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.

3Dnovator+	77.84 4	85.48 7	84.47 7	88.51 1	91.08 10	73.49 1	93.18 1	93.78 3	80.79 1	76.66 17	93.37 4	60.40 20	96.75 3	77.20 8	93.73 6	95.29 1

DeepC-MVS_fast	79.65 3	86.91 3	86.62 3	87.76 2	93.52 2	72.37 5	91.26 6	93.04 7	76.62 7	84.22 3	93.36 5	71.44 4	96.76 2	80.82 5	95.33 4	94.16 5
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS	79.81 2	87.08 2	86.88 2	87.69 3	91.16 9	72.32 6	90.31 11	93.94 2	77.12 5	82.82 5	94.23 1	72.13 3	97.09 1	84.83 2	95.37 3	93.65 7
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		85.89 6	85.39 6	87.38 4	93.59 1	72.63 3	92.74 2	93.18 6	76.78 6	80.73 7	93.82 3	64.33 12	96.29 6	82.67 4	90.69 14	93.23 9

PHI-MVS		86.43 4	86.17 5	87.24 5	90.88 12	70.96 9	92.27 4	94.07 1	72.45 15	85.22 2	91.90 10	69.47 5	96.42 5	83.28 3	95.94 1	94.35 4

CSCG		86.41 5	86.19 4	87.07 6	92.91 4	72.48 4	90.81 8	93.56 4	73.95 14	83.16 4	91.07 14	75.94 2	95.19 8	79.94 6	94.38 5	93.55 8

DeepPCF-MVS	80.84 1	88.10 1	88.56 1	86.73 7	92.24 6	69.03 12	89.57 15	93.39 5	77.53 3	89.79 1	94.12 2	78.98 1	96.58 4	85.66 1	95.72 2	94.58 3

3Dnovator	76.31 5	83.38 10	82.31 11	86.59 8	87.94 30	72.94 2	90.64 9	92.14 9	77.21 4	75.47 19	92.83 6	58.56 22	94.72 13	73.24 12	92.71 7	92.13 15

MAR-MVS		81.84 15	80.70 15	85.27 9	91.32 8	71.53 7	89.82 14	90.92 15	69.77 23	78.50 12	86.21 35	62.36 16	94.52 17	65.36 17	92.05 8	89.77 20
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
PCF-MVS	73.52 7	80.38 20	78.84 20	85.01 10	87.71 31	68.99 13	83.65 35	91.46 13	63.00 36	77.77 14	90.28 18	66.10 9	95.09 12	61.40 24	88.22 17	90.94 17
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OMC-MVS		82.69 12	81.97 12	84.85 11	88.75 22	67.42 19	87.98 26	90.87 16	74.92 10	79.72 8	91.65 11	62.19 17	93.96 20	75.26 10	86.42 23	93.16 10

EPP-MVSNet		83.40 9	83.02 9	84.57 12	90.13 15	64.47 29	92.32 3	90.73 17	74.45 12	79.35 10	91.10 12	69.05 6	95.12 9	72.78 14	87.22 19	94.13 6

ACMP	74.13 6	81.51 16	80.57 16	84.36 13	89.42 19	68.69 15	89.97 13	91.50 12	74.46 11	75.04 21	90.41 17	53.82 26	94.54 15	77.56 7	82.91 26	89.86 19
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		82.31 13	81.65 13	84.29 14	88.47 28	67.73 18	85.81 30	92.35 8	75.78 8	78.33 13	86.58 34	64.01 14	94.35 18	76.05 9	87.48 18	90.79 18
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		81.99 14	81.23 14	84.26 15	90.94 11	70.18 10	91.10 7	89.32 22	71.51 16	78.66 11	88.28 26	65.26 10	95.10 11	64.74 19	91.23 12	87.51 31

IS-MVSNet		83.15 11	82.81 10	84.18 16	89.94 17	63.30 32	91.59 5	88.46 28	79.04 2	79.49 9	92.16 8	65.10 11	94.28 19	67.71 16	91.86 9	94.95 2

OpenMVS	72.83 10	79.77 21	78.33 22	84.09 17	85.17 35	69.91 11	90.57 10	90.97 14	66.70 27	72.17 24	91.91 9	54.70 25	93.96 20	61.81 23	90.95 13	88.41 26

AdaColmap		80.58 19	79.42 19	84.06 18	93.09 3	68.91 14	89.36 16	88.97 26	69.27 24	75.70 18	89.69 19	57.20 24	95.77 7	63.06 21	88.41 16	87.50 33

ACMM	73.20 8	80.78 18	79.84 18	83.58 19	89.31 20	68.37 16	89.99 12	91.60 10	70.28 18	77.25 15	89.66 20	53.37 28	93.53 25	74.24 11	82.85 27	88.85 23
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PAPR		80.80 17	79.93 17	83.38 20	90.02 16	63.60 31	84.81 33	90.50 18	67.73 26	74.57 22	87.61 29	66.55 8	93.87 22	69.11 15	88.51 15	92.67 12

LS3D		76.95 27	74.82 28	83.37 21	90.45 13	67.36 21	89.15 17	86.94 30	61.87 41	69.52 32	90.61 16	51.71 30	94.53 16	46.38 47	86.71 22	88.21 27

IB-MVS	68.01 14	75.85 33	73.36 33	83.31 22	84.76 36	66.03 24	83.38 36	85.06 33	70.21 20	69.40 33	81.05 45	45.76 36	94.66 14	65.10 18	75.49 40	89.25 21

MG-MVS		83.41 8	83.45 8	83.28 23	92.74 5	62.28 34	88.17 24	89.50 20	75.22 9	81.49 6	92.74 7	66.75 7	95.11 10	72.85 13	91.58 11	92.45 13

PLC	70.83 11	78.05 25	76.37 26	83.08 24	91.88 7	67.80 17	88.19 23	89.46 21	64.33 33	69.87 30	88.38 25	53.66 27	93.58 24	58.86 27	82.73 29	87.86 28
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	73.13 9	79.15 22	77.94 23	82.79 25	89.59 18	62.99 33	88.16 25	91.51 11	65.77 29	77.14 16	91.09 13	60.91 19	93.21 26	50.26 43	87.05 20	92.17 14
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
F-COLMAP		76.38 28	74.33 30	82.50 26	89.28 21	66.95 22	88.41 21	89.03 24	64.05 35	66.83 40	88.61 24	46.78 34	92.89 29	57.48 31	78.55 33	87.67 29

ACMH+	68.96 13	76.01 30	74.01 31	82.03 27	88.60 25	65.31 28	88.86 18	87.55 29	70.25 19	67.75 37	87.47 30	41.27 44	93.19 28	58.37 29	75.94 39	87.60 30

CNLPA		78.08 24	76.79 24	81.97 28	90.40 14	71.07 8	87.59 27	84.55 34	66.03 28	72.38 23	89.64 21	57.56 23	86.04 42	59.61 26	83.35 25	88.79 24

ACMH	67.68 15	75.89 32	73.93 32	81.77 29	88.71 23	66.61 23	88.62 20	89.01 25	69.81 22	66.78 41	86.70 33	41.95 43	91.51 32	55.64 35	78.14 34	87.17 35
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PAPM		77.68 26	76.40 25	81.51 30	87.29 32	61.85 36	83.78 34	89.59 19	64.74 32	71.23 26	88.70 23	62.59 15	93.66 23	52.66 41	87.03 21	89.01 22

LTVRE_ROB	69.57 12	76.25 29	74.54 29	81.41 31	88.60 25	64.38 30	79.24 43	89.12 23	70.76 17	69.79 31	87.86 27	49.09 32	93.20 27	56.21 34	80.16 32	86.65 38
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
COLMAP_ROB	66.92 16	73.01 37	70.41 40	80.81 32	87.13 33	65.63 27	88.30 22	84.19 36	62.96 37	63.80 44	87.69 28	38.04 45	92.56 31	46.66 46	74.91 42	84.24 41
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MSDG		73.36 34	70.99 36	80.49 33	84.51 37	65.80 26	80.71 38	86.13 32	65.70 30	65.46 42	83.74 40	44.60 39	90.91 35	51.13 42	76.89 35	84.74 40

test_0402		72.79 38	70.44 39	79.84 34	88.13 29	65.99 25	85.93 29	84.29 35	65.57 31	67.40 39	85.49 36	46.92 33	92.61 30	35.88 50	74.38 44	80.94 45

TAMVS		76.00 31	74.94 27	79.20 35	82.02 40	58.65 42	88.83 19	88.75 27	70.11 21	67.87 36	86.96 32	58.69 21	91.44 33	64.20 20	66.99 47	87.51 31

RPSCF		73.23 35	71.46 34	78.54 36	82.50 39	59.85 40	82.18 37	82.84 41	58.96 46	71.15 27	89.41 22	45.48 38	84.77 45	58.82 28	71.83 45	91.02 16

PatchmatchNet		73.12 36	71.33 35	78.49 37	83.18 38	60.85 38	79.63 40	78.57 47	64.13 34	71.73 25	79.81 47	51.20 31	85.97 43	57.40 32	76.36 36	88.66 25
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ITE_SJBPF				78.22 38	81.77 42	60.57 39		83.30 38	69.25 25	67.54 38	87.20 31	36.33 47	87.28 40	54.34 37	74.62 43	86.80 37

Vis-MVSNet (Re-imp)		78.36 23	78.45 21	78.07 39	88.64 24	51.78 49	86.70 28	79.63 46	74.14 13	75.11 20	90.83 15	61.29 18	89.75 36	58.10 30	91.60 10	92.69 11

OpenMVS_ROB	64.09 18	70.56 41	68.19 43	77.65 40	80.26 44	59.41 41	85.01 31	82.96 40	58.76 47	65.43 43	82.33 42	37.63 46	91.23 34	45.34 49	76.03 38	82.32 43

TDRefinement		67.49 46	64.34 47	76.92 41	73.47 49	61.07 37	84.86 32	82.98 39	59.77 45	58.30 48	85.13 38	26.06 49	87.89 38	47.92 44	60.59 50	81.81 44

PatchMatch-RL		72.38 39	70.90 37	76.80 42	88.60 25	67.38 20	79.53 41	76.17 49	62.75 39	69.36 34	82.00 44	45.51 37	84.89 44	53.62 38	80.58 31	78.12 49

CMPMVS	51.72 20	70.19 43	68.16 44	76.28 43	73.15 50	57.55 43	79.47 42	83.92 37	48.02 51	56.48 49	84.81 39	43.13 41	86.42 41	62.67 22	81.81 30	84.89 39
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		70.33 42	68.37 42	76.21 44	80.60 43	56.23 47	79.19 44	86.49 31	60.89 43	61.29 45	85.47 37	31.78 48	89.47 37	53.37 39	76.21 37	82.94 42

PVSNet	64.34 17	72.08 40	70.87 38	75.69 45	86.21 34	56.44 46	74.37 48	80.73 44	62.06 40	70.17 28	82.23 43	42.86 42	83.31 46	54.77 36	84.45 24	87.32 34

TinyColmap		67.30 47	64.81 46	74.76 46	81.92 41	56.68 45	80.29 39	81.49 42	60.33 44	56.27 50	83.22 41	24.77 50	87.66 39	45.52 48	69.47 46	79.95 46

EPMVS		69.02 45	68.16 44	71.59 47	79.61 45	49.80 52	77.40 45	66.93 54	62.82 38	70.01 29	79.05 48	45.79 35	77.86 48	56.58 33	75.26 41	87.13 36

PMMVS		69.34 44	68.67 41	71.35 48	75.67 48	62.03 35	75.17 47	73.46 50	50.00 50	68.68 35	79.05 48	52.07 29	78.13 47	61.16 25	82.77 28	73.90 50

LF4IMVS		64.02 48	62.19 48	69.50 49	70.90 51	53.29 48	76.13 46	77.18 48	52.65 49	58.59 46	80.98 46	23.55 51	76.52 50	53.06 40	66.66 48	78.68 48

PVSNet_0	57.27 19	61.67 49	59.27 49	68.85 50	79.61 45	57.44 44	68.01 51	73.44 51	55.93 48	58.54 47	70.41 51	44.58 40	77.55 49	47.01 45	35.91 53	71.55 51

ANet_high		50.57 51	46.10 51	63.99 51	48.67 55	39.13 55	70.99 49	80.85 43	61.39 42	31.18 52	57.70 52	17.02 52	73.65 53	31.22 52	15.89 58	79.18 47

FPMVS		53.68 50	51.64 50	59.81 52	65.08 52	51.03 51	69.48 50	69.58 52	41.46 52	40.67 51	72.32 50	16.46 53	70.00 55	24.24 54	65.42 49	58.40 53

wuykxyi23d		39.76 54	33.18 55	59.51 53	46.98 56	44.01 53	57.70 54	67.74 53	24.13 56	13.98 58	34.33 58	1.27 59	71.33 54	34.23 51	18.23 56	63.18 52

PMVS	37.38 21	44.16 53	40.28 53	55.82 54	40.82 58	42.54 54	65.12 52	63.99 55	34.43 53	24.48 54	57.12 53	3.92 56	76.17 51	17.10 57	55.52 52	48.75 54
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 52	41.86 52	55.16 55	77.03 47	51.52 50	32.50 56	80.52 45	32.46 54	27.12 53	35.02 57	9.52 55	75.50 52	22.31 55	60.21 51	38.45 57
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		38.26 55	35.42 54	46.79 56	58.74 53	35.48 56	59.65 53	51.25 56	32.45 55	23.44 56	47.53 55	2.04 58	58.96 56	25.60 53	18.09 57	45.92 56

MVE	26.22 22	30.37 56	25.89 56	43.81 57	44.55 57	35.46 57	28.87 57	39.07 57	18.20 57	18.58 57	40.18 56	2.68 57	47.37 57	17.07 58	23.78 55	48.60 55
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 58	40.17 59	26.90 59		24.59 58	17.44 58	23.95 55	48.61 54	9.77 54	26.48 58	18.06 56	24.47 54	28.83 58

wuyk23d		16.82 57	15.94 57	19.46 59	58.74 53	31.45 58	39.22 55	3.74 59	6.84 59	6.04 59	2.70 59	1.27 59	24.29 59	10.54 59	14.40 59	2.63 59

Test By Simon												64.33 12

High-resolution multi-view benchmark