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	95.06 1	97.73 1	98.39 1	96.95 1	96.33 51	96.94 35	98.30 20	94.90 15	98.61 1	97.73 3	97.97 25	98.57 23	95.74 4	99.24 1	98.70 4	98.72 7	98.70 2
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
TDRefinement		97.59 2	98.32 2	96.73 4	95.90 66	98.10 2	99.08 2	93.92 31	98.24 3	96.44 13	98.12 20	97.86 53	96.06 2	99.24 1	98.93 1	99.00 2	97.77 5

WR-MVS		97.53 3	98.20 3	96.76 3	96.93 29	98.17 1	98.60 10	96.67 7	96.39 15	94.46 32	99.14 1	98.92 10	94.57 15	99.06 3	98.80 2	99.32 1	96.92 28

SixPastTwentyTwo		97.36 4	97.73 10	96.92 2	97.36 13	96.15 56	98.29 21	94.43 23	96.50 13	96.96 7	98.74 5	98.74 18	96.04 3	99.03 5	97.74 16	98.44 23	97.22 14

PS-CasMVS		97.22 5	97.84 7	96.50 5	97.08 25	97.92 6	98.17 30	97.02 2	94.71 27	95.32 21	98.52 12	98.97 9	92.91 42	99.04 4	98.47 5	98.49 19	97.24 13

PEN-MVS		97.16 6	97.87 6	96.33 11	97.20 21	97.97 4	98.25 25	96.86 6	95.09 25	94.93 26	98.66 7	99.16 5	92.27 52	98.98 6	98.39 7	98.49 19	96.83 32

DTE-MVSNet		97.16 6	97.75 9	96.47 6	97.40 12	97.95 5	98.20 28	96.89 5	95.30 20	95.15 24	98.66 7	98.80 15	92.77 46	98.97 7	98.27 9	98.44 23	96.28 42

COLMAP_ROB	93.74 2	97.09 8	97.98 4	96.05 17	95.97 63	97.78 9	98.56 11	91.72 86	97.53 7	96.01 15	98.14 19	98.76 17	95.28 5	98.76 11	98.23 10	98.77 5	96.67 36
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		97.06 9	97.78 8	96.23 13	96.74 37	98.04 3	98.25 25	97.32 1	94.40 33	93.71 52	98.55 10	98.89 11	92.97 39	98.91 9	98.45 6	98.38 28	97.19 15

CP-MVSNet		96.97 10	97.42 14	96.44 7	97.06 26	97.82 8	98.12 33	96.98 3	93.50 47	95.21 23	97.98 24	98.44 26	92.83 45	98.93 8	98.37 8	98.46 22	96.91 29

DVP-MVS++		96.63 11	97.92 5	95.12 40	97.77 6	97.52 15	98.29 21	93.83 34	96.72 9	92.52 75	98.10 21	99.07 8	90.87 78	97.83 31	97.44 28	97.44 61	98.76 1

ACMH	90.17 8	96.61 12	97.69 12	95.35 30	95.29 81	96.94 35	98.43 14	92.05 74	98.04 4	95.38 19	98.07 22	99.25 4	93.23 33	98.35 16	97.16 39	97.72 51	96.00 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		96.56 13	96.73 24	96.36 9	98.99 1	97.90 7	97.79 43	95.64 10	92.78 62	92.54 74	96.23 72	95.02 129	94.31 18	98.43 15	98.12 11	98.89 3	98.58 3

ACMMPR		96.54 14	96.71 25	96.35 10	97.55 9	97.63 11	98.62 9	94.54 19	94.45 30	94.19 39	95.04 96	97.35 67	94.92 10	97.85 28	97.50 25	98.26 29	97.17 16

v7n		96.49 15	97.20 18	95.65 22	95.57 76	96.04 58	97.93 38	92.49 58	96.40 14	97.13 6	98.99 2	99.41 3	93.79 25	97.84 30	96.15 65	97.00 83	95.60 55

DeepC-MVS	92.47 4	96.44 16	96.75 23	96.08 16	97.57 7	97.19 31	97.96 37	94.28 24	95.29 21	94.92 27	98.31 17	96.92 78	93.69 27	96.81 67	96.50 56	98.06 40	96.27 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM	90.06 9	96.31 17	96.42 32	96.19 14	97.21 20	97.16 33	98.71 5	93.79 37	94.35 34	93.81 46	92.80 131	98.23 34	95.11 6	98.07 20	97.45 27	98.51 18	96.86 31
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	89.90 10	96.27 18	97.52 13	94.81 47	95.19 84	97.18 32	97.97 36	92.52 56	96.72 9	90.50 122	97.31 46	99.11 6	94.10 19	98.67 12	97.90 14	98.56 15	95.79 51

APDe-MVS		96.23 19	97.22 17	95.08 41	96.66 41	97.56 14	98.63 8	93.69 41	94.62 28	89.80 130	97.73 33	98.13 38	93.84 24	97.79 33	97.63 18	97.87 47	97.08 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CP-MVS		96.21 20	96.16 43	96.27 12	97.56 8	97.13 34	98.43 14	94.70 18	92.62 65	94.13 41	92.71 132	98.03 44	94.54 16	98.00 24	97.60 20	98.23 31	97.05 24

HFP-MVS		96.18 21	96.53 29	95.77 20	97.34 16	97.26 28	98.16 31	94.54 19	94.45 30	92.52 75	95.05 94	96.95 77	93.89 22	97.28 49	97.46 26	98.19 33	97.25 11

UniMVSNet_ETH3D		96.15 22	97.71 11	94.33 55	97.31 17	96.71 40	95.06 107	96.91 4	97.86 5	90.42 123	98.55 10	99.60 1	88.01 118	98.51 13	97.81 15	98.26 29	94.95 67

MP-MVS		96.13 23	95.93 47	96.37 8	98.19 3	97.31 27	98.49 13	94.53 22	91.39 97	94.38 35	94.32 109	96.43 91	94.59 14	97.75 35	97.44 28	98.04 41	96.88 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		96.12 24	96.27 39	95.93 18	97.20 21	97.60 12	98.64 7	93.74 38	92.47 69	93.13 66	93.23 124	98.06 41	94.51 17	97.99 25	97.57 22	98.39 27	96.99 25
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
DVP-MVS		96.10 25	97.23 16	94.79 49	96.28 54	97.49 16	97.90 39	93.60 43	95.47 18	89.57 136	97.32 45	97.72 56	93.89 22	97.74 36	97.53 23	97.51 57	97.34 9
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
LGP-MVS_train		96.10 25	96.29 36	95.87 19	96.72 38	97.35 26	98.43 14	93.83 34	90.81 111	92.67 73	95.05 94	98.86 13	95.01 7	98.11 18	97.37 35	98.52 17	96.50 38

CSCG		96.07 27	97.15 19	94.81 47	96.06 61	97.58 13	96.52 73	90.98 97	96.51 12	93.60 54	97.13 53	98.55 24	93.01 37	97.17 53	95.36 81	98.68 9	97.78 4

DPE-MVS		96.00 28	96.80 22	95.06 42	95.87 69	97.47 21	98.25 25	93.73 39	92.38 71	91.57 103	97.55 40	97.97 46	92.98 38	97.49 47	97.61 19	97.96 45	97.16 17
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		95.99 29	96.48 30	95.41 29	97.43 11	97.36 24	97.55 48	93.70 40	94.05 41	93.79 47	97.02 56	94.53 134	92.28 51	97.53 45	97.19 37	97.73 50	97.67 7
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
TSAR-MVS + MP.		95.99 29	96.57 28	95.31 32	96.87 30	96.50 47	98.71 5	91.58 87	93.25 53	92.71 70	96.86 58	96.57 89	93.92 20	98.09 19	97.91 13	98.08 38	96.81 33
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		95.96 31	96.59 27	95.23 35	96.67 40	96.52 46	97.86 41	93.28 47	95.27 23	93.46 56	96.26 69	98.85 14	92.89 43	97.09 54	96.37 60	97.22 75	95.78 52
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SteuartSystems-ACMMP		95.96 31	96.13 44	95.76 21	97.06 26	97.36 24	98.40 18	94.24 26	91.49 91	91.91 93	94.50 105	96.89 79	94.99 8	98.01 23	97.44 28	97.97 44	97.25 11
Skip Steuart: Steuart Systems R&D Blog.
ACMP	89.62 11	95.96 31	96.28 37	95.59 23	96.58 43	97.23 30	98.26 24	93.22 48	92.33 74	92.31 83	94.29 110	98.73 19	94.68 12	98.04 21	97.14 40	98.47 21	96.17 45
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PGM-MVS		95.90 34	95.72 51	96.10 15	97.53 10	97.45 22	98.55 12	94.12 28	90.25 114	93.71 52	93.20 125	97.18 71	94.63 13	97.68 39	97.34 36	98.08 38	96.97 26

PMVS	87.16 16	95.88 35	96.47 31	95.19 37	97.00 28	96.02 59	96.70 64	91.57 88	94.43 32	95.33 20	97.16 52	95.37 117	92.39 48	98.89 10	98.72 3	98.17 35	94.71 73
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		95.86 36	96.18 40	95.47 28	97.11 24	97.26 28	98.37 19	93.48 45	93.49 48	93.99 44	95.61 80	94.11 138	92.49 47	97.87 27	97.44 28	97.40 64	97.52 8

Gipuma		95.86 36	96.17 41	95.50 27	95.92 65	94.59 106	94.77 116	92.50 57	97.82 6	97.90 2	95.56 83	97.88 51	94.71 11	98.02 22	94.81 96	97.23 74	94.48 80
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LS3D		95.83 38	96.35 34	95.22 36	96.47 47	97.49 16	97.99 34	92.35 61	94.92 26	94.58 30	94.88 100	95.11 127	91.52 63	98.48 14	98.05 12	98.42 25	95.49 56

SD-MVS		95.77 39	96.17 41	95.30 33	96.72 38	96.19 55	97.01 56	93.04 49	94.03 42	92.71 70	96.45 67	96.78 86	93.91 21	96.79 68	95.89 71	98.42 25	97.09 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
SED-MVS		95.73 40	96.98 20	94.28 56	96.08 59	97.39 23	98.18 29	93.80 36	94.20 36	89.61 135	97.29 47	97.49 64	90.69 82	97.74 36	97.41 32	97.32 69	97.34 9

TranMVSNet+NR-MVSNet		95.72 41	96.42 32	94.91 46	96.21 55	96.77 39	96.90 61	94.99 13	92.62 65	91.92 92	98.51 13	98.63 21	90.82 79	97.27 50	96.83 45	98.63 12	94.31 81

DU-MVS		95.51 42	95.68 52	95.33 31	96.45 48	96.44 49	96.61 70	95.32 11	89.97 119	93.78 48	97.46 42	98.07 40	91.19 70	97.03 57	96.53 53	98.61 13	94.22 82

UniMVSNet (Re)		95.46 43	95.86 49	95.00 45	96.09 57	96.60 41	96.68 68	94.99 13	90.36 113	92.13 86	97.64 37	98.13 38	91.38 64	96.90 62	96.74 47	98.73 6	94.63 76

RPSCF		95.46 43	96.95 21	93.73 79	95.72 73	95.94 64	95.58 98	88.08 146	95.31 19	91.34 106	96.26 69	98.04 43	93.63 28	98.28 17	97.67 17	98.01 42	97.13 18

anonymousdsp		95.45 45	96.70 26	93.99 67	88.43 204	92.05 153	99.18 1	85.42 181	94.29 35	96.10 14	98.63 9	99.08 7	96.11 1	97.77 34	97.41 32	98.70 8	97.69 6

APD-MVS		95.38 46	95.68 52	95.03 43	97.30 18	96.90 37	97.83 42	93.92 31	89.40 126	90.35 124	95.41 87	97.69 58	92.97 39	97.24 52	97.17 38	97.83 48	95.96 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet_NR-MVSNet		95.34 47	95.51 56	95.14 39	95.80 71	96.55 42	96.61 70	94.79 16	90.04 118	93.78 48	97.51 41	97.25 68	91.19 70	96.68 70	96.31 62	98.65 11	94.22 82

X-MVS		95.33 48	95.13 64	95.57 25	97.35 14	97.48 18	98.43 14	94.28 24	92.30 75	93.28 59	86.89 189	96.82 82	91.87 57	97.85 28	97.59 21	98.19 33	96.95 27

MSP-MVS		95.32 49	96.28 37	94.19 59	96.87 30	97.77 10	98.27 23	93.88 33	94.15 40	89.63 134	95.36 88	98.37 29	90.73 80	94.37 118	97.53 23	95.77 122	96.40 39
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
3Dnovator+	92.82 3	95.22 50	95.16 62	95.29 34	96.17 56	96.55 42	97.64 45	94.02 30	94.16 39	94.29 37	92.09 138	93.71 145	91.90 55	96.68 70	96.51 54	97.70 53	96.40 39

HPM-MVS++		95.21 51	94.89 67	95.59 23	97.79 5	95.39 82	97.68 44	94.05 29	91.91 83	94.35 36	93.38 122	95.07 128	92.94 41	96.01 82	95.88 72	96.73 86	96.61 37

TSAR-MVS + ACMM		95.17 52	95.95 45	94.26 57	96.07 60	96.46 48	95.67 96	94.21 27	93.84 44	90.99 114	97.18 50	95.24 125	93.55 29	96.60 73	95.61 79	95.06 140	96.69 35

CPTT-MVS		95.00 53	94.52 76	95.57 25	96.84 34	96.78 38	97.88 40	93.67 42	92.20 76	92.35 82	85.87 196	97.56 63	94.98 9	96.96 60	96.07 68	97.70 53	96.18 44

SF-MVS		94.88 54	95.87 48	93.73 79	95.30 79	95.93 65	94.80 115	91.76 84	93.11 57	91.93 91	95.83 77	97.07 74	91.11 73	96.62 72	96.44 58	97.46 58	96.13 46

Baseline_NR-MVSNet		94.85 55	95.35 60	94.26 57	96.45 48	93.86 122	96.70 64	94.54 19	90.07 117	90.17 128	98.77 4	97.89 48	90.64 85	97.03 57	96.16 64	97.04 82	93.67 95

EG-PatchMatch MVS		94.81 56	95.53 55	93.97 68	95.89 68	94.62 104	95.55 100	88.18 144	92.77 63	94.88 28	97.04 55	98.61 22	93.31 30	96.89 63	95.19 86	95.99 115	93.56 98

CS-MVS		94.76 57	94.41 80	95.18 38	94.95 90	95.99 61	97.28 49	91.99 76	85.51 159	94.55 31	93.07 127	97.69 58	93.77 26	97.08 55	96.79 46	98.53 16	94.72 71

OMC-MVS		94.74 58	95.46 58	93.91 71	94.62 102	96.26 53	96.64 69	89.36 132	94.20 36	94.15 40	94.02 115	97.73 55	91.34 66	96.15 80	95.04 90	97.37 66	94.80 69

DeepC-MVS_fast	91.38 6	94.73 59	94.98 65	94.44 51	96.83 36	96.12 57	96.69 66	92.17 67	92.98 60	93.72 50	94.14 111	95.45 115	90.49 91	95.73 89	95.30 83	96.71 87	95.13 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		94.65 60	94.84 69	94.44 51	94.95 90	96.55 42	96.46 76	91.10 95	88.96 129	96.00 16	94.55 104	95.32 120	90.67 83	96.97 59	96.69 51	97.44 61	94.84 68

CS-MVS-test		94.63 61	94.30 86	95.02 44	94.63 100	95.71 72	98.15 32	92.13 69	85.62 158	94.22 38	93.63 120	97.63 62	93.08 36	97.50 46	96.51 54	97.88 46	93.50 99

pmmvs6		94.58 62	97.30 15	91.40 123	94.84 94	94.61 105	93.40 148	92.43 60	98.51 2	85.61 164	98.73 6	99.53 2	84.40 144	97.88 26	97.03 41	97.72 51	94.79 70

DeepPCF-MVS	90.68 7	94.56 63	94.92 66	94.15 60	94.11 115	95.71 72	97.03 55	90.65 102	93.39 51	94.08 42	95.29 91	94.15 137	93.21 34	95.22 102	94.92 94	95.82 121	95.75 53

NR-MVSNet		94.55 64	95.66 54	93.25 91	94.26 111	96.44 49	96.69 66	95.32 11	89.97 119	91.79 98	97.46 42	98.39 28	82.85 154	96.87 65	96.48 57	98.57 14	93.98 88

Vis-MVSNet		94.39 65	95.85 50	92.68 99	90.91 187	95.88 67	97.62 47	91.41 89	91.95 82	89.20 139	97.29 47	96.26 94	90.60 90	96.95 61	95.91 69	96.32 102	96.71 34
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TSAR-MVS + GP.		94.25 66	94.81 71	93.60 81	96.52 46	95.80 70	94.37 125	92.47 59	90.89 107	88.92 141	95.34 89	94.38 135	92.85 44	96.36 78	95.62 78	96.47 94	95.28 62

CNVR-MVS		94.24 67	94.47 77	93.96 69	96.56 44	95.67 74	96.43 77	91.95 78	92.08 79	91.28 108	90.51 148	95.35 118	91.20 69	96.34 79	95.50 80	96.34 100	95.88 50

EC-MVSNet		94.23 68	93.81 102	94.71 50	94.85 93	96.23 54	97.14 51	93.40 46	81.79 182	91.58 102	93.29 123	95.21 126	93.13 35	97.73 38	96.95 42	98.20 32	95.45 57

v1192		93.98 69	93.94 96	94.01 65	93.91 124	94.63 103	97.00 57	89.75 122	91.01 105	96.50 10	97.93 26	98.26 33	91.74 59	92.06 150	92.05 140	95.18 135	91.66 137

v10		93.96 70	94.12 93	93.77 78	93.37 138	95.45 78	96.83 63	91.13 94	89.70 123	95.02 25	97.88 29	98.23 34	91.27 67	92.39 145	92.18 136	94.99 142	93.00 108

CDPH-MVS		93.96 70	93.86 98	94.08 62	96.31 52	95.84 68	96.92 59	91.85 81	87.21 145	91.25 110	92.83 129	96.06 102	91.05 75	95.57 92	94.81 96	97.12 77	94.72 71

MVS_0304		93.92 72	93.81 102	94.05 64	96.06 61	96.00 60	96.43 77	92.76 54	85.99 156	94.43 34	94.04 114	97.08 73	88.12 117	94.65 113	94.20 110	96.47 94	94.71 73

MSLP-MVS++		93.91 73	94.30 86	93.45 83	95.51 77	95.83 69	93.12 155	91.93 80	91.45 94	91.40 105	87.42 184	96.12 101	93.27 31	96.57 74	96.40 59	95.49 125	96.29 41

v1921920		93.90 74	93.82 100	94.00 66	93.74 130	94.31 110	97.12 52	89.33 133	91.13 102	96.77 9	97.90 27	98.06 41	91.95 54	91.93 154	91.54 149	95.10 138	91.85 131

train_agg		93.89 75	93.46 113	94.40 53	97.35 14	93.78 124	97.63 46	92.19 66	88.12 136	90.52 121	93.57 121	95.78 108	92.31 50	94.78 110	93.46 121	96.36 98	94.70 75

v144192		93.89 75	93.85 99	93.94 70	93.50 135	94.33 109	97.12 52	89.49 127	90.89 107	96.49 11	97.78 31	98.27 32	91.89 56	92.17 149	91.70 146	95.19 134	91.78 134

v1240		93.89 75	93.72 104	94.09 61	93.98 120	94.31 110	97.12 52	89.37 131	90.74 112	96.92 8	98.05 23	97.89 48	92.15 53	91.53 160	91.60 147	94.99 142	91.93 129

NCCC		93.87 78	93.42 114	94.40 53	96.84 34	95.42 79	96.47 75	92.62 55	92.36 73	92.05 88	83.83 204	95.55 111	91.84 58	95.89 84	95.23 85	96.56 91	95.63 54

v1144		93.83 79	93.87 97	93.78 77	93.72 131	94.57 107	96.85 62	89.98 116	91.31 99	95.90 17	97.89 28	98.40 27	91.13 72	92.01 153	92.01 141	95.10 138	90.94 143

MVS_111021_HR		93.82 80	94.26 89	93.31 86	95.01 88	93.97 120	95.73 93	89.75 122	92.06 80	92.49 77	94.01 116	96.05 103	90.61 89	95.95 83	94.78 99	96.28 103	93.04 107

thisisatest0515		93.79 81	94.41 80	93.06 96	94.14 112	92.50 145	95.56 99	88.55 140	91.61 87	92.45 78	96.84 59	95.71 109	90.62 87	94.58 114	95.07 88	97.05 80	94.58 77

TAPA-MVS	88.94 13	93.78 82	94.31 85	93.18 93	94.14 112	95.99 61	95.74 92	86.98 163	93.43 50	93.88 45	90.16 155	96.88 80	91.05 75	94.33 119	93.95 112	97.28 72	95.40 58
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE		93.72 83	93.62 108	93.84 72	94.75 97	94.90 97	97.24 50	91.81 83	86.97 149	92.74 69	93.83 118	97.24 70	90.46 92	95.10 106	94.09 111	96.08 112	93.18 105

EPP-MVSNet		93.63 84	93.95 95	93.26 89	95.15 85	96.54 45	96.18 85	91.97 77	91.74 84	85.76 162	94.95 98	84.27 191	91.60 62	97.61 43	97.38 34	98.87 4	95.18 64

v8		93.60 85	93.82 100	93.34 84	93.13 145	95.06 90	96.39 79	90.75 100	89.90 121	94.03 43	97.70 35	98.21 36	91.08 74	92.36 146	91.47 150	94.63 150	92.07 125

MCST-MVS		93.60 85	93.40 116	93.83 73	95.30 79	95.40 81	96.49 74	90.87 98	90.08 116	91.72 99	90.28 153	95.99 104	91.69 60	93.94 128	92.99 127	96.93 84	95.13 65

PVSNet_Blended_VisFu		93.60 85	93.41 115	93.83 73	96.31 52	95.65 75	95.71 94	90.58 104	88.08 138	93.17 64	95.29 91	92.20 154	90.72 81	94.69 112	93.41 123	96.51 93	94.54 78

TransMVSNet (Re)		93.55 88	96.32 35	90.32 139	94.38 107	94.05 115	93.30 152	89.53 126	97.15 8	85.12 167	98.83 3	97.89 48	82.21 160	96.75 69	96.14 66	97.35 67	93.46 100

DCV-MVSNet		93.49 89	95.15 63	91.55 117	94.05 116	95.92 66	95.15 105	91.21 91	92.76 64	87.01 158	89.71 159	97.16 72	83.90 149	97.65 40	96.87 44	97.99 43	95.95 49

v2v482		93.42 90	93.49 112	93.32 85	93.44 137	94.05 115	96.36 82	89.76 121	91.41 96	95.24 22	97.63 38	98.34 30	90.44 93	91.65 158	91.76 145	94.69 147	89.62 155

sasdasda		93.38 91	94.36 82	92.24 105	93.94 122	96.41 51	94.18 134	90.47 105	93.07 58	88.47 147	88.66 170	93.78 142	88.80 106	95.74 87	95.75 75	97.57 55	97.13 18

canonicalmvs		93.38 91	94.36 82	92.24 105	93.94 122	96.41 51	94.18 134	90.47 105	93.07 58	88.47 147	88.66 170	93.78 142	88.80 106	95.74 87	95.75 75	97.57 55	97.13 18

3Dnovator	91.81 5	93.36 93	94.27 88	92.29 104	92.99 150	95.03 91	95.76 91	87.79 149	93.82 45	92.38 81	92.19 137	93.37 149	88.14 116	95.26 101	94.85 95	96.69 88	95.40 58

pm-mvs1		93.27 94	95.94 46	90.16 140	94.13 114	93.66 125	92.61 166	89.91 118	95.73 17	84.28 176	98.51 13	98.29 31	82.80 155	96.44 76	95.76 74	97.25 73	93.21 104

casdiffmvs_mvg		93.27 94	94.83 70	91.45 121	93.59 133	94.47 108	94.91 111	89.83 120	92.04 81	87.14 156	97.57 39	98.47 25	86.03 133	94.07 126	94.44 106	97.21 76	92.76 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test1111		93.25 96	94.43 78	91.88 111	95.09 87	94.97 95	94.58 121	92.81 51	93.60 46	83.79 179	97.17 51	89.25 176	87.59 120	97.54 44	96.57 52	97.42 63	91.89 130

Anonymous20231211		93.19 97	95.50 57	90.49 136	93.77 128	95.29 84	94.36 129	90.04 115	91.44 95	84.59 171	96.72 62	97.65 60	82.45 159	97.25 51	96.32 61	97.74 49	93.79 91

TinyColmap		93.17 98	93.33 117	93.00 97	93.84 126	92.76 139	94.75 118	88.90 136	93.97 43	97.48 4	95.28 93	95.29 121	88.37 112	95.31 100	91.58 148	94.65 149	89.10 159

MVS_111021_LR		93.15 99	93.65 106	92.56 100	93.89 125	92.28 148	95.09 106	86.92 165	91.26 101	92.99 68	94.46 107	96.22 97	90.64 85	95.11 105	93.45 122	95.85 119	92.74 115

CNLPA		93.14 100	93.67 105	92.53 101	94.62 102	94.73 100	95.00 110	86.57 170	92.85 61	92.43 79	90.94 143	94.67 131	90.35 94	95.41 95	93.70 118	96.23 106	93.37 102

PLC	87.27 15	93.08 101	92.92 121	93.26 89	94.67 98	95.03 91	94.38 124	90.10 110	91.69 85	92.14 85	87.24 185	93.91 140	91.61 61	95.05 107	94.73 102	96.67 89	92.80 111
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		93.07 102	93.05 120	93.10 94	95.90 66	95.41 80	95.88 88	91.94 79	84.77 165	93.36 57	94.05 113	95.25 124	86.25 131	94.33 119	93.94 113	95.30 128	93.58 97

TSAR-MVS + COLMAP		93.06 103	93.65 106	92.36 102	94.62 102	94.28 112	95.36 104	89.46 129	92.18 77	91.64 100	95.55 84	95.27 123	88.60 110	93.24 134	92.50 132	94.46 153	92.55 121

ECVR-MVS		93.05 104	94.25 90	91.65 114	94.76 95	95.23 85	94.26 132	92.80 52	92.49 67	83.90 177	96.75 61	89.99 167	86.84 125	97.62 41	96.72 48	97.32 69	90.92 144

Effi-MVS+		92.93 105	92.16 132	93.83 73	94.29 109	93.53 132	95.04 108	92.98 50	85.27 162	94.46 32	90.24 154	95.34 119	89.99 97	93.72 129	94.23 109	96.22 107	92.79 112

Fast-Effi-MVS+		92.93 105	92.64 125	93.27 88	93.81 127	93.88 121	95.90 87	90.61 103	83.98 171	92.71 70	92.81 130	96.22 97	90.67 83	94.90 109	93.92 114	95.92 117	92.77 113

HQP-MVS		92.87 107	92.49 126	93.31 86	95.75 72	95.01 94	95.64 97	91.06 96	88.54 133	91.62 101	88.16 176	96.25 95	89.47 101	92.26 148	91.81 143	96.34 100	95.40 58

FMVSNet1		92.86 108	95.26 61	90.06 142	92.40 164	95.16 87	94.37 125	92.22 63	93.18 56	82.16 189	96.76 60	97.48 65	81.85 164	95.32 97	94.98 91	97.34 68	93.93 89

CLD-MVS		92.81 109	94.32 84	91.05 127	95.39 78	95.31 83	95.82 90	81.44 204	89.40 126	91.94 90	95.86 75	97.36 66	85.83 134	95.35 96	94.59 104	95.85 119	92.34 122
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		92.76 110	93.25 118	92.19 107	94.91 92	95.56 76	95.86 89	92.12 70	88.10 137	82.71 184	93.15 126	88.30 179	88.86 105	97.29 48	96.95 42	98.66 10	93.38 101

FC-MVSNet-train		92.75 111	95.40 59	89.66 151	95.21 83	94.82 98	97.00 57	89.40 130	91.13 102	81.71 190	97.72 34	96.43 91	77.57 190	96.89 63	96.72 48	97.05 80	94.09 85

V42		92.67 112	93.50 111	91.71 113	91.41 178	92.96 137	95.71 94	85.00 182	89.67 124	93.22 62	97.67 36	98.01 45	91.02 77	92.65 141	92.12 138	93.86 161	91.42 138

PM-MVS		92.65 113	93.20 119	92.00 109	92.11 172	90.16 174	95.99 86	84.81 186	91.31 99	92.41 80	95.87 74	96.64 88	92.35 49	93.65 131	92.91 128	94.34 156	91.85 131

QAPM		92.57 114	93.51 110	91.47 120	92.91 152	94.82 98	93.01 157	87.51 153	91.49 91	91.21 111	92.24 135	91.70 157	88.74 108	94.54 116	94.39 108	95.41 126	95.37 61

MIMVSNet1		92.52 115	94.88 68	89.77 147	96.09 57	91.99 154	96.92 59	89.68 124	95.92 16	84.55 172	96.64 64	98.21 36	78.44 183	96.08 81	95.10 87	92.91 176	90.22 152

tfpnnormal		92.45 116	94.77 72	89.74 148	93.95 121	93.44 135	93.25 153	88.49 142	95.27 23	83.20 182	96.51 65	96.23 96	83.17 153	95.47 94	94.52 105	96.38 97	91.97 128

PCF-MVS	87.46 14	92.44 117	91.80 134	93.19 92	94.66 99	95.80 70	96.37 80	90.19 109	87.57 142	92.23 84	89.26 164	93.97 139	89.24 102	91.32 162	90.82 158	96.46 96	93.86 90
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		92.42 118	93.99 94	90.60 134	93.25 141	93.82 123	94.28 131	88.73 138	91.53 89	84.53 174	97.74 32	98.64 20	86.60 128	93.21 136	91.20 153	96.21 108	91.76 136
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		92.41 119	91.49 138	93.48 82	95.96 64	95.02 93	95.37 103	91.73 85	87.97 140	91.28 108	82.82 208	91.04 161	90.62 87	95.82 86	95.07 88	95.95 116	92.67 116

v148		92.38 120	92.78 123	91.91 110	92.86 153	92.13 151	94.84 113	87.03 162	91.47 93	93.07 67	96.92 57	98.89 11	90.10 96	92.05 151	89.69 166	93.56 164	88.27 168

pmmvs-eth3d		92.34 121	92.33 127	92.34 103	92.67 157	90.67 168	96.37 80	89.06 134	90.98 106	93.60 54	97.13 53	97.02 76	88.29 113	90.20 169	91.42 151	94.07 159	88.89 163

DELS-MVS		92.33 122	93.61 109	90.83 130	92.84 154	95.13 89	94.76 117	87.22 161	87.78 141	88.42 150	95.78 78	95.28 122	85.71 137	94.44 117	93.91 115	96.01 114	92.97 109
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
Effi-MVS+-dtu		92.32 123	91.66 136	93.09 95	95.13 86	94.73 100	94.57 122	92.14 68	81.74 183	90.33 125	88.13 177	95.91 105	89.24 102	94.23 124	93.65 120	97.12 77	93.23 103

MGCFI-Net		92.31 124	94.25 90	90.04 145	93.75 129	95.96 63	93.32 150	90.28 108	93.28 52	80.57 194	88.79 168	93.78 142	84.89 139	95.55 93	95.31 82	97.45 60	97.10 21

UGNet		92.31 124	94.70 73	89.53 153	90.99 186	95.53 77	96.19 84	92.10 72	91.35 98	85.76 162	95.31 90	95.48 114	76.84 195	95.22 102	94.79 98	95.32 127	95.19 63
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
USDC		92.17 126	92.17 131	92.18 108	92.93 151	92.22 149	93.66 142	87.41 156	93.49 48	97.99 1	94.10 112	96.68 87	86.46 129	92.04 152	89.18 172	94.61 151	87.47 171

ETV-MVS		92.12 127	90.44 146	94.08 62	96.36 50	93.63 127	96.27 83	92.00 75	78.90 202	92.13 86	85.29 198	89.85 170	90.26 95	97.07 56	96.29 63	97.46 58	92.04 126

IterMVS-LS		92.10 128	92.33 127	91.82 112	93.18 142	93.66 125	92.80 164	92.27 62	90.82 109	90.59 120	97.19 49	90.97 162	87.76 119	89.60 176	90.94 157	94.34 156	93.16 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		92.09 129	92.84 122	91.22 126	92.55 159	92.97 136	93.42 147	85.43 180	90.24 115	91.83 95	94.70 101	94.59 132	88.48 111	94.91 108	93.31 125	95.59 124	89.15 158

EIA-MVS		91.95 130	90.36 148	93.81 76	96.54 45	94.65 102	95.38 102	90.40 107	78.01 207	93.72 50	86.70 192	91.95 156	89.93 98	95.67 91	94.72 103	96.89 85	90.79 146

MAR-MVS		91.86 131	91.14 142	92.71 98	94.29 109	94.24 113	94.91 111	91.82 82	81.66 184	93.32 58	84.51 201	93.42 148	86.86 124	95.16 104	94.44 106	95.05 141	94.53 79
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
EU-MVSNet		91.63 132	92.73 124	90.35 138	88.36 205	87.89 185	96.53 72	81.51 203	92.45 70	91.82 96	96.44 68	97.05 75	93.26 32	94.10 125	88.94 177	90.61 183	92.24 123

FC-MVSNet-test		91.49 133	94.43 78	88.07 169	94.97 89	90.53 171	95.42 101	91.18 93	93.24 54	72.94 214	98.37 15	93.86 141	78.78 177	97.82 32	96.13 67	95.13 136	91.05 141

FA-MVS(training)		91.38 134	91.18 141	91.62 116	93.49 136	92.38 146	95.03 109	90.81 99	87.20 146	91.46 104	93.00 128	89.47 173	84.19 146	93.20 138	92.08 139	94.74 146	90.90 145

OpenMVS	89.22 12	91.09 135	91.42 139	90.71 132	92.79 156	93.61 129	92.74 165	85.47 179	86.10 155	90.73 115	85.71 197	93.07 152	86.69 127	94.07 126	93.34 124	95.86 118	94.02 87

FPMVS		90.81 136	91.60 137	89.88 146	92.52 160	88.18 181	93.31 151	83.62 192	91.59 88	88.45 149	88.96 167	89.73 172	86.96 122	96.42 77	95.69 77	94.43 154	90.65 147

DI_MVS_plusplus_trai		90.68 137	90.40 147	91.00 128	92.43 163	92.61 143	94.17 136	88.98 135	88.32 135	88.76 145	93.67 119	87.58 181	86.44 130	89.74 174	90.33 161	95.24 131	90.56 150

Vis-MVSNet (Re-imp)		90.68 137	92.18 130	88.92 158	94.63 100	92.75 140	92.91 160	91.20 92	89.21 128	75.01 210	93.96 117	89.07 177	82.72 157	95.88 85	95.30 83	97.08 79	89.08 160

DPM-MVS		90.67 139	89.86 152	91.63 115	95.29 81	94.16 114	94.52 123	89.63 125	89.59 125	89.67 133	81.95 210	88.64 178	85.75 136	90.46 167	90.43 160	94.91 144	93.77 92

diffmvs		90.44 140	92.23 129	88.35 165	91.36 180	91.38 160	92.45 170	84.84 185	89.88 122	85.09 168	96.69 63	97.71 57	83.33 152	90.01 173	88.96 176	93.03 174	91.00 142
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FMVSNet2		90.28 141	92.04 133	88.23 167	91.22 182	94.05 115	92.88 161	90.69 101	86.53 152	79.89 198	94.38 108	92.73 153	78.54 180	91.64 159	92.26 135	96.17 109	92.67 116

IterMVS-SCA-FT		90.24 142	89.37 158	91.26 125	92.50 161	92.11 152	91.69 181	87.48 154	87.05 148	91.82 96	95.76 79	87.25 182	91.36 65	89.02 181	85.53 192	92.68 177	88.90 162

MVS_Test		90.19 143	90.58 143	89.74 148	92.12 171	91.74 156	92.51 167	88.54 141	82.80 177	87.50 154	94.62 102	95.02 129	83.97 147	88.69 184	89.32 170	93.79 162	91.85 131

EPNet		90.17 144	89.07 160	91.45 121	97.25 19	90.62 170	94.84 113	93.54 44	80.96 186	91.85 94	86.98 188	85.88 187	77.79 187	92.30 147	92.58 131	93.41 166	94.20 84
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		90.09 145	90.12 150	90.05 143	92.40 164	92.74 141	91.74 177	85.89 175	80.54 189	90.30 126	88.54 172	95.51 112	84.69 142	92.64 142	90.25 162	95.28 129	90.61 148

PVSNet_Blended		90.09 145	90.12 150	90.05 143	92.40 164	92.74 141	91.74 177	85.89 175	80.54 189	90.30 126	88.54 172	95.51 112	84.69 142	92.64 142	90.25 162	95.28 129	90.61 148

pmmvs4		89.95 147	89.32 159	90.69 133	91.60 177	89.17 178	94.37 125	87.63 150	88.07 139	91.02 113	94.50 105	90.50 165	86.13 132	86.33 198	89.40 169	93.39 167	87.29 174

MDA-MVSNet-bldmvs		89.75 148	91.67 135	87.50 174	74.25 224	90.88 165	94.68 119	85.89 175	91.64 86	91.03 112	95.86 75	94.35 136	89.10 104	96.87 65	86.37 188	90.04 184	85.72 179

WB-MVS		89.70 149	94.13 92	84.54 194	88.16 207	92.57 144	88.90 201	88.32 143	96.67 11	73.61 213	98.29 18	98.80 15	80.60 170	95.73 89	92.18 136	87.66 196	84.64 182

tttt0517		89.64 150	88.05 171	91.49 119	93.52 134	91.65 157	93.67 141	87.53 151	82.77 178	89.39 138	90.37 152	70.05 216	88.21 114	93.71 130	93.79 116	96.63 90	94.04 86

PatchMatch-RL		89.59 151	88.80 164	90.51 135	92.20 170	88.00 184	91.72 179	86.64 167	84.75 166	88.25 151	87.10 187	90.66 164	89.85 100	93.23 135	92.28 134	94.41 155	85.60 180

Fast-Effi-MVS+-dtu		89.57 152	88.42 168	90.92 129	93.35 139	91.57 158	93.01 157	95.71 9	78.94 201	87.65 153	84.68 200	93.14 151	82.00 162	90.84 165	91.01 156	93.78 163	88.77 164

thisisatest0530		89.54 153	87.99 173	91.35 124	93.17 143	91.31 161	93.45 146	87.53 151	82.96 176	89.17 140	90.45 149	70.32 215	88.21 114	93.37 133	93.79 116	96.54 92	93.71 94

test2506		89.51 154	87.77 176	91.55 117	94.76 95	95.23 85	94.26 132	92.80 52	92.49 67	83.31 181	89.97 157	50.93 230	86.84 125	97.62 41	96.72 48	97.32 69	91.42 138

GBi-Net		89.35 155	90.58 143	87.91 170	91.22 182	94.05 115	92.88 161	90.05 112	79.40 193	78.60 201	90.58 145	87.05 183	78.54 180	95.32 97	94.98 91	96.17 109	92.67 116

test1		89.35 155	90.58 143	87.91 170	91.22 182	94.05 115	92.88 161	90.05 112	79.40 193	78.60 201	90.58 145	87.05 183	78.54 180	95.32 97	94.98 91	96.17 109	92.67 116

thres600view7		89.14 157	88.83 162	89.51 154	93.71 132	93.55 130	93.93 139	88.02 147	87.30 144	82.40 185	81.18 211	80.63 202	82.69 158	94.27 121	95.90 70	96.27 104	88.94 161

CVMVSNet		88.97 158	89.73 154	88.10 168	87.33 212	85.22 195	94.68 119	78.68 205	88.94 130	86.98 159	95.55 84	85.71 188	89.87 99	91.19 163	89.69 166	91.05 181	91.78 134

CANet_DTU		88.95 159	89.51 157	88.29 166	93.12 146	91.22 163	93.61 143	83.47 195	80.07 192	90.71 119	89.19 165	93.68 146	76.27 199	91.44 161	91.17 155	92.59 178	89.83 154

GA-MVS		88.76 160	88.04 172	89.59 152	92.32 167	91.46 159	92.28 172	86.62 168	83.82 173	89.84 129	92.51 134	81.94 196	83.53 151	89.41 178	89.27 171	92.95 175	87.90 169

pmmvs5		88.63 161	89.70 155	87.39 175	89.24 198	90.64 169	91.87 176	82.13 199	83.34 174	87.86 152	94.58 103	96.15 100	79.87 174	87.33 193	89.07 175	93.39 167	86.76 175

thres400		88.54 162	88.15 170	88.98 156	93.17 143	92.84 138	93.56 144	86.93 164	86.45 153	82.37 186	79.96 213	81.46 199	81.83 165	93.21 136	94.76 100	96.04 113	88.39 166

CDS-MVSNet		88.41 163	89.79 153	86.79 180	94.55 105	90.82 166	92.50 168	89.85 119	83.26 175	80.52 195	91.05 141	89.93 169	69.11 210	93.17 139	92.71 130	94.21 158	87.63 170
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gg-mvs-nofinetune		88.32 164	88.81 163	87.75 172	93.07 147	89.37 177	89.06 200	95.94 8	95.29 21	87.15 155	97.38 44	76.38 205	68.05 213	91.04 164	89.10 174	93.24 170	83.10 189

IterMVS		88.32 164	88.25 169	88.41 164	90.83 188	91.24 162	93.07 156	81.69 201	86.77 150	88.55 146	95.61 80	86.91 186	87.01 121	87.38 192	83.77 194	89.29 186	86.06 178
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres200		88.29 166	87.88 174	88.76 160	92.50 161	93.55 130	92.47 169	88.02 147	84.80 164	81.44 191	79.28 215	82.20 195	81.83 165	94.27 121	93.67 119	96.27 104	87.40 172

IB-MVS	86.01 17	88.24 167	87.63 177	88.94 157	92.03 173	91.77 155	92.40 171	85.58 178	78.24 204	84.85 169	71.99 220	93.45 147	83.96 148	93.48 132	92.33 133	94.84 145	92.15 124
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
MDTV_nov1_ep13_2view		88.22 168	87.85 175	88.65 162	91.40 179	86.75 189	94.07 137	84.97 183	88.86 132	93.20 63	96.11 73	96.21 99	83.70 150	87.29 194	80.29 201	84.56 205	79.46 202

test20.03		88.20 169	91.26 140	84.63 192	96.64 42	89.39 176	90.73 188	89.97 117	91.07 104	72.02 216	94.98 97	95.45 115	69.35 209	92.70 140	91.19 154	89.06 188	84.02 183

HyFIR lowres test		88.19 170	86.56 184	90.09 141	91.24 181	92.17 150	94.30 130	88.79 137	84.06 168	85.45 165	89.52 162	85.64 189	88.64 109	85.40 201	87.28 182	92.14 180	81.87 192

ET-MVSNet_ETH3D		88.06 171	85.75 189	90.74 131	92.82 155	90.68 167	93.77 140	88.59 139	81.22 185	89.78 131	89.15 166	66.79 223	84.29 145	91.72 157	91.34 152	95.22 132	89.36 157

tfpn200view9		87.94 172	87.51 179	88.44 163	92.28 168	93.63 127	93.35 149	88.11 145	80.90 187	80.89 192	78.25 216	82.25 193	79.65 176	94.27 121	94.76 100	96.36 98	88.48 165

FMVSNet3		87.90 173	88.63 166	87.04 176	89.78 196	93.46 134	91.62 182	90.05 112	79.40 193	78.60 201	90.58 145	87.05 183	77.07 194	88.03 189	89.86 165	95.12 137	92.04 126

MS-PatchMatch		87.72 174	88.62 167	86.66 181	90.81 189	88.18 181	90.92 185	82.25 198	85.86 157	80.40 196	90.14 156	89.29 175	84.93 138	89.39 179	89.12 173	90.67 182	88.34 167

Anonymous20231206		87.45 175	89.66 156	84.87 189	94.00 117	87.73 187	91.36 183	86.41 172	88.89 131	75.03 209	92.59 133	96.82 82	72.48 207	89.72 175	88.06 179	89.93 185	83.81 185

EPNet_dtu		87.40 176	86.27 185	88.72 161	95.68 74	83.37 201	92.09 174	90.08 111	78.11 206	91.29 107	86.33 193	89.74 171	75.39 202	89.07 180	87.89 180	87.81 193	89.38 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		86.96 177	87.58 178	86.24 183	93.07 147	90.44 172	89.24 199	86.85 166	85.14 163	77.26 207	90.45 149	76.09 207	75.79 200	91.80 156	91.81 143	95.20 133	87.35 173

baseline		86.71 178	88.89 161	84.16 195	87.85 208	85.23 194	89.82 193	77.69 208	84.03 170	84.75 170	94.91 99	94.59 132	77.19 193	86.57 197	86.51 187	87.66 196	90.36 151

CHOSEN 1792x2688		86.64 179	86.62 182	86.65 182	90.33 192	87.86 186	93.19 154	83.30 196	83.95 172	82.32 187	87.93 179	89.34 174	86.92 123	85.64 200	84.95 193	83.85 209	86.68 176

dmvs_re		86.51 180	86.14 187	86.95 178	93.07 147	86.11 191	92.01 175	86.04 174	72.70 217	79.10 199	75.37 219	89.99 167	78.10 186	94.56 115	93.01 126	93.35 169	91.26 140

testgi		86.49 181	90.31 149	82.03 199	95.63 75	88.18 181	93.47 145	84.89 184	93.23 55	69.54 220	87.16 186	97.96 47	60.66 217	91.90 155	89.90 164	87.99 191	83.84 184

thres100view900		86.46 182	86.00 188	86.99 177	92.28 168	91.03 164	91.09 184	84.49 188	80.90 187	80.89 192	78.25 216	82.25 193	77.57 190	90.17 170	92.84 129	95.63 123	86.57 177

gm-plane-assit		86.15 183	82.51 197	90.40 137	95.81 70	92.29 147	97.99 34	84.66 187	92.15 78	93.15 65	97.84 30	44.65 231	78.60 179	88.02 190	85.95 189	92.20 179	76.69 210

CMPMVS	66.55 18	85.55 184	87.46 180	83.32 196	84.99 214	81.97 206	79.19 221	75.93 210	79.32 196	88.82 143	85.09 199	91.07 160	82.12 161	92.56 144	89.63 168	88.84 189	92.56 120
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		85.32 185	81.58 199	89.69 150	90.36 191	84.79 197	86.72 212	92.22 63	75.38 212	90.73 115	90.41 151	67.88 220	84.86 140	83.76 204	85.74 190	93.24 170	83.14 187

baseline2		84.95 186	82.68 196	87.59 173	92.64 158	88.41 180	90.09 190	84.25 189	75.88 210	85.23 166	82.49 209	71.15 213	80.14 173	88.21 188	87.21 185	93.21 173	85.39 181

pmnet_mix02		84.85 187	86.58 183	82.83 197	90.19 193	81.10 209	88.52 204	78.58 206	91.50 90	80.32 197	96.48 66	95.86 106	75.42 201	85.17 202	76.44 210	83.91 208	79.51 201

MVSTER		84.79 188	83.79 192	85.96 185	89.14 199	89.80 175	89.39 197	82.99 197	74.16 216	82.78 183	85.97 195	66.81 222	76.84 195	90.77 166	88.83 178	94.66 148	90.19 153

MIMVSNet		84.76 189	86.75 181	82.44 198	91.71 176	85.95 192	89.74 195	89.49 127	85.28 161	69.69 219	87.93 179	90.88 163	64.85 215	88.26 187	87.74 181	89.18 187	81.24 193

SCA		84.69 190	81.10 200	88.87 159	89.02 200	90.31 173	92.21 173	92.09 73	82.72 179	89.68 132	86.83 190	73.08 209	85.80 135	80.50 212	77.51 207	84.45 207	76.80 209

new-patchmatchnet		84.45 191	88.75 165	79.43 205	93.28 140	81.87 207	81.68 218	83.48 194	94.47 29	71.53 217	98.33 16	97.88 51	58.61 220	90.35 168	77.33 208	87.99 191	81.05 195

PatchT		83.44 192	81.10 200	86.18 184	77.92 222	82.58 205	89.87 192	87.39 157	75.88 210	90.73 115	89.86 158	66.71 224	84.86 140	83.76 204	85.74 190	86.33 202	83.14 187

RPMNet		83.42 193	78.40 209	89.28 155	89.79 195	84.79 197	90.64 189	92.11 71	75.38 212	87.10 157	79.80 214	61.99 229	82.79 156	81.88 210	82.07 198	93.23 172	82.87 190

TAMVS		82.96 194	86.15 186	79.24 208	90.57 190	83.12 204	87.29 208	75.12 212	84.06 168	65.81 221	92.22 136	88.27 180	69.11 210	88.72 182	87.26 184	87.56 198	79.38 203

PatchmatchNet		82.44 195	78.69 208	86.83 179	89.81 194	81.55 208	90.78 187	87.27 160	82.39 181	88.85 142	88.31 175	70.96 214	81.90 163	78.58 216	74.33 216	82.35 213	74.69 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		82.33 196	79.66 203	85.45 187	88.83 202	83.88 199	90.09 190	81.98 200	79.07 200	88.82 143	88.70 169	73.77 208	78.41 184	80.29 214	76.08 211	84.56 205	75.83 211

CostFormer		82.15 197	79.54 204	85.20 188	88.92 201	85.70 193	90.87 186	86.26 173	79.19 199	83.87 178	87.89 181	69.20 218	76.62 197	77.50 219	75.28 213	84.69 204	82.02 191

PMMVS		81.93 198	83.48 194	80.12 204	72.35 225	75.05 218	88.54 203	64.01 217	77.02 209	82.22 188	87.51 183	91.12 159	79.70 175	86.59 195	86.64 186	93.88 160	80.41 196

pmmvs3		81.69 199	83.83 191	79.19 209	78.33 221	78.57 212	89.53 196	58.71 220	78.88 203	84.34 175	88.36 174	91.96 155	77.69 189	87.48 191	82.42 197	86.54 201	79.18 204

tpm		81.58 200	78.84 206	84.79 191	91.11 185	79.50 210	89.79 194	83.75 190	79.30 197	92.05 88	90.98 142	64.78 226	74.54 203	80.50 212	76.67 209	77.49 218	80.15 199

test0.0.03 1		81.51 201	83.30 195	79.42 206	93.99 118	86.50 190	85.93 216	87.32 158	78.16 205	61.62 222	80.78 212	81.78 197	59.87 218	88.40 186	87.27 183	87.78 195	80.19 198

dps		81.42 202	77.88 214	85.56 186	87.67 210	85.17 196	88.37 206	87.46 155	74.37 215	84.55 172	86.80 191	62.18 228	80.20 172	81.13 211	77.52 206	85.10 203	77.98 207

test-LLR		80.62 203	77.20 217	84.62 193	93.99 118	75.11 216	87.04 209	87.32 158	70.11 220	78.59 204	83.17 206	71.60 211	73.88 205	82.32 208	79.20 203	86.91 199	78.87 205

tpm cat1		80.03 204	75.93 220	84.81 190	89.31 197	83.26 203	88.86 202	86.55 171	79.24 198	86.10 161	84.22 202	63.62 227	77.37 192	73.43 220	70.88 219	80.67 214	76.87 208

N_pmnet		79.33 205	84.22 190	73.62 215	91.72 175	73.72 219	86.11 214	76.36 209	92.38 71	53.38 223	95.54 86	95.62 110	59.14 219	84.23 203	74.84 215	75.03 221	73.25 217

EPMVS		79.26 206	78.20 212	80.49 202	87.04 213	78.86 211	86.08 215	83.51 193	82.63 180	73.94 212	89.59 160	68.67 219	72.03 208	78.17 217	75.08 214	80.37 215	74.37 214

CHOSEN 280x420		79.24 207	78.26 211	80.38 203	79.60 220	68.80 224	89.32 198	75.38 211	77.25 208	78.02 206	75.57 218	76.17 206	81.19 168	88.61 185	81.39 199	78.79 216	80.03 200

ADS-MVSNet		79.11 208	79.38 205	78.80 211	81.90 218	75.59 215	84.36 217	83.69 191	87.31 143	76.76 208	87.58 182	76.90 204	68.55 212	78.70 215	75.56 212	77.53 217	74.07 215

FMVSNet5		79.08 209	78.83 207	79.38 207	87.52 211	86.78 188	87.64 207	78.15 207	69.54 222	70.64 218	65.97 223	65.44 225	63.87 216	90.17 170	90.46 159	88.48 190	83.45 186

tpmrst		78.81 210	76.18 219	81.87 200	88.56 203	77.45 213	86.74 211	81.52 202	80.08 191	83.48 180	90.84 144	66.88 221	74.54 203	73.04 221	71.02 218	76.38 219	73.95 216

test-mter		78.71 211	78.35 210	79.12 210	84.03 215	76.58 214	88.51 205	59.06 219	71.06 218	78.87 200	83.73 205	71.83 210	76.44 198	83.41 207	80.61 200	87.79 194	81.24 193

MVS-HIRNet		78.28 212	75.28 221	81.79 201	80.33 219	69.38 223	76.83 222	86.59 169	70.76 219	86.66 160	89.57 161	81.04 200	77.74 188	77.81 218	71.65 217	82.62 211	66.73 221

E-PMN		77.81 213	77.88 214	77.73 214	88.26 206	70.48 222	80.19 220	71.20 214	86.66 151	72.89 215	88.09 178	81.74 198	78.75 178	90.02 172	68.30 220	75.10 220	59.85 222

EMVS		77.65 214	77.49 216	77.83 212	87.75 209	71.02 221	81.13 219	70.54 215	86.38 154	74.52 211	89.38 163	80.19 203	78.22 185	89.48 177	67.13 221	74.83 222	58.84 223

TESTMET0.1,1		77.47 215	77.20 217	77.78 213	81.94 217	75.11 216	87.04 209	58.33 221	70.11 220	78.59 204	83.17 206	71.60 211	73.88 205	82.32 208	79.20 203	86.91 199	78.87 205

new_pmnet		76.65 216	83.52 193	68.63 216	82.60 216	72.08 220	76.76 223	64.17 216	84.41 167	49.73 225	91.77 139	91.53 158	56.16 221	86.59 195	83.26 196	82.37 212	75.02 212

MVE	60.41 19	73.21 217	80.84 202	64.30 217	56.34 226	57.24 226	75.28 225	72.76 213	87.14 147	41.39 227	86.31 194	85.30 190	80.66 169	86.17 199	83.36 195	59.35 224	80.38 197
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		69.86 218	82.14 198	55.52 218	75.19 223	63.08 225	75.52 224	60.97 218	88.50 134	25.11 229	91.77 139	96.44 90	25.43 223	88.70 183	79.34 202	70.93 223	67.17 220

GG-mvs-BLEND		54.28 219	77.89 213	26.72 221	0.37 231	83.31 202	70.04 226	0.39 227	74.71 214	5.36 230	68.78 221	83.06 192	0.62 227	83.73 206	78.99 205	83.55 210	72.68 219

test_method		43.16 220	51.13 222	33.85 219	7.35 228	12.38 229	51.70 228	11.91 223	62.51 224	47.64 226	62.49 224	80.78 201	28.84 222	59.55 224	34.48 223	55.68 225	45.72 224

testmvs		2.38 221	3.35 223	1.26 223	0.83 229	0.96 231	1.53 231	0.83 225	3.59 226	1.63 232	6.03 226	2.93 233	1.55 226	3.49 225	2.51 225	1.21 229	3.92 226

test123		2.16 222	2.82 224	1.41 222	0.62 230	1.18 230	1.53 231	0.82 226	2.78 227	2.27 231	4.18 227	1.98 234	1.64 225	2.58 226	3.01 224	1.56 228	4.00 225

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

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

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

TPM-MVS					94.35 108	93.52 133	92.94 159			89.43 137	84.20 203	90.07 166	80.21 171			94.56 152	93.77 92
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										97.21 5

9.14												93.19 150

SR-MVS					97.13 23			94.77 17				97.77 54

Anonymous202405211			94.63 74		94.51 106	94.96 96	93.94 138	91.35 90	90.82 109		95.60 82	95.85 107	81.74 167	96.47 75	95.84 73	97.39 65	92.85 110

our_test_3					91.78 174	88.87 179	94.37 125

ambc			94.61 75		98.09 4	95.14 88	91.71 180		94.18 38	96.46 12	96.26 69	96.30 93	91.26 68	94.70 111	92.00 142	93.45 165	93.67 95

MTAPA										94.88 28		96.88 80

MTMP										95.43 18		97.25 68

Patchmatch-RL test							8.96 230

tmp_tt				28.44 220	36.05 227	15.86 228	21.29 229	6.40 224	54.52 225	51.96 224	50.37 225	38.68 232	9.55 224	61.75 223	59.66 222	45.36 227

XVS					96.86 32	97.48 18	98.73 3			93.28 59		96.82 82				98.17 35

X-MVStestdata					96.86 32	97.48 18	98.73 3			93.28 59		96.82 82				98.17 35

mPP-MVS					98.24 2							97.65 60

NP-MVS									85.48 160

Patchmtry						83.74 200	86.72 212	92.22 63		90.73 115

DeepMVS_CX						47.68 227	53.20 227	19.21 222	63.24 223	26.96 228	66.50 222	69.82 217	66.91 214	64.27 222		54.91 226	72.72 218

High-resolution multi-view benchmark