Low-res many-view results

This table lists the benchmark results for the low-res many-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	low-res many-view	indoor	outdoor	lakeside	sand box	storage room	storage room 2	tunnel

P-MVSNet		97.42 17	94.13 29	99.61 4	98.93 16	99.94 1	90.70 29	97.55 29	99.94 1

AttMVS		98.10 5	95.45 16	99.86 1	99.84 1	99.87 3	93.29 15	97.60 27	99.87 2

3Dnovator	99.15 2	98.46 3	96.92 5	99.48 7	99.05 14	99.63 14	95.44 6	98.41 16	99.78 3

HY-MVS	98.23 9	98.26 4	96.37 6	99.52 6	99.21 11	99.65 11	93.56 13	99.18 2	99.69 4

3Dnovator+	98.92 3	97.89 7	95.99 9	99.16 12	98.56 27	99.31 21	94.11 9	97.86 22	99.63 5

OpenMVS	98.12 10	97.86 8	95.83 11	99.21 11	98.55 28	99.47 18	93.67 11	97.98 20	99.61 6

MVSNet		97.77 11	94.82 23	99.73 2	99.71 4	99.87 3	90.82 28	98.81 9	99.61 6

mvs_zhu_1030		97.36 18	94.00 30	99.61 4	99.39 9	99.82 6	88.90 37	99.10 3	99.60 8

test_1124		98.99 1	97.89 4	99.72 3	99.70 5	99.84 5	98.10 1	97.68 24	99.60 8

vp_mvsnet		92.70 53	82.77 71	99.32 9	99.36 10	99.00 24	66.62 78	98.92 7	99.58 10

LPCS		97.58 13	95.27 19	99.12 13	98.44 30	99.37 19	93.66 12	96.88 36	99.56 11

TAPA-MVS(SR)		97.85 9	96.03 8	99.07 15	98.53 29	99.13 23	93.55 14	98.51 14	99.55 12

tmmvs		97.58 13	95.49 14	98.98 18	98.85 18	98.54 28	93.76 10	97.22 32	99.53 13

MVSNet_plusplus		85.44 63	75.33 77	92.19 57	99.09 13	77.97 65	54.19 82	96.48 43	99.49 14

DeepC-MVS_fast	98.47 5	96.63 25	94.29 28	98.19 28	97.66 38	97.50 38	91.63 23	96.95 34	99.41 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PMVS	92.94 21	78.84 69	50.69 82	97.61 34	95.96 50	97.48 39	65.74 79	35.64 82	99.38 16
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tm-dncc		96.71 22	93.39 36	98.92 21	98.85 18	98.54 28	89.98 33	96.81 37	99.36 17

ACMH	98.42 6	95.63 33	92.35 43	97.81 31	95.84 51	98.24 31	88.19 42	96.51 41	99.36 17
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DeepC-MVS	98.90 4	97.16 20	95.29 18	98.41 26	98.16 34	97.76 34	92.20 21	98.38 17	99.32 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_LR		97.57 15	95.45 16	98.98 18	99.00 15	98.67 26	92.23 20	98.67 11	99.27 20

GSE		96.89 21	94.34 27	98.60 24	97.60 40	98.97 25	92.57 19	96.11 47	99.23 21

ACMH+	98.40 8	95.77 31	92.95 37	97.64 33	96.31 49	97.47 40	89.65 35	96.26 44	99.15 22

TAPA-MVS	97.92 13	97.67 12	95.54 13	99.09 14	98.77 20	99.34 20	93.04 16	98.04 19	99.15 22
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	98.42 6	96.59 26	94.42 25	98.03 29	97.86 36	97.10 41	91.48 24	97.36 30	99.14 24

Pnet-new-		98.02 6	95.95 10	99.40 8	99.49 8	99.61 15	94.31 7	97.59 28	99.11 25

OpenMVS_ROB	97.31 17	97.22 19	95.18 20	98.58 25	98.69 24	97.95 33	92.61 18	97.75 23	99.10 26

COLMAP(SR)		96.17 29	93.90 32	97.69 32	96.39 48	97.64 36	91.16 26	96.64 39	99.04 27

COLMAP_ROB	98.06 12	94.72 45	92.05 44	96.50 42	93.64 61	96.86 42	87.59 45	96.51 41	99.02 28
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CasMVSNet(SR_A)		95.29 36	89.69 55	99.02 16	98.59 25	99.48 17	83.78 59	95.60 50	98.99 29

ACMP	97.51 14	93.81 49	89.89 53	96.42 43	93.98 59	96.31 47	85.39 54	94.39 58	98.97 30
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP(base)		95.23 37	92.81 40	96.85 39	95.06 55	96.52 43	89.63 36	95.99 48	98.96 31

test_1120		98.89 2	98.31 1	99.28 10	99.17 12	99.79 7	97.81 2	98.81 9	98.87 32

LTVRE_ROB	99.19 1	97.54 16	95.77 12	98.71 23	98.01 35	99.31 21	96.08 5	95.46 53	98.82 33
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
R-MVSNet		96.56 27	94.43 24	97.98 30	97.61 39	97.54 37	90.39 31	98.47 15	98.78 34

PLC	97.35 16	94.93 40	92.81 40	96.35 44	94.63 56	95.74 49	90.06 32	95.56 52	98.69 35
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMM	98.09 11	94.80 42	91.85 45	96.76 40	95.11 54	96.48 44	87.94 44	95.76 49	98.69 35
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CasMVSNet(base)		94.85 41	88.68 57	98.96 20	98.70 23	99.67 8	82.37 61	94.99 55	98.50 37

BP-MVSNet		96.44 28	93.68 33	98.28 27	98.32 31	98.10 32	89.68 34	97.67 25	98.43 38
Christian Sormann, Patrick KnÃ¶belreiter, Andreas Kuhn, Mattia Rossi, Thomas Pock, Friedrich Fraundorfer: BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo. 3DV 2020
MVSCRF		94.80 42	92.89 38	96.08 46	93.77 60	96.33 46	86.18 50	99.60 1	98.14 39

PCF-MVS	96.03 18	94.34 47	90.51 51	96.89 37	96.93 47	95.66 50	87.59 45	93.42 61	98.08 40
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
F/T MVSNet+Gipuma		91.90 55	93.60 34	90.76 58	97.37 43	77.09 67	88.37 38	98.83 8	97.83 41

MVS_test_1		87.21 60	82.26 72	90.51 59	97.39 42	76.51 69	67.94 77	96.58 40	97.62 42

unMVSmet		94.27 48	90.80 49	96.58 41	98.30 32	93.88 54	86.93 49	94.67 56	97.57 43

CasMVSNet(SR_B)		95.57 34	90.42 52	99.00 17	99.52 7	99.94 1	84.68 58	96.16 45	97.54 44

test_1126		97.78 10	96.13 7	98.89 22	99.62 6	99.52 16	94.30 8	97.95 21	97.53 45

MVSNet + Gipuma		91.67 56	93.45 35	90.48 60	97.40 41	76.54 68	88.37 38	98.52 13	97.50 46

ANet-0.75		92.64 54	85.79 63	97.20 35	94.49 57	99.66 9	79.52 63	92.06 64	97.46 47

Cas-MVS_preliminary		86.14 61	81.69 73	89.11 61	92.91 62	77.14 66	71.77 74	91.61 65	97.27 48

PVSNet_0	95.53 19	94.76 44	92.88 39	96.02 47	97.06 45	94.33 51	88.13 43	97.63 26	96.66 49

ANet		94.58 46	91.17 47	96.86 38	94.49 57	99.66 9	85.16 55	97.18 33	96.43 50

A-TVSNet + Gipuma		95.08 39	93.98 31	95.81 49	95.21 53	96.00 48	91.24 25	96.73 38	96.22 51

confMetMVS		93.00 52	90.74 50	94.52 54	98.59 25	89.04 55	85.90 52	95.57 51	95.92 52

CPR_FA		88.38 57	88.27 58	88.46 62	88.24 67	81.63 59	87.03 48	89.51 73	95.52 53

MVSNet_++		76.28 73	63.91 81	84.54 65	87.95 68	70.45 72	33.49 84	94.32 59	95.21 54

PVSNet	97.47 15	95.18 38	94.37 26	95.72 52	97.83 37	94.18 52	90.68 30	98.06 18	95.13 55

CIDER		95.78 30	95.47 15	95.98 48	97.01 46	96.43 45	91.90 22	99.04 4	94.51 56
Qingshan Xu and Wenbing Tao: Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume. AAAI 2020
Pnet_fast		95.36 35	92.80 42	97.07 36	98.75 21	98.31 30	88.27 41	97.33 31	94.14 57

unsupervisedMVS_cas		87.73 58	80.56 75	92.51 56	90.02 66	94.04 53	72.99 72	88.12 75	93.47 58

IB-MVS	95.41 20	95.66 32	94.83 22	96.21 45	98.30 32	97.74 35	91.10 27	98.57 12	92.58 59
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
Pnet-eth		93.70 50	94.85 21	92.94 55	98.90 17	88.78 56	92.79 17	96.91 35	91.13 60

CCVNet		78.62 70	84.56 65	74.66 72	70.01 80	63.28 73	78.75 64	90.36 71	90.68 61

QQQNet		76.16 74	83.14 69	71.50 74	70.80 76	53.03 79	75.46 69	90.83 67	90.68 61

SVVNet		75.58 76	83.42 67	70.36 76	66.75 82	54.11 76	76.01 67	90.83 67	90.21 63

ternet		75.58 76	83.42 67	70.36 76	66.75 82	54.11 76	76.01 67	90.83 67	90.21 63

test_mvsss		82.49 68	73.62 79	88.40 63	90.79 65	84.94 57	59.37 80	87.86 76	89.47 65

metmvs_fine		87.49 59	89.83 54	85.92 64	91.28 64	78.30 64	83.54 60	96.12 46	88.18 66

Pnet-blend++		96.65 23	98.04 2	95.73 50	99.77 2	99.65 11	97.16 3	98.93 5	87.77 67

Pnet-blend		96.65 23	98.04 2	95.73 50	99.77 2	99.65 11	97.16 3	98.93 5	87.77 67

test_1205		93.55 51	91.37 46	95.00 53	98.73 22	98.60 27	88.30 40	94.43 57	87.67 69

hgnet		84.50 64	86.06 61	83.46 67	85.83 70	78.98 62	84.69 56	87.44 78	85.56 70

DPSNet		84.50 64	86.06 61	83.46 67	85.83 70	78.98 62	84.69 56	87.44 78	85.56 70

example		78.46 71	69.47 80	84.45 66	87.90 69	79.95 61	69.21 76	69.72 81	85.51 72

Snet		83.91 66	89.25 56	80.34 70	95.66 52	59.91 75	85.81 53	92.69 62	85.46 73

TVSNet		76.50 72	85.63 64	70.41 75	72.28 75	53.63 78	77.80 65	93.46 60	85.31 74

RMVSNet		85.99 62	91.12 48	82.56 69	92.56 63	71.89 71	87.23 47	95.02 54	83.24 75

A1Net		75.91 75	86.79 60	68.65 79	76.15 72	46.80 83	81.23 62	92.34 63	83.01 76

firsttry		75.37 78	82.99 70	70.28 78	67.26 81	62.42 74	75.38 70	90.60 70	81.16 77

test3		74.14 79	83.69 66	67.77 80	70.24 79	52.13 81	76.30 66	91.09 66	80.94 78

MVE	92.54 22	82.65 67	87.52 59	79.40 71	75.10 73	82.70 58	85.94 51	89.11 74	80.40 79
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SGNet		72.64 81	81.38 74	66.81 81	70.44 78	52.48 80	72.96 73	89.80 72	77.50 80

PSD-MVSNet		71.12 82	79.18 76	65.75 82	70.74 77	51.62 82	70.68 75	87.67 77	74.90 81

unMVSv1		73.78 80	74.27 78	73.45 73	74.10 74	75.59 70	73.49 71	75.05 80	70.66 82

test_robustmvs				32.21 84	49.22 85	30.09 84	47.07 83		17.32 83

FADENet		15.30 84	10.26 83	18.67 85	28.34 86	22.58 85	10.01 85	10.51 83	5.07 84

dnet		0.00 85	0.00 85	0.00 86	0.00 87	0.00 86	0.00 87	0.00 84	0.00 85

CMPMVS	77.52 23	27.18 83	1.74 84	44.14 83	51.76 84	80.67 60	3.48 86	0.00 84	0.00 85
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_MVS							58.19 81

UnsupFinetunedMVSNet					97.37 43

Low-resolution many-view benchmark