	98.62 18	99.01 72	99.36 84	97.18 99	99.93 50	99.90 1	96.81 24	98.67 73	99.77 52	93.92 79	99.89 70	99.27 31	99.94 44	99.96 58

XVS		98.70 23	98.55 22	99.15 51	99.94 14	97.50 81	99.94 45	98.42 122	96.22 39	99.41 40	99.78 50	94.34 65	99.96 43	98.92 45	99.95 40	99.99 12

CDPH-MVS		98.65 24	98.36 34	99.49 23	99.94 14	98.73 33	99.87 71	98.33 138	93.97 101	99.76 12	99.87 15	94.99 51	99.75 100	98.55 65	100.00 1	99.98 44

APD-MVS		98.62 25	98.35 35	99.41 31	99.90 33	98.51 49	99.87 71	98.36 135	94.08 94	99.74 13	99.73 64	94.08 75	99.74 104	99.42 27	99.99 13	99.99 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + GP.		98.60 26	98.51 24	98.86 81	99.73 61	96.63 113	99.97 12	97.92 181	98.07 5	98.76 69	99.55 85	95.00 50	99.94 59	99.91 4	97.68 125	99.99 12

PAPM		98.60 26	98.42 26	99.14 53	96.05 216	98.96 13	99.90 59	99.35 27	96.68 28	98.35 88	99.66 78	96.45 21	98.51 173	99.45 25	99.89 55	99.96 58

#test#		98.59 28	98.41 27	99.14 53	99.96 8	97.43 85	99.95 31	98.61 78	95.00 68	99.31 46	99.85 21	94.22 70	100.00 1	98.78 53	99.98 26	99.98 44

Regformer-3		98.58 29	98.41 27	99.10 59	99.84 46	97.57 77	99.66 145	98.52 93	95.79 49	99.01 60	99.77 52	94.40 61	99.75 100	98.82 50	99.83 62	99.98 44

HFP-MVS		98.56 30	98.37 32	99.14 53	99.96 8	97.43 85	99.95 31	98.61 78	94.77 73	99.31 46	99.85 21	94.22 70	100.00 1	98.70 56	99.98 26	99.98 44

Regformer-4		98.56 30	98.39 30	99.08 61	99.84 46	97.52 79	99.66 145	98.52 93	95.76 52	99.01 60	99.77 52	94.33 67	99.75 100	98.80 52	99.83 62	99.98 44

region2R		98.54 32	98.37 32	99.05 67	99.96 8	97.18 99	99.96 19	98.55 90	94.87 71	99.45 36	99.85 21	94.07 76	100.00 1	98.67 58	100.00 1	99.98 44

DELS-MVS		98.54 32	98.22 38	99.50 22	99.15 88	98.65 39	100.00 1	98.58 82	97.70 7	98.21 95	99.24 106	92.58 108	99.94 59	98.63 63	99.94 44	99.92 69
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
PAPR		98.52 34	98.16 42	99.58 15	99.97 3	98.77 26	99.95 31	98.43 114	95.35 62	98.03 99	99.75 61	94.03 77	99.98 32	98.11 76	99.83 62	99.99 12

ACMMPR		98.50 35	98.32 36	99.05 67	99.96 8	97.18 99	99.95 31	98.60 80	94.77 73	99.31 46	99.84 35	93.73 86	100.00 1	98.70 56	99.98 26	99.98 44

ACMMP_Plus		98.49 36	98.14 43	99.54 18	99.66 67	98.62 41	99.85 89	98.37 134	94.68 77	99.53 29	99.83 37	92.87 100	100.00 1	98.66 61	99.84 61	99.99 12

EPNet		98.49 36	98.40 29	98.77 84	99.62 69	96.80 111	99.90 59	99.51 20	97.60 8	99.20 51	99.36 100	93.71 87	99.91 65	97.99 82	98.71 106	99.61 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		98.45 38	98.32 36	98.87 80	99.96 8	96.62 114	99.97 12	98.39 128	94.43 83	98.90 64	99.87 15	94.30 68	100.00 1	99.04 40	99.99 13	99.99 12

PS-MVSNAJ		98.44 39	98.20 40	99.16 47	98.80 118	98.92 15	99.54 165	98.17 156	97.34 11	99.85 5	99.85 21	91.20 126	99.89 70	99.41 28	99.67 75	98.69 191

MVS_111021_LR		98.42 40	98.38 31	98.53 102	99.39 82	95.79 140	99.87 71	99.86 2	96.70 27	98.78 68	99.79 45	92.03 116	99.90 66	99.17 32	99.86 60	99.88 74

DP-MVS Recon		98.41 41	98.02 47	99.56 16	99.97 3	98.70 35	99.92 52	98.44 109	92.06 171	98.40 86	99.84 35	95.68 33	100.00 1	98.19 72	99.71 73	99.97 54

PHI-MVS		98.41 41	98.21 39	99.03 69	99.86 40	97.10 103	99.98 6	98.80 59	90.78 207	99.62 23	99.78 50	95.30 41	100.00 1	99.80 7	99.93 49	99.99 12

mPP-MVS		98.39 43	98.20 40	98.97 74	99.97 3	96.92 108	99.95 31	98.38 131	95.04 67	98.61 78	99.80 44	93.39 91	100.00 1	98.64 62	100.00 1	99.98 44

PGM-MVS		98.34 44	98.13 44	98.99 73	99.92 27	97.00 104	99.75 118	99.50 21	93.90 105	99.37 44	99.76 56	93.24 96	100.00 1	97.75 92	99.96 37	99.98 44

zzz-MVS		98.33 45	98.00 48	99.30 38	99.85 41	97.93 68	99.80 102	98.28 143	95.76 52	97.18 115	99.88 12	92.74 104	100.00 1	98.67 58	99.88 57	99.99 12

MTAPA		98.29 46	97.96 52	99.30 38	99.85 41	97.93 68	99.39 183	98.28 143	95.76 52	97.18 115	99.88 12	92.74 104	100.00 1	98.67 58	99.88 57	99.99 12

CANet		98.27 47	97.82 55	99.63 8	99.72 63	99.10 10	99.98 6	98.51 99	97.00 18	98.52 80	99.71 67	87.80 164	99.95 51	99.75 11	99.38 93	99.83 78

EI-MVSNet-Vis-set		98.27 47	98.11 45	98.75 85	99.83 49	96.59 116	99.40 180	98.51 99	95.29 64	98.51 81	99.76 56	93.60 90	99.71 108	98.53 66	99.52 86	99.95 63

APD-MVS_3200maxsize		98.25 49	98.08 46	98.78 83	99.81 51	96.60 115	99.82 97	98.30 141	93.95 103	99.37 44	99.77 52	92.84 101	99.76 97	98.95 42	99.92 51	99.97 54

xiu_mvs_v2_base		98.23 50	97.97 50	99.02 71	98.69 122	98.66 37	99.52 167	98.08 167	97.05 16	99.86 4	99.86 17	90.65 134	99.71 108	99.39 29	98.63 107	98.69 191

MP-MVS		98.23 50	97.97 50	99.03 69	99.94 14	97.17 102	99.95 31	98.39 128	94.70 76	98.26 93	99.81 43	91.84 120	100.00 1	98.85 49	99.97 35	99.93 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		98.14 52	97.99 49	98.60 95	99.80 52	96.27 123	99.36 187	98.50 103	95.21 66	98.30 90	99.75 61	93.29 95	99.73 107	98.37 70	99.30 96	99.81 80

PAPM_NR		98.12 53	97.93 53	98.70 87	99.94 14	96.13 132	99.82 97	98.43 114	94.56 79	97.52 108	99.70 69	94.40 61	99.98 32	97.00 106	99.98 26	99.99 12

WTY-MVS		98.10 54	97.60 60	99.60 13	98.92 104	99.28 5	99.89 64	99.52 18	95.58 58	98.24 94	99.39 97	93.33 92	99.74 104	97.98 84	95.58 167	99.78 85

MP-MVS-pluss		98.07 55	97.64 58	99.38 35	99.74 57	98.41 53	99.74 121	98.18 155	93.35 119	96.45 129	99.85 21	92.64 107	99.97 41	98.91 47	99.89 55	99.77 86
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
1121		98.03 56	97.57 62	99.40 33	99.74 57	98.21 58	98.31 280	98.62 76	92.78 135	99.53 29	99.83 37	95.08 45	100.00 1	94.36 147	99.92 51	99.99 12

HPM-MVS		97.96 57	97.72 56	98.68 88	99.84 46	96.39 122	99.90 59	98.17 156	92.61 146	98.62 77	99.57 84	91.87 119	99.67 115	98.87 48	99.99 13	99.99 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PVSNet_Blended		97.94 58	97.64 58	98.83 82	99.59 70	96.99 105	100.00 1	99.10 31	95.38 61	98.27 91	99.08 113	89.00 155	99.95 51	99.12 33	99.25 97	99.57 118

PLC	95.54 3	97.93 59	97.89 54	98.05 131	99.82 50	94.77 170	99.92 52	98.46 107	93.93 104	97.20 113	99.27 102	95.44 39	99.97 41	97.41 96	99.51 88	99.41 138
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
API-MVS		97.86 60	97.66 57	98.47 109	99.52 76	95.41 153	99.47 174	98.87 53	91.68 180	98.84 65	99.85 21	92.34 110	99.99 28	98.44 68	99.96 37	100.00 1

lupinMVS		97.85 61	97.60 60	98.62 93	97.28 186	97.70 75	99.99 3	97.55 210	95.50 60	99.43 38	99.67 76	90.92 132	98.71 159	98.40 69	99.62 78	99.45 133

0601test		97.83 62	97.37 66	99.21 42	99.18 86	97.98 66	99.64 152	99.27 29	91.43 188	97.88 102	98.99 120	95.84 31	99.84 89	98.82 50	95.32 171	99.79 83

alignmvs		97.81 63	97.33 68	99.25 40	98.77 120	98.66 37	99.99 3	98.44 109	94.40 84	98.41 84	99.47 91	93.65 88	99.42 136	98.57 64	94.26 188	99.67 99

HPM-MVS_fast		97.80 64	97.50 63	98.68 88	99.79 53	96.42 119	99.88 66	98.16 159	91.75 178	98.94 63	99.54 87	91.82 121	99.65 117	97.62 94	99.99 13	99.99 12

HY-MVS	92.50 7	97.79 65	97.17 73	99.63 8	98.98 97	99.32 3	97.49 301	99.52 18	95.69 56	98.32 89	97.41 189	93.32 93	99.77 95	98.08 79	95.75 164	99.81 80

CNLPA		97.76 66	97.38 65	98.92 78	99.53 75	96.84 109	99.87 71	98.14 162	93.78 109	96.55 126	99.69 72	92.28 111	99.98 32	97.13 102	99.44 91	99.93 66

ACMMP		97.74 67	97.44 64	98.66 90	99.92 27	96.13 132	99.18 204	99.45 22	94.84 72	96.41 132	99.71 67	91.40 123	99.99 28	97.99 82	98.03 121	99.87 75
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
DeepPCF-MVS	95.94 2	97.71 68	98.98 8	93.92 253	99.63 68	81.76 328	99.96 19	98.56 86	99.47 1	99.19 53	99.99 1	94.16 74	100.00 1	99.92 3	99.93 49	100.00 1

abl_6		97.67 69	97.34 67	98.66 90	99.68 66	96.11 136	99.68 140	98.14 162	93.80 108	99.27 49	99.70 69	88.65 160	99.98 32	97.46 95	99.72 72	99.89 72

CPTT-MVS		97.64 70	97.32 69	98.58 97	99.97 3	95.77 141	99.96 19	98.35 136	89.90 218	98.36 87	99.79 45	91.18 129	99.99 28	98.37 70	99.99 13	99.99 12

sss		97.57 71	97.03 78	99.18 44	98.37 136	98.04 64	99.73 127	99.38 26	93.46 117	98.76 69	99.06 114	91.21 125	99.89 70	96.33 114	97.01 143	99.62 107

MVS_0304		97.52 72	96.79 84	99.69 6	99.59 70	99.30 4	99.97 12	98.01 171	96.99 19	98.84 65	99.79 45	78.90 261	99.96 43	99.74 13	99.32 95	99.81 80

xiu_mvs_v1_base_debu		97.43 73	97.06 74	98.55 98	97.74 172	98.14 59	99.31 192	97.86 188	96.43 31	99.62 23	99.69 72	85.56 186	99.68 112	99.05 36	98.31 113	97.83 200

xiu_mvs_v1_base		97.43 73	97.06 74	98.55 98	97.74 172	98.14 59	99.31 192	97.86 188	96.43 31	99.62 23	99.69 72	85.56 186	99.68 112	99.05 36	98.31 113	97.83 200

xiu_mvs_v1_base_debi		97.43 73	97.06 74	98.55 98	97.74 172	98.14 59	99.31 192	97.86 188	96.43 31	99.62 23	99.69 72	85.56 186	99.68 112	99.05 36	98.31 113	97.83 200

MAR-MVS		97.43 73	97.19 71	98.15 127	99.47 79	94.79 169	99.05 222	98.76 60	92.65 144	98.66 75	99.82 40	88.52 161	99.98 32	98.12 75	99.63 77	99.67 99
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
114514_t		97.41 77	96.83 81	99.14 53	99.51 78	97.83 70	99.89 64	98.27 146	88.48 240	99.06 57	99.66 78	90.30 137	99.64 118	96.32 115	99.97 35	99.96 58

DWT-MVSNet_test		97.31 78	97.19 71	97.66 142	98.24 142	94.67 171	98.86 242	98.20 154	93.60 115	98.09 97	98.89 127	97.51 5	98.78 153	94.04 155	97.28 133	99.55 121

OMC-MVS		97.28 79	97.23 70	97.41 151	99.76 54	93.36 204	99.65 148	97.95 177	96.03 47	97.41 110	99.70 69	89.61 142	99.51 122	96.73 112	98.25 116	99.38 145

PVSNet_Blended_VisFu		97.27 80	96.81 82	98.66 90	98.81 117	96.67 112	99.92 52	98.64 72	94.51 80	96.38 133	98.49 166	89.05 154	99.88 76	97.10 104	98.34 111	99.43 136

jason		97.24 81	96.86 80	98.38 119	95.73 228	97.32 95	99.97 12	97.40 230	95.34 63	98.60 79	99.54 87	87.70 165	98.56 170	97.94 85	99.47 89	99.25 160
jason: jason.
AdaColmap		97.23 82	96.80 83	98.51 103	99.99 1	95.60 149	99.09 210	98.84 56	93.32 120	96.74 123	99.72 65	86.04 182	100.00 1	98.01 80	99.43 92	99.94 65

tfpn_ndepth		97.21 83	96.63 88	98.92 78	99.06 90	98.28 56	99.95 31	98.91 43	92.96 127	96.49 127	98.67 154	97.40 7	99.07 142	91.87 191	94.38 181	99.41 138

VNet		97.21 83	96.57 91	99.13 58	98.97 98	97.82 71	99.03 224	99.21 30	94.31 87	99.18 54	98.88 129	86.26 181	99.89 70	98.93 44	94.32 186	99.69 96

PVSNet	91.05 13	97.13 85	96.69 87	98.45 111	99.52 76	95.81 139	99.95 31	99.65 16	94.73 75	99.04 58	99.21 108	84.48 194	99.95 51	94.92 133	98.74 105	99.58 116

CSCG		97.10 86	97.04 77	97.27 156	99.89 36	91.92 238	99.90 59	99.07 34	88.67 237	95.26 154	99.82 40	93.17 98	99.98 32	98.15 74	99.47 89	99.90 71

canonicalmvs		97.09 87	96.32 95	99.39 34	98.93 102	98.95 14	99.72 132	97.35 234	94.45 81	97.88 102	99.42 93	86.71 176	99.52 121	98.48 67	93.97 199	99.72 93

PatchFormer-LS_test		97.01 88	96.79 84	97.69 141	98.26 141	94.80 167	98.66 258	98.13 164	93.70 112	97.86 104	98.80 145	95.54 35	98.67 161	94.12 154	96.00 156	99.60 111

thres200		96.96 89	96.21 98	99.22 41	98.97 98	98.84 22	99.85 89	99.71 5	93.17 123	96.26 134	98.88 129	89.87 140	99.51 122	94.26 151	94.91 174	99.31 153

MVSFormer		96.94 90	96.60 89	97.95 133	97.28 186	97.70 75	99.55 163	97.27 241	91.17 197	99.43 38	99.54 87	90.92 132	96.89 271	94.67 142	99.62 78	99.25 160

F-COLMAP		96.93 91	96.95 79	96.87 164	99.71 64	91.74 244	99.85 89	97.95 177	93.11 125	95.72 147	99.16 110	92.35 109	99.94 59	95.32 128	99.35 94	98.92 184

DeepC-MVS	94.51 4	96.92 92	96.40 94	98.45 111	99.16 87	95.90 138	99.66 145	98.06 168	96.37 37	94.37 173	99.49 90	83.29 203	99.90 66	97.63 93	99.61 81	99.55 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tfpn1000		96.90 93	96.29 96	98.74 86	99.00 95	98.09 62	99.92 52	98.91 43	92.08 168	95.85 140	98.65 156	97.39 8	98.83 149	90.56 207	94.23 189	99.31 153

1314		96.84 94	95.96 109	99.48 25	96.74 206	98.52 48	98.31 280	98.86 54	95.82 48	89.91 215	98.98 121	87.49 167	99.96 43	97.80 88	99.73 71	99.96 58

CHOSEN 1792x2688		96.81 95	96.53 92	97.64 143	98.91 106	93.07 210	99.65 148	99.80 3	95.64 57	95.39 151	98.86 133	84.35 196	99.90 66	96.98 107	99.16 99	99.95 63

tfpn200view9		96.79 96	95.99 104	99.19 43	98.94 100	98.82 23	99.78 106	99.71 5	92.86 128	96.02 137	98.87 131	89.33 143	99.50 124	93.84 158	94.57 175	99.27 158

thres400		96.78 97	95.99 104	99.16 47	98.94 100	98.82 23	99.78 106	99.71 5	92.86 128	96.02 137	98.87 131	89.33 143	99.50 124	93.84 158	94.57 175	99.16 169

CANet_DTU		96.76 98	96.15 99	98.60 95	98.78 119	97.53 78	99.84 92	97.63 202	97.25 13	99.20 51	99.64 80	81.36 230	99.98 32	92.77 180	98.89 101	98.28 194

PMMVS		96.76 98	96.76 86	96.76 167	98.28 139	92.10 233	99.91 56	97.98 174	94.12 92	99.53 29	99.39 97	86.93 175	98.73 157	96.95 109	97.73 123	99.45 133

thres100view900		96.74 100	95.92 112	99.18 44	98.90 107	98.77 26	99.74 121	99.71 5	92.59 148	95.84 141	98.86 133	89.25 145	99.50 124	93.84 158	94.57 175	99.27 158

TESTMET0.1,1		96.74 100	96.26 97	98.16 124	97.36 185	96.48 118	99.96 19	98.29 142	91.93 173	95.77 146	98.07 178	95.54 35	98.29 196	90.55 208	98.89 101	99.70 94

conf200view11		96.73 102	95.92 112	99.16 47	98.90 107	98.77 26	99.74 121	99.71 5	92.59 148	95.84 141	98.86 133	89.25 145	99.50 124	93.84 158	94.57 175	99.20 163

tfpn111		96.69 103	95.87 122	99.16 47	98.90 107	98.77 26	99.74 121	99.71 5	92.59 148	95.84 141	98.86 133	89.25 145	99.50 124	93.44 169	94.50 179	99.20 163

thres600view7		96.69 103	95.87 122	99.14 53	98.90 107	98.78 25	99.74 121	99.71 5	92.59 148	95.84 141	98.86 133	89.25 145	99.50 124	93.44 169	94.50 179	99.16 169

EPP-MVSNet		96.69 103	96.60 89	96.96 161	97.74 172	93.05 212	99.37 185	98.56 86	88.75 236	95.83 145	99.01 117	96.01 24	98.56 170	96.92 110	97.20 139	99.25 160

casdiffmvs		96.67 106	96.14 100	98.27 122	97.95 156	96.49 117	99.48 172	97.29 239	92.09 167	98.67 73	97.12 196	89.10 153	98.74 156	96.27 116	97.25 136	99.57 118

HyFIR lowres test		96.66 107	96.43 93	97.36 154	99.05 91	93.91 184	99.70 134	99.80 3	90.54 208	96.26 134	98.08 177	92.15 114	98.23 201	96.84 111	95.46 168	99.93 66

MVS		96.60 108	95.56 133	99.72 4	96.85 199	99.22 8	98.31 280	98.94 39	91.57 182	90.90 201	99.61 82	86.66 177	99.96 43	97.36 97	99.88 57	99.99 12

UA-Net		96.54 109	95.96 109	98.27 122	98.23 143	95.71 146	98.00 295	98.45 108	93.72 111	98.41 84	99.27 102	88.71 159	99.66 116	91.19 196	97.69 124	99.44 135

thresconf0.02		96.53 110	95.88 115	98.48 105	98.59 123	97.38 89	99.87 71	98.91 43	91.32 191	95.22 159	98.83 139	96.57 15	98.66 163	89.55 220	94.09 191	99.40 141

tfpn_n400		96.53 110	95.88 115	98.48 105	98.59 123	97.38 89	99.87 71	98.91 43	91.32 191	95.22 159	98.83 139	96.57 15	98.66 163	89.55 220	94.09 191	99.40 141

tfpnconf		96.53 110	95.88 115	98.48 105	98.59 123	97.38 89	99.87 71	98.91 43	91.32 191	95.22 159	98.83 139	96.57 15	98.66 163	89.55 220	94.09 191	99.40 141

tfpnview11		96.53 110	95.88 115	98.48 105	98.59 123	97.38 89	99.87 71	98.91 43	91.32 191	95.22 159	98.83 139	96.57 15	98.66 163	89.55 220	94.09 191	99.40 141

EPMVS		96.53 110	96.01 103	98.09 130	98.43 135	96.12 135	96.36 318	99.43 24	93.53 116	97.64 106	95.04 267	94.41 60	98.38 190	91.13 197	98.11 117	99.75 88

conf0.01		96.52 115	95.88 115	98.41 117	98.59 123	97.38 89	99.87 71	98.91 43	91.32 191	95.22 159	98.83 139	96.57 15	98.66 163	89.55 220	94.09 191	99.20 163

conf0.002		96.52 115	95.88 115	98.41 117	98.59 123	97.38 89	99.87 71	98.91 43	91.32 191	95.22 159	98.83 139	96.57 15	98.66 163	89.55 220	94.09 191	99.20 163

test-LLR		96.47 117	96.04 102	97.78 138	97.02 192	95.44 151	99.96 19	98.21 151	94.07 95	95.55 148	96.38 222	93.90 82	98.27 199	90.42 210	98.83 103	99.64 105

view600		96.46 118	95.59 128	99.06 63	98.87 112	98.60 42	99.69 135	99.71 5	92.20 161	95.23 155	98.80 145	89.17 149	99.43 132	92.29 182	94.37 182	99.16 169

view800		96.46 118	95.59 128	99.06 63	98.87 112	98.60 42	99.69 135	99.71 5	92.20 161	95.23 155	98.80 145	89.17 149	99.43 132	92.29 182	94.37 182	99.16 169

conf0.05thres1000		96.46 118	95.59 128	99.06 63	98.87 112	98.60 42	99.69 135	99.71 5	92.20 161	95.23 155	98.80 145	89.17 149	99.43 132	92.29 182	94.37 182	99.16 169

tfpn		96.46 118	95.59 128	99.06 63	98.87 112	98.60 42	99.69 135	99.71 5	92.20 161	95.23 155	98.80 145	89.17 149	99.43 132	92.29 182	94.37 182	99.16 169

MVS_Test		96.46 118	95.74 125	98.61 94	98.18 146	97.23 97	99.31 192	97.15 249	91.07 200	98.84 65	97.05 202	88.17 163	98.97 146	94.39 146	97.50 128	99.61 109

test-mter		96.39 123	95.93 111	97.78 138	97.02 192	95.44 151	99.96 19	98.21 151	91.81 177	95.55 148	96.38 222	95.17 42	98.27 199	90.42 210	98.83 103	99.64 105

CDS-MVSNet		96.34 124	96.07 101	97.13 158	97.37 184	94.96 163	99.53 166	97.91 182	91.55 183	95.37 152	98.32 173	95.05 47	97.13 255	93.80 162	95.75 164	99.30 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		96.32 125	95.98 106	97.35 155	97.93 157	94.82 166	99.47 174	98.15 161	91.83 176	95.09 165	99.11 111	91.37 124	97.47 226	93.47 168	97.43 129	99.74 89

3Dnovator+	91.53 11	96.31 126	95.24 141	99.52 20	96.88 198	98.64 40	99.72 132	98.24 148	95.27 65	88.42 251	98.98 121	82.76 206	99.94 59	97.10 104	99.83 62	99.96 58

Effi-MVS+		96.30 127	95.69 126	98.16 124	97.85 163	96.26 124	97.41 302	97.21 244	90.37 210	98.65 76	98.58 162	86.61 178	98.70 160	97.11 103	97.37 132	99.52 127

IS-MVSNet		96.29 128	95.90 114	97.45 149	98.13 149	94.80 167	99.08 212	97.61 207	92.02 172	95.54 150	98.96 123	90.64 135	98.08 206	93.73 165	97.41 131	99.47 132

3Dnovator	91.47 12	96.28 129	95.34 139	99.08 61	96.82 201	97.47 84	99.45 177	98.81 57	95.52 59	89.39 233	99.00 119	81.97 216	99.95 51	97.27 99	99.83 62	99.84 77

tpmrst		96.27 130	95.98 106	97.13 158	97.96 154	93.15 209	96.34 319	98.17 156	92.07 169	98.71 72	95.12 261	93.91 81	98.73 157	94.91 135	96.62 148	99.50 130

CostFormer		96.10 131	95.88 115	96.78 166	97.03 191	92.55 225	97.08 309	97.83 191	90.04 217	98.72 71	94.89 277	95.01 49	98.29 196	96.54 113	95.77 163	99.50 130

diffmvs		96.06 132	95.46 134	97.89 136	97.92 158	95.28 157	99.16 207	97.28 240	91.73 179	98.16 96	96.74 214	87.48 168	98.81 150	93.69 166	96.95 145	99.58 116

PVSNet_BlendedMVS		96.05 133	95.82 124	96.72 169	99.59 70	96.99 105	99.95 31	99.10 31	94.06 98	98.27 91	95.80 234	89.00 155	99.95 51	99.12 33	87.53 242	93.24 297

PatchMatch-RL		96.04 134	95.40 136	97.95 133	99.59 70	95.22 161	99.52 167	99.07 34	93.96 102	96.49 127	98.35 172	82.28 208	99.82 90	90.15 215	99.22 98	98.81 188

1112_ss		96.01 135	95.20 143	98.42 114	97.80 167	96.41 120	99.65 148	96.66 292	92.71 138	92.88 189	99.40 95	92.16 113	99.30 137	91.92 189	93.66 200	99.55 121

PatchmatchNet		95.94 136	95.45 135	97.39 153	97.83 165	94.41 174	96.05 323	98.40 126	92.86 128	97.09 117	95.28 258	94.21 73	98.07 208	89.26 228	98.11 117	99.70 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TAMVS		95.85 137	95.58 132	96.65 172	97.07 189	93.50 193	99.17 205	97.82 192	91.39 190	95.02 166	98.01 179	92.20 112	97.30 239	93.75 164	95.83 162	99.14 175

LS3D		95.84 138	95.11 146	98.02 132	99.85 41	95.10 162	98.74 247	98.50 103	87.22 262	93.66 180	99.86 17	87.45 169	99.95 51	90.94 202	99.81 68	99.02 182

Test_1112_low_res		95.72 139	94.83 149	98.42 114	97.79 168	96.41 120	99.65 148	96.65 293	92.70 139	92.86 190	96.13 230	92.15 114	99.30 137	91.88 190	93.64 201	99.55 121

Vis-MVSNet		95.72 139	95.15 145	97.45 149	97.62 178	94.28 176	99.28 197	98.24 148	94.27 89	96.84 120	98.94 126	79.39 253	98.76 155	93.25 172	98.49 108	99.30 155
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet_dtu		95.71 141	95.39 137	96.66 171	98.92 104	93.41 200	99.57 159	98.90 51	96.19 41	97.52 108	98.56 164	92.65 106	97.36 230	77.89 316	98.33 112	99.20 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		95.71 141	95.38 138	96.68 170	98.49 134	92.28 229	99.84 92	97.50 219	92.12 166	92.06 194	98.79 150	84.69 192	98.67 161	95.29 129	99.66 76	99.09 180

mvs_anonymous		95.65 143	95.03 147	97.53 145	98.19 145	95.74 143	99.33 189	97.49 220	90.87 204	90.47 205	97.10 198	88.23 162	97.16 249	95.92 121	97.66 126	99.68 97

mvs-test1		95.53 144	95.97 108	94.20 241	97.77 169	85.44 311	99.95 31	97.06 253	94.92 69	96.58 125	98.72 152	85.81 183	98.98 145	94.80 137	98.11 117	98.18 195

MVSTER		95.53 144	95.22 142	96.45 176	98.56 129	97.72 72	99.91 56	97.67 200	92.38 158	91.39 197	97.14 195	97.24 10	97.30 239	94.80 137	87.85 237	94.34 234

tpm2		95.47 146	95.18 144	96.35 181	96.91 196	91.70 248	96.96 312	97.93 179	88.04 247	98.44 83	95.40 246	93.32 93	97.97 211	94.00 156	95.61 166	99.38 145

QAPM		95.40 147	94.17 160	99.10 59	96.92 195	97.71 73	99.40 180	98.68 65	89.31 223	88.94 243	98.89 127	82.48 207	99.96 43	93.12 178	99.83 62	99.62 107

UGNet		95.33 148	94.57 154	97.62 144	98.55 130	94.85 165	98.67 255	99.32 28	95.75 55	96.80 122	96.27 226	72.18 301	99.96 43	94.58 144	99.05 100	98.04 198
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
BH-untuned		95.18 149	94.83 149	96.22 183	98.36 137	91.22 255	99.80 102	97.32 237	90.91 203	91.08 199	98.67 154	83.51 200	98.54 172	94.23 152	99.61 81	98.92 184

BH-RMVSNet		95.18 149	94.31 158	97.80 137	98.17 147	95.23 160	99.76 117	97.53 214	92.52 154	94.27 175	99.25 105	76.84 274	98.80 151	90.89 204	99.54 85	99.35 150

PCF-MVS	94.20 5	95.18 149	94.10 161	98.43 113	98.55 130	95.99 137	97.91 297	97.31 238	90.35 211	89.48 232	99.22 107	85.19 191	99.89 70	90.40 212	98.47 109	99.41 138
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tpmp4_e23		95.15 152	94.69 153	96.55 173	97.84 164	91.77 243	97.10 308	97.91 182	88.33 243	97.19 114	95.06 265	93.92 79	98.51 173	89.64 219	95.19 173	99.37 147

dp		95.05 153	94.43 156	96.91 162	97.99 153	92.73 219	96.29 320	97.98 174	89.70 221	95.93 139	94.67 285	93.83 85	98.45 179	86.91 258	96.53 150	99.54 125

Fast-Effi-MVS+		95.02 154	94.19 159	97.52 146	97.88 160	94.55 172	99.97 12	97.08 252	88.85 235	94.47 172	97.96 181	84.59 193	98.41 182	89.84 217	97.10 141	99.59 113

IB-MVS	92.85 6	94.99 155	93.94 163	98.16 124	97.72 176	95.69 148	99.99 3	98.81 57	94.28 88	92.70 191	96.90 206	95.08 45	99.17 141	96.07 118	73.88 325	99.60 111
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
XVG-OURS		94.82 156	94.74 151	95.06 205	98.00 152	89.19 283	99.08 212	97.55 210	94.10 93	94.71 168	99.62 81	80.51 244	99.74 104	96.04 119	93.06 208	96.25 210

ADS-MVSNet		94.79 157	94.02 162	97.11 160	97.87 161	93.79 186	94.24 330	98.16 159	90.07 215	96.43 130	94.48 290	90.29 138	98.19 203	87.44 244	97.23 137	99.36 148

XVG-OURS-SEG-HR		94.79 157	94.70 152	95.08 204	98.05 151	89.19 283	99.08 212	97.54 212	93.66 113	94.87 167	99.58 83	78.78 262	99.79 93	97.31 98	93.40 203	96.25 210

OpenMVS	90.15 15	94.77 159	93.59 170	98.33 120	96.07 215	97.48 83	99.56 161	98.57 84	90.46 209	86.51 272	98.95 125	78.57 264	99.94 59	93.86 157	99.74 70	97.57 204

LFMVS		94.75 160	93.56 172	98.30 121	99.03 92	95.70 147	98.74 247	97.98 174	87.81 249	98.47 82	99.39 97	67.43 321	99.53 120	98.01 80	95.20 172	99.67 99

ab-mvs		94.69 161	93.42 176	98.51 103	98.07 150	96.26 124	96.49 316	98.68 65	90.31 212	94.54 169	97.00 204	76.30 279	99.71 108	95.98 120	93.38 204	99.56 120

CVMVSNet		94.68 162	94.94 148	93.89 255	96.80 202	86.92 303	99.06 219	98.98 37	94.45 81	94.23 176	99.02 115	85.60 185	95.31 309	90.91 203	95.39 170	99.43 136

cascas		94.64 163	93.61 167	97.74 140	97.82 166	96.26 124	99.96 19	97.78 194	85.76 280	94.00 177	97.54 186	76.95 273	99.21 139	97.23 100	95.43 169	97.76 203

HQP-MVS		94.61 164	94.50 155	94.92 214	95.78 222	91.85 239	99.87 71	97.89 184	96.82 21	93.37 181	98.65 156	80.65 242	98.39 186	97.92 86	89.60 212	94.53 216

TR-MVS		94.54 165	93.56 172	97.49 147	97.96 154	94.34 175	98.71 250	97.51 218	90.30 213	94.51 171	98.69 153	75.56 284	98.77 154	92.82 179	95.99 157	99.35 150

DP-MVS		94.54 165	93.42 176	97.91 135	99.46 81	94.04 181	98.93 233	97.48 221	81.15 318	90.04 212	99.55 85	87.02 174	99.95 51	88.97 230	98.11 117	99.73 91

Effi-MVS+-dtu		94.53 167	95.30 140	92.22 291	97.77 169	82.54 322	99.59 157	97.06 253	94.92 69	95.29 153	95.37 251	85.81 183	97.89 216	94.80 137	97.07 142	96.23 212

HQP_MVS		94.49 168	94.36 157	94.87 217	95.71 231	91.74 244	99.84 92	97.87 186	96.38 34	93.01 185	98.59 160	80.47 246	98.37 191	97.79 89	89.55 215	94.52 218

TAPA-MVS	92.12 8	94.42 169	93.60 169	96.90 163	99.33 85	91.78 242	99.78 106	98.00 172	89.89 219	94.52 170	99.47 91	91.97 117	99.18 140	69.90 331	99.52 86	99.73 91
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Patchmatch-test1		94.39 170	93.46 174	97.17 157	97.10 188	94.44 173	98.86 242	98.32 139	93.30 121	96.17 136	95.38 249	76.48 278	97.34 232	88.12 238	97.43 129	99.74 89

MSDG		94.37 171	93.36 180	97.40 152	98.88 111	93.95 183	99.37 185	97.38 232	85.75 283	90.80 202	99.17 109	84.11 198	99.88 76	86.35 262	98.43 110	98.36 193

tpmvs		94.28 172	93.57 171	96.40 178	98.55 130	91.50 253	95.70 328	98.55 90	87.47 257	92.15 193	94.26 294	91.42 122	98.95 147	88.15 236	95.85 161	98.76 190

FIs		94.10 173	93.43 175	96.11 185	94.70 247	96.82 110	99.58 158	98.93 42	92.54 153	89.34 235	97.31 191	87.62 166	97.10 258	94.22 153	86.58 246	94.40 227

CLD-MVS		94.06 174	93.90 164	94.55 231	96.02 217	90.69 261	99.98 6	97.72 197	96.62 30	91.05 200	98.85 138	77.21 270	98.47 175	98.11 76	89.51 217	94.48 220
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test0.0.03 1		93.86 175	93.61 167	94.64 226	95.02 243	92.18 232	99.93 50	98.58 82	94.07 95	87.96 255	98.50 165	93.90 82	94.96 314	81.33 297	93.17 206	96.78 206

X-MVStestdata		93.83 176	92.06 198	99.15 51	99.94 14	97.50 81	99.94 45	98.42 122	96.22 39	99.41 40	41.37 363	94.34 65	99.96 43	98.92 45	99.95 40	99.99 12

GA-MVS		93.83 176	92.84 184	96.80 165	95.73 228	93.57 192	99.88 66	97.24 243	92.57 152	92.92 187	96.66 215	78.73 263	97.67 221	87.75 241	94.06 198	99.17 168

FC-MVSNet-test		93.81 178	93.15 182	95.80 193	94.30 252	96.20 129	99.42 179	98.89 52	92.33 159	89.03 242	97.27 193	87.39 170	96.83 275	93.20 173	86.48 247	94.36 230

ADS-MVSNet2		93.80 179	93.88 165	93.55 262	97.87 161	85.94 306	94.24 330	96.84 285	90.07 215	96.43 130	94.48 290	90.29 138	95.37 308	87.44 244	97.23 137	99.36 148

VDD-MVS		93.77 180	92.94 183	96.27 182	98.55 130	90.22 271	98.77 246	97.79 193	90.85 205	96.82 121	99.42 93	61.18 338	99.77 95	98.95 42	94.13 190	98.82 187

EI-MVSNet		93.73 181	93.40 179	94.74 222	96.80 202	92.69 220	99.06 219	97.67 200	88.96 231	91.39 197	99.02 115	88.75 158	97.30 239	91.07 198	87.85 237	94.22 241

Fast-Effi-MVS+-dtu		93.72 182	93.86 166	93.29 265	97.06 190	86.16 304	99.80 102	96.83 286	92.66 142	92.58 192	97.83 183	81.39 229	97.67 221	89.75 218	96.87 147	96.05 214

tpm		93.70 183	93.41 178	94.58 229	95.36 238	87.41 301	97.01 310	96.90 280	90.85 205	96.72 124	94.14 297	90.40 136	96.84 274	90.75 206	88.54 230	99.51 128

PS-MVSNAJss		93.64 184	93.31 181	94.61 227	92.11 306	92.19 231	99.12 208	97.38 232	92.51 155	88.45 247	96.99 205	91.20 126	97.29 242	94.36 147	87.71 239	94.36 230

gg-mvs-nofinetune		93.51 185	91.86 201	98.47 109	97.72 176	97.96 67	92.62 339	98.51 99	74.70 337	97.33 111	69.59 352	98.91 3	97.79 218	97.77 91	99.56 84	99.67 99

nrg030		93.51 185	92.53 190	96.45 176	94.36 250	97.20 98	99.81 99	97.16 248	91.60 181	89.86 218	97.46 187	86.37 180	97.68 220	95.88 122	80.31 285	94.46 221

tpm cat1		93.51 185	92.52 191	96.47 174	97.77 169	91.47 254	96.13 321	98.06 168	80.98 319	92.91 188	93.78 302	89.66 141	98.87 148	87.03 254	96.39 152	99.09 180

CR-MVSNet		93.45 188	92.62 188	95.94 188	96.29 211	92.66 221	92.01 342	96.23 301	92.62 145	96.94 118	93.31 308	91.04 130	96.03 299	79.23 309	95.96 158	99.13 177

OPM-MVS		93.21 189	92.80 185	94.44 234	93.12 289	90.85 260	99.77 111	97.61 207	96.19 41	91.56 196	98.65 156	75.16 289	98.47 175	93.78 163	89.39 218	93.99 261

VDDNet		93.12 190	91.91 200	96.76 167	96.67 209	92.65 223	98.69 252	98.21 151	82.81 303	97.75 105	99.28 101	61.57 336	99.48 130	98.09 78	94.09 191	98.15 196

Anonymous202405211		93.10 191	91.99 199	96.40 178	99.10 89	89.65 281	98.88 236	97.93 179	83.71 299	94.00 177	98.75 151	68.79 314	99.88 76	95.08 131	91.71 210	99.68 97

UniMVSNet (Re)		93.07 192	92.13 195	95.88 190	94.84 244	96.24 128	99.88 66	98.98 37	92.49 156	89.25 237	95.40 246	87.09 173	97.14 253	93.13 177	78.16 304	94.26 238

LPG-MVS_test		92.96 193	92.71 187	93.71 258	95.43 236	88.67 288	99.75 118	97.62 204	92.81 132	90.05 209	98.49 166	75.24 287	98.40 184	95.84 124	89.12 219	94.07 250

UniMVSNet_NR-MVSNet		92.95 194	92.11 196	95.49 195	94.61 248	95.28 157	99.83 96	99.08 33	91.49 184	89.21 239	96.86 209	87.14 172	96.73 278	93.20 173	77.52 310	94.46 221

ACMM	91.95 10	92.88 195	92.52 191	93.98 252	95.75 227	89.08 285	99.77 111	97.52 216	93.00 126	89.95 214	97.99 180	76.17 281	98.46 178	93.63 167	88.87 223	94.39 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		92.83 196	92.29 194	94.47 233	91.90 310	92.46 226	99.55 163	97.27 241	91.17 197	89.96 213	96.07 232	81.10 233	96.89 271	94.67 142	88.91 221	94.05 252

ACMP	92.05 9	92.74 197	92.42 193	93.73 256	95.91 221	88.72 287	99.81 99	97.53 214	94.13 91	87.00 264	98.23 174	74.07 295	98.47 175	96.22 117	88.86 224	93.99 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		92.70 198	91.55 204	96.16 184	95.09 239	96.20 129	98.88 236	99.00 36	91.02 202	91.82 195	95.29 257	76.05 283	97.96 213	95.62 127	81.19 274	94.30 236

FMVSNet3		92.69 199	91.58 203	95.99 187	98.29 138	97.42 87	99.26 199	97.62 204	89.80 220	89.68 224	95.32 253	81.62 225	96.27 290	87.01 255	85.65 250	94.29 237

IterMVS-LS		92.69 199	92.11 196	94.43 236	96.80 202	92.74 218	99.45 177	96.89 281	88.98 229	89.65 227	95.38 249	88.77 157	96.34 288	90.98 201	82.04 269	94.22 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		92.65 201	91.50 205	96.10 186	96.85 199	90.49 266	91.50 344	97.19 245	82.76 304	90.23 206	95.59 241	95.02 48	98.00 210	77.41 319	96.98 144	99.82 79

AllTest		92.48 202	91.64 202	95.00 208	99.01 93	88.43 292	98.94 232	96.82 288	86.50 271	88.71 244	98.47 170	74.73 291	99.88 76	85.39 271	96.18 153	96.71 207

DI_MVS_plusplus_test		92.48 202	90.60 217	98.11 129	91.88 311	96.13 132	99.64 152	97.73 195	92.69 140	76.02 319	93.79 301	70.49 309	99.07 142	95.88 122	97.26 135	99.14 175

DU-MVS		92.46 204	91.45 207	95.49 195	94.05 255	95.28 157	99.81 99	98.74 61	92.25 160	89.21 239	96.64 217	81.66 223	96.73 278	93.20 173	77.52 310	94.46 221

test_normal		92.44 205	90.54 218	98.12 128	91.85 312	96.18 131	99.68 140	97.73 195	92.66 142	75.76 323	93.74 303	70.49 309	99.04 144	95.71 126	97.27 134	99.13 177

LCM-MVSNet-Re		92.31 206	92.60 189	91.43 298	97.53 180	79.27 336	99.02 225	91.83 349	92.07 169	80.31 306	94.38 293	83.50 201	95.48 306	97.22 101	97.58 127	99.54 125

WR-MVS		92.31 206	91.25 208	95.48 197	94.45 249	95.29 156	99.60 156	98.68 65	90.10 214	88.07 254	96.89 207	80.68 241	96.80 277	93.14 176	79.67 295	94.36 230

COLMAP_ROB	90.47 14	92.18 208	91.49 206	94.25 240	99.00 95	88.04 297	98.42 275	96.70 291	82.30 308	88.43 249	99.01 117	76.97 272	99.85 81	86.11 265	96.50 151	94.86 215
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240529		92.10 209	90.65 215	96.47 174	98.82 116	90.61 263	98.72 249	98.67 68	75.54 334	93.90 179	98.58 162	66.23 324	99.90 66	94.70 141	90.67 211	98.90 186

pmmvs4		92.10 209	91.07 211	95.18 202	92.82 296	94.96 163	99.48 172	96.83 286	87.45 258	88.66 246	96.56 220	83.78 199	96.83 275	89.29 227	84.77 258	93.75 281

jajsoiax		91.92 211	91.18 209	94.15 242	91.35 318	90.95 258	99.00 226	97.42 227	92.61 146	87.38 260	97.08 199	72.46 300	97.36 230	94.53 145	88.77 225	94.13 247

XXY-MVS		91.82 212	90.46 219	95.88 190	93.91 258	95.40 154	98.87 240	97.69 199	88.63 239	87.87 256	97.08 199	74.38 294	97.89 216	91.66 193	84.07 261	94.35 233

mvs_tets		91.81 213	91.08 210	94.00 250	91.63 316	90.58 264	98.67 255	97.43 224	92.43 157	87.37 261	97.05 202	71.76 302	97.32 235	94.75 140	88.68 227	94.11 248

VPNet		91.81 213	90.46 219	95.85 192	94.74 246	95.54 150	98.98 227	98.59 81	92.14 165	90.77 203	97.44 188	68.73 316	97.54 224	94.89 136	77.89 306	94.46 221

RPSCF		91.80 215	92.79 186	88.83 317	98.15 148	69.87 340	98.11 291	96.60 295	83.93 298	94.33 174	99.27 102	79.60 252	99.46 131	91.99 188	93.16 207	97.18 205

PVSNet_0	88.03 19	91.80 215	90.27 227	96.38 180	98.27 140	90.46 267	99.94 45	99.61 17	93.99 100	86.26 278	97.39 190	71.13 307	99.89 70	98.77 54	67.05 335	98.79 189

anonymousdsp		91.79 217	90.92 212	94.41 237	90.76 323	92.93 215	98.93 233	97.17 247	89.08 225	87.46 259	95.30 254	78.43 267	96.92 270	92.38 181	88.73 226	93.39 292

JIA-IIPM		91.76 218	90.70 214	94.94 212	96.11 214	87.51 299	93.16 337	98.13 164	75.79 333	97.58 107	77.68 348	92.84 101	97.97 211	88.47 234	96.54 149	99.33 152

TranMVSNet+NR-MVSNet		91.68 219	90.61 216	94.87 217	93.69 262	93.98 182	99.69 135	98.65 69	91.03 201	88.44 248	96.83 213	80.05 250	96.18 293	90.26 214	76.89 317	94.45 226

NR-MVSNet		91.56 220	90.22 229	95.60 194	94.05 255	95.76 142	98.25 284	98.70 63	91.16 199	80.78 305	96.64 217	83.23 204	96.57 282	91.41 194	77.73 308	94.46 221

v1neww		91.44 221	90.28 225	94.91 215	93.50 267	93.43 196	99.73 127	97.06 253	87.55 251	90.08 207	95.11 262	81.98 214	97.32 235	87.41 246	80.15 288	93.99 261

v7new		91.44 221	90.28 225	94.91 215	93.50 267	93.43 196	99.73 127	97.06 253	87.55 251	90.08 207	95.11 262	81.98 214	97.32 235	87.41 246	80.15 288	93.99 261

v6		91.44 221	90.27 227	94.93 213	93.44 271	93.44 195	99.73 127	97.05 257	87.57 250	90.05 209	95.10 264	81.87 219	97.39 228	87.45 243	80.17 287	93.98 265

divwei89l23v2f112		91.37 224	90.15 232	95.00 208	93.35 277	93.78 189	99.78 106	97.05 257	87.54 253	89.73 223	94.89 277	82.24 209	97.21 245	86.91 258	79.90 294	94.00 258

v1141		91.36 225	90.14 233	95.00 208	93.33 279	93.79 186	99.78 106	97.05 257	87.52 255	89.75 222	94.89 277	82.13 210	97.21 245	86.84 261	80.00 292	94.00 258

v1		91.36 225	90.14 233	95.04 206	93.35 277	93.80 185	99.77 111	97.05 257	87.53 254	89.77 221	94.91 275	81.99 213	97.33 234	86.90 260	79.98 293	94.00 258

v2v482		91.30 227	90.07 236	95.01 207	93.13 287	93.79 186	99.77 111	97.02 263	88.05 246	89.25 237	95.37 251	80.73 240	97.15 251	87.28 250	80.04 291	94.09 249

WR-MVS_H		91.30 227	90.35 222	94.15 242	94.17 254	92.62 224	99.17 205	98.94 39	88.87 234	86.48 274	94.46 292	84.36 195	96.61 281	88.19 235	78.51 300	93.21 298

V42		91.28 229	90.12 235	94.74 222	93.42 273	93.46 194	99.68 140	97.02 263	87.36 259	89.85 219	95.05 266	81.31 231	97.34 232	87.34 249	80.07 290	93.40 291

CP-MVSNet		91.23 230	90.22 229	94.26 239	93.96 257	92.39 228	99.09 210	98.57 84	88.95 232	86.42 275	96.57 219	79.19 257	96.37 286	90.29 213	78.95 297	94.02 253

XVG-ACMP-BASELINE		91.22 231	90.75 213	92.63 278	93.73 261	85.61 308	98.52 267	97.44 223	92.77 136	89.90 216	96.85 210	66.64 323	98.39 186	92.29 182	88.61 228	93.89 273

v7		91.20 232	89.99 237	94.82 220	93.57 263	93.41 200	99.57 159	96.98 269	86.83 268	89.88 217	95.22 259	81.01 235	97.14 253	85.53 269	81.31 273	93.90 271

v1144		91.09 233	89.83 238	94.87 217	93.25 284	93.69 191	99.62 155	96.98 269	86.83 268	89.64 228	94.99 271	80.94 236	97.05 261	85.08 274	81.16 275	93.87 275

FMVSNet2		91.02 234	89.56 243	95.41 198	97.53 180	95.74 143	98.98 227	97.41 229	87.05 264	88.43 249	95.00 270	71.34 304	96.24 292	85.12 273	85.21 255	94.25 240

MVP-Stereo		90.93 235	90.45 221	92.37 288	91.25 320	88.76 286	98.05 294	96.17 303	87.27 261	84.04 292	95.30 254	78.46 266	97.27 244	83.78 283	99.70 74	91.09 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS		90.91 236	90.17 231	93.12 268	96.78 205	90.42 269	98.89 235	97.05 257	89.03 227	86.49 273	95.42 245	76.59 276	95.02 312	87.22 251	84.09 260	93.93 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		90.88 237	89.82 239	94.08 245	97.53 180	91.97 234	98.43 272	96.95 274	87.05 264	89.68 224	94.72 281	71.34 304	96.11 294	87.01 255	85.65 250	94.17 243

test1		90.88 237	89.82 239	94.08 245	97.53 180	91.97 234	98.43 272	96.95 274	87.05 264	89.68 224	94.72 281	71.34 304	96.11 294	87.01 255	85.65 250	94.17 243

Anonymous20240521		90.82 239	89.48 248	94.82 220	93.53 265	94.07 180	99.07 216	97.43 224	87.10 263	84.14 291	94.60 287	83.11 205	97.32 235	85.73 268	80.61 282	93.08 300

v144192		90.79 240	89.52 245	94.59 228	93.11 290	92.77 217	99.56 161	96.99 267	86.38 273	89.82 220	94.95 274	80.50 245	97.10 258	83.98 281	80.41 283	93.90 271

v148		90.70 241	89.63 241	93.92 253	92.97 293	90.97 257	99.75 118	96.89 281	87.51 256	88.27 252	95.01 268	81.67 222	97.04 262	87.40 248	77.17 314	93.75 281

MS-PatchMatch		90.65 242	90.30 224	91.71 297	94.22 253	85.50 310	98.24 285	97.70 198	88.67 237	86.42 275	96.37 224	67.82 320	98.03 209	83.62 284	99.62 78	91.60 318

ACMH	89.72 17	90.64 243	89.63 241	93.66 260	95.64 234	88.64 290	98.55 262	97.45 222	89.03 227	81.62 302	97.61 185	69.75 312	98.41 182	89.37 226	87.62 241	93.92 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-CasMVS		90.63 244	89.51 246	93.99 251	93.83 259	91.70 248	98.98 227	98.52 93	88.48 240	86.15 279	96.53 221	75.46 285	96.31 289	88.83 231	78.86 299	93.95 267

v1192		90.62 245	89.25 251	94.72 224	93.13 287	93.07 210	99.50 169	97.02 263	86.33 274	89.56 231	95.01 268	79.22 256	97.09 260	82.34 292	81.16 275	94.01 255

v8		90.54 246	89.17 252	94.66 225	93.43 272	93.40 203	99.20 202	96.94 277	85.76 280	87.56 258	94.51 288	81.96 217	97.19 247	84.94 275	78.25 303	93.38 293

v1921920		90.46 247	89.12 253	94.50 232	92.96 294	92.46 226	99.49 170	96.98 269	86.10 276	89.61 230	95.30 254	78.55 265	97.03 265	82.17 293	80.89 281	94.01 255

our_test_3		90.39 248	89.48 248	93.12 268	92.40 301	89.57 282	99.33 189	96.35 300	87.84 248	85.30 285	94.99 271	84.14 197	96.09 297	80.38 301	84.56 259	93.71 286

PatchT		90.38 249	88.75 261	95.25 200	95.99 218	90.16 272	91.22 346	97.54 212	76.80 329	97.26 112	86.01 343	91.88 118	96.07 298	66.16 339	95.91 160	99.51 128

ACMH+	89.98 16	90.35 250	89.54 244	92.78 276	95.99 218	86.12 305	98.81 244	97.18 246	89.38 222	83.14 297	97.76 184	68.42 318	98.43 180	89.11 229	86.05 249	93.78 280

Baseline_NR-MVSNet		90.33 251	89.51 246	92.81 275	92.84 295	89.95 277	99.77 111	93.94 340	84.69 293	89.04 241	95.66 239	81.66 223	96.52 283	90.99 200	76.98 315	91.97 314

MIMVSNet		90.30 252	88.67 263	95.17 203	96.45 210	91.64 250	92.39 340	97.15 249	85.99 277	90.50 204	93.19 310	66.95 322	94.86 316	82.01 294	93.43 202	99.01 183

LTVRE_ROB	88.28 18	90.29 253	89.05 256	94.02 248	95.08 240	90.15 273	97.19 307	97.43 224	84.91 290	83.99 293	97.06 201	74.00 296	98.28 198	84.08 279	87.71 239	93.62 287
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
v10		90.25 254	88.82 259	94.57 230	93.53 265	93.43 196	99.08 212	96.87 284	85.00 289	87.34 262	94.51 288	80.93 237	97.02 267	82.85 289	79.23 296	93.26 296

v1240		90.20 255	88.79 260	94.44 234	93.05 292	92.27 230	99.38 184	96.92 278	85.89 278	89.36 234	94.87 280	77.89 269	97.03 265	80.66 300	81.08 277	94.01 255

PEN-MVS		90.19 256	89.06 255	93.57 261	93.06 291	90.90 259	99.06 219	98.47 105	88.11 245	85.91 281	96.30 225	76.67 275	95.94 303	87.07 252	76.91 316	93.89 273

pmmvs5		90.17 257	89.09 254	93.40 263	92.10 307	89.77 280	99.74 121	95.58 314	85.88 279	87.24 263	95.74 236	73.41 298	96.48 284	88.54 232	83.56 264	93.95 267

EU-MVSNet		90.14 258	90.34 223	89.54 314	92.55 300	81.06 331	98.69 252	98.04 170	91.41 189	86.59 271	96.84 212	80.83 238	93.31 336	86.20 263	81.91 270	94.26 238

USDC		90.00 259	88.96 257	93.10 270	94.81 245	88.16 296	98.71 250	95.54 316	93.66 113	83.75 295	97.20 194	65.58 326	98.31 195	83.96 282	87.49 243	92.85 306

Anonymous20231211		89.86 260	88.44 265	94.13 244	98.93 102	90.68 262	98.54 264	98.26 147	76.28 330	86.73 268	95.54 242	70.60 308	97.56 223	90.82 205	80.27 286	94.15 246

OurMVSNet-221017-0		89.81 261	89.48 248	90.83 303	91.64 315	81.21 329	98.17 289	95.38 325	91.48 185	85.65 284	97.31 191	72.66 299	97.29 242	88.15 236	84.83 257	93.97 266

Patchmtry		89.70 262	88.49 264	93.33 264	96.24 213	89.94 279	91.37 345	96.23 301	78.22 326	87.69 257	93.31 308	91.04 130	96.03 299	80.18 303	82.10 268	94.02 253

v7n		89.65 263	88.29 269	93.72 257	92.22 304	90.56 265	99.07 216	97.10 251	85.42 288	86.73 268	94.72 281	80.06 249	97.13 255	81.14 298	78.12 305	93.49 289

ppachtmachnet_test		89.58 264	88.35 268	93.25 266	92.40 301	90.44 268	99.33 189	96.73 290	85.49 286	85.90 282	95.77 235	81.09 234	96.00 302	76.00 325	82.49 267	93.30 294

v52		89.55 265	88.41 266	92.98 271	92.32 303	90.01 275	98.88 236	96.89 281	84.51 294	86.89 265	94.22 295	79.23 255	97.16 249	84.46 277	78.27 302	91.76 316

V4		89.55 265	88.41 266	92.98 271	92.21 305	90.03 274	98.87 240	96.91 279	84.51 294	86.84 266	94.21 296	79.37 254	97.15 251	84.45 278	78.28 301	91.76 316

DTE-MVSNet		89.40 267	88.24 270	92.88 274	92.66 299	89.95 277	99.10 209	98.22 150	87.29 260	85.12 287	96.22 227	76.27 280	95.30 310	83.56 285	75.74 320	93.41 290

RPMNet		89.39 268	87.20 280	95.94 188	96.29 211	92.66 221	92.01 342	97.63 202	70.19 345	96.94 118	85.87 344	87.25 171	96.03 299	62.69 342	95.96 158	99.13 177

pm-mvs1		89.36 269	87.81 274	94.01 249	93.40 275	91.93 237	98.62 259	96.48 299	86.25 275	83.86 294	96.14 229	73.68 297	97.04 262	86.16 264	75.73 321	93.04 302

tfpnnormal		89.29 270	87.61 276	94.34 238	94.35 251	94.13 179	98.95 231	98.94 39	83.94 297	84.47 290	95.51 243	74.84 290	97.39 228	77.05 322	80.41 283	91.48 320

LF4IMVS		89.25 271	88.85 258	90.45 307	92.81 297	81.19 330	98.12 290	94.79 333	91.44 187	86.29 277	97.11 197	65.30 328	98.11 205	88.53 233	85.25 254	92.07 311

testpf		89.10 272	88.73 262	90.24 308	97.59 179	83.48 319	74.22 357	97.39 231	79.66 323	89.64 228	93.92 298	86.38 179	95.76 304	85.42 270	94.31 187	91.49 319

testgi		89.01 273	88.04 272	91.90 295	93.49 269	84.89 314	99.73 127	95.66 312	93.89 107	85.14 286	98.17 175	59.68 340	94.66 318	77.73 317	88.88 222	96.16 213

v748		88.94 274	87.72 275	92.61 279	91.91 309	87.50 300	99.07 216	96.97 272	84.76 291	85.79 283	93.63 305	79.19 257	97.04 262	83.16 287	75.03 324	93.28 295

Test4		88.80 275	85.91 284	97.48 148	87.33 334	95.72 145	99.29 196	97.04 262	92.82 131	70.35 337	91.46 317	44.37 352	97.43 227	93.37 171	97.17 140	99.29 157

SixPastTwentyTwo		88.73 276	88.01 273	90.88 301	91.85 312	82.24 324	98.22 287	95.18 331	88.97 230	82.26 300	96.89 207	71.75 303	96.67 280	84.00 280	82.98 265	93.72 285

FMVSNet1		88.50 277	86.64 281	94.08 245	95.62 235	91.97 234	98.43 272	96.95 274	83.00 302	86.08 280	94.72 281	59.09 341	96.11 294	81.82 296	84.07 261	94.17 243

FMVSNet5		88.32 278	87.47 278	90.88 301	96.90 197	88.39 294	97.28 306	95.68 311	82.60 305	84.67 289	92.40 314	79.83 251	91.16 338	76.39 324	81.51 272	93.09 299

DSMNet-mixed		88.28 279	88.24 270	88.42 320	89.64 329	75.38 338	98.06 293	89.86 354	85.59 285	88.20 253	92.14 315	76.15 282	91.95 337	78.46 314	96.05 155	97.92 199

K. test v3		88.05 280	87.24 279	90.47 306	91.82 314	82.23 325	98.96 230	97.42 227	89.05 226	76.93 316	95.60 240	68.49 317	95.42 307	85.87 267	81.01 279	93.75 281

TinyColmap		87.87 281	86.51 282	91.94 294	95.05 242	85.57 309	97.65 299	94.08 338	84.40 296	81.82 301	96.85 210	62.14 335	98.33 193	80.25 302	86.37 248	91.91 315

TransMVSNet (Re)		87.25 282	85.28 286	93.16 267	93.56 264	91.03 256	98.54 264	94.05 339	83.69 300	81.09 304	96.16 228	75.32 286	96.40 285	76.69 323	68.41 332	92.06 312

Patchmatch-RL test		86.90 283	85.98 283	89.67 313	84.45 339	75.59 337	89.71 348	92.43 346	86.89 267	77.83 314	90.94 319	94.22 70	93.63 333	87.75 241	69.61 329	99.79 83

LP		86.76 284	84.85 288	92.50 282	95.08 240	85.89 307	89.97 347	96.97 272	75.28 336	84.97 288	90.68 320	80.78 239	95.13 311	61.64 343	88.31 233	96.46 209

v18		86.59 285	84.57 289	92.65 277	93.41 274	93.43 196	98.69 252	95.55 315	82.44 306	74.71 325	87.68 331	82.11 211	94.21 319	80.14 304	66.37 338	90.32 327

v16		86.52 286	84.49 290	92.60 280	93.45 270	93.31 205	98.60 261	95.52 318	82.30 308	74.59 327	87.70 330	81.95 218	94.18 320	79.93 306	66.38 337	90.30 328

v17		86.51 287	84.49 290	92.57 281	93.38 276	93.29 206	98.61 260	95.54 316	82.32 307	74.69 326	87.63 332	82.03 212	94.17 321	80.02 305	66.17 339	90.26 329

test2356		86.43 288	87.59 277	82.95 327	85.90 336	69.43 341	99.79 105	96.63 294	85.76 280	83.44 296	94.99 271	80.45 248	86.52 349	68.12 336	93.21 205	92.90 303

Anonymous20231206		86.32 289	85.42 285	89.02 316	89.11 331	80.53 334	99.05 222	95.28 327	85.43 287	82.82 298	93.92 298	74.40 293	93.44 335	66.99 337	81.83 271	93.08 300

v15		86.26 290	84.19 293	92.47 283	93.29 281	93.28 207	98.53 266	95.47 319	82.24 310	74.34 328	87.34 334	81.71 221	94.07 322	79.39 307	65.42 340	90.06 335

V14		86.22 291	84.15 294	92.41 286	93.30 280	93.16 208	98.47 269	95.47 319	82.10 311	74.27 329	87.41 333	81.73 220	94.02 324	79.26 308	65.37 342	90.04 336

MVS-HIRNet		86.22 291	83.19 306	95.31 199	96.71 208	90.29 270	92.12 341	97.33 236	62.85 348	86.82 267	70.37 351	69.37 313	97.49 225	75.12 326	97.99 122	98.15 196

V9		86.16 293	84.07 295	92.43 284	93.27 283	93.04 213	98.40 276	95.45 321	81.98 313	74.18 331	87.31 335	81.58 227	94.06 323	79.12 311	65.33 343	90.20 332

v12		86.10 294	84.01 296	92.37 288	93.23 286	92.96 214	98.33 279	95.45 321	81.87 314	74.05 333	87.15 337	81.60 226	93.98 327	79.09 312	65.28 344	90.18 333

v11		86.09 295	83.98 299	92.42 285	93.29 281	93.41 200	98.52 267	95.30 326	81.73 316	74.27 329	87.20 336	81.24 232	93.85 331	77.68 318	66.61 336	90.00 337

v13		86.06 296	83.97 300	92.34 290	93.25 284	92.85 216	98.26 283	95.44 323	81.70 317	74.02 334	87.11 339	81.58 227	94.00 326	78.94 313	65.41 341	90.18 333

pmmvs6		85.69 297	83.84 302	91.26 300	90.00 328	84.41 316	97.82 298	96.15 304	75.86 332	81.29 303	95.39 248	61.21 337	96.87 273	83.52 286	73.29 327	92.50 308

test_0402		85.58 298	83.94 301	90.50 305	93.81 260	85.04 313	98.55 262	95.20 330	76.01 331	79.72 309	95.13 260	64.15 331	96.26 291	66.04 340	86.88 245	90.21 331

UnsupCasMVSNet_eth		85.52 299	83.99 297	90.10 310	89.36 330	83.51 318	96.65 314	97.99 173	89.14 224	75.89 321	93.83 300	63.25 333	93.92 328	81.92 295	67.90 334	92.88 305

MDA-MVSNet_test_wron		85.51 300	83.32 305	92.10 292	90.96 321	88.58 291	99.20 202	96.52 297	79.70 322	57.12 349	92.69 312	79.11 259	93.86 330	77.10 321	77.46 312	93.86 276

YYNet1		85.50 301	83.33 304	92.00 293	90.89 322	88.38 295	99.22 201	96.55 296	79.60 324	57.26 348	92.72 311	79.09 260	93.78 332	77.25 320	77.37 313	93.84 277

EG-PatchMatch MVS		85.35 302	83.81 303	89.99 312	90.39 325	81.89 327	98.21 288	96.09 305	81.78 315	74.73 324	93.72 304	51.56 349	97.12 257	79.16 310	88.61 228	90.96 323

testing_2		85.10 303	81.72 310	95.22 201	82.25 343	94.16 177	97.54 300	97.01 266	88.15 244	62.23 344	86.43 342	44.43 351	97.18 248	92.28 187	85.20 256	94.31 235

TDRefinement		84.76 304	82.56 308	91.38 299	74.58 349	84.80 315	97.36 303	94.56 336	84.73 292	80.21 307	96.12 231	63.56 332	98.39 186	87.92 239	63.97 345	90.95 324

CMPMVS	61.59 21	84.75 305	85.14 287	83.57 325	90.32 326	62.54 348	96.98 311	97.59 209	74.33 338	69.95 338	96.66 215	64.17 330	98.32 194	87.88 240	88.41 232	89.84 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		84.72 306	83.99 297	86.91 322	88.19 333	80.62 333	98.88 236	95.94 307	88.36 242	78.87 310	94.62 286	68.75 315	89.11 342	66.52 338	75.82 319	91.00 322

new_pmnet		84.49 307	82.92 307	89.21 315	90.03 327	82.60 321	96.89 313	95.62 313	80.59 320	75.77 322	89.17 322	65.04 329	94.79 317	72.12 328	81.02 278	90.23 330

MDA-MVSNet-bldmvs		84.09 308	81.52 312	91.81 296	91.32 319	88.00 298	98.67 255	95.92 308	80.22 321	55.60 350	93.32 307	68.29 319	93.60 334	73.76 327	76.61 318	93.82 279

pmmvs-eth3d		84.03 309	81.97 309	90.20 309	84.15 340	87.09 302	98.10 292	94.73 335	83.05 301	74.10 332	87.77 329	65.56 327	94.01 325	81.08 299	69.24 331	89.49 341

testus		83.91 310	84.49 290	82.17 329	85.68 337	66.11 345	99.68 140	93.53 344	86.55 270	82.60 299	94.91 275	56.70 344	88.19 345	68.46 333	92.31 209	92.21 310

OpenMVS_ROB	79.82 20	83.77 311	81.68 311	90.03 311	88.30 332	82.82 320	98.46 270	95.22 329	73.92 340	76.00 320	91.29 318	55.00 345	96.94 269	68.40 334	88.51 231	90.34 326

MIMVSNet1		82.58 312	80.51 314	88.78 318	86.68 335	84.20 317	96.65 314	95.41 324	78.75 325	78.59 312	92.44 313	51.88 348	89.76 341	65.26 341	78.95 297	92.38 309

new-patchmatchnet		81.19 313	79.34 315	86.76 323	82.86 342	80.36 335	97.92 296	95.27 328	82.09 312	72.02 335	86.87 340	62.81 334	90.74 340	71.10 329	63.08 346	89.19 343

PM-MVS		80.47 314	78.88 316	85.26 324	83.79 341	72.22 339	95.89 326	91.08 350	85.71 284	76.56 318	88.30 323	36.64 353	93.90 329	82.39 291	69.57 330	89.66 340

pmmvs3		80.27 315	77.77 319	87.76 321	80.32 344	82.43 323	98.23 286	91.97 348	72.74 341	78.75 311	87.97 326	57.30 343	90.99 339	70.31 330	62.37 347	89.87 338

N_pmnet		80.06 316	80.78 313	77.89 332	91.94 308	45.28 361	98.80 245	56.82 365	78.10 327	80.08 308	93.33 306	77.03 271	95.76 304	68.14 335	82.81 266	92.64 307

UnsupCasMVSNet_bld		79.97 317	77.03 320	88.78 318	85.62 338	81.98 326	93.66 335	97.35 234	75.51 335	70.79 336	83.05 345	48.70 350	94.91 315	78.31 315	60.29 349	89.46 342

1111		79.11 318	78.74 317	80.23 330	78.33 345	67.13 343	97.31 304	93.65 342	71.34 342	68.35 340	87.87 327	85.42 189	88.46 343	52.93 350	73.46 326	85.11 346

test1235678		78.45 319	77.88 318	80.16 331	77.83 347	62.18 349	98.36 277	93.45 345	77.46 328	69.08 339	88.23 324	60.33 339	85.41 350	58.46 346	77.68 309	92.90 303

test12356		75.26 320	75.12 321	75.67 336	74.02 350	60.60 351	96.43 317	92.15 347	74.17 339	66.35 342	88.11 325	52.29 347	84.36 352	57.41 347	75.12 322	82.05 347

.test1245		71.48 321	71.80 322	70.51 340	78.33 345	67.13 343	97.31 304	93.65 342	71.34 342	68.35 340	87.87 327	85.42 189	88.46 343	52.93 350	11.01 360	55.94 359

FPMVS		68.72 322	68.72 323	68.71 341	65.95 355	44.27 363	95.97 325	94.74 334	51.13 350	53.26 352	90.50 321	25.11 359	83.00 353	60.80 344	80.97 280	78.87 350

LCM-MVSNet		67.77 323	64.73 326	76.87 333	62.95 359	56.25 354	89.37 349	93.74 341	44.53 353	61.99 345	80.74 346	20.42 362	86.53 348	69.37 332	59.50 350	87.84 344

testmv		67.54 324	65.93 324	72.37 338	64.46 358	54.05 355	95.09 329	90.07 352	68.90 347	55.16 351	77.63 349	30.39 354	82.61 354	49.42 353	62.26 348	80.45 349

PMMVS2		67.15 325	64.15 327	76.14 334	70.56 353	62.07 350	93.89 333	87.52 358	58.09 349	60.02 346	78.32 347	22.38 360	84.54 351	59.56 345	47.03 351	81.80 348

Gipuma		66.95 326	65.00 325	72.79 337	91.52 317	67.96 342	66.16 358	95.15 332	47.89 351	58.54 347	67.99 354	29.74 356	87.54 347	50.20 352	77.83 307	62.87 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		65.23 327	62.94 328	72.13 339	44.90 363	50.03 359	81.05 353	89.42 357	38.45 355	48.51 354	99.90 11	54.09 346	78.70 356	91.84 192	18.26 359	87.64 345

no-one		63.48 328	59.26 329	76.14 334	66.71 354	65.06 346	92.75 338	89.92 353	68.96 346	46.96 355	66.55 355	21.74 361	87.68 346	57.07 348	22.69 358	75.68 352

PNet_i23d		56.44 329	53.54 330	65.14 344	65.34 356	50.33 358	89.06 350	79.57 360	45.77 352	35.75 359	68.95 353	10.75 366	74.40 357	48.48 354	38.20 352	70.70 353

ANet_high		56.10 330	52.24 331	67.66 342	49.27 362	56.82 353	83.94 352	82.02 359	70.47 344	33.28 360	64.54 356	17.23 364	69.16 360	45.59 357	23.85 357	77.02 351

PMVS	49.05 23	53.75 331	51.34 333	60.97 346	40.80 364	34.68 364	74.82 356	89.62 356	37.55 356	28.67 361	72.12 350	7.09 367	81.63 355	43.17 358	68.21 333	66.59 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		52.30 332	52.18 332	52.67 347	71.51 351	45.40 360	93.62 336	76.60 363	36.01 357	43.50 356	64.13 357	27.11 358	67.31 361	31.06 360	26.06 355	45.30 362

MVE	53.74 22	51.54 333	47.86 335	62.60 345	59.56 360	50.93 357	79.41 354	77.69 362	35.69 358	36.27 358	61.76 359	5.79 370	69.63 359	37.97 359	36.61 353	67.24 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		51.44 334	51.22 334	52.11 348	70.71 352	44.97 362	94.04 332	75.66 364	35.34 359	42.40 357	61.56 360	28.93 357	65.87 362	27.64 361	24.73 356	45.49 361

wuykxyi23d		50.36 335	45.43 336	65.16 343	51.13 361	51.75 356	77.46 355	78.42 361	41.45 354	26.98 362	54.30 362	6.13 368	74.03 358	46.82 356	26.19 354	69.71 354

testmvs		40.60 336	44.45 337	29.05 351	19.49 366	14.11 367	99.68 140	18.47 366	20.74 360	64.59 343	98.48 169	10.95 365	17.09 365	56.66 349	11.01 360	55.94 359

test123		37.68 337	39.14 339	33.31 349	19.94 365	24.83 366	98.36 277	9.75 367	15.53 361	51.31 353	87.14 338	19.62 363	17.74 364	47.10 355	3.47 363	57.36 358

pcd1.5k->3k		37.58 338	39.62 338	31.46 350	92.73 298	0.00 368	0.00 360	97.52 216	0.00 363	0.00 364	0.00 365	78.40 268	0.00 366	0.00 363	87.90 236	94.37 229

cdsmvs_eth3d_5k		23.43 339	31.24 340	0.00 353	0.00 367	0.00 368	0.00 360	98.09 166	0.00 363	0.00 364	99.67 76	83.37 202	0.00 366	0.00 363	0.00 364	0.00 364

wuyk23d		20.37 340	20.84 341	18.99 352	65.34 356	27.73 365	50.43 359	7.67 368	9.50 362	8.01 363	6.34 364	6.13 368	26.24 363	23.40 362	10.69 362	2.99 363

ab-mvs-re		8.28 341	11.04 342	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	99.40 95	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		7.60 342	10.13 343	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	91.20 126	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																99.59 113

test_part3							99.88 66		96.14 43		99.91 7		100.00 1	99.99 1

test_part2					99.89 36	99.25 6				99.49 33

test_part1								98.41 124				97.20 11			99.99 13	99.99 12

sam_mvs1												94.72 57				99.59 113

sam_mvs												94.25 69

semantic-postprocess				92.93 273	96.72 207	89.96 276		96.99 267	88.95 232	86.63 270	95.67 238	76.50 277	95.00 313	87.04 253	84.04 263	93.84 277

ambc				83.23 326	77.17 348	62.61 347	87.38 351	94.55 337		76.72 317	86.65 341	30.16 355	96.36 287	84.85 276	69.86 328	90.73 325

MTGPA								98.28 143

test_post1							95.78 327				59.23 361	93.20 97	97.74 219	91.06 199

test_post											63.35 358	94.43 59	98.13 204

patchmatchnet-post											91.70 316	95.12 43	97.95 214

GG-mvs-BLEND				98.54 101	98.21 144	98.01 65	93.87 334	98.52 93		97.92 101	97.92 182	99.02 2	97.94 215	98.17 73	99.58 83	99.67 99

MTMP							99.87 71	96.49 298

gm-plane-assit					96.97 194	93.76 190			91.47 186		98.96 123		98.79 152	94.92 133

test9_res														99.71 17	99.99 13	100.00 1

TEST9					99.92 27	98.92 15	99.96 19	98.43 114	93.90 105	99.71 16	99.86 17	95.88 30	99.85 81

test_8					99.92 27	98.88 18	99.96 19	98.43 114	94.35 85	99.69 18	99.85 21	95.94 27	99.85 81

agg_prior2														99.48 24	100.00 1	100.00 1

agg_prior					99.93 24	98.77 26		98.43 114		99.63 21			99.85 81

TestCases				95.00 208	99.01 93	88.43 292		96.82 288	86.50 271	88.71 244	98.47 170	74.73 291	99.88 76	85.39 271	96.18 153	96.71 207

test_prior4						98.05 63	99.94 45

test_prior2							99.95 31		95.78 50	99.73 14	99.76 56	96.00 25		99.78 9	100.00 1

test_prior				99.43 27	99.94 14	98.49 50		98.65 69					99.80 91			99.99 12

旧先验2							99.46 176		94.21 90	99.85 5			99.95 51	96.96 108

新几何2							99.40 180

新几何1				99.42 30	99.75 56	98.27 57		98.63 75	92.69 140	99.55 28	99.82 40	94.40 61	100.00 1	91.21 195	99.94 44	99.99 12

旧先验1					99.76 54	97.52 79		98.64 72			99.85 21	95.63 34			99.94 44	99.99 12

无先验							99.49 170	98.71 62	93.46 117				100.00 1	94.36 147		99.99 12

原ACMM2							99.90 59

原ACMM1				98.96 75	99.73 61	96.99 105		98.51 99	94.06 98	99.62 23	99.85 21	94.97 52	99.96 43	95.11 130	99.95 40	99.92 69

test222					99.55 74	97.41 88	99.34 188	98.55 90	91.86 175	99.27 49	99.83 37	93.84 84			99.95 40	99.99 12

testdata2													99.99 28	90.54 209

segment_acmp												96.68 14

testdata				98.42 114	99.47 79	95.33 155		98.56 86	93.78 109	99.79 10	99.85 21	93.64 89	99.94 59	94.97 132	99.94 44	100.00 1

testdata1							99.28 197		96.35 38

test12				99.43 27	99.74 57	98.56 46		98.40 126		99.65 20		94.76 56	99.75 100		99.98 26	99.99 12

plane_prior7					95.71 231	91.59 252

plane_prior6					95.76 226	91.72 247						80.47 246

plane_prior5								97.87 186					98.37 191	97.79 89	89.55 215	94.52 218

plane_prior4											98.59 160

plane_prior3						91.64 250			96.63 29	93.01 185

plane_prior2							99.84 92		96.38 34

plane_prior1					95.73 228

plane_prior						91.74 244	99.86 87		96.76 25						89.59 214

n2								0.00 369

nn								0.00 369

door-mid								89.69 355

lessismore_v0				90.53 304	90.58 324	80.90 332		95.80 309		77.01 315	95.84 233	66.15 325	96.95 268	83.03 288	75.05 323	93.74 284

LGP-MVS_train				93.71 258	95.43 236	88.67 288		97.62 204	92.81 132	90.05 209	98.49 166	75.24 287	98.40 184	95.84 124	89.12 219	94.07 250

test11								98.44 109

door								90.31 351

HQP5-MVS						91.85 239

HQP-NCC					95.78 222		99.87 71		96.82 21	93.37 181

ACMP_Plane					95.78 222		99.87 71		96.82 21	93.37 181

BP-MVS														97.92 86

HQP4-MVS										93.37 181			98.39 186			94.53 216

HQP3-MVS								97.89 184							89.60 212

HQP2-MVS												80.65 242

NP-MVS					95.77 225	91.79 241					98.65 156

MDTV_nov1_ep13_2view						96.26 124	96.11 322		91.89 174	98.06 98		94.40 61		94.30 150		99.67 99

MDTV_nov1_ep13			95.69 126		97.90 159	94.15 178	95.98 324	98.44 109	93.12 124	97.98 100	95.74 236	95.10 44	98.58 169	90.02 216	96.92 146

ACMMP++_ref															87.04 244

ACMMP++															88.23 234

Test By Simon												92.82 103

ITE_SJBPF				92.38 287	95.69 233	85.14 312		95.71 310	92.81 132	89.33 236	98.11 176	70.23 311	98.42 181	85.91 266	88.16 235	93.59 288

DeepMVS_CX				82.92 328	95.98 220	58.66 352		96.01 306	92.72 137	78.34 313	95.51 243	58.29 342	98.08 206	82.57 290	85.29 253	92.03 313

High-resolution multi-view benchmark