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

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
LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 11	83.19 111	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
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

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 96	96.34 31	83.95 10	95.77 11	94.72 8	95.81 17	93.78 9

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 74	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.
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 91	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
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 93	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
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

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 95	95.26 56	83.62 12	95.01 24	93.94 16	95.79 19	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	85.87 107	94.97 65	82.54 18	95.05 23	94.83 7	95.14 27	91.94 36

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
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 73	94.44 76	81.68 23	94.17 31	94.19 14	95.81 17	93.87 6

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	92.22 105	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)
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	92.05 110	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 77	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
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 82	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.
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 69	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
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	91.80 113	78.71 38	94.36 29	93.82 18	94.48 39	94.32 5

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

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 42	91.49 41

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 55	93.48 20	95.19 57	81.07 27	92.75 46	92.07 37	94.55 38	93.74 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
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	75.37 101	96.86 1	98.38 6	75.10 70	95.93 8	94.07 15	96.46 5	89.39 58

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 68	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
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	80.05 138	95.74 46	84.77 6	94.28 30	92.68 27	95.28 26	92.45 31

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 60	92.32 36	98.35 8	80.29 29	95.28 18	92.34 32	95.52 22	90.43 50

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 74	94.36 13	94.87 66	81.46 25	92.49 50	91.42 42	93.27 53	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
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	78.98 143	94.53 72	76.47 56	95.25 19	94.28 12	95.85 14	93.55 15

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 83	93.59 12

SixPastTwentyTwo		89.14 29	92.19 31	85.58 32	84.62 90	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

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 40	92.24 32
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
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	75.31 102	95.80 6	98.37 7	71.16 101	96.30 5	93.32 22	92.88 61	90.11 52

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	74.79 105	88.83 78	88.90 138	78.67 40	96.06 7	95.45 4	96.66 3	95.58 1

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	92.07 108	78.37 41	94.43 28	92.59 28	95.86 13	91.35 42

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 95	96.03 3	98.04 9	70.78 107	95.65 14	92.32 33	93.18 56	87.84 71

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	71.92 125	95.85 5	98.01 11	71.83 98	95.82 9	93.19 23	93.07 59	90.83 48

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 55	92.94 41	91.17 45	93.46 50	93.37 21

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 48	89.94 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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	74.83 104	95.97 4	98.47 4	70.38 108	95.70 13	92.43 31	93.05 60	88.78 64

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 85	92.07 108	80.28 30	90.97 71	91.41 44	93.17 57	91.69 38

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	75.16 103	94.87 9	97.68 15	71.05 103	96.16 6	93.18 24	92.85 62	89.64 56

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 58	88.30 93	91.42 42	91.12 88	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
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 98	96.35 30	77.66 48	92.63 48	90.84 47	94.84 32	91.68 39

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	85.74 109	86.92 147	78.02 43	93.03 40	92.21 35	95.39 25	92.21 34

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 54	89.94 58	95.14 61	78.71 38	91.45 60	88.21 75	95.96 12	93.44 18

RPSCF		88.05 46	92.61 17	82.73 66	84.24 95	88.40 43	90.04 74	66.29 104	91.46 13	82.29 62	88.93 76	96.01 39	79.38 32	95.15 21	94.90 6	94.15 41	93.40 19

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 58	89.90 59	97.72 13	77.91 45	91.69 55	90.04 55	93.95 45	92.47 28

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 68	90.84 122	80.26 31	90.62 74	90.19 54	92.36 70	92.03 35

test_part1		87.86 49	93.26 6	81.56 75	87.23 70	86.76 62	90.91 53	70.06 71	96.50 1	76.74 93	96.63 2	98.62 2	69.45 115	92.93 43	90.92 46	94.98 29	90.46 49

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 58	89.90 59	95.37 54	77.91 45	91.69 55	90.04 55	93.95 45	92.47 28

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 66	86.05 105	91.99 112	75.84 64	91.16 65	90.44 50	93.41 51	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
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 67	85.98 90	91.30 84	88.19 67

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 63	82.96 126	92.15 106	76.04 61	91.69 55	90.69 48	92.17 74	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	75.92 169	92.39 102	77.08 52	91.72 54	90.68 49	92.57 67	91.30 43

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 72	89.20 72	92.87 95	78.33 42	90.12 78	88.47 70	92.51 69	89.04 61

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 57	86.92 97	93.32 90	75.55 66	91.00 70	89.85 58	93.47 49	89.71 55

Gipuma		86.47 57	89.25 56	83.23 56	83.88 103	78.78 121	85.35 117	68.42 89	92.69 10	89.03 12	91.94 37	96.32 33	81.80 22	94.45 27	86.86 83	90.91 89	83.69 100
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
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	84.89 116	92.40 101	75.97 62	90.88 72	89.70 60	92.58 65	89.03 62

MSLP-MVS++		86.29 59	89.10 57	83.01 59	85.71 83	89.79 34	87.04 106	74.39 51	85.17 74	78.92 86	77.59 153	93.57 86	82.60 17	93.23 37	91.88 40	89.42 108	92.46 30

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	78.83 145	93.71 83	76.20 60	90.11 79	88.22 74	94.00 43	89.97 53
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
anonymousdsp		85.62 61	90.53 48	79.88 92	64.64 205	76.35 141	96.28 13	53.53 190	85.63 69	81.59 71	92.81 30	97.71 14	86.88 2	94.56 26	92.83 25	96.35 6	93.84 8

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 61	89.69 64	92.52 100	74.01 80	87.53 99	86.84 84	89.63 103	87.80 72

CNLPA		85.50 63	88.58 59	81.91 70	84.55 92	87.52 56	90.89 54	63.56 136	88.18 47	84.06 45	83.85 122	91.34 119	76.46 57	91.27 62	89.00 68	91.96 75	88.88 63

UniMVSNet_ETH3D		85.39 64	91.12 45	78.71 100	90.48 37	83.72 82	81.76 140	82.41 6	93.84 6	64.43 159	95.41 7	98.76 1	63.72 142	93.63 34	89.74 59	89.47 107	82.74 113

PLC	76.06 15	85.38 65	87.46 73	82.95 62	85.79 82	88.84 40	88.86 85	68.70 86	87.06 58	83.60 49	79.02 141	90.05 128	77.37 51	90.88 72	89.66 61	93.37 52	86.74 77
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + GP.		85.32 66	87.41 75	82.89 63	90.07 41	85.69 72	89.07 83	72.99 57	82.45 91	74.52 109	85.09 114	87.67 144	79.24 33	91.11 66	90.41 51	91.45 80	89.45 57

TranMVSNet+NR-MVSNet		85.23 67	89.38 55	80.39 90	88.78 53	83.77 81	87.40 99	76.75 35	85.47 70	68.99 141	95.18 8	97.55 17	67.13 127	91.61 58	89.13 67	93.26 54	82.95 110

HQP-MVS		85.02 68	86.41 82	83.40 55	89.19 48	86.59 64	91.28 49	71.60 64	82.79 89	83.48 52	78.65 147	93.54 87	72.55 89	86.49 110	85.89 93	92.28 73	90.95 47

UniMVSNet (Re)		84.95 69	88.53 60	80.78 81	87.82 64	84.21 78	88.03 91	76.50 38	81.18 109	69.29 139	92.63 34	96.83 23	69.07 116	91.23 64	89.60 62	93.97 44	84.00 98

CS-MVS-test		84.94 70	87.32 76	82.17 68	85.81 81	81.60 98	88.59 88	63.65 134	80.19 118	83.48 52	89.54 65	92.96 94	76.74 54	92.10 51	88.42 72	94.72 36	86.44 79

DU-MVS		84.88 71	88.27 66	80.92 79	88.30 57	83.59 84	87.06 104	78.35 20	80.64 114	70.49 133	92.67 32	96.91 22	68.13 120	91.79 52	89.29 66	93.20 55	83.02 107

MCST-MVS		84.79 72	86.48 80	82.83 64	87.30 67	87.03 61	90.46 69	69.33 79	83.14 86	82.21 65	81.69 134	92.14 107	75.09 71	87.27 102	84.78 104	92.58 65	89.30 59

MVS_0304		84.73 73	86.19 84	83.02 58	88.32 56	86.71 63	91.55 47	70.87 67	73.79 146	82.88 56	85.13 113	93.35 89	72.55 89	88.62 88	87.69 77	91.93 76	88.05 70

UniMVSNet_NR-MVSNet		84.62 74	88.00 69	80.68 85	88.18 59	83.83 80	87.06 104	76.47 39	81.46 105	70.49 133	93.24 23	95.56 49	68.13 120	90.43 75	88.47 70	93.78 47	83.02 107

EG-PatchMatch MVS		84.35 75	87.55 72	80.62 86	86.38 76	82.24 94	86.75 107	64.02 130	84.24 79	78.17 90	89.38 69	95.03 64	78.78 37	89.95 80	86.33 87	89.59 104	85.65 86

AdaColmap		84.15 76	85.14 97	83.00 60	89.08 49	87.14 60	90.56 62	70.90 66	82.40 92	80.41 75	73.82 180	84.69 156	75.19 69	91.58 59	89.90 57	91.87 77	86.48 78

PCF-MVS	76.59 14	84.11 77	85.27 94	82.76 65	86.12 78	88.30 44	91.24 50	69.10 80	82.36 93	84.45 44	77.56 154	90.40 127	72.91 88	85.88 115	83.88 112	92.72 64	88.53 65
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		83.95 78	86.10 86	81.44 76	84.62 90	80.29 109	90.51 65	68.05 94	84.07 82	80.38 76	84.74 117	91.37 118	74.23 76	90.37 76	87.25 79	90.86 90	84.59 91

TinyColmap		83.79 79	86.12 85	81.07 78	83.42 108	81.44 100	85.42 115	68.55 88	88.71 44	89.46 8	87.60 87	92.72 96	70.34 109	89.29 83	81.94 130	89.20 109	81.12 125

v1192		83.61 80	85.23 95	81.72 72	84.05 98	82.15 95	89.54 78	66.20 105	81.38 107	86.76 33	91.79 41	96.03 37	74.88 73	81.81 148	80.92 137	88.91 114	82.50 115

v1240		83.57 81	84.94 101	81.97 69	84.05 98	81.27 102	89.46 80	66.06 107	81.31 108	87.50 21	91.88 40	95.46 52	76.25 59	81.16 153	80.51 141	88.52 121	82.98 109

v1921920		83.49 82	84.94 101	81.80 71	83.78 104	81.20 104	89.50 79	65.91 110	81.64 100	87.18 25	91.70 42	95.39 53	75.85 63	81.56 151	80.27 143	88.60 118	82.80 111

v144192		83.43 83	84.97 100	81.63 74	83.43 107	81.23 103	89.42 81	66.04 109	81.45 106	86.40 35	91.46 45	95.70 47	75.76 65	82.14 144	80.23 144	88.74 115	82.57 114

Vis-MVSNet		83.32 84	88.12 68	77.71 109	77.91 155	83.44 86	90.58 60	69.49 76	81.11 110	67.10 153	89.85 61	91.48 117	71.71 99	91.34 61	89.37 64	89.48 106	90.26 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1144		83.22 85	85.01 98	81.14 77	83.76 105	81.60 98	88.95 84	65.58 115	81.89 97	85.80 37	91.68 43	95.84 42	74.04 79	82.12 145	80.56 140	88.70 117	81.41 123

MVS_111021_LR		83.20 86	85.33 93	80.73 84	82.88 115	78.23 126	89.61 77	65.23 117	82.08 95	81.19 73	85.31 111	92.04 111	75.22 68	89.50 81	85.90 92	90.24 93	84.23 94

v10		83.17 87	85.22 96	80.78 81	83.26 110	82.99 88	88.66 87	66.49 103	79.24 126	83.60 49	91.46 45	95.47 51	74.12 77	82.60 143	80.66 138	88.53 120	84.11 97

PVSNet_Blended_VisFu		83.00 88	84.16 112	81.65 73	82.17 122	86.01 68	88.03 91	71.23 65	76.05 139	79.54 82	83.88 121	83.44 157	77.49 50	87.38 100	84.93 102	91.41 81	87.40 75

NR-MVSNet		82.89 89	87.43 74	77.59 111	83.91 102	83.59 84	87.10 103	78.35 20	80.64 114	68.85 142	92.67 32	96.50 25	54.19 177	87.19 105	88.68 69	93.16 58	82.75 112

CANet		82.84 90	84.60 105	80.78 81	87.30 67	85.20 75	90.23 71	69.00 81	72.16 155	78.73 87	84.49 119	90.70 125	69.54 113	87.65 98	86.17 88	89.87 100	85.84 84

Baseline_NR-MVSNet		82.79 91	86.51 79	78.44 106	88.30 57	75.62 149	87.81 93	74.97 48	81.53 102	66.84 154	94.71 12	96.46 26	66.90 128	91.79 52	83.37 121	85.83 150	82.09 118

EPP-MVSNet		82.76 92	86.47 81	78.45 105	86.00 80	84.47 77	85.39 116	68.42 89	84.17 80	62.97 163	89.26 71	76.84 181	72.13 95	92.56 49	90.40 52	95.76 20	87.56 74

CLD-MVS		82.75 93	87.22 77	77.54 112	88.01 63	85.76 71	90.23 71	54.52 184	82.28 94	82.11 67	88.48 81	95.27 55	63.95 140	89.41 82	88.29 73	86.45 141	81.01 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		82.33 94	83.87 115	80.52 88	84.51 93	81.32 101	87.53 97	68.05 94	74.94 144	79.67 81	82.37 131	92.31 103	72.21 92	85.06 122	86.91 82	91.18 86	84.20 95

3Dnovator	79.41 10	82.21 95	86.07 87	77.71 109	79.31 140	84.61 76	87.18 101	61.02 157	85.65 68	76.11 96	85.07 115	85.38 154	70.96 105	87.22 103	86.47 86	91.66 78	88.12 69

v8		82.20 96	84.56 106	79.45 95	82.42 118	81.65 97	87.26 100	64.27 124	79.36 125	81.70 70	91.04 51	95.75 45	73.30 87	82.82 139	79.18 150	87.74 128	82.09 118

v2v482		82.20 96	84.26 109	79.81 93	82.67 117	80.18 110	87.67 96	63.96 132	81.69 99	84.73 42	91.27 48	96.33 32	72.05 96	81.94 147	79.56 147	87.79 127	78.84 143

Effi-MVS+-dtu		82.04 98	83.39 123	80.48 89	85.48 84	86.57 66	88.40 89	68.28 91	69.04 169	73.13 119	76.26 164	91.11 121	74.74 74	88.40 91	87.76 76	92.84 63	84.57 92

MAR-MVS		81.98 99	82.92 125	80.88 80	85.18 86	85.85 69	89.13 82	69.52 74	71.21 159	82.25 63	71.28 191	88.89 139	69.69 110	88.71 86	86.96 80	89.52 105	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
GeoE		81.92 100	83.87 115	79.66 94	84.64 89	79.87 111	89.75 76	65.90 111	76.12 138	75.87 98	84.62 118	92.23 104	71.96 97	86.83 107	83.60 115	89.83 101	83.81 99

IS_MVSNet		81.72 101	85.01 98	77.90 108	86.19 77	82.64 91	85.56 114	70.02 72	80.11 120	63.52 161	87.28 92	81.18 166	67.26 125	91.08 69	89.33 65	94.82 33	83.42 103

FPMVS		81.56 102	84.04 114	78.66 101	82.92 113	75.96 145	86.48 110	65.66 114	84.67 78	71.47 128	77.78 151	83.22 160	77.57 49	91.24 63	90.21 53	87.84 126	85.21 88

DPM-MVS		81.42 103	82.11 129	80.62 86	87.54 65	85.30 74	90.18 73	68.96 82	81.00 112	79.15 85	70.45 197	83.29 159	67.67 124	82.81 140	83.46 116	90.19 94	88.48 66

Fast-Effi-MVS+		81.42 103	83.82 117	78.62 102	82.24 120	80.62 107	87.72 94	63.51 137	73.01 148	74.75 106	83.80 123	92.70 97	73.44 85	88.15 95	85.26 97	90.05 95	83.17 104

DROMVSNet		81.42 103	83.82 117	78.62 102	82.24 120	80.62 107	87.72 94	63.51 137	73.01 148	74.75 106	83.80 123	92.70 97	73.44 85	88.15 95	85.26 97	90.05 95	83.17 104

USDC		81.39 106	83.07 124	79.43 96	81.48 126	78.95 120	82.62 135	66.17 106	87.45 54	90.73 4	82.40 130	93.65 85	66.57 130	83.63 135	77.97 153	89.00 112	77.45 151

MSDG		81.39 106	84.23 111	78.09 107	82.40 119	82.47 93	85.31 119	60.91 158	79.73 123	80.26 77	86.30 101	88.27 142	69.67 111	87.20 104	84.98 101	89.97 98	80.67 129

canonicalmvs		81.22 108	86.04 88	75.60 120	83.17 112	83.18 87	80.29 149	65.82 113	85.97 67	67.98 149	77.74 152	91.51 116	65.17 136	88.62 88	86.15 89	91.17 87	89.09 60

thisisatest0515		81.18 109	84.32 108	77.52 113	76.73 166	74.84 155	85.06 120	61.37 154	81.05 111	73.95 112	88.79 79	89.25 135	75.49 67	85.98 114	84.78 104	92.53 68	85.56 87

pmmvs6		80.46 110	88.34 65	71.26 144	81.96 123	77.51 130	77.54 164	68.83 84	93.72 7	55.92 178	93.94 18	98.03 10	55.94 167	89.21 84	85.61 94	87.36 132	80.38 131

QAPM		80.43 111	84.34 107	75.86 118	79.40 139	82.06 96	79.86 154	61.94 151	83.28 85	74.73 108	81.74 133	85.44 153	70.97 104	84.99 127	84.71 106	88.29 122	88.14 68

PM-MVS		80.42 112	83.63 120	76.67 115	78.04 152	72.37 165	87.14 102	60.18 163	80.13 119	71.75 126	86.12 104	93.92 82	77.08 52	86.56 109	85.12 100	85.83 150	81.18 124

DCV-MVSNet		80.04 113	85.67 92	73.48 135	82.91 114	81.11 105	80.44 148	66.06 107	85.01 75	62.53 166	78.84 144	94.43 77	58.51 158	88.66 87	85.91 91	90.41 92	85.73 85

casdiffmvs		79.93 114	84.11 113	75.05 125	81.41 128	78.99 119	82.95 132	62.90 145	81.53 102	68.60 146	91.94 37	96.03 37	65.84 134	82.89 138	77.07 161	88.59 119	80.34 135

CS-MVS		79.80 115	80.83 135	78.60 104	84.11 96	78.49 122	85.82 112	58.91 170	65.79 180	77.94 91	78.53 148	89.70 129	72.51 91	87.89 97	84.32 108	92.34 71	81.12 125

IterMVS-LS		79.79 116	82.56 127	76.56 117	81.83 124	77.85 128	79.90 153	69.42 78	78.93 128	71.21 129	90.47 53	85.20 155	70.86 106	80.54 158	80.57 139	86.15 143	84.36 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		79.71 117	83.74 119	75.01 127	79.31 140	82.68 90	84.79 122	60.06 164	75.43 142	69.09 140	86.13 103	89.38 132	67.16 126	85.12 121	83.87 113	89.65 102	83.57 101
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		79.64 118	82.06 130	76.83 114	80.05 134	72.64 163	87.47 98	66.59 102	80.83 113	73.50 115	89.32 70	93.20 91	67.78 122	80.78 156	81.64 133	85.58 153	76.01 153

UGNet		79.62 119	85.91 89	72.28 141	73.52 176	83.91 79	86.64 108	69.51 75	79.85 122	62.57 165	85.82 108	89.63 130	53.18 181	88.39 92	87.35 78	88.28 123	86.43 80
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
V42		79.59 120	83.59 121	74.93 130	69.61 189	77.05 137	86.59 109	55.84 179	78.42 130	77.29 92	89.84 62	95.08 62	74.12 77	83.05 136	80.11 145	86.12 144	81.59 122

Anonymous20231211		79.37 121	85.78 90	71.89 142	82.87 116	79.66 115	78.77 161	63.93 133	83.36 84	59.39 170	90.54 52	94.66 71	56.46 165	87.38 100	84.12 110	89.92 99	80.74 128

EPNet		79.36 122	79.44 139	79.27 99	89.51 46	77.20 135	88.35 90	77.35 32	68.27 171	74.29 110	76.31 162	79.22 171	59.63 154	85.02 126	85.45 96	86.49 140	84.61 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v148		79.33 123	82.32 128	75.84 119	80.14 133	75.74 146	81.98 139	57.06 176	81.51 104	79.36 84	89.42 67	96.42 28	71.32 100	81.54 152	75.29 170	85.20 155	76.32 152

FC-MVSNet-train		79.20 124	86.29 83	70.94 148	84.06 97	77.67 129	85.68 113	64.11 127	82.90 88	52.22 192	92.57 35	93.69 84	49.52 192	88.30 93	86.93 81	90.03 97	81.95 120

TransMVSNet (Re)		79.05 125	86.66 78	70.18 154	83.32 109	75.99 144	77.54 164	63.98 131	90.68 24	55.84 179	94.80 10	96.06 36	53.73 180	86.27 112	83.22 122	86.65 136	79.61 140

ETV-MVS		79.01 126	77.98 146	80.22 91	86.69 73	79.73 114	88.80 86	68.27 92	63.22 193	71.56 127	70.25 199	73.63 191	73.66 83	90.30 77	86.77 85	92.33 72	81.95 120

EIA-MVS		78.57 127	77.90 147	79.35 97	87.24 69	80.71 106	86.16 111	64.03 129	62.63 198	73.49 116	73.60 181	76.12 185	73.83 81	88.49 90	84.93 102	91.36 82	78.78 144

OpenMVS	75.38 16	78.44 128	81.39 133	74.99 128	80.46 131	79.85 112	79.99 151	58.31 173	77.34 133	73.85 113	77.19 157	82.33 164	68.60 119	84.67 129	81.95 129	88.72 116	86.40 81

pm-mvs1		78.21 129	85.68 91	69.50 159	80.38 132	75.73 147	76.25 172	65.04 118	87.59 52	54.47 183	93.16 25	95.99 41	54.20 176	86.37 111	82.98 125	86.64 137	77.96 149

FMVSNet1		78.20 130	84.83 103	70.46 152	78.62 147	79.03 118	77.90 163	67.53 99	83.02 87	55.10 181	87.19 94	93.18 92	55.65 170	85.57 116	83.39 118	87.98 125	82.40 116

DI_MVS_plusplus_trai		77.64 131	79.64 138	75.31 123	79.87 136	76.89 138	81.55 143	63.64 135	76.21 137	72.03 124	85.59 110	82.97 161	66.63 129	79.27 164	77.78 155	88.14 124	78.76 145

IterMVS-SCA-FT		77.23 132	79.18 141	74.96 129	76.67 167	79.85 112	75.58 181	61.34 155	73.10 147	73.79 114	86.23 102	79.61 170	79.00 36	80.28 160	75.50 169	83.41 167	79.70 139

tfpnnormal		77.16 133	84.26 109	68.88 162	81.02 129	75.02 152	76.52 171	63.30 140	87.29 55	52.40 190	91.24 49	93.97 80	54.85 174	85.46 119	81.08 135	85.18 156	75.76 156

Fast-Effi-MVS+-dtu		76.92 134	77.18 152	76.62 116	79.55 137	79.17 117	84.80 121	77.40 30	64.46 188	68.75 144	70.81 195	86.57 148	63.36 147	81.74 149	81.76 131	85.86 149	75.78 155

diffmvs		76.74 135	81.61 132	71.06 146	75.64 171	74.45 158	80.68 147	57.57 175	77.48 131	67.62 152	88.95 75	93.94 81	61.98 149	79.74 161	76.18 165	82.85 168	80.50 130

MVS_Test		76.72 136	79.40 140	73.60 134	78.85 146	74.99 153	79.91 152	61.56 153	69.67 163	72.44 120	85.98 106	90.78 123	63.50 145	78.30 166	75.74 168	85.33 154	80.31 136

MDA-MVSNet-bldmvs		76.51 137	82.87 126	69.09 161	50.71 216	74.72 157	84.05 126	60.27 162	81.62 101	71.16 130	88.21 83	91.58 114	69.62 112	92.78 45	77.48 158	78.75 178	73.69 164

EU-MVSNet		76.48 138	80.53 136	71.75 143	67.62 195	70.30 170	81.74 141	54.06 187	75.47 141	71.01 131	80.10 136	93.17 93	73.67 82	83.73 134	77.85 154	82.40 169	83.07 106

PVSNet_BlendedMVS		76.45 139	78.12 144	74.49 131	76.76 160	78.46 123	79.65 155	63.26 141	65.42 184	73.15 117	75.05 174	88.96 136	66.51 131	82.73 141	77.66 156	87.61 129	78.60 146

PVSNet_Blended		76.45 139	78.12 144	74.49 131	76.76 160	78.46 123	79.65 155	63.26 141	65.42 184	73.15 117	75.05 174	88.96 136	66.51 131	82.73 141	77.66 156	87.61 129	78.60 146

Vis-MVSNet (Re-imp)		76.15 141	80.84 134	70.68 149	83.66 106	74.80 156	81.66 142	69.59 73	80.48 117	46.94 201	87.44 89	80.63 168	53.14 182	86.87 106	84.56 107	89.12 110	71.12 169

PatchMatch-RL		76.05 142	76.64 156	75.36 122	77.84 156	69.87 173	81.09 145	63.43 139	71.66 157	68.34 148	71.70 187	81.76 165	74.98 72	84.83 128	83.44 117	86.45 141	73.22 166

pmmvs4		75.92 143	77.48 151	74.10 133	78.21 151	70.94 167	84.06 125	64.78 120	75.13 143	68.47 147	84.12 120	83.32 158	64.74 139	75.93 178	79.14 151	84.31 160	73.77 163

FC-MVSNet-test		75.91 144	83.59 121	66.95 173	76.63 168	69.07 175	85.33 118	64.97 119	84.87 77	41.95 206	93.17 24	87.04 146	47.78 195	91.09 68	85.56 95	85.06 157	74.34 159

tttt0517		75.86 145	76.23 160	75.42 121	75.55 172	74.06 159	82.73 133	60.31 160	69.24 165	70.24 135	79.18 140	58.79 209	72.17 93	84.49 130	83.08 123	91.54 79	84.80 89

CVMVSNet		75.65 146	77.62 150	73.35 138	71.95 182	69.89 172	83.04 131	60.84 159	69.12 167	68.76 143	79.92 139	78.93 173	73.64 84	81.02 154	81.01 136	81.86 172	83.43 102

thisisatest0530		75.54 147	75.95 164	75.05 125	75.08 173	73.56 160	82.15 138	60.31 160	69.17 166	69.32 138	79.02 141	58.78 210	72.17 93	83.88 133	83.08 123	91.30 84	84.20 95

IB-MVS	71.28 17	75.21 148	77.00 154	73.12 139	76.76 160	77.45 131	83.05 130	58.92 169	63.01 194	64.31 160	59.99 212	87.57 145	68.64 118	86.26 113	82.34 128	87.05 135	82.36 117
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		75.04 149	78.45 142	71.07 145	77.27 157	77.96 127	83.88 127	58.00 174	64.11 189	68.67 145	75.65 171	88.37 141	53.92 179	82.05 146	81.11 134	84.67 158	79.88 138

GA-MVS		75.01 150	76.39 158	73.39 136	78.37 148	75.66 148	80.03 150	58.40 172	70.51 161	75.85 99	83.24 125	76.14 184	63.75 141	77.28 170	76.62 164	83.97 162	75.30 158

ET-MVSNet_ETH3D		74.71 151	74.19 171	75.31 123	79.22 142	75.29 150	82.70 134	64.05 128	65.45 183	70.96 132	77.15 158	57.70 211	65.89 133	84.40 131	81.65 132	89.03 111	77.67 150

FMVSNet2		74.43 152	79.70 137	68.27 165	76.76 160	77.36 132	75.77 176	65.36 116	72.28 153	52.97 187	81.92 132	85.61 152	52.73 185	80.66 157	79.73 146	86.04 145	80.37 132

thres600view7		74.34 153	78.43 143	69.56 158	80.47 130	76.28 142	78.65 162	62.56 147	77.39 132	52.53 188	74.03 178	76.78 182	55.90 169	85.06 122	85.19 99	87.25 133	74.29 160

IterMVS		73.62 154	76.53 157	70.23 153	71.83 183	77.18 136	80.69 146	53.22 191	72.23 154	66.62 155	85.21 112	78.96 172	69.54 113	76.28 177	71.63 180	79.45 175	74.25 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet1		73.40 155	81.85 131	63.55 185	72.90 179	64.37 189	84.58 123	53.60 189	90.84 20	53.92 184	87.75 86	96.10 34	45.31 198	85.37 120	79.32 149	70.98 193	69.18 178

HyFIR lowres test		73.29 156	74.14 172	72.30 140	73.08 178	78.33 125	83.12 129	62.41 149	63.81 190	62.13 167	76.67 161	78.50 174	71.09 102	74.13 182	77.47 159	81.98 171	70.10 173

GBi-Net		73.17 157	77.64 148	67.95 168	76.76 160	77.36 132	75.77 176	64.57 121	62.99 195	51.83 193	76.05 165	77.76 177	52.73 185	85.57 116	83.39 118	86.04 145	80.37 132

test1		73.17 157	77.64 148	67.95 168	76.76 160	77.36 132	75.77 176	64.57 121	62.99 195	51.83 193	76.05 165	77.76 177	52.73 185	85.57 116	83.39 118	86.04 145	80.37 132

thres400		73.13 159	76.99 155	68.62 163	79.46 138	74.93 154	77.23 166	61.23 156	75.54 140	52.31 191	72.20 186	77.10 180	54.89 172	82.92 137	82.62 127	86.57 139	73.66 165

CDS-MVSNet		73.07 160	77.02 153	68.46 164	81.62 125	72.89 162	79.56 157	70.78 68	69.56 164	52.52 189	77.37 156	81.12 167	42.60 200	84.20 132	83.93 111	83.65 163	70.07 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MDTV_nov1_ep13_2view		72.96 161	75.59 165	69.88 155	71.15 186	64.86 188	82.31 137	54.45 185	76.30 136	78.32 89	86.52 99	91.58 114	61.35 150	76.80 171	66.83 191	71.70 186	66.26 182

gg-mvs-nofinetune		72.68 162	75.21 168	69.73 156	81.48 126	69.04 176	70.48 193	76.67 36	86.92 59	67.80 151	88.06 84	64.67 199	42.12 202	77.60 168	73.65 173	79.81 174	66.57 181

thres200		72.41 163	76.00 163	68.21 166	78.28 149	76.28 142	74.94 182	62.56 147	72.14 156	51.35 196	69.59 201	76.51 183	54.89 172	85.06 122	80.51 141	87.25 133	71.92 168

tfpn200view9		72.01 164	75.40 166	68.06 167	77.97 153	76.44 140	77.04 168	62.67 146	66.81 174	50.82 197	67.30 203	75.67 187	52.46 188	85.06 122	82.64 126	87.41 131	73.86 162

EPNet_dtu		71.90 165	73.03 177	70.59 150	78.28 149	61.64 194	82.44 136	64.12 126	63.26 192	69.74 136	71.47 189	82.41 162	51.89 189	78.83 165	78.01 152	77.07 179	75.60 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gm-plane-assit		71.56 166	69.99 181	73.39 136	84.43 94	73.21 161	90.42 70	51.36 197	84.08 81	76.00 97	91.30 47	37.09 223	59.01 156	73.65 185	70.24 184	79.09 177	60.37 198

CMPMVS	55.74 18	71.56 166	76.26 159	66.08 178	68.11 193	63.91 191	63.17 207	50.52 199	68.79 170	75.49 100	70.78 196	85.67 151	63.54 144	81.58 150	77.20 160	75.63 180	85.86 83
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		71.40 168	75.20 169	66.97 172	75.00 174	76.59 139	74.29 183	64.57 121	62.99 195	51.83 193	76.05 165	77.76 177	51.49 190	76.58 174	77.03 162	84.62 159	79.43 141

MS-PatchMatch		71.18 169	73.99 173	67.89 170	77.16 158	71.76 166	77.18 167	56.38 178	67.35 172	55.04 182	74.63 176	75.70 186	62.38 148	76.62 173	75.97 167	79.22 176	75.90 154

test20.03		69.91 170	76.20 161	62.58 186	84.01 100	67.34 181	75.67 180	65.88 112	79.98 121	40.28 210	82.65 127	89.31 134	39.63 205	77.41 169	73.28 174	69.98 194	63.40 189

thres100view900		69.86 171	72.97 178	66.24 175	77.97 153	72.49 164	73.29 186	59.12 167	66.81 174	50.82 197	67.30 203	75.67 187	50.54 191	78.24 167	79.40 148	85.71 152	70.88 170

baseline1		69.62 172	73.55 175	65.02 184	78.95 145	70.39 169	71.38 192	62.03 150	70.97 160	47.95 200	78.47 149	68.19 197	47.77 196	79.65 163	76.94 163	82.05 170	70.27 172

CR-MVSNet		69.56 173	68.34 186	70.99 147	72.78 181	67.63 179	64.47 205	67.74 97	59.93 204	72.30 121	80.10 136	56.77 213	65.04 137	71.64 190	72.91 176	83.61 165	69.40 176

baseline		69.33 174	75.37 167	62.28 188	66.54 201	66.67 184	73.95 185	48.07 200	66.10 177	59.26 171	82.45 128	86.30 149	54.44 175	74.42 181	73.25 175	71.42 189	78.43 148

pmmvs5		68.91 175	74.35 170	62.56 187	67.45 197	66.78 183	71.70 189	51.47 196	67.17 173	56.25 177	82.41 129	88.59 140	47.21 197	73.21 188	74.23 171	81.30 173	68.03 180

CHOSEN 1792x2688		68.80 176	71.09 179	66.13 177	69.11 191	68.89 177	78.98 160	54.68 182	61.63 200	56.69 175	71.56 188	78.39 175	67.69 123	72.13 189	72.01 179	69.63 196	73.02 167

baseline2		68.71 177	68.34 186	69.14 160	75.69 170	69.70 174	76.60 170	55.53 181	60.13 203	62.07 168	66.76 205	60.35 204	60.77 151	76.53 176	74.03 172	84.19 161	70.88 170

SCA		68.54 178	67.52 188	69.73 156	67.79 194	75.04 151	76.96 169	68.94 83	66.41 176	67.86 150	74.03 178	60.96 202	65.55 135	68.99 198	65.67 192	71.30 191	61.54 197

testgi		68.20 179	76.05 162	59.04 192	79.99 135	67.32 182	81.16 144	51.78 195	84.91 76	39.36 211	73.42 182	95.19 57	32.79 211	76.54 175	70.40 183	69.14 197	64.55 185

MVSTER		68.08 180	69.73 182	66.16 176	66.33 203	70.06 171	75.71 179	52.36 193	55.18 212	58.64 172	70.23 200	56.72 214	57.34 162	79.68 162	76.03 166	86.61 138	80.20 137

Anonymous20231206		67.28 181	73.41 176	60.12 191	76.45 169	63.61 192	74.21 184	56.52 177	76.35 135	42.23 205	75.81 170	90.47 126	41.51 203	74.52 179	69.97 185	69.83 195	63.17 190

RPMNet		67.02 182	63.99 197	70.56 151	71.55 184	67.63 179	75.81 174	69.44 77	59.93 204	63.24 162	64.32 207	47.51 222	59.68 153	70.37 195	69.64 186	83.64 164	68.49 179

CostFormer		66.81 183	66.94 189	66.67 174	72.79 180	68.25 178	79.55 158	55.57 180	65.52 182	62.77 164	76.98 159	60.09 205	56.73 164	65.69 206	62.35 195	72.59 185	69.71 175

PatchT		66.25 184	66.76 190	65.67 181	55.87 211	60.75 195	70.17 194	59.00 168	59.80 206	72.30 121	78.68 146	54.12 218	65.04 137	71.64 190	72.91 176	71.63 188	69.40 176

dps		65.14 185	64.50 195	65.89 180	71.41 185	65.81 187	71.44 191	61.59 152	58.56 207	61.43 169	75.45 172	52.70 220	58.06 160	69.57 197	64.65 193	71.39 190	64.77 184

MDTV_nov1_ep13		64.96 186	64.77 194	65.18 183	67.08 198	62.46 193	75.80 175	51.10 198	62.27 199	69.74 136	74.12 177	62.65 200	55.64 171	68.19 200	62.16 199	71.70 186	61.57 196

PatchmatchNet		64.81 187	63.74 198	66.06 179	69.21 190	58.62 198	73.16 187	60.01 165	65.92 178	66.19 157	76.27 163	59.09 206	60.45 152	66.58 203	61.47 201	67.33 200	58.24 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		64.79 188	62.74 202	67.17 171	74.61 175	65.91 186	76.18 173	59.32 166	64.88 187	66.41 156	71.21 192	53.56 219	59.17 155	61.53 210	58.16 204	67.33 200	63.95 186

MIMVSNet		63.02 189	69.02 184	56.01 197	68.20 192	59.26 197	70.01 196	53.79 188	71.56 158	41.26 209	71.38 190	82.38 163	36.38 207	71.43 192	67.32 190	66.45 202	59.83 200

TAMVS		63.02 189	69.30 183	55.70 199	70.12 187	56.89 200	69.63 197	45.13 203	70.23 162	38.00 212	77.79 150	75.15 189	42.60 200	74.48 180	72.81 178	68.70 198	57.75 205

tpm		62.79 191	63.25 199	62.26 189	70.09 188	53.78 203	71.65 190	47.31 201	65.72 181	76.70 94	80.62 135	56.40 216	48.11 194	64.20 208	58.54 202	59.70 206	63.47 188

pmmvs3		62.72 192	68.71 185	55.74 198	50.74 215	57.10 199	70.05 195	28.82 213	61.57 202	57.39 174	71.19 193	85.73 150	53.96 178	73.36 187	69.43 187	73.47 184	62.55 192

pmnet_mix02		62.60 193	70.81 180	53.02 204	66.56 200	50.44 210	62.81 208	46.84 202	79.13 127	43.76 204	87.45 88	90.75 124	39.85 204	70.48 194	57.09 205	58.27 208	60.32 199

new-patchmatchnet		62.59 194	73.79 174	49.53 208	76.98 159	53.57 204	53.46 216	54.64 183	85.43 71	28.81 215	91.94 37	96.41 29	25.28 213	76.80 171	53.66 211	57.99 209	58.69 202

test-LLR		62.15 195	59.46 211	65.29 182	79.07 143	52.66 206	69.46 199	62.93 143	50.76 215	53.81 185	63.11 209	58.91 207	52.87 183	66.54 204	62.34 196	73.59 182	61.87 194

PMMVS		61.98 196	65.61 192	57.74 194	45.03 217	51.76 208	69.54 198	35.05 210	55.49 211	55.32 180	68.23 202	78.39 175	58.09 159	70.21 196	71.56 181	83.42 166	63.66 187

test0.0.03 1		61.79 197	65.33 193	57.65 195	79.07 143	64.09 190	68.51 202	62.93 143	61.59 201	33.71 214	61.58 211	71.58 195	33.43 210	70.95 193	68.68 188	68.26 199	58.82 201

MVS-HIRNet		59.74 198	58.74 214	60.92 190	57.74 210	45.81 214	56.02 214	58.69 171	55.69 210	65.17 158	70.86 194	71.66 193	56.75 163	61.11 211	53.74 210	71.17 192	52.28 209

tpmrst		59.42 199	60.02 209	58.71 193	67.56 196	53.10 205	66.99 203	51.88 194	63.80 191	57.68 173	76.73 160	56.49 215	48.73 193	56.47 214	55.55 207	59.43 207	58.02 204

test-mter		59.39 200	61.59 204	56.82 196	53.21 212	54.82 202	73.12 188	26.57 215	53.19 213	56.31 176	64.71 206	60.47 203	56.36 166	68.69 199	64.27 194	75.38 181	65.00 183

E-PMN		59.07 201	62.79 201	54.72 200	67.01 199	47.81 213	60.44 211	43.40 204	72.95 150	44.63 203	70.42 198	73.17 192	58.73 157	80.97 155	51.98 212	54.14 212	42.26 214

EMVS		58.97 202	62.63 203	54.70 201	66.26 204	48.71 211	61.74 209	42.71 205	72.80 152	46.00 202	73.01 185	71.66 193	57.91 161	80.41 159	50.68 214	53.55 213	41.11 215

TESTMET0.1,1		57.21 203	59.46 211	54.60 202	50.95 214	52.66 206	69.46 199	26.91 214	50.76 215	53.81 185	63.11 209	58.91 207	52.87 183	66.54 204	62.34 196	73.59 182	61.87 194

ADS-MVSNet		56.89 204	61.09 205	52.00 206	59.48 208	48.10 212	58.02 212	54.37 186	72.82 151	49.19 199	75.32 173	65.97 198	37.96 206	59.34 213	54.66 209	52.99 214	51.42 210

EPMVS		56.62 205	59.77 210	52.94 205	62.41 206	50.55 209	60.66 210	52.83 192	65.15 186	41.80 207	77.46 155	57.28 212	42.68 199	59.81 212	54.82 208	57.23 210	53.35 208

FMVSNet5		56.37 206	60.14 208	51.98 207	60.83 207	59.58 196	66.85 204	42.37 206	52.68 214	41.33 208	47.09 215	54.68 217	35.28 208	73.88 183	70.77 182	65.24 203	62.26 193

CHOSEN 280x420		56.32 207	58.85 213	53.36 203	51.63 213	39.91 217	69.12 201	38.61 209	56.29 209	36.79 213	48.84 214	62.59 201	63.39 146	73.61 186	67.66 189	60.61 204	63.07 191

N_pmnet		54.95 208	65.90 191	42.18 209	66.37 202	43.86 216	57.92 213	39.79 208	79.54 124	17.24 220	86.31 100	87.91 143	25.44 212	64.68 207	51.76 213	46.33 215	47.23 212

new_pmnet		52.29 209	63.16 200	39.61 211	58.89 209	44.70 215	48.78 218	34.73 211	65.88 179	17.85 219	73.42 182	80.00 169	23.06 214	67.00 202	62.28 198	54.36 211	48.81 211

MVE	41.12 19	51.80 210	60.92 206	41.16 210	35.21 219	34.14 219	48.45 219	41.39 207	69.11 168	19.53 218	63.33 208	73.80 190	63.56 143	67.19 201	61.51 200	38.85 216	57.38 206
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		48.13 211	64.06 196	29.55 212	44.06 218	36.69 218	51.95 217	29.97 212	74.75 145	8.90 222	76.02 168	91.24 120	7.53 216	73.78 184	55.91 206	34.87 217	40.01 216

GG-mvs-BLEND		41.63 212	60.36 207	19.78 213	0.14 224	66.04 185	55.66 215	0.17 221	57.64 208	2.42 223	51.82 213	69.42 196	0.28 220	64.11 209	58.29 203	60.02 205	55.18 207

test_method		22.69 213	26.99 215	17.67 214	2.13 221	4.31 222	27.50 220	4.53 217	37.94 217	24.52 217	36.20 217	51.40 221	15.26 215	29.86 216	17.09 216	32.07 218	12.16 217

test123		1.06 214	1.41 216	0.64 216	0.39 222	0.48 223	0.52 225	0.25 220	1.11 221	1.37 224	2.01 220	1.98 226	0.87 218	1.43 218	1.27 217	0.46 222	1.62 219

testmvs		0.93 215	1.37 217	0.41 217	0.36 223	0.36 224	0.62 224	0.39 219	1.48 220	0.18 225	2.41 219	1.31 227	0.41 219	1.25 219	1.08 218	0.48 221	1.68 218

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										87.10 29

9.14												89.43 131

SR-MVS					91.82 13			80.80 7				95.53 50

Anonymous202405211			84.68 104		83.92 101	79.45 116	79.03 159	67.79 96	82.01 96		88.77 80	92.58 99	55.93 168	86.68 108	84.26 109	88.92 113	78.98 142

our_test_3					73.27 177	70.91 168	83.26 128

ambc			88.38 62		91.62 17	87.97 51	84.48 124		88.64 45	87.93 16	87.38 90	94.82 69	74.53 75	89.14 85	83.86 114	85.94 148	86.84 76

MTAPA										89.37 9		94.85 67

MTMP										90.54 5		95.16 59

Patchmatch-RL test							4.13 223

tmp_tt				13.54 215	16.73 220	6.42 221	8.49 222	2.36 218	28.69 219	27.44 216	18.40 218	13.51 225	3.70 217	33.23 215	36.26 215	22.54 220

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 98	84.98 87	85.78 70	90.50 66	66.88 101	77.08 134	74.02 111	73.29 184	89.34 133	68.94 117			90.49 91	85.98 82

mPP-MVS					93.05 4							95.77 44

NP-MVS									78.65 129

Patchmtry						56.88 201	64.47 205	67.74 97		72.30 121

DeepMVS_CX						17.78 220	20.40 221	6.69 216	31.41 218	9.80 221	38.61 216	34.88 224	33.78 209	28.41 217		23.59 219	45.77 213

High-resolution multi-view benchmark