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	98.82 1	99.76 1	99.75 1	99.77 7	99.87 16	99.71 10	99.77 8	99.76 19	99.52 2	99.80 3	99.79 21	99.91 1	99.56 13	99.83 3	99.75 4	99.86 9	99.75 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
pmmvs6		99.74 2	99.75 1	99.73 11	99.92 5	99.67 15	99.76 10	99.84 11	99.59 1	99.52 24	99.87 11	99.91 1	99.43 27	99.87 1	99.81 2	99.89 6	99.52 10

SixPastTwentyTwo		99.70 3	99.59 4	99.82 2	99.93 3	99.80 1	99.86 2	99.87 6	98.87 11	99.79 5	99.85 14	99.33 65	99.74 5	99.85 2	99.82 1	99.74 24	99.63 5

v7n		99.68 4	99.61 3	99.76 8	99.89 12	99.74 7	99.87 1	99.82 13	99.20 6	99.71 6	99.96 1	99.73 13	99.76 3	99.58 20	99.59 16	99.52 47	99.46 15

anonymousdsp		99.64 5	99.55 6	99.74 10	99.87 16	99.56 25	99.82 3	99.73 23	98.54 16	99.71 6	99.92 4	99.84 7	99.61 9	99.70 9	99.63 9	99.69 33	99.64 3

UniMVSNet_ETH3D		99.61 6	99.59 4	99.63 13	99.96 1	99.70 11	99.53 35	99.86 8	99.28 5	99.48 30	99.44 54	99.86 5	99.01 69	99.78 4	99.76 3	99.90 2	99.33 21

WR-MVS		99.61 6	99.44 8	99.82 2	99.92 5	99.80 1	99.80 4	99.89 1	98.54 16	99.66 13	99.78 22	99.16 86	99.68 7	99.70 9	99.63 9	99.94 1	99.49 13

PEN-MVS		99.54 8	99.30 15	99.83 1	99.92 5	99.76 4	99.80 4	99.88 3	97.60 62	99.71 6	99.59 36	99.52 43	99.75 4	99.64 15	99.51 19	99.90 2	99.46 15

TDRefinement		99.54 8	99.50 7	99.60 17	99.70 67	99.35 45	99.77 8	99.58 51	99.40 4	99.28 49	99.66 26	99.41 54	99.55 15	99.74 8	99.65 8	99.70 30	99.25 26

DTE-MVSNet		99.52 10	99.27 16	99.82 2	99.93 3	99.77 3	99.79 6	99.87 6	97.89 44	99.70 11	99.55 45	99.21 77	99.77 2	99.65 13	99.43 23	99.90 2	99.36 19

PS-CasMVS		99.50 11	99.23 19	99.82 2	99.92 5	99.75 6	99.78 7	99.89 1	97.30 73	99.71 6	99.60 34	99.23 73	99.71 6	99.65 13	99.55 18	99.90 2	99.56 8

WR-MVS_H		99.48 12	99.23 19	99.76 8	99.91 9	99.76 4	99.75 11	99.88 3	97.27 76	99.58 17	99.56 41	99.24 72	99.56 13	99.60 18	99.60 15	99.88 8	99.58 7

pm-mvs1		99.47 13	99.38 9	99.57 21	99.82 28	99.49 29	99.63 23	99.65 39	98.88 10	99.31 43	99.85 14	99.02 105	99.23 46	99.60 18	99.58 17	99.80 15	99.22 33

MIMVSNet1		99.46 14	99.34 10	99.60 17	99.83 23	99.68 14	99.74 14	99.71 27	98.20 26	99.41 35	99.86 13	99.66 25	99.41 30	99.50 24	99.39 26	99.50 52	99.10 44

TransMVSNet (Re)		99.45 15	99.32 13	99.61 15	99.88 14	99.60 20	99.75 11	99.63 43	99.11 7	99.28 49	99.83 18	98.35 140	99.27 43	99.70 9	99.62 13	99.84 10	99.03 52

ACMH	97.81 6	99.44 16	99.33 11	99.56 22	99.81 32	99.42 36	99.73 15	99.58 51	99.02 8	99.10 73	99.41 59	99.69 19	99.60 10	99.45 28	99.26 36	99.55 43	99.05 49
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.39 17	99.04 28	99.80 6	99.91 9	99.70 11	99.75 11	99.88 3	96.82 97	99.68 12	99.32 62	98.86 114	99.68 7	99.57 21	99.47 20	99.89 6	99.52 10

COLMAP_ROB	98.29 2	99.37 18	99.25 17	99.51 30	99.74 58	99.12 73	99.56 32	99.39 87	98.96 9	99.17 61	99.44 54	99.63 32	99.58 11	99.48 26	99.27 35	99.60 40	98.81 78
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	97.88 4	99.33 19	99.15 23	99.53 29	99.73 63	99.05 81	99.49 40	99.40 85	98.42 19	99.55 21	99.71 24	99.89 3	99.49 19	99.14 43	98.81 67	99.54 44	99.02 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		99.32 20	99.33 11	99.31 57	99.87 16	99.65 18	99.63 23	99.75 21	97.76 46	97.29 196	99.87 11	99.63 32	99.52 16	99.66 12	99.63 9	99.77 20	99.12 40

UA-Net		99.30 21	99.22 21	99.39 44	99.94 2	99.66 17	98.91 111	99.86 8	97.74 52	98.74 115	99.00 89	99.60 37	99.17 54	99.50 24	99.39 26	99.70 30	99.64 3

ACMH+	97.53 7	99.29 22	99.20 22	99.40 43	99.81 32	99.22 62	99.59 29	99.50 68	98.64 15	98.29 149	99.21 74	99.69 19	99.57 12	99.53 23	99.33 31	99.66 34	98.81 78

Vis-MVSNet		99.25 23	99.32 13	99.17 67	99.65 78	99.55 27	99.63 23	99.33 103	98.16 27	99.29 46	99.65 30	99.77 10	97.56 144	99.44 30	99.14 41	99.58 41	99.51 12
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TranMVSNet+NR-MVSNet		99.23 24	98.91 35	99.61 15	99.81 32	99.45 33	99.47 42	99.68 30	97.28 75	99.39 36	99.54 46	99.08 101	99.45 22	99.09 49	98.84 64	99.83 11	99.04 50

CSCG		99.23 24	99.15 23	99.32 56	99.83 23	99.45 33	98.97 103	99.21 124	98.83 12	99.04 83	99.43 56	99.64 30	99.26 44	98.85 75	98.20 103	99.62 38	99.62 6

Gipuma		99.22 26	98.86 39	99.64 12	99.70 67	99.24 56	99.17 85	99.63 43	99.52 2	99.89 1	96.54 175	99.14 90	99.93 1	99.42 31	99.15 40	99.52 47	99.04 50
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tfpnnormal		99.19 27	98.90 36	99.54 26	99.81 32	99.55 27	99.60 27	99.54 59	98.53 18	99.23 53	98.40 110	98.23 143	99.40 31	99.29 36	99.36 29	99.63 37	98.95 64

Baseline_NR-MVSNet		99.18 28	98.87 37	99.54 26	99.74 58	99.56 25	99.36 57	99.62 48	96.53 118	99.29 46	99.85 14	98.64 132	99.40 31	99.03 60	99.63 9	99.83 11	98.86 73

thisisatest0515		99.16 29	98.94 33	99.41 38	99.75 52	99.43 35	99.36 57	99.63 43	97.68 58	99.35 38	99.31 63	98.90 111	99.09 63	98.95 65	99.20 37	99.27 83	99.11 41

CS-MVS-test		99.16 29	98.78 44	99.60 17	99.80 37	99.72 9	99.69 16	99.73 23	95.88 140	99.51 26	98.53 107	99.54 41	99.21 48	99.24 39	99.43 23	99.66 34	99.15 39

CS-MVS		99.15 31	98.75 46	99.62 14	99.76 48	99.73 8	99.60 27	99.75 21	95.67 147	99.50 27	98.53 107	99.39 59	99.29 40	99.21 41	99.46 22	99.79 18	99.29 24

APDe-MVS		99.15 31	98.95 30	99.39 44	99.77 43	99.28 53	99.52 36	99.54 59	97.22 80	99.06 77	99.20 75	99.64 30	99.05 67	99.14 43	99.02 51	99.39 65	99.17 37

FC-MVSNet-train		99.13 33	99.05 27	99.21 62	99.87 16	99.57 24	99.67 18	99.60 50	96.75 103	98.28 150	99.48 50	99.52 43	98.10 122	99.47 27	99.37 28	99.76 22	99.21 34

NR-MVSNet		99.10 34	98.68 56	99.58 20	99.89 12	99.23 59	99.35 61	99.63 43	96.58 111	99.36 37	99.05 83	98.67 130	99.46 20	99.63 16	98.73 77	99.80 15	98.88 72

DVP-MVS++		99.09 35	99.25 17	98.90 101	99.53 107	99.37 43	99.17 85	99.48 73	98.28 24	97.95 170	99.54 46	99.88 4	98.13 121	99.08 50	98.94 55	99.15 96	99.65 2

DVP-MVS		99.09 35	99.07 26	99.12 74	99.55 100	99.40 38	99.36 57	99.44 84	97.75 49	98.23 153	99.23 71	99.80 8	98.97 71	99.08 50	98.96 52	99.19 91	99.25 26
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
UniMVSNet (Re)		99.08 37	98.69 54	99.54 26	99.75 52	99.33 48	99.29 69	99.64 42	96.75 103	99.48 30	99.30 65	98.69 125	99.26 44	98.94 67	98.76 73	99.78 19	99.02 54

ACMMPR		99.05 38	98.72 50	99.44 32	99.79 38	99.12 73	99.35 61	99.56 54	97.74 52	99.21 55	97.72 137	99.55 40	99.29 40	98.90 73	98.81 67	99.41 64	99.19 35

DU-MVS		99.04 39	98.59 60	99.56 22	99.74 58	99.23 59	99.29 69	99.63 43	96.58 111	99.55 21	99.05 83	98.68 127	99.36 35	99.03 60	98.60 84	99.77 20	98.97 59

TSAR-MVS + MP.		99.02 40	98.95 30	99.11 77	99.23 157	98.79 117	99.51 37	98.73 165	97.50 66	98.56 126	99.03 86	99.59 38	99.16 56	99.29 36	99.17 39	99.50 52	99.24 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v10		99.01 41	98.66 57	99.41 38	99.52 112	99.39 39	99.57 31	99.66 37	97.59 63	99.32 42	99.88 9	99.23 73	99.50 18	97.77 140	97.98 114	98.92 126	98.78 83

EG-PatchMatch MVS		99.01 41	98.77 45	99.28 61	99.64 81	98.90 110	98.81 123	99.27 114	96.55 115	99.71 6	99.31 63	99.66 25	99.17 54	99.28 38	99.11 43	99.10 98	98.57 99

PVSNet_Blended_VisFu		98.98 43	98.79 42	99.21 62	99.76 48	99.34 46	99.35 61	99.35 99	97.12 86	99.46 32	99.56 41	98.89 112	98.08 126	99.05 54	98.58 86	99.27 83	98.98 58

HFP-MVS		98.97 44	98.70 52	99.29 59	99.67 72	98.98 93	99.13 91	99.53 62	97.76 46	98.90 98	98.07 125	99.50 49	99.14 59	98.64 87	98.78 71	99.37 67	99.18 36

UniMVSNet_NR-MVSNet		98.97 44	98.46 70	99.56 22	99.76 48	99.34 46	99.29 69	99.61 49	96.55 115	99.55 21	99.05 83	97.96 151	99.36 35	98.84 76	98.50 92	99.81 14	98.97 59

casdiffmvs_mvg		98.96 46	98.87 37	99.07 80	99.82 28	99.36 44	99.36 57	99.22 121	98.13 29	97.74 177	99.42 57	99.46 52	98.59 94	98.39 99	98.95 54	99.71 29	98.39 117
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		98.96 46	98.45 73	99.56 22	99.88 14	99.70 11	99.68 17	99.78 16	94.15 181	98.97 87	98.26 117	99.21 77	99.35 37	99.30 35	99.14 41	99.73 25	99.40 18

SED-MVS		98.94 48	98.95 30	98.91 100	99.43 128	99.38 41	99.12 93	99.46 78	97.05 89	98.43 141	99.23 71	99.79 9	97.99 129	99.05 54	98.94 55	99.05 112	99.23 31

ACMMP_NAP		98.94 48	98.72 50	99.21 62	99.67 72	99.08 76	99.26 74	99.39 87	96.84 94	98.88 102	98.22 118	99.68 21	98.82 80	99.06 53	98.90 58	99.25 86	99.25 26

v1144		98.94 48	98.53 65	99.42 36	99.62 85	99.03 87	99.58 30	99.36 96	97.99 35	99.49 29	99.91 8	99.20 80	99.51 17	97.61 145	97.85 121	98.95 121	98.10 140

v8		98.94 48	98.60 58	99.35 53	99.54 104	99.39 39	99.55 33	99.67 34	97.48 67	99.13 69	99.81 19	99.10 97	99.39 33	97.86 135	97.89 119	98.81 135	98.66 92

SteuartSystems-ACMMP		98.94 48	98.52 66	99.43 35	99.79 38	99.13 72	99.33 65	99.55 56	96.17 133	99.04 83	97.53 143	99.65 29	99.46 20	99.04 59	98.76 73	99.44 59	99.35 20
Skip Steuart: Steuart Systems R&D Blog.
v1192		98.91 53	98.48 69	99.41 38	99.61 89	99.03 87	99.64 20	99.25 118	97.91 41	99.58 17	99.92 4	99.07 103	99.45 22	97.55 149	97.68 135	98.93 123	98.23 130

FMVSNet1		98.90 54	99.10 25	98.67 126	99.54 104	99.48 30	99.22 79	99.66 37	98.39 22	97.50 184	99.66 26	99.04 104	96.58 165	99.05 54	99.03 48	99.52 47	99.08 46

ACMM	96.66 11	98.90 54	98.44 75	99.44 32	99.74 58	98.95 99	99.47 42	99.55 56	97.66 60	99.09 74	96.43 177	99.41 54	99.35 37	98.95 65	98.67 80	99.45 57	99.03 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		98.89 56	98.79 42	98.99 93	99.82 28	99.41 37	99.18 84	99.31 109	96.92 91	98.54 128	98.58 105	98.84 117	97.46 146	99.45 28	99.29 33	99.65 36	99.08 46

v1921920		98.89 56	98.46 70	99.39 44	99.58 93	99.04 85	99.64 20	99.17 130	97.91 41	99.64 15	99.92 4	98.99 109	99.44 25	97.44 156	97.57 144	98.84 133	98.35 120

GeoE		98.88 58	98.43 78	99.41 38	99.83 23	99.24 56	99.51 37	99.82 13	96.55 115	99.22 54	98.76 97	99.22 76	98.96 72	98.55 90	98.15 105	99.10 98	98.56 102

v144192		98.88 58	98.46 70	99.37 51	99.56 99	99.03 87	99.61 26	99.26 115	97.79 45	99.58 17	99.88 9	99.11 95	99.43 27	97.38 161	97.61 140	98.80 136	98.43 114

SMA-MVS		98.87 60	98.73 49	99.04 86	99.72 64	99.05 81	98.64 134	99.17 130	96.31 128	98.80 109	99.07 81	99.70 18	98.67 88	98.93 70	98.82 65	99.23 89	99.23 31
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMP	96.54 13	98.87 60	98.40 81	99.41 38	99.74 58	98.88 111	99.29 69	99.50 68	96.85 93	98.96 90	97.05 159	99.66 25	99.43 27	98.98 64	98.60 84	99.52 47	98.81 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		98.86 62	98.57 63	99.19 65	99.86 20	99.67 15	99.39 51	99.71 27	97.53 65	98.69 118	95.85 188	98.48 135	97.75 138	99.57 21	99.41 25	99.72 26	99.48 14

v1240		98.86 62	98.41 80	99.38 49	99.59 91	99.05 81	99.65 19	99.14 135	97.68 58	99.66 13	99.93 3	98.72 124	99.45 22	97.38 161	97.72 133	98.79 137	98.35 120

CP-MVS		98.86 62	98.43 78	99.36 52	99.68 70	98.97 97	99.19 82	99.46 78	96.60 109	99.20 56	97.11 158	99.51 47	99.15 58	98.92 71	98.82 65	99.45 57	99.08 46

v2v482		98.85 65	98.40 81	99.38 49	99.65 78	98.98 93	99.55 33	99.39 87	97.92 40	99.35 38	99.85 14	99.14 90	99.39 33	97.50 151	97.78 124	98.98 118	97.60 156

DPE-MVS		98.84 66	98.69 54	99.00 90	99.05 176	99.26 54	99.19 82	99.35 99	95.85 142	98.74 115	99.27 67	99.66 25	98.30 114	98.90 73	98.93 57	99.37 67	99.00 56
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OPM-MVS		98.84 66	98.59 60	99.12 74	99.52 112	98.50 142	99.13 91	99.22 121	97.76 46	98.76 111	98.70 99	99.61 35	98.90 75	98.67 85	98.37 97	99.19 91	98.57 99
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test20.03		98.84 66	98.74 48	98.95 96	99.77 43	99.33 48	99.21 81	99.46 78	97.29 74	98.88 102	99.65 30	99.10 97	97.07 156	99.11 46	98.76 73	99.32 76	97.98 144

casdiffmvs		98.84 66	98.75 46	98.94 99	99.75 52	99.21 63	99.33 65	99.04 145	98.04 31	97.46 187	99.72 23	99.72 15	98.60 92	98.30 111	98.37 97	99.48 54	97.92 146
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LGP-MVS_train		98.84 66	98.33 87	99.44 32	99.78 41	98.98 93	99.39 51	99.55 56	95.41 152	98.90 98	97.51 144	99.68 21	99.44 25	99.03 60	98.81 67	99.57 42	98.91 68

RPSCF		98.84 66	98.81 41	98.89 103	99.37 136	98.95 99	98.51 146	98.85 158	97.73 54	98.33 146	98.97 91	99.14 90	98.95 73	99.18 42	98.68 79	99.31 77	98.99 57

ACMMP		98.82 72	98.33 87	99.39 44	99.77 43	99.14 71	99.37 54	99.54 59	96.47 122	99.03 85	96.26 181	99.52 43	99.28 42	98.92 71	98.80 70	99.37 67	99.16 38
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
V42		98.81 73	98.49 68	99.18 66	99.52 112	98.92 105	99.50 39	99.29 111	97.43 70	98.97 87	99.81 19	99.00 108	99.30 39	97.93 131	98.01 112	98.51 161	98.34 124

LS3D		98.79 74	98.52 66	99.12 74	99.64 81	99.09 75	99.24 77	99.46 78	97.75 49	98.93 96	97.47 146	98.23 143	97.98 130	99.36 32	99.30 32	99.46 55	98.42 115

MP-MVS		98.78 75	98.30 89	99.34 55	99.75 52	98.95 99	99.26 74	99.46 78	95.78 146	99.17 61	96.98 163	99.72 15	99.06 66	98.84 76	98.74 76	99.33 73	99.11 41
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.77 76	98.45 73	99.15 70	99.68 70	98.94 103	99.49 40	99.31 109	97.95 37	98.91 97	99.65 30	99.62 34	99.18 51	97.99 129	97.64 139	98.33 166	97.38 161

test1111		98.75 77	98.14 102	99.46 31	99.86 20	99.63 19	99.47 42	99.68 30	98.34 23	98.76 111	99.66 26	90.92 195	99.23 46	99.77 5	99.71 5	99.75 23	98.95 64

ECVR-MVS		98.74 78	98.15 100	99.42 36	99.83 23	99.58 22	99.37 54	99.67 34	98.02 33	98.85 106	99.59 36	91.66 193	99.10 61	99.77 5	99.70 6	99.72 26	98.73 85

SD-MVS		98.73 79	98.54 64	98.95 96	99.14 166	98.76 120	98.46 150	99.14 135	97.71 56	98.56 126	98.06 127	99.61 35	98.85 79	98.56 89	97.74 130	99.54 44	99.32 22
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
MSP-MVS		98.72 80	98.60 58	98.87 105	99.67 72	99.33 48	99.15 88	99.26 115	96.99 90	97.90 173	98.19 120	99.74 12	98.29 115	97.69 143	98.96 52	98.96 119	99.27 25
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		98.69 81	98.09 107	99.39 44	99.76 48	99.07 77	99.30 68	99.51 66	94.76 163	99.18 60	96.70 170	99.51 47	99.20 49	98.79 81	98.71 78	99.39 65	99.11 41

pmmvs-eth3d		98.68 82	98.14 102	99.29 59	99.49 117	98.45 145	99.45 47	99.38 92	97.21 81	99.50 27	99.65 30	99.21 77	99.16 56	97.11 168	97.56 145	98.79 137	97.82 150

EU-MVSNet		98.68 82	98.94 33	98.37 146	99.14 166	98.74 122	99.64 20	98.20 190	98.21 25	99.17 61	99.66 26	99.18 83	99.08 64	99.11 46	98.86 60	95.00 203	98.83 75

PMVS	92.51 17	98.66 84	98.86 39	98.43 142	99.26 152	98.98 93	98.60 140	98.59 174	97.73 54	99.45 33	99.38 60	98.54 134	95.24 183	99.62 17	99.61 14	99.42 61	98.17 137
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepC-MVS_fast	97.38 8	98.65 85	98.34 86	99.02 89	99.33 140	98.29 152	98.99 101	98.71 167	97.40 71	99.31 43	98.20 119	99.40 57	98.54 101	98.33 108	98.18 104	99.23 89	98.58 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	98.16 3	98.65 85	98.35 85	99.00 90	99.59 91	98.70 125	98.90 115	99.36 96	97.97 36	99.09 74	96.55 174	99.09 99	97.97 131	98.70 84	98.65 82	99.12 97	98.81 78

TSAR-MVS + ACMM		98.64 87	98.58 62	98.72 120	99.17 164	98.63 131	98.69 129	99.10 142	97.69 57	98.30 148	99.12 79	99.38 60	98.70 87	98.45 94	97.51 147	98.35 165	99.25 26

DELS-MVS		98.63 88	98.70 52	98.55 138	99.24 156	99.04 85	98.96 104	98.52 177	96.83 96	98.38 143	99.58 39	99.68 21	97.06 157	98.74 83	98.44 94	99.10 98	98.59 96
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
QAPM		98.62 89	98.40 81	98.89 103	99.57 98	98.80 116	98.63 135	99.35 99	96.82 97	98.60 122	98.85 96	99.08 101	98.09 124	98.31 109	98.21 101	99.08 104	98.72 86

EPP-MVSNet		98.61 90	98.19 97	99.11 77	99.86 20	99.60 20	99.44 48	99.53 62	97.37 72	96.85 201	98.69 100	93.75 186	99.18 51	99.22 40	99.35 30	99.82 13	99.32 22

3Dnovator+	97.85 5	98.61 90	98.14 102	99.15 70	99.62 85	98.37 149	99.10 94	99.51 66	98.04 31	98.98 86	96.07 185	98.75 123	98.55 99	98.51 92	98.40 95	99.17 93	98.82 76

X-MVS		98.59 92	97.99 113	99.30 58	99.75 52	99.07 77	99.17 85	99.50 68	96.62 107	98.95 92	93.95 204	99.37 61	99.11 60	98.94 67	98.86 60	99.35 71	99.09 45

MVS_111021_HR		98.58 93	98.26 92	98.96 95	99.32 143	98.81 114	98.48 148	98.99 150	96.81 99	99.16 64	98.07 125	99.23 73	98.89 77	98.43 96	98.27 100	98.90 128	98.24 129

MVS_0304		98.57 94	98.36 84	98.82 112	99.72 64	98.94 103	98.92 109	99.14 135	96.76 102	99.33 41	98.30 114	99.73 13	96.74 161	98.05 126	97.79 123	99.08 104	98.97 59

PM-MVS		98.57 94	98.24 94	98.95 96	99.26 152	98.59 134	99.03 98	98.74 164	96.84 94	99.44 34	99.13 78	98.31 142	98.75 85	98.03 127	98.21 101	98.48 162	98.58 97

PHI-MVS		98.57 94	98.20 96	99.00 90	99.48 119	98.91 107	98.68 130	99.17 130	94.97 159	99.27 51	98.33 112	99.33 65	98.05 127	98.82 79	98.62 83	99.34 72	98.38 118

HPM-MVS++		98.56 97	98.08 108	99.11 77	99.53 107	98.61 133	99.02 100	99.32 107	96.29 130	99.06 77	97.23 153	99.50 49	98.77 83	98.15 122	97.90 117	98.96 119	98.90 69

TSAR-MVS + GP.		98.54 98	98.29 91	98.82 112	99.28 150	98.59 134	97.73 189	99.24 120	95.93 139	98.59 123	99.07 81	99.17 84	98.86 78	98.44 95	98.10 107	99.26 85	98.72 86

UGNet		98.52 99	99.00 29	97.96 167	99.58 93	99.26 54	99.27 73	99.40 85	98.07 30	98.28 150	98.76 97	99.71 17	92.24 211	98.94 67	98.85 62	99.00 117	99.43 17
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
Anonymous20231206		98.50 100	98.03 110	99.05 84	99.50 115	99.01 90	99.15 88	99.26 115	96.38 126	99.12 71	99.50 49	99.12 93	98.60 92	97.68 144	97.24 158	98.66 145	97.30 165

CLD-MVS		98.48 101	98.15 100	98.86 108	99.53 107	98.35 150	98.55 143	97.83 199	96.02 138	98.97 87	99.08 80	99.75 11	99.03 68	98.10 125	97.33 154	99.28 81	98.44 113
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet		98.47 102	98.30 89	98.67 126	99.65 78	98.87 112	98.82 122	99.01 148	96.14 134	99.29 46	98.86 94	99.01 106	96.54 166	98.36 103	98.08 109	98.72 141	98.80 82

APD-MVS		98.47 102	97.97 114	99.05 84	99.64 81	98.91 107	98.94 106	99.45 83	94.40 174	98.77 110	97.26 152	99.41 54	98.21 118	98.67 85	98.57 89	99.31 77	98.57 99
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Vis-MVSNet (Re-imp)		98.46 104	98.23 95	98.73 119	99.81 32	99.29 52	98.79 124	99.50 68	96.20 132	96.03 207	98.29 115	96.98 166	98.54 101	99.11 46	99.08 44	99.70 30	98.62 94

Fast-Effi-MVS+		98.42 105	97.79 120	99.15 70	99.69 69	98.66 129	98.94 106	99.68 30	94.49 168	99.05 79	98.06 127	98.86 114	98.48 104	98.18 119	97.78 124	99.05 112	98.54 105

ETV-MVS		98.41 106	97.76 121	99.17 67	99.58 93	99.01 90	98.91 111	99.50 68	93.33 194	99.31 43	96.82 167	98.42 138	98.17 120	99.13 45	99.08 44	99.54 44	98.56 102

MVS_111021_LR		98.39 107	98.11 105	98.71 122	99.08 173	98.54 140	98.23 171	98.56 176	96.57 113	99.13 69	98.41 109	98.86 114	98.65 90	98.23 117	97.87 120	98.65 147	98.28 126

pmmvs5		98.37 108	97.81 119	99.03 87	99.46 121	98.97 97	99.03 98	98.96 152	95.85 142	99.05 79	99.45 53	98.66 131	98.79 82	96.02 185	97.52 146	98.87 130	98.21 133

OMC-MVS		98.35 109	98.10 106	98.64 132	98.85 183	97.99 171	98.56 142	98.21 188	97.26 78	98.87 104	98.54 106	99.27 71	98.43 106	98.34 106	97.66 136	98.92 126	97.65 155

canonicalmvs		98.34 110	97.92 116	98.83 110	99.45 123	99.21 63	98.37 158	99.53 62	97.06 88	97.74 177	96.95 165	95.05 183	98.36 109	98.77 82	98.85 62	99.51 51	99.53 9

CHOSEN 1792x2688		98.31 111	98.02 111	98.66 128	99.55 100	98.57 137	99.38 53	99.25 118	98.42 19	98.48 136	99.58 39	99.85 6	98.31 113	95.75 188	95.71 183	96.96 190	98.27 128

CPTT-MVS		98.28 112	97.51 134	99.16 69	99.54 104	98.78 118	98.96 104	99.36 96	96.30 129	98.89 101	93.10 208	99.30 68	99.20 49	98.35 105	97.96 115	99.03 115	98.82 76

TinyColmap		98.27 113	97.62 131	99.03 87	99.29 148	97.79 180	98.92 109	98.95 153	97.48 67	99.52 24	98.65 102	97.86 153	98.90 75	98.34 106	97.27 156	98.64 148	95.97 185

diffmvs		98.26 114	98.16 98	98.39 144	99.61 89	98.78 118	98.79 124	98.61 172	97.94 38	97.11 200	99.51 48	99.52 43	97.61 142	96.55 177	96.93 164	98.61 150	97.87 148
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
USDC		98.26 114	97.57 132	99.06 81	99.42 131	97.98 173	98.83 119	98.85 158	97.57 64	99.59 16	99.15 77	98.59 133	98.99 70	97.42 157	96.08 182	98.69 144	96.23 183

SF-MVS		98.25 116	98.16 98	98.35 147	99.43 128	98.42 148	97.05 211	99.09 143	96.42 124	98.13 159	97.73 136	99.20 80	97.22 152	98.36 103	98.38 96	99.16 95	98.62 94

MCST-MVS		98.25 116	97.57 132	99.06 81	99.53 107	98.24 158	98.63 135	99.17 130	95.88 140	98.58 124	96.11 183	99.09 99	99.18 51	97.58 148	97.31 155	99.25 86	98.75 84

IterMVS-LS		98.23 118	97.66 127	98.90 101	99.63 84	99.38 41	99.07 95	99.48 73	97.75 49	98.81 108	99.37 61	94.57 185	97.88 135	96.54 178	97.04 161	98.53 158	98.97 59
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS	96.65 12	98.23 118	97.96 115	98.55 138	98.81 185	98.16 162	98.40 155	97.94 197	96.68 105	98.49 134	98.61 103	98.89 112	98.57 97	97.45 154	97.59 142	99.09 103	98.35 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNVR-MVS		98.22 120	97.76 121	98.76 117	99.33 140	98.26 156	98.48 148	98.88 156	96.22 131	98.47 138	95.79 189	99.33 65	98.35 110	98.37 102	97.99 113	99.03 115	98.38 118

IS_MVSNet		98.20 121	98.00 112	98.44 141	99.82 28	99.48 30	99.25 76	99.56 54	95.58 149	93.93 219	97.56 142	96.52 171	98.27 116	99.08 50	99.20 37	99.80 15	98.56 102

DeepPCF-MVS	96.68 10	98.20 121	98.26 92	98.12 160	97.03 222	98.11 165	98.44 152	97.70 201	96.77 101	98.52 130	98.91 92	99.17 84	98.58 96	98.41 98	98.02 111	98.46 163	98.46 110

MSDG		98.20 121	97.88 118	98.56 136	99.33 140	97.74 181	98.27 168	98.10 191	97.20 83	98.06 163	98.59 104	99.16 86	98.76 84	98.39 99	97.71 134	98.86 132	96.38 180

testgi		98.18 124	98.44 75	97.89 169	99.78 41	99.23 59	98.78 126	99.21 124	97.26 78	97.41 189	97.39 149	99.36 64	92.85 207	98.82 79	98.66 81	99.31 77	98.35 120

Effi-MVS+		98.11 125	97.29 140	99.06 81	99.62 85	98.55 138	98.16 174	99.80 15	94.64 164	99.15 67	96.59 172	97.43 159	98.44 105	97.46 153	97.90 117	99.17 93	98.45 112

FA-MVS(training)		98.08 126	97.68 125	98.56 136	99.14 166	98.69 126	98.41 153	99.83 12	95.85 142	98.57 125	97.95 132	96.92 168	96.85 159	98.51 92	98.09 108	98.54 156	97.74 151

HyFIR lowres test		98.08 126	97.16 149	99.14 73	99.72 64	98.91 107	99.41 49	99.58 51	97.93 39	98.82 107	99.24 69	95.81 177	98.73 86	95.16 199	95.13 192	98.60 152	97.94 145

EIA-MVS		98.03 128	97.20 146	98.99 93	99.66 75	99.24 56	98.53 145	99.52 65	91.56 210	99.25 52	95.34 193	98.78 120	97.72 139	98.38 101	98.58 86	99.28 81	98.54 105

train_agg		97.99 129	97.26 141	98.83 110	99.43 128	98.22 160	98.91 111	99.07 144	94.43 172	97.96 169	96.42 178	99.30 68	98.81 81	97.39 159	96.62 170	98.82 134	98.47 108

MSLP-MVS++		97.99 129	97.64 130	98.40 143	98.91 181	98.47 144	97.12 209	98.78 162	96.49 120	98.48 136	93.57 206	99.12 93	98.51 103	98.31 109	98.58 86	98.58 154	98.95 64

CDPH-MVS		97.99 129	97.23 144	98.87 105	99.58 93	98.29 152	98.83 119	99.20 126	93.76 188	98.11 161	96.11 183	99.16 86	98.23 117	97.80 138	97.22 159	99.29 80	98.28 126

FMVSNet2		97.94 132	98.08 108	97.77 175	98.71 189	99.21 63	98.62 137	99.47 75	96.62 107	96.37 206	99.20 75	97.70 155	94.39 194	97.39 159	97.75 129	99.08 104	98.70 89

PVSNet_BlendedMVS		97.93 133	97.66 127	98.25 153	99.30 145	98.67 127	98.31 163	97.95 195	94.30 178	98.75 113	97.63 139	98.76 121	96.30 173	98.29 112	97.78 124	98.93 123	98.18 135

PVSNet_Blended		97.93 133	97.66 127	98.25 153	99.30 145	98.67 127	98.31 163	97.95 195	94.30 178	98.75 113	97.63 139	98.76 121	96.30 173	98.29 112	97.78 124	98.93 123	98.18 135

OpenMVS	97.26 9	97.88 135	97.17 148	98.70 123	99.50 115	98.55 138	98.34 161	99.11 140	93.92 186	98.90 98	95.04 198	98.23 143	97.38 149	98.11 124	98.12 106	98.95 121	98.23 130

pmmvs4		97.87 136	97.02 153	98.86 108	99.20 158	97.68 184	98.89 116	99.03 146	96.57 113	99.12 71	99.03 86	97.26 163	98.42 107	95.16 199	96.34 174	98.53 158	97.10 172

NCCC		97.84 137	96.96 155	98.87 105	99.39 134	98.27 155	98.46 150	99.02 147	96.78 100	98.73 117	91.12 212	98.91 110	98.57 97	97.83 137	97.49 148	99.04 114	98.33 125

Effi-MVS+-dtu		97.78 138	97.37 138	98.26 151	99.25 154	98.50 142	97.89 183	99.19 129	94.51 166	98.16 157	95.93 186	98.80 119	95.97 176	98.27 116	97.38 151	99.10 98	98.23 130

MDA-MVSNet-bldmvs		97.75 139	97.26 141	98.33 148	99.35 139	98.45 145	99.32 67	97.21 206	97.90 43	99.05 79	99.01 88	96.86 169	99.08 64	99.36 32	92.97 202	95.97 199	96.25 182

CDS-MVSNet		97.75 139	97.68 125	97.83 173	99.08 173	98.20 161	98.68 130	98.61 172	95.63 148	97.80 175	99.24 69	96.93 167	94.09 199	97.96 130	97.82 122	98.71 142	97.99 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		97.75 139	97.26 141	98.32 150	98.58 197	97.86 176	97.80 185	98.09 192	96.49 120	98.49 134	96.15 182	98.08 146	98.35 110	98.00 128	97.03 162	98.61 150	97.21 169

PLC	95.63 15	97.73 142	97.01 154	98.57 135	99.10 170	97.80 179	97.72 190	98.77 163	96.34 127	98.38 143	93.46 207	98.06 147	98.66 89	97.90 133	97.65 138	98.77 139	97.90 147
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		97.69 143	97.15 150	98.33 148	99.27 151	98.43 147	98.25 169	99.29 111	95.00 158	97.39 191	98.86 94	98.00 150	97.14 154	95.38 194	96.22 176	98.62 149	98.15 139

GBi-Net		97.69 143	97.75 123	97.62 176	98.71 189	99.21 63	98.62 137	99.33 103	94.09 182	95.60 209	98.17 122	95.97 174	94.39 194	99.05 54	99.03 48	99.08 104	98.70 89

test1		97.69 143	97.75 123	97.62 176	98.71 189	99.21 63	98.62 137	99.33 103	94.09 182	95.60 209	98.17 122	95.97 174	94.39 194	99.05 54	99.03 48	99.08 104	98.70 89

CANet_DTU		97.65 146	97.50 136	97.82 174	99.19 161	98.08 167	98.41 153	98.67 169	94.40 174	99.16 64	98.32 113	98.69 125	93.96 201	97.87 134	97.61 140	97.51 186	97.56 158

IterMVS-SCA-FT		97.63 147	96.86 157	98.52 140	99.48 119	98.71 124	98.84 118	98.91 154	96.44 123	99.16 64	99.56 41	95.54 179	97.95 132	95.68 191	95.07 195	96.76 191	97.03 175

TSAR-MVS + COLMAP		97.62 148	97.31 139	97.98 165	98.47 203	97.39 188	98.29 165	98.25 187	96.68 105	97.54 183	98.87 93	98.04 149	97.08 155	96.78 172	96.26 175	98.26 169	97.12 171

MS-PatchMatch		97.60 149	97.22 145	98.04 164	98.67 193	97.18 193	97.91 181	98.28 186	95.82 145	98.34 145	97.66 138	98.38 139	97.77 137	97.10 169	97.25 157	97.27 188	97.18 170

PCF-MVS	95.58 16	97.60 149	96.67 158	98.69 124	99.44 126	98.23 159	98.37 158	98.81 160	93.01 198	98.22 154	97.97 131	99.59 38	98.20 119	95.72 190	95.08 193	99.08 104	97.09 174
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		97.58 151	96.65 161	98.66 128	99.30 145	97.99 171	97.88 184	98.65 170	94.58 165	98.66 119	94.65 202	99.15 89	98.59 94	96.10 183	95.59 184	98.90 128	98.50 107

DI_MVS_plusplus_trai		97.57 152	96.55 163	98.77 116	99.55 100	98.76 120	99.22 79	99.00 149	97.08 87	97.95 170	97.78 135	91.35 194	98.02 128	96.20 181	96.81 166	98.87 130	97.87 148

AdaColmap		97.57 152	96.57 162	98.74 118	99.25 154	98.01 169	98.36 160	98.98 151	94.44 171	98.47 138	92.44 209	97.91 152	98.62 91	98.19 118	97.74 130	98.73 140	97.28 166

baseline		97.50 154	97.51 134	97.50 180	99.18 162	97.38 189	98.00 177	98.00 194	96.52 119	97.49 185	99.28 66	99.43 53	95.31 182	95.27 196	96.22 176	96.99 189	98.47 108

IterMVS		97.40 155	96.67 158	98.25 153	99.45 123	98.66 129	98.87 117	98.73 165	96.40 125	98.94 95	99.56 41	95.26 181	97.58 143	95.38 194	94.70 197	95.90 200	96.72 178
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		97.38 156	96.15 171	98.82 112	99.39 134	98.34 151	98.65 133	98.88 156	90.80 217	98.86 105	92.35 210	95.13 182	98.09 124	98.84 76	98.88 59	99.06 111	98.71 88

CVMVSNet		97.38 156	97.39 137	97.37 183	98.58 197	97.72 182	98.70 128	97.42 204	97.21 81	95.95 208	99.46 52	93.31 189	97.38 149	97.60 146	97.78 124	96.18 196	98.66 92

new-patchmatchnet		97.26 158	96.12 172	98.58 134	99.55 100	98.63 131	99.14 90	97.04 208	98.80 13	99.19 58	99.92 4	99.19 82	98.92 74	95.51 193	87.04 211	97.66 183	93.73 201

MIMVSNet		97.24 159	97.15 150	97.36 184	99.03 177	98.52 141	98.55 143	99.73 23	94.94 162	94.94 216	97.98 130	97.37 161	93.66 202	97.60 146	97.34 153	98.23 172	96.29 181

PatchMatch-RL		97.24 159	96.45 166	98.17 157	98.70 192	97.57 187	97.31 204	98.48 180	94.42 173	98.39 142	95.74 190	96.35 173	97.88 135	97.75 141	97.48 149	98.24 171	95.87 186

thisisatest0530		97.20 161	95.95 176	98.66 128	99.46 121	98.84 113	98.29 165	99.20 126	94.51 166	98.25 152	97.42 147	85.03 210	97.68 140	98.43 96	98.56 90	99.08 104	98.89 71

tttt0517		97.18 162	95.92 177	98.65 131	99.49 117	98.92 105	98.29 165	99.20 126	94.37 176	98.17 155	97.37 150	84.72 213	97.68 140	98.55 90	98.56 90	99.10 98	98.95 64

MDTV_nov1_ep13_2view		97.12 163	96.19 170	98.22 156	99.13 169	98.05 168	99.24 77	99.47 75	97.61 61	99.15 67	99.59 36	99.01 106	98.40 108	94.87 202	90.14 205	93.91 206	94.04 200

MAR-MVS		97.12 163	96.28 169	98.11 161	98.94 179	97.22 191	97.65 194	99.38 92	90.93 216	98.15 158	95.17 195	97.13 164	96.48 169	97.71 142	97.40 150	98.06 176	98.40 116
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
Fast-Effi-MVS+-dtu		96.99 165	96.46 165	97.61 178	98.98 178	97.89 174	97.54 198	99.76 19	93.43 192	96.55 205	94.93 199	98.06 147	94.32 197	96.93 170	96.50 172	98.53 158	97.47 159

FPMVS		96.97 166	97.20 146	96.70 200	97.75 214	96.11 205	97.72 190	95.47 212	97.13 85	98.02 165	97.57 141	96.67 170	92.97 206	99.00 63	98.34 99	98.28 168	95.58 188

TAMVS		96.95 167	96.94 156	96.97 195	99.07 175	97.67 186	97.98 179	97.12 207	95.04 157	95.41 212	99.27 67	95.57 178	94.09 199	97.32 163	97.11 160	98.16 174	96.59 179

FMVSNet3		96.85 168	96.67 158	97.06 189	97.56 217	99.01 90	97.99 178	99.33 103	94.09 182	95.60 209	98.17 122	95.97 174	93.26 205	94.76 204	96.22 176	98.59 153	98.46 110

GA-MVS		96.84 169	95.86 179	97.98 165	99.16 165	98.29 152	97.91 181	98.64 171	95.14 155	97.71 179	98.04 129	88.90 198	96.50 168	96.41 180	96.61 171	97.97 180	97.60 156

CHOSEN 280x420		96.80 170	96.30 168	97.39 181	99.09 171	96.52 197	98.76 127	99.29 111	93.88 187	97.65 180	98.34 111	93.66 187	96.29 175	98.28 114	97.73 132	93.27 209	95.70 187

gg-mvs-nofinetune		96.77 171	96.52 164	97.06 189	99.66 75	97.82 178	97.54 198	99.86 8	98.69 14	98.61 121	99.94 2	89.62 196	88.37 219	97.55 149	96.67 168	98.30 167	95.35 189

DPM-MVS		96.73 172	95.70 182	97.95 168	98.93 180	97.26 190	97.39 203	98.44 182	95.47 151	97.62 181	90.71 213	98.47 137	97.03 158	95.02 201	95.27 189	98.26 169	97.67 153

baseline1		96.72 173	95.40 184	98.26 151	99.53 107	98.81 114	98.32 162	98.80 161	94.96 160	96.78 204	96.50 176	84.87 212	96.68 164	97.42 157	97.91 116	99.46 55	97.33 164

N_pmnet		96.68 174	95.70 182	97.84 172	99.42 131	98.00 170	99.35 61	98.21 188	98.40 21	98.13 159	99.42 57	99.30 68	97.44 148	94.00 208	88.79 206	94.47 205	91.96 207

pmnet_mix02		96.61 175	95.32 185	98.11 161	99.41 133	97.68 184	99.05 96	97.59 202	98.16 27	99.05 79	99.48 50	99.11 95	98.32 112	92.36 212	87.67 208	95.26 202	92.80 205

new_pmnet		96.59 176	96.40 167	96.81 197	98.24 210	95.46 214	97.71 192	94.75 215	96.92 91	96.80 203	99.23 71	97.81 154	96.69 162	96.58 176	95.16 191	96.69 192	93.64 202

PMMVS		96.47 177	95.81 180	97.23 185	97.38 219	95.96 209	97.31 204	96.91 209	93.21 195	97.93 172	97.14 156	97.64 157	95.70 178	95.24 197	96.18 179	98.17 173	95.33 190

EPNet		96.44 178	96.08 173	96.86 196	99.32 143	97.15 194	97.69 193	99.32 107	93.67 189	98.11 161	95.64 191	93.44 188	89.07 217	96.86 171	96.83 165	97.67 182	98.97 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres600view7		96.35 179	94.27 187	98.79 115	99.66 75	99.18 68	98.94 106	99.38 92	94.37 176	97.21 198	87.19 215	84.10 214	98.10 122	98.16 120	99.47 20	99.42 61	97.43 160

EPNet_dtu		96.31 180	95.96 175	96.72 199	99.18 162	95.39 215	97.03 212	99.13 139	93.02 197	99.35 38	97.23 153	97.07 165	90.70 216	95.74 189	95.08 193	94.94 204	98.16 138
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		96.30 181	95.87 178	96.80 198	97.66 216	96.48 198	97.93 180	93.80 216	93.40 193	98.54 128	98.27 116	97.50 158	97.37 151	97.49 152	93.11 201	95.52 201	94.85 194

PMMVS2		96.29 182	97.05 152	95.40 210	98.32 209	96.16 202	98.18 173	97.46 203	97.20 83	84.51 225	99.60 34	98.68 127	96.37 170	98.59 88	97.38 151	97.58 185	91.76 208

thres200		96.23 183	94.13 188	98.69 124	99.44 126	99.18 68	98.58 141	99.38 92	93.52 191	97.35 192	86.33 220	85.83 208	97.93 133	98.16 120	98.78 71	99.42 61	97.10 172

thres400		96.22 184	94.08 190	98.72 120	99.58 93	99.05 81	98.83 119	99.22 121	94.01 185	97.40 190	86.34 219	84.91 211	97.93 133	97.85 136	99.08 44	99.37 67	97.28 166

tfpn200view9		96.17 185	94.08 190	98.60 133	99.37 136	99.18 68	98.68 130	99.39 87	92.02 204	97.30 194	86.53 217	86.34 205	97.45 147	98.15 122	99.08 44	99.43 60	97.28 166

CMPMVS	74.71 19	96.17 185	96.06 174	96.30 204	97.41 218	94.52 218	94.83 220	95.46 213	91.57 209	97.26 197	94.45 203	98.33 141	94.98 185	98.28 114	97.59 142	97.86 181	97.68 152
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2506		96.12 187	93.35 200	99.35 53	99.83 23	99.58 22	99.37 54	99.67 34	98.02 33	98.44 140	97.51 144	60.03 228	99.10 61	99.77 5	99.70 6	99.72 26	98.86 73

IB-MVS	95.85 14	95.87 188	94.88 186	97.02 192	99.09 171	98.25 157	97.16 206	97.38 205	91.97 207	97.77 176	83.61 222	97.29 162	92.03 214	97.16 167	97.66 136	98.66 145	98.20 134
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
test0.0.03 1		95.81 189	95.77 181	95.85 209	99.20 158	98.15 164	97.49 202	98.50 178	92.24 200	92.74 222	96.82 167	92.70 190	88.60 218	97.31 165	97.01 163	98.57 155	96.19 184

thres100view900		95.74 190	93.66 199	98.17 157	99.37 136	98.59 134	98.10 175	98.33 185	92.02 204	97.30 194	86.53 217	86.34 205	96.69 162	96.77 173	98.47 93	99.24 88	96.89 176

ET-MVSNet_ETH3D		95.72 191	93.85 195	97.89 169	97.30 220	98.09 166	98.19 172	98.40 183	94.46 170	98.01 168	96.71 169	77.85 224	96.76 160	96.08 184	96.39 173	98.70 143	97.36 162

baseline2		95.58 192	94.04 192	97.38 182	98.80 186	98.16 162	97.14 207	97.80 200	91.45 211	97.49 185	95.22 194	83.63 215	94.98 185	96.42 179	96.66 169	98.06 176	96.76 177

PatchT		95.49 193	93.29 201	98.06 163	98.65 194	96.20 201	98.91 111	99.73 23	92.00 206	98.50 131	96.67 171	83.25 216	96.34 171	94.40 205	95.50 185	96.21 195	95.04 192

CR-MVSNet		95.38 194	93.01 202	98.16 159	98.63 195	95.85 211	97.64 195	99.78 16	91.27 213	98.50 131	96.84 166	82.16 217	96.34 171	94.40 205	95.50 185	98.05 178	95.04 192

MVSTER		95.38 194	93.99 194	97.01 193	98.83 184	98.95 99	96.62 213	99.14 135	92.17 202	97.44 188	97.29 151	77.88 223	91.63 215	97.45 154	96.18 179	98.41 164	97.99 142

MVS-HIRNet		94.86 196	93.83 196	96.07 205	97.07 221	94.00 219	94.31 221	99.17 130	91.23 215	98.17 155	98.69 100	97.43 159	95.66 179	94.05 207	91.92 203	92.04 216	89.46 216

test-LLR		94.79 197	93.71 197	96.06 206	99.20 158	96.16 202	96.31 215	98.50 178	89.98 218	94.08 217	97.01 160	86.43 203	92.20 212	96.76 174	95.31 187	96.05 197	94.31 197

RPMNet		94.72 198	92.01 207	97.88 171	98.56 200	95.85 211	97.78 186	99.70 29	91.27 213	98.33 146	93.69 205	81.88 218	94.91 188	92.60 210	94.34 199	98.01 179	94.46 196

gm-plane-assit		94.62 199	91.39 209	98.39 144	99.90 11	99.47 32	99.40 50	99.65 39	97.44 69	99.56 20	99.68 25	59.40 229	94.23 198	96.17 182	94.77 196	97.61 184	92.79 206

test-mter		94.62 199	94.02 193	95.32 211	97.72 215	96.75 195	96.23 217	95.67 211	89.83 221	93.23 221	96.99 162	85.94 207	92.66 210	97.32 163	96.11 181	96.44 193	95.22 191

FMVSNet5		94.57 201	92.77 203	96.67 201	97.88 212	98.72 123	97.54 198	98.70 168	88.64 222	95.11 214	86.90 216	81.77 219	93.27 204	97.92 132	98.07 110	97.50 187	97.34 163

SCA		94.53 202	91.95 208	97.55 179	98.58 197	97.86 176	98.49 147	99.68 30	95.11 156	99.07 76	95.87 187	87.24 201	96.53 167	89.77 215	87.08 210	92.96 211	90.69 211

MDTV_nov1_ep13		94.47 203	92.15 205	97.17 186	98.54 202	96.42 199	98.10 175	98.89 155	94.49 168	98.02 165	97.41 148	86.49 202	95.56 180	90.85 213	87.95 207	93.91 206	91.45 210

TESTMET0.1,1		94.44 204	93.71 197	95.30 212	97.84 213	96.16 202	96.31 215	95.32 214	89.98 218	94.08 217	97.01 160	86.43 203	92.20 212	96.76 174	95.31 187	96.05 197	94.31 197

ADS-MVSNet		94.41 205	92.13 206	97.07 188	98.86 182	96.60 196	98.38 157	98.47 181	96.13 136	98.02 165	96.98 163	87.50 200	95.87 177	89.89 214	87.58 209	92.79 213	90.27 213

tpm		93.89 206	91.21 210	97.03 191	98.36 207	96.07 206	97.53 201	99.65 39	92.24 200	98.64 120	97.23 153	74.67 227	94.64 192	92.68 209	90.73 204	93.37 208	94.82 195

PatchmatchNet		93.88 207	91.08 211	97.14 187	98.75 188	96.01 208	98.25 169	99.39 87	94.95 161	98.96 90	96.32 179	85.35 209	95.50 181	88.89 216	85.89 214	91.99 217	90.15 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		93.67 208	90.82 212	96.99 194	98.62 196	96.39 200	98.40 155	99.11 140	95.54 150	97.87 174	97.14 156	81.27 221	94.97 187	88.54 218	86.80 212	92.95 212	90.06 215

MVE	82.47 18	93.12 209	94.09 189	91.99 215	90.79 223	82.50 224	93.93 222	96.30 210	96.06 137	88.81 223	98.19 120	96.38 172	97.56 144	97.24 166	95.18 190	84.58 223	93.07 203
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CostFormer		92.75 210	89.49 214	96.55 202	98.78 187	95.83 213	97.55 197	98.59 174	91.83 208	97.34 193	96.31 180	78.53 222	94.50 193	86.14 219	84.92 215	92.54 214	92.84 204

tpmrst		92.45 211	89.48 215	95.92 208	98.43 205	95.03 216	97.14 207	97.92 198	94.16 180	97.56 182	97.86 134	81.63 220	93.56 203	85.89 220	82.86 216	90.91 221	88.95 218

dps		92.35 212	88.78 217	96.52 203	98.21 211	95.94 210	97.78 186	98.38 184	89.88 220	96.81 202	95.07 197	75.31 226	94.70 191	88.62 217	86.21 213	93.21 210	90.41 212

E-PMN		92.28 213	90.12 213	94.79 213	98.56 200	90.90 221	95.16 219	93.68 217	95.36 153	95.10 215	96.56 173	89.05 197	95.24 183	95.21 198	81.84 218	90.98 219	81.94 220

EMVS		91.84 214	89.39 216	94.70 214	98.44 204	90.84 222	95.27 218	93.53 218	95.18 154	95.26 213	95.62 192	87.59 199	94.77 190	94.87 202	80.72 219	90.95 220	80.88 221

tpm cat1		91.52 215	87.70 218	95.97 207	98.33 208	94.98 217	97.06 210	98.03 193	92.11 203	98.03 164	94.77 201	77.19 225	92.71 208	83.56 221	82.24 217	91.67 218	89.04 217

test_method		77.69 216	85.40 219	68.69 216	42.66 225	55.39 226	82.17 225	52.05 220	92.83 199	84.52 224	94.88 200	95.41 180	65.37 220	92.49 211	79.32 220	85.36 222	87.50 219

GG-mvs-BLEND		65.66 217	92.62 204	34.20 218	1.45 228	93.75 220	85.40 224	1.64 224	91.37 212	17.21 227	87.25 214	94.78 184	3.25 224	95.64 192	93.80 200	96.27 194	91.74 209

testmvs		9.73 218	13.38 220	5.48 220	3.62 226	4.12 227	6.40 228	3.19 223	14.92 223	7.68 229	22.10 223	13.89 231	6.83 222	13.47 222	10.38 222	5.14 226	14.81 222

test123		9.37 219	12.26 221	6.00 219	3.32 227	4.06 228	6.39 229	3.41 222	13.20 224	10.48 228	16.43 224	16.22 230	6.76 223	11.37 223	10.40 221	5.62 225	14.10 223

uanet_test		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

TPM-MVS					98.38 206	97.20 192	96.44 214			97.17 199	95.17 195	98.68 127	92.69 209			98.11 175	97.67 153

RE-MVS-def										99.88 2

9.14												98.83 118

SR-MVS					99.62 85			99.47 75				99.40 57

Anonymous202405211			98.44 75		99.79 38	99.32 51	99.05 96	99.34 102	96.59 110		97.95 132	97.68 156	97.16 153	99.36 32	99.28 34	99.61 39	98.90 69

our_test_3					99.29 148	97.72 182	98.98 102

ambc			97.89 117		99.45 123	97.88 175	97.78 186		97.27 76	99.80 3	98.99 90	98.48 135	98.55 99	97.80 138	96.68 167	98.54 156	98.10 140

MTAPA										99.19 58		99.68 21

MTMP										99.20 56		99.54 41

Patchmatch-RL test							32.47 227

tmp_tt				65.28 217	82.24 224	71.50 225	70.81 226	23.21 221	96.14 134	81.70 226	85.98 221	92.44 191	49.84 221	95.81 187	94.36 198	83.86 224

XVS					99.77 43	99.07 77	99.46 45			98.95 92		99.37 61				99.33 73

X-MVStestdata					99.77 43	99.07 77	99.46 45			98.95 92		99.37 61				99.33 73

mPP-MVS					99.75 52							99.49 51

NP-MVS									93.07 196

Patchmtry						96.05 207	97.64 195	99.78 16		98.50 131

DeepMVS_CX						87.86 223	92.27 223	61.98 219	93.64 190	93.62 220	91.17 211	91.67 192	94.90 189	95.99 186		92.48 215	94.18 199

High-resolution multi-view benchmark