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

MVSNet		91.32 8	84.12 17	96.13 4	98.67 3	99.19 1	73.29 21	94.95 3	90.53 35

CasMVSNet(SR_B)		89.61 15	77.50 31	97.68 1	98.62 4	99.07 2	66.98 34	88.02 27	95.36 10

mvs_zhu_1030		90.16 12	81.94 21	95.63 5	94.87 13	97.32 3	69.88 29	94.00 8	94.70 16

3Dnovator	96.53 2	93.94 1	90.03 3	96.55 2	96.08 8	96.95 4	85.43 3	94.62 6	96.62 3

CasMVSNet(base)		85.15 28	74.67 37	92.14 23	88.81 31	96.80 5	62.49 43	86.85 31	90.81 34

CasMVSNet(SR_A)		84.65 31	76.33 36	90.20 29	84.56 37	96.27 6	65.84 37	86.81 32	89.77 38

OpenMVS	94.22 8	91.21 10	86.10 9	94.61 8	93.08 20	95.94 7	79.58 10	92.61 11	94.82 15

LPCS		88.71 22	80.50 24	94.18 13	91.38 24	95.63 8	75.15 20	85.84 36	95.53 9

Pnet-blend++		89.08 17	85.48 11	91.49 25	94.54 15	95.61 9	78.52 11	92.44 12	84.31 42

Pnet-blend		89.08 17	85.48 11	91.49 25	94.54 15	95.61 9	78.52 11	92.44 12	84.31 42

3Dnovator+	96.13 3	92.29 5	87.63 5	95.40 6	94.86 14	95.31 11	82.29 6	92.97 9	96.03 5

LTVRE_ROB	96.88 1	91.35 7	86.15 8	94.81 7	95.87 9	94.76 12	85.06 4	87.25 30	93.80 21
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
GSE		86.24 26	77.00 32	92.39 22	89.33 30	93.63 13	71.06 27	82.94 42	94.22 19

AttMVS		91.69 6	84.94 15	96.20 3	99.02 1	93.24 14	77.86 14	92.02 16	96.33 4

PMVS	89.60 17	72.53 51	40.67 72	93.77 16	92.28 22	93.18 15	48.78 53	32.55 80	95.84 6
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_1126		91.28 9	86.68 7	94.36 12	96.53 7	93.15 16	81.50 7	91.85 17	93.39 23

TAPA-MVS	93.32 12	89.43 16	84.97 14	92.40 21	92.59 21	92.76 17	77.17 16	92.77 10	91.86 28
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS_fast	94.34 7	88.57 24	82.00 20	92.95 19	91.15 27	92.65 18	75.30 19	88.70 24	95.05 13
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tm-dncc		88.78 20	80.08 25	94.58 9	95.72 10	92.39 19	72.62 24	87.54 28	95.64 8

tmmvs		91.01 11	85.87 10	94.43 10	95.72 10	92.39 19	80.42 8	91.32 18	95.17 12

DeepC-MVS	95.41 4	89.62 14	85.22 13	92.56 20	91.17 26	92.38 21	78.02 13	92.43 14	94.13 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	94.58 5	89.02 19	82.96 19	93.05 17	91.93 23	92.37 22	76.15 18	89.78 22	94.87 14

ANet-0.75		70.67 52	47.80 65	85.91 39	81.25 47	92.34 23	38.12 63	57.47 63	84.15 44

ANet		78.35 44	67.13 47	85.83 40	81.24 48	92.34 23	57.90 47	76.37 48	83.92 45

P-MVSNet		88.77 21	81.05 22	93.92 14	95.26 12	91.17 25	72.77 23	89.32 23	95.32 11

COLMAP_ROB	94.48 6	85.43 27	78.72 27	89.90 30	84.17 39	91.10 26	68.97 32	88.47 26	94.42 18
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
vp_mvsnet		79.40 43	61.80 49	91.14 28	93.13 19	89.83 27	28.94 76	94.66 4	90.45 37

HY-MVS	91.43 15	88.66 23	84.44 16	91.47 27	91.38 24	89.67 28	76.53 17	92.35 15	93.36 24

Pnet-new-		92.40 4	89.38 4	94.42 11	97.52 5	88.91 29	83.32 5	95.44 1	96.83 2

COLMAP(base)		85.07 29	78.37 28	89.55 32	86.00 33	88.85 30	71.31 26	85.43 38	93.78 22

TAPA-MVS(SR)		89.94 13	87.22 6	91.74 24	93.91 18	88.71 31	80.31 9	94.13 7	92.61 25

test_1205		76.84 45	69.16 46	81.96 45	82.96 43	88.41 32	62.91 42	75.41 50	74.53 55

Pnet_fast		79.81 41	72.73 42	84.52 43	82.40 44	88.35 33	58.67 45	86.79 33	82.82 49

ACMP	92.54 13	80.70 39	71.88 43	86.59 37	80.49 49	88.25 34	63.03 41	80.72 44	91.01 32
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	93.33 11	81.98 36	74.23 38	87.15 36	82.31 45	88.08 35	65.33 38	83.12 41	91.07 31
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP(SR)		84.90 30	79.01 26	88.83 34	86.24 32	87.81 36	72.86 22	85.16 39	92.45 26

PCF-MVS	89.43 18	82.97 33	73.73 39	89.13 33	90.40 28	87.68 37	68.35 33	79.10 45	89.30 39
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_LR		81.05 38	72.92 41	86.47 38	84.32 38	87.64 38	58.56 46	87.27 29	87.46 40

PLC	91.02 16	83.72 32	77.86 29	87.63 35	83.31 41	87.52 39	70.85 28	84.87 40	92.08 27
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_1120		93.24 3	93.60 2	93.00 18	97.25 6	85.92 40	91.86 2	95.34 2	95.83 7

test_1124		93.84 2	93.94 1	93.78 15	98.74 2	85.65 41	93.23 1	94.65 5	96.94 1

A-TVSNet + Gipuma		72.66 50	60.91 51	80.50 48	75.15 55	84.76 42	59.05 44	62.76 59	81.58 51

OpenMVS_ROB	91.80 14	87.38 25	83.78 18	89.79 31	94.43 17	83.34 43	77.69 15	89.88 21	91.59 30

MVSCRF		75.50 48	69.78 45	79.31 51	77.67 54	82.99 44	49.66 51	89.90 20	77.28 53

R-MVSNet		80.37 40	76.91 33	82.68 44	83.10 42	81.84 45	65.20 39	88.62 25	83.11 47

CIDER		81.10 37	81.01 23	81.16 47	78.95 51	81.68 46	71.35 25	90.68 19	82.84 48
Qingshan Xu and Wenbing Tao: Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume. AAAI 2020
ACMH+	93.58 10	82.41 34	77.68 30	85.56 42	85.31 35	80.89 47	69.59 30	85.78 37	90.47 36

IB-MVS	85.98 20	79.62 42	76.62 34	81.62 46	85.10 36	80.82 48	66.55 35	86.69 35	78.96 52
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	93.61 9	82.01 35	76.51 35	85.67 41	85.78 34	80.27 49	66.29 36	86.73 34	90.97 33
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CMPMVS	73.10 23	25.96 83	1.21 84	42.46 68	48.37 68	79.01 50	2.43 85	0.00 84	0.00 85
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	81.89 21	75.64 47	69.86 44	79.48 50	78.69 52	74.99 51	63.13 40	76.60 47	84.77 41

BP-MVSNet		73.12 49	63.32 48	79.66 49	81.44 46	74.39 52	52.82 48	73.82 51	83.14 46
Christian Sormann, Patrick KnÃ¶belreiter, Andreas Kuhn, Mattia Rossi, Thomas Pock, Friedrich Fraundorfer: BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo. 3DV 2020
unsupervisedMVS_cas		61.20 54	49.84 63	68.78 54	63.77 59	73.15 53	38.43 62	61.26 60	69.42 56

PVSNet	86.72 19	76.20 46	73.67 40	77.88 52	79.20 50	72.57 54	69.58 31	77.77 46	81.87 50

test_mvsss		46.43 65	32.75 80	55.54 61	53.54 65	61.33 55	19.75 81	45.75 75	51.76 68

MVS_test_1		60.08 56	50.72 59	66.32 55	78.10 53	52.08 56	25.70 79	75.74 49	68.79 57

hgnet		44.78 66	46.10 66	43.90 65	55.34 63	51.87 57	37.81 64	54.38 65	24.49 80

DPSNet		44.78 66	46.10 66	43.90 65	55.34 63	51.87 57	37.81 64	54.38 65	24.49 80

example		36.07 80	28.13 82	41.37 70	57.61 62	48.26 59	28.65 78	27.61 82	18.25 82

Pnet-eth		61.19 55	57.15 55	63.88 56	83.71 40	47.10 60	41.45 59	72.85 53	60.82 60

CCVNet		46.86 64	39.85 76	51.54 62	50.35 67	46.44 61	36.28 68	43.42 77	57.82 62

MVSNet_plusplus		65.61 53	51.21 58	75.21 53	89.61 29	44.24 62	19.91 80	82.51 43	91.77 29

CPR_FA		50.17 61	52.38 56	48.69 64	44.83 70	42.31 63	43.51 58	61.26 60	58.94 61

MVE	73.61 22	42.73 68	50.51 60	37.54 77	28.32 83	41.81 64	47.01 55	54.00 67	42.50 73
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
F/T MVSNet+Gipuma		59.72 57	61.47 50	58.55 57	70.70 56	40.01 65	52.07 49	70.87 54	64.95 58

unMVSmet		49.47 63	58.19 53	43.66 67	50.58 66	39.49 66	47.78 54	68.60 55	40.90 74

MVSNet + Gipuma		58.38 58	60.00 52	57.30 58	68.90 58	39.34 67	51.70 50	68.30 56	63.65 59

RMVSNet		42.28 70	49.56 64	37.43 78	46.49 69	36.28 68	46.14 56	52.98 68	29.51 77

Snet		53.20 59	57.22 54	50.52 63	62.60 60	36.23 69	49.14 52	65.30 58	52.73 66

TVSNet		41.69 71	43.53 68	40.47 73	34.72 74	35.30 70	36.98 66	50.08 69	51.38 69

unMVSv1		30.79 82	29.07 81	31.95 82	33.31 75	35.18 71	28.91 77	29.23 81	27.34 79

MVSNet_++		50.06 62	40.45 73	56.46 59	57.82 61	34.95 72	7.47 84	73.43 52	76.61 54

test3		40.01 76	42.34 69	38.45 75	33.05 76	34.68 73	36.66 67	48.03 70	47.61 70

SVVNet		40.48 74	40.38 74	40.54 71	30.67 81	34.64 74	33.96 71	46.80 72	56.31 64

ternet		40.48 74	40.38 74	40.54 71	30.67 81	34.64 74	33.96 71	46.80 72	56.31 64

QQQNet		41.37 72	40.86 71	41.70 69	32.74 78	34.55 76	34.93 70	46.80 72	57.82 62

SGNet		36.94 77	37.96 77	36.27 79	31.95 79	33.84 77	32.37 73	43.54 76	43.01 72

firsttry		36.63 79	33.24 79	38.88 74	37.30 73	33.37 78	30.72 74	35.75 79	45.97 71

PSD-MVSNet		35.22 81	35.50 78	35.04 80	31.65 80	32.64 79	29.99 75	41.01 78	40.82 75

Cas-MVS_preliminary		50.29 60	41.63 70	56.06 60	43.18 71	30.41 80	35.30 69	47.96 71	94.60 17

confMetMVS		40.92 73	49.99 62	34.88 81	42.05 72	28.82 81	45.31 57	54.67 64	33.76 76

A1Net		42.71 69	50.00 61	37.85 76	32.83 77	28.57 82	41.36 60	58.63 62	52.14 67

metmvs_fine		36.70 78	52.31 57	26.30 83	26.70 84	24.23 83	38.64 61	65.98 57	27.95 78

test_robustmvs				7.77 84	12.93 85	6.88 84	12.31 83		3.50 83

FADENet		1.00 84	1.26 83	0.82 85	1.49 86	0.55 85	1.56 86	0.96 83	0.41 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

test_MVS							18.95 82

UnsupFinetunedMVSNet					70.70 56

Low-resolution many-view benchmark