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		93.16 1	95.57 1	90.36 1	88.79 52	93.57 1	97.27 1	78.23 22	95.55 2	93.00 1	93.98 17	96.01 39	87.53 1	97.69 1	96.81 1	97.33 1	95.34 3

PMVS	79.51 9	90.23 14	92.67 14	87.39 21	90.16 39	88.75 41	93.64 36	75.78 44	90.00 33	83.70 48	92.97 28	92.22 102	86.13 4	97.01 3	96.79 2	94.94 30	90.96 46
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 11	83.19 109	86.35 67	93.60 37	78.79 19	95.48 4	91.79 2	93.08 26	97.21 21	86.34 3	97.06 2	96.27 3	95.46 23	95.56 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
UA-Net		89.02 33	91.44 39	86.20 28	94.88 1	89.84 33	94.76 29	77.45 29	85.41 72	74.79 103	88.83 77	88.90 134	78.67 40	96.06 7	95.45 4	96.66 3	95.58 1

COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 12	88.98 51	92.86 2	95.51 20	72.17 59	94.95 5	91.27 3	94.11 16	97.77 12	84.22 8	96.49 4	95.27 5	96.79 2	93.60 11
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		88.05 46	92.61 17	82.73 66	84.24 94	88.40 43	90.04 74	66.29 105	91.46 13	82.29 61	88.93 75	96.01 39	79.38 32	95.15 21	94.90 6	94.15 40	93.40 19

zzz-MVS		90.38 11	91.35 41	89.25 5	93.08 3	86.59 64	96.45 11	79.00 16	90.23 28	89.30 10	85.87 106	94.97 65	82.54 18	95.05 23	94.83 7	95.14 27	91.94 36

CP-MVS		91.09 5	92.33 25	89.65 2	92.16 10	90.41 27	96.46 10	80.38 8	88.26 46	89.17 11	87.00 95	96.34 31	83.95 10	95.77 11	94.72 8	95.81 17	93.78 9

ACMMP		90.63 8	92.40 20	88.56 9	91.24 28	91.60 6	96.49 9	77.53 27	87.89 49	86.87 31	87.24 92	96.46 26	82.87 16	95.59 15	94.50 9	96.35 6	93.51 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
ACMM	80.67 7	90.67 7	92.46 19	88.57 8	91.35 22	89.93 31	96.34 12	77.36 31	90.17 29	86.88 30	87.32 90	96.63 24	83.32 13	95.79 10	94.49 10	96.19 9	92.91 25
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS		90.84 6	91.95 34	89.55 3	92.92 5	90.90 19	96.56 6	79.60 11	86.83 60	88.75 13	89.00 73	94.38 78	84.01 9	94.94 25	94.34 11	95.45 24	93.24 22
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
X-MVS		89.36 28	90.73 47	87.77 17	91.50 20	91.23 8	96.76 4	78.88 18	87.29 55	87.14 26	78.98 140	94.53 72	76.47 55	95.25 19	94.28 12	95.85 14	93.55 15

ACMMPR		91.30 4	92.88 11	89.46 4	91.92 11	91.61 5	96.60 5	79.46 14	90.08 31	88.53 14	89.54 65	95.57 48	84.25 7	95.24 20	94.27 13	95.97 11	93.85 7

HFP-MVS		90.32 13	92.37 22	87.94 14	91.46 21	90.91 18	95.69 18	79.49 12	89.94 34	83.50 51	89.06 72	94.44 76	81.68 23	94.17 31	94.19 14	95.81 17	93.87 6

WR-MVS		89.79 23	93.66 4	85.27 37	91.32 23	88.27 45	93.49 38	79.86 10	92.75 9	75.37 99	96.86 1	98.38 6	75.10 69	95.93 8	94.07 15	96.46 5	89.39 58

PGM-MVS		90.42 10	91.58 37	89.05 6	91.77 14	91.06 13	96.51 7	78.94 17	85.41 72	87.67 19	87.02 94	95.26 56	83.62 12	95.01 24	93.94 16	95.79 19	93.40 19

SteuartSystems-ACMMP		90.00 17	91.73 35	87.97 13	91.21 29	90.29 28	96.51 7	78.00 24	86.33 63	85.32 41	88.23 81	94.67 70	82.08 21	95.13 22	93.88 17	94.72 36	93.59 12
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		89.86 19	91.96 33	87.42 20	91.00 30	90.08 29	96.00 16	76.61 37	89.28 35	87.73 18	90.04 57	91.80 110	78.71 38	94.36 29	93.82 18	94.48 38	94.32 5

SMA-MVS		90.13 15	92.26 27	87.64 18	91.68 16	90.44 26	95.22 24	77.34 33	90.79 22	87.80 17	90.42 55	92.05 107	79.05 35	93.89 33	93.59 19	94.77 34	94.62 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
ACMP	80.00 8	90.12 16	92.30 26	87.58 19	90.83 34	91.10 12	94.96 28	76.06 41	87.47 53	85.33 40	88.91 76	97.65 16	82.13 20	95.31 17	93.44 20	96.14 10	92.22 33
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		90.56 9	92.38 21	88.43 10	90.88 32	91.15 11	95.35 22	77.65 26	86.26 65	87.23 24	90.45 54	97.35 18	83.20 14	95.44 16	93.41 21	96.28 8	92.63 26

PS-CasMVS		89.07 32	93.23 7	84.21 51	92.44 8	88.23 47	90.54 63	82.95 3	90.50 25	75.31 100	95.80 6	98.37 7	71.16 98	96.30 5	93.32 22	92.88 60	90.11 52

WR-MVS_H		88.99 35	93.28 5	83.99 54	91.92 11	89.13 39	91.95 46	83.23 1	90.14 30	71.92 122	95.85 5	98.01 11	71.83 95	95.82 9	93.19 23	93.07 58	90.83 48

CP-MVSNet		88.71 41	92.63 15	84.13 52	92.39 9	88.09 49	90.47 68	82.86 4	88.79 43	75.16 101	94.87 9	97.68 15	71.05 100	96.16 6	93.18 24	92.85 61	89.64 56

anonymousdsp		85.62 61	90.53 48	79.88 92	64.64 202	76.35 138	96.28 13	53.53 187	85.63 69	81.59 70	92.81 30	97.71 14	86.88 2	94.56 26	92.83 25	96.35 6	93.84 8

SD-MVS		89.91 18	92.23 30	87.19 22	91.31 24	89.79 34	94.31 32	75.34 47	89.26 38	81.79 68	92.68 31	95.08 62	83.88 11	93.10 39	92.69 26	96.54 4	93.02 23
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
CPTT-MVS		89.63 25	90.52 49	88.59 7	90.95 31	90.74 21	95.71 17	79.13 15	87.70 51	85.68 39	80.05 135	95.74 46	84.77 6	94.28 30	92.68 27	95.28 26	92.45 31

LS3D		89.02 33	91.69 36	85.91 30	89.72 43	90.81 20	92.56 44	71.69 63	90.83 21	87.24 23	89.71 63	92.07 105	78.37 41	94.43 28	92.59 28	95.86 13	91.35 42

DeepC-MVS	83.59 4	90.37 12	92.56 18	87.82 15	91.26 27	92.33 3	94.72 30	80.04 9	90.01 32	84.61 43	93.33 22	94.22 79	80.59 28	92.90 44	92.52 29	95.69 21	92.57 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + ACMM		89.14 29	92.11 32	85.67 31	89.27 47	90.61 24	90.98 51	79.48 13	88.86 41	79.80 80	93.01 27	93.53 88	83.17 15	92.75 46	92.45 30	91.32 82	93.59 12

PEN-MVS		88.86 39	92.92 9	84.11 53	92.92 5	88.05 50	90.83 55	82.67 5	91.04 18	74.83 102	95.97 4	98.47 4	70.38 105	95.70 13	92.43 31	93.05 59	88.78 64

ACMH+	79.05 11	89.62 26	93.08 8	85.58 32	88.58 55	89.26 38	92.18 45	74.23 52	93.55 8	82.66 59	92.32 36	98.35 8	80.29 29	95.28 18	92.34 32	95.52 22	90.43 50

DTE-MVSNet		88.99 35	92.77 12	84.59 43	93.31 2	88.10 48	90.96 52	83.09 2	91.38 14	76.21 94	96.03 3	98.04 9	70.78 104	95.65 14	92.32 33	93.18 55	87.84 71

TSAR-MVS + MP.		89.67 24	92.25 28	86.65 26	91.53 18	90.98 17	96.15 14	73.30 56	87.88 50	81.83 67	92.92 29	95.15 60	82.23 19	93.58 35	92.25 34	94.87 31	93.01 24
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
3Dnovator+	83.71 3	88.13 44	90.00 52	85.94 29	86.82 72	91.06 13	94.26 33	75.39 46	88.85 42	85.76 38	85.74 108	86.92 143	78.02 43	93.03 40	92.21 35	95.39 25	92.21 34

APD-MVS		89.14 29	91.25 44	86.67 25	91.73 15	91.02 15	95.50 21	77.74 25	84.04 83	79.47 83	91.48 44	94.85 67	81.14 26	92.94 41	92.20 36	94.47 39	92.24 32
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DPE-MVS		89.81 22	92.34 24	86.86 24	89.69 44	91.00 16	95.53 19	76.91 34	88.18 47	83.43 54	93.48 20	95.19 57	81.07 27	92.75 46	92.07 37	94.55 37	93.74 10

OPM-MVS		89.82 21	92.24 29	86.99 23	90.86 33	89.35 37	95.07 27	75.91 43	91.16 16	86.87 31	91.07 50	97.29 19	79.13 34	93.32 36	91.99 38	94.12 41	91.49 41

APDe-MVS		89.85 20	92.91 10	86.29 27	90.47 38	91.34 7	96.04 15	76.41 40	91.11 17	78.50 88	93.44 21	95.82 43	81.55 24	93.16 38	91.90 39	94.77 34	93.58 14

MSLP-MVS++		86.29 59	89.10 57	83.01 59	85.71 82	89.79 34	87.04 104	74.39 51	85.17 74	78.92 86	77.59 149	93.57 86	82.60 17	93.23 37	91.88 40	89.42 105	92.46 30

SixPastTwentyTwo		89.14 29	92.19 31	85.58 32	84.62 89	82.56 92	90.53 64	71.93 60	91.95 12	85.89 36	94.22 14	97.25 20	85.42 5	95.73 12	91.71 41	95.08 28	91.89 37

DVP-MVS		89.40 27	92.69 13	85.56 34	89.01 50	89.85 32	93.72 35	75.42 45	92.28 11	80.49 73	94.36 13	94.87 66	81.46 25	92.49 50	91.42 42	93.27 52	93.54 16
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MSP-MVS		88.51 42	91.36 40	85.19 39	90.63 36	92.01 4	95.29 23	77.52 28	90.48 26	80.21 78	90.21 56	96.08 35	76.38 57	88.30 93	91.42 42	91.12 87	91.01 45
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		88.74 40	89.54 54	87.80 16	92.58 7	85.69 72	95.10 26	78.01 23	87.08 57	87.66 20	87.89 84	92.07 105	80.28 30	90.97 70	91.41 44	93.17 56	91.69 38

SED-MVS		88.96 37	92.37 22	84.99 40	88.64 54	89.65 36	95.11 25	75.98 42	90.73 23	80.15 79	94.21 15	94.51 75	76.59 54	92.94 41	91.17 45	93.46 49	93.37 21

test_part1		87.86 49	93.26 6	81.56 74	87.23 70	86.76 62	90.91 53	70.06 71	96.50 1	76.74 92	96.63 2	98.62 2	69.45 112	92.93 43	90.92 46	94.98 29	90.46 49

OMC-MVS		88.16 43	91.34 42	84.46 46	86.85 71	90.63 23	93.01 41	67.00 100	90.35 27	87.40 22	86.86 97	96.35 30	77.66 48	92.63 48	90.84 47	94.84 32	91.68 39

CNVR-MVS		86.93 53	88.98 58	84.54 44	90.11 40	87.41 57	93.23 40	73.47 55	86.31 64	82.25 62	82.96 123	92.15 103	76.04 60	91.69 54	90.69 48	92.17 73	91.64 40

NCCC		86.74 54	87.97 70	85.31 36	90.64 35	87.25 58	93.27 39	74.59 49	86.50 61	83.72 47	75.92 165	92.39 100	77.08 52	91.72 53	90.68 49	92.57 67	91.30 43

DeepC-MVS_fast	81.78 5	87.38 51	89.64 53	84.75 41	89.89 42	90.70 22	92.74 43	74.45 50	86.02 66	82.16 65	86.05 104	91.99 109	75.84 63	91.16 64	90.44 50	93.41 50	91.09 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		85.32 66	87.41 75	82.89 63	90.07 41	85.69 72	89.07 82	72.99 57	82.45 91	74.52 106	85.09 113	87.67 140	79.24 33	91.11 65	90.41 51	91.45 79	89.45 57

EPP-MVSNet		82.76 91	86.47 80	78.45 102	86.00 80	84.47 77	85.39 113	68.42 89	84.17 80	62.97 160	89.26 70	76.84 177	72.13 93	92.56 49	90.40 52	95.76 20	87.56 74

FPMVS		81.56 100	84.04 113	78.66 100	82.92 111	75.96 142	86.48 108	65.66 114	84.67 78	71.47 125	77.78 147	83.22 156	77.57 49	91.24 62	90.21 53	87.84 123	85.21 87

DeepPCF-MVS	81.61 6	87.95 48	90.29 51	85.22 38	87.48 66	90.01 30	93.79 34	73.54 54	88.93 40	83.89 46	89.40 67	90.84 119	80.26 31	90.62 73	90.19 54	92.36 70	92.03 35

xxxxxxxxxxxxxcwj		88.03 47	91.29 43	84.22 49	88.17 60	87.90 52	90.80 56	71.80 61	89.28 35	82.70 57	89.90 59	97.72 13	77.91 45	91.69 54	90.04 55	93.95 44	92.47 28

SF-MVS		87.85 50	90.95 46	84.22 49	88.17 60	87.90 52	90.80 56	71.80 61	89.28 35	82.70 57	89.90 59	95.37 54	77.91 45	91.69 54	90.04 55	93.95 44	92.47 28

AdaColmap		84.15 75	85.14 96	83.00 60	89.08 49	87.14 60	90.56 62	70.90 66	82.40 92	80.41 74	73.82 176	84.69 152	75.19 68	91.58 58	89.90 57	91.87 76	86.48 78

CDPH-MVS		86.66 56	88.52 61	84.48 45	89.61 45	88.27 45	92.86 42	72.69 58	80.55 116	82.71 56	86.92 96	93.32 90	75.55 65	91.00 69	89.85 58	93.47 48	89.71 55

UniMVSNet_ETH3D		85.39 64	91.12 45	78.71 99	90.48 37	83.72 82	81.76 137	82.41 6	93.84 6	64.43 156	95.41 7	98.76 1	63.72 139	93.63 34	89.74 59	89.47 104	82.74 110

PHI-MVS		86.37 58	88.14 67	84.30 47	86.65 74	87.56 55	90.76 58	70.16 70	82.55 90	89.65 7	84.89 115	92.40 99	75.97 61	90.88 71	89.70 60	92.58 65	89.03 62

PLC	76.06 15	85.38 65	87.46 73	82.95 62	85.79 81	88.84 40	88.86 84	68.70 86	87.06 58	83.60 49	79.02 138	90.05 125	77.37 51	90.88 71	89.66 61	93.37 51	86.74 77
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UniMVSNet (Re)		84.95 69	88.53 60	80.78 81	87.82 64	84.21 78	88.03 90	76.50 38	81.18 109	69.29 136	92.63 34	96.83 23	69.07 113	91.23 63	89.60 62	93.97 43	84.00 97

ACMH	78.40 12	88.94 38	92.62 16	84.65 42	86.45 75	87.16 59	91.47 48	68.79 85	95.49 3	89.74 6	93.55 19	98.50 3	77.96 44	94.14 32	89.57 63	93.49 47	89.94 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet		83.32 83	88.12 68	77.71 106	77.91 152	83.44 86	90.58 60	69.49 76	81.11 110	67.10 150	89.85 61	91.48 114	71.71 96	91.34 60	89.37 64	89.48 103	90.26 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS_MVSNet		81.72 99	85.01 97	77.90 105	86.19 77	82.64 91	85.56 111	70.02 72	80.11 119	63.52 158	87.28 91	81.18 162	67.26 122	91.08 68	89.33 65	94.82 33	83.42 101

DU-MVS		84.88 70	88.27 66	80.92 79	88.30 57	83.59 84	87.06 102	78.35 20	80.64 114	70.49 130	92.67 32	96.91 22	68.13 117	91.79 51	89.29 66	93.20 54	83.02 104

TranMVSNet+NR-MVSNet		85.23 67	89.38 55	80.39 90	88.78 53	83.77 81	87.40 97	76.75 35	85.47 70	68.99 138	95.18 8	97.55 17	67.13 124	91.61 57	89.13 67	93.26 53	82.95 107

CNLPA		85.50 63	88.58 59	81.91 69	84.55 91	87.52 56	90.89 54	63.56 135	88.18 47	84.06 45	83.85 120	91.34 116	76.46 56	91.27 61	89.00 68	91.96 74	88.88 63

NR-MVSNet		82.89 88	87.43 74	77.59 108	83.91 100	83.59 84	87.10 101	78.35 20	80.64 114	68.85 139	92.67 32	96.50 25	54.19 174	87.19 103	88.68 69	93.16 57	82.75 109

train_agg		86.67 55	87.73 71	85.43 35	91.51 19	82.72 89	94.47 31	74.22 53	81.71 98	81.54 71	89.20 71	92.87 94	78.33 42	90.12 77	88.47 70	92.51 69	89.04 61

UniMVSNet_NR-MVSNet		84.62 73	88.00 69	80.68 85	88.18 59	83.83 80	87.06 102	76.47 39	81.46 105	70.49 130	93.24 23	95.56 49	68.13 117	90.43 74	88.47 70	93.78 46	83.02 104

CLD-MVS		82.75 92	87.22 76	77.54 109	88.01 63	85.76 71	90.23 71	54.52 181	82.28 94	82.11 66	88.48 80	95.27 55	63.95 137	89.41 81	88.29 72	86.45 138	81.01 124
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TAPA-MVS	78.00 13	85.88 60	88.37 63	82.96 61	84.69 88	88.62 42	90.62 59	64.22 125	89.15 39	88.05 15	78.83 142	93.71 83	76.20 59	90.11 78	88.22 73	94.00 42	89.97 53
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		88.12 45	91.45 38	84.23 48	88.12 62	90.59 25	90.57 61	68.60 87	91.37 15	83.45 53	89.94 58	95.14 61	78.71 38	91.45 59	88.21 74	95.96 12	93.44 18

Effi-MVS+-dtu		82.04 97	83.39 120	80.48 89	85.48 84	86.57 66	88.40 88	68.28 91	69.04 166	73.13 116	76.26 160	91.11 118	74.74 73	88.40 90	87.76 75	92.84 62	84.57 91

MVS_0304		84.73 72	86.19 83	83.02 58	88.32 56	86.71 63	91.55 47	70.87 67	73.79 144	82.88 55	85.13 112	93.35 89	72.55 88	88.62 87	87.69 76	91.93 75	88.05 70

UGNet		79.62 115	85.91 88	72.28 138	73.52 173	83.91 79	86.64 106	69.51 75	79.85 121	62.57 162	85.82 107	89.63 126	53.18 178	88.39 91	87.35 77	88.28 120	86.43 79
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
MVS_111021_HR		83.95 77	86.10 85	81.44 75	84.62 89	80.29 107	90.51 65	68.05 94	84.07 82	80.38 76	84.74 116	91.37 115	74.23 75	90.37 75	87.25 78	90.86 89	84.59 90

MAR-MVS		81.98 98	82.92 122	80.88 80	85.18 86	85.85 69	89.13 81	69.52 74	71.21 156	82.25 62	71.28 187	88.89 135	69.69 107	88.71 85	86.96 79	89.52 102	87.57 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
FC-MVSNet-train		79.20 121	86.29 82	70.94 145	84.06 95	77.67 126	85.68 110	64.11 127	82.90 88	52.22 189	92.57 35	93.69 84	49.52 189	88.30 93	86.93 80	90.03 95	81.95 117

Effi-MVS+		82.33 93	83.87 114	80.52 88	84.51 92	81.32 100	87.53 95	68.05 94	74.94 142	79.67 81	82.37 128	92.31 101	72.21 90	85.06 119	86.91 81	91.18 85	84.20 94

Gipuma		86.47 57	89.25 56	83.23 56	83.88 101	78.78 119	85.35 114	68.42 89	92.69 10	89.03 12	91.94 37	96.32 33	81.80 22	94.45 27	86.86 82	90.91 88	83.69 98
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
TSAR-MVS + COLMAP		85.51 62	88.36 64	82.19 67	86.05 79	87.69 54	90.50 66	70.60 69	86.40 62	82.33 60	89.69 64	92.52 98	74.01 79	87.53 97	86.84 83	89.63 100	87.80 72

ETV-MVS		79.01 123	77.98 142	80.22 91	86.69 73	79.73 112	88.80 85	68.27 92	63.22 190	71.56 124	70.25 195	73.63 188	73.66 82	90.30 76	86.77 84	92.33 71	81.95 117

3Dnovator	79.41 10	82.21 94	86.07 86	77.71 106	79.31 137	84.61 76	87.18 99	61.02 155	85.65 68	76.11 95	85.07 114	85.38 150	70.96 102	87.22 101	86.47 85	91.66 77	88.12 69

EG-PatchMatch MVS		84.35 74	87.55 72	80.62 86	86.38 76	82.24 94	86.75 105	64.02 130	84.24 79	78.17 90	89.38 68	95.03 64	78.78 37	89.95 79	86.33 86	89.59 101	85.65 85

CANet		82.84 89	84.60 104	80.78 81	87.30 67	85.20 75	90.23 71	69.00 81	72.16 152	78.73 87	84.49 117	90.70 122	69.54 110	87.65 96	86.17 87	89.87 98	85.84 83

canonicalmvs		81.22 105	86.04 87	75.60 117	83.17 110	83.18 87	80.29 146	65.82 113	85.97 67	67.98 146	77.74 148	91.51 113	65.17 133	88.62 87	86.15 88	91.17 86	89.09 60

v7n		87.11 52	90.46 50	83.19 57	85.22 85	83.69 83	90.03 75	68.20 93	91.01 19	86.71 34	94.80 10	98.46 5	77.69 47	91.10 66	85.98 89	91.30 83	88.19 67

DCV-MVSNet		80.04 110	85.67 91	73.48 132	82.91 112	81.11 104	80.44 145	66.06 108	85.01 75	62.53 163	78.84 141	94.43 77	58.51 155	88.66 86	85.91 90	90.41 91	85.73 84

MVS_111021_LR		83.20 85	85.33 92	80.73 84	82.88 113	78.23 123	89.61 76	65.23 117	82.08 95	81.19 72	85.31 110	92.04 108	75.22 67	89.50 80	85.90 91	90.24 92	84.23 93

HQP-MVS		85.02 68	86.41 81	83.40 55	89.19 48	86.59 64	91.28 49	71.60 64	82.79 89	83.48 52	78.65 144	93.54 87	72.55 88	86.49 107	85.89 92	92.28 72	90.95 47

pmmvs6		80.46 107	88.34 65	71.26 141	81.96 120	77.51 127	77.54 161	68.83 84	93.72 7	55.92 175	93.94 18	98.03 10	55.94 164	89.21 83	85.61 93	87.36 129	80.38 128

FC-MVSNet-test		75.91 141	83.59 118	66.95 170	76.63 165	69.07 172	85.33 115	64.97 119	84.87 77	41.95 203	93.17 24	87.04 142	47.78 192	91.09 67	85.56 94	85.06 154	74.34 156

EPNet		79.36 118	79.44 135	79.27 98	89.51 46	77.20 132	88.35 89	77.35 32	68.27 168	74.29 107	76.31 158	79.22 167	59.63 151	85.02 123	85.45 95	86.49 137	84.61 89
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Fast-Effi-MVS+		81.42 101	83.82 115	78.62 101	82.24 118	80.62 106	87.72 93	63.51 136	73.01 146	74.75 104	83.80 121	92.70 96	73.44 84	88.15 95	85.26 96	90.05 94	83.17 102

thres600view7		74.34 150	78.43 139	69.56 155	80.47 127	76.28 139	78.65 159	62.56 145	77.39 131	52.53 185	74.03 174	76.78 178	55.90 166	85.06 119	85.19 97	87.25 130	74.29 157

PM-MVS		80.42 109	83.63 117	76.67 112	78.04 149	72.37 162	87.14 100	60.18 161	80.13 118	71.75 123	86.12 103	93.92 82	77.08 52	86.56 106	85.12 98	85.83 147	81.18 121

MSDG		81.39 103	84.23 110	78.09 104	82.40 117	82.47 93	85.31 116	60.91 156	79.73 122	80.26 77	86.30 100	88.27 138	69.67 108	87.20 102	84.98 99	89.97 96	80.67 126

EIA-MVS		78.57 124	77.90 143	79.35 96	87.24 69	80.71 105	86.16 109	64.03 129	62.63 195	73.49 113	73.60 177	76.12 181	73.83 80	88.49 89	84.93 100	91.36 81	78.78 141

PVSNet_Blended_VisFu		83.00 87	84.16 111	81.65 72	82.17 119	86.01 68	88.03 90	71.23 65	76.05 137	79.54 82	83.88 119	83.44 153	77.49 50	87.38 98	84.93 100	91.41 80	87.40 75

thisisatest0515		81.18 106	84.32 107	77.52 110	76.73 163	74.84 152	85.06 117	61.37 152	81.05 111	73.95 109	88.79 78	89.25 131	75.49 66	85.98 111	84.78 102	92.53 68	85.56 86

MCST-MVS		84.79 71	86.48 79	82.83 64	87.30 67	87.03 61	90.46 69	69.33 79	83.14 86	82.21 64	81.69 131	92.14 104	75.09 70	87.27 100	84.78 102	92.58 65	89.30 59

QAPM		80.43 108	84.34 106	75.86 115	79.40 136	82.06 96	79.86 151	61.94 149	83.28 85	74.73 105	81.74 130	85.44 149	70.97 101	84.99 124	84.71 104	88.29 119	88.14 68

Vis-MVSNet (Re-imp)		76.15 138	80.84 131	70.68 146	83.66 104	74.80 153	81.66 139	69.59 73	80.48 117	46.94 198	87.44 88	80.63 164	53.14 179	86.87 104	84.56 105	89.12 107	71.12 166

CS-MVS		79.35 119	77.74 144	81.22 76	85.59 83	79.85 109	88.78 86	66.61 102	67.63 169	80.41 74	67.82 199	75.07 186	73.27 86	88.31 92	84.36 106	92.63 64	81.18 121

Anonymous202405211			84.68 103		83.92 99	79.45 114	79.03 156	67.79 96	82.01 96		88.77 79	92.58 97	55.93 165	86.68 105	84.26 107	88.92 110	78.98 139

Anonymous20231211		79.37 117	85.78 89	71.89 139	82.87 114	79.66 113	78.77 158	63.93 133	83.36 84	59.39 167	90.54 52	94.66 71	56.46 162	87.38 98	84.12 108	89.92 97	80.74 125

CDS-MVSNet		73.07 157	77.02 150	68.46 161	81.62 122	72.89 159	79.56 154	70.78 68	69.56 161	52.52 186	77.37 152	81.12 163	42.60 197	84.20 129	83.93 109	83.65 160	70.07 171
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PCF-MVS	76.59 14	84.11 76	85.27 93	82.76 65	86.12 78	88.30 44	91.24 50	69.10 80	82.36 93	84.45 44	77.56 150	90.40 124	72.91 87	85.88 112	83.88 110	92.72 63	88.53 65
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		79.71 113	83.74 116	75.01 124	79.31 137	82.68 90	84.79 119	60.06 162	75.43 140	69.09 137	86.13 102	89.38 128	67.16 123	85.12 118	83.87 111	89.65 99	83.57 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
ambc			88.38 62		91.62 17	87.97 51	84.48 121		88.64 45	87.93 16	87.38 89	94.82 69	74.53 74	89.14 84	83.86 112	85.94 145	86.84 76

DPM-MVS		81.42 101	82.11 126	80.62 86	87.54 65	85.30 74	90.18 73	68.96 82	81.00 112	79.15 85	70.45 193	83.29 155	67.67 121	82.81 137	83.46 113	90.19 93	88.48 66

PatchMatch-RL		76.05 139	76.64 153	75.36 119	77.84 153	69.87 170	81.09 142	63.43 137	71.66 154	68.34 145	71.70 183	81.76 161	74.98 71	84.83 125	83.44 114	86.45 138	73.22 163

GBi-Net		73.17 154	77.64 145	67.95 165	76.76 157	77.36 129	75.77 173	64.57 121	62.99 192	51.83 190	76.05 161	77.76 173	52.73 182	85.57 113	83.39 115	86.04 142	80.37 129

test1		73.17 154	77.64 145	67.95 165	76.76 157	77.36 129	75.77 173	64.57 121	62.99 192	51.83 190	76.05 161	77.76 173	52.73 182	85.57 113	83.39 115	86.04 142	80.37 129

FMVSNet1		78.20 127	84.83 102	70.46 149	78.62 144	79.03 116	77.90 160	67.53 99	83.02 87	55.10 178	87.19 93	93.18 92	55.65 167	85.57 113	83.39 115	87.98 122	82.40 113

Baseline_NR-MVSNet		82.79 90	86.51 78	78.44 103	88.30 57	75.62 146	87.81 92	74.97 48	81.53 102	66.84 151	94.71 12	96.46 26	66.90 125	91.79 51	83.37 118	85.83 147	82.09 115

TransMVSNet (Re)		79.05 122	86.66 77	70.18 151	83.32 107	75.99 141	77.54 161	63.98 131	90.68 24	55.84 176	94.80 10	96.06 36	53.73 177	86.27 109	83.22 119	86.65 133	79.61 137

thisisatest0530		75.54 144	75.95 161	75.05 122	75.08 170	73.56 157	82.15 135	60.31 158	69.17 163	69.32 135	79.02 138	58.78 207	72.17 91	83.88 130	83.08 120	91.30 83	84.20 94

tttt0517		75.86 142	76.23 157	75.42 118	75.55 169	74.06 156	82.73 130	60.31 158	69.24 162	70.24 132	79.18 137	58.79 206	72.17 91	84.49 127	83.08 120	91.54 78	84.80 88

pm-mvs1		78.21 126	85.68 90	69.50 156	80.38 129	75.73 144	76.25 169	65.04 118	87.59 52	54.47 180	93.16 25	95.99 41	54.20 173	86.37 108	82.98 122	86.64 134	77.96 146

tfpn200view9		72.01 161	75.40 163	68.06 164	77.97 150	76.44 137	77.04 165	62.67 144	66.81 172	50.82 194	67.30 200	75.67 183	52.46 185	85.06 119	82.64 123	87.41 128	73.86 159

thres400		73.13 156	76.99 152	68.62 160	79.46 135	74.93 151	77.23 163	61.23 154	75.54 138	52.31 188	72.20 182	77.10 176	54.89 169	82.92 134	82.62 124	86.57 136	73.66 162

IB-MVS	71.28 17	75.21 145	77.00 151	73.12 136	76.76 157	77.45 128	83.05 127	58.92 167	63.01 191	64.31 157	59.99 209	87.57 141	68.64 115	86.26 110	82.34 125	87.05 132	82.36 114
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
OpenMVS	75.38 16	78.44 125	81.39 130	74.99 125	80.46 128	79.85 109	79.99 148	58.31 170	77.34 132	73.85 110	77.19 153	82.33 160	68.60 116	84.67 126	81.95 126	88.72 113	86.40 80

TinyColmap		83.79 78	86.12 84	81.07 78	83.42 106	81.44 99	85.42 112	68.55 88	88.71 44	89.46 8	87.60 86	92.72 95	70.34 106	89.29 82	81.94 127	89.20 106	81.12 123

Fast-Effi-MVS+-dtu		76.92 131	77.18 149	76.62 113	79.55 134	79.17 115	84.80 118	77.40 30	64.46 185	68.75 141	70.81 191	86.57 144	63.36 144	81.74 146	81.76 128	85.86 146	75.78 152

ET-MVSNet_ETH3D		74.71 148	74.19 168	75.31 120	79.22 139	75.29 147	82.70 131	64.05 128	65.45 180	70.96 129	77.15 154	57.70 208	65.89 130	84.40 128	81.65 129	89.03 108	77.67 147

pmmvs-eth3d		79.64 114	82.06 127	76.83 111	80.05 131	72.64 160	87.47 96	66.59 103	80.83 113	73.50 112	89.32 69	93.20 91	67.78 119	80.78 153	81.64 130	85.58 150	76.01 150

CANet_DTU		75.04 146	78.45 138	71.07 142	77.27 154	77.96 124	83.88 124	58.00 171	64.11 186	68.67 142	75.65 167	88.37 137	53.92 176	82.05 143	81.11 131	84.67 155	79.88 135

tfpnnormal		77.16 130	84.26 108	68.88 159	81.02 126	75.02 149	76.52 168	63.30 138	87.29 55	52.40 187	91.24 49	93.97 80	54.85 171	85.46 116	81.08 132	85.18 153	75.76 153

CVMVSNet		75.65 143	77.62 147	73.35 135	71.95 179	69.89 169	83.04 128	60.84 157	69.12 164	68.76 140	79.92 136	78.93 169	73.64 83	81.02 151	81.01 133	81.86 169	83.43 100

v1192		83.61 79	85.23 94	81.72 71	84.05 96	82.15 95	89.54 77	66.20 106	81.38 107	86.76 33	91.79 41	96.03 37	74.88 72	81.81 145	80.92 134	88.91 111	82.50 112

v10		83.17 86	85.22 95	80.78 81	83.26 108	82.99 88	88.66 87	66.49 104	79.24 125	83.60 49	91.46 45	95.47 51	74.12 76	82.60 140	80.66 135	88.53 117	84.11 96

IterMVS-LS		79.79 112	82.56 124	76.56 114	81.83 121	77.85 125	79.90 150	69.42 78	78.93 127	71.21 126	90.47 53	85.20 151	70.86 103	80.54 155	80.57 136	86.15 140	84.36 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		83.22 84	85.01 97	81.14 77	83.76 103	81.60 98	88.95 83	65.58 115	81.89 97	85.80 37	91.68 43	95.84 42	74.04 78	82.12 142	80.56 137	88.70 114	81.41 120

v1240		83.57 80	84.94 100	81.97 68	84.05 96	81.27 101	89.46 79	66.06 108	81.31 108	87.50 21	91.88 40	95.46 52	76.25 58	81.16 150	80.51 138	88.52 118	82.98 106

thres200		72.41 160	76.00 160	68.21 163	78.28 146	76.28 139	74.94 179	62.56 145	72.14 153	51.35 193	69.59 197	76.51 179	54.89 169	85.06 119	80.51 138	87.25 130	71.92 165

v1921920		83.49 81	84.94 100	81.80 70	83.78 102	81.20 103	89.50 78	65.91 111	81.64 100	87.18 25	91.70 42	95.39 53	75.85 62	81.56 148	80.27 140	88.60 115	82.80 108

v144192		83.43 82	84.97 99	81.63 73	83.43 105	81.23 102	89.42 80	66.04 110	81.45 106	86.40 35	91.46 45	95.70 47	75.76 64	82.14 141	80.23 141	88.74 112	82.57 111

V42		79.59 116	83.59 118	74.93 127	69.61 186	77.05 134	86.59 107	55.84 176	78.42 129	77.29 91	89.84 62	95.08 62	74.12 76	83.05 133	80.11 142	86.12 141	81.59 119

FMVSNet2		74.43 149	79.70 133	68.27 162	76.76 157	77.36 129	75.77 173	65.36 116	72.28 150	52.97 184	81.92 129	85.61 148	52.73 182	80.66 154	79.73 143	86.04 142	80.37 129

v2v482		82.20 95	84.26 108	79.81 93	82.67 115	80.18 108	87.67 94	63.96 132	81.69 99	84.73 42	91.27 48	96.33 32	72.05 94	81.94 144	79.56 144	87.79 124	78.84 140

thres100view900		69.86 168	72.97 175	66.24 172	77.97 150	72.49 161	73.29 183	59.12 165	66.81 172	50.82 194	67.30 200	75.67 183	50.54 188	78.24 164	79.40 145	85.71 149	70.88 167

MIMVSNet1		73.40 152	81.85 128	63.55 182	72.90 176	64.37 186	84.58 120	53.60 186	90.84 20	53.92 181	87.75 85	96.10 34	45.31 195	85.37 117	79.32 146	70.98 190	69.18 175

v8		82.20 95	84.56 105	79.45 94	82.42 116	81.65 97	87.26 98	64.27 124	79.36 124	81.70 69	91.04 51	95.75 45	73.30 85	82.82 136	79.18 147	87.74 125	82.09 115

pmmvs4		75.92 140	77.48 148	74.10 130	78.21 148	70.94 164	84.06 122	64.78 120	75.13 141	68.47 144	84.12 118	83.32 154	64.74 136	75.93 175	79.14 148	84.31 157	73.77 160

EPNet_dtu		71.90 162	73.03 174	70.59 147	78.28 146	61.64 191	82.44 133	64.12 126	63.26 189	69.74 133	71.47 185	82.41 158	51.89 186	78.83 162	78.01 149	77.07 176	75.60 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		81.39 103	83.07 121	79.43 95	81.48 123	78.95 118	82.62 132	66.17 107	87.45 54	90.73 4	82.40 127	93.65 85	66.57 127	83.63 132	77.97 150	89.00 109	77.45 148

EU-MVSNet		76.48 135	80.53 132	71.75 140	67.62 192	70.30 167	81.74 138	54.06 184	75.47 139	71.01 128	80.10 133	93.17 93	73.67 81	83.73 131	77.85 151	82.40 166	83.07 103

DI_MVS_plusplus_trai		77.64 128	79.64 134	75.31 120	79.87 133	76.89 135	81.55 140	63.64 134	76.21 136	72.03 121	85.59 109	82.97 157	66.63 126	79.27 161	77.78 152	88.14 121	78.76 142

PVSNet_BlendedMVS		76.45 136	78.12 140	74.49 128	76.76 157	78.46 120	79.65 152	63.26 139	65.42 181	73.15 114	75.05 170	88.96 132	66.51 128	82.73 138	77.66 153	87.61 126	78.60 143

PVSNet_Blended		76.45 136	78.12 140	74.49 128	76.76 157	78.46 120	79.65 152	63.26 139	65.42 181	73.15 114	75.05 170	88.96 132	66.51 128	82.73 138	77.66 153	87.61 126	78.60 143

MDA-MVSNet-bldmvs		76.51 134	82.87 123	69.09 158	50.71 213	74.72 154	84.05 123	60.27 160	81.62 101	71.16 127	88.21 82	91.58 111	69.62 109	92.78 45	77.48 155	78.75 175	73.69 161

HyFIR lowres test		73.29 153	74.14 169	72.30 137	73.08 175	78.33 122	83.12 126	62.41 147	63.81 187	62.13 164	76.67 157	78.50 170	71.09 99	74.13 179	77.47 156	81.98 168	70.10 170

CMPMVS	55.74 18	71.56 163	76.26 156	66.08 175	68.11 190	63.91 188	63.17 204	50.52 196	68.79 167	75.49 98	70.78 192	85.67 147	63.54 141	81.58 147	77.20 157	75.63 177	85.86 82
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
casdiffmvs		79.93 111	84.11 112	75.05 122	81.41 125	78.99 117	82.95 129	62.90 143	81.53 102	68.60 143	91.94 37	96.03 37	65.84 131	82.89 135	77.07 158	88.59 116	80.34 132

FMVSNet3		71.40 165	75.20 166	66.97 169	75.00 171	76.59 136	74.29 180	64.57 121	62.99 192	51.83 190	76.05 161	77.76 173	51.49 187	76.58 171	77.03 159	84.62 156	79.43 138

baseline1		69.62 169	73.55 172	65.02 181	78.95 142	70.39 166	71.38 189	62.03 148	70.97 157	47.95 197	78.47 145	68.19 194	47.77 193	79.65 160	76.94 160	82.05 167	70.27 169

GA-MVS		75.01 147	76.39 155	73.39 133	78.37 145	75.66 145	80.03 147	58.40 169	70.51 158	75.85 97	83.24 122	76.14 180	63.75 138	77.28 167	76.62 161	83.97 159	75.30 155

diffmvs		76.74 132	81.61 129	71.06 143	75.64 168	74.45 155	80.68 144	57.57 172	77.48 130	67.62 149	88.95 74	93.94 81	61.98 146	79.74 158	76.18 162	82.85 165	80.50 127

MVSTER		68.08 177	69.73 179	66.16 173	66.33 200	70.06 168	75.71 176	52.36 190	55.18 209	58.64 169	70.23 196	56.72 211	57.34 159	79.68 159	76.03 163	86.61 135	80.20 134

MS-PatchMatch		71.18 166	73.99 170	67.89 167	77.16 155	71.76 163	77.18 164	56.38 175	67.35 170	55.04 179	74.63 172	75.70 182	62.38 145	76.62 170	75.97 164	79.22 173	75.90 151

MVS_Test		76.72 133	79.40 136	73.60 131	78.85 143	74.99 150	79.91 149	61.56 151	69.67 160	72.44 117	85.98 105	90.78 120	63.50 142	78.30 163	75.74 165	85.33 151	80.31 133

IterMVS-SCA-FT		77.23 129	79.18 137	74.96 126	76.67 164	79.85 109	75.58 178	61.34 153	73.10 145	73.79 111	86.23 101	79.61 166	79.00 36	80.28 157	75.50 166	83.41 164	79.70 136

v148		79.33 120	82.32 125	75.84 116	80.14 130	75.74 143	81.98 136	57.06 173	81.51 104	79.36 84	89.42 66	96.42 28	71.32 97	81.54 149	75.29 167	85.20 152	76.32 149

pmmvs5		68.91 172	74.35 167	62.56 184	67.45 194	66.78 180	71.70 186	51.47 193	67.17 171	56.25 174	82.41 126	88.59 136	47.21 194	73.21 185	74.23 168	81.30 170	68.03 177

baseline2		68.71 174	68.34 183	69.14 157	75.69 167	69.70 171	76.60 167	55.53 178	60.13 200	62.07 165	66.76 202	60.35 201	60.77 148	76.53 173	74.03 169	84.19 158	70.88 167

gg-mvs-nofinetune		72.68 159	75.21 165	69.73 153	81.48 123	69.04 173	70.48 190	76.67 36	86.92 59	67.80 148	88.06 83	64.67 196	42.12 199	77.60 165	73.65 170	79.81 171	66.57 178

test20.03		69.91 167	76.20 158	62.58 183	84.01 98	67.34 178	75.67 177	65.88 112	79.98 120	40.28 207	82.65 124	89.31 130	39.63 202	77.41 166	73.28 171	69.98 191	63.40 186

baseline		69.33 171	75.37 164	62.28 185	66.54 198	66.67 181	73.95 182	48.07 197	66.10 175	59.26 168	82.45 125	86.30 145	54.44 172	74.42 178	73.25 172	71.42 186	78.43 145

CR-MVSNet		69.56 170	68.34 183	70.99 144	72.78 178	67.63 176	64.47 202	67.74 97	59.93 201	72.30 118	80.10 133	56.77 210	65.04 134	71.64 187	72.91 173	83.61 162	69.40 173

PatchT		66.25 181	66.76 187	65.67 178	55.87 208	60.75 192	70.17 191	59.00 166	59.80 203	72.30 118	78.68 143	54.12 215	65.04 134	71.64 187	72.91 173	71.63 185	69.40 173

TAMVS		63.02 186	69.30 180	55.70 196	70.12 184	56.89 197	69.63 194	45.13 200	70.23 159	38.00 209	77.79 146	75.15 185	42.60 197	74.48 177	72.81 175	68.70 195	57.75 202

CHOSEN 1792x2688		68.80 173	71.09 176	66.13 174	69.11 188	68.89 174	78.98 157	54.68 179	61.63 197	56.69 172	71.56 184	78.39 171	67.69 120	72.13 186	72.01 176	69.63 193	73.02 164

IterMVS		73.62 151	76.53 154	70.23 150	71.83 180	77.18 133	80.69 143	53.22 188	72.23 151	66.62 152	85.21 111	78.96 168	69.54 110	76.28 174	71.63 177	79.45 172	74.25 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PMMVS		61.98 193	65.61 189	57.74 191	45.03 214	51.76 205	69.54 195	35.05 207	55.49 208	55.32 177	68.23 198	78.39 171	58.09 156	70.21 193	71.56 178	83.42 163	63.66 184

FMVSNet5		56.37 203	60.14 205	51.98 204	60.83 204	59.58 193	66.85 201	42.37 203	52.68 211	41.33 205	47.09 212	54.68 214	35.28 205	73.88 180	70.77 179	65.24 200	62.26 190

testgi		68.20 176	76.05 159	59.04 189	79.99 132	67.32 179	81.16 141	51.78 192	84.91 76	39.36 208	73.42 178	95.19 57	32.79 208	76.54 172	70.40 180	69.14 194	64.55 182

gm-plane-assit		71.56 163	69.99 178	73.39 133	84.43 93	73.21 158	90.42 70	51.36 194	84.08 81	76.00 96	91.30 47	37.09 219	59.01 153	73.65 182	70.24 181	79.09 174	60.37 195

Anonymous20231206		67.28 178	73.41 173	60.12 188	76.45 166	63.61 189	74.21 181	56.52 174	76.35 134	42.23 202	75.81 166	90.47 123	41.51 200	74.52 176	69.97 182	69.83 192	63.17 187

RPMNet		67.02 179	63.99 194	70.56 148	71.55 181	67.63 176	75.81 171	69.44 77	59.93 201	63.24 159	64.32 204	47.51 218	59.68 150	70.37 192	69.64 183	83.64 161	68.49 176

pmmvs3		62.72 189	68.71 182	55.74 195	50.74 212	57.10 196	70.05 192	28.82 210	61.57 199	57.39 171	71.19 189	85.73 146	53.96 175	73.36 184	69.43 184	73.47 181	62.55 189

test0.0.03 1		61.79 194	65.33 190	57.65 192	79.07 140	64.09 187	68.51 199	62.93 141	61.59 198	33.71 211	61.58 208	71.58 192	33.43 207	70.95 190	68.68 185	68.26 196	58.82 198

CHOSEN 280x420		56.32 204	58.85 210	53.36 200	51.63 210	39.91 214	69.12 198	38.61 206	56.29 206	36.79 210	48.84 211	62.59 198	63.39 143	73.61 183	67.66 186	60.61 201	63.07 188

MIMVSNet		63.02 186	69.02 181	56.01 194	68.20 189	59.26 194	70.01 193	53.79 185	71.56 155	41.26 206	71.38 186	82.38 159	36.38 204	71.43 189	67.32 187	66.45 199	59.83 197

MDTV_nov1_ep13_2view		72.96 158	75.59 162	69.88 152	71.15 183	64.86 185	82.31 134	54.45 182	76.30 135	78.32 89	86.52 98	91.58 111	61.35 147	76.80 168	66.83 188	71.70 183	66.26 179

SCA		68.54 175	67.52 185	69.73 153	67.79 191	75.04 148	76.96 166	68.94 83	66.41 174	67.86 147	74.03 174	60.96 199	65.55 132	68.99 195	65.67 189	71.30 188	61.54 194

dps		65.14 182	64.50 192	65.89 177	71.41 182	65.81 184	71.44 188	61.59 150	58.56 204	61.43 166	75.45 168	52.70 217	58.06 157	69.57 194	64.65 190	71.39 187	64.77 181

test-mter		59.39 197	61.59 201	56.82 193	53.21 209	54.82 199	73.12 185	26.57 212	53.19 210	56.31 173	64.71 203	60.47 200	56.36 163	68.69 196	64.27 191	75.38 178	65.00 180

CostFormer		66.81 180	66.94 186	66.67 171	72.79 177	68.25 175	79.55 155	55.57 177	65.52 179	62.77 161	76.98 155	60.09 202	56.73 161	65.69 203	62.35 192	72.59 182	69.71 172

test-LLR		62.15 192	59.46 208	65.29 179	79.07 140	52.66 203	69.46 196	62.93 141	50.76 212	53.81 182	63.11 206	58.91 204	52.87 180	66.54 201	62.34 193	73.59 179	61.87 191

TESTMET0.1,1		57.21 200	59.46 208	54.60 199	50.95 211	52.66 203	69.46 196	26.91 211	50.76 212	53.81 182	63.11 206	58.91 204	52.87 180	66.54 201	62.34 193	73.59 179	61.87 191

new_pmnet		52.29 206	63.16 197	39.61 208	58.89 206	44.70 212	48.78 215	34.73 208	65.88 177	17.85 215	73.42 178	80.00 165	23.06 211	67.00 199	62.28 195	54.36 208	48.81 208

MDTV_nov1_ep13		64.96 183	64.77 191	65.18 180	67.08 195	62.46 190	75.80 172	51.10 195	62.27 196	69.74 133	74.12 173	62.65 197	55.64 168	68.19 197	62.16 196	71.70 183	61.57 193

MVE	41.12 19	51.80 207	60.92 203	41.16 207	35.21 216	34.14 216	48.45 216	41.39 204	69.11 165	19.53 214	63.33 205	73.80 187	63.56 140	67.19 198	61.51 197	38.85 213	57.38 203
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PatchmatchNet		64.81 184	63.74 195	66.06 176	69.21 187	58.62 195	73.16 184	60.01 163	65.92 176	66.19 154	76.27 159	59.09 203	60.45 149	66.58 200	61.47 198	67.33 197	58.24 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		62.79 188	63.25 196	62.26 186	70.09 185	53.78 200	71.65 187	47.31 198	65.72 178	76.70 93	80.62 132	56.40 213	48.11 191	64.20 205	58.54 199	59.70 203	63.47 185

GG-mvs-BLEND		41.63 209	60.36 204	19.78 210	0.14 220	66.04 182	55.66 212	0.17 217	57.64 205	2.42 219	51.82 210	69.42 193	0.28 216	64.11 206	58.29 200	60.02 202	55.18 204

tpm cat1		64.79 185	62.74 199	67.17 168	74.61 172	65.91 183	76.18 170	59.32 164	64.88 184	66.41 153	71.21 188	53.56 216	59.17 152	61.53 207	58.16 201	67.33 197	63.95 183

pmnet_mix02		62.60 190	70.81 177	53.02 201	66.56 197	50.44 207	62.81 205	46.84 199	79.13 126	43.76 201	87.45 87	90.75 121	39.85 201	70.48 191	57.09 202	58.27 205	60.32 196

PMMVS2		48.13 208	64.06 193	29.55 209	44.06 215	36.69 215	51.95 214	29.97 209	74.75 143	8.90 218	76.02 164	91.24 117	7.53 212	73.78 181	55.91 203	34.87 214	40.01 213

tpmrst		59.42 196	60.02 206	58.71 190	67.56 193	53.10 202	66.99 200	51.88 191	63.80 188	57.68 170	76.73 156	56.49 212	48.73 190	56.47 211	55.55 204	59.43 204	58.02 201

EPMVS		56.62 202	59.77 207	52.94 202	62.41 203	50.55 206	60.66 207	52.83 189	65.15 183	41.80 204	77.46 151	57.28 209	42.68 196	59.81 209	54.82 205	57.23 207	53.35 205

ADS-MVSNet		56.89 201	61.09 202	52.00 203	59.48 205	48.10 209	58.02 209	54.37 183	72.82 148	49.19 196	75.32 169	65.97 195	37.96 203	59.34 210	54.66 206	52.99 211	51.42 207

MVS-HIRNet		59.74 195	58.74 211	60.92 187	57.74 207	45.81 211	56.02 211	58.69 168	55.69 207	65.17 155	70.86 190	71.66 190	56.75 160	61.11 208	53.74 207	71.17 189	52.28 206

new-patchmatchnet		62.59 191	73.79 171	49.53 205	76.98 156	53.57 201	53.46 213	54.64 180	85.43 71	28.81 212	91.94 37	96.41 29	25.28 210	76.80 168	53.66 208	57.99 206	58.69 199

E-PMN		59.07 198	62.79 198	54.72 197	67.01 196	47.81 210	60.44 208	43.40 201	72.95 147	44.63 200	70.42 194	73.17 189	58.73 154	80.97 152	51.98 209	54.14 209	42.26 211

N_pmnet		54.95 205	65.90 188	42.18 206	66.37 199	43.86 213	57.92 210	39.79 205	79.54 123	17.24 216	86.31 99	87.91 139	25.44 209	64.68 204	51.76 210	46.33 212	47.23 209

EMVS		58.97 199	62.63 200	54.70 198	66.26 201	48.71 208	61.74 206	42.71 202	72.80 149	46.00 199	73.01 181	71.66 190	57.91 158	80.41 156	50.68 211	53.55 210	41.11 212

tmp_tt				13.54 211	16.73 217	6.42 218	8.49 218	2.36 214	28.69 215	27.44 213	18.40 214	13.51 221	3.70 213	33.23 212	36.26 212	22.54 216

test123		1.06 210	1.41 212	0.64 212	0.39 218	0.48 219	0.52 221	0.25 216	1.11 217	1.37 220	2.01 216	1.98 222	0.87 214	1.43 214	1.27 213	0.46 218	1.62 215

testmvs		0.93 211	1.37 213	0.41 213	0.36 219	0.36 220	0.62 220	0.39 215	1.48 216	0.18 221	2.41 215	1.31 223	0.41 215	1.25 215	1.08 214	0.48 217	1.68 214

uanet_test		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

sosnet-low-res		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

sosnet		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

RE-MVS-def										87.10 29

9.14												89.43 127

SR-MVS					91.82 13			80.80 7				95.53 50

our_test_3					73.27 174	70.91 165	83.26 125

MTAPA										89.37 9		94.85 67

MTMP										90.54 5		95.16 59

Patchmatch-RL test							4.13 219

XVS					91.28 25	91.23 8	96.89 2			87.14 26		94.53 72				95.84 15

X-MVStestdata					91.28 25	91.23 8	96.89 2			87.14 26		94.53 72				95.84 15

abl_6				79.30 97	84.98 87	85.78 70	90.50 66	66.88 101	77.08 133	74.02 108	73.29 180	89.34 129	68.94 114			90.49 90	85.98 81

mPP-MVS					93.05 4							95.77 44

NP-MVS									78.65 128

Patchmtry						56.88 198	64.47 202	67.74 97		72.30 118

DeepMVS_CX						17.78 217	20.40 217	6.69 213	31.41 214	9.80 217	38.61 213	34.88 220	33.78 206	28.41 213		23.59 215	45.77 210

High-resolution multi-view benchmark