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 53	83.76 2	93.79 1	61.08 19	89.57 2	86.19 1	90.06 10	93.01 25	76.72 2	94.71 1	92.72 1	93.47 1	91.56 1

COLMAP_ROB	75.87 2	84.34 2	89.80 2	77.97 12	75.52 51	82.76 4	90.39 20	54.21 54	89.37 3	83.18 2	89.90 12	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 29	92.67 6	64.48 1	83.13 32	82.32 3	80.89 72	92.97 26	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 13	81.92 8	92.57 8	63.14 5	84.35 20	79.45 12	83.37 45	92.04 36	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 24	92.68 5	63.48 3	81.43 46	80.21 9	81.95 61	90.76 54	72.86 6	90.14 19	89.30 11	90.92 19	88.59 18
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 25	82.01 7	92.29 11	60.35 26	82.20 40	78.32 16	80.59 73	93.14 23	70.67 15	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 25	79.95 5	80.69 17	81.09 15	92.40 10	63.06 6	79.38 60	80.21 9	80.31 75	91.44 41	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 34	81.03 16	89.78 24	61.16 18	80.15 56	80.44 6	84.83 36	94.19 16	70.52 18	90.70 11	87.19 23	91.71 8	87.37 29

ACMM	71.24 7	82.85 9	86.59 11	78.50 10	80.10 26	78.59 31	91.77 12	60.76 23	84.43 18	76.49 25	81.58 66	93.50 18	70.45 19	91.38 7	89.42 9	91.42 16	87.22 31
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
zzz-MVS		82.61 10	85.04 32	79.79 6	82.59 4	73.90 58	92.42 9	62.39 12	84.54 17	80.21 9	79.86 79	90.74 55	70.63 16	90.01 21	89.71 8	90.48 21	86.49 36

HFP-MVS		82.37 11	86.28 14	77.81 15	79.94 27	80.96 18	91.13 15	63.30 4	84.04 22	71.81 39	82.39 56	89.59 71	69.16 24	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 19	78.62 35	84.34 1	90.30 22	63.54 2	83.10 33	71.30 44	86.91 23	90.54 61	67.12 30	87.81 35	87.05 24	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 34	79.19 7	80.73 16	80.86 21	91.68 13	62.59 10	82.55 37	75.53 29	73.88 112	92.28 32	73.74 5	90.07 20	87.65 19	90.87 20	87.74 24

ACMP	70.35 9	82.17 14	86.45 13	77.18 18	79.33 28	81.00 17	89.27 28	58.63 32	81.35 48	75.46 30	82.97 51	95.08 13	68.90 25	90.49 14	87.43 22	91.48 14	86.84 33
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		82.16 15	85.55 27	78.21 11	80.48 19	79.28 30	92.65 7	61.03 20	80.55 54	77.00 23	81.80 64	90.71 56	68.73 26	90.25 17	87.94 18	89.36 28	88.30 21
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		82.15 16	85.93 20	77.74 16	80.13 24	80.25 26	91.01 16	60.61 24	85.54 11	78.61 15	83.21 48	86.96 99	65.95 34	88.10 32	87.59 20	90.11 22	89.83 4
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SD-MVS		82.13 17	86.80 8	76.67 20	80.36 22	80.66 22	89.48 26	56.93 35	82.50 38	67.55 68	87.05 21	91.40 43	72.84 7	88.66 28	88.32 16	92.85 2	89.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
LTVRE_ROB	75.99 1	82.04 18	87.16 4	76.07 23	63.57 119	70.27 76	86.48 41	62.99 7	89.00 5	80.32 7	86.25 26	91.04 48	74.66 4	92.58 3	90.29 4	88.42 35	90.72 2
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 25	77.06 42	77.23 39	87.77 38	56.25 42	83.33 31	67.18 73	89.48 14	87.94 83	77.70 1	93.02 2	92.57 2	88.13 37	86.00 39
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		81.79 20	85.72 23	77.21 17	79.15 32	79.68 28	91.62 14	59.66 28	83.55 28	77.74 19	83.72 43	87.34 92	65.36 35	88.61 29	87.56 21	89.73 27	89.58 5

X-MVS		81.61 21	84.73 36	77.97 12	80.31 23	81.29 12	93.53 2	62.50 11	81.41 47	77.45 20	72.04 122	90.19 64	62.50 54	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 29	78.21 34	89.84 23	58.67 31	85.25 12	76.26 26	85.28 33	92.88 27	66.03 33	87.20 38	85.40 28	88.86 32	85.58 43

TSAR-MVS + MP.		81.23 23	86.13 16	75.52 26	80.74 15	83.22 3	90.55 17	55.12 49	80.87 51	67.62 67	88.01 16	92.38 31	70.61 17	86.64 40	83.10 43	88.51 33	88.67 15
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + ACMM		81.20 24	86.92 6	74.52 29	77.60 38	82.29 5	84.41 48	62.95 8	82.99 34	64.03 81	87.71 17	89.17 73	71.98 11	88.19 31	88.10 17	86.18 55	89.95 3

APDe-MVS		81.08 25	86.12 17	75.20 27	79.25 30	80.91 19	90.38 21	57.05 34	85.83 10	66.07 77	87.34 20	91.27 44	69.45 20	85.99 44	82.55 45	88.98 31	88.95 12

DPE-MVS		81.01 26	85.18 29	76.15 22	78.58 36	80.64 23	89.77 25	57.92 33	81.66 45	73.45 33	86.84 24	89.80 69	69.33 22	85.40 46	82.91 44	87.87 39	89.01 11

APD-MVS		80.60 27	84.63 37	75.91 24	81.22 11	81.48 10	90.49 18	58.81 30	77.54 66	67.49 69	85.90 28	89.82 68	69.43 21	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 28	82.57 51	77.96 14	81.99 7	72.76 63	90.48 19	61.31 15	80.85 52	77.90 18	81.93 62	87.01 96	68.20 28	84.15 56	85.27 30	87.85 40	86.00 39

DVP-MVS		80.31 29	85.60 26	74.15 33	76.23 46	78.39 32	86.62 40	55.79 45	86.47 9	71.32 43	90.96 7	89.02 75	69.28 23	84.62 55	81.64 54	85.66 60	88.09 22

ACMH+	67.97 10	80.15 30	86.16 15	73.14 40	73.82 60	76.41 42	83.59 53	54.82 52	87.35 6	70.86 48	86.98 22	96.27 4	66.50 31	89.17 25	83.39 40	89.26 29	83.56 49

OMC-MVS		79.95 31	85.28 28	73.74 36	72.95 63	80.10 27	87.87 37	48.13 81	84.62 16	79.42 13	80.27 76	92.49 29	64.14 44	87.25 37	85.11 31	89.92 25	87.10 32

SED-MVS		79.70 32	85.16 30	73.34 38	75.83 50	78.11 35	88.77 33	56.45 39	84.85 14	69.45 60	90.70 9	88.38 78	63.16 49	85.12 51	81.28 57	86.40 53	87.63 25

MSP-MVS		79.65 33	84.28 41	74.25 31	78.92 33	81.86 9	89.07 29	60.49 25	83.85 26	70.05 55	85.12 34	90.92 52	62.99 51	81.15 77	81.64 54	83.99 68	85.42 45

DeepPCF-MVS	71.57 5	79.49 34	84.05 42	74.17 32	74.14 57	80.88 20	89.33 27	56.24 43	82.41 39	71.58 41	82.27 57	86.47 102	66.47 32	84.80 53	84.16 36	87.26 45	87.34 30

LS3D		79.33 35	84.03 43	73.84 34	75.37 52	78.09 36	83.30 54	52.94 61	84.42 19	76.01 27	84.16 39	87.44 91	65.34 36	86.30 41	82.08 52	90.09 23	85.70 41

3Dnovator+	72.94 4	78.78 36	83.05 48	73.80 35	70.70 75	81.34 11	88.33 34	56.01 44	81.33 49	72.87 37	78.06 92	81.15 127	63.83 46	87.39 36	85.82 26	91.06 18	86.28 38

UA-Net		78.65 37	83.96 44	72.46 42	84.87 1	76.15 43	89.06 30	55.70 46	77.25 67	53.14 113	79.73 81	82.09 125	59.69 70	92.21 5	90.93 3	92.32 3	89.36 6

xxxxxxxxxxxxxcwj		78.58 38	83.87 45	72.41 43	76.04 48	75.72 46	83.86 50	51.81 65	84.00 24	70.65 51	81.27 68	95.06 14	64.64 41	83.28 64	80.28 59	87.44 43	87.49 27

DeepC-MVS_fast	71.40 6	78.48 39	82.92 49	73.31 39	76.44 45	82.23 6	87.59 39	56.56 38	77.79 64	68.91 64	77.00 96	87.32 93	61.90 56	85.40 46	84.37 33	88.46 34	86.33 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.36 40	83.47 47	72.41 43	76.04 48	75.72 46	83.86 50	51.81 65	84.00 24	70.65 51	81.27 68	92.22 33	64.64 41	83.28 64	80.28 59	87.44 43	87.49 27

WR-MVS		78.32 41	86.09 19	69.25 61	76.22 47	72.33 70	85.71 44	59.02 29	86.66 7	51.41 119	92.91 2	96.76 1	53.09 105	90.21 18	85.30 29	90.05 24	78.46 75

ACMH	66.19 11	78.12 42	84.55 38	70.63 52	69.62 81	72.40 69	80.77 70	46.43 95	89.24 4	77.99 17	87.42 19	95.83 9	62.95 52	86.27 42	78.24 71	86.00 58	82.46 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
train_agg		77.83 43	80.47 61	74.77 28	80.92 14	69.60 77	88.87 32	56.32 41	74.03 83	71.03 46	83.67 44	87.68 86	64.75 40	83.70 58	81.85 53	86.71 49	82.73 50

NCCC		77.82 44	80.72 60	74.43 30	79.24 31	75.72 46	88.06 35	56.36 40	79.61 58	73.22 35	67.75 135	87.05 95	63.09 50	85.62 45	84.00 37	86.62 50	85.30 46

CNVR-MVS		77.79 45	81.57 55	73.38 37	78.37 37	75.91 44	87.97 36	55.11 50	79.41 59	70.98 47	74.70 110	86.43 103	61.77 57	85.10 52	83.73 39	86.10 57	85.68 42

WR-MVS_H		77.56 46	85.88 21	67.86 65	80.54 18	74.32 55	83.23 55	61.78 13	83.47 29	47.46 139	91.81 6	95.84 8	50.50 116	90.44 16	84.37 33	83.63 73	80.89 61

RPSCF		77.56 46	84.51 39	69.46 60	65.17 108	74.36 54	79.74 75	47.45 84	84.01 23	72.89 36	77.89 93	90.67 57	65.14 38	88.25 30	89.74 7	86.38 54	86.64 35

PS-CasMVS		77.46 48	85.80 22	67.73 67	81.24 10	72.88 62	80.63 71	61.28 16	84.14 21	50.53 125	92.13 4	96.76 1	50.12 119	91.02 9	84.46 32	82.60 85	79.19 68

DTE-MVSNet		77.28 49	84.87 35	68.42 63	82.94 3	72.70 65	81.60 65	61.78 13	85.03 13	51.40 120	92.11 5	96.00 6	49.42 123	89.73 23	82.52 47	83.39 77	75.98 85

SixPastTwentyTwo		77.24 50	83.65 46	69.78 56	65.14 109	64.85 98	77.44 85	47.74 83	82.76 36	68.52 65	87.65 18	93.31 20	71.68 13	89.49 24	82.41 48	88.14 36	85.05 47

CDPH-MVS		77.22 51	81.05 59	72.75 41	77.29 40	77.46 38	86.36 42	54.02 56	73.00 88	69.75 58	77.78 94	88.90 76	61.31 61	84.09 57	82.54 46	87.79 41	83.57 48

PEN-MVS		77.06 52	85.05 31	67.74 66	82.29 6	72.59 66	80.86 69	61.03 20	84.66 15	50.08 128	92.19 3	96.59 3	49.12 124	89.83 22	82.35 49	83.06 78	77.14 81

CP-MVSNet		77.01 53	85.04 32	67.65 68	81.16 12	72.72 64	80.54 72	61.18 17	82.09 41	50.41 126	90.81 8	95.89 7	50.03 120	90.86 10	84.30 35	82.56 87	78.65 74

CSCG		76.95 54	82.08 53	70.97 48	73.32 62	78.35 33	81.08 68	47.19 86	83.47 29	69.82 57	80.44 74	87.19 94	64.59 43	81.01 80	77.26 79	89.83 26	86.84 33

CNLPA		76.67 55	81.72 54	70.77 51	70.75 73	76.68 41	86.14 43	46.11 97	81.82 43	74.68 31	76.37 98	86.23 105	62.92 53	85.28 49	83.29 41	84.02 67	82.40 52

MSLP-MVS++		76.66 56	82.32 52	70.06 54	70.51 76	80.27 25	79.77 74	55.58 47	77.79 64	63.09 82	67.25 139	89.50 72	71.01 14	88.10 32	85.74 27	80.39 98	87.56 26

TSAR-MVS + COLMAP		75.85 57	81.06 57	69.77 57	71.15 69	76.90 40	82.93 57	52.43 63	79.25 61	70.13 53	82.78 52	87.00 97	60.02 66	80.30 84	79.61 64	81.95 91	81.61 57

HQP-MVS		75.81 58	78.91 67	72.18 45	77.41 39	75.38 50	84.75 45	53.35 58	76.12 71	73.32 34	69.48 127	88.07 81	57.76 78	79.42 89	78.44 68	86.48 51	85.50 44

PLC	64.88 15	75.76 59	80.22 62	70.57 53	70.46 77	77.75 37	82.01 63	48.84 76	80.74 53	70.85 49	71.32 124	84.82 115	63.69 47	84.73 54	82.35 49	87.54 42	79.80 65
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_part1		75.44 60	84.35 40	65.04 88	71.41 68	70.75 74	78.24 79	47.16 87	90.52 1	56.65 98	93.19 1	95.21 12	49.99 121	82.20 72	77.98 73	86.80 48	80.63 62

TAPA-MVS	66.11 12	75.37 61	79.24 65	70.86 49	67.63 90	74.09 56	83.17 56	44.75 111	81.82 43	80.83 5	65.61 150	88.04 82	61.58 58	83.21 66	80.12 61	87.17 46	81.82 55
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 62	78.37 68	71.43 46	71.13 70	72.46 68	82.28 62	50.55 69	73.39 86	79.05 14	73.65 114	87.50 90	61.98 55	81.10 78	78.48 67	83.60 74	81.99 53

anonymousdsp		74.76 63	82.59 50	65.63 82	45.61 198	61.13 120	89.06 30	32.58 186	74.11 82	59.55 89	84.06 40	94.12 17	75.24 3	88.94 27	86.95 25	91.74 7	88.81 13

AdaColmap		74.73 64	77.57 73	71.40 47	76.90 43	75.76 45	84.54 47	53.08 60	76.20 70	66.64 76	66.06 148	78.16 142	61.32 60	85.37 48	82.20 51	85.95 59	79.27 67

v7n		74.47 65	81.06 57	66.77 73	66.98 94	67.10 80	76.76 89	45.88 99	81.98 42	67.43 70	88.38 15	95.67 10	61.38 59	80.76 82	73.49 98	82.21 89	80.06 64

PCF-MVS	65.25 14	73.99 66	76.74 78	70.79 50	71.61 67	75.33 51	83.76 52	50.40 70	74.88 74	74.50 32	67.60 136	85.36 112	58.30 76	78.61 92	74.25 93	86.15 56	81.13 60
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MCST-MVS		73.84 67	77.44 74	69.63 59	73.75 61	74.73 53	81.38 67	48.58 77	74.77 75	69.16 62	71.97 123	86.20 106	59.50 71	78.51 93	74.06 95	85.42 61	81.85 54

MVS_0304		73.74 68	77.16 76	69.74 58	74.24 56	73.47 59	84.70 46	49.62 71	62.26 139	67.27 71	75.87 101	87.57 88	57.49 81	81.20 76	79.50 65	85.10 62	80.27 63

TSAR-MVS + GP.		73.42 69	76.31 79	70.05 55	77.15 41	71.13 73	81.59 66	54.11 55	69.84 112	58.65 92	66.20 147	78.77 139	65.29 37	83.65 59	83.14 42	83.54 75	81.47 58

Gipuma		73.40 70	79.27 64	66.55 77	63.64 118	59.35 128	70.28 122	45.92 98	83.79 27	71.78 40	84.04 41	93.07 24	68.69 27	87.90 34	76.76 81	78.98 109	69.96 117
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		72.37 71	76.12 82	68.00 64	68.55 86	64.30 106	82.93 57	48.98 75	74.25 80	65.39 78	73.59 115	84.11 119	59.48 72	82.61 69	78.38 69	82.66 84	75.59 87

TinyColmap		71.85 72	76.11 83	66.87 72	66.07 99	65.34 93	74.35 102	49.30 74	79.93 57	75.93 28	75.66 103	87.74 85	54.72 96	80.66 83	70.42 117	80.85 96	73.02 101

UniMVSNet_ETH3D		71.84 73	81.36 56	60.74 107	76.46 44	66.01 87	66.49 140	60.24 27	86.58 8	41.87 162	90.04 11	96.02 5	43.72 150	85.14 50	77.30 78	75.64 129	68.40 131

TranMVSNet+NR-MVSNet		71.66 74	79.23 66	62.83 100	72.54 65	65.64 89	74.77 100	55.27 48	75.91 72	45.50 151	89.55 13	94.25 15	45.96 141	82.74 68	77.03 80	82.96 80	69.48 124

MVS_111021_LR		71.60 75	75.21 86	67.38 69	67.42 91	62.44 113	81.73 64	46.24 96	70.89 99	66.80 75	73.19 117	84.98 113	60.09 65	81.94 73	77.77 76	82.00 90	75.29 88

EG-PatchMatch MVS		71.50 76	76.82 77	65.30 85	70.74 74	66.50 84	74.23 104	43.25 120	72.02 91	59.11 90	79.85 80	86.88 100	63.95 45	80.29 85	75.25 89	80.51 97	76.98 82

DPM-MVS		71.35 77	73.50 103	68.84 62	74.93 54	73.35 60	84.07 49	50.56 68	71.91 92	67.06 74	61.21 169	77.02 147	52.64 108	74.15 111	75.14 90	83.79 71	81.74 56

UniMVSNet (Re)		71.29 78	78.14 69	63.30 95	70.29 78	66.57 83	75.98 91	54.74 53	70.20 105	46.20 149	85.08 35	93.21 21	48.19 129	82.50 70	78.33 70	84.40 65	71.08 112

CLD-MVS		71.24 79	78.12 70	63.20 97	74.03 58	71.60 71	82.82 59	32.91 183	74.23 81	69.32 61	79.65 82	91.54 39	47.02 137	81.22 75	79.01 66	73.09 144	69.63 120
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 80	75.09 88	66.39 78	72.57 64	71.53 72	82.38 61	47.10 88	59.81 145	59.81 88	74.97 106	84.37 118	54.25 99	79.89 88	77.64 77	82.25 88	77.40 79

v1192		71.06 81	74.87 90	66.61 75	66.38 96	65.80 88	78.27 78	45.28 104	70.19 106	70.79 50	83.37 45	91.79 37	58.76 75	70.86 144	69.02 123	80.16 100	73.08 99

DU-MVS		71.03 82	77.92 71	62.98 99	70.81 71	65.48 91	73.93 107	56.76 36	69.95 110	46.77 145	85.70 31	93.49 19	46.91 138	83.47 60	77.82 75	82.72 83	69.54 121

v1240		70.94 83	74.52 94	66.76 74	66.54 95	64.40 102	77.76 82	45.29 103	70.05 108	71.45 42	83.36 47	90.96 50	60.37 63	70.50 147	68.68 124	79.14 107	73.68 96

v1921920		70.82 84	74.46 96	66.58 76	66.33 97	64.35 105	77.72 83	45.07 106	70.39 102	71.18 45	83.15 49	90.62 59	59.97 67	70.90 142	68.43 131	79.19 106	73.39 97

UniMVSNet_NR-MVSNet		70.82 84	77.44 74	63.11 98	71.75 66	66.02 86	73.93 107	55.00 51	70.90 98	46.77 145	86.68 25	91.54 39	46.91 138	81.07 79	76.32 85	84.28 66	69.54 121

PVSNet_Blended_VisFu		70.70 86	73.62 102	67.28 71	63.53 120	72.96 61	77.97 80	52.10 64	63.65 133	62.66 84	71.14 125	73.46 157	63.55 48	79.35 91	75.34 88	83.90 69	79.43 66

v144192		70.68 87	74.40 98	66.34 79	65.94 101	64.38 103	77.63 84	45.18 105	69.97 109	70.11 54	82.70 54	90.77 53	59.84 69	71.43 140	68.46 127	79.31 105	73.08 99

FPMVS		70.46 88	74.89 89	65.28 86	69.09 84	61.42 118	77.07 87	46.92 91	76.73 69	53.53 109	67.33 137	75.07 153	67.23 29	83.41 62	81.54 56	77.86 116	78.73 72

v1144		70.45 89	74.50 95	65.73 81	65.74 103	64.88 97	77.33 86	44.16 113	70.59 101	69.63 59	83.15 49	91.42 42	57.79 77	71.29 141	68.53 126	79.72 103	71.63 111

v10		70.25 90	74.59 93	65.19 87	65.32 106	66.46 85	76.60 90	44.84 109	67.38 121	67.21 72	82.75 53	90.56 60	57.70 79	71.69 137	68.63 125	79.44 104	74.67 91

Effi-MVS+-dtu		70.10 91	73.76 101	65.82 80	70.23 79	74.92 52	79.47 76	44.49 112	56.98 160	54.34 103	64.26 154	84.78 116	59.97 67	80.96 81	80.38 58	86.44 52	74.05 94

MAR-MVS		70.00 92	72.28 114	67.34 70	69.89 80	72.57 67	80.09 73	49.49 73	60.28 144	69.03 63	59.29 179	80.79 129	54.68 97	78.39 95	76.00 86	80.87 95	78.67 73
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
Vis-MVSNet		69.95 93	77.69 72	60.91 106	60.67 134	66.71 81	77.94 81	48.58 77	69.10 115	45.78 150	80.21 77	83.58 122	53.41 104	82.92 67	80.11 62	79.08 108	81.21 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		69.51 94	76.17 80	61.74 104	68.38 89	66.60 82	71.77 113	46.98 89	73.60 85	41.79 163	82.06 60	69.65 168	52.51 109	83.41 62	79.94 63	89.02 30	77.94 77

3Dnovator	65.69 13	69.43 95	75.74 85	62.06 103	60.78 133	70.50 75	75.85 94	39.57 153	74.44 77	57.41 95	75.91 99	77.73 144	55.34 92	76.86 99	75.61 87	83.44 76	79.14 69

Effi-MVS+		69.04 96	73.01 107	64.40 91	67.20 92	64.83 99	74.87 99	43.97 115	63.33 135	60.90 86	73.06 118	85.79 109	55.61 90	73.58 117	76.41 84	83.84 70	74.09 93

v2v482		69.01 97	73.39 105	63.89 93	63.86 113	62.99 110	75.26 97	42.05 128	70.22 104	68.46 66	82.64 55	91.61 38	55.38 91	70.89 143	66.93 145	78.30 112	68.48 130

MSDG		68.98 98	73.31 106	63.92 92	67.08 93	68.27 78	75.41 96	40.77 144	67.61 119	64.89 79	75.75 102	78.96 136	53.70 101	76.72 101	73.95 96	81.71 93	71.93 109

v8		68.77 99	73.50 103	63.26 96	63.74 116	64.47 101	74.22 105	42.07 127	67.30 122	64.89 79	82.08 59	90.23 63	56.50 88	71.85 136	66.57 146	78.14 113	72.02 107

NR-MVSNet		68.66 100	76.15 81	59.93 111	65.49 104	65.48 91	74.42 101	56.76 36	69.95 110	45.38 152	85.70 31	91.13 45	34.68 179	74.52 110	76.75 82	82.83 82	69.49 123

USDC		68.53 101	71.82 119	64.68 89	63.53 120	61.87 116	70.12 123	46.98 89	77.89 63	76.58 24	68.55 132	86.88 100	50.50 116	73.73 114	65.62 148	80.39 98	68.21 133

IS_MVSNet		68.20 102	74.41 97	60.96 105	68.55 86	64.36 104	71.47 115	48.33 79	70.11 107	43.30 158	80.90 71	74.54 155	47.19 136	81.25 74	77.97 74	86.94 47	71.76 110

Baseline_NR-MVSNet		68.15 103	75.12 87	60.02 110	70.81 71	55.67 150	75.88 93	53.40 57	71.25 95	43.96 156	85.88 29	92.68 28	45.76 142	83.47 60	68.34 132	70.34 161	68.58 129

Fast-Effi-MVS+		67.71 104	72.54 111	62.07 102	63.83 114	63.68 107	75.74 95	39.94 150	60.89 143	54.29 104	73.00 119	86.19 107	56.85 84	78.46 94	73.23 99	81.74 92	72.36 105

thisisatest0515		66.95 105	72.29 113	60.72 108	56.37 157	56.05 148	71.08 116	38.81 157	67.59 120	53.26 112	78.21 90	79.79 134	60.11 64	75.69 106	73.02 101	84.69 63	75.66 86

EPNet		66.87 106	68.89 130	64.53 90	73.97 59	61.13 120	78.46 77	61.03 20	56.78 162	53.41 110	66.91 142	70.91 163	43.49 151	76.08 105	76.68 83	76.81 120	73.73 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		66.37 107	74.37 99	57.04 128	65.89 102	65.06 94	62.58 157	42.55 122	76.82 68	46.87 144	67.33 137	86.38 104	45.49 144	76.77 100	71.85 107	78.87 110	76.35 83

QAPM		66.36 108	72.76 110	58.90 116	59.57 141	65.01 95	64.05 153	41.17 139	73.09 87	56.82 97	69.42 128	77.78 143	55.07 94	73.00 122	72.07 105	76.71 121	78.96 70

casdiffmvs		66.19 109	72.34 112	59.02 115	62.75 124	60.61 126	69.06 128	41.38 136	69.49 113	54.11 105	84.00 42	89.74 70	49.12 124	70.74 146	62.70 163	77.70 118	69.14 127

V42		65.79 110	72.11 117	58.42 120	51.89 179	58.69 130	73.80 109	34.50 175	65.40 129	57.10 96	79.54 84	89.09 74	57.51 80	71.98 134	67.83 140	75.70 127	72.26 106

IterMVS-LS		65.76 111	70.85 124	59.81 113	65.33 105	57.78 135	64.63 150	48.02 82	65.65 128	51.05 122	81.31 67	77.47 145	54.94 95	69.46 155	69.36 122	74.90 133	74.95 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 112	71.25 123	59.14 114	58.92 147	54.88 159	73.66 111	38.55 160	66.12 126	49.91 130	69.87 126	86.97 98	60.61 62	76.30 103	74.75 91	73.19 142	69.83 118

UGNet		65.61 113	74.79 91	54.91 137	54.54 173	68.20 79	70.97 119	48.21 80	67.14 124	41.67 164	74.15 111	80.65 130	36.10 174	79.39 90	77.99 72	77.95 115	76.01 84
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		65.54 114	71.79 120	58.24 123	59.68 140	65.55 90	70.99 117	38.69 159	62.29 138	49.27 133	75.03 105	81.42 126	50.93 112	73.71 116	71.35 108	79.90 102	73.20 98
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		65.36 115	70.09 127	59.85 112	63.05 123	53.61 162	74.29 103	46.45 94	68.14 118	51.45 118	78.83 88	85.78 110	49.87 122	70.44 148	70.45 116	74.00 137	63.38 153

v148		64.92 116	70.58 126	58.32 121	59.89 138	57.09 141	66.04 142	35.27 174	69.11 114	60.66 87	79.57 83	90.93 51	53.91 100	69.81 154	62.22 164	74.14 135	65.31 145

CS-MVS		64.89 117	64.54 147	65.31 84	68.39 88	61.63 117	75.90 92	43.28 119	54.33 167	63.06 83	55.59 192	62.41 182	56.76 85	77.69 97	70.80 115	84.46 64	69.33 125

FC-MVSNet-train		64.87 118	74.76 92	53.33 141	65.24 107	58.05 132	69.69 125	41.92 131	70.99 97	32.62 185	85.75 30	88.23 79	32.10 189	77.61 98	74.41 92	78.43 111	68.25 132

pmmvs6		64.78 119	75.82 84	51.89 147	62.41 125	57.13 140	60.24 164	45.59 101	82.90 35	34.69 178	84.83 36	93.18 22	36.22 173	76.43 102	71.13 111	72.21 148	65.12 146

OpenMVS	60.79 16	64.42 120	69.72 128	58.23 124	61.63 129	62.17 114	64.11 152	37.54 168	67.17 123	55.71 102	65.89 149	74.89 154	52.67 107	72.20 132	68.29 134	77.73 117	77.39 80

DCV-MVSNet		64.34 121	72.84 109	54.42 139	63.79 115	62.09 115	62.50 158	42.72 121	74.32 79	41.34 165	66.96 140	88.57 77	39.18 162	75.20 108	70.35 118	77.01 119	72.37 104

ETV-MVS		64.30 122	64.76 145	63.77 94	68.59 85	62.49 112	77.02 88	45.31 102	49.27 189	50.88 123	56.23 190	59.91 191	57.12 83	80.19 87	74.23 94	83.68 72	71.03 113

Anonymous20231211		63.69 123	72.86 108	53.00 144	63.72 117	60.25 127	60.33 163	40.96 140	72.49 89	38.91 168	81.77 65	88.17 80	37.60 168	73.30 119	68.01 137	76.47 125	66.06 142

TransMVSNet (Re)		63.49 124	73.86 100	51.39 153	64.26 112	56.07 147	61.17 161	42.23 125	78.81 62	34.80 176	85.94 27	90.63 58	34.35 183	72.73 127	67.98 138	71.50 151	64.84 147

DI_MVS_plusplus_trai		63.43 125	67.54 134	58.63 117	62.34 126	58.06 131	65.75 146	42.15 126	63.05 136	53.28 111	75.88 100	75.92 150	50.18 118	68.04 158	64.20 154	78.07 114	67.65 134

EIA-MVS		63.24 126	64.16 150	62.16 101	69.30 83	63.20 109	72.40 112	40.82 143	48.31 195	51.50 117	59.63 177	62.23 183	57.33 82	78.00 96	71.94 106	81.59 94	65.82 143

Fast-Effi-MVS+-dtu		63.22 127	65.55 139	60.49 109	61.24 131	64.70 100	74.15 106	53.24 59	51.46 174	49.67 131	58.03 185	78.42 140	48.05 131	72.03 133	71.14 110	76.60 124	63.09 154

IterMVS-SCA-FT		62.67 128	68.00 132	56.45 134	56.92 155	64.92 96	57.51 176	38.12 161	59.44 146	53.62 108	74.74 109	71.60 160	64.84 39	70.24 150	65.27 150	67.70 169	69.83 118

diffmvs		62.64 129	69.66 129	54.46 138	56.19 160	55.06 156	67.36 137	36.74 172	64.18 132	50.58 124	79.54 84	87.55 89	45.13 146	68.04 158	63.20 159	70.78 156	70.02 116

MVS_Test		62.58 130	67.46 135	56.89 130	59.52 142	55.90 149	64.94 148	38.83 156	57.08 159	56.55 100	76.53 97	84.49 117	47.45 133	66.95 161	62.01 165	74.04 136	69.27 126

MDA-MVSNet-bldmvs		62.46 131	72.13 116	51.19 155	34.32 208	56.10 146	68.65 130	38.85 155	69.05 116	49.50 132	78.17 91	85.43 111	51.32 110	86.67 39	67.40 143	64.46 174	62.08 156

pm-mvs1		61.97 132	72.01 118	50.25 161	60.64 135	55.23 154	58.67 171	42.44 123	74.40 78	33.63 182	81.03 70	89.86 67	34.87 178	72.93 125	67.95 139	71.28 152	62.65 155

FMVSNet1		61.92 133	71.36 121	50.90 157	57.67 154	59.29 129	59.48 169	44.14 114	70.24 103	34.72 177	75.45 104	84.94 114	36.75 170	72.33 129	68.45 128	72.66 145	68.83 128

PVSNet_BlendedMVS		61.75 134	65.07 143	57.87 126	56.27 158	60.99 123	65.81 144	43.75 116	51.27 178	54.08 106	62.12 164	78.84 137	50.67 113	71.49 138	63.91 156	76.64 122	66.86 136

PVSNet_Blended		61.75 134	65.07 143	57.87 126	56.27 158	60.99 123	65.81 144	43.75 116	51.27 178	54.08 106	62.12 164	78.84 137	50.67 113	71.49 138	63.91 156	76.64 122	66.86 136

tttt0517		61.44 136	63.85 151	58.62 118	55.20 169	55.61 151	68.80 129	38.02 164	55.70 164	50.01 129	66.93 141	48.90 202	56.69 86	73.84 113	71.10 112	82.99 79	74.89 90

tfpnnormal		61.41 137	71.33 122	49.83 162	61.73 128	54.90 158	58.52 172	41.24 137	75.20 73	32.00 190	82.13 58	87.87 84	35.63 177	72.75 126	66.30 147	69.87 162	60.14 161

IB-MVS	57.02 17	61.37 138	65.39 140	56.69 131	56.65 156	60.85 125	70.70 120	37.90 166	49.37 188	45.37 153	48.75 203	79.14 135	53.55 103	76.26 104	70.85 114	75.97 126	72.50 103
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		61.22 139	67.07 136	54.40 140	59.89 138	63.62 108	70.98 118	36.77 171	50.49 181	47.15 140	62.45 162	80.81 128	37.90 167	71.87 135	70.09 120	73.69 138	70.19 115

pmmvs4		61.12 140	64.61 146	57.04 128	60.88 132	52.15 167	70.59 121	44.82 110	61.35 142	46.91 143	72.08 121	73.27 158	46.79 140	65.06 164	67.76 141	72.28 146	60.58 160

thisisatest0530		61.02 141	63.44 156	58.19 125	54.75 171	55.09 155	68.03 134	38.02 164	55.45 165	49.06 134	66.58 145	48.69 203	56.69 86	73.07 120	71.10 112	82.60 85	74.14 92

Vis-MVSNet (Re-imp)		60.99 142	67.78 133	53.06 143	64.66 111	53.49 163	67.40 135	49.52 72	68.55 117	28.00 198	79.53 86	71.41 162	33.08 187	75.30 107	71.28 109	75.69 128	54.91 174

PatchMatch-RL		60.96 143	63.00 159	58.57 119	59.16 146	52.18 166	67.38 136	41.99 129	57.85 154	48.16 135	53.55 199	69.77 167	59.47 73	73.73 114	72.49 104	75.27 132	61.44 158

GA-MVS		60.73 144	64.24 149	56.64 132	59.38 145	57.45 139	65.07 147	36.65 173	57.39 157	58.17 93	73.43 116	69.10 171	47.38 134	64.47 169	63.63 158	73.19 142	64.22 150

CVMVSNet		60.45 145	63.72 153	56.63 133	54.82 170	53.75 160	68.41 131	41.95 130	55.07 166	48.03 136	58.08 184	68.67 172	55.09 93	69.14 156	68.34 132	71.51 150	72.97 102

ET-MVSNet_ETH3D		60.33 146	62.10 162	58.27 122	58.61 150	58.05 132	68.06 132	41.20 138	51.40 175	51.10 121	64.06 155	49.42 201	50.61 115	74.72 109	70.29 119	80.05 101	66.74 138

FC-MVSNet-test		60.28 147	70.83 125	47.96 173	54.69 172	47.12 181	68.06 132	41.68 135	71.42 93	23.73 205	84.70 38	77.41 146	28.92 193	82.33 71	73.08 100	70.68 157	59.77 163

EU-MVSNet		59.77 148	66.07 137	52.42 145	47.81 188	51.86 169	62.98 156	32.28 188	62.08 140	47.10 141	59.94 175	83.42 123	53.08 106	70.06 153	63.19 160	71.26 154	71.96 108

IterMVS		59.24 149	64.45 148	53.16 142	50.98 181	61.29 119	66.51 139	32.85 184	58.17 150	46.31 148	72.58 120	70.23 165	54.26 98	64.81 167	60.24 169	68.04 168	63.81 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		59.15 150	62.28 161	55.49 136	52.42 177	62.59 111	71.76 114	39.74 151	50.25 183	41.92 161	62.88 160	69.16 170	55.85 89	62.77 174	67.18 144	71.27 153	61.11 159

thres600view7		58.87 151	65.91 138	50.66 159	61.27 130	56.32 144	59.88 167	40.63 146	64.88 130	32.10 189	64.82 151	69.83 166	36.72 171	72.99 123	72.55 103	73.34 140	59.97 162

CMPMVS	45.32 18	58.10 152	65.24 142	49.76 163	47.88 187	46.86 184	48.16 201	32.82 185	58.06 151	61.35 85	59.64 176	80.00 131	47.27 135	70.15 151	64.10 155	61.08 177	77.85 78
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 153	63.54 155	51.74 149	50.13 183	46.56 185	66.95 138	33.41 181	63.52 134	58.77 91	74.84 107	84.10 120	43.12 152	65.95 163	54.69 183	58.04 183	55.13 173

CDS-MVSNet		57.90 154	63.57 154	51.28 154	62.30 127	53.17 164	64.70 149	51.61 67	57.41 156	32.75 184	63.73 156	70.53 164	27.12 195	72.49 128	73.02 101	69.22 165	54.68 175
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet2		57.80 155	65.39 140	48.94 169	55.88 161	57.61 136	57.26 177	42.37 124	58.21 149	33.19 183	68.36 133	75.55 152	34.58 180	66.91 162	64.55 152	70.38 158	66.56 139

thres400		57.25 156	63.73 152	49.70 164	60.19 137	54.95 157	58.16 173	39.60 152	62.42 137	31.98 192	62.33 163	69.20 169	35.96 175	70.07 152	68.03 136	72.28 146	59.12 165

gm-plane-assit		56.76 157	57.64 176	55.73 135	66.01 100	55.45 153	74.96 98	30.54 193	73.71 84	56.04 101	81.81 63	30.91 218	43.83 148	58.77 184	54.71 182	63.02 176	48.13 190

MIMVSNet1		56.72 158	68.69 131	42.76 186	46.70 194	42.81 191	69.13 126	30.52 194	81.01 50	32.00 190	74.82 108	91.10 47	26.83 197	73.98 112	64.72 151	51.40 195	52.38 179

EPNet_dtu		56.63 159	60.77 168	51.80 148	55.47 166	44.63 186	69.83 124	38.74 158	50.27 182	47.64 137	58.01 186	72.27 159	33.71 185	68.60 157	67.72 142	65.39 172	63.86 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		56.54 160	63.26 157	48.70 170	55.88 161	57.61 136	57.26 177	41.75 132	49.06 190	32.37 186	61.81 166	67.02 174	34.58 180	72.33 129	68.45 128	70.38 158	66.56 139

test1		56.54 160	63.26 157	48.70 170	55.88 161	57.61 136	57.26 177	41.75 132	49.06 190	32.37 186	61.81 166	67.02 174	34.58 180	72.33 129	68.45 128	70.38 158	66.56 139

gg-mvs-nofinetune		56.45 162	61.04 166	51.10 156	63.42 122	49.40 177	53.71 188	52.52 62	74.77 75	46.93 142	77.31 95	53.88 195	26.42 199	62.51 175	57.81 175	63.60 175	51.57 182

thres200		56.35 163	62.36 160	49.34 166	58.87 148	56.32 144	55.91 181	40.63 146	58.51 148	31.34 193	58.81 183	67.31 173	35.96 175	72.99 123	65.51 149	73.34 140	57.07 168

MS-PatchMatch		56.31 164	60.22 171	51.73 150	60.53 136	55.53 152	63.41 154	37.18 169	51.34 177	37.44 170	60.53 173	62.19 184	45.52 143	64.25 170	63.17 161	66.33 170	64.56 148

tfpn200view9		56.07 165	61.85 163	49.34 166	58.57 151	56.48 143	58.01 175	40.72 145	53.23 168	31.01 194	56.41 188	66.40 178	34.18 184	73.02 121	68.06 135	73.53 139	59.35 164

FMVSNet3		54.77 166	60.73 169	47.81 174	54.29 174	56.88 142	55.89 182	41.75 132	49.06 190	32.37 186	61.81 166	67.02 174	33.67 186	62.88 173	61.96 166	68.88 166	65.53 144

thres100view900		53.88 167	59.19 172	47.68 175	58.57 151	52.74 165	54.45 185	38.07 163	53.23 168	31.01 194	56.41 188	66.40 178	32.80 188	65.03 166	64.43 153	71.18 155	56.10 171

CR-MVSNet		53.82 168	55.40 179	51.98 146	51.57 180	50.23 172	45.00 203	44.97 107	46.90 197	52.60 114	67.91 134	46.99 210	48.37 127	59.15 182	59.53 172	69.38 164	57.07 168

baseline2		53.55 169	55.19 180	51.62 151	55.27 168	51.95 168	60.89 162	34.23 176	46.69 199	42.47 159	53.56 198	50.01 198	45.33 145	64.63 168	61.22 167	71.56 149	58.28 167

test20.03		53.49 170	60.95 167	44.78 182	64.73 110	47.25 180	61.58 160	43.30 118	65.86 127	22.85 206	66.87 144	79.85 132	22.99 201	62.38 176	56.95 177	53.25 192	47.46 192

baseline		53.46 171	61.55 164	44.01 183	45.83 197	48.77 178	57.26 177	28.75 197	49.99 184	38.85 169	68.78 130	75.65 151	38.30 164	60.80 177	59.78 171	55.10 190	67.07 135

MVSTER		53.08 172	56.39 178	49.21 168	47.19 190	51.08 170	60.14 166	31.74 190	40.63 207	38.97 167	55.78 191	46.74 211	42.47 155	67.29 160	62.99 162	74.73 134	70.23 114

CHOSEN 1792x2688		52.99 173	56.91 177	48.42 172	47.32 189	50.10 174	64.18 151	33.85 178	45.46 202	36.95 172	55.20 195	66.49 177	51.20 111	59.28 180	59.81 170	57.01 185	61.99 157

baseline1		52.90 174	58.38 173	46.51 181	58.87 148	50.01 175	54.17 186	40.45 149	56.81 161	29.25 197	62.72 161	58.99 192	30.25 191	65.05 165	60.57 168	66.07 171	54.54 176

CostFormer		52.59 175	55.14 181	49.61 165	52.72 175	50.40 171	66.28 141	33.78 179	52.85 170	43.43 157	66.30 146	51.37 197	41.78 158	54.92 194	51.18 190	59.68 179	58.98 166

SCA		52.47 176	53.97 183	50.71 158	46.95 193	57.79 134	60.18 165	46.89 92	51.92 173	46.71 147	60.73 170	49.97 199	47.69 132	56.39 191	52.98 187	55.82 187	48.03 191

testgi		51.94 177	61.37 165	40.94 189	58.38 153	47.03 182	65.88 143	30.49 195	70.87 100	22.64 207	57.53 187	87.59 87	18.30 207	63.01 172	54.32 184	49.93 198	49.27 185

tpm cat1		50.98 178	51.28 189	50.62 160	55.74 164	49.92 176	63.13 155	38.12 161	52.38 172	47.61 138	60.11 174	44.51 214	44.86 147	51.31 201	47.49 197	54.25 191	53.24 178

RPMNet		50.92 179	50.32 191	51.62 151	50.25 182	50.23 172	59.16 170	46.70 93	46.90 197	42.39 160	48.97 202	37.23 217	41.78 158	57.30 189	56.18 179	69.44 163	55.43 172

pmmvs5		50.64 180	58.01 174	42.05 187	47.01 192	43.67 189	49.27 199	29.43 196	50.77 180	33.83 181	68.69 131	76.16 149	27.82 194	57.53 188	57.07 176	64.95 173	52.18 180

PatchT		50.55 181	53.55 185	47.05 179	37.59 207	42.26 192	50.55 196	37.56 167	46.37 200	52.60 114	66.91 142	43.54 216	48.37 127	59.15 182	59.53 172	55.62 188	57.07 168

Anonymous20231206		50.28 182	57.94 175	41.35 188	55.45 167	43.65 190	58.06 174	34.12 177	62.02 141	24.25 204	59.33 178	79.80 133	24.49 200	59.55 178	54.28 185	51.74 194	46.94 194

dps		49.71 183	51.97 187	47.07 178	52.37 178	47.00 183	53.02 191	40.52 148	44.91 203	41.23 166	64.55 152	44.27 215	40.12 161	57.71 187	51.97 189	55.14 189	53.41 177

MDTV_nov1_ep13		49.60 184	51.57 188	47.31 176	46.28 195	44.61 187	59.82 168	30.96 191	48.80 194	50.20 127	59.26 180	52.38 196	38.56 163	56.20 192	49.70 193	58.04 183	50.01 183

PatchmatchNet		48.67 185	50.10 192	46.99 180	48.29 186	41.00 193	55.54 183	38.94 154	51.38 176	45.15 154	63.22 158	48.45 205	42.83 153	53.80 198	48.50 196	51.19 197	44.37 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
new-patchmatchnet		47.33 186	60.49 170	31.99 204	55.69 165	33.86 205	36.84 212	33.31 182	72.36 90	14.33 212	80.09 78	92.14 34	13.27 209	63.54 171	40.09 204	38.51 206	41.32 203

tpm		46.67 187	49.20 197	43.72 184	49.60 184	36.60 202	53.93 187	26.84 198	52.70 171	58.05 94	69.04 129	47.96 206	30.06 192	48.33 204	42.76 200	43.88 201	47.01 193

pmmvs3		46.64 188	54.13 182	37.90 196	31.23 212	40.68 194	49.83 198	15.34 209	46.31 201	36.34 174	53.15 200	74.40 156	36.36 172	58.43 185	56.64 178	58.32 182	49.29 184

TAMVS		46.64 188	53.62 184	38.49 194	49.56 185	36.87 199	53.16 190	25.76 200	56.33 163	22.55 208	60.72 171	61.80 186	27.12 195	59.50 179	58.33 174	52.79 193	41.82 202

test-LLR		46.01 190	45.06 206	47.11 177	59.39 143	36.72 200	51.28 193	40.95 141	36.41 211	34.45 179	46.14 205	47.02 208	38.00 165	51.78 199	48.53 194	58.60 180	48.84 187

MIMVSNet		45.83 191	53.46 186	36.94 197	45.38 200	39.50 196	52.20 192	30.68 192	57.09 158	24.53 203	55.22 194	71.54 161	21.74 203	55.81 193	51.08 191	47.11 199	43.96 197

test0.0.03 1		45.40 192	49.55 195	40.57 191	59.39 143	44.36 188	53.37 189	40.95 141	47.14 196	19.23 209	45.49 207	60.24 189	19.24 205	54.82 195	51.98 188	51.21 196	42.82 199

PMMVS		45.37 193	49.29 196	40.79 190	27.75 213	35.07 204	50.88 195	19.88 206	39.27 209	35.78 175	50.11 201	61.29 187	42.04 156	54.13 197	55.95 180	68.43 167	49.19 186

MVS-HIRNet		44.56 194	45.52 204	43.44 185	40.98 203	31.03 209	39.52 211	36.96 170	42.80 205	44.37 155	53.80 197	60.04 190	41.85 157	47.97 206	41.08 202	56.99 186	41.95 201

test-mter		44.18 195	47.60 199	40.18 192	33.20 209	39.03 197	55.28 184	13.91 211	39.07 210	36.63 173	48.09 204	49.52 200	41.12 160	54.55 196	50.91 192	60.97 178	52.03 181

EMVS		43.85 196	49.91 193	36.77 199	45.46 199	32.70 206	44.09 205	25.33 201	57.88 153	26.62 199	58.99 182	61.14 188	42.77 154	70.26 149	38.52 208	36.38 208	29.87 209

E-PMN		43.83 197	49.81 194	36.84 198	46.09 196	31.86 208	42.77 207	25.85 199	57.76 155	25.53 200	55.50 193	62.47 181	43.77 149	70.78 145	39.51 205	37.04 207	30.79 208

tpmrst		43.31 198	46.14 202	40.02 193	47.05 191	36.48 203	48.01 202	32.17 189	49.50 187	37.26 171	63.66 157	47.04 207	31.98 190	42.00 210	40.55 203	43.64 202	43.75 198

TESTMET0.1,1		41.79 199	45.06 206	37.97 195	31.32 211	36.72 200	51.28 193	14.17 210	36.41 211	34.45 179	46.14 205	47.02 208	38.00 165	51.78 199	48.53 194	58.60 180	48.84 187

ADS-MVSNet		40.61 200	46.31 200	33.96 202	40.70 204	30.42 210	40.42 209	33.44 180	58.01 152	30.87 196	63.05 159	54.48 194	22.67 202	44.35 209	39.23 207	35.64 209	34.64 206

CHOSEN 280x420		40.24 201	44.14 209	35.69 200	32.36 210	23.58 213	50.30 197	21.21 205	40.94 206	18.84 210	32.75 211	48.65 204	48.13 130	59.16 181	55.31 181	43.28 203	48.62 189

EPMVS		40.11 202	44.96 208	34.44 201	41.55 202	32.65 207	41.74 208	32.39 187	49.89 186	24.83 201	64.44 153	46.38 212	26.57 198	44.75 208	39.47 206	39.59 205	37.16 205

FMVSNet5		39.83 203	45.08 205	33.71 203	39.24 205	39.56 195	48.77 200	23.55 203	39.45 208	24.55 202	33.73 210	44.57 213	20.97 204	58.27 186	54.23 186	45.16 200	45.77 195

N_pmnet		39.50 204	51.01 190	26.09 206	44.48 201	25.59 212	40.20 210	21.49 204	64.20 131	7.98 215	73.86 113	76.67 148	13.66 208	50.17 202	36.69 210	28.71 211	29.86 210

MVE	28.01 19	35.86 205	43.56 210	26.88 205	22.33 215	19.75 215	30.85 215	23.88 202	49.90 185	10.48 213	43.64 208	61.87 185	48.99 126	47.26 207	42.15 201	24.76 212	40.37 204
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 206	45.64 203	24.22 207	38.59 206	25.83 211	31.87 214	19.24 207	49.06 190	9.01 214	54.34 196	64.73 180	12.46 210	49.21 203	44.91 198	34.17 210	31.41 207

PMMVS2		34.11 207	48.55 198	17.26 208	25.45 214	20.72 214	35.08 213	16.26 208	58.71 147	4.16 217	59.22 181	78.40 141	3.65 211	57.24 190	38.31 209	18.94 213	27.28 211

GG-mvs-BLEND		31.54 208	46.27 201	14.37 209	0.07 219	48.65 179	42.97 206	0.08 216	44.04 204	1.21 219	39.77 209	57.94 193	0.15 215	48.19 205	42.82 199	41.70 204	42.46 200

test123		0.53 209	0.60 212	0.46 211	0.22 217	0.25 218	0.33 220	0.13 215	0.66 215	1.37 218	1.10 214	0.00 222	0.43 213	0.68 213	0.61 212	0.26 216	0.88 213

testmvs		0.47 210	0.69 211	0.21 212	0.17 218	0.17 219	0.35 219	0.16 214	0.66 215	0.18 220	1.05 215	0.99 221	0.27 214	0.62 214	0.54 213	0.15 217	0.77 214

uanet_test		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 222	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet-low-res		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 222	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 222	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

RE-MVS-def										70.04 56

9.14												83.64 121

SR-MVS					81.31 9			62.63 9				91.11 46

Anonymous202405211			72.22 115		66.19 98	61.09 122	62.23 159	45.87 100	71.25 95		79.33 87	86.16 108	37.36 169	73.54 118	69.84 121	75.45 130	64.32 149

our_test_3					52.72 175	53.66 161	69.11 127

ambc			79.96 63		74.57 55	75.48 49	73.75 110		80.32 55	72.34 38	78.46 89	92.41 30	59.05 74	80.24 86	73.95 96	75.41 131	78.85 71

MTAPA										80.26 8		90.53 62

MTMP										82.07 4		91.00 49

Patchmatch-RL test							2.05 218

tmp_tt				7.47 210	8.89 216	3.32 217	4.35 217	1.14 213	15.58 214	15.76 211	8.50 213	5.90 220	2.00 212	20.02 211	21.51 211	12.70 214

XVS					80.47 20	81.29 12	93.33 3			77.45 20		90.19 64				91.52 11

X-MVStestdata					80.47 20	81.29 12	93.33 3			77.45 20		90.19 64				91.52 11

abl_6				65.41 83	69.37 82	74.02 57	82.50 60	47.39 85	66.39 125	56.63 99	60.61 172	82.76 124	53.68 102			82.92 81	78.39 76

mPP-MVS					82.97 2							92.12 35

NP-MVS									71.39 94

Patchmtry						37.73 198	45.00 203	44.97 107		52.60 114

DeepMVS_CX						8.52 216	9.75 216	3.19 212	16.70 213	5.02 216	23.06 212	19.33 219	18.69 206	13.75 212		11.34 215	25.07 212

High-resolution multi-view benchmark