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	99.39 1	99.90 1	99.87 1	99.93 1	99.97 2	99.82 8	99.91 3	99.92 38	99.75 4	99.93 6	99.89 30	100.00 1	99.87 2	99.93 3	99.82 10	99.96 3	99.90 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
v7n		99.89 2	99.86 3	99.93 1	99.97 2	99.83 4	99.93 1	99.96 12	99.77 3	99.89 18	99.99 1	99.86 76	99.84 5	99.89 11	99.81 11	99.97 1	99.88 7

SixPastTwentyTwo		99.89 2	99.85 5	99.93 1	99.97 2	99.88 1	99.92 2	99.97 1	99.66 13	99.94 5	99.94 11	99.74 108	99.81 7	99.97 1	99.89 1	99.96 3	99.89 5

pmmvs6		99.88 4	99.87 1	99.89 9	99.97 2	99.76 22	99.89 5	99.96 12	99.82 2	99.90 16	99.92 16	99.95 25	99.68 32	99.93 3	99.88 3	99.95 7	99.86 13

anonymousdsp		99.87 5	99.86 3	99.88 13	99.95 10	99.75 28	99.90 4	99.96 12	99.69 7	99.83 52	99.96 4	99.99 3	99.74 22	99.95 2	99.83 7	99.91 25	99.88 7

FC-MVSNet-test		99.84 6	99.80 6	99.89 9	99.96 7	99.83 4	99.84 16	99.95 23	99.37 49	99.77 69	99.95 6	99.96 14	99.85 3	99.93 3	99.83 7	99.95 7	99.72 42

WB-MVS		99.82 7	99.76 9	99.89 9	99.94 23	99.82 8	99.79 29	99.93 27	99.67 10	99.97 2	99.83 45	99.78 105	99.79 12	99.72 39	99.70 22	99.95 7	99.78 28

UniMVSNet_ETH3D		99.81 8	99.79 7	99.85 19	99.98 1	99.76 22	99.73 48	99.96 12	99.68 9	99.87 30	99.59 85	99.91 56	99.58 52	99.90 10	99.85 6	99.96 3	99.81 21

TDRefinement		99.81 8	99.76 9	99.86 16	99.83 89	99.53 63	99.89 5	99.91 44	99.73 5	99.88 24	99.83 45	99.96 14	99.76 17	99.91 9	99.81 11	99.86 42	99.59 71

WR-MVS		99.79 10	99.68 14	99.91 5	99.95 10	99.83 4	99.87 9	99.96 12	99.39 47	99.93 6	99.87 35	99.29 151	99.77 15	99.83 22	99.72 20	99.97 1	99.82 18

MIMVSNet1		99.79 10	99.75 11	99.84 22	99.89 43	99.83 4	99.84 16	99.89 54	99.31 55	99.93 6	99.92 16	99.97 9	99.68 32	99.89 11	99.64 28	99.82 56	99.66 56

pm-mvs1		99.77 12	99.69 13	99.86 16	99.94 23	99.68 37	99.84 16	99.93 27	99.59 22	99.87 30	99.92 16	99.21 154	99.65 38	99.88 15	99.77 16	99.93 21	99.78 28

PEN-MVS		99.77 12	99.65 19	99.91 5	99.95 10	99.80 16	99.86 10	99.97 1	99.08 83	99.89 18	99.69 68	99.68 117	99.84 5	99.81 27	99.64 28	99.95 7	99.81 21

EU-MVSNet		99.76 14	99.74 12	99.78 42	99.82 94	99.81 13	99.88 7	99.87 60	99.31 55	99.75 77	99.91 23	99.76 107	99.78 13	99.84 21	99.74 19	99.56 139	99.81 21

Vis-MVSNet		99.76 14	99.78 8	99.75 52	99.92 31	99.77 21	99.83 19	99.85 71	99.43 41	99.85 43	99.84 42	100.00 1	99.13 118	99.83 22	99.66 25	99.90 29	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		99.75 16	99.66 18	99.85 19	99.87 54	99.86 2	99.83 19	99.91 44	98.84 119	99.92 10	99.57 87	99.85 82	99.60 47	99.82 25	99.79 13	99.94 16	99.87 11

CS-MVS-test		99.75 16	99.67 15	99.84 22	99.91 35	99.85 3	99.85 13	99.92 38	98.75 129	99.89 18	99.64 75	99.95 25	99.55 55	99.89 11	99.79 13	99.92 22	99.83 16

DTE-MVSNet		99.75 16	99.61 25	99.92 4	99.95 10	99.81 13	99.86 10	99.96 12	99.18 72	99.92 10	99.66 71	99.45 136	99.85 3	99.80 28	99.56 34	99.96 3	99.79 27

tfpnnormal		99.74 19	99.63 22	99.86 16	99.93 28	99.75 28	99.80 28	99.89 54	99.31 55	99.88 24	99.43 108	99.66 120	99.77 15	99.80 28	99.71 21	99.92 22	99.76 33

DeepC-MVS	99.05 5	99.74 19	99.64 20	99.84 22	99.90 40	99.39 96	99.79 29	99.81 100	99.69 7	99.90 16	99.87 35	99.98 5	99.81 7	99.62 55	99.32 62	99.83 53	99.65 59
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thisisatest0515		99.73 21	99.67 15	99.81 32	99.93 28	99.74 30	99.68 57	99.91 44	99.59 22	99.88 24	99.73 57	99.81 92	99.55 55	99.59 56	99.53 39	99.89 32	99.70 50

PS-CasMVS		99.73 21	99.59 31	99.90 8	99.95 10	99.80 16	99.85 13	99.97 1	98.95 103	99.86 36	99.73 57	99.36 143	99.81 7	99.83 22	99.67 24	99.95 7	99.83 16

WR-MVS_H		99.73 21	99.61 25	99.88 13	99.95 10	99.82 8	99.83 19	99.96 12	99.01 94	99.84 47	99.71 65	99.41 142	99.74 22	99.77 33	99.70 22	99.95 7	99.82 18

TransMVSNet (Re)		99.72 24	99.59 31	99.88 13	99.95 10	99.76 22	99.88 7	99.94 24	99.58 24	99.92 10	99.90 27	98.55 171	99.65 38	99.89 11	99.76 17	99.95 7	99.70 50

ACMH	99.11 4	99.72 24	99.63 22	99.84 22	99.87 54	99.59 50	99.83 19	99.88 59	99.46 38	99.87 30	99.66 71	99.95 25	99.76 17	99.73 38	99.47 48	99.84 48	99.52 103
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		99.70 26	99.67 15	99.74 58	99.94 23	99.71 33	99.82 24	99.91 44	99.14 80	99.53 135	99.70 66	99.88 68	99.33 90	99.88 15	99.61 33	99.94 16	99.77 30

EC-MVSNet		99.70 26	99.57 34	99.85 19	99.95 10	99.81 13	99.85 13	99.93 27	98.39 167	99.76 72	99.48 105	99.94 35	99.70 30	99.85 19	99.66 25	99.91 25	99.87 11

COLMAP_ROB	99.18 2	99.70 26	99.60 29	99.81 32	99.84 83	99.37 103	99.76 36	99.84 80	99.54 30	99.82 55	99.64 75	99.95 25	99.75 19	99.79 30	99.56 34	99.83 53	99.37 133
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	98.94 6	99.69 29	99.59 31	99.81 32	99.88 49	99.41 93	99.75 40	99.86 64	99.43 41	99.80 59	99.54 91	99.97 9	99.73 25	99.82 25	99.52 41	99.85 45	99.43 119

test20.03		99.68 30	99.60 29	99.76 48	99.91 35	99.70 36	99.68 57	99.87 60	99.05 90	99.88 24	99.92 16	99.88 68	99.50 68	99.77 33	99.42 55	99.75 77	99.49 105

CP-MVSNet		99.68 30	99.51 43	99.89 9	99.95 10	99.76 22	99.83 19	99.96 12	98.83 123	99.84 47	99.65 74	99.09 157	99.80 10	99.78 31	99.62 32	99.95 7	99.82 18

casdiffmvs_mvg		99.67 32	99.61 25	99.74 58	99.94 23	99.60 46	99.62 71	99.77 123	99.54 30	99.67 110	99.82 48	99.80 98	99.52 62	99.40 77	99.51 42	99.91 25	99.59 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		99.66 33	99.64 20	99.67 70	99.91 35	99.71 33	99.61 72	99.79 111	99.41 43	99.91 14	99.85 40	99.61 123	99.00 128	99.67 46	99.42 55	99.81 59	99.81 21

v10		99.65 34	99.51 43	99.81 32	99.83 89	99.61 45	99.75 40	99.94 24	99.56 26	99.76 72	99.94 11	99.60 125	99.73 25	99.11 135	99.01 105	99.85 45	99.74 37

CHOSEN 1792x2688		99.65 34	99.55 37	99.77 47	99.93 28	99.60 46	99.79 29	99.92 38	99.73 5	99.74 83	99.93 14	99.98 5	99.80 10	98.83 175	99.01 105	99.45 157	99.76 33

UA-Net		99.64 36	99.62 24	99.66 72	99.97 2	99.82 8	99.14 160	99.96 12	98.95 103	99.52 141	99.38 117	99.86 76	99.55 55	99.72 39	99.66 25	99.80 63	99.94 1

GeoE		99.63 37	99.51 43	99.78 42	99.91 35	99.57 53	99.78 32	99.97 1	99.23 63	99.72 93	99.72 61	99.80 98	99.50 68	99.45 74	99.10 91	99.79 66	99.71 48

Baseline_NR-MVSNet		99.62 38	99.48 48	99.78 42	99.85 77	99.76 22	99.59 77	99.82 92	98.84 119	99.88 24	99.91 23	99.04 158	99.61 45	99.46 67	99.78 15	99.94 16	99.60 69

pmmvs-eth3d		99.61 39	99.48 48	99.75 52	99.87 54	99.30 119	99.75 40	99.89 54	99.23 63	99.85 43	99.88 34	99.97 9	99.49 73	99.46 67	99.01 105	99.68 99	99.52 103

v1144		99.61 39	99.43 56	99.82 27	99.88 49	99.41 93	99.76 36	99.86 64	99.64 16	99.84 47	99.95 6	99.49 134	99.74 22	99.00 145	98.93 117	99.84 48	99.58 80

v8		99.61 39	99.45 54	99.79 41	99.80 100	99.59 50	99.73 48	99.93 27	99.48 36	99.77 69	99.90 27	99.48 135	99.67 35	99.11 135	98.89 121	99.84 48	99.73 39

casdiffmvs		99.61 39	99.55 37	99.68 69	99.89 43	99.53 63	99.64 65	99.68 151	99.51 33	99.62 119	99.90 27	99.96 14	99.37 84	99.28 102	99.25 65	99.88 34	99.44 116
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CSCG		99.61 39	99.52 42	99.71 63	99.89 43	99.62 43	99.52 93	99.76 132	99.61 20	99.69 102	99.73 57	99.96 14	99.57 53	99.27 105	98.62 151	99.81 59	99.85 15

v1192		99.60 44	99.41 60	99.82 27	99.89 43	99.43 86	99.81 26	99.84 80	99.63 18	99.85 43	99.95 6	99.35 146	99.72 27	99.01 143	98.90 120	99.82 56	99.58 80

APDe-MVS		99.60 44	99.48 48	99.73 61	99.85 77	99.51 74	99.75 40	99.85 71	99.17 73	99.81 58	99.56 89	99.94 35	99.44 80	99.42 76	99.22 66	99.67 101	99.54 95
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
v1921920		99.59 46	99.40 63	99.82 27	99.88 49	99.45 81	99.81 26	99.83 85	99.65 14	99.86 36	99.95 6	99.29 151	99.75 19	98.98 149	98.86 125	99.78 68	99.59 71

TranMVSNet+NR-MVSNet		99.59 46	99.42 59	99.80 37	99.87 54	99.55 57	99.64 65	99.86 64	99.05 90	99.88 24	99.72 61	99.33 149	99.64 42	99.47 66	99.14 77	99.91 25	99.67 55

EG-PatchMatch MVS		99.59 46	99.49 47	99.70 66	99.82 94	99.26 126	99.39 122	99.83 85	98.99 97	99.93 6	99.54 91	99.92 50	99.51 64	99.78 31	99.50 43	99.73 88	99.41 123

pmmvs5		99.58 49	99.47 51	99.70 66	99.84 83	99.50 75	99.58 81	99.80 108	98.98 100	99.73 90	99.92 16	99.81 92	99.49 73	99.28 102	99.05 99	99.77 72	99.73 39

v144192		99.58 49	99.39 64	99.80 37	99.87 54	99.44 83	99.77 33	99.84 80	99.64 16	99.86 36	99.93 14	99.35 146	99.72 27	98.92 155	98.82 129	99.74 83	99.66 56

v148		99.58 49	99.43 56	99.76 48	99.87 54	99.40 95	99.76 36	99.85 71	99.48 36	99.83 52	99.82 48	99.83 87	99.51 64	99.20 119	98.82 129	99.75 77	99.45 113

v1240		99.58 49	99.38 67	99.82 27	99.89 43	99.49 76	99.82 24	99.83 85	99.63 18	99.86 36	99.96 4	98.92 164	99.75 19	99.15 130	98.96 114	99.76 74	99.56 87

V42		99.57 53	99.41 60	99.75 52	99.84 83	99.37 103	99.73 48	99.83 85	99.41 43	99.75 77	99.89 30	99.42 140	99.60 47	99.15 130	98.96 114	99.76 74	99.65 59

TSAR-MVS + MP.		99.56 54	99.54 40	99.58 88	99.69 145	99.14 148	99.73 48	99.45 188	99.50 34	99.35 172	99.60 83	99.93 42	99.50 68	99.56 58	99.37 59	99.77 72	99.64 62
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v2v482		99.56 54	99.35 69	99.81 32	99.87 54	99.35 109	99.75 40	99.85 71	99.56 26	99.87 30	99.95 6	99.44 138	99.66 36	98.91 158	98.76 135	99.86 42	99.45 113

Gipuma		99.55 56	99.23 88	99.91 5	99.87 54	99.52 70	99.86 10	99.93 27	99.87 1	99.96 3	96.72 212	99.55 130	99.97 1	99.77 33	99.46 50	99.87 40	99.74 37
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DVP-MVS		99.53 57	99.51 43	99.55 97	99.82 94	99.58 52	99.54 89	99.78 116	99.28 61	99.21 182	99.70 66	99.97 9	99.32 93	99.32 90	99.14 77	99.64 113	99.58 80
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
NR-MVSNet		99.52 58	99.29 78	99.80 37	99.96 7	99.38 99	99.55 85	99.81 100	98.86 116	99.87 30	99.51 101	98.81 166	99.72 27	99.86 18	99.04 101	99.89 32	99.54 95

ACMMPR		99.51 59	99.32 73	99.72 62	99.87 54	99.33 112	99.61 72	99.85 71	99.19 70	99.73 90	98.73 166	99.95 25	99.61 45	99.35 84	99.14 77	99.66 103	99.58 80

UniMVSNet (Re)		99.50 60	99.29 78	99.75 52	99.86 69	99.47 79	99.51 96	99.82 92	98.90 111	99.89 18	99.64 75	99.00 159	99.55 55	99.32 90	99.08 94	99.90 29	99.59 71

FMVSNet1		99.50 60	99.57 34	99.42 119	99.67 154	99.65 40	99.60 76	99.91 44	99.40 45	99.39 165	99.83 45	99.27 153	98.14 169	99.68 43	99.50 43	99.81 59	99.68 52

HyFIR lowres test		99.50 60	99.26 82	99.80 37	99.95 10	99.62 43	99.76 36	99.97 1	99.67 10	99.56 131	99.94 11	98.40 174	99.78 13	98.84 174	98.59 154	99.76 74	99.72 42

PM-MVS		99.49 63	99.43 56	99.57 92	99.76 122	99.34 111	99.53 90	99.77 123	98.93 107	99.75 77	99.46 106	99.83 87	99.11 120	99.72 39	99.29 64	99.49 152	99.46 112

Anonymous20231206		99.48 64	99.31 75	99.69 68	99.79 104	99.57 53	99.63 69	99.79 111	98.88 113	99.91 14	99.72 61	99.93 42	99.59 49	99.24 108	98.63 150	99.43 161	99.18 150

DU-MVS		99.48 64	99.26 82	99.75 52	99.85 77	99.38 99	99.50 100	99.81 100	98.86 116	99.89 18	99.51 101	98.98 160	99.59 49	99.46 67	98.97 112	99.87 40	99.63 63

RPSCF		99.48 64	99.45 54	99.52 104	99.73 138	99.33 112	99.13 161	99.77 123	99.33 53	99.47 152	99.39 116	99.92 50	99.36 85	99.63 52	99.13 85	99.63 116	99.41 123

ACMMP_NAP		99.47 67	99.33 71	99.63 80	99.85 77	99.28 124	99.56 84	99.83 85	98.75 129	99.48 149	99.03 153	99.95 25	99.47 79	99.48 63	99.19 69	99.57 135	99.59 71

Anonymous20231211		99.47 67	99.39 64	99.57 92	99.89 43	99.60 46	99.50 100	99.69 145	98.91 110	99.62 119	99.17 139	99.35 146	98.86 142	99.63 52	99.46 50	99.84 48	99.62 66

SteuartSystems-ACMMP		99.47 67	99.22 91	99.76 48	99.88 49	99.36 105	99.65 64	99.84 80	98.47 154	99.80 59	98.68 169	99.96 14	99.68 32	99.37 81	99.06 96	99.72 92	99.66 56
Skip Steuart: Steuart Systems R&D Blog.
ACMM	98.37 12	99.47 67	99.23 88	99.74 58	99.86 69	99.19 142	99.68 57	99.86 64	99.16 77	99.71 99	98.52 179	99.95 25	99.62 44	99.35 84	99.02 103	99.74 83	99.42 122
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		99.46 71	99.57 34	99.33 141	99.75 126	99.57 53	99.44 113	99.81 100	99.38 48	98.56 213	99.81 52	99.99 3	98.79 147	99.33 88	99.13 85	99.62 122	99.81 21

HFP-MVS		99.46 71	99.30 76	99.65 74	99.82 94	99.25 130	99.50 100	99.82 92	99.23 63	99.58 129	98.86 157	99.94 35	99.56 54	99.14 133	99.12 89	99.63 116	99.56 87

LGP-MVS_train		99.46 71	99.18 100	99.78 42	99.87 54	99.25 130	99.71 55	99.87 60	98.02 186	99.79 63	98.90 156	99.96 14	99.66 36	99.49 62	99.17 73	99.79 66	99.49 105

SED-MVS		99.45 74	99.46 53	99.42 119	99.77 117	99.57 53	99.42 116	99.80 108	99.06 87	99.38 166	99.66 71	99.96 14	98.65 155	99.31 92	99.14 77	99.53 144	99.55 92

ETV-MVS		99.45 74	99.32 73	99.60 85	99.79 104	99.60 46	99.40 121	99.78 116	97.88 192	99.83 52	99.33 120	99.70 115	98.97 131	99.74 36	99.43 54	99.84 48	99.58 80

ACMP	98.32 13	99.44 76	99.18 100	99.75 52	99.83 89	99.18 143	99.64 65	99.83 85	98.81 125	99.79 63	98.42 188	99.96 14	99.64 42	99.46 67	98.98 111	99.74 83	99.44 116
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		99.43 77	99.23 88	99.67 70	99.92 31	99.76 22	99.64 65	99.93 27	99.06 87	99.68 109	97.77 199	98.97 161	98.97 131	99.72 39	99.54 38	99.88 34	99.81 21

SMA-MVS		99.43 77	99.41 60	99.45 115	99.82 94	99.31 117	99.02 175	99.59 167	99.06 87	99.34 175	99.53 97	99.96 14	99.38 83	99.29 97	99.13 85	99.53 144	99.59 71
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
testgi		99.43 77	99.47 51	99.38 128	99.90 40	99.67 39	99.30 140	99.73 140	98.64 142	99.53 135	99.52 99	99.90 59	98.08 172	99.65 50	99.40 58	99.75 77	99.55 92

DELS-MVS		99.42 80	99.53 41	99.29 145	99.52 182	99.43 86	99.42 116	99.28 203	99.16 77	99.72 93	99.82 48	99.97 9	98.17 166	99.56 58	99.16 74	99.65 105	99.59 71
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
3Dnovator	99.16 3	99.42 80	99.22 91	99.65 74	99.78 109	99.13 152	99.50 100	99.85 71	99.40 45	99.80 59	98.59 175	99.79 102	99.30 97	99.20 119	99.06 96	99.71 95	99.35 136

DPE-MVS		99.41 82	99.36 68	99.47 111	99.66 155	99.48 77	99.46 111	99.75 137	98.65 138	99.41 162	99.67 69	99.95 25	98.82 143	99.21 116	99.14 77	99.72 92	99.40 128
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UniMVSNet_NR-MVSNet		99.41 82	99.12 112	99.76 48	99.86 69	99.48 77	99.50 100	99.81 100	98.84 119	99.89 18	99.45 107	98.32 177	99.59 49	99.22 112	98.89 121	99.90 29	99.63 63

CP-MVS		99.41 82	99.20 96	99.65 74	99.80 100	99.23 137	99.44 113	99.75 137	98.60 147	99.74 83	98.66 170	99.93 42	99.48 76	99.33 88	99.16 74	99.73 88	99.48 108

QAPM		99.41 82	99.21 95	99.64 79	99.78 109	99.16 145	99.51 96	99.85 71	99.20 67	99.72 93	99.43 108	99.81 92	99.25 102	98.87 164	98.71 142	99.71 95	99.30 141

UGNet		99.40 86	99.61 25	99.16 164	99.88 49	99.64 41	99.61 72	99.77 123	99.31 55	99.63 118	99.33 120	99.93 42	96.46 206	99.63 52	99.53 39	99.63 116	99.89 5
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
Vis-MVSNet (Re-imp)		99.40 86	99.28 80	99.55 97	99.92 31	99.68 37	99.31 135	99.87 60	98.69 135	99.16 184	99.08 148	98.64 170	99.20 106	99.65 50	99.46 50	99.83 53	99.72 42

OPM-MVS		99.39 88	99.22 91	99.59 86	99.76 122	98.82 176	99.51 96	99.79 111	99.17 73	99.53 135	99.31 125	99.95 25	99.35 86	99.22 112	98.79 134	99.60 127	99.27 144
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Fast-Effi-MVS+		99.39 88	99.18 100	99.63 80	99.86 69	99.28 124	99.45 112	99.91 44	98.47 154	99.61 122	99.50 103	99.57 127	99.17 107	99.24 108	98.66 147	99.78 68	99.59 71

LS3D		99.39 88	99.28 80	99.52 104	99.77 117	99.39 96	99.55 85	99.82 92	98.93 107	99.64 116	98.52 179	99.67 119	98.58 159	99.74 36	99.63 30	99.75 77	99.06 166

diffmvs		99.38 91	99.33 71	99.45 115	99.87 54	99.39 96	99.28 144	99.58 170	99.55 28	99.50 145	99.85 40	99.85 82	98.94 137	98.58 187	98.68 145	99.51 149	99.39 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CANet		99.36 92	99.39 64	99.34 140	99.80 100	99.35 109	99.41 120	99.47 186	99.20 67	99.74 83	99.54 91	99.68 117	98.05 174	99.23 110	98.97 112	99.57 135	99.73 39

MVS_0304		99.36 92	99.35 69	99.37 134	99.85 77	99.36 105	99.39 122	99.56 172	99.36 51	99.75 77	99.23 131	99.90 59	97.97 180	99.00 145	98.83 128	99.69 98	99.77 30

ACMMP		99.36 92	99.06 120	99.71 63	99.86 69	99.36 105	99.63 69	99.85 71	98.33 169	99.72 93	97.73 201	99.94 35	99.53 59	99.37 81	99.13 85	99.65 105	99.56 87
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
SD-MVS		99.35 95	99.26 82	99.46 113	99.66 155	99.15 147	98.92 186	99.67 155	99.55 28	99.35 172	98.83 159	99.91 56	99.35 86	99.19 122	98.53 156	99.78 68	99.68 52
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
MP-MVS		99.35 95	99.09 118	99.65 74	99.84 83	99.22 138	99.59 77	99.78 116	98.13 178	99.67 110	98.44 184	99.93 42	99.43 82	99.31 92	99.09 93	99.60 127	99.49 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
pmmvs4		99.34 97	99.15 107	99.57 92	99.77 117	98.90 169	99.51 96	99.77 123	99.07 85	99.73 90	99.72 61	99.84 85	99.07 122	98.85 169	98.39 165	99.55 142	99.27 144

EPP-MVSNet		99.34 97	99.10 116	99.62 84	99.94 23	99.74 30	99.66 63	99.80 108	99.07 85	98.93 195	99.61 80	96.13 192	99.49 73	99.67 46	99.63 30	99.92 22	99.86 13

TSAR-MVS + GP.		99.33 99	99.17 104	99.51 106	99.71 143	99.00 164	98.84 194	99.71 142	98.23 175	99.74 83	99.53 97	99.90 59	99.35 86	99.38 80	98.85 126	99.72 92	99.31 139

PHI-MVS		99.33 99	99.19 98	99.49 109	99.69 145	99.25 130	99.27 145	99.59 167	98.44 158	99.78 67	99.15 140	99.92 50	98.95 136	99.39 78	99.04 101	99.64 113	99.18 150

MSP-MVS		99.32 101	99.26 82	99.38 128	99.76 122	99.54 60	99.42 116	99.72 141	98.92 109	98.84 203	98.96 155	99.96 14	98.91 138	98.72 182	99.14 77	99.63 116	99.58 80
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
PGM-MVS		99.32 101	98.99 129	99.71 63	99.86 69	99.31 117	99.59 77	99.86 64	97.51 201	99.75 77	98.23 191	99.94 35	99.53 59	99.29 97	99.08 94	99.65 105	99.54 95

DeepC-MVS_fast	98.69 9	99.32 101	99.13 110	99.53 100	99.63 161	98.78 179	99.53 90	99.33 201	99.08 83	99.77 69	99.18 138	99.89 62	99.29 98	99.00 145	98.70 143	99.65 105	99.30 141
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSDG		99.32 101	99.09 118	99.58 88	99.75 126	98.74 183	99.36 127	99.54 175	99.14 80	99.72 93	99.24 129	99.89 62	99.51 64	99.30 94	98.76 135	99.62 122	98.54 185

TSAR-MVS + ACMM		99.31 105	99.26 82	99.37 134	99.66 155	98.97 167	99.20 153	99.56 172	99.33 53	99.19 183	99.54 91	99.91 56	99.32 93	99.12 134	98.34 168	99.29 175	99.65 59

3Dnovator+	98.92 7	99.31 105	99.03 124	99.63 80	99.77 117	98.90 169	99.52 93	99.81 100	99.37 49	99.72 93	98.03 196	99.73 111	99.32 93	98.99 148	98.81 132	99.67 101	99.36 134

X-MVS		99.30 107	98.99 129	99.66 72	99.85 77	99.30 119	99.49 107	99.82 92	98.32 170	99.69 102	97.31 210	99.93 42	99.50 68	99.37 81	99.16 74	99.60 127	99.53 98

MVS_111021_HR		99.30 107	99.14 108	99.48 110	99.58 178	99.25 130	99.27 145	99.61 162	98.74 131	99.66 113	99.02 154	99.84 85	99.33 90	99.20 119	98.76 135	99.44 158	99.18 150

TAPA-MVS	98.54 10	99.30 107	99.24 87	99.36 139	99.44 197	98.77 181	99.00 177	99.41 192	99.23 63	99.60 124	99.50 103	99.86 76	99.15 114	99.29 97	98.95 116	99.56 139	99.08 162
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 107	99.01 128	99.63 80	99.75 126	98.89 172	99.35 130	99.60 164	98.53 152	99.86 36	99.57 87	99.94 35	99.52 62	98.96 150	98.10 181	99.70 97	99.08 162
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
USDC		99.29 111	98.98 131	99.65 74	99.72 140	98.87 174	99.47 109	99.66 158	99.35 52	99.87 30	99.58 86	99.87 75	99.51 64	98.85 169	97.93 187	99.65 105	98.38 189

PMVS	94.32 17	99.27 112	99.55 37	98.94 181	99.60 170	99.43 86	99.39 122	99.54 175	98.99 97	99.69 102	99.60 83	99.81 92	95.68 211	99.88 15	99.83 7	99.73 88	99.31 139
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FA-MVS(training)		99.26 113	99.12 112	99.44 117	99.60 170	99.26 126	99.24 150	99.97 1	98.84 119	99.76 72	99.43 108	98.74 167	98.47 162	99.39 78	99.10 91	99.57 135	99.07 165

MVS_111021_LR		99.25 114	99.13 110	99.39 124	99.50 190	99.14 148	99.23 151	99.50 183	98.67 136	99.61 122	99.12 144	99.81 92	99.16 110	99.28 102	98.67 146	99.35 171	99.21 149

ECVR-MVS		99.24 115	98.74 154	99.82 27	99.95 10	99.78 18	99.67 61	99.93 27	99.45 39	99.80 59	99.86 38	92.58 210	99.65 38	99.93 3	99.88 3	99.94 16	99.71 48

baseline		99.24 115	99.30 76	99.17 163	99.78 109	99.14 148	99.10 165	99.69 145	98.97 101	99.49 147	99.84 42	99.88 68	97.99 179	98.85 169	98.73 140	98.98 190	99.72 42

EIA-MVS		99.23 117	99.03 124	99.47 111	99.83 89	99.64 41	99.16 157	99.81 100	97.11 208	99.65 115	98.44 184	99.78 105	98.61 158	99.46 67	99.22 66	99.75 77	99.59 71

HPM-MVS++		99.23 117	98.98 131	99.53 100	99.75 126	99.02 162	99.44 113	99.77 123	98.65 138	99.52 141	98.72 167	99.92 50	99.33 90	98.77 180	98.40 164	99.40 165	99.36 134

PMMVS2		99.23 117	99.22 91	99.24 152	99.80 100	99.14 148	99.50 100	99.82 92	99.12 82	98.41 219	99.91 23	99.98 5	98.51 160	99.48 63	98.76 135	99.38 167	98.14 197

test1111		99.21 120	98.67 160	99.84 22	99.96 7	99.82 8	99.72 52	99.94 24	99.54 30	99.78 67	99.89 30	91.89 213	99.69 31	99.93 3	99.89 1	99.95 7	99.75 35

CPTT-MVS		99.21 120	98.89 141	99.58 88	99.72 140	99.12 155	99.30 140	99.76 132	98.62 143	99.66 113	97.51 206	99.89 62	99.48 76	99.01 143	98.64 149	99.58 134	99.40 128

TinyColmap		99.21 120	98.89 141	99.59 86	99.61 166	98.61 191	99.47 109	99.67 155	99.02 93	99.82 55	99.15 140	99.74 108	99.35 86	99.17 128	98.33 169	99.63 116	98.22 195

Effi-MVS+		99.20 123	98.93 136	99.50 108	99.79 104	99.26 126	98.82 197	99.96 12	98.37 168	99.60 124	99.12 144	98.36 175	99.05 125	98.93 153	98.82 129	99.78 68	99.68 52

PVSNet_BlendedMVS		99.20 123	99.17 104	99.23 153	99.69 145	99.33 112	99.04 170	99.13 206	98.41 163	99.79 63	99.33 120	99.36 143	98.10 170	99.29 97	98.87 123	99.65 105	99.56 87

PVSNet_Blended		99.20 123	99.17 104	99.23 153	99.69 145	99.33 112	99.04 170	99.13 206	98.41 163	99.79 63	99.33 120	99.36 143	98.10 170	99.29 97	98.87 123	99.65 105	99.56 87

MCST-MVS		99.17 126	98.82 149	99.57 92	99.75 126	98.70 187	99.25 149	99.69 145	98.62 143	99.59 126	98.54 177	99.79 102	99.53 59	98.48 191	98.15 177	99.64 113	99.43 119

APD-MVS		99.17 126	98.92 137	99.46 113	99.78 109	99.24 135	99.34 131	99.78 116	97.79 195	99.48 149	98.25 190	99.88 68	98.77 148	99.18 125	98.92 118	99.63 116	99.18 150
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.82 8	99.17 126	98.85 145	99.53 100	99.75 126	99.06 160	99.36 127	99.82 92	98.28 172	99.76 72	98.47 181	99.61 123	98.91 138	98.80 177	98.70 143	99.60 127	99.04 170

IterMVS-LS		99.16 129	98.82 149	99.57 92	99.87 54	99.71 33	99.58 81	99.92 38	99.24 62	99.71 99	99.73 57	95.79 193	98.91 138	98.82 176	98.66 147	99.43 161	99.77 30
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	98.38 11	99.16 129	99.20 96	99.12 168	99.20 214	98.71 186	98.85 193	99.06 209	99.17 73	98.96 194	99.61 80	99.86 76	99.29 98	99.17 128	98.72 141	99.36 169	99.15 158

IterMVS-SCA-FT		99.15 131	98.96 133	99.38 128	99.87 54	99.54 60	99.53 90	99.79 111	98.94 105	99.82 55	99.92 16	97.65 184	98.82 143	98.95 152	98.26 171	98.45 200	99.47 111

CDS-MVSNet		99.15 131	99.10 116	99.21 159	99.59 175	99.22 138	99.48 108	99.47 186	98.89 112	99.41 162	99.84 42	98.11 180	97.76 183	99.26 107	99.01 105	99.57 135	99.38 131
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		99.15 131	99.12 112	99.19 161	99.92 31	99.73 32	99.55 85	99.86 64	98.45 157	96.91 225	98.74 165	98.33 176	99.02 127	99.54 60	99.47 48	99.88 34	99.61 68

dmvs_re		99.14 134	98.76 152	99.58 88	99.75 126	99.38 99	99.30 140	99.68 151	96.94 213	99.74 83	97.70 202	99.20 155	99.29 98	99.22 112	99.35 60	99.73 88	99.55 92

MDA-MVSNet-bldmvs		99.11 135	99.11 115	99.12 168	99.91 35	99.38 99	99.77 33	98.72 213	99.31 55	99.85 43	99.43 108	98.26 178	99.48 76	99.85 19	98.47 159	96.99 211	99.08 162

OMC-MVS		99.11 135	98.95 134	99.29 145	99.37 203	98.57 193	99.19 154	99.20 205	98.87 115	99.58 129	99.13 142	99.88 68	99.00 128	99.19 122	98.46 160	99.43 161	98.57 184

MVS_Test		99.09 137	98.92 137	99.29 145	99.61 166	99.07 159	99.04 170	99.81 100	98.58 149	99.37 169	99.74 55	98.87 165	98.41 164	98.61 186	98.01 185	99.50 151	99.57 86

CNVR-MVS		99.08 138	98.83 146	99.37 134	99.61 166	98.74 183	99.15 158	99.54 175	98.59 148	99.37 169	98.15 193	99.88 68	99.08 121	98.91 158	98.46 160	99.48 153	99.06 166

IterMVS		99.08 138	98.90 140	99.29 145	99.87 54	99.53 63	99.52 93	99.77 123	98.94 105	99.75 77	99.91 23	97.52 188	98.72 152	98.86 167	98.14 178	98.09 203	99.43 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 140	99.19 98	98.93 183	99.02 219	99.53 63	99.31 135	99.84 80	98.86 116	98.88 198	99.64 75	98.44 173	96.92 200	99.35 84	99.00 109	99.61 124	99.53 98

CVMVSNet		99.06 141	98.88 144	99.28 149	99.52 182	99.53 63	99.42 116	99.69 145	98.74 131	98.27 221	99.89 30	95.48 198	99.44 80	99.46 67	99.33 61	99.32 174	99.75 35

CDPH-MVS		99.05 142	98.63 161	99.54 99	99.75 126	98.78 179	99.59 77	99.68 151	97.79 195	99.37 169	98.20 192	99.86 76	99.14 116	98.58 187	98.01 185	99.68 99	99.16 156

TAMVS		99.05 142	99.02 127	99.08 173	99.69 145	99.22 138	99.33 132	99.32 202	99.16 77	98.97 193	99.87 35	97.36 189	97.76 183	99.21 116	99.00 109	99.44 158	99.33 137

CANet_DTU		99.03 144	99.18 100	98.87 186	99.58 178	99.03 161	99.18 155	99.41 192	98.65 138	99.74 83	99.55 90	99.71 112	96.13 209	99.19 122	98.92 118	99.17 184	99.18 150

Effi-MVS+-dtu		99.01 145	99.05 121	98.98 177	99.60 170	99.13 152	99.03 174	99.61 162	98.52 153	99.01 189	98.53 178	99.83 87	96.95 199	99.48 63	98.59 154	99.66 103	99.25 148

sasdasda		99.00 146	98.68 158	99.37 134	99.68 151	99.42 90	98.94 184	99.89 54	99.00 95	98.99 190	98.43 186	95.69 194	98.96 134	99.18 125	99.18 70	99.74 83	99.88 7

canonicalmvs		99.00 146	98.68 158	99.37 134	99.68 151	99.42 90	98.94 184	99.89 54	99.00 95	98.99 190	98.43 186	95.69 194	98.96 134	99.18 125	99.18 70	99.74 83	99.88 7

MIMVSNet		99.00 146	99.03 124	98.97 180	99.32 209	99.32 116	99.39 122	99.91 44	98.41 163	98.76 206	99.24 129	99.17 156	97.13 193	99.30 94	98.80 133	99.29 175	99.01 171

CHOSEN 280x420		98.99 149	98.91 139	99.07 174	99.77 117	99.26 126	99.55 85	99.92 38	98.62 143	98.67 210	99.62 79	97.20 190	98.44 163	99.50 61	99.18 70	98.08 204	98.99 174

MGCFI-Net		98.98 150	98.69 157	99.33 141	99.68 151	99.42 90	98.95 182	99.90 53	99.04 92	98.88 198	98.45 183	95.64 196	98.81 145	99.15 130	99.21 68	99.75 77	99.90 2

SF-MVS		98.96 151	98.95 134	98.98 177	99.64 160	98.89 172	98.00 223	99.58 170	98.42 161	99.08 188	98.63 172	99.83 87	98.04 176	99.02 142	98.76 135	99.52 146	99.13 159

GBi-Net		98.96 151	99.05 121	98.85 187	99.02 219	99.53 63	99.31 135	99.78 116	98.13 178	98.48 215	99.43 108	97.58 185	96.92 200	99.68 43	99.50 43	99.61 124	99.53 98

test1		98.96 151	99.05 121	98.85 187	99.02 219	99.53 63	99.31 135	99.78 116	98.13 178	98.48 215	99.43 108	97.58 185	96.92 200	99.68 43	99.50 43	99.61 124	99.53 98

PCF-MVS	97.86 15	98.95 154	98.53 166	99.44 117	99.70 144	98.80 178	98.96 179	99.69 145	98.65 138	99.59 126	99.33 120	99.94 35	99.12 119	98.01 201	97.11 198	99.59 133	97.83 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 155	98.71 156	99.21 159	99.52 182	98.22 209	98.97 178	99.53 180	98.76 127	99.50 145	98.59 175	99.56 129	98.68 153	98.63 185	98.45 162	99.05 187	98.73 181

AdaColmap		98.93 156	98.53 166	99.39 124	99.52 182	98.65 190	99.11 164	99.59 167	98.08 182	99.44 155	97.46 208	99.45 136	99.24 103	98.92 155	98.44 163	99.44 158	98.73 181

MSLP-MVS++		98.92 157	98.73 155	99.14 165	99.44 197	99.00 164	98.36 213	99.35 198	98.82 124	99.38 166	96.06 214	99.79 102	99.07 122	98.88 163	99.05 99	99.27 177	99.53 98

new_pmnet		98.91 158	98.89 141	98.94 181	99.51 188	98.27 205	99.15 158	98.66 214	99.17 73	99.48 149	99.79 53	99.80 98	98.49 161	99.23 110	98.20 175	98.34 201	97.74 205

train_agg		98.89 159	98.48 171	99.38 128	99.69 145	98.76 182	99.31 135	99.60 164	97.71 197	98.98 192	97.89 197	99.89 62	99.29 98	98.32 192	97.59 194	99.42 164	99.16 156

NCCC		98.88 160	98.42 172	99.42 119	99.62 162	98.81 177	99.10 165	99.54 175	98.76 127	99.53 135	95.97 215	99.80 98	99.16 110	98.49 190	98.06 184	99.55 142	99.05 168

PLC	97.83 16	98.88 160	98.52 168	99.30 144	99.45 195	98.60 192	98.65 203	99.49 184	98.66 137	99.59 126	96.33 213	99.59 126	99.17 107	98.87 164	98.53 156	99.46 155	99.05 168
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 162	98.60 162	99.13 166	99.66 155	98.72 185	99.37 126	99.06 209	98.44 158	99.76 72	99.74 55	99.55 130	99.15 114	99.04 140	96.00 206	97.80 205	98.72 183

Fast-Effi-MVS+-dtu		98.82 163	98.80 151	98.84 189	99.51 188	98.90 169	98.96 179	99.91 44	98.29 171	99.11 187	98.47 181	99.63 122	96.03 210	99.21 116	98.12 179	99.52 146	99.01 171

CNLPA		98.82 163	98.52 168	99.18 162	99.21 213	98.50 197	98.73 201	99.34 200	98.73 133	99.56 131	97.55 205	99.42 140	99.06 124	98.93 153	98.10 181	99.21 183	98.38 189

PatchMatch-RL		98.80 165	98.52 168	99.12 168	99.38 202	98.70 187	98.56 206	99.55 174	97.81 194	99.34 175	97.57 204	99.31 150	98.67 154	99.27 105	98.62 151	99.22 182	98.35 191

thisisatest0530		98.78 166	98.26 175	99.39 124	99.78 109	99.43 86	99.07 167	99.64 160	98.44 158	99.42 160	99.22 132	92.68 209	98.63 156	99.30 94	99.14 77	99.80 63	99.60 69

tttt0517		98.77 167	98.25 177	99.38 128	99.79 104	99.46 80	99.07 167	99.64 160	98.40 166	99.38 166	99.21 134	92.54 211	98.63 156	99.34 87	99.14 77	99.80 63	99.62 66

DI_MVS_plusplus_trai		98.74 168	98.08 185	99.51 106	99.79 104	99.29 123	99.61 72	99.60 164	99.20 67	99.46 153	99.09 147	92.93 203	98.97 131	98.27 195	98.35 167	99.65 105	99.45 113

TSAR-MVS + COLMAP		98.74 168	98.58 164	98.93 183	99.29 210	98.23 206	99.04 170	99.24 204	98.79 126	98.80 205	99.37 118	99.71 112	98.06 173	98.02 200	97.46 196	99.16 185	98.48 187

MDTV_nov1_ep13_2view		98.73 170	98.31 174	99.22 156	99.75 126	99.24 135	99.75 40	99.93 27	99.31 55	99.84 47	99.86 38	99.81 92	99.31 96	97.40 209	94.77 208	96.73 213	97.81 202

PMMVS		98.71 171	98.55 165	98.90 185	99.28 211	98.45 199	98.53 209	99.45 188	97.67 199	99.15 186	98.76 163	99.54 132	97.79 182	98.77 180	98.23 173	99.16 185	98.46 188

HQP-MVS		98.70 172	98.19 181	99.28 149	99.61 166	98.52 195	98.71 202	99.35 198	97.97 189	99.53 135	97.38 209	99.85 82	99.14 116	97.53 205	96.85 202	99.36 169	99.26 147

N_pmnet		98.64 173	98.23 180	99.11 171	99.78 109	99.25 130	99.75 40	99.39 196	99.65 14	99.70 101	99.78 54	99.89 62	98.81 145	97.60 204	94.28 209	97.24 210	97.15 209

CMPMVS	76.62 19	98.64 173	98.60 162	98.68 194	99.33 207	97.07 222	98.11 221	98.50 215	97.69 198	99.26 178	98.35 189	99.66 120	97.62 186	99.43 75	99.02 103	99.24 180	99.01 171
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 175	98.75 153	98.49 200	98.10 225	99.44 83	99.02 175	99.78 116	98.13 178	98.48 215	99.43 108	97.58 185	96.16 208	98.85 169	98.39 165	99.40 165	99.41 123

GA-MVS		98.59 176	98.15 182	99.09 172	99.59 175	99.13 152	98.84 194	99.52 182	98.61 146	99.35 172	99.67 69	93.03 202	97.73 185	98.90 162	98.26 171	99.51 149	99.48 108

MAR-MVS		98.54 177	98.15 182	98.98 177	99.37 203	98.09 212	98.56 206	99.65 159	96.11 223	99.27 177	97.16 211	99.50 133	98.03 177	98.87 164	98.23 173	99.01 188	99.13 159
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
new-patchmatchnet		98.49 178	97.60 187	99.53 100	99.90 40	99.55 57	99.77 33	99.48 185	99.67 10	99.86 36	99.98 3	99.98 5	99.50 68	96.90 211	91.52 215	98.67 197	95.62 215

FPMVS		98.48 179	98.83 146	98.07 210	99.09 217	97.98 215	99.07 167	98.04 221	98.99 97	99.22 181	98.85 158	99.43 139	93.79 219	99.66 48	99.11 90	99.24 180	97.76 203

MVS-HIRNet		98.45 180	98.25 177	98.69 193	99.12 215	97.81 221	98.55 208	99.85 71	98.58 149	99.67 110	99.61 80	99.86 76	97.46 189	97.95 202	96.37 204	97.49 207	97.56 206

test0.0.03 1		98.41 181	98.41 173	98.40 204	99.62 162	99.16 145	98.87 191	99.41 192	97.15 206	96.60 227	99.31 125	97.00 191	96.55 205	98.91 158	98.51 158	99.37 168	98.82 178

gg-mvs-nofinetune		98.40 182	98.26 175	98.57 198	99.83 89	98.86 175	98.77 200	99.97 1	99.57 25	99.99 1	99.99 1	93.81 200	93.50 220	98.91 158	98.20 175	99.33 173	98.52 186

baseline1		98.39 183	97.59 188	99.31 143	99.78 109	99.45 81	99.13 161	99.53 180	98.06 184	98.87 200	98.63 172	90.04 217	98.76 149	98.85 169	98.84 127	99.81 59	99.28 143

pmnet_mix02		98.28 184	97.48 190	99.22 156	99.78 109	99.12 155	99.68 57	99.39 196	99.49 35	99.86 36	99.82 48	99.89 62	99.23 104	95.54 214	92.36 212	97.38 208	96.14 213

PatchT		98.11 185	97.12 196	99.26 151	99.65 159	98.34 203	99.57 83	99.97 1	97.48 202	99.43 157	99.04 152	90.84 215	98.15 167	98.04 198	97.78 188	98.82 194	98.30 192

DPM-MVS		98.10 186	97.32 194	99.01 176	99.52 182	97.92 216	98.47 211	99.45 188	98.25 173	98.91 196	93.99 219	99.69 116	98.73 151	96.29 213	96.32 205	99.00 189	98.77 179

EPNet_dtu		98.09 187	98.25 177	97.91 212	99.58 178	98.02 214	98.19 218	99.67 155	97.94 190	99.74 83	99.07 150	98.71 169	93.40 221	97.50 206	97.09 199	96.89 212	99.44 116
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 188	98.11 184	98.00 211	99.60 170	98.99 166	98.38 212	99.68 151	98.18 177	98.85 202	97.89 197	95.60 197	92.72 222	98.30 193	98.10 181	98.76 195	99.72 42
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 189	96.80 199	99.22 156	99.60 170	98.23 206	98.91 187	99.97 1	96.89 216	99.43 157	99.10 146	89.24 220	98.15 167	98.04 198	97.78 188	99.26 178	98.30 192

thres200		97.87 190	96.56 201	99.39 124	99.76 122	99.52 70	99.13 161	99.76 132	96.88 218	98.66 211	92.87 223	88.77 223	99.16 110	99.11 135	99.42 55	99.88 34	99.33 137

baseline2		97.87 190	97.18 195	98.67 195	99.34 206	99.17 144	98.48 210	98.82 212	97.08 209	98.83 204	98.75 164	89.47 219	97.03 198	98.67 184	98.27 170	99.52 146	98.83 177

thres600view7		97.86 192	96.53 204	99.41 122	99.84 83	99.52 70	99.36 127	99.76 132	97.32 204	98.38 220	93.24 220	87.25 225	99.23 104	99.11 135	99.75 18	99.88 34	99.48 108

tfpn200view9		97.85 193	96.54 202	99.38 128	99.74 136	99.52 70	99.17 156	99.76 132	96.10 224	98.70 208	92.99 221	89.10 221	99.00 128	99.11 135	99.56 34	99.88 34	99.41 123

thres400		97.82 194	96.47 205	99.40 123	99.81 99	99.44 83	99.29 143	99.69 145	97.15 206	98.57 212	92.82 224	87.96 224	99.16 110	98.96 150	99.55 37	99.86 42	99.41 123

IB-MVS	98.10 14	97.76 195	97.40 193	98.18 206	99.62 162	99.11 157	98.24 216	98.35 217	96.56 220	99.44 155	91.28 225	98.96 163	93.84 218	98.09 197	98.62 151	99.56 139	99.18 150
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
test-LLR		97.74 196	97.46 191	98.08 208	99.62 162	98.37 201	98.26 214	99.41 192	97.03 210	97.38 223	99.54 91	92.89 204	95.12 215	98.78 178	97.68 192	98.65 198	97.90 199

RPMNet		97.70 197	96.54 202	99.06 175	99.57 181	98.23 206	98.95 182	99.97 1	96.89 216	99.49 147	99.13 142	89.63 218	97.09 195	96.68 212	97.02 200	99.26 178	98.19 196

thres100view900		97.69 198	96.37 206	99.23 153	99.74 136	99.21 141	98.81 198	99.43 191	96.10 224	98.70 208	92.99 221	89.10 221	98.88 141	98.58 187	99.31 63	99.82 56	99.27 144

FMVSNet5		97.69 198	96.98 197	98.53 199	98.53 223	99.36 105	98.90 190	99.54 175	96.38 221	98.44 218	95.38 217	90.08 216	97.05 197	99.46 67	99.06 96	98.73 196	99.12 161

MVE	91.08 18	97.68 200	97.65 186	97.71 218	98.46 224	91.62 228	97.92 224	98.86 211	98.73 133	97.99 222	98.64 171	99.96 14	99.17 107	99.59 56	97.75 190	93.87 227	97.27 207
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 201	97.57 189	97.75 216	98.90 222	98.56 194	98.15 219	98.45 216	96.92 215	96.84 226	99.52 99	92.53 212	95.24 214	99.04 140	98.12 179	98.90 192	98.29 194

TESTMET0.1,1		97.62 202	97.46 191	97.81 214	99.07 218	98.37 201	98.26 214	98.35 217	97.03 210	97.38 223	99.54 91	92.89 204	95.12 215	98.78 178	97.68 192	98.65 198	97.90 199

test2506		97.57 203	95.67 212	99.78 42	99.95 10	99.78 18	99.67 61	99.93 27	99.45 39	99.55 134	99.20 135	71.73 232	99.65 38	99.93 3	99.88 3	99.94 16	99.72 42

MVSTER		97.55 204	96.75 200	98.48 201	99.46 194	99.54 60	98.24 216	99.77 123	97.56 200	99.41 162	99.31 125	84.86 227	94.66 217	98.86 167	97.75 190	99.34 172	99.38 131

ET-MVSNet_ETH3D		97.44 205	96.29 207	98.78 190	97.93 226	98.95 168	98.91 187	99.09 208	98.00 187	99.24 179	98.83 159	84.62 228	98.02 178	97.43 208	97.38 197	99.48 153	98.84 176

MDTV_nov1_ep13		97.41 206	96.26 208	98.76 191	99.47 192	98.43 200	99.26 148	99.82 92	98.06 184	99.23 180	99.22 132	92.86 206	98.05 174	95.33 216	93.66 211	96.73 213	96.26 212

ADS-MVSNet		97.29 207	96.17 209	98.59 197	99.59 175	98.70 187	99.32 133	99.86 64	98.47 154	99.56 131	99.08 148	98.16 179	97.34 191	92.92 218	91.17 216	95.91 216	94.72 218

SCA		97.25 208	96.05 210	98.64 196	99.36 205	99.02 162	99.27 145	99.96 12	98.25 173	99.69 102	98.71 168	94.66 199	97.95 181	93.95 217	92.35 213	95.64 217	95.40 217

gm-plane-assit		96.82 209	94.84 217	99.13 166	99.95 10	99.78 18	99.69 56	99.92 38	99.19 70	99.84 47	99.92 16	72.93 231	96.44 207	98.21 196	97.01 201	98.92 191	96.87 211

PatchmatchNet		96.81 210	95.41 214	98.43 203	99.43 199	98.30 204	99.23 151	99.93 27	98.19 176	99.64 116	98.81 162	93.50 201	97.43 190	92.89 219	90.78 218	94.94 222	95.41 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.76 211	95.30 216	98.46 202	99.42 200	98.47 198	99.32 133	99.91 44	98.42 161	99.51 143	99.07 150	92.81 207	97.12 194	92.39 220	91.71 214	95.51 218	94.20 220

E-PMN		96.72 212	95.78 211	97.81 214	99.45 195	95.46 225	98.14 220	98.33 219	97.99 188	98.73 207	98.09 194	98.97 161	97.54 188	97.45 207	91.09 217	94.70 224	91.40 223

tpm		96.56 213	94.68 218	98.74 192	99.12 215	97.90 217	98.79 199	99.93 27	96.79 219	99.69 102	99.19 137	81.48 230	97.56 187	95.46 215	93.97 210	97.37 209	97.99 198

EMVS		96.47 214	95.38 215	97.74 217	99.42 200	95.37 226	98.07 222	98.27 220	97.85 193	98.90 197	97.48 207	98.73 168	97.20 192	97.21 210	90.39 219	94.59 226	90.65 224

tpmrst		96.18 215	94.47 219	98.18 206	99.52 182	97.89 218	98.96 179	99.79 111	98.07 183	99.16 184	99.30 128	92.69 208	96.69 203	90.76 222	88.85 222	94.96 221	93.69 221

CostFormer		95.61 216	93.35 222	98.24 205	99.48 191	98.03 213	98.65 203	99.83 85	96.93 214	99.42 160	98.83 159	83.65 229	97.08 196	90.39 223	89.54 221	94.94 222	96.11 214

dps		95.59 217	93.46 221	98.08 208	99.33 207	98.22 209	98.87 191	99.70 143	96.17 222	98.87 200	97.75 200	86.85 226	96.60 204	91.24 221	89.62 220	95.10 220	94.34 219

tpm cat1		95.52 218	93.49 220	97.88 213	99.28 211	97.87 219	98.65 203	99.77 123	97.27 205	99.46 153	98.04 195	90.99 214	95.46 212	88.57 224	88.14 223	94.64 225	93.54 222

test_method		91.96 219	95.51 213	87.82 220	70.84 228	82.79 229	92.13 228	87.74 223	98.88 113	95.40 228	99.20 135	98.04 181	85.65 224	97.71 203	94.95 207	95.13 219	97.00 210

GG-mvs-BLEND		70.44 220	96.91 198	39.57 221	3.32 231	96.51 223	91.01 229	4.05 227	97.03 210	33.20 230	94.67 218	97.75 183	7.59 227	98.28 194	96.85 202	98.24 202	97.26 208

testmvs		22.33 221	29.66 223	13.79 222	8.97 229	10.35 230	15.53 232	8.09 226	32.51 226	19.87 231	45.18 226	30.56 234	17.05 226	29.96 225	24.74 224	13.21 228	34.30 225

test123		21.52 222	28.47 224	13.42 223	7.29 230	10.12 231	15.70 231	8.31 225	31.54 227	19.34 232	36.33 227	37.40 233	17.14 225	27.45 226	23.17 225	12.73 229	33.30 226

uanet_test		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet-low-res		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

TPM-MVS					99.47 192	97.86 220	97.79 225			98.49 214	97.62 203	99.83 87	95.33 213			98.90 192	98.77 179
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										99.96 3

9.14												99.57 127

SR-MVS					99.73 138			99.74 139				99.88 68

Anonymous202405211			99.14 108		99.87 54	99.55 57	99.50 100	99.70 143	98.55 151		98.61 174	98.46 172	98.76 149	99.66 48	99.50 43	99.85 45	99.63 63

our_test_3					99.75 126	99.11 157	99.74 47

ambc			98.83 146		99.72 140	98.52 195	98.84 194		98.96 102	99.92 10	99.34 119	99.74 108	99.04 126	98.68 183	97.57 195	99.46 155	98.99 174

MTAPA										99.62 119		99.95 25

MTMP										99.53 135		99.92 50

Patchmatch-RL test							65.75 230

tmp_tt				88.14 219	96.68 227	91.91 227	93.70 227	61.38 224	99.61 20	90.51 229	99.40 115	99.71 112	90.32 223	99.22 112	99.44 53	96.25 215

XVS					99.86 69	99.30 119	99.72 52			99.69 102		99.93 42				99.60 127

X-MVStestdata					99.86 69	99.30 119	99.72 52			99.69 102		99.93 42				99.60 127

mPP-MVS					99.84 83							99.92 50

NP-MVS									97.37 203

Patchmtry						98.19 211	98.91 187	99.97 1		99.43 157

DeepMVS_CX						96.39 224	97.15 226	88.89 222	97.94 190	99.51 143	95.71 216	97.88 182	98.19 165	98.92 155		97.73 206	97.75 204

High-resolution multi-view benchmark