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.

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.
CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 45	98.87 5	98.47 148	98.81 58	97.72 4	98.76 33	99.16 42	97.05 2	99.78 74	98.06 25	99.66 42	99.69 35

segment_acmp												96.85 3

MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 22	98.87 5	98.41 154	98.68 94	97.04 38	98.52 44	98.80 84	96.78 4	99.83 43	97.93 28	99.61 48	99.74 25

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

NCCC		98.61 13	98.35 20	99.38 10	99.28 60	98.61 10	98.45 149	98.76 72	97.82 3	98.45 48	98.93 73	96.65 6	99.83 43	97.38 57	99.41 76	99.71 32

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	97.60 278	98.17 23	99.85 2	99.64 53

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 78	98.12 37	99.04 51	99.09 18	93.32 173	98.83 29	99.10 48	96.54 8	99.83 43	97.70 43	99.76 23	99.59 61

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 63	96.80 83	98.71 111	99.05 22	97.28 21	98.84 27	99.28 25	96.47 9	99.40 125	98.52 14	99.70 37	99.47 77

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 27	99.14 37	98.66 104	96.84 43	99.56 2	99.31 21	96.34 10	99.70 91	98.32 20	99.73 34	99.73 27

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 71	97.25 69	98.11 190	98.29 164	97.19 30	98.99 20	99.02 58	96.22 11	99.67 96	98.52 14	98.56 110	99.51 69

TEST9					99.31 47	98.50 12	97.92 207	98.73 81	92.63 193	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	97.92 207	98.73 81	92.98 184	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	97.89 215	98.72 83	92.98 184	97.70 84	98.66 97	96.20 12	99.80 57

agg_prior1		97.95 46	97.51 55	99.28 20	99.30 52	98.38 17	97.81 222	98.72 83	93.16 178	97.57 93	98.66 97	96.14 15	99.81 50	96.63 84	99.56 61	99.66 48

Regformer-2		98.69 7	98.52 8	99.19 28	99.35 37	98.01 41	98.37 157	98.81 58	97.48 11	99.21 9	99.21 32	96.13 16	99.80 57	98.40 18	99.73 34	99.75 20

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 214	99.00 86	89.54 275	97.43 246	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

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 55	98.23 28	97.92 207	98.72 83	92.38 210	97.59 92	98.64 99	96.09 18	99.79 69	96.59 85	99.57 55	99.68 41

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 37	97.81 50	98.37 157	98.76 72	97.49 10	99.20 10	99.21 32	96.08 19	99.79 69	98.42 16	99.73 34	99.75 20

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

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

HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 28	99.26 17	98.58 117	97.52 7	99.41 3	98.78 85	96.00 23	99.79 69	97.79 38	99.59 52	99.69 35

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 71	97.32 64	97.91 210	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

test_prior3		98.22 42	97.90 43	99.19 28	99.31 47	98.22 30	97.80 223	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_prior2							97.80 223		96.12 63	97.89 75	98.69 92	95.96 25		96.89 71	99.60 49

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 31	98.44 14	97.91 210	98.67 101	92.57 197	98.77 32	98.85 79	95.93 27	99.72 86	95.56 118	99.69 38	99.68 41

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

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		94.06 227	92.30 244	99.34 13	99.70 15	98.35 22	99.29 14	98.88 46	97.40 14	98.46 45	43.50 333	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 35	97.37 63	98.40 155	98.79 66	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-3		98.59 16	98.50 11	98.86 57	99.43 35	97.05 74	98.40 155	98.68 94	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++		98.58 18	98.25 29	99.55 1	99.50 27	99.08 1	98.72 110	98.66 104	97.51 8	98.15 55	98.83 81	95.70 33	99.92 13	97.53 52	99.67 39	99.66 48

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

旧先验1					99.29 55	97.48 59		98.70 90			99.09 52	95.56 35			99.47 69	99.61 56

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

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 26	98.72 7	98.80 89	98.82 55	94.52 124	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
mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 39	99.28 16	98.81 58	96.24 58	98.35 52	99.23 29	95.46 38	99.94 3	97.42 55	99.81 8	99.77 14

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 32	96.49 96	98.30 168	98.69 91	97.21 28	98.84 27	99.36 16	95.41 39	99.78 74	98.62 6	99.65 43	99.80 3

ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 21	98.95 3	98.82 80	98.81 58	95.80 72	99.16 12	99.47 4	95.37 40	99.92 13	97.89 32	99.75 29	99.79 4

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 149	99.22 27	99.32 7	93.04 181	97.02 107	98.92 75	95.36 41	99.91 22	97.43 54	99.64 45	99.52 66

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 25	98.35 22	98.33 161	98.89 43	92.62 194	98.05 60	98.94 72	95.34 42	99.65 98	96.04 100	99.42 75	99.19 104

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

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 64	98.04 39	98.50 145	98.78 68	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
MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 4	98.43 152	98.78 68	94.10 134	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
EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 33	96.32 103	98.28 170	98.68 94	97.17 31	98.74 34	99.37 12	95.25 45	99.79 69	98.57 8	99.54 64	99.73 27

原ACMM1				98.65 65	99.32 45	96.62 89		98.67 101	93.27 176	97.81 77	98.97 65	95.18 47	99.83 43	93.84 161	99.46 72	99.50 71

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 46	99.44 4	98.82 55	94.46 128	98.94 21	99.20 35	95.16 48	99.74 85	97.58 47	99.85 2	99.77 14

test12				99.18 32	99.16 76	98.19 32		98.53 125		98.07 59		95.13 49	99.72 86		99.56 61	99.63 55

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 49	99.53 1	98.80 65	94.63 121	98.61 40	98.97 65	95.13 49	99.77 79	97.65 44	99.83 7	99.79 4

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 61	96.90 80	97.95 205	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

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 86	97.66 52	97.75 227	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
PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 55	96.31 105	98.14 185	98.76 72	92.41 208	96.39 136	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
MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 8	98.55 137	98.74 76	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 76	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

1121		97.37 77	96.77 87	99.16 35	99.34 39	97.99 44	98.19 179	98.68 94	90.14 258	98.01 66	98.97 65	94.80 56	99.87 35	93.36 172	99.46 72	99.61 56

Test By Simon												94.64 57

新几何1				99.16 35	99.34 39	98.01 41		98.69 91	90.06 260	98.13 56	98.95 71	94.60 58	99.89 27	91.97 213	99.47 69	99.59 61

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 33	99.22 27	98.79 66	96.13 62	97.92 73	99.23 29	94.54 59	99.94 3	96.74 81	99.78 14	99.73 27

pcd_1.5k_mvsjas		7.88 317	10.50 318	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	94.51 60	0.00 340	0.00 337	0.00 338	0.00 336

PS-MVSNAJss		96.43 110	96.26 105	96.92 162	95.84 275	95.08 153	99.16 35	98.50 134	95.87 70	93.84 210	98.34 127	94.51 60	98.61 198	96.88 74	93.45 212	97.06 191

PS-MVSNAJ		97.73 55	97.77 44	97.62 126	98.68 106	95.58 133	97.34 255	98.51 129	97.29 20	98.66 37	97.88 161	94.51 60	99.90 25	97.87 33	99.17 86	97.39 184

API-MVS		97.41 74	97.25 65	97.91 109	98.70 103	96.80 83	98.82 80	98.69 91	94.53 123	98.11 57	98.28 131	94.50 63	99.57 112	94.12 155	99.49 67	97.37 185

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
testdata				98.26 89	99.20 74	95.36 142		98.68 94	91.89 221	98.60 41	99.10 48	94.44 65	99.82 48	94.27 151	99.44 74	99.58 63

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 133	98.61 112	95.46 139	97.44 244	98.46 139	97.15 32	98.65 38	98.15 141	94.33 66	99.80 57	97.84 36	98.66 106	97.41 182

PAPR		96.84 97	96.24 106	98.65 65	98.72 102	96.92 79	97.36 253	98.57 118	93.33 172	96.67 123	97.57 189	94.30 67	99.56 114	91.05 232	98.59 108	99.47 77

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 27	96.41 99	98.63 124	98.60 111	95.18 98	97.06 105	98.06 147	94.26 68	99.57 112	93.80 163	98.87 96	99.52 66

test222					99.23 70	97.17 72	97.40 247	98.66 104	88.68 284	98.05 60	98.96 69	94.14 69			99.53 65	99.61 56

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 109	95.38 141	99.33 13	98.31 159	93.61 164	97.19 99	99.07 55	94.05 70	99.23 135	96.89 71	98.43 117	99.37 87

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 33	94.95 158	98.55 137	98.62 110	93.02 182	96.17 140	98.58 105	94.01 71	99.81 50	93.95 158	98.90 93	99.14 112

TAPA-MVS	93.98 7	95.35 155	94.56 164	97.74 118	99.13 79	94.83 172	98.33 161	98.64 109	86.62 293	96.29 138	98.61 100	94.00 72	99.29 131	80.00 305	99.41 76	99.09 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MG-MVS		97.81 53	97.60 49	98.44 80	99.12 80	95.97 115	97.75 227	98.78 68	96.89 42	98.46 45	99.22 31	93.90 73	99.68 95	94.81 137	99.52 66	99.67 46

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 142	95.98 111	98.20 176	98.33 158	93.67 162	96.95 108	98.49 111	93.54 74	98.42 228	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
TAMVS		97.02 90	96.79 84	97.70 122	98.06 141	95.31 146	98.52 140	98.31 159	93.95 142	97.05 106	98.61 100	93.49 75	98.52 211	95.33 124	97.81 137	99.29 96

abl_6		98.30 40	98.03 38	99.13 38	99.56 24	97.76 51	99.13 40	98.82 55	96.14 61	99.26 6	99.37 12	93.33 76	99.93 9	96.96 67	99.67 39	99.69 35

CNLPA		97.45 70	97.03 75	98.73 60	99.05 81	97.44 62	98.07 194	98.53 125	95.32 93	96.80 120	98.53 107	93.32 77	99.72 86	94.31 150	99.31 82	99.02 121

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 42	95.92 122	98.28 170	98.59 112	95.52 83	97.97 69	99.10 48	93.28 78	99.49 119	95.09 132	98.88 94	99.19 104

UA-Net		97.96 45	97.62 48	98.98 49	98.86 93	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

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 73	99.12 42	98.81 58	92.34 211	98.09 58	99.08 54	93.01 80	99.92 13	96.06 99	99.77 17	99.75 20

114514_t		96.93 93	96.27 104	98.92 53	99.50 27	97.63 54	98.85 75	98.90 41	84.80 305	97.77 78	99.11 46	92.84 81	99.66 97	94.85 135	99.77 17	99.47 77

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

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 148	99.25 64	95.35 144	98.26 172	99.26 8	94.28 130	97.94 71	97.46 193	92.74 83	99.81 50	96.88 74	93.32 215	96.20 270

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 64	95.35 144	97.28 259	99.26 8	93.13 179	97.94 71	98.21 138	92.74 83	99.81 50	96.88 74	99.40 78	99.27 98

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 123	95.97 115	98.74 105	98.07 210	94.27 131	98.44 49	98.07 146	92.48 85	99.26 132	96.43 92	98.19 125	99.16 109

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 139	95.76 129	99.47 2	98.40 149	94.98 107	98.79 30	98.83 81	92.34 86	98.41 235	96.91 69	99.59 52	99.34 88

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 139	95.76 129	97.68 232	97.76 224	94.50 125	98.79 30	98.61 100	92.34 86	99.30 130	97.58 47	99.59 52	99.31 91

CHOSEN 280x420		97.18 84	97.18 69	97.20 142	98.81 97	93.27 227	95.78 302	99.15 17	95.25 96	96.79 121	98.11 144	92.29 88	99.07 154	98.56 9	99.85 2	99.25 100

canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 103	98.38 17	99.34 11	98.39 151	96.76 45	97.67 86	97.40 196	92.26 89	99.49 119	98.28 22	96.28 171	99.08 118

IterMVS-LS		95.46 147	95.21 142	96.22 217	98.12 137	93.72 219	98.32 165	98.13 193	93.71 155	94.26 189	97.31 203	92.24 90	98.10 257	94.63 139	90.12 243	96.84 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		95.96 124	95.83 117	96.36 209	97.93 148	93.70 220	98.12 188	98.27 165	93.70 157	95.07 153	99.02 58	92.23 91	98.54 204	94.68 138	93.46 210	96.84 213

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 93	96.89 82	98.31 166	98.71 88	95.26 95	97.67 86	98.56 106	92.21 92	99.78 74	95.89 104	96.85 153	99.48 76

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 132	96.72 87	97.37 251	98.43 146	93.71 155	97.65 89	98.02 149	92.20 93	99.25 133	96.87 77	97.79 138	99.19 104

1112_ss		96.63 102	96.00 113	98.50 75	98.56 114	96.37 100	98.18 183	98.10 205	92.92 186	94.84 158	98.43 115	92.14 94	99.58 111	94.35 148	96.51 162	99.56 65

LS3D		97.16 85	96.66 92	98.68 63	98.53 117	97.19 71	98.93 62	98.90 41	92.83 191	95.99 145	99.37 12	92.12 95	99.87 35	93.67 166	99.57 55	98.97 126

nrg030		96.28 118	95.72 120	97.96 108	96.90 210	98.15 35	99.39 5	98.31 159	95.47 84	94.42 178	98.35 123	92.09 96	98.69 192	97.50 53	89.05 257	97.04 193

mvs_anonymous		96.70 101	96.53 97	97.18 144	98.19 132	93.78 215	98.31 166	98.19 179	94.01 137	94.47 169	98.27 134	92.08 97	98.46 220	97.39 56	97.91 132	99.31 91

FC-MVSNet-test		96.42 111	96.05 110	97.53 134	96.95 206	97.27 66	99.36 8	99.23 12	95.83 71	93.93 205	98.37 121	92.00 98	98.32 244	96.02 101	92.72 223	97.00 195

FIs		96.51 108	96.12 109	97.67 125	97.13 199	97.54 58	99.36 8	99.22 14	95.89 69	94.03 203	98.35 123	91.98 99	98.44 225	96.40 93	92.76 222	97.01 194

sss		97.39 75	96.98 77	98.61 67	98.60 113	96.61 91	98.22 174	98.93 35	93.97 141	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	98.70 114	98.39 151	89.45 277	94.52 167	99.35 18	91.85 101	99.85 40	92.89 191	98.88 94	99.68 41

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 114	95.94 119	97.71 229	98.07 210	92.10 217	94.79 162	97.29 204	91.75 102	99.56 114	94.17 153	96.50 163	99.58 63

UniMVSNet_NR-MVSNet		95.71 135	95.15 144	97.40 138	96.84 213	96.97 76	98.74 105	99.24 10	95.16 99	93.88 207	97.72 177	91.68 103	98.31 246	95.81 107	87.25 283	96.92 200

UniMVSNet (Re)		95.78 132	95.19 143	97.58 132	96.99 205	97.47 60	98.79 94	99.18 15	95.60 79	93.92 206	97.04 225	91.68 103	98.48 215	95.80 109	87.66 278	96.79 217

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 118	97.00 75	98.14 185	98.21 175	93.95 142	96.72 122	97.99 153	91.58 105	99.76 81	94.51 145	96.54 161	98.95 130

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 119	95.98 111	97.86 218	98.51 129	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 185

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 119	95.98 111	97.86 218	98.51 129	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 185

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 119	95.98 111	97.86 218	98.51 129	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 185

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 105	96.90 80	98.66 122	98.68 94	92.40 209	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
CANet		98.05 43	97.76 45	98.90 55	98.73 100	97.27 66	98.35 159	98.78 68	97.37 19	97.72 83	98.96 69	91.53 110	99.92 13	98.79 3	99.65 43	99.51 69

EPNet		97.28 80	96.87 81	98.51 74	94.98 293	96.14 108	98.90 64	97.02 279	98.28 1	95.99 145	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
1314		96.25 120	95.73 119	97.79 116	97.13 199	95.55 137	98.19 179	98.59 112	93.47 168	92.03 255	97.82 169	91.33 112	99.49 119	94.62 140	98.44 115	98.32 164

PAPM		94.95 172	94.00 193	97.78 117	97.04 202	95.65 131	96.03 297	98.25 170	91.23 245	94.19 194	97.80 171	91.27 113	98.86 182	82.61 300	97.61 143	98.84 135

jason		97.32 79	97.08 73	98.06 104	97.45 178	95.59 132	97.87 217	97.91 220	94.79 114	98.55 43	98.83 81	91.12 114	99.23 135	97.58 47	99.60 49	99.34 88
jason: jason.
IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 95	96.36 101	99.19 33	97.97 217	95.39 88	97.23 98	98.99 64	91.11 115	98.93 172	94.60 141	98.59 108	99.47 77

PMMVS		96.60 103	96.33 102	97.41 137	97.90 150	93.93 211	97.35 254	98.41 147	92.84 190	97.76 79	97.45 195	91.10 116	99.20 137	96.26 95	97.91 132	99.11 114

MVS		94.67 193	93.54 222	98.08 101	96.88 211	96.56 93	98.19 179	98.50 134	78.05 319	92.69 238	98.02 149	91.07 117	99.63 103	90.09 244	98.36 119	98.04 169

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 125	95.97 115	98.58 130	98.25 170	91.74 225	95.29 152	97.23 207	91.03 118	99.15 140	92.90 189	97.96 131	98.97 126

Effi-MVS+-dtu		96.29 116	96.56 94	95.51 239	97.89 151	90.22 269	98.80 89	98.10 205	96.57 52	96.45 135	96.66 250	90.81 119	98.91 174	95.72 111	97.99 130	97.40 183

mvs-test1		96.60 103	96.68 91	96.37 208	97.89 151	91.81 246	98.56 135	98.10 205	96.57 52	96.52 128	97.94 156	90.81 119	99.45 124	95.72 111	98.01 129	97.86 173

alignmvs		97.56 65	97.07 74	99.01 46	98.66 107	98.37 20	98.83 78	98.06 212	96.74 46	98.00 68	97.65 182	90.80 121	99.48 123	98.37 19	96.56 160	99.19 104

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 76	96.69 88	98.01 199	98.89 43	94.44 129	96.83 116	98.68 94	90.69 122	99.76 81	94.36 147	99.29 83	98.98 125

cdsmvs_eth3d_5k		23.98 313	31.98 313	0.00 328	0.00 341	0.00 342	0.00 333	98.59 112	0.00 337	0.00 338	98.61 100	90.60 123	0.00 340	0.00 337	0.00 338	0.00 336

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 64	96.93 78	98.83 78	98.75 75	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
WR-MVS_H		95.05 166	94.46 168	96.81 165	96.86 212	95.82 128	99.24 20	99.24 10	93.87 146	92.53 243	96.84 244	90.37 125	98.24 252	93.24 175	87.93 273	96.38 265

EPNet_dtu		95.21 161	94.95 153	95.99 224	96.17 259	90.45 267	98.16 184	97.27 269	96.77 44	93.14 229	98.33 128	90.34 126	98.42 228	85.57 293	98.81 100	99.09 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VNet		97.79 54	97.40 61	98.96 51	98.88 91	97.55 57	98.63 124	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

MSDG		95.93 126	95.30 139	97.83 113	98.90 88	95.36 142	96.83 281	98.37 154	91.32 240	94.43 177	98.73 91	90.27 128	99.60 106	90.05 247	98.82 99	98.52 151

LCM-MVSNet-Re		95.22 160	95.32 137	94.91 262	98.18 134	87.85 298	98.75 101	95.66 309	95.11 101	88.96 279	96.85 243	90.26 129	97.65 276	95.65 116	98.44 115	99.22 103

diffmvs		96.32 115	95.74 118	98.07 103	98.26 126	96.14 108	98.53 139	98.23 173	90.10 259	96.88 115	97.73 174	90.16 130	99.15 140	93.90 160	97.85 136	98.91 132

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 100	95.46 139	99.20 31	98.30 162	94.96 109	96.60 124	98.87 78	90.05 131	98.59 200	93.67 166	98.60 107	99.46 81

MDTV_nov1_ep13_2view						84.26 306	96.89 277		90.97 248	97.90 74		89.89 132		93.91 159		99.18 108

tpmrst		95.63 139	95.69 125	95.44 245	97.54 170	88.54 291	96.97 269	97.56 232	93.50 167	97.52 95	96.93 240	89.49 133	99.16 139	95.25 129	96.42 164	98.64 147

sam_mvs1												89.45 134

patchmatchnet-post											95.10 285	89.42 135	98.89 178

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 154	98.52 11	99.37 7	98.71 88	97.09 37	92.99 233	99.13 44	89.36 136	99.89 27	96.97 65	99.57 55	99.71 32

NR-MVSNet		94.98 170	94.16 182	97.44 136	96.53 227	97.22 70	98.74 105	98.95 32	94.96 109	89.25 277	97.69 178	89.32 137	98.18 254	94.59 142	87.40 280	96.92 200

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 42	95.56 135	97.38 249	99.65 2	92.34 211	97.61 90	98.20 139	89.29 138	99.10 151	96.97 65	97.60 144	99.77 14

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 156	97.64 53	99.35 10	99.06 20	97.02 39	93.75 212	99.16 42	89.25 139	99.92 13	97.22 59	99.75 29	99.64 53

PatchmatchNet		95.71 135	95.52 128	96.29 215	97.58 167	90.72 262	96.84 280	97.52 238	94.06 135	97.08 102	96.96 233	89.24 140	98.90 177	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.
MDTV_nov1_ep13			95.40 129		97.48 173	88.34 293	96.85 279	97.29 267	93.74 152	97.48 96	97.26 205	89.18 141	99.05 155	91.92 215	97.43 146

test_djsdf		96.00 123	95.69 125	96.93 160	95.72 279	95.49 138	99.47 2	98.40 149	94.98 107	94.58 165	97.86 162	89.16 142	98.41 235	96.91 69	94.12 197	96.88 209

QAPM		96.29 116	95.40 129	98.96 51	97.85 153	97.60 56	99.23 21	98.93 35	89.76 269	93.11 230	99.02 58	89.11 143	99.93 9	91.99 212	99.62 47	99.34 88

pmmvs4		94.69 189	93.99 195	96.81 165	95.74 277	95.94 119	97.40 247	97.67 228	90.42 253	93.37 221	97.59 187	89.08 144	98.20 253	92.97 184	91.67 234	96.30 269

sam_mvs												88.99 145

Patchmatch-test		94.42 206	93.68 215	96.63 183	97.60 165	91.76 248	94.83 312	97.49 250	89.45 277	94.14 197	97.10 213	88.99 145	98.83 185	85.37 295	98.13 127	99.29 96

Patchmatch-RL test		91.49 266	90.85 258	93.41 286	91.37 311	84.40 305	92.81 320	95.93 307	91.87 223	87.25 285	94.87 286	88.99 145	96.53 305	92.54 200	82.00 302	99.30 94

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 227	97.74 158	91.74 250	98.69 115	98.15 190	95.56 81	94.92 156	97.68 181	88.98 148	98.79 189	93.19 177	97.78 139	97.20 189

BH-untuned		95.95 125	95.72 120	96.65 180	98.55 116	92.26 240	98.23 173	97.79 223	93.73 153	94.62 164	98.01 151	88.97 149	99.00 163	93.04 182	98.51 111	98.68 143

XVG-OURS		96.55 107	96.41 99	96.99 154	98.75 99	93.76 216	97.50 243	98.52 127	95.67 76	96.83 116	99.30 24	88.95 150	99.53 117	95.88 105	96.26 172	97.69 177

v17		92.08 255	90.94 255	95.48 242	96.34 242	94.83 172	98.81 86	97.52 238	89.95 263	85.32 296	93.24 298	88.91 151	96.91 291	88.76 272	79.63 310	94.71 297

v18		92.10 254	90.97 254	95.50 240	96.34 242	94.85 161	98.82 80	97.52 238	89.99 261	85.31 298	93.26 297	88.90 152	96.92 290	88.82 271	79.77 309	94.73 295

v16		92.08 255	90.94 255	95.49 241	96.38 238	94.84 170	98.81 86	97.51 241	89.94 264	85.25 299	93.28 296	88.86 153	96.91 291	88.70 273	79.78 308	94.72 296

PVSNet	91.96 18	96.35 113	96.15 108	96.96 157	99.17 75	92.05 243	96.08 294	98.68 94	93.69 158	97.75 80	97.80 171	88.86 153	99.69 94	94.26 152	99.01 89	99.15 110

divwei89l23v2f112		94.76 183	94.12 187	96.67 178	96.28 252	94.85 161	98.69 115	98.12 195	92.44 205	94.29 187	96.94 236	88.85 155	98.48 215	92.67 194	88.79 266	96.67 235

v13		91.88 261	90.69 263	95.43 247	96.33 246	94.78 182	98.75 101	97.50 244	89.68 272	84.93 305	92.98 305	88.84 156	96.83 295	88.14 279	79.09 313	94.69 298

test_post											31.83 336	88.83 157	98.91 174

v1neww		94.83 177	94.22 177	96.68 175	96.39 235	94.85 161	98.87 71	98.11 200	92.45 203	94.45 170	97.06 220	88.82 158	98.54 204	92.93 186	88.91 260	96.65 240

v7new		94.83 177	94.22 177	96.68 175	96.39 235	94.85 161	98.87 71	98.11 200	92.45 203	94.45 170	97.06 220	88.82 158	98.54 204	92.93 186	88.91 260	96.65 240

v12		91.89 260	90.70 262	95.43 247	96.31 250	94.80 177	98.76 100	97.50 244	89.76 269	84.95 304	93.00 304	88.82 158	96.82 297	88.23 278	79.00 316	94.68 300

v8		94.47 204	93.77 208	96.57 192	96.36 239	94.83 172	99.05 50	98.19 179	91.92 220	93.16 226	96.97 232	88.82 158	98.48 215	91.69 220	87.79 276	96.39 264

V14		91.93 258	90.76 260	95.42 250	96.33 246	94.81 176	98.77 97	97.51 241	89.86 267	85.09 301	93.13 299	88.80 162	96.83 295	88.32 276	79.06 314	94.60 302

v1141		94.75 185	94.11 188	96.67 178	96.27 254	94.86 160	98.69 115	98.12 195	92.43 206	94.31 184	96.94 236	88.78 163	98.48 215	92.63 196	88.85 264	96.67 235

v15		91.94 257	90.77 259	95.43 247	96.31 250	94.83 172	98.77 97	97.50 244	89.92 265	85.13 300	93.08 301	88.76 164	96.86 293	88.40 275	79.10 312	94.61 301

v6		94.83 177	94.21 179	96.69 172	96.36 239	94.85 161	98.87 71	98.11 200	92.46 198	94.44 176	97.05 224	88.76 164	98.57 202	92.95 185	88.92 259	96.65 240

V9		91.91 259	90.73 261	95.45 244	96.32 249	94.80 177	98.77 97	97.50 244	89.81 268	85.03 303	93.08 301	88.76 164	96.86 293	88.24 277	79.03 315	94.69 298

v1		94.75 185	94.11 188	96.69 172	96.27 254	94.87 159	98.69 115	98.12 195	92.43 206	94.32 183	96.94 236	88.71 167	98.54 204	92.66 195	88.84 265	96.67 235

BH-w/o		95.38 153	95.08 146	96.26 216	98.34 122	91.79 247	97.70 230	97.43 255	92.87 189	94.24 191	97.22 208	88.66 168	98.84 183	91.55 222	97.70 142	98.16 167

tpmvs		94.60 196	94.36 173	95.33 254	97.46 175	88.60 289	96.88 278	97.68 227	91.29 242	93.80 211	96.42 259	88.58 169	99.24 134	91.06 230	96.04 178	98.17 166

DU-MVS		95.42 150	94.76 161	97.40 138	96.53 227	96.97 76	98.66 122	98.99 27	95.43 86	93.88 207	97.69 178	88.57 170	98.31 246	95.81 107	87.25 283	96.92 200

Baseline_NR-MVSNet		94.35 209	93.81 204	95.96 225	96.20 257	94.05 209	98.61 127	96.67 295	91.44 232	93.85 209	97.60 186	88.57 170	98.14 255	94.39 146	86.93 286	95.68 283

PCF-MVS	93.45 11	94.68 192	93.43 228	98.42 83	98.62 111	96.77 85	95.48 304	98.20 178	84.63 306	93.34 222	98.32 129	88.55 172	99.81 50	84.80 296	98.96 91	98.68 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v148		94.29 212	93.76 210	95.91 227	96.10 263	92.93 234	98.58 130	97.97 217	92.59 196	93.47 220	96.95 234	88.53 173	98.32 244	92.56 198	87.06 285	96.49 261

v11		91.85 262	90.68 264	95.36 252	96.34 242	94.74 184	98.80 89	97.43 255	89.60 275	85.09 301	93.03 303	88.53 173	96.75 298	87.37 282	79.96 307	94.58 303

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 47	96.51 95	97.91 210	99.06 20	93.72 154	96.92 112	98.06 147	88.50 175	99.65 98	91.77 218	99.00 90	98.66 145

V42		94.78 182	94.14 184	96.70 171	96.33 246	95.22 148	98.97 59	98.09 208	92.32 213	94.31 184	97.06 220	88.39 176	98.55 203	92.90 189	88.87 262	96.34 267

v7n		94.19 217	93.43 228	96.47 201	95.90 271	94.38 200	99.26 17	98.34 157	91.99 219	92.76 237	97.13 212	88.31 177	98.52 211	89.48 260	87.70 277	96.52 257

TranMVSNet+NR-MVSNet		95.14 164	94.48 166	97.11 149	96.45 232	96.36 101	99.03 52	99.03 23	95.04 105	93.58 214	97.93 157	88.27 178	98.03 262	94.13 154	86.90 288	96.95 199

MVSTER		96.06 122	95.72 120	97.08 151	98.23 128	95.93 121	98.73 108	98.27 165	94.86 113	95.07 153	98.09 145	88.21 179	98.54 204	96.59 85	93.46 210	96.79 217

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 52	94.56 194	98.05 195	99.71 1	93.57 165	97.09 101	98.91 76	88.17 180	99.89 27	96.87 77	99.56 61	99.81 2

CR-MVSNet		94.76 183	94.15 183	96.59 188	97.00 203	93.43 224	94.96 308	97.56 232	92.46 198	96.93 110	96.24 262	88.15 181	97.88 273	87.38 281	96.65 157	98.46 154

Patchmtry		93.22 242	92.35 243	95.84 230	96.77 215	93.09 233	94.66 314	97.56 232	87.37 291	92.90 234	96.24 262	88.15 181	97.90 269	87.37 282	90.10 244	96.53 256

v7		94.69 189	94.04 190	96.62 185	96.41 234	94.79 180	98.78 96	98.13 193	91.89 221	94.30 186	97.16 210	88.13 183	98.45 222	91.96 214	89.65 248	96.61 245

v10		94.29 212	93.55 221	96.51 198	96.39 235	94.80 177	98.99 55	98.19 179	91.35 238	93.02 232	96.99 230	88.09 184	98.41 235	90.50 240	88.41 269	96.33 268

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 107	96.23 106	99.22 27	99.00 25	96.63 51	98.04 62	99.21 32	88.05 185	99.35 129	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
v1144		94.59 198	93.92 198	96.60 187	96.21 256	94.78 182	98.59 128	98.14 192	91.86 224	94.21 193	97.02 227	87.97 186	98.41 235	91.72 219	89.57 249	96.61 245

PatchT		93.06 245	91.97 247	96.35 210	96.69 221	92.67 236	94.48 315	97.08 274	86.62 293	97.08 102	92.23 315	87.94 187	97.90 269	78.89 309	96.69 155	98.49 153

V4		94.18 219	93.52 223	96.13 221	95.89 272	94.31 202	99.23 21	98.22 174	91.42 233	92.82 236	96.89 241	87.93 188	98.52 211	91.51 223	87.81 274	95.58 285

ADS-MVSNet2		94.58 199	94.40 172	95.11 259	98.00 143	88.74 286	96.04 295	97.30 266	90.15 256	96.47 133	96.64 252	87.89 189	97.56 280	90.08 245	97.06 149	99.02 121

ADS-MVSNet		95.00 167	94.45 170	96.63 183	98.00 143	91.91 245	96.04 295	97.74 226	90.15 256	96.47 133	96.64 252	87.89 189	98.96 167	90.08 245	97.06 149	99.02 121

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 153	98.77 98	93.76 216	97.79 225	98.50 134	95.45 85	96.94 109	99.09 52	87.87 191	99.55 116	96.76 80	95.83 181	97.74 174

test_post1							96.68 284				30.43 337	87.85 192	98.69 192	92.59 197

v52		94.18 219	93.52 223	96.13 221	95.95 270	94.29 203	99.23 21	98.21 175	91.42 233	92.84 235	96.89 241	87.85 192	98.53 210	91.51 223	87.81 274	95.57 286

pcd1.5k->3k		39.42 311	41.78 312	32.35 324	96.17 259	0.00 342	0.00 333	98.54 122	0.00 337	0.00 338	0.00 339	87.78 194	0.00 340	0.00 337	93.56 209	97.06 191

test-LLR		95.10 165	94.87 159	95.80 232	96.77 215	89.70 273	96.91 273	95.21 311	95.11 101	94.83 160	95.72 278	87.71 195	98.97 164	93.06 180	98.50 112	98.72 139

test0.0.03 1		94.08 225	93.51 225	95.80 232	95.53 285	92.89 235	97.38 249	95.97 305	95.11 101	92.51 245	96.66 250	87.71 195	96.94 289	87.03 284	93.67 205	97.57 179

JIA-IIPM		93.35 237	92.49 241	95.92 226	96.48 231	90.65 264	95.01 307	96.96 285	85.93 299	96.08 141	87.33 320	87.70 197	98.78 190	91.35 226	95.58 183	98.34 162

v2v482		94.69 189	94.03 191	96.65 180	96.17 259	94.79 180	98.67 120	98.08 209	92.72 192	94.00 204	97.16 210	87.69 198	98.45 222	92.91 188	88.87 262	96.72 225

PatchFormer-LS_test		95.47 146	95.27 140	96.08 223	97.59 166	90.66 263	98.10 192	97.34 262	93.98 140	96.08 141	96.15 267	87.65 199	99.12 144	95.27 128	95.24 185	98.44 156

v748		93.75 233	93.06 233	95.82 231	95.73 278	92.64 237	99.25 19	98.24 172	91.60 228	92.22 252	96.52 255	87.60 200	98.46 220	90.64 237	85.72 295	96.36 266

CVMVSNet		95.43 149	96.04 111	93.57 285	97.93 148	83.62 307	98.12 188	98.59 112	95.68 75	96.56 125	99.02 58	87.51 201	97.51 281	93.56 169	97.44 145	99.60 59

WR-MVS		95.15 163	94.46 168	97.22 141	96.67 223	96.45 97	98.21 175	98.81 58	94.15 132	93.16 226	97.69 178	87.51 201	98.30 248	95.29 127	88.62 267	96.90 207

anonymousdsp		95.42 150	94.91 158	96.94 159	95.10 292	95.90 125	99.14 37	98.41 147	93.75 150	93.16 226	97.46 193	87.50 203	98.41 235	95.63 117	94.03 199	96.50 260

v144192		94.39 208	93.70 213	96.48 200	96.06 265	94.35 201	98.58 130	98.16 189	91.45 231	94.33 182	97.02 227	87.50 203	98.45 222	91.08 229	89.11 256	96.63 243

EU-MVSNet		93.66 234	94.14 184	92.25 294	95.96 269	83.38 308	98.52 140	98.12 195	94.69 115	92.61 240	98.13 143	87.36 205	96.39 307	91.82 216	90.00 245	96.98 196

CP-MVSNet		94.94 174	94.30 175	96.83 164	96.72 220	95.56 135	99.11 43	98.95 32	93.89 144	92.42 248	97.90 159	87.19 206	98.12 256	94.32 149	88.21 270	96.82 216

HQP_MVS		96.14 121	95.90 115	96.85 163	97.42 179	94.60 192	98.80 89	98.56 119	97.28 21	95.34 149	98.28 131	87.09 207	99.03 160	96.07 97	94.27 189	96.92 200

plane_prior6					97.35 184	94.61 190						87.09 207

RPSCF		94.87 176	95.40 129	93.26 289	98.89 90	82.06 313	98.33 161	98.06 212	90.30 255	96.56 125	99.26 27	87.09 207	99.49 119	93.82 162	96.32 170	98.24 165

RPMNet		92.52 249	91.17 252	96.59 188	97.00 203	93.43 224	94.96 308	97.26 270	82.27 312	96.93 110	92.12 316	86.98 210	97.88 273	76.32 314	96.65 157	98.46 154

v1192		94.32 210	93.58 220	96.53 196	96.10 263	94.45 196	98.50 145	98.17 187	91.54 229	94.19 194	97.06 220	86.95 211	98.43 227	90.14 243	89.57 249	96.70 229

CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 136	96.07 110	97.98 202	98.21 175	97.24 27	97.13 100	98.93 73	86.88 212	99.91 22	95.00 133	99.37 80	98.66 145

HQP2-MVS												86.75 213

HQP-MVS		95.72 134	95.40 129	96.69 172	97.20 193	94.25 205	98.05 195	98.46 139	96.43 54	94.45 170	97.73 174	86.75 213	98.96 167	95.30 125	94.18 193	96.86 212

OpenMVS	93.04 13	95.83 130	95.00 148	98.32 87	97.18 196	97.32 64	99.21 30	98.97 28	89.96 262	91.14 260	99.05 57	86.64 215	99.92 13	93.38 171	99.47 69	97.73 175

YYNet1		90.70 275	89.39 276	94.62 272	94.79 297	90.65 264	97.20 262	97.46 251	87.54 290	72.54 321	95.74 275	86.51 216	96.66 303	86.00 290	86.76 290	96.54 255

MDA-MVSNet_test_wron		90.71 274	89.38 277	94.68 270	94.83 296	90.78 261	97.19 263	97.46 251	87.60 289	72.41 322	95.72 278	86.51 216	96.71 302	85.92 291	86.80 289	96.56 253

v1921920		94.20 216	93.47 227	96.40 207	95.98 268	94.08 208	98.52 140	98.15 190	91.33 239	94.25 190	97.20 209	86.41 218	98.42 228	90.04 248	89.39 254	96.69 234

COLMAP_ROB	93.27 12	95.33 157	94.87 159	96.71 169	99.29 55	93.24 229	98.58 130	98.11 200	89.92 265	93.57 215	99.10 48	86.37 219	99.79 69	90.78 234	98.10 128	97.09 190
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVP-Stereo		94.28 214	93.92 198	95.35 253	94.95 294	92.60 238	97.97 203	97.65 229	91.61 227	90.68 266	97.09 215	86.32 220	98.42 228	89.70 255	99.34 81	95.02 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CLD-MVS		95.62 140	95.34 134	96.46 204	97.52 172	93.75 218	97.27 260	98.46 139	95.53 82	94.42 178	98.00 152	86.21 221	98.97 164	96.25 96	94.37 187	96.66 238
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpm cat1		93.36 236	92.80 237	95.07 260	97.58 167	87.97 296	96.76 282	97.86 221	82.17 313	93.53 216	96.04 270	86.13 222	99.13 143	89.24 263	95.87 180	98.10 168

MVS_0304		97.70 57	97.25 65	99.07 43	98.90 88	97.83 48	98.20 176	98.74 76	97.51 8	98.03 63	99.06 56	86.12 223	99.93 9	99.02 1	99.64 45	99.44 84

PEN-MVS		94.42 206	93.73 212	96.49 199	96.28 252	94.84 170	99.17 34	99.00 25	93.51 166	92.23 251	97.83 168	86.10 224	97.90 269	92.55 199	86.92 287	96.74 222

v1240		94.06 227	93.29 231	96.34 212	96.03 267	93.90 212	98.44 150	98.17 187	91.18 247	94.13 198	97.01 229	86.05 225	98.42 228	89.13 265	89.50 252	96.70 229

CostFormer		94.95 172	94.73 162	95.60 238	97.28 187	89.06 282	97.53 241	96.89 287	89.66 273	96.82 118	96.72 248	86.05 225	98.95 171	95.53 119	96.13 177	98.79 137

ACMM	93.85 9	95.69 137	95.38 133	96.61 186	97.61 164	93.84 214	98.91 63	98.44 143	95.25 96	94.28 188	98.47 113	86.04 227	99.12 144	95.50 120	93.95 202	96.87 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DTE-MVSNet		93.98 229	93.26 232	96.14 220	96.06 265	94.39 199	99.20 31	98.86 51	93.06 180	91.78 256	97.81 170	85.87 228	97.58 279	90.53 239	86.17 292	96.46 263

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 189	97.27 66	98.94 61	99.23 12	95.13 100	95.51 148	97.32 202	85.73 229	98.91 174	97.33 58	89.55 251	96.89 208

EPMVS		94.99 168	94.48 166	96.52 197	97.22 191	91.75 249	97.23 261	91.66 328	94.11 133	97.28 97	96.81 245	85.70 230	98.84 183	93.04 182	97.28 147	98.97 126

TransMVSNet (Re)		92.67 247	91.51 251	96.15 219	96.58 225	94.65 185	98.90 64	96.73 291	90.86 249	89.46 275	97.86 162	85.62 231	98.09 259	86.45 287	81.12 305	95.71 282

dp		94.15 222	93.90 200	94.90 263	97.31 186	86.82 303	96.97 269	97.19 273	91.22 246	96.02 144	96.61 254	85.51 232	99.02 162	90.00 249	94.30 188	98.85 133

LPG-MVS_test		95.62 140	95.34 134	96.47 201	97.46 175	93.54 221	98.99 55	98.54 122	94.67 117	94.36 180	98.77 87	85.39 233	99.11 148	95.71 113	94.15 195	96.76 220

LGP-MVS_train				96.47 201	97.46 175	93.54 221		98.54 122	94.67 117	94.36 180	98.77 87	85.39 233	99.11 148	95.71 113	94.15 195	96.76 220

PS-CasMVS		94.67 193	93.99 195	96.71 169	96.68 222	95.26 147	99.13 40	99.03 23	93.68 160	92.33 249	97.95 155	85.35 235	98.10 257	93.59 168	88.16 272	96.79 217

ab-mvs		96.42 111	95.71 123	98.55 71	98.63 110	96.75 86	97.88 216	98.74 76	93.84 147	96.54 127	98.18 140	85.34 236	99.75 83	95.93 103	96.35 169	99.15 110

N_pmnet		87.12 290	87.77 287	85.17 310	95.46 287	61.92 332	97.37 251	70.66 339	85.83 300	88.73 281	96.04 270	85.33 237	97.76 275	80.02 304	90.48 242	95.84 278

OPM-MVS		95.69 137	95.33 136	96.76 167	96.16 262	94.63 187	98.43 152	98.39 151	96.64 50	95.02 155	98.78 85	85.15 238	99.05 155	95.21 131	94.20 192	96.60 247

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 124	94.64 186	98.19 179	97.45 253	94.56 122	96.03 143	98.61 100	85.02 239	99.12 144	90.68 236	99.06 88	99.30 94

DSMNet-mixed		92.52 249	92.58 240	92.33 293	94.15 301	82.65 311	98.30 168	94.26 321	89.08 282	92.65 239	95.73 276	85.01 240	95.76 310	86.24 288	97.76 140	98.59 149

LTVRE_ROB	92.95 15	94.60 196	93.90 200	96.68 175	97.41 182	94.42 197	98.52 140	98.59 112	91.69 226	91.21 259	98.35 123	84.87 241	99.04 159	91.06 230	93.44 213	96.60 247
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
XXY-MVS		95.20 162	94.45 170	97.46 135	96.75 218	96.56 93	98.86 74	98.65 108	93.30 175	93.27 223	98.27 134	84.85 242	98.87 180	94.82 136	91.26 239	96.96 197

AllTest		95.24 159	94.65 163	96.99 154	99.25 64	93.21 230	98.59 128	98.18 182	91.36 236	93.52 217	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 170

TestCases				96.99 154	99.25 64	93.21 230		98.18 182	91.36 236	93.52 217	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 170

view600		95.60 142	94.93 154	97.62 126	99.05 81	94.85 161	99.09 45	97.01 281	95.36 89	96.52 128	97.37 197	84.55 245	99.59 107	89.07 266	96.39 165	98.40 157

view800		95.60 142	94.93 154	97.62 126	99.05 81	94.85 161	99.09 45	97.01 281	95.36 89	96.52 128	97.37 197	84.55 245	99.59 107	89.07 266	96.39 165	98.40 157

conf0.05thres1000		95.60 142	94.93 154	97.62 126	99.05 81	94.85 161	99.09 45	97.01 281	95.36 89	96.52 128	97.37 197	84.55 245	99.59 107	89.07 266	96.39 165	98.40 157

tfpn		95.60 142	94.93 154	97.62 126	99.05 81	94.85 161	99.09 45	97.01 281	95.36 89	96.52 128	97.37 197	84.55 245	99.59 107	89.07 266	96.39 165	98.40 157

pm-mvs1		93.94 230	93.06 233	96.59 188	96.49 230	95.16 149	98.95 60	98.03 216	92.32 213	91.08 261	97.84 165	84.54 249	98.41 235	92.16 205	86.13 294	96.19 271

ACMP	93.49 10	95.34 156	94.98 150	96.43 205	97.67 160	93.48 223	98.73 108	98.44 143	94.94 112	92.53 243	98.53 107	84.50 250	99.14 142	95.48 121	94.00 200	96.66 238
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LP		91.12 270	89.99 272	94.53 273	96.35 241	88.70 287	93.86 319	97.35 261	84.88 304	90.98 262	94.77 287	84.40 251	97.43 282	75.41 317	91.89 232	97.47 180

FMVSNet3		94.97 171	94.26 176	97.11 149	98.18 134	96.62 89	98.56 135	98.26 169	93.67 162	94.09 199	97.10 213	84.25 252	98.01 263	92.08 207	92.14 226	96.70 229

cascas		94.63 195	93.86 202	96.93 160	96.91 209	94.27 204	96.00 298	98.51 129	85.55 301	94.54 166	96.23 264	84.20 253	98.87 180	95.80 109	96.98 152	97.66 178

tpm		94.13 223	93.80 205	95.12 258	96.50 229	87.91 297	97.44 244	95.89 308	92.62 194	96.37 137	96.30 261	84.13 254	98.30 248	93.24 175	91.66 235	99.14 112

IterMVS		94.09 224	93.85 203	94.80 268	97.99 145	90.35 268	97.18 264	98.12 195	93.68 160	92.46 247	97.34 201	84.05 255	97.41 283	92.51 201	91.33 236	96.62 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test1		95.32 158	94.97 152	96.35 210	97.67 160	91.29 255	97.33 256	97.60 230	94.68 116	96.92 112	96.95 234	83.97 256	98.50 214	91.33 227	98.32 121	99.25 100

semantic-postprocess				94.85 265	97.98 147	90.56 266		98.11 200	93.75 150	92.58 241	97.48 192	83.91 257	97.41 283	92.48 202	91.30 237	96.58 249

TR-MVS		94.94 174	94.20 180	97.17 145	97.75 157	94.14 207	97.59 238	97.02 279	92.28 215	95.75 147	97.64 184	83.88 258	98.96 167	89.77 251	96.15 176	98.40 157

jajsoiax		95.45 148	95.03 147	96.73 168	95.42 288	94.63 187	99.14 37	98.52 127	95.74 73	93.22 224	98.36 122	83.87 259	98.65 196	96.95 68	94.04 198	96.91 205

Anonymous20231206		91.66 265	91.10 253	93.33 287	94.02 303	87.35 300	98.58 130	97.26 270	90.48 250	90.16 269	96.31 260	83.83 260	96.53 305	79.36 307	89.90 246	96.12 272

tpm2		94.19 217	93.76 210	95.46 243	97.23 190	89.04 283	97.31 258	96.85 290	87.08 292	96.21 139	96.79 246	83.75 261	98.74 191	92.43 203	96.23 174	98.59 149

mvs_tets		95.41 152	95.00 148	96.65 180	95.58 283	94.42 197	99.00 54	98.55 121	95.73 74	93.21 225	98.38 120	83.45 262	98.63 197	97.09 63	94.00 200	96.91 205

tpmp4_e23		93.91 231	93.42 230	95.38 251	97.62 163	88.59 290	97.52 242	97.34 262	87.94 288	94.17 196	96.79 246	82.91 263	99.05 155	90.62 238	95.91 179	98.50 152

OurMVSNet-221017-0		94.21 215	94.00 193	94.85 265	95.60 282	89.22 280	98.89 68	97.43 255	95.29 94	92.18 253	98.52 110	82.86 264	98.59 200	93.46 170	91.76 233	96.74 222

UGNet		96.78 99	96.30 103	98.19 94	98.24 127	95.89 126	98.88 70	98.93 35	97.39 16	96.81 119	97.84 165	82.60 265	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
pmmvs5		93.65 235	92.97 235	95.68 236	95.49 286	92.37 239	98.20 176	97.28 268	89.66 273	92.58 241	97.26 205	82.14 266	98.09 259	93.18 178	90.95 240	96.58 249

DWT-MVSNet_test		94.82 180	94.36 173	96.20 218	97.35 184	90.79 260	98.34 160	96.57 298	92.91 187	95.33 151	96.44 258	82.00 267	99.12 144	94.52 144	95.78 182	98.70 141

ACMH	92.88 16	94.55 200	93.95 197	96.34 212	97.63 162	93.26 228	98.81 86	98.49 138	93.43 169	89.74 272	98.53 107	81.91 268	99.08 153	93.69 164	93.30 216	96.70 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				95.44 245	97.42 179	91.32 254		97.50 244	95.09 104	93.59 213	98.35 123	81.70 269	98.88 179	89.71 254	93.39 214	96.12 272

GBi-Net		94.49 202	93.80 205	96.56 193	98.21 129	95.00 154	98.82 80	98.18 182	92.46 198	94.09 199	97.07 217	81.16 270	97.95 266	92.08 207	92.14 226	96.72 225

test1		94.49 202	93.80 205	96.56 193	98.21 129	95.00 154	98.82 80	98.18 182	92.46 198	94.09 199	97.07 217	81.16 270	97.95 266	92.08 207	92.14 226	96.72 225

FMVSNet2		94.47 204	93.61 218	97.04 152	98.21 129	96.43 98	98.79 94	98.27 165	92.46 198	93.50 219	97.09 215	81.16 270	98.00 264	91.09 228	91.93 230	96.70 229

GA-MVS		94.81 181	94.03 191	97.14 146	97.15 198	93.86 213	96.76 282	97.58 231	94.00 138	94.76 163	97.04 225	80.91 273	98.48 215	91.79 217	96.25 173	99.09 115

SixPastTwentyTwo		93.34 238	92.86 236	94.75 269	95.67 280	89.41 278	98.75 101	96.67 295	93.89 144	90.15 270	98.25 136	80.87 274	98.27 251	90.90 233	90.64 241	96.57 251

ACMH+	92.99 14	94.30 211	93.77 208	95.88 229	97.81 155	92.04 244	98.71 111	98.37 154	93.99 139	90.60 267	98.47 113	80.86 275	99.05 155	92.75 193	92.40 225	96.55 254

gg-mvs-nofinetune		92.21 252	90.58 266	97.13 147	96.75 218	95.09 152	95.85 300	89.40 331	85.43 302	94.50 168	81.98 324	80.80 276	98.40 241	92.16 205	98.33 120	97.88 172

test20.03		90.89 273	90.38 267	92.43 292	93.48 304	88.14 295	98.33 161	97.56 232	93.40 170	87.96 283	96.71 249	80.69 277	94.13 316	79.15 308	86.17 292	95.01 293

test_normal		94.72 188	93.59 219	98.11 99	95.30 290	95.95 118	97.91 210	97.39 260	94.64 120	85.70 294	95.88 273	80.52 278	99.36 128	96.69 82	98.30 122	99.01 124

DI_MVS_plusplus_test		94.74 187	93.62 217	98.09 100	95.34 289	95.92 122	98.09 193	97.34 262	94.66 119	85.89 291	95.91 272	80.49 279	99.38 127	96.66 83	98.22 123	98.97 126

VPNet		94.99 168	94.19 181	97.40 138	97.16 197	96.57 92	98.71 111	98.97 28	95.67 76	94.84 158	98.24 137	80.36 280	98.67 195	96.46 90	87.32 281	96.96 197

GG-mvs-BLEND				96.59 188	96.34 242	94.98 157	96.51 292	88.58 332		93.10 231	94.34 292	80.34 281	98.05 261	89.53 258	96.99 151	96.74 222

PVSNet_0	88.72 19	91.28 268	90.03 271	95.00 261	97.99 145	87.29 301	94.84 311	98.50 134	92.06 218	89.86 271	95.19 282	79.81 282	99.39 126	92.27 204	69.79 324	98.33 163

MS-PatchMatch		93.84 232	93.63 216	94.46 277	96.18 258	89.45 276	97.76 226	98.27 165	92.23 216	92.13 254	97.49 191	79.50 283	98.69 192	89.75 253	99.38 79	95.25 288

MVS-HIRNet		89.46 282	88.40 284	92.64 291	97.58 167	82.15 312	94.16 318	93.05 327	75.73 321	90.90 263	82.52 323	79.42 284	98.33 243	83.53 298	98.68 102	97.43 181

MDA-MVSNet-bldmvs		89.97 279	88.35 285	94.83 267	95.21 291	91.34 253	97.64 235	97.51 241	88.36 286	71.17 323	96.13 268	79.22 285	96.63 304	83.65 297	86.27 291	96.52 257

XVG-ACMP-BASELINE		94.54 201	94.14 184	95.75 235	96.55 226	91.65 251	98.11 190	98.44 143	94.96 109	94.22 192	97.90 159	79.18 286	99.11 148	94.05 157	93.85 203	96.48 262

TESTMET0.1,1		94.18 219	93.69 214	95.63 237	96.92 207	89.12 281	96.91 273	94.78 316	93.17 177	94.88 157	96.45 257	78.52 287	98.92 173	93.09 179	98.50 112	98.85 133

pmmvs-eth3d		90.36 277	89.05 280	94.32 279	91.10 312	92.12 241	97.63 237	96.95 286	88.86 283	84.91 306	93.13 299	78.32 288	96.74 299	88.70 273	81.81 304	94.09 309

IB-MVS	91.98 17	93.27 240	91.97 247	97.19 143	97.47 174	93.41 226	97.09 267	95.99 304	93.32 173	92.47 246	95.73 276	78.06 289	99.53 117	94.59 142	82.98 300	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
LF4IMVS		93.14 244	92.79 238	94.20 280	95.88 273	88.67 288	97.66 234	97.07 275	93.81 149	91.71 257	97.65 182	77.96 290	98.81 187	91.47 225	91.92 231	95.12 289

test-mter		94.08 225	93.51 225	95.80 232	96.77 215	89.70 273	96.91 273	95.21 311	92.89 188	94.83 160	95.72 278	77.69 291	98.97 164	93.06 180	98.50 112	98.72 139

USDC		93.33 239	92.71 239	95.21 255	96.83 214	90.83 259	96.91 273	97.50 244	93.84 147	90.72 265	98.14 142	77.69 291	98.82 186	89.51 259	93.21 219	95.97 276

test_0402		91.32 267	90.27 269	94.48 275	96.60 224	91.12 257	98.50 145	97.22 272	86.10 297	88.30 282	96.98 231	77.65 293	97.99 265	78.13 311	92.94 221	94.34 305

K. test v3		92.55 248	91.91 249	94.48 275	95.64 281	89.24 279	99.07 49	94.88 315	94.04 136	86.78 287	97.59 187	77.64 294	97.64 277	92.08 207	89.43 253	96.57 251

TDRefinement		91.06 271	89.68 274	95.21 255	85.35 323	91.49 252	98.51 144	97.07 275	91.47 230	88.83 280	97.84 165	77.31 295	99.09 152	92.79 192	77.98 317	95.04 291

new_pmnet		90.06 278	89.00 281	93.22 290	94.18 300	88.32 294	96.42 293	96.89 287	86.19 295	85.67 295	93.62 294	77.18 296	97.10 287	81.61 302	89.29 255	94.23 306

new-patchmatchnet		88.50 287	87.45 288	91.67 296	90.31 314	85.89 304	97.16 265	97.33 265	89.47 276	83.63 308	92.77 309	76.38 297	95.06 314	82.70 299	77.29 318	94.06 310

lessismore_v0				94.45 278	94.93 295	88.44 292		91.03 329		86.77 288	97.64 184	76.23 298	98.42 228	90.31 242	85.64 296	96.51 259

TinyColmap		92.31 251	91.53 250	94.65 271	96.92 207	89.75 272	96.92 271	96.68 294	90.45 252	89.62 273	97.85 164	76.06 299	98.81 187	86.74 285	92.51 224	95.41 287

pmmvs6		91.77 264	90.63 265	95.17 257	94.69 299	91.24 256	98.67 120	97.92 219	86.14 296	89.62 273	97.56 190	75.79 300	98.34 242	90.75 235	84.56 299	95.94 277

MIMVSNet		93.26 241	92.21 245	96.41 206	97.73 159	93.13 232	95.65 303	97.03 278	91.27 244	94.04 202	96.06 269	75.33 301	97.19 286	86.56 286	96.23 174	98.92 131

UnsupCasMVSNet_eth		90.99 272	89.92 273	94.19 281	94.08 302	89.83 271	97.13 266	98.67 101	93.69 158	85.83 293	96.19 266	75.15 302	96.74 299	89.14 264	79.41 311	96.00 275

LFMVS		95.86 129	94.98 150	98.47 78	98.87 92	96.32 103	98.84 77	96.02 303	93.40 170	98.62 39	99.20 35	74.99 303	99.63 103	97.72 42	97.20 148	99.46 81

testpf		88.74 285	89.09 278	87.69 303	95.78 276	83.16 310	84.05 330	94.13 324	85.22 303	90.30 268	94.39 291	74.92 304	95.80 309	89.77 251	93.28 218	84.10 324

CMPMVS	66.06 21	89.70 280	89.67 275	89.78 299	93.19 305	76.56 318	97.00 268	98.35 156	80.97 315	81.57 312	97.75 173	74.75 305	98.61 198	89.85 250	93.63 207	94.17 307
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		91.81 263	90.92 257	94.49 274	97.21 192	92.09 242	98.00 201	97.55 236	89.31 280	90.86 264	95.61 281	74.48 306	95.32 312	85.57 293	89.70 247	96.07 274

testgi		93.06 245	92.45 242	94.88 264	96.43 233	89.90 270	98.75 101	97.54 237	95.60 79	91.63 258	97.91 158	74.46 307	97.02 288	86.10 289	93.67 205	97.72 176

VDD-MVS		95.82 131	95.23 141	97.61 131	98.84 96	93.98 210	98.68 119	97.40 258	95.02 106	97.95 70	99.34 19	74.37 308	99.78 74	98.64 4	96.80 154	99.08 118

FMVSNet1		93.19 243	92.07 246	96.56 193	97.54 170	95.00 154	98.82 80	98.18 182	90.38 254	92.27 250	97.07 217	73.68 309	97.95 266	89.36 262	91.30 237	96.72 225

VDDNet		95.36 154	94.53 165	97.86 111	98.10 138	95.13 151	98.85 75	97.75 225	90.46 251	98.36 51	99.39 7	73.27 310	99.64 100	97.98 27	96.58 159	98.81 136

test2356		88.68 286	88.61 282	88.87 301	89.90 316	78.23 316	95.11 306	96.66 297	88.66 285	89.06 278	94.33 293	73.14 311	92.56 323	75.56 316	95.11 186	95.81 280

test1235678		86.26 292	85.81 291	87.62 304	86.97 321	75.00 323	96.55 290	96.32 302	86.08 298	81.32 313	92.98 305	73.10 312	92.05 324	71.64 320	87.32 281	95.81 280

testus		88.91 284	89.08 279	88.40 302	91.39 310	76.05 319	96.56 288	96.48 299	89.38 279	89.39 276	95.17 284	70.94 313	93.56 319	77.04 313	95.41 184	95.61 284

DeepMVS_CX				86.78 306	97.09 201	72.30 325		95.17 314	75.92 320	84.34 307	95.19 282	70.58 314	95.35 311	79.98 306	89.04 258	92.68 315

OpenMVS_ROB	86.42 20	89.00 283	87.43 289	93.69 284	93.08 306	89.42 277	97.91 210	96.89 287	78.58 318	85.86 292	94.69 288	69.48 315	98.29 250	77.13 312	93.29 217	93.36 314

1111		84.94 293	84.30 294	86.86 305	87.59 319	75.10 321	96.63 285	96.43 300	82.53 310	80.75 314	92.91 307	68.94 316	93.79 317	68.24 323	84.66 298	91.70 316

.test1245		73.05 302	76.31 300	63.27 323	87.59 319	75.10 321	96.63 285	96.43 300	82.53 310	80.75 314	92.91 307	68.94 316	93.79 317	68.24 323	12.72 335	20.91 333

EG-PatchMatch MVS		91.13 269	90.12 270	94.17 282	94.73 298	89.00 284	98.13 187	97.81 222	89.22 281	85.32 296	96.46 256	67.71 318	98.42 228	87.89 280	93.82 204	95.08 290

MIMVSNet1		89.67 281	88.28 286	93.82 283	92.81 308	91.08 258	98.01 199	97.45 253	87.95 287	87.90 284	95.87 274	67.63 319	94.56 315	78.73 310	88.18 271	95.83 279

pmmvs3		86.67 291	84.86 293	92.11 295	88.16 318	87.19 302	96.63 285	94.75 317	79.88 317	87.22 286	92.75 310	66.56 320	95.20 313	81.24 303	76.56 320	93.96 311

test12356		83.47 295	83.37 295	83.78 311	84.43 324	70.09 328	95.12 305	95.60 310	82.98 308	78.89 316	92.43 314	64.99 321	91.41 326	70.36 321	85.55 297	89.82 318

tmp_tt		68.90 304	66.97 304	74.68 319	50.78 338	59.95 334	87.13 326	83.47 337	38.80 333	62.21 327	96.23 264	64.70 322	76.91 336	88.91 270	30.49 333	87.19 321

UnsupCasMVSNet_bld		87.17 289	85.12 292	93.31 288	91.94 309	88.77 285	94.92 310	98.30 162	84.30 307	82.30 309	90.04 317	63.96 323	97.25 285	85.85 292	74.47 323	93.93 312

testing_2		90.61 276	88.50 283	96.95 158	90.08 315	95.57 134	97.69 231	98.06 212	93.02 182	76.55 317	92.48 313	61.18 324	98.44 225	95.45 122	91.98 229	96.84 213

Test4		92.21 252	90.34 268	97.82 115	92.83 307	95.87 127	97.94 206	98.05 215	94.50 125	82.12 310	94.48 289	59.54 325	98.54 204	95.39 123	98.22 123	99.06 120

PM-MVS		87.77 288	86.55 290	91.40 297	91.03 313	83.36 309	96.92 271	95.18 313	91.28 243	86.48 290	93.42 295	53.27 326	96.74 299	89.43 261	81.97 303	94.11 308

Anonymous20231211		83.69 294	81.50 296	90.26 298	89.23 317	80.10 315	97.97 203	97.06 277	72.79 323	82.05 311	92.57 311	50.28 327	96.32 308	76.15 315	75.38 321	94.37 304

testmv		78.74 296	77.35 297	82.89 313	78.16 332	69.30 329	95.87 299	94.65 318	81.11 314	70.98 324	87.11 321	46.31 328	90.42 327	65.28 326	76.72 319	88.95 319

ambc				89.49 300	86.66 322	75.78 320	92.66 321	96.72 292		86.55 289	92.50 312	46.01 329	97.90 269	90.32 241	82.09 301	94.80 294

Gipuma		78.40 298	76.75 299	83.38 312	95.54 284	80.43 314	79.42 331	97.40 258	64.67 325	73.46 320	80.82 326	45.65 330	93.14 321	66.32 325	87.43 279	76.56 329
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		64.07 308	63.26 309	66.53 322	81.73 327	58.81 337	91.85 322	84.75 336	51.93 332	59.09 329	75.13 330	43.32 331	79.09 335	42.03 333	39.47 330	61.69 330

E-PMN		64.94 307	64.25 307	67.02 321	82.28 326	59.36 336	91.83 323	85.63 335	52.69 330	60.22 328	77.28 329	41.06 332	80.12 334	46.15 332	41.14 329	61.57 331

FPMVS		77.62 300	77.14 298	79.05 315	79.25 329	60.97 333	95.79 301	95.94 306	65.96 324	67.93 325	94.40 290	37.73 333	88.88 329	68.83 322	88.46 268	87.29 320

PMMVS2		77.95 299	75.44 302	85.46 308	82.54 325	74.95 324	94.23 317	93.08 326	72.80 322	74.68 319	87.38 319	36.36 334	91.56 325	73.95 318	63.94 325	89.87 317

no-one		74.41 301	70.76 303	85.35 309	79.88 328	76.83 317	94.68 313	94.22 322	80.33 316	63.81 326	79.73 327	35.45 335	93.36 320	71.78 319	36.99 332	85.86 323

LCM-MVSNet		78.70 297	76.24 301	86.08 307	77.26 333	71.99 326	94.34 316	96.72 292	61.62 327	76.53 318	89.33 318	33.91 336	92.78 322	81.85 301	74.60 322	93.46 313

ANet_high		69.08 303	65.37 305	80.22 314	65.99 336	71.96 327	90.91 324	90.09 330	82.62 309	49.93 332	78.39 328	29.36 337	81.75 332	62.49 329	38.52 331	86.95 322

PNet_i23d		67.70 305	65.07 306	75.60 317	78.61 330	59.61 335	89.14 325	88.24 333	61.83 326	52.37 330	80.89 325	18.91 338	84.91 331	62.70 328	52.93 327	82.28 325

PMVS	61.03 23	65.95 306	63.57 308	73.09 320	57.90 337	51.22 338	85.05 329	93.93 325	54.45 329	44.32 333	83.57 322	13.22 339	89.15 328	58.68 330	81.00 306	78.91 328
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		20.95 315	23.72 316	12.64 326	13.54 340	8.19 340	96.55 290	6.13 342	7.48 336	16.74 336	37.98 335	12.97 340	6.05 338	16.69 335	5.43 337	23.68 332

wuyk23d		30.17 312	30.18 314	30.16 325	78.61 330	43.29 339	66.79 332	14.21 340	17.31 334	14.82 337	11.93 338	11.55 341	41.43 337	37.08 334	19.30 334	5.76 335

wuykxyi23d		63.73 309	58.86 311	78.35 316	67.62 335	67.90 330	86.56 327	87.81 334	58.26 328	42.49 334	70.28 332	11.55 341	85.05 330	63.66 327	41.50 328	82.11 326

MVE	62.14 22	63.28 310	59.38 310	74.99 318	74.33 334	65.47 331	85.55 328	80.50 338	52.02 331	51.10 331	75.00 331	10.91 343	80.50 333	51.60 331	53.40 326	78.99 327
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.48 314	24.95 315	11.09 327	14.89 339	6.47 341	96.56 288	9.87 341	7.55 335	17.93 335	39.02 334	9.43 344	5.90 339	16.56 336	12.72 335	20.91 333

sosnet-low-res		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

sosnet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

uncertanet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

Regformer		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

ab-mvs-re		8.20 316	10.94 317	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	98.43 115	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

uanet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

MTGPA								98.74 76

MTMP								94.14 323

gm-plane-assit					95.88 273	87.47 299			89.74 271		96.94 236		99.19 138	93.32 174

test9_res														96.39 94	99.57 55	99.69 35

agg_prior2														95.87 106	99.57 55	99.68 41

agg_prior					99.30 52	98.38 17		98.72 83		97.57 93			99.81 50

test_prior4						98.01 41	97.86 218

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

旧先验2							97.57 240		91.30 241	98.67 36			99.80 57	95.70 115

新几何2							97.64 235

无先验							97.58 239	98.72 83	91.38 235				99.87 35	93.36 172		99.60 59

原ACMM2							97.67 233

testdata2													99.89 27	91.65 221

testdata1							97.32 257		96.34 57

plane_prior7					97.42 179	94.63 187

plane_prior5								98.56 119					99.03 160	96.07 97	94.27 189	96.92 200

plane_prior4											98.28 131

plane_prior3						94.61 190			97.02 39	95.34 149

plane_prior2							98.80 89		97.28 21

plane_prior1					97.37 183

plane_prior						94.60 192	98.44 150		96.74 46						94.22 191

n2								0.00 343

nn								0.00 343

door-mid								94.37 320

test11								98.66 104

door								94.64 319

HQP5-MVS						94.25 205

HQP-NCC					97.20 193		98.05 195		96.43 54	94.45 170

ACMP_Plane					97.20 193		98.05 195		96.43 54	94.45 170

BP-MVS														95.30 125

HQP4-MVS										94.45 170			98.96 167			96.87 210

HQP3-MVS								98.46 139							94.18 193

NP-MVS					97.28 187	94.51 195					97.73 174

ACMMP++_ref															92.97 220

ACMMP++															93.61 208

High-resolution multi-view benchmark