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.

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

CDS-MVSNet		0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2	0.88 2

test_djsdf		1.42 4	1.42 4	1.42 5	1.42 5	1.42 5	1.42 4	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5

MVSFormer		1.42 4	1.42 4	1.42 5	1.42 5	1.42 5	1.42 4	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5

jason		1.42 4	1.42 4	1.42 5	1.42 5	1.42 5	1.42 4	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5
jason: jason.
lupinMVS		1.42 4	1.42 4	1.42 5	1.42 5	1.42 5	1.42 4	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5	1.42 5

cascas		2.00 8	2.00 8	2.00 9	2.00 9	2.00 9	2.00 8	2.00 10	2.00 9	2.00 9	2.00 9	2.00 9	2.00 9	2.00 9	2.00 9	2.00 9

UnsupCasMVSNet_bld		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

WTY-MVS		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

HY-MVS	5.90 1	5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

sss		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

Test_1112_low_res		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

1112_ss		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

ab-mvs		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

UnsupCasMVSNet_eth		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

ab-mvs-re		5.90 9	5.90 9	5.90 10	5.90 10	5.90 10	5.90 9	5.90 12	5.90 10	5.90 10	5.90 10	5.90 11	5.90 10	5.90 10	5.90 11	5.90 11

XXY-MVS		15.00 18	15.00 19	15.00 20	15.00 21	15.00 22	15.00 20	15.00 23	15.00 21	15.00 22	15.00 20	15.00 23	15.00 21	15.00 22	15.00 22	15.00 22

BH-RMVSNet		40.00 19	40.00 20	40.00 22	40.00 22	40.00 23	40.00 21	40.00 38	40.00 22	40.00 24	40.00 21	40.00 39	40.00 23	40.00 23	40.00 32	40.00 30

BH-untuned		40.00 19	40.00 20	40.00 22	40.00 22	40.00 23	40.00 21	40.00 38	40.00 22	40.00 24	40.00 21	40.00 39	40.00 23	40.00 23	40.00 32	40.00 30

TR-MVS		40.00 19	40.00 20	40.00 22	40.00 22	40.00 23	40.00 21	40.00 38	40.00 22	40.00 24	40.00 21	40.00 39	40.00 23	40.00 23	40.00 32	40.00 30

BH-w/o		40.00 19	40.00 20	40.00 22	40.00 22	40.00 23	40.00 21	40.00 38	40.00 22	40.00 24	40.00 21	40.00 39	40.00 23	40.00 23	40.00 32	40.00 30

GBi-Net		44.67 23	49.24 24	30.96 21	47.53 26	47.57 27	126.09 25	8.00 21	76.99 26	26.09 23	73.50 25	11.27 22	30.55 22	61.12 27	12.58 21	14.75 21

dp		87.20 24	113.80 27	7.42 19	10.50 20	8.88 19	298.82 29	2.70 11	510.35 43	7.79 19	172.93 28	3.37 10	9.16 19	14.00 21	3.71 10	4.23 10

new_pmnet		88.14 25	98.62 25	56.70 26	98.14 28	56.07 28	246.79 27	15.37 24	148.06 27	50.77 28	143.13 26	23.35 24	62.65 27	93.26 28	68.71 39	51.38 39

N_pmnet		88.14 25	98.62 25	56.70 26	98.14 28	56.07 28	246.79 27	15.37 24	148.06 27	50.77 28	143.13 26	23.35 24	62.65 27	93.26 28	68.71 39	51.38 39

UGNet		110.62 27	124.04 28	70.34 28	82.03 27	74.27 30	325.54 30	25.25 27	207.16 29	69.28 30	194.38 29	27.53 26	95.79 37	147.76 31	37.43 24	40.97 34
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
FMVSNet2		124.76 28	140.93 29	76.26 30	107.44 31	100.17 36	329.88 31	27.12 31	259.44 34	74.30 31	204.34 30	30.74 27	92.41 29	196.98 33	38.15 25	36.19 23

FMVSNet3		124.76 28	140.93 29	76.26 30	107.44 31	100.17 36	329.88 31	27.12 31	259.44 34	74.30 31	204.34 30	30.74 27	92.41 29	196.98 33	38.15 25	36.19 23

FMVSNet1		124.76 28	140.93 29	76.26 30	107.44 31	100.17 36	329.88 31	27.12 31	259.44 34	74.30 31	204.34 30	30.74 27	92.41 29	196.98 33	38.15 25	36.19 23

test1		124.76 28	140.93 29	76.26 30	107.44 31	100.17 36	329.88 31	27.12 31	259.44 34	74.30 31	204.34 30	30.74 27	92.41 29	196.98 33	38.15 25	36.19 23

MIMVSNet1		124.76 28	140.93 29	76.26 30	107.44 31	100.17 36	329.88 31	27.12 31	259.44 34	74.30 31	204.34 30	30.74 27	92.41 29	196.98 33	38.15 25	36.19 23

MIMVSNet		124.76 28	140.93 29	76.26 30	107.44 31	100.17 36	329.88 31	27.12 31	259.44 34	74.30 31	204.34 30	30.74 27	92.41 29	196.98 33	38.15 25	36.19 23

FMVSNet5		124.76 28	140.93 29	76.26 30	107.44 31	100.17 36	329.88 31	27.12 31	259.44 34	74.30 31	204.34 30	30.74 27	92.41 29	196.98 33	38.15 25	36.19 23

DSMNet-mixed		131.33 35	143.00 36	96.33 40	99.00 30	81.00 31	201.00 26	45.00 42	335.00 41	99.00 42	251.00 41	38.00 38	110.00 41	120.00 30	99.00 41	98.00 41

PVSNet_Blended		136.90 36	155.40 37	81.40 37	111.00 38	96.20 33	407.00 38	25.90 28	251.60 31	85.10 39	240.50 38	37.00 35	99.90 38	199.80 40	40.70 36	48.10 36

PVSNet_Blended_VisFu		136.90 36	155.40 37	81.40 37	111.00 38	96.20 33	407.00 38	25.90 28	251.60 31	85.10 39	240.50 38	37.00 35	99.90 38	199.80 40	40.70 36	48.10 36

PVSNet_BlendedMVS		136.90 36	155.40 37	81.40 37	111.00 38	96.20 33	407.00 38	25.90 28	251.60 31	85.10 39	240.50 38	37.00 35	99.90 38	199.80 40	40.70 36	48.10 36

DP-MVS		264.78 39	296.02 40	171.04 41	191.70 41	183.96 43	827.10 45	78.36 44	438.24 42	176.64 43	435.12 42	65.04 44	230.52 43	334.02 43	117.96 42	98.64 42

HyFIR lowres test		317.00 40	397.69 41	74.93 29	1190.04 76	86.11 32	1576.45 55	22.83 26	228.70 30	78.08 38	225.04 37	33.24 34	94.29 36	187.60 32	37.22 23	44.39 35

RPSCF		394.81 41	445.63 42	242.35 42	276.55 43	253.63 44	1207.27 51	90.69 47	715.56 46	241.57 45	684.87 45	111.96 49	320.02 44	548.38 46	133.81 44	153.39 44

EG-PatchMatch MVS		437.03 42	493.12 43	268.77 43	264.80 42	260.20 45	1491.80 53	98.00 49	723.20 47	238.80 44	722.40 46	63.10 43	382.00 46	629.00 47	207.00 53	164.10 46

PatchmatchNet		492.52 43	551.22 44	316.40 45	400.20 48	357.90 48	1277.10 52	115.80 54	1034.60 54	315.90 47	874.00 49	155.10 54	402.20 48	629.50 48	158.80 45	189.10 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LP		495.57 44	552.82 45	323.82 47	377.78 47	343.94 47	1099.61 50	67.44 43	1450.39 64	417.42 52	790.39 47	130.18 51	472.61 50	519.53 45	122.60 43	154.91 45

DELS-MVS		514.57 45	587.73 47	295.12 44	343.03 44	328.09 46	1540.48 54	116.22 55	988.59 49	325.44 48	906.22 50	119.86 50	365.72 45	788.60 54	161.09 46	191.55 48

DP-MVS Recon		516.25 46	581.17 46	321.48 46	361.02 45	429.00 52	1995.36 59	78.48 45	796.20 48	283.32 46	796.02 48	96.78 48	382.98 47	636.72 49	186.66 50	152.46 43

CR-MVSNet		612.36 47	617.81 48	596.01 61	611.29 59	600.33 61	790.30 43	513.12 87	579.21 44	552.71 61	567.40 43	662.54 95	531.67 52	503.93 44	779.80 78	656.02 73

IterMVS		615.05 48	691.66 49	385.23 50	516.40 53	454.80 53	1863.80 57	102.10 51	1142.20 56	364.10 50	1093.20 61	135.00 52	480.40 51	826.90 55	181.20 47	220.50 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IB-MVS	872.44 4	616.41 49	700.96 50	362.78 48	426.10 51	409.25 50	1822.69 56	124.42 57	1186.23 57	366.85 51	1079.42 59	190.60 59	451.78 49	930.72 59	181.59 48	227.30 51
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
ACMH+	729.97 2	661.38 50	754.00 51	383.51 49	368.96 46	376.38 49	2483.74 64	82.42 46	1089.25 55	333.39 49	1219.68 62	66.53 45	573.60 55	957.86 60	184.14 49	200.56 49

Gipuma		689.75 51	767.00 52	458.00 52	613.00 60	549.00 59	1943.00 58	187.00 67	1230.00 58	518.00 58	1092.00 60	203.00 64	564.00 54	892.00 58	225.00 55	261.00 52
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
IterMVS-LS		758.94 52	856.56 55	466.10 53	606.60 57	546.80 57	2309.90 61	122.20 56	1402.80 62	463.50 55	1348.20 64	192.60 60	574.60 56	1028.60 66	234.60 57	276.90 54
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		773.62 53	869.37 57	486.35 55	603.45 56	547.15 58	2532.14 65	106.92 53	1434.95 63	428.08 53	1360.56 65	165.15 56	628.96 58	984.09 61	209.01 54	282.94 55

RPMNet		804.30 54	835.64 53	710.26 68	885.90 68	895.90 74	455.98 41	668.73 92	1297.17 59	1002.26 79	1001.67 57	655.98 93	756.98 62	667.90 51	885.21 87	477.90 69

JIA-IIPM		804.30 54	835.64 53	710.26 68	885.90 68	895.90 74	455.98 41	668.73 92	1297.17 59	1002.26 79	1001.67 57	655.98 93	756.98 62	667.90 51	885.21 87	477.90 69

CSCG		826.03 56	963.29 59	414.24 51	421.52 50	426.11 51	2230.35 60	179.52 66	1827.44 70	733.43 70	933.12 52	281.25 70	545.23 53	1800.35 78	262.64 59	271.38 53

PVSNet	1207.94 6	829.56 57	937.18 58	506.69 56	645.55 61	585.72 60	2477.98 63	130.52 58	1578.29 66	509.50 57	1460.01 66	194.90 61	637.19 59	1170.78 69	267.07 61	297.17 57

PatchT		856.34 58	860.99 56	842.38 72	1290.90 79	880.90 72	790.30 43	1267.30 109	579.21 44	923.90 72	567.40 43	662.54 95	990.23 70	887.56 57	779.80 78	656.02 73

K. test v3		877.74 59	980.16 60	570.46 60	1316.85 81	813.15 70	2565.52 66	103.78 52	1509.49 65	548.33 60	1258.90 63	219.93 68	587.82 57	1102.37 68	196.30 51	310.42 59

MVSTER		890.06 60	1005.91 65	542.52 57	736.13 65	653.90 64	2616.56 67	145.22 61	1692.10 69	555.69 62	1554.72 68	266.25 69	660.02 60	1219.66 70	266.84 60	313.64 60

PMVS	863.65 3	957.08 61	1054.11 66	666.00 66	401.00 49	495.00 55	3790.00 72	279.00 71	1607.00 68	521.00 59	1745.00 70	90.00 46	1105.00 77	639.00 50	415.00 69	398.00 67
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMH	993.80 5	967.92 62	1106.11 68	553.33 58	494.00 52	534.00 56	3581.00 70	136.00 59	1585.00 67	478.00 56	1871.00 71	90.00 46	839.00 65	1460.00 72	260.00 58	287.00 56
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PVSNet_0	1661.03 9	968.54 63	1066.82 67	673.70 67	706.94 64	695.69 65	2669.02 68	153.52 63	2108.17 75	605.54 64	1679.14 69	213.26 67	886.75 67	1236.76 71	229.01 56	438.67 68

OurMVSNet-221017-0		999.00 64	999.00 61	999.00 73	999.00 71	999.00 76	999.00 46	999.00 105	999.00 50	999.00 75	999.00 53	999.00 102	999.00 72	999.00 62	999.00 93	999.00 92

EPMVS		999.00 64	999.00 61	999.00 73	999.00 71	999.00 76	999.00 46	999.00 105	999.00 50	999.00 75	999.00 53	999.00 102	999.00 72	999.00 62	999.00 93	999.00 92

TinyColmap		1000.00 66	1000.00 63	1000.00 75	1000.00 73	1000.00 78	1000.00 48	1000.00 107	1000.00 52	1000.00 77	1000.00 55	1000.00 104	1000.00 74	1000.00 64	1000.00 95	1000.00 94

USDC		1000.00 66	1000.00 63	1000.00 75	1000.00 73	1000.00 78	1000.00 48	1000.00 107	1000.00 52	1000.00 77	1000.00 55	1000.00 104	1000.00 74	1000.00 64	1000.00 95	1000.00 94

ACMP	1246.57 7	1086.24 68	1235.17 70	639.45 62	596.53 55	639.91 62	3904.24 73	152.04 62	1851.54 71	566.58 63	1997.95 73	138.50 53	938.30 68	1605.46 74	303.70 64	340.14 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Patchmtry		1090.75 69	1198.88 69	766.38 71	1290.90 79	485.90 54	2423.98 62	1267.30 109	1297.17 59	923.90 72	923.40 51	1215.54 107	990.23 70	667.90 51	779.80 78	823.02 80

test_0402		1110.00 70	1260.00 71	660.00 64	900.00 70	780.00 69	3300.00 69	210.00 69	2040.00 74	690.00 66	1950.00 72	300.00 73	810.00 64	1620.00 75	330.00 65	390.00 66

MVS_111021_LR		1136.45 71	1298.72 72	649.64 63	756.17 66	726.26 67	3785.94 71	138.15 60	2190.64 76	629.63 65	2064.39 74	165.45 57	874.83 66	1678.43 77	279.70 62	347.82 63

ACMM	1311.08 8	1165.06 72	1332.72 73	662.07 65	584.90 54	715.99 66	4373.59 75	153.99 64	1880.27 72	709.02 68	2316.97 75	188.63 58	939.89 69	1484.30 73	302.78 63	330.34 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MG-MVS		1256.50 73	1432.00 74	730.00 70	610.00 58	760.00 68	4720.00 76	169.00 65	1920.00 73	723.00 69	2510.00 78	201.00 63	1050.00 76	1645.00 76	390.00 68	380.00 64

FPMVS		1407.00 74	1495.59 75	1141.23 78	648.01 62	893.85 73	4124.00 74	503.81 86	3219.04 80	742.72 71	2498.84 77	157.98 55	1727.83 82	832.93 56	732.98 76	802.00 77

DeepC-MVS_fast	2190.19 12	1535.59 75	1886.82 77	481.89 54	847.73 67	830.16 71	7943.84 92	189.23 68	2398.83 77	691.73 67	2341.13 76	209.16 66	230.25 42	1999.25 79	360.46 66	385.25 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	2144.59 11	1658.33 76	1869.33 76	1025.33 77	1030.00 75	1023.00 80	5311.00 78	359.00 73	3044.00 79	937.00 74	2892.00 79	332.00 74	1440.00 79	2312.00 80	607.00 70	613.00 72
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
F-COLMAP		1878.17 77	2113.11 78	1173.33 81	1231.00 77	1174.00 81	6297.00 80	392.00 76	3356.00 82	1087.00 81	3082.00 80	361.00 78	1670.00 81	2523.00 82	689.00 72	676.00 75

OpenMVS_ROB	1936.51 10	1959.51 78	2224.88 80	1163.39 80	1253.91 78	1434.22 84	7065.71 82	272.76 70	2472.29 78	1647.54 101	3538.72 83	200.37 62	1496.48 80	2575.17 83	997.45 92	559.48 71

CMPMVS	2623.62 15	1983.08 79	2156.14 79	1463.89 87	1414.80 83	1517.00 86	5239.20 77	867.40 98	3310.30 81	1128.70 83	3489.90 82	366.75 79	2030.20 88	2427.60 81	1160.60 98	844.46 83
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVS-HIRNet		2125.39 80	2452.68 81	1143.53 79	2938.33 104	1446.92 85	6083.19 79	391.74 75	3801.71 84	1202.10 85	3439.72 81	492.73 83	1304.35 78	3048.25 86	676.32 71	679.32 76

PLC	2858.82 16	2192.92 81	2474.00 82	1349.67 83	1407.00 82	1356.00 83	7164.00 83	475.00 84	3970.00 85	1237.00 87	3765.00 86	451.00 80	1891.00 85	3006.00 85	791.00 81	802.00 77
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OpenMVS	2352.88 13	2263.08 82	2530.89 83	1459.67 86	1767.00 88	1591.00 89	7932.00 91	399.00 77	3643.00 83	1176.00 84	3569.00 84	297.00 72	1927.00 87	3302.00 87	693.00 74	861.00 84

PCF-MVS	4335.85 20	2272.23 83	2597.03 84	1297.84 82	1701.20 85	1332.37 82	8105.11 93	451.45 80	4031.84 86	1099.10 82	3966.50 87	467.45 81	1734.05 83	2701.68 84	848.94 84	827.11 81
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HyFIR		2388.40 84	2698.92 85	1456.81 85	2052.82 98	1649.07 90	6711.52 81	420.09 78	4302.41 87	1563.48 95	4427.41 90	655.47 92	1831.71 84	3415.95 88	741.16 77	889.66 86

3Dnovator	2482.06 14	2531.17 85	2839.56 86	1606.00 88	1732.00 86	1557.00 88	8375.00 95	483.00 85	4370.00 88	1371.00 90	4200.00 88	350.00 76	2207.00 89	3877.00 92	798.00 82	1054.00 96

tpmrst		2557.50 86	2847.12 87	1688.66 91	1784.08 89	1778.25 99	7445.50 89	342.14 72	4471.54 89	1534.55 94	4382.10 89	530.10 86	2361.21 92	4142.81 93	991.22 91	926.51 88

AdaColmap		2570.77 87	2948.61 88	1437.24 84	1873.64 90	1526.60 87	7819.77 90	373.97 74	4970.45 92	1479.29 92	4648.46 92	525.80 85	1904.97 86	4150.42 94	695.69 75	880.14 85

DeepPCF-MVS	3452.42 18	2670.71 88	3021.57 89	1618.12 89	2377.67 100	1751.39 98	8915.70 98	567.44 89	4569.74 90	1236.84 86	4615.63 91	513.23 84	2291.32 90	3515.48 89	882.39 86	811.66 79

LF4IMVS		2699.50 89	3049.67 90	1649.00 90	1755.00 87	1689.00 92	8330.00 94	464.00 82	4997.00 93	1528.00 93	4763.00 93	568.00 88	2296.00 91	4215.00 95	827.00 83	962.00 90

Vis-MVSNet		2877.42 90	3170.00 91	1999.67 96	1673.00 84	2042.00 103	8593.00 96	474.00 83	6452.00 103	1268.00 88	5125.00 94	339.00 75	3004.00 99	4222.00 96	384.00 67	953.00 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SteuartSystems-ACMMP		2891.50 91	3284.78 92	1711.67 92	1954.00 91	1655.00 91	10117.00 100	445.00 79	5102.00 94	1375.00 91	5126.00 95	351.00 77	2638.00 96	4402.00 97	691.00 73	842.00 82
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	3414.41 17	3146.67 92	3537.00 93	1975.67 94	2310.00 99	2005.00 102	10100.00 99	626.00 91	5753.00 95	1803.00 103	5179.00 96	487.00 82	2622.00 95	4550.00 98	1025.00 97	1300.00 99

DeepC-MVS	3917.13 19	3155.06 93	3601.83 94	1814.72 93	2515.52 102	1941.53 100	10432.50 101	589.42 90	4789.45 91	1298.65 89	7677.31 101	578.35 89	2589.53 93	3611.78 91	923.52 89	913.10 87
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 94	3806.99 95	2078.63 98	2535.47 103	2499.25 105	8816.16 97	456.05 81	6255.60 102	1664.52 102	6704.88 99	629.95 90	2768.38 97	6343.93 102	856.35 85	968.25 91
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tpm cat1		3617.85 95	4007.23 96	2449.73 99	2012.71 93	1698.05 93	7351.19 84	869.29 99	6132.06 97	1588.28 96	8007.42 102	1281.09 108	3888.11 103	7472.04 105	1351.00 100	1763.02 104

tpm2		3617.85 95	4007.23 96	2449.73 99	2012.71 93	1698.05 93	7351.19 84	869.29 99	6132.06 97	1588.28 96	8007.42 102	1281.09 108	3888.11 103	7472.04 105	1351.00 100	1763.02 104

tpmp4_e23		3617.85 95	4007.23 96	2449.73 99	2012.71 93	1698.05 93	7351.19 84	869.29 99	6132.06 97	1588.28 96	8007.42 102	1281.09 108	3888.11 103	7472.04 105	1351.00 100	1763.02 104

CostFormer		3617.85 95	4007.23 96	2449.73 99	2012.71 93	1698.05 93	7351.19 84	869.29 99	6132.06 97	1588.28 96	8007.42 102	1281.09 108	3888.11 103	7472.04 105	1351.00 100	1763.02 104

tpm		3617.85 95	4007.23 96	2449.73 99	2012.71 93	1698.05 93	7351.19 84	869.29 99	6132.06 97	1588.28 96	8007.42 102	1281.09 108	3888.11 103	7472.04 105	1351.00 100	1763.02 104

MVS_111021_HR		3742.80 100	4330.34 101	1980.20 95	2484.31 101	2259.52 104	14973.80 105	515.90 88	6500.13 104	1949.21 105	6146.89 97	535.66 87	2593.66 94	4880.76 100	986.37 90	1087.43 97

MAR-MVS		3973.16 101	4387.92 102	2728.86 104	3345.27 105	3341.39 108	11403.90 102	798.76 96	6555.62 106	2569.29 106	6514.86 98	834.97 100	3237.43 100	5891.08 101	1577.54 106	1607.76 100
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		4380.83 102	4906.56 103	2803.67 105	3844.00 106	3116.00 107	13454.00 103	901.00 104	6511.00 105	2794.00 107	7365.00 100	754.00 97	3634.00 101	6622.00 103	1914.00 108	1661.00 103

OMC-MVS		5022.26 103	5748.82 104	2842.58 106	4905.27 108	3101.27 106	14933.60 104	812.24 97	9278.40 107	2959.29 108	8800.17 107	1092.24 106	3806.30 102	7568.54 110	1389.60 105	1620.18 102

CLD-MVS		7338.25 104	8341.00 105	4330.00 107	4902.00 107	4991.00 109	23318.00 107	728.00 95	14437.00 110	4378.00 110	13379.00 108	824.00 99	5708.00 108	11429.00 112	1674.00 107	2291.00 109
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		7684.71 105	8776.23 106	4410.16 108	5298.55 109	5631.53 110	34329.90 109	1504.57 111	11554.00 109	3896.41 109	15111.00 109	812.19 98	5985.63 109	3516.20 90	2963.23 109	1613.31 101

MVE	10021.00 22	10550.67 106	10002.44 107	12195.33 110	6242.00 110	8768.00 112	30896.00 108	4451.00 113	10331.00 108	6247.00 111	18296.00 110	283.00 71	21484.00 111	7364.00 104	5912.00 111	6334.00 111
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CNLPA		10738.67 107	11772.44 108	7637.33 109	9120.00 111	8206.00 111	22252.00 106	2010.00 112	24984.00 111	7458.00 112	22596.00 111	2940.00 113	10260.00 110	11100.00 111	3492.00 110	4446.00 110

LTVRE_ROB	20565.47 23	23378.08 108	22805.00 109	25097.33 111	15500.00 112	19201.00 113	42712.00 110	6055.00 114	38388.00 112	16926.00 113	38534.00 112	7071.00 114	35653.00 112	29475.00 113	10584.00 112	20438.00 113
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
MSDG		32606.17 109	34443.44 110	27094.33 112	29249.00 113	25246.00 114	51144.00 111	6705.00 115	42886.00 113	22888.00 114	48534.00 113	13263.00 115	37507.00 113	79298.00 114	16024.00 113	18530.00 112

PHI-MVS		99999.00 110	99999.00 111	99999.00 113	99999.00 114	99999.00 115	99999.00 112	99999.00 116	99999.00 114	99999.00 115	99999.00 114	99999.00 116	99999.00 114	99999.00 115	99999.00 114	99999.00 114

114514_t		99999.00 110	99999.00 111	99999.00 113	99999.00 114	99999.00 115	99999.00 112	99999.00 116	99999.00 114	99999.00 115	99999.00 114	99999.00 116	99999.00 114	99999.00 115	99999.00 114	99999.00 114

XVG-OURS		999999.00 112	999999.00 113	999999.00 115	999999.00 116	999999.00 117	999999.00 114	999999.00 118	999999.00 116	999999.00 117	999999.00 116	999999.00 118	999999.00 116	999999.00 117	999999.00 116	999999.00 116

XVG-ACMP-BASELINE		999999.00 112	999999.00 113	999999.00 115	999999.00 116	999999.00 117	999999.00 114	999999.00 118	999999.00 116	999999.00 117	999999.00 116	999999.00 118	999999.00 116	999999.00 117	999999.00 116	999999.00 116

XVG-OURS-SEG-HR		999999.00 112	999999.00 113	999999.00 115	999999.00 116	999999.00 117	999999.00 114	999999.00 118	999999.00 116	999999.00 117	999999.00 116	999999.00 118	999999.00 116	999999.00 117	999999.00 116	999999.00 116

wuykxyi23d		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

wuyk23d		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PNet_i23d		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

ANet_high		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PatchFormer_test		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

Test4		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

CPTT-MVS		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PatchMatch-RL		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

HPM-MVS_fast		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PAPR		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

CP-MVS		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

NCCC		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PT_06_test		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

EPNet		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

EPNet_dtu		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

ACMMP		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119
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
IS-MVSNet		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

HPM-MVS		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PAPM_NR		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

LPG-MVS_test		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

EPP-MVSNet		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

Vis-MVSNet (Re-imp)		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PAPM		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PatchFormer-LS_test		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

PMMVS		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

no-one		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

TDRefinement		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

testpf		10000000.00 115	10000000.00 116	10000000.00 118	10000000.00 119	10000000.00 120	10000000.00 117	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

APD-MVS		1000000000.00 143	1000000000.00 144	1000000000.00 147	1000000000.00 150	1000000000.00 150	1000000000.00 147	1000000000.00 151	1000000000.00 150	1000000000.00 152	1000000000.00 149	1000000000.00 152	1000000000.00 149	1000000000.00 150	1000000000.00 151	1000000000.00 149

lessismore_v0				569.20 59	667.25 63	644.68 63		96.45 48		428.86 54	1514.17 67	204.21 65	756.58 61	1061.60 67	197.79 52	306.34 58

NP-MVS											10000000.00 119

HQP2-MVS												10000000.00 121

HQP3-MVS								10000000.00 121							10000000.00 119

HQP4-MVS										10000000.00 120			10000000.00 119			10000000.00 119

BP-MVS														10000000.00 120

ACMP_Plane					10000000.00 119		10000000.00 117		10000000.00 119	10000000.00 120

MDTV_nov1_ep13			9.99 18		9.99 19	9.99 20	9.99 18	9.99 22	9.99 19	9.99 20	9.99 19	9.99 20	9.99 20	9.99 19	9.99 20

HQP-NCC					10000000.00 119		10000000.00 117		10000000.00 119	10000000.00 120

HQP5-MVS						10000000.00 120

MDTV_nov1_ep13_2view						9.99 20	9.99 18		9.99 19	9.99 20		9.99 20		9.99 19		9.99 20

Test By Simon												990.00 101

door								99.00 50

ACMMP++_ref															10000000.00 119

LGP-MVS_train				10000000.00 118	10000000.00 119	10000000.00 120		10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 121	10000000.00 119	10000000.00 120	10000000.00 119	10000000.00 119

ACMMP++															10000000.00 119

HQP-MVS												10000000.00 121

DeepMVS_CX				1.00 4	1.00 4	1.00 4		1.00 4	1.00 4	1.00 4	1.00 4	1.00 4	1.00 4	1.00 4	1.00 4	1.00 4

ITE_SJBPF				2014.00 97	1998.00 92	1992.00 101		701.00 94	5989.00 96	1812.00 104	3612.00 85	650.00 91	2852.00 98	4601.00 99	1195.00 99	1198.00 98

door-mid								1.42 5

High-resolution multi-view benchmark