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 47	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 13	75.52 45	82.76 4	90.39 21	54.21 50	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 18
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 29	92.67 6	64.48 1	83.13 26	82.32 3	80.89 84	92.97 28	72.51 9	91.74 6	90.02 6	91.40 17	89.14 8

ACMMPR		83.94 4	87.20 3	80.14 4	81.04 12	81.92 8	92.57 8	63.14 5	84.35 17	79.45 13	83.37 51	92.04 37	72.82 8	90.66 12	88.96 12	91.80 6	89.13 9

MP-MVS		83.50 5	86.11 19	80.45 3	82.58 5	80.60 23	92.68 5	63.48 3	81.43 39	80.21 9	81.95 73	90.76 63	72.86 6	90.14 19	89.30 11	90.92 19	88.59 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		83.17 6	86.75 10	79.01 8	80.11 24	82.01 7	92.29 11	60.35 25	82.20 34	78.32 16	80.59 85	93.14 24	70.67 16	91.30 8	89.36 10	92.30 4	88.62 17
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 25	79.95 5	80.69 16	81.09 15	92.40 10	63.06 6	79.38 58	80.21 9	80.31 87	91.44 47	71.75 12	90.46 15	88.53 15	91.57 9	88.50 20

LGP-MVS_train		82.91 8	86.50 12	78.72 9	78.72 34	81.03 16	89.78 25	61.16 17	80.15 52	80.44 6	84.83 36	94.19 17	70.52 19	90.70 11	87.19 23	91.71 8	87.37 26

ACMM	71.24 7	82.85 9	86.59 11	78.50 10	80.10 25	78.59 31	91.77 12	60.76 23	84.43 15	76.49 25	81.58 79	93.50 19	70.45 20	91.38 7	89.42 9	91.42 16	87.22 28
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
zzz-MVS		82.61 10	85.04 29	79.79 6	82.59 4	73.90 54	92.42 9	62.39 11	84.54 14	80.21 9	79.86 91	90.74 64	70.63 17	90.01 21	89.71 8	90.48 21	86.49 33

SMA-MVS		82.46 11	86.30 14	77.99 12	80.13 23	80.41 24	91.20 15	60.85 22	85.43 9	79.65 12	84.19 40	87.27 105	66.00 36	88.65 29	87.80 19	90.12 22	90.34 4

HFP-MVS		82.37 12	86.28 15	77.81 16	79.94 26	80.96 18	91.13 16	63.30 4	84.04 19	71.81 38	82.39 65	89.59 83	69.16 23	89.08 26	88.83 14	91.49 13	89.10 10

DeepC-MVS	73.80 3	82.34 13	86.87 7	77.06 19	78.62 35	84.34 1	90.30 23	63.54 2	83.10 27	71.30 42	86.91 23	90.54 71	67.12 32	87.81 35	87.05 24	91.46 15	88.37 21
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 14	85.00 31	79.19 7	80.73 15	80.86 21	91.68 13	62.59 9	82.55 31	75.53 29	73.88 124	92.28 34	73.74 5	90.07 20	87.65 20	90.87 20	87.74 24

ACMP	70.35 9	82.17 15	86.45 13	77.18 18	79.33 27	81.00 17	89.27 29	58.63 30	81.35 41	75.46 30	82.97 56	95.08 12	68.90 25	90.49 14	87.43 22	91.48 14	86.84 30
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		82.16 16	85.55 26	78.21 11	80.48 18	79.28 30	92.65 7	61.03 19	80.55 49	77.00 23	81.80 78	90.71 65	68.73 26	90.25 17	87.94 18	89.36 28	88.30 22
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		82.13 17	86.80 8	76.67 20	80.36 21	80.66 22	89.48 26	56.93 33	82.50 32	67.55 67	87.05 19	91.40 50	72.84 7	88.66 28	88.32 16	92.85 2	89.04 11

LTVRE_ROB	75.99 1	82.04 18	87.16 4	76.07 22	63.57 122	70.27 70	86.48 39	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 35	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 19	87.08 5	75.68 24	77.06 41	77.23 37	87.77 37	56.25 39	83.33 25	67.18 78	89.48 10	87.94 95	77.70 1	93.02 2	92.57 2	88.13 37	86.00 36
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_Plus		81.79 20	85.72 23	77.21 17	79.15 32	79.68 28	91.62 14	59.66 26	83.55 22	77.74 19	83.72 47	87.34 103	65.36 37	88.61 30	87.56 21	89.73 27	89.58 6

X-MVS		81.61 21	84.73 33	77.97 13	80.31 22	81.29 12	93.53 2	62.50 10	81.41 40	77.45 20	72.04 134	90.19 76	62.50 52	90.57 13	88.87 13	91.54 10	88.73 14

OPM-MVS		81.44 22	85.68 24	76.49 21	79.27 28	78.21 33	89.84 24	58.67 29	85.25 10	76.26 26	85.28 32	92.88 29	66.03 35	87.20 38	85.40 28	88.86 32	85.58 40

TSAR-MVS + MP.		81.23 23	86.13 17	75.52 25	80.74 14	83.22 3	90.55 17	55.12 45	80.87 45	67.62 66	88.01 13	92.38 33	70.61 18	86.64 40	83.10 43	88.51 33	88.67 15

TSAR-MVS + ACMM		81.20 24	86.92 6	74.52 29	77.60 37	82.29 5	84.41 46	62.95 8	82.99 28	64.03 90	87.71 14	89.17 86	71.98 11	88.19 32	88.10 17	86.18 51	89.95 5

APDe-MVS		81.08 25	86.12 18	75.20 27	79.25 29	80.91 19	90.38 22	57.05 32	85.83 8	66.07 83	87.34 17	91.27 54	69.45 21	85.99 44	82.55 44	88.98 31	88.95 12

APD-MVS		80.60 26	84.63 34	75.91 23	81.22 10	81.48 10	90.49 19	58.81 28	77.54 64	67.49 69	85.90 27	89.82 82	69.43 22	86.08 43	83.80 38	88.01 38	87.77 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		80.44 27	82.57 46	77.96 15	81.99 7	72.76 58	90.48 20	61.31 14	80.85 46	77.90 18	81.93 74	87.01 108	68.20 28	84.15 53	85.27 30	87.85 39	86.00 36

ESAPD		80.30 28	84.42 37	75.49 26	79.20 31	79.76 27	89.40 27	58.51 31	81.15 43	69.56 54	85.14 33	88.71 90	68.92 24	85.26 49	82.30 50	87.35 42	88.64 16

ACMH+	67.97 10	80.15 29	86.16 16	73.14 38	73.82 53	76.41 40	83.59 48	54.82 48	87.35 6	70.86 46	86.98 22	96.27 5	66.50 33	89.17 25	83.39 40	89.26 29	83.56 47

OMC-MVS		79.95 30	85.28 27	73.74 35	72.95 56	80.10 26	87.87 36	48.13 76	84.62 13	79.42 14	80.27 88	92.49 31	64.14 43	87.25 37	85.11 31	89.92 25	87.10 29

HSP-MVS		79.66 31	84.23 38	74.34 31	78.92 33	81.86 9	90.55 17	60.49 24	80.19 51	69.08 57	85.12 34	90.92 61	62.99 49	81.15 74	78.00 67	83.99 61	92.37 1

DeepPCF-MVS	71.57 5	79.49 32	84.05 39	74.17 32	74.14 50	80.88 20	89.33 28	56.24 40	82.41 33	71.58 40	82.27 66	86.47 113	66.47 34	84.80 51	84.16 36	87.26 43	87.34 27

LS3D		79.33 33	84.03 40	73.84 33	75.37 46	78.09 34	83.30 49	52.94 57	84.42 16	76.01 27	84.16 42	87.44 102	65.34 38	86.30 41	82.08 52	90.09 23	85.70 38

3Dnovator+	72.94 4	78.78 34	83.05 43	73.80 34	70.70 69	81.34 11	88.33 33	56.01 41	81.33 42	72.87 36	78.06 102	81.15 139	63.83 45	87.39 36	85.82 26	91.06 18	86.28 35

UA-Net		78.65 35	83.96 41	72.46 40	84.87 1	76.15 41	89.06 30	55.70 42	77.25 65	53.14 123	79.73 93	82.09 137	59.69 67	92.21 5	90.93 3	92.32 3	89.36 7

DeepC-MVS_fast	71.40 6	78.48 36	82.92 44	73.31 37	76.44 43	82.23 6	87.59 38	56.56 36	77.79 62	68.91 59	77.00 108	87.32 104	61.90 54	85.40 46	84.37 33	88.46 34	86.33 34
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 37	86.09 20	69.25 57	76.22 44	72.33 65	85.71 42	59.02 27	86.66 7	51.41 128	92.91 1	96.76 2	53.09 115	90.21 18	85.30 29	90.05 24	78.46 73

ACMH	66.19 11	78.12 38	84.55 35	70.63 48	69.62 75	72.40 64	80.77 66	46.43 88	89.24 4	77.99 17	87.42 16	95.83 9	62.95 50	86.27 42	78.24 66	86.00 54	82.46 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
train_agg		77.83 39	80.47 58	74.77 28	80.92 13	69.60 71	88.87 32	56.32 38	74.03 82	71.03 44	83.67 48	87.68 98	64.75 41	83.70 57	81.85 53	86.71 46	82.73 49

NCCC		77.82 40	80.72 57	74.43 30	79.24 30	75.72 44	88.06 34	56.36 37	79.61 56	73.22 34	67.75 148	87.05 107	63.09 48	85.62 45	84.00 37	86.62 47	85.30 42

CNVR-MVS		77.79 41	81.57 51	73.38 36	78.37 36	75.91 42	87.97 35	55.11 46	79.41 57	70.98 45	74.70 122	86.43 114	61.77 55	85.10 50	83.73 39	86.10 53	85.68 39

WR-MVS_H		77.56 42	85.88 21	67.86 61	80.54 17	74.32 51	83.23 50	61.78 12	83.47 23	47.46 145	91.81 5	95.84 8	50.50 124	90.44 16	84.37 33	83.63 64	80.89 59

RPSCF		77.56 42	84.51 36	69.46 56	65.17 99	74.36 50	79.74 71	47.45 79	84.01 20	72.89 35	77.89 103	90.67 66	65.14 40	88.25 31	89.74 7	86.38 50	86.64 32

PS-CasMVS		77.46 44	85.80 22	67.73 63	81.24 9	72.88 57	80.63 67	61.28 15	84.14 18	50.53 132	92.13 3	96.76 2	50.12 127	91.02 9	84.46 32	82.60 77	79.19 66

DTE-MVSNet		77.28 45	84.87 32	68.42 58	82.94 3	72.70 60	81.60 61	61.78 12	85.03 11	51.40 129	92.11 4	96.00 6	49.42 130	89.73 23	82.52 46	83.39 68	75.98 84

SixPastTwentyTwo		77.24 46	83.65 42	69.78 52	65.14 100	64.85 91	77.44 82	47.74 78	82.76 30	68.52 60	87.65 15	93.31 21	71.68 13	89.49 24	82.41 47	88.14 36	85.05 43

CDPH-MVS		77.22 47	81.05 56	72.75 39	77.29 39	77.46 36	86.36 40	54.02 52	73.00 87	69.75 52	77.78 105	88.90 89	61.31 59	84.09 56	82.54 45	87.79 40	83.57 46

PEN-MVS		77.06 48	85.05 28	67.74 62	82.29 6	72.59 61	80.86 65	61.03 19	84.66 12	50.08 136	92.19 2	96.59 4	49.12 131	89.83 22	82.35 48	83.06 71	77.14 80

CP-MVSNet		77.01 49	85.04 29	67.65 64	81.16 11	72.72 59	80.54 68	61.18 16	82.09 35	50.41 133	90.81 6	95.89 7	50.03 128	90.86 10	84.30 35	82.56 78	78.65 72

CSCG		76.95 50	82.08 48	70.97 44	73.32 55	78.35 32	81.08 64	47.19 81	83.47 23	69.82 51	80.44 86	87.19 106	64.59 42	81.01 77	77.26 73	89.83 26	86.84 30

CNLPA		76.67 51	81.72 49	70.77 47	70.75 67	76.68 39	86.14 41	46.11 90	81.82 37	74.68 31	76.37 111	86.23 116	62.92 51	85.28 48	83.29 41	84.02 60	82.40 51

MSLP-MVS++		76.66 52	82.32 47	70.06 50	70.51 70	80.27 25	79.77 70	55.58 43	77.79 62	63.09 93	67.25 152	89.50 84	71.01 15	88.10 33	85.74 27	80.39 89	87.56 25

TSAR-MVS + COLMAP		75.85 53	81.06 54	69.77 53	71.15 62	76.90 38	82.93 53	52.43 59	79.25 59	70.13 49	82.78 57	87.00 109	60.02 63	80.30 83	79.61 59	81.95 83	81.61 55

HQP-MVS		75.81 54	78.91 65	72.18 41	77.41 38	75.38 46	84.75 43	53.35 54	76.12 69	73.32 33	69.48 139	88.07 93	57.76 78	79.42 87	78.44 63	86.48 48	85.50 41

PLC	64.88 15	75.76 55	80.22 59	70.57 49	70.46 71	77.75 35	82.01 59	48.84 71	80.74 48	70.85 47	71.32 136	84.82 126	63.69 46	84.73 52	82.35 48	87.54 41	79.80 63
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 56	79.24 63	70.86 45	67.63 81	74.09 52	83.17 52	44.75 102	81.82 37	80.83 5	65.61 161	88.04 94	61.58 56	83.21 63	80.12 56	87.17 44	81.82 54
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 57	78.37 66	71.43 42	71.13 63	72.46 63	82.28 58	50.55 64	73.39 85	79.05 15	73.65 126	87.50 101	61.98 53	81.10 75	78.48 62	83.60 65	81.99 52

anonymousdsp		74.76 58	82.59 45	65.63 78	45.61 215	61.13 128	89.06 30	32.58 205	74.11 81	59.55 102	84.06 44	94.12 18	75.24 3	88.94 27	86.95 25	91.74 7	88.81 13

AdaColmap		74.73 59	77.57 71	71.40 43	76.90 42	75.76 43	84.54 45	53.08 56	76.20 68	66.64 82	66.06 159	78.16 155	61.32 58	85.37 47	82.20 51	85.95 55	79.27 65

v7n		74.47 60	81.06 54	66.77 69	66.98 85	67.10 74	76.76 85	45.88 92	81.98 36	67.43 71	88.38 12	95.67 10	61.38 57	80.76 80	73.49 95	82.21 81	80.06 61

PCF-MVS	65.25 14	73.99 61	76.74 76	70.79 46	71.61 61	75.33 47	83.76 47	50.40 65	74.88 73	74.50 32	67.60 149	85.36 123	58.30 74	78.61 90	74.25 90	86.15 52	81.13 58
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v52		73.95 62	81.43 53	65.22 82	54.85 183	63.32 112	78.90 73	38.00 178	80.00 54	68.32 62	87.02 20	94.98 15	68.14 30	84.11 54	75.63 83	83.12 69	84.96 44

V4		73.95 62	81.44 52	65.22 82	54.86 182	63.31 113	78.89 74	38.00 178	80.03 53	68.29 63	87.02 20	95.00 13	68.15 29	84.11 54	75.62 84	83.12 69	84.95 45

MCST-MVS		73.84 64	77.44 72	69.63 55	73.75 54	74.73 49	81.38 63	48.58 72	74.77 74	69.16 56	71.97 135	86.20 117	59.50 69	78.51 91	74.06 91	85.42 56	81.85 53

MVS_0304		73.74 65	77.16 74	69.74 54	74.24 49	73.47 55	84.70 44	49.62 66	62.26 159	67.27 75	75.87 114	87.57 100	57.49 83	81.20 73	79.50 60	85.10 57	80.27 60

TSAR-MVS + GP.		73.42 66	76.31 78	70.05 51	77.15 40	71.13 68	81.59 62	54.11 51	69.84 119	58.65 105	66.20 158	78.77 152	65.29 39	83.65 58	83.14 42	83.54 66	81.47 56

Gipuma		73.40 67	79.27 62	66.55 73	63.64 121	59.35 134	70.28 131	45.92 91	83.79 21	71.78 39	84.04 45	93.07 26	68.69 27	87.90 34	76.76 76	78.98 101	69.96 121
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
casdiffmvs		72.53 68	76.39 77	68.02 59	71.10 64	64.54 95	83.21 51	51.41 63	74.16 80	63.47 92	77.74 106	88.15 92	56.55 91	78.30 94	73.87 94	82.41 79	77.98 75

MVS_111021_HR		72.37 69	76.12 81	68.00 60	68.55 78	64.30 104	82.93 53	48.98 70	74.25 78	65.39 84	73.59 127	84.11 130	59.48 70	82.61 66	78.38 64	82.66 76	75.59 85

TinyColmap		71.85 70	76.11 82	66.87 68	66.07 89	65.34 86	74.35 107	49.30 69	79.93 55	75.93 28	75.66 116	87.74 97	54.72 106	80.66 82	70.42 112	80.85 87	73.02 103

TranMVSNet+NR-MVSNet		71.66 71	79.23 64	62.83 110	72.54 58	65.64 82	74.77 105	55.27 44	75.91 70	45.50 156	89.55 9	94.25 16	45.96 147	82.74 65	77.03 75	82.96 72	69.48 127

MVS_111021_LR		71.60 72	75.21 87	67.38 65	67.42 82	62.44 122	81.73 60	46.24 89	70.89 102	66.80 81	73.19 129	84.98 124	60.09 62	81.94 69	77.77 71	82.00 82	75.29 86

EG-PatchMatch MVS		71.50 73	76.82 75	65.30 80	70.74 68	66.50 78	74.23 109	43.25 117	72.02 90	59.11 103	79.85 92	86.88 111	63.95 44	80.29 84	75.25 87	80.51 88	76.98 81

UniMVSNet (Re)		71.29 74	78.14 67	63.30 100	70.29 72	66.57 77	75.98 90	54.74 49	70.20 112	46.20 154	85.08 35	93.21 22	48.19 135	82.50 67	78.33 65	84.40 58	71.08 118

v748		71.27 75	79.41 61	61.76 114	60.62 145	61.73 125	68.46 140	40.71 155	80.76 47	61.02 98	87.12 18	95.00 13	59.62 68	80.67 81	70.67 110	80.14 92	79.93 62

CLD-MVS		71.24 76	78.12 68	63.20 102	74.03 51	71.60 66	82.82 55	32.91 202	74.23 79	69.32 55	79.65 94	91.54 45	47.02 143	81.22 72	79.01 61	73.09 155	69.63 123
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet		71.07 77	75.09 89	66.39 74	72.57 57	71.53 67	82.38 57	47.10 82	59.81 167	59.81 101	74.97 119	84.37 129	54.25 109	79.89 86	77.64 72	82.25 80	77.40 78

v1192		71.06 78	74.87 92	66.61 71	66.38 87	65.80 81	78.27 76	45.28 95	70.19 113	70.79 48	83.37 51	91.79 40	58.76 73	70.86 161	69.02 118	80.16 91	73.08 101

DU-MVS		71.03 79	77.92 69	62.98 108	70.81 65	65.48 84	73.93 117	56.76 34	69.95 117	46.77 151	85.70 30	93.49 20	46.91 144	83.47 59	77.82 70	82.72 75	69.54 124

v1240		70.94 80	74.52 99	66.76 70	66.54 86	64.40 98	77.76 79	45.29 94	70.05 115	71.45 41	83.36 53	90.96 59	60.37 61	70.50 164	68.68 120	79.14 99	73.68 96

v1921920		70.82 81	74.46 101	66.58 72	66.33 88	64.35 103	77.72 80	45.07 97	70.39 106	71.18 43	83.15 54	90.62 68	59.97 64	70.90 159	68.43 128	79.19 98	73.39 98

UniMVSNet_NR-MVSNet		70.82 81	77.44 72	63.11 103	71.75 59	66.02 80	73.93 117	55.00 47	70.90 101	46.77 151	86.68 24	91.54 45	46.91 144	81.07 76	76.32 80	84.28 59	69.54 124

Anonymous20231211		70.76 83	81.58 50	58.13 131	71.63 60	60.40 133	70.12 132	52.15 60	92.79 1	36.20 177	88.89 11	98.03 1	40.61 166	80.86 79	75.96 82	78.08 118	74.11 90

PVSNet_Blended_VisFu		70.70 84	73.62 108	67.28 67	63.53 124	72.96 56	77.97 77	52.10 61	63.65 150	62.66 95	71.14 137	73.46 169	63.55 47	79.35 89	75.34 86	83.90 62	79.43 64

v144192		70.68 85	74.40 103	66.34 75	65.94 91	64.38 100	77.63 81	45.18 96	69.97 116	70.11 50	82.70 60	90.77 62	59.84 66	71.43 154	68.46 124	79.31 97	73.08 101

v13		70.58 86	75.49 85	64.87 86	64.66 104	64.58 94	76.18 88	43.69 111	72.34 89	67.65 65	84.36 39	92.01 38	58.05 75	73.57 115	67.06 146	78.96 102	74.48 89

FPMVS		70.46 87	74.89 91	65.28 81	69.09 77	61.42 126	77.07 84	46.92 85	76.73 67	53.53 120	67.33 150	75.07 164	67.23 31	83.41 61	81.54 54	77.86 123	78.73 70

v1144		70.45 88	74.50 100	65.73 77	65.74 93	64.88 90	77.33 83	44.16 105	70.59 105	69.63 53	83.15 54	91.42 49	57.79 77	71.29 158	68.53 123	79.72 94	71.63 116

v12		70.39 89	75.25 86	64.73 87	64.60 106	64.47 96	76.00 89	43.55 113	71.92 91	67.51 68	84.15 43	91.88 39	57.83 76	73.32 116	67.00 147	78.87 103	74.02 93

v10		70.25 90	74.59 97	65.19 84	65.32 97	66.46 79	76.60 86	44.84 100	67.38 128	67.21 77	82.75 59	90.56 70	57.70 79	71.69 148	68.63 121	79.44 95	74.67 88

V9		70.20 91	75.02 90	64.58 89	64.49 107	64.36 101	75.80 94	43.40 114	71.53 92	67.35 74	83.95 46	91.73 42	57.63 81	73.04 119	66.96 148	78.79 105	73.61 97

Effi-MVS+-dtu		70.10 92	73.76 107	65.82 76	70.23 73	74.92 48	79.47 72	44.49 104	56.98 182	54.34 116	64.26 171	84.78 127	59.97 64	80.96 78	80.38 55	86.44 49	74.05 92

v11		70.10 92	74.82 93	64.58 89	64.83 102	64.39 99	75.89 91	43.18 119	71.34 95	67.75 64	84.19 40	91.75 41	57.23 85	71.46 153	66.85 151	78.60 108	73.78 94

MAR-MVS		70.00 94	72.28 125	67.34 66	69.89 74	72.57 62	80.09 69	49.49 68	60.28 165	69.03 58	59.29 194	80.79 141	54.68 107	78.39 93	76.00 81	80.87 86	78.67 71
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 95	74.77 95	64.41 92	64.39 108	64.25 105	75.59 96	43.25 117	71.12 99	67.14 79	83.65 49	91.58 44	57.40 84	72.75 127	66.90 150	78.70 106	73.15 100

Vis-MVSNet		69.95 96	77.69 70	60.91 117	60.67 143	66.71 75	77.94 78	48.58 72	69.10 121	45.78 155	80.21 89	83.58 134	53.41 114	82.92 64	80.11 57	79.08 100	81.21 57
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v7		69.81 97	73.94 105	65.00 85	65.33 95	65.07 87	76.60 86	43.66 112	67.36 129	67.25 76	82.76 58	90.57 69	57.70 79	71.69 148	68.63 121	79.44 95	71.52 117

v15		69.80 98	74.53 98	64.27 94	64.30 109	64.15 106	75.40 98	43.12 120	70.71 104	66.98 80	83.41 50	91.43 48	57.21 86	72.48 132	66.84 152	78.62 107	72.72 105

EPP-MVSNet		69.51 99	76.17 79	61.74 115	68.38 80	66.60 76	71.77 123	46.98 83	73.60 84	41.79 167	82.06 72	69.65 182	52.51 118	83.41 61	79.94 58	89.02 30	77.94 76

3Dnovator	65.69 13	69.43 100	75.74 84	62.06 113	60.78 142	70.50 69	75.85 93	39.57 165	74.44 76	57.41 108	75.91 112	77.73 157	55.34 102	76.86 97	75.61 85	83.44 67	79.14 67

Effi-MVS+		69.04 101	73.01 118	64.40 93	67.20 83	64.83 92	74.87 104	43.97 107	63.33 153	60.90 99	73.06 130	85.79 120	55.61 100	73.58 114	76.41 79	83.84 63	74.09 91

v2v482		69.01 102	73.39 110	63.89 96	63.86 114	62.99 117	75.26 99	42.05 131	70.22 111	68.46 61	82.64 61	91.61 43	55.38 101	70.89 160	66.93 149	78.30 113	68.48 137

v1		68.98 103	73.38 111	63.84 97	64.12 111	62.97 118	74.95 103	41.52 140	70.28 109	67.47 70	82.49 62	91.37 51	56.59 88	71.43 154	66.51 159	78.41 110	68.62 133

MSDG		68.98 103	73.31 114	63.92 95	67.08 84	68.27 72	75.41 97	40.77 151	67.61 127	64.89 85	75.75 115	78.96 149	53.70 111	76.72 99	73.95 92	81.71 85	71.93 113

v1141		68.97 105	73.38 111	63.83 98	64.11 112	62.97 118	74.96 100	41.52 140	70.29 107	67.36 73	82.47 63	91.37 51	56.59 88	71.43 154	66.49 161	78.41 110	68.61 135

divwei89l23v2f112		68.97 105	73.38 111	63.83 98	64.11 112	62.97 118	74.96 100	41.52 140	70.29 107	67.39 72	82.47 63	91.37 51	56.59 88	71.42 157	66.50 160	78.40 112	68.62 133

v8		68.77 107	73.50 109	63.26 101	63.74 116	64.47 96	74.22 113	42.07 129	67.30 130	64.89 85	82.08 71	90.23 73	56.50 93	71.85 147	66.57 156	78.14 114	72.02 111

NR-MVSNet		68.66 108	76.15 80	59.93 120	65.49 94	65.48 84	74.42 106	56.76 34	69.95 117	45.38 157	85.70 30	91.13 55	34.68 186	74.52 106	76.75 77	82.83 74	69.49 126

v17		68.55 109	73.23 115	63.08 104	63.67 120	63.84 107	74.05 115	42.28 126	66.34 137	63.93 91	81.91 75	89.83 81	56.50 93	71.97 141	66.55 157	78.08 118	72.18 109

USDC		68.53 110	71.82 129	64.68 88	63.53 124	61.87 124	70.12 132	46.98 83	77.89 61	76.58 24	68.55 143	86.88 111	50.50 124	73.73 111	65.62 165	80.39 89	68.21 139

v16		68.33 111	73.03 117	62.86 109	63.57 122	63.83 108	73.98 116	42.30 125	65.58 144	62.94 94	81.82 76	89.37 85	56.36 97	71.91 142	66.52 158	77.99 121	72.17 110

v1neww		68.32 112	72.82 119	63.07 105	63.73 117	63.12 114	74.23 109	40.99 146	67.21 131	64.83 88	82.09 69	90.20 74	56.49 95	71.86 144	66.61 153	78.14 114	68.65 131

v7new		68.32 112	72.82 119	63.07 105	63.73 117	63.12 114	74.23 109	40.99 146	67.21 131	64.83 88	82.09 69	90.20 74	56.49 95	71.86 144	66.61 153	78.14 114	68.65 131

v6		68.32 112	72.82 119	63.07 105	63.73 117	63.11 116	74.23 109	40.99 146	67.21 131	64.86 87	82.11 68	90.19 76	56.51 92	71.86 144	66.61 153	78.14 114	68.66 130

IS_MVSNet		68.20 115	74.41 102	60.96 116	68.55 78	64.36 101	71.47 125	48.33 74	70.11 114	43.30 164	80.90 83	74.54 167	47.19 142	81.25 71	77.97 69	86.94 45	71.76 114

Baseline_NR-MVSNet		68.15 116	75.12 88	60.02 119	70.81 65	55.67 162	75.88 92	53.40 53	71.25 96	43.96 161	85.88 28	92.68 30	45.76 148	83.47 59	68.34 129	70.34 176	68.58 136

v18		67.99 117	72.63 123	62.57 111	63.32 127	63.64 110	73.58 122	42.07 129	64.75 147	62.64 96	81.36 80	89.01 88	56.02 98	71.57 150	66.41 162	77.80 124	71.69 115

Fast-Effi-MVS+		67.71 118	72.54 124	62.07 112	63.83 115	63.68 109	75.74 95	39.94 162	60.89 164	54.29 117	73.00 131	86.19 118	56.85 87	78.46 92	73.23 96	81.74 84	72.36 107

EPNet		66.87 119	68.89 140	64.53 91	73.97 52	61.13 128	78.46 75	61.03 19	56.78 183	53.41 121	66.91 153	70.91 174	43.49 156	76.08 103	76.68 78	76.81 126	73.73 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		66.37 120	74.37 104	57.04 134	65.89 92	65.06 88	62.58 164	42.55 121	76.82 66	46.87 150	67.33 150	86.38 115	45.49 150	76.77 98	71.85 102	78.87 103	76.35 82

QAPM		66.36 121	72.76 122	58.90 124	59.57 151	65.01 89	64.05 160	41.17 145	73.09 86	56.82 110	69.42 140	77.78 156	55.07 104	73.00 123	72.07 101	76.71 127	78.96 68

V42		65.79 122	72.11 127	58.42 127	51.89 194	58.69 136	73.80 119	34.50 192	65.40 145	57.10 109	79.54 97	89.09 87	57.51 82	71.98 140	67.83 139	75.70 132	72.26 108

IterMVS-LS		65.76 123	70.85 134	59.81 122	65.33 95	57.78 140	64.63 157	48.02 77	65.65 142	51.05 131	81.31 81	77.47 158	54.94 105	69.46 171	69.36 115	74.90 136	74.95 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 124	71.25 133	59.14 123	58.92 160	54.88 168	73.66 121	38.55 174	66.12 139	49.91 137	69.87 138	86.97 110	60.61 60	76.30 101	74.75 88	73.19 153	69.83 122

UGNet		65.61 125	74.79 94	54.91 143	54.54 186	68.20 73	70.97 128	48.21 75	67.14 135	41.67 168	74.15 123	80.65 142	36.10 181	79.39 88	77.99 68	77.95 122	76.01 83
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		65.54 126	71.79 130	58.24 129	59.68 150	65.55 83	70.99 126	38.69 173	62.29 158	49.27 140	75.03 118	81.42 138	50.93 121	73.71 113	71.35 103	79.90 93	73.20 99
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		65.36 127	70.09 138	59.85 121	63.05 129	53.61 172	74.29 108	46.45 87	68.14 125	51.45 127	78.83 99	85.78 121	49.87 129	70.44 165	70.45 111	74.00 141	63.38 155

v148		64.92 128	70.58 136	58.32 128	59.89 148	57.09 147	66.04 148	35.27 191	69.11 120	60.66 100	79.57 96	90.93 60	53.91 110	69.81 170	62.22 181	74.14 139	65.31 148

FC-MVSNet-train		64.87 129	74.76 96	53.33 146	65.24 98	58.05 139	69.69 135	41.92 135	70.99 100	32.62 190	85.75 29	88.23 91	32.10 207	77.61 96	74.41 89	78.43 109	68.25 138

pmmvs6		64.78 130	75.82 83	51.89 153	62.41 131	57.13 146	60.24 172	45.59 93	82.90 29	34.69 182	84.83 36	93.18 23	36.22 180	76.43 100	71.13 107	72.21 160	65.12 149

OpenMVS	60.79 16	64.42 131	69.72 139	58.23 130	61.63 136	62.17 123	64.11 159	37.54 182	67.17 134	55.71 115	65.89 160	74.89 165	52.67 117	72.20 138	68.29 131	77.73 125	77.39 79

no-one		64.33 132	73.23 115	53.94 145	38.32 227	50.78 185	56.78 195	27.44 216	61.95 162	56.77 111	64.60 168	93.12 25	71.12 14	81.91 70	77.19 74	73.20 152	83.04 48

TransMVSNet (Re)		63.49 133	73.86 106	51.39 159	64.26 110	56.07 159	61.17 169	42.23 127	78.81 60	34.80 180	85.94 26	90.63 67	34.35 193	72.73 129	67.98 137	71.50 163	64.84 150

DI_MVS_plusplus_trai		63.43 134	67.54 144	58.63 125	62.34 132	58.06 138	65.75 152	42.15 128	63.05 154	53.28 122	75.88 113	75.92 162	50.18 126	68.04 175	64.20 172	78.07 120	67.65 140

Fast-Effi-MVS+-dtu		63.22 135	65.55 150	60.49 118	61.24 138	64.70 93	74.15 114	53.24 55	51.46 199	49.67 138	58.03 200	78.42 153	48.05 137	72.03 139	71.14 106	76.60 130	63.09 156

MVS_Test		62.58 136	67.46 145	56.89 136	59.52 154	55.90 160	64.94 155	38.83 170	57.08 181	56.55 113	76.53 109	84.49 128	47.45 138	66.95 177	62.01 182	74.04 140	69.27 128

MDA-MVSNet-bldmvs		62.46 137	72.13 126	51.19 161	34.32 231	56.10 157	68.65 139	38.85 167	69.05 122	49.50 139	78.17 101	85.43 122	51.32 119	86.67 39	67.40 144	64.46 190	62.08 159

pm-mvs1		61.97 138	72.01 128	50.25 168	60.64 144	55.23 165	58.67 180	42.44 123	74.40 77	33.63 186	81.03 82	89.86 80	34.87 185	72.93 126	67.95 138	71.28 164	62.65 158

conf0.05thres1000		61.96 139	70.38 137	52.13 151	63.31 128	58.12 137	62.09 165	42.45 122	75.50 71	33.07 188	77.89 103	69.72 181	37.32 172	77.88 95	70.72 109	74.55 138	62.82 157

FMVSNet1		61.92 140	71.36 131	50.90 164	57.67 170	59.29 135	59.48 176	44.14 106	70.24 110	34.72 181	75.45 117	84.94 125	36.75 176	72.33 135	68.45 125	72.66 157	68.83 129

PVSNet_BlendedMVS		61.75 141	65.07 155	57.87 132	56.27 173	60.99 130	65.81 150	43.75 109	51.27 202	54.08 118	62.12 181	78.84 150	50.67 122	71.49 151	63.91 174	76.64 128	66.86 142

PVSNet_Blended		61.75 141	65.07 155	57.87 132	56.27 173	60.99 130	65.81 150	43.75 109	51.27 202	54.08 118	62.12 181	78.84 150	50.67 122	71.49 151	63.91 174	76.64 128	66.86 142

tfpnnormal		61.41 143	71.33 132	49.83 169	61.73 135	54.90 167	58.52 181	41.24 143	75.20 72	32.00 198	82.13 67	87.87 96	35.63 184	72.75 127	66.30 163	69.87 177	60.14 164

IB-MVS	57.02 17	61.37 144	65.39 152	56.69 137	56.65 171	60.85 132	70.70 129	37.90 180	49.37 211	45.37 158	48.75 223	79.14 147	53.55 113	76.26 102	70.85 108	75.97 131	72.50 106
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		61.22 145	67.07 146	54.40 144	59.89 148	63.62 111	70.98 127	36.77 186	50.49 205	47.15 146	62.45 179	80.81 140	37.90 171	71.87 143	70.09 113	73.69 142	70.19 120

pmmvs4		61.12 146	64.61 158	57.04 134	60.88 141	52.15 181	70.59 130	44.82 101	61.35 163	46.91 149	72.08 133	73.27 170	46.79 146	65.06 180	67.76 140	72.28 158	60.58 163

Vis-MVSNet (Re-imp)		60.99 147	67.78 143	53.06 148	64.66 104	53.49 173	67.40 143	49.52 67	68.55 123	28.00 214	79.53 98	71.41 173	33.08 202	75.30 105	71.28 105	75.69 133	54.91 190

PatchMatch-RL		60.96 148	63.00 172	58.57 126	59.16 159	52.18 180	67.38 144	41.99 132	57.85 176	48.16 141	53.55 214	69.77 180	59.47 71	73.73 111	72.49 100	75.27 135	61.44 161

GA-MVS		60.73 149	64.24 162	56.64 138	59.38 158	57.45 144	65.07 153	36.65 187	57.39 179	58.17 106	73.43 128	69.10 185	47.38 139	64.47 184	63.63 176	73.19 153	64.22 152

CVMVSNet		60.45 150	63.72 165	56.63 139	54.82 184	53.75 170	68.41 141	41.95 134	55.07 187	48.03 142	58.08 199	68.67 186	55.09 103	69.14 173	68.34 129	71.51 162	72.97 104

FC-MVSNet-test		60.28 151	70.83 135	47.96 189	54.69 185	47.12 197	68.06 142	41.68 139	71.42 93	23.73 224	84.70 38	77.41 159	28.92 210	82.33 68	73.08 97	70.68 171	59.77 166

EU-MVSNet		59.77 152	66.07 148	52.42 150	47.81 206	51.86 183	62.98 163	32.28 207	62.08 160	47.10 147	59.94 191	83.42 135	53.08 116	70.06 169	63.19 177	71.26 166	71.96 112

Anonymous20240521		59.71 153	68.87 141	49.02 181	65.03 101	56.32 153	55.33 199	44.72 103	66.10 140	28.99 212	79.63 95	85.99 119	32.30 206	70.80 162	65.01 167	67.78 184	58.27 175

diffmvs		59.30 154	64.79 157	52.90 149	54.48 187	50.17 189	64.98 154	36.44 189	60.16 166	50.33 134	76.51 110	74.56 166	44.99 151	62.52 190	62.37 180	66.18 187	67.22 141

IterMVS		59.24 155	64.45 159	53.16 147	50.98 197	61.29 127	66.51 146	32.85 203	58.17 172	46.31 153	72.58 132	70.23 176	54.26 108	64.81 183	60.24 185	68.04 183	63.81 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view800		59.22 156	66.23 147	51.03 163	61.99 134	56.71 149	60.53 170	41.20 144	66.26 138	32.46 192	66.68 156	69.93 177	36.77 175	74.52 106	70.00 114	73.24 151	59.56 168

HyFIR lowres test		59.15 157	62.28 174	55.49 141	52.42 192	62.59 121	71.76 124	39.74 163	50.25 207	41.92 166	62.88 176	69.16 184	55.85 99	62.77 189	67.18 145	71.27 165	61.11 162

thres600view7		58.87 158	65.91 149	50.66 165	61.27 137	56.32 153	59.88 174	40.63 158	64.88 146	32.10 197	64.82 166	69.83 179	36.72 177	72.99 124	72.55 99	73.34 149	59.97 165

view600		58.47 159	65.42 151	50.36 167	61.04 140	55.84 161	59.33 177	40.34 161	64.46 148	32.31 196	64.78 167	69.85 178	36.46 178	72.46 133	71.31 104	72.68 156	59.26 172

CMPMVS	45.32 18	58.10 160	65.24 154	49.76 170	47.88 205	46.86 200	48.16 225	32.82 204	58.06 173	61.35 97	59.64 192	80.00 143	47.27 141	70.15 167	64.10 173	61.08 194	77.85 77
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 161	63.54 167	51.74 155	50.13 201	46.56 201	66.95 145	33.41 200	63.52 151	58.77 104	74.84 120	84.10 131	43.12 157	65.95 179	54.69 198	58.04 200	55.13 189

CDS-MVSNet		57.90 162	63.57 166	51.28 160	62.30 133	53.17 174	64.70 156	51.61 62	57.41 178	32.75 189	63.73 172	70.53 175	27.12 213	72.49 130	73.02 98	69.22 180	54.68 192
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet2		57.80 163	65.39 152	48.94 182	55.88 175	57.61 141	57.26 192	42.37 124	58.21 171	33.19 187	68.36 145	75.55 163	34.58 187	66.91 178	64.55 170	70.38 173	66.56 144

tfpn		57.74 164	63.03 171	51.58 158	62.87 130	57.28 145	61.53 168	41.99 132	67.67 126	32.52 191	68.13 146	43.08 232	36.94 174	76.07 104	69.31 116	73.62 143	59.68 167

thres400		57.25 165	63.73 164	49.70 171	60.19 147	54.95 166	58.16 182	39.60 164	62.42 157	31.98 200	62.33 180	69.20 183	35.96 182	70.07 168	68.03 136	72.28 158	59.12 173

tfpn_n400		57.07 166	64.44 160	48.48 185	59.55 152	52.25 178	57.99 189	38.85 167	71.25 96	29.07 209	65.20 163	63.07 197	34.41 190	73.99 108	67.52 142	70.99 168	57.83 176

tfpnconf		57.07 166	64.44 160	48.48 185	59.55 152	52.25 178	57.99 189	38.85 167	71.25 96	29.07 209	65.20 163	63.07 197	34.41 190	73.99 108	67.52 142	70.99 168	57.83 176

gm-plane-assit		56.76 168	57.64 188	55.73 140	66.01 90	55.45 164	74.96 100	30.54 212	73.71 83	56.04 114	81.81 77	30.91 239	43.83 154	58.77 202	54.71 197	63.02 192	48.13 209

MIMVSNet1		56.72 169	68.69 142	42.76 204	46.70 211	42.81 207	69.13 137	30.52 213	81.01 44	32.00 198	74.82 121	91.10 56	26.83 215	73.98 110	64.72 169	51.40 212	52.38 195

tfpnview11		56.69 170	63.86 163	48.33 188	59.46 155	52.35 177	57.85 191	38.80 171	68.21 124	29.07 209	65.20 163	63.07 197	34.36 192	73.21 117	68.72 119	70.44 172	56.28 185

EPNet_dtu		56.63 171	60.77 181	51.80 154	55.47 180	44.63 202	69.83 134	38.74 172	50.27 206	47.64 143	58.01 201	72.27 171	33.71 199	68.60 174	67.72 141	65.39 188	63.86 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		56.54 172	63.26 168	48.70 183	55.88 175	57.61 141	57.26 192	41.75 136	49.06 212	32.37 193	61.81 183	67.02 188	34.58 187	72.33 135	68.45 125	70.38 173	66.56 144

test1		56.54 172	63.26 168	48.70 183	55.88 175	57.61 141	57.26 192	41.75 136	49.06 212	32.37 193	61.81 183	67.02 188	34.58 187	72.33 135	68.45 125	70.38 173	66.56 144

gg-mvs-nofinetune		56.45 174	61.04 178	51.10 162	63.42 126	49.40 192	53.71 207	52.52 58	74.77 74	46.93 148	77.31 107	53.88 213	26.42 217	62.51 191	57.81 190	63.60 191	51.57 199

thres200		56.35 175	62.36 173	49.34 173	58.87 161	56.32 153	55.91 196	40.63 158	58.51 169	31.34 201	58.81 198	67.31 187	35.96 182	72.99 124	65.51 166	73.34 149	57.07 181

MS-PatchMatch		56.31 176	60.22 184	51.73 156	60.53 146	55.53 163	63.41 161	37.18 183	51.34 201	37.44 172	60.53 188	62.19 201	45.52 149	64.25 185	63.17 178	66.33 186	64.56 151

tfpn1000		56.13 177	63.18 170	47.91 190	58.34 168	53.03 175	58.87 179	38.14 175	65.64 143	27.09 215	65.41 162	59.49 209	33.41 201	73.14 118	69.08 117	71.63 161	56.46 184

conf200view11		56.07 178	61.85 175	49.32 175	58.57 162	56.49 150	58.01 184	40.73 152	53.23 190	30.91 204	56.41 203	66.40 192	34.18 194	73.03 120	68.06 132	73.54 144	59.36 169

tfpn200view9		56.07 178	61.85 175	49.34 173	58.57 162	56.48 152	58.01 184	40.72 154	53.23 190	31.01 202	56.41 203	66.40 192	34.18 194	73.02 122	68.06 132	73.53 146	59.35 171

tfpn111		55.56 180	60.91 180	49.32 175	58.57 162	56.49 150	58.01 184	40.73 152	53.23 190	30.91 204	49.82 220	66.40 192	34.18 194	73.03 120	68.06 132	73.54 144	59.36 169

tpmp4_e23		55.21 181	55.01 197	55.44 142	61.24 138	53.77 169	69.57 136	43.81 108	55.88 185	51.16 130	60.15 189	45.66 226	44.99 151	59.13 201	53.13 202	61.88 193	57.35 179

FMVSNet3		54.77 182	60.73 182	47.81 191	54.29 188	56.88 148	55.89 197	41.75 136	49.06 212	32.37 193	61.81 183	67.02 188	33.67 200	62.88 188	61.96 183	68.88 181	65.53 147

thres100view900		53.88 183	59.19 185	47.68 192	58.57 162	52.74 176	54.45 203	38.07 177	53.23 190	31.01 202	56.41 203	66.40 192	32.80 203	65.03 181	64.43 171	71.18 167	56.10 186

CR-MVSNet		53.82 184	55.40 195	51.98 152	51.57 196	50.23 187	45.00 228	44.97 98	46.90 219	52.60 124	67.91 147	46.99 223	48.37 133	59.15 199	59.53 187	69.38 179	57.07 181

conf0.01		53.73 185	57.58 189	49.24 178	58.35 167	56.17 156	58.01 184	40.65 156	53.23 190	30.80 207	51.96 216	43.35 231	34.18 194	72.49 130	68.06 132	73.43 147	57.80 178

test20.03		53.49 186	60.95 179	44.78 201	64.73 103	47.25 196	61.58 167	43.30 116	65.86 141	22.85 225	66.87 155	79.85 144	22.99 219	62.38 192	56.95 192	53.25 208	47.46 210

MVSTER		53.08 187	56.39 192	49.21 180	47.19 208	51.08 184	60.14 173	31.74 209	40.63 230	38.97 171	55.78 206	46.74 224	42.47 160	67.29 176	62.99 179	74.73 137	70.23 119

CHOSEN 1792x2688		52.99 188	56.91 191	48.42 187	47.32 207	50.10 190	64.18 158	33.85 197	45.46 224	36.95 174	55.20 209	66.49 191	51.20 120	59.28 197	59.81 186	57.01 203	61.99 160

conf0.002		52.78 189	55.83 193	49.22 179	58.28 169	56.09 158	58.01 184	40.64 157	53.23 190	30.79 208	50.10 219	36.15 236	34.18 194	72.40 134	65.72 164	73.41 148	57.11 180

CostFormer		52.59 190	55.14 196	49.61 172	52.72 190	50.40 186	66.28 147	33.78 198	52.85 196	43.43 162	66.30 157	51.37 215	41.78 163	54.92 214	51.18 207	59.68 196	58.98 174

testgi		51.94 191	61.37 177	40.94 208	58.38 166	47.03 198	65.88 149	30.49 214	70.87 103	22.64 226	57.53 202	87.59 99	18.30 225	63.01 187	54.32 199	49.93 215	49.27 203

tfpn_ndepth		51.52 192	57.21 190	44.88 199	54.05 189	52.14 182	53.58 208	37.07 184	55.55 186	24.73 220	47.12 225	56.92 211	28.92 210	69.22 172	64.80 168	70.94 170	54.74 191

tpm cat1		50.98 193	51.28 207	50.62 166	55.74 178	49.92 191	63.13 162	38.12 176	52.38 198	47.61 144	60.11 190	44.51 228	44.86 153	51.31 224	47.49 217	54.25 207	53.24 194

RPMNet		50.92 194	50.32 210	51.62 157	50.25 200	50.23 187	59.16 178	46.70 86	46.90 219	42.39 165	48.97 222	37.23 233	41.78 163	57.30 210	56.18 194	69.44 178	55.43 188

pmmvs5		50.64 195	58.01 186	42.05 205	47.01 210	43.67 205	49.27 221	29.43 215	50.77 204	33.83 185	68.69 142	76.16 161	27.82 212	57.53 209	57.07 191	64.95 189	52.18 196

PatchT		50.55 196	53.55 203	47.05 196	37.59 230	42.26 209	50.55 218	37.56 181	46.37 221	52.60 124	66.91 153	43.54 230	48.37 133	59.15 199	59.53 187	55.62 205	57.07 181

Anonymous20231206		50.28 197	57.94 187	41.35 207	55.45 181	43.65 206	58.06 183	34.12 196	62.02 161	24.25 223	59.33 193	79.80 145	24.49 218	59.55 195	54.28 200	51.74 211	46.94 212

thresconf0.02		49.98 198	53.83 201	45.48 198	56.47 172	49.38 193	52.01 213	36.49 188	63.51 152	28.04 213	49.82 220	36.72 235	32.63 204	64.84 182	60.66 184	67.22 185	51.91 198

dps		49.71 199	51.97 205	47.07 195	52.37 193	47.00 199	53.02 211	40.52 160	44.91 225	41.23 169	64.55 169	44.27 229	40.12 167	57.71 208	51.97 205	55.14 206	53.41 193

MDTV_nov1_ep13		49.60 200	51.57 206	47.31 193	46.28 212	44.61 203	59.82 175	30.96 210	48.80 216	50.20 135	59.26 195	52.38 214	38.56 168	56.20 212	49.70 212	58.04 200	50.01 201

LP		49.44 201	55.77 194	42.05 205	38.31 228	42.61 208	51.74 214	36.31 190	58.35 170	40.36 170	68.52 144	60.77 206	37.08 173	58.27 206	51.76 206	48.51 216	50.13 200

PatchmatchNet		48.67 202	50.10 211	46.99 197	48.29 204	41.00 210	55.54 198	38.94 166	51.38 200	45.15 159	63.22 174	48.45 218	42.83 158	53.80 220	48.50 215	51.19 214	44.37 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DWT-MVSNet_training		48.57 203	47.93 219	49.31 177	51.79 195	48.05 195	61.84 166	34.33 195	41.94 228	43.42 163	50.35 217	34.74 238	47.30 140	52.62 221	52.08 203	57.20 202	55.74 187

new-patchmatchnet		47.33 204	60.49 183	31.99 226	55.69 179	33.86 227	36.84 237	33.31 201	72.36 88	14.33 237	80.09 90	92.14 35	13.27 233	63.54 186	40.09 227	38.51 229	41.32 221

tpm		46.67 205	49.20 216	43.72 202	49.60 202	36.60 223	53.93 206	26.84 217	52.70 197	58.05 107	69.04 141	47.96 219	30.06 209	48.33 228	42.76 222	43.88 223	47.01 211

pmmvs3		46.64 206	54.13 200	37.90 215	31.23 236	40.68 211	49.83 220	15.34 233	46.31 222	36.34 176	53.15 215	74.40 168	36.36 179	58.43 204	56.64 193	58.32 199	49.29 202

TAMVS		46.64 206	53.62 202	38.49 213	49.56 203	36.87 220	53.16 210	25.76 219	56.33 184	22.55 228	60.72 186	61.80 203	27.12 213	59.50 196	58.33 189	52.79 209	41.82 220

test-LLR		46.01 208	45.06 227	47.11 194	59.39 156	36.72 221	51.28 215	40.95 149	36.41 235	34.45 183	46.14 227	47.02 221	38.00 169	51.78 222	48.53 213	58.60 197	48.84 205

MIMVSNet		45.83 209	53.46 204	36.94 216	45.38 217	39.50 213	52.20 212	30.68 211	57.09 180	24.53 222	55.22 208	71.54 172	21.74 221	55.81 213	51.08 208	47.11 219	43.96 215

test0.0.03 1		45.40 210	49.55 214	40.57 210	59.39 156	44.36 204	53.37 209	40.95 149	47.14 218	19.23 231	45.49 229	60.24 207	19.24 223	54.82 215	51.98 204	51.21 213	42.82 217

PMMVS		45.37 211	49.29 215	40.79 209	27.75 237	35.07 225	50.88 217	19.88 228	39.27 232	35.78 178	50.11 218	61.29 204	42.04 161	54.13 219	55.95 195	68.43 182	49.19 204

test1235678		44.92 212	54.19 198	34.11 221	41.53 220	37.95 217	54.27 204	23.09 223	53.64 188	22.14 229	53.92 211	84.05 132	16.41 228	60.66 193	50.30 210	47.20 217	38.84 224

testmv		44.91 213	54.17 199	34.11 221	41.52 221	37.93 218	54.27 204	23.09 223	53.61 189	22.14 229	53.89 212	84.00 133	16.41 228	60.64 194	50.29 211	47.20 217	38.83 225

MVS-HIRNet		44.56 214	45.52 225	43.44 203	40.98 222	31.03 232	39.52 236	36.96 185	42.80 227	44.37 160	53.80 213	60.04 208	41.85 162	47.97 230	41.08 225	56.99 204	41.95 219

test-mter		44.18 215	47.60 220	40.18 211	33.20 232	39.03 214	55.28 200	13.91 235	39.07 233	36.63 175	48.09 224	49.52 216	41.12 165	54.55 216	50.91 209	60.97 195	52.03 197

EMVS		43.85 216	49.91 212	36.77 218	45.46 216	32.70 229	44.09 230	25.33 220	57.88 175	26.62 216	58.99 197	61.14 205	42.77 159	70.26 166	38.52 232	36.38 231	29.87 233

E-PMN		43.83 217	49.81 213	36.84 217	46.09 214	31.86 231	42.77 232	25.85 218	57.76 177	25.53 217	55.50 207	62.47 200	43.77 155	70.78 163	39.51 229	37.04 230	30.79 232

tpmrst		43.31 218	46.14 223	40.02 212	47.05 209	36.48 224	48.01 226	32.17 208	49.50 210	37.26 173	63.66 173	47.04 220	31.98 208	42.00 235	40.55 226	43.64 224	43.75 216

TESTMET0.1,1		41.79 219	45.06 227	37.97 214	31.32 235	36.72 221	51.28 215	14.17 234	36.41 235	34.45 183	46.14 227	47.02 221	38.00 169	51.78 222	48.53 213	58.60 197	48.84 205

testus		41.61 220	50.54 209	31.20 228	38.11 229	38.92 215	49.10 222	17.60 230	48.25 217	25.05 218	41.45 231	79.34 146	13.18 234	58.28 205	47.10 218	44.17 222	40.41 222

testpf		41.44 221	38.52 234	44.85 200	46.17 213	38.68 216	60.29 171	43.31 115	24.28 237	35.09 179	39.52 233	34.84 237	32.39 205	43.79 234	39.89 228	51.88 210	48.65 207

ADS-MVSNet		40.61 222	46.31 221	33.96 223	40.70 223	30.42 233	40.42 234	33.44 199	58.01 174	30.87 206	63.05 175	54.48 212	22.67 220	44.35 233	39.23 231	35.64 232	34.64 228

CHOSEN 280x420		40.24 223	44.14 231	35.69 219	32.36 234	23.58 238	50.30 219	21.21 227	40.94 229	18.84 232	32.75 236	48.65 217	48.13 136	59.16 198	55.31 196	43.28 225	48.62 208

EPMVS		40.11 224	44.96 229	34.44 220	41.55 219	32.65 230	41.74 233	32.39 206	49.89 209	24.83 219	64.44 170	46.38 225	26.57 216	44.75 232	39.47 230	39.59 227	37.16 226

FMVSNet5		39.83 225	45.08 226	33.71 224	39.24 224	39.56 212	48.77 223	23.55 222	39.45 231	24.55 221	33.73 235	44.57 227	20.97 222	58.27 206	54.23 201	45.16 220	45.77 213

1111		39.71 226	44.86 230	33.71 224	50.45 198	28.51 234	55.07 201	34.43 193	62.60 155	17.59 233	62.60 177	28.17 240	14.69 230	54.19 217	41.91 224	30.02 234	36.03 227

test12356		39.53 227	49.18 217	28.26 230	32.93 233	33.64 228	48.68 224	15.96 232	46.26 223	16.21 235	46.46 226	79.07 148	17.13 226	58.60 203	48.30 216	38.67 228	31.96 230

N_pmnet		39.50 228	51.01 208	26.09 232	44.48 218	25.59 237	40.20 235	21.49 226	64.20 149	7.98 240	73.86 125	76.67 160	13.66 232	50.17 226	36.69 234	28.71 235	29.86 234

test2356		35.97 229	39.61 233	31.71 227	38.85 225	34.29 226	45.78 227	22.27 225	38.89 234	22.59 227	37.67 234	37.07 234	16.57 227	50.72 225	45.45 219	44.20 221	33.19 229

MVE	28.01 19	35.86 230	43.56 232	26.88 231	22.33 239	19.75 240	30.85 240	23.88 221	49.90 208	10.48 238	43.64 230	61.87 202	48.99 132	47.26 231	42.15 223	24.76 236	40.37 223
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 231	45.64 224	24.22 233	38.59 226	25.83 236	31.87 239	19.24 229	49.06 212	9.01 239	54.34 210	64.73 196	12.46 235	49.21 227	44.91 220	34.17 233	31.41 231

PMMVS2		34.11 232	48.55 218	17.26 234	25.45 238	20.72 239	35.08 238	16.26 231	58.71 168	4.16 242	59.22 196	78.40 154	3.65 236	57.24 211	38.31 233	18.94 237	27.28 235

GG-mvs-BLEND		31.54 233	46.27 222	14.37 235	0.07 243	48.65 194	42.97 231	0.08 240	44.04 226	1.21 244	39.77 232	57.94 210	0.15 240	48.19 229	42.82 221	41.70 226	42.46 218

.test1245		31.52 234	33.91 235	28.73 229	50.45 198	28.51 234	55.07 201	34.43 193	62.60 155	17.59 233	62.60 177	28.17 240	14.69 230	54.19 217	0.54 237	0.15 241	0.77 238

test123		0.53 235	0.60 237	0.46 237	0.22 241	0.25 243	0.33 245	0.13 239	0.66 240	1.37 243	1.10 239	0.00 245	0.43 238	0.68 238	0.61 236	0.26 240	0.88 237

testmvs		0.47 236	0.69 236	0.21 238	0.17 242	0.17 244	0.35 244	0.16 238	0.66 240	0.18 245	1.05 240	0.99 244	0.27 239	0.62 239	0.54 237	0.15 241	0.77 238

sosnet-low-res		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 245	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

sosnet		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 245	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

our_test_3					52.72 190	53.66 171	69.11 138

ambc			79.96 60		74.57 48	75.48 45	73.75 120		80.32 50	72.34 37	78.46 100	92.41 32	59.05 72	80.24 85	73.95 92	75.41 134	78.85 69

MTAPA										80.26 8		90.53 72

MTMP										82.07 4		91.00 58

Patchmatch-RL test							2.05 243

tmp_tt				7.47 236	8.89 240	3.32 242	4.35 242	1.14 237	15.58 239	15.76 236	8.50 238	5.90 243	2.00 237	20.02 236	21.51 235	12.70 238

XVS					80.47 19	81.29 12	93.33 3			77.45 20		90.19 76				91.52 11

X-MVStestdata					80.47 19	81.29 12	93.33 3			77.45 20		90.19 76				91.52 11

abl_6				65.41 79	69.37 76	74.02 53	82.50 56	47.39 80	66.39 136	56.63 112	60.61 187	82.76 136	53.68 112			82.92 73	78.39 74

mPP-MVS					82.97 2							92.12 36

NP-MVS									71.39 94

Patchmtry						37.73 219	45.00 228	44.97 98		52.60 124

DeepMVS_CX						8.52 241	9.75 241	3.19 236	16.70 238	5.02 241	23.06 237	19.33 242	18.69 224	13.75 237		11.34 239	25.07 236

High-resolution multi-view benchmark