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.

TDRefinement		86.29 1	90.77 1	81.06 1	75.10 46	83.76 2	93.79 1	61.08 18	89.57 2	86.19 1	90.06 7	93.01 27	76.72 2	94.71 1	92.72 1	93.47 1	91.56 2

COLMAP_ROB	75.87 2	84.34 2	89.80 2	77.97 12	75.52 44	82.76 4	90.39 20	54.21 49	89.37 3	83.18 2	89.90 8	95.58 11	72.34 10	92.31 4	90.04 5	92.17 5	88.61 17
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVS		84.06 3	86.79 9	80.86 2	81.81 8	79.66 28	92.67 6	64.48 1	83.13 25	82.32 3	80.89 83	92.97 28	72.51 9	91.74 6	90.02 6	91.40 17	89.14 7

ACMMPR		83.94 4	87.20 3	80.14 4	81.04 12	81.92 8	92.57 8	63.14 5	84.35 16	79.45 12	83.37 50	92.04 37	72.82 8	90.66 12	88.96 12	91.80 6	89.13 8

MP-MVS		83.50 5	86.11 18	80.45 3	82.58 5	80.60 23	92.68 5	63.48 3	81.43 38	80.21 9	81.95 72	90.76 63	72.86 6	90.14 19	89.30 11	90.92 19	88.59 18

ACMMP		83.17 6	86.75 10	79.01 8	80.11 23	82.01 7	92.29 11	60.35 24	82.20 33	78.32 15	80.59 84	93.14 24	70.67 16	91.30 8	89.36 10	92.30 4	88.62 16
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
PGM-MVS		83.03 7	85.67 24	79.95 5	80.69 16	81.09 15	92.40 10	63.06 6	79.38 57	80.21 9	80.31 86	91.44 47	71.75 12	90.46 15	88.53 15	91.57 9	88.50 19

LGP-MVS_train		82.91 8	86.50 12	78.72 9	78.72 33	81.03 16	89.78 24	61.16 17	80.15 51	80.44 6	84.83 36	94.19 17	70.52 19	90.70 11	87.19 22	91.71 8	87.37 25

ACMM	71.24 7	82.85 9	86.59 11	78.50 10	80.10 24	78.59 30	91.77 12	60.76 22	84.43 14	76.49 24	81.58 78	93.50 19	70.45 20	91.38 7	89.42 9	91.42 16	87.22 27
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MPTG		82.61 10	85.04 28	79.79 6	82.59 4	73.90 53	92.42 9	62.39 11	84.54 13	80.21 9	79.86 90	90.74 64	70.63 17	90.01 21	89.71 8	90.48 21	86.49 32

HFP-MVS		82.37 11	86.28 14	77.81 15	79.94 25	80.96 18	91.13 15	63.30 4	84.04 18	71.81 37	82.39 64	89.59 83	69.16 23	89.08 26	88.83 14	91.49 13	89.10 9

DeepC-MVS	73.80 3	82.34 12	86.87 7	77.06 18	78.62 34	84.34 1	90.30 22	63.54 2	83.10 26	71.30 41	86.91 23	90.54 71	67.12 32	87.81 34	87.05 23	91.46 15	88.37 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CPTT-MVS		82.32 13	85.00 30	79.19 7	80.73 15	80.86 21	91.68 13	62.59 9	82.55 30	75.53 28	73.88 121	92.28 34	73.74 5	90.07 20	87.65 19	90.87 20	87.74 23

ACMP	70.35 9	82.17 14	86.45 13	77.18 17	79.33 26	81.00 17	89.27 28	58.63 29	81.35 40	75.46 29	82.97 55	95.08 12	68.90 25	90.49 14	87.43 21	91.48 14	86.84 29
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		82.16 15	85.55 25	78.21 11	80.48 18	79.28 29	92.65 7	61.03 19	80.55 48	77.00 22	81.80 77	90.71 65	68.73 26	90.25 17	87.94 18	89.36 27	88.30 21
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		82.13 16	86.80 8	76.67 19	80.36 21	80.66 22	89.48 25	56.93 32	82.50 31	67.55 66	87.05 19	91.40 50	72.84 7	88.66 28	88.32 16	92.85 2	89.04 10

LTVRE_ROB	75.99 1	82.04 17	87.16 4	76.07 21	63.57 119	70.27 69	86.48 38	62.99 7	89.00 5	80.32 7	86.25 25	91.04 57	74.66 4	92.58 3	90.29 4	88.42 34	90.72 3
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
PMVS	70.37 8	81.82 18	87.08 5	75.68 23	77.06 40	77.23 36	87.77 36	56.25 38	83.33 24	67.18 77	89.48 10	87.94 94	77.70 1	93.02 2	92.57 2	88.13 36	86.00 35
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_Plus		81.79 19	85.72 22	77.21 16	79.15 31	79.68 27	91.62 14	59.66 25	83.55 21	77.74 18	83.72 46	87.34 102	65.36 36	88.61 29	87.56 20	89.73 26	89.58 5

X-MVS		81.61 20	84.73 32	77.97 12	80.31 22	81.29 12	93.53 2	62.50 10	81.41 39	77.45 19	72.04 131	90.19 76	62.50 51	90.57 13	88.87 13	91.54 10	88.73 13

OPM-MVS		81.44 21	85.68 23	76.49 20	79.27 27	78.21 32	89.84 23	58.67 28	85.25 9	76.26 25	85.28 32	92.88 29	66.03 35	87.20 37	85.40 27	88.86 31	85.58 39

TSAR-MVS + MP.		81.23 22	86.13 16	75.52 24	80.74 14	83.22 3	90.55 16	55.12 44	80.87 44	67.62 65	88.01 13	92.38 33	70.61 18	86.64 39	83.10 42	88.51 32	88.67 14

TSAR-MVS + ACMM		81.20 23	86.92 6	74.52 28	77.60 36	82.29 5	84.41 45	62.95 8	82.99 27	64.03 89	87.71 14	89.17 86	71.98 11	88.19 31	88.10 17	86.18 50	89.95 4

APDe-MVS		81.08 24	86.12 17	75.20 26	79.25 28	80.91 19	90.38 21	57.05 31	85.83 8	66.07 82	87.34 17	91.27 54	69.45 21	85.99 43	82.55 43	88.98 30	88.95 11

APD-MVS		80.60 25	84.63 33	75.91 22	81.22 10	81.48 10	90.49 18	58.81 27	77.54 63	67.49 68	85.90 27	89.82 82	69.43 22	86.08 42	83.80 37	88.01 37	87.77 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		80.44 26	82.57 45	77.96 14	81.99 7	72.76 57	90.48 19	61.31 14	80.85 45	77.90 17	81.93 73	87.01 106	68.20 28	84.15 52	85.27 29	87.85 38	86.00 35

ESAPD		80.30 27	84.42 36	75.49 25	79.20 30	79.76 26	89.40 26	58.51 30	81.15 42	69.56 53	85.14 33	88.71 90	68.92 24	85.26 48	82.30 49	87.35 41	88.64 15

ACMH+	67.97 10	80.15 28	86.16 15	73.14 37	73.82 52	76.41 39	83.59 47	54.82 47	87.35 6	70.86 45	86.98 22	96.27 5	66.50 33	89.17 25	83.39 39	89.26 28	83.56 46

OMC-MVS		79.95 29	85.28 26	73.74 34	72.95 55	80.10 25	87.87 35	48.13 74	84.62 12	79.42 13	80.27 87	92.49 31	64.14 42	87.25 36	85.11 30	89.92 24	87.10 28

HSP-MVS		79.66 30	84.23 37	74.34 30	78.92 32	81.86 9	90.55 16	60.49 23	80.19 50	69.08 56	85.12 34	90.92 61	62.99 48	81.15 73	78.00 66	83.99 60	92.37 1

DeepPCF-MVS	71.57 5	79.49 31	84.05 38	74.17 31	74.14 49	80.88 20	89.33 27	56.24 39	82.41 32	71.58 39	82.27 65	86.47 111	66.47 34	84.80 50	84.16 35	87.26 42	87.34 26

LS3D		79.33 32	84.03 39	73.84 32	75.37 45	78.09 33	83.30 48	52.94 56	84.42 15	76.01 26	84.16 41	87.44 101	65.34 37	86.30 40	82.08 51	90.09 22	85.70 37

3Dnovator+	72.94 4	78.78 33	83.05 42	73.80 33	70.70 67	81.34 11	88.33 32	56.01 40	81.33 41	72.87 35	78.06 100	81.15 136	63.83 44	87.39 35	85.82 25	91.06 18	86.28 34

UA-Net		78.65 34	83.96 40	72.46 39	84.87 1	76.15 40	89.06 29	55.70 41	77.25 64	53.14 121	79.73 92	82.09 134	59.69 66	92.21 5	90.93 3	92.32 3	89.36 6

DeepC-MVS_fast	71.40 6	78.48 35	82.92 43	73.31 36	76.44 42	82.23 6	87.59 37	56.56 35	77.79 61	68.91 58	77.00 105	87.32 103	61.90 53	85.40 45	84.37 32	88.46 33	86.33 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS		78.32 36	86.09 19	69.25 56	76.22 43	72.33 64	85.71 41	59.02 26	86.66 7	51.41 126	92.91 1	96.76 2	53.09 113	90.21 18	85.30 28	90.05 23	78.46 72

ACMH	66.19 11	78.12 37	84.55 34	70.63 47	69.62 73	72.40 63	80.77 64	46.43 86	89.24 4	77.99 16	87.42 16	95.83 9	62.95 49	86.27 41	78.24 65	86.00 53	82.46 49
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
train_agg		77.83 38	80.47 57	74.77 27	80.92 13	69.60 70	88.87 31	56.32 37	74.03 80	71.03 43	83.67 47	87.68 97	64.75 40	83.70 56	81.85 52	86.71 45	82.73 48

NCCC		77.82 39	80.72 56	74.43 29	79.24 29	75.72 43	88.06 33	56.36 36	79.61 55	73.22 33	67.75 145	87.05 105	63.09 47	85.62 44	84.00 36	86.62 46	85.30 41

CNVR-MVS		77.79 40	81.57 50	73.38 35	78.37 35	75.91 41	87.97 34	55.11 45	79.41 56	70.98 44	74.70 119	86.43 112	61.77 54	85.10 49	83.73 38	86.10 52	85.68 38

WR-MVS_H		77.56 41	85.88 20	67.86 59	80.54 17	74.32 50	83.23 49	61.78 12	83.47 22	47.46 143	91.81 5	95.84 8	50.50 122	90.44 16	84.37 32	83.63 63	80.89 58

RPSCF		77.56 41	84.51 35	69.46 55	65.17 97	74.36 49	79.74 69	47.45 77	84.01 19	72.89 34	77.89 101	90.67 66	65.14 39	88.25 30	89.74 7	86.38 49	86.64 31

PS-CasMVS		77.46 43	85.80 21	67.73 61	81.24 9	72.88 56	80.63 65	61.28 15	84.14 17	50.53 130	92.13 3	96.76 2	50.12 125	91.02 9	84.46 31	82.60 76	79.19 65

DTE-MVSNet		77.28 44	84.87 31	68.42 57	82.94 3	72.70 59	81.60 59	61.78 12	85.03 10	51.40 127	92.11 4	96.00 6	49.42 128	89.73 23	82.52 45	83.39 67	75.98 82

SixPastTwentyTwo		77.24 45	83.65 41	69.78 51	65.14 98	64.85 90	77.44 80	47.74 76	82.76 29	68.52 59	87.65 15	93.31 21	71.68 13	89.49 24	82.41 46	88.14 35	85.05 42

CDPH-MVS		77.22 46	81.05 55	72.75 38	77.29 38	77.46 35	86.36 39	54.02 51	73.00 85	69.75 51	77.78 103	88.90 89	61.31 58	84.09 55	82.54 44	87.79 39	83.57 45

PEN-MVS		77.06 47	85.05 27	67.74 60	82.29 6	72.59 60	80.86 63	61.03 19	84.66 11	50.08 134	92.19 2	96.59 4	49.12 129	89.83 22	82.35 47	83.06 70	77.14 78

CP-MVSNet		77.01 48	85.04 28	67.65 62	81.16 11	72.72 58	80.54 66	61.18 16	82.09 34	50.41 131	90.81 6	95.89 7	50.03 126	90.86 10	84.30 34	82.56 77	78.65 71

CSCG		76.95 49	82.08 47	70.97 43	73.32 54	78.35 31	81.08 62	47.19 79	83.47 22	69.82 50	80.44 85	87.19 104	64.59 41	81.01 76	77.26 72	89.83 25	86.84 29

CNLPA		76.67 50	81.72 48	70.77 46	70.75 65	76.68 38	86.14 40	46.11 88	81.82 36	74.68 30	76.37 108	86.23 114	62.92 50	85.28 47	83.29 40	84.02 59	82.40 50

MSLP-MVS++		76.66 51	82.32 46	70.06 49	70.51 68	80.27 24	79.77 68	55.58 42	77.79 61	63.09 91	67.25 149	89.50 84	71.01 15	88.10 32	85.74 26	80.39 87	87.56 24

TSAR-MVS + COLMAP		75.85 52	81.06 53	69.77 52	71.15 61	76.90 37	82.93 51	52.43 58	79.25 58	70.13 48	82.78 56	87.00 107	60.02 62	80.30 82	79.61 58	81.95 81	81.61 54

HQP-MVS		75.81 53	78.91 64	72.18 40	77.41 37	75.38 45	84.75 42	53.35 53	76.12 68	73.32 32	69.48 136	88.07 92	57.76 77	79.42 86	78.44 62	86.48 47	85.50 40

PLC	64.88 15	75.76 54	80.22 58	70.57 48	70.46 69	77.75 34	82.01 57	48.84 69	80.74 47	70.85 46	71.32 133	84.82 123	63.69 45	84.73 51	82.35 47	87.54 40	79.80 62
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	66.11 12	75.37 55	79.24 62	70.86 44	67.63 79	74.09 51	83.17 50	44.75 100	81.82 36	80.83 5	65.61 158	88.04 93	61.58 55	83.21 62	80.12 55	87.17 43	81.82 53
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 56	78.37 65	71.43 41	71.13 62	72.46 62	82.28 56	50.55 62	73.39 83	79.05 14	73.65 123	87.50 100	61.98 52	81.10 74	78.48 61	83.60 64	81.99 51

anonymousdsp		74.76 57	82.59 44	65.63 76	45.61 211	61.13 126	89.06 29	32.58 202	74.11 79	59.55 100	84.06 43	94.12 18	75.24 3	88.94 27	86.95 24	91.74 7	88.81 12

AdaColmap		74.73 58	77.57 70	71.40 42	76.90 41	75.76 42	84.54 44	53.08 55	76.20 67	66.64 81	66.06 156	78.16 152	61.32 57	85.37 46	82.20 50	85.95 54	79.27 64

v7n		74.47 59	81.06 53	66.77 67	66.98 83	67.10 73	76.76 83	45.88 90	81.98 35	67.43 70	88.38 12	95.67 10	61.38 56	80.76 79	73.49 93	82.21 79	80.06 60

PCF-MVS	65.25 14	73.99 60	76.74 75	70.79 45	71.61 60	75.33 46	83.76 46	50.40 63	74.88 72	74.50 31	67.60 146	85.36 120	58.30 73	78.61 89	74.25 89	86.15 51	81.13 57
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v52		73.95 61	81.43 52	65.22 80	54.85 180	63.32 110	78.90 71	38.00 175	80.00 53	68.32 61	87.02 20	94.98 15	68.14 30	84.11 53	75.63 82	83.12 68	84.96 43

V4		73.95 61	81.44 51	65.22 80	54.86 179	63.31 111	78.89 72	38.00 175	80.03 52	68.29 62	87.02 20	95.00 13	68.15 29	84.11 53	75.62 83	83.12 68	84.95 44

MCST-MVS		73.84 63	77.44 71	69.63 54	73.75 53	74.73 48	81.38 61	48.58 70	74.77 73	69.16 55	71.97 132	86.20 115	59.50 68	78.51 90	74.06 90	85.42 55	81.85 52

MVS_0304		73.74 64	77.16 73	69.74 53	74.24 48	73.47 54	84.70 43	49.62 64	62.26 156	67.27 74	75.87 111	87.57 99	57.49 82	81.20 72	79.50 59	85.10 56	80.27 59

TSAR-MVS + GP.		73.42 65	76.31 76	70.05 50	77.15 39	71.13 67	81.59 60	54.11 50	69.84 117	58.65 103	66.20 155	78.77 149	65.29 38	83.65 57	83.14 41	83.54 65	81.47 55

Gipuma		73.40 66	79.27 61	66.55 71	63.64 118	59.35 132	70.28 129	45.92 89	83.79 20	71.78 38	84.04 44	93.07 26	68.69 27	87.90 33	76.76 75	78.98 99	69.96 119
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		72.37 67	76.12 79	68.00 58	68.55 76	64.30 102	82.93 51	48.98 68	74.25 77	65.39 83	73.59 124	84.11 127	59.48 69	82.61 65	78.38 63	82.66 75	75.59 83

TinyColmap		71.85 68	76.11 80	66.87 66	66.07 87	65.34 85	74.35 105	49.30 67	79.93 54	75.93 27	75.66 113	87.74 96	54.72 104	80.66 81	70.42 110	80.85 85	73.02 101

TranMVSNet+NR-MVSNet		71.66 69	79.23 63	62.83 108	72.54 57	65.64 81	74.77 103	55.27 43	75.91 69	45.50 154	89.55 9	94.25 16	45.96 145	82.74 64	77.03 74	82.96 71	69.48 125

MVS_111021_LR		71.60 70	75.21 85	67.38 63	67.42 80	62.44 120	81.73 58	46.24 87	70.89 100	66.80 80	73.19 126	84.98 121	60.09 61	81.94 68	77.77 70	82.00 80	75.29 84

EG-PatchMatch MVS		71.50 71	76.82 74	65.30 78	70.74 66	66.50 77	74.23 107	43.25 114	72.02 88	59.11 101	79.85 91	86.88 109	63.95 43	80.29 83	75.25 86	80.51 86	76.98 79

UniMVSNet (Re)		71.29 72	78.14 66	63.30 98	70.29 70	66.57 76	75.98 88	54.74 48	70.20 110	46.20 152	85.08 35	93.21 22	48.19 133	82.50 66	78.33 64	84.40 57	71.08 116

v748		71.27 73	79.41 60	61.76 112	60.62 142	61.73 123	68.46 137	40.71 152	80.76 46	61.02 96	87.12 18	95.00 13	59.62 67	80.67 80	70.67 108	80.14 90	79.93 61

CLD-MVS		71.24 74	78.12 67	63.20 100	74.03 50	71.60 65	82.82 53	32.91 199	74.23 78	69.32 54	79.65 93	91.54 45	47.02 141	81.22 71	79.01 60	73.09 153	69.63 121
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		71.23 75	81.58 49	59.15 121	71.63 59	60.40 131	70.12 130	52.15 59	92.79 1	42.31 164	88.89 11	98.03 1	40.61 164	80.86 78	75.96 81	78.08 116	74.11 88

CANet		71.07 76	75.09 87	66.39 72	72.57 56	71.53 66	82.38 55	47.10 80	59.81 164	59.81 99	74.97 116	84.37 126	54.25 107	79.89 85	77.64 71	82.25 78	77.40 76

v1192		71.06 77	74.87 90	66.61 69	66.38 85	65.80 80	78.27 74	45.28 93	70.19 111	70.79 47	83.37 50	91.79 40	58.76 72	70.86 159	69.02 116	80.16 89	73.08 99

DU-MVS		71.03 78	77.92 68	62.98 106	70.81 63	65.48 83	73.93 115	56.76 33	69.95 115	46.77 149	85.70 30	93.49 20	46.91 142	83.47 58	77.82 69	82.72 74	69.54 122

v1240		70.94 79	74.52 97	66.76 68	66.54 84	64.40 96	77.76 77	45.29 92	70.05 113	71.45 40	83.36 52	90.96 59	60.37 60	70.50 161	68.68 118	79.14 97	73.68 94

v1921920		70.82 80	74.46 99	66.58 70	66.33 86	64.35 101	77.72 78	45.07 95	70.39 104	71.18 42	83.15 53	90.62 68	59.97 63	70.90 157	68.43 126	79.19 96	73.39 96

UniMVSNet_NR-MVSNet		70.82 80	77.44 71	63.11 101	71.75 58	66.02 79	73.93 115	55.00 46	70.90 99	46.77 149	86.68 24	91.54 45	46.91 142	81.07 75	76.32 79	84.28 58	69.54 122

PVSNet_Blended_VisFu		70.70 82	73.62 106	67.28 65	63.53 121	72.96 55	77.97 75	52.10 60	63.65 147	62.66 93	71.14 134	73.46 166	63.55 46	79.35 88	75.34 85	83.90 61	79.43 63

v144192		70.68 83	74.40 101	66.34 73	65.94 89	64.38 98	77.63 79	45.18 94	69.97 114	70.11 49	82.70 59	90.77 62	59.84 65	71.43 152	68.46 122	79.31 95	73.08 99

v13		70.58 84	75.49 83	64.87 84	64.66 101	64.58 93	76.18 86	43.69 108	72.34 87	67.65 64	84.36 39	92.01 38	58.05 74	73.57 113	67.06 144	78.96 100	74.48 87

FPMVS		70.46 85	74.89 89	65.28 79	69.09 75	61.42 124	77.07 82	46.92 83	76.73 66	53.53 118	67.33 147	75.07 161	67.23 31	83.41 60	81.54 53	77.86 121	78.73 69

v1144		70.45 86	74.50 98	65.73 75	65.74 91	64.88 89	77.33 81	44.16 102	70.59 103	69.63 52	83.15 53	91.42 49	57.79 76	71.29 156	68.53 121	79.72 92	71.63 114

v12		70.39 87	75.25 84	64.73 85	64.60 103	64.47 94	76.00 87	43.55 110	71.92 89	67.51 67	84.15 42	91.88 39	57.83 75	73.32 114	67.00 145	78.87 101	74.02 91

v10		70.25 88	74.59 95	65.19 82	65.32 95	66.46 78	76.60 84	44.84 98	67.38 126	67.21 76	82.75 58	90.56 70	57.70 78	71.69 146	68.63 119	79.44 93	74.67 86

V9		70.20 89	75.02 88	64.58 87	64.49 104	64.36 99	75.80 92	43.40 111	71.53 90	67.35 73	83.95 45	91.73 42	57.63 80	73.04 117	66.96 146	78.79 103	73.61 95

Effi-MVS+-dtu		70.10 90	73.76 105	65.82 74	70.23 71	74.92 47	79.47 70	44.49 101	56.98 179	54.34 114	64.26 168	84.78 124	59.97 63	80.96 77	80.38 54	86.44 48	74.05 90

v11		70.10 90	74.82 91	64.58 87	64.83 99	64.39 97	75.89 89	43.18 116	71.34 93	67.75 63	84.19 40	91.75 41	57.23 84	71.46 151	66.85 149	78.60 106	73.78 92

MAR-MVS		70.00 92	72.28 123	67.34 64	69.89 72	72.57 61	80.09 67	49.49 66	60.28 162	69.03 57	59.29 191	80.79 138	54.68 105	78.39 92	76.00 80	80.87 84	78.67 70
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
V14		69.99 93	74.77 93	64.41 90	64.39 105	64.25 103	75.59 94	43.25 114	71.12 97	67.14 78	83.65 48	91.58 44	57.40 83	72.75 125	66.90 148	78.70 104	73.15 98

Vis-MVSNet		69.95 94	77.69 69	60.91 115	60.67 140	66.71 74	77.94 76	48.58 70	69.10 119	45.78 153	80.21 88	83.58 131	53.41 112	82.92 63	80.11 56	79.08 98	81.21 56
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v7		69.81 95	73.94 103	65.00 83	65.33 93	65.07 86	76.60 84	43.66 109	67.36 127	67.25 75	82.76 57	90.57 69	57.70 78	71.69 146	68.63 119	79.44 93	71.52 115

v15		69.80 96	74.53 96	64.27 92	64.30 106	64.15 104	75.40 96	43.12 117	70.71 102	66.98 79	83.41 49	91.43 48	57.21 85	72.48 130	66.84 150	78.62 105	72.72 103

EPP-MVSNet		69.51 97	76.17 77	61.74 113	68.38 78	66.60 75	71.77 121	46.98 81	73.60 82	41.79 166	82.06 71	69.65 179	52.51 116	83.41 60	79.94 57	89.02 29	77.94 74

3Dnovator	65.69 13	69.43 98	75.74 82	62.06 111	60.78 139	70.50 68	75.85 91	39.57 162	74.44 75	57.41 106	75.91 109	77.73 154	55.34 100	76.86 95	75.61 84	83.44 66	79.14 66

Effi-MVS+		69.04 99	73.01 116	64.40 91	67.20 81	64.83 91	74.87 102	43.97 104	63.33 150	60.90 97	73.06 127	85.79 117	55.61 98	73.58 112	76.41 78	83.84 62	74.09 89

v2v482		69.01 100	73.39 108	63.89 94	63.86 111	62.99 115	75.26 97	42.05 128	70.22 109	68.46 60	82.64 60	91.61 43	55.38 99	70.89 158	66.93 147	78.30 111	68.48 135

v1		68.98 101	73.38 109	63.84 95	64.12 108	62.97 116	74.95 101	41.52 137	70.28 107	67.47 69	82.49 61	91.37 51	56.59 87	71.43 152	66.51 157	78.41 108	68.62 131

MSDG		68.98 101	73.31 112	63.92 93	67.08 82	68.27 71	75.41 95	40.77 148	67.61 125	64.89 84	75.75 112	78.96 146	53.70 109	76.72 97	73.95 91	81.71 83	71.93 111

v1141		68.97 103	73.38 109	63.83 96	64.11 109	62.97 116	74.96 98	41.52 137	70.29 105	67.36 72	82.47 62	91.37 51	56.59 87	71.43 152	66.49 159	78.41 108	68.61 133

divwei89l23v2f112		68.97 103	73.38 109	63.83 96	64.11 109	62.97 116	74.96 98	41.52 137	70.29 105	67.39 71	82.47 62	91.37 51	56.59 87	71.42 155	66.50 158	78.40 110	68.62 131

v8		68.77 105	73.50 107	63.26 99	63.74 113	64.47 94	74.22 111	42.07 126	67.30 128	64.89 84	82.08 70	90.23 73	56.50 91	71.85 145	66.57 154	78.14 112	72.02 109

NR-MVSNet		68.66 106	76.15 78	59.93 118	65.49 92	65.48 83	74.42 104	56.76 33	69.95 115	45.38 155	85.70 30	91.13 55	34.68 184	74.52 104	76.75 76	82.83 73	69.49 124

v17		68.55 107	73.23 113	63.08 102	63.67 117	63.84 105	74.05 113	42.28 123	66.34 135	63.93 90	81.91 74	89.83 81	56.50 91	71.97 139	66.55 155	78.08 116	72.18 107

USDC		68.53 108	71.82 127	64.68 86	63.53 121	61.87 122	70.12 130	46.98 81	77.89 60	76.58 23	68.55 140	86.88 109	50.50 122	73.73 109	65.62 163	80.39 87	68.21 137

v16		68.33 109	73.03 115	62.86 107	63.57 119	63.83 106	73.98 114	42.30 122	65.58 141	62.94 92	81.82 75	89.37 85	56.36 95	71.91 140	66.52 156	77.99 119	72.17 108

v1neww		68.32 110	72.82 117	63.07 103	63.73 114	63.12 112	74.23 107	40.99 143	67.21 129	64.83 87	82.09 68	90.20 74	56.49 93	71.86 142	66.61 151	78.14 112	68.65 129

v7new		68.32 110	72.82 117	63.07 103	63.73 114	63.12 112	74.23 107	40.99 143	67.21 129	64.83 87	82.09 68	90.20 74	56.49 93	71.86 142	66.61 151	78.14 112	68.65 129

v6		68.32 110	72.82 117	63.07 103	63.73 114	63.11 114	74.23 107	40.99 143	67.21 129	64.86 86	82.11 67	90.19 76	56.51 90	71.86 142	66.61 151	78.14 112	68.66 128

IS_MVSNet		68.20 113	74.41 100	60.96 114	68.55 76	64.36 99	71.47 123	48.33 72	70.11 112	43.30 162	80.90 82	74.54 164	47.19 140	81.25 70	77.97 68	86.94 44	71.76 112

Baseline_NR-MVSNet		68.15 114	75.12 86	60.02 117	70.81 63	55.67 159	75.88 90	53.40 52	71.25 94	43.96 159	85.88 28	92.68 30	45.76 146	83.47 58	68.34 127	70.34 174	68.58 134

v18		67.99 115	72.63 121	62.57 109	63.32 124	63.64 108	73.58 120	42.07 126	64.75 144	62.64 94	81.36 79	89.01 88	56.02 96	71.57 148	66.41 160	77.80 122	71.69 113

Fast-Effi-MVS+		67.71 116	72.54 122	62.07 110	63.83 112	63.68 107	75.74 93	39.94 159	60.89 161	54.29 115	73.00 128	86.19 116	56.85 86	78.46 91	73.23 94	81.74 82	72.36 105

EPNet		66.87 117	68.89 138	64.53 89	73.97 51	61.13 126	78.46 73	61.03 19	56.78 180	53.41 119	66.91 150	70.91 171	43.49 154	76.08 101	76.68 77	76.81 124	73.73 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		66.37 118	74.37 102	57.04 132	65.89 90	65.06 87	62.58 161	42.55 118	76.82 65	46.87 148	67.33 147	86.38 113	45.49 148	76.77 96	71.85 100	78.87 101	76.35 80

QAPM		66.36 119	72.76 120	58.90 123	59.57 148	65.01 88	64.05 157	41.17 142	73.09 84	56.82 108	69.42 137	77.78 153	55.07 102	73.00 121	72.07 99	76.71 125	78.96 67

V42		65.79 120	72.11 125	58.42 126	51.89 190	58.69 134	73.80 117	34.50 189	65.40 142	57.10 107	79.54 95	89.09 87	57.51 81	71.98 138	67.83 137	75.70 130	72.26 106

IterMVS-LS		65.76 121	70.85 132	59.81 120	65.33 93	57.78 138	64.63 154	48.02 75	65.65 139	51.05 129	81.31 80	77.47 155	54.94 103	69.46 168	69.36 113	74.90 134	74.95 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 122	71.25 131	59.14 122	58.92 157	54.88 165	73.66 119	38.55 171	66.12 137	49.91 135	69.87 135	86.97 108	60.61 59	76.30 99	74.75 87	73.19 151	69.83 120

UGNet		65.61 123	74.79 92	54.91 141	54.54 183	68.20 72	70.97 126	48.21 73	67.14 133	41.67 167	74.15 120	80.65 139	36.10 179	79.39 87	77.99 67	77.95 120	76.01 81
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
DELS-MVS		65.54 124	71.79 128	58.24 128	59.68 147	65.55 82	70.99 124	38.69 170	62.29 155	49.27 138	75.03 115	81.42 135	50.93 119	73.71 111	71.35 101	79.90 91	73.20 97
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
pmmvs-eth3d		65.36 125	70.09 136	59.85 119	63.05 126	53.61 168	74.29 106	46.45 85	68.14 123	51.45 125	78.83 97	85.78 118	49.87 127	70.44 162	70.45 109	74.00 139	63.38 153

v148		64.92 126	70.58 134	58.32 127	59.89 145	57.09 145	66.04 145	35.27 188	69.11 118	60.66 98	79.57 94	90.93 60	53.91 108	69.81 167	62.22 178	74.14 137	65.31 146

FC-MVSNet-train		64.87 127	74.76 94	53.33 144	65.24 96	58.05 137	69.69 133	41.92 132	70.99 98	32.62 188	85.75 29	88.23 91	32.10 204	77.61 94	74.41 88	78.43 107	68.25 136

pmmvs6		64.78 128	75.82 81	51.89 151	62.41 128	57.13 144	60.24 169	45.59 91	82.90 28	34.69 180	84.83 36	93.18 23	36.22 178	76.43 98	71.13 105	72.21 158	65.12 147

OpenMVS	60.79 16	64.42 129	69.72 137	58.23 129	61.63 133	62.17 121	64.11 156	37.54 179	67.17 132	55.71 113	65.89 157	74.89 162	52.67 115	72.20 136	68.29 129	77.73 123	77.39 77

no-one		64.33 130	73.23 113	53.94 143	38.32 223	50.78 181	56.78 192	27.44 213	61.95 159	56.77 109	64.60 165	93.12 25	71.12 14	81.91 69	77.19 73	73.20 150	83.04 47

TransMVSNet (Re)		63.49 131	73.86 104	51.39 157	64.26 107	56.07 156	61.17 166	42.23 124	78.81 59	34.80 178	85.94 26	90.63 67	34.35 191	72.73 127	67.98 135	71.50 161	64.84 148

DI_MVS_plusplus_trai		63.43 132	67.54 141	58.63 124	62.34 129	58.06 136	65.75 149	42.15 125	63.05 151	53.28 120	75.88 110	75.92 159	50.18 124	68.04 172	64.20 169	78.07 118	67.65 138

Fast-Effi-MVS+-dtu		63.22 133	65.55 147	60.49 116	61.24 135	64.70 92	74.15 112	53.24 54	51.46 196	49.67 136	58.03 197	78.42 150	48.05 135	72.03 137	71.14 104	76.60 128	63.09 154

MVS_Test		62.58 134	67.46 142	56.89 134	59.52 151	55.90 157	64.94 152	38.83 167	57.08 178	56.55 111	76.53 106	84.49 125	47.45 136	66.95 174	62.01 179	74.04 138	69.27 126

MDA-MVSNet-bldmvs		62.46 135	72.13 124	51.19 159	34.32 227	56.10 154	68.65 136	38.85 164	69.05 120	49.50 137	78.17 99	85.43 119	51.32 117	86.67 38	67.40 142	64.46 187	62.08 157

pm-mvs1		61.97 136	72.01 126	50.25 166	60.64 141	55.23 162	58.67 177	42.44 120	74.40 76	33.63 184	81.03 81	89.86 80	34.87 183	72.93 124	67.95 136	71.28 162	62.65 156

conf0.05thres1000		61.96 137	70.38 135	52.13 149	63.31 125	58.12 135	62.09 162	42.45 119	75.50 70	33.07 186	77.89 101	69.72 178	37.32 170	77.88 93	70.72 107	74.55 136	62.82 155

FMVSNet1		61.92 138	71.36 129	50.90 162	57.67 167	59.29 133	59.48 173	44.14 103	70.24 108	34.72 179	75.45 114	84.94 122	36.75 174	72.33 133	68.45 123	72.66 155	68.83 127

PVSNet_BlendedMVS		61.75 139	65.07 152	57.87 130	56.27 170	60.99 128	65.81 147	43.75 106	51.27 199	54.08 116	62.12 178	78.84 147	50.67 120	71.49 149	63.91 171	76.64 126	66.86 140

PVSNet_Blended		61.75 139	65.07 152	57.87 130	56.27 170	60.99 128	65.81 147	43.75 106	51.27 199	54.08 116	62.12 178	78.84 147	50.67 120	71.49 149	63.91 171	76.64 126	66.86 140

tfpnnormal		61.41 141	71.33 130	49.83 167	61.73 132	54.90 164	58.52 178	41.24 140	75.20 71	32.00 196	82.13 66	87.87 95	35.63 182	72.75 125	66.30 161	69.87 175	60.14 162

IB-MVS	57.02 17	61.37 142	65.39 149	56.69 135	56.65 168	60.85 130	70.70 127	37.90 177	49.37 208	45.37 156	48.75 220	79.14 144	53.55 111	76.26 100	70.85 106	75.97 129	72.50 104
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
CANet_DTU		61.22 143	67.07 143	54.40 142	59.89 145	63.62 109	70.98 125	36.77 183	50.49 202	47.15 144	62.45 176	80.81 137	37.90 169	71.87 141	70.09 111	73.69 140	70.19 118

pmmvs4		61.12 144	64.61 155	57.04 132	60.88 138	52.15 177	70.59 128	44.82 99	61.35 160	46.91 147	72.08 130	73.27 167	46.79 144	65.06 177	67.76 138	72.28 156	60.58 161

Vis-MVSNet (Re-imp)		60.99 145	67.78 140	53.06 146	64.66 101	53.49 169	67.40 140	49.52 65	68.55 121	28.00 211	79.53 96	71.41 170	33.08 200	75.30 103	71.28 103	75.69 131	54.91 187

PatchMatch-RL		60.96 146	63.00 169	58.57 125	59.16 156	52.18 176	67.38 141	41.99 129	57.85 173	48.16 139	53.55 211	69.77 177	59.47 70	73.73 109	72.49 98	75.27 133	61.44 159

GA-MVS		60.73 147	64.24 159	56.64 136	59.38 155	57.45 142	65.07 150	36.65 184	57.39 176	58.17 104	73.43 125	69.10 182	47.38 137	64.47 181	63.63 173	73.19 151	64.22 150

CVMVSNet		60.45 148	63.72 162	56.63 137	54.82 181	53.75 167	68.41 138	41.95 131	55.07 184	48.03 140	58.08 196	68.67 183	55.09 101	69.14 170	68.34 127	71.51 160	72.97 102

FC-MVSNet-test		60.28 149	70.83 133	47.96 186	54.69 182	47.12 193	68.06 139	41.68 136	71.42 91	23.73 221	84.70 38	77.41 156	28.92 207	82.33 67	73.08 95	70.68 169	59.77 164

EU-MVSNet		59.77 150	66.07 145	52.42 148	47.81 202	51.86 179	62.98 160	32.28 204	62.08 157	47.10 145	59.94 188	83.42 132	53.08 114	70.06 166	63.19 174	71.26 164	71.96 110

diffmvs		59.30 151	64.79 154	52.90 147	54.48 184	50.17 185	64.98 151	36.44 186	60.16 163	50.33 132	76.51 107	74.56 163	44.99 149	62.52 187	62.37 177	66.18 184	67.22 139

IterMVS		59.24 152	64.45 156	53.16 145	50.98 193	61.29 125	66.51 143	32.85 200	58.17 169	46.31 151	72.58 129	70.23 173	54.26 106	64.81 180	60.24 182	68.04 181	63.81 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view800		59.22 153	66.23 144	51.03 161	61.99 131	56.71 147	60.53 167	41.20 141	66.26 136	32.46 190	66.68 153	69.93 174	36.77 173	74.52 104	70.00 112	73.24 149	59.56 166

HyFIR lowres test		59.15 154	62.28 171	55.49 139	52.42 188	62.59 119	71.76 122	39.74 160	50.25 204	41.92 165	62.88 173	69.16 181	55.85 97	62.77 186	67.18 143	71.27 163	61.11 160

thres600view7		58.87 155	65.91 146	50.66 163	61.27 134	56.32 151	59.88 171	40.63 155	64.88 143	32.10 195	64.82 163	69.83 176	36.72 175	72.99 122	72.55 97	73.34 147	59.97 163

view600		58.47 156	65.42 148	50.36 165	61.04 137	55.84 158	59.33 174	40.34 158	64.46 145	32.31 194	64.78 164	69.85 175	36.46 176	72.46 131	71.31 102	72.68 154	59.26 170

CMPMVS	45.32 18	58.10 157	65.24 151	49.76 168	47.88 201	46.86 196	48.16 221	32.82 201	58.06 170	61.35 95	59.64 189	80.00 140	47.27 139	70.15 164	64.10 170	61.08 191	77.85 75
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 158	63.54 164	51.74 153	50.13 197	46.56 197	66.95 142	33.41 197	63.52 148	58.77 102	74.84 117	84.10 128	43.12 155	65.95 176	54.69 195	58.04 197	55.13 186

CDS-MVSNet		57.90 159	63.57 163	51.28 158	62.30 130	53.17 170	64.70 153	51.61 61	57.41 175	32.75 187	63.73 169	70.53 172	27.12 210	72.49 128	73.02 96	69.22 178	54.68 189
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet2		57.80 160	65.39 149	48.94 179	55.88 172	57.61 139	57.26 189	42.37 121	58.21 168	33.19 185	68.36 142	75.55 160	34.58 185	66.91 175	64.55 167	70.38 171	66.56 142

tfpn		57.74 161	63.03 168	51.58 156	62.87 127	57.28 143	61.53 165	41.99 129	67.67 124	32.52 189	68.13 143	43.08 229	36.94 172	76.07 102	69.31 114	73.62 141	59.68 165

thres400		57.25 162	63.73 161	49.70 169	60.19 144	54.95 163	58.16 179	39.60 161	62.42 154	31.98 198	62.33 177	69.20 180	35.96 180	70.07 165	68.03 134	72.28 156	59.12 171

tfpn_n400		57.07 163	64.44 157	48.48 182	59.55 149	52.25 174	57.99 186	38.85 164	71.25 94	29.07 207	65.20 160	63.07 194	34.41 188	73.99 106	67.52 140	70.99 166	57.83 173

tfpnconf		57.07 163	64.44 157	48.48 182	59.55 149	52.25 174	57.99 186	38.85 164	71.25 94	29.07 207	65.20 160	63.07 194	34.41 188	73.99 106	67.52 140	70.99 166	57.83 173

gm-plane-assit		56.76 165	57.64 185	55.73 138	66.01 88	55.45 161	74.96 98	30.54 209	73.71 81	56.04 112	81.81 76	30.91 236	43.83 152	58.77 199	54.71 194	63.02 189	48.13 206

MIMVSNet1		56.72 166	68.69 139	42.76 201	46.70 207	42.81 203	69.13 135	30.52 210	81.01 43	32.00 196	74.82 118	91.10 56	26.83 212	73.98 108	64.72 166	51.40 209	52.38 192

tfpnview11		56.69 167	63.86 160	48.33 185	59.46 152	52.35 173	57.85 188	38.80 168	68.21 122	29.07 207	65.20 160	63.07 194	34.36 190	73.21 115	68.72 117	70.44 170	56.28 182

EPNet_dtu		56.63 168	60.77 178	51.80 152	55.47 177	44.63 198	69.83 132	38.74 169	50.27 203	47.64 141	58.01 198	72.27 168	33.71 197	68.60 171	67.72 139	65.39 185	63.86 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		56.54 169	63.26 165	48.70 180	55.88 172	57.61 139	57.26 189	41.75 133	49.06 209	32.37 191	61.81 180	67.02 185	34.58 185	72.33 133	68.45 123	70.38 171	66.56 142

test1		56.54 169	63.26 165	48.70 180	55.88 172	57.61 139	57.26 189	41.75 133	49.06 209	32.37 191	61.81 180	67.02 185	34.58 185	72.33 133	68.45 123	70.38 171	66.56 142

gg-mvs-nofinetune		56.45 171	61.04 175	51.10 160	63.42 123	49.40 188	53.71 203	52.52 57	74.77 73	46.93 146	77.31 104	53.88 210	26.42 214	62.51 188	57.81 187	63.60 188	51.57 196

thres200		56.35 172	62.36 170	49.34 171	58.87 158	56.32 151	55.91 193	40.63 155	58.51 166	31.34 199	58.81 195	67.31 184	35.96 180	72.99 122	65.51 164	73.34 147	57.07 178

MS-PatchMatch		56.31 173	60.22 181	51.73 154	60.53 143	55.53 160	63.41 158	37.18 180	51.34 198	37.44 171	60.53 185	62.19 198	45.52 147	64.25 182	63.17 175	66.33 183	64.56 149

tfpn1000		56.13 174	63.18 167	47.91 187	58.34 165	53.03 171	58.87 176	38.14 172	65.64 140	27.09 212	65.41 159	59.49 206	33.41 199	73.14 116	69.08 115	71.63 159	56.46 181

conf200view11		56.07 175	61.85 172	49.32 173	58.57 159	56.49 148	58.01 181	40.73 149	53.23 187	30.91 202	56.41 200	66.40 189	34.18 192	73.03 118	68.06 130	73.54 142	59.36 167

tfpn200view9		56.07 175	61.85 172	49.34 171	58.57 159	56.48 150	58.01 181	40.72 151	53.23 187	31.01 200	56.41 200	66.40 189	34.18 192	73.02 120	68.06 130	73.53 144	59.35 169

tfpn111		55.56 177	60.91 177	49.32 173	58.57 159	56.49 148	58.01 181	40.73 149	53.23 187	30.91 202	49.82 217	66.40 189	34.18 192	73.03 118	68.06 130	73.54 142	59.36 167

tpmp4_e23		55.21 178	55.01 194	55.44 140	61.24 135	53.77 166	69.57 134	43.81 105	55.88 182	51.16 128	60.15 186	45.66 223	44.99 149	59.13 198	53.13 199	61.88 190	57.35 176

FMVSNet3		54.77 179	60.73 179	47.81 188	54.29 185	56.88 146	55.89 194	41.75 133	49.06 209	32.37 191	61.81 180	67.02 185	33.67 198	62.88 185	61.96 180	68.88 179	65.53 145

thres100view900		53.88 180	59.19 182	47.68 189	58.57 159	52.74 172	54.45 199	38.07 174	53.23 187	31.01 200	56.41 200	66.40 189	32.80 201	65.03 178	64.43 168	71.18 165	56.10 183

CR-MVSNet		53.82 181	55.40 192	51.98 150	51.57 192	50.23 183	45.00 224	44.97 96	46.90 216	52.60 122	67.91 144	46.99 220	48.37 131	59.15 196	59.53 184	69.38 177	57.07 178

conf0.01		53.73 182	57.58 186	49.24 176	58.35 164	56.17 153	58.01 181	40.65 153	53.23 187	30.80 205	51.96 213	43.35 228	34.18 192	72.49 128	68.06 130	73.43 145	57.80 175

test20.03		53.49 183	60.95 176	44.78 198	64.73 100	47.25 192	61.58 164	43.30 113	65.86 138	22.85 222	66.87 152	79.85 141	22.99 216	62.38 189	56.95 189	53.25 205	47.46 207

MVSTER		53.08 184	56.39 189	49.21 178	47.19 204	51.08 180	60.14 170	31.74 206	40.63 227	38.97 170	55.78 203	46.74 221	42.47 158	67.29 173	62.99 176	74.73 135	70.23 117

CHOSEN 1792x2688		52.99 185	56.91 188	48.42 184	47.32 203	50.10 186	64.18 155	33.85 194	45.46 221	36.95 173	55.20 206	66.49 188	51.20 118	59.28 194	59.81 183	57.01 200	61.99 158

conf0.002		52.78 186	55.83 190	49.22 177	58.28 166	56.09 155	58.01 181	40.64 154	53.23 187	30.79 206	50.10 216	36.15 233	34.18 192	72.40 132	65.72 162	73.41 146	57.11 177

CostFormer		52.59 187	55.14 193	49.61 170	52.72 187	50.40 182	66.28 144	33.78 195	52.85 193	43.43 160	66.30 154	51.37 212	41.78 161	54.92 211	51.18 204	59.68 193	58.98 172

testgi		51.94 188	61.37 174	40.94 205	58.38 163	47.03 194	65.88 146	30.49 211	70.87 101	22.64 223	57.53 199	87.59 98	18.30 222	63.01 184	54.32 196	49.93 212	49.27 200

tfpn_ndepth		51.52 189	57.21 187	44.88 196	54.05 186	52.14 178	53.58 204	37.07 181	55.55 183	24.73 217	47.12 222	56.92 208	28.92 207	69.22 169	64.80 165	70.94 168	54.74 188

tpm cat1		50.98 190	51.28 204	50.62 164	55.74 175	49.92 187	63.13 159	38.12 173	52.38 195	47.61 142	60.11 187	44.51 225	44.86 151	51.31 221	47.49 214	54.25 204	53.24 191

RPMNet		50.92 191	50.32 207	51.62 155	50.25 196	50.23 183	59.16 175	46.70 84	46.90 216	42.39 163	48.97 219	37.23 230	41.78 161	57.30 207	56.18 191	69.44 176	55.43 185

pmmvs5		50.64 192	58.01 183	42.05 202	47.01 206	43.67 201	49.27 217	29.43 212	50.77 201	33.83 183	68.69 139	76.16 158	27.82 209	57.53 206	57.07 188	64.95 186	52.18 193

PatchT		50.55 193	53.55 200	47.05 193	37.59 226	42.26 205	50.55 214	37.56 178	46.37 218	52.60 122	66.91 150	43.54 227	48.37 131	59.15 196	59.53 184	55.62 202	57.07 178

Anonymous20231206		50.28 194	57.94 184	41.35 204	55.45 178	43.65 202	58.06 180	34.12 193	62.02 158	24.25 220	59.33 190	79.80 142	24.49 215	59.55 192	54.28 197	51.74 208	46.94 209

thresconf0.02		49.98 195	53.83 198	45.48 195	56.47 169	49.38 189	52.01 209	36.49 185	63.51 149	28.04 210	49.82 217	36.72 232	32.63 202	64.84 179	60.66 181	67.22 182	51.91 195

dps		49.71 196	51.97 202	47.07 192	52.37 189	47.00 195	53.02 207	40.52 157	44.91 222	41.23 168	64.55 166	44.27 226	40.12 165	57.71 205	51.97 202	55.14 203	53.41 190

MDTV_nov1_ep13		49.60 197	51.57 203	47.31 190	46.28 208	44.61 199	59.82 172	30.96 207	48.80 213	50.20 133	59.26 192	52.38 211	38.56 166	56.20 209	49.70 209	58.04 197	50.01 198

LP		49.44 198	55.77 191	42.05 202	38.31 224	42.61 204	51.74 210	36.31 187	58.35 167	40.36 169	68.52 141	60.77 203	37.08 171	58.27 203	51.76 203	48.51 213	50.13 197

PatchmatchNet		48.67 199	50.10 208	46.99 194	48.29 200	41.00 206	55.54 195	38.94 163	51.38 197	45.15 157	63.22 171	48.45 215	42.83 156	53.80 217	48.50 212	51.19 211	44.37 211
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DWT-MVSNet_training		48.57 200	47.93 216	49.31 175	51.79 191	48.05 191	61.84 163	34.33 192	41.94 225	43.42 161	50.35 214	34.74 235	47.30 138	52.62 218	52.08 200	57.20 199	55.74 184

new-patchmatchnet		47.33 201	60.49 180	31.99 223	55.69 176	33.86 223	36.84 233	33.31 198	72.36 86	14.33 234	80.09 89	92.14 35	13.27 230	63.54 183	40.09 224	38.51 226	41.32 218

tpm		46.67 202	49.20 213	43.72 199	49.60 198	36.60 219	53.93 202	26.84 214	52.70 194	58.05 105	69.04 138	47.96 216	30.06 206	48.33 225	42.76 219	43.88 220	47.01 208

pmmvs3		46.64 203	54.13 197	37.90 212	31.23 232	40.68 207	49.83 216	15.34 230	46.31 219	36.34 175	53.15 212	74.40 165	36.36 177	58.43 201	56.64 190	58.32 196	49.29 199

TAMVS		46.64 203	53.62 199	38.49 210	49.56 199	36.87 216	53.16 206	25.76 216	56.33 181	22.55 225	60.72 183	61.80 200	27.12 210	59.50 193	58.33 186	52.79 206	41.82 217

test-LLR		46.01 205	45.06 224	47.11 191	59.39 153	36.72 217	51.28 211	40.95 146	36.41 232	34.45 181	46.14 224	47.02 218	38.00 167	51.78 219	48.53 210	58.60 194	48.84 202

MIMVSNet		45.83 206	53.46 201	36.94 213	45.38 213	39.50 209	52.20 208	30.68 208	57.09 177	24.53 219	55.22 205	71.54 169	21.74 218	55.81 210	51.08 205	47.11 216	43.96 212

test0.0.03 1		45.40 207	49.55 211	40.57 207	59.39 153	44.36 200	53.37 205	40.95 146	47.14 215	19.23 228	45.49 226	60.24 204	19.24 220	54.82 212	51.98 201	51.21 210	42.82 214

PMMVS		45.37 208	49.29 212	40.79 206	27.75 233	35.07 221	50.88 213	19.88 225	39.27 229	35.78 176	50.11 215	61.29 201	42.04 159	54.13 216	55.95 192	68.43 180	49.19 201

test1235678		44.92 209	54.19 195	34.11 218	41.53 216	37.95 213	54.27 200	23.09 220	53.64 185	22.14 226	53.92 208	84.05 129	16.41 225	60.66 190	50.30 207	47.20 214	38.84 221

testmv		44.91 210	54.17 196	34.11 218	41.52 217	37.93 214	54.27 200	23.09 220	53.61 186	22.14 226	53.89 209	84.00 130	16.41 225	60.64 191	50.29 208	47.20 214	38.83 222

MVS-HIRNet		44.56 211	45.52 222	43.44 200	40.98 218	31.03 228	39.52 232	36.96 182	42.80 224	44.37 158	53.80 210	60.04 205	41.85 160	47.97 227	41.08 222	56.99 201	41.95 216

test-mter		44.18 212	47.60 217	40.18 208	33.20 228	39.03 210	55.28 196	13.91 232	39.07 230	36.63 174	48.09 221	49.52 213	41.12 163	54.55 213	50.91 206	60.97 192	52.03 194

EMVS		43.85 213	49.91 209	36.77 215	45.46 212	32.70 225	44.09 226	25.33 217	57.88 172	26.62 213	58.99 194	61.14 202	42.77 157	70.26 163	38.52 229	36.38 228	29.87 230

E-PMN		43.83 214	49.81 210	36.84 214	46.09 210	31.86 227	42.77 228	25.85 215	57.76 174	25.53 214	55.50 204	62.47 197	43.77 153	70.78 160	39.51 226	37.04 227	30.79 229

tpmrst		43.31 215	46.14 220	40.02 209	47.05 205	36.48 220	48.01 222	32.17 205	49.50 207	37.26 172	63.66 170	47.04 217	31.98 205	42.00 232	40.55 223	43.64 221	43.75 213

TESTMET0.1,1		41.79 216	45.06 224	37.97 211	31.32 231	36.72 217	51.28 211	14.17 231	36.41 232	34.45 181	46.14 224	47.02 218	38.00 167	51.78 219	48.53 210	58.60 194	48.84 202

testus		41.61 217	50.54 206	31.20 225	38.11 225	38.92 211	49.10 218	17.60 227	48.25 214	25.05 215	41.45 228	79.34 143	13.18 231	58.28 202	47.10 215	44.17 219	40.41 219

testpf		41.44 218	38.52 231	44.85 197	46.17 209	38.68 212	60.29 168	43.31 112	24.28 234	35.09 177	39.52 230	34.84 234	32.39 203	43.79 231	39.89 225	51.88 207	48.65 204

ADS-MVSNet		40.61 219	46.31 218	33.96 220	40.70 219	30.42 229	40.42 230	33.44 196	58.01 171	30.87 204	63.05 172	54.48 209	22.67 217	44.35 230	39.23 228	35.64 229	34.64 225

CHOSEN 280x420		40.24 220	44.14 228	35.69 216	32.36 230	23.58 234	50.30 215	21.21 224	40.94 226	18.84 229	32.75 233	48.65 214	48.13 134	59.16 195	55.31 193	43.28 222	48.62 205

EPMVS		40.11 221	44.96 226	34.44 217	41.55 215	32.65 226	41.74 229	32.39 203	49.89 206	24.83 216	64.44 167	46.38 222	26.57 213	44.75 229	39.47 227	39.59 224	37.16 223

FMVSNet5		39.83 222	45.08 223	33.71 221	39.24 220	39.56 208	48.77 219	23.55 219	39.45 228	24.55 218	33.73 232	44.57 224	20.97 219	58.27 203	54.23 198	45.16 217	45.77 210

1111		39.71 223	44.86 227	33.71 221	50.45 194	28.51 230	55.07 197	34.43 190	62.60 152	17.59 230	62.60 174	28.17 237	14.69 227	54.19 214	41.91 221	30.02 231	36.03 224

test12356		39.53 224	49.18 214	28.26 227	32.93 229	33.64 224	48.68 220	15.96 229	46.26 220	16.21 232	46.46 223	79.07 145	17.13 223	58.60 200	48.30 213	38.67 225	31.96 227

N_pmnet		39.50 225	51.01 205	26.09 229	44.48 214	25.59 233	40.20 231	21.49 223	64.20 146	7.98 237	73.86 122	76.67 157	13.66 229	50.17 223	36.69 231	28.71 232	29.86 231

test2356		35.97 226	39.61 230	31.71 224	38.85 221	34.29 222	45.78 223	22.27 222	38.89 231	22.59 224	37.67 231	37.07 231	16.57 224	50.72 222	45.45 216	44.20 218	33.19 226

MVE	28.01 19	35.86 227	43.56 229	26.88 228	22.33 235	19.75 236	30.85 236	23.88 218	49.90 205	10.48 235	43.64 227	61.87 199	48.99 130	47.26 228	42.15 220	24.76 233	40.37 220
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 228	45.64 221	24.22 230	38.59 222	25.83 232	31.87 235	19.24 226	49.06 209	9.01 236	54.34 207	64.73 193	12.46 232	49.21 224	44.91 217	34.17 230	31.41 228

PMMVS2		34.11 229	48.55 215	17.26 231	25.45 234	20.72 235	35.08 234	16.26 228	58.71 165	4.16 239	59.22 193	78.40 151	3.65 233	57.24 208	38.31 230	18.94 234	27.28 232

GG-mvs-BLEND		31.54 230	46.27 219	14.37 232	0.07 239	48.65 190	42.97 227	0.08 237	44.04 223	1.21 241	39.77 229	57.94 207	0.15 237	48.19 226	42.82 218	41.70 223	42.46 215

.test1245		31.52 231	33.91 232	28.73 226	50.45 194	28.51 230	55.07 197	34.43 190	62.60 152	17.59 230	62.60 174	28.17 237	14.69 227	54.19 214	0.54 234	0.15 238	0.77 235

test123		0.53 232	0.60 234	0.46 234	0.22 237	0.25 239	0.33 241	0.13 236	0.66 237	1.37 240	1.10 236	0.00 242	0.43 235	0.68 235	0.61 233	0.26 237	0.88 234

testmvs		0.47 233	0.69 233	0.21 235	0.17 238	0.17 240	0.35 240	0.16 235	0.66 237	0.18 242	1.05 237	0.99 241	0.27 236	0.62 236	0.54 234	0.15 238	0.77 235

sosnet-low-res		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 242	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

sosnet		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 242	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

ambc			79.96 59		74.57 47	75.48 44	73.75 118		80.32 49	72.34 36	78.46 98	92.41 32	59.05 71	80.24 84	73.95 91	75.41 132	78.85 68

MTAPA										80.26 8		90.53 72

MTMP										82.07 4		91.00 58

Patchmatch-RL test							2.05 239

tmp_tt				7.47 233	8.89 236	3.32 238	4.35 238	1.14 234	15.58 236	15.76 233	8.50 235	5.90 240	2.00 234	20.02 233	21.51 232	12.70 235

XVS					80.47 19	81.29 12	93.33 3			77.45 19		90.19 76				91.52 11

X-MVStestdata					80.47 19	81.29 12	93.33 3			77.45 19		90.19 76				91.52 11

abl_6				65.41 77	69.37 74	74.02 52	82.50 54	47.39 78	66.39 134	56.63 110	60.61 184	82.76 133	53.68 110			82.92 72	78.39 73

mPP-MVS					82.97 2							92.12 36

NP-MVS									71.39 92

Patchmtry						37.73 215	45.00 224	44.97 96		52.60 122

DeepMVS_CX						8.52 237	9.75 237	3.19 233	16.70 235	5.02 238	23.06 234	19.33 239	18.69 221	13.75 234		11.34 236	25.07 233

High-resolution multi-view benchmark