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	97.71 1	99.33 1	99.47 1	99.16 7	99.16 41	99.11 14	99.39 12	99.16 11	99.26 2	99.22 5	99.51 18	99.75 4	98.54 15	99.71 1	99.47 3	99.52 12	99.46 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
SixPastTwentyTwo		99.25 2	99.20 3	99.32 1	99.53 14	99.32 8	99.64 2	99.19 10	98.05 10	99.19 6	99.74 4	98.96 49	99.03 2	99.69 2	99.58 1	99.32 25	99.06 6

WR-MVS		99.22 3	99.15 5	99.30 2	99.54 10	99.62 1	99.63 4	99.45 1	97.75 14	98.47 22	99.71 5	99.05 39	98.88 4	99.54 5	99.49 2	99.81 1	98.87 9

PS-CasMVS		99.08 4	98.90 11	99.28 3	99.65 3	99.56 4	99.59 6	99.39 3	96.36 35	98.83 14	99.46 21	99.09 32	98.62 10	99.51 7	99.36 8	99.63 3	98.97 7

PEN-MVS		99.08 4	98.95 8	99.23 5	99.65 3	99.59 2	99.64 2	99.34 6	96.68 27	98.65 17	99.43 23	99.33 16	98.47 17	99.50 8	99.32 9	99.60 5	98.79 11

v7n		99.03 6	99.03 7	99.02 9	99.09 52	99.11 14	99.57 9	98.82 19	98.21 9	99.25 3	99.84 2	99.59 6	98.76 6	99.23 19	98.83 32	98.63 71	98.40 33

DTE-MVSNet		99.03 6	98.88 12	99.21 6	99.66 2	99.59 2	99.62 5	99.34 6	96.92 23	98.52 19	99.36 29	98.98 45	98.57 13	99.49 9	99.23 12	99.56 9	98.55 25

TDRefinement		99.00 8	99.13 6	98.86 10	98.99 62	99.05 19	99.58 7	98.29 49	98.96 4	97.96 37	99.40 26	98.67 75	98.87 5	99.60 3	99.46 4	99.46 18	98.74 14

WR-MVS_H		98.97 9	98.82 14	99.14 8	99.56 8	99.56 4	99.54 11	99.42 2	96.07 40	98.37 24	99.34 31	99.09 32	98.43 18	99.45 10	99.41 5	99.53 10	98.86 10

UniMVSNet_ETH3D		98.93 10	99.20 3	98.63 22	99.54 10	99.33 7	98.73 63	99.37 4	98.87 5	97.86 39	99.27 35	99.78 2	96.59 85	99.52 6	99.40 6	99.67 2	98.21 41

CP-MVSNet		98.91 11	98.61 19	99.25 4	99.63 5	99.50 6	99.55 10	99.36 5	95.53 66	98.77 16	99.11 42	98.64 78	98.57 13	99.42 11	99.28 11	99.61 4	98.78 12

anonymousdsp		98.85 12	98.88 12	98.83 11	98.69 82	98.20 78	99.68 1	97.35 122	97.09 22	98.98 10	99.86 1	99.43 10	98.94 3	99.28 14	99.19 13	99.33 23	99.08 5

pmmvs6		98.77 13	99.35 2	98.09 43	98.32 101	98.92 25	98.57 70	99.03 12	99.36 1	96.86 83	99.77 3	99.86 1	96.20 100	99.56 4	99.39 7	99.59 6	98.61 22

ACMH	95.26 7	98.75 14	98.93 9	98.54 25	98.86 67	99.01 21	99.58 7	98.10 68	98.67 6	97.30 61	99.18 39	99.42 11	98.40 19	99.19 21	98.86 30	98.99 48	98.19 42
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	96.84 2	98.75 14	98.82 14	98.66 20	99.14 45	98.79 39	99.30 17	97.67 95	98.33 8	97.82 41	99.20 38	99.18 30	98.76 6	99.27 17	98.96 22	99.29 27	98.03 46
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UA-Net		98.66 16	98.60 22	98.73 15	99.83 1	99.28 9	98.56 72	99.24 8	96.04 41	97.12 70	98.44 78	98.95 50	98.17 28	99.15 24	99.00 21	99.48 17	99.33 3

DeepC-MVS	96.08 5	98.58 17	98.49 24	98.68 18	99.37 26	98.52 64	99.01 35	98.17 63	97.17 21	98.25 27	99.56 15	99.62 5	98.29 22	98.40 62	98.09 70	98.97 50	98.08 45
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TranMVSNet+NR-MVSNet		98.45 18	98.22 31	98.72 17	99.32 31	99.06 17	98.99 36	98.89 14	95.52 67	97.53 50	99.42 25	98.83 62	98.01 34	98.55 54	98.34 57	99.57 8	97.80 57

CSCG		98.45 18	98.61 19	98.26 37	99.11 49	99.06 17	98.17 92	97.49 108	97.93 12	97.37 58	98.88 55	99.29 18	98.10 29	98.40 62	97.51 87	99.32 25	99.16 4

DVP-MVS++		98.44 20	98.92 10	97.88 63	99.17 39	99.00 22	98.89 46	98.26 51	97.54 17	96.05 117	99.35 30	99.76 3	96.34 95	98.79 37	98.65 41	98.56 77	99.35 2

Gipuma		98.43 21	98.15 34	98.76 14	99.00 61	98.29 75	97.91 107	98.06 70	99.02 3	99.50 1	96.33 128	98.67 75	99.22 1	99.02 27	98.02 75	98.88 62	97.66 65
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ACMH+	94.90 8	98.40 22	98.71 17	98.04 53	98.93 64	98.84 32	99.30 17	97.86 87	97.78 13	94.19 172	98.77 65	99.39 13	98.61 11	99.33 13	99.07 14	99.33 23	97.81 56

ACMMPR		98.31 23	98.07 38	98.60 23	99.58 6	98.83 33	99.09 27	98.48 31	96.25 37	97.03 74	96.81 116	99.09 32	98.39 20	98.55 54	98.45 49	99.01 45	98.53 28

APDe-MVS		98.29 24	98.42 26	98.14 40	99.45 21	98.90 26	99.18 23	98.30 47	95.96 47	95.13 148	98.79 62	99.25 25	97.92 38	98.80 35	98.71 36	98.85 64	98.54 26

DVP-MVS		98.27 25	98.61 19	97.87 64	99.17 39	99.03 20	99.07 29	98.17 63	96.75 26	94.35 167	98.92 51	99.58 7	97.86 41	98.67 46	98.70 37	98.63 71	98.63 20
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
TransMVSNet (Re)		98.23 26	98.72 16	97.66 77	98.22 110	98.73 50	98.66 66	98.03 75	98.60 7	96.40 102	99.60 12	98.24 99	95.26 122	99.19 21	99.05 17	99.36 20	97.64 66

DU-MVS		98.23 26	97.74 54	98.81 12	99.23 33	98.77 41	98.76 57	98.88 15	94.10 114	98.50 20	98.87 57	98.32 96	97.99 35	98.40 62	98.08 73	99.49 16	97.64 66

UniMVSNet (Re)		98.23 26	97.85 47	98.67 19	99.15 42	98.87 28	98.74 60	98.84 17	94.27 112	97.94 38	99.01 45	98.39 92	97.82 42	98.35 67	98.29 62	99.51 15	97.78 58

MIMVSNet1		98.22 29	98.51 23	97.87 64	99.40 25	98.82 37	99.31 16	98.53 28	97.39 18	96.59 93	99.31 33	99.23 27	94.76 132	98.93 32	98.67 39	98.63 71	97.25 90

HFP-MVS		98.17 30	98.02 39	98.35 35	99.36 27	98.62 56	98.79 56	98.46 34	96.24 38	96.53 95	97.13 112	98.98 45	98.02 33	98.20 70	98.42 51	98.95 54	98.54 26

Baseline_NR-MVSNet		98.17 30	97.90 44	98.48 29	99.23 33	98.59 57	98.83 53	98.73 24	93.97 119	96.95 77	99.66 7	98.23 101	97.90 39	98.40 62	99.06 16	99.25 29	97.42 82

TSAR-MVS + MP.		98.15 32	98.23 30	98.06 51	98.47 92	98.16 84	99.23 20	96.87 138	95.58 61	96.72 86	98.41 79	99.06 36	98.05 32	98.99 29	98.90 26	99.00 46	98.51 29
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
pm-mvs1		98.14 33	98.66 18	97.53 86	97.93 132	98.49 66	98.14 94	98.19 59	97.95 11	96.17 113	99.63 10	98.85 58	95.41 120	98.91 33	98.89 27	99.34 22	97.86 55

SMA-MVS		98.13 34	98.22 31	98.02 56	99.44 23	98.73 50	98.24 89	97.87 86	95.22 74	96.76 85	98.66 71	99.35 15	97.03 70	98.53 57	98.39 53	98.80 66	98.69 16
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		98.12 35	98.08 37	98.18 39	99.34 28	98.74 49	98.97 38	98.00 77	95.13 78	96.90 78	97.54 101	99.27 22	97.18 64	98.72 42	98.45 49	98.68 70	98.69 16

UniMVSNet_NR-MVSNet		98.12 35	97.56 62	98.78 13	99.13 47	98.89 27	98.76 57	98.78 20	93.81 122	98.50 20	98.81 61	97.64 122	97.99 35	98.18 73	97.92 77	99.53 10	97.64 66

ACMM	94.29 11	98.12 35	97.71 55	98.59 24	99.51 16	98.58 59	99.24 19	98.25 52	96.22 39	96.90 78	95.01 152	98.89 55	98.52 16	98.66 47	98.32 60	99.13 36	98.28 39
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SteuartSystems-ACMMP		98.06 38	97.78 52	98.39 33	99.54 10	98.79 39	98.94 42	98.42 36	93.98 118	95.85 124	96.66 122	99.25 25	98.61 11	98.71 44	98.38 54	98.97 50	98.67 19
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		98.05 39	98.46 25	97.57 82	99.01 58	98.99 23	98.82 55	98.24 53	95.76 56	94.70 159	98.96 47	99.49 9	96.19 101	98.74 38	98.65 41	98.46 85	98.63 20

OPM-MVS		98.01 40	98.01 40	98.00 58	99.11 49	98.12 87	98.68 64	97.72 93	96.65 29	96.68 90	98.40 80	99.28 21	97.44 55	98.20 70	97.82 83	98.40 91	97.58 71
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		98.01 40	98.42 26	97.54 85	96.89 179	98.82 37	99.14 24	97.59 98	96.30 36	97.04 73	99.26 36	98.83 62	96.01 106	98.73 40	98.21 64	98.58 76	98.75 13
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		98.00 42	97.38 67	98.73 15	98.72 77	99.15 11	99.12 26	98.76 21	91.58 152	98.15 31	96.70 120	98.72 74	98.20 24	98.64 50	98.92 24	99.43 19	97.97 49

NR-MVSNet		98.00 42	97.88 45	98.13 41	98.33 99	98.77 41	98.83 53	98.88 15	94.10 114	97.46 55	98.87 57	98.58 83	95.78 109	99.13 25	98.16 68	99.52 12	97.53 74

CP-MVS		98.00 42	97.57 61	98.50 26	99.47 20	98.56 61	98.91 44	98.38 42	94.71 93	97.01 75	95.20 148	99.06 36	98.20 24	98.61 51	98.46 46	99.02 43	98.40 33

DPE-MVS		97.99 45	98.12 35	97.84 67	98.65 86	98.86 29	98.86 50	98.05 73	94.18 113	95.49 141	98.90 53	99.33 16	97.11 66	98.53 57	98.65 41	98.86 63	98.39 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMMP		97.99 45	97.60 60	98.45 31	99.53 14	98.83 33	99.13 25	98.30 47	94.57 99	96.39 106	95.32 146	98.95 50	98.37 21	98.61 51	98.47 45	99.00 46	98.45 30
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
MP-MVS		97.98 47	97.53 63	98.50 26	99.56 8	98.58 59	98.97 38	98.39 41	93.49 125	97.14 67	96.08 135	99.23 27	98.06 31	98.50 59	98.38 54	98.90 58	98.44 31
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EG-PatchMatch MVS		97.98 47	97.92 42	98.04 53	98.84 70	98.04 95	97.90 108	96.83 141	95.07 80	98.79 15	99.07 43	99.37 14	97.88 40	98.74 38	98.16 68	98.01 113	96.96 98

ACMP	94.03 12	97.97 49	97.61 59	98.39 33	99.43 24	98.51 65	98.97 38	98.06 70	94.63 97	96.10 115	96.12 134	99.20 29	98.63 9	98.68 45	98.20 67	99.14 33	97.93 52
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CS-MVS-test		97.96 50	97.38 67	98.64 21	98.57 88	99.13 12	99.36 13	98.66 25	91.67 151	98.17 30	96.91 115	98.84 60	97.99 35	98.80 35	98.88 28	99.08 41	97.43 81

LGP-MVS_train		97.96 50	97.53 63	98.45 31	99.45 21	98.64 55	99.09 27	98.27 50	92.99 137	96.04 118	96.57 123	99.29 18	98.66 8	98.73 40	98.42 51	99.19 31	98.09 44

LS3D		97.93 52	97.80 49	98.08 47	99.20 36	98.77 41	98.89 46	97.92 82	96.59 30	96.99 76	96.71 119	97.14 134	96.39 94	99.04 26	98.96 22	99.10 40	97.39 83

SD-MVS		97.84 53	97.78 52	97.90 61	98.33 99	98.06 92	97.95 104	97.80 92	96.03 45	96.72 86	97.57 99	99.18 30	97.50 53	97.88 76	97.08 100	99.11 38	98.68 18
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
RPSCF		97.83 54	98.27 28	97.31 97	98.23 108	98.06 92	97.44 133	95.79 170	96.90 24	95.81 126	98.76 66	98.61 82	97.70 47	98.90 34	98.36 56	98.90 58	98.29 36

thisisatest0515		97.82 55	97.67 56	97.99 59	98.49 91	98.07 91	98.48 77	98.06 70	95.35 72	97.74 43	98.83 60	97.61 123	96.74 77	97.53 93	98.30 61	98.43 90	98.01 48

PGM-MVS		97.82 55	97.25 73	98.48 29	99.54 10	98.75 48	99.02 31	98.35 45	92.41 141	96.84 84	95.39 145	98.99 44	98.24 23	98.43 61	98.34 57	98.90 58	98.41 32

PMVS	90.51 17	97.77 57	97.98 41	97.53 86	98.68 83	98.14 86	97.67 118	97.03 133	96.43 31	98.38 23	98.72 68	97.03 136	94.44 137	99.37 12	99.30 10	98.98 49	96.86 105
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSP-MVS		97.67 58	97.88 45	97.43 92	99.34 28	98.99 23	98.87 49	98.12 66	95.63 58	94.16 173	97.45 102	99.50 8	96.44 93	96.35 131	98.70 37	97.65 129	98.57 24
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
tfpnnormal		97.66 59	97.79 50	97.52 88	98.32 101	98.53 63	98.45 80	97.69 94	97.59 16	96.12 114	97.79 94	96.70 140	95.69 113	98.35 67	98.34 57	98.85 64	97.22 93

FC-MVSNet-train		97.65 60	98.16 33	97.05 109	98.85 68	98.85 30	99.34 14	98.08 69	94.50 104	94.41 165	99.21 37	98.80 66	92.66 162	98.98 30	98.85 31	98.96 52	97.94 51

v10		97.64 61	97.26 72	98.08 47	98.07 121	98.56 61	98.86 50	98.18 61	94.48 105	98.24 28	99.56 15	98.98 45	97.72 46	96.05 141	96.26 128	97.42 137	96.93 99

DROMVSNet		97.63 62	96.88 95	98.50 26	98.74 76	99.16 10	99.33 15	98.83 18	88.77 181	96.62 92	96.48 125	97.75 115	98.19 26	99.00 28	98.76 34	99.29 27	98.27 40

X-MVS		97.60 63	97.00 90	98.29 36	99.50 17	98.76 44	98.90 45	98.37 43	94.67 96	96.40 102	91.47 196	98.78 68	97.60 52	98.55 54	98.50 44	98.96 52	98.29 36

3Dnovator+	96.20 4	97.58 64	97.14 81	98.10 42	98.98 63	97.85 107	98.60 69	98.33 46	96.41 33	97.23 65	94.66 161	97.26 131	96.91 74	97.91 75	97.87 79	98.53 80	98.03 46

DCV-MVSNet		97.56 65	97.63 58	97.47 90	98.41 96	99.12 13	98.63 67	98.57 26	95.71 57	95.60 138	93.79 176	98.01 110	94.25 139	99.16 23	98.88 28	99.35 21	98.74 14

HPM-MVS++		97.56 65	97.11 85	98.09 43	99.18 38	97.95 102	98.57 70	98.20 57	94.08 116	97.25 64	95.96 139	98.81 65	97.13 65	97.51 94	97.30 97	98.21 101	98.15 43

FC-MVSNet-test		97.54 67	98.26 29	96.70 126	98.87 66	97.79 114	98.49 76	98.56 27	96.04 41	90.39 202	99.65 8	98.67 75	95.15 124	99.23 19	99.07 14	98.73 69	97.39 83

TSAR-MVS + ACMM		97.54 67	97.79 50	97.26 98	98.23 108	98.10 90	97.71 116	97.88 85	95.97 46	95.57 140	98.71 69	98.57 84	97.36 58	97.74 82	96.81 109	96.83 163	98.59 23

DeepC-MVS_fast	95.38 6	97.53 69	97.30 71	97.79 72	98.83 71	97.64 117	98.18 90	97.14 129	95.57 62	97.83 40	97.10 113	98.80 66	96.53 90	97.41 97	97.32 95	98.24 100	97.26 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1192		97.52 70	97.03 89	98.09 43	98.31 104	98.01 97	98.96 41	97.25 125	95.22 74	98.89 12	99.64 9	98.83 62	97.68 48	95.63 148	95.91 138	97.47 133	95.97 133

v1144		97.51 71	97.05 88	98.04 53	98.26 106	97.98 99	98.88 48	97.42 116	95.38 71	98.56 18	99.59 14	99.01 43	97.65 49	95.77 145	96.06 135	97.47 133	95.56 145

v8		97.51 71	97.16 79	97.91 60	97.99 128	98.48 67	98.76 57	98.17 63	94.54 103	97.69 45	99.48 20	98.76 71	97.63 51	96.10 140	96.14 130	97.20 147	96.64 112

v1921920		97.50 73	97.00 90	98.07 49	98.20 112	97.94 105	99.03 30	97.06 131	95.29 73	99.01 9	99.62 11	98.73 73	97.74 45	95.52 151	95.78 143	97.39 139	96.12 129

Anonymous20231211		97.49 74	97.91 43	97.00 112	98.31 104	98.72 52	98.27 87	97.84 89	94.76 92	94.77 158	98.14 87	98.38 94	93.60 149	98.96 31	98.66 40	99.22 30	97.77 60

v144192		97.49 74	96.99 92	98.07 49	98.11 120	97.95 102	99.02 31	97.21 126	94.90 88	98.88 13	99.53 17	98.89 55	97.75 44	95.59 149	95.90 139	97.43 136	96.16 127

test1111		97.48 76	97.20 76	97.81 71	98.78 74	98.85 30	98.68 64	98.40 37	96.68 27	94.84 156	99.13 41	90.32 187	97.01 71	99.27 17	99.05 17	99.19 31	97.10 95

GeoE		97.48 76	96.84 99	98.22 38	99.01 58	98.39 70	98.85 52	98.76 21	92.37 142	97.53 50	97.58 98	98.23 101	97.11 66	97.57 92	96.98 103	98.10 109	96.78 108

APD-MVS		97.47 78	97.16 79	97.84 67	99.32 31	98.39 70	98.47 79	98.21 56	92.08 147	95.23 145	96.68 121	98.90 53	96.99 72	98.20 70	98.21 64	98.80 66	97.67 64
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended_VisFu		97.44 79	97.14 81	97.79 72	99.15 42	98.44 68	98.32 85	97.66 96	93.74 124	97.73 44	98.79 62	96.93 139	95.64 118	97.69 84	96.91 106	98.25 99	97.50 77

PHI-MVS		97.44 79	97.17 78	97.74 75	98.14 117	98.41 69	98.03 100	97.50 106	92.07 148	98.01 36	97.33 107	98.62 81	96.02 105	98.34 69	98.21 64	98.76 68	97.24 92

v1240		97.43 81	96.87 98	98.09 43	98.25 107	97.92 106	99.02 31	97.06 131	94.77 91	99.09 8	99.68 6	98.51 87	97.78 43	95.25 156	95.81 141	97.32 143	96.13 128

ECVR-MVS		97.40 82	97.11 85	97.73 76	98.66 84	98.83 33	98.50 74	98.40 37	96.04 41	95.00 154	98.95 49	91.07 184	96.70 79	99.28 14	99.04 19	99.14 33	96.58 114

FMVSNet1		97.40 82	98.09 36	96.60 131	97.80 146	98.76 44	98.26 88	98.50 30	96.79 25	93.13 190	99.28 34	98.64 78	92.90 160	97.67 86	97.86 80	99.02 43	97.64 66

casdiffmvs_mvg		97.34 84	97.65 57	96.97 113	97.74 149	98.33 73	98.45 80	97.18 127	95.81 52	93.92 177	99.04 44	99.05 39	95.48 119	97.00 114	97.71 86	99.07 42	96.63 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v2v482		97.33 85	96.84 99	97.90 61	98.19 113	97.83 108	98.74 60	97.44 114	95.42 70	98.23 29	99.46 21	98.84 60	97.46 54	95.51 152	96.10 133	97.36 141	94.72 154

EPP-MVSNet		97.29 86	96.88 95	97.76 74	98.70 79	99.10 16	98.92 43	98.36 44	95.12 79	93.36 188	97.39 104	91.00 185	97.65 49	98.72 42	98.91 25	99.58 7	97.92 53

MVS_111021_HR		97.27 87	97.11 85	97.46 91	98.46 93	97.82 111	97.50 129	96.86 139	94.97 84	97.13 69	96.99 114	98.39 92	96.82 76	97.65 89	97.38 90	98.02 112	96.56 117

SF-MVS		97.26 88	97.43 65	97.05 109	98.80 73	97.83 108	96.02 181	97.44 114	94.98 83	95.74 130	97.16 110	98.45 91	95.72 111	97.85 77	97.97 76	98.60 74	97.78 58

TSAR-MVS + GP.		97.26 88	97.33 70	97.18 103	98.21 111	98.06 92	96.38 172	97.66 96	93.92 121	95.23 145	98.48 76	98.33 95	97.41 56	97.63 90	97.35 91	98.18 103	97.57 72

OMC-MVS		97.23 90	97.21 75	97.25 101	97.85 137	97.52 126	97.92 106	95.77 171	95.83 51	97.09 72	97.86 92	98.52 86	96.62 83	97.51 94	96.65 115	98.26 97	96.57 115

3Dnovator	96.31 3	97.22 91	97.19 77	97.25 101	98.14 117	97.95 102	98.03 100	96.77 144	96.42 32	97.14 67	95.11 149	97.59 124	95.14 126	97.79 80	97.72 84	98.26 97	97.76 62

MVS_0304		97.18 92	96.84 99	97.58 81	99.15 42	98.19 79	98.11 95	97.81 91	92.36 143	98.06 34	97.43 103	99.06 36	94.24 140	96.80 119	96.54 119	98.12 107	97.52 75

canonicalmvs		97.11 93	96.88 95	97.38 93	98.34 98	98.72 52	97.52 128	97.94 80	95.60 59	95.01 153	94.58 162	94.50 164	96.59 85	97.84 78	98.03 74	98.90 58	98.91 8

V42		97.10 94	96.97 93	97.26 98	97.64 152	97.60 119	98.45 80	95.99 160	94.44 106	97.35 59	99.40 26	98.63 80	97.34 60	96.33 134	96.38 125	96.82 165	96.00 131

CPTT-MVS		97.08 95	96.25 113	98.05 52	99.21 35	98.30 74	98.54 73	97.98 78	94.28 110	95.89 123	89.57 205	98.54 85	98.18 27	97.82 79	97.32 95	98.54 78	97.91 54

DeepPCF-MVS	94.55 10	97.05 96	97.13 84	96.95 115	96.06 193	97.12 143	98.01 102	95.44 177	95.18 76	97.50 52	97.86 92	98.08 106	97.31 62	97.23 102	97.00 102	97.36 141	97.45 79

QAPM		97.04 97	97.14 81	96.93 117	97.78 148	98.02 96	97.36 138	96.72 145	94.68 95	96.23 108	97.21 109	97.68 120	95.70 112	97.37 98	97.24 99	97.78 122	97.77 60

CNVR-MVS		97.03 98	96.77 104	97.34 94	98.89 65	97.67 116	97.64 121	97.17 128	94.40 108	95.70 134	94.02 171	98.76 71	96.49 92	97.78 81	97.29 98	98.12 107	97.47 78

casdiffmvs		97.00 99	97.36 69	96.59 132	97.65 151	97.98 99	98.06 97	96.81 142	95.78 54	92.77 196	99.40 26	99.26 24	95.65 117	96.70 123	96.39 124	98.59 75	95.99 132
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v148		96.99 100	96.70 106	97.34 94	97.89 135	97.23 135	98.33 84	96.96 134	95.57 62	97.12 70	98.99 46	99.40 12	97.23 63	96.22 137	95.45 148	96.50 170	94.02 166

DELS-MVS		96.90 101	97.24 74	96.50 137	97.85 137	98.18 80	97.88 111	95.92 163	93.48 126	95.34 143	98.86 59	98.94 52	94.03 143	97.33 100	97.04 101	98.00 114	96.85 106
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
MVS_111021_LR		96.86 102	96.72 105	97.03 111	97.80 146	97.06 146	97.04 152	95.51 176	94.55 100	97.47 53	97.35 106	97.68 120	96.66 81	97.11 107	96.73 111	97.69 126	96.57 115

PM-MVS		96.85 103	96.62 108	97.11 105	97.13 174	96.51 159	98.29 86	94.65 194	94.84 89	98.12 32	98.59 72	97.20 132	97.41 56	96.24 136	96.41 123	97.09 152	96.56 117

pmmvs-eth3d		96.84 104	96.22 115	97.56 83	97.63 154	96.38 166	98.74 60	96.91 137	94.63 97	98.26 26	99.43 23	98.28 97	96.58 87	94.52 166	95.54 146	97.24 145	94.75 153

CANet		96.81 105	96.50 109	97.17 104	99.10 51	97.96 101	97.86 112	97.51 104	91.30 155	97.75 42	97.64 96	97.89 113	93.39 153	96.98 115	96.73 111	97.40 138	96.99 97

Fast-Effi-MVS+		96.80 106	95.92 126	97.84 67	98.57 88	97.46 129	98.06 97	98.24 53	89.64 176	97.57 49	96.45 126	97.35 129	96.73 78	97.22 103	96.64 116	97.86 119	96.65 111

MCST-MVS		96.79 107	96.08 119	97.62 79	98.78 74	97.52 126	98.01 102	97.32 123	93.20 129	95.84 125	93.97 173	98.12 104	97.34 60	96.34 132	95.88 140	98.45 86	97.51 76

UGNet		96.79 107	97.82 48	95.58 159	97.57 157	98.39 70	98.48 77	97.84 89	95.85 50	94.68 160	97.91 91	99.07 35	87.12 201	97.71 83	97.51 87	97.80 120	98.29 36
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
TAPA-MVS	93.96 13	96.79 107	96.70 106	96.90 119	97.64 152	97.58 120	97.54 127	94.50 196	95.14 77	96.64 91	96.76 118	97.90 112	96.63 82	95.98 142	96.14 130	98.45 86	97.39 83
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		96.73 110	96.92 94	96.51 136	98.70 79	97.57 122	97.64 121	92.07 203	93.10 135	96.31 107	98.29 82	99.02 42	95.99 107	97.20 104	96.47 121	98.37 93	96.81 107
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
train_agg		96.68 111	95.93 125	97.56 83	99.08 53	97.16 139	98.44 83	97.37 119	91.12 159	95.18 147	95.43 144	98.48 89	97.36 58	96.48 128	95.52 147	97.95 117	97.34 87

CDPH-MVS		96.68 111	95.99 122	97.48 89	99.13 47	97.64 117	98.08 96	97.46 110	90.56 165	95.13 148	94.87 157	98.27 98	96.56 88	97.09 108	96.45 122	98.54 78	97.08 96

MSLP-MVS++		96.66 113	96.46 112	96.89 120	98.02 123	97.71 115	95.57 188	96.96 134	94.36 109	96.19 112	91.37 197	98.24 99	97.07 68	97.69 84	97.89 78	97.52 132	97.95 50

TinyColmap		96.64 114	96.07 120	97.32 96	97.84 142	96.40 163	97.63 123	96.25 155	95.86 49	98.98 10	97.94 90	96.34 147	96.17 102	97.30 101	95.38 151	97.04 154	93.24 173

IS_MVSNet		96.62 115	96.48 111	96.78 124	98.46 93	98.68 54	98.61 68	98.24 53	92.23 144	89.63 206	95.90 140	94.40 165	96.23 97	98.65 48	98.77 33	99.52 12	96.76 109

NCCC		96.56 116	95.68 128	97.59 80	99.04 57	97.54 125	97.67 118	97.56 102	94.84 89	96.10 115	87.91 208	98.09 105	96.98 73	97.20 104	96.80 110	98.21 101	97.38 86

ETV-MVS		96.54 117	95.27 136	98.02 56	99.07 55	97.48 128	98.16 93	98.19 59	87.33 196	97.58 48	92.67 185	95.93 153	96.22 98	98.49 60	98.46 46	98.91 57	96.50 120

Effi-MVS+		96.46 118	95.28 135	97.85 66	98.64 87	97.16 139	97.15 150	98.75 23	90.27 169	98.03 35	93.93 174	96.21 148	96.55 89	96.34 132	96.69 114	97.97 116	96.33 123

IterMVS-LS		96.35 119	95.85 127	96.93 117	97.53 158	98.00 98	97.37 136	97.97 79	95.49 69	96.71 89	98.94 50	93.23 172	94.82 131	93.15 185	95.05 154	97.17 149	97.12 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
USDC		96.30 120	95.64 130	97.07 107	97.62 155	96.35 168	97.17 149	95.71 172	95.52 67	99.17 7	98.11 88	97.46 126	95.67 114	95.44 154	93.60 174	97.09 152	92.99 177

Vis-MVSNet (Re-imp)		96.29 121	96.50 109	96.05 145	97.96 131	97.83 108	97.30 141	97.86 87	93.14 131	88.90 209	96.80 117	95.28 157	95.15 124	98.37 66	98.25 63	99.12 37	95.84 135

MSDG		96.27 122	96.17 118	96.38 141	97.85 137	96.27 169	96.55 169	94.41 197	94.55 100	95.62 137	97.56 100	97.80 114	96.22 98	97.17 106	96.27 127	97.67 128	93.60 170

CNLPA		96.24 123	95.97 123	96.57 134	97.48 163	97.10 145	96.75 162	94.95 188	94.92 87	96.20 111	94.81 158	96.61 142	96.25 96	96.94 116	95.64 144	97.79 121	95.74 141

EIA-MVS		96.23 124	94.85 148	97.84 67	99.08 53	98.21 77	97.69 117	98.03 75	85.68 206	98.09 33	91.75 195	97.07 135	95.66 116	97.58 91	97.72 84	98.47 84	95.91 134

PLC	92.55 15	96.10 125	95.36 132	96.96 114	98.13 119	96.88 150	96.49 170	96.67 149	94.07 117	95.71 133	91.14 198	96.09 150	96.84 75	96.70 123	96.58 118	97.92 118	96.03 130
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test20.03		96.08 126	96.80 102	95.25 168	99.19 37	97.58 120	97.24 146	97.56 102	94.95 86	91.91 197	98.58 73	98.03 108	87.88 197	97.43 96	96.94 105	97.69 126	94.05 165

FA-MVS(training)		96.07 127	95.59 131	96.63 129	98.00 127	97.44 130	97.36 138	98.53 28	92.21 145	95.97 120	96.18 132	94.22 168	92.98 157	96.79 120	96.70 113	96.95 159	95.56 145

TSAR-MVS + COLMAP		96.05 128	95.94 124	96.18 144	97.46 164	96.41 162	97.26 145	95.83 167	94.69 94	95.30 144	98.31 81	96.52 143	94.71 133	95.48 153	94.87 156	96.54 169	95.33 148

EU-MVSNet		96.03 129	96.23 114	95.80 153	95.48 206	94.18 187	98.99 36	91.51 205	97.22 20	97.66 46	99.15 40	98.51 87	98.08 30	95.92 143	92.88 181	93.09 193	95.72 142

PCF-MVS	92.69 14	95.98 130	95.05 143	97.06 108	98.43 95	97.56 123	97.76 114	96.65 150	89.95 174	95.70 134	96.18 132	98.48 89	95.74 110	93.64 177	93.35 178	98.09 111	96.18 126
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		95.97 131	95.01 145	97.08 106	98.72 77	97.19 137	97.07 151	96.69 148	91.49 153	95.77 129	92.19 191	97.93 111	96.15 103	94.66 163	94.16 165	98.10 109	97.45 79

Effi-MVS+-dtu		95.94 132	95.08 142	96.94 116	98.54 90	97.38 131	96.66 166	97.89 84	88.68 182	95.92 121	92.90 184	97.28 130	94.18 142	96.68 125	96.13 132	98.45 86	96.51 119

diffmvs		95.86 133	96.21 116	95.44 162	97.25 172	96.85 153	96.99 154	95.23 182	94.96 85	92.82 195	98.89 54	98.85 58	93.52 151	94.21 172	94.25 164	96.84 162	95.49 147
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
AdaColmap		95.85 134	94.65 151	97.26 98	98.70 79	97.20 136	97.33 140	97.30 124	91.28 157	95.90 122	88.16 207	96.17 149	96.60 84	97.34 99	96.82 108	97.71 123	95.60 144

FMVSNet2		95.77 135	96.20 117	95.27 166	96.77 182	98.18 80	97.28 142	97.90 83	93.12 132	91.37 199	98.25 84	96.05 151	90.04 182	94.96 161	95.94 137	98.28 94	96.90 100

OpenMVS	94.63 9	95.75 136	95.04 144	96.58 133	97.85 137	97.55 124	96.71 164	96.07 157	90.15 172	96.47 97	90.77 203	95.95 152	94.41 138	97.01 113	96.95 104	98.00 114	96.90 100

pmmvs5		95.70 137	95.22 137	96.26 142	96.55 188	97.24 134	97.50 129	94.99 187	90.95 161	96.87 80	98.47 77	97.40 127	94.45 136	92.86 186	94.98 155	97.23 146	94.64 156

Anonymous20231206		95.69 138	95.68 128	95.70 155	98.32 101	96.95 148	97.37 136	96.65 150	93.33 127	93.61 182	98.70 70	98.03 108	91.04 171	95.07 159	94.59 163	97.20 147	93.09 176

MAR-MVS		95.51 139	94.49 155	96.71 125	97.92 133	96.40 163	96.72 163	98.04 74	86.74 200	96.72 86	92.52 188	95.14 159	94.02 144	96.81 118	96.54 119	96.85 160	97.25 90
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
DI_MVS_plusplus_trai		95.48 140	94.51 153	96.61 130	97.13 174	97.30 133	98.05 99	96.79 143	93.75 123	95.08 151	96.38 127	89.76 189	94.95 127	93.97 176	94.82 160	97.64 130	95.63 143

MDA-MVSNet-bldmvs		95.45 141	95.20 138	95.74 154	94.24 211	96.38 166	97.93 105	94.80 189	95.56 65	96.87 80	98.29 82	95.24 158	96.50 91	98.65 48	90.38 193	94.09 187	91.93 181

PVSNet_BlendedMVS		95.44 142	95.09 140	95.86 151	97.31 169	97.13 141	96.31 175	95.01 185	88.55 185	96.23 108	94.55 165	97.75 115	92.56 164	96.42 129	95.44 149	97.71 123	95.81 136

PVSNet_Blended		95.44 142	95.09 140	95.86 151	97.31 169	97.13 141	96.31 175	95.01 185	88.55 185	96.23 108	94.55 165	97.75 115	92.56 164	96.42 129	95.44 149	97.71 123	95.81 136

pmmvs4		95.37 144	94.25 156	96.67 128	97.01 177	95.28 181	97.60 124	96.07 157	93.11 133	97.29 62	98.09 89	94.23 167	95.21 123	91.56 197	93.91 171	96.82 165	93.59 171

MVS_Test		95.34 145	94.88 147	95.89 150	96.93 178	96.84 154	96.66 166	97.08 130	90.06 173	94.02 174	97.61 97	96.64 141	93.59 150	92.73 189	94.02 169	97.03 155	96.24 124

GBi-Net		95.21 146	95.35 133	95.04 171	96.77 182	98.18 80	97.28 142	97.58 99	88.43 187	90.28 203	96.01 136	92.43 175	90.04 182	97.67 86	97.86 80	98.28 94	96.90 100

test1		95.21 146	95.35 133	95.04 171	96.77 182	98.18 80	97.28 142	97.58 99	88.43 187	90.28 203	96.01 136	92.43 175	90.04 182	97.67 86	97.86 80	98.28 94	96.90 100

IterMVS-SCA-FT		95.16 148	93.95 160	96.56 135	97.89 135	96.69 156	96.94 156	96.05 159	93.06 136	97.35 59	98.79 62	91.45 180	95.93 108	92.78 187	91.00 191	95.22 183	93.91 168

HyFIR lowres test		95.05 149	93.54 165	96.81 123	97.81 145	96.88 150	98.18 90	97.46 110	94.28 110	94.98 155	96.57 123	92.89 174	96.15 103	90.90 202	91.87 187	96.28 175	91.35 182

CHOSEN 1792x2688		94.98 150	94.69 150	95.31 164	97.27 171	95.58 178	97.90 108	95.56 175	95.03 81	93.77 181	95.65 142	99.29 18	95.30 121	91.51 198	91.28 190	92.05 201	94.50 158

CANet_DTU		94.96 151	94.62 152	95.35 163	98.03 122	96.11 171	96.92 158	95.60 174	88.59 184	97.27 63	95.27 147	96.50 144	88.77 193	95.53 150	95.59 145	95.54 181	94.78 152

CDS-MVSNet		94.91 152	95.17 139	94.60 179	97.85 137	96.21 170	96.90 160	96.39 153	90.81 162	93.40 186	97.24 108	94.54 163	85.78 207	96.25 135	96.15 129	97.26 144	95.01 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DPM-MVS		94.86 153	93.90 162	95.99 147	98.19 113	96.52 158	96.29 177	95.95 161	93.11 133	94.61 162	88.17 206	96.44 145	93.77 148	93.33 180	93.54 176	97.11 151	96.22 125

MS-PatchMatch		94.84 154	94.76 149	94.94 174	96.38 189	94.69 186	95.90 183	94.03 199	92.49 140	93.81 179	95.79 141	96.38 146	94.54 134	94.70 162	94.85 157	94.97 185	94.43 160

thisisatest0530		94.81 155	93.06 171	96.85 122	98.01 124	97.18 138	96.93 157	97.36 120	89.73 175	95.80 127	94.98 153	77.88 210	94.89 128	96.73 122	97.35 91	98.13 106	97.54 73

tttt0517		94.81 155	93.04 172	96.88 121	98.15 116	97.37 132	96.99 154	97.36 120	89.51 177	95.74 130	94.89 155	77.53 212	94.89 128	96.94 116	97.35 91	98.17 104	97.70 63

testgi		94.81 155	96.05 121	93.35 190	99.06 56	96.87 152	97.57 126	96.70 147	95.77 55	88.60 211	93.19 182	98.87 57	81.21 215	97.03 112	96.64 116	96.97 158	93.99 167

PatchMatch-RL		94.79 158	93.75 164	96.00 146	96.80 181	95.00 183	95.47 193	95.25 181	90.68 164	95.80 127	92.97 183	93.64 170	95.67 114	96.13 139	95.81 141	96.99 157	92.01 180

FPMVS		94.70 159	94.99 146	94.37 181	95.84 199	93.20 192	96.00 182	91.93 204	95.03 81	94.64 161	94.68 159	93.29 171	90.95 172	98.07 74	97.34 94	96.85 160	93.29 172

new-patchmatchnet		94.48 160	94.02 158	95.02 173	97.51 162	95.00 183	95.68 187	94.26 198	97.32 19	95.73 132	99.60 12	98.22 103	91.30 167	94.13 173	84.41 203	95.65 180	89.45 193

IterMVS		94.48 160	93.46 167	95.66 156	97.52 159	96.43 160	97.20 147	94.73 192	92.91 139	96.44 98	98.75 67	91.10 182	94.53 135	92.10 193	90.10 195	93.51 190	92.84 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDTV_nov1_ep13_2view		94.39 162	93.34 168	95.63 157	97.23 173	95.33 180	97.76 114	96.84 140	94.55 100	97.47 53	98.96 47	97.70 118	93.88 145	92.27 191	86.81 201	90.56 203	87.73 201

Fast-Effi-MVS+-dtu		94.34 163	93.26 170	95.62 158	97.82 143	95.97 174	95.86 184	99.01 13	86.88 198	93.39 187	90.83 201	95.46 156	90.61 176	94.46 168	94.68 161	97.01 156	94.51 157

thres600view7		94.34 163	92.31 180	96.70 126	98.19 113	98.12 87	97.85 113	97.45 112	91.49 153	93.98 176	84.27 211	82.02 201	94.24 140	97.04 109	98.76 34	98.49 82	94.47 159

EPNet		94.33 165	93.52 166	95.27 166	98.81 72	94.71 185	96.77 161	98.20 57	88.12 190	96.53 95	92.53 187	91.19 181	85.25 211	95.22 157	95.26 152	96.09 178	97.63 70
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		94.29 166	91.43 188	97.64 78	98.66 84	98.83 33	98.50 74	98.40 37	96.04 41	94.45 164	94.88 156	55.05 226	96.70 79	99.28 14	99.04 19	99.14 33	96.87 104

GA-MVS		94.18 167	92.98 173	95.58 159	97.36 166	96.42 161	96.21 178	95.86 164	90.29 168	95.08 151	96.19 131	85.37 193	92.82 161	94.01 175	94.14 166	96.16 177	94.41 161

gg-mvs-nofinetune		94.13 168	93.93 161	94.37 181	97.99 128	95.86 175	95.45 196	99.22 9	97.61 15	95.10 150	99.50 19	84.50 194	81.73 214	95.31 155	94.12 167	96.71 168	90.59 186

baseline		94.07 169	94.50 154	93.57 188	96.34 190	93.40 191	95.56 191	92.39 202	92.07 148	94.00 175	98.24 85	97.51 125	89.19 188	91.75 195	92.72 182	93.96 189	95.79 138

FMVSNet3		94.06 170	93.85 163	94.31 184	95.46 207	97.80 113	96.34 173	97.58 99	88.43 187	90.28 203	96.01 136	92.43 175	88.67 194	91.82 194	93.96 170	97.53 131	96.50 120

thres400		94.04 171	91.94 183	96.50 137	97.98 130	97.82 111	97.66 120	96.96 134	90.96 160	94.20 170	83.24 212	82.82 199	93.80 146	96.50 127	98.09 70	98.38 92	94.15 163

CVMVSNet		94.01 172	94.25 156	93.73 187	94.36 210	92.44 195	97.45 132	88.56 208	95.59 60	93.06 193	98.88 55	90.03 188	94.84 130	94.08 174	93.45 177	94.09 187	95.31 149

thres200		93.98 173	91.90 184	96.40 140	97.66 150	98.12 87	97.20 147	97.45 112	90.16 171	93.82 178	83.08 213	83.74 197	93.80 146	97.04 109	97.48 89	98.49 82	93.70 169

baseline1		93.89 174	92.82 175	95.14 170	97.62 155	96.97 147	96.12 179	96.36 154	91.30 155	91.53 198	94.68 159	80.72 203	90.80 174	95.71 146	96.29 126	98.44 89	94.09 164

tfpn200view9		93.80 175	91.75 185	96.20 143	97.52 159	98.15 85	97.48 131	97.47 109	87.65 192	93.56 184	83.03 214	84.12 195	92.62 163	97.04 109	98.09 70	98.52 81	94.17 162

MIMVSNet		93.68 176	93.96 159	93.35 190	97.82 143	96.08 172	96.34 173	98.46 34	91.28 157	86.67 216	94.95 154	94.87 161	84.39 212	94.53 164	94.65 162	96.45 172	91.34 183

pmnet_mix02		93.59 177	92.65 176	94.69 177	96.76 185	94.16 188	97.03 153	93.00 201	95.79 53	96.03 119	98.91 52	97.69 119	92.99 156	90.03 205	84.10 205	92.35 199	87.89 200

EPNet_dtu		93.45 178	92.51 178	94.55 180	98.39 97	91.67 204	95.46 194	97.50 106	86.56 201	97.38 57	93.52 177	94.20 169	85.82 206	93.31 182	92.53 183	92.72 195	95.76 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	92.44 16	93.33 179	92.15 182	94.70 176	97.42 165	96.39 165	95.57 188	94.67 193	86.40 204	93.59 183	78.28 218	95.76 155	89.59 187	95.88 144	95.98 136	97.39 139	96.34 122
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
ET-MVSNet_ETH3D		93.18 180	90.80 191	95.95 148	96.05 194	96.07 173	96.92 158	96.51 152	89.34 178	95.63 136	94.08 170	72.31 221	93.13 154	94.33 170	94.83 158	97.44 135	94.65 155

thres100view900		92.93 181	90.89 190	95.31 164	97.52 159	96.82 155	96.41 171	95.08 183	87.65 192	93.56 184	83.03 214	84.12 195	91.12 170	94.53 164	96.91 106	98.17 104	93.21 174

N_pmnet		92.46 182	92.38 179	92.55 196	97.91 134	93.47 190	97.42 134	94.01 200	96.40 34	88.48 212	98.50 75	98.07 107	88.14 196	91.04 201	84.30 204	89.35 208	84.85 207

TAMVS		92.46 182	93.34 168	91.44 204	97.03 176	93.84 189	94.68 206	90.60 206	90.44 167	85.31 217	97.14 111	93.03 173	85.78 207	94.34 169	93.67 173	95.22 183	90.93 185

CMPMVS	71.81 19	92.34 184	92.85 174	91.75 202	92.70 215	90.43 209	88.84 218	88.56 208	85.87 205	94.35 167	90.98 199	95.89 154	91.14 169	96.14 138	94.83 158	94.93 186	95.78 139
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline2		92.06 185	89.82 194	94.68 178	97.32 167	95.72 176	94.97 203	95.08 183	84.75 209	94.34 169	90.68 204	77.75 211	90.13 181	93.38 178	93.58 175	96.25 176	92.90 178

MVSTER		91.97 186	90.31 192	93.91 185	96.81 180	96.91 149	94.22 207	95.64 173	84.98 207	92.98 194	93.42 178	72.56 219	86.64 205	95.11 158	93.89 172	97.16 150	95.31 149

CR-MVSNet		91.94 187	88.50 197	95.94 149	96.14 192	92.08 199	95.23 199	98.47 32	84.30 211	96.44 98	94.58 162	75.57 213	92.92 158	90.22 203	92.22 184	96.43 173	90.56 187

gm-plane-assit		91.85 188	87.91 199	96.44 139	99.14 45	98.25 76	99.02 31	97.38 118	95.57 62	98.31 25	99.34 31	51.00 227	88.93 191	93.16 184	91.57 188	95.85 179	86.50 204

PMMVS		91.67 189	91.47 187	91.91 201	89.43 220	88.61 215	94.99 202	85.67 213	87.50 194	93.80 180	94.42 168	94.88 160	90.71 175	92.26 192	92.96 180	96.83 163	89.65 191

CHOSEN 280x420		91.55 190	90.27 193	93.05 193	94.61 209	88.01 216	96.56 168	94.62 195	88.04 191	94.20 170	92.66 186	86.60 191	90.82 173	95.06 160	91.89 186	87.49 213	89.61 192

PatchT		91.40 191	88.54 196	94.74 175	91.48 219	92.18 198	97.42 134	97.51 104	84.96 208	96.44 98	94.16 169	75.47 214	92.92 158	90.22 203	92.22 184	92.66 198	90.56 187

pmmvs3		91.20 192	91.40 189	90.96 206	91.71 218	91.08 205	95.41 197	81.34 217	87.36 195	94.57 163	95.02 151	94.30 166	90.42 177	94.28 171	89.26 197	92.30 200	88.49 198

test0.0.03 1		91.17 193	91.50 186	90.80 207	98.01 124	95.46 179	94.22 207	95.80 168	86.55 202	81.75 219	90.83 201	87.93 190	78.48 216	94.51 167	94.11 168	96.50 170	91.08 184

SCA		91.15 194	87.65 201	95.23 169	96.15 191	95.68 177	96.68 165	98.18 61	90.46 166	97.21 66	92.44 189	80.17 205	93.51 152	86.04 212	83.58 208	89.68 207	85.21 206

new_pmnet		90.85 195	92.26 181	89.21 210	93.68 214	89.05 214	93.20 215	84.16 216	92.99 137	84.25 218	97.72 95	94.60 162	86.80 204	93.20 183	91.30 189	93.21 191	86.94 203

RPMNet		90.52 196	86.27 210	95.48 161	95.95 197	92.08 199	95.55 192	98.12 66	84.30 211	95.60 138	87.49 209	72.78 218	91.24 168	87.93 207	89.34 196	96.41 174	89.98 190

MDTV_nov1_ep13		90.30 197	87.32 205	93.78 186	96.00 196	92.97 193	95.46 194	95.39 178	88.61 183	95.41 142	94.45 167	80.39 204	89.87 185	86.58 210	83.54 209	90.56 203	84.71 208

PatchmatchNet		89.98 198	86.23 211	94.36 183	96.56 187	91.90 203	96.07 180	96.72 145	90.18 170	96.87 80	93.36 181	78.06 209	91.46 166	84.71 216	81.40 213	88.45 210	83.97 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		89.89 199	87.70 200	92.43 198	95.52 204	90.91 207	95.57 188	95.33 179	93.19 130	91.21 200	93.41 179	82.12 200	89.05 189	86.21 211	83.77 207	87.92 211	84.31 209

tpm		89.84 200	86.81 207	93.36 189	96.60 186	91.92 202	95.02 201	97.39 117	86.79 199	96.54 94	95.03 150	69.70 222	87.66 198	88.79 206	86.19 202	86.95 215	89.27 194

test-LLR		89.77 201	87.47 203	92.45 197	98.01 124	89.77 211	93.25 213	95.80 168	81.56 216	89.19 207	92.08 192	79.59 206	85.77 209	91.47 199	89.04 199	92.69 196	88.75 195

FMVSNet5		89.65 202	87.60 202	92.04 200	95.63 203	96.61 157	94.82 205	94.75 190	80.11 220	87.72 214	77.73 219	73.81 217	83.81 213	95.64 147	96.08 134	95.49 182	93.21 174

EPMVS		89.28 203	86.28 209	92.79 195	96.01 195	92.00 201	95.83 185	95.85 166	90.78 163	91.00 201	94.58 162	74.65 215	88.93 191	85.00 214	82.88 211	89.09 209	84.09 211

test-mter		89.16 204	88.14 198	90.37 208	94.79 208	91.05 206	93.60 212	85.26 214	81.65 215	88.32 213	92.22 190	79.35 208	87.03 202	92.28 190	90.12 194	93.19 192	90.29 189

CostFormer		89.06 205	85.65 212	93.03 194	95.88 198	92.40 196	95.30 198	95.86 164	86.49 203	93.12 192	93.40 180	74.18 216	88.25 195	82.99 217	81.46 212	89.77 206	88.66 197

MVS-HIRNet		88.72 206	86.49 208	91.33 205	91.81 217	85.66 217	87.02 220	96.25 155	81.48 218	94.82 157	96.31 130	92.14 178	90.32 179	87.60 208	83.82 206	87.74 212	78.42 216

TESTMET0.1,1		88.60 207	87.47 203	89.93 209	94.23 212	89.77 211	93.25 213	84.47 215	81.56 216	89.19 207	92.08 192	79.59 206	85.77 209	91.47 199	89.04 199	92.69 196	88.75 195

dps		88.36 208	84.32 215	93.07 192	93.86 213	92.29 197	94.89 204	95.93 162	83.50 213	93.13 190	91.87 194	67.79 224	90.32 179	85.99 213	83.22 210	90.28 205	85.56 205

tpmrst		87.60 209	84.13 216	91.66 203	95.65 202	89.73 213	93.77 210	94.74 191	88.85 180	93.35 189	95.60 143	72.37 220	87.40 199	81.24 218	78.19 215	85.02 218	82.90 215

tpm cat1		87.19 210	82.78 217	92.33 199	95.66 201	90.61 208	94.19 209	95.27 180	86.97 197	94.38 166	90.91 200	69.40 223	87.21 200	79.57 220	77.82 216	87.25 214	84.18 210

E-PMN		86.94 211	85.10 213	89.09 212	95.77 200	83.54 220	89.89 217	86.55 210	92.18 146	87.34 215	94.02 171	83.42 198	89.63 186	93.32 181	77.11 217	85.33 216	72.09 217

EMVS		86.63 212	84.48 214	89.15 211	95.51 205	83.66 219	90.19 216	86.14 212	91.78 150	88.68 210	93.83 175	81.97 202	89.05 189	92.76 188	76.09 218	85.31 217	71.28 218

PMMVS2		86.47 213	92.62 177	79.29 214	92.01 216	85.63 218	93.74 211	86.37 211	93.95 120	54.18 224	98.19 86	97.39 128	58.46 217	96.57 126	93.07 179	90.99 202	83.55 214

MVE	72.99 18	85.37 214	89.43 195	80.63 213	74.43 221	71.94 222	88.25 219	89.81 207	93.27 128	67.32 222	96.32 129	91.83 179	90.40 178	93.36 179	90.79 192	73.55 221	88.49 198
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		61.30 215	70.45 218	50.62 215	22.69 223	30.92 224	68.31 223	25.76 219	80.56 219	68.71 220	82.80 216	91.08 183	44.64 218	80.50 219	56.70 219	73.64 220	70.58 219

GG-mvs-BLEND		61.03 216	87.02 206	30.71 217	0.74 226	90.01 210	78.90 222	0.74 223	84.56 210	9.46 225	79.17 217	90.69 186	1.37 222	91.74 196	89.13 198	93.04 194	83.83 213

testmvs		4.99 217	6.88 219	2.78 219	1.73 224	2.04 226	3.10 226	1.71 221	7.27 222	3.92 227	12.18 221	6.71 228	3.31 221	6.94 221	5.51 221	2.94 223	7.51 220

test123		4.41 218	5.71 220	2.88 218	1.28 225	2.21 225	3.09 227	1.65 222	6.35 223	4.98 226	8.53 222	3.88 229	3.46 220	5.79 222	5.71 220	2.85 224	7.50 221

uanet_test		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet-low-res		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

RE-MVS-def										99.38 2

9.14												96.98 138

SR-MVS					99.33 30			98.40 37				98.90 53

Anonymous202405211			97.39 66		98.85 68	98.59 57	97.89 110	97.93 81	94.41 107		97.37 105	96.99 137	93.09 155	98.61 51	98.46 46	99.11 38	97.27 88

our_test_3					97.32 167	95.13 182	97.59 125

ambc			96.78 103		99.01 58	97.11 144	95.73 186		95.91 48	99.25 3	98.56 74	97.17 133	97.04 69	96.76 121	95.22 153	96.72 167	96.73 110

MTAPA										97.43 56		99.27 22

MTMP										97.63 47		99.03 41

Patchmatch-RL test							17.42 225

tmp_tt				45.72 216	60.00 222	38.74 223	45.50 224	12.18 220	79.58 221	68.42 221	67.62 220	65.04 225	22.12 219	84.83 215	78.72 214	66.08 222

XVS					99.48 18	98.76 44	99.22 21			96.40 102		98.78 68				98.94 55

X-MVStestdata					99.48 18	98.76 44	99.22 21			96.40 102		98.78 68				98.94 55

mPP-MVS					99.58 6							98.98 45

NP-MVS									89.27 179

Patchmtry						92.70 194	95.23 199	98.47 32		96.44 98

DeepMVS_CX						72.99 221	80.14 221	37.34 218	83.46 214	60.13 223	84.40 210	85.48 192	86.93 203	87.22 209		79.61 219	87.32 202

High-resolution multi-view benchmark