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 54	98.20 3	99.93 1	99.98 2	96.82 9	100.00 1	99.75 9	100.00 1	99.99 11

NCCC		99.37 2	99.25 2	99.71 4	99.96 8	99.15 7	99.97 12	98.62 65	98.02 6	99.90 2	99.95 3	97.33 7	100.00 1	99.54 18	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 61	98.47 2	99.13 52	99.92 5	96.38 12	100.00 1	99.74 11	100.00 1	100.00 1

MSLP-MVS++		99.13 4	99.01 6	99.49 21	99.94 14	98.46 48	99.98 6	98.86 44	97.10 15	99.80 8	99.94 4	95.92 19	100.00 1	99.51 19	100.00 1	100.00 1

HSP-MVS		99.07 5	99.11 4	98.95 72	99.93 24	97.24 83	99.95 31	98.32 126	97.50 10	99.52 31	99.88 9	97.43 6	99.71 102	99.50 20	99.98 23	99.89 69

HPM-MVS++		99.07 5	98.88 10	99.63 7	99.90 33	99.02 10	99.95 31	98.56 75	97.56 9	99.44 34	99.85 18	95.38 28	100.00 1	99.31 27	99.99 13	99.87 72

APDe-MVS		99.06 7	98.91 9	99.51 19	99.94 14	98.76 28	99.91 54	98.39 116	97.20 14	99.46 32	99.85 18	95.53 26	99.79 87	99.86 3	100.00 1	99.99 11

SteuartSystems-ACMMP		99.02 8	98.97 8	99.18 41	98.72 111	97.71 66	99.98 6	98.44 98	96.85 20	99.80 8	99.91 6	97.57 4	99.85 76	99.44 23	99.99 13	99.99 11
Skip Steuart: Steuart Systems R&D Blog.
CHOSEN 280x420		99.01 9	99.03 5	98.95 72	99.38 80	98.87 17	98.46 249	99.42 24	97.03 17	99.02 56	99.09 109	99.35 1	98.21 186	99.73 13	99.78 66	99.77 82

test_prior3		98.99 10	98.84 11	99.43 25	99.94 14	98.49 46	99.95 31	98.65 58	95.78 48	99.73 13	99.76 53	96.00 15	99.80 85	99.78 7	100.00 1	99.99 11

TSAR-MVS + MP.		98.93 11	98.77 12	99.41 29	99.74 54	98.67 32	99.77 99	98.38 119	96.73 26	99.88 3	99.74 60	94.89 43	99.59 113	99.80 5	99.98 23	99.97 51

SD-MVS		98.92 12	98.70 13	99.56 14	99.70 62	98.73 29	99.94 44	98.34 124	96.38 34	99.81 7	99.76 53	94.59 46	99.98 29	99.84 4	99.96 34	99.97 51

MG-MVS		98.91 13	98.65 15	99.68 6	99.94 14	99.07 9	99.64 139	99.44 22	97.33 12	99.00 59	99.72 62	94.03 65	99.98 29	98.73 51	100.00 1	100.00 1

train_agg		98.88 14	98.65 15	99.59 12	99.92 27	98.92 13	99.96 19	98.43 104	94.35 83	99.71 15	99.86 14	95.94 17	99.85 76	99.69 16	99.98 23	99.99 11

agg_prior1		98.88 14	98.66 14	99.54 16	99.93 24	98.77 23	99.96 19	98.43 104	94.63 76	99.63 20	99.85 18	95.79 21	99.85 76	99.72 14	99.99 13	99.99 11

agg_prior3		98.84 16	98.62 17	99.47 24	99.92 27	98.56 42	99.96 19	98.43 104	94.07 93	99.67 18	99.85 18	96.05 13	99.85 76	99.69 16	99.98 23	99.99 11

DeepC-MVS_fast	96.59 1	98.81 17	98.54 22	99.62 10	99.90 33	98.85 18	99.24 183	98.47 94	98.14 4	99.08 53	99.91 6	93.09 87	100.00 1	99.04 37	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 18	98.60 19	99.36 34	99.85 38	98.34 50	99.87 67	98.52 82	96.05 43	99.41 37	99.79 42	94.93 41	99.76 91	99.07 32	99.90 50	99.99 11

Regformer-2		98.78 19	98.59 20	99.36 34	99.85 38	98.32 51	99.87 67	98.52 82	96.04 44	99.41 37	99.79 42	94.92 42	99.76 91	99.05 33	99.90 50	99.98 41

MVS_111021_HR		98.72 20	98.62 17	99.01 68	99.36 81	97.18 86	99.93 49	99.90 1	96.81 24	98.67 70	99.77 49	93.92 67	99.89 66	99.27 28	99.94 41	99.96 55

XVS		98.70 21	98.55 21	99.15 47	99.94 14	97.50 74	99.94 44	98.42 112	96.22 39	99.41 37	99.78 47	94.34 53	99.96 40	98.92 42	99.95 37	99.99 11

CDPH-MVS		98.65 22	98.36 32	99.49 21	99.94 14	98.73 29	99.87 67	98.33 125	93.97 99	99.76 11	99.87 12	94.99 39	99.75 94	98.55 61	100.00 1	99.98 41

APD-MVS		98.62 23	98.35 33	99.41 29	99.90 33	98.51 45	99.87 67	98.36 122	94.08 92	99.74 12	99.73 61	94.08 63	99.74 98	99.42 24	99.99 13	99.99 11
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + GP.		98.60 24	98.51 23	98.86 76	99.73 58	96.63 100	99.97 12	97.92 166	98.07 5	98.76 66	99.55 82	95.00 38	99.94 56	99.91 2	97.68 122	99.99 11

PAPM		98.60 24	98.42 24	99.14 49	96.05 199	98.96 11	99.90 57	99.35 26	96.68 28	98.35 84	99.66 75	96.45 11	98.51 157	99.45 22	99.89 52	99.96 55

#test#		98.59 26	98.41 25	99.14 49	99.96 8	97.43 78	99.95 31	98.61 67	95.00 66	99.31 43	99.85 18	94.22 58	100.00 1	98.78 49	99.98 23	99.98 41

Regformer-3		98.58 27	98.41 25	99.10 55	99.84 43	97.57 70	99.66 132	98.52 82	95.79 47	99.01 57	99.77 49	94.40 49	99.75 94	98.82 47	99.83 59	99.98 41

HFP-MVS		98.56 28	98.37 30	99.14 49	99.96 8	97.43 78	99.95 31	98.61 67	94.77 71	99.31 43	99.85 18	94.22 58	100.00 1	98.70 52	99.98 23	99.98 41

Regformer-4		98.56 28	98.39 28	99.08 57	99.84 43	97.52 72	99.66 132	98.52 82	95.76 50	99.01 57	99.77 49	94.33 55	99.75 94	98.80 48	99.83 59	99.98 41

region2R		98.54 30	98.37 30	99.05 63	99.96 8	97.18 86	99.96 19	98.55 79	94.87 69	99.45 33	99.85 18	94.07 64	100.00 1	98.67 54	100.00 1	99.98 41

DELS-MVS		98.54 30	98.22 36	99.50 20	99.15 84	98.65 35	100.00 1	98.58 71	97.70 7	98.21 91	99.24 103	92.58 96	99.94 56	98.63 59	99.94 41	99.92 66
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 32	98.16 40	99.58 13	99.97 3	98.77 23	99.95 31	98.43 104	95.35 60	98.03 94	99.75 58	94.03 65	99.98 29	98.11 72	99.83 59	99.99 11

ACMMPR		98.50 33	98.32 34	99.05 63	99.96 8	97.18 86	99.95 31	98.60 69	94.77 71	99.31 43	99.84 32	93.73 74	100.00 1	98.70 52	99.98 23	99.98 41

ACMMP_Plus		98.49 34	98.14 41	99.54 16	99.66 64	98.62 37	99.85 77	98.37 121	94.68 75	99.53 28	99.83 34	92.87 88	100.00 1	98.66 57	99.84 58	99.99 11

EPNet		98.49 34	98.40 27	98.77 79	99.62 66	96.80 98	99.90 57	99.51 19	97.60 8	99.20 48	99.36 97	93.71 75	99.91 62	97.99 78	98.71 103	99.61 104
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		98.45 36	98.32 34	98.87 75	99.96 8	96.62 101	99.97 12	98.39 116	94.43 81	98.90 61	99.87 12	94.30 56	100.00 1	99.04 37	99.99 13	99.99 11

PS-MVSNAJ		98.44 37	98.20 38	99.16 44	98.80 108	98.92 13	99.54 151	98.17 142	97.34 11	99.85 5	99.85 18	91.20 114	99.89 66	99.41 25	99.67 72	98.69 173

MVS_111021_LR		98.42 38	98.38 29	98.53 96	99.39 79	95.79 127	99.87 67	99.86 2	96.70 27	98.78 65	99.79 42	92.03 104	99.90 63	99.17 29	99.86 57	99.88 71

DP-MVS Recon		98.41 39	98.02 45	99.56 14	99.97 3	98.70 31	99.92 51	98.44 98	92.06 164	98.40 82	99.84 32	95.68 22	100.00 1	98.19 68	99.71 70	99.97 51

PHI-MVS		98.41 39	98.21 37	99.03 65	99.86 37	97.10 90	99.98 6	98.80 49	90.78 192	99.62 22	99.78 47	95.30 29	100.00 1	99.80 5	99.93 46	99.99 11

mPP-MVS		98.39 41	98.20 38	98.97 70	99.97 3	96.92 95	99.95 31	98.38 119	95.04 65	98.61 74	99.80 41	93.39 79	100.00 1	98.64 58	100.00 1	99.98 41

PGM-MVS		98.34 42	98.13 42	98.99 69	99.92 27	97.00 91	99.75 106	99.50 20	93.90 103	99.37 41	99.76 53	93.24 84	100.00 1	97.75 88	99.96 34	99.98 41

MPTG		98.33 43	98.00 46	99.30 36	99.85 38	97.93 61	99.80 90	98.28 130	95.76 50	97.18 110	99.88 9	92.74 92	100.00 1	98.67 54	99.88 54	99.99 11

MTAPA		98.29 44	97.96 50	99.30 36	99.85 38	97.93 61	99.39 168	98.28 130	95.76 50	97.18 110	99.88 9	92.74 92	100.00 1	98.67 54	99.88 54	99.99 11

CANet		98.27 45	97.82 53	99.63 7	99.72 60	99.10 8	99.98 6	98.51 88	97.00 18	98.52 76	99.71 64	87.80 150	99.95 48	99.75 9	99.38 90	99.83 75

EI-MVSNet-Vis-set		98.27 45	98.11 43	98.75 80	99.83 46	96.59 103	99.40 165	98.51 88	95.29 62	98.51 77	99.76 53	93.60 78	99.71 102	98.53 62	99.52 83	99.95 60

APD-MVS_3200maxsize		98.25 47	98.08 44	98.78 78	99.81 48	96.60 102	99.82 85	98.30 128	93.95 101	99.37 41	99.77 49	92.84 89	99.76 91	98.95 39	99.92 48	99.97 51

xiu_mvs_v2_base		98.23 48	97.97 48	99.02 67	98.69 112	98.66 33	99.52 153	98.08 153	97.05 16	99.86 4	99.86 14	90.65 122	99.71 102	99.39 26	98.63 104	98.69 173

MP-MVS		98.23 48	97.97 48	99.03 65	99.94 14	97.17 89	99.95 31	98.39 116	94.70 74	98.26 89	99.81 40	91.84 108	100.00 1	98.85 46	99.97 32	99.93 63

EI-MVSNet-UG-set		98.14 50	97.99 47	98.60 89	99.80 49	96.27 110	99.36 172	98.50 92	95.21 64	98.30 86	99.75 58	93.29 83	99.73 101	98.37 66	99.30 93	99.81 77

PAPM_NR		98.12 51	97.93 51	98.70 81	99.94 14	96.13 119	99.82 85	98.43 104	94.56 77	97.52 103	99.70 66	94.40 49	99.98 29	97.00 102	99.98 23	99.99 11

WTY-MVS		98.10 52	97.60 58	99.60 11	98.92 96	99.28 5	99.89 62	99.52 17	95.58 56	98.24 90	99.39 94	93.33 80	99.74 98	97.98 80	95.58 163	99.78 81

MP-MVS-pluss		98.07 53	97.64 56	99.38 33	99.74 54	98.41 49	99.74 109	98.18 141	93.35 117	96.45 123	99.85 18	92.64 95	99.97 38	98.91 44	99.89 52	99.77 82
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
1121		98.03 54	97.57 60	99.40 31	99.74 54	98.21 53	98.31 259	98.62 65	92.78 131	99.53 28	99.83 34	95.08 33	100.00 1	94.36 140	99.92 48	99.99 11

HPM-MVS		97.96 55	97.72 54	98.68 82	99.84 43	96.39 109	99.90 57	98.17 142	92.61 142	98.62 73	99.57 81	91.87 107	99.67 109	98.87 45	99.99 13	99.99 11

PVSNet_Blended		97.94 56	97.64 56	98.83 77	99.59 67	96.99 92	100.00 1	99.10 29	95.38 59	98.27 87	99.08 110	89.00 141	99.95 48	99.12 30	99.25 94	99.57 110

PLC	95.54 3	97.93 57	97.89 52	98.05 119	99.82 47	94.77 156	99.92 51	98.46 96	93.93 102	97.20 108	99.27 99	95.44 27	99.97 38	97.41 92	99.51 85	99.41 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
API-MVS		97.86 58	97.66 55	98.47 99	99.52 73	95.41 140	99.47 159	98.87 43	91.68 172	98.84 62	99.85 18	92.34 98	99.99 25	98.44 64	99.96 34	100.00 1

lupinMVS		97.85 59	97.60 58	98.62 87	97.28 169	97.70 68	99.99 3	97.55 195	95.50 58	99.43 35	99.67 73	90.92 120	98.71 149	98.40 65	99.62 75	99.45 124

alignmvs		97.81 60	97.33 65	99.25 38	98.77 110	98.66 33	99.99 3	98.44 98	94.40 82	98.41 80	99.47 88	93.65 76	99.42 129	98.57 60	94.26 181	99.67 94

HPM-MVS_fast		97.80 61	97.50 61	98.68 82	99.79 50	96.42 106	99.88 64	98.16 145	91.75 171	98.94 60	99.54 84	91.82 109	99.65 111	97.62 90	99.99 13	99.99 11

HY-MVS	92.50 7	97.79 62	97.17 70	99.63 7	98.98 90	99.32 3	97.49 280	99.52 17	95.69 54	98.32 85	97.41 174	93.32 81	99.77 89	98.08 75	95.75 160	99.81 77

CNLPA		97.76 63	97.38 63	98.92 74	99.53 72	96.84 96	99.87 67	98.14 148	93.78 107	96.55 121	99.69 69	92.28 99	99.98 29	97.13 98	99.44 88	99.93 63

ACMMP		97.74 64	97.44 62	98.66 84	99.92 27	96.13 119	99.18 187	99.45 21	94.84 70	96.41 126	99.71 64	91.40 111	99.99 25	97.99 78	98.03 118	99.87 72
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 65	98.98 7	93.92 236	99.63 65	81.76 308	99.96 19	98.56 75	99.47 1	99.19 50	99.99 1	94.16 62	100.00 1	99.92 1	99.93 46	100.00 1

abl_6		97.67 66	97.34 64	98.66 84	99.68 63	96.11 123	99.68 127	98.14 148	93.80 106	99.27 46	99.70 66	88.65 146	99.98 29	97.46 91	99.72 69	99.89 69

CPTT-MVS		97.64 67	97.32 66	98.58 91	99.97 3	95.77 128	99.96 19	98.35 123	89.90 203	98.36 83	99.79 42	91.18 117	99.99 25	98.37 66	99.99 13	99.99 11

sss		97.57 68	97.03 75	99.18 41	98.37 120	98.04 58	99.73 114	99.38 25	93.46 115	98.76 66	99.06 111	91.21 113	99.89 66	96.33 110	97.01 140	99.62 102

MVS_0304		97.52 69	96.79 81	99.69 5	99.59 67	99.30 4	99.97 12	98.01 157	96.99 19	98.84 62	99.79 42	78.90 244	99.96 40	99.74 11	99.32 92	99.81 77

xiu_mvs_v1_base_debu		97.43 70	97.06 71	98.55 92	97.74 155	98.14 54	99.31 175	97.86 173	96.43 31	99.62 22	99.69 69	85.56 172	99.68 106	99.05 33	98.31 110	97.83 182

xiu_mvs_v1_base		97.43 70	97.06 71	98.55 92	97.74 155	98.14 54	99.31 175	97.86 173	96.43 31	99.62 22	99.69 69	85.56 172	99.68 106	99.05 33	98.31 110	97.83 182

xiu_mvs_v1_base_debi		97.43 70	97.06 71	98.55 92	97.74 155	98.14 54	99.31 175	97.86 173	96.43 31	99.62 22	99.69 69	85.56 172	99.68 106	99.05 33	98.31 110	97.83 182

MAR-MVS		97.43 70	97.19 68	98.15 115	99.47 76	94.79 155	99.05 204	98.76 50	92.65 140	98.66 71	99.82 37	88.52 147	99.98 29	98.12 71	99.63 74	99.67 94
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 74	96.83 78	99.14 49	99.51 75	97.83 63	99.89 62	98.27 133	88.48 225	99.06 54	99.66 75	90.30 125	99.64 112	96.32 111	99.97 32	99.96 55

DWT-MVSNet_test		97.31 75	97.19 68	97.66 129	98.24 126	94.67 157	98.86 223	98.20 140	93.60 113	98.09 92	98.89 123	97.51 5	98.78 144	94.04 148	97.28 131	99.55 112

OMC-MVS		97.28 76	97.23 67	97.41 138	99.76 51	93.36 189	99.65 135	97.95 163	96.03 45	97.41 105	99.70 66	89.61 130	99.51 116	96.73 108	98.25 113	99.38 131

PVSNet_Blended_VisFu		97.27 77	96.81 79	98.66 84	98.81 107	96.67 99	99.92 51	98.64 61	94.51 78	96.38 127	98.49 151	89.05 140	99.88 72	97.10 100	98.34 108	99.43 127

jason		97.24 78	96.86 77	98.38 107	95.73 211	97.32 82	99.97 12	97.40 214	95.34 61	98.60 75	99.54 84	87.70 151	98.56 154	97.94 81	99.47 86	99.25 145
jason: jason.
AdaColmap		97.23 79	96.80 80	98.51 97	99.99 1	95.60 136	99.09 193	98.84 46	93.32 118	96.74 118	99.72 62	86.04 167	100.00 1	98.01 76	99.43 89	99.94 62

VNet		97.21 80	96.57 87	99.13 54	98.97 91	97.82 64	99.03 206	99.21 28	94.31 85	99.18 51	98.88 125	86.26 166	99.89 66	98.93 41	94.32 179	99.69 92

PVSNet	91.05 13	97.13 81	96.69 84	98.45 101	99.52 73	95.81 126	99.95 31	99.65 15	94.73 73	99.04 55	99.21 105	84.48 180	99.95 48	94.92 127	98.74 102	99.58 109

CSCG		97.10 82	97.04 74	97.27 143	99.89 36	91.92 223	99.90 57	99.07 32	88.67 222	95.26 146	99.82 37	93.17 86	99.98 29	98.15 70	99.47 86	99.90 68

canonicalmvs		97.09 83	96.32 91	99.39 32	98.93 95	98.95 12	99.72 119	97.35 218	94.45 79	97.88 97	99.42 90	86.71 161	99.52 115	98.48 63	93.97 185	99.72 89

PatchFormer-LS_test		97.01 84	96.79 81	97.69 128	98.26 125	94.80 153	98.66 238	98.13 150	93.70 110	97.86 98	98.80 134	95.54 24	98.67 151	94.12 147	96.00 152	99.60 106

thres200		96.96 85	96.21 93	99.22 39	98.97 91	98.84 19	99.85 77	99.71 5	93.17 120	96.26 128	98.88 125	89.87 128	99.51 116	94.26 144	94.91 169	99.31 139

MVSFormer		96.94 86	96.60 85	97.95 121	97.28 169	97.70 68	99.55 149	97.27 223	91.17 182	99.43 35	99.54 84	90.92 120	96.89 253	94.67 135	99.62 75	99.25 145

F-COLMAP		96.93 87	96.95 76	96.87 151	99.71 61	91.74 229	99.85 77	97.95 163	93.11 122	95.72 139	99.16 107	92.35 97	99.94 56	95.32 123	99.35 91	98.92 167

DeepC-MVS	94.51 4	96.92 88	96.40 90	98.45 101	99.16 83	95.90 125	99.66 132	98.06 154	96.37 37	94.37 159	99.49 87	83.29 188	99.90 63	97.63 89	99.61 78	99.55 112
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1314		96.84 89	95.96 103	99.48 23	96.74 189	98.52 44	98.31 259	98.86 44	95.82 46	89.91 199	98.98 117	87.49 153	99.96 40	97.80 84	99.73 68	99.96 55

CHOSEN 1792x2688		96.81 90	96.53 88	97.64 130	98.91 98	93.07 195	99.65 135	99.80 3	95.64 55	95.39 143	98.86 129	84.35 182	99.90 63	96.98 103	99.16 96	99.95 60

tfpn200view9		96.79 91	95.99 98	99.19 40	98.94 93	98.82 20	99.78 94	99.71 5	92.86 124	96.02 131	98.87 127	89.33 131	99.50 118	93.84 151	94.57 170	99.27 143

thres400		96.78 92	95.99 98	99.16 44	98.94 93	98.82 20	99.78 94	99.71 5	92.86 124	96.02 131	98.87 127	89.33 131	99.50 118	93.84 151	94.57 170	99.16 152

CANet_DTU		96.76 93	96.15 94	98.60 89	98.78 109	97.53 71	99.84 80	97.63 187	97.25 13	99.20 48	99.64 77	81.36 214	99.98 29	92.77 171	98.89 98	98.28 176

PMMVS		96.76 93	96.76 83	96.76 154	98.28 123	92.10 218	99.91 54	97.98 160	94.12 90	99.53 28	99.39 94	86.93 160	98.73 147	96.95 105	97.73 120	99.45 124

thres100view900		96.74 95	95.92 106	99.18 41	98.90 99	98.77 23	99.74 109	99.71 5	92.59 144	95.84 134	98.86 129	89.25 133	99.50 118	93.84 151	94.57 170	99.27 143

TESTMET0.1,1		96.74 95	96.26 92	98.16 112	97.36 168	96.48 105	99.96 19	98.29 129	91.93 166	95.77 138	98.07 163	95.54 24	98.29 180	90.55 196	98.89 98	99.70 90

conf200view11		96.73 97	95.92 106	99.16 44	98.90 99	98.77 23	99.74 109	99.71 5	92.59 144	95.84 134	98.86 129	89.25 133	99.50 118	93.84 151	94.57 170	99.20 149

thres600view7		96.69 98	95.87 110	99.14 49	98.90 99	98.78 22	99.74 109	99.71 5	92.59 144	95.84 134	98.86 129	89.25 133	99.50 118	93.44 161	94.50 174	99.16 152

EPP-MVSNet		96.69 98	96.60 85	96.96 148	97.74 155	93.05 197	99.37 170	98.56 75	88.75 221	95.83 137	99.01 114	96.01 14	98.56 154	96.92 106	97.20 136	99.25 145

HyFIR lowres test		96.66 100	96.43 89	97.36 141	99.05 85	93.91 169	99.70 121	99.80 3	90.54 193	96.26 128	98.08 162	92.15 102	98.23 185	96.84 107	95.46 164	99.93 63

MVS		96.60 101	95.56 120	99.72 3	96.85 182	99.22 6	98.31 259	98.94 37	91.57 174	90.90 185	99.61 79	86.66 162	99.96 40	97.36 93	99.88 54	99.99 11

UA-Net		96.54 102	95.96 103	98.27 110	98.23 127	95.71 133	98.00 274	98.45 97	93.72 109	98.41 80	99.27 99	88.71 145	99.66 110	91.19 186	97.69 121	99.44 126

EPMVS		96.53 103	96.01 97	98.09 118	98.43 119	96.12 122	96.36 297	99.43 23	93.53 114	97.64 100	95.04 249	94.41 48	98.38 174	91.13 187	98.11 114	99.75 84

test-LLR		96.47 104	96.04 96	97.78 125	97.02 175	95.44 138	99.96 19	98.21 137	94.07 93	95.55 140	96.38 206	93.90 70	98.27 183	90.42 198	98.83 100	99.64 100

view600		96.46 105	95.59 115	99.06 59	98.87 103	98.60 38	99.69 122	99.71 5	92.20 156	95.23 147	98.80 134	89.17 136	99.43 125	92.29 173	94.37 175	99.16 152

view800		96.46 105	95.59 115	99.06 59	98.87 103	98.60 38	99.69 122	99.71 5	92.20 156	95.23 147	98.80 134	89.17 136	99.43 125	92.29 173	94.37 175	99.16 152

conf0.05thres1000		96.46 105	95.59 115	99.06 59	98.87 103	98.60 38	99.69 122	99.71 5	92.20 156	95.23 147	98.80 134	89.17 136	99.43 125	92.29 173	94.37 175	99.16 152

tfpn		96.46 105	95.59 115	99.06 59	98.87 103	98.60 38	99.69 122	99.71 5	92.20 156	95.23 147	98.80 134	89.17 136	99.43 125	92.29 173	94.37 175	99.16 152

MVS_Test		96.46 105	95.74 112	98.61 88	98.18 130	97.23 84	99.31 175	97.15 232	91.07 185	98.84 62	97.05 186	88.17 149	98.97 138	94.39 139	97.50 125	99.61 104

test-mter		96.39 110	95.93 105	97.78 125	97.02 175	95.44 138	99.96 19	98.21 137	91.81 170	95.55 140	96.38 206	95.17 30	98.27 183	90.42 198	98.83 100	99.64 100

CDS-MVSNet		96.34 111	96.07 95	97.13 145	97.37 167	94.96 149	99.53 152	97.91 167	91.55 175	95.37 144	98.32 158	95.05 35	97.13 237	93.80 155	95.75 160	99.30 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		96.32 112	95.98 100	97.35 142	97.93 141	94.82 152	99.47 159	98.15 147	91.83 169	95.09 151	99.11 108	91.37 112	97.47 209	93.47 160	97.43 127	99.74 85

3Dnovator+	91.53 11	96.31 113	95.24 127	99.52 18	96.88 181	98.64 36	99.72 119	98.24 134	95.27 63	88.42 235	98.98 117	82.76 190	99.94 56	97.10 100	99.83 59	99.96 55

Effi-MVS+		96.30 114	95.69 113	98.16 112	97.85 146	96.26 111	97.41 281	97.21 226	90.37 195	98.65 72	98.58 148	86.61 163	98.70 150	97.11 99	97.37 130	99.52 118

IS-MVSNet		96.29 115	95.90 108	97.45 136	98.13 133	94.80 153	99.08 195	97.61 192	92.02 165	95.54 142	98.96 119	90.64 123	98.08 190	93.73 158	97.41 129	99.47 123

3Dnovator	91.47 12	96.28 116	95.34 125	99.08 57	96.82 184	97.47 77	99.45 162	98.81 47	95.52 57	89.39 217	99.00 116	81.97 200	99.95 48	97.27 95	99.83 59	99.84 74

tpmrst		96.27 117	95.98 100	97.13 145	97.96 139	93.15 194	96.34 298	98.17 142	92.07 162	98.71 69	95.12 243	93.91 69	98.73 147	94.91 129	96.62 144	99.50 121

CostFormer		96.10 118	95.88 109	96.78 153	97.03 174	92.55 210	97.08 288	97.83 176	90.04 202	98.72 68	94.89 258	95.01 37	98.29 180	96.54 109	95.77 159	99.50 121

PVSNet_BlendedMVS		96.05 119	95.82 111	96.72 156	99.59 67	96.99 92	99.95 31	99.10 29	94.06 96	98.27 87	95.80 218	89.00 141	99.95 48	99.12 30	87.53 226	93.24 276

PatchMatch-RL		96.04 120	95.40 122	97.95 121	99.59 67	95.22 147	99.52 153	99.07 32	93.96 100	96.49 122	98.35 157	82.28 192	99.82 84	90.15 204	99.22 95	98.81 170

1112_ss		96.01 121	95.20 129	98.42 104	97.80 150	96.41 107	99.65 135	96.66 274	92.71 134	92.88 173	99.40 92	92.16 101	99.30 130	91.92 180	93.66 186	99.55 112

PatchmatchNet		95.94 122	95.45 121	97.39 140	97.83 148	94.41 160	96.05 303	98.40 114	92.86 124	97.09 112	95.28 240	94.21 61	98.07 192	89.26 211	98.11 114	99.70 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TAMVS		95.85 123	95.58 119	96.65 159	97.07 172	93.50 178	99.17 188	97.82 177	91.39 181	95.02 152	98.01 164	92.20 100	97.30 221	93.75 157	95.83 158	99.14 158

LS3D		95.84 124	95.11 132	98.02 120	99.85 38	95.10 148	98.74 228	98.50 92	87.22 246	93.66 164	99.86 14	87.45 154	99.95 48	90.94 192	99.81 65	99.02 165

Test_1112_low_res		95.72 125	94.83 135	98.42 104	97.79 151	96.41 107	99.65 135	96.65 275	92.70 135	92.86 174	96.13 214	92.15 102	99.30 130	91.88 181	93.64 187	99.55 112

Vis-MVSNet		95.72 125	95.15 131	97.45 136	97.62 161	94.28 162	99.28 180	98.24 134	94.27 87	96.84 115	98.94 122	79.39 236	98.76 146	93.25 163	98.49 105	99.30 140
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet_dtu		95.71 127	95.39 123	96.66 158	98.92 96	93.41 185	99.57 145	98.90 41	96.19 41	97.52 103	98.56 149	92.65 94	97.36 213	77.89 297	98.33 109	99.20 149
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		95.71 127	95.38 124	96.68 157	98.49 118	92.28 214	99.84 80	97.50 204	92.12 161	92.06 178	98.79 139	84.69 178	98.67 151	95.29 124	99.66 73	99.09 163

mvs_anonymous		95.65 129	95.03 133	97.53 132	98.19 129	95.74 130	99.33 174	97.49 205	90.87 189	90.47 189	97.10 182	88.23 148	97.16 231	95.92 116	97.66 123	99.68 93

mvs-test1		95.53 130	95.97 102	94.20 225	97.77 152	85.44 291	99.95 31	97.06 236	94.92 67	96.58 120	98.72 140	85.81 169	98.98 137	94.80 131	98.11 114	98.18 177

MVSTER		95.53 130	95.22 128	96.45 162	98.56 113	97.72 65	99.91 54	97.67 185	92.38 153	91.39 181	97.14 180	97.24 8	97.30 221	94.80 131	87.85 221	94.34 216

tpm2		95.47 132	95.18 130	96.35 166	96.91 179	91.70 233	96.96 291	97.93 165	88.04 232	98.44 79	95.40 228	93.32 81	97.97 195	94.00 149	95.61 162	99.38 131

QAPM		95.40 133	94.17 147	99.10 55	96.92 178	97.71 66	99.40 165	98.68 55	89.31 208	88.94 227	98.89 123	82.48 191	99.96 40	93.12 169	99.83 59	99.62 102

UGNet		95.33 134	94.57 140	97.62 131	98.55 114	94.85 151	98.67 235	99.32 27	95.75 53	96.80 117	96.27 210	72.18 284	99.96 40	94.58 137	99.05 97	98.04 180
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
diffmvs		95.25 135	94.26 145	98.23 111	98.13 133	96.59 103	99.12 190	97.18 228	85.78 263	97.64 100	96.70 198	85.92 168	98.87 140	90.40 200	97.45 126	99.24 148

BH-untuned		95.18 136	94.83 135	96.22 168	98.36 121	91.22 240	99.80 90	97.32 221	90.91 188	91.08 183	98.67 142	83.51 185	98.54 156	94.23 145	99.61 78	98.92 167

BH-RMVSNet		95.18 136	94.31 144	97.80 124	98.17 131	95.23 146	99.76 105	97.53 199	92.52 149	94.27 161	99.25 102	76.84 257	98.80 142	90.89 194	99.54 82	99.35 136

PCF-MVS	94.20 5	95.18 136	94.10 148	98.43 103	98.55 114	95.99 124	97.91 276	97.31 222	90.35 196	89.48 216	99.22 104	85.19 177	99.89 66	90.40 200	98.47 106	99.41 129
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tpmp4_e23		95.15 139	94.69 139	96.55 160	97.84 147	91.77 228	97.10 287	97.91 167	88.33 228	97.19 109	95.06 247	93.92 67	98.51 157	89.64 208	95.19 168	99.37 133

dp		95.05 140	94.43 142	96.91 149	97.99 138	92.73 204	96.29 299	97.98 160	89.70 206	95.93 133	94.67 266	93.83 73	98.45 163	86.91 241	96.53 146	99.54 116

Fast-Effi-MVS+		95.02 141	94.19 146	97.52 133	97.88 143	94.55 158	99.97 12	97.08 235	88.85 220	94.47 158	97.96 166	84.59 179	98.41 166	89.84 206	97.10 138	99.59 108

IB-MVS	92.85 6	94.99 142	93.94 150	98.16 112	97.72 159	95.69 135	99.99 3	98.81 47	94.28 86	92.70 175	96.90 190	95.08 33	99.17 134	96.07 113	73.88 305	99.60 106
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		94.82 143	94.74 137	95.06 190	98.00 137	89.19 263	99.08 195	97.55 195	94.10 91	94.71 154	99.62 78	80.51 227	99.74 98	96.04 114	93.06 194	96.25 192

ADS-MVSNet		94.79 144	94.02 149	97.11 147	97.87 144	93.79 171	94.24 310	98.16 145	90.07 200	96.43 124	94.48 270	90.29 126	98.19 187	87.44 227	97.23 134	99.36 134

XVG-OURS-SEG-HR		94.79 144	94.70 138	95.08 189	98.05 136	89.19 263	99.08 195	97.54 197	93.66 111	94.87 153	99.58 80	78.78 245	99.79 87	97.31 94	93.40 189	96.25 192

OpenMVS	90.15 15	94.77 146	93.59 157	98.33 108	96.07 198	97.48 76	99.56 147	98.57 73	90.46 194	86.51 255	98.95 121	78.57 247	99.94 56	93.86 150	99.74 67	97.57 186

LFMVS		94.75 147	93.56 159	98.30 109	99.03 86	95.70 134	98.74 228	97.98 160	87.81 233	98.47 78	99.39 94	67.43 302	99.53 114	98.01 76	95.20 167	99.67 94

ab-mvs		94.69 148	93.42 163	98.51 97	98.07 135	96.26 111	96.49 295	98.68 55	90.31 197	94.54 155	97.00 188	76.30 262	99.71 102	95.98 115	93.38 190	99.56 111

CVMVSNet		94.68 149	94.94 134	93.89 238	96.80 185	86.92 283	99.06 201	98.98 35	94.45 79	94.23 162	99.02 112	85.60 171	95.31 289	90.91 193	95.39 166	99.43 127

cascas		94.64 150	93.61 154	97.74 127	97.82 149	96.26 111	99.96 19	97.78 179	85.76 264	94.00 163	97.54 171	76.95 256	99.21 132	97.23 96	95.43 165	97.76 185

HQP-MVS		94.61 151	94.50 141	94.92 199	95.78 205	91.85 224	99.87 67	97.89 169	96.82 21	93.37 165	98.65 143	80.65 225	98.39 170	97.92 82	89.60 196	94.53 198

TR-MVS		94.54 152	93.56 159	97.49 134	97.96 139	94.34 161	98.71 230	97.51 203	90.30 198	94.51 157	98.69 141	75.56 267	98.77 145	92.82 170	95.99 153	99.35 136

DP-MVS		94.54 152	93.42 163	97.91 123	99.46 78	94.04 166	98.93 215	97.48 206	81.15 300	90.04 196	99.55 82	87.02 159	99.95 48	88.97 213	98.11 114	99.73 87

Effi-MVS+-dtu		94.53 154	95.30 126	92.22 272	97.77 152	82.54 302	99.59 143	97.06 236	94.92 67	95.29 145	95.37 233	85.81 169	97.89 200	94.80 131	97.07 139	96.23 194

HQP_MVS		94.49 155	94.36 143	94.87 202	95.71 214	91.74 229	99.84 80	97.87 171	96.38 34	93.01 169	98.59 146	80.47 229	98.37 175	97.79 85	89.55 199	94.52 200

TAPA-MVS	92.12 8	94.42 156	93.60 156	96.90 150	99.33 82	91.78 227	99.78 94	98.00 158	89.89 204	94.52 156	99.47 88	91.97 105	99.18 133	69.90 311	99.52 83	99.73 87
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Patchmatch-test1		94.39 157	93.46 161	97.17 144	97.10 171	94.44 159	98.86 223	98.32 126	93.30 119	96.17 130	95.38 231	76.48 261	97.34 215	88.12 221	97.43 127	99.74 85

MSDG		94.37 158	93.36 167	97.40 139	98.88 102	93.95 168	99.37 170	97.38 216	85.75 267	90.80 186	99.17 106	84.11 183	99.88 72	86.35 245	98.43 107	98.36 175

tpmvs		94.28 159	93.57 158	96.40 164	98.55 114	91.50 238	95.70 308	98.55 79	87.47 241	92.15 177	94.26 274	91.42 110	98.95 139	88.15 219	95.85 157	98.76 172

FIs		94.10 160	93.43 162	96.11 170	94.70 230	96.82 97	99.58 144	98.93 40	92.54 148	89.34 219	97.31 176	87.62 152	97.10 240	94.22 146	86.58 230	94.40 209

CLD-MVS		94.06 161	93.90 151	94.55 215	96.02 200	90.69 246	99.98 6	97.72 182	96.62 30	91.05 184	98.85 133	77.21 253	98.47 159	98.11 72	89.51 201	94.48 202
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		93.86 162	93.61 154	94.64 210	95.02 226	92.18 217	99.93 49	98.58 71	94.07 93	87.96 239	98.50 150	93.90 70	94.96 294	81.33 279	93.17 192	96.78 188

X-MVStestdata		93.83 163	92.06 185	99.15 47	99.94 14	97.50 74	99.94 44	98.42 112	96.22 39	99.41 37	41.37 344	94.34 53	99.96 40	98.92 42	99.95 37	99.99 11

GA-MVS		93.83 163	92.84 171	96.80 152	95.73 211	93.57 177	99.88 64	97.24 225	92.57 147	92.92 171	96.66 199	78.73 246	97.67 205	87.75 224	94.06 184	99.17 151

FC-MVSNet-test		93.81 165	93.15 169	95.80 178	94.30 235	96.20 116	99.42 164	98.89 42	92.33 154	89.03 226	97.27 178	87.39 155	96.83 257	93.20 164	86.48 231	94.36 212

ADS-MVSNet2		93.80 166	93.88 152	93.55 245	97.87 144	85.94 286	94.24 310	96.84 268	90.07 200	96.43 124	94.48 270	90.29 126	95.37 288	87.44 227	97.23 134	99.36 134

VDD-MVS		93.77 167	92.94 170	96.27 167	98.55 114	90.22 253	98.77 227	97.79 178	90.85 190	96.82 116	99.42 90	61.18 318	99.77 89	98.95 39	94.13 182	98.82 169

EI-MVSNet		93.73 168	93.40 166	94.74 206	96.80 185	92.69 205	99.06 201	97.67 185	88.96 216	91.39 181	99.02 112	88.75 144	97.30 221	91.07 188	87.85 221	94.22 223

Fast-Effi-MVS+-dtu		93.72 169	93.86 153	93.29 248	97.06 173	86.16 284	99.80 90	96.83 269	92.66 138	92.58 176	97.83 168	81.39 213	97.67 205	89.75 207	96.87 143	96.05 196

tpm		93.70 170	93.41 165	94.58 213	95.36 221	87.41 281	97.01 289	96.90 263	90.85 190	96.72 119	94.14 277	90.40 124	96.84 256	90.75 195	88.54 214	99.51 119

PS-MVSNAJss		93.64 171	93.31 168	94.61 211	92.11 286	92.19 216	99.12 190	97.38 216	92.51 150	88.45 231	96.99 189	91.20 114	97.29 224	94.36 140	87.71 223	94.36 212

gg-mvs-nofinetune		93.51 172	91.86 187	98.47 99	97.72 159	97.96 60	92.62 319	98.51 88	74.70 317	97.33 106	69.59 333	98.91 3	97.79 202	97.77 87	99.56 81	99.67 94

nrg030		93.51 172	92.53 177	96.45 162	94.36 233	97.20 85	99.81 87	97.16 231	91.60 173	89.86 202	97.46 172	86.37 165	97.68 204	95.88 117	80.31 266	94.46 203

tpm cat1		93.51 172	92.52 178	96.47 161	97.77 152	91.47 239	96.13 301	98.06 154	80.98 301	92.91 172	93.78 282	89.66 129	98.87 140	87.03 237	96.39 148	99.09 163

CR-MVSNet		93.45 175	92.62 175	95.94 173	96.29 194	92.66 206	92.01 322	96.23 282	92.62 141	96.94 113	93.31 288	91.04 118	96.03 280	79.23 290	95.96 154	99.13 160

OPM-MVS		93.21 176	92.80 172	94.44 218	93.12 271	90.85 245	99.77 99	97.61 192	96.19 41	91.56 180	98.65 143	75.16 272	98.47 159	93.78 156	89.39 202	93.99 242

VDDNet		93.12 177	91.91 186	96.76 154	96.67 192	92.65 208	98.69 232	98.21 137	82.81 285	97.75 99	99.28 98	61.57 316	99.48 123	98.09 74	94.09 183	98.15 178

UniMVSNet (Re)		93.07 178	92.13 182	95.88 175	94.84 227	96.24 115	99.88 64	98.98 35	92.49 151	89.25 221	95.40 228	87.09 158	97.14 235	93.13 168	78.16 284	94.26 220

LPG-MVS_test		92.96 179	92.71 174	93.71 241	95.43 219	88.67 268	99.75 106	97.62 189	92.81 128	90.05 193	98.49 151	75.24 270	98.40 168	95.84 119	89.12 203	94.07 231

UniMVSNet_NR-MVSNet		92.95 180	92.11 183	95.49 180	94.61 231	95.28 144	99.83 84	99.08 31	91.49 176	89.21 223	96.86 193	87.14 157	96.73 260	93.20 164	77.52 290	94.46 203

ACMM	91.95 10	92.88 181	92.52 178	93.98 235	95.75 210	89.08 265	99.77 99	97.52 201	93.00 123	89.95 198	97.99 165	76.17 264	98.46 162	93.63 159	88.87 207	94.39 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		92.83 182	92.29 181	94.47 217	91.90 290	92.46 211	99.55 149	97.27 223	91.17 182	89.96 197	96.07 216	81.10 217	96.89 253	94.67 135	88.91 205	94.05 233

ACMP	92.05 9	92.74 183	92.42 180	93.73 239	95.91 204	88.72 267	99.81 87	97.53 199	94.13 89	87.00 248	98.23 159	74.07 278	98.47 159	96.22 112	88.86 208	93.99 242
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		92.70 184	91.55 190	96.16 169	95.09 222	96.20 116	98.88 218	99.00 34	91.02 187	91.82 179	95.29 239	76.05 266	97.96 197	95.62 122	81.19 256	94.30 218

FMVSNet3		92.69 185	91.58 189	95.99 172	98.29 122	97.42 80	99.26 182	97.62 189	89.80 205	89.68 208	95.32 235	81.62 209	96.27 272	87.01 238	85.65 234	94.29 219

IterMVS-LS		92.69 185	92.11 183	94.43 220	96.80 185	92.74 203	99.45 162	96.89 264	88.98 214	89.65 211	95.38 231	88.77 143	96.34 270	90.98 191	82.04 251	94.22 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		92.65 187	91.50 191	96.10 171	96.85 182	90.49 249	91.50 324	97.19 227	82.76 286	90.23 190	95.59 224	95.02 36	98.00 194	77.41 300	96.98 141	99.82 76

AllTest		92.48 188	91.64 188	95.00 193	99.01 87	88.43 272	98.94 214	96.82 271	86.50 254	88.71 228	98.47 155	74.73 274	99.88 72	85.39 253	96.18 149	96.71 189

DI_MVS_plusplus_test		92.48 188	90.60 202	98.11 117	91.88 291	96.13 119	99.64 139	97.73 180	92.69 136	76.02 299	93.79 281	70.49 291	99.07 135	95.88 117	97.26 133	99.14 158

DU-MVS		92.46 190	91.45 193	95.49 180	94.05 238	95.28 144	99.81 87	98.74 51	92.25 155	89.21 223	96.64 201	81.66 207	96.73 260	93.20 164	77.52 290	94.46 203

test_normal		92.44 191	90.54 203	98.12 116	91.85 292	96.18 118	99.68 127	97.73 180	92.66 138	75.76 303	93.74 283	70.49 291	99.04 136	95.71 121	97.27 132	99.13 160

LCM-MVSNet-Re		92.31 192	92.60 176	91.43 279	97.53 163	79.27 316	99.02 207	91.83 331	92.07 162	80.31 286	94.38 273	83.50 186	95.48 286	97.22 97	97.58 124	99.54 116

WR-MVS		92.31 192	91.25 194	95.48 182	94.45 232	95.29 143	99.60 142	98.68 55	90.10 199	88.07 238	96.89 191	80.68 224	96.80 259	93.14 167	79.67 275	94.36 212

COLMAP_ROB	90.47 14	92.18 194	91.49 192	94.25 224	99.00 89	88.04 277	98.42 254	96.70 273	82.30 290	88.43 233	99.01 114	76.97 255	99.85 76	86.11 248	96.50 147	94.86 197
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs4		92.10 195	91.07 197	95.18 187	92.82 278	94.96 149	99.48 158	96.83 269	87.45 242	88.66 230	96.56 204	83.78 184	96.83 257	89.29 210	84.77 242	93.75 262

jajsoiax		91.92 196	91.18 195	94.15 226	91.35 298	90.95 243	99.00 208	97.42 211	92.61 142	87.38 244	97.08 183	72.46 283	97.36 213	94.53 138	88.77 209	94.13 228

XXY-MVS		91.82 197	90.46 204	95.88 175	93.91 241	95.40 141	98.87 221	97.69 184	88.63 224	87.87 240	97.08 183	74.38 277	97.89 200	91.66 183	84.07 244	94.35 215

mvs_tets		91.81 198	91.08 196	94.00 233	91.63 296	90.58 247	98.67 235	97.43 209	92.43 152	87.37 245	97.05 186	71.76 285	97.32 218	94.75 134	88.68 211	94.11 229

VPNet		91.81 198	90.46 204	95.85 177	94.74 229	95.54 137	98.98 209	98.59 70	92.14 160	90.77 187	97.44 173	68.73 297	97.54 207	94.89 130	77.89 286	94.46 203

RPSCF		91.80 200	92.79 173	88.83 298	98.15 132	69.87 320	98.11 270	96.60 277	83.93 281	94.33 160	99.27 99	79.60 235	99.46 124	91.99 179	93.16 193	97.18 187

PVSNet_0	88.03 19	91.80 200	90.27 212	96.38 165	98.27 124	90.46 250	99.94 44	99.61 16	93.99 98	86.26 261	97.39 175	71.13 290	99.89 66	98.77 50	67.05 315	98.79 171

anonymousdsp		91.79 202	90.92 198	94.41 221	90.76 303	92.93 200	98.93 215	97.17 230	89.08 210	87.46 243	95.30 236	78.43 250	96.92 252	92.38 172	88.73 210	93.39 272

JIA-IIPM		91.76 203	90.70 200	94.94 197	96.11 197	87.51 279	93.16 317	98.13 150	75.79 314	97.58 102	77.68 329	92.84 89	97.97 195	88.47 217	96.54 145	99.33 138

TranMVSNet+NR-MVSNet		91.68 204	90.61 201	94.87 202	93.69 245	93.98 167	99.69 122	98.65 58	91.03 186	88.44 232	96.83 197	80.05 233	96.18 275	90.26 203	76.89 297	94.45 208

NR-MVSNet		91.56 205	90.22 214	95.60 179	94.05 238	95.76 129	98.25 263	98.70 53	91.16 184	80.78 285	96.64 201	83.23 189	96.57 264	91.41 184	77.73 288	94.46 203

v1neww		91.44 206	90.28 210	94.91 200	93.50 249	93.43 181	99.73 114	97.06 236	87.55 235	90.08 191	95.11 244	81.98 198	97.32 218	87.41 229	80.15 268	93.99 242

v7new		91.44 206	90.28 210	94.91 200	93.50 249	93.43 181	99.73 114	97.06 236	87.55 235	90.08 191	95.11 244	81.98 198	97.32 218	87.41 229	80.15 268	93.99 242

v6		91.44 206	90.27 212	94.93 198	93.44 253	93.44 180	99.73 114	97.05 240	87.57 234	90.05 193	95.10 246	81.87 203	97.39 211	87.45 226	80.17 267	93.98 246

divwei89l23v2f112		91.37 209	90.15 217	95.00 193	93.35 259	93.78 174	99.78 94	97.05 240	87.54 237	89.73 207	94.89 258	82.24 193	97.21 227	86.91 241	79.90 274	94.00 239

v1141		91.36 210	90.14 218	95.00 193	93.33 261	93.79 171	99.78 94	97.05 240	87.52 239	89.75 206	94.89 258	82.13 194	97.21 227	86.84 244	80.00 272	94.00 239

v1		91.36 210	90.14 218	95.04 191	93.35 259	93.80 170	99.77 99	97.05 240	87.53 238	89.77 205	94.91 256	81.99 197	97.33 217	86.90 243	79.98 273	94.00 239

v2v482		91.30 212	90.07 221	95.01 192	93.13 269	93.79 171	99.77 99	97.02 246	88.05 231	89.25 221	95.37 233	80.73 223	97.15 233	87.28 233	80.04 271	94.09 230

WR-MVS_H		91.30 212	90.35 207	94.15 226	94.17 237	92.62 209	99.17 188	98.94 37	88.87 219	86.48 257	94.46 272	84.36 181	96.61 263	88.19 218	78.51 280	93.21 277

V42		91.28 214	90.12 220	94.74 206	93.42 255	93.46 179	99.68 127	97.02 246	87.36 243	89.85 203	95.05 248	81.31 215	97.34 215	87.34 232	80.07 270	93.40 271

CP-MVSNet		91.23 215	90.22 214	94.26 223	93.96 240	92.39 213	99.09 193	98.57 73	88.95 217	86.42 258	96.57 203	79.19 240	96.37 268	90.29 202	78.95 277	94.02 234

XVG-ACMP-BASELINE		91.22 216	90.75 199	92.63 259	93.73 244	85.61 288	98.52 246	97.44 208	92.77 132	89.90 200	96.85 194	66.64 304	98.39 170	92.29 173	88.61 212	93.89 254

v7		91.20 217	89.99 222	94.82 205	93.57 246	93.41 185	99.57 145	96.98 252	86.83 251	89.88 201	95.22 241	81.01 218	97.14 235	85.53 251	81.31 255	93.90 252

v1144		91.09 218	89.83 223	94.87 202	93.25 266	93.69 176	99.62 141	96.98 252	86.83 251	89.64 212	94.99 253	80.94 219	97.05 243	85.08 256	81.16 257	93.87 256

FMVSNet2		91.02 219	89.56 228	95.41 183	97.53 163	95.74 130	98.98 209	97.41 213	87.05 247	88.43 233	95.00 252	71.34 287	96.24 274	85.12 255	85.21 239	94.25 222

MVP-Stereo		90.93 220	90.45 206	92.37 269	91.25 300	88.76 266	98.05 273	96.17 284	87.27 245	84.04 272	95.30 236	78.46 249	97.27 226	83.78 265	99.70 71	91.09 299
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS		90.91 221	90.17 216	93.12 250	96.78 188	90.42 251	98.89 217	97.05 240	89.03 212	86.49 256	95.42 227	76.59 259	95.02 292	87.22 234	84.09 243	93.93 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		90.88 222	89.82 224	94.08 228	97.53 163	91.97 219	98.43 251	96.95 257	87.05 247	89.68 208	94.72 262	71.34 287	96.11 276	87.01 238	85.65 234	94.17 225

test1		90.88 222	89.82 224	94.08 228	97.53 163	91.97 219	98.43 251	96.95 257	87.05 247	89.68 208	94.72 262	71.34 287	96.11 276	87.01 238	85.65 234	94.17 225

v144192		90.79 224	89.52 230	94.59 212	93.11 272	92.77 202	99.56 147	96.99 250	86.38 256	89.82 204	94.95 255	80.50 228	97.10 240	83.98 263	80.41 264	93.90 252

v148		90.70 225	89.63 226	93.92 236	92.97 275	90.97 242	99.75 106	96.89 264	87.51 240	88.27 236	95.01 250	81.67 206	97.04 244	87.40 231	77.17 294	93.75 262

MS-PatchMatch		90.65 226	90.30 209	91.71 278	94.22 236	85.50 290	98.24 264	97.70 183	88.67 222	86.42 258	96.37 208	67.82 301	98.03 193	83.62 266	99.62 75	91.60 296

ACMH	89.72 17	90.64 227	89.63 226	93.66 243	95.64 217	88.64 270	98.55 242	97.45 207	89.03 212	81.62 282	97.61 170	69.75 294	98.41 166	89.37 209	87.62 225	93.92 251
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-CasMVS		90.63 228	89.51 231	93.99 234	93.83 242	91.70 233	98.98 209	98.52 82	88.48 225	86.15 262	96.53 205	75.46 268	96.31 271	88.83 214	78.86 279	93.95 248

v1192		90.62 229	89.25 234	94.72 208	93.13 269	93.07 195	99.50 155	97.02 246	86.33 257	89.56 215	95.01 250	79.22 239	97.09 242	82.34 274	81.16 257	94.01 236

v8		90.54 230	89.17 235	94.66 209	93.43 254	93.40 188	99.20 185	96.94 260	85.76 264	87.56 242	94.51 268	81.96 201	97.19 229	84.94 257	78.25 283	93.38 273

v1921920		90.46 231	89.12 236	94.50 216	92.96 276	92.46 211	99.49 156	96.98 252	86.10 259	89.61 214	95.30 236	78.55 248	97.03 247	82.17 275	80.89 263	94.01 236

PatchT		90.38 232	88.75 244	95.25 185	95.99 201	90.16 254	91.22 326	97.54 197	76.80 311	97.26 107	86.01 324	91.88 106	96.07 279	66.16 319	95.91 156	99.51 119

ACMH+	89.98 16	90.35 233	89.54 229	92.78 257	95.99 201	86.12 285	98.81 225	97.18 228	89.38 207	83.14 277	97.76 169	68.42 299	98.43 164	89.11 212	86.05 233	93.78 261

Baseline_NR-MVSNet		90.33 234	89.51 231	92.81 256	92.84 277	89.95 259	99.77 99	93.94 322	84.69 276	89.04 225	95.66 222	81.66 207	96.52 265	90.99 190	76.98 295	91.97 292

MIMVSNet		90.30 235	88.67 246	95.17 188	96.45 193	91.64 235	92.39 320	97.15 232	85.99 260	90.50 188	93.19 290	66.95 303	94.86 296	82.01 276	93.43 188	99.01 166

LTVRE_ROB	88.28 18	90.29 236	89.05 239	94.02 231	95.08 223	90.15 255	97.19 286	97.43 209	84.91 273	83.99 273	97.06 185	74.00 279	98.28 182	84.08 261	87.71 223	93.62 267
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		90.25 237	88.82 242	94.57 214	93.53 248	93.43 181	99.08 195	96.87 267	85.00 272	87.34 246	94.51 268	80.93 220	97.02 249	82.85 271	79.23 276	93.26 275

v1240		90.20 238	88.79 243	94.44 218	93.05 274	92.27 215	99.38 169	96.92 261	85.89 261	89.36 218	94.87 261	77.89 252	97.03 247	80.66 282	81.08 259	94.01 236

PEN-MVS		90.19 239	89.06 238	93.57 244	93.06 273	90.90 244	99.06 201	98.47 94	88.11 230	85.91 264	96.30 209	76.67 258	95.94 283	87.07 235	76.91 296	93.89 254

pmmvs5		90.17 240	89.09 237	93.40 246	92.10 287	89.77 262	99.74 109	95.58 295	85.88 262	87.24 247	95.74 219	73.41 281	96.48 266	88.54 215	83.56 247	93.95 248

EU-MVSNet		90.14 241	90.34 208	89.54 295	92.55 282	81.06 311	98.69 232	98.04 156	91.41 180	86.59 254	96.84 196	80.83 221	93.31 316	86.20 246	81.91 252	94.26 220

USDC		90.00 242	88.96 240	93.10 251	94.81 228	88.16 276	98.71 230	95.54 297	93.66 111	83.75 275	97.20 179	65.58 306	98.31 179	83.96 264	87.49 227	92.85 284

OurMVSNet-221017-0		89.81 243	89.48 233	90.83 284	91.64 295	81.21 309	98.17 268	95.38 306	91.48 177	85.65 266	97.31 176	72.66 282	97.29 224	88.15 219	84.83 241	93.97 247

Patchmtry		89.70 244	88.49 247	93.33 247	96.24 196	89.94 261	91.37 325	96.23 282	78.22 308	87.69 241	93.31 288	91.04 118	96.03 280	80.18 284	82.10 250	94.02 234

v7n		89.65 245	88.29 250	93.72 240	92.22 284	90.56 248	99.07 199	97.10 234	85.42 271	86.73 252	94.72 262	80.06 232	97.13 237	81.14 280	78.12 285	93.49 269

v52		89.55 246	88.41 248	92.98 252	92.32 283	90.01 257	98.88 218	96.89 264	84.51 277	86.89 249	94.22 275	79.23 238	97.16 231	84.46 259	78.27 282	91.76 294

V4		89.55 246	88.41 248	92.98 252	92.21 285	90.03 256	98.87 221	96.91 262	84.51 277	86.84 250	94.21 276	79.37 237	97.15 233	84.45 260	78.28 281	91.76 294

DTE-MVSNet		89.40 248	88.24 251	92.88 255	92.66 281	89.95 259	99.10 192	98.22 136	87.29 244	85.12 268	96.22 211	76.27 263	95.30 290	83.56 267	75.74 300	93.41 270

RPMNet		89.39 249	87.20 261	95.94 173	96.29 194	92.66 206	92.01 322	97.63 187	70.19 325	96.94 113	85.87 325	87.25 156	96.03 280	62.69 322	95.96 154	99.13 160

pm-mvs1		89.36 250	87.81 255	94.01 232	93.40 257	91.93 222	98.62 239	96.48 281	86.25 258	83.86 274	96.14 213	73.68 280	97.04 244	86.16 247	75.73 301	93.04 280

tfpnnormal		89.29 251	87.61 257	94.34 222	94.35 234	94.13 165	98.95 213	98.94 37	83.94 280	84.47 271	95.51 225	74.84 273	97.39 211	77.05 303	80.41 264	91.48 298

LF4IMVS		89.25 252	88.85 241	90.45 288	92.81 279	81.19 310	98.12 269	94.79 314	91.44 179	86.29 260	97.11 181	65.30 308	98.11 189	88.53 216	85.25 238	92.07 289

testpf		89.10 253	88.73 245	90.24 289	97.59 162	83.48 299	74.22 337	97.39 215	79.66 305	89.64 212	93.92 278	86.38 164	95.76 284	85.42 252	94.31 180	91.49 297

testgi		89.01 254	88.04 253	91.90 276	93.49 251	84.89 294	99.73 114	95.66 293	93.89 105	85.14 267	98.17 160	59.68 320	94.66 298	77.73 298	88.88 206	96.16 195

v748		88.94 255	87.72 256	92.61 260	91.91 289	87.50 280	99.07 199	96.97 255	84.76 274	85.79 265	93.63 285	79.19 240	97.04 244	83.16 269	75.03 304	93.28 274

Test4		88.80 256	85.91 265	97.48 135	87.33 314	95.72 132	99.29 179	97.04 245	92.82 127	70.35 317	91.46 297	44.37 332	97.43 210	93.37 162	97.17 137	99.29 142

SixPastTwentyTwo		88.73 257	88.01 254	90.88 282	91.85 292	82.24 304	98.22 266	95.18 312	88.97 215	82.26 280	96.89 191	71.75 286	96.67 262	84.00 262	82.98 248	93.72 266

FMVSNet1		88.50 258	86.64 262	94.08 228	95.62 218	91.97 219	98.43 251	96.95 257	83.00 284	86.08 263	94.72 262	59.09 321	96.11 276	81.82 278	84.07 244	94.17 225

FMVSNet5		88.32 259	87.47 259	90.88 282	96.90 180	88.39 274	97.28 285	95.68 292	82.60 287	84.67 270	92.40 294	79.83 234	91.16 319	76.39 305	81.51 254	93.09 278

DSMNet-mixed		88.28 260	88.24 251	88.42 301	89.64 309	75.38 318	98.06 272	89.86 336	85.59 269	88.20 237	92.14 295	76.15 265	91.95 318	78.46 295	96.05 151	97.92 181

K. test v3		88.05 261	87.24 260	90.47 287	91.82 294	82.23 305	98.96 212	97.42 211	89.05 211	76.93 296	95.60 223	68.49 298	95.42 287	85.87 250	81.01 261	93.75 262

TinyColmap		87.87 262	86.51 263	91.94 275	95.05 225	85.57 289	97.65 278	94.08 320	84.40 279	81.82 281	96.85 194	62.14 315	98.33 177	80.25 283	86.37 232	91.91 293

TransMVSNet (Re)		87.25 263	85.28 267	93.16 249	93.56 247	91.03 241	98.54 244	94.05 321	83.69 282	81.09 284	96.16 212	75.32 269	96.40 267	76.69 304	68.41 312	92.06 290

Patchmatch-RL test		86.90 264	85.98 264	89.67 294	84.45 319	75.59 317	89.71 328	92.43 328	86.89 250	77.83 294	90.94 299	94.22 58	93.63 313	87.75 224	69.61 309	99.79 80

LP		86.76 265	84.85 269	92.50 263	95.08 223	85.89 287	89.97 327	96.97 255	75.28 316	84.97 269	90.68 300	80.78 222	95.13 291	61.64 324	88.31 217	96.46 191

v18		86.59 266	84.57 270	92.65 258	93.41 256	93.43 181	98.69 232	95.55 296	82.44 288	74.71 305	87.68 311	82.11 195	94.21 299	80.14 285	66.37 318	90.32 305

v16		86.52 267	84.49 271	92.60 261	93.45 252	93.31 190	98.60 241	95.52 299	82.30 290	74.59 307	87.70 310	81.95 202	94.18 300	79.93 287	66.38 317	90.30 306

v17		86.51 268	84.49 271	92.57 262	93.38 258	93.29 191	98.61 240	95.54 297	82.32 289	74.69 306	87.63 312	82.03 196	94.17 301	80.02 286	66.17 319	90.26 307

test2356		86.43 269	87.59 258	82.95 309	85.90 316	69.43 321	99.79 93	96.63 276	85.76 264	83.44 276	94.99 253	80.45 231	86.52 330	68.12 316	93.21 191	92.90 281

Anonymous20231206		86.32 270	85.42 266	89.02 297	89.11 311	80.53 314	99.05 204	95.28 308	85.43 270	82.82 278	93.92 278	74.40 276	93.44 315	66.99 317	81.83 253	93.08 279

v15		86.26 271	84.19 274	92.47 264	93.29 263	93.28 192	98.53 245	95.47 300	82.24 292	74.34 308	87.34 314	81.71 205	94.07 302	79.39 288	65.42 320	90.06 313

V14		86.22 272	84.15 275	92.41 267	93.30 262	93.16 193	98.47 248	95.47 300	82.10 293	74.27 309	87.41 313	81.73 204	94.02 304	79.26 289	65.37 322	90.04 314

MVS-HIRNet		86.22 272	83.19 287	95.31 184	96.71 191	90.29 252	92.12 321	97.33 220	62.85 328	86.82 251	70.37 332	69.37 295	97.49 208	75.12 306	97.99 119	98.15 178

V9		86.16 274	84.07 276	92.43 265	93.27 265	93.04 198	98.40 255	95.45 302	81.98 295	74.18 311	87.31 315	81.58 211	94.06 303	79.12 292	65.33 323	90.20 310

v12		86.10 275	84.01 277	92.37 269	93.23 268	92.96 199	98.33 258	95.45 302	81.87 296	74.05 313	87.15 317	81.60 210	93.98 307	79.09 293	65.28 324	90.18 311

v11		86.09 276	83.98 280	92.42 266	93.29 263	93.41 185	98.52 246	95.30 307	81.73 298	74.27 309	87.20 316	81.24 216	93.85 311	77.68 299	66.61 316	90.00 315

v13		86.06 277	83.97 281	92.34 271	93.25 266	92.85 201	98.26 262	95.44 304	81.70 299	74.02 314	87.11 319	81.58 211	94.00 306	78.94 294	65.41 321	90.18 311

pmmvs6		85.69 278	83.84 283	91.26 281	90.00 308	84.41 296	97.82 277	96.15 285	75.86 313	81.29 283	95.39 230	61.21 317	96.87 255	83.52 268	73.29 307	92.50 286

test_0402		85.58 279	83.94 282	90.50 286	93.81 243	85.04 293	98.55 242	95.20 311	76.01 312	79.72 289	95.13 242	64.15 311	96.26 273	66.04 320	86.88 229	90.21 309

UnsupCasMVSNet_eth		85.52 280	83.99 278	90.10 291	89.36 310	83.51 298	96.65 293	97.99 159	89.14 209	75.89 301	93.83 280	63.25 313	93.92 308	81.92 277	67.90 314	92.88 283

MDA-MVSNet_test_wron		85.51 281	83.32 286	92.10 273	90.96 301	88.58 271	99.20 185	96.52 279	79.70 304	57.12 330	92.69 292	79.11 242	93.86 310	77.10 302	77.46 292	93.86 257

YYNet1		85.50 282	83.33 285	92.00 274	90.89 302	88.38 275	99.22 184	96.55 278	79.60 306	57.26 329	92.72 291	79.09 243	93.78 312	77.25 301	77.37 293	93.84 258

EG-PatchMatch MVS		85.35 283	83.81 284	89.99 293	90.39 305	81.89 307	98.21 267	96.09 286	81.78 297	74.73 304	93.72 284	51.56 329	97.12 239	79.16 291	88.61 212	90.96 301

testing_2		85.10 284	81.72 291	95.22 186	82.25 323	94.16 163	97.54 279	97.01 249	88.15 229	62.23 325	86.43 322	44.43 331	97.18 230	92.28 178	85.20 240	94.31 217

TDRefinement		84.76 285	82.56 289	91.38 280	74.58 330	84.80 295	97.36 282	94.56 317	84.73 275	80.21 287	96.12 215	63.56 312	98.39 170	87.92 222	63.97 325	90.95 302

CMPMVS	61.59 21	84.75 286	85.14 268	83.57 306	90.32 306	62.54 329	96.98 290	97.59 194	74.33 318	69.95 318	96.66 199	64.17 310	98.32 178	87.88 223	88.41 216	89.84 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		84.72 287	83.99 278	86.91 303	88.19 313	80.62 313	98.88 218	95.94 288	88.36 227	78.87 290	94.62 267	68.75 296	89.11 323	66.52 318	75.82 299	91.00 300

new_pmnet		84.49 288	82.92 288	89.21 296	90.03 307	82.60 301	96.89 292	95.62 294	80.59 302	75.77 302	89.17 302	65.04 309	94.79 297	72.12 308	81.02 260	90.23 308

MDA-MVSNet-bldmvs		84.09 289	81.52 293	91.81 277	91.32 299	88.00 278	98.67 235	95.92 289	80.22 303	55.60 331	93.32 287	68.29 300	93.60 314	73.76 307	76.61 298	93.82 260

pmmvs-eth3d		84.03 290	81.97 290	90.20 290	84.15 320	87.09 282	98.10 271	94.73 316	83.05 283	74.10 312	87.77 309	65.56 307	94.01 305	81.08 281	69.24 311	89.49 320

testus		83.91 291	84.49 271	82.17 311	85.68 317	66.11 326	99.68 127	93.53 326	86.55 253	82.60 279	94.91 256	56.70 324	88.19 326	68.46 313	92.31 195	92.21 288

OpenMVS_ROB	79.82 20	83.77 292	81.68 292	90.03 292	88.30 312	82.82 300	98.46 249	95.22 310	73.92 320	76.00 300	91.29 298	55.00 325	96.94 251	68.40 314	88.51 215	90.34 304

MIMVSNet1		82.58 293	80.51 295	88.78 299	86.68 315	84.20 297	96.65 293	95.41 305	78.75 307	78.59 292	92.44 293	51.88 328	89.76 322	65.26 321	78.95 277	92.38 287

new-patchmatchnet		81.19 294	79.34 296	86.76 304	82.86 322	80.36 315	97.92 275	95.27 309	82.09 294	72.02 315	86.87 320	62.81 314	90.74 321	71.10 309	63.08 326	89.19 322

PM-MVS		80.47 295	78.88 297	85.26 305	83.79 321	72.22 319	95.89 306	91.08 332	85.71 268	76.56 298	88.30 303	36.64 333	93.90 309	82.39 273	69.57 310	89.66 318

pmmvs3		80.27 296	77.77 300	87.76 302	80.32 325	82.43 303	98.23 265	91.97 330	72.74 321	78.75 291	87.97 306	57.30 323	90.99 320	70.31 310	62.37 327	89.87 316

N_pmnet		80.06 297	80.78 294	77.89 314	91.94 288	45.28 342	98.80 226	56.82 347	78.10 309	80.08 288	93.33 286	77.03 254	95.76 284	68.14 315	82.81 249	92.64 285

UnsupCasMVSNet_bld		79.97 298	77.03 301	88.78 299	85.62 318	81.98 306	93.66 315	97.35 218	75.51 315	70.79 316	83.05 326	48.70 330	94.91 295	78.31 296	60.29 330	89.46 321

1111		79.11 299	78.74 298	80.23 312	78.33 326	67.13 323	97.31 283	93.65 324	71.34 322	68.35 321	87.87 307	85.42 175	88.46 324	52.93 331	73.46 306	85.11 325

test1235678		78.45 300	77.88 299	80.16 313	77.83 328	62.18 330	98.36 256	93.45 327	77.46 310	69.08 320	88.23 304	60.33 319	85.41 331	58.46 327	77.68 289	92.90 281

test12356		75.26 301	75.12 302	75.67 318	74.02 331	60.60 332	96.43 296	92.15 329	74.17 319	66.35 323	88.11 305	52.29 327	84.36 333	57.41 328	75.12 302	82.05 326

Anonymous20231211		74.17 302	71.17 304	83.17 308	80.58 324	67.02 325	96.27 300	94.45 319	57.31 330	69.60 319	86.25 323	33.67 334	92.96 317	61.86 323	60.50 329	89.54 319

.test1245		71.48 303	71.80 303	70.51 322	78.33 326	67.13 323	97.31 283	93.65 324	71.34 322	68.35 321	87.87 307	85.42 175	88.46 324	52.93 331	11.01 341	55.94 338

FPMVS		68.72 304	68.72 305	68.71 323	65.95 336	44.27 344	95.97 305	94.74 315	51.13 331	53.26 333	90.50 301	25.11 340	83.00 334	60.80 325	80.97 262	78.87 329

LCM-MVSNet		67.77 305	64.73 308	76.87 315	62.95 340	56.25 335	89.37 329	93.74 323	44.53 334	61.99 326	80.74 327	20.42 343	86.53 329	69.37 312	59.50 331	87.84 323

testmv		67.54 306	65.93 306	72.37 320	64.46 339	54.05 336	95.09 309	90.07 334	68.90 327	55.16 332	77.63 330	30.39 335	82.61 335	49.42 334	62.26 328	80.45 328

PMMVS2		67.15 307	64.15 309	76.14 316	70.56 334	62.07 331	93.89 313	87.52 340	58.09 329	60.02 327	78.32 328	22.38 341	84.54 332	59.56 326	47.03 332	81.80 327

Gipuma		66.95 308	65.00 307	72.79 319	91.52 297	67.96 322	66.16 338	95.15 313	47.89 332	58.54 328	67.99 335	29.74 337	87.54 328	50.20 333	77.83 287	62.87 336
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		65.23 309	62.94 310	72.13 321	44.90 344	50.03 340	81.05 333	89.42 339	38.45 336	48.51 335	99.90 8	54.09 326	78.70 337	91.84 182	18.26 340	87.64 324

no-one		63.48 310	59.26 311	76.14 316	66.71 335	65.06 327	92.75 318	89.92 335	68.96 326	46.96 336	66.55 336	21.74 342	87.68 327	57.07 329	22.69 339	75.68 331

PNet_i23d		56.44 311	53.54 312	65.14 326	65.34 337	50.33 339	89.06 330	79.57 342	45.77 333	35.75 340	68.95 334	10.75 347	74.40 338	48.48 335	38.20 333	70.70 332

ANet_high		56.10 312	52.24 313	67.66 324	49.27 343	56.82 334	83.94 332	82.02 341	70.47 324	33.28 341	64.54 337	17.23 345	69.16 341	45.59 338	23.85 338	77.02 330

PMVS	49.05 23	53.75 313	51.34 315	60.97 328	40.80 345	34.68 345	74.82 336	89.62 338	37.55 337	28.67 342	72.12 331	7.09 348	81.63 336	43.17 339	68.21 313	66.59 335
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		52.30 314	52.18 314	52.67 329	71.51 332	45.40 341	93.62 316	76.60 345	36.01 338	43.50 337	64.13 338	27.11 339	67.31 342	31.06 341	26.06 336	45.30 341

MVE	53.74 22	51.54 315	47.86 317	62.60 327	59.56 341	50.93 338	79.41 334	77.69 344	35.69 339	36.27 339	61.76 340	5.79 351	69.63 340	37.97 340	36.61 334	67.24 334
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		51.44 316	51.22 316	52.11 330	70.71 333	44.97 343	94.04 312	75.66 346	35.34 340	42.40 338	61.56 341	28.93 338	65.87 343	27.64 342	24.73 337	45.49 340

wuykxyi23d		50.36 317	45.43 318	65.16 325	51.13 342	51.75 337	77.46 335	78.42 343	41.45 335	26.98 343	54.30 343	6.13 349	74.03 339	46.82 337	26.19 335	69.71 333

testmvs		40.60 318	44.45 319	29.05 333	19.49 347	14.11 348	99.68 127	18.47 348	20.74 341	64.59 324	98.48 154	10.95 346	17.09 346	56.66 330	11.01 341	55.94 338

test123		37.68 319	39.14 321	33.31 331	19.94 346	24.83 347	98.36 256	9.75 349	15.53 342	51.31 334	87.14 318	19.62 344	17.74 345	47.10 336	3.47 344	57.36 337

pcd1.5k->3k		37.58 320	39.62 320	31.46 332	92.73 280	0.00 349	0.00 340	97.52 201	0.00 344	0.00 345	0.00 346	78.40 251	0.00 347	0.00 344	87.90 220	94.37 211

cdsmvs_eth3d_5k		23.43 321	31.24 322	0.00 335	0.00 348	0.00 349	0.00 340	98.09 152	0.00 344	0.00 345	99.67 73	83.37 187	0.00 347	0.00 344	0.00 345	0.00 343

wuyk23d		20.37 322	20.84 323	18.99 334	65.34 337	27.73 346	50.43 339	7.67 350	9.50 343	8.01 344	6.34 345	6.13 349	26.24 344	23.40 343	10.69 343	2.99 342

ab-mvs-re		8.28 323	11.04 324	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	99.40 92	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

pcd_1.5k_mvsjas		7.60 324	10.13 325	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	91.20 114	0.00 347	0.00 344	0.00 345	0.00 343

sosnet-low-res		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

sosnet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uncertanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

Regformer		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

ESAPD								98.44 98

sam_mvs1												94.72 45

sam_mvs												94.25 57

semantic-postprocess				92.93 254	96.72 190	89.96 258		96.99 250	88.95 217	86.63 253	95.67 221	76.50 260	95.00 293	87.04 236	84.04 246	93.84 258

ambc				83.23 307	77.17 329	62.61 328	87.38 331	94.55 318		76.72 297	86.65 321	30.16 336	96.36 269	84.85 258	69.86 308	90.73 303

MTGPA								98.28 130

test_post1							95.78 307				59.23 342	93.20 85	97.74 203	91.06 189

test_post											63.35 339	94.43 47	98.13 188

patchmatchnet-post											91.70 296	95.12 31	97.95 198

GG-mvs-BLEND				98.54 95	98.21 128	98.01 59	93.87 314	98.52 82		97.92 96	97.92 167	99.02 2	97.94 199	98.17 69	99.58 80	99.67 94

MTMP								96.49 280

gm-plane-assit					96.97 177	93.76 175			91.47 178		98.96 119		98.79 143	94.92 127

test9_res														99.71 15	99.99 13	100.00 1

TEST9					99.92 27	98.92 13	99.96 19	98.43 104	93.90 103	99.71 15	99.86 14	95.88 20	99.85 76

test_8					99.92 27	98.88 16	99.96 19	98.43 104	94.35 83	99.69 17	99.85 18	95.94 17	99.85 76

agg_prior2														99.48 21	100.00 1	100.00 1

agg_prior					99.93 24	98.77 23		98.43 104		99.63 20			99.85 76

TestCases				95.00 193	99.01 87	88.43 272		96.82 271	86.50 254	88.71 228	98.47 155	74.73 274	99.88 72	85.39 253	96.18 149	96.71 189

test_prior4						98.05 57	99.94 44

test_prior2							99.95 31		95.78 48	99.73 13	99.76 53	96.00 15		99.78 7	100.00 1

test_prior				99.43 25	99.94 14	98.49 46		98.65 58					99.80 85			99.99 11

旧先验2							99.46 161		94.21 88	99.85 5			99.95 48	96.96 104

新几何2							99.40 165

新几何1				99.42 28	99.75 53	98.27 52		98.63 64	92.69 136	99.55 27	99.82 37	94.40 49	100.00 1	91.21 185	99.94 41	99.99 11

旧先验1					99.76 51	97.52 72		98.64 61			99.85 18	95.63 23			99.94 41	99.99 11

无先验							99.49 156	98.71 52	93.46 115				100.00 1	94.36 140		99.99 11

原ACMM2							99.90 57

原ACMM1				98.96 71	99.73 58	96.99 92		98.51 88	94.06 96	99.62 22	99.85 18	94.97 40	99.96 40	95.11 125	99.95 37	99.92 66

test222					99.55 71	97.41 81	99.34 173	98.55 79	91.86 168	99.27 46	99.83 34	93.84 72			99.95 37	99.99 11

testdata2													99.99 25	90.54 197

segment_acmp												96.68 10

testdata				98.42 104	99.47 76	95.33 142		98.56 75	93.78 107	99.79 10	99.85 18	93.64 77	99.94 56	94.97 126	99.94 41	100.00 1

testdata1							99.28 180		96.35 38

test12				99.43 25	99.74 54	98.56 42		98.40 114		99.65 19		94.76 44	99.75 94		99.98 23	99.99 11

plane_prior7					95.71 214	91.59 237

plane_prior6					95.76 209	91.72 232						80.47 229

plane_prior5								97.87 171					98.37 175	97.79 85	89.55 199	94.52 200

plane_prior4											98.59 146

plane_prior3						91.64 235			96.63 29	93.01 169

plane_prior2							99.84 80		96.38 34

plane_prior1					95.73 211

plane_prior						91.74 229	99.86 76		96.76 25						89.59 198

n2								0.00 351

nn								0.00 351

door-mid								89.69 337

lessismore_v0				90.53 285	90.58 304	80.90 312		95.80 290		77.01 295	95.84 217	66.15 305	96.95 250	83.03 270	75.05 303	93.74 265

LGP-MVS_train				93.71 241	95.43 219	88.67 268		97.62 189	92.81 128	90.05 193	98.49 151	75.24 270	98.40 168	95.84 119	89.12 203	94.07 231

test11								98.44 98

door								90.31 333

HQP5-MVS						91.85 224

HQP-NCC					95.78 205		99.87 67		96.82 21	93.37 165

ACMP_Plane					95.78 205		99.87 67		96.82 21	93.37 165

BP-MVS														97.92 82

HQP4-MVS										93.37 165			98.39 170			94.53 198

HQP3-MVS								97.89 169							89.60 196

HQP2-MVS												80.65 225

NP-MVS					95.77 208	91.79 226					98.65 143

MDTV_nov1_ep13_2view						96.26 111	96.11 302		91.89 167	98.06 93		94.40 49		94.30 143		99.67 94

MDTV_nov1_ep13			95.69 113		97.90 142	94.15 164	95.98 304	98.44 98	93.12 121	97.98 95	95.74 219	95.10 32	98.58 153	90.02 205	96.92 142

ACMMP++_ref															87.04 228

ACMMP++															88.23 218

Test By Simon												92.82 91

ITE_SJBPF				92.38 268	95.69 216	85.14 292		95.71 291	92.81 128	89.33 220	98.11 161	70.23 293	98.42 165	85.91 249	88.16 219	93.59 268

DeepMVS_CX				82.92 310	95.98 203	58.66 333		96.01 287	92.72 133	78.34 293	95.51 225	58.29 322	98.08 190	82.57 272	85.29 237	92.03 291

High-resolution multi-view benchmark