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 59	98.20 3	99.93 1	99.98 2	96.82 12	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 8	99.97 12	98.62 70	98.02 6	99.90 2	99.95 3	97.33 9	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 66	98.47 2	99.13 53	99.92 5	96.38 18	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 49	99.98 6	98.86 49	97.10 15	99.80 8	99.94 4	95.92 25	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 89	99.95 31	98.32 132	97.50 10	99.52 31	99.88 9	97.43 6	99.71 102	99.50 20	99.98 24	99.89 70

HPM-MVS++		99.07 5	98.88 10	99.63 7	99.90 33	99.02 11	99.95 31	98.56 80	97.56 9	99.44 35	99.85 18	95.38 34	100.00 1	99.31 27	99.99 13	99.87 73

APDe-MVS		99.06 7	98.91 9	99.51 19	99.94 14	98.76 29	99.91 56	98.39 122	97.20 14	99.46 33	99.85 18	95.53 32	99.79 87	99.86 3	100.00 1	99.99 11

SteuartSystems-ACMMP		99.02 8	98.97 8	99.18 41	98.72 114	97.71 69	99.98 6	98.44 103	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 81	98.87 18	98.46 254	99.42 24	97.03 17	99.02 57	99.09 109	99.35 1	98.21 191	99.73 13	99.78 67	99.77 83

test_prior3		98.99 10	98.84 11	99.43 25	99.94 14	98.49 47	99.95 31	98.65 63	95.78 48	99.73 13	99.76 53	96.00 21	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 55	98.67 33	99.77 104	98.38 125	96.73 26	99.88 3	99.74 60	94.89 49	99.59 113	99.80 5	99.98 24	99.97 52

SD-MVS		98.92 12	98.70 13	99.56 14	99.70 63	98.73 30	99.94 45	98.34 130	96.38 34	99.81 7	99.76 53	94.59 52	99.98 29	99.84 4	99.96 35	99.97 52

MG-MVS		98.91 13	98.65 15	99.68 6	99.94 14	99.07 10	99.64 144	99.44 22	97.33 12	99.00 60	99.72 62	94.03 71	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 14	99.96 19	98.43 109	94.35 83	99.71 15	99.86 14	95.94 23	99.85 76	99.69 16	99.98 24	99.99 11

agg_prior1		98.88 14	98.66 14	99.54 16	99.93 24	98.77 24	99.96 19	98.43 109	94.63 76	99.63 20	99.85 18	95.79 27	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 43	99.96 19	98.43 109	94.07 93	99.67 18	99.85 18	96.05 19	99.85 76	99.69 16	99.98 24	99.99 11

DeepC-MVS_fast	96.59 1	98.81 17	98.54 22	99.62 10	99.90 33	98.85 19	99.24 188	98.47 99	98.14 4	99.08 54	99.91 6	93.09 93	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 39	98.34 51	99.87 69	98.52 87	96.05 43	99.41 38	99.79 42	94.93 47	99.76 91	99.07 32	99.90 51	99.99 11

Regformer-2		98.78 19	98.59 20	99.36 34	99.85 39	98.32 52	99.87 69	98.52 87	96.04 44	99.41 38	99.79 42	94.92 48	99.76 91	99.05 33	99.90 51	99.98 42

MVS_111021_HR		98.72 20	98.62 17	99.01 68	99.36 82	97.18 92	99.93 50	99.90 1	96.81 24	98.67 71	99.77 49	93.92 73	99.89 66	99.27 28	99.94 42	99.96 56

XVS		98.70 21	98.55 21	99.15 47	99.94 14	97.50 77	99.94 45	98.42 117	96.22 39	99.41 38	99.78 47	94.34 59	99.96 40	98.92 42	99.95 38	99.99 11

CDPH-MVS		98.65 22	98.36 32	99.49 21	99.94 14	98.73 30	99.87 69	98.33 131	93.97 99	99.76 11	99.87 12	94.99 45	99.75 94	98.55 61	100.00 1	99.98 42

APD-MVS		98.62 23	98.35 33	99.41 29	99.90 33	98.51 46	99.87 69	98.36 128	94.08 92	99.74 12	99.73 61	94.08 69	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 77	99.73 59	96.63 106	99.97 12	97.92 172	98.07 5	98.76 67	99.55 82	95.00 44	99.94 56	99.91 2	97.68 123	99.99 11

PAPM		98.60 24	98.42 24	99.14 49	96.05 205	98.96 12	99.90 59	99.35 26	96.68 28	98.35 85	99.66 75	96.45 17	98.51 162	99.45 22	99.89 53	99.96 56

#test#		98.59 26	98.41 25	99.14 49	99.96 8	97.43 81	99.95 31	98.61 72	95.00 66	99.31 44	99.85 18	94.22 64	100.00 1	98.78 49	99.98 24	99.98 42

Regformer-3		98.58 27	98.41 25	99.10 55	99.84 44	97.57 73	99.66 137	98.52 87	95.79 47	99.01 58	99.77 49	94.40 55	99.75 94	98.82 47	99.83 60	99.98 42

HFP-MVS		98.56 28	98.37 30	99.14 49	99.96 8	97.43 81	99.95 31	98.61 72	94.77 71	99.31 44	99.85 18	94.22 64	100.00 1	98.70 52	99.98 24	99.98 42

Regformer-4		98.56 28	98.39 28	99.08 57	99.84 44	97.52 75	99.66 137	98.52 87	95.76 50	99.01 58	99.77 49	94.33 61	99.75 94	98.80 48	99.83 60	99.98 42

region2R		98.54 30	98.37 30	99.05 63	99.96 8	97.18 92	99.96 19	98.55 84	94.87 69	99.45 34	99.85 18	94.07 70	100.00 1	98.67 54	100.00 1	99.98 42

DELS-MVS		98.54 30	98.22 36	99.50 20	99.15 85	98.65 36	100.00 1	98.58 76	97.70 7	98.21 92	99.24 103	92.58 102	99.94 56	98.63 59	99.94 42	99.92 67
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 24	99.95 31	98.43 109	95.35 60	98.03 95	99.75 58	94.03 71	99.98 29	98.11 72	99.83 60	99.99 11

ACMMPR		98.50 33	98.32 34	99.05 63	99.96 8	97.18 92	99.95 31	98.60 74	94.77 71	99.31 44	99.84 32	93.73 80	100.00 1	98.70 52	99.98 24	99.98 42

ACMMP_Plus		98.49 34	98.14 41	99.54 16	99.66 65	98.62 38	99.85 82	98.37 127	94.68 75	99.53 28	99.83 34	92.87 94	100.00 1	98.66 57	99.84 59	99.99 11

EPNet		98.49 34	98.40 27	98.77 80	99.62 67	96.80 104	99.90 59	99.51 19	97.60 8	99.20 49	99.36 97	93.71 81	99.91 62	97.99 78	98.71 104	99.61 105
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 76	99.96 8	96.62 107	99.97 12	98.39 122	94.43 81	98.90 62	99.87 12	94.30 62	100.00 1	99.04 37	99.99 13	99.99 11

PS-MVSNAJ		98.44 37	98.20 38	99.16 44	98.80 111	98.92 14	99.54 156	98.17 148	97.34 11	99.85 5	99.85 18	91.20 120	99.89 66	99.41 25	99.67 73	98.69 179

MVS_111021_LR		98.42 38	98.38 29	98.53 98	99.39 80	95.79 133	99.87 69	99.86 2	96.70 27	98.78 66	99.79 42	92.03 110	99.90 63	99.17 29	99.86 58	99.88 72

DP-MVS Recon		98.41 39	98.02 45	99.56 14	99.97 3	98.70 32	99.92 52	98.44 103	92.06 166	98.40 83	99.84 32	95.68 28	100.00 1	98.19 68	99.71 71	99.97 52

PHI-MVS		98.41 39	98.21 37	99.03 65	99.86 38	97.10 96	99.98 6	98.80 54	90.78 197	99.62 22	99.78 47	95.30 35	100.00 1	99.80 5	99.93 47	99.99 11

mPP-MVS		98.39 41	98.20 38	98.97 70	99.97 3	96.92 101	99.95 31	98.38 125	95.04 65	98.61 75	99.80 41	93.39 85	100.00 1	98.64 58	100.00 1	99.98 42

PGM-MVS		98.34 42	98.13 42	98.99 69	99.92 27	97.00 97	99.75 111	99.50 20	93.90 103	99.37 42	99.76 53	93.24 90	100.00 1	97.75 88	99.96 35	99.98 42

MPTG		98.33 43	98.00 46	99.30 36	99.85 39	97.93 64	99.80 95	98.28 136	95.76 50	97.18 111	99.88 9	92.74 98	100.00 1	98.67 54	99.88 55	99.99 11

MTAPA		98.29 44	97.96 50	99.30 36	99.85 39	97.93 64	99.39 173	98.28 136	95.76 50	97.18 111	99.88 9	92.74 98	100.00 1	98.67 54	99.88 55	99.99 11

CANet		98.27 45	97.82 53	99.63 7	99.72 61	99.10 9	99.98 6	98.51 93	97.00 18	98.52 77	99.71 64	87.80 156	99.95 48	99.75 9	99.38 91	99.83 76

EI-MVSNet-Vis-set		98.27 45	98.11 43	98.75 81	99.83 47	96.59 109	99.40 170	98.51 93	95.29 62	98.51 78	99.76 53	93.60 84	99.71 102	98.53 62	99.52 84	99.95 61

APD-MVS_3200maxsize		98.25 47	98.08 44	98.78 79	99.81 49	96.60 108	99.82 90	98.30 134	93.95 101	99.37 42	99.77 49	92.84 95	99.76 91	98.95 39	99.92 49	99.97 52

xiu_mvs_v2_base		98.23 48	97.97 48	99.02 67	98.69 115	98.66 34	99.52 158	98.08 159	97.05 16	99.86 4	99.86 14	90.65 128	99.71 102	99.39 26	98.63 105	98.69 179

MP-MVS		98.23 48	97.97 48	99.03 65	99.94 14	97.17 95	99.95 31	98.39 122	94.70 74	98.26 90	99.81 40	91.84 114	100.00 1	98.85 46	99.97 33	99.93 64

EI-MVSNet-UG-set		98.14 50	97.99 47	98.60 91	99.80 50	96.27 116	99.36 177	98.50 97	95.21 64	98.30 87	99.75 58	93.29 89	99.73 101	98.37 66	99.30 94	99.81 78

PAPM_NR		98.12 51	97.93 51	98.70 83	99.94 14	96.13 125	99.82 90	98.43 109	94.56 77	97.52 104	99.70 66	94.40 55	99.98 29	97.00 102	99.98 24	99.99 11

WTY-MVS		98.10 52	97.60 58	99.60 11	98.92 99	99.28 5	99.89 64	99.52 17	95.58 56	98.24 91	99.39 94	93.33 86	99.74 98	97.98 80	95.58 164	99.78 82

MP-MVS-pluss		98.07 53	97.64 56	99.38 33	99.74 55	98.41 50	99.74 114	98.18 147	93.35 117	96.45 125	99.85 18	92.64 101	99.97 38	98.91 44	99.89 53	99.77 83
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
1121		98.03 54	97.57 60	99.40 31	99.74 55	98.21 55	98.31 264	98.62 70	92.78 132	99.53 28	99.83 34	95.08 39	100.00 1	94.36 140	99.92 49	99.99 11

HPM-MVS		97.96 55	97.72 54	98.68 84	99.84 44	96.39 115	99.90 59	98.17 148	92.61 143	98.62 74	99.57 81	91.87 113	99.67 109	98.87 45	99.99 13	99.99 11

PVSNet_Blended		97.94 56	97.64 56	98.83 78	99.59 68	96.99 98	100.00 1	99.10 29	95.38 59	98.27 88	99.08 110	89.00 147	99.95 48	99.12 30	99.25 95	99.57 111

PLC	95.54 3	97.93 57	97.89 52	98.05 124	99.82 48	94.77 162	99.92 52	98.46 101	93.93 102	97.20 109	99.27 99	95.44 33	99.97 38	97.41 92	99.51 86	99.41 130
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	98.47 104	99.52 74	95.41 146	99.47 164	98.87 48	91.68 174	98.84 63	99.85 18	92.34 104	99.99 25	98.44 64	99.96 35	100.00 1

lupinMVS		97.85 59	97.60 58	98.62 89	97.28 175	97.70 71	99.99 3	97.55 201	95.50 58	99.43 36	99.67 73	90.92 126	98.71 151	98.40 65	99.62 76	99.45 125

alignmvs		97.81 60	97.33 65	99.25 38	98.77 113	98.66 34	99.99 3	98.44 103	94.40 82	98.41 81	99.47 88	93.65 82	99.42 129	98.57 60	94.26 183	99.67 95

HPM-MVS_fast		97.80 61	97.50 61	98.68 84	99.79 51	96.42 112	99.88 66	98.16 151	91.75 173	98.94 61	99.54 84	91.82 115	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 93	99.32 3	97.49 285	99.52 17	95.69 54	98.32 86	97.41 179	93.32 87	99.77 89	98.08 75	95.75 161	99.81 78

CNLPA		97.76 63	97.38 63	98.92 74	99.53 73	96.84 102	99.87 69	98.14 154	93.78 107	96.55 122	99.69 69	92.28 105	99.98 29	97.13 98	99.44 89	99.93 64

ACMMP		97.74 64	97.44 62	98.66 86	99.92 27	96.13 125	99.18 192	99.45 21	94.84 70	96.41 128	99.71 64	91.40 117	99.99 25	97.99 78	98.03 119	99.87 73
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	93.92 241	99.63 66	81.76 314	99.96 19	98.56 80	99.47 1	99.19 51	99.99 1	94.16 68	100.00 1	99.92 1	99.93 47	100.00 1

abl_6		97.67 66	97.34 64	98.66 86	99.68 64	96.11 129	99.68 132	98.14 154	93.80 106	99.27 47	99.70 66	88.65 152	99.98 29	97.46 91	99.72 70	99.89 70

CPTT-MVS		97.64 67	97.32 66	98.58 93	99.97 3	95.77 134	99.96 19	98.35 129	89.90 208	98.36 84	99.79 42	91.18 123	99.99 25	98.37 66	99.99 13	99.99 11

sss		97.57 68	97.03 75	99.18 41	98.37 126	98.04 61	99.73 119	99.38 25	93.46 115	98.76 67	99.06 111	91.21 119	99.89 66	96.33 110	97.01 141	99.62 103

MVS_0304		97.52 69	96.79 81	99.69 5	99.59 68	99.30 4	99.97 12	98.01 163	96.99 19	98.84 63	99.79 42	78.90 250	99.96 40	99.74 11	99.32 93	99.81 78

xiu_mvs_v1_base_debu		97.43 70	97.06 71	98.55 94	97.74 161	98.14 56	99.31 180	97.86 179	96.43 31	99.62 22	99.69 69	85.56 178	99.68 106	99.05 33	98.31 111	97.83 188

xiu_mvs_v1_base		97.43 70	97.06 71	98.55 94	97.74 161	98.14 56	99.31 180	97.86 179	96.43 31	99.62 22	99.69 69	85.56 178	99.68 106	99.05 33	98.31 111	97.83 188

xiu_mvs_v1_base_debi		97.43 70	97.06 71	98.55 94	97.74 161	98.14 56	99.31 180	97.86 179	96.43 31	99.62 22	99.69 69	85.56 178	99.68 106	99.05 33	98.31 111	97.83 188

MAR-MVS		97.43 70	97.19 68	98.15 120	99.47 77	94.79 161	99.05 209	98.76 55	92.65 141	98.66 72	99.82 37	88.52 153	99.98 29	98.12 71	99.63 75	99.67 95
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 76	97.83 66	99.89 64	98.27 139	88.48 230	99.06 55	99.66 75	90.30 131	99.64 112	96.32 111	99.97 33	99.96 56

DWT-MVSNet_test		97.31 75	97.19 68	97.66 134	98.24 132	94.67 163	98.86 228	98.20 146	93.60 113	98.09 93	98.89 123	97.51 5	98.78 146	94.04 148	97.28 132	99.55 113

OMC-MVS		97.28 76	97.23 67	97.41 143	99.76 52	93.36 195	99.65 140	97.95 169	96.03 45	97.41 106	99.70 66	89.61 136	99.51 116	96.73 108	98.25 114	99.38 136

PVSNet_Blended_VisFu		97.27 77	96.81 79	98.66 86	98.81 110	96.67 105	99.92 52	98.64 66	94.51 78	96.38 129	98.49 156	89.05 146	99.88 72	97.10 100	98.34 109	99.43 128

jason		97.24 78	96.86 77	98.38 112	95.73 217	97.32 88	99.97 12	97.40 220	95.34 61	98.60 76	99.54 84	87.70 157	98.56 159	97.94 81	99.47 87	99.25 151
jason: jason.
AdaColmap		97.23 79	96.80 80	98.51 99	99.99 1	95.60 142	99.09 198	98.84 51	93.32 118	96.74 119	99.72 62	86.04 173	100.00 1	98.01 76	99.43 90	99.94 63

tfpn_ndepth		97.21 80	96.63 85	98.92 74	99.06 86	98.28 53	99.95 31	98.91 41	92.96 124	96.49 123	98.67 145	97.40 7	99.07 135	91.87 182	94.38 176	99.41 130

VNet		97.21 80	96.57 88	99.13 54	98.97 94	97.82 67	99.03 211	99.21 28	94.31 85	99.18 52	98.88 125	86.26 172	99.89 66	98.93 41	94.32 181	99.69 93

PVSNet	91.05 13	97.13 82	96.69 84	98.45 106	99.52 74	95.81 132	99.95 31	99.65 15	94.73 73	99.04 56	99.21 105	84.48 186	99.95 48	94.92 127	98.74 103	99.58 110

CSCG		97.10 83	97.04 74	97.27 148	99.89 36	91.92 229	99.90 59	99.07 32	88.67 227	95.26 149	99.82 37	93.17 92	99.98 29	98.15 70	99.47 87	99.90 69

canonicalmvs		97.09 84	96.32 92	99.39 32	98.93 98	98.95 13	99.72 124	97.35 224	94.45 79	97.88 98	99.42 90	86.71 167	99.52 115	98.48 63	93.97 191	99.72 90

PatchFormer-LS_test		97.01 85	96.79 81	97.69 133	98.26 131	94.80 159	98.66 243	98.13 156	93.70 110	97.86 99	98.80 137	95.54 30	98.67 153	94.12 147	96.00 153	99.60 107

thres200		96.96 86	96.21 95	99.22 39	98.97 94	98.84 20	99.85 82	99.71 5	93.17 120	96.26 130	98.88 125	89.87 134	99.51 116	94.26 144	94.91 170	99.31 144

MVSFormer		96.94 87	96.60 86	97.95 126	97.28 175	97.70 71	99.55 154	97.27 229	91.17 187	99.43 36	99.54 84	90.92 126	96.89 258	94.67 135	99.62 76	99.25 151

F-COLMAP		96.93 88	96.95 76	96.87 156	99.71 62	91.74 235	99.85 82	97.95 169	93.11 122	95.72 142	99.16 107	92.35 103	99.94 56	95.32 123	99.35 92	98.92 173

DeepC-MVS	94.51 4	96.92 89	96.40 91	98.45 106	99.16 84	95.90 131	99.66 137	98.06 160	96.37 37	94.37 165	99.49 87	83.29 194	99.90 63	97.63 89	99.61 79	99.55 113
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 90	96.29 93	98.74 82	99.00 91	98.09 59	99.92 52	98.91 41	92.08 163	95.85 136	98.65 147	97.39 8	98.83 143	90.56 197	94.23 184	99.31 144

1314		96.84 91	95.96 105	99.48 23	96.74 195	98.52 45	98.31 264	98.86 49	95.82 46	89.91 205	98.98 117	87.49 159	99.96 40	97.80 84	99.73 69	99.96 56

CHOSEN 1792x2688		96.81 92	96.53 89	97.64 135	98.91 101	93.07 201	99.65 140	99.80 3	95.64 55	95.39 146	98.86 129	84.35 188	99.90 63	96.98 103	99.16 97	99.95 61

tfpn200view9		96.79 93	95.99 100	99.19 40	98.94 96	98.82 21	99.78 99	99.71 5	92.86 125	96.02 133	98.87 127	89.33 137	99.50 118	93.84 151	94.57 171	99.27 149

thres400		96.78 94	95.99 100	99.16 44	98.94 96	98.82 21	99.78 99	99.71 5	92.86 125	96.02 133	98.87 127	89.33 137	99.50 118	93.84 151	94.57 171	99.16 158

CANet_DTU		96.76 95	96.15 96	98.60 91	98.78 112	97.53 74	99.84 85	97.63 193	97.25 13	99.20 49	99.64 77	81.36 220	99.98 29	92.77 171	98.89 99	98.28 182

PMMVS		96.76 95	96.76 83	96.76 159	98.28 129	92.10 224	99.91 56	97.98 166	94.12 90	99.53 28	99.39 94	86.93 166	98.73 149	96.95 105	97.73 121	99.45 125

thres100view900		96.74 97	95.92 108	99.18 41	98.90 102	98.77 24	99.74 114	99.71 5	92.59 145	95.84 137	98.86 129	89.25 139	99.50 118	93.84 151	94.57 171	99.27 149

TESTMET0.1,1		96.74 97	96.26 94	98.16 117	97.36 174	96.48 111	99.96 19	98.29 135	91.93 168	95.77 141	98.07 168	95.54 30	98.29 185	90.55 198	98.89 99	99.70 91

conf200view11		96.73 99	95.92 108	99.16 44	98.90 102	98.77 24	99.74 114	99.71 5	92.59 145	95.84 137	98.86 129	89.25 139	99.50 118	93.84 151	94.57 171	99.20 155

thres600view7		96.69 100	95.87 115	99.14 49	98.90 102	98.78 23	99.74 114	99.71 5	92.59 145	95.84 137	98.86 129	89.25 139	99.50 118	93.44 161	94.50 175	99.16 158

EPP-MVSNet		96.69 100	96.60 86	96.96 153	97.74 161	93.05 203	99.37 175	98.56 80	88.75 226	95.83 140	99.01 114	96.01 20	98.56 159	96.92 106	97.20 137	99.25 151

HyFIR lowres test		96.66 102	96.43 90	97.36 146	99.05 87	93.91 175	99.70 126	99.80 3	90.54 198	96.26 130	98.08 167	92.15 108	98.23 190	96.84 107	95.46 165	99.93 64

MVS		96.60 103	95.56 125	99.72 3	96.85 188	99.22 7	98.31 264	98.94 37	91.57 176	90.90 191	99.61 79	86.66 168	99.96 40	97.36 93	99.88 55	99.99 11

UA-Net		96.54 104	95.96 105	98.27 115	98.23 133	95.71 139	98.00 279	98.45 102	93.72 109	98.41 81	99.27 99	88.71 151	99.66 110	91.19 187	97.69 122	99.44 127

tfpn_n400		96.53 105	95.88 111	98.48 101	98.59 116	97.38 85	99.87 69	98.91 41	91.32 184	95.22 154	98.83 134	96.57 14	98.66 155	89.55 211	94.09 186	99.40 133

tfpnconf		96.53 105	95.88 111	98.48 101	98.59 116	97.38 85	99.87 69	98.91 41	91.32 184	95.22 154	98.83 134	96.57 14	98.66 155	89.55 211	94.09 186	99.40 133

tfpnview11		96.53 105	95.88 111	98.48 101	98.59 116	97.38 85	99.87 69	98.91 41	91.32 184	95.22 154	98.83 134	96.57 14	98.66 155	89.55 211	94.09 186	99.40 133

EPMVS		96.53 105	96.01 99	98.09 123	98.43 125	96.12 128	96.36 302	99.43 23	93.53 114	97.64 101	95.04 254	94.41 54	98.38 179	91.13 188	98.11 115	99.75 85

test-LLR		96.47 109	96.04 98	97.78 130	97.02 181	95.44 144	99.96 19	98.21 143	94.07 93	95.55 143	96.38 211	93.90 76	98.27 188	90.42 200	98.83 101	99.64 101

view600		96.46 110	95.59 120	99.06 59	98.87 106	98.60 39	99.69 127	99.71 5	92.20 157	95.23 150	98.80 137	89.17 142	99.43 125	92.29 173	94.37 177	99.16 158

view800		96.46 110	95.59 120	99.06 59	98.87 106	98.60 39	99.69 127	99.71 5	92.20 157	95.23 150	98.80 137	89.17 142	99.43 125	92.29 173	94.37 177	99.16 158

conf0.05thres1000		96.46 110	95.59 120	99.06 59	98.87 106	98.60 39	99.69 127	99.71 5	92.20 157	95.23 150	98.80 137	89.17 142	99.43 125	92.29 173	94.37 177	99.16 158

tfpn		96.46 110	95.59 120	99.06 59	98.87 106	98.60 39	99.69 127	99.71 5	92.20 157	95.23 150	98.80 137	89.17 142	99.43 125	92.29 173	94.37 177	99.16 158

MVS_Test		96.46 110	95.74 117	98.61 90	98.18 136	97.23 90	99.31 180	97.15 238	91.07 190	98.84 63	97.05 191	88.17 155	98.97 139	94.39 139	97.50 126	99.61 105

test-mter		96.39 115	95.93 107	97.78 130	97.02 181	95.44 144	99.96 19	98.21 143	91.81 172	95.55 143	96.38 211	95.17 36	98.27 188	90.42 200	98.83 101	99.64 101

CDS-MVSNet		96.34 116	96.07 97	97.13 150	97.37 173	94.96 155	99.53 157	97.91 173	91.55 177	95.37 147	98.32 163	95.05 41	97.13 242	93.80 155	95.75 161	99.30 146
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		96.32 117	95.98 102	97.35 147	97.93 147	94.82 158	99.47 164	98.15 153	91.83 171	95.09 157	99.11 108	91.37 118	97.47 214	93.47 160	97.43 128	99.74 86

3Dnovator+	91.53 11	96.31 118	95.24 132	99.52 18	96.88 187	98.64 37	99.72 124	98.24 140	95.27 63	88.42 241	98.98 117	82.76 196	99.94 56	97.10 100	99.83 60	99.96 56

Effi-MVS+		96.30 119	95.69 118	98.16 117	97.85 152	96.26 117	97.41 286	97.21 232	90.37 200	98.65 73	98.58 153	86.61 169	98.70 152	97.11 99	97.37 131	99.52 119

IS-MVSNet		96.29 120	95.90 110	97.45 141	98.13 139	94.80 159	99.08 200	97.61 198	92.02 167	95.54 145	98.96 119	90.64 129	98.08 195	93.73 158	97.41 130	99.47 124

3Dnovator	91.47 12	96.28 121	95.34 130	99.08 57	96.82 190	97.47 80	99.45 167	98.81 52	95.52 57	89.39 223	99.00 116	81.97 206	99.95 48	97.27 95	99.83 60	99.84 75

tpmrst		96.27 122	95.98 102	97.13 150	97.96 145	93.15 200	96.34 303	98.17 148	92.07 164	98.71 70	95.12 248	93.91 75	98.73 149	94.91 129	96.62 145	99.50 122

CostFormer		96.10 123	95.88 111	96.78 158	97.03 180	92.55 216	97.08 293	97.83 182	90.04 207	98.72 69	94.89 263	95.01 43	98.29 185	96.54 109	95.77 160	99.50 122

PVSNet_BlendedMVS		96.05 124	95.82 116	96.72 161	99.59 68	96.99 98	99.95 31	99.10 29	94.06 96	98.27 88	95.80 223	89.00 147	99.95 48	99.12 30	87.53 232	93.24 282

PatchMatch-RL		96.04 125	95.40 127	97.95 126	99.59 68	95.22 153	99.52 158	99.07 32	93.96 100	96.49 123	98.35 162	82.28 198	99.82 84	90.15 206	99.22 96	98.81 176

1112_ss		96.01 126	95.20 134	98.42 109	97.80 156	96.41 113	99.65 140	96.66 280	92.71 135	92.88 179	99.40 92	92.16 107	99.30 130	91.92 180	93.66 192	99.55 113

PatchmatchNet		95.94 127	95.45 126	97.39 145	97.83 154	94.41 166	96.05 308	98.40 120	92.86 125	97.09 113	95.28 245	94.21 67	98.07 197	89.26 216	98.11 115	99.70 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TAMVS		95.85 128	95.58 124	96.65 164	97.07 178	93.50 184	99.17 193	97.82 183	91.39 183	95.02 158	98.01 169	92.20 106	97.30 226	93.75 157	95.83 159	99.14 164

LS3D		95.84 129	95.11 137	98.02 125	99.85 39	95.10 154	98.74 233	98.50 97	87.22 251	93.66 170	99.86 14	87.45 160	99.95 48	90.94 193	99.81 66	99.02 171

Test_1112_low_res		95.72 130	94.83 140	98.42 109	97.79 157	96.41 113	99.65 140	96.65 281	92.70 136	92.86 180	96.13 219	92.15 108	99.30 130	91.88 181	93.64 193	99.55 113

Vis-MVSNet		95.72 130	95.15 136	97.45 141	97.62 167	94.28 168	99.28 185	98.24 140	94.27 87	96.84 116	98.94 122	79.39 242	98.76 148	93.25 163	98.49 106	99.30 146
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet_dtu		95.71 132	95.39 128	96.66 163	98.92 99	93.41 191	99.57 150	98.90 46	96.19 41	97.52 104	98.56 154	92.65 100	97.36 218	77.89 302	98.33 110	99.20 155
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		95.71 132	95.38 129	96.68 162	98.49 124	92.28 220	99.84 85	97.50 210	92.12 162	92.06 184	98.79 142	84.69 184	98.67 153	95.29 124	99.66 74	99.09 169

mvs_anonymous		95.65 134	95.03 138	97.53 137	98.19 135	95.74 136	99.33 179	97.49 211	90.87 194	90.47 195	97.10 187	88.23 154	97.16 236	95.92 116	97.66 124	99.68 94

mvs-test1		95.53 135	95.97 104	94.20 230	97.77 158	85.44 297	99.95 31	97.06 242	94.92 67	96.58 121	98.72 143	85.81 175	98.98 138	94.80 131	98.11 115	98.18 183

MVSTER		95.53 135	95.22 133	96.45 167	98.56 119	97.72 68	99.91 56	97.67 191	92.38 154	91.39 187	97.14 185	97.24 10	97.30 226	94.80 131	87.85 227	94.34 222

tpm2		95.47 137	95.18 135	96.35 171	96.91 185	91.70 239	96.96 296	97.93 171	88.04 237	98.44 80	95.40 233	93.32 87	97.97 200	94.00 149	95.61 163	99.38 136

QAPM		95.40 138	94.17 152	99.10 55	96.92 184	97.71 69	99.40 170	98.68 60	89.31 213	88.94 233	98.89 123	82.48 197	99.96 40	93.12 169	99.83 60	99.62 103

UGNet		95.33 139	94.57 145	97.62 136	98.55 120	94.85 157	98.67 240	99.32 27	95.75 53	96.80 118	96.27 215	72.18 290	99.96 40	94.58 137	99.05 98	98.04 186
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		95.25 140	94.26 150	98.23 116	98.13 139	96.59 109	99.12 195	97.18 234	85.78 268	97.64 101	96.70 203	85.92 174	98.87 141	90.40 202	97.45 127	99.24 154

BH-untuned		95.18 141	94.83 140	96.22 173	98.36 127	91.22 246	99.80 95	97.32 227	90.91 193	91.08 189	98.67 145	83.51 191	98.54 161	94.23 145	99.61 79	98.92 173

BH-RMVSNet		95.18 141	94.31 149	97.80 129	98.17 137	95.23 152	99.76 110	97.53 205	92.52 150	94.27 167	99.25 102	76.84 263	98.80 144	90.89 195	99.54 83	99.35 141

PCF-MVS	94.20 5	95.18 141	94.10 153	98.43 108	98.55 120	95.99 130	97.91 281	97.31 228	90.35 201	89.48 222	99.22 104	85.19 183	99.89 66	90.40 202	98.47 107	99.41 130
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tpmp4_e23		95.15 144	94.69 144	96.55 165	97.84 153	91.77 234	97.10 292	97.91 173	88.33 233	97.19 110	95.06 252	93.92 73	98.51 162	89.64 210	95.19 169	99.37 138

dp		95.05 145	94.43 147	96.91 154	97.99 144	92.73 210	96.29 304	97.98 166	89.70 211	95.93 135	94.67 271	93.83 79	98.45 168	86.91 246	96.53 147	99.54 117

Fast-Effi-MVS+		95.02 146	94.19 151	97.52 138	97.88 149	94.55 164	99.97 12	97.08 241	88.85 225	94.47 164	97.96 171	84.59 185	98.41 171	89.84 208	97.10 139	99.59 109

IB-MVS	92.85 6	94.99 147	93.94 155	98.16 117	97.72 165	95.69 141	99.99 3	98.81 52	94.28 86	92.70 181	96.90 195	95.08 39	99.17 134	96.07 113	73.88 311	99.60 107
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		94.82 148	94.74 142	95.06 195	98.00 143	89.19 269	99.08 200	97.55 201	94.10 91	94.71 160	99.62 78	80.51 233	99.74 98	96.04 114	93.06 200	96.25 198

ADS-MVSNet		94.79 149	94.02 154	97.11 152	97.87 150	93.79 177	94.24 315	98.16 151	90.07 205	96.43 126	94.48 275	90.29 132	98.19 192	87.44 232	97.23 135	99.36 139

XVG-OURS-SEG-HR		94.79 149	94.70 143	95.08 194	98.05 142	89.19 269	99.08 200	97.54 203	93.66 111	94.87 159	99.58 80	78.78 251	99.79 87	97.31 94	93.40 195	96.25 198

OpenMVS	90.15 15	94.77 151	93.59 162	98.33 113	96.07 204	97.48 79	99.56 152	98.57 78	90.46 199	86.51 261	98.95 121	78.57 253	99.94 56	93.86 150	99.74 68	97.57 192

LFMVS		94.75 152	93.56 164	98.30 114	99.03 88	95.70 140	98.74 233	97.98 166	87.81 238	98.47 79	99.39 94	67.43 308	99.53 114	98.01 76	95.20 168	99.67 95

ab-mvs		94.69 153	93.42 168	98.51 99	98.07 141	96.26 117	96.49 300	98.68 60	90.31 202	94.54 161	97.00 193	76.30 268	99.71 102	95.98 115	93.38 196	99.56 112

CVMVSNet		94.68 154	94.94 139	93.89 243	96.80 191	86.92 289	99.06 206	98.98 35	94.45 79	94.23 168	99.02 112	85.60 177	95.31 294	90.91 194	95.39 167	99.43 128

cascas		94.64 155	93.61 159	97.74 132	97.82 155	96.26 117	99.96 19	97.78 185	85.76 269	94.00 169	97.54 176	76.95 262	99.21 132	97.23 96	95.43 166	97.76 191

HQP-MVS		94.61 156	94.50 146	94.92 204	95.78 211	91.85 230	99.87 69	97.89 175	96.82 21	93.37 171	98.65 147	80.65 231	98.39 175	97.92 82	89.60 202	94.53 204

TR-MVS		94.54 157	93.56 164	97.49 139	97.96 145	94.34 167	98.71 235	97.51 209	90.30 203	94.51 163	98.69 144	75.56 273	98.77 147	92.82 170	95.99 154	99.35 141

DP-MVS		94.54 157	93.42 168	97.91 128	99.46 79	94.04 172	98.93 220	97.48 212	81.15 305	90.04 202	99.55 82	87.02 165	99.95 48	88.97 218	98.11 115	99.73 88

Effi-MVS+-dtu		94.53 159	95.30 131	92.22 277	97.77 158	82.54 308	99.59 148	97.06 242	94.92 67	95.29 148	95.37 238	85.81 175	97.89 205	94.80 131	97.07 140	96.23 200

HQP_MVS		94.49 160	94.36 148	94.87 207	95.71 220	91.74 235	99.84 85	97.87 177	96.38 34	93.01 175	98.59 151	80.47 235	98.37 180	97.79 85	89.55 205	94.52 206

TAPA-MVS	92.12 8	94.42 161	93.60 161	96.90 155	99.33 83	91.78 233	99.78 99	98.00 164	89.89 209	94.52 162	99.47 88	91.97 111	99.18 133	69.90 316	99.52 84	99.73 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Patchmatch-test1		94.39 162	93.46 166	97.17 149	97.10 177	94.44 165	98.86 228	98.32 132	93.30 119	96.17 132	95.38 236	76.48 267	97.34 220	88.12 226	97.43 128	99.74 86

MSDG		94.37 163	93.36 172	97.40 144	98.88 105	93.95 174	99.37 175	97.38 222	85.75 272	90.80 192	99.17 106	84.11 189	99.88 72	86.35 250	98.43 108	98.36 181

tpmvs		94.28 164	93.57 163	96.40 169	98.55 120	91.50 244	95.70 313	98.55 84	87.47 246	92.15 183	94.26 279	91.42 116	98.95 140	88.15 224	95.85 158	98.76 178

FIs		94.10 165	93.43 167	96.11 175	94.70 236	96.82 103	99.58 149	98.93 40	92.54 149	89.34 225	97.31 181	87.62 158	97.10 245	94.22 146	86.58 236	94.40 215

CLD-MVS		94.06 166	93.90 156	94.55 220	96.02 206	90.69 252	99.98 6	97.72 188	96.62 30	91.05 190	98.85 133	77.21 259	98.47 164	98.11 72	89.51 207	94.48 208
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		93.86 167	93.61 159	94.64 215	95.02 232	92.18 223	99.93 50	98.58 76	94.07 93	87.96 245	98.50 155	93.90 76	94.96 299	81.33 284	93.17 198	96.78 194

X-MVStestdata		93.83 168	92.06 190	99.15 47	99.94 14	97.50 77	99.94 45	98.42 117	96.22 39	99.41 38	41.37 349	94.34 59	99.96 40	98.92 42	99.95 38	99.99 11

GA-MVS		93.83 168	92.84 176	96.80 157	95.73 217	93.57 183	99.88 66	97.24 231	92.57 148	92.92 177	96.66 204	78.73 252	97.67 210	87.75 229	94.06 190	99.17 157

FC-MVSNet-test		93.81 170	93.15 174	95.80 183	94.30 241	96.20 122	99.42 169	98.89 47	92.33 155	89.03 232	97.27 183	87.39 161	96.83 262	93.20 164	86.48 237	94.36 218

ADS-MVSNet2		93.80 171	93.88 157	93.55 250	97.87 150	85.94 292	94.24 315	96.84 274	90.07 205	96.43 126	94.48 275	90.29 132	95.37 293	87.44 232	97.23 135	99.36 139

VDD-MVS		93.77 172	92.94 175	96.27 172	98.55 120	90.22 259	98.77 232	97.79 184	90.85 195	96.82 117	99.42 90	61.18 324	99.77 89	98.95 39	94.13 185	98.82 175

EI-MVSNet		93.73 173	93.40 171	94.74 211	96.80 191	92.69 211	99.06 206	97.67 191	88.96 221	91.39 187	99.02 112	88.75 150	97.30 226	91.07 189	87.85 227	94.22 229

Fast-Effi-MVS+-dtu		93.72 174	93.86 158	93.29 253	97.06 179	86.16 290	99.80 95	96.83 275	92.66 139	92.58 182	97.83 173	81.39 219	97.67 210	89.75 209	96.87 144	96.05 202

tpm		93.70 175	93.41 170	94.58 218	95.36 227	87.41 287	97.01 294	96.90 269	90.85 195	96.72 120	94.14 282	90.40 130	96.84 261	90.75 196	88.54 220	99.51 120

PS-MVSNAJss		93.64 176	93.31 173	94.61 216	92.11 292	92.19 222	99.12 195	97.38 222	92.51 151	88.45 237	96.99 194	91.20 120	97.29 229	94.36 140	87.71 229	94.36 218

gg-mvs-nofinetune		93.51 177	91.86 192	98.47 104	97.72 165	97.96 63	92.62 324	98.51 93	74.70 322	97.33 107	69.59 338	98.91 3	97.79 207	97.77 87	99.56 82	99.67 95

nrg030		93.51 177	92.53 182	96.45 167	94.36 239	97.20 91	99.81 92	97.16 237	91.60 175	89.86 208	97.46 177	86.37 171	97.68 209	95.88 117	80.31 272	94.46 209

tpm cat1		93.51 177	92.52 183	96.47 166	97.77 158	91.47 245	96.13 306	98.06 160	80.98 306	92.91 178	93.78 287	89.66 135	98.87 141	87.03 242	96.39 149	99.09 169

CR-MVSNet		93.45 180	92.62 180	95.94 178	96.29 200	92.66 212	92.01 327	96.23 288	92.62 142	96.94 114	93.31 293	91.04 124	96.03 285	79.23 295	95.96 155	99.13 166

OPM-MVS		93.21 181	92.80 177	94.44 223	93.12 277	90.85 251	99.77 104	97.61 198	96.19 41	91.56 186	98.65 147	75.16 278	98.47 164	93.78 156	89.39 208	93.99 248

VDDNet		93.12 182	91.91 191	96.76 159	96.67 198	92.65 214	98.69 237	98.21 143	82.81 290	97.75 100	99.28 98	61.57 322	99.48 123	98.09 74	94.09 186	98.15 184

UniMVSNet (Re)		93.07 183	92.13 187	95.88 180	94.84 233	96.24 121	99.88 66	98.98 35	92.49 152	89.25 227	95.40 233	87.09 164	97.14 240	93.13 168	78.16 290	94.26 226

LPG-MVS_test		92.96 184	92.71 179	93.71 246	95.43 225	88.67 274	99.75 111	97.62 195	92.81 129	90.05 199	98.49 156	75.24 276	98.40 173	95.84 119	89.12 209	94.07 237

UniMVSNet_NR-MVSNet		92.95 185	92.11 188	95.49 185	94.61 237	95.28 150	99.83 89	99.08 31	91.49 178	89.21 229	96.86 198	87.14 163	96.73 265	93.20 164	77.52 296	94.46 209

ACMM	91.95 10	92.88 186	92.52 183	93.98 240	95.75 216	89.08 271	99.77 104	97.52 207	93.00 123	89.95 204	97.99 170	76.17 270	98.46 167	93.63 159	88.87 213	94.39 216
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		92.83 187	92.29 186	94.47 222	91.90 296	92.46 217	99.55 154	97.27 229	91.17 187	89.96 203	96.07 221	81.10 223	96.89 258	94.67 135	88.91 211	94.05 239

ACMP	92.05 9	92.74 188	92.42 185	93.73 244	95.91 210	88.72 273	99.81 92	97.53 205	94.13 89	87.00 254	98.23 164	74.07 284	98.47 164	96.22 112	88.86 214	93.99 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		92.70 189	91.55 195	96.16 174	95.09 228	96.20 122	98.88 223	99.00 34	91.02 192	91.82 185	95.29 244	76.05 272	97.96 202	95.62 122	81.19 262	94.30 224

FMVSNet3		92.69 190	91.58 194	95.99 177	98.29 128	97.42 83	99.26 187	97.62 195	89.80 210	89.68 214	95.32 240	81.62 215	96.27 277	87.01 243	85.65 240	94.29 225

IterMVS-LS		92.69 190	92.11 188	94.43 225	96.80 191	92.74 209	99.45 167	96.89 270	88.98 219	89.65 217	95.38 236	88.77 149	96.34 275	90.98 192	82.04 257	94.22 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		92.65 192	91.50 196	96.10 176	96.85 188	90.49 255	91.50 329	97.19 233	82.76 291	90.23 196	95.59 229	95.02 42	98.00 199	77.41 305	96.98 142	99.82 77

AllTest		92.48 193	91.64 193	95.00 198	99.01 89	88.43 278	98.94 219	96.82 277	86.50 259	88.71 234	98.47 160	74.73 280	99.88 72	85.39 258	96.18 150	96.71 195

DI_MVS_plusplus_test		92.48 193	90.60 207	98.11 122	91.88 297	96.13 125	99.64 144	97.73 186	92.69 137	76.02 305	93.79 286	70.49 297	99.07 135	95.88 117	97.26 134	99.14 164

DU-MVS		92.46 195	91.45 198	95.49 185	94.05 244	95.28 150	99.81 92	98.74 56	92.25 156	89.21 229	96.64 206	81.66 213	96.73 265	93.20 164	77.52 296	94.46 209

test_normal		92.44 196	90.54 208	98.12 121	91.85 298	96.18 124	99.68 132	97.73 186	92.66 139	75.76 309	93.74 288	70.49 297	99.04 137	95.71 121	97.27 133	99.13 166

LCM-MVSNet-Re		92.31 197	92.60 181	91.43 284	97.53 169	79.27 322	99.02 212	91.83 337	92.07 164	80.31 292	94.38 278	83.50 192	95.48 291	97.22 97	97.58 125	99.54 117

WR-MVS		92.31 197	91.25 199	95.48 187	94.45 238	95.29 149	99.60 147	98.68 60	90.10 204	88.07 244	96.89 196	80.68 230	96.80 264	93.14 167	79.67 281	94.36 218

COLMAP_ROB	90.47 14	92.18 199	91.49 197	94.25 229	99.00 91	88.04 283	98.42 259	96.70 279	82.30 295	88.43 239	99.01 114	76.97 261	99.85 76	86.11 253	96.50 148	94.86 203
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs4		92.10 200	91.07 202	95.18 192	92.82 284	94.96 155	99.48 163	96.83 275	87.45 247	88.66 236	96.56 209	83.78 190	96.83 262	89.29 215	84.77 248	93.75 268

jajsoiax		91.92 201	91.18 200	94.15 231	91.35 304	90.95 249	99.00 213	97.42 217	92.61 143	87.38 250	97.08 188	72.46 289	97.36 218	94.53 138	88.77 215	94.13 234

XXY-MVS		91.82 202	90.46 209	95.88 180	93.91 247	95.40 147	98.87 226	97.69 190	88.63 229	87.87 246	97.08 188	74.38 283	97.89 205	91.66 184	84.07 250	94.35 221

mvs_tets		91.81 203	91.08 201	94.00 238	91.63 302	90.58 253	98.67 240	97.43 215	92.43 153	87.37 251	97.05 191	71.76 291	97.32 223	94.75 134	88.68 217	94.11 235

VPNet		91.81 203	90.46 209	95.85 182	94.74 235	95.54 143	98.98 214	98.59 75	92.14 161	90.77 193	97.44 178	68.73 303	97.54 212	94.89 130	77.89 292	94.46 209

RPSCF		91.80 205	92.79 178	88.83 303	98.15 138	69.87 326	98.11 275	96.60 283	83.93 286	94.33 166	99.27 99	79.60 241	99.46 124	91.99 179	93.16 199	97.18 193

PVSNet_0	88.03 19	91.80 205	90.27 217	96.38 170	98.27 130	90.46 256	99.94 45	99.61 16	93.99 98	86.26 267	97.39 180	71.13 296	99.89 66	98.77 50	67.05 321	98.79 177

anonymousdsp		91.79 207	90.92 203	94.41 226	90.76 309	92.93 206	98.93 220	97.17 236	89.08 215	87.46 249	95.30 241	78.43 256	96.92 257	92.38 172	88.73 216	93.39 278

JIA-IIPM		91.76 208	90.70 205	94.94 202	96.11 203	87.51 285	93.16 322	98.13 156	75.79 319	97.58 103	77.68 334	92.84 95	97.97 200	88.47 222	96.54 146	99.33 143

TranMVSNet+NR-MVSNet		91.68 209	90.61 206	94.87 207	93.69 251	93.98 173	99.69 127	98.65 63	91.03 191	88.44 238	96.83 202	80.05 239	96.18 280	90.26 205	76.89 303	94.45 214

NR-MVSNet		91.56 210	90.22 219	95.60 184	94.05 244	95.76 135	98.25 268	98.70 58	91.16 189	80.78 291	96.64 206	83.23 195	96.57 269	91.41 185	77.73 294	94.46 209

v1neww		91.44 211	90.28 215	94.91 205	93.50 255	93.43 187	99.73 119	97.06 242	87.55 240	90.08 197	95.11 249	81.98 204	97.32 223	87.41 234	80.15 274	93.99 248

v7new		91.44 211	90.28 215	94.91 205	93.50 255	93.43 187	99.73 119	97.06 242	87.55 240	90.08 197	95.11 249	81.98 204	97.32 223	87.41 234	80.15 274	93.99 248

v6		91.44 211	90.27 217	94.93 203	93.44 259	93.44 186	99.73 119	97.05 246	87.57 239	90.05 199	95.10 251	81.87 209	97.39 216	87.45 231	80.17 273	93.98 252

divwei89l23v2f112		91.37 214	90.15 222	95.00 198	93.35 265	93.78 180	99.78 99	97.05 246	87.54 242	89.73 213	94.89 263	82.24 199	97.21 232	86.91 246	79.90 280	94.00 245

v1141		91.36 215	90.14 223	95.00 198	93.33 267	93.79 177	99.78 99	97.05 246	87.52 244	89.75 212	94.89 263	82.13 200	97.21 232	86.84 249	80.00 278	94.00 245

v1		91.36 215	90.14 223	95.04 196	93.35 265	93.80 176	99.77 104	97.05 246	87.53 243	89.77 211	94.91 261	81.99 203	97.33 222	86.90 248	79.98 279	94.00 245

v2v482		91.30 217	90.07 226	95.01 197	93.13 275	93.79 177	99.77 104	97.02 252	88.05 236	89.25 227	95.37 238	80.73 229	97.15 238	87.28 238	80.04 277	94.09 236

WR-MVS_H		91.30 217	90.35 212	94.15 231	94.17 243	92.62 215	99.17 193	98.94 37	88.87 224	86.48 263	94.46 277	84.36 187	96.61 268	88.19 223	78.51 286	93.21 283

V42		91.28 219	90.12 225	94.74 211	93.42 261	93.46 185	99.68 132	97.02 252	87.36 248	89.85 209	95.05 253	81.31 221	97.34 220	87.34 237	80.07 276	93.40 277

CP-MVSNet		91.23 220	90.22 219	94.26 228	93.96 246	92.39 219	99.09 198	98.57 78	88.95 222	86.42 264	96.57 208	79.19 246	96.37 273	90.29 204	78.95 283	94.02 240

XVG-ACMP-BASELINE		91.22 221	90.75 204	92.63 264	93.73 250	85.61 294	98.52 251	97.44 214	92.77 133	89.90 206	96.85 199	66.64 310	98.39 175	92.29 173	88.61 218	93.89 260

v7		91.20 222	89.99 227	94.82 210	93.57 252	93.41 191	99.57 150	96.98 258	86.83 256	89.88 207	95.22 246	81.01 224	97.14 240	85.53 256	81.31 261	93.90 258

v1144		91.09 223	89.83 228	94.87 207	93.25 272	93.69 182	99.62 146	96.98 258	86.83 256	89.64 218	94.99 258	80.94 225	97.05 248	85.08 261	81.16 263	93.87 262

FMVSNet2		91.02 224	89.56 233	95.41 188	97.53 169	95.74 136	98.98 214	97.41 219	87.05 252	88.43 239	95.00 257	71.34 293	96.24 279	85.12 260	85.21 245	94.25 228

MVP-Stereo		90.93 225	90.45 211	92.37 274	91.25 306	88.76 272	98.05 278	96.17 290	87.27 250	84.04 278	95.30 241	78.46 255	97.27 231	83.78 270	99.70 72	91.09 305
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS		90.91 226	90.17 221	93.12 255	96.78 194	90.42 257	98.89 222	97.05 246	89.03 217	86.49 262	95.42 232	76.59 265	95.02 297	87.22 239	84.09 249	93.93 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		90.88 227	89.82 229	94.08 233	97.53 169	91.97 225	98.43 256	96.95 263	87.05 252	89.68 214	94.72 267	71.34 293	96.11 281	87.01 243	85.65 240	94.17 231

test1		90.88 227	89.82 229	94.08 233	97.53 169	91.97 225	98.43 256	96.95 263	87.05 252	89.68 214	94.72 267	71.34 293	96.11 281	87.01 243	85.65 240	94.17 231

v144192		90.79 229	89.52 235	94.59 217	93.11 278	92.77 208	99.56 152	96.99 256	86.38 261	89.82 210	94.95 260	80.50 234	97.10 245	83.98 268	80.41 270	93.90 258

v148		90.70 230	89.63 231	93.92 241	92.97 281	90.97 248	99.75 111	96.89 270	87.51 245	88.27 242	95.01 255	81.67 212	97.04 249	87.40 236	77.17 300	93.75 268

MS-PatchMatch		90.65 231	90.30 214	91.71 283	94.22 242	85.50 296	98.24 269	97.70 189	88.67 227	86.42 264	96.37 213	67.82 307	98.03 198	83.62 271	99.62 76	91.60 302

ACMH	89.72 17	90.64 232	89.63 231	93.66 248	95.64 223	88.64 276	98.55 247	97.45 213	89.03 217	81.62 288	97.61 175	69.75 300	98.41 171	89.37 214	87.62 231	93.92 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-CasMVS		90.63 233	89.51 236	93.99 239	93.83 248	91.70 239	98.98 214	98.52 87	88.48 230	86.15 268	96.53 210	75.46 274	96.31 276	88.83 219	78.86 285	93.95 254

v1192		90.62 234	89.25 239	94.72 213	93.13 275	93.07 201	99.50 160	97.02 252	86.33 262	89.56 221	95.01 255	79.22 245	97.09 247	82.34 279	81.16 263	94.01 242

v8		90.54 235	89.17 240	94.66 214	93.43 260	93.40 194	99.20 190	96.94 266	85.76 269	87.56 248	94.51 273	81.96 207	97.19 234	84.94 262	78.25 289	93.38 279

v1921920		90.46 236	89.12 241	94.50 221	92.96 282	92.46 217	99.49 161	96.98 258	86.10 264	89.61 220	95.30 241	78.55 254	97.03 252	82.17 280	80.89 269	94.01 242

PatchT		90.38 237	88.75 249	95.25 190	95.99 207	90.16 260	91.22 331	97.54 203	76.80 316	97.26 108	86.01 329	91.88 112	96.07 284	66.16 324	95.91 157	99.51 120

ACMH+	89.98 16	90.35 238	89.54 234	92.78 262	95.99 207	86.12 291	98.81 230	97.18 234	89.38 212	83.14 283	97.76 174	68.42 305	98.43 169	89.11 217	86.05 239	93.78 267

Baseline_NR-MVSNet		90.33 239	89.51 236	92.81 261	92.84 283	89.95 265	99.77 104	93.94 328	84.69 281	89.04 231	95.66 227	81.66 213	96.52 270	90.99 191	76.98 301	91.97 298

MIMVSNet		90.30 240	88.67 251	95.17 193	96.45 199	91.64 241	92.39 325	97.15 238	85.99 265	90.50 194	93.19 295	66.95 309	94.86 301	82.01 281	93.43 194	99.01 172

LTVRE_ROB	88.28 18	90.29 241	89.05 244	94.02 236	95.08 229	90.15 261	97.19 291	97.43 215	84.91 278	83.99 279	97.06 190	74.00 285	98.28 187	84.08 266	87.71 229	93.62 273
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		90.25 242	88.82 247	94.57 219	93.53 254	93.43 187	99.08 200	96.87 273	85.00 277	87.34 252	94.51 273	80.93 226	97.02 254	82.85 276	79.23 282	93.26 281

v1240		90.20 243	88.79 248	94.44 223	93.05 280	92.27 221	99.38 174	96.92 267	85.89 266	89.36 224	94.87 266	77.89 258	97.03 252	80.66 287	81.08 265	94.01 242

PEN-MVS		90.19 244	89.06 243	93.57 249	93.06 279	90.90 250	99.06 206	98.47 99	88.11 235	85.91 270	96.30 214	76.67 264	95.94 288	87.07 240	76.91 302	93.89 260

pmmvs5		90.17 245	89.09 242	93.40 251	92.10 293	89.77 268	99.74 114	95.58 301	85.88 267	87.24 253	95.74 224	73.41 287	96.48 271	88.54 220	83.56 253	93.95 254

EU-MVSNet		90.14 246	90.34 213	89.54 300	92.55 288	81.06 317	98.69 237	98.04 162	91.41 182	86.59 260	96.84 201	80.83 227	93.31 321	86.20 251	81.91 258	94.26 226

USDC		90.00 247	88.96 245	93.10 256	94.81 234	88.16 282	98.71 235	95.54 303	93.66 111	83.75 281	97.20 184	65.58 312	98.31 184	83.96 269	87.49 233	92.85 290

OurMVSNet-221017-0		89.81 248	89.48 238	90.83 289	91.64 301	81.21 315	98.17 273	95.38 312	91.48 179	85.65 272	97.31 181	72.66 288	97.29 229	88.15 224	84.83 247	93.97 253

Patchmtry		89.70 249	88.49 252	93.33 252	96.24 202	89.94 267	91.37 330	96.23 288	78.22 313	87.69 247	93.31 293	91.04 124	96.03 285	80.18 289	82.10 256	94.02 240

v7n		89.65 250	88.29 255	93.72 245	92.22 290	90.56 254	99.07 204	97.10 240	85.42 276	86.73 258	94.72 267	80.06 238	97.13 242	81.14 285	78.12 291	93.49 275

v52		89.55 251	88.41 253	92.98 257	92.32 289	90.01 263	98.88 223	96.89 270	84.51 282	86.89 255	94.22 280	79.23 244	97.16 236	84.46 264	78.27 288	91.76 300

V4		89.55 251	88.41 253	92.98 257	92.21 291	90.03 262	98.87 226	96.91 268	84.51 282	86.84 256	94.21 281	79.37 243	97.15 238	84.45 265	78.28 287	91.76 300

DTE-MVSNet		89.40 253	88.24 256	92.88 260	92.66 287	89.95 265	99.10 197	98.22 142	87.29 249	85.12 274	96.22 216	76.27 269	95.30 295	83.56 272	75.74 306	93.41 276

RPMNet		89.39 254	87.20 266	95.94 178	96.29 200	92.66 212	92.01 327	97.63 193	70.19 330	96.94 114	85.87 330	87.25 162	96.03 285	62.69 327	95.96 155	99.13 166

pm-mvs1		89.36 255	87.81 260	94.01 237	93.40 263	91.93 228	98.62 244	96.48 287	86.25 263	83.86 280	96.14 218	73.68 286	97.04 249	86.16 252	75.73 307	93.04 286

tfpnnormal		89.29 256	87.61 262	94.34 227	94.35 240	94.13 171	98.95 218	98.94 37	83.94 285	84.47 277	95.51 230	74.84 279	97.39 216	77.05 308	80.41 270	91.48 304

LF4IMVS		89.25 257	88.85 246	90.45 293	92.81 285	81.19 316	98.12 274	94.79 320	91.44 181	86.29 266	97.11 186	65.30 314	98.11 194	88.53 221	85.25 244	92.07 295

testpf		89.10 258	88.73 250	90.24 294	97.59 168	83.48 305	74.22 342	97.39 221	79.66 310	89.64 218	93.92 283	86.38 170	95.76 289	85.42 257	94.31 182	91.49 303

testgi		89.01 259	88.04 258	91.90 281	93.49 257	84.89 300	99.73 119	95.66 299	93.89 105	85.14 273	98.17 165	59.68 326	94.66 303	77.73 303	88.88 212	96.16 201

v748		88.94 260	87.72 261	92.61 265	91.91 295	87.50 286	99.07 204	96.97 261	84.76 279	85.79 271	93.63 290	79.19 246	97.04 249	83.16 274	75.03 310	93.28 280

Test4		88.80 261	85.91 270	97.48 140	87.33 320	95.72 138	99.29 184	97.04 251	92.82 128	70.35 323	91.46 302	44.37 338	97.43 215	93.37 162	97.17 138	99.29 148

SixPastTwentyTwo		88.73 262	88.01 259	90.88 287	91.85 298	82.24 310	98.22 271	95.18 318	88.97 220	82.26 286	96.89 196	71.75 292	96.67 267	84.00 267	82.98 254	93.72 272

FMVSNet1		88.50 263	86.64 267	94.08 233	95.62 224	91.97 225	98.43 256	96.95 263	83.00 289	86.08 269	94.72 267	59.09 327	96.11 281	81.82 283	84.07 250	94.17 231

FMVSNet5		88.32 264	87.47 264	90.88 287	96.90 186	88.39 280	97.28 290	95.68 298	82.60 292	84.67 276	92.40 299	79.83 240	91.16 324	76.39 310	81.51 260	93.09 284

DSMNet-mixed		88.28 265	88.24 256	88.42 306	89.64 315	75.38 324	98.06 277	89.86 342	85.59 274	88.20 243	92.14 300	76.15 271	91.95 323	78.46 300	96.05 152	97.92 187

K. test v3		88.05 266	87.24 265	90.47 292	91.82 300	82.23 311	98.96 217	97.42 217	89.05 216	76.93 302	95.60 228	68.49 304	95.42 292	85.87 255	81.01 267	93.75 268

TinyColmap		87.87 267	86.51 268	91.94 280	95.05 231	85.57 295	97.65 283	94.08 326	84.40 284	81.82 287	96.85 199	62.14 321	98.33 182	80.25 288	86.37 238	91.91 299

TransMVSNet (Re)		87.25 268	85.28 272	93.16 254	93.56 253	91.03 247	98.54 249	94.05 327	83.69 287	81.09 290	96.16 217	75.32 275	96.40 272	76.69 309	68.41 318	92.06 296

Patchmatch-RL test		86.90 269	85.98 269	89.67 299	84.45 325	75.59 323	89.71 333	92.43 334	86.89 255	77.83 300	90.94 304	94.22 64	93.63 318	87.75 229	69.61 315	99.79 81

LP		86.76 270	84.85 274	92.50 268	95.08 229	85.89 293	89.97 332	96.97 261	75.28 321	84.97 275	90.68 305	80.78 228	95.13 296	61.64 329	88.31 223	96.46 197

v18		86.59 271	84.57 275	92.65 263	93.41 262	93.43 187	98.69 237	95.55 302	82.44 293	74.71 311	87.68 316	82.11 201	94.21 304	80.14 290	66.37 324	90.32 311

v16		86.52 272	84.49 276	92.60 266	93.45 258	93.31 196	98.60 246	95.52 305	82.30 295	74.59 313	87.70 315	81.95 208	94.18 305	79.93 292	66.38 323	90.30 312

v17		86.51 273	84.49 276	92.57 267	93.38 264	93.29 197	98.61 245	95.54 303	82.32 294	74.69 312	87.63 317	82.03 202	94.17 306	80.02 291	66.17 325	90.26 313

test2356		86.43 274	87.59 263	82.95 314	85.90 322	69.43 327	99.79 98	96.63 282	85.76 269	83.44 282	94.99 258	80.45 237	86.52 335	68.12 321	93.21 197	92.90 287

Anonymous20231206		86.32 275	85.42 271	89.02 302	89.11 317	80.53 320	99.05 209	95.28 314	85.43 275	82.82 284	93.92 283	74.40 282	93.44 320	66.99 322	81.83 259	93.08 285

v15		86.26 276	84.19 279	92.47 269	93.29 269	93.28 198	98.53 250	95.47 306	82.24 297	74.34 314	87.34 319	81.71 211	94.07 307	79.39 293	65.42 326	90.06 319

V14		86.22 277	84.15 280	92.41 272	93.30 268	93.16 199	98.47 253	95.47 306	82.10 298	74.27 315	87.41 318	81.73 210	94.02 309	79.26 294	65.37 328	90.04 320

MVS-HIRNet		86.22 277	83.19 292	95.31 189	96.71 197	90.29 258	92.12 326	97.33 226	62.85 333	86.82 257	70.37 337	69.37 301	97.49 213	75.12 311	97.99 120	98.15 184

V9		86.16 279	84.07 281	92.43 270	93.27 271	93.04 204	98.40 260	95.45 308	81.98 300	74.18 317	87.31 320	81.58 217	94.06 308	79.12 297	65.33 329	90.20 316

v12		86.10 280	84.01 282	92.37 274	93.23 274	92.96 205	98.33 263	95.45 308	81.87 301	74.05 319	87.15 322	81.60 216	93.98 312	79.09 298	65.28 330	90.18 317

v11		86.09 281	83.98 285	92.42 271	93.29 269	93.41 191	98.52 251	95.30 313	81.73 303	74.27 315	87.20 321	81.24 222	93.85 316	77.68 304	66.61 322	90.00 321

v13		86.06 282	83.97 286	92.34 276	93.25 272	92.85 207	98.26 267	95.44 310	81.70 304	74.02 320	87.11 324	81.58 217	94.00 311	78.94 299	65.41 327	90.18 317

pmmvs6		85.69 283	83.84 288	91.26 286	90.00 314	84.41 302	97.82 282	96.15 291	75.86 318	81.29 289	95.39 235	61.21 323	96.87 260	83.52 273	73.29 313	92.50 292

test_0402		85.58 284	83.94 287	90.50 291	93.81 249	85.04 299	98.55 247	95.20 317	76.01 317	79.72 295	95.13 247	64.15 317	96.26 278	66.04 325	86.88 235	90.21 315

UnsupCasMVSNet_eth		85.52 285	83.99 283	90.10 296	89.36 316	83.51 304	96.65 298	97.99 165	89.14 214	75.89 307	93.83 285	63.25 319	93.92 313	81.92 282	67.90 320	92.88 289

MDA-MVSNet_test_wron		85.51 286	83.32 291	92.10 278	90.96 307	88.58 277	99.20 190	96.52 285	79.70 309	57.12 336	92.69 297	79.11 248	93.86 315	77.10 307	77.46 298	93.86 263

YYNet1		85.50 287	83.33 290	92.00 279	90.89 308	88.38 281	99.22 189	96.55 284	79.60 311	57.26 335	92.72 296	79.09 249	93.78 317	77.25 306	77.37 299	93.84 264

EG-PatchMatch MVS		85.35 288	83.81 289	89.99 298	90.39 311	81.89 313	98.21 272	96.09 292	81.78 302	74.73 310	93.72 289	51.56 335	97.12 244	79.16 296	88.61 218	90.96 307

testing_2		85.10 289	81.72 296	95.22 191	82.25 329	94.16 169	97.54 284	97.01 255	88.15 234	62.23 331	86.43 327	44.43 337	97.18 235	92.28 178	85.20 246	94.31 223

TDRefinement		84.76 290	82.56 294	91.38 285	74.58 336	84.80 301	97.36 287	94.56 323	84.73 280	80.21 293	96.12 220	63.56 318	98.39 175	87.92 227	63.97 331	90.95 308

CMPMVS	61.59 21	84.75 291	85.14 273	83.57 311	90.32 312	62.54 335	96.98 295	97.59 200	74.33 323	69.95 324	96.66 204	64.17 316	98.32 183	87.88 228	88.41 222	89.84 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		84.72 292	83.99 283	86.91 308	88.19 319	80.62 319	98.88 223	95.94 294	88.36 232	78.87 296	94.62 272	68.75 302	89.11 328	66.52 323	75.82 305	91.00 306

new_pmnet		84.49 293	82.92 293	89.21 301	90.03 313	82.60 307	96.89 297	95.62 300	80.59 307	75.77 308	89.17 307	65.04 315	94.79 302	72.12 313	81.02 266	90.23 314

MDA-MVSNet-bldmvs		84.09 294	81.52 298	91.81 282	91.32 305	88.00 284	98.67 240	95.92 295	80.22 308	55.60 337	93.32 292	68.29 306	93.60 319	73.76 312	76.61 304	93.82 266

pmmvs-eth3d		84.03 295	81.97 295	90.20 295	84.15 326	87.09 288	98.10 276	94.73 322	83.05 288	74.10 318	87.77 314	65.56 313	94.01 310	81.08 286	69.24 317	89.49 326

testus		83.91 296	84.49 276	82.17 316	85.68 323	66.11 332	99.68 132	93.53 332	86.55 258	82.60 285	94.91 261	56.70 330	88.19 331	68.46 318	92.31 201	92.21 294

OpenMVS_ROB	79.82 20	83.77 297	81.68 297	90.03 297	88.30 318	82.82 306	98.46 254	95.22 316	73.92 325	76.00 306	91.29 303	55.00 331	96.94 256	68.40 319	88.51 221	90.34 310

MIMVSNet1		82.58 298	80.51 300	88.78 304	86.68 321	84.20 303	96.65 298	95.41 311	78.75 312	78.59 298	92.44 298	51.88 334	89.76 327	65.26 326	78.95 283	92.38 293

new-patchmatchnet		81.19 299	79.34 301	86.76 309	82.86 328	80.36 321	97.92 280	95.27 315	82.09 299	72.02 321	86.87 325	62.81 320	90.74 326	71.10 314	63.08 332	89.19 328

PM-MVS		80.47 300	78.88 302	85.26 310	83.79 327	72.22 325	95.89 311	91.08 338	85.71 273	76.56 304	88.30 308	36.64 339	93.90 314	82.39 278	69.57 316	89.66 324

pmmvs3		80.27 301	77.77 305	87.76 307	80.32 331	82.43 309	98.23 270	91.97 336	72.74 326	78.75 297	87.97 311	57.30 329	90.99 325	70.31 315	62.37 333	89.87 322

N_pmnet		80.06 302	80.78 299	77.89 319	91.94 294	45.28 348	98.80 231	56.82 353	78.10 314	80.08 294	93.33 291	77.03 260	95.76 289	68.14 320	82.81 255	92.64 291

UnsupCasMVSNet_bld		79.97 303	77.03 306	88.78 304	85.62 324	81.98 312	93.66 320	97.35 224	75.51 320	70.79 322	83.05 331	48.70 336	94.91 300	78.31 301	60.29 336	89.46 327

1111		79.11 304	78.74 303	80.23 317	78.33 332	67.13 329	97.31 288	93.65 330	71.34 327	68.35 327	87.87 312	85.42 181	88.46 329	52.93 336	73.46 312	85.11 331

test1235678		78.45 305	77.88 304	80.16 318	77.83 334	62.18 336	98.36 261	93.45 333	77.46 315	69.08 326	88.23 309	60.33 325	85.41 336	58.46 332	77.68 295	92.90 287

test12356		75.26 306	75.12 307	75.67 323	74.02 337	60.60 338	96.43 301	92.15 335	74.17 324	66.35 329	88.11 310	52.29 333	84.36 338	57.41 333	75.12 308	82.05 332

Anonymous20231211		74.17 307	71.17 309	83.17 313	80.58 330	67.02 331	96.27 305	94.45 325	57.31 335	69.60 325	86.25 328	33.67 340	92.96 322	61.86 328	60.50 335	89.54 325

.test1245		71.48 308	71.80 308	70.51 327	78.33 332	67.13 329	97.31 288	93.65 330	71.34 327	68.35 327	87.87 312	85.42 181	88.46 329	52.93 336	11.01 347	55.94 344

FPMVS		68.72 309	68.72 310	68.71 328	65.95 342	44.27 350	95.97 310	94.74 321	51.13 336	53.26 339	90.50 306	25.11 346	83.00 339	60.80 330	80.97 268	78.87 335

LCM-MVSNet		67.77 310	64.73 313	76.87 320	62.95 346	56.25 341	89.37 334	93.74 329	44.53 339	61.99 332	80.74 332	20.42 349	86.53 334	69.37 317	59.50 337	87.84 329

testmv		67.54 311	65.93 311	72.37 325	64.46 345	54.05 342	95.09 314	90.07 340	68.90 332	55.16 338	77.63 335	30.39 341	82.61 340	49.42 339	62.26 334	80.45 334

PMMVS2		67.15 312	64.15 314	76.14 321	70.56 340	62.07 337	93.89 318	87.52 346	58.09 334	60.02 333	78.32 333	22.38 347	84.54 337	59.56 331	47.03 338	81.80 333

Gipuma		66.95 313	65.00 312	72.79 324	91.52 303	67.96 328	66.16 343	95.15 319	47.89 337	58.54 334	67.99 340	29.74 343	87.54 333	50.20 338	77.83 293	62.87 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		65.23 314	62.94 315	72.13 326	44.90 350	50.03 346	81.05 338	89.42 345	38.45 341	48.51 341	99.90 8	54.09 332	78.70 342	91.84 183	18.26 346	87.64 330

no-one		63.48 315	59.26 316	76.14 321	66.71 341	65.06 333	92.75 323	89.92 341	68.96 331	46.96 342	66.55 341	21.74 348	87.68 332	57.07 334	22.69 345	75.68 337

PNet_i23d		56.44 316	53.54 317	65.14 331	65.34 343	50.33 345	89.06 335	79.57 348	45.77 338	35.75 346	68.95 339	10.75 353	74.40 343	48.48 340	38.20 339	70.70 338

ANet_high		56.10 317	52.24 318	67.66 329	49.27 349	56.82 340	83.94 337	82.02 347	70.47 329	33.28 347	64.54 342	17.23 351	69.16 346	45.59 343	23.85 344	77.02 336

PMVS	49.05 23	53.75 318	51.34 320	60.97 333	40.80 351	34.68 351	74.82 341	89.62 344	37.55 342	28.67 348	72.12 336	7.09 354	81.63 341	43.17 344	68.21 319	66.59 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		52.30 319	52.18 319	52.67 334	71.51 338	45.40 347	93.62 321	76.60 351	36.01 343	43.50 343	64.13 343	27.11 345	67.31 347	31.06 346	26.06 342	45.30 347

MVE	53.74 22	51.54 320	47.86 322	62.60 332	59.56 347	50.93 344	79.41 339	77.69 350	35.69 344	36.27 345	61.76 345	5.79 357	69.63 345	37.97 345	36.61 340	67.24 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		51.44 321	51.22 321	52.11 335	70.71 339	44.97 349	94.04 317	75.66 352	35.34 345	42.40 344	61.56 346	28.93 344	65.87 348	27.64 347	24.73 343	45.49 346

wuykxyi23d		50.36 322	45.43 323	65.16 330	51.13 348	51.75 343	77.46 340	78.42 349	41.45 340	26.98 349	54.30 348	6.13 355	74.03 344	46.82 342	26.19 341	69.71 339

testmvs		40.60 323	44.45 324	29.05 338	19.49 353	14.11 354	99.68 132	18.47 354	20.74 346	64.59 330	98.48 159	10.95 352	17.09 351	56.66 335	11.01 347	55.94 344

test123		37.68 324	39.14 326	33.31 336	19.94 352	24.83 353	98.36 261	9.75 355	15.53 347	51.31 340	87.14 323	19.62 350	17.74 350	47.10 341	3.47 350	57.36 343

pcd1.5k->3k		37.58 325	39.62 325	31.46 337	92.73 286	0.00 355	0.00 345	97.52 207	0.00 349	0.00 351	0.00 351	78.40 257	0.00 352	0.00 349	87.90 226	94.37 217

cdsmvs_eth3d_5k		23.43 326	31.24 327	0.00 340	0.00 354	0.00 355	0.00 345	98.09 158	0.00 349	0.00 351	99.67 73	83.37 193	0.00 352	0.00 349	0.00 351	0.00 349

wuyk23d		20.37 327	20.84 328	18.99 339	65.34 343	27.73 352	50.43 344	7.67 356	9.50 348	8.01 350	6.34 350	6.13 355	26.24 349	23.40 348	10.69 349	2.99 348

ab-mvs-re		8.28 328	11.04 329	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	99.40 92	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

pcd_1.5k_mvsjas		7.60 329	10.13 330	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	91.20 120	0.00 352	0.00 349	0.00 351	0.00 349

sosnet-low-res		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

sosnet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uncertanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

Regformer		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

test_part2					99.89 36	99.25 6				99.49 32

test_part1								98.41 119				97.20 11			99.99 13	99.99 11

test_all								98.44 103

sam_mvs1												94.72 51

sam_mvs												94.25 63

semantic-postprocess				92.93 259	96.72 196	89.96 264		96.99 256	88.95 222	86.63 259	95.67 226	76.50 266	95.00 298	87.04 241	84.04 252	93.84 264

ambc				83.23 312	77.17 335	62.61 334	87.38 336	94.55 324		76.72 303	86.65 326	30.16 342	96.36 274	84.85 263	69.86 314	90.73 309

MTGPA								98.28 136

test_post1							95.78 312				59.23 347	93.20 91	97.74 208	91.06 190

test_post											63.35 344	94.43 53	98.13 193

patchmatchnet-post											91.70 301	95.12 37	97.95 203

GG-mvs-BLEND				98.54 97	98.21 134	98.01 62	93.87 319	98.52 87		97.92 97	97.92 172	99.02 2	97.94 204	98.17 69	99.58 81	99.67 95

MTMP								96.49 286

gm-plane-assit					96.97 183	93.76 181			91.47 180		98.96 119		98.79 145	94.92 127

test9_res														99.71 15	99.99 13	100.00 1

TEST9					99.92 27	98.92 14	99.96 19	98.43 109	93.90 103	99.71 15	99.86 14	95.88 26	99.85 76

test_8					99.92 27	98.88 17	99.96 19	98.43 109	94.35 83	99.69 17	99.85 18	95.94 23	99.85 76

agg_prior2														99.48 21	100.00 1	100.00 1

agg_prior					99.93 24	98.77 24		98.43 109		99.63 20			99.85 76

TestCases				95.00 198	99.01 89	88.43 278		96.82 277	86.50 259	88.71 234	98.47 160	74.73 280	99.88 72	85.39 258	96.18 150	96.71 195

test_prior4						98.05 60	99.94 45

test_prior2							99.95 31		95.78 48	99.73 13	99.76 53	96.00 21		99.78 7	100.00 1

test_prior				99.43 25	99.94 14	98.49 47		98.65 63					99.80 85			99.99 11

旧先验2							99.46 166		94.21 88	99.85 5			99.95 48	96.96 104

新几何2							99.40 170

新几何1				99.42 28	99.75 54	98.27 54		98.63 69	92.69 137	99.55 27	99.82 37	94.40 55	100.00 1	91.21 186	99.94 42	99.99 11

旧先验1					99.76 52	97.52 75		98.64 66			99.85 18	95.63 29			99.94 42	99.99 11

无先验							99.49 161	98.71 57	93.46 115				100.00 1	94.36 140		99.99 11

原ACMM2							99.90 59

原ACMM1				98.96 71	99.73 59	96.99 98		98.51 93	94.06 96	99.62 22	99.85 18	94.97 46	99.96 40	95.11 125	99.95 38	99.92 67

test222					99.55 72	97.41 84	99.34 178	98.55 84	91.86 170	99.27 47	99.83 34	93.84 78			99.95 38	99.99 11

testdata2													99.99 25	90.54 199

segment_acmp												96.68 13

testdata				98.42 109	99.47 77	95.33 148		98.56 80	93.78 107	99.79 10	99.85 18	93.64 83	99.94 56	94.97 126	99.94 42	100.00 1

testdata1							99.28 185		96.35 38

test12				99.43 25	99.74 55	98.56 43		98.40 120		99.65 19		94.76 50	99.75 94		99.98 24	99.99 11

plane_prior7					95.71 220	91.59 243

plane_prior6					95.76 215	91.72 238						80.47 235

plane_prior5								97.87 177					98.37 180	97.79 85	89.55 205	94.52 206

plane_prior4											98.59 151

plane_prior3						91.64 241			96.63 29	93.01 175

plane_prior2							99.84 85		96.38 34

plane_prior1					95.73 217

plane_prior						91.74 235	99.86 81		96.76 25						89.59 204

n2								0.00 357

nn								0.00 357

door-mid								89.69 343

lessismore_v0				90.53 290	90.58 310	80.90 318		95.80 296		77.01 301	95.84 222	66.15 311	96.95 255	83.03 275	75.05 309	93.74 271

LGP-MVS_train				93.71 246	95.43 225	88.67 274		97.62 195	92.81 129	90.05 199	98.49 156	75.24 276	98.40 173	95.84 119	89.12 209	94.07 237

test11								98.44 103

door								90.31 339

HQP5-MVS						91.85 230

HQP-NCC					95.78 211		99.87 69		96.82 21	93.37 171

ACMP_Plane					95.78 211		99.87 69		96.82 21	93.37 171

BP-MVS														97.92 82

HQP4-MVS										93.37 171			98.39 175			94.53 204

HQP3-MVS								97.89 175							89.60 202

HQP2-MVS												80.65 231

NP-MVS					95.77 214	91.79 232					98.65 147

MDTV_nov1_ep13_2view						96.26 117	96.11 307		91.89 169	98.06 94		94.40 55		94.30 143		99.67 95

MDTV_nov1_ep13			95.69 118		97.90 148	94.15 170	95.98 309	98.44 103	93.12 121	97.98 96	95.74 224	95.10 38	98.58 158	90.02 207	96.92 143

ACMMP++_ref															87.04 234

ACMMP++															88.23 224

Test By Simon												92.82 97

ITE_SJBPF				92.38 273	95.69 222	85.14 298		95.71 297	92.81 129	89.33 226	98.11 166	70.23 299	98.42 170	85.91 254	88.16 225	93.59 274

DeepMVS_CX				82.92 315	95.98 209	58.66 339		96.01 293	92.72 134	78.34 299	95.51 230	58.29 328	98.08 195	82.57 277	85.29 243	92.03 297

High-resolution multi-view benchmark