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.

LTVRE_ROB	95.06 1	97.73 1	98.39 2	96.95 1	96.33 51	96.94 35	98.30 22	94.90 15	98.61 2	97.73 3	97.97 24	98.57 23	95.74 4	99.24 1	98.70 4	98.72 8	98.70 1
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
TDRefinement		97.59 2	98.32 3	96.73 4	95.90 67	98.10 2	99.08 2	93.92 32	98.24 4	96.44 13	98.12 20	97.86 52	96.06 2	99.24 1	98.93 1	99.00 2	97.77 4

WR-MVS		97.53 3	98.20 4	96.76 3	96.93 29	98.17 1	98.60 10	96.67 7	96.39 14	94.46 32	99.14 1	98.92 11	94.57 15	99.06 3	98.80 2	99.32 1	96.92 26

SixPastTwentyTwo		97.36 4	97.73 10	96.92 2	97.36 13	96.15 55	98.29 23	94.43 24	96.50 12	96.96 7	98.74 6	98.74 18	96.04 3	99.03 5	97.74 18	98.44 23	97.22 14

PS-CasMVS		97.22 5	97.84 7	96.50 5	97.08 25	97.92 6	98.17 31	97.02 2	94.71 26	95.32 21	98.52 13	98.97 9	92.91 40	99.04 4	98.47 6	98.49 19	97.24 13

PEN-MVS		97.16 6	97.87 6	96.33 12	97.20 21	97.97 4	98.25 26	96.86 6	95.09 24	94.93 26	98.66 8	99.16 6	92.27 50	98.98 6	98.39 8	98.49 19	96.83 30

DTE-MVSNet		97.16 6	97.75 9	96.47 6	97.40 12	97.95 5	98.20 29	96.89 5	95.30 19	95.15 24	98.66 8	98.80 16	92.77 44	98.97 7	98.27 10	98.44 23	96.28 41

COLMAP_ROB	93.74 2	97.09 8	97.98 5	96.05 18	95.97 63	97.78 9	98.56 11	91.72 82	97.53 8	96.01 15	98.14 19	98.76 17	95.28 5	98.76 12	98.23 11	98.77 6	96.67 35
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		97.06 9	97.78 8	96.23 14	96.74 37	98.04 3	98.25 26	97.32 1	94.40 33	93.71 52	98.55 11	98.89 12	92.97 37	98.91 9	98.45 7	98.38 28	97.19 15

CP-MVSNet		96.97 10	97.42 14	96.44 7	97.06 26	97.82 8	98.12 33	96.98 3	93.50 46	95.21 23	97.98 23	98.44 25	92.83 43	98.93 8	98.37 9	98.46 22	96.91 27

test_part1		96.91 11	98.63 1	94.90 45	94.62 98	97.75 11	98.33 21	93.88 34	98.92 1	93.11 67	99.06 2	99.66 1	90.49 89	98.84 11	98.61 5	98.97 3	97.60 7

ACMH	90.17 8	96.61 12	97.69 12	95.35 31	95.29 83	96.94 35	98.43 15	92.05 70	98.04 5	95.38 19	98.07 21	99.25 5	93.23 33	98.35 17	97.16 40	97.72 50	96.00 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		96.56 13	96.73 24	96.36 10	98.99 1	97.90 7	97.79 43	95.64 10	92.78 60	92.54 75	96.23 68	95.02 126	94.31 18	98.43 16	98.12 12	98.89 4	98.58 2

ACMMPR		96.54 14	96.71 25	96.35 11	97.55 9	97.63 12	98.62 9	94.54 19	94.45 30	94.19 38	95.04 93	97.35 64	94.92 10	97.85 30	97.50 27	98.26 29	97.17 16

v7n		96.49 15	97.20 18	95.65 23	95.57 77	96.04 57	97.93 38	92.49 55	96.40 13	97.13 6	98.99 3	99.41 4	93.79 26	97.84 32	96.15 61	97.00 77	95.60 55

DeepC-MVS	92.47 4	96.44 16	96.75 23	96.08 17	97.57 7	97.19 31	97.96 37	94.28 25	95.29 20	94.92 27	98.31 18	96.92 76	93.69 27	96.81 62	96.50 51	98.06 39	96.27 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM	90.06 9	96.31 17	96.42 32	96.19 15	97.21 20	97.16 33	98.71 5	93.79 38	94.35 34	93.81 46	92.80 124	98.23 33	95.11 6	98.07 22	97.45 29	98.51 18	96.86 29
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	89.90 10	96.27 18	97.52 13	94.81 46	95.19 86	97.18 32	97.97 36	92.52 53	96.72 10	90.50 121	97.31 44	99.11 7	94.10 20	98.67 13	97.90 15	98.56 16	95.79 51

APDe-MVS		96.23 19	97.22 17	95.08 40	96.66 41	97.56 15	98.63 8	93.69 42	94.62 27	89.80 130	97.73 32	98.13 37	93.84 25	97.79 34	97.63 20	97.87 46	97.08 21

CP-MVS		96.21 20	96.16 43	96.27 13	97.56 8	97.13 34	98.43 15	94.70 18	92.62 63	94.13 41	92.71 125	98.03 43	94.54 16	98.00 26	97.60 22	98.23 31	97.05 22

zzz-MVS		96.18 21	96.01 46	96.38 8	98.30 2	96.18 54	98.51 13	94.48 23	94.56 28	94.81 30	91.73 134	96.96 74	94.30 19	98.09 20	97.83 16	97.91 45	96.73 32

HFP-MVS		96.18 21	96.53 29	95.77 21	97.34 16	97.26 28	98.16 32	94.54 19	94.45 30	92.52 76	95.05 91	96.95 75	93.89 23	97.28 45	97.46 28	98.19 32	97.25 11

UniMVSNet_ETH3D		96.15 23	97.71 11	94.33 53	97.31 17	96.71 40	95.06 107	96.91 4	97.86 6	90.42 122	98.55 11	99.60 2	88.01 117	98.51 14	97.81 17	98.26 29	94.95 66

MP-MVS		96.13 24	95.93 49	96.37 9	98.19 4	97.31 27	98.49 14	94.53 22	91.39 92	94.38 35	94.32 106	96.43 89	94.59 14	97.75 36	97.44 30	98.04 40	96.88 28
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		96.12 25	96.27 39	95.93 19	97.20 21	97.60 13	98.64 7	93.74 39	92.47 65	93.13 66	93.23 119	98.06 40	94.51 17	97.99 27	97.57 24	98.39 27	96.99 23
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
DVP-MVS		96.10 26	97.23 16	94.79 48	96.28 54	97.49 16	97.90 39	93.60 44	95.47 17	89.57 136	97.32 43	97.72 55	93.89 23	97.74 37	97.53 25	97.51 55	97.34 9
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
LGP-MVS_train		96.10 26	96.29 36	95.87 20	96.72 38	97.35 26	98.43 15	93.83 36	90.81 106	92.67 74	95.05 91	98.86 14	95.01 7	98.11 19	97.37 36	98.52 17	96.50 37

CSCG		96.07 28	97.15 19	94.81 46	96.06 61	97.58 14	96.52 72	90.98 94	96.51 11	93.60 54	97.13 50	98.55 24	93.01 35	97.17 49	95.36 77	98.68 10	97.78 3

DPE-MVS		96.00 29	96.80 22	95.06 41	95.87 70	97.47 21	98.25 26	93.73 40	92.38 67	91.57 103	97.55 38	97.97 45	92.98 36	97.49 43	97.61 21	97.96 44	97.16 17
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		95.99 30	96.48 30	95.41 30	97.43 11	97.36 24	97.55 48	93.70 41	94.05 41	93.79 47	97.02 53	94.53 131	92.28 49	97.53 42	97.19 38	97.73 49	97.67 6
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
TSAR-MVS + MP.		95.99 30	96.57 28	95.31 33	96.87 30	96.50 47	98.71 5	91.58 83	93.25 51	92.71 71	96.86 55	96.57 87	93.92 21	98.09 20	97.91 14	98.08 37	96.81 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		95.96 32	96.59 27	95.23 36	96.67 40	96.52 46	97.86 41	93.28 47	95.27 22	93.46 56	96.26 65	98.85 15	92.89 41	97.09 50	96.37 56	97.22 70	95.78 52

SteuartSystems-ACMMP		95.96 32	96.13 45	95.76 22	97.06 26	97.36 24	98.40 19	94.24 27	91.49 86	91.91 94	94.50 102	96.89 77	94.99 8	98.01 25	97.44 30	97.97 43	97.25 11
Skip Steuart: Steuart Systems R&D Blog.
ACMP	89.62 11	95.96 32	96.28 37	95.59 24	96.58 43	97.23 30	98.26 25	93.22 48	92.33 70	92.31 83	94.29 107	98.73 19	94.68 12	98.04 23	97.14 41	98.47 21	96.17 44
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PGM-MVS		95.90 35	95.72 53	96.10 16	97.53 10	97.45 22	98.55 12	94.12 29	90.25 109	93.71 52	93.20 120	97.18 68	94.63 13	97.68 39	97.34 37	98.08 37	96.97 24

PMVS	87.16 16	95.88 36	96.47 31	95.19 38	97.00 28	96.02 58	96.70 63	91.57 84	94.43 32	95.33 20	97.16 49	95.37 115	92.39 46	98.89 10	98.72 3	98.17 34	94.71 71
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		95.86 37	96.18 40	95.47 29	97.11 24	97.26 28	98.37 20	93.48 46	93.49 47	93.99 44	95.61 77	94.11 136	92.49 45	97.87 29	97.44 30	97.40 61	97.52 8

Gipuma		95.86 37	96.17 41	95.50 28	95.92 66	94.59 102	94.77 115	92.50 54	97.82 7	97.90 2	95.56 80	97.88 50	94.71 11	98.02 24	94.81 91	97.23 69	94.48 78
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LS3D		95.83 39	96.35 34	95.22 37	96.47 47	97.49 16	97.99 34	92.35 58	94.92 25	94.58 31	94.88 97	95.11 124	91.52 61	98.48 15	98.05 13	98.42 25	95.49 56

SD-MVS		95.77 40	96.17 41	95.30 34	96.72 38	96.19 53	97.01 54	93.04 49	94.03 42	92.71 71	96.45 63	96.78 84	93.91 22	96.79 63	95.89 67	98.42 25	97.09 20
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
SED-MVS		95.73 41	96.98 20	94.28 55	96.08 59	97.39 23	98.18 30	93.80 37	94.20 36	89.61 135	97.29 45	97.49 61	90.69 80	97.74 37	97.41 33	97.32 66	97.34 9

TranMVSNet+NR-MVSNet		95.72 42	96.42 32	94.91 44	96.21 55	96.77 39	96.90 59	94.99 13	92.62 63	91.92 93	98.51 14	98.63 21	90.82 77	97.27 46	96.83 45	98.63 13	94.31 79

DU-MVS		95.51 43	95.68 54	95.33 32	96.45 48	96.44 49	96.61 69	95.32 11	89.97 114	93.78 48	97.46 40	98.07 39	91.19 68	97.03 52	96.53 49	98.61 14	94.22 80

UniMVSNet (Re)		95.46 44	95.86 51	95.00 43	96.09 57	96.60 41	96.68 67	94.99 13	90.36 108	92.13 86	97.64 36	98.13 37	91.38 62	96.90 57	96.74 46	98.73 7	94.63 74

RPSCF		95.46 44	96.95 21	93.73 78	95.72 74	95.94 61	95.58 98	88.08 139	95.31 18	91.34 105	96.26 65	98.04 42	93.63 28	98.28 18	97.67 19	98.01 41	97.13 18

anonymousdsp		95.45 46	96.70 26	93.99 66	88.43 196	92.05 145	99.18 1	85.42 173	94.29 35	96.10 14	98.63 10	99.08 8	96.11 1	97.77 35	97.41 33	98.70 9	97.69 5

APD-MVS		95.38 47	95.68 54	95.03 42	97.30 18	96.90 37	97.83 42	93.92 32	89.40 121	90.35 123	95.41 84	97.69 57	92.97 37	97.24 48	97.17 39	97.83 47	95.96 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet_NR-MVSNet		95.34 48	95.51 58	95.14 39	95.80 72	96.55 42	96.61 69	94.79 16	90.04 113	93.78 48	97.51 39	97.25 65	91.19 68	96.68 65	96.31 58	98.65 12	94.22 80

X-MVS		95.33 49	95.13 66	95.57 26	97.35 14	97.48 18	98.43 15	94.28 25	92.30 71	93.28 59	86.89 180	96.82 80	91.87 55	97.85 30	97.59 23	98.19 32	96.95 25

MSP-MVS		95.32 50	96.28 37	94.19 58	96.87 30	97.77 10	98.27 24	93.88 34	94.15 40	89.63 134	95.36 85	98.37 28	90.73 78	94.37 111	97.53 25	95.77 116	96.40 38
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
3Dnovator+	92.82 3	95.22 51	95.16 64	95.29 35	96.17 56	96.55 42	97.64 45	94.02 31	94.16 39	94.29 37	92.09 131	93.71 141	91.90 53	96.68 65	96.51 50	97.70 52	96.40 38

HPM-MVS++		95.21 52	94.89 69	95.59 24	97.79 6	95.39 79	97.68 44	94.05 30	91.91 78	94.35 36	93.38 118	95.07 125	92.94 39	96.01 78	95.88 68	96.73 80	96.61 36

TSAR-MVS + ACMM		95.17 53	95.95 47	94.26 56	96.07 60	96.46 48	95.67 95	94.21 28	93.84 44	90.99 113	97.18 48	95.24 123	93.55 29	96.60 69	95.61 74	95.06 135	96.69 34

xxxxxxxxxxxxxcwj		95.03 54	96.14 44	93.73 78	95.30 80	95.93 62	94.80 113	91.76 79	93.11 55	91.93 91	95.83 73	98.96 10	91.11 71	96.62 67	96.44 53	97.46 56	96.13 45

CPTT-MVS		95.00 55	94.52 77	95.57 26	96.84 34	96.78 38	97.88 40	93.67 43	92.20 72	92.35 82	85.87 188	97.56 60	94.98 9	96.96 55	96.07 64	97.70 52	96.18 43

SF-MVS		94.88 56	95.87 50	93.73 78	95.30 80	95.93 62	94.80 113	91.76 79	93.11 55	91.93 91	95.83 73	97.07 71	91.11 71	96.62 67	96.44 53	97.46 56	96.13 45

Baseline_NR-MVSNet		94.85 57	95.35 62	94.26 56	96.45 48	93.86 117	96.70 63	94.54 19	90.07 112	90.17 127	98.77 5	97.89 47	90.64 83	97.03 52	96.16 60	97.04 76	93.67 92

EG-PatchMatch MVS		94.81 58	95.53 57	93.97 67	95.89 69	94.62 100	95.55 100	88.18 137	92.77 61	94.88 28	97.04 52	98.61 22	93.31 30	96.89 58	95.19 81	95.99 109	93.56 95

OMC-MVS		94.74 59	95.46 60	93.91 70	94.62 98	96.26 52	96.64 68	89.36 125	94.20 36	94.15 40	94.02 112	97.73 54	91.34 64	96.15 76	95.04 85	97.37 63	94.80 68

DeepC-MVS_fast	91.38 6	94.73 60	94.98 67	94.44 49	96.83 36	96.12 56	96.69 65	92.17 64	92.98 58	93.72 50	94.14 108	95.45 113	90.49 89	95.73 85	95.30 78	96.71 81	95.13 64
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		94.65 61	94.84 71	94.44 49	94.95 91	96.55 42	96.46 75	91.10 92	88.96 124	96.00 16	94.55 101	95.32 118	90.67 81	96.97 54	96.69 48	97.44 59	94.84 67

pmmvs6		94.58 62	97.30 15	91.40 118	94.84 93	94.61 101	93.40 143	92.43 57	98.51 3	85.61 161	98.73 7	99.53 3	84.40 139	97.88 28	97.03 42	97.72 50	94.79 69

DeepPCF-MVS	90.68 7	94.56 63	94.92 68	94.15 59	94.11 111	95.71 70	97.03 53	90.65 98	93.39 50	94.08 42	95.29 88	94.15 135	93.21 34	95.22 96	94.92 89	95.82 115	95.75 53

NR-MVSNet		94.55 64	95.66 56	93.25 91	94.26 107	96.44 49	96.69 65	95.32 11	89.97 114	91.79 99	97.46 40	98.39 27	82.85 148	96.87 60	96.48 52	98.57 15	93.98 86

Vis-MVSNet		94.39 65	95.85 52	92.68 99	90.91 179	95.88 65	97.62 47	91.41 85	91.95 77	89.20 138	97.29 45	96.26 92	90.60 88	96.95 56	95.91 65	96.32 96	96.71 33
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TSAR-MVS + GP.		94.25 66	94.81 72	93.60 81	96.52 46	95.80 68	94.37 123	92.47 56	90.89 102	88.92 140	95.34 86	94.38 133	92.85 42	96.36 74	95.62 73	96.47 88	95.28 61

CNVR-MVS		94.24 67	94.47 78	93.96 68	96.56 44	95.67 71	96.43 76	91.95 73	92.08 75	91.28 107	90.51 142	95.35 116	91.20 67	96.34 75	95.50 76	96.34 94	95.88 50

v1192		93.98 68	93.94 90	94.01 64	93.91 119	94.63 99	97.00 55	89.75 115	91.01 100	96.50 10	97.93 25	98.26 32	91.74 57	92.06 141	92.05 131	95.18 130	91.66 133

v10		93.96 69	94.12 87	93.77 77	93.37 131	95.45 75	96.83 61	91.13 91	89.70 118	95.02 25	97.88 28	98.23 33	91.27 65	92.39 136	92.18 129	94.99 137	93.00 104

CDPH-MVS		93.96 69	93.86 92	94.08 61	96.31 52	95.84 66	96.92 57	91.85 76	87.21 140	91.25 109	92.83 122	96.06 100	91.05 74	95.57 87	94.81 91	97.12 71	94.72 70

MVS_0304		93.92 71	93.81 96	94.05 63	96.06 61	96.00 59	96.43 76	92.76 51	85.99 151	94.43 34	94.04 111	97.08 70	88.12 116	94.65 107	94.20 104	96.47 88	94.71 71

MSLP-MVS++		93.91 72	94.30 84	93.45 83	95.51 78	95.83 67	93.12 149	91.93 75	91.45 89	91.40 104	87.42 175	96.12 99	93.27 31	96.57 70	96.40 55	95.49 120	96.29 40

v1921920		93.90 73	93.82 94	94.00 65	93.74 125	94.31 105	97.12 50	89.33 126	91.13 97	96.77 9	97.90 26	98.06 40	91.95 52	91.93 145	91.54 140	95.10 133	91.85 127

train_agg		93.89 74	93.46 106	94.40 51	97.35 14	93.78 119	97.63 46	92.19 63	88.12 131	90.52 120	93.57 117	95.78 106	92.31 48	94.78 104	93.46 115	96.36 92	94.70 73

v144192		93.89 74	93.85 93	93.94 69	93.50 129	94.33 104	97.12 50	89.49 120	90.89 102	96.49 11	97.78 30	98.27 31	91.89 54	92.17 140	91.70 137	95.19 129	91.78 130

v1240		93.89 74	93.72 97	94.09 60	93.98 116	94.31 105	97.12 50	89.37 124	90.74 107	96.92 8	98.05 22	97.89 47	92.15 51	91.53 151	91.60 138	94.99 137	91.93 126

NCCC		93.87 77	93.42 107	94.40 51	96.84 34	95.42 76	96.47 74	92.62 52	92.36 69	92.05 88	83.83 195	95.55 109	91.84 56	95.89 80	95.23 80	96.56 85	95.63 54

v1144		93.83 78	93.87 91	93.78 76	93.72 126	94.57 103	96.85 60	89.98 110	91.31 94	95.90 17	97.89 27	98.40 26	91.13 70	92.01 144	92.01 132	95.10 133	90.94 137

MVS_111021_HR		93.82 79	94.26 86	93.31 86	95.01 89	93.97 115	95.73 92	89.75 115	92.06 76	92.49 77	94.01 113	96.05 101	90.61 87	95.95 79	94.78 94	96.28 97	93.04 103

thisisatest0515		93.79 80	94.41 80	93.06 96	94.14 108	92.50 138	95.56 99	88.55 134	91.61 82	92.45 78	96.84 56	95.71 107	90.62 85	94.58 108	95.07 83	97.05 74	94.58 75

TAPA-MVS	88.94 13	93.78 81	94.31 83	93.18 93	94.14 108	95.99 60	95.74 91	86.98 156	93.43 49	93.88 45	90.16 149	96.88 78	91.05 74	94.33 112	93.95 106	97.28 67	95.40 57
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE		93.72 82	93.62 101	93.84 71	94.75 94	94.90 93	97.24 49	91.81 78	86.97 143	92.74 70	93.83 115	97.24 67	90.46 91	95.10 100	94.09 105	96.08 106	93.18 101

EPP-MVSNet		93.63 83	93.95 89	93.26 89	95.15 87	96.54 45	96.18 84	91.97 72	91.74 79	85.76 159	94.95 95	84.27 183	91.60 60	97.61 41	97.38 35	98.87 5	95.18 63

v8		93.60 84	93.82 94	93.34 84	93.13 138	95.06 85	96.39 78	90.75 96	89.90 116	94.03 43	97.70 34	98.21 35	91.08 73	92.36 137	91.47 141	94.63 144	92.07 121

MCST-MVS		93.60 84	93.40 109	93.83 72	95.30 80	95.40 78	96.49 73	90.87 95	90.08 111	91.72 100	90.28 147	95.99 102	91.69 58	93.94 120	92.99 120	96.93 78	95.13 64

PVSNet_Blended_VisFu		93.60 84	93.41 108	93.83 72	96.31 52	95.65 72	95.71 93	90.58 100	88.08 133	93.17 64	95.29 88	92.20 150	90.72 79	94.69 106	93.41 117	96.51 87	94.54 76

TransMVSNet (Re)		93.55 87	96.32 35	90.32 134	94.38 104	94.05 110	93.30 146	89.53 119	97.15 9	85.12 164	98.83 4	97.89 47	82.21 154	96.75 64	96.14 62	97.35 64	93.46 96

DCV-MVSNet		93.49 88	95.15 65	91.55 114	94.05 112	95.92 64	95.15 105	91.21 88	92.76 62	87.01 155	89.71 152	97.16 69	83.90 143	97.65 40	96.87 44	97.99 42	95.95 49

v2v482		93.42 89	93.49 105	93.32 85	93.44 130	94.05 110	96.36 81	89.76 114	91.41 91	95.24 22	97.63 37	98.34 29	90.44 92	91.65 149	91.76 136	94.69 141	89.62 147

canonicalmvs		93.38 90	94.36 81	92.24 105	93.94 118	96.41 51	94.18 130	90.47 101	93.07 57	88.47 146	88.66 162	93.78 140	88.80 106	95.74 84	95.75 71	97.57 54	97.13 18

3Dnovator	91.81 5	93.36 91	94.27 85	92.29 104	92.99 142	95.03 86	95.76 90	87.79 142	93.82 45	92.38 81	92.19 130	93.37 145	88.14 115	95.26 95	94.85 90	96.69 82	95.40 57

pm-mvs1		93.27 92	95.94 48	90.16 135	94.13 110	93.66 120	92.61 159	89.91 112	95.73 16	84.28 173	98.51 14	98.29 30	82.80 149	96.44 72	95.76 70	97.25 68	93.21 100

Anonymous20231211		93.19 93	95.50 59	90.49 131	93.77 123	95.29 81	94.36 127	90.04 109	91.44 90	84.59 168	96.72 58	97.65 58	82.45 153	97.25 47	96.32 57	97.74 48	93.79 89

TinyColmap		93.17 94	93.33 110	93.00 97	93.84 121	92.76 133	94.75 117	88.90 130	93.97 43	97.48 4	95.28 90	95.29 119	88.37 111	95.31 94	91.58 139	94.65 143	89.10 151

MVS_111021_LR		93.15 95	93.65 99	92.56 100	93.89 120	92.28 140	95.09 106	86.92 158	91.26 96	92.99 69	94.46 104	96.22 95	90.64 83	95.11 99	93.45 116	95.85 113	92.74 110

CNLPA		93.14 96	93.67 98	92.53 101	94.62 98	94.73 96	95.00 109	86.57 163	92.85 59	92.43 79	90.94 137	94.67 128	90.35 93	95.41 89	93.70 112	96.23 100	93.37 98

PLC	87.27 15	93.08 97	92.92 114	93.26 89	94.67 95	95.03 86	94.38 122	90.10 104	91.69 80	92.14 85	87.24 176	93.91 138	91.61 59	95.05 101	94.73 97	96.67 83	92.80 107
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		93.07 98	93.05 113	93.10 94	95.90 67	95.41 77	95.88 87	91.94 74	84.77 158	93.36 57	94.05 110	95.25 122	86.25 128	94.33 112	93.94 107	95.30 123	93.58 94

TSAR-MVS + COLMAP		93.06 99	93.65 99	92.36 102	94.62 98	94.28 107	95.36 104	89.46 122	92.18 73	91.64 101	95.55 81	95.27 121	88.60 109	93.24 126	92.50 125	94.46 146	92.55 116

Effi-MVS+		92.93 100	92.16 125	93.83 72	94.29 105	93.53 127	95.04 108	92.98 50	85.27 155	94.46 32	90.24 148	95.34 117	89.99 97	93.72 121	94.23 103	96.22 101	92.79 108

Fast-Effi-MVS+		92.93 100	92.64 118	93.27 88	93.81 122	93.88 116	95.90 86	90.61 99	83.98 164	92.71 71	92.81 123	96.22 95	90.67 81	94.90 103	93.92 108	95.92 111	92.77 109

HQP-MVS		92.87 102	92.49 119	93.31 86	95.75 73	95.01 89	95.64 96	91.06 93	88.54 128	91.62 102	88.16 167	96.25 93	89.47 101	92.26 139	91.81 134	96.34 94	95.40 57

FMVSNet1		92.86 103	95.26 63	90.06 137	92.40 156	95.16 82	94.37 123	92.22 60	93.18 54	82.16 183	96.76 57	97.48 62	81.85 158	95.32 91	94.98 86	97.34 65	93.93 87

CLD-MVS		92.81 104	94.32 82	91.05 122	95.39 79	95.31 80	95.82 89	81.44 196	89.40 121	91.94 90	95.86 71	97.36 63	85.83 130	95.35 90	94.59 99	95.85 113	92.34 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		92.76 105	93.25 111	92.19 106	94.91 92	95.56 73	95.86 88	92.12 66	88.10 132	82.71 178	93.15 121	88.30 171	88.86 105	97.29 44	96.95 43	98.66 11	93.38 97

FC-MVSNet-train		92.75 106	95.40 61	89.66 145	95.21 85	94.82 94	97.00 55	89.40 123	91.13 97	81.71 184	97.72 33	96.43 89	77.57 181	96.89 58	96.72 47	97.05 74	94.09 83

V42		92.67 107	93.50 104	91.71 112	91.41 170	92.96 131	95.71 93	85.00 174	89.67 119	93.22 62	97.67 35	98.01 44	91.02 76	92.65 132	92.12 130	93.86 154	91.42 134

PM-MVS		92.65 108	93.20 112	92.00 108	92.11 164	90.16 166	95.99 85	84.81 178	91.31 94	92.41 80	95.87 70	96.64 86	92.35 47	93.65 123	92.91 121	94.34 149	91.85 127

QAPM		92.57 109	93.51 103	91.47 116	92.91 144	94.82 94	93.01 151	87.51 146	91.49 86	91.21 110	92.24 128	91.70 153	88.74 107	94.54 109	94.39 102	95.41 121	95.37 60

MIMVSNet1		92.52 110	94.88 70	89.77 141	96.09 57	91.99 146	96.92 57	89.68 117	95.92 15	84.55 169	96.64 60	98.21 35	78.44 175	96.08 77	95.10 82	92.91 168	90.22 144

tfpnnormal		92.45 111	94.77 73	89.74 142	93.95 117	93.44 129	93.25 147	88.49 136	95.27 22	83.20 176	96.51 61	96.23 94	83.17 147	95.47 88	94.52 100	96.38 91	91.97 125

PCF-MVS	87.46 14	92.44 112	91.80 127	93.19 92	94.66 96	95.80 68	96.37 79	90.19 103	87.57 137	92.23 84	89.26 157	93.97 137	89.24 102	91.32 153	90.82 149	96.46 90	93.86 88
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		92.42 113	93.99 88	90.60 129	93.25 134	93.82 118	94.28 129	88.73 132	91.53 84	84.53 171	97.74 31	98.64 20	86.60 125	93.21 128	91.20 144	96.21 102	91.76 132

AdaColmap		92.41 114	91.49 131	93.48 82	95.96 64	95.02 88	95.37 103	91.73 81	87.97 135	91.28 107	82.82 199	91.04 157	90.62 85	95.82 83	95.07 83	95.95 110	92.67 111

v148		92.38 115	92.78 116	91.91 109	92.86 145	92.13 143	94.84 111	87.03 155	91.47 88	93.07 68	96.92 54	98.89 12	90.10 96	92.05 142	89.69 157	93.56 157	88.27 160

pmmvs-eth3d		92.34 116	92.33 120	92.34 103	92.67 149	90.67 160	96.37 79	89.06 127	90.98 101	93.60 54	97.13 50	97.02 73	88.29 112	90.20 160	91.42 142	94.07 152	88.89 155

DELS-MVS		92.33 117	93.61 102	90.83 125	92.84 146	95.13 84	94.76 116	87.22 154	87.78 136	88.42 148	95.78 75	95.28 120	85.71 133	94.44 110	93.91 109	96.01 108	92.97 105
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
Effi-MVS+-dtu		92.32 118	91.66 129	93.09 95	95.13 88	94.73 96	94.57 120	92.14 65	81.74 175	90.33 124	88.13 168	95.91 103	89.24 102	94.23 117	93.65 114	97.12 71	93.23 99

UGNet		92.31 119	94.70 74	89.53 147	90.99 178	95.53 74	96.19 83	92.10 68	91.35 93	85.76 159	95.31 87	95.48 112	76.84 186	95.22 96	94.79 93	95.32 122	95.19 62
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
USDC		92.17 120	92.17 124	92.18 107	92.93 143	92.22 141	93.66 137	87.41 149	93.49 47	97.99 1	94.10 109	96.68 85	86.46 126	92.04 143	89.18 163	94.61 145	87.47 163

ETV-MVS		92.12 121	90.44 138	94.08 61	96.36 50	93.63 122	96.27 82	92.00 71	78.90 195	92.13 86	85.29 190	89.85 164	90.26 95	97.07 51	96.29 59	97.46 56	92.04 123

IterMVS-LS		92.10 122	92.33 120	91.82 111	93.18 135	93.66 120	92.80 157	92.27 59	90.82 104	90.59 119	97.19 47	90.97 158	87.76 118	89.60 167	90.94 148	94.34 149	93.16 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		92.09 123	92.84 115	91.22 121	92.55 151	92.97 130	93.42 142	85.43 172	90.24 110	91.83 96	94.70 98	94.59 129	88.48 110	94.91 102	93.31 119	95.59 119	89.15 150

CS-MVS		92.04 124	90.08 144	94.32 54	95.94 65	94.95 92	96.72 62	91.36 86	80.81 181	94.18 39	81.09 203	90.28 162	90.30 94	95.84 82	95.57 75	97.41 60	92.05 122

EIA-MVS		91.95 125	90.36 140	93.81 75	96.54 45	94.65 98	95.38 102	90.40 102	78.01 200	93.72 50	86.70 183	91.95 152	89.93 98	95.67 86	94.72 98	96.89 79	90.79 138

MAR-MVS		91.86 126	91.14 134	92.71 98	94.29 105	94.24 108	94.91 110	91.82 77	81.66 176	93.32 58	84.51 193	93.42 144	86.86 123	95.16 98	94.44 101	95.05 136	94.53 77
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
EU-MVSNet		91.63 127	92.73 117	90.35 133	88.36 197	87.89 177	96.53 71	81.51 195	92.45 66	91.82 97	96.44 64	97.05 72	93.26 32	94.10 118	88.94 168	90.61 175	92.24 119

FC-MVSNet-test		91.49 128	94.43 79	88.07 163	94.97 90	90.53 163	95.42 101	91.18 90	93.24 52	72.94 205	98.37 16	93.86 139	78.78 169	97.82 33	96.13 63	95.13 131	91.05 135

OpenMVS	89.22 12	91.09 129	91.42 132	90.71 127	92.79 148	93.61 124	92.74 158	85.47 171	86.10 150	90.73 114	85.71 189	93.07 148	86.69 124	94.07 119	93.34 118	95.86 112	94.02 85

FPMVS		90.81 130	91.60 130	89.88 140	92.52 152	88.18 173	93.31 145	83.62 184	91.59 83	88.45 147	88.96 160	89.73 166	86.96 121	96.42 73	95.69 72	94.43 147	90.65 139

DI_MVS_plusplus_trai		90.68 131	90.40 139	91.00 123	92.43 155	92.61 137	94.17 131	88.98 128	88.32 130	88.76 144	93.67 116	87.58 173	86.44 127	89.74 165	90.33 152	95.24 126	90.56 142

Vis-MVSNet (Re-imp)		90.68 131	92.18 123	88.92 152	94.63 97	92.75 134	92.91 153	91.20 89	89.21 123	75.01 202	93.96 114	89.07 169	82.72 151	95.88 81	95.30 78	97.08 73	89.08 152

DPM-MVS		90.67 133	89.86 145	91.63 113	95.29 83	94.16 109	94.52 121	89.63 118	89.59 120	89.67 133	81.95 201	88.64 170	85.75 132	90.46 158	90.43 151	94.91 139	93.77 90

diffmvs		90.44 134	92.23 122	88.35 159	91.36 172	91.38 152	92.45 163	84.84 177	89.88 117	85.09 165	96.69 59	97.71 56	83.33 146	90.01 164	88.96 167	93.03 166	91.00 136

FMVSNet2		90.28 135	92.04 126	88.23 161	91.22 174	94.05 110	92.88 154	90.69 97	86.53 146	79.89 191	94.38 105	92.73 149	78.54 172	91.64 150	92.26 128	96.17 103	92.67 111

IterMVS-SCA-FT		90.24 136	89.37 151	91.26 120	92.50 153	92.11 144	91.69 173	87.48 147	87.05 142	91.82 97	95.76 76	87.25 174	91.36 63	89.02 172	85.53 183	92.68 169	88.90 154

MVS_Test		90.19 137	90.58 135	89.74 142	92.12 163	91.74 148	92.51 160	88.54 135	82.80 170	87.50 152	94.62 99	95.02 126	83.97 141	88.69 175	89.32 161	93.79 155	91.85 127

EPNet		90.17 138	89.07 153	91.45 117	97.25 19	90.62 162	94.84 111	93.54 45	80.96 178	91.85 95	86.98 179	85.88 179	77.79 178	92.30 138	92.58 124	93.41 159	94.20 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		90.09 139	90.12 142	90.05 138	92.40 156	92.74 135	91.74 169	85.89 167	80.54 182	90.30 125	88.54 163	95.51 110	84.69 137	92.64 133	90.25 153	95.28 124	90.61 140

PVSNet_Blended		90.09 139	90.12 142	90.05 138	92.40 156	92.74 135	91.74 169	85.89 167	80.54 182	90.30 125	88.54 163	95.51 110	84.69 137	92.64 133	90.25 153	95.28 124	90.61 140

pmmvs4		89.95 141	89.32 152	90.69 128	91.60 169	89.17 170	94.37 123	87.63 143	88.07 134	91.02 112	94.50 102	90.50 161	86.13 129	86.33 189	89.40 160	93.39 160	87.29 166

MDA-MVSNet-bldmvs		89.75 142	91.67 128	87.50 168	74.25 215	90.88 157	94.68 118	85.89 167	91.64 81	91.03 111	95.86 71	94.35 134	89.10 104	96.87 60	86.37 179	90.04 176	85.72 171

tttt0517		89.64 143	88.05 164	91.49 115	93.52 128	91.65 149	93.67 136	87.53 144	82.77 171	89.39 137	90.37 146	70.05 208	88.21 113	93.71 122	93.79 110	96.63 84	94.04 84

PatchMatch-RL		89.59 144	88.80 157	90.51 130	92.20 162	88.00 176	91.72 171	86.64 160	84.75 159	88.25 149	87.10 178	90.66 160	89.85 100	93.23 127	92.28 127	94.41 148	85.60 172

Fast-Effi-MVS+-dtu		89.57 145	88.42 161	90.92 124	93.35 132	91.57 150	93.01 151	95.71 9	78.94 194	87.65 151	84.68 192	93.14 147	82.00 156	90.84 156	91.01 147	93.78 156	88.77 156

thisisatest0530		89.54 146	87.99 166	91.35 119	93.17 136	91.31 153	93.45 141	87.53 144	82.96 169	89.17 139	90.45 143	70.32 207	88.21 113	93.37 125	93.79 110	96.54 86	93.71 91

GBi-Net		89.35 147	90.58 135	87.91 164	91.22 174	94.05 110	92.88 154	90.05 106	79.40 186	78.60 193	90.58 139	87.05 175	78.54 172	95.32 91	94.98 86	96.17 103	92.67 111

test1		89.35 147	90.58 135	87.91 164	91.22 174	94.05 110	92.88 154	90.05 106	79.40 186	78.60 193	90.58 139	87.05 175	78.54 172	95.32 91	94.98 86	96.17 103	92.67 111

thres600view7		89.14 149	88.83 155	89.51 148	93.71 127	93.55 125	93.93 134	88.02 140	87.30 139	82.40 179	81.18 202	80.63 194	82.69 152	94.27 114	95.90 66	96.27 98	88.94 153

CVMVSNet		88.97 150	89.73 147	88.10 162	87.33 203	85.22 186	94.68 118	78.68 197	88.94 125	86.98 156	95.55 81	85.71 180	89.87 99	91.19 154	89.69 157	91.05 173	91.78 130

CANet_DTU		88.95 151	89.51 150	88.29 160	93.12 139	91.22 155	93.61 138	83.47 187	80.07 185	90.71 118	89.19 158	93.68 142	76.27 190	91.44 152	91.17 146	92.59 170	89.83 146

GA-MVS		88.76 152	88.04 165	89.59 146	92.32 159	91.46 151	92.28 165	86.62 161	83.82 166	89.84 129	92.51 127	81.94 188	83.53 145	89.41 169	89.27 162	92.95 167	87.90 161

pmmvs5		88.63 153	89.70 148	87.39 169	89.24 190	90.64 161	91.87 168	82.13 191	83.34 167	87.86 150	94.58 100	96.15 98	79.87 166	87.33 184	89.07 166	93.39 160	86.76 167

thres400		88.54 154	88.15 163	88.98 150	93.17 136	92.84 132	93.56 139	86.93 157	86.45 147	82.37 180	79.96 205	81.46 191	81.83 159	93.21 128	94.76 95	96.04 107	88.39 158

CDS-MVSNet		88.41 155	89.79 146	86.79 173	94.55 102	90.82 158	92.50 161	89.85 113	83.26 168	80.52 188	91.05 135	89.93 163	69.11 201	93.17 130	92.71 123	94.21 151	87.63 162
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gg-mvs-nofinetune		88.32 156	88.81 156	87.75 166	93.07 140	89.37 169	89.06 192	95.94 8	95.29 20	87.15 153	97.38 42	76.38 197	68.05 204	91.04 155	89.10 165	93.24 162	83.10 180

IterMVS		88.32 156	88.25 162	88.41 158	90.83 180	91.24 154	93.07 150	81.69 193	86.77 144	88.55 145	95.61 77	86.91 178	87.01 120	87.38 183	83.77 185	89.29 178	86.06 170
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres200		88.29 158	87.88 167	88.76 154	92.50 153	93.55 125	92.47 162	88.02 140	84.80 157	81.44 185	79.28 207	82.20 187	81.83 159	94.27 114	93.67 113	96.27 98	87.40 164

IB-MVS	86.01 17	88.24 159	87.63 169	88.94 151	92.03 165	91.77 147	92.40 164	85.58 170	78.24 197	84.85 166	71.99 211	93.45 143	83.96 142	93.48 124	92.33 126	94.84 140	92.15 120
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
MDTV_nov1_ep13_2view		88.22 160	87.85 168	88.65 156	91.40 171	86.75 181	94.07 132	84.97 175	88.86 127	93.20 63	96.11 69	96.21 97	83.70 144	87.29 185	80.29 192	84.56 196	79.46 193

test20.03		88.20 161	91.26 133	84.63 185	96.64 42	89.39 168	90.73 180	89.97 111	91.07 99	72.02 207	94.98 94	95.45 113	69.35 200	92.70 131	91.19 145	89.06 180	84.02 174

HyFIR lowres test		88.19 162	86.56 176	90.09 136	91.24 173	92.17 142	94.30 128	88.79 131	84.06 161	85.45 162	89.52 155	85.64 181	88.64 108	85.40 192	87.28 173	92.14 172	81.87 183

ET-MVSNet_ETH3D		88.06 163	85.75 180	90.74 126	92.82 147	90.68 159	93.77 135	88.59 133	81.22 177	89.78 131	89.15 159	66.79 215	84.29 140	91.72 148	91.34 143	95.22 127	89.36 149

tfpn200view9		87.94 164	87.51 171	88.44 157	92.28 160	93.63 122	93.35 144	88.11 138	80.90 179	80.89 186	78.25 208	82.25 185	79.65 168	94.27 114	94.76 95	96.36 92	88.48 157

FMVSNet3		87.90 165	88.63 159	87.04 170	89.78 188	93.46 128	91.62 174	90.05 106	79.40 186	78.60 193	90.58 139	87.05 175	77.07 185	88.03 180	89.86 156	95.12 132	92.04 123

MS-PatchMatch		87.72 166	88.62 160	86.66 174	90.81 181	88.18 173	90.92 177	82.25 190	85.86 152	80.40 189	90.14 150	89.29 168	84.93 134	89.39 170	89.12 164	90.67 174	88.34 159

Anonymous20231206		87.45 167	89.66 149	84.87 182	94.00 113	87.73 179	91.36 175	86.41 165	88.89 126	75.03 201	92.59 126	96.82 80	72.48 198	89.72 166	88.06 170	89.93 177	83.81 176

EPNet_dtu		87.40 168	86.27 177	88.72 155	95.68 75	83.37 192	92.09 167	90.08 105	78.11 199	91.29 106	86.33 184	89.74 165	75.39 193	89.07 171	87.89 171	87.81 185	89.38 148
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		86.96 169	87.58 170	86.24 176	93.07 140	90.44 164	89.24 191	86.85 159	85.14 156	77.26 199	90.45 143	76.09 199	75.79 191	91.80 147	91.81 134	95.20 128	87.35 165

baseline		86.71 170	88.89 154	84.16 187	87.85 199	85.23 185	89.82 185	77.69 200	84.03 163	84.75 167	94.91 96	94.59 129	77.19 184	86.57 188	86.51 178	87.66 188	90.36 143

CHOSEN 1792x2688		86.64 171	86.62 174	86.65 175	90.33 184	87.86 178	93.19 148	83.30 188	83.95 165	82.32 181	87.93 170	89.34 167	86.92 122	85.64 191	84.95 184	83.85 200	86.68 168

testgi		86.49 172	90.31 141	82.03 191	95.63 76	88.18 173	93.47 140	84.89 176	93.23 53	69.54 211	87.16 177	97.96 46	60.66 208	91.90 146	89.90 155	87.99 183	83.84 175

thres100view900		86.46 173	86.00 179	86.99 171	92.28 160	91.03 156	91.09 176	84.49 180	80.90 179	80.89 186	78.25 208	82.25 185	77.57 181	90.17 161	92.84 122	95.63 117	86.57 169

gm-plane-assit		86.15 174	82.51 188	90.40 132	95.81 71	92.29 139	97.99 34	84.66 179	92.15 74	93.15 65	97.84 29	44.65 222	78.60 171	88.02 181	85.95 180	92.20 171	76.69 201

CMPMVS	66.55 18	85.55 175	87.46 172	83.32 188	84.99 205	81.97 197	79.19 212	75.93 202	79.32 189	88.82 142	85.09 191	91.07 156	82.12 155	92.56 135	89.63 159	88.84 181	92.56 115
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		85.32 176	81.58 190	89.69 144	90.36 183	84.79 188	86.72 203	92.22 60	75.38 205	90.73 114	90.41 145	67.88 212	84.86 135	83.76 195	85.74 181	93.24 162	83.14 178

baseline2		84.95 177	82.68 187	87.59 167	92.64 150	88.41 172	90.09 182	84.25 181	75.88 203	85.23 163	82.49 200	71.15 205	80.14 165	88.21 179	87.21 176	93.21 165	85.39 173

pmnet_mix02		84.85 178	86.58 175	82.83 189	90.19 185	81.10 200	88.52 195	78.58 198	91.50 85	80.32 190	96.48 62	95.86 104	75.42 192	85.17 193	76.44 201	83.91 199	79.51 192

MVSTER		84.79 179	83.79 183	85.96 178	89.14 191	89.80 167	89.39 189	82.99 189	74.16 209	82.78 177	85.97 187	66.81 214	76.84 186	90.77 157	88.83 169	94.66 142	90.19 145

MIMVSNet		84.76 180	86.75 173	82.44 190	91.71 168	85.95 183	89.74 187	89.49 120	85.28 154	69.69 210	87.93 170	90.88 159	64.85 206	88.26 178	87.74 172	89.18 179	81.24 184

SCA		84.69 181	81.10 191	88.87 153	89.02 192	90.31 165	92.21 166	92.09 69	82.72 172	89.68 132	86.83 181	73.08 201	85.80 131	80.50 203	77.51 198	84.45 198	76.80 200

new-patchmatchnet		84.45 182	88.75 158	79.43 197	93.28 133	81.87 198	81.68 209	83.48 186	94.47 29	71.53 208	98.33 17	97.88 50	58.61 211	90.35 159	77.33 199	87.99 183	81.05 186

PatchT		83.44 183	81.10 191	86.18 177	77.92 213	82.58 196	89.87 184	87.39 150	75.88 203	90.73 114	89.86 151	66.71 216	84.86 135	83.76 195	85.74 181	86.33 193	83.14 178

RPMNet		83.42 184	78.40 200	89.28 149	89.79 187	84.79 188	90.64 181	92.11 67	75.38 205	87.10 154	79.80 206	61.99 221	82.79 150	81.88 201	82.07 189	93.23 164	82.87 181

TAMVS		82.96 185	86.15 178	79.24 200	90.57 182	83.12 195	87.29 199	75.12 204	84.06 161	65.81 212	92.22 129	88.27 172	69.11 201	88.72 173	87.26 175	87.56 189	79.38 194

PatchmatchNet		82.44 186	78.69 199	86.83 172	89.81 186	81.55 199	90.78 179	87.27 153	82.39 174	88.85 141	88.31 166	70.96 206	81.90 157	78.58 207	74.33 207	82.35 204	74.69 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		82.33 187	79.66 194	85.45 180	88.83 194	83.88 190	90.09 182	81.98 192	79.07 193	88.82 142	88.70 161	73.77 200	78.41 176	80.29 205	76.08 202	84.56 196	75.83 202

CostFormer		82.15 188	79.54 195	85.20 181	88.92 193	85.70 184	90.87 178	86.26 166	79.19 192	83.87 174	87.89 172	69.20 210	76.62 188	77.50 210	75.28 204	84.69 195	82.02 182

PMMVS		81.93 189	83.48 185	80.12 196	72.35 216	75.05 209	88.54 194	64.01 209	77.02 202	82.22 182	87.51 174	91.12 155	79.70 167	86.59 186	86.64 177	93.88 153	80.41 187

pmmvs3		81.69 190	83.83 182	79.19 201	78.33 212	78.57 203	89.53 188	58.71 212	78.88 196	84.34 172	88.36 165	91.96 151	77.69 180	87.48 182	82.42 188	86.54 192	79.18 195

tpm		81.58 191	78.84 197	84.79 184	91.11 177	79.50 201	89.79 186	83.75 182	79.30 190	92.05 88	90.98 136	64.78 218	74.54 194	80.50 203	76.67 200	77.49 209	80.15 190

test0.0.03 1		81.51 192	83.30 186	79.42 198	93.99 114	86.50 182	85.93 207	87.32 151	78.16 198	61.62 213	80.78 204	81.78 189	59.87 209	88.40 177	87.27 174	87.78 187	80.19 189

dps		81.42 193	77.88 205	85.56 179	87.67 201	85.17 187	88.37 197	87.46 148	74.37 208	84.55 169	86.80 182	62.18 220	80.20 164	81.13 202	77.52 197	85.10 194	77.98 198

test-LLR		80.62 194	77.20 208	84.62 186	93.99 114	75.11 207	87.04 200	87.32 151	70.11 212	78.59 196	83.17 197	71.60 203	73.88 196	82.32 199	79.20 194	86.91 190	78.87 196

tpm cat1		80.03 195	75.93 211	84.81 183	89.31 189	83.26 194	88.86 193	86.55 164	79.24 191	86.10 158	84.22 194	63.62 219	77.37 183	73.43 211	70.88 210	80.67 205	76.87 199

N_pmnet		79.33 196	84.22 181	73.62 207	91.72 167	73.72 210	86.11 205	76.36 201	92.38 67	53.38 214	95.54 83	95.62 108	59.14 210	84.23 194	74.84 206	75.03 212	73.25 208

EPMVS		79.26 197	78.20 203	80.49 194	87.04 204	78.86 202	86.08 206	83.51 185	82.63 173	73.94 204	89.59 153	68.67 211	72.03 199	78.17 208	75.08 205	80.37 206	74.37 205

CHOSEN 280x420		79.24 198	78.26 202	80.38 195	79.60 211	68.80 215	89.32 190	75.38 203	77.25 201	78.02 198	75.57 210	76.17 198	81.19 162	88.61 176	81.39 190	78.79 207	80.03 191

ADS-MVSNet		79.11 199	79.38 196	78.80 203	81.90 209	75.59 206	84.36 208	83.69 183	87.31 138	76.76 200	87.58 173	76.90 196	68.55 203	78.70 206	75.56 203	77.53 208	74.07 206

FMVSNet5		79.08 200	78.83 198	79.38 199	87.52 202	86.78 180	87.64 198	78.15 199	69.54 214	70.64 209	65.97 214	65.44 217	63.87 207	90.17 161	90.46 150	88.48 182	83.45 177

tpmrst		78.81 201	76.18 210	81.87 192	88.56 195	77.45 204	86.74 202	81.52 194	80.08 184	83.48 175	90.84 138	66.88 213	74.54 194	73.04 212	71.02 209	76.38 210	73.95 207

test-mter		78.71 202	78.35 201	79.12 202	84.03 206	76.58 205	88.51 196	59.06 211	71.06 210	78.87 192	83.73 196	71.83 202	76.44 189	83.41 198	80.61 191	87.79 186	81.24 184

MVS-HIRNet		78.28 203	75.28 212	81.79 193	80.33 210	69.38 214	76.83 213	86.59 162	70.76 211	86.66 157	89.57 154	81.04 192	77.74 179	77.81 209	71.65 208	82.62 202	66.73 212

E-PMN		77.81 204	77.88 205	77.73 206	88.26 198	70.48 213	80.19 211	71.20 206	86.66 145	72.89 206	88.09 169	81.74 190	78.75 170	90.02 163	68.30 211	75.10 211	59.85 213

EMVS		77.65 205	77.49 207	77.83 204	87.75 200	71.02 212	81.13 210	70.54 207	86.38 148	74.52 203	89.38 156	80.19 195	78.22 177	89.48 168	67.13 212	74.83 213	58.84 214

TESTMET0.1,1		77.47 206	77.20 208	77.78 205	81.94 208	75.11 207	87.04 200	58.33 213	70.11 212	78.59 196	83.17 197	71.60 203	73.88 196	82.32 199	79.20 194	86.91 190	78.87 196

new_pmnet		76.65 207	83.52 184	68.63 208	82.60 207	72.08 211	76.76 214	64.17 208	84.41 160	49.73 216	91.77 132	91.53 154	56.16 212	86.59 186	83.26 187	82.37 203	75.02 203

MVE	60.41 19	73.21 208	80.84 193	64.30 209	56.34 217	57.24 217	75.28 216	72.76 205	87.14 141	41.39 218	86.31 185	85.30 182	80.66 163	86.17 190	83.36 186	59.35 215	80.38 188
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		69.86 209	82.14 189	55.52 210	75.19 214	63.08 216	75.52 215	60.97 210	88.50 129	25.11 220	91.77 132	96.44 88	25.43 214	88.70 174	79.34 193	70.93 214	67.17 211

GG-mvs-BLEND		54.28 210	77.89 204	26.72 213	0.37 222	83.31 193	70.04 217	0.39 219	74.71 207	5.36 221	68.78 212	83.06 184	0.62 218	83.73 197	78.99 196	83.55 201	72.68 210

test_method		43.16 211	51.13 213	33.85 211	7.35 219	12.38 220	51.70 219	11.91 215	62.51 216	47.64 217	62.49 215	80.78 193	28.84 213	59.55 215	34.48 214	55.68 216	45.72 215

testmvs		2.38 212	3.35 214	1.26 215	0.83 220	0.96 222	1.53 222	0.83 217	3.59 218	1.63 223	6.03 217	2.93 224	1.55 217	3.49 216	2.51 216	1.21 220	3.92 217

test123		2.16 213	2.82 215	1.41 214	0.62 221	1.18 221	1.53 222	0.82 218	2.78 219	2.27 222	4.18 218	1.98 225	1.64 216	2.58 217	3.01 215	1.56 219	4.00 216

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										97.21 5

9.14												93.19 146

SR-MVS					97.13 23			94.77 17				97.77 53

Anonymous202405211			94.63 75		94.51 103	94.96 91	93.94 133	91.35 87	90.82 104		95.60 79	95.85 105	81.74 161	96.47 71	95.84 69	97.39 62	92.85 106

our_test_3					91.78 166	88.87 171	94.37 123

ambc			94.61 76		98.09 5	95.14 83	91.71 172		94.18 38	96.46 12	96.26 65	96.30 91	91.26 66	94.70 105	92.00 133	93.45 158	93.67 92

MTAPA										94.88 28		96.88 78

MTMP										95.43 18		97.25 65

Patchmatch-RL test							8.96 221

tmp_tt				28.44 212	36.05 218	15.86 219	21.29 220	6.40 216	54.52 217	51.96 215	50.37 216	38.68 223	9.55 215	61.75 214	59.66 213	45.36 218

XVS					96.86 32	97.48 18	98.73 3			93.28 59		96.82 80				98.17 34

X-MVStestdata					96.86 32	97.48 18	98.73 3			93.28 59		96.82 80				98.17 34

abl_6				91.88 110	93.76 124	94.98 90	95.64 96	88.97 129	86.20 149	90.00 128	86.31 185	94.50 132	87.31 119			95.60 118	92.48 117

mPP-MVS					98.24 3							97.65 58

NP-MVS									85.48 153

Patchmtry						83.74 191	86.72 203	92.22 60		90.73 114

DeepMVS_CX						47.68 218	53.20 218	19.21 214	63.24 215	26.96 219	66.50 213	69.82 209	66.91 205	64.27 213		54.91 217	72.72 209

High-resolution multi-view benchmark