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.

fmvsm_s_conf0.5_n_5		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.5_n_4		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_l_conf0.5_n_3		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.5_n_3		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.5_n_2		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.1_n_2		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_l_conf0.5_n_a		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_l_conf0.5_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.1_n_a		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.1_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.5_n_a		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

fmvsm_s_conf0.5_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

MM		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvsmconf0.1_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvsmconf0.01_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvsmconf_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvsmvis_n_1920		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvsm_n_1920		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_cas_vis1_n_1920		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_vis1_n_1920		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_vis1_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvs1_n		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

mvsany_test1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

APD_test1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_vis1_rt		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_vis3_rt		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvs2		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvs1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_fmvs3		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

mvsany_test3		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

testf1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

APD_test2		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_f		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

SF-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

MVS_0304		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

train_agg		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

HFP-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

region2R		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

1314		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

QAPM		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

h-mvs33		0.30 42	0.30 43	0.30 47	0.30 54	0.30 54	0.30 54	0.30 55	0.30 54	0.30 53	0.30 51	0.30 53	0.30 54	0.30 52	0.30 52	0.30 52

hse-mvs2		0.30 42	0.30 43	0.30 47	0.30 54	0.30 54	0.30 54	0.30 55	0.30 54	0.30 53	0.30 51	0.30 53	0.30 54	0.30 52	0.30 52	0.30 52

CL-MVSNet_self_test		0.30 42	0.30 43	0.30 47	0.30 54	0.30 54	0.30 54	0.30 55	0.30 54	0.30 53	0.30 51	0.30 53	0.30 54	0.30 52	0.30 52	0.30 52

AUN-MVS		0.30 42	0.30 43	0.30 47	0.30 54	0.30 54	0.30 54	0.30 55	0.30 54	0.30 53	0.30 51	0.30 53	0.30 54	0.30 52	0.30 52	0.30 52

LCM-MVSNet		0.37 46	0.36 47	0.37 51	0.37 58	0.37 58	0.37 58	0.32 59	0.37 58	0.37 57	0.37 55	0.37 57	0.37 58	0.37 56	0.37 56	0.37 56

v144192		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v1921920		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v1192		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v1144		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v148		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v7n		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v1240		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v8		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v10		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

v2v482		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

V42		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

GA-MVS		0.40 47	0.40 48	0.40 52	0.40 59	0.40 59	0.40 59	0.40 61	0.40 59	0.40 58	0.40 56	0.40 58	0.40 59	0.40 57	0.40 57	0.40 57

Fast-Effi-MVS+		0.41 59	0.40 48	0.41 64	0.41 71	0.41 71	0.41 71	0.35 60	0.41 71	0.41 70	0.41 68	0.41 70	0.41 71	0.41 69	0.41 69	0.41 69

Fast-Effi-MVS+-dtu		0.44 60	0.44 61	0.44 65	0.44 72	0.44 72	0.44 72	0.44 73	0.44 72	0.44 71	0.44 69	0.44 71	0.44 72	0.44 70	0.44 70	0.44 70

Effi-MVS+		0.58 61	0.56 62	0.62 68	0.53 73	0.62 75	0.53 73	0.53 74	0.53 73	0.62 74	0.62 72	0.53 72	0.62 75	0.62 72	0.53 71	0.62 73

Effi-MVS+-dtu		0.59 62	0.61 64	0.53 66	0.68 80	0.53 73	0.53 73	0.68 81	0.68 80	0.53 72	0.53 70	0.68 79	0.53 73	0.68 78	0.53 71	0.53 71

KD-MVS_self_test		0.60 63	0.60 63	0.60 67	0.60 74	0.60 74	0.60 75	0.60 75	0.60 74	0.60 73	0.60 71	0.60 73	0.60 74	0.60 71	0.60 73	0.60 72

EIA-MVS		0.62 64	0.62 65	0.62 68	0.62 75	0.62 75	0.62 76	0.62 76	0.62 75	0.62 74	0.62 72	0.62 74	0.62 75	0.62 72	0.62 74	0.62 73

ETV-MVS		0.62 64	0.62 65	0.62 68	0.62 75	0.62 75	0.62 76	0.62 76	0.62 75	0.62 74	0.62 72	0.62 74	0.62 75	0.62 72	0.62 74	0.62 73

CS-MVS		0.62 64	0.62 65	0.62 68	0.62 75	0.62 75	0.62 76	0.62 76	0.62 75	0.62 74	0.62 72	0.62 74	0.62 75	0.62 72	0.62 74	0.62 73

SPE-MVS-test		0.62 64	0.62 65	0.62 68	0.62 75	0.62 75	0.62 76	0.62 76	0.62 75	0.62 74	0.62 72	0.62 74	0.62 75	0.62 72	0.62 74	0.62 73

EC-MVSNet		0.62 64	0.62 65	0.62 68	0.62 75	0.62 75	0.62 76	0.62 76	0.62 75	0.62 74	0.62 72	0.62 74	0.62 75	0.62 72	0.62 74	0.62 73

CDS-MVSNet		0.88 69	0.88 70	0.88 74	0.88 81	0.88 81	0.88 81	0.88 82	0.88 81	0.88 80	0.88 78	0.88 80	0.88 81	0.88 79	0.88 79	0.88 79
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		0.88 69	0.88 70	0.88 74	0.88 81	0.88 81	0.88 81	0.88 82	0.88 81	0.88 80	0.88 78	0.88 80	0.88 81	0.88 79	0.88 79	0.88 79

LCM-MVSNet-Re		0.89 71	0.89 72	0.89 76	0.89 83	0.89 83	0.89 83	0.89 84	0.89 83	0.89 82	0.89 80	0.89 82	0.89 83	0.89 81	0.89 81	0.89 81

MDA-MVSNet-bldmvs		1.20 72	1.20 73	1.20 78	1.20 85	1.20 86	1.20 84	1.20 87	1.20 85	1.20 84	1.20 82	1.20 84	1.20 85	1.20 83	1.20 83	1.20 83

YYNet1		1.20 72	1.20 73	1.20 78	1.20 85	1.20 86	1.20 84	1.20 87	1.20 85	1.20 84	1.20 82	1.20 84	1.20 85	1.20 83	1.20 83	1.20 83

MDA-MVSNet_test_wron		1.20 72	1.20 73	1.20 78	1.20 85	1.20 86	1.20 84	1.20 87	1.20 85	1.20 84	1.20 82	1.20 84	1.20 85	1.20 83	1.20 83	1.20 83

KD-MVS_2432*1600		1.30 75	1.30 76	1.30 81	1.30 88	1.30 89	1.30 87	1.30 90	1.30 88	1.30 87	1.30 85	1.30 87	1.30 88	1.30 86	1.30 86	1.30 86

miper_refine_blended		1.30 75	1.30 76	1.30 81	1.30 88	1.30 89	1.30 87	1.30 90	1.30 88	1.30 87	1.30 85	1.30 87	1.30 88	1.30 86	1.30 86	1.30 86

EI-MVSNet-UG-set		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

EI-MVSNet-Vis-set		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

EI-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

Regformer		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

CVMVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

EU-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

PS-CasMVS		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

UniMVSNet_NR-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

PEN-MVS		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

TransMVSNet (Re)		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

DTE-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

DU-MVS		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

UniMVSNet (Re)		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

CP-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

WR-MVS_H		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

WR-MVS		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

NR-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

Baseline_NR-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

TranMVSNet+NR-MVSNet		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

MVSFormer		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

jason		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88
jason: jason.
lupinMVS		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

test_djsdf		1.42 77	1.42 78	1.42 83	1.42 90	1.42 91	1.42 89	1.42 92	1.42 90	1.42 89	1.42 87	1.42 89	1.42 90	1.42 88	1.42 88	1.42 88

reproduce_monomvs		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

mmtdpeth		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

reproduce_model		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

reproduce-ours		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

our_new_method		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

mmdepth		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

monomultidepth		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

mvs5depth		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

MVStest1		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

ttmdpeth		1.48 100	1.48 101	1.48 106	1.48 114	1.48 114	1.48 112	1.48 117	1.48 113	1.48 112	1.48 110	1.48 112	1.48 113	1.48 111	1.48 111	1.48 111

alignmvs		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

nrg030		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

LFMVS		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

VDD-MVS		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

VDDNet		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

VPNet		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

VNet		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

VPA-MVSNet		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

cascas		2.00 110	2.00 111	2.00 116	2.00 124	2.00 124	2.00 122	2.00 127	2.00 123	2.00 122	2.00 120	2.00 122	2.00 123	2.00 121	2.00 121	2.00 121

UnsupCasMVSNet_eth		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

UnsupCasMVSNet_bld		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

WTY-MVS		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

HY-MVS	5.90 1	5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

sss		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

Test_1112_low_res		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

1112_ss		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

ab-mvs-re		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

ab-mvs		5.90 119	5.90 120	5.90 125	5.90 133	5.90 133	5.90 132	5.90 139	5.90 133	5.90 131	5.90 129	5.90 133	5.90 132	5.90 130	5.90 133	5.90 133

CDPH-MVS		6.66 128	6.66 129	6.66 134	6.66 142	6.66 142	6.66 141	6.66 148	6.66 142	6.66 140	6.66 138	6.66 142	6.66 141	6.66 139	6.66 142	6.66 142

xiu_mvs_v1_base_debu		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

xiu_mvs_v2_base		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

xiu_mvs_v1_base		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

xiu_mvs_v1_base_debi		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

cdsmvs_eth3d_5k		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

pcd_1.5k_mvsjas		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

PS-MVSNAJss		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

PS-MVSNAJ		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

jajsoiax		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

mvs_tets		10.28 129	10.28 131	10.28 136	10.28 144	10.28 146	10.28 144	10.28 151	10.28 145	10.28 144	10.28 140	10.28 146	10.28 144	10.28 142	10.28 145	10.28 144

SR-MVS-dyc-post		11.55 139	11.55 141	11.55 148	11.55 155	11.55 157	11.55 154	11.55 163	11.55 155	11.55 155	11.55 150	11.55 157	11.55 154	11.55 152	11.55 155	11.55 154

XXY-MVS		15.00 140	15.00 142	15.00 149	15.00 156	15.00 159	15.00 155	15.00 165	15.00 156	15.00 157	15.00 151	15.00 161	15.00 156	15.00 154	15.00 157	15.00 156

test_method		18.29 141	20.89 143	10.50 146	15.00 156	13.00 158	55.00 161	3.00 137	34.00 158	11.50 154	32.50 152	5.00 132	13.50 155	27.00 156	5.00 132	5.00 132

GeoE		19.41 142	22.09 144	11.37 147	17.21 158	11.30 156	50.50 160	4.30 138	33.60 157	11.70 156	44.50 157	7.30 143	18.90 157	22.60 155	7.10 143	3.90 130

BH-w/o		40.00 143	40.00 145	40.00 151	40.00 159	40.00 160	40.00 156	40.00 184	40.00 159	40.00 159	40.00 153	40.00 178	40.00 159	40.00 157	40.00 171	40.00 167

BH-untuned		40.00 143	40.00 145	40.00 151	40.00 159	40.00 160	40.00 156	40.00 184	40.00 159	40.00 159	40.00 153	40.00 178	40.00 159	40.00 157	40.00 171	40.00 167

BH-RMVSNet		40.00 143	40.00 145	40.00 151	40.00 159	40.00 160	40.00 156	40.00 184	40.00 159	40.00 159	40.00 153	40.00 178	40.00 159	40.00 157	40.00 171	40.00 167

TR-MVS		40.00 143	40.00 145	40.00 151	40.00 159	40.00 160	40.00 156	40.00 184	40.00 159	40.00 159	40.00 153	40.00 178	40.00 159	40.00 157	40.00 171	40.00 167

GBi-Net		44.67 147	49.24 149	30.96 150	47.53 163	47.57 164	126.09 162	8.00 149	76.99 163	26.09 158	73.50 159	11.27 156	30.55 158	61.12 161	12.58 156	14.75 155

dp		87.20 148	113.80 152	7.42 135	10.50 154	8.88 143	298.82 169	2.70 136	510.35 188	7.79 141	172.93 162	3.37 131	9.16 142	14.00 153	3.71 131	4.23 131

new_pmnet		88.14 149	98.62 150	56.70 155	98.14 170	56.07 165	246.79 164	15.37 166	148.06 164	50.77 163	143.13 160	23.35 162	62.65 163	93.26 162	68.71 181	51.38 177

N_pmnet		88.14 149	98.62 150	56.70 155	98.14 170	56.07 165	246.79 164	15.37 166	148.06 164	50.77 163	143.13 160	23.35 162	62.65 163	93.26 162	68.71 181	51.38 177

CANet		110.62 151	124.04 153	70.34 158	82.03 167	74.27 170	325.54 170	25.25 169	207.16 166	69.28 168	194.38 163	27.53 164	95.79 172	147.76 166	37.43 161	40.97 171

CANet_DTU		110.62 151	124.04 153	70.34 158	82.03 167	74.27 170	325.54 170	25.25 169	207.16 166	69.28 168	194.38 163	27.53 164	95.79 172	147.76 166	37.43 161	40.97 171

UGNet		110.62 151	124.04 153	70.34 158	82.03 167	74.27 170	325.54 170	25.25 169	207.16 166	69.28 168	194.38 163	27.53 164	95.79 172	147.76 166	37.43 161	40.97 171
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
ACMMP_NAP		118.81 154	133.18 156	75.70 161	74.28 165	73.29 168	284.50 166	11.35 161	238.58 170	65.86 166	255.35 178	14.55 159	115.69 180	222.18 180	31.94 159	38.12 165

FMVSNet5		124.76 155	140.93 157	76.26 163	107.44 173	100.17 177	329.88 173	27.12 175	259.44 175	74.30 171	204.34 166	30.74 167	92.41 165	196.98 169	38.15 164	36.19 158

test1		124.76 155	140.93 157	76.26 163	107.44 173	100.17 177	329.88 173	27.12 175	259.44 175	74.30 171	204.34 166	30.74 167	92.41 165	196.98 169	38.15 164	36.19 158

FMVSNet3		124.76 155	140.93 157	76.26 163	107.44 173	100.17 177	329.88 173	27.12 175	259.44 175	74.30 171	204.34 166	30.74 167	92.41 165	196.98 169	38.15 164	36.19 158

FMVSNet2		124.76 155	140.93 157	76.26 163	107.44 173	100.17 177	329.88 173	27.12 175	259.44 175	74.30 171	204.34 166	30.74 167	92.41 165	196.98 169	38.15 164	36.19 158

FMVSNet1		124.76 155	140.93 157	76.26 163	107.44 173	100.17 177	329.88 173	27.12 175	259.44 175	74.30 171	204.34 166	30.74 167	92.41 165	196.98 169	38.15 164	36.19 158

MIMVSNet1		124.76 155	140.93 157	76.26 163	107.44 173	100.17 177	329.88 173	27.12 175	259.44 175	74.30 171	204.34 166	30.74 167	92.41 165	196.98 169	38.15 164	36.19 158

MIMVSNet		124.76 155	140.93 157	76.26 163	107.44 173	100.17 177	329.88 173	27.12 175	259.44 175	74.30 171	204.34 166	30.74 167	92.41 165	196.98 169	38.15 164	36.19 158

DSMNet-mixed		131.33 162	143.00 164	96.33 173	99.00 172	81.00 173	201.00 163	45.00 189	335.00 182	99.00 181	251.00 177	38.00 177	110.00 179	120.00 165	99.00 187	98.00 183

PVSNet_Blended_VisFu		136.90 163	155.40 165	81.40 170	111.00 180	96.20 174	407.00 180	25.90 172	251.60 172	85.10 178	240.50 174	37.00 174	99.90 175	199.80 176	40.70 175	48.10 174

PVSNet_BlendedMVS		136.90 163	155.40 165	81.40 170	111.00 180	96.20 174	407.00 180	25.90 172	251.60 172	85.10 178	240.50 174	37.00 174	99.90 175	199.80 176	40.70 175	48.10 174

PVSNet_Blended		136.90 163	155.40 165	81.40 170	111.00 180	96.20 174	407.00 180	25.90 172	251.60 172	85.10 178	240.50 174	37.00 174	99.90 175	199.80 176	40.70 175	48.10 174

BP-MVS1		202.65 166	232.15 168	114.16 174	183.40 184	141.78 184	655.87 188	24.69 168	372.56 183	109.76 182	356.89 180	47.15 182	139.51 182	284.15 182	54.86 178	61.20 179

FE-MVS		207.10 167	234.26 169	125.61 175	180.34 183	153.23 185	635.64 187	30.05 182	378.38 184	127.68 183	377.08 181	48.95 183	154.43 183	266.67 181	63.56 179	69.18 180

GDP-MVS		219.79 168	249.11 170	131.85 176	187.15 185	155.97 186	667.31 189	30.57 183	398.95 185	131.26 184	387.86 182	58.83 185	164.55 184	315.00 183	65.03 180	75.04 181

FA-MVS(test-final)		261.15 169	293.29 171	164.72 177	236.23 188	199.99 191	800.05 195	42.35 188	475.87 187	155.47 185	470.67 184	58.49 184	205.24 186	319.44 184	81.03 184	88.93 182

DP-MVS		264.78 170	296.02 172	171.04 179	191.70 186	183.96 188	827.10 196	78.36 200	438.24 186	176.64 187	435.12 183	65.04 187	230.52 189	334.02 185	117.96 195	98.64 184

kuosan		267.89 171	301.05 173	168.41 178	244.05 189	177.28 187	667.98 190	65.76 191	533.41 189	157.79 186	492.01 185	104.69 200	226.72 187	364.47 187	79.31 183	101.21 185

WB-MVS		316.44 172	357.41 174	193.51 181	257.06 190	213.32 194	914.45 198	49.03 190	600.77 195	186.42 189	634.00 197	75.03 190	251.35 191	411.39 188	88.55 185	115.87 187

SSC-MVS		320.23 173	364.09 175	188.66 180	260.78 191	193.84 189	975.93 204	73.11 196	596.72 194	185.29 188	589.02 191	74.47 189	246.01 190	427.42 191	94.06 186	126.12 188

UWE-MVS-28		327.73 174	369.67 176	201.90 182	281.09 197	210.75 192	832.98 197	74.67 199	680.98 202	198.23 190	613.42 194	112.56 203	259.80 192	418.76 189	114.35 189	135.15 194

testing91		347.05 175	389.92 177	218.43 184	275.92 195	241.43 196	923.71 200	71.70 192	667.65 200	217.04 194	629.00 195	113.33 204	281.39 194	495.50 194	115.45 191	132.47 191

testing11		348.86 176	393.09 178	216.15 183	275.24 194	239.53 195	915.55 199	72.35 193	679.89 201	215.75 193	633.79 196	118.08 205	276.62 193	509.56 202	117.61 194	132.30 190

dongtai		360.45 177	401.64 179	236.89 187	303.10 198	262.16 202	963.12 201	72.78 194	622.82 198	199.37 191	708.06 200	130.96 208	310.90 197	506.46 201	108.11 188	137.60 195

Syy-MVS		370.76 178	415.60 181	236.25 186	310.54 199	210.76 193	965.13 202	98.58 215	746.06 207	266.26 201	788.85 207	91.37 194	364.76 205	357.17 186	116.42 192	133.22 192

tmp_tt		371.08 179	412.78 180	246.00 190	231.00 187	197.00 190	1293.00 223	376.00 264	756.00 209	609.00 242	71.00 158	106.00 201	195.00 185	102.00 164	171.00 212	346.00 238

UWE-MVS		387.98 180	438.66 183	235.93 185	349.32 204	257.61 200	1081.50 212	82.45 204	637.74 199	257.12 198	738.54 202	134.25 209	308.00 195	531.93 204	135.11 198	142.17 196

myMVS_eth3d		390.90 181	437.44 182	251.28 192	310.54 199	255.86 199	965.13 202	98.58 215	746.06 207	266.26 201	788.85 207	154.95 216	364.76 205	490.19 193	116.42 192	133.22 192

RPSCF		394.81 182	445.63 184	242.35 188	276.55 196	253.63 198	1207.27 220	90.69 209	715.56 203	241.57 197	684.87 198	111.96 202	320.02 199	548.38 206	133.81 197	153.39 200

WB-MVSnew		404.84 183	458.00 185	245.36 189	374.70 208	320.27 210	1193.74 219	72.90 195	773.77 210	238.30 195	688.22 199	90.05 193	308.59 196	532.76 205	157.54 206	107.21 186

testing99		408.82 184	462.15 186	248.86 191	322.45 201	269.42 203	1118.93 215	73.26 197	719.36 205	262.28 199	772.10 204	150.98 215	327.08 200	601.98 210	137.99 199	150.07 198

testing3		418.10 185	469.06 187	265.23 195	335.69 202	290.63 205	1240.88 221	93.72 210	717.99 204	215.31 192	811.97 213	232.41 247	358.74 203	458.67 192	114.88 190	146.32 197

myMVS_eth3d28		418.40 186	473.07 188	254.39 193	261.18 192	313.04 206	1172.79 218	74.16 198	929.18 218	264.68 200	745.72 203	139.03 213	318.91 198	549.18 207	121.71 196	131.21 189

testing222		430.52 187	487.50 191	259.56 194	379.87 212	282.69 204	1138.34 216	81.30 202	880.86 214	281.58 204	834.27 215	135.83 211	333.22 201	514.90 203	140.56 200	162.76 201

EG-PatchMatch MVS		437.03 188	493.12 192	268.77 196	264.80 193	260.20 201	1491.80 227	98.00 214	723.20 206	238.80 196	722.40 201	63.10 186	382.00 209	629.00 214	207.00 221	164.10 204

ADS-MVSNet2		443.33 189	482.84 189	324.79 208	434.94 221	387.73 219	1110.57 213	87.69 206	887.02 215	282.92 205	779.29 205	98.57 198	422.76 217	502.40 195	162.19 210	163.87 202

ADS-MVSNet		443.33 189	482.84 189	324.79 208	434.94 221	387.73 219	1110.57 213	87.69 206	887.02 215	282.92 205	779.29 205	98.57 198	422.76 217	502.40 195	162.19 210	163.87 202

ETVMVS		443.50 191	498.07 193	279.78 197	378.05 211	321.57 211	1163.68 217	87.12 205	897.50 217	269.81 203	822.83 214	141.20 214	334.53 202	568.21 209	154.21 201	183.26 208

MVP-Stereo		473.58 192	533.67 194	293.33 199	376.00 209	318.00 207	1461.00 225	96.00 211	835.00 212	290.00 208	804.00 210	97.00 196	383.00 211	688.00 222	156.00 203	179.00 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		473.58 192	533.67 194	293.33 199	376.00 209	318.00 207	1461.00 225	96.00 211	835.00 212	290.00 208	804.00 210	97.00 196	383.00 211	688.00 222	156.00 203	179.00 206

testing3-2		489.51 194	555.88 197	290.42 198	362.21 206	319.27 209	1424.84 224	88.81 208	964.80 219	309.62 211	879.13 217	174.38 223	363.98 204	643.82 218	155.29 202	188.01 209

PatchmatchNet		492.52 195	551.22 196	316.40 206	400.20 217	357.90 217	1277.10 222	115.80 222	1034.60 228	315.90 212	874.00 216	155.10 217	402.20 214	629.50 215	158.80 207	189.10 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmvs		501.65 196	567.42 198	304.33 202	395.39 216	356.32 216	1512.04 228	216.53 251	994.15 221	350.16 217	804.16 212	119.38 206	380.98 208	555.14 208	159.87 208	175.69 205

DELS-MVS		514.57 197	587.73 200	295.12 201	343.03 203	328.09 214	1540.48 229	116.22 223	988.59 220	325.44 215	906.22 218	119.86 207	365.72 207	788.60 226	161.09 209	191.55 212
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
DP-MVS Recon		516.25 198	581.17 199	321.48 207	361.02 205	429.00 223	1995.36 235	78.48 201	796.20 211	283.32 207	796.02 209	96.78 195	382.98 210	636.72 216	186.66 218	152.46 199

gg-mvs-nofinetune		594.69 199	605.37 201	562.67 229	617.29 237	500.33 227	772.30 191	413.12 272	579.21 190	552.71 236	567.40 186	662.54 291	531.67 221	503.93 197	779.80 269	656.02 253

SR-MVS		610.10 200	610.10 202	610.10 234	610.10 231	610.10 244	610.10 185	610.10 296	610.10 196	610.10 243	610.10 192	610.10 279	610.10 237	610.10 211	610.10 252	610.10 250

CR-MVSNet		612.36 201	617.81 204	596.01 232	611.29 233	600.33 241	790.30 192	513.12 287	579.21 190	552.71 236	567.40 186	662.54 291	531.67 221	503.93 197	779.80 269	656.02 253

IterMVS		615.05 202	691.66 205	385.23 212	516.40 224	454.80 224	1863.80 233	102.10 219	1142.20 230	364.10 218	1093.20 232	135.00 210	480.40 220	826.90 227	181.20 215	220.50 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IB-MVS	872.44 4	616.41 203	700.96 206	362.78 210	426.10 220	409.25 221	1822.69 232	124.42 234	1186.23 231	366.85 219	1079.42 230	190.60 235	451.78 219	930.72 231	181.59 216	227.30 217
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
WBMVS		655.88 204	770.84 209	310.98 205	391.79 215	353.00 215	2280.68 237	99.17 218	1776.79 251	301.20 210	1123.23 233	187.34 233	390.28 213	620.39 213	156.98 205	189.66 211

ACMH+	729.97 2	661.38 205	754.00 207	383.51 211	368.96 207	376.38 218	2483.74 249	82.42 203	1089.25 229	333.39 216	1219.68 234	66.53 188	573.60 234	957.86 232	184.14 217	200.56 215

SSC-MVS3.2		667.82 206	787.71 210	308.16 203	385.94 213	327.65 212	1718.51 230	117.00 224	1942.46 256	317.12 213	1488.73 247	183.89 224	404.82 215	763.70 224	172.02 213	192.01 213

UBG		667.82 206	787.71 210	308.16 203	385.94 213	327.65 212	1718.51 230	117.00 224	1942.46 256	317.12 213	1488.73 247	183.89 224	404.82 215	763.70 224	172.02 213	192.01 213

Gipuma		689.75 208	767.00 208	458.00 221	613.00 236	549.00 239	1943.00 234	187.00 248	1230.00 232	518.00 233	1092.00 231	203.00 241	564.00 226	892.00 230	225.00 231	261.00 225
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
IterMVS-LS		758.94 209	856.56 214	466.10 222	606.60 229	546.80 237	2309.90 238	122.20 233	1402.80 236	463.50 223	1348.20 236	192.60 236	574.60 235	1028.60 242	234.60 233	276.90 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet		766.12 210	859.03 215	487.38 225	595.68 226	536.14 229	2346.27 239	142.51 239	1488.08 238	422.43 220	1361.32 238	163.65 220	628.60 239	1003.74 241	207.61 222	297.41 231

SixPastTwentyTwo		773.62 211	869.37 217	486.35 224	603.45 228	547.15 238	2532.14 250	106.92 221	1434.95 237	428.08 221	1360.56 237	165.15 221	628.96 240	984.09 233	209.01 223	282.94 228

mvs_anonymous		774.90 212	882.28 218	452.77 214	636.50 238	537.20 230	2348.80 240	120.30 226	1504.30 239	474.40 224	1371.10 239	186.90 226	564.70 227	1078.90 244	219.30 224	256.40 218

casdiffmvs		774.90 212	882.28 218	452.77 214	636.50 238	537.20 230	2348.80 240	120.30 226	1504.30 239	474.40 224	1371.10 239	186.90 226	564.70 227	1078.90 244	219.30 224	256.40 218
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		774.90 212	882.28 218	452.77 214	636.50 238	537.20 230	2348.80 240	120.30 226	1504.30 239	474.40 224	1371.10 239	186.90 226	564.70 227	1078.90 244	219.30 224	256.40 218
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		774.90 212	882.28 218	452.77 214	636.50 238	537.20 230	2348.80 240	120.30 226	1504.30 239	474.40 224	1371.10 239	186.90 226	564.70 227	1078.90 244	219.30 224	256.40 218

baseline1		774.90 212	882.28 218	452.77 214	636.50 238	537.20 230	2348.80 240	120.30 226	1504.30 239	474.40 224	1371.10 239	186.90 226	564.70 227	1078.90 244	219.30 224	256.40 218

casdiffmvs_mvg		774.90 212	882.28 218	452.77 214	636.50 238	537.20 230	2348.80 240	120.30 226	1504.30 239	474.40 224	1371.10 239	186.90 226	564.70 227	1078.90 244	219.30 224	256.40 218
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		774.90 212	882.28 218	452.77 214	636.50 238	537.20 230	2348.80 240	120.30 226	1504.30 239	474.40 224	1371.10 239	186.90 226	564.70 227	1078.90 244	219.30 224	256.40 218

RPMNet		787.26 219	829.13 212	661.63 238	885.90 253	895.90 261	455.98 183	610.10 296	1297.17 233	1002.26 265	1001.67 228	655.98 289	611.10 238	667.90 219	885.21 281	477.90 247

JIA-IIPM		804.30 220	835.64 213	710.26 243	885.90 253	895.90 261	455.98 183	668.73 301	1297.17 233	1002.26 265	1001.67 228	655.98 289	756.98 244	667.90 219	885.21 281	477.90 247

CSCG		826.03 221	963.29 226	414.24 213	421.52 219	426.11 222	2230.35 236	179.52 247	1827.44 252	733.43 253	933.12 220	281.25 249	545.23 225	1800.35 261	262.64 235	271.38 226

PVSNet	1207.94 6	829.56 222	937.18 225	506.69 226	645.55 245	585.72 240	2477.98 248	130.52 235	1578.29 247	509.50 232	1460.01 246	194.90 237	637.19 241	1170.78 252	267.07 237	297.17 230

PatchT		856.34 223	860.99 216	842.38 248	1290.90 268	880.90 259	790.30 192	1267.30 319	579.21 190	923.90 255	567.40 186	662.54 291	990.23 252	887.56 229	779.80 269	656.02 253

K. test v3		877.74 224	980.16 227	570.46 231	1316.85 270	813.15 256	2565.52 251	103.78 220	1509.49 246	548.33 235	1258.90 235	219.93 246	587.82 236	1102.37 251	196.30 219	310.42 233

MVSTER		890.06 225	1005.91 234	542.52 227	736.13 250	653.90 248	2616.56 252	145.22 241	1692.10 250	555.69 239	1554.72 250	266.25 248	660.02 242	1219.66 253	266.84 236	313.64 234

PMVS	863.65 3	957.08 226	1054.11 235	666.00 240	401.00 218	495.00 226	3790.00 257	279.00 253	1607.00 249	521.00 234	1745.00 252	90.00 191	1105.00 263	639.00 217	415.00 248	398.00 245
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMH	993.80 5	967.92 227	1106.11 237	553.33 228	494.00 223	534.00 228	3581.00 255	136.00 236	1585.00 248	478.00 231	1871.00 254	90.00 191	839.00 246	1460.00 255	260.00 234	287.00 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PVSNet_0	1661.03 9	968.54 228	1066.82 236	673.70 241	706.94 248	695.69 249	2669.02 253	153.52 243	2108.17 259	605.54 241	1679.14 251	213.26 245	886.75 249	1236.76 254	229.01 232	438.67 246

OurMVSNet-221017-0		999.00 229	999.00 228	999.00 249	999.00 258	999.00 263	999.00 205	999.00 312	999.00 222	999.00 258	999.00 221	999.00 304	999.00 254	999.00 234	999.00 288	999.00 285

EPMVS		999.00 229	999.00 228	999.00 249	999.00 258	999.00 263	999.00 205	999.00 312	999.00 222	999.00 258	999.00 221	999.00 304	999.00 254	999.00 234	999.00 288	999.00 285

uanet_test		1000.00 231	1000.00 230	1000.00 252	1000.00 261	1000.00 266	1000.00 208	1000.00 315	1000.00 224	1000.00 261	1000.00 224	1000.00 307	1000.00 257	1000.00 237	1000.00 291	1000.00 288

sosnet		1000.00 231	1000.00 230	1000.00 252	1000.00 261	1000.00 266	1000.00 208	1000.00 315	1000.00 224	1000.00 261	1000.00 224	1000.00 307	1000.00 257	1000.00 237	1000.00 291	1000.00 288

USDC		1000.00 231	1000.00 230	1000.00 252	1000.00 261	1000.00 266	1000.00 208	1000.00 315	1000.00 224	1000.00 261	1000.00 224	1000.00 307	1000.00 257	1000.00 237	1000.00 291	1000.00 288

TinyColmap		1000.00 231	1000.00 230	1000.00 252	1000.00 261	1000.00 266	1000.00 208	1000.00 315	1000.00 224	1000.00 261	1000.00 224	1000.00 307	1000.00 257	1000.00 237	1000.00 291	1000.00 288

ACMP	1246.57 7	1086.24 235	1235.17 239	639.45 235	596.53 227	639.91 246	3904.24 258	152.04 242	1851.54 253	566.58 240	1997.95 256	138.50 212	938.30 250	1605.46 257	303.70 243	340.14 236
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Patchmtry		1090.75 236	1198.88 238	766.38 247	1290.90 268	485.90 225	2423.98 247	1267.30 319	1297.17 233	923.90 255	923.40 219	1215.54 312	990.23 252	667.90 219	779.80 269	823.02 267

test_0402		1110.00 237	1260.00 240	660.00 237	900.00 256	780.00 255	3300.00 254	210.00 250	2040.00 258	690.00 248	1950.00 255	300.00 252	810.00 245	1620.00 258	330.00 244	390.00 244

MVS_111021_LR		1136.45 238	1298.72 241	649.64 236	756.17 251	726.26 252	3785.94 256	138.15 238	2190.64 260	629.63 246	2064.39 257	165.45 222	874.83 248	1678.43 260	279.70 238	347.82 239

ACMM	1311.08 8	1165.06 239	1332.72 242	662.07 239	584.90 225	715.99 251	4373.59 262	153.99 244	1880.27 254	709.02 250	2316.97 258	188.63 234	939.89 251	1484.30 256	302.78 242	330.34 235
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSLP-MVS++		1242.93 240	1411.08 243	738.48 245	712.71 249	712.68 250	4168.17 260	136.50 237	2276.40 261	613.28 245	2368.60 261	161.61 219	1130.38 264	1964.54 264	297.88 240	372.39 241

MG-MVS		1256.50 241	1432.00 244	730.00 244	610.00 230	760.00 253	4720.00 263	169.00 245	1920.00 255	723.00 252	2510.00 264	201.00 239	1050.00 261	1645.00 259	390.00 247	380.00 242

ACMMPR		1296.60 242	1480.82 245	743.96 246	899.25 255	763.47 254	4305.47 261	143.90 240	2530.94 264	683.99 247	2346.34 260	201.65 240	1098.25 262	1920.59 263	295.21 239	370.16 240

TSAR-MVS + GP.		1386.84 243	1622.84 247	678.81 242	952.13 257	840.17 258	5358.21 266	170.75 246	2650.47 265	713.54 251	2394.28 262	209.42 244	855.56 247	1854.36 262	302.42 241	340.71 237

FPMVS		1407.00 244	1495.59 246	1141.23 257	648.01 246	893.85 260	4124.00 259	503.81 286	3219.04 267	742.72 254	2498.84 263	157.98 218	1727.83 270	832.93 228	732.98 268	802.00 262

DeepC-MVS_fast	2190.19 12	1535.59 245	1886.82 249	481.89 223	847.73 252	830.16 257	7943.84 282	189.23 249	2398.83 262	691.73 249	2341.13 259	209.16 243	230.25 188	1999.25 265	360.46 245	385.25 243
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	2144.59 11	1658.33 246	1869.33 248	1025.33 256	1030.00 265	1023.00 270	5311.00 265	359.00 262	3044.00 266	937.00 257	2892.00 265	332.00 256	1440.00 267	2312.00 267	607.00 251	613.00 252
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
F-COLMAP		1878.17 247	2113.11 250	1173.33 260	1231.00 266	1174.00 271	6297.00 268	392.00 267	3356.00 269	1087.00 267	3082.00 266	361.00 260	1670.00 269	2523.00 269	689.00 258	676.00 257

OpenMVS_ROB	1936.51 10	1959.51 248	2224.88 252	1163.39 259	1253.91 267	1434.22 274	7065.71 272	272.76 252	2472.29 263	1647.54 297	3538.72 269	200.37 238	1496.48 268	2575.17 270	997.45 287	559.48 249

CMPMVS	2623.62 15	1983.08 249	2156.14 251	1463.89 270	1414.80 272	1517.00 276	5239.20 264	867.40 306	3310.30 268	1128.70 269	3489.90 268	366.75 261	2030.20 281	2427.60 268	1160.60 300	844.46 270
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVS-HIRNet		2125.39 250	2452.68 253	1143.53 258	2938.33 313	1446.92 275	6083.19 267	391.74 266	3801.71 272	1202.10 271	3439.72 267	492.73 270	1304.35 266	3048.25 274	676.32 257	679.32 258

PLC	2858.82 16	2192.92 251	2474.00 254	1349.67 262	1407.00 271	1356.00 273	7164.00 273	475.00 279	3970.00 273	1237.00 274	3765.00 272	451.00 267	1891.00 275	3006.00 273	791.00 274	802.00 262
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OpenMVS	2352.88 13	2263.08 252	2530.89 255	1459.67 269	1767.00 283	1591.00 279	7932.00 281	399.00 268	3643.00 270	1176.00 270	3569.00 270	297.00 251	1927.00 277	3302.00 275	693.00 263	861.00 271

PCF-MVS	4335.85 20	2272.23 253	2597.03 256	1297.84 261	1701.20 275	1332.37 272	8105.11 283	451.45 275	4031.84 274	1099.10 268	3966.50 273	467.45 268	1734.05 271	2701.68 272	848.94 278	827.11 268
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CHOSEN 280x420		2340.81 254	2643.03 257	1434.14 263	2025.55 292	1629.36 283	6542.40 269	412.49 269	4219.70 276	1547.02 285	4328.52 275	650.74 286	1798.26 272	3333.17 276	727.72 265	874.79 272

CHOSEN 1792x2688		2340.81 254	2643.03 257	1434.14 263	2025.55 292	1629.36 283	6542.40 269	412.49 269	4219.70 276	1547.02 285	4328.52 275	650.74 286	1798.26 272	3333.17 276	727.72 265	874.79 272

HyFIR lowres test		2340.81 254	2643.03 257	1434.14 263	2025.55 292	1629.36 283	6542.40 269	412.49 269	4219.70 276	1547.02 285	4328.52 275	650.74 286	1798.26 272	3333.17 276	727.72 265	874.79 272

3Dnovator	2482.06 14	2531.17 257	2839.56 260	1606.00 271	1732.00 279	1557.00 278	8375.00 285	483.00 280	4370.00 279	1371.00 280	4200.00 274	350.00 258	2207.00 282	3877.00 282	798.00 275	1054.00 292

tpmrst		2557.50 258	2847.12 261	1688.66 274	1784.08 284	1778.25 293	7445.50 279	342.14 259	4471.54 280	1534.55 284	4382.10 278	530.10 274	2361.21 285	4142.81 286	991.22 286	926.51 277

AdaColmap		2570.77 259	2948.61 262	1437.24 266	1873.64 285	1526.60 277	7819.77 280	373.97 263	4970.45 283	1479.29 282	4648.46 280	525.80 273	1904.97 276	4150.42 287	695.69 264	880.14 275

SD-MVS		2654.53 260	3055.56 265	1451.45 267	1725.60 276	1598.35 280	9203.30 289	315.86 256	5016.02 285	1363.85 277	4894.28 283	387.23 262	2023.43 278	3973.88 283	620.04 254	732.56 259
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DeepPCF-MVS	3452.42 18	2670.71 261	3021.57 263	1618.12 272	2377.67 299	1751.39 292	8915.70 288	567.44 294	4569.74 281	1236.84 273	4615.63 279	513.23 271	2291.32 283	3515.48 279	882.39 280	811.66 264

LF4IMVS		2699.50 262	3049.67 264	1649.00 273	1755.00 280	1689.00 287	8330.00 284	464.00 277	4997.00 284	1528.00 283	4763.00 282	568.00 277	2296.00 284	4215.00 288	827.00 277	962.00 282

EGC-MVSNET		2819.75 263	3096.89 266	1988.33 284	3105.00 314	2241.00 304	9480.00 293	640.00 300	4209.00 275	1583.00 290	4740.00 281	547.00 276	2797.00 297	2644.00 271	924.00 284	927.00 278

Vis-MVSNet		2877.42 264	3170.00 267	1999.67 285	1673.00 274	2042.00 302	8593.00 286	474.00 278	6452.00 298	1268.00 275	5125.00 286	339.00 257	3004.00 299	4222.00 289	384.00 246	953.00 281
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SteuartSystems-ACMMP		2891.50 265	3284.78 268	1711.67 275	1954.00 286	1655.00 286	10117.00 302	445.00 274	5102.00 288	1375.00 281	5126.00 287	351.00 259	2638.00 295	4402.00 290	691.00 261	842.00 269
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	3414.41 17	3146.67 266	3537.00 269	1975.67 282	2310.00 297	2005.00 301	10100.00 301	626.00 299	5753.00 289	1803.00 300	5179.00 288	487.00 269	2622.00 293	4550.00 291	1025.00 295	1300.00 299

DeepC-MVS	3917.13 19	3155.06 267	3601.83 270	1814.72 280	2515.52 302	1941.53 297	10432.50 303	589.42 295	4789.45 282	1298.65 276	7677.31 304	578.35 278	2589.53 289	3611.78 281	923.52 283	913.10 276
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DPM-MVS		3268.00 268	3762.89 271	1783.33 278	1756.00 281	1806.00 294	12319.00 305	307.00 254	6319.00 296	1557.00 288	5763.00 289	302.00 253	2609.00 291	5061.00 294	482.00 249	935.00 279

OPM-MVS		3268.00 268	3762.89 271	1783.33 278	1756.00 281	1806.00 294	12319.00 305	307.00 254	6319.00 296	1557.00 288	5763.00 289	302.00 253	2609.00 291	5061.00 294	482.00 249	935.00 279
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
TAPA-MVS	6309.84 21	3374.90 270	3806.99 273	2078.63 287	2535.47 303	2499.25 312	8816.16 287	456.05 276	6255.60 295	1664.52 298	6704.88 294	629.95 284	2768.38 296	6343.93 308	856.35 279	968.25 283
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + MP.		3528.01 271	4065.89 278	1914.35 281	2349.10 298	2141.30 303	12362.70 307	417.60 273	6717.80 302	1759.40 299	6497.50 292	518.18 272	2629.38 294	5164.32 296	806.40 276	972.38 284
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tpm2		3617.85 272	4007.23 274	2449.73 292	2012.71 288	1698.05 288	7351.19 275	869.29 307	6132.06 291	1588.28 291	8007.42 307	1281.09 313	3888.11 311	7472.04 312	1351.00 302	1763.02 308

tpm cat1		3617.85 272	4007.23 274	2449.73 292	2012.71 288	1698.05 288	7351.19 275	869.29 307	6132.06 291	1588.28 291	8007.42 307	1281.09 313	3888.11 311	7472.04 312	1351.00 302	1763.02 308

CostFormer		3617.85 272	4007.23 274	2449.73 292	2012.71 288	1698.05 288	7351.19 275	869.29 307	6132.06 291	1588.28 291	8007.42 307	1281.09 313	3888.11 311	7472.04 312	1351.00 302	1763.02 308

tpm		3617.85 272	4007.23 274	2449.73 292	2012.71 288	1698.05 288	7351.19 275	869.29 307	6132.06 291	1588.28 291	8007.42 307	1281.09 313	3888.11 311	7472.04 312	1351.00 302	1763.02 308

MVS_111021_HR		3742.80 276	4330.34 279	1980.20 283	2484.31 301	2259.52 305	14973.80 312	515.90 293	6500.13 299	1949.21 302	6146.89 291	535.66 275	2593.66 290	4880.76 293	986.37 285	1087.43 293

MAR-MVS		3973.16 277	4387.92 280	2728.86 298	3345.27 315	3341.39 319	11403.90 304	798.76 304	6555.62 301	2569.29 312	6514.86 293	834.97 298	3237.43 308	5891.08 299	1577.54 307	1607.76 300
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
DVP-MVS++		3981.10 278	4731.32 283	1730.42 276	2191.51 295	1949.98 298	16473.90 314	355.33 260	7172.90 307	1622.98 295	7879.79 305	434.33 265	2428.11 287	5762.12 297	689.04 259	813.18 265

DVP-MVS		3981.10 278	4731.32 283	1730.42 276	2191.51 295	1949.98 298	16473.90 314	355.33 260	7172.90 307	1622.98 295	7879.79 305	434.33 265	2428.11 287	5762.12 297	689.04 259	813.18 265
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
GST-MVS		4000.00 280	4533.33 281	2400.00 291	2400.00 300	2400.00 306	7200.00 274	2400.00 323	7200.00 309	2400.00 311	7200.00 299	2400.00 317	2400.00 286	7200.00 310	2400.00 314	2400.00 313

SED-MVS		4062.74 281	4671.13 282	2237.56 288	2643.17 304	2465.14 307	13996.40 309	493.26 281	7619.38 310	2107.24 303	7416.93 301	610.24 281	3088.29 304	6075.14 304	1078.39 296	1159.26 294

MSP-MVS		4296.20 282	4911.12 286	2451.45 296	2725.60 309	2598.35 313	16203.30 313	515.86 289	7016.02 303	2363.85 307	6894.28 295	887.23 299	3023.43 300	5973.88 300	1620.04 308	1732.56 304
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
API-MVS		4380.83 283	4906.56 285	2803.67 299	3844.00 316	3116.00 318	13454.00 308	901.00 311	6511.00 300	2794.00 313	7365.00 300	754.00 295	3634.00 309	6622.00 309	1914.00 313	1661.00 303

OMC-MVS		5022.26 284	5748.82 287	2842.58 300	4905.27 318	3101.27 317	14933.60 311	812.24 305	9278.40 314	2959.29 314	8800.17 311	1092.24 311	3806.30 310	7568.54 316	1389.60 306	1620.18 302

CLD-MVS		7338.25 285	8341.00 288	4330.00 301	4902.00 317	4991.00 320	23318.00 317	728.00 303	14437.00 324	4378.00 316	13379.00 319	824.00 297	5708.00 315	11429.00 325	1674.00 312	2291.00 312
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		7684.71 286	8776.23 289	4410.16 302	5298.55 319	5631.53 321	34329.90 319	1504.57 321	11554.00 323	3896.41 315	15111.00 320	812.19 296	5985.63 316	3516.20 280	2963.23 315	1613.31 301

ZNCC-MVS		9999.00 287	9999.00 290	9999.00 304	9999.00 322	9999.00 324	9999.00 294	9999.00 327	9999.00 315	9999.00 319	9999.00 312	9999.00 320	9999.00 317	9999.00 317	9999.00 318	9999.00 316

patch_mono-2		10000.00 288	10000.00 291	10000.00 305	10000.00 323	10000.00 325	10000.00 295	10000.00 328	10000.00 316	10000.00 320	10000.00 313	10000.00 321	10000.00 318	10000.00 318	10000.00 319	10000.00 317

dcpmvs_2		10000.00 288	10000.00 291	10000.00 305	10000.00 323	10000.00 325	10000.00 295	10000.00 328	10000.00 316	10000.00 320	10000.00 313	10000.00 321	10000.00 318	10000.00 318	10000.00 319	10000.00 317

DCPMVS		10000.00 288	10000.00 291	10000.00 305	10000.00 323	10000.00 325	10000.00 295	10000.00 328	10000.00 316	10000.00 320	10000.00 313	10000.00 321	10000.00 318	10000.00 318	10000.00 319	10000.00 317

UA-Net		10000.00 288	10000.00 291	10000.00 305	10000.00 323	10000.00 325	10000.00 295	10000.00 328	10000.00 316	10000.00 320	10000.00 313	10000.00 321	10000.00 318	10000.00 318	10000.00 319	10000.00 317

uncertanet		10000.00 288	10000.00 291	10000.00 305	10000.00 323	10000.00 325	10000.00 295	10000.00 328	10000.00 316	10000.00 320	10000.00 313	10000.00 321	10000.00 318	10000.00 318	10000.00 319	10000.00 317

uanet		10000.00 288	10000.00 291	10000.00 305	10000.00 323	10000.00 325	10000.00 295	10000.00 328	10000.00 316	10000.00 320	10000.00 313	10000.00 321	10000.00 318	10000.00 318	10000.00 319	10000.00 317

MVE	10021.00 22	10550.67 294	10002.44 297	12195.33 311	6242.00 320	8768.00 323	30896.00 318	4451.00 324	10331.00 322	6247.00 317	18296.00 321	283.00 250	21484.00 325	7364.00 311	5912.00 317	6334.00 315
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CNLPA		10738.67 295	11772.44 298	7637.33 303	9120.00 321	8206.00 322	22252.00 316	2010.00 322	24984.00 325	7458.00 318	22596.00 322	2940.00 318	10260.00 324	11100.00 324	3492.00 316	4446.00 314

LTVRE_ROB	20565.47 23	23378.08 296	22805.00 299	25097.33 312	15500.00 329	19201.00 331	42712.00 320	6055.00 325	38388.00 326	16926.00 326	38534.00 323	7071.00 319	35653.00 326	29475.00 326	10584.00 325	20438.00 324
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
MSDG		32606.17 297	34443.44 300	27094.33 313	29249.00 330	25246.00 332	51144.00 321	6705.00 326	42886.00 327	22888.00 327	48534.00 324	13263.00 327	37507.00 327	79298.00 327	16024.00 326	18530.00 323

114514_t		99999.00 298	99999.00 301	99999.00 314	99999.00 331	99999.00 333	99999.00 322	99999.00 334	99999.00 328	99999.00 328	99999.00 325	99999.00 328	99999.00 328	99999.00 328	99999.00 327	99999.00 325

PHI-MVS		99999.00 298	99999.00 301	99999.00 314	99999.00 331	99999.00 333	99999.00 322	99999.00 334	99999.00 328	99999.00 328	99999.00 325	99999.00 328	99999.00 328	99999.00 328	99999.00 327	99999.00 325

MP-MVS-pluss		833433.63 300	1111219.60 306	75.70 161	74.28 165	73.29 168	284.50 166	11.35 161	238.58 170	65.86 166	255.35 178	14.55 159	115.69 180	10000000.00 333	31.94 159	38.12 165
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MTAPA		833442.10 301	1111233.77 307	67.09 157	65.59 164	64.81 167	284.50 166	10000000.00 339	219.97 169	59.62 165	229.89 173	12.72 158	103.00 178	203.67 179	27.93 158	33.46 157

XVG-OURS-SEG-HR		999999.00 302	999999.00 303	999999.00 316	999999.00 333	999999.00 335	999999.00 324	999999.00 336	999999.00 330	999999.00 330	999999.00 327	999999.00 330	999999.00 330	999999.00 330	999999.00 329	999999.00 327

XVG-OURS		999999.00 302	999999.00 303	999999.00 316	999999.00 333	999999.00 335	999999.00 324	999999.00 336	999999.00 330	999999.00 330	999999.00 327	999999.00 330	999999.00 330	999999.00 330	999999.00 329	999999.00 327

XVG-ACMP-BASELINE		999999.00 302	999999.00 303	999999.00 316	999999.00 333	999999.00 335	999999.00 324	999999.00 336	999999.00 330	999999.00 330	999999.00 327	999999.00 330	999999.00 330	999999.00 330	999999.00 329	999999.00 327

MVSMamba_PlusPlus		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

MGCFI-Net		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

sasdasda		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

dmvs_re		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

SDMVSNet		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

dmvs_testset		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

sd_testset		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

balanced_conf03		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

MonoMVSNet		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test2506		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test1111		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

ECVR-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test_blank		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

tt0805		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Anonymous20240521		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

cl22		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

miper_ehance_all_eth		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

miper_enhance_ethall		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

cl____		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

DIV-MVS_self_test		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

eth_miper_zixun_eth		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

ET-MVSNet_ETH3D		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

UniMVSNet_ETH3D		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

miper_lstm_enhance		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

D2MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test_yl		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

thisisatest0530		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Anonymous20240529		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Anonymous202405211		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

DCV-MVSNet		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

tttt0517		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

our_test_3		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

thisisatest0515		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

ppachtmachnet_test		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

SMA-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPE-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
thres100view900		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

tfpnnormal		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

tfpn200view9		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

c3_l		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

IterMVS-SCA-FT		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

pmmvs6		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

pmmvs5		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Anonymous20231211		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

pmmvs-eth3d		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Anonymous20231206		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

SCA		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Patchmatch-test		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Patchmatch-RL test		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

canonicalmvs		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

anonymousdsp		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

FIs		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

FC-MVSNet-test		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

sosnet-low-res		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

AllTest		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

RRT-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

mamv4		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

HPM-MVS++		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

XVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

pm-mvs1		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

X-MVStestdata		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

APDe-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

pmmvs4		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test-LLR		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

TESTMET0.1,1		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test-mter		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

testgi		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test20.03		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

thres600view7		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

MP-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

thres400		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test123		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

thres200		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test0.0.03 1		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

pmmvs3		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

EMVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

E-PMN		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PGM-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

MCST-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

MVS_Test		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PMMVS2		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PM-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

HQP_MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

mPP-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

mvsmamba		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

HPM-MVS_fast		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

HPM-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
LPG-MVS_test		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

EPNet_dtu		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

NCCC		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

CP-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

CPTT-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

HQP-MVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

IS-MVSNet		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PAPM_NR		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PAPR		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

Vis-MVSNet (Re-imp)		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PatchMatch-RL		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

TDRefinement		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

EPP-MVSNet		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PMMVS		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

PAPM		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

ACMMP		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330
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
ANet_high		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

wuyk23d		10000000.00 305	10000000.00 308	10000000.00 319	10000000.00 336	10000000.00 338	10000000.00 327	10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

APD-MVS		1000000000.00 414	1000000000.00 417	1000000000.00 430	1000000000.00 455	1000000000.00 455	1000000000.00 441	1000000000.00 455	1000000000.00 448	1000000000.00 449	1000000000.00 446	1000000000.00 451	1000000000.00 449	1000000000.00 448	1000000000.00 447	1000000000.00 445
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						248.46 197								422.23 190

FOURS1					2643.17 304	2465.14 307	13996.40 309	493.26 281	7619.38 310	2107.24 303

MSC_two_6792asdad				2237.56 288	2643.17 304	2465.14 307		493.26 281					3088.29 304	6075.14 304	1078.39 296	1159.26 294

PC_three_1452									7619.38 310	2107.24 303	7416.93 301	610.24 281	3088.29 304	6075.14 304	1078.39 296	1159.26 294

No_MVS				2237.56 288	2643.17 304	2465.14 307		493.26 281					3088.29 304	6075.14 304	1078.39 296	1159.26 294

test_one_0601					2643.17 304	2465.14 307		493.26 281	7619.38 310	2107.24 303	7416.93 301	610.24 281

eth-test2					1.42 90

eth-test					10000000.00 336

ZD-MVS					1584.00 273	1821.00 296		378.00 265	3721.00 271	1231.00 272	1833.00 253	321.00 255	1224.00 265	2103.00 266	692.00 262

RE-MVS-def			610.10 202		610.10 231	610.10 244	610.10 185	610.10 296	610.10 196	610.10 243	610.10 192	610.10 279		610.10 211	610.10 252	610.10 250

IU-MVS					2725.60 309	2598.35 313		515.86 289	7016.02 303	2363.85 307				5973.88 300	1620.04 308	1732.56 304

OPU-MVS				2451.45 296	2725.60 309	2598.35 313	9203.30 289				6894.28 295	887.23 299	3023.43 300	5973.88 300	1620.04 308	1732.56 304

test_241102_TWO								515.86 289	7016.02 303	2363.85 307	6894.28 295	887.23 299	3023.43 300	5973.88 300	1620.04 308	1732.56 304

test_241102_ONE					2725.60 309	2598.35 313		515.86 289	7016.02 303	2363.85 307	6894.28 295	887.23 299	3023.43 300

9.14			0.00 1		0.00 1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

save fliter					0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_0728_THIRD									5016.02 285	1363.85 277	4894.28 283	387.23 262	2023.43 278	3973.88 283	620.04 254	732.56 259

test_0728_SECOND				1451.45 267	1725.60 276	1598.35 280	9203.30 289	315.86 256					2023.43 278	3973.88 283	620.04 254	732.56 259

test0726					1725.60 276	1598.35 280	9203.30 289	315.86 256	5016.02 285	1363.85 277	4894.28 283	387.23 262

GSMVS																10000000.00 330

test_part2					10000000.00 336	10000000.00 338				10000000.00 333

sam_mvs1												10000000.00 333				10000000.00 330

sam_mvs												10000000.00 333

ambc				999.00 249	999.00 258	999.00 263	999.00 205	999.00 312		999.00 258	999.00 221	999.00 304	999.00 254	999.00 234	999.00 288	999.00 285

MTGPA								10000000.00 339

test_post1							10000000.00 327				10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333

test_post											10000000.00 330	10000000.00 333	10000000.00 333

patchmatchnet-post											10000000.00 330	10000000.00 333	10000000.00 333

GG-mvs-BLEND				596.01 232	611.29 233	600.33 241	790.30 192	513.12 287		552.71 236	567.40 186	662.54 291	531.67 221	503.93 197	779.80 269	656.02 253

MTMP							10000000.00 327	10000000.00 339

gm-plane-assit					611.29 233	600.33 241			579.21 190		567.40 186		531.67 221	503.93 197

test9_res														0.00 1	0.00 1	0.00 1

TEST9					0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test_8					0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

agg_prior2														0.00 1	0.00 1	0.00 1

agg_prior					0.00 1	0.00 1		0.00 1		0.00 1			0.00 1

TestCases				10000000.00 319	10000000.00 336	10000000.00 338		10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test_prior4						0.00 1	0.00 1

test_prior2							0.00 1		0.00 1	0.00 1	0.00 1	0.00 1		0.00 1	0.00 1

test_prior				0.00 1	0.00 1	0.00 1		0.00 1					0.00 1			0.00 1

旧先验2							0.00 1		0.00 1	0.00 1			0.00 1	0.00 1

新几何2							0.00 1

新几何1				0.00 1	0.00 1	0.00 1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

旧先验1					0.00 1	0.00 1		0.00 1			0.00 1	0.00 1			0.00 1	0.00 1

无先验							0.00 1	0.00 1	0.00 1				0.00 1	0.00 1		0.00 1

原ACMM2							0.00 1

原ACMM1				0.00 1	0.00 1	0.00 1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

test222					0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1			0.00 1	0.00 1

testdata2													0.00 1	0.00 1

segment_acmp												0.00 1

testdata				0.00 1	0.00 1	0.00 1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

testdata1							0.00 1		0.00 1

test12				0.00 1	0.00 1	0.00 1		0.00 1		0.00 1		0.00 1	0.00 1		0.00 1	0.00 1

plane_prior7					10000000.00 336	10000000.00 338

plane_prior6					10000000.00 336	10000000.00 338						10000000.00 333

plane_prior5								10000000.00 339					10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

plane_prior4											10000000.00 330

plane_prior3						10000000.00 338			10000000.00 333	10000000.00 333

plane_prior2							10000000.00 327		10000000.00 333

plane_prior1					10000000.00 336

plane_prior						1.00 84	2.00 122		2.00 123						2.00 121

n2								14.20 164

nn								1.42 92

door-mid								1.42 92

lessismore_v0				569.20 230	667.25 247	644.68 247		96.45 213		428.86 222	1514.17 249	204.21 242	756.58 243	1061.60 243	197.79 220	306.34 232

LGP-MVS_train				10000000.00 319	10000000.00 336	10000000.00 338		10000000.00 339	10000000.00 333	10000000.00 333	10000000.00 330	10000000.00 333	10000000.00 333	10000000.00 333	10000000.00 332	10000000.00 330

test11								1.00 85

door								99.00 217

HQP5-MVS						10000000.00 338

HQP-NCC					10000000.00 336		10000000.00 327		10000000.00 333	10000000.00 333

ACMP_Plane					10000000.00 336		10000000.00 327		10000000.00 333	10000000.00 333

BP-MVS														10000000.00 333

HQP4-MVS										10000000.00 333			10000000.00 333			10000000.00 330

HQP3-MVS								10000000.00 339							10000000.00 332

HQP2-MVS												10000000.00 333

NP-MVS					10000000.00 336	10000000.00 338					10000000.00 330

MDTV_nov1_ep13_2view						9.99 144	9.99 142		9.99 143	9.99 142		9.99 144		9.99 140		9.99 143

MDTV_nov1_ep13			9.99 130		9.99 143	9.99 144	9.99 142	9.99 150	9.99 143	9.99 142	9.99 139	9.99 144	9.99 143	9.99 140	9.99 144

ACMMP++_ref															10000000.00 332

ACMMP++															10000000.00 332

Test By Simon												990.00 303

ITE_SJBPF				2014.00 286	1998.00 287	1992.00 300		701.00 302	5989.00 290	1812.00 301	3612.00 271	650.00 285	2852.00 298	4601.00 292	1195.00 301	1198.00 298

DeepMVS_CX				1.00 77	1.00 84	1.00 84		1.00 85	1.00 84	1.00 83	1.00 81	1.00 83	1.00 84	1.00 82	1.00 82	1.00 82

High-resolution multi-view benchmark