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.

DeepPCF-MVS	95.94 2	97.71 66	98.98 8	93.92 245	99.63 67	81.76 318	99.96 19	98.56 83	99.47 1	99.19 52	99.99 1	94.16 72	100.00 1	99.92 3	99.93 48	100.00 1

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 62	98.20 3	99.93 1	99.98 2	96.82 13	100.00 1	99.75 11	100.00 1	99.99 11

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 73	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 20	100.00 1	100.00 1

MSLP-MVS++		99.13 5	99.01 6	99.49 22	99.94 14	98.46 50	99.98 6	98.86 52	97.10 15	99.80 8	99.94 4	95.92 29	100.00 1	99.51 21	100.00 1	100.00 1

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 69	98.47 2	99.13 54	99.92 5	96.38 22	100.00 1	99.74 13	100.00 1	100.00 1

test_part3							99.88 66		96.14 43		99.91 6		100.00 1	99.99 1

ESAPD		99.18 4	98.99 7	99.75 3	99.89 36	99.25 6	99.88 66	98.41 121	96.14 43	99.49 32	99.91 6	97.20 11	100.00 1	99.99 1	99.99 13	99.99 11

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 42	98.72 115	97.71 70	99.98 6	98.44 106	96.85 20	99.80 8	99.91 6	97.57 4	99.85 78	99.44 25	99.99 13	99.99 11
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	96.59 1	98.81 18	98.54 23	99.62 11	99.90 33	98.85 20	99.24 193	98.47 102	98.14 4	99.08 55	99.91 6	93.09 97	100.00 1	99.04 39	99.99 13	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tmp_tt		65.23 318	62.94 319	72.13 330	44.90 354	50.03 350	81.05 343	89.42 348	38.45 346	48.51 345	99.90 10	54.09 336	78.70 347	91.84 185	18.26 350	87.64 336

HSP-MVS		99.07 6	99.11 4	98.95 73	99.93 24	97.24 93	99.95 31	98.32 135	97.50 10	99.52 31	99.88 11	97.43 6	99.71 104	99.50 22	99.98 25	99.89 71

MPTG		98.33 44	98.00 47	99.30 37	99.85 40	97.93 65	99.80 100	98.28 139	95.76 52	97.18 112	99.88 11	92.74 102	100.00 1	98.67 56	99.88 56	99.99 11

MTAPA		98.29 45	97.96 51	99.30 37	99.85 40	97.93 65	99.39 178	98.28 139	95.76 52	97.18 112	99.88 11	92.74 102	100.00 1	98.67 56	99.88 56	99.99 11

CDPH-MVS		98.65 23	98.36 33	99.49 22	99.94 14	98.73 31	99.87 71	98.33 134	93.97 101	99.76 11	99.87 14	94.99 49	99.75 96	98.55 63	100.00 1	99.98 43

CP-MVS		98.45 37	98.32 35	98.87 77	99.96 8	96.62 111	99.97 12	98.39 125	94.43 83	98.90 63	99.87 14	94.30 66	100.00 1	99.04 39	99.99 13	99.99 11

xiu_mvs_v2_base		98.23 49	97.97 49	99.02 68	98.69 116	98.66 35	99.52 163	98.08 162	97.05 16	99.86 4	99.86 16	90.65 132	99.71 104	99.39 28	98.63 106	98.69 185

TEST9					99.92 27	98.92 15	99.96 19	98.43 111	93.90 105	99.71 15	99.86 16	95.88 30	99.85 78

train_agg		98.88 15	98.65 16	99.59 13	99.92 27	98.92 15	99.96 19	98.43 111	94.35 85	99.71 15	99.86 16	95.94 27	99.85 78	99.69 18	99.98 25	99.99 11

LS3D		95.84 133	95.11 141	98.02 129	99.85 40	95.10 158	98.74 238	98.50 100	87.22 256	93.66 174	99.86 16	87.45 164	99.95 50	90.94 195	99.81 67	99.02 177

MP-MVS-pluss		98.07 54	97.64 57	99.38 34	99.74 56	98.41 51	99.74 119	98.18 150	93.35 119	96.45 126	99.85 20	92.64 105	99.97 40	98.91 46	99.89 54	99.77 84
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_8					99.92 27	98.88 18	99.96 19	98.43 111	94.35 85	99.69 17	99.85 20	95.94 27	99.85 78

agg_prior3		98.84 17	98.62 18	99.47 25	99.92 27	98.56 44	99.96 19	98.43 111	94.07 95	99.67 18	99.85 20	96.05 23	99.85 78	99.69 18	99.98 25	99.99 11

agg_prior1		98.88 15	98.66 15	99.54 17	99.93 24	98.77 25	99.96 19	98.43 111	94.63 78	99.63 20	99.85 20	95.79 31	99.85 78	99.72 16	99.99 13	99.99 11

HFP-MVS		98.56 29	98.37 31	99.14 50	99.96 8	97.43 82	99.95 31	98.61 75	94.77 73	99.31 45	99.85 20	94.22 68	100.00 1	98.70 54	99.98 25	99.98 43

region2R		98.54 31	98.37 31	99.05 64	99.96 8	97.18 96	99.96 19	98.55 87	94.87 71	99.45 35	99.85 20	94.07 74	100.00 1	98.67 56	100.00 1	99.98 43

PS-MVSNAJ		98.44 38	98.20 39	99.16 45	98.80 112	98.92 15	99.54 161	98.17 151	97.34 11	99.85 5	99.85 20	91.20 124	99.89 68	99.41 27	99.67 74	98.69 185

#test#		98.59 27	98.41 26	99.14 50	99.96 8	97.43 82	99.95 31	98.61 75	95.00 68	99.31 45	99.85 20	94.22 68	100.00 1	98.78 51	99.98 25	99.98 43

HPM-MVS++		99.07 6	98.88 11	99.63 8	99.90 33	99.02 12	99.95 31	98.56 83	97.56 9	99.44 36	99.85 20	95.38 38	100.00 1	99.31 29	99.99 13	99.87 74

旧先验1					99.76 53	97.52 76		98.64 69			99.85 20	95.63 33			99.94 43	99.99 11

原ACMM1				98.96 72	99.73 60	96.99 102		98.51 96	94.06 98	99.62 22	99.85 20	94.97 50	99.96 42	95.11 127	99.95 39	99.92 68

testdata				98.42 111	99.47 78	95.33 152		98.56 83	93.78 109	99.79 10	99.85 20	93.64 87	99.94 58	94.97 128	99.94 43	100.00 1

APDe-MVS		99.06 8	98.91 10	99.51 20	99.94 14	98.76 30	99.91 56	98.39 125	97.20 14	99.46 34	99.85 20	95.53 36	99.79 89	99.86 5	100.00 1	99.99 11

API-MVS		97.86 59	97.66 56	98.47 106	99.52 75	95.41 150	99.47 169	98.87 51	91.68 176	98.84 64	99.85 20	92.34 108	99.99 27	98.44 66	99.96 36	100.00 1

ACMMPR		98.50 34	98.32 35	99.05 64	99.96 8	97.18 96	99.95 31	98.60 77	94.77 73	99.31 45	99.84 34	93.73 84	100.00 1	98.70 54	99.98 25	99.98 43

DP-MVS Recon		98.41 40	98.02 46	99.56 15	99.97 3	98.70 33	99.92 52	98.44 106	92.06 168	98.40 84	99.84 34	95.68 32	100.00 1	98.19 70	99.71 72	99.97 53

ACMMP_Plus		98.49 35	98.14 42	99.54 17	99.66 66	98.62 39	99.85 87	98.37 130	94.68 77	99.53 28	99.83 36	92.87 98	100.00 1	98.66 59	99.84 60	99.99 11

test222					99.55 73	97.41 85	99.34 183	98.55 87	91.86 172	99.27 48	99.83 36	93.84 82			99.95 39	99.99 11

1121		98.03 55	97.57 61	99.40 32	99.74 56	98.21 56	98.31 269	98.62 73	92.78 134	99.53 28	99.83 36	95.08 43	100.00 1	94.36 142	99.92 50	99.99 11

新几何1				99.42 29	99.75 55	98.27 55		98.63 72	92.69 139	99.55 27	99.82 39	94.40 59	100.00 1	91.21 188	99.94 43	99.99 11

CSCG		97.10 84	97.04 75	97.27 152	99.89 36	91.92 233	99.90 59	99.07 32	88.67 232	95.26 150	99.82 39	93.17 96	99.98 31	98.15 72	99.47 88	99.90 70

MAR-MVS		97.43 71	97.19 69	98.15 124	99.47 78	94.79 165	99.05 214	98.76 58	92.65 143	98.66 73	99.82 39	88.52 157	99.98 31	98.12 73	99.63 76	99.67 96
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
MP-MVS		98.23 49	97.97 49	99.03 66	99.94 14	97.17 99	99.95 31	98.39 125	94.70 76	98.26 91	99.81 42	91.84 118	100.00 1	98.85 48	99.97 34	99.93 65

mPP-MVS		98.39 42	98.20 39	98.97 71	99.97 3	96.92 105	99.95 31	98.38 128	95.04 67	98.61 76	99.80 43	93.39 89	100.00 1	98.64 60	100.00 1	99.98 43

MVS_0304		97.52 70	96.79 82	99.69 6	99.59 69	99.30 4	99.97 12	98.01 166	96.99 19	98.84 64	99.79 44	78.90 254	99.96 42	99.74 13	99.32 94	99.81 79

Regformer-1		98.79 19	98.60 20	99.36 35	99.85 40	98.34 52	99.87 71	98.52 90	96.05 45	99.41 39	99.79 44	94.93 51	99.76 93	99.07 34	99.90 52	99.99 11

Regformer-2		98.78 20	98.59 21	99.36 35	99.85 40	98.32 53	99.87 71	98.52 90	96.04 46	99.41 39	99.79 44	94.92 52	99.76 93	99.05 35	99.90 52	99.98 43

CPTT-MVS		97.64 68	97.32 67	98.58 94	99.97 3	95.77 138	99.96 19	98.35 132	89.90 213	98.36 85	99.79 44	91.18 127	99.99 27	98.37 68	99.99 13	99.99 11

MVS_111021_LR		98.42 39	98.38 30	98.53 99	99.39 81	95.79 137	99.87 71	99.86 2	96.70 27	98.78 67	99.79 44	92.03 114	99.90 65	99.17 31	99.86 59	99.88 73

XVS		98.70 22	98.55 22	99.15 48	99.94 14	97.50 78	99.94 45	98.42 119	96.22 39	99.41 39	99.78 49	94.34 63	99.96 42	98.92 44	99.95 39	99.99 11

PHI-MVS		98.41 40	98.21 38	99.03 66	99.86 39	97.10 100	99.98 6	98.80 57	90.78 202	99.62 22	99.78 49	95.30 39	100.00 1	99.80 7	99.93 48	99.99 11

Regformer-3		98.58 28	98.41 26	99.10 56	99.84 45	97.57 74	99.66 142	98.52 90	95.79 49	99.01 59	99.77 51	94.40 59	99.75 96	98.82 49	99.83 61	99.98 43

Regformer-4		98.56 29	98.39 29	99.08 58	99.84 45	97.52 76	99.66 142	98.52 90	95.76 52	99.01 59	99.77 51	94.33 65	99.75 96	98.80 50	99.83 61	99.98 43

APD-MVS_3200maxsize		98.25 48	98.08 45	98.78 80	99.81 50	96.60 112	99.82 95	98.30 137	93.95 103	99.37 43	99.77 51	92.84 99	99.76 93	98.95 41	99.92 50	99.97 53

MVS_111021_HR		98.72 21	98.62 18	99.01 69	99.36 83	97.18 96	99.93 50	99.90 1	96.81 24	98.67 72	99.77 51	93.92 77	99.89 68	99.27 30	99.94 43	99.96 57

EI-MVSNet-Vis-set		98.27 46	98.11 44	98.75 82	99.83 48	96.59 113	99.40 175	98.51 96	95.29 64	98.51 79	99.76 55	93.60 88	99.71 104	98.53 64	99.52 85	99.95 62

test_prior3		98.99 11	98.84 12	99.43 26	99.94 14	98.49 48	99.95 31	98.65 66	95.78 50	99.73 13	99.76 55	96.00 25	99.80 87	99.78 9	100.00 1	99.99 11

test_prior2							99.95 31		95.78 50	99.73 13	99.76 55	96.00 25		99.78 9	100.00 1

SD-MVS		98.92 13	98.70 14	99.56 15	99.70 64	98.73 31	99.94 45	98.34 133	96.38 34	99.81 7	99.76 55	94.59 56	99.98 31	99.84 6	99.96 36	99.97 53

PGM-MVS		98.34 43	98.13 43	98.99 70	99.92 27	97.00 101	99.75 116	99.50 20	93.90 105	99.37 43	99.76 55	93.24 94	100.00 1	97.75 90	99.96 36	99.98 43

EI-MVSNet-UG-set		98.14 51	97.99 48	98.60 92	99.80 51	96.27 120	99.36 182	98.50 100	95.21 66	98.30 88	99.75 60	93.29 93	99.73 103	98.37 68	99.30 95	99.81 79

PAPR		98.52 33	98.16 41	99.58 14	99.97 3	98.77 25	99.95 31	98.43 111	95.35 62	98.03 96	99.75 60	94.03 75	99.98 31	98.11 74	99.83 61	99.99 11

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 30	99.74 56	98.67 34	99.77 109	98.38 128	96.73 26	99.88 3	99.74 62	94.89 53	99.59 115	99.80 7	99.98 25	99.97 53

APD-MVS		98.62 24	98.35 34	99.41 30	99.90 33	98.51 47	99.87 71	98.36 131	94.08 94	99.74 12	99.73 63	94.08 73	99.74 100	99.42 26	99.99 13	99.99 11
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 149	99.44 22	97.33 12	99.00 61	99.72 64	94.03 75	99.98 31	98.73 53	100.00 1	100.00 1

AdaColmap		97.23 80	96.80 81	98.51 100	99.99 1	95.60 146	99.09 203	98.84 54	93.32 120	96.74 120	99.72 64	86.04 177	100.00 1	98.01 78	99.43 91	99.94 64

CANet		98.27 46	97.82 54	99.63 8	99.72 62	99.10 10	99.98 6	98.51 96	97.00 18	98.52 78	99.71 66	87.80 160	99.95 50	99.75 11	99.38 92	99.83 77

ACMMP		97.74 65	97.44 63	98.66 87	99.92 27	96.13 129	99.18 197	99.45 21	94.84 72	96.41 129	99.71 66	91.40 121	99.99 27	97.99 80	98.03 120	99.87 74
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
abl_6		97.67 67	97.34 65	98.66 87	99.68 65	96.11 133	99.68 137	98.14 157	93.80 108	99.27 48	99.70 68	88.65 156	99.98 31	97.46 93	99.72 71	99.89 71

PAPM_NR		98.12 52	97.93 52	98.70 84	99.94 14	96.13 129	99.82 95	98.43 111	94.56 79	97.52 105	99.70 68	94.40 59	99.98 31	97.00 104	99.98 25	99.99 11

OMC-MVS		97.28 77	97.23 68	97.41 147	99.76 53	93.36 199	99.65 145	97.95 172	96.03 47	97.41 107	99.70 68	89.61 140	99.51 118	96.73 110	98.25 115	99.38 140

xiu_mvs_v1_base_debu		97.43 71	97.06 72	98.55 95	97.74 165	98.14 57	99.31 185	97.86 182	96.43 31	99.62 22	99.69 71	85.56 182	99.68 108	99.05 35	98.31 112	97.83 194

xiu_mvs_v1_base		97.43 71	97.06 72	98.55 95	97.74 165	98.14 57	99.31 185	97.86 182	96.43 31	99.62 22	99.69 71	85.56 182	99.68 108	99.05 35	98.31 112	97.83 194

xiu_mvs_v1_base_debi		97.43 71	97.06 72	98.55 95	97.74 165	98.14 57	99.31 185	97.86 182	96.43 31	99.62 22	99.69 71	85.56 182	99.68 108	99.05 35	98.31 112	97.83 194

CNLPA		97.76 64	97.38 64	98.92 75	99.53 74	96.84 106	99.87 71	98.14 157	93.78 109	96.55 123	99.69 71	92.28 109	99.98 31	97.13 100	99.44 90	99.93 65

cdsmvs_eth3d_5k		23.43 330	31.24 331	0.00 344	0.00 358	0.00 359	0.00 350	98.09 161	0.00 354	0.00 355	99.67 75	83.37 197	0.00 357	0.00 354	0.00 355	0.00 355

lupinMVS		97.85 60	97.60 59	98.62 90	97.28 179	97.70 72	99.99 3	97.55 204	95.50 60	99.43 37	99.67 75	90.92 130	98.71 153	98.40 67	99.62 77	99.45 128

114514_t		97.41 75	96.83 79	99.14 50	99.51 77	97.83 67	99.89 64	98.27 142	88.48 235	99.06 56	99.66 77	90.30 135	99.64 114	96.32 113	99.97 34	99.96 57

PAPM		98.60 25	98.42 25	99.14 50	96.05 209	98.96 13	99.90 59	99.35 26	96.68 28	98.35 86	99.66 77	96.45 21	98.51 167	99.45 24	99.89 54	99.96 57

CANet_DTU		96.76 96	96.15 97	98.60 92	98.78 113	97.53 75	99.84 90	97.63 196	97.25 13	99.20 50	99.64 79	81.36 224	99.98 31	92.77 173	98.89 100	98.28 188

XVG-OURS		94.82 152	94.74 146	95.06 199	98.00 147	89.19 273	99.08 205	97.55 204	94.10 93	94.71 164	99.62 80	80.51 237	99.74 100	96.04 116	93.06 204	96.25 204

MVS		96.60 104	95.56 129	99.72 4	96.85 192	99.22 8	98.31 269	98.94 37	91.57 178	90.90 195	99.61 81	86.66 172	99.96 42	97.36 95	99.88 56	99.99 11

XVG-OURS-SEG-HR		94.79 153	94.70 147	95.08 198	98.05 146	89.19 273	99.08 205	97.54 206	93.66 113	94.87 163	99.58 82	78.78 255	99.79 89	97.31 96	93.40 199	96.25 204

HPM-MVS		97.96 56	97.72 55	98.68 85	99.84 45	96.39 119	99.90 59	98.17 151	92.61 145	98.62 75	99.57 83	91.87 117	99.67 111	98.87 47	99.99 13	99.99 11

TSAR-MVS + GP.		98.60 25	98.51 24	98.86 78	99.73 60	96.63 110	99.97 12	97.92 175	98.07 5	98.76 68	99.55 84	95.00 48	99.94 58	99.91 4	97.68 124	99.99 11

DP-MVS		94.54 161	93.42 172	97.91 132	99.46 80	94.04 176	98.93 225	97.48 215	81.15 310	90.04 206	99.55 84	87.02 169	99.95 50	88.97 223	98.11 116	99.73 89

MVSFormer		96.94 88	96.60 87	97.95 130	97.28 179	97.70 72	99.55 159	97.27 232	91.17 192	99.43 37	99.54 86	90.92 130	96.89 263	94.67 137	99.62 77	99.25 155

jason		97.24 79	96.86 78	98.38 116	95.73 221	97.32 92	99.97 12	97.40 223	95.34 63	98.60 77	99.54 86	87.70 161	98.56 164	97.94 83	99.47 88	99.25 155
jason: jason.
HPM-MVS_fast		97.80 62	97.50 62	98.68 85	99.79 52	96.42 116	99.88 66	98.16 154	91.75 175	98.94 62	99.54 86	91.82 119	99.65 113	97.62 92	99.99 13	99.99 11

DeepC-MVS	94.51 4	96.92 90	96.40 92	98.45 108	99.16 85	95.90 135	99.66 142	98.06 163	96.37 37	94.37 169	99.49 89	83.29 198	99.90 65	97.63 91	99.61 80	99.55 116
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
alignmvs		97.81 61	97.33 66	99.25 39	98.77 114	98.66 35	99.99 3	98.44 106	94.40 84	98.41 82	99.47 90	93.65 86	99.42 131	98.57 62	94.26 184	99.67 96

TAPA-MVS	92.12 8	94.42 165	93.60 165	96.90 159	99.33 84	91.78 237	99.78 104	98.00 167	89.89 214	94.52 166	99.47 90	91.97 115	99.18 135	69.90 321	99.52 85	99.73 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		97.09 85	96.32 93	99.39 33	98.93 99	98.95 14	99.72 129	97.35 227	94.45 81	97.88 99	99.42 92	86.71 171	99.52 117	98.48 65	93.97 195	99.72 91

VDD-MVS		93.77 176	92.94 179	96.27 176	98.55 124	90.22 263	98.77 237	97.79 187	90.85 200	96.82 118	99.42 92	61.18 328	99.77 91	98.95 41	94.13 186	98.82 181

1112_ss		96.01 130	95.20 138	98.42 111	97.80 160	96.41 117	99.65 145	96.66 283	92.71 137	92.88 183	99.40 94	92.16 111	99.30 132	91.92 182	93.66 196	99.55 116

ab-mvs-re		8.28 332	11.04 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	99.40 94	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

LFMVS		94.75 156	93.56 168	98.30 118	99.03 89	95.70 144	98.74 238	97.98 169	87.81 243	98.47 80	99.39 96	67.43 312	99.53 116	98.01 78	95.20 169	99.67 96

WTY-MVS		98.10 53	97.60 59	99.60 12	98.92 100	99.28 5	99.89 64	99.52 17	95.58 58	98.24 92	99.39 96	93.33 90	99.74 100	97.98 82	95.58 165	99.78 83

PMMVS		96.76 96	96.76 84	96.76 163	98.28 133	92.10 228	99.91 56	97.98 169	94.12 92	99.53 28	99.39 96	86.93 170	98.73 151	96.95 107	97.73 122	99.45 128

EPNet		98.49 35	98.40 28	98.77 81	99.62 68	96.80 108	99.90 59	99.51 19	97.60 8	99.20 50	99.36 99	93.71 85	99.91 64	97.99 80	98.71 105	99.61 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VDDNet		93.12 186	91.91 195	96.76 163	96.67 202	92.65 218	98.69 242	98.21 146	82.81 295	97.75 101	99.28 100	61.57 326	99.48 125	98.09 76	94.09 187	98.15 190

UA-Net		96.54 105	95.96 106	98.27 119	98.23 137	95.71 143	98.00 284	98.45 105	93.72 111	98.41 82	99.27 101	88.71 155	99.66 112	91.19 189	97.69 123	99.44 130

RPSCF		91.80 209	92.79 182	88.83 307	98.15 142	69.87 330	98.11 280	96.60 286	83.93 291	94.33 170	99.27 101	79.60 245	99.46 126	91.99 181	93.16 203	97.18 199

PLC	95.54 3	97.93 58	97.89 53	98.05 128	99.82 49	94.77 166	99.92 52	98.46 104	93.93 104	97.20 110	99.27 101	95.44 37	99.97 40	97.41 94	99.51 87	99.41 133
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-RMVSNet		95.18 145	94.31 153	97.80 133	98.17 141	95.23 156	99.76 115	97.53 208	92.52 152	94.27 171	99.25 104	76.84 267	98.80 146	90.89 197	99.54 84	99.35 145

DELS-MVS		98.54 31	98.22 37	99.50 21	99.15 86	98.65 37	100.00 1	98.58 79	97.70 7	98.21 93	99.24 105	92.58 106	99.94 58	98.63 61	99.94 43	99.92 68
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
PCF-MVS	94.20 5	95.18 145	94.10 157	98.43 110	98.55 124	95.99 134	97.91 286	97.31 231	90.35 206	89.48 226	99.22 106	85.19 187	99.89 68	90.40 204	98.47 108	99.41 133
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet	91.05 13	97.13 83	96.69 85	98.45 108	99.52 75	95.81 136	99.95 31	99.65 15	94.73 75	99.04 57	99.21 107	84.48 190	99.95 50	94.92 129	98.74 104	99.58 113

MSDG		94.37 167	93.36 176	97.40 148	98.88 106	93.95 178	99.37 180	97.38 225	85.75 277	90.80 196	99.17 108	84.11 193	99.88 74	86.35 255	98.43 109	98.36 187

F-COLMAP		96.93 89	96.95 77	96.87 160	99.71 63	91.74 239	99.85 87	97.95 172	93.11 124	95.72 143	99.16 109	92.35 107	99.94 58	95.32 125	99.35 93	98.92 179

Vis-MVSNet (Re-imp)		96.32 121	95.98 103	97.35 151	97.93 151	94.82 162	99.47 169	98.15 156	91.83 173	95.09 161	99.11 110	91.37 122	97.47 219	93.47 162	97.43 129	99.74 87

CHOSEN 280x420		99.01 10	99.03 5	98.95 73	99.38 82	98.87 19	98.46 259	99.42 24	97.03 17	99.02 58	99.09 111	99.35 1	98.21 196	99.73 15	99.78 68	99.77 84

PVSNet_Blended		97.94 57	97.64 57	98.83 79	99.59 69	96.99 102	100.00 1	99.10 29	95.38 61	98.27 89	99.08 112	89.00 151	99.95 50	99.12 32	99.25 96	99.57 114

sss		97.57 69	97.03 76	99.18 42	98.37 130	98.04 62	99.73 124	99.38 25	93.46 117	98.76 68	99.06 113	91.21 123	99.89 68	96.33 112	97.01 142	99.62 104

EI-MVSNet		93.73 177	93.40 175	94.74 215	96.80 195	92.69 215	99.06 211	97.67 194	88.96 226	91.39 191	99.02 114	88.75 154	97.30 231	91.07 191	87.85 231	94.22 235

CVMVSNet		94.68 158	94.94 143	93.89 247	96.80 195	86.92 293	99.06 211	98.98 35	94.45 81	94.23 172	99.02 114	85.60 181	95.31 299	90.91 196	95.39 168	99.43 131

EPP-MVSNet		96.69 101	96.60 87	96.96 157	97.74 165	93.05 207	99.37 180	98.56 83	88.75 231	95.83 141	99.01 116	96.01 24	98.56 164	96.92 108	97.20 138	99.25 155

COLMAP_ROB	90.47 14	92.18 203	91.49 201	94.25 233	99.00 92	88.04 287	98.42 264	96.70 282	82.30 300	88.43 243	99.01 116	76.97 265	99.85 78	86.11 258	96.50 149	94.86 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
3Dnovator	91.47 12	96.28 125	95.34 134	99.08 58	96.82 194	97.47 81	99.45 172	98.81 55	95.52 59	89.39 227	99.00 118	81.97 210	99.95 50	97.27 97	99.83 61	99.84 76

1314		96.84 92	95.96 106	99.48 24	96.74 199	98.52 46	98.31 269	98.86 52	95.82 48	89.91 209	98.98 119	87.49 163	99.96 42	97.80 86	99.73 70	99.96 57

3Dnovator+	91.53 11	96.31 122	95.24 136	99.52 19	96.88 191	98.64 38	99.72 129	98.24 143	95.27 65	88.42 245	98.98 119	82.76 200	99.94 58	97.10 102	99.83 61	99.96 57

gm-plane-assit					96.97 187	93.76 185			91.47 182		98.96 121		98.79 147	94.92 129

IS-MVSNet		96.29 124	95.90 111	97.45 145	98.13 143	94.80 163	99.08 205	97.61 201	92.02 169	95.54 146	98.96 121	90.64 133	98.08 200	93.73 160	97.41 131	99.47 127

OpenMVS	90.15 15	94.77 155	93.59 166	98.33 117	96.07 208	97.48 80	99.56 157	98.57 81	90.46 204	86.51 265	98.95 123	78.57 257	99.94 58	93.86 152	99.74 69	97.57 198

Vis-MVSNet		95.72 134	95.15 140	97.45 145	97.62 171	94.28 172	99.28 190	98.24 143	94.27 89	96.84 117	98.94 124	79.39 246	98.76 150	93.25 165	98.49 107	99.30 150
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DWT-MVSNet_test		97.31 76	97.19 69	97.66 138	98.24 136	94.67 167	98.86 233	98.20 149	93.60 115	98.09 94	98.89 125	97.51 5	98.78 148	94.04 150	97.28 133	99.55 116

QAPM		95.40 142	94.17 156	99.10 56	96.92 188	97.71 70	99.40 175	98.68 63	89.31 218	88.94 237	98.89 125	82.48 201	99.96 42	93.12 171	99.83 61	99.62 104

VNet		97.21 81	96.57 89	99.13 55	98.97 95	97.82 68	99.03 216	99.21 28	94.31 87	99.18 53	98.88 127	86.26 176	99.89 68	98.93 43	94.32 182	99.69 94

thres200		96.96 87	96.21 96	99.22 40	98.97 95	98.84 21	99.85 87	99.71 5	93.17 122	96.26 131	98.88 127	89.87 138	99.51 118	94.26 146	94.91 171	99.31 148

tfpn200view9		96.79 94	95.99 101	99.19 41	98.94 97	98.82 22	99.78 104	99.71 5	92.86 127	96.02 134	98.87 129	89.33 141	99.50 120	93.84 153	94.57 172	99.27 153

thres400		96.78 95	95.99 101	99.16 45	98.94 97	98.82 22	99.78 104	99.71 5	92.86 127	96.02 134	98.87 129	89.33 141	99.50 120	93.84 153	94.57 172	99.16 164

conf200view11		96.73 100	95.92 109	99.16 45	98.90 103	98.77 25	99.74 119	99.71 5	92.59 147	95.84 138	98.86 131	89.25 143	99.50 120	93.84 153	94.57 172	99.20 159

thres100view900		96.74 98	95.92 109	99.18 42	98.90 103	98.77 25	99.74 119	99.71 5	92.59 147	95.84 138	98.86 131	89.25 143	99.50 120	93.84 153	94.57 172	99.27 153

thres600view7		96.69 101	95.87 119	99.14 50	98.90 103	98.78 24	99.74 119	99.71 5	92.59 147	95.84 138	98.86 131	89.25 143	99.50 120	93.44 163	94.50 176	99.16 164

CHOSEN 1792x2688		96.81 93	96.53 90	97.64 139	98.91 102	93.07 205	99.65 145	99.80 3	95.64 57	95.39 147	98.86 131	84.35 192	99.90 65	96.98 105	99.16 98	99.95 62

CLD-MVS		94.06 170	93.90 160	94.55 224	96.02 210	90.69 256	99.98 6	97.72 191	96.62 30	91.05 194	98.85 135	77.21 263	98.47 169	98.11 74	89.51 211	94.48 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
conf0.01		96.52 111	95.88 112	98.41 114	98.59 117	97.38 86	99.87 71	98.91 41	91.32 186	95.22 155	98.83 136	96.57 15	98.66 157	89.55 213	94.09 187	99.20 159

conf0.002		96.52 111	95.88 112	98.41 114	98.59 117	97.38 86	99.87 71	98.91 41	91.32 186	95.22 155	98.83 136	96.57 15	98.66 157	89.55 213	94.09 187	99.20 159

thresconf0.02		96.53 106	95.88 112	98.48 102	98.59 117	97.38 86	99.87 71	98.91 41	91.32 186	95.22 155	98.83 136	96.57 15	98.66 157	89.55 213	94.09 187	99.40 136

tfpn_n400		96.53 106	95.88 112	98.48 102	98.59 117	97.38 86	99.87 71	98.91 41	91.32 186	95.22 155	98.83 136	96.57 15	98.66 157	89.55 213	94.09 187	99.40 136

tfpnconf		96.53 106	95.88 112	98.48 102	98.59 117	97.38 86	99.87 71	98.91 41	91.32 186	95.22 155	98.83 136	96.57 15	98.66 157	89.55 213	94.09 187	99.40 136

tfpnview11		96.53 106	95.88 112	98.48 102	98.59 117	97.38 86	99.87 71	98.91 41	91.32 186	95.22 155	98.83 136	96.57 15	98.66 157	89.55 213	94.09 187	99.40 136

view600		96.46 114	95.59 124	99.06 60	98.87 107	98.60 40	99.69 132	99.71 5	92.20 159	95.23 151	98.80 142	89.17 146	99.43 127	92.29 175	94.37 178	99.16 164

view800		96.46 114	95.59 124	99.06 60	98.87 107	98.60 40	99.69 132	99.71 5	92.20 159	95.23 151	98.80 142	89.17 146	99.43 127	92.29 175	94.37 178	99.16 164

conf0.05thres1000		96.46 114	95.59 124	99.06 60	98.87 107	98.60 40	99.69 132	99.71 5	92.20 159	95.23 151	98.80 142	89.17 146	99.43 127	92.29 175	94.37 178	99.16 164

tfpn		96.46 114	95.59 124	99.06 60	98.87 107	98.60 40	99.69 132	99.71 5	92.20 159	95.23 151	98.80 142	89.17 146	99.43 127	92.29 175	94.37 178	99.16 164

PatchFormer-LS_test		97.01 86	96.79 82	97.69 137	98.26 135	94.80 163	98.66 248	98.13 159	93.70 112	97.86 100	98.80 142	95.54 34	98.67 155	94.12 149	96.00 154	99.60 108

BH-w/o		95.71 136	95.38 133	96.68 166	98.49 128	92.28 224	99.84 90	97.50 213	92.12 164	92.06 188	98.79 147	84.69 188	98.67 155	95.29 126	99.66 75	99.09 175

mvs-test1		95.53 139	95.97 105	94.20 234	97.77 162	85.44 301	99.95 31	97.06 245	94.92 69	96.58 122	98.72 148	85.81 179	98.98 140	94.80 133	98.11 116	98.18 189

TR-MVS		94.54 161	93.56 168	97.49 143	97.96 149	94.34 171	98.71 240	97.51 212	90.30 208	94.51 167	98.69 149	75.56 277	98.77 149	92.82 172	95.99 155	99.35 145

tfpn_ndepth		97.21 81	96.63 86	98.92 75	99.06 87	98.28 54	99.95 31	98.91 41	92.96 126	96.49 124	98.67 150	97.40 7	99.07 137	91.87 184	94.38 177	99.41 133

BH-untuned		95.18 145	94.83 144	96.22 177	98.36 131	91.22 250	99.80 100	97.32 230	90.91 198	91.08 193	98.67 150	83.51 195	98.54 166	94.23 147	99.61 80	98.92 179

tfpn1000		96.90 91	96.29 94	98.74 83	99.00 92	98.09 60	99.92 52	98.91 41	92.08 165	95.85 137	98.65 152	97.39 8	98.83 145	90.56 199	94.23 185	99.31 148

OPM-MVS		93.21 185	92.80 181	94.44 227	93.12 281	90.85 255	99.77 109	97.61 201	96.19 41	91.56 190	98.65 152	75.16 282	98.47 169	93.78 158	89.39 212	93.99 254

NP-MVS					95.77 218	91.79 236					98.65 152

HQP-MVS		94.61 160	94.50 150	94.92 208	95.78 215	91.85 234	99.87 71	97.89 178	96.82 21	93.37 175	98.65 152	80.65 235	98.39 180	97.92 84	89.60 206	94.53 210

HQP_MVS		94.49 164	94.36 152	94.87 211	95.71 224	91.74 239	99.84 90	97.87 180	96.38 34	93.01 179	98.59 156	80.47 239	98.37 185	97.79 87	89.55 209	94.52 212

plane_prior4											98.59 156

Effi-MVS+		96.30 123	95.69 122	98.16 121	97.85 156	96.26 121	97.41 291	97.21 235	90.37 205	98.65 74	98.58 158	86.61 173	98.70 154	97.11 101	97.37 132	99.52 122

EPNet_dtu		95.71 136	95.39 132	96.66 167	98.92 100	93.41 195	99.57 155	98.90 49	96.19 41	97.52 105	98.56 159	92.65 104	97.36 223	77.89 307	98.33 111	99.20 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test0.0.03 1		93.86 171	93.61 163	94.64 219	95.02 236	92.18 227	99.93 50	98.58 79	94.07 95	87.96 249	98.50 160	93.90 80	94.96 304	81.33 289	93.17 202	96.78 200

LPG-MVS_test		92.96 188	92.71 183	93.71 250	95.43 229	88.67 278	99.75 116	97.62 198	92.81 131	90.05 203	98.49 161	75.24 280	98.40 178	95.84 121	89.12 213	94.07 243

LGP-MVS_train				93.71 250	95.43 229	88.67 278		97.62 198	92.81 131	90.05 203	98.49 161	75.24 280	98.40 178	95.84 121	89.12 213	94.07 243

PVSNet_Blended_VisFu		97.27 78	96.81 80	98.66 87	98.81 111	96.67 109	99.92 52	98.64 69	94.51 80	96.38 130	98.49 161	89.05 150	99.88 74	97.10 102	98.34 110	99.43 131

testmvs		40.60 327	44.45 328	29.05 342	19.49 357	14.11 358	99.68 137	18.47 357	20.74 351	64.59 334	98.48 164	10.95 356	17.09 356	56.66 340	11.01 351	55.94 350

AllTest		92.48 197	91.64 197	95.00 202	99.01 90	88.43 282	98.94 224	96.82 280	86.50 264	88.71 238	98.47 165	74.73 284	99.88 74	85.39 263	96.18 151	96.71 201

TestCases				95.00 202	99.01 90	88.43 282		96.82 280	86.50 264	88.71 238	98.47 165	74.73 284	99.88 74	85.39 263	96.18 151	96.71 201

PatchMatch-RL		96.04 129	95.40 131	97.95 130	99.59 69	95.22 157	99.52 163	99.07 32	93.96 102	96.49 124	98.35 167	82.28 202	99.82 86	90.15 208	99.22 97	98.81 182

CDS-MVSNet		96.34 120	96.07 98	97.13 154	97.37 177	94.96 159	99.53 162	97.91 176	91.55 179	95.37 148	98.32 168	95.05 45	97.13 247	93.80 157	95.75 162	99.30 150
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMP	92.05 9	92.74 192	92.42 189	93.73 248	95.91 214	88.72 277	99.81 97	97.53 208	94.13 91	87.00 258	98.23 169	74.07 288	98.47 169	96.22 114	88.86 218	93.99 254
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testgi		89.01 263	88.04 262	91.90 285	93.49 261	84.89 304	99.73 124	95.66 302	93.89 107	85.14 277	98.17 170	59.68 330	94.66 308	77.73 308	88.88 216	96.16 207

ITE_SJBPF				92.38 277	95.69 226	85.14 302		95.71 300	92.81 131	89.33 230	98.11 171	70.23 303	98.42 175	85.91 259	88.16 229	93.59 280

HyFIR lowres test		96.66 103	96.43 91	97.36 150	99.05 88	93.91 179	99.70 131	99.80 3	90.54 203	96.26 131	98.08 172	92.15 112	98.23 195	96.84 109	95.46 166	99.93 65

TESTMET0.1,1		96.74 98	96.26 95	98.16 121	97.36 178	96.48 115	99.96 19	98.29 138	91.93 170	95.77 142	98.07 173	95.54 34	98.29 190	90.55 200	98.89 100	99.70 92

TAMVS		95.85 132	95.58 128	96.65 168	97.07 182	93.50 188	99.17 198	97.82 186	91.39 185	95.02 162	98.01 174	92.20 110	97.30 231	93.75 159	95.83 160	99.14 170

ACMM	91.95 10	92.88 190	92.52 187	93.98 244	95.75 220	89.08 275	99.77 109	97.52 210	93.00 125	89.95 208	97.99 175	76.17 274	98.46 172	93.63 161	88.87 217	94.39 222
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+		95.02 150	94.19 155	97.52 142	97.88 153	94.55 168	99.97 12	97.08 244	88.85 230	94.47 168	97.96 176	84.59 189	98.41 176	89.84 210	97.10 140	99.59 110

GG-mvs-BLEND				98.54 98	98.21 138	98.01 63	93.87 324	98.52 90		97.92 98	97.92 177	99.02 2	97.94 209	98.17 71	99.58 82	99.67 96

Fast-Effi-MVS+-dtu		93.72 178	93.86 162	93.29 257	97.06 183	86.16 294	99.80 100	96.83 278	92.66 141	92.58 186	97.83 178	81.39 223	97.67 215	89.75 211	96.87 145	96.05 208

ACMH+	89.98 16	90.35 242	89.54 238	92.78 266	95.99 211	86.12 295	98.81 235	97.18 237	89.38 217	83.14 287	97.76 179	68.42 309	98.43 174	89.11 222	86.05 243	93.78 273

ACMH	89.72 17	90.64 236	89.63 235	93.66 252	95.64 227	88.64 280	98.55 252	97.45 216	89.03 222	81.62 292	97.61 180	69.75 304	98.41 176	89.37 219	87.62 235	93.92 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		94.64 159	93.61 163	97.74 136	97.82 159	96.26 121	99.96 19	97.78 188	85.76 274	94.00 173	97.54 181	76.95 266	99.21 134	97.23 98	95.43 167	97.76 197

nrg030		93.51 181	92.53 186	96.45 171	94.36 243	97.20 95	99.81 97	97.16 240	91.60 177	89.86 212	97.46 182	86.37 175	97.68 214	95.88 119	80.31 276	94.46 215

VPNet		91.81 207	90.46 213	95.85 186	94.74 239	95.54 147	98.98 219	98.59 78	92.14 163	90.77 197	97.44 183	68.73 307	97.54 217	94.89 132	77.89 296	94.46 215

HY-MVS	92.50 7	97.79 63	97.17 71	99.63 8	98.98 94	99.32 3	97.49 290	99.52 17	95.69 56	98.32 87	97.41 184	93.32 91	99.77 91	98.08 77	95.75 162	99.81 79

PVSNet_0	88.03 19	91.80 209	90.27 221	96.38 174	98.27 134	90.46 260	99.94 45	99.61 16	93.99 100	86.26 271	97.39 185	71.13 300	99.89 68	98.77 52	67.05 325	98.79 183

FIs		94.10 169	93.43 171	96.11 179	94.70 240	96.82 107	99.58 154	98.93 40	92.54 151	89.34 229	97.31 186	87.62 162	97.10 250	94.22 148	86.58 240	94.40 221

OurMVSNet-221017-0		89.81 252	89.48 242	90.83 293	91.64 305	81.21 319	98.17 278	95.38 315	91.48 181	85.65 276	97.31 186	72.66 292	97.29 234	88.15 229	84.83 251	93.97 259

FC-MVSNet-test		93.81 174	93.15 178	95.80 187	94.30 245	96.20 126	99.42 174	98.89 50	92.33 157	89.03 236	97.27 188	87.39 165	96.83 267	93.20 166	86.48 241	94.36 224

USDC		90.00 251	88.96 249	93.10 260	94.81 238	88.16 286	98.71 240	95.54 306	93.66 113	83.75 285	97.20 189	65.58 316	98.31 189	83.96 274	87.49 237	92.85 296

MVSTER		95.53 139	95.22 137	96.45 171	98.56 123	97.72 69	99.91 56	97.67 194	92.38 156	91.39 191	97.14 190	97.24 10	97.30 231	94.80 133	87.85 231	94.34 228

LF4IMVS		89.25 261	88.85 250	90.45 297	92.81 289	81.19 320	98.12 279	94.79 323	91.44 183	86.29 270	97.11 191	65.30 318	98.11 199	88.53 226	85.25 248	92.07 301

mvs_anonymous		95.65 138	95.03 142	97.53 141	98.19 139	95.74 140	99.33 184	97.49 214	90.87 199	90.47 199	97.10 192	88.23 158	97.16 241	95.92 118	97.66 125	99.68 95

jajsoiax		91.92 205	91.18 204	94.15 235	91.35 308	90.95 253	99.00 218	97.42 220	92.61 145	87.38 254	97.08 193	72.46 293	97.36 223	94.53 140	88.77 219	94.13 240

XXY-MVS		91.82 206	90.46 213	95.88 184	93.91 251	95.40 151	98.87 231	97.69 193	88.63 234	87.87 250	97.08 193	74.38 287	97.89 210	91.66 186	84.07 254	94.35 227

LTVRE_ROB	88.28 18	90.29 245	89.05 248	94.02 240	95.08 233	90.15 265	97.19 296	97.43 218	84.91 283	83.99 283	97.06 195	74.00 289	98.28 192	84.08 271	87.71 233	93.62 279
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
mvs_tets		91.81 207	91.08 205	94.00 242	91.63 306	90.58 257	98.67 245	97.43 218	92.43 155	87.37 255	97.05 196	71.76 295	97.32 228	94.75 136	88.68 221	94.11 241

MVS_Test		96.46 114	95.74 121	98.61 91	98.18 140	97.23 94	99.31 185	97.15 241	91.07 195	98.84 64	97.05 196	88.17 159	98.97 141	94.39 141	97.50 127	99.61 106

ab-mvs		94.69 157	93.42 172	98.51 100	98.07 145	96.26 121	96.49 305	98.68 63	90.31 207	94.54 165	97.00 198	76.30 272	99.71 104	95.98 117	93.38 200	99.56 115

PS-MVSNAJss		93.64 180	93.31 177	94.61 220	92.11 296	92.19 226	99.12 200	97.38 225	92.51 153	88.45 241	96.99 199	91.20 124	97.29 234	94.36 142	87.71 233	94.36 224

IB-MVS	92.85 6	94.99 151	93.94 159	98.16 121	97.72 169	95.69 145	99.99 3	98.81 55	94.28 88	92.70 185	96.90 200	95.08 43	99.17 136	96.07 115	73.88 315	99.60 108
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
WR-MVS		92.31 201	91.25 203	95.48 191	94.45 242	95.29 153	99.60 152	98.68 63	90.10 209	88.07 248	96.89 201	80.68 234	96.80 269	93.14 169	79.67 285	94.36 224

SixPastTwentyTwo		88.73 266	88.01 263	90.88 291	91.85 302	82.24 314	98.22 276	95.18 321	88.97 225	82.26 290	96.89 201	71.75 296	96.67 272	84.00 272	82.98 258	93.72 278

UniMVSNet_NR-MVSNet		92.95 189	92.11 192	95.49 189	94.61 241	95.28 154	99.83 94	99.08 31	91.49 180	89.21 233	96.86 203	87.14 167	96.73 270	93.20 166	77.52 300	94.46 215

XVG-ACMP-BASELINE		91.22 225	90.75 208	92.63 268	93.73 254	85.61 298	98.52 256	97.44 217	92.77 135	89.90 210	96.85 204	66.64 314	98.39 180	92.29 175	88.61 222	93.89 266

TinyColmap		87.87 271	86.51 272	91.94 284	95.05 235	85.57 299	97.65 288	94.08 329	84.40 289	81.82 291	96.85 204	62.14 325	98.33 187	80.25 293	86.37 242	91.91 305

EU-MVSNet		90.14 250	90.34 217	89.54 304	92.55 292	81.06 321	98.69 242	98.04 165	91.41 184	86.59 264	96.84 206	80.83 231	93.31 326	86.20 256	81.91 262	94.26 232

TranMVSNet+NR-MVSNet		91.68 213	90.61 210	94.87 211	93.69 255	93.98 177	99.69 132	98.65 66	91.03 196	88.44 242	96.83 207	80.05 243	96.18 285	90.26 207	76.89 307	94.45 220

diffmvs		95.25 144	94.26 154	98.23 120	98.13 143	96.59 113	99.12 200	97.18 237	85.78 273	97.64 102	96.70 208	85.92 178	98.87 143	90.40 204	97.45 128	99.24 158

GA-MVS		93.83 172	92.84 180	96.80 161	95.73 221	93.57 187	99.88 66	97.24 234	92.57 150	92.92 181	96.66 209	78.73 256	97.67 215	87.75 234	94.06 194	99.17 163

CMPMVS	61.59 21	84.75 295	85.14 277	83.57 315	90.32 316	62.54 339	96.98 300	97.59 203	74.33 328	69.95 328	96.66 209	64.17 320	98.32 188	87.88 233	88.41 226	89.84 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DU-MVS		92.46 199	91.45 202	95.49 189	94.05 248	95.28 154	99.81 97	98.74 59	92.25 158	89.21 233	96.64 211	81.66 217	96.73 270	93.20 166	77.52 300	94.46 215

NR-MVSNet		91.56 214	90.22 223	95.60 188	94.05 248	95.76 139	98.25 273	98.70 61	91.16 194	80.78 295	96.64 211	83.23 199	96.57 274	91.41 187	77.73 298	94.46 215

CP-MVSNet		91.23 224	90.22 223	94.26 232	93.96 250	92.39 223	99.09 203	98.57 81	88.95 227	86.42 268	96.57 213	79.19 250	96.37 278	90.29 206	78.95 287	94.02 246

pmmvs4		92.10 204	91.07 206	95.18 196	92.82 288	94.96 159	99.48 168	96.83 278	87.45 252	88.66 240	96.56 214	83.78 194	96.83 267	89.29 220	84.77 252	93.75 274

PS-CasMVS		90.63 237	89.51 240	93.99 243	93.83 252	91.70 243	98.98 219	98.52 90	88.48 235	86.15 272	96.53 215	75.46 278	96.31 281	88.83 224	78.86 289	93.95 260

test-LLR		96.47 113	96.04 99	97.78 134	97.02 185	95.44 148	99.96 19	98.21 146	94.07 95	95.55 144	96.38 216	93.90 80	98.27 193	90.42 202	98.83 102	99.64 102

test-mter		96.39 119	95.93 108	97.78 134	97.02 185	95.44 148	99.96 19	98.21 146	91.81 174	95.55 144	96.38 216	95.17 40	98.27 193	90.42 202	98.83 102	99.64 102

MS-PatchMatch		90.65 235	90.30 218	91.71 287	94.22 246	85.50 300	98.24 274	97.70 192	88.67 232	86.42 268	96.37 218	67.82 311	98.03 203	83.62 276	99.62 77	91.60 308

PEN-MVS		90.19 248	89.06 247	93.57 253	93.06 283	90.90 254	99.06 211	98.47 102	88.11 240	85.91 274	96.30 219	76.67 268	95.94 293	87.07 245	76.91 306	93.89 266

UGNet		95.33 143	94.57 149	97.62 140	98.55 124	94.85 161	98.67 245	99.32 27	95.75 55	96.80 119	96.27 220	72.18 294	99.96 42	94.58 139	99.05 99	98.04 192
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
DTE-MVSNet		89.40 257	88.24 260	92.88 264	92.66 291	89.95 269	99.10 202	98.22 145	87.29 254	85.12 278	96.22 221	76.27 273	95.30 300	83.56 277	75.74 310	93.41 282

TransMVSNet (Re)		87.25 272	85.28 276	93.16 258	93.56 257	91.03 251	98.54 254	94.05 330	83.69 292	81.09 294	96.16 222	75.32 279	96.40 277	76.69 314	68.41 322	92.06 302

pm-mvs1		89.36 259	87.81 264	94.01 241	93.40 267	91.93 232	98.62 249	96.48 290	86.25 268	83.86 284	96.14 223	73.68 290	97.04 254	86.16 257	75.73 311	93.04 292

Test_1112_low_res		95.72 134	94.83 144	98.42 111	97.79 161	96.41 117	99.65 145	96.65 284	92.70 138	92.86 184	96.13 224	92.15 112	99.30 132	91.88 183	93.64 197	99.55 116

TDRefinement		84.76 294	82.56 298	91.38 289	74.58 340	84.80 305	97.36 292	94.56 326	84.73 285	80.21 297	96.12 225	63.56 322	98.39 180	87.92 232	63.97 335	90.95 314

test_djsdf		92.83 191	92.29 190	94.47 226	91.90 300	92.46 221	99.55 159	97.27 232	91.17 192	89.96 207	96.07 226	81.10 227	96.89 263	94.67 137	88.91 215	94.05 245

lessismore_v0				90.53 294	90.58 314	80.90 322		95.80 299		77.01 305	95.84 227	66.15 315	96.95 260	83.03 280	75.05 313	93.74 277

PVSNet_BlendedMVS		96.05 128	95.82 120	96.72 165	99.59 69	96.99 102	99.95 31	99.10 29	94.06 98	98.27 89	95.80 228	89.00 151	99.95 50	99.12 32	87.53 236	93.24 288

pmmvs5		90.17 249	89.09 246	93.40 255	92.10 297	89.77 272	99.74 119	95.58 304	85.88 272	87.24 257	95.74 229	73.41 291	96.48 276	88.54 225	83.56 257	93.95 260

MDTV_nov1_ep13			95.69 122		97.90 152	94.15 174	95.98 314	98.44 106	93.12 123	97.98 97	95.74 229	95.10 42	98.58 163	90.02 209	96.92 144

semantic-postprocess				92.93 263	96.72 200	89.96 268		96.99 259	88.95 227	86.63 263	95.67 231	76.50 270	95.00 303	87.04 246	84.04 256	93.84 270

Baseline_NR-MVSNet		90.33 243	89.51 240	92.81 265	92.84 287	89.95 269	99.77 109	93.94 331	84.69 286	89.04 235	95.66 232	81.66 217	96.52 275	90.99 193	76.98 305	91.97 304

K. test v3		88.05 270	87.24 269	90.47 296	91.82 304	82.23 315	98.96 222	97.42 220	89.05 221	76.93 306	95.60 233	68.49 308	95.42 297	85.87 260	81.01 271	93.75 274

Patchmatch-test		92.65 196	91.50 200	96.10 180	96.85 192	90.49 259	91.50 334	97.19 236	82.76 296	90.23 200	95.59 234	95.02 46	98.00 204	77.41 310	96.98 143	99.82 78

tfpnnormal		89.29 260	87.61 266	94.34 231	94.35 244	94.13 175	98.95 223	98.94 37	83.94 290	84.47 281	95.51 235	74.84 283	97.39 221	77.05 313	80.41 274	91.48 310

DeepMVS_CX				82.92 319	95.98 213	58.66 343		96.01 296	92.72 136	78.34 303	95.51 235	58.29 332	98.08 200	82.57 282	85.29 247	92.03 303

IterMVS		90.91 230	90.17 225	93.12 259	96.78 198	90.42 261	98.89 227	97.05 249	89.03 222	86.49 266	95.42 237	76.59 269	95.02 302	87.22 244	84.09 253	93.93 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		93.07 187	92.13 191	95.88 184	94.84 237	96.24 125	99.88 66	98.98 35	92.49 154	89.25 231	95.40 238	87.09 168	97.14 245	93.13 170	78.16 294	94.26 232

tpm2		95.47 141	95.18 139	96.35 175	96.91 189	91.70 243	96.96 301	97.93 174	88.04 242	98.44 81	95.40 238	93.32 91	97.97 205	94.00 151	95.61 164	99.38 140

pmmvs6		85.69 287	83.84 292	91.26 290	90.00 318	84.41 306	97.82 287	96.15 294	75.86 323	81.29 293	95.39 240	61.21 327	96.87 265	83.52 278	73.29 317	92.50 298

Patchmatch-test1		94.39 166	93.46 170	97.17 153	97.10 181	94.44 169	98.86 233	98.32 135	93.30 121	96.17 133	95.38 241	76.48 271	97.34 225	88.12 231	97.43 129	99.74 87

IterMVS-LS		92.69 194	92.11 192	94.43 229	96.80 195	92.74 213	99.45 172	96.89 273	88.98 224	89.65 221	95.38 241	88.77 153	96.34 280	90.98 194	82.04 261	94.22 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		94.53 163	95.30 135	92.22 281	97.77 162	82.54 312	99.59 153	97.06 245	94.92 69	95.29 149	95.37 243	85.81 179	97.89 210	94.80 133	97.07 141	96.23 206

v2v482		91.30 221	90.07 230	95.01 201	93.13 279	93.79 181	99.77 109	97.02 255	88.05 241	89.25 231	95.37 243	80.73 233	97.15 243	87.28 243	80.04 281	94.09 242

FMVSNet3		92.69 194	91.58 198	95.99 181	98.29 132	97.42 84	99.26 192	97.62 198	89.80 215	89.68 218	95.32 245	81.62 219	96.27 282	87.01 248	85.65 244	94.29 231

MVP-Stereo		90.93 229	90.45 215	92.37 278	91.25 310	88.76 276	98.05 283	96.17 293	87.27 255	84.04 282	95.30 246	78.46 259	97.27 236	83.78 275	99.70 73	91.09 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
anonymousdsp		91.79 211	90.92 207	94.41 230	90.76 313	92.93 210	98.93 225	97.17 239	89.08 220	87.46 253	95.30 246	78.43 260	96.92 262	92.38 174	88.73 220	93.39 284

v1921920		90.46 240	89.12 245	94.50 225	92.96 286	92.46 221	99.49 166	96.98 261	86.10 269	89.61 224	95.30 246	78.55 258	97.03 257	82.17 285	80.89 273	94.01 248

VPA-MVSNet		92.70 193	91.55 199	96.16 178	95.09 232	96.20 126	98.88 228	99.00 34	91.02 197	91.82 189	95.29 249	76.05 276	97.96 207	95.62 124	81.19 266	94.30 230

PatchmatchNet		95.94 131	95.45 130	97.39 149	97.83 158	94.41 170	96.05 313	98.40 123	92.86 127	97.09 114	95.28 250	94.21 71	98.07 202	89.26 221	98.11 116	99.70 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v7		91.20 226	89.99 231	94.82 214	93.57 256	93.41 195	99.57 155	96.98 261	86.83 261	89.88 211	95.22 251	81.01 228	97.14 245	85.53 261	81.31 265	93.90 264

test_0402		85.58 288	83.94 291	90.50 295	93.81 253	85.04 303	98.55 252	95.20 320	76.01 322	79.72 299	95.13 252	64.15 321	96.26 283	66.04 330	86.88 239	90.21 321

tpmrst		96.27 126	95.98 103	97.13 154	97.96 149	93.15 204	96.34 308	98.17 151	92.07 166	98.71 71	95.12 253	93.91 79	98.73 151	94.91 131	96.62 146	99.50 125

v1neww		91.44 215	90.28 219	94.91 209	93.50 259	93.43 191	99.73 124	97.06 245	87.55 245	90.08 201	95.11 254	81.98 208	97.32 228	87.41 239	80.15 278	93.99 254

v7new		91.44 215	90.28 219	94.91 209	93.50 259	93.43 191	99.73 124	97.06 245	87.55 245	90.08 201	95.11 254	81.98 208	97.32 228	87.41 239	80.15 278	93.99 254

v6		91.44 215	90.27 221	94.93 207	93.44 263	93.44 190	99.73 124	97.05 249	87.57 244	90.05 203	95.10 256	81.87 213	97.39 221	87.45 236	80.17 277	93.98 258

tpmp4_e23		95.15 148	94.69 148	96.55 169	97.84 157	91.77 238	97.10 297	97.91 176	88.33 238	97.19 111	95.06 257	93.92 77	98.51 167	89.64 212	95.19 170	99.37 142

V42		91.28 223	90.12 229	94.74 215	93.42 265	93.46 189	99.68 137	97.02 255	87.36 253	89.85 213	95.05 258	81.31 225	97.34 225	87.34 242	80.07 280	93.40 283

EPMVS		96.53 106	96.01 100	98.09 127	98.43 129	96.12 132	96.36 307	99.43 23	93.53 116	97.64 102	95.04 259	94.41 58	98.38 184	91.13 190	98.11 116	99.75 86

v1192		90.62 238	89.25 243	94.72 217	93.13 279	93.07 205	99.50 165	97.02 255	86.33 267	89.56 225	95.01 260	79.22 249	97.09 252	82.34 284	81.16 267	94.01 248

v148		90.70 234	89.63 235	93.92 245	92.97 285	90.97 252	99.75 116	96.89 273	87.51 250	88.27 246	95.01 260	81.67 216	97.04 254	87.40 241	77.17 304	93.75 274

FMVSNet2		91.02 228	89.56 237	95.41 192	97.53 173	95.74 140	98.98 219	97.41 222	87.05 257	88.43 243	95.00 262	71.34 297	96.24 284	85.12 265	85.21 249	94.25 234

v1144		91.09 227	89.83 232	94.87 211	93.25 276	93.69 186	99.62 151	96.98 261	86.83 261	89.64 222	94.99 263	80.94 229	97.05 253	85.08 266	81.16 267	93.87 268

test2356		86.43 278	87.59 267	82.95 318	85.90 326	69.43 331	99.79 103	96.63 285	85.76 274	83.44 286	94.99 263	80.45 241	86.52 340	68.12 326	93.21 201	92.90 293

v144192		90.79 233	89.52 239	94.59 221	93.11 282	92.77 212	99.56 157	96.99 259	86.38 266	89.82 214	94.95 265	80.50 238	97.10 250	83.98 273	80.41 274	93.90 264

v1		91.36 219	90.14 227	95.04 200	93.35 269	93.80 180	99.77 109	97.05 249	87.53 248	89.77 215	94.91 266	81.99 207	97.33 227	86.90 253	79.98 283	94.00 251

testus		83.91 300	84.49 280	82.17 320	85.68 327	66.11 336	99.68 137	93.53 335	86.55 263	82.60 289	94.91 266	56.70 334	88.19 336	68.46 323	92.31 205	92.21 300

v1141		91.36 219	90.14 227	95.00 202	93.33 271	93.79 181	99.78 104	97.05 249	87.52 249	89.75 216	94.89 268	82.13 204	97.21 237	86.84 254	80.00 282	94.00 251

divwei89l23v2f112		91.37 218	90.15 226	95.00 202	93.35 269	93.78 184	99.78 104	97.05 249	87.54 247	89.73 217	94.89 268	82.24 203	97.21 237	86.91 251	79.90 284	94.00 251

CostFormer		96.10 127	95.88 112	96.78 162	97.03 184	92.55 220	97.08 298	97.83 185	90.04 212	98.72 70	94.89 268	95.01 47	98.29 190	96.54 111	95.77 161	99.50 125

v1240		90.20 247	88.79 252	94.44 227	93.05 284	92.27 225	99.38 179	96.92 270	85.89 271	89.36 228	94.87 271	77.89 262	97.03 257	80.66 292	81.08 269	94.01 248

v7n		89.65 254	88.29 259	93.72 249	92.22 294	90.56 258	99.07 209	97.10 243	85.42 281	86.73 262	94.72 272	80.06 242	97.13 247	81.14 290	78.12 295	93.49 281

GBi-Net		90.88 231	89.82 233	94.08 237	97.53 173	91.97 229	98.43 261	96.95 266	87.05 257	89.68 218	94.72 272	71.34 297	96.11 286	87.01 248	85.65 244	94.17 237

test1		90.88 231	89.82 233	94.08 237	97.53 173	91.97 229	98.43 261	96.95 266	87.05 257	89.68 218	94.72 272	71.34 297	96.11 286	87.01 248	85.65 244	94.17 237

FMVSNet1		88.50 267	86.64 271	94.08 237	95.62 228	91.97 229	98.43 261	96.95 266	83.00 294	86.08 273	94.72 272	59.09 331	96.11 286	81.82 288	84.07 254	94.17 237

dp		95.05 149	94.43 151	96.91 158	97.99 148	92.73 214	96.29 309	97.98 169	89.70 216	95.93 136	94.67 276	93.83 83	98.45 173	86.91 251	96.53 148	99.54 120

test20.03		84.72 296	83.99 287	86.91 312	88.19 323	80.62 323	98.88 228	95.94 297	88.36 237	78.87 300	94.62 277	68.75 306	89.11 333	66.52 328	75.82 309	91.00 312

v8		90.54 239	89.17 244	94.66 218	93.43 264	93.40 198	99.20 195	96.94 269	85.76 274	87.56 252	94.51 278	81.96 211	97.19 239	84.94 267	78.25 293	93.38 285

v10		90.25 246	88.82 251	94.57 223	93.53 258	93.43 191	99.08 205	96.87 276	85.00 282	87.34 256	94.51 278	80.93 230	97.02 259	82.85 281	79.23 286	93.26 287

ADS-MVSNet2		93.80 175	93.88 161	93.55 254	97.87 154	85.94 296	94.24 320	96.84 277	90.07 210	96.43 127	94.48 280	90.29 136	95.37 298	87.44 237	97.23 136	99.36 143

ADS-MVSNet		94.79 153	94.02 158	97.11 156	97.87 154	93.79 181	94.24 320	98.16 154	90.07 210	96.43 127	94.48 280	90.29 136	98.19 197	87.44 237	97.23 136	99.36 143

WR-MVS_H		91.30 221	90.35 216	94.15 235	94.17 247	92.62 219	99.17 198	98.94 37	88.87 229	86.48 267	94.46 282	84.36 191	96.61 273	88.19 228	78.51 290	93.21 289

LCM-MVSNet-Re		92.31 201	92.60 185	91.43 288	97.53 173	79.27 326	99.02 217	91.83 340	92.07 166	80.31 296	94.38 283	83.50 196	95.48 296	97.22 99	97.58 126	99.54 120

tpmvs		94.28 168	93.57 167	96.40 173	98.55 124	91.50 248	95.70 318	98.55 87	87.47 251	92.15 187	94.26 284	91.42 120	98.95 142	88.15 229	95.85 159	98.76 184

v52		89.55 255	88.41 257	92.98 261	92.32 293	90.01 267	98.88 228	96.89 273	84.51 287	86.89 259	94.22 285	79.23 248	97.16 241	84.46 269	78.27 292	91.76 306

V4		89.55 255	88.41 257	92.98 261	92.21 295	90.03 266	98.87 231	96.91 271	84.51 287	86.84 260	94.21 286	79.37 247	97.15 243	84.45 270	78.28 291	91.76 306

tpm		93.70 179	93.41 174	94.58 222	95.36 231	87.41 291	97.01 299	96.90 272	90.85 200	96.72 121	94.14 287	90.40 134	96.84 266	90.75 198	88.54 224	99.51 123

Anonymous20231206		86.32 279	85.42 275	89.02 306	89.11 321	80.53 324	99.05 214	95.28 317	85.43 280	82.82 288	93.92 288	74.40 286	93.44 325	66.99 327	81.83 263	93.08 291

testpf		89.10 262	88.73 254	90.24 298	97.59 172	83.48 309	74.22 347	97.39 224	79.66 315	89.64 222	93.92 288	86.38 174	95.76 294	85.42 262	94.31 183	91.49 309

UnsupCasMVSNet_eth		85.52 289	83.99 287	90.10 300	89.36 320	83.51 308	96.65 303	97.99 168	89.14 219	75.89 311	93.83 290	63.25 323	93.92 318	81.92 287	67.90 324	92.88 295

DI_MVS_plusplus_test		92.48 197	90.60 211	98.11 126	91.88 301	96.13 129	99.64 149	97.73 189	92.69 139	76.02 309	93.79 291	70.49 301	99.07 137	95.88 119	97.26 135	99.14 170

tpm cat1		93.51 181	92.52 187	96.47 170	97.77 162	91.47 249	96.13 311	98.06 163	80.98 311	92.91 182	93.78 292	89.66 139	98.87 143	87.03 247	96.39 150	99.09 175

test_normal		92.44 200	90.54 212	98.12 125	91.85 302	96.18 128	99.68 137	97.73 189	92.66 141	75.76 313	93.74 293	70.49 301	99.04 139	95.71 123	97.27 134	99.13 172

EG-PatchMatch MVS		85.35 292	83.81 293	89.99 302	90.39 315	81.89 317	98.21 277	96.09 295	81.78 307	74.73 314	93.72 294	51.56 339	97.12 249	79.16 301	88.61 222	90.96 313

v748		88.94 264	87.72 265	92.61 269	91.91 299	87.50 290	99.07 209	96.97 264	84.76 284	85.79 275	93.63 295	79.19 250	97.04 254	83.16 279	75.03 314	93.28 286

N_pmnet		80.06 306	80.78 303	77.89 323	91.94 298	45.28 352	98.80 236	56.82 356	78.10 319	80.08 298	93.33 296	77.03 264	95.76 294	68.14 325	82.81 259	92.64 297

MDA-MVSNet-bldmvs		84.09 298	81.52 302	91.81 286	91.32 309	88.00 288	98.67 245	95.92 298	80.22 313	55.60 341	93.32 297	68.29 310	93.60 324	73.76 317	76.61 308	93.82 272

CR-MVSNet		93.45 184	92.62 184	95.94 182	96.29 204	92.66 216	92.01 332	96.23 291	92.62 144	96.94 115	93.31 298	91.04 128	96.03 290	79.23 300	95.96 156	99.13 172

Patchmtry		89.70 253	88.49 256	93.33 256	96.24 206	89.94 271	91.37 335	96.23 291	78.22 318	87.69 251	93.31 298	91.04 128	96.03 290	80.18 294	82.10 260	94.02 246

MIMVSNet		90.30 244	88.67 255	95.17 197	96.45 203	91.64 245	92.39 330	97.15 241	85.99 270	90.50 198	93.19 300	66.95 313	94.86 306	82.01 286	93.43 198	99.01 178

YYNet1		85.50 291	83.33 294	92.00 283	90.89 312	88.38 285	99.22 194	96.55 287	79.60 316	57.26 339	92.72 301	79.09 253	93.78 322	77.25 311	77.37 303	93.84 270

MDA-MVSNet_test_wron		85.51 290	83.32 295	92.10 282	90.96 311	88.58 281	99.20 195	96.52 288	79.70 314	57.12 340	92.69 302	79.11 252	93.86 320	77.10 312	77.46 302	93.86 269

MIMVSNet1		82.58 302	80.51 304	88.78 308	86.68 325	84.20 307	96.65 303	95.41 314	78.75 317	78.59 302	92.44 303	51.88 338	89.76 332	65.26 331	78.95 287	92.38 299

FMVSNet5		88.32 268	87.47 268	90.88 291	96.90 190	88.39 284	97.28 295	95.68 301	82.60 297	84.67 280	92.40 304	79.83 244	91.16 329	76.39 315	81.51 264	93.09 290

DSMNet-mixed		88.28 269	88.24 260	88.42 310	89.64 319	75.38 328	98.06 282	89.86 345	85.59 279	88.20 247	92.14 305	76.15 275	91.95 328	78.46 305	96.05 153	97.92 193

patchmatchnet-post											91.70 306	95.12 41	97.95 208

Test4		88.80 265	85.91 274	97.48 144	87.33 324	95.72 142	99.29 189	97.04 254	92.82 130	70.35 327	91.46 307	44.37 342	97.43 220	93.37 164	97.17 139	99.29 152

OpenMVS_ROB	79.82 20	83.77 301	81.68 301	90.03 301	88.30 322	82.82 310	98.46 259	95.22 319	73.92 330	76.00 310	91.29 308	55.00 335	96.94 261	68.40 324	88.51 225	90.34 316

Patchmatch-RL test		86.90 273	85.98 273	89.67 303	84.45 329	75.59 327	89.71 338	92.43 337	86.89 260	77.83 304	90.94 309	94.22 68	93.63 323	87.75 234	69.61 319	99.79 82

LP		86.76 274	84.85 278	92.50 272	95.08 233	85.89 297	89.97 337	96.97 264	75.28 326	84.97 279	90.68 310	80.78 232	95.13 301	61.64 334	88.31 227	96.46 203

FPMVS		68.72 313	68.72 314	68.71 332	65.95 346	44.27 354	95.97 315	94.74 324	51.13 341	53.26 343	90.50 311	25.11 350	83.00 344	60.80 335	80.97 272	78.87 341

new_pmnet		84.49 297	82.92 297	89.21 305	90.03 317	82.60 311	96.89 302	95.62 303	80.59 312	75.77 312	89.17 312	65.04 319	94.79 307	72.12 318	81.02 270	90.23 320

PM-MVS		80.47 304	78.88 306	85.26 314	83.79 331	72.22 329	95.89 316	91.08 341	85.71 278	76.56 308	88.30 313	36.64 343	93.90 319	82.39 283	69.57 320	89.66 330

test1235678		78.45 309	77.88 308	80.16 322	77.83 338	62.18 340	98.36 266	93.45 336	77.46 320	69.08 330	88.23 314	60.33 329	85.41 341	58.46 337	77.68 299	92.90 293

test12356		75.26 310	75.12 311	75.67 327	74.02 341	60.60 342	96.43 306	92.15 338	74.17 329	66.35 333	88.11 315	52.29 337	84.36 343	57.41 338	75.12 312	82.05 338

pmmvs3		80.27 305	77.77 309	87.76 311	80.32 335	82.43 313	98.23 275	91.97 339	72.74 331	78.75 301	87.97 316	57.30 333	90.99 330	70.31 320	62.37 337	89.87 328

1111		79.11 308	78.74 307	80.23 321	78.33 336	67.13 333	97.31 293	93.65 333	71.34 332	68.35 331	87.87 317	85.42 185	88.46 334	52.93 341	73.46 316	85.11 337

.test1245		71.48 312	71.80 312	70.51 331	78.33 336	67.13 333	97.31 293	93.65 333	71.34 332	68.35 331	87.87 317	85.42 185	88.46 334	52.93 341	11.01 351	55.94 350

pmmvs-eth3d		84.03 299	81.97 299	90.20 299	84.15 330	87.09 292	98.10 281	94.73 325	83.05 293	74.10 322	87.77 319	65.56 317	94.01 315	81.08 291	69.24 321	89.49 332

v16		86.52 276	84.49 280	92.60 270	93.45 262	93.31 200	98.60 251	95.52 308	82.30 300	74.59 317	87.70 320	81.95 212	94.18 310	79.93 297	66.38 327	90.30 318

v18		86.59 275	84.57 279	92.65 267	93.41 266	93.43 191	98.69 242	95.55 305	82.44 298	74.71 315	87.68 321	82.11 205	94.21 309	80.14 295	66.37 328	90.32 317

v17		86.51 277	84.49 280	92.57 271	93.38 268	93.29 201	98.61 250	95.54 306	82.32 299	74.69 316	87.63 322	82.03 206	94.17 311	80.02 296	66.17 329	90.26 319

V14		86.22 281	84.15 284	92.41 276	93.30 272	93.16 203	98.47 258	95.47 309	82.10 303	74.27 319	87.41 323	81.73 214	94.02 314	79.26 299	65.37 332	90.04 326

v15		86.26 280	84.19 283	92.47 273	93.29 273	93.28 202	98.53 255	95.47 309	82.24 302	74.34 318	87.34 324	81.71 215	94.07 312	79.39 298	65.42 330	90.06 325

V9		86.16 283	84.07 285	92.43 274	93.27 275	93.04 208	98.40 265	95.45 311	81.98 305	74.18 321	87.31 325	81.58 221	94.06 313	79.12 302	65.33 333	90.20 322

v11		86.09 285	83.98 289	92.42 275	93.29 273	93.41 195	98.52 256	95.30 316	81.73 308	74.27 319	87.20 326	81.24 226	93.85 321	77.68 309	66.61 326	90.00 327

v12		86.10 284	84.01 286	92.37 278	93.23 278	92.96 209	98.33 268	95.45 311	81.87 306	74.05 323	87.15 327	81.60 220	93.98 317	79.09 303	65.28 334	90.18 323

test123		37.68 328	39.14 330	33.31 340	19.94 356	24.83 357	98.36 266	9.75 358	15.53 352	51.31 344	87.14 328	19.62 354	17.74 355	47.10 346	3.47 354	57.36 349

v13		86.06 286	83.97 290	92.34 280	93.25 276	92.85 211	98.26 272	95.44 313	81.70 309	74.02 324	87.11 329	81.58 221	94.00 316	78.94 304	65.41 331	90.18 323

new-patchmatchnet		81.19 303	79.34 305	86.76 313	82.86 332	80.36 325	97.92 285	95.27 318	82.09 304	72.02 325	86.87 330	62.81 324	90.74 331	71.10 319	63.08 336	89.19 334

ambc				83.23 316	77.17 339	62.61 338	87.38 341	94.55 327		76.72 307	86.65 331	30.16 346	96.36 279	84.85 268	69.86 318	90.73 315

testing_2		85.10 293	81.72 300	95.22 195	82.25 333	94.16 173	97.54 289	97.01 258	88.15 239	62.23 335	86.43 332	44.43 341	97.18 240	92.28 180	85.20 250	94.31 229

Anonymous20231211		74.17 311	71.17 313	83.17 317	80.58 334	67.02 335	96.27 310	94.45 328	57.31 340	69.60 329	86.25 333	33.67 344	92.96 327	61.86 333	60.50 339	89.54 331

PatchT		90.38 241	88.75 253	95.25 194	95.99 211	90.16 264	91.22 336	97.54 206	76.80 321	97.26 109	86.01 334	91.88 116	96.07 289	66.16 329	95.91 158	99.51 123

RPMNet		89.39 258	87.20 270	95.94 182	96.29 204	92.66 216	92.01 332	97.63 196	70.19 335	96.94 115	85.87 335	87.25 166	96.03 290	62.69 332	95.96 156	99.13 172

UnsupCasMVSNet_bld		79.97 307	77.03 310	88.78 308	85.62 328	81.98 316	93.66 325	97.35 227	75.51 325	70.79 326	83.05 336	48.70 340	94.91 305	78.31 306	60.29 340	89.46 333

LCM-MVSNet		67.77 314	64.73 317	76.87 324	62.95 350	56.25 345	89.37 339	93.74 332	44.53 344	61.99 336	80.74 337	20.42 353	86.53 339	69.37 322	59.50 341	87.84 335

PMMVS2		67.15 316	64.15 318	76.14 325	70.56 344	62.07 341	93.89 323	87.52 349	58.09 339	60.02 337	78.32 338	22.38 351	84.54 342	59.56 336	47.03 342	81.80 339

JIA-IIPM		91.76 212	90.70 209	94.94 206	96.11 207	87.51 289	93.16 327	98.13 159	75.79 324	97.58 104	77.68 339	92.84 99	97.97 205	88.47 227	96.54 147	99.33 147

testmv		67.54 315	65.93 315	72.37 329	64.46 349	54.05 346	95.09 319	90.07 343	68.90 337	55.16 342	77.63 340	30.39 345	82.61 345	49.42 344	62.26 338	80.45 340

PMVS	49.05 23	53.75 322	51.34 324	60.97 337	40.80 355	34.68 355	74.82 346	89.62 347	37.55 347	28.67 352	72.12 341	7.09 358	81.63 346	43.17 349	68.21 323	66.59 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS-HIRNet		86.22 281	83.19 296	95.31 193	96.71 201	90.29 262	92.12 331	97.33 229	62.85 338	86.82 261	70.37 342	69.37 305	97.49 218	75.12 316	97.99 121	98.15 190

gg-mvs-nofinetune		93.51 181	91.86 196	98.47 106	97.72 169	97.96 64	92.62 329	98.51 96	74.70 327	97.33 108	69.59 343	98.91 3	97.79 212	97.77 89	99.56 83	99.67 96

PNet_i23d		56.44 320	53.54 321	65.14 335	65.34 347	50.33 349	89.06 340	79.57 351	45.77 343	35.75 350	68.95 344	10.75 357	74.40 348	48.48 345	38.20 343	70.70 344

Gipuma		66.95 317	65.00 316	72.79 328	91.52 307	67.96 332	66.16 348	95.15 322	47.89 342	58.54 338	67.99 345	29.74 347	87.54 338	50.20 343	77.83 297	62.87 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		63.48 319	59.26 320	76.14 325	66.71 345	65.06 337	92.75 328	89.92 344	68.96 336	46.96 346	66.55 346	21.74 352	87.68 337	57.07 339	22.69 349	75.68 343

ANet_high		56.10 321	52.24 322	67.66 333	49.27 353	56.82 344	83.94 342	82.02 350	70.47 334	33.28 351	64.54 347	17.23 355	69.16 351	45.59 348	23.85 348	77.02 342

E-PMN		52.30 323	52.18 323	52.67 338	71.51 342	45.40 351	93.62 326	76.60 354	36.01 348	43.50 347	64.13 348	27.11 349	67.31 352	31.06 351	26.06 346	45.30 353

test_post											63.35 349	94.43 57	98.13 198

MVE	53.74 22	51.54 324	47.86 326	62.60 336	59.56 351	50.93 348	79.41 344	77.69 353	35.69 349	36.27 349	61.76 350	5.79 361	69.63 350	37.97 350	36.61 344	67.24 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		51.44 325	51.22 325	52.11 339	70.71 343	44.97 353	94.04 322	75.66 355	35.34 350	42.40 348	61.56 351	28.93 348	65.87 353	27.64 352	24.73 347	45.49 352

test_post1							95.78 317				59.23 352	93.20 95	97.74 213	91.06 192

wuykxyi23d		50.36 326	45.43 327	65.16 334	51.13 352	51.75 347	77.46 345	78.42 352	41.45 345	26.98 353	54.30 353	6.13 359	74.03 349	46.82 347	26.19 345	69.71 345

X-MVStestdata		93.83 172	92.06 194	99.15 48	99.94 14	97.50 78	99.94 45	98.42 119	96.22 39	99.41 39	41.37 354	94.34 63	99.96 42	98.92 44	99.95 39	99.99 11

wuyk23d		20.37 331	20.84 332	18.99 343	65.34 347	27.73 356	50.43 349	7.67 359	9.50 353	8.01 354	6.34 355	6.13 359	26.24 354	23.40 353	10.69 353	2.99 354

pcd_1.5k_mvsjas		7.60 333	10.13 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	91.20 124	0.00 357	0.00 354	0.00 355	0.00 355

pcd1.5k->3k		37.58 329	39.62 329	31.46 341	92.73 290	0.00 359	0.00 350	97.52 210	0.00 354	0.00 355	0.00 356	78.40 261	0.00 357	0.00 354	87.90 230	94.37 223

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																99.59 110

test_part2					99.89 36	99.25 6				99.49 32

test_part1								98.41 121				97.20 11			99.99 13	99.99 11

sam_mvs1												94.72 55				99.59 110

sam_mvs												94.25 67

MTGPA								98.28 139

MTMP								96.49 289

test9_res														99.71 17	99.99 13	100.00 1

agg_prior2														99.48 23	100.00 1	100.00 1

agg_prior					99.93 24	98.77 25		98.43 111		99.63 20			99.85 78

test_prior4						98.05 61	99.94 45

test_prior				99.43 26	99.94 14	98.49 48		98.65 66					99.80 87			99.99 11

旧先验2							99.46 171		94.21 90	99.85 5			99.95 50	96.96 106

新几何2							99.40 175

无先验							99.49 166	98.71 60	93.46 117				100.00 1	94.36 142		99.99 11

原ACMM2							99.90 59

testdata2													99.99 27	90.54 201

segment_acmp												96.68 14

testdata1							99.28 190		96.35 38

test12				99.43 26	99.74 56	98.56 44		98.40 123		99.65 19		94.76 54	99.75 96		99.98 25	99.99 11

plane_prior7					95.71 224	91.59 247

plane_prior6					95.76 219	91.72 242						80.47 239

plane_prior5								97.87 180					98.37 185	97.79 87	89.55 209	94.52 212

plane_prior3						91.64 245			96.63 29	93.01 179

plane_prior2							99.84 90		96.38 34

plane_prior1					95.73 221

plane_prior						91.74 239	99.86 86		96.76 25						89.59 208

n2								0.00 360

nn								0.00 360

door-mid								89.69 346

test11								98.44 106

door								90.31 342

HQP5-MVS						91.85 234

HQP-NCC					95.78 215		99.87 71		96.82 21	93.37 175

ACMP_Plane					95.78 215		99.87 71		96.82 21	93.37 175

BP-MVS														97.92 84

HQP4-MVS										93.37 175			98.39 180			94.53 210

HQP3-MVS								97.89 178							89.60 206

HQP2-MVS												80.65 235

MDTV_nov1_ep13_2view						96.26 121	96.11 312		91.89 171	98.06 95		94.40 59		94.30 145		99.67 96

ACMMP++_ref															87.04 238

ACMMP++															88.23 228

Test By Simon												92.82 101

High-resolution multi-view benchmark