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.

MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 8	98.55 140	98.74 77	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

MTAPA		98.58 18	98.29 26	99.46 6	99.76 1	98.64 8	98.90 64	98.74 77	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 34	99.23 21	98.93 35	96.15 60	98.94 21	99.17 39	95.91 28	99.94 3	97.55 50	99.79 10	99.78 7

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 35	99.23 21	98.95 32	96.10 65	98.93 25	99.19 38	95.70 33	99.94 3	97.62 45	99.79 10	99.78 7

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 33	99.22 27	98.79 67	96.13 62	97.92 73	99.23 29	94.54 59	99.94 3	96.74 81	99.78 14	99.73 27

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 38	98.99 55	99.49 5	95.43 86	99.03 15	99.32 20	95.56 35	99.94 3	96.80 79	99.77 17	99.78 7

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 39	99.28 16	98.81 59	96.24 58	98.35 52	99.23 29	95.46 38	99.94 3	97.42 55	99.81 8	99.77 14

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 46	99.34 11	98.87 48	95.96 68	98.60 41	99.13 44	96.05 22	99.94 3	97.77 39	99.86 1	99.77 14

MVS_0304		97.70 57	97.25 65	99.07 43	98.90 91	97.83 48	98.20 179	98.74 77	97.51 8	98.03 63	99.06 56	86.12 223	99.93 9	99.02 1	99.64 45	99.44 84

abl_6		98.30 40	98.03 38	99.13 38	99.56 24	97.76 51	99.13 40	98.82 56	96.14 61	99.26 6	99.37 12	93.33 76	99.93 9	96.96 67	99.67 39	99.69 35

QAPM		96.29 116	95.40 129	98.96 51	97.85 157	97.60 56	99.23 21	98.93 35	89.76 273	93.11 234	99.02 58	89.11 143	99.93 9	91.99 212	99.62 47	99.34 88

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 55	99.03 52	99.41 6	95.98 67	97.60 91	99.36 16	94.45 64	99.93 9	97.14 61	98.85 97	99.70 34
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
CANet		98.05 43	97.76 45	98.90 55	98.73 104	97.27 66	98.35 162	98.78 69	97.37 19	97.72 83	98.96 69	91.53 110	99.92 13	98.79 3	99.65 43	99.51 69

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 4	98.43 155	98.78 69	94.10 138	97.69 85	99.42 5	95.25 45	99.92 13	98.09 24	99.80 9	99.67 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 21	98.95 3	98.82 81	98.81 59	95.80 72	99.16 12	99.47 4	95.37 40	99.92 13	97.89 32	99.75 29	99.79 4

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 25	99.23 21	98.96 30	96.10 65	98.94 21	99.17 39	96.06 20	99.92 13	97.62 45	99.78 14	99.75 20

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 25	98.98 58	98.96 30	95.65 78	98.94 21	99.17 39	96.06 20	99.92 13	97.21 60	99.78 14	99.75 20

HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 27	99.08 1	98.72 111	98.66 105	97.51 8	98.15 55	98.83 81	95.70 33	99.92 13	97.53 52	99.67 39	99.66 48

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 73	99.12 42	98.81 59	92.34 215	98.09 58	99.08 54	93.01 80	99.92 13	96.06 99	99.77 17	99.75 20

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 160	97.64 53	99.35 10	99.06 20	97.02 39	93.75 216	99.16 42	89.25 139	99.92 13	97.22 59	99.75 29	99.64 53

OpenMVS	93.04 13	95.83 130	95.00 148	98.32 87	97.18 200	97.32 64	99.21 30	98.97 28	89.96 266	91.14 264	99.05 57	86.64 215	99.92 13	93.38 171	99.47 69	97.73 178

CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 140	96.07 110	97.98 206	98.21 176	97.24 27	97.13 100	98.93 73	86.88 212	99.91 22	95.00 133	99.37 80	98.66 145

PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 61	95.91 124	98.63 127	99.16 16	94.48 131	97.67 86	98.88 77	92.80 82	99.91 22	97.11 62	99.12 87	99.50 71

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 149	99.22 27	99.32 7	93.04 185	97.02 107	98.92 75	95.36 41	99.91 22	97.43 54	99.64 45	99.52 66

PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 110	95.58 133	97.34 259	98.51 130	97.29 20	98.66 37	97.88 161	94.51 60	99.90 25	97.87 33	99.17 86	97.39 187

UGNet		96.78 99	96.30 103	98.19 94	98.24 131	95.89 126	98.88 70	98.93 35	97.39 16	96.81 119	97.84 165	82.60 269	99.90 25	96.53 88	99.49 67	98.79 137
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
XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 22	99.29 14	98.88 46	97.40 14	98.46 45	99.20 35	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

X-MVStestdata		94.06 231	92.30 248	99.34 13	99.70 15	98.35 22	99.29 14	98.88 46	97.40 14	98.46 45	43.50 337	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

新几何1				99.16 35	99.34 39	98.01 41		98.69 92	90.06 264	98.13 56	98.95 71	94.60 58	99.89 27	91.97 213	99.47 69	99.59 61

testdata2													99.89 27	91.65 221

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 52	94.56 198	98.05 199	99.71 1	93.57 169	97.09 101	98.91 76	88.17 180	99.89 27	96.87 77	99.56 61	99.81 2

EPNet		97.28 80	96.87 81	98.51 74	94.98 297	96.14 108	98.90 64	97.02 281	98.28 1	95.99 149	99.11 46	91.36 111	99.89 27	96.98 64	99.19 85	99.50 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 158	98.52 11	99.37 7	98.71 89	97.09 37	92.99 237	99.13 44	89.36 136	99.89 27	96.97 65	99.57 55	99.71 32

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 86	97.66 52	97.75 231	98.89 43	97.71 6	98.33 53	98.97 65	94.97 52	99.88 34	98.42 16	99.76 23	99.42 85
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
无先验							97.58 243	98.72 84	91.38 239				99.87 35	93.36 172		99.60 59

1121		97.37 77	96.77 87	99.16 35	99.34 39	97.99 44	98.19 183	98.68 95	90.14 262	98.01 66	98.97 65	94.80 56	99.87 35	93.36 172	99.46 72	99.61 56

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 71	98.43 16	99.10 44	98.87 48	97.38 17	99.35 5	99.40 6	97.78 1	99.87 35	97.77 39	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 64	96.93 78	98.83 79	98.75 76	96.96 41	96.89 114	99.50 3	90.46 124	99.87 35	97.84 36	99.76 23	99.52 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		97.16 85	96.66 92	98.68 63	98.53 121	97.19 71	98.93 62	98.90 41	92.83 195	95.99 149	99.37 12	92.12 95	99.87 35	93.67 166	99.57 55	98.97 126

sss		97.39 75	96.98 77	98.61 67	98.60 117	96.61 91	98.22 177	98.93 35	93.97 145	98.01 66	98.48 112	91.98 99	99.85 40	96.45 91	98.15 126	99.39 86

DP-MVS		96.59 105	95.93 114	98.57 69	99.34 39	96.19 107	98.70 115	98.39 152	89.45 281	94.52 171	99.35 18	91.85 101	99.85 40	92.89 191	98.88 94	99.68 41

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 23	98.96 2	99.39 5	98.93 35	97.38 17	99.41 3	99.54 1	96.66 5	99.84 42	98.86 2	99.85 2	99.87 1

原ACMM1				98.65 65	99.32 45	96.62 89		98.67 102	93.27 180	97.81 77	98.97 65	95.18 47	99.83 43	93.84 161	99.46 72	99.50 71

VNet		97.79 54	97.40 61	98.96 51	98.88 94	97.55 57	98.63 127	98.93 35	96.74 46	99.02 16	98.84 80	90.33 127	99.83 43	98.53 10	96.66 156	99.50 71

MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 22	98.87 5	98.41 157	98.68 95	97.04 38	98.52 44	98.80 84	96.78 4	99.83 43	97.93 28	99.61 48	99.74 25

NCCC		98.61 13	98.35 20	99.38 10	99.28 60	98.61 10	98.45 152	98.76 73	97.82 3	98.45 48	98.93 73	96.65 6	99.83 43	97.38 57	99.41 76	99.71 32

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 78	98.12 37	99.04 51	99.09 18	93.32 177	98.83 29	99.10 48	96.54 8	99.83 43	97.70 43	99.76 23	99.59 61

testdata				98.26 89	99.20 74	95.36 142		98.68 95	91.89 225	98.60 41	99.10 48	94.44 65	99.82 48	94.27 151	99.44 74	99.58 63

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 64	98.04 39	98.50 148	98.78 69	97.72 4	98.92 26	99.28 25	95.27 44	99.82 48	97.55 50	99.77 17	99.69 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		97.95 46	97.51 55	99.28 20	99.30 52	98.38 17	97.81 226	98.72 84	93.16 182	97.57 93	98.66 97	96.14 15	99.81 50	96.63 84	99.56 61	99.66 48

agg_prior					99.30 52	98.38 17		98.72 84		97.57 93			99.81 50

UA-Net		97.96 45	97.62 48	98.98 49	98.86 96	97.47 60	98.89 68	99.08 19	96.67 49	98.72 35	99.54 1	93.15 79	99.81 50	94.87 134	98.83 98	99.65 50

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 152	99.25 64	95.35 144	98.26 175	99.26 8	94.28 134	97.94 71	97.46 193	92.74 83	99.81 50	96.88 74	93.32 219	96.20 274

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 64	95.35 144	97.28 263	99.26 8	93.13 183	97.94 71	98.21 138	92.74 83	99.81 50	96.88 74	99.40 78	99.27 98

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 33	94.95 161	98.55 140	98.62 111	93.02 186	96.17 144	98.58 105	94.01 71	99.81 50	93.95 158	98.90 93	99.14 112

PCF-MVS	93.45 11	94.68 196	93.43 232	98.42 83	98.62 115	96.77 85	95.48 308	98.20 179	84.63 310	93.34 226	98.32 129	88.55 172	99.81 50	84.80 300	98.96 91	98.68 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 123	95.98 111	97.86 222	98.51 130	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 189

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 135	98.61 116	95.46 139	97.44 248	98.46 140	97.15 32	98.65 38	98.15 141	94.33 66	99.80 57	97.84 36	98.66 106	97.41 185

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 123	95.98 111	97.86 222	98.51 130	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 189

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 123	95.98 111	97.86 222	98.51 130	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 189

TEST9					99.31 47	98.50 12	97.92 211	98.73 82	92.63 197	97.74 81	98.68 94	96.20 12	99.80 57

train_agg		97.97 44	97.52 54	99.33 15	99.31 47	98.50 12	97.92 211	98.73 82	92.98 188	97.74 81	98.68 94	96.20 12	99.80 57	96.59 85	99.57 55	99.68 41

test_8					99.29 55	98.44 14	97.89 219	98.72 84	92.98 188	97.70 84	98.66 97	96.20 12	99.80 57

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 35	97.37 63	98.40 158	98.79 67	97.46 12	99.09 13	99.31 21	95.86 31	99.80 57	98.64 4	99.76 23	99.79 4

Regformer-2		98.69 7	98.52 8	99.19 28	99.35 37	98.01 41	98.37 160	98.81 59	97.48 11	99.21 9	99.21 32	96.13 16	99.80 57	98.40 18	99.73 34	99.75 20

旧先验2							97.57 244		91.30 245	98.67 36			99.80 57	95.70 115

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 27	97.92 45	99.15 36	98.81 59	96.24 58	99.20 10	99.37 12	95.30 43	99.80 57	97.73 41	99.67 39	99.72 30

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 26	98.72 7	98.80 90	98.82 56	94.52 128	99.23 8	99.25 28	95.54 37	99.80 57	96.52 89	99.77 17	99.74 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 28	99.26 17	98.58 118	97.52 7	99.41 3	98.78 85	96.00 23	99.79 69	97.79 38	99.59 52	99.69 35

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 55	98.23 28	97.92 211	98.72 84	92.38 214	97.59 92	98.64 99	96.09 18	99.79 69	96.59 85	99.57 55	99.68 41

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 33	96.32 103	98.28 173	98.68 95	97.17 31	98.74 34	99.37 12	95.25 45	99.79 69	98.57 8	99.54 64	99.73 27

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 37	97.81 50	98.37 160	98.76 73	97.49 10	99.20 10	99.21 32	96.08 19	99.79 69	98.42 16	99.73 34	99.75 20

COLMAP_ROB	93.27 12	95.33 159	94.87 159	96.71 173	99.29 55	93.24 233	98.58 133	98.11 201	89.92 269	93.57 219	99.10 48	86.37 219	99.79 69	90.78 234	98.10 128	97.09 194
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 32	96.49 96	98.30 171	98.69 92	97.21 28	98.84 27	99.36 16	95.41 39	99.78 74	98.62 6	99.65 43	99.80 3

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 99	93.98 214	98.68 120	97.40 259	95.02 110	97.95 70	99.34 19	74.37 312	99.78 74	98.64 4	96.80 154	99.08 118

CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 45	98.87 5	98.47 151	98.81 59	97.72 4	98.76 33	99.16 42	97.05 2	99.78 74	98.06 25	99.66 42	99.69 35

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 96	96.89 82	98.31 169	98.71 89	95.26 98	97.67 86	98.56 106	92.21 92	99.78 74	95.89 104	96.85 153	99.48 76

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 55	96.31 105	98.14 189	98.76 73	92.41 212	96.39 140	98.31 130	94.92 53	99.78 74	94.06 156	98.77 101	99.23 102
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Regformer-3		98.59 16	98.50 11	98.86 57	99.43 35	97.05 74	98.40 158	98.68 95	97.43 13	99.06 14	99.31 21	95.80 32	99.77 79	98.62 6	99.76 23	99.78 7

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 49	99.53 1	98.80 66	94.63 125	98.61 40	98.97 65	95.13 49	99.77 79	97.65 44	99.83 7	99.79 4

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 122	97.00 75	98.14 189	98.21 176	93.95 146	96.72 122	97.99 153	91.58 105	99.76 81	94.51 145	96.54 161	98.95 130

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 76	96.69 88	98.01 203	98.89 43	94.44 133	96.83 116	98.68 94	90.69 122	99.76 81	94.36 147	99.29 83	98.98 125

ab-mvs		96.42 111	95.71 123	98.55 71	98.63 114	96.75 86	97.88 220	98.74 77	93.84 151	96.54 130	98.18 140	85.34 236	99.75 83	95.93 103	96.35 170	99.15 110

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 109	96.90 80	98.66 125	98.68 95	92.40 213	97.07 104	97.96 154	91.54 109	99.75 83	93.68 165	98.92 92	98.69 142
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
HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 46	99.44 4	98.82 56	94.46 132	98.94 21	99.20 35	95.16 48	99.74 85	97.58 47	99.85 2	99.77 14

AllTest		95.24 163	94.65 164	96.99 158	99.25 64	93.21 234	98.59 131	98.18 183	91.36 240	93.52 221	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 172

TestCases				96.99 158	99.25 64	93.21 234		98.18 183	91.36 240	93.52 221	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 172

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 31	98.44 14	97.91 214	98.67 102	92.57 201	98.77 32	98.85 79	95.93 27	99.72 86	95.56 118	99.69 38	99.68 41

test12				99.18 32	99.16 76	98.19 32		98.53 126		98.07 59		95.13 49	99.72 86		99.56 61	99.63 55

CNLPA		97.45 70	97.03 75	98.73 60	99.05 81	97.44 62	98.07 198	98.53 126	95.32 96	96.80 120	98.53 107	93.32 77	99.72 86	94.31 150	99.31 82	99.02 121

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 27	99.14 37	98.66 105	96.84 43	99.56 2	99.31 21	96.34 10	99.70 91	98.32 20	99.73 34	99.73 27

test_prior3		98.22 42	97.90 43	99.19 28	99.31 47	98.22 30	97.80 227	98.84 53	96.12 63	97.89 75	98.69 92	95.96 25	99.70 91	96.89 71	99.60 49	99.65 50

test_prior				99.19 28	99.31 47	98.22 30		98.84 53					99.70 91			99.65 50

PVSNet	91.96 18	96.35 113	96.15 108	96.96 161	99.17 75	92.05 247	96.08 298	98.68 95	93.69 162	97.75 80	97.80 171	88.86 153	99.69 94	94.26 152	99.01 89	99.15 110

MG-MVS		97.81 53	97.60 49	98.44 80	99.12 80	95.97 115	97.75 231	98.78 69	96.89 42	98.46 45	99.22 31	93.90 73	99.68 95	94.81 137	99.52 66	99.67 46

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 71	97.25 69	98.11 194	98.29 165	97.19 30	98.99 20	99.02 58	96.22 11	99.67 96	98.52 14	98.56 110	99.51 69

114514_t		96.93 93	96.27 104	98.92 53	99.50 27	97.63 54	98.85 75	98.90 41	84.80 309	97.77 78	99.11 46	92.84 81	99.66 97	94.85 135	99.77 17	99.47 77

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 25	98.35 22	98.33 164	98.89 43	92.62 198	98.05 60	98.94 72	95.34 42	99.65 98	96.04 100	99.42 75	99.19 104

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 47	96.51 95	97.91 214	99.06 20	93.72 158	96.92 112	98.06 147	88.50 175	99.65 98	91.77 218	99.00 90	98.66 145

VDDNet		95.36 156	94.53 169	97.86 111	98.10 142	95.13 151	98.85 75	97.75 226	90.46 255	98.36 51	99.39 7	73.27 314	99.64 100	97.98 27	96.58 159	98.81 136

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 71	97.32 64	97.91 214	99.58 3	97.20 29	98.33 53	99.00 63	95.99 24	99.64 100	98.05 26	99.76 23	99.69 35

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 218	99.00 86	89.54 279	97.43 250	98.87 48	98.16 2	99.26 6	99.38 11	96.12 17	99.64 100	98.30 21	99.77 17	99.72 30

LFMVS		95.86 129	94.98 150	98.47 78	98.87 95	96.32 103	98.84 78	96.02 308	93.40 174	98.62 39	99.20 35	74.99 307	99.63 103	97.72 42	97.20 148	99.46 81

MVS		94.67 197	93.54 226	98.08 101	96.88 215	96.56 93	98.19 183	98.50 135	78.05 323	92.69 242	98.02 149	91.07 117	99.63 103	90.09 244	98.36 119	98.04 171

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 61	96.90 80	97.95 209	99.58 3	97.14 33	98.44 49	99.01 62	95.03 51	99.62 105	97.91 29	99.75 29	99.50 71

MSDG		95.93 126	95.30 139	97.83 113	98.90 91	95.36 142	96.83 285	98.37 155	91.32 244	94.43 181	98.73 91	90.27 128	99.60 106	90.05 247	98.82 99	98.52 151

view600		95.60 142	94.93 154	97.62 128	99.05 81	94.85 165	99.09 45	97.01 283	95.36 90	96.52 132	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

view800		95.60 142	94.93 154	97.62 128	99.05 81	94.85 165	99.09 45	97.01 283	95.36 90	96.52 132	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

conf0.05thres1000		95.60 142	94.93 154	97.62 128	99.05 81	94.85 165	99.09 45	97.01 283	95.36 90	96.52 132	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

tfpn		95.60 142	94.93 154	97.62 128	99.05 81	94.85 165	99.09 45	97.01 283	95.36 90	96.52 132	97.37 197	84.55 246	99.59 107	89.07 266	96.39 166	98.40 157

thres600view7		95.49 146	94.77 161	97.67 125	98.98 88	95.02 154	98.85 75	96.90 291	95.38 89	96.63 124	96.90 241	84.29 253	99.59 107	88.65 275	96.33 171	98.40 157

1112_ss		96.63 102	96.00 113	98.50 75	98.56 118	96.37 100	98.18 187	98.10 206	92.92 190	94.84 162	98.43 115	92.14 94	99.58 112	94.35 148	96.51 162	99.56 65

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 27	96.41 99	98.63 127	98.60 112	95.18 102	97.06 105	98.06 147	94.26 68	99.57 113	93.80 163	98.87 96	99.52 66

API-MVS		97.41 74	97.25 65	97.91 109	98.70 107	96.80 83	98.82 81	98.69 92	94.53 127	98.11 57	98.28 131	94.50 63	99.57 113	94.12 155	99.49 67	97.37 189

tfpn200view9		95.32 160	94.62 165	97.43 139	98.94 89	94.98 158	98.68 120	96.93 289	95.33 94	96.55 128	96.53 256	84.23 255	99.56 115	88.11 281	96.29 173	97.76 176

thres400		95.38 154	94.62 165	97.65 127	98.94 89	94.98 158	98.68 120	96.93 289	95.33 94	96.55 128	96.53 256	84.23 255	99.56 115	88.11 281	96.29 173	98.40 157

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 118	95.94 119	97.71 233	98.07 211	92.10 221	94.79 166	97.29 204	91.75 102	99.56 115	94.17 153	96.50 163	99.58 63

PAPR		96.84 97	96.24 106	98.65 65	98.72 106	96.92 79	97.36 257	98.57 119	93.33 176	96.67 123	97.57 189	94.30 67	99.56 115	91.05 232	98.59 108	99.47 77

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 157	98.77 102	93.76 220	97.79 229	98.50 135	95.45 85	96.94 109	99.09 52	87.87 191	99.55 119	96.76 80	95.83 185	97.74 177

thres200		95.25 162	94.57 167	97.28 144	98.81 100	94.92 162	98.20 179	97.11 275	95.24 101	96.54 130	96.22 269	84.58 245	99.53 120	87.93 283	96.50 163	97.39 187

XVG-OURS		96.55 107	96.41 99	96.99 158	98.75 103	93.76 220	97.50 247	98.52 128	95.67 76	96.83 116	99.30 24	88.95 150	99.53 120	95.88 105	96.26 176	97.69 180

IB-MVS	91.98 17	93.27 244	91.97 251	97.19 147	97.47 178	93.41 230	97.09 271	95.99 309	93.32 177	92.47 250	95.73 280	78.06 293	99.53 120	94.59 142	82.98 304	98.62 148
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
canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 107	98.38 17	99.34 11	98.39 152	96.76 45	97.67 86	97.40 196	92.26 89	99.49 123	98.28 22	96.28 175	99.08 118

1314		96.25 120	95.73 119	97.79 116	97.13 203	95.55 137	98.19 183	98.59 113	93.47 172	92.03 259	97.82 169	91.33 112	99.49 123	94.62 140	98.44 115	98.32 166

RPSCF		94.87 180	95.40 129	93.26 293	98.89 93	82.06 317	98.33 164	98.06 213	90.30 259	96.56 126	99.26 27	87.09 207	99.49 123	93.82 162	96.32 172	98.24 167

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 42	95.92 122	98.28 173	98.59 113	95.52 83	97.97 69	99.10 48	93.28 78	99.49 123	95.09 132	98.88 94	99.19 104

alignmvs		97.56 65	97.07 74	99.01 46	98.66 111	98.37 20	98.83 79	98.06 213	96.74 46	98.00 68	97.65 182	90.80 121	99.48 127	98.37 19	96.56 160	99.19 104

mvs-test1		96.60 103	96.68 91	96.37 212	97.89 155	91.81 250	98.56 138	98.10 206	96.57 52	96.52 132	97.94 156	90.81 119	99.45 128	95.72 111	98.01 129	97.86 175

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 63	96.80 83	98.71 112	99.05 22	97.28 21	98.84 27	99.28 25	96.47 9	99.40 129	98.52 14	99.70 37	99.47 77

PVSNet_0	88.72 19	91.28 272	90.03 275	95.00 265	97.99 149	87.29 305	94.84 315	98.50 135	92.06 222	89.86 275	95.19 286	79.81 286	99.39 130	92.27 204	69.79 328	98.33 165

DI_MVS_plusplus_test		94.74 191	93.62 221	98.09 100	95.34 293	95.92 122	98.09 197	97.34 263	94.66 123	85.89 295	95.91 276	80.49 283	99.38 131	96.66 83	98.22 123	98.97 126

test_normal		94.72 192	93.59 223	98.11 99	95.30 294	95.95 118	97.91 214	97.39 261	94.64 124	85.70 298	95.88 277	80.52 282	99.36 132	96.69 82	98.30 122	99.01 124

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 111	96.23 106	99.22 27	99.00 25	96.63 51	98.04 62	99.21 32	88.05 185	99.35 133	96.01 102	99.21 84	99.45 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
lupinMVS		97.44 71	97.22 68	98.12 98	98.07 143	95.76 129	97.68 236	97.76 225	94.50 129	98.79 30	98.61 100	92.34 86	99.30 134	97.58 47	99.59 52	99.31 91

TAPA-MVS	93.98 7	95.35 157	94.56 168	97.74 118	99.13 79	94.83 176	98.33 164	98.64 110	86.62 297	96.29 142	98.61 100	94.00 72	99.29 135	80.00 309	99.41 76	99.09 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_Test		97.28 80	97.00 76	98.13 97	98.33 127	95.97 115	98.74 106	98.07 211	94.27 135	98.44 49	98.07 146	92.48 85	99.26 136	96.43 92	98.19 125	99.16 109

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 136	96.72 87	97.37 255	98.43 147	93.71 159	97.65 89	98.02 149	92.20 93	99.25 137	96.87 77	97.79 138	99.19 104

tpmvs		94.60 200	94.36 177	95.33 258	97.46 179	88.60 293	96.88 282	97.68 228	91.29 246	93.80 215	96.42 262	88.58 169	99.24 138	91.06 230	96.04 182	98.17 168

jason		97.32 79	97.08 73	98.06 104	97.45 182	95.59 132	97.87 221	97.91 221	94.79 118	98.55 43	98.83 81	91.12 114	99.23 139	97.58 47	99.60 49	99.34 88
jason: jason.
EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 113	95.38 141	99.33 13	98.31 160	93.61 168	97.19 99	99.07 55	94.05 70	99.23 139	96.89 71	98.43 117	99.37 87

PMMVS		96.60 103	96.33 102	97.41 140	97.90 154	93.93 215	97.35 258	98.41 148	92.84 194	97.76 79	97.45 195	91.10 116	99.20 141	96.26 95	97.91 132	99.11 114

gm-plane-assit					95.88 277	87.47 303			89.74 275		96.94 236		99.19 142	93.32 174

tpmrst		95.63 139	95.69 125	95.44 249	97.54 174	88.54 295	96.97 273	97.56 233	93.50 171	97.52 95	96.93 240	89.49 133	99.16 143	95.25 129	96.42 165	98.64 147

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 129	95.97 115	98.58 133	98.25 171	91.74 229	95.29 156	97.23 207	91.03 118	99.15 144	92.90 189	97.96 131	98.97 126

diffmvs		96.32 115	95.74 118	98.07 103	98.26 130	96.14 108	98.53 142	98.23 174	90.10 263	96.88 115	97.73 174	90.16 130	99.15 144	93.90 160	97.85 136	98.91 132

ACMP	93.49 10	95.34 158	94.98 150	96.43 209	97.67 164	93.48 227	98.73 109	98.44 144	94.94 116	92.53 247	98.53 107	84.50 251	99.14 146	95.48 121	94.00 204	96.66 242
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tpm cat1		93.36 240	92.80 241	95.07 264	97.58 171	87.97 300	96.76 286	97.86 222	82.17 317	93.53 220	96.04 274	86.13 222	99.13 147	89.24 263	95.87 184	98.10 170

DWT-MVSNet_test		94.82 184	94.36 177	96.20 222	97.35 188	90.79 264	98.34 163	96.57 303	92.91 191	95.33 155	96.44 261	82.00 271	99.12 148	94.52 144	95.78 186	98.70 141

PatchFormer-LS_test		95.47 147	95.27 140	96.08 227	97.59 170	90.66 267	98.10 196	97.34 263	93.98 144	96.08 145	96.15 271	87.65 199	99.12 148	95.27 128	95.24 189	98.44 156

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 128	94.64 190	98.19 183	97.45 254	94.56 126	96.03 147	98.61 100	85.02 239	99.12 148	90.68 236	99.06 88	99.30 94

ACMM	93.85 9	95.69 137	95.38 133	96.61 190	97.61 168	93.84 218	98.91 63	98.44 144	95.25 99	94.28 192	98.47 113	86.04 227	99.12 148	95.50 120	93.95 206	96.87 214
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-ACMP-BASELINE		94.54 205	94.14 188	95.75 239	96.55 230	91.65 255	98.11 194	98.44 144	94.96 113	94.22 196	97.90 159	79.18 290	99.11 152	94.05 157	93.85 207	96.48 266

LPG-MVS_test		95.62 140	95.34 134	96.47 205	97.46 179	93.54 225	98.99 55	98.54 123	94.67 121	94.36 184	98.77 87	85.39 233	99.11 152	95.71 113	94.15 199	96.76 224

LGP-MVS_train				96.47 205	97.46 179	93.54 225		98.54 123	94.67 121	94.36 184	98.77 87	85.39 233	99.11 152	95.71 113	94.15 199	96.76 224

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 42	95.56 135	97.38 253	99.65 2	92.34 215	97.61 90	98.20 139	89.29 138	99.10 155	96.97 65	97.60 144	99.77 14

TDRefinement		91.06 275	89.68 278	95.21 259	85.35 327	91.49 256	98.51 147	97.07 277	91.47 234	88.83 284	97.84 165	77.31 299	99.09 156	92.79 192	77.98 321	95.04 295

ACMH	92.88 16	94.55 204	93.95 201	96.34 216	97.63 166	93.26 232	98.81 87	98.49 139	93.43 173	89.74 276	98.53 107	81.91 272	99.08 157	93.69 164	93.30 220	96.70 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		97.18 84	97.18 69	97.20 146	98.81 100	93.27 231	95.78 306	99.15 17	95.25 99	96.79 121	98.11 144	92.29 88	99.07 158	98.56 9	99.85 2	99.25 100

OPM-MVS		95.69 137	95.33 136	96.76 171	96.16 266	94.63 191	98.43 155	98.39 152	96.64 50	95.02 159	98.78 85	85.15 238	99.05 159	95.21 131	94.20 196	96.60 251

tpmp4_e23		93.91 235	93.42 234	95.38 255	97.62 167	88.59 294	97.52 246	97.34 263	87.94 292	94.17 200	96.79 247	82.91 267	99.05 159	90.62 238	95.91 183	98.50 152

MDTV_nov1_ep13			95.40 129		97.48 177	88.34 297	96.85 283	97.29 268	93.74 156	97.48 96	97.26 205	89.18 141	99.05 159	91.92 215	97.43 146

ACMH+	92.99 14	94.30 215	93.77 212	95.88 233	97.81 159	92.04 248	98.71 112	98.37 155	93.99 143	90.60 271	98.47 113	80.86 279	99.05 159	92.75 193	92.40 229	96.55 258

LTVRE_ROB	92.95 15	94.60 200	93.90 204	96.68 179	97.41 186	94.42 201	98.52 143	98.59 113	91.69 230	91.21 263	98.35 123	84.87 241	99.04 163	91.06 230	93.44 217	96.60 251
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
HQP_MVS		96.14 121	95.90 115	96.85 167	97.42 183	94.60 196	98.80 90	98.56 120	97.28 21	95.34 153	98.28 131	87.09 207	99.03 164	96.07 97	94.27 193	96.92 204

plane_prior5								98.56 120					99.03 164	96.07 97	94.27 193	96.92 204

dp		94.15 226	93.90 204	94.90 267	97.31 190	86.82 307	96.97 273	97.19 274	91.22 250	96.02 148	96.61 255	85.51 232	99.02 166	90.00 249	94.30 192	98.85 133

BH-untuned		95.95 125	95.72 120	96.65 184	98.55 120	92.26 244	98.23 176	97.79 224	93.73 157	94.62 168	98.01 151	88.97 149	99.00 167	93.04 182	98.51 111	98.68 143

test-LLR		95.10 169	94.87 159	95.80 236	96.77 219	89.70 277	96.91 277	95.21 316	95.11 105	94.83 164	95.72 282	87.71 195	98.97 168	93.06 180	98.50 112	98.72 139

test-mter		94.08 229	93.51 229	95.80 236	96.77 219	89.70 277	96.91 277	95.21 316	92.89 192	94.83 164	95.72 282	77.69 295	98.97 168	93.06 180	98.50 112	98.72 139

CLD-MVS		95.62 140	95.34 134	96.46 208	97.52 176	93.75 222	97.27 264	98.46 140	95.53 82	94.42 182	98.00 152	86.21 221	98.97 168	96.25 96	94.37 191	96.66 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ADS-MVSNet		95.00 171	94.45 174	96.63 187	98.00 147	91.91 249	96.04 299	97.74 227	90.15 260	96.47 137	96.64 253	87.89 189	98.96 171	90.08 245	97.06 149	99.02 121

HQP4-MVS										94.45 174			98.96 171			96.87 214

TR-MVS		94.94 178	94.20 184	97.17 149	97.75 161	94.14 211	97.59 242	97.02 281	92.28 219	95.75 151	97.64 184	83.88 262	98.96 171	89.77 251	96.15 180	98.40 157

HQP-MVS		95.72 134	95.40 129	96.69 176	97.20 197	94.25 209	98.05 199	98.46 140	96.43 54	94.45 174	97.73 174	86.75 213	98.96 171	95.30 125	94.18 197	96.86 216

CostFormer		94.95 176	94.73 163	95.60 242	97.28 191	89.06 286	97.53 245	96.89 292	89.66 277	96.82 118	96.72 249	86.05 225	98.95 175	95.53 119	96.13 181	98.79 137

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 98	96.36 101	99.19 33	97.97 218	95.39 88	97.23 98	98.99 64	91.11 115	98.93 176	94.60 141	98.59 108	99.47 77

TESTMET0.1,1		94.18 223	93.69 218	95.63 241	96.92 211	89.12 285	96.91 277	94.78 321	93.17 181	94.88 161	96.45 260	78.52 291	98.92 177	93.09 179	98.50 112	98.85 133

Effi-MVS+-dtu		96.29 116	96.56 94	95.51 243	97.89 155	90.22 273	98.80 90	98.10 206	96.57 52	96.45 139	96.66 251	90.81 119	98.91 178	95.72 111	97.99 130	97.40 186

test_post											31.83 340	88.83 157	98.91 178

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 193	97.27 66	98.94 61	99.23 12	95.13 104	95.51 152	97.32 202	85.73 229	98.91 178	97.33 58	89.55 255	96.89 212

PatchmatchNet		95.71 135	95.52 128	96.29 219	97.58 171	90.72 266	96.84 284	97.52 239	94.06 139	97.08 102	96.96 233	89.24 140	98.90 181	92.03 211	98.37 118	99.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											95.10 289	89.42 135	98.89 182

ITE_SJBPF				95.44 249	97.42 183	91.32 258		97.50 245	95.09 108	93.59 217	98.35 123	81.70 273	98.88 183	89.71 254	93.39 218	96.12 276

cascas		94.63 199	93.86 206	96.93 164	96.91 213	94.27 208	96.00 302	98.51 130	85.55 305	94.54 170	96.23 267	84.20 257	98.87 184	95.80 109	96.98 152	97.66 181

XXY-MVS		95.20 166	94.45 174	97.46 137	96.75 222	96.56 93	98.86 74	98.65 109	93.30 179	93.27 227	98.27 134	84.85 242	98.87 184	94.82 136	91.26 243	96.96 201

PAPM		94.95 176	94.00 197	97.78 117	97.04 206	95.65 131	96.03 301	98.25 171	91.23 249	94.19 198	97.80 171	91.27 113	98.86 186	82.61 304	97.61 143	98.84 135

BH-w/o		95.38 154	95.08 146	96.26 220	98.34 126	91.79 251	97.70 234	97.43 256	92.87 193	94.24 195	97.22 208	88.66 168	98.84 187	91.55 222	97.70 142	98.16 169

EPMVS		94.99 172	94.48 170	96.52 201	97.22 195	91.75 253	97.23 265	91.66 333	94.11 137	97.28 97	96.81 246	85.70 230	98.84 187	93.04 182	97.28 147	98.97 126

Patchmatch-test		94.42 210	93.68 219	96.63 187	97.60 169	91.76 252	94.83 316	97.49 251	89.45 281	94.14 201	97.10 213	88.99 145	98.83 189	85.37 299	98.13 127	99.29 96

USDC		93.33 243	92.71 243	95.21 259	96.83 218	90.83 263	96.91 277	97.50 245	93.84 151	90.72 269	98.14 142	77.69 295	98.82 190	89.51 259	93.21 223	95.97 280

TinyColmap		92.31 255	91.53 254	94.65 275	96.92 211	89.75 276	96.92 275	96.68 299	90.45 256	89.62 277	97.85 164	76.06 303	98.81 191	86.74 289	92.51 228	95.41 291

LF4IMVS		93.14 248	92.79 242	94.20 284	95.88 277	88.67 292	97.66 238	97.07 277	93.81 153	91.71 261	97.65 182	77.96 294	98.81 191	91.47 225	91.92 235	95.12 293

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 231	97.74 162	91.74 254	98.69 116	98.15 191	95.56 81	94.92 160	97.68 181	88.98 148	98.79 193	93.19 177	97.78 139	97.20 193

JIA-IIPM		93.35 241	92.49 245	95.92 230	96.48 235	90.65 268	95.01 311	96.96 287	85.93 303	96.08 145	87.33 324	87.70 197	98.78 194	91.35 226	95.58 187	98.34 164

tpm2		94.19 221	93.76 214	95.46 247	97.23 194	89.04 287	97.31 262	96.85 295	87.08 296	96.21 143	96.79 247	83.75 265	98.74 195	92.43 203	96.23 178	98.59 149

test_post1							96.68 288				30.43 341	87.85 192	98.69 196	92.59 197

MS-PatchMatch		93.84 236	93.63 220	94.46 281	96.18 262	89.45 280	97.76 230	98.27 166	92.23 220	92.13 258	97.49 191	79.50 287	98.69 196	89.75 253	99.38 79	95.25 292

nrg030		96.28 118	95.72 120	97.96 108	96.90 214	98.15 35	99.39 5	98.31 160	95.47 84	94.42 182	98.35 123	92.09 96	98.69 196	97.50 53	89.05 261	97.04 197

VPNet		94.99 172	94.19 185	97.40 141	97.16 201	96.57 92	98.71 112	98.97 28	95.67 76	94.84 162	98.24 137	80.36 284	98.67 199	96.46 90	87.32 285	96.96 201

jajsoiax		95.45 149	95.03 147	96.73 172	95.42 292	94.63 191	99.14 37	98.52 128	95.74 73	93.22 228	98.36 122	83.87 263	98.65 200	96.95 68	94.04 202	96.91 209

mvs_tets		95.41 153	95.00 148	96.65 184	95.58 287	94.42 201	99.00 54	98.55 122	95.73 74	93.21 229	98.38 120	83.45 266	98.63 201	97.09 63	94.00 204	96.91 209

PS-MVSNAJss		96.43 110	96.26 105	96.92 166	95.84 279	95.08 153	99.16 35	98.50 135	95.87 70	93.84 214	98.34 127	94.51 60	98.61 202	96.88 74	93.45 216	97.06 195

CMPMVS	66.06 21	89.70 284	89.67 279	89.78 303	93.19 309	76.56 322	97.00 272	98.35 157	80.97 319	81.57 316	97.75 173	74.75 309	98.61 202	89.85 250	93.63 211	94.17 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OurMVSNet-221017-0		94.21 219	94.00 197	94.85 269	95.60 286	89.22 284	98.89 68	97.43 256	95.29 97	92.18 257	98.52 110	82.86 268	98.59 204	93.46 170	91.76 237	96.74 226

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 104	95.46 139	99.20 31	98.30 163	94.96 113	96.60 125	98.87 78	90.05 131	98.59 204	93.67 166	98.60 107	99.46 81

v6		94.83 181	94.21 183	96.69 176	96.36 243	94.85 165	98.87 71	98.11 201	92.46 202	94.44 180	97.05 224	88.76 164	98.57 206	92.95 185	88.92 263	96.65 244

V42		94.78 186	94.14 188	96.70 175	96.33 250	95.22 148	98.97 59	98.09 209	92.32 217	94.31 188	97.06 220	88.39 176	98.55 207	92.90 189	88.87 266	96.34 271

v1neww		94.83 181	94.22 181	96.68 179	96.39 239	94.85 165	98.87 71	98.11 201	92.45 207	94.45 174	97.06 220	88.82 158	98.54 208	92.93 186	88.91 264	96.65 244

v7new		94.83 181	94.22 181	96.68 179	96.39 239	94.85 165	98.87 71	98.11 201	92.45 207	94.45 174	97.06 220	88.82 158	98.54 208	92.93 186	88.91 264	96.65 244

v1		94.75 189	94.11 192	96.69 176	96.27 258	94.87 163	98.69 116	98.12 196	92.43 210	94.32 187	96.94 236	88.71 167	98.54 208	92.66 195	88.84 269	96.67 239

EI-MVSNet		95.96 124	95.83 117	96.36 213	97.93 152	93.70 224	98.12 192	98.27 166	93.70 161	95.07 157	99.02 58	92.23 91	98.54 208	94.68 138	93.46 214	96.84 217

Test4		92.21 256	90.34 272	97.82 115	92.83 311	95.87 127	97.94 210	98.05 216	94.50 129	82.12 314	94.48 293	59.54 329	98.54 208	95.39 123	98.22 123	99.06 120

MVSTER		96.06 122	95.72 120	97.08 155	98.23 132	95.93 121	98.73 109	98.27 166	94.86 117	95.07 157	98.09 145	88.21 179	98.54 208	96.59 85	93.46 214	96.79 221

v52		94.18 223	93.52 227	96.13 225	95.95 274	94.29 207	99.23 21	98.21 176	91.42 237	92.84 239	96.89 242	87.85 192	98.53 214	91.51 223	87.81 278	95.57 290

v7n		94.19 221	93.43 232	96.47 205	95.90 275	94.38 204	99.26 17	98.34 158	91.99 223	92.76 241	97.13 212	88.31 177	98.52 215	89.48 260	87.70 281	96.52 261

V4		94.18 223	93.52 227	96.13 225	95.89 276	94.31 206	99.23 21	98.22 175	91.42 237	92.82 240	96.89 242	87.93 188	98.52 215	91.51 223	87.81 278	95.58 289

TAMVS		97.02 90	96.79 84	97.70 122	98.06 145	95.31 146	98.52 143	98.31 160	93.95 146	97.05 106	98.61 100	93.49 75	98.52 215	95.33 124	97.81 137	99.29 96

Patchmatch-test1		95.32 160	94.97 152	96.35 214	97.67 164	91.29 259	97.33 260	97.60 231	94.68 120	96.92 112	96.95 234	83.97 260	98.50 218	91.33 227	98.32 121	99.25 100

v1141		94.75 189	94.11 192	96.67 182	96.27 258	94.86 164	98.69 116	98.12 196	92.43 210	94.31 188	96.94 236	88.78 163	98.48 219	92.63 196	88.85 268	96.67 239

divwei89l23v2f112		94.76 187	94.12 191	96.67 182	96.28 256	94.85 165	98.69 116	98.12 196	92.44 209	94.29 191	96.94 236	88.85 155	98.48 219	92.67 194	88.79 270	96.67 239

v8		94.47 208	93.77 212	96.57 196	96.36 243	94.83 176	99.05 50	98.19 180	91.92 224	93.16 230	96.97 232	88.82 158	98.48 219	91.69 220	87.79 280	96.39 268

GA-MVS		94.81 185	94.03 195	97.14 150	97.15 202	93.86 217	96.76 286	97.58 232	94.00 142	94.76 167	97.04 225	80.91 277	98.48 219	91.79 217	96.25 177	99.09 115

UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 209	97.47 60	98.79 95	99.18 15	95.60 79	93.92 210	97.04 225	91.68 103	98.48 219	95.80 109	87.66 282	96.79 221

v748		93.75 237	93.06 237	95.82 235	95.73 282	92.64 241	99.25 19	98.24 173	91.60 232	92.22 256	96.52 258	87.60 200	98.46 224	90.64 237	85.72 299	96.36 270

mvs_anonymous		96.70 101	96.53 97	97.18 148	98.19 136	93.78 219	98.31 169	98.19 180	94.01 141	94.47 173	98.27 134	92.08 97	98.46 224	97.39 56	97.91 132	99.31 91

v144192		94.39 212	93.70 217	96.48 204	96.06 269	94.35 205	98.58 133	98.16 190	91.45 235	94.33 186	97.02 227	87.50 203	98.45 226	91.08 229	89.11 260	96.63 247

v7		94.69 193	94.04 194	96.62 189	96.41 238	94.79 184	98.78 97	98.13 194	91.89 225	94.30 190	97.16 210	88.13 183	98.45 226	91.96 214	89.65 252	96.61 249

v2v482		94.69 193	94.03 195	96.65 184	96.17 263	94.79 184	98.67 123	98.08 210	92.72 196	94.00 208	97.16 210	87.69 198	98.45 226	92.91 188	88.87 266	96.72 229

FIs		96.51 108	96.12 109	97.67 125	97.13 203	97.54 58	99.36 8	99.22 14	95.89 69	94.03 207	98.35 123	91.98 99	98.44 229	96.40 93	92.76 226	97.01 198

testing_2		90.61 280	88.50 287	96.95 162	90.08 319	95.57 134	97.69 235	98.06 213	93.02 186	76.55 321	92.48 317	61.18 328	98.44 229	95.45 122	91.98 233	96.84 217

v1192		94.32 214	93.58 224	96.53 200	96.10 267	94.45 200	98.50 148	98.17 188	91.54 233	94.19 198	97.06 220	86.95 211	98.43 231	90.14 243	89.57 253	96.70 233

MVP-Stereo		94.28 218	93.92 202	95.35 257	94.95 298	92.60 242	97.97 207	97.65 230	91.61 231	90.68 270	97.09 215	86.32 220	98.42 232	89.70 255	99.34 81	95.02 296
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1921920		94.20 220	93.47 231	96.40 211	95.98 272	94.08 212	98.52 143	98.15 191	91.33 243	94.25 194	97.20 209	86.41 218	98.42 232	90.04 248	89.39 258	96.69 238

v1240		94.06 231	93.29 235	96.34 216	96.03 271	93.90 216	98.44 153	98.17 188	91.18 251	94.13 202	97.01 229	86.05 225	98.42 232	89.13 265	89.50 256	96.70 233

lessismore_v0				94.45 282	94.93 299	88.44 296		91.03 334		86.77 292	97.64 184	76.23 302	98.42 232	90.31 242	85.64 300	96.51 263

EPNet_dtu		95.21 165	94.95 153	95.99 228	96.17 263	90.45 271	98.16 188	97.27 270	96.77 44	93.14 233	98.33 128	90.34 126	98.42 232	85.57 297	98.81 100	99.09 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		91.13 273	90.12 274	94.17 286	94.73 302	89.00 288	98.13 191	97.81 223	89.22 285	85.32 300	96.46 259	67.71 322	98.42 232	87.89 284	93.82 208	95.08 294

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 146	95.98 111	98.20 179	98.33 159	93.67 166	96.95 108	98.49 111	93.54 74	98.42 232	95.24 130	97.74 141	99.31 91
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
anonymousdsp		95.42 151	94.91 158	96.94 163	95.10 296	95.90 125	99.14 37	98.41 148	93.75 154	93.16 230	97.46 193	87.50 203	98.41 239	95.63 117	94.03 203	96.50 264

v1144		94.59 202	93.92 202	96.60 191	96.21 260	94.78 186	98.59 131	98.14 193	91.86 228	94.21 197	97.02 227	87.97 186	98.41 239	91.72 219	89.57 253	96.61 249

pm-mvs1		93.94 234	93.06 237	96.59 192	96.49 234	95.16 149	98.95 60	98.03 217	92.32 217	91.08 265	97.84 165	84.54 250	98.41 239	92.16 205	86.13 298	96.19 275

v10		94.29 216	93.55 225	96.51 202	96.39 239	94.80 181	98.99 55	98.19 180	91.35 242	93.02 236	96.99 230	88.09 184	98.41 239	90.50 240	88.41 273	96.33 272

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 143	95.76 129	99.47 2	98.40 150	94.98 111	98.79 30	98.83 81	92.34 86	98.41 239	96.91 69	99.59 52	99.34 88

test_djsdf		96.00 123	95.69 125	96.93 164	95.72 283	95.49 138	99.47 2	98.40 150	94.98 111	94.58 169	97.86 162	89.16 142	98.41 239	96.91 69	94.12 201	96.88 213

gg-mvs-nofinetune		92.21 256	90.58 270	97.13 151	96.75 222	95.09 152	95.85 304	89.40 336	85.43 306	94.50 172	81.98 328	80.80 280	98.40 245	92.16 205	98.33 120	97.88 174

pmmvs6		91.77 268	90.63 269	95.17 261	94.69 303	91.24 260	98.67 123	97.92 220	86.14 300	89.62 277	97.56 190	75.79 304	98.34 246	90.75 235	84.56 303	95.94 281

MVS-HIRNet		89.46 286	88.40 288	92.64 295	97.58 171	82.15 316	94.16 322	93.05 332	75.73 325	90.90 267	82.52 327	79.42 288	98.33 247	83.53 302	98.68 102	97.43 184

FC-MVSNet-test		96.42 111	96.05 110	97.53 136	96.95 210	97.27 66	99.36 8	99.23 12	95.83 71	93.93 209	98.37 121	92.00 98	98.32 248	96.02 101	92.72 227	97.00 199

v148		94.29 216	93.76 214	95.91 231	96.10 267	92.93 238	98.58 133	97.97 218	92.59 200	93.47 224	96.95 234	88.53 173	98.32 248	92.56 198	87.06 289	96.49 265

UniMVSNet_NR-MVSNet		95.71 135	95.15 144	97.40 141	96.84 217	96.97 76	98.74 106	99.24 10	95.16 103	93.88 211	97.72 177	91.68 103	98.31 250	95.81 107	87.25 287	96.92 204

DU-MVS		95.42 151	94.76 162	97.40 141	96.53 231	96.97 76	98.66 125	98.99 27	95.43 86	93.88 211	97.69 178	88.57 170	98.31 250	95.81 107	87.25 287	96.92 204

WR-MVS		95.15 167	94.46 172	97.22 145	96.67 227	96.45 97	98.21 178	98.81 59	94.15 136	93.16 230	97.69 178	87.51 201	98.30 252	95.29 127	88.62 271	96.90 211

tpm		94.13 227	93.80 209	95.12 262	96.50 233	87.91 301	97.44 248	95.89 313	92.62 198	96.37 141	96.30 264	84.13 258	98.30 252	93.24 175	91.66 239	99.14 112

OpenMVS_ROB	86.42 20	89.00 287	87.43 293	93.69 288	93.08 310	89.42 281	97.91 214	96.89 292	78.58 322	85.86 296	94.69 292	69.48 319	98.29 254	77.13 316	93.29 221	93.36 318

SixPastTwentyTwo		93.34 242	92.86 240	94.75 273	95.67 284	89.41 282	98.75 102	96.67 300	93.89 148	90.15 274	98.25 136	80.87 278	98.27 255	90.90 233	90.64 245	96.57 255

WR-MVS_H		95.05 170	94.46 172	96.81 169	96.86 216	95.82 128	99.24 20	99.24 10	93.87 150	92.53 247	96.84 245	90.37 125	98.24 256	93.24 175	87.93 277	96.38 269

pmmvs4		94.69 193	93.99 199	96.81 169	95.74 281	95.94 119	97.40 251	97.67 229	90.42 257	93.37 225	97.59 187	89.08 144	98.20 257	92.97 184	91.67 238	96.30 273

NR-MVSNet		94.98 174	94.16 186	97.44 138	96.53 231	97.22 70	98.74 106	98.95 32	94.96 113	89.25 281	97.69 178	89.32 137	98.18 258	94.59 142	87.40 284	96.92 204

Baseline_NR-MVSNet		94.35 213	93.81 208	95.96 229	96.20 261	94.05 213	98.61 130	96.67 300	91.44 236	93.85 213	97.60 186	88.57 170	98.14 259	94.39 146	86.93 290	95.68 287

CP-MVSNet		94.94 178	94.30 179	96.83 168	96.72 224	95.56 135	99.11 43	98.95 32	93.89 148	92.42 252	97.90 159	87.19 206	98.12 260	94.32 149	88.21 274	96.82 220

PS-CasMVS		94.67 197	93.99 199	96.71 173	96.68 226	95.26 147	99.13 40	99.03 23	93.68 164	92.33 253	97.95 155	85.35 235	98.10 261	93.59 168	88.16 276	96.79 221

IterMVS-LS		95.46 148	95.21 142	96.22 221	98.12 141	93.72 223	98.32 168	98.13 194	93.71 159	94.26 193	97.31 203	92.24 90	98.10 261	94.63 139	90.12 247	96.84 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs5		93.65 239	92.97 239	95.68 240	95.49 290	92.37 243	98.20 179	97.28 269	89.66 277	92.58 245	97.26 205	82.14 270	98.09 263	93.18 178	90.95 244	96.58 253

TransMVSNet (Re)		92.67 251	91.51 255	96.15 223	96.58 229	94.65 189	98.90 64	96.73 296	90.86 253	89.46 279	97.86 162	85.62 231	98.09 263	86.45 291	81.12 309	95.71 286

GG-mvs-BLEND				96.59 192	96.34 246	94.98 158	96.51 296	88.58 337		93.10 235	94.34 296	80.34 285	98.05 265	89.53 258	96.99 151	96.74 226

TranMVSNet+NR-MVSNet		95.14 168	94.48 170	97.11 153	96.45 236	96.36 101	99.03 52	99.03 23	95.04 109	93.58 218	97.93 157	88.27 178	98.03 266	94.13 154	86.90 292	96.95 203

FMVSNet3		94.97 175	94.26 180	97.11 153	98.18 138	96.62 89	98.56 138	98.26 170	93.67 166	94.09 203	97.10 213	84.25 254	98.01 267	92.08 207	92.14 230	96.70 233

FMVSNet2		94.47 208	93.61 222	97.04 156	98.21 133	96.43 98	98.79 95	98.27 166	92.46 202	93.50 223	97.09 215	81.16 274	98.00 268	91.09 228	91.93 234	96.70 233

test_0402		91.32 271	90.27 273	94.48 279	96.60 228	91.12 261	98.50 148	97.22 273	86.10 301	88.30 286	96.98 231	77.65 297	97.99 269	78.13 315	92.94 225	94.34 309

GBi-Net		94.49 206	93.80 209	96.56 197	98.21 133	95.00 155	98.82 81	98.18 183	92.46 202	94.09 203	97.07 217	81.16 274	97.95 270	92.08 207	92.14 230	96.72 229

test1		94.49 206	93.80 209	96.56 197	98.21 133	95.00 155	98.82 81	98.18 183	92.46 202	94.09 203	97.07 217	81.16 274	97.95 270	92.08 207	92.14 230	96.72 229

FMVSNet1		93.19 247	92.07 250	96.56 197	97.54 174	95.00 155	98.82 81	98.18 183	90.38 258	92.27 254	97.07 217	73.68 313	97.95 270	89.36 262	91.30 241	96.72 229

ambc				89.49 304	86.66 326	75.78 324	92.66 325	96.72 297		86.55 293	92.50 316	46.01 333	97.90 273	90.32 241	82.09 305	94.80 298

PEN-MVS		94.42 210	93.73 216	96.49 203	96.28 256	94.84 174	99.17 34	99.00 25	93.51 170	92.23 255	97.83 168	86.10 224	97.90 273	92.55 199	86.92 291	96.74 226

Patchmtry		93.22 246	92.35 247	95.84 234	96.77 219	93.09 237	94.66 318	97.56 233	87.37 295	92.90 238	96.24 265	88.15 181	97.90 273	87.37 286	90.10 248	96.53 260

PatchT		93.06 249	91.97 251	96.35 214	96.69 225	92.67 240	94.48 319	97.08 276	86.62 297	97.08 102	92.23 319	87.94 187	97.90 273	78.89 313	96.69 155	98.49 153

CR-MVSNet		94.76 187	94.15 187	96.59 192	97.00 207	93.43 228	94.96 312	97.56 233	92.46 202	96.93 110	96.24 265	88.15 181	97.88 277	87.38 285	96.65 157	98.46 154

RPMNet		92.52 253	91.17 256	96.59 192	97.00 207	93.43 228	94.96 312	97.26 271	82.27 316	96.93 110	92.12 320	86.98 210	97.88 277	76.32 318	96.65 157	98.46 154

N_pmnet		87.12 294	87.77 291	85.17 314	95.46 291	61.92 336	97.37 255	70.66 344	85.83 304	88.73 285	96.04 274	85.33 237	97.76 279	80.02 308	90.48 246	95.84 282

LCM-MVSNet-Re		95.22 164	95.32 137	94.91 266	98.18 138	87.85 302	98.75 102	95.66 314	95.11 105	88.96 283	96.85 244	90.26 129	97.65 280	95.65 116	98.44 115	99.22 103

K. test v3		92.55 252	91.91 253	94.48 279	95.64 285	89.24 283	99.07 49	94.88 320	94.04 140	86.78 291	97.59 187	77.64 298	97.64 281	92.08 207	89.43 257	96.57 255

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 42	98.36 21	98.90 64	98.85 52	97.28 21	99.72 1	99.39 7	96.63 7	97.60 282	98.17 23	99.85 2	99.64 53

DTE-MVSNet		93.98 233	93.26 236	96.14 224	96.06 269	94.39 203	99.20 31	98.86 51	93.06 184	91.78 260	97.81 170	85.87 228	97.58 283	90.53 239	86.17 296	96.46 267

ADS-MVSNet2		94.58 203	94.40 176	95.11 263	98.00 147	88.74 290	96.04 299	97.30 267	90.15 260	96.47 137	96.64 253	87.89 189	97.56 284	90.08 245	97.06 149	99.02 121

CVMVSNet		95.43 150	96.04 111	93.57 289	97.93 152	83.62 311	98.12 192	98.59 113	95.68 75	96.56 126	99.02 58	87.51 201	97.51 285	93.56 169	97.44 145	99.60 59

LP		91.12 274	89.99 276	94.53 277	96.35 245	88.70 291	93.86 323	97.35 262	84.88 308	90.98 266	94.77 291	84.40 252	97.43 286	75.41 321	91.89 236	97.47 183

semantic-postprocess				94.85 269	97.98 151	90.56 270		98.11 201	93.75 154	92.58 245	97.48 192	83.91 261	97.41 287	92.48 202	91.30 241	96.58 253

IterMVS		94.09 228	93.85 207	94.80 272	97.99 149	90.35 272	97.18 268	98.12 196	93.68 164	92.46 251	97.34 201	84.05 259	97.41 287	92.51 201	91.33 240	96.62 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UnsupCasMVSNet_bld		87.17 293	85.12 296	93.31 292	91.94 313	88.77 289	94.92 314	98.30 163	84.30 311	82.30 313	90.04 321	63.96 327	97.25 289	85.85 296	74.47 327	93.93 316

MIMVSNet		93.26 245	92.21 249	96.41 210	97.73 163	93.13 236	95.65 307	97.03 280	91.27 248	94.04 206	96.06 273	75.33 305	97.19 290	86.56 290	96.23 178	98.92 131

new_pmnet		90.06 282	89.00 285	93.22 294	94.18 304	88.32 298	96.42 297	96.89 292	86.19 299	85.67 299	93.62 298	77.18 300	97.10 291	81.61 306	89.29 259	94.23 310

testgi		93.06 249	92.45 246	94.88 268	96.43 237	89.90 274	98.75 102	97.54 238	95.60 79	91.63 262	97.91 158	74.46 311	97.02 292	86.10 293	93.67 209	97.72 179

test0.0.03 1		94.08 229	93.51 229	95.80 236	95.53 289	92.89 239	97.38 253	95.97 310	95.11 105	92.51 249	96.66 251	87.71 195	96.94 293	87.03 288	93.67 209	97.57 182

v18		92.10 258	90.97 258	95.50 244	96.34 246	94.85 165	98.82 81	97.52 239	89.99 265	85.31 302	93.26 301	88.90 152	96.92 294	88.82 271	79.77 313	94.73 299

v17		92.08 259	90.94 259	95.48 246	96.34 246	94.83 176	98.81 87	97.52 239	89.95 267	85.32 300	93.24 302	88.91 151	96.91 295	88.76 272	79.63 314	94.71 301

v16		92.08 259	90.94 259	95.49 245	96.38 242	94.84 174	98.81 87	97.51 242	89.94 268	85.25 303	93.28 300	88.86 153	96.91 295	88.70 273	79.78 312	94.72 300

v15		91.94 261	90.77 263	95.43 251	96.31 254	94.83 176	98.77 98	97.50 245	89.92 269	85.13 304	93.08 305	88.76 164	96.86 297	88.40 276	79.10 316	94.61 305

V9		91.91 263	90.73 265	95.45 248	96.32 253	94.80 181	98.77 98	97.50 245	89.81 272	85.03 307	93.08 305	88.76 164	96.86 297	88.24 278	79.03 319	94.69 302

v13		91.88 265	90.69 267	95.43 251	96.33 250	94.78 186	98.75 102	97.50 245	89.68 276	84.93 309	92.98 309	88.84 156	96.83 299	88.14 280	79.09 317	94.69 302

V14		91.93 262	90.76 264	95.42 254	96.33 250	94.81 180	98.77 98	97.51 242	89.86 271	85.09 305	93.13 303	88.80 162	96.83 299	88.32 277	79.06 318	94.60 306

v12		91.89 264	90.70 266	95.43 251	96.31 254	94.80 181	98.76 101	97.50 245	89.76 273	84.95 308	93.00 308	88.82 158	96.82 301	88.23 279	79.00 320	94.68 304

v11		91.85 266	90.68 268	95.36 256	96.34 246	94.74 188	98.80 90	97.43 256	89.60 279	85.09 305	93.03 307	88.53 173	96.75 302	87.37 286	79.96 311	94.58 307

pmmvs-eth3d		90.36 281	89.05 284	94.32 283	91.10 316	92.12 245	97.63 241	96.95 288	88.86 287	84.91 310	93.13 303	78.32 292	96.74 303	88.70 273	81.81 308	94.09 313

PM-MVS		87.77 292	86.55 294	91.40 301	91.03 317	83.36 313	96.92 275	95.18 318	91.28 247	86.48 294	93.42 299	53.27 330	96.74 303	89.43 261	81.97 307	94.11 312

UnsupCasMVSNet_eth		90.99 276	89.92 277	94.19 285	94.08 306	89.83 275	97.13 270	98.67 102	93.69 162	85.83 297	96.19 270	75.15 306	96.74 303	89.14 264	79.41 315	96.00 279

MDA-MVSNet_test_wron		90.71 278	89.38 281	94.68 274	94.83 300	90.78 265	97.19 267	97.46 252	87.60 293	72.41 326	95.72 282	86.51 216	96.71 306	85.92 295	86.80 293	96.56 257

YYNet1		90.70 279	89.39 280	94.62 276	94.79 301	90.65 268	97.20 266	97.46 252	87.54 294	72.54 325	95.74 279	86.51 216	96.66 307	86.00 294	86.76 294	96.54 259

MDA-MVSNet-bldmvs		89.97 283	88.35 289	94.83 271	95.21 295	91.34 257	97.64 239	97.51 242	88.36 290	71.17 327	96.13 272	79.22 289	96.63 308	83.65 301	86.27 295	96.52 261

Anonymous20231206		91.66 269	91.10 257	93.33 291	94.02 307	87.35 304	98.58 133	97.26 271	90.48 254	90.16 273	96.31 263	83.83 264	96.53 309	79.36 311	89.90 250	96.12 276

Patchmatch-RL test		91.49 270	90.85 262	93.41 290	91.37 315	84.40 309	92.81 324	95.93 312	91.87 227	87.25 289	94.87 290	88.99 145	96.53 309	92.54 200	82.00 306	99.30 94

EU-MVSNet		93.66 238	94.14 188	92.25 298	95.96 273	83.38 312	98.52 143	98.12 196	94.69 119	92.61 244	98.13 143	87.36 205	96.39 311	91.82 216	90.00 249	96.98 200

Anonymous20231211		83.69 298	81.50 300	90.26 302	89.23 321	80.10 319	97.97 207	97.06 279	72.79 327	82.05 315	92.57 315	50.28 331	96.32 312	76.15 319	75.38 325	94.37 308

testpf		88.74 289	89.09 282	87.69 307	95.78 280	83.16 314	84.05 334	94.13 329	85.22 307	90.30 272	94.39 295	74.92 308	95.80 313	89.77 251	93.28 222	84.10 328

DSMNet-mixed		92.52 253	92.58 244	92.33 297	94.15 305	82.65 315	98.30 171	94.26 326	89.08 286	92.65 243	95.73 280	85.01 240	95.76 314	86.24 292	97.76 140	98.59 149

DeepMVS_CX				86.78 310	97.09 205	72.30 329		95.17 319	75.92 324	84.34 311	95.19 286	70.58 318	95.35 315	79.98 310	89.04 262	92.68 319

FMVSNet5		91.81 267	90.92 261	94.49 278	97.21 196	92.09 246	98.00 205	97.55 237	89.31 284	90.86 268	95.61 285	74.48 310	95.32 316	85.57 297	89.70 251	96.07 278

pmmvs3		86.67 295	84.86 297	92.11 299	88.16 322	87.19 306	96.63 289	94.75 322	79.88 321	87.22 290	92.75 314	66.56 324	95.20 317	81.24 307	76.56 324	93.96 315

new-patchmatchnet		88.50 291	87.45 292	91.67 300	90.31 318	85.89 308	97.16 269	97.33 266	89.47 280	83.63 312	92.77 313	76.38 301	95.06 318	82.70 303	77.29 322	94.06 314

MIMVSNet1		89.67 285	88.28 290	93.82 287	92.81 312	91.08 262	98.01 203	97.45 254	87.95 291	87.90 288	95.87 278	67.63 323	94.56 319	78.73 314	88.18 275	95.83 283

test20.03		90.89 277	90.38 271	92.43 296	93.48 308	88.14 299	98.33 164	97.56 233	93.40 174	87.96 287	96.71 250	80.69 281	94.13 320	79.15 312	86.17 296	95.01 297

1111		84.94 297	84.30 298	86.86 309	87.59 323	75.10 325	96.63 289	96.43 305	82.53 314	80.75 318	92.91 311	68.94 320	93.79 321	68.24 327	84.66 302	91.70 320

.test1245		73.05 306	76.31 304	63.27 327	87.59 323	75.10 325	96.63 289	96.43 305	82.53 314	80.75 318	92.91 311	68.94 320	93.79 321	68.24 327	12.72 339	20.91 337

testus		88.91 288	89.08 283	88.40 306	91.39 314	76.05 323	96.56 292	96.48 304	89.38 283	89.39 280	95.17 288	70.94 317	93.56 323	77.04 317	95.41 188	95.61 288

no-one		74.41 305	70.76 307	85.35 313	79.88 332	76.83 321	94.68 317	94.22 327	80.33 320	63.81 330	79.73 331	35.45 339	93.36 324	71.78 323	36.99 336	85.86 327

Gipuma		78.40 302	76.75 303	83.38 316	95.54 288	80.43 318	79.42 335	97.40 259	64.67 329	73.46 324	80.82 330	45.65 334	93.14 325	66.32 329	87.43 283	76.56 333
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		78.70 301	76.24 305	86.08 311	77.26 337	71.99 330	94.34 320	96.72 297	61.62 331	76.53 322	89.33 322	33.91 340	92.78 326	81.85 305	74.60 326	93.46 317

test2356		88.68 290	88.61 286	88.87 305	89.90 320	78.23 320	95.11 310	96.66 302	88.66 289	89.06 282	94.33 297	73.14 315	92.56 327	75.56 320	95.11 190	95.81 284

test1235678		86.26 296	85.81 295	87.62 308	86.97 325	75.00 327	96.55 294	96.32 307	86.08 302	81.32 317	92.98 309	73.10 316	92.05 328	71.64 324	87.32 285	95.81 284

PMMVS2		77.95 303	75.44 306	85.46 312	82.54 329	74.95 328	94.23 321	93.08 331	72.80 326	74.68 323	87.38 323	36.36 338	91.56 329	73.95 322	63.94 329	89.87 321

test12356		83.47 299	83.37 299	83.78 315	84.43 328	70.09 332	95.12 309	95.60 315	82.98 312	78.89 320	92.43 318	64.99 325	91.41 330	70.36 325	85.55 301	89.82 322

testmv		78.74 300	77.35 301	82.89 317	78.16 336	69.30 333	95.87 303	94.65 323	81.11 318	70.98 328	87.11 325	46.31 332	90.42 331	65.28 330	76.72 323	88.95 323

PMVS	61.03 23	65.95 310	63.57 312	73.09 324	57.90 341	51.22 342	85.05 333	93.93 330	54.45 333	44.32 337	83.57 326	13.22 343	89.15 332	58.68 334	81.00 310	78.91 332
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		77.62 304	77.14 302	79.05 319	79.25 333	60.97 337	95.79 305	95.94 311	65.96 328	67.93 329	94.40 294	37.73 337	88.88 333	68.83 326	88.46 272	87.29 324

wuykxyi23d		63.73 313	58.86 315	78.35 320	67.62 339	67.90 334	86.56 331	87.81 339	58.26 332	42.49 338	70.28 336	11.55 345	85.05 334	63.66 331	41.50 332	82.11 330

PNet_i23d		67.70 309	65.07 310	75.60 321	78.61 334	59.61 339	89.14 329	88.24 338	61.83 330	52.37 334	80.89 329	18.91 342	84.91 335	62.70 332	52.93 331	82.28 329

ANet_high		69.08 307	65.37 309	80.22 318	65.99 340	71.96 331	90.91 328	90.09 335	82.62 313	49.93 336	78.39 332	29.36 341	81.75 336	62.49 333	38.52 335	86.95 326

MVE	62.14 22	63.28 314	59.38 314	74.99 322	74.33 338	65.47 335	85.55 332	80.50 343	52.02 335	51.10 335	75.00 335	10.91 347	80.50 337	51.60 335	53.40 330	78.99 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		64.94 311	64.25 311	67.02 325	82.28 330	59.36 340	91.83 327	85.63 340	52.69 334	60.22 332	77.28 333	41.06 336	80.12 338	46.15 336	41.14 333	61.57 335

EMVS		64.07 312	63.26 313	66.53 326	81.73 331	58.81 341	91.85 326	84.75 341	51.93 336	59.09 333	75.13 334	43.32 335	79.09 339	42.03 337	39.47 334	61.69 334

tmp_tt		68.90 308	66.97 308	74.68 323	50.78 342	59.95 338	87.13 330	83.47 342	38.80 337	62.21 331	96.23 267	64.70 326	76.91 340	88.91 270	30.49 337	87.19 325

wuyk23d		30.17 316	30.18 318	30.16 329	78.61 334	43.29 343	66.79 336	14.21 345	17.31 338	14.82 341	11.93 342	11.55 345	41.43 341	37.08 338	19.30 338	5.76 339

test123		20.95 319	23.72 320	12.64 330	13.54 344	8.19 344	96.55 294	6.13 347	7.48 340	16.74 340	37.98 339	12.97 344	6.05 342	16.69 339	5.43 341	23.68 336

testmvs		21.48 318	24.95 319	11.09 331	14.89 343	6.47 345	96.56 292	9.87 346	7.55 339	17.93 339	39.02 338	9.43 348	5.90 343	16.56 340	12.72 339	20.91 337

cdsmvs_eth3d_5k		23.98 317	31.98 317	0.00 332	0.00 345	0.00 346	0.00 337	98.59 113	0.00 341	0.00 342	98.61 100	90.60 123	0.00 344	0.00 341	0.00 342	0.00 340

pcd_1.5k_mvsjas		7.88 321	10.50 322	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	94.51 60	0.00 344	0.00 341	0.00 342	0.00 340

pcd1.5k->3k		39.42 315	41.78 316	32.35 328	96.17 263	0.00 346	0.00 337	98.54 123	0.00 341	0.00 342	0.00 343	87.78 194	0.00 344	0.00 341	93.56 213	97.06 195

sosnet-low-res		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sosnet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uncertanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

Regformer		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

ab-mvs-re		8.20 320	10.94 321	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	98.43 115	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

ESAPD								98.84 53

sam_mvs1												89.45 134

sam_mvs												88.99 145

MTGPA								98.74 77

MTMP								94.14 328

test9_res														96.39 94	99.57 55	99.69 35

agg_prior2														95.87 106	99.57 55	99.68 41

test_prior4						98.01 41	97.86 222

test_prior2							97.80 227		96.12 63	97.89 75	98.69 92	95.96 25		96.89 71	99.60 49

新几何2							97.64 239

旧先验1					99.29 55	97.48 59		98.70 91			99.09 52	95.56 35			99.47 69	99.61 56

原ACMM2							97.67 237

test222					99.23 70	97.17 72	97.40 251	98.66 105	88.68 288	98.05 60	98.96 69	94.14 69			99.53 65	99.61 56

segment_acmp												96.85 3

testdata1							97.32 261		96.34 57

plane_prior7					97.42 183	94.63 191

plane_prior6					97.35 188	94.61 194						87.09 207

plane_prior4											98.28 131

plane_prior3						94.61 194			97.02 39	95.34 153

plane_prior2							98.80 90		97.28 21

plane_prior1					97.37 187

plane_prior						94.60 196	98.44 153		96.74 46						94.22 195

n2								0.00 348

nn								0.00 348

door-mid								94.37 325

test11								98.66 105

door								94.64 324

HQP5-MVS						94.25 209

HQP-NCC					97.20 197		98.05 199		96.43 54	94.45 174

ACMP_Plane					97.20 197		98.05 199		96.43 54	94.45 174

BP-MVS														95.30 125

HQP3-MVS								98.46 140							94.18 197

HQP2-MVS												86.75 213

NP-MVS					97.28 191	94.51 199					97.73 174

MDTV_nov1_ep13_2view						84.26 310	96.89 281		90.97 252	97.90 74		89.89 132		93.91 159		99.18 108

ACMMP++_ref															92.97 224

ACMMP++															93.61 212

Test By Simon												94.64 57

High-resolution multi-view benchmark