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.

DVP-MVS		98.86 3	98.97 2	98.75 2	99.43 13	99.63 1	99.25 12	97.81 1	98.62 1	97.69 1	97.59 20	99.90 1	98.93 5	98.99 3	98.42 11	99.37 52	99.62 3
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
SED-MVS		98.90 1	99.07 1	98.69 3	99.38 19	99.61 2	99.33 7	97.80 3	98.25 7	97.60 2	98.87 3	99.89 2	98.67 18	99.02 2	98.26 17	99.36 54	99.61 5

MSP-MVS		98.73 5	98.93 4	98.50 6	99.44 12	99.57 3	99.36 3	97.65 8	98.14 11	96.51 15	98.49 6	99.65 7	98.67 18	98.60 13	98.42 11	99.40 46	99.63 1
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
DPE-MVS		98.75 4	98.91 5	98.57 4	99.21 24	99.54 4	99.42 2	97.78 5	97.49 31	96.84 9	98.94 1	99.82 4	98.59 21	98.90 9	98.22 18	99.56 10	99.48 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		98.87 2	98.96 3	98.77 1	99.58 2	99.53 5	99.44 1	97.81 1	98.22 9	97.33 4	98.70 4	99.33 9	98.86 8	98.96 5	98.40 13	99.63 3	99.57 8

CSCG		97.44 32	97.18 40	97.75 28	99.47 6	99.52 6	98.55 31	95.41 41	97.69 24	95.72 20	94.29 53	95.53 62	98.10 31	96.20 101	97.38 51	99.24 72	99.62 3

SteuartSystems-ACMMP		98.38 14	98.71 9	97.99 24	99.34 21	99.46 7	99.34 5	97.33 25	97.31 35	94.25 30	98.06 13	99.17 18	98.13 28	98.98 4	98.46 9	99.55 11	99.54 9
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		98.66 6	98.89 6	98.39 9	99.60 1	99.41 8	99.00 20	97.63 12	97.78 17	95.83 19	98.33 10	99.83 3	98.85 10	98.93 7	98.56 6	99.41 43	99.40 14
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
ACMMP_NAP		98.20 18	98.49 12	97.85 26	99.50 4	99.40 9	99.26 11	97.64 11	97.47 33	92.62 46	97.59 20	99.09 21	98.71 16	98.82 11	97.86 33	99.40 46	99.19 39

zzz-MVS		98.43 11	98.31 23	98.57 4	99.48 5	99.40 9	99.32 8	97.62 13	97.70 22	96.67 11	96.59 32	99.09 21	98.86 8	98.65 12	97.56 43	99.45 30	99.17 45

ACMMPR		98.40 12	98.49 12	98.28 14	99.41 14	99.40 9	99.36 3	97.35 22	98.30 5	95.02 26	97.79 17	98.39 37	99.04 2	98.26 27	98.10 22	99.50 21	99.22 35

XVS					96.60 68	99.35 12	96.82 65			90.85 57		98.72 29				99.46 26

X-MVStestdata					96.60 68	99.35 12	96.82 65			90.85 57		98.72 29				99.46 26

X-MVS		97.84 24	98.19 27	97.42 31	99.40 15	99.35 12	99.06 17	97.25 26	97.38 34	90.85 57	96.06 36	98.72 29	98.53 24	98.41 22	98.15 21	99.46 26	99.28 24

PGM-MVS		97.81 25	98.11 28	97.46 30	99.55 3	99.34 15	99.32 8	94.51 46	96.21 60	93.07 37	98.05 14	97.95 42	98.82 12	98.22 30	97.89 32	99.48 22	99.09 53

HFP-MVS		98.48 9	98.62 10	98.32 12	99.39 18	99.33 16	99.27 10	97.42 19	98.27 6	95.25 24	98.34 9	98.83 26	99.08 1	98.26 27	98.08 24	99.48 22	99.26 29

CP-MVS		98.32 17	98.34 21	98.29 13	99.34 21	99.30 17	99.15 14	97.35 22	97.49 31	95.58 22	97.72 18	98.62 33	98.82 12	98.29 25	97.67 38	99.51 19	99.28 24

MVS_111021_HR		97.04 39	98.20 26	95.69 53	98.44 46	99.29 18	96.59 74	93.20 59	97.70 22	89.94 76	98.46 7	96.89 47	96.71 63	98.11 37	97.95 28	99.27 67	99.01 68

ACMMP		97.37 33	97.48 35	97.25 32	98.88 37	99.28 19	98.47 34	96.86 35	97.04 44	92.15 47	97.57 23	96.05 60	97.67 38	97.27 59	95.99 87	99.46 26	99.14 50
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
DeepC-MVS	94.87 4	96.76 47	96.50 52	97.05 36	98.21 49	99.28 19	98.67 27	97.38 21	97.31 35	90.36 69	89.19 100	93.58 69	98.19 27	98.31 24	98.50 7	99.51 19	99.36 17
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		98.52 7	98.77 8	98.23 16	98.15 50	99.26 21	98.79 26	97.59 16	98.52 2	96.25 16	97.99 15	99.75 5	99.01 3	98.27 26	97.97 27	99.59 4	99.63 1
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
APD-MVS		98.36 15	98.32 22	98.41 8	99.47 6	99.26 21	99.12 15	97.77 6	96.73 49	96.12 17	97.27 28	98.88 24	98.46 25	98.47 17	98.39 14	99.52 14	99.22 35
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		96.06 53	96.04 59	96.07 50	97.77 56	99.25 23	98.10 42	93.26 56	94.42 103	92.79 43	88.52 107	93.48 70	95.06 89	98.51 15	98.83 1	99.45 30	99.28 24
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
PHI-MVS		97.78 26	98.44 17	97.02 37	98.73 38	99.25 23	98.11 41	95.54 40	96.66 52	92.79 43	98.52 5	99.38 8	97.50 42	97.84 45	98.39 14	99.45 30	99.03 65

CANet		96.84 44	97.20 38	96.42 42	97.92 54	99.24 25	98.60 29	93.51 53	97.11 41	93.07 37	91.16 82	97.24 45	96.21 71	98.24 29	98.05 25	99.22 78	99.35 18

MVS_0304		96.31 49	96.91 47	95.62 54	97.21 64	99.20 26	98.55 31	93.10 61	97.04 44	89.73 78	90.30 92	96.35 53	95.71 77	98.14 34	97.93 31	99.38 49	99.40 14

MP-MVS		98.09 22	98.30 24	97.84 27	99.34 21	99.19 27	99.23 13	97.40 20	97.09 42	93.03 40	97.58 22	98.85 25	98.57 23	98.44 20	97.69 37	99.48 22	99.23 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + MP.		98.49 8	98.78 7	98.15 20	98.14 51	99.17 28	99.34 5	97.18 30	98.44 4	95.72 20	97.84 16	99.28 11	98.87 7	99.05 1	98.05 25	99.66 1	99.60 6
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		98.34 16	98.47 14	98.18 17	99.46 8	99.15 29	99.10 16	97.69 7	97.67 25	94.93 27	97.62 19	99.70 6	98.60 20	98.45 18	97.46 46	99.31 61	99.26 29

QAPM		96.78 46	97.14 42	96.36 44	99.05 30	99.14 30	98.02 43	93.26 56	97.27 37	90.84 60	91.16 82	97.31 44	97.64 40	97.70 49	98.20 19	99.33 56	99.18 43

MSLP-MVS++		98.04 23	97.93 32	98.18 17	99.10 28	99.09 31	98.34 36	96.99 33	97.54 30	96.60 13	94.82 49	98.45 35	98.89 6	97.46 55	98.77 4	99.17 87	99.37 16

xxxxxxxxxxxxxcwj		97.07 38	95.99 60	98.33 10	99.45 9	99.05 32	98.27 37	97.65 8	97.73 18	97.02 7	98.18 11	81.99 143	98.11 29	98.15 32	97.62 39	99.45 30	99.19 39

SF-MVS		98.39 13	98.45 16	98.33 10	99.45 9	99.05 32	98.27 37	97.65 8	97.73 18	97.02 7	98.18 11	99.25 14	98.11 29	98.15 32	97.62 39	99.45 30	99.19 39

TSAR-MVS + ACMM		97.71 28	98.60 11	96.66 41	98.64 41	99.05 32	98.85 25	97.23 28	98.45 3	89.40 84	97.51 24	99.27 13	96.88 59	98.53 14	97.81 35	98.96 115	99.59 7

MCST-MVS		98.20 18	98.36 18	98.01 23	99.40 15	99.05 32	99.00 20	97.62 13	97.59 29	93.70 34	97.42 27	99.30 10	98.77 14	98.39 23	97.48 45	99.59 4	99.31 23

CNVR-MVS		98.47 10	98.46 15	98.48 7	99.40 15	99.05 32	99.02 19	97.54 17	97.73 18	96.65 12	97.20 29	99.13 19	98.85 10	98.91 8	98.10 22	99.41 43	99.08 54

NCCC		98.10 21	98.05 30	98.17 19	99.38 19	99.05 32	99.00 20	97.53 18	98.04 13	95.12 25	94.80 50	99.18 17	98.58 22	98.49 16	97.78 36	99.39 48	98.98 72

CPTT-MVS		97.78 26	97.54 33	98.05 22	98.91 35	99.05 32	99.00 20	96.96 34	97.14 40	95.92 18	95.50 42	98.78 28	98.99 4	97.20 61	96.07 82	98.54 152	99.04 64

DeepC-MVS_fast	96.13 1	98.13 20	98.27 25	97.97 25	99.16 27	99.03 39	99.05 18	97.24 27	98.22 9	94.17 32	95.82 38	98.07 39	98.69 17	98.83 10	98.80 2	99.52 14	99.10 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	93.79 8	97.08 37	97.20 38	96.95 38	99.09 29	99.03 39	98.20 40	93.33 54	97.99 14	93.82 33	90.61 90	96.80 49	97.82 35	97.90 44	98.78 3	99.47 25	99.26 29

PVSNet_BlendedMVS		95.41 60	95.28 70	95.57 55	97.42 60	99.02 41	95.89 95	93.10 61	96.16 61	93.12 35	91.99 71	85.27 120	94.66 94	98.09 38	97.34 52	99.24 72	99.08 54

PVSNet_Blended		95.41 60	95.28 70	95.57 55	97.42 60	99.02 41	95.89 95	93.10 61	96.16 61	93.12 35	91.99 71	85.27 120	94.66 94	98.09 38	97.34 52	99.24 72	99.08 54

IS_MVSNet		95.28 62	96.43 54	93.94 85	95.30 89	99.01 43	95.90 93	91.12 87	94.13 108	87.50 100	91.23 81	94.45 66	94.17 103	98.45 18	98.50 7	99.65 2	99.23 33

CS-MVS		96.23 52	97.15 41	95.16 61	95.01 99	98.98 44	97.13 57	90.68 92	96.00 68	91.21 54	94.03 54	96.48 51	97.35 45	98.00 41	97.43 47	99.55 11	99.15 47

MVS_111021_LR		97.16 36	98.01 31	96.16 47	98.47 44	98.98 44	96.94 61	93.89 49	97.64 27	91.44 51	98.89 2	96.41 52	97.20 49	98.02 40	97.29 56	99.04 110	98.85 87

PVSNet_Blended_VisFu		94.77 72	95.54 66	93.87 87	96.48 71	98.97 46	94.33 122	91.84 76	94.93 95	90.37 68	85.04 130	94.99 63	90.87 150	98.12 36	97.30 54	99.30 63	99.45 13

OpenMVS	92.33 11	95.50 55	95.22 72	95.82 52	98.98 31	98.97 46	97.67 51	93.04 64	94.64 99	89.18 88	84.44 135	94.79 64	96.79 60	97.23 60	97.61 41	99.24 72	98.88 83

tfpn200view9		93.64 94	92.57 114	94.89 68	95.33 87	98.94 48	96.82 65	92.31 67	92.63 128	88.29 92	87.21 111	78.01 157	97.12 53	96.82 71	95.85 92	99.45 30	98.56 99

DeepPCF-MVS	95.28 2	97.00 40	98.35 20	95.42 58	97.30 62	98.94 48	94.82 113	96.03 39	98.24 8	92.11 48	95.80 39	98.64 32	95.51 84	98.95 6	98.66 5	96.78 185	99.20 38

thres600view7		93.49 99	92.37 125	94.79 73	95.42 84	98.93 50	96.58 75	92.31 67	93.04 122	87.88 97	86.62 117	76.94 163	97.09 54	96.82 71	95.63 97	99.45 30	98.63 96

thres200		93.62 95	92.54 115	94.88 69	95.36 86	98.93 50	96.75 69	92.31 67	92.84 125	88.28 94	86.99 113	77.81 159	97.13 51	96.82 71	95.92 88	99.45 30	98.49 105

TSAR-MVS + GP.		97.45 31	98.36 18	96.39 43	95.56 83	98.93 50	97.74 49	93.31 55	97.61 28	94.24 31	98.44 8	99.19 16	98.03 33	97.60 51	97.41 49	99.44 38	99.33 20

train_agg		97.65 29	98.06 29	97.18 34	98.94 33	98.91 53	98.98 24	97.07 32	96.71 50	90.66 62	97.43 26	99.08 23	98.20 26	97.96 42	97.14 57	99.22 78	99.19 39

thres400		93.56 97	92.43 122	94.87 70	95.40 85	98.91 53	96.70 71	92.38 66	92.93 124	88.19 96	86.69 116	77.35 160	97.13 51	96.75 76	95.85 92	99.42 42	98.56 99

LS3D		95.46 58	95.14 73	95.84 51	97.91 55	98.90 55	98.58 30	97.79 4	97.07 43	83.65 116	88.71 103	88.64 101	97.82 35	97.49 54	97.42 48	99.26 71	97.72 136

CHOSEN 1792x2688		92.66 108	92.49 118	92.85 102	97.13 65	98.89 56	95.90 93	88.50 120	95.32 86	83.31 117	71.99 191	88.96 99	94.10 105	96.69 78	96.49 71	98.15 165	99.10 51

EIA-MVS		95.50 55	96.19 57	94.69 75	94.83 102	98.88 57	95.93 92	91.50 83	94.47 102	89.43 82	93.14 60	92.72 74	97.05 55	97.82 48	97.13 58	99.43 41	99.15 47

CDPH-MVS		96.84 44	97.49 34	96.09 48	98.92 34	98.85 58	98.61 28	95.09 42	96.00 68	87.29 101	95.45 44	97.42 43	97.16 50	97.83 46	97.94 29	99.44 38	98.92 78

3Dnovator+	93.91 7	97.23 35	97.22 37	97.24 33	98.89 36	98.85 58	98.26 39	93.25 58	97.99 14	95.56 23	90.01 96	98.03 41	98.05 32	97.91 43	98.43 10	99.44 38	99.35 18

Vis-MVSNet		92.77 106	95.00 78	90.16 131	94.10 120	98.79 60	94.76 115	88.26 122	92.37 137	79.95 132	88.19 109	91.58 78	84.38 193	97.59 52	97.58 42	99.52 14	98.91 81
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ETV-MVS		96.31 49	97.47 36	94.96 67	94.79 103	98.78 61	96.08 87	91.41 84	96.16 61	90.50 64	95.76 40	96.20 57	97.39 43	98.42 21	97.82 34	99.57 8	99.18 43

AdaColmap		97.53 30	96.93 45	98.24 15	99.21 24	98.77 62	98.47 34	97.34 24	96.68 51	96.52 14	95.11 47	96.12 58	98.72 15	97.19 63	96.24 78	99.17 87	98.39 112

thres100view900		93.55 98	92.47 121	94.81 72	95.33 87	98.74 63	96.78 68	92.30 70	92.63 128	88.29 92	87.21 111	78.01 157	96.78 61	96.38 91	95.92 88	99.38 49	98.40 111

abl_6				96.82 40	98.60 42	98.74 63	97.74 49	93.73 50	96.25 58	94.37 29	94.55 52	98.60 34	97.25 47			99.27 67	98.61 97

PCF-MVS	93.95 6	95.65 54	95.14 73	96.25 45	97.73 58	98.73 65	97.59 52	97.13 31	92.50 132	89.09 90	89.85 97	96.65 50	96.90 58	94.97 133	94.89 117	99.08 100	98.38 113
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
canonicalmvs		95.25 64	95.45 68	95.00 65	95.27 91	98.72 66	96.89 62	89.82 103	96.51 53	90.84 60	93.72 57	86.01 115	97.66 39	95.78 113	97.94 29	99.54 13	99.50 10

HyFIR lowres test		92.03 112	91.55 136	92.58 103	97.13 65	98.72 66	94.65 117	86.54 140	93.58 117	82.56 120	67.75 202	90.47 86	95.67 78	95.87 109	95.54 100	98.91 120	98.93 77

tttt0517		94.52 78	95.44 69	93.44 95	94.51 112	98.68 68	94.61 118	90.72 89	95.61 82	86.84 105	93.78 56	89.26 95	94.74 91	97.02 69	94.86 118	99.20 84	98.87 85

OMC-MVS		97.00 40	96.92 46	97.09 35	98.69 39	98.66 69	97.85 47	95.02 43	98.09 12	94.47 28	93.15 59	96.90 46	97.38 44	97.16 64	96.82 67	99.13 94	97.65 137

TAPA-MVS	94.18 5	96.38 48	96.49 53	96.25 45	98.26 48	98.66 69	98.00 44	94.96 44	97.17 39	89.48 81	92.91 63	96.35 53	97.53 41	96.59 82	95.90 90	99.28 65	97.82 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0530		94.54 77	95.47 67	93.46 94	94.51 112	98.65 71	94.66 116	90.72 89	95.69 80	86.90 104	93.80 55	89.44 92	94.74 91	96.98 70	94.86 118	99.19 85	98.85 87

Vis-MVSNet (Re-imp)		94.46 79	96.24 56	92.40 104	95.23 92	98.64 72	95.56 101	90.99 88	94.42 103	85.02 110	90.88 88	94.65 65	88.01 172	98.17 31	98.37 16	99.57 8	98.53 102

EPP-MVSNet		95.27 63	96.18 58	94.20 83	94.88 101	98.64 72	94.97 109	90.70 91	95.34 85	89.67 80	91.66 78	93.84 67	95.42 86	97.32 58	97.00 60	99.58 6	99.47 12

UGNet		94.92 65	96.63 50	92.93 101	96.03 77	98.63 74	94.53 119	91.52 82	96.23 59	90.03 73	92.87 64	96.10 59	86.28 182	96.68 79	96.60 70	99.16 90	99.32 22
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
CNLPA		96.90 42	96.28 55	97.64 29	98.56 43	98.63 74	96.85 64	96.60 37	97.73 18	97.08 6	89.78 98	96.28 56	97.80 37	96.73 77	96.63 69	98.94 117	98.14 123

UA-Net		93.96 88	95.95 61	91.64 112	96.06 76	98.59 76	95.29 103	90.00 99	91.06 152	82.87 118	90.64 89	98.06 40	86.06 183	98.14 34	98.20 19	99.58 6	96.96 157

casdiffmvs		94.38 83	94.15 93	94.64 77	94.70 109	98.51 77	96.03 90	91.66 79	95.70 78	89.36 85	86.48 119	85.03 125	96.60 66	97.40 56	97.30 54	99.52 14	98.67 94

IB-MVS	89.56 15	91.71 117	92.50 117	90.79 124	95.94 79	98.44 78	87.05 194	91.38 85	93.15 121	92.98 41	84.78 131	85.14 123	78.27 201	92.47 172	94.44 135	99.10 98	99.08 54
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		93.92 89	96.57 51	90.83 122	95.63 81	98.39 79	96.99 60	87.38 131	96.26 57	71.97 175	96.31 34	93.02 71	94.53 97	97.38 57	96.83 66	98.49 155	97.79 129

MVS_Test		94.82 68	95.66 63	93.84 88	94.79 103	98.35 80	96.49 78	89.10 114	96.12 64	87.09 103	92.58 66	90.61 85	96.48 67	96.51 89	96.89 64	99.11 97	98.54 101

diffmvs		94.31 84	94.21 88	94.42 80	94.64 110	98.28 81	96.36 81	91.56 80	96.77 48	88.89 91	88.97 101	84.23 129	96.01 75	96.05 105	96.41 73	99.05 109	98.79 91

EPNet		96.27 51	96.97 44	95.46 57	98.47 44	98.28 81	97.41 54	93.67 51	95.86 74	92.86 42	97.51 24	93.79 68	91.76 135	97.03 68	97.03 59	98.61 148	99.28 24
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DI_MVS_plusplus_trai		94.01 87	93.63 102	94.44 79	94.54 111	98.26 83	97.51 53	90.63 93	95.88 73	89.34 86	80.54 154	89.36 93	95.48 85	96.33 95	96.27 77	99.17 87	98.78 92

PLC	94.95 3	97.37 33	96.77 49	98.07 21	98.97 32	98.21 84	97.94 46	96.85 36	97.66 26	97.58 3	93.33 58	96.84 48	98.01 34	97.13 65	96.20 80	99.09 99	98.01 124
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		96.86 43	96.82 48	96.91 39	98.08 52	98.20 85	98.52 33	97.20 29	97.24 38	91.42 52	91.84 75	98.45 35	97.25 47	97.07 66	97.40 50	98.95 116	97.55 140

Anonymous202405211			92.18 127		95.04 98	98.20 85	96.14 85	91.79 78	93.93 109		74.60 173	88.38 104	96.48 67	95.17 129	95.82 95	99.00 111	99.15 47

MAR-MVS		95.50 55	95.60 64	95.39 59	98.67 40	98.18 87	95.89 95	89.81 104	94.55 101	91.97 49	92.99 61	90.21 88	97.30 46	96.79 74	97.49 44	98.72 138	98.99 70
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
gg-mvs-nofinetune		86.17 186	88.57 160	83.36 194	93.44 130	98.15 88	96.58 75	72.05 208	74.12 212	49.23 216	64.81 206	90.85 83	89.90 165	97.83 46	96.84 65	98.97 114	97.41 144

PatchMatch-RL		94.69 74	94.41 84	95.02 64	97.63 59	98.15 88	94.50 120	91.99 73	95.32 86	91.31 53	95.47 43	83.44 135	96.02 74	96.56 83	95.23 109	98.69 141	96.67 164

Effi-MVS+		92.93 105	93.86 97	91.86 108	94.07 121	98.09 90	95.59 100	85.98 147	94.27 106	79.54 136	91.12 85	81.81 144	96.71 63	96.67 80	96.06 83	99.27 67	98.98 72

Anonymous20231211		93.49 99	92.33 126	94.84 71	94.78 105	98.00 91	96.11 86	91.85 75	94.86 96	90.91 56	74.69 172	89.18 96	96.73 62	94.82 134	95.51 101	98.67 142	99.24 32

CHOSEN 280x420		95.46 58	97.01 43	93.66 91	97.28 63	97.98 92	96.40 80	85.39 155	96.10 65	91.07 55	96.53 33	96.34 55	95.61 81	97.65 50	96.95 62	96.21 186	97.49 141

baseline1		94.59 76	94.47 83	94.72 74	95.16 94	97.97 93	96.07 88	91.94 74	94.86 96	89.98 74	91.60 79	85.87 117	95.64 79	97.07 66	96.90 63	99.52 14	97.06 156

baseline		94.83 67	95.82 62	93.68 90	94.75 106	97.80 94	96.51 77	88.53 119	97.02 46	89.34 86	92.93 62	92.18 76	94.69 93	95.78 113	96.08 81	98.27 163	98.97 76

GeoE		92.52 110	92.64 113	92.39 105	93.96 122	97.76 95	96.01 91	85.60 152	93.23 120	83.94 113	81.56 148	84.80 126	95.63 80	96.22 99	95.83 94	99.19 85	99.07 58

ACMP	92.88 9	94.43 80	94.38 85	94.50 78	96.01 78	97.69 96	95.85 98	92.09 72	95.74 77	89.12 89	95.14 46	82.62 141	94.77 90	95.73 115	94.67 122	99.14 93	99.06 59
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ET-MVSNet_ETH3D		93.34 101	94.33 87	92.18 107	83.26 208	97.66 97	96.72 70	89.89 102	95.62 81	87.17 102	96.00 37	83.69 134	96.99 56	93.78 149	95.34 105	99.06 105	98.18 122

TSAR-MVS + COLMAP		94.79 70	94.51 82	95.11 62	96.50 70	97.54 98	97.99 45	94.54 45	97.81 16	85.88 107	96.73 31	81.28 147	96.99 56	96.29 96	95.21 110	98.76 137	96.73 163

LGP-MVS_train		94.12 85	94.62 80	93.53 92	96.44 72	97.54 98	97.40 55	91.84 76	94.66 98	81.09 129	95.70 41	83.36 136	95.10 88	96.36 94	95.71 96	99.32 58	99.03 65

baseline2		93.01 104	94.17 91	91.64 112	92.83 139	97.49 100	93.40 134	87.53 129	93.67 115	86.07 106	91.83 76	86.58 109	91.36 139	96.38 91	95.06 112	98.67 142	98.20 121

CLD-MVS		94.79 70	94.36 86	95.30 60	95.21 93	97.46 101	97.23 56	92.24 71	96.43 54	91.77 50	92.69 65	84.31 128	96.06 72	95.52 119	95.03 113	99.31 61	99.06 59
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
COLMAP_ROB	90.49 14	93.27 103	92.71 112	93.93 86	97.75 57	97.44 102	96.07 88	93.17 60	95.40 84	83.86 114	83.76 139	88.72 100	93.87 108	94.25 145	94.11 140	98.87 123	95.28 178
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MSDG		94.82 68	93.73 100	96.09 48	98.34 47	97.43 103	97.06 58	96.05 38	95.84 75	90.56 63	86.30 124	89.10 98	95.55 83	96.13 104	95.61 98	99.00 111	95.73 172

OPM-MVS		93.61 96	92.43 122	95.00 65	96.94 67	97.34 104	97.78 48	94.23 47	89.64 164	85.53 108	88.70 104	82.81 139	96.28 70	96.28 97	95.00 116	99.24 72	97.22 149

SCA		90.92 128	93.04 111	88.45 150	93.72 128	97.33 105	92.77 143	76.08 199	96.02 67	78.26 140	91.96 73	90.86 82	93.99 107	90.98 188	90.04 189	95.88 190	94.06 188

EPMVS		90.88 129	92.12 128	89.44 141	94.71 107	97.24 106	93.55 130	76.81 194	95.89 72	81.77 124	91.49 80	86.47 111	93.87 108	90.21 191	90.07 188	95.92 189	93.49 195

HQP-MVS		94.43 80	94.57 81	94.27 82	96.41 73	97.23 107	96.89 62	93.98 48	95.94 71	83.68 115	95.01 48	84.46 127	95.58 82	95.47 121	94.85 121	99.07 102	99.00 69

Fast-Effi-MVS+		91.87 114	92.08 129	91.62 114	92.91 137	97.21 108	94.93 110	84.60 166	93.61 116	81.49 127	83.50 140	78.95 152	96.62 65	96.55 84	96.22 79	99.16 90	98.51 103

Effi-MVS+-dtu		91.78 116	93.59 104	89.68 139	92.44 143	97.11 109	94.40 121	84.94 162	92.43 133	75.48 157	91.09 86	83.75 133	93.55 116	96.61 81	95.47 102	97.24 181	98.67 94

MDTV_nov1_ep13		91.57 120	93.18 109	89.70 137	93.39 131	96.97 110	93.53 131	80.91 185	95.70 78	81.86 123	92.40 68	89.93 89	93.25 121	91.97 181	90.80 184	95.25 199	94.46 182

ACMH+	90.88 12	91.41 123	91.13 139	91.74 111	95.11 96	96.95 111	93.13 139	89.48 110	92.42 134	79.93 133	85.13 129	78.02 156	93.82 110	93.49 156	93.88 146	98.94 117	97.99 125

MS-PatchMatch		91.82 115	92.51 116	91.02 118	95.83 80	96.88 112	95.05 107	84.55 168	93.85 112	82.01 122	82.51 145	91.71 77	90.52 157	95.07 131	93.03 162	98.13 166	94.52 180

TDRefinement		89.07 155	88.15 164	90.14 133	95.16 94	96.88 112	95.55 102	90.20 97	89.68 163	76.42 151	76.67 164	74.30 173	84.85 190	93.11 162	91.91 180	98.64 147	94.47 181

ACMH	90.77 13	91.51 122	91.63 135	91.38 115	95.62 82	96.87 114	91.76 167	89.66 106	91.58 147	78.67 138	86.73 115	78.12 155	93.77 111	94.59 136	94.54 131	98.78 135	98.98 72
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PatchmatchNet		90.56 132	92.49 118	88.31 153	93.83 126	96.86 115	92.42 151	76.50 196	95.96 70	78.31 139	91.96 73	89.66 91	93.48 117	90.04 193	89.20 192	95.32 196	93.73 193
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-mter		90.95 127	93.54 107	87.93 164	90.28 162	96.80 116	91.44 169	82.68 178	92.15 142	74.37 168	89.57 99	88.23 106	90.88 149	96.37 93	94.31 137	97.93 172	97.37 145

CDS-MVSNet		92.77 106	93.60 103	91.80 110	92.63 141	96.80 116	95.24 105	89.14 113	90.30 161	84.58 111	86.76 114	90.65 84	90.42 158	95.89 108	96.49 71	98.79 134	98.32 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMM	92.75 10	94.41 82	93.84 98	95.09 63	96.41 73	96.80 116	94.88 112	93.54 52	96.41 55	90.16 70	92.31 69	83.11 137	96.32 69	96.22 99	94.65 123	99.22 78	97.35 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		93.85 90	93.91 95	93.78 89	94.94 100	96.79 119	94.29 123	91.13 86	93.84 113	88.26 95	90.40 91	85.23 122	94.65 96	96.54 85	95.31 106	99.38 49	99.28 24

PMMVS		94.61 75	95.56 65	93.50 93	94.30 116	96.74 120	94.91 111	89.56 108	95.58 83	87.72 98	96.15 35	92.86 72	96.06 72	95.47 121	95.02 114	98.43 160	97.09 152

ADS-MVSNet		89.80 144	91.33 138	88.00 162	94.43 114	96.71 121	92.29 155	74.95 204	96.07 66	77.39 143	88.67 105	86.09 114	93.26 120	88.44 197	89.57 191	95.68 192	93.81 192

MVSTER		94.89 66	95.07 76	94.68 76	94.71 107	96.68 122	97.00 59	90.57 94	95.18 92	93.05 39	95.21 45	86.41 112	93.72 112	97.59 52	95.88 91	99.00 111	98.50 104

GG-mvs-BLEND		66.17 207	94.91 79	32.63 213	1.32 221	96.64 123	91.40 170	0.85 219	94.39 105	2.20 222	90.15 95	95.70 61	2.27 218	96.39 90	95.44 103	97.78 174	95.68 173

TAMVS		90.54 134	90.87 144	90.16 131	91.48 149	96.61 124	93.26 137	86.08 145	87.71 181	81.66 126	83.11 143	84.04 130	90.42 158	94.54 137	94.60 126	98.04 170	95.48 176

test-LLR		91.62 119	93.56 105	89.35 143	93.31 133	96.57 125	92.02 163	87.06 135	92.34 138	75.05 164	90.20 93	88.64 101	90.93 146	96.19 102	94.07 141	97.75 176	96.90 160

TESTMET0.1,1		91.07 126	93.56 105	88.17 154	90.43 158	96.57 125	92.02 163	82.83 177	92.34 138	75.05 164	90.20 93	88.64 101	90.93 146	96.19 102	94.07 141	97.75 176	96.90 160

GA-MVS		89.28 150	90.75 145	87.57 171	91.77 147	96.48 127	92.29 155	87.58 128	90.61 158	65.77 197	84.48 134	76.84 164	89.46 166	95.84 110	93.68 151	98.52 153	97.34 147

Fast-Effi-MVS+-dtu		91.19 125	93.64 101	88.33 152	92.19 145	96.46 128	93.99 126	81.52 183	92.59 130	71.82 176	92.17 70	85.54 118	91.68 136	95.73 115	94.64 124	98.80 132	98.34 114

USDC		90.69 130	90.52 146	90.88 121	94.17 119	96.43 129	95.82 99	86.76 137	93.92 110	76.27 153	86.49 118	74.30 173	93.67 115	95.04 132	93.36 155	98.61 148	94.13 185

RPSCF		94.05 86	94.00 94	94.12 84	96.20 75	96.41 130	96.61 73	91.54 81	95.83 76	89.73 78	96.94 30	92.80 73	95.35 87	91.63 184	90.44 186	95.27 198	93.94 189

FC-MVSNet-test		91.63 118	93.82 99	89.08 144	92.02 146	96.40 131	93.26 137	87.26 132	93.72 114	77.26 144	88.61 106	89.86 90	85.50 186	95.72 117	95.02 114	99.16 90	97.44 143

test0.0.03 1		91.97 113	93.91 95	89.72 136	93.31 133	96.40 131	91.34 172	87.06 135	93.86 111	81.67 125	91.15 84	89.16 97	86.02 184	95.08 130	95.09 111	98.91 120	96.64 166

UniMVSNet_ETH3D		88.47 161	86.00 191	91.35 116	91.55 148	96.29 133	92.53 148	88.81 115	85.58 195	82.33 121	67.63 203	66.87 204	94.04 106	91.49 185	95.24 108	98.84 126	98.92 78

EG-PatchMatch MVS		86.68 182	87.24 178	86.02 187	90.58 157	96.26 134	91.08 176	81.59 181	84.96 196	69.80 190	71.35 195	75.08 170	84.23 194	94.24 146	93.35 156	98.82 127	95.46 177

dps		90.11 142	89.37 155	90.98 119	93.89 124	96.21 135	93.49 132	77.61 192	91.95 143	92.74 45	88.85 102	78.77 154	92.37 128	87.71 200	87.71 197	95.80 191	94.38 183

thisisatest0515		90.12 141	92.06 130	87.85 165	90.03 165	96.17 136	87.83 191	87.45 130	91.71 146	77.15 145	85.40 128	84.01 131	85.74 185	95.41 123	93.30 158	98.88 122	98.43 107

UniMVSNet (Re)		90.03 143	89.61 151	90.51 127	89.97 167	96.12 137	92.32 153	89.26 111	90.99 153	80.95 130	78.25 161	75.08 170	91.14 142	93.78 149	93.87 147	99.41 43	99.21 37

CostFormer		90.69 130	90.48 147	90.93 120	94.18 118	96.08 138	94.03 125	78.20 190	93.47 118	89.96 75	90.97 87	80.30 148	93.72 112	87.66 201	88.75 193	95.51 195	96.12 168

FMVSNet3		93.79 93	94.17 91	93.35 98	91.21 154	95.99 139	96.62 72	88.68 116	95.23 89	90.40 65	86.39 120	91.16 79	94.11 104	95.96 106	96.67 68	99.07 102	97.79 129

tpmrst		88.86 159	89.62 150	87.97 163	94.33 115	95.98 140	92.62 147	76.36 197	94.62 100	76.94 147	85.98 125	82.80 140	92.80 125	86.90 203	87.15 199	94.77 203	93.93 190

anonymousdsp		88.90 157	91.00 141	86.44 183	88.74 193	95.97 141	90.40 182	82.86 176	88.77 171	67.33 195	81.18 150	81.44 146	90.22 161	96.23 98	94.27 138	99.12 96	99.16 46

Patchmtry						95.96 142	93.36 135	75.99 200		75.19 161

CR-MVSNet		90.16 140	91.96 132	88.06 158	93.32 132	95.95 143	93.36 135	75.99 200	92.40 135	75.19 161	83.18 141	85.37 119	92.05 130	95.21 127	94.56 129	98.47 157	97.08 154

RPMNet		90.19 139	92.03 131	88.05 159	93.46 129	95.95 143	93.41 133	74.59 205	92.40 135	75.91 155	84.22 136	86.41 112	92.49 126	94.42 141	93.85 148	98.44 158	96.96 157

SixPastTwentyTwo		88.37 162	89.47 152	87.08 177	90.01 166	95.93 145	87.41 192	85.32 156	90.26 162	70.26 184	86.34 123	71.95 183	90.93 146	92.89 167	91.72 181	98.55 151	97.22 149

GBi-Net		93.81 91	94.18 89	93.38 96	91.34 151	95.86 146	96.22 82	88.68 116	95.23 89	90.40 65	86.39 120	91.16 79	94.40 100	96.52 86	96.30 74	99.21 81	97.79 129

test1		93.81 91	94.18 89	93.38 96	91.34 151	95.86 146	96.22 82	88.68 116	95.23 89	90.40 65	86.39 120	91.16 79	94.40 100	96.52 86	96.30 74	99.21 81	97.79 129

FMVSNet2		93.30 102	93.36 108	93.22 100	91.34 151	95.86 146	96.22 82	88.24 123	95.15 93	89.92 77	81.64 147	89.36 93	94.40 100	96.77 75	96.98 61	99.21 81	97.79 129

UniMVSNet_NR-MVSNet		90.35 136	89.96 148	90.80 123	89.66 170	95.83 149	92.48 149	90.53 95	90.96 154	79.57 134	79.33 158	77.14 161	93.21 122	92.91 166	94.50 134	99.37 52	99.05 62

DCV-MVSNet		94.76 73	95.12 75	94.35 81	95.10 97	95.81 150	96.46 79	89.49 109	96.33 56	90.16 70	92.55 67	90.26 87	95.83 76	95.52 119	96.03 85	99.06 105	99.33 20

LTVRE_ROB	87.32 16	87.55 174	88.25 163	86.73 180	90.66 156	95.80 151	93.05 140	84.77 163	83.35 201	60.32 209	83.12 142	67.39 202	93.32 119	94.36 143	94.86 118	98.28 162	98.87 85
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
tfpnnormal		88.50 160	87.01 182	90.23 129	91.36 150	95.78 152	92.74 144	90.09 98	83.65 200	76.33 152	71.46 194	69.58 195	91.84 133	95.54 118	94.02 143	99.06 105	99.03 65

pm-mvs1		89.19 153	89.02 156	89.38 142	90.40 159	95.74 153	92.05 161	88.10 125	86.13 191	77.70 141	73.72 181	79.44 151	88.97 169	95.81 112	94.51 133	99.08 100	97.78 134

MIMVSNet		88.99 156	91.07 140	86.57 182	86.78 202	95.62 154	91.20 175	75.40 202	90.65 157	76.57 149	84.05 137	82.44 142	91.01 145	95.84 110	95.38 104	98.48 156	93.50 194

DU-MVS		89.67 146	88.84 157	90.63 126	89.26 180	95.61 155	92.48 149	89.91 100	91.22 150	79.57 134	77.72 162	71.18 187	93.21 122	92.53 170	94.57 128	99.35 55	99.05 62

NR-MVSNet		89.34 149	88.66 158	90.13 134	90.40 159	95.61 155	93.04 141	89.91 100	91.22 150	78.96 137	77.72 162	68.90 198	89.16 168	94.24 146	93.95 144	99.32 58	98.99 70

testgi		89.42 147	91.50 137	87.00 179	92.40 144	95.59 157	89.15 188	85.27 159	92.78 126	72.42 173	91.75 77	76.00 166	84.09 195	94.38 142	93.82 150	98.65 146	96.15 167

PatchT		89.13 154	91.71 133	86.11 186	92.92 136	95.59 157	83.64 202	75.09 203	91.87 144	75.19 161	82.63 144	85.06 124	92.05 130	95.21 127	94.56 129	97.76 175	97.08 154

WR-MVS_H		87.93 168	87.85 171	88.03 161	89.62 171	95.58 159	90.47 181	85.55 153	87.20 186	76.83 148	74.42 176	72.67 181	86.37 181	93.22 161	93.04 161	99.33 56	98.83 89

pmmvs5		87.83 172	88.09 165	87.51 174	89.59 173	95.48 160	89.75 186	84.73 164	86.07 193	71.44 178	80.57 153	70.09 193	90.74 153	94.47 139	92.87 166	98.82 127	97.10 151

EPNet_dtu		92.45 111	95.02 77	89.46 140	98.02 53	95.47 161	94.79 114	92.62 65	94.97 94	70.11 186	94.76 51	92.61 75	84.07 196	95.94 107	95.56 99	97.15 182	95.82 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_part1		91.21 124	89.47 152	93.24 99	94.26 117	95.45 162	95.26 104	88.36 121	88.49 174	90.04 72	72.61 188	82.82 138	93.69 114	93.25 160	94.62 125	97.84 173	99.06 59

CVMVSNet		89.77 145	91.66 134	87.56 172	93.21 135	95.45 162	91.94 166	89.22 112	89.62 165	69.34 192	83.99 138	85.90 116	84.81 191	94.30 144	95.28 107	96.85 184	97.09 152

TinyColmap		89.42 147	88.58 159	90.40 128	93.80 127	95.45 162	93.96 127	86.54 140	92.24 140	76.49 150	80.83 151	70.44 190	93.37 118	94.45 140	93.30 158	98.26 164	93.37 196

tpm cat1		88.90 157	87.78 173	90.22 130	93.88 125	95.39 165	93.79 128	78.11 191	92.55 131	89.43 82	81.31 149	79.84 150	91.40 138	84.95 204	86.34 202	94.68 205	94.09 186

V42		88.31 163	87.95 169	88.73 147	89.44 175	95.34 166	92.23 157	87.21 133	88.83 169	74.49 167	74.89 171	73.43 178	90.41 160	92.08 179	92.77 169	98.60 150	98.33 115

v2v482		88.25 164	87.71 174	88.88 145	89.23 184	95.28 167	92.10 159	87.89 127	88.69 172	73.31 171	75.32 168	71.64 184	91.89 132	92.10 178	92.92 164	98.86 125	97.99 125

WR-MVS		87.93 168	88.09 165	87.75 166	89.26 180	95.28 167	90.81 178	86.69 138	88.90 168	75.29 160	74.31 177	73.72 176	85.19 189	92.26 173	93.32 157	99.27 67	98.81 90

FMVSNet1		91.54 121	90.93 142	92.26 106	90.35 161	95.27 169	95.22 106	87.16 134	91.37 149	87.62 99	75.45 167	83.84 132	94.43 98	96.52 86	96.30 74	98.82 127	97.74 135

TranMVSNet+NR-MVSNet		89.23 152	88.48 161	90.11 135	89.07 186	95.25 170	92.91 142	90.43 96	90.31 160	77.10 146	76.62 165	71.57 185	91.83 134	92.12 176	94.59 127	99.32 58	98.92 78

v148		87.51 175	86.79 184	88.36 151	89.39 177	95.21 171	89.84 185	88.20 124	87.61 183	77.56 142	73.38 184	70.32 192	86.80 178	90.70 189	92.31 176	98.37 161	97.98 127

v1144		87.92 170	87.79 172	88.07 156	89.27 179	95.15 172	92.17 158	85.62 151	88.52 173	71.52 177	73.80 180	72.40 182	91.06 144	93.54 155	92.80 167	98.81 130	98.33 115

CP-MVSNet		87.89 171	87.27 177	88.62 148	89.30 178	95.06 173	90.60 180	85.78 149	87.43 185	75.98 154	74.60 173	68.14 201	90.76 151	93.07 164	93.60 152	99.30 63	98.98 72

CMPMVS	65.18 17	84.76 192	83.10 198	86.69 181	95.29 90	95.05 174	88.37 189	85.51 154	80.27 207	71.31 179	68.37 200	73.85 175	85.25 187	87.72 199	87.75 196	94.38 206	88.70 206
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v8		88.21 165	87.94 170	88.51 149	89.62 171	95.01 175	92.31 154	84.99 161	88.94 167	74.70 166	75.03 169	73.51 177	90.67 154	92.11 177	92.74 170	98.80 132	98.24 119

IterMVS-LS		92.56 109	93.18 109	91.84 109	93.90 123	94.97 176	94.99 108	86.20 144	94.18 107	82.68 119	85.81 126	87.36 108	94.43 98	95.31 125	96.02 86	98.87 123	98.60 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		90.55 133	89.91 149	91.30 117	90.26 163	94.95 177	92.73 145	87.94 126	93.44 119	85.35 109	82.28 146	76.09 165	93.02 124	93.56 154	92.26 178	98.51 154	96.77 162

v7n		86.43 184	86.52 188	86.33 184	87.91 197	94.93 178	90.15 184	83.05 174	86.57 188	70.21 185	71.48 193	66.78 205	87.72 173	94.19 148	92.96 163	98.92 119	98.76 93

PS-CasMVS		87.33 178	86.68 187	88.10 155	89.22 185	94.93 178	90.35 183	85.70 150	86.44 190	74.01 169	73.43 183	66.59 207	90.04 162	92.92 165	93.52 153	99.28 65	98.91 81

v144192		87.40 177	87.20 179	87.64 168	88.89 188	94.88 180	91.65 168	84.70 165	87.80 180	71.17 181	73.20 185	70.91 188	90.75 152	92.69 168	92.49 173	98.71 139	98.43 107

v1192		87.51 175	87.31 176	87.74 167	89.04 187	94.87 181	92.07 160	85.03 160	88.49 174	70.32 183	72.65 187	70.35 191	91.21 141	93.59 151	92.80 167	98.78 135	98.42 109

v1921920		87.31 179	87.13 180	87.52 173	88.87 190	94.72 182	91.96 165	84.59 167	88.28 176	69.86 189	72.50 189	70.03 194	91.10 143	93.33 158	92.61 172	98.71 139	98.44 106

v10		88.00 166	87.96 168	88.05 159	89.44 175	94.68 183	92.36 152	83.35 173	89.37 166	72.96 172	73.98 179	72.79 180	91.35 140	93.59 151	92.88 165	98.81 130	98.42 109

MDTV_nov1_ep13_2view		86.30 185	88.27 162	84.01 192	87.71 199	94.67 184	88.08 190	76.78 195	90.59 159	68.66 194	80.46 155	80.12 149	87.58 176	89.95 194	88.20 195	95.25 199	93.90 191

v1240		86.89 181	86.75 186	87.06 178	88.75 192	94.65 185	91.30 174	84.05 169	87.49 184	68.94 193	71.96 192	68.86 199	90.65 155	93.33 158	92.72 171	98.67 142	98.24 119

tpm		87.95 167	89.44 154	86.21 185	92.53 142	94.62 186	91.40 170	76.36 197	91.46 148	69.80 190	87.43 110	75.14 168	91.55 137	89.85 195	90.60 185	95.61 193	96.96 157

MVS-HIRNet		85.36 190	86.89 183	83.57 193	90.13 164	94.51 187	83.57 203	72.61 207	88.27 177	71.22 180	68.97 198	81.81 144	88.91 170	93.08 163	91.94 179	94.97 202	89.64 205

PEN-MVS		87.22 180	86.50 189	88.07 156	88.88 189	94.44 188	90.99 177	86.21 142	86.53 189	73.66 170	74.97 170	66.56 208	89.42 167	91.20 187	93.48 154	99.24 72	98.31 118

TransMVSNet (Re)		87.73 173	86.79 184	88.83 146	90.76 155	94.40 189	91.33 173	89.62 107	84.73 197	75.41 159	72.73 186	71.41 186	86.80 178	94.53 138	93.93 145	99.06 105	95.83 170

pmmvs6		85.98 188	84.89 196	87.25 176	88.83 191	94.35 190	89.36 187	85.30 158	78.51 209	75.44 158	62.71 208	75.41 167	87.65 174	93.58 153	92.40 175	96.89 183	97.29 148

IterMVS		90.20 138	92.43 122	87.61 170	92.82 140	94.31 191	94.11 124	81.54 182	92.97 123	69.90 188	84.71 132	88.16 107	89.96 164	95.25 126	94.17 139	97.31 180	97.46 142
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		90.24 137	92.48 120	87.63 169	92.85 138	94.30 192	93.79 128	81.47 184	92.66 127	69.95 187	84.66 133	88.38 104	89.99 163	95.39 124	94.34 136	97.74 178	97.63 138

pmnet_mix02		86.12 187	87.12 181	84.96 190	89.82 168	94.12 193	84.88 200	86.63 139	91.78 145	65.60 198	80.76 152	76.98 162	86.61 180	87.29 202	84.80 205	96.21 186	94.09 186

DTE-MVSNet		86.67 183	86.09 190	87.35 175	88.45 195	94.08 194	90.65 179	86.05 146	86.13 191	72.19 174	74.58 175	66.77 206	87.61 175	90.31 190	93.12 160	99.13 94	97.62 139

our_test_3					89.78 169	93.84 195	85.59 197

Baseline_NR-MVSNet		89.27 151	88.01 167	90.73 125	89.26 180	93.71 196	92.71 146	89.78 105	90.73 155	81.28 128	73.53 182	72.85 179	92.30 129	92.53 170	93.84 149	99.07 102	98.88 83

MDA-MVSNet-bldmvs		80.11 199	80.24 202	79.94 199	77.01 211	93.21 197	78.86 209	85.94 148	82.71 204	60.86 206	79.71 157	51.77 217	83.71 197	75.60 209	86.37 201	93.28 207	92.35 197

Anonymous20231206		83.84 195	85.19 194	82.26 196	87.38 200	92.87 198	85.49 198	83.65 171	86.07 193	63.44 204	68.42 199	69.01 197	75.45 204	93.34 157	92.44 174	98.12 168	94.20 184

N_pmnet		84.80 191	85.10 195	84.45 191	89.25 183	92.86 199	84.04 201	86.21 142	88.78 170	66.73 196	72.41 190	74.87 172	85.21 188	88.32 198	86.45 200	95.30 197	92.04 199

EU-MVSNet		85.62 189	87.65 175	83.24 195	88.54 194	92.77 200	87.12 193	85.32 156	86.71 187	64.54 200	78.52 160	75.11 169	78.35 200	92.25 174	92.28 177	95.58 194	95.93 169

FMVSNet5		90.36 135	90.93 142	89.70 137	87.99 196	92.25 201	92.03 162	83.51 172	92.20 141	84.13 112	85.59 127	86.48 110	92.43 127	94.61 135	94.52 132	98.13 166	90.85 202

test20.03		82.92 197	85.52 192	79.90 200	87.75 198	91.84 202	82.80 204	82.99 175	82.65 205	60.32 209	78.90 159	70.50 189	67.10 208	92.05 180	90.89 183	98.44 158	91.80 200

PM-MVS		84.72 193	84.47 197	85.03 189	84.67 204	91.57 203	86.27 196	82.31 180	87.65 182	70.62 182	76.54 166	56.41 215	88.75 171	92.59 169	89.85 190	97.54 179	96.66 165

pmmvs-eth3d		84.33 194	82.94 199	85.96 188	84.16 205	90.94 204	86.55 195	83.79 170	84.25 198	75.85 156	70.64 196	56.43 214	87.44 177	92.20 175	90.41 187	97.97 171	95.68 173

MIMVSNet1		80.03 200	80.93 201	78.97 201	72.46 214	90.73 205	80.81 207	82.44 179	80.39 206	63.64 202	57.57 209	64.93 209	76.37 202	91.66 183	91.55 182	98.07 169	89.70 204

new-patchmatchnet		78.49 202	78.19 205	78.84 202	84.13 206	90.06 206	77.11 211	80.39 186	79.57 208	59.64 212	66.01 204	55.65 216	75.62 203	84.55 205	80.70 207	96.14 188	90.77 203

gm-plane-assit		83.26 196	85.29 193	80.89 197	89.52 174	89.89 207	70.26 213	78.24 189	77.11 210	58.01 213	74.16 178	66.90 203	90.63 156	97.20 61	96.05 84	98.66 145	95.68 173

new_pmnet		81.53 198	82.68 200	80.20 198	83.47 207	89.47 208	82.21 206	78.36 188	87.86 179	60.14 211	67.90 201	69.43 196	82.03 198	89.22 196	87.47 198	94.99 201	87.39 207

DeepMVS_CX						86.86 209	79.50 208	70.43 210	90.73 155	63.66 201	80.36 156	60.83 210	79.68 199	76.23 208		89.46 210	86.53 208

pmmvs3		79.16 201	80.12 203	78.05 203	79.36 209	86.59 210	78.13 210	73.87 206	76.42 211	57.51 214	70.59 197	57.02 213	84.66 192	90.10 192	88.32 194	94.75 204	91.77 201

ambc			73.83 207		76.23 212	85.13 211	82.27 205		84.16 199	65.58 199	52.82 211	23.31 222	73.55 205	91.41 186	85.26 204	92.97 208	94.70 179

FPMVS		75.84 203	74.59 206	77.29 204	86.92 201	83.89 212	85.01 199	80.05 187	82.91 203	60.61 208	65.25 205	60.41 211	63.86 209	75.60 209	73.60 211	87.29 213	80.47 210

PMMVS2		64.36 208	65.94 210	62.52 209	67.37 215	77.44 213	64.39 215	69.32 213	61.47 214	34.59 217	46.09 212	41.03 218	48.02 215	74.56 211	78.23 208	91.43 209	82.76 209

tmp_tt				66.88 207	86.07 203	73.86 214	68.22 214	33.38 216	96.88 47	80.67 131	88.23 108	78.82 153	49.78 213	82.68 207	77.47 209	83.19 215

Gipuma		68.35 205	66.71 208	70.27 205	74.16 213	68.78 215	63.93 216	71.77 209	83.34 202	54.57 215	34.37 213	31.88 219	68.69 207	83.30 206	85.53 203	88.48 211	79.78 211
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		72.96 204	78.68 204	66.28 208	50.17 218	64.90 216	75.45 212	50.90 215	87.89 178	62.54 205	62.98 207	68.34 200	70.45 206	91.90 182	82.41 206	88.19 212	92.35 197

PMVS	63.12 18	67.27 206	66.39 209	68.30 206	77.98 210	60.24 217	59.53 217	76.82 193	66.65 213	60.74 207	54.39 210	59.82 212	51.24 212	73.92 212	70.52 212	83.48 214	79.17 212
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.86 19	49.54 211	51.43 211	47.33 212	44.14 219	59.20 218	36.45 220	60.59 214	41.47 217	31.14 218	29.58 214	17.06 223	48.52 214	62.22 213	74.63 210	63.12 218	75.87 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		49.98 210	46.76 213	53.74 211	64.96 216	51.29 219	37.81 219	69.35 212	51.83 215	22.69 220	29.57 215	25.06 220	57.28 210	44.81 215	56.11 214	70.32 217	68.64 215

E-PMN		50.67 209	47.85 212	53.96 210	64.13 217	50.98 220	38.06 218	69.51 211	51.40 216	24.60 219	29.46 216	24.39 221	56.07 211	48.17 214	59.70 213	71.40 216	70.84 214

testmvs		12.09 212	16.94 214	6.42 214	3.15 220	6.08 221	9.51 222	3.84 217	21.46 218	5.31 221	27.49 217	6.76 224	10.89 216	17.06 216	15.01 215	5.84 219	24.75 216

test123		9.58 213	13.53 215	4.97 215	1.31 222	5.47 222	8.32 223	2.95 218	18.14 219	2.03 223	20.82 218	2.34 225	10.60 217	10.00 217	14.16 216	4.60 220	23.77 217

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										63.50 203

9.14												99.28 11

SR-MVS					99.45 9			97.61 15				99.20 15

MTAPA										96.83 10		99.12 20

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 221

mPP-MVS					99.21 24							98.29 38

NP-MVS									95.32 86

High-resolution multi-view benchmark