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.

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

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

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_prior3		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	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_prior1		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	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_prior					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

#test#		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	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_prior3		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	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_prior				0.00 1	0.00 1	0.00 1		0.00 1					0.00 1			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

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

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

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

1121		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	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

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

abl_6		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	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

LCM-MVSNet		0.37 13	0.36 13	0.37 18	0.37 23	0.37 24	0.37 23	0.32 24	0.37 23	0.37 23	0.37 21	0.37 23	0.37 24	0.37 22	0.37 22	0.37 23

v144192		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v1921920		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v1192		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v1144		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v148		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v748		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v7n		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v1141		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v1neww		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v7new		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v1240		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v18		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v17		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v16		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

divwei89l23v2f112		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v15		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v13		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v12		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v8		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v7		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v6		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v11		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v52		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

V14		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v10		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

V4		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v2v482		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

v1		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

V42		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

V9		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

GA-MVS		0.40 14	0.40 14	0.40 19	0.40 24	0.40 25	0.40 24	0.40 26	0.40 24	0.40 24	0.40 22	0.40 24	0.40 25	0.40 23	0.40 23	0.40 24

Fast-Effi-MVS+		0.41 45	0.40 14	0.41 50	0.41 55	0.41 56	0.41 55	0.35 25	0.41 55	0.41 55	0.41 53	0.41 55	0.41 56	0.41 54	0.41 54	0.41 55

Fast-Effi-MVS+-dtu		0.44 46	0.44 46	0.44 51	0.44 56	0.44 57	0.44 56	0.44 57	0.44 56	0.44 56	0.44 54	0.44 56	0.44 57	0.44 55	0.44 55	0.44 56

Effi-MVS+		0.58 47	0.56 47	0.62 53	0.53 57	0.62 59	0.53 57	0.53 58	0.53 57	0.62 58	0.62 56	0.53 57	0.62 59	0.62 56	0.53 56	0.62 58

Effi-MVS+-dtu		0.59 48	0.61 48	0.53 52	0.68 58	0.53 58	0.53 57	0.68 59	0.68 58	0.53 57	0.53 55	0.68 58	0.53 58	0.68 57	0.53 56	0.53 57

mvs-test1		0.68 49	0.68 49	0.68 54	0.68 58	0.68 60	0.68 59	0.68 59	0.68 58	0.68 59	0.68 57	0.68 58	0.68 60	0.68 57	0.68 58	0.68 59

CDS-MVSNet		0.88 50	0.88 50	0.88 55	0.88 60	0.88 61	0.88 60	0.88 61	0.88 60	0.88 60	0.88 58	0.88 60	0.88 61	0.88 59	0.88 59	0.88 60
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		0.88 50	0.88 50	0.88 55	0.88 60	0.88 61	0.88 60	0.88 61	0.88 60	0.88 60	0.88 58	0.88 60	0.88 61	0.88 59	0.88 59	0.88 60

LCM-MVSNet-Re		0.89 52	0.89 52	0.89 57	0.89 62	0.89 63	0.89 62	0.89 63	0.89 62	0.89 62	0.89 60	0.89 62	0.89 63	0.89 61	0.89 61	0.89 62

DeepMVS_CX				1.00 58	1.00 63	1.00 64		1.00 64	1.00 63	1.00 63	1.00 61	1.00 63	1.00 64	1.00 62	1.00 62	1.00 63

MDA-MVSNet-bldmvs		1.20 53	1.20 53	1.20 59	1.20 64	1.20 66	1.20 63	1.20 66	1.20 64	1.20 64	1.20 62	1.20 64	1.20 65	1.20 63	1.20 63	1.20 64

YYNet1		1.20 53	1.20 53	1.20 59	1.20 64	1.20 66	1.20 63	1.20 66	1.20 64	1.20 64	1.20 62	1.20 64	1.20 65	1.20 63	1.20 63	1.20 64

MDA-MVSNet_test_wron		1.20 53	1.20 53	1.20 59	1.20 64	1.20 66	1.20 63	1.20 66	1.20 64	1.20 64	1.20 62	1.20 64	1.20 65	1.20 63	1.20 63	1.20 64

EI-MVSNet-UG-set		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

EI-MVSNet-Vis-set		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

Regformer-3		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

Regformer-4		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

Regformer-1		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

Regformer-2		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

EI-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

Regformer		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

CVMVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

EU-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

PS-CasMVS		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

UniMVSNet_NR-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

PEN-MVS		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

TransMVSNet (Re)		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

DTE-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

DU-MVS		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

UniMVSNet (Re)		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

CP-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

WR-MVS_H		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

WR-MVS		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

NR-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

Baseline_NR-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

TranMVSNet+NR-MVSNet		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

MVSFormer		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

jason		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67
jason: jason.
lupinMVS		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

test_djsdf		1.42 56	1.42 56	1.42 62	1.42 67	1.42 69	1.42 66	1.42 69	1.42 67	1.42 67	1.42 65	1.42 67	1.42 68	1.42 66	1.42 66	1.42 67

alignmvs		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

nrg030		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

LFMVS		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

VDD-MVS		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

VDDNet		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

VPNet		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

VNet		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

VPA-MVSNet		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

cascas		2.00 83	2.00 83	2.00 89	2.00 94	2.00 96	2.00 93	2.00 98	2.00 94	2.00 94	2.00 92	2.00 94	2.00 95	2.00 93	2.00 93	2.00 94

UnsupCasMVSNet_eth		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

UnsupCasMVSNet_bld		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

WTY-MVS		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

HY-MVS	5.90 1	5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

sss		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

Test_1112_low_res		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

1112_ss		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

ab-mvs-re		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

ab-mvs		5.90 92	5.90 92	5.90 98	5.90 103	5.90 105	5.90 103	5.90 108	5.90 104	5.90 103	5.90 101	5.90 104	5.90 104	5.90 102	5.90 104	5.90 104

CDPH-MVS		6.66 101	6.66 101	6.66 107	6.66 112	6.66 114	6.66 112	6.66 117	6.66 113	6.66 112	6.66 110	6.66 113	6.66 113	6.66 111	6.66 113	6.66 113

MDTV_nov1_ep13			9.99 102		9.99 113	9.99 116	9.99 113	9.99 119	9.99 114	9.99 114	9.99 111	9.99 114	9.99 115	9.99 112	9.99 114

xiu_mvs_v1_base_debu		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

xiu_mvs_v2_base		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

xiu_mvs_v1_base		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

xiu_mvs_v1_base_debi		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

cdsmvs_eth3d_5k		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

pcd_1.5k_mvsjas		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

pcd1.5k->3k		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

PS-MVSNAJss		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

PS-MVSNAJ		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

jajsoiax		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

mvs_tets		10.28 102	10.28 103	10.28 109	10.28 114	10.28 118	10.28 115	10.28 120	10.28 116	10.28 116	10.28 112	10.28 116	10.28 116	10.28 114	10.28 115	10.28 115

dp		87.20 119	113.80 122	7.42 108	10.50 125	8.88 115	298.82 139	2.70 107	510.35 155	7.79 113	172.93 132	3.37 103	9.16 114	14.00 125	3.71 103	4.23 103

XXY-MVS		15.00 113	15.00 114	15.00 120	15.00 126	15.00 129	15.00 126	15.00 134	15.00 127	15.00 127	15.00 123	15.00 132	15.00 127	15.00 126	15.00 127	15.00 127

BH-w/o		40.00 114	40.00 115	40.00 122	40.00 127	40.00 130	40.00 127	40.00 151	40.00 128	40.00 129	40.00 124	40.00 150	40.00 129	40.00 127	40.00 143	40.00 139

BH-untuned		40.00 114	40.00 115	40.00 122	40.00 127	40.00 130	40.00 127	40.00 151	40.00 128	40.00 129	40.00 124	40.00 150	40.00 129	40.00 127	40.00 143	40.00 139

BH-RMVSNet		40.00 114	40.00 115	40.00 122	40.00 127	40.00 130	40.00 127	40.00 151	40.00 128	40.00 129	40.00 124	40.00 150	40.00 129	40.00 127	40.00 143	40.00 139

TR-MVS		40.00 114	40.00 115	40.00 122	40.00 127	40.00 130	40.00 127	40.00 151	40.00 128	40.00 129	40.00 124	40.00 150	40.00 129	40.00 127	40.00 143	40.00 139

GBi-Net		44.67 118	49.24 119	30.96 121	47.53 131	47.57 134	126.09 131	8.00 118	76.99 132	26.09 128	73.50 129	11.27 127	30.55 128	61.12 131	12.58 126	14.75 126

zzz-MVS		833442.10 238	1111233.77 242	67.09 128	65.59 132	64.81 137	284.50 135	10000000.00 263	219.97 139	59.62 135	229.89 144	12.72 128	103.00 149	203.67 150	27.93 128	33.46 128

MTAPA		833442.10 238	1111233.77 242	67.09 128	65.59 132	64.81 137	284.50 135	10000000.00 263	219.97 139	59.62 135	229.89 144	12.72 128	103.00 149	203.67 150	27.93 128	33.46 128

MP-MVS-pluss		833433.63 237	1111219.60 241	75.70 134	74.28 134	73.29 139	284.50 135	11.35 131	238.58 141	65.86 137	255.35 150	14.55 130	115.69 152	10000000.00 259	31.94 130	38.12 137
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		118.81 126	133.18 127	75.70 134	74.28 134	73.29 139	284.50 135	11.35 131	238.58 141	65.86 137	255.35 150	14.55 130	115.69 152	222.18 152	31.94 130	38.12 137

CANet		110.62 122	124.04 123	70.34 130	82.03 136	74.27 141	325.54 140	25.25 137	207.16 135	69.28 139	194.38 133	27.53 135	95.79 142	147.76 136	37.43 132	40.97 143

CANet_DTU		110.62 122	124.04 123	70.34 130	82.03 136	74.27 141	325.54 140	25.25 137	207.16 135	69.28 139	194.38 133	27.53 135	95.79 142	147.76 136	37.43 132	40.97 143

MVS_0304		110.62 122	124.04 123	70.34 130	82.03 136	74.27 141	325.54 140	25.25 137	207.16 135	69.28 139	194.38 133	27.53 135	95.79 142	147.76 136	37.43 132	40.97 143

UGNet		110.62 122	124.04 123	70.34 130	82.03 136	74.27 141	325.54 140	25.25 137	207.16 135	69.28 139	194.38 133	27.53 135	95.79 142	147.76 136	37.43 132	40.97 143
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
new_pmnet		88.14 120	98.62 120	56.70 126	98.14 140	56.07 135	246.79 133	15.37 135	148.06 133	50.77 133	143.13 130	23.35 133	62.65 133	93.26 132	68.71 150	51.38 150

N_pmnet		88.14 120	98.62 120	56.70 126	98.14 140	56.07 135	246.79 133	15.37 135	148.06 133	50.77 133	143.13 130	23.35 133	62.65 133	93.26 132	68.71 150	51.38 150

DSMNet-mixed		131.33 134	143.00 135	96.33 146	99.00 142	81.00 145	201.00 132	45.00 155	335.00 153	99.00 153	251.00 149	38.00 149	110.00 151	120.00 135	99.00 152	98.00 152

FMVSNet5		124.76 127	140.93 128	76.26 136	107.44 143	100.17 149	329.88 144	27.12 144	259.44 146	74.30 143	204.34 137	30.74 139	92.41 135	196.98 140	38.15 136	36.19 130

test1		124.76 127	140.93 128	76.26 136	107.44 143	100.17 149	329.88 144	27.12 144	259.44 146	74.30 143	204.34 137	30.74 139	92.41 135	196.98 140	38.15 136	36.19 130

FMVSNet3		124.76 127	140.93 128	76.26 136	107.44 143	100.17 149	329.88 144	27.12 144	259.44 146	74.30 143	204.34 137	30.74 139	92.41 135	196.98 140	38.15 136	36.19 130

FMVSNet2		124.76 127	140.93 128	76.26 136	107.44 143	100.17 149	329.88 144	27.12 144	259.44 146	74.30 143	204.34 137	30.74 139	92.41 135	196.98 140	38.15 136	36.19 130

FMVSNet1		124.76 127	140.93 128	76.26 136	107.44 143	100.17 149	329.88 144	27.12 144	259.44 146	74.30 143	204.34 137	30.74 139	92.41 135	196.98 140	38.15 136	36.19 130

MIMVSNet1		124.76 127	140.93 128	76.26 136	107.44 143	100.17 149	329.88 144	27.12 144	259.44 146	74.30 143	204.34 137	30.74 139	92.41 135	196.98 140	38.15 136	36.19 130

MIMVSNet		124.76 127	140.93 128	76.26 136	107.44 143	100.17 149	329.88 144	27.12 144	259.44 146	74.30 143	204.34 137	30.74 139	92.41 135	196.98 140	38.15 136	36.19 130

PVSNet_Blended_VisFu		136.90 135	155.40 136	81.40 143	111.00 150	96.20 146	407.00 151	25.90 141	251.60 143	85.10 150	240.50 146	37.00 146	99.90 146	199.80 147	40.70 147	48.10 147

PVSNet_BlendedMVS		136.90 135	155.40 136	81.40 143	111.00 150	96.20 146	407.00 151	25.90 141	251.60 143	85.10 150	240.50 146	37.00 146	99.90 146	199.80 147	40.70 147	48.10 147

PVSNet_Blended		136.90 135	155.40 136	81.40 143	111.00 150	96.20 146	407.00 151	25.90 141	251.60 143	85.10 150	240.50 146	37.00 146	99.90 146	199.80 147	40.70 147	48.10 147

DP-MVS		264.78 138	296.02 139	171.04 147	191.70 153	183.96 156	827.10 160	78.36 157	438.24 154	176.64 154	435.12 152	65.04 155	230.52 156	334.02 153	117.96 153	98.64 153

tmp_tt		371.08 139	412.78 140	246.00 149	231.00 154	197.00 157	1293.00 172	376.00 204	756.00 162	609.00 189	71.00 128	106.00 164	195.00 154	102.00 134	171.00 163	346.00 186

EG-PatchMatch MVS		437.03 141	493.12 144	268.77 150	264.80 155	260.20 159	1491.80 175	98.00 166	723.20 161	238.80 155	722.40 159	63.10 154	382.00 160	629.00 163	207.00 170	164.10 159

RPSCF		394.81 140	445.63 141	242.35 148	276.55 156	253.63 158	1207.27 170	90.69 162	715.56 160	241.57 156	684.87 158	111.96 165	320.02 157	548.38 161	133.81 155	153.39 155

DELS-MVS		514.57 149	587.73 151	295.12 153	343.03 157	328.09 162	1540.48 177	116.22 172	988.59 168	325.44 163	906.22 168	119.86 167	365.72 158	788.60 172	161.09 160	191.55 164
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		516.25 150	581.17 150	321.48 156	361.02 158	429.00 171	1995.36 181	78.48 158	796.20 163	283.32 159	796.02 163	96.78 159	382.98 161	636.72 165	186.66 167	152.46 154

ACMH+	729.97 2	661.38 155	754.00 156	383.51 161	368.96 159	376.38 166	2483.74 192	82.42 159	1089.25 176	333.39 164	1219.68 182	66.53 156	573.60 181	957.86 178	184.14 166	200.56 165

MVP-Stereo		473.58 144	533.67 145	293.33 151	376.00 160	318.00 160	1461.00 173	96.00 163	835.00 164	290.00 160	804.00 164	97.00 160	383.00 162	688.00 170	156.00 156	179.00 161
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		473.58 144	533.67 145	293.33 151	376.00 160	318.00 160	1461.00 173	96.00 163	835.00 164	290.00 160	804.00 164	97.00 160	383.00 162	688.00 170	156.00 156	179.00 161

LP		495.57 147	552.82 148	323.82 157	377.78 162	343.94 163	1099.61 167	67.44 156	1450.39 185	417.42 168	790.39 162	130.18 168	472.61 168	519.53 160	122.60 154	154.91 156

tpmvs		501.65 148	567.42 149	304.33 154	395.39 163	356.32 164	1512.04 176	216.53 196	994.15 169	350.16 165	804.16 166	119.38 166	380.98 159	555.14 162	159.87 159	175.69 160

PatchmatchNet		492.52 146	551.22 147	316.40 155	400.20 164	357.90 165	1277.10 171	115.80 171	1034.60 175	315.90 162	874.00 167	155.10 171	402.20 164	629.50 164	158.80 158	189.10 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PMVS	863.65 3	957.08 172	1054.11 178	666.00 186	401.00 165	495.00 174	3790.00 200	279.00 198	1607.00 195	521.00 181	1745.00 196	90.00 157	1105.00 208	639.00 166	415.00 195	398.00 193
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CSCG		826.03 167	963.29 170	414.24 163	421.52 166	426.11 170	2230.35 182	179.52 192	1827.44 197	733.43 198	933.12 170	281.25 196	545.23 174	1800.35 204	262.64 182	271.38 174

IB-MVS	872.44 4	616.41 154	700.96 155	362.78 160	426.10 167	409.25 169	1822.69 178	124.42 179	1186.23 178	366.85 167	1079.42 179	190.60 183	451.78 167	930.72 177	181.59 165	227.30 167
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
ADS-MVSNet2		443.33 142	482.84 142	324.79 158	434.94 168	387.73 167	1110.57 168	87.69 160	887.02 166	282.92 157	779.29 160	98.57 162	422.76 165	502.40 154	162.19 161	163.87 157

ADS-MVSNet		443.33 142	482.84 142	324.79 158	434.94 168	387.73 167	1110.57 168	87.69 160	887.02 166	282.92 157	779.29 160	98.57 162	422.76 165	502.40 154	162.19 161	163.87 157

ACMH	993.80 5	967.92 173	1106.11 180	553.33 176	494.00 170	534.00 176	3581.00 198	136.00 181	1585.00 194	478.00 178	1871.00 197	90.00 157	839.00 192	1460.00 198	260.00 181	287.00 177
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		615.05 153	691.66 154	385.23 162	516.40 171	454.80 172	1863.80 179	102.10 168	1142.20 177	364.10 166	1093.20 181	135.00 169	480.40 169	826.90 173	181.20 164	220.50 166
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMM	1311.08 8	1165.06 184	1332.72 185	662.07 185	584.90 172	715.99 195	4373.59 205	153.99 189	1880.27 199	709.02 195	2316.97 201	188.63 182	939.89 197	1484.30 199	302.78 189	330.34 183
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
new-patchmatchnet		766.12 158	859.03 161	487.38 173	595.68 173	536.14 177	2346.27 184	142.51 184	1488.08 186	422.43 169	1361.32 186	163.65 174	628.60 184	1003.74 186	207.61 171	297.41 179

ACMP	1246.57 7	1086.24 180	1235.17 182	639.45 182	596.53 174	639.91 190	3904.24 201	152.04 187	1851.54 198	566.58 187	1997.95 199	138.50 170	938.30 196	1605.46 200	303.70 190	340.14 184
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SixPastTwentyTwo		773.62 159	869.37 163	486.35 172	603.45 175	547.15 184	2532.14 193	106.92 170	1434.95 184	428.08 170	1360.56 185	165.15 175	628.96 185	984.09 179	209.01 172	282.94 176

IterMVS-LS		758.94 157	856.56 160	466.10 170	606.60 176	546.80 183	2309.90 183	122.20 178	1402.80 183	463.50 172	1348.20 184	192.60 184	574.60 182	1028.60 187	234.60 180	276.90 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MG-MVS		1256.50 186	1432.00 187	730.00 191	610.00 177	760.00 197	4720.00 206	169.00 190	1920.00 200	723.00 197	2510.00 207	201.00 187	1050.00 206	1645.00 202	390.00 194	380.00 190

GG-mvs-BLEND				596.01 180	611.29 178	600.33 187	790.30 157	513.12 221		552.71 183	567.40 153	662.54 226	531.67 170	503.93 156	779.80 208	656.02 199

gm-plane-assit					611.29 178	600.33 187			579.21 156		567.40 153		531.67 170	503.93 156

CR-MVSNet		612.36 152	617.81 153	596.01 180	611.29 178	600.33 187	790.30 157	513.12 221	579.21 156	552.71 183	567.40 153	662.54 226	531.67 170	503.93 156	779.80 208	656.02 199

Gipuma		689.75 156	767.00 157	458.00 169	613.00 181	549.00 185	1943.00 180	187.00 193	1230.00 179	518.00 180	1092.00 180	203.00 189	564.00 175	892.00 176	225.00 178	261.00 173
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
gg-mvs-nofinetune		594.69 151	605.37 152	562.67 177	617.29 182	500.33 175	772.30 156	413.12 211	579.21 156	552.71 183	567.40 153	662.54 226	531.67 170	503.93 156	779.80 208	656.02 199

mvs_anonymous		774.90 160	882.28 164	452.77 164	636.50 183	537.20 178	2348.80 185	120.30 173	1504.30 187	474.40 173	1371.10 187	186.90 177	564.70 176	1078.90 189	219.30 173	256.40 168

casdiffmvs		774.90 160	882.28 164	452.77 164	636.50 183	537.20 178	2348.80 185	120.30 173	1504.30 187	474.40 173	1371.10 187	186.90 177	564.70 176	1078.90 189	219.30 173	256.40 168

diffmvs		774.90 160	882.28 164	452.77 164	636.50 183	537.20 178	2348.80 185	120.30 173	1504.30 187	474.40 173	1371.10 187	186.90 177	564.70 176	1078.90 189	219.30 173	256.40 168

casdiffmvs1		774.90 160	882.28 164	452.77 164	636.50 183	537.20 178	2348.80 185	120.30 173	1504.30 187	474.40 173	1371.10 187	186.90 177	564.70 176	1078.90 189	219.30 173	256.40 168

diffmvs1		774.90 160	882.28 164	452.77 164	636.50 183	537.20 178	2348.80 185	120.30 173	1504.30 187	474.40 173	1371.10 187	186.90 177	564.70 176	1078.90 189	219.30 173	256.40 168

PVSNet	1207.94 6	829.56 168	937.18 169	506.69 174	645.55 188	585.72 186	2477.98 191	130.52 180	1578.29 193	509.50 179	1460.01 192	194.90 185	637.19 186	1170.78 195	267.07 184	297.17 178

FPMVS		1407.00 189	1495.59 189	1141.23 203	648.01 189	893.85 204	4124.00 202	503.81 220	3219.04 210	742.72 199	2498.84 206	157.98 172	1727.83 214	832.93 174	732.98 207	802.00 206

lessismore_v0				569.20 178	667.25 190	644.68 191		96.45 165		428.86 171	1514.17 193	204.21 190	756.58 188	1061.60 188	197.79 169	306.34 180

PVSNet_0	1661.03 9	968.54 174	1066.82 179	673.70 187	706.94 191	695.69 193	2669.02 196	153.52 188	2108.17 202	605.54 188	1679.14 195	213.26 193	886.75 195	1236.76 197	229.01 179	438.67 194

MSLP-MVS++		1242.93 185	1411.08 186	738.48 192	712.71 192	712.68 194	4168.17 203	136.50 182	2276.40 204	613.28 190	2368.60 204	161.61 173	1130.38 209	1964.54 207	297.88 187	372.39 189

MVSTER		890.06 171	1005.91 177	542.52 175	736.13 193	653.90 192	2616.56 195	145.22 186	1692.10 196	555.69 186	1554.72 194	266.25 195	660.02 187	1219.66 196	266.84 183	313.64 182

MVS_111021_LR		1136.45 183	1298.72 184	649.64 183	756.17 194	726.26 196	3785.94 199	138.15 183	2190.64 203	629.63 191	2064.39 200	165.45 176	874.83 194	1678.43 203	279.70 185	347.82 187

DeepC-MVS_fast	2190.19 12	1535.59 190	1886.82 192	481.89 171	847.73 195	830.16 201	7943.84 226	189.23 194	2398.83 205	691.73 194	2341.13 202	209.16 191	230.25 155	1999.25 208	360.46 192	385.25 191
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
JIA-IIPM		804.30 165	835.64 158	710.26 189	885.90 196	895.90 205	455.98 154	668.73 228	1297.17 180	1002.26 209	1001.67 177	655.98 224	756.98 189	667.90 167	885.21 220	477.90 195

RPMNet		804.30 165	835.64 158	710.26 189	885.90 196	895.90 205	455.98 154	668.73 228	1297.17 180	1002.26 209	1001.67 177	655.98 224	756.98 189	667.90 167	885.21 220	477.90 195

ACMMPR		1296.60 187	1480.82 188	743.96 193	899.25 198	763.47 198	4305.47 204	143.90 185	2530.94 207	683.99 192	2346.34 203	201.65 188	1098.25 207	1920.59 206	295.21 186	370.16 188

test_0402		1110.00 182	1260.00 183	660.00 184	900.00 199	780.00 199	3300.00 197	210.00 195	2040.00 201	690.00 193	1950.00 198	300.00 199	810.00 191	1620.00 201	330.00 191	390.00 192

TSAR-MVS + GP.		1386.84 188	1622.84 190	678.81 188	952.13 200	840.17 202	5358.21 209	170.75 191	2650.47 208	713.54 196	2394.28 205	209.42 192	855.56 193	1854.36 205	302.42 188	340.71 185

ambc				999.00 196	999.00 201	999.00 207	999.00 161	999.00 241		999.00 203	999.00 171	999.00 236	999.00 200	999.00 180	999.00 226	999.00 225

OurMVSNet-221017-0		999.00 175	999.00 172	999.00 196	999.00 201	999.00 207	999.00 161	999.00 241	999.00 170	999.00 203	999.00 171	999.00 236	999.00 200	999.00 180	999.00 226	999.00 225

EPMVS		999.00 175	999.00 172	999.00 196	999.00 201	999.00 207	999.00 161	999.00 241	999.00 170	999.00 203	999.00 171	999.00 236	999.00 200	999.00 180	999.00 226	999.00 225

sosnet		1000.00 177	1000.00 174	1000.00 199	1000.00 204	1000.00 210	1000.00 164	1000.00 244	1000.00 172	1000.00 206	1000.00 174	1000.00 239	1000.00 203	1000.00 183	1000.00 229	1000.00 228

USDC		1000.00 177	1000.00 174	1000.00 199	1000.00 204	1000.00 210	1000.00 164	1000.00 244	1000.00 172	1000.00 206	1000.00 174	1000.00 239	1000.00 203	1000.00 183	1000.00 229	1000.00 228

TinyColmap		1000.00 177	1000.00 174	1000.00 199	1000.00 204	1000.00 210	1000.00 164	1000.00 244	1000.00 172	1000.00 206	1000.00 174	1000.00 239	1000.00 203	1000.00 183	1000.00 229	1000.00 228

COLMAP_ROB	2144.59 11	1658.33 191	1869.33 191	1025.33 202	1030.00 207	1023.00 213	5311.00 208	359.00 202	3044.00 209	937.00 202	2892.00 208	332.00 201	1440.00 211	2312.00 209	607.00 197	613.00 198
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
F-COLMAP		1878.17 192	2113.11 193	1173.33 206	1231.00 208	1174.00 214	6297.00 211	392.00 206	3356.00 212	1087.00 211	3082.00 209	361.00 205	1670.00 213	2523.00 211	689.00 200	676.00 203

OpenMVS_ROB	1936.51 10	1959.51 193	2224.88 195	1163.39 205	1253.91 209	1434.22 217	7065.71 215	272.76 197	2472.29 206	1647.54 235	3538.72 212	200.37 186	1496.48 212	2575.17 212	997.45 225	559.48 197

Patchmtry		1090.75 181	1198.88 181	766.38 194	1290.90 210	485.90 173	2423.98 190	1267.30 247	1297.17 180	923.90 200	923.40 169	1215.54 243	990.23 198	667.90 167	779.80 208	823.02 209

PatchT		856.34 169	860.99 162	842.38 195	1290.90 210	880.90 203	790.30 157	1267.30 247	579.21 156	923.90 200	567.40 153	662.54 226	990.23 198	887.56 175	779.80 208	656.02 199

K. test v3		877.74 170	980.16 171	570.46 179	1316.85 212	813.15 200	2565.52 194	103.78 169	1509.49 192	548.33 182	1258.90 183	219.93 194	587.82 183	1102.37 194	196.30 168	310.42 181

PLC	2858.82 16	2192.92 196	2474.00 197	1349.67 208	1407.00 213	1356.00 216	7164.00 216	475.00 218	3970.00 215	1237.00 217	3765.00 215	451.00 208	1891.00 219	3006.00 214	791.00 213	802.00 206
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CMPMVS	2623.62 15	1983.08 194	2156.14 194	1463.89 215	1414.80 214	1517.00 219	5239.20 207	867.40 234	3310.30 211	1128.70 213	3489.90 211	366.75 206	2030.20 223	2427.60 210	1160.60 233	844.46 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet		2877.42 208	3170.00 209	1999.67 226	1673.00 215	2042.00 240	8593.00 230	474.00 217	6452.00 237	1268.00 218	5125.00 226	339.00 202	3004.00 237	4222.00 228	384.00 193	953.00 221
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PCF-MVS	4335.85 20	2272.23 198	2597.03 199	1297.84 207	1701.20 216	1332.37 215	8105.11 227	451.45 214	4031.84 216	1099.10 212	3966.50 216	467.45 209	1734.05 215	2701.68 213	848.94 217	827.11 210
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SD-MVS		2654.53 205	3055.56 208	1451.45 213	1725.60 217	1598.35 223	9203.30 233	315.86 200	5016.02 226	1363.85 220	4894.28 225	387.23 207	2023.43 222	3973.88 224	620.04 198	732.56 205

3Dnovator	2482.06 14	2531.17 202	2839.56 203	1606.00 216	1732.00 218	1557.00 221	8375.00 229	483.00 219	4370.00 220	1371.00 221	4200.00 217	350.00 203	2207.00 224	3877.00 223	798.00 214	1054.00 231

LF4IMVS		2699.50 207	3049.67 207	1649.00 218	1755.00 219	1689.00 228	8330.00 228	464.00 216	4997.00 225	1528.00 224	4763.00 224	568.00 217	2296.00 226	4215.00 227	827.00 216	962.00 222

OPM-MVS		3268.00 212	3762.89 213	1783.33 221	1756.00 220	1806.00 236	12319.00 241	307.00 199	6319.00 236	1557.00 229	5763.00 229	302.00 200	2609.00 231	5061.00 233	482.00 196	935.00 220

OpenMVS	2352.88 13	2263.08 197	2530.89 198	1459.67 214	1767.00 221	1591.00 222	7932.00 225	399.00 207	3643.00 213	1176.00 214	3569.00 213	297.00 198	1927.00 221	3302.00 216	693.00 202	861.00 213

tpmrst		2557.50 203	2847.12 204	1688.66 219	1784.08 222	1778.25 235	7445.50 223	342.14 201	4471.54 221	1534.55 225	4382.10 221	530.10 215	2361.21 227	4142.81 225	991.22 224	926.51 219

AdaColmap		2570.77 204	2948.61 205	1437.24 212	1873.64 223	1526.60 220	7819.77 224	373.97 203	4970.45 224	1479.29 223	4648.46 223	525.80 214	1904.97 220	4150.42 226	695.69 203	880.14 217

SteuartSystems-ACMMP		2891.50 209	3284.78 210	1711.67 220	1954.00 224	1655.00 227	10117.00 238	445.00 213	5102.00 227	1375.00 222	5126.00 227	351.00 204	2638.00 234	4402.00 229	691.00 201	842.00 211
Skip Steuart: Steuart Systems R&D Blog.
ITE_SJBPF				2014.00 227	1998.00 225	1992.00 238		701.00 230	5989.00 229	1812.00 239	3612.00 214	650.00 220	2852.00 236	4601.00 231	1195.00 234	1198.00 233

tpm2		3617.85 215	4007.23 215	2449.73 230	2012.71 226	1698.05 229	7351.19 218	869.29 235	6132.06 230	1588.28 230	8007.42 238	1281.09 244	3888.11 242	7472.04 241	1351.00 235	1763.02 240

tpm cat1		3617.85 215	4007.23 215	2449.73 230	2012.71 226	1698.05 229	7351.19 218	869.29 235	6132.06 230	1588.28 230	8007.42 238	1281.09 244	3888.11 242	7472.04 241	1351.00 235	1763.02 240

tpmp4_e23		3617.85 215	4007.23 215	2449.73 230	2012.71 226	1698.05 229	7351.19 218	869.29 235	6132.06 230	1588.28 230	8007.42 238	1281.09 244	3888.11 242	7472.04 241	1351.00 235	1763.02 240

CostFormer		3617.85 215	4007.23 215	2449.73 230	2012.71 226	1698.05 229	7351.19 218	869.29 235	6132.06 230	1588.28 230	8007.42 238	1281.09 244	3888.11 242	7472.04 241	1351.00 235	1763.02 240

tpm		3617.85 215	4007.23 215	2449.73 230	2012.71 226	1698.05 229	7351.19 218	869.29 235	6132.06 230	1588.28 230	8007.42 238	1281.09 244	3888.11 242	7472.04 241	1351.00 235	1763.02 240

CHOSEN 280x420		2340.81 199	2643.03 200	1434.14 209	2025.55 231	1629.36 224	6542.40 212	412.49 208	4219.70 217	1547.02 226	4328.52 218	650.74 221	1798.26 216	3333.17 217	727.72 204	874.79 214

CHOSEN 1792x2688		2340.81 199	2643.03 200	1434.14 209	2025.55 231	1629.36 224	6542.40 212	412.49 208	4219.70 217	1547.02 226	4328.52 218	650.74 221	1798.26 216	3333.17 217	727.72 204	874.79 214

HyFIR lowres test		2340.81 199	2643.03 200	1434.14 209	2025.55 231	1629.36 224	6542.40 212	412.49 208	4219.70 217	1547.02 226	4328.52 218	650.74 221	1798.26 216	3333.17 217	727.72 204	874.79 214

3Dnovator+	3414.41 17	3146.67 210	3537.00 211	1975.67 224	2310.00 234	2005.00 239	10100.00 237	626.00 227	5753.00 228	1803.00 238	5179.00 228	487.00 210	2622.00 232	4550.00 230	1025.00 232	1300.00 234

TSAR-MVS + MP.		3528.01 214	4065.89 220	1914.35 223	2349.10 235	2141.30 241	12362.70 242	417.60 212	6717.80 241	1759.40 237	6497.50 231	518.18 213	2629.38 233	5164.32 234	806.40 215	972.38 224

DeepPCF-MVS	3452.42 18	2670.71 206	3021.57 206	1618.12 217	2377.67 236	1751.39 234	8915.70 232	567.44 225	4569.74 222	1236.84 216	4615.63 222	513.23 212	2291.32 225	3515.48 220	882.39 219	811.66 208

GST-MVS		4000.00 222	4533.33 223	2400.00 229	2400.00 237	2400.00 243	7200.00 217	2400.00 251	7200.00 243	2400.00 242	7200.00 235	2400.00 249	2400.00 228	7200.00 239	2400.00 245	2400.00 246

MVS_111021_HR		3742.80 220	4330.34 221	1980.20 225	2484.31 238	2259.52 242	14973.80 245	515.90 224	6500.13 238	1949.21 240	6146.89 230	535.66 216	2593.66 230	4880.76 232	986.37 223	1087.43 232

DeepC-MVS	3917.13 19	3155.06 211	3601.83 212	1814.72 222	2515.52 239	1941.53 237	10432.50 239	589.42 226	4789.45 223	1298.65 219	7677.31 237	578.35 218	2589.53 229	3611.78 222	923.52 222	913.10 218
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS	6309.84 21	3374.90 213	3806.99 214	2078.63 228	2535.47 240	2499.25 244	8816.16 231	456.05 215	6255.60 235	1664.52 236	6704.88 233	629.95 219	2768.38 235	6343.93 237	856.35 218	968.25 223
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HSP-MVS		4296.20 223	4911.12 225	2451.45 235	2725.60 241	2598.35 245	16203.30 246	515.86 223	7016.02 242	2363.85 241	6894.28 234	887.23 234	3023.43 238	5973.88 236	1620.04 242	1732.56 239

MVS-HIRNet		2125.39 195	2452.68 196	1143.53 204	2938.33 242	1446.92 218	6083.19 210	391.74 205	3801.71 214	1202.10 215	3439.72 210	492.73 211	1304.35 210	3048.25 215	676.32 199	679.32 204

MAR-MVS		3973.16 221	4387.92 222	2728.86 236	3345.27 243	3341.39 248	11403.90 240	798.76 232	6555.62 240	2569.29 243	6514.86 232	834.97 233	3237.43 239	5891.08 235	1577.54 241	1607.76 235
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
API-MVS		4380.83 224	4906.56 224	2803.67 237	3844.00 244	3116.00 247	13454.00 243	901.00 240	6511.00 239	2794.00 244	7365.00 236	754.00 230	3634.00 240	6622.00 238	1914.00 244	1661.00 238

CLD-MVS		7338.25 226	8341.00 227	4330.00 239	4902.00 245	4991.00 249	23318.00 248	728.00 231	14437.00 250	4378.00 247	13379.00 247	824.00 232	5708.00 247	11429.00 251	1674.00 243	2291.00 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OMC-MVS		5022.26 225	5748.82 226	2842.58 238	4905.27 246	3101.27 246	14933.60 244	812.24 233	9278.40 244	2959.29 245	8800.17 243	1092.24 242	3806.30 241	7568.54 246	1389.60 240	1620.18 237

LS3D		7684.71 227	8776.23 228	4410.16 240	5298.55 247	5631.53 250	34329.90 250	1504.57 249	11554.00 249	3896.41 246	15111.00 248	812.19 231	5985.63 248	3516.20 221	2963.23 246	1613.31 236

MVE	10021.00 22	10550.67 231	10002.44 232	12195.33 245	6242.00 248	8768.00 252	30896.00 249	4451.00 252	10331.00 248	6247.00 248	18296.00 249	283.00 197	21484.00 253	7364.00 240	5912.00 248	6334.00 248
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CNLPA		10738.67 232	11772.44 233	7637.33 241	9120.00 249	8206.00 251	22252.00 247	2010.00 250	24984.00 251	7458.00 249	22596.00 250	2940.00 250	10260.00 252	11100.00 250	3492.00 247	4446.00 247

UA-Net		10000.00 228	10000.00 229	10000.00 242	10000.00 250	10000.00 253	10000.00 234	10000.00 255	10000.00 245	10000.00 250	10000.00 244	10000.00 252	10000.00 249	10000.00 247	10000.00 249	10000.00 249

uncertanet		10000.00 228	10000.00 229	10000.00 242	10000.00 250	10000.00 253	10000.00 234	10000.00 255	10000.00 245	10000.00 250	10000.00 244	10000.00 252	10000.00 249	10000.00 247	10000.00 249	10000.00 249

uanet		10000.00 228	10000.00 229	10000.00 242	10000.00 250	10000.00 253	10000.00 234	10000.00 255	10000.00 245	10000.00 250	10000.00 244	10000.00 252	10000.00 249	10000.00 247	10000.00 249	10000.00 249

LTVRE_ROB	20565.47 23	23378.08 233	22805.00 234	25097.33 246	15500.00 253	19201.00 256	42712.00 251	6055.00 253	38388.00 252	16926.00 253	38534.00 251	7071.00 251	35653.00 254	29475.00 252	10584.00 252	20438.00 253
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		32606.17 234	34443.44 235	27094.33 247	29249.00 254	25246.00 257	51144.00 252	6705.00 254	42886.00 253	22888.00 254	48534.00 252	13263.00 255	37507.00 255	79298.00 253	16024.00 253	18530.00 252

114514_t		99999.00 235	99999.00 236	99999.00 248	99999.00 255	99999.00 258	99999.00 253	99999.00 258	99999.00 254	99999.00 255	99999.00 253	99999.00 256	99999.00 256	99999.00 254	99999.00 254	99999.00 254

PHI-MVS		99999.00 235	99999.00 236	99999.00 248	99999.00 255	99999.00 258	99999.00 253	99999.00 258	99999.00 254	99999.00 255	99999.00 253	99999.00 256	99999.00 256	99999.00 254	99999.00 254	99999.00 254

XVG-OURS-SEG-HR		999999.00 240	999999.00 238	999999.00 250	999999.00 257	999999.00 260	999999.00 255	999999.00 260	999999.00 256	999999.00 257	999999.00 255	999999.00 258	999999.00 258	999999.00 256	999999.00 256	999999.00 256

XVG-OURS		999999.00 240	999999.00 238	999999.00 250	999999.00 257	999999.00 260	999999.00 255	999999.00 260	999999.00 256	999999.00 257	999999.00 255	999999.00 258	999999.00 258	999999.00 256	999999.00 256	999999.00 256

XVG-ACMP-BASELINE		999999.00 240	999999.00 238	999999.00 250	999999.00 257	999999.00 260	999999.00 255	999999.00 260	999999.00 256	999999.00 257	999999.00 255	999999.00 258	999999.00 258	999999.00 256	999999.00 256	999999.00 256

0601test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

thisisatest0530		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Anonymous20240529		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Anonymous202405211		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Anonymous20240521		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tttt0517		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

our_test_3		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

thisisatest0515		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

ppachtmachnet_test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

SMA-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpn111		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

conf0.01		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

ESAPD		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

conf0.002		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

thresconf0.02		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpn_n400		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpnconf		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpnview11		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpn1000		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test_part2					10000000.00 260	10000000.00 263				10000000.00 260

tfpn_ndepth		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

conf200view11		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

thres100view900		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpnnormal		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpn200view9		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

view600		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

view800		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

conf0.05thres1000		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

tfpn		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

v1.0		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

semantic-postprocess				10000000.00 253	10000000.00 260	10000000.00 263		10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

pmmvs6		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

pmmvs5		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Anonymous20231211		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

pmmvs-eth3d		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Anonymous20231206		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Patchmatch-test1		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Patchmatch-test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Patchmatch-RL test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

canonicalmvs		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

anonymousdsp		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

FIs		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

FC-MVSNet-test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

sosnet-low-res		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

AllTest		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

TestCases				10000000.00 253	10000000.00 260	10000000.00 263		10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

testing_2		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test_normal		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

HPM-MVS++		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

XVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

pm-mvs1		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

X-MVStestdata		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

APDe-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

APD-MVS_3200maxsize		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

pmmvs4		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test-LLR		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

TESTMET0.1,1		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test-mter		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

testgi		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test20.03		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

thres600view7		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

1111		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

.test1245		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

MP-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

thres400		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test123		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

thres200		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test0.0.03 1		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test12356		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

testus		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

pmmvs3		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

testmv		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

EMVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

E-PMN		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test2356		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

test1235678		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PGM-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

MCST-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

MVS_Test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PMMVS2		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PM-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

HQP_MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

plane_prior7					10000000.00 260	10000000.00 263

plane_prior6					10000000.00 260	10000000.00 263						10000000.00 261

plane_prior1					10000000.00 260

mPP-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

DI_MVS_plusplus_test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

DWT-MVSNet_test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Test4		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

HPM-MVS_fast		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PatchFormer-LS_test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

testpf		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

HPM-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
LPG-MVS_test		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

LGP-MVS_train				10000000.00 253	10000000.00 260	10000000.00 263		10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

EPNet_dtu		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP-NCC					10000000.00 260		10000000.00 258		10000000.00 259	10000000.00 260

ACMP_Plane					10000000.00 260		10000000.00 258		10000000.00 259	10000000.00 260

CNVR-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

NCCC		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

CP-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

NP-MVS					10000000.00 260	10000000.00 263					10000000.00 258

no-one		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

CPTT-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

HQP-MVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

IS-MVSNet		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PAPM_NR		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PAPR		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

Vis-MVSNet (Re-imp)		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PatchMatch-RL		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

TDRefinement		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

EPP-MVSNet		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PMMVS		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PAPM		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

ACMMP		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259
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		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

PNet_i23d		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

wuyk23d		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

wuykxyi23d		10000000.00 243	10000000.00 244	10000000.00 253	10000000.00 260	10000000.00 263	10000000.00 258	10000000.00 263	10000000.00 259	10000000.00 260	10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

APD-MVS		1000000000.00 354	1000000000.00 355	1000000000.00 368	1000000000.00 381	1000000000.00 384	1000000000.00 375	1000000000.00 384	1000000000.00 377	1000000000.00 379	1000000000.00 377	1000000000.00 383	1000000000.00 381	1000000000.00 378	1000000000.00 378	1000000000.00 378
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GSMVS																10000000.00 259

test_part1				10000000.00 253		10000000.00 263	10000000.00 258	10000000.00 263				10000000.00 261	10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

sam_mvs1												10000000.00 261				10000000.00 259

sam_mvs												10000000.00 261

MTGPA								10000000.00 263

test_post1							10000000.00 258				10000000.00 258	10000000.00 261	10000000.00 261	10000000.00 259

test_post											10000000.00 258	10000000.00 261	10000000.00 261

patchmatchnet-post											10000000.00 258	10000000.00 261	10000000.00 261

MTMP							10000000.00 258	10000000.00 263

test9_res														0.00 1	0.00 1	0.00 1

agg_prior2														0.00 1	0.00 1	0.00 1

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

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

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

testdata2													0.00 1	0.00 1

segment_acmp												0.00 1

testdata1							0.00 1		0.00 1

plane_prior5								10000000.00 263					10000000.00 261	10000000.00 259	10000000.00 259	10000000.00 259

plane_prior4											10000000.00 258

plane_prior3						10000000.00 263			10000000.00 259	10000000.00 260

plane_prior2							10000000.00 258		10000000.00 259

plane_prior						1.00 64	2.00 93		2.00 94						2.00 93

n2								14.20 133

nn								1.42 69

door-mid								1.42 69

test11								1.00 64

door								99.00 167

HQP5-MVS						10000000.00 263

BP-MVS														10000000.00 259

HQP4-MVS										10000000.00 260			10000000.00 261			10000000.00 259

HQP3-MVS								10000000.00 263							10000000.00 259

HQP2-MVS												10000000.00 261

MDTV_nov1_ep13_2view						9.99 116	9.99 113		9.99 114	9.99 114		9.99 114		9.99 112		9.99 114

ACMMP++_ref															10000000.00 259

ACMMP++															10000000.00 259

Test By Simon												990.00 235

High-resolution multi-view benchmark