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 42	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 11	99.19 6	99.74 4	98.96 53	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 15	98.47 22	99.71 5	99.05 43	98.88 4	99.54 5	99.49 2	99.81 1	98.87 11

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 38	98.83 14	99.46 21	99.09 37	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 30	98.65 17	99.43 23	99.33 18	98.47 17	99.50 8	99.32 9	99.60 5	98.79 13

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 73	98.40 35

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 26	98.52 19	99.36 29	98.98 49	98.57 13	99.49 9	99.23 12	99.56 9	98.55 27

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 36	99.40 26	98.67 79	98.87 5	99.60 3	99.46 4	99.46 18	98.74 16

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 44	98.37 24	99.34 32	99.09 37	98.43 18	99.45 10	99.41 5	99.53 10	98.86 12

UniMVSNet_ETH3D		98.93 10	99.20 3	98.63 22	99.54 10	99.33 7	98.73 64	99.37 4	98.87 5	97.86 38	99.27 37	99.78 2	96.59 85	99.52 6	99.40 6	99.67 2	98.21 43

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 73	98.77 16	99.11 45	98.64 83	98.57 13	99.42 11	99.28 11	99.61 4	98.78 14

anonymousdsp		98.85 12	98.88 12	98.83 11	98.69 82	98.20 82	99.68 1	97.35 125	97.09 24	98.98 10	99.86 1	99.43 12	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	98.32 102	98.92 25	98.57 71	99.03 12	99.36 1	96.86 82	99.77 3	99.86 1	96.20 101	99.56 4	99.39 7	99.59 6	98.61 24

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 60	99.18 42	99.42 13	98.40 19	99.19 21	98.86 30	98.99 48	98.19 44
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 97	98.33 8	97.82 40	99.20 41	99.18 34	98.76 6	99.27 17	98.96 22	99.29 27	98.03 49
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 73	99.24 8	96.04 45	97.12 69	98.44 84	98.95 54	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 67	99.01 35	98.17 63	97.17 23	98.25 27	99.56 15	99.62 5	98.29 22	98.40 63	98.09 70	98.97 50	98.08 47
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 74	97.53 49	99.42 25	98.83 66	98.01 34	98.55 55	98.34 57	99.57 8	97.80 60

CSCG		98.45 18	98.61 19	98.26 37	99.11 49	99.06 17	98.17 93	97.49 110	97.93 13	97.37 57	98.88 59	99.29 21	98.10 29	98.40 63	97.51 92	99.32 25	99.16 4

DVP-MVS++		98.44 20	98.92 10	97.88 63	99.17 40	99.00 22	98.89 46	98.26 51	97.54 18	96.05 117	99.35 30	99.76 3	96.34 96	98.79 37	98.65 41	98.56 79	99.35 2

Gipuma		98.43 21	98.15 34	98.76 14	99.00 61	98.29 79	97.91 108	98.06 70	99.02 3	99.50 1	96.33 134	98.67 79	99.22 1	99.02 27	98.02 76	98.88 63	97.66 68
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 89	97.78 14	94.19 177	98.77 70	99.39 15	98.61 11	99.33 13	99.07 14	99.33 23	97.81 59

ACMMPR		98.31 23	98.07 39	98.60 23	99.58 6	98.83 33	99.09 27	98.48 31	96.25 41	97.03 73	96.81 122	99.09 37	98.39 20	98.55 55	98.45 49	99.01 45	98.53 30

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 51	95.13 149	98.79 67	99.25 28	97.92 38	98.80 35	98.71 36	98.85 66	98.54 28
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS		98.27 25	98.61 19	97.87 64	99.17 40	99.03 20	99.07 29	98.17 63	96.75 29	94.35 172	98.92 55	99.58 7	97.86 41	98.67 46	98.70 37	98.63 73	98.63 22
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	98.22 112	98.73 51	98.66 67	98.03 75	98.60 7	96.40 102	99.60 12	98.24 104	95.26 125	99.19 21	99.05 17	99.36 20	97.64 69

DU-MVS		98.23 26	97.74 55	98.81 12	99.23 34	98.77 42	98.76 58	98.88 15	94.10 122	98.50 20	98.87 61	98.32 101	97.99 35	98.40 63	98.08 73	99.49 16	97.64 69

UniMVSNet (Re)		98.23 26	97.85 48	98.67 19	99.15 43	98.87 28	98.74 61	98.84 17	94.27 120	97.94 37	99.01 48	98.39 97	97.82 42	98.35 68	98.29 62	99.51 15	97.78 61

MIMVSNet1		98.22 29	98.51 23	97.87 64	99.40 25	98.82 37	99.31 16	98.53 28	97.39 19	96.59 93	99.31 34	99.23 30	94.76 137	98.93 32	98.67 39	98.63 73	97.25 92

HFP-MVS		98.17 30	98.02 40	98.35 35	99.36 27	98.62 59	98.79 57	98.46 34	96.24 42	96.53 95	97.13 118	98.98 49	98.02 33	98.20 71	98.42 51	98.95 54	98.54 28

Baseline_NR-MVSNet		98.17 30	97.90 45	98.48 29	99.23 34	98.59 60	98.83 54	98.73 24	93.97 127	96.95 76	99.66 7	98.23 106	97.90 39	98.40 63	99.06 16	99.25 29	97.42 84

TSAR-MVS + MP.		98.15 32	98.23 30	98.06 51	98.47 92	98.16 87	99.23 20	96.87 142	95.58 68	96.72 86	98.41 85	99.06 41	98.05 32	98.99 29	98.90 26	99.00 46	98.51 31
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 85	97.93 136	98.49 69	98.14 96	98.19 59	97.95 12	96.17 113	99.63 10	98.85 62	95.41 123	98.91 33	98.89 27	99.34 22	97.86 58

SMA-MVS		98.13 34	98.22 31	98.02 56	99.44 23	98.73 51	98.24 90	97.87 88	95.22 81	96.76 85	98.66 76	99.35 17	97.03 70	98.53 58	98.39 53	98.80 68	98.69 18
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 38	98.18 39	99.34 28	98.74 50	98.97 38	98.00 77	95.13 85	96.90 77	97.54 108	99.27 25	97.18 64	98.72 42	98.45 49	98.68 72	98.69 18

UniMVSNet_NR-MVSNet		98.12 35	97.56 64	98.78 13	99.13 47	98.89 27	98.76 58	98.78 20	93.81 130	98.50 20	98.81 65	97.64 127	97.99 35	98.18 74	97.92 79	99.53 10	97.64 69

ACMM	94.29 11	98.12 35	97.71 56	98.59 24	99.51 16	98.58 62	99.24 19	98.25 52	96.22 43	96.90 77	95.01 158	98.89 59	98.52 16	98.66 48	98.32 60	99.13 36	98.28 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SteuartSystems-ACMMP		98.06 38	97.78 53	98.39 33	99.54 10	98.79 39	98.94 42	98.42 36	93.98 126	95.85 124	96.66 128	99.25 28	98.61 11	98.71 44	98.38 54	98.97 50	98.67 21
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		98.05 39	98.46 25	97.57 81	99.01 58	98.99 23	98.82 56	98.24 53	95.76 60	94.70 162	98.96 50	99.49 10	96.19 102	98.74 38	98.65 41	98.46 87	98.63 22

OPM-MVS		98.01 40	98.01 41	98.00 58	99.11 49	98.12 90	98.68 65	97.72 95	96.65 32	96.68 90	98.40 86	99.28 24	97.44 55	98.20 71	97.82 86	98.40 94	97.58 74
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 84	96.89 188	98.82 37	99.14 24	97.59 100	96.30 40	97.04 72	99.26 39	98.83 66	96.01 108	98.73 40	98.21 64	98.58 78	98.75 15
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		98.00 42	97.38 70	98.73 15	98.72 77	99.15 11	99.12 26	98.76 21	91.58 160	98.15 31	96.70 126	98.72 78	98.20 24	98.64 51	98.92 24	99.43 19	97.97 52

NR-MVSNet		98.00 42	97.88 46	98.13 41	98.33 100	98.77 42	98.83 54	98.88 15	94.10 122	97.46 54	98.87 61	98.58 88	95.78 111	99.13 25	98.16 68	99.52 12	97.53 77

CP-MVS		98.00 42	97.57 63	98.50 26	99.47 20	98.56 64	98.91 44	98.38 42	94.71 100	97.01 74	95.20 154	99.06 41	98.20 24	98.61 52	98.46 46	99.02 43	98.40 35

DPE-MVS		97.99 45	98.12 35	97.84 67	98.65 86	98.86 29	98.86 50	98.05 73	94.18 121	95.49 141	98.90 57	99.33 18	97.11 66	98.53 58	98.65 41	98.86 65	98.39 37
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 62	98.45 31	99.53 14	98.83 33	99.13 25	98.30 47	94.57 107	96.39 106	95.32 152	98.95 54	98.37 21	98.61 52	98.47 45	99.00 46	98.45 32
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 65	98.50 26	99.56 8	98.58 62	98.97 38	98.39 41	93.49 134	97.14 66	96.08 141	99.23 30	98.06 31	98.50 60	98.38 54	98.90 58	98.44 33
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 43	98.04 53	98.84 70	98.04 99	97.90 109	96.83 145	95.07 87	98.79 15	99.07 46	99.37 16	97.88 40	98.74 38	98.16 68	98.01 115	96.96 100

ACMP	94.03 12	97.97 49	97.61 61	98.39 33	99.43 24	98.51 68	98.97 38	98.06 70	94.63 105	96.10 115	96.12 140	99.20 33	98.63 9	98.68 45	98.20 67	99.14 33	97.93 55
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SPE-MVS-test		97.96 50	97.38 70	98.64 21	98.57 88	99.13 12	99.36 13	98.66 25	91.67 159	98.17 30	96.91 121	98.84 64	97.99 35	98.80 35	98.88 28	99.08 41	97.43 83

LGP-MVS_train		97.96 50	97.53 65	98.45 31	99.45 21	98.64 57	99.09 27	98.27 50	92.99 146	96.04 118	96.57 129	99.29 21	98.66 8	98.73 40	98.42 51	99.19 31	98.09 46

LS3D		97.93 52	97.80 50	98.08 47	99.20 37	98.77 42	98.89 46	97.92 83	96.59 33	96.99 75	96.71 125	97.14 139	96.39 95	99.04 26	98.96 22	99.10 40	97.39 85

SD-MVS		97.84 53	97.78 53	97.90 61	98.33 100	98.06 95	97.95 105	97.80 93	96.03 49	96.72 86	97.57 106	99.18 34	97.50 53	97.88 78	97.08 105	99.11 38	98.68 20
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
RPSCF		97.83 54	98.27 28	97.31 98	98.23 110	98.06 95	97.44 137	95.79 176	96.90 27	95.81 126	98.76 71	98.61 87	97.70 47	98.90 34	98.36 56	98.90 58	98.29 38

thisisatest0515		97.82 55	97.67 57	97.99 59	98.49 91	98.07 94	98.48 78	98.06 70	95.35 79	97.74 42	98.83 64	97.61 128	96.74 77	97.53 98	98.30 61	98.43 93	98.01 51

PGM-MVS		97.82 55	97.25 77	98.48 29	99.54 10	98.75 49	99.02 31	98.35 45	92.41 150	96.84 83	95.39 151	98.99 48	98.24 23	98.43 62	98.34 57	98.90 58	98.41 34

PMVS	90.51 17	97.77 57	97.98 42	97.53 85	98.68 83	98.14 89	97.67 121	97.03 137	96.43 34	98.38 23	98.72 73	97.03 141	94.44 142	99.37 12	99.30 10	98.98 49	96.86 107
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSP-MVS		97.67 58	97.88 46	97.43 92	99.34 28	98.99 23	98.87 49	98.12 66	95.63 64	94.16 179	97.45 109	99.50 9	96.44 94	96.35 137	98.70 37	97.65 134	98.57 26
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 51	97.52 87	98.32 102	98.53 66	98.45 81	97.69 96	97.59 17	96.12 114	97.79 100	96.70 146	95.69 115	98.35 68	98.34 57	98.85 66	97.22 95

FC-MVSNet-train		97.65 60	98.16 33	97.05 111	98.85 68	98.85 30	99.34 14	98.08 69	94.50 112	94.41 169	99.21 40	98.80 70	92.66 171	98.98 30	98.85 31	98.96 52	97.94 54

v10		97.64 61	97.26 75	98.08 47	98.07 124	98.56 64	98.86 50	98.18 61	94.48 113	98.24 28	99.56 15	98.98 49	97.72 46	96.05 148	96.26 133	97.42 143	96.93 101

EC-MVSNet		97.63 62	96.88 101	98.50 26	98.74 76	99.16 10	99.33 15	98.83 18	88.77 189	96.62 92	96.48 131	97.75 120	98.19 26	99.00 28	98.76 34	99.29 27	98.27 42

X-MVS		97.60 63	97.00 95	98.29 36	99.50 17	98.76 45	98.90 45	98.37 43	94.67 104	96.40 102	91.47 205	98.78 72	97.60 52	98.55 55	98.50 44	98.96 52	98.29 38

3Dnovator+	96.20 4	97.58 64	97.14 86	98.10 42	98.98 63	97.85 112	98.60 70	98.33 46	96.41 36	97.23 64	94.66 167	97.26 136	96.91 74	97.91 77	97.87 82	98.53 82	98.03 49

DCV-MVSNet		97.56 65	97.63 60	97.47 90	98.41 96	99.12 13	98.63 68	98.57 26	95.71 63	95.60 138	93.79 184	98.01 115	94.25 144	99.16 23	98.88 28	99.35 21	98.74 16

HPM-MVS++		97.56 65	97.11 90	98.09 43	99.18 39	97.95 106	98.57 71	98.20 57	94.08 124	97.25 63	95.96 145	98.81 69	97.13 65	97.51 99	97.30 102	98.21 104	98.15 45

FC-MVSNet-test		97.54 67	98.26 29	96.70 129	98.87 66	97.79 119	98.49 77	98.56 27	96.04 45	90.39 211	99.65 8	98.67 79	95.15 127	99.23 19	99.07 14	98.73 71	97.39 85

TSAR-MVS + ACMM		97.54 67	97.79 51	97.26 99	98.23 110	98.10 93	97.71 118	97.88 87	95.97 50	95.57 140	98.71 74	98.57 89	97.36 58	97.74 87	96.81 114	96.83 171	98.59 25

DeepC-MVS_fast	95.38 6	97.53 69	97.30 74	97.79 72	98.83 71	97.64 122	98.18 91	97.14 133	95.57 69	97.83 39	97.10 119	98.80 70	96.53 91	97.41 102	97.32 100	98.24 103	97.26 91
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 94	98.09 43	98.31 105	98.01 101	98.96 41	97.25 128	95.22 81	98.89 12	99.64 9	98.83 66	97.68 48	95.63 155	95.91 144	97.47 139	95.97 139

v1144		97.51 71	97.05 93	98.04 53	98.26 108	97.98 103	98.88 48	97.42 118	95.38 78	98.56 18	99.59 14	99.01 47	97.65 49	95.77 152	96.06 141	97.47 139	95.56 153

v8		97.51 71	97.16 84	97.91 60	97.99 132	98.48 70	98.76 58	98.17 63	94.54 111	97.69 44	99.48 20	98.76 75	97.63 51	96.10 147	96.14 135	97.20 154	96.64 115

v1921920		97.50 73	97.00 95	98.07 49	98.20 114	97.94 109	99.03 30	97.06 135	95.29 80	99.01 9	99.62 11	98.73 77	97.74 45	95.52 159	95.78 150	97.39 145	96.12 134

Anonymous20231211		97.49 74	97.91 44	97.00 114	98.31 105	98.72 53	98.27 88	97.84 91	94.76 99	94.77 161	98.14 93	98.38 99	93.60 156	98.96 31	98.66 40	99.22 30	97.77 63

v144192		97.49 74	96.99 97	98.07 49	98.11 123	97.95 106	99.02 31	97.21 129	94.90 95	98.88 13	99.53 17	98.89 59	97.75 44	95.59 157	95.90 145	97.43 142	96.16 132

test1111		97.48 76	97.20 80	97.81 71	98.78 74	98.85 30	98.68 65	98.40 37	96.68 30	94.84 158	99.13 44	90.32 197	97.01 71	99.27 17	99.05 17	99.19 31	97.10 97

GeoE		97.48 76	96.84 106	98.22 38	99.01 58	98.39 73	98.85 53	98.76 21	92.37 151	97.53 49	97.58 105	98.23 106	97.11 66	97.57 97	96.98 108	98.10 111	96.78 110

APD-MVS		97.47 78	97.16 84	97.84 67	99.32 31	98.39 73	98.47 80	98.21 56	92.08 155	95.23 145	96.68 127	98.90 57	96.99 72	98.20 71	98.21 64	98.80 68	97.67 67
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended_VisFu		97.44 79	97.14 86	97.79 72	99.15 43	98.44 71	98.32 86	97.66 98	93.74 133	97.73 43	98.79 67	96.93 144	95.64 120	97.69 89	96.91 111	98.25 102	97.50 79

PHI-MVS		97.44 79	97.17 83	97.74 75	98.14 120	98.41 72	98.03 101	97.50 108	92.07 156	98.01 35	97.33 113	98.62 86	96.02 107	98.34 70	98.21 64	98.76 70	97.24 94

v1240		97.43 81	96.87 105	98.09 43	98.25 109	97.92 110	99.02 31	97.06 135	94.77 98	99.09 8	99.68 6	98.51 92	97.78 43	95.25 164	95.81 148	97.32 150	96.13 133

viewmacassd2359aftdt		97.42 82	97.67 57	97.13 106	98.20 114	98.06 95	98.16 94	97.16 132	97.27 21	95.23 145	99.29 35	99.48 11	96.05 106	96.73 127	96.66 120	98.00 116	96.17 131

ECVR-MVS		97.40 83	97.11 90	97.73 76	98.66 84	98.83 33	98.50 75	98.40 37	96.04 45	95.00 156	98.95 52	91.07 194	96.70 79	99.28 14	99.04 19	99.14 33	96.58 117

FMVSNet1		97.40 83	98.09 36	96.60 134	97.80 150	98.76 45	98.26 89	98.50 30	96.79 28	93.13 196	99.28 36	98.64 83	92.90 168	97.67 91	97.86 83	99.02 43	97.64 69

MVS_0304		97.38 85	97.26 75	97.51 88	99.28 33	98.79 39	98.86 50	97.79 94	94.68 102	96.79 84	97.69 102	95.75 162	93.91 151	98.10 75	97.76 87	98.45 88	98.08 47

casdiffmvs_mvg		97.34 86	97.65 59	96.97 115	97.74 153	98.33 77	98.45 81	97.18 130	95.81 56	93.92 183	99.04 47	99.05 43	95.48 122	97.00 120	97.71 90	99.07 42	96.63 116
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v2v482		97.33 87	96.84 106	97.90 61	98.19 116	97.83 113	98.74 61	97.44 116	95.42 77	98.23 29	99.46 21	98.84 64	97.46 54	95.51 160	96.10 138	97.36 148	94.72 164

EPP-MVSNet		97.29 88	96.88 101	97.76 74	98.70 79	99.10 16	98.92 43	98.36 44	95.12 86	93.36 194	97.39 110	91.00 195	97.65 49	98.72 42	98.91 25	99.58 7	97.92 56

MVS_111021_HR		97.27 89	97.11 90	97.46 91	98.46 93	97.82 116	97.50 133	96.86 143	94.97 91	97.13 68	96.99 120	98.39 97	96.82 76	97.65 94	97.38 95	98.02 114	96.56 120

SF-MVS		97.26 90	97.43 68	97.05 111	98.80 73	97.83 113	96.02 189	97.44 116	94.98 90	95.74 130	97.16 116	98.45 96	95.72 113	97.85 79	97.97 78	98.60 76	97.78 61

TSAR-MVS + GP.		97.26 90	97.33 73	97.18 104	98.21 113	98.06 95	96.38 180	97.66 98	93.92 129	95.23 145	98.48 82	98.33 100	97.41 56	97.63 95	97.35 96	98.18 106	97.57 75

OMC-MVS		97.23 92	97.21 79	97.25 102	97.85 141	97.52 131	97.92 107	95.77 177	95.83 55	97.09 71	97.86 98	98.52 91	96.62 83	97.51 99	96.65 121	98.26 100	96.57 118

3Dnovator	96.31 3	97.22 93	97.19 82	97.25 102	98.14 120	97.95 106	98.03 101	96.77 149	96.42 35	97.14 66	95.11 155	97.59 129	95.14 129	97.79 84	97.72 88	98.26 100	97.76 65

sasdasda		97.11 94	96.88 101	97.38 93	98.34 98	98.72 53	97.52 131	97.94 80	95.60 65	95.01 154	94.58 169	94.50 172	96.59 85	97.84 80	98.03 74	98.90 58	98.91 9

canonicalmvs		97.11 94	96.88 101	97.38 93	98.34 98	98.72 53	97.52 131	97.94 80	95.60 65	95.01 154	94.58 169	94.50 172	96.59 85	97.84 80	98.03 74	98.90 58	98.91 9

V42		97.10 96	96.97 98	97.26 99	97.64 157	97.60 124	98.45 81	95.99 165	94.44 114	97.35 58	99.40 26	98.63 85	97.34 60	96.33 140	96.38 130	96.82 173	96.00 136

CPTT-MVS		97.08 97	96.25 120	98.05 52	99.21 36	98.30 78	98.54 74	97.98 78	94.28 118	95.89 123	89.57 214	98.54 90	98.18 27	97.82 83	97.32 100	98.54 80	97.91 57

DeepPCF-MVS	94.55 10	97.05 98	97.13 89	96.95 117	96.06 203	97.12 149	98.01 103	95.44 185	95.18 83	97.50 51	97.86 98	98.08 111	97.31 62	97.23 108	97.00 107	97.36 148	97.45 81

QAPM		97.04 99	97.14 86	96.93 119	97.78 152	98.02 100	97.36 143	96.72 150	94.68 102	96.23 108	97.21 115	97.68 125	95.70 114	97.37 103	97.24 104	97.78 127	97.77 63

CNVR-MVS		97.03 100	96.77 111	97.34 95	98.89 65	97.67 121	97.64 124	97.17 131	94.40 116	95.70 134	94.02 179	98.76 75	96.49 93	97.78 85	97.29 103	98.12 110	97.47 80

viewmanbaseed2359cas		97.01 101	97.20 80	96.79 126	98.06 125	97.90 111	97.80 115	96.78 148	96.34 39	94.82 159	98.80 66	99.15 36	95.50 121	96.14 144	96.07 140	97.79 125	96.00 136

casdiffmvs		97.00 102	97.36 72	96.59 135	97.65 156	97.98 103	98.06 98	96.81 146	95.78 58	92.77 204	99.40 26	99.26 27	95.65 119	96.70 129	96.39 129	98.59 77	95.99 138
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		96.99 103	96.70 113	97.34 95	97.89 139	97.23 141	98.33 85	96.96 138	95.57 69	97.12 69	98.99 49	99.40 14	97.23 63	96.22 143	95.45 155	96.50 179	94.02 176

viewmsd2359difaftdt		96.92 104	97.45 67	96.31 146	97.53 163	97.42 136	97.70 119	95.37 187	96.93 25	94.18 178	99.27 37	99.52 8	95.11 130	97.33 105	95.90 145	97.98 119	95.79 144

DELS-MVS		96.90 105	97.24 78	96.50 140	97.85 141	98.18 83	97.88 112	95.92 169	93.48 135	95.34 143	98.86 63	98.94 56	94.03 147	97.33 105	97.04 106	98.00 116	96.85 108
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		96.86 106	96.72 112	97.03 113	97.80 150	97.06 152	97.04 159	95.51 184	94.55 108	97.47 52	97.35 112	97.68 125	96.66 81	97.11 113	96.73 116	97.69 131	96.57 118

PM-MVS		96.85 107	96.62 115	97.11 107	97.13 183	96.51 167	98.29 87	94.65 203	94.84 96	98.12 32	98.59 78	97.20 137	97.41 56	96.24 142	96.41 128	97.09 159	96.56 120

pmmvs-eth3d		96.84 108	96.22 122	97.56 82	97.63 159	96.38 174	98.74 61	96.91 141	94.63 105	98.26 26	99.43 23	98.28 102	96.58 88	94.52 175	95.54 153	97.24 152	94.75 163

CANet		96.81 109	96.50 116	97.17 105	99.10 51	97.96 105	97.86 113	97.51 106	91.30 163	97.75 41	97.64 103	97.89 118	93.39 160	96.98 121	96.73 116	97.40 144	96.99 99

Fast-Effi-MVS+		96.80 110	95.92 133	97.84 67	98.57 88	97.46 134	98.06 98	98.24 53	89.64 184	97.57 48	96.45 132	97.35 134	96.73 78	97.22 109	96.64 122	97.86 123	96.65 114

MCST-MVS		96.79 111	96.08 126	97.62 79	98.78 74	97.52 131	98.01 103	97.32 126	93.20 138	95.84 125	93.97 181	98.12 109	97.34 60	96.34 138	95.88 147	98.45 88	97.51 78

UGNet		96.79 111	97.82 49	95.58 167	97.57 162	98.39 73	98.48 78	97.84 91	95.85 54	94.68 163	97.91 97	99.07 40	87.12 211	97.71 88	97.51 92	97.80 124	98.29 38
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	96.79 111	96.70 113	96.90 121	97.64 157	97.58 125	97.54 130	94.50 205	95.14 84	96.64 91	96.76 124	97.90 117	96.63 82	95.98 149	96.14 135	98.45 88	97.39 85
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		96.73 114	96.92 99	96.51 139	98.70 79	97.57 127	97.64 124	92.07 212	93.10 144	96.31 107	98.29 88	99.02 46	95.99 109	97.20 110	96.47 126	98.37 96	96.81 109
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MGCFI-Net		96.69 115	96.89 100	96.44 142	98.30 107	98.63 58	97.39 140	97.90 84	95.72 62	91.16 209	94.65 168	94.55 170	95.04 131	97.78 85	98.00 77	98.87 64	98.93 8

train_agg		96.68 116	95.93 132	97.56 82	99.08 53	97.16 145	98.44 84	97.37 122	91.12 167	95.18 148	95.43 150	98.48 94	97.36 58	96.48 134	95.52 154	97.95 121	97.34 89

CDPH-MVS		96.68 116	95.99 129	97.48 89	99.13 47	97.64 122	98.08 97	97.46 112	90.56 173	95.13 149	94.87 163	98.27 103	96.56 89	97.09 114	96.45 127	98.54 80	97.08 98

MSLP-MVS++		96.66 118	96.46 119	96.89 122	98.02 127	97.71 120	95.57 196	96.96 138	94.36 117	96.19 112	91.37 206	98.24 104	97.07 68	97.69 89	97.89 80	97.52 137	97.95 53

TinyColmap		96.64 119	96.07 127	97.32 97	97.84 146	96.40 171	97.63 126	96.25 160	95.86 53	98.98 10	97.94 96	96.34 153	96.17 103	97.30 107	95.38 158	97.04 162	93.24 183

IS_MVSNet		96.62 120	96.48 118	96.78 127	98.46 93	98.68 56	98.61 69	98.24 53	92.23 152	89.63 216	95.90 146	94.40 174	96.23 98	98.65 49	98.77 33	99.52 12	96.76 111

NCCC		96.56 121	95.68 136	97.59 80	99.04 57	97.54 130	97.67 121	97.56 104	94.84 96	96.10 115	87.91 217	98.09 110	96.98 73	97.20 110	96.80 115	98.21 104	97.38 88

WB-MVS		96.54 122	98.09 36	94.73 184	96.68 195	98.34 76	94.77 215	97.39 119	98.12 10	89.72 215	98.95 52	99.32 20	93.33 161	98.67 46	97.88 81	96.47 181	95.38 156

ETV-MVS		96.54 122	95.27 144	98.02 56	99.07 55	97.48 133	98.16 94	98.19 59	87.33 204	97.58 47	92.67 193	95.93 159	96.22 99	98.49 61	98.46 46	98.91 57	96.50 123

Effi-MVS+		96.46 124	95.28 143	97.85 66	98.64 87	97.16 145	97.15 156	98.75 23	90.27 177	98.03 34	93.93 182	96.21 154	96.55 90	96.34 138	96.69 119	97.97 120	96.33 126

IterMVS-LS		96.35 125	95.85 135	96.93 119	97.53 163	98.00 102	97.37 141	97.97 79	95.49 76	96.71 89	98.94 54	93.23 181	94.82 136	93.15 194	95.05 161	97.17 156	97.12 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs_AUTHOR		96.30 126	96.79 109	95.73 161	97.43 172	97.06 152	97.24 151	95.65 179	95.76 60	92.97 202	99.35 30	99.21 32	93.99 150	95.61 156	94.85 164	97.09 159	95.65 150

USDC		96.30 126	95.64 138	97.07 109	97.62 160	96.35 176	97.17 155	95.71 178	95.52 74	99.17 7	98.11 94	97.46 131	95.67 116	95.44 162	93.60 182	97.09 159	92.99 187

Vis-MVSNet (Re-imp)		96.29 128	96.50 116	96.05 150	97.96 135	97.83 113	97.30 146	97.86 89	93.14 140	88.90 219	96.80 123	95.28 164	95.15 127	98.37 67	98.25 63	99.12 37	95.84 141

MSDG		96.27 129	96.17 125	96.38 145	97.85 141	96.27 178	96.55 177	94.41 206	94.55 108	95.62 137	97.56 107	97.80 119	96.22 99	97.17 112	96.27 132	97.67 133	93.60 180

CNLPA		96.24 130	95.97 130	96.57 137	97.48 170	97.10 151	96.75 170	94.95 197	94.92 94	96.20 111	94.81 164	96.61 148	96.25 97	96.94 122	95.64 151	97.79 125	95.74 148

EIA-MVS		96.23 131	94.85 156	97.84 67	99.08 53	98.21 81	97.69 120	98.03 75	85.68 214	98.09 33	91.75 204	97.07 140	95.66 118	97.58 96	97.72 88	98.47 86	95.91 140

PLC	92.55 15	96.10 132	95.36 140	96.96 116	98.13 122	96.88 157	96.49 178	96.67 154	94.07 125	95.71 133	91.14 207	96.09 156	96.84 75	96.70 129	96.58 124	97.92 122	96.03 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test20.03		96.08 133	96.80 108	95.25 176	99.19 38	97.58 125	97.24 151	97.56 104	94.95 93	91.91 205	98.58 79	98.03 113	87.88 207	97.43 101	96.94 110	97.69 131	94.05 175

FA-MVS(training)		96.07 134	95.59 139	96.63 132	98.00 131	97.44 135	97.36 143	98.53 28	92.21 153	95.97 120	96.18 138	94.22 177	92.98 165	96.79 125	96.70 118	96.95 167	95.56 153

TSAR-MVS + COLMAP		96.05 135	95.94 131	96.18 149	97.46 171	96.41 170	97.26 150	95.83 173	94.69 101	95.30 144	98.31 87	96.52 149	94.71 138	95.48 161	94.87 163	96.54 178	95.33 158

EU-MVSNet		96.03 136	96.23 121	95.80 159	95.48 216	94.18 197	98.99 36	91.51 214	97.22 22	97.66 45	99.15 43	98.51 92	98.08 30	95.92 150	92.88 190	93.09 203	95.72 149

PCF-MVS	92.69 14	95.98 137	95.05 151	97.06 110	98.43 95	97.56 128	97.76 116	96.65 155	89.95 182	95.70 134	96.18 138	98.48 94	95.74 112	93.64 186	93.35 187	98.09 113	96.18 130
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		95.97 138	95.01 153	97.08 108	98.72 77	97.19 143	97.07 158	96.69 153	91.49 161	95.77 129	92.19 199	97.93 116	96.15 104	94.66 172	94.16 173	98.10 111	97.45 81

Effi-MVS+-dtu		95.94 139	95.08 150	96.94 118	98.54 90	97.38 137	96.66 174	97.89 86	88.68 190	95.92 121	92.90 192	97.28 135	94.18 146	96.68 131	96.13 137	98.45 88	96.51 122

diffmvs		95.86 140	96.21 123	95.44 170	97.25 180	96.85 160	96.99 162	95.23 191	94.96 92	92.82 203	98.89 58	98.85 62	93.52 158	94.21 181	94.25 172	96.84 170	95.49 155
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		95.85 141	94.65 159	97.26 99	98.70 79	97.20 142	97.33 145	97.30 127	91.28 165	95.90 122	88.16 216	96.17 155	96.60 84	97.34 104	96.82 113	97.71 128	95.60 152

viewmambaseed2359dif		95.80 142	95.87 134	95.73 161	97.17 182	96.55 165	97.15 156	95.60 181	93.77 131	93.06 199	98.63 77	98.66 82	94.03 147	94.76 170	93.36 186	97.37 147	95.34 157

FMVSNet2		95.77 143	96.20 124	95.27 174	96.77 191	98.18 83	97.28 147	97.90 84	93.12 141	91.37 207	98.25 90	96.05 157	90.04 191	94.96 169	95.94 143	98.28 97	96.90 102

OpenMVS	94.63 9	95.75 144	95.04 152	96.58 136	97.85 141	97.55 129	96.71 172	96.07 162	90.15 180	96.47 97	90.77 212	95.95 158	94.41 143	97.01 119	96.95 109	98.00 116	96.90 102

pmmvs5		95.70 145	95.22 145	96.26 147	96.55 198	97.24 140	97.50 133	94.99 196	90.95 169	96.87 79	98.47 83	97.40 132	94.45 141	92.86 195	94.98 162	97.23 153	94.64 166

Anonymous20231206		95.69 146	95.68 136	95.70 163	98.32 102	96.95 155	97.37 141	96.65 155	93.33 136	93.61 188	98.70 75	98.03 113	91.04 180	95.07 167	94.59 171	97.20 154	93.09 186

MAR-MVS		95.51 147	94.49 163	96.71 128	97.92 137	96.40 171	96.72 171	98.04 74	86.74 208	96.72 86	92.52 196	95.14 166	94.02 149	96.81 124	96.54 125	96.85 168	97.25 92
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_pp		95.48 148	94.51 161	96.61 133	97.13 183	97.30 139	98.05 100	96.79 147	93.75 132	95.08 152	96.38 133	89.76 199	94.95 132	93.97 185	94.82 168	97.64 135	95.63 151

MDA-MVSNet-bldmvs		95.45 149	95.20 146	95.74 160	94.24 221	96.38 174	97.93 106	94.80 198	95.56 72	96.87 79	98.29 88	95.24 165	96.50 92	98.65 49	90.38 202	94.09 197	91.93 191

PVSNet_BlendedMVS		95.44 150	95.09 148	95.86 157	97.31 177	97.13 147	96.31 183	95.01 194	88.55 193	96.23 108	94.55 173	97.75 120	92.56 173	96.42 135	95.44 156	97.71 128	95.81 142

PVSNet_Blended		95.44 150	95.09 148	95.86 157	97.31 177	97.13 147	96.31 183	95.01 194	88.55 193	96.23 108	94.55 173	97.75 120	92.56 173	96.42 135	95.44 156	97.71 128	95.81 142

pmmvs4		95.37 152	94.25 164	96.67 131	97.01 186	95.28 191	97.60 127	96.07 162	93.11 142	97.29 61	98.09 95	94.23 176	95.21 126	91.56 206	93.91 179	96.82 173	93.59 181

MVS_Test		95.34 153	94.88 155	95.89 156	96.93 187	96.84 161	96.66 174	97.08 134	90.06 181	94.02 180	97.61 104	96.64 147	93.59 157	92.73 198	94.02 177	97.03 163	96.24 127

GBi-Net		95.21 154	95.35 141	95.04 179	96.77 191	98.18 83	97.28 147	97.58 101	88.43 195	90.28 212	96.01 142	92.43 185	90.04 191	97.67 91	97.86 83	98.28 97	96.90 102

test1		95.21 154	95.35 141	95.04 179	96.77 191	98.18 83	97.28 147	97.58 101	88.43 195	90.28 212	96.01 142	92.43 185	90.04 191	97.67 91	97.86 83	98.28 97	96.90 102

IterMVS-SCA-FT		95.16 156	93.95 168	96.56 138	97.89 139	96.69 163	96.94 164	96.05 164	93.06 145	97.35 58	98.79 67	91.45 190	95.93 110	92.78 196	91.00 200	95.22 193	93.91 178

HyFIR lowres test		95.05 157	93.54 173	96.81 125	97.81 149	96.88 157	98.18 91	97.46 112	94.28 118	94.98 157	96.57 129	92.89 184	96.15 104	90.90 211	91.87 196	96.28 185	91.35 192

CHOSEN 1792x2688		94.98 158	94.69 158	95.31 172	97.27 179	95.58 187	97.90 109	95.56 183	95.03 88	93.77 187	95.65 148	99.29 21	95.30 124	91.51 207	91.28 199	92.05 211	94.50 168

CANet_DTU		94.96 159	94.62 160	95.35 171	98.03 126	96.11 180	96.92 166	95.60 181	88.59 192	97.27 62	95.27 153	96.50 150	88.77 203	95.53 158	95.59 152	95.54 191	94.78 162

CDS-MVSNet		94.91 160	95.17 147	94.60 188	97.85 141	96.21 179	96.90 168	96.39 158	90.81 170	93.40 192	97.24 114	94.54 171	85.78 217	96.25 141	96.15 134	97.26 151	95.01 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DPM-MVS		94.86 161	93.90 170	95.99 152	98.19 116	96.52 166	96.29 185	95.95 166	93.11 142	94.61 165	88.17 215	96.44 151	93.77 155	93.33 189	93.54 184	97.11 158	96.22 128

MS-PatchMatch		94.84 162	94.76 157	94.94 182	96.38 199	94.69 196	95.90 191	94.03 208	92.49 149	93.81 185	95.79 147	96.38 152	94.54 139	94.70 171	94.85 164	94.97 195	94.43 170

thisisatest0530		94.81 163	93.06 179	96.85 124	98.01 128	97.18 144	96.93 165	97.36 123	89.73 183	95.80 127	94.98 159	77.88 220	94.89 133	96.73 127	97.35 96	98.13 109	97.54 76

tttt0517		94.81 163	93.04 180	96.88 123	98.15 119	97.37 138	96.99 162	97.36 123	89.51 185	95.74 130	94.89 161	77.53 222	94.89 133	96.94 122	97.35 96	98.17 107	97.70 66

testgi		94.81 163	96.05 128	93.35 199	99.06 56	96.87 159	97.57 129	96.70 152	95.77 59	88.60 221	93.19 190	98.87 61	81.21 225	97.03 118	96.64 122	96.97 166	93.99 177

PatchMatch-RL		94.79 166	93.75 172	96.00 151	96.80 190	95.00 193	95.47 201	95.25 190	90.68 172	95.80 127	92.97 191	93.64 179	95.67 116	96.13 146	95.81 148	96.99 165	92.01 190

FPMVS		94.70 167	94.99 154	94.37 190	95.84 209	93.20 202	96.00 190	91.93 213	95.03 88	94.64 164	94.68 165	93.29 180	90.95 181	98.07 76	97.34 99	96.85 168	93.29 182

new-patchmatchnet		94.48 168	94.02 166	95.02 181	97.51 168	95.00 193	95.68 195	94.26 207	97.32 20	95.73 132	99.60 12	98.22 108	91.30 176	94.13 182	84.41 212	95.65 190	89.45 203

IterMVS		94.48 168	93.46 175	95.66 164	97.52 165	96.43 168	97.20 153	94.73 201	92.91 148	96.44 98	98.75 72	91.10 192	94.53 140	92.10 202	90.10 204	93.51 200	92.84 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDTV_nov1_ep13_2view		94.39 170	93.34 176	95.63 165	97.23 181	95.33 190	97.76 116	96.84 144	94.55 108	97.47 52	98.96 50	97.70 123	93.88 152	92.27 200	86.81 210	90.56 213	87.73 211

Fast-Effi-MVS+-dtu		94.34 171	93.26 178	95.62 166	97.82 147	95.97 183	95.86 192	99.01 13	86.88 206	93.39 193	90.83 210	95.46 163	90.61 185	94.46 177	94.68 169	97.01 164	94.51 167

thres600view7		94.34 171	92.31 189	96.70 129	98.19 116	98.12 90	97.85 114	97.45 114	91.49 161	93.98 182	84.27 220	82.02 211	94.24 145	97.04 115	98.76 34	98.49 84	94.47 169

EPNet		94.33 173	93.52 174	95.27 174	98.81 72	94.71 195	96.77 169	98.20 57	88.12 198	96.53 95	92.53 195	91.19 191	85.25 221	95.22 165	95.26 159	96.09 188	97.63 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		94.29 174	91.43 197	97.64 78	98.66 84	98.83 33	98.50 75	98.40 37	96.04 45	94.45 168	94.88 162	55.05 236	96.70 79	99.28 14	99.04 19	99.14 33	96.87 106

GA-MVS		94.18 175	92.98 181	95.58 167	97.36 174	96.42 169	96.21 186	95.86 170	90.29 176	95.08 152	96.19 137	85.37 203	92.82 169	94.01 184	94.14 174	96.16 187	94.41 171

gg-mvs-nofinetune		94.13 176	93.93 169	94.37 190	97.99 132	95.86 184	95.45 204	99.22 9	97.61 16	95.10 151	99.50 19	84.50 204	81.73 224	95.31 163	94.12 175	96.71 176	90.59 196

baseline		94.07 177	94.50 162	93.57 197	96.34 200	93.40 201	95.56 199	92.39 211	92.07 156	94.00 181	98.24 91	97.51 130	89.19 197	91.75 204	92.72 191	93.96 199	95.79 144

FMVSNet3		94.06 178	93.85 171	94.31 193	95.46 217	97.80 118	96.34 181	97.58 101	88.43 195	90.28 212	96.01 142	92.43 185	88.67 204	91.82 203	93.96 178	97.53 136	96.50 123

thres400		94.04 179	91.94 192	96.50 140	97.98 134	97.82 116	97.66 123	96.96 138	90.96 168	94.20 175	83.24 222	82.82 209	93.80 153	96.50 133	98.09 70	98.38 95	94.15 173

dmvs_re		94.02 180	92.39 187	95.91 155	97.71 154	95.43 189	97.00 161	95.94 167	82.49 223	94.61 165	83.69 221	93.01 183	92.71 170	97.83 82	97.56 91	97.50 138	96.73 112

CVMVSNet		94.01 181	94.25 164	93.73 196	94.36 220	92.44 205	97.45 136	88.56 217	95.59 67	93.06 199	98.88 59	90.03 198	94.84 135	94.08 183	93.45 185	94.09 197	95.31 159

thres200		93.98 182	91.90 193	96.40 144	97.66 155	98.12 90	97.20 153	97.45 114	90.16 179	93.82 184	83.08 223	83.74 207	93.80 153	97.04 115	97.48 94	98.49 84	93.70 179

baseline1		93.89 183	92.82 183	95.14 178	97.62 160	96.97 154	96.12 187	96.36 159	91.30 163	91.53 206	94.68 165	80.72 213	90.80 183	95.71 153	96.29 131	98.44 92	94.09 174

tfpn200view9		93.80 184	91.75 194	96.20 148	97.52 165	98.15 88	97.48 135	97.47 111	87.65 200	93.56 190	83.03 224	84.12 205	92.62 172	97.04 115	98.09 70	98.52 83	94.17 172

MIMVSNet		93.68 185	93.96 167	93.35 199	97.82 147	96.08 181	96.34 181	98.46 34	91.28 165	86.67 226	94.95 160	94.87 168	84.39 222	94.53 173	94.65 170	96.45 182	91.34 193

pmnet_mix02		93.59 186	92.65 184	94.69 186	96.76 194	94.16 198	97.03 160	93.00 210	95.79 57	96.03 119	98.91 56	97.69 124	92.99 164	90.03 214	84.10 214	92.35 209	87.89 210

EPNet_dtu		93.45 187	92.51 186	94.55 189	98.39 97	91.67 214	95.46 202	97.50 108	86.56 209	97.38 56	93.52 185	94.20 178	85.82 216	93.31 191	92.53 192	92.72 205	95.76 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	92.44 16	93.33 188	92.15 191	94.70 185	97.42 173	96.39 173	95.57 196	94.67 202	86.40 212	93.59 189	78.28 228	95.76 161	89.59 196	95.88 151	95.98 142	97.39 145	96.34 125
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		93.18 189	90.80 200	95.95 153	96.05 204	96.07 182	96.92 166	96.51 157	89.34 186	95.63 136	94.08 178	72.31 231	93.13 162	94.33 179	94.83 166	97.44 141	94.65 165

thres100view900		92.93 190	90.89 199	95.31 172	97.52 165	96.82 162	96.41 179	95.08 192	87.65 200	93.56 190	83.03 224	84.12 205	91.12 179	94.53 173	96.91 111	98.17 107	93.21 184

N_pmnet		92.46 191	92.38 188	92.55 205	97.91 138	93.47 200	97.42 138	94.01 209	96.40 37	88.48 222	98.50 81	98.07 112	88.14 206	91.04 210	84.30 213	89.35 218	84.85 217

TAMVS		92.46 191	93.34 176	91.44 213	97.03 185	93.84 199	94.68 216	90.60 215	90.44 175	85.31 227	97.14 117	93.03 182	85.78 217	94.34 178	93.67 181	95.22 193	90.93 195

CMPMVS	71.81 19	92.34 193	92.85 182	91.75 211	92.70 225	90.43 219	88.84 228	88.56 217	85.87 213	94.35 172	90.98 208	95.89 160	91.14 178	96.14 144	94.83 166	94.93 196	95.78 146
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline2		92.06 194	89.82 203	94.68 187	97.32 175	95.72 185	94.97 212	95.08 192	84.75 217	94.34 174	90.68 213	77.75 221	90.13 190	93.38 187	93.58 183	96.25 186	92.90 188

MVSTER		91.97 195	90.31 201	93.91 194	96.81 189	96.91 156	94.22 217	95.64 180	84.98 215	92.98 201	93.42 186	72.56 229	86.64 215	95.11 166	93.89 180	97.16 157	95.31 159

CR-MVSNet		91.94 196	88.50 206	95.94 154	96.14 202	92.08 209	95.23 207	98.47 32	84.30 219	96.44 98	94.58 169	75.57 223	92.92 166	90.22 212	92.22 193	96.43 183	90.56 197

gm-plane-assit		91.85 197	87.91 208	96.44 142	99.14 45	98.25 80	99.02 31	97.38 121	95.57 69	98.31 25	99.34 32	51.00 237	88.93 200	93.16 193	91.57 197	95.85 189	86.50 214

PMMVS		91.67 198	91.47 196	91.91 210	89.43 230	88.61 225	94.99 211	85.67 222	87.50 202	93.80 186	94.42 176	94.88 167	90.71 184	92.26 201	92.96 189	96.83 171	89.65 201

CHOSEN 280x420		91.55 199	90.27 202	93.05 202	94.61 219	88.01 226	96.56 176	94.62 204	88.04 199	94.20 175	92.66 194	86.60 201	90.82 182	95.06 168	91.89 195	87.49 223	89.61 202

PatchT		91.40 200	88.54 205	94.74 183	91.48 229	92.18 208	97.42 138	97.51 106	84.96 216	96.44 98	94.16 177	75.47 224	92.92 166	90.22 212	92.22 193	92.66 208	90.56 197

pmmvs3		91.20 201	91.40 198	90.96 215	91.71 228	91.08 215	95.41 205	81.34 226	87.36 203	94.57 167	95.02 157	94.30 175	90.42 186	94.28 180	89.26 206	92.30 210	88.49 208

test0.0.03 1		91.17 202	91.50 195	90.80 216	98.01 128	95.46 188	94.22 217	95.80 174	86.55 210	81.75 229	90.83 210	87.93 200	78.48 226	94.51 176	94.11 176	96.50 179	91.08 194

SCA		91.15 203	87.65 210	95.23 177	96.15 201	95.68 186	96.68 173	98.18 61	90.46 174	97.21 65	92.44 197	80.17 215	93.51 159	86.04 221	83.58 217	89.68 217	85.21 216

new_pmnet		90.85 204	92.26 190	89.21 219	93.68 224	89.05 224	93.20 225	84.16 225	92.99 146	84.25 228	97.72 101	94.60 169	86.80 214	93.20 192	91.30 198	93.21 201	86.94 213

RPMNet		90.52 205	86.27 219	95.48 169	95.95 207	92.08 209	95.55 200	98.12 66	84.30 219	95.60 138	87.49 218	72.78 228	91.24 177	87.93 216	89.34 205	96.41 184	89.98 200

MDTV_nov1_ep13		90.30 206	87.32 214	93.78 195	96.00 206	92.97 203	95.46 202	95.39 186	88.61 191	95.41 142	94.45 175	80.39 214	89.87 194	86.58 219	83.54 218	90.56 213	84.71 218

PatchmatchNet		89.98 207	86.23 220	94.36 192	96.56 197	91.90 213	96.07 188	96.72 150	90.18 178	96.87 79	93.36 189	78.06 219	91.46 175	84.71 225	81.40 222	88.45 220	83.97 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		89.89 208	87.70 209	92.43 207	95.52 214	90.91 217	95.57 196	95.33 188	93.19 139	91.21 208	93.41 187	82.12 210	89.05 198	86.21 220	83.77 216	87.92 221	84.31 219

tpm		89.84 209	86.81 216	93.36 198	96.60 196	91.92 212	95.02 210	97.39 119	86.79 207	96.54 94	95.03 156	69.70 232	87.66 208	88.79 215	86.19 211	86.95 225	89.27 204

test-LLR		89.77 210	87.47 212	92.45 206	98.01 128	89.77 221	93.25 223	95.80 174	81.56 225	89.19 217	92.08 200	79.59 216	85.77 219	91.47 208	89.04 208	92.69 206	88.75 205

FMVSNet5		89.65 211	87.60 211	92.04 209	95.63 213	96.61 164	94.82 214	94.75 199	80.11 229	87.72 224	77.73 229	73.81 227	83.81 223	95.64 154	96.08 139	95.49 192	93.21 184

EPMVS		89.28 212	86.28 218	92.79 204	96.01 205	92.00 211	95.83 193	95.85 172	90.78 171	91.00 210	94.58 169	74.65 225	88.93 200	85.00 223	82.88 220	89.09 219	84.09 221

test-mter		89.16 213	88.14 207	90.37 217	94.79 218	91.05 216	93.60 222	85.26 223	81.65 224	88.32 223	92.22 198	79.35 218	87.03 212	92.28 199	90.12 203	93.19 202	90.29 199

CostFormer		89.06 214	85.65 221	93.03 203	95.88 208	92.40 206	95.30 206	95.86 170	86.49 211	93.12 198	93.40 188	74.18 226	88.25 205	82.99 226	81.46 221	89.77 216	88.66 207

MVS-HIRNet		88.72 215	86.49 217	91.33 214	91.81 227	85.66 227	87.02 230	96.25 160	81.48 227	94.82 159	96.31 136	92.14 188	90.32 188	87.60 217	83.82 215	87.74 222	78.42 226

TESTMET0.1,1		88.60 216	87.47 212	89.93 218	94.23 222	89.77 221	93.25 223	84.47 224	81.56 225	89.19 217	92.08 200	79.59 216	85.77 219	91.47 208	89.04 208	92.69 206	88.75 205

dps		88.36 217	84.32 224	93.07 201	93.86 223	92.29 207	94.89 213	95.93 168	83.50 221	93.13 196	91.87 202	67.79 234	90.32 188	85.99 222	83.22 219	90.28 215	85.56 215

tpmrst		87.60 218	84.13 225	91.66 212	95.65 212	89.73 223	93.77 220	94.74 200	88.85 188	93.35 195	95.60 149	72.37 230	87.40 209	81.24 227	78.19 224	85.02 228	82.90 225

tpm cat1		87.19 219	82.78 226	92.33 208	95.66 211	90.61 218	94.19 219	95.27 189	86.97 205	94.38 170	90.91 209	69.40 233	87.21 210	79.57 229	77.82 225	87.25 224	84.18 220

E-PMN		86.94 220	85.10 222	89.09 221	95.77 210	83.54 230	89.89 227	86.55 219	92.18 154	87.34 225	94.02 179	83.42 208	89.63 195	93.32 190	77.11 226	85.33 226	72.09 227

EMVS		86.63 221	84.48 223	89.15 220	95.51 215	83.66 229	90.19 226	86.14 221	91.78 158	88.68 220	93.83 183	81.97 212	89.05 198	92.76 197	76.09 227	85.31 227	71.28 228

PMMVS2		86.47 222	92.62 185	79.29 223	92.01 226	85.63 228	93.74 221	86.37 220	93.95 128	54.18 234	98.19 92	97.39 133	58.46 227	96.57 132	93.07 188	90.99 212	83.55 224

MVE	72.99 18	85.37 223	89.43 204	80.63 222	74.43 231	71.94 232	88.25 229	89.81 216	93.27 137	67.32 232	96.32 135	91.83 189	90.40 187	93.36 188	90.79 201	73.55 231	88.49 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		61.30 224	70.45 227	50.62 224	22.69 233	30.92 234	68.31 233	25.76 228	80.56 228	68.71 230	82.80 226	91.08 193	44.64 228	80.50 228	56.70 228	73.64 230	70.58 229

GG-mvs-BLEND		61.03 225	87.02 215	30.71 226	0.74 236	90.01 220	78.90 232	0.74 232	84.56 218	9.46 235	79.17 227	90.69 196	1.37 232	91.74 205	89.13 207	93.04 204	83.83 223

testmvs		4.99 226	6.88 228	2.78 228	1.73 234	2.04 236	3.10 236	1.71 230	7.27 231	3.92 237	12.18 231	6.71 238	3.31 231	6.94 230	5.51 230	2.94 233	7.51 230

test123		4.41 227	5.71 229	2.88 227	1.28 235	2.21 235	3.09 237	1.65 231	6.35 232	4.98 236	8.53 232	3.88 239	3.46 230	5.79 231	5.71 229	2.85 234	7.50 231

uanet_test		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet-low-res		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

TPM-MVS					97.49 169	96.32 177	95.05 209			94.36 171	91.82 203	96.92 145	88.89 202			96.67 177	96.22 128
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										99.38 2

9.14												96.98 143

SR-MVS					99.33 30			98.40 37				98.90 57

Anonymous202405211			97.39 69		98.85 68	98.59 60	97.89 111	97.93 82	94.41 115		97.37 111	96.99 142	93.09 163	98.61 52	98.46 46	99.11 38	97.27 90

our_test_3					97.32 175	95.13 192	97.59 128

ambc			96.78 110		99.01 58	97.11 150	95.73 194		95.91 52	99.25 3	98.56 80	97.17 138	97.04 69	96.76 126	95.22 160	96.72 175	96.73 112

MTAPA										97.43 55		99.27 25

MTMP										97.63 46		99.03 45

Patchmatch-RL test							17.42 235

tmp_tt				45.72 225	60.00 232	38.74 233	45.50 234	12.18 229	79.58 230	68.42 231	67.62 230	65.04 235	22.12 229	84.83 224	78.72 223	66.08 232

XVS					99.48 18	98.76 45	99.22 21			96.40 102		98.78 72				98.94 55

X-MVStestdata					99.48 18	98.76 45	99.22 21			96.40 102		98.78 72				98.94 55

mPP-MVS					99.58 6							98.98 49

NP-MVS									89.27 187

Patchmtry						92.70 204	95.23 207	98.47 32		96.44 98

DeepMVS_CX						72.99 231	80.14 231	37.34 227	83.46 222	60.13 233	84.40 219	85.48 202	86.93 213	87.22 218		79.61 229	87.32 212

High-resolution multi-view benchmark