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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

Fast-Effi-MVS+-dtu		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

Effi-MVS+-dtu		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	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	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	0.00 1

v144192		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v1921920		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v1192		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v1144		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v148		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v748		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v7n		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v1141		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v1neww		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v7new		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v1240		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v18		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v17		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v16		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

divwei89l23v2f112		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v15		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v13		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v12		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v8		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v7		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v6		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v11		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v52		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

V14		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v10		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

V4		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v2v482		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

v1		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

V42		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

V9		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

GA-MVS		0.40 5	0.40 5	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 6	0.40 5	0.40 5	0.40 6	0.40 6

Fast-Effi-MVS+		0.41 36	0.41 36	0.41 37	0.41 37	0.41 37	0.41 37	0.41 37	0.41 37	0.41 37	0.41 37	0.41 37	0.41 37	0.41 36	0.41 36	0.41 37	0.41 37

Effi-MVS+		0.53 37	0.53 37	0.53 38	0.53 38	0.53 38	0.53 38	0.53 38	0.53 38	0.53 38	0.53 38	0.53 38	0.53 38	0.53 37	0.53 37	0.53 38	0.53 38

CDS-MVSNet		0.88 38	0.88 38	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 38	0.88 38	0.88 39	0.88 39
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		0.88 38	0.88 38	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 39	0.88 38	0.88 38	0.88 39	0.88 39

MDA-MVSNet-bldmvs		1.20 40	1.20 40	1.20 41	1.20 41	1.20 42	1.20 42	1.20 42	1.20 42	1.20 42	1.20 42	1.20 42	1.20 42	1.20 41	1.20 40	1.20 42	1.20 42

CVMVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

EU-MVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

PS-CasMVS		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

UniMVSNet_NR-MVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

PEN-MVS		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

TransMVSNet (Re)		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

DTE-MVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

DU-MVS		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

UniMVSNet (Re)		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

CP-MVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

WR-MVS_H		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

WR-MVS		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

NR-MVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

Baseline_NR-MVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

TranMVSNet+NR-MVSNet		1.42 41	1.42 41	1.42 42	1.42 42	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 43	1.42 42	1.42 41	1.42 43	1.42 43

CDPH-MVS		6.66 56	6.66 56	6.66 57	6.66 57	6.66 58	6.66 58	6.66 58	6.66 58	6.66 58	6.66 58	6.66 58	6.66 58	6.66 57	6.66 56	6.66 58	6.66 58

MDTV_nov1_ep13_2view		9.99 57	9.99 57	9.99 58	9.99 58	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 58	9.99 57	9.99 59	9.99 59

MDTV_nov1_ep13		9.99 57	9.99 57	9.99 58	9.99 58	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 59	9.99 58	9.99 57	9.99 59	9.99 59

GBi-Net		39.84 59	35.75 59	44.63 60	43.04 60	49.88 61	51.06 61	86.51 62	35.34 61	17.20 61	29.83 61	15.85 61	43.43 63	35.35 60	35.70 59	26.51 63	48.28 63

X-MVS		52.20 60	52.20 60	52.20 61	52.20 61	52.20 62	52.20 62	52.20 61	52.20 62	52.20 71	52.20 62	52.20 80	52.20 64	52.20 61	52.20 60	52.20 64	52.20 64

new_pmnet		75.00 61	74.67 61	75.39 62	85.42 64	96.03 66	98.77 65	173.23 63	69.57 63	36.19 62	56.93 63	117.43 84	32.98 61	85.38 69	68.41 61	25.74 61	28.95 61

N_pmnet		75.00 61	74.67 61	75.39 62	85.42 64	96.03 66	98.77 65	173.23 63	69.57 63	36.19 62	56.93 63	117.43 84	32.98 61	85.38 69	68.41 61	25.74 61	28.95 61

CANet		100.44 63	93.07 63	109.04 64	111.90 66	131.06 70	119.40 67	222.42 72	80.48 65	36.93 64	70.80 65	40.92 66	89.90 65	100.59 71	101.42 64	73.68 69	126.25 65

CANet_DTU		100.44 63	93.07 63	109.04 64	111.90 66	131.06 70	119.40 67	222.42 72	80.48 65	36.93 64	70.80 65	40.92 66	89.90 65	100.59 71	101.42 64	73.68 69	126.25 65

MVS_0304		100.44 63	93.07 63	109.04 64	111.90 66	131.06 70	119.40 67	222.42 72	80.48 65	36.93 64	70.80 65	40.92 66	89.90 65	100.59 71	101.42 64	73.68 69	126.25 65

UGNet		100.44 63	93.07 63	109.04 64	111.90 66	131.06 70	119.40 67	222.42 72	80.48 65	36.93 64	70.80 65	40.92 66	89.90 65	100.59 71	101.42 64	73.68 69	126.25 65
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
FMVSNet5		116.86 67	110.47 69	124.32 68	131.47 72	157.41 74	150.67 73	199.64 65	115.30 72	56.61 77	97.70 74	51.77 70	117.26 69	77.26 62	113.14 70	90.25 76	160.68 72

test1		116.86 67	110.47 69	124.32 68	131.47 72	157.41 74	150.67 73	199.64 65	115.30 72	56.61 77	97.70 74	51.77 70	117.26 69	77.26 62	113.14 70	90.25 76	160.68 72

FMVSNet3		116.86 67	110.47 69	124.32 68	131.47 72	157.41 74	150.67 73	199.64 65	115.30 72	56.61 77	97.70 74	51.77 70	117.26 69	77.26 62	113.14 70	90.25 76	160.68 72

FMVSNet2		116.86 67	110.47 69	124.32 68	131.47 72	157.41 74	150.67 73	199.64 65	115.30 72	56.61 77	97.70 74	51.77 70	117.26 69	77.26 62	113.14 70	90.25 76	160.68 72

FMVSNet1		116.86 67	110.47 69	124.32 68	131.47 72	157.41 74	150.67 73	199.64 65	115.30 72	56.61 77	97.70 74	51.77 70	117.26 69	77.26 62	113.14 70	90.25 76	160.68 72

MIMVSNet1		116.86 67	110.47 69	124.32 68	131.47 72	157.41 74	150.67 73	199.64 65	115.30 72	56.61 77	97.70 74	51.77 70	117.26 69	77.26 62	113.14 70	90.25 76	160.68 72

MIMVSNet		116.86 67	110.47 69	124.32 68	131.47 72	157.41 74	150.67 73	199.64 65	115.30 72	56.61 77	97.70 74	51.77 70	117.26 69	77.26 62	113.14 70	90.25 76	160.68 72

ACMMP_Plus		119.64 74	106.58 67	134.88 75	123.82 70	127.44 68	145.38 71	298.43 80	116.16 79	43.72 69	77.82 69	29.30 63	131.24 76	117.82 78	106.21 68	66.69 67	171.29 79

MPTG		119.64 74	106.58 67	134.88 75	123.82 70	127.44 68	145.38 71	298.43 80	116.16 79	43.72 69	77.82 69	29.30 63	131.24 76	117.82 78	106.21 68	66.69 67	171.29 79

PVSNet_Blended_VisFu		129.22 76	115.76 76	144.92 77	140.60 79	162.80 81	166.50 80	281.20 77	114.70 69	55.50 74	96.20 71	51.80 77	140.60 78	114.70 75	114.70 77	85.10 73	155.40 69

PVSNet_BlendedMVS		129.22 76	115.76 76	144.92 77	140.60 79	162.80 81	166.50 80	281.20 77	114.70 69	55.50 74	96.20 71	51.80 77	140.60 78	114.70 75	114.70 77	85.10 73	155.40 69

PVSNet_Blended		129.22 76	115.76 76	144.92 77	140.60 79	162.80 81	166.50 80	281.20 77	114.70 69	55.50 74	96.20 71	51.80 77	140.60 78	114.70 75	114.70 77	85.10 73	155.40 69

SixPastTwentyTwo		210.90 79	189.75 79	235.58 80	225.41 82	284.88 84	267.17 83	485.88 83	164.28 82	76.51 84	137.62 81	76.69 83	214.49 81	206.28 80	208.00 80	144.02 84	250.52 81

RPSCF		376.53 80	341.80 80	417.05 82	418.60 85	457.01 89	467.46 88	825.25 89	336.12 85	159.32 89	260.40 85	151.13 89	386.94 83	364.25 82	352.10 81	244.72 85	471.62 84

EG-PatchMatch MVS		402.98 81	364.36 81	448.05 83	435.50 86	473.00 90	489.60 90	910.20 90	338.60 86	127.30 85	205.10 82	123.50 86	434.60 85	450.70 87	447.70 84	291.10 87	511.90 85

LP		413.85 82	365.67 82	470.06 84	505.99 91	560.16 92	484.94 89	977.47 91	310.10 83	140.33 86	226.97 83	133.09 87	397.21 84	428.08 84	451.30 85	314.39 89	450.02 83

MS-PatchMatch		438.85 83	388.71 83	497.33 85	479.00 87	527.00 91	558.00 93	988.00 92	379.00 88	174.00 91	278.00 87	162.00 90	494.00 89	418.00 83	412.00 82	291.00 86	545.00 88

ADS-MVSNet		452.36 84	406.02 84	506.42 88	534.76 94	628.22 95	552.79 92	1009.99 98	333.98 84	149.26 87	246.67 84	149.01 88	447.03 87	465.35 88	501.18 88	344.69 91	517.75 86

PatchmatchNet		452.63 85	410.20 85	502.13 87	520.70 93	614.40 93	546.00 91	1018.80 99	379.00 88	167.50 90	277.10 86	169.40 91	437.10 86	437.10 85	457.50 86	322.70 90	536.90 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DELS-MVS		458.84 86	423.31 86	500.30 86	511.99 92	616.59 94	578.86 94	991.10 93	355.79 87	158.99 88	319.09 89	190.62 93	453.64 88	437.73 86	439.52 83	307.20 88	603.81 89
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
dps		534.60 87	472.40 87	607.16 90	690.38 101	718.85 98	636.03 95	1231.66 101	422.74 90	198.65 93	325.68 90	194.97 94	512.90 90	509.81 90	513.65 89	373.33 93	621.11 90

gm-plane-assit		551.84 88	503.49 89	608.24 91	411.90 84	370.41 85	267.30 84	710.53 85	454.42 93	785.72 127	491.23 100	326.12 108	881.92 104	538.27 94	650.05 97	592.08 106	693.94 92

IB-MVS	750.90 3	552.99 89	511.38 93	601.53 89	606.02 97	735.75 100	688.75 98	1183.86 100	436.09 92	210.63 96	397.40 92	233.85 97	551.05 91	521.77 91	533.78 91	368.88 92	721.05 97
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
IterMVS		554.60 90	488.63 88	631.57 95	636.90 98	700.80 96	674.70 97	1271.10 103	430.50 91	207.10 95	370.50 91	202.70 95	611.80 94	534.20 93	545.30 92	387.80 94	636.40 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GG-mvs-BLEND		557.53 91	503.49 89	620.58 92	485.90 88	370.41 85	267.30 84	710.53 85	454.42 93	785.72 127	491.23 100	326.12 108	881.92 104	538.27 94	650.05 97	592.08 106	693.94 92

gg-mvs-nofinetune		557.53 91	503.49 89	620.58 92	485.90 88	370.41 85	267.30 84	710.53 85	454.42 93	785.72 127	491.23 100	326.12 108	881.92 104	538.27 94	650.05 97	592.08 106	693.94 92

CR-MVSNet		557.53 91	503.49 89	620.58 92	485.90 88	370.41 85	267.30 84	710.53 85	454.42 93	785.72 127	491.23 100	326.12 108	881.92 104	538.27 94	650.05 97	592.08 106	693.94 92

Gipuma		587.77 94	529.57 94	655.67 96	637.00 99	733.00 99	749.00 99	1245.00 102	525.00 99	269.00 101	445.00 96	253.00 99	637.00 96	525.00 92	525.00 90	397.00 95	701.00 96
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_Test		654.54 95	583.51 95	737.40 98	721.80 102	822.10 102	808.60 101	1494.00 105	542.20 101	259.90 99	441.60 94	257.70 101	689.10 98	620.80 98	617.70 94	451.00 99	782.50 98

diffmvs		654.54 95	583.51 95	737.40 98	721.80 102	822.10 102	808.60 101	1494.00 105	542.20 101	259.90 99	441.60 94	257.70 101	689.10 98	620.80 98	617.70 94	451.00 99	782.50 98

ACMH+	702.88 1	670.82 97	589.81 97	765.33 100	662.52 100	701.42 97	795.24 100	1698.33 108	598.81 104	226.55 97	403.02 93	187.13 92	786.82 101	724.80 103	646.56 96	422.52 97	866.95 101

new-patchmatchnet		712.65 98	632.59 98	806.05 103	787.79 104	936.25 107	882.47 104	1795.72 109	528.60 100	244.29 98	471.90 97	257.36 100	681.47 97	705.16 102	659.05 101	444.55 98	869.82 102

IterMVS-LS		714.97 99	638.00 100	804.77 102	793.60 105	914.20 105	886.50 106	1612.70 107	588.30 103	285.30 102	491.20 99	278.40 104	759.20 100	677.40 101	681.30 102	491.30 101	835.20 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CSCG		727.26 100	663.25 101	801.94 101	590.65 96	811.25 101	854.12 103	1805.32 110	816.43 109	388.25 107	612.42 108	345.37 114	621.61 95	507.46 89	471.55 87	551.65 104	1078.31 112

MVSTER		834.63 101	737.03 103	948.49 104	988.86 107	1140.51 117	1129.34 115	1832.80 111	682.42 105	319.01 103	550.98 106	311.35 107	872.51 103	763.95 104	757.83 103	548.41 103	952.20 104

PMVS	743.55 2	836.66 102	637.90 99	1068.55 111	1306.50 118	875.12 104	1166.70 116	2629.00 114	512.24 98	196.22 92	282.69 88	237.15 98	597.87 93	842.47 105	765.54 104	514.99 102	950.09 103
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
RPMNet		858.01 103	961.99 109	736.69 97	550.05 95	1120.24 115	1212.05 117	463.05 82	485.90 97	550.05 120	580.05 107	890.05 137	559.05 92	1485.90 119	1185.90 117	1085.90 123	985.90 106

PatchT		878.42 104	785.05 104	987.36 105	1050.05 114	922.05 106	1111.05 113	677.05 84	950.05 112	1285.90 151	750.05 114	640.05 125	850.05 102	950.05 106	950.05 106	950.05 117	333.05 82

ACMH	935.68 4	948.23 105	857.00 105	1054.67 110	946.00 106	970.00 108	1125.00 114	2467.00 112	823.00 110	330.00 105	622.00 109	211.00 96	891.00 108	970.00 108	1061.00 114	569.00 105	1342.00 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSLP-MVS++		993.91 106	732.21 102	1299.24 115	1631.22 121	1345.26 119	1480.05 120	2524.20 113	735.65 106	463.57 118	499.89 104	302.78 105	1292.66 117	675.99 100	612.79 93	403.71 96	953.11 105

EPMVS		999.00 107	999.00 110	999.00 106	999.00 108	999.00 109	999.00 107	999.00 94	999.00 114	999.00 135	999.00 121	999.00 142	999.00 110	999.00 109	999.00 109	999.00 118	999.00 107

sosnet		1000.00 108	1000.00 112	1000.00 107	1000.00 110	1000.00 111	1000.00 109	1000.00 95	1000.00 116	1000.00 137	1000.00 123	1000.00 144	1000.00 112	1000.00 111	1000.00 111	1000.00 120	1000.00 109

USDC		1000.00 108	1000.00 112	1000.00 107	1000.00 110	1000.00 111	1000.00 109	1000.00 95	1000.00 116	1000.00 137	1000.00 123	1000.00 144	1000.00 112	1000.00 111	1000.00 111	1000.00 120	1000.00 109

TinyColmap		1000.00 108	1000.00 112	1000.00 107	1000.00 110	1000.00 111	1000.00 109	1000.00 95	1000.00 116	1000.00 137	1000.00 123	1000.00 144	1000.00 112	1000.00 111	1000.00 111	1000.00 120	1000.00 109

ACMM	1129.18 5	1047.56 111	912.90 106	1204.67 112	1040.21 113	1016.71 114	1105.32 112	2918.67 117	813.91 108	327.22 104	665.34 110	267.72 103	1192.58 116	1091.35 115	991.88 108	643.99 111	1543.40 121
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_111021_LR		1055.24 112	916.36 107	1217.27 113	1129.93 116	1268.26 118	1313.88 119	2635.76 115	923.19 111	396.92 112	704.88 112	327.66 112	1136.32 115	965.38 107	946.95 105	690.83 114	1278.20 113

ACMP	1170.86 6	1091.37 113	943.23 108	1264.21 114	1112.10 115	1138.94 116	1276.90 118	2675.36 116	989.62 113	426.15 116	742.77 113	349.09 116	1454.51 121	1047.36 114	969.90 107	640.22 110	1364.94 116
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TSAR-MVS + GP.		1239.27 114	1094.74 117	1407.89 116	1220.99 117	1845.47 124	1680.98 123	3191.81 118	1098.41 119	362.98 106	679.12 111	339.31 113	1323.06 118	1173.24 118	1093.83 115	667.49 112	1433.78 117

ACMMPR		1265.65 115	1083.59 116	1478.06 118	1441.06 120	1438.61 122	1540.68 122	3284.05 120	1219.68 121	458.81 117	803.22 116	346.91 115	1421.66 120	1126.11 117	1150.51 116	722.08 115	1500.09 119

DeepC-MVS_fast	2081.98 8	1311.87 116	1172.50 118	1474.47 117	1411.78 119	1397.23 121	1519.03 121	3401.40 121	1201.29 120	417.90 114	801.95 115	976.25 141	1409.37 119	1114.52 116	1201.12 118	687.33 113	1515.14 120
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FPMVS		1356.65 117	1072.60 115	1688.04 119	1840.79 122	1397.01 120	1793.24 124	4397.86 125	804.66 107	408.78 113	538.65 105	407.36 117	894.84 109	1587.73 120	1286.38 119	792.73 116	1486.39 118

OpenMVS	2049.89 7	1895.92 118	1721.29 119	2099.67 120	2064.00 123	2419.00 127	2138.00 125	4572.00 126	1541.00 127	548.00 119	937.00 118	580.00 123	1983.00 123	1971.00 126	1993.00 124	1293.00 130	2608.00 125

MVS-HIRNet		1975.98 119	1797.02 121	2184.76 121	2111.07 124	2443.03 128	2503.43 127	4063.71 122	1684.46 130	711.34 125	1456.05 133	848.70 135	2396.26 133	1900.93 121	1915.01 122	1322.75 131	2330.99 122

CMPMVS	2507.20 10	2083.60 120	1983.13 124	2200.81 122	2582.70 132	3097.30 135	2332.10 126	4364.50 124	1390.00 122	653.68 123	997.75 120	742.65 130	1880.20 122	2298.80 133	2413.60 136	1391.70 133	2941.80 130
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PCF-MVS	3738.24 13	2129.60 121	1746.89 120	2576.10 128	2258.22 128	2263.67 125	2650.06 132	6361.78 139	1676.15 129	695.66 124	1209.77 127	737.88 128	2237.28 124	2083.02 128	1835.31 120	1253.58 129	2422.45 123
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CHOSEN 280x420		2225.10 122	1992.66 125	2496.28 123	2192.10 125	2641.81 129	2621.41 129	5345.34 130	2116.46 135	1002.26 140	1683.84 137	837.43 131	2338.65 126	1920.94 123	2020.28 125	1477.92 135	2727.86 126

CHOSEN 1792x2688		2225.10 122	1992.66 125	2496.28 123	2192.10 125	2641.81 129	2621.41 129	5345.34 130	2116.46 135	1002.26 140	1683.84 137	837.43 131	2338.65 126	1920.94 123	2020.28 125	1477.92 135	2727.86 126

HyFIR lowres test		2225.10 122	1992.66 125	2496.28 123	2192.10 125	2641.81 129	2621.41 129	5345.34 130	2116.46 135	1002.26 140	1683.84 137	837.43 131	2338.65 126	1920.94 123	2020.28 125	1477.92 135	2727.86 126

AdaColmap		2288.90 125	2080.73 129	2531.77 126	2485.98 131	2757.87 132	2974.47 134	4914.62 127	2255.54 138	804.63 131	1611.14 136	842.30 134	2657.83 136	2056.92 127	1960.62 123	1353.09 132	3080.73 131

3Dnovator	2220.89 9	2303.77 126	2037.00 128	2615.00 129	2617.00 133	2967.00 134	2616.00 128	5882.00 137	1928.00 133	589.00 121	907.00 117	528.00 122	2443.00 135	2370.00 135	2392.00 135	1543.00 139	3167.00 132

SD-MVS		2324.36 127	1910.97 122	2806.65 136	2945.31 136	3305.03 140	3267.51 137	5962.25 138	1896.37 132	848.15 132	1224.25 128	661.55 126	2707.30 137	1913.40 122	1871.41 121	1109.38 124	2504.79 124

DeepPCF-MVS	3033.31 11	2362.79 128	2183.15 130	2572.38 127	2349.28 129	2383.16 126	2740.92 133	5212.97 129	2043.02 134	751.44 126	1466.47 134	1327.42 152	2904.33 138	2573.45 139	2227.77 129	1475.33 134	3260.73 134

Vis-MVSNet		2390.77 129	1976.57 123	2874.00 137	2644.00 134	3248.00 138	3040.00 135	7371.00 143	1627.00 128	421.00 115	1200.00 126	622.00 124	2251.00 125	2326.00 134	2052.00 128	1517.00 138	2761.00 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SteuartSystems-ACMMP		2615.00 130	2242.57 131	3049.50 138	2828.00 135	2801.00 133	3323.00 139	6648.00 141	2491.00 139	1023.00 143	1605.00 135	697.00 127	2926.00 140	2502.00 136	2279.00 130	1549.00 140	3323.00 136
Skip Steuart: Steuart Systems R&D Blog.
tpm cat1		2652.13 131	2606.66 135	2705.18 131	3151.86 138	6902.39 151	3612.17 142	5463.78 133	1457.76 123	391.31 108	1358.61 129	446.44 118	2360.09 129	2296.48 129	2290.90 131	1251.84 125	3494.04 137

tpmp4_e23		2652.13 131	2606.66 135	2705.18 131	3151.86 138	6902.39 151	3612.17 142	5463.78 133	1457.76 123	391.31 108	1358.61 129	446.44 118	2360.09 129	2296.48 129	2290.90 131	1251.84 125	3494.04 137

CostFormer		2652.13 131	2606.66 135	2705.18 131	3151.86 138	6902.39 151	3612.17 142	5463.78 133	1457.76 123	391.31 108	1358.61 129	446.44 118	2360.09 129	2296.48 129	2290.90 131	1251.84 125	3494.04 137

tpm		2652.13 131	2606.66 135	2705.18 131	3151.86 138	6902.39 151	3612.17 142	5463.78 133	1457.76 123	391.31 108	1358.61 129	446.44 118	2360.09 129	2296.48 129	2290.90 131	1251.84 125	3494.04 137

tpmrst		2688.71 135	2604.46 134	2787.00 135	2472.96 130	3190.36 136	3313.69 138	5003.06 128	1837.26 131	1097.38 146	1721.89 141	1012.20 147	2415.09 134	2970.27 145	2736.80 142	2419.79 150	4762.44 147

COLMAP_ROB	3798.28 14	2690.62 136	2704.29 141	2674.67 130	3138.00 137	3522.00 142	3826.00 147	3219.00 119	2702.00 144	1138.00 149	2260.00 148	1212.00 151	2968.00 141	2936.00 144	2571.00 139	1759.00 145	3727.00 144
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TAPA-MVS	5463.68 16	2764.21 137	2434.28 132	3149.14 139	3645.15 146	3257.60 139	3452.71 141	6526.32 140	2591.85 142	651.36 122	1964.77 146	947.44 140	2924.41 139	2523.36 137	2452.68 137	1694.86 143	3302.28 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator+	3173.67 12	2938.77 138	2519.43 133	3428.00 140	3256.00 142	3708.00 143	3443.00 140	7516.00 144	2525.00 140	1121.00 147	972.00 119	925.00 139	3192.00 142	2899.00 142	2897.00 144	2040.00 147	3710.00 143

OPM-MVS		3051.54 139	2661.57 139	3506.50 141	3532.00 145	3332.00 141	3744.00 146	7839.00 147	2772.00 145	985.00 134	1919.00 145	738.00 129	3319.00 143	2901.00 143	2715.00 141	1620.00 142	4254.00 146

TSAR-MVS + ACMM		3184.92 140	2679.45 140	3774.64 146	3302.51 143	4168.07 145	4256.74 149	8947.31 149	2634.53 143	1026.25 144	1723.72 142	1043.87 148	3387.29 144	2794.29 140	2687.41 140	1727.73 144	3704.25 142

MVS_111021_HR		3192.50 141	2773.91 142	3680.86 143	3515.58 144	3815.81 144	4020.82 148	7766.11 145	2775.23 146	1300.96 153	2221.19 147	1117.25 150	3420.01 145	2873.98 141	2835.60 143	2061.69 148	3778.31 145

HSP-MVS		3241.28 142	2825.26 143	3726.65 145	3945.31 148	4305.03 146	4267.51 151	6962.25 142	2896.37 147	1068.15 145	1824.25 144	1061.55 149	4307.30 148	3013.40 146	3171.41 145	1809.38 146	3504.79 141

PLC	4785.50 15	3275.54 143	3030.86 144	3561.00 142	4093.00 149	4596.00 148	5174.00 154	4267.00 123	3570.00 149	1495.00 154	2940.00 151	1594.00 154	3947.00 147	3817.00 148	3343.00 147	2390.00 149	1356.00 115
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		3961.62 144	3726.55 145	4235.86 147	4107.62 150	6522.83 150	4862.93 153	7956.64 148	3380.98 148	1665.13 155	3231.54 152	1650.34 155	4357.29 149	3198.61 147	3209.42 146	2465.52 151	4892.15 148
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
TSAR-MVS + MP.		4216.58 145	4674.68 147	3682.13 144	3752.63 147	4315.48 147	4264.77 150	7782.51 146	2583.81 141	1125.68 148	16722.30 158	882.60 136	3589.10 146	2523.80 138	2486.45 138	1578.08 141	3208.34 133

OMC-MVS		4439.92 146	3906.78 146	5061.92 149	4748.70 152	5679.38 149	5855.03 155	9923.00 150	4023.36 152	1740.95 156	1693.78 140	1693.78 156	4658.45 150	4254.77 149	4135.74 149	3445.36 153	5866.63 150

DeepC-MVS	5581.15 17	4824.92 147	5020.53 148	4596.70 148	4714.52 151	3205.14 137	3108.50 136	10234.20 152	3823.34 151	1885.23 157	9002.42 155	2045.34 158	4715.23 151	7652.65 152	3993.41 148	2922.51 152	5421.41 149
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + COLMAP		6088.23 148	5315.97 149	6989.21 150	6522.51 153	8025.15 155	7938.31 156	15276.20 153	5401.97 154	1914.31 158	3802.65 153	1979.48 157	6704.11 153	5217.07 150	5137.16 150	3579.82 154	7648.29 151

CLD-MVS		6986.85 149	6222.57 150	7878.50 151	7707.00 154	8884.00 156	9025.00 157	15607.00 154	6148.00 155	2571.00 159	4822.00 154	2082.00 159	7875.00 155	6389.00 151	6449.00 151	4486.00 155	8784.00 153
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		7481.83 150	6596.02 151	8515.28 152	9871.71 155	9844.03 157	9225.16 158	18308.40 155	5364.90 153	1267.41 150	2381.61 149	1512.79 153	7225.72 154	8875.69 153	9516.28 152	5193.26 156	8676.83 152

ACMMP		9999.00 151	9999.00 152	9999.00 153	9999.00 157	9999.00 158	9999.00 159	9999.00 151	9999.00 156	9999.00 161	9999.00 156	9999.00 161	9999.00 156	9999.00 154	9999.00 153	9999.00 158	9999.00 154
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
MVE	11086.50 18	13278.69 152	12303.57 153	14416.33 154	9890.00 156	17107.00 159	4634.00 152	59884.00 158	3666.00 150	966.00 133	1792.00 143	896.00 138	5680.00 152	20677.00 157	23030.00 156	5444.00 157	18957.00 155
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CNLPA		17763.08 153	15907.71 154	19927.67 155	19334.00 158	22387.00 160	22896.00 160	38668.00 156	15772.00 157	7632.00 160	13229.00 157	7123.00 160	19334.00 157	15773.00 155	15700.00 154	11702.00 159	21370.00 156

MSDG		31705.85 154	29167.57 156	34667.17 156	44079.00 159	28314.00 162	44778.00 162	43648.00 157	31045.00 159	14849.00 162	26179.00 159	13752.00 163	38088.00 159	30859.00 158	30835.00 157	22561.00 161	43189.00 158

LTVRE_ROB	38377.89 19	49656.08 155	18936.71 155	85495.33 157	306852.00 161	22526.00 161	29206.00 161	112098.00 160	30022.00 158	17348.00 163	2824.00 150	11239.00 162	29457.00 158	20569.00 156	19941.00 155	18011.00 160	25436.00 157
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
PHI-MVS		99999.00 156	99999.00 157	99999.00 158	99999.00 160	99999.00 163	99999.00 163	99999.00 159	99999.00 160	99999.00 164	99999.00 160	99999.00 164	99999.00 160	99999.00 159	99999.00 158	99999.00 162	99999.00 159

tfpn200view9		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

view600		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

view800		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

conf0.05thres1000		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

tfpn		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

ESAPD		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

pmmvs6		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

pmmvs5		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

Anonymous20231211		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

pmmvs-eth3d		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

Anonymous20231206		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

canonicalmvs		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

anonymousdsp		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

FC-MVSNet-train		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

UA-Net		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

FC-MVSNet-test		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

sosnet-low-res		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

DI_MVS_plusplus_trai		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

HPM-MVS++		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

pm-mvs1		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

APDe-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

pmmvs4		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test-LLR		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

TESTMET0.1,1		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test-mter		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

testgi		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test20.03		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

thres600view7		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

1111		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

.test1245		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

MP-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

testmvs		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

thres400		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test123		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

thres200		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test0.0.03 1		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test12356		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

testus		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

pmmvs3		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

testmv		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

EMVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

E-PMN		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test2356		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

test1235678		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

PGM-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

MCST-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

PMMVS2		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

PM-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

DWT-MVSNet_training		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

testpf		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

LGP-MVS_train		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

EPNet_dtu		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

NCCC		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

CP-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

no-one		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

CPTT-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

HQP-MVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

IS_MVSNet		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

Vis-MVSNet (Re-imp)		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

PatchMatch-RL		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

TDRefinement		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

EPP-MVSNet		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

PMMVS		10000000.00 157	10000000.00 158	10000000.00 159	10000000.00 162	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 165	10000000.00 161	10000000.00 160	10000000.00 159	10000000.00 163	10000000.00 160

APD-MVS		1000000000.00 222	1000000000.00 223	1000000000.00 224	1000000000.00 228	1000000000.00 229	1000000000.00 230	1000000000.00 226	1000000000.00 227	1000000000.00 231	1000000000.00 226	1000000000.00 231	1000000000.00 226	1000000000.00 225	1000000000.00 224	1000000000.00 228	1000000000.00 225
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ambc			999.00 110		999.00 108	999.00 109	999.00 107		999.00 114	999.00 135	999.00 121	999.00 142	999.00 110	999.00 109	999.00 109	999.00 118	999.00 107

MTAPA										41.98 68		28.43 62

MTMP										10000000.00 165		31.71 65

Patchmatch-RL test							10000000.00 164

tmp_tt				299.50 81	365.00 83	54.00 65	649.00 96	241.00 76	159.00 81	204.00 94	479.00 98	308.00 106	243.00 82	246.00 81	82.00 63	95.00 83

XVS					52.20 61	52.20 62	52.20 62			52.20 71		52.20 80				52.20 64

X-MVStestdata					52.20 61	52.20 62	52.20 62			52.20 71		52.20 80				52.20 64

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

mPP-MVS					10000000.00 162							10000000.00 165

NP-MVS									10000000.00 161

Patchmtry						1825.90 123	885.90 105	1405.90 104		1285.90 151

DeepMVS_CX						1.00 41	1.00 41	1.00 41	1.00 41	1.00 41	1.00 41	1.00 41	1.00 41	1.00 40		1.00 41	1.00 41

High-resolution multi-view benchmark